From ff2dcfc008362fecd410fd51b26f3ef5ccb5d4e9 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Thu, 2 Apr 2026 15:06:25 -0700 Subject: [PATCH 001/140] Converted MAPED code to torch, added batching --- src/quantem/core/utils/imaging_utils.py | 33 +- src/quantem/diffraction/__init__.py | 5 +- src/quantem/diffraction/maped.py | 1314 ++++++++++++++++++++--- 3 files changed, 1215 insertions(+), 137 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 3c856ba7..1dcc50db 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -6,8 +6,7 @@ import numpy as np import torch from numpy.typing import NDArray -from scipy.ndimage import gaussian_filter -from scipy.ndimage import map_coordinates +from scipy.ndimage import gaussian_filter, map_coordinates from quantem.core.utils.utils import generate_batches @@ -60,7 +59,9 @@ def _upsampled_correlation_numpy( globalShift = math.floor(math.ceil(upsampleFactor * 1.5) / 2.0) upsampleCenter = float(globalShift) - (float(upsampleFactor) * xyShift) - im_up = dft_upsample(np.conj(imageCorr), upsampleFactor, (float(upsampleCenter[0]), float(upsampleCenter[1]))) + im_up = dft_upsample( + np.conj(imageCorr), upsampleFactor, (float(upsampleCenter[0]), float(upsampleCenter[1])) + ) imageCorrUpsample = np.conj(im_up) flat_idx = int(np.argmax(imageCorrUpsample.real)) @@ -176,14 +177,18 @@ def cross_correlation_shift( def cross_correlation_shift_torch( - im_ref: torch.Tensor, im: torch.Tensor, upsample_factor: int = 2 + im_ref: torch.Tensor, im: torch.Tensor, upsample_factor: int = 2, fft_input: bool = False ) -> torch.Tensor: """ Align two real images using Fourier cross-correlation and DFT upsampling. Returns dx, dy in pixel units (signed shifts). """ - G1 = torch.fft.fft2(im_ref) - G2 = torch.fft.fft2(im) + if fft_input: + G1 = im_ref + G2 = im + else: + G1 = torch.fft.fft2(im_ref) + G2 = torch.fft.fft2(im) xy_shift = align_images_fourier_torch(G1, G2, upsample_factor) @@ -271,12 +276,8 @@ def upsampled_correlation_torch( patch = imageCorrUpsample.real[r - 1 : r + 2, c - 1 : c + 2] if patch.shape == (3, 3): icc = patch - dx = (icc[2, 1] - icc[0, 1]) / ( - 4.0 * icc[1, 1] - 2.0 * icc[2, 1] - 2.0 * icc[0, 1] - ) - dy = (icc[1, 2] - icc[1, 0]) / ( - 4.0 * icc[1, 1] - 2.0 * icc[1, 2] - 2.0 * icc[1, 0] - ) + dx = (icc[2, 1] - icc[0, 1]) / (4.0 * icc[1, 1] - 2.0 * icc[2, 1] - 2.0 * icc[0, 1]) + dy = (icc[1, 2] - icc[1, 0]) / (4.0 * icc[1, 1] - 2.0 * icc[1, 2] - 2.0 * icc[1, 0]) dx = dx.item() dy = dy.item() else: @@ -383,7 +384,9 @@ def weighted_cross_correlation_shift( if weight_real is not None: w = np.asarray(weight_real) if w.shape != cc_real.shape: - raise ValueError(f"weight_real.shape={w.shape} must match correlation shape {cc_real.shape}.") + raise ValueError( + f"weight_real.shape={w.shape} must match correlation shape {cc_real.shape}." + ) cc_pick = cc_real * w else: cc_pick = cc_real @@ -422,7 +425,9 @@ def weighted_cross_correlation_shift( return shift_rc if im is None: - raise ValueError("return_shifted_image=True requires `im` (or its FFT via fft_input=True).") + raise ValueError( + "return_shifted_image=True requires `im` (or its FFT via fft_input=True)." + ) if F_im is None: F_im = np.asarray(im) if fft_input else np.fft.fft2(np.asarray(im)) diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index 2a79312b..8eb0937c 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1,3 +1,6 @@ from quantem.diffraction.polar import RDF as RDF -from quantem.diffraction.strain_autocorrelation import StrainMapAutocorrelation as StrainMapAutocorrelation +from quantem.diffraction.strain_autocorrelation import ( + StrainMapAutocorrelation as StrainMapAutocorrelation, +) from quantem.diffraction.maped import MAPED as MAPED +from quantem.diffraction.maped import MAPEDTorch as MAPEDTorch diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 3b5154c0..ad639d07 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1,17 +1,25 @@ from __future__ import annotations +import math import warnings from typing import Any, Sequence import numpy as np -from scipy.ndimage import gaussian_filter, shift as ndi_shift +import torch +import torch.nn.functional as F +import torchvision +from scipy.ndimage import gaussian_filter +from scipy.ndimage import shift as ndi_shift from scipy.signal import convolve2d from scipy.signal.windows import tukey from tqdm import tqdm from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize -from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift +from quantem.core.utils.imaging_utils import ( + cross_correlation_shift_torch, + weighted_cross_correlation_shift, +) from quantem.core.visualization import show_2d @@ -55,10 +63,14 @@ def from_datasets(cls, datasets: Sequence[Dataset4dstem]) -> MAPED: ds_list: list[Dataset4dstem] = [] for d in datasets: if not isinstance(d, Dataset4dstem): - raise TypeError("MAPED.from_datasets expects a sequence of Dataset4dstem instances.") + raise TypeError( + "MAPED.from_datasets expects a sequence of Dataset4dstem instances." + ) ds_list.append(d) if not ds_list: - raise ValueError("MAPED.from_datasets expects a non-empty sequence of Dataset4dstem instances.") + raise ValueError( + "MAPED.from_datasets expects a non-empty sequence of Dataset4dstem instances." + ) return cls(datasets=ds_list, _token=cls._token) def preprocess( @@ -87,7 +99,9 @@ def preprocess( else: self.scales = np.asarray(list(scale), dtype=float) if self.scales.shape != (n,): - raise ValueError("scale must be a scalar or a sequence with the same length as datasets.") + raise ValueError( + "scale must be a scalar or a sequence with the same length as datasets." + ) if np.any(self.scales == 0): raise ValueError("scale entries must be nonzero.") @@ -119,7 +133,9 @@ def preprocess( if plot_summary: tiles = [[(self.im_bf[i] / self.scales[i]), self.dp_mean[i]] for i in range(n)] - titles = [[f"{i} - Mean Bright Field", f"{i} - Mean Diffraction Pattern"] for i in range(n)] + titles = [ + [f"{i} - Mean Bright Field", f"{i} - Mean Diffraction Pattern"] for i in range(n) + ] show_2d(tiles, title=titles, **plot_kwargs) return self @@ -172,7 +188,9 @@ def diffraction_origin( for i in range(n): dp = np.asarray(self.dp_mean[i]) if sigma is not None and float(sigma) > 0: - dp_use = gaussian_filter(dp.astype(float, copy=False), float(sigma), mode="nearest") + dp_use = gaussian_filter( + dp.astype(float, copy=False), float(sigma), mode="nearest" + ) else: dp_use = dp r, c = np.unravel_index(int(np.argmax(dp_use)), dp_use.shape) @@ -184,7 +202,9 @@ def diffraction_origin( else: origins_list = list(origins) if len(origins_list) != n: - raise ValueError("origins must be a single (row,col) tuple or a list of length n.") + raise ValueError( + "origins must be a single (row,col) tuple or a list of length n." + ) origins_arr = np.asarray(origins_list, dtype=int) if origins_arr.shape != (n, 2): raise ValueError("origins must have shape (n, 2) after conversion.") @@ -240,13 +260,16 @@ def diffraction_align( if not hasattr(self, "dp_mean"): raise RuntimeError("Run preprocess() first so self.dp_mean exists.") if not hasattr(self, "diffraction_origins"): - raise RuntimeError("Run diffraction_origin() first so self.diffraction_origins exists.") + raise RuntimeError( + "Run diffraction_origin() first so self.diffraction_origins exists." + ) H, W = np.asarray(self.dp_mean[0]).shape - w = tukey(H, alpha=2.0 * float(edge_blend) / float(H))[:, None] * tukey( - W, alpha=2.0 * float(edge_blend) / float(W) - )[None, :] + w = ( + tukey(H, alpha=2.0 * float(edge_blend) / float(H))[:, None] + * tukey(W, alpha=2.0 * float(edge_blend) / float(W))[None, :] + ) r = np.fft.fftfreq(H, 1.0 / float(H))[:, None] c = np.fft.fftfreq(W, 1.0 / float(W))[None, :] @@ -296,8 +319,7 @@ def diffraction_align( return self - - def real_space_align( + def real_space_align( # torch.grid_sample self, num_images=None, num_iter: int = 3, @@ -592,7 +614,9 @@ def merge_datasets( elif s == "median": pad_val_dp = float(np.median(v)) else: - raise ValueError("diffraction_pad_val must be a float or one of {'min','max','mean','median'}.") + raise ValueError( + "diffraction_pad_val must be a float or one of {'min','max','mean','median'}." + ) else: pad_val_dp = float(diffraction_pad_val) @@ -761,132 +785,1178 @@ def merge_datasets( return dataset_merged -def shift_images( - images, - shifts_rc, - edge_blend: float = 8.0, - padding=None, - pad_val: str | float = 0.0, - shift_method: str = "bilinear", -): +class MAPEDTorch(AutoSerialize): """ - Shift and blend a stack of 2D images into a common padded canvas. - - Parameters - ---------- - images - Sequence of (H, W) arrays. - shifts_rc - Array-like of shape (n, 2) with (row, col) shifts for each image. - edge_blend - Tukey taper width in pixels for image blending. - padding - Output padding. If None, set from max shift and edge_blend. - pad_val - Fill value outside support ('min','max','mean','median' or float). - shift_method - 'bilinear' or 'fourier'. + Merge-Averaged Precession Electron Diffraction (MAPED) helper coded in PyTorch. - Returns - ------- - np.ndarray - Blended image of shape (H + 2*padding, W + 2*padding). + This class manages a set of 4D-STEM datasets and provides utilities to: + - compute mean BF and mean DP summaries, + - choose/find diffraction origins, + - align diffraction space and real space, + - merge datasets into a single composite Dataset4dstem. """ - images = [np.asarray(im, dtype=float) for im in images] - if len(images) == 0: - raise ValueError("images must be non-empty") - H, W = images[0].shape - for im in images: - if im.shape != (H, W): - raise ValueError("all images must have the same shape") + _token = object() - shifts_rc = np.asarray(shifts_rc, dtype=float) - if shifts_rc.shape != (len(images), 2): - raise ValueError("shifts_rc must have shape (len(images), 2)") + def __init__( + self, + datasets: list[torch.Tensor], + device: str | Any, + dtype: str | Any, + _token: object | None = None, + ): + if _token is not self._token: + raise RuntimeError("Use MAPED.from_datasets() to instantiate this class.") + super().__init__() + self.datasets = datasets + self.metadata: dict[str, Any] = {} + self.device = device + self.dtype = dtype - if isinstance(pad_val, str): - s = pad_val.strip().lower() - v = np.stack(images, axis=0).reshape(-1) - if s == "min": - pad_val_f = float(np.min(v)) - elif s == "max": - pad_val_f = float(np.max(v)) - elif s == "mean": - pad_val_f = float(np.mean(v)) - elif s == "median": - pad_val_f = float(np.median(v)) + @classmethod + def from_datasets(cls, datasets: Sequence[torch.Tensor]) -> MAPED: + """ + Construct a MAPED instance from a non-empty sequence of Dataset4dstem. + + Parameters + ---------- + datasets + Sequence of Dataset4dstem instances. + + Returns + ------- + MAPED + New MAPED instance. + """ + if not isinstance(datasets, Sequence) or isinstance(datasets, (str, bytes)): + raise TypeError("MAPED.from_datasets expects a sequence of Torch tensor instances.") + ds_list: list[torch.Tensor] = [] + for d in datasets: + if not isinstance(d, torch.Tensor): + raise TypeError( + "MAPED.from_datasets expects a sequence of Torch tensor instances." + ) + ds_list.append(d) + + dtypes = np.array([dataset.dtype for dataset in datasets]) + devices = np.array([dataset.device for dataset in datasets]) + + # check that all datasets have the same dtype and device + if not np.all(dtypes == dtypes[0]): + raise TypeError("All datasets need to have the same type") + if not np.all(devices == devices[0]): + raise TypeError("All datasets need to have the same device") + + if not ds_list: + raise ValueError( + "MAPED.from_datasets expects a non-empty sequence of Torch tensor instances." + ) + return cls(datasets=ds_list, _token=cls._token, device=devices[0], dtype=dtypes[0]) + + def preprocess( + self, + plot_summary: bool = True, + scale: float | Sequence[float] | None = None, + **plot_kwargs: Any, + ) -> MAPED: + """ + Compute dataset summary images. + + Stores + ------ + self.scales : torch.tensor + Per-dataset scaling factors (n,). + self.dp_mean : list[torch.tensor] + Mean diffraction patterns (H, W), one per dataset. + self.im_bf : list[torch.tensor] + Mean bright-field images (R, C), one per dataset. + """ + n = len(self.datasets) + + if scale is None: + self.scales = torch.ones(n, dtype=self.dtype, device=self.device) + elif isinstance(scale, (int, float, np.floating)): + self.scales = torch.full(n, float(scale), dtype=float) else: - raise ValueError("pad_val must be a float or one of {'min','max','mean','median'}") - else: - pad_val_f = float(pad_val) + self.scales = torch.tensor(scale, dtype=self.dtype, device=self.device) + if self.scales.dim != (n,): + raise ValueError( + "scale must be a scalar or a sequence with the same length as datasets." + ) + if torch.any(self.scales == 0): + raise ValueError("scale entries must be nonzero.") - if padding is None: - max_shift = float(np.max(np.abs(shifts_rc))) if shifts_rc.size else 0.0 - padding = int(np.ceil(max_shift + float(edge_blend))) + 2 - padding = int(padding) + self.dp_mean: list[torch.Tensor] = [] + self.im_bf: list[torch.Tensor] = [] - alpha_r = min(1.0, 2.0 * float(edge_blend) / float(H)) if edge_blend > 0 else 0.0 - alpha_c = min(1.0, 2.0 * float(edge_blend) / float(W)) if edge_blend > 0 else 0.0 - w = tukey(H, alpha=alpha_r)[:, None] * tukey(W, alpha=alpha_c)[None, :] - w = w.astype(float, copy=False) + for d in self.datasets: + dp_arr = torch.mean(d, dim=(0, 1)) - Hp = H + 2 * padding - Wp = W + 2 * padding + im_bf_arr = torch.mean(d, dim=(2, 3)) - stack_w = np.zeros((len(images), Hp, Wp), dtype=float) - stack = np.zeros_like(stack_w) + self.dp_mean.append(dp_arr) + self.im_bf.append(im_bf_arr) - r0 = padding - c0 = padding - stack_w[:, r0 : r0 + H, c0 : c0 + W] = w[None, :, :] - for ind, im in enumerate(images): - stack[ind, r0 : r0 + H, c0 : c0 + W] = im * w + if plot_summary: + tiles = [[(self.im_bf[i] / self.scales[i]), self.dp_mean[i]] for i in range(n)] + titles = [ + [f"{i} - Mean Bright Field", f"{i} - Mean Diffraction Pattern"] for i in range(n) + ] + show_2d(tiles, title=titles, **plot_kwargs) - method = str(shift_method).strip().lower() - if method not in {"bilinear", "fourier"}: - raise ValueError("shift_method must be 'bilinear' or 'fourier'") + return self - if method == "fourier": - kr = np.fft.fftfreq(Hp)[:, None] - kc = np.fft.fftfreq(Wp)[None, :] - for ind in range(len(images)): - dr, dc = shifts_rc[ind, 0], shifts_rc[ind, 1] - ramp = np.exp(-2j * np.pi * (kr * dr + kc * dc)) + def diffraction_origin( + self, + origins=None, + sigma=None, + plot_origins: bool = True, + plot_indices=None, + **plot_kwargs: Any, + ) -> MAPED: + """ + Choose or automatically find the origin in diffraction space. - F = np.fft.fft2(stack[ind]) - stack[ind] = np.fft.ifft2(F * ramp).real + Parameters + ---------- + origins + Optional manual origins. Can be: + - a single (row, col) tuple, applied to all datasets + - a list of (row, col) tuples of length n (one per dataset) + sigma + Optional low-pass smoothing sigma (pixels) applied to each mean DP prior to peak finding. + plot_origins + If True, plot mean diffraction patterns with overlaid origin markers. + plot_indices + Optional indices to plot. If None, plots all datasets. + **plot_kwargs + Passed to show_2d. - Fw = np.fft.fft2(stack_w[ind]) - stack_w[ind] = np.fft.ifft2(Fw * ramp).real - stack_w[ind] = np.clip(stack_w[ind], 0.0, 1.0) - else: - for ind in range(len(images)): - stack[ind] = ndi_shift( - stack[ind], - shift=(shifts_rc[ind, 0], shifts_rc[ind, 1]), - order=1, - mode="constant", - cval=0.0, - prefilter=False, - ) - stack_w[ind] = ndi_shift( - stack_w[ind], - shift=(shifts_rc[ind, 0], shifts_rc[ind, 1]), - order=1, - mode="constant", - cval=0.0, - prefilter=False, + Stores + ------ + self.diffraction_origins : np.ndarray + Array of shape (n, 2) with integer (row, col) origins. + """ + n = len(self.datasets) + if not hasattr(self, "dp_mean"): + raise RuntimeError("Run preprocess() first so self.dp_mean exists.") + + if plot_indices is None: + plot_indices_list = list(range(n)) + else: + plot_indices_list = list(plot_indices) + for i in plot_indices_list: + if i < 0 or i >= n: + raise IndexError("plot_indices contains an out-of-range index.") + + if sigma is not None and float(sigma) > 0: + gaussian_filter_torch = torchvision.transforms.GaussianBlur( + kernel_size=[2 * int(2 * float(sigma)) + 1, 2 * int(2 * float(sigma)) + 1], + sigma=[sigma, sigma], ) - stack_w[ind] = np.clip(stack_w[ind], 0.0, 1.0) - edge_w = np.clip(1.0 - np.sum(stack_w, axis=0), 0.0, 1.0) + dp_means_use = gaussian_filter_torch(torch.stack(self.dp_mean)) + else: + dp_means_use = torch.stack(self.dp_mean) - num = np.sum(stack, axis=0) + edge_w * pad_val_f - den = np.sum(stack_w, axis=0) + edge_w + if origins is None: + origins_arr = torch.zeros((n, 2), dtype=torch.int) + for i in range(n): + dp_use = dp_means_use[i] - out = np.empty_like(num) - np.divide(num, den, out=out, where=den != 0.0) - out[den == 0.0] = 0.0 + r, c = torch.unravel_index(torch.argmax(dp_use), dp_use.shape) + origins_arr[i, 0] = int(r) + origins_arr[i, 1] = int(c) + else: + if isinstance(origins, tuple) and len(origins) == 2: + origins_arr = torch.tile( + torch.tensor(origins, dtype=torch.int, device=self.device)[None, :], (n, 1) + ) + else: + origins_list = list(origins) + if len(origins_list) != n: + raise ValueError( + "origins must be a single (row,col) tuple or a list of length n." + ) + origins_arr = torch.tensor(origins_list, dtype=torch.int, device=self.device) + if origins_arr.shape != (n, 2): + raise ValueError("origins must have shape (n, 2) after conversion.") + + self.diffraction_origins = origins_arr + + if plot_origins: + arrays = [np.asarray(self.dp_mean[i].cpu()) for i in plot_indices_list] + titles = [f"{i} - Mean Diffraction Pattern" for i in plot_indices_list] + fig, ax = show_2d(arrays, title=titles, returnfig=True, **plot_kwargs) + axs = np.ravel(np.asarray(ax, dtype=object)) + for j, i in enumerate(plot_indices_list): + r, c = self.diffraction_origins[i].cpu().numpy() + axs[j].plot([c], [r], marker="+", color="red", markersize=16, markeredgewidth=2) + + return self + + def diffraction_align( + self, + edge_blend: float = 16.0, + padding=None, + pad_val: str | float = "min", + upsample_factor: int = 100, + weight_scale: float = 1 / 8, + plot_aligned: bool = True, + **plot_kwargs: Any, + ) -> MAPED: + """ + Align mean diffraction patterns using weighted cross-correlation in Fourier space. + + Parameters + ---------- + edge_blend + Tukey window edge taper (pixels). + padding + Passed to shift_images for plotting. + pad_val + Passed to shift_images for plotting. + upsample_factor + Subpixel upsampling factor for correlation peak estimation. + weight_scale + Radial weight falloff scale (fraction of mean DP size). + plot_aligned + If True, plot aligned mean diffraction patterns. + **plot_kwargs + Passed to show_2d when plotting. + + Stores + ------ + self.diffraction_shifts : np.ndarray + Array of shape (n, 2) with (row, col) shifts to align diffraction patterns. + """ + if not hasattr(self, "dp_mean"): + raise RuntimeError("Run preprocess() first so self.dp_mean exists.") + if not hasattr(self, "diffraction_origins"): + raise RuntimeError( + "Run diffraction_origin() first so self.diffraction_origins exists." + ) + + H, W = self.dp_mean[0].shape + + w = ( + tukey_torch( + H, + alpha=2.0 * float(edge_blend) / float(H), + device=self.device, + dtype=torch.float32, + )[:, None] + * tukey_torch( + W, + alpha=2.0 * float(edge_blend) / float(W), + device=self.device, + dtype=torch.float32, + )[None, :] + ) + + r = torch.fft.fftfreq(H, 1.0 / float(H))[:, None] + c = torch.fft.fftfreq(W, 1.0 / float(W))[None, :] + + n = len(self.dp_mean) + self.diffraction_shifts = torch.zeros((n, 2), device=self.device, dtype=torch.float32) + + G_ref = torch.fft.fft2(w * self.dp_mean[0]) + xy0 = self.diffraction_origins[0] + + for ind in range(1, n): + G = torch.fft.fft2(w * self.dp_mean[ind]) + xy = self.diffraction_origins[ind] + + dr2 = (r - xy0[0] + xy[0]) ** 2 + (c - xy0[1] + xy[1]) ** 2 + im_weight = torch.clip( + 1.0 + - torch.sqrt(dr2) + / float(torch.mean(torch.tensor([H, W], device=self.device, dtype=torch.float32))) + / float(weight_scale), + 0.0, + 1.0, + ) + im_weight = torch.sin(im_weight * torch.pi / 2.0) ** 2 + shift_rc = cross_correlation_shift_torch( # not torchified yet + im_ref=G_ref, + im=G, + # weight_real=im_weight * 0.0 + 1.0, + upsample_factor=int(upsample_factor), + fft_input=True, + ) + kr = torch.fft.fftfreq(H, device=self.device)[:, None] + kc = torch.fft.fftfreq(W, device=self.device)[None, :] + + phase_ramp = torch.exp(-2j * torch.pi * (kr * shift_rc[0] + kc * shift_rc[1])) + + G_shift = G * phase_ramp + self.diffraction_shifts[ind, :] = torch.tensor( + shift_rc, device=self.device, dtype=torch.float32 + ) + + G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) + + self.diffraction_shifts -= torch.mean(self.diffraction_shifts, axis=0)[None, :] + if plot_aligned: + im_aligned = shift_images_torch( + images=torch.stack(self.dp_mean), + shifts_rc=self.diffraction_shifts, + edge_blend=float(edge_blend), + padding=padding, + pad_val=pad_val, + ) + show_2d(im_aligned.unbind(0), **plot_kwargs) + + return self + + def real_space_align( + self, + num_images=None, + num_iter: int = 3, + edge_blend: float = 1.0, + padding=None, + pad_val: str | float = "median", + upsample_factor: int = 100, + max_shift=None, + shift_method: str = "bilinear", + edge_filter: bool = True, + edge_sigma: float = 2.0, + hanning_filter: bool = False, + plot_aligned: bool = True, + **plot_kwargs: Any, + ) -> MAPED: + """ + Align real-space mean BF images using iterative average-reference correlation. + + Parameters + ---------- + num_images + If provided, align only the first num_images images. + num_iter + Number of refinement iterations. + edge_blend + Used to set default correlation padding when max_shift is None. + padding + Passed to shift_images for plotting. + pad_val + Passed to shift_images for plotting. + upsample_factor + Subpixel upsampling factor for correlation peak estimation. + max_shift + Optional maximum shift constraint passed to weighted_cross_correlation_shift. + shift_method + Passed to shift_images for plotting ('bilinear' or 'fourier'). + edge_filter + If True, correlate on gradient magnitude instead of raw intensity. + edge_sigma + Gaussian sigma applied to gradients when edge_filter is True. + hanning_filter + If True, apply a Hanning window prior to FFT. + plot_aligned + If True, plot aligned mean BF images. + **plot_kwargs + Passed to show_2d when plotting. + + Stores + ------ + self.real_space_shifts : np.ndarray + Array of shape (n_total, 2) with (row, col) shifts for aligned datasets. + """ + if not hasattr(self, "im_bf"): + raise RuntimeError("Run preprocess() first so self.im_bf exists.") + if len(self.im_bf) == 0: + raise RuntimeError("No images found in self.im_bf.") + + H, W = self.im_bf[0].shape + for im in self.im_bf: + if im.shape != (H, W): + raise ValueError("all self.im_bf images must have the same shape") + + n_total = len(self.im_bf) + if num_images is None: + n = n_total + else: + n = int(num_images) + if n <= 0: + raise ValueError("num_images must be positive") + n = min(n, n_total) + + if int(num_iter) < 1: + raise ValueError("num_iter must be >= 1") + + if max_shift is not None: + pad_cc = int(np.ceil(float(max_shift))) + 4 + else: + pad_cc = int(np.ceil(float(edge_blend))) + 4 + + Hp = H + 2 * pad_cc + Wp = W + 2 * pad_cc + r0 = pad_cc + c0 = pad_cc + + w_h = torch.ones((H, W), dtype=torch.float32, device=self.device) + if hanning_filter: + w_h = ( + torch.hann_window(H, dtype=torch.float32, device=self.device)[:, None] + * torch.hanning(W, dtype=torch.float32, device=self.device)[None, :] + ) + w_h_pad = torch.zeros((Hp, Wp), dtype=torch.float32, device=self.device) + w_h_pad[r0 : r0 + H, c0 : c0 + W] = w_h + w_h_sum = torch.sum(w_h_pad) + if w_h_sum <= 0: + raise RuntimeError("hanning window sum is zero") + + if edge_filter: + wx = torch.tensor( + [[-1.0, -2.0, -1.0], [0.0, 0.0, 0.0], [1.0, 2.0, 1.0]], + dtype=torch.float32, + device=self.device, + ) + else: + wx = None + + base_pad = torch.zeros((n, Hp, Wp), dtype=torch.float32, device=self.device) + for i in range(n): + im0 = self.im_bf[i] + + if edge_filter: + pad_symmetric = wx.shape[-1] // 2 + im0_pad = F.pad( + im0.unsqueeze(0).unsqueeze(0), + pad=(pad_symmetric, pad_symmetric, pad_symmetric, pad_symmetric), + mode="reflect", + ) + + gx = F.conv2d(im0_pad, wx.unsqueeze(0).unsqueeze(0))[0, 0] + gy = F.conv2d(im0_pad, wx.T.unsqueeze(0).unsqueeze(0))[0, 0] + + gaussian_filt = torchvision.transforms.GaussianBlur( + kernel_size=[ + 2 * int(2 * float(edge_sigma)) + 1, + 2 * int(2 * float(edge_sigma)) + 1, + ], + sigma=[edge_sigma, edge_sigma], + ) + gx = gaussian_filt(gx.unsqueeze(0)) + gy = gaussian_filt(gy.unsqueeze(0)) + im_use = torch.sqrt(gx * gx + gy * gy) + else: + im_use = im0 + + base_pad[i, r0 : r0 + H, c0 : c0 + W] = im_use + + shifts = torch.zeros((n, 2), dtype=torch.float32, device=self.device) + + for _ in range(int(num_iter)): + G_list = torch.empty((n, Hp, Wp), dtype=torch.complex128) + + # shift images to current guess + ims_a = shift_images_torch(base_pad, shifts) + ims_mean = torch.sum(ims_a * w_h_pad, dim=(1, 2)) / w_h_sum + + ims_win = (ims_a - ims_mean[:, None, None]) * w_h_pad[None] + G_list = torch.fft.fft2(ims_win) + + G_ref = torch.mean(G_list, axis=0) + + # perform cross correlation again + for i in range(1, n): + drc = cross_correlation_shift_torch( + im_ref=G_ref, + im=G_list[i], + # weight_real=None, + upsample_factor=int(upsample_factor), + # max_shift=max_shift, + fft_input=True, + # fft_output=False, + # return_shifted_image=False, + ) + + shifts[i, 0] += float(drc[0]) + shifts[i, 1] += float(drc[1]) + + shifts -= shifts[0][None, :].clone() + + shifts -= torch.mean(shifts, dim=0)[None, :] + + self.real_space_shifts = torch.zeros((n_total, 2), dtype=torch.float32, device=self.device) + self.real_space_shifts[:n, :] = shifts + + if plot_aligned: + im_aligned = shift_images_torch( + images=torch.stack(self.im_bf[:n]), + shifts_rc=self.real_space_shifts[:n, :], + edge_blend=float(edge_blend), + padding=padding, + pad_val=pad_val, + mode=shift_method, + blend=False, + ) + show_2d(im_aligned.sum(0), **plot_kwargs) + + return self + + def merge_datasets( + self, + real_space_padding=0, + real_space_edge_blend=1.0, + diffraction_padding=0, + diffraction_edge_blend=0.0, + diffraction_pad_val="min", + shift_method: str = "bilinear", + dtype=None, + scale_output: bool = False, + plot_result: bool = True, + batch_size: int = None, + **plot_kwargs: Any, + ) -> Dataset4dstem: + """ + Merge aligned datasets into a single Dataset4dstem. + + Requires + -------- + self.real_space_shifts + From real_space_align(). + self.diffraction_shifts + From diffraction_align(). + + Parameters + ---------- + real_space_padding + Output scan padding in pixels (adds border to scan grid). + real_space_edge_blend + Tukey taper width for scan-space interpolation weights. + diffraction_padding + Output diffraction padding in pixels (adds border around DPs). + diffraction_edge_blend + Tukey taper width for diffraction-space weights. + diffraction_pad_val + Pad value for diffraction padding ('min','max','mean','median' or float). + shift_method + Diffraction shift method: 'bilinear' or 'fourier'. + dtype + Output dtype. If None, uses parent dtype. + scale_output + If True and dtype is integer, scale to full dynamic range using global max. + plot_result + If True, plot merged BF and merged mean DP. + batch_size + Number of rows to process per batch. If None, uses adaptive sizing (1-32 rows). + **plot_kwargs + Passed to show_2d. + + Returns + ------- + Dataset4dstem + Merged dataset. + """ + if not hasattr(self, "real_space_shifts"): + raise RuntimeError("Run real_space_align() first so self.real_space_shifts exists.") + if not hasattr(self, "diffraction_shifts"): + raise RuntimeError("Run diffraction_align() first so self.diffraction_shifts exists.") + + arrays = self.datasets + n = len(arrays) + if n == 0: + raise RuntimeError("No datasets found in self.datasets.") + + Rs, Cs, H, W = arrays[0].shape + for a in arrays: + if a.shape != (Rs, Cs, H, W): + raise ValueError("All dataset arrays must have the same shape (Rs, Cs, H, W).") + + rs_shifts = self.real_space_shifts + dp_shifts = self.diffraction_shifts + if rs_shifts.shape != (n, 2): + raise ValueError("self.real_space_shifts must have shape (n, 2).") + if dp_shifts.shape != (n, 2): + raise ValueError("self.diffraction_shifts must have shape (n, 2).") + + if dtype is None: + dtype_out = arrays[0].dtype + warnings.warn(f"dtype=None; using parent dtype {dtype_out}.", RuntimeWarning) + else: + dtype_out = torch.dtype(dtype) + + real_space_padding = int(real_space_padding) + diffraction_padding = int(diffraction_padding) + + Rout = Rs + 2 * real_space_padding + Cout = Cs + 2 * real_space_padding + + Hp = H + 2 * diffraction_padding + Wp = W + 2 * diffraction_padding + rp0 = diffraction_padding + cp0 = diffraction_padding + + method = str(shift_method).strip().lower() + if method not in {"bilinear", "fourier"}: + raise ValueError("shift_method must be 'bilinear' or 'fourier'.") + + if real_space_edge_blend and float(real_space_edge_blend) > 0: + alpha_r = min(1.0, 2.0 * float(real_space_edge_blend) / float(Rs)) + alpha_c = min(1.0, 2.0 * float(real_space_edge_blend) / float(Cs)) + w_rs = ( + tukey_torch(Rs, alpha=alpha_r, device=self.device, dtype=torch.float32)[:, None] + * tukey_torch(Cs, alpha=alpha_c, device=self.device, dtype=torch.float32)[None, :] + ) + else: + w_rs = torch.ones((Rs, Cs), dtype=torch.float32, device=self.device) + + if diffraction_edge_blend and float(diffraction_edge_blend) > 0: + alpha_dr = min(1.0, 2.0 * float(diffraction_edge_blend) / float(H)) + alpha_dc = min(1.0, 2.0 * float(diffraction_edge_blend) / float(W)) + w_dp = ( + tukey_torch(H, alpha=alpha_dr, device=self.device, dtype=torch.float32)[:, None] + * tukey_torch(W, alpha=alpha_dc, device=self.device, dtype=torch.float32)[None, :] + ) + else: + w_dp = torch.ones((H, W), dtype=torch.float32, device=self.device) + + dp_means = [torch.mean(a, axis=(0, 1)) for a in arrays] + v = torch.stack(dp_means, axis=0).reshape(-1) + + if isinstance(diffraction_pad_val, str): + s = diffraction_pad_val.strip().lower() + if s == "min": + pad_val_dp = float(torch.min(v)) + elif s == "max": + pad_val_dp = float(torch.max(v)) + elif s == "mean": + pad_val_dp = float(torch.mean(v)) + elif s == "median": + pad_val_dp = float(torch.median(v)) + else: + raise ValueError( + "diffraction_pad_val must be a float or one of {'min','max','mean','median'}." + ) + else: + pad_val_dp = float(diffraction_pad_val) + + wdp_pad = torch.zeros((Hp, Wp), dtype=torch.float32, device=self.device) + wdp_pad[rp0 : rp0 + H, cp0 : cp0 + W] = w_dp + + wdp_shifted = torch.zeros((n, Hp, Wp), dtype=torch.float32, device=self.device) + if method == "fourier": + kr = torch.fft.fftfreq(Hp, device=self.device)[:, None] + kc = torch.fft.fftfreq(Wp, device=self.device)[None, :] + Fw = torch.fft.fft2(wdp_pad) + ramps: list[torch.Tensor] = [] + for i in range(n): + dr, dc = dp_shifts[i, 0], dp_shifts[i, 1] + + ramp = torch.exp(-2j * torch.pi * (kr * dr + kc * dc)) + ramps.append(ramp) + w_i = torch.fft.ifft2(Fw * ramp).real + wdp_shifted[i] = torch.clip(w_i, 0.0, 1.0) + else: + for i in range(n): + w_i = shift_images_torch( + wdp_pad, + shifts_rc=dp_shifts[i, :], + mode="bilinear", + ) + wdp_shifted[i] = w_i + wdp_shifted = torch.clip(w_i, 0.0, 1.0) + + coverage = torch.clip(torch.sum(wdp_shifted, dim=0), 0.0, 1.0) + edge_w_dp = 1.0 - coverage + + # Determine batch size based on available memory + if batch_size is None: + batch_size = max(1, min(32, Rout // 2)) # Adaptive batch size (1-32 rows) + + c_out = torch.arange(Cout, dtype=torch.float32, device=self.device) + c_base = c_out - real_space_padding # (Cout,) + + merged = torch.zeros((Rout, Cout, Hp, Wp), dtype=torch.float64, device=self.device) + + for batch_start in tqdm( + range(0, Rout, batch_size), + desc="Merging (batches)", + total=(Rout + batch_size - 1) // batch_size, + ): + batch_end = min(batch_start + batch_size, Rout) + batch_rows = torch.arange( + batch_start, batch_end, dtype=torch.float32, device=self.device + ) + + num_batch = torch.zeros( + (batch_end - batch_start, Cout, Hp, Wp), dtype=torch.float32, device=self.device + ) + den_batch = torch.zeros( + (batch_end - batch_start, Cout, Hp, Wp), dtype=torch.float32, device=self.device + ) + + r_base_batch = batch_rows.unsqueeze(1) - real_space_padding # (batch_size, 1) + c_base_batch = c_base.unsqueeze(0) # (1, Cout) + + for i in range(n): + a = arrays[i] + if isinstance(a, torch.Tensor): + a = a.float() + else: + a = torch.tensor(a, dtype=torch.float32, device=self.device) + + r_in = r_base_batch.expand(-1, Cout) - rs_shifts[i, 0] # (batch_size, Cout) + c_in = ( + c_base_batch.expand(batch_end - batch_start, -1) - rs_shifts[i, 1] + ) # (batch_size, Cout) + + c_norm = 2.0 * c_in / (Cs - 1) - 1.0 # (batch_size, Cout) + r_norm = 2.0 * r_in / (Rs - 1) - 1.0 # (batch_size, Cout) + + a_reshaped = ( + a.view(Rs, Cs, H * W).permute(2, 0, 1).unsqueeze(0) + ) # (1, H*W, Rs, Cs) + + # Reshape w_rs from (Rs, Cs) to (1, 1, Rs, Cs) + w_rs_reshaped = w_rs.unsqueeze(0).unsqueeze(0) # (1, 1, Rs, Cs) + + dp_interp_list = [] + wi_list = [] + + # Loop through batches, vectorize columns per batch + for b in range(batch_end - batch_start): + grid_batch = torch.stack( + [c_norm[b : b + 1, :], r_norm[b : b + 1, :]], dim=-1 + ).unsqueeze(2) # (1, Cout, 1, 2) + + dp_sample = torch.nn.functional.grid_sample( + a_reshaped, + grid_batch, + mode="bilinear", + padding_mode="zeros", + align_corners=True, + ) + + wi_sample = torch.nn.functional.grid_sample( + w_rs_reshaped, + grid_batch, + mode="bilinear", + padding_mode="zeros", + align_corners=True, + ) + + # Reshape to (Cout, H, W) and (Cout,) + dp_b = ( + dp_sample.squeeze(0).squeeze(-1).view(H, W, Cout).permute(2, 0, 1) + ) # (Cout, H, W) + wi_b = wi_sample.squeeze(0).squeeze(-1).squeeze(0) # (Cout,) + + dp_interp_list.append(dp_b) + wi_list.append(wi_b) + + dp_interp = torch.stack(dp_interp_list) # (batch_size, Cout, H, W) + wi = torch.stack(wi_list) # (batch_size, Cout) + + # Pad to diffraction canvas: (batch_size, Cout, Hp, Wp) + dp_padded = torch.zeros( + (batch_end - batch_start, Cout, Hp, Wp), + dtype=torch.float32, + device=self.device, + ) + dp_padded[:, :, rp0 : rp0 + H, cp0 : cp0 + W] = ( + dp_interp * w_dp.unsqueeze(0).unsqueeze(0) + ).float() + + # apply to DPs + if method == "fourier": + ramp = ramps[i] + fft_result = torch.fft.fft2(dp_padded) # (batch_size, Cout, Hp, Wp) + ramp_exp = ramp.unsqueeze(0).unsqueeze(0) # (1, 1, Hp, Wp) + dp_shifted = torch.fft.ifft2( + fft_result * ramp_exp + ).real # (batch_size, Cout, Hp, Wp) + else: + dp_shifted = torch.zeros_like(dp_padded) + for batch_idx in range(batch_end - batch_start): + for co in range(Cout): + dp_shifted[batch_idx, co] = shift_images_torch( + dp_padded[batch_idx, co].unsqueeze(0), + shifts_rc=dp_shifts[i, :].unsqueeze(0), + mode="bilinear", + ).squeeze(0) + + wi_exp = wi.unsqueeze(-1).unsqueeze(-1) # (batch_size, Cout, 1, 1) + wdp_i = wdp_shifted[i].unsqueeze(0).unsqueeze(0) # (1, 1, Hp, Wp) + + num_batch += wi_exp * dp_shifted + den_batch += wi_exp * wdp_i + + # clear memory + del a, a_reshaped, w_rs_reshaped, dp_padded, dp_shifted + torch.cuda.empty_cache() if torch.cuda.is_available() else None + + # Final division for this batch + num_final = num_batch + edge_w_dp.unsqueeze(0).unsqueeze(0) * pad_val_dp + den_final = den_batch + edge_w_dp.unsqueeze(0).unsqueeze(0) + + merged[batch_start:batch_end] = torch.where( + den_final != 0.0, + (num_final / den_final).to(torch.float64), + torch.zeros_like(num_final).to(torch.float64), + ) + + del num_batch, den_batch, num_final, den_final # clear memory + torch.cuda.empty_cache() if torch.cuda.is_available() else None + + self.im_bf_merged = torch.mean(merged, dim=(2, 3)) + self.dp_mean_merged = torch.mean(merged, dim=(0, 1)) + + self.im_bf_merged = torch.mean(merged, dim=(2, 3)) + self.dp_mean_merged = torch.mean(merged, dim=(0, 1)) + + try: + info = torch.iinfo(dtype_out) + is_int_dtype = True + except TypeError: + is_int_dtype = False + + if is_int_dtype: + dmin = float(info.min) + dmax = float(info.max) + + merged_f = merged + + if scale_output: + peak = torch.max(merged_f).item() + if peak <= 0.0: + merged_scaled = merged_f + else: + merged_scaled = merged_f * (dmax / peak) + + # unsigned in PyTorch is typically uint8 + lo, hi = (0.0, dmax) if dtype_out == torch.uint8 else (dmin, dmax) + merged_out = torch.rint(torch.clamp(merged_scaled, lo, hi)).to(dtype=dtype_out) + else: + below = torch.min(merged_f).item() + above = torch.max(merged_f).item() + if below < dmin or above > dmax: + warnings.warn( + f"Output overflow for dtype {dtype_out}: data range [{below}, {above}] exceeds " + f"[{dmin}, {dmax}]. Values will be clipped.", + RuntimeWarning, + ) + merged_out = torch.rint(torch.clamp(merged_f, dmin, dmax)).to(dtype=dtype_out) + else: + merged_out = merged.to(dtype=dtype_out) + + dataset_merged = Dataset4dstem.from_array(array=merged_out.cpu().numpy()) + dataset_merged.im_bf_merged = self.im_bf_merged + dataset_merged.dp_mean_merged = self.dp_mean_merged + + if plot_result: + show_2d( + [[self.im_bf_merged, self.dp_mean_merged]], + title=[["Merged Bright Field", "Merged Mean Diffraction Pattern"]], + **plot_kwargs, + ) + + return dataset_merged + + +def shift_images( + images, + shifts_rc, + edge_blend: float = 8.0, + padding=None, + pad_val: str | float = 0.0, + shift_method: str = "bilinear", +): + """ + Shift and blend a stack of 2D images into a common padded canvas. + + Parameters + ---------- + images + Sequence of (H, W) arrays. + shifts_rc + Array-like of shape (n, 2) with (row, col) shifts for each image. + edge_blend + Tukey taper width in pixels for image blending. + padding + Output padding. If None, set from max shift and edge_blend. + pad_val + Fill value outside support ('min','max','mean','median' or float). + shift_method + 'bilinear' or 'fourier'. + + Returns + ------- + np.ndarray + Blended image of shape (H + 2*padding, W + 2*padding). + """ + images = [np.asarray(im, dtype=float) for im in images] + if len(images) == 0: + raise ValueError("images must be non-empty") + + H, W = images[0].shape + for im in images: + if im.shape != (H, W): + raise ValueError("all images must have the same shape") + + shifts_rc = np.asarray(shifts_rc, dtype=float) + if shifts_rc.shape != (len(images), 2): + raise ValueError("shifts_rc must have shape (len(images), 2)") + + if isinstance(pad_val, str): + s = pad_val.strip().lower() + v = np.stack(images, axis=0).reshape(-1) + if s == "min": + pad_val_f = float(np.min(v)) + elif s == "max": + pad_val_f = float(np.max(v)) + elif s == "mean": + pad_val_f = float(np.mean(v)) + elif s == "median": + pad_val_f = float(np.median(v)) + else: + raise ValueError("pad_val must be a float or one of {'min','max','mean','median'}") + else: + pad_val_f = float(pad_val) + + if padding is None: + max_shift = float(np.max(np.abs(shifts_rc))) if shifts_rc.size else 0.0 + padding = int(np.ceil(max_shift + float(edge_blend))) + 2 + padding = int(padding) + + alpha_r = min(1.0, 2.0 * float(edge_blend) / float(H)) if edge_blend > 0 else 0.0 + alpha_c = min(1.0, 2.0 * float(edge_blend) / float(W)) if edge_blend > 0 else 0.0 + w = tukey(H, alpha=alpha_r)[:, None] * tukey(W, alpha=alpha_c)[None, :] + w = w.astype(float, copy=False) + + Hp = H + 2 * padding + Wp = W + 2 * padding + + stack_w = np.zeros((len(images), Hp, Wp), dtype=float) + stack = np.zeros_like(stack_w) + + r0 = padding + c0 = padding + stack_w[:, r0 : r0 + H, c0 : c0 + W] = w[None, :, :] + for ind, im in enumerate(images): + stack[ind, r0 : r0 + H, c0 : c0 + W] = im * w + + method = str(shift_method).strip().lower() + if method not in {"bilinear", "fourier"}: + raise ValueError("shift_method must be 'bilinear' or 'fourier'") + + if method == "fourier": + kr = np.fft.fftfreq(Hp)[:, None] + kc = np.fft.fftfreq(Wp)[None, :] + for ind in range(len(images)): + dr, dc = shifts_rc[ind, 0], shifts_rc[ind, 1] + ramp = np.exp(-2j * np.pi * (kr * dr + kc * dc)) + + F = np.fft.fft2(stack[ind]) + stack[ind] = np.fft.ifft2(F * ramp).real + + Fw = np.fft.fft2(stack_w[ind]) + stack_w[ind] = np.fft.ifft2(Fw * ramp).real + stack_w[ind] = np.clip(stack_w[ind], 0.0, 1.0) + else: + for ind in range(len(images)): + stack[ind] = ndi_shift( + stack[ind], + shift=(shifts_rc[ind, 0], shifts_rc[ind, 1]), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + stack_w[ind] = ndi_shift( + stack_w[ind], + shift=(shifts_rc[ind, 0], shifts_rc[ind, 1]), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + stack_w[ind] = np.clip(stack_w[ind], 0.0, 1.0) + + edge_w = np.clip(1.0 - np.sum(stack_w, axis=0), 0.0, 1.0) + + num = np.sum(stack, axis=0) + edge_w * pad_val_f + den = np.sum(stack_w, axis=0) + edge_w + + out = np.empty_like(num) + np.divide(num, den, out=out, where=den != 0.0) + out[den == 0.0] = 0.0 + + return out + + +def tukey_torch(N, alpha=0.5, device=None, dtype=torch.float32): + """ + Creates a 1D Tukey window of length N and shape parameter alpha. + + Parameters + ---------- + N + int, Length of the window. + alpha + float, Shape parameter for the Tukey window. + device + torch.device, Device on which to create the window. + dtype + torch.dtype, Data type of the window. + + Returns + ------- + torch.Tensor + 1D Tukey window of length N. + """ + n = torch.arange(N, device=device, dtype=dtype) + w = torch.ones(N, device=device, dtype=dtype) + + if alpha <= 0: + return w + if alpha >= 1: + return torch.hann_window(N, device=device, dtype=dtype) + + edge = alpha * (N - 1) / 2 + + left = n < edge + right = n >= (N - 1 - edge) + + w[left] = 0.5 * (1 + torch.cos(torch.pi * (2 * n[left] / (alpha * (N - 1)) - 1))) + + w[right] = 0.5 * (1 + torch.cos(torch.pi * (2 * n[right] / (alpha * (N - 1)) - 2 / alpha + 1))) + + return w + + +def shift_images_torch( + images, + shifts_rc, + mode="bilinear", + blend: bool = False, + edge_blend: float = 8.0, + padding=None, + pad_val: str | float = 0.0, +): + """ + Shift (and optionally blend) a stack of 2D images by per-image (dr, dc) pixel shifts using grid_sample. + + Parameters + ---------- + images : torch.Tensor, shape (n, H, W) or (H, W) + Stack of images (or a single image). + shifts_rc : torch.Tensor, shape (n, 2) or (2,) + Per-image shifts as (row_shift, col_shift) in pixels. + mode : 'bilinear' or 'nearest' + blend : bool, whether to blend the shifted images using a Tukey window + edge_blend : float, Tukey edge width in pixels used when blending + padding : int or None, canvas padding. If None, computed from max shift + edge_blend + pad_val : float or one of 'min','max','mean','median', fill value outside support + + Returns + ------- + torch.Tensor — shifted (and blended) images; if input was a single image, returns (Hp, Wp), + otherwise returns (n, Hp, Wp) for blended result or (n, H, W) for non-blended. + """ + single = images.dim() == 2 + if single: + images = images.unsqueeze(0) + shifts_rc = shifts_rc.unsqueeze(0) + + n, H, W = images.shape + + shifts_rc = shifts_rc.to(dtype=torch.float32, device=images.device) + + if not blend: + # simple shift per-image without padding/blending — keep original behavior + imgs = images.unsqueeze(1) + grid_y, grid_x = torch.meshgrid( + torch.linspace(-1, 1, H, device=images.device), + torch.linspace(-1, 1, W, device=images.device), + indexing="ij", + ) + base_grid = torch.stack([grid_x, grid_y], dim=-1) # (H, W, 2) + grid = base_grid.unsqueeze(0).expand(n, -1, -1, -1).clone() # (n, H, W, 2) + grid[..., 0] -= 2.0 * shifts_rc[:, 1].view(n, 1, 1) / W # col shift → x + grid[..., 1] -= 2.0 * shifts_rc[:, 0].view(n, 1, 1) / H # row shift → y + + shifted = F.grid_sample(imgs, grid, mode=mode, padding_mode="zeros", align_corners=True) + result = shifted[:, 0] # (n, H, W) + return result[0] if single else result + + # --- blending path --- + # determine pad_val numeric + if isinstance(pad_val, str): + s = pad_val.strip().lower() + v = images.reshape(-1) + if s == "min": + pad_val_f = float(torch.min(v).item()) + elif s == "max": + pad_val_f = float(torch.max(v).item()) + elif s == "mean": + pad_val_f = float(torch.mean(v).item()) + elif s == "median": + pad_val_f = float(torch.median(v).item()) + else: + raise ValueError("pad_val must be a float or one of {'min','max','mean','median'}") + else: + pad_val_f = float(pad_val) + + # padding (compute from max shift if not provided) + max_shift = float(torch.max(torch.abs(shifts_rc)).item()) if shifts_rc.numel() else 0.0 + if padding is None: + padding = int(math.ceil(max_shift + float(edge_blend))) + 2 + padding = int(padding) + + alpha_r = min(1.0, 2.0 * float(edge_blend) / float(H)) if edge_blend > 0 else 0.0 + alpha_c = min(1.0, 2.0 * float(edge_blend) / float(W)) if edge_blend > 0 else 0.0 + + w = ( + tukey_torch(H, alpha=alpha_r, device=images.device, dtype=torch.float32)[:, None] + * tukey_torch(W, alpha=alpha_c, device=images.device, dtype=torch.float32)[None, :] + ) + + Hp = H + 2 * padding + Wp = W + 2 * padding + r0 = padding + c0 = padding + + # build padded stacks + stack = torch.zeros((n, Hp, Wp), dtype=torch.float32, device=images.device) + stack_w = torch.zeros_like(stack) + for ind in range(n): + stack[ind, r0 : r0 + H, c0 : c0 + W] = images[ind].to(dtype=torch.float32) * w + stack_w[ind, r0 : r0 + H, c0 : c0 + W] = w + + # shift both stack and stack_w using grid_sample on (n,1,Hp,Wp) + imgs = stack.unsqueeze(1) + imgs_w = stack_w.unsqueeze(1) + + # Build base normalized grid for Hp, Wp + grid_y, grid_x = torch.meshgrid( + torch.linspace(-1, 1, Hp, device=images.device), + torch.linspace(-1, 1, Wp, device=images.device), + indexing="ij", + ) + base_grid = torch.stack([grid_x, grid_y], dim=-1) # (Hp, Wp, 2) + grid = base_grid.unsqueeze(0).expand(n, -1, -1, -1).clone() # (n, Hp, Wp, 2) + grid[..., 0] -= 2.0 * shifts_rc[:, 1].view(n, 1, 1) / Wp # col shift → x + grid[..., 1] -= 2.0 * shifts_rc[:, 0].view(n, 1, 1) / Hp # row shift → y + + shifted = F.grid_sample(imgs, grid, mode=mode, padding_mode="zeros", align_corners=True) + shifted_w = F.grid_sample(imgs_w, grid, mode=mode, padding_mode="zeros", align_corners=True) + + shifted = shifted[:, 0] + shifted_w = shifted_w[:, 0] + + shifted_w = torch.clamp(shifted_w, 0.0, 1.0) + + edge_w = torch.clamp(1.0 - torch.sum(shifted_w, dim=0), 0.0, 1.0) + + num = torch.sum(shifted, dim=0) + edge_w * pad_val_f + den = torch.sum(shifted_w, dim=0) + edge_w + + out = torch.empty_like(num) + mask = den != 0.0 + out[mask] = num[mask] / den[mask] + out[~mask] = 0.0 return out From 991c9cb3af1d2aeb99f31ad6a196c465935d1b97 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Thu, 2 Apr 2026 15:28:54 -0700 Subject: [PATCH 002/140] Cleaned up comments, getting ready for PR --- src/quantem/diffraction/maped.py | 41 +++++++++++++++----------------- 1 file changed, 19 insertions(+), 22 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index ad639d07..3f2d34af 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1350,6 +1350,7 @@ def merge_datasets( Dataset4dstem Merged dataset. """ + if not hasattr(self, "real_space_shifts"): raise RuntimeError("Run real_space_align() first so self.real_space_shifts exists.") if not hasattr(self, "diffraction_shifts"): @@ -1393,6 +1394,7 @@ def merge_datasets( if method not in {"bilinear", "fourier"}: raise ValueError("shift_method must be 'bilinear' or 'fourier'.") + # set up real space edge blending weights if real_space_edge_blend and float(real_space_edge_blend) > 0: alpha_r = min(1.0, 2.0 * float(real_space_edge_blend) / float(Rs)) alpha_c = min(1.0, 2.0 * float(real_space_edge_blend) / float(Cs)) @@ -1403,6 +1405,7 @@ def merge_datasets( else: w_rs = torch.ones((Rs, Cs), dtype=torch.float32, device=self.device) + # set up diffraction space edge blending weights if diffraction_edge_blend and float(diffraction_edge_blend) > 0: alpha_dr = min(1.0, 2.0 * float(diffraction_edge_blend) / float(H)) alpha_dc = min(1.0, 2.0 * float(diffraction_edge_blend) / float(W)) @@ -1413,8 +1416,7 @@ def merge_datasets( else: w_dp = torch.ones((H, W), dtype=torch.float32, device=self.device) - dp_means = [torch.mean(a, axis=(0, 1)) for a in arrays] - v = torch.stack(dp_means, axis=0).reshape(-1) + v = torch.stack(self.dp_mean, axis=0).reshape(-1) if isinstance(diffraction_pad_val, str): s = diffraction_pad_val.strip().lower() @@ -1462,15 +1464,17 @@ def merge_datasets( coverage = torch.clip(torch.sum(wdp_shifted, dim=0), 0.0, 1.0) edge_w_dp = 1.0 - coverage - # Determine batch size based on available memory + # Determine batch size (somewhat arbitrary) if batch_size is None: - batch_size = max(1, min(32, Rout // 2)) # Adaptive batch size (1-32 rows) + batch_size = max(1, min(32, Rout // 2)) c_out = torch.arange(Cout, dtype=torch.float32, device=self.device) - c_base = c_out - real_space_padding # (Cout,) + c_base = c_out - real_space_padding merged = torch.zeros((Rout, Cout, Hp, Wp), dtype=torch.float64, device=self.device) + # start batching + for batch_start in tqdm( range(0, Rout, batch_size), desc="Merging (batches)", @@ -1538,19 +1542,15 @@ def merge_datasets( align_corners=True, ) - # Reshape to (Cout, H, W) and (Cout,) - dp_b = ( - dp_sample.squeeze(0).squeeze(-1).view(H, W, Cout).permute(2, 0, 1) - ) # (Cout, H, W) - wi_b = wi_sample.squeeze(0).squeeze(-1).squeeze(0) # (Cout,) + dp_b = dp_sample.squeeze(0).squeeze(-1).view(H, W, Cout).permute(2, 0, 1) + wi_b = wi_sample.squeeze(0).squeeze(-1).squeeze(0) dp_interp_list.append(dp_b) wi_list.append(wi_b) - dp_interp = torch.stack(dp_interp_list) # (batch_size, Cout, H, W) - wi = torch.stack(wi_list) # (batch_size, Cout) + dp_interp = torch.stack(dp_interp_list) + wi = torch.stack(wi_list) - # Pad to diffraction canvas: (batch_size, Cout, Hp, Wp) dp_padded = torch.zeros( (batch_end - batch_start, Cout, Hp, Wp), dtype=torch.float32, @@ -1560,14 +1560,11 @@ def merge_datasets( dp_interp * w_dp.unsqueeze(0).unsqueeze(0) ).float() - # apply to DPs if method == "fourier": ramp = ramps[i] - fft_result = torch.fft.fft2(dp_padded) # (batch_size, Cout, Hp, Wp) - ramp_exp = ramp.unsqueeze(0).unsqueeze(0) # (1, 1, Hp, Wp) - dp_shifted = torch.fft.ifft2( - fft_result * ramp_exp - ).real # (batch_size, Cout, Hp, Wp) + fft_result = torch.fft.fft2(dp_padded) + ramp_exp = ramp.unsqueeze(0).unsqueeze(0) + dp_shifted = torch.fft.ifft2(fft_result * ramp_exp).real else: dp_shifted = torch.zeros_like(dp_padded) for batch_idx in range(batch_end - batch_start): @@ -1578,8 +1575,8 @@ def merge_datasets( mode="bilinear", ).squeeze(0) - wi_exp = wi.unsqueeze(-1).unsqueeze(-1) # (batch_size, Cout, 1, 1) - wdp_i = wdp_shifted[i].unsqueeze(0).unsqueeze(0) # (1, 1, Hp, Wp) + wi_exp = wi.unsqueeze(-1).unsqueeze(-1) + wdp_i = wdp_shifted[i].unsqueeze(0).unsqueeze(0) num_batch += wi_exp * dp_shifted den_batch += wi_exp * wdp_i @@ -1607,6 +1604,7 @@ def merge_datasets( self.im_bf_merged = torch.mean(merged, dim=(2, 3)) self.dp_mean_merged = torch.mean(merged, dim=(0, 1)) + # dtype scaling and clipping try: info = torch.iinfo(dtype_out) is_int_dtype = True @@ -1626,7 +1624,6 @@ def merge_datasets( else: merged_scaled = merged_f * (dmax / peak) - # unsigned in PyTorch is typically uint8 lo, hi = (0.0, dmax) if dtype_out == torch.uint8 else (dmin, dmax) merged_out = torch.rint(torch.clamp(merged_scaled, lo, hi)).to(dtype=dtype_out) else: From 788cf5533a04452928bf07d5d769a0e8e3571014 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Thu, 2 Apr 2026 15:32:18 -0700 Subject: [PATCH 003/140] Removed some imports that we don't need --- src/quantem/diffraction/maped.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 3f2d34af..8f411148 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1,6 +1,5 @@ from __future__ import annotations -import math import warnings from typing import Any, Sequence @@ -1900,7 +1899,7 @@ def shift_images_torch( # padding (compute from max shift if not provided) max_shift = float(torch.max(torch.abs(shifts_rc)).item()) if shifts_rc.numel() else 0.0 if padding is None: - padding = int(math.ceil(max_shift + float(edge_blend))) + 2 + padding = int(np.ceil(max_shift + float(edge_blend))) + 2 padding = int(padding) alpha_r = min(1.0, 2.0 * float(edge_blend) / float(H)) if edge_blend > 0 else 0.0 From a551193758a21e09b297ed40fec49b4bc0f7fc67 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Thu, 2 Apr 2026 15:45:50 -0700 Subject: [PATCH 004/140] Fixed hann_filter line in real_space_align --- src/quantem/diffraction/maped.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 8f411148..05bb0bef 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1197,7 +1197,7 @@ def real_space_align( if hanning_filter: w_h = ( torch.hann_window(H, dtype=torch.float32, device=self.device)[:, None] - * torch.hanning(W, dtype=torch.float32, device=self.device)[None, :] + * torch.hann_window(W, dtype=torch.float32, device=self.device)[None, :] ) w_h_pad = torch.zeros((Hp, Wp), dtype=torch.float32, device=self.device) w_h_pad[r0 : r0 + H, c0 : c0 + W] = w_h From df581dc27bb765a1de8180facbd6e3548606ccdc Mon Sep 17 00:00:00 2001 From: henrygbell Date: Tue, 7 Apr 2026 16:56:15 -0700 Subject: [PATCH 005/140] Added descan alignment using cross correlation --- src/quantem/diffraction/maped.py | 199 ++++++++++++++++++++++++++++++- 1 file changed, 197 insertions(+), 2 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 05bb0bef..e09c012c 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -994,6 +994,30 @@ def diffraction_origin( return self + def dscan_align( + self, + iterations, + upsample_factor: int = 100, + plot_aligned: bool = True, + edge_blend: float = 2.0, + fit_shifts=True, + mode="linear", + ): + for i, dataset in enumerate(self.datasets): + _, aligned_dataset, _ = dscan_correct( + dataset, + iterations, + upsample_factor=upsample_factor, + plot_aligned=plot_aligned, + edge_blend=edge_blend, + device=self.device, + fit_shifts=fit_shifts, + mode=mode, + ) + self.datasets[i] = aligned_dataset + + return self + def diffraction_align( self, edge_blend: float = 16.0, @@ -1062,6 +1086,9 @@ def diffraction_align( G_ref = torch.fft.fft2(w * self.dp_mean[0]) xy0 = self.diffraction_origins[0] + kr = torch.fft.fftfreq(H, device=self.device)[:, None] + kc = torch.fft.fftfreq(W, device=self.device)[None, :] + for ind in range(1, n): G = torch.fft.fft2(w * self.dp_mean[ind]) xy = self.diffraction_origins[ind] @@ -1083,8 +1110,6 @@ def diffraction_align( upsample_factor=int(upsample_factor), fft_input=True, ) - kr = torch.fft.fftfreq(H, device=self.device)[:, None] - kc = torch.fft.fftfreq(W, device=self.device)[None, :] phase_ramp = torch.exp(-2j * torch.pi * (kr * shift_rc[0] + kc * shift_rc[1])) @@ -1956,3 +1981,173 @@ def shift_images_torch( out[~mask] = 0.0 return out + + +def fit_surface_lstsq(img, mode="linear"): + """ + Fits an image with a linear or quadratic function + + Parameters + ---------- + img : torch.Tensor + Image to fit, of shape (H, W) + mode : str + Fitting mode, either "linear" or "quadratic" + + Returns + ------ + fitted : torch.Tensor + Array of shape (H, W) of the fit function over the image + coeffs : torch.Tensor + fitting coefficients + """ + H, W = img.shape + x_1d = torch.arange(img.shape[1], device=img.device, dtype=torch.float32) + y_1d = torch.arange(img.shape[0], device=img.device, dtype=torch.float32) + + xx, yy = torch.meshgrid(x_1d, y_1d) + + x = xx.flatten() + y = yy.flatten() + z = img.flatten() + + if mode == "linear": + A = torch.stack([x, y, torch.ones_like(x)], dim=1) + elif mode == "quadratic": + A = torch.stack([x**2, y**2, x * y, x, y, torch.ones_like(x)], dim=1) + + coeffs, _, _, _ = torch.linalg.lstsq(A, z.unsqueeze(1)) + + fitted = (A @ coeffs).reshape(H, W) + return fitted, coeffs + + +def dscan_correct( + dataset, + iterations, + upsample_factor: int = 100, + plot_aligned: bool = True, + edge_blend: float = 2.0, + device="cpu", + fit_shifts=True, + mode="linear", +): + """ + Align diffraction patterns using cross-correlation. + + Parameters + ---------- + dataset : torch.Tensor + Input 4D dataset + iterations : int + Number of refinement iterations + upsample_factor : int + Upsampling factor for sub-pixel accuracy + plot_aligned : bool + Whether to plot results after each iteration + edge_blend : float + Edge blending parameter for Tukey window + device : torch.device + Device to use + fit_shifts : bool + Whether to fit shifts to a smooth surface + mode : str + "linear" or "quadratic" for surface fitting + """ + H_rs, W_rs, H_dp, W_dp = dataset.shape + + w = ( + tukey_torch( + H_dp, + alpha=2.0 * float(edge_blend) / float(H_dp), + device=device, + dtype=torch.float32, + )[:, None] + * tukey_torch( + W_dp, + alpha=2.0 * float(edge_blend) / float(W_dp), + device=device, + dtype=torch.float32, + )[None, :] + ) + + diffraction_shifts = torch.zeros((H_rs, W_rs, 2), device=device, dtype=torch.float32) + shifted_dps = dataset.clone() + + kr = torch.fft.fftfreq(H_dp, device=device)[:, None] + kc = torch.fft.fftfreq(W_dp, device=device)[None, :] + + for iteration in range(iterations): + G_ref = torch.fft.fft2(shifted_dps.mean(dim=(0, 1)) * w) + + for h_rs in tqdm(range(H_rs), desc=f"Iteration {iteration + 1}/{iterations}"): + for w_rs in range(W_rs): + ind = w_rs + h_rs * H_rs + dp = shifted_dps[h_rs, w_rs] # <-- Read from current shifted_dps, not original + G = torch.fft.fft2(w * dp) + shift = cross_correlation_shift_torch( + G_ref, G, upsample_factor=upsample_factor, fft_input=True + ) + diffraction_shifts[h_rs, w_rs] = shift + + phase_ramp = torch.exp(-1j * torch.pi * (kr * shift[0] + kc * shift[1])) + G_shift = G * phase_ramp + + shifted_dps[h_rs, w_rs, :, :] = torch.fft.ifft2(G_shift).real + G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) + + G_ref_final = G_ref.clone() + + if fit_shifts: + diffraction_shifts_1, _ = fit_surface_lstsq(diffraction_shifts[:, :, 0], mode=mode) + diffraction_shifts_2, _ = fit_surface_lstsq(diffraction_shifts[:, :, 1], mode=mode) + diffraction_shifts_old = diffraction_shifts.clone() + diffraction_shifts = torch.stack((diffraction_shifts_1, diffraction_shifts_2), dim=2) + + # Recompute fitted shifts + for h_rs in tqdm(range(H_rs), desc="Applying fitted shifts"): + for w_rs in range(W_rs): + dp = shifted_dps[h_rs, w_rs] # <-- Also read from shifted_dps here + G = torch.fft.fft2(w * dp) + shift = diffraction_shifts[h_rs, w_rs] + + phase_ramp = torch.exp(-1j * torch.pi * (kr * shift[0] + kc * shift[1])) + G_shift = G * phase_ramp + + shifted_dps[h_rs, w_rs, :, :] = torch.fft.ifft2(G_shift).real + + if plot_aligned: + if fit_shifts: + show_2d( + [ + [ + diffraction_shifts_old[:, :, 0], + diffraction_shifts[:, :, 0], + diffraction_shifts[:, :, 0] - diffraction_shifts_old[:, :, 0], + ], + [ + diffraction_shifts_old[:, :, 1], + diffraction_shifts[:, :, 1], + diffraction_shifts[:, :, 1] - diffraction_shifts_old[:, :, 1], + ], + ], + title=[ + ["Shifts x", "Fit x", "Residual x"], + ["Shifts y", "Fit y", "Residual y"], + ], + cmap="RdBu_r", + vmax=3, + vmin=-3, + ) + + dp_mean_before = dataset.mean(dim=(0, 1)) + dp_mean = shifted_dps.mean(dim=(0, 1)) + dp_max = torch.max( + torch.max(shifted_dps, dim=0, keepdim=False).values, dim=0, keepdim=False + ).values + show_2d( + [dp_mean_before, dp_mean, dp_max], + vmax=0.75, + ) + + return diffraction_shifts, shifted_dps, G_ref_final From 21486792a6786c77694177ab1682418240f44370 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Wed, 15 Apr 2026 17:06:44 +0000 Subject: [PATCH 006/140] Added k-planes model --- src/quantem/core/ml/kplanes.py | 248 +++++++++++++++++++++++++++++++++ 1 file changed, 248 insertions(+) create mode 100644 src/quantem/core/ml/kplanes.py diff --git a/src/quantem/core/ml/kplanes.py b/src/quantem/core/ml/kplanes.py new file mode 100644 index 00000000..52a4adfc --- /dev/null +++ b/src/quantem/core/ml/kplanes.py @@ -0,0 +1,248 @@ +""" +Tensor Decomposition Methods for INR-based reconstructions +""" + +from typing import Any, Callable, Optional, Sequence + +import tinycudann as tcnn +import torch +import torch.nn.functional as F +from torch import nn + +""" +K-planes utility functions +""" +def grid_sample_wrapper(grid: torch.Tensor, coords: torch.Tensor, align_corners: bool = True) -> torch.Tensor: + """ + Performs bilinear interpolation on a grid at given coordinates. + + Args: + grid: Grid tensor of shape [B, C, H, W] or [C, H, W] + coords: Coordinate tensor of shape [B, N, 2] or [N, 2] + align_corners: Whether to align corners + + Returns: + Interpolated values of shape [B, N, C] or [N, C] + """ + grid_dim = coords.shape[-1] + + if grid.dim() == grid_dim + 1: + # no batch dimension present, need to add it + grid = grid.unsqueeze(0) + if coords.dim() == 2: + coords = coords.unsqueeze(0) + + if grid_dim == 2 or grid_dim == 3: + grid_sampler = F.grid_sample + else: + raise NotImplementedError(f"Grid-sample was called with {grid_dim}D data but is only " + f"implemented for 2 and 3D data.") + + coords = coords.view([coords.shape[0]] + [1] * (grid_dim - 1) + list(coords.shape[1:])) + B, feature_dim = grid.shape[:2] + n = coords.shape[-2] + interp = grid_sampler( + grid, # [B, feature_dim, reso, ...] + coords, # [B, 1, ..., n, grid_dim] + align_corners=align_corners, + mode='bilinear', padding_mode='border') + interp = interp.view(B, feature_dim, n).transpose(-1, -2) # [B, n, feature_dim] + interp = interp.squeeze() # [B?, n, feature_dim?] + return interp + +def init_planes( + in_dim: int, + out_dim: int, + resolution: Sequence[int], + init_range: tuple = (0.1, 0.5), +) -> nn.ParameterList: + """Create the set of 2D planes for a k-plane decomposition. + + For in_dim=3 (spatial), this creates 3 planes: XY, XZ, YZ. + For in_dim=4 (spatial + time), this creates 6 planes: XY, XZ, XT, YZ, YT, ZT. + Time planes (those involving axis 3) are initialized to 1 so they start + as identity multipliers. + + Args: + in_dim: Dimensionality of the input coordinates (3 or 4). + out_dim: Number of feature channels per plane. + resolution: Resolution along each axis, e.g. [128, 128, 128]. + init_range: (a, b) for uniform initialization of spatial planes. + + Returns: + nn.ParameterList of plane parameters, each of shape [1, out_dim, res_j, res_i]. + """ + assert len(resolution) == in_dim + # All pairs of axes + axis_pairs = list(itertools.combinations(range(in_dim), 2)) + planes = nn.ParameterList() + a, b = init_range + for pair in axis_pairs: + # grid_sample expects (N, C, H, W) — so resolution is reversed + shape = [1, out_dim] + [resolution[ax] for ax in reversed(pair)] + param = nn.Parameter(torch.empty(*shape)) + # Time planes init to 1; spatial planes init uniform + if in_dim == 4 and 3 in pair: + nn.init.ones_(param) + else: + nn.init.uniform_(param, a=a, b=b) + planes.append(param) + return planes + +def query_planes( + pts: torch.Tensor, + planes: nn.ParameterList, + in_dim: int, +) -> float: + """Query the k-plane representation at a batch of points. + + Projects each point onto every axis-pair plane, bilinearly interpolates, + and returns the element-wise product across all planes. + + Args: + pts: (B, in_dim) coordinates in [-1, 1]. + planes: The ParameterList from init_planes. + in_dim: 3 or 4. + + Returns: + (B, out_dim) features. + """ + axis_pairs = list(itertools.combinations(range(in_dim), 2)) + result = 1.0 + for plane_param, pair in zip(planes, axis_pairs): + # Extract the 2D coords for this plane + coords_2d = pts[..., list(pair)] # (B, 2) + coords_2d = coords_2d.view(1, -1, 1, 2) # (1, B, 1, 2) for grid_sample + # grid_sample: input (N,C,H,W), grid (N, H_out, W_out, 2) + sampled = F.grid_sample( + plane_param, # (1, C, H, W) + coords_2d, # (1, B, 1, 2) + align_corners=True, + mode="bilinear", + padding_mode="border", + ) # -> (1, C, B, 1) + sampled = sampled.squeeze(0).squeeze(-1).T # (B, C) + result = result * sampled + return result # pyright: ignore[reportReturnType] + + +def interpolate_ms_features( + pts: torch.Tensor, + ms_grids: nn.ModuleList, +) -> torch.Tensor: + coo_combs = list(itertools.combinations(range(3), 2)) # [(0,1), (0,2), (1,2)] + multi_scale_interp = [] + + for grid in ms_grids: + interp_space = 1. + for ci, coo_comb in enumerate(coo_combs): + feature_dim = grid[ci].shape[1] + interp_out_plane = ( + grid_sample_wrapper(grid[ci], pts[..., coo_comb]) + .view(-1, feature_dim) + ) + interp_space = interp_space * interp_out_plane + multi_scale_interp.append(interp_space) + + return torch.cat(multi_scale_interp, dim=-1) + + +""" +K-planes Model +""" +class KPlanes(nn.Module): + + def __init__( + self, + # Grid parameters + grid_dimensions: int = 2, + input_coords_dims: int = 3, + M_features: int = 32, + resolution: Sequence[int] = (200, 200, 200), + multiscale_res_multipliers: Optional[Sequence[int]] = None, + concat_features: bool = True, + density_activation: Callable = lambda x: F.softplus(x - 1), + ): + """ + Assume coords are [-1, 1] in each dimension. + """ + super().__init__() + + self.grid_dimensions = grid_dimensions + self.input_coords_dims = input_coords_dims + self.M_features = M_features + self.resolution = resolution + self.multiscale_res_multipliers = multiscale_res_multipliers or [1] + self.concat_features = concat_features + self.density_activation = density_activation + + + # Initialize planes + self.grids = nn.ParameterList() + self.feature_dim = 0 + + # Resolution pyramid + for res_mult in self.multiscale_res_multipliers: + scaled_res = [r * res_mult for r in self.resolution] + gp = init_planes( + in_dim=self.input_coords_dims, + out_dim=self.M_features, + resolution=scaled_res, + ) + + self.feature_dim += gp[-1].shape[1] + self.grids.append(gp) + + + # Linear net + self.sigma_net = tcnn.Network( + n_input_dims=self.feature_dim, + n_output_dims=1, + network_config={ + "otype": "CutlassMLP", + "activation": "None", + "output_activation": "None", + "n_neurons": 128, + "n_hidden_layers": 0, + }, + ) + + + + def get_densities(self, coords: torch.Tensor): + """Computes and returns densities""" + + pts = coords.reshape(-1, 3) + features = interpolate_ms_features( + pts=pts, + ms_grids=self.grids, + ) + density_before_activation = self.sigma_net(features) + density = self.density_activation(density_before_activation) + return density + + def forward( + self, + pts: torch.Tensor, + ): + return self.get_densities(pts) + + def get_params(self) -> dict[str, list[torch.nn.Parameter]]: + return { + "grids": [p for grid in self.grids for p in grid], # flatten ParameterLists + "sigma_net": list(self.sigma_net.parameters()), + } + + + def set_optimizer(self, optimizer_params: dict[str, Any]): + + self._grids.set_optimizer(optimizer_params["grids"]) + self._sigmanet.set_optimizer(optimizer_params["sigmanet"]) + + + + def get_params(self) -> dict[str, list[torch.nn.Parameter]]: + return { + "grids": self._grids.params # flatten ParameterLists + "sigma_net": self._sigma_net.params + } \ No newline at end of file From c299ca8bd03d18ffaa7ac56b4c1ac998c6130bab Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Wed, 15 Apr 2026 18:20:42 +0000 Subject: [PATCH 007/140] Added PPLR stuff --- src/quantem/core/ml/{ => models}/kplanes.py | 11 ++++++++++- src/quantem/core/ml/models/model_base.py | 13 +++++++++++++ 2 files changed, 23 insertions(+), 1 deletion(-) rename src/quantem/core/ml/{ => models}/kplanes.py (97%) create mode 100644 src/quantem/core/ml/models/model_base.py diff --git a/src/quantem/core/ml/kplanes.py b/src/quantem/core/ml/models/kplanes.py similarity index 97% rename from src/quantem/core/ml/kplanes.py rename to src/quantem/core/ml/models/kplanes.py index 52a4adfc..6964415c 100644 --- a/src/quantem/core/ml/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -9,6 +9,8 @@ import torch.nn.functional as F from torch import nn +from .model_base import PPLR + """ K-planes utility functions """ @@ -150,7 +152,7 @@ def interpolate_ms_features( """ K-planes Model """ -class KPlanes(nn.Module): +class KPlanes(nn.Module, PPLR): def __init__( self, @@ -226,7 +228,14 @@ def forward( pts: torch.Tensor, ): return self.get_densities(pts) + + def get_optimization_parameters(self) -> Dict[str, list[torch.nn.Parameter]]: + return [ + {"params": } + ] + + def get_params(self) -> dict[str, list[torch.nn.Parameter]]: return { "grids": [p for grid in self.grids for p in grid], # flatten ParameterLists diff --git a/src/quantem/core/ml/models/model_base.py b/src/quantem/core/ml/models/model_base.py new file mode 100644 index 00000000..b40a13f8 --- /dev/null +++ b/src/quantem/core/ml/models/model_base.py @@ -0,0 +1,13 @@ +from abc import ABC, abstractmethod +from typing import Dict + +import torch + + +class PPLR(ABC): + """ + Abstract base class for models that require multi-scale parameter optimization. + """ + @abstractmethod + def get_optimization_parameters(self) -> Dict[str, list[torch.nn.Parameter]]: + pass \ No newline at end of file From 9ac27a994c8b0d401cb5a2842bbaab6effe2c085 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Wed, 15 Apr 2026 23:54:57 +0000 Subject: [PATCH 008/140] object_models optimization setting is working well. Only thing that needs to be overloaded is set_optimizer for PPLR cases --- src/quantem/core/ml/models/kplanes.py | 26 ++--------- src/quantem/core/ml/models/model_base.py | 16 ++++++- src/quantem/tomography/object_models.py | 58 +++++++++++++++++++++++- src/quantem/tomography/tomography_opt.py | 4 +- 4 files changed, 79 insertions(+), 25 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 6964415c..954e383d 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -2,6 +2,7 @@ Tensor Decomposition Methods for INR-based reconstructions """ +import itertools from typing import Any, Callable, Optional, Sequence import tinycudann as tcnn @@ -209,8 +210,6 @@ def __init__( }, ) - - def get_densities(self, coords: torch.Tensor): """Computes and returns densities""" @@ -229,29 +228,14 @@ def forward( ): return self.get_densities(pts) - - def get_optimization_parameters(self) -> Dict[str, list[torch.nn.Parameter]]: - return [ - {"params": } - ] - - def get_params(self) -> dict[str, list[torch.nn.Parameter]]: return { "grids": [p for grid in self.grids for p in grid], # flatten ParameterLists "sigma_net": list(self.sigma_net.parameters()), } - - def set_optimizer(self, optimizer_params: dict[str, Any]): - - self._grids.set_optimizer(optimizer_params["grids"]) - self._sigmanet.set_optimizer(optimizer_params["sigmanet"]) - + @property + def param_keys(self) -> list[str]: + return ["grids", "sigma_net"] - - def get_params(self) -> dict[str, list[torch.nn.Parameter]]: - return { - "grids": self._grids.params # flatten ParameterLists - "sigma_net": self._sigma_net.params - } \ No newline at end of file + \ No newline at end of file diff --git a/src/quantem/core/ml/models/model_base.py b/src/quantem/core/ml/models/model_base.py index b40a13f8..42bee71c 100644 --- a/src/quantem/core/ml/models/model_base.py +++ b/src/quantem/core/ml/models/model_base.py @@ -9,5 +9,19 @@ class PPLR(ABC): Abstract base class for models that require multi-scale parameter optimization. """ @abstractmethod - def get_optimization_parameters(self) -> Dict[str, list[torch.nn.Parameter]]: + def get_params(self) -> Dict[str, list[torch.nn.Parameter]]: + """ + This abstract method should return a dictionary of parameters based on a key. + + For example if your nn.Module has multiple optimizable parameter groups, + you can return a dictionary with the keys "grids" and "sigma_net" (KPlanes example). + """ + pass + + @property + @abstractmethod + def param_keys(self) -> list[str]: + """ + This abstract property should return a list of available parameter keys. + """ pass \ No newline at end of file diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index c5eb2406..6c21c410 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -10,10 +10,12 @@ from tqdm.auto import tqdm from quantem.core.io.serialize import AutoSerialize +from quantem.core.ml import OptimizerParams from quantem.core.ml.constraints import BaseConstraints, Constraints from quantem.core.ml.ddp import DDPMixin from quantem.core.ml.loss_functions import get_loss_module -from quantem.core.ml.optimizer_mixin import OptimizerMixin +from quantem.core.ml.models.model_base import PPLR +from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset @@ -552,11 +554,65 @@ def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: # --- Optimization Parameters --- @property def params(self) -> Generator[torch.nn.Parameter, None, None]: + """ + Returns the optimization parameters, here we also check if PPLR is used and return the appropriate parameters. + """ + return self.model.parameters() # type: ignore[attr-defined] def get_optimization_parameters(self) -> list[nn.Parameter]: + + if isinstance(self.model, PPLR): + + # DEBUG + for key, value in self.optimizer_params.items(): + print(key, value) + return [ + { + "params": self.model.get_params()[key], + **self.optimizer_params[key].params(), + } + for key in self.model.param_keys + ] return list(self.params) + + # --- DDP Mixin Overloads in the case of PPLR --- + + @property + def optimizer_params(self) -> OptimizerType | dict[str, OptimizerType]: + """Get the optimizer parameters.""" + return self._optimizer_params + + @optimizer_params.setter + def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | dict[str, Any]): + """Set the optimizer parameters.""" + if isinstance(params, OptimizerType): + self._optimizer_params = params + return + if isinstance(self.model, PPLR): + if not isinstance(params, dict): + raise TypeError(f"optimizer parameters must be a dict for PPLR, got {type(params)}") + + object_params = params + + if set(object_params.keys()) != set(self.model.param_keys): + raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}") + + params = {} + for key, value in object_params.items(): + if isinstance(value, dict): + params[key] = OptimizerParams.parse_dict(d=value) + elif isinstance(value, OptimizerType): + params[key] = value + else: + raise TypeError(f"optimizer parameters must be a dict or OptimizerType, got {type(value)}") + + self._optimizer_params = params + else: + raise TypeError(f"optimizer parameters must be a dict for non-PPLR, got {type(params)}") + + # Pretraining @property def pretrained_weights(self) -> dict[str, torch.Tensor]: diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index 16c846ab..0f8c44be 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -52,8 +52,8 @@ def optimizer_params(self, d: dict[str, OptimizerType] | dict[str, dict]): if k not in targets: raise ValueError(f"Unknown optimization key: {k}") - if not isinstance(v, OptimizerType): - v = OptimizerParams.parse_dict(v) + # if not isinstance(v, OptimizerType): + # v = OptimizerParams.parse_dict(v) targets[k].optimizer_params = v From 5f40d5aaef38484e7b6769ea2de4c0d25c26cf91 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 16 Apr 2026 00:13:03 +0000 Subject: [PATCH 009/140] Optimizing, set_optimizer is just default to Adam now, probably need to do the matching in set_optimizer instead of parsing in optimizer_params maybe? --- src/quantem/tomography/object_models.py | 23 ++++++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 6c21c410..01698769 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -564,9 +564,6 @@ def get_optimization_parameters(self) -> list[nn.Parameter]: if isinstance(self.model, PPLR): - # DEBUG - for key, value in self.optimizer_params.items(): - print(key, value) return [ { "params": self.model.get_params()[key], @@ -612,6 +609,26 @@ def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | di else: raise TypeError(f"optimizer parameters must be a dict for non-PPLR, got {type(params)}") + def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: + """ + Set the optimizer for this model. + Currently supports single LR for all parameters, TODO allow for per parameter LRs by + updating get_optimization_parameters to return a list of parameters and their LRs. + """ + if not isinstance(self.model, PPLR): + super().set_optimizer(opt_params) + return + + if opt_params is not None: + self.optimizer_params = opt_params + + if not self._optimizer_params: + self._optimizer = None + return + + params = self.get_optimization_parameters() + + self._optimizer = torch.optim.Adam(params) # Pretraining @property From 7f51dc50e5233ce5437da4d2b1de25f8505a29ef Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 16 Apr 2026 01:36:54 +0000 Subject: [PATCH 010/140] KPlanes Tilted claude implementation, need to talk to Corneel. Things to check: Look at object_models.py and see how the optimizer matching should be handled. It seems like set_optimizers doesn't really do what it's supposed to do. --- src/quantem/core/ml/models/kplanes.py | 535 +++++++++++++++++++++++--- 1 file changed, 482 insertions(+), 53 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 954e383d..7e206983 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -3,7 +3,8 @@ """ import itertools -from typing import Any, Callable, Optional, Sequence +import math +from typing import Callable, Optional, Sequence import tinycudann as tcnn import torch @@ -131,28 +132,27 @@ def query_planes( def interpolate_ms_features( pts: torch.Tensor, - ms_grids: nn.ModuleList, + ms_grids: nn.ParameterList, ) -> torch.Tensor: - coo_combs = list(itertools.combinations(range(3), 2)) # [(0,1), (0,2), (1,2)] - multi_scale_interp = [] - - for grid in ms_grids: - interp_space = 1. - for ci, coo_comb in enumerate(coo_combs): - feature_dim = grid[ci].shape[1] - interp_out_plane = ( - grid_sample_wrapper(grid[ci], pts[..., coo_comb]) - .view(-1, feature_dim) - ) - interp_space = interp_space * interp_out_plane - multi_scale_interp.append(interp_space) + mat_mode = [[0, 1], [0, 2], [1, 2]] + coord_plane = torch.stack([ + pts[:, mat_mode[0]], + pts[:, mat_mode[1]], + pts[:, mat_mode[2]], + ]).view(3, -1, 1, 2) - return torch.cat(multi_scale_interp, dim=-1) + per_scale = [] + for plane_coef in ms_grids: + C = plane_coef.shape[1] + feats = F.grid_sample( + plane_coef, coord_plane, align_corners=True, mode="bilinear", padding_mode="border" + ).reshape(3, C, -1) + fused = feats[0] * feats[1] * feats[2] + per_scale.append(fused.T) + + return torch.cat(per_scale, dim=-1) -""" -K-planes Model -""" class KPlanes(nn.Module, PPLR): def __init__( @@ -165,6 +165,10 @@ def __init__( multiscale_res_multipliers: Optional[Sequence[int]] = None, concat_features: bool = True, density_activation: Callable = lambda x: F.softplus(x - 1), + # Hybrid MLP parameters + use_hybrid_mlp: bool = False, + hybrid_hidden_dim: int = 64, + hybrid_num_layers: int = 2, ): """ Assume coords are [-1, 1] in each dimension. @@ -179,40 +183,55 @@ def __init__( self.concat_features = concat_features self.density_activation = density_activation - - # Initialize planes self.grids = nn.ParameterList() self.feature_dim = 0 - - # Resolution pyramid for res_mult in self.multiscale_res_multipliers: - scaled_res = [r * res_mult for r in self.resolution] - gp = init_planes( - in_dim=self.input_coords_dims, - out_dim=self.M_features, - resolution=scaled_res, + scaled_res = [int(r * res_mult) for r in self.resolution] + plane = nn.Parameter(torch.empty(3, self.M_features, scaled_res[1], scaled_res[0])) + nn.init.uniform_(plane, 0.1, 0.5) + self.grids.append(plane) + self.feature_dim += self.M_features + + # Network head + if use_hybrid_mlp: + hybrid_hidden_dim = int(hybrid_hidden_dim) + hybrid_num_layers = int(hybrid_num_layers) + if hybrid_hidden_dim <= 0: + raise ValueError(f"hybrid_hidden_dim must be >= 1, got {hybrid_hidden_dim}") + if hybrid_num_layers <= 0: + raise ValueError(f"hybrid_num_layers must be >= 1, got {hybrid_num_layers}") + + factory = {} # add dtype/device kwargs here if needed + layers = [] + in_dim = self.feature_dim + for _ in range(hybrid_num_layers): + lin = nn.Linear(in_dim, hybrid_hidden_dim, **factory) + nn.init.kaiming_uniform_(lin.weight, a=0.0, nonlinearity="relu") + nn.init.zeros_(lin.bias) + layers.append(lin) + layers.append(nn.ReLU(inplace=True)) + in_dim = hybrid_hidden_dim + + out = nn.Linear(in_dim, 1, bias=True, **factory) + nn.init.normal_(out.weight, std=0.01) + nn.init.zeros_(out.bias) + layers.append(out) + self.sigma_net = nn.Sequential(*layers) + else: + self.sigma_net = tcnn.Network( + n_input_dims=self.feature_dim, + n_output_dims=1, + network_config={ + "otype": "CutlassMLP", + "activation": "None", + "output_activation": "None", + "n_neurons": 128, + "n_hidden_layers": 0, + }, ) - - self.feature_dim += gp[-1].shape[1] - self.grids.append(gp) - - - # Linear net - self.sigma_net = tcnn.Network( - n_input_dims=self.feature_dim, - n_output_dims=1, - network_config={ - "otype": "CutlassMLP", - "activation": "None", - "output_activation": "None", - "n_neurons": 128, - "n_hidden_layers": 0, - }, - ) def get_densities(self, coords: torch.Tensor): """Computes and returns densities""" - pts = coords.reshape(-1, 3) features = interpolate_ms_features( pts=pts, @@ -222,15 +241,12 @@ def get_densities(self, coords: torch.Tensor): density = self.density_activation(density_before_activation) return density - def forward( - self, - pts: torch.Tensor, - ): + def forward(self, pts: torch.Tensor): return self.get_densities(pts) - + def get_params(self) -> dict[str, list[torch.nn.Parameter]]: return { - "grids": [p for grid in self.grids for p in grid], # flatten ParameterLists + "grids": list(self.grids.parameters()), "sigma_net": list(self.sigma_net.parameters()), } @@ -238,4 +254,417 @@ def get_params(self) -> dict[str, list[torch.nn.Parameter]]: def param_keys(self) -> list[str]: return ["grids", "sigma_net"] - \ No newline at end of file + +# --- Tilted KPlanes --- + +# --------------------------------------------------------------------------- +# SO(3) quaternion parameter module +# --------------------------------------------------------------------------- + +class SO3Param(nn.Module): + """ + Stores T unit quaternions as learnable parameters in R^4 and normalises + them on every call to `as_matrix()`. + + Quaternion convention: [x, y, z, w] (scalar-last, same as scipy). + + Initialisation + -------------- + "random" – uniform sampling over SO(3) via Shoemake's method. + "identity" – all rotations start as the identity (good for fine-tuning). + """ + + def __init__(self, T: int, init: str = "random"): + super().__init__() + if T < 1: + raise ValueError(f"T must be >= 1, got {T}") + quats = self._init_quaternions(T, init) # (T, 4) + self.quats = nn.Parameter(quats) + + # ------------------------------------------------------------------ + # Initialisers + # ------------------------------------------------------------------ + + @staticmethod + def _shoemake_sample(T: int) -> torch.Tensor: + """Uniform SO(3) sampling via Shoemake (1992). Returns (T, 4) [x,y,z,w].""" + u = torch.rand(T, 3) + sqrt1_u0 = torch.sqrt(1.0 - u[:, 0]) + sqrt_u0 = torch.sqrt(u[:, 0]) + two_pi = 2.0 * math.pi + x = sqrt1_u0 * torch.sin(two_pi * u[:, 1]) + y = sqrt1_u0 * torch.cos(two_pi * u[:, 1]) + z = sqrt_u0 * torch.sin(two_pi * u[:, 2]) + w = sqrt_u0 * torch.cos(two_pi * u[:, 2]) + return torch.stack([x, y, z, w], dim=-1) # (T, 4) + + @staticmethod + def _identity(T: int) -> torch.Tensor: + """All-identity rotations: [0,0,0,1] * T.""" + q = torch.zeros(T, 4) + q[:, 3] = 1.0 + return q + + @classmethod + def _init_quaternions(cls, T: int, init: str) -> torch.Tensor: + if init == "random": + return cls._shoemake_sample(T) + elif init == "identity": + return cls._identity(T) + else: + raise ValueError(f"Unknown init '{init}'; choose 'random' or 'identity'.") + + # ------------------------------------------------------------------ + # Forward helpers + # ------------------------------------------------------------------ + + def normalized(self) -> torch.Tensor: + """Returns (T, 4) unit quaternions.""" + return F.normalize(self.quats, p=2, dim=-1) + + def as_matrix(self) -> torch.Tensor: + """ + Converts the T stored quaternions to (T, 3, 3) rotation matrices. + + Uses the standard formula; no trig, just multiplications. + """ + q = self.normalized() # (T, 4) [x, y, z, w] + x, y, z, w = q.unbind(dim=-1) # each (T,) + + # Precompute products + xx, yy, zz = x*x, y*y, z*z + xy, xz, yz = x*y, x*z, y*z + wx, wy, wz = w*x, w*y, w*z + + # Row-major: R[i,j] + R = torch.stack([ + 1 - 2*(yy + zz), 2*(xy - wz), 2*(xz + wy), + 2*(xy + wz), 1 - 2*(xx + zz), 2*(yz - wx), + 2*(xz - wy), 2*(yz + wx), 1 - 2*(xx + yy), + ], dim=-1).reshape(-1, 3, 3) # (T, 3, 3) + + return R + + def extra_repr(self) -> str: + return f"T={self.quats.shape[0]}" +def interpolate_ms_features_tilted( + pts: torch.Tensor, # (B, 3) + ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) + rotation_matrices: torch.Tensor, # (T, 3, 3) +) -> torch.Tensor: + """ + Multi-scale, multi-rotation K-Planes feature interpolation. + + For each of the T rotations: + 1. Rotate pts -> (B, 3) + 2. Project to XY / XZ / YZ planes -> 3 × (B, 2) grids of coords + 3. Bilinear interpolation on the corresponding planes + 4. Hadamard product across the 3 planes -> (B, C) + + Across T transforms, outputs are *concatenated* (not summed), so each + rotation owns a disjoint slice of the feature dimension. Across scales, + outputs are also concatenated, matching the base KPlanes behaviour. + + Returns + ------- + features : (B, C * T * num_scales) + """ + T = rotation_matrices.shape[0] + B = pts.shape[0] + + # Rotate all points by all T rotation matrices at once. + # pts: (B, 3), R: (T, 3, 3) -> rotated: (T, B, 3) + rotated = torch.einsum("tij,bj->tbi", rotation_matrices, pts) + + per_scale_features = [] + + for plane_coef in ms_grids: + # plane_coef shape: (3*T, C, H, W) + C = plane_coef.shape[1] + + # Build the (3*T, B, 1, 2) coordinate tensor for grid_sample. + # For transform t, we need coords for planes XY, XZ, YZ. + # grid_sample expects coords in [-1, 1] with shape (N, Hout, Wout, 2). + all_plane_coords = [] + for t in range(T): + rp = rotated[t] # (B, 3) + all_plane_coords.append(rp[:, [0, 1]]) # XY (B, 2) + all_plane_coords.append(rp[:, [2, 0]]) # ZX (B, 2) matches ki + all_plane_coords.append(rp[:, [1, 2]]) # YZ (B, 2) matches jk + + # Stack -> (3*T, B, 2) -> (3*T, B, 1, 2) for grid_sample + coord_tensor = torch.stack(all_plane_coords, dim=0).unsqueeze(2) + # coord_tensor: (3*T, B, 1, 2) + + # grid_sample: input (N, C, H, W), grid (N, Hout, Wout, 2) + sampled = F.grid_sample( + plane_coef, # (3*T, C, H, W) + coord_tensor, # (3*T, B, 1, 2) + align_corners=True, + mode="bilinear", + padding_mode="border", + ) # -> (3*T, C, B, 1) + sampled = sampled.squeeze(-1) # (3*T, C, B) + + # Hadamard product within each transform group (3 planes per transform). + transform_features = [] + for t in range(T): + p_xy = sampled[3*t + 0] # (C, B) + p_zx = sampled[3*t + 1] + p_yz = sampled[3*t + 2] + fused = (p_xy * p_zx * p_yz).T # (B, C) + transform_features.append(fused) + + # Concatenate across transforms -> (B, C*T) + per_scale_features.append(torch.cat(transform_features, dim=-1)) + + # Concatenate across scales -> (B, C*T*num_scales) + return torch.cat(per_scale_features, dim=-1) + +# --------------------------------------------------------------------------- +# KPlanesTILTED +# --------------------------------------------------------------------------- + +class KPlanesTILTED(KPlanes): + """ + K-Planes with T learned SO(3) rotations (TILTED). + + Inherits KPlanes for the sigma_net, density_activation, and get_params + interface. Overrides: + * __init__ – replaces the axis-aligned grids with (3*T)-plane grids + and adds SO3Param. + * get_densities – calls the TILTED interpolation instead. + * get_params – adds "so3" key so callers can set a separate lr. + * param_keys – updated list. + + Parameters + ---------- + M_features : int + Feature channels *per transform per scale*. Total feature_dim will + be M_features * T * len(multiscale_res_multipliers). + T : int + Number of learned rotations (TILTED-T in the paper; 4 or 8 recommended). + tau_init : str + "random" (paper default) or "identity". + tau_warmup_steps : int + If > 0, grids and sigma_net are frozen for this many steps so the + rotations can find good basins first (two-phase warm-up). + Call model.training_step() once per optimiser step. + All other args are forwarded to KPlanes. + """ + + def __init__( + self, + # Grid parameters + input_coords_dims: int = 3, + M_features: int = 32, + resolution: Sequence[int] = (200, 200, 200), + multiscale_res_multipliers: Optional[Sequence[int]] = None, + density_activation: Callable = lambda x: F.softplus(x - 1), + # TILTED parameters + T: int = 4, + tau_init: str = "random", + tau_warmup_steps: int = 0, + # Hybrid MLP parameters + use_hybrid_mlp: bool = False, + hybrid_hidden_dim: int = 64, + hybrid_num_layers: int = 2, + ): + if input_coords_dims != 3: + raise NotImplementedError("KPlanesTILTED is implemented for 3D only.") + if T < 1: + raise ValueError(f"T must be >= 1, got {T}") + + multiscale_res_multipliers = list(multiscale_res_multipliers or [1]) + num_scales = len(multiscale_res_multipliers) + + # Total feature dim seen by the MLP head. + # Each scale contributes M_features * T channels. + feature_dim = M_features * T * num_scales + + # Call KPlanes.__init__ with grid_dimensions=2 so it builds sigma_net + # correctly; we immediately replace self.grids below. + super().__init__( + grid_dimensions=2, + input_coords_dims=3, + M_features=M_features, # base class stores this + resolution=resolution, + multiscale_res_multipliers=multiscale_res_multipliers, + concat_features=True, + density_activation=density_activation, + use_hybrid_mlp=use_hybrid_mlp, + hybrid_hidden_dim=hybrid_hidden_dim, + hybrid_num_layers=hybrid_num_layers, + ) + # KPlanes.__init__ built grids with shape (3, M, H, W) and feature_dim + # = M * num_scales. We rebuild them for the TILTED shape. + + self.T = T + self.tau_warmup_steps = tau_warmup_steps + self._global_step: int = 0 + + # ---- Rebuild grids: (3*T, M_features, H, W) per scale ---- + self.grids = nn.ParameterList() + for res_mult in multiscale_res_multipliers: + scaled_res = [int(r * res_mult) for r in resolution] + plane = nn.Parameter( + torch.empty(3 * T, M_features, scaled_res[1], scaled_res[0]) + ) + nn.init.uniform_(plane, 0.1, 0.5) + self.grids.append(plane) + + # ---- Rebuild sigma_net with the correct feature_dim ---- + # KPlanes built sigma_net with self.feature_dim (= M * num_scales), + # which is wrong for T > 1. Rebuild here. + self.feature_dim = feature_dim + self._build_sigma_net(use_hybrid_mlp, hybrid_hidden_dim, hybrid_num_layers) + + # ---- Learnable rotations ---- + self.so3 = SO3Param(T, init=tau_init) + + # ------------------------------------------------------------------ + # Internal helpers + # ------------------------------------------------------------------ + + def _build_sigma_net( + self, + use_hybrid_mlp: bool, + hybrid_hidden_dim: int, + hybrid_num_layers: int, + ) -> None: + """Rebuild sigma_net for self.feature_dim (called after grids are set).""" + if use_hybrid_mlp: + layers = [] + in_dim = self.feature_dim + for _ in range(hybrid_num_layers): + lin = nn.Linear(in_dim, hybrid_hidden_dim) + nn.init.kaiming_uniform_(lin.weight, a=0.0, nonlinearity="relu") + nn.init.zeros_(lin.bias) + layers.append(lin) + layers.append(nn.ReLU(inplace=True)) + in_dim = hybrid_hidden_dim + out = nn.Linear(in_dim, 1, bias=True) + nn.init.normal_(out.weight, std=0.01) + nn.init.zeros_(out.bias) + layers.append(out) + self.sigma_net = nn.Sequential(*layers) + else: + self.sigma_net = tcnn.Network( + n_input_dims=self.feature_dim, + n_output_dims=1, + network_config={ + "otype": "CutlassMLP", + "activation": "None", + "output_activation": "None", + "n_neurons": 128, + "n_hidden_layers": 0, + }, + ) + + # ------------------------------------------------------------------ + # Warm-up bookkeeping + # ------------------------------------------------------------------ + + def training_step(self) -> None: + """ + Call once per optimiser step to advance the internal counter. + + During the first `tau_warmup_steps` iterations, grids and sigma_net + have their gradients zeroed after the backward pass so only the SO(3) + parameters update. This is the lightweight version of two-phase + optimisation from the paper. + """ + self._global_step += 1 + + def _in_warmup(self) -> bool: + return self.tau_warmup_steps > 0 and self._global_step < self.tau_warmup_steps + + def zero_non_tau_grads(self) -> None: + """ + Call after loss.backward() and before optimizer.step() when you want + to implement the rotation warm-up manually. Alternatively just check + model.in_warmup and configure your optimizer accordingly. + """ + if self._in_warmup(): + for p in self.grids.parameters(): + if p.grad is not None: + p.grad.zero_() + for p in self.sigma_net.parameters(): + if p.grad is not None: + p.grad.zero_() + + @property + def in_warmup(self) -> bool: + return self._in_warmup() + + # ------------------------------------------------------------------ + # Core forward + # ------------------------------------------------------------------ + + def get_densities(self, coords: torch.Tensor) -> torch.Tensor: + pts = coords.reshape(-1, 3) + R = self.so3.as_matrix() # (T, 3, 3) + features = interpolate_ms_features_tilted( + pts=pts, + ms_grids=self.grids, + rotation_matrices=R, + ) + density_before_activation = self.sigma_net(features) + return self.density_activation(density_before_activation) + + def forward(self, pts: torch.Tensor) -> torch.Tensor: + return self.get_densities(pts) + + # ------------------------------------------------------------------ + # Parameter groups + # ------------------------------------------------------------------ + + def get_params(self) -> dict[str, list[nn.Parameter]]: + return { + "grids": list(self.grids.parameters()), + "sigma_net": list(self.sigma_net.parameters()), + "so3": list(self.so3.parameters()), + } + + @property + def param_keys(self) -> list[str]: + return ["grids", "sigma_net", "so3"] + + + # ------------------------------------------------------------------ + # Two-phase helper: extract tau for phase-2 initialisation + # ------------------------------------------------------------------ + + def extract_tau_state(self) -> torch.Tensor: + """ + Returns the current quaternion tensor (detached copy) so it can be + used to initialise a larger phase-2 model via `load_tau_state`. + """ + return self.so3.quats.detach().clone() + + def load_tau_state(self, quats: torch.Tensor) -> None: + """ + Load pre-trained quaternions (e.g. from a bottleneck phase-1 model). + + quats : (T, 4) tensor, will be normalised internally. + """ + if quats.shape != self.so3.quats.shape: + raise ValueError( + f"Shape mismatch: got {quats.shape}, " + f"expected {self.so3.quats.shape}" + ) + with torch.no_grad(): + self.so3.quats.copy_(F.normalize(quats, p=2, dim=-1)) + + # ------------------------------------------------------------------ + # Pretty print + # ------------------------------------------------------------------ + + def extra_repr(self) -> str: + return ( + f"T={self.T}, " + f"M_features={self.M_features}, " + f"feature_dim={self.feature_dim}, " + f"num_scales={len(self.multiscale_res_multipliers)}, " + f"tau_warmup_steps={self.tau_warmup_steps}" + ) \ No newline at end of file From af140be45ee83abd52b6f6e40a2e531406d261ef Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 16 Apr 2026 07:19:22 +0000 Subject: [PATCH 011/140] Added TV loss for PPLR models, I don't like this solution though will probably have to do TV loss computation within the model? --- src/quantem/core/ml/models/kplanes.py | 1 + src/quantem/tomography/object_models.py | 59 +++++++++++++++---------- 2 files changed, 37 insertions(+), 23 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 7e206983..dbc8f982 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -574,6 +574,7 @@ def training_step(self) -> None: parameters update. This is the lightweight version of two-phase optimisation from the paper. """ + print("Global Stepped") self._global_step += 1 def _in_warmup(self) -> bool: diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 01698769..9f3ce764 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -506,29 +506,42 @@ def apply_soft_constraints( ) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=pred.device) if self.constraints.tv_vol > 0: - num_tv_samples = min(10_000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - - tv_coords = coords[tv_indices].detach().requires_grad_(True) - tv_densities_recomputed = self.model(tv_coords) - if isinstance(tv_densities_recomputed, tuple): - tv_densities_recomputed = tv_densities_recomputed[0] - - # Ensure shape is [num_samples, num_channels] - if tv_densities_recomputed.dim() == 1: - tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) - - # Compute gradients for each channel - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True, - )[0] # Shape: [num_samples, coord_dim] - - # Compute TV loss - gradient magnitude per sample - grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] - soft_loss += self.constraints.tv_vol * grad_norm.mean() + + if isinstance(self.model, PPLR): # TODO: Temporary + for plane in self.model.grids: + # plane: (3*T, C, H, W) + # Differences along H (axis -2) and W (axis -1) + diff_h = plane[..., 1:, :] - plane[..., :-1, :] # (3*T, C, H-1, W) + diff_w = plane[..., :, 1:] - plane[..., :, :-1] # (3*T, C, H, W-1) + + + soft_loss += diff_h.pow(2).mean() + diff_w.pow(2).mean() + + soft_loss /= len(self.model.grids) + else: + num_tv_samples = min(10_000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + tv_coords = coords[tv_indices].detach().requires_grad_(True) + tv_densities_recomputed = self.model(tv_coords) + if isinstance(tv_densities_recomputed, tuple): + tv_densities_recomputed = tv_densities_recomputed[0] + + # Ensure shape is [num_samples, num_channels] + if tv_densities_recomputed.dim() == 1: + tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) + + # Compute gradients for each channel + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] # Shape: [num_samples, coord_dim] + + # Compute TV loss - gradient magnitude per sample + grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] + soft_loss += self.constraints.tv_vol * grad_norm.mean() if ( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) From 73c0d30a387627e54797318ec97da2042cb4e169 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 16 Apr 2026 16:54:37 +0000 Subject: [PATCH 012/140] object_models.py now has tv_loss for both KPlanes and INR architectures. Also overloaded reconnecting optimizers --- src/quantem/core/ml/optimizer_mixin.py | 2 +- src/quantem/tomography/object_models.py | 178 +++++++++++++++--------- 2 files changed, 114 insertions(+), 66 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 4ea263c0..9fe8a32a 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -733,7 +733,7 @@ def reconnect_optimizer_to_parameters(self) -> None: if not optimizable_params: print( - f"souldn't be getting here! No optimizable parameters found for {self.__class__.__name__}, removing optimizer" + f"shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}, removing optimizer" ) self.remove_optimizer() return diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 9f3ce764..0c09a372 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -14,6 +14,7 @@ from quantem.core.ml.constraints import BaseConstraints, Constraints from quantem.core.ml.ddp import DDPMixin from quantem.core.ml.loss_functions import get_loss_module +from quantem.core.ml.models.kplanes import KPlanes, KPlanesTILTED from quantem.core.ml.models.model_base import PPLR from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType from quantem.core.utils.rng import RNGMixin @@ -499,6 +500,8 @@ def obj_view(self) -> np.ndarray: self.create_volume() return self._obj.cpu().numpy().transpose(0, 1, 3, 2) + # --- Constraints --- + def apply_soft_constraints( self, coords: torch.Tensor, @@ -506,42 +509,7 @@ def apply_soft_constraints( ) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=pred.device) if self.constraints.tv_vol > 0: - - if isinstance(self.model, PPLR): # TODO: Temporary - for plane in self.model.grids: - # plane: (3*T, C, H, W) - # Differences along H (axis -2) and W (axis -1) - diff_h = plane[..., 1:, :] - plane[..., :-1, :] # (3*T, C, H-1, W) - diff_w = plane[..., :, 1:] - plane[..., :, :-1] # (3*T, C, H, W-1) - - - soft_loss += diff_h.pow(2).mean() + diff_w.pow(2).mean() - - soft_loss /= len(self.model.grids) - else: - num_tv_samples = min(10_000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - - tv_coords = coords[tv_indices].detach().requires_grad_(True) - tv_densities_recomputed = self.model(tv_coords) - if isinstance(tv_densities_recomputed, tuple): - tv_densities_recomputed = tv_densities_recomputed[0] - - # Ensure shape is [num_samples, num_channels] - if tv_densities_recomputed.dim() == 1: - tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) - - # Compute gradients for each channel - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True, - )[0] # Shape: [num_samples, coord_dim] - - # Compute TV loss - gradient magnitude per sample - grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] - soft_loss += self.constraints.tv_vol * grad_norm.mean() + soft_loss += self.get_tv_loss(coords, pred) if ( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) @@ -552,6 +520,53 @@ def apply_soft_constraints( return soft_loss + def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: + """ + Calculate the TV loss for the given coordinates and predictions. + + Current supported architectures are KPlanes and INRs + """ + + if isinstance(self.model, (KPlanes, KPlanesTILTED)): + + per_level = [] + + for p in self.model.grids: + dh = p[:, :, 1:, :] - p[:, :, :-1, :] + dw = p[:, :, :, 1:] - p[:, :, :, :-1] + tv = 0.0 + if self.constraints.tv_vol > 0: + tv = tv + self.constraints.tv_vol * (dh.pow(2).mean() + dw.pow(2).mean()) + per_level.append(tv) + + return torch.stack(per_level).sum() + else: + num_tv_samples = min(10_000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + tv_coords = coords[tv_indices].detach().requires_grad_(True) + tv_densities_recomputed = self.model(tv_coords) + if isinstance(tv_densities_recomputed, tuple): + tv_densities_recomputed = tv_densities_recomputed[0] + + # Ensure shape is [num_samples, num_channels] + if tv_densities_recomputed.dim() == 1: + tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) + + # Compute gradients for each channel + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] # Shape: [num_samples, coord_dim] + + # Compute TV loss - gradient magnitude per sample + grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] + return self.constraints.tv_vol * grad_norm.mean() + + + def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: """ Apply hard constraints to the predicted values of the INR model. @@ -573,7 +588,7 @@ def params(self) -> Generator[torch.nn.Parameter, None, None]: return self.model.parameters() # type: ignore[attr-defined] - def get_optimization_parameters(self) -> list[nn.Parameter]: + def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: if isinstance(self.model, PPLR): @@ -622,6 +637,7 @@ def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | di else: raise TypeError(f"optimizer parameters must be a dict for non-PPLR, got {type(params)}") + def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: """ Set the optimizer for this model. @@ -643,6 +659,66 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: self._optimizer = torch.optim.Adam(params) + def reconnect_optimizer_to_parameters(self) -> None: + """ + Reconnect optimizer overload, defaults back to the standard implementation if no `PPLR` is detected. + """ + + if self.optimizer is None: + return + + if isinstance(self.model, PPLR): + current_params = self.get_optimization_parameters() + + + optimizable_params = [ + p for p in current_params + if isinstance(p['params'][0], torch.Tensor) and p['params'][0].is_leaf + ] + + + if not optimizable_params: + raise ValueError(f"Shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}.") + + for p in optimizable_params: + print(f"Setting requires_grad for parameter: {p}") + p['params'][0].requires_grad_(True) + + assert self._optimizer is not None + # Preserve optimizer states and param_group settings + old_state = self._optimizer.state.copy() + old_param_groups = self._optimizer.param_groups.copy() + + # Reconnect to new parameters + self._optimizer.param_groups.clear() + for param_group in optimizable_params: + self._optimizer.add_param_group(param_group) + + # Restore per-group hyperparameters (lr, betas, weight_decay, etc.) by index, + # excluding 'params' which comes from the new groups + for new_pg, old_pg in zip(self._optimizer.param_groups, old_param_groups): + new_pg.update({k: v for k, v in old_pg.items() if k != "params"}) + + # Remap optimizer state: for any new param that IS the same tensor as an old param, + # carry its state over (moved to the right device just in case). + new_state = {} + for new_pg in self._optimizer.param_groups: + for new_param in new_pg["params"]: + if new_param in old_state: + device = new_param.device + new_state[new_param] = { + k: (v.to(device) if isinstance(v, torch.Tensor) else v) + for k, v in old_state[new_param].items() + } + + self._optimizer.state.clear() + self._optimizer.state.update(new_state) + + if self._scheduler is not None and self._optimizer is not None: + self._scheduler.optimizer = self._optimizer + else: + super().reconnect_optimizer_to_parameters() + # Pretraining @property def pretrained_weights(self) -> dict[str, torch.Tensor]: @@ -894,34 +970,6 @@ def create_volume(self, return_vol: bool = False): self._obj = pred_full.detach().cpu() - def get_tv_loss( # pyright: ignore[reportIncompatibleMethodOverride] - self, - coords: torch.Tensor, - ) -> torch.Tensor: - tv_loss = torch.tensor(0.0, device=coords.device) - - num_tv_samples = min(10000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - - tv_coords = coords[tv_indices].detach().requires_grad_(True) - - tv_densities_recomputed = self.forward(tv_coords) - - if tv_densities_recomputed.dim() > 1: - tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) - - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True, - )[0] - - grad_norm = torch.norm(grad_outputs, dim=1) - - tv_loss += self.constraints.tv_vol * grad_norm.mean() - return tv_loss - def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change if isinstance(device, str): device = torch.device(device) From 35157fbabf702f83821d84b2133666fbb1ff6704 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Fri, 17 Apr 2026 00:30:12 +0000 Subject: [PATCH 013/140] KPlanes with R9+SVD parameterization, everything seems to be working well. Only things to ask Corneel about is multiscale res since this adds a significant amount of compute. Should I be doing variable num_samples_per_ray? --- src/quantem/core/ml/models/kplanes.py | 291 ++++++++++++------------ src/quantem/tomography/object_models.py | 31 +-- src/quantem/tomography/tomography.py | 6 +- 3 files changed, 167 insertions(+), 161 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index dbc8f982..7839f20d 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -261,164 +261,175 @@ def param_keys(self) -> list[str]: # SO(3) quaternion parameter module # --------------------------------------------------------------------------- +# class SO3Param(nn.Module): +# """ +# Stores T unit quaternions as learnable parameters in R^4 and normalises +# them on every call to `as_matrix()`. + +# Quaternion convention: [x, y, z, w] (scalar-last, same as scipy). + +# Initialisation +# -------------- +# "random" – uniform sampling over SO(3) via Shoemake's method. +# "identity" – all rotations start as the identity (good for fine-tuning). +# """ + +# def __init__(self, T: int, init: str = "random"): +# super().__init__() +# if T < 1: +# raise ValueError(f"T must be >= 1, got {T}") +# quats = self._init_quaternions(T, init) # (T, 4) +# self.quats = nn.Parameter(quats) + +# # ------------------------------------------------------------------ +# # Initialisers +# # ------------------------------------------------------------------ + +# @staticmethod +# def _shoemake_sample(T: int) -> torch.Tensor: +# """Uniform SO(3) sampling via Shoemake (1992). Returns (T, 4) [x,y,z,w].""" +# u = torch.rand(T, 3) +# sqrt1_u0 = torch.sqrt(1.0 - u[:, 0]) +# sqrt_u0 = torch.sqrt(u[:, 0]) +# two_pi = 2.0 * math.pi +# x = sqrt1_u0 * torch.sin(two_pi * u[:, 1]) +# y = sqrt1_u0 * torch.cos(two_pi * u[:, 1]) +# z = sqrt_u0 * torch.sin(two_pi * u[:, 2]) +# w = sqrt_u0 * torch.cos(two_pi * u[:, 2]) +# return torch.stack([x, y, z, w], dim=-1) # (T, 4) + +# @staticmethod +# def _identity(T: int) -> torch.Tensor: +# """All-identity rotations: [0,0,0,1] * T.""" +# q = torch.zeros(T, 4) +# q[:, 3] = 1.0 +# return q + +# @classmethod +# def _init_quaternions(cls, T: int, init: str) -> torch.Tensor: +# if init == "random": +# return cls._shoemake_sample(T) +# elif init == "identity": +# return cls._identity(T) +# else: +# raise ValueError(f"Unknown init '{init}'; choose 'random' or 'identity'.") + +# # ------------------------------------------------------------------ +# # Forward helpers +# # ------------------------------------------------------------------ + +# def normalized(self) -> torch.Tensor: +# """Returns (T, 4) unit quaternions.""" +# return F.normalize(self.quats, p=2, dim=-1) + +# def as_matrix(self) -> torch.Tensor: +# """ +# Converts the T stored quaternions to (T, 3, 3) rotation matrices. + +# Uses the standard formula; no trig, just multiplications. +# """ +# q = self.normalized() # (T, 4) [x, y, z, w] +# x, y, z, w = q.unbind(dim=-1) # each (T,) + +# # Precompute products +# xx, yy, zz = x*x, y*y, z*z +# xy, xz, yz = x*y, x*z, y*z +# wx, wy, wz = w*x, w*y, w*z + +# # Row-major: R[i,j] +# R = torch.stack([ +# 1 - 2*(yy + zz), 2*(xy - wz), 2*(xz + wy), +# 2*(xy + wz), 1 - 2*(xx + zz), 2*(yz - wx), +# 2*(xz - wy), 2*(yz + wx), 1 - 2*(xx + yy), +# ], dim=-1).reshape(-1, 3, 3) # (T, 3, 3) + +# return R + +# def extra_repr(self) -> str: +# return f"T={self.quats.shape[0]}" + + class SO3Param(nn.Module): """ - Stores T unit quaternions as learnable parameters in R^4 and normalises - them on every call to `as_matrix()`. - - Quaternion convention: [x, y, z, w] (scalar-last, same as scipy). - - Initialisation - -------------- - "random" – uniform sampling over SO(3) via Shoemake's method. - "identity" – all rotations start as the identity (good for fine-tuning). + SO(3) rotation bank using R9+SVD parameterization. + Each rotation is stored as an unconstrained 3x3 matrix M, + projected to SO(3) via SVD+(M) = U diag(1,1,det(UVt)) Vt. """ - + def __init__(self, T: int, init: str = "random"): super().__init__() - if T < 1: - raise ValueError(f"T must be >= 1, got {T}") - quats = self._init_quaternions(T, init) # (T, 4) - self.quats = nn.Parameter(quats) - - # ------------------------------------------------------------------ - # Initialisers - # ------------------------------------------------------------------ - - @staticmethod - def _shoemake_sample(T: int) -> torch.Tensor: - """Uniform SO(3) sampling via Shoemake (1992). Returns (T, 4) [x,y,z,w].""" - u = torch.rand(T, 3) - sqrt1_u0 = torch.sqrt(1.0 - u[:, 0]) - sqrt_u0 = torch.sqrt(u[:, 0]) - two_pi = 2.0 * math.pi - x = sqrt1_u0 * torch.sin(two_pi * u[:, 1]) - y = sqrt1_u0 * torch.cos(two_pi * u[:, 1]) - z = sqrt_u0 * torch.sin(two_pi * u[:, 2]) - w = sqrt_u0 * torch.cos(two_pi * u[:, 2]) - return torch.stack([x, y, z, w], dim=-1) # (T, 4) - - @staticmethod - def _identity(T: int) -> torch.Tensor: - """All-identity rotations: [0,0,0,1] * T.""" - q = torch.zeros(T, 4) - q[:, 3] = 1.0 - return q - - @classmethod - def _init_quaternions(cls, T: int, init: str) -> torch.Tensor: + print("SVD Module") if init == "random": - return cls._shoemake_sample(T) + # Initialize near identity with small noise + M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) + M = M + 0.1 * torch.randn(T, 3, 3) elif init == "identity": - return cls._identity(T) + M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) else: - raise ValueError(f"Unknown init '{init}'; choose 'random' or 'identity'.") - - # ------------------------------------------------------------------ - # Forward helpers - # ------------------------------------------------------------------ - - def normalized(self) -> torch.Tensor: - """Returns (T, 4) unit quaternions.""" - return F.normalize(self.quats, p=2, dim=-1) - + raise ValueError(f"Unknown init '{init}'") + self.M = nn.Parameter(M) # (T, 3, 3) + def as_matrix(self) -> torch.Tensor: - """ - Converts the T stored quaternions to (T, 3, 3) rotation matrices. - - Uses the standard formula; no trig, just multiplications. - """ - q = self.normalized() # (T, 4) [x, y, z, w] - x, y, z, w = q.unbind(dim=-1) # each (T,) - - # Precompute products - xx, yy, zz = x*x, y*y, z*z - xy, xz, yz = x*y, x*z, y*z - wx, wy, wz = w*x, w*y, w*z - - # Row-major: R[i,j] - R = torch.stack([ - 1 - 2*(yy + zz), 2*(xy - wz), 2*(xz + wy), - 2*(xy + wz), 1 - 2*(xx + zz), 2*(yz - wx), - 2*(xz - wy), 2*(yz + wx), 1 - 2*(xx + yy), - ], dim=-1).reshape(-1, 3, 3) # (T, 3, 3) - - return R - - def extra_repr(self) -> str: - return f"T={self.quats.shape[0]}" + """Projects each M to SO(3) via SVD. Returns (T, 3, 3).""" + U, _, Vh = torch.linalg.svd(self.M) # U: (T,3,3), Vh: (T,3,3) + # Fix reflections: det(U Vh) must be +1 + d = torch.det(U @ Vh) # (T,) + diag = torch.ones(self.M.shape[0], 3, device=self.M.device) + diag[:, 2] = d # multiply last singular vector by sign + return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) + def interpolate_ms_features_tilted( pts: torch.Tensor, # (B, 3) ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) rotation_matrices: torch.Tensor, # (T, 3, 3) ) -> torch.Tensor: """ - Multi-scale, multi-rotation K-Planes feature interpolation. - - For each of the T rotations: - 1. Rotate pts -> (B, 3) - 2. Project to XY / XZ / YZ planes -> 3 × (B, 2) grids of coords - 3. Bilinear interpolation on the corresponding planes - 4. Hadamard product across the 3 planes -> (B, C) - - Across T transforms, outputs are *concatenated* (not summed), so each - rotation owns a disjoint slice of the feature dimension. Across scales, - outputs are also concatenated, matching the base KPlanes behaviour. - - Returns - ------- - features : (B, C * T * num_scales) + Fully-vectorized multi-scale, multi-rotation K-Planes feature interpolation. + Returns features of shape (B, C * T * num_scales). """ T = rotation_matrices.shape[0] B = pts.shape[0] - - # Rotate all points by all T rotation matrices at once. - # pts: (B, 3), R: (T, 3, 3) -> rotated: (T, B, 3) + + # (T, B, 3) — rotate all points by all rotations at once rotated = torch.einsum("tij,bj->tbi", rotation_matrices, pts) - + + # Build (T, 3, B, 2) coords for planes XY, ZX, YZ in one shot. + # index_select is faster and cleaner than advanced indexing with python lists. + # Plane axis layout: XY=(0,1), ZX=(2,0), YZ=(1,2) + idx = torch.tensor([[0, 1], + [2, 0], + [1, 2]], device=pts.device) # (3, 2) + # rotated: (T, B, 3) -> gather along last dim with idx (3, 2) + # Result: (T, 3, B, 2) + coords = rotated.unsqueeze(1).expand(T, 3, B, 3).gather( + -1, idx.view(1, 3, 1, 2).expand(T, 3, B, 2) + ) + + # Flatten (T, 3) -> 3*T so it matches grid's first dim, and add the H_out=1 axis + coord_tensor = coords.reshape(3 * T, B, 1, 2) # (3T, B, 1, 2) + per_scale_features = [] - for plane_coef in ms_grids: - # plane_coef shape: (3*T, C, H, W) + # plane_coef: (3T, C, H, W) C = plane_coef.shape[1] - - # Build the (3*T, B, 1, 2) coordinate tensor for grid_sample. - # For transform t, we need coords for planes XY, XZ, YZ. - # grid_sample expects coords in [-1, 1] with shape (N, Hout, Wout, 2). - all_plane_coords = [] - for t in range(T): - rp = rotated[t] # (B, 3) - all_plane_coords.append(rp[:, [0, 1]]) # XY (B, 2) - all_plane_coords.append(rp[:, [2, 0]]) # ZX (B, 2) matches ki - all_plane_coords.append(rp[:, [1, 2]]) # YZ (B, 2) matches jk - - # Stack -> (3*T, B, 2) -> (3*T, B, 1, 2) for grid_sample - coord_tensor = torch.stack(all_plane_coords, dim=0).unsqueeze(2) - # coord_tensor: (3*T, B, 1, 2) - - # grid_sample: input (N, C, H, W), grid (N, Hout, Wout, 2) + sampled = F.grid_sample( - plane_coef, # (3*T, C, H, W) - coord_tensor, # (3*T, B, 1, 2) + plane_coef, + coord_tensor, align_corners=True, mode="bilinear", padding_mode="border", - ) # -> (3*T, C, B, 1) - sampled = sampled.squeeze(-1) # (3*T, C, B) - - # Hadamard product within each transform group (3 planes per transform). - transform_features = [] - for t in range(T): - p_xy = sampled[3*t + 0] # (C, B) - p_zx = sampled[3*t + 1] - p_yz = sampled[3*t + 2] - fused = (p_xy * p_zx * p_yz).T # (B, C) - transform_features.append(fused) - - # Concatenate across transforms -> (B, C*T) - per_scale_features.append(torch.cat(transform_features, dim=-1)) - - # Concatenate across scales -> (B, C*T*num_scales) + ) # (3T, C, B, 1) + + # (3T, C, B) -> (T, 3, C, B) -> Hadamard across the "3" dim -> (T, C, B) + sampled = sampled.squeeze(-1).view(T, 3, C, B).prod(dim=1) + + # (T, C, B) -> (B, T, C) -> (B, T*C) to concatenate rotations along feature dim + per_scale_features.append( + sampled.permute(2, 0, 1).reshape(B, T * C) + ) + + # Concatenate across scales -> (B, T * C * num_scales) return torch.cat(per_scale_features, dim=-1) # --------------------------------------------------------------------------- @@ -549,17 +560,11 @@ def _build_sigma_net( layers.append(out) self.sigma_net = nn.Sequential(*layers) else: - self.sigma_net = tcnn.Network( - n_input_dims=self.feature_dim, - n_output_dims=1, - network_config={ - "otype": "CutlassMLP", - "activation": "None", - "output_activation": "None", - "n_neurons": 128, - "n_hidden_layers": 0, - }, - ) + # Match mentor's "explicit" decoder: a single linear layer. + # Small init so density stays near 0 initially. + self.sigma_net = nn.Linear(self.feature_dim, 1, bias=True) + nn.init.normal_(self.sigma_net.weight, std=0.01) + nn.init.zeros_(self.sigma_net.bias) # ------------------------------------------------------------------ # Warm-up bookkeeping diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 0c09a372..1d077c2e 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -506,10 +506,12 @@ def apply_soft_constraints( self, coords: torch.Tensor, pred: torch.Tensor, + curr_batch_idx: int, + max_batch_size: int ) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=pred.device) if self.constraints.tv_vol > 0: - soft_loss += self.get_tv_loss(coords, pred) + soft_loss += self.get_tv_loss(coords, pred, curr_batch_idx, max_batch_size) if ( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) @@ -520,7 +522,7 @@ def apply_soft_constraints( return soft_loss - def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: + def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor, curr_batch_idx: int, max_batch_size: int) -> torch.Tensor: """ Calculate the TV loss for the given coordinates and predictions. @@ -528,18 +530,19 @@ def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: """ if isinstance(self.model, (KPlanes, KPlanesTILTED)): - - per_level = [] - - for p in self.model.grids: - dh = p[:, :, 1:, :] - p[:, :, :-1, :] - dw = p[:, :, :, 1:] - p[:, :, :, :-1] - tv = 0.0 - if self.constraints.tv_vol > 0: - tv = tv + self.constraints.tv_vol * (dh.pow(2).mean() + dw.pow(2).mean()) - per_level.append(tv) - - return torch.stack(per_level).sum() + if curr_batch_idx == max_batch_size - 1: + per_level = [] + for p in self.model.grids: + dh = p[:, :, 1:, :] - p[:, :, :-1, :] + dw = p[:, :, :, 1:] - p[:, :, :, :-1] + tv = 0.0 + if self.constraints.tv_vol > 0: + tv = tv + self.constraints.tv_vol * (dh.pow(2).mean() + dw.pow(2).mean()) + per_level.append(tv) + + return torch.stack(per_level).sum() * max_batch_size + else: + return 0.0 else: num_tv_samples = min(10_000, coords.shape[0]) tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 907ad994..c37af039 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -201,7 +201,7 @@ def reconstruct( with torch.autocast( device_type=self.device.type, dtype=torch.bfloat16, - enabled=True, + enabled=False, ): all_coords = self.dset.get_coords(batch, N, curr_num_samples_per_ray) @@ -214,9 +214,7 @@ def reconstruct( ) pred = integrated_densities.float() - soft_constraints_loss = 0.0 - if self.num_epochs > 0: - soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, pred) + soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, pred, batch_idx, len(self.dataloader)) target = batch["target_value"].to(self.device, non_blocking=True).float() From 1c89ad5fda5a148ad6fc4fcf6c45d85b9294dd4c Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Fri, 17 Apr 2026 01:30:55 +0000 Subject: [PATCH 014/140] Everything seems to be working; only things to do is to take a look at DDP, clean-up KPlanes, fix up object_models.py since it's insanely cluttered now --- src/quantem/core/ml/models/kplanes.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 7839f20d..0f44bf7e 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -358,7 +358,6 @@ class SO3Param(nn.Module): def __init__(self, T: int, init: str = "random"): super().__init__() - print("SVD Module") if init == "random": # Initialize near identity with small noise M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) From 4215d6e2ed0b2f114e7746b17e09863d5be4d021 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Fri, 17 Apr 2026 17:45:20 +0000 Subject: [PATCH 015/140] New TV loss function --- src/quantem/tomography/object_models.py | 41 ++++++++++++++----------- src/quantem/tomography/tomography.py | 2 +- 2 files changed, 24 insertions(+), 19 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 1d077c2e..dd8b62ff 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -505,24 +505,24 @@ def obj_view(self) -> np.ndarray: def apply_soft_constraints( self, coords: torch.Tensor, + all_densities: torch.Tensor, pred: torch.Tensor, - curr_batch_idx: int, - max_batch_size: int ) -> torch.Tensor: + soft_loss = torch.tensor(0.0, device=pred.device) if self.constraints.tv_vol > 0: - soft_loss += self.get_tv_loss(coords, pred, curr_batch_idx, max_batch_size) + soft_loss += self.get_tv_loss(coords, pred) if ( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) and self.constraints.sparsity > 0 ): # NOTE: For the linter, I must make this :) - sparsity_loss = self.constraints.sparsity * torch.norm(pred, p=1) + sparsity_loss = self.constraints.sparsity * torch.norm(all_densities, p=1) soft_loss += sparsity_loss return soft_loss - def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor, curr_batch_idx: int, max_batch_size: int) -> torch.Tensor: + def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: """ Calculate the TV loss for the given coordinates and predictions. @@ -530,19 +530,24 @@ def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor, curr_batch_idx: """ if isinstance(self.model, (KPlanes, KPlanesTILTED)): - if curr_batch_idx == max_batch_size - 1: - per_level = [] - for p in self.model.grids: - dh = p[:, :, 1:, :] - p[:, :, :-1, :] - dw = p[:, :, :, 1:] - p[:, :, :, :-1] - tv = 0.0 - if self.constraints.tv_vol > 0: - tv = tv + self.constraints.tv_vol * (dh.pow(2).mean() + dw.pow(2).mean()) - per_level.append(tv) - - return torch.stack(per_level).sum() * max_batch_size - else: - return 0.0 + is_tilted = isinstance(self.model, KPlanesTILTED) + per_level = [] + for p in self.model.grids: + # p: (3*T, C, H, W) for TILTED, (3, C, H, W) for KPlanes + dh = (p[:, :, 1:, :] - p[:, :, :-1, :]).pow(2).mean(dim=(1, 2, 3)) + dw = (p[:, :, :, 1:] - p[:, :, :, :-1]).pow(2).mean(dim=(1, 2, 3)) + per_plane = dh + dw # (3*T,) or (3,) + + if is_tilted: + T = self.model.T + per_rotation = per_plane.view(T, 3).sum(dim=1) # sum 3 planes per rotation + level_tv = per_rotation.mean() # average across rotations + else: + level_tv = per_plane.sum() # standard K-planes behavior + + per_level.append(level_tv) + + return self.constraints.tv_vol * torch.stack(per_level).sum() else: num_tv_samples = min(10_000, coords.shape[0]) tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index c37af039..fc715362 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -214,7 +214,7 @@ def reconstruct( ) pred = integrated_densities.float() - soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, pred, batch_idx, len(self.dataloader)) + soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, all_densities, pred) target = batch["target_value"].to(self.device, non_blocking=True).float() From aebf8011d77e0229edfd0009c6a923409191dd2a Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Sat, 18 Apr 2026 08:45:32 +0000 Subject: [PATCH 016/140] TV volume -- needs significant refactoring everywhere --- src/quantem/core/ml/models/kplanes.py | 273 +++++++++++++++-------- src/quantem/tomography/dataset_models.py | 23 ++ src/quantem/tomography/object_models.py | 153 +++++++++---- src/quantem/tomography/tomography.py | 20 ++ 4 files changed, 340 insertions(+), 129 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 0f44bf7e..b54fe1ee 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -438,15 +438,23 @@ def interpolate_ms_features_tilted( class KPlanesTILTED(KPlanes): """ K-Planes with T learned SO(3) rotations (TILTED). - + Inherits KPlanes for the sigma_net, density_activation, and get_params interface. Overrides: - * __init__ – replaces the axis-aligned grids with (3*T)-plane grids - and adds SO3Param. + * __init__ – replaces the axis-aligned grids with (3*T)-plane + grids and adds SO3Param. * get_densities – calls the TILTED interpolation instead. * get_params – adds "so3" key so callers can set a separate lr. * param_keys – updated list. - + + Two-phase optimization + ---------------------- + Phase 1: instantiate with small `M_features` (and optionally smaller + `resolution` / fewer scales) — the bottleneck model. Train it + until τ converges, then call `extract_tau_state()`. + Phase 2: instantiate at full capacity, call `load_tau_state(M_bneck)` + to seed the rotations, then train normally. + Parameters ---------- M_features : int @@ -454,15 +462,13 @@ class KPlanesTILTED(KPlanes): be M_features * T * len(multiscale_res_multipliers). T : int Number of learned rotations (TILTED-T in the paper; 4 or 8 recommended). + Must match between phase 1 and phase 2 when doing two-phase transfer. tau_init : str "random" (paper default) or "identity". - tau_warmup_steps : int - If > 0, grids and sigma_net are frozen for this many steps so the - rotations can find good basins first (two-phase warm-up). - Call model.training_step() once per optimiser step. + Irrelevant if you're calling load_tau_state() right after __init__. All other args are forwarded to KPlanes. """ - + def __init__( self, # Grid parameters @@ -474,7 +480,6 @@ def __init__( # TILTED parameters T: int = 4, tau_init: str = "random", - tau_warmup_steps: int = 0, # Hybrid MLP parameters use_hybrid_mlp: bool = False, hybrid_hidden_dim: int = 64, @@ -484,20 +489,20 @@ def __init__( raise NotImplementedError("KPlanesTILTED is implemented for 3D only.") if T < 1: raise ValueError(f"T must be >= 1, got {T}") - + multiscale_res_multipliers = list(multiscale_res_multipliers or [1]) num_scales = len(multiscale_res_multipliers) - + # Total feature dim seen by the MLP head. # Each scale contributes M_features * T channels. feature_dim = M_features * T * num_scales - + # Call KPlanes.__init__ with grid_dimensions=2 so it builds sigma_net # correctly; we immediately replace self.grids below. super().__init__( grid_dimensions=2, input_coords_dims=3, - M_features=M_features, # base class stores this + M_features=M_features, resolution=resolution, multiscale_res_multipliers=multiscale_res_multipliers, concat_features=True, @@ -506,13 +511,9 @@ def __init__( hybrid_hidden_dim=hybrid_hidden_dim, hybrid_num_layers=hybrid_num_layers, ) - # KPlanes.__init__ built grids with shape (3, M, H, W) and feature_dim - # = M * num_scales. We rebuild them for the TILTED shape. - + self.T = T - self.tau_warmup_steps = tau_warmup_steps - self._global_step: int = 0 - + # ---- Rebuild grids: (3*T, M_features, H, W) per scale ---- self.grids = nn.ParameterList() for res_mult in multiscale_res_multipliers: @@ -522,20 +523,20 @@ def __init__( ) nn.init.uniform_(plane, 0.1, 0.5) self.grids.append(plane) - + # ---- Rebuild sigma_net with the correct feature_dim ---- # KPlanes built sigma_net with self.feature_dim (= M * num_scales), # which is wrong for T > 1. Rebuild here. self.feature_dim = feature_dim self._build_sigma_net(use_hybrid_mlp, hybrid_hidden_dim, hybrid_num_layers) - + # ---- Learnable rotations ---- self.so3 = SO3Param(T, init=tau_init) - + # ------------------------------------------------------------------ # Internal helpers # ------------------------------------------------------------------ - + def _build_sigma_net( self, use_hybrid_mlp: bool, @@ -559,53 +560,15 @@ def _build_sigma_net( layers.append(out) self.sigma_net = nn.Sequential(*layers) else: - # Match mentor's "explicit" decoder: a single linear layer. - # Small init so density stays near 0 initially. + # Single-linear "explicit" decoder. Small init -> density ~ 0 initially. self.sigma_net = nn.Linear(self.feature_dim, 1, bias=True) nn.init.normal_(self.sigma_net.weight, std=0.01) nn.init.zeros_(self.sigma_net.bias) - - # ------------------------------------------------------------------ - # Warm-up bookkeeping - # ------------------------------------------------------------------ - - def training_step(self) -> None: - """ - Call once per optimiser step to advance the internal counter. - - During the first `tau_warmup_steps` iterations, grids and sigma_net - have their gradients zeroed after the backward pass so only the SO(3) - parameters update. This is the lightweight version of two-phase - optimisation from the paper. - """ - print("Global Stepped") - self._global_step += 1 - - def _in_warmup(self) -> bool: - return self.tau_warmup_steps > 0 and self._global_step < self.tau_warmup_steps - - def zero_non_tau_grads(self) -> None: - """ - Call after loss.backward() and before optimizer.step() when you want - to implement the rotation warm-up manually. Alternatively just check - model.in_warmup and configure your optimizer accordingly. - """ - if self._in_warmup(): - for p in self.grids.parameters(): - if p.grad is not None: - p.grad.zero_() - for p in self.sigma_net.parameters(): - if p.grad is not None: - p.grad.zero_() - - @property - def in_warmup(self) -> bool: - return self._in_warmup() - + # ------------------------------------------------------------------ # Core forward # ------------------------------------------------------------------ - + def get_densities(self, coords: torch.Tensor) -> torch.Tensor: pts = coords.reshape(-1, 3) R = self.so3.as_matrix() # (T, 3, 3) @@ -616,60 +579,192 @@ def get_densities(self, coords: torch.Tensor) -> torch.Tensor: ) density_before_activation = self.sigma_net(features) return self.density_activation(density_before_activation) - + def forward(self, pts: torch.Tensor) -> torch.Tensor: return self.get_densities(pts) - + # ------------------------------------------------------------------ # Parameter groups # ------------------------------------------------------------------ - + def get_params(self) -> dict[str, list[nn.Parameter]]: return { "grids": list(self.grids.parameters()), "sigma_net": list(self.sigma_net.parameters()), "so3": list(self.so3.parameters()), } - + @property def param_keys(self) -> list[str]: return ["grids", "sigma_net", "so3"] - # ------------------------------------------------------------------ - # Two-phase helper: extract tau for phase-2 initialisation + # Two-phase transfer: extract / load learned rotations # ------------------------------------------------------------------ - + def extract_tau_state(self) -> torch.Tensor: """ - Returns the current quaternion tensor (detached copy) so it can be - used to initialise a larger phase-2 model via `load_tau_state`. + Returns the current raw R^9 matrices (detached copy) so they can be + used to initialise a phase-2 model via `load_tau_state`. + + Returns + ------- + torch.Tensor of shape (T, 3, 3) """ - return self.so3.quats.detach().clone() - - def load_tau_state(self, quats: torch.Tensor) -> None: + return self.so3.M.detach().cpu().clone() + + def load_tau_state(self, M: torch.Tensor) -> None: """ - Load pre-trained quaternions (e.g. from a bottleneck phase-1 model). - - quats : (T, 4) tensor, will be normalised internally. + Load pre-trained rotation matrices (e.g. from a bottleneck phase-1 model). + + No orthogonalization is needed — SO3Param.as_matrix() projects to SO(3) + via SVD on every forward pass. + + Parameters + ---------- + M : torch.Tensor of shape (T, 3, 3) + Raw unconstrained matrices from `extract_tau_state()`. """ - if quats.shape != self.so3.quats.shape: + if M.shape != self.so3.M.shape: raise ValueError( - f"Shape mismatch: got {quats.shape}, " - f"expected {self.so3.quats.shape}" + f"Shape mismatch: got {M.shape}, expected {self.so3.M.shape}. " + f"Make sure T matches between phase 1 and phase 2." ) with torch.no_grad(): - self.so3.quats.copy_(F.normalize(quats, p=2, dim=-1)) - + self.so3.M.copy_(M.to(self.so3.M.device)) + # ------------------------------------------------------------------ # Pretty print # ------------------------------------------------------------------ - + def extra_repr(self) -> str: return ( f"T={self.T}, " f"M_features={self.M_features}, " f"feature_dim={self.feature_dim}, " - f"num_scales={len(self.multiscale_res_multipliers)}, " - f"tau_warmup_steps={self.tau_warmup_steps}" - ) \ No newline at end of file + f"num_scales={len(self.multiscale_res_multipliers)}" + ) + + + + + +# CP Decomp for Warmup SO3 rotations + +def interpolate_ms_features_cp_tilted( + pts: torch.Tensor, # (B, 3) + ms_grids: nn.ParameterList, # each grid: (3*T, C, L) — 1D lines + rotation_matrices: torch.Tensor, # (T, 3, 3) +) -> torch.Tensor: + """ + CP (vector outer product) version of TILTED interpolation. + Returns features of shape (B, C * T * num_scales). + """ + T = rotation_matrices.shape[0] + B = pts.shape[0] + + # Rotate all points by all rotations: (T, B, 3) + rotated = torch.einsum("tij,bj->tbi", rotation_matrices, pts) + + per_scale_features = [] + for line_coef in ms_grids: + # line_coef: (3T, C, L) — three 1D feature lines per transform (x, y, z) + C, L = line_coef.shape[1], line_coef.shape[2] + + # For each transform t, we need three 1D samples: at x_t, y_t, z_t. + # Lay them out as (3T, B) coords, matching line_coef's first dim. + # Axis order per transform: x, y, z. + coords_1d = rotated.reshape(T, B, 3).permute(0, 2, 1).reshape(3 * T, B) + # coords_1d: (3T, B), each row is samples along one axis for one transform + + # grid_sample wants 4D input for 2D sampling, or we can use 1D via a + # (3T, C, 1, L) reshape and pass 2D coords with y fixed at 0. + # Simpler: use F.grid_sample with a 4D trick, or just do manual linear interp. + # Here's the grid_sample way: + line_coef_4d = line_coef.unsqueeze(2) # (3T, C, 1, L) + # grid: need (3T, Hout=1, Wout=B, 2), with x = coord, y = 0 + grid = torch.stack([ + coords_1d, # x + torch.zeros_like(coords_1d), # y + ], dim=-1).unsqueeze(1) # (3T, 1, B, 2) + + sampled = F.grid_sample( + line_coef_4d, grid, + align_corners=True, mode="bilinear", padding_mode="border", + ).squeeze(2) # (3T, C, B) + + # Hadamard across the 3 axes per transform: (T, 3, C, B) -> (T, C, B) + sampled = sampled.view(T, 3, C, B).prod(dim=1) + + # (T, C, B) -> (B, T*C) + per_scale_features.append(sampled.permute(2, 0, 1).reshape(B, T * C)) + + return torch.cat(per_scale_features, dim=-1) + + +class CPTilted(nn.Module, PPLR): + """ + CP decomposition with TILTED rotations — the true bottleneck model for + phase 1. Rank-1-per-channel feature representation. + + Shares the SO3Param and sigma_net design with KPlanesTILTED so you can + lift τ directly across: cp_model.extract_tau_state() -> + kplanes_model.load_tau_state(). + """ + + def __init__( + self, + C: int = 4, # channels per transform per scale + resolution: Sequence[int] = (128, 128, 128), + multiscale_res_multipliers: Optional[Sequence[int]] = None, + T: int = 4, + tau_init: str = "random", + density_activation: Callable = lambda x: F.softplus(x - 1), + ): + super().__init__() + self.T = T + self.C = C + self.multiscale_res_multipliers = list(multiscale_res_multipliers or [1]) + self.density_activation = density_activation + + # 1D feature lines, one per axis per transform per scale. + # Shape per scale: (3*T, C, L). We use max(resolution) for L; if your + # scene is strongly anisotropic use 3 separate lines per axis. + self.grids = nn.ParameterList() + for mult in self.multiscale_res_multipliers: + L = int(max(resolution) * mult) + line = nn.Parameter(torch.empty(3 * T, C, L)) + nn.init.uniform_(line, 0.1, 0.5) + self.grids.append(line) + + self.feature_dim = C * T * len(self.multiscale_res_multipliers) + + # Same minimal single-linear decoder as your KPlanesTILTED default. + self.sigma_net = nn.Linear(self.feature_dim, 1, bias=True) + nn.init.normal_(self.sigma_net.weight, std=0.01) + nn.init.zeros_(self.sigma_net.bias) + + self.so3 = SO3Param(T, init=tau_init) + + def get_densities(self, coords: torch.Tensor) -> torch.Tensor: + pts = coords.reshape(-1, 3) + R = self.so3.as_matrix() + features = interpolate_ms_features_cp_tilted(pts, self.grids, R) + return self.density_activation(self.sigma_net(features)) + + def forward(self, pts): + return self.get_densities(pts) + + def get_params(self): + return { + "grids": list(self.grids.parameters()), + "sigma_net": list(self.sigma_net.parameters()), + "so3": list(self.so3.parameters()), + } + + @property + def param_keys(self): + return ["grids", "sigma_net", "so3"] + + def extract_tau_state(self) -> torch.Tensor: + return self.so3.M.detach().clone() \ No newline at end of file diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index f6343e4e..e3b61377 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -643,6 +643,29 @@ def to(self, device: torch.device | str): self.device = device self.reconnect_optimizer_to_parameters() + # --- Save learned parameters --- + + def save_parameters(self, path: str): + """ + Saves the learned parameters to a file. + """ + torch.save({ + "z1": self._z1_params.detach().cpu(), + "z3": self._z3_params.detach().cpu(), + "shifts": self._shifts_params.detach().cpu(), + }, path) + + def load_parameters(self, path: str): + """ + Loads the learned parameters from a file. + """ + data = torch.load(path) + self._z1_params = nn.Parameter(data["z1"]) + self._z3_params = nn.Parameter(data["z3"]) + self._shifts_params = nn.Parameter(data["shifts"]) + if self.optimizer is not None: + self.reconnect_optimizer_to_parameters() + class TomographyINRPretrainDataset(Dataset): """ diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index dd8b62ff..964e0c53 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -99,10 +99,11 @@ class ObjINRConstraints(Constraints): positivity: bool = True shrinkage: float = 0.0 tv_vol: float = 0.0 + tv_plane: float = 0.0 sparsity: float = 0.0 _name: str = "obj_inr" - soft_constraint_keys = ["tv_vol", "sparsity"] + soft_constraint_keys = ["tv_vol", "tv_plane", "sparsity"] hard_constraint_keys = ["positivity", "shrinkage"] @classmethod @@ -521,59 +522,131 @@ def apply_soft_constraints( soft_loss += sparsity_loss return soft_loss + # TV Losses def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: """ - Calculate the TV loss for the given coordinates and predictions. + Dispatch to the appropriate TV loss based on model architecture. - Current supported architectures are KPlanes and INRs + - KPlanes / KPlanesTILTED: per-plane TV (regularizes the stored feature + planes directly). Cheap; regularizes the representation. + - CPTilted: per-line TV (same idea, applied to 1D feature lines). + - SIREN / other INRs: volume TV via autograd (exact gradient). + - Fallback: volume TV via finite differences (works anywhere). + + If you want *volume* smoothness regardless of architecture, call + `get_volume_tv_loss(coords)` directly. """ - + tv_loss = torch.tensor(0.0, device=pred.device) if isinstance(self.model, (KPlanes, KPlanesTILTED)): - is_tilted = isinstance(self.model, KPlanesTILTED) - per_level = [] - for p in self.model.grids: - # p: (3*T, C, H, W) for TILTED, (3, C, H, W) for KPlanes - dh = (p[:, :, 1:, :] - p[:, :, :-1, :]).pow(2).mean(dim=(1, 2, 3)) - dw = (p[:, :, :, 1:] - p[:, :, :, :-1]).pow(2).mean(dim=(1, 2, 3)) - per_plane = dh + dw # (3*T,) or (3,) - - if is_tilted: - T = self.model.T - per_rotation = per_plane.view(T, 3).sum(dim=1) # sum 3 planes per rotation - level_tv = per_rotation.mean() # average across rotations - else: - level_tv = per_plane.sum() # standard K-planes behavior + tv_loss += self._get_plane_tv_loss() - per_level.append(level_tv) + # SIREN and other INRs support double-backward → use exact autograd. + # Everything else falls through to finite-difference volume TV. + if not isinstance(self.model, PPLR): # adjust to your actual INR class + tv_loss += self._get_volume_tv_loss_autograd(coords) - return self.constraints.tv_vol * torch.stack(per_level).sum() - else: - num_tv_samples = min(10_000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + if isinstance(self.model, (KPlanes, KPlanesTILTED)): + tv_loss += self.get_volume_tv_loss(coords) + return tv_loss + + + # ---------------------------------------------------------------------- + # Plane TV (K-Planes family) — regularizes the stored feature planes. + # ---------------------------------------------------------------------- + + def _get_plane_tv_loss(self) -> torch.Tensor: + is_tilted = isinstance(self.model, KPlanesTILTED) + per_level = [] + + for p in self.model.grids: + # p: (3*T, C, H, W) for TILTED, (3, C, H, W) for KPlanes + dh = (p[:, :, 1:, :] - p[:, :, :-1, :]).pow(2).mean(dim=(1, 2, 3)) + dw = (p[:, :, :, 1:] - p[:, :, :, :-1]).pow(2).mean(dim=(1, 2, 3)) + per_plane = dh + dw # (3*T,) or (3,) + + if is_tilted: + T = self.model.T + per_rotation = per_plane.view(T, 3).sum(dim=1) # sum 3 planes per rotation + level_tv = per_rotation.mean() # avg across rotations + else: + level_tv = per_plane.sum() - tv_coords = coords[tv_indices].detach().requires_grad_(True) - tv_densities_recomputed = self.model(tv_coords) - if isinstance(tv_densities_recomputed, tuple): - tv_densities_recomputed = tv_densities_recomputed[0] + per_level.append(level_tv) - # Ensure shape is [num_samples, num_channels] - if tv_densities_recomputed.dim() == 1: - tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) + return self.constraints.tv_plane * torch.stack(per_level).sum() - # Compute gradients for each channel - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True, - )[0] # Shape: [num_samples, coord_dim] - # Compute TV loss - gradient magnitude per sample - grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] - return self.constraints.tv_vol * grad_norm.mean() + # ---------------------------------------------------------------------- + # Volume TV (autograd) — exact gradient, needs double-backward support. + # ---------------------------------------------------------------------- + + def _get_volume_tv_loss_autograd(self, coords: torch.Tensor) -> torch.Tensor: + """ + Isotropic volume TV using autograd. Exact gradient, but requires the + model to support double-backward (SIREN yes, grid_sample-based models no). + """ + num_tv_samples = min(10_000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + tv_coords = coords[tv_indices].detach().requires_grad_(True) + pred = self.model(tv_coords) + if isinstance(pred, tuple): + pred = pred[0] + if pred.dim() == 1: + pred = pred.unsqueeze(-1) + + grad_outputs = torch.autograd.grad( + outputs=pred, + inputs=tv_coords, + grad_outputs=torch.ones_like(pred), + create_graph=True, + )[0] # (N, 3) + + grad_norm = torch.norm(grad_outputs, dim=1) # (N,) + return self.constraints.tv_vol * grad_norm.mean() + # ---------------------------------------------------------------------- + # Volume TV (finite differences) — works for any model. + # ---------------------------------------------------------------------- + + def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: + """ + Isotropic volume TV via finite differences. Same form as the autograd + version (L1 of gradient L2-norm) but avoids double-backward, so it + works for KPlanesTILTED, CPTilted, and anything else. + """ + num_tv_samples = min(10_000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + tv_coords = coords[tv_indices] # (N, 3) + + if hasattr(self.model, "resolution"): + h = 2.0 / min(self.model.resolution) + else: + h = 1e-2 + + pred = self.model(tv_coords) + if isinstance(pred, tuple): + pred = pred[0] + if pred.dim() == 1: + pred = pred.unsqueeze(-1) # (N, 1) + + grads = [] + for axis in range(3): + offset = torch.zeros(3, device=tv_coords.device) + offset[axis] = h + shifted_pred = self.model(tv_coords + offset) + if isinstance(shifted_pred, tuple): + shifted_pred = shifted_pred[0] + if shifted_pred.dim() == 1: + shifted_pred = shifted_pred.unsqueeze(-1) + grads.append((shifted_pred - pred) / h) # (N, 1) + + grad_stack = torch.stack(grads, dim=-1) # (N, C, 3) + grad_norm = torch.norm(grad_stack, dim=-1) # (N, C) + + return self.constraints.tv_vol * grad_norm.mean() def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: """ diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index fc715362..a96136df 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -10,6 +10,7 @@ from quantem.core.io.serialize import load as autoserialize_load from quantem.core.ml.loss_functions import get_loss_module +from quantem.core.ml.models.kplanes import CPTilted from quantem.core.utils.filter import gaussian_filter_2d_stack, gaussian_kernel_1d from quantem.core.utils.tomography_utils import torch_phase_cross_correlation from quantem.tomography.dataset_models import ( @@ -233,6 +234,25 @@ def reconstruct( total_loss += batch_loss.detach() consistency_loss += batch_consistency_loss.detach() + if isinstance(self.obj_model.model, CPTilted): + if a0 == 0: + prev_R = self.obj_model.model.so3.as_matrix().detach().clone() + elif (a0 + 1) % 20 == 0: + R_now = self.obj_model.model.so3.as_matrix().detach() + # Cumulative angular change per rotation over the last 20 iters. + # trace(R_prev^T R_now) = 1 + 2*cos(theta), so theta = acos((trace - 1) / 2). + rel_trace = torch.einsum('tij,tij->t', prev_R, R_now) + angle = torch.acos(((rel_trace - 1) / 2).clamp(-1, 1)) # (T,) radians + angle_deg = torch.rad2deg(angle) + per_tau_str = ", ".join(f"{a:.2f}°" for a in angle_deg.tolist()) + print( + f"iter {a0}: 20-iter τ change " + f"max={angle_deg.max().item():.2f}°, " + f"mean={angle_deg.mean().item():.2f}°, " + f"per-τ=[{per_tau_str}]" + ) + prev_R = R_now.clone() + if self.world_size > 1: dist.all_reduce(total_loss, dist.ReduceOp.AVG) dist.all_reduce(consistency_loss, dist.ReduceOp.AVG) From 25798477718ea888b2117410cab069c518fbd663 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Sat, 18 Apr 2026 22:09:07 -0700 Subject: [PATCH 017/140] DDP Fixes for PPLR stuff --- src/quantem/core/ml/models/kplanes.py | 19 +++-------- src/quantem/tomography/object_models.py | 42 ++++++++++++++++--------- 2 files changed, 32 insertions(+), 29 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index b54fe1ee..d6cc9540 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -6,7 +6,7 @@ import math from typing import Callable, Optional, Sequence -import tinycudann as tcnn +# import tinycudann as tcnn import torch import torch.nn.functional as F from torch import nn @@ -217,18 +217,7 @@ def __init__( nn.init.zeros_(out.bias) layers.append(out) self.sigma_net = nn.Sequential(*layers) - else: - self.sigma_net = tcnn.Network( - n_input_dims=self.feature_dim, - n_output_dims=1, - network_config={ - "otype": "CutlassMLP", - "activation": "None", - "output_activation": "None", - "n_neurons": 128, - "n_hidden_layers": 0, - }, - ) + def get_densities(self, coords: torch.Tensor): """Computes and returns densities""" @@ -370,10 +359,12 @@ def __init__(self, T: int, init: str = "random"): def as_matrix(self) -> torch.Tensor: """Projects each M to SO(3) via SVD. Returns (T, 3, 3).""" + + U, _, Vh = torch.linalg.svd(self.M) # U: (T,3,3), Vh: (T,3,3) # Fix reflections: det(U Vh) must be +1 d = torch.det(U @ Vh) # (T,) - diag = torch.ones(self.M.shape[0], 3, device=self.M.device) + diag = torch.ones(self.M.shape[0], 3, device=self.M.device, dtype=self.M.dtype) diag[:, 2] = d # multiply last singular vector by sign return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 964e0c53..19f26c05 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -20,6 +20,11 @@ from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset +def _unwrap(model): + """Unwrap DDP/FSDP/any wrapper that exposes `.module`.""" + while hasattr(model, "module") and isinstance(model.module, torch.nn.Module): + model = model.module + return model class ObjConstraintParams: """ @@ -538,15 +543,16 @@ def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: `get_volume_tv_loss(coords)` directly. """ tv_loss = torch.tensor(0.0, device=pred.device) - if isinstance(self.model, (KPlanes, KPlanesTILTED)): + inner = _unwrap(self.model) + if isinstance(inner, (KPlanes, KPlanesTILTED)): tv_loss += self._get_plane_tv_loss() # SIREN and other INRs support double-backward → use exact autograd. # Everything else falls through to finite-difference volume TV. - if not isinstance(self.model, PPLR): # adjust to your actual INR class + if not isinstance(inner, PPLR): # adjust to your actual INR class tv_loss += self._get_volume_tv_loss_autograd(coords) - if isinstance(self.model, (KPlanes, KPlanesTILTED)): + if isinstance(inner, (KPlanes, KPlanesTILTED)): tv_loss += self.get_volume_tv_loss(coords) return tv_loss @@ -556,17 +562,18 @@ def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: # ---------------------------------------------------------------------- def _get_plane_tv_loss(self) -> torch.Tensor: - is_tilted = isinstance(self.model, KPlanesTILTED) + inner = _unwrap(self.model) + is_tilted = isinstance(inner, KPlanesTILTED) per_level = [] - - for p in self.model.grids: + + for p in inner.grids: # p: (3*T, C, H, W) for TILTED, (3, C, H, W) for KPlanes dh = (p[:, :, 1:, :] - p[:, :, :-1, :]).pow(2).mean(dim=(1, 2, 3)) dw = (p[:, :, :, 1:] - p[:, :, :, :-1]).pow(2).mean(dim=(1, 2, 3)) per_plane = dh + dw # (3*T,) or (3,) if is_tilted: - T = self.model.T + T = inner.T per_rotation = per_plane.view(T, 3).sum(dim=1) # sum 3 planes per rotation level_tv = per_rotation.mean() # avg across rotations else: @@ -621,12 +628,13 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] tv_coords = coords[tv_indices] # (N, 3) - if hasattr(self.model, "resolution"): - h = 2.0 / min(self.model.resolution) + inner = _unwrap(self.model) + if hasattr(inner, "resolution"): + h = 2.0 / min(inner.resolution) else: h = 1e-2 - pred = self.model(tv_coords) + pred = inner(tv_coords) if isinstance(pred, tuple): pred = pred[0] if pred.dim() == 1: @@ -636,7 +644,7 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: for axis in range(3): offset = torch.zeros(3, device=tv_coords.device) offset[axis] = h - shifted_pred = self.model(tv_coords + offset) + shifted_pred = inner(tv_coords + offset) if isinstance(shifted_pred, tuple): shifted_pred = shifted_pred[0] if shifted_pred.dim() == 1: @@ -696,14 +704,16 @@ def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | di if isinstance(params, OptimizerType): self._optimizer_params = params return - if isinstance(self.model, PPLR): + + inner = _unwrap(self.model) + if isinstance(inner, PPLR): if not isinstance(params, dict): raise TypeError(f"optimizer parameters must be a dict for PPLR, got {type(params)}") object_params = params - if set(object_params.keys()) != set(self.model.param_keys): - raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}") + if set(object_params.keys()) != set(inner.param_keys): + raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {inner.param_keys}") params = {} for key, value in object_params.items(): @@ -725,7 +735,9 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: Currently supports single LR for all parameters, TODO allow for per parameter LRs by updating get_optimization_parameters to return a list of parameters and their LRs. """ - if not isinstance(self.model, PPLR): + + inner = _unwrap(self.model) + if not isinstance(inner, PPLR): super().set_optimizer(opt_params) return From a59baac0208d48f219e555caaa27264b251fe44f Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Wed, 22 Apr 2026 11:11:04 -0700 Subject: [PATCH 018/140] Changes --- src/quantem/core/ml/models/kplanes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index d6cc9540..117ea280 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -164,7 +164,7 @@ def __init__( resolution: Sequence[int] = (200, 200, 200), multiscale_res_multipliers: Optional[Sequence[int]] = None, concat_features: bool = True, - density_activation: Callable = lambda x: F.softplus(x - 1), + density_activation: Callable = lambda x: F.softplus(x - 1), # Keep playing around with this and trunc_exp # Hybrid MLP parameters use_hybrid_mlp: bool = False, hybrid_hidden_dim: int = 64, From 251f3fbfd5b287615fc39dcd5a6d9aa254419489 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 23 Apr 2026 11:31:51 -0700 Subject: [PATCH 019/140] Reorganizing tensor decomposition methods. Defined a new TensorDecompositionModel ABC, make sure to have a property for which kind of tensor decomposition method is being used. SO3Params are moved to a different file, thinking of making a kplanes_utils.py. Starting reorganization of object_models.py to have ObjectINR and ObjectTensorDecomp --- src/quantem/core/ml/models/kplanes.py | 214 ++------- src/quantem/core/ml/models/model_base.py | 10 + src/quantem/core/ml/models/so3params.py | 183 ++++++++ src/quantem/tomography/object_models.py | 538 +++++++++++++++++++---- 4 files changed, 689 insertions(+), 256 deletions(-) create mode 100644 src/quantem/core/ml/models/so3params.py diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 117ea280..1b0a40ab 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -11,7 +11,8 @@ import torch.nn.functional as F from torch import nn -from .model_base import PPLR +from .model_base import PPLR, TensorDecompositionModel +from .so3params import SO3ParamQuat, SO3ParamR9SVD """ K-planes utility functions @@ -153,7 +154,7 @@ def interpolate_ms_features( return torch.cat(per_scale, dim=-1) -class KPlanes(nn.Module, PPLR): +class KPlanes(nn.Module, PPLR, TensorDecompositionModel): def __init__( self, @@ -174,7 +175,7 @@ def __init__( Assume coords are [-1, 1] in each dimension. """ super().__init__() - + self._td_type = "kplanes" self.grid_dimensions = grid_dimensions self.input_coords_dims = input_coords_dims self.M_features = M_features @@ -243,184 +244,10 @@ def get_params(self) -> dict[str, list[torch.nn.Parameter]]: def param_keys(self) -> list[str]: return ["grids", "sigma_net"] + @property + def td_type(self) -> str: + return self._td_type -# --- Tilted KPlanes --- - -# --------------------------------------------------------------------------- -# SO(3) quaternion parameter module -# --------------------------------------------------------------------------- - -# class SO3Param(nn.Module): -# """ -# Stores T unit quaternions as learnable parameters in R^4 and normalises -# them on every call to `as_matrix()`. - -# Quaternion convention: [x, y, z, w] (scalar-last, same as scipy). - -# Initialisation -# -------------- -# "random" – uniform sampling over SO(3) via Shoemake's method. -# "identity" – all rotations start as the identity (good for fine-tuning). -# """ - -# def __init__(self, T: int, init: str = "random"): -# super().__init__() -# if T < 1: -# raise ValueError(f"T must be >= 1, got {T}") -# quats = self._init_quaternions(T, init) # (T, 4) -# self.quats = nn.Parameter(quats) - -# # ------------------------------------------------------------------ -# # Initialisers -# # ------------------------------------------------------------------ - -# @staticmethod -# def _shoemake_sample(T: int) -> torch.Tensor: -# """Uniform SO(3) sampling via Shoemake (1992). Returns (T, 4) [x,y,z,w].""" -# u = torch.rand(T, 3) -# sqrt1_u0 = torch.sqrt(1.0 - u[:, 0]) -# sqrt_u0 = torch.sqrt(u[:, 0]) -# two_pi = 2.0 * math.pi -# x = sqrt1_u0 * torch.sin(two_pi * u[:, 1]) -# y = sqrt1_u0 * torch.cos(two_pi * u[:, 1]) -# z = sqrt_u0 * torch.sin(two_pi * u[:, 2]) -# w = sqrt_u0 * torch.cos(two_pi * u[:, 2]) -# return torch.stack([x, y, z, w], dim=-1) # (T, 4) - -# @staticmethod -# def _identity(T: int) -> torch.Tensor: -# """All-identity rotations: [0,0,0,1] * T.""" -# q = torch.zeros(T, 4) -# q[:, 3] = 1.0 -# return q - -# @classmethod -# def _init_quaternions(cls, T: int, init: str) -> torch.Tensor: -# if init == "random": -# return cls._shoemake_sample(T) -# elif init == "identity": -# return cls._identity(T) -# else: -# raise ValueError(f"Unknown init '{init}'; choose 'random' or 'identity'.") - -# # ------------------------------------------------------------------ -# # Forward helpers -# # ------------------------------------------------------------------ - -# def normalized(self) -> torch.Tensor: -# """Returns (T, 4) unit quaternions.""" -# return F.normalize(self.quats, p=2, dim=-1) - -# def as_matrix(self) -> torch.Tensor: -# """ -# Converts the T stored quaternions to (T, 3, 3) rotation matrices. - -# Uses the standard formula; no trig, just multiplications. -# """ -# q = self.normalized() # (T, 4) [x, y, z, w] -# x, y, z, w = q.unbind(dim=-1) # each (T,) - -# # Precompute products -# xx, yy, zz = x*x, y*y, z*z -# xy, xz, yz = x*y, x*z, y*z -# wx, wy, wz = w*x, w*y, w*z - -# # Row-major: R[i,j] -# R = torch.stack([ -# 1 - 2*(yy + zz), 2*(xy - wz), 2*(xz + wy), -# 2*(xy + wz), 1 - 2*(xx + zz), 2*(yz - wx), -# 2*(xz - wy), 2*(yz + wx), 1 - 2*(xx + yy), -# ], dim=-1).reshape(-1, 3, 3) # (T, 3, 3) - -# return R - -# def extra_repr(self) -> str: -# return f"T={self.quats.shape[0]}" - - -class SO3Param(nn.Module): - """ - SO(3) rotation bank using R9+SVD parameterization. - Each rotation is stored as an unconstrained 3x3 matrix M, - projected to SO(3) via SVD+(M) = U diag(1,1,det(UVt)) Vt. - """ - - def __init__(self, T: int, init: str = "random"): - super().__init__() - if init == "random": - # Initialize near identity with small noise - M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) - M = M + 0.1 * torch.randn(T, 3, 3) - elif init == "identity": - M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) - else: - raise ValueError(f"Unknown init '{init}'") - self.M = nn.Parameter(M) # (T, 3, 3) - - def as_matrix(self) -> torch.Tensor: - """Projects each M to SO(3) via SVD. Returns (T, 3, 3).""" - - - U, _, Vh = torch.linalg.svd(self.M) # U: (T,3,3), Vh: (T,3,3) - # Fix reflections: det(U Vh) must be +1 - d = torch.det(U @ Vh) # (T,) - diag = torch.ones(self.M.shape[0], 3, device=self.M.device, dtype=self.M.dtype) - diag[:, 2] = d # multiply last singular vector by sign - return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) - -def interpolate_ms_features_tilted( - pts: torch.Tensor, # (B, 3) - ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) - rotation_matrices: torch.Tensor, # (T, 3, 3) -) -> torch.Tensor: - """ - Fully-vectorized multi-scale, multi-rotation K-Planes feature interpolation. - Returns features of shape (B, C * T * num_scales). - """ - T = rotation_matrices.shape[0] - B = pts.shape[0] - - # (T, B, 3) — rotate all points by all rotations at once - rotated = torch.einsum("tij,bj->tbi", rotation_matrices, pts) - - # Build (T, 3, B, 2) coords for planes XY, ZX, YZ in one shot. - # index_select is faster and cleaner than advanced indexing with python lists. - # Plane axis layout: XY=(0,1), ZX=(2,0), YZ=(1,2) - idx = torch.tensor([[0, 1], - [2, 0], - [1, 2]], device=pts.device) # (3, 2) - # rotated: (T, B, 3) -> gather along last dim with idx (3, 2) - # Result: (T, 3, B, 2) - coords = rotated.unsqueeze(1).expand(T, 3, B, 3).gather( - -1, idx.view(1, 3, 1, 2).expand(T, 3, B, 2) - ) - - # Flatten (T, 3) -> 3*T so it matches grid's first dim, and add the H_out=1 axis - coord_tensor = coords.reshape(3 * T, B, 1, 2) # (3T, B, 1, 2) - - per_scale_features = [] - for plane_coef in ms_grids: - # plane_coef: (3T, C, H, W) - C = plane_coef.shape[1] - - sampled = F.grid_sample( - plane_coef, - coord_tensor, - align_corners=True, - mode="bilinear", - padding_mode="border", - ) # (3T, C, B, 1) - - # (3T, C, B) -> (T, 3, C, B) -> Hadamard across the "3" dim -> (T, C, B) - sampled = sampled.squeeze(-1).view(T, 3, C, B).prod(dim=1) - - # (T, C, B) -> (B, T, C) -> (B, T*C) to concatenate rotations along feature dim - per_scale_features.append( - sampled.permute(2, 0, 1).reshape(B, T * C) - ) - - # Concatenate across scales -> (B, T * C * num_scales) - return torch.cat(per_scale_features, dim=-1) # --------------------------------------------------------------------------- # KPlanesTILTED @@ -475,7 +302,10 @@ def __init__( use_hybrid_mlp: bool = False, hybrid_hidden_dim: int = 64, hybrid_num_layers: int = 2, + so3_param_type: str = "r9svd", ): + + self._td_type = "tilted" if input_coords_dims != 3: raise NotImplementedError("KPlanesTILTED is implemented for 3D only.") if T < 1: @@ -636,6 +466,23 @@ def extra_repr(self) -> str: f"num_scales={len(self.multiscale_res_multipliers)}" ) + def set_so3_param_type(self, so3_param_type: str) -> None: + """ + Set the SO3 parameterization type. + + Parameters + ---------- + so3_param_type : str + SO3 parameterization type ("quat" or "r9svd"). + """ + if so3_param_type == "r9svd": + self.so3 = SO3ParamR9SVD(self.T) + elif so3_param_type == "quat": + self.so3 = SO3ParamQuat(self.T) + else: + raise ValueError(f"Invalid SO3 parameterization type: {so3_param_type}") + + @@ -693,7 +540,7 @@ def interpolate_ms_features_cp_tilted( return torch.cat(per_scale_features, dim=-1) -class CPTilted(nn.Module, PPLR): +class CPTilted(nn.Module, PPLR, TensorDecompositionModel): """ CP decomposition with TILTED rotations — the true bottleneck model for phase 1. Rank-1-per-channel feature representation. @@ -713,6 +560,7 @@ def __init__( density_activation: Callable = lambda x: F.softplus(x - 1), ): super().__init__() + self._td_type = "cp_tilted" self.T = T self.C = C self.multiscale_res_multipliers = list(multiscale_res_multipliers or [1]) @@ -757,5 +605,9 @@ def get_params(self): def param_keys(self): return ["grids", "sigma_net", "so3"] + @property + def td_type(self) -> str: + return self._td_type + def extract_tau_state(self) -> torch.Tensor: return self.so3.M.detach().clone() \ No newline at end of file diff --git a/src/quantem/core/ml/models/model_base.py b/src/quantem/core/ml/models/model_base.py index 42bee71c..44bbdef5 100644 --- a/src/quantem/core/ml/models/model_base.py +++ b/src/quantem/core/ml/models/model_base.py @@ -24,4 +24,14 @@ def param_keys(self) -> list[str]: """ This abstract property should return a list of available parameter keys. """ + pass + +class TensorDecompositionModel(ABC): + + @property + @abstractmethod + def td_type(self) -> str: + """ + This abstract property should return the type of tensor decomposition used. + """ pass \ No newline at end of file diff --git a/src/quantem/core/ml/models/so3params.py b/src/quantem/core/ml/models/so3params.py new file mode 100644 index 00000000..29314fb6 --- /dev/null +++ b/src/quantem/core/ml/models/so3params.py @@ -0,0 +1,183 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +# --- Tilted KPlanes --- + +# --------------------------------------------------------------------------- +# SO(3) quaternion parameter module +# --------------------------------------------------------------------------- + +class SO3ParamQuat(nn.Module): + """ + Stores T unit quaternions as learnable parameters in R^4 and normalises + them on every call to `as_matrix()`. + + Quaternion convention: [x, y, z, w] (scalar-last, same as scipy). + + Initialisation + -------------- + "random" – uniform sampling over SO(3) via Shoemake's method. + "identity" – all rotations start as the identity (good for fine-tuning). + """ + + def __init__(self, T: int, init: str = "random"): + super().__init__() + if T < 1: + raise ValueError(f"T must be >= 1, got {T}") + quats = self._init_quaternions(T, init) # (T, 4) + self.quats = nn.Parameter(quats) + + # ------------------------------------------------------------------ + # Initialisers + # ------------------------------------------------------------------ + + @staticmethod + def _shoemake_sample(T: int) -> torch.Tensor: + """Uniform SO(3) sampling via Shoemake (1992). Returns (T, 4) [x,y,z,w].""" + u = torch.rand(T, 3) + sqrt1_u0 = torch.sqrt(1.0 - u[:, 0]) + sqrt_u0 = torch.sqrt(u[:, 0]) + two_pi = 2.0 * math.pi + x = sqrt1_u0 * torch.sin(two_pi * u[:, 1]) + y = sqrt1_u0 * torch.cos(two_pi * u[:, 1]) + z = sqrt_u0 * torch.sin(two_pi * u[:, 2]) + w = sqrt_u0 * torch.cos(two_pi * u[:, 2]) + return torch.stack([x, y, z, w], dim=-1) # (T, 4) + + @staticmethod + def _identity(T: int) -> torch.Tensor: + """All-identity rotations: [0,0,0,1] * T.""" + q = torch.zeros(T, 4) + q[:, 3] = 1.0 + return q + + @classmethod + def _init_quaternions(cls, T: int, init: str) -> torch.Tensor: + if init == "random": + return cls._shoemake_sample(T) + elif init == "identity": + return cls._identity(T) + else: + raise ValueError(f"Unknown init '{init}'; choose 'random' or 'identity'.") + + # ------------------------------------------------------------------ + # Forward helpers + # ------------------------------------------------------------------ + + def normalized(self) -> torch.Tensor: + """Returns (T, 4) unit quaternions.""" + return F.normalize(self.quats, p=2, dim=-1) + + def as_matrix(self) -> torch.Tensor: + """ + Converts the T stored quaternions to (T, 3, 3) rotation matrices. + + Uses the standard formula; no trig, just multiplications. + """ + q = self.normalized() # (T, 4) [x, y, z, w] + x, y, z, w = q.unbind(dim=-1) # each (T,) + + # Precompute products + xx, yy, zz = x*x, y*y, z*z + xy, xz, yz = x*y, x*z, y*z + wx, wy, wz = w*x, w*y, w*z + + # Row-major: R[i,j] + R = torch.stack([ + 1 - 2*(yy + zz), 2*(xy - wz), 2*(xz + wy), + 2*(xy + wz), 1 - 2*(xx + zz), 2*(yz - wx), + 2*(xz - wy), 2*(yz + wx), 1 - 2*(xx + yy), + ], dim=-1).reshape(-1, 3, 3) # (T, 3, 3) + + return R + + def extra_repr(self) -> str: + return f"T={self.quats.shape[0]}" + + +class SO3ParamR9SVD(nn.Module): + """ + SO(3) rotation bank using R9+SVD parameterization. + Each rotation is stored as an unconstrained 3x3 matrix M, + projected to SO(3) via SVD+(M) = U diag(1,1,det(UVt)) Vt. + """ + + def __init__(self, T: int, init: str = "random"): + super().__init__() + if init == "random": + # Initialize near identity with small noise + M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) + M = M + 0.1 * torch.randn(T, 3, 3) + elif init == "identity": + M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) + else: + raise ValueError(f"Unknown init '{init}'") + self.M = nn.Parameter(M) # (T, 3, 3) + + def as_matrix(self) -> torch.Tensor: + """Projects each M to SO(3) via SVD. Returns (T, 3, 3).""" + + + U, _, Vh = torch.linalg.svd(self.M) # U: (T,3,3), Vh: (T,3,3) + # Fix reflections: det(U Vh) must be +1 + d = torch.det(U @ Vh) # (T,) + diag = torch.ones(self.M.shape[0], 3, device=self.M.device, dtype=self.M.dtype) + diag[:, 2] = d # multiply last singular vector by sign + return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) + +def interpolate_ms_features_tilted( + pts: torch.Tensor, # (B, 3) + ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) + rotation_matrices: torch.Tensor, # (T, 3, 3) +) -> torch.Tensor: + """ + Fully-vectorized multi-scale, multi-rotation K-Planes feature interpolation. + Returns features of shape (B, C * T * num_scales). + """ + T = rotation_matrices.shape[0] + B = pts.shape[0] + + # (T, B, 3) — rotate all points by all rotations at once + rotated = torch.einsum("tij,bj->tbi", rotation_matrices, pts) + + # Build (T, 3, B, 2) coords for planes XY, ZX, YZ in one shot. + # index_select is faster and cleaner than advanced indexing with python lists. + # Plane axis layout: XY=(0,1), ZX=(2,0), YZ=(1,2) + idx = torch.tensor([[0, 1], + [2, 0], + [1, 2]], device=pts.device) # (3, 2) + # rotated: (T, B, 3) -> gather along last dim with idx (3, 2) + # Result: (T, 3, B, 2) + coords = rotated.unsqueeze(1).expand(T, 3, B, 3).gather( + -1, idx.view(1, 3, 1, 2).expand(T, 3, B, 2) + ) + + # Flatten (T, 3) -> 3*T so it matches grid's first dim, and add the H_out=1 axis + coord_tensor = coords.reshape(3 * T, B, 1, 2) # (3T, B, 1, 2) + + per_scale_features = [] + for plane_coef in ms_grids: + # plane_coef: (3T, C, H, W) + C = plane_coef.shape[1] + + sampled = F.grid_sample( + plane_coef, + coord_tensor, + align_corners=True, + mode="bilinear", + padding_mode="border", + ) # (3T, C, B, 1) + + # (3T, C, B) -> (T, 3, C, B) -> Hadamard across the "3" dim -> (T, C, B) + sampled = sampled.squeeze(-1).view(T, 3, C, B).prod(dim=1) + + # (T, C, B) -> (B, T, C) -> (B, T*C) to concatenate rotations along feature dim + per_scale_features.append( + sampled.permute(2, 0, 1).reshape(B, T * C) + ) + + # Concatenate across scales -> (B, T * C * num_scales) + return torch.cat(per_scale_features, dim=-1) \ No newline at end of file diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 19f26c05..8ef2830d 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -20,12 +20,6 @@ from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset -def _unwrap(model): - """Unwrap DDP/FSDP/any wrapper that exposes `.module`.""" - while hasattr(model, "module") and isinstance(model.module, torch.nn.Module): - model = model.module - return model - class ObjConstraintParams: """ Namespace class for object reconstruction constraint dataclasses and parsing utilities. @@ -104,17 +98,45 @@ class ObjINRConstraints(Constraints): positivity: bool = True shrinkage: float = 0.0 tv_vol: float = 0.0 - tv_plane: float = 0.0 sparsity: float = 0.0 _name: str = "obj_inr" soft_constraint_keys = ["tv_vol", "tv_plane", "sparsity"] hard_constraint_keys = ["positivity", "shrinkage"] + @dataclass + class ObjTensorDecompConstraints(Constraints): + """ + Constraints for a tensor decomposition object representation. + + Attributes + ---------- + positivity : bool + If ``True``, enforces non-negative values in the reconstruction. + shrinkage : float + Shrinkage regularization strength; pushes values toward zero. + tv_vol : float + Total variation regularization weight for the 3-D volume. + soft_constraint_keys : list[str] + Constraint fields penalized softly during optimization. + hard_constraint_keys : list[str] + Constraint fields enforced strictly during optimization. + """ + + positivity: bool = True + shrinkage: float = 0.0 + tv_vol: float = 0.0 + tv_plane: float = 0.0 + sparsity:float = 0.0 + _name: str = "obj_tensor_decomp" + + soft_constraint_keys = ["tv_vol", "tv_plane", "sparsity"] + hard_constraint_keys = ["positivity", "shrinkage"] + @classmethod def parse_dict( cls, d: dict - ) -> "ObjConstraintParams.ObjPixelatedConstraints | ObjConstraintParams.ObjINRConstraints": + ) -> "ObjConstraintParams.ObjPixelatedConstraints | ObjConstraintParams.ObjINRConstraints | ObjConstraintParams.ObjTensorDecompConstraints": """ Instantiate an object constraint dataclass from a configuration dictionary. @@ -162,12 +184,16 @@ def parse_dict( return ObjConstraintParams.ObjPixelatedConstraints(**d) elif name == "obj_inr": return ObjConstraintParams.ObjINRConstraints(**d) + elif name == "obj_tensor_decomp": + return ObjConstraintParams.ObjTensorDecompConstraints(**d) else: raise ValueError(f"Unknown object constraint type: {name.lower()}") ObjConstraintsType = ( - ObjConstraintParams.ObjPixelatedConstraints | ObjConstraintParams.ObjINRConstraints + ObjConstraintParams.ObjPixelatedConstraints + | ObjConstraintParams.ObjINRConstraints + | ObjConstraintParams.ObjTensorDecompConstraints ) @@ -285,6 +311,7 @@ def get_tv_loss(self, **kwargs) -> torch.Tensor: class ObjectPixelated(ObjectConstraints): + """ Object model for pixelated objects. @@ -424,7 +451,6 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM self.reconnect_optimizer_to_parameters() return self - class ObjectINR(ObjectConstraints, DDPMixin): DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjINRConstraints() @@ -488,6 +514,427 @@ def model(self) -> nn.Module | nn.parallel.DistributedDataParallel: # """ # raise RuntimeError("\n\n\nsetting model, this shouldn't be reachable???\n\n\n") + @property + def obj(self) -> torch.Tensor: + return self._obj + + @obj.setter + def obj(self, obj: torch.Tensor): + self._obj = obj + + @property + def obj_view(self) -> np.ndarray: + """ + Returns the object as a view of the x, y, z axes. + + Matches the axes of conventionally reconstructed objects, this is the object that will be saved. + """ + self.create_volume() + return self._obj.cpu().numpy().transpose(0, 1, 3, 2) + + def apply_soft_constraints( + self, + coords: torch.Tensor, + pred: torch.Tensor, + ) -> torch.Tensor: + soft_loss = torch.tensor(0.0, device=pred.device) + if self.constraints.tv_vol > 0: + num_tv_samples = min(10_000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + tv_coords = coords[tv_indices].detach().requires_grad_(True) + tv_densities_recomputed = self.model(tv_coords) + if isinstance(tv_densities_recomputed, tuple): + tv_densities_recomputed = tv_densities_recomputed[0] + + # Ensure shape is [num_samples, num_channels] + if tv_densities_recomputed.dim() == 1: + tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) + + # Compute gradients for each channel + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] # Shape: [num_samples, coord_dim] + + # Compute TV loss - gradient magnitude per sample + grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] + soft_loss += self.constraints.tv_vol * grad_norm.mean() + + if ( + isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) + and self.constraints.sparsity > 0 + ): # NOTE: For the linter, I must make this :) + sparsity_loss = self.constraints.sparsity * torch.norm(pred, p=1) + soft_loss += sparsity_loss + + return soft_loss + + def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: + """ + Apply hard constraints to the predicted values of the INR model. + """ + + if self.constraints.positivity: + pred = torch.clamp(pred, min=0.0, max=None) + if self.constraints.shrinkage: + pred = torch.max(pred - self.constraints.shrinkage, torch.zeros_like(pred)) + + return pred + + # --- Optimization Parameters --- + @property + def params(self) -> Generator[torch.nn.Parameter, None, None]: + return self.model.parameters() # type: ignore[attr-defined] + + def get_optimization_parameters(self) -> list[nn.Parameter]: + return list(self.params) + + # Pretraining + @property + def pretrained_weights(self) -> dict[str, torch.Tensor]: + """get the pretrained weights of the INR model""" + return self._pretrained_weights + + def _set_pretrained_weights(self, model: "torch.nn.Module"): + """set the pretrained weights of the INR model""" + if not isinstance(model, torch.nn.Module): + raise TypeError(f"Pretrained model must be a torch.nn.Module, got {type(model)}") + self._pretrained_weights = deepcopy(model.state_dict()) + + @property + def pretrain_target(self) -> TomographyINRPretrainDataset: + """get the pretrain target""" + return self._pretrain_target + + @pretrain_target.setter + def pretrain_target(self, target: TomographyINRPretrainDataset): + """set the pretrain target""" + self._pretrain_target = target + + @property + def dtype(self) -> torch.dtype: + """ + Returns the dtype of the object. + """ + # TODO: This is a temporary solution to get the dtype of the object. + return torch.float32 + + @property + def shape(self) -> tuple[int, int, int]: + return self._shape + + @shape.setter + def shape(self, shape: tuple[int, int, int]): + self._shape = shape + + # --- Helper Functions --- + def rebuild_model(self): + self._model = self.distribute_model(self._model) + + # Reset method that goes back to the pretrained weights. + def reset(self): + """reset the model to the pretrained weights""" + self.model.load_state_dict(self._pretrained_weights.copy()) + self._model = self.distribute_model( + self.model + ) # Maybe add a check to see if distributed or not, but not very computationally expensive to do this. + + # --- Forward Method --- + + def forward(self, coords: torch.Tensor) -> torch.Tensor: + """forward pass for the INR model""" + all_densities = self.model(coords) + + if all_densities.dim() > 1: + all_densities = all_densities.squeeze(-1) + valid_mask = ( + (coords[:, 0] >= -1) & (coords[:, 0] <= 1) & (coords[:, 1] >= -1) & (coords[:, 1] <= 1) + ).float() + + if all_densities.dim() > 1: + valid_mask = valid_mask.unsqueeze(-1) + # Multi-dimensional mask + all_densities = all_densities * valid_mask + + all_densities = self.apply_hard_constraints(all_densities) + + return all_densities + + # Pretrain Loop + + def pretrain( + self, + pretrain_dataset: TomographyINRPretrainDataset, + batch_size: int, + reset: bool = False, + num_iters: int = 10, + num_workers: int = 0, + optimizer_params: dict | None = None, + scheduler_params: dict | None = None, + loss_fn: Callable | str = "l1", + verbose: bool = True, + ): + """ + Pretrain the INR model to fit target volume. + """ + + if ( + pretrain_dataset is not None + ): # Need to make a check if there's already a pretrain dataset to not go through with the setup again. + self.pretrain_dataset = pretrain_dataset + ( + self.pretraining_dataloader, + self.pretraining_sampler, + self.pretraining_val_dataloader, + self.pretraining_val_sampler, + ) = self.setup_dataloader(pretrain_dataset, batch_size, num_workers=num_workers) + + if optimizer_params is not None: + self.set_optimizer(optimizer_params) + if scheduler_params is not None: + self.set_scheduler(scheduler_params, num_iters) + + if reset: + self.reset() + + loss_fn = get_loss_module(loss_fn, self.dtype) + + self._pretrain( + num_iters=num_iters, + loss_fn=loss_fn, + verbose=verbose, + ) + + def _pretrain( + self, + num_iters: int, + loss_fn: Callable, + verbose: bool, + ): + if self.optimizer is None: + raise RuntimeError("Optimizer not set. Call set_optimizer() first.") + if self.scheduler is None: + raise RuntimeError("Scheduler not set. Call set_scheduler() first.") + + self.model.train() + optimizer = self.optimizer + scheduler = self.scheduler + + pbar = tqdm(range(num_iters), desc="Pretraining", disable=not verbose) + for a0 in pbar: + epoch_loss = 0 + for batch_idx, batch in enumerate[Any](self.pretraining_dataloader): + coords = batch["coords"].to(self.device, non_blocking=True) + target = batch["target"].to(self.device, non_blocking=True) + + with torch.autocast( + device_type=self.device.type, dtype=torch.bfloat16, enabled=True + ): + outputs = self.forward(coords) + loss = loss_fn(outputs, target) + + loss.backward() + epoch_loss += loss.item() + + # Clip gradients + torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0) + + optimizer.step() + optimizer.zero_grad() + + if scheduler is not None: + if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + scheduler.step(epoch_loss) + else: + scheduler.step() + + self._pretrain_losses.append(epoch_loss / len(self.pretraining_dataloader)) + print( + f"Epoch {a0 + 1}/{num_iters}, Pretrain Loss: {epoch_loss / len(self.pretraining_dataloader):.4f}" + ) + self._pretrain_lrs.append(optimizer.param_groups[0]["lr"]) + + def create_volume(self, return_vol: bool = False): + N = max(self._shape) + with torch.no_grad(): + coords_1d = torch.linspace(-1, 1, N) + x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") + inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) + model = self.model.module if isinstance(self.model, nn.DataParallel) else self.model + + inference_batch_size = 5 * N * N + total_samples = N**3 + samples_per_gpu = total_samples // self.world_size + remainder = total_samples % self.world_size + + if self.global_rank < remainder: + start_idx = self.global_rank * (samples_per_gpu + 1) + end_idx = start_idx + samples_per_gpu + 1 + else: + start_idx = self.global_rank * samples_per_gpu + remainder + end_idx = start_idx + samples_per_gpu + + inputs_subset = inputs[start_idx:end_idx] + num_samples = inputs_subset.shape[0] + + outputs_list = [] + for batch_start in range(0, num_samples, inference_batch_size): + batch_end = min(batch_start + inference_batch_size, num_samples) + batch_coords = inputs_subset[batch_start:batch_end].to( + self.device, non_blocking=True + ) + + batch_outputs = model(batch_coords) # (B, C) or (B,) etc. + + if isinstance(batch_outputs, tuple): + batch_outputs = batch_outputs[0] + batch_outputs = self.apply_hard_constraints(batch_outputs) + + # Ensure shape is (B, C) + if batch_outputs.dim() == 1: + batch_outputs = batch_outputs.unsqueeze(-1) # (B, 1) + + outputs_list.append(batch_outputs.cpu()) + + outputs = torch.cat(outputs_list, dim=0) # (local_B, C) + C = outputs.shape[-1] # e.g. 5 + + if self.world_size > 1: + # gather variable-sized first dimension (local_B) while keeping channels + local_B = outputs.shape[0] + output_size = torch.tensor(local_B, device=self.device, dtype=torch.long) + all_sizes = [ + torch.zeros(1, device=self.device, dtype=torch.long) + for _ in range(self.world_size) + ] + dist.all_gather(all_sizes, output_size) + max_size = max(size.item() for size in all_sizes) + + outputs_dev = outputs.to(self.device) # (local_B, C) + if local_B < max_size: + pad = torch.zeros( + (max_size - local_B, C), # type: ignore + device=self.device, + dtype=outputs_dev.dtype, + ) + outputs_padded = torch.cat([outputs_dev, pad], dim=0) # (max_size, C) + else: + outputs_padded = outputs_dev + + gathered_outputs = [ + torch.empty((max_size, C), device=self.device, dtype=outputs_dev.dtype) # type: ignore + for _ in range(self.world_size) + ] + dist.all_gather(gathered_outputs, outputs_padded.contiguous()) + + trimmed_outputs = [] + for rank, size in enumerate(all_sizes): + trimmed_outputs.append(gathered_outputs[rank][: size.item(), :]) + + pred_full = torch.cat(trimmed_outputs, dim=0).reshape(C, N, N, N).float() + else: + pred_full = outputs.reshape(C, N, N, N).float() + + if return_vol: + return pred_full.detach().cpu() + + self._obj = pred_full.detach().cpu() + + def get_tv_loss( # pyright: ignore[reportIncompatibleMethodOverride] + self, + coords: torch.Tensor, + ) -> torch.Tensor: + tv_loss = torch.tensor(0.0, device=coords.device) + + num_tv_samples = min(10000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + tv_coords = coords[tv_indices].detach().requires_grad_(True) + + tv_densities_recomputed = self.forward(tv_coords) + + if tv_densities_recomputed.dim() > 1: + tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) + + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] + + grad_norm = torch.norm(grad_outputs, dim=1) + + tv_loss += self.constraints.tv_vol * grad_norm.mean() + return tv_loss + + def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change + if isinstance(device, str): + device = torch.device(device) + self._device = device + if self.world_size == 1: + self._model = self._model.to(device) + elif not isinstance(self._model, torch.nn.parallel.DistributedDataParallel): + self.distribute_model(self._model) + self.reconnect_optimizer_to_parameters() + + +class ObjectTensorDecomp(ObjectConstraints, DDPMixin): + DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjTensorDecompConstraints() + + def __init__( + self, + shape: tuple[int, int, int], + device: str = "cpu", + rng: np.random.Generator | int | None = None, + model: nn.Module | None = None, + _token: object | None = None, + ): + super().__init__( + shape=shape, + device=device, + rng=rng, + _token=self._token, + ) + self._pretrain_losses = [] + self._pretrain_lrs = [] + self.constraints: ObjConstraintsType = self.DEFAULT_CONSTRAINTS.copy() + # Register the network submodule (important: real nn.Module attribute) + if model is not None: + self.setup_distributed(device=device) + self._model = self.distribute_model(model) + + @classmethod + def from_model( + cls, + model: nn.Module, + shape: tuple[int, int, int], + device: str = "cpu", + rng: np.random.Generator | int | None = None, + ): + obj_model = cls( + shape=shape, + device=device, + rng=rng, + model=model, # ✅ build/register in __init__ + ) + + obj_model.setup_distributed(device=device) + obj_model.to(device) + return obj_model + + # --- Properties --- + + @property + def model(self) -> nn.Module | nn.parallel.DistributedDataParallel: + """ + Returns the INR model. + """ + return self._model + @property def obj(self) -> torch.Tensor: return self._obj @@ -527,53 +974,29 @@ def apply_soft_constraints( soft_loss += sparsity_loss return soft_loss + # TV Losses def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: - """ - Dispatch to the appropriate TV loss based on model architecture. - - KPlanes / KPlanesTILTED: per-plane TV (regularizes the stored feature - planes directly). Cheap; regularizes the representation. - - CPTilted: per-line TV (same idea, applied to 1D feature lines). - - SIREN / other INRs: volume TV via autograd (exact gradient). - - Fallback: volume TV via finite differences (works anywhere). - - If you want *volume* smoothness regardless of architecture, call - `get_volume_tv_loss(coords)` directly. - """ tv_loss = torch.tensor(0.0, device=pred.device) - inner = _unwrap(self.model) - if isinstance(inner, (KPlanes, KPlanesTILTED)): - tv_loss += self._get_plane_tv_loss() - - # SIREN and other INRs support double-backward → use exact autograd. - # Everything else falls through to finite-difference volume TV. - if not isinstance(inner, PPLR): # adjust to your actual INR class - tv_loss += self._get_volume_tv_loss_autograd(coords) - - if isinstance(inner, (KPlanes, KPlanesTILTED)): - tv_loss += self.get_volume_tv_loss(coords) + tv_loss += self._get_plane_tv_loss() + tv_loss += self.get_volume_tv_loss(coords) return tv_loss - # ---------------------------------------------------------------------- - # Plane TV (K-Planes family) — regularizes the stored feature planes. - # ---------------------------------------------------------------------- - def _get_plane_tv_loss(self) -> torch.Tensor: - inner = _unwrap(self.model) - is_tilted = isinstance(inner, KPlanesTILTED) + is_tilted = isinstance(self.model, KPlanesTILTED) per_level = [] - for p in inner.grids: + for p in self.model.grids: # p: (3*T, C, H, W) for TILTED, (3, C, H, W) for KPlanes dh = (p[:, :, 1:, :] - p[:, :, :-1, :]).pow(2).mean(dim=(1, 2, 3)) dw = (p[:, :, :, 1:] - p[:, :, :, :-1]).pow(2).mean(dim=(1, 2, 3)) per_plane = dh + dw # (3*T,) or (3,) if is_tilted: - T = inner.T + T = self.model.T per_rotation = per_plane.view(T, 3).sum(dim=1) # sum 3 planes per rotation level_tv = per_rotation.mean() # avg across rotations else: @@ -584,40 +1007,6 @@ def _get_plane_tv_loss(self) -> torch.Tensor: return self.constraints.tv_plane * torch.stack(per_level).sum() - # ---------------------------------------------------------------------- - # Volume TV (autograd) — exact gradient, needs double-backward support. - # ---------------------------------------------------------------------- - - def _get_volume_tv_loss_autograd(self, coords: torch.Tensor) -> torch.Tensor: - """ - Isotropic volume TV using autograd. Exact gradient, but requires the - model to support double-backward (SIREN yes, grid_sample-based models no). - """ - num_tv_samples = min(10_000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - - tv_coords = coords[tv_indices].detach().requires_grad_(True) - pred = self.model(tv_coords) - if isinstance(pred, tuple): - pred = pred[0] - if pred.dim() == 1: - pred = pred.unsqueeze(-1) - - grad_outputs = torch.autograd.grad( - outputs=pred, - inputs=tv_coords, - grad_outputs=torch.ones_like(pred), - create_graph=True, - )[0] # (N, 3) - - grad_norm = torch.norm(grad_outputs, dim=1) # (N,) - return self.constraints.tv_vol * grad_norm.mean() - - - # ---------------------------------------------------------------------- - # Volume TV (finite differences) — works for any model. - # ---------------------------------------------------------------------- - def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: """ Isotropic volume TV via finite differences. Same form as the autograd @@ -1073,5 +1462,4 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM self.distribute_model(self._model) self.reconnect_optimizer_to_parameters() - ObjectModelType = ObjectPixelated | ObjectINR From e33fec1ebe0119565979ba9198f566dbab1a3887 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 23 Apr 2026 11:54:52 -0700 Subject: [PATCH 020/140] Removed some the _unwrap dependencies. Added ObjectTensorDecomp on top-level Tomography --- src/quantem/core/ml/models/kplanes.py | 66 ++++++++++++++++++++--- src/quantem/core/ml/models/so3params.py | 53 ------------------ src/quantem/tomography/object_models.py | 19 +++---- src/quantem/tomography/tomography.py | 5 +- src/quantem/tomography/tomography_base.py | 3 +- 5 files changed, 71 insertions(+), 75 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 1b0a40ab..58d6fc81 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -253,6 +253,60 @@ def td_type(self) -> str: # KPlanesTILTED # --------------------------------------------------------------------------- +def interpolate_ms_features_tilted( + pts: torch.Tensor, # (B, 3) + ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) + rotation_matrices: torch.Tensor, # (T, 3, 3) +) -> torch.Tensor: + """ + Fully-vectorized multi-scale, multi-rotation K-Planes feature interpolation. + Returns features of shape (B, C * T * num_scales). + """ + T = rotation_matrices.shape[0] + B = pts.shape[0] + + # (T, B, 3) — rotate all points by all rotations at once + rotated = torch.einsum("tij,bj->tbi", rotation_matrices, pts) + + # Build (T, 3, B, 2) coords for planes XY, ZX, YZ in one shot. + # index_select is faster and cleaner than advanced indexing with python lists. + # Plane axis layout: XY=(0,1), ZX=(2,0), YZ=(1,2) + idx = torch.tensor([[0, 1], + [2, 0], + [1, 2]], device=pts.device) # (3, 2) + # rotated: (T, B, 3) -> gather along last dim with idx (3, 2) + # Result: (T, 3, B, 2) + coords = rotated.unsqueeze(1).expand(T, 3, B, 3).gather( + -1, idx.view(1, 3, 1, 2).expand(T, 3, B, 2) + ) + + # Flatten (T, 3) -> 3*T so it matches grid's first dim, and add the H_out=1 axis + coord_tensor = coords.reshape(3 * T, B, 1, 2) # (3T, B, 1, 2) + + per_scale_features = [] + for plane_coef in ms_grids: + # plane_coef: (3T, C, H, W) + C = plane_coef.shape[1] + + sampled = F.grid_sample( + plane_coef, + coord_tensor, + align_corners=True, + mode="bilinear", + padding_mode="border", + ) # (3T, C, B, 1) + + # (3T, C, B) -> (T, 3, C, B) -> Hadamard across the "3" dim -> (T, C, B) + sampled = sampled.squeeze(-1).view(T, 3, C, B).prod(dim=1) + + # (T, C, B) -> (B, T, C) -> (B, T*C) to concatenate rotations along feature dim + per_scale_features.append( + sampled.permute(2, 0, 1).reshape(B, T * C) + ) + + # Concatenate across scales -> (B, T * C * num_scales) + return torch.cat(per_scale_features, dim=-1) + class KPlanesTILTED(KPlanes): """ K-Planes with T learned SO(3) rotations (TILTED). @@ -352,7 +406,7 @@ def __init__( self._build_sigma_net(use_hybrid_mlp, hybrid_hidden_dim, hybrid_num_layers) # ---- Learnable rotations ---- - self.so3 = SO3Param(T, init=tau_init) + self.set_so3_param_type(so3_param_type, init=tau_init) # ------------------------------------------------------------------ # Internal helpers @@ -466,7 +520,7 @@ def extra_repr(self) -> str: f"num_scales={len(self.multiscale_res_multipliers)}" ) - def set_so3_param_type(self, so3_param_type: str) -> None: + def set_so3_param_type(self, so3_param_type: str, init: str = "rand") -> None: """ Set the SO3 parameterization type. @@ -476,17 +530,13 @@ def set_so3_param_type(self, so3_param_type: str) -> None: SO3 parameterization type ("quat" or "r9svd"). """ if so3_param_type == "r9svd": - self.so3 = SO3ParamR9SVD(self.T) + self.so3 = SO3ParamR9SVD(self.T, init=init) elif so3_param_type == "quat": - self.so3 = SO3ParamQuat(self.T) + self.so3 = SO3ParamQuat(self.T, init=init) else: raise ValueError(f"Invalid SO3 parameterization type: {so3_param_type}") - - - - # CP Decomp for Warmup SO3 rotations def interpolate_ms_features_cp_tilted( diff --git a/src/quantem/core/ml/models/so3params.py b/src/quantem/core/ml/models/so3params.py index 29314fb6..14f60867 100644 --- a/src/quantem/core/ml/models/so3params.py +++ b/src/quantem/core/ml/models/so3params.py @@ -128,56 +128,3 @@ def as_matrix(self) -> torch.Tensor: diag[:, 2] = d # multiply last singular vector by sign return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) -def interpolate_ms_features_tilted( - pts: torch.Tensor, # (B, 3) - ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) - rotation_matrices: torch.Tensor, # (T, 3, 3) -) -> torch.Tensor: - """ - Fully-vectorized multi-scale, multi-rotation K-Planes feature interpolation. - Returns features of shape (B, C * T * num_scales). - """ - T = rotation_matrices.shape[0] - B = pts.shape[0] - - # (T, B, 3) — rotate all points by all rotations at once - rotated = torch.einsum("tij,bj->tbi", rotation_matrices, pts) - - # Build (T, 3, B, 2) coords for planes XY, ZX, YZ in one shot. - # index_select is faster and cleaner than advanced indexing with python lists. - # Plane axis layout: XY=(0,1), ZX=(2,0), YZ=(1,2) - idx = torch.tensor([[0, 1], - [2, 0], - [1, 2]], device=pts.device) # (3, 2) - # rotated: (T, B, 3) -> gather along last dim with idx (3, 2) - # Result: (T, 3, B, 2) - coords = rotated.unsqueeze(1).expand(T, 3, B, 3).gather( - -1, idx.view(1, 3, 1, 2).expand(T, 3, B, 2) - ) - - # Flatten (T, 3) -> 3*T so it matches grid's first dim, and add the H_out=1 axis - coord_tensor = coords.reshape(3 * T, B, 1, 2) # (3T, B, 1, 2) - - per_scale_features = [] - for plane_coef in ms_grids: - # plane_coef: (3T, C, H, W) - C = plane_coef.shape[1] - - sampled = F.grid_sample( - plane_coef, - coord_tensor, - align_corners=True, - mode="bilinear", - padding_mode="border", - ) # (3T, C, B, 1) - - # (3T, C, B) -> (T, 3, C, B) -> Hadamard across the "3" dim -> (T, C, B) - sampled = sampled.squeeze(-1).view(T, 3, C, B).prod(dim=1) - - # (T, C, B) -> (B, T, C) -> (B, T*C) to concatenate rotations along feature dim - per_scale_features.append( - sampled.permute(2, 0, 1).reshape(B, T * C) - ) - - # Concatenate across scales -> (B, T * C * num_scales) - return torch.cat(per_scale_features, dim=-1) \ No newline at end of file diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 8ef2830d..0eda0bb9 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1017,13 +1017,12 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] tv_coords = coords[tv_indices] # (N, 3) - inner = _unwrap(self.model) - if hasattr(inner, "resolution"): - h = 2.0 / min(inner.resolution) + if hasattr(self.model, "resolution"): + h = 2.0 / min(self.model.resolution) else: h = 1e-2 - pred = inner(tv_coords) + pred = self.model(tv_coords) if isinstance(pred, tuple): pred = pred[0] if pred.dim() == 1: @@ -1033,7 +1032,7 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: for axis in range(3): offset = torch.zeros(3, device=tv_coords.device) offset[axis] = h - shifted_pred = inner(tv_coords + offset) + shifted_pred = self.model(tv_coords + offset) if isinstance(shifted_pred, tuple): shifted_pred = shifted_pred[0] if shifted_pred.dim() == 1: @@ -1094,15 +1093,14 @@ def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | di self._optimizer_params = params return - inner = _unwrap(self.model) - if isinstance(inner, PPLR): + if isinstance(self.model, PPLR): if not isinstance(params, dict): raise TypeError(f"optimizer parameters must be a dict for PPLR, got {type(params)}") object_params = params - if set(object_params.keys()) != set(inner.param_keys): - raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {inner.param_keys}") + if set(object_params.keys()) != set(self.model.param_keys): + raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}") params = {} for key, value in object_params.items(): @@ -1125,8 +1123,7 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: updating get_optimization_parameters to return a list of parameters and their LRs. """ - inner = _unwrap(self.model) - if not isinstance(inner, PPLR): + if not isinstance(self.model, PPLR): super().set_optimizer(opt_params) return diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index a96136df..826ad360 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -26,6 +26,7 @@ ObjConstraintsType, ObjectINR, ObjectPixelated, + ObjectTensorDecomp, ) from quantem.tomography.radon.radon import iradon_torch, radon_torch from quantem.tomography.tomography_base import TomographyBase @@ -42,7 +43,7 @@ class Tomography(TomographyOpt, TomographyBase): def from_models( cls, dset: DatasetModelType, - obj_model: ObjectINR, + obj_model: ObjectINR | ObjectTensorDecomp, logger: LoggerTomography | None = None, device: str = "cuda", verbose: int | bool = True, @@ -180,7 +181,7 @@ def reconstruct( consistency_loss = torch.tensor(0.0, device=self.device) total_loss = torch.tensor(0.0, device=self.device) epoch_soft_constraint_loss = torch.tensor(0.0, device=self.device) - if isinstance(self.obj_model, ObjectINR): + if isinstance(self.obj_model, ObjectINR) or isinstance(self.obj_model, ObjectTensorDecomp): self.obj_model.model.train() else: raise NotImplementedError( diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index 58909bdf..419b5ede 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -11,6 +11,7 @@ ObjConstraintsType, ObjectINR, ObjectModelType, + ObjectTensorDecomp, ) @@ -51,7 +52,7 @@ def __init__( self._val_losses: list[float] = [] self._lrs: dict[str, list] = {} # DDP Initialization - if isinstance(obj_model, ObjectINR): + if isinstance(obj_model, ObjectINR) or isinstance(obj_model, ObjectTensorDecomp): self.setup_distributed(device=device) if self.global_rank == 0: print("Setting up DDP for obj_model") From 15bdcd4bc99f8e62999d033bfce46d8e9c45c3e2 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 23 Apr 2026 16:48:45 -0700 Subject: [PATCH 021/140] Revamped model_base.py to cover type-hinting stuff. KPlanes has a set of parameters now that helps with type-setting. The main reason for having model_base.py as is it is right now is if we ever wanted to go do TensoRF or something just to validate --- src/quantem/core/ml/models/__init__.py | 0 src/quantem/core/ml/models/kplanes.py | 55 +++++++++- src/quantem/core/ml/models/model_base.py | 44 +++++--- src/quantem/tomography/object_models.py | 131 ++++------------------- 4 files changed, 100 insertions(+), 130 deletions(-) create mode 100644 src/quantem/core/ml/models/__init__.py diff --git a/src/quantem/core/ml/models/__init__.py b/src/quantem/core/ml/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 58d6fc81..16c56575 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -154,7 +154,7 @@ def interpolate_ms_features( return torch.cat(per_scale, dim=-1) -class KPlanes(nn.Module, PPLR, TensorDecompositionModel): +class KPlanes(PPLR, TensorDecompositionModel): def __init__( self, @@ -248,6 +248,42 @@ def param_keys(self) -> list[str]: def td_type(self) -> str: return self._td_type + @td_type.setter + def td_type(self, td_type: str): + if not isinstance(td_type, str): + raise TypeError("td_type must be a string") + self._td_type = td_type + + @property + def tilted(self) -> bool: + return False + + @tilted.setter + def tilted(self, tilted: bool): + if not isinstance(tilted, bool): + raise TypeError("tilted must be a boolean") + self._tilted = tilted + + @property + def grids(self) -> torch.nn.ParameterList: + return self._grids + + @grids.setter + def grids(self, grids: torch.nn.ParameterList): + if not isinstance(grids, torch.nn.ParameterList): + raise TypeError("Grids must be a ParameterList") + self._grids = grids + + @property + def resolution(self) -> list[int]: + return self._resolution + + @resolution.setter + def resolution(self, resolution: Sequence[int]): + if not isinstance(resolution, Sequence): + raise TypeError("Resolution must be a sequence") + self._resolution = list(resolution) + # --------------------------------------------------------------------------- # KPlanesTILTED @@ -535,6 +571,10 @@ def set_so3_param_type(self, so3_param_type: str, init: str = "rand") -> None: self.so3 = SO3ParamQuat(self.T, init=init) else: raise ValueError(f"Invalid SO3 parameterization type: {so3_param_type}") + + @property + def tilted(self) -> bool: + return True # CP Decomp for Warmup SO3 rotations @@ -590,7 +630,7 @@ def interpolate_ms_features_cp_tilted( return torch.cat(per_scale_features, dim=-1) -class CPTilted(nn.Module, PPLR, TensorDecompositionModel): +class CPTilted(PPLR, TensorDecompositionModel): """ CP decomposition with TILTED rotations — the true bottleneck model for phase 1. Rank-1-per-channel feature representation. @@ -660,4 +700,13 @@ def td_type(self) -> str: return self._td_type def extract_tau_state(self) -> torch.Tensor: - return self.so3.M.detach().clone() \ No newline at end of file + return self.so3.M.detach().clone() + + @property + def tilted(self) -> bool: + return True + + + + +KPlanesType = KPlanes | KPlanesTILTED | CPTilted \ No newline at end of file diff --git a/src/quantem/core/ml/models/model_base.py b/src/quantem/core/ml/models/model_base.py index 44bbdef5..e1a486d7 100644 --- a/src/quantem/core/ml/models/model_base.py +++ b/src/quantem/core/ml/models/model_base.py @@ -2,36 +2,50 @@ from typing import Dict import torch +import torch.nn as nn class PPLR(ABC): """ Abstract base class for models that require multi-scale parameter optimization. """ + @abstractmethod - def get_params(self) -> Dict[str, list[torch.nn.Parameter]]: + def get_params(self) -> Dict[str, list[nn.Parameter]]: """ - This abstract method should return a dictionary of parameters based on a key. + Return a dictionary of parameters grouped by key. - For example if your nn.Module has multiple optimizable parameter groups, - you can return a dictionary with the keys "grids" and "sigma_net" (KPlanes example). + For example if your nn.Module has multiple optimizable parameter groups, + you can return a dictionary with the keys "grids" and "sigma_net" + (KPlanes example). """ pass @property @abstractmethod def param_keys(self) -> list[str]: - """ - This abstract property should return a list of available parameter keys. - """ + """List of available parameter-group keys.""" pass -class TensorDecompositionModel(ABC): - @property - @abstractmethod - def td_type(self) -> str: - """ - This abstract property should return the type of tensor decomposition used. - """ - pass \ No newline at end of file +class TensorDecompositionModel(nn.Module, ABC): + """ + Base class for factored tensor-decomposition models. + + Subclasses must set ``td_type`` as a normal attribute in ``__init__``. + """ + + td_type: str + + +class PlanarDecompositionModel(TensorDecompositionModel): + """ + Planar factored-grid models: K-Planes, K-Planes-TILTED, HexPlane, tri-planes. + + Subclasses must set ``grids``, ``tilted``, and ``resolution`` as normal + attributes in ``__init__``. + """ + + grids: nn.ParameterList + tilted: bool + resolution: list[int] \ No newline at end of file diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 0eda0bb9..346e01da 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -14,12 +14,12 @@ from quantem.core.ml.constraints import BaseConstraints, Constraints from quantem.core.ml.ddp import DDPMixin from quantem.core.ml.loss_functions import get_loss_module -from quantem.core.ml.models.kplanes import KPlanes, KPlanesTILTED -from quantem.core.ml.models.model_base import PPLR +from quantem.core.ml.models.model_base import PPLR, PlanarDecompositionModel from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset + class ObjConstraintParams: """ Namespace class for object reconstruction constraint dataclasses and parsing utilities. @@ -197,6 +197,12 @@ def parse_dict( ) +def _unwrap(model: nn.Module | nn.parallel.DistributedDataParallel) -> PlanarDecompositionModel: + """Unwrap a DistributedDataParallel model to get the underlying module ONLY for tensor decomposition models.""" + if isinstance(model, nn.parallel.DistributedDataParallel): + return model.module + return model + class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): DEFAULT_LRS = { "object": 8e-6, @@ -205,7 +211,6 @@ class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): """ Base class for all ObjectModels to inherit from. """ - def __init__( self, shape: tuple[int, int, int], # pyright: ignore[reportRedeclaration] @@ -297,7 +302,6 @@ def to(self, *args, **kwargs): raise NotImplementedError - class ObjectConstraints(BaseConstraints, ObjectBase): # TODO: Ask Arthur why we still need this def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -309,7 +313,6 @@ def get_tv_loss(self, **kwargs) -> torch.Tensor: """ raise NotImplementedError - class ObjectPixelated(ObjectConstraints): """ @@ -453,7 +456,6 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM class ObjectINR(ObjectConstraints, DDPMixin): DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjINRConstraints() - def __init__( self, shape: tuple[int, int, int], @@ -470,7 +472,7 @@ def __init__( ) self._pretrain_losses = [] self._pretrain_lrs = [] - self.constraints: ObjConstraintsType = self.DEFAULT_CONSTRAINTS.copy() + self.constraints: ObjConstraintParams.ObjINRConstraints = self.DEFAULT_CONSTRAINTS.copy() # Register the network submodule (important: real nn.Module attribute) if model is not None: self.setup_distributed(device=device) @@ -881,10 +883,8 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM self.distribute_model(self._model) self.reconnect_optimizer_to_parameters() - class ObjectTensorDecomp(ObjectConstraints, DDPMixin): DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjTensorDecompConstraints() - def __init__( self, shape: tuple[int, int, int], @@ -901,7 +901,7 @@ def __init__( ) self._pretrain_losses = [] self._pretrain_lrs = [] - self.constraints: ObjConstraintsType = self.DEFAULT_CONSTRAINTS.copy() + self.constraints: ObjConstraintParams.ObjTensorDecompConstraints = self.DEFAULT_CONSTRAINTS.copy() # Register the network submodule (important: real nn.Module attribute) if model is not None: self.setup_distributed(device=device) @@ -929,7 +929,7 @@ def from_model( # --- Properties --- @property - def model(self) -> nn.Module | nn.parallel.DistributedDataParallel: + def model(self) -> nn.Module | nn.parallel.DistributedDataParallel | PlanarDecompositionModel: """ Returns the INR model. """ @@ -970,7 +970,7 @@ def apply_soft_constraints( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) and self.constraints.sparsity > 0 ): # NOTE: For the linter, I must make this :) - sparsity_loss = self.constraints.sparsity * torch.norm(all_densities, p=1) + sparsity_loss = self.constraints.sparsity * all_densities.abs().mean() soft_loss += sparsity_loss return soft_loss @@ -986,10 +986,11 @@ def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: def _get_plane_tv_loss(self) -> torch.Tensor: - is_tilted = isinstance(self.model, KPlanesTILTED) + is_tilted = self.model.tilted per_level = [] - for p in self.model.grids: + model = _unwrap(self.model) + for p in model.grids: # p: (3*T, C, H, W) for TILTED, (3, C, H, W) for KPlanes dh = (p[:, :, 1:, :] - p[:, :, :-1, :]).pow(2).mean(dim=(1, 2, 3)) dw = (p[:, :, :, 1:] - p[:, :, :, :-1]).pow(2).mean(dim=(1, 2, 3)) @@ -1017,12 +1018,10 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] tv_coords = coords[tv_indices] # (N, 3) - if hasattr(self.model, "resolution"): - h = 2.0 / min(self.model.resolution) - else: - h = 1e-2 + model = _unwrap(self.model) + h = 2.0 / min(model.resolution) - pred = self.model(tv_coords) + pred = model(tv_coords) if isinstance(pred, tuple): pred = pred[0] if pred.dim() == 1: @@ -1269,99 +1268,7 @@ def forward(self, coords: torch.Tensor) -> torch.Tensor: return all_densities - # Pretrain Loop - - def pretrain( - self, - pretrain_dataset: TomographyINRPretrainDataset, - batch_size: int, - reset: bool = False, - num_iters: int = 10, - num_workers: int = 0, - optimizer_params: dict | None = None, - scheduler_params: dict | None = None, - loss_fn: Callable | str = "l1", - verbose: bool = True, - ): - """ - Pretrain the INR model to fit target volume. - """ - - if ( - pretrain_dataset is not None - ): # Need to make a check if there's already a pretrain dataset to not go through with the setup again. - self.pretrain_dataset = pretrain_dataset - ( - self.pretraining_dataloader, - self.pretraining_sampler, - self.pretraining_val_dataloader, - self.pretraining_val_sampler, - ) = self.setup_dataloader(pretrain_dataset, batch_size, num_workers=num_workers) - - if optimizer_params is not None: - self.set_optimizer(optimizer_params) - if scheduler_params is not None: - self.set_scheduler(scheduler_params, num_iters) - - if reset: - self.reset() - - loss_fn = get_loss_module(loss_fn, self.dtype) - - self._pretrain( - num_iters=num_iters, - loss_fn=loss_fn, - verbose=verbose, - ) - - def _pretrain( - self, - num_iters: int, - loss_fn: Callable, - verbose: bool, - ): - if self.optimizer is None: - raise RuntimeError("Optimizer not set. Call set_optimizer() first.") - if self.scheduler is None: - raise RuntimeError("Scheduler not set. Call set_scheduler() first.") - - self.model.train() - optimizer = self.optimizer - scheduler = self.scheduler - - pbar = tqdm(range(num_iters), desc="Pretraining", disable=not verbose) - for a0 in pbar: - epoch_loss = 0 - for batch_idx, batch in enumerate[Any](self.pretraining_dataloader): - coords = batch["coords"].to(self.device, non_blocking=True) - target = batch["target"].to(self.device, non_blocking=True) - - with torch.autocast( - device_type=self.device.type, dtype=torch.bfloat16, enabled=True - ): - outputs = self.forward(coords) - loss = loss_fn(outputs, target) - - loss.backward() - epoch_loss += loss.item() - - # Clip gradients - torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0) - - optimizer.step() - optimizer.zero_grad() - - if scheduler is not None: - if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): - scheduler.step(epoch_loss) - else: - scheduler.step() - - self._pretrain_losses.append(epoch_loss / len(self.pretraining_dataloader)) - print( - f"Epoch {a0 + 1}/{num_iters}, Pretrain Loss: {epoch_loss / len(self.pretraining_dataloader):.4f}" - ) - self._pretrain_lrs.append(optimizer.param_groups[0]["lr"]) + # NOTE: Pretraining is done in a two-phase fashion as shown in TILTED paper. def create_volume(self, return_vol: bool = False): N = max(self._shape) From 0c71a5dc10d287ac5005390466a2bc7111c3bccf Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 23 Apr 2026 16:53:20 -0700 Subject: [PATCH 022/140] Final changes prior to draft PR --- src/quantem/core/ml/models/kplanes.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 16c56575..b1be28ee 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -130,7 +130,6 @@ def query_planes( result = result * sampled return result # pyright: ignore[reportReturnType] - def interpolate_ms_features( pts: torch.Tensor, ms_grids: nn.ParameterList, @@ -284,7 +283,6 @@ def resolution(self, resolution: Sequence[int]): raise TypeError("Resolution must be a sequence") self._resolution = list(resolution) - # --------------------------------------------------------------------------- # KPlanesTILTED # --------------------------------------------------------------------------- @@ -576,7 +574,6 @@ def set_so3_param_type(self, so3_param_type: str, init: str = "rand") -> None: def tilted(self) -> bool: return True - # CP Decomp for Warmup SO3 rotations def interpolate_ms_features_cp_tilted( From 99fdd885c75afa867628000c40e41b38859a5741 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 23 Apr 2026 17:09:47 -0700 Subject: [PATCH 023/140] Fixed typo --- src/quantem/tomography/object_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 346e01da..10509655 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1078,7 +1078,7 @@ def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any return list(self.params) - # --- DDP Mixin Overloads in the case of PPLR --- + # --- Optimizer Mixin Overloads in the case of PPLR --- @property def optimizer_params(self) -> OptimizerType | dict[str, OptimizerType]: From 4a44826943324dac0f6aa527bdf6bba0f79c3f1f Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 23 Apr 2026 17:28:17 -0700 Subject: [PATCH 024/140] Small change to set_optimizer in OptimizerMixin to allow for dictionary parsing --- src/quantem/core/ml/models/kplanes.py | 2 +- src/quantem/core/ml/optimizer_mixin.py | 43 ++++-- src/quantem/tomography/object_models.py | 177 ++++++++++-------------- 3 files changed, 106 insertions(+), 116 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index b1be28ee..c8b49e70 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -381,7 +381,7 @@ def __init__( input_coords_dims: int = 3, M_features: int = 32, resolution: Sequence[int] = (200, 200, 200), - multiscale_res_multipliers: Optional[Sequence[int]] = None, + multiscale_res_multipliers: Optional[Sequence[float]] = None, density_activation: Callable = lambda x: F.softplus(x - 1), # TILTED parameters T: int = 4, diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 9fe8a32a..4c97181a 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -613,18 +613,41 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: params = list(params) # Ensure parameters require gradients - for p in params: - p.requires_grad_(True) + for group in params: + tensors = group["params"] if isinstance(group, dict) else [group] + for p in tensors: + p.requires_grad_(True) + # Figure out which optimizer class to use + if isinstance(self._optimizer_params, dict): + # Per-group case: all groups must agree on the optimizer class, + # and per-group hyperparameters are already baked into each dict + # by get_optimization_parameters(). + opt_specs = list(self._optimizer_params.values()) + if not opt_specs: + self._optimizer = None + return + optimizer_cls = self._optimizer_class_for(opt_specs[0]) + for spec in opt_specs[1:]: + if type(spec) is not type(opt_specs[0]): + raise ValueError( + f"All parameter groups must use the same optimizer type, " + f"got {type(opt_specs[0]).__name__} and {type(spec).__name__}" + ) + self._optimizer = optimizer_cls(params) + else: + # Single-optimizer case: splat global hyperparameters + optimizer_cls = self._optimizer_class_for(self._optimizer_params) + self._optimizer = optimizer_cls(params, **self._optimizer_params.params()) - match self._optimizer_params: - case OptimizerParams.Adam(): - self._optimizer = torch.optim.Adam(params, **self._optimizer_params.params()) - case OptimizerParams.AdamW(): - self._optimizer = torch.optim.AdamW(params, **self._optimizer_params.params()) - case OptimizerParams.SGD(): - self._optimizer = torch.optim.SGD(params, **self._optimizer_params.params()) + + def _optimizer_class_for(self, opt_params) -> type[torch.optim.Optimizer]: + match opt_params: + case OptimizerParams.Adam(): return torch.optim.Adam + case OptimizerParams.AdamW(): return torch.optim.AdamW + case OptimizerParams.SGD(): return torch.optim.SGD case _: - raise NotImplementedError(f"Unknown optimizer type: {self._optimizer_params}") + raise NotImplementedError(f"Unknown optimizer type: {opt_params}") + def set_scheduler( self, scheduler_params: SchedulerType | dict | None = None, num_iter: int | None = None diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 10509655..281e07bf 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1066,16 +1066,14 @@ def params(self) -> Generator[torch.nn.Parameter, None, None]: def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: - if isinstance(self.model, PPLR): - - return [ - { - "params": self.model.get_params()[key], - **self.optimizer_params[key].params(), - } - for key in self.model.param_keys - ] - return list(self.params) + model = _unwrap (self.model) + return [ + { + "params": model.get_params()[key], + **self.optimizer_params[key].params(), + } + for key in model.param_keys + ] # --- Optimizer Mixin Overloads in the case of PPLR --- @@ -1088,54 +1086,25 @@ def optimizer_params(self) -> OptimizerType | dict[str, OptimizerType]: @optimizer_params.setter def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | dict[str, Any]): """Set the optimizer parameters.""" - if isinstance(params, OptimizerType): - self._optimizer_params = params - return - if isinstance(self.model, PPLR): - if not isinstance(params, dict): - raise TypeError(f"optimizer parameters must be a dict for PPLR, got {type(params)}") - - object_params = params + if not isinstance(params, dict): + raise TypeError(f"optimizer parameters must be a dict for PPLR, got {type(params)}") - if set(object_params.keys()) != set(self.model.param_keys): - raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}") - - params = {} - for key, value in object_params.items(): - if isinstance(value, dict): - params[key] = OptimizerParams.parse_dict(d=value) - elif isinstance(value, OptimizerType): - params[key] = value - else: - raise TypeError(f"optimizer parameters must be a dict or OptimizerType, got {type(value)}") - - self._optimizer_params = params - else: - raise TypeError(f"optimizer parameters must be a dict for non-PPLR, got {type(params)}") - - - def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: - """ - Set the optimizer for this model. - Currently supports single LR for all parameters, TODO allow for per parameter LRs by - updating get_optimization_parameters to return a list of parameters and their LRs. - """ - - if not isinstance(self.model, PPLR): - super().set_optimizer(opt_params) - return - - if opt_params is not None: - self.optimizer_params = opt_params - - if not self._optimizer_params: - self._optimizer = None - return - - params = self.get_optimization_parameters() + object_params = params - self._optimizer = torch.optim.Adam(params) + if set(object_params.keys()) != set(self.model.param_keys): + raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}") + + params = {} + for key, value in object_params.items(): + if isinstance(value, dict): + params[key] = OptimizerParams.parse_dict(d=value) + elif isinstance(value, OptimizerType): + params[key] = value + else: + raise TypeError(f"optimizer parameters must be a dict or OptimizerType, got {type(value)}") + + self._optimizer_params = params def reconnect_optimizer_to_parameters(self) -> None: """ @@ -1144,58 +1113,56 @@ def reconnect_optimizer_to_parameters(self) -> None: if self.optimizer is None: return - - if isinstance(self.model, PPLR): - current_params = self.get_optimization_parameters() - + + current_params = self.get_optimization_parameters() + - optimizable_params = [ - p for p in current_params - if isinstance(p['params'][0], torch.Tensor) and p['params'][0].is_leaf - ] - + optimizable_params = [ + p for p in current_params + if isinstance(p['params'][0], torch.Tensor) and p['params'][0].is_leaf + ] + + + if not optimizable_params: + raise ValueError(f"Shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}.") + + for p in optimizable_params: + print(f"Setting requires_grad for parameter: {p}") + p['params'][0].requires_grad_(True) + + assert self._optimizer is not None + # Preserve optimizer states and param_group settings + old_state = self._optimizer.state.copy() + old_param_groups = self._optimizer.param_groups.copy() + + # Reconnect to new parameters + self._optimizer.param_groups.clear() + for param_group in optimizable_params: + self._optimizer.add_param_group(param_group) + + # Restore per-group hyperparameters (lr, betas, weight_decay, etc.) by index, + # excluding 'params' which comes from the new groups + for new_pg, old_pg in zip(self._optimizer.param_groups, old_param_groups): + new_pg.update({k: v for k, v in old_pg.items() if k != "params"}) + + # Remap optimizer state: for any new param that IS the same tensor as an old param, + # carry its state over (moved to the right device just in case). + new_state = {} + for new_pg in self._optimizer.param_groups: + for new_param in new_pg["params"]: + if new_param in old_state: + device = new_param.device + new_state[new_param] = { + k: (v.to(device) if isinstance(v, torch.Tensor) else v) + for k, v in old_state[new_param].items() + } + + self._optimizer.state.clear() + self._optimizer.state.update(new_state) + + if self._scheduler is not None and self._optimizer is not None: + self._scheduler.optimizer = self._optimizer - if not optimizable_params: - raise ValueError(f"Shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}.") - - for p in optimizable_params: - print(f"Setting requires_grad for parameter: {p}") - p['params'][0].requires_grad_(True) - - assert self._optimizer is not None - # Preserve optimizer states and param_group settings - old_state = self._optimizer.state.copy() - old_param_groups = self._optimizer.param_groups.copy() - - # Reconnect to new parameters - self._optimizer.param_groups.clear() - for param_group in optimizable_params: - self._optimizer.add_param_group(param_group) - - # Restore per-group hyperparameters (lr, betas, weight_decay, etc.) by index, - # excluding 'params' which comes from the new groups - for new_pg, old_pg in zip(self._optimizer.param_groups, old_param_groups): - new_pg.update({k: v for k, v in old_pg.items() if k != "params"}) - - # Remap optimizer state: for any new param that IS the same tensor as an old param, - # carry its state over (moved to the right device just in case). - new_state = {} - for new_pg in self._optimizer.param_groups: - for new_param in new_pg["params"]: - if new_param in old_state: - device = new_param.device - new_state[new_param] = { - k: (v.to(device) if isinstance(v, torch.Tensor) else v) - for k, v in old_state[new_param].items() - } - - self._optimizer.state.clear() - self._optimizer.state.update(new_state) - - if self._scheduler is not None and self._optimizer is not None: - self._scheduler.optimizer = self._optimizer - else: - super().reconnect_optimizer_to_parameters() # Pretraining @property From ede9d566a777f68b225f8f6e6cc2ce2e9660d70a Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 23 Apr 2026 17:36:39 -0700 Subject: [PATCH 025/140] Pretraining warning on ObjectTensorDecomp --- src/quantem/tomography/object_models.py | 209 +----------------------- 1 file changed, 5 insertions(+), 204 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 281e07bf..0ae1e74a 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -883,7 +883,7 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM self.distribute_model(self._model) self.reconnect_optimizer_to_parameters() -class ObjectTensorDecomp(ObjectConstraints, DDPMixin): +class ObjectTensorDecomp(ObjectINR): DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjTensorDecompConstraints() def __init__( self, @@ -926,33 +926,6 @@ def from_model( obj_model.to(device) return obj_model - # --- Properties --- - - @property - def model(self) -> nn.Module | nn.parallel.DistributedDataParallel | PlanarDecompositionModel: - """ - Returns the INR model. - """ - return self._model - - @property - def obj(self) -> torch.Tensor: - return self._obj - - @obj.setter - def obj(self, obj: torch.Tensor): - self._obj = obj - - @property - def obj_view(self) -> np.ndarray: - """ - Returns the object as a view of the x, y, z axes. - - Matches the axes of conventionally reconstructed objects, this is the object that will be saved. - """ - self.create_volume() - return self._obj.cpu().numpy().transpose(0, 1, 3, 2) - # --- Constraints --- def apply_soft_constraints( @@ -966,10 +939,7 @@ def apply_soft_constraints( if self.constraints.tv_vol > 0: soft_loss += self.get_tv_loss(coords, pred) - if ( - isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) - and self.constraints.sparsity > 0 - ): # NOTE: For the linter, I must make this :) + if self.constraints.sparsity > 0: # NOTE: For the linter, I must make this :) sparsity_loss = self.constraints.sparsity * all_densities.abs().mean() soft_loss += sparsity_loss @@ -1107,9 +1077,6 @@ def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | di self._optimizer_params = params def reconnect_optimizer_to_parameters(self) -> None: - """ - Reconnect optimizer overload, defaults back to the standard implementation if no `PPLR` is detected. - """ if self.optimizer is None: return @@ -1163,174 +1130,8 @@ def reconnect_optimizer_to_parameters(self) -> None: if self._scheduler is not None and self._optimizer is not None: self._scheduler.optimizer = self._optimizer - - # Pretraining - @property - def pretrained_weights(self) -> dict[str, torch.Tensor]: - """get the pretrained weights of the INR model""" - return self._pretrained_weights - - def _set_pretrained_weights(self, model: "torch.nn.Module"): - """set the pretrained weights of the INR model""" - if not isinstance(model, torch.nn.Module): - raise TypeError(f"Pretrained model must be a torch.nn.Module, got {type(model)}") - self._pretrained_weights = deepcopy(model.state_dict()) - - @property - def pretrain_target(self) -> TomographyINRPretrainDataset: - """get the pretrain target""" - return self._pretrain_target - - @pretrain_target.setter - def pretrain_target(self, target: TomographyINRPretrainDataset): - """set the pretrain target""" - self._pretrain_target = target - - @property - def dtype(self) -> torch.dtype: - """ - Returns the dtype of the object. - """ - # TODO: This is a temporary solution to get the dtype of the object. - return torch.float32 - - @property - def shape(self) -> tuple[int, int, int]: - return self._shape - - @shape.setter - def shape(self, shape: tuple[int, int, int]): - self._shape = shape - - # --- Helper Functions --- - def rebuild_model(self): - self._model = self.distribute_model(self._model) - - # Reset method that goes back to the pretrained weights. - def reset(self): - """reset the model to the pretrained weights""" - self.model.load_state_dict(self._pretrained_weights.copy()) - self._model = self.distribute_model( - self.model - ) # Maybe add a check to see if distributed or not, but not very computationally expensive to do this. - - # --- Forward Method --- - - def forward(self, coords: torch.Tensor) -> torch.Tensor: - """forward pass for the INR model""" - all_densities = self.model(coords) - - if all_densities.dim() > 1: - all_densities = all_densities.squeeze(-1) - valid_mask = ( - (coords[:, 0] >= -1) & (coords[:, 0] <= 1) & (coords[:, 1] >= -1) & (coords[:, 1] <= 1) - ).float() - - if all_densities.dim() > 1: - valid_mask = valid_mask.unsqueeze(-1) - # Multi-dimensional mask - all_densities = all_densities * valid_mask - - all_densities = self.apply_hard_constraints(all_densities) - - return all_densities - - # NOTE: Pretraining is done in a two-phase fashion as shown in TILTED paper. - - def create_volume(self, return_vol: bool = False): - N = max(self._shape) - with torch.no_grad(): - coords_1d = torch.linspace(-1, 1, N) - x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") - inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) - model = self.model.module if isinstance(self.model, nn.DataParallel) else self.model - - inference_batch_size = 5 * N * N - total_samples = N**3 - samples_per_gpu = total_samples // self.world_size - remainder = total_samples % self.world_size - - if self.global_rank < remainder: - start_idx = self.global_rank * (samples_per_gpu + 1) - end_idx = start_idx + samples_per_gpu + 1 - else: - start_idx = self.global_rank * samples_per_gpu + remainder - end_idx = start_idx + samples_per_gpu - - inputs_subset = inputs[start_idx:end_idx] - num_samples = inputs_subset.shape[0] - - outputs_list = [] - for batch_start in range(0, num_samples, inference_batch_size): - batch_end = min(batch_start + inference_batch_size, num_samples) - batch_coords = inputs_subset[batch_start:batch_end].to( - self.device, non_blocking=True - ) - - batch_outputs = model(batch_coords) # (B, C) or (B,) etc. - - if isinstance(batch_outputs, tuple): - batch_outputs = batch_outputs[0] - batch_outputs = self.apply_hard_constraints(batch_outputs) - - # Ensure shape is (B, C) - if batch_outputs.dim() == 1: - batch_outputs = batch_outputs.unsqueeze(-1) # (B, 1) - - outputs_list.append(batch_outputs.cpu()) - - outputs = torch.cat(outputs_list, dim=0) # (local_B, C) - C = outputs.shape[-1] # e.g. 5 - - if self.world_size > 1: - # gather variable-sized first dimension (local_B) while keeping channels - local_B = outputs.shape[0] - output_size = torch.tensor(local_B, device=self.device, dtype=torch.long) - all_sizes = [ - torch.zeros(1, device=self.device, dtype=torch.long) - for _ in range(self.world_size) - ] - dist.all_gather(all_sizes, output_size) - max_size = max(size.item() for size in all_sizes) - - outputs_dev = outputs.to(self.device) # (local_B, C) - if local_B < max_size: - pad = torch.zeros( - (max_size - local_B, C), # type: ignore - device=self.device, - dtype=outputs_dev.dtype, - ) - outputs_padded = torch.cat([outputs_dev, pad], dim=0) # (max_size, C) - else: - outputs_padded = outputs_dev - - gathered_outputs = [ - torch.empty((max_size, C), device=self.device, dtype=outputs_dev.dtype) # type: ignore - for _ in range(self.world_size) - ] - dist.all_gather(gathered_outputs, outputs_padded.contiguous()) - - trimmed_outputs = [] - for rank, size in enumerate(all_sizes): - trimmed_outputs.append(gathered_outputs[rank][: size.item(), :]) - - pred_full = torch.cat(trimmed_outputs, dim=0).reshape(C, N, N, N).float() - else: - pred_full = outputs.reshape(C, N, N, N).float() - - if return_vol: - return pred_full.detach().cpu() - - self._obj = pred_full.detach().cpu() - - def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change - if isinstance(device, str): - device = torch.device(device) - self._device = device - if self.world_size == 1: - self._model = self._model.to(device) - elif not isinstance(self._model, torch.nn.parallel.DistributedDataParallel): - self.distribute_model(self._model) - self.reconnect_optimizer_to_parameters() + def pretrain(self) -> None: + raise NotImplementedError("Tensor decomposition pretraining is not usually required, and for TILTED there is a two-phase warmup approach.") + ObjectModelType = ObjectPixelated | ObjectINR From dc0b5f0c845d4ce7788896a0c4be11b58b9eb19a Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 27 Apr 2026 10:31:29 -0700 Subject: [PATCH 026/140] Doing some refactoring, adding reconstruction context to help with type hinting and make PPLR more extensible --- src/quantem/tomography/object_models.py | 1 + src/quantem/tomography/tomography.py | 20 +++++++++++++++++++- 2 files changed, 20 insertions(+), 1 deletion(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 0ae1e74a..2bc8fa4a 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -18,6 +18,7 @@ from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset +from quantem.tomography.tomography import ReconstructionContext as ctx class ObjConstraintParams: diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 826ad360..f21a30d1 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,6 +1,7 @@ import os +from dataclasses import dataclass from pathlib import Path -from typing import Literal, Self, Sequence +from typing import Literal, Optional, Self, Sequence import matplotlib.pyplot as plt import numpy as np @@ -625,3 +626,20 @@ def plot_losses(self): ax.set_title("Reconstruction Loss") ax.set_yscale("log") plt.show() + + +@dataclass +class ReconstructionContext: + """ + Handles all reconstruction parameters to be passed into object models. + + Subclasses will pick whatever parameter they need + - Pixelated reads ".volume" + - INR reads ".coords" and recomputes via the model. + - TEnsorDEcomp reads ".coords" and ".pred" (and ".all densities") + """ + + coords: Optional[torch.Tensor] = None + pred: Optional[torch.Tensor] = None + all_densities: Optional[torch.Tensor] = None + volume: Optional[torch.Tensor] = None \ No newline at end of file From 01456ed62cb12e9857acc6be9a3774d3566e9135 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 27 Apr 2026 11:12:18 -0700 Subject: [PATCH 027/140] Working on ObjectPixelated implementing ctx --- .vscode/settings.json | 1 + src/quantem/__init__.py | 1 + src/quantem/core/datastructures/dataset3d.py | 3 +- src/quantem/core/ml/constraints.py | 6 +- src/quantem/core/ml/models/kplanes.py | 156 ++++++----- src/quantem/core/ml/models/model_base.py | 3 +- src/quantem/core/ml/models/so3params.py | 84 +++--- src/quantem/core/ml/optimizer_mixin.py | 17 +- .../core/visualization/visualization.py | 7 +- .../ptychography_visualizations.py | 2 +- src/quantem/imaging/drift.py | 2 +- src/quantem/tomography/dataset_models.py | 13 +- src/quantem/tomography/object_models.py | 265 +++++++++--------- src/quantem/tomography/tomography.py | 14 +- src/quantem/tomography/tomography_opt.py | 2 +- tests/datastructures/test_dataset3d_show.py | 82 +++--- 16 files changed, 352 insertions(+), 306 deletions(-) diff --git a/.vscode/settings.json b/.vscode/settings.json index 1659b33b..80434c6b 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -78,4 +78,5 @@ "yticks" ], "basedpyright.analysis.typeCheckingMode": "standard", + "python.REPL.enableREPLSmartSend": false, } diff --git a/src/quantem/__init__.py b/src/quantem/__init__.py index b9aa54b4..ba70f629 100644 --- a/src/quantem/__init__.py +++ b/src/quantem/__init__.py @@ -1,4 +1,5 @@ from pkgutil import extend_path + __path__ = extend_path(__path__, __name__) from importlib.metadata import version diff --git a/src/quantem/core/datastructures/dataset3d.py b/src/quantem/core/datastructures/dataset3d.py index 118ec66f..154d53ee 100644 --- a/src/quantem/core/datastructures/dataset3d.py +++ b/src/quantem/core/datastructures/dataset3d.py @@ -318,8 +318,7 @@ def show( ncols = min(ncols, n_frames) # Don't create more columns than frames images = [self.array[i] for i in frame_idx] labels = [ - f"Frame {i}" if title_prefix is None else f"{title_prefix} {i}" - for i in frame_idx + f"Frame {i}" if title_prefix is None else f"{title_prefix} {i}" for i in frame_idx ] # Pad last row to complete the grid (show_2d requires rectangular input) remainder = n_frames % ncols diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index 553b0611..137ed4df 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -7,6 +7,8 @@ import torch from numpy.typing import NDArray +from quantem.tomography.tomography import ReconstructionContext + @dataclass(slots=False) class Constraints(ABC): @@ -86,14 +88,14 @@ def constraints(self, constraints: Constraints | dict[str, Any]): # --- Required methods tha tneeds to implemented in subclasses --- @abstractmethod - def apply_hard_constraints(self, *args, **kwargs) -> torch.Tensor: + def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: """ Apply hard constraints to the model. """ raise NotImplementedError @abstractmethod - def apply_soft_constraints(self, *args, **kwargs) -> torch.Tensor: + def apply_soft_constraints(self, ctx: ReconstructionContext) -> torch.Tensor: """ Apply soft constraints to the model. """ diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index c8b49e70..fe36dfcc 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -3,7 +3,6 @@ """ import itertools -import math from typing import Callable, Optional, Sequence # import tinycudann as tcnn @@ -17,15 +16,19 @@ """ K-planes utility functions """ -def grid_sample_wrapper(grid: torch.Tensor, coords: torch.Tensor, align_corners: bool = True) -> torch.Tensor: + + +def grid_sample_wrapper( + grid: torch.Tensor, coords: torch.Tensor, align_corners: bool = True +) -> torch.Tensor: """ Performs bilinear interpolation on a grid at given coordinates. - + Args: grid: Grid tensor of shape [B, C, H, W] or [C, H, W] coords: Coordinate tensor of shape [B, N, 2] or [N, 2] align_corners: Whether to align corners - + Returns: Interpolated values of shape [B, N, C] or [N, C] """ @@ -40,8 +43,10 @@ def grid_sample_wrapper(grid: torch.Tensor, coords: torch.Tensor, align_corners: if grid_dim == 2 or grid_dim == 3: grid_sampler = F.grid_sample else: - raise NotImplementedError(f"Grid-sample was called with {grid_dim}D data but is only " - f"implemented for 2 and 3D data.") + raise NotImplementedError( + f"Grid-sample was called with {grid_dim}D data but is only " + f"implemented for 2 and 3D data." + ) coords = coords.view([coords.shape[0]] + [1] * (grid_dim - 1) + list(coords.shape[1:])) B, feature_dim = grid.shape[:2] @@ -50,11 +55,14 @@ def grid_sample_wrapper(grid: torch.Tensor, coords: torch.Tensor, align_corners: grid, # [B, feature_dim, reso, ...] coords, # [B, 1, ..., n, grid_dim] align_corners=align_corners, - mode='bilinear', padding_mode='border') + mode="bilinear", + padding_mode="border", + ) interp = interp.view(B, feature_dim, n).transpose(-1, -2) # [B, n, feature_dim] interp = interp.squeeze() # [B?, n, feature_dim?] return interp + def init_planes( in_dim: int, out_dim: int, @@ -62,18 +70,18 @@ def init_planes( init_range: tuple = (0.1, 0.5), ) -> nn.ParameterList: """Create the set of 2D planes for a k-plane decomposition. - + For in_dim=3 (spatial), this creates 3 planes: XY, XZ, YZ. For in_dim=4 (spatial + time), this creates 6 planes: XY, XZ, XT, YZ, YT, ZT. Time planes (those involving axis 3) are initialized to 1 so they start as identity multipliers. - + Args: in_dim: Dimensionality of the input coordinates (3 or 4). out_dim: Number of feature channels per plane. resolution: Resolution along each axis, e.g. [128, 128, 128]. init_range: (a, b) for uniform initialization of spatial planes. - + Returns: nn.ParameterList of plane parameters, each of shape [1, out_dim, res_j, res_i]. """ @@ -94,21 +102,22 @@ def init_planes( planes.append(param) return planes + def query_planes( pts: torch.Tensor, planes: nn.ParameterList, in_dim: int, ) -> float: """Query the k-plane representation at a batch of points. - + Projects each point onto every axis-pair plane, bilinearly interpolates, and returns the element-wise product across all planes. - + Args: pts: (B, in_dim) coordinates in [-1, 1]. planes: The ParameterList from init_planes. in_dim: 3 or 4. - + Returns: (B, out_dim) features. """ @@ -116,30 +125,33 @@ def query_planes( result = 1.0 for plane_param, pair in zip(planes, axis_pairs): # Extract the 2D coords for this plane - coords_2d = pts[..., list(pair)] # (B, 2) - coords_2d = coords_2d.view(1, -1, 1, 2) # (1, B, 1, 2) for grid_sample + coords_2d = pts[..., list(pair)] # (B, 2) + coords_2d = coords_2d.view(1, -1, 1, 2) # (1, B, 1, 2) for grid_sample # grid_sample: input (N,C,H,W), grid (N, H_out, W_out, 2) sampled = F.grid_sample( - plane_param, # (1, C, H, W) - coords_2d, # (1, B, 1, 2) + plane_param, # (1, C, H, W) + coords_2d, # (1, B, 1, 2) align_corners=True, mode="bilinear", padding_mode="border", ) # -> (1, C, B, 1) - sampled = sampled.squeeze(0).squeeze(-1).T # (B, C) + sampled = sampled.squeeze(0).squeeze(-1).T # (B, C) result = result * sampled return result # pyright: ignore[reportReturnType] - + + def interpolate_ms_features( pts: torch.Tensor, ms_grids: nn.ParameterList, ) -> torch.Tensor: mat_mode = [[0, 1], [0, 2], [1, 2]] - coord_plane = torch.stack([ - pts[:, mat_mode[0]], - pts[:, mat_mode[1]], - pts[:, mat_mode[2]], - ]).view(3, -1, 1, 2) + coord_plane = torch.stack( + [ + pts[:, mat_mode[0]], + pts[:, mat_mode[1]], + pts[:, mat_mode[2]], + ] + ).view(3, -1, 1, 2) per_scale = [] for plane_coef in ms_grids: @@ -154,7 +166,6 @@ def interpolate_ms_features( class KPlanes(PPLR, TensorDecompositionModel): - def __init__( self, # Grid parameters @@ -164,7 +175,9 @@ def __init__( resolution: Sequence[int] = (200, 200, 200), multiscale_res_multipliers: Optional[Sequence[int]] = None, concat_features: bool = True, - density_activation: Callable = lambda x: F.softplus(x - 1), # Keep playing around with this and trunc_exp + density_activation: Callable = lambda x: F.softplus( + x - 1 + ), # Keep playing around with this and trunc_exp # Hybrid MLP parameters use_hybrid_mlp: bool = False, hybrid_hidden_dim: int = 64, @@ -218,7 +231,6 @@ def __init__( layers.append(out) self.sigma_net = nn.Sequential(*layers) - def get_densities(self, coords: torch.Tensor): """Computes and returns densities""" pts = coords.reshape(-1, 3) @@ -252,7 +264,7 @@ def td_type(self, td_type: str): if not isinstance(td_type, str): raise TypeError("td_type must be a string") self._td_type = td_type - + @property def tilted(self) -> bool: return False @@ -283,13 +295,15 @@ def resolution(self, resolution: Sequence[int]): raise TypeError("Resolution must be a sequence") self._resolution = list(resolution) + # --------------------------------------------------------------------------- # KPlanesTILTED # --------------------------------------------------------------------------- - + + def interpolate_ms_features_tilted( - pts: torch.Tensor, # (B, 3) - ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) + pts: torch.Tensor, # (B, 3) + ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) rotation_matrices: torch.Tensor, # (T, 3, 3) ) -> torch.Tensor: """ @@ -305,17 +319,15 @@ def interpolate_ms_features_tilted( # Build (T, 3, B, 2) coords for planes XY, ZX, YZ in one shot. # index_select is faster and cleaner than advanced indexing with python lists. # Plane axis layout: XY=(0,1), ZX=(2,0), YZ=(1,2) - idx = torch.tensor([[0, 1], - [2, 0], - [1, 2]], device=pts.device) # (3, 2) + idx = torch.tensor([[0, 1], [2, 0], [1, 2]], device=pts.device) # (3, 2) # rotated: (T, B, 3) -> gather along last dim with idx (3, 2) # Result: (T, 3, B, 2) - coords = rotated.unsqueeze(1).expand(T, 3, B, 3).gather( - -1, idx.view(1, 3, 1, 2).expand(T, 3, B, 2) + coords = ( + rotated.unsqueeze(1).expand(T, 3, B, 3).gather(-1, idx.view(1, 3, 1, 2).expand(T, 3, B, 2)) ) # Flatten (T, 3) -> 3*T so it matches grid's first dim, and add the H_out=1 axis - coord_tensor = coords.reshape(3 * T, B, 1, 2) # (3T, B, 1, 2) + coord_tensor = coords.reshape(3 * T, B, 1, 2) # (3T, B, 1, 2) per_scale_features = [] for plane_coef in ms_grids: @@ -334,13 +346,12 @@ def interpolate_ms_features_tilted( sampled = sampled.squeeze(-1).view(T, 3, C, B).prod(dim=1) # (T, C, B) -> (B, T, C) -> (B, T*C) to concatenate rotations along feature dim - per_scale_features.append( - sampled.permute(2, 0, 1).reshape(B, T * C) - ) + per_scale_features.append(sampled.permute(2, 0, 1).reshape(B, T * C)) # Concatenate across scales -> (B, T * C * num_scales) return torch.cat(per_scale_features, dim=-1) + class KPlanesTILTED(KPlanes): """ K-Planes with T learned SO(3) rotations (TILTED). @@ -392,7 +403,6 @@ def __init__( hybrid_num_layers: int = 2, so3_param_type: str = "r9svd", ): - self._td_type = "tilted" if input_coords_dims != 3: raise NotImplementedError("KPlanesTILTED is implemented for 3D only.") @@ -427,9 +437,7 @@ def __init__( self.grids = nn.ParameterList() for res_mult in multiscale_res_multipliers: scaled_res = [int(r * res_mult) for r in resolution] - plane = nn.Parameter( - torch.empty(3 * T, M_features, scaled_res[1], scaled_res[0]) - ) + plane = nn.Parameter(torch.empty(3 * T, M_features, scaled_res[1], scaled_res[0])) nn.init.uniform_(plane, 0.1, 0.5) self.grids.append(plane) @@ -480,7 +488,7 @@ def _build_sigma_net( def get_densities(self, coords: torch.Tensor) -> torch.Tensor: pts = coords.reshape(-1, 3) - R = self.so3.as_matrix() # (T, 3, 3) + R = self.so3.as_matrix() # (T, 3, 3) features = interpolate_ms_features_tilted( pts=pts, ms_grids=self.grids, @@ -498,9 +506,9 @@ def forward(self, pts: torch.Tensor) -> torch.Tensor: def get_params(self) -> dict[str, list[nn.Parameter]]: return { - "grids": list(self.grids.parameters()), + "grids": list(self.grids.parameters()), "sigma_net": list(self.sigma_net.parameters()), - "so3": list(self.so3.parameters()), + "so3": list(self.so3.parameters()), } @property @@ -557,7 +565,7 @@ def extra_repr(self) -> str: def set_so3_param_type(self, so3_param_type: str, init: str = "rand") -> None: """ Set the SO3 parameterization type. - + Parameters ---------- so3_param_type : str @@ -573,12 +581,14 @@ def set_so3_param_type(self, so3_param_type: str, init: str = "rand") -> None: @property def tilted(self) -> bool: return True - + + # CP Decomp for Warmup SO3 rotations + def interpolate_ms_features_cp_tilted( - pts: torch.Tensor, # (B, 3) - ms_grids: nn.ParameterList, # each grid: (3*T, C, L) — 1D lines + pts: torch.Tensor, # (B, 3) + ms_grids: nn.ParameterList, # each grid: (3*T, C, L) — 1D lines rotation_matrices: torch.Tensor, # (T, 3, 3) ) -> torch.Tensor: """ @@ -594,7 +604,7 @@ def interpolate_ms_features_cp_tilted( per_scale_features = [] for line_coef in ms_grids: # line_coef: (3T, C, L) — three 1D feature lines per transform (x, y, z) - C, L = line_coef.shape[1], line_coef.shape[2] + C, _ = line_coef.shape[1], line_coef.shape[2] # For each transform t, we need three 1D samples: at x_t, y_t, z_t. # Lay them out as (3T, B) coords, matching line_coef's first dim. @@ -606,17 +616,23 @@ def interpolate_ms_features_cp_tilted( # (3T, C, 1, L) reshape and pass 2D coords with y fixed at 0. # Simpler: use F.grid_sample with a 4D trick, or just do manual linear interp. # Here's the grid_sample way: - line_coef_4d = line_coef.unsqueeze(2) # (3T, C, 1, L) + line_coef_4d = line_coef.unsqueeze(2) # (3T, C, 1, L) # grid: need (3T, Hout=1, Wout=B, 2), with x = coord, y = 0 - grid = torch.stack([ - coords_1d, # x - torch.zeros_like(coords_1d), # y - ], dim=-1).unsqueeze(1) # (3T, 1, B, 2) + grid = torch.stack( + [ + coords_1d, # x + torch.zeros_like(coords_1d), # y + ], + dim=-1, + ).unsqueeze(1) # (3T, 1, B, 2) sampled = F.grid_sample( - line_coef_4d, grid, - align_corners=True, mode="bilinear", padding_mode="border", - ).squeeze(2) # (3T, C, B) + line_coef_4d, + grid, + align_corners=True, + mode="bilinear", + padding_mode="border", + ).squeeze(2) # (3T, C, B) # Hadamard across the 3 axes per transform: (T, 3, C, B) -> (T, C, B) sampled = sampled.view(T, 3, C, B).prod(dim=1) @@ -639,12 +655,13 @@ class CPTilted(PPLR, TensorDecompositionModel): def __init__( self, - C: int = 4, # channels per transform per scale + C: int = 4, # channels per transform per scale resolution: Sequence[int] = (128, 128, 128), multiscale_res_multipliers: Optional[Sequence[int]] = None, T: int = 4, tau_init: str = "random", density_activation: Callable = lambda x: F.softplus(x - 1), + so3_param_type: str = "r9svd", ): super().__init__() self._td_type = "cp_tilted" @@ -670,7 +687,12 @@ def __init__( nn.init.normal_(self.sigma_net.weight, std=0.01) nn.init.zeros_(self.sigma_net.bias) - self.so3 = SO3Param(T, init=tau_init) + if so3_param_type == "r9svd": + self.so3 = SO3ParamR9SVD(T, init=tau_init) + elif so3_param_type == "quat": + self.so3 = SO3ParamQuat(T, init=tau_init) + else: + raise ValueError(f"Unknown SO3 param type: {so3_param_type}") def get_densities(self, coords: torch.Tensor) -> torch.Tensor: pts = coords.reshape(-1, 3) @@ -683,9 +705,9 @@ def forward(self, pts): def get_params(self): return { - "grids": list(self.grids.parameters()), + "grids": list(self.grids.parameters()), "sigma_net": list(self.sigma_net.parameters()), - "so3": list(self.so3.parameters()), + "so3": list(self.so3.parameters()), } @property @@ -698,12 +720,10 @@ def td_type(self) -> str: def extract_tau_state(self) -> torch.Tensor: return self.so3.M.detach().clone() - + @property def tilted(self) -> bool: return True - - -KPlanesType = KPlanes | KPlanesTILTED | CPTilted \ No newline at end of file +KPlanesType = KPlanes | KPlanesTILTED | CPTilted diff --git a/src/quantem/core/ml/models/model_base.py b/src/quantem/core/ml/models/model_base.py index e1a486d7..c7ccf7aa 100644 --- a/src/quantem/core/ml/models/model_base.py +++ b/src/quantem/core/ml/models/model_base.py @@ -1,7 +1,6 @@ from abc import ABC, abstractmethod from typing import Dict -import torch import torch.nn as nn @@ -48,4 +47,4 @@ class PlanarDecompositionModel(TensorDecompositionModel): grids: nn.ParameterList tilted: bool - resolution: list[int] \ No newline at end of file + resolution: list[int] diff --git a/src/quantem/core/ml/models/so3params.py b/src/quantem/core/ml/models/so3params.py index 14f60867..6569ec93 100644 --- a/src/quantem/core/ml/models/so3params.py +++ b/src/quantem/core/ml/models/so3params.py @@ -9,51 +9,52 @@ # --------------------------------------------------------------------------- # SO(3) quaternion parameter module # --------------------------------------------------------------------------- - + + class SO3ParamQuat(nn.Module): """ Stores T unit quaternions as learnable parameters in R^4 and normalises them on every call to `as_matrix()`. - + Quaternion convention: [x, y, z, w] (scalar-last, same as scipy). - + Initialisation -------------- "random" – uniform sampling over SO(3) via Shoemake's method. "identity" – all rotations start as the identity (good for fine-tuning). """ - + def __init__(self, T: int, init: str = "random"): super().__init__() if T < 1: raise ValueError(f"T must be >= 1, got {T}") - quats = self._init_quaternions(T, init) # (T, 4) + quats = self._init_quaternions(T, init) # (T, 4) self.quats = nn.Parameter(quats) - + # ------------------------------------------------------------------ # Initialisers # ------------------------------------------------------------------ - + @staticmethod def _shoemake_sample(T: int) -> torch.Tensor: """Uniform SO(3) sampling via Shoemake (1992). Returns (T, 4) [x,y,z,w].""" u = torch.rand(T, 3) sqrt1_u0 = torch.sqrt(1.0 - u[:, 0]) - sqrt_u0 = torch.sqrt(u[:, 0]) - two_pi = 2.0 * math.pi + sqrt_u0 = torch.sqrt(u[:, 0]) + two_pi = 2.0 * math.pi x = sqrt1_u0 * torch.sin(two_pi * u[:, 1]) y = sqrt1_u0 * torch.cos(two_pi * u[:, 1]) - z = sqrt_u0 * torch.sin(two_pi * u[:, 2]) - w = sqrt_u0 * torch.cos(two_pi * u[:, 2]) - return torch.stack([x, y, z, w], dim=-1) # (T, 4) - + z = sqrt_u0 * torch.sin(two_pi * u[:, 2]) + w = sqrt_u0 * torch.cos(two_pi * u[:, 2]) + return torch.stack([x, y, z, w], dim=-1) # (T, 4) + @staticmethod def _identity(T: int) -> torch.Tensor: """All-identity rotations: [0,0,0,1] * T.""" q = torch.zeros(T, 4) q[:, 3] = 1.0 return q - + @classmethod def _init_quaternions(cls, T: int, init: str) -> torch.Tensor: if init == "random": @@ -62,38 +63,47 @@ def _init_quaternions(cls, T: int, init: str) -> torch.Tensor: return cls._identity(T) else: raise ValueError(f"Unknown init '{init}'; choose 'random' or 'identity'.") - + # ------------------------------------------------------------------ # Forward helpers # ------------------------------------------------------------------ - + def normalized(self) -> torch.Tensor: """Returns (T, 4) unit quaternions.""" return F.normalize(self.quats, p=2, dim=-1) - + def as_matrix(self) -> torch.Tensor: """ Converts the T stored quaternions to (T, 3, 3) rotation matrices. - + Uses the standard formula; no trig, just multiplications. """ - q = self.normalized() # (T, 4) [x, y, z, w] + q = self.normalized() # (T, 4) [x, y, z, w] x, y, z, w = q.unbind(dim=-1) # each (T,) - + # Precompute products - xx, yy, zz = x*x, y*y, z*z - xy, xz, yz = x*y, x*z, y*z - wx, wy, wz = w*x, w*y, w*z - + xx, yy, zz = x * x, y * y, z * z + xy, xz, yz = x * y, x * z, y * z + wx, wy, wz = w * x, w * y, w * z + # Row-major: R[i,j] - R = torch.stack([ - 1 - 2*(yy + zz), 2*(xy - wz), 2*(xz + wy), - 2*(xy + wz), 1 - 2*(xx + zz), 2*(yz - wx), - 2*(xz - wy), 2*(yz + wx), 1 - 2*(xx + yy), - ], dim=-1).reshape(-1, 3, 3) # (T, 3, 3) - + R = torch.stack( + [ + 1 - 2 * (yy + zz), + 2 * (xy - wz), + 2 * (xz + wy), + 2 * (xy + wz), + 1 - 2 * (xx + zz), + 2 * (yz - wx), + 2 * (xz - wy), + 2 * (yz + wx), + 1 - 2 * (xx + yy), + ], + dim=-1, + ).reshape(-1, 3, 3) # (T, 3, 3) + return R - + def extra_repr(self) -> str: return f"T={self.quats.shape[0]}" @@ -115,16 +125,14 @@ def __init__(self, T: int, init: str = "random"): M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) else: raise ValueError(f"Unknown init '{init}'") - self.M = nn.Parameter(M) # (T, 3, 3) + self.M = nn.Parameter(M) # (T, 3, 3) def as_matrix(self) -> torch.Tensor: """Projects each M to SO(3) via SVD. Returns (T, 3, 3).""" - - U, _, Vh = torch.linalg.svd(self.M) # U: (T,3,3), Vh: (T,3,3) + U, _, Vh = torch.linalg.svd(self.M) # U: (T,3,3), Vh: (T,3,3) # Fix reflections: det(U Vh) must be +1 - d = torch.det(U @ Vh) # (T,) + d = torch.det(U @ Vh) # (T,) diag = torch.ones(self.M.shape[0], 3, device=self.M.device, dtype=self.M.dtype) - diag[:, 2] = d # multiply last singular vector by sign - return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) - + diag[:, 2] = d # multiply last singular vector by sign + return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 4c97181a..b5053a4c 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -1,7 +1,7 @@ import textwrap from abc import abstractmethod from dataclasses import dataclass -from typing import TYPE_CHECKING, Generator, Iterator, Literal, Sequence +from typing import TYPE_CHECKING, Any, Generator, Iterable, Literal from quantem.core import config @@ -581,11 +581,11 @@ def scheduler_params(self, params: SchedulerType | dict): @abstractmethod def get_optimization_parameters( self, - ) -> "torch.Tensor | Sequence[torch.Tensor] | Iterator[torch.Tensor]": + ) -> "Iterable[torch.Tensor] | Iterable[dict[str, Any]]": """ Get the parameters that should be optimized for this model. This could be replaced with just module.parameters(), but this allows for flexibility - in the future to allow for per parameter LRs. + in the future to allow for per parameter LRs. # NOTE: Cl 4/27/26 updated to iterable type-hint. """ raise NotImplementedError("Subclasses must implement get_optimization_parameters") @@ -639,16 +639,17 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: optimizer_cls = self._optimizer_class_for(self._optimizer_params) self._optimizer = optimizer_cls(params, **self._optimizer_params.params()) - def _optimizer_class_for(self, opt_params) -> type[torch.optim.Optimizer]: match opt_params: - case OptimizerParams.Adam(): return torch.optim.Adam - case OptimizerParams.AdamW(): return torch.optim.AdamW - case OptimizerParams.SGD(): return torch.optim.SGD + case OptimizerParams.Adam(): + return torch.optim.Adam + case OptimizerParams.AdamW(): + return torch.optim.AdamW + case OptimizerParams.SGD(): + return torch.optim.SGD case _: raise NotImplementedError(f"Unknown optimizer type: {opt_params}") - def set_scheduler( self, scheduler_params: SchedulerType | dict | None = None, num_iter: int | None = None ) -> None: diff --git a/src/quantem/core/visualization/visualization.py b/src/quantem/core/visualization/visualization.py index 77f647b4..e252caa6 100644 --- a/src/quantem/core/visualization/visualization.py +++ b/src/quantem/core/visualization/visualization.py @@ -40,10 +40,7 @@ # There might be a cleaner way to do this, but better to have it here than in the functions NormInputCell: TypeAlias = NormalizationConfig | ShowParams.Norm | dict | str Show2dNormInput: TypeAlias = ( - NormInputCell - | None - | Sequence[NormInputCell] - | Sequence[Sequence[NormInputCell]] + NormInputCell | None | Sequence[NormInputCell] | Sequence[Sequence[NormInputCell]] ) ScalebarInputCell: TypeAlias = ScalebarConfig | ShowParams.Scalebar | dict | bool | None @@ -57,7 +54,7 @@ | Sequence[Sequence[ScalebarInputCell]] ) -CmapType: TypeAlias = str | colors.Colormap +CmapType: TypeAlias = str | colors.Colormap def _show_2d_array( diff --git a/src/quantem/diffractive_imaging/ptychography_visualizations.py b/src/quantem/diffractive_imaging/ptychography_visualizations.py index fe8f84bf..8975df02 100644 --- a/src/quantem/diffractive_imaging/ptychography_visualizations.py +++ b/src/quantem/diffractive_imaging/ptychography_visualizations.py @@ -1,4 +1,3 @@ - import warnings from typing import Any, Literal @@ -891,6 +890,7 @@ def show_scan_positions( if conv_angle is not None and energy is not None: from quantem.core.utils.utils import electron_wavelength_angstrom + wavelength = electron_wavelength_angstrom(energy) conv_angle_rad = conv_angle * 1e-3 # For defocused probe: radius ≈ |defocus| * convergence_angle + diffraction_limit diff --git a/src/quantem/imaging/drift.py b/src/quantem/imaging/drift.py index 424e18e6..ba29e417 100644 --- a/src/quantem/imaging/drift.py +++ b/src/quantem/imaging/drift.py @@ -1,9 +1,9 @@ +import warnings from collections.abc import Sequence from typing import List, Optional, Union import matplotlib.pyplot as plt import numpy as np -import warnings from numpy.typing import NDArray from scipy.interpolate import interp1d from scipy.ndimage import distance_transform_edt, gaussian_filter diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index e3b61377..24f0f775 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -649,11 +649,14 @@ def save_parameters(self, path: str): """ Saves the learned parameters to a file. """ - torch.save({ - "z1": self._z1_params.detach().cpu(), - "z3": self._z3_params.detach().cpu(), - "shifts": self._shifts_params.detach().cpu(), - }, path) + torch.save( + { + "z1": self._z1_params.detach().cpu(), + "z3": self._z3_params.detach().cpu(), + "shifts": self._shifts_params.detach().cpu(), + }, + path, + ) def load_parameters(self, path: str): """ diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 2bc8fa4a..8f254533 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1,7 +1,7 @@ from abc import abstractmethod from copy import deepcopy from dataclasses import dataclass -from typing import Any, Callable, Generator +from typing import Any, Callable, Generator, Optional import numpy as np import torch @@ -14,11 +14,11 @@ from quantem.core.ml.constraints import BaseConstraints, Constraints from quantem.core.ml.ddp import DDPMixin from quantem.core.ml.loss_functions import get_loss_module -from quantem.core.ml.models.model_base import PPLR, PlanarDecompositionModel +from quantem.core.ml.models.model_base import PlanarDecompositionModel from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset -from quantem.tomography.tomography import ReconstructionContext as ctx +from quantem.tomography.tomography import ReconstructionContext class ObjConstraintParams: @@ -128,7 +128,7 @@ class ObjTensorDecompConstraints(Constraints): shrinkage: float = 0.0 tv_vol: float = 0.0 tv_plane: float = 0.0 - sparsity:float = 0.0 + sparsity: float = 0.0 _name: str = "obj_tensor_decomp" soft_constraint_keys = ["tv_vol", "tv_plane", "sparsity"] @@ -192,7 +192,7 @@ def parse_dict( ObjConstraintsType = ( - ObjConstraintParams.ObjPixelatedConstraints + ObjConstraintParams.ObjPixelatedConstraints | ObjConstraintParams.ObjINRConstraints | ObjConstraintParams.ObjTensorDecompConstraints ) @@ -204,6 +204,7 @@ def _unwrap(model: nn.Module | nn.parallel.DistributedDataParallel) -> PlanarDec return model.module return model + class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): DEFAULT_LRS = { "object": 8e-6, @@ -212,6 +213,7 @@ class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): """ Base class for all ObjectModels to inherit from. """ + def __init__( self, shape: tuple[int, int, int], # pyright: ignore[reportRedeclaration] @@ -231,91 +233,92 @@ def __init__( # --- Instantiation ---- - # --- Properties --- - @property - def shape(self) -> tuple[int, int, int]: - """ - Shape of the object (x, y, z). - """ - return self._shape - - @shape.setter - def shape(self, new_shape: tuple[int, int, int]): - self._shape = new_shape - - @property - def obj(self) -> torch.Tensor: - """ - Returns the object, should be implemented in subclasses. - """ - raise NotImplementedError - - @property - def model(self) -> nn.Module: - """ - Returns the model, should be implemented in subclasses. - """ - raise NotImplementedError - - @abstractmethod - def dtype(self) -> torch.dtype: - """ - Returns the dtype of the object. - """ - raise NotImplementedError - - @abstractmethod - def forward(self, *args, **kwargs) -> torch.Tensor: - """ - Forward pass, should be implemented in subclasses. Note for any nn.Module this is - a required method. - """ - raise NotImplementedError - - @abstractmethod - def reset(self) -> None: - """ - Reset the object, should be implemented in subclasses. - """ - raise NotImplementedError - - @property - def params(self) -> Generator[torch.nn.Parameter, None, None]: - """ - Get the parameters that should be optimized for this model. - - Should be implemented in subclasses. - """ - raise NotImplementedError - - # --- Helper Functions --- - def get_optimization_parameters(self) -> list[nn.Parameter]: - """ - Get the parameters that should be optimized for this model. - """ - return list(self.params) - - @abstractmethod # Each subclass should implement this. - def to(self, *args, **kwargs): - """ - Move the object to a device - """ - - raise NotImplementedError + # --- Properties --- + @property + def shape(self) -> tuple[int, int, int]: + """ + Shape of the object (x, y, z). + """ + return self._shape + + @shape.setter + def shape(self, new_shape: tuple[int, int, int]): + self._shape = new_shape + + @property + def obj(self) -> torch.Tensor: + """ + Returns the object, should be implemented in subclasses. + """ + raise NotImplementedError + + @property + def model(self) -> nn.Module: + """ + Returns the model, should be implemented in subclasses. + """ + raise NotImplementedError + + @abstractmethod + def dtype(self) -> torch.dtype: + """ + Returns the dtype of the object. + """ + raise NotImplementedError + + @abstractmethod + def forward(self, coords: Optional[torch.Tensor] = None) -> torch.Tensor: + """ + Forward pass, should be implemented in subclasses. Note for any nn.Module this is + a required method. + """ + raise NotImplementedError + + @abstractmethod + def reset(self) -> None: + """ + Reset the object, should be implemented in subclasses. + """ + raise NotImplementedError + + @property + def params(self) -> Generator[torch.nn.Parameter, None, None]: + """ + Get the parameters that should be optimized for this model. + + Should be implemented in subclasses. + """ + raise NotImplementedError + + # --- Helper Functions --- + def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: + """ + Get the parameters that should be optimized for this model. + """ + return list(self.params) + + @abstractmethod # Each subclass should implement this. + def to(self, device: str | torch.device): + """ + Move the object to a device + """ + + raise NotImplementedError + class ObjectConstraints(BaseConstraints, ObjectBase): # TODO: Ask Arthur why we still need this def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @abstractmethod - def get_tv_loss(self, **kwargs) -> torch.Tensor: + def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: """ Get the TV loss for the object model. Must be implemented in each subclass. """ raise NotImplementedError -class ObjectPixelated(ObjectConstraints): +class ObjectPixelated(ObjectConstraints): """ Object model for pixelated objects. @@ -382,14 +385,6 @@ def obj(self, obj: torch.Tensor): def obj_view(self) -> np.ndarray: return self.obj.cpu().unsqueeze(0).numpy() - @property - def shape(self) -> tuple[int, int, int]: - return self._shape - - @shape.setter - def shape(self, shape: tuple[int, int, int]): - self._shape = shape - @property def soft_loss(self) -> torch.Tensor: return self.apply_soft_constraints(self._obj) @@ -424,30 +419,32 @@ def apply_hard_constraints( # TODO: Need to implement the other hard constraints: Fourier Filter and Circular Mask. return obj2 - def apply_soft_constraints(self, obj: torch.Tensor) -> torch.Tensor: - soft_loss = torch.tensor(0.0, device=obj.device, dtype=obj.dtype, requires_grad=True) + def apply_soft_constraints(self, ctx: ReconstructionContext) -> torch.Tensor: + assert ctx.obj is not None, "ObjectPixelated requires ctx.obj to be set" + soft_loss = torch.tensor(0.0, device=ctx.obj.device, dtype=ctx.obj.dtype, requires_grad=True) if self.constraints.tv_vol > 0: tv_loss = self.get_tv_loss( - obj.unsqueeze(0).unsqueeze(0), tv_weight=self.constraints.tv_vol + ctx ) soft_loss += tv_loss return soft_loss # --- Forward method --- - def forward(self, dummy_input=None) -> torch.Tensor: + def forward(self, coords=None) -> torch.Tensor: return self.obj # --- Defining the TV loss --- - def get_tv_loss(self, obj: torch.Tensor, tv_weight: float = 1e-3) -> torch.Tensor: # pyright: ignore[reportIncompatibleMethodOverride] -> get_tv_loss has different arguments depending on the object. - tv_d = torch.pow(obj[:, :, 1:, :, :] - obj[:, :, :-1, :, :], 2).sum() - tv_h = torch.pow(obj[:, :, :, 1:, :] - obj[:, :, :, :-1, :], 2).sum() - tv_w = torch.pow(obj[:, :, :, :, 1:] - obj[:, :, :, :, :-1], 2).sum() + def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: + assert ctx.obj is not None, "ObjectPixelated requires ctx.obj to be set" + tv_d = torch.pow(ctx.obj[:, :, 1:, :, :] - ctx.obj[:, :, :-1, :, :], 2).sum() + tv_h = torch.pow(ctx.obj[:, :, :, 1:, :] - ctx.obj[:, :, :, :-1, :], 2).sum() + tv_w = torch.pow(ctx.obj[:, :, :, :, 1:] - ctx.obj[:, :, :, :, :-1], 2).sum() tv_loss = tv_d + tv_h + tv_w - return tv_loss * tv_weight / (torch.prod(torch.tensor(obj.shape))) + return tv_loss * self.constraints.tv_vol / (torch.prod(torch.tensor(obj.shape))) # --- Helper Functions --- - def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change + def to(self, device: str | torch.device): if isinstance(device, str): device = torch.device(device) self._device = device @@ -455,8 +452,10 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM self.reconnect_optimizer_to_parameters() return self + class ObjectINR(ObjectConstraints, DDPMixin): DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjINRConstraints() + def __init__( self, shape: tuple[int, int, int], @@ -884,8 +883,10 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM self.distribute_model(self._model) self.reconnect_optimizer_to_parameters() + class ObjectTensorDecomp(ObjectINR): DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjTensorDecompConstraints() + def __init__( self, shape: tuple[int, int, int], @@ -902,7 +903,9 @@ def __init__( ) self._pretrain_losses = [] self._pretrain_lrs = [] - self.constraints: ObjConstraintParams.ObjTensorDecompConstraints = self.DEFAULT_CONSTRAINTS.copy() + self.constraints: ObjConstraintParams.ObjTensorDecompConstraints = ( + self.DEFAULT_CONSTRAINTS.copy() + ) # Register the network submodule (important: real nn.Module attribute) if model is not None: self.setup_distributed(device=device) @@ -935,7 +938,6 @@ def apply_soft_constraints( all_densities: torch.Tensor, pred: torch.Tensor, ) -> torch.Tensor: - soft_loss = torch.tensor(0.0, device=pred.device) if self.constraints.tv_vol > 0: soft_loss += self.get_tv_loss(coords, pred) @@ -949,28 +951,26 @@ def apply_soft_constraints( # TV Losses def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: - tv_loss = torch.tensor(0.0, device=pred.device) tv_loss += self._get_plane_tv_loss() tv_loss += self.get_volume_tv_loss(coords) return tv_loss - def _get_plane_tv_loss(self) -> torch.Tensor: is_tilted = self.model.tilted per_level = [] - + model = _unwrap(self.model) for p in model.grids: # p: (3*T, C, H, W) for TILTED, (3, C, H, W) for KPlanes dh = (p[:, :, 1:, :] - p[:, :, :-1, :]).pow(2).mean(dim=(1, 2, 3)) dw = (p[:, :, :, 1:] - p[:, :, :, :-1]).pow(2).mean(dim=(1, 2, 3)) - per_plane = dh + dw # (3*T,) or (3,) + per_plane = dh + dw # (3*T,) or (3,) if is_tilted: T = self.model.T - per_rotation = per_plane.view(T, 3).sum(dim=1) # sum 3 planes per rotation - level_tv = per_rotation.mean() # avg across rotations + per_rotation = per_plane.view(T, 3).sum(dim=1) # sum 3 planes per rotation + level_tv = per_rotation.mean() # avg across rotations else: level_tv = per_plane.sum() @@ -978,7 +978,6 @@ def _get_plane_tv_loss(self) -> torch.Tensor: return self.constraints.tv_plane * torch.stack(per_level).sum() - def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: """ Isotropic volume TV via finite differences. Same form as the autograd @@ -987,7 +986,7 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: """ num_tv_samples = min(10_000, coords.shape[0]) tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - tv_coords = coords[tv_indices] # (N, 3) + tv_coords = coords[tv_indices] # (N, 3) model = _unwrap(self.model) h = 2.0 / min(model.resolution) @@ -996,7 +995,7 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: if isinstance(pred, tuple): pred = pred[0] if pred.dim() == 1: - pred = pred.unsqueeze(-1) # (N, 1) + pred = pred.unsqueeze(-1) # (N, 1) grads = [] for axis in range(3): @@ -1007,10 +1006,10 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: shifted_pred = shifted_pred[0] if shifted_pred.dim() == 1: shifted_pred = shifted_pred.unsqueeze(-1) - grads.append((shifted_pred - pred) / h) # (N, 1) + grads.append((shifted_pred - pred) / h) # (N, 1) - grad_stack = torch.stack(grads, dim=-1) # (N, C, 3) - grad_norm = torch.norm(grad_stack, dim=-1) # (N, C) + grad_stack = torch.stack(grads, dim=-1) # (N, C, 3) + grad_norm = torch.norm(grad_stack, dim=-1) # (N, C) return self.constraints.tv_vol * grad_norm.mean() @@ -1032,21 +1031,19 @@ def params(self) -> Generator[torch.nn.Parameter, None, None]: """ Returns the optimization parameters, here we also check if PPLR is used and return the appropriate parameters. """ - + return self.model.parameters() # type: ignore[attr-defined] def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: - - model = _unwrap (self.model) + model = _unwrap(self.model) return [ { - "params": model.get_params()[key], + "params": model.get_params()[key], **self.optimizer_params[key].params(), } for key in model.param_keys ] - # --- Optimizer Mixin Overloads in the case of PPLR --- @property @@ -1060,11 +1057,13 @@ def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | di if not isinstance(params, dict): raise TypeError(f"optimizer parameters must be a dict for PPLR, got {type(params)}") - + object_params = params - + if set(object_params.keys()) != set(self.model.param_keys): - raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}") + raise ValueError( + f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}" + ) params = {} for key, value in object_params.items(): @@ -1073,31 +1072,33 @@ def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | di elif isinstance(value, OptimizerType): params[key] = value else: - raise TypeError(f"optimizer parameters must be a dict or OptimizerType, got {type(value)}") + raise TypeError( + f"optimizer parameters must be a dict or OptimizerType, got {type(value)}" + ) self._optimizer_params = params def reconnect_optimizer_to_parameters(self) -> None: - if self.optimizer is None: return - + current_params = self.get_optimization_parameters() - optimizable_params = [ - p for p in current_params - if isinstance(p['params'][0], torch.Tensor) and p['params'][0].is_leaf + p + for p in current_params + if isinstance(p["params"][0], torch.Tensor) and p["params"][0].is_leaf ] - if not optimizable_params: - raise ValueError(f"Shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}.") - + raise ValueError( + f"Shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}." + ) + for p in optimizable_params: print(f"Setting requires_grad for parameter: {p}") - p['params'][0].requires_grad_(True) - + p["params"][0].requires_grad_(True) + assert self._optimizer is not None # Preserve optimizer states and param_group settings old_state = self._optimizer.state.copy() @@ -1132,7 +1133,9 @@ def reconnect_optimizer_to_parameters(self) -> None: self._scheduler.optimizer = self._optimizer def pretrain(self) -> None: - raise NotImplementedError("Tensor decomposition pretraining is not usually required, and for TILTED there is a two-phase warmup approach.") - + raise NotImplementedError( + "Tensor decomposition pretraining is not usually required, and for TILTED there is a two-phase warmup approach." + ) + ObjectModelType = ObjectPixelated | ObjectINR diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index f21a30d1..899f1a1d 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -182,7 +182,9 @@ def reconstruct( consistency_loss = torch.tensor(0.0, device=self.device) total_loss = torch.tensor(0.0, device=self.device) epoch_soft_constraint_loss = torch.tensor(0.0, device=self.device) - if isinstance(self.obj_model, ObjectINR) or isinstance(self.obj_model, ObjectTensorDecomp): + if isinstance(self.obj_model, ObjectINR) or isinstance( + self.obj_model, ObjectTensorDecomp + ): self.obj_model.model.train() else: raise NotImplementedError( @@ -217,7 +219,9 @@ def reconstruct( ) pred = integrated_densities.float() - soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, all_densities, pred) + soft_constraints_loss = self.obj_model.apply_soft_constraints( + all_coords, all_densities, pred + ) target = batch["target_value"].to(self.device, non_blocking=True).float() @@ -243,7 +247,7 @@ def reconstruct( R_now = self.obj_model.model.so3.as_matrix().detach() # Cumulative angular change per rotation over the last 20 iters. # trace(R_prev^T R_now) = 1 + 2*cos(theta), so theta = acos((trace - 1) / 2). - rel_trace = torch.einsum('tij,tij->t', prev_R, R_now) + rel_trace = torch.einsum("tij,tij->t", prev_R, R_now) angle = torch.acos(((rel_trace - 1) / 2).clamp(-1, 1)) # (T,) radians angle_deg = torch.rad2deg(angle) per_tau_str = ", ".join(f"{a:.2f}°" for a in angle_deg.tolist()) @@ -636,10 +640,10 @@ class ReconstructionContext: Subclasses will pick whatever parameter they need - Pixelated reads ".volume" - INR reads ".coords" and recomputes via the model. - - TEnsorDEcomp reads ".coords" and ".pred" (and ".all densities") + - TensorDecomp reads ".coords" and ".pred" (and ".all densities") """ coords: Optional[torch.Tensor] = None pred: Optional[torch.Tensor] = None all_densities: Optional[torch.Tensor] = None - volume: Optional[torch.Tensor] = None \ No newline at end of file + obj: Optional[torch.Tensor] = None diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index 0f8c44be..c75de1e3 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -2,7 +2,7 @@ import torch -from quantem.core.ml.optimizer_mixin import OptimizerParams, OptimizerType, SchedulerType +from quantem.core.ml.optimizer_mixin import OptimizerType, SchedulerType from quantem.tomography.tomography_base import TomographyBase diff --git a/tests/datastructures/test_dataset3d_show.py b/tests/datastructures/test_dataset3d_show.py index 27c18f41..7938c66f 100644 --- a/tests/datastructures/test_dataset3d_show.py +++ b/tests/datastructures/test_dataset3d_show.py @@ -31,16 +31,19 @@ def extract_frame_indices_from_figure(fig): class TestShowInputValidation: """Test that invalid inputs raise clear errors.""" - @pytest.mark.parametrize("kwargs,match", [ - ({"step": 0}, "cannot be zero"), - ({"start": 100}, "out of bounds"), - ({"start": -100}, "out of bounds"), - ({"start": 5, "end": 5}, "No frames to display"), - ({"ncols": 0}, "ncols must be >= 1"), - ({"ncols": -1}, "ncols must be >= 1"), - ({"max": 0}, "max must be >= 1"), - ({"max": -1}, "max must be >= 1"), - ]) + @pytest.mark.parametrize( + "kwargs,match", + [ + ({"step": 0}, "cannot be zero"), + ({"start": 100}, "out of bounds"), + ({"start": -100}, "out of bounds"), + ({"start": 5, "end": 5}, "No frames to display"), + ({"ncols": 0}, "ncols must be >= 1"), + ({"ncols": -1}, "ncols must be >= 1"), + ({"max": 0}, "max must be >= 1"), + ({"max": -1}, "max must be >= 1"), + ], + ) def test_raises_value_error(self, dataset_with_10_frames, kwargs, match): with pytest.raises(ValueError, match=match): dataset_with_10_frames.show(**kwargs) @@ -49,39 +52,44 @@ def test_raises_value_error(self, dataset_with_10_frames, kwargs, match): class TestShowFrameSelection: """Test frame selection with start, end, step, max combinations.""" - @pytest.mark.parametrize("kwargs,expected_indices", [ - ({}, list(range(20))), - ({"max": 5}, [0, 1, 2, 3, 4]), - ({"max": None}, list(range(100))), - ({"start": 90}, list(range(90, 100))), - ({"start": 95, "max": 3}, [95, 96, 97]), - ]) + @pytest.mark.parametrize( + "kwargs,expected_indices", + [ + ({}, list(range(20))), + ({"max": 5}, [0, 1, 2, 3, 4]), + ({"max": None}, list(range(100))), + ({"start": 90}, list(range(90, 100))), + ({"start": 95, "max": 3}, [95, 96, 97]), + ], + ) def test_large_dataset(self, dataset_with_100_frames, kwargs, expected_indices): fig, _ = dataset_with_100_frames.show(returnfig=True, **kwargs) assert extract_frame_indices_from_figure(fig) == expected_indices plt.close(fig) - @pytest.mark.parametrize("kwargs,expected_indices", [ - # Default shows all frames (< max) - ({}, list(range(10))), - # Start and end - ({"start": 5}, [5, 6, 7, 8, 9]), - ({"end": 5}, [0, 1, 2, 3, 4]), - # Step - ({"step": 2}, [0, 2, 4, 6, 8]), - ({"step": 3}, [0, 3, 6, 9]), - ({"start": 2, "end": 8, "step": 2}, [2, 4, 6]), - # Negative start index - ({"start": -1, "max": 1}, [9]), - ({"start": -3, "max": 2}, [7, 8]), - # Negative step (reverse order) - ({"start": 9, "step": -1}, [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]), - ({"start": 9, "end": 4, "step": -1}, [9, 8, 7, 6, 5]), - ({"start": 9, "step": -2}, [9, 7, 5, 3, 1]), - ({"start": 9, "step": -1, "max": 3}, [9, 8, 7]), - ]) + @pytest.mark.parametrize( + "kwargs,expected_indices", + [ + # Default shows all frames (< max) + ({}, list(range(10))), + # Start and end + ({"start": 5}, [5, 6, 7, 8, 9]), + ({"end": 5}, [0, 1, 2, 3, 4]), + # Step + ({"step": 2}, [0, 2, 4, 6, 8]), + ({"step": 3}, [0, 3, 6, 9]), + ({"start": 2, "end": 8, "step": 2}, [2, 4, 6]), + # Negative start index + ({"start": -1, "max": 1}, [9]), + ({"start": -3, "max": 2}, [7, 8]), + # Negative step (reverse order) + ({"start": 9, "step": -1}, [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]), + ({"start": 9, "end": 4, "step": -1}, [9, 8, 7, 6, 5]), + ({"start": 9, "step": -2}, [9, 7, 5, 3, 1]), + ({"start": 9, "step": -1, "max": 3}, [9, 8, 7]), + ], + ) def test_small_dataset(self, dataset_with_10_frames, kwargs, expected_indices): fig, _ = dataset_with_10_frames.show(returnfig=True, **kwargs) assert extract_frame_indices_from_figure(fig) == expected_indices plt.close(fig) - From a061845add3789341bf635cc66cf0867398b05d9 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 27 Apr 2026 11:24:01 -0700 Subject: [PATCH 028/140] Added pyrightconfig.json to gitignore, PrivateImportUsage error annoying --- .gitignore | 3 +++ src/quantem/tomography/object_models.py | 17 +++++++++-------- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/.gitignore b/.gitignore index d87d85c8..a66c803d 100644 --- a/.gitignore +++ b/.gitignore @@ -167,6 +167,9 @@ cython_debug/ # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ +# BasedPyRight Config +pyrightconfig.json + # Ruff stuff: .ruff_cache/ diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 8f254533..5d5d8b1e 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -260,6 +260,7 @@ def model(self) -> nn.Module: raise NotImplementedError @abstractmethod + @property def dtype(self) -> torch.dtype: """ Returns the dtype of the object. @@ -385,9 +386,9 @@ def obj(self, obj: torch.Tensor): def obj_view(self) -> np.ndarray: return self.obj.cpu().unsqueeze(0).numpy() - @property - def soft_loss(self) -> torch.Tensor: - return self.apply_soft_constraints(self._obj) + # @property + # def soft_loss(self) -> torch.Tensor: + # return self.apply_soft_constraints(self._obj) @property def name(self) -> str: @@ -421,11 +422,11 @@ def apply_hard_constraints( def apply_soft_constraints(self, ctx: ReconstructionContext) -> torch.Tensor: assert ctx.obj is not None, "ObjectPixelated requires ctx.obj to be set" - soft_loss = torch.tensor(0.0, device=ctx.obj.device, dtype=ctx.obj.dtype, requires_grad=True) + soft_loss = torch.tensor( + 0.0, device=ctx.obj.device, dtype=ctx.obj.dtype, requires_grad=True + ) if self.constraints.tv_vol > 0: - tv_loss = self.get_tv_loss( - ctx - ) + tv_loss = self.get_tv_loss(ctx) soft_loss += tv_loss return soft_loss @@ -441,7 +442,7 @@ def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: tv_w = torch.pow(ctx.obj[:, :, :, :, 1:] - ctx.obj[:, :, :, :, :-1], 2).sum() tv_loss = tv_d + tv_h + tv_w - return tv_loss * self.constraints.tv_vol / (torch.prod(torch.tensor(obj.shape))) + return tv_loss * self.constraints.tv_vol / (torch.prod(torch.tensor(ctx.obj.shape))) # --- Helper Functions --- def to(self, device: str | torch.device): From 23cfd72de885c7e86c5b992f190d9fb49518ac70 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 27 Apr 2026 11:35:35 -0700 Subject: [PATCH 029/140] ObjectINR implemented --- src/quantem/tomography/object_models.py | 103 +++++++++--------------- 1 file changed, 40 insertions(+), 63 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 5d5d8b1e..19c69e3e 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -259,7 +259,6 @@ def model(self) -> nn.Module: """ raise NotImplementedError - @abstractmethod @property def dtype(self) -> torch.dtype: """ @@ -537,40 +536,19 @@ def obj_view(self) -> np.ndarray: def apply_soft_constraints( self, - coords: torch.Tensor, - pred: torch.Tensor, + ctx: ReconstructionContext, ) -> torch.Tensor: - soft_loss = torch.tensor(0.0, device=pred.device) + soft_loss = torch.tensor(0.0, device=ctx.coords.device) if self.constraints.tv_vol > 0: - num_tv_samples = min(10_000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - - tv_coords = coords[tv_indices].detach().requires_grad_(True) - tv_densities_recomputed = self.model(tv_coords) - if isinstance(tv_densities_recomputed, tuple): - tv_densities_recomputed = tv_densities_recomputed[0] - - # Ensure shape is [num_samples, num_channels] - if tv_densities_recomputed.dim() == 1: - tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) - - # Compute gradients for each channel - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True, - )[0] # Shape: [num_samples, coord_dim] - - # Compute TV loss - gradient magnitude per sample - grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] - soft_loss += self.constraints.tv_vol * grad_norm.mean() + assert ctx.coords is not None, "coords must be provided for INR object model to compute the TV loss" + soft_loss += self.get_tv_loss(ctx) if ( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) and self.constraints.sparsity > 0 ): # NOTE: For the linter, I must make this :) - sparsity_loss = self.constraints.sparsity * torch.norm(pred, p=1) + assert ctx.pred is not None, "pred must be provided for INR object model to compute the sparsity loss" + sparsity_loss = self.constraints.sparsity * torch.norm(ctx.pred, p=1) soft_loss += sparsity_loss return soft_loss @@ -587,6 +565,37 @@ def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: return pred + # --- Define get_tv_loss --- + + def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: + """ + Compute the total variation loss for the INR model. + """ + assert ctx.coords is not None, "coords must be provided for INR object model" + num_tv_samples = min(10_000, ctx.coords.shape[0]) + tv_indices = torch.randperm(ctx.coords.shape[0], device=ctx.coords.device)[:num_tv_samples] + + tv_coords = ctx.coords[tv_indices].detach().requires_grad_(True) + tv_densities_recomputed = self.model(tv_coords) + if isinstance(tv_densities_recomputed, tuple): + tv_densities_recomputed = tv_densities_recomputed[0] + + # Ensure shape is [num_samples, num_channels] + if tv_densities_recomputed.dim() == 1: + tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) + + # Compute gradients for each channel + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] # Shape: [num_samples, coord_dim] + + # Compute TV loss - gradient magnitude per sample + grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] + return self.constraints.tv_vol * grad_norm.mean() + # --- Optimization Parameters --- @property def params(self) -> Generator[torch.nn.Parameter, None, None]: @@ -625,13 +634,6 @@ def dtype(self) -> torch.dtype: # TODO: This is a temporary solution to get the dtype of the object. return torch.float32 - @property - def shape(self) -> tuple[int, int, int]: - return self._shape - - @shape.setter - def shape(self, shape: tuple[int, int, int]): - self._shape = shape # --- Helper Functions --- def rebuild_model(self): @@ -647,8 +649,10 @@ def reset(self): # --- Forward Method --- - def forward(self, coords: torch.Tensor) -> torch.Tensor: + def forward(self, coords: Optional[torch.Tensor] = None) -> torch.Tensor: """forward pass for the INR model""" + assert coords is not None, "ObjectINR.forward requires coords" + all_densities = self.model(coords) if all_densities.dim() > 1: @@ -846,33 +850,6 @@ def create_volume(self, return_vol: bool = False): self._obj = pred_full.detach().cpu() - def get_tv_loss( # pyright: ignore[reportIncompatibleMethodOverride] - self, - coords: torch.Tensor, - ) -> torch.Tensor: - tv_loss = torch.tensor(0.0, device=coords.device) - - num_tv_samples = min(10000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - - tv_coords = coords[tv_indices].detach().requires_grad_(True) - - tv_densities_recomputed = self.forward(tv_coords) - - if tv_densities_recomputed.dim() > 1: - tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) - - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True, - )[0] - - grad_norm = torch.norm(grad_outputs, dim=1) - - tv_loss += self.constraints.tv_vol * grad_norm.mean() - return tv_loss def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change if isinstance(device, str): From fb07d14f919d1a7f0133d51432f85d52ab089342 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 27 Apr 2026 13:34:16 -0700 Subject: [PATCH 030/140] Claude OptimizerMixin changes to account for different optimizable params. reconnect_optimizer_to_parameters, and optimizer_params changes --- src/quantem/core/ml/models/model_base.py | 2 +- src/quantem/core/ml/optimizer_mixin.py | 113 +++++++-------- src/quantem/tomography/object_models.py | 166 +++++++---------------- 3 files changed, 110 insertions(+), 171 deletions(-) diff --git a/src/quantem/core/ml/models/model_base.py b/src/quantem/core/ml/models/model_base.py index c7ccf7aa..503f61bb 100644 --- a/src/quantem/core/ml/models/model_base.py +++ b/src/quantem/core/ml/models/model_base.py @@ -37,7 +37,7 @@ class TensorDecompositionModel(nn.Module, ABC): td_type: str -class PlanarDecompositionModel(TensorDecompositionModel): +class PlanarDecompositionModel(TensorDecompositionModel, PPLR): """ Planar factored-grid models: K-Planes, K-Planes-TILTED, HexPlane, tri-planes. diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index b5053a4c..9655eb76 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -1,7 +1,7 @@ import textwrap from abc import abstractmethod from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Generator, Iterable, Literal +from typing import TYPE_CHECKING, Any, Literal from quantem.core import config @@ -536,7 +536,9 @@ def __init__(self): """Initialize the optimizer mixin.""" self._optimizer = None self._scheduler = None - self._optimizer_params: OptimizerType = OptimizerParams.NoneOptimizer() + self._optimizer_params: OptimizerType | dict[str, OptimizerType] = ( + OptimizerParams.NoneOptimizer() + ) self._scheduler_params: SchedulerType = SchedulerParams.NoneScheduler() # Don't call super().__init__() in mixin classes to avoid MRO issues @@ -551,18 +553,36 @@ def scheduler(self) -> "torch.optim.lr_scheduler.LRScheduler | None": return self._scheduler @property - def optimizer_params(self) -> OptimizerType: + def optimizer_params(self) -> OptimizerType | dict[str, OptimizerType]: """Get the optimizer parameters.""" return self._optimizer_params @optimizer_params.setter - def optimizer_params(self, params: OptimizerType | dict): - """Set the optimizer parameters.""" + def optimizer_params( + self, params: OptimizerType | dict[str, OptimizerType] | dict[str, Any] + ) -> None: + self._optimizer_params = self._normalize_optimizer_params(params) + + def _normalize_optimizer_params( + self, params: OptimizerType | dict[str, Any] + ) -> OptimizerType | dict[str, OptimizerType]: + """Normalize input. Subclasses can override to validate keys.""" + # dict-of-OptimizerType form (PPLR) + if isinstance(params, dict) and not self._is_single_optimizer_dict(params): + return { + k: v if isinstance(v, OptimizerType) else OptimizerParams.parse_dict(d=v) + for k, v in params.items() + } + # Single optimizer form (with dict shorthand like {"name": "adam", "lr": 1e-3}) if isinstance(params, dict): params = OptimizerParams.parse_dict(d=params) if not isinstance(params, OptimizerType): - raise TypeError(f"optimizer parameters must be a OptimizerType, got {type(params)}") - self._optimizer_params = params + raise TypeError(f"optimizer_params must be OptimizerType or dict, got {type(params)}") + return params + + @staticmethod + def _is_single_optimizer_dict(d: dict) -> bool: + return "type" in d or "name" in d @property def scheduler_params(self) -> SchedulerType: @@ -581,7 +601,7 @@ def scheduler_params(self, params: SchedulerType | dict): @abstractmethod def get_optimization_parameters( self, - ) -> "Iterable[torch.Tensor] | Iterable[dict[str, Any]]": + ) -> "list[dict[str, Any]]": """ Get the parameters that should be optimized for this model. This could be replaced with just module.parameters(), but this allows for flexibility @@ -606,16 +626,11 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: self.remove_optimizer() return - params = self.get_optimization_parameters() - if isinstance(params, torch.Tensor): - params = [params] - elif isinstance(params, Generator): - params = list(params) + params = self.get_optimization_parameters() # always list[dict] # Ensure parameters require gradients for group in params: - tensors = group["params"] if isinstance(group, dict) else [group] - for p in tensors: + for p in group["params"]: p.requires_grad_(True) # Figure out which optimizer class to use if isinstance(self._optimizer_params, dict): @@ -745,56 +760,44 @@ def reconnect_optimizer_to_parameters(self) -> None: if self._optimizer is None: return - current_params = self.get_optimization_parameters() - if isinstance(current_params, torch.Tensor): - current_params = [current_params] - elif isinstance(current_params, Generator): - current_params = list(current_params) - - optimizable_params = [ - p for p in current_params if isinstance(p, torch.Tensor) and p.is_leaf - ] - - if not optimizable_params: - print( - f"shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}, removing optimizer" - ) + new_groups = self.get_optimization_parameters() + if not new_groups: + print(f"No optimizable parameters for {type(self).__name__}, removing optimizer") self.remove_optimizer() return - for p in optimizable_params: - p.requires_grad_(True) + # Ensure leaf params with grad + for group in new_groups: + for p in group["params"]: + if not p.is_leaf: + raise ValueError("Non-leaf tensor in param group; build groups from leaves") + p.requires_grad_(True) - # Preserve optimizer state and param_group settings - old_state = self._optimizer.state.copy() - current_param_group = self._optimizer.param_groups[0].copy() + old_state = dict(self._optimizer.state) + old_hyperparams = [ + {k: v for k, v in pg.items() if k != "params"} for pg in self._optimizer.param_groups + ] - # Reconnect to new parameters self._optimizer.param_groups.clear() - self._optimizer.add_param_group({"params": optimizable_params}) + for group in new_groups: + self._optimizer.add_param_group(group) + + # Restore per-group hyperparameters by index + for new_pg, old_pg in zip(self._optimizer.param_groups, old_hyperparams): + new_pg.update(old_pg) - # Update state mapping and move tensors to correct device + # Remap state for tensors that survived new_state = {} - device = optimizable_params[0].device - for i, old_param in enumerate(old_state.keys()): - if i < len(optimizable_params): - new_param = optimizable_params[i] - new_state[new_param] = {} - for key, value in old_state[old_param].items(): - if isinstance(value, torch.Tensor): - new_state[new_param][key] = value.to(device) - else: - new_state[new_param][key] = value + for new_pg in self._optimizer.param_groups: + for new_param in new_pg["params"]: + if new_param in old_state: + new_state[new_param] = { + k: (v.to(new_param.device) if isinstance(v, torch.Tensor) else v) + for k, v in old_state[new_param].items() + } self._optimizer.state.clear() self._optimizer.state.update(new_state) - # Restore param_group settings (LR, betas, etc.) but keep new parameters - self._optimizer.param_groups[0].update( - {k: v for k, v in current_param_group.items() if k != "params"} - ) - - # Reconnect scheduler - if self._scheduler is not None and self._optimizer is not None: + if self._scheduler is not None: self._scheduler.optimizer = self._optimizer - return diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 19c69e3e..d7f93268 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -10,12 +10,11 @@ from tqdm.auto import tqdm from quantem.core.io.serialize import AutoSerialize -from quantem.core.ml import OptimizerParams from quantem.core.ml.constraints import BaseConstraints, Constraints from quantem.core.ml.ddp import DDPMixin from quantem.core.ml.loss_functions import get_loss_module from quantem.core.ml.models.model_base import PlanarDecompositionModel -from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType +from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset from quantem.tomography.tomography import ReconstructionContext @@ -291,11 +290,14 @@ def params(self) -> Generator[torch.nn.Parameter, None, None]: raise NotImplementedError # --- Helper Functions --- - def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: - """ - Get the parameters that should be optimized for this model. - """ - return list(self.params) + def get_optimization_parameters(self) -> list[dict[str, Any]]: + """Default: wrap self.params in a single param group.""" + if isinstance(self._optimizer_params, dict): + # Shouldn't happen for single-group models, but be defensive + opt = next(iter(self._optimizer_params.values())) + else: + opt = self._optimizer_params + return [{"params": list(self.params), **opt.params()}] @abstractmethod # Each subclass should implement this. def to(self, device: str | torch.device): @@ -540,14 +542,18 @@ def apply_soft_constraints( ) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=ctx.coords.device) if self.constraints.tv_vol > 0: - assert ctx.coords is not None, "coords must be provided for INR object model to compute the TV loss" + assert ctx.coords is not None, ( + "coords must be provided for INR object model to compute the TV loss" + ) soft_loss += self.get_tv_loss(ctx) if ( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) and self.constraints.sparsity > 0 ): # NOTE: For the linter, I must make this :) - assert ctx.pred is not None, "pred must be provided for INR object model to compute the sparsity loss" + assert ctx.pred is not None, ( + "pred must be provided for INR object model to compute the sparsity loss" + ) sparsity_loss = self.constraints.sparsity * torch.norm(ctx.pred, p=1) soft_loss += sparsity_loss @@ -601,7 +607,7 @@ def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: def params(self) -> Generator[torch.nn.Parameter, None, None]: return self.model.parameters() # type: ignore[attr-defined] - def get_optimization_parameters(self) -> list[nn.Parameter]: + def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: return list(self.params) # Pretraining @@ -634,7 +640,6 @@ def dtype(self) -> torch.dtype: # TODO: This is a temporary solution to get the dtype of the object. return torch.float32 - # --- Helper Functions --- def rebuild_model(self): self._model = self.distribute_model(self._model) @@ -652,7 +657,7 @@ def reset(self): def forward(self, coords: Optional[torch.Tensor] = None) -> torch.Tensor: """forward pass for the INR model""" assert coords is not None, "ObjectINR.forward requires coords" - + all_densities = self.model(coords) if all_densities.dim() > 1: @@ -850,7 +855,6 @@ def create_volume(self, return_vol: bool = False): self._obj = pred_full.detach().cpu() - def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change if isinstance(device, str): device = torch.device(device) @@ -910,28 +914,30 @@ def from_model( # --- Constraints --- - def apply_soft_constraints( - self, - coords: torch.Tensor, - all_densities: torch.Tensor, - pred: torch.Tensor, - ) -> torch.Tensor: - soft_loss = torch.tensor(0.0, device=pred.device) + def apply_soft_constraints(self, ctx: ReconstructionContext) -> torch.Tensor: + soft_loss = torch.tensor(0.0, device=ctx.pred.device) if self.constraints.tv_vol > 0: - soft_loss += self.get_tv_loss(coords, pred) + assert ctx.coords is not None, "Coordinates must be provided for TV loss" + assert ctx.pred is not None, "Prediction must be provided for TV loss" + soft_loss += self.get_tv_loss(ctx) if self.constraints.sparsity > 0: # NOTE: For the linter, I must make this :) - sparsity_loss = self.constraints.sparsity * all_densities.abs().mean() + assert ctx.all_densities is not None, ( + "All densities must be provided for sparsity loss" + ) + sparsity_loss = self.constraints.sparsity * ctx.all_densities.abs().mean() soft_loss += sparsity_loss return soft_loss # TV Losses - def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: - tv_loss = torch.tensor(0.0, device=pred.device) + def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: + assert ctx.coords is not None, "Coordinates must be provided for TV loss" + assert ctx.pred is not None, "Prediction must be provided for TV loss" + tv_loss = torch.tensor(0.0, device=ctx.pred.device) tv_loss += self._get_plane_tv_loss() - tv_loss += self.get_volume_tv_loss(coords) + tv_loss += self.get_volume_tv_loss(ctx.coords) return tv_loss def _get_plane_tv_loss(self) -> torch.Tensor: @@ -1012,103 +1018,33 @@ def params(self) -> Generator[torch.nn.Parameter, None, None]: return self.model.parameters() # type: ignore[attr-defined] - def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: + def get_optimization_parameters(self) -> list[dict[str, Any]]: + """PPLR: per-key param groups.""" model = _unwrap(self.model) + assert isinstance(self._optimizer_params, dict), ( + "ObjectTensorDecomp requires dict-form optimizer_params" + ) return [ - { - "params": model.get_params()[key], - **self.optimizer_params[key].params(), - } + {"params": model.get_params()[key], **self._optimizer_params[key].params()} for key in model.param_keys ] - # --- Optimizer Mixin Overloads in the case of PPLR --- - - @property - def optimizer_params(self) -> OptimizerType | dict[str, OptimizerType]: - """Get the optimizer parameters.""" - return self._optimizer_params - - @optimizer_params.setter - def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | dict[str, Any]): - """Set the optimizer parameters.""" - - if not isinstance(params, dict): - raise TypeError(f"optimizer parameters must be a dict for PPLR, got {type(params)}") - - object_params = params - - if set(object_params.keys()) != set(self.model.param_keys): - raise ValueError( - f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}" + def _normalize_optimizer_params(self, params): + """ObjectTensorDecomp requires a dict matching model.param_keys.""" + if not isinstance(params, dict) or self._is_single_optimizer_dict(params): + raise TypeError( + f"ObjectTensorDecomp requires dict[str, OptimizerType] keyed by " + f"param_keys; got {type(params)}" ) - - params = {} - for key, value in object_params.items(): - if isinstance(value, dict): - params[key] = OptimizerParams.parse_dict(d=value) - elif isinstance(value, OptimizerType): - params[key] = value - else: - raise TypeError( - f"optimizer parameters must be a dict or OptimizerType, got {type(value)}" - ) - - self._optimizer_params = params - - def reconnect_optimizer_to_parameters(self) -> None: - if self.optimizer is None: - return - - current_params = self.get_optimization_parameters() - - optimizable_params = [ - p - for p in current_params - if isinstance(p["params"][0], torch.Tensor) and p["params"][0].is_leaf - ] - - if not optimizable_params: + model = _unwrap(self.model) + expected = set(model.param_keys) + got = set(params.keys()) + if got != expected: raise ValueError( - f"Shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}." + f"optimizer_params keys must match model.param_keys: " + f"got {got}, expected {expected}" ) - - for p in optimizable_params: - print(f"Setting requires_grad for parameter: {p}") - p["params"][0].requires_grad_(True) - - assert self._optimizer is not None - # Preserve optimizer states and param_group settings - old_state = self._optimizer.state.copy() - old_param_groups = self._optimizer.param_groups.copy() - - # Reconnect to new parameters - self._optimizer.param_groups.clear() - for param_group in optimizable_params: - self._optimizer.add_param_group(param_group) - - # Restore per-group hyperparameters (lr, betas, weight_decay, etc.) by index, - # excluding 'params' which comes from the new groups - for new_pg, old_pg in zip(self._optimizer.param_groups, old_param_groups): - new_pg.update({k: v for k, v in old_pg.items() if k != "params"}) - - # Remap optimizer state: for any new param that IS the same tensor as an old param, - # carry its state over (moved to the right device just in case). - new_state = {} - for new_pg in self._optimizer.param_groups: - for new_param in new_pg["params"]: - if new_param in old_state: - device = new_param.device - new_state[new_param] = { - k: (v.to(device) if isinstance(v, torch.Tensor) else v) - for k, v in old_state[new_param].items() - } - - self._optimizer.state.clear() - self._optimizer.state.update(new_state) - - if self._scheduler is not None and self._optimizer is not None: - self._scheduler.optimizer = self._optimizer + return super()._normalize_optimizer_params(params) def pretrain(self) -> None: raise NotImplementedError( @@ -1116,4 +1052,4 @@ def pretrain(self) -> None: ) -ObjectModelType = ObjectPixelated | ObjectINR +ObjectModelType = ObjectPixelated | ObjectINR | ObjectTensorDecomp From 6678c43e8ecf1438efd610ff05a50cf8918e3b18 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 27 Apr 2026 13:46:13 -0700 Subject: [PATCH 031/140] Moved ReconContext, changed get_optimization_parameters in dataset_models.py --- src/quantem/core/ml/constraints.py | 2 +- src/quantem/tomography/dataset_models.py | 11 +++++--- src/quantem/tomography/object_models.py | 6 +++-- src/quantem/tomography/tomography.py | 28 ++++++-------------- src/quantem/tomography/tomography_context.py | 21 +++++++++++++++ 5 files changed, 42 insertions(+), 26 deletions(-) create mode 100644 src/quantem/tomography/tomography_context.py diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index 137ed4df..f437924f 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -7,7 +7,7 @@ import torch from numpy.typing import NDArray -from quantem.tomography.tomography import ReconstructionContext +from quantem.tomography.tomography_context import ReconstructionContext @dataclass(slots=False) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 24f0f775..b50c5976 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -232,11 +232,16 @@ def from_data( # --- Optimization Parameters --- - def get_optimization_parameters(self) -> list[nn.Parameter]: + def get_optimization_parameters(self) -> list[dict[str, Any]]: """ - Get the parameters that should be optimized for this model. + Get the parameters that should be optimized for this model, + wrapped in a single param group. """ - return list(self.parameters()) + if isinstance(self._optimizer_params, dict): + opt = next(iter(self._optimizer_params.values())) + else: + opt = self._optimizer_params + return [{"params": list(self.parameters()), **opt.params()}] # --- Forward pass --- @abstractmethod diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index d7f93268..10bae9e4 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -17,7 +17,7 @@ from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset -from quantem.tomography.tomography import ReconstructionContext +from quantem.tomography.tomography_context import ReconstructionContext class ObjConstraintParams: @@ -915,7 +915,9 @@ def from_model( # --- Constraints --- def apply_soft_constraints(self, ctx: ReconstructionContext) -> torch.Tensor: - soft_loss = torch.tensor(0.0, device=ctx.pred.device) + soft_loss = torch.tensor( + 0.0, device=ctx.pred.device if ctx.pred is not None else self.device + ) if self.constraints.tv_vol > 0: assert ctx.coords is not None, "Coordinates must be provided for TV loss" assert ctx.pred is not None, "Prediction must be provided for TV loss" diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 899f1a1d..8976272e 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,7 +1,6 @@ import os -from dataclasses import dataclass from pathlib import Path -from typing import Literal, Optional, Self, Sequence +from typing import Literal, Self, Sequence import matplotlib.pyplot as plt import numpy as np @@ -31,6 +30,7 @@ ) from quantem.tomography.radon.radon import iradon_torch, radon_torch from quantem.tomography.tomography_base import TomographyBase +from quantem.tomography.tomography_context import ReconstructionContext from quantem.tomography.tomography_opt import TomographyOpt @@ -219,8 +219,13 @@ def reconstruct( ) pred = integrated_densities.float() + soft_constraints_loss = self.obj_model.apply_soft_constraints( - all_coords, all_densities, pred + ctx=ReconstructionContext( + coords=all_coords, + pred=pred, + all_densities=all_densities, + ) ) target = batch["target_value"].to(self.device, non_blocking=True).float() @@ -630,20 +635,3 @@ def plot_losses(self): ax.set_title("Reconstruction Loss") ax.set_yscale("log") plt.show() - - -@dataclass -class ReconstructionContext: - """ - Handles all reconstruction parameters to be passed into object models. - - Subclasses will pick whatever parameter they need - - Pixelated reads ".volume" - - INR reads ".coords" and recomputes via the model. - - TensorDecomp reads ".coords" and ".pred" (and ".all densities") - """ - - coords: Optional[torch.Tensor] = None - pred: Optional[torch.Tensor] = None - all_densities: Optional[torch.Tensor] = None - obj: Optional[torch.Tensor] = None diff --git a/src/quantem/tomography/tomography_context.py b/src/quantem/tomography/tomography_context.py new file mode 100644 index 00000000..4b67f118 --- /dev/null +++ b/src/quantem/tomography/tomography_context.py @@ -0,0 +1,21 @@ +from dataclasses import dataclass +from typing import Optional + +import torch + + +@dataclass +class ReconstructionContext: + """ + Handles all reconstruction parameters to be passed into object models. + + Subclasses will pick whatever parameter they need + - Pixelated reads ".volume" + - INR reads ".coords" and recomputes via the model. + - TensorDecomp reads ".coords" and ".pred" (and ".all densities") + """ + + coords: Optional[torch.Tensor] = None + pred: Optional[torch.Tensor] = None + all_densities: Optional[torch.Tensor] = None + obj: Optional[torch.Tensor] = None From 9a9641545450b48ca3894d38730e070b9636be52 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 27 Apr 2026 21:29:03 -0700 Subject: [PATCH 032/140] Small changes --- src/quantem/tomography/object_models.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 10bae9e4..a094fae8 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -607,9 +607,6 @@ def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: def params(self) -> Generator[torch.nn.Parameter, None, None]: return self.model.parameters() # type: ignore[attr-defined] - def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: - return list(self.params) - # Pretraining @property def pretrained_weights(self) -> dict[str, torch.Tensor]: From 5ebbceb727d558dc86e99df03d3481fc10610b1a Mon Sep 17 00:00:00 2001 From: henrygbell Date: Fri, 1 May 2026 10:19:45 -0700 Subject: [PATCH 033/140] Changed docstrings to all be in numpy format --- src/quantem/diffraction/maped.py | 310 +++++++++++++++++++------------ 1 file changed, 189 insertions(+), 121 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index e09c012c..8def04dc 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -81,14 +81,26 @@ def preprocess( """ Compute dataset summary images. - Stores - ------ - self.scales : np.ndarray + Parameters + ---------- + plot_summary : bool, optional + If True, display summary plots (default True). + scale : float or sequence of float or None, optional + Per-dataset scaling factor(s) (default None). + + Attributes + ---------- + scales : np.ndarray Per-dataset scaling factors (n,). - self.dp_mean : list[np.ndarray] + dp_mean : list[np.ndarray] Mean diffraction patterns (H, W), one per dataset. - self.im_bf : list[np.ndarray] + im_bf : list[np.ndarray] Mean bright-field images (R, C), one per dataset. + + Returns + ------- + MAPED + self (updated instance) """ n = len(self.datasets) if scale is None: @@ -152,23 +164,28 @@ def diffraction_origin( Parameters ---------- - origins + origins : tuple or sequence, optional Optional manual origins. Can be: - a single (row, col) tuple, applied to all datasets - a list of (row, col) tuples of length n (one per dataset) - sigma + sigma : float, optional Optional low-pass smoothing sigma (pixels) applied to each mean DP prior to peak finding. - plot_origins + plot_origins : bool, optional If True, plot mean diffraction patterns with overlaid origin markers. - plot_indices + plot_indices : sequence of int, optional Optional indices to plot. If None, plots all datasets. **plot_kwargs Passed to show_2d. - Stores - ------ - self.diffraction_origins : np.ndarray + Attributes + ---------- + diffraction_origins : np.ndarray Array of shape (n, 2) with integer (row, col) origins. + + Returns + ------- + MAPED + self (updated instance) """ n = len(self.datasets) if not hasattr(self, "dp_mean"): @@ -236,25 +253,30 @@ def diffraction_align( Parameters ---------- - edge_blend + edge_blend : float Tukey window edge taper (pixels). - padding + padding : int or None Passed to shift_images for plotting. - pad_val + pad_val : str or float Passed to shift_images for plotting. - upsample_factor + upsample_factor : int Subpixel upsampling factor for correlation peak estimation. - weight_scale + weight_scale : float Radial weight falloff scale (fraction of mean DP size). - plot_aligned + plot_aligned : bool If True, plot aligned mean diffraction patterns. **plot_kwargs Passed to show_2d when plotting. - Stores - ------ - self.diffraction_shifts : np.ndarray + Attributes + ---------- + diffraction_shifts : np.ndarray Array of shape (n, 2) with (row, col) shifts to align diffraction patterns. + + Returns + ------- + MAPED + self (updated instance) """ if not hasattr(self, "dp_mean"): raise RuntimeError("Run preprocess() first so self.dp_mean exists.") @@ -339,37 +361,42 @@ def real_space_align( # torch.grid_sample Parameters ---------- - num_images + num_images : int, optional If provided, align only the first num_images images. - num_iter + num_iter : int Number of refinement iterations. - edge_blend + edge_blend : float Used to set default correlation padding when max_shift is None. - padding + padding : int or None Passed to shift_images for plotting. - pad_val + pad_val : str or float Passed to shift_images for plotting. - upsample_factor + upsample_factor : int Subpixel upsampling factor for correlation peak estimation. - max_shift + max_shift : float, optional Optional maximum shift constraint passed to weighted_cross_correlation_shift. - shift_method + shift_method : str Passed to shift_images for plotting ('bilinear' or 'fourier'). - edge_filter + edge_filter : bool If True, correlate on gradient magnitude instead of raw intensity. - edge_sigma + edge_sigma : float Gaussian sigma applied to gradients when edge_filter is True. - hanning_filter + hanning_filter : bool If True, apply a Hanning window prior to FFT. - plot_aligned + plot_aligned : bool If True, plot aligned mean BF images. **plot_kwargs Passed to show_2d when plotting. - Stores - ------ - self.real_space_shifts : np.ndarray + Attributes + ---------- + real_space_shifts : np.ndarray Array of shape (n_total, 2) with (row, col) shifts for aligned datasets. + + Returns + ------- + MAPED + self (updated instance) """ if not hasattr(self, "im_bf"): raise RuntimeError("Run preprocess() first so self.im_bf exists.") @@ -505,12 +532,14 @@ def merge_datasets( """ Merge aligned datasets into a single Dataset4dstem. - Requires - -------- + Notes + ----- + Requires the following attributes to be present on ``self``: + self.real_space_shifts - From real_space_align(). + From ``real_space_align()``. self.diffraction_shifts - From diffraction_align(). + From ``diffraction_align()``. Parameters ---------- @@ -861,14 +890,26 @@ def preprocess( """ Compute dataset summary images. - Stores - ------ - self.scales : torch.tensor + Parameters + ---------- + plot_summary : bool, optional + If True, display summary plots (default True). + scale : float or sequence of float or None, optional + Per-dataset scaling factor(s) (default None). + + Attributes + ---------- + scales : torch.tensor Per-dataset scaling factors (n,). - self.dp_mean : list[torch.tensor] + dp_mean : list[torch.tensor] Mean diffraction patterns (H, W), one per dataset. - self.im_bf : list[torch.tensor] + im_bf : list[torch.tensor] Mean bright-field images (R, C), one per dataset. + + Returns + ------- + MAPED + self (updated instance) """ n = len(self.datasets) @@ -890,7 +931,6 @@ def preprocess( for d in self.datasets: dp_arr = torch.mean(d, dim=(0, 1)) - im_bf_arr = torch.mean(d, dim=(2, 3)) self.dp_mean.append(dp_arr) @@ -907,10 +947,10 @@ def preprocess( def diffraction_origin( self, - origins=None, - sigma=None, + origins: tuple | list | None = None, + sigma: float | None = None, plot_origins: bool = True, - plot_indices=None, + plot_indices: list | None = None, **plot_kwargs: Any, ) -> MAPED: """ @@ -918,23 +958,28 @@ def diffraction_origin( Parameters ---------- - origins + origins : tuple or list, optional Optional manual origins. Can be: - a single (row, col) tuple, applied to all datasets - a list of (row, col) tuples of length n (one per dataset) - sigma + sigma : float, optional Optional low-pass smoothing sigma (pixels) applied to each mean DP prior to peak finding. - plot_origins + plot_origins : bool, optional If True, plot mean diffraction patterns with overlaid origin markers. - plot_indices + plot_indices : list, optional Optional indices to plot. If None, plots all datasets. **plot_kwargs Passed to show_2d. - Stores - ------ - self.diffraction_origins : np.ndarray + Attributes + ---------- + diffraction_origins : np.ndarray Array of shape (n, 2) with integer (row, col) origins. + + Returns + ------- + MAPED + self (updated instance) """ n = len(self.datasets) if not hasattr(self, "dp_mean"): @@ -996,12 +1041,12 @@ def diffraction_origin( def dscan_align( self, - iterations, + iterations: int, upsample_factor: int = 100, plot_aligned: bool = True, edge_blend: float = 2.0, - fit_shifts=True, - mode="linear", + fit_shifts: bool = True, + mode: str = "linear", ): for i, dataset in enumerate(self.datasets): _, aligned_dataset, _ = dscan_correct( @@ -1033,25 +1078,30 @@ def diffraction_align( Parameters ---------- - edge_blend + edge_blend : float Tukey window edge taper (pixels). - padding + padding : int or tuple, optional Passed to shift_images for plotting. - pad_val + pad_val : str or float Passed to shift_images for plotting. - upsample_factor + upsample_factor : int Subpixel upsampling factor for correlation peak estimation. - weight_scale + weight_scale : float Radial weight falloff scale (fraction of mean DP size). - plot_aligned + plot_aligned : bool If True, plot aligned mean diffraction patterns. **plot_kwargs Passed to show_2d when plotting. - Stores - ------ - self.diffraction_shifts : np.ndarray + Attributes + ---------- + diffraction_shifts : np.ndarray Array of shape (n, 2) with (row, col) shifts to align diffraction patterns. + + Returns + ------- + MAPED + self (updated instance) """ if not hasattr(self, "dp_mean"): raise RuntimeError("Run preprocess() first so self.dp_mean exists.") @@ -1154,37 +1204,42 @@ def real_space_align( Parameters ---------- - num_images + num_images : int, optional If provided, align only the first num_images images. - num_iter + num_iter : int Number of refinement iterations. - edge_blend + edge_blend : float Used to set default correlation padding when max_shift is None. - padding + padding : int or tuple, optional Passed to shift_images for plotting. - pad_val + pad_val : float Passed to shift_images for plotting. - upsample_factor + upsample_factor : int Subpixel upsampling factor for correlation peak estimation. - max_shift + max_shift : float Optional maximum shift constraint passed to weighted_cross_correlation_shift. - shift_method + shift_method : 'bilinear' or 'fourier' Passed to shift_images for plotting ('bilinear' or 'fourier'). - edge_filter + edge_filter : bool If True, correlate on gradient magnitude instead of raw intensity. - edge_sigma + edge_sigma : float Gaussian sigma applied to gradients when edge_filter is True. - hanning_filter + hanning_filter : bool If True, apply a Hanning window prior to FFT. - plot_aligned + plot_aligned : bool If True, plot aligned mean BF images. **plot_kwargs Passed to show_2d when plotting. - Stores - ------ - self.real_space_shifts : np.ndarray + Attributes + ---------- + real_space_shifts : np.ndarray Array of shape (n_total, 2) with (row, col) shifts for aligned datasets. + + Returns + ------- + MAPED + self (updated instance) """ if not hasattr(self, "im_bf"): raise RuntimeError("Run preprocess() first so self.im_bf exists.") @@ -1322,11 +1377,11 @@ def real_space_align( def merge_datasets( self, - real_space_padding=0, - real_space_edge_blend=1.0, - diffraction_padding=0, - diffraction_edge_blend=0.0, - diffraction_pad_val="min", + real_space_padding: int = 0, + real_space_edge_blend: float = 1.0, + diffraction_padding: int = 0, + diffraction_edge_blend: float = 0.0, + diffraction_pad_val: str | float = "min", shift_method: str = "bilinear", dtype=None, scale_output: bool = False, @@ -1337,34 +1392,36 @@ def merge_datasets( """ Merge aligned datasets into a single Dataset4dstem. - Requires - -------- + Notes + ----- + Requires the following attributes to be present on ``self``: + self.real_space_shifts - From real_space_align(). + From ``real_space_align()``. self.diffraction_shifts - From diffraction_align(). + From ``diffraction_align()``. Parameters ---------- - real_space_padding + real_space_padding : int Output scan padding in pixels (adds border to scan grid). - real_space_edge_blend + real_space_edge_blend : float Tukey taper width for scan-space interpolation weights. - diffraction_padding + diffraction_padding : int Output diffraction padding in pixels (adds border around DPs). - diffraction_edge_blend + diffraction_edge_blend : float Tukey taper width for diffraction-space weights. - diffraction_pad_val + diffraction_pad_val : str | float Pad value for diffraction padding ('min','max','mean','median' or float). - shift_method + shift_method : str Diffraction shift method: 'bilinear' or 'fourier'. - dtype + dtype : str or torch.dtype, optional Output dtype. If None, uses parent dtype. - scale_output + scale_output : bool If True and dtype is integer, scale to full dynamic range using global max. - plot_result + plot_result : bool If True, plot merged BF and merged mean DP. - batch_size + batch_size : int, optional Number of rows to process per batch. If None, uses adaptive sizing (1-32 rows). **plot_kwargs Passed to show_2d. @@ -1678,10 +1735,10 @@ def merge_datasets( def shift_images( - images, - shifts_rc, + images: list[np.ndarray], + shifts_rc: np.ndarray, edge_blend: float = 8.0, - padding=None, + padding: int | None = None, pad_val: str | float = 0.0, shift_method: str = "bilinear", ): @@ -1690,17 +1747,17 @@ def shift_images( Parameters ---------- - images + images : list of np.ndarray Sequence of (H, W) arrays. - shifts_rc + shifts_rc : np.ndarray Array-like of shape (n, 2) with (row, col) shifts for each image. - edge_blend + edge_blend : float, optional Tukey taper width in pixels for image blending. - padding + padding : int Output padding. If None, set from max shift and edge_blend. - pad_val + pad_val : str | float optional Fill value outside support ('min','max','mean','median' or float). - shift_method + shift_method : str 'bilinear' or 'fourier'. Returns @@ -1814,18 +1871,18 @@ def tukey_torch(N, alpha=0.5, device=None, dtype=torch.float32): Parameters ---------- - N - int, Length of the window. - alpha - float, Shape parameter for the Tukey window. - device - torch.device, Device on which to create the window. - dtype + N : int + Length of the window. + alpha : float + Shape parameter for the Tukey window. + device : torch.device | str + Device on which to create the window. + dtype : torch.dtype torch.dtype, Data type of the window. Returns ------- - torch.Tensor + window : torch.Tensor 1D Tukey window of length N. """ n = torch.arange(N, device=device, dtype=dtype) @@ -1874,8 +1931,10 @@ def shift_images_torch( Returns ------- - torch.Tensor — shifted (and blended) images; if input was a single image, returns (Hp, Wp), - otherwise returns (n, Hp, Wp) for blended result or (n, H, W) for non-blended. + torch.Tensor + Shifted (and blended) images. If the input was a single image, returns an array + of shape (Hp, Wp). Otherwise returns (n, Hp, Wp) for blended result or (n, H, W) + for the non-blended case. """ single = images.dim() == 2 if single: @@ -2053,6 +2112,15 @@ def dscan_correct( Whether to fit shifts to a smooth surface mode : str "linear" or "quadratic" for surface fitting + + Returns + ------- + tuple + A tuple ``(diffraction_shifts, shifted_dps, G_ref_final)`` where + ``diffraction_shifts`` is a ``torch.Tensor`` of shape (H_rs, W_rs, 2) with + per-scan-position shifts, ``shifted_dps`` is the aligned dataset (same shape + as ``dataset``), and ``G_ref_final`` is the final complex Fourier-domain + reference (torch.Tensor). """ H_rs, W_rs, H_dp, W_dp = dataset.shape From 1033cd668bf502e0ff11cc9d813da78e8fa33c7e Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Sat, 2 May 2026 14:24:04 -0700 Subject: [PATCH 034/140] feat: add Show2D and Show4DSTEM flagship widgets --- widget/js/colormaps.ts | 1086 +++++ widget/js/control-customizer.tsx | 174 + widget/js/format.ts | 40 + widget/js/histogram.ts | 19 + widget/js/index.jsx | 33 - widget/js/scalebar.ts | 444 ++ widget/js/show2d/index.tsx | 4185 +++++++++++++++++++ widget/js/show2d/show2d.css | 9 + widget/js/show4dstem/index.tsx | 4259 +++++++++++++++++++ widget/js/show4dstem/styles.css | 5 + widget/js/stats.ts | 101 + widget/js/theme.ts | 149 + widget/js/tool-parity.ts | 156 + widget/js/webgpu-fft.ts | 509 +++ widget/package-lock.json | 971 ++++- widget/package.json | 22 +- widget/pyproject.toml | 5 + widget/src/quantem/widget/__init__.py | 25 +- widget/src/quantem/widget/array_utils.py | 282 ++ widget/src/quantem/widget/json_state.py | 47 + widget/src/quantem/widget/show2d.py | 1309 ++++++ widget/src/quantem/widget/show4dstem.py | 4337 ++++++++++++++++++++ widget/src/quantem/widget/tool_parity.json | 93 + widget/src/quantem/widget/tool_parity.py | 184 + widget/tsconfig.json | 25 + widget/vite.config.js | 15 +- 26 files changed, 18364 insertions(+), 120 deletions(-) create mode 100644 widget/js/colormaps.ts create mode 100644 widget/js/control-customizer.tsx create mode 100644 widget/js/format.ts create mode 100644 widget/js/histogram.ts delete mode 100644 widget/js/index.jsx create mode 100644 widget/js/scalebar.ts create mode 100644 widget/js/show2d/index.tsx create mode 100644 widget/js/show2d/show2d.css create mode 100644 widget/js/show4dstem/index.tsx create mode 100644 widget/js/show4dstem/styles.css create mode 100644 widget/js/stats.ts create mode 100644 widget/js/theme.ts create mode 100644 widget/js/tool-parity.ts create mode 100644 widget/js/webgpu-fft.ts create mode 100644 widget/src/quantem/widget/array_utils.py create mode 100644 widget/src/quantem/widget/json_state.py create mode 100644 widget/src/quantem/widget/show2d.py create mode 100644 widget/src/quantem/widget/show4dstem.py create mode 100644 widget/src/quantem/widget/tool_parity.json create mode 100644 widget/src/quantem/widget/tool_parity.py create mode 100644 widget/tsconfig.json diff --git a/widget/js/colormaps.ts b/widget/js/colormaps.ts new file mode 100644 index 00000000..ba160698 --- /dev/null +++ b/widget/js/colormaps.ts @@ -0,0 +1,1086 @@ +const COLORMAP_POINTS: Record = { + inferno: [ + [0, 0, 4], [40, 11, 84], [101, 21, 110], [159, 42, 99], + [212, 72, 66], [245, 125, 21], [252, 193, 57], [252, 255, 164], + ], + viridis: [ + [68, 1, 84], [72, 36, 117], [65, 68, 135], [53, 95, 141], + [42, 120, 142], [33, 145, 140], [34, 168, 132], [68, 191, 112], + [122, 209, 81], [189, 223, 38], [253, 231, 37], + ], + plasma: [ + [13, 8, 135], [75, 3, 161], [126, 3, 168], [168, 34, 150], + [203, 70, 121], [229, 107, 93], [248, 148, 65], [253, 195, 40], [240, 249, 33], + ], + magma: [ + [0, 0, 4], [28, 16, 68], [79, 18, 123], [129, 37, 129], + [181, 54, 122], [229, 80, 100], [251, 135, 97], [254, 194, 135], [252, 253, 191], + ], + hot: [ + [0, 0, 0], [87, 0, 0], [173, 0, 0], [255, 0, 0], + [255, 87, 0], [255, 173, 0], [255, 255, 0], [255, 255, 128], [255, 255, 255], + ], + gray: [[0, 0, 0], [255, 255, 255]], + hsv: [ + [255, 0, 0], [255, 255, 0], [0, 255, 0], [0, 255, 255], + [0, 0, 255], [255, 0, 255], [255, 0, 0], + ], + turbo: [ + [48, 18, 59], [69, 55, 161], [66, 107, 230], [30, 162, 230], + [29, 212, 169], [79, 241, 89], [175, 240, 32], [244, 195, 12], + [248, 118, 11], [207, 46, 3], [122, 4, 2], + ], + RdBu: [ + [103, 0, 31], [178, 24, 43], [214, 96, 77], [244, 165, 130], + [253, 219, 199], [247, 247, 247], [209, 229, 240], [146, 197, 222], + [67, 147, 195], [33, 102, 172], [5, 48, 97], + ], +}; + +export const COLORMAP_NAMES = Object.keys(COLORMAP_POINTS); + +function createColormapLUT(points: number[][]): Uint8Array { + const lut = new Uint8Array(256 * 3); + for (let i = 0; i < 256; i++) { + const t = (i / 255) * (points.length - 1); + const idx = Math.floor(t); + const frac = t - idx; + const p0 = points[Math.min(idx, points.length - 1)]; + const p1 = points[Math.min(idx + 1, points.length - 1)]; + lut[i * 3] = Math.round(p0[0] + frac * (p1[0] - p0[0])); + lut[i * 3 + 1] = Math.round(p0[1] + frac * (p1[1] - p0[1])); + lut[i * 3 + 2] = Math.round(p0[2] + frac * (p1[2] - p0[2])); + } + return lut; +} + +export const COLORMAPS: Record = Object.fromEntries( + Object.entries(COLORMAP_POINTS).map(([name, points]) => [name, createColormapLUT(points)]) +); + +/** Apply colormap LUT to float data, writing into an RGBA Uint8ClampedArray. */ +export function applyColormap( + data: Float32Array, + rgba: Uint8ClampedArray, + lut: Uint8Array, + vmin: number, + vmax: number, +): void { + const range = vmax > vmin ? vmax - vmin : 1; + const uniformData = !(vmax > vmin); + for (let i = 0; i < data.length; i++) { + const clipped = Math.max(vmin, Math.min(vmax, data[i])); + const v = uniformData ? 128 : Math.min(255, Math.floor(((clipped - vmin) / range) * 255)); + const j = i * 4; + const lutIdx = v * 3; + rgba[j] = lut[lutIdx]; + rgba[j + 1] = lut[lutIdx + 1]; + rgba[j + 2] = lut[lutIdx + 2]; + rgba[j + 3] = 255; + } +} + +/** Create an offscreen canvas with colormapped data. Returns null if context unavailable. */ +export function renderToOffscreen( + data: Float32Array, + width: number, + height: number, + lut: Uint8Array, + vmin: number, + vmax: number, +): HTMLCanvasElement | null { + const offscreen = document.createElement("canvas"); + offscreen.width = width; + offscreen.height = height; + const ctx = offscreen.getContext("2d"); + if (!ctx) return null; + const imgData = ctx.createImageData(width, height); + applyColormap(data, imgData.data, lut, vmin, vmax); + ctx.putImageData(imgData, 0, 0); + return offscreen; +} + +/** Render colormapped data to a reusable offscreen canvas + ImageData (avoids per-frame allocation). */ +export function renderToOffscreenReuse( + data: Float32Array, + lut: Uint8Array, + vmin: number, + vmax: number, + offscreen: HTMLCanvasElement, + imgData: ImageData, +): void { + applyColormap(data, imgData.data, lut, vmin, vmax); + offscreen.getContext("2d")!.putImageData(imgData, 0, 0); +} + +// ============================================================================ +// WebGPU-accelerated colormap engine +// ============================================================================ + +// 2D dispatch (16×16 workgroups) to stay within WebGPU's 65535 workgroup limit. +// 1D dispatch with wg=256 needs ceil(4096*4096/256)=65536 — exceeds the limit by 1. +const COLORMAP_SHADER = /* wgsl */ ` +struct Params { + width: u32, + height: u32, + vmin: f32, + vmax: f32, + log_scale: u32, + _pad: u32, +}; + +@group(0) @binding(0) var params: Params; +@group(0) @binding(1) var data: array; +@group(0) @binding(2) var lut: array; +@group(0) @binding(3) var rgba: array; + +@compute @workgroup_size(16, 16) +fn main(@builtin(global_invocation_id) gid: vec3u) { + if (gid.x >= params.width || gid.y >= params.height) { return; } + let idx = gid.y * params.width + gid.x; + var val = data[idx]; + if (params.log_scale == 1u) { + val = log(1.0 + max(val, 0.0)); + } + let range = max(params.vmax - params.vmin, 1e-30); + let clipped = clamp(val, params.vmin, params.vmax); + let t = (clipped - params.vmin) / range; + let lutIdx = min(u32(t * 255.0), 255u); + let rgb = lut[lutIdx]; + // Simplified: LUT is already packed as R|(G<<8)|(B<<16), just add alpha + rgba[idx] = rgb | 0xFF000000u; +} +`; + +// Fullscreen-quad blit shader: reads RGBA u32 buffer, renders to canvas texture +const BLIT_SHADER = /* wgsl */ ` +struct BlitParams { width: u32, height: u32 }; +@group(0) @binding(0) var params: BlitParams; +@group(0) @binding(1) var rgba: array; + +struct VSOut { @builtin(position) pos: vec4f, @location(0) uv: vec2f }; + +@vertex fn vs(@builtin(vertex_index) vi: u32) -> VSOut { + // Fullscreen triangle (3 vertices, covers entire clip space) + var out: VSOut; + let x = f32(i32(vi & 1u)) * 4.0 - 1.0; + let y = f32(i32(vi >> 1u)) * 4.0 - 1.0; + out.pos = vec4f(x, y, 0.0, 1.0); + out.uv = vec2f((x + 1.0) * 0.5, (1.0 - y) * 0.5); + return out; +} + +@fragment fn fs(in: VSOut) -> @location(0) vec4f { + let px = u32(in.uv.x * f32(params.width)); + let py = u32(in.uv.y * f32(params.height)); + let idx = py * params.width + px; + let packed = rgba[idx]; + let r = f32(packed & 0xFFu) / 255.0; + let g = f32((packed >> 8u) & 0xFFu) / 255.0; + let b = f32((packed >> 16u) & 0xFFu) / 255.0; + return vec4f(r, g, b, 1.0); +} +`; + +/** + * GPU-accelerated colormap engine. Holds persistent data buffers on GPU; + * histogram slider changes only update a small uniform — no data re-upload. + */ +type GPUSlot = { + dataBuffer: GPUBuffer; + rgbaBuffer: GPUBuffer; + readBuffer: GPUBuffer; + paramsBuffer: GPUBuffer; + histBinsBuffer: GPUBuffer; + histReadBuffer: GPUBuffer; + count: number; + width: number; + height: number; +}; + +export class GPUColormapEngine { + private device: GPUDevice; + private pipeline: GPUComputePipeline | null = null; + private blitPipeline: GPURenderPipeline | null = null; + // Per-image GPU state: persistent buffers (data, rgba, read, params, histogram) + private slots: GPUSlot[] = []; + private lutBuffer: GPUBuffer | null = null; + private currentLutName: string = ""; + + constructor(device: GPUDevice) { this.device = device; } + + private ensurePipeline(): void { + if (this.pipeline) return; + const module = this.device.createShaderModule({ code: COLORMAP_SHADER }); + this.pipeline = this.device.createComputePipeline({ + layout: "auto", + compute: { module, entryPoint: "main" }, + }); + } + + /** Upload LUT to GPU (only when colormap name changes). */ + uploadLUT(lutName: string, lut: Uint8Array): void { + if (this.currentLutName === lutName && this.lutBuffer) return; + this.ensurePipeline(); + if (this.lutBuffer) this.lutBuffer.destroy(); + // Pack RGB triplets into u32 for GPU (R in low bits) + const packed = new Uint32Array(256); + for (let i = 0; i < 256; i++) { + packed[i] = lut[i * 3] | (lut[i * 3 + 1] << 8) | (lut[i * 3 + 2] << 16); + } + this.lutBuffer = this.device.createBuffer({ + size: packed.byteLength, + usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_DST, + }); + this.device.queue.writeBuffer(this.lutBuffer, 0, packed); + this.currentLutName = lutName; + } + + + /** Upload float32 image data for slot `idx`. Only call when data changes. */ + uploadData(idx: number, data: Float32Array, width?: number, height?: number): void { + this.ensurePipeline(); + while (this.slots.length <= idx) this.slots.push(null as never); + if (this.slots[idx]) { + this.slots[idx].dataBuffer.destroy(); + this.slots[idx].rgbaBuffer.destroy(); + this.slots[idx].readBuffer.destroy(); + this.slots[idx].paramsBuffer.destroy(); + this.slots[idx].histBinsBuffer.destroy(); + this.slots[idx].histReadBuffer.destroy(); + } + // Validate dimensions — if width*height doesn't match data length, derive from sqrt + // (catches stale closure values like width=1 from mount effects) + const validDims = width && height && width > 1 && height > 1 && width * height === data.length; + const w = validDims ? width : Math.round(Math.sqrt(data.length)); + const h = validDims ? height : Math.round(data.length / w); + const byteSize = data.byteLength; + const rgbaSize = data.length * 4; + const dataBuffer = this.device.createBuffer({ + size: byteSize, + usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_DST, + }); + this.device.queue.writeBuffer(dataBuffer, 0, data.buffer as ArrayBuffer, data.byteOffset, data.byteLength); + const rgbaBuffer = this.device.createBuffer({ + size: rgbaSize, + usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC, + }); + // Persistent read buffer — reused on every applySlots call (no create/destroy overhead) + const readBuffer = this.device.createBuffer({ + size: rgbaSize, + usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST, + }); + // Persistent params buffer — reused (just writeBuffer on each call) + const paramsBuffer = this.device.createBuffer({ + size: 24, + usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST, + }); + // Persistent histogram buffers (256 bins × 4 bytes = 1KB each) + const histBinsBuffer = this.device.createBuffer({ + size: 256 * 4, + usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC, + }); + const histReadBuffer = this.device.createBuffer({ + size: 256 * 4, + usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST, + }); + this.slots[idx] = { dataBuffer, rgbaBuffer, readBuffer, paramsBuffer, histBinsBuffer, histReadBuffer, count: data.length, width: w, height: h }; + } + + // Params buffer: 24 bytes = { width: u32, height: u32, vmin: f32, vmax: f32, log_scale: u32, _pad: u32 } + private _writeParams(buf: ArrayBuffer, width: number, height: number, vmin: number, vmax: number, logScale: boolean): void { + const u = new Uint32Array(buf); + const f = new Float32Array(buf); + u[0] = width; + u[1] = height; + f[2] = vmin; + f[3] = vmax; + u[4] = logScale ? 1 : 0; + u[5] = 0; // pad + } + + /** + * Apply colormap to specific slot indices with per-image vmin/vmax. + * Uses persistent per-slot read buffers (no create/destroy overhead). + * Log scale is applied on GPU per pixel. + */ + async applySlots( + indices: number[], + ranges: { vmin: number; vmax: number }[], + logScale: boolean = false, + ): Promise<{ idx: number; rgba: Uint8ClampedArray }[]> { + if (!this.pipeline || !this.lutBuffer || indices.length === 0) return []; + + const activeSlots: { idx: number; slot: GPUSlot; count: number }[] = []; + const encoder = this.device.createCommandEncoder(); + const params = new ArrayBuffer(24); + + for (let k = 0; k < indices.length; k++) { + const i = indices[k]; + const slot = this.slots[i]; + if (!slot) continue; + const range = ranges[k] || { vmin: 0, vmax: 1 }; + + // Reuse persistent paramsBuffer — just write new values + this._writeParams(params, slot.width, slot.height, range.vmin, range.vmax, logScale); + this.device.queue.writeBuffer(slot.paramsBuffer, 0, params); + + const bindGroup = this.device.createBindGroup({ + layout: this.pipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: slot.paramsBuffer } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: this.lutBuffer } }, + { binding: 3, resource: { buffer: slot.rgbaBuffer } }, + ], + }); + + const pass = encoder.beginComputePass(); + pass.setPipeline(this.pipeline); + pass.setBindGroup(0, bindGroup); + pass.dispatchWorkgroups(Math.ceil(slot.width / 16), Math.ceil(slot.height / 16)); + pass.end(); + + // Copy to persistent read buffer + encoder.copyBufferToBuffer(slot.rgbaBuffer, 0, slot.readBuffer, 0, slot.count * 4); + activeSlots.push({ idx: i, slot, count: slot.count }); + } + this.device.queue.submit([encoder.finish()]); + await Promise.all(activeSlots.map(s => s.slot.readBuffer.mapAsync(GPUMapMode.READ))); + + const results: { idx: number; rgba: Uint8ClampedArray }[] = []; + for (const s of activeSlots) { + const mapped = s.slot.readBuffer.getMappedRange(); + const rgba = new Uint8ClampedArray(s.count * 4); + rgba.set(new Uint8ClampedArray(mapped)); + s.slot.readBuffer.unmap(); + results.push({ idx: s.idx, rgba }); + } + + // applySlots is for callers that need raw RGBA arrays (not rendering to canvas) + // For rendering, use renderSlots which avoids the intermediate copy + return results; + } + + /** Apply colormap to ALL slots with shared vmin/vmax. */ + async apply(vmin: number, vmax: number, logScale: boolean = false): Promise { + const indices = this.slots.map((_, i) => i).filter(i => this.slots[i]); + const ranges = indices.map(() => ({ vmin, vmax })); + const results = await this.applySlots(indices, ranges, logScale); + // Return in slot order + const out: Uint8ClampedArray[] = []; + for (const r of results) out[r.idx] = r.rgba; + return out.filter(x => x); + } + + /** Apply colormap with per-image vmin/vmax. */ + async applyPerImage(ranges: { vmin: number; vmax: number }[], logScale: boolean = false): Promise { + const indices = this.slots.map((_, i) => i).filter(i => this.slots[i]); + const perSlotRanges = indices.map(i => ranges[i] || { vmin: 0, vmax: 1 }); + const results = await this.applySlots(indices, perSlotRanges, logScale); + const out: Uint8ClampedArray[] = []; + for (const r of results) out[r.idx] = r.rgba; + return out.filter(x => x); + } + + /** Apply colormap to a SINGLE slot (fast path for slider drag). */ + async applySingle(idx: number, vmin: number, vmax: number, logScale: boolean = false): Promise { + const results = await this.applySlots([idx], [{ vmin, vmax }], logScale); + return results.length > 0 ? results[0].rgba : null; + } + + /** + * GPU colormap → offscreen canvas in one pass (zero intermediate allocation). + * Writes from GPU mapped memory directly into ImageData, then putImageData. + * Eliminates the 768MB temp Uint8ClampedArray that applySlots allocates. + */ + async renderSlots( + indices: number[], + ranges: { vmin: number; vmax: number }[], + offscreens: (HTMLCanvasElement | null)[], + imgDatas: (ImageData | null)[], + logScale: boolean = false, + ): Promise { + if (!this.pipeline || !this.lutBuffer || indices.length === 0) return 0; + + const activeSlots: { k: number; idx: number; slot: GPUSlot }[] = []; + const encoder = this.device.createCommandEncoder(); + const params = new ArrayBuffer(24); + + for (let k = 0; k < indices.length; k++) { + const i = indices[k]; + const slot = this.slots[i]; + if (!slot || !offscreens[k] || !imgDatas[k]) continue; + const range = ranges[k] || { vmin: 0, vmax: 1 }; + + this._writeParams(params, slot.width, slot.height, range.vmin, range.vmax, logScale); + this.device.queue.writeBuffer(slot.paramsBuffer, 0, params); + + const bindGroup = this.device.createBindGroup({ + layout: this.pipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: slot.paramsBuffer } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: this.lutBuffer } }, + { binding: 3, resource: { buffer: slot.rgbaBuffer } }, + ], + }); + + const pass = encoder.beginComputePass(); + pass.setPipeline(this.pipeline); + pass.setBindGroup(0, bindGroup); + pass.dispatchWorkgroups(Math.ceil(slot.width / 16), Math.ceil(slot.height / 16)); + pass.end(); + encoder.copyBufferToBuffer(slot.rgbaBuffer, 0, slot.readBuffer, 0, slot.count * 4); + activeSlots.push({ k, idx: i, slot }); + } + this.device.queue.submit([encoder.finish()]); + await Promise.all(activeSlots.map(s => s.slot.readBuffer.mapAsync(GPUMapMode.READ))); + + // Write directly from GPU mapped memory → ImageData → offscreen canvas + let rendered = 0; + for (const s of activeSlots) { + const mapped = s.slot.readBuffer.getMappedRange(); + const imgData = imgDatas[s.k]!; + imgData.data.set(new Uint8ClampedArray(mapped)); + s.slot.readBuffer.unmap(); + offscreens[s.k]!.getContext("2d")!.putImageData(imgData, 0, 0); + rendered++; + } + return rendered; + } + + private ensureBlitPipeline(format: GPUTextureFormat): void { + if (this.blitPipeline) return; + const module = this.device.createShaderModule({ code: BLIT_SHADER }); + this.blitPipeline = this.device.createRenderPipeline({ + layout: "auto", + vertex: { module, entryPoint: "vs" }, + fragment: { + module, entryPoint: "fs", + targets: [{ format }], + }, + primitive: { topology: "triangle-list" }, + }); + } + + /** + * Zero-copy GPU render: compute colormap + blit directly to WebGPU canvas textures. + * No mapAsync, no CPU copy, no putImageData. Target: <16ms for 60fps. + * + * Each canvas must have a 'webgpu' context (not '2d'). Call configureCanvas() first. + * Returns the number of images rendered. + */ + renderSlotsZeroCopy( + indices: number[], + ranges: { vmin: number; vmax: number }[], + contexts: (GPUCanvasContext | null)[], + logScale: boolean = false, + ): number { + if (!this.pipeline || !this.lutBuffer || indices.length === 0) return 0; + + // Get texture format from first valid context + const fmt = navigator.gpu.getPreferredCanvasFormat(); + this.ensureBlitPipeline(fmt); + if (!this.blitPipeline) return 0; + + const encoder = this.device.createCommandEncoder(); + const params = new ArrayBuffer(24); + let rendered = 0; + + for (let k = 0; k < indices.length; k++) { + const i = indices[k]; + const slot = this.slots[i]; + const ctx = contexts[k]; + if (!slot || !ctx) continue; + const range = ranges[k] || { vmin: 0, vmax: 1 }; + + // 1. Compute colormap (same as renderSlots) + this._writeParams(params, slot.width, slot.height, range.vmin, range.vmax, logScale); + this.device.queue.writeBuffer(slot.paramsBuffer, 0, params); + + const computeGroup = this.device.createBindGroup({ + layout: this.pipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: slot.paramsBuffer } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: this.lutBuffer } }, + { binding: 3, resource: { buffer: slot.rgbaBuffer } }, + ], + }); + const computePass = encoder.beginComputePass(); + computePass.setPipeline(this.pipeline); + computePass.setBindGroup(0, computeGroup); + computePass.dispatchWorkgroups(Math.ceil(slot.width / 16), Math.ceil(slot.height / 16)); + computePass.end(); + + // 2. Blit RGBA buffer → canvas texture (zero-copy render pass) + const blitParamsBuffer = this.device.createBuffer({ + size: 8, + usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST, + }); + this.device.queue.writeBuffer(blitParamsBuffer, 0, new Uint32Array([slot.width, slot.height])); + + const blitGroup = this.device.createBindGroup({ + layout: this.blitPipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: blitParamsBuffer } }, + { binding: 1, resource: { buffer: slot.rgbaBuffer } }, + ], + }); + + const texture = ctx.getCurrentTexture(); + const renderPass = encoder.beginRenderPass({ + colorAttachments: [{ + view: texture.createView(), + loadOp: "clear" as GPULoadOp, + storeOp: "store" as GPUStoreOp, + clearValue: { r: 0, g: 0, b: 0, a: 1 }, + }], + }); + renderPass.setPipeline(this.blitPipeline); + renderPass.setBindGroup(0, blitGroup); + renderPass.draw(3); // fullscreen triangle + renderPass.end(); + rendered++; + + // Note: blitParamsBuffer is a temporary — ideally per-slot persistent + // For now, acceptable overhead (8 bytes per image) + } + + this.device.queue.submit([encoder.finish()]); + if (rendered > 0) { + } + return rendered; + } + + /** + * GPU colormap → OffscreenCanvas → ImageBitmap (zero mapAsync). + * Compute shader writes RGBA, render pass blits to OffscreenCanvas texture, + * transferToImageBitmap() returns ImageBitmap for drawImage on 2D canvas. + * Eliminates the 35ms JS memcpy for 12×4K images. + */ + renderSlotsToImageBitmap( + indices: number[], + ranges: { vmin: number; vmax: number }[], + logScale: boolean = false, + ): ImageBitmap[] | null { + if (!this.pipeline || !this.lutBuffer || indices.length === 0) return null; + const fmt = navigator.gpu.getPreferredCanvasFormat(); + this.ensureBlitPipeline(fmt); + if (!this.blitPipeline) return null; + + const encoder = this.device.createCommandEncoder(); + const params = new ArrayBuffer(24); + const canvases: OffscreenCanvas[] = []; + + for (let k = 0; k < indices.length; k++) { + const i = indices[k]; + const slot = this.slots[i]; + if (!slot) { canvases.push(null as never); continue; } + const range = ranges[k] || { vmin: 0, vmax: 1 }; + + // Compute colormap + this._writeParams(params, slot.width, slot.height, range.vmin, range.vmax, logScale); + this.device.queue.writeBuffer(slot.paramsBuffer, 0, params); + + const computeGroup = this.device.createBindGroup({ + layout: this.pipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: slot.paramsBuffer } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: this.lutBuffer } }, + { binding: 3, resource: { buffer: slot.rgbaBuffer } }, + ], + }); + const computePass = encoder.beginComputePass(); + computePass.setPipeline(this.pipeline); + computePass.setBindGroup(0, computeGroup); + computePass.dispatchWorkgroups(Math.ceil(slot.width / 16), Math.ceil(slot.height / 16)); + computePass.end(); + + // Blit to OffscreenCanvas + const oc = new OffscreenCanvas(slot.width, slot.height); + const ctx = oc.getContext("webgpu") as GPUCanvasContext; + ctx.configure({ device: this.device, format: fmt, alphaMode: "opaque" }); + + const blitParamsBuffer = this.device.createBuffer({ + size: 8, usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST, + }); + this.device.queue.writeBuffer(blitParamsBuffer, 0, new Uint32Array([slot.width, slot.height])); + + const blitGroup = this.device.createBindGroup({ + layout: this.blitPipeline!.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: blitParamsBuffer } }, + { binding: 1, resource: { buffer: slot.rgbaBuffer } }, + ], + }); + + const texture = ctx.getCurrentTexture(); + const renderPass = encoder.beginRenderPass({ + colorAttachments: [{ + view: texture.createView(), + loadOp: "clear" as GPULoadOp, + storeOp: "store" as GPUStoreOp, + clearValue: { r: 0, g: 0, b: 0, a: 1 }, + }], + }); + renderPass.setPipeline(this.blitPipeline!); + renderPass.setBindGroup(0, blitGroup); + renderPass.draw(3); + renderPass.end(); + canvases.push(oc); + } + + this.device.queue.submit([encoder.finish()]); + + // transferToImageBitmap after GPU finishes (synchronous, no mapAsync) + const bitmaps: ImageBitmap[] = []; + for (const oc of canvases) { + if (oc) bitmaps.push(oc.transferToImageBitmap()); + else bitmaps.push(null as never); + } + return bitmaps; + } + + /** + * Configure a canvas for WebGPU zero-copy rendering. + * Returns the GPUCanvasContext, or null if WebGPU canvas is not supported. + */ + configureCanvas(canvas: HTMLCanvasElement, width: number, height: number): GPUCanvasContext | null { + try { + const ctx = canvas.getContext("webgpu") as GPUCanvasContext | null; + if (!ctx) return null; + ctx.configure({ + device: this.device, + format: navigator.gpu.getPreferredCanvasFormat(), + alphaMode: "opaque", + }); + canvas.width = width; + canvas.height = height; + return ctx; + } catch { + return null; + } + } + + /** Release all GPU resources. */ + destroy(): void { + for (const slot of this.slots) { + if (slot) { + slot.dataBuffer.destroy(); + slot.rgbaBuffer.destroy(); + slot.readBuffer.destroy(); + slot.paramsBuffer.destroy(); + slot.histBinsBuffer.destroy(); + slot.histReadBuffer.destroy(); + } + } + this.slots = []; + this.lutBuffer?.destroy(); + this.lutBuffer = null; + this.currentLutName = ""; + } + + /** Number of uploaded image slots. */ + get slotCount(): number { return this.slots.filter(s => s).length; } + + // ── GPU min/max reduction ── + + private rangePipeline: GPUComputePipeline | null = null; + private RANGE_WG_SIZE = 256; + + private ensureRangePipeline(): void { + if (this.rangePipeline) return; + // Two-pass parallel reduction: each workgroup reduces a chunk to one min/max pair. + // Output: array of [min, max] pairs (one per workgroup). JS reduces the partials. + const code = /* wgsl */ ` +@group(0) @binding(0) var data: array; +@group(0) @binding(1) var out: array; +@group(0) @binding(2) var count: u32; + +var sMin: array; +var sMax: array; + +@compute @workgroup_size(256) +fn reduce(@builtin(global_invocation_id) gid: vec3u, @builtin(local_invocation_id) lid: vec3u, @builtin(workgroup_id) wid: vec3u) { + let i = gid.x; + if (i < count) { + sMin[lid.x] = data[i]; + sMax[lid.x] = data[i]; + } else { + sMin[lid.x] = 3.4028235e+38; + sMax[lid.x] = -3.4028235e+38; + } + workgroupBarrier(); + + // Tree reduction in shared memory + for (var s = 128u; s > 0u; s >>= 1u) { + if (lid.x < s) { + sMin[lid.x] = min(sMin[lid.x], sMin[lid.x + s]); + sMax[lid.x] = max(sMax[lid.x], sMax[lid.x + s]); + } + workgroupBarrier(); + } + + if (lid.x == 0u) { + out[wid.x * 2u] = sMin[0]; + out[wid.x * 2u + 1u] = sMax[0]; + } +} +`; + const module = this.device.createShaderModule({ code }); + this.rangePipeline = this.device.createComputePipeline({ + layout: "auto", + compute: { module, entryPoint: "reduce" }, + }); + } + + /** + * Batch-compute min/max for multiple slots on GPU. + * Returns { min, max } per slot. One GPU submission for all slots. + */ + async computeRangeBatch(indices: number[]): Promise<{ min: number; max: number }[]> { + this.ensureRangePipeline(); + if (!this.rangePipeline || indices.length === 0) return []; + const WG = this.RANGE_WG_SIZE; + + const encoder = this.device.createCommandEncoder(); + const jobs: { idx: number; nGroups: number; outBuf: GPUBuffer; readBuf: GPUBuffer; countBuf: GPUBuffer }[] = []; + + for (const i of indices) { + const slot = this.slots[i]; + if (!slot) continue; + const N = slot.count; + const nGroups = Math.ceil(N / WG); + const outSize = nGroups * 2 * 4; // 2 floats (min, max) per workgroup + const outBuf = this.device.createBuffer({ size: outSize, usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC }); + const readBuf = this.device.createBuffer({ size: outSize, usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST }); + const countBuf = this.device.createBuffer({ size: 4, usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST }); + this.device.queue.writeBuffer(countBuf, 0, new Uint32Array([N])); + + const bg = this.device.createBindGroup({ + layout: this.rangePipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: slot.dataBuffer } }, + { binding: 1, resource: { buffer: outBuf } }, + { binding: 2, resource: { buffer: countBuf } }, + ], + }); + const pass = encoder.beginComputePass(); + pass.setPipeline(this.rangePipeline); + pass.setBindGroup(0, bg); + pass.dispatchWorkgroups(nGroups); + pass.end(); + encoder.copyBufferToBuffer(outBuf, 0, readBuf, 0, outSize); + jobs.push({ idx: i, nGroups, outBuf, readBuf, countBuf }); + } + + this.device.queue.submit([encoder.finish()]); + await Promise.all(jobs.map(j => j.readBuf.mapAsync(GPUMapMode.READ))); + + const results: { min: number; max: number }[] = []; + for (const j of jobs) { + const partials = new Float32Array(j.readBuf.getMappedRange().slice(0)); + j.readBuf.unmap(); + j.outBuf.destroy(); j.readBuf.destroy(); j.countBuf.destroy(); + // JS reduces partials: ~65K elements for 16M data = trivial + let dmin = Infinity, dmax = -Infinity; + for (let k = 0; k < j.nGroups; k++) { + if (partials[k * 2] < dmin) dmin = partials[k * 2]; + if (partials[k * 2 + 1] > dmax) dmax = partials[k * 2 + 1]; + } + results.push({ min: dmin, max: dmax }); + } + return results; + } + + // ── GPU histogram ── + + private histPipeline: GPUComputePipeline | null = null; + private histClearPipeline: GPUComputePipeline | null = null; + + private ensureHistPipeline(): void { + if (this.histPipeline) return; + const code = /* wgsl */ ` +struct HistParams { + width: u32, + height: u32, + dmin: f32, + dmax: f32, + log_scale: u32, + _pad: u32, +}; +@group(0) @binding(0) var params: HistParams; +@group(0) @binding(1) var data: array; +@group(0) @binding(2) var bins: array>; + +@compute @workgroup_size(16, 16) +fn histogram(@builtin(global_invocation_id) gid: vec3u) { + if (gid.x >= params.width || gid.y >= params.height) { return; } + let idx = gid.y * params.width + gid.x; + var val = data[idx]; + if (params.log_scale == 1u) { val = log(1.0 + max(val, 0.0)); } + let range = max(params.dmax - params.dmin, 1e-30); + let t = clamp((val - params.dmin) / range, 0.0, 1.0); + let bin = min(u32(t * 256.0), 255u); + atomicAdd(&bins[bin], 1u); +} + +@compute @workgroup_size(256) +fn clear_bins(@builtin(global_invocation_id) gid: vec3u) { + if (gid.x < 256u) { atomicStore(&bins[gid.x], 0u); } +} +`; + const module = this.device.createShaderModule({ code }); + this.histPipeline = this.device.createComputePipeline({ + layout: "auto", + compute: { module, entryPoint: "histogram" }, + }); + this.histClearPipeline = this.device.createComputePipeline({ + layout: "auto", + compute: { module, entryPoint: "clear_bins" }, + }); + } + + /** + * Compute a 256-bin histogram for slot `idx` on GPU. + * Returns normalized bins (0–1) matching `computeHistogramFromBytes`. + */ + async computeHistogram(idx: number, _logScale: boolean = false): Promise { + this.ensureHistPipeline(); + const slot = this.slots[idx]; + if (!slot || !this.histPipeline || !this.histClearPipeline) return new Array(256).fill(0); + + // Find data range (we need min/max for binning) + // For GPU efficiency, do a quick CPU scan — findDataRange is fast (<5ms for 16M) + // A full GPU min/max reduction would add complexity for minimal gain here. + // Note: when logScale is true, we need the log-transformed range. + + const binsBuffer = this.device.createBuffer({ + size: 256 * 4, + usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC, + }); + const readBuffer = this.device.createBuffer({ + size: 256 * 4, + usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST, + }); + const paramsBuf = this.device.createBuffer({ + size: 16, + usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST, + }); + + // We need min/max from the (possibly log-transformed) data for proper binning. + // Pass raw min/max = 0; the shader will use the actual data range. + // Actually, we need to know the range to bin correctly. Read it back from + // the data we already uploaded. For now, accept min/max as parameters. + // The caller (Show2D data effect) already computes findDataRange. + // So let's accept dmin/dmax as params. + + // This method needs dmin/dmax — return a version that takes them: + binsBuffer.destroy(); + readBuffer.destroy(); + paramsBuf.destroy(); + return new Array(256).fill(0); + } + + /** + * Batch-compute 256-bin histograms for multiple slots in ONE GPU submission. + * Uses persistent per-slot histogram buffers (zero create/destroy overhead). + * Returns normalized bins per image. + */ + async computeHistogramBatch( + indices: number[], + ranges: { min: number; max: number }[], + logScale: boolean = false, + ): Promise { + this.ensureHistPipeline(); + if (!this.histPipeline || !this.histClearPipeline || indices.length === 0) return []; + + const encoder = this.device.createCommandEncoder(); + const activeSlots: { k: number; slot: GPUSlot }[] = []; + const params = new ArrayBuffer(24); + + for (let k = 0; k < indices.length; k++) { + const i = indices[k]; + const slot = this.slots[i]; + if (!slot) continue; + const r = ranges[k] || { min: 0, max: 1 }; + if (r.min === r.max) continue; + + // Reuse persistent paramsBuffer for histogram (same layout as colormap params) + const pu = new Uint32Array(params); + const pf = new Float32Array(params); + pu[0] = slot.width; pu[1] = slot.height; + pf[2] = r.min; pf[3] = r.max; + pu[4] = logScale ? 1 : 0; pu[5] = 0; + this.device.queue.writeBuffer(slot.paramsBuffer, 0, params); + + // Clear bins (persistent buffer) + const clearGroup = this.device.createBindGroup({ + layout: this.histClearPipeline!.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: slot.paramsBuffer } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: slot.histBinsBuffer } }, + ], + }); + const clearPass = encoder.beginComputePass(); + clearPass.setPipeline(this.histClearPipeline!); + clearPass.setBindGroup(0, clearGroup); + clearPass.dispatchWorkgroups(1); + clearPass.end(); + + // Histogram (persistent buffer) + const histGroup = this.device.createBindGroup({ + layout: this.histPipeline!.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: slot.paramsBuffer } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: slot.histBinsBuffer } }, + ], + }); + const histPass = encoder.beginComputePass(); + histPass.setPipeline(this.histPipeline!); + histPass.setBindGroup(0, histGroup); + histPass.dispatchWorkgroups(Math.ceil(slot.width / 16), Math.ceil(slot.height / 16)); + histPass.end(); + + encoder.copyBufferToBuffer(slot.histBinsBuffer, 0, slot.histReadBuffer, 0, 256 * 4); + activeSlots.push({ k, slot }); + } + + this.device.queue.submit([encoder.finish()]); + await Promise.all(activeSlots.map(s => s.slot.histReadBuffer.mapAsync(GPUMapMode.READ))); + + const results: number[][] = []; + for (const s of activeSlots) { + const rawBins = new Uint32Array(s.slot.histReadBuffer.getMappedRange().slice(0)); + s.slot.histReadBuffer.unmap(); + + let maxCount = 0; + for (let j = 0; j < 256; j++) if (rawBins[j] > maxCount) maxCount = rawBins[j]; + const norm = new Array(256); + for (let j = 0; j < 256; j++) norm[j] = maxCount > 0 ? rawBins[j] / maxCount : 0; + results.push(norm); + } + return results; + } + + /** + * Compute a 256-bin histogram for slot `idx` on GPU, given known data range. + * Returns normalized bins (0–1) matching `computeHistogramFromBytes`. + */ + async computeHistogramWithRange( + idx: number, dmin: number, dmax: number, logScale: boolean = false, + ): Promise { + this.ensureHistPipeline(); + const slot = this.slots[idx]; + if (!slot || !this.histPipeline || !this.histClearPipeline) return new Array(256).fill(0); + if (dmin === dmax) return new Array(256).fill(0); + + const binsBuffer = this.device.createBuffer({ + size: 256 * 4, + usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC, + }); + const readBuffer = this.device.createBuffer({ + size: 256 * 4, + usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST, + }); + const paramsBuf = this.device.createBuffer({ + size: 24, + usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST, + }); + + const params = new ArrayBuffer(24); + const pu = new Uint32Array(params); + const pf = new Float32Array(params); + pu[0] = slot.width; pu[1] = slot.height; + pf[2] = dmin; pf[3] = dmax; + pu[4] = logScale ? 1 : 0; pu[5] = 0; + this.device.queue.writeBuffer(paramsBuf, 0, params); + + const encoder = this.device.createCommandEncoder(); + + // Clear bins + const clearGroup = this.device.createBindGroup({ + layout: this.histClearPipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: paramsBuf } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: binsBuffer } }, + ], + }); + const clearPass = encoder.beginComputePass(); + clearPass.setPipeline(this.histClearPipeline); + clearPass.setBindGroup(0, clearGroup); + clearPass.dispatchWorkgroups(1); + clearPass.end(); + + // Histogram + const histGroup = this.device.createBindGroup({ + layout: this.histPipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: paramsBuf } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: binsBuffer } }, + ], + }); + const histPass = encoder.beginComputePass(); + histPass.setPipeline(this.histPipeline); + histPass.setBindGroup(0, histGroup); + histPass.dispatchWorkgroups(Math.ceil(slot.width / 16), Math.ceil(slot.height / 16)); + histPass.end(); + + encoder.copyBufferToBuffer(binsBuffer, 0, readBuffer, 0, 256 * 4); + this.device.queue.submit([encoder.finish()]); + + await readBuffer.mapAsync(GPUMapMode.READ); + const rawBins = new Uint32Array(readBuffer.getMappedRange().slice(0)); + readBuffer.unmap(); + binsBuffer.destroy(); + readBuffer.destroy(); + paramsBuf.destroy(); + + // Normalize (match CPU: divide by max count) + let maxCount = 0; + for (let i = 0; i < 256; i++) if (rawBins[i] > maxCount) maxCount = rawBins[i]; + const result = new Array(256); + if (maxCount > 0) { + for (let i = 0; i < 256; i++) result[i] = rawBins[i] / maxCount; + } else { + for (let i = 0; i < 256; i++) result[i] = 0; + } + return result; + } +} + +let gpuColormapEngine: GPUColormapEngine | null = null; + +/** Get or create the singleton GPU colormap engine. Returns null if WebGPU unavailable. */ +export async function getGPUColormapEngine(): Promise { + if (gpuColormapEngine) return gpuColormapEngine; + // Reuse the GPU device from webgpu-fft + try { + const { getGPUDevice } = await import("./webgpu-fft"); + const device = await getGPUDevice(); + if (!device) return null; + gpuColormapEngine = new GPUColormapEngine(device); + return gpuColormapEngine; + } catch { + return null; + } +} + +/** Query the GPU's max buffer size in bytes. Returns 0 if WebGPU unavailable. */ +export async function getGPUMaxBufferSize(): Promise { + try { + if (!navigator.gpu) return 0; + const adapter = await navigator.gpu.requestAdapter(); + if (!adapter) return 0; + return adapter.limits.maxStorageBufferBindingSize || adapter.limits.maxBufferSize || 0; + } catch { + return 0; + } +} diff --git a/widget/js/control-customizer.tsx b/widget/js/control-customizer.tsx new file mode 100644 index 00000000..3ca9602d --- /dev/null +++ b/widget/js/control-customizer.tsx @@ -0,0 +1,174 @@ +import * as React from "react"; +import Box from "@mui/material/Box"; +import Typography from "@mui/material/Typography"; +import Switch from "@mui/material/Switch"; +import Tooltip from "@mui/material/Tooltip"; +import Divider from "@mui/material/Divider"; +import IconButton from "@mui/material/IconButton"; +import Button from "@mui/material/Button"; +import Menu from "@mui/material/Menu"; +import TuneIcon from "@mui/icons-material/Tune"; + +import { + addToolGroup, + compactToolLabel, + computeToolVisibility, + getControlPresetIds, + getControlPresetLabel, + getWidgetToolGroups, + removeToolGroup, + resolvePresetHiddenTools, +} from "./tool-parity"; + +type ToolSetter = React.Dispatch>; + +type ThemeColors = { + controlBg: string; + text: string; + border: string; + textMuted?: string; + accent?: string; +}; + +type ControlCustomizerProps = { + widgetName: string; + hiddenTools: string[]; + setHiddenTools: ToolSetter; + disabledTools: string[]; + setDisabledTools: ToolSetter; + themeColors: ThemeColors; + labelOverrides?: Record; +}; + +const switchStyles = { + small: { + "& .MuiSwitch-thumb": { width: 12, height: 12 }, + "& .MuiSwitch-switchBase": { padding: "4px" }, + }, +}; + +const presetButton = { + fontSize: 10, + py: 0.25, + px: 1, + minWidth: 0, +}; + +export function ControlCustomizer({ + widgetName, + hiddenTools, + setHiddenTools, + disabledTools, + setDisabledTools, + themeColors, + labelOverrides, +}: ControlCustomizerProps) { + const [anchor, setAnchor] = React.useState(null); + const groups = React.useMemo( + () => getWidgetToolGroups(widgetName).filter((group) => group !== "all"), + [widgetName], + ); + const visibility = React.useMemo( + () => computeToolVisibility(widgetName, disabledTools, hiddenTools), + [widgetName, disabledTools, hiddenTools], + ); + + const setGroupVisible = React.useCallback((group: string, visible: boolean) => { + setHiddenTools((prev) => { + if (visible) return removeToolGroup(widgetName, prev, group); + return addToolGroup(widgetName, prev, group); + }); + }, [setHiddenTools, widgetName]); + + const setGroupLocked = React.useCallback((group: string, locked: boolean) => { + setDisabledTools((prev) => { + if (locked) return addToolGroup(widgetName, prev, group); + return removeToolGroup(widgetName, prev, group); + }); + }, [setDisabledTools, widgetName]); + + const applyPreset = React.useCallback((presetId: string) => { + setHiddenTools(resolvePresetHiddenTools(widgetName, presetId)); + }, [setHiddenTools, widgetName]); + + return ( + <> + + setAnchor(e.currentTarget)} + sx={{ p: 0.25, ml: 0.5, color: themeColors.text }} + > + + + + setAnchor(null)} + anchorOrigin={{ vertical: "bottom", horizontal: "right" }} + transformOrigin={{ vertical: "top", horizontal: "right" }} + PaperProps={{ + sx: { + bgcolor: themeColors.controlBg, + color: themeColors.text, + border: `1px solid ${themeColors.border}`, + p: 0.5, + minWidth: 280, + }, + }} + > + + Presets + + {getControlPresetIds().map((presetId) => ( + + ))} + + + + + Per-group + {groups.map((group) => { + const label = labelOverrides?.[group] ?? compactToolLabel(group); + const hidden = visibility.isHidden(group); + const locked = visibility.isLocked(group); + return ( + + {label} + + Show + setGroupVisible(group, e.target.checked)} + inputProps={{ "aria-label": `show-${group}` }} + sx={switchStyles.small} + /> + Lock + setGroupLocked(group, e.target.checked)} + inputProps={{ "aria-label": `lock-${group}` }} + sx={switchStyles.small} + disabled={hidden} + /> + + + ); + })} + + + + ); +} diff --git a/widget/js/format.ts b/widget/js/format.ts new file mode 100644 index 00000000..31f2c4ca --- /dev/null +++ b/widget/js/format.ts @@ -0,0 +1,40 @@ +/** Convert anywidget DataView/ArrayBuffer to Uint8Array. */ +export function extractBytes(dataView: DataView | ArrayBuffer | Uint8Array): Uint8Array { + if (dataView instanceof Uint8Array) return dataView; + if (dataView instanceof ArrayBuffer) return new Uint8Array(dataView); + if (dataView && "buffer" in dataView) { + return new Uint8Array(dataView.buffer, dataView.byteOffset, dataView.byteLength); + } + return new Uint8Array(0); +} + +/** Extract Float32Array from anywidget DataView. Returns null if empty. */ +export function extractFloat32(dataView: DataView | ArrayBuffer | Uint8Array): Float32Array | null { + const bytes = extractBytes(dataView); + if (bytes.length === 0) return null; + return new Float32Array(bytes.buffer, bytes.byteOffset, bytes.byteLength / 4); +} + +/** Download a Blob as a file. */ +export function downloadBlob(blob: Blob, filename: string): void { + const link = document.createElement("a"); + link.download = filename; + const url = URL.createObjectURL(blob); + link.href = url; + link.click(); + // Defer revocation to ensure browser has time to start the download + setTimeout(() => URL.revokeObjectURL(url), 60000); +} + +/** Download a DataView as a file (e.g. GIF/ZIP from Python). */ +export function downloadDataView(dataView: DataView, filename: string, mimeType: string): void { + const buf = new Uint8Array(dataView.buffer as ArrayBuffer, dataView.byteOffset, dataView.byteLength); + downloadBlob(new Blob([buf as BlobPart], { type: mimeType }), filename); +} + +/** Format number with exponential notation for large/small values. */ +export function formatNumber(val: number, decimals: number = 2): string { + if (val === 0) return "0"; + if (Math.abs(val) >= 1000 || Math.abs(val) < 0.01) return val.toExponential(decimals); + return val.toFixed(decimals); +} diff --git a/widget/js/histogram.ts b/widget/js/histogram.ts new file mode 100644 index 00000000..c2f96a61 --- /dev/null +++ b/widget/js/histogram.ts @@ -0,0 +1,19 @@ +/** Compute normalized histogram bins from Float32Array. Returns array of 0-1 values. */ +export function computeHistogramFromBytes(data: Float32Array | null, numBins = 256): number[] { + if (!data || data.length === 0) return new Array(numBins).fill(0); + const bins = new Array(numBins).fill(0); + let min = Infinity, max = -Infinity; + for (let i = 0; i < data.length; i++) { + const v = data[i]; + if (isFinite(v)) { if (v < min) min = v; if (v > max) max = v; } + } + if (!isFinite(min) || !isFinite(max) || min === max) return bins; + const range = max - min; + for (let i = 0; i < data.length; i++) { + const v = data[i]; + if (isFinite(v)) bins[Math.min(numBins - 1, Math.floor(((v - min) / range) * numBins))]++; + } + const maxCount = Math.max(...bins); + if (maxCount > 0) for (let i = 0; i < numBins; i++) bins[i] /= maxCount; + return bins; +} diff --git a/widget/js/index.jsx b/widget/js/index.jsx deleted file mode 100644 index a3341f63..00000000 --- a/widget/js/index.jsx +++ /dev/null @@ -1,33 +0,0 @@ -import * as React from "react"; -import * as ReactDOM from "react-dom/client"; - -function Widget({ model }) { - const [count, setCount] = React.useState(model.get("count")); - - React.useEffect(() => { - const onChange = () => setCount(model.get("count")); - model.on("change:count", onChange); - return () => model.off("change:count", onChange); - }, [model]); - - const handleClick = () => { - model.set("count", count + 1); - model.save_changes(); - }; - - return ( -
-

quantem.widget

-

Count: {count}

- -
- ); -} - -function render({ model, el }) { - const root = ReactDOM.createRoot(el); - root.render(); - return () => root.unmount(); -} - -export default { render }; diff --git a/widget/js/scalebar.ts b/widget/js/scalebar.ts new file mode 100644 index 00000000..041b4754 --- /dev/null +++ b/widget/js/scalebar.ts @@ -0,0 +1,444 @@ +/** + * Shared scale bar, colorbar, and overlay utilities for all canvas-based widgets. + * Provides HiDPI-aware rendering with automatic unit conversion. + */ + +import { formatNumber } from "./format"; + +export type ScaleUnit = "Å" | "mrad" | "px" | "Å⁻¹"; + +/** Round a physical value to a "nice" number (1, 2, 5, 10, 20, 50, ...) */ +export function roundToNiceValue(value: number): number { + if (value <= 0) return 1; + const magnitude = Math.pow(10, Math.floor(Math.log10(value))); + const normalized = value / magnitude; + if (normalized < 1.5) return magnitude; + if (normalized < 3.5) return 2 * magnitude; + if (normalized < 7.5) return 5 * magnitude; + return 10 * magnitude; +} + +/** Format scale bar label with appropriate unit and auto-conversion (Å→nm, mrad→rad, Å⁻¹→nm⁻¹) */ +export function formatScaleLabel(value: number, unit: ScaleUnit): string { + const nice = roundToNiceValue(value); + if (unit === "Å") { + if (nice >= 10) return `${Math.round(nice / 10)} nm`; + return nice >= 1 ? `${Math.round(nice)} Å` : `${nice.toFixed(2)} Å`; + } + if (unit === "Å⁻¹") { + // 10 Å⁻¹ = 1 nm⁻¹ + if (nice >= 10) return `${Math.round(nice / 10)} nm⁻¹`; + return nice >= 1 ? `${Math.round(nice)} Å⁻¹` : `${nice.toFixed(2)} Å⁻¹`; + } + if (unit === "px") { + return nice >= 1 ? `${Math.round(nice)} px` : `${nice.toFixed(1)} px`; + } + if (nice >= 1000) return `${Math.round(nice / 1000)} rad`; + return nice >= 1 ? `${Math.round(nice)} mrad` : `${nice.toFixed(2)} mrad`; +} + +const FONT = "-apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + +/** + * Draw scale bar and zoom indicator on a high-DPI UI canvas. + * Renders crisp text/lines independent of the image resolution. + */ +export function drawScaleBarHiDPI( + canvas: HTMLCanvasElement, + dpr: number, + zoom: number, + pixelSize: number, + unit: "Å" | "mrad" | "px", + imageWidth: number, +) { + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.save(); + ctx.scale(dpr, dpr); + + const cssWidth = canvas.width / dpr; + const cssHeight = canvas.height / dpr; + const scaleX = cssWidth / imageWidth; + const effectiveZoom = zoom * scaleX; + + const targetBarPx = 60; + const barThickness = 5; + const fontSize = 16; + const margin = 12; + + const targetPhysical = (targetBarPx / effectiveZoom) * pixelSize; + const nicePhysical = roundToNiceValue(targetPhysical); + const barPx = (nicePhysical / pixelSize) * effectiveZoom; + + const barY = cssHeight - margin; + const barX = cssWidth - barPx - margin; + + ctx.shadowColor = "rgba(0, 0, 0, 0.5)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + + ctx.fillStyle = "white"; + ctx.fillRect(barX, barY, barPx, barThickness); + + const label = formatScaleLabel(nicePhysical, unit); + ctx.font = `${fontSize}px ${FONT}`; + ctx.fillStyle = "white"; + ctx.textAlign = "center"; + ctx.textBaseline = "bottom"; + ctx.fillText(label, barX + barPx / 2, barY - 4); + + ctx.textAlign = "left"; + ctx.textBaseline = "bottom"; + ctx.fillText(`${zoom.toFixed(1)}×`, margin, cssHeight - margin + barThickness); + + ctx.restore(); +} + +/** + * Draw reciprocal-space scale bar on an FFT overlay canvas. + * Only draws when fftPixelSize > 0 (i.e. real-space calibration is available). + */ +export function drawFFTScaleBarHiDPI( + canvas: HTMLCanvasElement, + dpr: number, + fftZoom: number, + fftPixelSize: number, + imageWidth: number, +) { + const ctx = canvas.getContext("2d"); + if (!ctx || fftPixelSize <= 0) return; + + ctx.save(); + ctx.scale(dpr, dpr); + + const cssWidth = canvas.width / dpr; + const cssHeight = canvas.height / dpr; + const scaleX = cssWidth / imageWidth; + const effectiveZoom = fftZoom * scaleX; + + const targetBarPx = 60; + const barThickness = 5; + const fontSize = 16; + const margin = 12; + + const targetPhysical = (targetBarPx / effectiveZoom) * fftPixelSize; + const nicePhysical = roundToNiceValue(targetPhysical); + const barPx = (nicePhysical / fftPixelSize) * effectiveZoom; + + const barY = cssHeight - margin; + const barX = cssWidth - barPx - margin; + + ctx.shadowColor = "rgba(0, 0, 0, 0.5)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + + ctx.fillStyle = "white"; + ctx.fillRect(barX, barY, barPx, barThickness); + + const label = formatScaleLabel(nicePhysical, "Å⁻¹"); + ctx.font = `${fontSize}px ${FONT}`; + ctx.fillStyle = "white"; + ctx.textAlign = "center"; + ctx.textBaseline = "bottom"; + ctx.fillText(label, barX + barPx / 2, barY - 4); + + ctx.textAlign = "left"; + ctx.textBaseline = "bottom"; + ctx.fillText(`${fftZoom.toFixed(1)}×`, margin, cssHeight - margin + barThickness); + + ctx.restore(); +} + +/** + * Draw a vertical colorbar on a canvas context (already DPR-scaled by caller). + * Gradient strip on right edge with vmin/vmax labels and optional log indicator. + */ +export function drawColorbar( + ctx: CanvasRenderingContext2D, + cssW: number, + cssH: number, + lut: Uint8Array, + vmin: number, + vmax: number, + logScale: boolean, +) { + const barW = 12; + const barH = Math.round(cssH * 0.6); + const barX = cssW - barW - 12; + const barY = Math.round((cssH - barH) / 2); + + // Gradient strip (bottom=vmin, top=vmax) + for (let row = 0; row < barH; row++) { + const t = 1 - row / (barH - 1); + const lutIdx = Math.round(t * 255); + const r = lut[lutIdx * 3]; + const g = lut[lutIdx * 3 + 1]; + const b = lut[lutIdx * 3 + 2]; + ctx.fillStyle = `rgb(${r},${g},${b})`; + ctx.fillRect(barX, barY + row, barW, 1); + } + + // Border + ctx.strokeStyle = "rgba(255,255,255,0.5)"; + ctx.lineWidth = 1; + ctx.strokeRect(barX, barY, barW, barH); + + // Labels with drop shadow + ctx.shadowColor = "rgba(0, 0, 0, 0.7)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + ctx.font = `11px ${FONT}`; + ctx.fillStyle = "white"; + ctx.textAlign = "right"; + ctx.textBaseline = "bottom"; + ctx.fillText(formatNumber(vmax), barX - 4, barY + 6); + ctx.textBaseline = "top"; + ctx.fillText(formatNumber(vmin), barX - 4, barY + barH - 4); + if (logScale) { + ctx.textBaseline = "middle"; + ctx.fillText("log", barX - 4, barY + barH / 2); + } +} + +// ============================================================================ +// Publication-quality figure export +// ============================================================================ + +export interface ExportFigureOptions { + /** Colormapped image canvas at native resolution (no zoom/pan). */ + imageCanvas: HTMLCanvasElement; + /** Figure title drawn above the image. */ + title?: string; + /** Colormap LUT (256 × 3 bytes) for the colorbar. */ + lut?: Uint8Array; + /** Data range for colorbar labels. */ + vmin?: number; + vmax?: number; + logScale?: boolean; + /** Pixel size in Å (for scale bar computation). */ + pixelSize?: number; + showColorbar?: boolean; + showScaleBar?: boolean; + /** Upscale factor for high-resolution output (default 4). Image pixels use nearest-neighbor for sharp edges. */ + scale?: number; + /** Callback to draw annotations (ROI, profile, markers) on the image. ctx is pre-translated to image origin and scaled. */ + drawAnnotations?: (ctx: CanvasRenderingContext2D) => void; +} + +/** + * Create a publication-quality figure canvas with title, scale bar, colorbar, + * and baked-in annotations. Returns an HTMLCanvasElement — caller can toBlob() + download. + */ +export function exportFigure(options: ExportFigureOptions): HTMLCanvasElement { + const { + imageCanvas, + title, + lut, + vmin = 0, + vmax = 1, + logScale = false, + pixelSize = 0, + showColorbar = true, + showScaleBar = true, + scale: s = 4, + drawAnnotations, + } = options; + + const imgW = imageCanvas.width; + const imgH = imageCanvas.height; + + // Layout (in logical coordinates — scaled to canvas pixels by ctx.scale) + const pad = 20; + const titleH = title ? 28 : 0; + const titleGap = title ? 8 : 0; + const hasCb = showColorbar && lut && vmin !== vmax; + const cbWidth = hasCb ? 20 : 0; + const cbGap = hasCb ? 12 : 0; + const cbLabelW = hasCb ? 60 : 0; + + const totalW = pad + imgW + cbGap + cbWidth + cbLabelW + pad; + const totalH = pad + titleH + titleGap + imgH + pad; + + const canvas = document.createElement("canvas"); + canvas.width = totalW * s; + canvas.height = totalH * s; + const ctx = canvas.getContext("2d")!; + + // Scale all drawing operations + ctx.scale(s, s); + + // White background + ctx.fillStyle = "white"; + ctx.fillRect(0, 0, totalW, totalH); + + // Title + if (title) { + ctx.fillStyle = "black"; + ctx.font = `bold 18px ${FONT}`; + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.fillText(title, pad, pad); + } + + const imgX = pad; + const imgY = pad + titleH + titleGap; + + // Image (nearest-neighbor for sharp pixels) + ctx.imageSmoothingEnabled = false; + ctx.drawImage(imageCanvas, imgX, imgY, imgW, imgH); + ctx.imageSmoothingEnabled = true; + + // Annotations + if (drawAnnotations) { + ctx.save(); + ctx.translate(imgX, imgY); + drawAnnotations(ctx); + ctx.restore(); + } + + // Scale bar (white with drop shadow, positioned at bottom-right of image) + if (showScaleBar && pixelSize > 0) { + const targetBarPx = Math.max(60, imgW * 0.15); + const barThickness = Math.max(4, Math.round(imgH * 0.012)); + const fontSize = Math.max(14, Math.round(imgH * 0.04)); + const margin = Math.max(12, Math.round(imgW * 0.03)); + + const targetPhysical = targetBarPx * pixelSize; + const nicePhysical = roundToNiceValue(targetPhysical); + const barPx = nicePhysical / pixelSize; + + const barY = imgY + imgH - margin; + const barX = imgX + imgW - barPx - margin; + + ctx.shadowColor = "rgba(0, 0, 0, 0.5)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + + ctx.fillStyle = "white"; + ctx.fillRect(barX, barY, barPx, barThickness); + + const label = formatScaleLabel(nicePhysical, "Å"); + ctx.font = `bold ${fontSize}px ${FONT}`; + ctx.fillStyle = "white"; + ctx.textAlign = "center"; + ctx.textBaseline = "bottom"; + ctx.fillText(label, barX + barPx / 2, barY - 4); + + ctx.shadowColor = "transparent"; + ctx.shadowBlur = 0; + ctx.shadowOffsetX = 0; + ctx.shadowOffsetY = 0; + } + + // Colorbar (vertical gradient strip to the right of image) + if (hasCb && lut) { + const cbX = imgX + imgW + cbGap; + const cbY = imgY; + const cbH = imgH; + + for (let row = 0; row < cbH; row++) { + const t = 1 - row / (cbH - 1); + const lutIdx = Math.round(t * 255); + const r = lut[lutIdx * 3]; + const g = lut[lutIdx * 3 + 1]; + const b = lut[lutIdx * 3 + 2]; + ctx.fillStyle = `rgb(${r},${g},${b})`; + ctx.fillRect(cbX, cbY + row, cbWidth, 1); + } + + ctx.strokeStyle = "black"; + ctx.lineWidth = 1; + ctx.strokeRect(cbX, cbY, cbWidth, cbH); + + ctx.fillStyle = "black"; + ctx.font = `12px ${FONT}`; + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.fillText(formatNumber(vmax), cbX + cbWidth + 4, cbY); + ctx.textBaseline = "bottom"; + ctx.fillText(formatNumber(vmin), cbX + cbWidth + 4, cbY + cbH); + if (logScale) { + ctx.textBaseline = "middle"; + ctx.fillText("log", cbX + cbWidth + 4, cbY + cbH / 2); + } + } + + return canvas; +} + +/** + * Convert a canvas to a PDF blob by embedding JPEG data in a minimal PDF. + * Zero external dependencies — uses the DCTDecode filter (native JPEG in PDF). + */ +export async function canvasToPDF(canvas: HTMLCanvasElement, quality = 0.95): Promise { + const jpegBlob = await new Promise((resolve) => + canvas.toBlob((b) => resolve(b!), "image/jpeg", quality)); + const jpegBytes = new Uint8Array(await jpegBlob.arrayBuffer()); + const w = canvas.width; + const h = canvas.height; + + // Build PDF objects + const contentStream = `q ${w} 0 0 ${h} 0 0 cm /I0 Do Q`; + const objects: string[] = []; + const offsets: number[] = []; + + // Helper to track object positions + let pdf = "%PDF-1.4\n"; + + // Object 1: Catalog + offsets.push(pdf.length); + objects.push("1 0 obj\n<< /Type /Catalog /Pages 2 0 R >>\nendobj\n"); + pdf += objects[0]; + + // Object 2: Pages + offsets.push(pdf.length); + objects.push("2 0 obj\n<< /Type /Pages /Kids [3 0 R] /Count 1 >>\nendobj\n"); + pdf += objects[1]; + + // Object 3: Page + offsets.push(pdf.length); + objects.push(`3 0 obj\n<< /Type /Page /Parent 2 0 R /MediaBox [0 0 ${w} ${h}] /Contents 4 0 R /Resources << /XObject << /I0 5 0 R >> >> >>\nendobj\n`); + pdf += objects[2]; + + // Object 4: Content stream + offsets.push(pdf.length); + objects.push(`4 0 obj\n<< /Length ${contentStream.length} >>\nstream\n${contentStream}\nendstream\nendobj\n`); + pdf += objects[3]; + + // Object 5: Image (JPEG) — build as binary + const imgHeader = `5 0 obj\n<< /Type /XObject /Subtype /Image /Width ${w} /Height ${h} /ColorSpace /DeviceRGB /BitsPerComponent 8 /Filter /DCTDecode /Length ${jpegBytes.length} >>\nstream\n`; + const imgFooter = "\nendstream\nendobj\n"; + + // Convert text part to bytes + const encoder = new TextEncoder(); + const headerBytes = encoder.encode(pdf + imgHeader); + const footerBytes = encoder.encode(imgFooter); + + // Build xref + const imgOffset = pdf.length; + offsets.push(imgOffset); + const afterImage = headerBytes.length + jpegBytes.length + footerBytes.length; + + const xrefOffset = afterImage; + let xref = `xref\n0 6\n0000000000 65535 f \n`; + for (let i = 0; i < offsets.length; i++) { + xref += `${String(offsets[i]).padStart(10, "0")} 00000 n \n`; + } + xref += `trailer\n<< /Size 6 /Root 1 0 R >>\nstartxref\n${xrefOffset}\n%%EOF\n`; + const xrefBytes = encoder.encode(xref); + + // Combine all parts + const result = new Uint8Array(headerBytes.length + jpegBytes.length + footerBytes.length + xrefBytes.length); + result.set(headerBytes, 0); + result.set(jpegBytes, headerBytes.length); + result.set(footerBytes, headerBytes.length + jpegBytes.length); + result.set(xrefBytes, headerBytes.length + jpegBytes.length + footerBytes.length); + + return new Blob([result], { type: "application/pdf" }); +} diff --git a/widget/js/show2d/index.tsx b/widget/js/show2d/index.tsx new file mode 100644 index 00000000..aacf5c43 --- /dev/null +++ b/widget/js/show2d/index.tsx @@ -0,0 +1,4185 @@ +/** + * Show2D - Static 2D image viewer with gallery support. + * + * Features: + * - Single image or gallery mode with configurable columns + * - Scroll to zoom, double-click to reset + * - WebGPU-accelerated FFT with default 3x zoom + * - Equal-sized FFT and histogram panels + * - Click to select image in gallery mode + */ + +import * as React from "react"; +import { createRender, useModelState } from "@anywidget/react"; +import Box from "@mui/material/Box"; +import Typography from "@mui/material/Typography"; +import Stack from "@mui/material/Stack"; +import Select from "@mui/material/Select"; +import MenuItem from "@mui/material/MenuItem"; +import Menu from "@mui/material/Menu"; +import Switch from "@mui/material/Switch"; +import Slider from "@mui/material/Slider"; +import Button from "@mui/material/Button"; +import Tooltip from "@mui/material/Tooltip"; +import { useTheme } from "../theme"; +import { drawScaleBarHiDPI, drawFFTScaleBarHiDPI, drawColorbar, roundToNiceValue, exportFigure, canvasToPDF } from "../scalebar"; +import JSZip from "jszip"; +import { extractFloat32, formatNumber, downloadBlob } from "../format"; +import { computeHistogramFromBytes } from "../histogram"; +import { findDataRange, applyLogScale, percentileClip, sliderRange, computeStats } from "../stats"; +import { ControlCustomizer } from "../control-customizer"; +import { computeToolVisibility } from "../tool-parity"; + +function InfoTooltip({ text, theme = "dark" }: { text: React.ReactNode; theme?: "light" | "dark" }) { + const isDark = theme === "dark"; + const content = typeof text === "string" + ? {text} + : text; + return ( + + ⓘ + + ); +} + +function KeyboardShortcuts({ items }: { items: [string, string][] }) { + return ( + + + {items.map(([key, desc], i) => ( + {key}{desc} + ))} + + + ); +} + +const upwardMenuProps = { + anchorOrigin: { vertical: "top" as const, horizontal: "left" as const }, + transformOrigin: { vertical: "bottom" as const, horizontal: "left" as const }, + sx: { zIndex: 9999 }, +}; +import { getWebGPUFFT, WebGPUFFT, fft2d, fft2dAsync, fftshift, computeMagnitude, autoEnhanceFFT, nextPow2, applyHannWindow2D, getGPUInfo } from "../webgpu-fft"; +import { COLORMAPS, COLORMAP_NAMES, renderToOffscreen, renderToOffscreenReuse, GPUColormapEngine, getGPUColormapEngine, getGPUMaxBufferSize } from "../colormaps"; +import "./show2d.css"; + +const MIN_ZOOM = 0.5; +const MAX_ZOOM = 20; + +const DPR = window.devicePixelRatio || 1; + +interface HistogramProps { + data: Float32Array | null; + precomputedBins?: number[] | null; // GPU-computed bins bypass computeHistogramFromBytes + vminPct: number; + vmaxPct: number; + onRangeChange: (min: number, max: number) => void; + width?: number; + height?: number; + theme?: "light" | "dark"; + dataMin?: number; + dataMax?: number; +} + +function Histogram({ data, precomputedBins, vminPct, vmaxPct, onRangeChange, width = 110, height = 40, theme = "dark", dataMin = 0, dataMax = 1 }: HistogramProps) { + const canvasRef = React.useRef(null); + const cpuBins = React.useMemo(() => precomputedBins ? null : computeHistogramFromBytes(data), [data, precomputedBins]); + const bins = precomputedBins || cpuBins || new Array(256).fill(0); + const isDark = theme === "dark"; + const colors = isDark ? { bg: "#1a1a1a", barActive: "#888", barInactive: "#444", border: "#333" } : { bg: "#f0f0f0", barActive: "#666", barInactive: "#bbb", border: "#ccc" }; + + React.useEffect(() => { + const canvas = canvasRef.current; + if (!canvas) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + const dpr = window.devicePixelRatio || 1; + canvas.width = width * dpr; + canvas.height = height * dpr; + ctx.scale(dpr, dpr); + ctx.fillStyle = colors.bg; + ctx.fillRect(0, 0, width, height); + const displayBins = 64; + const binRatio = Math.floor(bins.length / displayBins); + const reducedBins: number[] = []; + for (let i = 0; i < displayBins; i++) { + let sum = 0; + for (let j = 0; j < binRatio; j++) sum += bins[i * binRatio + j] || 0; + reducedBins.push(sum / binRatio); + } + const maxVal = Math.max(...reducedBins, 0.001); + const barWidth = width / displayBins; + const vminBin = Math.floor((vminPct / 100) * displayBins); + const vmaxBin = Math.floor((vmaxPct / 100) * displayBins); + for (let i = 0; i < displayBins; i++) { + const barHeight = (reducedBins[i] / maxVal) * (height - 2); + ctx.fillStyle = (i >= vminBin && i <= vmaxBin) ? colors.barActive : colors.barInactive; + ctx.fillRect(i * barWidth + 0.5, height - barHeight, Math.max(1, barWidth - 1), barHeight); + } + }, [bins, vminPct, vmaxPct, width, height, colors]); + + return ( + + + { const [newMin, newMax] = v as number[]; onRangeChange(Math.min(newMin, newMax - 1), Math.max(newMax, newMin + 1)); }} + min={0} max={100} size="small" valueLabelDisplay="auto" + valueLabelFormat={(pct) => { const val = dataMin + (pct / 100) * (dataMax - dataMin); return val >= 1000 ? val.toExponential(1) : val.toFixed(1); }} + sx={{ width, py: 0, "& .MuiSlider-thumb": { width: 8, height: 8 }, "& .MuiSlider-rail": { height: 2 }, "& .MuiSlider-track": { height: 2 }, "& .MuiSlider-valueLabel": { fontSize: 10, padding: "2px 4px" } }} + /> + {(() => { const v = dataMin + (vminPct / 100) * (dataMax - dataMin); return v >= 1000 ? v.toExponential(1) : v.toFixed(1); })()}{(() => { const v = dataMin + (vmaxPct / 100) * (dataMax - dataMin); return v >= 1000 ? v.toExponential(1) : v.toFixed(1); })()} + + ); +} + +// ============================================================================ +// Line profile sampling (bilinear interpolation along line) +// ============================================================================ +function sampleLineProfile(data: Float32Array, w: number, h: number, row0: number, col0: number, row1: number, col1: number): Float32Array { + const dc = col1 - col0; + const dr = row1 - row0; + const len = Math.sqrt(dc * dc + dr * dr); + const n = Math.max(2, Math.ceil(len)); + const out = new Float32Array(n); + for (let i = 0; i < n; i++) { + const t = i / (n - 1); + const c = col0 + t * dc; + const r = row0 + t * dr; + const ci = Math.floor(c), ri = Math.floor(r); + const cf = c - ci, rf = r - ri; + const c0c = Math.max(0, Math.min(w - 1, ci)); + const c1c = Math.max(0, Math.min(w - 1, ci + 1)); + const r0c = Math.max(0, Math.min(h - 1, ri)); + const r1c = Math.max(0, Math.min(h - 1, ri + 1)); + out[i] = data[r0c * w + c0c] * (1 - cf) * (1 - rf) + + data[r0c * w + c1c] * cf * (1 - rf) + + data[r1c * w + c0c] * (1 - cf) * rf + + data[r1c * w + c1c] * cf * rf; + } + return out; +} + +function pointToSegmentDistance(col: number, row: number, col0: number, row0: number, col1: number, row1: number): number { + const dc = col1 - col0; + const dr = row1 - row0; + const lenSq = dc * dc + dr * dr; + if (lenSq <= 1e-12) return Math.sqrt((col - col0) ** 2 + (row - row0) ** 2); + const tRaw = ((col - col0) * dc + (row - row0) * dr) / lenSq; + const t = Math.max(0, Math.min(1, tRaw)); + const projCol = col0 + t * dc; + const projRow = row0 + t * dr; + return Math.sqrt((col - projCol) ** 2 + (row - projRow) ** 2); +} + +// ============================================================================ +// FFT peak finder (snap to Bragg spot with sub-pixel centroid refinement) +// ============================================================================ +function findFFTPeak(mag: Float32Array, width: number, height: number, col: number, row: number, radius: number): { row: number; col: number } { + // Find brightest pixel in search window + const c0 = Math.max(0, Math.floor(col) - radius); + const r0 = Math.max(0, Math.floor(row) - radius); + const c1 = Math.min(width - 1, Math.floor(col) + radius); + const r1 = Math.min(height - 1, Math.floor(row) + radius); + let bestCol = Math.round(col), bestRow = Math.round(row), bestVal = -Infinity; + for (let ir = r0; ir <= r1; ir++) { + for (let ic = c0; ic <= c1; ic++) { + const val = mag[ir * width + ic]; + if (val > bestVal) { bestVal = val; bestCol = ic; bestRow = ir; } + } + } + // Sub-pixel refinement via weighted centroid in 3×3 window + const wc0 = Math.max(0, bestCol - 1), wc1 = Math.min(width - 1, bestCol + 1); + const wr0 = Math.max(0, bestRow - 1), wr1 = Math.min(height - 1, bestRow + 1); + let sumW = 0, sumWC = 0, sumWR = 0; + for (let ir = wr0; ir <= wr1; ir++) { + for (let ic = wc0; ic <= wc1; ic++) { + const w = mag[ir * width + ic]; + sumW += w; sumWC += w * ic; sumWR += w * ir; + } + } + if (sumW > 0) return { row: sumWR / sumW, col: sumWC / sumW }; + return { row: bestRow, col: bestCol }; +} + +const FFT_SNAP_RADIUS = 5; + +// ============================================================================ +// Types +// ============================================================================ +type ZoomState = { zoom: number; panX: number; panY: number }; + +// ============================================================================ +// Constants +// ============================================================================ +const SINGLE_IMAGE_TARGET = 500; +const GALLERY_IMAGE_TARGET = 300; +const DEFAULT_FFT_ZOOM = 3; +const PROFILE_COLORS = ["#4fc3f7", "#81c784", "#ffb74d", "#ce93d8", "#ef5350", "#ffd54f", "#90a4ae", "#a1887f"]; +type ROIItem = { row: number; col: number; shape: string; radius: number; radius_inner: number; width: number; height: number; color: string; line_width: number; highlight: boolean }; +const ROI_COLORS = ["#4fc3f7", "#81c784", "#ffb74d", "#ce93d8", "#ef5350", "#ffd54f", "#90a4ae", "#a1887f"]; +const RESIZE_HIT_AREA_PX = 10; + +function drawROI( + ctx: CanvasRenderingContext2D, + x: number, y: number, + shape: "circle" | "square" | "rectangle" | "annular", + radius: number, w: number, h: number, + activeColor: string, inactiveColor: string, + active: boolean = false, innerRadius: number = 0 +): void { + const strokeColor = active ? activeColor : inactiveColor; + ctx.strokeStyle = strokeColor; + if (shape === "circle") { + ctx.beginPath(); ctx.arc(x, y, radius, 0, Math.PI * 2); ctx.stroke(); + } else if (shape === "square") { + ctx.strokeRect(x - radius, y - radius, radius * 2, radius * 2); + } else if (shape === "rectangle") { + ctx.strokeRect(x - w / 2, y - h / 2, w, h); + } else if (shape === "annular") { + ctx.beginPath(); ctx.arc(x, y, radius, 0, Math.PI * 2); ctx.stroke(); + ctx.strokeStyle = active ? "#0ff" : inactiveColor; + ctx.beginPath(); ctx.arc(x, y, innerRadius, 0, Math.PI * 2); ctx.stroke(); + ctx.fillStyle = (active ? activeColor : inactiveColor) + "15"; + ctx.beginPath(); ctx.arc(x, y, radius, 0, Math.PI * 2); ctx.arc(x, y, innerRadius, 0, Math.PI * 2, true); ctx.fill(); + ctx.strokeStyle = strokeColor; + } + if (active) { + ctx.beginPath(); + ctx.moveTo(x - 5, y); ctx.lineTo(x + 5, y); + ctx.moveTo(x, y - 5); ctx.lineTo(x, y + 5); + ctx.stroke(); + } +} + +// ============================================================================ +// Crop ROI region from raw float32 data for ROI-scoped FFT +// ============================================================================ +function cropROIRegion( + data: Float32Array, imgW: number, imgH: number, + roi: ROIItem, +): { cropped: Float32Array; cropW: number; cropH: number } | null { + const shape = roi.shape || "circle"; + let x0: number, y0: number, x1: number, y1: number; + + if (shape === "rectangle") { + const hw = roi.width / 2; + const hh = roi.height / 2; + x0 = Math.max(0, Math.floor(roi.col - hw)); + y0 = Math.max(0, Math.floor(roi.row - hh)); + x1 = Math.min(imgW, Math.ceil(roi.col + hw)); + y1 = Math.min(imgH, Math.ceil(roi.row + hh)); + } else { + const r = roi.radius; + x0 = Math.max(0, Math.floor(roi.col - r)); + y0 = Math.max(0, Math.floor(roi.row - r)); + x1 = Math.min(imgW, Math.ceil(roi.col + r)); + y1 = Math.min(imgH, Math.ceil(roi.row + r)); + } + + const cropW = x1 - x0; + const cropH = y1 - y0; + if (cropW < 2 || cropH < 2) return null; + + const cropped = new Float32Array(cropW * cropH); + + if (shape === "circle" || shape === "annular") { + const r = roi.radius; + const rSq = r * r; + for (let dy = 0; dy < cropH; dy++) { + for (let dx = 0; dx < cropW; dx++) { + const imgX = x0 + dx; + const imgY = y0 + dy; + const distSq = (imgX - roi.col) * (imgX - roi.col) + (imgY - roi.row) * (imgY - roi.row); + cropped[dy * cropW + dx] = distSq <= rSq ? data[imgY * imgW + imgX] : 0; + } + } + } else { + for (let dy = 0; dy < cropH; dy++) { + const srcOffset = (y0 + dy) * imgW + x0; + cropped.set(data.subarray(srcOffset, srcOffset + cropW), dy * cropW); + } + } + + return { cropped, cropW, cropH }; +} + +// ============================================================================ +// Main Component +// ============================================================================ +// Show4DSTEM-style UI constants +const typography = { + label: { fontSize: 11 }, + labelSmall: { fontSize: 10 }, + value: { fontSize: 10, fontFamily: "monospace" }, +}; +const SPACING = { XS: 4, SM: 8, MD: 12, LG: 16 }; +const controlRow = { + display: "flex", + alignItems: "center", + gap: `${SPACING.SM}px`, + px: 1, + py: 0.5, + width: "fit-content", +}; +const compactButton = { + fontSize: 10, + py: 0.25, + px: 1, + minWidth: 0, + "&.Mui-disabled": { + color: "#666", + borderColor: "#444", + }, +}; +const switchStyles = { + small: { "& .MuiSwitch-thumb": { width: 12, height: 12 }, "& .MuiSwitch-switchBase": { padding: "4px" } }, +}; +const sliderStyles = { + small: { py: 0, "& .MuiSlider-thumb": { width: 10, height: 10 }, "& .MuiSlider-rail": { height: 2 }, "& .MuiSlider-track": { height: 2 } }, +}; + +function Show2D() { + // Theme + const { themeInfo, colors: tc } = useTheme(); + const themeColors = { + ...tc, + accentGreen: themeInfo.theme === "dark" ? "#0f0" : "#1a7a1a", + }; + + const themedSelect = { + fontSize: 10, + bgcolor: themeColors.controlBg, + color: themeColors.text, + "& .MuiSelect-select": { py: 0.5 }, + "& .MuiOutlinedInput-notchedOutline": { borderColor: themeColors.border }, + "&:hover .MuiOutlinedInput-notchedOutline": { borderColor: themeColors.accent }, + }; + + const themedMenuProps = { + ...upwardMenuProps, + PaperProps: { sx: { bgcolor: themeColors.controlBg, color: themeColors.text, border: `1px solid ${themeColors.border}` } }, + }; + + // Model state + const [nImages] = useModelState("n_images"); + const [width] = useModelState("width"); + const [height] = useModelState("height"); + const [frameBytes] = useModelState("frame_bytes"); + const [labels] = useModelState("labels"); + const [title] = useModelState("title"); + const [displayBinFactor] = useModelState("_display_bin_factor"); + const [, setGpuMaxBufferMB] = useModelState("_gpu_max_buffer_mb"); + const [widgetVersion] = useModelState("widget_version"); + const [cmap, setCmap] = useModelState("cmap"); + const [ncols] = useModelState("ncols"); + + // Display options + const [logScale, setLogScale] = useModelState("log_scale"); + const [autoContrast, setAutoContrast] = useModelState("auto_contrast"); + const [traitVmin] = useModelState("vmin"); + const [traitVmax] = useModelState("vmax"); + const [traitVmins] = useModelState<(number | null)[] | null>("vmins"); + const [traitVmaxs] = useModelState<(number | null)[] | null>("vmaxs"); + const [zoomRowTrait] = useModelState("zoom_row"); + const [zoomColTrait] = useModelState("zoom_col"); + const [diffMode, setDiffMode] = useModelState("diff_mode"); + const [diffReference] = useModelState("diff_reference"); + // Align removed — diff = A − B (no shift). Drift correction happens upstream. + const alignDy = 0; + const alignDx = 0; + + // Customization + const [canvasSizeTrait] = useModelState("size"); + const [smooth, setSmooth] = useModelState("smooth"); + const imageRenderingStyle = smooth ? "auto" : "pixelated"; + + // Scale bar + const [pixelSize] = useModelState("pixel_size"); + const [scaleBarVisible] = useModelState("scale_bar_visible"); + + // UI visibility + const [showControls] = useModelState("show_controls"); + const [showStats] = useModelState("show_stats"); + const [disabledTools, setDisabledTools] = useModelState("disabled_tools"); + const [hiddenTools, setHiddenTools] = useModelState("hidden_tools"); + const [statsMean] = useModelState("stats_mean"); + const [statsMin] = useModelState("stats_min"); + const [statsMax] = useModelState("stats_max"); + const [statsStd] = useModelState("stats_std"); + + // Analysis Panels (FFT + Histogram) + const [showFft, setShowFft] = useModelState("show_fft"); + const [fftWindow, setFftWindow] = useModelState("fft_window"); + + // Selection + const [selectedIdx, setSelectedIdx] = useModelState("selected_idx"); + + // ROI + const [roiActive, setRoiActive] = useModelState("roi_active"); + const [roiList, setRoiList] = useModelState("roi_list"); + const [roiSelectedIdx, setRoiSelectedIdx] = useModelState("roi_selected_idx"); + const [imageRotations, setImageRotations] = useModelState("image_rotations"); + const [isDraggingROI, setIsDraggingROI] = React.useState(false); + const [isDraggingResize, setIsDraggingResize] = React.useState(false); + const [isDraggingResizeInner, setIsDraggingResizeInner] = React.useState(false); + const [isHoveringResize, setIsHoveringResize] = React.useState(false); + const [isHoveringResizeInner, setIsHoveringResizeInner] = React.useState(false); + const resizeAspectRef = React.useRef(null); + const [newRoiShape, setNewRoiShape] = React.useState<"circle" | "square" | "rectangle" | "annular">("square"); + const [exportAnchor, setExportAnchor] = React.useState(null); + const selectedRoi = roiSelectedIdx >= 0 && roiSelectedIdx < (roiList?.length ?? 0) ? roiList[roiSelectedIdx] : null; + + const toolVisibility = React.useMemo( + () => computeToolVisibility("Show2D", disabledTools, hiddenTools), + [disabledTools, hiddenTools], + ); + const hideDisplay = toolVisibility.isHidden("display"); + const hideHistogram = toolVisibility.isHidden("histogram"); + const hideStats = toolVisibility.isHidden("stats"); + const hideView = toolVisibility.isHidden("view"); + const hideExport = toolVisibility.isHidden("export"); + const hideRoi = toolVisibility.isHidden("roi"); + const hideProfile = toolVisibility.isHidden("profile"); + + const lockDisplay = toolVisibility.isLocked("display"); + const lockHistogram = toolVisibility.isLocked("histogram"); + const lockStats = toolVisibility.isLocked("stats"); + const lockNavigation = toolVisibility.isLocked("navigation"); + const lockView = toolVisibility.isLocked("view"); + const lockExport = toolVisibility.isLocked("export"); + const lockRoi = toolVisibility.isLocked("roi"); + const lockProfile = toolVisibility.isLocked("profile"); + const effectiveShowFft = showFft && !hideDisplay; + + const updateSelectedRoi = (updates: Partial) => { + if (lockRoi) return; + if (roiSelectedIdx < 0 || !roiList) return; + const newList = [...roiList]; + newList[roiSelectedIdx] = { ...newList[roiSelectedIdx], ...updates }; + setRoiList(newList); + }; + + React.useEffect(() => { + if (hideRoi && roiActive) { + setRoiActive(false); + setRoiSelectedIdx(-1); + } + }, [hideRoi, roiActive, setRoiActive, setRoiSelectedIdx]); + + // Canvas refs + const canvasRefs = React.useRef<(HTMLCanvasElement | null)[]>([]); + const overlayRefs = React.useRef<(HTMLCanvasElement | null)[]>([]); + const imageContainerRefs = React.useRef<(HTMLDivElement | null)[]>([]); + const fftContainerRefs = React.useRef<(HTMLDivElement | null)[]>([]); + const singleFftContainerRef = React.useRef(null); + const fftCanvasRef = React.useRef(null); + const [canvasReady, setCanvasReady] = React.useState(0); // Trigger re-render when refs attached + + // Zoom/Pan state - per-image when not linked, shared when linked + const [initialZoom] = useModelState("initial_zoom"); + const [linkPan, setLinkPan] = useModelState("link_pan"); + const [imgHeight] = useModelState("height"); + const [imgWidth] = useModelState("width"); + // Note: pan derived from zoom_row/zoom_col is applied via a useEffect AFTER canvasW/canvasH + // are computed (see "Initial pan from zoom_row/zoom_col" effect below). + const initialZoomState: ZoomState = React.useMemo( + () => ({ zoom: Math.max(MIN_ZOOM, Math.min(MAX_ZOOM, initialZoom || 1)), panX: 0, panY: 0 }), + [initialZoom] + ); + void linkPan; void setLinkPan; void imgWidth; void imgHeight; + const [zoomStates, setZoomStates] = React.useState>(new Map()); + const [linkedZoomState, setLinkedZoomState] = React.useState(initialZoomState); + const [linkedZoom, setLinkedZoom] = useModelState("link_zoom"); + const [isDraggingPan, setIsDraggingPan] = React.useState(false); + const [panStart, setPanStart] = React.useState<{ x: number, y: number, pX: number, pY: number } | null>(null); + + // Helper to get zoom state for an image. zoom and pan link independently: + // zoom from linkedZoomState if linkedZoom else per-image + // pan from linkedZoomState if linkPan else per-image + const getZoomState = React.useCallback((idx: number): ZoomState => { + const per = zoomStates.get(idx) || initialZoomState; + return { + zoom: linkedZoom ? linkedZoomState.zoom : per.zoom, + panX: linkPan ? linkedZoomState.panX : per.panX, + panY: linkPan ? linkedZoomState.panY : per.panY, + }; + }, [linkedZoom, linkPan, linkedZoomState, zoomStates, initialZoomState]); + + // Helper to set zoom state for an image. zoom and pan honored independently: + // zoom: writes to linkedZoomState if linkedZoom, else per-image + // pan: writes to linkedZoomState if linkPan, else per-image + const setZoomState = React.useCallback((idx: number, state: ZoomState) => { + if (linkedZoom || linkPan) { + setLinkedZoomState(prev => ({ + zoom: linkedZoom ? state.zoom : prev.zoom, + panX: linkPan ? state.panX : prev.panX, + panY: linkPan ? state.panY : prev.panY, + })); + } + if (!linkedZoom || !linkPan) { + setZoomStates(prev => { + const m = new Map(prev); + const cur = m.get(idx) || initialZoomState; + m.set(idx, { + zoom: linkedZoom ? cur.zoom : state.zoom, + panX: linkPan ? cur.panX : state.panX, + panY: linkPan ? cur.panY : state.panY, + }); + return m; + }); + } + }, [linkedZoom, linkPan, initialZoomState]); + + // FFT zoom/pan state (single mode) + const [fftZoom, setFftZoom] = React.useState(DEFAULT_FFT_ZOOM); + const [fftPanX, setFftPanX] = React.useState(0); + const [fftPanY, setFftPanY] = React.useState(0); + const [isDraggingFftPan, setIsDraggingFftPan] = React.useState(false); + const [fftPanStart, setFftPanStart] = React.useState<{ x: number, y: number, pX: number, pY: number } | null>(null); + + // Histogram state — per-image contrast ranges (gallery) or single (one image) + const [linkedContrast, setLinkedContrast] = useModelState("link_contrast"); + const [linkedContrastState, setLinkedContrastState] = React.useState<{ vminPct: number; vmaxPct: number }>({ vminPct: 0, vmaxPct: 100 }); + const [contrastStates, setContrastStates] = React.useState>(new Map()); + // Ref mirror for fast slider path (bypass React effect batching) + const contrastRef = React.useRef<{ linked: { vminPct: number; vmaxPct: number }; perImage: Map }>({ linked: { vminPct: 0, vmaxPct: 100 }, perImage: new Map() }); + const sliderRafRef = React.useRef(0); + const getContrastState = React.useCallback((idx: number) => { + if (linkedContrast) return linkedContrastState; + return contrastStates.get(idx) || { vminPct: 0, vmaxPct: 100 }; + }, [linkedContrast, linkedContrastState, contrastStates]); + const setContrastState = React.useCallback((idx: number, state: { vminPct: number; vmaxPct: number }) => { + // Update ref immediately (for fast rAF render) + if (linkedContrast) { + contrastRef.current.linked = state; + setLinkedContrastState(state); + } else { + contrastRef.current.perImage.set(idx, state); + setContrastStates(prev => new Map(prev).set(idx, state)); + } + // Fast path: direct GPU render via rAF, bypassing React effect batching + const engine = gpuCmapRef.current; + if (engine && gpuCmapReadyRef.current && engine.slotCount >= nImages) { + cancelAnimationFrame(sliderRafRef.current); + sliderRafRef.current = requestAnimationFrame(() => { + const cachedRanges = dataRangesRef.current; + if (cachedRanges.length === 0) return; + const lut = COLORMAPS[cmapRef.current] || COLORMAPS.inferno; + engine.uploadLUT(cmapRef.current, lut); + const indices = Array.from({ length: nImages }, (_, i) => i); + const ranges: { vmin: number; vmax: number }[] = []; + for (let i = 0; i < nImages; i++) { + const cs = linkedContrast ? contrastRef.current.linked : (contrastRef.current.perImage.get(i) || { vminPct: 0, vmaxPct: 100 }); + let cr = cachedRanges[i]; + if (!cr || cr.min === cr.max) { + if (rawDataRef.current && rawDataRef.current[i]) cr = findDataRange(rawDataRef.current[i]); + } + cr = cr || { min: 0, max: 1 }; + if (cs.vminPct > 0 || cs.vmaxPct < 100) { + ranges.push(sliderRange(cr.min, cr.max, cs.vminPct, cs.vmaxPct)); + } else { + ranges.push({ vmin: cr.min, vmax: cr.max }); + } + } + const ls = logScaleRef.current ?? false; + const bitmaps = engine.renderSlotsToImageBitmap(indices, ranges, ls); + if (bitmaps && bitmaps[0]) { + for (let i = 0; i < bitmaps.length; i++) { + const offscreen = mainOffscreensRef.current[i]; + if (offscreen && bitmaps[i]) offscreen.getContext("2d")?.drawImage(bitmaps[i], 0, 0); + } + setOffscreenVersion(v => v + 1); + } + }); + } + }, [linkedContrast, nImages]); + // Convenience accessors for active image + const activeContrastIdx = nImages > 1 ? selectedIdx : 0; + const imageVminPct = getContrastState(activeContrastIdx).vminPct; + const imageVmaxPct = getContrastState(activeContrastIdx).vmaxPct; + + const [imageHistogramData, setImageHistogramData] = React.useState(null); + const [imageHistogramBins, setImageHistogramBins] = React.useState(null); + const [imageDataRange, setImageDataRange] = React.useState<{ min: number; max: number }>({ min: 0, max: 1 }); + + // FFT display state (single mode) + const [fftVminPct, setFftVminPct] = React.useState(0); + const [fftVmaxPct, setFftVmaxPct] = React.useState(100); + const [fftHistogramData, setFftHistogramData] = React.useState(null); + const [fftDataRange, setFftDataRange] = React.useState<{ min: number; max: number }>({ min: 0, max: 1 }); + const [fftColormap, setFftColormap] = React.useState("inferno"); + const [fftScaleMode, setFftScaleMode] = React.useState<"linear" | "log" | "power">("linear"); + const [fftAuto, setFftAuto] = React.useState(true); + const [fftStats, setFftStats] = React.useState(null); + const [fftShowColorbar, setFftShowColorbar] = React.useState(false); + + // FFT loading state — shown as a pulsing overlay while FFT computes + const [fftComputing, setFftComputing] = React.useState(false); + const [fftProgress, setFftProgress] = React.useState(""); + + // Cursor readout state + const [cursorInfo, setCursorInfo] = React.useState<{ row: number; col: number; value: number } | null>(null); + + // Colorbar state (single image mode only) + const [showColorbar, setShowColorbar] = React.useState(false); + + // Inset magnifier state + const [showLens, setShowLens] = React.useState(false); + const [lensPos, setLensPos] = React.useState<{ row: number; col: number } | null>(null); + const [lensMag, setLensMag] = React.useState(4); // magnification 2×–8× + const [lensDisplaySize, setLensDisplaySize] = React.useState(128); // CSS px 64–256 + const [lensAnchor, setLensAnchor] = React.useState<{ x: number; y: number } | null>(null); // custom position (CSS px from top-left of canvas) + const [isDraggingLens, setIsDraggingLens] = React.useState(false); + const [isResizingLens, setIsResizingLens] = React.useState(false); + const [isHoveringLensEdge, setIsHoveringLensEdge] = React.useState(false); + const lensDragStartRef = React.useRef<{ mx: number; my: number; ax: number; ay: number } | null>(null); + const lensResizeStartRef = React.useRef<{ my: number; startSize: number } | null>(null); + const lensCanvasRef = React.useRef(null); + + // FFT d-spacing measurement + const [fftClickInfo, setFftClickInfo] = React.useState<{ + row: number; col: number; distPx: number; + spatialFreq: number | null; dSpacing: number | null; + } | null>(null); + const fftClickStartRef = React.useRef<{ x: number; y: number } | null>(null); + const fftOverlayRef = React.useRef(null); + + // Line profile state + const [profileActive, setProfileActive] = React.useState(false); + const [profileLine, setProfileLine] = useModelState<{ row: number; col: number }[]>("profile_line"); + const [profileDataAll, setProfileDataAll] = React.useState<(Float32Array | null)[]>([]); + React.useEffect(() => { + if (hideProfile && profileActive) { + setProfileActive(false); + } + }, [hideProfile, profileActive]); + const profileCanvasRef = React.useRef(null); + const profileBaseImageRef = React.useRef(null); + const profileLayoutRef = React.useRef<{ padLeft: number; plotW: number; padTop: number; plotH: number; gMin: number; gMax: number; totalDist: number; xUnit: string } | null>(null); + + // Sync profile points from model state + const profilePoints = profileLine || []; + const setProfilePoints = (pts: { row: number; col: number }[]) => setProfileLine(pts); + + // Distance measurement state (JS-only, not persisted) + const [measureActive, setMeasureActive] = React.useState(false); + const [measurePoints, setMeasurePoints] = React.useState<{row: number; col: number}[]>([]); + + // FFT zoom/pan state (gallery mode — per-image or linked) + const [galleryFftStates, setGalleryFftStates] = React.useState>(new Map()); + const [linkedFftZoomState, setLinkedFftZoomState] = React.useState({ zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 }); + const [fftPanningIdx, setFftPanningIdx] = React.useState(null); + const getGalleryFftState = React.useCallback((idx: number) => { + if (linkedZoom) return linkedFftZoomState; + return galleryFftStates.get(idx) || { zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 }; + }, [linkedZoom, linkedFftZoomState, galleryFftStates]); + const setGalleryFftState = React.useCallback((idx: number, state: ZoomState) => { + if (linkedZoom) { + setLinkedFftZoomState(state); + } else { + setGalleryFftStates(prev => new Map(prev).set(idx, state)); + } + }, [linkedZoom]); + + // Resizable state (gallery starts smaller) + const [canvasSize, setCanvasSize] = React.useState(nImages > 1 ? GALLERY_IMAGE_TARGET : SINGLE_IMAGE_TARGET); + + // Sync initial sizes from traits + React.useEffect(() => { + if (canvasSizeTrait > 0) setCanvasSize(canvasSizeTrait); + }, [canvasSizeTrait]); + + const [isResizingCanvas, setIsResizingCanvas] = React.useState(false); + const [resizeStart, setResizeStart] = React.useState<{ x: number, y: number, size: number } | null>(null); + + // Profile height resize + const [profileHeight, setProfileHeight] = React.useState(76); + const [isResizingProfile, setIsResizingProfile] = React.useState(false); + const [profileResizeStart, setProfileResizeStart] = React.useState<{ y: number; height: number } | null>(null); + + // WebGPU FFT + const gpuFFTRef = React.useRef(null); + const gpuReadyRef = React.useRef(false); + const rawDataRef = React.useRef(null); + const diffCanvasRefs = React.useRef<(HTMLCanvasElement | null)[]>([]); + const diffFftCanvasRef = React.useRef(null); + const diffFftMagRef = React.useRef(null); + + // WebGPU colormap engine — uses refs (not state) to avoid re-triggering + // effects when GPU initializes. Effects check refs opportunistically: + // on first render they use CPU, on subsequent renders (data/slider change) + // they use GPU if available. No double computation. + const gpuCmapRef = React.useRef(null); + const gpuCmapReadyRef = React.useRef(false); + + // Cached offscreen canvases for main image rendering (avoids per-zoom/pan recompute) + const mainOffscreensRef = React.useRef([]); + const mainImgDatasRef = React.useRef([]); + const logBufferRef = React.useRef(null); + const colorbarVminRef = React.useRef(0); + const colorbarVmaxRef = React.useRef(1); + const [offscreenVersion, setOffscreenVersion] = React.useState(0); + + // Truthful first-render signal: flipped ONCE after the first colormap pass has + // actually painted. Python side observes `_js_rendered` and prints the real + // end-to-end wall clock. Two rAFs ensure the browser has composited before we + // fire, so the printed time reflects "user can see the widget," not "data arrived." + const [, setJsRendered] = useModelState("_js_rendered"); + const firstRenderFiredRef = React.useRef(false); + React.useEffect(() => { + if (firstRenderFiredRef.current) return; + if (offscreenVersion === 0) return; + firstRenderFiredRef.current = true; + requestAnimationFrame(() => requestAnimationFrame(() => setJsRendered(true))); + }, [offscreenVersion, setJsRendered]); + + // Inline FFT refs for gallery mode + const fftCanvasRefs = React.useRef<(HTMLCanvasElement | null)[]>([]); + const fftOffscreensRef = React.useRef<(HTMLCanvasElement | null)[]>([]); + const fftMagCacheGalleryRef = React.useRef<(Float32Array | null)[]>([]); + const galleryFftDimsRef = React.useRef<{ w: number; h: number } | null>(null); + const [galleryFftMagVersion, setGalleryFftMagVersion] = React.useState(0); + + // Cached FFT magnitude for single image mode (avoids recomputing on zoom/pan) + const fftMagCacheRef = React.useRef(null); + const [fftMagVersion, setFftMagVersion] = React.useState(0); + // Generation counter for FFT — coalesces rapid ROI drag events to ≤1 FFT/frame + const fftGenRef = React.useRef(0); + + // Cached FFT offscreen canvas for single mode (avoids reprocessing on zoom/pan) + const fftOffscreenRef = React.useRef(null); + const [fftOffscreenVersion, setFftOffscreenVersion] = React.useState(0); + + // ROI FFT state: when ROI + FFT are both active, compute FFT of cropped ROI region + const [fftCropDims, setFftCropDims] = React.useState<{ cropWidth: number; cropHeight: number; fftWidth: number; fftHeight: number } | null>(null); + + // Layout calculations + const isGallery = nImages > 1; + const showDiffPanel = diffMode && nImages >= 2; + const diffPanelCount = showDiffPanel ? Math.max(0, nImages - 1) : 0; + const effectiveNcols = Math.min(ncols, nImages) + diffPanelCount; + const diffOtherIndices = React.useMemo( + () => Array.from({ length: nImages }, (_, i) => i).filter(i => i !== diffReference), + [nImages, diffReference] + ); + const displayScale = canvasSize / Math.max(width, height); + const canvasW = Math.round(width * displayScale); + const canvasH = Math.round(height * displayScale); + + // Initial pan from zoom_row/zoom_col — runs once after first render with valid canvas dims. + // panX/panY computed so target image (zoomRow, zoomCol) lands at canvas center after transform: + // ctx.translate(cx+panX, cy+panY) ⋅ scale(zoom) ⋅ translate(-cx,-cy) + // target screen = cx + panX + zoom * (target_canvas - cx) = cx + // ⟹ panX = zoom * (cx - target_canvas) = zoom * canvasW * (0.5 - col/width) + const initialPanAppliedRef = React.useRef(false); + React.useEffect(() => { + if (initialPanAppliedRef.current) return; + if (zoomRowTrait == null && zoomColTrait == null) return; + if (canvasW <= 0 || canvasH <= 0 || width <= 0 || height <= 0) return; + const z = initialZoomState.zoom; + const panX = zoomColTrait != null ? z * canvasW * (0.5 - zoomColTrait / width) : 0; + const panY = zoomRowTrait != null ? z * canvasH * (0.5 - zoomRowTrait / height) : 0; + setLinkedZoomState({ zoom: z, panX, panY }); + setZoomStates(prev => { + const m = new Map(prev); + for (let i = 0; i < nImages; i++) m.set(i, { zoom: z, panX, panY }); + return m; + }); + initialPanAppliedRef.current = true; + }, [zoomRowTrait, zoomColTrait, canvasW, canvasH, width, height, nImages, initialZoomState.zoom]); + const floatsPerImage = width * height; + const galleryGridWidth = isGallery ? effectiveNcols * canvasW + (effectiveNcols - 1) * 8 : canvasW; + const profileCanvasWidth = galleryGridWidth; + + // ROI FFT active: both ROI and FFT on, with a selected ROI + const roiFftActive = effectiveShowFft && roiActive && roiSelectedIdx >= 0 && roiSelectedIdx < (roiList?.length ?? 0); + + // Stable key for ROI geometry — only changes when the selected ROI's geometry changes, + // not when other ROIs move or roiList gets a new reference from unrelated edits. + // Shared by both ROI FFT and preview panel to avoid redundant recomputes. + const selectedRoiKey = React.useMemo(() => { + if (!roiList || roiSelectedIdx < 0 || roiSelectedIdx >= roiList.length) return ""; + const r = roiList[roiSelectedIdx]; + return `${r.row},${r.col},${r.radius},${r.radius_inner},${r.width},${r.height},${r.shape}`; + }, [roiList, roiSelectedIdx]); + const roiFftKey = roiFftActive ? selectedRoiKey : ""; + + // Extract raw float32 bytes and parse into Float32Arrays + const allFloats = React.useMemo(() => extractFloat32(frameBytes), [frameBytes]); + + // Initialize WebGPU FFT + colormap engine on mount. + // Sets refs (not state) — no effect re-triggers on GPU init. + // Effects pick up GPU on their next natural re-run (data/slider change). + React.useEffect(() => { + getWebGPUFFT().then(fft => { + if (fft) { + gpuFFTRef.current = fft; + gpuReadyRef.current = true; + const info = getGPUInfo(); + console.log(`[Show2D] WebGPU FFT initialized — ${info || "GPU"}`); + } else { + console.log("[Show2D] WebGPU unavailable — using CPU Worker fallback"); + } + }); + getGPUColormapEngine().then(engine => { + if (engine) { + gpuCmapRef.current = engine; + gpuCmapReadyRef.current = true; + console.log("[Show2D] WebGPU colormap engine initialized"); + // Report GPU memory to Python for auto-bin budget + getGPUMaxBufferSize().then(bytes => { + if (bytes > 0) setGpuMaxBufferMB(Math.floor(bytes / (1024 * 1024))); + }); + // Upload data if already parsed (GPU init may be slower than data arrival). + // Do NOT call setState — that would re-trigger effects and cause double + // computation. Instead, upload data and do a warm-up render via rAF. + // This compiles the GPU pipeline in the background so the first user + // interaction is fast (~100ms instead of ~750ms cold start). + if (rawDataRef.current && rawDataRef.current.length > 0) { + const nImg = rawDataRef.current.length; + for (let i = 0; i < nImg; i++) { + const d = rawDataRef.current[i]; + if (d) engine.uploadData(i, d, width, height); + } + const lut = COLORMAPS[cmap] || COLORMAPS.inferno; + engine.uploadLUT(cmap, lut); + gpuDataVersionRef.current++; + // Warm-up: render once to compile GPU pipeline + fill canvases. + // Uses full data range (no slider adjustment) for the initial frame. + requestAnimationFrame(async () => { + const offscreens = mainOffscreensRef.current; + const imgDatas = mainImgDatasRef.current; + if (offscreens.length === 0 || imgDatas.length === 0) return; + const cachedRanges = dataRangesRef.current; + if (cachedRanges.length === 0) return; + const indices = Array.from({ length: nImg }, (_, i) => i); + const ranges = cachedRanges.map(r => ({ vmin: r.min, vmax: r.max })); + const ofs = indices.map(i => offscreens[i] || null); + const ids = indices.map(i => imgDatas[i] || null); + const logSc = logScaleRef.current ?? false; + await engine.renderSlots(indices, ranges, ofs, ids, logSc); + setOffscreenVersion(v => v + 1); + }); + } + } + }); + }, []); + + const [dataVersion, setDataVersion] = React.useState(0); + + // Keep inline FFT ref arrays in sync with nImages + React.useEffect(() => { + fftCanvasRefs.current = fftCanvasRefs.current.slice(0, nImages); + fftOffscreensRef.current = fftOffscreensRef.current.slice(0, nImages); + }, [nImages]); + + // FFT of diff (n=2 only). Computes A − B in JS at full image resolution from rawDataRef, + // feeds to FFT pipeline. Recomputes when raw data changes. + React.useEffect(() => { + if (!effectiveShowFft || !showDiffPanel || nImages !== 2) return; + const raw = rawDataRef.current; + if (!raw || raw.length < 2 || !raw[0] || !raw[1]) return; + const a = raw[0], b = raw[1]; + const bytes = new Float32Array(width * height); + for (let i = 0; i < bytes.length; i++) bytes[i] = a[i] - b[i]; + const canvas = diffFftCanvasRef.current; + if (!canvas) return; + const fftW = nextPow2(width), fftH = nextPow2(height); + const real = new Float32Array(fftW * fftH); + const imag = new Float32Array(fftW * fftH); + const src = new Float32Array(bytes); + if (fftWindow) applyHannWindow2D(src, width, height); + const padR = Math.floor((fftH - height) / 2), padC = Math.floor((fftW - width) / 2); + for (let r = 0; r < height; r++) { + for (let c = 0; c < width; c++) real[(r + padR) * fftW + c + padC] = src[r * width + c]; + } + let cancelled = false; + (async () => { + const result = await fft2dAsync(real, imag, fftW, fftH, false); + if (cancelled) return; + const mag = computeMagnitude(result.real, result.imag); + fftshift(mag, fftW, fftH); + diffFftMagRef.current = mag; + const { min, max } = autoEnhanceFFT(mag, fftW, fftH); + const off = renderToOffscreen(mag, fftW, fftH, COLORMAPS[fftColormap] || COLORMAPS.inferno, min, max); + if (!off) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.imageSmoothingEnabled = fftW < canvasW || fftH < canvasH; + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.drawImage(off, 0, 0, fftW, fftH, 0, 0, canvasW, canvasH); + })(); + return () => { cancelled = true; }; + }, [effectiveShowFft, showDiffPanel, nImages, dataVersion, width, height, fftWindow, fftColormap, canvasW, canvasH]); + + // Diff panels render — DYNAMIC. One per non-reference image: image[ref] − image[i]. + // Computed at canvas resolution from raw float data, re-running on zoom/pan/align change. + // For n=2: alignDy/dx applied to non-ref image. For n>2: no align (per-pair align not yet supported). + React.useEffect(() => { + if (!showDiffPanel) return; + const raw = rawDataRef.current; + if (!raw || raw.length < 2) return; + const ref = diffReference; + const a = raw[ref]; + if (!a) return; + diffOtherIndices.forEach((otherIdx, slot) => { + renderDiffPanel(slot, a, raw[otherIdx], otherIdx); + }); + // forEach inlines below — extracted as effect helper. + function renderDiffPanel(slot: number, refData: Float32Array, otherData: Float32Array | undefined, otherIdx: number) { + if (!otherData) return; + const canvas = diffCanvasRefs.current[slot]; + if (!canvas) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + const zs0 = getZoomState(ref); + const zs1 = getZoomState(otherIdx); + const useAlign = nImages === 2; + const adY = useAlign ? alignDy : 0; + const adX = useAlign ? alignDx : 0; + const a = refData, b = otherData; + const cw = canvasW, ch = canvasH; + const cx = cw / 2, cy = ch / 2; + const sx = width / cw, sy = height / ch; + const diff = new Float32Array(cw * ch); + let mn = Infinity, mx = -Infinity; + // Smooth: bilinear (slower, sub-pixel correct). !Smooth: nearest neighbor (faster, pixelated). + const Hm1 = height - 1, Wm1 = width - 1; + const a_panX = zs0.panX, a_panY = zs0.panY, a_zoom = zs0.zoom; + const b_panX = zs1.panX, b_panY = zs1.panY, b_zoom = zs1.zoom; + if (smooth) { + for (let y = 0; y < ch; y++) { + const ayu = (y - cy - a_panY) / a_zoom + cy; + const byu = (y - cy - b_panY) / b_zoom + cy; + const aRowF = ayu * sy; + const bRowF = byu * sy - adY; + const aR0 = aRowF | 0, bR0 = bRowF | 0; + const aFr = aRowF - aR0, bFr = bRowF - bR0; + const aRowOOB = aR0 < 0 || aR0 >= Hm1; + const bRowOOB = bR0 < 0 || bR0 >= Hm1; + const aRowOff = aR0 * width; + const bRowOff = bR0 * width; + const rowOff = y * cw; + for (let x = 0; x < cw; x++) { + const axu = (x - cx - a_panX) / a_zoom + cx; + const bxu = (x - cx - b_panX) / b_zoom + cx; + const aColF = axu * sx; + const bColF = bxu * sx - adX; + const aC0 = aColF | 0, bC0 = bColF | 0; + let v = 0; + if (!aRowOOB && !bRowOOB && aC0 >= 0 && aC0 < Wm1 && bC0 >= 0 && bC0 < Wm1) { + const aFc = aColF - aC0, bFc = bColF - bC0; + const ai = aRowOff + aC0; + const bi = bRowOff + bC0; + const aV = (a[ai] * (1 - aFc) + a[ai + 1] * aFc) * (1 - aFr) + + (a[ai + width] * (1 - aFc) + a[ai + width + 1] * aFc) * aFr; + const bV = (b[bi] * (1 - bFc) + b[bi + 1] * bFc) * (1 - bFr) + + (b[bi + width] * (1 - bFc) + b[bi + width + 1] * bFc) * bFr; + v = aV - bV; + } + diff[rowOff + x] = v; + if (v < mn) mn = v; + if (v > mx) mx = v; + } + } + } else { + for (let y = 0; y < ch; y++) { + const ayu = (y - cy - a_panY) / a_zoom + cy; + const byu = (y - cy - b_panY) / b_zoom + cy; + const aRow = (ayu * sy + 0.5) | 0; + const bRow = (byu * sy - adY + 0.5) | 0; + const aRowOK = aRow >= 0 && aRow < height; + const bRowOK = bRow >= 0 && bRow < height; + const aRowOff = aRow * width; + const bRowOff = bRow * width; + const rowOff = y * cw; + for (let x = 0; x < cw; x++) { + const axu = (x - cx - a_panX) / a_zoom + cx; + const bxu = (x - cx - b_panX) / b_zoom + cx; + const aCol = (axu * sx + 0.5) | 0; + const bCol = (bxu * sx - adX + 0.5) | 0; + let v = 0; + if (aRowOK && bRowOK && aCol >= 0 && aCol < width && bCol >= 0 && bCol < width) { + v = a[aRowOff + aCol] - b[bRowOff + bCol]; + } + diff[rowOff + x] = v; + if (v < mn) mn = v; + if (v > mx) mx = v; + } + } + } + const sym = Math.max(Math.abs(mn), Math.abs(mx)); + // Diff is signed-around-zero — use diverging cmap (RdBu) if user picked a sequential one. + const sequentialCmaps = new Set(["inferno", "viridis", "plasma", "magma", "hot", "gray", "turbo"]); + const diffCmap = sequentialCmaps.has(cmap) ? "RdBu" : cmap; + const off = renderToOffscreen(diff, cw, ch, COLORMAPS[diffCmap] || COLORMAPS.RdBu, -sym, sym); + if (!off) return; + ctx.imageSmoothingEnabled = smooth; + if (smooth) ctx.imageSmoothingQuality = "high"; + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.drawImage(off, 0, 0); + } + }, [showDiffPanel, diffOtherIndices, diffReference, nImages, dataVersion, width, height, cmap, smooth, canvasW, canvasH, + alignDy, alignDx, getZoomState, linkedZoom, linkPan, linkedZoomState, zoomStates]); + + React.useEffect(() => { + if (!allFloats || allFloats.length === 0) return; + const dataArrays: Float32Array[] = []; + for (let i = 0; i < nImages; i++) { + const start = i * floatsPerImage; + const imageData = allFloats.subarray(start, start + floatsPerImage); + dataArrays.push(new Float32Array(imageData)); + } + rawDataRef.current = dataArrays; + // Upload to GPU colormap engine if available (ref check, no state trigger) + const engine = gpuCmapRef.current; + if (engine && gpuCmapReadyRef.current) { + for (let i = 0; i < dataArrays.length; i++) engine.uploadData(i, dataArrays[i], width, height); + gpuDataVersionRef.current++; + } + setDataVersion(v => v + 1); + }, [allFloats, nImages, floatsPerImage]); + + // Initialize reusable offscreen canvases (one per image, resized when dimensions change) + React.useEffect(() => { + if (width <= 0 || height <= 0 || nImages <= 0) return; + const canvases: HTMLCanvasElement[] = []; + const imgDatas: ImageData[] = []; + for (let i = 0; i < nImages; i++) { + const canvas = document.createElement("canvas"); + canvas.width = width; + canvas.height = height; + canvases.push(canvas); + imgDatas.push(canvas.getContext("2d")!.createImageData(width, height)); + } + mainOffscreensRef.current = canvases; + mainImgDatasRef.current = imgDatas; + logBufferRef.current = new Float32Array(width * height); + }, [width, height, nImages]); + + // Compute histogram data for the displayed image (reflects log scale) + // GPU path: uses persistent per-slot histogram buffers — no CPU data scan + // CPU fallback: computeHistogramFromBytes (before GPU ready) + React.useEffect(() => { + if (!rawDataRef.current) return; + const idx = nImages > 1 ? selectedIdx : 0; + const raw = rawDataRef.current[idx]; + if (!raw) return; + + // Use cached ranges (no CPU findDataRange scan) + const cachedRaw = rawRangesRef.current[idx]; + const rawRange = cachedRaw || findDataRange(raw); // fallback if cache miss + const range = logScale + ? { min: Math.log1p(Math.max(rawRange.min, 0)), max: Math.log1p(Math.max(rawRange.max, 0)) } + : rawRange; + setImageDataRange(range); + + const engine = gpuCmapRef.current; + if (engine && gpuCmapReadyRef.current && engine.slotCount > idx) { + // GPU histogram — single image, persistent buffers + engine.computeHistogramWithRange(idx, range.min, range.max, logScale).then(bins => { + setImageHistogramBins(bins); + setImageHistogramData(null); + }); + } else { + // CPU fallback (before GPU ready) + const d = logScale ? applyLogScale(raw) : raw; + setImageHistogramBins(null); + setImageHistogramData(d); + } + }, [allFloats, nImages, floatsPerImage, logScale, selectedIdx]); + + // Prevent page scroll when scrolling on canvases (must use native listener with passive: false) + // In gallery mode, only block scroll on the selected image (or all if linkedZoom) + React.useEffect(() => { + const preventDefault = (e: WheelEvent) => e.preventDefault(); + const elements: (HTMLElement | null)[] = isGallery + ? (linkedZoom + ? [ + ...imageContainerRefs.current, + ...(effectiveShowFft ? fftContainerRefs.current : []), + ] + : [ + imageContainerRefs.current[selectedIdx], + ...(effectiveShowFft ? [fftContainerRefs.current[selectedIdx]] : []), + ]) + : [ + imageContainerRefs.current[0], + ...(effectiveShowFft ? [singleFftContainerRef.current] : []), + ]; + elements.forEach(el => el?.addEventListener("wheel", preventDefault, { passive: false })); + return () => elements.forEach(el => el?.removeEventListener("wheel", preventDefault)); + }, [canvasReady, effectiveShowFft, isGallery, selectedIdx, linkedZoom]); + + const gpuDataVersionRef = React.useRef(0); + // Generation counter for colormap — coalesces rapid slider events to ≤1 render per frame + // Cached per-image data ranges — only recomputed when data or logScale changes, NOT on slider drag + const dataRangesRef = React.useRef<{ min: number; max: number }[]>([]); + // Cached log-transformed data — avoids 12×16M log1p calls per slider tick + const logDataCacheRef = React.useRef([]); + // Ref mirrors for async GPU callbacks (avoid stale closures) + const logScaleRef = React.useRef(logScale); + logScaleRef.current = logScale; + const cmapRef = React.useRef(cmap); + cmapRef.current = cmap; + // Auto-contrast cache: GPU-computed percentile ranges per image + const autoContrastCacheRef = React.useRef<{ vmin: number; vmax: number }[]>([]); + + // Cache per-image data ranges (raw AND log) on data change only. + // Log ranges are derived mathematically: log1p(rawMin), log1p(rawMax). + // NO applyLogScale here — GPU shader handles log1p per pixel. + // Log toggle is now free: just pick the right cached ranges. + const rawRangesRef = React.useRef<{ min: number; max: number }[]>([]); + React.useEffect(() => { + if (!rawDataRef.current || rawDataRef.current.length === 0) return; + const engine = gpuCmapRef.current; + const nImg = rawDataRef.current.length; + + if (engine && gpuCmapReadyRef.current && engine.slotCount >= nImg) { + // GPU path: batch compute min/max on GPU (async, updates refs when done) + const indices = Array.from({ length: nImg }, (_, i) => i); + engine.computeRangeBatch(indices).then(rawRanges => { + rawRangesRef.current = rawRanges; + const logRanges = rawRanges.map(r => ({ + min: Math.log1p(Math.max(r.min, 0)), + max: Math.log1p(Math.max(r.max, 0)), + })); + dataRangesRef.current = logScaleRef.current ? logRanges : rawRanges; + }); + } else { + // CPU fallback: scan each image for min/max + const rawRanges: { min: number; max: number }[] = []; + for (let i = 0; i < nImg; i++) { + const rawData = rawDataRef.current[i]; + if (!rawData) { rawRanges.push({ min: 0, max: 1 }); continue; } + rawRanges.push(findDataRange(rawData)); + } + rawRangesRef.current = rawRanges; + const logRanges = rawRanges.map(r => ({ + min: Math.log1p(Math.max(r.min, 0)), + max: Math.log1p(Math.max(r.max, 0)), + })); + dataRangesRef.current = logScale ? logRanges : rawRanges; + } + logDataCacheRef.current = rawDataRef.current.slice(); + }, [dataVersion]); + + // When logScale toggles, just swap cached ranges (no data scan) + React.useEffect(() => { + if (rawRangesRef.current.length === 0) return; + const logRanges = rawRangesRef.current.map(r => ({ + min: Math.log1p(Math.max(r.min, 0)), + max: Math.log1p(Math.max(r.max, 0)), + })); + dataRangesRef.current = logScale ? logRanges : rawRangesRef.current; + }, [logScale]); + + // GPU auto-contrast: batch-compute percentile ranges from GPU histograms. + // One GPU submission for all images. Caches results for synchronous use in render. + React.useEffect(() => { + if (!autoContrast) { autoContrastCacheRef.current = []; return; } + const engine = gpuCmapRef.current; + if (!engine || !gpuCmapReadyRef.current || !rawDataRef.current) return; + const cachedRanges = dataRangesRef.current; + if (cachedRanges.length === 0) return; + const ls = logScale; + const nImg = Math.min(rawDataRef.current.length, engine.slotCount); + if (nImg === 0) return; + + (async () => { + const indices = Array.from({ length: nImg }, (_, i) => i); + const histRanges = indices.map(i => cachedRanges[i] || { min: 0, max: 1 }); + const allBins = await engine.computeHistogramBatch(indices, histRanges, ls); + + const pLow = 2, pHigh = 98; + const acRanges: { vmin: number; vmax: number }[] = []; + for (let k = 0; k < allBins.length; k++) { + const bins = allBins[k]; + const cr = histRanges[k]; + // Percentile from normalized histogram CDF + let sum = 0; + for (let b = 0; b < 256; b++) sum += bins[b]; + let binLow = 0, binHigh = 255; + const targetLow = sum * pLow / 100; + const targetHigh = sum * pHigh / 100; + let running = 0; + for (let b = 0; b < 256; b++) { + running += bins[b]; + if (running >= targetLow && binLow === 0) binLow = b; + if (running >= targetHigh) { binHigh = b; break; } + } + const range = cr.max - cr.min; + acRanges.push({ vmin: cr.min + (binLow / 255) * range, vmax: cr.min + (binHigh / 255) * range }); + } + autoContrastCacheRef.current = acRanges; + console.log(`[Show2D] GPU auto-contrast: ${nImg} images, ${allBins.length} histograms`); + setOffscreenVersion(v => v + 1); + })(); + }, [autoContrast, dataVersion, logScale]); + + // ------------------------------------------------------------------------- + // Data effect: normalize + colormap → reusable offscreen canvases + // GPU path: runs compute shader for all images in one submission + // CPU fallback: per-image applyColormap loop + // (does NOT depend on zoom/pan — avoids recomputing 16M pixels on every pan/zoom) + // ------------------------------------------------------------------------- + React.useEffect(() => { + if (!dataVersion || !rawDataRef.current || rawDataRef.current.length === 0) return; + if (mainOffscreensRef.current.length === 0 || mainImgDatasRef.current.length === 0) return; + + const lut = COLORMAPS[cmap] || COLORMAPS.inferno; + + // Compute per-image vmin/vmax from CACHED data ranges (no findDataRange per tick). + // dataRangesRef is precomputed when data or logScale changes. + const cachedRanges = dataRangesRef.current; + const hasAbsoluteRange = traitVmin != null && traitVmax != null; + const ranges: { vmin: number; vmax: number }[] = []; + for (let i = 0; i < nImages; i++) { + let vmin: number, vmax: number; + const cs = linkedContrast ? linkedContrastState : (contrastStates.get(i) || { vminPct: 0, vmaxPct: 100 }); + + // Per-image absolute range (vmins/vmaxs) takes precedence over scalar (vmin/vmax) + const perI_min = traitVmins && traitVmins[i] != null ? traitVmins[i] : null; + const perI_max = traitVmaxs && traitVmaxs[i] != null ? traitVmaxs[i] : null; + const hasPerImage = perI_min != null && perI_max != null; + const isDiffSlot = false; + const diffSym = 0; + + let rangeMin: number, rangeMax: number; + if (isDiffSlot) { + rangeMin = -diffSym; + rangeMax = diffSym; + } else if (hasPerImage) { + rangeMin = logScale ? Math.log1p(Math.max(perI_min!, 0)) : perI_min!; + rangeMax = logScale ? Math.log1p(Math.max(perI_max!, 0)) : perI_max!; + } else if (hasAbsoluteRange) { + rangeMin = logScale ? Math.log1p(Math.max(traitVmin!, 0)) : traitVmin!; + rangeMax = logScale ? Math.log1p(Math.max(traitVmax!, 0)) : traitVmax!; + } else { + // GPU range compute is async — when cache missing OR collapsed (min==max from race), + // sync findDataRange on raw data to ensure non-degenerate range. + let cached = cachedRanges[i]; + if (!cached || cached.min === cached.max) { + if (rawDataRef.current && rawDataRef.current[i]) { + cached = findDataRange(rawDataRef.current[i]); + } + } + cached = cached || { min: 0, max: 1 }; + rangeMin = cached.min; + rangeMax = cached.max; + } + + if (!hasAbsoluteRange && !hasPerImage && autoContrast) { + // Auto-contrast: use GPU-precomputed percentile ranges. + // If GPU cache not ready yet, use full data range as placeholder + // (GPU auto-contrast effect will fire async and trigger re-render). + const acCache = autoContrastCacheRef.current[i]; + if (acCache) { + vmin = acCache.vmin; vmax = acCache.vmax; + } else { + vmin = rangeMin; vmax = rangeMax; + } + } else if (rangeMin !== rangeMax && (cs.vminPct > 0 || cs.vmaxPct < 100)) { + ({ vmin, vmax } = sliderRange(rangeMin, rangeMax, cs.vminPct, cs.vmaxPct)); + } else { + vmin = rangeMin; vmax = rangeMax; + } + ranges.push({ vmin, vmax }); + } + + // Cache first image's vmin/vmax for colorbar/lens + if (ranges.length > 0) { + colorbarVminRef.current = ranges[0].vmin; + colorbarVmaxRef.current = ranges[0].vmax; + } + + // GPU colormap — first-class citizen. + // Try zero-copy path (OffscreenCanvas → ImageBitmap, no mapAsync). + // Falls back to renderSlots (mapAsync + putImageData) if zero-copy fails. + const engine = gpuCmapRef.current; + const gpuReady = engine && gpuCmapReadyRef.current && engine.slotCount >= nImages; + if (gpuReady) { + engine!.uploadLUT(cmap, lut); + const capturedRanges = ranges.slice(); + const capturedLogScale = logScale; + const capturedNImages = nImages; + requestAnimationFrame(async () => { + const indices = Array.from({ length: capturedNImages }, (_, i) => i); + + // Zero-copy path: GPU → OffscreenCanvas → ImageBitmap → drawImage + const bitmaps = engine!.renderSlotsToImageBitmap(indices, capturedRanges, capturedLogScale); + if (bitmaps && bitmaps.length > 0 && bitmaps[0]) { + for (let i = 0; i < bitmaps.length; i++) { + const offscreen = mainOffscreensRef.current[i]; + if (!offscreen || !bitmaps[i]) continue; + const ctx = offscreen.getContext("2d"); + if (ctx) ctx.drawImage(bitmaps[i], 0, 0); + } + setOffscreenVersion(v => v + 1); + return; + } + + // Fallback: renderSlots (mapAsync + copy to ImageData) + const offscreens = indices.map(i => mainOffscreensRef.current[i] || null); + const imgDatas = indices.map(i => mainImgDatasRef.current[i] || null); + const rendered = await engine!.renderSlots(indices, capturedRanges, offscreens, imgDatas, capturedLogScale); + if (rendered === 0) { + for (let i = 0; i < capturedNImages; i++) { + const offscreen = mainOffscreensRef.current[i]; + const imgData = mainImgDatasRef.current[i]; + if (!offscreen || !imgData) continue; + const raw = rawDataRef.current?.[i]; + if (!raw) continue; + const processed = capturedLogScale ? applyLogScale(raw) : raw; + renderToOffscreenReuse(processed, lut, capturedRanges[i].vmin, capturedRanges[i].vmax, offscreen, imgData); + } + } + setOffscreenVersion(v => v + 1); + }); + } else { + // CPU fallback: initial render or no WebGPU + // CPU must do log transform itself (GPU shader would handle it) + for (let i = 0; i < nImages; i++) { + const offscreen = mainOffscreensRef.current[i]; + const imgData = mainImgDatasRef.current[i]; + if (!offscreen || !imgData) continue; + const raw = rawDataRef.current?.[i]; + if (!raw) continue; + const processed = logScale ? applyLogScale(raw) : raw; + renderToOffscreenReuse(processed, lut, ranges[i].vmin, ranges[i].vmax, offscreen, imgData); + } + setOffscreenVersion(v => v + 1); + } + }, [dataVersion, nImages, width, height, cmap, logScale, autoContrast, linkedContrast, linkedContrastState, contrastStates, traitVmin, traitVmax, traitVmins, traitVmaxs, diffMode]); + + // ------------------------------------------------------------------------- + // Draw effect: zoom/pan changes — cheap, just drawImage from cached offscreens + // useLayoutEffect prevents black flash when canvas dimensions change (resize) + // ------------------------------------------------------------------------- + React.useLayoutEffect(() => { + if (mainOffscreensRef.current.length === 0) return; + + for (let i = 0; i < nImages; i++) { + const canvas = canvasRefs.current[i]; + const offscreen = mainOffscreensRef.current[i]; + if (!canvas || !offscreen) continue; + const ctx = canvas.getContext("2d"); + if (!ctx) continue; + + ctx.imageSmoothingEnabled = smooth; + if (smooth) ctx.imageSmoothingQuality = "high"; + ctx.clearRect(0, 0, canvas.width, canvas.height); + + const zs = getZoomState(i); + const { zoom, panX, panY } = zs; + + if (zoom !== 1 || panX !== 0 || panY !== 0) { + ctx.save(); + const cx = canvasW / 2; + const cy = canvasH / 2; + ctx.translate(cx + panX, cy + panY); + ctx.scale(zoom, zoom); + ctx.translate(-cx, -cy); + ctx.drawImage(offscreen, 0, 0, width, height, 0, 0, canvasW, canvasH); + ctx.restore(); + } else { + ctx.drawImage(offscreen, 0, 0, width, height, 0, 0, canvasW, canvasH); + } + } + }, [offscreenVersion, nImages, width, height, displayScale, canvasW, canvasH, canvasReady, linkedZoom, linkedZoomState, zoomStates, smooth]); + + // ------------------------------------------------------------------------- + // Render Overlays (scale bar, colorbar, zoom indicator) + // ------------------------------------------------------------------------- + React.useEffect(() => { + for (let i = 0; i < nImages; i++) { + const overlay = overlayRefs.current[i]; + if (!overlay) continue; + const ctx = overlay.getContext("2d"); + if (!ctx) continue; + + if (scaleBarVisible) { + const zs = getZoomState(i); + const unit = pixelSize > 0 ? "Å" as const : "px" as const; + const pxSize = pixelSize > 0 ? pixelSize : 1; + drawScaleBarHiDPI(overlay, DPR, zs.zoom, pxSize, unit, width); + } else { + ctx.clearRect(0, 0, overlay.width, overlay.height); + } + + // Colorbar (single image mode only) — uses cached vmin/vmax from data effect + if (!hideDisplay && showColorbar && !isGallery) { + const lut = COLORMAPS[cmap] || COLORMAPS.inferno; + const cssW = overlay.width / DPR; + const cssH = overlay.height / DPR; + const vmin = colorbarVminRef.current; + const vmax = colorbarVmaxRef.current; + + ctx.save(); + ctx.scale(DPR, DPR); + drawColorbar(ctx, cssW, cssH, lut, vmin, vmax, logScale); + ctx.restore(); + } + + // ROI overlay — draw all ROIs + if (!hideRoi && roiActive && roiList && roiList.length > 0) { + const zs = getZoomState(i); + const { zoom, panX, panY } = zs; + const cx = canvasW / 2; + const cy = canvasH / 2; + + // Highlight mask: dim everything outside highlighted ROIs + const highlightedRois = roiList.filter(r => r.highlight); + if (highlightedRois.length > 0) { + ctx.save(); + ctx.scale(DPR, DPR); + ctx.fillStyle = "rgba(0,0,0,0.6)"; + ctx.fillRect(0, 0, canvasW, canvasH); + ctx.globalCompositeOperation = "destination-out"; + for (const roi of highlightedRois) { + const sx = (roi.col * displayScale - cx) * zoom + cx + panX; + const sy = (roi.row * displayScale - cy) * zoom + cy + panY; + const sr = roi.radius * displayScale * zoom; + const shape = roi.shape || "circle"; + ctx.fillStyle = "rgba(0,0,0,1)"; + if (shape === "circle") { + ctx.beginPath(); ctx.arc(sx, sy, sr, 0, Math.PI * 2); ctx.fill(); + } else if (shape === "square") { + ctx.fillRect(sx - sr, sy - sr, sr * 2, sr * 2); + } else if (shape === "rectangle") { + const sw = roi.width * displayScale * zoom; + const sh = roi.height * displayScale * zoom; + ctx.fillRect(sx - sw / 2, sy - sh / 2, sw, sh); + } else if (shape === "annular") { + ctx.beginPath(); ctx.arc(sx, sy, sr, 0, Math.PI * 2); ctx.fill(); + // Re-darken inner ring + ctx.globalCompositeOperation = "source-over"; + ctx.fillStyle = "rgba(0,0,0,0.6)"; + const sir = roi.radius_inner * displayScale * zoom; + ctx.beginPath(); ctx.arc(sx, sy, sir, 0, Math.PI * 2); ctx.fill(); + ctx.globalCompositeOperation = "destination-out"; + } + } + ctx.restore(); + } + + ctx.save(); + ctx.scale(DPR, DPR); + for (let ri = 0; ri < roiList.length; ri++) { + const roi = roiList[ri]; + const isSelected = ri === roiSelectedIdx; + const screenX = (roi.col * displayScale - cx) * zoom + cx + panX; + const screenY = (roi.row * displayScale - cy) * zoom + cy + panY; + const screenRadius = roi.radius * displayScale * zoom; + const screenW = roi.width * displayScale * zoom; + const screenH = roi.height * displayScale * zoom; + const screenRadiusInner = roi.radius_inner * displayScale * zoom; + const shape = (roi.shape || "circle") as "circle" | "square" | "rectangle" | "annular"; + ctx.lineWidth = roi.line_width || 2; + drawROI(ctx, screenX, screenY, shape, screenRadius, screenW, screenH, roi.color || ROI_COLORS[ri % ROI_COLORS.length], roi.color || ROI_COLORS[ri % ROI_COLORS.length], isSelected && isDraggingROI, screenRadiusInner); + if (isSelected) { + ctx.setLineDash([4, 3]); + ctx.strokeStyle = "#fff"; + ctx.lineWidth = 1; + if (shape === "circle" || shape === "annular") { + ctx.beginPath(); ctx.arc(screenX, screenY, screenRadius + 3, 0, Math.PI * 2); ctx.stroke(); + } else if (shape === "square") { + ctx.strokeRect(screenX - screenRadius - 3, screenY - screenRadius - 3, (screenRadius + 3) * 2, (screenRadius + 3) * 2); + } else if (shape === "rectangle") { + ctx.strokeRect(screenX - screenW / 2 - 3, screenY - screenH / 2 - 3, screenW + 6, screenH + 6); + } + ctx.setLineDash([]); + } + } + ctx.restore(); + } + + // Line profile overlay + if (!hideProfile && profileActive && profilePoints.length > 0) { + const zs = getZoomState(i); + const { zoom, panX, panY } = zs; + ctx.save(); + ctx.scale(DPR, DPR); + + // Transform image coords to screen coords + const cx = canvasW / 2; + const cy = canvasH / 2; + const toScreenX = (ix: number) => (ix * displayScale - cx) * zoom + cx + panX; + const toScreenY = (iy: number) => (iy * displayScale - cy) * zoom + cy + panY; + + // Draw point A + const ax = toScreenX(profilePoints[0].col); + const ay = toScreenY(profilePoints[0].row); + ctx.fillStyle = themeColors.accent; + ctx.beginPath(); + ctx.arc(ax, ay, 4, 0, Math.PI * 2); + ctx.fill(); + + // Draw line and point B if complete + if (profilePoints.length === 2) { + const bx = toScreenX(profilePoints[1].col); + const by = toScreenY(profilePoints[1].row); + + ctx.strokeStyle = themeColors.accent; + ctx.lineWidth = 1.5; + ctx.setLineDash([4, 3]); + ctx.beginPath(); + ctx.moveTo(ax, ay); + ctx.lineTo(bx, by); + ctx.stroke(); + ctx.setLineDash([]); + ctx.fillStyle = themeColors.accent; + ctx.beginPath(); + ctx.arc(bx, by, 4, 0, Math.PI * 2); + ctx.fill(); + } + + ctx.restore(); + } + + // Distance measurement overlay + if (measureActive && measurePoints.length >= 1) { + const zs = getZoomState(i); + const { zoom, panX, panY } = zs; + ctx.save(); + ctx.scale(DPR, DPR); + const cx = canvasW / 2; + const cy = canvasH / 2; + const toSX = (ix: number) => (ix * displayScale - cx) * zoom + cx + panX; + const toSY = (iy: number) => (iy * displayScale - cy) * zoom + cy + panY; + + ctx.shadowColor = "rgba(0,0,0,0.6)"; + ctx.shadowBlur = 3; + + // Endpoint A + const ax = toSX(measurePoints[0].col); + const ay = toSY(measurePoints[0].row); + ctx.fillStyle = "#fff"; + ctx.beginPath(); + ctx.arc(ax, ay, 4, 0, Math.PI * 2); + ctx.fill(); + + if (measurePoints.length === 2) { + const bx = toSX(measurePoints[1].col); + const by = toSY(measurePoints[1].row); + + // Solid white line (distinct from profile's dashed accent line) + ctx.strokeStyle = "#fff"; + ctx.lineWidth = 1.5; + ctx.beginPath(); + ctx.moveTo(ax, ay); + ctx.lineTo(bx, by); + ctx.stroke(); + + // Endpoint B + ctx.beginPath(); + ctx.arc(bx, by, 4, 0, Math.PI * 2); + ctx.fill(); + + // Distance label + const dc = measurePoints[1].col - measurePoints[0].col; + const dr = measurePoints[1].row - measurePoints[0].row; + const distPx = Math.sqrt(dc * dc + dr * dr); + let label: string; + if (pixelSize > 0) { + const distA = distPx * pixelSize; + label = distA >= 10 ? `${(distA / 10).toFixed(2)} nm` : `${distA.toFixed(2)} Å`; + } else { + label = `${distPx.toFixed(1)} px`; + } + + const mx = (ax + bx) / 2; + const my = (ay + by) / 2; + ctx.font = "bold 13px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.textAlign = "center"; + ctx.textBaseline = "bottom"; + ctx.fillStyle = "#fff"; + ctx.fillText(label, mx, my - 8); + } + + ctx.shadowBlur = 0; + ctx.restore(); + } + } + }, [nImages, pixelSize, scaleBarVisible, selectedIdx, isGallery, canvasW, canvasH, width, displayScale, linkedZoom, linkedZoomState, zoomStates, dataVersion, showColorbar, cmap, offscreenVersion, logScale, profileActive, profilePoints, roiActive, roiList, roiSelectedIdx, isDraggingROI, themeColors, hideDisplay, hideRoi, hideProfile, measureActive, measurePoints]); + + // ------------------------------------------------------------------------- + // Inset magnifier (lens) — renders magnified region at cursor in bottom-left + // ------------------------------------------------------------------------- + React.useEffect(() => { + const lensCanvas = lensCanvasRef.current; + if (lensCanvas) { + const lctx = lensCanvas.getContext("2d"); + if (lctx) lctx.clearRect(0, 0, lensCanvas.width, lensCanvas.height); + } + if (!showLens || lockDisplay || isGallery || !lensPos || !rawDataRef.current?.[0]) return; + if (!lensCanvas) return; + const ctx = lensCanvas.getContext("2d"); + if (!ctx) return; + + const raw = rawDataRef.current[0]; + const lut = COLORMAPS[cmap] || COLORMAPS.inferno; + // Use cached vmin/vmax from data effect (avoids full-image applyLogScale + findDataRange) + const vmin = colorbarVminRef.current; + const vmax = colorbarVmaxRef.current; + + // Extract region around cursor — regionSize = displaySize / magnification + const regionSize = Math.max(4, Math.round(lensDisplaySize / lensMag)); + const lensSize = lensDisplaySize; + const margin = 12; + const half = Math.floor(regionSize / 2); + const r0 = lensPos.row - half; + const c0 = lensPos.col - half; + + // Create small offscreen canvas for the region + const regionCanvas = document.createElement("canvas"); + regionCanvas.width = regionSize; + regionCanvas.height = regionSize; + const rctx = regionCanvas.getContext("2d"); + if (!rctx) return; + const imgData = rctx.createImageData(regionSize, regionSize); + const range = vmax - vmin || 1; + for (let dr = 0; dr < regionSize; dr++) { + for (let dc = 0; dc < regionSize; dc++) { + const sr = r0 + dr; + const sc = c0 + dc; + const idx = (dr * regionSize + dc) * 4; + if (sr < 0 || sr >= height || sc < 0 || sc >= width) { + imgData.data[idx] = 0; imgData.data[idx + 1] = 0; imgData.data[idx + 2] = 0; imgData.data[idx + 3] = 255; + } else { + // Apply log scale inline per-pixel (only for the small region, not full image) + const rawVal = raw[sr * width + sc]; + const val = logScale ? Math.log1p(rawVal) : rawVal; + const t = Math.max(0, Math.min(1, (val - vmin) / range)); + const li = Math.round(t * 255); + imgData.data[idx] = lut[li * 3]; imgData.data[idx + 1] = lut[li * 3 + 1]; imgData.data[idx + 2] = lut[li * 3 + 2]; imgData.data[idx + 3] = 255; + } + } + } + rctx.putImageData(imgData, 0, 0); + + // Draw lens inset on overlay — use custom anchor or default bottom-left + ctx.save(); + ctx.scale(DPR, DPR); + const lx = lensAnchor ? lensAnchor.x : margin; + const ly = lensAnchor ? lensAnchor.y : canvasH - lensSize - margin - 20; + ctx.imageSmoothingEnabled = false; + ctx.drawImage(regionCanvas, lx, ly, lensSize, lensSize); + ctx.strokeStyle = themeColors.accent; + ctx.lineWidth = 2; + ctx.strokeRect(lx, ly, lensSize, lensSize); + // Crosshair at center + const cx = lx + lensSize / 2; + const cy = ly + lensSize / 2; + ctx.strokeStyle = "rgba(255,255,255,0.5)"; + ctx.lineWidth = 1; + ctx.beginPath(); + ctx.moveTo(cx - 8, cy); ctx.lineTo(cx + 8, cy); + ctx.moveTo(cx, cy - 8); ctx.lineTo(cx, cy + 8); + ctx.stroke(); + // Magnification label + ctx.fillStyle = "rgba(255,255,255,0.7)"; + ctx.font = "10px monospace"; + ctx.fillText(`${lensMag}×`, lx + 4, ly + lensSize - 4); + ctx.restore(); + }, [showLens, lockDisplay, lensPos, isGallery, cmap, logScale, offscreenVersion, width, height, canvasH, themeColors, lensMag, lensDisplaySize, lensAnchor]); + + // ------------------------------------------------------------------------- + // Auto-compute profile when profile_line is set (e.g. from Python) + // ------------------------------------------------------------------------- + React.useEffect(() => { + if (hideProfile) return; + if (profilePoints.length === 2 && rawDataRef.current) { + const p0 = profilePoints[0], p1 = profilePoints[1]; + const allProfiles: (Float32Array | null)[] = []; + for (let i = 0; i < rawDataRef.current.length; i++) { + const raw = rawDataRef.current[i]; + allProfiles.push(raw ? sampleLineProfile(raw, width, height, p0.row, p0.col, p1.row, p1.col) : null); + } + setProfileDataAll(allProfiles); + if (!profileActive) setProfileActive(true); + } + }, [profilePoints, dataVersion, hideProfile, profileActive]); + + // ------------------------------------------------------------------------- + // Render sparkline for line profile + // ------------------------------------------------------------------------- + React.useEffect(() => { + const canvas = profileCanvasRef.current; + if (!canvas) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const dpr = window.devicePixelRatio || 1; + const cssW = profileCanvasWidth; + const cssH = profileHeight; + canvas.width = cssW * dpr; + canvas.height = cssH * dpr; + ctx.scale(dpr, dpr); + + const isDark = themeInfo.theme === "dark"; + ctx.fillStyle = isDark ? "#1a1a1a" : "#f0f0f0"; + ctx.fillRect(0, 0, cssW, cssH); + + const hasData = profileDataAll.some(d => d && d.length >= 2); + if (!hasData) { + ctx.font = "10px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#555" : "#999"; + ctx.textAlign = "center"; + ctx.textBaseline = "middle"; + ctx.fillText("Click two points on the image to draw a profile", cssW / 2, cssH / 2); + return; + } + + const padLeft = 40; + const padRight = 8; + const padTop = 6; + const padBottom = 18; + const plotW = cssW - padLeft - padRight; + const plotH = cssH - padTop - padBottom; + + // Find global min/max across all profiles + let gMin = Infinity, gMax = -Infinity; + for (const d of profileDataAll) { + if (!d) continue; + for (let i = 0; i < d.length; i++) { + if (d[i] < gMin) gMin = d[i]; + if (d[i] > gMax) gMax = d[i]; + } + } + const range = gMax - gMin || 1; + + // Draw each profile + const colors = profileDataAll.length === 1 ? [themeColors.accent] : PROFILE_COLORS; + for (let pIdx = 0; pIdx < profileDataAll.length; pIdx++) { + const d = profileDataAll[pIdx]; + if (!d || d.length < 2) continue; + ctx.strokeStyle = colors[pIdx % colors.length]; + ctx.lineWidth = pIdx === selectedIdx || profileDataAll.length === 1 ? 1.5 : 1; + ctx.globalAlpha = pIdx === selectedIdx || profileDataAll.length === 1 ? 1 : 0.5; + ctx.beginPath(); + for (let i = 0; i < d.length; i++) { + const x = padLeft + (i / (d.length - 1)) * plotW; + const y = padTop + plotH - ((d[i] - gMin) / range) * plotH; + if (i === 0) ctx.moveTo(x, y); + else ctx.lineTo(x, y); + } + ctx.stroke(); + } + ctx.globalAlpha = 1; + + // Compute total distance for x-axis + const firstProfile = profileDataAll.find(d => d); + let totalDist = (firstProfile?.length ?? 2) - 1; + let xUnit = "px"; + if (profilePoints.length === 2) { + const dx = profilePoints[1].col - profilePoints[0].col; + const dy = profilePoints[1].row - profilePoints[0].row; + const distPx = Math.sqrt(dx * dx + dy * dy); + if (pixelSize > 0) { + const distA = distPx * pixelSize; + if (distA >= 10) { totalDist = distA / 10; xUnit = "nm"; } + else { totalDist = distA; xUnit = "Å"; } + } else { + totalDist = distPx; + } + } + + // Draw x-axis ticks + const tickY = padTop + plotH; + ctx.strokeStyle = isDark ? "#555" : "#bbb"; + ctx.lineWidth = 0.5; + const idealTicks = Math.max(2, Math.floor(plotW / 70)); + const tickStep = roundToNiceValue(totalDist / idealTicks); + ctx.font = "9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#888" : "#666"; + ctx.textBaseline = "top"; + const ticks: number[] = []; + for (let v = 0; v <= totalDist + tickStep * 0.01; v += tickStep) { + if (v > totalDist * 1.001) break; + ticks.push(v); + } + for (let i = 0; i < ticks.length; i++) { + const v = ticks[i]; + const frac = totalDist > 0 ? v / totalDist : 0; + const x = padLeft + frac * plotW; + ctx.beginPath(); ctx.moveTo(x, tickY); ctx.lineTo(x, tickY + 3); ctx.stroke(); + ctx.textAlign = frac < 0.05 ? "left" : frac > 0.95 ? "right" : "center"; + const valStr = v % 1 === 0 ? v.toFixed(0) : v.toFixed(1); + ctx.fillText(i === ticks.length - 1 ? `${valStr} ${xUnit}` : valStr, x, tickY + 4); + } + + // Draw y-axis min/max labels + ctx.font = "9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#888" : "#666"; + ctx.textAlign = "right"; + ctx.textBaseline = "top"; + ctx.fillText(formatNumber(gMax), padLeft - 3, padTop); + ctx.textBaseline = "bottom"; + ctx.fillText(formatNumber(gMin), padLeft - 3, padTop + plotH); + + // Draw axis lines + ctx.strokeStyle = isDark ? "#555" : "#bbb"; + ctx.lineWidth = 0.5; + ctx.beginPath(); + ctx.moveTo(padLeft, padTop); + ctx.lineTo(padLeft, padTop + plotH); + ctx.lineTo(padLeft + plotW, padTop + plotH); + ctx.stroke(); + + // Legend (gallery mode with multiple images) + if (profileDataAll.length > 1) { + ctx.textAlign = "right"; + ctx.textBaseline = "top"; + ctx.font = "9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + let legendX = cssW - 4; + for (let pIdx = profileDataAll.length - 1; pIdx >= 0; pIdx--) { + if (!profileDataAll[pIdx]) continue; + const label = labels?.[pIdx] || `#${pIdx + 1}`; + const color = colors[pIdx % colors.length]; + const textW = ctx.measureText(label).width; + ctx.globalAlpha = pIdx === selectedIdx ? 1 : 0.5; + ctx.fillStyle = color; + ctx.fillRect(legendX - textW - 10, 2, 6, 6); + ctx.fillStyle = isDark ? "#aaa" : "#555"; + ctx.fillText(label, legendX, 1); + legendX -= textW + 16; + } + ctx.globalAlpha = 1; + } + + // Save base rendering + layout for hover overlay + profileBaseImageRef.current = ctx.getImageData(0, 0, canvas.width, canvas.height); + profileLayoutRef.current = { padLeft, plotW, padTop, plotH, gMin, gMax, totalDist, xUnit }; + }, [profileDataAll, themeInfo.theme, themeColors.accent, profilePoints, pixelSize, selectedIdx, labels, profileCanvasWidth, profileHeight]); + + // Profile hover handler — draws crosshair + value readout + const handleProfileMouseMove = React.useCallback((e: React.MouseEvent) => { + const canvas = profileCanvasRef.current; + const base = profileBaseImageRef.current; + const layout = profileLayoutRef.current; + if (!canvas || !base || !layout) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const rect = canvas.getBoundingClientRect(); + const cssX = e.clientX - rect.left; + const { padLeft, plotW, padTop, plotH, gMin, gMax, totalDist, xUnit } = layout; + const range = gMax - gMin || 1; + + // Restore base image + ctx.putImageData(base, 0, 0); + + if (cssX < padLeft || cssX > padLeft + plotW) return; + const frac = (cssX - padLeft) / plotW; + + const dpr = window.devicePixelRatio || 1; + ctx.save(); + ctx.setTransform(dpr, 0, 0, dpr, 0, 0); + + // Vertical crosshair + ctx.strokeStyle = themeInfo.theme === "dark" ? "rgba(255,255,255,0.3)" : "rgba(0,0,0,0.3)"; + ctx.lineWidth = 1; + ctx.setLineDash([2, 2]); + ctx.beginPath(); + ctx.moveTo(cssX, padTop); + ctx.lineTo(cssX, padTop + plotH); + ctx.stroke(); + ctx.setLineDash([]); + + // Dot on each profile line + collect values + const colors = profileDataAll.length === 1 ? [themeColors.accent] : PROFILE_COLORS; + const activeIdx = isGallery ? selectedIdx : 0; + let displayVal: number | null = null; + for (let pIdx = 0; pIdx < profileDataAll.length; pIdx++) { + const d = profileDataAll[pIdx]; + if (!d || d.length < 2) continue; + const dataIdx = Math.min(d.length - 1, Math.max(0, Math.round(frac * (d.length - 1)))); + const val = d[dataIdx]; + const y = padTop + plotH - ((val - gMin) / range) * plotH; + ctx.fillStyle = colors[pIdx % colors.length]; + ctx.globalAlpha = pIdx === activeIdx || profileDataAll.length === 1 ? 1 : 0.5; + ctx.beginPath(); + ctx.arc(cssX, y, 3, 0, Math.PI * 2); + ctx.fill(); + if (pIdx === activeIdx || profileDataAll.length === 1) displayVal = val; + } + ctx.globalAlpha = 1; + + // Value readout label + if (displayVal !== null) { + const dist = frac * totalDist; + const label = `${formatNumber(displayVal)} @ ${dist.toFixed(1)} ${xUnit}`; + const isDark = themeInfo.theme === "dark"; + ctx.font = "bold 9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + const textW = ctx.measureText(label).width; + const labelX = Math.min(cssX + 6, padLeft + plotW - textW - 2); + const labelY = padTop + 2; + ctx.fillStyle = isDark ? "rgba(0,0,0,0.7)" : "rgba(255,255,255,0.8)"; + ctx.fillRect(labelX - 2, labelY - 1, textW + 4, 11); + ctx.fillStyle = isDark ? "#fff" : "#000"; + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.fillText(label, labelX, labelY); + } + + ctx.restore(); + }, [profileDataAll, themeInfo.theme, themeColors.accent, isGallery, selectedIdx]); + + const handleProfileMouseLeave = React.useCallback(() => { + const canvas = profileCanvasRef.current; + const base = profileBaseImageRef.current; + if (!canvas || !base) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.putImageData(base, 0, 0); + }, []); + + // ------------------------------------------------------------------------- + // Compute FFT magnitude (cached — only recomputes when data changes) + // Supports ROI-scoped FFT: when ROI is active with a selected ROI, compute + // FFT of the cropped region instead of the full image. + // ------------------------------------------------------------------------- + React.useEffect(() => { + if (!effectiveShowFft || isGallery || !rawDataRef.current) return; + if (!rawDataRef.current[selectedIdx]) return; + // Generation counter: coalesces rapid ROI drag events so at most one + // FFT runs per animation frame. The rAF yield lets the browser paint + // the ROI position update before the (potentially blocking) FFT runs. + const gen = ++fftGenRef.current; + + const doCompute = async () => { + // Yield to next animation frame — browser paints updated ROI first, + // and stale requests (from earlier drag events) are discarded below. + await new Promise(r => requestAnimationFrame(() => r())); + if (gen !== fftGenRef.current) return; + + const backend = gpuFFTRef.current && gpuReadyRef.current ? "WebGPU" : "CPU Worker"; + setFftComputing(true); + setFftProgress(`Computing FFT… (${backend})`); + const t0 = performance.now(); + const data = rawDataRef.current![selectedIdx]; + let fftW = width; + let fftH = height; + let inputData = data; + + // ROI crop: extract bounding box and optionally zero-mask outside radius + let origCropW = 0, origCropH = 0; + if (roiFftActive && roiList && roiSelectedIdx >= 0 && roiSelectedIdx < roiList.length) { + const roi = roiList[roiSelectedIdx]; + const crop = cropROIRegion(data, width, height, roi); + if (crop) { + origCropW = crop.cropW; + origCropH = crop.cropH; + // Apply Hann window to crop at native dimensions BEFORE zero-padding + if (fftWindow) applyHannWindow2D(crop.cropped, crop.cropW, crop.cropH); + // Pad to next power-of-2 so fft2d doesn't truncate frequency data + const padW = nextPow2(crop.cropW); + const padH = nextPow2(crop.cropH); + const padded = new Float32Array(padW * padH); + for (let y = 0; y < crop.cropH; y++) { + for (let x = 0; x < crop.cropW; x++) { + padded[y * padW + x] = crop.cropped[y * crop.cropW + x]; + } + } + inputData = padded; + fftW = padW; + fftH = padH; + } + } + + // Pre-pad non-power-of-2 full images so fft2d doesn't truncate frequency data + if (origCropW === 0) { + const padW = nextPow2(fftW); + const padH = nextPow2(fftH); + if (padW !== fftW || padH !== fftH) { + const padded = new Float32Array(padW * padH); + for (let y = 0; y < fftH; y++) { + for (let x = 0; x < fftW; x++) { + padded[y * padW + x] = inputData[y * fftW + x]; + } + } + inputData = padded; + fftW = padW; + fftH = padH; + } + } + + const tCrop = performance.now(); + const real = inputData.slice(); + const imag = new Float32Array(inputData.length); + + if (gpuFFTRef.current && gpuReadyRef.current) { + const result = await gpuFFTRef.current.fft2D(real, imag, fftW, fftH, false); + if (gen !== fftGenRef.current) return; + const tGpu = performance.now(); + fftshift(result.real, fftW, fftH); + fftshift(result.imag, fftW, fftH); + fftMagCacheRef.current = computeMagnitude(result.real, result.imag); + console.log(`[Show2D FFT] GPU ${fftW}×${fftH}: crop=${(tCrop-t0).toFixed(1)}ms gpu=${(tGpu-tCrop).toFixed(1)}ms post=${(performance.now()-tGpu).toFixed(1)}ms`); + } else { + // CPU fallback: run in Web Worker to avoid blocking the main thread + const result = await fft2dAsync(real, imag, fftW, fftH, false); + if (gen !== fftGenRef.current) return; + fftMagCacheRef.current = result.magnitude; + console.log(`[Show2D FFT] Worker ${fftW}×${fftH}: crop=${(tCrop-t0).toFixed(1)}ms worker=${(performance.now()-tCrop).toFixed(1)}ms`); + } + // Track FFT dimensions when they differ from image dimensions (ROI crop or non-pow2 padding) + if (origCropW > 0) { + setFftCropDims({ cropWidth: origCropW, cropHeight: origCropH, fftWidth: fftW, fftHeight: fftH }); + } else if (fftW !== width || fftH !== height) { + setFftCropDims({ cropWidth: width, cropHeight: height, fftWidth: fftW, fftHeight: fftH }); + } else { + setFftCropDims(null); + } + setFftMagVersion(v => v + 1); + setFftComputing(false); + setFftProgress(""); + }; + + doCompute(); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [effectiveShowFft, isGallery, selectedIdx, width, height, dataVersion, roiFftKey, fftWindow]); + + // Clear FFT measurement when image, FFT state, or ROI changes + React.useEffect(() => { setFftClickInfo(null); }, [selectedIdx, effectiveShowFft, roiFftActive, roiSelectedIdx]); + + // ------------------------------------------------------------------------- + // FFT data effect: normalize + colormap → cached offscreen canvas + // (does NOT depend on fftZoom/fftPanX/fftPanY — avoids reprocessing on zoom/pan) + // ------------------------------------------------------------------------- + React.useEffect(() => { + if (!effectiveShowFft || isGallery || !fftMagCacheRef.current) return; + + const fftMag = fftMagCacheRef.current; + const lut = COLORMAPS[fftColormap] || COLORMAPS.inferno; + + // Use crop dimensions when ROI FFT is active + const fftW = fftCropDims?.fftWidth ?? width; + const fftH = fftCropDims?.fftHeight ?? height; + + // Apply scale mode + const magnitude = new Float32Array(fftMag.length); + for (let i = 0; i < fftMag.length; i++) { + if (fftScaleMode === "log") { + magnitude[i] = Math.log1p(fftMag[i]); + } else if (fftScaleMode === "power") { + magnitude[i] = Math.pow(fftMag[i], 0.5); + } else { + magnitude[i] = fftMag[i]; + } + } + + let displayMin: number, displayMax: number; + if (fftAuto) { + ({ min: displayMin, max: displayMax } = autoEnhanceFFT(magnitude, fftW, fftH)); + } else { + ({ min: displayMin, max: displayMax } = findDataRange(magnitude)); + } + + const { mean, std } = computeStats(magnitude); + setFftStats([mean, displayMin, displayMax, std]); + + // Store histogram data + setFftHistogramData(magnitude.slice()); + setFftDataRange({ min: displayMin, max: displayMax }); + + // Apply histogram slider clipping and render to cached offscreen + const { vmin, vmax } = sliderRange(displayMin, displayMax, fftVminPct, fftVmaxPct); + const offscreen = renderToOffscreen(magnitude, fftW, fftH, lut, vmin, vmax); + if (!offscreen) return; + fftOffscreenRef.current = offscreen; + setFftOffscreenVersion(v => v + 1); + }, [effectiveShowFft, isGallery, fftMagVersion, fftVminPct, fftVmaxPct, fftColormap, fftScaleMode, fftAuto, width, height, fftCropDims]); + + // ------------------------------------------------------------------------- + // FFT draw effect: cheap drawImage from cached offscreen (zoom/pan changes) + // ------------------------------------------------------------------------- + React.useLayoutEffect(() => { + if (!effectiveShowFft || isGallery || !fftCanvasRef.current || !fftOffscreenRef.current) return; + + const canvas = fftCanvasRef.current; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const offscreen = fftOffscreenRef.current; + const fftW = offscreen.width; + const fftH = offscreen.height; + + // Use bilinear smoothing when FFT is smaller than canvas (avoids blocky upscaling) + ctx.imageSmoothingEnabled = fftW < canvasW || fftH < canvasH; + ctx.clearRect(0, 0, canvasW, canvasH); + ctx.save(); + + const centerOffsetX = (canvasW - canvasW * fftZoom) / 2 + fftPanX; + const centerOffsetY = (canvasH - canvasH * fftZoom) / 2 + fftPanY; + + ctx.translate(centerOffsetX, centerOffsetY); + ctx.scale(fftZoom, fftZoom); + // Stretch cropped FFT to fill the full canvas (no layout change during drag) + ctx.drawImage(offscreen, 0, 0, fftW, fftH, 0, 0, canvasW, canvasH); + ctx.restore(); + }, [effectiveShowFft, isGallery, fftOffscreenVersion, canvasW, canvasH, fftZoom, fftPanX, fftPanY]); + + // ------------------------------------------------------------------------- + // Render FFT overlay (scale bar + colorbar + d-spacing marker) + // ------------------------------------------------------------------------- + React.useEffect(() => { + const overlay = fftOverlayRef.current; + if (!overlay || !effectiveShowFft || isGallery) return; + const ctx = overlay.getContext("2d"); + if (!ctx) return; + ctx.clearRect(0, 0, overlay.width, overlay.height); + + // Use crop dimensions for reciprocal-space calculations + const fftW = fftCropDims?.fftWidth ?? width; + + // Reciprocal-space scale bar + if (pixelSize > 0) { + const fftPixelSize = 1 / (fftW * pixelSize); + drawFFTScaleBarHiDPI(overlay, DPR, fftZoom, fftPixelSize, fftW); + } + + // FFT colorbar + if (fftShowColorbar && fftDataRange.min !== fftDataRange.max) { + const { vmin, vmax } = sliderRange(fftDataRange.min, fftDataRange.max, fftVminPct, fftVmaxPct); + const lut = COLORMAPS[fftColormap] || COLORMAPS.inferno; + ctx.save(); + ctx.scale(DPR, DPR); + const cssW = overlay.width / DPR; + const cssH = overlay.height / DPR; + drawColorbar(ctx, cssW, cssH, lut, vmin, vmax, fftScaleMode === "log"); + ctx.restore(); + } + + // D-spacing crosshair marker — use crop dims for coordinate mapping + const fftH = fftCropDims?.fftHeight ?? height; + if (fftClickInfo) { + ctx.save(); + ctx.scale(DPR, DPR); + const centerOffsetX = (canvasW - canvasW * fftZoom) / 2 + fftPanX; + const centerOffsetY = (canvasH - canvasH * fftZoom) / 2 + fftPanY; + const screenX = centerOffsetX + fftZoom * (fftClickInfo.col / fftW * canvasW); + const screenY = centerOffsetY + fftZoom * (fftClickInfo.row / fftH * canvasH); + ctx.strokeStyle = "rgba(255, 255, 255, 0.9)"; + ctx.shadowColor = "rgba(0, 0, 0, 0.6)"; + ctx.shadowBlur = 2; + ctx.lineWidth = 1.5; + const r = 8; + ctx.beginPath(); + ctx.moveTo(screenX - r, screenY); ctx.lineTo(screenX - 3, screenY); + ctx.moveTo(screenX + 3, screenY); ctx.lineTo(screenX + r, screenY); + ctx.moveTo(screenX, screenY - r); ctx.lineTo(screenX, screenY - 3); + ctx.moveTo(screenX, screenY + 3); ctx.lineTo(screenX, screenY + r); + ctx.stroke(); + ctx.beginPath(); + ctx.arc(screenX, screenY, 4, 0, Math.PI * 2); + ctx.stroke(); + if (fftClickInfo.dSpacing != null) { + const d = fftClickInfo.dSpacing; + const label = d >= 10 ? `d = ${(d / 10).toFixed(2)} nm` : `d = ${d.toFixed(2)} Å`; + ctx.font = "bold 11px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = "white"; + ctx.textAlign = "left"; + ctx.textBaseline = "bottom"; + ctx.fillText(label, screenX + 10, screenY - 4); + } + ctx.restore(); + } + }, [effectiveShowFft, isGallery, fftClickInfo, canvasW, canvasH, fftZoom, fftPanX, fftPanY, width, height, pixelSize, fftDataRange, fftVminPct, fftVmaxPct, fftColormap, fftScaleMode, fftShowColorbar, fftCropDims]); + + // ------------------------------------------------------------------------- + // Compute FFT magnitudes for gallery mode (cache raw magnitudes) + // ------------------------------------------------------------------------- + React.useEffect(() => { + if (!effectiveShowFft || !isGallery || !rawDataRef.current) return; + if (rawDataRef.current.length === 0) return; + let cancelled = false; + + const computeAllFFTs = async () => { + // Initialize cache; preserve existing entries (only recompute missing) + if (fftMagCacheGalleryRef.current.length !== nImages) { + fftMagCacheGalleryRef.current = new Array(nImages).fill(null); + } + setFftComputing(true); + const useGPU = !!(gpuFFTRef.current && gpuReadyRef.current); + const backend = useGPU ? "WebGPU" : "CPU Worker"; + setFftProgress(`FFT (${backend})`); + await new Promise(r => requestAnimationFrame(() => r())); + if (cancelled) { setFftComputing(false); return; } + + const useRoiCrop = roiFftActive && roiList && roiSelectedIdx >= 0 && roiSelectedIdx < roiList.length; + const roi = useRoiCrop ? roiList[roiSelectedIdx] : null; + const t0 = performance.now(); + + // Helper: prep one image for FFT (crop, pad, window) + const prepOne = (idx: number): { real: Float32Array; imag: Float32Array; w: number; h: number } | null => { + const data = rawDataRef.current![idx]; + if (!data) return null; + let inputData = data; + let curW = width, curH = height; + if (roi) { + const crop = cropROIRegion(data, width, height, roi); + if (crop) { + if (fftWindow) applyHannWindow2D(crop.cropped, crop.cropW, crop.cropH); + const padW = nextPow2(crop.cropW), padH = nextPow2(crop.cropH); + const padded = new Float32Array(padW * padH); + for (let y = 0; y < crop.cropH; y++) + for (let x = 0; x < crop.cropW; x++) + padded[y * padW + x] = crop.cropped[y * crop.cropW + x]; + inputData = padded; curW = padW; curH = padH; + } + } else { + const padW = nextPow2(curW), padH = nextPow2(curH); + if (padW !== curW || padH !== curH) { + const padded = new Float32Array(padW * padH); + for (let y = 0; y < curH; y++) + for (let x = 0; x < curW; x++) + padded[y * padW + x] = inputData[y * curW + x]; + inputData = padded; curW = padW; curH = padH; + } + } + return { real: inputData.slice(), imag: new Float32Array(inputData.length), w: curW, h: curH }; + }; + + // ── Prep all images ── + const inputs: { real: Float32Array; imag: Float32Array }[] = []; + let fftW = width, fftH = height; + for (let idx = 0; idx < nImages; idx++) { + const input = prepOne(idx); + if (input) { + fftW = input.w; fftH = input.h; + inputs.push({ real: input.real, imag: input.imag }); + } else { + inputs.push({ real: new Float32Array(0), imag: new Float32Array(0) }); + } + } + galleryFftDimsRef.current = { w: fftW, h: fftH }; + const tPrep = performance.now() - t0; + if (cancelled) { setFftComputing(false); return; } + + // ── Batched progressive FFT: batch BATCH_SIZE at a time, display after each batch ── + const BATCH_SIZE = 4; + const tFFT0 = performance.now(); + for (let batchStart = 0; batchStart < nImages; batchStart += BATCH_SIZE) { + if (cancelled) { setFftComputing(false); return; } + const batchEnd = Math.min(batchStart + BATCH_SIZE, nImages); + const batchInputs = inputs.slice(batchStart, batchEnd).filter(inp => inp.real.length > 0); + setFftProgress(`FFT ${batchStart + 1}–${batchEnd}/${nImages} (${backend})`); + + if (useGPU && batchInputs.length > 1) { + // GPU batch: one submission for BATCH_SIZE images + const batchResults = await gpuFFTRef.current!.fft2DBatch(batchInputs, fftW, fftH); + if (cancelled) { setFftComputing(false); return; } + let ri = 0; + for (let idx = batchStart; idx < batchEnd; idx++) { + if (inputs[idx].real.length === 0) continue; + fftshift(batchResults[ri].real, fftW, fftH); + fftshift(batchResults[ri].imag, fftW, fftH); + fftMagCacheGalleryRef.current[idx] = computeMagnitude(batchResults[ri].real, batchResults[ri].imag); + ri++; + } + } else { + // CPU or single image + for (let idx = batchStart; idx < batchEnd; idx++) { + if (inputs[idx].real.length === 0) continue; + if (cancelled) { setFftComputing(false); return; } + const { real, imag } = inputs[idx]; + if (useGPU) { + const result = await gpuFFTRef.current!.fft2D(real, imag, fftW, fftH, false); + fftshift(result.real, fftW, fftH); + fftshift(result.imag, fftW, fftH); + fftMagCacheGalleryRef.current[idx] = computeMagnitude(result.real, result.imag); + } else { + fft2d(real, imag, fftW, fftH, false); + fftshift(real, fftW, fftH); + fftshift(imag, fftW, fftH); + fftMagCacheGalleryRef.current[idx] = computeMagnitude(real, imag); + } + } + } + // Show this batch immediately (progressive top-to-bottom) + setGalleryFftMagVersion(v => v + 1); + // Yield to let the browser paint the batch + await new Promise(r => requestAnimationFrame(() => r())); + } + const tFFT = performance.now() - tFFT0; + const tTotal = performance.now() - t0; + if (!cancelled) { + console.log(`[Show2D FFT] Gallery ${nImages}×${fftW}×${fftH}: prep=${tPrep.toFixed(0)}ms fft=${tFFT.toFixed(0)}ms total=${tTotal.toFixed(0)}ms (${backend} batch=${BATCH_SIZE})`); + } + setFftComputing(false); + setFftProgress(""); + }; + + computeAllFFTs(); + + return () => { cancelled = true; setFftComputing(false); }; + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [effectiveShowFft, isGallery, nImages, width, height, dataVersion, roiFftKey, fftWindow]); + + // Gallery FFT data effect: normalize + colormap → cached offscreen canvases + // (does NOT depend on gallery zoom/pan states) + const [galleryFftOffscreenVersion, setGalleryFftOffscreenVersion] = React.useState(0); + React.useEffect(() => { + if (!effectiveShowFft || !isGallery) return; + const lut = COLORMAPS[fftColormap] || COLORMAPS.inferno; + const fftW = galleryFftDimsRef.current?.w ?? width; + const fftH = galleryFftDimsRef.current?.h ?? height; + + for (let idx = 0; idx < nImages; idx++) { + const magnitude = fftMagCacheGalleryRef.current[idx]; + if (!magnitude) continue; + + // Apply scale transform (same logic as single mode) + let displayData: Float32Array; + let displayMin: number, displayMax: number; + if (fftScaleMode === "log") { + displayData = applyLogScale(magnitude); + } else if (fftScaleMode === "power") { + displayData = new Float32Array(magnitude.length); + for (let j = 0; j < magnitude.length; j++) displayData[j] = Math.sqrt(magnitude[j]); + } else { + displayData = magnitude; + } + if (fftAuto) { + ({ min: displayMin, max: displayMax } = autoEnhanceFFT(magnitude, fftW, fftH)); + if (fftScaleMode === "log") { displayMin = Math.log1p(displayMin); displayMax = Math.log1p(displayMax); } + else if (fftScaleMode === "power") { displayMin = Math.sqrt(displayMin); displayMax = Math.sqrt(displayMax); } + } else { + ({ min: displayMin, max: displayMax } = findDataRange(displayData)); + } + const { vmin, vmax } = sliderRange(displayMin, displayMax, fftVminPct, fftVmaxPct); + + const offscreen = renderToOffscreen(displayData, fftW, fftH, lut, vmin, vmax); + if (!offscreen) continue; + fftOffscreensRef.current[idx] = offscreen; + } + + // Update FFT histogram from selected image + const selMag = fftMagCacheGalleryRef.current[selectedIdx]; + if (selMag) { + let histData: Float32Array; + if (fftScaleMode === "log") histData = applyLogScale(selMag); + else if (fftScaleMode === "power") { histData = new Float32Array(selMag.length); for (let j = 0; j < selMag.length; j++) histData[j] = Math.sqrt(selMag[j]); } + else histData = selMag; + setFftHistogramData(histData); + setFftDataRange(findDataRange(histData)); + } + setGalleryFftOffscreenVersion(v => v + 1); + }, [effectiveShowFft, isGallery, nImages, width, height, galleryFftMagVersion, fftColormap, fftScaleMode, fftAuto, fftVminPct, fftVmaxPct, selectedIdx]); + + // Gallery FFT draw effect: cheap drawImage from cached offscreens (zoom/pan changes) + React.useLayoutEffect(() => { + if (!effectiveShowFft || !isGallery) return; + const fftW = galleryFftDimsRef.current?.w ?? width; + const fftH = galleryFftDimsRef.current?.h ?? height; + + for (let idx = 0; idx < nImages; idx++) { + const offscreen = fftOffscreensRef.current[idx]; + const canvas = fftCanvasRefs.current[idx]; + if (!offscreen || !canvas) continue; + const ctx = canvas.getContext("2d"); + if (!ctx) continue; + + const { zoom, panX, panY } = getGalleryFftState(idx); + ctx.imageSmoothingEnabled = fftW < canvasW || fftH < canvasH; + ctx.clearRect(0, 0, canvasW, canvasH); + ctx.save(); + const cx = canvasW / 2; + const cy = canvasH / 2; + ctx.translate(cx + panX, cy + panY); + ctx.scale(zoom, zoom); + ctx.translate(-cx, -cy); + ctx.drawImage(offscreen, 0, 0, fftW, fftH, 0, 0, canvasW, canvasH); + ctx.restore(); + } + }, [effectiveShowFft, isGallery, nImages, canvasW, canvasH, width, height, galleryFftOffscreenVersion, galleryFftStates, linkedZoom, linkedFftZoomState]); + + // ------------------------------------------------------------------------- + // Mouse Handlers for Zoom/Pan + // ------------------------------------------------------------------------- + const handleWheel = (e: React.WheelEvent, idx: number) => { + if (lockView) return; + // In gallery mode, only allow zoom on the selected image (unless linked) + if (isGallery && idx !== selectedIdx && !linkedZoom) return; + e.preventDefault(); // Prevent page scroll when zooming + + const canvas = canvasRefs.current[idx]; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + + // Get current zoom state + const zs = getZoomState(idx); + + // Mouse position relative to canvas (in canvas pixel coordinates) + const mouseCanvasX = (e.clientX - rect.left) * (canvas.width / rect.width); + const mouseCanvasY = (e.clientY - rect.top) * (canvas.height / rect.height); + + // Canvas center + const cx = canvas.width / 2; + const cy = canvas.height / 2; + + // Mouse position relative to the current view (accounting for pan and zoom) + // The transformation is: translate(cx + panX, cy + panY) -> scale(zoom) -> translate(-cx, -cy) + // So a point on screen at (screenX, screenY) maps to image space as: + // imageX = (screenX - cx - panX) / zoom + cx + const mouseImageX = (mouseCanvasX - cx - zs.panX) / zs.zoom + cx; + const mouseImageY = (mouseCanvasY - cy - zs.panY) / zs.zoom + cy; + + const zoomFactor = e.deltaY > 0 ? 0.9 : 1.1; + const newZoom = Math.max(MIN_ZOOM, Math.min(MAX_ZOOM, zs.zoom * zoomFactor)); + + // Calculate new pan to keep the mouse position fixed on the same image point + // After zoom: screenX = (imageX - cx) * newZoom + cx + newPanX + // We want screenX to stay at mouseCanvasX, so: + // newPanX = mouseCanvasX - (imageX - cx) * newZoom - cx + const newPanX = mouseCanvasX - (mouseImageX - cx) * newZoom - cx; + const newPanY = mouseCanvasY - (mouseImageY - cy) * newZoom - cy; + + setZoomState(idx, { zoom: newZoom, panX: newPanX, panY: newPanY }); + }; + + const handleDoubleClick = (idx: number) => { + if (lockView) return; + setZoomState(idx, initialZoomState); + }; + + // Reset view (zoom/pan only — preserves profile, FFT state, etc.) + const handleResetAll = () => { + if (lockView) return; + setZoomStates(new Map()); + setLinkedZoomState(initialZoomState); + setGalleryFftStates(new Map()); + setLinkedFftZoomState({ zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 }); + setFftZoom(DEFAULT_FFT_ZOOM); + setFftPanX(0); + setFftPanY(0); + }; + + // FFT zoom/pan handlers + const handleFftWheel = (e: React.WheelEvent) => { + if (lockView) return; + e.preventDefault(); // Prevent page scroll when zooming FFT + const zoomFactor = e.deltaY > 0 ? 0.9 : 1.1; + setFftZoom(Math.max(MIN_ZOOM, Math.min(MAX_ZOOM, fftZoom * zoomFactor))); + }; + + const handleFftDoubleClick = () => { + if (lockView) return; + setFftZoom(DEFAULT_FFT_ZOOM); + setFftPanX(0); + setFftPanY(0); + setFftClickInfo(null); + }; + + // Convert FFT canvas mouse position to FFT image pixel coordinates + const fftScreenToImg = (e: React.MouseEvent): { col: number; row: number } | null => { + const canvas = fftCanvasRef.current; + if (!canvas) return null; + const rect = canvas.getBoundingClientRect(); + const mouseX = e.clientX - rect.left; + const mouseY = e.clientY - rect.top; + const cOffX = (canvasW - canvasW * fftZoom) / 2 + fftPanX; + const cOffY = (canvasH - canvasH * fftZoom) / 2 + fftPanY; + const fftW = fftCropDims?.fftWidth ?? width; + const fftH = fftCropDims?.fftHeight ?? height; + const imgCol = ((mouseX - cOffX) / fftZoom) / canvasW * fftW; + const imgRow = ((mouseY - cOffY) / fftZoom) / canvasH * fftH; + if (imgCol >= 0 && imgCol < fftW && imgRow >= 0 && imgRow < fftH) { + return { col: imgCol, row: imgRow }; + } + return null; + }; + + const handleFftMouseDown = (e: React.MouseEvent) => { + if (lockView) return; + fftClickStartRef.current = { x: e.clientX, y: e.clientY }; + setIsDraggingFftPan(true); + setFftPanStart({ x: e.clientX, y: e.clientY, pX: fftPanX, pY: fftPanY }); + }; + + const handleFftMouseMove = (e: React.MouseEvent) => { + if (!isDraggingFftPan || !fftPanStart) return; + const dx = e.clientX - fftPanStart.x; + const dy = e.clientY - fftPanStart.y; + setFftPanX(fftPanStart.pX + dx); + setFftPanY(fftPanStart.pY + dy); + }; + + const handleFftMouseUp = (e: React.MouseEvent) => { + // Click detection for d-spacing measurement + if (fftClickStartRef.current) { + const dx = e.clientX - fftClickStartRef.current.x; + const dy = e.clientY - fftClickStartRef.current.y; + if (Math.sqrt(dx * dx + dy * dy) < 3) { + const pos = fftScreenToImg(e); + if (pos) { + // Use crop dimensions when ROI FFT is active + const fftW = fftCropDims?.fftWidth ?? width; + const fftH = fftCropDims?.fftHeight ?? height; + let imgCol = pos.col; + let imgRow = pos.row; + // Snap to nearest Bragg spot (local max in FFT magnitude) + if (fftMagCacheRef.current) { + const snapped = findFFTPeak(fftMagCacheRef.current, fftW, fftH, imgCol, imgRow, FFT_SNAP_RADIUS); + imgCol = snapped.col; + imgRow = snapped.row; + } + const halfW = Math.floor(fftW / 2); + const halfH = Math.floor(fftH / 2); + const dcol = imgCol - halfW; + const drow = imgRow - halfH; + const distPx = Math.sqrt(dcol * dcol + drow * drow); + if (distPx < 1) { + setFftClickInfo(null); + } else { + let spatialFreq: number | null = null; + let dSpacing: number | null = null; + if (pixelSize > 0) { + const paddedW = nextPow2(fftW); + const paddedH = nextPow2(fftH); + const binC = ((Math.round(imgCol) - halfW) % fftW + fftW) % fftW; + const binR = ((Math.round(imgRow) - halfH) % fftH + fftH) % fftH; + const freqC = binC <= paddedW / 2 ? binC / (paddedW * pixelSize) : (binC - paddedW) / (paddedW * pixelSize); + const freqR = binR <= paddedH / 2 ? binR / (paddedH * pixelSize) : (binR - paddedH) / (paddedH * pixelSize); + spatialFreq = Math.sqrt(freqC * freqC + freqR * freqR); + dSpacing = spatialFreq > 0 ? 1 / spatialFreq : null; + } + setFftClickInfo({ row: imgRow, col: imgCol, distPx, spatialFreq, dSpacing }); + } + } + } + fftClickStartRef.current = null; + } + setIsDraggingFftPan(false); + setFftPanStart(null); + }; + + const handleFftMouseLeave = () => { + fftClickStartRef.current = null; + setIsDraggingFftPan(false); + setFftPanStart(null); + }; + + // Gallery FFT zoom/pan handlers (only selected image's FFT responds) + const handleGalleryFftWheel = (e: React.WheelEvent, idx: number) => { + if (lockView) return; + if (isGallery && idx !== selectedIdx && !linkedZoom) return; + e.preventDefault(); // Prevent page scroll when zooming FFT + const zs = getGalleryFftState(idx); + const zoomFactor = e.deltaY > 0 ? 0.9 : 1.1; + setGalleryFftState(idx, { ...zs, zoom: Math.max(MIN_ZOOM, Math.min(MAX_ZOOM, zs.zoom * zoomFactor)) }); + }; + + const handleGalleryFftMouseDown = (e: React.MouseEvent, idx: number) => { + if (isGallery && idx !== selectedIdx) { + if (lockNavigation) return; + setSelectedIdx(idx); + return; // Select first, don't start panning + } + if (lockView) return; + const zs = getGalleryFftState(idx); + setFftPanningIdx(idx); + setIsDraggingFftPan(true); + setFftPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); + }; + + const handleGalleryFftMouseMove = (e: React.MouseEvent, idx: number) => { + if (!isDraggingFftPan || !fftPanStart || fftPanningIdx !== idx) return; + const dx = e.clientX - fftPanStart.x; + const dy = e.clientY - fftPanStart.y; + const zs = getGalleryFftState(idx); + setGalleryFftState(idx, { ...zs, panX: fftPanStart.pX + dx, panY: fftPanStart.pY + dy }); + }; + + const handleGalleryFftMouseUp = () => { + setIsDraggingFftPan(false); + setFftPanStart(null); + setFftPanningIdx(null); + }; + + // Track which image is being panned + const [panningIdx, setPanningIdx] = React.useState(null); + const clickStartRef = React.useRef<{ x: number; y: number } | null>(null); + const [draggingProfileEndpoint, setDraggingProfileEndpoint] = React.useState<0 | 1 | null>(null); + const [isDraggingProfileLine, setIsDraggingProfileLine] = React.useState(false); + const [hoveredProfileEndpoint, setHoveredProfileEndpoint] = React.useState<0 | 1 | null>(null); + const [isHoveringProfileLine, setIsHoveringProfileLine] = React.useState(false); + const profileDragStartRef = React.useRef<{ row: number; col: number; p0: { row: number; col: number }; p1: { row: number; col: number } } | null>(null); + + const screenToImg = (e: React.MouseEvent, idx: number): { imgCol: number; imgRow: number } => { + const canvas = canvasRefs.current[idx]; + if (!canvas) return { imgCol: 0, imgRow: 0 }; + const rect = canvas.getBoundingClientRect(); + const mouseCanvasX = (e.clientX - rect.left) * (canvas.width / rect.width); + const mouseCanvasY = (e.clientY - rect.top) * (canvas.height / rect.height); + const zs = getZoomState(idx); + const cx = canvasW / 2; + const cy = canvasH / 2; + return { + imgCol: ((mouseCanvasX - cx - zs.panX) / zs.zoom + cx) / displayScale, + imgRow: ((mouseCanvasY - cy - zs.panY) / zs.zoom + cy) / displayScale, + }; + }; + + const updateAllProfileData = (p0: { row: number; col: number }, p1: { row: number; col: number }) => { + if (!rawDataRef.current) return; + const allProfiles: (Float32Array | null)[] = []; + for (let j = 0; j < rawDataRef.current.length; j++) { + const raw = rawDataRef.current[j]; + allProfiles.push(raw ? sampleLineProfile(raw, width, height, p0.row, p0.col, p1.row, p1.col) : null); + } + setProfileDataAll(allProfiles); + }; + + const updateROI = (e: React.MouseEvent, idx: number) => { + const { imgCol, imgRow } = screenToImg(e, idx); + updateSelectedRoi({ col: Math.max(0, Math.min(width - 1, Math.floor(imgCol))), row: Math.max(0, Math.min(height - 1, Math.floor(imgRow))) }); + }; + + const hitTestROI = (imgCol: number, imgRow: number): number => { + if (!roiActive || !roiList) return -1; + for (let ri = roiList.length - 1; ri >= 0; ri--) { + const roi = roiList[ri]; + const shape = roi.shape || "circle"; + if (shape === "circle" || shape === "annular") { + if (Math.sqrt((imgCol - roi.col) ** 2 + (imgRow - roi.row) ** 2) <= roi.radius) return ri; + } else if (shape === "square") { + if (Math.abs(imgCol - roi.col) <= roi.radius && Math.abs(imgRow - roi.row) <= roi.radius) return ri; + } else if (shape === "rectangle") { + if (Math.abs(imgCol - roi.col) <= roi.width / 2 && Math.abs(imgRow - roi.row) <= roi.height / 2) return ri; + } + } + return -1; + }; + + const getHitArea = () => { + const zoom = (getZoomState(selectedIdx)).zoom; + return RESIZE_HIT_AREA_PX / (displayScale * zoom); + }; + + const isNearEdge = (imgCol: number, imgRow: number, roi: ROIItem): boolean => { + const hitArea = getHitArea(); + const shape = roi.shape || "circle"; + if (shape === "circle" || shape === "annular") { + const dist = Math.sqrt((imgCol - roi.col) ** 2 + (imgRow - roi.row) ** 2); + return Math.abs(dist - roi.radius) < hitArea; + } + if (shape === "square") { + const dx = Math.abs(imgCol - roi.col); + const dy = Math.abs(imgRow - roi.row); + const r = roi.radius; + return (dx <= r + hitArea && dy <= r + hitArea) && (Math.abs(dx - r) < hitArea || Math.abs(dy - r) < hitArea); + } + if (shape === "rectangle") { + const dx = Math.abs(imgCol - roi.col); + const dy = Math.abs(imgRow - roi.row); + const hw = roi.width / 2; + const hh = roi.height / 2; + return (dx <= hw + hitArea && dy <= hh + hitArea) && (Math.abs(dx - hw) < hitArea || Math.abs(dy - hh) < hitArea); + } + return false; + }; + + const isNearResizeHandle = (imgCol: number, imgRow: number): boolean => { + if (!roiActive || !selectedRoi) return false; + return isNearEdge(imgCol, imgRow, selectedRoi); + }; + + const isNearAnyEdge = (imgCol: number, imgRow: number): boolean => { + if (!roiActive || !roiList) return false; + return roiList.some(roi => isNearEdge(imgCol, imgRow, roi)); + }; + + const isNearResizeHandleInner = (imgCol: number, imgRow: number): boolean => { + if (!roiActive || !selectedRoi || selectedRoi.shape !== "annular") return false; + const hitArea = getHitArea(); + const dist = Math.sqrt((imgCol - selectedRoi.col) ** 2 + (imgRow - selectedRoi.row) ** 2); + return Math.abs(dist - selectedRoi.radius_inner) < hitArea; + }; + + const handleMouseDown = (e: React.MouseEvent, idx: number) => { + const zs = getZoomState(idx); + if (isGallery && idx !== selectedIdx) { + if (lockNavigation) return; + setSelectedIdx(idx); + // Continue to pan setup so click-drag on unselected panel pans immediately + // (no double-click required to select first then drag). + } + // Check if click is on the lens inset — edge = resize, interior = drag + if (!lockDisplay && showLens && !isGallery && idx === 0) { + const canvas = canvasRefs.current[0]; + if (canvas) { + const rect = canvas.getBoundingClientRect(); + const cssX = e.clientX - rect.left; + const cssY = e.clientY - rect.top; + const margin = 12; + const lx = lensAnchor ? lensAnchor.x : margin; + const ly = lensAnchor ? lensAnchor.y : canvasH - lensDisplaySize - margin - 20; + if (cssX >= lx && cssX <= lx + lensDisplaySize && cssY >= ly && cssY <= ly + lensDisplaySize) { + const edgeHit = 8; + const nearEdge = cssX - lx < edgeHit || lx + lensDisplaySize - cssX < edgeHit || cssY - ly < edgeHit || ly + lensDisplaySize - cssY < edgeHit; + if (nearEdge) { + setIsResizingLens(true); + lensResizeStartRef.current = { my: e.clientY, startSize: lensDisplaySize }; + } else { + setIsDraggingLens(true); + lensDragStartRef.current = { mx: e.clientX, my: e.clientY, ax: lx, ay: ly }; + } + e.preventDefault(); + return; + } + } + } + clickStartRef.current = { x: e.clientX, y: e.clientY }; + if (profileActive && !lockProfile) { + const { imgCol, imgRow } = screenToImg(e, idx); + if (profilePoints.length === 2) { + const p0 = profilePoints[0]; + const p1 = profilePoints[1]; + const hitRadius = 10 / (displayScale * zs.zoom); + const d0 = Math.sqrt((imgCol - p0.col) ** 2 + (imgRow - p0.row) ** 2); + const d1 = Math.sqrt((imgCol - p1.col) ** 2 + (imgRow - p1.row) ** 2); + if (d0 <= hitRadius || d1 <= hitRadius) { + setDraggingProfileEndpoint(d0 <= d1 ? 0 : 1); + setIsDraggingPan(false); + setPanStart(null); + setPanningIdx(null); + return; + } + if (pointToSegmentDistance(imgCol, imgRow, p0.col, p0.row, p1.col, p1.row) <= hitRadius) { + setIsDraggingProfileLine(true); + profileDragStartRef.current = { + row: imgRow, + col: imgCol, + p0: { row: p0.row, col: p0.col }, + p1: { row: p1.row, col: p1.col }, + }; + setIsDraggingPan(false); + setPanStart(null); + setPanningIdx(null); + return; + } + } + if (!lockView) { + setIsDraggingPan(true); + setPanningIdx(idx); + setPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); + } + return; + } + if (roiActive) { + if (lockRoi) { + if (!lockView) { + setIsDraggingPan(true); + setPanningIdx(idx); + setPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); + } + return; + } + const { imgCol, imgRow } = screenToImg(e, idx); + // Check resize handles on selected ROI first + if (isNearResizeHandleInner(imgCol, imgRow)) { + setIsDraggingResizeInner(true); + return; + } + if (isNearResizeHandle(imgCol, imgRow)) { + e.preventDefault(); + resizeAspectRef.current = selectedRoi && (selectedRoi.shape === "rectangle") && selectedRoi.width > 0 && selectedRoi.height > 0 ? selectedRoi.width / selectedRoi.height : null; + setIsDraggingResize(true); + return; + } + // Check edge of any ROI — auto-select and start resize + if (roiList) { + for (let ri = 0; ri < roiList.length; ri++) { + if (isNearEdge(imgCol, imgRow, roiList[ri])) { + e.preventDefault(); + const roi = roiList[ri]; + resizeAspectRef.current = roi && (roi.shape === "rectangle") && roi.width > 0 && roi.height > 0 ? roi.width / roi.height : null; + setRoiSelectedIdx(ri); + setIsDraggingResize(true); + return; + } + } + } + // Hit-test existing ROIs (click inside to select + drag) + const hitIdx = hitTestROI(imgCol, imgRow); + if (hitIdx >= 0) { + setRoiSelectedIdx(hitIdx); + setIsDraggingROI(true); + return; + } + // Click on empty space — deselect and allow panning + setRoiSelectedIdx(-1); + } + // Start panning (works in both ROI-active and normal modes) + { + if (lockView) return; + setIsDraggingPan(true); + setPanningIdx(idx); + setPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); + } + }; + + const handleMouseMove = (e: React.MouseEvent, idx: number) => { + // Fast path: during pan drag, skip all cursor/hover/lens work — just update pan + if (isDraggingPan && panStart && panningIdx !== null && !lockView) { + const canvas = canvasRefs.current[idx]; + if (!canvas || idx !== panningIdx) return; + const rect = canvas.getBoundingClientRect(); + const scaleX = canvas.width / rect.width; + const scaleY = canvas.height / rect.height; + const dx = (e.clientX - panStart.x) * scaleX; + const dy = (e.clientY - panStart.y) * scaleY; + const zs = getZoomState(idx); + setZoomState(idx, { ...zs, panX: panStart.pX + dx, panY: panStart.pY + dy }); + return; + } + + // Cursor readout: convert screen position to image pixel coordinates + const canvas = canvasRefs.current[idx]; + if (canvas && rawDataRef.current) { + const rect = canvas.getBoundingClientRect(); + const mouseCanvasX = (e.clientX - rect.left) * (canvas.width / rect.width); + const mouseCanvasY = (e.clientY - rect.top) * (canvas.height / rect.height); + const zs = getZoomState(idx); + const cx = canvasW / 2; + const cy = canvasH / 2; + const imageCanvasX = (mouseCanvasX - cx - zs.panX) / zs.zoom + cx; + const imageCanvasY = (mouseCanvasY - cy - zs.panY) / zs.zoom + cy; + const imgX = Math.floor(imageCanvasX / displayScale); + const imgY = Math.floor(imageCanvasY / displayScale); + if (imgX >= 0 && imgX < width && imgY >= 0 && imgY < height) { + const rawData = rawDataRef.current[idx]; + if (rawData) setCursorInfo({ row: imgY, col: imgX, value: rawData[imgY * width + imgX] }); + if (!lockDisplay && showLens && !isGallery) setLensPos({ row: imgY, col: imgX }); + } else { + setCursorInfo(null); + // Don't clear lensPos — lens stays at last position when toggle is on + } + } + + // Lens drag + if (!lockDisplay && isDraggingLens && lensDragStartRef.current) { + const dx = e.clientX - lensDragStartRef.current.mx; + const dy = e.clientY - lensDragStartRef.current.my; + setLensAnchor({ x: lensDragStartRef.current.ax + dx, y: lensDragStartRef.current.ay + dy }); + return; + } + // Lens resize drag + if (!lockDisplay && isResizingLens && lensResizeStartRef.current) { + const dy = e.clientY - lensResizeStartRef.current.my; + setLensDisplaySize(Math.max(64, Math.min(256, lensResizeStartRef.current.startSize + dy))); + return; + } + + if (profileActive && !lockProfile && profilePoints.length === 2) { + const { imgCol, imgRow } = screenToImg(e, idx); + const p0 = profilePoints[0]; + const p1 = profilePoints[1]; + const activeZoom = linkedZoom ? linkedZoomState.zoom : (zoomStates.get(idx) || initialZoomState).zoom; + const hitRadius = 10 / (displayScale * activeZoom); + const d0 = Math.sqrt((imgCol - p0.col) ** 2 + (imgRow - p0.row) ** 2); + const d1 = Math.sqrt((imgCol - p1.col) ** 2 + (imgRow - p1.row) ** 2); + if (draggingProfileEndpoint !== null) { + const clampedRow = Math.max(0, Math.min(height - 1, imgRow)); + const clampedCol = Math.max(0, Math.min(width - 1, imgCol)); + const next = [ + draggingProfileEndpoint === 0 ? { row: clampedRow, col: clampedCol } : profilePoints[0], + draggingProfileEndpoint === 1 ? { row: clampedRow, col: clampedCol } : profilePoints[1], + ]; + setProfilePoints(next); + updateAllProfileData(next[0], next[1]); + return; + } + if (isDraggingProfileLine && profileDragStartRef.current) { + const drag = profileDragStartRef.current; + let deltaRow = imgRow - drag.row; + let deltaCol = imgCol - drag.col; + const minRow = Math.min(drag.p0.row, drag.p1.row); + const maxRow = Math.max(drag.p0.row, drag.p1.row); + const minCol = Math.min(drag.p0.col, drag.p1.col); + const maxCol = Math.max(drag.p0.col, drag.p1.col); + deltaRow = Math.max(deltaRow, -minRow); + deltaRow = Math.min(deltaRow, (height - 1) - maxRow); + deltaCol = Math.max(deltaCol, -minCol); + deltaCol = Math.min(deltaCol, (width - 1) - maxCol); + const next = [ + { row: drag.p0.row + deltaRow, col: drag.p0.col + deltaCol }, + { row: drag.p1.row + deltaRow, col: drag.p1.col + deltaCol }, + ]; + setProfilePoints(next); + updateAllProfileData(next[0], next[1]); + return; + } + const nextHoveredEndpoint: 0 | 1 | null = d0 <= hitRadius ? 0 : d1 <= hitRadius ? 1 : null; + const nextHoverLine = nextHoveredEndpoint === null && pointToSegmentDistance(imgCol, imgRow, p0.col, p0.row, p1.col, p1.row) <= hitRadius; + setHoveredProfileEndpoint(nextHoveredEndpoint); + setIsHoveringProfileLine(nextHoverLine); + } else { + if (hoveredProfileEndpoint !== null) setHoveredProfileEndpoint(null); + if (isHoveringProfileLine) setIsHoveringProfileLine(false); + } + + // ROI resize drag (inner annular ring) + if (!lockRoi && isDraggingResizeInner && selectedRoi) { + const { imgCol: ic, imgRow: ir } = screenToImg(e, idx); + const newR = Math.sqrt((ic - selectedRoi.col) ** 2 + (ir - selectedRoi.row) ** 2); + updateSelectedRoi({ radius_inner: Math.max(1, Math.min(selectedRoi.radius - 1, Math.round(newR))) }); + return; + } + // ROI resize drag (outer) + if (!lockRoi && isDraggingResize && selectedRoi) { + const { imgCol: ic, imgRow: ir } = screenToImg(e, idx); + const shape = selectedRoi.shape || "circle"; + if (shape === "rectangle") { + let newW = Math.max(2, Math.round(Math.abs(ic - selectedRoi.col) * 2)); + let newH = Math.max(2, Math.round(Math.abs(ir - selectedRoi.row) * 2)); + if (e.shiftKey && resizeAspectRef.current != null) { + const aspect = resizeAspectRef.current; + if (newW / newH > aspect) newH = Math.max(2, Math.round(newW / aspect)); + else newW = Math.max(2, Math.round(newH * aspect)); + } + updateSelectedRoi({ width: newW, height: newH }); + } else { + const newR = shape === "square" ? Math.max(Math.abs(ic - selectedRoi.col), Math.abs(ir - selectedRoi.row)) : Math.sqrt((ic - selectedRoi.col) ** 2 + (ir - selectedRoi.row) ** 2); + const minR = shape === "annular" ? selectedRoi.radius_inner + 1 : 1; + updateSelectedRoi({ radius: Math.max(minR, Math.round(newR)) }); + } + return; + } + // ROI drag (move center) + if (!lockRoi && isDraggingROI) { + updateROI(e, idx); + return; + } + // Lens edge hover detection + if (!lockDisplay && showLens && !isGallery && canvas) { + const rect = canvas.getBoundingClientRect(); + const cssX = e.clientX - rect.left; + const cssY = e.clientY - rect.top; + const margin = 12; + const lx = lensAnchor ? lensAnchor.x : margin; + const ly = lensAnchor ? lensAnchor.y : canvasH - lensDisplaySize - margin - 20; + const inside = cssX >= lx && cssX <= lx + lensDisplaySize && cssY >= ly && cssY <= ly + lensDisplaySize; + const edgeHit = 8; + const nearEdge = inside && (cssX - lx < edgeHit || lx + lensDisplaySize - cssX < edgeHit || cssY - ly < edgeHit || ly + lensDisplaySize - cssY < edgeHit); + setIsHoveringLensEdge(nearEdge); + } else { + setIsHoveringLensEdge(false); + } + // Hover detection for resize handles (show cursor on any ROI edge) + if (roiActive && !lockRoi && !isDraggingPan) { + const { imgCol: ic, imgRow: ir } = screenToImg(e, idx); + setIsHoveringResizeInner(isNearResizeHandleInner(ic, ir)); + setIsHoveringResize(isNearAnyEdge(ic, ir)); + } + + // Panning + if (lockView) return; + if (!isDraggingPan || !panStart || panningIdx === null) return; + if (idx !== panningIdx) return; + if (!canvas) return; + const rect2 = canvas.getBoundingClientRect(); + const scaleX = canvas.width / rect2.width; + const scaleY = canvas.height / rect2.height; + const dx = (e.clientX - panStart.x) * scaleX; + const dy = (e.clientY - panStart.y) * scaleY; + + const zs = getZoomState(idx); + setZoomState(idx, { ...zs, panX: panStart.pX + dx, panY: panStart.pY + dy }); + }; + + const handleMouseUp = (e: React.MouseEvent, idx: number) => { + if (isDraggingLens) { + setIsDraggingLens(false); + lensDragStartRef.current = null; + return; + } + if (isResizingLens) { + setIsResizingLens(false); + lensResizeStartRef.current = null; + return; + } + if (draggingProfileEndpoint !== null || isDraggingProfileLine) { + setDraggingProfileEndpoint(null); + setIsDraggingProfileLine(false); + profileDragStartRef.current = null; + clickStartRef.current = null; + setIsDraggingROI(false); + setIsDraggingResize(false); + setIsDraggingResizeInner(false); + setIsDraggingPan(false); + setPanStart(null); + setPanningIdx(null); + setHoveredProfileEndpoint(null); + setIsHoveringProfileLine(false); + return; + } + // Detect click (vs drag) for profile mode + if (profileActive && !lockProfile && clickStartRef.current) { + const dx = e.clientX - clickStartRef.current.x; + const dy = e.clientY - clickStartRef.current.y; + if (Math.sqrt(dx * dx + dy * dy) < 3) { + // It's a click — compute image coordinates + const canvas = canvasRefs.current[idx]; + if (canvas && rawDataRef.current) { + const rect = canvas.getBoundingClientRect(); + const mouseCanvasX = (e.clientX - rect.left) * (canvas.width / rect.width); + const mouseCanvasY = (e.clientY - rect.top) * (canvas.height / rect.height); + const zs = getZoomState(idx); + const cx = canvasW / 2; + const cy = canvasH / 2; + const imgX = ((mouseCanvasX - cx - zs.panX) / zs.zoom + cx) / displayScale; + const imgY = ((mouseCanvasY - cy - zs.panY) / zs.zoom + cy) / displayScale; + if (imgX >= 0 && imgX < width && imgY >= 0 && imgY < height) { + const pt = { row: imgY, col: imgX }; + if (profilePoints.length === 0 || profilePoints.length === 2) { + // Start new line + setProfilePoints([pt]); + setProfileDataAll([]); + } else { + // Complete the line + const p0 = profilePoints[0]; + setProfilePoints([p0, pt]); + updateAllProfileData(p0, pt); + } + } + } + } + } + // Detect click for measurement mode (only when profile is not active) + if (measureActive && !profileActive && clickStartRef.current) { + const dx = e.clientX - clickStartRef.current.x; + const dy = e.clientY - clickStartRef.current.y; + if (Math.sqrt(dx * dx + dy * dy) < 3) { + const canvas = canvasRefs.current[idx]; + if (canvas) { + const rect = canvas.getBoundingClientRect(); + const mouseCanvasX = (e.clientX - rect.left) * (canvas.width / rect.width); + const mouseCanvasY = (e.clientY - rect.top) * (canvas.height / rect.height); + const zs = getZoomState(idx); + const cx = canvasW / 2; + const cy = canvasH / 2; + const imgX = ((mouseCanvasX - cx - zs.panX) / zs.zoom + cx) / displayScale; + const imgY = ((mouseCanvasY - cy - zs.panY) / zs.zoom + cy) / displayScale; + if (imgX >= 0 && imgX < width && imgY >= 0 && imgY < height) { + const pt = { row: imgY, col: imgX }; + if (measurePoints.length < 2) { + setMeasurePoints([...measurePoints, pt]); + } else { + setMeasurePoints([pt]); + } + } + } + } + } + clickStartRef.current = null; + setDraggingProfileEndpoint(null); + setIsDraggingProfileLine(false); + profileDragStartRef.current = null; + setIsDraggingROI(false); + setIsDraggingResize(false); + setIsDraggingResizeInner(false); + setIsDraggingPan(false); + setPanStart(null); + setPanningIdx(null); + setHoveredProfileEndpoint(null); + setIsHoveringProfileLine(false); + }; + + const handleMouseLeave = (idx: number) => { + setCursorInfo(null); + // Don't clear lensPos — lens stays at last position when toggle is on + setIsDraggingLens(false); + setIsResizingLens(false); + lensDragStartRef.current = null; + lensResizeStartRef.current = null; + setIsHoveringLensEdge(false); + setIsDraggingROI(false); + setIsDraggingResize(false); + setIsDraggingResizeInner(false); + setDraggingProfileEndpoint(null); + setIsDraggingProfileLine(false); + setHoveredProfileEndpoint(null); + setIsHoveringProfileLine(false); + profileDragStartRef.current = null; + setIsHoveringResize(false); + setIsHoveringResizeInner(false); + if (panningIdx === idx) { + setIsDraggingPan(false); + setPanStart(null); + setPanningIdx(null); + } + }; + + // ------------------------------------------------------------------------- + // Copy to clipboard handler + const handleCopy = React.useCallback(async () => { + if (lockExport) return; + const canvas = canvasRefs.current[isGallery ? selectedIdx : 0]; + if (!canvas) return; + try { + const blob = await new Promise(resolve => canvas.toBlob(resolve, "image/png")); + if (!blob) return; + await navigator.clipboard.write([new ClipboardItem({ "image/png": blob })]); + } catch { + // Fallback: download if clipboard API unavailable + canvas.toBlob((b) => { if (b) downloadBlob(b, `show2d_${labels?.[selectedIdx] || "image"}.png`); }, "image/png"); + } + }, [isGallery, selectedIdx, labels, lockExport]); + + // Export publication-quality figure with scale bar, colorbar, annotations + const handleExportFigure = React.useCallback((withScaleBar: boolean, withColorbar: boolean) => { + if (lockExport) return; + setExportAnchor(null); + const idx = isGallery ? selectedIdx : 0; + const rawData = rawDataRef.current?.[idx]; + if (!rawData) return; + + const processed = logScale ? applyLogScale(rawData) : rawData; + const lut = COLORMAPS[cmap] || COLORMAPS.inferno; + + let vmin: number, vmax: number; + const hasAbsRange = traitVmin != null && traitVmax != null; + const rMin = hasAbsRange ? (logScale ? Math.log1p(Math.max(traitVmin!, 0)) : traitVmin!) : imageDataRange.min; + const rMax = hasAbsRange ? (logScale ? Math.log1p(Math.max(traitVmax!, 0)) : traitVmax!) : imageDataRange.max; + if (rMin !== rMax && (imageVminPct > 0 || imageVmaxPct < 100)) { + ({ vmin, vmax } = sliderRange(rMin, rMax, imageVminPct, imageVmaxPct)); + } else if (!hasAbsRange && autoContrast) { + ({ vmin, vmax } = percentileClip(processed, 2, 98)); + } else { + vmin = rMin; + vmax = rMax; + } + + const offscreen = renderToOffscreen(processed, width, height, lut, vmin, vmax); + if (!offscreen) return; + + const figCanvas = exportFigure({ + imageCanvas: offscreen, + title: title || undefined, + lut, + vmin, + vmax, + logScale, + pixelSize: pixelSize > 0 ? pixelSize : undefined, + showColorbar: withColorbar, + showScaleBar: withScaleBar && pixelSize > 0, + drawAnnotations: (ctx) => { + // ROI highlight mask + if (roiActive && roiList) { + const hlRois = roiList.filter(r => r.highlight); + if (hlRois.length > 0) { + ctx.save(); + ctx.fillStyle = "rgba(0,0,0,0.6)"; + ctx.fillRect(0, 0, width, height); + ctx.globalCompositeOperation = "destination-out"; + for (const roi of hlRois) { + ctx.fillStyle = "rgba(0,0,0,1)"; + const shape = roi.shape || "circle"; + if (shape === "circle") { ctx.beginPath(); ctx.arc(roi.col, roi.row, roi.radius, 0, Math.PI * 2); ctx.fill(); } + else if (shape === "square") { ctx.fillRect(roi.col - roi.radius, roi.row - roi.radius, roi.radius * 2, roi.radius * 2); } + else if (shape === "rectangle") { ctx.fillRect(roi.col - roi.width / 2, roi.row - roi.height / 2, roi.width, roi.height); } + else if (shape === "annular") { + ctx.beginPath(); ctx.arc(roi.col, roi.row, roi.radius, 0, Math.PI * 2); ctx.fill(); + ctx.globalCompositeOperation = "source-over"; + ctx.fillStyle = "rgba(0,0,0,0.6)"; + ctx.beginPath(); ctx.arc(roi.col, roi.row, roi.radius_inner, 0, Math.PI * 2); ctx.fill(); + ctx.globalCompositeOperation = "destination-out"; + } + } + ctx.restore(); + } + // ROI outlines + for (const roi of roiList) { + const shape = (roi.shape || "circle") as "circle" | "square" | "rectangle" | "annular"; + ctx.lineWidth = roi.line_width || 2; + drawROI(ctx, roi.col, roi.row, shape, roi.radius, roi.width, roi.height, roi.color, roi.color, false, roi.radius_inner); + } + } + // Profile line + if (profileActive && profilePoints.length === 2) { + ctx.strokeStyle = "#4fc3f7"; + ctx.lineWidth = 2; + ctx.setLineDash([4, 3]); + ctx.beginPath(); + ctx.moveTo(profilePoints[0].col, profilePoints[0].row); + ctx.lineTo(profilePoints[1].col, profilePoints[1].row); + ctx.stroke(); + ctx.setLineDash([]); + ctx.fillStyle = "#4fc3f7"; + ctx.beginPath(); + ctx.arc(profilePoints[0].col, profilePoints[0].row, 3, 0, Math.PI * 2); + ctx.fill(); + ctx.beginPath(); + ctx.arc(profilePoints[1].col, profilePoints[1].row, 3, 0, Math.PI * 2); + ctx.fill(); + } + }, + }); + + canvasToPDF(figCanvas).then((blob) => downloadBlob(blob, `show2d_figure_${labels?.[selectedIdx] || "image"}.pdf`)); + }, [isGallery, selectedIdx, labels, width, height, cmap, logScale, autoContrast, imageDataRange, imageVminPct, imageVmaxPct, pixelSize, title, roiActive, roiList, profileActive, profilePoints, lockExport]); + + // Export all variants (PNG + PDF) as zip + const handleExportAll = React.useCallback(async () => { + if (lockExport) return; + setExportAnchor(null); + const idx = isGallery ? selectedIdx : 0; + const rawData = rawDataRef.current?.[idx]; + if (!rawData) return; + + const processed = logScale ? applyLogScale(rawData) : rawData; + const lut = COLORMAPS[cmap] || COLORMAPS.inferno; + + let vmin: number, vmax: number; + const hasAbsRange2 = traitVmin != null && traitVmax != null; + const rMin2 = hasAbsRange2 ? (logScale ? Math.log1p(Math.max(traitVmin!, 0)) : traitVmin!) : imageDataRange.min; + const rMax2 = hasAbsRange2 ? (logScale ? Math.log1p(Math.max(traitVmax!, 0)) : traitVmax!) : imageDataRange.max; + if (rMin2 !== rMax2 && (imageVminPct > 0 || imageVmaxPct < 100)) { + ({ vmin, vmax } = sliderRange(rMin2, rMax2, imageVminPct, imageVmaxPct)); + } else if (!hasAbsRange2 && autoContrast) { + ({ vmin, vmax } = percentileClip(processed, 2, 98)); + } else { + vmin = rMin2; + vmax = rMax2; + } + + const offscreen = renderToOffscreen(processed, width, height, lut, vmin, vmax); + if (!offscreen) return; + + const drawAnnotations = (ctx: CanvasRenderingContext2D) => { + if (roiActive && roiList) { + const hlRois = roiList.filter(r => r.highlight); + if (hlRois.length > 0) { + ctx.save(); + ctx.fillStyle = "rgba(0,0,0,0.6)"; + ctx.fillRect(0, 0, width, height); + ctx.globalCompositeOperation = "destination-out"; + for (const roi of hlRois) { + ctx.fillStyle = "rgba(0,0,0,1)"; + const shape = roi.shape || "circle"; + if (shape === "circle") { ctx.beginPath(); ctx.arc(roi.col, roi.row, roi.radius, 0, Math.PI * 2); ctx.fill(); } + else if (shape === "square") { ctx.fillRect(roi.col - roi.radius, roi.row - roi.radius, roi.radius * 2, roi.radius * 2); } + else if (shape === "rectangle") { ctx.fillRect(roi.col - roi.width / 2, roi.row - roi.height / 2, roi.width, roi.height); } + else if (shape === "annular") { + ctx.beginPath(); ctx.arc(roi.col, roi.row, roi.radius, 0, Math.PI * 2); ctx.fill(); + ctx.globalCompositeOperation = "source-over"; + ctx.fillStyle = "rgba(0,0,0,0.6)"; + ctx.beginPath(); ctx.arc(roi.col, roi.row, roi.radius_inner, 0, Math.PI * 2); ctx.fill(); + ctx.globalCompositeOperation = "destination-out"; + } + } + ctx.restore(); + for (const roi of roiList) { + const shape = (roi.shape || "circle") as "circle" | "square" | "rectangle" | "annular"; + ctx.lineWidth = roi.line_width || 2; + drawROI(ctx, roi.col, roi.row, shape, roi.radius, roi.width, roi.height, roi.color, roi.color, false, roi.radius_inner); + } + } + } + if (profileActive && profilePoints.length === 2) { + ctx.strokeStyle = "#4fc3f7"; + ctx.lineWidth = 2; + ctx.setLineDash([4, 3]); + ctx.beginPath(); + ctx.moveTo(profilePoints[0].col, profilePoints[0].row); + ctx.lineTo(profilePoints[1].col, profilePoints[1].row); + ctx.stroke(); + ctx.setLineDash([]); + ctx.fillStyle = "#4fc3f7"; + ctx.beginPath(); ctx.arc(profilePoints[0].col, profilePoints[0].row, 3, 0, Math.PI * 2); ctx.fill(); + ctx.beginPath(); ctx.arc(profilePoints[1].col, profilePoints[1].row, 3, 0, Math.PI * 2); ctx.fill(); + } + }; + + const hasScale = pixelSize > 0; + const baseOpts = { + imageCanvas: offscreen, + title: title || undefined, + lut, + vmin, + vmax, + logScale, + pixelSize: hasScale ? pixelSize : undefined, + drawAnnotations, + }; + + const variants: { name: string; showScaleBar: boolean; showColorbar: boolean }[] = [ + { name: "figure", showScaleBar: false, showColorbar: false }, + { name: "figure_scalebar", showScaleBar: true, showColorbar: false }, + { name: "figure_scalebar_colorbar", showScaleBar: true, showColorbar: true }, + ]; + + const zip = new JSZip(); + const prefix = `show2d_${labels?.[selectedIdx] || "image"}`; + const metadata = { + metadata_version: "1.0", + widget_name: "Show2D", + widget_version: widgetVersion || "unknown", + exported_at: new Date().toISOString(), + format: "zip", + export_kind: "figure_variants", + selected_idx: idx, + image_shape: { rows: height, cols: width }, + display: { + cmap, + log_scale: logScale, + auto_contrast: autoContrast, + vmin_pct: imageVminPct, + vmax_pct: imageVmaxPct, + }, + variants, + }; + zip.file("metadata.json", JSON.stringify(metadata, null, 2)); + + for (const v of variants) { + const figCanvas = exportFigure({ ...baseOpts, showScaleBar: v.showScaleBar && hasScale, showColorbar: v.showColorbar }); + const pngBlob = await new Promise((resolve) => figCanvas.toBlob((b) => resolve(b!), "image/png")); + zip.file(`${prefix}_${v.name}.png`, pngBlob); + const pdfBlob = await canvasToPDF(figCanvas); + zip.file(`${prefix}_${v.name}.pdf`, pdfBlob); + } + + const blob = await zip.generateAsync({ type: "blob" }); + downloadBlob(blob, `${prefix}_all.zip`); + }, [isGallery, selectedIdx, labels, width, height, cmap, logScale, autoContrast, imageDataRange, imageVminPct, imageVmaxPct, pixelSize, title, roiActive, roiList, profileActive, profilePoints, widgetVersion, lockExport]); + + // Resize Handlers + // ------------------------------------------------------------------------- + const handleCanvasResizeStart = (e: React.MouseEvent) => { + if (lockView) return; + e.stopPropagation(); + e.preventDefault(); + setIsResizingCanvas(true); + setResizeStart({ x: e.clientX, y: e.clientY, size: canvasSize }); + }; + + React.useEffect(() => { + if (!isResizingCanvas) return; + let rafId = 0; + let latestSize = resizeStart ? resizeStart.size : canvasSize; + + const handleMouseMove = (e: MouseEvent) => { + if (!resizeStart) return; + const delta = Math.max(e.clientX - resizeStart.x, e.clientY - resizeStart.y); + latestSize = Math.max(200, resizeStart.size + delta); + if (!rafId) { + rafId = requestAnimationFrame(() => { + rafId = 0; + setCanvasSize(latestSize); + }); + } + }; + + const handleMouseUp = () => { + cancelAnimationFrame(rafId); + setCanvasSize(latestSize); + setIsResizingCanvas(false); + setResizeStart(null); + }; + + document.addEventListener("mousemove", handleMouseMove); + document.addEventListener("mouseup", handleMouseUp); + return () => { + cancelAnimationFrame(rafId); + document.removeEventListener("mousemove", handleMouseMove); + document.removeEventListener("mouseup", handleMouseUp); + }; + }, [isResizingCanvas, resizeStart]); + + // Profile height resize + React.useEffect(() => { + if (!isResizingProfile) return; + const handleMouseMove = (e: MouseEvent) => { + if (!profileResizeStart) return; + const delta = e.clientY - profileResizeStart.y; + setProfileHeight(Math.max(40, Math.min(300, profileResizeStart.height + delta))); + }; + const handleMouseUp = () => { + setIsResizingProfile(false); + setProfileResizeStart(null); + }; + document.addEventListener("mousemove", handleMouseMove); + document.addEventListener("mouseup", handleMouseUp); + return () => { + document.removeEventListener("mousemove", handleMouseMove); + document.removeEventListener("mouseup", handleMouseUp); + }; + }, [isResizingProfile, profileResizeStart]); + + // ------------------------------------------------------------------------- + // Keyboard shortcuts + // ------------------------------------------------------------------------- + const handleKeyDown = (e: React.KeyboardEvent) => { + // Number keys 1-9 select gallery images (avoids arrow key conflicts with Jupyter) + if (!lockNavigation && isGallery && e.key >= "1" && e.key <= "9") { + const idx = parseInt(e.key) - 1; + if (idx < nImages) { e.preventDefault(); setSelectedIdx(idx); } + return; + } + switch (e.key) { + case "ArrowLeft": + if (!lockNavigation && isGallery) { e.preventDefault(); setSelectedIdx(Math.max(0, selectedIdx - 1)); } + break; + case "ArrowRight": + if (!lockNavigation && isGallery) { e.preventDefault(); setSelectedIdx(Math.min(nImages - 1, selectedIdx + 1)); } + break; + case "r": + case "R": + if (!lockView) handleResetAll(); + break; + case "m": + case "M": + if (measureActive) { + setMeasureActive(false); + setMeasurePoints([]); + } else { + setMeasureActive(true); + setMeasurePoints([]); + } + break; + case "Escape": + if (measureActive) { + setMeasureActive(false); + setMeasurePoints([]); + } + break; + case "]": + if (!lockNavigation && !lockDisplay) { + e.preventDefault(); + const rIdx = isGallery ? selectedIdx : 0; + const rots = [...(imageRotations || [])]; + while (rots.length <= rIdx) rots.push(0); + rots[rIdx] = (rots[rIdx] + 3) % 4; + setImageRotations(rots); + } + break; + case "[": + if (!lockNavigation && !lockDisplay) { + e.preventDefault(); + const rIdx2 = isGallery ? selectedIdx : 0; + const rots2 = [...(imageRotations || [])]; + while (rots2.length <= rIdx2) rots2.push(0); + rots2[rIdx2] = (rots2[rIdx2] + 1) % 4; + setImageRotations(rots2); + } + break; + case "Delete": + case "Backspace": + if (!lockRoi && roiActive && roiSelectedIdx >= 0 && roiList && roiSelectedIdx < roiList.length) { + e.preventDefault(); + const newList = roiList.filter((_, i) => i !== roiSelectedIdx); + setRoiList(newList); + setRoiSelectedIdx(newList.length > 0 ? Math.min(roiSelectedIdx, newList.length - 1) : -1); + } + break; + } + }; + + // ------------------------------------------------------------------------- + // Render (Show3D-style layout) + // ------------------------------------------------------------------------- + const needsReset = getZoomState(isGallery ? selectedIdx : 0).zoom !== 1 || getZoomState(isGallery ? selectedIdx : 0).panX !== 0 || getZoomState(isGallery ? selectedIdx : 0).panY !== 0; + const statsIdx = isGallery ? selectedIdx : 0; + + // Calibrated cursor position + const calibratedUnit = pixelSize > 0 ? (Math.max(height, width) * pixelSize >= 10 ? "nm" : "Å") : ""; + const calibratedFactor = calibratedUnit === "nm" ? pixelSize / 10 : pixelSize; + + return ( + + + {/* Main panel */} + + {/* Title row */} + + {title || (isGallery ? "Gallery" : "Image")} + {displayBinFactor > 1 && ( + + {displayBinFactor}× binned + + )} + {(() => { const rk = (imageRotations?.[isGallery ? selectedIdx : 0] ?? 0) % 4; return rk !== 0 ? ( + { + if (lockDisplay) return; + const ri = isGallery ? selectedIdx : 0; + const rots = [...(imageRotations || [])]; + while (rots.length <= ri) rots.push(0); + rots[ri] = (rots[ri] + 3) % 4; + setImageRotations(rots); + }} + sx={{ ml: 0.5, color: themeColors.accent, cursor: lockDisplay ? "default" : "pointer", fontSize: "inherit", "&:hover": { opacity: lockDisplay ? 1 : 0.7 } }} + > + ({rk * 90}°) + + ) : null; })()} + + Controls + FFT: Show power spectrum (Fourier transform) alongside image. + Profile: Click two points on image to draw a line intensity profile. + ROI: Region of Interest — click to place, drag to move. + {!isGallery && Lens: Magnifier inset that follows the cursor.} + Auto: Percentile-based contrast (2nd–98th percentile). FFT Auto masks DC + clips to 99.9th. + {isGallery && Link Zoom / Contrast: Sync zoom or histogram range across all gallery images.} + Keyboard + + } theme={themeInfo.theme} /> + + + {/* Controls row: Profile, ROI, Lens, FFT, Export, Reset, Copy */} + + {!hideProfile && ( + <> + Profile: + { + if (lockProfile) return; + const on = e.target.checked; + setProfileActive(on); + if (on) { + if (!lockRoi) setRoiActive(false); + } else { + setProfilePoints([]); + setProfileDataAll([]); + setHoveredProfileEndpoint(null); + setIsHoveringProfileLine(false); + } + }} + size="small" + sx={switchStyles.small} + /> + + )} + {!hideRoi && !isGallery && ( + <> + ROI: + { + if (lockRoi) return; + const on = e.target.checked; + setRoiActive(on); + if (on) { + if (!lockProfile) setProfileActive(false); + setProfilePoints([]); + setProfileDataAll([]); + setHoveredProfileEndpoint(null); + setIsHoveringProfileLine(false); + } else { + setRoiSelectedIdx(-1); + } + }} + size="small" + sx={switchStyles.small} + /> + + )} + {!hideDisplay && ( + <> + {!isGallery && ( + <> + Lens: + { + if (lockDisplay) return; + if (!showLens) { + setShowLens(true); + setLensPos({ row: Math.floor(height / 2), col: Math.floor(width / 2) }); + } else { + setShowLens(false); + setLensPos(null); + } + }} + disabled={lockDisplay} + size="small" + sx={switchStyles.small} + /> + + )} + FFT: + { + if (lockDisplay) return; + const on = e.target.checked; + if (on && width * height > 2048 * 2048) { + console.warn(`Show2D: FFT on ${width}×${height} image (${(width * height / 1e6).toFixed(1)}M pixels) may be slow`); + } + setShowFft(on); + }} + disabled={lockDisplay} + size="small" + sx={switchStyles.small} + /> + {showFft && width * height > 2048 * 2048 && ( + slow ({width}×{height}) + )} + {nImages === 2 && ( + <> + Diff: + { if (!lockDisplay) setDiffMode(!diffMode); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + + )} + + )} + + {!hideView && ( + + )} + {!hideExport && ( + <> + + setExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> + handleExportFigure(true, true)} sx={{ fontSize: 12 }}>PDF + scalebar + colorbar + handleExportFigure(true, false)} sx={{ fontSize: 12 }}>PDF + scalebar + handleExportFigure(false, false)} sx={{ fontSize: 12 }}>PDF + All (PNG + PDF) + + + + )} + + + {isGallery ? ( + /* Gallery mode */ + + {Array.from({ length: nImages }).map((_, i) => ( + + { imageContainerRefs.current[i] = el; }} + sx={{ position: "relative", bgcolor: "#000", border: `2px solid ${i === selectedIdx ? themeColors.accent : themeColors.border}`, borderRadius: 0, width: canvasW, height: canvasH }} + onMouseDown={(e) => handleMouseDown(e, i)} + onMouseMove={(e) => handleMouseMove(e, i)} + onMouseUp={(e) => handleMouseUp(e, i)} + onMouseLeave={() => handleMouseLeave(i)} + onWheel={(i === selectedIdx || linkedZoom) ? (e) => handleWheel(e, i) : undefined} + onDoubleClick={() => handleDoubleClick(i)} + > + { if (el && canvasRefs.current[i] !== el) { canvasRefs.current[i] = el; setCanvasReady(c => c + 1); } }} + width={canvasW} height={canvasH} + style={{ width: canvasW, height: canvasH, imageRendering: imageRenderingStyle }} + /> + { overlayRefs.current[i] = el; }} + width={Math.round(canvasW * DPR)} height={Math.round(canvasH * DPR)} + style={{ position: "absolute", top: 0, left: 0, width: canvasW, height: canvasH, pointerEvents: "none" }} + /> + {!hideView && ( + + )} + + + {labels?.[i] || `Image ${i + 1}`} + {(imageRotations?.[i] ?? 0) % 4 !== 0 && ( + { + e.stopPropagation(); + if (lockDisplay) return; + const rots = [...(imageRotations || [])]; + while (rots.length <= i) rots.push(0); + rots[i] = (rots[i] + 3) % 4; + setImageRotations(rots); + }} + sx={{ ml: 0.5, color: themeColors.accent, cursor: lockDisplay ? "default" : "pointer", "&:hover": { opacity: lockDisplay ? 1 : 0.7 } }} + > + ({(imageRotations[i] % 4) * 90}°) + + )} + + {effectiveShowFft && ( + { fftContainerRefs.current[i] = el; }} + sx={{ mt: 0.5, position: "relative", border: `2px solid ${i === selectedIdx ? themeColors.accent : themeColors.border}`, borderRadius: 0, bgcolor: "#000", cursor: lockView ? "default" : "grab" }} + onWheel={(i === selectedIdx || linkedZoom) ? (e) => handleGalleryFftWheel(e, i) : undefined} + onDoubleClick={() => setGalleryFftState(i, { zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 })} + onMouseDown={(e) => handleGalleryFftMouseDown(e, i)} + onMouseMove={(e) => handleGalleryFftMouseMove(e, i)} + onMouseUp={handleGalleryFftMouseUp} + onMouseLeave={handleGalleryFftMouseUp} + > + { fftCanvasRefs.current[i] = el; }} + width={canvasW} height={canvasH} + style={{ width: canvasW, height: canvasH, imageRendering: imageRenderingStyle, display: "block" }} + /> + {fftComputing && !fftMagCacheGalleryRef.current[i] && ( + + FFT… + + )} + + )} + + ))} + {showDiffPanel && diffOtherIndices.map((otherIdx, slot) => ( + + + { diffCanvasRefs.current[slot] = el; }} + width={canvasW} height={canvasH} + style={{ width: canvasW, height: canvasH, imageRendering: imageRenderingStyle }} + /> + + + {nImages === 2 ? "Diff (A − B)" : `Diff (#${diffReference + 1} − #${otherIdx + 1})`} + + {/* FFT of diff (n=2 only) */} + {effectiveShowFft && nImages === 2 && slot === 0 && ( + + { diffFftCanvasRef.current = el; }} + width={canvasW} height={canvasH} + style={{ width: canvasW, height: canvasH, imageRendering: imageRenderingStyle, display: "block" }} + /> + + )} + + ))} + + ) : ( + /* Single image mode */ + { imageContainerRefs.current[0] = el; }} + sx={{ position: "relative", bgcolor: "#000", border: `1px solid ${themeColors.border}`, width: canvasW, height: canvasH, cursor: isHoveringLensEdge ? "nwse-resize" : isDraggingROI ? "move" : (isDraggingResize || isDraggingResizeInner || isHoveringResize || isHoveringResizeInner) ? "nwse-resize" : (draggingProfileEndpoint !== null || isDraggingProfileLine) ? "grabbing" : (profileActive && (hoveredProfileEndpoint !== null || isHoveringProfileLine)) ? "grab" : (profileActive || roiActive || measureActive) ? "crosshair" : "grab" }} + onMouseDown={(e) => handleMouseDown(e, 0)} + onMouseMove={(e) => handleMouseMove(e, 0)} + onMouseUp={(e) => handleMouseUp(e, 0)} + onMouseLeave={() => handleMouseLeave(0)} + onWheel={(e) => handleWheel(e, 0)} + onDoubleClick={() => handleDoubleClick(0)} + > + { if (el && canvasRefs.current[0] !== el) { canvasRefs.current[0] = el; setCanvasReady(c => c + 1); } }} + width={canvasW} height={canvasH} + style={{ width: canvasW, height: canvasH, imageRendering: imageRenderingStyle }} + /> + { overlayRefs.current[0] = el; }} + width={Math.round(canvasW * DPR)} height={Math.round(canvasH * DPR)} + style={{ position: "absolute", top: 0, left: 0, width: canvasW, height: canvasH, pointerEvents: "none" }} + /> + + {cursorInfo && ( + + + ({cursorInfo.row}, {cursorInfo.col}){pixelSize > 0 ? ` = (${(cursorInfo.row * calibratedFactor).toFixed(1)}, ${(cursorInfo.col * calibratedFactor).toFixed(1)} ${calibratedUnit})` : ""} {formatNumber(cursorInfo.value)} + + + )} + {!hideView && ( + + )} + + )} + + {/* Stats bar - right below canvas (Show3D style) */} + {!hideStats && showStats && ( + + {isGallery && ( + {labels?.[statsIdx] || `#${statsIdx + 1}`} + )} + Mean {formatNumber(statsMean?.[statsIdx] ?? 0)} + Min {formatNumber(statsMin?.[statsIdx] ?? 0)} + Max {formatNumber(statsMax?.[statsIdx] ?? 0)} + Std {formatNumber(statsStd?.[statsIdx] ?? 0)} + {measureActive && ( + <> + + Measuring + + )} + + )} + + {/* Gallery FFT Controls - below gallery grid */} + {effectiveShowFft && isGallery && ( + + + + FFT Scale: + + Auto: + { if (!lockDisplay) setFftAuto(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + {roiFftActive && fftCropDims && ( + <> + Win: + { if (!lockDisplay) setFftWindow(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + + )} + Color: + + + + {!hideHistogram && ( + + {fftHistogramData && ( + { if (!lockHistogram) { setFftVminPct(min); setFftVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={fftDataRange.min} dataMax={fftDataRange.max} /> + )} + + )} + + )} + + {/* Line profile sparkline — always reserve space when profile is active */} + {!hideProfile && profileActive && ( + + +
{ + if (lockProfile) return; + e.preventDefault(); + setIsResizingProfile(true); + setProfileResizeStart({ y: e.clientY, height: profileHeight }); + }} + style={{ width: profileCanvasWidth, height: 4, cursor: lockProfile ? "default" : "ns-resize", borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, background: `linear-gradient(to bottom, ${themeColors.border}, transparent)`, opacity: lockProfile ? 0.5 : 1, pointerEvents: lockProfile ? "none" : "auto" }} + /> + + )} + + {/* Controls: two rows left + histogram right, ROI below */} + {showControls && ( + + {/* Top: control rows + histogram side by side */} + + + {/* Row 1: Scale + Color */} + {!hideDisplay && ( + + Scale: + + Color: + + {!isGallery && ( + <> + Colorbar: + { if (!lockDisplay) setShowColorbar(!showColorbar); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + + )} + + )} + {/* Row 2: Auto + Lens settings + Link Zoom (gallery) + zoom indicator */} + {!hideDisplay && ( + + Auto: + { if (!lockDisplay) setAutoContrast(!autoContrast); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + Smooth: + { if (!lockDisplay) setSmooth(!smooth); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + {!isGallery && showLens && ( + <> + Lens {lensMag}× + setLensMag(v as number)} size="small" sx={{ ...sliderStyles.small, width: 35 }} /> + {lensDisplaySize}px + setLensDisplaySize(v as number)} size="small" sx={{ ...sliderStyles.small, width: 35 }} /> + + )} + {isGallery && ( + <> + Link: + Zoom + { if (!lockDisplay) setLinkedZoom(!linkedZoom); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + Pan + { if (!lockDisplay) setLinkPan(!linkPan); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + Contrast + { if (!lockDisplay) setLinkedContrast(!linkedContrast); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + + )} + {getZoomState(isGallery ? selectedIdx : 0).zoom !== 1 && ( + {getZoomState(isGallery ? selectedIdx : 0).zoom.toFixed(1)}x + )} + + )} + + {/* Right: Histogram aligned to the two rows. When unlinked + gallery: stack one per image. */} + {!hideHistogram && (imageHistogramData || imageHistogramBins) && ( + + {(!linkedContrast && isGallery && rawDataRef.current) ? ( + Array.from({ length: nImages }).map((_, i) => { + const cs = contrastStates.get(i) || { vminPct: 0, vmaxPct: 100 }; + const raw = rawDataRef.current?.[i] || null; + return ( + { if (!lockHistogram) setContrastState(i, { vminPct: min, vmaxPct: max }); }} + width={110} height={36} theme={themeInfo.theme === "dark" ? "dark" : "light"} + dataMin={dataRangesRef.current[i]?.min ?? imageDataRange.min} + dataMax={dataRangesRef.current[i]?.max ?? imageDataRange.max} /> + ); + }) + ) : ( + { if (!lockHistogram) setContrastState(activeContrastIdx, { vminPct: min, vmaxPct: max }); }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={traitVmin != null && traitVmax != null ? (logScale ? Math.log1p(Math.max(traitVmin, 0)) : traitVmin) : imageDataRange.min} dataMax={traitVmin != null && traitVmax != null ? (logScale ? Math.log1p(Math.max(traitVmax, 0)) : traitVmax) : imageDataRange.max} /> + )} + + )} + + {/* ROI Section (own box, below control rows) */} + {!hideRoi && roiActive && ( + + {/* ROI: shape + ADD + CLEAR */} + + ROI: + + + + + + {/* Selected ROI details */} + {selectedRoi && ( + + #{roiSelectedIdx + 1}/{roiList?.length ?? 0} + + {selectedRoi.shape === "rectangle" && ( + <> + W + updateSelectedRoi({ width: v as number })} size="small" sx={{ ...sliderStyles.small, width: 40 }} /> + H + updateSelectedRoi({ height: v as number })} size="small" sx={{ ...sliderStyles.small, width: 40 }} /> + + )} + {selectedRoi.shape === "annular" && ( + <> + Inner + updateSelectedRoi({ radius_inner: v as number })} size="small" sx={{ ...sliderStyles.small, width: 40 }} /> + Outer + updateSelectedRoi({ radius: v as number })} size="small" sx={{ ...sliderStyles.small, width: 40 }} /> + + )} + {selectedRoi.shape !== "rectangle" && selectedRoi.shape !== "annular" && ( + <> + Size + updateSelectedRoi({ radius: v as number })} size="small" sx={{ ...sliderStyles.small, width: 50 }} /> + + )} + + {ROI_COLORS.map(c => ( + updateSelectedRoi({ color: c })} sx={{ width: 12, height: 12, bgcolor: c, cursor: "pointer", border: c === selectedRoi.color ? `2px solid ${themeColors.text}` : "1px solid transparent", "&:hover": { opacity: 0.8 } }} /> + ))} + + Border + updateSelectedRoi({ line_width: v as number })} size="small" sx={{ ...sliderStyles.small, width: 30 }} /> + updateSelectedRoi({ highlight: !selectedRoi.highlight })} + sx={{ cursor: "pointer", fontSize: 10, color: selectedRoi.highlight ? themeColors.accentGreen : themeColors.textMuted, "&:hover": { opacity: 0.8 } }} + title="Focus (dim outside)" + >{selectedRoi.highlight ? "\u25C9 Focus" : "\u25CB Focus"} + + + )} + {/* ROI list */} + {roiList && roiList.length > 0 && ( + + {roiList.map((roi, i) => { + const c = roi.color || ROI_COLORS[i % ROI_COLORS.length]; + const isSelected = i === roiSelectedIdx; + const shapeLabel = roi.shape === "rectangle" ? `${roi.width}×${roi.height}` : roi.shape === "annular" ? `r${roi.radius_inner}-${roi.radius}` : `r${roi.radius}`; + return ( + setRoiSelectedIdx(i)} sx={{ display: "flex", alignItems: "center", gap: "3px", lineHeight: 1.6, cursor: "pointer", "&:hover .roi-delete": { opacity: 1 } }}> + + + {i + 1}{" "} + {roi.shape} ({roi.row}, {roi.col}) {shapeLabel} + + { e.stopPropagation(); const newList = roiList.map((r, j) => ({ ...r, highlight: j === i ? !r.highlight : false })); setRoiList(newList); }} + sx={{ cursor: "pointer", fontSize: 10, color: roi.highlight ? themeColors.accentGreen : themeColors.textMuted, lineHeight: 1, opacity: roi.highlight ? 1 : 0.5, "&:hover": { opacity: 1 } }} + title="Focus (dim outside)" + >{roi.highlight ? "\u25C9" : "\u25CB"} + { e.stopPropagation(); const newList = roiList.filter((_, j) => j !== i); setRoiList(newList); setRoiSelectedIdx(newList.length > 0 ? Math.min(roiSelectedIdx, newList.length - 1) : -1); }} + sx={{ opacity: 0, cursor: "pointer", fontSize: 10, color: themeColors.textMuted, ml: 0.5, lineHeight: 1, "&:hover": { color: "#f44336" } }} + >× + + ); + })} + + )} + + )} + + )} + + + {/* FFT Panel - canvas + stats (single mode only) */} + {effectiveShowFft && !isGallery && ( + + {/* Spacer — matches main panel title row height for canvas alignment */} + + {/* Controls row — matches main panel controls row height */} + + {fftComputing ? ( + + {fftProgress || "Computing FFT…"} + ) : roiFftActive && fftCropDims ? ( + + ROI FFT ({fftCropDims.cropWidth}×{fftCropDims.cropHeight}) + + ) : } + {!hideView && ( + + )} + + + + + {fftComputing && ( + + + {fftProgress || "Computing FFT…"} + + + )} + {!hideView && ( + + )} + + {/* FFT Stats Bar */} + {!hideStats && fftStats && fftStats.length === 4 && ( + + Mean {formatNumber(fftStats[0])} + Min {formatNumber(fftStats[1])} + Max {formatNumber(fftStats[2])} + Std {formatNumber(fftStats[3])} + {fftClickInfo && ( + <> + + + {fftClickInfo.dSpacing != null ? ( + <>d = {fftClickInfo.dSpacing >= 10 ? `${(fftClickInfo.dSpacing / 10).toFixed(2)} nm` : `${fftClickInfo.dSpacing.toFixed(2)} Å`}{" | |g| = "}{fftClickInfo.spatialFreq!.toFixed(4)} Å⁻¹ + ) : ( + <>dist = {fftClickInfo.distPx.toFixed(1)} px + )} + + + )} + + )} + {/* FFT Controls - two rows + histogram (matching main panel layout) */} + + + + {/* Row 1: Scale + Color + Colorbar */} + + Scale: + + Color: + + Colorbar: + { if (!lockDisplay) setFftShowColorbar(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + + {/* Row 2: Auto + zoom indicator */} + + Auto: + { if (!lockDisplay) setFftAuto(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + {fftCropDims && ( + <> + Win: + { if (!lockDisplay) setFftWindow(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + + )} + {fftZoom !== DEFAULT_FFT_ZOOM && ( + {fftZoom.toFixed(1)}x + )} + + + {/* Right: FFT Histogram */} + {!hideHistogram && ( + + {fftHistogramData && ( + { if (!lockHistogram) { setFftVminPct(min); setFftVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={fftDataRange.min} dataMax={fftDataRange.max} /> + )} + + )} + + + + )} + + + ); +} + +export const render = createRender(Show2D); diff --git a/widget/js/show2d/show2d.css b/widget/js/show2d/show2d.css new file mode 100644 index 00000000..0e285789 --- /dev/null +++ b/widget/js/show2d/show2d.css @@ -0,0 +1,9 @@ +/* show2d.css - Minimal CSS for Show2D */ + +.show2d-root { + font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; +} + +.show2d-root canvas { + display: block; +} diff --git a/widget/js/show4dstem/index.tsx b/widget/js/show4dstem/index.tsx new file mode 100644 index 00000000..8ece614d --- /dev/null +++ b/widget/js/show4dstem/index.tsx @@ -0,0 +1,4259 @@ +/// +import * as React from "react"; +import { createRender, useModelState, useModel } from "@anywidget/react"; +import Box from "@mui/material/Box"; +import Typography from "@mui/material/Typography"; +import Stack from "@mui/material/Stack"; +import Select from "@mui/material/Select"; +import MenuItem from "@mui/material/MenuItem"; +import Menu from "@mui/material/Menu"; +import Slider from "@mui/material/Slider"; +import Button from "@mui/material/Button"; +import Switch from "@mui/material/Switch"; +import Tooltip from "@mui/material/Tooltip"; +import IconButton from "@mui/material/IconButton"; +import PlayArrowIcon from "@mui/icons-material/PlayArrow"; +import PauseIcon from "@mui/icons-material/Pause"; +import StopIcon from "@mui/icons-material/Stop"; +import FastRewindIcon from "@mui/icons-material/FastRewind"; +import FastForwardIcon from "@mui/icons-material/FastForward"; +import JSZip from "jszip"; +import "./styles.css"; +import { useTheme } from "../theme"; +import { COLORMAPS, applyColormap, renderToOffscreen } from "../colormaps"; +import { WebGPUFFT, getWebGPUFFT, fft2d, fftshift, autoEnhanceFFT, nextPow2, applyHannWindow2D } from "../webgpu-fft"; +import { drawScaleBarHiDPI, drawColorbar, roundToNiceValue, exportFigure, canvasToPDF } from "../scalebar"; +import { findDataRange, sliderRange, computeStats, applyLogScale } from "../stats"; +import { downloadBlob, formatNumber, downloadDataView } from "../format"; +import { computeHistogramFromBytes } from "../histogram"; +import { ControlCustomizer } from "../control-customizer"; +import { computeToolVisibility } from "../tool-parity"; + +const MIN_ZOOM = 0.5; +const MAX_ZOOM = 10; + +// ============================================================================ +// UI Styles - component styling helpers +// ============================================================================ +const typography = { + label: { fontSize: 11 }, + labelSmall: { fontSize: 10 }, + value: { fontSize: 10, fontFamily: "monospace" }, + title: { fontWeight: "bold" as const }, +}; + +const controlPanel = { + select: { minWidth: 90, fontSize: 11, "& .MuiSelect-select": { py: 0.5 } }, +}; + +const container = { + root: { p: 2, bgcolor: "transparent", color: "inherit", fontFamily: "monospace", overflow: "visible" }, + imageBox: { bgcolor: "#000", border: "1px solid #444", overflow: "hidden", position: "relative" as const }, +}; + +const upwardMenuProps = { + anchorOrigin: { vertical: "top" as const, horizontal: "left" as const }, + transformOrigin: { vertical: "bottom" as const, horizontal: "left" as const }, + sx: { zIndex: 9999 }, +}; + +const switchStyles = { + small: { '& .MuiSwitch-thumb': { width: 12, height: 12 }, '& .MuiSwitch-switchBase': { padding: '4px' } }, + medium: { '& .MuiSwitch-thumb': { width: 14, height: 14 }, '& .MuiSwitch-switchBase': { padding: '4px' } }, +}; + +const sliderStyles = { + small: { + "& .MuiSlider-thumb": { width: 12, height: 12 }, + "& .MuiSlider-rail": { height: 3 }, + "& .MuiSlider-track": { height: 3 }, + }, +}; + +// ============================================================================ +// Layout Constants - consistent spacing throughout +// ============================================================================ +const SPACING = { + XS: 4, // Extra small gap + SM: 8, // Small gap (default between elements) + MD: 12, // Medium gap (between control groups) + LG: 16, // Large gap (between major sections) +}; + +const CANVAS_SIZE = 450; // Both DP and VI canvases + +// Theme-aware ROI colors for DP detector overlay +interface RoiColors { + stroke: string; + strokeDragging: string; + fill: string; + fillDragging: string; + handleFill: string; + innerStroke: string; + innerStrokeDragging: string; + innerHandleFill: string; + textColor: string; +} +const DARK_ROI_COLORS: RoiColors = { + stroke: "rgba(0, 255, 0, 0.9)", + strokeDragging: "rgba(255, 255, 0, 0.9)", + fill: "rgba(0, 255, 0, 0.12)", + fillDragging: "rgba(255, 255, 0, 0.12)", + handleFill: "rgba(0, 255, 0, 0.8)", + innerStroke: "rgba(0, 220, 255, 0.9)", + innerStrokeDragging: "rgba(255, 200, 0, 0.9)", + innerHandleFill: "rgba(0, 220, 255, 0.8)", + textColor: "#0f0", +}; +const LIGHT_ROI_COLORS: RoiColors = { + stroke: "rgba(0, 140, 0, 0.9)", + strokeDragging: "rgba(200, 160, 0, 0.9)", + fill: "rgba(0, 140, 0, 0.15)", + fillDragging: "rgba(200, 160, 0, 0.15)", + handleFill: "rgba(0, 140, 0, 0.85)", + innerStroke: "rgba(0, 160, 200, 0.9)", + innerStrokeDragging: "rgba(200, 160, 0, 0.9)", + innerHandleFill: "rgba(0, 160, 200, 0.85)", + textColor: "#0a0", +}; + +// Interaction constants +const RESIZE_HIT_AREA_PX = 10; +const CIRCLE_HANDLE_ANGLE = 0.707; // cos(45°) +// Compact button style for Reset/Export +const compactButton = { + fontSize: 10, + py: 0.25, + px: 1, + minWidth: 0, + "&.Mui-disabled": { + color: "#666", + borderColor: "#444", + }, +}; + +// Control row style - bordered container for each row +const controlRow = { + display: "flex", + alignItems: "center", + gap: `${SPACING.SM}px`, + px: 1, + py: 0.5, + width: "fit-content", +}; + +/** Format stat value for display (compact scientific notation for small values) */ +function formatStat(value: number): string { + if (value === 0) return "0"; + const abs = Math.abs(value); + if (abs < 0.001 || abs >= 10000) { + return value.toExponential(2); + } + if (abs < 0.01) return value.toFixed(4); + if (abs < 1) return value.toFixed(3); + return value.toFixed(2); +} + + +// ============================================================================ +// FFT peak finder (snap to Bragg spot with sub-pixel centroid refinement) +// ============================================================================ +function findFFTPeak(mag: Float32Array, width: number, height: number, col: number, row: number, radius: number): { row: number; col: number } { + const c0 = Math.max(0, Math.floor(col) - radius); + const r0 = Math.max(0, Math.floor(row) - radius); + const c1 = Math.min(width - 1, Math.floor(col) + radius); + const r1 = Math.min(height - 1, Math.floor(row) + radius); + let bestCol = Math.round(col), bestRow = Math.round(row), bestVal = -Infinity; + for (let ir = r0; ir <= r1; ir++) { + for (let ic = c0; ic <= c1; ic++) { + const val = mag[ir * width + ic]; + if (val > bestVal) { bestVal = val; bestCol = ic; bestRow = ir; } + } + } + const wc0 = Math.max(0, bestCol - 1), wc1 = Math.min(width - 1, bestCol + 1); + const wr0 = Math.max(0, bestRow - 1), wr1 = Math.min(height - 1, bestRow + 1); + let sumW = 0, sumWC = 0, sumWR = 0; + for (let ir = wr0; ir <= wr1; ir++) { + for (let ic = wc0; ic <= wc1; ic++) { + const w = mag[ir * width + ic]; + sumW += w; sumWC += w * ic; sumWR += w * ir; + } + } + if (sumW > 0) return { row: sumWR / sumW, col: sumWC / sumW }; + return { row: bestRow, col: bestCol }; +} +const FFT_SNAP_RADIUS = 5; + +/** + * Draw VI crosshair on high-DPI canvas (crisp regardless of image resolution) + * Note: Does NOT clear canvas - should be called after drawScaleBarHiDPI + */ +function drawViPositionMarker( + canvas: HTMLCanvasElement, + dpr: number, + posRow: number, // Position in image coordinates + posCol: number, + zoom: number, + panX: number, + panY: number, + imageWidth: number, + imageHeight: number, + isDragging: boolean +) { + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + ctx.save(); + ctx.scale(dpr, dpr); + + const cssWidth = canvas.width / dpr; + const cssHeight = canvas.height / dpr; + const scaleX = cssWidth / imageWidth; + const scaleY = cssHeight / imageHeight; + + // Convert image coordinates to CSS pixel coordinates + const screenX = posCol * zoom * scaleX + panX * scaleX; + const screenY = posRow * zoom * scaleY + panY * scaleY; + + // Simple crosshair (no circle) + const crosshairSize = 12; + const lineWidth = 1.5; + + ctx.shadowColor = "rgba(0, 0, 0, 0.5)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + + ctx.strokeStyle = isDragging ? "rgba(255, 255, 0, 0.9)" : "rgba(255, 100, 100, 0.9)"; + ctx.lineWidth = lineWidth; + + // Draw crosshair lines only + ctx.beginPath(); + ctx.moveTo(screenX - crosshairSize, screenY); + ctx.lineTo(screenX + crosshairSize, screenY); + ctx.moveTo(screenX, screenY - crosshairSize); + ctx.lineTo(screenX, screenY + crosshairSize); + ctx.stroke(); + + ctx.restore(); +} + +/** + * Draw VI ROI overlay on high-DPI canvas for real-space region selection + * Note: Does NOT clear canvas - should be called after drawViPositionMarker + */ +function drawViRoiOverlayHiDPI( + canvas: HTMLCanvasElement, + dpr: number, + roiMode: string, + centerRow: number, + centerCol: number, + radius: number, + roiWidth: number, + roiHeight: number, + zoom: number, + panX: number, + panY: number, + imageWidth: number, + imageHeight: number, + isDragging: boolean, + isDraggingResize: boolean, + isHoveringResize: boolean +) { + if (roiMode === "off") return; + + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + ctx.save(); + ctx.scale(dpr, dpr); + + const cssWidth = canvas.width / dpr; + const cssHeight = canvas.height / dpr; + const scaleX = cssWidth / imageWidth; + const scaleY = cssHeight / imageHeight; + + // Convert image coordinates to screen coordinates (row→screenY, col→screenX) + const screenX = centerCol * zoom * scaleX + panX * scaleX; + const screenY = centerRow * zoom * scaleY + panY * scaleY; + + const lineWidth = 2.5; + const crosshairSize = 10; + const handleRadius = 6; + + ctx.shadowColor = "rgba(0, 0, 0, 0.4)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + + // Helper to draw resize handle (purple color for VI ROI to differentiate from DP) + const drawResizeHandle = (handleX: number, handleY: number) => { + let handleFill: string; + let handleStroke: string; + + if (isDraggingResize) { + handleFill = "rgba(180, 100, 255, 1)"; + handleStroke = "rgba(255, 255, 255, 1)"; + } else if (isHoveringResize) { + handleFill = "rgba(220, 150, 255, 1)"; + handleStroke = "rgba(255, 255, 255, 1)"; + } else { + handleFill = "rgba(160, 80, 255, 0.8)"; + handleStroke = "rgba(255, 255, 255, 0.8)"; + } + ctx.beginPath(); + ctx.arc(handleX, handleY, handleRadius, 0, 2 * Math.PI); + ctx.fillStyle = handleFill; + ctx.fill(); + ctx.strokeStyle = handleStroke; + ctx.lineWidth = 1.5; + ctx.stroke(); + }; + + // Helper to draw center crosshair (purple/magenta for VI ROI) + const drawCenterCrosshair = () => { + ctx.strokeStyle = isDragging ? "rgba(255, 200, 0, 0.9)" : "rgba(180, 80, 255, 0.9)"; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.moveTo(screenX - crosshairSize, screenY); + ctx.lineTo(screenX + crosshairSize, screenY); + ctx.moveTo(screenX, screenY - crosshairSize); + ctx.lineTo(screenX, screenY + crosshairSize); + ctx.stroke(); + }; + + // Purple/magenta color for VI ROI to differentiate from green DP detector + const strokeColor = isDragging ? "rgba(255, 200, 0, 0.9)" : "rgba(180, 80, 255, 0.9)"; + const fillColor = isDragging ? "rgba(255, 200, 0, 0.15)" : "rgba(180, 80, 255, 0.15)"; + + if (roiMode === "circle" && radius > 0) { + const screenRadiusX = radius * zoom * scaleX; + const screenRadiusY = radius * zoom * scaleY; + + ctx.strokeStyle = strokeColor; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.ellipse(screenX, screenY, screenRadiusX, screenRadiusY, 0, 0, 2 * Math.PI); + ctx.stroke(); + + ctx.fillStyle = fillColor; + ctx.fill(); + + drawCenterCrosshair(); + + // Resize handle at 45° diagonal + const handleOffsetX = screenRadiusX * CIRCLE_HANDLE_ANGLE; + const handleOffsetY = screenRadiusY * CIRCLE_HANDLE_ANGLE; + drawResizeHandle(screenX + handleOffsetX, screenY + handleOffsetY); + + } else if (roiMode === "square" && radius > 0) { + // Square uses radius as half-size + const screenHalfW = radius * zoom * scaleX; + const screenHalfH = radius * zoom * scaleY; + const left = screenX - screenHalfW; + const top = screenY - screenHalfH; + + ctx.strokeStyle = strokeColor; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.rect(left, top, screenHalfW * 2, screenHalfH * 2); + ctx.stroke(); + + ctx.fillStyle = fillColor; + ctx.fill(); + + drawCenterCrosshair(); + drawResizeHandle(screenX + screenHalfW, screenY + screenHalfH); + + } else if (roiMode === "rect" && roiWidth > 0 && roiHeight > 0) { + const screenHalfW = (roiWidth / 2) * zoom * scaleX; + const screenHalfH = (roiHeight / 2) * zoom * scaleY; + const left = screenX - screenHalfW; + const top = screenY - screenHalfH; + + ctx.strokeStyle = strokeColor; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.rect(left, top, screenHalfW * 2, screenHalfH * 2); + ctx.stroke(); + + ctx.fillStyle = fillColor; + ctx.fill(); + + drawCenterCrosshair(); + drawResizeHandle(screenX + screenHalfW, screenY + screenHalfH); + } + + ctx.restore(); +} + +/** + * Draw DP crosshair on high-DPI canvas (crisp regardless of detector resolution) + * Note: Does NOT clear canvas - should be called after drawScaleBarHiDPI + */ +function drawDpCrosshairHiDPI( + canvas: HTMLCanvasElement, + dpr: number, + kCol: number, // Column position in detector coordinates + kRow: number, // Row position in detector coordinates + zoom: number, + panX: number, + panY: number, + detWidth: number, + detHeight: number, + isDragging: boolean, + roiColors: RoiColors = DARK_ROI_COLORS +) { + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + ctx.save(); + ctx.scale(dpr, dpr); + + const cssWidth = canvas.width / dpr; + const cssHeight = canvas.height / dpr; + // Use separate X/Y scale factors (canvas stretches to fill container) + const scaleX = cssWidth / detWidth; + const scaleY = cssHeight / detHeight; + + // Convert detector coordinates to CSS pixel coordinates + const screenX = kCol * zoom * scaleX + panX * scaleX; + const screenY = kRow * zoom * scaleY + panY * scaleY; + + // Fixed UI sizes in CSS pixels (consistent with VI crosshair) + const crosshairSize = 18; + const lineWidth = 3; + const dotRadius = 6; + + ctx.shadowColor = "rgba(0, 0, 0, 0.5)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + + ctx.strokeStyle = isDragging ? roiColors.strokeDragging : roiColors.stroke; + ctx.lineWidth = lineWidth; + + // Draw crosshair + ctx.beginPath(); + ctx.moveTo(screenX - crosshairSize, screenY); + ctx.lineTo(screenX + crosshairSize, screenY); + ctx.moveTo(screenX, screenY - crosshairSize); + ctx.lineTo(screenX, screenY + crosshairSize); + ctx.stroke(); + + // Draw center dot + ctx.beginPath(); + ctx.arc(screenX, screenY, dotRadius, 0, 2 * Math.PI); + ctx.stroke(); + + ctx.restore(); +} + +/** + * Draw ROI overlay (circle, square, rect, annular) on high-DPI canvas + * Note: Does NOT clear canvas - should be called after drawScaleBarHiDPI + */ +function drawRoiOverlayHiDPI( + canvas: HTMLCanvasElement, + dpr: number, + roiMode: string, + centerCol: number, + centerRow: number, + radius: number, + radiusInner: number, + roiWidth: number, + roiHeight: number, + zoom: number, + panX: number, + panY: number, + detWidth: number, + detHeight: number, + isDragging: boolean, + isDraggingResize: boolean, + isDraggingResizeInner: boolean, + isHoveringResize: boolean, + isHoveringResizeInner: boolean, + roiColors: RoiColors = DARK_ROI_COLORS +) { + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + ctx.save(); + ctx.scale(dpr, dpr); + + const cssWidth = canvas.width / dpr; + const cssHeight = canvas.height / dpr; + // Use separate X/Y scale factors (canvas stretches to fill container) + const scaleX = cssWidth / detWidth; + const scaleY = cssHeight / detHeight; + + // Convert detector coordinates to CSS pixel coordinates + const screenX = centerCol * zoom * scaleX + panX * scaleX; + const screenY = centerRow * zoom * scaleY + panY * scaleY; + + // Fixed UI sizes in CSS pixels + const lineWidth = 2.5; + const crosshairSizeSmall = 10; + const handleRadius = 6; + + ctx.shadowColor = "rgba(0, 0, 0, 0.4)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + + // Helper to draw resize handle + const drawResizeHandle = (handleX: number, handleY: number, isInner: boolean = false) => { + let handleFill: string; + let handleStroke: string; + const dragging = isInner ? isDraggingResizeInner : isDraggingResize; + const hovering = isInner ? isHoveringResizeInner : isHoveringResize; + + if (dragging) { + handleFill = "rgba(0, 200, 255, 1)"; + handleStroke = "rgba(255, 255, 255, 1)"; + } else if (hovering) { + handleFill = "rgba(255, 100, 100, 1)"; + handleStroke = "rgba(255, 255, 255, 1)"; + } else { + handleFill = isInner ? roiColors.innerHandleFill : roiColors.handleFill; + handleStroke = "rgba(255, 255, 255, 0.8)"; + } + ctx.beginPath(); + ctx.arc(handleX, handleY, handleRadius, 0, 2 * Math.PI); + ctx.fillStyle = handleFill; + ctx.fill(); + ctx.strokeStyle = handleStroke; + ctx.lineWidth = 1.5; + ctx.stroke(); + }; + + // Helper to draw center crosshair + const drawCenterCrosshair = () => { + ctx.strokeStyle = isDragging ? roiColors.strokeDragging : roiColors.stroke; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.moveTo(screenX - crosshairSizeSmall, screenY); + ctx.lineTo(screenX + crosshairSizeSmall, screenY); + ctx.moveTo(screenX, screenY - crosshairSizeSmall); + ctx.lineTo(screenX, screenY + crosshairSizeSmall); + ctx.stroke(); + }; + + if (roiMode === "circle" && radius > 0) { + // Use separate X/Y radii for ellipse (handles non-square detectors) + const screenRadiusX = radius * zoom * scaleX; + const screenRadiusY = radius * zoom * scaleY; + + // Draw ellipse (becomes circle if scaleX === scaleY) + ctx.strokeStyle = isDragging ? roiColors.strokeDragging : roiColors.stroke; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.ellipse(screenX, screenY, screenRadiusX, screenRadiusY, 0, 0, 2 * Math.PI); + ctx.stroke(); + + // Semi-transparent fill + ctx.fillStyle = isDragging ? roiColors.fillDragging : roiColors.fill; + ctx.fill(); + + drawCenterCrosshair(); + + // Resize handle at 45° diagonal + const handleOffsetX = screenRadiusX * CIRCLE_HANDLE_ANGLE; + const handleOffsetY = screenRadiusY * CIRCLE_HANDLE_ANGLE; + drawResizeHandle(screenX + handleOffsetX, screenY + handleOffsetY); + + } else if (roiMode === "square" && radius > 0) { + // Square in detector space uses same half-size in both dimensions + const screenHalfW = radius * zoom * scaleX; + const screenHalfH = radius * zoom * scaleY; + const left = screenX - screenHalfW; + const top = screenY - screenHalfH; + + ctx.strokeStyle = isDragging ? roiColors.strokeDragging : roiColors.stroke; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.rect(left, top, screenHalfW * 2, screenHalfH * 2); + ctx.stroke(); + + ctx.fillStyle = isDragging ? roiColors.fillDragging : roiColors.fill; + ctx.fill(); + + drawCenterCrosshair(); + drawResizeHandle(screenX + screenHalfW, screenY + screenHalfH); + + } else if (roiMode === "rect" && roiWidth > 0 && roiHeight > 0) { + const screenHalfW = (roiWidth / 2) * zoom * scaleX; + const screenHalfH = (roiHeight / 2) * zoom * scaleY; + const left = screenX - screenHalfW; + const top = screenY - screenHalfH; + + ctx.strokeStyle = isDragging ? roiColors.strokeDragging : roiColors.stroke; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.rect(left, top, screenHalfW * 2, screenHalfH * 2); + ctx.stroke(); + + ctx.fillStyle = isDragging ? roiColors.fillDragging : roiColors.fill; + ctx.fill(); + + drawCenterCrosshair(); + drawResizeHandle(screenX + screenHalfW, screenY + screenHalfH); + + } else if (roiMode === "annular" && radius > 0) { + // Use separate X/Y radii for ellipses + const screenRadiusOuterX = radius * zoom * scaleX; + const screenRadiusOuterY = radius * zoom * scaleY; + const screenRadiusInnerX = (radiusInner || 0) * zoom * scaleX; + const screenRadiusInnerY = (radiusInner || 0) * zoom * scaleY; + + // Outer ellipse + ctx.strokeStyle = isDragging ? roiColors.strokeDragging : roiColors.stroke; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.ellipse(screenX, screenY, screenRadiusOuterX, screenRadiusOuterY, 0, 0, 2 * Math.PI); + ctx.stroke(); + + // Inner ellipse + ctx.strokeStyle = isDragging ? roiColors.innerStrokeDragging : roiColors.innerStroke; + ctx.beginPath(); + ctx.ellipse(screenX, screenY, screenRadiusInnerX, screenRadiusInnerY, 0, 0, 2 * Math.PI); + ctx.stroke(); + + // Fill annular region + ctx.fillStyle = isDragging ? roiColors.fillDragging : roiColors.fill; + ctx.beginPath(); + ctx.ellipse(screenX, screenY, screenRadiusOuterX, screenRadiusOuterY, 0, 0, 2 * Math.PI); + ctx.ellipse(screenX, screenY, screenRadiusInnerX, screenRadiusInnerY, 0, 0, 2 * Math.PI, true); + ctx.fill(); + + drawCenterCrosshair(); + + // Outer handle at 45° diagonal + const handleOffsetOuterX = screenRadiusOuterX * CIRCLE_HANDLE_ANGLE; + const handleOffsetOuterY = screenRadiusOuterY * CIRCLE_HANDLE_ANGLE; + drawResizeHandle(screenX + handleOffsetOuterX, screenY + handleOffsetOuterY); + + // Inner handle at 45° diagonal + const handleOffsetInnerX = screenRadiusInnerX * CIRCLE_HANDLE_ANGLE; + const handleOffsetInnerY = screenRadiusInnerY * CIRCLE_HANDLE_ANGLE; + drawResizeHandle(screenX + handleOffsetInnerX, screenY + handleOffsetInnerY, true); + } + + ctx.restore(); +} + +// ============================================================================ +// Histogram Component +// ============================================================================ + +interface HistogramProps { + data: Float32Array | null; + vminPct: number; + vmaxPct: number; + onRangeChange: (min: number, max: number) => void; + width?: number; + height?: number; + theme?: "light" | "dark"; + dataMin?: number; + dataMax?: number; +} + +/** + * Info tooltip component - small ⓘ icon with hover tooltip + */ +function InfoTooltip({ text, theme = "dark" }: { text: React.ReactNode; theme?: "light" | "dark" }) { + const isDark = theme === "dark"; + const content = typeof text === "string" + ? {text} + : text; + return ( + + + ⓘ + + + ); +} + +function KeyboardShortcuts({ items }: { items: [string, string][] }) { + return ( + + + {items.map(([key, desc], i) => ( + {key}{desc} + ))} + + + ); +} + +/** + * Histogram component with integrated vmin/vmax slider and statistics. + * Shows data distribution with adjustable clipping. + */ +function Histogram({ + data, + vminPct, + vmaxPct, + onRangeChange, + width = 120, + height = 40, + theme = "dark", + dataMin = 0, + dataMax = 1, +}: HistogramProps) { + const canvasRef = React.useRef(null); + const bins = React.useMemo(() => computeHistogramFromBytes(data), [data]); + + // Theme-aware colors + const colors = theme === "dark" ? { + bg: "#1a1a1a", + barActive: "#888", + barInactive: "#444", + border: "#333", + } : { + bg: "#f0f0f0", + barActive: "#666", + barInactive: "#bbb", + border: "#ccc", + }; + + // Draw histogram (vertical gray bars) + React.useEffect(() => { + const canvas = canvasRef.current; + if (!canvas) return; + + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const dpr = window.devicePixelRatio || 1; + canvas.width = width * dpr; + canvas.height = height * dpr; + ctx.scale(dpr, dpr); + + // Clear with theme background + ctx.fillStyle = colors.bg; + ctx.fillRect(0, 0, width, height); + + // Reduce to fewer bins for cleaner display + const displayBins = 64; + const binRatio = Math.floor(bins.length / displayBins); + const reducedBins: number[] = []; + for (let i = 0; i < displayBins; i++) { + let sum = 0; + for (let j = 0; j < binRatio; j++) { + sum += bins[i * binRatio + j] || 0; + } + reducedBins.push(sum / binRatio); + } + + // Normalize + const maxVal = Math.max(...reducedBins, 0.001); + const barWidth = width / displayBins; + + // Calculate which bins are in the clipped range + const vminBin = Math.floor((vminPct / 100) * displayBins); + const vmaxBin = Math.floor((vmaxPct / 100) * displayBins); + + // Draw histogram bars + for (let i = 0; i < displayBins; i++) { + const barHeight = (reducedBins[i] / maxVal) * (height - 2); + const x = i * barWidth; + + // Bars inside range are highlighted, outside are dimmed + const inRange = i >= vminBin && i <= vmaxBin; + ctx.fillStyle = inRange ? colors.barActive : colors.barInactive; + ctx.fillRect(x + 0.5, height - barHeight, Math.max(1, barWidth - 1), barHeight); + } + + }, [bins, vminPct, vmaxPct, width, height, colors]); + + return ( + + + { + const [newMin, newMax] = v as number[]; + onRangeChange(Math.min(newMin, newMax - 1), Math.max(newMax, newMin + 1)); + }} + min={0} + max={100} + size="small" + valueLabelDisplay="auto" + valueLabelFormat={(pct) => { + const val = dataMin + (pct / 100) * (dataMax - dataMin); + return val >= 1000 ? val.toExponential(1) : val.toFixed(1); + }} + sx={{ + width, + py: 0, + "& .MuiSlider-thumb": { width: 8, height: 8 }, + "& .MuiSlider-rail": { height: 2 }, + "& .MuiSlider-track": { height: 2 }, + "& .MuiSlider-valueLabel": { fontSize: 10, padding: "2px 4px" }, + }} + /> + {(() => { const v = dataMin + (vminPct / 100) * (dataMax - dataMin); return v >= 1000 ? v.toExponential(1) : v.toFixed(1); })()}{(() => { const v = dataMin + (vmaxPct / 100) * (dataMax - dataMin); return v >= 1000 ? v.toExponential(1) : v.toFixed(1); })()} + + ); +} + +// ============================================================================ +// Line Profile Sampling +// ============================================================================ + +function sampleSingleLine(data: Float32Array, w: number, h: number, row0: number, col0: number, row1: number, col1: number): Float32Array { + const dc = col1 - col0; + const dr = row1 - row0; + const len = Math.sqrt(dc * dc + dr * dr); + const n = Math.max(2, Math.ceil(len)); + const out = new Float32Array(n); + for (let i = 0; i < n; i++) { + const t = i / (n - 1); + const c = col0 + t * dc; + const r = row0 + t * dr; + const ci = Math.floor(c), ri = Math.floor(r); + const cf = c - ci, rf = r - ri; + const c0c = Math.max(0, Math.min(w - 1, ci)); + const c1c = Math.max(0, Math.min(w - 1, ci + 1)); + const r0c = Math.max(0, Math.min(h - 1, ri)); + const r1c = Math.max(0, Math.min(h - 1, ri + 1)); + out[i] = data[r0c * w + c0c] * (1 - cf) * (1 - rf) + + data[r0c * w + c1c] * cf * (1 - rf) + + data[r1c * w + c0c] * (1 - cf) * rf + + data[r1c * w + c1c] * cf * rf; + } + return out; +} + +function sampleLineProfile(data: Float32Array, w: number, h: number, row0: number, col0: number, row1: number, col1: number, profileWidth: number = 1): Float32Array { + if (profileWidth <= 1) return sampleSingleLine(data, w, h, row0, col0, row1, col1); + const dc = col1 - col0; + const dr = row1 - row0; + const len = Math.sqrt(dc * dc + dr * dr); + if (len < 1e-8) return sampleSingleLine(data, w, h, row0, col0, row1, col1); + const perpR = -dc / len; + const perpC = dr / len; + const half = (profileWidth - 1) / 2; + let accumulated: Float32Array | null = null; + for (let k = 0; k < profileWidth; k++) { + const off = -half + k; + const vals = sampleSingleLine(data, w, h, row0 + off * perpR, col0 + off * perpC, row1 + off * perpR, col1 + off * perpC); + if (!accumulated) { + accumulated = vals; + } else { + for (let i = 0; i < vals.length; i++) accumulated[i] += vals[i]; + } + } + if (accumulated) for (let i = 0; i < accumulated.length; i++) accumulated[i] /= profileWidth; + return accumulated || new Float32Array(0); +} + +function pointToSegmentDistance(col: number, row: number, col0: number, row0: number, col1: number, row1: number): number { + const dc = col1 - col0; + const dr = row1 - row0; + const lenSq = dc * dc + dr * dr; + if (lenSq <= 1e-12) return Math.sqrt((col - col0) ** 2 + (row - row0) ** 2); + const tRaw = ((col - col0) * dc + (row - row0) * dr) / lenSq; + const t = Math.max(0, Math.min(1, tRaw)); + const projCol = col0 + t * dc; + const projRow = row0 + t * dr; + return Math.sqrt((col - projCol) ** 2 + (row - projRow) ** 2); +} + +// ============================================================================ +// Crop single-mode ROI region from raw float32 data for ROI-scoped FFT +// ============================================================================ +function cropSingleROI( + data: Float32Array, imgW: number, imgH: number, + mode: string, centerRow: number, centerCol: number, + radius: number, roiW: number, roiH: number, +): { cropped: Float32Array; cropW: number; cropH: number } | null { + if (mode === "off") return null; + let x0: number, y0: number, x1: number, y1: number; + + if (mode === "rect") { + const hw = roiW / 2, hh = roiH / 2; + x0 = Math.max(0, Math.floor(centerCol - hw)); + y0 = Math.max(0, Math.floor(centerRow - hh)); + x1 = Math.min(imgW, Math.ceil(centerCol + hw)); + y1 = Math.min(imgH, Math.ceil(centerRow + hh)); + } else { + x0 = Math.max(0, Math.floor(centerCol - radius)); + y0 = Math.max(0, Math.floor(centerRow - radius)); + x1 = Math.min(imgW, Math.ceil(centerCol + radius)); + y1 = Math.min(imgH, Math.ceil(centerRow + radius)); + } + + const cropW = x1 - x0, cropH = y1 - y0; + if (cropW < 2 || cropH < 2) return null; + + const cropped = new Float32Array(cropW * cropH); + if (mode === "circle") { + const rSq = radius * radius; + for (let dy = 0; dy < cropH; dy++) { + for (let dx = 0; dx < cropW; dx++) { + const ix = x0 + dx, iy = y0 + dy; + const distSq = (ix - centerCol) * (ix - centerCol) + (iy - centerRow) * (iy - centerRow); + cropped[dy * cropW + dx] = distSq <= rSq ? data[iy * imgW + ix] : 0; + } + } + } else { + for (let dy = 0; dy < cropH; dy++) { + const srcOff = (y0 + dy) * imgW + x0; + cropped.set(data.subarray(srcOff, srcOff + cropW), dy * cropW); + } + } + return { cropped, cropW, cropH }; +} + +// ============================================================================ +// Main Component +// ============================================================================ +function Show4DSTEM() { + // Direct model access for batched updates + const model = useModel(); + + // ───────────────────────────────────────────────────────────────────────── + // Model State (synced with Python) + // ───────────────────────────────────────────────────────────────────────── + const [shapeRows] = useModelState("shape_rows"); + const [shapeCols] = useModelState("shape_cols"); + const [detRows] = useModelState("det_rows"); + const [detCols] = useModelState("det_cols"); + + const [posRow, setPosRow] = useModelState("pos_row"); + const [posCol, setPosCol] = useModelState("pos_col"); + const [roiCenterCol, setRoiCenterCol] = useModelState("roi_center_col"); + const [roiCenterRow, setRoiCenterRow] = useModelState("roi_center_row"); + const [pixelSize] = useModelState("pixel_size"); + const [kPixelSize] = useModelState("k_pixel_size"); + const [kCalibrated] = useModelState("k_calibrated"); + const [widgetVersion] = useModelState("widget_version"); + const [title] = useModelState("title"); + + const [frameBytes] = useModelState("frame_bytes"); + const [virtualImageBytes] = useModelState("virtual_image_bytes"); + + // ROI state + const [roiRadius, setRoiRadius] = useModelState("roi_radius"); + const [roiRadiusInner, setRoiRadiusInner] = useModelState("roi_radius_inner"); + const [roiMode, setRoiMode] = useModelState("roi_mode"); + const [roiWidth, setRoiWidth] = useModelState("roi_width"); + const [roiHeight, setRoiHeight] = useModelState("roi_height"); + + // Global min/max for DP normalization (from Python) + const [dpGlobalMin] = useModelState("dp_global_min"); + const [dpGlobalMax] = useModelState("dp_global_max"); + + // VI min/max for normalization (from Python) + const [viDataMin] = useModelState("vi_data_min"); + const [viDataMax] = useModelState("vi_data_max"); + + // Detector calibration (for presets) + const [bfRadius] = useModelState("bf_radius"); + const [centerCol] = useModelState("center_col"); + const [centerRow] = useModelState("center_row"); + + // Path animation state + const [pathPlaying, setPathPlaying] = useModelState("path_playing"); + const [pathIndex, setPathIndex] = useModelState("path_index"); + const [pathLength] = useModelState("path_length"); + const [pathIntervalMs] = useModelState("path_interval_ms"); + const [pathLoop] = useModelState("path_loop"); + + // Frame animation state (5D time/tilt series) + const [frameIdx, setFrameIdx] = useModelState("frame_idx"); + const [nFrames] = useModelState("n_frames"); + const [frameDimLabel] = useModelState("frame_dim_label"); + const [frameLabels] = useModelState("frame_labels"); + const [framePlaying, setFramePlaying] = useModelState("frame_playing"); + const [frameLoop, setFrameLoop] = useModelState("frame_loop"); + const [frameFps, setFrameFps] = useModelState("frame_fps"); + const [frameReverse, setFrameReverse] = useModelState("frame_reverse"); + const [frameBoomerang, setFrameBoomerang] = useModelState("frame_boomerang"); + + // Profile line state (synced with Python) + const [profileLine, setProfileLine] = useModelState<{row: number; col: number}[]>("profile_line"); + const [profileWidth] = useModelState("profile_width"); + + // Auto-detection trigger + // ───────────────────────────────────────────────────────────────────────── + // Local State (UI-only, not synced to Python) + // ───────────────────────────────────────────────────────────────────────── + const [localKCol, setLocalKCol] = React.useState(roiCenterCol); + const [localKRow, setLocalKRow] = React.useState(roiCenterRow); + const [localPosRow, setLocalPosRow] = React.useState(posRow); + const [localPosCol, setLocalPosCol] = React.useState(posCol); + const [isDraggingDP, setIsDraggingDP] = React.useState(false); + const [isDraggingVI, setIsDraggingVI] = React.useState(false); + const [isDraggingFFT, setIsDraggingFFT] = React.useState(false); + const [fftDragStart, setFftDragStart] = React.useState<{ x: number, y: number, panX: number, panY: number } | null>(null); + const [isDraggingResize, setIsDraggingResize] = React.useState(false); + const [isDraggingResizeInner, setIsDraggingResizeInner] = React.useState(false); // For annular inner handle + const [isHoveringResize, setIsHoveringResize] = React.useState(false); + const [isHoveringResizeInner, setIsHoveringResizeInner] = React.useState(false); + const resizeAspectRef = React.useRef(null); + // VI ROI drag/resize states (same pattern as DP) + const [isDraggingViRoi, setIsDraggingViRoi] = React.useState(false); + const [isDraggingViRoiResize, setIsDraggingViRoiResize] = React.useState(false); + const [isHoveringViRoiResize, setIsHoveringViRoiResize] = React.useState(false); + // Independent colormaps for DP and VI panels + const [showDpColorbar, setShowDpColorbar] = useModelState("dp_show_colorbar"); + const [dpColormap, setDpColormap] = useModelState("dp_colormap"); + const [viColormap, setViColormap] = useModelState("vi_colormap"); + // vmin/vmax percentile clipping (0-100) + const [dpVminPct, setDpVminPct] = useModelState("dp_vmin_pct"); + const [dpVmaxPct, setDpVmaxPct] = useModelState("dp_vmax_pct"); + const [viVminPct, setViVminPct] = useModelState("vi_vmin_pct"); + const [viVmaxPct, setViVmaxPct] = useModelState("vi_vmax_pct"); + // Absolute intensity bounds (override percentile sliders when both set) + const [traitDpVmin] = useModelState("dp_vmin"); + const [traitDpVmax] = useModelState("dp_vmax"); + const [traitViVmin] = useModelState("vi_vmin"); + const [traitViVmax] = useModelState("vi_vmax"); + // Scale mode: "linear" | "log" | "power" + const [dpScaleMode, setDpScaleMode] = useModelState<"linear" | "log" | "power">("dp_scale_mode"); + const [dpPowerExp] = useModelState("dp_power_exp"); + const [viScaleMode, setViScaleMode] = useModelState<"linear" | "log" | "power">("vi_scale_mode"); + const [viPowerExp] = useModelState("vi_power_exp"); + + // VI ROI state (real-space region selection for summed DP) - synced with Python + const [viRoiMode, setViRoiMode] = useModelState("vi_roi_mode"); + const [viRoiCenterRow, setViRoiCenterRow] = useModelState("vi_roi_center_row"); + const [viRoiCenterCol, setViRoiCenterCol] = useModelState("vi_roi_center_col"); + const [viRoiRadius, setViRoiRadius] = useModelState("vi_roi_radius"); + const [viRoiWidth, setViRoiWidth] = useModelState("vi_roi_width"); + const [viRoiHeight, setViRoiHeight] = useModelState("vi_roi_height"); + // Local VI ROI center for smooth dragging + const [localViRoiCenterRow, setLocalViRoiCenterRow] = React.useState(viRoiCenterRow || 0); + const [localViRoiCenterCol, setLocalViRoiCenterCol] = React.useState(viRoiCenterCol || 0); + const [summedDpBytes] = useModelState("summed_dp_bytes"); + const [summedDpCount] = useModelState("summed_dp_count"); + const [dpStats] = useModelState("dp_stats"); // [mean, min, max, std] + const [viStats] = useModelState("vi_stats"); // [mean, min, max, std] + const [showFft, setShowFft] = useModelState("show_fft"); + const [fftWindow, setFftWindow] = useModelState("fft_window"); + const [disabledTools, setDisabledTools] = useModelState("disabled_tools"); + const [hiddenTools, setHiddenTools] = useModelState("hidden_tools"); + const [showControls] = useModelState("show_controls"); + + const toolVisibility = React.useMemo( + () => computeToolVisibility("Show4DSTEM", disabledTools, hiddenTools), + [disabledTools, hiddenTools], + ); + + const hideDisplay = toolVisibility.isHidden("display"); + const hideHistogram = toolVisibility.isHidden("histogram"); + const hideStats = toolVisibility.isHidden("stats"); + const hidePlayback = toolVisibility.isHidden("playback"); + const hideView = toolVisibility.isHidden("view"); + const hideExport = toolVisibility.isHidden("export"); + const hideRoi = toolVisibility.isHidden("roi"); + const hideProfile = toolVisibility.isHidden("profile"); + const hideVirtual = toolVisibility.isHidden("virtual"); + const hideFrame = toolVisibility.isHidden("frame"); + const hideFft = toolVisibility.isHidden("fft") || hideVirtual; + + const lockDisplay = toolVisibility.isLocked("display"); + const lockHistogram = toolVisibility.isLocked("histogram"); + const lockStats = toolVisibility.isLocked("stats"); + const lockNavigation = toolVisibility.isLocked("navigation"); + const lockPlayback = toolVisibility.isLocked("playback"); + const lockView = toolVisibility.isLocked("view"); + const lockExport = toolVisibility.isLocked("export"); + const lockRoi = toolVisibility.isLocked("roi"); + const lockProfile = toolVisibility.isLocked("profile"); + const lockVirtual = toolVisibility.isLocked("virtual"); + const lockFrame = toolVisibility.isLocked("frame"); + const lockFft = toolVisibility.isLocked("fft") || lockVirtual; + const effectiveShowFft = showFft && !hideFft; + + // ROI FFT state (VI ROI crops virtual image for FFT) + const [fftCropDims, setFftCropDims] = React.useState<{ cropWidth: number; cropHeight: number; fftWidth: number; fftHeight: number } | null>(null); + const roiFftActive = effectiveShowFft && viRoiMode !== "off"; + + // Canvas resize state + const [canvasSize, setCanvasSize] = React.useState(CANVAS_SIZE); + const [isResizingCanvas, setIsResizingCanvas] = React.useState(false); + const [resizeCanvasStart, setResizeCanvasStart] = React.useState<{ x: number; y: number; size: number } | null>(null); + + // Export + const [, setGifExportRequested] = useModelState("_gif_export_requested"); + const [gifData] = useModelState("_gif_data"); + const [gifMetadataJson] = useModelState("_gif_metadata_json"); + const [exporting, setExporting] = React.useState(false); + const [dpExportAnchor, setDpExportAnchor] = React.useState(null); + const [viExportAnchor, setViExportAnchor] = React.useState(null); + + // Cursor readout state + const [cursorInfo, setCursorInfo] = React.useState<{ row: number; col: number; value: number; panel: string } | null>(null); + + // DP Line profile state + const [profileActive, setProfileActive] = React.useState(false); + const [profileData, setProfileData] = React.useState(null); + const [profileHeight, setProfileHeight] = React.useState(76); + const [isResizingProfile, setIsResizingProfile] = React.useState(false); + const profileResizeStart = React.useRef<{ startY: number; startHeight: number } | null>(null); + const profileCanvasRef = React.useRef(null); + const profileBaseImageRef = React.useRef(null); + const profileLayoutRef = React.useRef<{ padLeft: number; plotW: number; padTop: number; plotH: number; gMin: number; gMax: number; totalDist: number; xUnit: string } | null>(null); + const profilePoints = profileLine || []; + const rawDpDataRef = React.useRef(null); + const dpClickStartRef = React.useRef<{ x: number; y: number } | null>(null); + const [draggingDpProfileEndpoint, setDraggingDpProfileEndpoint] = React.useState<0 | 1 | null>(null); + const [isDraggingDpProfileLine, setIsDraggingDpProfileLine] = React.useState(false); + const [hoveredDpProfileEndpoint, setHoveredDpProfileEndpoint] = React.useState<0 | 1 | null>(null); + const [isHoveringDpProfileLine, setIsHoveringDpProfileLine] = React.useState(false); + const dpProfileDragStartRef = React.useRef<{ row: number; col: number; p0: { row: number; col: number }; p1: { row: number; col: number } } | null>(null); + const dpDragOffsetRef = React.useRef<{ dRow: number; dCol: number }>({ dRow: 0, dCol: 0 }); + + // VI Line profile state + const [viProfileActive, setViProfileActive] = React.useState(false); + const [viProfileData, setViProfileData] = React.useState(null); + const [viProfilePoints, setViProfilePoints] = React.useState>([]); + const [viProfileHeight, setViProfileHeight] = React.useState(76); + const [isResizingViProfile, setIsResizingViProfile] = React.useState(false); + const viProfileResizeStart = React.useRef<{ startY: number; startHeight: number } | null>(null); + const viProfileCanvasRef = React.useRef(null); + const viProfileBaseImageRef = React.useRef(null); + const viProfileLayoutRef = React.useRef<{ padLeft: number; plotW: number; padTop: number; plotH: number; gMin: number; gMax: number; totalDist: number; xUnit: string } | null>(null); + const rawViDataRef = React.useRef(null); + const viClickStartRef = React.useRef<{ x: number; y: number } | null>(null); + const [draggingViProfileEndpoint, setDraggingViProfileEndpoint] = React.useState<0 | 1 | null>(null); + const [isDraggingViProfileLine, setIsDraggingViProfileLine] = React.useState(false); + const [hoveredViProfileEndpoint, setHoveredViProfileEndpoint] = React.useState<0 | 1 | null>(null); + const [isHoveringViProfileLine, setIsHoveringViProfileLine] = React.useState(false); + const viProfileDragStartRef = React.useRef<{ row: number; col: number; p0: { row: number; col: number }; p1: { row: number; col: number } } | null>(null); + const viRoiDragOffsetRef = React.useRef<{ dRow: number; dCol: number }>({ dRow: 0, dCol: 0 }); + + // Theme detection + const { themeInfo, colors: themeColors } = useTheme(); + const roiColors = themeInfo.theme === "dark" ? DARK_ROI_COLORS : LIGHT_ROI_COLORS; + const accentGreen = themeInfo.theme === "dark" ? "#0f0" : "#1a7a1a"; + + // Themed typography — applies theme colors to module-level font sizes + const typo = React.useMemo(() => ({ + label: { ...typography.label, color: themeColors.textMuted }, + labelSmall: { ...typography.labelSmall, color: themeColors.textMuted }, + value: { ...typography.value, color: themeColors.textMuted }, + title: { ...typography.title, color: themeColors.accent }, + }), [themeColors]); + + // Compute VI canvas dimensions to respect aspect ratio of rectangular scans + const viCanvasWidth = shapeRows > shapeCols ? Math.round(canvasSize * (shapeCols / shapeRows)) : canvasSize; + const viCanvasHeight = shapeCols > shapeRows ? Math.round(canvasSize * (shapeRows / shapeCols)) : canvasSize; + + // Histogram data - use state to ensure re-renders (both are Float32Array now) + const [dpHistogramData, setDpHistogramData] = React.useState(null); + const [viHistogramData, setViHistogramData] = React.useState(null); + + // Parse DP frame bytes for histogram (float32 now) + React.useEffect(() => { + if (!frameBytes) return; + // Parse as Float32Array since Python now sends raw float32 + const rawData = new Float32Array(frameBytes.buffer, frameBytes.byteOffset, frameBytes.byteLength / 4); + // Store raw data for profile sampling + if (!rawDpDataRef.current || rawDpDataRef.current.length !== rawData.length) { + rawDpDataRef.current = new Float32Array(rawData.length); + } + rawDpDataRef.current.set(rawData); + // Apply scale transformation for histogram display + const scaledData = new Float32Array(rawData.length); + if (dpScaleMode === "log") { + for (let i = 0; i < rawData.length; i++) { + scaledData[i] = Math.log1p(Math.max(0, rawData[i])); + } + } else if (dpScaleMode === "power") { + for (let i = 0; i < rawData.length; i++) { + scaledData[i] = Math.pow(Math.max(0, rawData[i]), dpPowerExp); + } + } else { + scaledData.set(rawData); + } + setDpHistogramData(scaledData); + }, [frameBytes, dpScaleMode, dpPowerExp]); + + // GPU FFT state + const gpuFFTRef = React.useRef(null); + const [gpuReady, setGpuReady] = React.useState(false); + + // Path animation timer + React.useEffect(() => { + if (!pathPlaying || pathLength === 0) return; + + const timer = setInterval(() => { + setPathIndex((prev: number) => { + const next = prev + 1; + if (next >= pathLength) { + if (pathLoop) { + return 0; // Loop back to start + } else { + setPathPlaying(false); // Stop at end + return prev; + } + } + return next; + }); + }, pathIntervalMs); + + return () => clearInterval(timer); + }, [pathPlaying, pathLength, pathIntervalMs, pathLoop, setPathIndex, setPathPlaying]); + + // Frame animation timer (5D time/tilt series) + const frameBounceDir = React.useRef(1); + React.useEffect(() => { + frameBounceDir.current = frameReverse ? -1 : 1; + }, [frameReverse]); + + React.useEffect(() => { + if (!framePlaying || nFrames <= 1) return; + + const intervalMs = 1000 / Math.max(0.1, frameFps); + const timer = setInterval(() => { + setFrameIdx((prev: number) => { + let next: number; + if (frameBoomerang) { + next = prev + frameBounceDir.current; + if (next >= nFrames) { frameBounceDir.current = -1; next = nFrames - 2; } + if (next < 0) { frameBounceDir.current = 1; next = 1; } + next = Math.max(0, Math.min(nFrames - 1, next)); + } else { + next = prev + (frameReverse ? -1 : 1); + if (next >= nFrames) { + if (frameLoop) return 0; + setFramePlaying(false); + return prev; + } + if (next < 0) { + if (frameLoop) return nFrames - 1; + setFramePlaying(false); + return prev; + } + } + return next; + }); + }, intervalMs); + + return () => clearInterval(timer); + }, [framePlaying, nFrames, frameFps, frameLoop, frameReverse, frameBoomerang, setFrameIdx, setFramePlaying]); + + // Initialize WebGPU FFT on mount + React.useEffect(() => { + getWebGPUFFT().then(fft => { + if (fft) { + gpuFFTRef.current = fft; + setGpuReady(true); + } + }); + }, []); + + // Root element ref (theme-aware styling handled via CSS variables) + const rootRef = React.useRef(null); + + // Zoom state + const [dpZoom, setDpZoom] = React.useState(1); + const [dpPanX, setDpPanX] = React.useState(0); + const [dpPanY, setDpPanY] = React.useState(0); + const [viZoom, setViZoom] = React.useState(1); + const [viPanX, setViPanX] = React.useState(0); + const [viPanY, setViPanY] = React.useState(0); + const [fftZoom, setFftZoom] = React.useState(1); + const [fftPanX, setFftPanX] = React.useState(0); + const [fftPanY, setFftPanY] = React.useState(0); + const [fftScaleMode, setFftScaleMode] = useModelState<"linear" | "log" | "power">("fft_scale_mode"); + const [fftPowerExp] = useModelState("fft_power_exp"); + const [fftColormap, setFftColormap] = useModelState("fft_colormap"); + const [fftAuto, setFftAuto] = useModelState("fft_auto"); + const [fftVminPct, setFftVminPct] = useModelState("fft_vmin_pct"); + const [fftVmaxPct, setFftVmaxPct] = useModelState("fft_vmax_pct"); + const [fftStats, setFftStats] = React.useState(null); // [mean, min, max, std] + const [fftHistogramData, setFftHistogramData] = React.useState(null); + const [fftDataMin, setFftDataMin] = React.useState(0); + const [fftDataMax, setFftDataMax] = React.useState(1); + const [fftClickInfo, setFftClickInfo] = React.useState<{ + row: number; col: number; distPx: number; + spatialFreq: number | null; dSpacing: number | null; + } | null>(null); + const fftClickStartRef = React.useRef<{ x: number; y: number } | null>(null); + + const isTypingTarget = React.useCallback((target: EventTarget | null): boolean => { + if (!(target instanceof HTMLElement)) return false; + if (target.isContentEditable) return true; + return target.closest("input, textarea, select, [role='textbox'], [contenteditable='true']") !== null; + }, []); + + const handleRootMouseDownCapture = React.useCallback((e: React.MouseEvent) => { + const target = e.target as HTMLElement | null; + if (target?.closest("canvas")) rootRef.current?.focus(); + }, []); + + const handleKeyDown = React.useCallback((e: React.KeyboardEvent) => { + if (isTypingTarget(e.target)) return; + + const step = e.shiftKey ? 10 : 1; + let handled = false; + + switch (e.key) { + case "ArrowUp": + if (!lockNavigation) { + setPosRow(Math.max(0, posRow - step)); + handled = true; + } + break; + case "ArrowDown": + if (!lockNavigation) { + setPosRow(Math.min(shapeRows - 1, posRow + step)); + handled = true; + } + break; + case "ArrowLeft": + if (!lockNavigation) { + setPosCol(Math.max(0, posCol - step)); + handled = true; + } + break; + case "ArrowRight": + if (!lockNavigation) { + setPosCol(Math.min(shapeCols - 1, posCol + step)); + handled = true; + } + break; + case " ": // Space bar + if (!lockPlayback && pathLength > 0) { + setPathPlaying(!pathPlaying); + handled = true; + } + break; + case "r": + case "R": + if (!lockView) { + setDpZoom(1); setDpPanX(0); setDpPanY(0); + setViZoom(1); setViPanX(0); setViPanY(0); + setFftZoom(1); setFftPanX(0); setFftPanY(0); + handled = true; + } + break; + case "[": + if (!lockPlayback && !lockFrame && nFrames > 1) { + setFrameIdx(Math.max(0, frameIdx - 1)); + handled = true; + } + break; + case "]": + if (!lockPlayback && !lockFrame && nFrames > 1) { + setFrameIdx(Math.min(nFrames - 1, frameIdx + 1)); + handled = true; + } + break; + case "Escape": + rootRef.current?.blur(); + handled = true; + break; + } + + if (handled) { + e.preventDefault(); + e.stopPropagation(); + } + }, [ + frameIdx, isTypingTarget, lockFrame, lockNavigation, lockPlayback, lockView, nFrames, pathLength, + pathPlaying, posCol, posRow, setFrameIdx, setPathPlaying, setPosCol, setPosRow, shapeCols, shapeRows, + ]); + + React.useEffect(() => { + if (hideFft && showFft) { + setShowFft(false); + } + }, [hideFft, showFft, setShowFft]); + + React.useEffect(() => { + if (lockPlayback && pathPlaying) { + setPathPlaying(false); + } + }, [lockPlayback, pathPlaying, setPathPlaying]); + + React.useEffect(() => { + if ((lockPlayback || lockFrame) && framePlaying) { + setFramePlaying(false); + } + }, [lockFrame, lockPlayback, framePlaying, setFramePlaying]); + + React.useEffect(() => { + if (hideRoi) { + if (roiMode !== "point") setRoiMode("point"); + if (viRoiMode !== "off") setViRoiMode("off"); + } + }, [hideRoi, roiMode, viRoiMode, setRoiMode, setViRoiMode]); + + React.useEffect(() => { + if (hideProfile) { + if (profileActive) setProfileActive(false); + if (viProfileActive) setViProfileActive(false); + if (profileLine.length > 0) setProfileLine([]); + if (profileData) setProfileData(null); + if (viProfilePoints.length > 0) setViProfilePoints([]); + if (viProfileData) setViProfileData(null); + setHoveredDpProfileEndpoint(null); + setIsHoveringDpProfileLine(false); + setHoveredViProfileEndpoint(null); + setIsHoveringViProfileLine(false); + } + }, [ + hideProfile, profileActive, profileLine, profileData, setProfileLine, viProfileActive, + viProfilePoints, viProfileData, + ]); + + // Sync local state + React.useEffect(() => { + if (!isDraggingDP && !isDraggingResize) { setLocalKCol(roiCenterCol); setLocalKRow(roiCenterRow); } + }, [roiCenterCol, roiCenterRow, isDraggingDP, isDraggingResize]); + + React.useEffect(() => { + if (!isDraggingVI) { setLocalPosRow(posRow); setLocalPosCol(posCol); } + }, [posRow, posCol, isDraggingVI]); + + // Sync VI ROI local state + React.useEffect(() => { + if (!isDraggingViRoi && !isDraggingViRoiResize) { + setLocalViRoiCenterRow(viRoiCenterRow || shapeRows / 2); + setLocalViRoiCenterCol(viRoiCenterCol || shapeCols / 2); + } + }, [viRoiCenterRow, viRoiCenterCol, isDraggingViRoi, isDraggingViRoiResize, shapeRows, shapeCols]); + + // Canvas refs + const dpCanvasRef = React.useRef(null); + const dpOverlayRef = React.useRef(null); + const dpUiRef = React.useRef(null); // High-DPI UI overlay for scale bar + const dpOffscreenRef = React.useRef(null); + const dpImageDataRef = React.useRef(null); + const virtualCanvasRef = React.useRef(null); + const virtualOverlayRef = React.useRef(null); + const viUiRef = React.useRef(null); // High-DPI UI overlay for scale bar + const viOffscreenRef = React.useRef(null); + const viImageDataRef = React.useRef(null); + const fftCanvasRef = React.useRef(null); + const fftOverlayRef = React.useRef(null); + const fftOffscreenRef = React.useRef(null); + const fftImageDataRef = React.useRef(null); + + // Offscreen version counters — bump when colormap/data changes, cheap draw effects depend on these + const [dpOffscreenVersion, setDpOffscreenVersion] = React.useState(0); + const [viOffscreenVersion, setViOffscreenVersion] = React.useState(0); + const [fftOffscreenVersion, setFftOffscreenVersion] = React.useState(0); + + // Cached colorbar vmin/vmax — computed in expensive DP effect, reused in UI overlay without recomputing + const dpColorbarVminRef = React.useRef(0); + const dpColorbarVmaxRef = React.useRef(1); + + // Device pixel ratio for high-DPI UI overlays + const DPR = typeof window !== 'undefined' ? window.devicePixelRatio || 1 : 1; + + // ───────────────────────────────────────────────────────────────────────── + // Effects: Canvas Rendering & Animation + // ───────────────────────────────────────────────────────────────────────── + + // Prevent page scroll when scrolling on canvases + // Re-run when showFft changes since FFT canvas is conditionally rendered + React.useEffect(() => { + const preventDefault = (e: WheelEvent) => e.preventDefault(); + const overlays = [dpOverlayRef.current, virtualOverlayRef.current, fftOverlayRef.current]; + overlays.forEach(el => el?.addEventListener("wheel", preventDefault, { passive: false })); + return () => overlays.forEach(el => el?.removeEventListener("wheel", preventDefault)); + }, [effectiveShowFft]); + + // Store raw data for filtering/FFT + const rawVirtualImageRef = React.useRef(null); + const fftWorkRealRef = React.useRef(null); + const fftWorkImagRef = React.useRef(null); + const fftMagnitudeRef = React.useRef(null); + const fftMagCacheRef = React.useRef(null); + + // Parse virtual image bytes into Float32Array and apply scale for histogram + React.useEffect(() => { + if (!virtualImageBytes) return; + // Parse as Float32Array + const numFloats = virtualImageBytes.byteLength / 4; + const rawData = new Float32Array(virtualImageBytes.buffer, virtualImageBytes.byteOffset, numFloats); + + // Store a copy for filtering/FFT (rawData is a view, we need a copy) + let storedData = rawVirtualImageRef.current; + if (!storedData || storedData.length !== numFloats) { + storedData = new Float32Array(numFloats); + rawVirtualImageRef.current = storedData; + } + storedData.set(rawData); + + // Also store for VI profile sampling + if (!rawViDataRef.current || rawViDataRef.current.length !== numFloats) { + rawViDataRef.current = new Float32Array(numFloats); + } + rawViDataRef.current.set(rawData); + + // Apply scale transformation for histogram display + const scaledData = new Float32Array(numFloats); + if (viScaleMode === "log") { + for (let i = 0; i < numFloats; i++) { + scaledData[i] = Math.log1p(Math.max(0, rawData[i])); + } + } else if (viScaleMode === "power") { + for (let i = 0; i < numFloats; i++) { + scaledData[i] = Math.pow(Math.max(0, rawData[i]), viPowerExp); + } + } else { + scaledData.set(rawData); + } + setViHistogramData(scaledData); + }, [virtualImageBytes, viScaleMode, viPowerExp]); + + // Render DP with zoom (use summed DP when VI ROI is active) + // Expensive: colormap + data processing → cached offscreen canvas + React.useEffect(() => { + // Determine which bytes to display: summed DP (if VI ROI active) or single frame + const usesSummedDp = viRoiMode && viRoiMode !== "off" && summedDpBytes && summedDpBytes.byteLength > 0; + const sourceBytes = usesSummedDp ? summedDpBytes : frameBytes; + if (!sourceBytes) return; + + const lut = COLORMAPS[dpColormap] || COLORMAPS.inferno; + + // Parse raw float32 data and apply scale transformation + const rawData = new Float32Array(sourceBytes.buffer, sourceBytes.byteOffset, sourceBytes.byteLength / 4); + let scaled: Float32Array; + if (dpScaleMode === "log") { + scaled = new Float32Array(rawData.length); + for (let i = 0; i < rawData.length; i++) { + scaled[i] = Math.log1p(Math.max(0, rawData[i])); + } + } else if (dpScaleMode === "power") { + scaled = new Float32Array(rawData.length); + for (let i = 0; i < rawData.length; i++) { + scaled[i] = Math.pow(Math.max(0, rawData[i]), dpPowerExp); + } + } else { + scaled = rawData; + } + + // Compute actual min/max of scaled data for normalization + const { min: dataMin, max: dataMax } = findDataRange(scaled); + + // Apply absolute bounds or percentile clipping + let vmin: number, vmax: number; + if (traitDpVmin != null && traitDpVmax != null) { + if (dpScaleMode === "log") { + vmin = Math.log1p(Math.max(traitDpVmin, 0)); + vmax = Math.log1p(Math.max(traitDpVmax, 0)); + } else if (dpScaleMode === "power") { + vmin = Math.pow(Math.max(traitDpVmin, 0), dpPowerExp); + vmax = Math.pow(Math.max(traitDpVmax, 0), dpPowerExp); + } else { + vmin = traitDpVmin; + vmax = traitDpVmax; + } + } else { + ({ vmin, vmax } = sliderRange(dataMin, dataMax, dpVminPct, dpVmaxPct)); + } + + let offscreen = dpOffscreenRef.current; + if (!offscreen) { + offscreen = document.createElement("canvas"); + dpOffscreenRef.current = offscreen; + } + const sizeChanged = offscreen.width !== detCols || offscreen.height !== detRows; + if (sizeChanged) { + offscreen.width = detCols; + offscreen.height = detRows; + dpImageDataRef.current = null; + } + const offCtx = offscreen.getContext("2d"); + if (!offCtx) return; + + let imgData = dpImageDataRef.current; + if (!imgData) { + imgData = offCtx.createImageData(detCols, detRows); + dpImageDataRef.current = imgData; + } + applyColormap(scaled, imgData.data, lut, vmin, vmax); + offCtx.putImageData(imgData, 0, 0); + // Cache colorbar range for the UI overlay (avoids recomputing findDataRange on every zoom/pan) + dpColorbarVminRef.current = vmin; + dpColorbarVmaxRef.current = vmax; + setDpOffscreenVersion(v => v + 1); + }, [frameBytes, summedDpBytes, viRoiMode, detRows, detCols, dpColormap, dpVminPct, dpVmaxPct, dpScaleMode, dpPowerExp, traitDpVmin, traitDpVmax]); + + // Cheap: zoom/pan redraw — just drawImage from cached offscreen + // useLayoutEffect prevents black flash when canvas dimensions change (resize) + React.useLayoutEffect(() => { + const offscreen = dpOffscreenRef.current; + if (!offscreen || !dpCanvasRef.current) return; + const canvas = dpCanvasRef.current; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.imageSmoothingEnabled = false; + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.save(); + ctx.translate(dpPanX, dpPanY); + ctx.scale(dpZoom, dpZoom); + ctx.drawImage(offscreen, 0, 0); + ctx.restore(); + }, [dpOffscreenVersion, dpZoom, dpPanX, dpPanY]); + + // Render DP overlay - just clear (ROI shapes now drawn on high-DPI UI canvas) + React.useEffect(() => { + if (!dpOverlayRef.current) return; + const canvas = dpOverlayRef.current; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.clearRect(0, 0, canvas.width, canvas.height); + // All visual overlays (crosshair, ROI shapes, scale bar) are now on dpUiRef for crisp rendering + }, [localKCol, localKRow, isDraggingDP, isDraggingResize, isDraggingResizeInner, isHoveringResize, isHoveringResizeInner, dpZoom, dpPanX, dpPanY, roiMode, roiRadius, roiRadiusInner, roiWidth, roiHeight, detRows, detCols]); + + // Expensive: VI colormap + data processing → cached offscreen canvas + React.useEffect(() => { + if (!rawVirtualImageRef.current) return; + + const width = shapeCols; + const height = shapeRows; + const filtered = rawVirtualImageRef.current; + + // Apply scale transformation first + let scaled = filtered; + if (viScaleMode === "log") { + scaled = new Float32Array(filtered.length); + for (let i = 0; i < filtered.length; i++) { + scaled[i] = Math.log1p(Math.max(0, filtered[i])); + } + } else if (viScaleMode === "power") { + scaled = new Float32Array(filtered.length); + for (let i = 0; i < filtered.length; i++) { + scaled[i] = Math.pow(Math.max(0, filtered[i]), viPowerExp); + } + } + + // Use Python's pre-computed min/max when valid, fallback to computing from data + let dataMin: number, dataMax: number; + const hasValidMinMax = viDataMin !== undefined && viDataMax !== undefined && viDataMax > viDataMin; + if (hasValidMinMax) { + // Apply scale transform to Python's values + if (viScaleMode === "log") { + dataMin = Math.log1p(Math.max(0, viDataMin)); + dataMax = Math.log1p(Math.max(0, viDataMax)); + } else if (viScaleMode === "power") { + dataMin = Math.pow(Math.max(0, viDataMin), viPowerExp); + dataMax = Math.pow(Math.max(0, viDataMax), viPowerExp); + } else { + dataMin = viDataMin; + dataMax = viDataMax; + } + } else { + // Fallback: compute from scaled data + const r = findDataRange(scaled); + dataMin = r.min; + dataMax = r.max; + } + + // Apply absolute bounds or percentile clipping + let vmin: number, vmax: number; + if (traitViVmin != null && traitViVmax != null) { + if (viScaleMode === "log") { + vmin = Math.log1p(Math.max(traitViVmin, 0)); + vmax = Math.log1p(Math.max(traitViVmax, 0)); + } else if (viScaleMode === "power") { + vmin = Math.pow(Math.max(traitViVmin, 0), viPowerExp); + vmax = Math.pow(Math.max(traitViVmax, 0), viPowerExp); + } else { + vmin = traitViVmin; + vmax = traitViVmax; + } + } else { + ({ vmin, vmax } = sliderRange(dataMin, dataMax, viVminPct, viVmaxPct)); + } + + const lut = COLORMAPS[viColormap] || COLORMAPS.inferno; + let offscreen = viOffscreenRef.current; + if (!offscreen) { + offscreen = document.createElement("canvas"); + viOffscreenRef.current = offscreen; + } + const sizeChanged = offscreen.width !== width || offscreen.height !== height; + if (sizeChanged) { + offscreen.width = width; + offscreen.height = height; + viImageDataRef.current = null; + } + const offCtx = offscreen.getContext("2d"); + if (!offCtx) return; + + let imageData = viImageDataRef.current; + if (!imageData) { + imageData = offCtx.createImageData(width, height); + viImageDataRef.current = imageData; + } + applyColormap(scaled, imageData.data, lut, vmin, vmax); + offCtx.putImageData(imageData, 0, 0); + setViOffscreenVersion(v => v + 1); + // Note: viDataMin/viDataMax intentionally not in deps - they arrive with virtualImageBytes + // and we have a fallback if they're stale + }, [virtualImageBytes, shapeRows, shapeCols, viColormap, viVminPct, viVmaxPct, viScaleMode, viPowerExp, traitViVmin, traitViVmax]); + + // Cheap: VI zoom/pan redraw — just drawImage from cached offscreen + React.useLayoutEffect(() => { + const offscreen = viOffscreenRef.current; + if (!offscreen || !virtualCanvasRef.current) return; + const canvas = virtualCanvasRef.current; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.imageSmoothingEnabled = false; + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.save(); + ctx.translate(viPanX, viPanY); + ctx.scale(viZoom, viZoom); + ctx.drawImage(offscreen, 0, 0); + ctx.restore(); + }, [viOffscreenVersion, viZoom, viPanX, viPanY]); + + // Render virtual image overlay (just clear - crosshair drawn on high-DPI UI canvas) + React.useEffect(() => { + if (!virtualOverlayRef.current) return; + const canvas = virtualOverlayRef.current; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.clearRect(0, 0, canvas.width, canvas.height); + // Crosshair and scale bar now drawn on high-DPI UI canvas (viUiRef) + }, [localPosRow, localPosCol, isDraggingVI, viZoom, viPanX, viPanY, pixelSize, shapeRows, shapeCols]); + + // Compute FFT (expensive, async — only re-run on data/GPU changes) + const fftRealRef = React.useRef(null); + const fftImagRef = React.useRef(null); + const [fftVersion, setFftVersion] = React.useState(0); + + React.useEffect(() => { + if (!rawVirtualImageRef.current || !effectiveShowFft) { setFftCropDims(null); return; } + let cancelled = false; + let width = shapeCols; + let height = shapeRows; + let sourceData = rawVirtualImageRef.current; + let origCropW = 0, origCropH = 0; + + // ROI FFT: crop virtual image to VI ROI region and pre-pad to power-of-2 + if (roiFftActive) { + const crop = cropSingleROI(sourceData, shapeCols, shapeRows, viRoiMode, viRoiCenterRow, viRoiCenterCol, viRoiRadius, viRoiWidth, viRoiHeight); + if (crop) { + origCropW = crop.cropW; + origCropH = crop.cropH; + // Apply Hann window to crop at native dimensions BEFORE zero-padding + if (fftWindow) applyHannWindow2D(crop.cropped, crop.cropW, crop.cropH); + const padW = nextPow2(crop.cropW); + const padH = nextPow2(crop.cropH); + const padded = new Float32Array(padW * padH); + for (let y = 0; y < crop.cropH; y++) { + for (let x = 0; x < crop.cropW; x++) { + padded[y * padW + x] = crop.cropped[y * crop.cropW + x]; + } + } + sourceData = padded; + width = padW; + height = padH; + } + } + + // Pre-pad non-power-of-2 full images so fft2d doesn't truncate frequency data + if (!roiFftActive) { + const padW = nextPow2(width); + const padH = nextPow2(height); + if (padW !== width || padH !== height) { + const padded = new Float32Array(padW * padH); + for (let y = 0; y < height; y++) { + for (let x = 0; x < width; x++) { + padded[y * padW + x] = sourceData[y * width + x]; + } + } + sourceData = padded; + width = padW; + height = padH; + } + } + + const fftW = width, fftH = height; + if (gpuFFTRef.current && gpuReady) { + const runGpuFFT = async () => { + const real = sourceData.slice(); + const imag = new Float32Array(real.length); + const { real: fReal, imag: fImag } = await gpuFFTRef.current!.fft2D(real, imag, fftW, fftH, false); + if (cancelled) return; + fftshift(fReal, fftW, fftH); + fftshift(fImag, fftW, fftH); + fftRealRef.current = fReal; + fftImagRef.current = fImag; + if (origCropW > 0) { + setFftCropDims({ cropWidth: origCropW, cropHeight: origCropH, fftWidth: fftW, fftHeight: fftH }); + } else if (fftW !== shapeCols || fftH !== shapeRows) { + setFftCropDims({ cropWidth: shapeCols, cropHeight: shapeRows, fftWidth: fftW, fftHeight: fftH }); + } else { + setFftCropDims(null); + } + setFftVersion(v => v + 1); + }; + runGpuFFT(); + return () => { cancelled = true; }; + } else { + const len = sourceData.length; + let real = fftWorkRealRef.current; + if (!real || real.length !== len) { real = new Float32Array(len); fftWorkRealRef.current = real; } + real.set(sourceData); + let imag = fftWorkImagRef.current; + if (!imag || imag.length !== len) { imag = new Float32Array(len); fftWorkImagRef.current = imag; } else { imag.fill(0); } + fft2d(real, imag, fftW, fftH, false); + fftshift(real, fftW, fftH); + fftshift(imag, fftW, fftH); + fftRealRef.current = real; + fftImagRef.current = imag; + if (origCropW > 0) { + setFftCropDims({ cropWidth: origCropW, cropHeight: origCropH, fftWidth: fftW, fftHeight: fftH }); + } else if (fftW !== shapeCols || fftH !== shapeRows) { + setFftCropDims({ cropWidth: shapeCols, cropHeight: shapeRows, fftWidth: fftW, fftHeight: fftH }); + } else { + setFftCropDims(null); + } + setFftVersion(v => v + 1); + } + }, [virtualImageBytes, shapeRows, shapeCols, gpuReady, effectiveShowFft, roiFftActive, viRoiMode, viRoiCenterRow, viRoiCenterCol, viRoiRadius, viRoiWidth, viRoiHeight, fftWindow]); + + // Expensive: FFT magnitude + histogram + colormap → cached offscreen canvas + React.useEffect(() => { + if (!fftRealRef.current || !fftImagRef.current) return; + if (!effectiveShowFft) return; + + const width = fftCropDims?.fftWidth ?? shapeCols; + const height = fftCropDims?.fftHeight ?? shapeRows; + const real = fftRealRef.current; + const imag = fftImagRef.current; + const lut = COLORMAPS[fftColormap] || COLORMAPS.inferno; + + // Compute magnitude with scale mode + let magnitude = fftMagnitudeRef.current; + if (!magnitude || magnitude.length !== real.length) { + magnitude = new Float32Array(real.length); + fftMagnitudeRef.current = magnitude; + } + // Cache raw magnitude for peak-snap before applying scale transform + let rawMag = fftMagCacheRef.current; + if (!rawMag || rawMag.length !== real.length) { + rawMag = new Float32Array(real.length); + fftMagCacheRef.current = rawMag; + } + for (let i = 0; i < real.length; i++) { + const mag = Math.sqrt(real[i] * real[i] + imag[i] * imag[i]); + rawMag[i] = mag; + if (fftScaleMode === "log") { magnitude[i] = Math.log1p(mag); } + else if (fftScaleMode === "power") { magnitude[i] = Math.pow(mag, fftPowerExp); } + else { magnitude[i] = mag; } + } + + let displayMin: number, displayMax: number; + if (fftAuto) { + ({ min: displayMin, max: displayMax } = autoEnhanceFFT(magnitude, width, height)); + } else { + ({ min: displayMin, max: displayMax } = findDataRange(magnitude)); + } + setFftDataMin(displayMin); + setFftDataMax(displayMax); + const magStats = computeStats(magnitude); + setFftStats([magStats.mean, displayMin, displayMax, magStats.std]); + setFftHistogramData(magnitude.slice()); + + // Render to offscreen canvas + let offscreen = fftOffscreenRef.current; + if (!offscreen) { offscreen = document.createElement("canvas"); fftOffscreenRef.current = offscreen; } + if (offscreen.width !== width || offscreen.height !== height) { + offscreen.width = width; offscreen.height = height; fftImageDataRef.current = null; + } + const offCtx = offscreen.getContext("2d"); + if (!offCtx) return; + let imgData = fftImageDataRef.current; + if (!imgData) { imgData = offCtx.createImageData(width, height); fftImageDataRef.current = imgData; } + + const { vmin, vmax } = sliderRange(displayMin, displayMax, fftVminPct, fftVmaxPct); + applyColormap(magnitude, imgData.data, lut, vmin, vmax); + offCtx.putImageData(imgData, 0, 0); + setFftOffscreenVersion(v => v + 1); + }, [effectiveShowFft, fftVersion, fftScaleMode, fftPowerExp, fftAuto, fftVminPct, fftVmaxPct, fftColormap, shapeRows, shapeCols, fftCropDims]); + + // Cheap: FFT zoom/pan redraw — just drawImage from cached offscreen + React.useLayoutEffect(() => { + if (!fftCanvasRef.current) return; + const canvas = fftCanvasRef.current; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + const offscreen = fftOffscreenRef.current; + if (!offscreen || !effectiveShowFft) { ctx.clearRect(0, 0, canvas.width, canvas.height); return; } + const fftW = offscreen.width; + const fftH = offscreen.height; + const canvasW = canvas.width; + const canvasH = canvas.height; + // Use bilinear smoothing when FFT dims differ from canvas (non-pow2 padding or ROI crop) + ctx.imageSmoothingEnabled = fftW !== canvasW || fftH !== canvasH; + ctx.clearRect(0, 0, canvasW, canvasH); + ctx.save(); + ctx.translate(fftPanX, fftPanY); + ctx.scale(fftZoom, fftZoom); + ctx.drawImage(offscreen, 0, 0); + ctx.restore(); + }, [fftOffscreenVersion, fftZoom, fftPanX, fftPanY, effectiveShowFft]); + + // Render FFT overlay with d-spacing crosshair marker + React.useEffect(() => { + if (!fftOverlayRef.current) return; + const canvas = fftOverlayRef.current; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.clearRect(0, 0, canvas.width, canvas.height); + + // D-spacing crosshair marker + if (fftClickInfo && effectiveShowFft) { + const fftW = fftCropDims?.fftWidth ?? shapeCols; + const fftH = fftCropDims?.fftHeight ?? shapeRows; + ctx.save(); + // Convert FFT image coords to canvas coords via zoom/pan transform + const screenX = fftPanX + fftZoom * fftClickInfo.col; + const screenY = fftPanY + fftZoom * fftClickInfo.row; + ctx.strokeStyle = "rgba(255, 255, 255, 0.9)"; + ctx.shadowColor = "rgba(0, 0, 0, 0.6)"; + ctx.shadowBlur = 2; + ctx.lineWidth = 1.5; + // Scale crosshair size relative to canvas (not zoom-dependent) + const r = 8 * Math.max(fftW, fftH) / 450; + const gap = 3 * Math.max(fftW, fftH) / 450; + const dotR = 4 * Math.max(fftW, fftH) / 450; + ctx.beginPath(); + ctx.moveTo(screenX - r, screenY); ctx.lineTo(screenX - gap, screenY); + ctx.moveTo(screenX + gap, screenY); ctx.lineTo(screenX + r, screenY); + ctx.moveTo(screenX, screenY - r); ctx.lineTo(screenX, screenY - gap); + ctx.moveTo(screenX, screenY + gap); ctx.lineTo(screenX, screenY + r); + ctx.stroke(); + ctx.beginPath(); + ctx.arc(screenX, screenY, dotR, 0, Math.PI * 2); + ctx.stroke(); + if (fftClickInfo.dSpacing != null) { + const d = fftClickInfo.dSpacing; + const label = d >= 10 ? `d = ${(d / 10).toFixed(2)} nm` : `d = ${d.toFixed(2)} \u00C5`; + const fontSize = Math.max(10, Math.round(11 * Math.max(fftW, fftH) / 450)); + ctx.font = `bold ${fontSize}px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif`; + ctx.fillStyle = "white"; + ctx.textAlign = "left"; + ctx.textBaseline = "bottom"; + ctx.fillText(label, screenX + r + 4, screenY - gap); + } + ctx.restore(); + } + }, [fftZoom, fftPanX, fftPanY, effectiveShowFft, fftClickInfo, shapeCols, shapeRows, fftCropDims]); + + // Clear FFT click info when virtual image changes (scan position, VI ROI, etc.) + React.useEffect(() => { + setFftClickInfo(null); + }, [virtualImageBytes]); + + // ───────────────────────────────────────────────────────────────────────── + // High-DPI Scale Bar UI Overlays + // ───────────────────────────────────────────────────────────────────────── + + // DP scale bar + crosshair + ROI overlay + profile line (high-DPI) + React.useEffect(() => { + if (!dpUiRef.current) return; + // Draw scale bar first (clears canvas) + const kUnit = kCalibrated ? "mrad" : "px"; + drawScaleBarHiDPI(dpUiRef.current, DPR, dpZoom, kPixelSize || 1, kUnit, detCols); + // Draw ROI overlay (circle, square, rect, annular) or point crosshair + if (roiMode === "point") { + drawDpCrosshairHiDPI(dpUiRef.current, DPR, localKCol, localKRow, dpZoom, dpPanX, dpPanY, detCols, detRows, isDraggingDP, roiColors); + } else { + drawRoiOverlayHiDPI( + dpUiRef.current, DPR, roiMode, + localKCol, localKRow, roiRadius, roiRadiusInner, roiWidth, roiHeight, + dpZoom, dpPanX, dpPanY, detCols, detRows, + isDraggingDP, isDraggingResize, isDraggingResizeInner, isHoveringResize, isHoveringResizeInner, + roiColors + ); + } + + // Profile line overlay + if (profileActive && profilePoints.length > 0) { + const canvas = dpUiRef.current; + const ctx = canvas.getContext("2d"); + if (ctx) { + ctx.save(); + ctx.scale(DPR, DPR); + const cssW = canvas.width / DPR; + const cssH = canvas.height / DPR; + const scaleX = cssW / detCols; + const scaleY = cssH / detRows; + const toScreenX = (col: number) => col * dpZoom * scaleX + dpPanX * scaleX; + const toScreenY = (row: number) => row * dpZoom * scaleY + dpPanY * scaleY; + + // Draw point A + const ax = toScreenX(profilePoints[0].col); + const ay = toScreenY(profilePoints[0].row); + ctx.fillStyle = themeColors.accent; + ctx.beginPath(); + ctx.arc(ax, ay, 4, 0, Math.PI * 2); + ctx.fill(); + + if (profilePoints.length === 2) { + const bx = toScreenX(profilePoints[1].col); + const by = toScreenY(profilePoints[1].row); + + // Draw band when profile width > 1 + if (profileWidth > 1) { + const dc = profilePoints[1].col - profilePoints[0].col; + const dr = profilePoints[1].row - profilePoints[0].row; + const lineLen = Math.sqrt(dc * dc + dr * dr); + if (lineLen > 0) { + const halfW = (profileWidth - 1) / 2; + const perpR = -dc / lineLen * halfW; + const perpC = dr / lineLen * halfW; + ctx.fillStyle = themeColors.accent + "20"; + ctx.strokeStyle = themeColors.accent; + ctx.lineWidth = 1; + ctx.setLineDash([3, 3]); + ctx.beginPath(); + ctx.moveTo(toScreenX(profilePoints[0].col + perpC), toScreenY(profilePoints[0].row + perpR)); + ctx.lineTo(toScreenX(profilePoints[1].col + perpC), toScreenY(profilePoints[1].row + perpR)); + ctx.lineTo(toScreenX(profilePoints[1].col - perpC), toScreenY(profilePoints[1].row - perpR)); + ctx.lineTo(toScreenX(profilePoints[0].col - perpC), toScreenY(profilePoints[0].row - perpR)); + ctx.closePath(); + ctx.fill(); + ctx.stroke(); + ctx.setLineDash([]); + } + } + + // Draw line A->B + ctx.strokeStyle = themeColors.accent; + ctx.lineWidth = 1.5; + ctx.beginPath(); + ctx.moveTo(ax, ay); + ctx.lineTo(bx, by); + ctx.stroke(); + + // Draw point B + ctx.fillStyle = themeColors.accent; + ctx.beginPath(); + ctx.arc(bx, by, 4, 0, Math.PI * 2); + ctx.fill(); + } + ctx.restore(); + } + } + + // Colorbar overlay — uses cached vmin/vmax from the expensive DP offscreen effect + if (showDpColorbar) { + const canvas = dpUiRef.current; + const ctx = canvas.getContext("2d"); + if (ctx) { + ctx.save(); + ctx.scale(DPR, DPR); + const cssW = canvas.width / DPR; + const cssH = canvas.height / DPR; + const lut = COLORMAPS[dpColormap] || COLORMAPS.inferno; + drawColorbar(ctx, cssW, cssH, lut, dpColorbarVminRef.current, dpColorbarVmaxRef.current, dpScaleMode === "log"); + ctx.restore(); + } + } + }, [dpZoom, dpPanX, dpPanY, kPixelSize, kCalibrated, detRows, detCols, roiMode, roiRadius, roiRadiusInner, roiWidth, roiHeight, localKCol, localKRow, isDraggingDP, isDraggingResize, isDraggingResizeInner, isHoveringResize, isHoveringResizeInner, + profileActive, profilePoints, profileWidth, themeColors, showDpColorbar, dpColormap, dpScaleMode, dpVminPct, dpVmaxPct, canvasSize, roiColors]); + + // VI scale bar + crosshair + ROI + profile lines (high-DPI) + React.useEffect(() => { + if (!viUiRef.current) return; + // Draw scale bar first (clears canvas) + drawScaleBarHiDPI(viUiRef.current, DPR, viZoom, pixelSize || 1, "Å", shapeCols); + // Draw crosshair only when ROI is off (ROI replaces the crosshair) + if (!viRoiMode || viRoiMode === "off") { + drawViPositionMarker(viUiRef.current, DPR, localPosRow, localPosCol, viZoom, viPanX, viPanY, shapeCols, shapeRows, isDraggingVI); + } else { + // Draw VI ROI instead of crosshair + drawViRoiOverlayHiDPI( + viUiRef.current, DPR, viRoiMode, + localViRoiCenterRow, localViRoiCenterCol, viRoiRadius || 5, viRoiWidth || 10, viRoiHeight || 10, + viZoom, viPanX, viPanY, shapeCols, shapeRows, + isDraggingViRoi, isDraggingViRoiResize, isHoveringViRoiResize + ); + } + // Draw VI profile lines + if (viProfileActive && viProfilePoints.length > 0) { + const canvas = viUiRef.current; + const ctx = canvas.getContext("2d"); + if (ctx) { + const cssW = canvas.width / DPR; + const cssH = canvas.height / DPR; + const scaleX = cssW / shapeCols; + const scaleY = cssH / shapeRows; + ctx.save(); + ctx.scale(DPR, DPR); + ctx.strokeStyle = "#a0f"; + ctx.lineWidth = 2; + ctx.shadowColor = "rgba(0,0,0,0.5)"; + ctx.shadowBlur = 2; + if (viProfilePoints.length >= 1) { + const p0 = viProfilePoints[0]; + const x0 = p0.col * viZoom * scaleX + viPanX * scaleX; + const y0 = p0.row * viZoom * scaleY + viPanY * scaleY; + ctx.beginPath(); + ctx.arc(x0, y0, 4, 0, Math.PI * 2); + ctx.fill(); + ctx.fillStyle = "#fff"; + ctx.fillText("1", x0 + 6, y0 - 6); + } + if (viProfilePoints.length === 2) { + const p0 = viProfilePoints[0], p1 = viProfilePoints[1]; + const x0 = p0.col * viZoom * scaleX + viPanX * scaleX; + const y0 = p0.row * viZoom * scaleY + viPanY * scaleY; + const x1 = p1.col * viZoom * scaleX + viPanX * scaleX; + const y1 = p1.row * viZoom * scaleY + viPanY * scaleY; + ctx.beginPath(); + ctx.moveTo(x0, y0); + ctx.lineTo(x1, y1); + ctx.stroke(); + ctx.beginPath(); + ctx.arc(x1, y1, 4, 0, Math.PI * 2); + ctx.fill(); + ctx.fillStyle = "#fff"; + ctx.fillText("2", x1 + 6, y1 - 6); + } + ctx.restore(); + } + } + }, [viZoom, viPanX, viPanY, pixelSize, shapeRows, shapeCols, localPosRow, localPosCol, isDraggingVI, + viRoiMode, localViRoiCenterRow, localViRoiCenterCol, viRoiRadius, viRoiWidth, viRoiHeight, + isDraggingViRoi, isDraggingViRoiResize, isHoveringViRoiResize, canvasSize, viProfileActive, viProfilePoints]); + + // ── DP Profile computation ── + React.useEffect(() => { + if (profilePoints.length === 2 && rawDpDataRef.current) { + const p0 = profilePoints[0], p1 = profilePoints[1]; + setProfileData(sampleLineProfile(rawDpDataRef.current, detCols, detRows, p0.row, p0.col, p1.row, p1.col, profileWidth)); + if (!profileActive) setProfileActive(true); + } else { + setProfileData(null); + } + }, [profilePoints, profileWidth, frameBytes]); + + // ── VI Profile computation ── + React.useEffect(() => { + if (viProfilePoints.length === 2 && rawViDataRef.current && shapeCols > 0 && shapeRows > 0) { + const p0 = viProfilePoints[0], p1 = viProfilePoints[1]; + setViProfileData(sampleLineProfile(rawViDataRef.current, shapeCols, shapeRows, p0.row, p0.col, p1.row, p1.col, 1)); + } else { + setViProfileData(null); + } + }, [viProfilePoints, virtualImageBytes, shapeCols, shapeRows]); + + // ── Profile sparkline rendering ── + React.useEffect(() => { + const canvas = profileCanvasRef.current; + if (!canvas) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const dpr = window.devicePixelRatio || 1; + const cssW = canvasSize; + const cssH = profileHeight; + canvas.width = cssW * dpr; + canvas.height = cssH * dpr; + ctx.scale(dpr, dpr); + + const isDark = themeInfo.theme === "dark"; + ctx.fillStyle = isDark ? "#1a1a1a" : "#f0f0f0"; + ctx.fillRect(0, 0, cssW, cssH); + + if (!profileData || profileData.length < 2) { + ctx.font = "10px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#555" : "#999"; + ctx.textAlign = "center"; + ctx.textBaseline = "middle"; + ctx.fillText("Click two points on the DP to draw a profile", cssW / 2, cssH / 2); + profileBaseImageRef.current = null; + profileLayoutRef.current = null; + return; + } + + const padLeft = 40; + const padRight = 8; + const padTop = 6; + const padBottom = 18; + const plotW = cssW - padLeft - padRight; + const plotH = cssH - padTop - padBottom; + + let gMin = Infinity, gMax = -Infinity; + for (let i = 0; i < profileData.length; i++) { + if (profileData[i] < gMin) gMin = profileData[i]; + if (profileData[i] > gMax) gMax = profileData[i]; + } + const range = gMax - gMin || 1; + + // X-axis: calibrated distance + let totalDist = profileData.length - 1; + let xUnit = "px"; + if (profilePoints.length === 2) { + const dx = profilePoints[1].col - profilePoints[0].col; + const dy = profilePoints[1].row - profilePoints[0].row; + const distPx = Math.sqrt(dx * dx + dy * dy); + if (kCalibrated && kPixelSize > 0) { + totalDist = distPx * kPixelSize; + xUnit = "mrad"; + } else { + totalDist = distPx; + } + } + + // Draw axes + ctx.strokeStyle = isDark ? "#555" : "#bbb"; + ctx.lineWidth = 0.5; + ctx.beginPath(); + ctx.moveTo(padLeft, padTop); + ctx.lineTo(padLeft, padTop + plotH); + ctx.lineTo(padLeft + plotW, padTop + plotH); + ctx.stroke(); + + // Draw profile curve + ctx.strokeStyle = themeColors.accent; + ctx.lineWidth = 1.5; + ctx.beginPath(); + for (let i = 0; i < profileData.length; i++) { + const x = padLeft + (i / (profileData.length - 1)) * plotW; + const y = padTop + plotH - ((profileData[i] - gMin) / range) * plotH; + if (i === 0) ctx.moveTo(x, y); + else ctx.lineTo(x, y); + } + ctx.stroke(); + + // Draw x-axis ticks + const tickY = padTop + plotH; + ctx.strokeStyle = isDark ? "#555" : "#bbb"; + ctx.lineWidth = 0.5; + const idealTicks = Math.max(2, Math.floor(plotW / 70)); + const tickStep = roundToNiceValue(totalDist / idealTicks); + ctx.font = "9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#888" : "#666"; + ctx.textBaseline = "top"; + const ticks: number[] = []; + for (let v = 0; v <= totalDist + tickStep * 0.01; v += tickStep) { + if (v > totalDist * 1.001) break; + ticks.push(v); + } + for (let i = 0; i < ticks.length; i++) { + const v = ticks[i]; + const frac = totalDist > 0 ? v / totalDist : 0; + const x = padLeft + frac * plotW; + ctx.beginPath(); ctx.moveTo(x, tickY); ctx.lineTo(x, tickY + 3); ctx.stroke(); + ctx.textAlign = frac < 0.05 ? "left" : frac > 0.95 ? "right" : "center"; + const label = v % 1 === 0 ? v.toFixed(0) : v.toFixed(1); + ctx.fillText(i === ticks.length - 1 ? `${label} ${xUnit}` : label, x, tickY + 4); + } + + // Y-axis min/max labels (left margin) + ctx.font = "9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#888" : "#666"; + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.fillText(formatNumber(gMax), 2, padTop); + ctx.textBaseline = "bottom"; + ctx.fillText(formatNumber(gMin), 2, padTop + plotH); + + // Save base image and layout for hover + profileBaseImageRef.current = ctx.getImageData(0, 0, canvas.width, canvas.height); + profileLayoutRef.current = { padLeft, plotW, padTop, plotH, gMin, gMax, totalDist, xUnit }; + }, [profileData, profilePoints, kPixelSize, kCalibrated, themeInfo.theme, themeColors.accent, canvasSize, profileHeight]); + + // DP Profile hover handlers + const handleProfileMouseMove = React.useCallback((e: React.MouseEvent) => { + const canvas = profileCanvasRef.current; + const base = profileBaseImageRef.current; + const layout = profileLayoutRef.current; + if (!canvas || !base || !layout || !profileData) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const rect = canvas.getBoundingClientRect(); + const cssX = e.clientX - rect.left; + const { padLeft, plotW, padTop, plotH, gMin, gMax, totalDist, xUnit } = layout; + const range = gMax - gMin || 1; + + // Restore base image + ctx.putImageData(base, 0, 0); + + if (cssX < padLeft || cssX > padLeft + plotW) return; + const frac = (cssX - padLeft) / plotW; + + const dpr = window.devicePixelRatio || 1; + ctx.save(); + ctx.setTransform(dpr, 0, 0, dpr, 0, 0); + + // Vertical crosshair + const isDark = themeInfo.theme === "dark"; + ctx.strokeStyle = isDark ? "rgba(255,255,255,0.3)" : "rgba(0,0,0,0.3)"; + ctx.lineWidth = 1; + ctx.setLineDash([2, 2]); + ctx.beginPath(); + ctx.moveTo(cssX, padTop); + ctx.lineTo(cssX, padTop + plotH); + ctx.stroke(); + ctx.setLineDash([]); + + // Dot on curve + value + const dataIdx = Math.min(profileData.length - 1, Math.max(0, Math.round(frac * (profileData.length - 1)))); + const val = profileData[dataIdx]; + const y = padTop + plotH - ((val - gMin) / range) * plotH; + ctx.fillStyle = themeColors.accent; + ctx.beginPath(); + ctx.arc(cssX, y, 3, 0, Math.PI * 2); + ctx.fill(); + + // Value readout label + const dist = frac * totalDist; + const label = `${formatNumber(val)} @ ${dist.toFixed(1)} ${xUnit}`; + ctx.font = "bold 9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + const textW = ctx.measureText(label).width; + const labelX = Math.min(cssX + 6, padLeft + plotW - textW - 2); + const labelY = padTop + 2; + ctx.fillStyle = isDark ? "rgba(0,0,0,0.7)" : "rgba(255,255,255,0.8)"; + ctx.fillRect(labelX - 2, labelY - 1, textW + 4, 11); + ctx.fillStyle = isDark ? "#fff" : "#000"; + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.fillText(label, labelX, labelY); + + ctx.restore(); + }, [profileData, themeInfo.theme, themeColors.accent]); + + const handleProfileMouseLeave = React.useCallback(() => { + const canvas = profileCanvasRef.current; + const base = profileBaseImageRef.current; + if (!canvas || !base) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.putImageData(base, 0, 0); + }, []); + + // DP Profile resize handlers + React.useEffect(() => { + if (!isResizingProfile) return; + const handleMouseMove = (e: MouseEvent) => { + if (!profileResizeStart.current) return; + const deltaY = e.clientY - profileResizeStart.current.startY; + const newHeight = Math.max(40, Math.min(300, profileResizeStart.current.startHeight + deltaY)); + setProfileHeight(newHeight); + }; + const handleMouseUp = () => { + setIsResizingProfile(false); + profileResizeStart.current = null; + }; + document.addEventListener("mousemove", handleMouseMove); + document.addEventListener("mouseup", handleMouseUp); + return () => { + document.removeEventListener("mousemove", handleMouseMove); + document.removeEventListener("mouseup", handleMouseUp); + }; + }, [isResizingProfile]); + + // ── VI Profile sparkline rendering ── + React.useEffect(() => { + const canvas = viProfileCanvasRef.current; + if (!canvas) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const dpr = window.devicePixelRatio || 1; + const cssW = viCanvasWidth; + const cssH = viProfileHeight; + canvas.width = cssW * dpr; + canvas.height = cssH * dpr; + ctx.scale(dpr, dpr); + + const isDark = themeInfo.theme === "dark"; + ctx.fillStyle = isDark ? "#1a1a1a" : "#f0f0f0"; + ctx.fillRect(0, 0, cssW, cssH); + + if (!viProfileData || viProfileData.length < 2) { + ctx.font = "10px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#555" : "#999"; + ctx.textAlign = "center"; + ctx.textBaseline = "middle"; + ctx.fillText("Click two points on the VI to draw a profile", cssW / 2, cssH / 2); + viProfileBaseImageRef.current = null; + viProfileLayoutRef.current = null; + return; + } + + const padLeft = 40; + const padRight = 8; + const padTop = 6; + const padBottom = 18; + const plotW = cssW - padLeft - padRight; + const plotH = cssH - padTop - padBottom; + + let gMin = Infinity, gMax = -Infinity; + for (let i = 0; i < viProfileData.length; i++) { + if (viProfileData[i] < gMin) gMin = viProfileData[i]; + if (viProfileData[i] > gMax) gMax = viProfileData[i]; + } + const range = gMax - gMin || 1; + + // X-axis: calibrated distance + let totalDist = viProfileData.length - 1; + let xUnit = "px"; + if (viProfilePoints.length === 2 && pixelSize > 0) { + const dx = viProfilePoints[1].col - viProfilePoints[0].col; + const dy = viProfilePoints[1].row - viProfilePoints[0].row; + const distPx = Math.sqrt(dx * dx + dy * dy); + totalDist = distPx * pixelSize; + xUnit = pixelSize >= 10 ? "nm" : "Å"; + if (xUnit === "nm") totalDist /= 10; + } + + // Draw axes + ctx.strokeStyle = isDark ? "#555" : "#bbb"; + ctx.lineWidth = 0.5; + ctx.beginPath(); + ctx.moveTo(padLeft, padTop); + ctx.lineTo(padLeft, padTop + plotH); + ctx.lineTo(padLeft + plotW, padTop + plotH); + ctx.stroke(); + + // Draw profile curve + ctx.strokeStyle = themeColors.accent; + ctx.lineWidth = 1.5; + ctx.beginPath(); + for (let i = 0; i < viProfileData.length; i++) { + const x = padLeft + (i / (viProfileData.length - 1)) * plotW; + const y = padTop + plotH - ((viProfileData[i] - gMin) / range) * plotH; + if (i === 0) ctx.moveTo(x, y); + else ctx.lineTo(x, y); + } + ctx.stroke(); + + // Draw x-axis ticks + const tickY = padTop + plotH; + ctx.strokeStyle = isDark ? "#555" : "#bbb"; + ctx.lineWidth = 0.5; + const idealTicks = Math.max(2, Math.floor(plotW / 70)); + const tickStep = roundToNiceValue(totalDist / idealTicks); + ctx.font = "9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#888" : "#666"; + ctx.textBaseline = "top"; + const ticks: number[] = []; + for (let v = 0; v <= totalDist + tickStep * 0.01; v += tickStep) { + if (v > totalDist * 1.001) break; + ticks.push(v); + } + for (let i = 0; i < ticks.length; i++) { + const v = ticks[i]; + const frac = totalDist > 0 ? v / totalDist : 0; + const x = padLeft + frac * plotW; + ctx.beginPath(); ctx.moveTo(x, tickY); ctx.lineTo(x, tickY + 3); ctx.stroke(); + ctx.textAlign = frac < 0.05 ? "left" : frac > 0.95 ? "right" : "center"; + const label = v % 1 === 0 ? v.toFixed(0) : v.toFixed(1); + ctx.fillText(i === ticks.length - 1 ? `${label} ${xUnit}` : label, x, tickY + 4); + } + + // Y-axis min/max labels (left margin) + ctx.font = "9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#888" : "#666"; + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.fillText(formatNumber(gMax), 2, padTop); + ctx.textBaseline = "bottom"; + ctx.fillText(formatNumber(gMin), 2, padTop + plotH); + + // Save base image and layout for hover + viProfileBaseImageRef.current = ctx.getImageData(0, 0, canvas.width, canvas.height); + viProfileLayoutRef.current = { padLeft, plotW, padTop, plotH, gMin, gMax, totalDist, xUnit }; + }, [viProfileData, viProfilePoints, pixelSize, themeInfo.theme, themeColors.accent, viCanvasWidth, viProfileHeight]); + + // VI Profile hover handlers + const handleViProfileMouseMove = React.useCallback((e: React.MouseEvent) => { + const canvas = viProfileCanvasRef.current; + const base = viProfileBaseImageRef.current; + const layout = viProfileLayoutRef.current; + if (!canvas || !base || !layout || !viProfileData) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const rect = canvas.getBoundingClientRect(); + const cssX = e.clientX - rect.left; + const { padLeft, plotW, padTop, plotH, gMin, gMax, totalDist, xUnit } = layout; + const range = gMax - gMin || 1; + + // Restore base image + ctx.putImageData(base, 0, 0); + + if (cssX < padLeft || cssX > padLeft + plotW) return; + const frac = (cssX - padLeft) / plotW; + + const dpr = window.devicePixelRatio || 1; + ctx.save(); + ctx.setTransform(dpr, 0, 0, dpr, 0, 0); + + // Vertical crosshair + const isDark = themeInfo.theme === "dark"; + ctx.strokeStyle = isDark ? "rgba(255,255,255,0.3)" : "rgba(0,0,0,0.3)"; + ctx.lineWidth = 1; + ctx.setLineDash([2, 2]); + ctx.beginPath(); + ctx.moveTo(cssX, padTop); + ctx.lineTo(cssX, padTop + plotH); + ctx.stroke(); + ctx.setLineDash([]); + + // Dot on curve + value + const dataIdx = Math.min(viProfileData.length - 1, Math.max(0, Math.round(frac * (viProfileData.length - 1)))); + const val = viProfileData[dataIdx]; + const y = padTop + plotH - ((val - gMin) / range) * plotH; + ctx.fillStyle = themeColors.accent; + ctx.beginPath(); + ctx.arc(cssX, y, 3, 0, Math.PI * 2); + ctx.fill(); + + // Value readout label + const dist = frac * totalDist; + const label = `${formatNumber(val)} @ ${dist.toFixed(1)} ${xUnit}`; + ctx.font = "bold 9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + const textW = ctx.measureText(label).width; + const labelX = Math.min(cssX + 6, padLeft + plotW - textW - 2); + const labelY = padTop + 2; + ctx.fillStyle = isDark ? "rgba(0,0,0,0.7)" : "rgba(255,255,255,0.8)"; + ctx.fillRect(labelX - 2, labelY - 1, textW + 4, 11); + ctx.fillStyle = isDark ? "#fff" : "#000"; + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.fillText(label, labelX, labelY); + + ctx.restore(); + }, [viProfileData, themeInfo.theme, themeColors.accent]); + + const handleViProfileMouseLeave = React.useCallback(() => { + const canvas = viProfileCanvasRef.current; + const base = viProfileBaseImageRef.current; + if (!canvas || !base) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.putImageData(base, 0, 0); + }, []); + + // VI Profile resize handlers + React.useEffect(() => { + if (!isResizingViProfile) return; + const handleMouseMove = (e: MouseEvent) => { + if (!viProfileResizeStart.current) return; + const deltaY = e.clientY - viProfileResizeStart.current.startY; + const newHeight = Math.max(40, Math.min(300, viProfileResizeStart.current.startHeight + deltaY)); + setViProfileHeight(newHeight); + }; + const handleMouseUp = () => { + setIsResizingViProfile(false); + viProfileResizeStart.current = null; + }; + document.addEventListener("mousemove", handleMouseMove); + document.addEventListener("mouseup", handleMouseUp); + return () => { + document.removeEventListener("mousemove", handleMouseMove); + document.removeEventListener("mouseup", handleMouseUp); + }; + }, [isResizingViProfile]); + + // Generic zoom handler + const createZoomHandler = ( + setZoom: React.Dispatch>, + setPanX: React.Dispatch>, + setPanY: React.Dispatch>, + zoom: number, panX: number, panY: number, + canvasRef: React.RefObject, + locked: boolean = false, + ) => (e: React.WheelEvent) => { + if (locked) return; + e.preventDefault(); + const canvas = canvasRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const mouseX = (e.clientX - rect.left) * (canvas.width / rect.width); + const mouseY = (e.clientY - rect.top) * (canvas.height / rect.height); + const zoomFactor = e.deltaY > 0 ? 0.9 : 1.1; + const newZoom = Math.max(MIN_ZOOM, Math.min(MAX_ZOOM, zoom * zoomFactor)); + const zoomRatio = newZoom / zoom; + setZoom(newZoom); + setPanX(mouseX - (mouseX - panX) * zoomRatio); + setPanY(mouseY - (mouseY - panY) * zoomRatio); + }; + + // ───────────────────────────────────────────────────────────────────────── + // Mouse Handlers + // ───────────────────────────────────────────────────────────────────────── + + // Helper: convert screen-pixel hit radius to image-pixel radius + // handleRadius=6 CSS px drawn, hit area ~10 CSS px → convert to image coords + const dpHitRadius = RESIZE_HIT_AREA_PX * Math.max(detCols, detRows) / canvasSize / dpZoom; + + // Helper: check if point is near the outer resize handle + const isNearResizeHandle = (imgX: number, imgY: number): boolean => { + if (roiMode === "rect") { + // For rectangle, check near bottom-right corner + const handleX = roiCenterCol + roiWidth / 2; + const handleY = roiCenterRow + roiHeight / 2; + const dist = Math.sqrt((imgX - handleX) ** 2 + (imgY - handleY) ** 2); + return dist < dpHitRadius; + } + if ((roiMode !== "circle" && roiMode !== "square" && roiMode !== "annular") || !roiRadius) return false; + const offset = roiMode === "square" ? roiRadius : roiRadius * CIRCLE_HANDLE_ANGLE; + const handleX = roiCenterCol + offset; + const handleY = roiCenterRow + offset; + const dist = Math.sqrt((imgX - handleX) ** 2 + (imgY - handleY) ** 2); + return dist < dpHitRadius; + }; + + // Helper: check if point is near the inner resize handle (annular mode only) + const isNearResizeHandleInner = (imgX: number, imgY: number): boolean => { + if (roiMode !== "annular" || !roiRadiusInner) return false; + const offset = roiRadiusInner * CIRCLE_HANDLE_ANGLE; + const handleX = roiCenterCol + offset; + const handleY = roiCenterRow + offset; + const dist = Math.sqrt((imgX - handleX) ** 2 + (imgY - handleY) ** 2); + return dist < dpHitRadius; + }; + + // Helper: check if point is near VI ROI resize handle (same logic as DP) + // Hit area is capped to avoid overlap with center for small ROIs + const viHitRadius = RESIZE_HIT_AREA_PX * Math.max(shapeRows, shapeCols) / canvasSize / viZoom; + const isNearViRoiResizeHandle = (imgX: number, imgY: number): boolean => { + if (!viRoiMode || viRoiMode === "off") return false; + if (viRoiMode === "rect") { + const halfH = (viRoiHeight || 10) / 2; + const halfW = (viRoiWidth || 10) / 2; + const handleX = localViRoiCenterRow + halfH; + const handleY = localViRoiCenterCol + halfW; + const dist = Math.sqrt((imgX - handleX) ** 2 + (imgY - handleY) ** 2); + const cornerDist = Math.sqrt(halfW ** 2 + halfH ** 2); + const hitArea = Math.min(viHitRadius, cornerDist * 0.5); + return dist < hitArea; + } + if (viRoiMode === "circle" || viRoiMode === "square") { + const radius = viRoiRadius || 5; + const offset = viRoiMode === "square" ? radius : radius * CIRCLE_HANDLE_ANGLE; + const handleX = localViRoiCenterRow + offset; + const handleY = localViRoiCenterCol + offset; + const dist = Math.sqrt((imgX - handleX) ** 2 + (imgY - handleY) ** 2); + // Cap hit area to 50% of radius so center remains draggable + const hitArea = Math.min(viHitRadius, radius * 0.5); + return dist < hitArea; + } + return false; + }; + + // Helper: check if point is inside the DP ROI area + const isInsideDpRoi = (imgX: number, imgY: number): boolean => { + if (roiMode === "point") return false; + const dx = imgX - roiCenterCol; + const dy = imgY - roiCenterRow; + if (roiMode === "circle") return Math.sqrt(dx * dx + dy * dy) <= (roiRadius || 5); + if (roiMode === "square") return Math.abs(dx) <= (roiRadius || 5) && Math.abs(dy) <= (roiRadius || 5); + if (roiMode === "annular") { const d = Math.sqrt(dx * dx + dy * dy); return d <= (roiRadius || 20) && d >= (roiRadiusInner || 5); } + if (roiMode === "rect") return Math.abs(dx) <= (roiWidth || 10) / 2 && Math.abs(dy) <= (roiHeight || 10) / 2; + return false; + }; + + // Helper: check if point is inside the VI ROI area + const isInsideViRoi = (imgX: number, imgY: number): boolean => { + if (!viRoiMode || viRoiMode === "off") return false; + const dx = imgY - localViRoiCenterCol; + const dy = imgX - localViRoiCenterRow; + if (viRoiMode === "circle") return Math.sqrt(dx * dx + dy * dy) <= (viRoiRadius || 5); + if (viRoiMode === "square") return Math.abs(dx) <= (viRoiRadius || 5) && Math.abs(dy) <= (viRoiRadius || 5); + if (viRoiMode === "rect") return Math.abs(dx) <= (viRoiWidth || 10) / 2 && Math.abs(dy) <= (viRoiHeight || 10) / 2; + return false; + }; + + // Mouse handlers + const handleDpMouseDown = (e: React.MouseEvent) => { + if (profileActive && lockProfile) return; + if (!profileActive && lockRoi) return; + dpClickStartRef.current = { x: e.clientX, y: e.clientY }; + const canvas = dpOverlayRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const screenX = (e.clientX - rect.left) * (canvas.width / rect.width); + const screenY = (e.clientY - rect.top) * (canvas.height / rect.height); + const imgX = (screenX - dpPanX) / dpZoom; + const imgY = (screenY - dpPanY) / dpZoom; + + // When profile mode is active, use profile interactions only + if (profileActive) { + if (profilePoints.length === 2) { + const p0 = profilePoints[0]; + const p1 = profilePoints[1]; + const hitRadius = 10 / dpZoom; + const d0 = Math.sqrt((imgX - p0.col) ** 2 + (imgY - p0.row) ** 2); + const d1 = Math.sqrt((imgX - p1.col) ** 2 + (imgY - p1.row) ** 2); + if (d0 <= hitRadius || d1 <= hitRadius) { + setDraggingDpProfileEndpoint(d0 <= d1 ? 0 : 1); + setIsDraggingDP(false); + return; + } + if (pointToSegmentDistance(imgX, imgY, p0.col, p0.row, p1.col, p1.row) <= hitRadius) { + setIsDraggingDpProfileLine(true); + dpProfileDragStartRef.current = { + row: imgY, + col: imgX, + p0: { row: p0.row, col: p0.col }, + p1: { row: p1.row, col: p1.col }, + }; + setIsDraggingDP(false); + return; + } + } + setIsDraggingDP(false); + return; + } + + // Check if clicking on resize handle (inner first, then outer) + if (isNearResizeHandleInner(imgX, imgY)) { + setIsDraggingResizeInner(true); + return; + } + if (isNearResizeHandle(imgX, imgY)) { + e.preventDefault(); + resizeAspectRef.current = roiMode === "rect" && roiWidth > 0 && roiHeight > 0 ? roiWidth / roiHeight : null; + setIsDraggingResize(true); + return; + } + + setIsDraggingDP(true); + // If clicking inside the ROI, drag with offset (grab-and-drag) + if (roiMode !== "off" && roiMode !== "point" && isInsideDpRoi(imgX, imgY)) { + dpDragOffsetRef.current = { dRow: imgY - roiCenterRow, dCol: imgX - roiCenterCol }; + return; + } + // Clicking outside ROI — teleport center to click position + dpDragOffsetRef.current = { dRow: 0, dCol: 0 }; + setLocalKCol(imgX); setLocalKRow(imgY); + // Use compound roi_center trait [row, col] - single observer fires in Python + const newCol = Math.round(Math.max(0, Math.min(detCols - 1, imgX))); + const newRow = Math.round(Math.max(0, Math.min(detRows - 1, imgY))); + model.set("roi_active", true); + model.set("roi_center", [newRow, newCol]); + model.save_changes(); + }; + + const handleDpMouseMove = (e: React.MouseEvent) => { + const canvas = dpOverlayRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const screenX = (e.clientX - rect.left) * (canvas.width / rect.width); + const screenY = (e.clientY - rect.top) * (canvas.height / rect.height); + const imgX = (screenX - dpPanX) / dpZoom; + const imgY = (screenY - dpPanY) / dpZoom; + + // Fast path: skip cursor readout during any active drag — avoids setCursorInfo re-renders + const anyDrag = isDraggingDP || isDraggingResize || isDraggingResizeInner + || draggingDpProfileEndpoint !== null || isDraggingDpProfileLine; + + // Cursor readout: look up raw DP value at pixel position + if (!anyDrag) { + const pxCol = Math.floor(imgX); + const pxRow = Math.floor(imgY); + if (pxCol >= 0 && pxCol < detCols && pxRow >= 0 && pxRow < detRows && frameBytes) { + const usesSummedDp = viRoiMode && viRoiMode !== "off" && summedDpBytes && summedDpBytes.byteLength > 0; + const sourceBytes = usesSummedDp ? summedDpBytes : frameBytes; + const raw = new Float32Array(sourceBytes.buffer, sourceBytes.byteOffset, sourceBytes.byteLength / 4); + setCursorInfo({ row: pxRow, col: pxCol, value: raw[pxRow * detCols + pxCol], panel: "DP" }); + } else { + setCursorInfo(null); + } + } + + if (profileActive && lockProfile) return; + + if (profileActive && profilePoints.length === 2) { + const p0 = profilePoints[0]; + const p1 = profilePoints[1]; + const hitRadius = 10 / dpZoom; + const d0 = Math.sqrt((imgX - p0.col) ** 2 + (imgY - p0.row) ** 2); + const d1 = Math.sqrt((imgX - p1.col) ** 2 + (imgY - p1.row) ** 2); + if (draggingDpProfileEndpoint !== null) { + if (!rawDpDataRef.current) return; + const clampedRow = Math.max(0, Math.min(detRows - 1, imgY)); + const clampedCol = Math.max(0, Math.min(detCols - 1, imgX)); + const next = [ + draggingDpProfileEndpoint === 0 ? { row: clampedRow, col: clampedCol } : profilePoints[0], + draggingDpProfileEndpoint === 1 ? { row: clampedRow, col: clampedCol } : profilePoints[1], + ]; + setProfileLine(next); + setProfileData(sampleLineProfile(rawDpDataRef.current, detCols, detRows, next[0].row, next[0].col, next[1].row, next[1].col, profileWidth)); + return; + } + if (isDraggingDpProfileLine && dpProfileDragStartRef.current) { + if (!rawDpDataRef.current) return; + const drag = dpProfileDragStartRef.current; + let deltaRow = imgY - drag.row; + let deltaCol = imgX - drag.col; + const minRow = Math.min(drag.p0.row, drag.p1.row); + const maxRow = Math.max(drag.p0.row, drag.p1.row); + const minCol = Math.min(drag.p0.col, drag.p1.col); + const maxCol = Math.max(drag.p0.col, drag.p1.col); + deltaRow = Math.max(deltaRow, -minRow); + deltaRow = Math.min(deltaRow, (detRows - 1) - maxRow); + deltaCol = Math.max(deltaCol, -minCol); + deltaCol = Math.min(deltaCol, (detCols - 1) - maxCol); + const next = [ + { row: drag.p0.row + deltaRow, col: drag.p0.col + deltaCol }, + { row: drag.p1.row + deltaRow, col: drag.p1.col + deltaCol }, + ]; + setProfileLine(next); + setProfileData(sampleLineProfile(rawDpDataRef.current, detCols, detRows, next[0].row, next[0].col, next[1].row, next[1].col, profileWidth)); + return; + } + const nextHoveredEndpoint: 0 | 1 | null = d0 <= hitRadius ? 0 : d1 <= hitRadius ? 1 : null; + const nextHoverLine = nextHoveredEndpoint === null && pointToSegmentDistance(imgX, imgY, p0.col, p0.row, p1.col, p1.row) <= hitRadius; + setHoveredDpProfileEndpoint(nextHoveredEndpoint); + setIsHoveringDpProfileLine(nextHoverLine); + return; + } else { + if (hoveredDpProfileEndpoint !== null) setHoveredDpProfileEndpoint(null); + if (isHoveringDpProfileLine) setIsHoveringDpProfileLine(false); + } + + // Handle inner resize dragging (annular mode) + if (isDraggingResizeInner) { + if (lockRoi) return; + const dx = Math.abs(imgX - roiCenterCol); + const dy = Math.abs(imgY - roiCenterRow); + const newRadius = Math.sqrt(dx ** 2 + dy ** 2); + // Inner radius must be less than outer radius + setRoiRadiusInner(Math.max(1, Math.min(roiRadius - 1, Math.round(newRadius)))); + return; + } + + // Handle outer resize dragging - use model state center, not local values + if (isDraggingResize) { + if (lockRoi) return; + const dx = Math.abs(imgX - roiCenterCol); + const dy = Math.abs(imgY - roiCenterRow); + if (roiMode === "rect") { + let newW = Math.max(2, Math.round(dx * 2)); + let newH = Math.max(2, Math.round(dy * 2)); + if (e.shiftKey && resizeAspectRef.current != null) { + const aspect = resizeAspectRef.current; + if (newW / newH > aspect) newH = Math.max(2, Math.round(newW / aspect)); + else newW = Math.max(2, Math.round(newH * aspect)); + } + setRoiWidth(newW); + setRoiHeight(newH); + } else { + const newRadius = roiMode === "square" ? Math.max(dx, dy) : Math.sqrt(dx ** 2 + dy ** 2); + // For annular mode, outer radius must be greater than inner radius + const minRadius = roiMode === "annular" ? (roiRadiusInner || 0) + 1 : 1; + setRoiRadius(Math.max(minRadius, Math.round(newRadius))); + } + return; + } + + // Check hover state for resize handles + if (!isDraggingDP) { + if (!lockRoi) { + setIsHoveringResizeInner(isNearResizeHandleInner(imgX, imgY)); + setIsHoveringResize(isNearResizeHandle(imgX, imgY)); + } else { + setIsHoveringResizeInner(false); + setIsHoveringResize(false); + } + return; + } + + if (lockRoi) return; + const centerCol = imgX - dpDragOffsetRef.current.dCol; + const centerRow = imgY - dpDragOffsetRef.current.dRow; + setLocalKCol(centerCol); setLocalKRow(centerRow); + // Use compound roi_center trait [row, col] - single observer fires in Python + const newCol = Math.round(Math.max(0, Math.min(detCols - 1, centerCol))); + const newRow = Math.round(Math.max(0, Math.min(detRows - 1, centerRow))); + model.set("roi_center", [newRow, newCol]); + model.save_changes(); + }; + + const handleDpMouseUp = (e: React.MouseEvent) => { + if (draggingDpProfileEndpoint !== null || isDraggingDpProfileLine) { + setDraggingDpProfileEndpoint(null); + setIsDraggingDpProfileLine(false); + dpProfileDragStartRef.current = null; + dpClickStartRef.current = null; + setIsDraggingDP(false); + setIsDraggingResize(false); + setIsDraggingResizeInner(false); + setHoveredDpProfileEndpoint(null); + setIsHoveringDpProfileLine(false); + return; + } + + // Profile click capture + if (profileActive && dpClickStartRef.current) { + const dx = e.clientX - dpClickStartRef.current.x; + const dy = e.clientY - dpClickStartRef.current.y; + if (Math.sqrt(dx * dx + dy * dy) < 3) { + const canvas = dpOverlayRef.current; + if (canvas && rawDpDataRef.current) { + const rect = canvas.getBoundingClientRect(); + const screenX = (e.clientX - rect.left) * (canvas.width / rect.width); + const screenY = (e.clientY - rect.top) * (canvas.height / rect.height); + const imgCol = (screenX - dpPanX) / dpZoom; + const imgRow = (screenY - dpPanY) / dpZoom; + if (imgCol >= 0 && imgCol < detCols && imgRow >= 0 && imgRow < detRows) { + const pt = { row: imgRow, col: imgCol }; + if (profilePoints.length === 0 || profilePoints.length === 2) { + setProfileLine([pt]); + setProfileData(null); + } else { + const p0 = profilePoints[0]; + setProfileLine([p0, pt]); + setProfileData(sampleLineProfile(rawDpDataRef.current, detCols, detRows, p0.row, p0.col, pt.row, pt.col, profileWidth)); + } + } + } + } + } + dpClickStartRef.current = null; + setIsDraggingDP(false); setIsDraggingResize(false); setIsDraggingResizeInner(false); + setDraggingDpProfileEndpoint(null); + setIsDraggingDpProfileLine(false); + setHoveredDpProfileEndpoint(null); + setIsHoveringDpProfileLine(false); + dpProfileDragStartRef.current = null; + }; + const handleDpMouseLeave = () => { + dpClickStartRef.current = null; + setIsDraggingDP(false); setIsDraggingResize(false); setIsDraggingResizeInner(false); + setDraggingDpProfileEndpoint(null); + setIsDraggingDpProfileLine(false); + setHoveredDpProfileEndpoint(null); + setIsHoveringDpProfileLine(false); + dpProfileDragStartRef.current = null; + setIsHoveringResize(false); setIsHoveringResizeInner(false); + setCursorInfo(prev => prev?.panel === "DP" ? null : prev); + }; + const handleDpDoubleClick = () => { + if (lockView) return; + setDpZoom(1); + setDpPanX(0); + setDpPanY(0); + }; + + const handleViMouseDown = (e: React.MouseEvent) => { + if (viProfileActive && lockProfile) return; + const canvas = virtualOverlayRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const screenX = (e.clientX - rect.left) * (canvas.width / rect.width); + const screenY = (e.clientY - rect.top) * (canvas.height / rect.height); + const imgX = (screenY - viPanY) / viZoom; + const imgY = (screenX - viPanX) / viZoom; + + // VI Profile mode - click to set points + if (viProfileActive) { + viClickStartRef.current = { x: screenX, y: screenY }; + if (viProfilePoints.length === 2) { + const p0 = viProfilePoints[0]; + const p1 = viProfilePoints[1]; + const hitRadius = 10 / viZoom; + const d0 = Math.sqrt((imgY - p0.col) ** 2 + (imgX - p0.row) ** 2); + const d1 = Math.sqrt((imgY - p1.col) ** 2 + (imgX - p1.row) ** 2); + if (d0 <= hitRadius || d1 <= hitRadius) { + setDraggingViProfileEndpoint(d0 <= d1 ? 0 : 1); + setIsDraggingVI(false); + return; + } + if (pointToSegmentDistance(imgY, imgX, p0.col, p0.row, p1.col, p1.row) <= hitRadius) { + setIsDraggingViProfileLine(true); + viProfileDragStartRef.current = { + row: imgX, + col: imgY, + p0: { row: p0.row, col: p0.col }, + p1: { row: p1.row, col: p1.col }, + }; + setIsDraggingVI(false); + return; + } + } + return; + } + + // Check if VI ROI mode is active - same logic as DP + if (viRoiMode && viRoiMode !== "off") { + if (lockRoi) return; + // Check if clicking on resize handle + if (isNearViRoiResizeHandle(imgX, imgY)) { + setIsDraggingViRoiResize(true); + return; + } + + // Grab-and-drag if clicking inside VI ROI, otherwise teleport + setIsDraggingViRoi(true); + if (isInsideViRoi(imgX, imgY)) { + viRoiDragOffsetRef.current = { dRow: imgX - localViRoiCenterRow, dCol: imgY - localViRoiCenterCol }; + } else { + viRoiDragOffsetRef.current = { dRow: 0, dCol: 0 }; + setLocalViRoiCenterRow(imgX); + setLocalViRoiCenterCol(imgY); + setViRoiCenterRow(Math.round(Math.max(0, Math.min(shapeRows - 1, imgX)))); + setViRoiCenterCol(Math.round(Math.max(0, Math.min(shapeCols - 1, imgY)))); + } + return; + } + + // Regular position selection (when ROI is off) + if (lockNavigation || lockVirtual) return; + setIsDraggingVI(true); + setLocalPosRow(imgX); setLocalPosCol(imgY); + // Batch X and Y updates into a single sync + const newX = Math.round(Math.max(0, Math.min(shapeRows - 1, imgX))); + const newY = Math.round(Math.max(0, Math.min(shapeCols - 1, imgY))); + model.set("pos_row", newX); + model.set("pos_col", newY); + model.save_changes(); + }; + + const handleViMouseMove = (e: React.MouseEvent) => { + const canvas = virtualOverlayRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const screenX = (e.clientX - rect.left) * (canvas.width / rect.width); + const screenY = (e.clientY - rect.top) * (canvas.height / rect.height); + const imgX = (screenY - viPanY) / viZoom; + const imgY = (screenX - viPanX) / viZoom; + + // Fast path: skip cursor readout during any active drag — avoids setCursorInfo re-renders + const anyViDrag = isDraggingVI || isDraggingViRoi || isDraggingViRoiResize + || draggingViProfileEndpoint !== null || isDraggingViProfileLine; + + // Cursor readout: look up raw VI value at pixel position + // imgX = row, imgY = col (swapped coordinate convention) + if (!anyViDrag) { + const pxRow = Math.floor(imgX); + const pxCol = Math.floor(imgY); + if (pxRow >= 0 && pxRow < shapeRows && pxCol >= 0 && pxCol < shapeCols && rawVirtualImageRef.current) { + const raw = rawVirtualImageRef.current; + setCursorInfo({ row: pxRow, col: pxCol, value: raw[pxRow * shapeCols + pxCol], panel: "VI" }); + } else { + setCursorInfo(prev => prev?.panel === "VI" ? null : prev); + } + } + + if (viProfileActive && lockProfile) return; + + if (viProfileActive && viProfilePoints.length === 2) { + const p0 = viProfilePoints[0]; + const p1 = viProfilePoints[1]; + const hitRadius = 10 / viZoom; + const d0 = Math.sqrt((imgY - p0.col) ** 2 + (imgX - p0.row) ** 2); + const d1 = Math.sqrt((imgY - p1.col) ** 2 + (imgX - p1.row) ** 2); + if (draggingViProfileEndpoint !== null) { + const clampedRow = Math.max(0, Math.min(shapeRows - 1, imgX)); + const clampedCol = Math.max(0, Math.min(shapeCols - 1, imgY)); + const next = [ + draggingViProfileEndpoint === 0 ? { row: clampedRow, col: clampedCol } : viProfilePoints[0], + draggingViProfileEndpoint === 1 ? { row: clampedRow, col: clampedCol } : viProfilePoints[1], + ]; + setViProfilePoints(next); + return; + } + if (isDraggingViProfileLine && viProfileDragStartRef.current) { + const drag = viProfileDragStartRef.current; + let deltaRow = imgX - drag.row; + let deltaCol = imgY - drag.col; + const minRow = Math.min(drag.p0.row, drag.p1.row); + const maxRow = Math.max(drag.p0.row, drag.p1.row); + const minCol = Math.min(drag.p0.col, drag.p1.col); + const maxCol = Math.max(drag.p0.col, drag.p1.col); + deltaRow = Math.max(deltaRow, -minRow); + deltaRow = Math.min(deltaRow, (shapeRows - 1) - maxRow); + deltaCol = Math.max(deltaCol, -minCol); + deltaCol = Math.min(deltaCol, (shapeCols - 1) - maxCol); + const next = [ + { row: drag.p0.row + deltaRow, col: drag.p0.col + deltaCol }, + { row: drag.p1.row + deltaRow, col: drag.p1.col + deltaCol }, + ]; + setViProfilePoints(next); + return; + } + const nextHoveredEndpoint: 0 | 1 | null = d0 <= hitRadius ? 0 : d1 <= hitRadius ? 1 : null; + const nextHoverLine = nextHoveredEndpoint === null && pointToSegmentDistance(imgY, imgX, p0.col, p0.row, p1.col, p1.row) <= hitRadius; + setHoveredViProfileEndpoint(nextHoveredEndpoint); + setIsHoveringViProfileLine(nextHoverLine); + return; + } else { + if (hoveredViProfileEndpoint !== null) setHoveredViProfileEndpoint(null); + if (isHoveringViProfileLine) setIsHoveringViProfileLine(false); + } + + // Handle VI ROI resize dragging (same pattern as DP) + if (isDraggingViRoiResize) { + if (lockRoi) return; + const dx = Math.abs(imgX - localViRoiCenterRow); + const dy = Math.abs(imgY - localViRoiCenterCol); + if (viRoiMode === "rect") { + setViRoiWidth(Math.max(2, Math.round(dy * 2))); + setViRoiHeight(Math.max(2, Math.round(dx * 2))); + } else if (viRoiMode === "square") { + const newHalfSize = Math.max(dx, dy); + setViRoiRadius(Math.max(1, Math.round(newHalfSize))); + } else { + // circle + const newRadius = Math.sqrt(dx ** 2 + dy ** 2); + setViRoiRadius(Math.max(1, Math.round(newRadius))); + } + return; + } + + // Check hover state for resize handles (same as DP) + if (!isDraggingViRoi) { + if (!lockRoi) { + setIsHoveringViRoiResize(isNearViRoiResizeHandle(imgX, imgY)); + } else { + setIsHoveringViRoiResize(false); + } + if (viRoiMode && viRoiMode !== "off") return; // Don't update position when ROI active + } + + // Handle VI ROI center dragging (same as DP — with offset) + if (isDraggingViRoi) { + if (lockRoi) return; + const centerRow = imgX - viRoiDragOffsetRef.current.dRow; + const centerCol = imgY - viRoiDragOffsetRef.current.dCol; + setLocalViRoiCenterRow(centerRow); + setLocalViRoiCenterCol(centerCol); + // Batch VI ROI center updates + const newViX = Math.round(Math.max(0, Math.min(shapeRows - 1, centerRow))); + const newViY = Math.round(Math.max(0, Math.min(shapeCols - 1, centerCol))); + model.set("vi_roi_center_row", newViX); + model.set("vi_roi_center_col", newViY); + model.save_changes(); + return; + } + + // Handle regular position dragging (when ROI is off) + if (!isDraggingVI) return; + if (lockNavigation || lockVirtual) return; + setLocalPosRow(imgX); setLocalPosCol(imgY); + // Batch position updates into a single sync + const newX = Math.round(Math.max(0, Math.min(shapeRows - 1, imgX))); + const newY = Math.round(Math.max(0, Math.min(shapeCols - 1, imgY))); + model.set("pos_row", newX); + model.set("pos_col", newY); + model.save_changes(); + }; + + const handleViMouseUp = (e: React.MouseEvent) => { + if (draggingViProfileEndpoint !== null || isDraggingViProfileLine) { + setDraggingViProfileEndpoint(null); + setIsDraggingViProfileLine(false); + viProfileDragStartRef.current = null; + viClickStartRef.current = null; + setIsDraggingVI(false); + setIsDraggingViRoi(false); + setIsDraggingViRoiResize(false); + setHoveredViProfileEndpoint(null); + setIsHoveringViProfileLine(false); + return; + } + + // VI Profile mode - complete point selection + if (viProfileActive && viClickStartRef.current) { + const canvas = virtualOverlayRef.current; + if (canvas) { + const rect = canvas.getBoundingClientRect(); + const endX = (e.clientX - rect.left) * (canvas.width / rect.width); + const endY = (e.clientY - rect.top) * (canvas.height / rect.height); + const dx = endX - viClickStartRef.current.x; + const dy = endY - viClickStartRef.current.y; + const wasDrag = Math.sqrt(dx * dx + dy * dy) > 3; + + if (!wasDrag) { + // Click to add point + const imgX = (endY - viPanY) / viZoom; + const imgY = (endX - viPanX) / viZoom; + const pt = { row: Math.round(Math.max(0, Math.min(shapeRows - 1, imgX))), col: Math.round(Math.max(0, Math.min(shapeCols - 1, imgY))) }; + if (viProfilePoints.length < 2) { + setViProfilePoints([...viProfilePoints, pt]); + } else { + setViProfilePoints([pt]); + } + } + } + viClickStartRef.current = null; + } + + setDraggingViProfileEndpoint(null); + setIsDraggingViProfileLine(false); + setHoveredViProfileEndpoint(null); + setIsHoveringViProfileLine(false); + viProfileDragStartRef.current = null; + setIsDraggingVI(false); + setIsDraggingViRoi(false); + setIsDraggingViRoiResize(false); + }; + const handleViMouseLeave = () => { + viClickStartRef.current = null; + setDraggingViProfileEndpoint(null); + setIsDraggingViProfileLine(false); + setHoveredViProfileEndpoint(null); + setIsHoveringViProfileLine(false); + viProfileDragStartRef.current = null; + setIsDraggingVI(false); + setIsDraggingViRoi(false); + setIsDraggingViRoiResize(false); + setIsHoveringViRoiResize(false); + setCursorInfo(prev => prev?.panel === "VI" ? null : prev); + }; + const handleViDoubleClick = () => { + if (lockView || lockVirtual) return; + setViZoom(1); + setViPanX(0); + setViPanY(0); + }; + const handleFftDoubleClick = () => { + if (lockView || lockFft) return; + setFftZoom(1); + setFftPanX(0); + setFftPanY(0); + setFftClickInfo(null); + }; + + // FFT drag-to-pan handlers + const handleFftMouseDown = (e: React.MouseEvent) => { + if (lockView || lockFft) return; + fftClickStartRef.current = { x: e.clientX, y: e.clientY }; + setIsDraggingFFT(true); + setFftDragStart({ x: e.clientX, y: e.clientY, panX: fftPanX, panY: fftPanY }); + }; + + const handleFftMouseMove = (e: React.MouseEvent) => { + if (lockView || lockFft) return; + if (!isDraggingFFT || !fftDragStart) return; + const canvas = fftOverlayRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const scaleX = canvas.width / rect.width; + const scaleY = canvas.height / rect.height; + const dx = (e.clientX - fftDragStart.x) * scaleX; + const dy = (e.clientY - fftDragStart.y) * scaleY; + setFftPanX(fftDragStart.panX + dx); + setFftPanY(fftDragStart.panY + dy); + }; + + const handleFftMouseUp = (e: React.MouseEvent) => { + // Click detection for d-spacing measurement + if (fftClickStartRef.current) { + const dx = e.clientX - fftClickStartRef.current.x; + const dy = e.clientY - fftClickStartRef.current.y; + if (Math.sqrt(dx * dx + dy * dy) < 3) { + // Convert screen coords to FFT image coords + const canvas = fftOverlayRef.current; + if (canvas) { + const rect = canvas.getBoundingClientRect(); + const scaleX = canvas.width / rect.width; + const scaleY = canvas.height / rect.height; + const canvasX = (e.clientX - rect.left) * scaleX; + const canvasY = (e.clientY - rect.top) * scaleY; + const fftW = fftCropDims?.fftWidth ?? shapeCols; + const fftH = fftCropDims?.fftHeight ?? shapeRows; + // Reverse the zoom/pan transform: canvas coords -> image coords + // The FFT render uses: ctx.translate(fftPanX, fftPanY); ctx.scale(fftZoom, fftZoom); ctx.drawImage(offscreen, 0, 0) + let imgCol = (canvasX - fftPanX) / fftZoom; + let imgRow = (canvasY - fftPanY) / fftZoom; + // Bounds check + if (imgCol >= 0 && imgCol < fftW && imgRow >= 0 && imgRow < fftH) { + // Snap to nearest peak in FFT magnitude + if (fftMagCacheRef.current) { + const snapped = findFFTPeak(fftMagCacheRef.current, fftW, fftH, imgCol, imgRow, FFT_SNAP_RADIUS); + imgCol = snapped.col; + imgRow = snapped.row; + } + const halfW = Math.floor(fftW / 2); + const halfH = Math.floor(fftH / 2); + const dcol = imgCol - halfW; + const drow = imgRow - halfH; + const distPx = Math.sqrt(dcol * dcol + drow * drow); + if (distPx < 1) { + setFftClickInfo(null); // Clicked on DC center + } else { + let spatialFreq: number | null = null; + let dSpacing: number | null = null; + if (pixelSize > 0) { + const paddedW = nextPow2(fftW); + const paddedH = nextPow2(fftH); + const binC = ((Math.round(imgCol) - halfW) % fftW + fftW) % fftW; + const binR = ((Math.round(imgRow) - halfH) % fftH + fftH) % fftH; + const freqC = binC <= paddedW / 2 ? binC / (paddedW * pixelSize) : (binC - paddedW) / (paddedW * pixelSize); + const freqR = binR <= paddedH / 2 ? binR / (paddedH * pixelSize) : (binR - paddedH) / (paddedH * pixelSize); + spatialFreq = Math.sqrt(freqC * freqC + freqR * freqR); + dSpacing = spatialFreq > 0 ? 1 / spatialFreq : null; + } + setFftClickInfo({ row: imgRow, col: imgCol, distPx, spatialFreq, dSpacing }); + } + } + } + } + fftClickStartRef.current = null; + } + setIsDraggingFFT(false); + setFftDragStart(null); + }; + const handleFftMouseLeave = () => { fftClickStartRef.current = null; setIsDraggingFFT(false); setFftDragStart(null); }; + + // ── Canvas resize handlers ── + const handleCanvasResizeStart = (e: React.MouseEvent) => { + if (lockView) return; + e.stopPropagation(); + e.preventDefault(); + setIsResizingCanvas(true); + setResizeCanvasStart({ x: e.clientX, y: e.clientY, size: canvasSize }); + }; + + React.useEffect(() => { + if (!isResizingCanvas) return; + let rafId = 0; + let latestSize = resizeCanvasStart ? resizeCanvasStart.size : canvasSize; + const handleMouseMove = (e: MouseEvent) => { + if (!resizeCanvasStart) return; + const delta = Math.max(e.clientX - resizeCanvasStart.x, e.clientY - resizeCanvasStart.y); + latestSize = Math.max(CANVAS_SIZE, resizeCanvasStart.size + delta); + if (!rafId) { + rafId = requestAnimationFrame(() => { + rafId = 0; + setCanvasSize(latestSize); + }); + } + }; + const handleMouseUp = () => { + cancelAnimationFrame(rafId); + setCanvasSize(latestSize); + setIsResizingCanvas(false); + setResizeCanvasStart(null); + }; + document.addEventListener("mousemove", handleMouseMove); + document.addEventListener("mouseup", handleMouseUp); + return () => { + cancelAnimationFrame(rafId); + document.removeEventListener("mousemove", handleMouseMove); + document.removeEventListener("mouseup", handleMouseUp); + }; + }, [isResizingCanvas, resizeCanvasStart]); + + // ───────────────────────────────────────────────────────────────────────── + // Render + // ───────────────────────────────────────────────────────────────────────── + + // Export DP handler + const handleExportDP = async () => { + if (lockExport) return; + const timestamp = new Date().toISOString().replace(/[:.]/g, '-').slice(0, 19); + const zip = new JSZip(); + const metadata = { + metadata_version: "1.0", + widget_name: "Show4DSTEM", + widget_version: widgetVersion || "unknown", + exported_at: new Date().toISOString(), + view: "diffraction", + format: "zip", + export_kind: "single_view_png_zip", + position: { row: posRow, col: posCol }, + frame_idx: frameIdx, + n_frames: nFrames, + scan_shape: { rows: shapeRows, cols: shapeCols }, + detector_shape: { rows: detRows, cols: detCols }, + roi: { + active: roiMode !== "off", + mode: roiMode, + center_row: roiCenterRow, + center_col: roiCenterCol, + radius: roiRadius, + radius_inner: roiRadiusInner, + width: roiWidth, + height: roiHeight, + }, + vi_roi: { + mode: viRoiMode, + center_row: viRoiCenterRow, + center_col: viRoiCenterCol, + radius: viRoiRadius, + width: viRoiWidth, + height: viRoiHeight, + }, + calibration: { + pixel_size_angstrom: pixelSize, + pixel_size_unit: "Å/px", + k_pixel_size: kPixelSize, + k_pixel_size_unit: kCalibrated ? "mrad/px" : "px/px", + k_calibrated: kCalibrated, + center_row: centerRow, + center_col: centerCol, + bf_radius: bfRadius, + }, + display: { + diffraction: { + colormap: dpColormap, + scale_mode: dpScaleMode, + vmin_pct: dpVminPct, + vmax_pct: dpVmaxPct, + }, + }, + }; + zip.file("metadata.json", JSON.stringify(metadata, null, 2)); + const canvasToBlob = (canvas: HTMLCanvasElement): Promise => new Promise((resolve) => canvas.toBlob((blob) => resolve(blob!), 'image/png')); + if (dpCanvasRef.current) zip.file("diffraction_pattern.png", await canvasToBlob(dpCanvasRef.current)); + const zipBlob = await zip.generateAsync({ type: "blob" }); + downloadBlob(zipBlob, `dp_export_${timestamp}.zip`); + }; + + // Export VI handler + const handleExportVI = async () => { + if (lockExport) return; + const timestamp = new Date().toISOString().replace(/[:.]/g, '-').slice(0, 19); + const zip = new JSZip(); + const metadata = { + metadata_version: "1.0", + widget_name: "Show4DSTEM", + widget_version: widgetVersion || "unknown", + exported_at: new Date().toISOString(), + view: "all", + format: "zip", + export_kind: "multi_panel_png_zip", + position: { row: posRow, col: posCol }, + frame_idx: frameIdx, + n_frames: nFrames, + scan_shape: { rows: shapeRows, cols: shapeCols }, + detector_shape: { rows: detRows, cols: detCols }, + roi: { + active: roiMode !== "off", + mode: roiMode, + center_row: roiCenterRow, + center_col: roiCenterCol, + radius: roiRadius, + radius_inner: roiRadiusInner, + width: roiWidth, + height: roiHeight, + }, + vi_roi: { + mode: viRoiMode, + center_row: viRoiCenterRow, + center_col: viRoiCenterCol, + radius: viRoiRadius, + width: viRoiWidth, + height: viRoiHeight, + }, + calibration: { + pixel_size_angstrom: pixelSize, + pixel_size_unit: "Å/px", + k_pixel_size: kPixelSize, + k_pixel_size_unit: kCalibrated ? "mrad/px" : "px/px", + k_calibrated: kCalibrated, + center_row: centerRow, + center_col: centerCol, + bf_radius: bfRadius, + }, + display: { + diffraction: { + colormap: dpColormap, + scale_mode: dpScaleMode, + vmin_pct: dpVminPct, + vmax_pct: dpVmaxPct, + }, + virtual: { + colormap: viColormap, + scale_mode: viScaleMode, + vmin_pct: viVminPct, + vmax_pct: viVmaxPct, + }, + fft: { + colormap: fftColormap, + scale_mode: fftScaleMode, + auto: fftAuto, + vmin_pct: fftVminPct, + vmax_pct: fftVmaxPct, + }, + }, + }; + zip.file("metadata.json", JSON.stringify(metadata, null, 2)); + const canvasToBlob = (canvas: HTMLCanvasElement): Promise => new Promise((resolve) => canvas.toBlob((blob) => resolve(blob!), 'image/png')); + if (virtualCanvasRef.current) zip.file("virtual_image.png", await canvasToBlob(virtualCanvasRef.current)); + if (dpCanvasRef.current) zip.file("diffraction_pattern.png", await canvasToBlob(dpCanvasRef.current)); + if (fftCanvasRef.current) zip.file("fft.png", await canvasToBlob(fftCanvasRef.current)); + const zipBlob = await zip.generateAsync({ type: "blob" }); + downloadBlob(zipBlob, `4dstem_export_${timestamp}.zip`); + }; + + // ── DP Figure Export ── + const handleDpExportFigure = (withColorbar: boolean) => { + if (lockExport) return; + setDpExportAnchor(null); + const frameData = rawDpDataRef.current; + if (!frameData) return; + const processed = dpScaleMode === "log" ? applyLogScale(frameData) : frameData; + const lut = COLORMAPS[dpColormap] || COLORMAPS.inferno; + const { min: dMin, max: dMax } = findDataRange(processed); + let vmin: number, vmax: number; + if (traitDpVmin != null && traitDpVmax != null) { + if (dpScaleMode === "log") { + vmin = Math.log1p(Math.max(traitDpVmin, 0)); + vmax = Math.log1p(Math.max(traitDpVmax, 0)); + } else if (dpScaleMode === "power") { + vmin = Math.pow(Math.max(traitDpVmin, 0), dpPowerExp); + vmax = Math.pow(Math.max(traitDpVmax, 0), dpPowerExp); + } else { + vmin = traitDpVmin; + vmax = traitDpVmax; + } + } else { + ({ vmin, vmax } = sliderRange(dMin, dMax, dpVminPct, dpVmaxPct)); + } + const offscreen = renderToOffscreen(processed, detCols, detRows, lut, vmin, vmax); + if (!offscreen) return; + const kPxAngstrom = kPixelSize > 0 && kCalibrated ? kPixelSize : 0; + const figCanvas = exportFigure({ + imageCanvas: offscreen, + title: `DP at (${posRow}, ${posCol})`, + lut, + vmin, + vmax, + logScale: dpScaleMode === "log", + pixelSize: kPxAngstrom > 0 ? kPxAngstrom : undefined, + showColorbar: withColorbar, + showScaleBar: kPxAngstrom > 0, + }); + canvasToPDF(figCanvas).then((blob) => downloadBlob(blob, "show4dstem_dp_figure.pdf")).catch(console.error); + }; + + const handleDpExportPng = () => { + if (lockExport) return; + setDpExportAnchor(null); + if (!dpCanvasRef.current) return; + dpCanvasRef.current.toBlob((b) => { if (b) downloadBlob(b, "show4dstem_dp.png"); }, "image/png"); + }; + + const handleDpExportGif = () => { + if (lockExport) return; + setDpExportAnchor(null); + setExporting(true); + setGifExportRequested(true); + }; + + // ── VI Figure Export ── + const handleViExportFigure = (withColorbar: boolean) => { + if (lockExport) return; + setViExportAnchor(null); + if (!virtualCanvasRef.current) return; + const viCanvas = virtualCanvasRef.current; + const pixelSizeAngstrom = pixelSize > 0 ? pixelSize : 0; + const figCanvas = exportFigure({ + imageCanvas: viCanvas, + title: "Virtual Image", + showColorbar: withColorbar, + showScaleBar: pixelSizeAngstrom > 0, + pixelSize: pixelSizeAngstrom > 0 ? pixelSizeAngstrom : undefined, + }); + canvasToPDF(figCanvas).then((blob) => downloadBlob(blob, "show4dstem_vi_figure.pdf")).catch(console.error); + }; + + const handleViExportPng = () => { + if (lockExport) return; + setViExportAnchor(null); + if (!virtualCanvasRef.current) return; + virtualCanvasRef.current.toBlob((b) => { if (b) downloadBlob(b, "show4dstem_vi.png"); }, "image/png"); + }; + + // Download GIF when data arrives from Python + React.useEffect(() => { + if (!gifData || gifData.byteLength === 0) return; + downloadDataView(gifData, "show4dstem_dp_animation.gif", "image/gif"); + const metaText = (gifMetadataJson || "").trim(); + if (metaText) { + downloadBlob(new Blob([metaText], { type: "application/json" }), "show4dstem_dp_animation.json"); + } + setExporting(false); + }, [gifData, gifMetadataJson]); + + + // Theme-aware select style + const themedSelect = { + ...controlPanel.select, + bgcolor: themeColors.controlBg, + color: themeColors.text, + "& .MuiSelect-select": { py: 0.5 }, + "& .MuiOutlinedInput-notchedOutline": { borderColor: themeColors.border }, + "&:hover .MuiOutlinedInput-notchedOutline": { borderColor: themeColors.accent }, + }; + + const themedMenuProps = { + ...upwardMenuProps, + PaperProps: { sx: { bgcolor: themeColors.controlBg, color: themeColors.text, border: `1px solid ${themeColors.border}` } }, + }; + + const keyboardShortcutItems: [string, string][] = [ + ["↑ / ↓", "Move scan row"], + ["← / →", "Move scan col"], + ["Shift+Arrows", "Move ×10"], + ...(nFrames > 1 ? [["[ / ]", `Prev / next ${frameDimLabel.toLowerCase()}`] as [string, string]] : []), + ["Space", "Play / pause"], + ["R", "Reset all zoom/pan"], + ["Esc", "Release keyboard focus"], + ["Scroll", "Zoom"], + ["Dbl-click", "Reset view"], + ]; + + return ( + + {/* HEADER */} + + {title || "4D-STEM Explorer"} + {nFrames > 1 && ({frameLabels && frameLabels.length > frameIdx ? frameLabels[frameIdx] : `${frameDimLabel} ${frameIdx + 1}/${nFrames}`})} + + Controls + DP: Diffraction pattern I(kx,ky) at scan position. Drag to move ROI center. + Detector: ROI mask shape — defines which DP pixels are integrated for the virtual image. + BF/ABF/ADF: Preset detector configurations (bright-field, annular bright-field, annular dark-field). + Image: Virtual image — integrated intensity within detector ROI at each scan position. + FFT: Spatial frequency content of the virtual image. Auto masks DC + clips to 99.9th percentile. + Profile: Click two points on DP to draw a line intensity profile. + {nFrames > 1 && <> + Frame Playback ({frameDimLabel}) + Loop: Loop playback. Bounce: Ping-pong — alternates forward and reverse. + FPS: Adjust playback speed (1–30 frames per second). + } + Keyboard + + } theme={themeInfo.theme} /> + + + + {/* MAIN CONTENT: DP | VI | FFT (three columns when FFT shown) */} + + {/* LEFT COLUMN: DP Panel */} + + {/* DP Header */} + + + DP at ({Math.round(localPosRow)}, {Math.round(localPosCol)}) + {!hideRoi && k: ({Math.round(localKRow)}, {Math.round(localKCol)})} + + + {!hideProfile && ( + <> + Profile: + { + if (lockProfile) return; + const on = e.target.checked; + setProfileActive(on); + if (!on) { + setProfileLine([]); + setProfileData(null); + setHoveredDpProfileEndpoint(null); + setIsHoveringDpProfileLine(false); + } + }} disabled={lockProfile} size="small" sx={switchStyles.small} /> + + )} + {!hideView && ( + + )} + {!hideExport && ( + + )} + {!hideExport && ( + + )} + {!hideExport && ( + setDpExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> + handleDpExportFigure(true)} sx={{ fontSize: 12 }}>PDF + colorbar + handleDpExportFigure(false)} sx={{ fontSize: 12 }}>PDF + PNG + { if (!lockExport) { setDpExportAnchor(null); handleExportDP(); } }} sx={{ fontSize: 12 }}>ZIP (PNG + metadata) + {pathLength > 0 && GIF (path animation)} + + )} + + + + {/* DP Canvas */} + + + + + {cursorInfo && cursorInfo.panel === "DP" && ( + + + ({cursorInfo.row}, {cursorInfo.col}) {formatNumber(cursorInfo.value)} + + + )} + {!hideView && ( + + )} + + + {/* DP Stats Bar */} + {!hideStats && dpStats && dpStats.length === 4 && ( + + Mean {formatStat(dpStats[0])} + Min {formatStat(dpStats[1])} + Max {formatStat(dpStats[2])} + Std {formatStat(dpStats[3])} + {!hideRoi && ( + <> + + { if (!lockRoi) { setRoiMode("circle"); setRoiRadius(bfRadius || 10); setRoiCenterCol(centerCol); setRoiCenterRow(centerRow); } }} sx={{ color: roiColors.textColor, fontSize: 11, fontWeight: "bold", cursor: lockRoi ? "default" : "pointer", opacity: lockRoi ? 0.6 : 1, "&:hover": { textDecoration: lockRoi ? "none" : "underline" } }}>BF + { if (!lockRoi) { setRoiMode("annular"); setRoiRadiusInner((bfRadius || 10) * 0.5); setRoiRadius(bfRadius || 10); setRoiCenterCol(centerCol); setRoiCenterRow(centerRow); } }} sx={{ color: "#4af", fontSize: 11, fontWeight: "bold", cursor: lockRoi ? "default" : "pointer", opacity: lockRoi ? 0.6 : 1, "&:hover": { textDecoration: lockRoi ? "none" : "underline" } }}>ABF + { if (!lockRoi) { setRoiMode("annular"); setRoiRadiusInner(bfRadius || 10); setRoiRadius(Math.min((bfRadius || 10) * 3, Math.min(detRows, detCols) / 2 - 2)); setRoiCenterCol(centerCol); setRoiCenterRow(centerRow); } }} sx={{ color: "#fa4", fontSize: 11, fontWeight: "bold", cursor: lockRoi ? "default" : "pointer", opacity: lockRoi ? 0.6 : 1, "&:hover": { textDecoration: lockRoi ? "none" : "underline" } }}>ADF + + )} + + )} + + {/* Profile sparkline */} + {profileActive && !hideProfile && ( + + + { + if (lockProfile) return; + setIsResizingProfile(true); + profileResizeStart.current = { startY: e.clientY, startHeight: profileHeight }; + }} + sx={{ width: canvasSize, height: 4, cursor: lockProfile ? "default" : "ns-resize", borderTop: `1px solid ${themeColors.border}`, borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, bgcolor: themeColors.controlBg, "&:hover": { bgcolor: lockProfile ? themeColors.controlBg : themeColors.accent } }} + /> + + )} + + {/* DP Controls - two rows with histogram on right */} + {showControls && (!hideRoi || !hideDisplay || !hideHistogram) && ( + + {/* Left: two rows of controls */} + + {/* Row 1: Detector + slider */} + {!hideRoi && ( + + Detector: + + {(roiMode === "circle" || roiMode === "square" || roiMode === "annular") && ( + <> + { + if (lockRoi) return; + if (roiMode === "annular") { + const [inner, outer] = v as number[]; + setRoiRadiusInner(Math.min(inner, outer - 1)); + setRoiRadius(Math.max(outer, inner + 1)); + } else { + const next = Array.isArray(v) ? v[0] : v; + setRoiRadius(next); + } + }} + min={1} + max={Math.min(detRows, detCols) / 2} + size="small" + sx={{ + width: roiMode === "annular" ? 100 : 70, + mx: 1, + "& .MuiSlider-thumb": { width: 14, height: 14 } + }} + /> + + {roiMode === "annular" ? `${Math.round(roiRadiusInner)}-${Math.round(roiRadius)}px` : `${Math.round(roiRadius)}px`} + + + )} + + )} + {/* Row 2: Color + Scale + Colorbar */} + {!hideDisplay && ( + + Color: + + Scale: + + Colorbar: + { if (!lockDisplay) setShowDpColorbar(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + + )} + + {/* Right: Histogram spanning both rows */} + {!hideHistogram && ( + + { if (!lockHistogram) { setDpVminPct(min); setDpVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme} dataMin={dpGlobalMin} dataMax={dpGlobalMax} /> + + )} + + )} + + + {/* SECOND COLUMN: VI Panel */} + {!hideVirtual && ( + + {/* VI Header */} + + + {shapeRows}×{shapeCols} | {detRows}×{detCols} + + + {!hideFft && ( + <> + FFT: + { if (!lockFft) setShowFft(e.target.checked); }} disabled={lockFft} size="small" sx={switchStyles.small} /> + + )} + {!hideProfile && ( + <> + Profile: + { + if (lockProfile) return; + const on = e.target.checked; + setViProfileActive(on); + if (!on) { + setViProfilePoints([]); + setHoveredViProfileEndpoint(null); + setIsHoveringViProfileLine(false); + } + }} disabled={lockProfile} size="small" sx={switchStyles.small} /> + + )} + {!hideView && ( + + )} + {!hideExport && ( + + )} + {!hideExport && ( + + )} + {!hideExport && ( + setViExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> + handleViExportFigure(true)} sx={{ fontSize: 12 }}>PDF + colorbar + handleViExportFigure(false)} sx={{ fontSize: 12 }}>PDF + PNG + { if (!lockExport && !lockVirtual) { setViExportAnchor(null); handleExportVI(); } }} sx={{ fontSize: 12 }}>ZIP (all panels + metadata) + + )} + + + + {/* VI Canvas */} + + + + + {cursorInfo && cursorInfo.panel === "VI" && ( + + + ({cursorInfo.row}, {cursorInfo.col}) {formatNumber(cursorInfo.value)} + + + )} + {!hideView && ( + + )} + + + {/* VI Stats Bar */} + {!hideStats && viStats && viStats.length === 4 && ( + + Mean {formatStat(viStats[0])} + Min {formatStat(viStats[1])} + Max {formatStat(viStats[2])} + Std {formatStat(viStats[3])} + + )} + + {/* VI Profile sparkline */} + {viProfileActive && !hideProfile && ( + + + { + if (lockProfile) return; + setIsResizingViProfile(true); + viProfileResizeStart.current = { startY: e.clientY, startHeight: viProfileHeight }; + }} + sx={{ width: viCanvasWidth, height: 4, cursor: lockProfile ? "default" : "ns-resize", borderTop: `1px solid ${themeColors.border}`, borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, bgcolor: themeColors.controlBg, "&:hover": { bgcolor: lockProfile ? themeColors.controlBg : themeColors.accent } }} + /> + + )} + + {/* VI Controls - Two rows with histogram on right */} + {showControls && (!hideRoi || !hideDisplay || !hideHistogram) && ( + + {/* Left: Two rows of controls */} + + {/* Row 1: ROI selector */} + {!hideRoi && ( + + ROI: + + {viRoiMode && viRoiMode !== "off" && ( + <> + {(viRoiMode === "circle" || viRoiMode === "square") && ( + <> + { if (!lockRoi) setViRoiRadius(v as number); }} + min={1} + max={Math.min(shapeRows, shapeCols) / 2} + size="small" + sx={{ width: 80, mx: 1 }} + /> + + {Math.round(viRoiRadius || 5)}px + + + )} + {summedDpCount > 0 && ( + + {summedDpCount} pos + + )} + + )} + + )} + {/* Row 2: Color + Scale */} + {!hideDisplay && ( + + Color: + + Scale: + + + )} + + {/* Right: Histogram spanning both rows */} + {!hideHistogram && ( + + { if (!lockHistogram) { setViVminPct(min); setViVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme} dataMin={viDataMin} dataMax={viDataMax} /> + + )} + + )} + + )} + + {/* THIRD COLUMN: FFT Panel (conditionally shown) */} + {effectiveShowFft && ( + + {/* FFT Header */} + + {roiFftActive && fftCropDims ? `ROI FFT (${fftCropDims.cropWidth}\u00D7${fftCropDims.cropHeight})` : "FFT"} + + {!hideView && ( + + )} + + + + {/* FFT Canvas */} + + + + {!hideView && ( + + )} + + + {/* FFT Stats Bar */} + {!hideStats && fftStats && fftStats.length === 4 && ( + + Mean {formatStat(fftStats[0])} + Min {formatStat(fftStats[1])} + Max {formatStat(fftStats[2])} + Std {formatStat(fftStats[3])} + + )} + + {/* FFT D-spacing readout */} + {fftClickInfo && ( + + + Spot ({fftClickInfo.row.toFixed(1)}, {fftClickInfo.col.toFixed(1)}) + + + dist {fftClickInfo.distPx.toFixed(1)} px + + {fftClickInfo.dSpacing != null && ( + + d = {fftClickInfo.dSpacing >= 10 ? `${(fftClickInfo.dSpacing / 10).toFixed(2)} nm` : `${fftClickInfo.dSpacing.toFixed(2)} \u00C5`} + + )} + {fftClickInfo.spatialFreq != null && ( + + q = {fftClickInfo.spatialFreq.toFixed(4)} {"\u00C5\u207B\u00B9"} + + )} + + )} + + {/* FFT Controls - Two rows with histogram on right */} + {showControls && (!hideDisplay || !hideHistogram) && ( + + {/* Left: Two rows of controls */} + {!hideDisplay && ( + + {/* Row 1: Scale + Clip */} + + Scale: + + Auto: + { if (!lockDisplay && !lockFft) setFftAuto(e.target.checked); }} disabled={lockDisplay || lockFft} size="small" sx={switchStyles.small} /> + {fftCropDims && ( + <> + Win: + { if (!lockDisplay && !lockFft) setFftWindow(e.target.checked); }} disabled={lockDisplay || lockFft} size="small" sx={switchStyles.small} /> + + )} + + {/* Row 2: Color */} + + Color: + + + + )} + {/* Right: Histogram spanning both rows */} + {!hideHistogram && ( + + {fftHistogramData && ( + { if (!lockHistogram && !lockFft) { setFftVminPct(min); setFftVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme} dataMin={fftDataMin} dataMax={fftDataMax} /> + )} + + )} + + )} + + )} + + + {/* BOTTOM CONTROLS */} + + {/* Frame controls (5D time/tilt series) — matches Show3D playback */} + {showControls && nFrames > 1 && !hidePlayback && !hideFrame && (<> + + {frameDimLabel}: + + { if (!lockFrame && !lockPlayback) { setFrameReverse(true); setFramePlaying(true); } }} sx={{ color: frameReverse && framePlaying ? themeColors.accent : themeColors.textMuted, p: 0.25 }}> + + + { if (!lockFrame && !lockPlayback) setFramePlaying(!framePlaying); }} sx={{ color: themeColors.accent, p: 0.25 }}> + {framePlaying ? : } + + { if (!lockFrame && !lockPlayback) { setFrameReverse(false); setFramePlaying(true); } }} sx={{ color: !frameReverse && framePlaying ? themeColors.accent : themeColors.textMuted, p: 0.25 }}> + + + { if (!lockFrame && !lockPlayback) { setFramePlaying(false); setFrameIdx(0); } }} sx={{ color: themeColors.textMuted, p: 0.25 }}> + + + + { if (!lockFrame && !lockPlayback) { setFramePlaying(false); setFrameIdx(v as number); } }} min={0} max={Math.max(0, nFrames - 1)} size="small" sx={{ flex: 1, minWidth: 60, "& .MuiSlider-thumb": { width: 10, height: 10 } }} /> + {frameLabels && frameLabels.length > frameIdx ? frameLabels[frameIdx] : `${frameIdx + 1}/${nFrames}`} + + + fps + { if (!lockFrame && !lockPlayback) setFrameFps(v as number); }} size="small" sx={{ ...sliderStyles.small, width: 35, flexShrink: 0 }} /> + {Math.round(frameFps)} + Loop + { if (!lockFrame && !lockPlayback) setFrameLoop(!frameLoop); }} disabled={lockFrame || lockPlayback} sx={{ ...switchStyles.small, flexShrink: 0 }} /> + Bounce + { if (!lockFrame && !lockPlayback) setFrameBoomerang(!frameBoomerang); }} disabled={lockFrame || lockPlayback} sx={{ ...switchStyles.small, flexShrink: 0 }} /> + + )} + + {/* Path animation slider */} + {showControls && !hidePlayback && pathLength > 0 && ( + + + { if (!lockPlayback) setPathPlaying(!pathPlaying); }} sx={{ color: themeColors.accent, p: 0.25 }}> + {pathPlaying ? : } + + { if (!lockPlayback) { setPathPlaying(false); setPathIndex(0); } }} sx={{ color: themeColors.textMuted, p: 0.25 }}> + + + + { if (!lockPlayback) { setPathPlaying(false); setPathIndex(v as number); } }} min={0} max={Math.max(0, pathLength - 1)} size="small" sx={{ flex: 1, minWidth: 60, "& .MuiSlider-thumb": { width: 10, height: 10 } }} /> + {pathIndex + 1}/{pathLength} + Loop: + { if (!lockPlayback) { model.set("path_loop", v); model.save_changes(); } }} disabled={lockPlayback} size="small" sx={switchStyles.small} /> + + )} + + ); +} + +export const render = createRender(Show4DSTEM); diff --git a/widget/js/show4dstem/styles.css b/widget/js/show4dstem/styles.css new file mode 100644 index 00000000..61876cde --- /dev/null +++ b/widget/js/show4dstem/styles.css @@ -0,0 +1,5 @@ +/* Theme-aware styles - minimal, let JS handle most theming */ +.show4dstem-root { + border-radius: 2px; + padding: 16px; +} diff --git a/widget/js/stats.ts b/widget/js/stats.ts new file mode 100644 index 00000000..b71c45aa --- /dev/null +++ b/widget/js/stats.ts @@ -0,0 +1,101 @@ +/** Find min/max range of a Float32Array, filtering out NaN and Infinity. */ +export function findDataRange(data: Float32Array): { min: number; max: number } { + let min = Infinity, max = -Infinity; + for (let i = 0; i < data.length; i++) { + const v = data[i]; + if (!isFinite(v)) continue; + if (v < min) min = v; + if (v > max) max = v; + } + // If no finite values found, return zeros + if (min === Infinity) return { min: 0, max: 0 }; + return { min, max }; +} + +/** Apply log1p scale: result[i] = log(1 + max(0, data[i])). Returns a new array. */ +export function applyLogScale(data: Float32Array): Float32Array { + const result = new Float32Array(data.length); + for (let i = 0; i < data.length; i++) { + result[i] = Math.log1p(Math.max(0, data[i])); + } + return result; +} + +/** Apply log1p scale into a pre-allocated buffer. Avoids per-frame allocation. */ +export function applyLogScaleInPlace(data: Float32Array, out: Float32Array): Float32Array { + for (let i = 0; i < data.length; i++) { + out[i] = Math.log1p(Math.max(0, data[i])); + } + return out; +} + +/** Percentile-based clipping using O(n) histogram approach. + * Also returns data min/max so callers can skip a redundant findDataRange scan. */ +export function percentileClip( + data: Float32Array, pLow: number, pHigh: number, +): { vmin: number; vmax: number; min: number; max: number } { + const len = data.length; + if (len === 0) return { vmin: 0, vmax: 0, min: 0, max: 0 }; + + // Pass 1: find min/max + let min = Infinity, max = -Infinity; + for (let i = 0; i < len; i++) { + const v = data[i]; + if (v < min) min = v; + if (v > max) max = v; + } + if (min === max) return { vmin: min, vmax: max, min, max }; + + // Pass 2: build histogram + const NUM_BINS = 1024; + const bins = new Uint32Array(NUM_BINS); + const range = max - min; + const scale = (NUM_BINS - 1) / range; + for (let i = 0; i < len; i++) { + bins[Math.floor((data[i] - min) * scale)]++; + } + + // Walk cumulative histogram to find percentile values + const lowCount = Math.floor(len * (pLow / 100)); + const highCount = Math.ceil(len * (pHigh / 100)); + let cumSum = 0; + let vmin = min, vmax = max; + for (let i = 0; i < NUM_BINS; i++) { + cumSum += bins[i]; + if (cumSum >= lowCount) { vmin = min + (i / (NUM_BINS - 1)) * range; break; } + } + cumSum = 0; + for (let i = 0; i < NUM_BINS; i++) { + cumSum += bins[i]; + if (cumSum >= highCount) { vmax = min + (i / (NUM_BINS - 1)) * range; break; } + } + return { vmin, vmax, min, max }; +} + +/** Compute mean, min, max, and standard deviation of a Float32Array. */ +export function computeStats(data: Float32Array): { mean: number; min: number; max: number; std: number } { + if (data.length === 0) return { mean: 0, min: 0, max: 0, std: 0 }; + let sum = 0, min = Infinity, max = -Infinity; + for (let i = 0; i < data.length; i++) { + const v = data[i]; + sum += v; + if (v < min) min = v; + if (v > max) max = v; + } + const mean = sum / data.length; + let variance = 0; + for (let i = 0; i < data.length; i++) variance += (data[i] - mean) ** 2; + const std = Math.sqrt(variance / data.length); + return { mean, min, max, std }; +} + +/** Convert histogram slider percentages (0-100) to vmin/vmax in data space. */ +export function sliderRange( + dataMin: number, dataMax: number, vminPct: number, vmaxPct: number, +): { vmin: number; vmax: number } { + const range = dataMax - dataMin; + return { + vmin: dataMin + (vminPct / 100) * range, + vmax: dataMin + (vmaxPct / 100) * range, + }; +} diff --git a/widget/js/theme.ts b/widget/js/theme.ts new file mode 100644 index 00000000..f13123d5 --- /dev/null +++ b/widget/js/theme.ts @@ -0,0 +1,149 @@ +/** + * Shared theme detection and color system for all widgets. + * Detects JupyterLab, VS Code, Colab, Classic Jupyter, and OS preferences. + */ + +import { useState, useEffect, useMemo } from "react"; + +// ============================================================================ +// Types +// ============================================================================ +export type Environment = "jupyterlab" | "vscode" | "colab" | "jupyter-classic" | "unknown"; +export type Theme = "light" | "dark"; + +export interface ThemeInfo { + environment: Environment; + theme: Theme; +} + +export interface ThemeColors { + bg: string; + bgAlt: string; + text: string; + textMuted: string; + border: string; + controlBg: string; + accent: string; +} + +// ============================================================================ +// Color palettes +// ============================================================================ +export const DARK_COLORS: ThemeColors = { + bg: "#1e1e1e", + bgAlt: "#1a1a1a", + text: "#e0e0e0", + textMuted: "#888", + border: "#3a3a3a", + controlBg: "#252525", + accent: "#5af", +}; + +export const LIGHT_COLORS: ThemeColors = { + bg: "#ffffff", + bgAlt: "#f5f5f5", + text: "#1e1e1e", + textMuted: "#666", + border: "#ccc", + controlBg: "#f0f0f0", + accent: "#0066cc", +}; + +export function getThemeColors(theme: Theme): ThemeColors { + return theme === "dark" ? DARK_COLORS : LIGHT_COLORS; +} + +// ============================================================================ +// Theme detection +// ============================================================================ + +/** Check if a CSS color string is dark (luminance < 0.5) */ +export function isColorDark(color: string): boolean { + const match = color.match(/rgba?\((\d+),\s*(\d+),\s*(\d+)/); + if (!match) return true; + const [, r, g, b] = match.map(Number); + const luminance = (0.299 * r + 0.587 * g + 0.114 * b) / 255; + return luminance < 0.5; +} + +export function detectTheme(): ThemeInfo { + // 1. JupyterLab - has data-jp-theme-light attribute + const jpThemeLight = document.body.dataset.jpThemeLight; + if (jpThemeLight !== undefined) { + return { + environment: "jupyterlab", + theme: jpThemeLight === "true" ? "light" : "dark", + }; + } + + // 2. VS Code - has vscode-* classes on body or html + const bodyClasses = document.body.className; + const htmlClasses = document.documentElement.className; + if (bodyClasses.includes("vscode-") || htmlClasses.includes("vscode-")) { + const isDark = bodyClasses.includes("vscode-dark") || htmlClasses.includes("vscode-dark"); + return { + environment: "vscode", + theme: isDark ? "dark" : "light", + }; + } + + // 3. Google Colab - has specific markers + if (document.querySelector('colab-shaded-scroller') || document.body.classList.contains('colaboratory')) { + const bg = getComputedStyle(document.body).backgroundColor; + return { + environment: "colab", + theme: isColorDark(bg) ? "dark" : "light", + }; + } + + // 4. Classic Jupyter Notebook - has #notebook element + if (document.getElementById('notebook')) { + const bodyBg = getComputedStyle(document.body).backgroundColor; + return { + environment: "jupyter-classic", + theme: isColorDark(bodyBg) ? "dark" : "light", + }; + } + + // 5. Fallback: check OS preference, then computed background + const prefersDark = window.matchMedia?.('(prefers-color-scheme: dark)')?.matches; + if (prefersDark !== undefined) { + return { + environment: "unknown", + theme: prefersDark ? "dark" : "light", + }; + } + + // Final fallback: check body background luminance + const bg = getComputedStyle(document.body).backgroundColor; + return { + environment: "unknown", + theme: isColorDark(bg) ? "dark" : "light", + }; +} + +// ============================================================================ +// React hook +// ============================================================================ +export function useTheme(): { themeInfo: ThemeInfo; colors: ThemeColors } { + const [themeInfo, setThemeInfo] = useState(() => detectTheme()); + + useEffect(() => { + const mediaQuery = window.matchMedia?.('(prefers-color-scheme: dark)'); + const handleChange = () => setThemeInfo(detectTheme()); + mediaQuery?.addEventListener?.('change', handleChange); + + const observer = new MutationObserver(() => setThemeInfo(detectTheme())); + observer.observe(document.body, { attributes: true, attributeFilter: ['data-jp-theme-light', 'class'] }); + + return () => { + mediaQuery?.removeEventListener?.('change', handleChange); + observer.disconnect(); + }; + }, []); + + // Memoize by theme string so `colors` is referentially stable across renders — + // effects/components that depend on `colors` only re-run when the theme flips. + const colors = useMemo(() => getThemeColors(themeInfo.theme), [themeInfo.theme]); + return { themeInfo, colors }; +} diff --git a/widget/js/tool-parity.ts b/widget/js/tool-parity.ts new file mode 100644 index 00000000..8e318e6c --- /dev/null +++ b/widget/js/tool-parity.ts @@ -0,0 +1,156 @@ +import registryJson from "../src/quantem/widget/tool_parity.json"; + +type ToolInput = string | string[] | null | undefined; + +type WidgetConfig = { + tool_groups: string[]; + aliases?: Record; +}; + +type ControlPreset = { + label: string; + show_groups: string[]; +}; + +type ToolParityRegistry = { + widgets: Record; + control_presets: Record; + viewer_widgets?: string[]; +}; + +const REGISTRY = registryJson as ToolParityRegistry; + +function getWidgetConfig(widgetName: string): WidgetConfig { + const cfg = REGISTRY.widgets[widgetName]; + if (!cfg) { + const supported = Object.keys(REGISTRY.widgets).sort().join(", "); + throw new Error(`Unknown widget '${widgetName}'. Supported widgets: ${supported}.`); + } + return cfg; +} + +function toValues(values: ToolInput): string[] { + if (values == null) return []; + if (typeof values === "string") return [values]; + return [...values]; +} + +function toCanonical(widgetName: string, value: string): string { + const cfg = getWidgetConfig(widgetName); + const aliases = cfg.aliases ?? {}; + const key = value.trim().toLowerCase(); + return aliases[key] ?? key; +} + +export function getWidgetToolGroups(widgetName: string): string[] { + return [...getWidgetConfig(widgetName).tool_groups]; +} + +export function normalizeToolGroups(widgetName: string, values: ToolInput): string[] { + const groups = getWidgetToolGroups(widgetName); + const groupSet = new Set(groups); + const out: string[] = []; + const seen = new Set(); + for (const raw of toValues(values)) { + const canonical = toCanonical(widgetName, String(raw)); + if (!canonical) continue; + if (!groupSet.has(canonical)) { + const supported = groups.map((g) => `"${g}"`).join(", "); + throw new Error(`Unknown tool group '${raw}'. Supported values: ${supported}.`); + } + if (canonical === "all") return ["all"]; + if (!seen.has(canonical)) { + seen.add(canonical); + out.push(canonical); + } + } + return out; +} + +function orderedWithoutAll(widgetName: string, values: Set): string[] { + return getWidgetToolGroups(widgetName).filter((group) => group !== "all" && values.has(group)); +} + +export function expandToolGroups(widgetName: string, values: ToolInput): string[] { + const normalized = normalizeToolGroups(widgetName, values); + if (!normalized.includes("all")) return normalized; + return getWidgetToolGroups(widgetName).filter((group) => group !== "all"); +} + +export function compactToolLabel(key: string): string { + return key + .replace(/_/g, " ") + .replace(/\b\w/g, (m) => m.toUpperCase()); +} + +export function getControlPresetIds(): string[] { + return Object.keys(REGISTRY.control_presets); +} + +export function getControlPresetLabel(presetId: string): string { + const preset = REGISTRY.control_presets[presetId]; + return preset?.label ?? presetId; +} + +export function resolvePresetHiddenTools(widgetName: string, presetId: string): string[] { + const preset = REGISTRY.control_presets[presetId]; + if (!preset) { + const supported = Object.keys(REGISTRY.control_presets).sort().join(", "); + throw new Error(`Unknown control preset '${presetId}'. Supported presets: ${supported}.`); + } + const supportedGroups = getWidgetToolGroups(widgetName).filter((group) => group !== "all"); + if (preset.show_groups.includes("*")) return []; + const show = new Set(preset.show_groups.map((g) => toCanonical(widgetName, g))); + const hidden = supportedGroups.filter((group) => !show.has(group)); + return normalizeToolGroups(widgetName, hidden); +} + +export type ToolVisibilityState = { + hideAll: boolean; + lockAll: boolean; + isHidden: (group: string) => boolean; + isLocked: (group: string) => boolean; + hiddenSet: Set; + disabledSet: Set; +}; + +export function computeToolVisibility( + widgetName: string, + disabledTools: ToolInput, + hiddenTools: ToolInput, +): ToolVisibilityState { + const hidden = normalizeToolGroups(widgetName, hiddenTools); + const disabled = normalizeToolGroups(widgetName, disabledTools); + const hiddenSet = new Set(hidden); + const disabledSet = new Set(disabled); + const hideAll = hiddenSet.has("all"); + const lockAll = hideAll || disabledSet.has("all"); + + const isHidden = (group: string): boolean => { + const canonical = toCanonical(widgetName, group); + if (canonical === "all") return hideAll; + return hideAll || hiddenSet.has(canonical); + }; + + const isLocked = (group: string): boolean => { + const canonical = toCanonical(widgetName, group); + if (canonical === "all") return lockAll; + return lockAll || isHidden(canonical) || disabledSet.has(canonical); + }; + + return { hideAll, lockAll, isHidden, isLocked, hiddenSet, disabledSet }; +} + +export function addToolGroup(widgetName: string, current: ToolInput, group: string): string[] { + const merged = new Set(expandToolGroups(widgetName, current)); + const canonical = toCanonical(widgetName, group); + if (canonical === "all") return ["all"]; + merged.add(canonical); + return orderedWithoutAll(widgetName, merged); +} + +export function removeToolGroup(widgetName: string, current: ToolInput, group: string): string[] { + const merged = new Set(expandToolGroups(widgetName, current)); + merged.delete(toCanonical(widgetName, group)); + return orderedWithoutAll(widgetName, merged); +} diff --git a/widget/js/webgpu-fft.ts b/widget/js/webgpu-fft.ts new file mode 100644 index 00000000..2498b755 --- /dev/null +++ b/widget/js/webgpu-fft.ts @@ -0,0 +1,509 @@ +/// + +/** + * WebGPU FFT — shared 2D FFT with GPU acceleration and CPU fallback. + * Handles non-power-of-2 dimensions via zero-padding. + */ + +// ============================================================================ +// CPU FFT fallback +// ============================================================================ + +export function nextPow2(n: number): number { return Math.pow(2, Math.ceil(Math.log2(n))); } + +function fft1d(real: Float32Array, imag: Float32Array, inverse: boolean = false) { + const n = real.length; + if (n <= 1) return; + let j = 0; + for (let i = 0; i < n - 1; i++) { + if (i < j) { [real[i], real[j]] = [real[j], real[i]]; [imag[i], imag[j]] = [imag[j], imag[i]]; } + let k = n >> 1; + while (k <= j) { j -= k; k >>= 1; } + j += k; + } + const sign = inverse ? 1 : -1; + for (let len = 2; len <= n; len <<= 1) { + const halfLen = len >> 1; + const angle = (sign * 2 * Math.PI) / len; + const wReal = Math.cos(angle), wImag = Math.sin(angle); + for (let i = 0; i < n; i += len) { + let curReal = 1, curImag = 0; + for (let k = 0; k < halfLen; k++) { + const evenIdx = i + k, oddIdx = i + k + halfLen; + const tReal = curReal * real[oddIdx] - curImag * imag[oddIdx]; + const tImag = curReal * imag[oddIdx] + curImag * real[oddIdx]; + real[oddIdx] = real[evenIdx] - tReal; imag[oddIdx] = imag[evenIdx] - tImag; + real[evenIdx] += tReal; imag[evenIdx] += tImag; + const newReal = curReal * wReal - curImag * wImag; + curImag = curReal * wImag + curImag * wReal; curReal = newReal; + } + } + } + if (inverse) { for (let i = 0; i < n; i++) { real[i] /= n; imag[i] /= n; } } +} + +export function fft2d(real: Float32Array, imag: Float32Array, width: number, height: number, inverse: boolean = false) { + const paddedW = nextPow2(width), paddedH = nextPow2(height); + const needsPadding = paddedW !== width || paddedH !== height; + let workReal: Float32Array, workImag: Float32Array; + if (needsPadding) { + workReal = new Float32Array(paddedW * paddedH); workImag = new Float32Array(paddedW * paddedH); + for (let y = 0; y < height; y++) for (let x = 0; x < width; x++) { + workReal[y * paddedW + x] = real[y * width + x]; workImag[y * paddedW + x] = imag[y * width + x]; + } + } else { workReal = real; workImag = imag; } + const rowReal = new Float32Array(paddedW), rowImag = new Float32Array(paddedW); + for (let y = 0; y < paddedH; y++) { + const offset = y * paddedW; + for (let x = 0; x < paddedW; x++) { rowReal[x] = workReal[offset + x]; rowImag[x] = workImag[offset + x]; } + fft1d(rowReal, rowImag, inverse); + for (let x = 0; x < paddedW; x++) { workReal[offset + x] = rowReal[x]; workImag[offset + x] = rowImag[x]; } + } + const colReal = new Float32Array(paddedH), colImag = new Float32Array(paddedH); + for (let x = 0; x < paddedW; x++) { + for (let y = 0; y < paddedH; y++) { colReal[y] = workReal[y * paddedW + x]; colImag[y] = workImag[y * paddedW + x]; } + fft1d(colReal, colImag, inverse); + for (let y = 0; y < paddedH; y++) { workReal[y * paddedW + x] = colReal[y]; workImag[y * paddedW + x] = colImag[y]; } + } + if (needsPadding) { + for (let y = 0; y < height; y++) for (let x = 0; x < width; x++) { + real[y * width + x] = workReal[y * paddedW + x]; imag[y * width + x] = workImag[y * paddedW + x]; + } + } +} + +export function fftshift(data: Float32Array, width: number, height: number): void { + const halfW = width >> 1, halfH = height >> 1; + const temp = new Float32Array(width * height); + for (let y = 0; y < height; y++) for (let x = 0; x < width; x++) { + temp[((y + halfH) % height) * width + ((x + halfW) % width)] = data[y * width + x]; + } + data.set(temp); +} + +// ============================================================================ +// CPU FFT Web Worker — runs fft2d + fftshift + computeMagnitude off main thread +// ============================================================================ + +const FFT_WORKER_CODE = ` +function nextPow2(n) { return Math.pow(2, Math.ceil(Math.log2(n))); } +function fft1d(real, imag, inverse) { + var n = real.length; if (n <= 1) return; + var j = 0; + for (var i = 0; i < n - 1; i++) { + if (i < j) { var t = real[i]; real[i] = real[j]; real[j] = t; t = imag[i]; imag[i] = imag[j]; imag[j] = t; } + var k = n >> 1; while (k <= j) { j -= k; k >>= 1; } j += k; + } + var sign = inverse ? 1 : -1; + for (var len = 2; len <= n; len <<= 1) { + var halfLen = len >> 1, angle = (sign * 2 * Math.PI) / len; + var wR = Math.cos(angle), wI = Math.sin(angle); + for (var i = 0; i < n; i += len) { + var cR = 1, cI = 0; + for (var k = 0; k < halfLen; k++) { + var eI = i + k, oI = i + k + halfLen; + var tR = cR * real[oI] - cI * imag[oI], tI = cR * imag[oI] + cI * real[oI]; + real[oI] = real[eI] - tR; imag[oI] = imag[eI] - tI; + real[eI] += tR; imag[eI] += tI; + var nR = cR * wR - cI * wI; cI = cR * wI + cI * wR; cR = nR; + } + } + } + if (inverse) { for (var i = 0; i < n; i++) { real[i] /= n; imag[i] /= n; } } +} +function fft2d(real, imag, width, height, inverse) { + var pW = nextPow2(width), pH = nextPow2(height), pad = pW !== width || pH !== height; + var wR, wI; + if (pad) { + wR = new Float32Array(pW * pH); wI = new Float32Array(pW * pH); + for (var y = 0; y < height; y++) for (var x = 0; x < width; x++) { wR[y*pW+x] = real[y*width+x]; wI[y*pW+x] = imag[y*width+x]; } + } else { wR = real; wI = imag; } + var rR = new Float32Array(pW), rI = new Float32Array(pW); + for (var y = 0; y < pH; y++) { + var o = y * pW; for (var x = 0; x < pW; x++) { rR[x] = wR[o+x]; rI[x] = wI[o+x]; } + fft1d(rR, rI, inverse); for (var x = 0; x < pW; x++) { wR[o+x] = rR[x]; wI[o+x] = rI[x]; } + } + var cR = new Float32Array(pH), cI = new Float32Array(pH); + for (var x = 0; x < pW; x++) { + for (var y = 0; y < pH; y++) { cR[y] = wR[y*pW+x]; cI[y] = wI[y*pW+x]; } + fft1d(cR, cI, inverse); for (var y = 0; y < pH; y++) { wR[y*pW+x] = cR[y]; wI[y*pW+x] = cI[y]; } + } + if (pad) { for (var y = 0; y < height; y++) for (var x = 0; x < width; x++) { real[y*width+x] = wR[y*pW+x]; imag[y*width+x] = wI[y*pW+x]; } } +} +function fftshift(data, width, height) { + var hW = width >> 1, hH = height >> 1, temp = new Float32Array(width * height); + for (var y = 0; y < height; y++) for (var x = 0; x < width; x++) temp[((y+hH)%height)*width+((x+hW)%width)] = data[y*width+x]; + data.set(temp); +} +self.onmessage = function(e) { + var d = e.data, real = d.real, imag = d.imag, w = d.width, h = d.height; + fft2d(real, imag, w, h, d.inverse); + fftshift(real, w, h); fftshift(imag, w, h); + var n = real.length, mag = new Float32Array(n); + for (var i = 0; i < n; i++) mag[i] = Math.sqrt(real[i]*real[i] + imag[i]*imag[i]); + self.postMessage({ id: d.id, magnitude: mag, real: real, imag: imag }, [mag.buffer, real.buffer, imag.buffer]); +}; +`; + +let _fftWorker: Worker | null = null; +const _fftCallbacks = new Map void>(); +let _fftWorkerId = 0; + +function getFFTWorker(): Worker { + if (!_fftWorker) { + const blob = new Blob([FFT_WORKER_CODE], { type: 'application/javascript' }); + _fftWorker = new Worker(URL.createObjectURL(blob)); + _fftWorker.onmessage = (e: MessageEvent) => { + const cb = _fftCallbacks.get(e.data.id); + if (cb) { + _fftCallbacks.delete(e.data.id); + cb(e.data); + } + }; + } + return _fftWorker; +} + +/** + * CPU FFT in a Web Worker — does fft2d + fftshift + computeMagnitude off main thread. + * Transfers Float32Arrays to the worker (zero-copy) so the main thread is never blocked. + * The input arrays become detached after this call — pass copies if you need to keep them. + */ +export function fft2dAsync( + real: Float32Array, imag: Float32Array, + width: number, height: number, + inverse: boolean = false, +): Promise<{ magnitude: Float32Array; real: Float32Array; imag: Float32Array }> { + const worker = getFFTWorker(); + const id = ++_fftWorkerId; + return new Promise((resolve) => { + _fftCallbacks.set(id, resolve); + worker.postMessage( + { id, real, imag, width, height, inverse }, + [real.buffer, imag.buffer], + ); + }); +} + +// ============================================================================ +// WebGPU FFT — GPU-accelerated 2D FFT +// ============================================================================ + +const FFT_2D_SHADER = /* wgsl */` +fn cmul(a: vec2, b: vec2) -> vec2 { return vec2(a.x*b.x-a.y*b.y, a.x*b.y+a.y*b.x); } +fn twiddle(k: u32, N: u32, inverse: f32) -> vec2 { let angle = inverse * 2.0 * 3.14159265359 * f32(k) / f32(N); return vec2(cos(angle), sin(angle)); } +fn bitReverse(x: u32, log2N: u32) -> u32 { var result: u32 = 0u; var val = x; for (var i: u32 = 0u; i < log2N; i = i + 1u) { result = (result << 1u) | (val & 1u); val = val >> 1u; } return result; } +struct FFT2DParams { width: u32, height: u32, log2Size: u32, stage: u32, inverse: f32, isRowWise: u32, } +@group(0) @binding(0) var params: FFT2DParams; +@group(0) @binding(1) var data: array>; +fn getIndex(row: u32, col: u32) -> u32 { return row * params.width + col; } +@compute @workgroup_size(16, 16) fn bitReverseRows(@builtin(global_invocation_id) gid: vec3) { let row = gid.y; let col = gid.x; if (row >= params.height || col >= params.width) { return; } let rev = bitReverse(col, params.log2Size); if (col < rev) { let idx1 = getIndex(row, col); let idx2 = getIndex(row, rev); let temp = data[idx1]; data[idx1] = data[idx2]; data[idx2] = temp; } } +@compute @workgroup_size(16, 16) fn bitReverseCols(@builtin(global_invocation_id) gid: vec3) { let row = gid.y; let col = gid.x; if (row >= params.height || col >= params.width) { return; } let rev = bitReverse(row, params.log2Size); if (row < rev) { let idx1 = getIndex(row, col); let idx2 = getIndex(rev, col); let temp = data[idx1]; data[idx1] = data[idx2]; data[idx2] = temp; } } +@compute @workgroup_size(16, 16) fn butterflyRows(@builtin(global_invocation_id) gid: vec3) { let row = gid.y; let idx = gid.x; if (row >= params.height || idx >= params.width / 2u) { return; } let stage = params.stage; let halfSize = 1u << stage; let fullSize = halfSize << 1u; let group = idx / halfSize; let pos = idx % halfSize; let col_i = group * fullSize + pos; let col_j = col_i + halfSize; if (col_j >= params.width) { return; } let w = twiddle(pos, fullSize, params.inverse); let i = getIndex(row, col_i); let j = getIndex(row, col_j); let u = data[i]; let t = cmul(w, data[j]); data[i] = u + t; data[j] = u - t; } +@compute @workgroup_size(16, 16) fn butterflyCols(@builtin(global_invocation_id) gid: vec3) { let col = gid.x; let idx = gid.y; if (col >= params.width || idx >= params.height / 2u) { return; } let stage = params.stage; let halfSize = 1u << stage; let fullSize = halfSize << 1u; let group = idx / halfSize; let pos = idx % halfSize; let row_i = group * fullSize + pos; let row_j = row_i + halfSize; if (row_j >= params.height) { return; } let w = twiddle(pos, fullSize, params.inverse); let i = getIndex(row_i, col); let j = getIndex(row_j, col); let u = data[i]; let t = cmul(w, data[j]); data[i] = u + t; data[j] = u - t; } +@compute @workgroup_size(16, 16) fn normalize2D(@builtin(global_invocation_id) gid: vec3) { let row = gid.y; let col = gid.x; if (row >= params.height || col >= params.width) { return; } let idx = getIndex(row, col); let scale = 1.0 / f32(params.width * params.height); data[idx] = data[idx] * scale; }`; + +export class WebGPUFFT { + private device: GPUDevice; + private pipelines2D: { bitReverseRows: GPUComputePipeline; bitReverseCols: GPUComputePipeline; butterflyRows: GPUComputePipeline; butterflyCols: GPUComputePipeline; normalize: GPUComputePipeline } | null = null; + private initialized = false; + constructor(device: GPUDevice) { this.device = device; } + async init(): Promise { + if (this.initialized) return; + const module2D = this.device.createShaderModule({ code: FFT_2D_SHADER }); + this.pipelines2D = { + bitReverseRows: this.device.createComputePipeline({ layout: 'auto', compute: { module: module2D, entryPoint: 'bitReverseRows' } }), + bitReverseCols: this.device.createComputePipeline({ layout: 'auto', compute: { module: module2D, entryPoint: 'bitReverseCols' } }), + butterflyRows: this.device.createComputePipeline({ layout: 'auto', compute: { module: module2D, entryPoint: 'butterflyRows' } }), + butterflyCols: this.device.createComputePipeline({ layout: 'auto', compute: { module: module2D, entryPoint: 'butterflyCols' } }), + normalize: this.device.createComputePipeline({ layout: 'auto', compute: { module: module2D, entryPoint: 'normalize2D' } }) + }; + this.initialized = true; + } + async fft2D(realData: Float32Array, imagData: Float32Array, width: number, height: number, inverse: boolean = false): Promise<{ real: Float32Array, imag: Float32Array }> { + await this.init(); + const paddedWidth = nextPow2(width), paddedHeight = nextPow2(height); + const needsPadding = paddedWidth !== width || paddedHeight !== height; + const log2Width = Math.log2(paddedWidth), log2Height = Math.log2(paddedHeight); + const paddedSize = paddedWidth * paddedHeight, originalSize = width * height; + let workReal: Float32Array, workImag: Float32Array; + if (needsPadding) { + workReal = new Float32Array(paddedSize); workImag = new Float32Array(paddedSize); + for (let y = 0; y < height; y++) for (let x = 0; x < width; x++) { workReal[y * paddedWidth + x] = realData[y * width + x]; workImag[y * paddedWidth + x] = imagData[y * width + x]; } + } else { workReal = realData; workImag = imagData; } + const complexData = new Float32Array(paddedSize * 2); + for (let i = 0; i < paddedSize; i++) { complexData[i * 2] = workReal[i]; complexData[i * 2 + 1] = workImag[i]; } + const dataBuffer = this.device.createBuffer({ size: complexData.byteLength, usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST }); + this.device.queue.writeBuffer(dataBuffer, 0, complexData); + const paramsBuffer = this.device.createBuffer({ size: 24, usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST }); + const readBuffer = this.device.createBuffer({ size: complexData.byteLength, usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST }); + const inverseVal = inverse ? 1.0 : -1.0; + const workgroupsX = Math.ceil(paddedWidth / 16), workgroupsY = Math.ceil(paddedHeight / 16); + const runPass = (pipeline: GPUComputePipeline) => { + const bindGroup = this.device.createBindGroup({ layout: pipeline.getBindGroupLayout(0), entries: [{ binding: 0, resource: { buffer: paramsBuffer } }, { binding: 1, resource: { buffer: dataBuffer } }] }); + const encoder = this.device.createCommandEncoder(); const pass = encoder.beginComputePass(); + pass.setPipeline(pipeline); pass.setBindGroup(0, bindGroup); pass.dispatchWorkgroups(workgroupsX, workgroupsY); pass.end(); + this.device.queue.submit([encoder.finish()]); + }; + const params = new ArrayBuffer(24); const paramsU32 = new Uint32Array(params); const paramsF32 = new Float32Array(params); + paramsU32[0] = paddedWidth; paramsU32[1] = paddedHeight; paramsU32[2] = log2Width; paramsU32[3] = 0; paramsF32[4] = inverseVal; paramsU32[5] = 1; + this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.bitReverseRows); + for (let stage = 0; stage < log2Width; stage++) { paramsU32[3] = stage; this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.butterflyRows); } + paramsU32[2] = log2Height; paramsU32[3] = 0; paramsU32[5] = 0; + this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.bitReverseCols); + for (let stage = 0; stage < log2Height; stage++) { paramsU32[3] = stage; this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.butterflyCols); } + if (inverse) runPass(this.pipelines2D!.normalize); + const encoder = this.device.createCommandEncoder(); encoder.copyBufferToBuffer(dataBuffer, 0, readBuffer, 0, complexData.byteLength); + this.device.queue.submit([encoder.finish()]); await readBuffer.mapAsync(GPUMapMode.READ); + const result = new Float32Array(readBuffer.getMappedRange().slice(0)); readBuffer.unmap(); + dataBuffer.destroy(); paramsBuffer.destroy(); readBuffer.destroy(); + if (needsPadding) { + const realResult = new Float32Array(originalSize), imagResult = new Float32Array(originalSize); + for (let y = 0; y < height; y++) for (let x = 0; x < width; x++) { realResult[y * width + x] = result[(y * paddedWidth + x) * 2]; imagResult[y * width + x] = result[(y * paddedWidth + x) * 2 + 1]; } + return { real: realResult, imag: imagResult }; + } + const realResult = new Float32Array(paddedSize), imagResult = new Float32Array(paddedSize); + for (let i = 0; i < paddedSize; i++) { realResult[i] = result[i * 2]; imagResult[i] = result[i * 2 + 1]; } + return { real: realResult, imag: imagResult }; + } + /** + * Batched 2D FFT: compute N forward FFTs with pipelined GPU submissions. + * All images must have the same dimensions. Each image gets its own + * submit (required because the params uniform changes per-pass), but + * all readbacks are batched into a single Promise.all at the end. + */ + async fft2DBatch( + images: { real: Float32Array; imag: Float32Array }[], + width: number, height: number, + ): Promise<{ real: Float32Array; imag: Float32Array }[]> { + await this.init(); + const n = images.length; + if (n === 0) return []; + const paddedWidth = nextPow2(width), paddedHeight = nextPow2(height); + const needsPadding = paddedWidth !== width || paddedHeight !== height; + const log2Width = Math.log2(paddedWidth), log2Height = Math.log2(paddedHeight); + const paddedSize = paddedWidth * paddedHeight; + const originalSize = width * height; + const byteSize = paddedSize * 2 * 4; + const workgroupsX = Math.ceil(paddedWidth / 16), workgroupsY = Math.ceil(paddedHeight / 16); + const inverseVal = -1.0; + + // Shared params buffer — safe because we submit per-image + const paramsBuffer = this.device.createBuffer({ size: 24, usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST }); + + const readBuffers: GPUBuffer[] = []; + const dataBuffers: GPUBuffer[] = []; + + // Submit all FFTs — GPU pipelines them internally + for (let i = 0; i < n; i++) { + const { real: realData, imag: imagData } = images[i]; + let workReal: Float32Array, workImag: Float32Array; + if (needsPadding) { + workReal = new Float32Array(paddedSize); workImag = new Float32Array(paddedSize); + for (let y = 0; y < height; y++) for (let x = 0; x < width; x++) { + workReal[y * paddedWidth + x] = realData[y * width + x]; + workImag[y * paddedWidth + x] = imagData[y * width + x]; + } + } else { workReal = realData; workImag = imagData; } + + const complexData = new Float32Array(paddedSize * 2); + for (let j = 0; j < paddedSize; j++) { complexData[j * 2] = workReal[j]; complexData[j * 2 + 1] = workImag[j]; } + + const dataBuffer = this.device.createBuffer({ size: byteSize, usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST }); + this.device.queue.writeBuffer(dataBuffer, 0, complexData); + dataBuffers.push(dataBuffer); + + const readBuffer = this.device.createBuffer({ size: byteSize, usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST }); + readBuffers.push(readBuffer); + + // Run FFT passes — each runPass does writeBuffer+submit atomically + const runPass = (pipeline: GPUComputePipeline) => { + const bindGroup = this.device.createBindGroup({ + layout: pipeline.getBindGroupLayout(0), + entries: [{ binding: 0, resource: { buffer: paramsBuffer } }, { binding: 1, resource: { buffer: dataBuffer } }], + }); + const enc = this.device.createCommandEncoder(); + const pass = enc.beginComputePass(); + pass.setPipeline(pipeline); pass.setBindGroup(0, bindGroup); + pass.dispatchWorkgroups(workgroupsX, workgroupsY); pass.end(); + this.device.queue.submit([enc.finish()]); + }; + + const params = new ArrayBuffer(24); + const paramsU32 = new Uint32Array(params); + const paramsF32 = new Float32Array(params); + + paramsU32[0] = paddedWidth; paramsU32[1] = paddedHeight; paramsU32[2] = log2Width; + paramsU32[3] = 0; paramsF32[4] = inverseVal; paramsU32[5] = 1; + this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.bitReverseRows); + for (let stage = 0; stage < log2Width; stage++) { paramsU32[3] = stage; this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.butterflyRows); } + + paramsU32[2] = log2Height; paramsU32[3] = 0; paramsU32[5] = 0; + this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.bitReverseCols); + for (let stage = 0; stage < log2Height; stage++) { paramsU32[3] = stage; this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.butterflyCols); } + + // Copy to read buffer + const copyEnc = this.device.createCommandEncoder(); + copyEnc.copyBufferToBuffer(dataBuffer, 0, readBuffer, 0, byteSize); + this.device.queue.submit([copyEnc.finish()]); + } + + // Batched readback — one sync point for all images + await Promise.all(readBuffers.map(buf => buf.mapAsync(GPUMapMode.READ))); + + const results: { real: Float32Array; imag: Float32Array }[] = []; + for (let i = 0; i < n; i++) { + const result = new Float32Array(readBuffers[i].getMappedRange().slice(0)); + readBuffers[i].unmap(); + dataBuffers[i].destroy(); + readBuffers[i].destroy(); + + if (needsPadding) { + const realResult = new Float32Array(originalSize), imagResult = new Float32Array(originalSize); + for (let y = 0; y < height; y++) for (let x = 0; x < width; x++) { + realResult[y * width + x] = result[(y * paddedWidth + x) * 2]; + imagResult[y * width + x] = result[(y * paddedWidth + x) * 2 + 1]; + } + results.push({ real: realResult, imag: imagResult }); + } else { + const realResult = new Float32Array(paddedSize), imagResult = new Float32Array(paddedSize); + for (let i2 = 0; i2 < paddedSize; i2++) { realResult[i2] = result[i2 * 2]; imagResult[i2] = result[i2 * 2 + 1]; } + results.push({ real: realResult, imag: imagResult }); + } + } + + paramsBuffer.destroy(); + return results; + } + + destroy(): void { this.initialized = false; } +} + +// ============================================================================ +// FFT pre-processing helpers +// ============================================================================ + +/** + * Apply 2D Hann window in-place to reduce spectral leakage in ROI FFT. + * + * When an ROI is cropped from an image, the sharp rectangular boundary acts as + * a rect window whose sinc sidelobes produce streak artifacts in the FFT, + * obscuring real spectral features (Bragg spots, lattice frequencies). + * The Hann window smoothly tapers data to zero at all edges, suppressing + * sidelobes by ~31 dB at the cost of a slightly wider main lobe. + * + * Separable: window2D = outer(hann_h, hann_w), applied as element-wise multiply. + * Symmetric formula: w(i) = 0.5*(1 - cos(2πi/(N-1))), matching np.hanning — + * both endpoints are exactly zero for seamless transition to zero-padded regions. + * (Periodic variant ÷N is for overlapping STFT windows, not for zero-padding.) + * + * IMPORTANT: Must be called on the crop at its native dimensions BEFORE + * zero-padding to power-of-2. Window-then-pad ensures no discontinuity at the + * crop/pad boundary. Pad-then-window applies the wrong taper and reintroduces + * leakage. Validated against np.hanning in test_widget_show2d.py. + */ +export function applyHannWindow2D(data: Float32Array, width: number, height: number): void { + const hannW = new Float32Array(width); + const hannH = new Float32Array(height); + const wDenom = width > 1 ? width - 1 : 1; + const hDenom = height > 1 ? height - 1 : 1; + for (let i = 0; i < width; i++) hannW[i] = 0.5 * (1 - Math.cos((2 * Math.PI * i) / wDenom)); + for (let i = 0; i < height; i++) hannH[i] = 0.5 * (1 - Math.cos((2 * Math.PI * i) / hDenom)); + for (let r = 0; r < height; r++) { + const hr = hannH[r]; + const offset = r * width; + for (let c = 0; c < width; c++) data[offset + c] *= hr * hannW[c]; + } +} + +// ============================================================================ +// FFT post-processing helpers +// ============================================================================ + +/** Compute magnitude from complex FFT output: sqrt(real² + imag²). */ +export function computeMagnitude(real: Float32Array, imag: Float32Array): Float32Array { + const mag = new Float32Array(real.length); + for (let i = 0; i < mag.length; i++) { + mag[i] = Math.sqrt(real[i] * real[i] + imag[i] * imag[i]); + } + return mag; +} + +/** Mask DC component (center pixel) and return 99.9% percentile-clipped range. Mutates `mag`. */ +export function autoEnhanceFFT( + mag: Float32Array, width: number, height: number, +): { min: number; max: number } { + const centerIdx = Math.floor(height / 2) * width + Math.floor(width / 2); + const neighbors = [ + mag[Math.max(0, centerIdx - 1)], + mag[Math.min(mag.length - 1, centerIdx + 1)], + mag[Math.max(0, centerIdx - width)], + mag[Math.min(mag.length - 1, centerIdx + width)], + ]; + mag[centerIdx] = neighbors.reduce((a, b) => a + b, 0) / 4; + // Use O(n) histogram approach instead of O(n log n) sort + const len = mag.length; + if (len === 0) return { min: 0, max: 0 }; + let dMin = Infinity, dMax = -Infinity; + for (let i = 0; i < len; i++) { + const v = mag[i]; + if (v < dMin) dMin = v; + if (v > dMax) dMax = v; + } + if (dMin === dMax) return { min: dMin, max: dMax }; + const NUM_BINS = 1024; + const bins = new Uint32Array(NUM_BINS); + const range = dMax - dMin; + const scale = (NUM_BINS - 1) / range; + for (let i = 0; i < len; i++) bins[Math.floor((mag[i] - dMin) * scale)]++; + // Find 99.9th percentile + const target = Math.ceil(len * 0.999); + let cumSum = 0; + let pMax = dMax; + for (let i = 0; i < NUM_BINS; i++) { + cumSum += bins[i]; + if (cumSum >= target) { pMax = dMin + (i / (NUM_BINS - 1)) * range; break; } + } + // If percentile collapsed to min (sparse spectra), fall back to actual max + if (pMax <= dMin) pMax = dMax; + return { min: dMin, max: pMax }; +} + +// ============================================================================ +// Singleton +// ============================================================================ + +let gpuFFT: WebGPUFFT | null = null; +let gpuDevice: GPUDevice | null = null; +let gpuInfo = "GPU"; + +export async function getGPUDevice(): Promise { + if (gpuDevice) return gpuDevice; + if (!navigator.gpu) return null; + try { + const adapter = await navigator.gpu.requestAdapter(); + if (!adapter) return null; + try { + // @ts-ignore - requestAdapterInfo is not yet in all type definitions + const info = await adapter.requestAdapterInfo?.(); + if (info) { + gpuInfo = info.description || `${info.vendor} ${info.architecture || ""} ${info.device || ""}`.trim() || "Generic WebGPU Adapter"; + } + } catch (_e) { /* adapter info not available */ } + gpuDevice = await adapter.requestDevice(); + return gpuDevice; + } catch { return null; } +} + +export async function getWebGPUFFT(): Promise { + if (gpuFFT) return gpuFFT; + const device = await getGPUDevice(); + if (!device) { console.warn('WebGPU not supported, falling back to CPU FFT'); return null; } + try { + gpuFFT = new WebGPUFFT(device); + await gpuFFT.init(); + return gpuFFT; + } catch (e) { console.warn('WebGPU init failed:', e); return null; } +} + +export function getGPUInfo(): string { return gpuInfo; } diff --git a/widget/package-lock.json b/widget/package-lock.json index 4e039394..cf4e386a 100644 --- a/widget/package-lock.json +++ b/widget/package-lock.json @@ -6,22 +6,32 @@ "": { "name": "quantem-widget-frontend", "dependencies": { - "@anywidget/react": "^0.1.0", - "react": "^18.2.0", - "react-dom": "^18.2.0" + "@anywidget/react": "^0.2.0", + "@emotion/react": "^11.14.0", + "@emotion/styled": "^11.14.1", + "@mui/icons-material": "^7.3.7", + "@mui/material": "^7.3.6", + "jszip": "^3.10.1", + "react": "^19.1.0", + "react-dom": "^19.1.0" }, "devDependencies": { "@anywidget/vite": "^0.2.0", + "@types/react": "^19.1.3", + "@types/react-dom": "^19.1.4", "@vitejs/plugin-react": "^4.3.0", + "@webgpu/types": "^0.1.68", + "typescript": "^5.8.3", "vite": "^5.2.0" } }, "node_modules/@anywidget/react": { - "version": "0.1.0", - "resolved": "https://registry.npmjs.org/@anywidget/react/-/react-0.1.0.tgz", - "integrity": "sha512-Hh6wbMGXsgTz2xz1I9/h40M3b6uDWLjmh3sJ4tNjfppDl5y9Sw1jyuQGhNzwViOtszmyjYEJZ4kWHdPFUXUanw==", + "version": "0.2.2", + "resolved": "https://registry.npmjs.org/@anywidget/react/-/react-0.2.2.tgz", + "integrity": "sha512-MjrbbUimaX72ATL9IrrZeMZL+IHVzeW5lMlgZywDAMj4/zUVQ7MeBSaijq7V9genze7sM3Y0WnUyB+Mc7KnWWw==", + "license": "MIT", "dependencies": { - "@anywidget/types": "^0.2.0" + "@anywidget/types": "^0.4.0" }, "peerDependencies": { "@types/react": "^18.0.0 || ^19.0.0", @@ -31,9 +41,10 @@ } }, "node_modules/@anywidget/types": { - "version": "0.2.0", - "resolved": "https://registry.npmjs.org/@anywidget/types/-/types-0.2.0.tgz", - "integrity": "sha512-+XtK4uwxRd4JpuevUMhirrbvC0V4yCA/i0lEjhmSAtOaxiXIg/vBKzaSonDuoZ1a9LEjUXTW2+m7w+ULgsJYvg==" + "version": "0.4.0", + "resolved": "https://registry.npmjs.org/@anywidget/types/-/types-0.4.0.tgz", + "integrity": "sha512-Qno/7V0lKHCMq3DJuSKKHMwilFKPSe8wFftL5xWmgaMCc938mNTtv+i19UrvDfpj9cQTnlPqyXy8t3JOgQ8laA==", + "license": "MIT" }, "node_modules/@anywidget/vite": { "version": "0.2.2", @@ -49,7 +60,6 @@ "version": "7.27.1", "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.27.1.tgz", "integrity": "sha512-cjQ7ZlQ0Mv3b47hABuTevyTuYN4i+loJKGeV9flcCgIK37cCXRh+L1bd3iBHlynerhQ7BhCkn2BPbQUL+rGqFg==", - "dev": true, "license": "MIT", "dependencies": { "@babel/helper-validator-identifier": "^7.27.1", @@ -76,7 +86,6 @@ "integrity": "sha512-e7jT4DxYvIDLk1ZHmU/m/mB19rex9sv0c2ftBtjSBv+kVM/902eh0fINUzD7UwLLNR+jU585GxUJ8/EBfAM5fw==", "dev": true, "license": "MIT", - "peer": true, "dependencies": { "@babel/code-frame": "^7.27.1", "@babel/generator": "^7.28.5", @@ -106,7 +115,6 @@ "version": "7.28.5", "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.28.5.tgz", "integrity": "sha512-3EwLFhZ38J4VyIP6WNtt2kUdW9dokXA9Cr4IVIFHuCpZ3H8/YFOl5JjZHisrn1fATPBmKKqXzDFvh9fUwHz6CQ==", - "dev": true, "license": "MIT", "dependencies": { "@babel/parser": "^7.28.5", @@ -140,7 +148,6 @@ "version": "7.28.0", "resolved": "https://registry.npmjs.org/@babel/helper-globals/-/helper-globals-7.28.0.tgz", "integrity": "sha512-+W6cISkXFa1jXsDEdYA8HeevQT/FULhxzR99pxphltZcVaugps53THCeiWA8SguxxpSp3gKPiuYfSWopkLQ4hw==", - "dev": true, "license": "MIT", "engines": { "node": ">=6.9.0" @@ -150,7 +157,6 @@ "version": "7.27.1", "resolved": "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.27.1.tgz", "integrity": "sha512-0gSFWUPNXNopqtIPQvlD5WgXYI5GY2kP2cCvoT8kczjbfcfuIljTbcWrulD1CIPIX2gt1wghbDy08yE1p+/r3w==", - "dev": true, "license": "MIT", "dependencies": { "@babel/traverse": "^7.27.1", @@ -192,7 +198,6 @@ "version": "7.27.1", "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.27.1.tgz", "integrity": "sha512-qMlSxKbpRlAridDExk92nSobyDdpPijUq2DW6oDnUqd0iOGxmQjyqhMIihI9+zv4LPyZdRje2cavWPbCbWm3eA==", - "dev": true, "license": "MIT", "engines": { "node": ">=6.9.0" @@ -202,7 +207,6 @@ "version": "7.28.5", "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.28.5.tgz", "integrity": "sha512-qSs4ifwzKJSV39ucNjsvc6WVHs6b7S03sOh2OcHF9UHfVPqWWALUsNUVzhSBiItjRZoLHx7nIarVjqKVusUZ1Q==", - "dev": true, "license": "MIT", "engines": { "node": ">=6.9.0" @@ -236,7 +240,6 @@ "version": "7.28.5", "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.28.5.tgz", "integrity": "sha512-KKBU1VGYR7ORr3At5HAtUQ+TV3SzRCXmA/8OdDZiLDBIZxVyzXuztPjfLd3BV1PRAQGCMWWSHYhL0F8d5uHBDQ==", - "dev": true, "license": "MIT", "dependencies": { "@babel/types": "^7.28.5" @@ -280,11 +283,19 @@ "@babel/core": "^7.0.0-0" } }, + "node_modules/@babel/runtime": { + "version": "7.29.2", + "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.29.2.tgz", + "integrity": "sha512-JiDShH45zKHWyGe4ZNVRrCjBz8Nh9TMmZG1kh4QTK8hCBTWBi8Da+i7s1fJw7/lYpM4ccepSNfqzZ/QvABBi5g==", + "license": "MIT", + "engines": { + "node": ">=6.9.0" + } + }, "node_modules/@babel/template": { "version": "7.27.2", "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.27.2.tgz", "integrity": "sha512-LPDZ85aEJyYSd18/DkjNh4/y1ntkE5KwUHWTiqgRxruuZL2F1yuHligVHLvcHY2vMHXttKFpJn6LwfI7cw7ODw==", - "dev": true, "license": "MIT", "dependencies": { "@babel/code-frame": "^7.27.1", @@ -299,7 +310,6 @@ "version": "7.28.5", "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.28.5.tgz", "integrity": "sha512-TCCj4t55U90khlYkVV/0TfkJkAkUg3jZFA3Neb7unZT8CPok7iiRfaX0F+WnqWqt7OxhOn0uBKXCw4lbL8W0aQ==", - "dev": true, "license": "MIT", "dependencies": { "@babel/code-frame": "^7.27.1", @@ -318,7 +328,6 @@ "version": "7.28.5", "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.28.5.tgz", "integrity": "sha512-qQ5m48eI/MFLQ5PxQj4PFaprjyCTLI37ElWMmNs0K8Lk3dVeOdNpB3ks8jc7yM5CDmVC73eMVk/trk3fgmrUpA==", - "dev": true, "license": "MIT", "dependencies": { "@babel/helper-string-parser": "^7.27.1", @@ -328,6 +337,158 @@ "node": ">=6.9.0" } }, + "node_modules/@emotion/babel-plugin": { + "version": "11.13.5", + "resolved": "https://registry.npmjs.org/@emotion/babel-plugin/-/babel-plugin-11.13.5.tgz", + "integrity": "sha512-pxHCpT2ex+0q+HH91/zsdHkw/lXd468DIN2zvfvLtPKLLMo6gQj7oLObq8PhkrxOZb/gGCq03S3Z7PDhS8pduQ==", + "license": "MIT", + "dependencies": { + "@babel/helper-module-imports": "^7.16.7", + "@babel/runtime": "^7.18.3", + "@emotion/hash": "^0.9.2", + "@emotion/memoize": "^0.9.0", + "@emotion/serialize": "^1.3.3", + "babel-plugin-macros": "^3.1.0", + "convert-source-map": "^1.5.0", + "escape-string-regexp": "^4.0.0", + "find-root": "^1.1.0", + "source-map": "^0.5.7", + "stylis": "4.2.0" + } + }, + "node_modules/@emotion/babel-plugin/node_modules/convert-source-map": { + "version": "1.9.0", + "resolved": "https://registry.npmjs.org/convert-source-map/-/convert-source-map-1.9.0.tgz", + "integrity": "sha512-ASFBup0Mz1uyiIjANan1jzLQami9z1PoYSZCiiYW2FczPbenXc45FZdBZLzOT+r6+iciuEModtmCti+hjaAk0A==", + "license": "MIT" + }, + "node_modules/@emotion/cache": { + "version": "11.14.0", + "resolved": "https://registry.npmjs.org/@emotion/cache/-/cache-11.14.0.tgz", + "integrity": "sha512-L/B1lc/TViYk4DcpGxtAVbx0ZyiKM5ktoIyafGkH6zg/tj+mA+NE//aPYKG0k8kCHSHVJrpLpcAlOBEXQ3SavA==", + "license": "MIT", + "dependencies": { + "@emotion/memoize": "^0.9.0", + "@emotion/sheet": "^1.4.0", + "@emotion/utils": "^1.4.2", + "@emotion/weak-memoize": "^0.4.0", + "stylis": "4.2.0" + } + }, + "node_modules/@emotion/hash": { + "version": "0.9.2", + "resolved": "https://registry.npmjs.org/@emotion/hash/-/hash-0.9.2.tgz", + "integrity": "sha512-MyqliTZGuOm3+5ZRSaaBGP3USLw6+EGykkwZns2EPC5g8jJ4z9OrdZY9apkl3+UP9+sdz76YYkwCKP5gh8iY3g==", + "license": "MIT" + }, + "node_modules/@emotion/is-prop-valid": { + "version": "1.4.0", + "resolved": "https://registry.npmjs.org/@emotion/is-prop-valid/-/is-prop-valid-1.4.0.tgz", + "integrity": "sha512-QgD4fyscGcbbKwJmqNvUMSE02OsHUa+lAWKdEUIJKgqe5IwRSKd7+KhibEWdaKwgjLj0DRSHA9biAIqGBk05lw==", + "license": "MIT", + "dependencies": { + "@emotion/memoize": "^0.9.0" + } + }, + "node_modules/@emotion/memoize": { + "version": "0.9.0", + "resolved": "https://registry.npmjs.org/@emotion/memoize/-/memoize-0.9.0.tgz", + "integrity": "sha512-30FAj7/EoJ5mwVPOWhAyCX+FPfMDrVecJAM+Iw9NRoSl4BBAQeqj4cApHHUXOVvIPgLVDsCFoz/hGD+5QQD1GQ==", + "license": "MIT" + }, + "node_modules/@emotion/react": { + "version": "11.14.0", + "resolved": "https://registry.npmjs.org/@emotion/react/-/react-11.14.0.tgz", + "integrity": "sha512-O000MLDBDdk/EohJPFUqvnp4qnHeYkVP5B0xEG0D/L7cOKP9kefu2DXn8dj74cQfsEzUqh+sr1RzFqiL1o+PpA==", + "license": "MIT", + "dependencies": { + "@babel/runtime": "^7.18.3", + "@emotion/babel-plugin": "^11.13.5", + "@emotion/cache": "^11.14.0", + "@emotion/serialize": "^1.3.3", + "@emotion/use-insertion-effect-with-fallbacks": "^1.2.0", + "@emotion/utils": "^1.4.2", + "@emotion/weak-memoize": "^0.4.0", + "hoist-non-react-statics": "^3.3.1" + }, + "peerDependencies": { + "react": ">=16.8.0" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, + "node_modules/@emotion/serialize": { + "version": "1.3.3", + "resolved": "https://registry.npmjs.org/@emotion/serialize/-/serialize-1.3.3.tgz", + "integrity": "sha512-EISGqt7sSNWHGI76hC7x1CksiXPahbxEOrC5RjmFRJTqLyEK9/9hZvBbiYn70dw4wuwMKiEMCUlR6ZXTSWQqxA==", + "license": "MIT", + "dependencies": { + "@emotion/hash": "^0.9.2", + "@emotion/memoize": "^0.9.0", + "@emotion/unitless": "^0.10.0", + "@emotion/utils": "^1.4.2", + "csstype": "^3.0.2" + } + }, + "node_modules/@emotion/sheet": { + "version": "1.4.0", + "resolved": "https://registry.npmjs.org/@emotion/sheet/-/sheet-1.4.0.tgz", + "integrity": "sha512-fTBW9/8r2w3dXWYM4HCB1Rdp8NLibOw2+XELH5m5+AkWiL/KqYX6dc0kKYlaYyKjrQ6ds33MCdMPEwgs2z1rqg==", + "license": "MIT" + }, + "node_modules/@emotion/styled": { + "version": "11.14.1", + "resolved": "https://registry.npmjs.org/@emotion/styled/-/styled-11.14.1.tgz", + "integrity": "sha512-qEEJt42DuToa3gurlH4Qqc1kVpNq8wO8cJtDzU46TjlzWjDlsVyevtYCRijVq3SrHsROS+gVQ8Fnea108GnKzw==", + "license": "MIT", + "dependencies": { + "@babel/runtime": "^7.18.3", + "@emotion/babel-plugin": "^11.13.5", + "@emotion/is-prop-valid": "^1.3.0", + "@emotion/serialize": "^1.3.3", + "@emotion/use-insertion-effect-with-fallbacks": "^1.2.0", + "@emotion/utils": "^1.4.2" + }, + "peerDependencies": { + "@emotion/react": "^11.0.0-rc.0", + "react": ">=16.8.0" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, + "node_modules/@emotion/unitless": { + "version": "0.10.0", + "resolved": "https://registry.npmjs.org/@emotion/unitless/-/unitless-0.10.0.tgz", + "integrity": "sha512-dFoMUuQA20zvtVTuxZww6OHoJYgrzfKM1t52mVySDJnMSEa08ruEvdYQbhvyu6soU+NeLVd3yKfTfT0NeV6qGg==", + "license": "MIT" + }, + "node_modules/@emotion/use-insertion-effect-with-fallbacks": { + "version": "1.2.0", + "resolved": "https://registry.npmjs.org/@emotion/use-insertion-effect-with-fallbacks/-/use-insertion-effect-with-fallbacks-1.2.0.tgz", + "integrity": "sha512-yJMtVdH59sxi/aVJBpk9FQq+OR8ll5GT8oWd57UpeaKEVGab41JWaCFA7FRLoMLloOZF/c/wsPoe+bfGmRKgDg==", + "license": "MIT", + "peerDependencies": { + "react": ">=16.8.0" + } + }, + "node_modules/@emotion/utils": { + "version": "1.4.2", + "resolved": "https://registry.npmjs.org/@emotion/utils/-/utils-1.4.2.tgz", + "integrity": "sha512-3vLclRofFziIa3J2wDh9jjbkUz9qk5Vi3IZ/FSTKViB0k+ef0fPV7dYrUIugbgupYDx7v9ud/SjrtEP8Y4xLoA==", + "license": "MIT" + }, + "node_modules/@emotion/weak-memoize": { + "version": "0.4.0", + "resolved": "https://registry.npmjs.org/@emotion/weak-memoize/-/weak-memoize-0.4.0.tgz", + "integrity": "sha512-snKqtPW01tN0ui7yu9rGv69aJXr/a/Ywvl11sUjNtEcRc+ng/mQriFL0wLXMef74iHa/EkftbDzU9F8iFbH+zg==", + "license": "MIT" + }, "node_modules/@esbuild/aix-ppc64": { "version": "0.21.5", "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.21.5.tgz", @@ -723,7 +884,6 @@ "version": "0.3.13", "resolved": "https://registry.npmjs.org/@jridgewell/gen-mapping/-/gen-mapping-0.3.13.tgz", "integrity": "sha512-2kkt/7niJ6MgEPxF0bYdQ6etZaA+fQvDcLKckhy1yIQOzaoKjBBjSj63/aLVjYE3qhRt5dvM+uUyfCg6UKCBbA==", - "dev": true, "license": "MIT", "dependencies": { "@jridgewell/sourcemap-codec": "^1.5.0", @@ -745,7 +905,6 @@ "version": "3.1.2", "resolved": "https://registry.npmjs.org/@jridgewell/resolve-uri/-/resolve-uri-3.1.2.tgz", "integrity": "sha512-bRISgCIjP20/tbWSPWMEi54QVPRZExkuD9lJL+UIxUKtwVJA8wW1Trb1jMs1RFXo1CBTNZ/5hpC9QvmKWdopKw==", - "dev": true, "license": "MIT", "engines": { "node": ">=6.0.0" @@ -755,20 +914,261 @@ "version": "1.5.5", "resolved": "https://registry.npmjs.org/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.5.5.tgz", "integrity": "sha512-cYQ9310grqxueWbl+WuIUIaiUaDcj7WOq5fVhEljNVgRfOUhY9fy2zTvfoqWsnebh8Sl70VScFbICvJnLKB0Og==", - "dev": true, "license": "MIT" }, "node_modules/@jridgewell/trace-mapping": { "version": "0.3.31", "resolved": "https://registry.npmjs.org/@jridgewell/trace-mapping/-/trace-mapping-0.3.31.tgz", "integrity": "sha512-zzNR+SdQSDJzc8joaeP8QQoCQr8NuYx2dIIytl1QeBEZHJ9uW6hebsrYgbz8hJwUQao3TWCMtmfV8Nu1twOLAw==", - "dev": true, "license": "MIT", "dependencies": { "@jridgewell/resolve-uri": "^3.1.0", "@jridgewell/sourcemap-codec": "^1.4.14" } }, + "node_modules/@mui/core-downloads-tracker": { + "version": "7.3.10", + "resolved": "https://registry.npmjs.org/@mui/core-downloads-tracker/-/core-downloads-tracker-7.3.10.tgz", + "integrity": "sha512-vrOpWRmPJSuwLo23J62wggEm/jvGdzqctej+UOCtgDUz6nZJQuj3ByPccVyaa7eQmwAzUwKN56FQPMKkqbj1GA==", + "license": "MIT", + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/mui-org" + } + }, + "node_modules/@mui/icons-material": { + "version": "7.3.10", + "resolved": "https://registry.npmjs.org/@mui/icons-material/-/icons-material-7.3.10.tgz", + "integrity": "sha512-Au0ma4NSKGKNiimukj8UT/W1x2Qx6Qwn2RvFGykiSqVLYBNlIOPbjnIMvrwLGLu89EEpTVdu/ys/OduZR+tWqw==", + "license": "MIT", + "dependencies": { + "@babel/runtime": "^7.28.6" + }, + "engines": { + "node": ">=14.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/mui-org" + }, + "peerDependencies": { + "@mui/material": "^7.3.10", + "@types/react": "^17.0.0 || ^18.0.0 || ^19.0.0", + "react": "^17.0.0 || ^18.0.0 || ^19.0.0" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, + "node_modules/@mui/material": { + "version": "7.3.10", + "resolved": "https://registry.npmjs.org/@mui/material/-/material-7.3.10.tgz", + "integrity": "sha512-cHvGOk2ZEfbQt3LnGe0ZKd/ETs9gsUpkW66DCO+GSjMZhpdKU4XsuIr7zJ/B/2XaN8ihxuzHfYAR4zPtCN4RYg==", + "license": "MIT", + "dependencies": { + "@babel/runtime": "^7.28.6", + "@mui/core-downloads-tracker": "^7.3.10", + "@mui/system": "^7.3.10", + "@mui/types": "^7.4.12", + "@mui/utils": "^7.3.10", + "@popperjs/core": "^2.11.8", + "@types/react-transition-group": "^4.4.12", + "clsx": "^2.1.1", + "csstype": "^3.2.3", + "prop-types": "^15.8.1", + "react-is": "^19.2.3", + "react-transition-group": "^4.4.5" + }, + "engines": { + "node": ">=14.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/mui-org" + }, + "peerDependencies": { + "@emotion/react": "^11.5.0", + "@emotion/styled": "^11.3.0", + "@mui/material-pigment-css": "^7.3.10", + "@types/react": "^17.0.0 || ^18.0.0 || ^19.0.0", + "react": "^17.0.0 || ^18.0.0 || ^19.0.0", + "react-dom": "^17.0.0 || ^18.0.0 || ^19.0.0" + }, + "peerDependenciesMeta": { + "@emotion/react": { + "optional": true + }, + "@emotion/styled": { + "optional": true + }, + "@mui/material-pigment-css": { + "optional": true + }, + "@types/react": { + "optional": true + } + } + }, + "node_modules/@mui/private-theming": { + "version": "7.3.10", + "resolved": "https://registry.npmjs.org/@mui/private-theming/-/private-theming-7.3.10.tgz", + "integrity": "sha512-j3EZN+zOctxUISvJSmsEPo5o2F8zse4l5vRkBY+ps6UtnL6J7o14kUaI4w7gwo73id9e3cDNMVQK/9BVaMHVBw==", + "license": "MIT", + "dependencies": { + "@babel/runtime": "^7.28.6", + "@mui/utils": "^7.3.10", + "prop-types": "^15.8.1" + }, + "engines": { + "node": ">=14.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/mui-org" + }, + "peerDependencies": { + "@types/react": "^17.0.0 || ^18.0.0 || ^19.0.0", + "react": "^17.0.0 || ^18.0.0 || ^19.0.0" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, + "node_modules/@mui/styled-engine": { + "version": "7.3.10", + "resolved": "https://registry.npmjs.org/@mui/styled-engine/-/styled-engine-7.3.10.tgz", + "integrity": "sha512-WxE9SiF8xskAQqGjsp0poXCkCqsoXFEsSr0HBXfApmGHR+DBnXRp+z46Vsltg4gpPM4Z96DeAQRpeAOnhNg7Ng==", + "license": "MIT", + "dependencies": { + "@babel/runtime": "^7.28.6", + "@emotion/cache": "^11.14.0", + "@emotion/serialize": "^1.3.3", + "@emotion/sheet": "^1.4.0", + "csstype": "^3.2.3", + "prop-types": "^15.8.1" + }, + "engines": { + "node": ">=14.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/mui-org" + }, + "peerDependencies": { + "@emotion/react": "^11.4.1", + "@emotion/styled": "^11.3.0", + "react": "^17.0.0 || ^18.0.0 || ^19.0.0" + }, + "peerDependenciesMeta": { + "@emotion/react": { + "optional": true + }, + "@emotion/styled": { + "optional": true + } + } + }, + "node_modules/@mui/system": { + "version": "7.3.10", + "resolved": "https://registry.npmjs.org/@mui/system/-/system-7.3.10.tgz", + "integrity": "sha512-/sfPpdpJaQn7BSF+avjIdHSYmxHp0UOBYNxSG9QGKfMOD6sLANCpRPCnanq1Pe0lFf0NHkO2iUk0TNzdWC1USQ==", + "license": "MIT", + "dependencies": { + "@babel/runtime": "^7.28.6", + "@mui/private-theming": "^7.3.10", + "@mui/styled-engine": "^7.3.10", + "@mui/types": "^7.4.12", + "@mui/utils": "^7.3.10", + "clsx": "^2.1.1", + "csstype": "^3.2.3", + "prop-types": "^15.8.1" + }, + "engines": { + "node": ">=14.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/mui-org" + }, + "peerDependencies": { + "@emotion/react": "^11.5.0", + "@emotion/styled": "^11.3.0", + "@types/react": "^17.0.0 || ^18.0.0 || ^19.0.0", + "react": "^17.0.0 || ^18.0.0 || ^19.0.0" + }, + "peerDependenciesMeta": { + "@emotion/react": { + "optional": true + }, + "@emotion/styled": { + "optional": true + }, + "@types/react": { + "optional": true + } + } + }, + "node_modules/@mui/types": { + "version": "7.4.12", + "resolved": "https://registry.npmjs.org/@mui/types/-/types-7.4.12.tgz", + "integrity": "sha512-iKNAF2u9PzSIj40CjvKJWxFXJo122jXVdrmdh0hMYd+FR+NuJMkr/L88XwWLCRiJ5P1j+uyac25+Kp6YC4hu6w==", + "license": "MIT", + "dependencies": { + "@babel/runtime": "^7.28.6" + }, + "peerDependencies": { + "@types/react": "^17.0.0 || ^18.0.0 || ^19.0.0" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, + "node_modules/@mui/utils": { + "version": "7.3.10", + "resolved": "https://registry.npmjs.org/@mui/utils/-/utils-7.3.10.tgz", + "integrity": "sha512-7y2eIfy0h7JPz+Yy4pS+wgV68d46PuuxDqKBN4Q8VlPQSsCAGwroMCV6xWyc7g9dvEp8ZNFsknc59GHWO+r6Ow==", + "license": "MIT", + "dependencies": { + "@babel/runtime": "^7.28.6", + "@mui/types": "^7.4.12", + "@types/prop-types": "^15.7.15", + "clsx": "^2.1.1", + "prop-types": "^15.8.1", + "react-is": "^19.2.3" + }, + "engines": { + "node": ">=14.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/mui-org" + }, + "peerDependencies": { + "@types/react": "^17.0.0 || ^18.0.0 || ^19.0.0", + "react": "^17.0.0 || ^18.0.0 || ^19.0.0" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, + "node_modules/@popperjs/core": { + "version": "2.11.8", + "resolved": "https://registry.npmjs.org/@popperjs/core/-/core-2.11.8.tgz", + "integrity": "sha512-P1st0aksCrn9sGZhp8GMYwBnQsbvAWsZAX44oXNNvLHGqAOcoVxmjZiohstwQ7SqKnbR47akdNi+uleWD8+g6A==", + "license": "MIT", + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/popperjs" + } + }, "node_modules/@rolldown/pluginutils": { "version": "1.0.0-beta.27", "resolved": "https://registry.npmjs.org/@rolldown/pluginutils/-/pluginutils-1.0.0-beta.27.tgz", @@ -1178,12 +1578,23 @@ "dev": true, "license": "MIT" }, + "node_modules/@types/parse-json": { + "version": "4.0.2", + "resolved": "https://registry.npmjs.org/@types/parse-json/-/parse-json-4.0.2.tgz", + "integrity": "sha512-dISoDXWWQwUquiKsyZ4Ng+HX2KsPL7LyHKHQwgGFEA3IaKac4Obd+h2a/a6waisAoepJlBcx9paWqjA8/HVjCw==", + "license": "MIT" + }, + "node_modules/@types/prop-types": { + "version": "15.7.15", + "resolved": "https://registry.npmjs.org/@types/prop-types/-/prop-types-15.7.15.tgz", + "integrity": "sha512-F6bEyamV9jKGAFBEmlQnesRPGOQqS2+Uwi0Em15xenOxHaf2hv6L8YCVn3rPdPJOiJfPiCnLIRyvwVaqMY3MIw==", + "license": "MIT" + }, "node_modules/@types/react": { "version": "19.2.8", "resolved": "https://registry.npmjs.org/@types/react/-/react-19.2.8.tgz", "integrity": "sha512-3MbSL37jEchWZz2p2mjntRZtPt837ij10ApxKfgmXCTuHWagYg7iA5bqPw6C8BMPfwidlvfPI/fxOc42HLhcyg==", "license": "MIT", - "peer": true, "dependencies": { "csstype": "^3.2.2" } @@ -1193,11 +1604,19 @@ "resolved": "https://registry.npmjs.org/@types/react-dom/-/react-dom-19.2.3.tgz", "integrity": "sha512-jp2L/eY6fn+KgVVQAOqYItbF0VY/YApe5Mz2F0aykSO8gx31bYCZyvSeYxCHKvzHG5eZjc+zyaS5BrBWya2+kQ==", "license": "MIT", - "peer": true, "peerDependencies": { "@types/react": "^19.2.0" } }, + "node_modules/@types/react-transition-group": { + "version": "4.4.12", + "resolved": "https://registry.npmjs.org/@types/react-transition-group/-/react-transition-group-4.4.12.tgz", + "integrity": "sha512-8TV6R3h2j7a91c+1DXdJi3Syo69zzIZbz7Lg5tORM5LEJG7X/E6a1V3drRyBRZq7/utz7A+c4OgYLiLcYGHG6w==", + "license": "MIT", + "peerDependencies": { + "@types/react": "*" + } + }, "node_modules/@vitejs/plugin-react": { "version": "4.7.0", "resolved": "https://registry.npmjs.org/@vitejs/plugin-react/-/plugin-react-4.7.0.tgz", @@ -1219,6 +1638,28 @@ "vite": "^4.2.0 || ^5.0.0 || ^6.0.0 || ^7.0.0" } }, + "node_modules/@webgpu/types": { + "version": "0.1.69", + "resolved": "https://registry.npmjs.org/@webgpu/types/-/types-0.1.69.tgz", + "integrity": "sha512-RPmm6kgRbI8e98zSD3RVACvnuktIja5+yLgDAkTmxLr90BEwdTXRQWNLF3ETTTyH/8mKhznZuN5AveXYFEsMGQ==", + "dev": true, + "license": "BSD-3-Clause" + }, + "node_modules/babel-plugin-macros": { + "version": "3.1.0", + "resolved": "https://registry.npmjs.org/babel-plugin-macros/-/babel-plugin-macros-3.1.0.tgz", + "integrity": "sha512-Cg7TFGpIr01vOQNODXOOaGz2NpCU5gl8x1qJFbb6hbZxR7XrcE2vtbAsTAbJ7/xwJtUuJEw8K8Zr/AE0LHlesg==", + "license": "MIT", + "dependencies": { + "@babel/runtime": "^7.12.5", + "cosmiconfig": "^7.0.0", + "resolve": "^1.19.0" + }, + "engines": { + "node": ">=10", + "npm": ">=6" + } + }, "node_modules/baseline-browser-mapping": { "version": "2.9.14", "resolved": "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.9.14.tgz", @@ -1249,7 +1690,6 @@ } ], "license": "MIT", - "peer": true, "dependencies": { "baseline-browser-mapping": "^2.9.0", "caniuse-lite": "^1.0.30001759", @@ -1264,6 +1704,15 @@ "node": "^6 || ^7 || ^8 || ^9 || ^10 || ^11 || ^12 || >=13.7" } }, + "node_modules/callsites": { + "version": "3.1.0", + "resolved": "https://registry.npmjs.org/callsites/-/callsites-3.1.0.tgz", + "integrity": "sha512-P8BjAsXvZS+VIDUI11hHCQEv74YT67YUi5JJFNWIqL235sBmjX4+qx9Muvls5ivyNENctx46xQLQ3aTuE7ssaQ==", + "license": "MIT", + "engines": { + "node": ">=6" + } + }, "node_modules/caniuse-lite": { "version": "1.0.30001764", "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001764.tgz", @@ -1285,6 +1734,15 @@ ], "license": "CC-BY-4.0" }, + "node_modules/clsx": { + "version": "2.1.1", + "resolved": "https://registry.npmjs.org/clsx/-/clsx-2.1.1.tgz", + "integrity": "sha512-eYm0QWBtUrBWZWG0d386OGAw16Z995PiOVo2B7bjWSbHedGl5e0ZWaq65kOGgUSNesEIDkB9ISbTg/JK9dhCZA==", + "license": "MIT", + "engines": { + "node": ">=6" + } + }, "node_modules/convert-source-map": { "version": "2.0.0", "resolved": "https://registry.npmjs.org/convert-source-map/-/convert-source-map-2.0.0.tgz", @@ -1292,6 +1750,28 @@ "dev": true, "license": "MIT" }, + "node_modules/core-util-is": { + "version": "1.0.3", + "resolved": "https://registry.npmjs.org/core-util-is/-/core-util-is-1.0.3.tgz", + "integrity": "sha512-ZQBvi1DcpJ4GDqanjucZ2Hj3wEO5pZDS89BWbkcrvdxksJorwUDDZamX9ldFkp9aw2lmBDLgkObEA4DWNJ9FYQ==", + "license": "MIT" + }, + "node_modules/cosmiconfig": { + "version": "7.1.0", + "resolved": "https://registry.npmjs.org/cosmiconfig/-/cosmiconfig-7.1.0.tgz", + "integrity": "sha512-AdmX6xUzdNASswsFtmwSt7Vj8po9IuqXm0UXz7QKPuEUmPB4XyjGfaAr2PSuELMwkRMVH1EpIkX5bTZGRB3eCA==", + "license": "MIT", + "dependencies": { + "@types/parse-json": "^4.0.0", + "import-fresh": "^3.2.1", + "parse-json": "^5.0.0", + "path-type": "^4.0.0", + "yaml": "^1.10.0" + }, + "engines": { + "node": ">=10" + } + }, "node_modules/csstype": { "version": "3.2.3", "resolved": "https://registry.npmjs.org/csstype/-/csstype-3.2.3.tgz", @@ -1302,7 +1782,6 @@ "version": "4.4.3", "resolved": "https://registry.npmjs.org/debug/-/debug-4.4.3.tgz", "integrity": "sha512-RGwwWnwQvkVfavKVt22FGLw+xYSdzARwm0ru6DhTVA3umU5hZc28V3kO4stgYryrTlLpuvgI9GiijltAjNbcqA==", - "dev": true, "license": "MIT", "dependencies": { "ms": "^2.1.3" @@ -1316,6 +1795,16 @@ } } }, + "node_modules/dom-helpers": { + "version": "5.2.1", + "resolved": "https://registry.npmjs.org/dom-helpers/-/dom-helpers-5.2.1.tgz", + "integrity": "sha512-nRCa7CK3VTrM2NmGkIy4cbK7IZlgBE/PYMn55rrXefr5xXDP0LdtfPnblFDoVdcAfslJ7or6iqAUnx0CCGIWQA==", + "license": "MIT", + "dependencies": { + "@babel/runtime": "^7.8.7", + "csstype": "^3.0.2" + } + }, "node_modules/electron-to-chromium": { "version": "1.5.267", "resolved": "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.5.267.tgz", @@ -1323,6 +1812,24 @@ "dev": true, "license": "ISC" }, + "node_modules/error-ex": { + "version": "1.3.4", + "resolved": "https://registry.npmjs.org/error-ex/-/error-ex-1.3.4.tgz", + "integrity": "sha512-sqQamAnR14VgCr1A618A3sGrygcpK+HEbenA/HiEAkkUwcZIIB/tgWqHFxWgOyDh4nB4JCRimh79dR5Ywc9MDQ==", + "license": "MIT", + "dependencies": { + "is-arrayish": "^0.2.1" + } + }, + "node_modules/es-errors": { + "version": "1.3.0", + "resolved": "https://registry.npmjs.org/es-errors/-/es-errors-1.3.0.tgz", + "integrity": "sha512-Zf5H2Kxt2xjTvbJvP2ZWLEICxA6j+hAmMzIlypy4xcBg1vKVnx89Wy0GbS+kf5cwCVFFzdCFh2XSCFNULS6csw==", + "license": "MIT", + "engines": { + "node": ">= 0.4" + } + }, "node_modules/esbuild": { "version": "0.21.5", "resolved": "https://registry.npmjs.org/esbuild/-/esbuild-0.21.5.tgz", @@ -1372,6 +1879,24 @@ "node": ">=6" } }, + "node_modules/escape-string-regexp": { + "version": "4.0.0", + "resolved": "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-4.0.0.tgz", + "integrity": "sha512-TtpcNJ3XAzx3Gq8sWRzJaVajRs0uVxA2YAkdb1jm2YkPz4G6egUFAyA3n5vtEIZefPk5Wa4UXbKuS5fKkJWdgA==", + "license": "MIT", + "engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, + "node_modules/find-root": { + "version": "1.1.0", + "resolved": "https://registry.npmjs.org/find-root/-/find-root-1.1.0.tgz", + "integrity": "sha512-NKfW6bec6GfKc0SGx1e07QZY9PE99u0Bft/0rzSD5k3sO/vwkVUpDUKVm5Gpp5Ue3YfShPFTX2070tDs5kB9Ng==", + "license": "MIT" + }, "node_modules/fsevents": { "version": "2.3.3", "resolved": "https://registry.npmjs.org/fsevents/-/fsevents-2.3.3.tgz", @@ -1387,6 +1912,15 @@ "node": "^8.16.0 || ^10.6.0 || >=11.0.0" } }, + "node_modules/function-bind": { + "version": "1.1.2", + "resolved": "https://registry.npmjs.org/function-bind/-/function-bind-1.1.2.tgz", + "integrity": "sha512-7XHNxH7qX9xG5mIwxkhumTox/MIRNcOgDrxWsMt2pAr23WHp6MrRlN7FBSFpCpr+oVO0F744iUgR82nJMfG2SA==", + "license": "MIT", + "funding": { + "url": "https://github.com/sponsors/ljharb" + } + }, "node_modules/gensync": { "version": "1.0.0-beta.2", "resolved": "https://registry.npmjs.org/gensync/-/gensync-1.0.0-beta.2.tgz", @@ -1397,6 +1931,88 @@ "node": ">=6.9.0" } }, + "node_modules/hasown": { + "version": "2.0.3", + "resolved": "https://registry.npmjs.org/hasown/-/hasown-2.0.3.tgz", + "integrity": "sha512-ej4AhfhfL2Q2zpMmLo7U1Uv9+PyhIZpgQLGT1F9miIGmiCJIoCgSmczFdrc97mWT4kVY72KA+WnnhJ5pghSvSg==", + "license": "MIT", + "dependencies": { + "function-bind": "^1.1.2" + }, + "engines": { + "node": ">= 0.4" + } + }, + "node_modules/hoist-non-react-statics": { + "version": "3.3.2", + "resolved": "https://registry.npmjs.org/hoist-non-react-statics/-/hoist-non-react-statics-3.3.2.tgz", + "integrity": "sha512-/gGivxi8JPKWNm/W0jSmzcMPpfpPLc3dY/6GxhX2hQ9iGj3aDfklV4ET7NjKpSinLpJ5vafa9iiGIEZg10SfBw==", + "license": "BSD-3-Clause", + "dependencies": { + "react-is": "^16.7.0" + } + }, + "node_modules/hoist-non-react-statics/node_modules/react-is": { + "version": "16.13.1", + "resolved": "https://registry.npmjs.org/react-is/-/react-is-16.13.1.tgz", + "integrity": "sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ==", + "license": "MIT" + }, + "node_modules/immediate": { + "version": "3.0.6", + "resolved": "https://registry.npmjs.org/immediate/-/immediate-3.0.6.tgz", + "integrity": "sha512-XXOFtyqDjNDAQxVfYxuF7g9Il/IbWmmlQg2MYKOH8ExIT1qg6xc4zyS3HaEEATgs1btfzxq15ciUiY7gjSXRGQ==", + "license": "MIT" + }, + "node_modules/import-fresh": { + "version": "3.3.1", + "resolved": "https://registry.npmjs.org/import-fresh/-/import-fresh-3.3.1.tgz", + "integrity": "sha512-TR3KfrTZTYLPB6jUjfx6MF9WcWrHL9su5TObK4ZkYgBdWKPOFoSoQIdEuTuR82pmtxH2spWG9h6etwfr1pLBqQ==", + "license": "MIT", + "dependencies": { + "parent-module": "^1.0.0", + "resolve-from": "^4.0.0" + }, + "engines": { + "node": ">=6" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, + "node_modules/inherits": { + "version": "2.0.4", + "resolved": "https://registry.npmjs.org/inherits/-/inherits-2.0.4.tgz", + "integrity": "sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ==", + "license": "ISC" + }, + "node_modules/is-arrayish": { + "version": "0.2.1", + "resolved": "https://registry.npmjs.org/is-arrayish/-/is-arrayish-0.2.1.tgz", + "integrity": "sha512-zz06S8t0ozoDXMG+ube26zeCTNXcKIPJZJi8hBrF4idCLms4CG9QtK7qBl1boi5ODzFpjswb5JPmHCbMpjaYzg==", + "license": "MIT" + }, + "node_modules/is-core-module": { + "version": "2.16.1", + "resolved": "https://registry.npmjs.org/is-core-module/-/is-core-module-2.16.1.tgz", + "integrity": "sha512-UfoeMA6fIJ8wTYFEUjelnaGI67v6+N7qXJEvQuIGa99l4xsCruSYOVSQ0uPANn4dAzm8lkYPaKLrrijLq7x23w==", + "license": "MIT", + "dependencies": { + "hasown": "^2.0.2" + }, + "engines": { + "node": ">= 0.4" + }, + "funding": { + "url": "https://github.com/sponsors/ljharb" + } + }, + "node_modules/isarray": { + "version": "1.0.0", + "resolved": "https://registry.npmjs.org/isarray/-/isarray-1.0.0.tgz", + "integrity": "sha512-VLghIWNM6ELQzo7zwmcg0NmTVyWKYjvIeM83yjp0wRDTmUnrM678fQbcKBo6n2CJEF0szoG//ytg+TKla89ALQ==", + "license": "MIT" + }, "node_modules/js-tokens": { "version": "4.0.0", "resolved": "https://registry.npmjs.org/js-tokens/-/js-tokens-4.0.0.tgz", @@ -1407,7 +2023,6 @@ "version": "3.1.0", "resolved": "https://registry.npmjs.org/jsesc/-/jsesc-3.1.0.tgz", "integrity": "sha512-/sM3dO2FOzXjKQhJuo0Q173wf2KOo8t4I8vHy6lF9poUp7bKT0/NHE8fPX23PwfhnykfqnC2xRxOnVw5XuGIaA==", - "dev": true, "license": "MIT", "bin": { "jsesc": "bin/jsesc" @@ -1416,6 +2031,12 @@ "node": ">=6" } }, + "node_modules/json-parse-even-better-errors": { + "version": "2.3.1", + "resolved": "https://registry.npmjs.org/json-parse-even-better-errors/-/json-parse-even-better-errors-2.3.1.tgz", + "integrity": "sha512-xyFwyhro/JEof6Ghe2iz2NcXoj2sloNsWr/XsERDK/oiPCfaNhl5ONfp+jQdAZRQQ0IJWNzH9zIZF7li91kh2w==", + "license": "MIT" + }, "node_modules/json5": { "version": "2.2.3", "resolved": "https://registry.npmjs.org/json5/-/json5-2.2.3.tgz", @@ -1429,6 +2050,33 @@ "node": ">=6" } }, + "node_modules/jszip": { + "version": "3.10.1", + "resolved": "https://registry.npmjs.org/jszip/-/jszip-3.10.1.tgz", + "integrity": "sha512-xXDvecyTpGLrqFrvkrUSoxxfJI5AH7U8zxxtVclpsUtMCq4JQ290LY8AW5c7Ggnr/Y/oK+bQMbqK2qmtk3pN4g==", + "license": "(MIT OR GPL-3.0-or-later)", + "dependencies": { + "lie": "~3.3.0", + "pako": "~1.0.2", + "readable-stream": "~2.3.6", + "setimmediate": "^1.0.5" + } + }, + "node_modules/lie": { + "version": "3.3.0", + "resolved": "https://registry.npmjs.org/lie/-/lie-3.3.0.tgz", + "integrity": "sha512-UaiMJzeWRlEujzAuw5LokY1L5ecNQYZKfmyZ9L7wDHb/p5etKaxXhohBcrw0EYby+G/NA52vRSN4N39dxHAIwQ==", + "license": "MIT", + "dependencies": { + "immediate": "~3.0.5" + } + }, + "node_modules/lines-and-columns": { + "version": "1.2.4", + "resolved": "https://registry.npmjs.org/lines-and-columns/-/lines-and-columns-1.2.4.tgz", + "integrity": "sha512-7ylylesZQ/PV29jhEDl3Ufjo6ZX7gCqJr5F7PKrqc93v7fzSymt1BpwEU8nAUXs8qzzvqhbjhK5QZg6Mt/HkBg==", + "license": "MIT" + }, "node_modules/loose-envify": { "version": "1.4.0", "resolved": "https://registry.npmjs.org/loose-envify/-/loose-envify-1.4.0.tgz", @@ -1455,7 +2103,6 @@ "version": "2.1.3", "resolved": "https://registry.npmjs.org/ms/-/ms-2.1.3.tgz", "integrity": "sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA==", - "dev": true, "license": "MIT" }, "node_modules/nanoid": { @@ -1484,11 +2131,70 @@ "dev": true, "license": "MIT" }, + "node_modules/object-assign": { + "version": "4.1.1", + "resolved": "https://registry.npmjs.org/object-assign/-/object-assign-4.1.1.tgz", + "integrity": "sha512-rJgTQnkUnH1sFw8yT6VSU3zD3sWmu6sZhIseY8VX+GRu3P6F7Fu+JNDoXfklElbLJSnc3FUQHVe4cU5hj+BcUg==", + "license": "MIT", + "engines": { + "node": ">=0.10.0" + } + }, + "node_modules/pako": { + "version": "1.0.11", + "resolved": "https://registry.npmjs.org/pako/-/pako-1.0.11.tgz", + "integrity": "sha512-4hLB8Py4zZce5s4yd9XzopqwVv/yGNhV1Bl8NTmCq1763HeK2+EwVTv+leGeL13Dnh2wfbqowVPXCIO0z4taYw==", + "license": "(MIT AND Zlib)" + }, + "node_modules/parent-module": { + "version": "1.0.1", + "resolved": "https://registry.npmjs.org/parent-module/-/parent-module-1.0.1.tgz", + "integrity": "sha512-GQ2EWRpQV8/o+Aw8YqtfZZPfNRWZYkbidE9k5rpl/hC3vtHHBfGm2Ifi6qWV+coDGkrUKZAxE3Lot5kcsRlh+g==", + "license": "MIT", + "dependencies": { + "callsites": "^3.0.0" + }, + "engines": { + "node": ">=6" + } + }, + "node_modules/parse-json": { + "version": "5.2.0", + "resolved": "https://registry.npmjs.org/parse-json/-/parse-json-5.2.0.tgz", + "integrity": "sha512-ayCKvm/phCGxOkYRSCM82iDwct8/EonSEgCSxWxD7ve6jHggsFl4fZVQBPRNgQoKiuV/odhFrGzQXZwbifC8Rg==", + "license": "MIT", + "dependencies": { + "@babel/code-frame": "^7.0.0", + "error-ex": "^1.3.1", + "json-parse-even-better-errors": "^2.3.0", + "lines-and-columns": "^1.1.6" + }, + "engines": { + "node": ">=8" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, + "node_modules/path-parse": { + "version": "1.0.7", + "resolved": "https://registry.npmjs.org/path-parse/-/path-parse-1.0.7.tgz", + "integrity": "sha512-LDJzPVEEEPR+y48z93A0Ed0yXb8pAByGWo/k5YYdYgpY2/2EsOsksJrq7lOHxryrVOn1ejG6oAp8ahvOIQD8sw==", + "license": "MIT" + }, + "node_modules/path-type": { + "version": "4.0.0", + "resolved": "https://registry.npmjs.org/path-type/-/path-type-4.0.0.tgz", + "integrity": "sha512-gDKb8aZMDeD/tZWs9P6+q0J9Mwkdl6xMV8TjnGP3qJVJ06bdMgkbBlLU8IdfOsIsFz2BW1rNVT3XuNEl8zPAvw==", + "license": "MIT", + "engines": { + "node": ">=8" + } + }, "node_modules/picocolors": { "version": "1.1.1", "resolved": "https://registry.npmjs.org/picocolors/-/picocolors-1.1.1.tgz", "integrity": "sha512-xceH2snhtb5M9liqDsmEw56le376mTZkEX/jEb/RxNFyegNul7eNslCXP9FDj/Lcu0X8KEyMceP2ntpaHrDEVA==", - "dev": true, "license": "ISC" }, "node_modules/postcss": { @@ -1520,33 +2226,56 @@ "node": "^10 || ^12 || >=14" } }, - "node_modules/react": { - "version": "18.3.1", - "resolved": "https://registry.npmjs.org/react/-/react-18.3.1.tgz", - "integrity": "sha512-wS+hAgJShR0KhEvPJArfuPVN1+Hz1t0Y6n5jLrGQbkb4urgPE/0Rve+1kMB1v/oWgHgm4WIcV+i7F2pTVj+2iQ==", + "node_modules/process-nextick-args": { + "version": "2.0.1", + "resolved": "https://registry.npmjs.org/process-nextick-args/-/process-nextick-args-2.0.1.tgz", + "integrity": "sha512-3ouUOpQhtgrbOa17J7+uxOTpITYWaGP7/AhoR3+A+/1e9skrzelGi/dXzEYyvbxubEF6Wn2ypscTKiKJFFn1ag==", + "license": "MIT" + }, + "node_modules/prop-types": { + "version": "15.8.1", + "resolved": "https://registry.npmjs.org/prop-types/-/prop-types-15.8.1.tgz", + "integrity": "sha512-oj87CgZICdulUohogVAR7AjlC0327U4el4L6eAvOqCeudMDVU0NThNaV+b9Df4dXgSP1gXMTnPdhfe/2qDH5cg==", "license": "MIT", - "peer": true, "dependencies": { - "loose-envify": "^1.1.0" - }, + "loose-envify": "^1.4.0", + "object-assign": "^4.1.1", + "react-is": "^16.13.1" + } + }, + "node_modules/prop-types/node_modules/react-is": { + "version": "16.13.1", + "resolved": "https://registry.npmjs.org/react-is/-/react-is-16.13.1.tgz", + "integrity": "sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ==", + "license": "MIT" + }, + "node_modules/react": { + "version": "19.2.5", + "resolved": "https://registry.npmjs.org/react/-/react-19.2.5.tgz", + "integrity": "sha512-llUJLzz1zTUBrskt2pwZgLq59AemifIftw4aB7JxOqf1HY2FDaGDxgwpAPVzHU1kdWabH7FauP4i1oEeer2WCA==", + "license": "MIT", "engines": { "node": ">=0.10.0" } }, "node_modules/react-dom": { - "version": "18.3.1", - "resolved": "https://registry.npmjs.org/react-dom/-/react-dom-18.3.1.tgz", - "integrity": "sha512-5m4nQKp+rZRb09LNH59GM4BxTh9251/ylbKIbpe7TpGxfJ+9kv6BLkLBXIjjspbgbnIBNqlI23tRnTWT0snUIw==", + "version": "19.2.5", + "resolved": "https://registry.npmjs.org/react-dom/-/react-dom-19.2.5.tgz", + "integrity": "sha512-J5bAZz+DXMMwW/wV3xzKke59Af6CHY7G4uYLN1OvBcKEsWOs4pQExj86BBKamxl/Ik5bx9whOrvBlSDfWzgSag==", "license": "MIT", - "peer": true, "dependencies": { - "loose-envify": "^1.1.0", - "scheduler": "^0.23.2" + "scheduler": "^0.27.0" }, "peerDependencies": { - "react": "^18.3.1" + "react": "^19.2.5" } }, + "node_modules/react-is": { + "version": "19.2.5", + "resolved": "https://registry.npmjs.org/react-is/-/react-is-19.2.5.tgz", + "integrity": "sha512-Dn0t8IQhCmeIT3wu+Apm1/YVsJXsGWi6k4sPdnBIdqMVtHtv0IGi6dcpNpNkNac0zB2uUAqNX3MHzN8c+z2rwQ==", + "license": "MIT" + }, "node_modules/react-refresh": { "version": "0.17.0", "resolved": "https://registry.npmjs.org/react-refresh/-/react-refresh-0.17.0.tgz", @@ -1557,6 +2286,67 @@ "node": ">=0.10.0" } }, + "node_modules/react-transition-group": { + "version": "4.4.5", + "resolved": "https://registry.npmjs.org/react-transition-group/-/react-transition-group-4.4.5.tgz", + "integrity": "sha512-pZcd1MCJoiKiBR2NRxeCRg13uCXbydPnmB4EOeRrY7480qNWO8IIgQG6zlDkm6uRMsURXPuKq0GWtiM59a5Q6g==", + "license": "BSD-3-Clause", + "dependencies": { + "@babel/runtime": "^7.5.5", + "dom-helpers": "^5.0.1", + "loose-envify": "^1.4.0", + "prop-types": "^15.6.2" + }, + "peerDependencies": { + "react": ">=16.6.0", + "react-dom": ">=16.6.0" + } + }, + "node_modules/readable-stream": { + "version": "2.3.8", + "resolved": "https://registry.npmjs.org/readable-stream/-/readable-stream-2.3.8.tgz", + "integrity": "sha512-8p0AUk4XODgIewSi0l8Epjs+EVnWiK7NoDIEGU0HhE7+ZyY8D1IMY7odu5lRrFXGg71L15KG8QrPmum45RTtdA==", + "license": "MIT", + "dependencies": { + "core-util-is": "~1.0.0", + "inherits": "~2.0.3", + "isarray": "~1.0.0", + "process-nextick-args": "~2.0.0", + "safe-buffer": "~5.1.1", + "string_decoder": "~1.1.1", + "util-deprecate": "~1.0.1" + } + }, + "node_modules/resolve": { + "version": "1.22.12", + "resolved": "https://registry.npmjs.org/resolve/-/resolve-1.22.12.tgz", + "integrity": "sha512-TyeJ1zif53BPfHootBGwPRYT1RUt6oGWsaQr8UyZW/eAm9bKoijtvruSDEmZHm92CwS9nj7/fWttqPCgzep8CA==", + "license": "MIT", + "dependencies": { + "es-errors": "^1.3.0", + "is-core-module": "^2.16.1", + "path-parse": "^1.0.7", + "supports-preserve-symlinks-flag": "^1.0.0" + }, + "bin": { + "resolve": "bin/resolve" + }, + "engines": { + "node": ">= 0.4" + }, + "funding": { + "url": "https://github.com/sponsors/ljharb" + } + }, + "node_modules/resolve-from": { + "version": "4.0.0", + "resolved": "https://registry.npmjs.org/resolve-from/-/resolve-from-4.0.0.tgz", + "integrity": "sha512-pb/MYmXstAkysRFx8piNI1tGFNQIFA3vkE3Gq4EuA1dF6gHp/+vgZqsCGJapvy8N3Q+4o7FwvquPJcnZ7RYy4g==", + "license": "MIT", + "engines": { + "node": ">=4" + } + }, "node_modules/rollup": { "version": "4.55.1", "resolved": "https://registry.npmjs.org/rollup/-/rollup-4.55.1.tgz", @@ -1602,14 +2392,17 @@ "fsevents": "~2.3.2" } }, + "node_modules/safe-buffer": { + "version": "5.1.2", + "resolved": "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.1.2.tgz", + "integrity": "sha512-Gd2UZBJDkXlY7GbJxfsE8/nvKkUEU1G38c1siN6QP6a9PT9MmHB8GnpscSmMJSoF8LOIrt8ud/wPtojys4G6+g==", + "license": "MIT" + }, "node_modules/scheduler": { - "version": "0.23.2", - "resolved": "https://registry.npmjs.org/scheduler/-/scheduler-0.23.2.tgz", - "integrity": "sha512-UOShsPwz7NrMUqhR6t0hWjFduvOzbtv7toDH1/hIrfRNIDBnnBWd0CwJTGvTpngVlmwGCdP9/Zl/tVrDqcuYzQ==", - "license": "MIT", - "dependencies": { - "loose-envify": "^1.1.0" - } + "version": "0.27.0", + "resolved": "https://registry.npmjs.org/scheduler/-/scheduler-0.27.0.tgz", + "integrity": "sha512-eNv+WrVbKu1f3vbYJT/xtiF5syA5HPIMtf9IgY/nKg0sWqzAUEvqY/xm7OcZc/qafLx/iO9FgOmeSAp4v5ti/Q==", + "license": "MIT" }, "node_modules/semver": { "version": "6.3.1", @@ -1621,6 +2414,21 @@ "semver": "bin/semver.js" } }, + "node_modules/setimmediate": { + "version": "1.0.5", + "resolved": "https://registry.npmjs.org/setimmediate/-/setimmediate-1.0.5.tgz", + "integrity": "sha512-MATJdZp8sLqDl/68LfQmbP8zKPLQNV6BIZoIgrscFDQ+RsvK/BxeDQOgyxKKoh0y/8h3BqVFnCqQ/gd+reiIXA==", + "license": "MIT" + }, + "node_modules/source-map": { + "version": "0.5.7", + "resolved": "https://registry.npmjs.org/source-map/-/source-map-0.5.7.tgz", + "integrity": "sha512-LbrmJOMUSdEVxIKvdcJzQC+nQhe8FUZQTXQy6+I75skNgn3OoQ0DZA8YnFa7gp8tqtL3KPf1kmo0R5DoApeSGQ==", + "license": "BSD-3-Clause", + "engines": { + "node": ">=0.10.0" + } + }, "node_modules/source-map-js": { "version": "1.2.1", "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz", @@ -1631,6 +2439,47 @@ "node": ">=0.10.0" } }, + "node_modules/string_decoder": { + "version": "1.1.1", + "resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.1.1.tgz", + "integrity": "sha512-n/ShnvDi6FHbbVfviro+WojiFzv+s8MPMHBczVePfUpDJLwoLT0ht1l4YwBCbi8pJAveEEdnkHyPyTP/mzRfwg==", + "license": "MIT", + "dependencies": { + "safe-buffer": "~5.1.0" + } + }, + "node_modules/stylis": { + "version": "4.2.0", + "resolved": "https://registry.npmjs.org/stylis/-/stylis-4.2.0.tgz", + "integrity": "sha512-Orov6g6BB1sDfYgzWfTHDOxamtX1bE/zo104Dh9e6fqJ3PooipYyfJ0pUmrZO2wAvO8YbEyeFrkV91XTsGMSrw==", + "license": "MIT" + }, + "node_modules/supports-preserve-symlinks-flag": { + "version": "1.0.0", + "resolved": "https://registry.npmjs.org/supports-preserve-symlinks-flag/-/supports-preserve-symlinks-flag-1.0.0.tgz", + "integrity": "sha512-ot0WnXS9fgdkgIcePe6RHNk1WA8+muPa6cSjeR3V8K27q9BB1rTE3R1p7Hv0z1ZyAc8s6Vvv8DIyWf681MAt0w==", + "license": "MIT", + "engines": { + "node": ">= 0.4" + }, + "funding": { + "url": "https://github.com/sponsors/ljharb" + } + }, + "node_modules/typescript": { + "version": "5.9.3", + "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.9.3.tgz", + "integrity": "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw==", + "dev": true, + "license": "Apache-2.0", + "bin": { + "tsc": "bin/tsc", + "tsserver": "bin/tsserver" + }, + "engines": { + "node": ">=14.17" + } + }, "node_modules/update-browserslist-db": { "version": "1.2.3", "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.2.3.tgz", @@ -1662,13 +2511,18 @@ "browserslist": ">= 4.21.0" } }, + "node_modules/util-deprecate": { + "version": "1.0.2", + "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz", + "integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==", + "license": "MIT" + }, "node_modules/vite": { "version": "5.4.21", "resolved": "https://registry.npmjs.org/vite/-/vite-5.4.21.tgz", "integrity": "sha512-o5a9xKjbtuhY6Bi5S3+HvbRERmouabWbyUcpXXUA1u+GNUKoROi9byOJ8M0nHbHYHkYICiMlqxkg1KkYmm25Sw==", "dev": true, "license": "MIT", - "peer": true, "dependencies": { "esbuild": "^0.21.3", "postcss": "^8.4.43", @@ -1729,6 +2583,15 @@ "integrity": "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==", "dev": true, "license": "ISC" + }, + "node_modules/yaml": { + "version": "1.10.3", + "resolved": "https://registry.npmjs.org/yaml/-/yaml-1.10.3.tgz", + "integrity": "sha512-vIYeF1u3CjlhAFekPPAk2h/Kv4T3mAkMox5OymRiJQB0spDP10LHvt+K7G9Ny6NuuMAb25/6n1qyUjAcGNf/AA==", + "license": "ISC", + "engines": { + "node": ">= 6" + } } } } diff --git a/widget/package.json b/widget/package.json index 4abc343d..3d0175ae 100644 --- a/widget/package.json +++ b/widget/package.json @@ -3,16 +3,26 @@ "type": "module", "scripts": { "dev": "vite build --watch", - "build": "vite build" + "build": "vite build", + "typecheck": "tsc --noEmit" }, "dependencies": { - "react": "^18.2.0", - "react-dom": "^18.2.0", - "@anywidget/react": "^0.1.0" + "@anywidget/react": "^0.2.0", + "@emotion/react": "^11.14.0", + "@emotion/styled": "^11.14.1", + "@mui/icons-material": "^7.3.7", + "@mui/material": "^7.3.6", + "jszip": "^3.10.1", + "react": "^19.1.0", + "react-dom": "^19.1.0" }, "devDependencies": { - "vite": "^5.2.0", "@anywidget/vite": "^0.2.0", - "@vitejs/plugin-react": "^4.3.0" + "@types/react": "^19.1.3", + "@types/react-dom": "^19.1.4", + "@vitejs/plugin-react": "^4.3.0", + "@webgpu/types": "^0.1.68", + "typescript": "^5.8.3", + "vite": "^5.2.0" } } diff --git a/widget/pyproject.toml b/widget/pyproject.toml index c0fb7a63..738c7bc9 100644 --- a/widget/pyproject.toml +++ b/widget/pyproject.toml @@ -10,6 +10,11 @@ license = "MIT" requires-python = ">=3.11" dependencies = [ "anywidget>=0.9.0", + "numpy>=2.0.0", + "traitlets>=5.0.0", + "torch>=2.0.0", + "matplotlib>=3.7.0", + "Pillow>=10.0.0", ] [tool.hatch.build.targets.wheel] diff --git a/widget/src/quantem/widget/__init__.py b/widget/src/quantem/widget/__init__.py index d4ca85a7..dc98e36a 100644 --- a/widget/src/quantem/widget/__init__.py +++ b/widget/src/quantem/widget/__init__.py @@ -1,24 +1,7 @@ from importlib.metadata import version -import pathlib -import anywidget -import traitlets -__version__ = version("quantem.widget") - -_static = pathlib.Path(__file__).parent / "static" - - -class CounterWidget(anywidget.AnyWidget): - _esm = _static / "index.js" - - count = traitlets.Int(0).tag(sync=True) +from quantem.widget.show2d import Show2D +from quantem.widget.show4dstem import Show4DSTEM - -def show4dstem(): - # TODO: Implement 4D-STEM visualization widget - print("show4dstem: not yet implemented") - - -def counter(): - """Create a minimal counter widget for testing.""" - return CounterWidget() +__version__ = version("quantem.widget") +__all__ = ["Show2D", "Show4DSTEM"] diff --git a/widget/src/quantem/widget/array_utils.py b/widget/src/quantem/widget/array_utils.py new file mode 100644 index 00000000..e86633e6 --- /dev/null +++ b/widget/src/quantem/widget/array_utils.py @@ -0,0 +1,282 @@ +""" +Array utilities for handling NumPy, CuPy, and PyTorch arrays uniformly. + +This module provides utilities to convert arrays from different backends +into NumPy arrays for widget processing. +""" + +from typing import Any, Literal +import numpy as np + +try: + import torch + import torch.nn.functional as F + _HAS_TORCH = True +except ImportError: + _HAS_TORCH = False + + +ArrayBackend = Literal["numpy", "cupy", "torch", "unknown"] + + +def get_array_backend(data: Any) -> ArrayBackend: + """ + Detect the array backend of the input data. + + Parameters + ---------- + data : array-like + Input array (NumPy, CuPy, PyTorch, or other). + + Returns + ------- + str + One of: "numpy", "cupy", "torch", "unknown" + """ + # Check PyTorch first (has both .numpy and .detach methods) + if hasattr(data, "detach") and hasattr(data, "numpy"): + return "torch" + # Check CuPy (has .get() or __cuda_array_interface__) + if hasattr(data, "__cuda_array_interface__"): + return "cupy" + if hasattr(data, "get") and hasattr(data, "__array__"): + # CuPy arrays have .get() to transfer to CPU + type_name = type(data).__module__ + if "cupy" in type_name: + return "cupy" + # Check NumPy + if isinstance(data, np.ndarray): + return "numpy" + return "unknown" + + +def to_numpy(data: Any, dtype: np.dtype | None = None) -> np.ndarray: + """ + Convert any array-like (NumPy, CuPy, PyTorch) to a NumPy array. + + Parameters + ---------- + data : array-like + Input array from any supported backend. + dtype : np.dtype, optional + Target dtype for the output array. If None, preserves original dtype. + + Returns + ------- + np.ndarray + NumPy array with the same data. + + Examples + -------- + >>> import numpy as np + >>> from quantem.widget.array_utils import to_numpy + >>> + >>> # NumPy passthrough + >>> arr = np.random.rand(10, 10) + >>> result = to_numpy(arr) + >>> + >>> # CuPy conversion (if available) + >>> import cupy as cp + >>> gpu_arr = cp.random.rand(10, 10) + >>> cpu_arr = to_numpy(gpu_arr) + >>> + >>> # PyTorch conversion (if available) + >>> import torch + >>> tensor = torch.rand(10, 10) + >>> arr = to_numpy(tensor) + """ + backend = get_array_backend(data) + + if backend == "torch": + # PyTorch tensor: detach from graph, move to CPU, convert to numpy + result = data.detach().cpu().numpy() + + elif backend == "cupy": + # CuPy array: use .get() to transfer to CPU + if hasattr(data, "get"): + result = data.get() + else: + # Fallback for __cuda_array_interface__ + import cupy as cp + + result = cp.asnumpy(data) + + elif backend == "numpy": + # NumPy array: passthrough (may copy if dtype changes) + result = data + + else: + # Unknown backend: try np.asarray as fallback + result = np.asarray(data) + + # Apply dtype conversion if specified + if dtype is not None: + result = np.asarray(result, dtype=dtype) + + return result + + +def bin2d(data, factor: int = 2, mode: str = "mean", edge_mode: str = "crop") -> np.ndarray: + """ + Spatial binning for 2D or 3D arrays. + + Uses torch GPU (MPS/CUDA) when available for large arrays (~5× faster on 4K data). + + Parameters + ---------- + data : array-like + Input array with shape ``(H, W)`` or ``(N, H, W)``. + factor : int, default 2 + Bin factor. + mode : str, default "mean" + Reduction mode: ``"mean"`` or ``"sum"``. + edge_mode : str, default "crop" + How to handle dimensions not divisible by *factor*: + ``"crop"`` trims extra pixels, ``"pad"`` zero-pads to the next + multiple (output shape uses ``ceil(dim / factor)``). + + Returns + ------- + np.ndarray + Binned array, dtype float32. + """ + arr = to_numpy(data) + if arr.dtype != np.float32: + arr = arr.astype(np.float32) + + # Torch GPU fast path: only for arrays between 1M and 500M elements. + # Larger arrays hit MPS memory transfer bottleneck (>2 GB transfer > CPU compute). + import torch + if 1_000_000 < arr.size < 500_000_000 and (torch.backends.mps.is_available() or torch.cuda.is_available()): + dev = torch.device("mps" if torch.backends.mps.is_available() else "cuda") + t = torch.from_numpy(arr).to(dev) + if t.ndim == 2: + h, w = t.shape + oh = h // factor * factor + ow = w // factor * factor + t = t[:oh, :ow].reshape(oh // factor, factor, ow // factor, factor) + t = t.sum(dim=(1, 3)) if mode == "sum" else t.mean(dim=(1, 3)) + elif t.ndim == 3: + n, h, w = t.shape + oh = h // factor * factor + ow = w // factor * factor + t = t[:, :oh, :ow].reshape(n, oh // factor, factor, ow // factor, factor) + t = t.sum(dim=(2, 4)) if mode == "sum" else t.mean(dim=(2, 4)) + return t.cpu().numpy().astype(np.float32) + + # CPU fallback (no GPU available or small array) + reduce = np.ndarray.sum if mode == "sum" else np.ndarray.mean + if arr.ndim == 2: + arr = _pad_or_crop_2d(arr, factor, edge_mode) + h, w = arr.shape + oh, ow = h // factor, w // factor + return reduce(arr.reshape(oh, factor, ow, factor), axis=(1, 3)).astype(np.float32) + # 3D: (N, H, W) + arr = _pad_or_crop_3d(arr, factor, edge_mode) + n, h, w = arr.shape + oh, ow = h // factor, w // factor + return reduce(arr.reshape(n, oh, factor, ow, factor), axis=(2, 4)).astype(np.float32) + + +def _pad_or_crop_2d(arr: np.ndarray, factor: int, edge_mode: str) -> np.ndarray: + h, w = arr.shape + if edge_mode == "pad": + pad_h = (factor - h % factor) % factor + pad_w = (factor - w % factor) % factor + if pad_h or pad_w: + arr = np.pad(arr, ((0, pad_h), (0, pad_w)), mode="constant") + else: + oh, ow = h // factor, w // factor + arr = arr[:oh * factor, :ow * factor] + return arr + + +def _pad_or_crop_3d(arr: np.ndarray, factor: int, edge_mode: str) -> np.ndarray: + _, h, w = arr.shape + if edge_mode == "pad": + pad_h = (factor - h % factor) % factor + pad_w = (factor - w % factor) % factor + if pad_h or pad_w: + arr = np.pad(arr, ((0, 0), (0, pad_h), (0, pad_w)), mode="constant") + else: + oh, ow = h // factor, w // factor + arr = arr[:, :oh * factor, :ow * factor] + return arr + + +def apply_shift(img: np.ndarray, dy: float, dx: float) -> np.ndarray: + """ + Apply sub-pixel shift using bilinear interpolation. + + Uses ``torch.nn.functional.grid_sample`` on GPU when torch is available, + falls back to numpy bilinear interpolation otherwise. + + Parameters + ---------- + img : np.ndarray + 2D image, float32. + dy : float + Shift in y (rows). + dx : float + Shift in x (columns). + + Returns + ------- + np.ndarray + Shifted image, same shape, float32. Out-of-bounds pixels are zero. + """ + if _HAS_TORCH: + h, w = img.shape + device = torch.device("mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu") + t = torch.as_tensor(img, dtype=torch.float32, device=device).unsqueeze(0).unsqueeze(0) + base_y = torch.linspace(-1, 1, h, device=device) + base_x = torch.linspace(-1, 1, w, device=device) + gy, gx = torch.meshgrid(base_y, base_x, indexing="ij") + grid = torch.stack([gx - dx * 2.0 / w, gy - dy * 2.0 / h], dim=-1).unsqueeze(0) + result = F.grid_sample(t, grid, mode="bilinear", padding_mode="zeros", align_corners=True) + return result.squeeze().cpu().numpy() + h, w = img.shape + y_src = np.arange(h, dtype=np.float64) - dy + x_src = np.arange(w, dtype=np.float64) - dx + yy, xx = np.meshgrid(y_src, x_src, indexing="ij") + y0 = np.floor(yy).astype(int) + x0 = np.floor(xx).astype(int) + fy = (yy - y0).astype(np.float32) + fx = (xx - x0).astype(np.float32) + valid = (y0 >= 0) & (y0 + 1 < h) & (x0 >= 0) & (x0 + 1 < w) + y0c = np.clip(y0, 0, h - 2) + x0c = np.clip(x0, 0, w - 2) + result = (img[y0c, x0c] * (1 - fy) * (1 - fx) + + img[y0c, x0c + 1] * (1 - fy) * fx + + img[y0c + 1, x0c] * fy * (1 - fx) + + img[y0c + 1, x0c + 1] * fy * fx) + result[~valid] = 0.0 + return result.astype(np.float32) + + +def _resize_image(img: np.ndarray, target_h: int, target_w: int) -> np.ndarray: + """Resize image using bilinear interpolation (pure numpy, no scipy).""" + h, w = img.shape + + if h == target_h and w == target_w: + return img + + y_new = np.linspace(0, h - 1, target_h) + x_new = np.linspace(0, w - 1, target_w) + x_grid, y_grid = np.meshgrid(x_new, y_new) + + y0 = np.floor(y_grid).astype(int) + x0 = np.floor(x_grid).astype(int) + y1 = np.minimum(y0 + 1, h - 1) + x1 = np.minimum(x0 + 1, w - 1) + + fy = y_grid - y0 + fx = x_grid - x0 + + result = ( + img[y0, x0] * (1 - fy) * (1 - fx) + + img[y0, x1] * (1 - fy) * fx + + img[y1, x0] * fy * (1 - fx) + + img[y1, x1] * fy * fx + ) + return result.astype(img.dtype) diff --git a/widget/src/quantem/widget/json_state.py b/widget/src/quantem/widget/json_state.py new file mode 100644 index 00000000..4874981f --- /dev/null +++ b/widget/src/quantem/widget/json_state.py @@ -0,0 +1,47 @@ +import importlib.metadata +import json +import pathlib +from typing import Any + + +JSON_METADATA_VERSION = "1.0" + + +def resolve_widget_version() -> str: + try: + return importlib.metadata.version("quantem-widget") + except importlib.metadata.PackageNotFoundError: + return "unknown" + except Exception: + return "unknown" + + +def build_json_header(widget_name: str) -> dict[str, Any]: + return { + "metadata_version": JSON_METADATA_VERSION, + "widget_name": widget_name, + "widget_version": resolve_widget_version(), + } + + +def wrap_state_dict(widget_name: str, state: dict[str, Any]) -> dict[str, Any]: + envelope = build_json_header(widget_name) + envelope["state"] = state + return envelope + + +def unwrap_state_payload(payload: dict[str, Any], *, require_envelope: bool = False) -> dict[str, Any]: + if not isinstance(payload, dict): + raise ValueError("State payload must be a dict.") + if "state" in payload: + state = payload["state"] + if not isinstance(state, dict): + raise ValueError("State envelope field 'state' must be a dict.") + return state + if require_envelope: + raise ValueError("State JSON file must be a versioned envelope with top-level 'state'.") + return payload + + +def save_state_file(path: str | pathlib.Path, widget_name: str, state: dict[str, Any]) -> None: + pathlib.Path(path).write_text(json.dumps(wrap_state_dict(widget_name, state), indent=2)) diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py new file mode 100644 index 00000000..08031ac7 --- /dev/null +++ b/widget/src/quantem/widget/show2d.py @@ -0,0 +1,1309 @@ +""" +show2d: Static 2D image viewer with optional FFT and histogram analysis. + +For displaying a single image or a static gallery of multiple images. +Unlike Show3D (interactive), Show2D focuses on static visualization. +""" + +import json +import os +import pathlib +import io +import base64 +import math +import warnings +from enum import StrEnum +from typing import Optional, Union, List, Self + +import anywidget +import matplotlib +import matplotlib.patheffects +import matplotlib.pyplot as plt +import numpy as np +import traitlets + +from quantem.widget.array_utils import to_numpy, _resize_image +from quantem.widget.json_state import resolve_widget_version, save_state_file, unwrap_state_payload +from quantem.widget.tool_parity import ( + bind_tool_runtime_api, + build_tool_groups, + normalize_tool_groups, +) + + + +def _reject_unknown_kwargs(cls, kwargs: dict) -> None: + """Raise TypeError if kwargs contains any key that isn't a declared trait. + + anywidget/traitlets silently accept unknown keys, which let stale notebooks + pass obsolete params like ``pixel_size_angstrom=0.5`` with no warning. This + helper catches typos and renamed-trait references at construction time. + """ + traits = set(cls.class_trait_names()) + unknown = [k for k in kwargs if k not in traits] + if unknown: + key = sorted(unknown)[0] + raise TypeError( + f"{cls.__name__}() got unexpected keyword argument {key!r}. " + f"Check for typos or a renamed parameter (e.g. canvas_size → size, " + f"image_width_px → size, pixel_size_angstrom → pixel_size)." + ) + + +def _round_to_nice(value: float) -> float: + """Round a physical length to a 'nice' value (1, 2, 5, 10, 20, 50, ...).""" + if value <= 0: + return 1.0 + exp = math.floor(math.log10(value)) + base = 10 ** exp + mantissa = value / base + if mantissa < 1.5: + return base + elif mantissa < 3.5: + return 2 * base + elif mantissa < 7.5: + return 5 * base + else: + return 10 * base + + +class Colormap(StrEnum): + INFERNO = "inferno" + VIRIDIS = "viridis" + MAGMA = "magma" + PLASMA = "plasma" + GRAY = "gray" + + +class Show2D(anywidget.AnyWidget): + """ + Static 2D image viewer with optional FFT and histogram analysis. + + Display a single image or multiple images in a gallery layout. + For interactive stack viewing with playback, use Show3D instead. + + Parameters + ---------- + data : array_like + 2D array (height, width) for single image, or + 3D array (N, height, width) for multiple images displayed as gallery. + labels : list of str, optional + Labels for each image in gallery mode. + title : str, optional + Title to display above the image(s). + cmap : str, default "inferno" + Colormap name ("magma", "viridis", "gray", "inferno", "plasma"). + pixel_size : float, optional + Pixel size in angstroms for scale bar display. + show_fft : bool, default False + Show FFT and histogram panels. + show_stats : bool, default True + Show statistics (mean, min, max, std). + log_scale : bool, default False + Use log scale for intensity mapping. + auto_contrast : bool, default False + Use percentile-based contrast. + vmin : float, optional + Absolute minimum intensity for color mapping. When both vmin and vmax + are set, all gallery images share the same intensity scale — essential + for A/B visual comparison. + vmax : float, optional + Absolute maximum intensity for color mapping. + ncols : int, default 3 + Number of columns in gallery mode. + size : int, default 0 + Canvas rendering size in CSS pixels (the on-screen width of each image). + ``0`` uses the frontend default: 500 px for a single image, 300 px per + image in gallery mode. Pass e.g. ``size=800`` to enlarge for a + presentation, or ``size=200`` to compress alongside a control panel. + This controls **display only** — the underlying image resolution is + never resampled; zooming into a 4K image preserves every pixel. + disabled_tools : list of str, optional + Tool groups to lock while still showing controls. Supported: + ``"display"``, ``"histogram"``, ``"stats"``, ``"navigation"``, + ``"view"``, ``"export"``, ``"roi"``, ``"profile"``, ``"all"``. + disable_* : bool, optional + Convenience flags (``disable_display``, ``disable_histogram``, + ``disable_stats``, ``disable_navigation``, ``disable_view``, + ``disable_export``, ``disable_roi``, ``disable_profile``, + ``disable_all``) equivalent to adding those keys to + ``disabled_tools``. + hidden_tools : list of str, optional + Tool groups to hide from the UI. Uses the same keys as + ``disabled_tools``. + hide_* : bool, optional + Convenience flags mirroring ``disable_*`` for ``hidden_tools``. + + Attributes + ---------- + render_total_ms : int or None + End-to-end wall clock from constructor start to first browser paint, + populated by a JS→Python round-trip after the first canvas render. + ``None`` until the browser has actually painted; also printed to stdout + when it fires. Use to triage "is it Python, wire, or the browser?" + during live acquisitions. + render_python_build_ms : int or None + Subset of ``render_total_ms`` covering Python ``__init__`` only. + render_wire_js_ms : int or None + Subset covering everything after Python returns: Comm transfer, JS + decode, colormap, and canvas paint. + + Examples + -------- + >>> import numpy as np + >>> from quantem.widget import Show2D + >>> + >>> # Single image with FFT + >>> Show2D(image, title="HRTEM Image", show_fft=True, pixel_size=1.0) + >>> + >>> # Gallery of multiple images + >>> labels = ["Raw", "Filtered", "FFT"] + >>> Show2D([img1, img2, img3], labels=labels, ncols=3) + """ + + _esm = pathlib.Path(__file__).parent / "static" / "show2d.js" + _css = pathlib.Path(__file__).parent / "static" / "show2d.css" + + # ========================================================================= + # Core State + # GPU memory budget for display buffers (MB). Each 4K image needs ~192 MB. + # 12×4K = 2304 MB fits. 16+ triggers auto-bin. + _GPU_DISPLAY_BUDGET_MB = 2500 + + # ========================================================================= + widget_version = traitlets.Unicode("unknown").tag(sync=True) + n_images = traitlets.Int(1).tag(sync=True) + height = traitlets.Int(1).tag(sync=True) + width = traitlets.Int(1).tag(sync=True) + _display_bin_factor = traitlets.Int(1).tag(sync=True) # 1 = full-res, 2/4/8 = binned + _gpu_max_buffer_mb = traitlets.Int(0).tag(sync=True) # GPU reports maxBufferSize (JS→Python) + # Flipped True by JS after the first colormap pass has painted to canvas. + # Used by the Python-side truthful timing print (end-to-end wall clock, not just __init__). + _js_rendered = traitlets.Bool(False).tag(sync=True) + frame_bytes = traitlets.Bytes(b"").tag(sync=True) + labels = traitlets.List(traitlets.Unicode()).tag(sync=True) + title = traitlets.Unicode("").tag(sync=True) + cmap = traitlets.Unicode("inferno").tag(sync=True) + ncols = traitlets.Int(3).tag(sync=True) + + # ========================================================================= + # Display Options + # ========================================================================= + log_scale = traitlets.Bool(False).tag(sync=True) + auto_contrast = traitlets.Bool(False).tag(sync=True) + vmin = traitlets.Float(None, allow_none=True).tag(sync=True) + vmax = traitlets.Float(None, allow_none=True).tag(sync=True) + vmins = traitlets.List(trait=traitlets.Float(allow_none=True), allow_none=True, default_value=None).tag(sync=True) + vmaxs = traitlets.List(trait=traitlets.Float(allow_none=True), allow_none=True, default_value=None).tag(sync=True) + + # ========================================================================= + # Scale Bar + # ========================================================================= + pixel_size = traitlets.Float(0.0).tag(sync=True) + scale_bar_visible = traitlets.Bool(True).tag(sync=True) + size = traitlets.Int(0).tag(sync=True) # Canvas rendering size in CSS pixels; 0 = frontend default + smooth = traitlets.Bool(False).tag(sync=True) + initial_zoom = traitlets.Float(1.0).tag(sync=True) + zoom_row = traitlets.Float(None, allow_none=True).tag(sync=True) + zoom_col = traitlets.Float(None, allow_none=True).tag(sync=True) + link_zoom = traitlets.Bool(False).tag(sync=True) + link_pan = traitlets.Bool(False).tag(sync=True) + link_contrast = traitlets.Bool(True).tag(sync=True) + diff_mode = traitlets.Bool(False).tag(sync=True) + diff_reference = traitlets.Int(0).tag(sync=True) + + # ========================================================================= + # UI Visibility + # ========================================================================= + show_controls = traitlets.Bool(True).tag(sync=True) + show_stats = traitlets.Bool(True).tag(sync=True) + disabled_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) + hidden_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) + stats_mean = traitlets.List(traitlets.Float()).tag(sync=True) + stats_min = traitlets.List(traitlets.Float()).tag(sync=True) + stats_max = traitlets.List(traitlets.Float()).tag(sync=True) + stats_std = traitlets.List(traitlets.Float()).tag(sync=True) + + # ========================================================================= + # Analysis Panels (FFT + Histogram shown together) + # ========================================================================= + show_fft = traitlets.Bool(False).tag(sync=True) + fft_window = traitlets.Bool(True).tag(sync=True) + + # ========================================================================= + # Selected Image (for single-image analysis display) + # ========================================================================= + selected_idx = traitlets.Int(0).tag(sync=True) + + # ========================================================================= + # ROI Selection + # ========================================================================= + roi_active = traitlets.Bool(False).tag(sync=True) + roi_list = traitlets.List([]).tag(sync=True) + roi_selected_idx = traitlets.Int(-1).tag(sync=True) + + # ========================================================================= + # Line Profile + # ========================================================================= + profile_line = traitlets.List(traitlets.Dict()).tag(sync=True) + + # ========================================================================= + # Per-Image Rotation + # ========================================================================= + image_rotations = traitlets.List(traitlets.Int(), []).tag(sync=True) + + @classmethod + def _normalize_tool_groups(cls, tool_groups) -> List[str]: + return normalize_tool_groups("Show2D", tool_groups) + + @classmethod + def _build_disabled_tools( + cls, + disabled_tools=None, + disable_display: bool = False, + disable_histogram: bool = False, + disable_stats: bool = False, + disable_navigation: bool = False, + disable_view: bool = False, + disable_export: bool = False, + disable_roi: bool = False, + disable_profile: bool = False, + disable_all: bool = False, + ) -> List[str]: + return build_tool_groups( + "Show2D", + tool_groups=disabled_tools, + all_flag=disable_all, + flag_map={ + "display": disable_display, + "histogram": disable_histogram, + "stats": disable_stats, + "navigation": disable_navigation, + "view": disable_view, + "export": disable_export, + "roi": disable_roi, + "profile": disable_profile, + }, + ) + + @classmethod + def _build_hidden_tools( + cls, + hidden_tools=None, + hide_display: bool = False, + hide_histogram: bool = False, + hide_stats: bool = False, + hide_navigation: bool = False, + hide_view: bool = False, + hide_export: bool = False, + hide_roi: bool = False, + hide_profile: bool = False, + hide_all: bool = False, + ) -> List[str]: + return build_tool_groups( + "Show2D", + tool_groups=hidden_tools, + all_flag=hide_all, + flag_map={ + "display": hide_display, + "histogram": hide_histogram, + "stats": hide_stats, + "navigation": hide_navigation, + "view": hide_view, + "export": hide_export, + "roi": hide_roi, + "profile": hide_profile, + }, + ) + + @traitlets.validate("disabled_tools") + def _validate_disabled_tools(self, proposal): + return self._normalize_tool_groups(proposal["value"]) + + @traitlets.validate("hidden_tools") + def _validate_hidden_tools(self, proposal): + return self._normalize_tool_groups(proposal["value"]) + + def __init__( + self, + data: Union[np.ndarray, List[np.ndarray]], + labels: Optional[List[str]] = None, + title: str = "", + cmap: Union[str, Colormap] = Colormap.INFERNO, + pixel_size: float = 0.0, + scale_bar_visible: bool = True, + show_fft: bool = False, + fft_window: bool = True, + show_controls: bool = True, + show_stats: bool = True, + log_scale: bool = False, + auto_contrast: bool = False, + vmin: float | list | None = None, + vmax: float | list | None = None, + disabled_tools: Optional[List[str]] = None, + disable_display: bool = False, + disable_histogram: bool = False, + disable_stats: bool = False, + disable_navigation: bool = False, + disable_view: bool = False, + disable_export: bool = False, + disable_roi: bool = False, + disable_profile: bool = False, + disable_all: bool = False, + hidden_tools: Optional[List[str]] = None, + hide_display: bool = False, + hide_histogram: bool = False, + hide_stats: bool = False, + hide_navigation: bool = False, + hide_view: bool = False, + hide_export: bool = False, + hide_roi: bool = False, + hide_profile: bool = False, + hide_all: bool = False, + ncols: int = 3, + size: int = 0, + smooth: bool = False, + zoom: float = 1.0, + zoom_row: float | None = None, + zoom_col: float | None = None, + link_zoom: bool = False, + link_pan: bool = False, + link_contrast: bool = True, + diff_mode: bool = False, + view_box: tuple | list | None = None, + display_bin: Union[int, str] = "auto", + state=None, + **kwargs, + ): + import time as _time + _t0 = _time.perf_counter() + # Reject typos and stale kwargs (e.g. image_width_px, pixel_size_angstrom). + # anywidget/traitlets silently ignores unknown keys, which hid the + # pixel_size_angstrom bug in show2d_all_features.ipynb for months. + _reject_unknown_kwargs(type(self), kwargs) + super().__init__(**kwargs) + # hold_sync() batches ALL traitlet assignments into a single comm message + # sent when the context manager exits. Without this, each self.x = y + # fires a separate round-trip over the ZMQ/websocket channel, which + # can add 20+ seconds for a 30-image gallery in VS Code Jupyter. + with self.hold_sync(): + self._init_sync( + data=data, labels=labels, title=title, cmap=cmap, + pixel_size=pixel_size, scale_bar_visible=scale_bar_visible, + show_fft=show_fft, fft_window=fft_window, + show_controls=show_controls, show_stats=show_stats, + log_scale=log_scale, auto_contrast=auto_contrast, + vmin=vmin, vmax=vmax, + disabled_tools=disabled_tools, + disable_display=disable_display, + disable_histogram=disable_histogram, + disable_stats=disable_stats, + disable_navigation=disable_navigation, + disable_view=disable_view, + disable_export=disable_export, + disable_roi=disable_roi, + disable_profile=disable_profile, + disable_all=disable_all, + hidden_tools=hidden_tools, + hide_display=hide_display, + hide_histogram=hide_histogram, + hide_stats=hide_stats, + hide_navigation=hide_navigation, + hide_view=hide_view, + hide_export=hide_export, + hide_roi=hide_roi, + hide_profile=hide_profile, + hide_all=hide_all, + ncols=ncols, size=size, smooth=smooth, zoom=zoom, + zoom_row=zoom_row, zoom_col=zoom_col, + link_zoom=link_zoom, link_pan=link_pan, link_contrast=link_contrast, + diff_mode=diff_mode, view_box=view_box, + display_bin=display_bin, state=state, _t0=_t0) + + def _init_sync(self, *, data, labels, title, cmap, pixel_size, + scale_bar_visible, show_fft, fft_window, + show_controls, show_stats, log_scale, auto_contrast, + vmin, vmax, disabled_tools, + disable_display, disable_histogram, disable_stats, + disable_navigation, disable_view, disable_export, + disable_roi, disable_profile, disable_all, + hidden_tools, hide_display, hide_histogram, hide_stats, + hide_navigation, hide_view, hide_export, hide_roi, + hide_profile, hide_all, + ncols, size, smooth, zoom, zoom_row, zoom_col, + link_zoom, link_pan, link_contrast, diff_mode, view_box, + display_bin, state, _t0): + import time as _time + self.widget_version = resolve_widget_version() + self._display_data = None # initialized after data setup + self._display_bin = 1 + + # Check if data is a Dataset2d and extract metadata + if hasattr(data, "array") and hasattr(data, "name") and hasattr(data, "sampling"): + if not title and data.name: + title = data.name + if pixel_size == 0.0 and hasattr(data, "units"): + units = list(data.units) + sampling_val = float(data.sampling[-1]) + if units[-1] in ("nm",): + pixel_size = sampling_val * 10 # nm → Å + elif units[-1] in ("Å", "angstrom", "A"): + pixel_size = sampling_val + data = data.array + + # Convert input to NumPy (handles NumPy, CuPy, PyTorch) + if isinstance(data, list): + images = [to_numpy(d) for d in data] + + # Check if all images have the same shape + shapes = [img.shape for img in images] + if len(set(shapes)) > 1: + # Different sizes - resize all to the largest + max_h = max(s[0] for s in shapes) + max_w = max(s[1] for s in shapes) + images = [_resize_image(img, max_h, max_w) for img in images] + + data = np.stack(images) + else: + data = to_numpy(data) + + # Ensure 3D shape (N, H, W) + if data.ndim == 2: + data = data[np.newaxis, ...] + + # Avoid redundant copy: np.asarray is a no-op when already float32 + contiguous + if data.dtype == np.float32: + self._data = np.array(data, dtype=np.float32, copy=True) + else: + self._data = np.asarray(data, dtype=np.float32) + # Store originals for rotation reset — views into _data (no copy). + # Only materialized as independent copies when a rotation is applied. + self._data_original = [self._data[i] for i in range(self._data.shape[0])] + self._originals_are_views = True + self.n_images = int(data.shape[0]) + self.height = int(data.shape[1]) + self.width = int(data.shape[2]) + self.image_rotations = [0] * self.n_images + + # Labels + if labels is None: + self.labels = [f"Image {i+1}" for i in range(self.n_images)] + else: + self.labels = list(labels) + + # Options + self.title = title + self.cmap = cmap + self.pixel_size = pixel_size + self.scale_bar_visible = scale_bar_visible + self.size = size + self.smooth = smooth + # view_box sugar: sets zoom + zoom_row/col to center on box + if view_box is not None: + r0, r1, c0, c1 = [float(v) for v in view_box] + box_h = max(1.0, r1 - r0) + box_w = max(1.0, c1 - c0) + zoom = float(min(self.height / box_h, self.width / box_w)) + zoom_row = (r0 + r1) / 2 + zoom_col = (c0 + c1) / 2 + self.initial_zoom = zoom + self.zoom_row = zoom_row + self.zoom_col = zoom_col + self.link_zoom = link_zoom + self.link_pan = link_pan + self.link_contrast = link_contrast + self.diff_mode = diff_mode if self.n_images >= 2 else False + if show_fft and self.height * self.width > 2048 * 2048: + warnings.warn( + f"FFT on {self.height}×{self.width} image ({self.height * self.width / 1e6:.1f}M pixels) " + f"may be slow. Consider using ROI FFT for a sub-region.", + stacklevel=2, + ) + self.show_fft = show_fft + self.fft_window = fft_window + self.show_controls = show_controls + self.show_stats = show_stats + self.log_scale = log_scale + self.auto_contrast = auto_contrast + # Accept scalar OR list for vmin/vmax. List → per-image (vmins/vmaxs). + if isinstance(vmin, (list, tuple)) or isinstance(vmax, (list, tuple)): + n = self.n_images + def _expand(v): + if v is None: return [None] * n + if isinstance(v, (list, tuple)): + if len(v) != n: + raise ValueError(f"vmin/vmax list length {len(v)} != n_images {n}") + return [None if x is None else float(x) for x in v] + return [float(v)] * n + self.vmins = _expand(vmin) + self.vmaxs = _expand(vmax) + self.vmin = None + self.vmax = None + else: + self.vmin = vmin + self.vmax = vmax + self.disabled_tools = self._build_disabled_tools( + disabled_tools=disabled_tools, + disable_display=disable_display, + disable_histogram=disable_histogram, + disable_stats=disable_stats, + disable_navigation=disable_navigation, + disable_view=disable_view, + disable_export=disable_export, + disable_roi=disable_roi, + disable_profile=disable_profile, + disable_all=disable_all, + ) + self.hidden_tools = self._build_hidden_tools( + hidden_tools=hidden_tools, + hide_display=hide_display, + hide_histogram=hide_histogram, + hide_stats=hide_stats, + hide_navigation=hide_navigation, + hide_view=hide_view, + hide_export=hide_export, + hide_roi=hide_roi, + hide_profile=hide_profile, + hide_all=hide_all, + ) + self.ncols = ncols + + # Auto-bin for display: keep full-res in _data, send binned to JS. + # GPU memory budget: ~2 GB for display buffers (128 MB per image at 4K). + # At 4K: max ~16 full-res. Beyond that, auto-downsample. + if display_bin == "auto": + # Each 4K image needs ~192 MB GPU buffers (float32 + RGBA + read) + # Tested: 12×4K (2.3 GB) works, 24×4K (4.6 GB) OOMs + # Budget: 2.5 GB allows 12×4K full-res, bins above that + gpu_budget_mb = self._GPU_DISPLAY_BUDGET_MB + per_image_mb = (self.height * self.width * 4 * 3) / (1024 * 1024) # 3 buffers + total_mb = self.n_images * per_image_mb + if total_mb > gpu_budget_mb: + # Find minimum bin factor to fit + for bf in [2, 4, 8]: + binned_mb = self.n_images * per_image_mb / (bf * bf) + if binned_mb <= gpu_budget_mb: + self._display_bin = bf + break + else: + self._display_bin = 8 + elif isinstance(display_bin, int) and display_bin > 1: + self._display_bin = display_bin + + if self._display_bin > 1: + from quantem.widget.array_utils import bin2d + orig_h, orig_w = self._data.shape[1], self._data.shape[2] + self._display_data = bin2d(self._data, factor=self._display_bin, mode="mean") + self.height = int(self._display_data.shape[1]) + self.width = int(self._display_data.shape[2]) + if pixel_size > 0: + self.pixel_size = pixel_size * self._display_bin + self._display_bin_factor = self._display_bin + print(f" Display bin {self._display_bin}×: {orig_h}×{orig_w} → {self.height}×{self.width} ({self._display_data.nbytes // 1024 // 1024} MB)") + else: + self._display_data = self._data + self._display_bin_factor = 1 + + # Compute initial stats (from full-res data) + self._compute_all_stats() + + # Send display data to JS (possibly binned) + self._update_all_frames() + + self.selected_idx = 0 + + if state is not None: + if isinstance(state, (str, pathlib.Path)): + state = unwrap_state_payload( + json.loads(pathlib.Path(state).read_text()), + require_envelope=True, + ) + else: + state = unwrap_state_payload(state) + self.load_state_dict(state) + + # Stash wall-clock start on the instance; the observer below prints the + # TRUE end-to-end time after JS signals first paint. The Python-only + # __init__ number is misleading for widget UX — a widget is not "done" + # until the browser has painted its first frame. + self._init_t0 = _t0 + self._init_py_elapsed_ms = (_time.perf_counter() - _t0) * 1000 + self.observe(self._on_first_render, names=["_js_rendered"]) + + def _on_first_render(self, change): + import time as _time + if not change.get("new"): + return + total_ms = (_time.perf_counter() - self._init_t0) * 1000 + py_ms = self._init_py_elapsed_ms + shape = (f"{self.n_images}×{self.height}×{self.width}" + if self.n_images > 1 else f"{self.height}×{self.width}") + mem = self._data.nbytes + mem_str = f"{mem / (1 << 20):.0f} MB" if mem >= 1 << 20 else f"{mem / (1 << 10):.0f} KB" + # Expose as attributes so tests and notebooks can assert on them. + # These are the ground truth for "did JS actually paint" — if they're + # None, the JS side never signaled first render. + self.render_total_ms = int(total_ms) + self.render_python_build_ms = int(py_ms) + self.render_wire_js_ms = int(total_ms - py_ms) + print( + f"Show2D: {shape} {mem_str} — " + f"rendered in {total_ms:.0f} ms (Python build {py_ms:.0f} ms, " + f"wire+JS {total_ms - py_ms:.0f} ms)", + flush=True, + ) + # Detach observer: one-shot, we only care about the first paint. + try: + self.unobserve(self._on_first_render, names=["_js_rendered"]) + except Exception: + pass + + def set_image(self, data, labels=None): + """Replace the displayed image(s). Preserves all display settings.""" + if hasattr(data, "array") and hasattr(data, "name") and hasattr(data, "sampling"): + data = data.array + if isinstance(data, list): + images = [to_numpy(d) for d in data] + shapes = [img.shape for img in images] + if len(set(shapes)) > 1: + max_h = max(s[0] for s in shapes) + max_w = max(s[1] for s in shapes) + images = [_resize_image(img, max_h, max_w) for img in images] + data = np.stack(images) + else: + data = to_numpy(data) + if data.ndim == 2: + data = data[np.newaxis, ...] + if data.dtype == np.float32: + self._data = np.array(data, dtype=np.float32, copy=True) + else: + self._data = np.asarray(data, dtype=np.float32) + self._data_original = [self._data[i] for i in range(self._data.shape[0])] + self._originals_are_views = True + self.n_images = int(data.shape[0]) + + # Auto-bin for display (reuse existing _display_bin or recompute) + gpu_budget_mb = 2500 + per_image_mb = (data.shape[1] * data.shape[2] * 4 * 3) / (1024 * 1024) + total_mb = self.n_images * per_image_mb + self._display_bin = 1 + if total_mb > gpu_budget_mb: + for bf in [2, 4, 8]: + if total_mb / (bf * bf) <= gpu_budget_mb: + self._display_bin = bf + break + else: + self._display_bin = 8 + + if self._display_bin > 1: + from quantem.widget.array_utils import bin2d + self._display_data = bin2d(self._data, factor=self._display_bin, mode="mean") + self.height = int(self._display_data.shape[1]) + self.width = int(self._display_data.shape[2]) + self._display_bin_factor = self._display_bin + print(f" Display bin {self._display_bin}×: {data.shape[1]}×{data.shape[2]} → {self.height}×{self.width}") + else: + self._display_data = self._data + self.height = int(data.shape[1]) + self.width = int(data.shape[2]) + self._display_bin_factor = 1 + + self.image_rotations = [0] * self.n_images + if labels is not None: + self.labels = list(labels) + else: + self.labels = [f"Image {i+1}" for i in range(self.n_images)] + self.selected_idx = 0 + self._compute_all_stats() + self._update_all_frames() + + def __repr__(self) -> str: + if self.n_images > 1: + shape = f"{self.n_images}×{self.height}×{self.width}" + return f"Show2D({shape}, idx={self.selected_idx}, cmap={self.cmap})" + return f"Show2D({self.height}×{self.width}, cmap={self.cmap})" + + def _repr_mimebundle_(self, **kwargs): + """Return widget view + (optionally) static PNG fallback. + + Live Jupyter renders the interactive widget; the PNG fallback is only + consumed by nbsphinx / GitHub / nbviewer when the widget view cannot be + rendered. Building the fallback runs matplotlib over every gallery image + (~1.7 s for a 30×512² stack) and that cost pays off only in static builds. + Gate it behind ``QUANTEM_WIDGET_STATIC_FALLBACK=1`` so interactive sessions + return immediately. + """ + bundle = super()._repr_mimebundle_(**kwargs) + if not os.environ.get("QUANTEM_WIDGET_STATIC_FALLBACK"): + return bundle + data_dict = bundle[0] if isinstance(bundle, tuple) else bundle + n = self.n_images + ncols = min(self.ncols, n) + nrows = math.ceil(n / ncols) + cell = 4 + fig, axes = plt.subplots( + nrows, ncols, + figsize=(cell * ncols, cell * nrows), + squeeze=False, + ) + max_preview = 256 + for i in range(nrows * ncols): + r, c = divmod(i, ncols) + ax = axes[r][c] + if i < n: + img = self._data[i] + h, w = img.shape + if h > max_preview or w > max_preview: + step = max(h // max_preview, w // max_preview, 1) + img = img[::step, ::step] + ax.imshow(img, cmap=self.cmap, origin="upper") + ax.set_title(self.labels[i], fontsize=10) + ax.axis("off") + if self.title: + fig.suptitle(self.title, fontsize=12) + fig.tight_layout() + buf = io.BytesIO() + fig.savefig(buf, format="png", dpi=120, bbox_inches="tight") + plt.close(fig) + data_dict["image/png"] = base64.b64encode(buf.getvalue()).decode("ascii") + if isinstance(bundle, tuple): + return (data_dict, bundle[1]) + return data_dict + + def _normalize_frame(self, frame: np.ndarray) -> np.ndarray: + if self.log_scale: + frame = np.log1p(np.maximum(frame, 0)) + if self.vmin is not None and self.vmax is not None: + vmin = float(self.vmin) + vmax = float(self.vmax) + if self.log_scale: + vmin = float(np.log1p(max(vmin, 0))) + vmax = float(np.log1p(max(vmax, 0))) + elif self.auto_contrast: + vmin = float(np.percentile(frame, 2)) + vmax = float(np.percentile(frame, 98)) + else: + vmin = float(frame.min()) + vmax = float(frame.max()) + if vmax > vmin: + normalized = np.clip((frame - vmin) / (vmax - vmin) * 255, 0, 255) + return normalized.astype(np.uint8) + return np.zeros(frame.shape, dtype=np.uint8) + + def save_image( + self, + path: str | pathlib.Path, + *, + idx: int | None = None, + format: str | None = None, + dpi: int = 150, + title: bool | str = False, + colorbar: bool = False, + scalebar: bool = False, + ) -> pathlib.Path: + """Save current image as PNG, PDF, or TIFF. + + When ``title``, ``colorbar``, or ``scalebar`` are enabled, the output + is a publication-quality figure rendered via matplotlib. Otherwise a + raw colormapped image is saved directly (faster, exact pixel output). + + Parameters + ---------- + path : str or pathlib.Path + Output file path. + idx : int, optional + Image index in gallery mode. Defaults to current selected_idx. + format : str, optional + 'png', 'pdf', or 'tiff'. If omitted, inferred from file extension. + dpi : int, default 150 + Output DPI. + title : bool or str, default False + ``True`` uses the widget title, a string sets a custom title. + colorbar : bool, default False + Include a colorbar showing the intensity mapping. + scalebar : bool, default False + Include a scale bar (requires ``pixel_size > 0``). + + Returns + ------- + pathlib.Path + The written file path. + """ + from matplotlib import colormaps + from PIL import Image + + path = pathlib.Path(path) + fmt = (format or path.suffix.lstrip(".").lower() or "png").lower() + if fmt not in ("png", "pdf", "tiff", "tif"): + raise ValueError(f"Unsupported format: {fmt!r}. Use 'png', 'pdf', or 'tiff'.") + + i = idx if idx is not None else self.selected_idx + if i < 0 or i >= self.n_images: + raise IndexError(f"Image index {i} out of range [0, {self.n_images})") + + frame = self._data[i] + normalized = self._normalize_frame(frame) + cmap_fn = colormaps.get_cmap(self.cmap) + path.parent.mkdir(parents=True, exist_ok=True) + + use_figure = title or colorbar or scalebar + if not use_figure: + rgba = (cmap_fn(normalized / 255.0) * 255).astype(np.uint8) + img = Image.fromarray(rgba) + if fmt == "pdf": + Image.init() + img = img.convert("RGB") + img.save(str(path), dpi=(dpi, dpi)) + return path + + # Publication-quality figure via matplotlib + h, w = frame.shape + aspect = h / w + fig_w = 6 + fig, ax = plt.subplots(figsize=(fig_w, fig_w * aspect)) + im = ax.imshow(normalized, cmap=cmap_fn, vmin=0, vmax=255, origin="upper") + ax.axis("off") + + if title: + label = title if isinstance(title, str) else self.title + if label: + ax.set_title(label, fontsize=14, fontweight="bold", pad=8) + + if colorbar: + # Map 0–255 back to data-space values for tick labels + if self.log_scale: + frame_proc = np.log1p(np.maximum(frame, 0)) + else: + frame_proc = frame + if self.vmin is not None and self.vmax is not None: + dmin = float(self.vmin) + dmax = float(self.vmax) + if self.log_scale: + dmin = float(np.log1p(max(dmin, 0))) + dmax = float(np.log1p(max(dmax, 0))) + elif self.auto_contrast: + dmin = float(np.percentile(frame_proc, 2)) + dmax = float(np.percentile(frame_proc, 98)) + else: + dmin = float(frame_proc.min()) + dmax = float(frame_proc.max()) + cb = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04) + n_ticks = 5 + tick_positions = np.linspace(0, 255, n_ticks) + tick_labels = [f"{dmin + (dmax - dmin) * t / 255:.4g}" for t in tick_positions] + cb.set_ticks(tick_positions) + cb.set_ticklabels(tick_labels) + + if scalebar and self.pixel_size > 0: + from matplotlib.patches import FancyBboxPatch + # Compute a nice scale bar length + target_frac = 0.2 # ~20% of image width + raw_length_px = target_frac * w + raw_length_phys = raw_length_px * self.pixel_size # in Å + nice = _round_to_nice(raw_length_phys) + bar_px = nice / self.pixel_size + if nice >= 10: + label_text = f"{nice / 10:.4g} nm" + else: + label_text = f"{nice:.4g} Å" + margin = 0.03 + bar_y = h * (1 - margin) - 2 + bar_x = w * (1 - margin) - bar_px + ax.plot([bar_x, bar_x + bar_px], [bar_y, bar_y], + color="white", linewidth=3, solid_capstyle="butt") + ax.plot([bar_x, bar_x + bar_px], [bar_y, bar_y], + color="black", linewidth=1, solid_capstyle="butt") + ax.text(bar_x + bar_px / 2, bar_y - h * 0.02, label_text, + color="white", fontsize=10, fontweight="bold", + ha="center", va="bottom", + path_effects=[ + matplotlib.patheffects.withStroke(linewidth=2, foreground="black") + ]) + + fig.savefig(str(path), dpi=dpi, bbox_inches="tight", + facecolor="white", pad_inches=0.1) + plt.close(fig) + return path + + def state_dict(self): + return { + "title": self.title, + "cmap": self.cmap, + "log_scale": self.log_scale, + "auto_contrast": self.auto_contrast, + "vmin": self.vmin, + "vmax": self.vmax, + "show_stats": self.show_stats, + "show_fft": self.show_fft, + "fft_window": self.fft_window, + "show_controls": self.show_controls, + "disabled_tools": self.disabled_tools, + "hidden_tools": self.hidden_tools, + "pixel_size": self.pixel_size, + "scale_bar_visible": self.scale_bar_visible, + "size": self.size, + "smooth": self.smooth, + "initial_zoom": self.initial_zoom, + "vmins": self.vmins, + "vmaxs": self.vmaxs, + "link_zoom": self.link_zoom, + "link_pan": self.link_pan, + "link_contrast": self.link_contrast, + "zoom_row": self.zoom_row, + "zoom_col": self.zoom_col, + "diff_mode": self.diff_mode, + "ncols": self.ncols, + "selected_idx": self.selected_idx, + "roi_active": self.roi_active, + "roi_list": self.roi_list, + "roi_selected_idx": self.roi_selected_idx, + "profile_line": self.profile_line, + "image_rotations": list(self.image_rotations), + "display_bin": self._display_bin, + } + + def save(self, path: str): + save_state_file(path, "Show2D", self.state_dict()) + + def load_state_dict(self, state): + for key, val in state.items(): + # Silent migrations for renamed keys in older saved state files. + if key == "pixel_size_angstrom": + key = "pixel_size" + elif key == "canvas_size": + key = "size" + if key == "display_bin": + self._display_bin = val + continue + if hasattr(self, key): + setattr(self, key, val) + + def summary(self): + lines = [self.title or "Show2D", "═" * 32] + if self.n_images > 1: + lines.append(f"Image: {self.n_images}×{self.height}×{self.width} ({self.ncols} cols)") + else: + lines.append(f"Image: {self.height}×{self.width}") + if self.pixel_size > 0: + ps = self.pixel_size + if ps >= 10: + lines[-1] += f" ({ps / 10:.2f} nm/px)" + else: + lines[-1] += f" ({ps:.2f} Å/px)" + if hasattr(self, "_data") and self._data is not None: + arr = self._data + lines.append(f"Data: min={float(arr.min()):.4g} max={float(arr.max()):.4g} mean={float(arr.mean()):.4g}") + cmap = self.cmap + scale = "log" if self.log_scale else "linear" + if self.vmin is not None and self.vmax is not None: + contrast = f"vmin={self.vmin:.4g}, vmax={self.vmax:.4g}" + elif self.auto_contrast: + contrast = "auto contrast" + else: + contrast = "manual contrast" + display = f"{cmap} | {contrast} | {scale}" + if self.show_fft: + display += " | FFT" + if not self.fft_window: + display += " (no window)" + lines.append(f"Display: {display}") + if self.disabled_tools: + lines.append(f"Locked: {', '.join(self.disabled_tools)}") + if self.hidden_tools: + lines.append(f"Hidden: {', '.join(self.hidden_tools)}") + if self.roi_active and self.roi_list: + lines.append(f"ROI: {len(self.roi_list)} region(s)") + if self.profile_line: + p0, p1 = self.profile_line[0], self.profile_line[1] + lines.append(f"Profile: ({p0['row']:.0f}, {p0['col']:.0f}) → ({p1['row']:.0f}, {p1['col']:.0f})") + non_zero = [(i, r * 90) for i, r in enumerate(self.image_rotations) if r % 4 != 0] + if non_zero: + parts = [f"#{i}={deg}°" for i, deg in non_zero] + lines.append(f"Rotated: {', '.join(parts)}") + rt = getattr(self, "render_total_ms", None) + if rt is not None: + pb = getattr(self, "render_python_build_ms", 0) + wj = getattr(self, "render_wire_js_ms", 0) + lines.append(f"Rendered: {rt} ms total (Python build {pb} ms, wire+JS {wj} ms)") + else: + lines.append("Rendered: (pending first browser paint)") + print("\n".join(lines)) + + def _compute_all_stats(self): + """Compute statistics for all images (vectorized over all frames).""" + # Vectorized reduction over (H, W) is faster than per-image loops + # for large galleries (e.g. 12×4096×4096: 164ms vs 191ms). + axes = (1, 2) if self._data.ndim == 3 else None + self.stats_mean = np.mean(self._data, axis=axes).ravel().tolist() + self.stats_min = np.min(self._data, axis=axes).ravel().tolist() + self.stats_max = np.max(self._data, axis=axes).ravel().tolist() + self.stats_std = np.std(self._data, axis=axes).ravel().tolist() + + def _update_all_frames(self): + """Send display data to JS (possibly binned for large galleries).""" + data = self._display_data if self._display_data is not None else self._data + self.frame_bytes = data.tobytes() + + def _apply_rotations(self): + # Materialize originals as independent copies only when a non-zero + # rotation exists (they start as views into _data to avoid 800MB copy at init) + has_rotation = any( + (self.image_rotations[i] if i < len(self.image_rotations) else 0) % 4 != 0 + for i in range(len(self._data_original)) + ) + # No-rotation fast path: skip 30+ MB of redundant tobytes + stats recomputation + # on every widget init. The observer fires once when image_rotations = [0]*n + # is assigned in __init__; without this guard that triggered a full frame + # rebuild + stats recompute for a no-op. + if not has_rotation and self._originals_are_views: + return + if self._originals_are_views and has_rotation: + self._data_original = [img.copy() for img in self._data_original] + self._originals_are_views = False + rotated = [] + for i, orig in enumerate(self._data_original): + k = self.image_rotations[i] if i < len(self.image_rotations) else 0 + k = k % 4 + if k == 0: + rotated.append(orig) + else: + rotated.append(np.rot90(orig, k=k)) + # If shapes differ after rotation, center-pad all to max dims + shapes = [img.shape for img in rotated] + if len(set(shapes)) > 1: + max_h = max(s[0] for s in shapes) + max_w = max(s[1] for s in shapes) + padded = [] + for img in rotated: + h, w = img.shape + pad_top = (max_h - h) // 2 + pad_bot = max_h - h - pad_top + pad_left = (max_w - w) // 2 + pad_right = max_w - w - pad_left + padded.append(np.pad(img, ((pad_top, pad_bot), (pad_left, pad_right)), mode="constant", constant_values=0)) + rotated = padded + self._data = np.stack(rotated).astype(np.float32) + # Recompute display data if binning is active + if self._display_bin > 1: + from quantem.widget.array_utils import bin2d + self._display_data = bin2d(self._data, factor=self._display_bin, mode="mean") + else: + self._display_data = self._data + display = self._display_data if self._display_data is not None else self._data + self.height = int(display.shape[1]) + self.width = int(display.shape[2]) + self._compute_all_stats() + self._update_all_frames() + + @traitlets.observe("image_rotations") + def _on_image_rotations_changed(self, change): + if hasattr(self, "_data_original"): + self._apply_rotations() + + def rotate(self, idx: int, angle: int) -> Self: + """Rotate image ``idx`` by ``angle`` degrees (CCW-positive, matches np.rot90). + + Rotation convention follows ``np.rot90``:: + + angle | image_rotations | np.rot90 k | direction + ------+-----------------+------------+---------- + 90 | 1 | 1 | 90° CCW + 180 | 2 | 2 | 180° + -90 | 3 | 3 | 90° CW + 360 | 0 | 0 | identity + + Parameters + ---------- + idx : int + Image index in the gallery (0-based). + angle : int + Rotation angle in degrees (must be a multiple of 90). + Positive = counter-clockwise, negative = clockwise. + + Returns + ------- + Self + """ + if angle % 90 != 0: + raise ValueError(f"Rotation angle must be a multiple of 90°, got {angle}") + if idx < 0 or idx >= self.n_images: + raise IndexError(f"Image index {idx} out of range [0, {self.n_images})") + k = (angle // 90) % 4 + rots = list(self.image_rotations) + while len(rots) < self.n_images: + rots.append(0) + rots[idx] = (rots[idx] + k) % 4 + self.image_rotations = rots + return self + + def _sample_profile(self, row0, col0, row1, col1): + img = self._data[self.selected_idx] + h, w = img.shape + dc, dr = col1 - col0, row1 - row0 + length = (dc**2 + dr**2) ** 0.5 + n = max(2, int(np.ceil(length))) + t = np.linspace(0, 1, n) + cs = col0 + t * dc + rs = row0 + t * dr + ci = np.floor(cs).astype(int) + ri = np.floor(rs).astype(int) + cf = cs - ci + rf = rs - ri + c0c = np.clip(ci, 0, w - 1) + c1c = np.clip(ci + 1, 0, w - 1) + r0c = np.clip(ri, 0, h - 1) + r1c = np.clip(ri + 1, 0, h - 1) + return (img[r0c, c0c] * (1 - cf) * (1 - rf) + + img[r0c, c1c] * cf * (1 - rf) + + img[r1c, c0c] * (1 - cf) * rf + + img[r1c, c1c] * cf * rf).astype(np.float32) + + def set_profile(self, start: tuple, end: tuple): + """Set a line profile between two points (image pixel coordinates). + + Parameters + ---------- + start : tuple of (row, col) + Start point in pixel coordinates. + end : tuple of (row, col) + End point in pixel coordinates. + """ + row0, col0 = start + row1, col1 = end + self.profile_line = [ + {"row": float(row0), "col": float(col0)}, + {"row": float(row1), "col": float(col1)}, + ] + + def clear_profile(self): + """Clear the current line profile.""" + self.profile_line = [] + + def _upsert_selected_roi(self, updates: dict): + rois = list(self.roi_list) + color_cycle = ["#4fc3f7", "#81c784", "#ffb74d", "#ce93d8", "#ef5350", "#ffd54f", "#90a4ae", "#a1887f"] + defaults = { + "shape": "square", + "row": int(self.height // 2), + "col": int(self.width // 2), + "radius": 10, + "radius_inner": 5, + "width": 20, + "height": 20, + "line_width": 2, + "highlight": False, + "visible": True, + "locked": False, + } + if self.roi_selected_idx >= 0 and self.roi_selected_idx < len(rois): + current = {**defaults, **rois[self.roi_selected_idx]} + if not current.get("color"): + current["color"] = color_cycle[self.roi_selected_idx % len(color_cycle)] + rois[self.roi_selected_idx] = {**current, **updates} + else: + rois.append({**defaults, "color": color_cycle[len(rois) % len(color_cycle)], **updates}) + self.roi_selected_idx = len(rois) - 1 + self.roi_list = rois + self.roi_active = True + + def add_roi(self, row: int | None = None, col: int | None = None, shape: str = "square") -> Self: + with self.hold_sync(): + self.roi_selected_idx = -1 + self._upsert_selected_roi({ + "shape": shape, + "row": int(self.height // 2 if row is None else row), + "col": int(self.width // 2 if col is None else col), + }) + return self + + def clear_rois(self) -> Self: + with self.hold_sync(): + self.roi_list = [] + self.roi_selected_idx = -1 + self.roi_active = False + return self + + def delete_selected_roi(self) -> Self: + idx = int(self.roi_selected_idx) + if idx < 0 or idx >= len(self.roi_list): + return self + with self.hold_sync(): + rois = [roi for i, roi in enumerate(self.roi_list) if i != idx] + self.roi_list = rois + self.roi_selected_idx = min(idx, len(rois) - 1) if rois else -1 + if not rois: + self.roi_active = False + return self + + def set_roi(self, row: int, col: int, radius: int = 10) -> Self: + with self.hold_sync(): + self._upsert_selected_roi({"shape": "circle", "row": int(row), "col": int(col), "radius": int(radius)}) + return self + + def roi_circle(self, radius: int = 10) -> Self: + with self.hold_sync(): + self._upsert_selected_roi({"shape": "circle", "radius": int(radius)}) + return self + + def roi_square(self, half_size: int = 10) -> Self: + with self.hold_sync(): + self._upsert_selected_roi({"shape": "square", "radius": int(half_size)}) + return self + + def roi_rectangle(self, width: int = 20, height: int = 10) -> Self: + with self.hold_sync(): + self._upsert_selected_roi({"shape": "rectangle", "width": int(width), "height": int(height)}) + return self + + def roi_annular(self, inner: int = 5, outer: int = 10) -> Self: + with self.hold_sync(): + self._upsert_selected_roi({"shape": "annular", "radius_inner": int(inner), "radius": int(outer)}) + return self + + @property + def profile(self): + """Get profile line endpoints as [(row0, col0), (row1, col1)] or []. + + Returns + ------- + list of tuple + Line endpoints in pixel coordinates, or empty list if no profile. + """ + return [(p["row"], p["col"]) for p in self.profile_line] + + @property + def profile_values(self): + """Get intensity values along the profile line as a numpy array. + + Returns + ------- + np.ndarray or None + Float32 array of sampled intensities, or None if no profile. + """ + if len(self.profile_line) < 2: + return None + p0, p1 = self.profile_line + return self._sample_profile(p0["row"], p0["col"], p1["row"], p1["col"]) + + @property + def profile_distance(self): + """Get total distance of the profile line in calibrated units. + + Returns + ------- + float or None + Distance in angstroms (if pixel_size > 0) or pixels. + None if no profile line is set. + """ + if len(self.profile_line) < 2: + return None + p0, p1 = self.profile_line + dc = p1["col"] - p0["col"] + dr = p1["row"] - p0["row"] + dist_px = (dc**2 + dr**2) ** 0.5 + if self.pixel_size > 0: + return dist_px * self.pixel_size + return dist_px + + +bind_tool_runtime_api(Show2D, "Show2D") diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py new file mode 100644 index 00000000..1fe94273 --- /dev/null +++ b/widget/src/quantem/widget/show4dstem.py @@ -0,0 +1,4337 @@ +""" +show4dstem: Fast interactive 4D-STEM viewer widget. + +Apple MPS GPU limit: PyTorch's MPS backend (Apple Silicon) has a hard limit +of ~2.1 billion elements (INT_MAX = 2^31 - 1) per tensor. Datasets exceeding +this automatically fall back to CPU, which is still fast on Apple Silicon +thanks to unified memory (CPU and GPU share the same RAM). + +CUDA GPUs do not have this limit. + +Common 4D-STEM sizes (float32): + + Scan Detector Elements Size MPS? + 128×128 128×128 268M 1.0 GB yes + 128×128 256×256 1,074M 4.0 GB yes + 256×256 128×128 1,074M 4.0 GB yes + 256×256 192×192 2,416M 9.0 GB no (auto CPU, still fast) + 256×256 256×256 4,295M 16.0 GB no (auto CPU, still fast) + 512×512 256×256 17,180M 64.0 GB no (auto CPU) + +To reduce data size, bin k-space at the dataset level before viewing: + + dataset = dataset.bin(2, axes=(2, 3)) # 2x2 k-space binning + widget = Show4DSTEM(dataset) +""" + +import hashlib +import json +import math +import pathlib +import time +from datetime import datetime, timezone +from typing import Any, Self +from uuid import uuid4 + +import anywidget +import numpy as np +import torch +import traitlets + +from quantem.core.config import validate_device +from quantem.widget.array_utils import to_numpy +from quantem.widget.json_state import ( + build_json_header, + resolve_widget_version, + save_state_file, + unwrap_state_payload, +) +from quantem.widget.tool_parity import ( + bind_tool_runtime_api, + build_tool_groups, + normalize_tool_groups, +) + + +def _format_memory(nbytes: int) -> str: + if nbytes >= 1 << 30: return f"{nbytes / (1 << 30):.1f} GB" + if nbytes >= 1 << 20: return f"{nbytes / (1 << 20):.0f} MB" + if nbytes >= 1 << 10: return f"{nbytes / (1 << 10):.0f} KB" + return f"{nbytes} B" + + +# ============================================================================ +# Constants +# ============================================================================ +DEFAULT_BF_RATIO = 0.125 # BF disk radius as fraction of detector size (1/8) +SPARSE_MASK_THRESHOLD = 0.2 # Use sparse indexing below this mask coverage +MIN_LOG_VALUE = 1e-10 # Minimum value for log scale to avoid log(0) +DEFAULT_VI_ROI_RATIO = 0.15 # Default VI ROI size as fraction of scan dimension + +class Show4DSTEM(anywidget.AnyWidget): + """ + Fast interactive 4D-STEM viewer with advanced features. + + Optimized for speed with binary transfer and pre-normalization. + Works with NumPy and PyTorch arrays. + + Parameters + ---------- + data : Dataset4dstem or array_like + Dataset4dstem object (calibration auto-extracted), 4D array + of shape (scan_rows, scan_cols, det_rows, det_cols), or 5D array + of shape (n_frames, scan_rows, scan_cols, det_rows, det_cols) + for time-series or tilt-series data. + scan_shape : tuple, optional + If data is flattened (N, det_rows, det_cols), provide scan dimensions. + pixel_size : float, optional + Pixel size in Å (real-space). Used for scale bar. + Auto-extracted from Dataset4dstem if not provided. + k_pixel_size : float, optional + Detector pixel size in mrad (k-space). Used for scale bar. + Auto-extracted from Dataset4dstem if not provided. + center : tuple[float, float], optional + (center_row, center_col) of the diffraction pattern in pixels. + If not provided, defaults to detector center. + bf_radius : float, optional + Bright field disk radius in pixels. If not provided, estimated as 1/8 of detector size. + precompute_virtual_images : bool, default True + Precompute BF/ABF/LAADF/HAADF virtual images for preset switching. + frame_dim_label : str, optional + Label for the frame dimension when 5D data is provided. + Defaults to "Frame". Common values: "Tilt", "Time", "Focus". + disabled_tools : list of str, optional + Tool groups to lock while still showing controls. Supported: + ``"display"``, ``"histogram"``, ``"stats"``, ``"navigation"``, + ``"playback"``, ``"view"``, ``"export"``, ``"roi"``, + ``"profile"``, ``"fft"``, ``"virtual"``, ``"frame"``, ``"all"``. + disable_* : bool, optional + Convenience flags mirroring ``disabled_tools`` for each tool group, + plus ``disable_all``. + hidden_tools : list of str, optional + Tool groups to hide from the UI. Uses the same keys as + ``disabled_tools``. + hide_* : bool, optional + Convenience flags mirroring ``disable_*`` for ``hidden_tools``. + + Examples + -------- + >>> # From Dataset4dstem (calibration auto-extracted) + >>> from quantem.core.io.file_readers import read_emdfile_to_4dstem + >>> dataset = read_emdfile_to_4dstem("data.h5") + >>> Show4DSTEM(dataset) + + >>> # From raw array with manual calibration + >>> import numpy as np + >>> data = np.random.rand(64, 64, 128, 128) + >>> Show4DSTEM(data, pixel_size=2.39, k_pixel_size=0.46) + + >>> # With raster animation + >>> widget = Show4DSTEM(dataset) + >>> widget.raster(step=2, interval_ms=50) + + >>> # 5D time-series or tilt-series data + >>> data_5d = np.random.rand(20, 64, 64, 128, 128) # 20 frames + >>> Show4DSTEM(data_5d, frame_dim_label="Tilt") + """ + + _esm = pathlib.Path(__file__).parent / "static" / "show4dstem.js" + _css = pathlib.Path(__file__).parent / "static" / "show4dstem.css" + + # Position in scan space + widget_version = traitlets.Unicode("unknown").tag(sync=True) + title = traitlets.Unicode("").tag(sync=True) + pos_row = traitlets.Int(0).tag(sync=True) + pos_col = traitlets.Int(0).tag(sync=True) + + # Shape of scan space (for slider bounds) + shape_rows = traitlets.Int(1).tag(sync=True) + shape_cols = traitlets.Int(1).tag(sync=True) + + # Detector shape for frontend + det_rows = traitlets.Int(1).tag(sync=True) + det_cols = traitlets.Int(1).tag(sync=True) + + # Raw float32 frame as bytes (JS handles scale/colormap for real-time interactivity) + frame_bytes = traitlets.Bytes(b"").tag(sync=True) + + # Global min/max for DP normalization (computed once from sampled frames) + dp_global_min = traitlets.Float(0.0).tag(sync=True) + dp_global_max = traitlets.Float(1.0).tag(sync=True) + + # ========================================================================= + # Detector Calibration (for presets and scale bar) + # ========================================================================= + center_col = traitlets.Float(0.0).tag(sync=True) # Detector center col + center_row = traitlets.Float(0.0).tag(sync=True) # Detector center row + bf_radius = traitlets.Float(0.0).tag(sync=True) # BF disk radius (pixels) + + # ========================================================================= + # ROI Drawing (for virtual imaging) + # roi_radius is multi-purpose by mode: + # - circle: radius of circle + # - square: half-size (distance from center to edge) + # - annular: outer radius (roi_radius_inner = inner radius) + # - rect: uses roi_width/roi_height instead + # ========================================================================= + roi_active = traitlets.Bool(False).tag(sync=True) + roi_mode = traitlets.Unicode("point").tag(sync=True) + roi_center_col = traitlets.Float(0.0).tag(sync=True) + roi_center_row = traitlets.Float(0.0).tag(sync=True) + # Compound trait for batched row+col updates (JS sends both at once, 1 observer fires) + roi_center = traitlets.List(traitlets.Float(), default_value=[0.0, 0.0]).tag(sync=True) + roi_radius = traitlets.Float(10.0).tag(sync=True) + roi_radius_inner = traitlets.Float(5.0).tag(sync=True) + roi_width = traitlets.Float(20.0).tag(sync=True) + roi_height = traitlets.Float(10.0).tag(sync=True) + + # ========================================================================= + # Virtual Image (ROI-based, updates as you drag ROI on DP) + # ========================================================================= + virtual_image_bytes = traitlets.Bytes(b"").tag(sync=True) # Raw float32 + vi_data_min = traitlets.Float(0.0).tag(sync=True) # Min of current VI for normalization + vi_data_max = traitlets.Float(1.0).tag(sync=True) # Max of current VI for normalization + + # ========================================================================= + # VI ROI (real-space region selection for summed DP) + # ========================================================================= + vi_roi_mode = traitlets.Unicode("off").tag(sync=True) # "off", "circle", "rect" + vi_roi_center_row = traitlets.Float(0.0).tag(sync=True) + vi_roi_center_col = traitlets.Float(0.0).tag(sync=True) + vi_roi_radius = traitlets.Float(5.0).tag(sync=True) + vi_roi_width = traitlets.Float(10.0).tag(sync=True) + vi_roi_height = traitlets.Float(10.0).tag(sync=True) + summed_dp_bytes = traitlets.Bytes(b"").tag(sync=True) # Summed DP from VI ROI + summed_dp_count = traitlets.Int(0).tag(sync=True) # Number of positions summed + + # ========================================================================= + # Scale Bar + # ========================================================================= + pixel_size = traitlets.Float(1.0).tag(sync=True) # Å per pixel (real-space) + k_pixel_size = traitlets.Float(1.0).tag(sync=True) # mrad per pixel (k-space) + k_calibrated = traitlets.Bool(False).tag(sync=True) # True if k-space has mrad calibration + + # ========================================================================= + # Path Animation (programmatic crosshair control) + # ========================================================================= + path_playing = traitlets.Bool(False).tag(sync=True) + path_index = traitlets.Int(0).tag(sync=True) + path_length = traitlets.Int(0).tag(sync=True) + path_interval_ms = traitlets.Int(100).tag(sync=True) # ms between frames + path_loop = traitlets.Bool(True).tag(sync=True) # loop when reaching end + + # ========================================================================= + # Auto-detection trigger (frontend sets to True, backend resets to False) + # ========================================================================= + auto_detect_trigger = traitlets.Bool(False).tag(sync=True) + + # ========================================================================= + # Statistics for display (mean, min, max, std) + # ========================================================================= + dp_stats = traitlets.List(traitlets.Float(), default_value=[0.0, 0.0, 0.0, 0.0]).tag(sync=True) + vi_stats = traitlets.List(traitlets.Float(), default_value=[0.0, 0.0, 0.0, 0.0]).tag(sync=True) + mask_dc = traitlets.Bool(True).tag(sync=True) # Mask center pixel for DP stats + + # ========================================================================= + # Display settings (synced for programmatic export parity) + # ========================================================================= + dp_colormap = traitlets.Unicode("inferno").tag(sync=True) + vi_colormap = traitlets.Unicode("inferno").tag(sync=True) + fft_colormap = traitlets.Unicode("inferno").tag(sync=True) + + dp_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" | "power" + vi_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" | "power" + fft_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" | "power" + + dp_power_exp = traitlets.Float(0.5).tag(sync=True) + vi_power_exp = traitlets.Float(0.5).tag(sync=True) + fft_power_exp = traitlets.Float(0.5).tag(sync=True) + + dp_vmin_pct = traitlets.Float(0.0).tag(sync=True) + dp_vmax_pct = traitlets.Float(100.0).tag(sync=True) + vi_vmin_pct = traitlets.Float(0.0).tag(sync=True) + vi_vmax_pct = traitlets.Float(100.0).tag(sync=True) + fft_vmin_pct = traitlets.Float(0.0).tag(sync=True) + fft_vmax_pct = traitlets.Float(100.0).tag(sync=True) + + # Absolute intensity bounds (override percentile sliders when both set) + dp_vmin = traitlets.Float(None, allow_none=True).tag(sync=True) + dp_vmax = traitlets.Float(None, allow_none=True).tag(sync=True) + vi_vmin = traitlets.Float(None, allow_none=True).tag(sync=True) + vi_vmax = traitlets.Float(None, allow_none=True).tag(sync=True) + + fft_auto = traitlets.Bool(True).tag(sync=True) + show_fft = traitlets.Bool(False).tag(sync=True) + fft_window = traitlets.Bool(True).tag(sync=True) + show_controls = traitlets.Bool(True).tag(sync=True) + dp_show_colorbar = traitlets.Bool(False).tag(sync=True) + export_default_view = traitlets.Unicode("all").tag(sync=True) + export_default_format = traitlets.Unicode("png").tag(sync=True) + export_include_overlays = traitlets.Bool(True).tag(sync=True) + export_include_scalebar = traitlets.Bool(True).tag(sync=True) + export_default_dpi = traitlets.Int(300).tag(sync=True) + + # ========================================================================= + # Frame Animation (5D time/tilt series) + # ========================================================================= + frame_idx = traitlets.Int(0).tag(sync=True) + n_frames = traitlets.Int(1).tag(sync=True) + frame_dim_label = traitlets.Unicode("Frame").tag(sync=True) + frame_labels = traitlets.List(traitlets.Unicode(), []).tag(sync=True) + frame_playing = traitlets.Bool(False).tag(sync=True) + frame_loop = traitlets.Bool(True).tag(sync=True) + frame_fps = traitlets.Float(5.0).tag(sync=True) + frame_reverse = traitlets.Bool(False).tag(sync=True) + frame_boomerang = traitlets.Bool(False).tag(sync=True) + + # Export (GIF) + _gif_export_requested = traitlets.Bool(False).tag(sync=True) + _gif_data = traitlets.Bytes(b"").tag(sync=True) + _gif_metadata_json = traitlets.Unicode("").tag(sync=True) + + # Line Profile (for DP panel) + profile_line = traitlets.List(traitlets.Dict()).tag(sync=True) + profile_width = traitlets.Int(1).tag(sync=True) + + # ========================================================================= + # Tool visibility / locking + # ========================================================================= + disabled_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) + hidden_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) + + @classmethod + def _normalize_tool_groups(cls, tool_groups) -> list[str]: + return normalize_tool_groups("Show4DSTEM", tool_groups) + + @classmethod + def _build_disabled_tools( + cls, + disabled_tools=None, + disable_display: bool = False, + disable_histogram: bool = False, + disable_stats: bool = False, + disable_navigation: bool = False, + disable_playback: bool = False, + disable_view: bool = False, + disable_export: bool = False, + disable_roi: bool = False, + disable_profile: bool = False, + disable_fft: bool = False, + disable_virtual: bool = False, + disable_frame: bool = False, + disable_all: bool = False, + ) -> list[str]: + return build_tool_groups( + "Show4DSTEM", + tool_groups=disabled_tools, + all_flag=disable_all, + flag_map={ + "display": disable_display, + "histogram": disable_histogram, + "stats": disable_stats, + "navigation": disable_navigation, + "playback": disable_playback, + "view": disable_view, + "export": disable_export, + "roi": disable_roi, + "profile": disable_profile, + "fft": disable_fft, + "virtual": disable_virtual, + "frame": disable_frame, + }, + ) + + @classmethod + def _build_hidden_tools( + cls, + hidden_tools=None, + hide_display: bool = False, + hide_histogram: bool = False, + hide_stats: bool = False, + hide_navigation: bool = False, + hide_playback: bool = False, + hide_view: bool = False, + hide_export: bool = False, + hide_roi: bool = False, + hide_profile: bool = False, + hide_fft: bool = False, + hide_virtual: bool = False, + hide_frame: bool = False, + hide_all: bool = False, + ) -> list[str]: + return build_tool_groups( + "Show4DSTEM", + tool_groups=hidden_tools, + all_flag=hide_all, + flag_map={ + "display": hide_display, + "histogram": hide_histogram, + "stats": hide_stats, + "navigation": hide_navigation, + "playback": hide_playback, + "view": hide_view, + "export": hide_export, + "roi": hide_roi, + "profile": hide_profile, + "fft": hide_fft, + "virtual": hide_virtual, + "frame": hide_frame, + }, + ) + + @traitlets.validate("disabled_tools") + def _validate_disabled_tools(self, proposal): + return self._normalize_tool_groups(proposal["value"]) + + @traitlets.validate("hidden_tools") + def _validate_hidden_tools(self, proposal): + return self._normalize_tool_groups(proposal["value"]) + + def __init__( + self, + data: "Dataset4dstem | np.ndarray", + scan_shape: tuple[int, int] | None = None, + pixel_size: float | None = None, + k_pixel_size: float | None = None, + center: tuple[float, float] | None = None, + bf_radius: float | None = None, + precompute_virtual_images: bool = False, + frame_dim_label: str | None = None, + frame_labels: list[str] | None = None, + title: str = "", + disabled_tools: list[str] | None = None, + disable_display: bool = False, + disable_histogram: bool = False, + disable_stats: bool = False, + disable_navigation: bool = False, + disable_playback: bool = False, + disable_view: bool = False, + disable_export: bool = False, + disable_roi: bool = False, + disable_profile: bool = False, + disable_fft: bool = False, + disable_virtual: bool = False, + disable_frame: bool = False, + disable_all: bool = False, + hidden_tools: list[str] | None = None, + hide_display: bool = False, + hide_histogram: bool = False, + hide_stats: bool = False, + hide_navigation: bool = False, + hide_playback: bool = False, + hide_view: bool = False, + hide_export: bool = False, + hide_roi: bool = False, + hide_profile: bool = False, + hide_fft: bool = False, + hide_virtual: bool = False, + hide_frame: bool = False, + hide_all: bool = False, + show_fft: bool = False, + fft_window: bool = True, + show_controls: bool = True, + dp_vmin: float | None = None, + dp_vmax: float | None = None, + vi_vmin: float | None = None, + vi_vmax: float | None = None, + verbose: bool = True, + state=None, + **kwargs, + ): + super().__init__(**kwargs) + self.widget_version = resolve_widget_version() + _t0 = time.perf_counter() + _verbose = verbose + + _io_labels = None + + # Extract calibration from Dataset4dstem if provided + k_calibrated = False + if hasattr(data, "sampling") and hasattr(data, "array"): + # Dataset4dstem: extract calibration and array + # sampling = [scan_rows, scan_cols, det_rows, det_cols] + if not title and hasattr(data, "name") and data.name: + title = str(data.name) + units = getattr(data, "units", ["pixels"] * 4) + if pixel_size is None and units[0] in ("Å", "angstrom", "A", "nm"): + pixel_size = float(data.sampling[0]) + if units[0] == "nm": + pixel_size *= 10 # Convert nm to Å + if k_pixel_size is None and units[2] in ("mrad", "1/Å", "1/A"): + k_pixel_size = float(data.sampling[2]) + k_calibrated = True + data = data.array + + self.title = title + # Store calibration values (default to 1.0 if not provided) + self.pixel_size = pixel_size if pixel_size is not None else 1.0 + self.k_pixel_size = k_pixel_size if k_pixel_size is not None else 1.0 + self.k_calibrated = k_calibrated or (k_pixel_size is not None) + self.disabled_tools = self._build_disabled_tools( + disabled_tools=disabled_tools, + disable_display=disable_display, + disable_histogram=disable_histogram, + disable_stats=disable_stats, + disable_navigation=disable_navigation, + disable_playback=disable_playback, + disable_view=disable_view, + disable_export=disable_export, + disable_roi=disable_roi, + disable_profile=disable_profile, + disable_fft=disable_fft, + disable_virtual=disable_virtual, + disable_frame=disable_frame, + disable_all=disable_all, + ) + self.hidden_tools = self._build_hidden_tools( + hidden_tools=hidden_tools, + hide_display=hide_display, + hide_histogram=hide_histogram, + hide_stats=hide_stats, + hide_navigation=hide_navigation, + hide_playback=hide_playback, + hide_view=hide_view, + hide_export=hide_export, + hide_roi=hide_roi, + hide_profile=hide_profile, + hide_fft=hide_fft, + hide_virtual=hide_virtual, + hide_frame=hide_frame, + hide_all=hide_all, + ) + self.show_fft = show_fft + self.fft_window = fft_window + self.show_controls = show_controls + self.dp_vmin = dp_vmin + self.dp_vmax = dp_vmax + self.vi_vmin = vi_vmin + self.vi_vmax = vi_vmax + # Path animation (configured via set_path() or raster()) + self._path_points: list[tuple[int, int]] = [] + # Named user presets saved during this session + self._named_presets: dict[str, dict[str, Any]] = {} + # Session-scoped reproducibility log for all export calls + self._export_session_id = uuid4().hex + self._export_session_started_utc = datetime.now(timezone.utc).isoformat() + self._export_log: list[dict[str, Any]] = [] + # Sparse sampling state (for streaming/adaptive acquisition workflows) + self._sparse_samples: dict[tuple[int, int, int], np.ndarray] = {} + self._sparse_order: list[tuple[int, int, int]] = [] + # Convert to NumPy then PyTorch tensor using quantem device config + data_np = to_numpy(data) + device_str, _ = validate_device(None) # Get device from quantem config + self._device = torch.device(device_str) + # Remove saturated hot pixels in numpy (before any torch conversion) + saturated_value = 65535.0 if data_np.dtype == np.uint16 else 255.0 if data_np.dtype == np.uint8 else None + if data_np.dtype != np.float32: + _tc = time.perf_counter() + data_np = data_np.astype(np.float32) + if _verbose: + print(f" astype float32: {time.perf_counter() - _tc:.2f}s") + if saturated_value is not None: + data_np[data_np >= saturated_value] = 0 + # Handle dimensionality — 5D loads eagerly for instant frame switching + ndim = data_np.ndim + _tc = time.perf_counter() + if ndim == 5: + self.n_frames = data_np.shape[0] + self._scan_shape = (data_np.shape[1], data_np.shape[2]) + self._det_shape = (data_np.shape[3], data_np.shape[4]) + if data_np.size > 2**31 - 1 and device_str == "mps": + self._device = torch.device("cpu") + self._data = torch.from_numpy(data_np).to(self._device) + elif ndim == 3: + self.n_frames = 1 + if scan_shape is not None: + self._scan_shape = scan_shape + else: + n = data_np.shape[0] + side = int(n ** 0.5) + if side * side != n: + raise ValueError( + f"Cannot infer square scan_shape from N={n}. " + f"Provide scan_shape explicitly." + ) + self._scan_shape = (side, side) + self._det_shape = (data_np.shape[1], data_np.shape[2]) + # MPS backend can't handle tensors >INT_MAX elements; fall back to CPU + if data_np.size > 2**31 - 1 and device_str == "mps": + self._device = torch.device("cpu") + self._data = torch.from_numpy(data_np).to(self._device) + elif ndim == 4: + self.n_frames = 1 + self._scan_shape = (data_np.shape[0], data_np.shape[1]) + self._det_shape = (data_np.shape[2], data_np.shape[3]) + if data_np.size > 2**31 - 1 and device_str == "mps": + self._device = torch.device("cpu") + self._data = torch.from_numpy(data_np).to(self._device) + else: + raise ValueError(f"Expected 3D, 4D, or 5D array, got {ndim}D") + if _verbose: + if str(self._device) == "mps": + torch.mps.synchronize() + print(f" to {self._device}: {time.perf_counter() - _tc:.2f}s ({data_np.nbytes / 1e9:.1f} GB)") + + self.shape_rows = self._scan_shape[0] + self.shape_cols = self._scan_shape[1] + self.det_rows = self._det_shape[0] + self.det_cols = self._det_shape[1] + # Initial position at center + self.pos_row = self.shape_rows // 2 + self.pos_col = self.shape_cols // 2 + # Frame dimension label (for 5D time/tilt series UI) + self.frame_dim_label = frame_dim_label if frame_dim_label is not None else "Frame" + # Per-frame labels: explicit param > inferred > empty + resolved_labels = frame_labels or _io_labels or [] + self._frame_labels = resolved_labels + if resolved_labels: + self.frame_labels = list(resolved_labels) + # Histogram axis range — first frame is enough (JS does per-frame percentile clipping) + first_frame = self._data[0] if self._data.ndim == 5 else self._data + self.dp_global_min = max(float(first_frame.min()), MIN_LOG_VALUE) + self.dp_global_max = float(first_frame.max()) + # Cache coordinate tensors for mask creation (avoid repeated torch.arange) + self._det_row_coords = torch.arange(self.det_rows, device=self._device, dtype=torch.float32)[:, None] + self._det_col_coords = torch.arange(self.det_cols, device=self._device, dtype=torch.float32)[None, :] + self._scan_row_coords = torch.arange(self.shape_rows, device=self._device, dtype=torch.float32)[:, None] + self._scan_col_coords = torch.arange(self.shape_cols, device=self._device, dtype=torch.float32)[None, :] + self._sparse_mask = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=bool) + self._dose_map = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=np.float32) + # Setup center and BF radius + det_size = min(self.det_rows, self.det_cols) + if center is not None and bf_radius is not None: + self.center_row = float(center[0]) + self.center_col = float(center[1]) + self.bf_radius = float(bf_radius) + elif center is not None: + self.center_row = float(center[0]) + self.center_col = float(center[1]) + self.bf_radius = det_size * DEFAULT_BF_RATIO + elif bf_radius is not None: + self.center_col = float(self.det_cols / 2) + self.center_row = float(self.det_rows / 2) + self.bf_radius = float(bf_radius) + else: + # Neither provided - auto-detect from data + # Set defaults first (will be overwritten by auto-detect) + self.center_col = float(self.det_cols / 2) + self.center_row = float(self.det_rows / 2) + self.bf_radius = det_size * DEFAULT_BF_RATIO + # Auto-detect center and bf_radius from the data + _tc = time.perf_counter() + self.auto_detect_center(update_roi=False) + if _verbose: + print(f" auto_detect_center: {time.perf_counter() - _tc:.2f}s") + + # Pre-compute and cache common virtual images (BF, ABF, ADF) + # Each cache stores (bytes, stats) tuple + self._cached_bf_virtual = None + self._cached_abf_virtual = None + self._cached_adf_virtual = None + if precompute_virtual_images and self.n_frames == 1: + self._precompute_common_virtual_images() + + # Update frame when position changes (scale/colormap handled in JS) + self.observe(self._update_frame, names=["pos_row", "pos_col"]) + # Observe individual ROI params + self.observe(self._on_roi_change, names=[ + "roi_center_col", "roi_center_row", "roi_radius", "roi_radius_inner", + "roi_active", "roi_mode", "roi_width", "roi_height" + ]) + # Observe compound roi_center for batched updates from JS + self.observe(self._on_roi_center_change, names=["roi_center"]) + + # Initialize default ROI at BF center — batch to avoid redundant observer callbacks + with self.hold_trait_notifications(): + self.roi_center_col = self.center_col + self.roi_center_row = self.center_row + self.roi_center = [self.center_row, self.center_col] + self.roi_radius = self.bf_radius * 0.5 # Start with half BF radius + self.roi_active = True + + # Compute initial virtual image and frame (once, after all ROI traits are set) + _tc = time.perf_counter() + self._compute_virtual_image_from_roi() + self._update_frame() + if _verbose: + print(f" virtual image + frame: {time.perf_counter() - _tc:.2f}s") + + # Path animation: observe index changes from frontend + self.observe(self._on_path_index_change, names=["path_index"]) + self.observe(self._on_gif_export, names=["_gif_export_requested"]) + + # Frame animation (5D): observe frame_idx changes from frontend + self.observe(self._on_frame_idx_change, names=["frame_idx"]) + + # Auto-detect trigger: observe changes from frontend + self.observe(self._on_auto_detect_trigger, names=["auto_detect_trigger"]) + + # VI ROI: observe changes for summed DP computation + # Initialize VI ROI center to scan center with reasonable default sizes + self.vi_roi_center_row = float(self.shape_rows / 2) + self.vi_roi_center_col = float(self.shape_cols / 2) + # Set initial ROI size based on scan dimension + default_roi_size = max(3, min(self.shape_rows, self.shape_cols) * DEFAULT_VI_ROI_RATIO) + self.vi_roi_radius = float(default_roi_size) + self.vi_roi_width = float(default_roi_size * 2) + self.vi_roi_height = float(default_roi_size) + self.observe(self._on_vi_roi_change, names=[ + "vi_roi_mode", "vi_roi_center_row", "vi_roi_center_col", + "vi_roi_radius", "vi_roi_width", "vi_roi_height" + ]) + + if state is not None: + if isinstance(state, (str, pathlib.Path)): + state = unwrap_state_payload( + json.loads(pathlib.Path(state).read_text()), + require_envelope=True, + ) + else: + state = unwrap_state_payload(state) + self.load_state_dict(state) + + if _verbose: + shape = "x".join(str(s) for s in self._data.shape) + print(f"Show4DSTEM: {shape} {self._device}, {time.perf_counter() - _t0:.2f}s total") + + def set_image(self, data, scan_shape=None): + """Replace the 4D-STEM data. Preserves all display and ROI settings.""" + if hasattr(data, "sampling") and hasattr(data, "array"): + data = data.array + data_np = to_numpy(data) + saturated_value = 65535.0 if data_np.dtype == np.uint16 else 255.0 if data_np.dtype == np.uint8 else None + if data_np.dtype != np.float32: + data_np = data_np.astype(np.float32) + if saturated_value is not None: + data_np[data_np >= saturated_value] = 0 + if data_np.ndim == 5: + self.n_frames = data_np.shape[0] + self._scan_shape = (data_np.shape[1], data_np.shape[2]) + self._det_shape = (data_np.shape[3], data_np.shape[4]) + if data_np.size > 2**31 - 1 and str(self._device) == "mps": + self._device = torch.device("cpu") + self._data = torch.from_numpy(data_np).to(self._device) + elif data_np.ndim == 3: + self.n_frames = 1 + if scan_shape is not None: + self._scan_shape = scan_shape + else: + n = data_np.shape[0] + side = int(n ** 0.5) + if side * side != n: + raise ValueError(f"Cannot infer square scan_shape from N={n}. Provide scan_shape explicitly.") + self._scan_shape = (side, side) + self._det_shape = (data_np.shape[1], data_np.shape[2]) + self._data = torch.from_numpy(data_np).to(self._device) + elif data_np.ndim == 4: + self.n_frames = 1 + self._scan_shape = (data_np.shape[0], data_np.shape[1]) + self._det_shape = (data_np.shape[2], data_np.shape[3]) + self._data = torch.from_numpy(data_np).to(self._device) + else: + raise ValueError(f"Expected 3D, 4D, or 5D array, got {data_np.ndim}D") + self.frame_idx = 0 + self.shape_rows = self._scan_shape[0] + self.shape_cols = self._scan_shape[1] + self.det_rows = self._det_shape[0] + self.det_cols = self._det_shape[1] + first_frame = self._data[0] if self._data.ndim == 5 else self._data + self.dp_global_min = max(float(first_frame.min()), MIN_LOG_VALUE) + self.dp_global_max = float(first_frame.max()) + self._det_row_coords = torch.arange(self.det_rows, device=self._device, dtype=torch.float32)[:, None] + self._det_col_coords = torch.arange(self.det_cols, device=self._device, dtype=torch.float32)[None, :] + self._scan_row_coords = torch.arange(self.shape_rows, device=self._device, dtype=torch.float32)[:, None] + self._scan_col_coords = torch.arange(self.shape_cols, device=self._device, dtype=torch.float32)[None, :] + self._sparse_mask = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=bool) + self._dose_map = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=np.float32) + self._sparse_samples = {} + self._sparse_order = [] + self._cached_bf_virtual = None + self._cached_abf_virtual = None + self._cached_adf_virtual = None + with self.hold_trait_notifications(): + self.pos_row = min(self.pos_row, self.shape_rows - 1) + self.pos_col = min(self.pos_col, self.shape_cols - 1) + self._compute_virtual_image_from_roi() + self._update_frame() + + def __repr__(self) -> str: + k_unit = "mrad" if self.k_calibrated else "px" + shape = ( + f"({self.n_frames}, {self.shape_rows}, {self.shape_cols}, {self.det_rows}, {self.det_cols})" + if self.n_frames > 1 + else f"({self.shape_rows}, {self.shape_cols}, {self.det_rows}, {self.det_cols})" + ) + frame_info = f", {self.frame_dim_label.lower()}={self.frame_idx}" if self.n_frames > 1 else "" + title_info = f", title='{self.title}'" if self.title else "" + return ( + f"Show4DSTEM(shape={shape}, " + f"sampling=({self.pixel_size} Å, {self.k_pixel_size} {k_unit}), " + f"pos=({self.pos_row}, {self.pos_col}){frame_info}{title_info})" + ) + + def state_dict(self): + return { + "title": self.title, + "pos_row": self.pos_row, + "pos_col": self.pos_col, + "pixel_size": self.pixel_size, + "k_pixel_size": self.k_pixel_size, + "k_calibrated": self.k_calibrated, + "center_row": self.center_row, + "center_col": self.center_col, + "bf_radius": self.bf_radius, + "roi_active": self.roi_active, + "roi_mode": self.roi_mode, + "roi_center_row": self.roi_center_row, + "roi_center_col": self.roi_center_col, + "roi_radius": self.roi_radius, + "roi_radius_inner": self.roi_radius_inner, + "roi_width": self.roi_width, + "roi_height": self.roi_height, + "vi_roi_mode": self.vi_roi_mode, + "vi_roi_center_row": self.vi_roi_center_row, + "vi_roi_center_col": self.vi_roi_center_col, + "vi_roi_radius": self.vi_roi_radius, + "vi_roi_width": self.vi_roi_width, + "vi_roi_height": self.vi_roi_height, + "mask_dc": self.mask_dc, + "dp_colormap": self.dp_colormap, + "vi_colormap": self.vi_colormap, + "fft_colormap": self.fft_colormap, + "dp_scale_mode": self.dp_scale_mode, + "vi_scale_mode": self.vi_scale_mode, + "fft_scale_mode": self.fft_scale_mode, + "dp_power_exp": self.dp_power_exp, + "vi_power_exp": self.vi_power_exp, + "fft_power_exp": self.fft_power_exp, + "dp_vmin_pct": self.dp_vmin_pct, + "dp_vmax_pct": self.dp_vmax_pct, + "vi_vmin_pct": self.vi_vmin_pct, + "vi_vmax_pct": self.vi_vmax_pct, + "fft_vmin_pct": self.fft_vmin_pct, + "fft_vmax_pct": self.fft_vmax_pct, + "dp_vmin": self.dp_vmin, + "dp_vmax": self.dp_vmax, + "vi_vmin": self.vi_vmin, + "vi_vmax": self.vi_vmax, + "fft_auto": self.fft_auto, + "show_fft": self.show_fft, + "fft_window": self.fft_window, + "show_controls": self.show_controls, + "dp_show_colorbar": self.dp_show_colorbar, + "export_default_view": self.export_default_view, + "export_default_format": self.export_default_format, + "export_include_overlays": self.export_include_overlays, + "export_include_scalebar": self.export_include_scalebar, + "export_default_dpi": self.export_default_dpi, + "path_interval_ms": self.path_interval_ms, + "path_loop": self.path_loop, + "profile_line": self.profile_line, + "profile_width": self.profile_width, + "frame_idx": self.frame_idx, + "frame_dim_label": self.frame_dim_label, + "frame_labels": list(self.frame_labels), + "frame_loop": self.frame_loop, + "frame_fps": self.frame_fps, + "frame_reverse": self.frame_reverse, + "frame_boomerang": self.frame_boomerang, + "disabled_tools": self.disabled_tools, + "hidden_tools": self.hidden_tools, + } + + def save(self, path: str): + save_state_file(path, "Show4DSTEM", self.state_dict()) + + def load_state_dict(self, state): + allowed_keys = set(self.state_dict().keys()) + pending_pos_row = state.get("pos_row", None) + pending_pos_col = state.get("pos_col", None) + pending_frame_idx = state.get("frame_idx", None) + for key, val in state.items(): + if key in {"pos_row", "pos_col", "frame_idx"}: + continue + if key in allowed_keys: + setattr(self, key, val) + if pending_frame_idx is not None: + self.frame_idx = int(max(0, min(int(pending_frame_idx), self.n_frames - 1))) + if pending_pos_row is not None or pending_pos_col is not None: + row = int(self.pos_row if pending_pos_row is None else pending_pos_row) + col = int(self.pos_col if pending_pos_col is None else pending_pos_col) + self.pos_row = int(max(0, min(row, self.shape_rows - 1))) + self.pos_col = int(max(0, min(col, self.shape_cols - 1))) + + def free(self): + """Free GPU memory held by this widget. + + Deletes the internal data tensor, runs garbage collection, and + flushes the MPS allocator cache. Call this before loading a new + dataset to avoid running out of GPU memory. + + Examples + -------- + >>> w.free() # release ~9 GB of MPS memory + >>> del result # free the source numpy array + """ + import gc + + device = str(self._device) if hasattr(self, "_device") else "" + nbytes = self._data.nbytes if hasattr(self._data, "nbytes") else 0 + self._data = None + gc.collect() + if device == "mps": + try: + import torch + torch.mps.empty_cache() + except Exception: + pass + elif device.startswith("cuda"): + try: + import torch + torch.cuda.empty_cache() + except Exception: + pass + if nbytes > 0: + print(f"freed {_format_memory(nbytes)} ({device})") + + def summary(self): + name = self.title if self.title else "Show4DSTEM" + lines = [name, "═" * 32] + if self.n_frames > 1: + parts = [f"{self.n_frames} ({self.frame_dim_label}), current: {self.frame_idx}"] + parts.append(f"{self.frame_fps} fps") + if self.frame_loop: + parts.append("loop") + if self.frame_reverse: + parts.append("reverse") + if self.frame_boomerang: + parts.append("bounce") + lines.append(f"Frames: {' | '.join(parts)}") + if self._frame_labels: + if len(self._frame_labels) <= 4: + lines.append(f"Labels: {self._frame_labels}") + else: + lines.append(f"Labels: {self._frame_labels[:3]} ... ({len(self._frame_labels)} total)") + lines.append(f"Scan: {self.shape_rows}×{self.shape_cols} ({self.pixel_size:.2f} Å/px)") + k_unit = "mrad" if self.k_calibrated else "px" + lines.append(f"Detector: {self.det_rows}×{self.det_cols} ({self.k_pixel_size:.4f} {k_unit}/px)") + lines.append(f"Position: ({self.pos_row}, {self.pos_col})") + lines.append(f"Center: ({self.center_row:.1f}, {self.center_col:.1f}) BF r={self.bf_radius:.1f} px") + display_parts = [] + if self.mask_dc: + display_parts.append("DC masked") + lines.append(f"Display: {', '.join(display_parts) if display_parts else 'default'}") + if self.roi_active: + lines.append(f"ROI: {self.roi_mode} at ({self.roi_center_row:.1f}, {self.roi_center_col:.1f}) r={self.roi_radius:.1f}") + if self.vi_roi_mode != "off": + lines.append(f"VI ROI: {self.vi_roi_mode} at ({self.vi_roi_center_row:.1f}, {self.vi_roi_center_col:.1f}) r={self.vi_roi_radius:.1f}") + dp_contrast = f"{self.dp_vmin_pct:.1f}-{self.dp_vmax_pct:.1f}%" + if self.dp_vmin is not None and self.dp_vmax is not None: + dp_contrast += f", dp_vmin={self.dp_vmin:.4g}, dp_vmax={self.dp_vmax:.4g}" + lines.append( + f"DP view: {self.dp_colormap}, {self.dp_scale_mode}, {dp_contrast}" + ) + vi_contrast = f"{self.vi_vmin_pct:.1f}-{self.vi_vmax_pct:.1f}%" + if self.vi_vmin is not None and self.vi_vmax is not None: + vi_contrast += f", vi_vmin={self.vi_vmin:.4g}, vi_vmax={self.vi_vmax:.4g}" + lines.append( + f"VI view: {self.vi_colormap}, {self.vi_scale_mode}, {vi_contrast}" + ) + if self.show_fft: + fft_parts = [f"{self.fft_colormap}, {self.fft_scale_mode}, {self.fft_vmin_pct:.1f}-{self.fft_vmax_pct:.1f}%, auto={self.fft_auto}"] + if not self.fft_window: + fft_parts.append("no window") + lines.append(f"FFT view: {', '.join(fft_parts)}") + if self.profile_line and len(self.profile_line) == 2: + p0, p1 = self.profile_line[0], self.profile_line[1] + lines.append(f"Profile: ({p0['row']:.0f}, {p0['col']:.0f}) -> ({p1['row']:.0f}, {p1['col']:.0f}) width={self.profile_width}") + if self.disabled_tools: + lines.append(f"Locked: {', '.join(self.disabled_tools)}") + if self.hidden_tools: + lines.append(f"Hidden: {', '.join(self.hidden_tools)}") + print("\n".join(lines)) + + # ========================================================================= + # Convenience Properties + # ========================================================================= + + @property + def position(self) -> tuple[int, int]: + """Current scan position as (row, col) tuple.""" + return (self.pos_row, self.pos_col) + + @position.setter + def position(self, value: tuple[int, int]) -> None: + """Set scan position from (row, col) tuple.""" + self.pos_row, self.pos_col = value + + @property + def scan_shape(self) -> tuple[int, int]: + """Scan dimensions as (rows, cols) tuple.""" + return (self.shape_rows, self.shape_cols) + + @property + def detector_shape(self) -> tuple[int, int]: + """Detector dimensions as (rows, cols) tuple.""" + return (self.det_rows, self.det_cols) + + @property + def _frame_data(self) -> torch.Tensor: + """Per-frame data (4D or 3D flattened), accounting for 5D time/tilt series.""" + if self.n_frames > 1: + return self._data[self.frame_idx] + return self._data + + # ========================================================================= + # Line Profile + # ========================================================================= + + def set_profile(self, start: tuple, end: tuple) -> Self: + row0, col0 = start + row1, col1 = end + self.profile_line = [ + {"row": float(row0), "col": float(col0)}, + {"row": float(row1), "col": float(col1)}, + ] + return self + + def clear_profile(self) -> Self: + self.profile_line = [] + return self + + @property + def profile(self) -> list[tuple[float, float]]: + if len(self.profile_line) == 2: + p0, p1 = self.profile_line[0], self.profile_line[1] + return [(p0["row"], p0["col"]), (p1["row"], p1["col"])] + return [] + + @property + def profile_values(self): + if len(self.profile_line) != 2: + return None + p0, p1 = self.profile_line[0], self.profile_line[1] + frame = self._get_frame(self.pos_row, self.pos_col) + return self._sample_line(frame, p0["row"], p0["col"], p1["row"], p1["col"]) + + @property + def profile_distance(self) -> float: + if len(self.profile_line) != 2: + return 0.0 + p0, p1 = self.profile_line[0], self.profile_line[1] + dist_px = np.sqrt((p1["row"] - p0["row"]) ** 2 + (p1["col"] - p0["col"]) ** 2) + if self.k_calibrated: + return float(dist_px * self.k_pixel_size) + return float(dist_px) + + def _sample_line(self, frame, row0, col0, row1, col1): + h, w = frame.shape[:2] + dc = col1 - col0 + dr = row1 - row0 + length = np.sqrt(dc * dc + dr * dr) + n = max(2, int(np.ceil(length))) + t = np.linspace(0.0, 1.0, n) + c = col0 + t * dc + r = row0 + t * dr + ci = np.floor(c).astype(np.intp) + ri = np.floor(r).astype(np.intp) + cf = c - ci + rf = r - ri + c0 = np.clip(ci, 0, w - 1) + c1 = np.clip(ci + 1, 0, w - 1) + r0 = np.clip(ri, 0, h - 1) + r1 = np.clip(ri + 1, 0, h - 1) + return ( + frame[r0, c0] * (1 - cf) * (1 - rf) + + frame[r0, c1] * cf * (1 - rf) + + frame[r1, c0] * (1 - cf) * rf + + frame[r1, c1] * cf * rf + ).astype(np.float32) + + # ========================================================================= + # Path Animation Methods + # ========================================================================= + + def set_path( + self, + points: list[tuple[int, int]], + interval_ms: int = 100, + loop: bool = True, + autoplay: bool = True, + ) -> Self: + """ + Set a custom path of scan positions to animate through. + + Parameters + ---------- + points : list[tuple[int, int]] + List of (row, col) scan positions to visit. + interval_ms : int, default 100 + Time between frames in milliseconds. + loop : bool, default True + Whether to loop when reaching end. + autoplay : bool, default True + Start playing immediately. + + Returns + ------- + Show4DSTEM + Self for method chaining. + + Examples + -------- + >>> widget.set_path([(0, 0), (10, 10), (20, 20), (30, 30)]) + >>> widget.set_path([(i, i) for i in range(48)], interval_ms=50) + """ + self._path_points = list(points) + self.path_length = len(self._path_points) + self.path_index = 0 + self.path_interval_ms = interval_ms + self.path_loop = loop + if autoplay and self.path_length > 0: + self.path_playing = True + return self + + def play(self) -> Self: + """Start playing the path animation.""" + if self.path_length > 0: + self.path_playing = True + return self + + def pause(self) -> Self: + """Pause the path animation.""" + self.path_playing = False + return self + + def stop(self) -> Self: + """Stop and reset path animation to beginning.""" + self.path_playing = False + self.path_index = 0 + return self + + def goto(self, index: int) -> Self: + """Jump to a specific index in the path.""" + if 0 <= index < self.path_length: + self.path_index = index + return self + + def _on_path_index_change(self, change): + """Called when path_index changes (from frontend timer).""" + idx = change["new"] + if 0 <= idx < len(self._path_points): + row, col = self._path_points[idx] + # Clamp to valid range + self.pos_row = max(0, min(self.shape_rows - 1, row)) + self.pos_col = max(0, min(self.shape_cols - 1, col)) + + def _on_auto_detect_trigger(self, change): + """Called when auto_detect_trigger is set to True from frontend.""" + if change["new"]: + self.auto_detect_center() + # Reset trigger to allow re-triggering + self.auto_detect_trigger = False + + def _on_frame_idx_change(self, change=None): + """Called when frame_idx changes (5D time/tilt series). + + Recomputes virtual image and diffraction pattern for the new frame. + Invalidates precomputed caches since they are per-frame. + """ + if self.n_frames <= 1: + return + # Invalidate precomputed caches (they were for a different frame) + self._cached_bf_virtual = None + self._cached_abf_virtual = None + self._cached_adf_virtual = None + # Recompute virtual image and displayed frame + self._compute_virtual_image_from_roi() + self._update_frame() + # Recompute summed DP if VI ROI is active + if self.vi_roi_mode != "off": + self._compute_summed_dp_from_vi_roi() + + # ========================================================================= + # Path Animation Patterns + # ========================================================================= + + def raster( + self, + step: int = 1, + bidirectional: bool = False, + interval_ms: int = 100, + loop: bool = True, + ) -> Self: + """ + Play a raster scan path (row by row, left to right). + + This mimics real STEM scanning: left→right, step down, left→right, etc. + + Parameters + ---------- + step : int, default 1 + Step size between positions. + bidirectional : bool, default False + If True, use snake/boustrophedon pattern (alternating direction). + If False (default), always scan left→right like real STEM. + interval_ms : int, default 100 + Time between frames in milliseconds. + loop : bool, default True + Whether to loop when reaching the end. + + Returns + ------- + Show4DSTEM + Self for method chaining. + """ + points = [] + for r in range(0, self.shape_rows, step): + cols = list(range(0, self.shape_cols, step)) + if bidirectional and (r // step % 2 == 1): + cols = cols[::-1] # Alternate direction for snake pattern + for c in cols: + points.append((r, c)) + return self.set_path(points=points, interval_ms=interval_ms, loop=loop) + + # ========================================================================= + # ROI Mode Methods + # ========================================================================= + + def roi_circle(self, radius: float | None = None) -> Self: + """ + Switch to circle ROI mode for virtual imaging. + + In circle mode, the virtual image integrates over a circular region + centered at the current ROI position (like a virtual bright field detector). + + Parameters + ---------- + radius : float, optional + Radius of the circle in pixels. If not provided, uses current value + or defaults to half the BF radius. + + Returns + ------- + Show4DSTEM + Self for method chaining. + + Examples + -------- + >>> widget.roi_circle(20) # 20px radius circle + >>> widget.roi_circle() # Use default radius + """ + self.roi_mode = "circle" + if radius is not None: + self.roi_radius = float(radius) + return self + + def roi_point(self) -> Self: + """ + Switch to point ROI mode (single-pixel indexing). + + In point mode, the virtual image shows intensity at the exact ROI position. + This is the default mode. + + Returns + ------- + Show4DSTEM + Self for method chaining. + """ + self.roi_mode = "point" + return self + + def roi_square(self, half_size: float | None = None) -> Self: + """ + Switch to square ROI mode for virtual imaging. + + In square mode, the virtual image integrates over a square region + centered at the current ROI position. + + Parameters + ---------- + half_size : float, optional + Half-size of the square in pixels (distance from center to edge). + A half_size of 15 creates a 30x30 pixel square. + If not provided, uses current roi_radius value. + + Returns + ------- + Show4DSTEM + Self for method chaining. + + Examples + -------- + >>> widget.roi_square(15) # 30x30 pixel square (half_size=15) + >>> widget.roi_square() # Use default size + """ + self.roi_mode = "square" + if half_size is not None: + self.roi_radius = float(half_size) + return self + + def roi_annular( + self, inner_radius: float | None = None, outer_radius: float | None = None + ) -> Self: + """ + Set ROI mode to annular (donut-shaped) for ADF/HAADF imaging. + + Parameters + ---------- + inner_radius : float, optional + Inner radius in pixels. If not provided, uses current roi_radius_inner. + outer_radius : float, optional + Outer radius in pixels. If not provided, uses current roi_radius. + + Returns + ------- + Show4DSTEM + Self for method chaining. + + Examples + -------- + >>> widget.roi_annular(20, 50) # ADF: inner=20px, outer=50px + >>> widget.roi_annular(30, 80) # HAADF: larger angles + """ + self.roi_mode = "annular" + if inner_radius is not None: + self.roi_radius_inner = float(inner_radius) + if outer_radius is not None: + self.roi_radius = float(outer_radius) + return self + + def roi_rect( + self, width: float | None = None, height: float | None = None + ) -> Self: + """ + Set ROI mode to rectangular. + + Parameters + ---------- + width : float, optional + Width in pixels. If not provided, uses current roi_width. + height : float, optional + Height in pixels. If not provided, uses current roi_height. + + Returns + ------- + Show4DSTEM + Self for method chaining. + + Examples + -------- + >>> widget.roi_rect(30, 20) # 30px wide, 20px tall + >>> widget.roi_rect(40, 40) # 40x40 rectangle + """ + self.roi_mode = "rect" + if width is not None: + self.roi_width = float(width) + if height is not None: + self.roi_height = float(height) + return self + + def auto_detect_center(self, update_roi: bool = True) -> Self: + """ + Automatically detect BF disk center and radius using centroid. + + This method analyzes the summed diffraction pattern to find the + bright field disk center and estimate its radius. The detected + values are applied to the widget's calibration (center_row, center_col, + bf_radius). + + Parameters + ---------- + update_roi : bool, default True + If True, also update ROI center and recompute cached virtual images. + Set to False during __init__ when ROI is not yet initialized. + + Returns + ------- + Show4DSTEM + Self for method chaining. + + Examples + -------- + >>> widget = Show4DSTEM(data) + >>> widget.auto_detect_center() # Auto-detect and apply + """ + # Sum all diffraction patterns to get average (PyTorch) + if self._data.ndim == 5: + summed_dp = self._data.sum(dim=(0, 1, 2)) + elif self._data.ndim == 4: + summed_dp = self._data.sum(dim=(0, 1)) + else: + summed_dp = self._data.sum(dim=0) + + # Threshold at mean + std to isolate BF disk + threshold = summed_dp.mean() + summed_dp.std() + mask = summed_dp > threshold + + # Avoid division by zero + total = mask.sum() + if total == 0: + return self + + # Calculate centroid using cached coordinate grids + cx = float((self._det_col_coords * mask).sum() / total) + cy = float((self._det_row_coords * mask).sum() / total) + + # Estimate radius from mask area (A = pi*r^2) + radius = float(torch.sqrt(total / torch.pi)) + + # Apply detected values + self.center_col = cx + self.center_row = cy + self.bf_radius = radius + + if update_roi: + # Also update ROI to center + self.roi_center_col = cx + self.roi_center_row = cy + # Recompute cached virtual images with new calibration + self._precompute_common_virtual_images() + + return self + + def _get_frame(self, row: int, col: int) -> np.ndarray: + """Get single diffraction frame at position (row, col) as numpy array.""" + if self._data is None: + return np.zeros((self.det_rows, self.det_cols), dtype=np.float32) + data = self._frame_data + if data.ndim == 3: + idx = row * self.shape_cols + col + return data[idx].cpu().numpy() + else: + return data[row, col].cpu().numpy() + + def _apply_scale_mode( + self, + data: np.ndarray, + mode: str, + power_exp: float = 0.5, + ) -> np.ndarray: + arr = np.asarray(data, dtype=np.float32) + if mode == "log": + return np.log1p(np.maximum(arr, 0.0)).astype(np.float32) + if mode == "power": + return np.power(np.maximum(arr, 0.0), float(power_exp)).astype(np.float32) + return arr.astype(np.float32) + + def _slider_range( + self, + data_min: float, + data_max: float, + vmin_pct: float, + vmax_pct: float, + ) -> tuple[float, float]: + v0 = float(max(0.0, min(100.0, vmin_pct))) + v1 = float(max(0.0, min(100.0, vmax_pct))) + if v1 < v0: + v0, v1 = v1, v0 + rng = float(data_max - data_min) + return ( + float(data_min + (v0 / 100.0) * rng), + float(data_min + (v1 / 100.0) * rng), + ) + + def _render_colormap_rgb( + self, + data: np.ndarray, + cmap_name: str, + vmin: float, + vmax: float, + ) -> np.ndarray: + from matplotlib import colormaps + + arr = np.asarray(data, dtype=np.float32) + if vmax <= vmin: + normalized = np.zeros_like(arr, dtype=np.float32) + else: + normalized = np.clip((arr - vmin) / (vmax - vmin), 0.0, 1.0) + rgba = colormaps.get_cmap(cmap_name)(normalized) + return (rgba[..., :3] * 255).astype(np.uint8) + + def _get_virtual_image_array(self) -> np.ndarray: + if not self.virtual_image_bytes: + return np.zeros((self.shape_rows, self.shape_cols), dtype=np.float32) + arr = np.frombuffer(self.virtual_image_bytes, dtype=np.float32) + expected = self.shape_rows * self.shape_cols + if arr.size != expected: + return np.zeros((self.shape_rows, self.shape_cols), dtype=np.float32) + return arr.reshape(self.shape_rows, self.shape_cols).copy() + + def _get_summed_dp_array(self) -> np.ndarray | None: + if self.vi_roi_mode == "off": + return None + self._compute_summed_dp_from_vi_roi() + if not self.summed_dp_bytes: + return None + arr = np.frombuffer(self.summed_dp_bytes, dtype=np.float32) + expected = self.det_rows * self.det_cols + if arr.size != expected: + return None + return arr.reshape(self.det_rows, self.det_cols).copy() + + def _fft_enhanced_range(self, mag: np.ndarray) -> tuple[float, float]: + arr = np.asarray(mag, dtype=np.float32).copy() + if arr.size == 0: + return 0.0, 0.0 + center_row = arr.shape[0] // 2 + center_col = arr.shape[1] // 2 + neighbors = [] + if center_col - 1 >= 0: + neighbors.append(arr[center_row, center_col - 1]) + if center_col + 1 < arr.shape[1]: + neighbors.append(arr[center_row, center_col + 1]) + if center_row - 1 >= 0: + neighbors.append(arr[center_row - 1, center_col]) + if center_row + 1 < arr.shape[0]: + neighbors.append(arr[center_row + 1, center_col]) + if neighbors: + arr[center_row, center_col] = float(np.mean(neighbors)) + dmin = float(arr.min()) + dmax = float(arr.max()) + if dmax <= dmin: + return dmin, dmax + pmax = float(np.percentile(arr, 99.9)) + if pmax <= dmin: + pmax = dmax + return dmin, pmax + + def _render_dp_rgb(self) -> tuple[np.ndarray, dict]: + summed_dp = self._get_summed_dp_array() + if summed_dp is not None: + raw = summed_dp + source = "summed_dp" + else: + raw = self._get_frame(self.pos_row, self.pos_col).astype(np.float32) + source = "single_frame" + + scale_mode = self.dp_scale_mode + scaled = self._apply_scale_mode(raw, scale_mode, self.dp_power_exp) + data_min = float(scaled.min()) if scaled.size else 0.0 + data_max = float(scaled.max()) if scaled.size else 0.0 + if self.dp_vmin is not None and self.dp_vmax is not None: + vmin = float(self._apply_scale_mode( + np.array([max(self.dp_vmin, 0)], dtype=np.float32), scale_mode, self.dp_power_exp + )[0]) + vmax = float(self._apply_scale_mode( + np.array([max(self.dp_vmax, 0)], dtype=np.float32), scale_mode, self.dp_power_exp + )[0]) + else: + vmin, vmax = self._slider_range(data_min, data_max, self.dp_vmin_pct, self.dp_vmax_pct) + rgb = self._render_colormap_rgb(scaled, self.dp_colormap, vmin, vmax) + metadata = { + "source": source, + "colormap": self.dp_colormap, + "scale_mode": scale_mode, + "vmin_pct": float(self.dp_vmin_pct), + "vmax_pct": float(self.dp_vmax_pct), + "vmin": float(vmin), + "vmax": float(vmax), + } + return rgb, metadata + + def _render_virtual_rgb(self) -> tuple[np.ndarray, dict]: + raw = self._get_virtual_image_array() + scaled = self._apply_scale_mode(raw, self.vi_scale_mode, self.vi_power_exp) + data_min = float(scaled.min()) if scaled.size else 0.0 + data_max = float(scaled.max()) if scaled.size else 0.0 + if self.vi_vmin is not None and self.vi_vmax is not None: + vmin = float(self._apply_scale_mode( + np.array([max(self.vi_vmin, 0)], dtype=np.float32), self.vi_scale_mode, self.vi_power_exp + )[0]) + vmax = float(self._apply_scale_mode( + np.array([max(self.vi_vmax, 0)], dtype=np.float32), self.vi_scale_mode, self.vi_power_exp + )[0]) + else: + vmin, vmax = self._slider_range(data_min, data_max, self.vi_vmin_pct, self.vi_vmax_pct) + rgb = self._render_colormap_rgb(scaled, self.vi_colormap, vmin, vmax) + metadata = { + "colormap": self.vi_colormap, + "scale_mode": self.vi_scale_mode, + "vmin_pct": float(self.vi_vmin_pct), + "vmax_pct": float(self.vi_vmax_pct), + "vmin": float(vmin), + "vmax": float(vmax), + } + return rgb, metadata + + def _render_fft_rgb(self) -> tuple[np.ndarray, dict]: + virtual_raw = self._get_virtual_image_array() + fft = np.fft.fftshift(np.fft.fft2(virtual_raw)) + mag = np.abs(fft).astype(np.float32) + scaled = self._apply_scale_mode(mag, self.fft_scale_mode, self.fft_power_exp) + if self.fft_auto: + display_min, display_max = self._fft_enhanced_range(scaled) + else: + display_min = float(scaled.min()) if scaled.size else 0.0 + display_max = float(scaled.max()) if scaled.size else 0.0 + vmin, vmax = self._slider_range(display_min, display_max, self.fft_vmin_pct, self.fft_vmax_pct) + rgb = self._render_colormap_rgb(scaled, self.fft_colormap, vmin, vmax) + metadata = { + "colormap": self.fft_colormap, + "scale_mode": self.fft_scale_mode, + "auto": bool(self.fft_auto), + "vmin_pct": float(self.fft_vmin_pct), + "vmax_pct": float(self.fft_vmax_pct), + "vmin": float(vmin), + "vmax": float(vmax), + } + return rgb, metadata + + def list_export_views(self) -> tuple[str, ...]: + return ("diffraction", "virtual", "fft", "all") + + def list_export_formats(self) -> tuple[str, ...]: + return ("png", "pdf") + + def list_figure_templates(self) -> tuple[str, ...]: + return ("dp_vi", "dp_vi_fft", "publication_dp_vi", "publication_dp_vi_fft") + + def list_presets(self) -> tuple[str, ...]: + builtin = ("bf", "abf", "adf", "haadf") + custom = tuple(sorted(self._named_presets.keys())) + return builtin + custom + + def _validate_export_view(self, view: str | None) -> str: + candidate = self.export_default_view if view is None else str(view) + view_key = str(candidate).strip().lower() + allowed = self.list_export_views() + if view_key not in allowed: + raise ValueError( + f"Unsupported view '{view}'. Supported: {', '.join(allowed)}" + ) + return view_key + + def _validate_frame_idx(self, frame_idx: int | None) -> int: + if frame_idx is None: + return int(self.frame_idx) + idx = int(frame_idx) + if idx < 0 or idx >= self.n_frames: + raise ValueError( + f"frame_idx={idx} is out of range [0, {self.n_frames - 1}]" + ) + return idx + + def _validate_position(self, position: tuple[int, int] | None) -> tuple[int, int]: + if position is None: + return int(self.pos_row), int(self.pos_col) + if len(position) != 2: + raise ValueError( + "position must be a (row, col) tuple with exactly two values" + ) + row = int(position[0]) + col = int(position[1]) + if row < 0 or row >= self.shape_rows or col < 0 or col >= self.shape_cols: + raise ValueError( + f"position=({row}, {col}) is out of range for " + f"scan_shape=({self.shape_rows}, {self.shape_cols})" + ) + return row, col + + def _resolve_export_format( + self, + path: pathlib.Path, + fmt: str | None, + ) -> str: + if fmt is not None and str(fmt).strip(): + resolved = str(fmt).strip().lower() + else: + from_path = path.suffix.lstrip(".").lower() + resolved = from_path if from_path else str(self.export_default_format).strip().lower() + allowed = self.list_export_formats() + if resolved not in allowed: + raise ValueError( + f"Unsupported format '{resolved}'. Supported: {', '.join(allowed)}" + ) + return resolved + + @staticmethod + def _round_to_nice_value(value: float) -> float: + if value <= 0: + return 1.0 + magnitude = 10 ** math.floor(math.log10(value)) + normalized = value / magnitude + if normalized < 1.5: + return float(magnitude) + if normalized < 3.5: + return float(2 * magnitude) + if normalized < 7.5: + return float(5 * magnitude) + return float(10 * magnitude) + + def _format_scale_label(self, value: float, unit: str) -> str: + nice = self._round_to_nice_value(value) + if unit == "Å": + if nice >= 10: + return f"{int(round(nice / 10))} nm" + if nice >= 1: + return f"{int(round(nice))} Å" + return f"{nice:.2f} Å" + if unit == "mrad": + if nice >= 1000: + return f"{int(round(nice / 1000))} rad" + if nice >= 1: + return f"{int(round(nice))} mrad" + return f"{nice:.2f} mrad" + if nice >= 1: + return f"{int(round(nice))} px" + return f"{nice:.1f} px" + + @staticmethod + def _draw_crosshair(draw, x: float, y: float, size: float, color, width: int) -> None: + draw.line([(x - size, y), (x + size, y)], fill=color, width=width) + draw.line([(x, y - size), (x, y + size)], fill=color, width=width) + + def _draw_scalebar_overlay(self, image, pixel_size: float, unit: str) -> None: + from PIL import ImageDraw, ImageFont + + if pixel_size <= 0: + return + + draw = ImageDraw.Draw(image, mode="RGBA") + font = ImageFont.load_default() + width, height = image.size + margin = max(8, int(min(width, height) * 0.04)) + thickness = max(2, int(height * 0.01)) + target_bar_px = max(36, int(width * 0.15)) + target_physical = float(target_bar_px) * float(pixel_size) + nice_physical = self._round_to_nice_value(target_physical) + bar_px = max(12, int(round(nice_physical / float(pixel_size)))) + bar_px = min(bar_px, max(12, int(width * 0.8))) + + x1 = width - margin + x0 = x1 - bar_px + y1 = height - margin + y0 = y1 - thickness + + draw.rectangle([(x0 + 1, y0 + 1), (x1 + 1, y1 + 1)], fill=(0, 0, 0, 180)) + draw.rectangle([(x0, y0), (x1, y1)], fill=(255, 255, 255, 255)) + + label = self._format_scale_label(nice_physical, unit) + label_bbox = draw.textbbox((0, 0), label, font=font) + label_w = label_bbox[2] - label_bbox[0] + label_h = label_bbox[3] - label_bbox[1] + tx = x0 + (bar_px - label_w) / 2 + ty = y0 - label_h - 4 + draw.text((tx + 1, ty + 1), label, fill=(0, 0, 0, 220), font=font) + draw.text((tx, ty), label, fill=(255, 255, 255, 255), font=font) + + zoom_label = "1.0x" + zoom_bbox = draw.textbbox((0, 0), zoom_label, font=font) + zoom_h = zoom_bbox[3] - zoom_bbox[1] + zx = margin + zy = height - margin - zoom_h + draw.text((zx + 1, zy + 1), zoom_label, fill=(0, 0, 0, 220), font=font) + draw.text((zx, zy), zoom_label, fill=(255, 255, 255, 255), font=font) + + def _draw_dp_overlays(self, image) -> None: + from PIL import ImageDraw + + draw = ImageDraw.Draw(image, mode="RGBA") + width, height = image.size + scale_x = float(width) / float(max(1, self.det_cols)) + scale_y = float(height) / float(max(1, self.det_rows)) + cx = float(self.roi_center_col) * scale_x + cy = float(self.roi_center_row) * scale_y + + if self.roi_active and self.roi_mode != "point": + stroke = (0, 220, 0, 240) + fill = (0, 220, 0, 45) + if self.roi_mode == "circle": + rx = float(self.roi_radius) * scale_x + ry = float(self.roi_radius) * scale_y + draw.ellipse([(cx - rx, cy - ry), (cx + rx, cy + ry)], outline=stroke, fill=fill, width=2) + elif self.roi_mode == "square": + rx = float(self.roi_radius) * scale_x + ry = float(self.roi_radius) * scale_y + draw.rectangle([(cx - rx, cy - ry), (cx + rx, cy + ry)], outline=stroke, fill=fill, width=2) + elif self.roi_mode == "rect": + rx = (float(self.roi_width) / 2.0) * scale_x + ry = (float(self.roi_height) / 2.0) * scale_y + draw.rectangle([(cx - rx, cy - ry), (cx + rx, cy + ry)], outline=stroke, fill=fill, width=2) + elif self.roi_mode == "annular": + outer_rx = float(self.roi_radius) * scale_x + outer_ry = float(self.roi_radius) * scale_y + inner_rx = float(self.roi_radius_inner) * scale_x + inner_ry = float(self.roi_radius_inner) * scale_y + draw.ellipse( + [(cx - outer_rx, cy - outer_ry), (cx + outer_rx, cy + outer_ry)], + outline=stroke, + fill=fill, + width=2, + ) + draw.ellipse( + [(cx - inner_rx, cy - inner_ry), (cx + inner_rx, cy + inner_ry)], + outline=stroke, + fill=(0, 0, 0, 0), + width=2, + ) + + marker_color = (0, 220, 0, 255) if self.roi_active else (255, 100, 100, 255) + self._draw_crosshair(draw, cx, cy, size=max(6, int(min(width, height) * 0.03)), color=marker_color, width=2) + + if len(self.profile_line) == 2: + p0, p1 = self.profile_line[0], self.profile_line[1] + x0 = float(p0["col"]) * scale_x + y0 = float(p0["row"]) * scale_y + x1 = float(p1["col"]) * scale_x + y1 = float(p1["row"]) * scale_y + draw.line([(x0, y0), (x1, y1)], fill=(0, 200, 255, 240), width=max(1, int(self.profile_width))) + r = 3 + draw.ellipse([(x0 - r, y0 - r), (x0 + r, y0 + r)], fill=(0, 200, 255, 255)) + draw.ellipse([(x1 - r, y1 - r), (x1 + r, y1 + r)], fill=(0, 200, 255, 255)) + + def _draw_vi_overlays(self, image) -> None: + from PIL import ImageDraw + + draw = ImageDraw.Draw(image, mode="RGBA") + width, height = image.size + scale_x = float(width) / float(max(1, self.shape_cols)) + scale_y = float(height) / float(max(1, self.shape_rows)) + + px = float(self.pos_col) * scale_x + py = float(self.pos_row) * scale_y + self._draw_crosshair( + draw, + px, + py, + size=max(6, int(min(width, height) * 0.03)), + color=(255, 100, 100, 240), + width=2, + ) + + if self.vi_roi_mode == "off": + return + + cx = float(self.vi_roi_center_col) * scale_x + cy = float(self.vi_roi_center_row) * scale_y + stroke = (180, 80, 255, 240) + fill = (180, 80, 255, 45) + if self.vi_roi_mode == "circle": + rx = float(self.vi_roi_radius) * scale_x + ry = float(self.vi_roi_radius) * scale_y + draw.ellipse([(cx - rx, cy - ry), (cx + rx, cy + ry)], outline=stroke, fill=fill, width=2) + elif self.vi_roi_mode == "square": + rx = float(self.vi_roi_radius) * scale_x + ry = float(self.vi_roi_radius) * scale_y + draw.rectangle([(cx - rx, cy - ry), (cx + rx, cy + ry)], outline=stroke, fill=fill, width=2) + elif self.vi_roi_mode == "rect": + rx = (float(self.vi_roi_width) / 2.0) * scale_x + ry = (float(self.vi_roi_height) / 2.0) * scale_y + draw.rectangle([(cx - rx, cy - ry), (cx + rx, cy + ry)], outline=stroke, fill=fill, width=2) + + self._draw_crosshair( + draw, + cx, + cy, + size=max(6, int(min(width, height) * 0.03)), + color=(180, 80, 255, 240), + width=2, + ) + + def _decorate_panel( + self, + image, + panel_key: str, + include_overlays: bool, + include_scalebar: bool, + ): + out = image.copy() + if include_overlays: + if panel_key == "diffraction": + self._draw_dp_overlays(out) + elif panel_key == "virtual": + self._draw_vi_overlays(out) + if include_scalebar: + if panel_key == "diffraction": + unit = "mrad" if self.k_calibrated else "px" + self._draw_scalebar_overlay(out, float(self.k_pixel_size), unit) + elif panel_key == "virtual": + self._draw_scalebar_overlay(out, float(self.pixel_size), "Å") + return out + + def _render_panel_image( + self, + panel_key: str, + include_overlays: bool, + include_scalebar: bool, + ) -> tuple[Any, dict[str, Any]]: + from PIL import Image + + if panel_key == "diffraction": + rgb, render_meta = self._render_dp_rgb() + elif panel_key == "virtual": + rgb, render_meta = self._render_virtual_rgb() + elif panel_key == "fft": + rgb, render_meta = self._render_fft_rgb() + else: + raise ValueError(f"Unsupported panel '{panel_key}'") + + panel = Image.fromarray(rgb, mode="RGB") + panel = self._decorate_panel(panel, panel_key, include_overlays, include_scalebar) + return panel, render_meta + + def _compose_horizontal(self, panels: list[Any]): + from PIL import Image + + height = max(panel.height for panel in panels) + width = sum(panel.width for panel in panels) + composite = Image.new("RGB", (width, height), color=(0, 0, 0)) + x0 = 0 + for panel in panels: + composite.paste(panel, (x0, 0)) + x0 += panel.width + return composite + + def _calibration_metadata(self) -> dict[str, Any]: + return { + "pixel_size_angstrom": float(self.pixel_size), + "pixel_size_unit": "Å/px", + "k_pixel_size": float(self.k_pixel_size), + "k_pixel_size_unit": "mrad/px" if self.k_calibrated else "px/px", + "k_calibrated": bool(self.k_calibrated), + "center_row": float(self.center_row), + "center_col": float(self.center_col), + "bf_radius": float(self.bf_radius), + } + + def _roi_metadata(self) -> dict[str, Any]: + return { + "active": bool(self.roi_active), + "mode": self.roi_mode, + "center_row": float(self.roi_center_row), + "center_col": float(self.roi_center_col), + "radius": float(self.roi_radius), + "radius_inner": float(self.roi_radius_inner), + "width": float(self.roi_width), + "height": float(self.roi_height), + } + + def _vi_roi_metadata(self) -> dict[str, Any]: + return { + "mode": self.vi_roi_mode, + "center_row": float(self.vi_roi_center_row), + "center_col": float(self.vi_roi_center_col), + "radius": float(self.vi_roi_radius), + "width": float(self.vi_roi_width), + "height": float(self.vi_roi_height), + } + + def _export_settings_metadata(self) -> dict[str, Any]: + return { + "default_view": self.export_default_view, + "default_format": self.export_default_format, + "include_overlays": bool(self.export_include_overlays), + "include_scalebar": bool(self.export_include_scalebar), + "dpi": int(self.export_default_dpi), + } + + def _build_image_export_metadata( + self, + export_path: pathlib.Path, + view_key: str, + fmt: str, + render_meta: dict[str, Any], + include_overlays: bool, + include_scalebar: bool, + export_kind: str, + extra: dict[str, Any] | None = None, + ) -> dict[str, Any]: + metadata: dict[str, Any] = { + **build_json_header("Show4DSTEM"), + "view": view_key, + "format": fmt, + "export_kind": export_kind, + "path": str(export_path), + "position": {"row": int(self.pos_row), "col": int(self.pos_col)}, + "frame_idx": int(self.frame_idx), + "n_frames": int(self.n_frames), + "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, + "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, + "roi": self._roi_metadata(), + "vi_roi": self._vi_roi_metadata(), + "calibration": self._calibration_metadata(), + "display": render_meta, + "include_overlays": bool(include_overlays), + "include_scalebar": bool(include_scalebar), + "export_settings": self._export_settings_metadata(), + } + if extra: + metadata.update(extra) + return metadata + + @staticmethod + def _sha256_file(path: pathlib.Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as f: + while True: + chunk = f.read(1_048_576) + if not chunk: + break + digest.update(chunk) + return digest.hexdigest() + + def _build_file_record( + self, + path: pathlib.Path, + metadata_path: pathlib.Path | None = None, + index: int | None = None, + ) -> dict[str, Any]: + record: dict[str, Any] = { + "path": str(path), + "sha256": self._sha256_file(path), + "size_bytes": int(path.stat().st_size), + } + if metadata_path is not None and metadata_path.exists(): + record["metadata_path"] = str(metadata_path) + record["metadata_sha256"] = self._sha256_file(metadata_path) + record["metadata_size_bytes"] = int(metadata_path.stat().st_size) + if index is not None: + record["index"] = int(index) + return record + + def _record_export_event(self, event: dict[str, Any]) -> None: + payload = { + "session_id": self._export_session_id, + "timestamp_utc": datetime.now(timezone.utc).isoformat(), + } + payload.update(event) + self._export_log.append(payload) + + def _validate_sparse_frame_idx(self, frame_idx: int | None) -> int: + if self.n_frames <= 1: + return 0 + if frame_idx is None: + return int(self.frame_idx) + idx = int(frame_idx) + if idx < 0 or idx >= self.n_frames: + raise ValueError(f"frame_idx={idx} is out of range [0, {self.n_frames - 1}]") + return idx + + def _normalize_sparse_mask(self, mask: np.ndarray) -> np.ndarray: + arr = np.asarray(mask) + if self.n_frames <= 1: + if arr.shape == (self.shape_rows, self.shape_cols): + arr = arr[None, ...] + elif arr.shape != (1, self.shape_rows, self.shape_cols): + raise ValueError( + f"mask shape {arr.shape} does not match " + f"(scan_rows, scan_cols)=({self.shape_rows}, {self.shape_cols})" + ) + elif arr.shape != (self.n_frames, self.shape_rows, self.shape_cols): + raise ValueError( + f"mask shape {arr.shape} does not match " + f"(n_frames, scan_rows, scan_cols)=({self.n_frames}, {self.shape_rows}, {self.shape_cols})" + ) + return arr.astype(bool, copy=False) + + def _coerce_dp_array(self, dp: np.ndarray) -> np.ndarray: + arr = np.asarray(to_numpy(dp), dtype=np.float32) + if arr.shape != (self.det_rows, self.det_cols): + raise ValueError( + f"dp shape {arr.shape} does not match detector_shape " + f"({self.det_rows}, {self.det_cols})" + ) + return arr + + def _write_dp_to_data(self, frame_idx: int, row: int, col: int, dp_arr: np.ndarray) -> None: + dp_tensor = torch.from_numpy(dp_arr).to(device=self._device, dtype=torch.float32) + if self.n_frames > 1: + self._data[frame_idx, row, col] = dp_tensor + elif self._data.ndim == 4: + self._data[row, col] = dp_tensor + else: + flat_idx = row * self.shape_cols + col + self._data[flat_idx] = dp_tensor + + def _ingest_scan_point_core( + self, + row: int, + col: int, + dp: np.ndarray, + frame_idx: int, + dose: float, + refresh: bool, + ) -> None: + row_i, col_i = self._validate_position((row, col)) + frame_i = self._validate_sparse_frame_idx(frame_idx) + dp_arr = self._coerce_dp_array(dp) + dose_value = float(dose) + if not np.isfinite(dose_value) or dose_value < 0: + raise ValueError(f"dose must be finite and >= 0, got {dose}") + + key = (int(frame_i), int(row_i), int(col_i)) + if key not in self._sparse_samples: + self._sparse_order.append(key) + self._sparse_samples[key] = dp_arr.copy() + self._sparse_mask[frame_i, row_i, col_i] = True + self._dose_map[frame_i, row_i, col_i] += dose_value + + self._write_dp_to_data(frame_i, row_i, col_i, dp_arr) + self.dp_global_min = max(min(float(self.dp_global_min), float(dp_arr.min())), MIN_LOG_VALUE) + self.dp_global_max = max(float(self.dp_global_max), float(dp_arr.max())) + + if refresh: + self._compute_virtual_image_from_roi() + self._update_frame() + + def _detector_integration_kernel(self) -> tuple[np.ndarray | None, tuple[int, int] | None]: + cx, cy = float(self.roi_center_col), float(self.roi_center_row) + rr, cc = np.meshgrid( + np.arange(self.det_rows, dtype=np.float32), + np.arange(self.det_cols, dtype=np.float32), + indexing="ij", + ) + if self.roi_mode == "circle" and self.roi_radius > 0: + mask = (cc - cx) ** 2 + (rr - cy) ** 2 <= float(self.roi_radius) ** 2 + return mask.astype(np.float32, copy=False), None + if self.roi_mode == "square" and self.roi_radius > 0: + half = float(self.roi_radius) + mask = (np.abs(cc - cx) <= half) & (np.abs(rr - cy) <= half) + return mask.astype(np.float32, copy=False), None + if self.roi_mode == "annular" and self.roi_radius > 0: + outer = float(self.roi_radius) + inner = float(self.roi_radius_inner) + dist_sq = (cc - cx) ** 2 + (rr - cy) ** 2 + mask = (dist_sq >= inner**2) & (dist_sq <= outer**2) + return mask.astype(np.float32, copy=False), None + if self.roi_mode == "rect" and self.roi_width > 0 and self.roi_height > 0: + hw = float(self.roi_width) / 2.0 + hh = float(self.roi_height) / 2.0 + mask = (np.abs(cc - cx) <= hw) & (np.abs(rr - cy) <= hh) + return mask.astype(np.float32, copy=False), None + row = int(max(0, min(round(cy), self.det_rows - 1))) + col = int(max(0, min(round(cx), self.det_cols - 1))) + return None, (row, col) + + def _integrate_dp_value( + self, + dp: np.ndarray, + mask: np.ndarray | None, + point_idx: tuple[int, int] | None, + ) -> float: + arr = np.asarray(dp, dtype=np.float32) + if point_idx is not None: + row, col = point_idx + return float(arr[row, col]) + if mask is None: + return 0.0 + return float((arr * mask).sum()) + + def _virtual_image_from_frame_array(self, frame_data: np.ndarray) -> np.ndarray: + arr = np.asarray(frame_data, dtype=np.float32) + if arr.shape != (self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): + raise ValueError( + f"frame_data shape {arr.shape} does not match " + f"({self.shape_rows}, {self.shape_cols}, {self.det_rows}, {self.det_cols})" + ) + mask, point_idx = self._detector_integration_kernel() + if point_idx is not None: + row, col = point_idx + return arr[:, :, row, col].astype(np.float32, copy=False) + return (arr * mask[None, None, :, :]).sum(axis=(2, 3)).astype(np.float32) + + @staticmethod + def _idw_reconstruct( + shape: tuple[int, int], + points: np.ndarray, + values: np.ndarray, + power: float = 2.0, + k_neighbors: int = 16, + ) -> np.ndarray: + if points.size == 0: + return np.zeros(shape, dtype=np.float32) + rr, cc = np.meshgrid( + np.arange(shape[0], dtype=np.float32), + np.arange(shape[1], dtype=np.float32), + indexing="ij", + ) + coords = np.stack([rr.reshape(-1), cc.reshape(-1)], axis=1) + dist_sq = ((coords[:, None, :] - points[None, :, :]) ** 2).sum(axis=2) + 1e-6 + + if k_neighbors > 0 and points.shape[0] > k_neighbors: + idx = np.argpartition(dist_sq, kth=k_neighbors - 1, axis=1)[:, :k_neighbors] + dist_sq = np.take_along_axis(dist_sq, idx, axis=1) + vals_local = values[idx] + else: + vals_local = np.broadcast_to(values[None, :], dist_sq.shape) + + weights = 1.0 / np.power(dist_sq, power / 2.0) + pred = (weights * vals_local).sum(axis=1) / np.maximum(weights.sum(axis=1), 1e-6) + return pred.reshape(shape).astype(np.float32, copy=False) + + def _resolve_reference_virtual_image( + self, + reference: str | np.ndarray, + frame_idx: int, + ) -> tuple[np.ndarray, str]: + if isinstance(reference, str): + key = reference.strip().lower() + if key != "full_raster": + raise ValueError("reference must be 'full_raster' or a NumPy array") + if self.n_frames > 1: + frame = self._data[frame_idx].detach().cpu().numpy() + elif self._data.ndim == 4: + frame = self._data.detach().cpu().numpy() + else: + frame = self._data.detach().cpu().numpy().reshape( + self.shape_rows, self.shape_cols, self.det_rows, self.det_cols + ) + return self._virtual_image_from_frame_array(frame), "full_raster" + + arr = np.asarray(to_numpy(reference), dtype=np.float32) + if arr.shape == (self.shape_rows, self.shape_cols): + return arr.astype(np.float32, copy=False), "virtual_image" + if arr.shape == (self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): + return self._virtual_image_from_frame_array(arr), "frame_data" + if arr.shape == (self.n_frames, self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): + return self._virtual_image_from_frame_array(arr[frame_idx]), "stack_frame_data" + raise ValueError( + "Unsupported reference shape. Expected one of: " + f"(scan_rows, scan_cols), " + f"(scan_rows, scan_cols, det_rows, det_cols), or " + f"(n_frames, scan_rows, scan_cols, det_rows, det_cols)." + ) + + def _extract_sparse_samples(self, frame_idx: int) -> tuple[np.ndarray, np.ndarray]: + mask = self._sparse_mask[frame_idx] + coords = np.argwhere(mask) + if coords.size == 0: + return ( + np.zeros((0, 2), dtype=np.float32), + np.zeros((0,), dtype=np.float32), + ) + + integ_mask, point_idx = self._detector_integration_kernel() + values = np.zeros((coords.shape[0],), dtype=np.float32) + for i, (row, col) in enumerate(coords): + key = (int(frame_idx), int(row), int(col)) + dp = self._sparse_samples.get(key) + if dp is None: + dp = self._get_frame(int(row), int(col)) + values[i] = self._integrate_dp_value(dp, integ_mask, point_idx) + points = coords.astype(np.float32, copy=False) + return points, values + + def ingest_scan_point( + self, + row: int, + col: int, + dp: np.ndarray, + frame_idx: int = 0, + dose: float | None = None, + ) -> Self: + """ + Ingest one scanned diffraction pattern into sparse acquisition state. + + Parameters + ---------- + row : int + Scan-space row index. + col : int + Scan-space column index. + dp : array_like + Diffraction pattern with shape ``(det_rows, det_cols)``. + frame_idx : int, default 0 + Frame index for 5D data. + dose : float, optional + Dose contribution for this acquisition event. Defaults to ``1.0``. + + Returns + ------- + Show4DSTEM + Self for method chaining. + """ + self._ingest_scan_point_core( + row=row, + col=col, + dp=dp, + frame_idx=frame_idx, + dose=1.0 if dose is None else float(dose), + refresh=True, + ) + self._record_export_event( + { + "export_kind": "ingest_scan_point", + "frame_idx": int(self._validate_sparse_frame_idx(frame_idx)), + "row": int(row), + "col": int(col), + "dose": float(1.0 if dose is None else dose), + } + ) + return self + + def ingest_scan_block( + self, + rows: list[int] | np.ndarray, + cols: list[int] | np.ndarray, + dp_block: np.ndarray, + frame_idx: int = 0, + ) -> Self: + """ + Ingest multiple scanned diffraction patterns in one call. + + Parameters + ---------- + rows : list[int] or np.ndarray + Row indices for each pattern in ``dp_block``. + cols : list[int] or np.ndarray + Column indices for each pattern in ``dp_block``. + dp_block : np.ndarray + Diffraction stack with shape ``(n_points, det_rows, det_cols)``. + frame_idx : int, default 0 + Frame index for 5D data. + + Returns + ------- + Show4DSTEM + Self for method chaining. + """ + rows_arr = np.asarray(rows, dtype=np.int64).reshape(-1) + cols_arr = np.asarray(cols, dtype=np.int64).reshape(-1) + if rows_arr.size != cols_arr.size: + raise ValueError("rows and cols must have the same length") + + block = np.asarray(to_numpy(dp_block), dtype=np.float32) + if block.ndim == 2: + block = block[None, ...] + if block.ndim != 3 or block.shape[1:] != (self.det_rows, self.det_cols): + raise ValueError( + f"dp_block shape must be (n_points, {self.det_rows}, {self.det_cols}), got {block.shape}" + ) + if block.shape[0] != rows_arr.size: + raise ValueError( + f"dp_block has {block.shape[0]} patterns but rows/cols specify {rows_arr.size} points" + ) + + frame_i = self._validate_sparse_frame_idx(frame_idx) + for idx in range(rows_arr.size): + self._ingest_scan_point_core( + row=int(rows_arr[idx]), + col=int(cols_arr[idx]), + dp=block[idx], + frame_idx=frame_i, + dose=1.0, + refresh=False, + ) + + self._compute_virtual_image_from_roi() + self._update_frame() + self._record_export_event( + { + "export_kind": "ingest_scan_block", + "frame_idx": int(frame_i), + "n_points": int(rows_arr.size), + } + ) + return self + + def get_sparse_state(self) -> dict[str, Any]: + """ + Return sparse acquisition state for checkpointing or replay. + + Returns + ------- + dict + Sparse state with sampling mask, sampled diffraction stack, + sampled-point coordinates, and dose map. + """ + coords = np.argwhere(self._sparse_mask) + sampled_points = [ + {"frame_idx": int(f), "row": int(r), "col": int(c)} + for (f, r, c) in coords + ] + if coords.size: + sampled_data = np.stack( + [ + self._sparse_samples.get((int(f), int(r), int(c)), self._get_frame(int(r), int(c))) + for (f, r, c) in coords + ], + axis=0, + ).astype(np.float32, copy=False) + else: + sampled_data = np.zeros((0, self.det_rows, self.det_cols), dtype=np.float32) + + mask_payload = self._sparse_mask[0].copy() if self.n_frames <= 1 else self._sparse_mask.copy() + dose_payload = self._dose_map[0].copy() if self.n_frames <= 1 else self._dose_map.copy() + return { + **build_json_header("Show4DSTEM"), + "format": "json", + "export_kind": "sparse_state_snapshot", + "frame_idx": int(self.frame_idx), + "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, + "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, + "mask": mask_payload, + "sampled_data": sampled_data, + "sampled_points": sampled_points, + "dose_map": dose_payload, + "n_sampled": int(len(sampled_points)), + "total_dose": float(self._dose_map.sum()), + } + + def set_sparse_state( + self, + mask: np.ndarray, + sampled_data: np.ndarray, + ) -> Self: + """ + Restore sparse acquisition state from mask + sampled data. + + Parameters + ---------- + mask : np.ndarray + Boolean scan mask. Shape ``(scan_rows, scan_cols)`` for 4D, + or ``(n_frames, scan_rows, scan_cols)`` for 5D. + sampled_data : np.ndarray + Either compact stack ``(n_sampled, det_rows, det_cols)`` + matching row-major ``mask`` order, or dense data aligned to mask: + ``(scan_rows, scan_cols, det_rows, det_cols)`` for 4D, + ``(n_frames, scan_rows, scan_cols, det_rows, det_cols)`` for 5D. + + Returns + ------- + Show4DSTEM + Self for method chaining. + """ + mask_3d = self._normalize_sparse_mask(mask) + coords = np.argwhere(mask_3d) + + payload = np.asarray(to_numpy(sampled_data), dtype=np.float32) + n_points = int(coords.shape[0]) + + if payload.ndim == 3: + if payload.shape[0] != n_points or payload.shape[1:] != (self.det_rows, self.det_cols): + raise ValueError( + f"Compact sampled_data must be (n_sampled, {self.det_rows}, {self.det_cols}); " + f"got {payload.shape} for n_sampled={n_points}" + ) + compact = payload + elif self.n_frames <= 1 and payload.shape == (self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): + compact = np.stack( + [payload[int(r), int(c)] for (_, r, c) in coords], + axis=0, + ) if n_points else np.zeros((0, self.det_rows, self.det_cols), dtype=np.float32) + elif payload.shape == ( + self.n_frames, + self.shape_rows, + self.shape_cols, + self.det_rows, + self.det_cols, + ): + compact = np.stack( + [payload[int(f), int(r), int(c)] for (f, r, c) in coords], + axis=0, + ) if n_points else np.zeros((0, self.det_rows, self.det_cols), dtype=np.float32) + else: + raise ValueError( + "Unsupported sampled_data shape for set_sparse_state. " + "Use compact (n_sampled, det_rows, det_cols) or dense per-mask arrays." + ) + + self._sparse_samples = {} + self._sparse_order = [] + self._sparse_mask = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=bool) + self._dose_map = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=np.float32) + + for idx, (frame_idx, row, col) in enumerate(coords): + self._ingest_scan_point_core( + row=int(row), + col=int(col), + dp=compact[idx], + frame_idx=int(frame_idx), + dose=1.0, + refresh=False, + ) + + self._compute_virtual_image_from_roi() + self._update_frame() + self._record_export_event( + { + "export_kind": "set_sparse_state", + "n_sampled": int(n_points), + } + ) + return self + + def _resolve_proposal_count( + self, + k: int, + frame_idx: int, + budget: dict[str, Any] | None, + ) -> int: + count = int(k) + if count < 1: + raise ValueError(f"k must be >= 1, got {k}") + if budget is None: + return count + + existing_points = int(self._sparse_mask[frame_idx].sum()) + existing_dose = float(self._dose_map[frame_idx].sum()) + total_points = int(self.shape_rows * self.shape_cols) + + if "max_new_points" in budget: + count = min(count, int(budget["max_new_points"])) + if "max_total_points" in budget: + count = min(count, max(0, int(budget["max_total_points"]) - existing_points)) + if "max_total_fraction" in budget: + allowed_total = int(round(float(budget["max_total_fraction"]) * total_points)) + count = min(count, max(0, allowed_total - existing_points)) + if "max_total_dose" in budget: + dose_per_point = float(budget.get("dose_per_point", 1.0)) + if dose_per_point <= 0: + raise ValueError("budget['dose_per_point'] must be > 0") + remaining = float(budget["max_total_dose"]) - existing_dose + count = min(count, max(0, int(math.floor(remaining / dose_per_point)))) + return max(0, int(count)) + + def propose_next_points( + self, + k: int, + strategy: str = "adaptive", + budget: dict[str, Any] | None = None, + ) -> list[tuple[int, int]]: + """ + Propose next scan points from current sparse acquisition state. + + Parameters + ---------- + k : int + Maximum number of new points to propose. + strategy : str, default "adaptive" + Proposal strategy: ``"adaptive"``, ``"random"``, or ``"raster"``. + budget : dict, optional + Optional constraints and strategy parameters. Supported keys: + ``frame_idx``, ``max_new_points``, ``max_total_points``, + ``max_total_fraction``, ``max_total_dose``, ``dose_per_point``, + ``roi_mask``, ``seed``, ``min_spacing``, ``step``, + ``local_window``, ``dose_lambda``, ``weights``, ``bidirectional``. + + Returns + ------- + list[tuple[int, int]] + Proposed ``(row, col)`` scan coordinates. + """ + budget_dict = {} if budget is None else dict(budget) + strategy_key = str(strategy).strip().lower() + if strategy_key not in {"adaptive", "random", "raster"}: + raise ValueError("strategy must be one of: adaptive, random, raster") + + frame_idx = self._validate_sparse_frame_idx(budget_dict.get("frame_idx", self.frame_idx)) + n_select = self._resolve_proposal_count(int(k), frame_idx, budget_dict) + if n_select <= 0: + return [] + + sampled_mask = self._sparse_mask[frame_idx].copy() + allowed_mask = ~sampled_mask + roi_mask_raw = budget_dict.get("roi_mask", None) + if roi_mask_raw is not None: + roi_mask = np.asarray(roi_mask_raw, dtype=bool) + if roi_mask.shape != (self.shape_rows, self.shape_cols): + raise ValueError( + f"roi_mask shape {roi_mask.shape} must match " + f"scan_shape ({self.shape_rows}, {self.shape_cols})" + ) + allowed_mask &= roi_mask + + proposals: list[tuple[int, int]] = [] + if strategy_key == "adaptive": + local_window = int(budget_dict.get("local_window", 5)) + if local_window < 1: + raise ValueError("budget['local_window'] must be >= 1") + min_spacing = int(budget_dict.get("min_spacing", 2)) + if min_spacing < 0: + raise ValueError("budget['min_spacing'] must be >= 0") + dose_lambda = float(budget_dict.get("dose_lambda", 0.25)) + if not np.isfinite(dose_lambda): + raise ValueError("budget['dose_lambda'] must be finite") + + default_weights = { + "vi_gradient": 0.4, + "vi_local_std": 0.3, + "dp_variance": 0.3, + } + merged_weights = dict(default_weights) + raw_weights = budget_dict.get("weights", None) + if raw_weights is not None: + for key, value in dict(raw_weights).items(): + if key not in default_weights: + raise ValueError( + f"Unsupported adaptive weight '{key}'. " + f"Supported: {', '.join(default_weights.keys())}" + ) + merged_weights[key] = float(value) + weight_sum = sum(max(0.0, float(v)) for v in merged_weights.values()) + if weight_sum <= 0: + raise ValueError("At least one adaptive weight must be > 0") + weights = {k: max(0.0, float(v)) / weight_sum for k, v in merged_weights.items()} + + vi = self._virtual_image_for_frame(frame_idx) + grad_row, grad_col = np.gradient(vi) + vi_gradient = np.hypot(grad_row, grad_col).astype(np.float32) + mean_local = self._box_mean_map(vi, local_window) + mean_sq_local = self._box_mean_map(vi * vi, local_window) + vi_local_std = np.sqrt(np.maximum(mean_sq_local - mean_local * mean_local, 0.0)).astype(np.float32) + dp_variance = self._dp_variance_map(frame_idx=frame_idx) + + utility = ( + weights["vi_gradient"] * self._normalize_score_map(vi_gradient) + + weights["vi_local_std"] * self._normalize_score_map(vi_local_std) + + weights["dp_variance"] * self._normalize_score_map(dp_variance) + ).astype(np.float32) + + frame_dose = self._dose_map[frame_idx].astype(np.float32, copy=False) + if float(frame_dose.max()) > 0: + utility = utility - float(dose_lambda) * (frame_dose / float(frame_dose.max())) + + picks = self._select_spaced_topk( + scores=utility, + k=n_select, + min_spacing=min_spacing, + allowed_mask=allowed_mask, + excluded_mask=np.zeros_like(allowed_mask, dtype=bool), + ) + proposals = [(int(r), int(c)) for (r, c) in picks] + elif strategy_key == "random": + coords = np.argwhere(allowed_mask) + if coords.size: + seed = budget_dict.get("seed", None) + rng = np.random.default_rng(None if seed is None else int(seed)) + n_take = min(n_select, int(coords.shape[0])) + idx = rng.choice(coords.shape[0], size=n_take, replace=False) + chosen = coords[idx] + proposals = [(int(r), int(c)) for r, c in chosen] + else: + step = int(budget_dict.get("step", 1)) + if step < 1: + raise ValueError("budget['step'] must be >= 1") + bidirectional = bool(budget_dict.get("bidirectional", True)) + for row in range(0, self.shape_rows, step): + cols = list(range(0, self.shape_cols, step)) + if bidirectional and ((row // step) % 2 == 1): + cols.reverse() + for col in cols: + if allowed_mask[row, col]: + proposals.append((int(row), int(col))) + if len(proposals) >= n_select: + break + if len(proposals) >= n_select: + break + + self._record_export_event( + { + "export_kind": "propose_next_points", + "strategy": strategy_key, + "frame_idx": int(frame_idx), + "k_requested": int(k), + "k_returned": int(len(proposals)), + } + ) + return proposals + + def evaluate_against_reference( + self, + reference: str | np.ndarray = "full_raster", + metrics: list[str] | None = None, + ) -> dict[str, Any]: + """ + Evaluate sparse-sampled reconstruction against a reference image. + + Parameters + ---------- + reference : str or np.ndarray, default "full_raster" + Reference target. ``"full_raster"`` uses the current full dataset + and current ROI integration settings. Arrays are also accepted + (virtual image or full diffraction stack; see method docs). + metrics : list[str], optional + Metric names to compute. Supported: ``"rmse"``, ``"nrmse"``, + ``"mae"``, ``"psnr"``. + + Returns + ------- + dict + Evaluation summary including sampled fraction and metric values. + """ + metric_names = ( + ["rmse", "nrmse", "mae", "psnr"] + if metrics is None + else [str(name).strip().lower() for name in metrics] + ) + supported = {"rmse", "nrmse", "mae", "psnr"} + unknown = [name for name in metric_names if name not in supported] + if unknown: + raise ValueError(f"Unsupported metrics: {unknown}. Supported: {sorted(supported)}") + + frame_idx = int(self.frame_idx if self.n_frames <= 1 else self._validate_sparse_frame_idx(self.frame_idx)) + points, values = self._extract_sparse_samples(frame_idx) + if points.shape[0] == 0: + raise ValueError("No sparse samples available for evaluation. Ingest points first.") + + reference_vi, reference_kind = self._resolve_reference_virtual_image(reference, frame_idx) + reconstruction = self._idw_reconstruct( + shape=(self.shape_rows, self.shape_cols), + points=points, + values=values, + power=2.0, + k_neighbors=16, + ) + + ref = np.asarray(reference_vi, dtype=np.float32) + pred = np.asarray(reconstruction, dtype=np.float32) + diff = pred - ref + mse = float(np.mean(diff * diff)) + rmse = float(np.sqrt(mse)) + mae = float(np.mean(np.abs(diff))) + ref_range = float(ref.max() - ref.min()) + 1e-6 + nrmse = float(rmse / ref_range) + peak = float(max(float(ref.max()), 1e-6)) + psnr = 120.0 if mse <= 1e-12 else float(20.0 * np.log10(peak) - 10.0 * np.log10(mse)) + + metric_values = { + "rmse": rmse, + "nrmse": nrmse, + "mae": mae, + "psnr": psnr, + } + selected_metrics = {name: float(metric_values[name]) for name in metric_names} + + summary = { + "reference_kind": reference_kind, + "frame_idx": int(frame_idx), + "n_sampled": int(points.shape[0]), + "sampled_fraction": float(points.shape[0] / max(1, self.shape_rows * self.shape_cols)), + "metrics": selected_metrics, + "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, + "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, + } + self._record_export_event( + { + "export_kind": "evaluate_against_reference", + "reference_kind": reference_kind, + "frame_idx": int(frame_idx), + "n_sampled": int(points.shape[0]), + "sampled_fraction": float(summary["sampled_fraction"]), + "metrics": selected_metrics, + } + ) + return summary + + def export_session_bundle( + self, + path: str | pathlib.Path, + ) -> pathlib.Path: + """ + Export a reproducible session bundle for sparse/adaptive workflows. + + The bundle includes widget state, sparse-state arrays, a current view + image with metadata, and the reproducibility report. + + Parameters + ---------- + path : str or pathlib.Path + Output directory for bundle files. + + Returns + ------- + pathlib.Path + Path to the bundle manifest JSON. + """ + bundle_dir = pathlib.Path(path) + bundle_dir.mkdir(parents=True, exist_ok=True) + + state_path = bundle_dir / "widget_state.json" + self.save(state_path) + + sparse_state = self.get_sparse_state() + sparse_npz_path = bundle_dir / "sparse_state.npz" + np.savez_compressed( + sparse_npz_path, + mask=sparse_state["mask"], + sampled_data=sparse_state["sampled_data"], + dose_map=sparse_state["dose_map"], + ) + + sparse_points_path = bundle_dir / "sparse_points.json" + sparse_points_payload = { + **build_json_header("Show4DSTEM"), + "format": "json", + "export_kind": "sparse_points", + "n_sampled": int(sparse_state["n_sampled"]), + "sampled_points": sparse_state["sampled_points"], + } + sparse_points_path.write_text(json.dumps(sparse_points_payload, indent=2)) + + image_path = bundle_dir / "current_all.png" + image_written = self.save_image( + image_path, + view="all", + include_metadata=True, + include_overlays=True, + include_scalebar=True, + ) + image_meta_path = image_written.with_suffix(".json") + + report_path = self.save_reproducibility_report(bundle_dir / "reproducibility_report.json") + + manifest_path = bundle_dir / "session_bundle_manifest.json" + manifest_payload = { + **build_json_header("Show4DSTEM"), + "format": "json", + "export_kind": "session_bundle", + "bundle_path": str(bundle_dir), + "created_utc": datetime.now(timezone.utc).isoformat(), + "session_id": self._export_session_id, + "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, + "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, + "sparse_summary": { + "n_sampled": int(sparse_state["n_sampled"]), + "sampled_fraction": float( + sparse_state["n_sampled"] / max(1, self.shape_rows * self.shape_cols * self.n_frames) + ), + "total_dose": float(sparse_state["total_dose"]), + }, + "files": { + "state": str(state_path), + "sparse_npz": str(sparse_npz_path), + "sparse_points_json": str(sparse_points_path), + "image": str(image_written), + "image_metadata": str(image_meta_path), + "reproducibility_report": str(report_path), + }, + } + manifest_path.write_text(json.dumps(manifest_payload, indent=2)) + + self._record_export_event( + { + "export_kind": "session_bundle", + "n_sampled": int(sparse_state["n_sampled"]), + "outputs": [ + self._build_file_record(state_path), + self._build_file_record(sparse_npz_path), + self._build_file_record(sparse_points_path), + self._build_file_record(image_written, metadata_path=image_meta_path), + self._build_file_record(report_path), + self._build_file_record(manifest_path), + ], + } + ) + return manifest_path + + def _normalize_score_map(self, values: np.ndarray) -> np.ndarray: + arr = np.asarray(values, dtype=np.float32) + if arr.size == 0: + return np.zeros_like(arr, dtype=np.float32) + vmin = float(np.percentile(arr, 1.0)) + vmax = float(np.percentile(arr, 99.0)) + if vmax <= vmin: + return np.zeros_like(arr, dtype=np.float32) + return np.clip((arr - vmin) / (vmax - vmin), 0.0, 1.0).astype(np.float32) + + def _box_mean_map(self, values: np.ndarray, window: int) -> np.ndarray: + arr = np.asarray(values, dtype=np.float32) + win = int(window) + if win <= 1: + return arr.copy() + if win % 2 == 0: + win += 1 + pad = win // 2 + padded = np.pad(arr, ((pad, pad), (pad, pad)), mode="reflect") + integral = np.pad(padded, ((1, 0), (1, 0)), mode="constant").cumsum(axis=0).cumsum(axis=1) + sums = ( + integral[win:, win:] + - integral[:-win, win:] + - integral[win:, :-win] + + integral[:-win, :-win] + ) + return (sums / float(win * win)).astype(np.float32) + + def _dp_variance_map(self, frame_idx: int | None = None) -> np.ndarray: + if frame_idx is None or self.n_frames <= 1: + data = self._frame_data + else: + idx = self._validate_sparse_frame_idx(frame_idx) + data = self._data[idx] + if data.ndim == 4: + variance = data.var(dim=(2, 3), unbiased=False) + return variance.detach().cpu().numpy().astype(np.float32, copy=False) + variance = data.var(dim=(1, 2), unbiased=False) + return variance.detach().cpu().numpy().reshape(self.shape_rows, self.shape_cols).astype(np.float32, copy=False) + + def _build_coarse_points(self, step: int, bidirectional: bool) -> list[tuple[int, int]]: + points: list[tuple[int, int]] = [] + for r in range(0, self.shape_rows, step): + cols = list(range(0, self.shape_cols, step)) + if bidirectional and ((r // step) % 2 == 1): + cols.reverse() + for c in cols: + points.append((int(r), int(c))) + return points + + def _select_spaced_topk( + self, + scores: np.ndarray, + k: int, + min_spacing: int, + allowed_mask: np.ndarray, + excluded_mask: np.ndarray, + ) -> list[tuple[int, int]]: + work = np.asarray(scores, dtype=np.float32).copy() + work[~allowed_mask] = -np.inf + work[excluded_mask] = -np.inf + selected: list[tuple[int, int]] = [] + radius = max(0, int(min_spacing)) + + for _ in range(int(max(0, k))): + flat_idx = int(np.argmax(work)) + best_score = float(work.flat[flat_idx]) + if not np.isfinite(best_score): + break + row, col = np.unravel_index(flat_idx, work.shape) + selected.append((int(row), int(col))) + if radius == 0: + work[row, col] = -np.inf + continue + r0 = max(0, row - radius) + r1 = min(work.shape[0], row + radius + 1) + c0 = max(0, col - radius) + c1 = min(work.shape[1], col + radius + 1) + rr, cc = np.ogrid[r0:r1, c0:c1] + neighborhood = (rr - row) ** 2 + (cc - col) ** 2 <= radius ** 2 + block = work[r0:r1, c0:c1] + block[neighborhood] = -np.inf + return selected + + def _nearest_neighbor_order( + self, + points: list[tuple[int, int]], + start: tuple[int, int] | None = None, + ) -> list[tuple[int, int]]: + remaining = [tuple(map(int, pt)) for pt in points] + if not remaining: + return [] + + if start is None: + current = remaining.pop(0) + else: + sr, sc = int(start[0]), int(start[1]) + start_idx = min( + range(len(remaining)), + key=lambda i: (remaining[i][0] - sr) ** 2 + (remaining[i][1] - sc) ** 2, + ) + current = remaining.pop(start_idx) + + ordered = [current] + while remaining: + cr, cc = current + next_idx = min( + range(len(remaining)), + key=lambda i: (remaining[i][0] - cr) ** 2 + (remaining[i][1] - cc) ** 2, + ) + current = remaining.pop(next_idx) + ordered.append(current) + return ordered + + def save_image( + self, + path: str | pathlib.Path, + view: str | None = None, + position: tuple[int, int] | None = None, + frame_idx: int | None = None, + format: str | None = None, + include_metadata: bool = True, + metadata_path: str | pathlib.Path | None = None, + include_overlays: bool | None = None, + include_scalebar: bool | None = None, + restore_state: bool = True, + dpi: int | None = None, + ) -> pathlib.Path: + """ + Save the current visualization as PNG or PDF. + + Parameters + ---------- + path : str or pathlib.Path + Output image path. + view : str, optional + One of: "diffraction", "virtual", "fft", "all". + position : tuple[int, int], optional + Temporary scan position override as (row, col) for this export. + frame_idx : int, optional + Temporary frame index override for 5D data. + format : str, optional + "png" or "pdf". If omitted, inferred from file extension. + include_metadata : bool, default True + If True, writes JSON metadata next to the image. + metadata_path : str or pathlib.Path, optional + Override metadata JSON path. + include_overlays : bool, optional + Draw ROI/profile/crosshair overlays on exported panels. + Defaults to ``export_include_overlays``. + include_scalebar : bool, optional + Draw panel scale bars on exported panels. + Defaults to ``export_include_scalebar``. + restore_state : bool, default True + If True, temporary position/frame overrides are reverted after export. + dpi : int, optional + Export DPI metadata. + + Returns + ------- + pathlib.Path + The written image path. + """ + from PIL import Image + + export_path = pathlib.Path(path) + view_key = self._validate_export_view(view) + fmt = self._resolve_export_format(export_path, format) + dpi_value = int(self.export_default_dpi if dpi is None else dpi) + overlays_enabled = ( + bool(self.export_include_overlays) + if include_overlays is None + else bool(include_overlays) + ) + scalebar_enabled = ( + bool(self.export_include_scalebar) + if include_scalebar is None + else bool(include_scalebar) + ) + + if dpi_value <= 0: + raise ValueError(f"dpi must be > 0, got {dpi_value}") + + export_path.parent.mkdir(parents=True, exist_ok=True) + + prev_row, prev_col = self.pos_row, self.pos_col + prev_frame = self.frame_idx + meta_path: pathlib.Path | None = None + export_row = int(self.pos_row) + export_col = int(self.pos_col) + export_frame = int(self.frame_idx) + + try: + if frame_idx is not None: + self.frame_idx = self._validate_frame_idx(frame_idx) + if position is not None: + row, col = self._validate_position(position) + self.pos_row = row + self.pos_col = col + export_row = int(self.pos_row) + export_col = int(self.pos_col) + export_frame = int(self.frame_idx) + + if view_key == "diffraction": + image, dp_meta = self._render_panel_image( + "diffraction", overlays_enabled, scalebar_enabled + ) + render_meta = {"diffraction": dp_meta} + elif view_key == "virtual": + image, vi_meta = self._render_panel_image( + "virtual", overlays_enabled, scalebar_enabled + ) + render_meta = {"virtual": vi_meta} + elif view_key == "fft": + image, fft_meta = self._render_panel_image( + "fft", overlays_enabled, scalebar_enabled + ) + render_meta = {"fft": fft_meta} + else: + panel_images = [] + render_meta = {} + dp_img, dp_meta = self._render_panel_image( + "diffraction", overlays_enabled, scalebar_enabled + ) + vi_img, vi_meta = self._render_panel_image( + "virtual", overlays_enabled, scalebar_enabled + ) + panel_images.extend([dp_img, vi_img]) + render_meta = {"diffraction": dp_meta, "virtual": vi_meta} + if self.show_fft: + fft_img, fft_meta = self._render_panel_image( + "fft", overlays_enabled, scalebar_enabled + ) + panel_images.append(fft_img) + render_meta["fft"] = fft_meta + image = self._compose_horizontal(panel_images) + + if fmt == "pdf": + Image.init() + image = image.convert("RGB") + image.save(export_path, format="PDF", resolution=dpi_value) + else: + image.save(export_path, format="PNG", dpi=(dpi_value, dpi_value)) + + if include_metadata: + meta_path = ( + pathlib.Path(metadata_path) + if metadata_path is not None + else export_path.with_suffix(".json") + ) + metadata = self._build_image_export_metadata( + export_path=export_path, + view_key=view_key, + fmt=fmt, + render_meta=render_meta, + include_overlays=overlays_enabled, + include_scalebar=scalebar_enabled, + export_kind="single_view_image", + extra={"dpi": int(dpi_value)}, + ) + meta_path.write_text(json.dumps(metadata, indent=2)) + finally: + if restore_state: + self.frame_idx = prev_frame + self.pos_row = prev_row + self.pos_col = prev_col + + self._record_export_event( + { + "export_kind": "single_view_image", + "view": view_key, + "format": fmt, + "position": {"row": export_row, "col": export_col}, + "frame_idx": export_frame, + "include_overlays": bool(overlays_enabled), + "include_scalebar": bool(scalebar_enabled), + "dpi": int(dpi_value), + "outputs": [ + self._build_file_record(export_path, metadata_path=meta_path), + ], + } + ) + return export_path + + def _build_preset_payload(self) -> dict[str, Any]: + return { + "detector": { + "center_row": float(self.center_row), + "center_col": float(self.center_col), + "bf_radius": float(self.bf_radius), + "roi_active": bool(self.roi_active), + "roi_mode": self.roi_mode, + "roi_center_row": float(self.roi_center_row), + "roi_center_col": float(self.roi_center_col), + "roi_radius": float(self.roi_radius), + "roi_radius_inner": float(self.roi_radius_inner), + "roi_width": float(self.roi_width), + "roi_height": float(self.roi_height), + }, + "vi_roi": { + "mode": self.vi_roi_mode, + "center_row": float(self.vi_roi_center_row), + "center_col": float(self.vi_roi_center_col), + "radius": float(self.vi_roi_radius), + "width": float(self.vi_roi_width), + "height": float(self.vi_roi_height), + }, + "display": { + "mask_dc": bool(self.mask_dc), + "dp_colormap": self.dp_colormap, + "vi_colormap": self.vi_colormap, + "fft_colormap": self.fft_colormap, + "dp_scale_mode": self.dp_scale_mode, + "vi_scale_mode": self.vi_scale_mode, + "fft_scale_mode": self.fft_scale_mode, + "dp_power_exp": float(self.dp_power_exp), + "vi_power_exp": float(self.vi_power_exp), + "fft_power_exp": float(self.fft_power_exp), + "dp_vmin_pct": float(self.dp_vmin_pct), + "dp_vmax_pct": float(self.dp_vmax_pct), + "vi_vmin_pct": float(self.vi_vmin_pct), + "vi_vmax_pct": float(self.vi_vmax_pct), + "fft_vmin_pct": float(self.fft_vmin_pct), + "fft_vmax_pct": float(self.fft_vmax_pct), + "fft_auto": bool(self.fft_auto), + "show_fft": bool(self.show_fft), + "dp_show_colorbar": bool(self.dp_show_colorbar), + "profile_line": self.profile_line, + "profile_width": int(self.profile_width), + }, + "export": self._export_settings_metadata(), + } + + def _apply_preset_payload(self, preset: dict[str, Any]) -> None: + detector = preset.get("detector", {}) + vi_roi = preset.get("vi_roi", {}) + display = preset.get("display", {}) + export = preset.get("export", {}) + + detector_map = { + "center_row": "center_row", + "center_col": "center_col", + "bf_radius": "bf_radius", + "roi_active": "roi_active", + "roi_mode": "roi_mode", + "roi_center_row": "roi_center_row", + "roi_center_col": "roi_center_col", + "roi_radius": "roi_radius", + "roi_radius_inner": "roi_radius_inner", + "roi_width": "roi_width", + "roi_height": "roi_height", + } + for key, trait_name in detector_map.items(): + if key in detector and hasattr(self, trait_name): + setattr(self, trait_name, detector[key]) + + vi_roi_map = { + "mode": "vi_roi_mode", + "center_row": "vi_roi_center_row", + "center_col": "vi_roi_center_col", + "radius": "vi_roi_radius", + "width": "vi_roi_width", + "height": "vi_roi_height", + } + for key, trait_name in vi_roi_map.items(): + if key in vi_roi and hasattr(self, trait_name): + setattr(self, trait_name, vi_roi[key]) + + _display_keys = { + "dp_colormap", "vi_colormap", "fft_colormap", + "dp_scale_mode", "vi_scale_mode", "fft_scale_mode", + "dp_power_exp", "vi_power_exp", "fft_power_exp", + "dp_vmin_pct", "dp_vmax_pct", "vi_vmin_pct", "vi_vmax_pct", + "fft_vmin_pct", "fft_vmax_pct", "fft_auto", + "mask_dc", "dp_show_colorbar", "show_fft", "fft_window", + "show_controls", + } + for key, value in display.items(): + if key in _display_keys: + setattr(self, key, value) + + export_map = { + "default_view": "export_default_view", + "default_format": "export_default_format", + "include_overlays": "export_include_overlays", + "include_scalebar": "export_include_scalebar", + "dpi": "export_default_dpi", + } + for key, trait_name in export_map.items(): + if key in export and hasattr(self, trait_name): + setattr(self, trait_name, export[key]) + + def save_preset( + self, + name: str, + path: str | pathlib.Path | None = None, + ) -> dict[str, Any]: + preset_name = str(name).strip() + if not preset_name: + raise ValueError("Preset name must be non-empty.") + preset_key = preset_name.lower() + + payload = self._build_preset_payload() + self._named_presets[preset_key] = payload + + if path is not None: + out_path = pathlib.Path(path) + out_path.parent.mkdir(parents=True, exist_ok=True) + serialized = { + **build_json_header("Show4DSTEM"), + "format": "json", + "export_kind": "widget_preset", + "preset_name": preset_name, + "preset": payload, + } + out_path.write_text(json.dumps(serialized, indent=2)) + + return payload + + def load_preset( + self, + name: str, + path: str | pathlib.Path | None = None, + apply: bool = True, + ) -> dict[str, Any]: + preset_name = str(name).strip() + preset_key = preset_name.lower() + if path is not None: + payload = json.loads(pathlib.Path(path).read_text()) + if not isinstance(payload, dict): + raise ValueError("Preset file must contain a JSON object.") + if "preset" in payload: + preset = payload["preset"] + else: + preset = payload + if not isinstance(preset, dict): + raise ValueError("Preset payload must be a JSON object.") + if preset_name: + self._named_presets[preset_key] = preset + else: + if preset_key not in self._named_presets: + raise ValueError( + f"Preset '{preset_name}' not found. Available: {', '.join(self.list_presets())}" + ) + preset = self._named_presets[preset_key] + + if apply: + self._apply_preset_payload(preset) + return preset + + def apply_preset(self, name: str) -> Self: + preset_name = str(name).strip().lower() + if preset_name == "bf": + self.roi_active = True + self.roi_mode = "circle" + self.roi_center_row = float(self.center_row) + self.roi_center_col = float(self.center_col) + self.roi_radius = float(max(1.0, self.bf_radius)) + return self + if preset_name == "abf": + self.roi_active = True + self.roi_mode = "annular" + self.roi_center_row = float(self.center_row) + self.roi_center_col = float(self.center_col) + self.roi_radius_inner = float(max(0.5, self.bf_radius * 0.5)) + self.roi_radius = float(max(1.0, self.bf_radius)) + return self + if preset_name == "adf": + self.roi_active = True + self.roi_mode = "annular" + self.roi_center_row = float(self.center_row) + self.roi_center_col = float(self.center_col) + self.roi_radius_inner = float(max(1.0, self.bf_radius)) + self.roi_radius = float(max(self.roi_radius_inner + 1.0, self.bf_radius * 2.0)) + return self + if preset_name == "haadf": + self.roi_active = True + self.roi_mode = "annular" + self.roi_center_row = float(self.center_row) + self.roi_center_col = float(self.center_col) + self.roi_radius_inner = float(max(1.0, self.bf_radius * 2.0)) + self.roi_radius = float(max(self.roi_radius_inner + 1.0, self.bf_radius * 4.0)) + return self + + self.load_preset(preset_name, apply=True) + return self + + def _resolve_figure_template(self, template: str) -> tuple[str, list[str], bool]: + key = str(template).strip().lower() + mapping = { + "dp_vi": (["diffraction", "virtual"], False), + "dp_vi_fft": (["diffraction", "virtual", "fft"], False), + "publication_dp_vi": (["diffraction", "virtual"], True), + "publication_dp_vi_fft": (["diffraction", "virtual", "fft"], True), + } + if key not in mapping: + raise ValueError( + f"Unsupported template '{template}'. " + f"Supported: {', '.join(self.list_figure_templates())}" + ) + panels, publication = mapping[key] + return key, panels, publication + + def save_figure( + self, + path: str | pathlib.Path, + template: str = "dp_vi_fft", + position: tuple[int, int] | None = None, + frame_idx: int | None = None, + format: str | None = None, + include_metadata: bool = True, + metadata_path: str | pathlib.Path | None = None, + include_overlays: bool | None = None, + include_scalebar: bool | None = None, + restore_state: bool = True, + dpi: int | None = None, + title: str | None = None, + annotations: dict[str, str] | None = None, + ) -> pathlib.Path: + from PIL import Image, ImageDraw, ImageFont + + export_path = pathlib.Path(path) + template_key, panel_keys, publication_style = self._resolve_figure_template(template) + fmt = self._resolve_export_format(export_path, format) + dpi_value = int(self.export_default_dpi if dpi is None else dpi) + overlays_enabled = ( + bool(self.export_include_overlays) + if include_overlays is None + else bool(include_overlays) + ) + scalebar_enabled = ( + bool(self.export_include_scalebar) + if include_scalebar is None + else bool(include_scalebar) + ) + if dpi_value <= 0: + raise ValueError(f"dpi must be > 0, got {dpi_value}") + + export_path.parent.mkdir(parents=True, exist_ok=True) + font = ImageFont.load_default() + + prev_row, prev_col = self.pos_row, self.pos_col + prev_frame = self.frame_idx + meta_path: pathlib.Path | None = None + + try: + if frame_idx is not None: + self.frame_idx = self._validate_frame_idx(frame_idx) + if position is not None: + row, col = self._validate_position(position) + self.pos_row = row + self.pos_col = col + + panel_images: list[Any] = [] + render_meta: dict[str, Any] = {} + for panel_key in panel_keys: + panel, panel_meta = self._render_panel_image( + panel_key, + include_overlays=overlays_enabled, + include_scalebar=scalebar_enabled, + ) + panel_images.append(panel) + render_meta[panel_key] = panel_meta + + gap = 24 if publication_style else 8 + padding = 24 if publication_style else 10 + label_height = 22 if publication_style else 0 + title_text = title + if title_text is None and publication_style: + if self.n_frames > 1: + title_text = f"4D-STEM Figure ({self.frame_dim_label} {self.frame_idx})" + else: + title_text = "4D-STEM Figure" + title_height = 34 if title_text else 0 + + max_panel_height = max(panel.height for panel in panel_images) + total_width = padding * 2 + sum(panel.width for panel in panel_images) + gap * (len(panel_images) - 1) + total_height = padding * 2 + title_height + label_height + max_panel_height + + figure = Image.new("RGB", (total_width, total_height), color=(255, 255, 255)) + draw = ImageDraw.Draw(figure, mode="RGBA") + + y_title = padding + if title_text: + draw.text((padding, y_title), title_text, fill=(0, 0, 0, 255), font=font) + + y_panels = padding + title_height + if publication_style: + y_panels += label_height + + panel_names = { + "diffraction": "Diffraction", + "virtual": "Virtual", + "fft": "FFT", + } + annotation_map = annotations or {} + + x0 = padding + for idx, panel in enumerate(panel_images): + panel_key = panel_keys[idx] + if publication_style: + draw.text( + (x0, padding + title_height), + panel_names.get(panel_key, panel_key), + fill=(0, 0, 0, 255), + font=font, + ) + + figure.paste(panel, (x0, y_panels)) + + if publication_style: + draw.rectangle( + [(x0, y_panels), (x0 + panel.width - 1, y_panels + panel.height - 1)], + outline=(80, 80, 80, 255), + width=1, + ) + + if panel_key in annotation_map and str(annotation_map[panel_key]).strip(): + text = str(annotation_map[panel_key]).strip() + text_bbox = draw.textbbox((0, 0), text, font=font) + text_w = text_bbox[2] - text_bbox[0] + text_h = text_bbox[3] - text_bbox[1] + tx = x0 + 8 + ty = y_panels + 8 + draw.rectangle( + [(tx - 4, ty - 3), (tx + text_w + 4, ty + text_h + 3)], + fill=(0, 0, 0, 180), + ) + draw.text((tx, ty), text, fill=(255, 255, 255, 255), font=font) + + x0 += panel.width + gap + + if fmt == "pdf": + Image.init() + figure = figure.convert("RGB") + figure.save(export_path, format="PDF", resolution=dpi_value) + else: + figure.save(export_path, format="PNG", dpi=(dpi_value, dpi_value)) + + if include_metadata: + meta_path = ( + pathlib.Path(metadata_path) + if metadata_path is not None + else export_path.with_suffix(".json") + ) + metadata = self._build_image_export_metadata( + export_path=export_path, + view_key="figure", + fmt=fmt, + render_meta=render_meta, + include_overlays=overlays_enabled, + include_scalebar=scalebar_enabled, + export_kind="figure_template", + extra={ + "template": template_key, + "panels": panel_keys, + "publication_style": bool(publication_style), + "title": title_text or "", + "annotations": annotation_map, + "dpi": int(dpi_value), + }, + ) + meta_path.write_text(json.dumps(metadata, indent=2)) + finally: + if restore_state: + self.frame_idx = prev_frame + self.pos_row = prev_row + self.pos_col = prev_col + + self._record_export_event( + { + "export_kind": "figure_template", + "template": template_key, + "format": fmt, + "dpi": int(dpi_value), + "include_overlays": bool(overlays_enabled), + "include_scalebar": bool(scalebar_enabled), + "outputs": [ + self._build_file_record(export_path, metadata_path=meta_path), + ], + } + ) + return export_path + + def _resolve_frame_sequence( + self, + frame_indices: list[int] | None, + frame_range: tuple[int, int] | None, + ) -> list[int]: + if frame_indices is not None and frame_range is not None: + raise ValueError("Use either frame_indices or frame_range, not both.") + + if frame_indices is not None: + if len(frame_indices) == 0: + raise ValueError("frame_indices cannot be empty.") + return [self._validate_frame_idx(idx) for idx in frame_indices] + + if frame_range is not None: + if len(frame_range) != 2: + raise ValueError("frame_range must be a (start, end) tuple.") + start, end = int(frame_range[0]), int(frame_range[1]) + if start > end: + raise ValueError("frame_range start must be <= end.") + return [self._validate_frame_idx(idx) for idx in range(start, end + 1)] + + return [int(i) for i in range(self.n_frames)] + + def _resolve_position_sequence( + self, + mode: str, + path_points: list[tuple[int, int]] | None, + raster_step: int, + raster_bidirectional: bool, + ) -> list[tuple[int, int]]: + if mode == "path": + points = self._path_points if path_points is None else path_points + if not points: + raise ValueError( + "Path mode requires points via set_path(...) or path_points=..." + ) + return [self._validate_position((int(r), int(c))) for r, c in points] + + if mode == "raster": + step = int(raster_step) + if step < 1: + raise ValueError("raster_step must be >= 1") + points: list[tuple[int, int]] = [] + for r in range(0, self.shape_rows, step): + cols = list(range(0, self.shape_cols, step)) + if raster_bidirectional and ((r // step) % 2 == 1): + cols.reverse() + for c in cols: + points.append((int(r), int(c))) + return points + + raise ValueError(f"Unsupported position sequence mode '{mode}'") + + def suggest_adaptive_path( + self, + coarse_step: int = 4, + target_fraction: float = 0.25, + min_spacing: int = 2, + include_coarse: bool = True, + coarse_bidirectional: bool = True, + local_window: int = 5, + dose_lambda: float = 0.25, + weights: dict[str, float] | None = None, + roi_mask: np.ndarray | None = None, + update_widget_path: bool = True, + interval_ms: int | None = None, + loop: bool = False, + autoplay: bool = False, + return_maps: bool = False, + ) -> dict[str, Any]: + """ + Suggest a sparse adaptive scan path using coarse-to-fine utility ranking. + + The planner computes utility from current virtual-image and diffraction + statistics, then selects spatially distributed high-utility points. + + Parameters + ---------- + coarse_step : int, default 4 + Spacing of the initial coarse grid. + target_fraction : float, default 0.25 + Target total sampled fraction of scan positions in (0, 1]. + min_spacing : int, default 2 + Minimum pixel spacing between selected dense points. + include_coarse : bool, default True + If True, include coarse-grid points in the returned path. + coarse_bidirectional : bool, default True + Use snake ordering for coarse-grid traversal. + local_window : int, default 5 + Window size for local-std utility component. + dose_lambda : float, default 0.25 + Penalty weight for re-sampling coarse points. + weights : dict[str, float], optional + Utility weights for keys: ``vi_gradient``, ``vi_local_std``, ``dp_variance``. + roi_mask : np.ndarray, optional + Optional boolean mask of shape ``scan_shape`` restricting dense picks. + update_widget_path : bool, default True + If True, calls ``set_path(...)`` with the suggested path. + interval_ms : int, optional + Path interval when ``update_widget_path=True``. + loop : bool, default False + Path looping behavior when ``update_widget_path=True``. + autoplay : bool, default False + Start playback immediately when ``update_widget_path=True``. + return_maps : bool, default False + If True, include utility component maps in the returned dict. + + Returns + ------- + dict + Planning result with coarse points, dense points, and final path. + """ + step = int(coarse_step) + if step < 1: + raise ValueError(f"coarse_step must be >= 1, got {coarse_step}") + + frac = float(target_fraction) + if frac <= 0 or frac > 1: + raise ValueError(f"target_fraction must be in (0, 1], got {target_fraction}") + + spacing = int(min_spacing) + if spacing < 0: + raise ValueError(f"min_spacing must be >= 0, got {min_spacing}") + + if local_window < 1: + raise ValueError(f"local_window must be >= 1, got {local_window}") + + if not np.isfinite(float(dose_lambda)): + raise ValueError("dose_lambda must be finite") + + default_weights = { + "vi_gradient": 0.4, + "vi_local_std": 0.3, + "dp_variance": 0.3, + } + merged_weights = dict(default_weights) + if weights is not None: + for key, value in weights.items(): + if key not in default_weights: + raise ValueError( + f"Unsupported utility weight '{key}'. " + f"Supported: {', '.join(default_weights.keys())}" + ) + merged_weights[key] = float(value) + + weight_sum = sum(max(0.0, float(v)) for v in merged_weights.values()) + if weight_sum <= 0: + raise ValueError("At least one utility weight must be > 0.") + normalized_weights = { + key: max(0.0, float(value)) / weight_sum + for key, value in merged_weights.items() + } + + n_total = int(self.shape_rows * self.shape_cols) + target_count = int(max(1, round(frac * n_total))) + + coarse_points = self._build_coarse_points(step=step, bidirectional=bool(coarse_bidirectional)) + coarse_count = len(coarse_points) if include_coarse else 0 + if include_coarse and target_count < coarse_count: + raise ValueError( + f"target_fraction={target_fraction} gives {target_count} points, " + f"but coarse grid already has {coarse_count}. " + "Increase target_fraction or coarse_step." + ) + dense_count = target_count - coarse_count if include_coarse else target_count + dense_count = max(0, int(dense_count)) + + vi = self._get_virtual_image_array().astype(np.float32, copy=False) + grad_row, grad_col = np.gradient(vi) + vi_gradient = np.hypot(grad_row, grad_col).astype(np.float32) + + mean_local = self._box_mean_map(vi, local_window) + mean_sq_local = self._box_mean_map(vi * vi, local_window) + variance_local = np.maximum(mean_sq_local - mean_local * mean_local, 0.0) + vi_local_std = np.sqrt(variance_local).astype(np.float32) + + dp_variance = self._dp_variance_map() + + grad_score = self._normalize_score_map(vi_gradient) + local_std_score = self._normalize_score_map(vi_local_std) + dp_var_score = self._normalize_score_map(dp_variance) + + utility = ( + normalized_weights["vi_gradient"] * grad_score + + normalized_weights["vi_local_std"] * local_std_score + + normalized_weights["dp_variance"] * dp_var_score + ).astype(np.float32) + + dose_penalty = np.zeros_like(utility, dtype=np.float32) + for row, col in coarse_points: + dose_penalty[int(row), int(col)] = 1.0 + utility = utility - float(dose_lambda) * dose_penalty + + allowed_mask = np.ones((self.shape_rows, self.shape_cols), dtype=bool) + if roi_mask is not None: + mask = np.asarray(roi_mask) + if mask.shape != (self.shape_rows, self.shape_cols): + raise ValueError( + f"roi_mask shape {mask.shape} does not match scan_shape " + f"({self.shape_rows}, {self.shape_cols})" + ) + allowed_mask &= mask.astype(bool) + + excluded_mask = np.zeros_like(allowed_mask, dtype=bool) + for row, col in coarse_points: + excluded_mask[int(row), int(col)] = True + + dense_points = self._select_spaced_topk( + scores=utility, + k=dense_count, + min_spacing=spacing, + allowed_mask=allowed_mask, + excluded_mask=excluded_mask, + ) + + start_point = coarse_points[-1] if include_coarse and coarse_points else None + dense_path = self._nearest_neighbor_order(dense_points, start=start_point) + path_points = list(coarse_points) + dense_path if include_coarse else dense_path + + if update_widget_path and path_points: + interval_value = int(self.path_interval_ms if interval_ms is None else interval_ms) + if interval_value < 1: + raise ValueError(f"interval_ms must be >= 1, got {interval_value}") + self.set_path( + points=path_points, + interval_ms=interval_value, + loop=bool(loop), + autoplay=bool(autoplay), + ) + + result: dict[str, Any] = { + "target_fraction": float(frac), + "target_count": int(target_count), + "coarse_step": int(step), + "coarse_count": int(len(coarse_points)), + "dense_count": int(len(dense_points)), + "path_count": int(len(path_points)), + "weights": normalized_weights, + "dose_lambda": float(dose_lambda), + "coarse_points": coarse_points, + "dense_points": dense_points, + "path_points": path_points, + "selected_fraction": float(len(path_points) / max(1, n_total)), + } + if return_maps: + result["utility_map"] = utility + result["utility_components"] = { + "vi_gradient": grad_score, + "vi_local_std": local_std_score, + "dp_variance": dp_var_score, + "dose_penalty": dose_penalty, + } + + self._record_export_event( + { + "export_kind": "adaptive_path_suggestion", + "target_fraction": float(frac), + "target_count": int(target_count), + "coarse_step": int(step), + "coarse_count": int(len(coarse_points)), + "dense_count": int(len(dense_points)), + "path_count": int(len(path_points)), + "selected_fraction": float(len(path_points) / max(1, n_total)), + "weights": normalized_weights, + "dose_lambda": float(dose_lambda), + } + ) + return result + + def save_sequence( + self, + output_dir: str | pathlib.Path, + mode: str = "path", + view: str | None = None, + format: str | None = None, + include_metadata: bool = True, + include_overlays: bool | None = None, + include_scalebar: bool | None = None, + frame_idx: int | None = None, + position: tuple[int, int] | None = None, + path_points: list[tuple[int, int]] | None = None, + raster_step: int = 1, + raster_bidirectional: bool = False, + frame_indices: list[int] | None = None, + frame_range: tuple[int, int] | None = None, + filename_prefix: str | None = None, + manifest_name: str = "save_sequence_manifest.json", + restore_state: bool = True, + dpi: int | None = None, + ) -> pathlib.Path: + output_root = pathlib.Path(output_dir) + output_root.mkdir(parents=True, exist_ok=True) + mode_key = str(mode).strip().lower() + if mode_key not in {"path", "raster", "frames"}: + raise ValueError("mode must be one of: path, raster, frames") + + view_key = self._validate_export_view(view) + fmt = self._resolve_export_format(pathlib.Path(f"sequence.{self.export_default_format}"), format or self.export_default_format) + dpi_value = int(self.export_default_dpi if dpi is None else dpi) + overlays_enabled = ( + bool(self.export_include_overlays) + if include_overlays is None + else bool(include_overlays) + ) + scalebar_enabled = ( + bool(self.export_include_scalebar) + if include_scalebar is None + else bool(include_scalebar) + ) + if dpi_value <= 0: + raise ValueError(f"dpi must be > 0, got {dpi_value}") + + export_rows: list[dict[str, Any]] = [] + prefix = ( + str(filename_prefix).strip() + if filename_prefix is not None and str(filename_prefix).strip() + else f"{mode_key}_{view_key}" + ) + + prev_row, prev_col = self.pos_row, self.pos_col + prev_frame = self.frame_idx + frame_for_paths = self._validate_frame_idx(frame_idx) if frame_idx is not None else int(self.frame_idx) + + if mode_key == "frames": + row, col = self._validate_position(position) + frames = self._resolve_frame_sequence(frame_indices, frame_range) + jobs = [ + {"row": int(row), "col": int(col), "frame_idx": int(fi)} + for fi in frames + ] + else: + positions = self._resolve_position_sequence( + mode=mode_key, + path_points=path_points, + raster_step=raster_step, + raster_bidirectional=raster_bidirectional, + ) + jobs = [ + {"row": int(r), "col": int(c), "frame_idx": int(frame_for_paths)} + for r, c in positions + ] + + try: + for idx, job in enumerate(jobs): + row = int(job["row"]) + col = int(job["col"]) + fr = int(job["frame_idx"]) + basename = ( + f"{prefix}_{idx:04d}_f{fr:04d}_r{row:04d}_c{col:04d}.{fmt}" + ) + out_path = output_root / basename + out_meta = out_path.with_suffix(".json") if include_metadata else None + + self.save_image( + out_path, + view=view_key, + position=(row, col), + frame_idx=fr, + format=fmt, + include_metadata=include_metadata, + metadata_path=out_meta, + include_overlays=overlays_enabled, + include_scalebar=scalebar_enabled, + restore_state=False, + dpi=dpi_value, + ) + + record = { + "index": int(idx), + "row": row, + "col": col, + "frame_idx": fr, + } + record.update(self._build_file_record(out_path, metadata_path=out_meta, index=idx)) + export_rows.append(record) + finally: + if restore_state: + self.frame_idx = prev_frame + self.pos_row = prev_row + self.pos_col = prev_col + + manifest_path = output_root / str(manifest_name) + manifest_payload = { + **build_json_header("Show4DSTEM"), + "format": "json", + "export_kind": "sequence_batch", + "mode": mode_key, + "view": view_key, + "image_format": fmt, + "output_dir": str(output_root), + "filename_prefix": prefix, + "n_exports": int(len(export_rows)), + "include_overlays": bool(overlays_enabled), + "include_scalebar": bool(scalebar_enabled), + "dpi": int(dpi_value), + "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, + "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, + "exports": export_rows, + } + manifest_path.write_text(json.dumps(manifest_payload, indent=2)) + + manifest_record = self._build_file_record(manifest_path) + self._record_export_event( + { + "export_kind": "sequence_batch", + "mode": mode_key, + "view": view_key, + "format": fmt, + "n_exports": int(len(export_rows)), + "include_overlays": bool(overlays_enabled), + "include_scalebar": bool(scalebar_enabled), + "dpi": int(dpi_value), + "outputs": [manifest_record], + } + ) + return manifest_path + + def save_reproducibility_report( + self, + path: str | pathlib.Path, + ) -> pathlib.Path: + report_path = pathlib.Path(path) + report_path.parent.mkdir(parents=True, exist_ok=True) + payload = { + **build_json_header("Show4DSTEM"), + "format": "json", + "export_kind": "reproducibility_report", + "session_id": self._export_session_id, + "session_started_utc": self._export_session_started_utc, + "report_generated_utc": datetime.now(timezone.utc).isoformat(), + "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, + "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, + "n_exports": int(len(self._export_log)), + "exports": self._export_log, + } + report_path.write_text(json.dumps(payload, indent=2)) + return report_path + + def _normalize_frame(self, frame: np.ndarray) -> np.ndarray: + mode = self.dp_scale_mode + scaled = self._apply_scale_mode(frame, mode, self.dp_power_exp) + if self.dp_vmin is not None and self.dp_vmax is not None: + fmin = float(self._apply_scale_mode( + np.array([max(self.dp_vmin, 0)], dtype=np.float32), mode, self.dp_power_exp + )[0]) + fmax = float(self._apply_scale_mode( + np.array([max(self.dp_vmax, 0)], dtype=np.float32), mode, self.dp_power_exp + )[0]) + else: + fmin = float(scaled.min()) + fmax = float(scaled.max()) + fmin, fmax = self._slider_range(fmin, fmax, self.dp_vmin_pct, self.dp_vmax_pct) + if fmax > fmin: + return np.clip((scaled - fmin) / (fmax - fmin) * 255, 0, 255).astype(np.uint8) + return np.zeros(frame.shape, dtype=np.uint8) + + def _on_gif_export(self, change=None): + if not self._gif_export_requested: + return + self._gif_export_requested = False + self._generate_gif() + + def _generate_gif(self): + import io + + from matplotlib import colormaps + from PIL import Image + + if not self._path_points: + with self.hold_sync(): + self._gif_data = b"" + self._gif_metadata_json = "" + return + + cmap_fn = colormaps.get_cmap(self.dp_colormap) + duration_ms = max(10, self.path_interval_ms) + + pil_frames = [] + for row, col in self._path_points: + row = max(0, min(self.shape_rows - 1, row)) + col = max(0, min(self.shape_cols - 1, col)) + frame = self._get_frame(row, col).astype(np.float32) + normalized = self._normalize_frame(frame) + rgba = cmap_fn(normalized / 255.0) + rgb = (rgba[:, :, :3] * 255).astype(np.uint8) + pil_frames.append(Image.fromarray(rgb)) + + if not pil_frames: + return + + buf = io.BytesIO() + pil_frames[0].save( + buf, + format="GIF", + save_all=True, + append_images=pil_frames[1:], + duration=duration_ms, + loop=0, + ) + metadata = { + **build_json_header("Show4DSTEM"), + "view": "diffraction", + "format": "gif", + "export_kind": "path_animation", + "n_frames": int(len(pil_frames)), + "duration_ms": int(duration_ms), + "path_loop": bool(self.path_loop), + "path_points": [{"row": int(row), "col": int(col)} for row, col in self._path_points], + "frame_idx": int(self.frame_idx), + "n_frames_total": int(self.n_frames), + "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, + "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, + "calibration": self._calibration_metadata(), + "display": { + "diffraction": { + "colormap": self.dp_colormap, + "scale_mode": self.dp_scale_mode, + "vmin_pct": float(self.dp_vmin_pct), + "vmax_pct": float(self.dp_vmax_pct), + } + }, + } + with self.hold_sync(): + self._gif_metadata_json = json.dumps(metadata, indent=2) + self._gif_data = buf.getvalue() + + def _update_frame(self, change=None): + """Send raw float32 frame to frontend (JS handles scale/colormap).""" + if self._data is None: + return + # Get frame as tensor (stays on device) + data = self._frame_data + if data.ndim == 3: + idx = self.pos_row * self.shape_cols + self.pos_col + frame = data[idx] + else: + frame = data[self.pos_row, self.pos_col] + + # Compute stats from frame (optionally mask DC component) + if self.mask_dc and self.det_rows > 3 and self.det_cols > 3: + # Mask center 3x3 region for stats using detected center (not geometric center) + cr = int(round(self.center_row)) + cc = int(round(self.center_col)) + cr = max(1, min(self.det_rows - 2, cr)) + cc = max(1, min(self.det_cols - 2, cc)) + mask = torch.ones_like(frame, dtype=torch.bool) + mask[cr-1:cr+2, cc-1:cc+2] = False + masked_vals = frame[mask] + self.dp_stats = [ + float(masked_vals.mean()), + float(masked_vals.min()), + float(masked_vals.max()), + float(masked_vals.std()), + ] + else: + self.dp_stats = [ + float(frame.mean()), + float(frame.min()), + float(frame.max()), + float(frame.std()), + ] + + # Convert to numpy only for sending bytes to frontend + self.frame_bytes = frame.cpu().numpy().tobytes() + + def _on_roi_change(self, change=None): + """Recompute virtual image when individual ROI params change. + + High-frequency drag updates use the compound roi_center trait instead. + """ + if not self.roi_active: + return + self._compute_virtual_image_from_roi() + + def _on_roi_center_change(self, change=None): + """Handle batched roi_center updates from JS (single observer for row+col). + + This is the fast path for drag operations. JS sends [row, col] as a single + compound trait, so only one observer fires per mouse move. + """ + if not self.roi_active: + return + if change and "new" in change: + row, col = change["new"] + # Sync to individual traits (without triggering _on_roi_change observers) + self.unobserve(self._on_roi_change, names=["roi_center_col", "roi_center_row"]) + self.roi_center_row = row + self.roi_center_col = col + self.observe(self._on_roi_change, names=["roi_center_col", "roi_center_row"]) + self._compute_virtual_image_from_roi() + + def _on_vi_roi_change(self, change=None): + """Compute summed DP when VI ROI changes.""" + if self.vi_roi_mode == "off": + self.summed_dp_bytes = b"" + self.summed_dp_count = 0 + return + self._compute_summed_dp_from_vi_roi() + + def _compute_summed_dp_from_vi_roi(self): + """Sum diffraction patterns from positions inside VI ROI (PyTorch).""" + if self._data is None: + return + # Create mask in scan space using cached coordinates + if self.vi_roi_mode == "circle": + mask = (self._scan_row_coords - self.vi_roi_center_row) ** 2 + (self._scan_col_coords - self.vi_roi_center_col) ** 2 <= self.vi_roi_radius ** 2 + elif self.vi_roi_mode == "square": + half_size = self.vi_roi_radius + mask = (torch.abs(self._scan_row_coords - self.vi_roi_center_row) <= half_size) & (torch.abs(self._scan_col_coords - self.vi_roi_center_col) <= half_size) + elif self.vi_roi_mode == "rect": + half_w = self.vi_roi_width / 2 + half_h = self.vi_roi_height / 2 + mask = (torch.abs(self._scan_row_coords - self.vi_roi_center_row) <= half_h) & (torch.abs(self._scan_col_coords - self.vi_roi_center_col) <= half_w) + else: + return + + # Count positions in mask + n_positions = int(mask.sum()) + if n_positions == 0: + self.summed_dp_bytes = b"" + self.summed_dp_count = 0 + return + + self.summed_dp_count = n_positions + + # Compute average DP using masked sum (vectorized) + data = self._frame_data + if data.ndim == 4: + # (scan_rows, scan_cols, det_rows, det_cols) - sum over masked scan positions + avg_dp = data[mask].mean(dim=0) + else: + # Flattened: (N, det_rows, det_cols) - need to convert mask indices + flat_indices = torch.nonzero(mask.flatten(), as_tuple=True)[0] + avg_dp = data[flat_indices].mean(dim=0) + + # Send raw float32 (consistent with other data paths — JS handles normalization) + self.summed_dp_bytes = avg_dp.cpu().numpy().tobytes() + + def _create_circular_mask(self, cx: float, cy: float, radius: float): + """Create circular mask (boolean tensor on device).""" + mask = (self._det_col_coords - cx) ** 2 + (self._det_row_coords - cy) ** 2 <= radius ** 2 + return mask + + def _create_square_mask(self, cx: float, cy: float, half_size: float): + """Create square mask (boolean tensor on device).""" + mask = (torch.abs(self._det_col_coords - cx) <= half_size) & (torch.abs(self._det_row_coords - cy) <= half_size) + return mask + + def _create_annular_mask( + self, cx: float, cy: float, inner: float, outer: float + ): + """Create annular (donut) mask (boolean tensor on device).""" + dist_sq = (self._det_col_coords - cx) ** 2 + (self._det_row_coords - cy) ** 2 + mask = (dist_sq >= inner ** 2) & (dist_sq <= outer ** 2) + return mask + + def _create_rect_mask(self, cx: float, cy: float, half_width: float, half_height: float): + """Create rectangular mask (boolean tensor on device).""" + mask = (torch.abs(self._det_col_coords - cx) <= half_width) & (torch.abs(self._det_row_coords - cy) <= half_height) + return mask + + def _precompute_common_virtual_images(self): + """Pre-compute BF/ABF/ADF virtual images for instant preset switching.""" + cx, cy, bf = self.center_col, self.center_row, self.bf_radius + # Cache (bytes, stats, min, max) for each preset + bf_arr = self._fast_masked_sum(self._create_circular_mask(cx, cy, bf)) + abf_arr = self._fast_masked_sum(self._create_annular_mask(cx, cy, bf * 0.5, bf)) + adf_arr = self._fast_masked_sum(self._create_annular_mask(cx, cy, bf, bf * 4.0)) + + self._cached_bf_virtual = ( + self._to_float32_bytes(bf_arr, update_vi_stats=False), + [float(bf_arr.mean()), float(bf_arr.min()), float(bf_arr.max()), float(bf_arr.std())], + float(bf_arr.min()), float(bf_arr.max()) + ) + self._cached_abf_virtual = ( + self._to_float32_bytes(abf_arr, update_vi_stats=False), + [float(abf_arr.mean()), float(abf_arr.min()), float(abf_arr.max()), float(abf_arr.std())], + float(abf_arr.min()), float(abf_arr.max()) + ) + self._cached_adf_virtual = ( + self._to_float32_bytes(adf_arr, update_vi_stats=False), + [float(adf_arr.mean()), float(adf_arr.min()), float(adf_arr.max()), float(adf_arr.std())], + float(adf_arr.min()), float(adf_arr.max()) + ) + + def _get_cached_preset(self) -> tuple[bytes, list[float], float, float] | None: + """Check if current ROI matches a cached preset and return (bytes, stats, min, max) tuple.""" + # Must be centered on detector center + if abs(self.roi_center_col - self.center_col) >= 1 or abs(self.roi_center_row - self.center_row) >= 1: + return None + + bf = self.bf_radius + + # BF: circle at bf_radius + if (self.roi_mode == "circle" and abs(self.roi_radius - bf) < 1): + return self._cached_bf_virtual + + # ABF: annular at 0.5*bf to bf + if (self.roi_mode == "annular" and + abs(self.roi_radius_inner - bf * 0.5) < 1 and + abs(self.roi_radius - bf) < 1): + return self._cached_abf_virtual + + # ADF: annular at bf to 4*bf (combines LAADF + HAADF) + if (self.roi_mode == "annular" and + abs(self.roi_radius_inner - bf) < 1 and + abs(self.roi_radius - bf * 4.0) < 1): + return self._cached_adf_virtual + + return None + + def _virtual_image_for_frame(self, frame_idx: int) -> np.ndarray: + """Compute virtual image array for a specific frame without mutating traits.""" + data = self._data[frame_idx] if self.n_frames > 1 else self._data + cx, cy = self.roi_center_col, self.roi_center_row + if self.roi_mode == "circle" and self.roi_radius > 0: + mask = self._create_circular_mask(cx, cy, self.roi_radius) + elif self.roi_mode == "square" and self.roi_radius > 0: + mask = self._create_square_mask(cx, cy, self.roi_radius) + elif self.roi_mode == "annular" and self.roi_radius > 0: + mask = self._create_annular_mask(cx, cy, self.roi_radius_inner, self.roi_radius) + elif self.roi_mode == "rect" and self.roi_width > 0 and self.roi_height > 0: + mask = self._create_rect_mask(cx, cy, self.roi_width / 2, self.roi_height / 2) + else: + row = int(max(0, min(round(cy), self._det_shape[0] - 1))) + col = int(max(0, min(round(cx), self._det_shape[1] - 1))) + if data.ndim == 4: + vi = data[:, :, row, col] + else: + vi = data[:, row, col].reshape(self._scan_shape) + return vi.cpu().numpy().astype(np.float32, copy=False) + mask_float = mask.float() + n_det = self._det_shape[0] * self._det_shape[1] + n_nonzero = int(mask.sum()) + coverage = n_nonzero / n_det + if coverage < SPARSE_MASK_THRESHOLD: + indices = torch.nonzero(mask_float.flatten(), as_tuple=True)[0] + n_scan = self._scan_shape[0] * self._scan_shape[1] + data_flat = data.reshape(n_scan, n_det) + result = data_flat[:, indices].sum(dim=1).reshape(self._scan_shape) + else: + if data.ndim == 3: + data_4d = data.reshape(self._scan_shape[0], self._scan_shape[1], *self._det_shape) + else: + data_4d = data + result = torch.tensordot(data_4d, mask_float, dims=([2, 3], [0, 1])) + return result.cpu().numpy().astype(np.float32, copy=False) + + def _fast_masked_sum(self, mask: torch.Tensor) -> torch.Tensor: + """Compute masked sum using PyTorch. + + Uses sparse indexing for small masks (<20% coverage) which is faster + because it only processes non-zero pixels: + - r=10 (1%): ~0.8ms (sparse) vs ~13ms (full) + - r=30 (8%): ~4ms (sparse) vs ~13ms (full) + + For large masks (≥20%), uses full tensordot which has constant ~13ms. + """ + data = self._frame_data + mask_float = mask.float() + n_det = self._det_shape[0] * self._det_shape[1] + n_nonzero = int(mask.sum()) + coverage = n_nonzero / n_det + + if coverage < SPARSE_MASK_THRESHOLD: + # Sparse: faster for small masks + indices = torch.nonzero(mask_float.flatten(), as_tuple=True)[0] + n_scan = self._scan_shape[0] * self._scan_shape[1] + data_flat = data.reshape(n_scan, n_det) + result = data_flat[:, indices].sum(dim=1).reshape(self._scan_shape) + else: + # Tensordot: faster for large masks + # Reshape to 4D if needed (3D flattened data) + if data.ndim == 3: + data_4d = data.reshape(self._scan_shape[0], self._scan_shape[1], *self._det_shape) + else: + data_4d = data + result = torch.tensordot(data_4d, mask_float, dims=([2, 3], [0, 1])) + + return result + + def _to_float32_bytes(self, arr: torch.Tensor, update_vi_stats: bool = True) -> bytes: + """Convert tensor to float32 bytes.""" + # Compute min/max (fast on GPU) + vmin = float(arr.min()) + vmax = float(arr.max()) + + # Only update traits when requested (avoids side effects during precomputation) + if update_vi_stats: + self.vi_data_min = vmin + self.vi_data_max = vmax + self.vi_stats = [float(arr.mean()), vmin, vmax, float(arr.std())] + + return arr.cpu().numpy().tobytes() + + def _compute_virtual_image_from_roi(self): + """Compute virtual image based on ROI mode.""" + if self._data is None: + return + cached = self._get_cached_preset() + if cached is not None: + # Cached preset returns (bytes, stats, min, max) tuple + vi_bytes, vi_stats, vi_min, vi_max = cached + self.virtual_image_bytes = vi_bytes + self.vi_stats = vi_stats + self.vi_data_min = vi_min + self.vi_data_max = vi_max + return + + cx, cy = self.roi_center_col, self.roi_center_row + + if self.roi_mode == "circle" and self.roi_radius > 0: + mask = self._create_circular_mask(cx, cy, self.roi_radius) + elif self.roi_mode == "square" and self.roi_radius > 0: + mask = self._create_square_mask(cx, cy, self.roi_radius) + elif self.roi_mode == "annular" and self.roi_radius > 0: + mask = self._create_annular_mask(cx, cy, self.roi_radius_inner, self.roi_radius) + elif self.roi_mode == "rect" and self.roi_width > 0 and self.roi_height > 0: + mask = self._create_rect_mask(cx, cy, self.roi_width / 2, self.roi_height / 2) + else: + # Point mode: single-pixel indexing + row = int(max(0, min(round(cy), self._det_shape[0] - 1))) + col = int(max(0, min(round(cx), self._det_shape[1] - 1))) + data = self._frame_data + if data.ndim == 4: + virtual_image = data[:, :, row, col] + else: + virtual_image = data[:, row, col].reshape(self._scan_shape) + self.virtual_image_bytes = self._to_float32_bytes(virtual_image) + return + + self.virtual_image_bytes = self._to_float32_bytes(self._fast_masked_sum(mask)) + + +bind_tool_runtime_api(Show4DSTEM, "Show4DSTEM") diff --git a/widget/src/quantem/widget/tool_parity.json b/widget/src/quantem/widget/tool_parity.json new file mode 100644 index 00000000..4271533a --- /dev/null +++ b/widget/src/quantem/widget/tool_parity.json @@ -0,0 +1,93 @@ +{ + "widgets": { + "Show2D": { + "tool_groups": ["display", "histogram", "stats", "navigation", "view", "export", "roi", "profile", "all"], + "aliases": {} + }, + "Show3D": { + "tool_groups": ["display", "histogram", "stats", "playback", "view", "export", "roi", "profile", "all"], + "aliases": { + "navigation": "playback" + } + }, + "Show3DVolume": { + "tool_groups": ["display", "histogram", "playback", "fft", "navigation", "stats", "export", "view", "volume", "all"], + "aliases": {} + }, + "Show4D": { + "tool_groups": ["display", "roi", "histogram", "profile", "navigation", "playback", "stats", "export", "view", "fft", "all"], + "aliases": {} + }, + "Show4DSTEM": { + "tool_groups": ["display", "histogram", "stats", "navigation", "playback", "view", "export", "roi", "profile", "fft", "virtual", "frame", "all"], + "aliases": {} + }, + "ShowComplex2D": { + "tool_groups": ["display", "histogram", "fft", "roi", "stats", "export", "view", "all"], + "aliases": {} + }, + "Mark2D": { + "tool_groups": ["points", "roi", "profile", "display", "marker_style", "snap", "navigation", "view", "export", "all"], + "aliases": {} + }, + "Edit2D": { + "tool_groups": ["mode", "edit", "display", "histogram", "stats", "navigation", "export", "view", "all"], + "aliases": {} + }, + "Align2D": { + "tool_groups": ["alignment", "overlay", "display", "histogram", "stats", "export", "view", "all"], + "aliases": {} + }, + "Align2DBulk": { + "tool_groups": ["display", "histogram", "navigation", "stats", "view", "export", "all"], + "aliases": {} + }, + "Bin4D": { + "tool_groups": ["display", "binning", "mask", "preview", "stats", "export", "all"], + "aliases": {} + }, + "Browse": { + "tool_groups": ["navigation", "filter", "preview", "all"], + "aliases": {} + }, + "Bin2D": { + "tool_groups": ["display", "binning", "histogram", "stats", "navigation", "export", "all"], + "aliases": {} + }, + "Show1D": { + "tool_groups": ["display", "peaks", "stats", "export", "all"], + "aliases": {} + }, + "MetricExplorer": { + "tool_groups": ["display", "export", "all"], + "aliases": {} + }, + "ShowDiffraction": { + "tool_groups": ["display", "histogram", "stats", "navigation", "view", "export", "spots", "all"], + "aliases": {} + } + }, + "viewer_widgets": ["Show1D", "Show2D", "Show3D", "Show3DVolume", "Show4D", "Show4DSTEM", "ShowComplex2D"], + "control_presets": { + "all": { + "label": "All", + "show_groups": ["*"] + }, + "compact": { + "label": "Compact", + "show_groups": ["mode", "edit", "display", "navigation", "playback", "view", "export", "fft"] + }, + "mask_focus": { + "label": "Mask Focus", + "show_groups": ["edit", "display", "roi", "histogram", "stats", "navigation", "playback", "view", "export", "fft", "virtual", "frame"] + }, + "crop_focus": { + "label": "Crop Focus", + "show_groups": ["mode", "edit", "display", "histogram", "stats", "navigation", "view", "export"] + }, + "spectroscopy": { + "label": "Spectroscopy", + "show_groups": ["display", "peaks", "stats"] + } + } +} diff --git a/widget/src/quantem/widget/tool_parity.py b/widget/src/quantem/widget/tool_parity.py new file mode 100644 index 00000000..d5d4f84e --- /dev/null +++ b/widget/src/quantem/widget/tool_parity.py @@ -0,0 +1,184 @@ +"""Shared tool visibility/locking registry and helpers.""" + +from __future__ import annotations + +import json +import pathlib +from functools import lru_cache +from typing import Any + +_REGISTRY_PATH = pathlib.Path(__file__).with_name("tool_parity.json") + + +@lru_cache(maxsize=1) +def _load_registry() -> dict[str, Any]: + return json.loads(_REGISTRY_PATH.read_text()) + + +def get_widget_tool_groups(widget_name: str) -> tuple[str, ...]: + registry = _load_registry() + widgets = registry.get("widgets", {}) + if widget_name not in widgets: + supported = ", ".join(sorted(widgets)) + raise ValueError(f"Unknown widget {widget_name!r}. Supported widgets: {supported}.") + return tuple(str(v).strip().lower() for v in widgets[widget_name].get("tool_groups", [])) + + +def get_widget_tool_aliases(widget_name: str) -> dict[str, str]: + registry = _load_registry() + widgets = registry.get("widgets", {}) + if widget_name not in widgets: + supported = ", ".join(sorted(widgets)) + raise ValueError(f"Unknown widget {widget_name!r}. Supported widgets: {supported}.") + aliases = widgets[widget_name].get("aliases", {}) + return {str(k).strip().lower(): str(v).strip().lower() for k, v in aliases.items()} + + +def normalize_tool_groups(widget_name: str, tool_groups) -> list[str]: + if tool_groups is None: + return [] + if isinstance(tool_groups, str): + values = [tool_groups] + else: + values = list(tool_groups) + + order = get_widget_tool_groups(widget_name) + aliases = get_widget_tool_aliases(widget_name) + supported = set(order) + normalized: list[str] = [] + seen: set[str] = set() + + for raw in values: + key = str(raw).strip().lower() + if not key: + continue + key = aliases.get(key, key) + if key not in supported: + supported_values = ", ".join(f'"{k}"' for k in order) + raise ValueError( + f"Unknown tool group {raw!r}. Supported values: {supported_values}." + ) + if key == "all": + return ["all"] + if key not in seen: + seen.add(key) + normalized.append(key) + return normalized + + +def build_tool_groups( + widget_name: str, + *, + tool_groups=None, + all_flag: bool = False, + flag_map: dict[str, bool] | None = None, +) -> list[str]: + if all_flag: + return ["all"] + values: list[str] = [] + if tool_groups is not None: + if isinstance(tool_groups, str): + values.append(tool_groups) + else: + values.extend(tool_groups) + for key, enabled in (flag_map or {}).items(): + if enabled: + values.append(key) + return normalize_tool_groups(widget_name, values) + + +def resolve_control_preset_hidden_tools(widget_name: str, preset_id: str) -> list[str]: + preset_key = str(preset_id).strip().lower() + presets = _load_registry().get("control_presets", {}) + if preset_key not in presets: + supported = ", ".join(sorted(presets)) + raise ValueError(f"Unknown control preset {preset_id!r}. Supported presets: {supported}.") + + show_groups = [str(v).strip().lower() for v in presets[preset_key].get("show_groups", [])] + supported_groups = [g for g in get_widget_tool_groups(widget_name) if g != "all"] + if "*" in show_groups: + return [] + show_set = set(show_groups) + hidden = [group for group in supported_groups if group not in show_set] + return normalize_tool_groups(widget_name, hidden) + + +def _flatten_groups(groups: tuple[Any, ...]) -> list[Any]: + if len(groups) == 1 and isinstance(groups[0], (list, tuple, set)): + return list(groups[0]) + return list(groups) + + +def _expanded_without_all(widget_name: str, values) -> list[str]: + normalized = normalize_tool_groups(widget_name, values) + if "all" not in normalized: + return normalized + return [group for group in get_widget_tool_groups(widget_name) if group != "all"] + + +def _ordered_groups(widget_name: str, values: set[str]) -> list[str]: + return [group for group in get_widget_tool_groups(widget_name) if group != "all" and group in values] + + +def bind_tool_runtime_api(cls, widget_name: str) -> None: + """Attach runtime lock/hide helpers to a widget class.""" + + def set_disabled_tools(self, tool_groups) -> Any: + self.disabled_tools = normalize_tool_groups(widget_name, tool_groups) + return self + + def set_hidden_tools(self, tool_groups) -> Any: + self.hidden_tools = normalize_tool_groups(widget_name, tool_groups) + return self + + def lock_tool(self, *tool_groups) -> Any: + new_groups = _flatten_groups(tool_groups) + if not new_groups: + return self + current = _expanded_without_all(widget_name, self.disabled_tools) + requested = _expanded_without_all(widget_name, new_groups) + merged = set(current).union(requested) + self.disabled_tools = _ordered_groups(widget_name, merged) + return self + + def unlock_tool(self, *tool_groups) -> Any: + remove_groups = _flatten_groups(tool_groups) + if not remove_groups: + return self + current = set(_expanded_without_all(widget_name, self.disabled_tools)) + requested = set(_expanded_without_all(widget_name, remove_groups)) + current.difference_update(requested) + self.disabled_tools = _ordered_groups(widget_name, current) + return self + + def hide_tool(self, *tool_groups) -> Any: + new_groups = _flatten_groups(tool_groups) + if not new_groups: + return self + current = _expanded_without_all(widget_name, self.hidden_tools) + requested = _expanded_without_all(widget_name, new_groups) + merged = set(current).union(requested) + self.hidden_tools = _ordered_groups(widget_name, merged) + return self + + def show_tool(self, *tool_groups) -> Any: + remove_groups = _flatten_groups(tool_groups) + if not remove_groups: + return self + current = set(_expanded_without_all(widget_name, self.hidden_tools)) + requested = set(_expanded_without_all(widget_name, remove_groups)) + current.difference_update(requested) + self.hidden_tools = _ordered_groups(widget_name, current) + return self + + def apply_control_preset(self, preset: str) -> Any: + self.hidden_tools = resolve_control_preset_hidden_tools(widget_name, preset) + return self + + cls.set_disabled_tools = set_disabled_tools # type: ignore[attr-defined] + cls.set_hidden_tools = set_hidden_tools # type: ignore[attr-defined] + cls.lock_tool = lock_tool # type: ignore[attr-defined] + cls.unlock_tool = unlock_tool # type: ignore[attr-defined] + cls.hide_tool = hide_tool # type: ignore[attr-defined] + cls.show_tool = show_tool # type: ignore[attr-defined] + cls.apply_control_preset = apply_control_preset # type: ignore[attr-defined] diff --git a/widget/tsconfig.json b/widget/tsconfig.json new file mode 100644 index 00000000..8b4afe79 --- /dev/null +++ b/widget/tsconfig.json @@ -0,0 +1,25 @@ +{ + "include": [ + "js" + ], + "compilerOptions": { + "target": "ES2020", + "module": "ESNext", + "lib": [ + "ES2020", + "DOM", + "DOM.Iterable" + ], + "skipLibCheck": true, + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "resolveJsonModule": true, + "isolatedModules": true, + "noEmit": true, + "jsx": "react", + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "noFallthroughCasesInSwitch": true + } +} \ No newline at end of file diff --git a/widget/vite.config.js b/widget/vite.config.js index 8f303083..291b84aa 100644 --- a/widget/vite.config.js +++ b/widget/vite.config.js @@ -9,14 +9,17 @@ export default defineConfig({ }, build: { outDir: "src/quantem/widget/static", - lib: { - entry: "js/index.jsx", - formats: ["es"], - fileName: "index", - }, + emptyOutDir: true, rollupOptions: { + input: { + show2d: "js/show2d/index.tsx", + show4dstem: "js/show4dstem/index.tsx", + }, output: { - inlineDynamicImports: true, + entryFileNames: "[name].js", + assetFileNames: "[name][extname]", + format: "es", + inlineDynamicImports: false, }, }, }, From 9c3d93013973f9472b336f5def913142046cad6a Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Sat, 2 May 2026 14:29:29 -0700 Subject: [PATCH 035/140] refactor: modernize type hints in widget package (PEP 604/585) --- widget/src/quantem/widget/show2d.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py index 08031ac7..7c7ef16a 100644 --- a/widget/src/quantem/widget/show2d.py +++ b/widget/src/quantem/widget/show2d.py @@ -13,7 +13,7 @@ import math import warnings from enum import StrEnum -from typing import Optional, Union, List, Self +from typing import Self import anywidget import matplotlib @@ -253,7 +253,7 @@ class Show2D(anywidget.AnyWidget): image_rotations = traitlets.List(traitlets.Int(), []).tag(sync=True) @classmethod - def _normalize_tool_groups(cls, tool_groups) -> List[str]: + def _normalize_tool_groups(cls, tool_groups) -> list[str]: return normalize_tool_groups("Show2D", tool_groups) @classmethod @@ -269,7 +269,7 @@ def _build_disabled_tools( disable_roi: bool = False, disable_profile: bool = False, disable_all: bool = False, - ) -> List[str]: + ) -> list[str]: return build_tool_groups( "Show2D", tool_groups=disabled_tools, @@ -299,7 +299,7 @@ def _build_hidden_tools( hide_roi: bool = False, hide_profile: bool = False, hide_all: bool = False, - ) -> List[str]: + ) -> list[str]: return build_tool_groups( "Show2D", tool_groups=hidden_tools, @@ -326,10 +326,10 @@ def _validate_hidden_tools(self, proposal): def __init__( self, - data: Union[np.ndarray, List[np.ndarray]], - labels: Optional[List[str]] = None, + data: np.ndarray | list[np.ndarray], + labels: list[str | None] = None, title: str = "", - cmap: Union[str, Colormap] = Colormap.INFERNO, + cmap: str | Colormap = Colormap.INFERNO, pixel_size: float = 0.0, scale_bar_visible: bool = True, show_fft: bool = False, @@ -340,7 +340,7 @@ def __init__( auto_contrast: bool = False, vmin: float | list | None = None, vmax: float | list | None = None, - disabled_tools: Optional[List[str]] = None, + disabled_tools: list[str | None] = None, disable_display: bool = False, disable_histogram: bool = False, disable_stats: bool = False, @@ -350,7 +350,7 @@ def __init__( disable_roi: bool = False, disable_profile: bool = False, disable_all: bool = False, - hidden_tools: Optional[List[str]] = None, + hidden_tools: list[str | None] = None, hide_display: bool = False, hide_histogram: bool = False, hide_stats: bool = False, @@ -371,7 +371,7 @@ def __init__( link_contrast: bool = True, diff_mode: bool = False, view_box: tuple | list | None = None, - display_bin: Union[int, str] = "auto", + display_bin: int | str = "auto", state=None, **kwargs, ): From b245948629fc0ae3fb2d2ad21ee08cdce722c39f Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Sat, 2 May 2026 14:33:40 -0700 Subject: [PATCH 036/140] feat: first-class Dataset2d/Dataset3d support, drop cupy, improve errors --- widget/src/quantem/widget/array_utils.py | 291 +++++------------------ widget/src/quantem/widget/show2d.py | 15 +- widget/src/quantem/widget/show4dstem.py | 6 +- 3 files changed, 73 insertions(+), 239 deletions(-) diff --git a/widget/src/quantem/widget/array_utils.py b/widget/src/quantem/widget/array_utils.py index e86633e6..a38190e6 100644 --- a/widget/src/quantem/widget/array_utils.py +++ b/widget/src/quantem/widget/array_utils.py @@ -1,282 +1,111 @@ """ -Array utilities for handling NumPy, CuPy, and PyTorch arrays uniformly. - -This module provides utilities to convert arrays from different backends -into NumPy arrays for widget processing. +Array utilities for widgets. Supports NumPy + PyTorch input. """ -from typing import Any, Literal +from typing import Literal import numpy as np try: import torch - import torch.nn.functional as F _HAS_TORCH = True except ImportError: _HAS_TORCH = False -ArrayBackend = Literal["numpy", "cupy", "torch", "unknown"] - - -def get_array_backend(data: Any) -> ArrayBackend: - """ - Detect the array backend of the input data. +ArrayBackend = Literal["numpy", "torch", "unknown"] - Parameters - ---------- - data : array-like - Input array (NumPy, CuPy, PyTorch, or other). - Returns - ------- - str - One of: "numpy", "cupy", "torch", "unknown" - """ - # Check PyTorch first (has both .numpy and .detach methods) - if hasattr(data, "detach") and hasattr(data, "numpy"): +def get_array_backend(data) -> ArrayBackend: + """Detect array backend. Returns 'numpy', 'torch', or 'unknown'.""" + if _HAS_TORCH and isinstance(data, torch.Tensor): return "torch" - # Check CuPy (has .get() or __cuda_array_interface__) - if hasattr(data, "__cuda_array_interface__"): - return "cupy" - if hasattr(data, "get") and hasattr(data, "__array__"): - # CuPy arrays have .get() to transfer to CPU - type_name = type(data).__module__ - if "cupy" in type_name: - return "cupy" - # Check NumPy if isinstance(data, np.ndarray): return "numpy" return "unknown" -def to_numpy(data: Any, dtype: np.dtype | None = None) -> np.ndarray: - """ - Convert any array-like (NumPy, CuPy, PyTorch) to a NumPy array. +def to_numpy(data, dtype: np.dtype | None = None) -> np.ndarray: + """Convert NumPy or PyTorch array to NumPy. Parameters ---------- - data : array-like - Input array from any supported backend. + data : np.ndarray or torch.Tensor + Input array. dtype : np.dtype, optional - Target dtype for the output array. If None, preserves original dtype. + Target dtype. Returns ------- np.ndarray - NumPy array with the same data. Examples -------- >>> import numpy as np - >>> from quantem.widget.array_utils import to_numpy - >>> - >>> # NumPy passthrough - >>> arr = np.random.rand(10, 10) - >>> result = to_numpy(arr) - >>> - >>> # CuPy conversion (if available) - >>> import cupy as cp - >>> gpu_arr = cp.random.rand(10, 10) - >>> cpu_arr = to_numpy(gpu_arr) - >>> - >>> # PyTorch conversion (if available) + >>> to_numpy(np.zeros((4, 4))) >>> import torch - >>> tensor = torch.rand(10, 10) - >>> arr = to_numpy(tensor) + >>> to_numpy(torch.zeros(4, 4)) + + Raises + ------ + TypeError + If `data` is not a NumPy array or PyTorch tensor. """ backend = get_array_backend(data) - if backend == "torch": - # PyTorch tensor: detach from graph, move to CPU, convert to numpy result = data.detach().cpu().numpy() - - elif backend == "cupy": - # CuPy array: use .get() to transfer to CPU - if hasattr(data, "get"): - result = data.get() - else: - # Fallback for __cuda_array_interface__ - import cupy as cp - - result = cp.asnumpy(data) - elif backend == "numpy": - # NumPy array: passthrough (may copy if dtype changes) result = data - else: - # Unknown backend: try np.asarray as fallback - result = np.asarray(data) - - # Apply dtype conversion if specified + # Try np.asarray as last-resort fallback for things like Dataset arrays + try: + result = np.asarray(data) + except Exception as e: + raise TypeError( + f"to_numpy expected a NumPy array or PyTorch tensor, got {type(data).__name__}. " + f"Convert your input via np.asarray(...) or tensor.cpu().numpy() first." + ) from e if dtype is not None: result = np.asarray(result, dtype=dtype) - return result -def bin2d(data, factor: int = 2, mode: str = "mean", edge_mode: str = "crop") -> np.ndarray: - """ - Spatial binning for 2D or 3D arrays. - - Uses torch GPU (MPS/CUDA) when available for large arrays (~5× faster on 4K data). - - Parameters - ---------- - data : array-like - Input array with shape ``(H, W)`` or ``(N, H, W)``. - factor : int, default 2 - Bin factor. - mode : str, default "mean" - Reduction mode: ``"mean"`` or ``"sum"``. - edge_mode : str, default "crop" - How to handle dimensions not divisible by *factor*: - ``"crop"`` trims extra pixels, ``"pad"`` zero-pads to the next - multiple (output shape uses ``ceil(dim / factor)``). - - Returns - ------- - np.ndarray - Binned array, dtype float32. - """ - arr = to_numpy(data) - if arr.dtype != np.float32: - arr = arr.astype(np.float32) - - # Torch GPU fast path: only for arrays between 1M and 500M elements. - # Larger arrays hit MPS memory transfer bottleneck (>2 GB transfer > CPU compute). - import torch - if 1_000_000 < arr.size < 500_000_000 and (torch.backends.mps.is_available() or torch.cuda.is_available()): - dev = torch.device("mps" if torch.backends.mps.is_available() else "cuda") - t = torch.from_numpy(arr).to(dev) - if t.ndim == 2: - h, w = t.shape - oh = h // factor * factor - ow = w // factor * factor - t = t[:oh, :ow].reshape(oh // factor, factor, ow // factor, factor) - t = t.sum(dim=(1, 3)) if mode == "sum" else t.mean(dim=(1, 3)) - elif t.ndim == 3: - n, h, w = t.shape - oh = h // factor * factor - ow = w // factor * factor - t = t[:, :oh, :ow].reshape(n, oh // factor, factor, ow // factor, factor) - t = t.sum(dim=(2, 4)) if mode == "sum" else t.mean(dim=(2, 4)) - return t.cpu().numpy().astype(np.float32) - - # CPU fallback (no GPU available or small array) - reduce = np.ndarray.sum if mode == "sum" else np.ndarray.mean - if arr.ndim == 2: - arr = _pad_or_crop_2d(arr, factor, edge_mode) - h, w = arr.shape - oh, ow = h // factor, w // factor - return reduce(arr.reshape(oh, factor, ow, factor), axis=(1, 3)).astype(np.float32) - # 3D: (N, H, W) - arr = _pad_or_crop_3d(arr, factor, edge_mode) - n, h, w = arr.shape - oh, ow = h // factor, w // factor - return reduce(arr.reshape(n, oh, factor, ow, factor), axis=(2, 4)).astype(np.float32) - - -def _pad_or_crop_2d(arr: np.ndarray, factor: int, edge_mode: str) -> np.ndarray: - h, w = arr.shape - if edge_mode == "pad": - pad_h = (factor - h % factor) % factor - pad_w = (factor - w % factor) % factor - if pad_h or pad_w: - arr = np.pad(arr, ((0, pad_h), (0, pad_w)), mode="constant") - else: - oh, ow = h // factor, w // factor - arr = arr[:oh * factor, :ow * factor] - return arr - - -def _pad_or_crop_3d(arr: np.ndarray, factor: int, edge_mode: str) -> np.ndarray: - _, h, w = arr.shape - if edge_mode == "pad": - pad_h = (factor - h % factor) % factor - pad_w = (factor - w % factor) % factor - if pad_h or pad_w: - arr = np.pad(arr, ((0, 0), (0, pad_h), (0, pad_w)), mode="constant") - else: - oh, ow = h // factor, w // factor - arr = arr[:, :oh * factor, :ow * factor] - return arr - - -def apply_shift(img: np.ndarray, dy: float, dx: float) -> np.ndarray: - """ - Apply sub-pixel shift using bilinear interpolation. - - Uses ``torch.nn.functional.grid_sample`` on GPU when torch is available, - falls back to numpy bilinear interpolation otherwise. - - Parameters - ---------- - img : np.ndarray - 2D image, float32. - dy : float - Shift in y (rows). - dx : float - Shift in x (columns). - - Returns - ------- - np.ndarray - Shifted image, same shape, float32. Out-of-bounds pixels are zero. - """ - if _HAS_TORCH: - h, w = img.shape - device = torch.device("mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu") - t = torch.as_tensor(img, dtype=torch.float32, device=device).unsqueeze(0).unsqueeze(0) - base_y = torch.linspace(-1, 1, h, device=device) - base_x = torch.linspace(-1, 1, w, device=device) - gy, gx = torch.meshgrid(base_y, base_x, indexing="ij") - grid = torch.stack([gx - dx * 2.0 / w, gy - dy * 2.0 / h], dim=-1).unsqueeze(0) - result = F.grid_sample(t, grid, mode="bilinear", padding_mode="zeros", align_corners=True) - return result.squeeze().cpu().numpy() - h, w = img.shape - y_src = np.arange(h, dtype=np.float64) - dy - x_src = np.arange(w, dtype=np.float64) - dx - yy, xx = np.meshgrid(y_src, x_src, indexing="ij") - y0 = np.floor(yy).astype(int) - x0 = np.floor(xx).astype(int) - fy = (yy - y0).astype(np.float32) - fx = (xx - x0).astype(np.float32) - valid = (y0 >= 0) & (y0 + 1 < h) & (x0 >= 0) & (x0 + 1 < w) - y0c = np.clip(y0, 0, h - 2) - x0c = np.clip(x0, 0, w - 2) - result = (img[y0c, x0c] * (1 - fy) * (1 - fx) - + img[y0c, x0c + 1] * (1 - fy) * fx - + img[y0c + 1, x0c] * fy * (1 - fx) - + img[y0c + 1, x0c + 1] * fy * fx) - result[~valid] = 0.0 - return result.astype(np.float32) - - def _resize_image(img: np.ndarray, target_h: int, target_w: int) -> np.ndarray: - """Resize image using bilinear interpolation (pure numpy, no scipy).""" - h, w = img.shape + """Center-pad an image to (target_h, target_w) with zeros. + Used to align gallery images of different shapes to a common canvas. + """ + h, w = img.shape[-2:] if h == target_h and w == target_w: return img + pad_top = (target_h - h) // 2 + pad_bot = target_h - h - pad_top + pad_left = (target_w - w) // 2 + pad_right = target_w - w - pad_left + return np.pad(img, ((pad_top, pad_bot), (pad_left, pad_right)), mode="constant", constant_values=0) - y_new = np.linspace(0, h - 1, target_h) - x_new = np.linspace(0, w - 1, target_w) - x_grid, y_grid = np.meshgrid(x_new, y_new) - - y0 = np.floor(y_grid).astype(int) - x0 = np.floor(x_grid).astype(int) - y1 = np.minimum(y0 + 1, h - 1) - x1 = np.minimum(x0 + 1, w - 1) - fy = y_grid - y0 - fx = x_grid - x0 - - result = ( - img[y0, x0] * (1 - fy) * (1 - fx) + - img[y0, x1] * (1 - fy) * fx + - img[y1, x0] * fy * (1 - fx) + - img[y1, x1] * fy * fx - ) - return result.astype(img.dtype) +def apply_shift(img: np.ndarray, dy: float, dx: float) -> np.ndarray: + """Sub-pixel image shift via bilinear interpolation. Used for diff alignment.""" + if not _HAS_TORCH: + # Fallback: integer roll only + return np.roll(img, (int(round(dy)), int(round(dx))), axis=(-2, -1)) + t = torch.from_numpy(img).float() + if t.ndim == 2: + t = t.unsqueeze(0).unsqueeze(0) + h, w = t.shape[-2:] + y = torch.arange(h, dtype=torch.float32) - dy + x = torch.arange(w, dtype=torch.float32) - dx + yy, xx = torch.meshgrid(y, x, indexing="ij") + grid = torch.stack(((xx / (w - 1)) * 2 - 1, (yy / (h - 1)) * 2 - 1), dim=-1).unsqueeze(0) + out = torch.nn.functional.grid_sample(t, grid, mode="bilinear", padding_mode="border", align_corners=True) + return out.squeeze().numpy() + + +def bin2d(img: np.ndarray, factor: int) -> np.ndarray: + """Reduce 2D image by integer binning factor. Mean of f×f blocks.""" + if factor <= 1: + return img + h, w = img.shape[-2:] + h2, w2 = h - h % factor, w - w % factor + img = img[..., :h2, :w2] + return img.reshape(*img.shape[:-2], h2 // factor, factor, w2 // factor, factor).mean(axis=(-3, -1)) diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py index 7c7ef16a..cd2dff99 100644 --- a/widget/src/quantem/widget/show2d.py +++ b/widget/src/quantem/widget/show2d.py @@ -22,6 +22,7 @@ import numpy as np import traitlets +from quantem.core.datastructures import Dataset2d, Dataset3d from quantem.widget.array_utils import to_numpy, _resize_image from quantem.widget.json_state import resolve_widget_version, save_state_file, unwrap_state_payload from quantem.widget.tool_parity import ( @@ -438,8 +439,12 @@ def _init_sync(self, *, data, labels, title, cmap, pixel_size, self._display_data = None # initialized after data setup self._display_bin = 1 - # Check if data is a Dataset2d and extract metadata - if hasattr(data, "array") and hasattr(data, "name") and hasattr(data, "sampling"): + # First-class support for quantem Dataset2d / Dataset3d: + # extract array + auto-populate title, pixel_size from sampling+units. + # (Duck-typing fallback below covers any other object exposing the same API.) + if isinstance(data, (Dataset2d, Dataset3d)) or ( + hasattr(data, "array") and hasattr(data, "name") and hasattr(data, "sampling") + ): if not title and data.name: title = data.name if pixel_size == 0.0 and hasattr(data, "units"): @@ -451,7 +456,7 @@ def _init_sync(self, *, data, labels, title, cmap, pixel_size, pixel_size = sampling_val data = data.array - # Convert input to NumPy (handles NumPy, CuPy, PyTorch) + # Convert NumPy / PyTorch / list inputs to a NumPy array. if isinstance(data, list): images = [to_numpy(d) for d in data] @@ -532,7 +537,7 @@ def _expand(v): if v is None: return [None] * n if isinstance(v, (list, tuple)): if len(v) != n: - raise ValueError(f"vmin/vmax list length {len(v)} != n_images {n}") + raise ValueError(f"vmin/vmax list has length {len(v)} but n_images is {n}. Pass a list of length {n} or a scalar to apply uniformly.") return [None if x is None else float(x) for x in v] return [float(v)] * n self.vmins = _expand(vmin) @@ -1125,7 +1130,7 @@ def rotate(self, idx: int, angle: int) -> Self: Self """ if angle % 90 != 0: - raise ValueError(f"Rotation angle must be a multiple of 90°, got {angle}") + raise ValueError(f"Rotation angle must be a multiple of 90 (got {angle}). Use 0, 90, 180, 270, or -90, -180, -270.") if idx < 0 or idx >= self.n_images: raise IndexError(f"Image index {idx} out of range [0, {self.n_images})") k = (angle // 90) % 4 diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 1fe94273..c514be2f 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -566,7 +566,7 @@ def __init__( self._device = torch.device("cpu") self._data = torch.from_numpy(data_np).to(self._device) else: - raise ValueError(f"Expected 3D, 4D, or 5D array, got {ndim}D") + raise ValueError(f"Show4DSTEM expects a 3D ((N, det_h, det_w) flat-scan), 4D ((scan_h, scan_w, det_h, det_w)), or 5D ((n_frames, scan_h, scan_w, det_h, det_w)) array. Got {ndim}D. Reshape with array.reshape((scan_h, scan_w, det_h, det_w)) or pass a Dataset4dstem.") if _verbose: if str(self._device) == "mps": torch.mps.synchronize() @@ -729,7 +729,7 @@ def set_image(self, data, scan_shape=None): self._det_shape = (data_np.shape[2], data_np.shape[3]) self._data = torch.from_numpy(data_np).to(self._device) else: - raise ValueError(f"Expected 3D, 4D, or 5D array, got {data_np.ndim}D") + raise ValueError(f"Show4DSTEM expects a 3D, 4D, or 5D array. Got {data_np.ndim}D. See documentation for accepted shapes.") self.frame_idx = 0 self.shape_rows = self._scan_shape[0] self.shape_cols = self._scan_shape[1] @@ -1865,7 +1865,7 @@ def _render_panel_image( elif panel_key == "fft": rgb, render_meta = self._render_fft_rgb() else: - raise ValueError(f"Unsupported panel '{panel_key}'") + raise ValueError(f"Unsupported panel {panel_key!r}. Valid options: 'diffraction', 'virtual', 'fft', 'all'.") panel = Image.fromarray(rgb, mode="RGB") panel = self._decorate_panel(panel, panel_key, include_overlays, include_scalebar) From 43b54b2ab6b63e9c11baa0ab3f1aefc334d09e7c Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Sun, 3 May 2026 14:24:45 -0700 Subject: [PATCH 037/140] initial demo of Show2D and Show4DSTEM --- .gitignore | 16 +- widget/js/colormaps.ts | 16 +- widget/js/control-customizer.tsx | 174 - widget/js/{webgpu-fft.ts => fft.ts} | 75 +- widget/js/{scalebar.ts => figure.ts} | 33 +- widget/js/histogram.ts | 19 - widget/js/show2d/index.tsx | 362 +- widget/js/show2d/show2d.css | 9 - widget/js/show4dstem/index.tsx | 989 +++--- widget/js/show4dstem/styles.css | 5 - widget/js/stats.ts | 20 + widget/js/tool-parity.ts | 156 - widget/package-lock.json | 1186 +------ widget/package.json | 10 +- widget/src/quantem/widget/__init__.py | 9 +- widget/src/quantem/widget/array_utils.py | 92 +- widget/src/quantem/widget/show2d.py | 316 +- widget/src/quantem/widget/show4dstem.py | 3046 +++-------------- .../widget/{json_state.py => state.py} | 2 - widget/src/quantem/widget/tool_parity.json | 93 - widget/src/quantem/widget/tool_parity.py | 184 - widget/tests/test_fft_parity.py | 200 ++ widget/tests/test_state_dict.py | 168 + widget/vite.config.js | 26 - 24 files changed, 1778 insertions(+), 5428 deletions(-) delete mode 100644 widget/js/control-customizer.tsx rename widget/js/{webgpu-fft.ts => fft.ts} (89%) rename widget/js/{scalebar.ts => figure.ts} (92%) delete mode 100644 widget/js/histogram.ts delete mode 100644 widget/js/show2d/show2d.css delete mode 100644 widget/js/show4dstem/styles.css delete mode 100644 widget/js/tool-parity.ts rename widget/src/quantem/widget/{json_state.py => state.py} (96%) delete mode 100644 widget/src/quantem/widget/tool_parity.json delete mode 100644 widget/src/quantem/widget/tool_parity.py create mode 100644 widget/tests/test_fft_parity.py create mode 100644 widget/tests/test_state_dict.py delete mode 100644 widget/vite.config.js diff --git a/.gitignore b/.gitignore index d87d85c8..92d69e39 100644 --- a/.gitignore +++ b/.gitignore @@ -185,9 +185,23 @@ ipynb-playground/ *.h5 *.npy -# cursor +# cursor/CLI .cursor +.claude +CLAUDE.md +AGENTS.md +AGENT.md # widget (JS build artifacts) node_modules/ widget/src/quantem/widget/static/ + +# widget — local-only (per-developer notebooks, docs scratch, build/test scripts). +# Track only src/, js/, tests/test_*.py for now. +widget/.gitignore +widget/docs/ +widget/notebooks/ +widget/scripts/ +widget/tests/integration/ +widget/tests/snapshots/ + diff --git a/widget/js/colormaps.ts b/widget/js/colormaps.ts index ba160698..40a940b2 100644 --- a/widget/js/colormaps.ts +++ b/widget/js/colormaps.ts @@ -1,3 +1,7 @@ +// ============================================================================ +// Color palettes (LUT control points) +// ============================================================================ + const COLORMAP_POINTS: Record = { inferno: [ [0, 0, 4], [40, 11, 84], [101, 21, 110], [159, 42, 99], @@ -58,6 +62,10 @@ export const COLORMAPS: Record = Object.fromEntries( Object.entries(COLORMAP_POINTS).map(([name, points]) => [name, createColormapLUT(points)]) ); +// ============================================================================ +// CPU colormap (Float32 -> RGBA via 256-entry LUT) +// ============================================================================ + /** Apply colormap LUT to float data, writing into an RGBA Uint8ClampedArray. */ export function applyColormap( data: Float32Array, @@ -119,6 +127,10 @@ export function renderToOffscreenReuse( // 2D dispatch (16×16 workgroups) to stay within WebGPU's 65535 workgroup limit. // 1D dispatch with wg=256 needs ceil(4096*4096/256)=65536 — exceeds the limit by 1. +// ============================================================================ +// WebGPU colormap engine (compute shader, ~300x faster than CPU loop on 4K data) +// ============================================================================ + const COLORMAP_SHADER = /* wgsl */ ` struct Params { width: u32, @@ -1061,9 +1073,9 @@ let gpuColormapEngine: GPUColormapEngine | null = null; /** Get or create the singleton GPU colormap engine. Returns null if WebGPU unavailable. */ export async function getGPUColormapEngine(): Promise { if (gpuColormapEngine) return gpuColormapEngine; - // Reuse the GPU device from webgpu-fft + // Reuse the GPU device from fft try { - const { getGPUDevice } = await import("./webgpu-fft"); + const { getGPUDevice } = await import("./fft"); const device = await getGPUDevice(); if (!device) return null; gpuColormapEngine = new GPUColormapEngine(device); diff --git a/widget/js/control-customizer.tsx b/widget/js/control-customizer.tsx deleted file mode 100644 index 3ca9602d..00000000 --- a/widget/js/control-customizer.tsx +++ /dev/null @@ -1,174 +0,0 @@ -import * as React from "react"; -import Box from "@mui/material/Box"; -import Typography from "@mui/material/Typography"; -import Switch from "@mui/material/Switch"; -import Tooltip from "@mui/material/Tooltip"; -import Divider from "@mui/material/Divider"; -import IconButton from "@mui/material/IconButton"; -import Button from "@mui/material/Button"; -import Menu from "@mui/material/Menu"; -import TuneIcon from "@mui/icons-material/Tune"; - -import { - addToolGroup, - compactToolLabel, - computeToolVisibility, - getControlPresetIds, - getControlPresetLabel, - getWidgetToolGroups, - removeToolGroup, - resolvePresetHiddenTools, -} from "./tool-parity"; - -type ToolSetter = React.Dispatch>; - -type ThemeColors = { - controlBg: string; - text: string; - border: string; - textMuted?: string; - accent?: string; -}; - -type ControlCustomizerProps = { - widgetName: string; - hiddenTools: string[]; - setHiddenTools: ToolSetter; - disabledTools: string[]; - setDisabledTools: ToolSetter; - themeColors: ThemeColors; - labelOverrides?: Record; -}; - -const switchStyles = { - small: { - "& .MuiSwitch-thumb": { width: 12, height: 12 }, - "& .MuiSwitch-switchBase": { padding: "4px" }, - }, -}; - -const presetButton = { - fontSize: 10, - py: 0.25, - px: 1, - minWidth: 0, -}; - -export function ControlCustomizer({ - widgetName, - hiddenTools, - setHiddenTools, - disabledTools, - setDisabledTools, - themeColors, - labelOverrides, -}: ControlCustomizerProps) { - const [anchor, setAnchor] = React.useState(null); - const groups = React.useMemo( - () => getWidgetToolGroups(widgetName).filter((group) => group !== "all"), - [widgetName], - ); - const visibility = React.useMemo( - () => computeToolVisibility(widgetName, disabledTools, hiddenTools), - [widgetName, disabledTools, hiddenTools], - ); - - const setGroupVisible = React.useCallback((group: string, visible: boolean) => { - setHiddenTools((prev) => { - if (visible) return removeToolGroup(widgetName, prev, group); - return addToolGroup(widgetName, prev, group); - }); - }, [setHiddenTools, widgetName]); - - const setGroupLocked = React.useCallback((group: string, locked: boolean) => { - setDisabledTools((prev) => { - if (locked) return addToolGroup(widgetName, prev, group); - return removeToolGroup(widgetName, prev, group); - }); - }, [setDisabledTools, widgetName]); - - const applyPreset = React.useCallback((presetId: string) => { - setHiddenTools(resolvePresetHiddenTools(widgetName, presetId)); - }, [setHiddenTools, widgetName]); - - return ( - <> - - setAnchor(e.currentTarget)} - sx={{ p: 0.25, ml: 0.5, color: themeColors.text }} - > - - - - setAnchor(null)} - anchorOrigin={{ vertical: "bottom", horizontal: "right" }} - transformOrigin={{ vertical: "top", horizontal: "right" }} - PaperProps={{ - sx: { - bgcolor: themeColors.controlBg, - color: themeColors.text, - border: `1px solid ${themeColors.border}`, - p: 0.5, - minWidth: 280, - }, - }} - > - - Presets - - {getControlPresetIds().map((presetId) => ( - - ))} - - - - - Per-group - {groups.map((group) => { - const label = labelOverrides?.[group] ?? compactToolLabel(group); - const hidden = visibility.isHidden(group); - const locked = visibility.isLocked(group); - return ( - - {label} - - Show - setGroupVisible(group, e.target.checked)} - inputProps={{ "aria-label": `show-${group}` }} - sx={switchStyles.small} - /> - Lock - setGroupLocked(group, e.target.checked)} - inputProps={{ "aria-label": `lock-${group}` }} - sx={switchStyles.small} - disabled={hidden} - /> - - - ); - })} - - - - ); -} diff --git a/widget/js/webgpu-fft.ts b/widget/js/fft.ts similarity index 89% rename from widget/js/webgpu-fft.ts rename to widget/js/fft.ts index 2498b755..b2a72ea6 100644 --- a/widget/js/webgpu-fft.ts +++ b/widget/js/fft.ts @@ -85,63 +85,24 @@ export function fftshift(data: Float32Array, width: number, height: number): voi // CPU FFT Web Worker — runs fft2d + fftshift + computeMagnitude off main thread // ============================================================================ +// Build worker source by stringifying the same fft1d/fft2d/fftshift defined +// above. Single source of truth: fix a bug once, both paths get it. Pure +// functions only (no module-state closures), so .toString() captures the full +// behavior. Use Function.name in the onmessage body so minified names still +// match (esbuild may rename `fft2d` -> `a`; the .name property tracks rename). const FFT_WORKER_CODE = ` -function nextPow2(n) { return Math.pow(2, Math.ceil(Math.log2(n))); } -function fft1d(real, imag, inverse) { - var n = real.length; if (n <= 1) return; - var j = 0; - for (var i = 0; i < n - 1; i++) { - if (i < j) { var t = real[i]; real[i] = real[j]; real[j] = t; t = imag[i]; imag[i] = imag[j]; imag[j] = t; } - var k = n >> 1; while (k <= j) { j -= k; k >>= 1; } j += k; - } - var sign = inverse ? 1 : -1; - for (var len = 2; len <= n; len <<= 1) { - var halfLen = len >> 1, angle = (sign * 2 * Math.PI) / len; - var wR = Math.cos(angle), wI = Math.sin(angle); - for (var i = 0; i < n; i += len) { - var cR = 1, cI = 0; - for (var k = 0; k < halfLen; k++) { - var eI = i + k, oI = i + k + halfLen; - var tR = cR * real[oI] - cI * imag[oI], tI = cR * imag[oI] + cI * real[oI]; - real[oI] = real[eI] - tR; imag[oI] = imag[eI] - tI; - real[eI] += tR; imag[eI] += tI; - var nR = cR * wR - cI * wI; cI = cR * wI + cI * wR; cR = nR; - } - } - } - if (inverse) { for (var i = 0; i < n; i++) { real[i] /= n; imag[i] /= n; } } -} -function fft2d(real, imag, width, height, inverse) { - var pW = nextPow2(width), pH = nextPow2(height), pad = pW !== width || pH !== height; - var wR, wI; - if (pad) { - wR = new Float32Array(pW * pH); wI = new Float32Array(pW * pH); - for (var y = 0; y < height; y++) for (var x = 0; x < width; x++) { wR[y*pW+x] = real[y*width+x]; wI[y*pW+x] = imag[y*width+x]; } - } else { wR = real; wI = imag; } - var rR = new Float32Array(pW), rI = new Float32Array(pW); - for (var y = 0; y < pH; y++) { - var o = y * pW; for (var x = 0; x < pW; x++) { rR[x] = wR[o+x]; rI[x] = wI[o+x]; } - fft1d(rR, rI, inverse); for (var x = 0; x < pW; x++) { wR[o+x] = rR[x]; wI[o+x] = rI[x]; } - } - var cR = new Float32Array(pH), cI = new Float32Array(pH); - for (var x = 0; x < pW; x++) { - for (var y = 0; y < pH; y++) { cR[y] = wR[y*pW+x]; cI[y] = wI[y*pW+x]; } - fft1d(cR, cI, inverse); for (var y = 0; y < pH; y++) { wR[y*pW+x] = cR[y]; wI[y*pW+x] = cI[y]; } - } - if (pad) { for (var y = 0; y < height; y++) for (var x = 0; x < width; x++) { real[y*width+x] = wR[y*pW+x]; imag[y*width+x] = wI[y*pW+x]; } } -} -function fftshift(data, width, height) { - var hW = width >> 1, hH = height >> 1, temp = new Float32Array(width * height); - for (var y = 0; y < height; y++) for (var x = 0; x < width; x++) temp[((y+hH)%height)*width+((x+hW)%width)] = data[y*width+x]; - data.set(temp); -} +${nextPow2.toString()} +${fft1d.toString()} +${fft2d.toString()} +${fftshift.toString()} self.onmessage = function(e) { - var d = e.data, real = d.real, imag = d.imag, w = d.width, h = d.height; - fft2d(real, imag, w, h, d.inverse); - fftshift(real, w, h); fftshift(imag, w, h); - var n = real.length, mag = new Float32Array(n); - for (var i = 0; i < n; i++) mag[i] = Math.sqrt(real[i]*real[i] + imag[i]*imag[i]); - self.postMessage({ id: d.id, magnitude: mag, real: real, imag: imag }, [mag.buffer, real.buffer, imag.buffer]); + const d = e.data; + ${fft2d.name}(d.real, d.imag, d.width, d.height, d.inverse); + ${fftshift.name}(d.real, d.width, d.height); + ${fftshift.name}(d.imag, d.width, d.height); + const n = d.real.length, mag = new Float32Array(n); + for (let i = 0; i < n; i++) mag[i] = Math.sqrt(d.real[i]*d.real[i] + d.imag[i]*d.imag[i]); + self.postMessage({ id: d.id, magnitude: mag, real: d.real, imag: d.imag }, [mag.buffer, d.real.buffer, d.imag.buffer]); }; `; @@ -189,6 +150,10 @@ export function fft2dAsync( // WebGPU FFT — GPU-accelerated 2D FFT // ============================================================================ +// ============================================================================ +// WebGPU FFT (compute shader, GPU-resident) +// ============================================================================ + const FFT_2D_SHADER = /* wgsl */` fn cmul(a: vec2, b: vec2) -> vec2 { return vec2(a.x*b.x-a.y*b.y, a.x*b.y+a.y*b.x); } fn twiddle(k: u32, N: u32, inverse: f32) -> vec2 { let angle = inverse * 2.0 * 3.14159265359 * f32(k) / f32(N); return vec2(cos(angle), sin(angle)); } diff --git a/widget/js/scalebar.ts b/widget/js/figure.ts similarity index 92% rename from widget/js/scalebar.ts rename to widget/js/figure.ts index 041b4754..b1fd3f2f 100644 --- a/widget/js/scalebar.ts +++ b/widget/js/figure.ts @@ -5,8 +5,6 @@ import { formatNumber } from "./format"; -export type ScaleUnit = "Å" | "mrad" | "px" | "Å⁻¹"; - /** Round a physical value to a "nice" number (1, 2, 5, 10, 20, 50, ...) */ export function roundToNiceValue(value: number): number { if (value <= 0) return 1; @@ -18,23 +16,10 @@ export function roundToNiceValue(value: number): number { return 10 * magnitude; } -/** Format scale bar label with appropriate unit and auto-conversion (Å→nm, mrad→rad, Å⁻¹→nm⁻¹) */ -export function formatScaleLabel(value: number, unit: ScaleUnit): string { +/** Format scale bar label. Unit string is displayed verbatim - no conversion. */ +export function formatScaleLabel(value: number, unit: string): string { const nice = roundToNiceValue(value); - if (unit === "Å") { - if (nice >= 10) return `${Math.round(nice / 10)} nm`; - return nice >= 1 ? `${Math.round(nice)} Å` : `${nice.toFixed(2)} Å`; - } - if (unit === "Å⁻¹") { - // 10 Å⁻¹ = 1 nm⁻¹ - if (nice >= 10) return `${Math.round(nice / 10)} nm⁻¹`; - return nice >= 1 ? `${Math.round(nice)} Å⁻¹` : `${nice.toFixed(2)} Å⁻¹`; - } - if (unit === "px") { - return nice >= 1 ? `${Math.round(nice)} px` : `${nice.toFixed(1)} px`; - } - if (nice >= 1000) return `${Math.round(nice / 1000)} rad`; - return nice >= 1 ? `${Math.round(nice)} mrad` : `${nice.toFixed(2)} mrad`; + return nice >= 1 ? `${Math.round(nice)} ${unit}` : `${nice.toFixed(2)} ${unit}`; } const FONT = "-apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; @@ -48,7 +33,7 @@ export function drawScaleBarHiDPI( dpr: number, zoom: number, pixelSize: number, - unit: "Å" | "mrad" | "px", + unit: string, imageWidth: number, ) { const ctx = canvas.getContext("2d"); @@ -107,6 +92,7 @@ export function drawFFTScaleBarHiDPI( fftZoom: number, fftPixelSize: number, imageWidth: number, + unit: string = "1/px", ) { const ctx = canvas.getContext("2d"); if (!ctx || fftPixelSize <= 0) return; @@ -139,7 +125,7 @@ export function drawFFTScaleBarHiDPI( ctx.fillStyle = "white"; ctx.fillRect(barX, barY, barPx, barThickness); - const label = formatScaleLabel(nicePhysical, "Å⁻¹"); + const label = formatScaleLabel(nicePhysical, unit); ctx.font = `${fontSize}px ${FONT}`; ctx.fillStyle = "white"; ctx.textAlign = "center"; @@ -220,8 +206,10 @@ export interface ExportFigureOptions { vmin?: number; vmax?: number; logScale?: boolean; - /** Pixel size in Å (for scale bar computation). */ + /** Pixel size in user-supplied unit (for scale bar computation). */ pixelSize?: number; + /** Unit string for the scale bar label (e.g. "A", "nm", "mrad"). */ + pixelUnit?: string; showColorbar?: boolean; showScaleBar?: boolean; /** Upscale factor for high-resolution output (default 4). Image pixels use nearest-neighbor for sharp edges. */ @@ -243,6 +231,7 @@ export function exportFigure(options: ExportFigureOptions): HTMLCanvasElement { vmax = 1, logScale = false, pixelSize = 0, + pixelUnit = "pixels", showColorbar = true, showScaleBar = true, scale: s = 4, @@ -323,7 +312,7 @@ export function exportFigure(options: ExportFigureOptions): HTMLCanvasElement { ctx.fillStyle = "white"; ctx.fillRect(barX, barY, barPx, barThickness); - const label = formatScaleLabel(nicePhysical, "Å"); + const label = formatScaleLabel(nicePhysical, pixelUnit); ctx.font = `bold ${fontSize}px ${FONT}`; ctx.fillStyle = "white"; ctx.textAlign = "center"; diff --git a/widget/js/histogram.ts b/widget/js/histogram.ts deleted file mode 100644 index c2f96a61..00000000 --- a/widget/js/histogram.ts +++ /dev/null @@ -1,19 +0,0 @@ -/** Compute normalized histogram bins from Float32Array. Returns array of 0-1 values. */ -export function computeHistogramFromBytes(data: Float32Array | null, numBins = 256): number[] { - if (!data || data.length === 0) return new Array(numBins).fill(0); - const bins = new Array(numBins).fill(0); - let min = Infinity, max = -Infinity; - for (let i = 0; i < data.length; i++) { - const v = data[i]; - if (isFinite(v)) { if (v < min) min = v; if (v > max) max = v; } - } - if (!isFinite(min) || !isFinite(max) || min === max) return bins; - const range = max - min; - for (let i = 0; i < data.length; i++) { - const v = data[i]; - if (isFinite(v)) bins[Math.min(numBins - 1, Math.floor(((v - min) / range) * numBins))]++; - } - const maxCount = Math.max(...bins); - if (maxCount > 0) for (let i = 0; i < numBins; i++) bins[i] /= maxCount; - return bins; -} diff --git a/widget/js/show2d/index.tsx b/widget/js/show2d/index.tsx index aacf5c43..17120308 100644 --- a/widget/js/show2d/index.tsx +++ b/widget/js/show2d/index.tsx @@ -22,13 +22,10 @@ import Slider from "@mui/material/Slider"; import Button from "@mui/material/Button"; import Tooltip from "@mui/material/Tooltip"; import { useTheme } from "../theme"; -import { drawScaleBarHiDPI, drawFFTScaleBarHiDPI, drawColorbar, roundToNiceValue, exportFigure, canvasToPDF } from "../scalebar"; +import { drawScaleBarHiDPI, drawFFTScaleBarHiDPI, drawColorbar, roundToNiceValue, exportFigure, canvasToPDF } from "../figure"; import JSZip from "jszip"; import { extractFloat32, formatNumber, downloadBlob } from "../format"; -import { computeHistogramFromBytes } from "../histogram"; -import { findDataRange, applyLogScale, percentileClip, sliderRange, computeStats } from "../stats"; -import { ControlCustomizer } from "../control-customizer"; -import { computeToolVisibility } from "../tool-parity"; +import { computeHistogramFromBytes, findDataRange, applyLogScale, percentileClip, sliderRange, computeStats } from "../stats"; function InfoTooltip({ text, theme = "dark" }: { text: React.ReactNode; theme?: "light" | "dark" }) { const isDark = theme === "dark"; @@ -66,9 +63,8 @@ const upwardMenuProps = { transformOrigin: { vertical: "bottom" as const, horizontal: "left" as const }, sx: { zIndex: 9999 }, }; -import { getWebGPUFFT, WebGPUFFT, fft2d, fft2dAsync, fftshift, computeMagnitude, autoEnhanceFFT, nextPow2, applyHannWindow2D, getGPUInfo } from "../webgpu-fft"; +import { getWebGPUFFT, WebGPUFFT, fft2d, fft2dAsync, fftshift, computeMagnitude, autoEnhanceFFT, nextPow2, applyHannWindow2D, getGPUInfo } from "../fft"; import { COLORMAPS, COLORMAP_NAMES, renderToOffscreen, renderToOffscreenReuse, GPUColormapEngine, getGPUColormapEngine, getGPUMaxBufferSize } from "../colormaps"; -import "./show2d.css"; const MIN_ZOOM = 0.5; const MAX_ZOOM = 20; @@ -403,13 +399,12 @@ function Show2D() { // Scale bar const [pixelSize] = useModelState("pixel_size"); + const [pixelUnit] = useModelState("pixel_unit"); const [scaleBarVisible] = useModelState("scale_bar_visible"); // UI visibility const [showControls] = useModelState("show_controls"); const [showStats] = useModelState("show_stats"); - const [disabledTools, setDisabledTools] = useModelState("disabled_tools"); - const [hiddenTools, setHiddenTools] = useModelState("hidden_tools"); const [statsMean] = useModelState("stats_mean"); const [statsMin] = useModelState("stats_min"); const [statsMax] = useModelState("stats_max"); @@ -437,43 +432,15 @@ function Show2D() { const [exportAnchor, setExportAnchor] = React.useState(null); const selectedRoi = roiSelectedIdx >= 0 && roiSelectedIdx < (roiList?.length ?? 0) ? roiList[roiSelectedIdx] : null; - const toolVisibility = React.useMemo( - () => computeToolVisibility("Show2D", disabledTools, hiddenTools), - [disabledTools, hiddenTools], - ); - const hideDisplay = toolVisibility.isHidden("display"); - const hideHistogram = toolVisibility.isHidden("histogram"); - const hideStats = toolVisibility.isHidden("stats"); - const hideView = toolVisibility.isHidden("view"); - const hideExport = toolVisibility.isHidden("export"); - const hideRoi = toolVisibility.isHidden("roi"); - const hideProfile = toolVisibility.isHidden("profile"); - - const lockDisplay = toolVisibility.isLocked("display"); - const lockHistogram = toolVisibility.isLocked("histogram"); - const lockStats = toolVisibility.isLocked("stats"); - const lockNavigation = toolVisibility.isLocked("navigation"); - const lockView = toolVisibility.isLocked("view"); - const lockExport = toolVisibility.isLocked("export"); - const lockRoi = toolVisibility.isLocked("roi"); - const lockProfile = toolVisibility.isLocked("profile"); - const effectiveShowFft = showFft && !hideDisplay; + const effectiveShowFft = showFft; const updateSelectedRoi = (updates: Partial) => { - if (lockRoi) return; if (roiSelectedIdx < 0 || !roiList) return; const newList = [...roiList]; newList[roiSelectedIdx] = { ...newList[roiSelectedIdx], ...updates }; setRoiList(newList); }; - React.useEffect(() => { - if (hideRoi && roiActive) { - setRoiActive(false); - setRoiSelectedIdx(-1); - } - }, [hideRoi, roiActive, setRoiActive, setRoiSelectedIdx]); - // Canvas refs const canvasRefs = React.useRef<(HTMLCanvasElement | null)[]>([]); const overlayRefs = React.useRef<(HTMLCanvasElement | null)[]>([]); @@ -656,11 +623,6 @@ function Show2D() { const [profileActive, setProfileActive] = React.useState(false); const [profileLine, setProfileLine] = useModelState<{ row: number; col: number }[]>("profile_line"); const [profileDataAll, setProfileDataAll] = React.useState<(Float32Array | null)[]>([]); - React.useEffect(() => { - if (hideProfile && profileActive) { - setProfileActive(false); - } - }, [hideProfile, profileActive]); const profileCanvasRef = React.useRef(null); const profileBaseImageRef = React.useRef(null); const profileLayoutRef = React.useRef<{ padLeft: number; plotW: number; padTop: number; plotH: number; gMin: number; gMax: number; totalDist: number; xUnit: string } | null>(null); @@ -1408,7 +1370,7 @@ function Show2D() { if (scaleBarVisible) { const zs = getZoomState(i); - const unit = pixelSize > 0 ? "Å" as const : "px" as const; + const unit = pixelSize > 0 ? pixelUnit : "px"; const pxSize = pixelSize > 0 ? pixelSize : 1; drawScaleBarHiDPI(overlay, DPR, zs.zoom, pxSize, unit, width); } else { @@ -1416,7 +1378,7 @@ function Show2D() { } // Colorbar (single image mode only) — uses cached vmin/vmax from data effect - if (!hideDisplay && showColorbar && !isGallery) { + if (showColorbar && !isGallery) { const lut = COLORMAPS[cmap] || COLORMAPS.inferno; const cssW = overlay.width / DPR; const cssH = overlay.height / DPR; @@ -1430,7 +1392,7 @@ function Show2D() { } // ROI overlay — draw all ROIs - if (!hideRoi && roiActive && roiList && roiList.length > 0) { + if (roiActive && roiList && roiList.length > 0) { const zs = getZoomState(i); const { zoom, panX, panY } = zs; const cx = canvasW / 2; @@ -1503,7 +1465,7 @@ function Show2D() { } // Line profile overlay - if (!hideProfile && profileActive && profilePoints.length > 0) { + if (profileActive && profilePoints.length > 0) { const zs = getZoomState(i); const { zoom, panX, panY } = zs; ctx.save(); @@ -1609,7 +1571,7 @@ function Show2D() { ctx.restore(); } } - }, [nImages, pixelSize, scaleBarVisible, selectedIdx, isGallery, canvasW, canvasH, width, displayScale, linkedZoom, linkedZoomState, zoomStates, dataVersion, showColorbar, cmap, offscreenVersion, logScale, profileActive, profilePoints, roiActive, roiList, roiSelectedIdx, isDraggingROI, themeColors, hideDisplay, hideRoi, hideProfile, measureActive, measurePoints]); + }, [nImages, pixelSize, scaleBarVisible, selectedIdx, isGallery, canvasW, canvasH, width, displayScale, linkedZoom, linkedZoomState, zoomStates, dataVersion, showColorbar, cmap, offscreenVersion, logScale, profileActive, profilePoints, roiActive, roiList, roiSelectedIdx, isDraggingROI, themeColors, measureActive, measurePoints]); // ------------------------------------------------------------------------- // Inset magnifier (lens) — renders magnified region at cursor in bottom-left @@ -1620,7 +1582,7 @@ function Show2D() { const lctx = lensCanvas.getContext("2d"); if (lctx) lctx.clearRect(0, 0, lensCanvas.width, lensCanvas.height); } - if (!showLens || lockDisplay || isGallery || !lensPos || !rawDataRef.current?.[0]) return; + if (!showLens || isGallery || !lensPos || !rawDataRef.current?.[0]) return; if (!lensCanvas) return; const ctx = lensCanvas.getContext("2d"); if (!ctx) return; @@ -1690,13 +1652,12 @@ function Show2D() { ctx.font = "10px monospace"; ctx.fillText(`${lensMag}×`, lx + 4, ly + lensSize - 4); ctx.restore(); - }, [showLens, lockDisplay, lensPos, isGallery, cmap, logScale, offscreenVersion, width, height, canvasH, themeColors, lensMag, lensDisplaySize, lensAnchor]); + }, [showLens, lensPos, isGallery, cmap, logScale, offscreenVersion, width, height, canvasH, themeColors, lensMag, lensDisplaySize, lensAnchor]); // ------------------------------------------------------------------------- // Auto-compute profile when profile_line is set (e.g. from Python) // ------------------------------------------------------------------------- React.useEffect(() => { - if (hideProfile) return; if (profilePoints.length === 2 && rawDataRef.current) { const p0 = profilePoints[0], p1 = profilePoints[1]; const allProfiles: (Float32Array | null)[] = []; @@ -1707,7 +1668,7 @@ function Show2D() { setProfileDataAll(allProfiles); if (!profileActive) setProfileActive(true); } - }, [profilePoints, dataVersion, hideProfile, profileActive]); + }, [profilePoints, dataVersion, profileActive]); // ------------------------------------------------------------------------- // Render sparkline for line profile @@ -1785,9 +1746,8 @@ function Show2D() { const dy = profilePoints[1].row - profilePoints[0].row; const distPx = Math.sqrt(dx * dx + dy * dy); if (pixelSize > 0) { - const distA = distPx * pixelSize; - if (distA >= 10) { totalDist = distA / 10; xUnit = "nm"; } - else { totalDist = distA; xUnit = "Å"; } + totalDist = distPx * pixelSize; + xUnit = pixelUnit; } else { totalDist = distPx; } @@ -2408,7 +2368,6 @@ function Show2D() { // Mouse Handlers for Zoom/Pan // ------------------------------------------------------------------------- const handleWheel = (e: React.WheelEvent, idx: number) => { - if (lockView) return; // In gallery mode, only allow zoom on the selected image (unless linked) if (isGallery && idx !== selectedIdx && !linkedZoom) return; e.preventDefault(); // Prevent page scroll when zooming @@ -2449,13 +2408,11 @@ function Show2D() { }; const handleDoubleClick = (idx: number) => { - if (lockView) return; setZoomState(idx, initialZoomState); }; // Reset view (zoom/pan only — preserves profile, FFT state, etc.) const handleResetAll = () => { - if (lockView) return; setZoomStates(new Map()); setLinkedZoomState(initialZoomState); setGalleryFftStates(new Map()); @@ -2467,14 +2424,12 @@ function Show2D() { // FFT zoom/pan handlers const handleFftWheel = (e: React.WheelEvent) => { - if (lockView) return; e.preventDefault(); // Prevent page scroll when zooming FFT const zoomFactor = e.deltaY > 0 ? 0.9 : 1.1; setFftZoom(Math.max(MIN_ZOOM, Math.min(MAX_ZOOM, fftZoom * zoomFactor))); }; const handleFftDoubleClick = () => { - if (lockView) return; setFftZoom(DEFAULT_FFT_ZOOM); setFftPanX(0); setFftPanY(0); @@ -2501,7 +2456,6 @@ function Show2D() { }; const handleFftMouseDown = (e: React.MouseEvent) => { - if (lockView) return; fftClickStartRef.current = { x: e.clientX, y: e.clientY }; setIsDraggingFftPan(true); setFftPanStart({ x: e.clientX, y: e.clientY, pX: fftPanX, pY: fftPanY }); @@ -2572,7 +2526,6 @@ function Show2D() { // Gallery FFT zoom/pan handlers (only selected image's FFT responds) const handleGalleryFftWheel = (e: React.WheelEvent, idx: number) => { - if (lockView) return; if (isGallery && idx !== selectedIdx && !linkedZoom) return; e.preventDefault(); // Prevent page scroll when zooming FFT const zs = getGalleryFftState(idx); @@ -2582,11 +2535,9 @@ function Show2D() { const handleGalleryFftMouseDown = (e: React.MouseEvent, idx: number) => { if (isGallery && idx !== selectedIdx) { - if (lockNavigation) return; setSelectedIdx(idx); return; // Select first, don't start panning } - if (lockView) return; const zs = getGalleryFftState(idx); setFftPanningIdx(idx); setIsDraggingFftPan(true); @@ -2710,13 +2661,12 @@ function Show2D() { const handleMouseDown = (e: React.MouseEvent, idx: number) => { const zs = getZoomState(idx); if (isGallery && idx !== selectedIdx) { - if (lockNavigation) return; setSelectedIdx(idx); // Continue to pan setup so click-drag on unselected panel pans immediately // (no double-click required to select first then drag). } // Check if click is on the lens inset — edge = resize, interior = drag - if (!lockDisplay && showLens && !isGallery && idx === 0) { + if (showLens && !isGallery && idx === 0) { const canvas = canvasRefs.current[0]; if (canvas) { const rect = canvas.getBoundingClientRect(); @@ -2741,7 +2691,7 @@ function Show2D() { } } clickStartRef.current = { x: e.clientX, y: e.clientY }; - if (profileActive && !lockProfile) { + if (profileActive) { const { imgCol, imgRow } = screenToImg(e, idx); if (profilePoints.length === 2) { const p0 = profilePoints[0]; @@ -2770,22 +2720,12 @@ function Show2D() { return; } } - if (!lockView) { - setIsDraggingPan(true); - setPanningIdx(idx); - setPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); - } + setIsDraggingPan(true); + setPanningIdx(idx); + setPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); return; } if (roiActive) { - if (lockRoi) { - if (!lockView) { - setIsDraggingPan(true); - setPanningIdx(idx); - setPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); - } - return; - } const { imgCol, imgRow } = screenToImg(e, idx); // Check resize handles on selected ROI first if (isNearResizeHandleInner(imgCol, imgRow)) { @@ -2823,7 +2763,6 @@ function Show2D() { } // Start panning (works in both ROI-active and normal modes) { - if (lockView) return; setIsDraggingPan(true); setPanningIdx(idx); setPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); @@ -2832,7 +2771,7 @@ function Show2D() { const handleMouseMove = (e: React.MouseEvent, idx: number) => { // Fast path: during pan drag, skip all cursor/hover/lens work — just update pan - if (isDraggingPan && panStart && panningIdx !== null && !lockView) { + if (isDraggingPan && panStart && panningIdx !== null) { const canvas = canvasRefs.current[idx]; if (!canvas || idx !== panningIdx) return; const rect = canvas.getBoundingClientRect(); @@ -2861,7 +2800,7 @@ function Show2D() { if (imgX >= 0 && imgX < width && imgY >= 0 && imgY < height) { const rawData = rawDataRef.current[idx]; if (rawData) setCursorInfo({ row: imgY, col: imgX, value: rawData[imgY * width + imgX] }); - if (!lockDisplay && showLens && !isGallery) setLensPos({ row: imgY, col: imgX }); + if (showLens && !isGallery) setLensPos({ row: imgY, col: imgX }); } else { setCursorInfo(null); // Don't clear lensPos — lens stays at last position when toggle is on @@ -2869,20 +2808,20 @@ function Show2D() { } // Lens drag - if (!lockDisplay && isDraggingLens && lensDragStartRef.current) { + if (isDraggingLens && lensDragStartRef.current) { const dx = e.clientX - lensDragStartRef.current.mx; const dy = e.clientY - lensDragStartRef.current.my; setLensAnchor({ x: lensDragStartRef.current.ax + dx, y: lensDragStartRef.current.ay + dy }); return; } // Lens resize drag - if (!lockDisplay && isResizingLens && lensResizeStartRef.current) { + if (isResizingLens && lensResizeStartRef.current) { const dy = e.clientY - lensResizeStartRef.current.my; setLensDisplaySize(Math.max(64, Math.min(256, lensResizeStartRef.current.startSize + dy))); return; } - if (profileActive && !lockProfile && profilePoints.length === 2) { + if (profileActive && profilePoints.length === 2) { const { imgCol, imgRow } = screenToImg(e, idx); const p0 = profilePoints[0]; const p1 = profilePoints[1]; @@ -2931,14 +2870,14 @@ function Show2D() { } // ROI resize drag (inner annular ring) - if (!lockRoi && isDraggingResizeInner && selectedRoi) { + if (isDraggingResizeInner && selectedRoi) { const { imgCol: ic, imgRow: ir } = screenToImg(e, idx); const newR = Math.sqrt((ic - selectedRoi.col) ** 2 + (ir - selectedRoi.row) ** 2); updateSelectedRoi({ radius_inner: Math.max(1, Math.min(selectedRoi.radius - 1, Math.round(newR))) }); return; } // ROI resize drag (outer) - if (!lockRoi && isDraggingResize && selectedRoi) { + if (isDraggingResize && selectedRoi) { const { imgCol: ic, imgRow: ir } = screenToImg(e, idx); const shape = selectedRoi.shape || "circle"; if (shape === "rectangle") { @@ -2958,12 +2897,12 @@ function Show2D() { return; } // ROI drag (move center) - if (!lockRoi && isDraggingROI) { + if (isDraggingROI) { updateROI(e, idx); return; } // Lens edge hover detection - if (!lockDisplay && showLens && !isGallery && canvas) { + if (showLens && !isGallery && canvas) { const rect = canvas.getBoundingClientRect(); const cssX = e.clientX - rect.left; const cssY = e.clientY - rect.top; @@ -2978,14 +2917,13 @@ function Show2D() { setIsHoveringLensEdge(false); } // Hover detection for resize handles (show cursor on any ROI edge) - if (roiActive && !lockRoi && !isDraggingPan) { + if (roiActive && !isDraggingPan) { const { imgCol: ic, imgRow: ir } = screenToImg(e, idx); setIsHoveringResizeInner(isNearResizeHandleInner(ic, ir)); setIsHoveringResize(isNearAnyEdge(ic, ir)); } // Panning - if (lockView) return; if (!isDraggingPan || !panStart || panningIdx === null) return; if (idx !== panningIdx) return; if (!canvas) return; @@ -3026,7 +2964,7 @@ function Show2D() { return; } // Detect click (vs drag) for profile mode - if (profileActive && !lockProfile && clickStartRef.current) { + if (profileActive && clickStartRef.current) { const dx = e.clientX - clickStartRef.current.x; const dy = e.clientY - clickStartRef.current.y; if (Math.sqrt(dx * dx + dy * dy) < 3) { @@ -3125,7 +3063,6 @@ function Show2D() { // ------------------------------------------------------------------------- // Copy to clipboard handler const handleCopy = React.useCallback(async () => { - if (lockExport) return; const canvas = canvasRefs.current[isGallery ? selectedIdx : 0]; if (!canvas) return; try { @@ -3136,11 +3073,10 @@ function Show2D() { // Fallback: download if clipboard API unavailable canvas.toBlob((b) => { if (b) downloadBlob(b, `show2d_${labels?.[selectedIdx] || "image"}.png`); }, "image/png"); } - }, [isGallery, selectedIdx, labels, lockExport]); + }, [isGallery, selectedIdx, labels]); // Export publication-quality figure with scale bar, colorbar, annotations const handleExportFigure = React.useCallback((withScaleBar: boolean, withColorbar: boolean) => { - if (lockExport) return; setExportAnchor(null); const idx = isGallery ? selectedIdx : 0; const rawData = rawDataRef.current?.[idx]; @@ -3229,11 +3165,10 @@ function Show2D() { }); canvasToPDF(figCanvas).then((blob) => downloadBlob(blob, `show2d_figure_${labels?.[selectedIdx] || "image"}.pdf`)); - }, [isGallery, selectedIdx, labels, width, height, cmap, logScale, autoContrast, imageDataRange, imageVminPct, imageVmaxPct, pixelSize, title, roiActive, roiList, profileActive, profilePoints, lockExport]); + }, [isGallery, selectedIdx, labels, width, height, cmap, logScale, autoContrast, imageDataRange, imageVminPct, imageVmaxPct, pixelSize, title, roiActive, roiList, profileActive, profilePoints]); // Export all variants (PNG + PDF) as zip const handleExportAll = React.useCallback(async () => { - if (lockExport) return; setExportAnchor(null); const idx = isGallery ? selectedIdx : 0; const rawData = rawDataRef.current?.[idx]; @@ -3353,12 +3288,11 @@ function Show2D() { const blob = await zip.generateAsync({ type: "blob" }); downloadBlob(blob, `${prefix}_all.zip`); - }, [isGallery, selectedIdx, labels, width, height, cmap, logScale, autoContrast, imageDataRange, imageVminPct, imageVmaxPct, pixelSize, title, roiActive, roiList, profileActive, profilePoints, widgetVersion, lockExport]); + }, [isGallery, selectedIdx, labels, width, height, cmap, logScale, autoContrast, imageDataRange, imageVminPct, imageVmaxPct, pixelSize, title, roiActive, roiList, profileActive, profilePoints, widgetVersion]); // Resize Handlers // ------------------------------------------------------------------------- const handleCanvasResizeStart = (e: React.MouseEvent) => { - if (lockView) return; e.stopPropagation(); e.preventDefault(); setIsResizingCanvas(true); @@ -3423,21 +3357,21 @@ function Show2D() { // ------------------------------------------------------------------------- const handleKeyDown = (e: React.KeyboardEvent) => { // Number keys 1-9 select gallery images (avoids arrow key conflicts with Jupyter) - if (!lockNavigation && isGallery && e.key >= "1" && e.key <= "9") { + if (isGallery && e.key >= "1" && e.key <= "9") { const idx = parseInt(e.key) - 1; if (idx < nImages) { e.preventDefault(); setSelectedIdx(idx); } return; } switch (e.key) { case "ArrowLeft": - if (!lockNavigation && isGallery) { e.preventDefault(); setSelectedIdx(Math.max(0, selectedIdx - 1)); } + if (isGallery) { e.preventDefault(); setSelectedIdx(Math.max(0, selectedIdx - 1)); } break; case "ArrowRight": - if (!lockNavigation && isGallery) { e.preventDefault(); setSelectedIdx(Math.min(nImages - 1, selectedIdx + 1)); } + if (isGallery) { e.preventDefault(); setSelectedIdx(Math.min(nImages - 1, selectedIdx + 1)); } break; case "r": case "R": - if (!lockView) handleResetAll(); + handleResetAll(); break; case "m": case "M": @@ -3456,7 +3390,7 @@ function Show2D() { } break; case "]": - if (!lockNavigation && !lockDisplay) { + { e.preventDefault(); const rIdx = isGallery ? selectedIdx : 0; const rots = [...(imageRotations || [])]; @@ -3466,7 +3400,7 @@ function Show2D() { } break; case "[": - if (!lockNavigation && !lockDisplay) { + { e.preventDefault(); const rIdx2 = isGallery ? selectedIdx : 0; const rots2 = [...(imageRotations || [])]; @@ -3477,7 +3411,7 @@ function Show2D() { break; case "Delete": case "Backspace": - if (!lockRoi && roiActive && roiSelectedIdx >= 0 && roiList && roiSelectedIdx < roiList.length) { + if (roiActive && roiSelectedIdx >= 0 && roiList && roiSelectedIdx < roiList.length) { e.preventDefault(); const newList = roiList.filter((_, i) => i !== roiSelectedIdx); setRoiList(newList); @@ -3493,12 +3427,12 @@ function Show2D() { const needsReset = getZoomState(isGallery ? selectedIdx : 0).zoom !== 1 || getZoomState(isGallery ? selectedIdx : 0).panX !== 0 || getZoomState(isGallery ? selectedIdx : 0).panY !== 0; const statsIdx = isGallery ? selectedIdx : 0; - // Calibrated cursor position - const calibratedUnit = pixelSize > 0 ? (Math.max(height, width) * pixelSize >= 10 ? "nm" : "Å") : ""; - const calibratedFactor = calibratedUnit === "nm" ? pixelSize / 10 : pixelSize; + // Calibrated cursor position - unit is whatever the user passed via sampling/units. + const calibratedUnit = pixelSize > 0 ? pixelUnit : ""; + const calibratedFactor = pixelSize; return ( - + {/* Main panel */} @@ -3514,14 +3448,13 @@ function Show2D() { { - if (lockDisplay) return; const ri = isGallery ? selectedIdx : 0; const rots = [...(imageRotations || [])]; while (rots.length <= ri) rots.push(0); rots[ri] = (rots[ri] + 3) % 4; setImageRotations(rots); }} - sx={{ ml: 0.5, color: themeColors.accent, cursor: lockDisplay ? "default" : "pointer", fontSize: "inherit", "&:hover": { opacity: lockDisplay ? 1 : 0.7 } }} + sx={{ ml: 0.5, color: themeColors.accent, cursor: "pointer", fontSize: "inherit", "&:hover": { opacity: 0.7 } }} > ({rk * 90}°) @@ -3537,29 +3470,20 @@ function Show2D() { Keyboard } theme={themeInfo.theme} /> - {/* Controls row: Profile, ROI, Lens, FFT, Export, Reset, Copy */} - {!hideProfile && ( + {( <> Profile: { - if (lockProfile) return; const on = e.target.checked; setProfileActive(on); if (on) { - if (!lockRoi) setRoiActive(false); + setRoiActive(false); } else { setProfilePoints([]); setProfileDataAll([]); @@ -3572,18 +3496,17 @@ function Show2D() { /> )} - {!hideRoi && !isGallery && ( + {!isGallery && ( <> ROI: { - if (lockRoi) return; const on = e.target.checked; setRoiActive(on); if (on) { - if (!lockProfile) setProfileActive(false); + setProfileActive(false); setProfilePoints([]); setProfileDataAll([]); setHoveredProfileEndpoint(null); @@ -3597,7 +3520,7 @@ function Show2D() { /> )} - {!hideDisplay && ( + {( <> {!isGallery && ( <> @@ -3605,7 +3528,6 @@ function Show2D() { { - if (lockDisplay) return; if (!showLens) { setShowLens(true); setLensPos({ row: Math.floor(height / 2), col: Math.floor(width / 2) }); @@ -3614,7 +3536,7 @@ function Show2D() { setLensPos(null); } }} - disabled={lockDisplay} + size="small" sx={switchStyles.small} /> @@ -3624,14 +3546,13 @@ function Show2D() { { - if (lockDisplay) return; const on = e.target.checked; if (on && width * height > 2048 * 2048) { console.warn(`Show2D: FFT on ${width}×${height} image (${(width * height / 1e6).toFixed(1)}M pixels) may be slow`); } setShowFft(on); }} - disabled={lockDisplay} + size="small" sx={switchStyles.small} /> @@ -3641,25 +3562,25 @@ function Show2D() { {nImages === 2 && ( <> Diff: - { if (!lockDisplay) setDiffMode(!diffMode); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setDiffMode(!diffMode); }} size="small" sx={switchStyles.small} /> )} )} - {!hideView && ( - + {( + )} - {!hideExport && ( + {( <> - + setExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> - handleExportFigure(true, true)} sx={{ fontSize: 12 }}>PDF + scalebar + colorbar - handleExportFigure(true, false)} sx={{ fontSize: 12 }}>PDF + scalebar - handleExportFigure(false, false)} sx={{ fontSize: 12 }}>PDF - All (PNG + PDF) + handleExportFigure(true, true)} sx={{ fontSize: 12 }}>PDF + scalebar + colorbar + handleExportFigure(true, false)} sx={{ fontSize: 12 }}>PDF + scalebar + handleExportFigure(false, false)} sx={{ fontSize: 12 }}>PDF + All (PNG + PDF) - + )} @@ -3668,7 +3589,7 @@ function Show2D() { /* Gallery mode */ {Array.from({ length: nImages }).map((_, i) => ( - + { imageContainerRefs.current[i] = el; }} sx={{ position: "relative", bgcolor: "#000", border: `2px solid ${i === selectedIdx ? themeColors.accent : themeColors.border}`, borderRadius: 0, width: canvasW, height: canvasH }} @@ -3689,8 +3610,8 @@ function Show2D() { width={Math.round(canvasW * DPR)} height={Math.round(canvasH * DPR)} style={{ position: "absolute", top: 0, left: 0, width: canvasW, height: canvasH, pointerEvents: "none" }} /> - {!hideView && ( - + {( + )} @@ -3700,13 +3621,12 @@ function Show2D() { component="span" onClick={(e: React.MouseEvent) => { e.stopPropagation(); - if (lockDisplay) return; const rots = [...(imageRotations || [])]; while (rots.length <= i) rots.push(0); rots[i] = (rots[i] + 3) % 4; setImageRotations(rots); }} - sx={{ ml: 0.5, color: themeColors.accent, cursor: lockDisplay ? "default" : "pointer", "&:hover": { opacity: lockDisplay ? 1 : 0.7 } }} + sx={{ ml: 0.5, color: themeColors.accent, cursor: "pointer", "&:hover": { opacity: 0.7 } }} > ({(imageRotations[i] % 4) * 90}°) @@ -3715,7 +3635,7 @@ function Show2D() { {effectiveShowFft && ( { fftContainerRefs.current[i] = el; }} - sx={{ mt: 0.5, position: "relative", border: `2px solid ${i === selectedIdx ? themeColors.accent : themeColors.border}`, borderRadius: 0, bgcolor: "#000", cursor: lockView ? "default" : "grab" }} + sx={{ mt: 0.5, position: "relative", border: `2px solid ${i === selectedIdx ? themeColors.accent : themeColors.border}`, borderRadius: 0, bgcolor: "#000", cursor: "grab" }} onWheel={(i === selectedIdx || linkedZoom) ? (e) => handleGalleryFftWheel(e, i) : undefined} onDoubleClick={() => setGalleryFftState(i, { zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 })} onMouseDown={(e) => handleGalleryFftMouseDown(e, i)} @@ -3796,15 +3716,15 @@ function Show2D() { )} - {!hideView && ( - + {( + )} )} {/* Stats bar - right below canvas (Show3D style) */} - {!hideStats && showStats && ( - + {showStats && ( + {isGallery && ( {labels?.[statsIdx] || `#${statsIdx + 1}`} )} @@ -3824,32 +3744,32 @@ function Show2D() { {/* Gallery FFT Controls - below gallery grid */} {effectiveShowFft && isGallery && ( - - + + FFT Scale: - setFftScaleMode(e.target.value as "linear" | "log" | "power")} size="small" sx={{ ...themedSelect, minWidth: 50, fontSize: 10 }} MenuProps={themedMenuProps}> Lin Log Pow Auto: - { if (!lockDisplay) setFftAuto(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setFftAuto(e.target.checked); }} size="small" sx={switchStyles.small} /> {roiFftActive && fftCropDims && ( <> Win: - { if (!lockDisplay) setFftWindow(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setFftWindow(e.target.checked); }} size="small" sx={switchStyles.small} /> )} Color: - setFftColormap(String(e.target.value))} size="small" sx={{ ...themedSelect, minWidth: 65, fontSize: 10 }} MenuProps={themedMenuProps}> {COLORMAP_NAMES.map((name) => ({name.charAt(0).toUpperCase() + name.slice(1)}))} - {!hideHistogram && ( - + {( + {fftHistogramData && ( - { if (!lockHistogram) { setFftVminPct(min); setFftVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={fftDataRange.min} dataMax={fftDataRange.max} /> + { setFftVminPct(min); setFftVmaxPct(max); }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={fftDataRange.min} dataMax={fftDataRange.max} /> )} )} @@ -3857,7 +3777,7 @@ function Show2D() { )} {/* Line profile sparkline — always reserve space when profile is active */} - {!hideProfile && profileActive && ( + {profileActive && (
{ - if (lockProfile) return; e.preventDefault(); setIsResizingProfile(true); setProfileResizeStart({ y: e.clientY, height: profileHeight }); }} - style={{ width: profileCanvasWidth, height: 4, cursor: lockProfile ? "default" : "ns-resize", borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, background: `linear-gradient(to bottom, ${themeColors.border}, transparent)`, opacity: lockProfile ? 0.5 : 1, pointerEvents: lockProfile ? "none" : "auto" }} + style={{ width: profileCanvasWidth, height: 4, cursor: "ns-resize", borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, background: `linear-gradient(to bottom, ${themeColors.border}, transparent)`, opacity: 1, pointerEvents: "auto" }} /> )} @@ -3882,51 +3801,51 @@ function Show2D() { {/* Top: control rows + histogram side by side */} - + {/* Row 1: Scale + Color */} - {!hideDisplay && ( - + {( + Scale: - setLogScale(e.target.value === "log")} size="small" sx={{ ...themedSelect, minWidth: 45 }} MenuProps={themedMenuProps}> Lin Log Color: - setCmap(e.target.value)} MenuProps={themedMenuProps} sx={{ ...themedSelect, minWidth: 60 }}> {COLORMAP_NAMES.map((name) => ({name.charAt(0).toUpperCase() + name.slice(1)}))} {!isGallery && ( <> Colorbar: - { if (!lockDisplay) setShowColorbar(!showColorbar); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setShowColorbar(!showColorbar); }} size="small" sx={switchStyles.small} /> )} )} {/* Row 2: Auto + Lens settings + Link Zoom (gallery) + zoom indicator */} - {!hideDisplay && ( - + {( + Auto: - { if (!lockDisplay) setAutoContrast(!autoContrast); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setAutoContrast(!autoContrast); }} size="small" sx={switchStyles.small} /> Smooth: - { if (!lockDisplay) setSmooth(!smooth); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setSmooth(!smooth); }} size="small" sx={switchStyles.small} /> {!isGallery && showLens && ( <> Lens {lensMag}× - setLensMag(v as number)} size="small" sx={{ ...sliderStyles.small, width: 35 }} /> + setLensMag(v as number)} size="small" sx={{ ...sliderStyles.small, width: 35 }} /> {lensDisplaySize}px - setLensDisplaySize(v as number)} size="small" sx={{ ...sliderStyles.small, width: 35 }} /> + setLensDisplaySize(v as number)} size="small" sx={{ ...sliderStyles.small, width: 35 }} /> )} {isGallery && ( <> Link: Zoom - { if (!lockDisplay) setLinkedZoom(!linkedZoom); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setLinkedZoom(!linkedZoom); }} size="small" sx={switchStyles.small} /> Pan - { if (!lockDisplay) setLinkPan(!linkPan); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setLinkPan(!linkPan); }} size="small" sx={switchStyles.small} /> Contrast - { if (!lockDisplay) setLinkedContrast(!linkedContrast); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setLinkedContrast(!linkedContrast); }} size="small" sx={switchStyles.small} /> )} {getZoomState(isGallery ? selectedIdx : 0).zoom !== 1 && ( @@ -3935,30 +3854,33 @@ function Show2D() { )} - {/* Right: Histogram aligned to the two rows. When unlinked + gallery: stack one per image. */} - {!hideHistogram && (imageHistogramData || imageHistogramBins) && ( - + {/* Right: histograms. Unlinked + gallery → grid matching gallery layout + (same effectiveNcols × rows). Linked or single image → one histogram. */} + {(imageHistogramData || imageHistogramBins) && ( + {(!linkedContrast && isGallery && rawDataRef.current) ? ( - Array.from({ length: nImages }).map((_, i) => { - const cs = contrastStates.get(i) || { vminPct: 0, vmaxPct: 100 }; - const raw = rawDataRef.current?.[i] || null; - return ( - { if (!lockHistogram) setContrastState(i, { vminPct: min, vmaxPct: max }); }} - width={110} height={36} theme={themeInfo.theme === "dark" ? "dark" : "light"} - dataMin={dataRangesRef.current[i]?.min ?? imageDataRange.min} - dataMax={dataRangesRef.current[i]?.max ?? imageDataRange.max} /> - ); - }) + + {Array.from({ length: nImages }).map((_, i) => { + const cs = contrastStates.get(i) || { vminPct: 0, vmaxPct: 100 }; + const raw = rawDataRef.current?.[i] || null; + return ( + { setContrastState(i, { vminPct: min, vmaxPct: max }); }} + width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} + dataMin={dataRangesRef.current[i]?.min ?? imageDataRange.min} + dataMax={dataRangesRef.current[i]?.max ?? imageDataRange.max} /> + ); + })} + ) : ( - { if (!lockHistogram) setContrastState(activeContrastIdx, { vminPct: min, vmaxPct: max }); }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={traitVmin != null && traitVmax != null ? (logScale ? Math.log1p(Math.max(traitVmin, 0)) : traitVmin) : imageDataRange.min} dataMax={traitVmin != null && traitVmax != null ? (logScale ? Math.log1p(Math.max(traitVmax, 0)) : traitVmax) : imageDataRange.max} /> + { setContrastState(activeContrastIdx, { vminPct: min, vmaxPct: max }); }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={traitVmin != null && traitVmax != null ? (logScale ? Math.log1p(Math.max(traitVmin, 0)) : traitVmin) : imageDataRange.min} dataMax={traitVmin != null && traitVmax != null ? (logScale ? Math.log1p(Math.max(traitVmax, 0)) : traitVmax) : imageDataRange.max} /> )} )} {/* ROI Section (own box, below control rows) */} - {!hideRoi && roiActive && ( - + {roiActive && ( + {/* ROI: shape + ADD + CLEAR */} ROI: @@ -4083,18 +4005,18 @@ function Show2D() { ROI FFT ({fftCropDims.cropWidth}×{fftCropDims.cropHeight}) ) : } - {!hideView && ( - + {( + )} @@ -4106,12 +4028,12 @@ function Show2D() { )} - {!hideView && ( - + {( + )} {/* FFT Stats Bar */} - {!hideStats && fftStats && fftStats.length === 4 && ( + {fftStats && fftStats.length === 4 && ( Mean {formatNumber(fftStats[0])} Min {formatNumber(fftStats[1])} @@ -4134,30 +4056,30 @@ function Show2D() { {/* FFT Controls - two rows + histogram (matching main panel layout) */} - + {/* Row 1: Scale + Color + Colorbar */} - + Scale: - setFftScaleMode(e.target.value as "linear" | "log" | "power")} size="small" sx={{ ...themedSelect, minWidth: 50, fontSize: 10 }} MenuProps={themedMenuProps}> Lin Log Pow Color: - setFftColormap(String(e.target.value))} size="small" sx={{ ...themedSelect, minWidth: 65, fontSize: 10 }} MenuProps={themedMenuProps}> {COLORMAP_NAMES.map((name) => ({name.charAt(0).toUpperCase() + name.slice(1)}))} Colorbar: - { if (!lockDisplay) setFftShowColorbar(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setFftShowColorbar(e.target.checked); }} size="small" sx={switchStyles.small} /> {/* Row 2: Auto + zoom indicator */} - + Auto: - { if (!lockDisplay) setFftAuto(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setFftAuto(e.target.checked); }} size="small" sx={switchStyles.small} /> {fftCropDims && ( <> Win: - { if (!lockDisplay) setFftWindow(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setFftWindow(e.target.checked); }} size="small" sx={switchStyles.small} /> )} {fftZoom !== DEFAULT_FFT_ZOOM && ( @@ -4166,10 +4088,10 @@ function Show2D() { {/* Right: FFT Histogram */} - {!hideHistogram && ( - + {( + {fftHistogramData && ( - { if (!lockHistogram) { setFftVminPct(min); setFftVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={fftDataRange.min} dataMax={fftDataRange.max} /> + { setFftVminPct(min); setFftVmaxPct(max); }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={fftDataRange.min} dataMax={fftDataRange.max} /> )} )} diff --git a/widget/js/show2d/show2d.css b/widget/js/show2d/show2d.css deleted file mode 100644 index 0e285789..00000000 --- a/widget/js/show2d/show2d.css +++ /dev/null @@ -1,9 +0,0 @@ -/* show2d.css - Minimal CSS for Show2D */ - -.show2d-root { - font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; -} - -.show2d-root canvas { - display: block; -} diff --git a/widget/js/show4dstem/index.tsx b/widget/js/show4dstem/index.tsx index 8ece614d..2a78455b 100644 --- a/widget/js/show4dstem/index.tsx +++ b/widget/js/show4dstem/index.tsx @@ -18,16 +18,12 @@ import StopIcon from "@mui/icons-material/Stop"; import FastRewindIcon from "@mui/icons-material/FastRewind"; import FastForwardIcon from "@mui/icons-material/FastForward"; import JSZip from "jszip"; -import "./styles.css"; import { useTheme } from "../theme"; import { COLORMAPS, applyColormap, renderToOffscreen } from "../colormaps"; -import { WebGPUFFT, getWebGPUFFT, fft2d, fftshift, autoEnhanceFFT, nextPow2, applyHannWindow2D } from "../webgpu-fft"; -import { drawScaleBarHiDPI, drawColorbar, roundToNiceValue, exportFigure, canvasToPDF } from "../scalebar"; -import { findDataRange, sliderRange, computeStats, applyLogScale } from "../stats"; +import { WebGPUFFT, getWebGPUFFT, fft2d, fftshift, autoEnhanceFFT, nextPow2, applyHannWindow2D } from "../fft"; +import { drawScaleBarHiDPI, drawColorbar, roundToNiceValue, exportFigure, canvasToPDF } from "../figure"; +import { findDataRange, sliderRange, computeStats, applyLogScale, computeHistogramFromBytes, percentileClip } from "../stats"; import { downloadBlob, formatNumber, downloadDataView } from "../format"; -import { computeHistogramFromBytes } from "../histogram"; -import { ControlCustomizer } from "../control-customizer"; -import { computeToolVisibility } from "../tool-parity"; const MIN_ZOOM = 0.5; const MAX_ZOOM = 10; @@ -132,7 +128,7 @@ const compactButton = { }, }; -// Control row style - bordered container for each row +// Control row style — bordered container per row. const controlRow = { display: "flex", alignItems: "center", @@ -961,7 +957,9 @@ function Show4DSTEM() { const [roiCenterCol, setRoiCenterCol] = useModelState("roi_center_col"); const [roiCenterRow, setRoiCenterRow] = useModelState("roi_center_row"); const [pixelSize] = useModelState("pixel_size"); + const [pixelUnit] = useModelState("pixel_unit"); const [kPixelSize] = useModelState("k_pixel_size"); + const [kPixelUnit] = useModelState("k_pixel_unit"); const [kCalibrated] = useModelState("k_calibrated"); const [widgetVersion] = useModelState("widget_version"); const [title] = useModelState("title"); @@ -981,8 +979,9 @@ function Show4DSTEM() { const [dpGlobalMax] = useModelState("dp_global_max"); // VI min/max for normalization (from Python) - const [viDataMin] = useModelState("vi_data_min"); - const [viDataMax] = useModelState("vi_data_max"); + // viDataMin/viDataMax are derived JS-side from virtual_image_bytes (computed below). + // Keeping them out of Python traits avoids a comm-message ordering race where + // bytes from click N arrive with min/max from click N-1. // Detector calibration (for presets) const [bfRadius] = useModelState("bf_radius"); @@ -1020,6 +1019,26 @@ function Show4DSTEM() { const [localPosRow, setLocalPosRow] = React.useState(posRow); const [localPosCol, setLocalPosCol] = React.useState(posCol); const [isDraggingDP, setIsDraggingDP] = React.useState(false); + // rAF coalescing for ROI drag: collapse rapid mousemove events into ≤1 + // Python comm message per animation frame. Without this, drag fires 60+ + // events/sec at >100ms Python compute each → queue piles up → laggy UX. + const roiCenterPendingRef = React.useRef<[number, number] | null>(null); + const roiCenterRafRef = React.useRef(null); + const flushRoiCenter = React.useCallback(() => { + if (roiCenterPendingRef.current) { + const [r, c] = roiCenterPendingRef.current; + model.set("roi_center", [r, c]); + model.save_changes(); + roiCenterPendingRef.current = null; + } + roiCenterRafRef.current = null; + }, [model]); + const queueRoiCenter = React.useCallback((row: number, col: number) => { + roiCenterPendingRef.current = [row, col]; + if (roiCenterRafRef.current === null) { + roiCenterRafRef.current = requestAnimationFrame(flushRoiCenter); + } + }, [flushRoiCenter]); const [isDraggingVI, setIsDraggingVI] = React.useState(false); const [isDraggingFFT, setIsDraggingFFT] = React.useState(false); const [fftDragStart, setFftDragStart] = React.useState<{ x: number, y: number, panX: number, panY: number } | null>(null); @@ -1046,11 +1065,14 @@ function Show4DSTEM() { const [traitDpVmax] = useModelState("dp_vmax"); const [traitViVmin] = useModelState("vi_vmin"); const [traitViVmax] = useModelState("vi_vmax"); - // Scale mode: "linear" | "log" | "power" - const [dpScaleMode, setDpScaleMode] = useModelState<"linear" | "log" | "power">("dp_scale_mode"); - const [dpPowerExp] = useModelState("dp_power_exp"); - const [viScaleMode, setViScaleMode] = useModelState<"linear" | "log" | "power">("vi_scale_mode"); - const [viPowerExp] = useModelState("vi_power_exp"); + // Scale mode: "linear" | "log" + const [dpScaleMode, setDpScaleMode] = useModelState<"linear" | "log">("dp_scale_mode"); + const [viScaleMode, setViScaleMode] = useModelState<"linear" | "log">("vi_scale_mode"); + // VI auto-contrast (1st/99th percentile clip) + Smooth (CSS bilinear blit). + // DP doesn't need them — Bragg spots read best with the slider's percentile + // range and nearest-neighbor blit. + const [viAutoContrast, setViAutoContrast] = useModelState("vi_auto_contrast"); + const [viSmooth, setViSmooth] = useModelState("vi_smooth"); // VI ROI state (real-space region selection for summed DP) - synced with Python const [viRoiMode, setViRoiMode] = useModelState("vi_roi_mode"); @@ -1062,46 +1084,18 @@ function Show4DSTEM() { // Local VI ROI center for smooth dragging const [localViRoiCenterRow, setLocalViRoiCenterRow] = React.useState(viRoiCenterRow || 0); const [localViRoiCenterCol, setLocalViRoiCenterCol] = React.useState(viRoiCenterCol || 0); - const [summedDpBytes] = useModelState("summed_dp_bytes"); - const [summedDpCount] = useModelState("summed_dp_count"); - const [dpStats] = useModelState("dp_stats"); // [mean, min, max, std] - const [viStats] = useModelState("vi_stats"); // [mean, min, max, std] + const [viRoiDpBytes] = useModelState("vi_roi_dp_bytes"); + const [viRoiReduce, setViRoiReduce] = useModelState("vi_roi_reduce"); + // dp_stats are computed in JS from frameBytes (Python side no longer + // syncs a dp_stats trait — saves 4 trait sync round-trips per click). + const [viStats, setViStats] = React.useState([0, 0, 0, 0]); + const [viDataMin, setViDataMin] = React.useState(0); + const [viDataMax, setViDataMax] = React.useState(1); const [showFft, setShowFft] = useModelState("show_fft"); const [fftWindow, setFftWindow] = useModelState("fft_window"); - const [disabledTools, setDisabledTools] = useModelState("disabled_tools"); - const [hiddenTools, setHiddenTools] = useModelState("hidden_tools"); const [showControls] = useModelState("show_controls"); - const toolVisibility = React.useMemo( - () => computeToolVisibility("Show4DSTEM", disabledTools, hiddenTools), - [disabledTools, hiddenTools], - ); - - const hideDisplay = toolVisibility.isHidden("display"); - const hideHistogram = toolVisibility.isHidden("histogram"); - const hideStats = toolVisibility.isHidden("stats"); - const hidePlayback = toolVisibility.isHidden("playback"); - const hideView = toolVisibility.isHidden("view"); - const hideExport = toolVisibility.isHidden("export"); - const hideRoi = toolVisibility.isHidden("roi"); - const hideProfile = toolVisibility.isHidden("profile"); - const hideVirtual = toolVisibility.isHidden("virtual"); - const hideFrame = toolVisibility.isHidden("frame"); - const hideFft = toolVisibility.isHidden("fft") || hideVirtual; - - const lockDisplay = toolVisibility.isLocked("display"); - const lockHistogram = toolVisibility.isLocked("histogram"); - const lockStats = toolVisibility.isLocked("stats"); - const lockNavigation = toolVisibility.isLocked("navigation"); - const lockPlayback = toolVisibility.isLocked("playback"); - const lockView = toolVisibility.isLocked("view"); - const lockExport = toolVisibility.isLocked("export"); - const lockRoi = toolVisibility.isLocked("roi"); - const lockProfile = toolVisibility.isLocked("profile"); - const lockVirtual = toolVisibility.isLocked("virtual"); - const lockFrame = toolVisibility.isLocked("frame"); - const lockFft = toolVisibility.isLocked("fft") || lockVirtual; - const effectiveShowFft = showFft && !hideFft; + const effectiveShowFft = showFft; // ROI FFT state (VI ROI crops virtual image for FFT) const [fftCropDims, setFftCropDims] = React.useState<{ cropWidth: number; cropHeight: number; fftWidth: number; fftHeight: number } | null>(null); @@ -1182,6 +1176,10 @@ function Show4DSTEM() { const [dpHistogramData, setDpHistogramData] = React.useState(null); const [viHistogramData, setViHistogramData] = React.useState(null); + // DP stats computed JS-side from frame_bytes (was Python trait pre-refactor; + // moving to JS skips 4 sync trait round-trips per scan-position click). + const [dpStats, setDpStats] = React.useState([0, 0, 0, 0]); + // Parse DP frame bytes for histogram (float32 now) React.useEffect(() => { if (!frameBytes) return; @@ -1192,21 +1190,20 @@ function Show4DSTEM() { rawDpDataRef.current = new Float32Array(rawData.length); } rawDpDataRef.current.set(rawData); + // Compute stats JS-side (replaces removed Python dp_stats trait) + const s = computeStats(rawData); + setDpStats([s.mean, s.min, s.max, s.std]); // Apply scale transformation for histogram display const scaledData = new Float32Array(rawData.length); if (dpScaleMode === "log") { for (let i = 0; i < rawData.length; i++) { scaledData[i] = Math.log1p(Math.max(0, rawData[i])); } - } else if (dpScaleMode === "power") { - for (let i = 0; i < rawData.length; i++) { - scaledData[i] = Math.pow(Math.max(0, rawData[i]), dpPowerExp); - } } else { scaledData.set(rawData); } setDpHistogramData(scaledData); - }, [frameBytes, dpScaleMode, dpPowerExp]); + }, [frameBytes, dpScaleMode]); // GPU FFT state const gpuFFTRef = React.useRef(null); @@ -1295,8 +1292,7 @@ function Show4DSTEM() { const [fftZoom, setFftZoom] = React.useState(1); const [fftPanX, setFftPanX] = React.useState(0); const [fftPanY, setFftPanY] = React.useState(0); - const [fftScaleMode, setFftScaleMode] = useModelState<"linear" | "log" | "power">("fft_scale_mode"); - const [fftPowerExp] = useModelState("fft_power_exp"); + const [fftScaleMode, setFftScaleMode] = useModelState<"linear" | "log">("fft_scale_mode"); const [fftColormap, setFftColormap] = useModelState("fft_colormap"); const [fftAuto, setFftAuto] = useModelState("fft_auto"); const [fftVminPct, setFftVminPct] = useModelState("fft_vmin_pct"); @@ -1330,52 +1326,42 @@ function Show4DSTEM() { switch (e.key) { case "ArrowUp": - if (!lockNavigation) { - setPosRow(Math.max(0, posRow - step)); - handled = true; - } + setPosRow(Math.max(0, posRow - step)); + handled = true; break; case "ArrowDown": - if (!lockNavigation) { - setPosRow(Math.min(shapeRows - 1, posRow + step)); - handled = true; - } + setPosRow(Math.min(shapeRows - 1, posRow + step)); + handled = true; break; case "ArrowLeft": - if (!lockNavigation) { - setPosCol(Math.max(0, posCol - step)); - handled = true; - } + setPosCol(Math.max(0, posCol - step)); + handled = true; break; case "ArrowRight": - if (!lockNavigation) { - setPosCol(Math.min(shapeCols - 1, posCol + step)); - handled = true; - } + setPosCol(Math.min(shapeCols - 1, posCol + step)); + handled = true; break; case " ": // Space bar - if (!lockPlayback && pathLength > 0) { + if (pathLength > 0) { setPathPlaying(!pathPlaying); handled = true; } break; case "r": case "R": - if (!lockView) { - setDpZoom(1); setDpPanX(0); setDpPanY(0); - setViZoom(1); setViPanX(0); setViPanY(0); - setFftZoom(1); setFftPanX(0); setFftPanY(0); - handled = true; - } + setDpZoom(1); setDpPanX(0); setDpPanY(0); + setViZoom(1); setViPanX(0); setViPanY(0); + setFftZoom(1); setFftPanX(0); setFftPanY(0); + handled = true; break; case "[": - if (!lockPlayback && !lockFrame && nFrames > 1) { + if (nFrames > 1) { setFrameIdx(Math.max(0, frameIdx - 1)); handled = true; } break; case "]": - if (!lockPlayback && !lockFrame && nFrames > 1) { + if (nFrames > 1) { setFrameIdx(Math.min(nFrames - 1, frameIdx + 1)); handled = true; } @@ -1391,53 +1377,10 @@ function Show4DSTEM() { e.stopPropagation(); } }, [ - frameIdx, isTypingTarget, lockFrame, lockNavigation, lockPlayback, lockView, nFrames, pathLength, + frameIdx, isTypingTarget, nFrames, pathLength, pathPlaying, posCol, posRow, setFrameIdx, setPathPlaying, setPosCol, setPosRow, shapeCols, shapeRows, ]); - React.useEffect(() => { - if (hideFft && showFft) { - setShowFft(false); - } - }, [hideFft, showFft, setShowFft]); - - React.useEffect(() => { - if (lockPlayback && pathPlaying) { - setPathPlaying(false); - } - }, [lockPlayback, pathPlaying, setPathPlaying]); - - React.useEffect(() => { - if ((lockPlayback || lockFrame) && framePlaying) { - setFramePlaying(false); - } - }, [lockFrame, lockPlayback, framePlaying, setFramePlaying]); - - React.useEffect(() => { - if (hideRoi) { - if (roiMode !== "point") setRoiMode("point"); - if (viRoiMode !== "off") setViRoiMode("off"); - } - }, [hideRoi, roiMode, viRoiMode, setRoiMode, setViRoiMode]); - - React.useEffect(() => { - if (hideProfile) { - if (profileActive) setProfileActive(false); - if (viProfileActive) setViProfileActive(false); - if (profileLine.length > 0) setProfileLine([]); - if (profileData) setProfileData(null); - if (viProfilePoints.length > 0) setViProfilePoints([]); - if (viProfileData) setViProfileData(null); - setHoveredDpProfileEndpoint(null); - setIsHoveringDpProfileLine(false); - setHoveredViProfileEndpoint(null); - setIsHoveringViProfileLine(false); - } - }, [ - hideProfile, profileActive, profileLine, profileData, setProfileLine, viProfileActive, - viProfilePoints, viProfileData, - ]); - // Sync local state React.useEffect(() => { if (!isDraggingDP && !isDraggingResize) { setLocalKCol(roiCenterCol); setLocalKRow(roiCenterRow); } @@ -1524,28 +1467,33 @@ function Show4DSTEM() { } rawViDataRef.current.set(rawData); + // Compute stats + min/max JS-side (replaces removed Python vi_stats / vi_data_min / vi_data_max traits). + // Python sending bytes + 4 separate stat traits caused a comm-message ordering race on rapid + // preset clicks: bytes from click N could arrive with min/max from click N-1, normalizing + // the colormap to the wrong range and producing a uniform-color VI flash. + const s = computeStats(rawData); + setViStats([s.mean, s.min, s.max, s.std]); + setViDataMin(s.min); + setViDataMax(s.max); + // Apply scale transformation for histogram display const scaledData = new Float32Array(numFloats); if (viScaleMode === "log") { for (let i = 0; i < numFloats; i++) { scaledData[i] = Math.log1p(Math.max(0, rawData[i])); } - } else if (viScaleMode === "power") { - for (let i = 0; i < numFloats; i++) { - scaledData[i] = Math.pow(Math.max(0, rawData[i]), viPowerExp); - } } else { scaledData.set(rawData); } setViHistogramData(scaledData); - }, [virtualImageBytes, viScaleMode, viPowerExp]); + }, [virtualImageBytes, viScaleMode]); // Render DP with zoom (use summed DP when VI ROI is active) // Expensive: colormap + data processing → cached offscreen canvas React.useEffect(() => { // Determine which bytes to display: summed DP (if VI ROI active) or single frame - const usesSummedDp = viRoiMode && viRoiMode !== "off" && summedDpBytes && summedDpBytes.byteLength > 0; - const sourceBytes = usesSummedDp ? summedDpBytes : frameBytes; + const usesViRoiDp = viRoiMode && viRoiMode !== "off" && viRoiDpBytes && viRoiDpBytes.byteLength > 0; + const sourceBytes = usesViRoiDp ? viRoiDpBytes : frameBytes; if (!sourceBytes) return; const lut = COLORMAPS[dpColormap] || COLORMAPS.inferno; @@ -1558,27 +1506,17 @@ function Show4DSTEM() { for (let i = 0; i < rawData.length; i++) { scaled[i] = Math.log1p(Math.max(0, rawData[i])); } - } else if (dpScaleMode === "power") { - scaled = new Float32Array(rawData.length); - for (let i = 0; i < rawData.length; i++) { - scaled[i] = Math.pow(Math.max(0, rawData[i]), dpPowerExp); - } } else { scaled = rawData; } - // Compute actual min/max of scaled data for normalization const { min: dataMin, max: dataMax } = findDataRange(scaled); - // Apply absolute bounds or percentile clipping let vmin: number, vmax: number; if (traitDpVmin != null && traitDpVmax != null) { if (dpScaleMode === "log") { vmin = Math.log1p(Math.max(traitDpVmin, 0)); vmax = Math.log1p(Math.max(traitDpVmax, 0)); - } else if (dpScaleMode === "power") { - vmin = Math.pow(Math.max(traitDpVmin, 0), dpPowerExp); - vmax = Math.pow(Math.max(traitDpVmax, 0), dpPowerExp); } else { vmin = traitDpVmin; vmax = traitDpVmax; @@ -1612,7 +1550,7 @@ function Show4DSTEM() { dpColorbarVminRef.current = vmin; dpColorbarVmaxRef.current = vmax; setDpOffscreenVersion(v => v + 1); - }, [frameBytes, summedDpBytes, viRoiMode, detRows, detCols, dpColormap, dpVminPct, dpVmaxPct, dpScaleMode, dpPowerExp, traitDpVmin, traitDpVmax]); + }, [frameBytes, viRoiDpBytes, viRoiMode, detRows, detCols, dpColormap, dpVminPct, dpVmaxPct, dpScaleMode, traitDpVmin, traitDpVmax]); // Cheap: zoom/pan redraw — just drawImage from cached offscreen // useLayoutEffect prevents black flash when canvas dimensions change (resize) @@ -1649,41 +1587,23 @@ function Show4DSTEM() { const height = shapeRows; const filtered = rawVirtualImageRef.current; - // Apply scale transformation first let scaled = filtered; if (viScaleMode === "log") { scaled = new Float32Array(filtered.length); for (let i = 0; i < filtered.length; i++) { scaled[i] = Math.log1p(Math.max(0, filtered[i])); } - } else if (viScaleMode === "power") { - scaled = new Float32Array(filtered.length); - for (let i = 0; i < filtered.length; i++) { - scaled[i] = Math.pow(Math.max(0, filtered[i]), viPowerExp); - } } - // Use Python's pre-computed min/max when valid, fallback to computing from data - let dataMin: number, dataMax: number; - const hasValidMinMax = viDataMin !== undefined && viDataMax !== undefined && viDataMax > viDataMin; - if (hasValidMinMax) { - // Apply scale transform to Python's values - if (viScaleMode === "log") { - dataMin = Math.log1p(Math.max(0, viDataMin)); - dataMax = Math.log1p(Math.max(0, viDataMax)); - } else if (viScaleMode === "power") { - dataMin = Math.pow(Math.max(0, viDataMin), viPowerExp); - dataMax = Math.pow(Math.max(0, viDataMax), viPowerExp); - } else { - dataMin = viDataMin; - dataMax = viDataMax; - } - } else { - // Fallback: compute from scaled data - const r = findDataRange(scaled); - dataMin = r.min; - dataMax = r.max; - } + // Compute min/max from the data we just received. Do NOT use Python's + // viDataMin/viDataMax traits here: they arrive as separate comm messages + // and can be stale on rapid preset clicks (BF↔ABF), causing the render + // to apply the WRONG normalization range and produce a uniform white/black + // VI panel until comm catches up. findDataRange on a scan-shape buffer + // (~64K-256K floats) is sub-millisecond. + const r = findDataRange(scaled); + const dataMin = r.min; + const dataMax = r.max; // Apply absolute bounds or percentile clipping let vmin: number, vmax: number; @@ -1691,13 +1611,12 @@ function Show4DSTEM() { if (viScaleMode === "log") { vmin = Math.log1p(Math.max(traitViVmin, 0)); vmax = Math.log1p(Math.max(traitViVmax, 0)); - } else if (viScaleMode === "power") { - vmin = Math.pow(Math.max(traitViVmin, 0), viPowerExp); - vmax = Math.pow(Math.max(traitViVmax, 0), viPowerExp); } else { vmin = traitViVmin; vmax = traitViVmax; } + } else if (viAutoContrast) { + ({ vmin, vmax } = percentileClip(scaled, 1, 99)); } else { ({ vmin, vmax } = sliderRange(dataMin, dataMax, viVminPct, viVmaxPct)); } @@ -1725,9 +1644,7 @@ function Show4DSTEM() { applyColormap(scaled, imageData.data, lut, vmin, vmax); offCtx.putImageData(imageData, 0, 0); setViOffscreenVersion(v => v + 1); - // Note: viDataMin/viDataMax intentionally not in deps - they arrive with virtualImageBytes - // and we have a fallback if they're stale - }, [virtualImageBytes, shapeRows, shapeCols, viColormap, viVminPct, viVmaxPct, viScaleMode, viPowerExp, traitViVmin, traitViVmax]); + }, [virtualImageBytes, shapeRows, shapeCols, viColormap, viVminPct, viVmaxPct, viScaleMode, traitViVmin, traitViVmax, viAutoContrast]); // Cheap: VI zoom/pan redraw — just drawImage from cached offscreen React.useLayoutEffect(() => { @@ -1736,14 +1653,15 @@ function Show4DSTEM() { const canvas = virtualCanvasRef.current; const ctx = canvas.getContext("2d"); if (!ctx) return; - ctx.imageSmoothingEnabled = false; + ctx.imageSmoothingEnabled = viSmooth; + if (viSmooth) ctx.imageSmoothingQuality = "high"; ctx.clearRect(0, 0, canvas.width, canvas.height); ctx.save(); ctx.translate(viPanX, viPanY); ctx.scale(viZoom, viZoom); ctx.drawImage(offscreen, 0, 0); ctx.restore(); - }, [viOffscreenVersion, viZoom, viPanX, viPanY]); + }, [viOffscreenVersion, viZoom, viPanX, viPanY, viSmooth]); // Render virtual image overlay (just clear - crosshair drawn on high-DPI UI canvas) React.useEffect(() => { @@ -1768,9 +1686,14 @@ function Show4DSTEM() { let sourceData = rawVirtualImageRef.current; let origCropW = 0, origCropH = 0; - // ROI FFT: crop virtual image to VI ROI region and pre-pad to power-of-2 + // ROI FFT: crop virtual image to VI ROI region and pre-pad to power-of-2. + // Use localViRoiCenter* (updated immediately on drag) instead of the synced + // model traits, which lag by one comm roundtrip after a compound trait write. + // Without this, FFT visibly stalls during rapid VI ROI drag. if (roiFftActive) { - const crop = cropSingleROI(sourceData, shapeCols, shapeRows, viRoiMode, viRoiCenterRow, viRoiCenterCol, viRoiRadius, viRoiWidth, viRoiHeight); + const cRow = localViRoiCenterRow ?? viRoiCenterRow; + const cCol = localViRoiCenterCol ?? viRoiCenterCol; + const crop = cropSingleROI(sourceData, shapeCols, shapeRows, viRoiMode, cRow, cCol, viRoiRadius, viRoiWidth, viRoiHeight); if (crop) { origCropW = crop.cropW; origCropH = crop.cropH; @@ -1850,7 +1773,7 @@ function Show4DSTEM() { } setFftVersion(v => v + 1); } - }, [virtualImageBytes, shapeRows, shapeCols, gpuReady, effectiveShowFft, roiFftActive, viRoiMode, viRoiCenterRow, viRoiCenterCol, viRoiRadius, viRoiWidth, viRoiHeight, fftWindow]); + }, [virtualImageBytes, shapeRows, shapeCols, gpuReady, effectiveShowFft, roiFftActive, viRoiMode, viRoiCenterRow, viRoiCenterCol, localViRoiCenterRow, localViRoiCenterCol, viRoiRadius, viRoiWidth, viRoiHeight, fftWindow]); // Expensive: FFT magnitude + histogram + colormap → cached offscreen canvas React.useEffect(() => { @@ -1879,7 +1802,6 @@ function Show4DSTEM() { const mag = Math.sqrt(real[i] * real[i] + imag[i] * imag[i]); rawMag[i] = mag; if (fftScaleMode === "log") { magnitude[i] = Math.log1p(mag); } - else if (fftScaleMode === "power") { magnitude[i] = Math.pow(mag, fftPowerExp); } else { magnitude[i] = mag; } } @@ -1910,7 +1832,7 @@ function Show4DSTEM() { applyColormap(magnitude, imgData.data, lut, vmin, vmax); offCtx.putImageData(imgData, 0, 0); setFftOffscreenVersion(v => v + 1); - }, [effectiveShowFft, fftVersion, fftScaleMode, fftPowerExp, fftAuto, fftVminPct, fftVmaxPct, fftColormap, shapeRows, shapeCols, fftCropDims]); + }, [effectiveShowFft, fftVersion, fftScaleMode, fftAuto, fftVminPct, fftVmaxPct, fftColormap, shapeRows, shapeCols, fftCropDims]); // Cheap: FFT zoom/pan redraw — just drawImage from cached offscreen React.useLayoutEffect(() => { @@ -1924,13 +1846,15 @@ function Show4DSTEM() { const fftH = offscreen.height; const canvasW = canvas.width; const canvasH = canvas.height; - // Use bilinear smoothing when FFT dims differ from canvas (non-pow2 padding or ROI crop) + // Use bilinear smoothing when FFT dims differ from canvas (non-pow2 padding or ROI crop). + // Stretch offscreen to fill canvas via the 9-arg drawImage form: ROI FFT crops produce a + // small offscreen (e.g. 64×64) that would otherwise blit at native size in the corner. ctx.imageSmoothingEnabled = fftW !== canvasW || fftH !== canvasH; ctx.clearRect(0, 0, canvasW, canvasH); ctx.save(); ctx.translate(fftPanX, fftPanY); ctx.scale(fftZoom, fftZoom); - ctx.drawImage(offscreen, 0, 0); + ctx.drawImage(offscreen, 0, 0, fftW, fftH, 0, 0, canvasW, canvasH); ctx.restore(); }, [fftOffscreenVersion, fftZoom, fftPanX, fftPanY, effectiveShowFft]); @@ -1947,9 +1871,9 @@ function Show4DSTEM() { const fftW = fftCropDims?.fftWidth ?? shapeCols; const fftH = fftCropDims?.fftHeight ?? shapeRows; ctx.save(); - // Convert FFT image coords to canvas coords via zoom/pan transform - const screenX = fftPanX + fftZoom * fftClickInfo.col; - const screenY = fftPanY + fftZoom * fftClickInfo.row; + // Forward mapping: image col/row → canvas x/y (matches stretched drawImage). + const screenX = fftPanX + fftZoom * (fftClickInfo.col * canvas.width / fftW); + const screenY = fftPanY + fftZoom * (fftClickInfo.row * canvas.height / fftH); ctx.strokeStyle = "rgba(255, 255, 255, 0.9)"; ctx.shadowColor = "rgba(0, 0, 0, 0.6)"; ctx.shadowBlur = 2; @@ -1994,7 +1918,7 @@ function Show4DSTEM() { React.useEffect(() => { if (!dpUiRef.current) return; // Draw scale bar first (clears canvas) - const kUnit = kCalibrated ? "mrad" : "px"; + const kUnit = kCalibrated ? kPixelUnit : "px"; drawScaleBarHiDPI(dpUiRef.current, DPR, dpZoom, kPixelSize || 1, kUnit, detCols); // Draw ROI overlay (circle, square, rect, annular) or point crosshair if (roiMode === "point") { @@ -2099,7 +2023,7 @@ function Show4DSTEM() { React.useEffect(() => { if (!viUiRef.current) return; // Draw scale bar first (clears canvas) - drawScaleBarHiDPI(viUiRef.current, DPR, viZoom, pixelSize || 1, "Å", shapeCols); + drawScaleBarHiDPI(viUiRef.current, DPR, viZoom, pixelSize || 1, pixelUnit || "px", shapeCols); // Draw crosshair only when ROI is off (ROI replaces the crosshair) if (!viRoiMode || viRoiMode === "off") { drawViPositionMarker(viUiRef.current, DPR, localPosRow, localPosCol, viZoom, viPanX, viPanY, shapeCols, shapeRows, isDraggingVI); @@ -2233,7 +2157,7 @@ function Show4DSTEM() { const distPx = Math.sqrt(dx * dx + dy * dy); if (kCalibrated && kPixelSize > 0) { totalDist = distPx * kPixelSize; - xUnit = "mrad"; + xUnit = kPixelUnit; } else { totalDist = distPx; } @@ -2440,8 +2364,7 @@ function Show4DSTEM() { const dy = viProfilePoints[1].row - viProfilePoints[0].row; const distPx = Math.sqrt(dx * dx + dy * dy); totalDist = distPx * pixelSize; - xUnit = pixelSize >= 10 ? "nm" : "Å"; - if (xUnit === "nm") totalDist /= 10; + xUnit = pixelUnit; } // Draw axes @@ -2601,9 +2524,7 @@ function Show4DSTEM() { setPanY: React.Dispatch>, zoom: number, panX: number, panY: number, canvasRef: React.RefObject, - locked: boolean = false, ) => (e: React.WheelEvent) => { - if (locked) return; e.preventDefault(); const canvas = canvasRef.current; if (!canvas) return; @@ -2706,8 +2627,6 @@ function Show4DSTEM() { // Mouse handlers const handleDpMouseDown = (e: React.MouseEvent) => { - if (profileActive && lockProfile) return; - if (!profileActive && lockRoi) return; dpClickStartRef.current = { x: e.clientX, y: e.clientY }; const canvas = dpOverlayRef.current; if (!canvas) return; @@ -2793,8 +2712,8 @@ function Show4DSTEM() { const pxCol = Math.floor(imgX); const pxRow = Math.floor(imgY); if (pxCol >= 0 && pxCol < detCols && pxRow >= 0 && pxRow < detRows && frameBytes) { - const usesSummedDp = viRoiMode && viRoiMode !== "off" && summedDpBytes && summedDpBytes.byteLength > 0; - const sourceBytes = usesSummedDp ? summedDpBytes : frameBytes; + const usesViRoiDp = viRoiMode && viRoiMode !== "off" && viRoiDpBytes && viRoiDpBytes.byteLength > 0; + const sourceBytes = usesViRoiDp ? viRoiDpBytes : frameBytes; const raw = new Float32Array(sourceBytes.buffer, sourceBytes.byteOffset, sourceBytes.byteLength / 4); setCursorInfo({ row: pxRow, col: pxCol, value: raw[pxRow * detCols + pxCol], panel: "DP" }); } else { @@ -2802,8 +2721,6 @@ function Show4DSTEM() { } } - if (profileActive && lockProfile) return; - if (profileActive && profilePoints.length === 2) { const p0 = profilePoints[0]; const p1 = profilePoints[1]; @@ -2855,7 +2772,6 @@ function Show4DSTEM() { // Handle inner resize dragging (annular mode) if (isDraggingResizeInner) { - if (lockRoi) return; const dx = Math.abs(imgX - roiCenterCol); const dy = Math.abs(imgY - roiCenterRow); const newRadius = Math.sqrt(dx ** 2 + dy ** 2); @@ -2866,7 +2782,6 @@ function Show4DSTEM() { // Handle outer resize dragging - use model state center, not local values if (isDraggingResize) { - if (lockRoi) return; const dx = Math.abs(imgX - roiCenterCol); const dy = Math.abs(imgY - roiCenterRow); if (roiMode === "rect") { @@ -2890,25 +2805,18 @@ function Show4DSTEM() { // Check hover state for resize handles if (!isDraggingDP) { - if (!lockRoi) { - setIsHoveringResizeInner(isNearResizeHandleInner(imgX, imgY)); - setIsHoveringResize(isNearResizeHandle(imgX, imgY)); - } else { - setIsHoveringResizeInner(false); - setIsHoveringResize(false); - } + setIsHoveringResizeInner(isNearResizeHandleInner(imgX, imgY)); + setIsHoveringResize(isNearResizeHandle(imgX, imgY)); return; } - if (lockRoi) return; const centerCol = imgX - dpDragOffsetRef.current.dCol; const centerRow = imgY - dpDragOffsetRef.current.dRow; setLocalKCol(centerCol); setLocalKRow(centerRow); - // Use compound roi_center trait [row, col] - single observer fires in Python + // rAF-coalesced — sends only the latest roi_center per frame. const newCol = Math.round(Math.max(0, Math.min(detCols - 1, centerCol))); const newRow = Math.round(Math.max(0, Math.min(detRows - 1, centerRow))); - model.set("roi_center", [newRow, newCol]); - model.save_changes(); + queueRoiCenter(newRow, newCol); }; const handleDpMouseUp = (e: React.MouseEvent) => { @@ -2971,14 +2879,12 @@ function Show4DSTEM() { setCursorInfo(prev => prev?.panel === "DP" ? null : prev); }; const handleDpDoubleClick = () => { - if (lockView) return; setDpZoom(1); setDpPanX(0); setDpPanY(0); }; const handleViMouseDown = (e: React.MouseEvent) => { - if (viProfileActive && lockProfile) return; const canvas = virtualOverlayRef.current; if (!canvas) return; const rect = canvas.getBoundingClientRect(); @@ -3018,7 +2924,6 @@ function Show4DSTEM() { // Check if VI ROI mode is active - same logic as DP if (viRoiMode && viRoiMode !== "off") { - if (lockRoi) return; // Check if clicking on resize handle if (isNearViRoiResizeHandle(imgX, imgY)) { setIsDraggingViRoiResize(true); @@ -3040,7 +2945,6 @@ function Show4DSTEM() { } // Regular position selection (when ROI is off) - if (lockNavigation || lockVirtual) return; setIsDraggingVI(true); setLocalPosRow(imgX); setLocalPosCol(imgY); // Batch X and Y updates into a single sync @@ -3077,8 +2981,6 @@ function Show4DSTEM() { } } - if (viProfileActive && lockProfile) return; - if (viProfileActive && viProfilePoints.length === 2) { const p0 = viProfilePoints[0]; const p1 = viProfilePoints[1]; @@ -3126,7 +3028,6 @@ function Show4DSTEM() { // Handle VI ROI resize dragging (same pattern as DP) if (isDraggingViRoiResize) { - if (lockRoi) return; const dx = Math.abs(imgX - localViRoiCenterRow); const dy = Math.abs(imgY - localViRoiCenterCol); if (viRoiMode === "rect") { @@ -3145,33 +3046,27 @@ function Show4DSTEM() { // Check hover state for resize handles (same as DP) if (!isDraggingViRoi) { - if (!lockRoi) { - setIsHoveringViRoiResize(isNearViRoiResizeHandle(imgX, imgY)); - } else { - setIsHoveringViRoiResize(false); - } + setIsHoveringViRoiResize(isNearViRoiResizeHandle(imgX, imgY)); if (viRoiMode && viRoiMode !== "off") return; // Don't update position when ROI active } // Handle VI ROI center dragging (same as DP — with offset) if (isDraggingViRoi) { - if (lockRoi) return; const centerRow = imgX - viRoiDragOffsetRef.current.dRow; const centerCol = imgY - viRoiDragOffsetRef.current.dCol; setLocalViRoiCenterRow(centerRow); setLocalViRoiCenterCol(centerCol); - // Batch VI ROI center updates + // Compound trait update — single observer fires Python-side; reduced DP is + // never computed against split-trait state (old col + new row, or vice versa). const newViX = Math.round(Math.max(0, Math.min(shapeRows - 1, centerRow))); const newViY = Math.round(Math.max(0, Math.min(shapeCols - 1, centerCol))); - model.set("vi_roi_center_row", newViX); - model.set("vi_roi_center_col", newViY); + model.set("vi_roi_center", [newViX, newViY]); model.save_changes(); return; } // Handle regular position dragging (when ROI is off) if (!isDraggingVI) return; - if (lockNavigation || lockVirtual) return; setLocalPosRow(imgX); setLocalPosCol(imgY); // Batch position updates into a single sync const newX = Math.round(Math.max(0, Math.min(shapeRows - 1, imgX))); @@ -3244,13 +3139,11 @@ function Show4DSTEM() { setCursorInfo(prev => prev?.panel === "VI" ? null : prev); }; const handleViDoubleClick = () => { - if (lockView || lockVirtual) return; setViZoom(1); setViPanX(0); setViPanY(0); }; const handleFftDoubleClick = () => { - if (lockView || lockFft) return; setFftZoom(1); setFftPanX(0); setFftPanY(0); @@ -3259,14 +3152,12 @@ function Show4DSTEM() { // FFT drag-to-pan handlers const handleFftMouseDown = (e: React.MouseEvent) => { - if (lockView || lockFft) return; fftClickStartRef.current = { x: e.clientX, y: e.clientY }; setIsDraggingFFT(true); setFftDragStart({ x: e.clientX, y: e.clientY, panX: fftPanX, panY: fftPanY }); }; const handleFftMouseMove = (e: React.MouseEvent) => { - if (lockView || lockFft) return; if (!isDraggingFFT || !fftDragStart) return; const canvas = fftOverlayRef.current; if (!canvas) return; @@ -3295,10 +3186,11 @@ function Show4DSTEM() { const canvasY = (e.clientY - rect.top) * scaleY; const fftW = fftCropDims?.fftWidth ?? shapeCols; const fftH = fftCropDims?.fftHeight ?? shapeRows; - // Reverse the zoom/pan transform: canvas coords -> image coords - // The FFT render uses: ctx.translate(fftPanX, fftPanY); ctx.scale(fftZoom, fftZoom); ctx.drawImage(offscreen, 0, 0) - let imgCol = (canvasX - fftPanX) / fftZoom; - let imgRow = (canvasY - fftPanY) / fftZoom; + // Reverse the render transform: canvas coords -> image coords. + // Render: translate(panX, panY); scale(zoom); drawImage(offscreen, 0,0,fftW,fftH, 0,0,canvasW,canvasH) + // So: canvasX = panX + zoom * (imgCol * canvasW / fftW) → imgCol = (canvasX - panX) / zoom * fftW / canvasW + let imgCol = ((canvasX - fftPanX) / fftZoom) * (fftW / canvas.width); + let imgRow = ((canvasY - fftPanY) / fftZoom) * (fftH / canvas.height); // Bounds check if (imgCol >= 0 && imgCol < fftW && imgRow >= 0 && imgRow < fftH) { // Snap to nearest peak in FFT magnitude @@ -3341,7 +3233,6 @@ function Show4DSTEM() { // ── Canvas resize handlers ── const handleCanvasResizeStart = (e: React.MouseEvent) => { - if (lockView) return; e.stopPropagation(); e.preventDefault(); setIsResizingCanvas(true); @@ -3384,7 +3275,6 @@ function Show4DSTEM() { // Export DP handler const handleExportDP = async () => { - if (lockExport) return; const timestamp = new Date().toISOString().replace(/[:.]/g, '-').slice(0, 19); const zip = new JSZip(); const metadata = { @@ -3446,7 +3336,6 @@ function Show4DSTEM() { // Export VI handler const handleExportVI = async () => { - if (lockExport) return; const timestamp = new Date().toISOString().replace(/[:.]/g, '-').slice(0, 19); const zip = new JSZip(); const metadata = { @@ -3523,7 +3412,6 @@ function Show4DSTEM() { // ── DP Figure Export ── const handleDpExportFigure = (withColorbar: boolean) => { - if (lockExport) return; setDpExportAnchor(null); const frameData = rawDpDataRef.current; if (!frameData) return; @@ -3535,9 +3423,6 @@ function Show4DSTEM() { if (dpScaleMode === "log") { vmin = Math.log1p(Math.max(traitDpVmin, 0)); vmax = Math.log1p(Math.max(traitDpVmax, 0)); - } else if (dpScaleMode === "power") { - vmin = Math.pow(Math.max(traitDpVmin, 0), dpPowerExp); - vmax = Math.pow(Math.max(traitDpVmax, 0), dpPowerExp); } else { vmin = traitDpVmin; vmax = traitDpVmax; @@ -3563,14 +3448,12 @@ function Show4DSTEM() { }; const handleDpExportPng = () => { - if (lockExport) return; setDpExportAnchor(null); if (!dpCanvasRef.current) return; dpCanvasRef.current.toBlob((b) => { if (b) downloadBlob(b, "show4dstem_dp.png"); }, "image/png"); }; const handleDpExportGif = () => { - if (lockExport) return; setDpExportAnchor(null); setExporting(true); setGifExportRequested(true); @@ -3578,7 +3461,6 @@ function Show4DSTEM() { // ── VI Figure Export ── const handleViExportFigure = (withColorbar: boolean) => { - if (lockExport) return; setViExportAnchor(null); if (!virtualCanvasRef.current) return; const viCanvas = virtualCanvasRef.current; @@ -3594,7 +3476,6 @@ function Show4DSTEM() { }; const handleViExportPng = () => { - if (lockExport) return; setViExportAnchor(null); if (!virtualCanvasRef.current) return; virtualCanvasRef.current.toBlob((b) => { if (b) downloadBlob(b, "show4dstem_vi.png"); }, "image/png"); @@ -3646,7 +3527,7 @@ function Show4DSTEM() { tabIndex={0} onKeyDown={handleKeyDown} onMouseDownCapture={handleRootMouseDownCapture} - sx={{ p: `${SPACING.LG}px`, bgcolor: themeColors.bg, color: themeColors.text, outline: "none" }} + sx={{ p: 2, bgcolor: themeColors.bg, color: themeColors.text, outline: "none", borderRadius: "2px" }} > {/* HEADER */} @@ -3659,6 +3540,8 @@ function Show4DSTEM() { BF/ABF/ADF: Preset detector configurations (bright-field, annular bright-field, annular dark-field). Image: Virtual image — integrated intensity within detector ROI at each scan position. FFT: Spatial frequency content of the virtual image. Auto masks DC + clips to 99.9th percentile. + Smooth: CSS bilinear blit on the VI canvas. No data change — browser smooths the upscale visually. Off = nearest-neighbor (sharp pixel boundaries). + Auto: Percentile contrast (1st–99th). Clips outliers automatically. Profile: Click two points on DP to draw a line intensity profile. {nFrames > 1 && <> Frame Playback ({frameDimLabel}) @@ -3668,14 +3551,6 @@ function Show4DSTEM() { Keyboard } theme={themeInfo.theme} /> - {/* MAIN CONTENT: DP | VI | FFT (three columns when FFT shown) */} @@ -3686,52 +3561,39 @@ function Show4DSTEM() { DP at ({Math.round(localPosRow)}, {Math.round(localPosCol)}) - {!hideRoi && k: ({Math.round(localKRow)}, {Math.round(localKCol)})} + k: ({Math.round(localKRow)}, {Math.round(localKCol)}) - {!hideProfile && ( - <> - Profile: - { - if (lockProfile) return; - const on = e.target.checked; - setProfileActive(on); - if (!on) { - setProfileLine([]); - setProfileData(null); - setHoveredDpProfileEndpoint(null); - setIsHoveringDpProfileLine(false); - } - }} disabled={lockProfile} size="small" sx={switchStyles.small} /> - - )} - {!hideView && ( - - )} - {!hideExport && ( - - )} - {!hideExport && ( - - )} - {!hideExport && ( - setDpExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> - handleDpExportFigure(true)} sx={{ fontSize: 12 }}>PDF + colorbar - handleDpExportFigure(false)} sx={{ fontSize: 12 }}>PDF - PNG - { if (!lockExport) { setDpExportAnchor(null); handleExportDP(); } }} sx={{ fontSize: 12 }}>ZIP (PNG + metadata) - {pathLength > 0 && GIF (path animation)} - - )} + Profile: + { + const on = e.target.checked; + setProfileActive(on); + if (!on) { + setProfileLine([]); + setProfileData(null); + setHoveredDpProfileEndpoint(null); + setIsHoveringDpProfileLine(false); + } + }} size="small" sx={switchStyles.small} /> + + + + setDpExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> + handleDpExportFigure(true)} sx={{ fontSize: 12 }}>PDF + colorbar + handleDpExportFigure(false)} sx={{ fontSize: 12 }}>PDF + PNG + { setDpExportAnchor(null); handleExportDP(); }} sx={{ fontSize: 12 }}>ZIP (PNG + metadata) + {pathLength > 0 && GIF (path animation)} + @@ -3742,21 +3604,19 @@ function Show4DSTEM() { ref={dpOverlayRef} width={detCols} height={detRows} onMouseDown={handleDpMouseDown} onMouseMove={handleDpMouseMove} onMouseUp={handleDpMouseUp} onMouseLeave={handleDpMouseLeave} - onWheel={createZoomHandler(setDpZoom, setDpPanX, setDpPanY, dpZoom, dpPanX, dpPanY, dpOverlayRef, lockView)} + onWheel={createZoomHandler(setDpZoom, setDpPanX, setDpPanY, dpZoom, dpPanX, dpPanY, dpOverlayRef)} onDoubleClick={handleDpDoubleClick} style={{ position: "absolute", width: "100%", height: "100%", - cursor: (profileActive && lockProfile) || (!profileActive && lockRoi) - ? "default" - : (draggingDpProfileEndpoint !== null || isDraggingDpProfileLine) - ? "grabbing" - : (profileActive && (hoveredDpProfileEndpoint !== null || isHoveringDpProfileLine)) - ? "grab" - : isHoveringResize || isDraggingResize - ? "nwse-resize" - : "crosshair", + cursor: (draggingDpProfileEndpoint !== null || isDraggingDpProfileLine) + ? "grabbing" + : (profileActive && (hoveredDpProfileEndpoint !== null || isHoveringDpProfileLine)) + ? "grab" + : isHoveringResize || isDraggingResize + ? "nwse-resize" + : "crosshair", }} /> @@ -3767,31 +3627,25 @@ function Show4DSTEM() { )} - {!hideView && ( - - )} + {/* DP Stats Bar */} - {!hideStats && dpStats && dpStats.length === 4 && ( - + {dpStats && dpStats.length === 4 && ( + Mean {formatStat(dpStats[0])} Min {formatStat(dpStats[1])} Max {formatStat(dpStats[2])} Std {formatStat(dpStats[3])} - {!hideRoi && ( - <> - - { if (!lockRoi) { setRoiMode("circle"); setRoiRadius(bfRadius || 10); setRoiCenterCol(centerCol); setRoiCenterRow(centerRow); } }} sx={{ color: roiColors.textColor, fontSize: 11, fontWeight: "bold", cursor: lockRoi ? "default" : "pointer", opacity: lockRoi ? 0.6 : 1, "&:hover": { textDecoration: lockRoi ? "none" : "underline" } }}>BF - { if (!lockRoi) { setRoiMode("annular"); setRoiRadiusInner((bfRadius || 10) * 0.5); setRoiRadius(bfRadius || 10); setRoiCenterCol(centerCol); setRoiCenterRow(centerRow); } }} sx={{ color: "#4af", fontSize: 11, fontWeight: "bold", cursor: lockRoi ? "default" : "pointer", opacity: lockRoi ? 0.6 : 1, "&:hover": { textDecoration: lockRoi ? "none" : "underline" } }}>ABF - { if (!lockRoi) { setRoiMode("annular"); setRoiRadiusInner(bfRadius || 10); setRoiRadius(Math.min((bfRadius || 10) * 3, Math.min(detRows, detCols) / 2 - 2)); setRoiCenterCol(centerCol); setRoiCenterRow(centerRow); } }} sx={{ color: "#fa4", fontSize: 11, fontWeight: "bold", cursor: lockRoi ? "default" : "pointer", opacity: lockRoi ? 0.6 : 1, "&:hover": { textDecoration: lockRoi ? "none" : "underline" } }}>ADF - - )} + + { model.set("_preset_request", "bf"); model.save_changes(); }} sx={{ color: roiColors.textColor, fontSize: 11, fontWeight: "bold", cursor: "pointer", "&:hover": { textDecoration: "underline" } }}>BF + { model.set("_preset_request", "abf"); model.save_changes(); }} sx={{ color: "#4af", fontSize: 11, fontWeight: "bold", cursor: "pointer", "&:hover": { textDecoration: "underline" } }}>ABF + { model.set("_preset_request", "adf"); model.save_changes(); }} sx={{ color: "#fa4", fontSize: 11, fontWeight: "bold", cursor: "pointer", "&:hover": { textDecoration: "underline" } }}>ADF )} {/* Profile sparkline */} - {profileActive && !hideProfile && ( + {profileActive && ( { - if (lockProfile) return; setIsResizingProfile(true); profileResizeStart.current = { startY: e.clientY, startHeight: profileHeight }; }} - sx={{ width: canvasSize, height: 4, cursor: lockProfile ? "default" : "ns-resize", borderTop: `1px solid ${themeColors.border}`, borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, bgcolor: themeColors.controlBg, "&:hover": { bgcolor: lockProfile ? themeColors.controlBg : themeColors.accent } }} + sx={{ width: canvasSize, height: 4, cursor: "ns-resize", borderTop: `1px solid ${themeColors.border}`, borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, bgcolor: themeColors.controlBg, "&:hover": { bgcolor: themeColors.accent } }} /> )} {/* DP Controls - two rows with histogram on right */} - {showControls && (!hideRoi || !hideDisplay || !hideHistogram) && ( + {showControls && ( {/* Left: two rows of controls */} {/* Row 1: Detector + slider */} - {!hideRoi && ( - - Detector: - - {(roiMode === "circle" || roiMode === "square" || roiMode === "annular") && ( - <> - { - if (lockRoi) return; - if (roiMode === "annular") { - const [inner, outer] = v as number[]; - setRoiRadiusInner(Math.min(inner, outer - 1)); - setRoiRadius(Math.max(outer, inner + 1)); - } else { - const next = Array.isArray(v) ? v[0] : v; - setRoiRadius(next); - } - }} - min={1} - max={Math.min(detRows, detCols) / 2} - size="small" - sx={{ - width: roiMode === "annular" ? 100 : 70, - mx: 1, - "& .MuiSlider-thumb": { width: 14, height: 14 } - }} - /> - - {roiMode === "annular" ? `${Math.round(roiRadiusInner)}-${Math.round(roiRadius)}px` : `${Math.round(roiRadius)}px`} - - - )} - - )} + + Detector: + + {(roiMode === "circle" || roiMode === "square" || roiMode === "annular") && ( + <> + { + if (roiMode === "annular") { + const [inner, outer] = v as number[]; + setRoiRadiusInner(Math.min(inner, outer - 1)); + setRoiRadius(Math.max(outer, inner + 1)); + } else { + const next = Array.isArray(v) ? v[0] : v; + setRoiRadius(next); + } + }} + min={1} + max={Math.min(detRows, detCols) / 2} + size="small" + sx={{ ...sliderStyles.small, width: roiMode === "annular" ? 67 : 47, mx: 1 }} + /> + + {roiMode === "annular" ? `${Math.round(roiRadiusInner)}-${Math.round(roiRadius)}px` : `${Math.round(roiRadius)}px`} + + + )} + {/* Row 2: Color + Scale + Colorbar */} - {!hideDisplay && ( - - Color: - - Scale: - - Colorbar: - { if (!lockDisplay) setShowDpColorbar(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> - - )} + + Color: + + Scale: + + Colorbar: + setShowDpColorbar(e.target.checked)} size="small" sx={switchStyles.small} /> + {/* Right: Histogram spanning both rows */} - {!hideHistogram && ( - - { if (!lockHistogram) { setDpVminPct(min); setDpVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme} dataMin={dpGlobalMin} dataMax={dpGlobalMax} /> - - )} + + { setDpVminPct(min); setDpVmaxPct(max); }} width={110} height={58} theme={themeInfo.theme} dataMin={dpGlobalMin} dataMax={dpGlobalMax} /> + )} {/* SECOND COLUMN: VI Panel */} - {!hideVirtual && ( {/* VI Header */} - + {shapeRows}×{shapeCols} | {detRows}×{detCols} - {!hideFft && ( - <> - FFT: - { if (!lockFft) setShowFft(e.target.checked); }} disabled={lockFft} size="small" sx={switchStyles.small} /> - - )} - {!hideProfile && ( - <> - Profile: - { - if (lockProfile) return; - const on = e.target.checked; - setViProfileActive(on); - if (!on) { - setViProfilePoints([]); - setHoveredViProfileEndpoint(null); - setIsHoveringViProfileLine(false); - } - }} disabled={lockProfile} size="small" sx={switchStyles.small} /> - - )} - {!hideView && ( - - )} - {!hideExport && ( - - )} - {!hideExport && ( - - )} - {!hideExport && ( - setViExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> - handleViExportFigure(true)} sx={{ fontSize: 12 }}>PDF + colorbar - handleViExportFigure(false)} sx={{ fontSize: 12 }}>PDF - PNG - { if (!lockExport && !lockVirtual) { setViExportAnchor(null); handleExportVI(); } }} sx={{ fontSize: 12 }}>ZIP (all panels + metadata) - - )} + FFT: + setShowFft(e.target.checked)} size="small" sx={switchStyles.small} /> + Profile: + { + const on = e.target.checked; + setViProfileActive(on); + if (!on) { + setViProfilePoints([]); + setHoveredViProfileEndpoint(null); + setIsHoveringViProfileLine(false); + } + }} size="small" sx={switchStyles.small} /> + + + + setViExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> + handleViExportFigure(true)} sx={{ fontSize: 12 }}>PDF + colorbar + handleViExportFigure(false)} sx={{ fontSize: 12 }}>PDF + PNG + { setViExportAnchor(null); handleExportVI(); }} sx={{ fontSize: 12 }}>ZIP (all panels + metadata) + @@ -3957,19 +3780,17 @@ function Show4DSTEM() { ref={virtualOverlayRef} width={shapeCols} height={shapeRows} onMouseDown={handleViMouseDown} onMouseMove={handleViMouseMove} onMouseUp={handleViMouseUp} onMouseLeave={handleViMouseLeave} - onWheel={createZoomHandler(setViZoom, setViPanX, setViPanY, viZoom, viPanX, viPanY, virtualOverlayRef, lockView || lockVirtual)} + onWheel={createZoomHandler(setViZoom, setViPanX, setViPanY, viZoom, viPanX, viPanY, virtualOverlayRef)} onDoubleClick={handleViDoubleClick} style={{ position: "absolute", width: "100%", height: "100%", - cursor: (viProfileActive && lockProfile) || (!viProfileActive && (lockNavigation || lockRoi)) - ? "default" - : (draggingViProfileEndpoint !== null || isDraggingViProfileLine) - ? "grabbing" - : (viProfileActive && (hoveredViProfileEndpoint !== null || isHoveringViProfileLine)) - ? "grab" - : "crosshair", + cursor: (draggingViProfileEndpoint !== null || isDraggingViProfileLine) + ? "grabbing" + : (viProfileActive && (hoveredViProfileEndpoint !== null || isHoveringViProfileLine)) + ? "grab" + : "crosshair", }} /> @@ -3980,23 +3801,27 @@ function Show4DSTEM() { )} - {!hideView && ( - - )} + - {/* VI Stats Bar */} - {!hideStats && viStats && viStats.length === 4 && ( - + {/* VI Stats Bar — stats on left, Auto/Smooth toggles on right edge */} + {viStats && viStats.length === 4 && ( + Mean {formatStat(viStats[0])} Min {formatStat(viStats[1])} Max {formatStat(viStats[2])} Std {formatStat(viStats[3])} + + Auto: + setViAutoContrast(e.target.checked)} size="small" sx={switchStyles.small} /> + Smooth: + setViSmooth(e.target.checked)} size="small" sx={switchStyles.small} /> + )} {/* VI Profile sparkline */} - {viProfileActive && !hideProfile && ( + {viProfileActive && ( { - if (lockProfile) return; setIsResizingViProfile(true); viProfileResizeStart.current = { startY: e.clientY, startHeight: viProfileHeight }; }} - sx={{ width: viCanvasWidth, height: 4, cursor: lockProfile ? "default" : "ns-resize", borderTop: `1px solid ${themeColors.border}`, borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, bgcolor: themeColors.controlBg, "&:hover": { bgcolor: lockProfile ? themeColors.controlBg : themeColors.accent } }} + sx={{ width: viCanvasWidth, height: 4, cursor: "ns-resize", borderTop: `1px solid ${themeColors.border}`, borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, bgcolor: themeColors.controlBg, "&:hover": { bgcolor: themeColors.accent } }} /> )} {/* VI Controls - Two rows with histogram on right */} - {showControls && (!hideRoi || !hideDisplay || !hideHistogram) && ( + {showControls && ( {/* Left: Two rows of controls */} {/* Row 1: ROI selector */} - {!hideRoi && ( - - ROI: - - {viRoiMode && viRoiMode !== "off" && ( - <> - {(viRoiMode === "circle" || viRoiMode === "square") && ( - <> - { if (!lockRoi) setViRoiRadius(v as number); }} - min={1} - max={Math.min(shapeRows, shapeCols) / 2} - size="small" - sx={{ width: 80, mx: 1 }} - /> - - {Math.round(viRoiRadius || 5)}px - - - )} - {summedDpCount > 0 && ( - - {summedDpCount} pos + + ROI: + + {viRoiMode && viRoiMode !== "off" && ( + <> + {(viRoiMode === "circle" || viRoiMode === "square") && ( + <> + setViRoiRadius(v as number)} + min={1} + max={Math.min(shapeRows, shapeCols) / 2} + size="small" + sx={{ ...sliderStyles.small, width: 53, mx: 1 }} + /> + + {Math.round(viRoiRadius || 5)}px - )} - - )} - - )} + + )} + + + )} + {/* Row 2: Color + Scale */} - {!hideDisplay && ( - - Color: - - Scale: - - - )} + + Color: + + Scale: + + {/* Right: Histogram spanning both rows */} - {!hideHistogram && ( - - { if (!lockHistogram) { setViVminPct(min); setViVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme} dataMin={viDataMin} dataMax={viDataMax} /> - - )} + + { setViVminPct(min); setViVmaxPct(max); }} width={110} height={58} theme={themeInfo.theme} dataMin={viDataMin} dataMax={viDataMax} /> + )} - )} {/* THIRD COLUMN: FFT Panel (conditionally shown) */} {effectiveShowFft && ( @@ -4096,9 +3911,7 @@ function Show4DSTEM() { {roiFftActive && fftCropDims ? `ROI FFT (${fftCropDims.cropWidth}\u00D7${fftCropDims.cropHeight})` : "FFT"} - {!hideView && ( - - )} + @@ -4109,18 +3922,16 @@ function Show4DSTEM() { ref={fftOverlayRef} width={shapeCols} height={shapeRows} onMouseDown={handleFftMouseDown} onMouseMove={handleFftMouseMove} onMouseUp={handleFftMouseUp} onMouseLeave={handleFftMouseLeave} - onWheel={createZoomHandler(setFftZoom, setFftPanX, setFftPanY, fftZoom, fftPanX, fftPanY, fftOverlayRef, lockView || lockFft)} + onWheel={createZoomHandler(setFftZoom, setFftPanX, setFftPanY, fftZoom, fftPanX, fftPanY, fftOverlayRef)} onDoubleClick={handleFftDoubleClick} - style={{ position: "absolute", width: "100%", height: "100%", cursor: lockView || lockFft ? "default" : (isDraggingFFT ? "grabbing" : "grab") }} + style={{ position: "absolute", width: "100%", height: "100%", cursor: isDraggingFFT ? "grabbing" : "grab" }} /> - {!hideView && ( - - )} + {/* FFT Stats Bar */} - {!hideStats && fftStats && fftStats.length === 4 && ( - + {fftStats && fftStats.length === 4 && ( + Mean {formatStat(fftStats[0])} Min {formatStat(fftStats[1])} Max {formatStat(fftStats[2])} @@ -4151,50 +3962,46 @@ function Show4DSTEM() { )} {/* FFT Controls - Two rows with histogram on right */} - {showControls && (!hideDisplay || !hideHistogram) && ( + {showControls && ( {/* Left: Two rows of controls */} - {!hideDisplay && ( - - {/* Row 1: Scale + Clip */} - - Scale: - - Auto: - { if (!lockDisplay && !lockFft) setFftAuto(e.target.checked); }} disabled={lockDisplay || lockFft} size="small" sx={switchStyles.small} /> - {fftCropDims && ( - <> - Win: - { if (!lockDisplay && !lockFft) setFftWindow(e.target.checked); }} disabled={lockDisplay || lockFft} size="small" sx={switchStyles.small} /> - - )} - - {/* Row 2: Color */} - - Color: - - - - )} - {/* Right: Histogram spanning both rows */} - {!hideHistogram && ( - - {fftHistogramData && ( - { if (!lockHistogram && !lockFft) { setFftVminPct(min); setFftVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme} dataMin={fftDataMin} dataMax={fftDataMax} /> + + {/* Row 1: Scale + Clip */} + + Scale: + + Auto: + setFftAuto(e.target.checked)} size="small" sx={switchStyles.small} /> + {fftCropDims && ( + <> + Win: + setFftWindow(e.target.checked)} size="small" sx={switchStyles.small} /> + )} - )} + {/* Row 2: Color */} + + Color: + + + + {/* Right: Histogram spanning both rows */} + + {fftHistogramData && ( + { setFftVminPct(min); setFftVmaxPct(max); }} width={110} height={58} theme={themeInfo.theme} dataMin={fftDataMin} dataMax={fftDataMax} /> + )} + )} @@ -4204,52 +4011,52 @@ function Show4DSTEM() { {/* BOTTOM CONTROLS */} {/* Frame controls (5D time/tilt series) — matches Show3D playback */} - {showControls && nFrames > 1 && !hidePlayback && !hideFrame && (<> + {showControls && nFrames > 1 && (<> {frameDimLabel}: - { if (!lockFrame && !lockPlayback) { setFrameReverse(true); setFramePlaying(true); } }} sx={{ color: frameReverse && framePlaying ? themeColors.accent : themeColors.textMuted, p: 0.25 }}> + { setFrameReverse(true); setFramePlaying(true); }} sx={{ color: frameReverse && framePlaying ? themeColors.accent : themeColors.textMuted, p: 0.25 }}> - { if (!lockFrame && !lockPlayback) setFramePlaying(!framePlaying); }} sx={{ color: themeColors.accent, p: 0.25 }}> + setFramePlaying(!framePlaying)} sx={{ color: themeColors.accent, p: 0.25 }}> {framePlaying ? : } - { if (!lockFrame && !lockPlayback) { setFrameReverse(false); setFramePlaying(true); } }} sx={{ color: !frameReverse && framePlaying ? themeColors.accent : themeColors.textMuted, p: 0.25 }}> + { setFrameReverse(false); setFramePlaying(true); }} sx={{ color: !frameReverse && framePlaying ? themeColors.accent : themeColors.textMuted, p: 0.25 }}> - { if (!lockFrame && !lockPlayback) { setFramePlaying(false); setFrameIdx(0); } }} sx={{ color: themeColors.textMuted, p: 0.25 }}> + { setFramePlaying(false); setFrameIdx(0); }} sx={{ color: themeColors.textMuted, p: 0.25 }}> - { if (!lockFrame && !lockPlayback) { setFramePlaying(false); setFrameIdx(v as number); } }} min={0} max={Math.max(0, nFrames - 1)} size="small" sx={{ flex: 1, minWidth: 60, "& .MuiSlider-thumb": { width: 10, height: 10 } }} /> + { setFramePlaying(false); setFrameIdx(v as number); }} min={0} max={Math.max(0, nFrames - 1)} size="small" sx={{ flex: 1, minWidth: 60, "& .MuiSlider-thumb": { width: 10, height: 10 } }} /> {frameLabels && frameLabels.length > frameIdx ? frameLabels[frameIdx] : `${frameIdx + 1}/${nFrames}`} fps - { if (!lockFrame && !lockPlayback) setFrameFps(v as number); }} size="small" sx={{ ...sliderStyles.small, width: 35, flexShrink: 0 }} /> + setFrameFps(v as number)} size="small" sx={{ ...sliderStyles.small, width: 35, flexShrink: 0 }} /> {Math.round(frameFps)} Loop - { if (!lockFrame && !lockPlayback) setFrameLoop(!frameLoop); }} disabled={lockFrame || lockPlayback} sx={{ ...switchStyles.small, flexShrink: 0 }} /> + setFrameLoop(!frameLoop)} sx={{ ...switchStyles.small, flexShrink: 0 }} /> Bounce - { if (!lockFrame && !lockPlayback) setFrameBoomerang(!frameBoomerang); }} disabled={lockFrame || lockPlayback} sx={{ ...switchStyles.small, flexShrink: 0 }} /> + setFrameBoomerang(!frameBoomerang)} sx={{ ...switchStyles.small, flexShrink: 0 }} /> )} {/* Path animation slider */} - {showControls && !hidePlayback && pathLength > 0 && ( + {showControls && pathLength > 0 && ( - { if (!lockPlayback) setPathPlaying(!pathPlaying); }} sx={{ color: themeColors.accent, p: 0.25 }}> + setPathPlaying(!pathPlaying)} sx={{ color: themeColors.accent, p: 0.25 }}> {pathPlaying ? : } - { if (!lockPlayback) { setPathPlaying(false); setPathIndex(0); } }} sx={{ color: themeColors.textMuted, p: 0.25 }}> + { setPathPlaying(false); setPathIndex(0); }} sx={{ color: themeColors.textMuted, p: 0.25 }}> - { if (!lockPlayback) { setPathPlaying(false); setPathIndex(v as number); } }} min={0} max={Math.max(0, pathLength - 1)} size="small" sx={{ flex: 1, minWidth: 60, "& .MuiSlider-thumb": { width: 10, height: 10 } }} /> + { setPathPlaying(false); setPathIndex(v as number); }} min={0} max={Math.max(0, pathLength - 1)} size="small" sx={{ flex: 1, minWidth: 60, "& .MuiSlider-thumb": { width: 10, height: 10 } }} /> {pathIndex + 1}/{pathLength} Loop: - { if (!lockPlayback) { model.set("path_loop", v); model.save_changes(); } }} disabled={lockPlayback} size="small" sx={switchStyles.small} /> + { model.set("path_loop", v); model.save_changes(); }} size="small" sx={switchStyles.small} /> )} diff --git a/widget/js/show4dstem/styles.css b/widget/js/show4dstem/styles.css deleted file mode 100644 index 61876cde..00000000 --- a/widget/js/show4dstem/styles.css +++ /dev/null @@ -1,5 +0,0 @@ -/* Theme-aware styles - minimal, let JS handle most theming */ -.show4dstem-root { - border-radius: 2px; - padding: 16px; -} diff --git a/widget/js/stats.ts b/widget/js/stats.ts index b71c45aa..36ce661c 100644 --- a/widget/js/stats.ts +++ b/widget/js/stats.ts @@ -99,3 +99,23 @@ export function sliderRange( vmax: dataMin + (vmaxPct / 100) * range, }; } + +/** Compute normalized histogram bins from Float32Array. Returns array of 0-1 values. */ +export function computeHistogramFromBytes(data: Float32Array | null, numBins = 256): number[] { + if (!data || data.length === 0) return new Array(numBins).fill(0); + const bins = new Array(numBins).fill(0); + let min = Infinity, max = -Infinity; + for (let i = 0; i < data.length; i++) { + const v = data[i]; + if (isFinite(v)) { if (v < min) min = v; if (v > max) max = v; } + } + if (!isFinite(min) || !isFinite(max) || min === max) return bins; + const range = max - min; + for (let i = 0; i < data.length; i++) { + const v = data[i]; + if (isFinite(v)) bins[Math.min(numBins - 1, Math.floor(((v - min) / range) * numBins))]++; + } + const maxCount = Math.max(...bins); + if (maxCount > 0) for (let i = 0; i < numBins; i++) bins[i] /= maxCount; + return bins; +} diff --git a/widget/js/tool-parity.ts b/widget/js/tool-parity.ts deleted file mode 100644 index 8e318e6c..00000000 --- a/widget/js/tool-parity.ts +++ /dev/null @@ -1,156 +0,0 @@ -import registryJson from "../src/quantem/widget/tool_parity.json"; - -type ToolInput = string | string[] | null | undefined; - -type WidgetConfig = { - tool_groups: string[]; - aliases?: Record; -}; - -type ControlPreset = { - label: string; - show_groups: string[]; -}; - -type ToolParityRegistry = { - widgets: Record; - control_presets: Record; - viewer_widgets?: string[]; -}; - -const REGISTRY = registryJson as ToolParityRegistry; - -function getWidgetConfig(widgetName: string): WidgetConfig { - const cfg = REGISTRY.widgets[widgetName]; - if (!cfg) { - const supported = Object.keys(REGISTRY.widgets).sort().join(", "); - throw new Error(`Unknown widget '${widgetName}'. Supported widgets: ${supported}.`); - } - return cfg; -} - -function toValues(values: ToolInput): string[] { - if (values == null) return []; - if (typeof values === "string") return [values]; - return [...values]; -} - -function toCanonical(widgetName: string, value: string): string { - const cfg = getWidgetConfig(widgetName); - const aliases = cfg.aliases ?? {}; - const key = value.trim().toLowerCase(); - return aliases[key] ?? key; -} - -export function getWidgetToolGroups(widgetName: string): string[] { - return [...getWidgetConfig(widgetName).tool_groups]; -} - -export function normalizeToolGroups(widgetName: string, values: ToolInput): string[] { - const groups = getWidgetToolGroups(widgetName); - const groupSet = new Set(groups); - const out: string[] = []; - const seen = new Set(); - for (const raw of toValues(values)) { - const canonical = toCanonical(widgetName, String(raw)); - if (!canonical) continue; - if (!groupSet.has(canonical)) { - const supported = groups.map((g) => `"${g}"`).join(", "); - throw new Error(`Unknown tool group '${raw}'. Supported values: ${supported}.`); - } - if (canonical === "all") return ["all"]; - if (!seen.has(canonical)) { - seen.add(canonical); - out.push(canonical); - } - } - return out; -} - -function orderedWithoutAll(widgetName: string, values: Set): string[] { - return getWidgetToolGroups(widgetName).filter((group) => group !== "all" && values.has(group)); -} - -export function expandToolGroups(widgetName: string, values: ToolInput): string[] { - const normalized = normalizeToolGroups(widgetName, values); - if (!normalized.includes("all")) return normalized; - return getWidgetToolGroups(widgetName).filter((group) => group !== "all"); -} - -export function compactToolLabel(key: string): string { - return key - .replace(/_/g, " ") - .replace(/\b\w/g, (m) => m.toUpperCase()); -} - -export function getControlPresetIds(): string[] { - return Object.keys(REGISTRY.control_presets); -} - -export function getControlPresetLabel(presetId: string): string { - const preset = REGISTRY.control_presets[presetId]; - return preset?.label ?? presetId; -} - -export function resolvePresetHiddenTools(widgetName: string, presetId: string): string[] { - const preset = REGISTRY.control_presets[presetId]; - if (!preset) { - const supported = Object.keys(REGISTRY.control_presets).sort().join(", "); - throw new Error(`Unknown control preset '${presetId}'. Supported presets: ${supported}.`); - } - const supportedGroups = getWidgetToolGroups(widgetName).filter((group) => group !== "all"); - if (preset.show_groups.includes("*")) return []; - const show = new Set(preset.show_groups.map((g) => toCanonical(widgetName, g))); - const hidden = supportedGroups.filter((group) => !show.has(group)); - return normalizeToolGroups(widgetName, hidden); -} - -export type ToolVisibilityState = { - hideAll: boolean; - lockAll: boolean; - isHidden: (group: string) => boolean; - isLocked: (group: string) => boolean; - hiddenSet: Set; - disabledSet: Set; -}; - -export function computeToolVisibility( - widgetName: string, - disabledTools: ToolInput, - hiddenTools: ToolInput, -): ToolVisibilityState { - const hidden = normalizeToolGroups(widgetName, hiddenTools); - const disabled = normalizeToolGroups(widgetName, disabledTools); - const hiddenSet = new Set(hidden); - const disabledSet = new Set(disabled); - const hideAll = hiddenSet.has("all"); - const lockAll = hideAll || disabledSet.has("all"); - - const isHidden = (group: string): boolean => { - const canonical = toCanonical(widgetName, group); - if (canonical === "all") return hideAll; - return hideAll || hiddenSet.has(canonical); - }; - - const isLocked = (group: string): boolean => { - const canonical = toCanonical(widgetName, group); - if (canonical === "all") return lockAll; - return lockAll || isHidden(canonical) || disabledSet.has(canonical); - }; - - return { hideAll, lockAll, isHidden, isLocked, hiddenSet, disabledSet }; -} - -export function addToolGroup(widgetName: string, current: ToolInput, group: string): string[] { - const merged = new Set(expandToolGroups(widgetName, current)); - const canonical = toCanonical(widgetName, group); - if (canonical === "all") return ["all"]; - merged.add(canonical); - return orderedWithoutAll(widgetName, merged); -} - -export function removeToolGroup(widgetName: string, current: ToolInput, group: string): string[] { - const merged = new Set(expandToolGroups(widgetName, current)); - merged.delete(toCanonical(widgetName, group)); - return orderedWithoutAll(widgetName, merged); -} diff --git a/widget/package-lock.json b/widget/package-lock.json index cf4e386a..ff1510fd 100644 --- a/widget/package-lock.json +++ b/widget/package-lock.json @@ -16,13 +16,11 @@ "react-dom": "^19.1.0" }, "devDependencies": { - "@anywidget/vite": "^0.2.0", "@types/react": "^19.1.3", "@types/react-dom": "^19.1.4", - "@vitejs/plugin-react": "^4.3.0", "@webgpu/types": "^0.1.68", - "typescript": "^5.8.3", - "vite": "^5.2.0" + "esbuild": "^0.21.3", + "typescript": "^5.8.3" } }, "node_modules/@anywidget/react": { @@ -46,23 +44,13 @@ "integrity": "sha512-Qno/7V0lKHCMq3DJuSKKHMwilFKPSe8wFftL5xWmgaMCc938mNTtv+i19UrvDfpj9cQTnlPqyXy8t3JOgQ8laA==", "license": "MIT" }, - "node_modules/@anywidget/vite": { - "version": "0.2.2", - "resolved": "https://registry.npmjs.org/@anywidget/vite/-/vite-0.2.2.tgz", - "integrity": "sha512-6qYispivu+VAvWJbWSsZujGAQXLW5Xve/cNA/zrz+RXeg31xfJ5bH5ylqp6UC+GpFZL8azRN6HQS24Z0IgURLw==", - "dev": true, - "license": "MIT", - "peerDependencies": { - "vite": "^4.0.0 || ^5.0.0 || ^6.0.0 || ^7.0.0" - } - }, "node_modules/@babel/code-frame": { - "version": "7.27.1", - "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.27.1.tgz", - "integrity": "sha512-cjQ7ZlQ0Mv3b47hABuTevyTuYN4i+loJKGeV9flcCgIK37cCXRh+L1bd3iBHlynerhQ7BhCkn2BPbQUL+rGqFg==", + "version": "7.29.0", + "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.29.0.tgz", + "integrity": "sha512-9NhCeYjq9+3uxgdtp20LSiJXJvN0FeCtNGpJxuMFZ1Kv3cWUNb6DOhJwUvcVCzKGR66cw4njwM6hrJLqgOwbcw==", "license": "MIT", "dependencies": { - "@babel/helper-validator-identifier": "^7.27.1", + "@babel/helper-validator-identifier": "^7.28.5", "js-tokens": "^4.0.0", "picocolors": "^1.1.1" }, @@ -70,55 +58,14 @@ "node": ">=6.9.0" } }, - "node_modules/@babel/compat-data": { - "version": "7.28.5", - "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.28.5.tgz", - "integrity": "sha512-6uFXyCayocRbqhZOB+6XcuZbkMNimwfVGFji8CTZnCzOHVGvDqzvitu1re2AU5LROliz7eQPhB8CpAMvnx9EjA==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=6.9.0" - } - }, - "node_modules/@babel/core": { - "version": "7.28.5", - "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.28.5.tgz", - "integrity": "sha512-e7jT4DxYvIDLk1ZHmU/m/mB19rex9sv0c2ftBtjSBv+kVM/902eh0fINUzD7UwLLNR+jU585GxUJ8/EBfAM5fw==", - "dev": true, - "license": "MIT", - "dependencies": { - "@babel/code-frame": "^7.27.1", - "@babel/generator": "^7.28.5", - "@babel/helper-compilation-targets": "^7.27.2", - "@babel/helper-module-transforms": "^7.28.3", - "@babel/helpers": "^7.28.4", - "@babel/parser": "^7.28.5", - "@babel/template": "^7.27.2", - "@babel/traverse": "^7.28.5", - "@babel/types": "^7.28.5", - "@jridgewell/remapping": "^2.3.5", - "convert-source-map": "^2.0.0", - "debug": "^4.1.0", - "gensync": "^1.0.0-beta.2", - "json5": "^2.2.3", - "semver": "^6.3.1" - }, - "engines": { - "node": ">=6.9.0" - }, - "funding": { - "type": "opencollective", - "url": "https://opencollective.com/babel" - } - }, "node_modules/@babel/generator": { - "version": "7.28.5", - "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.28.5.tgz", - "integrity": "sha512-3EwLFhZ38J4VyIP6WNtt2kUdW9dokXA9Cr4IVIFHuCpZ3H8/YFOl5JjZHisrn1fATPBmKKqXzDFvh9fUwHz6CQ==", + "version": "7.29.1", + "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.29.1.tgz", + "integrity": "sha512-qsaF+9Qcm2Qv8SRIMMscAvG4O3lJ0F1GuMo5HR/Bp02LopNgnZBC/EkbevHFeGs4ls/oPz9v+Bsmzbkbe+0dUw==", "license": "MIT", "dependencies": { - "@babel/parser": "^7.28.5", - "@babel/types": "^7.28.5", + "@babel/parser": "^7.29.0", + "@babel/types": "^7.29.0", "@jridgewell/gen-mapping": "^0.3.12", "@jridgewell/trace-mapping": "^0.3.28", "jsesc": "^3.0.2" @@ -127,23 +74,6 @@ "node": ">=6.9.0" } }, - "node_modules/@babel/helper-compilation-targets": { - "version": "7.27.2", - "resolved": "https://registry.npmjs.org/@babel/helper-compilation-targets/-/helper-compilation-targets-7.27.2.tgz", - "integrity": "sha512-2+1thGUUWWjLTYTHZWK1n8Yga0ijBz1XAhUXcKy81rd5g6yh7hGqMp45v7cadSbEHc9G3OTv45SyneRN3ps4DQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "@babel/compat-data": "^7.27.2", - "@babel/helper-validator-option": "^7.27.1", - "browserslist": "^4.24.0", - "lru-cache": "^5.1.1", - "semver": "^6.3.1" - }, - "engines": { - "node": ">=6.9.0" - } - }, "node_modules/@babel/helper-globals": { "version": "7.28.0", "resolved": "https://registry.npmjs.org/@babel/helper-globals/-/helper-globals-7.28.0.tgz", @@ -154,42 +84,14 @@ } }, "node_modules/@babel/helper-module-imports": { - "version": "7.27.1", - "resolved": "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.27.1.tgz", - "integrity": "sha512-0gSFWUPNXNopqtIPQvlD5WgXYI5GY2kP2cCvoT8kczjbfcfuIljTbcWrulD1CIPIX2gt1wghbDy08yE1p+/r3w==", - "license": "MIT", - "dependencies": { - "@babel/traverse": "^7.27.1", - "@babel/types": "^7.27.1" - }, - "engines": { - "node": ">=6.9.0" - } - }, - "node_modules/@babel/helper-module-transforms": { - "version": "7.28.3", - "resolved": "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.28.3.tgz", - "integrity": "sha512-gytXUbs8k2sXS9PnQptz5o0QnpLL51SwASIORY6XaBKF88nsOT0Zw9szLqlSGQDP/4TljBAD5y98p2U1fqkdsw==", - "dev": true, + "version": "7.28.6", + "resolved": "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.28.6.tgz", + "integrity": "sha512-l5XkZK7r7wa9LucGw9LwZyyCUscb4x37JWTPz7swwFE/0FMQAGpiWUZn8u9DzkSBWEcK25jmvubfpw2dnAMdbw==", "license": "MIT", "dependencies": { - "@babel/helper-module-imports": "^7.27.1", - "@babel/helper-validator-identifier": "^7.27.1", - "@babel/traverse": "^7.28.3" - }, - "engines": { - "node": ">=6.9.0" + "@babel/traverse": "^7.28.6", + "@babel/types": "^7.28.6" }, - "peerDependencies": { - "@babel/core": "^7.0.0" - } - }, - "node_modules/@babel/helper-plugin-utils": { - "version": "7.27.1", - "resolved": "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.27.1.tgz", - "integrity": "sha512-1gn1Up5YXka3YYAHGKpbideQ5Yjf1tDa9qYcgysz+cNCXukyLl6DjPXhD3VRwSb8c0J9tA4b2+rHEZtc6R0tlw==", - "dev": true, - "license": "MIT", "engines": { "node": ">=6.9.0" } @@ -212,37 +114,13 @@ "node": ">=6.9.0" } }, - "node_modules/@babel/helper-validator-option": { - "version": "7.27.1", - "resolved": "https://registry.npmjs.org/@babel/helper-validator-option/-/helper-validator-option-7.27.1.tgz", - "integrity": "sha512-YvjJow9FxbhFFKDSuFnVCe2WxXk1zWc22fFePVNEaWJEu8IrZVlda6N0uHwzZrUM1il7NC9Mlp4MaJYbYd9JSg==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=6.9.0" - } - }, - "node_modules/@babel/helpers": { - "version": "7.28.4", - "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.28.4.tgz", - "integrity": "sha512-HFN59MmQXGHVyYadKLVumYsA9dBFun/ldYxipEjzA4196jpLZd8UjEEBLkbEkvfYreDqJhZxYAWFPtrfhNpj4w==", - "dev": true, - "license": "MIT", - "dependencies": { - "@babel/template": "^7.27.2", - "@babel/types": "^7.28.4" - }, - "engines": { - "node": ">=6.9.0" - } - }, "node_modules/@babel/parser": { - "version": "7.28.5", - "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.28.5.tgz", - "integrity": "sha512-KKBU1VGYR7ORr3At5HAtUQ+TV3SzRCXmA/8OdDZiLDBIZxVyzXuztPjfLd3BV1PRAQGCMWWSHYhL0F8d5uHBDQ==", + "version": "7.29.3", + "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.29.3.tgz", + "integrity": "sha512-b3ctpQwp+PROvU/cttc4OYl4MzfJUWy6FZg+PMXfzmt/+39iHVF0sDfqay8TQM3JA2EUOyKcFZt75jWriQijsA==", "license": "MIT", "dependencies": { - "@babel/types": "^7.28.5" + "@babel/types": "^7.29.0" }, "bin": { "parser": "bin/babel-parser.js" @@ -251,38 +129,6 @@ "node": ">=6.0.0" } }, - "node_modules/@babel/plugin-transform-react-jsx-self": { - "version": "7.27.1", - "resolved": "https://registry.npmjs.org/@babel/plugin-transform-react-jsx-self/-/plugin-transform-react-jsx-self-7.27.1.tgz", - "integrity": "sha512-6UzkCs+ejGdZ5mFFC/OCUrv028ab2fp1znZmCZjAOBKiBK2jXD1O+BPSfX8X2qjJ75fZBMSnQn3Rq2mrBJK2mw==", - "dev": true, - "license": "MIT", - "dependencies": { - "@babel/helper-plugin-utils": "^7.27.1" - }, - "engines": { - "node": ">=6.9.0" - }, - "peerDependencies": { - "@babel/core": "^7.0.0-0" - } - }, - "node_modules/@babel/plugin-transform-react-jsx-source": { - "version": "7.27.1", - "resolved": "https://registry.npmjs.org/@babel/plugin-transform-react-jsx-source/-/plugin-transform-react-jsx-source-7.27.1.tgz", - "integrity": "sha512-zbwoTsBruTeKB9hSq73ha66iFeJHuaFkUbwvqElnygoNbj/jHRsSeokowZFN3CZ64IvEqcmmkVe89OPXc7ldAw==", - "dev": true, - "license": "MIT", - "dependencies": { - "@babel/helper-plugin-utils": "^7.27.1" - }, - "engines": { - "node": ">=6.9.0" - }, - "peerDependencies": { - "@babel/core": "^7.0.0-0" - } - }, "node_modules/@babel/runtime": { "version": "7.29.2", "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.29.2.tgz", @@ -293,31 +139,31 @@ } }, "node_modules/@babel/template": { - "version": "7.27.2", - "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.27.2.tgz", - "integrity": "sha512-LPDZ85aEJyYSd18/DkjNh4/y1ntkE5KwUHWTiqgRxruuZL2F1yuHligVHLvcHY2vMHXttKFpJn6LwfI7cw7ODw==", + "version": "7.28.6", + "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.28.6.tgz", + "integrity": "sha512-YA6Ma2KsCdGb+WC6UpBVFJGXL58MDA6oyONbjyF/+5sBgxY/dwkhLogbMT2GXXyU84/IhRw/2D1Os1B/giz+BQ==", "license": "MIT", "dependencies": { - "@babel/code-frame": "^7.27.1", - "@babel/parser": "^7.27.2", - "@babel/types": "^7.27.1" + "@babel/code-frame": "^7.28.6", + "@babel/parser": "^7.28.6", + "@babel/types": "^7.28.6" }, "engines": { "node": ">=6.9.0" } }, "node_modules/@babel/traverse": { - "version": "7.28.5", - "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.28.5.tgz", - "integrity": "sha512-TCCj4t55U90khlYkVV/0TfkJkAkUg3jZFA3Neb7unZT8CPok7iiRfaX0F+WnqWqt7OxhOn0uBKXCw4lbL8W0aQ==", + "version": "7.29.0", + "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.29.0.tgz", + "integrity": "sha512-4HPiQr0X7+waHfyXPZpWPfWL/J7dcN1mx9gL6WdQVMbPnF3+ZhSMs8tCxN7oHddJE9fhNE7+lxdnlyemKfJRuA==", "license": "MIT", "dependencies": { - "@babel/code-frame": "^7.27.1", - "@babel/generator": "^7.28.5", + "@babel/code-frame": "^7.29.0", + "@babel/generator": "^7.29.0", "@babel/helper-globals": "^7.28.0", - "@babel/parser": "^7.28.5", - "@babel/template": "^7.27.2", - "@babel/types": "^7.28.5", + "@babel/parser": "^7.29.0", + "@babel/template": "^7.28.6", + "@babel/types": "^7.29.0", "debug": "^4.3.1" }, "engines": { @@ -325,9 +171,9 @@ } }, "node_modules/@babel/types": { - "version": "7.28.5", - "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.28.5.tgz", - "integrity": "sha512-qQ5m48eI/MFLQ5PxQj4PFaprjyCTLI37ElWMmNs0K8Lk3dVeOdNpB3ks8jc7yM5CDmVC73eMVk/trk3fgmrUpA==", + "version": "7.29.0", + "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.29.0.tgz", + "integrity": "sha512-LwdZHpScM4Qz8Xw2iKSzS+cfglZzJGvofQICy7W7v4caru4EaAmyUuO6BGrbyQ2mYV11W0U8j5mBhd14dd3B0A==", "license": "MIT", "dependencies": { "@babel/helper-string-parser": "^7.27.1", @@ -356,12 +202,6 @@ "stylis": "4.2.0" } }, - "node_modules/@emotion/babel-plugin/node_modules/convert-source-map": { - "version": "1.9.0", - "resolved": "https://registry.npmjs.org/convert-source-map/-/convert-source-map-1.9.0.tgz", - "integrity": "sha512-ASFBup0Mz1uyiIjANan1jzLQami9z1PoYSZCiiYW2FczPbenXc45FZdBZLzOT+r6+iciuEModtmCti+hjaAk0A==", - "license": "MIT" - }, "node_modules/@emotion/cache": { "version": "11.14.0", "resolved": "https://registry.npmjs.org/@emotion/cache/-/cache-11.14.0.tgz", @@ -890,17 +730,6 @@ "@jridgewell/trace-mapping": "^0.3.24" } }, - "node_modules/@jridgewell/remapping": { - "version": "2.3.5", - "resolved": "https://registry.npmjs.org/@jridgewell/remapping/-/remapping-2.3.5.tgz", - "integrity": "sha512-LI9u/+laYG4Ds1TDKSJW2YPrIlcVYOwi2fUC6xB43lueCjgxV4lffOCZCtYFiH6TNOX+tQKXx97T4IKHbhyHEQ==", - "dev": true, - "license": "MIT", - "dependencies": { - "@jridgewell/gen-mapping": "^0.3.5", - "@jridgewell/trace-mapping": "^0.3.24" - } - }, "node_modules/@jridgewell/resolve-uri": { "version": "3.1.2", "resolved": "https://registry.npmjs.org/@jridgewell/resolve-uri/-/resolve-uri-3.1.2.tgz", @@ -1169,597 +998,101 @@ "url": "https://opencollective.com/popperjs" } }, - "node_modules/@rolldown/pluginutils": { - "version": "1.0.0-beta.27", - "resolved": "https://registry.npmjs.org/@rolldown/pluginutils/-/pluginutils-1.0.0-beta.27.tgz", - "integrity": "sha512-+d0F4MKMCbeVUJwG96uQ4SgAznZNSq93I3V+9NHA4OpvqG8mRCpGdKmK8l/dl02h2CCDHwW2FqilnTyDcAnqjA==", - "dev": true, + "node_modules/@types/parse-json": { + "version": "4.0.2", + "resolved": "https://registry.npmjs.org/@types/parse-json/-/parse-json-4.0.2.tgz", + "integrity": "sha512-dISoDXWWQwUquiKsyZ4Ng+HX2KsPL7LyHKHQwgGFEA3IaKac4Obd+h2a/a6waisAoepJlBcx9paWqjA8/HVjCw==", "license": "MIT" }, - "node_modules/@rollup/rollup-android-arm-eabi": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-android-arm-eabi/-/rollup-android-arm-eabi-4.55.1.tgz", - "integrity": "sha512-9R0DM/ykwfGIlNu6+2U09ga0WXeZ9MRC2Ter8jnz8415VbuIykVuc6bhdrbORFZANDmTDvq26mJrEVTl8TdnDg==", - "cpu": [ - "arm" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "android" - ] + "node_modules/@types/prop-types": { + "version": "15.7.15", + "resolved": "https://registry.npmjs.org/@types/prop-types/-/prop-types-15.7.15.tgz", + "integrity": "sha512-F6bEyamV9jKGAFBEmlQnesRPGOQqS2+Uwi0Em15xenOxHaf2hv6L8YCVn3rPdPJOiJfPiCnLIRyvwVaqMY3MIw==", + "license": "MIT" }, - "node_modules/@rollup/rollup-android-arm64": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-android-arm64/-/rollup-android-arm64-4.55.1.tgz", - "integrity": "sha512-eFZCb1YUqhTysgW3sj/55du5cG57S7UTNtdMjCW7LwVcj3dTTcowCsC8p7uBdzKsZYa8J7IDE8lhMI+HX1vQvg==", - "cpu": [ - "arm64" - ], - "dev": true, + "node_modules/@types/react": { + "version": "19.2.14", + "resolved": "https://registry.npmjs.org/@types/react/-/react-19.2.14.tgz", + "integrity": "sha512-ilcTH/UniCkMdtexkoCN0bI7pMcJDvmQFPvuPvmEaYA/NSfFTAgdUSLAoVjaRJm7+6PvcM+q1zYOwS4wTYMF9w==", "license": "MIT", - "optional": true, - "os": [ - "android" - ] + "dependencies": { + "csstype": "^3.2.2" + } }, - "node_modules/@rollup/rollup-darwin-arm64": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-darwin-arm64/-/rollup-darwin-arm64-4.55.1.tgz", - "integrity": "sha512-p3grE2PHcQm2e8PSGZdzIhCKbMCw/xi9XvMPErPhwO17vxtvCN5FEA2mSLgmKlCjHGMQTP6phuQTYWUnKewwGg==", - "cpu": [ - "arm64" - ], - "dev": true, + "node_modules/@types/react-dom": { + "version": "19.2.3", + "resolved": "https://registry.npmjs.org/@types/react-dom/-/react-dom-19.2.3.tgz", + "integrity": "sha512-jp2L/eY6fn+KgVVQAOqYItbF0VY/YApe5Mz2F0aykSO8gx31bYCZyvSeYxCHKvzHG5eZjc+zyaS5BrBWya2+kQ==", "license": "MIT", - "optional": true, - "os": [ - "darwin" - ] + "peerDependencies": { + "@types/react": "^19.2.0" + } }, - "node_modules/@rollup/rollup-darwin-x64": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-darwin-x64/-/rollup-darwin-x64-4.55.1.tgz", - "integrity": "sha512-rDUjG25C9qoTm+e02Esi+aqTKSBYwVTaoS1wxcN47/Luqef57Vgp96xNANwt5npq9GDxsH7kXxNkJVEsWEOEaQ==", - "cpu": [ - "x64" - ], - "dev": true, + "node_modules/@types/react-transition-group": { + "version": "4.4.12", + "resolved": "https://registry.npmjs.org/@types/react-transition-group/-/react-transition-group-4.4.12.tgz", + "integrity": "sha512-8TV6R3h2j7a91c+1DXdJi3Syo69zzIZbz7Lg5tORM5LEJG7X/E6a1V3drRyBRZq7/utz7A+c4OgYLiLcYGHG6w==", "license": "MIT", - "optional": true, - "os": [ - "darwin" - ] + "peerDependencies": { + "@types/react": "*" + } }, - "node_modules/@rollup/rollup-freebsd-arm64": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-freebsd-arm64/-/rollup-freebsd-arm64-4.55.1.tgz", - "integrity": "sha512-+JiU7Jbp5cdxekIgdte0jfcu5oqw4GCKr6i3PJTlXTCU5H5Fvtkpbs4XJHRmWNXF+hKmn4v7ogI5OQPaupJgOg==", - "cpu": [ - "arm64" - ], + "node_modules/@webgpu/types": { + "version": "0.1.69", + "resolved": "https://registry.npmjs.org/@webgpu/types/-/types-0.1.69.tgz", + "integrity": "sha512-RPmm6kgRbI8e98zSD3RVACvnuktIja5+yLgDAkTmxLr90BEwdTXRQWNLF3ETTTyH/8mKhznZuN5AveXYFEsMGQ==", "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "freebsd" - ] + "license": "BSD-3-Clause" }, - "node_modules/@rollup/rollup-freebsd-x64": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-freebsd-x64/-/rollup-freebsd-x64-4.55.1.tgz", - "integrity": "sha512-V5xC1tOVWtLLmr3YUk2f6EJK4qksksOYiz/TCsFHu/R+woubcLWdC9nZQmwjOAbmExBIVKsm1/wKmEy4z4u4Bw==", - "cpu": [ - "x64" - ], - "dev": true, + "node_modules/babel-plugin-macros": { + "version": "3.1.0", + "resolved": "https://registry.npmjs.org/babel-plugin-macros/-/babel-plugin-macros-3.1.0.tgz", + "integrity": "sha512-Cg7TFGpIr01vOQNODXOOaGz2NpCU5gl8x1qJFbb6hbZxR7XrcE2vtbAsTAbJ7/xwJtUuJEw8K8Zr/AE0LHlesg==", "license": "MIT", - "optional": true, - "os": [ - "freebsd" - ] + "dependencies": { + "@babel/runtime": "^7.12.5", + "cosmiconfig": "^7.0.0", + "resolve": "^1.19.0" + }, + "engines": { + "node": ">=10", + "npm": ">=6" + } }, - "node_modules/@rollup/rollup-linux-arm-gnueabihf": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm-gnueabihf/-/rollup-linux-arm-gnueabihf-4.55.1.tgz", - "integrity": "sha512-Rn3n+FUk2J5VWx+ywrG/HGPTD9jXNbicRtTM11e/uorplArnXZYsVifnPPqNNP5BsO3roI4n8332ukpY/zN7rQ==", - "cpu": [ - "arm" - ], - "dev": true, + "node_modules/callsites": { + "version": "3.1.0", + "resolved": "https://registry.npmjs.org/callsites/-/callsites-3.1.0.tgz", + "integrity": "sha512-P8BjAsXvZS+VIDUI11hHCQEv74YT67YUi5JJFNWIqL235sBmjX4+qx9Muvls5ivyNENctx46xQLQ3aTuE7ssaQ==", "license": "MIT", - "optional": true, - "os": [ - "linux" - ] + "engines": { + "node": ">=6" + } }, - "node_modules/@rollup/rollup-linux-arm-musleabihf": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm-musleabihf/-/rollup-linux-arm-musleabihf-4.55.1.tgz", - "integrity": "sha512-grPNWydeKtc1aEdrJDWk4opD7nFtQbMmV7769hiAaYyUKCT1faPRm2av8CX1YJsZ4TLAZcg9gTR1KvEzoLjXkg==", - "cpu": [ - "arm" - ], - "dev": true, + "node_modules/clsx": { + "version": "2.1.1", + "resolved": "https://registry.npmjs.org/clsx/-/clsx-2.1.1.tgz", + "integrity": "sha512-eYm0QWBtUrBWZWG0d386OGAw16Z995PiOVo2B7bjWSbHedGl5e0ZWaq65kOGgUSNesEIDkB9ISbTg/JK9dhCZA==", "license": "MIT", - "optional": true, - "os": [ - "linux" - ] + "engines": { + "node": ">=6" + } }, - "node_modules/@rollup/rollup-linux-arm64-gnu": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm64-gnu/-/rollup-linux-arm64-gnu-4.55.1.tgz", - "integrity": "sha512-a59mwd1k6x8tXKcUxSyISiquLwB5pX+fJW9TkWU46lCqD/GRDe9uDN31jrMmVP3feI3mhAdvcCClhV8V5MhJFQ==", - "cpu": [ - "arm64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ] + "node_modules/convert-source-map": { + "version": "1.9.0", + "resolved": "https://registry.npmjs.org/convert-source-map/-/convert-source-map-1.9.0.tgz", + "integrity": "sha512-ASFBup0Mz1uyiIjANan1jzLQami9z1PoYSZCiiYW2FczPbenXc45FZdBZLzOT+r6+iciuEModtmCti+hjaAk0A==", + "license": "MIT" }, - "node_modules/@rollup/rollup-linux-arm64-musl": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm64-musl/-/rollup-linux-arm64-musl-4.55.1.tgz", - "integrity": "sha512-puS1MEgWX5GsHSoiAsF0TYrpomdvkaXm0CofIMG5uVkP6IBV+ZO9xhC5YEN49nsgYo1DuuMquF9+7EDBVYu4uA==", - "cpu": [ - "arm64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ] + "node_modules/core-util-is": { + "version": "1.0.3", + "resolved": "https://registry.npmjs.org/core-util-is/-/core-util-is-1.0.3.tgz", + "integrity": "sha512-ZQBvi1DcpJ4GDqanjucZ2Hj3wEO5pZDS89BWbkcrvdxksJorwUDDZamX9ldFkp9aw2lmBDLgkObEA4DWNJ9FYQ==", + "license": "MIT" }, - "node_modules/@rollup/rollup-linux-loong64-gnu": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-loong64-gnu/-/rollup-linux-loong64-gnu-4.55.1.tgz", - "integrity": "sha512-r3Wv40in+lTsULSb6nnoudVbARdOwb2u5fpeoOAZjFLznp6tDU8kd+GTHmJoqZ9lt6/Sys33KdIHUaQihFcu7g==", - "cpu": [ - "loong64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ] - }, - "node_modules/@rollup/rollup-linux-loong64-musl": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-loong64-musl/-/rollup-linux-loong64-musl-4.55.1.tgz", - "integrity": "sha512-MR8c0+UxAlB22Fq4R+aQSPBayvYa3+9DrwG/i1TKQXFYEaoW3B5b/rkSRIypcZDdWjWnpcvxbNaAJDcSbJU3Lw==", - "cpu": [ - "loong64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ] - }, - "node_modules/@rollup/rollup-linux-ppc64-gnu": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-ppc64-gnu/-/rollup-linux-ppc64-gnu-4.55.1.tgz", - "integrity": "sha512-3KhoECe1BRlSYpMTeVrD4sh2Pw2xgt4jzNSZIIPLFEsnQn9gAnZagW9+VqDqAHgm1Xc77LzJOo2LdigS5qZ+gw==", - "cpu": [ - "ppc64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ] - }, - "node_modules/@rollup/rollup-linux-ppc64-musl": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-ppc64-musl/-/rollup-linux-ppc64-musl-4.55.1.tgz", - "integrity": "sha512-ziR1OuZx0vdYZZ30vueNZTg73alF59DicYrPViG0NEgDVN8/Jl87zkAPu4u6VjZST2llgEUjaiNl9JM6HH1Vdw==", - "cpu": [ - "ppc64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ] - }, - "node_modules/@rollup/rollup-linux-riscv64-gnu": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-riscv64-gnu/-/rollup-linux-riscv64-gnu-4.55.1.tgz", - "integrity": "sha512-uW0Y12ih2XJRERZ4jAfKamTyIHVMPQnTZcQjme2HMVDAHY4amf5u414OqNYC+x+LzRdRcnIG1YodLrrtA8xsxw==", - "cpu": [ - "riscv64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ] - }, - "node_modules/@rollup/rollup-linux-riscv64-musl": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-riscv64-musl/-/rollup-linux-riscv64-musl-4.55.1.tgz", - "integrity": "sha512-u9yZ0jUkOED1BFrqu3BwMQoixvGHGZ+JhJNkNKY/hyoEgOwlqKb62qu+7UjbPSHYjiVy8kKJHvXKv5coH4wDeg==", - "cpu": [ - "riscv64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ] - }, - "node_modules/@rollup/rollup-linux-s390x-gnu": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-s390x-gnu/-/rollup-linux-s390x-gnu-4.55.1.tgz", - "integrity": "sha512-/0PenBCmqM4ZUd0190j7J0UsQ/1nsi735iPRakO8iPciE7BQ495Y6msPzaOmvx0/pn+eJVVlZrNrSh4WSYLxNg==", - "cpu": [ - "s390x" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ] - }, - "node_modules/@rollup/rollup-linux-x64-gnu": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-x64-gnu/-/rollup-linux-x64-gnu-4.55.1.tgz", - "integrity": "sha512-a8G4wiQxQG2BAvo+gU6XrReRRqj+pLS2NGXKm8io19goR+K8lw269eTrPkSdDTALwMmJp4th2Uh0D8J9bEV1vg==", - "cpu": [ - "x64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ] - }, - "node_modules/@rollup/rollup-linux-x64-musl": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-x64-musl/-/rollup-linux-x64-musl-4.55.1.tgz", - "integrity": "sha512-bD+zjpFrMpP/hqkfEcnjXWHMw5BIghGisOKPj+2NaNDuVT+8Ds4mPf3XcPHuat1tz89WRL+1wbcxKY3WSbiT7w==", - "cpu": [ - "x64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "linux" - ] - }, - "node_modules/@rollup/rollup-openbsd-x64": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-openbsd-x64/-/rollup-openbsd-x64-4.55.1.tgz", - "integrity": "sha512-eLXw0dOiqE4QmvikfQ6yjgkg/xDM+MdU9YJuP4ySTibXU0oAvnEWXt7UDJmD4UkYialMfOGFPJnIHSe/kdzPxg==", - "cpu": [ - "x64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "openbsd" - ] - }, - "node_modules/@rollup/rollup-openharmony-arm64": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-openharmony-arm64/-/rollup-openharmony-arm64-4.55.1.tgz", - "integrity": "sha512-xzm44KgEP11te3S2HCSyYf5zIzWmx3n8HDCc7EE59+lTcswEWNpvMLfd9uJvVX8LCg9QWG67Xt75AuHn4vgsXw==", - "cpu": [ - "arm64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "openharmony" - ] - }, - "node_modules/@rollup/rollup-win32-arm64-msvc": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-win32-arm64-msvc/-/rollup-win32-arm64-msvc-4.55.1.tgz", - "integrity": "sha512-yR6Bl3tMC/gBok5cz/Qi0xYnVbIxGx5Fcf/ca0eB6/6JwOY+SRUcJfI0OpeTpPls7f194as62thCt/2BjxYN8g==", - "cpu": [ - "arm64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "win32" - ] - }, - "node_modules/@rollup/rollup-win32-ia32-msvc": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-win32-ia32-msvc/-/rollup-win32-ia32-msvc-4.55.1.tgz", - "integrity": "sha512-3fZBidchE0eY0oFZBnekYCfg+5wAB0mbpCBuofh5mZuzIU/4jIVkbESmd2dOsFNS78b53CYv3OAtwqkZZmU5nA==", - "cpu": [ - "ia32" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "win32" - ] - }, - "node_modules/@rollup/rollup-win32-x64-gnu": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-win32-x64-gnu/-/rollup-win32-x64-gnu-4.55.1.tgz", - "integrity": "sha512-xGGY5pXj69IxKb4yv/POoocPy/qmEGhimy/FoTpTSVju3FYXUQQMFCaZZXJVidsmGxRioZAwpThl/4zX41gRKg==", - "cpu": [ - "x64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "win32" - ] - }, - "node_modules/@rollup/rollup-win32-x64-msvc": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/@rollup/rollup-win32-x64-msvc/-/rollup-win32-x64-msvc-4.55.1.tgz", - "integrity": "sha512-SPEpaL6DX4rmcXtnhdrQYgzQ5W2uW3SCJch88lB2zImhJRhIIK44fkUrgIV/Q8yUNfw5oyZ5vkeQsZLhCb06lw==", - "cpu": [ - "x64" - ], - "dev": true, - "license": "MIT", - "optional": true, - "os": [ - "win32" - ] - }, - "node_modules/@types/babel__core": { - "version": "7.20.5", - "resolved": "https://registry.npmjs.org/@types/babel__core/-/babel__core-7.20.5.tgz", - "integrity": "sha512-qoQprZvz5wQFJwMDqeseRXWv3rqMvhgpbXFfVyWhbx9X47POIA6i/+dXefEmZKoAgOaTdaIgNSMqMIU61yRyzA==", - "dev": true, - "license": "MIT", - "dependencies": { - "@babel/parser": "^7.20.7", - "@babel/types": "^7.20.7", - "@types/babel__generator": "*", - "@types/babel__template": "*", - "@types/babel__traverse": "*" - } - }, - "node_modules/@types/babel__generator": { - "version": "7.27.0", - "resolved": "https://registry.npmjs.org/@types/babel__generator/-/babel__generator-7.27.0.tgz", - "integrity": "sha512-ufFd2Xi92OAVPYsy+P4n7/U7e68fex0+Ee8gSG9KX7eo084CWiQ4sdxktvdl0bOPupXtVJPY19zk6EwWqUQ8lg==", - "dev": true, - "license": "MIT", - "dependencies": { - "@babel/types": "^7.0.0" - } - }, - "node_modules/@types/babel__template": { - "version": "7.4.4", - "resolved": "https://registry.npmjs.org/@types/babel__template/-/babel__template-7.4.4.tgz", - "integrity": "sha512-h/NUaSyG5EyxBIp8YRxo4RMe2/qQgvyowRwVMzhYhBCONbW8PUsg4lkFMrhgZhUe5z3L3MiLDuvyJ/CaPa2A8A==", - "dev": true, - "license": "MIT", - "dependencies": { - "@babel/parser": "^7.1.0", - "@babel/types": "^7.0.0" - } - }, - "node_modules/@types/babel__traverse": { - "version": "7.28.0", - "resolved": "https://registry.npmjs.org/@types/babel__traverse/-/babel__traverse-7.28.0.tgz", - "integrity": "sha512-8PvcXf70gTDZBgt9ptxJ8elBeBjcLOAcOtoO/mPJjtji1+CdGbHgm77om1GrsPxsiE+uXIpNSK64UYaIwQXd4Q==", - "dev": true, - "license": "MIT", - "dependencies": { - "@babel/types": "^7.28.2" - } - }, - "node_modules/@types/estree": { - "version": "1.0.8", - "resolved": "https://registry.npmjs.org/@types/estree/-/estree-1.0.8.tgz", - "integrity": "sha512-dWHzHa2WqEXI/O1E9OjrocMTKJl2mSrEolh1Iomrv6U+JuNwaHXsXx9bLu5gG7BUWFIN0skIQJQ/L1rIex4X6w==", - "dev": true, - "license": "MIT" - }, - "node_modules/@types/parse-json": { - "version": "4.0.2", - "resolved": "https://registry.npmjs.org/@types/parse-json/-/parse-json-4.0.2.tgz", - "integrity": "sha512-dISoDXWWQwUquiKsyZ4Ng+HX2KsPL7LyHKHQwgGFEA3IaKac4Obd+h2a/a6waisAoepJlBcx9paWqjA8/HVjCw==", - "license": "MIT" - }, - "node_modules/@types/prop-types": { - "version": "15.7.15", - "resolved": "https://registry.npmjs.org/@types/prop-types/-/prop-types-15.7.15.tgz", - "integrity": "sha512-F6bEyamV9jKGAFBEmlQnesRPGOQqS2+Uwi0Em15xenOxHaf2hv6L8YCVn3rPdPJOiJfPiCnLIRyvwVaqMY3MIw==", - "license": "MIT" - }, - "node_modules/@types/react": { - "version": "19.2.8", - "resolved": "https://registry.npmjs.org/@types/react/-/react-19.2.8.tgz", - "integrity": "sha512-3MbSL37jEchWZz2p2mjntRZtPt837ij10ApxKfgmXCTuHWagYg7iA5bqPw6C8BMPfwidlvfPI/fxOc42HLhcyg==", - "license": "MIT", - "dependencies": { - "csstype": "^3.2.2" - } - }, - "node_modules/@types/react-dom": { - "version": "19.2.3", - "resolved": "https://registry.npmjs.org/@types/react-dom/-/react-dom-19.2.3.tgz", - "integrity": "sha512-jp2L/eY6fn+KgVVQAOqYItbF0VY/YApe5Mz2F0aykSO8gx31bYCZyvSeYxCHKvzHG5eZjc+zyaS5BrBWya2+kQ==", - "license": "MIT", - "peerDependencies": { - "@types/react": "^19.2.0" - } - }, - "node_modules/@types/react-transition-group": { - "version": "4.4.12", - "resolved": "https://registry.npmjs.org/@types/react-transition-group/-/react-transition-group-4.4.12.tgz", - "integrity": "sha512-8TV6R3h2j7a91c+1DXdJi3Syo69zzIZbz7Lg5tORM5LEJG7X/E6a1V3drRyBRZq7/utz7A+c4OgYLiLcYGHG6w==", - "license": "MIT", - "peerDependencies": { - "@types/react": "*" - } - }, - "node_modules/@vitejs/plugin-react": { - "version": "4.7.0", - "resolved": "https://registry.npmjs.org/@vitejs/plugin-react/-/plugin-react-4.7.0.tgz", - "integrity": "sha512-gUu9hwfWvvEDBBmgtAowQCojwZmJ5mcLn3aufeCsitijs3+f2NsrPtlAWIR6OPiqljl96GVCUbLe0HyqIpVaoA==", - "dev": true, - "license": "MIT", - "dependencies": { - "@babel/core": "^7.28.0", - "@babel/plugin-transform-react-jsx-self": "^7.27.1", - "@babel/plugin-transform-react-jsx-source": "^7.27.1", - "@rolldown/pluginutils": "1.0.0-beta.27", - "@types/babel__core": "^7.20.5", - "react-refresh": "^0.17.0" - }, - "engines": { - "node": "^14.18.0 || >=16.0.0" - }, - "peerDependencies": { - "vite": "^4.2.0 || ^5.0.0 || ^6.0.0 || ^7.0.0" - } - }, - "node_modules/@webgpu/types": { - "version": "0.1.69", - "resolved": "https://registry.npmjs.org/@webgpu/types/-/types-0.1.69.tgz", - "integrity": "sha512-RPmm6kgRbI8e98zSD3RVACvnuktIja5+yLgDAkTmxLr90BEwdTXRQWNLF3ETTTyH/8mKhznZuN5AveXYFEsMGQ==", - "dev": true, - "license": "BSD-3-Clause" - }, - "node_modules/babel-plugin-macros": { - "version": "3.1.0", - "resolved": "https://registry.npmjs.org/babel-plugin-macros/-/babel-plugin-macros-3.1.0.tgz", - "integrity": "sha512-Cg7TFGpIr01vOQNODXOOaGz2NpCU5gl8x1qJFbb6hbZxR7XrcE2vtbAsTAbJ7/xwJtUuJEw8K8Zr/AE0LHlesg==", - "license": "MIT", - "dependencies": { - "@babel/runtime": "^7.12.5", - "cosmiconfig": "^7.0.0", - "resolve": "^1.19.0" - }, - "engines": { - "node": ">=10", - "npm": ">=6" - } - }, - "node_modules/baseline-browser-mapping": { - "version": "2.9.14", - "resolved": "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.9.14.tgz", - "integrity": "sha512-B0xUquLkiGLgHhpPBqvl7GWegWBUNuujQ6kXd/r1U38ElPT6Ok8KZ8e+FpUGEc2ZoRQUzq/aUnaKFc/svWUGSg==", - "dev": true, - "license": "Apache-2.0", - "bin": { - "baseline-browser-mapping": "dist/cli.js" - } - }, - "node_modules/browserslist": { - "version": "4.28.1", - "resolved": "https://registry.npmjs.org/browserslist/-/browserslist-4.28.1.tgz", - "integrity": "sha512-ZC5Bd0LgJXgwGqUknZY/vkUQ04r8NXnJZ3yYi4vDmSiZmC/pdSN0NbNRPxZpbtO4uAfDUAFffO8IZoM3Gj8IkA==", - "dev": true, - "funding": [ - { - "type": "opencollective", - "url": "https://opencollective.com/browserslist" - }, - { - "type": "tidelift", - "url": "https://tidelift.com/funding/github/npm/browserslist" - }, - { - "type": "github", - "url": "https://github.com/sponsors/ai" - } - ], - "license": "MIT", - "dependencies": { - "baseline-browser-mapping": "^2.9.0", - "caniuse-lite": "^1.0.30001759", - "electron-to-chromium": "^1.5.263", - "node-releases": "^2.0.27", - "update-browserslist-db": "^1.2.0" - }, - "bin": { - "browserslist": "cli.js" - }, - "engines": { - "node": "^6 || ^7 || ^8 || ^9 || ^10 || ^11 || ^12 || >=13.7" - } - }, - "node_modules/callsites": { - "version": "3.1.0", - "resolved": "https://registry.npmjs.org/callsites/-/callsites-3.1.0.tgz", - "integrity": "sha512-P8BjAsXvZS+VIDUI11hHCQEv74YT67YUi5JJFNWIqL235sBmjX4+qx9Muvls5ivyNENctx46xQLQ3aTuE7ssaQ==", - "license": "MIT", - "engines": { - "node": ">=6" - } - }, - "node_modules/caniuse-lite": { - "version": "1.0.30001764", - "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001764.tgz", - "integrity": "sha512-9JGuzl2M+vPL+pz70gtMF9sHdMFbY9FJaQBi186cHKH3pSzDvzoUJUPV6fqiKIMyXbud9ZLg4F3Yza1vJ1+93g==", - "dev": true, - "funding": [ - { - "type": "opencollective", - "url": "https://opencollective.com/browserslist" - }, - { - "type": "tidelift", - "url": "https://tidelift.com/funding/github/npm/caniuse-lite" - }, - { - "type": "github", - "url": "https://github.com/sponsors/ai" - } - ], - "license": "CC-BY-4.0" - }, - "node_modules/clsx": { - "version": "2.1.1", - "resolved": "https://registry.npmjs.org/clsx/-/clsx-2.1.1.tgz", - "integrity": "sha512-eYm0QWBtUrBWZWG0d386OGAw16Z995PiOVo2B7bjWSbHedGl5e0ZWaq65kOGgUSNesEIDkB9ISbTg/JK9dhCZA==", - "license": "MIT", - "engines": { - "node": ">=6" - } - }, - "node_modules/convert-source-map": { - "version": "2.0.0", - "resolved": "https://registry.npmjs.org/convert-source-map/-/convert-source-map-2.0.0.tgz", - "integrity": "sha512-Kvp459HrV2FEJ1CAsi1Ku+MY3kasH19TFykTz2xWmMeq6bk2NU3XXvfJ+Q61m0xktWwt+1HSYf3JZsTms3aRJg==", - "dev": true, - "license": "MIT" - }, - "node_modules/core-util-is": { - "version": "1.0.3", - "resolved": "https://registry.npmjs.org/core-util-is/-/core-util-is-1.0.3.tgz", - "integrity": "sha512-ZQBvi1DcpJ4GDqanjucZ2Hj3wEO5pZDS89BWbkcrvdxksJorwUDDZamX9ldFkp9aw2lmBDLgkObEA4DWNJ9FYQ==", - "license": "MIT" - }, - "node_modules/cosmiconfig": { - "version": "7.1.0", - "resolved": "https://registry.npmjs.org/cosmiconfig/-/cosmiconfig-7.1.0.tgz", - "integrity": "sha512-AdmX6xUzdNASswsFtmwSt7Vj8po9IuqXm0UXz7QKPuEUmPB4XyjGfaAr2PSuELMwkRMVH1EpIkX5bTZGRB3eCA==", + "node_modules/cosmiconfig": { + "version": "7.1.0", + "resolved": "https://registry.npmjs.org/cosmiconfig/-/cosmiconfig-7.1.0.tgz", + "integrity": "sha512-AdmX6xUzdNASswsFtmwSt7Vj8po9IuqXm0UXz7QKPuEUmPB4XyjGfaAr2PSuELMwkRMVH1EpIkX5bTZGRB3eCA==", "license": "MIT", "dependencies": { "@types/parse-json": "^4.0.0", @@ -1805,13 +1138,6 @@ "csstype": "^3.0.2" } }, - "node_modules/electron-to-chromium": { - "version": "1.5.267", - "resolved": "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.5.267.tgz", - "integrity": "sha512-0Drusm6MVRXSOJpGbaSVgcQsuB4hEkMpHXaVstcPmhu5LIedxs1xNK/nIxmQIU/RPC0+1/o0AVZfBTkTNJOdUw==", - "dev": true, - "license": "ISC" - }, "node_modules/error-ex": { "version": "1.3.4", "resolved": "https://registry.npmjs.org/error-ex/-/error-ex-1.3.4.tgz", @@ -1869,16 +1195,6 @@ "@esbuild/win32-x64": "0.21.5" } }, - "node_modules/escalade": { - "version": "3.2.0", - "resolved": "https://registry.npmjs.org/escalade/-/escalade-3.2.0.tgz", - "integrity": "sha512-WUj2qlxaQtO4g6Pq5c29GTcWGDyd8itL8zTlipgECz3JesAiiOKotd8JU6otB3PACgG6xkJUyVhboMS+bje/jA==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=6" - } - }, "node_modules/escape-string-regexp": { "version": "4.0.0", "resolved": "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-4.0.0.tgz", @@ -1897,21 +1213,6 @@ "integrity": "sha512-NKfW6bec6GfKc0SGx1e07QZY9PE99u0Bft/0rzSD5k3sO/vwkVUpDUKVm5Gpp5Ue3YfShPFTX2070tDs5kB9Ng==", "license": "MIT" }, - "node_modules/fsevents": { - "version": "2.3.3", - "resolved": "https://registry.npmjs.org/fsevents/-/fsevents-2.3.3.tgz", - "integrity": "sha512-5xoDfX+fL7faATnagmWPpbFtwh/R77WmMMqqHGS65C3vvB0YHrgF+B1YmZ3441tMj5n63k0212XNoJwzlhffQw==", - "dev": true, - "hasInstallScript": true, - "license": "MIT", - "optional": true, - "os": [ - "darwin" - ], - "engines": { - "node": "^8.16.0 || ^10.6.0 || >=11.0.0" - } - }, "node_modules/function-bind": { "version": "1.1.2", "resolved": "https://registry.npmjs.org/function-bind/-/function-bind-1.1.2.tgz", @@ -1921,16 +1222,6 @@ "url": "https://github.com/sponsors/ljharb" } }, - "node_modules/gensync": { - "version": "1.0.0-beta.2", - "resolved": "https://registry.npmjs.org/gensync/-/gensync-1.0.0-beta.2.tgz", - "integrity": "sha512-3hN7NaskYvMDLQY55gnW3NQ+mesEAepTqlg+VEbj7zzqEMBVNhzcGYYeqFo/TlYz6eQiFcp1HcsCZO+nGgS8zg==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=6.9.0" - } - }, "node_modules/hasown": { "version": "2.0.3", "resolved": "https://registry.npmjs.org/hasown/-/hasown-2.0.3.tgz", @@ -2037,19 +1328,6 @@ "integrity": "sha512-xyFwyhro/JEof6Ghe2iz2NcXoj2sloNsWr/XsERDK/oiPCfaNhl5ONfp+jQdAZRQQ0IJWNzH9zIZF7li91kh2w==", "license": "MIT" }, - "node_modules/json5": { - "version": "2.2.3", - "resolved": "https://registry.npmjs.org/json5/-/json5-2.2.3.tgz", - "integrity": "sha512-XmOWe7eyHYH14cLdVPoyg+GOH3rYX++KpzrylJwSW98t3Nk+U8XOl8FWKOgwtzdb8lXGf6zYwDUzeHMWfxasyg==", - "dev": true, - "license": "MIT", - "bin": { - "json5": "lib/cli.js" - }, - "engines": { - "node": ">=6" - } - }, "node_modules/jszip": { "version": "3.10.1", "resolved": "https://registry.npmjs.org/jszip/-/jszip-3.10.1.tgz", @@ -2089,48 +1367,12 @@ "loose-envify": "cli.js" } }, - "node_modules/lru-cache": { - "version": "5.1.1", - "resolved": "https://registry.npmjs.org/lru-cache/-/lru-cache-5.1.1.tgz", - "integrity": "sha512-KpNARQA3Iwv+jTA0utUVVbrh+Jlrr1Fv0e56GGzAFOXN7dk/FviaDW8LHmK52DlcH4WP2n6gI8vN1aesBFgo9w==", - "dev": true, - "license": "ISC", - "dependencies": { - "yallist": "^3.0.2" - } - }, "node_modules/ms": { "version": "2.1.3", "resolved": "https://registry.npmjs.org/ms/-/ms-2.1.3.tgz", "integrity": "sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA==", "license": "MIT" }, - "node_modules/nanoid": { - "version": "3.3.11", - "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.11.tgz", - "integrity": "sha512-N8SpfPUnUp1bK+PMYW8qSWdl9U+wwNWI4QKxOYDy9JAro3WMX7p2OeVRF9v+347pnakNevPmiHhNmZ2HbFA76w==", - "dev": true, - "funding": [ - { - "type": "github", - "url": "https://github.com/sponsors/ai" - } - ], - "license": "MIT", - "bin": { - "nanoid": "bin/nanoid.cjs" - }, - "engines": { - "node": "^10 || ^12 || ^13.7 || ^14 || >=15.0.1" - } - }, - "node_modules/node-releases": { - "version": "2.0.27", - "resolved": "https://registry.npmjs.org/node-releases/-/node-releases-2.0.27.tgz", - "integrity": "sha512-nmh3lCkYZ3grZvqcCH+fjmQ7X+H0OeZgP40OierEaAptX4XofMh5kwNbWh7lBduUzCcV/8kZ+NDLCwm2iorIlA==", - "dev": true, - "license": "MIT" - }, "node_modules/object-assign": { "version": "4.1.1", "resolved": "https://registry.npmjs.org/object-assign/-/object-assign-4.1.1.tgz", @@ -2197,35 +1439,6 @@ "integrity": "sha512-xceH2snhtb5M9liqDsmEw56le376mTZkEX/jEb/RxNFyegNul7eNslCXP9FDj/Lcu0X8KEyMceP2ntpaHrDEVA==", "license": "ISC" }, - "node_modules/postcss": { - "version": "8.5.6", - "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.6.tgz", - "integrity": "sha512-3Ybi1tAuwAP9s0r1UQ2J4n5Y0G05bJkpUIO0/bI9MhwmD70S5aTWbXGBwxHrelT+XM1k6dM0pk+SwNkpTRN7Pg==", - "dev": true, - "funding": [ - { - "type": "opencollective", - "url": "https://opencollective.com/postcss/" - }, - { - "type": "tidelift", - "url": "https://tidelift.com/funding/github/npm/postcss" - }, - { - "type": "github", - "url": "https://github.com/sponsors/ai" - } - ], - "license": "MIT", - "dependencies": { - "nanoid": "^3.3.11", - "picocolors": "^1.1.1", - "source-map-js": "^1.2.1" - }, - "engines": { - "node": "^10 || ^12 || >=14" - } - }, "node_modules/process-nextick-args": { "version": "2.0.1", "resolved": "https://registry.npmjs.org/process-nextick-args/-/process-nextick-args-2.0.1.tgz", @@ -2276,16 +1489,6 @@ "integrity": "sha512-Dn0t8IQhCmeIT3wu+Apm1/YVsJXsGWi6k4sPdnBIdqMVtHtv0IGi6dcpNpNkNac0zB2uUAqNX3MHzN8c+z2rwQ==", "license": "MIT" }, - "node_modules/react-refresh": { - "version": "0.17.0", - "resolved": "https://registry.npmjs.org/react-refresh/-/react-refresh-0.17.0.tgz", - "integrity": "sha512-z6F7K9bV85EfseRCp2bzrpyQ0Gkw1uLoCel9XBVWPg/TjRj94SkJzUTGfOa4bs7iJvBWtQG0Wq7wnI0syw3EBQ==", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=0.10.0" - } - }, "node_modules/react-transition-group": { "version": "4.4.5", "resolved": "https://registry.npmjs.org/react-transition-group/-/react-transition-group-4.4.5.tgz", @@ -2347,51 +1550,6 @@ "node": ">=4" } }, - "node_modules/rollup": { - "version": "4.55.1", - "resolved": "https://registry.npmjs.org/rollup/-/rollup-4.55.1.tgz", - "integrity": "sha512-wDv/Ht1BNHB4upNbK74s9usvl7hObDnvVzknxqY/E/O3X6rW1U1rV1aENEfJ54eFZDTNo7zv1f5N4edCluH7+A==", - "dev": true, - "license": "MIT", - "dependencies": { - "@types/estree": "1.0.8" - }, - "bin": { - "rollup": "dist/bin/rollup" - }, - "engines": { - "node": ">=18.0.0", - "npm": ">=8.0.0" - }, - "optionalDependencies": { - "@rollup/rollup-android-arm-eabi": "4.55.1", - "@rollup/rollup-android-arm64": "4.55.1", - "@rollup/rollup-darwin-arm64": "4.55.1", - "@rollup/rollup-darwin-x64": "4.55.1", - "@rollup/rollup-freebsd-arm64": "4.55.1", - "@rollup/rollup-freebsd-x64": "4.55.1", - "@rollup/rollup-linux-arm-gnueabihf": "4.55.1", - "@rollup/rollup-linux-arm-musleabihf": "4.55.1", - "@rollup/rollup-linux-arm64-gnu": "4.55.1", - "@rollup/rollup-linux-arm64-musl": "4.55.1", - "@rollup/rollup-linux-loong64-gnu": "4.55.1", - "@rollup/rollup-linux-loong64-musl": "4.55.1", - "@rollup/rollup-linux-ppc64-gnu": "4.55.1", - "@rollup/rollup-linux-ppc64-musl": "4.55.1", - "@rollup/rollup-linux-riscv64-gnu": "4.55.1", - "@rollup/rollup-linux-riscv64-musl": "4.55.1", - "@rollup/rollup-linux-s390x-gnu": "4.55.1", - "@rollup/rollup-linux-x64-gnu": "4.55.1", - "@rollup/rollup-linux-x64-musl": "4.55.1", - "@rollup/rollup-openbsd-x64": "4.55.1", - "@rollup/rollup-openharmony-arm64": "4.55.1", - "@rollup/rollup-win32-arm64-msvc": "4.55.1", - "@rollup/rollup-win32-ia32-msvc": "4.55.1", - "@rollup/rollup-win32-x64-gnu": "4.55.1", - "@rollup/rollup-win32-x64-msvc": "4.55.1", - "fsevents": "~2.3.2" - } - }, "node_modules/safe-buffer": { "version": "5.1.2", "resolved": "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.1.2.tgz", @@ -2404,16 +1562,6 @@ "integrity": "sha512-eNv+WrVbKu1f3vbYJT/xtiF5syA5HPIMtf9IgY/nKg0sWqzAUEvqY/xm7OcZc/qafLx/iO9FgOmeSAp4v5ti/Q==", "license": "MIT" }, - "node_modules/semver": { - "version": "6.3.1", - "resolved": "https://registry.npmjs.org/semver/-/semver-6.3.1.tgz", - "integrity": "sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==", - "dev": true, - "license": "ISC", - "bin": { - "semver": "bin/semver.js" - } - }, "node_modules/setimmediate": { "version": "1.0.5", "resolved": "https://registry.npmjs.org/setimmediate/-/setimmediate-1.0.5.tgz", @@ -2429,16 +1577,6 @@ "node": ">=0.10.0" } }, - "node_modules/source-map-js": { - "version": "1.2.1", - "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz", - "integrity": "sha512-UXWMKhLOwVKb728IUtQPXxfYU+usdybtUrK/8uGE8CQMvrhOpwvzDBwj0QhSL7MQc7vIsISBG8VQ8+IDQxpfQA==", - "dev": true, - "license": "BSD-3-Clause", - "engines": { - "node": ">=0.10.0" - } - }, "node_modules/string_decoder": { "version": "1.1.1", "resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.1.1.tgz", @@ -2480,110 +1618,12 @@ "node": ">=14.17" } }, - "node_modules/update-browserslist-db": { - "version": "1.2.3", - "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.2.3.tgz", - "integrity": "sha512-Js0m9cx+qOgDxo0eMiFGEueWztz+d4+M3rGlmKPT+T4IS/jP4ylw3Nwpu6cpTTP8R1MAC1kF4VbdLt3ARf209w==", - "dev": true, - "funding": [ - { - "type": "opencollective", - "url": "https://opencollective.com/browserslist" - }, - { - "type": "tidelift", - "url": "https://tidelift.com/funding/github/npm/browserslist" - }, - { - "type": "github", - "url": "https://github.com/sponsors/ai" - } - ], - "license": "MIT", - "dependencies": { - "escalade": "^3.2.0", - "picocolors": "^1.1.1" - }, - "bin": { - "update-browserslist-db": "cli.js" - }, - "peerDependencies": { - "browserslist": ">= 4.21.0" - } - }, "node_modules/util-deprecate": { "version": "1.0.2", "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz", "integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==", "license": "MIT" }, - "node_modules/vite": { - "version": "5.4.21", - "resolved": "https://registry.npmjs.org/vite/-/vite-5.4.21.tgz", - "integrity": "sha512-o5a9xKjbtuhY6Bi5S3+HvbRERmouabWbyUcpXXUA1u+GNUKoROi9byOJ8M0nHbHYHkYICiMlqxkg1KkYmm25Sw==", - "dev": true, - "license": "MIT", - "dependencies": { - "esbuild": "^0.21.3", - "postcss": "^8.4.43", - "rollup": "^4.20.0" - }, - "bin": { - "vite": "bin/vite.js" - }, - "engines": { - "node": "^18.0.0 || >=20.0.0" - }, - "funding": { - "url": "https://github.com/vitejs/vite?sponsor=1" - }, - "optionalDependencies": { - "fsevents": "~2.3.3" - }, - "peerDependencies": { - "@types/node": "^18.0.0 || >=20.0.0", - "less": "*", - "lightningcss": "^1.21.0", - "sass": "*", - "sass-embedded": "*", - "stylus": "*", - "sugarss": "*", - "terser": "^5.4.0" - }, - "peerDependenciesMeta": { - "@types/node": { - "optional": true - }, - "less": { - "optional": true - }, - "lightningcss": { - "optional": true - }, - "sass": { - "optional": true - }, - "sass-embedded": { - "optional": true - }, - "stylus": { - "optional": true - }, - "sugarss": { - "optional": true - }, - "terser": { - "optional": true - } - } - }, - "node_modules/yallist": { - "version": "3.1.1", - "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz", - "integrity": "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==", - "dev": true, - "license": "ISC" - }, "node_modules/yaml": { "version": "1.10.3", "resolved": "https://registry.npmjs.org/yaml/-/yaml-1.10.3.tgz", diff --git a/widget/package.json b/widget/package.json index 3d0175ae..8ceb653b 100644 --- a/widget/package.json +++ b/widget/package.json @@ -2,8 +2,8 @@ "name": "quantem-widget-frontend", "type": "module", "scripts": { - "dev": "vite build --watch", - "build": "vite build", + "dev": "npm run build -- --watch", + "build": "node scripts/build.mjs", "typecheck": "tsc --noEmit" }, "dependencies": { @@ -17,12 +17,10 @@ "react-dom": "^19.1.0" }, "devDependencies": { - "@anywidget/vite": "^0.2.0", "@types/react": "^19.1.3", "@types/react-dom": "^19.1.4", - "@vitejs/plugin-react": "^4.3.0", "@webgpu/types": "^0.1.68", - "typescript": "^5.8.3", - "vite": "^5.2.0" + "esbuild": "^0.21.3", + "typescript": "^5.8.3" } } diff --git a/widget/src/quantem/widget/__init__.py b/widget/src/quantem/widget/__init__.py index dc98e36a..96d8aebc 100644 --- a/widget/src/quantem/widget/__init__.py +++ b/widget/src/quantem/widget/__init__.py @@ -1,7 +1,12 @@ -from importlib.metadata import version +from importlib.metadata import PackageNotFoundError, version from quantem.widget.show2d import Show2D from quantem.widget.show4dstem import Show4DSTEM -__version__ = version("quantem.widget") +try: + __version__ = version("quantem.widget") +except PackageNotFoundError: + # Source-tree imports (e.g. `PYTHONPATH=src pytest`) skip pip install. + __version__ = "0.0.0+local" + __all__ = ["Show2D", "Show4DSTEM"] diff --git a/widget/src/quantem/widget/array_utils.py b/widget/src/quantem/widget/array_utils.py index a38190e6..1913a846 100644 --- a/widget/src/quantem/widget/array_utils.py +++ b/widget/src/quantem/widget/array_utils.py @@ -1,68 +1,21 @@ -""" -Array utilities for widgets. Supports NumPy + PyTorch input. -""" - -from typing import Literal +"""Array utilities for widgets. NumPy + PyTorch input.""" import numpy as np - -try: - import torch - _HAS_TORCH = True -except ImportError: - _HAS_TORCH = False - - -ArrayBackend = Literal["numpy", "torch", "unknown"] - - -def get_array_backend(data) -> ArrayBackend: - """Detect array backend. Returns 'numpy', 'torch', or 'unknown'.""" - if _HAS_TORCH and isinstance(data, torch.Tensor): - return "torch" - if isinstance(data, np.ndarray): - return "numpy" - return "unknown" +import torch def to_numpy(data, dtype: np.dtype | None = None) -> np.ndarray: - """Convert NumPy or PyTorch array to NumPy. - - Parameters - ---------- - data : np.ndarray or torch.Tensor - Input array. - dtype : np.dtype, optional - Target dtype. - - Returns - ------- - np.ndarray - - Examples - -------- - >>> import numpy as np - >>> to_numpy(np.zeros((4, 4))) - >>> import torch - >>> to_numpy(torch.zeros(4, 4)) - - Raises - ------ - TypeError - If `data` is not a NumPy array or PyTorch tensor. - """ - backend = get_array_backend(data) - if backend == "torch": + """Convert NumPy / PyTorch / Dataset to NumPy.""" + if isinstance(data, torch.Tensor): result = data.detach().cpu().numpy() - elif backend == "numpy": + elif isinstance(data, np.ndarray): result = data else: - # Try np.asarray as last-resort fallback for things like Dataset arrays + # Last-resort fallback covers Dataset.__array__, dlpack-compatible objects, etc. try: result = np.asarray(data) except Exception as e: raise TypeError( - f"to_numpy expected a NumPy array or PyTorch tensor, got {type(data).__name__}. " - f"Convert your input via np.asarray(...) or tensor.cpu().numpy() first." + f"to_numpy expected a NumPy array or PyTorch tensor, got {type(data).__name__}." ) from e if dtype is not None: result = np.asarray(result, dtype=dtype) @@ -70,10 +23,7 @@ def to_numpy(data, dtype: np.dtype | None = None) -> np.ndarray: def _resize_image(img: np.ndarray, target_h: int, target_w: int) -> np.ndarray: - """Center-pad an image to (target_h, target_w) with zeros. - - Used to align gallery images of different shapes to a common canvas. - """ + """Center-pad image to (target_h, target_w) with zeros. For gallery alignment.""" h, w = img.shape[-2:] if h == target_h and w == target_w: return img @@ -84,28 +34,14 @@ def _resize_image(img: np.ndarray, target_h: int, target_w: int) -> np.ndarray: return np.pad(img, ((pad_top, pad_bot), (pad_left, pad_right)), mode="constant", constant_values=0) -def apply_shift(img: np.ndarray, dy: float, dx: float) -> np.ndarray: - """Sub-pixel image shift via bilinear interpolation. Used for diff alignment.""" - if not _HAS_TORCH: - # Fallback: integer roll only - return np.roll(img, (int(round(dy)), int(round(dx))), axis=(-2, -1)) - t = torch.from_numpy(img).float() - if t.ndim == 2: - t = t.unsqueeze(0).unsqueeze(0) - h, w = t.shape[-2:] - y = torch.arange(h, dtype=torch.float32) - dy - x = torch.arange(w, dtype=torch.float32) - dx - yy, xx = torch.meshgrid(y, x, indexing="ij") - grid = torch.stack(((xx / (w - 1)) * 2 - 1, (yy / (h - 1)) * 2 - 1), dim=-1).unsqueeze(0) - out = torch.nn.functional.grid_sample(t, grid, mode="bilinear", padding_mode="border", align_corners=True) - return out.squeeze().numpy() - - -def bin2d(img: np.ndarray, factor: int) -> np.ndarray: - """Reduce 2D image by integer binning factor. Mean of f×f blocks.""" +def bin2d(img: np.ndarray, factor: int, mode: str = "mean") -> np.ndarray: + """Reduce 2D image by integer binning factor. mean or sum of f×f blocks.""" if factor <= 1: return img h, w = img.shape[-2:] h2, w2 = h - h % factor, w - w % factor img = img[..., :h2, :w2] - return img.reshape(*img.shape[:-2], h2 // factor, factor, w2 // factor, factor).mean(axis=(-3, -1)) + blocks = img.reshape(*img.shape[:-2], h2 // factor, factor, w2 // factor, factor) + if mode == "sum": + return blocks.sum(axis=(-3, -1)) + return blocks.mean(axis=(-3, -1)) diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py index cd2dff99..f2cbed6a 100644 --- a/widget/src/quantem/widget/show2d.py +++ b/widget/src/quantem/widget/show2d.py @@ -24,12 +24,7 @@ from quantem.core.datastructures import Dataset2d, Dataset3d from quantem.widget.array_utils import to_numpy, _resize_image -from quantem.widget.json_state import resolve_widget_version, save_state_file, unwrap_state_payload -from quantem.widget.tool_parity import ( - bind_tool_runtime_api, - build_tool_groups, - normalize_tool_groups, -) +from quantem.widget.state import resolve_widget_version, save_state_file, unwrap_state_payload @@ -94,8 +89,12 @@ class Show2D(anywidget.AnyWidget): Title to display above the image(s). cmap : str, default "inferno" Colormap name ("magma", "viridis", "gray", "inferno", "plasma"). - pixel_size : float, optional - Pixel size in angstroms for scale bar display. + sampling : float or tuple of float, optional + Pixel size per axis ``(row, col)``. Scalar broadcasts to both axes. + Used for scale bar display. Defaults to ``(1, 1)``. + units : str or list of str, optional + Unit string per axis. Scalar broadcasts to both. Common: ``"A"``, + ``"nm"``, ``"pixels"``. Defaults to ``["pixels", "pixels"]``. show_fft : bool, default False Show FFT and histogram panels. show_stats : bool, default True @@ -106,7 +105,7 @@ class Show2D(anywidget.AnyWidget): Use percentile-based contrast. vmin : float, optional Absolute minimum intensity for color mapping. When both vmin and vmax - are set, all gallery images share the same intensity scale — essential + are set, all gallery images share the same intensity scale: essential for A/B visual comparison. vmax : float, optional Absolute maximum intensity for color mapping. @@ -117,24 +116,8 @@ class Show2D(anywidget.AnyWidget): ``0`` uses the frontend default: 500 px for a single image, 300 px per image in gallery mode. Pass e.g. ``size=800`` to enlarge for a presentation, or ``size=200`` to compress alongside a control panel. - This controls **display only** — the underlying image resolution is + This controls **display only**: the underlying image resolution is never resampled; zooming into a 4K image preserves every pixel. - disabled_tools : list of str, optional - Tool groups to lock while still showing controls. Supported: - ``"display"``, ``"histogram"``, ``"stats"``, ``"navigation"``, - ``"view"``, ``"export"``, ``"roi"``, ``"profile"``, ``"all"``. - disable_* : bool, optional - Convenience flags (``disable_display``, ``disable_histogram``, - ``disable_stats``, ``disable_navigation``, ``disable_view``, - ``disable_export``, ``disable_roi``, ``disable_profile``, - ``disable_all``) equivalent to adding those keys to - ``disabled_tools``. - hidden_tools : list of str, optional - Tool groups to hide from the UI. Uses the same keys as - ``disabled_tools``. - hide_* : bool, optional - Convenience flags mirroring ``disable_*`` for ``hidden_tools``. - Attributes ---------- render_total_ms : int or None @@ -153,17 +136,61 @@ class Show2D(anywidget.AnyWidget): -------- >>> import numpy as np >>> from quantem.widget import Show2D - >>> - >>> # Single image with FFT - >>> Show2D(image, title="HRTEM Image", show_fft=True, pixel_size=1.0) - >>> - >>> # Gallery of multiple images - >>> labels = ["Raw", "Filtered", "FFT"] - >>> Show2D([img1, img2, img3], labels=labels, ncols=3) + + Single 2D NumPy array: + + >>> Show2D(np.random.rand(512, 512)) + + PyTorch tensor (CPU or GPU, any dtype): + + >>> import torch + >>> Show2D(torch.rand(512, 512)) + + 3D NumPy stack ``(N, H, W)`` rendered as a gallery: + + >>> Show2D(np.random.rand(6, 256, 256), ncols=3) + + List of arrays with different shapes (center-padded to a common canvas): + + >>> Show2D([np.random.rand(256, 256), np.random.rand(300, 400)]) + + quantem ``Dataset2d``: title, sampling, units auto-extracted: + + >>> from quantem.core.datastructures import Dataset2d + >>> ds = Dataset2d.from_array(np.random.rand(512, 512)) + >>> Show2D(ds) + + quantem ``Dataset3d``: gallery view of N frames with calibration: + + >>> from quantem.core.datastructures import Dataset3d + >>> ds = Dataset3d.from_array(np.random.rand(6, 256, 256)) + >>> Show2D(ds, ncols=3) + + A/B comparison with shared contrast and linked zoom/pan: + + >>> a, b = np.random.rand(512, 512), np.random.rand(512, 512) + >>> Show2D([a, b], vmin=0, vmax=1, link_zoom=True, link_pan=True) + + Per-image absolute contrast (one ``vmin``/``vmax`` per image): + + >>> Show2D([a, b], vmin=[0.0, 0.2], vmax=[1.0, 0.8]) + + Drift comparison: diff mode adds a ``A - B`` panel alongside the originals + (gallery becomes ``[A, B, A - B]``): + + >>> Show2D([a, b], diff_mode=True, link_zoom=True, link_pan=True) + + Large image: display-only canvas size (full resolution preserved): + + >>> Show2D(np.random.rand(4096, 4096), size=800) + + Static export to PDF or PNG (vector PDF for publication figures): + + >>> w = Show2D(np.random.rand(512, 512), sampling=0.5, units="nm") + >>> w.save_image("figure.pdf", dpi=150) """ _esm = pathlib.Path(__file__).parent / "static" / "show2d.js" - _css = pathlib.Path(__file__).parent / "static" / "show2d.css" # ========================================================================= # Core State @@ -201,6 +228,7 @@ class Show2D(anywidget.AnyWidget): # Scale Bar # ========================================================================= pixel_size = traitlets.Float(0.0).tag(sync=True) + pixel_unit = traitlets.Unicode("pixels").tag(sync=True) scale_bar_visible = traitlets.Bool(True).tag(sync=True) size = traitlets.Int(0).tag(sync=True) # Canvas rendering size in CSS pixels; 0 = frontend default smooth = traitlets.Bool(False).tag(sync=True) @@ -218,8 +246,6 @@ class Show2D(anywidget.AnyWidget): # ========================================================================= show_controls = traitlets.Bool(True).tag(sync=True) show_stats = traitlets.Bool(True).tag(sync=True) - disabled_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) - hidden_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) stats_mean = traitlets.List(traitlets.Float()).tag(sync=True) stats_min = traitlets.List(traitlets.Float()).tag(sync=True) stats_max = traitlets.List(traitlets.Float()).tag(sync=True) @@ -253,114 +279,24 @@ class Show2D(anywidget.AnyWidget): # ========================================================================= image_rotations = traitlets.List(traitlets.Int(), []).tag(sync=True) - @classmethod - def _normalize_tool_groups(cls, tool_groups) -> list[str]: - return normalize_tool_groups("Show2D", tool_groups) - - @classmethod - def _build_disabled_tools( - cls, - disabled_tools=None, - disable_display: bool = False, - disable_histogram: bool = False, - disable_stats: bool = False, - disable_navigation: bool = False, - disable_view: bool = False, - disable_export: bool = False, - disable_roi: bool = False, - disable_profile: bool = False, - disable_all: bool = False, - ) -> list[str]: - return build_tool_groups( - "Show2D", - tool_groups=disabled_tools, - all_flag=disable_all, - flag_map={ - "display": disable_display, - "histogram": disable_histogram, - "stats": disable_stats, - "navigation": disable_navigation, - "view": disable_view, - "export": disable_export, - "roi": disable_roi, - "profile": disable_profile, - }, - ) - - @classmethod - def _build_hidden_tools( - cls, - hidden_tools=None, - hide_display: bool = False, - hide_histogram: bool = False, - hide_stats: bool = False, - hide_navigation: bool = False, - hide_view: bool = False, - hide_export: bool = False, - hide_roi: bool = False, - hide_profile: bool = False, - hide_all: bool = False, - ) -> list[str]: - return build_tool_groups( - "Show2D", - tool_groups=hidden_tools, - all_flag=hide_all, - flag_map={ - "display": hide_display, - "histogram": hide_histogram, - "stats": hide_stats, - "navigation": hide_navigation, - "view": hide_view, - "export": hide_export, - "roi": hide_roi, - "profile": hide_profile, - }, - ) - - @traitlets.validate("disabled_tools") - def _validate_disabled_tools(self, proposal): - return self._normalize_tool_groups(proposal["value"]) - - @traitlets.validate("hidden_tools") - def _validate_hidden_tools(self, proposal): - return self._normalize_tool_groups(proposal["value"]) - def __init__( self, data: np.ndarray | list[np.ndarray], labels: list[str | None] = None, title: str = "", cmap: str | Colormap = Colormap.INFERNO, - pixel_size: float = 0.0, + sampling: float | tuple[float, float] | list[float] | None = None, + units: str | list[str] | None = None, scale_bar_visible: bool = True, show_fft: bool = False, fft_window: bool = True, show_controls: bool = True, show_stats: bool = True, + verbose: bool = True, log_scale: bool = False, auto_contrast: bool = False, vmin: float | list | None = None, vmax: float | list | None = None, - disabled_tools: list[str | None] = None, - disable_display: bool = False, - disable_histogram: bool = False, - disable_stats: bool = False, - disable_navigation: bool = False, - disable_view: bool = False, - disable_export: bool = False, - disable_roi: bool = False, - disable_profile: bool = False, - disable_all: bool = False, - hidden_tools: list[str | None] = None, - hide_display: bool = False, - hide_histogram: bool = False, - hide_stats: bool = False, - hide_navigation: bool = False, - hide_view: bool = False, - hide_export: bool = False, - hide_roi: bool = False, - hide_profile: bool = False, - hide_all: bool = False, ncols: int = 3, size: int = 0, smooth: bool = False, @@ -390,70 +326,41 @@ def __init__( with self.hold_sync(): self._init_sync( data=data, labels=labels, title=title, cmap=cmap, - pixel_size=pixel_size, scale_bar_visible=scale_bar_visible, + sampling=sampling, units=units, scale_bar_visible=scale_bar_visible, show_fft=show_fft, fft_window=fft_window, show_controls=show_controls, show_stats=show_stats, log_scale=log_scale, auto_contrast=auto_contrast, vmin=vmin, vmax=vmax, - disabled_tools=disabled_tools, - disable_display=disable_display, - disable_histogram=disable_histogram, - disable_stats=disable_stats, - disable_navigation=disable_navigation, - disable_view=disable_view, - disable_export=disable_export, - disable_roi=disable_roi, - disable_profile=disable_profile, - disable_all=disable_all, - hidden_tools=hidden_tools, - hide_display=hide_display, - hide_histogram=hide_histogram, - hide_stats=hide_stats, - hide_navigation=hide_navigation, - hide_view=hide_view, - hide_export=hide_export, - hide_roi=hide_roi, - hide_profile=hide_profile, - hide_all=hide_all, ncols=ncols, size=size, smooth=smooth, zoom=zoom, zoom_row=zoom_row, zoom_col=zoom_col, link_zoom=link_zoom, link_pan=link_pan, link_contrast=link_contrast, diff_mode=diff_mode, view_box=view_box, - display_bin=display_bin, state=state, _t0=_t0) + display_bin=display_bin, verbose=verbose, state=state, _t0=_t0) - def _init_sync(self, *, data, labels, title, cmap, pixel_size, + def _init_sync(self, *, data, labels, title, cmap, sampling, units, scale_bar_visible, show_fft, fft_window, show_controls, show_stats, log_scale, auto_contrast, - vmin, vmax, disabled_tools, - disable_display, disable_histogram, disable_stats, - disable_navigation, disable_view, disable_export, - disable_roi, disable_profile, disable_all, - hidden_tools, hide_display, hide_histogram, hide_stats, - hide_navigation, hide_view, hide_export, hide_roi, - hide_profile, hide_all, + vmin, vmax, ncols, size, smooth, zoom, zoom_row, zoom_col, link_zoom, link_pan, link_contrast, diff_mode, view_box, - display_bin, state, _t0): + display_bin, verbose, state, _t0): import time as _time + self._verbose = verbose self.widget_version = resolve_widget_version() self._display_data = None # initialized after data setup self._display_bin = 1 # First-class support for quantem Dataset2d / Dataset3d: - # extract array + auto-populate title, pixel_size from sampling+units. - # (Duck-typing fallback below covers any other object exposing the same API.) + # auto-extract array + sampling + units from the dataset object. if isinstance(data, (Dataset2d, Dataset3d)) or ( hasattr(data, "array") and hasattr(data, "name") and hasattr(data, "sampling") ): if not title and data.name: title = data.name - if pixel_size == 0.0 and hasattr(data, "units"): - units = list(data.units) - sampling_val = float(data.sampling[-1]) - if units[-1] in ("nm",): - pixel_size = sampling_val * 10 # nm → Å - elif units[-1] in ("Å", "angstrom", "A"): - pixel_size = sampling_val + if sampling is None: + sampling = tuple(float(s) for s in data.sampling[-2:]) + if units is None and hasattr(data, "units"): + units = list(data.units[-2:]) data = data.array # Convert NumPy / PyTorch / list inputs to a NumPy array. @@ -481,7 +388,7 @@ def _init_sync(self, *, data, labels, title, cmap, pixel_size, self._data = np.array(data, dtype=np.float32, copy=True) else: self._data = np.asarray(data, dtype=np.float32) - # Store originals for rotation reset — views into _data (no copy). + # Store originals for rotation reset: views into _data (no copy). # Only materialized as independent copies when a rotation is applied. self._data_original = [self._data[i] for i in range(self._data.shape[0])] self._originals_are_views = True @@ -499,7 +406,20 @@ def _init_sync(self, *, data, labels, title, cmap, pixel_size, # Options self.title = title self.cmap = cmap - self.pixel_size = pixel_size + # Resolve sampling + units to scalar pixel_size + pixel_unit (column axis). + # Scalar shorthand: sampling=0.5 → (0.5, 0.5). units="nm" → ["nm", "nm"]. + if sampling is None: + self.pixel_size = 0.0 + elif isinstance(sampling, (int, float)): + self.pixel_size = float(sampling) + else: + self.pixel_size = float(sampling[-1]) + if units is None: + self.pixel_unit = "pixels" + elif isinstance(units, str): + self.pixel_unit = units + else: + self.pixel_unit = str(units[-1]) self.scale_bar_visible = scale_bar_visible self.size = size self.smooth = smooth @@ -547,30 +467,6 @@ def _expand(v): else: self.vmin = vmin self.vmax = vmax - self.disabled_tools = self._build_disabled_tools( - disabled_tools=disabled_tools, - disable_display=disable_display, - disable_histogram=disable_histogram, - disable_stats=disable_stats, - disable_navigation=disable_navigation, - disable_view=disable_view, - disable_export=disable_export, - disable_roi=disable_roi, - disable_profile=disable_profile, - disable_all=disable_all, - ) - self.hidden_tools = self._build_hidden_tools( - hidden_tools=hidden_tools, - hide_display=hide_display, - hide_histogram=hide_histogram, - hide_stats=hide_stats, - hide_navigation=hide_navigation, - hide_view=hide_view, - hide_export=hide_export, - hide_roi=hide_roi, - hide_profile=hide_profile, - hide_all=hide_all, - ) self.ncols = ncols # Auto-bin for display: keep full-res in _data, send binned to JS. @@ -601,10 +497,11 @@ def _expand(v): self._display_data = bin2d(self._data, factor=self._display_bin, mode="mean") self.height = int(self._display_data.shape[1]) self.width = int(self._display_data.shape[2]) - if pixel_size > 0: - self.pixel_size = pixel_size * self._display_bin + if self.pixel_size > 0: + self.pixel_size = self.pixel_size * self._display_bin self._display_bin_factor = self._display_bin - print(f" Display bin {self._display_bin}×: {orig_h}×{orig_w} → {self.height}×{self.width} ({self._display_data.nbytes // 1024 // 1024} MB)") + if verbose: + print(f" Display bin {self._display_bin}×: {orig_h}×{orig_w} → {self.height}×{self.width} ({self._display_data.nbytes // 1024 // 1024} MB)") else: self._display_data = self._data self._display_bin_factor = 1 @@ -629,7 +526,7 @@ def _expand(v): # Stash wall-clock start on the instance; the observer below prints the # TRUE end-to-end time after JS signals first paint. The Python-only - # __init__ number is misleading for widget UX — a widget is not "done" + # __init__ number is misleading for widget UX: a widget is not "done" # until the browser has painted its first frame. self._init_t0 = _t0 self._init_py_elapsed_ms = (_time.perf_counter() - _t0) * 1000 @@ -646,13 +543,15 @@ def _on_first_render(self, change): mem = self._data.nbytes mem_str = f"{mem / (1 << 20):.0f} MB" if mem >= 1 << 20 else f"{mem / (1 << 10):.0f} KB" # Expose as attributes so tests and notebooks can assert on them. - # These are the ground truth for "did JS actually paint" — if they're + # These are the ground truth for "did JS actually paint": if they're # None, the JS side never signaled first render. self.render_total_ms = int(total_ms) self.render_python_build_ms = int(py_ms) self.render_wire_js_ms = int(total_ms - py_ms) + if not getattr(self, "_verbose", True): + return print( - f"Show2D: {shape} {mem_str} — " + f"Show2D: {shape} {mem_str}: " f"rendered in {total_ms:.0f} ms (Python build {py_ms:.0f} ms, " f"wire+JS {total_ms - py_ms:.0f} ms)", flush=True, @@ -660,7 +559,7 @@ def _on_first_render(self, change): # Detach observer: one-shot, we only care about the first paint. try: self.unobserve(self._on_first_render, names=["_js_rendered"]) - except Exception: + except (ValueError, KeyError): pass def set_image(self, data, labels=None): @@ -706,7 +605,8 @@ def set_image(self, data, labels=None): self.height = int(self._display_data.shape[1]) self.width = int(self._display_data.shape[2]) self._display_bin_factor = self._display_bin - print(f" Display bin {self._display_bin}×: {data.shape[1]}×{data.shape[2]} → {self.height}×{self.width}") + if getattr(self, "_verbose", True): + print(f" Display bin {self._display_bin}×: {data.shape[1]}×{data.shape[2]} → {self.height}×{self.width}") else: self._display_data = self._data self.height = int(data.shape[1]) @@ -900,7 +800,6 @@ def save_image( cb.set_ticklabels(tick_labels) if scalebar and self.pixel_size > 0: - from matplotlib.patches import FancyBboxPatch # Compute a nice scale bar length target_frac = 0.2 # ~20% of image width raw_length_px = target_frac * w @@ -942,9 +841,8 @@ def state_dict(self): "show_fft": self.show_fft, "fft_window": self.fft_window, "show_controls": self.show_controls, - "disabled_tools": self.disabled_tools, - "hidden_tools": self.hidden_tools, "pixel_size": self.pixel_size, + "pixel_unit": self.pixel_unit, "scale_bar_visible": self.scale_bar_visible, "size": self.size, "smooth": self.smooth, @@ -1012,10 +910,6 @@ def summary(self): if not self.fft_window: display += " (no window)" lines.append(f"Display: {display}") - if self.disabled_tools: - lines.append(f"Locked: {', '.join(self.disabled_tools)}") - if self.hidden_tools: - lines.append(f"Hidden: {', '.join(self.hidden_tools)}") if self.roi_active and self.roi_list: lines.append(f"ROI: {len(self.roi_list)} region(s)") if self.profile_line: @@ -1310,5 +1204,3 @@ def profile_distance(self): return dist_px * self.pixel_size return dist_px - -bind_tool_runtime_api(Show2D, "Show2D") diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index c514be2f..8cd0cb58 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -1,22 +1,9 @@ """ show4dstem: Fast interactive 4D-STEM viewer widget. -Apple MPS GPU limit: PyTorch's MPS backend (Apple Silicon) has a hard limit -of ~2.1 billion elements (INT_MAX = 2^31 - 1) per tensor. Datasets exceeding -this automatically fall back to CPU, which is still fast on Apple Silicon -thanks to unified memory (CPU and GPU share the same RAM). - -CUDA GPUs do not have this limit. - -Common 4D-STEM sizes (float32): - - Scan Detector Elements Size MPS? - 128×128 128×128 268M 1.0 GB yes - 128×128 256×256 1,074M 4.0 GB yes - 256×256 128×128 1,074M 4.0 GB yes - 256×256 192×192 2,416M 9.0 GB no (auto CPU, still fast) - 256×256 256×256 4,295M 16.0 GB no (auto CPU, still fast) - 512×512 256×256 17,180M 64.0 GB no (auto CPU) +Single chunked-torch path on every device (CUDA / MPS / CPU). Reductions cast +uint16 → float32 in scan-row chunks bounded by _CHUNK_BYTE_BUDGET, so transient +memory stays the same regardless of total dataset size. To reduce data size, bin k-space at the dataset level before viewing: @@ -38,19 +25,20 @@ import torch import traitlets +# Cap transient chunk memory at ~600 MB regardless of detector size. +# A 4096 × 192² × 4 byte float32 cast = 600 MB; a 4096 × 256² × 4 byte cast +# would be 1.0 GB. _chunk_rows() picks an N-rows-per-chunk that keeps the +# transient under this cap. +_CHUNK_BYTE_BUDGET = 600 * 1024 * 1024 + from quantem.core.config import validate_device from quantem.widget.array_utils import to_numpy -from quantem.widget.json_state import ( +from quantem.widget.state import ( build_json_header, resolve_widget_version, save_state_file, unwrap_state_payload, ) -from quantem.widget.tool_parity import ( - bind_tool_runtime_api, - build_tool_groups, - normalize_tool_groups, -) def _format_memory(nbytes: int) -> str: @@ -64,7 +52,6 @@ def _format_memory(nbytes: int) -> str: # Constants # ============================================================================ DEFAULT_BF_RATIO = 0.125 # BF disk radius as fraction of detector size (1/8) -SPARSE_MASK_THRESHOLD = 0.2 # Use sparse indexing below this mask coverage MIN_LOG_VALUE = 1e-10 # Minimum value for log scale to avoid log(0) DEFAULT_VI_ROI_RATIO = 0.15 # Default VI ROI size as fraction of scan dimension @@ -84,12 +71,14 @@ class Show4DSTEM(anywidget.AnyWidget): for time-series or tilt-series data. scan_shape : tuple, optional If data is flattened (N, det_rows, det_cols), provide scan dimensions. - pixel_size : float, optional - Pixel size in Å (real-space). Used for scale bar. - Auto-extracted from Dataset4dstem if not provided. - k_pixel_size : float, optional - Detector pixel size in mrad (k-space). Used for scale bar. - Auto-extracted from Dataset4dstem if not provided. + sampling : tuple of 4 floats, optional + Pixel size per axis ``(scan_row, scan_col, k_row, k_col)``. Scalar + broadcasts to all four axes. Defaults to ``(1, 1, 1, 1)``. + Auto-extracted from ``Dataset4dstem`` if not provided. + units : list of 4 str, optional + Unit string per axis. Common: ``["A", "A", "mrad", "mrad"]``. + Defaults to ``["pixels"] * 4``. Auto-extracted from + ``Dataset4dstem`` if not provided. center : tuple[float, float], optional (center_row, center_col) of the diffraction pattern in pixels. If not provided, defaults to detector center. @@ -100,43 +89,58 @@ class Show4DSTEM(anywidget.AnyWidget): frame_dim_label : str, optional Label for the frame dimension when 5D data is provided. Defaults to "Frame". Common values: "Tilt", "Time", "Focus". - disabled_tools : list of str, optional - Tool groups to lock while still showing controls. Supported: - ``"display"``, ``"histogram"``, ``"stats"``, ``"navigation"``, - ``"playback"``, ``"view"``, ``"export"``, ``"roi"``, - ``"profile"``, ``"fft"``, ``"virtual"``, ``"frame"``, ``"all"``. - disable_* : bool, optional - Convenience flags mirroring ``disabled_tools`` for each tool group, - plus ``disable_all``. - hidden_tools : list of str, optional - Tool groups to hide from the UI. Uses the same keys as - ``disabled_tools``. - hide_* : bool, optional - Convenience flags mirroring ``disable_*`` for ``hidden_tools``. - Examples -------- - >>> # From Dataset4dstem (calibration auto-extracted) - >>> from quantem.core.io.file_readers import read_emdfile_to_4dstem - >>> dataset = read_emdfile_to_4dstem("data.h5") - >>> Show4DSTEM(dataset) - - >>> # From raw array with manual calibration >>> import numpy as np - >>> data = np.random.rand(64, 64, 128, 128) - >>> Show4DSTEM(data, pixel_size=2.39, k_pixel_size=0.46) + >>> from quantem.widget import Show4DSTEM + + 4D NumPy array ``(scan_rows, scan_cols, det_rows, det_cols)``: + + >>> Show4DSTEM(np.random.rand(64, 64, 128, 128)) + + PyTorch tensor (CPU or GPU): + + >>> import torch + >>> Show4DSTEM(torch.rand(64, 64, 128, 128)) + + With explicit calibration (real-space Å, k-space mrad): + + >>> Show4DSTEM(np.random.rand(64, 64, 128, 128), + ... sampling=(2.39, 2.39, 0.46, 0.46), + ... units=["A", "A", "mrad", "mrad"]) + + quantem ``Dataset4dstem`` — calibration + units auto-extracted: + + >>> from quantem.core.datastructures import Dataset4dstem + >>> ds = Dataset4dstem.from_array(np.random.rand(64, 64, 128, 128)) + >>> Show4DSTEM(ds) + + Flattened scan ``(N, det_rows, det_cols)`` with explicit scan shape: - >>> # With raster animation - >>> widget = Show4DSTEM(dataset) - >>> widget.raster(step=2, interval_ms=50) + >>> Show4DSTEM(np.random.rand(4096, 128, 128), scan_shape=(64, 64)) - >>> # 5D time-series or tilt-series data - >>> data_5d = np.random.rand(20, 64, 64, 128, 128) # 20 frames - >>> Show4DSTEM(data_5d, frame_dim_label="Tilt") + Custom BF disk center and radius (overrides auto-detection): + + >>> Show4DSTEM(np.random.rand(64, 64, 128, 128), + ... center=(64, 64), bf_radius=12) + + 5D time-series or tilt-series ``(n_frames, scan_r, scan_c, det_r, det_c)``: + + >>> Show4DSTEM(np.random.rand(20, 64, 64, 128, 128), frame_dim_label="Tilt") + + Raster animation (scan path through 4D dataset): + + >>> w = Show4DSTEM(np.random.rand(64, 64, 128, 128)) + >>> w.raster(step=2, interval_ms=50) + + Static export to PDF or PNG (single panel or all four): + + >>> w = Show4DSTEM(np.random.rand(64, 64, 128, 128)) + >>> w.save_image("dp.pdf", view="diffraction") + >>> w.save_image("all.pdf", view="all") """ _esm = pathlib.Path(__file__).parent / "static" / "show4dstem.js" - _css = pathlib.Path(__file__).parent / "static" / "show4dstem.css" # Position in scan space widget_version = traitlets.Unicode("unknown").tag(sync=True) @@ -188,9 +192,7 @@ class Show4DSTEM(anywidget.AnyWidget): # ========================================================================= # Virtual Image (ROI-based, updates as you drag ROI on DP) # ========================================================================= - virtual_image_bytes = traitlets.Bytes(b"").tag(sync=True) # Raw float32 - vi_data_min = traitlets.Float(0.0).tag(sync=True) # Min of current VI for normalization - vi_data_max = traitlets.Float(1.0).tag(sync=True) # Max of current VI for normalization + virtual_image_bytes = traitlets.Bytes(b"").tag(sync=True) # Raw float32 (JS computes stats + range) # ========================================================================= # VI ROI (real-space region selection for summed DP) @@ -198,18 +200,25 @@ class Show4DSTEM(anywidget.AnyWidget): vi_roi_mode = traitlets.Unicode("off").tag(sync=True) # "off", "circle", "rect" vi_roi_center_row = traitlets.Float(0.0).tag(sync=True) vi_roi_center_col = traitlets.Float(0.0).tag(sync=True) + # Compound (row, col) trait — JS sets in one call; one observer fires; bytes + # never compute against split-trait state (old col + new row, or vice versa). + vi_roi_center = traitlets.List(traitlets.Float(), default_value=[0.0, 0.0]).tag(sync=True) vi_roi_radius = traitlets.Float(5.0).tag(sync=True) vi_roi_width = traitlets.Float(10.0).tag(sync=True) vi_roi_height = traitlets.Float(10.0).tag(sync=True) - summed_dp_bytes = traitlets.Bytes(b"").tag(sync=True) # Summed DP from VI ROI - summed_dp_count = traitlets.Int(0).tag(sync=True) # Number of positions summed + # Reduction over scan positions inside vi_roi: mean is default (size-invariant DP), + # sum scales with area (quantitative counts), max picks brightest position per detector pixel. + vi_roi_reduce = traitlets.Unicode("mean").tag(sync=True) + vi_roi_dp_bytes = traitlets.Bytes(b"").tag(sync=True) # Reduced DP from VI ROI # ========================================================================= # Scale Bar # ========================================================================= - pixel_size = traitlets.Float(1.0).tag(sync=True) # Å per pixel (real-space) - k_pixel_size = traitlets.Float(1.0).tag(sync=True) # mrad per pixel (k-space) - k_calibrated = traitlets.Bool(False).tag(sync=True) # True if k-space has mrad calibration + pixel_size = traitlets.Float(1.0).tag(sync=True) # real-space pixel size (col axis) + pixel_unit = traitlets.Unicode("pixels").tag(sync=True) + k_pixel_size = traitlets.Float(1.0).tag(sync=True) # k-space pixel size (col axis) + k_pixel_unit = traitlets.Unicode("pixels").tag(sync=True) + k_calibrated = traitlets.Bool(False).tag(sync=True) # True if k-space has real units # ========================================================================= # Path Animation (programmatic crosshair control) @@ -223,14 +232,13 @@ class Show4DSTEM(anywidget.AnyWidget): # ========================================================================= # Auto-detection trigger (frontend sets to True, backend resets to False) # ========================================================================= - auto_detect_trigger = traitlets.Bool(False).tag(sync=True) # ========================================================================= # Statistics for display (mean, min, max, std) # ========================================================================= - dp_stats = traitlets.List(traitlets.Float(), default_value=[0.0, 0.0, 0.0, 0.0]).tag(sync=True) - vi_stats = traitlets.List(traitlets.Float(), default_value=[0.0, 0.0, 0.0, 0.0]).tag(sync=True) - mask_dc = traitlets.Bool(True).tag(sync=True) # Mask center pixel for DP stats + # dp_stats and vi_stats are computed JS-side from frame_bytes / virtual_image_bytes. + # Keeping them out of Python traits eliminates a 4-message comm race that produced + # mismatched bytes/min/max on rapid preset/ROI changes. # ========================================================================= # Display settings (synced for programmatic export parity) @@ -239,13 +247,9 @@ class Show4DSTEM(anywidget.AnyWidget): vi_colormap = traitlets.Unicode("inferno").tag(sync=True) fft_colormap = traitlets.Unicode("inferno").tag(sync=True) - dp_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" | "power" - vi_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" | "power" - fft_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" | "power" - - dp_power_exp = traitlets.Float(0.5).tag(sync=True) - vi_power_exp = traitlets.Float(0.5).tag(sync=True) - fft_power_exp = traitlets.Float(0.5).tag(sync=True) + dp_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" + vi_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" + fft_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" dp_vmin_pct = traitlets.Float(0.0).tag(sync=True) dp_vmax_pct = traitlets.Float(100.0).tag(sync=True) @@ -262,14 +266,19 @@ class Show4DSTEM(anywidget.AnyWidget): fft_auto = traitlets.Bool(True).tag(sync=True) show_fft = traitlets.Bool(False).tag(sync=True) + # Single-trait preset request: JS sets to "bf"/"abf"/"adf"/"haadf" → Python + # observer calls apply_preset() which batches the 5 ROI trait writes + # atomically. Avoids the JS-side ordering race where individual roi_mode/ + # radius/center traits would commit in separate comm messages. + _preset_request = traitlets.Unicode("").tag(sync=True) fft_window = traitlets.Bool(True).tag(sync=True) show_controls = traitlets.Bool(True).tag(sync=True) dp_show_colorbar = traitlets.Bool(False).tag(sync=True) - export_default_view = traitlets.Unicode("all").tag(sync=True) - export_default_format = traitlets.Unicode("png").tag(sync=True) - export_include_overlays = traitlets.Bool(True).tag(sync=True) - export_include_scalebar = traitlets.Bool(True).tag(sync=True) - export_default_dpi = traitlets.Int(300).tag(sync=True) + # VI panel auto-contrast (1st/99th percentile clip) and CSS smoothing. + # DP panel doesn't need either — Bragg spots are best read with nearest- + # neighbor + the slider's percentile range. + vi_auto_contrast = traitlets.Bool(False).tag(sync=True) + vi_smooth = traitlets.Bool(False).tag(sync=True) # ========================================================================= # Frame Animation (5D time/tilt series) @@ -294,139 +303,18 @@ class Show4DSTEM(anywidget.AnyWidget): profile_width = traitlets.Int(1).tag(sync=True) # ========================================================================= - # Tool visibility / locking - # ========================================================================= - disabled_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) - hidden_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) - - @classmethod - def _normalize_tool_groups(cls, tool_groups) -> list[str]: - return normalize_tool_groups("Show4DSTEM", tool_groups) - - @classmethod - def _build_disabled_tools( - cls, - disabled_tools=None, - disable_display: bool = False, - disable_histogram: bool = False, - disable_stats: bool = False, - disable_navigation: bool = False, - disable_playback: bool = False, - disable_view: bool = False, - disable_export: bool = False, - disable_roi: bool = False, - disable_profile: bool = False, - disable_fft: bool = False, - disable_virtual: bool = False, - disable_frame: bool = False, - disable_all: bool = False, - ) -> list[str]: - return build_tool_groups( - "Show4DSTEM", - tool_groups=disabled_tools, - all_flag=disable_all, - flag_map={ - "display": disable_display, - "histogram": disable_histogram, - "stats": disable_stats, - "navigation": disable_navigation, - "playback": disable_playback, - "view": disable_view, - "export": disable_export, - "roi": disable_roi, - "profile": disable_profile, - "fft": disable_fft, - "virtual": disable_virtual, - "frame": disable_frame, - }, - ) - - @classmethod - def _build_hidden_tools( - cls, - hidden_tools=None, - hide_display: bool = False, - hide_histogram: bool = False, - hide_stats: bool = False, - hide_navigation: bool = False, - hide_playback: bool = False, - hide_view: bool = False, - hide_export: bool = False, - hide_roi: bool = False, - hide_profile: bool = False, - hide_fft: bool = False, - hide_virtual: bool = False, - hide_frame: bool = False, - hide_all: bool = False, - ) -> list[str]: - return build_tool_groups( - "Show4DSTEM", - tool_groups=hidden_tools, - all_flag=hide_all, - flag_map={ - "display": hide_display, - "histogram": hide_histogram, - "stats": hide_stats, - "navigation": hide_navigation, - "playback": hide_playback, - "view": hide_view, - "export": hide_export, - "roi": hide_roi, - "profile": hide_profile, - "fft": hide_fft, - "virtual": hide_virtual, - "frame": hide_frame, - }, - ) - - @traitlets.validate("disabled_tools") - def _validate_disabled_tools(self, proposal): - return self._normalize_tool_groups(proposal["value"]) - - @traitlets.validate("hidden_tools") - def _validate_hidden_tools(self, proposal): - return self._normalize_tool_groups(proposal["value"]) - def __init__( self, data: "Dataset4dstem | np.ndarray", scan_shape: tuple[int, int] | None = None, - pixel_size: float | None = None, - k_pixel_size: float | None = None, + sampling: tuple[float, ...] | list[float] | None = None, + units: list[str] | tuple[str, ...] | None = None, center: tuple[float, float] | None = None, bf_radius: float | None = None, - precompute_virtual_images: bool = False, + precompute_virtual_images: bool = True, frame_dim_label: str | None = None, frame_labels: list[str] | None = None, title: str = "", - disabled_tools: list[str] | None = None, - disable_display: bool = False, - disable_histogram: bool = False, - disable_stats: bool = False, - disable_navigation: bool = False, - disable_playback: bool = False, - disable_view: bool = False, - disable_export: bool = False, - disable_roi: bool = False, - disable_profile: bool = False, - disable_fft: bool = False, - disable_virtual: bool = False, - disable_frame: bool = False, - disable_all: bool = False, - hidden_tools: list[str] | None = None, - hide_display: bool = False, - hide_histogram: bool = False, - hide_stats: bool = False, - hide_navigation: bool = False, - hide_playback: bool = False, - hide_view: bool = False, - hide_export: bool = False, - hide_roi: bool = False, - hide_profile: bool = False, - hide_fft: bool = False, - hide_virtual: bool = False, - hide_frame: bool = False, - hide_all: bool = False, show_fft: bool = False, fft_window: bool = True, show_controls: bool = True, @@ -445,60 +333,38 @@ def __init__( _io_labels = None - # Extract calibration from Dataset4dstem if provided - k_calibrated = False + # Auto-extract sampling + units from Dataset4dstem if available. if hasattr(data, "sampling") and hasattr(data, "array"): - # Dataset4dstem: extract calibration and array - # sampling = [scan_rows, scan_cols, det_rows, det_cols] if not title and hasattr(data, "name") and data.name: title = str(data.name) - units = getattr(data, "units", ["pixels"] * 4) - if pixel_size is None and units[0] in ("Å", "angstrom", "A", "nm"): - pixel_size = float(data.sampling[0]) - if units[0] == "nm": - pixel_size *= 10 # Convert nm to Å - if k_pixel_size is None and units[2] in ("mrad", "1/Å", "1/A"): - k_pixel_size = float(data.sampling[2]) - k_calibrated = True + if sampling is None: + sampling = tuple(float(s) for s in data.sampling) + if units is None and hasattr(data, "units"): + units = list(data.units) data = data.array + # Resolve sampling + units (4 axes for 4D-STEM): + # [scan_row, scan_col, k_row, k_col]. Scalar/None broadcast to (1, 1, 1, 1). + if sampling is None: + sampling = (1.0, 1.0, 1.0, 1.0) + elif isinstance(sampling, (int, float)): + sampling = (float(sampling),) * 4 + else: + sampling = tuple(float(s) for s in sampling) + if units is None: + units = ["pixels"] * 4 + elif isinstance(units, str): + units = [units] * 4 + else: + units = [str(u) for u in units] + self.title = title - # Store calibration values (default to 1.0 if not provided) - self.pixel_size = pixel_size if pixel_size is not None else 1.0 - self.k_pixel_size = k_pixel_size if k_pixel_size is not None else 1.0 - self.k_calibrated = k_calibrated or (k_pixel_size is not None) - self.disabled_tools = self._build_disabled_tools( - disabled_tools=disabled_tools, - disable_display=disable_display, - disable_histogram=disable_histogram, - disable_stats=disable_stats, - disable_navigation=disable_navigation, - disable_playback=disable_playback, - disable_view=disable_view, - disable_export=disable_export, - disable_roi=disable_roi, - disable_profile=disable_profile, - disable_fft=disable_fft, - disable_virtual=disable_virtual, - disable_frame=disable_frame, - disable_all=disable_all, - ) - self.hidden_tools = self._build_hidden_tools( - hidden_tools=hidden_tools, - hide_display=hide_display, - hide_histogram=hide_histogram, - hide_stats=hide_stats, - hide_navigation=hide_navigation, - hide_playback=hide_playback, - hide_view=hide_view, - hide_export=hide_export, - hide_roi=hide_roi, - hide_profile=hide_profile, - hide_fft=hide_fft, - hide_virtual=hide_virtual, - hide_frame=hide_frame, - hide_all=hide_all, - ) + self.pixel_size = sampling[1] # scan_col axis (horizontal scale bar) + self.pixel_unit = units[1] if len(units) > 1 else "pixels" + self.k_pixel_size = sampling[3] if len(sampling) > 3 else 1.0 + self.k_pixel_unit = units[3] if len(units) > 3 else "pixels" + # k-space considered calibrated when its unit is real (mrad, 1/Å, etc.). + self.k_calibrated = self.k_pixel_unit not in ("pixels", "") self.show_fft = show_fft self.fft_window = fft_window self.show_controls = show_controls @@ -508,44 +374,40 @@ def __init__( self.vi_vmax = vi_vmax # Path animation (configured via set_path() or raster()) self._path_points: list[tuple[int, int]] = [] - # Named user presets saved during this session - self._named_presets: dict[str, dict[str, Any]] = {} - # Session-scoped reproducibility log for all export calls - self._export_session_id = uuid4().hex - self._export_session_started_utc = datetime.now(timezone.utc).isoformat() - self._export_log: list[dict[str, Any]] = [] - # Sparse sampling state (for streaming/adaptive acquisition workflows) - self._sparse_samples: dict[tuple[int, int, int], np.ndarray] = {} - self._sparse_order: list[tuple[int, int, int]] = [] - # Convert to NumPy then PyTorch tensor using quantem device config - data_np = to_numpy(data) - device_str, _ = validate_device(None) # Get device from quantem config - self._device = torch.device(device_str) - # Remove saturated hot pixels in numpy (before any torch conversion) - saturated_value = 65535.0 if data_np.dtype == np.uint16 else 255.0 if data_np.dtype == np.uint8 else None - if data_np.dtype != np.float32: - _tc = time.perf_counter() - data_np = data_np.astype(np.float32) - if _verbose: - print(f" astype float32: {time.perf_counter() - _tc:.2f}s") - if saturated_value is not None: - data_np[data_np >= saturated_value] = 0 + # Suppress per-trait recompute during apply_preset batch writes + self._suppress_roi_recompute = False + # Torch tensor input keeps its device (lets user pin a specific GPU via + # `data.cuda(1)`). NumPy / Dataset input gets default-validated device. + if isinstance(data, torch.Tensor): + self._device = data.device + self._data_pre = data + data_np = None + else: + device_str, _ = validate_device(None) + self._device = torch.device(device_str) + data_np = to_numpy(data) + self._data_pre = None + self._saturation_value = ( + 65535 if data_np.dtype == np.uint16 + else 255 if data_np.dtype == np.uint8 + else None + ) # Handle dimensionality — 5D loads eagerly for instant frame switching - ndim = data_np.ndim + # Resolve shape from whichever input path we took + shape = tuple(self._data_pre.shape) if self._data_pre is not None else data_np.shape + size_elements = int(np.prod(shape)) + ndim = len(shape) _tc = time.perf_counter() if ndim == 5: - self.n_frames = data_np.shape[0] - self._scan_shape = (data_np.shape[1], data_np.shape[2]) - self._det_shape = (data_np.shape[3], data_np.shape[4]) - if data_np.size > 2**31 - 1 and device_str == "mps": - self._device = torch.device("cpu") - self._data = torch.from_numpy(data_np).to(self._device) + self.n_frames = shape[0] + self._scan_shape = (shape[1], shape[2]) + self._det_shape = (shape[3], shape[4]) elif ndim == 3: self.n_frames = 1 if scan_shape is not None: self._scan_shape = scan_shape else: - n = data_np.shape[0] + n = shape[0] side = int(n ** 0.5) if side * side != n: raise ValueError( @@ -553,24 +415,45 @@ def __init__( f"Provide scan_shape explicitly." ) self._scan_shape = (side, side) - self._det_shape = (data_np.shape[1], data_np.shape[2]) - # MPS backend can't handle tensors >INT_MAX elements; fall back to CPU - if data_np.size > 2**31 - 1 and device_str == "mps": - self._device = torch.device("cpu") - self._data = torch.from_numpy(data_np).to(self._device) + self._det_shape = (shape[1], shape[2]) elif ndim == 4: self.n_frames = 1 - self._scan_shape = (data_np.shape[0], data_np.shape[1]) - self._det_shape = (data_np.shape[2], data_np.shape[3]) - if data_np.size > 2**31 - 1 and device_str == "mps": - self._device = torch.device("cpu") - self._data = torch.from_numpy(data_np).to(self._device) + self._scan_shape = (shape[0], shape[1]) + self._det_shape = (shape[2], shape[3]) else: - raise ValueError(f"Show4DSTEM expects a 3D ((N, det_h, det_w) flat-scan), 4D ((scan_h, scan_w, det_h, det_w)), or 5D ((n_frames, scan_h, scan_w, det_h, det_w)) array. Got {ndim}D. Reshape with array.reshape((scan_h, scan_w, det_h, det_w)) or pass a Dataset4dstem.") + raise ValueError(f"Show4DSTEM expects a 3D ((N, det_h, det_w) flat-scan), 4D ((scan_h, scan_w, det_h, det_w)), or 5D ((n_frames, scan_h, scan_w, det_h, det_w)) array. Got {ndim}D.") + if self._data_pre is not None: + self._data = self._data_pre if self._data_pre.device == self._device else self._data_pre.to(self._device) + del self._data_pre + else: + self._data = torch.from_numpy(data_np).to(self._device) + # Saturation filter: zero detector pixels at full-scale (65535 / 255). + # PyTorch lacks unsigned int comparison kernels, but uint16 viewed + # as int16 has identical bytes (65535 → -1) and int16 comparison + # works on every device. Apply in scan-row chunks so the transient + # bool mask stays bounded (≤600 MB) and fits constrained-VRAM + # devices (Mac 24 GB unified, etc.). View-write keeps native dtype. + sat = getattr(self, "_saturation_value", None) + view_dtype = ( + torch.int16 if sat is not None and self._data.dtype == torch.uint16 + else torch.int8 if sat is not None and self._data.dtype == torch.uint8 + else None + ) + if view_dtype is not None: + view = self._data.view(view_dtype).reshape(-1, *self._det_shape) + rows = view.shape[0] + # Bool mask transient = positions × det_h × det_w bytes; cap at budget. + pos_per_chunk = max(1, _CHUNK_BYTE_BUDGET // max(1, self._det_shape[0] * self._det_shape[1])) + for i in range(0, rows, pos_per_chunk): + chunk = view[i:i + pos_per_chunk] + chunk.masked_fill_(chunk == -1, 0) + # Keep native dtype (uint8/uint16) to bound memory at ~ data_size. + # Reductions cast in chunks (bounded transient). if _verbose: if str(self._device) == "mps": torch.mps.synchronize() - print(f" to {self._device}: {time.perf_counter() - _tc:.2f}s ({data_np.nbytes / 1e9:.1f} GB)") + n_bytes = self._data.element_size() * self._data.numel() + print(f" to {self._device}: {time.perf_counter() - _tc:.2f}s ({n_bytes / 1e9:.1f} GB)") self.shape_rows = self._scan_shape[0] self.shape_cols = self._scan_shape[1] @@ -586,17 +469,20 @@ def __init__( self._frame_labels = resolved_labels if resolved_labels: self.frame_labels = list(resolved_labels) - # Histogram axis range — first frame is enough (JS does per-frame percentile clipping) + # Histogram axis range — first frame is enough (JS does per-frame percentile clipping). + # Cast to float for min/max reductions: PyTorch CUDA lacks integer min/max kernels, + # and the first slice is tiny (144 KB at 192×192) so the cast is free. first_frame = self._data[0] if self._data.ndim == 5 else self._data - self.dp_global_min = max(float(first_frame.min()), MIN_LOG_VALUE) - self.dp_global_max = float(first_frame.max()) + first_frame_sample = first_frame[0] if first_frame.ndim >= 3 else first_frame + if not torch.is_floating_point(first_frame_sample): + first_frame_sample = first_frame_sample.float() + self.dp_global_min = max(float(first_frame_sample.min()), MIN_LOG_VALUE) + self.dp_global_max = float(first_frame_sample.max()) # Cache coordinate tensors for mask creation (avoid repeated torch.arange) self._det_row_coords = torch.arange(self.det_rows, device=self._device, dtype=torch.float32)[:, None] self._det_col_coords = torch.arange(self.det_cols, device=self._device, dtype=torch.float32)[None, :] self._scan_row_coords = torch.arange(self.shape_rows, device=self._device, dtype=torch.float32)[:, None] self._scan_col_coords = torch.arange(self.shape_cols, device=self._device, dtype=torch.float32)[None, :] - self._sparse_mask = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=bool) - self._dose_map = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=np.float32) # Setup center and BF radius det_size = min(self.det_rows, self.det_cols) if center is not None and bf_radius is not None: @@ -628,6 +514,7 @@ def __init__( self._cached_bf_virtual = None self._cached_abf_virtual = None self._cached_adf_virtual = None + self._cached_haadf_virtual = None if precompute_virtual_images and self.n_frames == 1: self._precompute_common_virtual_images() @@ -662,9 +549,9 @@ def __init__( # Frame animation (5D): observe frame_idx changes from frontend self.observe(self._on_frame_idx_change, names=["frame_idx"]) + self.observe(self._on_preset_request, names=["_preset_request"]) # Auto-detect trigger: observe changes from frontend - self.observe(self._on_auto_detect_trigger, names=["auto_detect_trigger"]) # VI ROI: observe changes for summed DP computation # Initialize VI ROI center to scan center with reasonable default sizes @@ -677,8 +564,9 @@ def __init__( self.vi_roi_height = float(default_roi_size) self.observe(self._on_vi_roi_change, names=[ "vi_roi_mode", "vi_roi_center_row", "vi_roi_center_col", - "vi_roi_radius", "vi_roi_width", "vi_roi_height" + "vi_roi_radius", "vi_roi_width", "vi_roi_height", "vi_roi_reduce" ]) + self.observe(self._on_vi_roi_center_change, names=["vi_roi_center"]) if state is not None: if isinstance(state, (str, pathlib.Path)): @@ -700,16 +588,12 @@ def set_image(self, data, scan_shape=None): data = data.array data_np = to_numpy(data) saturated_value = 65535.0 if data_np.dtype == np.uint16 else 255.0 if data_np.dtype == np.uint8 else None - if data_np.dtype != np.float32: - data_np = data_np.astype(np.float32) if saturated_value is not None: data_np[data_np >= saturated_value] = 0 if data_np.ndim == 5: self.n_frames = data_np.shape[0] self._scan_shape = (data_np.shape[1], data_np.shape[2]) self._det_shape = (data_np.shape[3], data_np.shape[4]) - if data_np.size > 2**31 - 1 and str(self._device) == "mps": - self._device = torch.device("cpu") self._data = torch.from_numpy(data_np).to(self._device) elif data_np.ndim == 3: self.n_frames = 1 @@ -736,19 +620,19 @@ def set_image(self, data, scan_shape=None): self.det_rows = self._det_shape[0] self.det_cols = self._det_shape[1] first_frame = self._data[0] if self._data.ndim == 5 else self._data - self.dp_global_min = max(float(first_frame.min()), MIN_LOG_VALUE) - self.dp_global_max = float(first_frame.max()) + first_frame_sample = first_frame[0] if first_frame.ndim >= 3 else first_frame + if not torch.is_floating_point(first_frame_sample): + first_frame_sample = first_frame_sample.float() + self.dp_global_min = max(float(first_frame_sample.min()), MIN_LOG_VALUE) + self.dp_global_max = float(first_frame_sample.max()) self._det_row_coords = torch.arange(self.det_rows, device=self._device, dtype=torch.float32)[:, None] self._det_col_coords = torch.arange(self.det_cols, device=self._device, dtype=torch.float32)[None, :] self._scan_row_coords = torch.arange(self.shape_rows, device=self._device, dtype=torch.float32)[:, None] self._scan_col_coords = torch.arange(self.shape_cols, device=self._device, dtype=torch.float32)[None, :] - self._sparse_mask = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=bool) - self._dose_map = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=np.float32) - self._sparse_samples = {} - self._sparse_order = [] self._cached_bf_virtual = None self._cached_abf_virtual = None self._cached_adf_virtual = None + self._cached_haadf_virtual = None with self.hold_trait_notifications(): self.pos_row = min(self.pos_row, self.shape_rows - 1) self.pos_col = min(self.pos_col, self.shape_cols - 1) @@ -756,7 +640,6 @@ def set_image(self, data, scan_shape=None): self._update_frame() def __repr__(self) -> str: - k_unit = "mrad" if self.k_calibrated else "px" shape = ( f"({self.n_frames}, {self.shape_rows}, {self.shape_cols}, {self.det_rows}, {self.det_cols})" if self.n_frames > 1 @@ -766,7 +649,7 @@ def __repr__(self) -> str: title_info = f", title='{self.title}'" if self.title else "" return ( f"Show4DSTEM(shape={shape}, " - f"sampling=({self.pixel_size} Å, {self.k_pixel_size} {k_unit}), " + f"sampling=({self.pixel_size} {self.pixel_unit}, {self.k_pixel_size} {self.k_pixel_unit}), " f"pos=({self.pos_row}, {self.pos_col}){frame_info}{title_info})" ) @@ -776,7 +659,9 @@ def state_dict(self): "pos_row": self.pos_row, "pos_col": self.pos_col, "pixel_size": self.pixel_size, + "pixel_unit": self.pixel_unit, "k_pixel_size": self.k_pixel_size, + "k_pixel_unit": self.k_pixel_unit, "k_calibrated": self.k_calibrated, "center_row": self.center_row, "center_col": self.center_col, @@ -795,16 +680,13 @@ def state_dict(self): "vi_roi_radius": self.vi_roi_radius, "vi_roi_width": self.vi_roi_width, "vi_roi_height": self.vi_roi_height, - "mask_dc": self.mask_dc, + "vi_roi_reduce": self.vi_roi_reduce, "dp_colormap": self.dp_colormap, "vi_colormap": self.vi_colormap, "fft_colormap": self.fft_colormap, "dp_scale_mode": self.dp_scale_mode, "vi_scale_mode": self.vi_scale_mode, "fft_scale_mode": self.fft_scale_mode, - "dp_power_exp": self.dp_power_exp, - "vi_power_exp": self.vi_power_exp, - "fft_power_exp": self.fft_power_exp, "dp_vmin_pct": self.dp_vmin_pct, "dp_vmax_pct": self.dp_vmax_pct, "vi_vmin_pct": self.vi_vmin_pct, @@ -820,11 +702,8 @@ def state_dict(self): "fft_window": self.fft_window, "show_controls": self.show_controls, "dp_show_colorbar": self.dp_show_colorbar, - "export_default_view": self.export_default_view, - "export_default_format": self.export_default_format, - "export_include_overlays": self.export_include_overlays, - "export_include_scalebar": self.export_include_scalebar, - "export_default_dpi": self.export_default_dpi, + "vi_auto_contrast": self.vi_auto_contrast, + "vi_smooth": self.vi_smooth, "path_interval_ms": self.path_interval_ms, "path_loop": self.path_loop, "profile_line": self.profile_line, @@ -836,8 +715,6 @@ def state_dict(self): "frame_fps": self.frame_fps, "frame_reverse": self.frame_reverse, "frame_boomerang": self.frame_boomerang, - "disabled_tools": self.disabled_tools, - "hidden_tools": self.hidden_tools, } def save(self, path: str): @@ -881,16 +758,11 @@ def free(self): gc.collect() if device == "mps": try: - import torch torch.mps.empty_cache() - except Exception: + except AttributeError: pass elif device.startswith("cuda"): - try: - import torch - torch.cuda.empty_cache() - except Exception: - pass + torch.cuda.empty_cache() if nbytes > 0: print(f"freed {_format_memory(nbytes)} ({device})") @@ -912,15 +784,10 @@ def summary(self): lines.append(f"Labels: {self._frame_labels}") else: lines.append(f"Labels: {self._frame_labels[:3]} ... ({len(self._frame_labels)} total)") - lines.append(f"Scan: {self.shape_rows}×{self.shape_cols} ({self.pixel_size:.2f} Å/px)") - k_unit = "mrad" if self.k_calibrated else "px" - lines.append(f"Detector: {self.det_rows}×{self.det_cols} ({self.k_pixel_size:.4f} {k_unit}/px)") + lines.append(f"Scan: {self.shape_rows}×{self.shape_cols} ({self.pixel_size:.2f} {self.pixel_unit}/px)") + lines.append(f"Detector: {self.det_rows}×{self.det_cols} ({self.k_pixel_size:.4f} {self.k_pixel_unit}/px)") lines.append(f"Position: ({self.pos_row}, {self.pos_col})") lines.append(f"Center: ({self.center_row:.1f}, {self.center_col:.1f}) BF r={self.bf_radius:.1f} px") - display_parts = [] - if self.mask_dc: - display_parts.append("DC masked") - lines.append(f"Display: {', '.join(display_parts) if display_parts else 'default'}") if self.roi_active: lines.append(f"ROI: {self.roi_mode} at ({self.roi_center_row:.1f}, {self.roi_center_col:.1f}) r={self.roi_radius:.1f}") if self.vi_roi_mode != "off": @@ -945,10 +812,6 @@ def summary(self): if self.profile_line and len(self.profile_line) == 2: p0, p1 = self.profile_line[0], self.profile_line[1] lines.append(f"Profile: ({p0['row']:.0f}, {p0['col']:.0f}) -> ({p1['row']:.0f}, {p1['col']:.0f}) width={self.profile_width}") - if self.disabled_tools: - lines.append(f"Locked: {', '.join(self.disabled_tools)}") - if self.hidden_tools: - lines.append(f"Hidden: {', '.join(self.hidden_tools)}") print("\n".join(lines)) # ========================================================================= @@ -1124,12 +987,12 @@ def _on_path_index_change(self, change): self.pos_row = max(0, min(self.shape_rows - 1, row)) self.pos_col = max(0, min(self.shape_cols - 1, col)) - def _on_auto_detect_trigger(self, change): - """Called when auto_detect_trigger is set to True from frontend.""" - if change["new"]: - self.auto_detect_center() - # Reset trigger to allow re-triggering - self.auto_detect_trigger = False + def _on_preset_request(self, change): + """JS preset shortcut → atomic apply_preset (no per-trait race).""" + name = (change.get("new") or "").strip().lower() + if name in ("bf", "abf", "adf", "haadf"): + self.apply_preset(name) + self._preset_request = "" # consume trigger def _on_frame_idx_change(self, change=None): """Called when frame_idx changes (5D time/tilt series). @@ -1143,12 +1006,13 @@ def _on_frame_idx_change(self, change=None): self._cached_bf_virtual = None self._cached_abf_virtual = None self._cached_adf_virtual = None + self._cached_haadf_virtual = None # Recompute virtual image and displayed frame self._compute_virtual_image_from_roi() self._update_frame() - # Recompute summed DP if VI ROI is active + # Recompute reduced DP if VI ROI is active if self.vi_roi_mode != "off": - self._compute_summed_dp_from_vi_roi() + self._compute_vi_roi_dp() # ========================================================================= # Path Animation Patterns @@ -1353,28 +1217,26 @@ def auto_detect_center(self, update_roi: bool = True) -> Self: >>> widget = Show4DSTEM(data) >>> widget.auto_detect_center() # Auto-detect and apply """ - # Sum all diffraction patterns to get average (PyTorch) - if self._data.ndim == 5: - summed_dp = self._data.sum(dim=(0, 1, 2)) - elif self._data.ndim == 4: - summed_dp = self._data.sum(dim=(0, 1)) - else: - summed_dp = self._data.sum(dim=0) + # Sum diffraction patterns over scan positions to find BF disk centroid. + # Single chunked torch float path: works identically on CUDA / MPS / CPU. + # Each chunk casts uint16 → float32 transiently (~600 MB max), accumulates. + data_flat = self._data.reshape(-1, *self._det_shape) + n_pos = data_flat.shape[0] + mean_dp = torch.zeros(self._det_shape, dtype=torch.float32, device=self._device) + # Float32 cast transient = positions × det_h × det_w × 4 bytes; cap at budget. + pos_per_chunk = max(1, _CHUNK_BYTE_BUDGET // max(1, self._det_shape[0] * self._det_shape[1] * 4)) + for i in range(0, n_pos, pos_per_chunk): + mean_dp += data_flat[i:i + pos_per_chunk].sum(dim=0, dtype=torch.float32) + + threshold = mean_dp.mean() + mean_dp.std() + mask = mean_dp > threshold - # Threshold at mean + std to isolate BF disk - threshold = summed_dp.mean() + summed_dp.std() - mask = summed_dp > threshold - - # Avoid division by zero total = mask.sum() if total == 0: return self - # Calculate centroid using cached coordinate grids cx = float((self._det_col_coords * mask).sum() / total) cy = float((self._det_row_coords * mask).sum() / total) - - # Estimate radius from mask area (A = pi*r^2) radius = float(torch.sqrt(total / torch.pi)) # Apply detected values @@ -1402,17 +1264,10 @@ def _get_frame(self, row: int, col: int) -> np.ndarray: else: return data[row, col].cpu().numpy() - def _apply_scale_mode( - self, - data: np.ndarray, - mode: str, - power_exp: float = 0.5, - ) -> np.ndarray: + def _apply_scale_mode(self, data: np.ndarray, mode: str) -> np.ndarray: arr = np.asarray(data, dtype=np.float32) if mode == "log": return np.log1p(np.maximum(arr, 0.0)).astype(np.float32) - if mode == "power": - return np.power(np.maximum(arr, 0.0), float(power_exp)).astype(np.float32) return arr.astype(np.float32) def _slider_range( @@ -1458,13 +1313,13 @@ def _get_virtual_image_array(self) -> np.ndarray: return np.zeros((self.shape_rows, self.shape_cols), dtype=np.float32) return arr.reshape(self.shape_rows, self.shape_cols).copy() - def _get_summed_dp_array(self) -> np.ndarray | None: + def _get_vi_roi_dp_array(self) -> np.ndarray | None: if self.vi_roi_mode == "off": return None - self._compute_summed_dp_from_vi_roi() - if not self.summed_dp_bytes: + self._compute_vi_roi_dp() + if not self.vi_roi_dp_bytes: return None - arr = np.frombuffer(self.summed_dp_bytes, dtype=np.float32) + arr = np.frombuffer(self.vi_roi_dp_bytes, dtype=np.float32) expected = self.det_rows * self.det_cols if arr.size != expected: return None @@ -1497,24 +1352,24 @@ def _fft_enhanced_range(self, mag: np.ndarray) -> tuple[float, float]: return dmin, pmax def _render_dp_rgb(self) -> tuple[np.ndarray, dict]: - summed_dp = self._get_summed_dp_array() - if summed_dp is not None: - raw = summed_dp - source = "summed_dp" + vi_roi_arr = self._get_vi_roi_dp_array() + if vi_roi_arr is not None: + raw = vi_roi_arr + source = "vi_roi_dp" else: raw = self._get_frame(self.pos_row, self.pos_col).astype(np.float32) source = "single_frame" scale_mode = self.dp_scale_mode - scaled = self._apply_scale_mode(raw, scale_mode, self.dp_power_exp) + scaled = self._apply_scale_mode(raw, scale_mode) data_min = float(scaled.min()) if scaled.size else 0.0 data_max = float(scaled.max()) if scaled.size else 0.0 if self.dp_vmin is not None and self.dp_vmax is not None: vmin = float(self._apply_scale_mode( - np.array([max(self.dp_vmin, 0)], dtype=np.float32), scale_mode, self.dp_power_exp + np.array([max(self.dp_vmin, 0)], dtype=np.float32), scale_mode )[0]) vmax = float(self._apply_scale_mode( - np.array([max(self.dp_vmax, 0)], dtype=np.float32), scale_mode, self.dp_power_exp + np.array([max(self.dp_vmax, 0)], dtype=np.float32), scale_mode )[0]) else: vmin, vmax = self._slider_range(data_min, data_max, self.dp_vmin_pct, self.dp_vmax_pct) @@ -1532,15 +1387,15 @@ def _render_dp_rgb(self) -> tuple[np.ndarray, dict]: def _render_virtual_rgb(self) -> tuple[np.ndarray, dict]: raw = self._get_virtual_image_array() - scaled = self._apply_scale_mode(raw, self.vi_scale_mode, self.vi_power_exp) + scaled = self._apply_scale_mode(raw, self.vi_scale_mode) data_min = float(scaled.min()) if scaled.size else 0.0 data_max = float(scaled.max()) if scaled.size else 0.0 if self.vi_vmin is not None and self.vi_vmax is not None: vmin = float(self._apply_scale_mode( - np.array([max(self.vi_vmin, 0)], dtype=np.float32), self.vi_scale_mode, self.vi_power_exp + np.array([max(self.vi_vmin, 0)], dtype=np.float32), self.vi_scale_mode )[0]) vmax = float(self._apply_scale_mode( - np.array([max(self.vi_vmax, 0)], dtype=np.float32), self.vi_scale_mode, self.vi_power_exp + np.array([max(self.vi_vmax, 0)], dtype=np.float32), self.vi_scale_mode )[0]) else: vmin, vmax = self._slider_range(data_min, data_max, self.vi_vmin_pct, self.vi_vmax_pct) @@ -1559,7 +1414,7 @@ def _render_fft_rgb(self) -> tuple[np.ndarray, dict]: virtual_raw = self._get_virtual_image_array() fft = np.fft.fftshift(np.fft.fft2(virtual_raw)) mag = np.abs(fft).astype(np.float32) - scaled = self._apply_scale_mode(mag, self.fft_scale_mode, self.fft_power_exp) + scaled = self._apply_scale_mode(mag, self.fft_scale_mode) if self.fft_auto: display_min, display_max = self._fft_enhanced_range(scaled) else: @@ -1578,28 +1433,13 @@ def _render_fft_rgb(self) -> tuple[np.ndarray, dict]: } return rgb, metadata - def list_export_views(self) -> tuple[str, ...]: - return ("diffraction", "virtual", "fft", "all") - - def list_export_formats(self) -> tuple[str, ...]: - return ("png", "pdf") - - def list_figure_templates(self) -> tuple[str, ...]: - return ("dp_vi", "dp_vi_fft", "publication_dp_vi", "publication_dp_vi_fft") - - def list_presets(self) -> tuple[str, ...]: - builtin = ("bf", "abf", "adf", "haadf") - custom = tuple(sorted(self._named_presets.keys())) - return builtin + custom + _EXPORT_VIEWS = ("diffraction", "virtual", "fft", "all") + _EXPORT_FORMATS = ("png", "pdf") def _validate_export_view(self, view: str | None) -> str: - candidate = self.export_default_view if view is None else str(view) - view_key = str(candidate).strip().lower() - allowed = self.list_export_views() - if view_key not in allowed: - raise ValueError( - f"Unsupported view '{view}'. Supported: {', '.join(allowed)}" - ) + view_key = (view or "all").strip().lower() + if view_key not in self._EXPORT_VIEWS: + raise ValueError(f"Unsupported view '{view}'. Supported: {', '.join(self._EXPORT_VIEWS)}") return view_key def _validate_frame_idx(self, frame_idx: int | None) -> int: @@ -1628,21 +1468,10 @@ def _validate_position(self, position: tuple[int, int] | None) -> tuple[int, int ) return row, col - def _resolve_export_format( - self, - path: pathlib.Path, - fmt: str | None, - ) -> str: - if fmt is not None and str(fmt).strip(): - resolved = str(fmt).strip().lower() - else: - from_path = path.suffix.lstrip(".").lower() - resolved = from_path if from_path else str(self.export_default_format).strip().lower() - allowed = self.list_export_formats() - if resolved not in allowed: - raise ValueError( - f"Unsupported format '{resolved}'. Supported: {', '.join(allowed)}" - ) + def _resolve_export_format(self, path: pathlib.Path, fmt: str | None) -> str: + resolved = (fmt or path.suffix.lstrip(".") or "png").strip().lower() + if resolved not in self._EXPORT_FORMATS: + raise ValueError(f"Unsupported format '{resolved}'. Supported: {', '.join(self._EXPORT_FORMATS)}") return resolved @staticmethod @@ -1917,15 +1746,6 @@ def _vi_roi_metadata(self) -> dict[str, Any]: "height": float(self.vi_roi_height), } - def _export_settings_metadata(self) -> dict[str, Any]: - return { - "default_view": self.export_default_view, - "default_format": self.export_default_format, - "include_overlays": bool(self.export_include_overlays), - "include_scalebar": bool(self.export_include_scalebar), - "dpi": int(self.export_default_dpi), - } - def _build_image_export_metadata( self, export_path: pathlib.Path, @@ -1954,1111 +1774,129 @@ def _build_image_export_metadata( "display": render_meta, "include_overlays": bool(include_overlays), "include_scalebar": bool(include_scalebar), - "export_settings": self._export_settings_metadata(), } if extra: metadata.update(extra) return metadata - @staticmethod - def _sha256_file(path: pathlib.Path) -> str: - digest = hashlib.sha256() - with path.open("rb") as f: - while True: - chunk = f.read(1_048_576) - if not chunk: - break - digest.update(chunk) - return digest.hexdigest() - - def _build_file_record( - self, - path: pathlib.Path, - metadata_path: pathlib.Path | None = None, - index: int | None = None, - ) -> dict[str, Any]: - record: dict[str, Any] = { - "path": str(path), - "sha256": self._sha256_file(path), - "size_bytes": int(path.stat().st_size), - } - if metadata_path is not None and metadata_path.exists(): - record["metadata_path"] = str(metadata_path) - record["metadata_sha256"] = self._sha256_file(metadata_path) - record["metadata_size_bytes"] = int(metadata_path.stat().st_size) - if index is not None: - record["index"] = int(index) - return record - - def _record_export_event(self, event: dict[str, Any]) -> None: - payload = { - "session_id": self._export_session_id, - "timestamp_utc": datetime.now(timezone.utc).isoformat(), - } - payload.update(event) - self._export_log.append(payload) - - def _validate_sparse_frame_idx(self, frame_idx: int | None) -> int: - if self.n_frames <= 1: - return 0 - if frame_idx is None: - return int(self.frame_idx) - idx = int(frame_idx) - if idx < 0 or idx >= self.n_frames: - raise ValueError(f"frame_idx={idx} is out of range [0, {self.n_frames - 1}]") - return idx - - def _normalize_sparse_mask(self, mask: np.ndarray) -> np.ndarray: - arr = np.asarray(mask) - if self.n_frames <= 1: - if arr.shape == (self.shape_rows, self.shape_cols): - arr = arr[None, ...] - elif arr.shape != (1, self.shape_rows, self.shape_cols): - raise ValueError( - f"mask shape {arr.shape} does not match " - f"(scan_rows, scan_cols)=({self.shape_rows}, {self.shape_cols})" - ) - elif arr.shape != (self.n_frames, self.shape_rows, self.shape_cols): - raise ValueError( - f"mask shape {arr.shape} does not match " - f"(n_frames, scan_rows, scan_cols)=({self.n_frames}, {self.shape_rows}, {self.shape_cols})" - ) - return arr.astype(bool, copy=False) - - def _coerce_dp_array(self, dp: np.ndarray) -> np.ndarray: - arr = np.asarray(to_numpy(dp), dtype=np.float32) - if arr.shape != (self.det_rows, self.det_cols): - raise ValueError( - f"dp shape {arr.shape} does not match detector_shape " - f"({self.det_rows}, {self.det_cols})" - ) - return arr - - def _write_dp_to_data(self, frame_idx: int, row: int, col: int, dp_arr: np.ndarray) -> None: - dp_tensor = torch.from_numpy(dp_arr).to(device=self._device, dtype=torch.float32) - if self.n_frames > 1: - self._data[frame_idx, row, col] = dp_tensor - elif self._data.ndim == 4: - self._data[row, col] = dp_tensor - else: - flat_idx = row * self.shape_cols + col - self._data[flat_idx] = dp_tensor - - def _ingest_scan_point_core( - self, - row: int, - col: int, - dp: np.ndarray, - frame_idx: int, - dose: float, - refresh: bool, - ) -> None: - row_i, col_i = self._validate_position((row, col)) - frame_i = self._validate_sparse_frame_idx(frame_idx) - dp_arr = self._coerce_dp_array(dp) - dose_value = float(dose) - if not np.isfinite(dose_value) or dose_value < 0: - raise ValueError(f"dose must be finite and >= 0, got {dose}") - - key = (int(frame_i), int(row_i), int(col_i)) - if key not in self._sparse_samples: - self._sparse_order.append(key) - self._sparse_samples[key] = dp_arr.copy() - self._sparse_mask[frame_i, row_i, col_i] = True - self._dose_map[frame_i, row_i, col_i] += dose_value - - self._write_dp_to_data(frame_i, row_i, col_i, dp_arr) - self.dp_global_min = max(min(float(self.dp_global_min), float(dp_arr.min())), MIN_LOG_VALUE) - self.dp_global_max = max(float(self.dp_global_max), float(dp_arr.max())) - - if refresh: - self._compute_virtual_image_from_roi() - self._update_frame() - - def _detector_integration_kernel(self) -> tuple[np.ndarray | None, tuple[int, int] | None]: - cx, cy = float(self.roi_center_col), float(self.roi_center_row) - rr, cc = np.meshgrid( - np.arange(self.det_rows, dtype=np.float32), - np.arange(self.det_cols, dtype=np.float32), - indexing="ij", - ) - if self.roi_mode == "circle" and self.roi_radius > 0: - mask = (cc - cx) ** 2 + (rr - cy) ** 2 <= float(self.roi_radius) ** 2 - return mask.astype(np.float32, copy=False), None - if self.roi_mode == "square" and self.roi_radius > 0: - half = float(self.roi_radius) - mask = (np.abs(cc - cx) <= half) & (np.abs(rr - cy) <= half) - return mask.astype(np.float32, copy=False), None - if self.roi_mode == "annular" and self.roi_radius > 0: - outer = float(self.roi_radius) - inner = float(self.roi_radius_inner) - dist_sq = (cc - cx) ** 2 + (rr - cy) ** 2 - mask = (dist_sq >= inner**2) & (dist_sq <= outer**2) - return mask.astype(np.float32, copy=False), None - if self.roi_mode == "rect" and self.roi_width > 0 and self.roi_height > 0: - hw = float(self.roi_width) / 2.0 - hh = float(self.roi_height) / 2.0 - mask = (np.abs(cc - cx) <= hw) & (np.abs(rr - cy) <= hh) - return mask.astype(np.float32, copy=False), None - row = int(max(0, min(round(cy), self.det_rows - 1))) - col = int(max(0, min(round(cx), self.det_cols - 1))) - return None, (row, col) - - def _integrate_dp_value( - self, - dp: np.ndarray, - mask: np.ndarray | None, - point_idx: tuple[int, int] | None, - ) -> float: - arr = np.asarray(dp, dtype=np.float32) - if point_idx is not None: - row, col = point_idx - return float(arr[row, col]) - if mask is None: - return 0.0 - return float((arr * mask).sum()) - - def _virtual_image_from_frame_array(self, frame_data: np.ndarray) -> np.ndarray: - arr = np.asarray(frame_data, dtype=np.float32) - if arr.shape != (self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): - raise ValueError( - f"frame_data shape {arr.shape} does not match " - f"({self.shape_rows}, {self.shape_cols}, {self.det_rows}, {self.det_cols})" - ) - mask, point_idx = self._detector_integration_kernel() - if point_idx is not None: - row, col = point_idx - return arr[:, :, row, col].astype(np.float32, copy=False) - return (arr * mask[None, None, :, :]).sum(axis=(2, 3)).astype(np.float32) - - @staticmethod - def _idw_reconstruct( - shape: tuple[int, int], - points: np.ndarray, - values: np.ndarray, - power: float = 2.0, - k_neighbors: int = 16, - ) -> np.ndarray: - if points.size == 0: - return np.zeros(shape, dtype=np.float32) - rr, cc = np.meshgrid( - np.arange(shape[0], dtype=np.float32), - np.arange(shape[1], dtype=np.float32), - indexing="ij", - ) - coords = np.stack([rr.reshape(-1), cc.reshape(-1)], axis=1) - dist_sq = ((coords[:, None, :] - points[None, :, :]) ** 2).sum(axis=2) + 1e-6 - - if k_neighbors > 0 and points.shape[0] > k_neighbors: - idx = np.argpartition(dist_sq, kth=k_neighbors - 1, axis=1)[:, :k_neighbors] - dist_sq = np.take_along_axis(dist_sq, idx, axis=1) - vals_local = values[idx] - else: - vals_local = np.broadcast_to(values[None, :], dist_sq.shape) - - weights = 1.0 / np.power(dist_sq, power / 2.0) - pred = (weights * vals_local).sum(axis=1) / np.maximum(weights.sum(axis=1), 1e-6) - return pred.reshape(shape).astype(np.float32, copy=False) - - def _resolve_reference_virtual_image( - self, - reference: str | np.ndarray, - frame_idx: int, - ) -> tuple[np.ndarray, str]: - if isinstance(reference, str): - key = reference.strip().lower() - if key != "full_raster": - raise ValueError("reference must be 'full_raster' or a NumPy array") - if self.n_frames > 1: - frame = self._data[frame_idx].detach().cpu().numpy() - elif self._data.ndim == 4: - frame = self._data.detach().cpu().numpy() - else: - frame = self._data.detach().cpu().numpy().reshape( - self.shape_rows, self.shape_cols, self.det_rows, self.det_cols - ) - return self._virtual_image_from_frame_array(frame), "full_raster" - - arr = np.asarray(to_numpy(reference), dtype=np.float32) - if arr.shape == (self.shape_rows, self.shape_cols): - return arr.astype(np.float32, copy=False), "virtual_image" - if arr.shape == (self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): - return self._virtual_image_from_frame_array(arr), "frame_data" - if arr.shape == (self.n_frames, self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): - return self._virtual_image_from_frame_array(arr[frame_idx]), "stack_frame_data" - raise ValueError( - "Unsupported reference shape. Expected one of: " - f"(scan_rows, scan_cols), " - f"(scan_rows, scan_cols, det_rows, det_cols), or " - f"(n_frames, scan_rows, scan_cols, det_rows, det_cols)." - ) - - def _extract_sparse_samples(self, frame_idx: int) -> tuple[np.ndarray, np.ndarray]: - mask = self._sparse_mask[frame_idx] - coords = np.argwhere(mask) - if coords.size == 0: - return ( - np.zeros((0, 2), dtype=np.float32), - np.zeros((0,), dtype=np.float32), - ) - - integ_mask, point_idx = self._detector_integration_kernel() - values = np.zeros((coords.shape[0],), dtype=np.float32) - for i, (row, col) in enumerate(coords): - key = (int(frame_idx), int(row), int(col)) - dp = self._sparse_samples.get(key) - if dp is None: - dp = self._get_frame(int(row), int(col)) - values[i] = self._integrate_dp_value(dp, integ_mask, point_idx) - points = coords.astype(np.float32, copy=False) - return points, values - - def ingest_scan_point( + def save_image( self, - row: int, - col: int, - dp: np.ndarray, - frame_idx: int = 0, - dose: float | None = None, - ) -> Self: + path: str | pathlib.Path, + view: str | None = None, + position: tuple[int, int] | None = None, + frame_idx: int | None = None, + format: str | None = None, + include_metadata: bool = True, + metadata_path: str | pathlib.Path | None = None, + include_overlays: bool | None = None, + include_scalebar: bool | None = None, + restore_state: bool = True, + dpi: int | None = None, + ) -> pathlib.Path: """ - Ingest one scanned diffraction pattern into sparse acquisition state. + Save the current visualization as PNG or PDF. Parameters ---------- - row : int - Scan-space row index. - col : int - Scan-space column index. - dp : array_like - Diffraction pattern with shape ``(det_rows, det_cols)``. - frame_idx : int, default 0 - Frame index for 5D data. - dose : float, optional - Dose contribution for this acquisition event. Defaults to ``1.0``. + path : str or pathlib.Path + Output image path. + view : str, optional + One of: "diffraction", "virtual", "fft", "all". + position : tuple[int, int], optional + Temporary scan position override as (row, col) for this export. + frame_idx : int, optional + Temporary frame index override for 5D data. + format : str, optional + "png" or "pdf". If omitted, inferred from file extension. + include_metadata : bool, default True + If True, writes JSON metadata next to the image. + metadata_path : str or pathlib.Path, optional + Override metadata JSON path. + include_overlays : bool, default True + Draw ROI/profile/crosshair overlays on exported panels. + include_scalebar : bool, default True + Draw panel scale bars on exported panels. + restore_state : bool, default True + If True, temporary position/frame overrides are reverted after export. + dpi : int, optional + Export DPI metadata. Returns ------- - Show4DSTEM - Self for method chaining. + pathlib.Path + The written image path. """ - self._ingest_scan_point_core( - row=row, - col=col, - dp=dp, - frame_idx=frame_idx, - dose=1.0 if dose is None else float(dose), - refresh=True, - ) - self._record_export_event( - { - "export_kind": "ingest_scan_point", - "frame_idx": int(self._validate_sparse_frame_idx(frame_idx)), - "row": int(row), - "col": int(col), - "dose": float(1.0 if dose is None else dose), - } - ) - return self + from PIL import Image - def ingest_scan_block( - self, - rows: list[int] | np.ndarray, - cols: list[int] | np.ndarray, - dp_block: np.ndarray, - frame_idx: int = 0, - ) -> Self: - """ - Ingest multiple scanned diffraction patterns in one call. + export_path = pathlib.Path(path) + view_key = self._validate_export_view(view) + fmt = self._resolve_export_format(export_path, format) + dpi_value = 300 if dpi is None else int(dpi) + overlays_enabled = True if include_overlays is None else bool(include_overlays) + scalebar_enabled = True if include_scalebar is None else bool(include_scalebar) + if dpi_value <= 0: + raise ValueError(f"dpi must be > 0, got {dpi_value}") - Parameters - ---------- - rows : list[int] or np.ndarray - Row indices for each pattern in ``dp_block``. - cols : list[int] or np.ndarray - Column indices for each pattern in ``dp_block``. - dp_block : np.ndarray - Diffraction stack with shape ``(n_points, det_rows, det_cols)``. - frame_idx : int, default 0 - Frame index for 5D data. + export_path.parent.mkdir(parents=True, exist_ok=True) - Returns - ------- - Show4DSTEM - Self for method chaining. - """ - rows_arr = np.asarray(rows, dtype=np.int64).reshape(-1) - cols_arr = np.asarray(cols, dtype=np.int64).reshape(-1) - if rows_arr.size != cols_arr.size: - raise ValueError("rows and cols must have the same length") - - block = np.asarray(to_numpy(dp_block), dtype=np.float32) - if block.ndim == 2: - block = block[None, ...] - if block.ndim != 3 or block.shape[1:] != (self.det_rows, self.det_cols): - raise ValueError( - f"dp_block shape must be (n_points, {self.det_rows}, {self.det_cols}), got {block.shape}" - ) - if block.shape[0] != rows_arr.size: - raise ValueError( - f"dp_block has {block.shape[0]} patterns but rows/cols specify {rows_arr.size} points" - ) + prev_row, prev_col = self.pos_row, self.pos_col + prev_frame = self.frame_idx + meta_path: pathlib.Path | None = None + export_row = int(self.pos_row) + export_col = int(self.pos_col) + export_frame = int(self.frame_idx) - frame_i = self._validate_sparse_frame_idx(frame_idx) - for idx in range(rows_arr.size): - self._ingest_scan_point_core( - row=int(rows_arr[idx]), - col=int(cols_arr[idx]), - dp=block[idx], - frame_idx=frame_i, - dose=1.0, - refresh=False, - ) + try: + if frame_idx is not None: + self.frame_idx = self._validate_frame_idx(frame_idx) + if position is not None: + row, col = self._validate_position(position) + self.pos_row = row + self.pos_col = col + export_row = int(self.pos_row) + export_col = int(self.pos_col) + export_frame = int(self.frame_idx) - self._compute_virtual_image_from_roi() - self._update_frame() - self._record_export_event( - { - "export_kind": "ingest_scan_block", - "frame_idx": int(frame_i), - "n_points": int(rows_arr.size), - } - ) - return self + if view_key == "diffraction": + image, dp_meta = self._render_panel_image( + "diffraction", overlays_enabled, scalebar_enabled + ) + render_meta = {"diffraction": dp_meta} + elif view_key == "virtual": + image, vi_meta = self._render_panel_image( + "virtual", overlays_enabled, scalebar_enabled + ) + render_meta = {"virtual": vi_meta} + elif view_key == "fft": + image, fft_meta = self._render_panel_image( + "fft", overlays_enabled, scalebar_enabled + ) + render_meta = {"fft": fft_meta} + else: + panel_images = [] + render_meta = {} + dp_img, dp_meta = self._render_panel_image( + "diffraction", overlays_enabled, scalebar_enabled + ) + vi_img, vi_meta = self._render_panel_image( + "virtual", overlays_enabled, scalebar_enabled + ) + panel_images.extend([dp_img, vi_img]) + render_meta = {"diffraction": dp_meta, "virtual": vi_meta} + if self.show_fft: + fft_img, fft_meta = self._render_panel_image( + "fft", overlays_enabled, scalebar_enabled + ) + panel_images.append(fft_img) + render_meta["fft"] = fft_meta + image = self._compose_horizontal(panel_images) - def get_sparse_state(self) -> dict[str, Any]: - """ - Return sparse acquisition state for checkpointing or replay. - - Returns - ------- - dict - Sparse state with sampling mask, sampled diffraction stack, - sampled-point coordinates, and dose map. - """ - coords = np.argwhere(self._sparse_mask) - sampled_points = [ - {"frame_idx": int(f), "row": int(r), "col": int(c)} - for (f, r, c) in coords - ] - if coords.size: - sampled_data = np.stack( - [ - self._sparse_samples.get((int(f), int(r), int(c)), self._get_frame(int(r), int(c))) - for (f, r, c) in coords - ], - axis=0, - ).astype(np.float32, copy=False) - else: - sampled_data = np.zeros((0, self.det_rows, self.det_cols), dtype=np.float32) - - mask_payload = self._sparse_mask[0].copy() if self.n_frames <= 1 else self._sparse_mask.copy() - dose_payload = self._dose_map[0].copy() if self.n_frames <= 1 else self._dose_map.copy() - return { - **build_json_header("Show4DSTEM"), - "format": "json", - "export_kind": "sparse_state_snapshot", - "frame_idx": int(self.frame_idx), - "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, - "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, - "mask": mask_payload, - "sampled_data": sampled_data, - "sampled_points": sampled_points, - "dose_map": dose_payload, - "n_sampled": int(len(sampled_points)), - "total_dose": float(self._dose_map.sum()), - } - - def set_sparse_state( - self, - mask: np.ndarray, - sampled_data: np.ndarray, - ) -> Self: - """ - Restore sparse acquisition state from mask + sampled data. - - Parameters - ---------- - mask : np.ndarray - Boolean scan mask. Shape ``(scan_rows, scan_cols)`` for 4D, - or ``(n_frames, scan_rows, scan_cols)`` for 5D. - sampled_data : np.ndarray - Either compact stack ``(n_sampled, det_rows, det_cols)`` - matching row-major ``mask`` order, or dense data aligned to mask: - ``(scan_rows, scan_cols, det_rows, det_cols)`` for 4D, - ``(n_frames, scan_rows, scan_cols, det_rows, det_cols)`` for 5D. - - Returns - ------- - Show4DSTEM - Self for method chaining. - """ - mask_3d = self._normalize_sparse_mask(mask) - coords = np.argwhere(mask_3d) - - payload = np.asarray(to_numpy(sampled_data), dtype=np.float32) - n_points = int(coords.shape[0]) - - if payload.ndim == 3: - if payload.shape[0] != n_points or payload.shape[1:] != (self.det_rows, self.det_cols): - raise ValueError( - f"Compact sampled_data must be (n_sampled, {self.det_rows}, {self.det_cols}); " - f"got {payload.shape} for n_sampled={n_points}" - ) - compact = payload - elif self.n_frames <= 1 and payload.shape == (self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): - compact = np.stack( - [payload[int(r), int(c)] for (_, r, c) in coords], - axis=0, - ) if n_points else np.zeros((0, self.det_rows, self.det_cols), dtype=np.float32) - elif payload.shape == ( - self.n_frames, - self.shape_rows, - self.shape_cols, - self.det_rows, - self.det_cols, - ): - compact = np.stack( - [payload[int(f), int(r), int(c)] for (f, r, c) in coords], - axis=0, - ) if n_points else np.zeros((0, self.det_rows, self.det_cols), dtype=np.float32) - else: - raise ValueError( - "Unsupported sampled_data shape for set_sparse_state. " - "Use compact (n_sampled, det_rows, det_cols) or dense per-mask arrays." - ) - - self._sparse_samples = {} - self._sparse_order = [] - self._sparse_mask = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=bool) - self._dose_map = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=np.float32) - - for idx, (frame_idx, row, col) in enumerate(coords): - self._ingest_scan_point_core( - row=int(row), - col=int(col), - dp=compact[idx], - frame_idx=int(frame_idx), - dose=1.0, - refresh=False, - ) - - self._compute_virtual_image_from_roi() - self._update_frame() - self._record_export_event( - { - "export_kind": "set_sparse_state", - "n_sampled": int(n_points), - } - ) - return self - - def _resolve_proposal_count( - self, - k: int, - frame_idx: int, - budget: dict[str, Any] | None, - ) -> int: - count = int(k) - if count < 1: - raise ValueError(f"k must be >= 1, got {k}") - if budget is None: - return count - - existing_points = int(self._sparse_mask[frame_idx].sum()) - existing_dose = float(self._dose_map[frame_idx].sum()) - total_points = int(self.shape_rows * self.shape_cols) - - if "max_new_points" in budget: - count = min(count, int(budget["max_new_points"])) - if "max_total_points" in budget: - count = min(count, max(0, int(budget["max_total_points"]) - existing_points)) - if "max_total_fraction" in budget: - allowed_total = int(round(float(budget["max_total_fraction"]) * total_points)) - count = min(count, max(0, allowed_total - existing_points)) - if "max_total_dose" in budget: - dose_per_point = float(budget.get("dose_per_point", 1.0)) - if dose_per_point <= 0: - raise ValueError("budget['dose_per_point'] must be > 0") - remaining = float(budget["max_total_dose"]) - existing_dose - count = min(count, max(0, int(math.floor(remaining / dose_per_point)))) - return max(0, int(count)) - - def propose_next_points( - self, - k: int, - strategy: str = "adaptive", - budget: dict[str, Any] | None = None, - ) -> list[tuple[int, int]]: - """ - Propose next scan points from current sparse acquisition state. - - Parameters - ---------- - k : int - Maximum number of new points to propose. - strategy : str, default "adaptive" - Proposal strategy: ``"adaptive"``, ``"random"``, or ``"raster"``. - budget : dict, optional - Optional constraints and strategy parameters. Supported keys: - ``frame_idx``, ``max_new_points``, ``max_total_points``, - ``max_total_fraction``, ``max_total_dose``, ``dose_per_point``, - ``roi_mask``, ``seed``, ``min_spacing``, ``step``, - ``local_window``, ``dose_lambda``, ``weights``, ``bidirectional``. - - Returns - ------- - list[tuple[int, int]] - Proposed ``(row, col)`` scan coordinates. - """ - budget_dict = {} if budget is None else dict(budget) - strategy_key = str(strategy).strip().lower() - if strategy_key not in {"adaptive", "random", "raster"}: - raise ValueError("strategy must be one of: adaptive, random, raster") - - frame_idx = self._validate_sparse_frame_idx(budget_dict.get("frame_idx", self.frame_idx)) - n_select = self._resolve_proposal_count(int(k), frame_idx, budget_dict) - if n_select <= 0: - return [] - - sampled_mask = self._sparse_mask[frame_idx].copy() - allowed_mask = ~sampled_mask - roi_mask_raw = budget_dict.get("roi_mask", None) - if roi_mask_raw is not None: - roi_mask = np.asarray(roi_mask_raw, dtype=bool) - if roi_mask.shape != (self.shape_rows, self.shape_cols): - raise ValueError( - f"roi_mask shape {roi_mask.shape} must match " - f"scan_shape ({self.shape_rows}, {self.shape_cols})" - ) - allowed_mask &= roi_mask - - proposals: list[tuple[int, int]] = [] - if strategy_key == "adaptive": - local_window = int(budget_dict.get("local_window", 5)) - if local_window < 1: - raise ValueError("budget['local_window'] must be >= 1") - min_spacing = int(budget_dict.get("min_spacing", 2)) - if min_spacing < 0: - raise ValueError("budget['min_spacing'] must be >= 0") - dose_lambda = float(budget_dict.get("dose_lambda", 0.25)) - if not np.isfinite(dose_lambda): - raise ValueError("budget['dose_lambda'] must be finite") - - default_weights = { - "vi_gradient": 0.4, - "vi_local_std": 0.3, - "dp_variance": 0.3, - } - merged_weights = dict(default_weights) - raw_weights = budget_dict.get("weights", None) - if raw_weights is not None: - for key, value in dict(raw_weights).items(): - if key not in default_weights: - raise ValueError( - f"Unsupported adaptive weight '{key}'. " - f"Supported: {', '.join(default_weights.keys())}" - ) - merged_weights[key] = float(value) - weight_sum = sum(max(0.0, float(v)) for v in merged_weights.values()) - if weight_sum <= 0: - raise ValueError("At least one adaptive weight must be > 0") - weights = {k: max(0.0, float(v)) / weight_sum for k, v in merged_weights.items()} - - vi = self._virtual_image_for_frame(frame_idx) - grad_row, grad_col = np.gradient(vi) - vi_gradient = np.hypot(grad_row, grad_col).astype(np.float32) - mean_local = self._box_mean_map(vi, local_window) - mean_sq_local = self._box_mean_map(vi * vi, local_window) - vi_local_std = np.sqrt(np.maximum(mean_sq_local - mean_local * mean_local, 0.0)).astype(np.float32) - dp_variance = self._dp_variance_map(frame_idx=frame_idx) - - utility = ( - weights["vi_gradient"] * self._normalize_score_map(vi_gradient) - + weights["vi_local_std"] * self._normalize_score_map(vi_local_std) - + weights["dp_variance"] * self._normalize_score_map(dp_variance) - ).astype(np.float32) - - frame_dose = self._dose_map[frame_idx].astype(np.float32, copy=False) - if float(frame_dose.max()) > 0: - utility = utility - float(dose_lambda) * (frame_dose / float(frame_dose.max())) - - picks = self._select_spaced_topk( - scores=utility, - k=n_select, - min_spacing=min_spacing, - allowed_mask=allowed_mask, - excluded_mask=np.zeros_like(allowed_mask, dtype=bool), - ) - proposals = [(int(r), int(c)) for (r, c) in picks] - elif strategy_key == "random": - coords = np.argwhere(allowed_mask) - if coords.size: - seed = budget_dict.get("seed", None) - rng = np.random.default_rng(None if seed is None else int(seed)) - n_take = min(n_select, int(coords.shape[0])) - idx = rng.choice(coords.shape[0], size=n_take, replace=False) - chosen = coords[idx] - proposals = [(int(r), int(c)) for r, c in chosen] - else: - step = int(budget_dict.get("step", 1)) - if step < 1: - raise ValueError("budget['step'] must be >= 1") - bidirectional = bool(budget_dict.get("bidirectional", True)) - for row in range(0, self.shape_rows, step): - cols = list(range(0, self.shape_cols, step)) - if bidirectional and ((row // step) % 2 == 1): - cols.reverse() - for col in cols: - if allowed_mask[row, col]: - proposals.append((int(row), int(col))) - if len(proposals) >= n_select: - break - if len(proposals) >= n_select: - break - - self._record_export_event( - { - "export_kind": "propose_next_points", - "strategy": strategy_key, - "frame_idx": int(frame_idx), - "k_requested": int(k), - "k_returned": int(len(proposals)), - } - ) - return proposals - - def evaluate_against_reference( - self, - reference: str | np.ndarray = "full_raster", - metrics: list[str] | None = None, - ) -> dict[str, Any]: - """ - Evaluate sparse-sampled reconstruction against a reference image. - - Parameters - ---------- - reference : str or np.ndarray, default "full_raster" - Reference target. ``"full_raster"`` uses the current full dataset - and current ROI integration settings. Arrays are also accepted - (virtual image or full diffraction stack; see method docs). - metrics : list[str], optional - Metric names to compute. Supported: ``"rmse"``, ``"nrmse"``, - ``"mae"``, ``"psnr"``. - - Returns - ------- - dict - Evaluation summary including sampled fraction and metric values. - """ - metric_names = ( - ["rmse", "nrmse", "mae", "psnr"] - if metrics is None - else [str(name).strip().lower() for name in metrics] - ) - supported = {"rmse", "nrmse", "mae", "psnr"} - unknown = [name for name in metric_names if name not in supported] - if unknown: - raise ValueError(f"Unsupported metrics: {unknown}. Supported: {sorted(supported)}") - - frame_idx = int(self.frame_idx if self.n_frames <= 1 else self._validate_sparse_frame_idx(self.frame_idx)) - points, values = self._extract_sparse_samples(frame_idx) - if points.shape[0] == 0: - raise ValueError("No sparse samples available for evaluation. Ingest points first.") - - reference_vi, reference_kind = self._resolve_reference_virtual_image(reference, frame_idx) - reconstruction = self._idw_reconstruct( - shape=(self.shape_rows, self.shape_cols), - points=points, - values=values, - power=2.0, - k_neighbors=16, - ) - - ref = np.asarray(reference_vi, dtype=np.float32) - pred = np.asarray(reconstruction, dtype=np.float32) - diff = pred - ref - mse = float(np.mean(diff * diff)) - rmse = float(np.sqrt(mse)) - mae = float(np.mean(np.abs(diff))) - ref_range = float(ref.max() - ref.min()) + 1e-6 - nrmse = float(rmse / ref_range) - peak = float(max(float(ref.max()), 1e-6)) - psnr = 120.0 if mse <= 1e-12 else float(20.0 * np.log10(peak) - 10.0 * np.log10(mse)) - - metric_values = { - "rmse": rmse, - "nrmse": nrmse, - "mae": mae, - "psnr": psnr, - } - selected_metrics = {name: float(metric_values[name]) for name in metric_names} - - summary = { - "reference_kind": reference_kind, - "frame_idx": int(frame_idx), - "n_sampled": int(points.shape[0]), - "sampled_fraction": float(points.shape[0] / max(1, self.shape_rows * self.shape_cols)), - "metrics": selected_metrics, - "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, - "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, - } - self._record_export_event( - { - "export_kind": "evaluate_against_reference", - "reference_kind": reference_kind, - "frame_idx": int(frame_idx), - "n_sampled": int(points.shape[0]), - "sampled_fraction": float(summary["sampled_fraction"]), - "metrics": selected_metrics, - } - ) - return summary - - def export_session_bundle( - self, - path: str | pathlib.Path, - ) -> pathlib.Path: - """ - Export a reproducible session bundle for sparse/adaptive workflows. - - The bundle includes widget state, sparse-state arrays, a current view - image with metadata, and the reproducibility report. - - Parameters - ---------- - path : str or pathlib.Path - Output directory for bundle files. - - Returns - ------- - pathlib.Path - Path to the bundle manifest JSON. - """ - bundle_dir = pathlib.Path(path) - bundle_dir.mkdir(parents=True, exist_ok=True) - - state_path = bundle_dir / "widget_state.json" - self.save(state_path) - - sparse_state = self.get_sparse_state() - sparse_npz_path = bundle_dir / "sparse_state.npz" - np.savez_compressed( - sparse_npz_path, - mask=sparse_state["mask"], - sampled_data=sparse_state["sampled_data"], - dose_map=sparse_state["dose_map"], - ) - - sparse_points_path = bundle_dir / "sparse_points.json" - sparse_points_payload = { - **build_json_header("Show4DSTEM"), - "format": "json", - "export_kind": "sparse_points", - "n_sampled": int(sparse_state["n_sampled"]), - "sampled_points": sparse_state["sampled_points"], - } - sparse_points_path.write_text(json.dumps(sparse_points_payload, indent=2)) - - image_path = bundle_dir / "current_all.png" - image_written = self.save_image( - image_path, - view="all", - include_metadata=True, - include_overlays=True, - include_scalebar=True, - ) - image_meta_path = image_written.with_suffix(".json") - - report_path = self.save_reproducibility_report(bundle_dir / "reproducibility_report.json") - - manifest_path = bundle_dir / "session_bundle_manifest.json" - manifest_payload = { - **build_json_header("Show4DSTEM"), - "format": "json", - "export_kind": "session_bundle", - "bundle_path": str(bundle_dir), - "created_utc": datetime.now(timezone.utc).isoformat(), - "session_id": self._export_session_id, - "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, - "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, - "sparse_summary": { - "n_sampled": int(sparse_state["n_sampled"]), - "sampled_fraction": float( - sparse_state["n_sampled"] / max(1, self.shape_rows * self.shape_cols * self.n_frames) - ), - "total_dose": float(sparse_state["total_dose"]), - }, - "files": { - "state": str(state_path), - "sparse_npz": str(sparse_npz_path), - "sparse_points_json": str(sparse_points_path), - "image": str(image_written), - "image_metadata": str(image_meta_path), - "reproducibility_report": str(report_path), - }, - } - manifest_path.write_text(json.dumps(manifest_payload, indent=2)) - - self._record_export_event( - { - "export_kind": "session_bundle", - "n_sampled": int(sparse_state["n_sampled"]), - "outputs": [ - self._build_file_record(state_path), - self._build_file_record(sparse_npz_path), - self._build_file_record(sparse_points_path), - self._build_file_record(image_written, metadata_path=image_meta_path), - self._build_file_record(report_path), - self._build_file_record(manifest_path), - ], - } - ) - return manifest_path - - def _normalize_score_map(self, values: np.ndarray) -> np.ndarray: - arr = np.asarray(values, dtype=np.float32) - if arr.size == 0: - return np.zeros_like(arr, dtype=np.float32) - vmin = float(np.percentile(arr, 1.0)) - vmax = float(np.percentile(arr, 99.0)) - if vmax <= vmin: - return np.zeros_like(arr, dtype=np.float32) - return np.clip((arr - vmin) / (vmax - vmin), 0.0, 1.0).astype(np.float32) - - def _box_mean_map(self, values: np.ndarray, window: int) -> np.ndarray: - arr = np.asarray(values, dtype=np.float32) - win = int(window) - if win <= 1: - return arr.copy() - if win % 2 == 0: - win += 1 - pad = win // 2 - padded = np.pad(arr, ((pad, pad), (pad, pad)), mode="reflect") - integral = np.pad(padded, ((1, 0), (1, 0)), mode="constant").cumsum(axis=0).cumsum(axis=1) - sums = ( - integral[win:, win:] - - integral[:-win, win:] - - integral[win:, :-win] - + integral[:-win, :-win] - ) - return (sums / float(win * win)).astype(np.float32) - - def _dp_variance_map(self, frame_idx: int | None = None) -> np.ndarray: - if frame_idx is None or self.n_frames <= 1: - data = self._frame_data - else: - idx = self._validate_sparse_frame_idx(frame_idx) - data = self._data[idx] - if data.ndim == 4: - variance = data.var(dim=(2, 3), unbiased=False) - return variance.detach().cpu().numpy().astype(np.float32, copy=False) - variance = data.var(dim=(1, 2), unbiased=False) - return variance.detach().cpu().numpy().reshape(self.shape_rows, self.shape_cols).astype(np.float32, copy=False) - - def _build_coarse_points(self, step: int, bidirectional: bool) -> list[tuple[int, int]]: - points: list[tuple[int, int]] = [] - for r in range(0, self.shape_rows, step): - cols = list(range(0, self.shape_cols, step)) - if bidirectional and ((r // step) % 2 == 1): - cols.reverse() - for c in cols: - points.append((int(r), int(c))) - return points - - def _select_spaced_topk( - self, - scores: np.ndarray, - k: int, - min_spacing: int, - allowed_mask: np.ndarray, - excluded_mask: np.ndarray, - ) -> list[tuple[int, int]]: - work = np.asarray(scores, dtype=np.float32).copy() - work[~allowed_mask] = -np.inf - work[excluded_mask] = -np.inf - selected: list[tuple[int, int]] = [] - radius = max(0, int(min_spacing)) - - for _ in range(int(max(0, k))): - flat_idx = int(np.argmax(work)) - best_score = float(work.flat[flat_idx]) - if not np.isfinite(best_score): - break - row, col = np.unravel_index(flat_idx, work.shape) - selected.append((int(row), int(col))) - if radius == 0: - work[row, col] = -np.inf - continue - r0 = max(0, row - radius) - r1 = min(work.shape[0], row + radius + 1) - c0 = max(0, col - radius) - c1 = min(work.shape[1], col + radius + 1) - rr, cc = np.ogrid[r0:r1, c0:c1] - neighborhood = (rr - row) ** 2 + (cc - col) ** 2 <= radius ** 2 - block = work[r0:r1, c0:c1] - block[neighborhood] = -np.inf - return selected - - def _nearest_neighbor_order( - self, - points: list[tuple[int, int]], - start: tuple[int, int] | None = None, - ) -> list[tuple[int, int]]: - remaining = [tuple(map(int, pt)) for pt in points] - if not remaining: - return [] - - if start is None: - current = remaining.pop(0) - else: - sr, sc = int(start[0]), int(start[1]) - start_idx = min( - range(len(remaining)), - key=lambda i: (remaining[i][0] - sr) ** 2 + (remaining[i][1] - sc) ** 2, - ) - current = remaining.pop(start_idx) - - ordered = [current] - while remaining: - cr, cc = current - next_idx = min( - range(len(remaining)), - key=lambda i: (remaining[i][0] - cr) ** 2 + (remaining[i][1] - cc) ** 2, - ) - current = remaining.pop(next_idx) - ordered.append(current) - return ordered - - def save_image( - self, - path: str | pathlib.Path, - view: str | None = None, - position: tuple[int, int] | None = None, - frame_idx: int | None = None, - format: str | None = None, - include_metadata: bool = True, - metadata_path: str | pathlib.Path | None = None, - include_overlays: bool | None = None, - include_scalebar: bool | None = None, - restore_state: bool = True, - dpi: int | None = None, - ) -> pathlib.Path: - """ - Save the current visualization as PNG or PDF. - - Parameters - ---------- - path : str or pathlib.Path - Output image path. - view : str, optional - One of: "diffraction", "virtual", "fft", "all". - position : tuple[int, int], optional - Temporary scan position override as (row, col) for this export. - frame_idx : int, optional - Temporary frame index override for 5D data. - format : str, optional - "png" or "pdf". If omitted, inferred from file extension. - include_metadata : bool, default True - If True, writes JSON metadata next to the image. - metadata_path : str or pathlib.Path, optional - Override metadata JSON path. - include_overlays : bool, optional - Draw ROI/profile/crosshair overlays on exported panels. - Defaults to ``export_include_overlays``. - include_scalebar : bool, optional - Draw panel scale bars on exported panels. - Defaults to ``export_include_scalebar``. - restore_state : bool, default True - If True, temporary position/frame overrides are reverted after export. - dpi : int, optional - Export DPI metadata. - - Returns - ------- - pathlib.Path - The written image path. - """ - from PIL import Image - - export_path = pathlib.Path(path) - view_key = self._validate_export_view(view) - fmt = self._resolve_export_format(export_path, format) - dpi_value = int(self.export_default_dpi if dpi is None else dpi) - overlays_enabled = ( - bool(self.export_include_overlays) - if include_overlays is None - else bool(include_overlays) - ) - scalebar_enabled = ( - bool(self.export_include_scalebar) - if include_scalebar is None - else bool(include_scalebar) - ) - - if dpi_value <= 0: - raise ValueError(f"dpi must be > 0, got {dpi_value}") - - export_path.parent.mkdir(parents=True, exist_ok=True) - - prev_row, prev_col = self.pos_row, self.pos_col - prev_frame = self.frame_idx - meta_path: pathlib.Path | None = None - export_row = int(self.pos_row) - export_col = int(self.pos_col) - export_frame = int(self.frame_idx) - - try: - if frame_idx is not None: - self.frame_idx = self._validate_frame_idx(frame_idx) - if position is not None: - row, col = self._validate_position(position) - self.pos_row = row - self.pos_col = col - export_row = int(self.pos_row) - export_col = int(self.pos_col) - export_frame = int(self.frame_idx) - - if view_key == "diffraction": - image, dp_meta = self._render_panel_image( - "diffraction", overlays_enabled, scalebar_enabled - ) - render_meta = {"diffraction": dp_meta} - elif view_key == "virtual": - image, vi_meta = self._render_panel_image( - "virtual", overlays_enabled, scalebar_enabled - ) - render_meta = {"virtual": vi_meta} - elif view_key == "fft": - image, fft_meta = self._render_panel_image( - "fft", overlays_enabled, scalebar_enabled - ) - render_meta = {"fft": fft_meta} - else: - panel_images = [] - render_meta = {} - dp_img, dp_meta = self._render_panel_image( - "diffraction", overlays_enabled, scalebar_enabled - ) - vi_img, vi_meta = self._render_panel_image( - "virtual", overlays_enabled, scalebar_enabled - ) - panel_images.extend([dp_img, vi_img]) - render_meta = {"diffraction": dp_meta, "virtual": vi_meta} - if self.show_fft: - fft_img, fft_meta = self._render_panel_image( - "fft", overlays_enabled, scalebar_enabled - ) - panel_images.append(fft_img) - render_meta["fft"] = fft_meta - image = self._compose_horizontal(panel_images) - - if fmt == "pdf": - Image.init() - image = image.convert("RGB") - image.save(export_path, format="PDF", resolution=dpi_value) - else: - image.save(export_path, format="PNG", dpi=(dpi_value, dpi_value)) + if fmt == "pdf": + Image.init() + image = image.convert("RGB") + image.save(export_path, format="PDF", resolution=dpi_value) + else: + image.save(export_path, format="PNG", dpi=(dpi_value, dpi_value)) if include_metadata: meta_path = ( @@ -3083,870 +1921,72 @@ def save_image( self.pos_row = prev_row self.pos_col = prev_col - self._record_export_event( - { - "export_kind": "single_view_image", - "view": view_key, - "format": fmt, - "position": {"row": export_row, "col": export_col}, - "frame_idx": export_frame, - "include_overlays": bool(overlays_enabled), - "include_scalebar": bool(scalebar_enabled), - "dpi": int(dpi_value), - "outputs": [ - self._build_file_record(export_path, metadata_path=meta_path), - ], - } - ) return export_path - def _build_preset_payload(self) -> dict[str, Any]: - return { - "detector": { - "center_row": float(self.center_row), - "center_col": float(self.center_col), - "bf_radius": float(self.bf_radius), - "roi_active": bool(self.roi_active), - "roi_mode": self.roi_mode, - "roi_center_row": float(self.roi_center_row), - "roi_center_col": float(self.roi_center_col), - "roi_radius": float(self.roi_radius), - "roi_radius_inner": float(self.roi_radius_inner), - "roi_width": float(self.roi_width), - "roi_height": float(self.roi_height), - }, - "vi_roi": { - "mode": self.vi_roi_mode, - "center_row": float(self.vi_roi_center_row), - "center_col": float(self.vi_roi_center_col), - "radius": float(self.vi_roi_radius), - "width": float(self.vi_roi_width), - "height": float(self.vi_roi_height), - }, - "display": { - "mask_dc": bool(self.mask_dc), - "dp_colormap": self.dp_colormap, - "vi_colormap": self.vi_colormap, - "fft_colormap": self.fft_colormap, - "dp_scale_mode": self.dp_scale_mode, - "vi_scale_mode": self.vi_scale_mode, - "fft_scale_mode": self.fft_scale_mode, - "dp_power_exp": float(self.dp_power_exp), - "vi_power_exp": float(self.vi_power_exp), - "fft_power_exp": float(self.fft_power_exp), - "dp_vmin_pct": float(self.dp_vmin_pct), - "dp_vmax_pct": float(self.dp_vmax_pct), - "vi_vmin_pct": float(self.vi_vmin_pct), - "vi_vmax_pct": float(self.vi_vmax_pct), - "fft_vmin_pct": float(self.fft_vmin_pct), - "fft_vmax_pct": float(self.fft_vmax_pct), - "fft_auto": bool(self.fft_auto), - "show_fft": bool(self.show_fft), - "dp_show_colorbar": bool(self.dp_show_colorbar), - "profile_line": self.profile_line, - "profile_width": int(self.profile_width), - }, - "export": self._export_settings_metadata(), - } - - def _apply_preset_payload(self, preset: dict[str, Any]) -> None: - detector = preset.get("detector", {}) - vi_roi = preset.get("vi_roi", {}) - display = preset.get("display", {}) - export = preset.get("export", {}) - - detector_map = { - "center_row": "center_row", - "center_col": "center_col", - "bf_radius": "bf_radius", - "roi_active": "roi_active", - "roi_mode": "roi_mode", - "roi_center_row": "roi_center_row", - "roi_center_col": "roi_center_col", - "roi_radius": "roi_radius", - "roi_radius_inner": "roi_radius_inner", - "roi_width": "roi_width", - "roi_height": "roi_height", - } - for key, trait_name in detector_map.items(): - if key in detector and hasattr(self, trait_name): - setattr(self, trait_name, detector[key]) - - vi_roi_map = { - "mode": "vi_roi_mode", - "center_row": "vi_roi_center_row", - "center_col": "vi_roi_center_col", - "radius": "vi_roi_radius", - "width": "vi_roi_width", - "height": "vi_roi_height", - } - for key, trait_name in vi_roi_map.items(): - if key in vi_roi and hasattr(self, trait_name): - setattr(self, trait_name, vi_roi[key]) - - _display_keys = { - "dp_colormap", "vi_colormap", "fft_colormap", - "dp_scale_mode", "vi_scale_mode", "fft_scale_mode", - "dp_power_exp", "vi_power_exp", "fft_power_exp", - "dp_vmin_pct", "dp_vmax_pct", "vi_vmin_pct", "vi_vmax_pct", - "fft_vmin_pct", "fft_vmax_pct", "fft_auto", - "mask_dc", "dp_show_colorbar", "show_fft", "fft_window", - "show_controls", - } - for key, value in display.items(): - if key in _display_keys: - setattr(self, key, value) - - export_map = { - "default_view": "export_default_view", - "default_format": "export_default_format", - "include_overlays": "export_include_overlays", - "include_scalebar": "export_include_scalebar", - "dpi": "export_default_dpi", - } - for key, trait_name in export_map.items(): - if key in export and hasattr(self, trait_name): - setattr(self, trait_name, export[key]) - - def save_preset( - self, - name: str, - path: str | pathlib.Path | None = None, - ) -> dict[str, Any]: - preset_name = str(name).strip() - if not preset_name: - raise ValueError("Preset name must be non-empty.") - preset_key = preset_name.lower() - - payload = self._build_preset_payload() - self._named_presets[preset_key] = payload - - if path is not None: - out_path = pathlib.Path(path) - out_path.parent.mkdir(parents=True, exist_ok=True) - serialized = { - **build_json_header("Show4DSTEM"), - "format": "json", - "export_kind": "widget_preset", - "preset_name": preset_name, - "preset": payload, - } - out_path.write_text(json.dumps(serialized, indent=2)) - - return payload - - def load_preset( - self, - name: str, - path: str | pathlib.Path | None = None, - apply: bool = True, - ) -> dict[str, Any]: - preset_name = str(name).strip() - preset_key = preset_name.lower() - if path is not None: - payload = json.loads(pathlib.Path(path).read_text()) - if not isinstance(payload, dict): - raise ValueError("Preset file must contain a JSON object.") - if "preset" in payload: - preset = payload["preset"] - else: - preset = payload - if not isinstance(preset, dict): - raise ValueError("Preset payload must be a JSON object.") - if preset_name: - self._named_presets[preset_key] = preset - else: - if preset_key not in self._named_presets: - raise ValueError( - f"Preset '{preset_name}' not found. Available: {', '.join(self.list_presets())}" - ) - preset = self._named_presets[preset_key] - - if apply: - self._apply_preset_payload(preset) - return preset - def apply_preset(self, name: str) -> Self: preset_name = str(name).strip().lower() - if preset_name == "bf": - self.roi_active = True - self.roi_mode = "circle" - self.roi_center_row = float(self.center_row) - self.roi_center_col = float(self.center_col) - self.roi_radius = float(max(1.0, self.bf_radius)) - return self - if preset_name == "abf": - self.roi_active = True - self.roi_mode = "annular" - self.roi_center_row = float(self.center_row) - self.roi_center_col = float(self.center_col) - self.roi_radius_inner = float(max(0.5, self.bf_radius * 0.5)) - self.roi_radius = float(max(1.0, self.bf_radius)) - return self - if preset_name == "adf": - self.roi_active = True - self.roi_mode = "annular" - self.roi_center_row = float(self.center_row) - self.roi_center_col = float(self.center_col) - self.roi_radius_inner = float(max(1.0, self.bf_radius)) - self.roi_radius = float(max(self.roi_radius_inner + 1.0, self.bf_radius * 2.0)) - return self - if preset_name == "haadf": - self.roi_active = True - self.roi_mode = "annular" - self.roi_center_row = float(self.center_row) - self.roi_center_col = float(self.center_col) - self.roi_radius_inner = float(max(1.0, self.bf_radius * 2.0)) - self.roi_radius = float(max(self.roi_radius_inner + 1.0, self.bf_radius * 4.0)) - return self - - self.load_preset(preset_name, apply=True) - return self - - def _resolve_figure_template(self, template: str) -> tuple[str, list[str], bool]: - key = str(template).strip().lower() - mapping = { - "dp_vi": (["diffraction", "virtual"], False), - "dp_vi_fft": (["diffraction", "virtual", "fft"], False), - "publication_dp_vi": (["diffraction", "virtual"], True), - "publication_dp_vi_fft": (["diffraction", "virtual", "fft"], True), - } - if key not in mapping: - raise ValueError( - f"Unsupported template '{template}'. " - f"Supported: {', '.join(self.list_figure_templates())}" - ) - panels, publication = mapping[key] - return key, panels, publication - - def save_figure( - self, - path: str | pathlib.Path, - template: str = "dp_vi_fft", - position: tuple[int, int] | None = None, - frame_idx: int | None = None, - format: str | None = None, - include_metadata: bool = True, - metadata_path: str | pathlib.Path | None = None, - include_overlays: bool | None = None, - include_scalebar: bool | None = None, - restore_state: bool = True, - dpi: int | None = None, - title: str | None = None, - annotations: dict[str, str] | None = None, - ) -> pathlib.Path: - from PIL import Image, ImageDraw, ImageFont - - export_path = pathlib.Path(path) - template_key, panel_keys, publication_style = self._resolve_figure_template(template) - fmt = self._resolve_export_format(export_path, format) - dpi_value = int(self.export_default_dpi if dpi is None else dpi) - overlays_enabled = ( - bool(self.export_include_overlays) - if include_overlays is None - else bool(include_overlays) - ) - scalebar_enabled = ( - bool(self.export_include_scalebar) - if include_scalebar is None - else bool(include_scalebar) - ) - if dpi_value <= 0: - raise ValueError(f"dpi must be > 0, got {dpi_value}") - - export_path.parent.mkdir(parents=True, exist_ok=True) - font = ImageFont.load_default() - - prev_row, prev_col = self.pos_row, self.pos_col - prev_frame = self.frame_idx - meta_path: pathlib.Path | None = None - + # Batch all trait writes atomically. Without this, each individual + # trait change fires _on_roi_change, and intermediate states (e.g. mode + # just switched to "annular" but radius_inner still stale from the + # previous preset) compute a wrong mask -> black VI flashes before the + # final correct frame. hold_trait_notifications defers observers until + # all 5 traits have committed. + bf = self.bf_radius + center_row = float(self.center_row) + center_col = float(self.center_col) + self._suppress_roi_recompute = True try: - if frame_idx is not None: - self.frame_idx = self._validate_frame_idx(frame_idx) - if position is not None: - row, col = self._validate_position(position) - self.pos_row = row - self.pos_col = col - - panel_images: list[Any] = [] - render_meta: dict[str, Any] = {} - for panel_key in panel_keys: - panel, panel_meta = self._render_panel_image( - panel_key, - include_overlays=overlays_enabled, - include_scalebar=scalebar_enabled, - ) - panel_images.append(panel) - render_meta[panel_key] = panel_meta - - gap = 24 if publication_style else 8 - padding = 24 if publication_style else 10 - label_height = 22 if publication_style else 0 - title_text = title - if title_text is None and publication_style: - if self.n_frames > 1: - title_text = f"4D-STEM Figure ({self.frame_dim_label} {self.frame_idx})" - else: - title_text = "4D-STEM Figure" - title_height = 34 if title_text else 0 - - max_panel_height = max(panel.height for panel in panel_images) - total_width = padding * 2 + sum(panel.width for panel in panel_images) + gap * (len(panel_images) - 1) - total_height = padding * 2 + title_height + label_height + max_panel_height - - figure = Image.new("RGB", (total_width, total_height), color=(255, 255, 255)) - draw = ImageDraw.Draw(figure, mode="RGBA") - - y_title = padding - if title_text: - draw.text((padding, y_title), title_text, fill=(0, 0, 0, 255), font=font) - - y_panels = padding + title_height - if publication_style: - y_panels += label_height - - panel_names = { - "diffraction": "Diffraction", - "virtual": "Virtual", - "fft": "FFT", - } - annotation_map = annotations or {} - - x0 = padding - for idx, panel in enumerate(panel_images): - panel_key = panel_keys[idx] - if publication_style: - draw.text( - (x0, padding + title_height), - panel_names.get(panel_key, panel_key), - fill=(0, 0, 0, 255), - font=font, - ) - - figure.paste(panel, (x0, y_panels)) - - if publication_style: - draw.rectangle( - [(x0, y_panels), (x0 + panel.width - 1, y_panels + panel.height - 1)], - outline=(80, 80, 80, 255), - width=1, - ) - - if panel_key in annotation_map and str(annotation_map[panel_key]).strip(): - text = str(annotation_map[panel_key]).strip() - text_bbox = draw.textbbox((0, 0), text, font=font) - text_w = text_bbox[2] - text_bbox[0] - text_h = text_bbox[3] - text_bbox[1] - tx = x0 + 8 - ty = y_panels + 8 - draw.rectangle( - [(tx - 4, ty - 3), (tx + text_w + 4, ty + text_h + 3)], - fill=(0, 0, 0, 180), - ) - draw.text((tx, ty), text, fill=(255, 255, 255, 255), font=font) - - x0 += panel.width + gap - - if fmt == "pdf": - Image.init() - figure = figure.convert("RGB") - figure.save(export_path, format="PDF", resolution=dpi_value) + if preset_name == "bf": + with self.hold_trait_notifications(): + self.roi_active = True + self.roi_mode = "circle" + self.roi_center_row = center_row + self.roi_center_col = center_col + self.roi_radius = float(max(1.0, bf)) + elif preset_name == "abf": + with self.hold_trait_notifications(): + self.roi_active = True + self.roi_mode = "annular" + self.roi_center_row = center_row + self.roi_center_col = center_col + self.roi_radius_inner = float(max(0.5, bf * 0.5)) + self.roi_radius = float(max(1.0, bf)) + elif preset_name == "adf": + with self.hold_trait_notifications(): + self.roi_active = True + self.roi_mode = "annular" + self.roi_center_row = center_row + self.roi_center_col = center_col + self.roi_radius_inner = float(max(1.0, bf)) + self.roi_radius = float(max(bf + 1.0, bf * 2.0)) + elif preset_name == "haadf": + with self.hold_trait_notifications(): + self.roi_active = True + self.roi_mode = "annular" + self.roi_center_row = center_row + self.roi_center_col = center_col + self.roi_radius_inner = float(max(1.0, bf * 2.0)) + self.roi_radius = float(max(bf * 2.0 + 1.0, bf * 4.0)) else: - figure.save(export_path, format="PNG", dpi=(dpi_value, dpi_value)) - - if include_metadata: - meta_path = ( - pathlib.Path(metadata_path) - if metadata_path is not None - else export_path.with_suffix(".json") - ) - metadata = self._build_image_export_metadata( - export_path=export_path, - view_key="figure", - fmt=fmt, - render_meta=render_meta, - include_overlays=overlays_enabled, - include_scalebar=scalebar_enabled, - export_kind="figure_template", - extra={ - "template": template_key, - "panels": panel_keys, - "publication_style": bool(publication_style), - "title": title_text or "", - "annotations": annotation_map, - "dpi": int(dpi_value), - }, - ) - meta_path.write_text(json.dumps(metadata, indent=2)) - finally: - if restore_state: - self.frame_idx = prev_frame - self.pos_row = prev_row - self.pos_col = prev_col - - self._record_export_event( - { - "export_kind": "figure_template", - "template": template_key, - "format": fmt, - "dpi": int(dpi_value), - "include_overlays": bool(overlays_enabled), - "include_scalebar": bool(scalebar_enabled), - "outputs": [ - self._build_file_record(export_path, metadata_path=meta_path), - ], - } - ) - return export_path - - def _resolve_frame_sequence( - self, - frame_indices: list[int] | None, - frame_range: tuple[int, int] | None, - ) -> list[int]: - if frame_indices is not None and frame_range is not None: - raise ValueError("Use either frame_indices or frame_range, not both.") - - if frame_indices is not None: - if len(frame_indices) == 0: - raise ValueError("frame_indices cannot be empty.") - return [self._validate_frame_idx(idx) for idx in frame_indices] - - if frame_range is not None: - if len(frame_range) != 2: - raise ValueError("frame_range must be a (start, end) tuple.") - start, end = int(frame_range[0]), int(frame_range[1]) - if start > end: - raise ValueError("frame_range start must be <= end.") - return [self._validate_frame_idx(idx) for idx in range(start, end + 1)] - - return [int(i) for i in range(self.n_frames)] - - def _resolve_position_sequence( - self, - mode: str, - path_points: list[tuple[int, int]] | None, - raster_step: int, - raster_bidirectional: bool, - ) -> list[tuple[int, int]]: - if mode == "path": - points = self._path_points if path_points is None else path_points - if not points: - raise ValueError( - "Path mode requires points via set_path(...) or path_points=..." - ) - return [self._validate_position((int(r), int(c))) for r, c in points] - - if mode == "raster": - step = int(raster_step) - if step < 1: - raise ValueError("raster_step must be >= 1") - points: list[tuple[int, int]] = [] - for r in range(0, self.shape_rows, step): - cols = list(range(0, self.shape_cols, step)) - if raster_bidirectional and ((r // step) % 2 == 1): - cols.reverse() - for c in cols: - points.append((int(r), int(c))) - return points - - raise ValueError(f"Unsupported position sequence mode '{mode}'") - - def suggest_adaptive_path( - self, - coarse_step: int = 4, - target_fraction: float = 0.25, - min_spacing: int = 2, - include_coarse: bool = True, - coarse_bidirectional: bool = True, - local_window: int = 5, - dose_lambda: float = 0.25, - weights: dict[str, float] | None = None, - roi_mask: np.ndarray | None = None, - update_widget_path: bool = True, - interval_ms: int | None = None, - loop: bool = False, - autoplay: bool = False, - return_maps: bool = False, - ) -> dict[str, Any]: - """ - Suggest a sparse adaptive scan path using coarse-to-fine utility ranking. - - The planner computes utility from current virtual-image and diffraction - statistics, then selects spatially distributed high-utility points. - - Parameters - ---------- - coarse_step : int, default 4 - Spacing of the initial coarse grid. - target_fraction : float, default 0.25 - Target total sampled fraction of scan positions in (0, 1]. - min_spacing : int, default 2 - Minimum pixel spacing between selected dense points. - include_coarse : bool, default True - If True, include coarse-grid points in the returned path. - coarse_bidirectional : bool, default True - Use snake ordering for coarse-grid traversal. - local_window : int, default 5 - Window size for local-std utility component. - dose_lambda : float, default 0.25 - Penalty weight for re-sampling coarse points. - weights : dict[str, float], optional - Utility weights for keys: ``vi_gradient``, ``vi_local_std``, ``dp_variance``. - roi_mask : np.ndarray, optional - Optional boolean mask of shape ``scan_shape`` restricting dense picks. - update_widget_path : bool, default True - If True, calls ``set_path(...)`` with the suggested path. - interval_ms : int, optional - Path interval when ``update_widget_path=True``. - loop : bool, default False - Path looping behavior when ``update_widget_path=True``. - autoplay : bool, default False - Start playback immediately when ``update_widget_path=True``. - return_maps : bool, default False - If True, include utility component maps in the returned dict. - - Returns - ------- - dict - Planning result with coarse points, dense points, and final path. - """ - step = int(coarse_step) - if step < 1: - raise ValueError(f"coarse_step must be >= 1, got {coarse_step}") - - frac = float(target_fraction) - if frac <= 0 or frac > 1: - raise ValueError(f"target_fraction must be in (0, 1], got {target_fraction}") - - spacing = int(min_spacing) - if spacing < 0: - raise ValueError(f"min_spacing must be >= 0, got {min_spacing}") - - if local_window < 1: - raise ValueError(f"local_window must be >= 1, got {local_window}") - - if not np.isfinite(float(dose_lambda)): - raise ValueError("dose_lambda must be finite") - - default_weights = { - "vi_gradient": 0.4, - "vi_local_std": 0.3, - "dp_variance": 0.3, - } - merged_weights = dict(default_weights) - if weights is not None: - for key, value in weights.items(): - if key not in default_weights: - raise ValueError( - f"Unsupported utility weight '{key}'. " - f"Supported: {', '.join(default_weights.keys())}" - ) - merged_weights[key] = float(value) - - weight_sum = sum(max(0.0, float(v)) for v in merged_weights.values()) - if weight_sum <= 0: - raise ValueError("At least one utility weight must be > 0.") - normalized_weights = { - key: max(0.0, float(value)) / weight_sum - for key, value in merged_weights.items() - } - - n_total = int(self.shape_rows * self.shape_cols) - target_count = int(max(1, round(frac * n_total))) - - coarse_points = self._build_coarse_points(step=step, bidirectional=bool(coarse_bidirectional)) - coarse_count = len(coarse_points) if include_coarse else 0 - if include_coarse and target_count < coarse_count: - raise ValueError( - f"target_fraction={target_fraction} gives {target_count} points, " - f"but coarse grid already has {coarse_count}. " - "Increase target_fraction or coarse_step." - ) - dense_count = target_count - coarse_count if include_coarse else target_count - dense_count = max(0, int(dense_count)) - - vi = self._get_virtual_image_array().astype(np.float32, copy=False) - grad_row, grad_col = np.gradient(vi) - vi_gradient = np.hypot(grad_row, grad_col).astype(np.float32) - - mean_local = self._box_mean_map(vi, local_window) - mean_sq_local = self._box_mean_map(vi * vi, local_window) - variance_local = np.maximum(mean_sq_local - mean_local * mean_local, 0.0) - vi_local_std = np.sqrt(variance_local).astype(np.float32) - - dp_variance = self._dp_variance_map() - - grad_score = self._normalize_score_map(vi_gradient) - local_std_score = self._normalize_score_map(vi_local_std) - dp_var_score = self._normalize_score_map(dp_variance) - - utility = ( - normalized_weights["vi_gradient"] * grad_score - + normalized_weights["vi_local_std"] * local_std_score - + normalized_weights["dp_variance"] * dp_var_score - ).astype(np.float32) - - dose_penalty = np.zeros_like(utility, dtype=np.float32) - for row, col in coarse_points: - dose_penalty[int(row), int(col)] = 1.0 - utility = utility - float(dose_lambda) * dose_penalty - - allowed_mask = np.ones((self.shape_rows, self.shape_cols), dtype=bool) - if roi_mask is not None: - mask = np.asarray(roi_mask) - if mask.shape != (self.shape_rows, self.shape_cols): raise ValueError( - f"roi_mask shape {mask.shape} does not match scan_shape " - f"({self.shape_rows}, {self.shape_cols})" - ) - allowed_mask &= mask.astype(bool) - - excluded_mask = np.zeros_like(allowed_mask, dtype=bool) - for row, col in coarse_points: - excluded_mask[int(row), int(col)] = True - - dense_points = self._select_spaced_topk( - scores=utility, - k=dense_count, - min_spacing=spacing, - allowed_mask=allowed_mask, - excluded_mask=excluded_mask, - ) - - start_point = coarse_points[-1] if include_coarse and coarse_points else None - dense_path = self._nearest_neighbor_order(dense_points, start=start_point) - path_points = list(coarse_points) + dense_path if include_coarse else dense_path - - if update_widget_path and path_points: - interval_value = int(self.path_interval_ms if interval_ms is None else interval_ms) - if interval_value < 1: - raise ValueError(f"interval_ms must be >= 1, got {interval_value}") - self.set_path( - points=path_points, - interval_ms=interval_value, - loop=bool(loop), - autoplay=bool(autoplay), - ) - - result: dict[str, Any] = { - "target_fraction": float(frac), - "target_count": int(target_count), - "coarse_step": int(step), - "coarse_count": int(len(coarse_points)), - "dense_count": int(len(dense_points)), - "path_count": int(len(path_points)), - "weights": normalized_weights, - "dose_lambda": float(dose_lambda), - "coarse_points": coarse_points, - "dense_points": dense_points, - "path_points": path_points, - "selected_fraction": float(len(path_points) / max(1, n_total)), - } - if return_maps: - result["utility_map"] = utility - result["utility_components"] = { - "vi_gradient": grad_score, - "vi_local_std": local_std_score, - "dp_variance": dp_var_score, - "dose_penalty": dose_penalty, - } - - self._record_export_event( - { - "export_kind": "adaptive_path_suggestion", - "target_fraction": float(frac), - "target_count": int(target_count), - "coarse_step": int(step), - "coarse_count": int(len(coarse_points)), - "dense_count": int(len(dense_points)), - "path_count": int(len(path_points)), - "selected_fraction": float(len(path_points) / max(1, n_total)), - "weights": normalized_weights, - "dose_lambda": float(dose_lambda), - } - ) - return result - - def save_sequence( - self, - output_dir: str | pathlib.Path, - mode: str = "path", - view: str | None = None, - format: str | None = None, - include_metadata: bool = True, - include_overlays: bool | None = None, - include_scalebar: bool | None = None, - frame_idx: int | None = None, - position: tuple[int, int] | None = None, - path_points: list[tuple[int, int]] | None = None, - raster_step: int = 1, - raster_bidirectional: bool = False, - frame_indices: list[int] | None = None, - frame_range: tuple[int, int] | None = None, - filename_prefix: str | None = None, - manifest_name: str = "save_sequence_manifest.json", - restore_state: bool = True, - dpi: int | None = None, - ) -> pathlib.Path: - output_root = pathlib.Path(output_dir) - output_root.mkdir(parents=True, exist_ok=True) - mode_key = str(mode).strip().lower() - if mode_key not in {"path", "raster", "frames"}: - raise ValueError("mode must be one of: path, raster, frames") - - view_key = self._validate_export_view(view) - fmt = self._resolve_export_format(pathlib.Path(f"sequence.{self.export_default_format}"), format or self.export_default_format) - dpi_value = int(self.export_default_dpi if dpi is None else dpi) - overlays_enabled = ( - bool(self.export_include_overlays) - if include_overlays is None - else bool(include_overlays) - ) - scalebar_enabled = ( - bool(self.export_include_scalebar) - if include_scalebar is None - else bool(include_scalebar) - ) - if dpi_value <= 0: - raise ValueError(f"dpi must be > 0, got {dpi_value}") - - export_rows: list[dict[str, Any]] = [] - prefix = ( - str(filename_prefix).strip() - if filename_prefix is not None and str(filename_prefix).strip() - else f"{mode_key}_{view_key}" - ) - - prev_row, prev_col = self.pos_row, self.pos_col - prev_frame = self.frame_idx - frame_for_paths = self._validate_frame_idx(frame_idx) if frame_idx is not None else int(self.frame_idx) - - if mode_key == "frames": - row, col = self._validate_position(position) - frames = self._resolve_frame_sequence(frame_indices, frame_range) - jobs = [ - {"row": int(row), "col": int(col), "frame_idx": int(fi)} - for fi in frames - ] - else: - positions = self._resolve_position_sequence( - mode=mode_key, - path_points=path_points, - raster_step=raster_step, - raster_bidirectional=raster_bidirectional, - ) - jobs = [ - {"row": int(r), "col": int(c), "frame_idx": int(frame_for_paths)} - for r, c in positions - ] - - try: - for idx, job in enumerate(jobs): - row = int(job["row"]) - col = int(job["col"]) - fr = int(job["frame_idx"]) - basename = ( - f"{prefix}_{idx:04d}_f{fr:04d}_r{row:04d}_c{col:04d}.{fmt}" - ) - out_path = output_root / basename - out_meta = out_path.with_suffix(".json") if include_metadata else None - - self.save_image( - out_path, - view=view_key, - position=(row, col), - frame_idx=fr, - format=fmt, - include_metadata=include_metadata, - metadata_path=out_meta, - include_overlays=overlays_enabled, - include_scalebar=scalebar_enabled, - restore_state=False, - dpi=dpi_value, + f"Unknown preset {name!r}. Choices: 'bf', 'abf', 'adf', 'haadf'." ) - - record = { - "index": int(idx), - "row": row, - "col": col, - "frame_idx": fr, - } - record.update(self._build_file_record(out_path, metadata_path=out_meta, index=idx)) - export_rows.append(record) finally: - if restore_state: - self.frame_idx = prev_frame - self.pos_row = prev_row - self.pos_col = prev_col - - manifest_path = output_root / str(manifest_name) - manifest_payload = { - **build_json_header("Show4DSTEM"), - "format": "json", - "export_kind": "sequence_batch", - "mode": mode_key, - "view": view_key, - "image_format": fmt, - "output_dir": str(output_root), - "filename_prefix": prefix, - "n_exports": int(len(export_rows)), - "include_overlays": bool(overlays_enabled), - "include_scalebar": bool(scalebar_enabled), - "dpi": int(dpi_value), - "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, - "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, - "exports": export_rows, - } - manifest_path.write_text(json.dumps(manifest_payload, indent=2)) - - manifest_record = self._build_file_record(manifest_path) - self._record_export_event( - { - "export_kind": "sequence_batch", - "mode": mode_key, - "view": view_key, - "format": fmt, - "n_exports": int(len(export_rows)), - "include_overlays": bool(overlays_enabled), - "include_scalebar": bool(scalebar_enabled), - "dpi": int(dpi_value), - "outputs": [manifest_record], - } - ) - return manifest_path + self._suppress_roi_recompute = False + # Single recompute with final, consistent state. + self._compute_virtual_image_from_roi() + return self - def save_reproducibility_report( - self, - path: str | pathlib.Path, - ) -> pathlib.Path: - report_path = pathlib.Path(path) - report_path.parent.mkdir(parents=True, exist_ok=True) - payload = { - **build_json_header("Show4DSTEM"), - "format": "json", - "export_kind": "reproducibility_report", - "session_id": self._export_session_id, - "session_started_utc": self._export_session_started_utc, - "report_generated_utc": datetime.now(timezone.utc).isoformat(), - "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, - "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, - "n_exports": int(len(self._export_log)), - "exports": self._export_log, - } - report_path.write_text(json.dumps(payload, indent=2)) - return report_path def _normalize_frame(self, frame: np.ndarray) -> np.ndarray: mode = self.dp_scale_mode - scaled = self._apply_scale_mode(frame, mode, self.dp_power_exp) + scaled = self._apply_scale_mode(frame, mode) if self.dp_vmin is not None and self.dp_vmax is not None: fmin = float(self._apply_scale_mode( - np.array([max(self.dp_vmin, 0)], dtype=np.float32), mode, self.dp_power_exp + np.array([max(self.dp_vmin, 0)], dtype=np.float32), mode )[0]) fmax = float(self._apply_scale_mode( - np.array([max(self.dp_vmax, 0)], dtype=np.float32), mode, self.dp_power_exp + np.array([max(self.dp_vmax, 0)], dtype=np.float32), mode )[0]) else: fmin = float(scaled.min()) @@ -4038,31 +2078,14 @@ def _update_frame(self, change=None): else: frame = data[self.pos_row, self.pos_col] - # Compute stats from frame (optionally mask DC component) - if self.mask_dc and self.det_rows > 3 and self.det_cols > 3: - # Mask center 3x3 region for stats using detected center (not geometric center) - cr = int(round(self.center_row)) - cc = int(round(self.center_col)) - cr = max(1, min(self.det_rows - 2, cr)) - cc = max(1, min(self.det_cols - 2, cc)) - mask = torch.ones_like(frame, dtype=torch.bool) - mask[cr-1:cr+2, cc-1:cc+2] = False - masked_vals = frame[mask] - self.dp_stats = [ - float(masked_vals.mean()), - float(masked_vals.min()), - float(masked_vals.max()), - float(masked_vals.std()), - ] - else: - self.dp_stats = [ - float(frame.mean()), - float(frame.min()), - float(frame.max()), - float(frame.std()), - ] - - # Convert to numpy only for sending bytes to frontend + # Cast small frame to float32 for stats and JS transfer. Bulk data + # stays in native dtype; only this single 192×192 (~144 KB) frame + # gets promoted. + if frame.dtype != torch.float32: + frame = frame.float() + # Stats compute moved to JS (frontend has frame_bytes; computeStats() in + # js/stats.ts does mean/min/max/std on the Float32Array directly, + # avoiding 4 sync trait round-trips per scan-position click). self.frame_bytes = frame.cpu().numpy().tobytes() def _on_roi_change(self, change=None): @@ -4072,6 +2095,8 @@ def _on_roi_change(self, change=None): """ if not self.roi_active: return + if getattr(self, "_suppress_roi_recompute", False): + return self._compute_virtual_image_from_roi() def _on_roi_center_change(self, change=None): @@ -4082,6 +2107,8 @@ def _on_roi_center_change(self, change=None): """ if not self.roi_active: return + if getattr(self, "_suppress_roi_recompute", False): + return if change and "new" in change: row, col = change["new"] # Sync to individual traits (without triggering _on_roi_change observers) @@ -4091,19 +2118,36 @@ def _on_roi_center_change(self, change=None): self.observe(self._on_roi_change, names=["roi_center_col", "roi_center_row"]) self._compute_virtual_image_from_roi() + def _on_vi_roi_center_change(self, change=None): + """Apply compound (row, col) update atomically (avoids split-trait race).""" + if change and "new" in change: + row, col = change["new"] + self.unobserve(self._on_vi_roi_change, names=["vi_roi_center_row", "vi_roi_center_col"]) + self.vi_roi_center_row = float(row) + self.vi_roi_center_col = float(col) + self.observe(self._on_vi_roi_change, names=["vi_roi_center_row", "vi_roi_center_col"]) + if self.vi_roi_mode == "off": + self.vi_roi_dp_bytes = b"" + return + self._compute_vi_roi_dp() + def _on_vi_roi_change(self, change=None): - """Compute summed DP when VI ROI changes.""" + """Recompute reduced DP when VI ROI or reduction changes.""" if self.vi_roi_mode == "off": - self.summed_dp_bytes = b"" - self.summed_dp_count = 0 + self.vi_roi_dp_bytes = b"" return - self._compute_summed_dp_from_vi_roi() + self._compute_vi_roi_dp() + + def _compute_vi_roi_dp(self): + """Reduce diffraction patterns over scan positions inside VI ROI. - def _compute_summed_dp_from_vi_roi(self): - """Sum diffraction patterns from positions inside VI ROI (PyTorch).""" + Reduction selected by `vi_roi_reduce`: + - "mean": average DP (size-invariant, default for region-of-interest analysis) + - "sum": total counts (scales with ROI area; use for quantitative integration) + - "max": brightest pixel per detector position across the region + """ if self._data is None: return - # Create mask in scan space using cached coordinates if self.vi_roi_mode == "circle": mask = (self._scan_row_coords - self.vi_roi_center_row) ** 2 + (self._scan_col_coords - self.vi_roi_center_col) ** 2 <= self.vi_roi_radius ** 2 elif self.vi_roi_mode == "square": @@ -4116,27 +2160,41 @@ def _compute_summed_dp_from_vi_roi(self): else: return - # Count positions in mask n_positions = int(mask.sum()) if n_positions == 0: - self.summed_dp_bytes = b"" - self.summed_dp_count = 0 + self.vi_roi_dp_bytes = b"" return - self.summed_dp_count = n_positions - - # Compute average DP using masked sum (vectorized) + reduce = self.vi_roi_reduce data = self._frame_data - if data.ndim == 4: - # (scan_rows, scan_cols, det_rows, det_cols) - sum over masked scan positions - avg_dp = data[mask].mean(dim=0) - else: - # Flattened: (N, det_rows, det_cols) - need to convert mask indices - flat_indices = torch.nonzero(mask.flatten(), as_tuple=True)[0] - avg_dp = data[flat_indices].mean(dim=0) + # Single chunked torch path. For each scan-row chunk: cast to float32 and + # broadcast-multiply by the mask (no `chunk[row_mask]` slab, which would + # roughly duplicate the chunk in memory when the mask is dense). Sum/mean + # use einsum over scan dims; max masks zero rows then takes amax. + data_4d = data if data.ndim == 4 else data.reshape(self._scan_shape[0], self._scan_shape[1], *self._det_shape) + rows_per_chunk = self._chunk_rows() + if reduce == "sum" or reduce == "mean": + dp = torch.zeros(self._det_shape, dtype=torch.float32, device=self._device) + else: # max + dp = torch.full(self._det_shape, -float("inf"), dtype=torch.float32, device=self._device) + for i in range(0, self._scan_shape[0], rows_per_chunk): + row_mask = mask[i:i + rows_per_chunk] + if not bool(row_mask.any()): + continue + chunk = data_4d[i:i + rows_per_chunk] + if not torch.is_floating_point(chunk): + chunk = chunk.float() + row_mask_f = row_mask.float() + if reduce == "max": + # Outside-mask positions become 0; doesn't affect amax provided + # the data has any non-negative pixels (true for detector counts). + dp = torch.maximum(dp, (chunk * row_mask_f[..., None, None]).amax(dim=(0, 1))) + else: + dp += torch.einsum("rcij,rc->ij", chunk, row_mask_f) + if reduce == "mean": + dp /= float(n_positions) - # Send raw float32 (consistent with other data paths — JS handles normalization) - self.summed_dp_bytes = avg_dp.cpu().numpy().tobytes() + self.vi_roi_dp_bytes = dp.cpu().numpy().tobytes() def _create_circular_mask(self, cx: float, cy: float, radius: float): """Create circular mask (boolean tensor on device).""" @@ -4162,31 +2220,24 @@ def _create_rect_mask(self, cx: float, cy: float, half_width: float, half_height return mask def _precompute_common_virtual_images(self): - """Pre-compute BF/ABF/ADF virtual images for instant preset switching.""" + """Pre-compute BF/ABF/ADF/HAADF virtual image bytes. Annular ranges match + apply_preset() so the cache always hits on preset clicks.""" cx, cy, bf = self.center_col, self.center_row, self.bf_radius - # Cache (bytes, stats, min, max) for each preset - bf_arr = self._fast_masked_sum(self._create_circular_mask(cx, cy, bf)) - abf_arr = self._fast_masked_sum(self._create_annular_mask(cx, cy, bf * 0.5, bf)) - adf_arr = self._fast_masked_sum(self._create_annular_mask(cx, cy, bf, bf * 4.0)) - - self._cached_bf_virtual = ( - self._to_float32_bytes(bf_arr, update_vi_stats=False), - [float(bf_arr.mean()), float(bf_arr.min()), float(bf_arr.max()), float(bf_arr.std())], - float(bf_arr.min()), float(bf_arr.max()) + self._cached_bf_virtual = self._to_float32_bytes( + self._fast_masked_sum(self._create_circular_mask(cx, cy, bf)) + ) + self._cached_abf_virtual = self._to_float32_bytes( + self._fast_masked_sum(self._create_annular_mask(cx, cy, bf * 0.5, bf)) ) - self._cached_abf_virtual = ( - self._to_float32_bytes(abf_arr, update_vi_stats=False), - [float(abf_arr.mean()), float(abf_arr.min()), float(abf_arr.max()), float(abf_arr.std())], - float(abf_arr.min()), float(abf_arr.max()) + self._cached_adf_virtual = self._to_float32_bytes( + self._fast_masked_sum(self._create_annular_mask(cx, cy, bf, bf * 2.0)) ) - self._cached_adf_virtual = ( - self._to_float32_bytes(adf_arr, update_vi_stats=False), - [float(adf_arr.mean()), float(adf_arr.min()), float(adf_arr.max()), float(adf_arr.std())], - float(adf_arr.min()), float(adf_arr.max()) + self._cached_haadf_virtual = self._to_float32_bytes( + self._fast_masked_sum(self._create_annular_mask(cx, cy, bf * 2.0, bf * 4.0)) ) - def _get_cached_preset(self) -> tuple[bytes, list[float], float, float] | None: - """Check if current ROI matches a cached preset and return (bytes, stats, min, max) tuple.""" + def _get_cached_preset(self) -> bytes | None: + """Return cached preset bytes if current ROI matches BF/ABF/ADF preset shape.""" # Must be centered on detector center if abs(self.roi_center_col - self.center_col) >= 1 or abs(self.roi_center_row - self.center_row) >= 1: return None @@ -4203,16 +2254,25 @@ def _get_cached_preset(self) -> tuple[bytes, list[float], float, float] | None: abs(self.roi_radius - bf) < 1): return self._cached_abf_virtual - # ADF: annular at bf to 4*bf (combines LAADF + HAADF) + # ADF: annular at bf to 2*bf if (self.roi_mode == "annular" and abs(self.roi_radius_inner - bf) < 1 and - abs(self.roi_radius - bf * 4.0) < 1): + abs(self.roi_radius - bf * 2.0) < 1): return self._cached_adf_virtual + # HAADF: annular at 2*bf to 4*bf + if (self.roi_mode == "annular" and + abs(self.roi_radius_inner - bf * 2.0) < 1 and + abs(self.roi_radius - bf * 4.0) < 1): + return self._cached_haadf_virtual + return None def _virtual_image_for_frame(self, frame_idx: int) -> np.ndarray: - """Compute virtual image array for a specific frame without mutating traits.""" + """Compute virtual image for a specific 5D frame without mutating traits. + + Single chunked-torch path matching _fast_masked_sum. + """ data = self._data[frame_idx] if self.n_frames > 1 else self._data cx, cy = self.roi_center_col, self.roi_center_row if self.roi_mode == "circle" and self.roi_radius > 0: @@ -4231,68 +2291,65 @@ def _virtual_image_for_frame(self, frame_idx: int) -> np.ndarray: else: vi = data[:, row, col].reshape(self._scan_shape) return vi.cpu().numpy().astype(np.float32, copy=False) - mask_float = mask.float() - n_det = self._det_shape[0] * self._det_shape[1] - n_nonzero = int(mask.sum()) - coverage = n_nonzero / n_det - if coverage < SPARSE_MASK_THRESHOLD: - indices = torch.nonzero(mask_float.flatten(), as_tuple=True)[0] - n_scan = self._scan_shape[0] * self._scan_shape[1] - data_flat = data.reshape(n_scan, n_det) - result = data_flat[:, indices].sum(dim=1).reshape(self._scan_shape) - else: - if data.ndim == 3: - data_4d = data.reshape(self._scan_shape[0], self._scan_shape[1], *self._det_shape) - else: - data_4d = data - result = torch.tensordot(data_4d, mask_float, dims=([2, 3], [0, 1])) - return result.cpu().numpy().astype(np.float32, copy=False) + data_4d = data if data.ndim == 4 else data.reshape(self._scan_shape[0], self._scan_shape[1], *self._det_shape) + mask_f = mask.float() + rows_per_chunk = self._chunk_rows() + out = torch.zeros(self._scan_shape, dtype=torch.float32, device=self._device) + for i in range(0, data_4d.shape[0], rows_per_chunk): + chunk = data_4d[i:i + rows_per_chunk] + if not torch.is_floating_point(chunk): + chunk = chunk.float() + out[i:i + rows_per_chunk] = torch.tensordot(chunk, mask_f, dims=([2, 3], [0, 1])) + return out.cpu().numpy().astype(np.float32, copy=False) + + def _chunk_rows(self) -> int: + """Pick rows-per-chunk so float32 transient stays under _CHUNK_BYTE_BUDGET. + + Float32 cast of one chunk = rows × scan_cols × det_h × det_w × 4 bytes. + Selected slabs (e.g. vi_roi reduce) inherit the same per-row budget. + """ + per_row = self._scan_shape[1] * self._det_shape[0] * self._det_shape[1] * 4 + return max(1, _CHUNK_BYTE_BUDGET // max(1, per_row)) def _fast_masked_sum(self, mask: torch.Tensor) -> torch.Tensor: - """Compute masked sum using PyTorch. - - Uses sparse indexing for small masks (<20% coverage) which is faster - because it only processes non-zero pixels: - - r=10 (1%): ~0.8ms (sparse) vs ~13ms (full) - - r=30 (8%): ~4ms (sparse) vs ~13ms (full) + """Sum data over scan positions weighted by detector mask. - For large masks (≥20%), uses full tensordot which has constant ~13ms. + Chunked tensordot. Per-chunk float32 cast bounded by _CHUNK_BYTE_BUDGET. + Identical math on CUDA / MPS / CPU. """ data = self._frame_data - mask_float = mask.float() - n_det = self._det_shape[0] * self._det_shape[1] - n_nonzero = int(mask.sum()) - coverage = n_nonzero / n_det - - if coverage < SPARSE_MASK_THRESHOLD: - # Sparse: faster for small masks - indices = torch.nonzero(mask_float.flatten(), as_tuple=True)[0] - n_scan = self._scan_shape[0] * self._scan_shape[1] - data_flat = data.reshape(n_scan, n_det) - result = data_flat[:, indices].sum(dim=1).reshape(self._scan_shape) + if data.ndim == 3: + data_4d = data.reshape(self._scan_shape[0], self._scan_shape[1], *self._det_shape) else: - # Tensordot: faster for large masks - # Reshape to 4D if needed (3D flattened data) - if data.ndim == 3: - data_4d = data.reshape(self._scan_shape[0], self._scan_shape[1], *self._det_shape) - else: - data_4d = data - result = torch.tensordot(data_4d, mask_float, dims=([2, 3], [0, 1])) - - return result - - def _to_float32_bytes(self, arr: torch.Tensor, update_vi_stats: bool = True) -> bytes: - """Convert tensor to float32 bytes.""" - # Compute min/max (fast on GPU) - vmin = float(arr.min()) - vmax = float(arr.max()) + data_4d = data + # Single chunked torch path. Per scan-row chunk: cast to float32, contract + # with mask via tensordot. Transient memory bounded by chunk size. Same + # code on CUDA / MPS / CPU. Identical results regardless of device. + mask_f = mask.float() + n_rows = data_4d.shape[0] + out = torch.zeros(self._scan_shape, dtype=torch.float32, device=self._device) + # Convert positions chunk size to row chunks based on scan width. + rows_per_chunk = self._chunk_rows() + for i in range(0, n_rows, rows_per_chunk): + chunk = data_4d[i:i + rows_per_chunk] + if not torch.is_floating_point(chunk): + chunk = chunk.float() + out[i:i + rows_per_chunk] = torch.tensordot(chunk, mask_f, dims=([2, 3], [0, 1])) + return out - # Only update traits when requested (avoids side effects during precomputation) - if update_vi_stats: - self.vi_data_min = vmin - self.vi_data_max = vmax - self.vi_stats = [float(arr.mean()), vmin, vmax, float(arr.std())] + def _to_float32_bytes(self, arr: torch.Tensor) -> bytes: + """Convert tensor (any numeric dtype) to float32 bytes for JS transfer. + Cast to float32 only at the small output. Integer reductions (uint16 sums, + int64 accumulators) get promoted here so the multi-GB raw data never gets + copied to float. Stats (min/max/mean/std) are computed JS-side from the + same Float32Array — keeping them out of separate traits avoids a + comm-message ordering race where bytes from click N arrive with stats + from click N-1, producing a wrong colormap normalization (uniform white + flash on rapid preset switching). + """ + if arr.dtype != torch.float32: + arr = arr.float() return arr.cpu().numpy().tobytes() def _compute_virtual_image_from_roi(self): @@ -4301,12 +2358,7 @@ def _compute_virtual_image_from_roi(self): return cached = self._get_cached_preset() if cached is not None: - # Cached preset returns (bytes, stats, min, max) tuple - vi_bytes, vi_stats, vi_min, vi_max = cached - self.virtual_image_bytes = vi_bytes - self.vi_stats = vi_stats - self.vi_data_min = vi_min - self.vi_data_max = vi_max + self.virtual_image_bytes = cached return cx, cy = self.roi_center_col, self.roi_center_row @@ -4333,5 +2385,3 @@ def _compute_virtual_image_from_roi(self): self.virtual_image_bytes = self._to_float32_bytes(self._fast_masked_sum(mask)) - -bind_tool_runtime_api(Show4DSTEM, "Show4DSTEM") diff --git a/widget/src/quantem/widget/json_state.py b/widget/src/quantem/widget/state.py similarity index 96% rename from widget/src/quantem/widget/json_state.py rename to widget/src/quantem/widget/state.py index 4874981f..d7710287 100644 --- a/widget/src/quantem/widget/json_state.py +++ b/widget/src/quantem/widget/state.py @@ -12,8 +12,6 @@ def resolve_widget_version() -> str: return importlib.metadata.version("quantem-widget") except importlib.metadata.PackageNotFoundError: return "unknown" - except Exception: - return "unknown" def build_json_header(widget_name: str) -> dict[str, Any]: diff --git a/widget/src/quantem/widget/tool_parity.json b/widget/src/quantem/widget/tool_parity.json deleted file mode 100644 index 4271533a..00000000 --- a/widget/src/quantem/widget/tool_parity.json +++ /dev/null @@ -1,93 +0,0 @@ -{ - "widgets": { - "Show2D": { - "tool_groups": ["display", "histogram", "stats", "navigation", "view", "export", "roi", "profile", "all"], - "aliases": {} - }, - "Show3D": { - "tool_groups": ["display", "histogram", "stats", "playback", "view", "export", "roi", "profile", "all"], - "aliases": { - "navigation": "playback" - } - }, - "Show3DVolume": { - "tool_groups": ["display", "histogram", "playback", "fft", "navigation", "stats", "export", "view", "volume", "all"], - "aliases": {} - }, - "Show4D": { - "tool_groups": ["display", "roi", "histogram", "profile", "navigation", "playback", "stats", "export", "view", "fft", "all"], - "aliases": {} - }, - "Show4DSTEM": { - "tool_groups": ["display", "histogram", "stats", "navigation", "playback", "view", "export", "roi", "profile", "fft", "virtual", "frame", "all"], - "aliases": {} - }, - "ShowComplex2D": { - "tool_groups": ["display", "histogram", "fft", "roi", "stats", "export", "view", "all"], - "aliases": {} - }, - "Mark2D": { - "tool_groups": ["points", "roi", "profile", "display", "marker_style", "snap", "navigation", "view", "export", "all"], - "aliases": {} - }, - "Edit2D": { - "tool_groups": ["mode", "edit", "display", "histogram", "stats", "navigation", "export", "view", "all"], - "aliases": {} - }, - "Align2D": { - "tool_groups": ["alignment", "overlay", "display", "histogram", "stats", "export", "view", "all"], - "aliases": {} - }, - "Align2DBulk": { - "tool_groups": ["display", "histogram", "navigation", "stats", "view", "export", "all"], - "aliases": {} - }, - "Bin4D": { - "tool_groups": ["display", "binning", "mask", "preview", "stats", "export", "all"], - "aliases": {} - }, - "Browse": { - "tool_groups": ["navigation", "filter", "preview", "all"], - "aliases": {} - }, - "Bin2D": { - "tool_groups": ["display", "binning", "histogram", "stats", "navigation", "export", "all"], - "aliases": {} - }, - "Show1D": { - "tool_groups": ["display", "peaks", "stats", "export", "all"], - "aliases": {} - }, - "MetricExplorer": { - "tool_groups": ["display", "export", "all"], - "aliases": {} - }, - "ShowDiffraction": { - "tool_groups": ["display", "histogram", "stats", "navigation", "view", "export", "spots", "all"], - "aliases": {} - } - }, - "viewer_widgets": ["Show1D", "Show2D", "Show3D", "Show3DVolume", "Show4D", "Show4DSTEM", "ShowComplex2D"], - "control_presets": { - "all": { - "label": "All", - "show_groups": ["*"] - }, - "compact": { - "label": "Compact", - "show_groups": ["mode", "edit", "display", "navigation", "playback", "view", "export", "fft"] - }, - "mask_focus": { - "label": "Mask Focus", - "show_groups": ["edit", "display", "roi", "histogram", "stats", "navigation", "playback", "view", "export", "fft", "virtual", "frame"] - }, - "crop_focus": { - "label": "Crop Focus", - "show_groups": ["mode", "edit", "display", "histogram", "stats", "navigation", "view", "export"] - }, - "spectroscopy": { - "label": "Spectroscopy", - "show_groups": ["display", "peaks", "stats"] - } - } -} diff --git a/widget/src/quantem/widget/tool_parity.py b/widget/src/quantem/widget/tool_parity.py deleted file mode 100644 index d5d4f84e..00000000 --- a/widget/src/quantem/widget/tool_parity.py +++ /dev/null @@ -1,184 +0,0 @@ -"""Shared tool visibility/locking registry and helpers.""" - -from __future__ import annotations - -import json -import pathlib -from functools import lru_cache -from typing import Any - -_REGISTRY_PATH = pathlib.Path(__file__).with_name("tool_parity.json") - - -@lru_cache(maxsize=1) -def _load_registry() -> dict[str, Any]: - return json.loads(_REGISTRY_PATH.read_text()) - - -def get_widget_tool_groups(widget_name: str) -> tuple[str, ...]: - registry = _load_registry() - widgets = registry.get("widgets", {}) - if widget_name not in widgets: - supported = ", ".join(sorted(widgets)) - raise ValueError(f"Unknown widget {widget_name!r}. Supported widgets: {supported}.") - return tuple(str(v).strip().lower() for v in widgets[widget_name].get("tool_groups", [])) - - -def get_widget_tool_aliases(widget_name: str) -> dict[str, str]: - registry = _load_registry() - widgets = registry.get("widgets", {}) - if widget_name not in widgets: - supported = ", ".join(sorted(widgets)) - raise ValueError(f"Unknown widget {widget_name!r}. Supported widgets: {supported}.") - aliases = widgets[widget_name].get("aliases", {}) - return {str(k).strip().lower(): str(v).strip().lower() for k, v in aliases.items()} - - -def normalize_tool_groups(widget_name: str, tool_groups) -> list[str]: - if tool_groups is None: - return [] - if isinstance(tool_groups, str): - values = [tool_groups] - else: - values = list(tool_groups) - - order = get_widget_tool_groups(widget_name) - aliases = get_widget_tool_aliases(widget_name) - supported = set(order) - normalized: list[str] = [] - seen: set[str] = set() - - for raw in values: - key = str(raw).strip().lower() - if not key: - continue - key = aliases.get(key, key) - if key not in supported: - supported_values = ", ".join(f'"{k}"' for k in order) - raise ValueError( - f"Unknown tool group {raw!r}. Supported values: {supported_values}." - ) - if key == "all": - return ["all"] - if key not in seen: - seen.add(key) - normalized.append(key) - return normalized - - -def build_tool_groups( - widget_name: str, - *, - tool_groups=None, - all_flag: bool = False, - flag_map: dict[str, bool] | None = None, -) -> list[str]: - if all_flag: - return ["all"] - values: list[str] = [] - if tool_groups is not None: - if isinstance(tool_groups, str): - values.append(tool_groups) - else: - values.extend(tool_groups) - for key, enabled in (flag_map or {}).items(): - if enabled: - values.append(key) - return normalize_tool_groups(widget_name, values) - - -def resolve_control_preset_hidden_tools(widget_name: str, preset_id: str) -> list[str]: - preset_key = str(preset_id).strip().lower() - presets = _load_registry().get("control_presets", {}) - if preset_key not in presets: - supported = ", ".join(sorted(presets)) - raise ValueError(f"Unknown control preset {preset_id!r}. Supported presets: {supported}.") - - show_groups = [str(v).strip().lower() for v in presets[preset_key].get("show_groups", [])] - supported_groups = [g for g in get_widget_tool_groups(widget_name) if g != "all"] - if "*" in show_groups: - return [] - show_set = set(show_groups) - hidden = [group for group in supported_groups if group not in show_set] - return normalize_tool_groups(widget_name, hidden) - - -def _flatten_groups(groups: tuple[Any, ...]) -> list[Any]: - if len(groups) == 1 and isinstance(groups[0], (list, tuple, set)): - return list(groups[0]) - return list(groups) - - -def _expanded_without_all(widget_name: str, values) -> list[str]: - normalized = normalize_tool_groups(widget_name, values) - if "all" not in normalized: - return normalized - return [group for group in get_widget_tool_groups(widget_name) if group != "all"] - - -def _ordered_groups(widget_name: str, values: set[str]) -> list[str]: - return [group for group in get_widget_tool_groups(widget_name) if group != "all" and group in values] - - -def bind_tool_runtime_api(cls, widget_name: str) -> None: - """Attach runtime lock/hide helpers to a widget class.""" - - def set_disabled_tools(self, tool_groups) -> Any: - self.disabled_tools = normalize_tool_groups(widget_name, tool_groups) - return self - - def set_hidden_tools(self, tool_groups) -> Any: - self.hidden_tools = normalize_tool_groups(widget_name, tool_groups) - return self - - def lock_tool(self, *tool_groups) -> Any: - new_groups = _flatten_groups(tool_groups) - if not new_groups: - return self - current = _expanded_without_all(widget_name, self.disabled_tools) - requested = _expanded_without_all(widget_name, new_groups) - merged = set(current).union(requested) - self.disabled_tools = _ordered_groups(widget_name, merged) - return self - - def unlock_tool(self, *tool_groups) -> Any: - remove_groups = _flatten_groups(tool_groups) - if not remove_groups: - return self - current = set(_expanded_without_all(widget_name, self.disabled_tools)) - requested = set(_expanded_without_all(widget_name, remove_groups)) - current.difference_update(requested) - self.disabled_tools = _ordered_groups(widget_name, current) - return self - - def hide_tool(self, *tool_groups) -> Any: - new_groups = _flatten_groups(tool_groups) - if not new_groups: - return self - current = _expanded_without_all(widget_name, self.hidden_tools) - requested = _expanded_without_all(widget_name, new_groups) - merged = set(current).union(requested) - self.hidden_tools = _ordered_groups(widget_name, merged) - return self - - def show_tool(self, *tool_groups) -> Any: - remove_groups = _flatten_groups(tool_groups) - if not remove_groups: - return self - current = set(_expanded_without_all(widget_name, self.hidden_tools)) - requested = set(_expanded_without_all(widget_name, remove_groups)) - current.difference_update(requested) - self.hidden_tools = _ordered_groups(widget_name, current) - return self - - def apply_control_preset(self, preset: str) -> Any: - self.hidden_tools = resolve_control_preset_hidden_tools(widget_name, preset) - return self - - cls.set_disabled_tools = set_disabled_tools # type: ignore[attr-defined] - cls.set_hidden_tools = set_hidden_tools # type: ignore[attr-defined] - cls.lock_tool = lock_tool # type: ignore[attr-defined] - cls.unlock_tool = unlock_tool # type: ignore[attr-defined] - cls.hide_tool = hide_tool # type: ignore[attr-defined] - cls.show_tool = show_tool # type: ignore[attr-defined] - cls.apply_control_preset = apply_control_preset # type: ignore[attr-defined] diff --git a/widget/tests/test_fft_parity.py b/widget/tests/test_fft_parity.py new file mode 100644 index 00000000..4e38b85f --- /dev/null +++ b/widget/tests/test_fft_parity.py @@ -0,0 +1,200 @@ +"""FFT parity: JS fft1d/fft2d/fftshift line-ported to Python, validated against numpy. + +Why ports instead of running the JS directly: pytest can't drive a TypeScript +module without a Node bridge or browser harness, both of which add fragility +and slow CI. Instead we mirror js/fft.ts:14-82 line-for-line in Python below +and assert against numpy.fft. If the JS algorithm has a bug, the line-port +inherits it and this test fails — surfacing the bug at unit-test speed. + +When js/fft.ts changes, update the ports here in the same commit. The +side-by-side structure makes drift visually obvious during review. +""" +import numpy as np + + +def _next_pow2(n: int) -> int: + p = 1 + while p < n: + p <<= 1 + return p + + +def _js_fft1d(real: np.ndarray, imag: np.ndarray, inverse: bool = False) -> None: + """Line-port of js/fft.ts fft1d. In-place. Iterative radix-2 Cooley-Tukey.""" + n = real.size + if n <= 1: + return + # Bit-reversal permutation. + j = 0 + for i in range(n - 1): + if i < j: + real[i], real[j] = real[j], real[i] + imag[i], imag[j] = imag[j], imag[i] + k = n >> 1 + while k <= j: + j -= k + k >>= 1 + j += k + sign = 1 if inverse else -1 + length = 2 + while length <= n: + half = length >> 1 + angle = (sign * 2 * np.pi) / length + w_real = np.cos(angle) + w_imag = np.sin(angle) + for i in range(0, n, length): + cur_real = 1.0 + cur_imag = 0.0 + for k in range(half): + even = i + k + odd = i + k + half + t_real = cur_real * real[odd] - cur_imag * imag[odd] + t_imag = cur_real * imag[odd] + cur_imag * real[odd] + real[odd] = real[even] - t_real + imag[odd] = imag[even] - t_imag + real[even] += t_real + imag[even] += t_imag + new_real = cur_real * w_real - cur_imag * w_imag + cur_imag = cur_real * w_imag + cur_imag * w_real + cur_real = new_real + length <<= 1 + if inverse: + real /= n + imag /= n + + +def _js_fft2d(real: np.ndarray, imag: np.ndarray, width: int, height: int, inverse: bool = False) -> None: + """Line-port of js/fft.ts fft2d. In-place on (height*width) flattened arrays.""" + padded_w = _next_pow2(width) + padded_h = _next_pow2(height) + needs_padding = padded_w != width or padded_h != height + if needs_padding: + work_real = np.zeros(padded_w * padded_h, dtype=np.float64) + work_imag = np.zeros(padded_w * padded_h, dtype=np.float64) + for y in range(height): + for x in range(width): + work_real[y * padded_w + x] = real[y * width + x] + work_imag[y * padded_w + x] = imag[y * width + x] + else: + work_real = real + work_imag = imag + row_real = np.empty(padded_w, dtype=np.float64) + row_imag = np.empty(padded_w, dtype=np.float64) + for y in range(padded_h): + offset = y * padded_w + row_real[:] = work_real[offset:offset + padded_w] + row_imag[:] = work_imag[offset:offset + padded_w] + _js_fft1d(row_real, row_imag, inverse) + work_real[offset:offset + padded_w] = row_real + work_imag[offset:offset + padded_w] = row_imag + col_real = np.empty(padded_h, dtype=np.float64) + col_imag = np.empty(padded_h, dtype=np.float64) + for x in range(padded_w): + for y in range(padded_h): + col_real[y] = work_real[y * padded_w + x] + col_imag[y] = work_imag[y * padded_w + x] + _js_fft1d(col_real, col_imag, inverse) + for y in range(padded_h): + work_real[y * padded_w + x] = col_real[y] + work_imag[y * padded_w + x] = col_imag[y] + if needs_padding: + for y in range(height): + for x in range(width): + real[y * width + x] = work_real[y * padded_w + x] + imag[y * width + x] = work_imag[y * padded_w + x] + + +def _js_fftshift(data: np.ndarray, width: int, height: int) -> None: + """Line-port of js/fft.ts fftshift. In-place.""" + half_w = width >> 1 + half_h = height >> 1 + temp = np.empty(width * height, dtype=data.dtype) + for y in range(height): + for x in range(width): + temp[((y + half_h) % height) * width + ((x + half_w) % width)] = data[y * width + x] + data[:] = temp + + +# --------------------------------------------------------------------------- + +def test_fft1d_matches_numpy_pow2(): + """1D FFT on power-of-2 input matches numpy.fft.fft.""" + rng = np.random.default_rng(0) + n = 64 + x = rng.standard_normal(n) + real = x.astype(np.float64).copy() + imag = np.zeros(n, dtype=np.float64) + _js_fft1d(real, imag, inverse=False) + js = real + 1j * imag + expected = np.fft.fft(x) + np.testing.assert_allclose(js, expected, atol=1e-9) + + +def test_fft1d_inverse_roundtrip(): + """fft1d(fft1d(x), inverse=True) ≈ x.""" + rng = np.random.default_rng(1) + n = 128 + x = rng.standard_normal(n) + real = x.astype(np.float64).copy() + imag = np.zeros(n, dtype=np.float64) + _js_fft1d(real, imag, inverse=False) + _js_fft1d(real, imag, inverse=True) + np.testing.assert_allclose(real, x, atol=1e-9) + np.testing.assert_allclose(imag, np.zeros(n), atol=1e-9) + + +def test_fft2d_matches_numpy_pow2(): + """2D FFT on power-of-2 dims matches numpy.fft.fft2.""" + rng = np.random.default_rng(2) + h, w = 32, 64 + img = rng.standard_normal((h, w)) + real = img.astype(np.float64).flatten() + imag = np.zeros(h * w, dtype=np.float64) + _js_fft2d(real, imag, w, h, inverse=False) + js = (real + 1j * imag).reshape(h, w) + expected = np.fft.fft2(img) + np.testing.assert_allclose(js, expected, atol=1e-9) + + +def test_fft2d_non_pow2_zero_pads(): + """Non-power-of-2 input gets zero-padded; FFT of padded matches numpy of padded.""" + rng = np.random.default_rng(3) + h, w = 30, 50 + img = rng.standard_normal((h, w)) + real = img.astype(np.float64).flatten() + imag = np.zeros(h * w, dtype=np.float64) + _js_fft2d(real, imag, w, h, inverse=False) + # JS contract: only the (h, w) region of the result is written back to the input arrays. + js = (real + 1j * imag).reshape(h, w) + pw, ph = _next_pow2(w), _next_pow2(h) + padded = np.zeros((ph, pw)) + padded[:h, :w] = img + expected = np.fft.fft2(padded)[:h, :w] + np.testing.assert_allclose(js, expected, atol=1e-9) + + +def test_fftshift_matches_numpy(): + """fftshift matches numpy.fft.fftshift on 2D data.""" + rng = np.random.default_rng(4) + h, w = 16, 16 + img = rng.standard_normal((h, w)) + flat = img.flatten().copy() + _js_fftshift(flat, w, h) + js_shifted = flat.reshape(h, w) + expected = np.fft.fftshift(img) + np.testing.assert_array_equal(js_shifted, expected) + + +def test_fft2d_then_fftshift_matches_numpy(): + """Combined FFT + fftshift matches numpy reference.""" + rng = np.random.default_rng(5) + h, w = 32, 32 + img = rng.standard_normal((h, w)) + real = img.astype(np.float64).flatten() + imag = np.zeros(h * w, dtype=np.float64) + _js_fft2d(real, imag, w, h, inverse=False) + _js_fftshift(real, w, h) + _js_fftshift(imag, w, h) + js = (real + 1j * imag).reshape(h, w) + expected = np.fft.fftshift(np.fft.fft2(img)) + np.testing.assert_allclose(js, expected, atol=1e-9) diff --git a/widget/tests/test_state_dict.py b/widget/tests/test_state_dict.py new file mode 100644 index 00000000..9247c979 --- /dev/null +++ b/widget/tests/test_state_dict.py @@ -0,0 +1,168 @@ +"""state_dict roundtrip tests for Show2D and Show4DSTEM. + +For each widget: +1. Construct with default data. +2. Mutate every trait in state_dict() to a non-default value. +3. Get state_dict. +4. Construct a fresh widget and load_state_dict. +5. Assert every trait on the restored widget equals what we set. + +Catches silent regressions when traits are added, renamed, or dropped without +updating the state_dict roundtrip path. +""" +import json +import pathlib + +import numpy as np +import pytest + +from quantem.widget import Show2D, Show4DSTEM + + +def _flip_value(default): + """Return a value distinct from `default` for the same type.""" + if isinstance(default, bool): + return not default + if isinstance(default, int): + return int(default) + 7 + if isinstance(default, float): + return float(default) + 0.123 + if isinstance(default, str): + return default + "_x" if default else "x" + if isinstance(default, list): + return [_flip_value(default[0])] if default else [0] + return default + + +def _mutate_state(state: dict) -> dict: + """Build a new state dict with every key changed to a non-default value.""" + out = {} + for k, v in state.items(): + # Skip values our flipper can't safely tweak (None defaults, nested dicts/lists-of-dicts, bytes). + if v is None or isinstance(v, (dict, bytes)): + out[k] = v + continue + # Lists hold structured items (dicts, tuples) for ROI / profile / labels; + # mutating them generically is fragile. The roundtrip-defaults test already + # covers list trait persistence — here we only mutate scalars. + if isinstance(v, list): + out[k] = v + continue + out[k] = _flip_value(v) + return out + + +# --------------------------------------------------------------------------- +# Show4DSTEM +# --------------------------------------------------------------------------- + +@pytest.fixture +def show4dstem_widget(): + data = np.random.default_rng(0).poisson(5, (8, 8, 16, 16)).astype(np.uint16) + data[:, :, 6:10, 6:10] += 500 # synthetic BF disk + return Show4DSTEM(data, verbose=False) + + +def test_show4dstem_state_dict_keys(show4dstem_widget): + """state_dict returns a non-empty dict of public traits.""" + s = show4dstem_widget.state_dict() + assert isinstance(s, dict) + assert len(s) > 10 + # Required keys for the widget's user-facing display state + for required in ("title", "dp_colormap", "vi_colormap", "roi_mode", "vi_roi_reduce"): + assert required in s, f"state_dict missing key {required!r}" + + +def test_show4dstem_state_dict_roundtrip_defaults(show4dstem_widget): + """save → load on default widget preserves state.""" + original = show4dstem_widget.state_dict() + data = np.random.default_rng(0).poisson(5, (8, 8, 16, 16)).astype(np.uint16) + data[:, :, 6:10, 6:10] += 500 + fresh = Show4DSTEM(data, state=original, verbose=False) + restored = fresh.state_dict() + for k in original: + assert restored[k] == original[k], f"{k}: {original[k]!r} -> {restored[k]!r}" + + +def test_show4dstem_state_dict_roundtrip_mutated(show4dstem_widget): + """Mutating every trait then roundtripping preserves the mutations.""" + # Position / frame indices are clamped to valid range by trait validators + # against the data dimensions; mutating them generically is meaningless here. + skip = {"pos_row", "pos_col", "frame_idx", "path_index", "path_length", + "vi_roi_center_row", "vi_roi_center_col"} + mutated = _mutate_state(show4dstem_widget.state_dict()) + show4dstem_widget.load_state_dict(mutated) + out = show4dstem_widget.state_dict() + for k, v in mutated.items(): + if k in skip: + continue + if isinstance(v, float): + assert abs(out[k] - v) < 1e-3, f"{k}: expected {v}, got {out[k]}" + else: + assert out[k] == v, f"{k}: expected {v!r}, got {out[k]!r}" + + +def test_show4dstem_save_and_load(tmp_path, show4dstem_widget): + """save() writes a versioned envelope JSON, state= kwarg loads it.""" + show4dstem_widget.dp_colormap = "viridis" + show4dstem_widget.vi_colormap = "magma" + show4dstem_widget.show_fft = True + path = tmp_path / "show4dstem_state.json" + show4dstem_widget.save(str(path)) + + payload = json.loads(path.read_text()) + assert payload["widget_name"] == "Show4DSTEM" + assert "metadata_version" in payload + assert "state" in payload + + data = np.random.default_rng(0).poisson(5, (8, 8, 16, 16)).astype(np.uint16) + data[:, :, 6:10, 6:10] += 500 + fresh = Show4DSTEM(data, state=str(path), verbose=False) + assert fresh.dp_colormap == "viridis" + assert fresh.vi_colormap == "magma" + assert fresh.show_fft is True + + +# --------------------------------------------------------------------------- +# Show2D +# --------------------------------------------------------------------------- + +@pytest.fixture +def show2d_widget(): + return Show2D(np.random.default_rng(0).standard_normal((32, 32)).astype(np.float32), verbose=False) + + +def test_show2d_state_dict_keys(show2d_widget): + s = show2d_widget.state_dict() + assert isinstance(s, dict) + assert len(s) > 5 + for required in ("cmap", "log_scale"): + assert required in s, f"state_dict missing key {required!r}" + + +def test_show2d_state_dict_roundtrip_defaults(show2d_widget): + original = show2d_widget.state_dict() + fresh = Show2D(np.random.default_rng(0).standard_normal((32, 32)).astype(np.float32), + state=original, verbose=False) + restored = fresh.state_dict() + for k in original: + if isinstance(original[k], float): + assert abs(restored[k] - original[k]) < 1e-3, f"{k}: {original[k]} -> {restored[k]}" + else: + assert restored[k] == original[k], f"{k}: {original[k]!r} -> {restored[k]!r}" + + +def test_show2d_save_and_load(tmp_path, show2d_widget): + show2d_widget.cmap = "viridis" + show2d_widget.log_scale = True + path = tmp_path / "show2d_state.json" + show2d_widget.save(str(path)) + + payload = json.loads(path.read_text()) + assert payload["widget_name"] == "Show2D" + assert "state" in payload + + fresh = Show2D(np.random.default_rng(0).standard_normal((32, 32)).astype(np.float32), + state=str(path), verbose=False) + assert fresh.cmap == "viridis" + assert fresh.log_scale is True diff --git a/widget/vite.config.js b/widget/vite.config.js deleted file mode 100644 index 291b84aa..00000000 --- a/widget/vite.config.js +++ /dev/null @@ -1,26 +0,0 @@ -import { defineConfig } from "vite"; -import anywidget from "@anywidget/vite"; -import react from "@vitejs/plugin-react"; - -export default defineConfig({ - plugins: [anywidget(), react()], - define: { - "process.env.NODE_ENV": JSON.stringify("production"), - }, - build: { - outDir: "src/quantem/widget/static", - emptyOutDir: true, - rollupOptions: { - input: { - show2d: "js/show2d/index.tsx", - show4dstem: "js/show4dstem/index.tsx", - }, - output: { - entryFileNames: "[name].js", - assetFileNames: "[name][extname]", - format: "es", - inlineDynamicImports: false, - }, - }, - }, -}); From ff510f958ad5f3d90554ef82bad2eac87f879736 Mon Sep 17 00:00:00 2001 From: quantem-bot Date: Mon, 4 May 2026 11:09:22 +0000 Subject: [PATCH 038/140] chore: update lock file --- uv.lock | 193 ++++++++++++++++++++++++++++++++------------------------ 1 file changed, 109 insertions(+), 84 deletions(-) diff --git a/uv.lock b/uv.lock index 43a03fd0..6932e160 100644 --- a/uv.lock +++ b/uv.lock @@ -51,16 +51,16 @@ wheels = [ [[package]] name = "anywidget" -version = "0.10.0" +version = "0.11.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "ipywidgets" }, { name = "psygnal" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/7e/00/8b5d3cc6146dd091abf1495869ca447f8a801b9938dbd13b15477a51c658/anywidget-0.10.0.tar.gz", hash = "sha256:4ec7cba129613af8d210654d6297c5c3fb5d76fcd2efea829fd58050d8b28802", size = 391132, upload-time = "2026-04-07T04:27:18.795Z" } +sdist = { url = "https://files.pythonhosted.org/packages/79/31/0491d707c674b34267f55d96d6a7148e55e7b6718a271686232cf295fbe2/anywidget-0.11.0.tar.gz", hash = "sha256:6695fbef9449cf8c27f421b96c5837aa37f909ec1f60cfa33add333e1b70b169", size = 426999, upload-time = "2026-04-27T23:42:09.576Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/e6/f1/80a173ddbb7753d3a3bf9fb7fa698280a5f418dedce2a8662e960807441b/anywidget-0.10.0-py3-none-any.whl", hash = "sha256:a001543b55be9b4e9c9783b2f1aa0d5be733b321723d9bf30975fce840730a57", size = 254744, upload-time = "2026-04-07T04:27:17.252Z" }, + { url = "https://files.pythonhosted.org/packages/8e/c2/8fec8e8e2eb920cc2280f569144080cd58622a2eda83bfa4c0c354a63264/anywidget-0.11.0-py3-none-any.whl", hash = "sha256:c574d9acc6503ad27b37a9acea48f957a8ba7c9c9876cfcb37898931c098ce9d", size = 317341, upload-time = "2026-04-27T23:42:08.356Z" }, ] [[package]] @@ -656,10 +656,10 @@ wheels = [ [[package]] name = "cuda-pathfinder" -version = "1.5.3" +version = "1.5.4" source = { registry = "https://pypi.org/simple" } wheels = [ - { url = "https://files.pythonhosted.org/packages/d3/d6/ac63065d33dd700fee7ebd7d287332401b54e31b9346e142f871e1f0b116/cuda_pathfinder-1.5.3-py3-none-any.whl", hash = "sha256:dff021123aedbb4117cc7ec81717bbfe198fb4e8b5f1ee57e0e084fec5c8577d", size = 49991, upload-time = "2026-04-14T20:09:27.037Z" }, + { url = "https://files.pythonhosted.org/packages/11/d0/c177e29701cf1d3008d7d2b16b5fc626592ce13bd535f8795c5f57187e0e/cuda_pathfinder-1.5.4-py3-none-any.whl", hash = "sha256:9563d3175ce1828531acf4b94e1c1c7d67208c347ca002493e2654878b26f4b7", size = 51657, upload-time = "2026-04-27T22:42:07.712Z" }, ] [[package]] @@ -922,11 +922,11 @@ wheels = [ [[package]] name = "fsspec" -version = "2026.3.0" +version = "2026.4.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/e1/cf/b50ddf667c15276a9ab15a70ef5f257564de271957933ffea49d2cdbcdfb/fsspec-2026.3.0.tar.gz", hash = "sha256:1ee6a0e28677557f8c2f994e3eea77db6392b4de9cd1f5d7a9e87a0ae9d01b41", size = 313547, upload-time = "2026-03-27T19:11:14.892Z" } +sdist = { url = "https://files.pythonhosted.org/packages/d5/8d/1c51c094345df128ca4a990d633fe1a0ff28726c9e6b3c41ba65087bba1d/fsspec-2026.4.0.tar.gz", hash = "sha256:301d8ac70ae90ef3ad05dcf94d6c3754a097f9b5fe4667d2787aa359ec7df7e4", size = 312760, upload-time = "2026-04-29T20:42:38.635Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/d5/1f/5f4a3cd9e4440e9d9bc78ad0a91a1c8d46b4d429d5239ebe6793c9fe5c41/fsspec-2026.3.0-py3-none-any.whl", hash = "sha256:d2ceafaad1b3457968ed14efa28798162f1638dbb5d2a6868a2db002a5ee39a4", size = 202595, upload-time = "2026-03-27T19:11:13.595Z" }, + { url = "https://files.pythonhosted.org/packages/d5/0c/043d5e551459da400957a1395e0febbf771446ff34291afcbe3d8be2a279/fsspec-2026.4.0-py3-none-any.whl", hash = "sha256:11ef7bb35dab8a394fde6e608221d5cf3e8499401c249bebaeaad760a1a8dec2", size = 203402, upload-time = "2026-04-29T20:42:36.842Z" }, ] [[package]] @@ -961,49 +961,49 @@ wheels = [ [[package]] name = "greenlet" -version = "3.4.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/86/94/a5935717b307d7c71fe877b52b884c6af707d2d2090db118a03fbd799369/greenlet-3.4.0.tar.gz", hash = "sha256:f50a96b64dafd6169e595a5c56c9146ef80333e67d4476a65a9c55f400fc22ff", size = 195913, upload-time = "2026-04-08T17:08:00.863Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/fb/c6/dba32cab7e3a625b011aa5647486e2d28423a48845a2998c126dd69c85e1/greenlet-3.4.0-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:805bebb4945094acbab757d34d6e1098be6de8966009ab9ca54f06ff492def58", size = 285504, upload-time = "2026-04-08T15:52:14.071Z" }, - { url = "https://files.pythonhosted.org/packages/54/f4/7cb5c2b1feb9a1f50e038be79980dfa969aa91979e5e3a18fdbcfad2c517/greenlet-3.4.0-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:439fc2f12b9b512d9dfa681c5afe5f6b3232c708d13e6f02c845e0d9f4c2d8c6", size = 605476, upload-time = "2026-04-08T16:24:37.064Z" }, - { url = "https://files.pythonhosted.org/packages/d6/af/b66ab0b2f9a4c5a867c136bf66d9599f34f21a1bcca26a2884a29c450bd9/greenlet-3.4.0-cp311-cp311-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a70ed1cb0295bee1df57b63bf7f46b4e56a5c93709eea769c1fec1bb23a95875", size = 618336, upload-time = "2026-04-08T16:30:56.59Z" }, - { url = "https://files.pythonhosted.org/packages/e5/5c/8c5633ece6ba611d64bf2770219a98dd439921d6424e4e8cf16b0ac74ea5/greenlet-3.4.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c660bce1940a1acae5f51f0a064f1bc785d07ea16efcb4bc708090afc4d69e83", size = 613515, upload-time = "2026-04-08T15:56:32.478Z" }, - { url = "https://files.pythonhosted.org/packages/a9/df/950d15bca0d90a0e7395eb777903060504cdb509b7b705631e8fb69ff415/greenlet-3.4.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:ee407d4d1ca9dc632265aee1c8732c4a2d60adff848057cdebfe5fe94eb2c8a2", size = 1574623, upload-time = "2026-04-08T16:26:18.596Z" }, - { url = "https://files.pythonhosted.org/packages/1a/e7/0839afab829fcb7333c9ff6d80c040949510055d2d4d63251f0d1c7c804e/greenlet-3.4.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:956215d5e355fffa7c021d168728321fd4d31fd730ac609b1653b450f6a4bc71", size = 1639579, upload-time = "2026-04-08T15:57:29.231Z" }, - { url = "https://files.pythonhosted.org/packages/d9/2b/b4482401e9bcaf9f5c97f67ead38db89c19520ff6d0d6699979c6efcc200/greenlet-3.4.0-cp311-cp311-win_amd64.whl", hash = "sha256:5cb614ace7c27571270354e9c9f696554d073f8aa9319079dcba466bbdead711", size = 238233, upload-time = "2026-04-08T17:02:54.286Z" }, - { url = "https://files.pythonhosted.org/packages/0c/4d/d8123a4e0bcd583d5cfc8ddae0bbe29c67aab96711be331a7cc935a35966/greenlet-3.4.0-cp311-cp311-win_arm64.whl", hash = "sha256:04403ac74fe295a361f650818de93be11b5038a78f49ccfb64d3b1be8fbf1267", size = 235045, upload-time = "2026-04-08T17:04:05.072Z" }, - { url = "https://files.pythonhosted.org/packages/65/8b/3669ad3b3f247a791b2b4aceb3aa5a31f5f6817bf547e4e1ff712338145a/greenlet-3.4.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:1a54a921561dd9518d31d2d3db4d7f80e589083063ab4d3e2e950756ef809e1a", size = 286902, upload-time = "2026-04-08T15:52:12.138Z" }, - { url = "https://files.pythonhosted.org/packages/38/3e/3c0e19b82900873e2d8469b590a6c4b3dfd2b316d0591f1c26b38a4879a5/greenlet-3.4.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:16dec271460a9a2b154e3b1c2fa1050ce6280878430320e85e08c166772e3f97", size = 606099, upload-time = "2026-04-08T16:24:38.408Z" }, - { url = "https://files.pythonhosted.org/packages/b5/33/99fef65e7754fc76a4ed14794074c38c9ed3394a5bd129d7f61b705f3168/greenlet-3.4.0-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:90036ce224ed6fe75508c1907a77e4540176dcf0744473627785dd519c6f9996", size = 618837, upload-time = "2026-04-08T16:30:58.298Z" }, - { url = "https://files.pythonhosted.org/packages/36/f7/229f3aed6948faa20e0616a0b8568da22e365ede6a54d7d369058b128afd/greenlet-3.4.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a1c4f6b453006efb8310affb2d132832e9bbb4fc01ce6df6b70d810d38f1f6dc", size = 615062, upload-time = "2026-04-08T15:56:33.766Z" }, - { url = "https://files.pythonhosted.org/packages/08/97/d988180011aa40135c46cd0d0cf01dd97f7162bae14139b4a3ef54889ba5/greenlet-3.4.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:9b2d9a138ffa0e306d0e2b72976d2fb10b97e690d40ab36a472acaab0838e2de", size = 1573511, upload-time = "2026-04-08T16:26:20.058Z" }, - { url = "https://files.pythonhosted.org/packages/d4/0f/a5a26fe152fb3d12e6a474181f6e9848283504d0afd095f353d85726374b/greenlet-3.4.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:8424683caf46eb0eb6f626cb95e008e8cc30d0cb675bdfa48200925c79b38a08", size = 1640396, upload-time = "2026-04-08T15:57:30.88Z" }, - { url = "https://files.pythonhosted.org/packages/42/cf/bb2c32d9a100e36ee9f6e38fad6b1e082b8184010cb06259b49e1266ca01/greenlet-3.4.0-cp312-cp312-win_amd64.whl", hash = "sha256:a0a53fb071531d003b075c444014ff8f8b1a9898d36bb88abd9ac7b3524648a2", size = 238892, upload-time = "2026-04-08T17:03:10.094Z" }, - { url = "https://files.pythonhosted.org/packages/b7/47/6c41314bac56e71436ce551c7fbe3cc830ed857e6aa9708dbb9c65142eb6/greenlet-3.4.0-cp312-cp312-win_arm64.whl", hash = "sha256:f38b81880ba28f232f1f675893a39cf7b6db25b31cc0a09bb50787ecf957e85e", size = 235599, upload-time = "2026-04-08T15:52:54.3Z" }, - { url = "https://files.pythonhosted.org/packages/7a/75/7e9cd1126a1e1f0cd67b0eda02e5221b28488d352684704a78ed505bd719/greenlet-3.4.0-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:43748988b097f9c6f09364f260741aa73c80747f63389824435c7a50bfdfd5c1", size = 285856, upload-time = "2026-04-08T15:52:45.82Z" }, - { url = "https://files.pythonhosted.org/packages/9d/c4/3e2df392e5cb199527c4d9dbcaa75c14edcc394b45040f0189f649631e3c/greenlet-3.4.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5566e4e2cd7a880e8c27618e3eab20f3494452d12fd5129edef7b2f7aa9a36d1", size = 610208, upload-time = "2026-04-08T16:24:39.674Z" }, - { url = "https://files.pythonhosted.org/packages/da/af/750cdfda1d1bd30a6c28080245be8d0346e669a98fdbae7f4102aa95fff3/greenlet-3.4.0-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1054c5a3c78e2ab599d452f23f7adafef55062a783a8e241d24f3b633ba6ff82", size = 621269, upload-time = "2026-04-08T16:30:59.767Z" }, - { url = "https://files.pythonhosted.org/packages/54/78/0cbc693622cd54ebe25207efbb3a0eb07c2639cb8594f6e3aaaa0bb077a8/greenlet-3.4.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f82cb6cddc27dd81c96b1506f4aa7def15070c3b2a67d4e46fd19016aacce6cf", size = 617549, upload-time = "2026-04-08T15:56:34.893Z" }, - { url = "https://files.pythonhosted.org/packages/ba/c0/8966767de01343c1ff47e8b855dc78e7d1a8ed2b7b9c83576a57e289f81d/greenlet-3.4.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:227a46251ecba4ff46ae742bc5ce95c91d5aceb4b02f885487aff269c127a729", size = 1575310, upload-time = "2026-04-08T16:26:21.671Z" }, - { url = "https://files.pythonhosted.org/packages/b8/38/bcdc71ba05e9a5fda87f63ffc2abcd1f15693b659346df994a48c968003d/greenlet-3.4.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:5b99e87be7eba788dd5b75ba1cde5639edffdec5f91fe0d734a249535ec3408c", size = 1640435, upload-time = "2026-04-08T15:57:32.572Z" }, - { url = "https://files.pythonhosted.org/packages/a1/c2/19b664b7173b9e4ef5f77e8cef9f14c20ec7fce7920dc1ccd7afd955d093/greenlet-3.4.0-cp313-cp313-win_amd64.whl", hash = "sha256:849f8bc17acd6295fcb5de8e46d55cc0e52381c56eaf50a2afd258e97bc65940", size = 238760, upload-time = "2026-04-08T17:04:03.878Z" }, - { url = "https://files.pythonhosted.org/packages/9b/96/795619651d39c7fbd809a522f881aa6f0ead504cc8201c3a5b789dfaef99/greenlet-3.4.0-cp313-cp313-win_arm64.whl", hash = "sha256:9390ad88b652b1903814eaabd629ca184db15e0eeb6fe8a390bbf8b9106ae15a", size = 235498, upload-time = "2026-04-08T17:05:00.584Z" }, - { url = "https://files.pythonhosted.org/packages/78/02/bde66806e8f169cf90b14d02c500c44cdbe02c8e224c9c67bafd1b8cadd1/greenlet-3.4.0-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:10a07aca6babdd18c16a3f4f8880acfffc2b88dfe431ad6aa5f5740759d7d75e", size = 286291, upload-time = "2026-04-08T17:09:34.307Z" }, - { url = "https://files.pythonhosted.org/packages/05/1f/39da1c336a87d47c58352fb8a78541ce63d63ae57c5b9dae1fe02801bbc2/greenlet-3.4.0-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:076e21040b3a917d3ce4ad68fb5c3c6b32f1405616c4a57aa83120979649bd3d", size = 656749, upload-time = "2026-04-08T16:24:41.721Z" }, - { url = "https://files.pythonhosted.org/packages/d3/6c/90ee29a4ee27af7aa2e2ec408799eeb69ee3fcc5abcecac6ddd07a5cd0f2/greenlet-3.4.0-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e82689eea4a237e530bb5cb41b180ef81fa2160e1f89422a67be7d90da67f615", size = 669084, upload-time = "2026-04-08T16:31:01.372Z" }, - { url = "https://files.pythonhosted.org/packages/07/49/d4cad6e5381a50947bb973d2f6cf6592621451b09368b8c20d9b8af49c5b/greenlet-3.4.0-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4df3b0b2289ec686d3c821a5fee44259c05cfe824dd5e6e12c8e5f5df23085cf", size = 665621, upload-time = "2026-04-08T15:56:35.995Z" }, - { url = "https://files.pythonhosted.org/packages/37/31/d1edd54f424761b5d47718822f506b435b6aab2f3f93b465441143ea5119/greenlet-3.4.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:8bff29d586ea415688f4cec96a591fcc3bf762d046a796cdadc1fdb6e7f2d5bf", size = 1622259, upload-time = "2026-04-08T16:26:23.201Z" }, - { url = "https://files.pythonhosted.org/packages/b0/c6/6d3f9cdcb21c4e12a79cb332579f1c6aa1af78eb68059c5a957c7812d95e/greenlet-3.4.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:8a569c2fb840c53c13a2b8967c63621fafbd1a0e015b9c82f408c33d626a2fda", size = 1686916, upload-time = "2026-04-08T15:57:34.282Z" }, - { url = "https://files.pythonhosted.org/packages/63/45/c1ca4a1ad975de4727e52d3ffe641ae23e1d7a8ffaa8ff7a0477e1827b92/greenlet-3.4.0-cp314-cp314-win_amd64.whl", hash = "sha256:207ba5b97ea8b0b60eb43ffcacf26969dd83726095161d676aac03ff913ee50d", size = 239821, upload-time = "2026-04-08T17:03:48.423Z" }, - { url = "https://files.pythonhosted.org/packages/71/c4/6f621023364d7e85a4769c014c8982f98053246d142420e0328980933ceb/greenlet-3.4.0-cp314-cp314-win_arm64.whl", hash = "sha256:f8296d4e2b92af34ebde81085a01690f26a51eb9ac09a0fcadb331eb36dbc802", size = 236932, upload-time = "2026-04-08T17:04:33.551Z" }, - { url = "https://files.pythonhosted.org/packages/d4/8f/18d72b629783f5e8d045a76f5325c1e938e659a9e4da79c7dcd10169a48d/greenlet-3.4.0-cp314-cp314t-macosx_11_0_universal2.whl", hash = "sha256:d70012e51df2dbbccfaf63a40aaf9b40c8bed37c3e3a38751c926301ce538ece", size = 294681, upload-time = "2026-04-08T15:52:35.778Z" }, - { url = "https://files.pythonhosted.org/packages/9e/ad/5fa86ec46769c4153820d58a04062285b3b9e10ba3d461ee257b68dcbf53/greenlet-3.4.0-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a58bec0751f43068cd40cff31bb3ca02ad6000b3a51ca81367af4eb5abc480c8", size = 658899, upload-time = "2026-04-08T16:24:43.32Z" }, - { url = "https://files.pythonhosted.org/packages/43/f0/4e8174ca0e87ae748c409f055a1ba161038c43cc0a5a6f1433a26ac2e5bf/greenlet-3.4.0-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:05fa0803561028f4b2e3b490ee41216a842eaee11aed004cc343a996d9523aa2", size = 665284, upload-time = "2026-04-08T16:31:02.833Z" }, - { url = "https://files.pythonhosted.org/packages/19/da/991cf7cd33662e2df92a1274b7eb4d61769294d38a1bba8a45f31364845e/greenlet-3.4.0-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e60d38719cb80b3ab5e85f9f1aed4960acfde09868af6762ccb27b260d68f4ed", size = 661861, upload-time = "2026-04-08T15:56:37.269Z" }, - { url = "https://files.pythonhosted.org/packages/36/c5/6c2c708e14db3d9caea4b459d8464f58c32047451142fe2cfd90e7458f41/greenlet-3.4.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:7f50c804733b43eded05ae694691c9aa68bca7d0a867d67d4a3f514742a2d53f", size = 1622182, upload-time = "2026-04-08T16:26:24.777Z" }, - { url = "https://files.pythonhosted.org/packages/7a/4c/50c5fed19378e11a29fabab1f6be39ea95358f4a0a07e115a51ca93385d8/greenlet-3.4.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:2d4f0635dc4aa638cda4b2f5a07ae9a2cff9280327b581a3fcb6f317b4fbc38a", size = 1685050, upload-time = "2026-04-08T15:57:36.453Z" }, - { url = "https://files.pythonhosted.org/packages/db/72/85ae954d734703ab48e622c59d4ce35d77ce840c265814af9c078cacc7aa/greenlet-3.4.0-cp314-cp314t-win_amd64.whl", hash = "sha256:1a4a48f24681300c640f143ba7c404270e1ebbbcf34331d7104a4ff40f8ea705", size = 245554, upload-time = "2026-04-08T17:03:50.044Z" }, +version = "3.5.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/3c/3f/dbf99fb14bfeb88c28f16729215478c0e265cacd6dc22270c8f31bb6892f/greenlet-3.5.0.tar.gz", hash = "sha256:d419647372241bc68e957bf38d5c1f98852155e4146bd1e4121adea81f4f01e4", size = 196995, upload-time = "2026-04-27T13:37:15.544Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/8b/0f/a91f143f356523ff682309732b175765a9bc2836fd7c081c2c67fedc1ad4/greenlet-3.5.0-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:8f1cc966c126639cd152fdaa52624d2655f492faa79e013fea161de3e6dda082", size = 284726, upload-time = "2026-04-27T12:20:51.402Z" }, + { url = "https://files.pythonhosted.org/packages/95/82/800646c7ffc5dbabd75ddd2f6b519bb898c0c9c969e5d0473bfe5d20bcce/greenlet-3.5.0-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:362624e6a8e5bca3b8233e45eef33903a100e9539a2b995c364d595dbc4018b3", size = 604264, upload-time = "2026-04-27T12:52:39.494Z" }, + { url = "https://files.pythonhosted.org/packages/ca/ac/354867c0bba812fc33b15bc55aedafedd0aee3c7dd91dfca22444157dc0c/greenlet-3.5.0-cp311-cp311-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:5ecd83806b0f4c2f53b1018e0005cd82269ea01d42befc0368730028d850ed1c", size = 616099, upload-time = "2026-04-27T12:59:39.623Z" }, + { url = "https://files.pythonhosted.org/packages/ff/b0/815bece7399e01cadb69014219eebd0042339875c59a59b0820a46ece356/greenlet-3.5.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0ff251e9a0279522e62f6176412869395a64ddf2b5c5f782ff609a8216a4e662", size = 615198, upload-time = "2026-04-27T12:25:25.928Z" }, + { url = "https://files.pythonhosted.org/packages/10/80/3b2c0a895d6698f6ddb31b07942ebfa982f3e30888bc5546a5b5990de8b2/greenlet-3.5.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:6d874e79afd41a96e11ff4c5d0bc90a80973e476fda1c2c64985667397df432b", size = 1574927, upload-time = "2026-04-27T12:53:25.81Z" }, + { url = "https://files.pythonhosted.org/packages/44/0e/f354af514a4c61454dbc68e44d47544a5a4d6317e30b77ddfa3a09f4c5f3/greenlet-3.5.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:0ed006e4b86c59de7467eb2601cd1b77b5a7d657d1ee55e30fe30d76451edba4", size = 1642683, upload-time = "2026-04-27T12:25:23.9Z" }, + { url = "https://files.pythonhosted.org/packages/fa/6a/87f38255201e993a1915265ebb80cd7c2c78b04a45744995abbf6b259fd8/greenlet-3.5.0-cp311-cp311-win_amd64.whl", hash = "sha256:703cb211b820dbffbbc55a16bfc6e4583a6e6e990f33a119d2cc8b83211119c8", size = 238115, upload-time = "2026-04-27T12:21:48.845Z" }, + { url = "https://files.pythonhosted.org/packages/e3/f8/450fe3c5938fa737ea4d22699772e6e34e8e24431a47bf4e8a1ceed4a98e/greenlet-3.5.0-cp311-cp311-win_arm64.whl", hash = "sha256:6c18dfb59c70f5a94acd271c72e90128c3c776e41e5f07767908c8c1b74ad339", size = 235017, upload-time = "2026-04-27T12:22:26.768Z" }, + { url = "https://files.pythonhosted.org/packages/ef/32/f2ce6d4cac3e55bc6173f92dbe627e782e1850f89d986c3606feb63aafa7/greenlet-3.5.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:db2910d3c809444e0a20147361f343fe2798e106af8d9d8506f5305302655a9f", size = 286228, upload-time = "2026-04-27T12:20:34.421Z" }, + { url = "https://files.pythonhosted.org/packages/b7/aa/caed9e5adf742315fc7be2a84196373aab4816e540e38ba0d76cb7584d68/greenlet-3.5.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3ec9ea74e7268ace7f9aab1b1a4e730193fc661b39a993cd91c606c32d4a3628", size = 601775, upload-time = "2026-04-27T12:52:41.045Z" }, + { url = "https://files.pythonhosted.org/packages/c7/af/90ae08497400a941595d12774447f752d3dfe0fbb012e35b76bc5c0ff37e/greenlet-3.5.0-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:54d243512da35485fc7a6bf3c178fdda6327a9d6506fcdd62b1abd1e41b2927b", size = 614436, upload-time = "2026-04-27T12:59:41.595Z" }, + { url = "https://files.pythonhosted.org/packages/2b/e0/2e13df68f367e2f9960616927d60857dd7e56aaadd59a47c644216b2f920/greenlet-3.5.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9d280a7f5c331622c69f97eb167f33577ff2d1df282c41cd15907fc0a3ca198c", size = 611388, upload-time = "2026-04-27T12:25:28.008Z" }, + { url = "https://files.pythonhosted.org/packages/82/f7/393c64055132ac0d488ef6be549253b7e6274194863967ddc0bc8f5b87b8/greenlet-3.5.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1eb67d5adefb5bd2e182d42678a328979a209e4e82eb93575708185d31d1f588", size = 1570768, upload-time = "2026-04-27T12:53:28.099Z" }, + { url = "https://files.pythonhosted.org/packages/b8/4b/eaf7735253522cf56d1b74d672a58f54fc114702ceaf05def59aae72f6e1/greenlet-3.5.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2628d6c86f6cb0cb45e0c3c54058bbec559f57eaae699447748cb3928150577e", size = 1635983, upload-time = "2026-04-27T12:25:26.903Z" }, + { url = "https://files.pythonhosted.org/packages/4c/fe/4fb3a0805bd5165da5ebf858da7cc01cce8061674106d2cf5bdab32cbfde/greenlet-3.5.0-cp312-cp312-win_amd64.whl", hash = "sha256:d4d9f0624c775f2dfc56ba54d515a8c771044346852a918b405914f6b19d7fd8", size = 238840, upload-time = "2026-04-27T12:23:54.806Z" }, + { url = "https://files.pythonhosted.org/packages/cb/cb/baa584cb00532126ffe12d9787db0a60c5a4f55c27bfe2666df5d4c30a32/greenlet-3.5.0-cp312-cp312-win_arm64.whl", hash = "sha256:83ed9f27f1680b50e89f40f6df348a290ea234b249a4003d366663a12eab94f2", size = 235615, upload-time = "2026-04-27T12:21:38.57Z" }, + { url = "https://files.pythonhosted.org/packages/0c/58/fc576f99037ce19c5aa16628e4c3226b6d1419f72a62c79f5f40576e6eb3/greenlet-3.5.0-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:5a5ed18de6a0f6cc7087f1563f6bd93fc7df1c19165ca01e9bde5a5dc281d106", size = 285066, upload-time = "2026-04-27T12:23:05.033Z" }, + { url = "https://files.pythonhosted.org/packages/4a/ba/b28ddbe6bfad6a8ac196ef0e8cff37bc65b79735995b9e410923fffeeb70/greenlet-3.5.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a717fbc46d8a354fa675f7c1e813485b6ba3885f9bef0cd56e5ba27d758ff5b", size = 604414, upload-time = "2026-04-27T12:52:42.358Z" }, + { url = "https://files.pythonhosted.org/packages/09/06/4b69f8f0b67603a8be2790e55107a190b376f2627fe0eaf5695d85ffb3cd/greenlet-3.5.0-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ddc090c5c1792b10246a78e8c2163ebbe04cf877f9d785c230a7b27b39ad038e", size = 617349, upload-time = "2026-04-27T12:59:43.32Z" }, + { url = "https://files.pythonhosted.org/packages/8a/17/a3918541fd0ddefe024a69de6d16aa7b46d36ac19562adaa63c7fa180eff/greenlet-3.5.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2094acd54b272cb6eae8c03dd87b3fa1820a4cef18d6889c378d503500a1dc13", size = 613927, upload-time = "2026-04-27T12:25:30.28Z" }, + { url = "https://files.pythonhosted.org/packages/ee/e1/bd0af6213c7dd33175d8a462d4c1fe1175124ebed4855bc1475a5b5242c2/greenlet-3.5.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5e05ba267789ea87b5a155cf0e810b1ab88bf18e9e8740813945ceb8ee4350ba", size = 1570893, upload-time = "2026-04-27T12:53:29.483Z" }, + { url = "https://files.pythonhosted.org/packages/9b/2a/0789702f864f5382cb476b93d7a9c823c10472658102ccd65f415747d2e2/greenlet-3.5.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0ecec963079cd58cbd14723582384f11f166fd58883c15dcbfb342e0bc9b5846", size = 1636060, upload-time = "2026-04-27T12:25:28.845Z" }, + { url = "https://files.pythonhosted.org/packages/b2/8f/22bf9df92bbff0eb07842b60f7e63bf7675a9742df628437a9f02d09137f/greenlet-3.5.0-cp313-cp313-win_amd64.whl", hash = "sha256:728d9667d8f2f586644b748dbd9bb67e50d6a9381767d1357714ea6825bb3bf5", size = 238740, upload-time = "2026-04-27T12:24:01.341Z" }, + { url = "https://files.pythonhosted.org/packages/b6/b7/9c5c3d653bd4ff614277c049ac676422e2c557db47b4fe43e6313fc005dc/greenlet-3.5.0-cp313-cp313-win_arm64.whl", hash = "sha256:47422135b1d308c14b2c6e758beedb1acd33bb91679f5670edf77bf46244722b", size = 235525, upload-time = "2026-04-27T12:23:12.308Z" }, + { url = "https://files.pythonhosted.org/packages/94/5e/a70f31e3e8d961c4ce589c15b28e4225d63704e431a23932a3808cbcc867/greenlet-3.5.0-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:f35807464c4c58c55f0d31dfa83c541a5615d825c2fe3d2b95360cf7c4e3c0a8", size = 285564, upload-time = "2026-04-27T12:23:08.555Z" }, + { url = "https://files.pythonhosted.org/packages/af/a6/046c0a28e21833e4086918218cfb3d8bed51c075a1b700f20b9d7861c0f4/greenlet-3.5.0-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:55fa7ea52771be44af0de27d8b80c02cd18c2c3cddde6c847ecebdf72418b6a1", size = 651166, upload-time = "2026-04-27T12:52:43.644Z" }, + { url = "https://files.pythonhosted.org/packages/47/f8/4af27f71c5ff32a7fbc516adb46370d9c4ae2bc7bd3dc7d066ac542b4b15/greenlet-3.5.0-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a97e4821aa710603f94de0da25f25096454d78ffdace5dc77f3a006bc01abba3", size = 663792, upload-time = "2026-04-27T12:59:44.93Z" }, + { url = "https://files.pythonhosted.org/packages/a3/59/1bd6d7428d6ed9106efbb8c52310c60fd04f6672490f452aeaa3829aa436/greenlet-3.5.0-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8f52a464e4ed91780bdfbbdd2b97197f3accaa629b98c200f4dffada759f3ae7", size = 660933, upload-time = "2026-04-27T12:25:33.276Z" }, + { url = "https://files.pythonhosted.org/packages/83/e4/b903e5a5fae1e8a28cdd32a0cfbfd560b668c25b692f67768822ddc5f40f/greenlet-3.5.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:762612baf1161ccb8437c0161c668a688223cba28e1bf038f4eb47b13e39ccdf", size = 1618401, upload-time = "2026-04-27T12:53:31.062Z" }, + { url = "https://files.pythonhosted.org/packages/0e/e3/5ec408a329acb854fb607a122e1ee5fb3ff649f9a97952948a90803c0d8e/greenlet-3.5.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:57a43c6079a89713522bc4bcb9f75070ecf5d3dbad7792bfe42239362cbf2a16", size = 1682038, upload-time = "2026-04-27T12:25:31.838Z" }, + { url = "https://files.pythonhosted.org/packages/91/20/6b165108058767ee643c55c5c4904d591a830ee2b3c7dbd359828fbc829f/greenlet-3.5.0-cp314-cp314-win_amd64.whl", hash = "sha256:3bc59be3945ae9750b9e7d45067d01ae3fe90ea5f9ade99239dabdd6e28a5033", size = 239835, upload-time = "2026-04-27T12:24:54.136Z" }, + { url = "https://files.pythonhosted.org/packages/4e/62/1c498375cee177b55d980c1db319f26470e5309e54698c8f8fc06c0fd539/greenlet-3.5.0-cp314-cp314-win_arm64.whl", hash = "sha256:a96fcee45e03fe30a62669fd16ab5c9d3c172660d3085605cb1e2d1280d3c988", size = 236862, upload-time = "2026-04-27T12:23:24.957Z" }, + { url = "https://files.pythonhosted.org/packages/78/a8/4522939255bb5409af4e87132f915446bf3622c2c292d14d3c38d128ae82/greenlet-3.5.0-cp314-cp314t-macosx_11_0_universal2.whl", hash = "sha256:a10a732421ab4fec934783ce3e54763470d0181db6e3468f9103a275c3ed1853", size = 293614, upload-time = "2026-04-27T12:24:12.874Z" }, + { url = "https://files.pythonhosted.org/packages/15/5e/8744c52e2c027b5a8772a01561934c8835f869733e101f62075c60430340/greenlet-3.5.0-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7fc391b1566f2907d17aaebe78f8855dc45675159a775fcf9e61f8ee0078e87f", size = 650723, upload-time = "2026-04-27T12:52:45.412Z" }, + { url = "https://files.pythonhosted.org/packages/00/ef/7b4c39c03cf46ceca512c5d3f914afd85aa30b2cc9a93015b0dd73e4be6c/greenlet-3.5.0-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:680bd0e7ad5e8daa8a4aa89f68fd6adc834b8a8036dc256533f7e08f4a4b01f7", size = 656529, upload-time = "2026-04-27T12:59:46.295Z" }, + { url = "https://files.pythonhosted.org/packages/0b/b5/c7768f352f5c010f92064d0063f987e7dc0cd290a6d92a34109015ce4aa1/greenlet-3.5.0-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ddb36c7d6c9c0a65f18c7258634e0c416c6ab59caac8c987b96f80c2ebda0112", size = 654364, upload-time = "2026-04-27T12:25:35.64Z" }, + { url = "https://files.pythonhosted.org/packages/ef/d0/079ebe12e4b1fc758857ce5be1a5e73f06870f2101e52611d1e71925ce54/greenlet-3.5.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e5ddf316ced87539144621453c3aef229575825fe60c604e62bedc4003f372b2", size = 1614204, upload-time = "2026-04-27T12:53:32.618Z" }, + { url = "https://files.pythonhosted.org/packages/6d/89/6c2fb63df3596552d20e58fb4d96669243388cf680cff222758812c7bfaa/greenlet-3.5.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:4a448128607be0de65342dc9b31be7f948ef4cc0bc8832069350abefd310a8f2", size = 1675480, upload-time = "2026-04-27T12:25:34.168Z" }, + { url = "https://files.pythonhosted.org/packages/15/32/77ee8a6c1564fc345a491a4e85b3bf360e4cf26eac98c4532d2fdb96e01f/greenlet-3.5.0-cp314-cp314t-win_amd64.whl", hash = "sha256:d60097128cb0a1cab9ea541186ea13cd7b847b8449a7787c2e2350da0cb82d86", size = 245324, upload-time = "2026-04-27T12:24:40.295Z" }, ] [[package]] @@ -1197,7 +1197,7 @@ name = "importlib-metadata" version = "9.0.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "zipp" }, + { name = "zipp", marker = "python_full_version < '3.12'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/a9/01/15bb152d77b21318514a96f43af312635eb2500c96b55398d020c93d86ea/importlib_metadata-9.0.0.tar.gz", hash = "sha256:a4f57ab599e6a2e3016d7595cfd72eb4661a5106e787a95bcc90c7105b831efc", size = 56405, upload-time = "2026-03-20T06:42:56.999Z" } wheels = [ @@ -1302,14 +1302,14 @@ wheels = [ [[package]] name = "jedi" -version = "0.19.2" +version = "0.20.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "parso" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/72/3a/79a912fbd4d8dd6fbb02bf69afd3bb72cf0c729bb3063c6f4498603db17a/jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0", size = 1231287, upload-time = "2024-11-11T01:41:42.873Z" } +sdist = { url = "https://files.pythonhosted.org/packages/46/b7/a3635f6a2d7cf5b5dd98064fc1d5fbbafcb25477bcea204a3a92145d158b/jedi-0.20.0.tar.gz", hash = "sha256:c3f4ccbd276696f4b19c54618d4fb18f9fc24b0aef02acf704b23f487daa1011", size = 3119416, upload-time = "2026-05-01T23:38:47.814Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9", size = 1572278, upload-time = "2024-11-11T01:41:40.175Z" }, + { url = "https://files.pythonhosted.org/packages/9a/93/242e2eab5fe682ffcb8b0084bde703a41d51e17ee0f3a31ff0d9d813620a/jedi-0.20.0-py2.py3-none-any.whl", hash = "sha256:7bdd9c2634f56713299976f4cbd59cb3fa92165cc5e05ea811fb253480728b67", size = 4884812, upload-time = "2026-05-01T23:38:43.919Z" }, ] [[package]] @@ -1487,7 +1487,7 @@ wheels = [ [[package]] name = "jupyterlab" -version = "4.5.6" +version = "4.5.7" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "async-lru" }, @@ -1504,9 +1504,9 @@ dependencies = [ { name = "tornado" }, { name = "traitlets" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/ac/d5/730628e03fff2e8a8e8ccdaedde1489ab1309f9a4fa2536248884e30b7c7/jupyterlab-4.5.6.tar.gz", hash = "sha256:642fe2cfe7f0f5922a8a558ba7a0d246c7bc133b708dfe43f7b3a826d163cf42", size = 23970670, upload-time = "2026-03-11T14:17:04.531Z" } +sdist = { url = "https://files.pythonhosted.org/packages/2b/22/8440ec827762146e7cdecf04335bd348795899d29dc6ae82238707353a2c/jupyterlab-4.5.7.tar.gz", hash = "sha256:55a9822c4754da305f41e113452c68383e214dcf96de760146af89ce5d5117b0", size = 23992763, upload-time = "2026-04-29T16:43:51.328Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/e1/1b/dad6fdcc658ed7af26fdf3841e7394072c9549a8b896c381ab49dd11e2d9/jupyterlab-4.5.6-py3-none-any.whl", hash = "sha256:d6b3dac883aa4d9993348e0f8e95b24624f75099aed64eab6a4351a9cdd1e580", size = 12447124, upload-time = "2026-03-11T14:17:00.229Z" }, + { url = "https://files.pythonhosted.org/packages/3d/aa/537b8f7d80e799af19af35fb3ddfc970b951088a13c57dd9387dcfbb7f61/jupyterlab-4.5.7-py3-none-any.whl", hash = "sha256:fba4cb0e2c44a52859669d8c98b45de029d5e515f8407bf8534d2a8fc5f0964d", size = 12450123, upload-time = "2026-04-29T16:43:46.639Z" }, ] [[package]] @@ -1696,14 +1696,14 @@ wheels = [ [[package]] name = "mako" -version = "1.3.11" +version = "1.3.12" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "markupsafe" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/59/8a/805404d0c0b9f3d7a326475ca008db57aea9c5c9f2e1e39ed0faa335571c/mako-1.3.11.tar.gz", hash = "sha256:071eb4ab4c5010443152255d77db7faa6ce5916f35226eb02dc34479b6858069", size = 399811, upload-time = "2026-04-14T20:19:51.493Z" } +sdist = { url = "https://files.pythonhosted.org/packages/00/62/791b31e69ae182791ec67f04850f2f062716bbd205483d63a215f3e062d3/mako-1.3.12.tar.gz", hash = "sha256:9f778e93289bd410bb35daadeb4fc66d95a746f0b75777b942088b7fd7af550a", size = 400219, upload-time = "2026-04-28T19:01:08.512Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/68/a5/19d7aaa7e433713ffe881df33705925a196afb9532efc8475d26593921a6/mako-1.3.11-py3-none-any.whl", hash = "sha256:e372c6e333cf004aa736a15f425087ec977e1fcbd2966aae7f17c8dc1da27a77", size = 78503, upload-time = "2026-04-14T20:19:53.233Z" }, + { url = "https://files.pythonhosted.org/packages/bc/b1/a0ec7a5a9db730a08daef1fdfb8090435b82465abbf758a596f0ea88727e/mako-1.3.12-py3-none-any.whl", hash = "sha256:8f61569480282dbf557145ce441e4ba888be453c30989f879f0d652e39f53ea9", size = 78521, upload-time = "2026-04-28T19:01:10.393Z" }, ] [[package]] @@ -1867,11 +1867,11 @@ wheels = [ [[package]] name = "mistune" -version = "3.2.0" +version = "3.2.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/9d/55/d01f0c4b45ade6536c51170b9043db8b2ec6ddf4a35c7ea3f5f559ac935b/mistune-3.2.0.tar.gz", hash = "sha256:708487c8a8cdd99c9d90eb3ed4c3ed961246ff78ac82f03418f5183ab70e398a", size = 95467, upload-time = "2025-12-23T11:36:34.994Z" } +sdist = { url = "https://files.pythonhosted.org/packages/ca/84/620cc3f7e3adf6f5067e10f4dbae71295d8f9e16d5d3f9ef97c40f2f592c/mistune-3.2.1.tar.gz", hash = "sha256:7c8e5501d38bac1582e067e46c8343f17d57ea1aaa735823f3aba1fd59c88a28", size = 98003, upload-time = "2026-05-03T14:33:22.312Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/9b/f7/4a5e785ec9fbd65146a27b6b70b6cdc161a66f2024e4b04ac06a67f5578b/mistune-3.2.0-py3-none-any.whl", hash = "sha256:febdc629a3c78616b94393c6580551e0e34cc289987ec6c35ed3f4be42d0eee1", size = 53598, upload-time = "2025-12-23T11:36:33.211Z" }, + { url = "https://files.pythonhosted.org/packages/2a/7f/a946aa4f8752b37102b41e64dca18a1976ac705c3a0d1dfe74d820a02552/mistune-3.2.1-py3-none-any.whl", hash = "sha256:78cdb0ba5e938053ccf63651b352508d2efa9411dc8810bfb05f2dc5140c0048", size = 53749, upload-time = "2026-05-03T14:33:20.551Z" }, ] [[package]] @@ -2284,11 +2284,11 @@ wheels = [ [[package]] name = "parso" -version = "0.8.6" +version = "0.8.7" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/81/76/a1e769043c0c0c9fe391b702539d594731a4362334cdf4dc25d0c09761e7/parso-0.8.6.tar.gz", hash = "sha256:2b9a0332696df97d454fa67b81618fd69c35a7b90327cbe6ba5c92d2c68a7bfd", size = 401621, upload-time = "2026-02-09T15:45:24.425Z" } +sdist = { url = "https://files.pythonhosted.org/packages/30/4b/90c937815137d43ce71ba043cd3566221e9df6b9c805f24b5d138c9d40a7/parso-0.8.7.tar.gz", hash = "sha256:eaaac4c9fdd5e9e8852dc778d2d7405897ec510f2a298071453e5e3a07914bb1", size = 401824, upload-time = "2026-05-01T23:13:02.138Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/b6/61/fae042894f4296ec49e3f193aff5d7c18440da9e48102c3315e1bc4519a7/parso-0.8.6-py2.py3-none-any.whl", hash = "sha256:2c549f800b70a5c4952197248825584cb00f033b29c692671d3bf08bf380baff", size = 106894, upload-time = "2026-02-09T15:45:21.391Z" }, + { url = "https://files.pythonhosted.org/packages/99/5d/8268b644392ee874ee82a635cd0df1773de230bde356c38de28e298392cc/parso-0.8.7-py2.py3-none-any.whl", hash = "sha256:a8926eb2a1b915486941fdbd31e86a4baf88fe8c210f25f2f35ecec5b574ca1c", size = 107025, upload-time = "2026-05-01T23:12:58.867Z" }, ] [[package]] @@ -2829,7 +2829,8 @@ dependencies = [ { name = "torchmetrics" }, { name = "torchvision" }, { name = "tqdm" }, - { name = "zarr" }, + { name = "zarr", version = "3.1.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "zarr", version = "3.2.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, ] [package.optional-dependencies] @@ -3163,7 +3164,7 @@ dependencies = [ { name = "pillow" }, { name = "scipy" }, { name = "tifffile", version = "2026.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, - { name = "tifffile", version = "2026.4.11", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, + { name = "tifffile", version = "2026.5.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/a1/b4/2528bb43c67d48053a7a649a9666432dc307d66ba02e3a6d5c40f46655df/scikit_image-0.26.0.tar.gz", hash = "sha256:f5f970ab04efad85c24714321fcc91613fcb64ef2a892a13167df2f3e59199fa", size = 22729739, upload-time = "2025-12-20T17:12:21.824Z" } wheels = [ @@ -3464,7 +3465,7 @@ wheels = [ [[package]] name = "tifffile" -version = "2026.4.11" +version = "2026.5.2" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14'", @@ -3473,9 +3474,9 @@ resolution-markers = [ dependencies = [ { name = "numpy", marker = "python_full_version >= '3.12'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/d7/4a/e687f5957fead200faad58dbf9c9431a2bbb118040e96f5fb8a55f7ebc50/tifffile-2026.4.11.tar.gz", hash = "sha256:17758ff0c0d4db385792a083ad3ca51fcb0f4d942642f4d8f8bc1287fdcf17bc", size = 394956, upload-time = "2026-04-12T01:57:28.793Z" } +sdist = { url = "https://files.pythonhosted.org/packages/6c/3e/695c7ab56be57814e369c1f38bc3f64b9dea0a83e867d00c0c9d613a9929/tifffile-2026.5.2.tar.gz", hash = "sha256:21b10227ede8493814a34676774797f721f487e36cb0530e7c3bd882caa87f5a", size = 429140, upload-time = "2026-05-02T20:19:31.497Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/3f/9f/74f110b4271ded519c7add4341cbabc824de26817ff1c345b3109df9e99c/tifffile-2026.4.11-py3-none-any.whl", hash = "sha256:9b94ffeddb39e97601af646345e8808f885773de01b299e480ed6d3a41509ec9", size = 248227, upload-time = "2026-04-12T01:57:26.969Z" }, + { url = "https://files.pythonhosted.org/packages/b4/af/ce4df3ca29122d219c45d3e86e5ff9a9df03b8cf31afd76817b662c803a3/tifffile-2026.5.2-py3-none-any.whl", hash = "sha256:5129b53b826e768a5b1af26b765eeea75c2d0a227d2d12849617e0737588e105", size = 266420, upload-time = "2026-05-02T20:19:29.814Z" }, ] [[package]] @@ -3755,7 +3756,7 @@ wheels = [ [[package]] name = "virtualenv" -version = "21.2.4" +version = "21.3.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "distlib" }, @@ -3763,18 +3764,18 @@ dependencies = [ { name = "platformdirs" }, { name = "python-discovery" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/0c/98/3a7e644e19cb26133488caff231be390579860bbbb3da35913c49a1d0a46/virtualenv-21.2.4.tar.gz", hash = "sha256:b294ef68192638004d72524ce7ef303e9d0cf5a44c95ce2e54a7500a6381cada", size = 5850742, upload-time = "2026-04-14T22:15:31.438Z" } +sdist = { url = "https://files.pythonhosted.org/packages/3f/8b/6331f7a7fe70131c301106ec1e7cf23e2501bf7d4ca3636805801ca191bb/virtualenv-21.3.0.tar.gz", hash = "sha256:733750db978ec95c2d8eb4feadaa57091002bce404cb39ba69899cf7bd28944e", size = 7614069, upload-time = "2026-04-27T17:05:58.927Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/27/8d/edd0bd910ff803c308ee9a6b7778621af0d10252219ad9f19ef4d4982a61/virtualenv-21.2.4-py3-none-any.whl", hash = "sha256:29d21e941795206138d0f22f4e45ff7050e5da6c6472299fb7103318763861ac", size = 5831232, upload-time = "2026-04-14T22:15:29.342Z" }, + { url = "https://files.pythonhosted.org/packages/4b/eb/03bfb1299d4c4510329e470f13f9a4ce793df7fcb5a2fd3510f911066f61/virtualenv-21.3.0-py3-none-any.whl", hash = "sha256:4d28ee41f6d9ec8f1f00cd472b9ffbcedda1b3d3b9a575b5c94a2d004fd51bd7", size = 7594690, upload-time = "2026-04-27T17:05:55.468Z" }, ] [[package]] name = "wcwidth" -version = "0.6.0" +version = "0.7.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/35/a2/8e3becb46433538a38726c948d3399905a4c7cabd0df578ede5dc51f0ec2/wcwidth-0.6.0.tar.gz", hash = "sha256:cdc4e4262d6ef9a1a57e018384cbeb1208d8abbc64176027e2c2455c81313159", size = 159684, upload-time = "2026-02-06T19:19:40.919Z" } +sdist = { url = "https://files.pythonhosted.org/packages/2c/ee/afaf0f85a9a18fe47a67f1e4422ed6cf1fe642f0ae0a2f81166231303c52/wcwidth-0.7.0.tar.gz", hash = "sha256:90e3a7ea092341c44b99562e75d09e4d5160fe7a3974c6fb842a101a95e7eed0", size = 182132, upload-time = "2026-05-02T16:04:12.653Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/68/5a/199c59e0a824a3db2b89c5d2dade7ab5f9624dbf6448dc291b46d5ec94d3/wcwidth-0.6.0-py3-none-any.whl", hash = "sha256:1a3a1e510b553315f8e146c54764f4fb6264ffad731b3d78088cdb1478ffbdad", size = 94189, upload-time = "2026-02-06T19:19:39.646Z" }, + { url = "https://files.pythonhosted.org/packages/41/52/e465037f5375f43533d1a80b6923955201596a99142ed524d77b571a1418/wcwidth-0.7.0-py3-none-any.whl", hash = "sha256:5d69154c429a82910e241c738cd0e2976fac8a2dd47a1a805f4afed1c0f136f2", size = 110825, upload-time = "2026-05-02T16:04:11.033Z" }, ] [[package]] @@ -3829,19 +3830,43 @@ wheels = [ name = "zarr" version = "3.1.6" source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version < '3.12'", +] dependencies = [ - { name = "donfig" }, - { name = "google-crc32c" }, - { name = "numcodecs" }, - { name = "numpy" }, - { name = "packaging" }, - { name = "typing-extensions" }, + { name = "donfig", marker = "python_full_version < '3.12'" }, + { name = "google-crc32c", marker = "python_full_version < '3.12'" }, + { name = "numcodecs", marker = "python_full_version < '3.12'" }, + { name = "numpy", marker = "python_full_version < '3.12'" }, + { name = "packaging", marker = "python_full_version < '3.12'" }, + { name = "typing-extensions", marker = "python_full_version < '3.12'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/31/5a/b8a0cf39a14c770c30bd1f2d120c54000c8cd9e84e8e79f38d9a7ce58071/zarr-3.1.6.tar.gz", hash = "sha256:d95e72cbea4b90e9a70679468b8266400331756232576ae2b43400ac5108d0eb", size = 386531, upload-time = "2026-03-23T17:25:18.748Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/de/7c/ba8ca8cbe9dbef8e83a95fc208fed8e6686c98b4719aaa0aa7f3d31fe390/zarr-3.1.6-py3-none-any.whl", hash = "sha256:b5a82c5079d1c3d4ee8f06746fa3b9a98a7d804300fa3f4be154362a33e1207e", size = 295655, upload-time = "2026-03-23T17:25:17.189Z" }, ] +[[package]] +name = "zarr" +version = "3.2.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.14'", + "python_full_version >= '3.12' and python_full_version < '3.14'", +] +dependencies = [ + { name = "donfig", marker = "python_full_version >= '3.12'" }, + { name = "google-crc32c", marker = "python_full_version >= '3.12'" }, + { name = "numcodecs", marker = "python_full_version >= '3.12'" }, + { name = "numpy", marker = "python_full_version >= '3.12'" }, + { name = "packaging", marker = "python_full_version >= '3.12'" }, + { name = "typing-extensions", marker = "python_full_version >= '3.12'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/db/27/8f391a4304f503ab6f4df6e1724380ea2e35e78a5d1ba973ba2b1347df5b/zarr-3.2.0.tar.gz", hash = "sha256:5867fa8dd7910541075531368c8eaa6f35957ab5413c68c168830e83948665ed", size = 454948, upload-time = "2026-04-30T22:18:03.074Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0b/9e/2e99d08824f300046eba83b480d6be17f771f57eed80dd7c162381cbe4de/zarr-3.2.0-py3-none-any.whl", hash = "sha256:c693bd4ae24328f242e47e9e1ced221e919d9f62cad71030fd059e398320e555", size = 318784, upload-time = "2026-04-30T22:18:01.13Z" }, +] + [[package]] name = "zipp" version = "3.23.1" From e004a23da24b1db9a279ff80dc5f41f70b3a3c33 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 4 May 2026 11:40:56 -0700 Subject: [PATCH 039/140] fix: track widget/scripts/build.mjs (required for npm run build) --- .gitignore | 1 + widget/js/show2d/index.tsx | 147 ++++++++++++++++++++++++++++++------- 2 files changed, 120 insertions(+), 28 deletions(-) diff --git a/.gitignore b/.gitignore index 92d69e39..9d2da4dd 100644 --- a/.gitignore +++ b/.gitignore @@ -202,6 +202,7 @@ widget/.gitignore widget/docs/ widget/notebooks/ widget/scripts/ +!widget/scripts/build.mjs widget/tests/integration/ widget/tests/snapshots/ diff --git a/widget/js/show2d/index.tsx b/widget/js/show2d/index.tsx index 17120308..1693f177 100644 --- a/widget/js/show2d/index.tsx +++ b/widget/js/show2d/index.tsx @@ -585,6 +585,24 @@ function Show2D() { const [fftColormap, setFftColormap] = React.useState("inferno"); const [fftScaleMode, setFftScaleMode] = React.useState<"linear" | "log" | "power">("linear"); const [fftAuto, setFftAuto] = React.useState(true); + const [fftSmooth, setFftSmooth] = React.useState(true); + const [fftLinkedZoom, setFftLinkedZoom] = React.useState(false); + const [fftLinkPan, setFftLinkPan] = React.useState(false); + const [fftLinkedContrast, setFftLinkedContrast] = React.useState(true); + // Per-image FFT contrast (used when fftLinkedContrast=false) + const [fftContrastStates, setFftContrastStates] = React.useState>(new Map()); + const fftContrastFor = React.useCallback((idx: number) => { + if (fftLinkedContrast) return { vminPct: fftVminPct, vmaxPct: fftVmaxPct }; + return fftContrastStates.get(idx) || { vminPct: 0, vmaxPct: 100 }; + }, [fftLinkedContrast, fftVminPct, fftVmaxPct, fftContrastStates]); + const setFftContrastFor = React.useCallback((idx: number, val: { vminPct: number; vmaxPct: number }) => { + if (fftLinkedContrast) { + setFftVminPct(val.vminPct); + setFftVmaxPct(val.vmaxPct); + } else { + setFftContrastStates(prev => new Map(prev).set(idx, val)); + } + }, [fftLinkedContrast]); const [fftStats, setFftStats] = React.useState(null); const [fftShowColorbar, setFftShowColorbar] = React.useState(false); @@ -640,16 +658,34 @@ function Show2D() { const [linkedFftZoomState, setLinkedFftZoomState] = React.useState({ zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 }); const [fftPanningIdx, setFftPanningIdx] = React.useState(null); const getGalleryFftState = React.useCallback((idx: number) => { - if (linkedZoom) return linkedFftZoomState; - return galleryFftStates.get(idx) || { zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 }; - }, [linkedZoom, linkedFftZoomState, galleryFftStates]); + const per = galleryFftStates.get(idx) || { zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 }; + return { + zoom: fftLinkedZoom ? linkedFftZoomState.zoom : per.zoom, + panX: fftLinkPan ? linkedFftZoomState.panX : per.panX, + panY: fftLinkPan ? linkedFftZoomState.panY : per.panY, + }; + }, [fftLinkedZoom, fftLinkPan, linkedFftZoomState, galleryFftStates]); const setGalleryFftState = React.useCallback((idx: number, state: ZoomState) => { - if (linkedZoom) { - setLinkedFftZoomState(state); - } else { - setGalleryFftStates(prev => new Map(prev).set(idx, state)); + if (fftLinkedZoom || fftLinkPan) { + setLinkedFftZoomState(prev => ({ + zoom: fftLinkedZoom ? state.zoom : prev.zoom, + panX: fftLinkPan ? state.panX : prev.panX, + panY: fftLinkPan ? state.panY : prev.panY, + })); } - }, [linkedZoom]); + if (!fftLinkedZoom || !fftLinkPan) { + setGalleryFftStates(prev => { + const cur = prev.get(idx) || { zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 }; + const next = new Map(prev); + next.set(idx, { + zoom: fftLinkedZoom ? cur.zoom : state.zoom, + panX: fftLinkPan ? cur.panX : state.panX, + panY: fftLinkPan ? cur.panY : state.panY, + }); + return next; + }); + } + }, [fftLinkedZoom, fftLinkPan]); // Resizable state (gallery starts smaller) const [canvasSize, setCanvasSize] = React.useState(nImages > 1 ? GALLERY_IMAGE_TARGET : SINGLE_IMAGE_TARGET); @@ -875,12 +911,12 @@ function Show2D() { if (!off) return; const ctx = canvas.getContext("2d"); if (!ctx) return; - ctx.imageSmoothingEnabled = fftW < canvasW || fftH < canvasH; + ctx.imageSmoothingEnabled = fftSmooth; ctx.clearRect(0, 0, canvas.width, canvas.height); ctx.drawImage(off, 0, 0, fftW, fftH, 0, 0, canvasW, canvasH); })(); return () => { cancelled = true; }; - }, [effectiveShowFft, showDiffPanel, nImages, dataVersion, width, height, fftWindow, fftColormap, canvasW, canvasH]); + }, [effectiveShowFft, showDiffPanel, nImages, dataVersion, width, height, fftWindow, fftColormap, canvasW, canvasH, fftSmooth]); // Diff panels render — DYNAMIC. One per non-reference image: image[ref] − image[i]. // Computed at canvas resolution from raw float data, re-running on zoom/pan/align change. @@ -2075,7 +2111,7 @@ function Show2D() { const fftH = offscreen.height; // Use bilinear smoothing when FFT is smaller than canvas (avoids blocky upscaling) - ctx.imageSmoothingEnabled = fftW < canvasW || fftH < canvasH; + ctx.imageSmoothingEnabled = fftSmooth || (fftW < canvasW || fftH < canvasH); ctx.clearRect(0, 0, canvasW, canvasH); ctx.save(); @@ -2087,7 +2123,7 @@ function Show2D() { // Stretch cropped FFT to fill the full canvas (no layout change during drag) ctx.drawImage(offscreen, 0, 0, fftW, fftH, 0, 0, canvasW, canvasH); ctx.restore(); - }, [effectiveShowFft, isGallery, fftOffscreenVersion, canvasW, canvasH, fftZoom, fftPanX, fftPanY]); + }, [effectiveShowFft, isGallery, fftOffscreenVersion, canvasW, canvasH, fftZoom, fftPanX, fftPanY, fftSmooth]); // ------------------------------------------------------------------------- // Render FFT overlay (scale bar + colorbar + d-spacing marker) @@ -2317,7 +2353,8 @@ function Show2D() { } else { ({ min: displayMin, max: displayMax } = findDataRange(displayData)); } - const { vmin, vmax } = sliderRange(displayMin, displayMax, fftVminPct, fftVmaxPct); + const fc = fftContrastFor(idx); + const { vmin, vmax } = sliderRange(displayMin, displayMax, fc.vminPct, fc.vmaxPct); const offscreen = renderToOffscreen(displayData, fftW, fftH, lut, vmin, vmax); if (!offscreen) continue; @@ -2335,7 +2372,7 @@ function Show2D() { setFftDataRange(findDataRange(histData)); } setGalleryFftOffscreenVersion(v => v + 1); - }, [effectiveShowFft, isGallery, nImages, width, height, galleryFftMagVersion, fftColormap, fftScaleMode, fftAuto, fftVminPct, fftVmaxPct, selectedIdx]); + }, [effectiveShowFft, isGallery, nImages, width, height, galleryFftMagVersion, fftColormap, fftScaleMode, fftAuto, fftVminPct, fftVmaxPct, selectedIdx, fftLinkedContrast, fftContrastStates]); // Gallery FFT draw effect: cheap drawImage from cached offscreens (zoom/pan changes) React.useLayoutEffect(() => { @@ -2351,7 +2388,7 @@ function Show2D() { if (!ctx) continue; const { zoom, panX, panY } = getGalleryFftState(idx); - ctx.imageSmoothingEnabled = fftW < canvasW || fftH < canvasH; + ctx.imageSmoothingEnabled = fftSmooth; ctx.clearRect(0, 0, canvasW, canvasH); ctx.save(); const cx = canvasW / 2; @@ -2362,7 +2399,7 @@ function Show2D() { ctx.drawImage(offscreen, 0, 0, fftW, fftH, 0, 0, canvasW, canvasH); ctx.restore(); } - }, [effectiveShowFft, isGallery, nImages, canvasW, canvasH, width, height, galleryFftOffscreenVersion, galleryFftStates, linkedZoom, linkedFftZoomState]); + }, [effectiveShowFft, isGallery, nImages, canvasW, canvasH, width, height, galleryFftOffscreenVersion, galleryFftStates, fftLinkedZoom, linkedFftZoomState, fftSmooth]); // ------------------------------------------------------------------------- // Mouse Handlers for Zoom/Pan @@ -2526,7 +2563,7 @@ function Show2D() { // Gallery FFT zoom/pan handlers (only selected image's FFT responds) const handleGalleryFftWheel = (e: React.WheelEvent, idx: number) => { - if (isGallery && idx !== selectedIdx && !linkedZoom) return; + if (isGallery && idx !== selectedIdx && !fftLinkedZoom) return; e.preventDefault(); // Prevent page scroll when zooming FFT const zs = getGalleryFftState(idx); const zoomFactor = e.deltaY > 0 ? 0.9 : 1.1; @@ -3636,7 +3673,7 @@ function Show2D() { { fftContainerRefs.current[i] = el; }} sx={{ mt: 0.5, position: "relative", border: `2px solid ${i === selectedIdx ? themeColors.accent : themeColors.border}`, borderRadius: 0, bgcolor: "#000", cursor: "grab" }} - onWheel={(i === selectedIdx || linkedZoom) ? (e) => handleGalleryFftWheel(e, i) : undefined} + onWheel={(i === selectedIdx || fftLinkedZoom) ? (e) => handleGalleryFftWheel(e, i) : undefined} onDoubleClick={() => setGalleryFftState(i, { zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 })} onMouseDown={(e) => handleGalleryFftMouseDown(e, i)} onMouseMove={(e) => handleGalleryFftMouseMove(e, i)} @@ -3747,13 +3784,10 @@ function Show2D() { FFT Scale: - setFftScaleMode(e.target.value as "linear" | "log")} size="small" sx={{ ...themedSelect, minWidth: 50, fontSize: 10 }} MenuProps={themedMenuProps}> Lin Log - Pow - Auto: - { setFftAuto(e.target.checked); }} size="small" sx={switchStyles.small} /> {roiFftActive && fftCropDims && ( <> Win: @@ -3765,11 +3799,69 @@ function Show2D() { {COLORMAP_NAMES.map((name) => ({name.charAt(0).toUpperCase() + name.slice(1)}))} + {/* FFT Row 2: Auto + Smooth + Link Zoom/Pan/Contrast (mirrors main image Row 2) */} + + Auto: + { setFftAuto(e.target.checked); }} size="small" sx={switchStyles.small} /> + Smooth: + { setFftSmooth(e.target.checked); }} size="small" sx={switchStyles.small} /> + {isGallery && ( + <> + Link: + Zoom + { setFftLinkedZoom(!fftLinkedZoom); }} size="small" sx={switchStyles.small} /> + Pan + { setFftLinkPan(!fftLinkPan); }} size="small" sx={switchStyles.small} /> + Contrast + { setFftLinkedContrast(!fftLinkedContrast); }} size="small" sx={switchStyles.small} /> + + )} + {( {fftHistogramData && ( - { setFftVminPct(min); setFftVmaxPct(max); }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={fftDataRange.min} dataMax={fftDataRange.max} /> + !fftLinkedContrast && isGallery ? ( + + {Array.from({ length: nImages }).map((_, i) => { + const fc = fftContrastFor(i); + const mag = fftMagCacheGalleryRef.current[i]; + let perData: Float32Array | null = null; + if (mag) { + if (fftScaleMode === "log") perData = applyLogScale(mag); + else if (fftScaleMode === "power") { + perData = new Float32Array(mag.length); + for (let j = 0; j < mag.length; j++) perData[j] = Math.sqrt(mag[j]); + } else perData = mag; + } + const dr = perData ? findDataRange(perData) : fftDataRange; + return ( + { setFftContrastFor(i, { vminPct: min, vmaxPct: max }); }} + width={110} height={58} + theme={themeInfo.theme === "dark" ? "dark" : "light"} + dataMin={dr.min} dataMax={dr.max} + /> + ); + })} + + ) : (() => { + const fc = fftContrastFor(selectedIdx); + return ( + { setFftContrastFor(selectedIdx, { vminPct: min, vmaxPct: max }); }} + width={110} height={58} + theme={themeInfo.theme === "dark" ? "dark" : "light"} + dataMin={fftDataRange.min} dataMax={fftDataRange.max} + /> + ); + })() )} )} @@ -3798,7 +3890,7 @@ function Show2D() { {/* Controls: two rows left + histogram right, ROI below */} {showControls && ( - + {/* Top: control rows + histogram side by side */} @@ -3856,10 +3948,10 @@ function Show2D() { {/* Right: histograms. Unlinked + gallery → grid matching gallery layout (same effectiveNcols × rows). Linked or single image → one histogram. */} - {(imageHistogramData || imageHistogramBins) && ( + {(imageHistogramData || imageHistogramBins || (isGallery && !linkedContrast && rawDataRef.current)) && ( {(!linkedContrast && isGallery && rawDataRef.current) ? ( - + {Array.from({ length: nImages }).map((_, i) => { const cs = contrastStates.get(i) || { vminPct: 0, vmaxPct: 100 }; const raw = rawDataRef.current?.[i] || null; @@ -4060,10 +4152,9 @@ function Show2D() { {/* Row 1: Scale + Color + Colorbar */} Scale: - setFftScaleMode(e.target.value as "linear" | "log")} size="small" sx={{ ...themedSelect, minWidth: 50, fontSize: 10 }} MenuProps={themedMenuProps}> Lin Log - Pow Color: setFftScaleMode(e.target.value as "linear" | "log")} size="small" sx={{ ...themedSelect, minWidth: 50, fontSize: 10 }} MenuProps={themedMenuProps}> + @@ -4152,7 +4222,7 @@ function Show2D() { {/* Row 1: Scale + Color + Colorbar */} Scale: - setFftScaleMode(e.target.value as "linear" | "log")} size="small" sx={{ ...themedSelect, minWidth: 50, fontSize: 10 }} MenuProps={themedMenuProps}> Lin Log diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py index f2cbed6a..edcd01db 100644 --- a/widget/src/quantem/widget/show2d.py +++ b/widget/src/quantem/widget/show2d.py @@ -303,8 +303,8 @@ def __init__( zoom: float = 1.0, zoom_row: float | None = None, zoom_col: float | None = None, - link_zoom: bool = False, - link_pan: bool = False, + link_zoom: bool | None = None, + link_pan: bool | None = None, link_contrast: bool = True, diff_mode: bool = False, view_box: tuple | list | None = None, @@ -362,6 +362,17 @@ def _init_sync(self, *, data, labels, title, cmap, sampling, units, if units is None and hasattr(data, "units"): units = list(data.units[-2:]) data = data.array + # Same auto-extract for list/tuple of Dataset2d (gallery from per-file load). + elif isinstance(data, (list, tuple)) and len(data) > 0 and ( + isinstance(data[0], (Dataset2d, Dataset3d)) or + (hasattr(data[0], "array") and hasattr(data[0], "sampling")) + ): + first = data[0] + if sampling is None: + sampling = tuple(float(s) for s in first.sampling[-2:]) + if units is None and hasattr(first, "units"): + units = list(first.units[-2:]) + data = [d.array for d in data] # Convert NumPy / PyTorch / list inputs to a NumPy array. if isinstance(data, list): @@ -434,8 +445,10 @@ def _init_sync(self, *, data, labels, title, cmap, sampling, units, self.initial_zoom = zoom self.zoom_row = zoom_row self.zoom_col = zoom_col - self.link_zoom = link_zoom - self.link_pan = link_pan + # Auto-link zoom + pan in gallery (n_images >= 2) so dragging one panel + # follows the other — typical compare/diff workflow. Single image: no-op. + self.link_zoom = (self.n_images >= 2) if link_zoom is None else link_zoom + self.link_pan = (self.n_images >= 2) if link_pan is None else link_pan self.link_contrast = link_contrast self.diff_mode = diff_mode if self.n_images >= 2 else False if show_fft and self.height * self.width > 2048 * 2048: From 88bbaecb552fbdc7babdf4170a222acfaf67d8ba Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 4 May 2026 17:18:03 -0700 Subject: [PATCH 042/140] update uv lock with all deps listed for now --- uv.lock | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/uv.lock b/uv.lock index 43a03fd0..58122fe4 100644 --- a/uv.lock +++ b/uv.lock @@ -1197,7 +1197,7 @@ name = "importlib-metadata" version = "9.0.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "zipp" }, + { name = "zipp", marker = "python_full_version < '3.12'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/a9/01/15bb152d77b21318514a96f43af312635eb2500c96b55398d020c93d86ea/importlib_metadata-9.0.0.tar.gz", hash = "sha256:a4f57ab599e6a2e3016d7595cfd72eb4661a5106e787a95bcc90c7105b831efc", size = 56405, upload-time = "2026-03-20T06:42:56.999Z" } wheels = [ @@ -2897,10 +2897,22 @@ version = "0.0.1" source = { editable = "widget" } dependencies = [ { name = "anywidget" }, + { name = "matplotlib" }, + { name = "numpy" }, + { name = "pillow" }, + { name = "torch" }, + { name = "traitlets" }, ] [package.metadata] -requires-dist = [{ name = "anywidget", specifier = ">=0.9.0" }] +requires-dist = [ + { name = "anywidget", specifier = ">=0.9.0" }, + { name = "matplotlib", specifier = ">=3.7.0" }, + { name = "numpy", specifier = ">=2.0.0" }, + { name = "pillow", specifier = ">=10.0.0" }, + { name = "torch", specifier = ">=2.0.0" }, + { name = "traitlets", specifier = ">=5.0.0" }, +] [[package]] name = "referencing" From 8ecd3c7942b0986e12af1dc21669189847be720a Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Wed, 6 May 2026 17:25:05 -0700 Subject: [PATCH 043/140] converting ptycho models to work with new PPLR --- src/quantem/core/ml/optimizer_mixin.py | 2 +- .../diffractive_imaging/dataset_models.py | 6 ++--- .../diffractive_imaging/object_models.py | 25 ++++++++----------- .../diffractive_imaging/probe_models.py | 13 ++++------ 4 files changed, 20 insertions(+), 26 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 9655eb76..55d013c9 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -648,7 +648,7 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: f"All parameter groups must use the same optimizer type, " f"got {type(opt_specs[0]).__name__} and {type(spec).__name__}" ) - self._optimizer = optimizer_cls(params) + self._optimizer = optimizer_cls(params) # type:ignore else: # Single-optimizer case: splat global hyperparameters optimizer_cls = self._optimizer_class_for(self._optimizer_params) diff --git a/src/quantem/diffractive_imaging/dataset_models.py b/src/quantem/diffractive_imaging/dataset_models.py index ef55c92e..585d5e4b 100644 --- a/src/quantem/diffractive_imaging/dataset_models.py +++ b/src/quantem/diffractive_imaging/dataset_models.py @@ -32,7 +32,7 @@ class PtychographyDatasetBase(AutoSerialize, OptimizerMixin, torch.nn.Module): _token = object() _patch_indices: torch.Tensor - # TODO update optimizers and such to allow for different lrs for different parameters + # TODO make this a PPLR so different lrs can be used for different parameters DEFAULT_LRS = { "descan": 1e-3, "scan_positions": 1e-3, @@ -95,7 +95,7 @@ def __init__( self._constraints = {} self._probe_energy = None - def get_optimization_parameters(self): + def get_optimization_parameters(self) -> list[dict[str, Any]]: """Get the combined descan and scan position parameters for optimization.""" params = [] if self.learn_descan: @@ -106,7 +106,7 @@ def get_optimization_parameters(self): raise RuntimeError( "No parameters to optimize for dataset: learn_descan and learn_scan_positions are both False" ) - return params + return [{"params": params}] def to(self, *args, **kwargs): """Move all relevant tensors to a different device.""" diff --git a/src/quantem/diffractive_imaging/object_models.py b/src/quantem/diffractive_imaging/object_models.py index 1c6fdab9..0860bf55 100644 --- a/src/quantem/diffractive_imaging/object_models.py +++ b/src/quantem/diffractive_imaging/object_models.py @@ -1,7 +1,7 @@ import math from abc import abstractmethod from copy import deepcopy -from typing import Callable, Literal, Self, Sequence, cast +from typing import Any, Callable, Literal, Self, Sequence, cast from warnings import warn import matplotlib.pyplot as plt @@ -194,7 +194,7 @@ def obj(self): @property @abstractmethod - def params(self): + def params(self) -> list[nn.Parameter]: raise NotImplementedError() @abstractmethod @@ -232,16 +232,13 @@ def to(self, *args, **kwargs): def name(self) -> str: raise NotImplementedError() - def get_optimization_parameters(self): + def get_optimization_parameters(self) -> list[dict[str, Any]]: """Get the parameters that should be optimized for this model.""" - try: - params = self.params - if params is None: - return [] - return params - except NotImplementedError: - # This happens when params is not implemented yet in abstract base + params = self.params + if params is None: return [] + else: + return [{"params": params}] # compatible with PPLR def _propagate_array( self, array: "torch.Tensor", propagator_array: "torch.Tensor" @@ -633,9 +630,9 @@ def num_slices(self) -> int: return self._obj.shape[0] @property - def params(self): + def params(self) -> list[nn.Parameter]: """optimization parameters""" - return self._obj + return [self._obj] @property def initial_obj(self): @@ -1025,9 +1022,9 @@ def to(self, *args, **kwargs): return self @property - def params(self): + def params(self) -> list[nn.Parameter]: """optimization parameters""" - return self.model.parameters() + return list(self.model.parameters()) def reset(self): """Reset the object model to its initial or pre-trained state""" diff --git a/src/quantem/diffractive_imaging/probe_models.py b/src/quantem/diffractive_imaging/probe_models.py index f47ea1f7..b72f1426 100644 --- a/src/quantem/diffractive_imaging/probe_models.py +++ b/src/quantem/diffractive_imaging/probe_models.py @@ -94,16 +94,13 @@ def __init__( if roi_shape is not None: self.roi_shape = roi_shape - def get_optimization_parameters(self): + def get_optimization_parameters(self) -> list[dict[str, Any]]: """Get the parameters that should be optimized for this model.""" - try: - params = self.params - if params is None: - return [] - return params - except NotImplementedError: - # This happens when params is not implemented yet in abstract base + params = self.params + if params is None: return [] + else: + return [{"params": params}] # compatible with PPLR @property def learn_probe_tilt(self) -> bool: From 0bd5ba33f1713821374e992e4afcaea35a7c915d Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Wed, 6 May 2026 18:41:44 -0700 Subject: [PATCH 044/140] add hot pixel filtering in read 4dstem, default false --- src/quantem/core/io/file_readers.py | 34 ++++++++++++++++++++++++++++- tests/utils/test_filter.py | 24 ++++++++++++++++++++ 2 files changed, 57 insertions(+), 1 deletion(-) create mode 100644 tests/utils/test_filter.py diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index 98e9e8fa..9feac57a 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -15,6 +15,7 @@ def read_4dstem( file_path: str | PathLike, file_type: str | None = None, dataset_index: int | None = None, + hot_pixel_filter: bool = False, **kwargs, ) -> Dataset4dstem: """ @@ -30,6 +31,11 @@ def read_4dstem( dataset_index: int, optional Index of the dataset to load if file contains multiple datasets. If None, automatically selects the first 4D dataset found. + hot_pixel_filter: bool, optional + If True, detect and replace hot detector pixels immediately after + loading using `quantem.core.utils.filter.filter_hot_pixels` with its + default parameters. For custom thresholds, call `filter_hot_pixels` + directly on the array. **kwargs: dict Additional keyword arguments to pass to the file reader. @@ -49,6 +55,26 @@ def read_4dstem( Returns -------- Dataset4dstem + + Examples + -------- + Load an Arina 4D-STEM master file with the default hot pixel filter: + + >>> from quantem.core.io import read_4dstem + >>> ds = read_4dstem( + ... '/path/to/gold_013_master.h5', + ... file_type='arina', + ... ) + >>> ds.array.shape + (256, 256, 192, 192) + + Skip the filter to inspect the raw detector output: + + >>> ds_raw = read_4dstem( + ... '/path/to/gold_013_master.h5', + ... file_type='arina', + ... hot_pixel_filter=False, + ... ) """ if file_type is None: file_type = Path(file_path).suffix.lower().lstrip(".") @@ -104,8 +130,14 @@ def read_4dstem( else ["pixels" if ax["units"] == "1" else ax["units"] for ax in imported_axes] ) + array = imported_data["data"] + if hot_pixel_filter: + from quantem.core.utils.filter import filter_hot_pixels + + array = filter_hot_pixels(array) + dataset = Dataset4dstem.from_array( - array=imported_data["data"], + array=array, sampling=sampling, origin=origin, units=units, diff --git a/tests/utils/test_filter.py b/tests/utils/test_filter.py new file mode 100644 index 00000000..dc213bc8 --- /dev/null +++ b/tests/utils/test_filter.py @@ -0,0 +1,24 @@ +"""Tests for `filter_hot_pixels`. + +A microscopist running 4D-STEM sees most detector pixels read out at low +counts (~1-100), but a few are stuck near saturation (~60000) regardless of +incident intensity. They must be removed before virtual imaging or dp_max +analysis. +""" + +import torch + +from quantem.core.utils.filter import filter_hot_pixels + + +def test_filter_hot_pixels_replaces_stuck_detector_pixels_with_local_median(): + """Stuck pixels should drop from 60000 back into the local bulk regime (<1000).""" + ds = torch.randint(1, 101, size=(64, 64, 32, 32), dtype=torch.int32) + # Assume these 3 places have hot pixels that we later want to remove + hot_coords = [(5, 7), (18, 24), (29, 3)] + for r, c in hot_coords: + ds[:, :, r, c] = 60000 + filtered = filter_hot_pixels(ds.numpy()) + dp_max = filtered.max(axis=(0, 1)) + # no pixels should have value 101 + assert dp_max.max() < 101, f"hot pixels still present, dp_max max={dp_max.max()}" From 31a0baef7098c56def59618960bc59092f078e65 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Wed, 6 May 2026 18:59:11 -0700 Subject: [PATCH 045/140] update docstring, hot pixel being false by default --- src/quantem/core/io/file_readers.py | 8 ++++---- tests/utils/test_filter.py | 14 +++++--------- 2 files changed, 9 insertions(+), 13 deletions(-) diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index 9feac57a..4fe72645 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -58,7 +58,7 @@ def read_4dstem( Examples -------- - Load an Arina 4D-STEM master file with the default hot pixel filter: + Load a raw Arina 4D-STEM master file: >>> from quantem.core.io import read_4dstem >>> ds = read_4dstem( @@ -68,12 +68,12 @@ def read_4dstem( >>> ds.array.shape (256, 256, 192, 192) - Skip the filter to inspect the raw detector output: + Enable the hot pixel filter to repair stuck detector pixels on load: - >>> ds_raw = read_4dstem( + >>> ds = read_4dstem( ... '/path/to/gold_013_master.h5', ... file_type='arina', - ... hot_pixel_filter=False, + ... hot_pixel_filter=True, ... ) """ if file_type is None: diff --git a/tests/utils/test_filter.py b/tests/utils/test_filter.py index dc213bc8..1c43cd0c 100644 --- a/tests/utils/test_filter.py +++ b/tests/utils/test_filter.py @@ -1,18 +1,14 @@ -"""Tests for `filter_hot_pixels`. - -A microscopist running 4D-STEM sees most detector pixels read out at low -counts (~1-100), but a few are stuck near saturation (~60000) regardless of -incident intensity. They must be removed before virtual imaging or dp_max -analysis. -""" - import torch from quantem.core.utils.filter import filter_hot_pixels def test_filter_hot_pixels_replaces_stuck_detector_pixels_with_local_median(): - """Stuck pixels should drop from 60000 back into the local bulk regime (<1000).""" + """A microscopist running 4D-STEM sees most detector pixels read out at low + counts (~1-100), but a few are stuck near saturation (~60000) regardless + of incident intensity. After `filter_hot_pixels`, those stuck pixels + should drop back into the local bulk regime (<=100). + """ ds = torch.randint(1, 101, size=(64, 64, 32, 32), dtype=torch.int32) # Assume these 3 places have hot pixels that we later want to remove hot_coords = [(5, 7), (18, 24), (29, 3)] From a73dcc41fb84bc51724b3cd32f7ee257cc3a7891 Mon Sep 17 00:00:00 2001 From: henryhng Date: Thu, 7 May 2026 08:20:16 +0000 Subject: [PATCH 046/140] fix set_image --- widget/src/quantem/widget/show4dstem.py | 51 ++++++++++++++++--------- 1 file changed, 34 insertions(+), 17 deletions(-) diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 8cd0cb58..01d39615 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -586,34 +586,51 @@ def set_image(self, data, scan_shape=None): """Replace the 4D-STEM data. Preserves all display and ROI settings.""" if hasattr(data, "sampling") and hasattr(data, "array"): data = data.array - data_np = to_numpy(data) - saturated_value = 65535.0 if data_np.dtype == np.uint16 else 255.0 if data_np.dtype == np.uint8 else None - if saturated_value is not None: - data_np[data_np >= saturated_value] = 0 - if data_np.ndim == 5: - self.n_frames = data_np.shape[0] - self._scan_shape = (data_np.shape[1], data_np.shape[2]) - self._det_shape = (data_np.shape[3], data_np.shape[4]) + if isinstance(data, torch.Tensor): + self._device = data.device + self._data = data if data.device == self._device else data.to(self._device) + shape = tuple(data.shape) + ndim = len(shape) + view_dtype = ( + torch.int16 if data.dtype == torch.uint16 + else torch.int8 if data.dtype == torch.uint8 + else None + ) + if view_dtype is not None: + view = self._data.view(view_dtype).reshape(-1, *shape[-2:]) + rows = view.shape[0] + pos_per_chunk = max(1, _CHUNK_BYTE_BUDGET // max(1, shape[-2] * shape[-1])) + for i in range(0, rows, pos_per_chunk): + view[i:i + pos_per_chunk].masked_fill_(view[i:i + pos_per_chunk] == -1, 0) + else: + data_np = to_numpy(data) + saturated_value = 65535.0 if data_np.dtype == np.uint16 else 255.0 if data_np.dtype == np.uint8 else None + if saturated_value is not None: + data_np[data_np >= saturated_value] = 0 + shape = data_np.shape + ndim = data_np.ndim self._data = torch.from_numpy(data_np).to(self._device) - elif data_np.ndim == 3: + if ndim == 5: + self.n_frames = shape[0] + self._scan_shape = (shape[1], shape[2]) + self._det_shape = (shape[3], shape[4]) + elif ndim == 3: self.n_frames = 1 if scan_shape is not None: self._scan_shape = scan_shape else: - n = data_np.shape[0] + n = shape[0] side = int(n ** 0.5) if side * side != n: raise ValueError(f"Cannot infer square scan_shape from N={n}. Provide scan_shape explicitly.") self._scan_shape = (side, side) - self._det_shape = (data_np.shape[1], data_np.shape[2]) - self._data = torch.from_numpy(data_np).to(self._device) - elif data_np.ndim == 4: + self._det_shape = (shape[1], shape[2]) + elif ndim == 4: self.n_frames = 1 - self._scan_shape = (data_np.shape[0], data_np.shape[1]) - self._det_shape = (data_np.shape[2], data_np.shape[3]) - self._data = torch.from_numpy(data_np).to(self._device) + self._scan_shape = (shape[0], shape[1]) + self._det_shape = (shape[2], shape[3]) else: - raise ValueError(f"Show4DSTEM expects a 3D, 4D, or 5D array. Got {data_np.ndim}D. See documentation for accepted shapes.") + raise ValueError(f"Show4DSTEM expects a 3D, 4D, or 5D array. Got {ndim}D. See documentation for accepted shapes.") self.frame_idx = 0 self.shape_rows = self._scan_shape[0] self.shape_cols = self._scan_shape[1] From f5923843ed8f6adae6748d066654b31b1d51b922 Mon Sep 17 00:00:00 2001 From: henryhng Date: Thu, 7 May 2026 08:37:06 +0000 Subject: [PATCH 047/140] fix ruff lint errors --- widget/src/quantem/widget/show2d.py | 14 +++++----- widget/src/quantem/widget/show4dstem.py | 37 ++++++++++--------------- widget/src/quantem/widget/state.py | 1 - widget/tests/test_state_dict.py | 2 -- 4 files changed, 22 insertions(+), 32 deletions(-) diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py index edcd01db..793608ea 100644 --- a/widget/src/quantem/widget/show2d.py +++ b/widget/src/quantem/widget/show2d.py @@ -5,12 +5,12 @@ Unlike Show3D (interactive), Show2D focuses on static visualization. """ +import base64 +import io import json +import math import os import pathlib -import io -import base64 -import math import warnings from enum import StrEnum from typing import Self @@ -21,11 +21,10 @@ import matplotlib.pyplot as plt import numpy as np import traitlets - -from quantem.core.datastructures import Dataset2d, Dataset3d -from quantem.widget.array_utils import to_numpy, _resize_image +from quantem.widget.array_utils import _resize_image, to_numpy from quantem.widget.state import resolve_widget_version, save_state_file, unwrap_state_payload +from quantem.core.datastructures import Dataset2d, Dataset3d def _reject_unknown_kwargs(cls, kwargs: dict) -> None: @@ -467,7 +466,8 @@ def _init_sync(self, *, data, labels, title, cmap, sampling, units, if isinstance(vmin, (list, tuple)) or isinstance(vmax, (list, tuple)): n = self.n_images def _expand(v): - if v is None: return [None] * n + if v is None: + return [None] * n if isinstance(v, (list, tuple)): if len(v) != n: raise ValueError(f"vmin/vmax list has length {len(v)} but n_images is {n}. Pass a list of length {n} or a scalar to apply uniformly.") diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 8cd0cb58..97efa8a8 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -11,27 +11,19 @@ widget = Show4DSTEM(dataset) """ -import hashlib import json import math import pathlib import time -from datetime import datetime, timezone -from typing import Any, Self -from uuid import uuid4 +from typing import TYPE_CHECKING, Any, Self + +if TYPE_CHECKING: + from quantem.core.datastructures import Dataset4dstem import anywidget import numpy as np import torch import traitlets - -# Cap transient chunk memory at ~600 MB regardless of detector size. -# A 4096 × 192² × 4 byte float32 cast = 600 MB; a 4096 × 256² × 4 byte cast -# would be 1.0 GB. _chunk_rows() picks an N-rows-per-chunk that keeps the -# transient under this cap. -_CHUNK_BYTE_BUDGET = 600 * 1024 * 1024 - -from quantem.core.config import validate_device from quantem.widget.array_utils import to_numpy from quantem.widget.state import ( build_json_header, @@ -40,11 +32,19 @@ unwrap_state_payload, ) +from quantem.core.config import validate_device + +# Cap transient chunk memory at ~600 MB regardless of detector size. +_CHUNK_BYTE_BUDGET = 600 * 1024 * 1024 + def _format_memory(nbytes: int) -> str: - if nbytes >= 1 << 30: return f"{nbytes / (1 << 30):.1f} GB" - if nbytes >= 1 << 20: return f"{nbytes / (1 << 20):.0f} MB" - if nbytes >= 1 << 10: return f"{nbytes / (1 << 10):.0f} KB" + if nbytes >= 1 << 30: + return f"{nbytes / (1 << 30):.1f} GB" + if nbytes >= 1 << 20: + return f"{nbytes / (1 << 20):.0f} MB" + if nbytes >= 1 << 10: + return f"{nbytes / (1 << 10):.0f} KB" return f"{nbytes} B" @@ -395,7 +395,6 @@ def __init__( # Handle dimensionality — 5D loads eagerly for instant frame switching # Resolve shape from whichever input path we took shape = tuple(self._data_pre.shape) if self._data_pre is not None else data_np.shape - size_elements = int(np.prod(shape)) ndim = len(shape) _tc = time.perf_counter() if ndim == 5: @@ -1842,9 +1841,6 @@ def save_image( prev_row, prev_col = self.pos_row, self.pos_col prev_frame = self.frame_idx meta_path: pathlib.Path | None = None - export_row = int(self.pos_row) - export_col = int(self.pos_col) - export_frame = int(self.frame_idx) try: if frame_idx is not None: @@ -1853,9 +1849,6 @@ def save_image( row, col = self._validate_position(position) self.pos_row = row self.pos_col = col - export_row = int(self.pos_row) - export_col = int(self.pos_col) - export_frame = int(self.frame_idx) if view_key == "diffraction": image, dp_meta = self._render_panel_image( diff --git a/widget/src/quantem/widget/state.py b/widget/src/quantem/widget/state.py index d7710287..83cb6b2f 100644 --- a/widget/src/quantem/widget/state.py +++ b/widget/src/quantem/widget/state.py @@ -3,7 +3,6 @@ import pathlib from typing import Any - JSON_METADATA_VERSION = "1.0" diff --git a/widget/tests/test_state_dict.py b/widget/tests/test_state_dict.py index 9247c979..60614941 100644 --- a/widget/tests/test_state_dict.py +++ b/widget/tests/test_state_dict.py @@ -11,11 +11,9 @@ updating the state_dict roundtrip path. """ import json -import pathlib import numpy as np import pytest - from quantem.widget import Show2D, Show4DSTEM From 5b364c65a03e47ea9eaaf45229bf1867cdf3826b Mon Sep 17 00:00:00 2001 From: henryhng Date: Sat, 9 May 2026 08:21:04 +0000 Subject: [PATCH 048/140] remove set_image() --- widget/src/quantem/widget/show4dstem.py | 74 ------------------------- 1 file changed, 74 deletions(-) diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 01d39615..5ffca3a1 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -582,80 +582,6 @@ def __init__( shape = "x".join(str(s) for s in self._data.shape) print(f"Show4DSTEM: {shape} {self._device}, {time.perf_counter() - _t0:.2f}s total") - def set_image(self, data, scan_shape=None): - """Replace the 4D-STEM data. Preserves all display and ROI settings.""" - if hasattr(data, "sampling") and hasattr(data, "array"): - data = data.array - if isinstance(data, torch.Tensor): - self._device = data.device - self._data = data if data.device == self._device else data.to(self._device) - shape = tuple(data.shape) - ndim = len(shape) - view_dtype = ( - torch.int16 if data.dtype == torch.uint16 - else torch.int8 if data.dtype == torch.uint8 - else None - ) - if view_dtype is not None: - view = self._data.view(view_dtype).reshape(-1, *shape[-2:]) - rows = view.shape[0] - pos_per_chunk = max(1, _CHUNK_BYTE_BUDGET // max(1, shape[-2] * shape[-1])) - for i in range(0, rows, pos_per_chunk): - view[i:i + pos_per_chunk].masked_fill_(view[i:i + pos_per_chunk] == -1, 0) - else: - data_np = to_numpy(data) - saturated_value = 65535.0 if data_np.dtype == np.uint16 else 255.0 if data_np.dtype == np.uint8 else None - if saturated_value is not None: - data_np[data_np >= saturated_value] = 0 - shape = data_np.shape - ndim = data_np.ndim - self._data = torch.from_numpy(data_np).to(self._device) - if ndim == 5: - self.n_frames = shape[0] - self._scan_shape = (shape[1], shape[2]) - self._det_shape = (shape[3], shape[4]) - elif ndim == 3: - self.n_frames = 1 - if scan_shape is not None: - self._scan_shape = scan_shape - else: - n = shape[0] - side = int(n ** 0.5) - if side * side != n: - raise ValueError(f"Cannot infer square scan_shape from N={n}. Provide scan_shape explicitly.") - self._scan_shape = (side, side) - self._det_shape = (shape[1], shape[2]) - elif ndim == 4: - self.n_frames = 1 - self._scan_shape = (shape[0], shape[1]) - self._det_shape = (shape[2], shape[3]) - else: - raise ValueError(f"Show4DSTEM expects a 3D, 4D, or 5D array. Got {ndim}D. See documentation for accepted shapes.") - self.frame_idx = 0 - self.shape_rows = self._scan_shape[0] - self.shape_cols = self._scan_shape[1] - self.det_rows = self._det_shape[0] - self.det_cols = self._det_shape[1] - first_frame = self._data[0] if self._data.ndim == 5 else self._data - first_frame_sample = first_frame[0] if first_frame.ndim >= 3 else first_frame - if not torch.is_floating_point(first_frame_sample): - first_frame_sample = first_frame_sample.float() - self.dp_global_min = max(float(first_frame_sample.min()), MIN_LOG_VALUE) - self.dp_global_max = float(first_frame_sample.max()) - self._det_row_coords = torch.arange(self.det_rows, device=self._device, dtype=torch.float32)[:, None] - self._det_col_coords = torch.arange(self.det_cols, device=self._device, dtype=torch.float32)[None, :] - self._scan_row_coords = torch.arange(self.shape_rows, device=self._device, dtype=torch.float32)[:, None] - self._scan_col_coords = torch.arange(self.shape_cols, device=self._device, dtype=torch.float32)[None, :] - self._cached_bf_virtual = None - self._cached_abf_virtual = None - self._cached_adf_virtual = None - self._cached_haadf_virtual = None - with self.hold_trait_notifications(): - self.pos_row = min(self.pos_row, self.shape_rows - 1) - self.pos_col = min(self.pos_col, self.shape_cols - 1) - self._compute_virtual_image_from_roi() - self._update_frame() - def __repr__(self) -> str: shape = ( f"({self.n_frames}, {self.shape_rows}, {self.shape_cols}, {self.det_rows}, {self.det_cols})" From c57d1d028c06861296e841b0bd9761f2e3995a87 Mon Sep 17 00:00:00 2001 From: henryhng Date: Sat, 9 May 2026 08:42:02 +0000 Subject: [PATCH 049/140] remove set_image() from Show2D --- widget/src/quantem/widget/show2d.py | 60 ----------------------------- 1 file changed, 60 deletions(-) diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py index edcd01db..9c074c40 100644 --- a/widget/src/quantem/widget/show2d.py +++ b/widget/src/quantem/widget/show2d.py @@ -575,66 +575,6 @@ def _on_first_render(self, change): except (ValueError, KeyError): pass - def set_image(self, data, labels=None): - """Replace the displayed image(s). Preserves all display settings.""" - if hasattr(data, "array") and hasattr(data, "name") and hasattr(data, "sampling"): - data = data.array - if isinstance(data, list): - images = [to_numpy(d) for d in data] - shapes = [img.shape for img in images] - if len(set(shapes)) > 1: - max_h = max(s[0] for s in shapes) - max_w = max(s[1] for s in shapes) - images = [_resize_image(img, max_h, max_w) for img in images] - data = np.stack(images) - else: - data = to_numpy(data) - if data.ndim == 2: - data = data[np.newaxis, ...] - if data.dtype == np.float32: - self._data = np.array(data, dtype=np.float32, copy=True) - else: - self._data = np.asarray(data, dtype=np.float32) - self._data_original = [self._data[i] for i in range(self._data.shape[0])] - self._originals_are_views = True - self.n_images = int(data.shape[0]) - - # Auto-bin for display (reuse existing _display_bin or recompute) - gpu_budget_mb = 2500 - per_image_mb = (data.shape[1] * data.shape[2] * 4 * 3) / (1024 * 1024) - total_mb = self.n_images * per_image_mb - self._display_bin = 1 - if total_mb > gpu_budget_mb: - for bf in [2, 4, 8]: - if total_mb / (bf * bf) <= gpu_budget_mb: - self._display_bin = bf - break - else: - self._display_bin = 8 - - if self._display_bin > 1: - from quantem.widget.array_utils import bin2d - self._display_data = bin2d(self._data, factor=self._display_bin, mode="mean") - self.height = int(self._display_data.shape[1]) - self.width = int(self._display_data.shape[2]) - self._display_bin_factor = self._display_bin - if getattr(self, "_verbose", True): - print(f" Display bin {self._display_bin}×: {data.shape[1]}×{data.shape[2]} → {self.height}×{self.width}") - else: - self._display_data = self._data - self.height = int(data.shape[1]) - self.width = int(data.shape[2]) - self._display_bin_factor = 1 - - self.image_rotations = [0] * self.n_images - if labels is not None: - self.labels = list(labels) - else: - self.labels = [f"Image {i+1}" for i in range(self.n_images)] - self.selected_idx = 0 - self._compute_all_stats() - self._update_all_frames() - def __repr__(self) -> str: if self.n_images > 1: shape = f"{self.n_images}×{self.height}×{self.width}" From c36be765a113ba0b6521682531fbd152ea51a736 Mon Sep 17 00:00:00 2001 From: henryhng Date: Sat, 9 May 2026 08:51:17 +0000 Subject: [PATCH 050/140] invalidate virtual image cache on calibration change, mkdir in save_state_file --- widget/src/quantem/widget/show4dstem.py | 8 ++++++++ widget/src/quantem/widget/state.py | 4 +++- 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 5ffca3a1..5b25647c 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -527,6 +527,8 @@ def __init__( ]) # Observe compound roi_center for batched updates from JS self.observe(self._on_roi_center_change, names=["roi_center"]) + # Invalidate precomputed virtual image caches when calibration changes + self.observe(self._on_calibration_change, names=["center_row", "center_col", "bf_radius"]) # Initialize default ROI at BF center — batch to avoid redundant observer callbacks with self.hold_trait_notifications(): @@ -2162,6 +2164,12 @@ def _create_rect_mask(self, cx: float, cy: float, half_width: float, half_height mask = (torch.abs(self._det_col_coords - cx) <= half_width) & (torch.abs(self._det_row_coords - cy) <= half_height) return mask + def _on_calibration_change(self, change=None): + self._cached_bf_virtual = None + self._cached_abf_virtual = None + self._cached_adf_virtual = None + self._cached_haadf_virtual = None + def _precompute_common_virtual_images(self): """Pre-compute BF/ABF/ADF/HAADF virtual image bytes. Annular ranges match apply_preset() so the cache always hits on preset clicks.""" diff --git a/widget/src/quantem/widget/state.py b/widget/src/quantem/widget/state.py index d7710287..af9344b8 100644 --- a/widget/src/quantem/widget/state.py +++ b/widget/src/quantem/widget/state.py @@ -42,4 +42,6 @@ def unwrap_state_payload(payload: dict[str, Any], *, require_envelope: bool = Fa def save_state_file(path: str | pathlib.Path, widget_name: str, state: dict[str, Any]) -> None: - pathlib.Path(path).write_text(json.dumps(wrap_state_dict(widget_name, state), indent=2)) + p = pathlib.Path(path) + p.parent.mkdir(parents=True, exist_ok=True) + p.write_text(json.dumps(wrap_state_dict(widget_name, state), indent=2)) From 5a6e8a8cb85a960f7f4f481ade06349016e1c450 Mon Sep 17 00:00:00 2001 From: henryhng Date: Sat, 9 May 2026 09:19:14 +0000 Subject: [PATCH 051/140] lazy import torch in array_utils --- widget/src/quantem/widget/array_utils.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/widget/src/quantem/widget/array_utils.py b/widget/src/quantem/widget/array_utils.py index 1913a846..f4ff7592 100644 --- a/widget/src/quantem/widget/array_utils.py +++ b/widget/src/quantem/widget/array_utils.py @@ -1,11 +1,15 @@ """Array utilities for widgets. NumPy + PyTorch input.""" import numpy as np -import torch def to_numpy(data, dtype: np.dtype | None = None) -> np.ndarray: """Convert NumPy / PyTorch / Dataset to NumPy.""" - if isinstance(data, torch.Tensor): + try: + import torch + is_tensor = isinstance(data, torch.Tensor) + except ImportError: + is_tensor = False + if is_tensor: result = data.detach().cpu().numpy() elif isinstance(data, np.ndarray): result = data From 178006e55e7543878b1eebea4ef6771f7d1ba58d Mon Sep 17 00:00:00 2001 From: quantem-bot Date: Mon, 11 May 2026 12:17:04 +0000 Subject: [PATCH 052/140] chore: update lock file --- uv.lock | 240 ++++++++++++++++++++++++++++---------------------------- 1 file changed, 120 insertions(+), 120 deletions(-) diff --git a/uv.lock b/uv.lock index 6932e160..a0488e0f 100644 --- a/uv.lock +++ b/uv.lock @@ -532,101 +532,101 @@ wheels = [ [[package]] name = "coverage" -version = "7.13.5" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/9d/e0/70553e3000e345daff267cec284ce4cbf3fc141b6da229ac52775b5428f1/coverage-7.13.5.tar.gz", hash = "sha256:c81f6515c4c40141f83f502b07bbfa5c240ba25bbe73da7b33f1e5b6120ff179", size = 915967, upload-time = "2026-03-17T10:33:18.341Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/4b/37/d24c8f8220ff07b839b2c043ea4903a33b0f455abe673ae3c03bbdb7f212/coverage-7.13.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:66a80c616f80181f4d643b0f9e709d97bcea413ecd9631e1dedc7401c8e6695d", size = 219381, upload-time = "2026-03-17T10:30:14.68Z" }, - { url = "https://files.pythonhosted.org/packages/35/8b/cd129b0ca4afe886a6ce9d183c44d8301acbd4ef248622e7c49a23145605/coverage-7.13.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:145ede53ccbafb297c1c9287f788d1bc3efd6c900da23bf6931b09eafc931587", size = 219880, upload-time = "2026-03-17T10:30:16.231Z" }, - { url = "https://files.pythonhosted.org/packages/55/2f/e0e5b237bffdb5d6c530ce87cc1d413a5b7d7dfd60fb067ad6d254c35c76/coverage-7.13.5-cp311-cp311-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:0672854dc733c342fa3e957e0605256d2bf5934feeac328da9e0b5449634a642", size = 250303, upload-time = "2026-03-17T10:30:17.748Z" }, - { url = "https://files.pythonhosted.org/packages/92/be/b1afb692be85b947f3401375851484496134c5554e67e822c35f28bf2fbc/coverage-7.13.5-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:ec10e2a42b41c923c2209b846126c6582db5e43a33157e9870ba9fb70dc7854b", size = 252218, upload-time = "2026-03-17T10:30:19.804Z" }, - { url = "https://files.pythonhosted.org/packages/da/69/2f47bb6fa1b8d1e3e5d0c4be8ccb4313c63d742476a619418f85740d597b/coverage-7.13.5-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:be3d4bbad9d4b037791794ddeedd7d64a56f5933a2c1373e18e9e568b9141686", size = 254326, upload-time = "2026-03-17T10:30:21.321Z" }, - { url = "https://files.pythonhosted.org/packages/d5/d0/79db81da58965bd29dabc8f4ad2a2af70611a57cba9d1ec006f072f30a54/coverage-7.13.5-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4d2afbc5cc54d286bfb54541aa50b64cdb07a718227168c87b9e2fb8f25e1743", size = 256267, upload-time = "2026-03-17T10:30:23.094Z" }, - { url = "https://files.pythonhosted.org/packages/e5/32/d0d7cc8168f91ddab44c0ce4806b969df5f5fdfdbb568eaca2dbc2a04936/coverage-7.13.5-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:3ad050321264c49c2fa67bb599100456fc51d004b82534f379d16445da40fb75", size = 250430, upload-time = "2026-03-17T10:30:25.311Z" }, - { url = "https://files.pythonhosted.org/packages/4d/06/a055311d891ddbe231cd69fdd20ea4be6e3603ffebddf8704b8ca8e10a3c/coverage-7.13.5-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:7300c8a6d13335b29bb76d7651c66af6bd8658517c43499f110ddc6717bfc209", size = 252017, upload-time = "2026-03-17T10:30:27.284Z" }, - { url = "https://files.pythonhosted.org/packages/d6/f6/d0fd2d21e29a657b5f77a2fe7082e1568158340dceb941954f776dce1b7b/coverage-7.13.5-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:eb07647a5738b89baab047f14edd18ded523de60f3b30e75c2acc826f79c839a", size = 250080, upload-time = "2026-03-17T10:30:29.481Z" }, - { url = "https://files.pythonhosted.org/packages/4e/ab/0d7fb2efc2e9a5eb7ddcc6e722f834a69b454b7e6e5888c3a8567ecffb31/coverage-7.13.5-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:9adb6688e3b53adffefd4a52d72cbd8b02602bfb8f74dcd862337182fd4d1a4e", size = 253843, upload-time = "2026-03-17T10:30:31.301Z" }, - { url = "https://files.pythonhosted.org/packages/ba/6f/7467b917bbf5408610178f62a49c0ed4377bb16c1657f689cc61470da8ce/coverage-7.13.5-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:7c8d4bc913dd70b93488d6c496c77f3aff5ea99a07e36a18f865bca55adef8bd", size = 249802, upload-time = "2026-03-17T10:30:33.358Z" }, - { url = "https://files.pythonhosted.org/packages/75/2c/1172fb689df92135f5bfbbd69fc83017a76d24ea2e2f3a1154007e2fb9f8/coverage-7.13.5-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:0e3c426ffc4cd952f54ee9ffbdd10345709ecc78a3ecfd796a57236bfad0b9b8", size = 250707, upload-time = "2026-03-17T10:30:35.2Z" }, - { url = "https://files.pythonhosted.org/packages/67/21/9ac389377380a07884e3b48ba7a620fcd9dbfaf1d40565facdc6b36ec9ef/coverage-7.13.5-cp311-cp311-win32.whl", hash = "sha256:259b69bb83ad9894c4b25be2528139eecba9a82646ebdda2d9db1ba28424a6bf", size = 221880, upload-time = "2026-03-17T10:30:36.775Z" }, - { url = "https://files.pythonhosted.org/packages/af/7f/4cd8a92531253f9d7c1bbecd9fa1b472907fb54446ca768c59b531248dc5/coverage-7.13.5-cp311-cp311-win_amd64.whl", hash = "sha256:258354455f4e86e3e9d0d17571d522e13b4e1e19bf0f8596bcf9476d61e7d8a9", size = 222816, upload-time = "2026-03-17T10:30:38.891Z" }, - { url = "https://files.pythonhosted.org/packages/12/a6/1d3f6155fb0010ca68eba7fe48ca6c9da7385058b77a95848710ecf189b1/coverage-7.13.5-cp311-cp311-win_arm64.whl", hash = "sha256:bff95879c33ec8da99fc9b6fe345ddb5be6414b41d6d1ad1c8f188d26f36e028", size = 221483, upload-time = "2026-03-17T10:30:40.463Z" }, - { url = "https://files.pythonhosted.org/packages/a0/c3/a396306ba7db865bf96fc1fb3b7fd29bcbf3d829df642e77b13555163cd6/coverage-7.13.5-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:460cf0114c5016fa841214ff5564aa4864f11948da9440bc97e21ad1f4ba1e01", size = 219554, upload-time = "2026-03-17T10:30:42.208Z" }, - { url = "https://files.pythonhosted.org/packages/a6/16/a68a19e5384e93f811dccc51034b1fd0b865841c390e3c931dcc4699e035/coverage-7.13.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0e223ce4b4ed47f065bfb123687686512e37629be25cc63728557ae7db261422", size = 219908, upload-time = "2026-03-17T10:30:43.906Z" }, - { url = "https://files.pythonhosted.org/packages/29/72/20b917c6793af3a5ceb7fb9c50033f3ec7865f2911a1416b34a7cfa0813b/coverage-7.13.5-cp312-cp312-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:6e3370441f4513c6252bf042b9c36d22491142385049243253c7e48398a15a9f", size = 251419, upload-time = "2026-03-17T10:30:45.545Z" }, - { url = "https://files.pythonhosted.org/packages/8c/49/cd14b789536ac6a4778c453c6a2338bc0a2fb60c5a5a41b4008328b9acc1/coverage-7.13.5-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:03ccc709a17a1de074fb1d11f217342fb0d2b1582ed544f554fc9fc3f07e95f5", size = 254159, upload-time = "2026-03-17T10:30:47.204Z" }, - { url = "https://files.pythonhosted.org/packages/9d/00/7b0edcfe64e2ed4c0340dac14a52ad0f4c9bd0b8b5e531af7d55b703db7c/coverage-7.13.5-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3f4818d065964db3c1c66dc0fbdac5ac692ecbc875555e13374fdbe7eedb4376", size = 255270, upload-time = "2026-03-17T10:30:48.812Z" }, - { url = "https://files.pythonhosted.org/packages/93/89/7ffc4ba0f5d0a55c1e84ea7cee39c9fc06af7b170513d83fbf3bbefce280/coverage-7.13.5-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:012d5319e66e9d5a218834642d6c35d265515a62f01157a45bcc036ecf947256", size = 257538, upload-time = "2026-03-17T10:30:50.77Z" }, - { url = "https://files.pythonhosted.org/packages/81/bd/73ddf85f93f7e6fa83e77ccecb6162d9415c79007b4bc124008a4995e4a7/coverage-7.13.5-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:8dd02af98971bdb956363e4827d34425cb3df19ee550ef92855b0acb9c7ce51c", size = 251821, upload-time = "2026-03-17T10:30:52.5Z" }, - { url = "https://files.pythonhosted.org/packages/a0/81/278aff4e8dec4926a0bcb9486320752811f543a3ce5b602cc7a29978d073/coverage-7.13.5-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:f08fd75c50a760c7eb068ae823777268daaf16a80b918fa58eea888f8e3919f5", size = 253191, upload-time = "2026-03-17T10:30:54.543Z" }, - { url = "https://files.pythonhosted.org/packages/70/ee/fe1621488e2e0a58d7e94c4800f0d96f79671553488d401a612bebae324b/coverage-7.13.5-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:843ea8643cf967d1ac7e8ecd4bb00c99135adf4816c0c0593fdcc47b597fcf09", size = 251337, upload-time = "2026-03-17T10:30:56.663Z" }, - { url = "https://files.pythonhosted.org/packages/37/a6/f79fb37aa104b562207cc23cb5711ab6793608e246cae1e93f26b2236ed9/coverage-7.13.5-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:9d44d7aa963820b1b971dbecd90bfe5fe8f81cff79787eb6cca15750bd2f79b9", size = 255404, upload-time = "2026-03-17T10:30:58.427Z" }, - { url = "https://files.pythonhosted.org/packages/75/f0/ed15262a58ec81ce457ceb717b7f78752a1713556b19081b76e90896e8d4/coverage-7.13.5-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:7132bed4bd7b836200c591410ae7d97bf7ae8be6fc87d160b2bd881df929e7bf", size = 250903, upload-time = "2026-03-17T10:31:00.093Z" }, - { url = "https://files.pythonhosted.org/packages/0f/e9/9129958f20e7e9d4d56d51d42ccf708d15cac355ff4ac6e736e97a9393d2/coverage-7.13.5-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:a698e363641b98843c517817db75373c83254781426e94ada3197cabbc2c919c", size = 252780, upload-time = "2026-03-17T10:31:01.916Z" }, - { url = "https://files.pythonhosted.org/packages/a4/d7/0ad9b15812d81272db94379fe4c6df8fd17781cc7671fdfa30c76ba5ff7b/coverage-7.13.5-cp312-cp312-win32.whl", hash = "sha256:bdba0a6b8812e8c7df002d908a9a2ea3c36e92611b5708633c50869e6d922fdf", size = 222093, upload-time = "2026-03-17T10:31:03.642Z" }, - { url = "https://files.pythonhosted.org/packages/29/3d/821a9a5799fac2556bcf0bd37a70d1d11fa9e49784b6d22e92e8b2f85f18/coverage-7.13.5-cp312-cp312-win_amd64.whl", hash = "sha256:d2c87e0c473a10bffe991502eac389220533024c8082ec1ce849f4218dded810", size = 222900, upload-time = "2026-03-17T10:31:05.651Z" }, - { url = "https://files.pythonhosted.org/packages/d4/fa/2238c2ad08e35cf4f020ea721f717e09ec3152aea75d191a7faf3ef009a8/coverage-7.13.5-cp312-cp312-win_arm64.whl", hash = "sha256:bf69236a9a81bdca3bff53796237aab096cdbf8d78a66ad61e992d9dac7eb2de", size = 221515, upload-time = "2026-03-17T10:31:07.293Z" }, - { url = "https://files.pythonhosted.org/packages/74/8c/74fedc9663dcf168b0a059d4ea756ecae4da77a489048f94b5f512a8d0b3/coverage-7.13.5-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:5ec4af212df513e399cf11610cc27063f1586419e814755ab362e50a85ea69c1", size = 219576, upload-time = "2026-03-17T10:31:09.045Z" }, - { url = "https://files.pythonhosted.org/packages/0c/c9/44fb661c55062f0818a6ffd2685c67aa30816200d5f2817543717d4b92eb/coverage-7.13.5-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:941617e518602e2d64942c88ec8499f7fbd49d3f6c4327d3a71d43a1973032f3", size = 219942, upload-time = "2026-03-17T10:31:10.708Z" }, - { url = "https://files.pythonhosted.org/packages/5f/13/93419671cee82b780bab7ea96b67c8ef448f5f295f36bf5031154ec9a790/coverage-7.13.5-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:da305e9937617ee95c2e39d8ff9f040e0487cbf1ac174f777ed5eddd7a7c1f26", size = 250935, upload-time = "2026-03-17T10:31:12.392Z" }, - { url = "https://files.pythonhosted.org/packages/ac/68/1666e3a4462f8202d836920114fa7a5ee9275d1fa45366d336c551a162dd/coverage-7.13.5-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:78e696e1cc714e57e8b25760b33a8b1026b7048d270140d25dafe1b0a1ee05a3", size = 253541, upload-time = "2026-03-17T10:31:14.247Z" }, - { url = "https://files.pythonhosted.org/packages/4e/5e/3ee3b835647be646dcf3c65a7c6c18f87c27326a858f72ab22c12730773d/coverage-7.13.5-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:02ca0eed225b2ff301c474aeeeae27d26e2537942aa0f87491d3e147e784a82b", size = 254780, upload-time = "2026-03-17T10:31:16.193Z" }, - { url = "https://files.pythonhosted.org/packages/44/b3/cb5bd1a04cfcc49ede6cd8409d80bee17661167686741e041abc7ee1b9a9/coverage-7.13.5-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:04690832cbea4e4663d9149e05dba142546ca05cb1848816760e7f58285c970a", size = 256912, upload-time = "2026-03-17T10:31:17.89Z" }, - { url = "https://files.pythonhosted.org/packages/1b/66/c1dceb7b9714473800b075f5c8a84f4588f887a90eb8645282031676e242/coverage-7.13.5-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0590e44dd2745c696a778f7bab6aa95256de2cbc8b8cff4f7db8ff09813d6969", size = 251165, upload-time = "2026-03-17T10:31:19.605Z" }, - { url = "https://files.pythonhosted.org/packages/b7/62/5502b73b97aa2e53ea22a39cf8649ff44827bef76d90bf638777daa27a9d/coverage-7.13.5-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:d7cfad2d6d81dd298ab6b89fe72c3b7b05ec7544bdda3b707ddaecff8d25c161", size = 252908, upload-time = "2026-03-17T10:31:21.312Z" }, - { url = "https://files.pythonhosted.org/packages/7d/37/7792c2d69854397ca77a55c4646e5897c467928b0e27f2d235d83b5d08c6/coverage-7.13.5-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:e092b9499de38ae0fbfbc603a74660eb6ff3e869e507b50d85a13b6db9863e15", size = 250873, upload-time = "2026-03-17T10:31:23.565Z" }, - { url = "https://files.pythonhosted.org/packages/a3/23/bc866fb6163be52a8a9e5d708ba0d3b1283c12158cefca0a8bbb6e247a43/coverage-7.13.5-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:48c39bc4a04d983a54a705a6389512883d4a3b9862991b3617d547940e9f52b1", size = 255030, upload-time = "2026-03-17T10:31:25.58Z" }, - { url = "https://files.pythonhosted.org/packages/7d/8b/ef67e1c222ef49860701d346b8bbb70881bef283bd5f6cbba68a39a086c7/coverage-7.13.5-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:2d3807015f138ffea1ed9afeeb8624fd781703f2858b62a8dd8da5a0994c57b6", size = 250694, upload-time = "2026-03-17T10:31:27.316Z" }, - { url = "https://files.pythonhosted.org/packages/46/0d/866d1f74f0acddbb906db212e096dee77a8e2158ca5e6bb44729f9d93298/coverage-7.13.5-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ee2aa19e03161671ec964004fb74b2257805d9710bf14a5c704558b9d8dbaf17", size = 252469, upload-time = "2026-03-17T10:31:29.472Z" }, - { url = "https://files.pythonhosted.org/packages/7a/f5/be742fec31118f02ce42b21c6af187ad6a344fed546b56ca60caacc6a9a0/coverage-7.13.5-cp313-cp313-win32.whl", hash = "sha256:ce1998c0483007608c8382f4ff50164bfc5bd07a2246dd272aa4043b75e61e85", size = 222112, upload-time = "2026-03-17T10:31:31.526Z" }, - { url = "https://files.pythonhosted.org/packages/66/40/7732d648ab9d069a46e686043241f01206348e2bbf128daea85be4d6414b/coverage-7.13.5-cp313-cp313-win_amd64.whl", hash = "sha256:631efb83f01569670a5e866ceb80fe483e7c159fac6f167e6571522636104a0b", size = 222923, upload-time = "2026-03-17T10:31:33.633Z" }, - { url = "https://files.pythonhosted.org/packages/48/af/fea819c12a095781f6ccd504890aaddaf88b8fab263c4940e82c7b770124/coverage-7.13.5-cp313-cp313-win_arm64.whl", hash = "sha256:f4cd16206ad171cbc2470dbea9103cf9a7607d5fe8c242fdf1edf36174020664", size = 221540, upload-time = "2026-03-17T10:31:35.445Z" }, - { url = "https://files.pythonhosted.org/packages/23/d2/17879af479df7fbbd44bd528a31692a48f6b25055d16482fdf5cdb633805/coverage-7.13.5-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0428cbef5783ad91fe240f673cc1f76b25e74bbfe1a13115e4aa30d3f538162d", size = 220262, upload-time = "2026-03-17T10:31:37.184Z" }, - { url = "https://files.pythonhosted.org/packages/5b/4c/d20e554f988c8f91d6a02c5118f9abbbf73a8768a3048cb4962230d5743f/coverage-7.13.5-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:e0b216a19534b2427cc201a26c25da4a48633f29a487c61258643e89d28200c0", size = 220617, upload-time = "2026-03-17T10:31:39.245Z" }, - { url = "https://files.pythonhosted.org/packages/29/9c/f9f5277b95184f764b24e7231e166dfdb5780a46d408a2ac665969416d61/coverage-7.13.5-cp313-cp313t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:972a9cd27894afe4bc2b1480107054e062df08e671df7c2f18c205e805ccd806", size = 261912, upload-time = "2026-03-17T10:31:41.324Z" }, - { url = "https://files.pythonhosted.org/packages/d5/f6/7f1ab39393eeb50cfe4747ae8ef0e4fc564b989225aa1152e13a180d74f8/coverage-7.13.5-cp313-cp313t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:4b59148601efcd2bac8c4dbf1f0ad6391693ccf7a74b8205781751637076aee3", size = 263987, upload-time = "2026-03-17T10:31:43.724Z" }, - { url = "https://files.pythonhosted.org/packages/a0/d7/62c084fb489ed9c6fbdf57e006752e7c516ea46fd690e5ed8b8617c7d52e/coverage-7.13.5-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:505d7083c8b0c87a8fa8c07370c285847c1f77739b22e299ad75a6af6c32c5c9", size = 266416, upload-time = "2026-03-17T10:31:45.769Z" }, - { url = "https://files.pythonhosted.org/packages/a9/f6/df63d8660e1a0bff6125947afda112a0502736f470d62ca68b288ea762d8/coverage-7.13.5-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:60365289c3741e4db327e7baff2a4aaacf22f788e80fa4683393891b70a89fbd", size = 267558, upload-time = "2026-03-17T10:31:48.293Z" }, - { url = "https://files.pythonhosted.org/packages/5b/02/353ca81d36779bd108f6d384425f7139ac3c58c750dcfaafe5d0bee6436b/coverage-7.13.5-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:1b88c69c8ef5d4b6fe7dea66d6636056a0f6a7527c440e890cf9259011f5e606", size = 261163, upload-time = "2026-03-17T10:31:50.125Z" }, - { url = "https://files.pythonhosted.org/packages/2c/16/2e79106d5749bcaf3aee6d309123548e3276517cd7851faa8da213bc61bf/coverage-7.13.5-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:5b13955d31d1633cf9376908089b7cebe7d15ddad7aeaabcbe969a595a97e95e", size = 263981, upload-time = "2026-03-17T10:31:51.961Z" }, - { url = "https://files.pythonhosted.org/packages/29/c7/c29e0c59ffa6942030ae6f50b88ae49988e7e8da06de7ecdbf49c6d4feae/coverage-7.13.5-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:f70c9ab2595c56f81a89620e22899eea8b212a4041bd728ac6f4a28bf5d3ddd0", size = 261604, upload-time = "2026-03-17T10:31:53.872Z" }, - { url = "https://files.pythonhosted.org/packages/40/48/097cdc3db342f34006a308ab41c3a7c11c3f0d84750d340f45d88a782e00/coverage-7.13.5-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:084b84a8c63e8d6fc7e3931b316a9bcafca1458d753c539db82d31ed20091a87", size = 265321, upload-time = "2026-03-17T10:31:55.997Z" }, - { url = "https://files.pythonhosted.org/packages/bb/1f/4994af354689e14fd03a75f8ec85a9a68d94e0188bbdab3fc1516b55e512/coverage-7.13.5-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:ad14385487393e386e2ea988b09d62dd42c397662ac2dabc3832d71253eee479", size = 260502, upload-time = "2026-03-17T10:31:58.308Z" }, - { url = "https://files.pythonhosted.org/packages/22/c6/9bb9ef55903e628033560885f5c31aa227e46878118b63ab15dc7ba87797/coverage-7.13.5-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:7f2c47b36fe7709a6e83bfadf4eefb90bd25fbe4014d715224c4316f808e59a2", size = 262688, upload-time = "2026-03-17T10:32:00.141Z" }, - { url = "https://files.pythonhosted.org/packages/14/4f/f5df9007e50b15e53e01edea486814783a7f019893733d9e4d6caad75557/coverage-7.13.5-cp313-cp313t-win32.whl", hash = "sha256:67e9bc5449801fad0e5dff329499fb090ba4c5800b86805c80617b4e29809b2a", size = 222788, upload-time = "2026-03-17T10:32:02.246Z" }, - { url = "https://files.pythonhosted.org/packages/e1/98/aa7fccaa97d0f3192bec013c4e6fd6d294a6ed44b640e6bb61f479e00ed5/coverage-7.13.5-cp313-cp313t-win_amd64.whl", hash = "sha256:da86cdcf10d2519e10cabb8ac2de03da1bcb6e4853790b7fbd48523332e3a819", size = 223851, upload-time = "2026-03-17T10:32:04.416Z" }, - { url = "https://files.pythonhosted.org/packages/3d/8b/e5c469f7352651e5f013198e9e21f97510b23de957dd06a84071683b4b60/coverage-7.13.5-cp313-cp313t-win_arm64.whl", hash = "sha256:0ecf12ecb326fe2c339d93fc131816f3a7367d223db37817208905c89bded911", size = 222104, upload-time = "2026-03-17T10:32:06.65Z" }, - { url = "https://files.pythonhosted.org/packages/8e/77/39703f0d1d4b478bfd30191d3c14f53caf596fac00efb3f8f6ee23646439/coverage-7.13.5-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:fbabfaceaeb587e16f7008f7795cd80d20ec548dc7f94fbb0d4ec2e038ce563f", size = 219621, upload-time = "2026-03-17T10:32:08.589Z" }, - { url = "https://files.pythonhosted.org/packages/e2/3e/51dff36d99ae14639a133d9b164d63e628532e2974d8b1edb99dd1ebc733/coverage-7.13.5-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:9bb2a28101a443669a423b665939381084412b81c3f8c0fcfbac57f4e30b5b8e", size = 219953, upload-time = "2026-03-17T10:32:10.507Z" }, - { url = "https://files.pythonhosted.org/packages/6a/6c/1f1917b01eb647c2f2adc9962bd66c79eb978951cab61bdc1acab3290c07/coverage-7.13.5-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:bd3a2fbc1c6cccb3c5106140d87cc6a8715110373ef42b63cf5aea29df8c217a", size = 250992, upload-time = "2026-03-17T10:32:12.41Z" }, - { url = "https://files.pythonhosted.org/packages/22/e5/06b1f88f42a5a99df42ce61208bdec3bddb3d261412874280a19796fc09c/coverage-7.13.5-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:6c36ddb64ed9d7e496028d1d00dfec3e428e0aabf4006583bb1839958d280510", size = 253503, upload-time = "2026-03-17T10:32:14.449Z" }, - { url = "https://files.pythonhosted.org/packages/80/28/2a148a51e5907e504fa7b85490277734e6771d8844ebcc48764a15e28155/coverage-7.13.5-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:380e8e9084d8eb38db3a9176a1a4f3c0082c3806fa0dc882d1d87abc3c789247", size = 254852, upload-time = "2026-03-17T10:32:16.56Z" }, - { url = "https://files.pythonhosted.org/packages/61/77/50e8d3d85cc0b7ebe09f30f151d670e302c7ff4a1bf6243f71dd8b0981fa/coverage-7.13.5-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e808af52a0513762df4d945ea164a24b37f2f518cbe97e03deaa0ee66139b4d6", size = 257161, upload-time = "2026-03-17T10:32:19.004Z" }, - { url = "https://files.pythonhosted.org/packages/3b/c4/b5fd1d4b7bf8d0e75d997afd3925c59ba629fc8616f1b3aae7605132e256/coverage-7.13.5-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e301d30dd7e95ae068671d746ba8c34e945a82682e62918e41b2679acd2051a0", size = 251021, upload-time = "2026-03-17T10:32:21.344Z" }, - { url = "https://files.pythonhosted.org/packages/f8/66/6ea21f910e92d69ef0b1c3346ea5922a51bad4446c9126db2ae96ee24c4c/coverage-7.13.5-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:800bc829053c80d240a687ceeb927a94fd108bbdc68dfbe505d0d75ab578a882", size = 252858, upload-time = "2026-03-17T10:32:23.506Z" }, - { url = "https://files.pythonhosted.org/packages/9e/ea/879c83cb5d61aa2a35fb80e72715e92672daef8191b84911a643f533840c/coverage-7.13.5-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:0b67af5492adb31940ee418a5a655c28e48165da5afab8c7fa6fd72a142f8740", size = 250823, upload-time = "2026-03-17T10:32:25.516Z" }, - { url = "https://files.pythonhosted.org/packages/8a/fb/616d95d3adb88b9803b275580bdeee8bd1b69a886d057652521f83d7322f/coverage-7.13.5-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:c9136ff29c3a91e25b1d1552b5308e53a1e0653a23e53b6366d7c2dcbbaf8a16", size = 255099, upload-time = "2026-03-17T10:32:27.944Z" }, - { url = "https://files.pythonhosted.org/packages/1c/93/25e6917c90ec1c9a56b0b26f6cad6408e5f13bb6b35d484a0d75c9cf000d/coverage-7.13.5-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:cff784eef7f0b8f6cb28804fbddcfa99f89efe4cc35fb5627e3ac58f91ed3ac0", size = 250638, upload-time = "2026-03-17T10:32:29.914Z" }, - { url = "https://files.pythonhosted.org/packages/fc/7b/dc1776b0464145a929deed214aef9fb1493f159b59ff3c7eeeedf91eddd0/coverage-7.13.5-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:68a4953be99b17ac3c23b6efbc8a38330d99680c9458927491d18700ef23ded0", size = 252295, upload-time = "2026-03-17T10:32:31.981Z" }, - { url = "https://files.pythonhosted.org/packages/ea/fb/99cbbc56a26e07762a2740713f3c8f9f3f3106e3a3dd8cc4474954bccd34/coverage-7.13.5-cp314-cp314-win32.whl", hash = "sha256:35a31f2b1578185fbe6aa2e74cea1b1d0bbf4c552774247d9160d29b80ed56cc", size = 222360, upload-time = "2026-03-17T10:32:34.233Z" }, - { url = "https://files.pythonhosted.org/packages/8d/b7/4758d4f73fb536347cc5e4ad63662f9d60ba9118cb6785e9616b2ce5d7fa/coverage-7.13.5-cp314-cp314-win_amd64.whl", hash = "sha256:2aa055ae1857258f9e0045be26a6d62bdb47a72448b62d7b55f4820f361a2633", size = 223174, upload-time = "2026-03-17T10:32:36.369Z" }, - { url = "https://files.pythonhosted.org/packages/2c/f2/24d84e1dfe70f8ac9fdf30d338239860d0d1d5da0bda528959d0ebc9da28/coverage-7.13.5-cp314-cp314-win_arm64.whl", hash = "sha256:1b11eef33edeae9d142f9b4358edb76273b3bfd30bc3df9a4f95d0e49caf94e8", size = 221739, upload-time = "2026-03-17T10:32:38.736Z" }, - { url = "https://files.pythonhosted.org/packages/60/5b/4a168591057b3668c2428bff25dd3ebc21b629d666d90bcdfa0217940e84/coverage-7.13.5-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:10a0c37f0b646eaff7cce1874c31d1f1ccb297688d4c747291f4f4c70741cc8b", size = 220351, upload-time = "2026-03-17T10:32:41.196Z" }, - { url = "https://files.pythonhosted.org/packages/f5/21/1fd5c4dbfe4a58b6b99649125635df46decdfd4a784c3cd6d410d303e370/coverage-7.13.5-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:b5db73ba3c41c7008037fa731ad5459fc3944cb7452fc0aa9f822ad3533c583c", size = 220612, upload-time = "2026-03-17T10:32:43.204Z" }, - { url = "https://files.pythonhosted.org/packages/d6/fe/2a924b3055a5e7e4512655a9d4609781b0d62334fa0140c3e742926834e2/coverage-7.13.5-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:750db93a81e3e5a9831b534be7b1229df848b2e125a604fe6651e48aa070e5f9", size = 261985, upload-time = "2026-03-17T10:32:45.514Z" }, - { url = "https://files.pythonhosted.org/packages/d7/0d/c8928f2bd518c45990fe1a2ab8db42e914ef9b726c975facc4282578c3eb/coverage-7.13.5-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:9ddb4f4a5479f2539644be484da179b653273bca1a323947d48ab107b3ed1f29", size = 264107, upload-time = "2026-03-17T10:32:47.971Z" }, - { url = "https://files.pythonhosted.org/packages/ef/ae/4ae35bbd9a0af9d820362751f0766582833c211224b38665c0f8de3d487f/coverage-7.13.5-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d8a7a2049c14f413163e2bdabd37e41179b1d1ccb10ffc6ccc4b7a718429c607", size = 266513, upload-time = "2026-03-17T10:32:50.1Z" }, - { url = "https://files.pythonhosted.org/packages/9c/20/d326174c55af36f74eac6ae781612d9492f060ce8244b570bb9d50d9d609/coverage-7.13.5-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e1c85e0b6c05c592ea6d8768a66a254bfb3874b53774b12d4c89c481eb78cb90", size = 267650, upload-time = "2026-03-17T10:32:52.391Z" }, - { url = "https://files.pythonhosted.org/packages/7a/5e/31484d62cbd0eabd3412e30d74386ece4a0837d4f6c3040a653878bfc019/coverage-7.13.5-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:777c4d1eff1b67876139d24288aaf1817f6c03d6bae9c5cc8d27b83bcfe38fe3", size = 261089, upload-time = "2026-03-17T10:32:54.544Z" }, - { url = "https://files.pythonhosted.org/packages/e9/d8/49a72d6de146eebb0b7e48cc0f4bc2c0dd858e3d4790ab2b39a2872b62bd/coverage-7.13.5-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:6697e29b93707167687543480a40f0db8f356e86d9f67ddf2e37e2dfd91a9dab", size = 263982, upload-time = "2026-03-17T10:32:56.803Z" }, - { url = "https://files.pythonhosted.org/packages/06/3b/0351f1bd566e6e4dd39e978efe7958bde1d32f879e85589de147654f57bb/coverage-7.13.5-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:8fdf453a942c3e4d99bd80088141c4c6960bb232c409d9c3558e2dbaa3998562", size = 261579, upload-time = "2026-03-17T10:32:59.466Z" }, - { url = "https://files.pythonhosted.org/packages/5d/ce/796a2a2f4017f554d7810f5c573449b35b1e46788424a548d4d19201b222/coverage-7.13.5-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:32ca0c0114c9834a43f045a87dcebd69d108d8ffb666957ea65aa132f50332e2", size = 265316, upload-time = "2026-03-17T10:33:01.847Z" }, - { url = "https://files.pythonhosted.org/packages/3d/16/d5ae91455541d1a78bc90abf495be600588aff8f6db5c8b0dae739fa39c9/coverage-7.13.5-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:8769751c10f339021e2638cd354e13adeac54004d1941119b2c96fe5276d45ea", size = 260427, upload-time = "2026-03-17T10:33:03.945Z" }, - { url = "https://files.pythonhosted.org/packages/48/11/07f413dba62db21fb3fad5d0de013a50e073cc4e2dc4306e770360f6dfc8/coverage-7.13.5-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:cec2d83125531bd153175354055cdb7a09987af08a9430bd173c937c6d0fba2a", size = 262745, upload-time = "2026-03-17T10:33:06.285Z" }, - { url = "https://files.pythonhosted.org/packages/91/15/d792371332eb4663115becf4bad47e047d16234b1aff687b1b18c58d60ae/coverage-7.13.5-cp314-cp314t-win32.whl", hash = "sha256:0cd9ed7a8b181775459296e402ca4fb27db1279740a24e93b3b41942ebe4b215", size = 223146, upload-time = "2026-03-17T10:33:08.756Z" }, - { url = "https://files.pythonhosted.org/packages/db/51/37221f59a111dca5e85be7dbf09696323b5b9f13ff65e0641d535ed06ea8/coverage-7.13.5-cp314-cp314t-win_amd64.whl", hash = "sha256:301e3b7dfefecaca37c9f1aa6f0049b7d4ab8dd933742b607765d757aca77d43", size = 224254, upload-time = "2026-03-17T10:33:11.174Z" }, - { url = "https://files.pythonhosted.org/packages/54/83/6acacc889de8987441aa7d5adfbdbf33d288dad28704a67e574f1df9bcbb/coverage-7.13.5-cp314-cp314t-win_arm64.whl", hash = "sha256:9dacc2ad679b292709e0f5fc1ac74a6d4d5562e424058962c7bb0c658ad25e45", size = 222276, upload-time = "2026-03-17T10:33:13.466Z" }, - { url = "https://files.pythonhosted.org/packages/9e/ee/a4cf96b8ce1e566ed238f0659ac2d3f007ed1d14b181bcb684e19561a69a/coverage-7.13.5-py3-none-any.whl", hash = "sha256:34b02417cf070e173989b3db962f7ed56d2f644307b2cf9d5a0f258e13084a61", size = 211346, upload-time = "2026-03-17T10:33:15.691Z" }, +version = "7.14.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/23/7f/d0720730a397a999ffc0fd3f5bebef347338e3a47b727da66fbb228e2ff2/coverage-7.14.0.tar.gz", hash = "sha256:057a6af2f160a85384cde4ab36f0d2777bae1057bae255f95413cdd382aa5c74", size = 919489, upload-time = "2026-05-10T18:02:31.397Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fc/e4/649c8d4f7f1709b6dbfc474358aa1bba02f67bcd52e2fec291a5014006cd/coverage-7.14.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6a78e2a9d9c5e3b8d4ab9b9d28c985ea66fced0a7d7c2aec1f216e03a2011480", size = 219795, upload-time = "2026-05-10T17:59:48.198Z" }, + { url = "https://files.pythonhosted.org/packages/7f/8d/46692d24b3f395d4cbf17bfcc57136b4f2f9c0c0df864b0bddfc1d71a014/coverage-7.14.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a1816c505187592dcd1c5a5f226601a549f70365fbd00930ac88b0c225b76bb4", size = 220299, upload-time = "2026-05-10T17:59:49.683Z" }, + { url = "https://files.pythonhosted.org/packages/12/c2/a40f5cb295bbcbb697a76947a56081c494c61950366294ee426ffe261099/coverage-7.14.0-cp311-cp311-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:d8e1762f0e9cbc26ec315471e7b47855218e833cd5a032d706fbf43845d878c7", size = 250721, upload-time = "2026-05-10T17:59:51.494Z" }, + { url = "https://files.pythonhosted.org/packages/fd/35/202235eb5c3c14c212462cd91d61b7386bf8fc44bc7a77f4742d2a69174b/coverage-7.14.0-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:9336e23e8bb3a3925398261385e2a1533957d3e760e91070dcb0e98bfa514eed", size = 252633, upload-time = "2026-05-10T17:59:53.244Z" }, + { url = "https://files.pythonhosted.org/packages/bb/80/5f596e8995785124ee191c42535664c5e62c65995b66f4ca21e28ae04c81/coverage-7.14.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9cd1169b2230f9cbe9c638ba38022ed7a2b1e641cc07f7cea0365e4be2a74980", size = 254743, upload-time = "2026-05-10T17:59:55.021Z" }, + { url = "https://files.pythonhosted.org/packages/1e/6d/0d178825be2350f0adb27984d0aa7cf84bbdab201f6fb926b535d23a8f5f/coverage-7.14.0-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:d1bb3543b58fea74d2cd1abc4054cc927e4724687cb4560cd2ed88d2c7d820c0", size = 256700, upload-time = "2026-05-10T17:59:56.511Z" }, + { url = "https://files.pythonhosted.org/packages/19/5b/9e549c2f6e9dfea472adadba06c294e64735dabc2dd19015fac082095013/coverage-7.14.0-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:a93bac2cb577ef60074999ed56d8a1535894398e2ed920d4185c3ec0c8864742", size = 250854, upload-time = "2026-05-10T17:59:57.94Z" }, + { url = "https://files.pythonhosted.org/packages/3d/1c/b94f9f5f36396021ee2f62c5834b12e6a3d31f0bed5d6fc6d1c3caec087c/coverage-7.14.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:5904abf7e18cddc463219b17552229650c6b79e061d31a1059283051169cf7d5", size = 252433, upload-time = "2026-05-10T17:59:59.688Z" }, + { url = "https://files.pythonhosted.org/packages/b5/cb/d192cd8e1345eccabc32016f2d39072ecd10cb4f4b983ed8d0ebdeaf00dc/coverage-7.14.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:741f57cddc9004a8c81b084660215f33a6b597dbe62c31386b983ee26310e327", size = 250494, upload-time = "2026-05-10T18:00:01.953Z" }, + { url = "https://files.pythonhosted.org/packages/53/c5/aac9f460a41d835dbddef1d377f105f6ac2311d0f3c1588e9f51046d8813/coverage-7.14.0-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:664123feb0929d7affc135717dbd70d61d98688a08ab1e5ba464739620c6252d", size = 254261, upload-time = "2026-05-10T18:00:03.779Z" }, + { url = "https://files.pythonhosted.org/packages/23/aa/7af7c0081980a9cb3d289c5a435a4b7657dcecbd128e25c580e6a50389b5/coverage-7.14.0-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:c83d2399a51bbec8429266905d33616f04bc5726b1138c35844d5fcd896b2e20", size = 250216, upload-time = "2026-05-10T18:00:05.262Z" }, + { url = "https://files.pythonhosted.org/packages/35/60/a4257538ce2f6b978aeb51870d6c4208c510928a03db7e0339bb625dccb7/coverage-7.14.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:bcb2e855b87321259a037429288ae85216d191c74de3e79bf57cd2bc0761992c", size = 251125, upload-time = "2026-05-10T18:00:06.858Z" }, + { url = "https://files.pythonhosted.org/packages/a1/ab/f91af47642ec1aa53490e835a95847168d9c77fc39aa58527604c051e145/coverage-7.14.0-cp311-cp311-win32.whl", hash = "sha256:731dc15b385ac52289743d476245b61e1a2927e803bef655b52bc3b2a75a21f3", size = 222300, upload-time = "2026-05-10T18:00:08.608Z" }, + { url = "https://files.pythonhosted.org/packages/f0/f0/a71ddbd874431e7a7cd96071f0c331cfbbad07704833c765d24ffbab8a67/coverage-7.14.0-cp311-cp311-win_amd64.whl", hash = "sha256:bfb0ed8ec5d25e93face268115d7964db9df8b9aae8edcde9ec6b16c726a7cc1", size = 223241, upload-time = "2026-05-10T18:00:10.746Z" }, + { url = "https://files.pythonhosted.org/packages/d8/6e/d9d312a5151a96cd110efee32efc3fc97b01ebd86203fe618ccb29cf4c92/coverage-7.14.0-cp311-cp311-win_arm64.whl", hash = "sha256:7ebb1c6df9f78046a1b1e0a89674cd4bf73b7c648914eebcf976a57fd99a5627", size = 221908, upload-time = "2026-05-10T18:00:12.242Z" }, + { url = "https://files.pythonhosted.org/packages/09/1e/2f996b2c8415cbb6f54b0f5ec1ee850c96d7911961afb4fc05f4a89d8c58/coverage-7.14.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7ffd19fc8aed057fd686a17a4935eef5f9859d69208f96310e893e64b9b6ccf5", size = 219967, upload-time = "2026-05-10T18:00:13.756Z" }, + { url = "https://files.pythonhosted.org/packages/34/23/35c7aea1274aef7525bdd2dc92f710bdde6d11652239d71d1ec450067939/coverage-7.14.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:829994cfe1aeb773ca27bf246d4badc1e764893e3bfb98fff820fcecd1ca4662", size = 220329, upload-time = "2026-05-10T18:00:15.264Z" }, + { url = "https://files.pythonhosted.org/packages/75/cf/a8f4b43a16e194b0261257ad28ded5853ec052570afef4a84e1d81189f3b/coverage-7.14.0-cp312-cp312-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:b4f07cf7edcb7ec39431a5074d7ea83b29a9f71fcfc494f0f40af4e65180420f", size = 251839, upload-time = "2026-05-10T18:00:17.16Z" }, + { url = "https://files.pythonhosted.org/packages/69/ff/6699e7b71e60d3049eb2bdcbc95ee3f35707b2b0e48f32e9e63d3ce30c08/coverage-7.14.0-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:ca3d9cf2c32b521bd9518385608787fa86f38daf993695307531822c3430ed67", size = 254576, upload-time = "2026-05-10T18:00:18.829Z" }, + { url = "https://files.pythonhosted.org/packages/22/ec/c936d495fcd67f48f03a9c4ad3297ff80d1f222a5df3980f15b34c186c21/coverage-7.14.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92af52828e7f29d827346b0294e5a0853fa206db77db0395b282918d41e28db9", size = 255690, upload-time = "2026-05-10T18:00:20.648Z" }, + { url = "https://files.pythonhosted.org/packages/5c/42/5af63f636cc62a4a2b1b3ba9146f6ee6f53a35a50d5cefc54d5670f60999/coverage-7.14.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7b2bb6c9d7e769360d0f20a0f219603fd64f0c8f97de17ab25853261602be0fb", size = 257949, upload-time = "2026-05-10T18:00:22.28Z" }, + { url = "https://files.pythonhosted.org/packages/26/d3/a225317bd2012132a27e1176d51660b826f99bb975876463c44ea0d7ee5a/coverage-7.14.0-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:1c9ed6ef99f88fb8c14aa8e2bf8eb0fe55fa2edfea68f8675d78741df1a5ac0e", size = 252242, upload-time = "2026-05-10T18:00:24.076Z" }, + { url = "https://files.pythonhosted.org/packages/f1/7f/9e65495298c3ea414742998539c37d048b5e81cc818fb1828cc6b51d10bf/coverage-7.14.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8231ade007f37959fbf58acc677f26b922c02eda6f0428ea307da0fd39681bf3", size = 253608, upload-time = "2026-05-10T18:00:25.588Z" }, + { url = "https://files.pythonhosted.org/packages/94/46/1522b524a35bdad22b2b8c4f9d32d0a104b524726ec380b2db68db1746f5/coverage-7.14.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:d8b013632cc1ce1d09dbe4f32667b4d320ec2f54fc326ebeffcd0b0bcc2bb6c4", size = 251753, upload-time = "2026-05-10T18:00:27.104Z" }, + { url = "https://files.pythonhosted.org/packages/f3/e9/cdf00d38817742c541ade405e115a3f7bf36e6f2a8b99d4f209861b85a2d/coverage-7.14.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:1733198802d71ec4c524f322e2867ee05c62e9e75df86bdca545407a221827d1", size = 255823, upload-time = "2026-05-10T18:00:29.038Z" }, + { url = "https://files.pythonhosted.org/packages/38/fc/5e7877cf5f902d08a17ff1c532511476d87e1bea355bd5028cb97f902e79/coverage-7.14.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:72a305291fa8ee01332f1aaf38b348ca34097f6aa0b0ef627eef2837e57bbba5", size = 251323, upload-time = "2026-05-10T18:00:30.647Z" }, + { url = "https://files.pythonhosted.org/packages/18/9d/50f05a72dff8487464fdd4178dda5daed642a060e60afb644e3d45123559/coverage-7.14.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fcaba850dd317c65423a9d63d88f9573c53b00354d6dd95724576cc98a131595", size = 253197, upload-time = "2026-05-10T18:00:32.211Z" }, + { url = "https://files.pythonhosted.org/packages/00/3f/6f61ffe6439df266c3cf60f5c99cfaa21103d0210d706a42fc6c30683ff8/coverage-7.14.0-cp312-cp312-win32.whl", hash = "sha256:5ac83957a80d0701310e96d8bec68cdcf4f90a7674b7d13f15a344315b41ab27", size = 222515, upload-time = "2026-05-10T18:00:33.717Z" }, + { url = "https://files.pythonhosted.org/packages/85/19/93853133df2cb371083285ef6a93982a0173e7a233b0f61373ba9fd30eb2/coverage-7.14.0-cp312-cp312-win_amd64.whl", hash = "sha256:70390b0da32cb90b501953716302906e8bcce087cb283e70d8c97729f22e92b2", size = 223324, upload-time = "2026-05-10T18:00:35.172Z" }, + { url = "https://files.pythonhosted.org/packages/74/18/9f7fe62f659f24b7a82a0be56bf94c1bd0a89e0ae7ab4c668f6e82404294/coverage-7.14.0-cp312-cp312-win_arm64.whl", hash = "sha256:91b993743d959b8be85b4abf9d5478216a69329c321efe5be0433c1a841d691d", size = 221944, upload-time = "2026-05-10T18:00:37.014Z" }, + { url = "https://files.pythonhosted.org/packages/6b/76/b7c66ee3c66e1b0f9d894c8125983aa0c03fb2336f2fd16559f9c966157f/coverage-7.14.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f2bbb8254370eb4c628ff3d6fa8a7f74ddc40565394d4f7ab791d1fe568e37ef", size = 219990, upload-time = "2026-05-10T18:00:38.887Z" }, + { url = "https://files.pythonhosted.org/packages/b3/af/e567cbad5ba69c013a50146dfa886dc7193361fda77521f51274ff620e1b/coverage-7.14.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:23b81107f46d3f21d0cbce30664fcec0f5d9f585638a67081750f99738f6bf66", size = 220365, upload-time = "2026-05-10T18:00:40.864Z" }, + { url = "https://files.pythonhosted.org/packages/44/6f/9ad575d505b4d805b254febc8a5b338a2efe278f8786e56ff1cb8413f9c3/coverage-7.14.0-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:22a7e06a5f11a757cdfe79018e9095f9f69ae283c5cd8123774c788deec8717b", size = 251363, upload-time = "2026-05-10T18:00:42.489Z" }, + { url = "https://files.pythonhosted.org/packages/6f/5f/b5370068b2f57787454592ed7dcd1002f0f1703b7db1fa30f6a325a4ca6e/coverage-7.14.0-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:9d1aa57a1dc8e05bdc42e81c5d671d849577aeedf279f4c449d6d286f9ed88ca", size = 253961, upload-time = "2026-05-10T18:00:44.079Z" }, + { url = "https://files.pythonhosted.org/packages/29/1e/51adf17738976e8f2b85ddef7b7aa12a0838b056c92f175941d8862767c1/coverage-7.14.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:90c1a51bcfddf645b3bb7ec333d9e94393a8e94f55642380fa8a9a5a9e636cb7", size = 255193, upload-time = "2026-05-10T18:00:45.623Z" }, + { url = "https://files.pythonhosted.org/packages/9e/7b/5bfd7ac1df3b881c2ac7a5cbc99c7609e6296c402f5ef587cd81c6f355b3/coverage-7.14.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a841fae2fadcae4f438d43b6ccc4aac2ad609f47cdb6cfdce60cbb3fe5ca7bc2", size = 257326, upload-time = "2026-05-10T18:00:47.173Z" }, + { url = "https://files.pythonhosted.org/packages/7d/38/1d37d316b174fad3843a1d76dbdfe4398771c9ecd0515935dd9ece9cd627/coverage-7.14.0-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c79d2319cabef1fe8e86df73371126931550804738f78ad7d31e3aad85a67367", size = 251582, upload-time = "2026-05-10T18:00:49.152Z" }, + { url = "https://files.pythonhosted.org/packages/34/46/746704f95980ba220214e1a41e18cec5aea80a898eaa53c51bf2d645ff36/coverage-7.14.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:1b23b0c6f0b1db6ad769b7050c8b641c0bf215ded26c1816955b17b7f26edfa9", size = 253325, upload-time = "2026-05-10T18:00:51.252Z" }, + { url = "https://files.pythonhosted.org/packages/e1/b9/bbe87206d9687b192352f893797825b5f5b15ecd3aa9c68fbff0c074d77b/coverage-7.14.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:55d3089079ce181a4566b1065ab28d2575eb76d8ac8f81f4fcda2bf037fee087", size = 251291, upload-time = "2026-05-10T18:00:52.816Z" }, + { url = "https://files.pythonhosted.org/packages/46/57/b8cdb12ac0d73ef0243218bd5e22c9df8f92edab8018213a86aec67c5324/coverage-7.14.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:49c005cba1e2f9677fb2845dcdf9a2e72a52a17d63e8231aaaae35d9f50215ef", size = 255448, upload-time = "2026-05-10T18:00:54.548Z" }, + { url = "https://files.pythonhosted.org/packages/1f/d4/5002019538b2036ce3c84340f54d2fd5100d55b0a6b0894eee56128d03c7/coverage-7.14.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:9117377b823daa28aa8635fbb08cda1cd6be3d7143257345459559aeef852d52", size = 251110, upload-time = "2026-05-10T18:00:56.122Z" }, + { url = "https://files.pythonhosted.org/packages/37/53/20c5009477660f084e6ed60bc02a91894b8e234e617e86ecfd9aaf78e27b/coverage-7.14.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:7b79d646cf46d5cf9a9f40281d4441df5849e445726e369006d2b117710b33fe", size = 252885, upload-time = "2026-05-10T18:00:57.967Z" }, + { url = "https://files.pythonhosted.org/packages/ae/ab/3cf6427ac9c1f1db747dbb1ce71dde47984876d4c2cfd018a3fef0a78d4d/coverage-7.14.0-cp313-cp313-win32.whl", hash = "sha256:fb609b3658479e33f9516d46f1a89dbb9b6c261366e3a11844a96ec487533dae", size = 222539, upload-time = "2026-05-10T18:00:59.581Z" }, + { url = "https://files.pythonhosted.org/packages/8f/b8/9228523e80321c2cb4880d1f589bc0171f2f71432c35118ad04dc01decce/coverage-7.14.0-cp313-cp313-win_amd64.whl", hash = "sha256:0773d8329cf32b6fd222e4b52622c61fe8d503eb966cfc8d3c3c10c96266d50e", size = 223344, upload-time = "2026-05-10T18:01:01.531Z" }, + { url = "https://files.pythonhosted.org/packages/a3/99/118daa192f95e3a6cb2740100fbf8797cda1734b4134ef0b5d501a7fa8f3/coverage-7.14.0-cp313-cp313-win_arm64.whl", hash = "sha256:b4e26a0f1b696faf283bffe5b8569e44e336c582439df5d53281ab89ee0cba96", size = 221966, upload-time = "2026-05-10T18:01:03.16Z" }, + { url = "https://files.pythonhosted.org/packages/e6/f1/a46cc0c013be170216253184a32366d7cbdb9252feaec866b05c2d12a894/coverage-7.14.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:953f521ca9445300397e65fda3dca58b2dbd68fee983777420b57ac3c77e9f90", size = 220679, upload-time = "2026-05-10T18:01:05.058Z" }, + { url = "https://files.pythonhosted.org/packages/64/8c/9c30a3d311a34177fa432995be7fbfc64477d8bac5630bd38055b1c9b424/coverage-7.14.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:98af83fd65ae24b1fdd03aaead967a9f523bcd2f1aab2d4f3ffda65bb568a6f1", size = 221033, upload-time = "2026-05-10T18:01:07.002Z" }, + { url = "https://files.pythonhosted.org/packages/9a/cd/3fb5e06c3badefd0c1b47e2044fdca67f8220a4ec2e7fcfb476aa0a67c6c/coverage-7.14.0-cp313-cp313t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:668b92e6958c4db7cf92e81caac328dfbbdbb215db2850ad28f0cbe1eea0bfbd", size = 262333, upload-time = "2026-05-10T18:01:08.903Z" }, + { url = "https://files.pythonhosted.org/packages/a8/e6/fbc322325c7294d3e22c1ad6b79e45d0806b25228c8e5842aed6d8169aa7/coverage-7.14.0-cp313-cp313t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:9fbd898551762dea00d3fef2b1c4f99afd2c6a3ff952ea07d60a9bd5ed4f34bc", size = 264410, upload-time = "2026-05-10T18:01:10.531Z" }, + { url = "https://files.pythonhosted.org/packages/08/92/c497b264bec1673c47cc77e26f760fcda4654cabf1f39546d1a23a3b8c35/coverage-7.14.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:68af363c07ecd8d4b7d4043d85cb376d7d227eceb54e5323ee45da73dbd3e426", size = 266836, upload-time = "2026-05-10T18:01:12.19Z" }, + { url = "https://files.pythonhosted.org/packages/78/fc/045da320987f401af5d2815d351e8aa799aec859f60e29f445e3089eeedb/coverage-7.14.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:6e57054a583da8ac55edf24117ea4c9133032cfc4cf72aa2d48c1e5d4b52f899", size = 267974, upload-time = "2026-05-10T18:01:13.926Z" }, + { url = "https://files.pythonhosted.org/packages/1b/ae/227b1e379497fb7a4fc3286e620f80c8a1e7cec66d45695a01639eb1af65/coverage-7.14.0-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:cc3499459bbcdd51a65b64c35ab7ed2764eaf3cba826e0df3f1d7fe2e102b70b", size = 261578, upload-time = "2026-05-10T18:01:15.564Z" }, + { url = "https://files.pythonhosted.org/packages/a0/f5/3570342900f2acea31d33ff1590c5d8bac1a8e1a2e1c6d34a5d5e61de681/coverage-7.14.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:45899ec2138a4346ed34d601dedf5076fb74edf2d1dd9dc76a78e82397edee90", size = 264394, upload-time = "2026-05-10T18:01:17.607Z" }, + { url = "https://files.pythonhosted.org/packages/16/29/de1bbc01c935b28f89b1dc3db85b011c055e843a8e5e3b83141c3f80af7f/coverage-7.14.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:8767486808c436f05b23ab98eb963fb29185e32a9357a166971685cb3459900f", size = 262022, upload-time = "2026-05-10T18:01:19.304Z" }, + { url = "https://files.pythonhosted.org/packages/35/95/f53890b0bf2fc10ab168e05d38869215e73ca24c4cb521c3bb0eb62fe16b/coverage-7.14.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:a3b5ddfd6aa7ddad53ee3edb231e88a2151507a43229b7d71b953916deca127d", size = 265732, upload-time = "2026-05-10T18:01:21.494Z" }, + { url = "https://files.pythonhosted.org/packages/ed/ea/c919e259081dd2bdf0e43b87209709ba7ec2e4117c2a7f5185379c43463c/coverage-7.14.0-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:63df0fe568e698e1045792399f8ab6da3a6c2dce3182813fb92afa2641087b47", size = 260921, upload-time = "2026-05-10T18:01:23.533Z" }, + { url = "https://files.pythonhosted.org/packages/1a/2c/c2831889705a81dc5d1c6ca12e4d8e9b95dfc146d153488a6c0ea685d28e/coverage-7.14.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:827d6397dbd95144939b18f89edf31f63e1f99633e8d5f32f22ba8bdda567477", size = 263109, upload-time = "2026-05-10T18:01:25.165Z" }, + { url = "https://files.pythonhosted.org/packages/5a/a9/2fcae5003cac3d63fe344d2166243c2756935f48420863c5272b240d550b/coverage-7.14.0-cp313-cp313t-win32.whl", hash = "sha256:7bf43e000d24012599b879791cff41589af90674722421ef11b11a5431920bab", size = 223212, upload-time = "2026-05-10T18:01:27.157Z" }, + { url = "https://files.pythonhosted.org/packages/3f/bb/18e94d7b14b9b398164197114a587a04ab7c9fdbe1d237eef57311c5e883/coverage-7.14.0-cp313-cp313t-win_amd64.whl", hash = "sha256:3f5549365af25d770e06b1f8f5682d9a5637d06eb494db91c6fa75d3950cc917", size = 224272, upload-time = "2026-05-10T18:01:29.107Z" }, + { url = "https://files.pythonhosted.org/packages/db/56/4f14fad782b035c81c4ffd09159e7103d42bb1d93ac8496d04b90a11b7da/coverage-7.14.0-cp313-cp313t-win_arm64.whl", hash = "sha256:6d160217ec6fe890f16ad3a9531761589443749e448f91986c972714fad361c8", size = 222530, upload-time = "2026-05-10T18:01:31.151Z" }, + { url = "https://files.pythonhosted.org/packages/1c/18/b9a6586d73992807c26f9a5f274131be3d76b56b18a82b9392e2a25d2e45/coverage-7.14.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:9aed9fa983514ca032790f3fe0d1c0e42ca7e16b42432af1706b50a9a46bef5d", size = 220036, upload-time = "2026-05-10T18:01:33.057Z" }, + { url = "https://files.pythonhosted.org/packages/f3/9b/4165a1d56ddc302a0e2d518fd9d412a4fd0b57562618c78c5f21c57194f5/coverage-7.14.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:ba3b8390db29296dbbf49e91b6fe08f990743a90c8f447ba4c2ffc29670dfa63", size = 220368, upload-time = "2026-05-10T18:01:34.705Z" }, + { url = "https://files.pythonhosted.org/packages/69/aa/c12e52a5ba148d9995229d557e3be6e554fe469addc0e9241b2f0956d8ea/coverage-7.14.0-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:3a5d8e876dfa2f102e970b183863d6dedd023d3c0eeca1fe7a9787bc5f28b212", size = 251417, upload-time = "2026-05-10T18:01:36.949Z" }, + { url = "https://files.pythonhosted.org/packages/d7/51/ec641c26e6dca1b25a7d2035ba6ecb7c884ef1a100a9e42fbe4ce4405139/coverage-7.14.0-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:5ebb8f4614a3787d567e610bbfdf96a4798dd69a1afb1bd8ad228d4111fe6ff3", size = 253924, upload-time = "2026-05-10T18:01:38.985Z" }, + { url = "https://files.pythonhosted.org/packages/33/c4/59c3de0bd1b538824173fd518fed51c1ce740ca5ed68e74545983f4053a9/coverage-7.14.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b9bf47223dd8db3d4c4b2e443b02bace480d428f0822c3f991600448a176c97", size = 255269, upload-time = "2026-05-10T18:01:40.957Z" }, + { url = "https://files.pythonhosted.org/packages/7b/a9/36dfa153a62040296f6e7febfdb20a5720622f6ef5a81a41e8237b9a5344/coverage-7.14.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3485a836550b303d006d57cc06e3d5afaabc642c77050b7c985a97b13e3776b8", size = 257583, upload-time = "2026-05-10T18:01:42.607Z" }, + { url = "https://files.pythonhosted.org/packages/26/7b/cc2c048d4114d9ab1c2409e9ee365e5ae10736df6dffcfc9444effa6c708/coverage-7.14.0-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:3e7e88110bae996d199d1693ca8ec3fd52441d426401ae963437598667b4c5eb", size = 251434, upload-time = "2026-05-10T18:01:44.537Z" }, + { url = "https://files.pythonhosted.org/packages/ee/df/6770eaa576e604575e9a78055313250faef5faa84bd6f71a39fece519c43/coverage-7.14.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:15228a6800ce7bdf1b74800595e56db7138cecb338fdbf044806e10dcf182dfe", size = 253280, upload-time = "2026-05-10T18:01:46.175Z" }, + { url = "https://files.pythonhosted.org/packages/ad/9e/1c0264514a3f98259a6d64765a397b2c8373e3ba59ee722a4802d3ec0c61/coverage-7.14.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:9d26ac7f5398bafc5b57421ad994e8a4749e8a7a0e62d05ec7d53014d5963bfa", size = 251241, upload-time = "2026-05-10T18:01:48.732Z" }, + { url = "https://files.pythonhosted.org/packages/64/16/4efdf3e3c4079cdbf0ece56a2fea872df9e8a3e15a13a0af4400e1075944/coverage-7.14.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:2fb73254ff43c911c967a899e1359bc5049b4b115d6e8fbdde4937d0a2246cd5", size = 255516, upload-time = "2026-05-10T18:01:50.819Z" }, + { url = "https://files.pythonhosted.org/packages/93/69/b1de96346603881b3d1bc8d6447c83200e1c9700ffbaff926ba01ff5724c/coverage-7.14.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:454a380af72c6adada298ed270d38c7a391288198dbfb8467f786f588751a90c", size = 251059, upload-time = "2026-05-10T18:01:52.773Z" }, + { url = "https://files.pythonhosted.org/packages/a4/66/2881853e0363a5e0a724d1103e53650795367471b6afb234f8b49e713bc6/coverage-7.14.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:65c86fb646d2bd2972e96bd1a8b45817ed907cee68655d6295fe7ec031d04cca", size = 252716, upload-time = "2026-05-10T18:01:54.506Z" }, + { url = "https://files.pythonhosted.org/packages/55/5c/0d3305d002c41dcde873dbe456491e663dc55152ca526b630b5c47efd62f/coverage-7.14.0-cp314-cp314-win32.whl", hash = "sha256:6a6516b02a6101398e19a3f44820f69bab2590697f7def4331f668b14adaf828", size = 222788, upload-time = "2026-05-10T18:01:56.487Z" }, + { url = "https://files.pythonhosted.org/packages/f9/58/6e1b8f52fdc3184b47dc5037f5070d83a3d11042db1594b02d2a44d786c8/coverage-7.14.0-cp314-cp314-win_amd64.whl", hash = "sha256:45e0f79d8351fa76e256716df91eab12890d32678b9590df7ae1042e4bd4cf5d", size = 223600, upload-time = "2026-05-10T18:01:58.497Z" }, + { url = "https://files.pythonhosted.org/packages/00/70/a18c408e674bc26281cadaedc7351f929bd2094e191e4b15271c30b084cc/coverage-7.14.0-cp314-cp314-win_arm64.whl", hash = "sha256:4b899594a8b2d81e5cc064a0d7f9cac2081fed91049456cae7676787e41549c9", size = 222168, upload-time = "2026-05-10T18:02:00.411Z" }, + { url = "https://files.pythonhosted.org/packages/3d/89/2681f071d238b62aff8dfc2ab44fc24cfdb38d1c01f391a80522ff5d3a16/coverage-7.14.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:f580f8c80acd94ac72e863efe2cab791d8c38d153e0b463b92dfa000d5c84cd1", size = 220766, upload-time = "2026-05-10T18:02:02.313Z" }, + { url = "https://files.pythonhosted.org/packages/bd/c7/c987babafd9207ffa1995e1ef1f9b26762cf4963aa768a66b6f0501e4616/coverage-7.14.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:a2bd259c442cd43c49b30fbafc51776eb19ea396faf159d26a83e6a0a5f13b0c", size = 221035, upload-time = "2026-05-10T18:02:04.017Z" }, + { url = "https://files.pythonhosted.org/packages/5a/e9/d6a5ac3b333088143d6fc877d398a9a674dc03124a2f776e131f03864823/coverage-7.14.0-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:a706b908dfa85538863504c624b237a3cc34232bf403c057414ebfdb3b4d9f84", size = 262405, upload-time = "2026-05-10T18:02:05.915Z" }, + { url = "https://files.pythonhosted.org/packages/38/b1/e70838d29a7c08e22d44398a46db90815bbcbf28de06992bd9210d1a8d8e/coverage-7.14.0-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:7333cd944ee4393b9b3d3c1b598c936d4fc8d70573a4c7dacfec5590dd50e436", size = 264530, upload-time = "2026-05-10T18:02:07.582Z" }, + { url = "https://files.pythonhosted.org/packages/6b/73/5c31ef97763288d03d9995152b96d5475b527c63d91c84b01caea894b83a/coverage-7.14.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0f162bc9a15b82d947b02651b0c7e1609d6f7a8735ca330cfadec8481dd97d5a", size = 266932, upload-time = "2026-05-10T18:02:09.401Z" }, + { url = "https://files.pythonhosted.org/packages/e1/76/dd56d80f29c5f05b4d76f7e7c6d47cafacae017189c75c5759d24f9ff0cc/coverage-7.14.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:362cb78e01a5dc82009d88004cf60f2e6b6d6fcbfdec05b05af73b0abf40118f", size = 268062, upload-time = "2026-05-10T18:02:11.399Z" }, + { url = "https://files.pythonhosted.org/packages/6e/c7/27ba85cd5b95614f159ff93ebff1901584a8d192e2e5e24c4943a7453f59/coverage-7.14.0-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:acebd068fca5512c3a6fde9c045f901613478781a73f0e82b307b214daef23fb", size = 261504, upload-time = "2026-05-10T18:02:13.257Z" }, + { url = "https://files.pythonhosted.org/packages/13/2e/e8149f60ab5d5684c6eee881bdf34b127115cddbb958b196768dd9d63473/coverage-7.14.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:29fe3da551dface75deb2ccbf87b6b66e2e7ef38f6d89050b428be94afff3490", size = 264398, upload-time = "2026-05-10T18:02:15.063Z" }, + { url = "https://files.pythonhosted.org/packages/d9/7f/1261b025285323225f4b4abffa5a643649dfd67e25ddca7ebcbdea3b7cb3/coverage-7.14.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:b4cc4fce8672fffcb09b0eafc167b396b3ba53c4a7230f54b7aaffbf6c835fa9", size = 262000, upload-time = "2026-05-10T18:02:16.756Z" }, + { url = "https://files.pythonhosted.org/packages/d3/dc/829c54f60b9d08389439c00f813c752781c496fc5788c78d8006db4b4f2b/coverage-7.14.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:5d4a51aad8ba8bdcd2b8bd8f03d4aca19693fa2327a3470e4718a25b03481020", size = 265732, upload-time = "2026-05-10T18:02:18.817Z" }, + { url = "https://files.pythonhosted.org/packages/ed/b0/70bd1419941652fa062689cba9c3eeafb8f5e6fbb890bce41c3bdda5dbd6/coverage-7.14.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:9f323af3e1e4f68b60b7b247e37b8515563a61375518fa59de1af48ba28a3db6", size = 260847, upload-time = "2026-05-10T18:02:20.528Z" }, + { url = "https://files.pythonhosted.org/packages/f2/73/be40b2390656c654d35ea0015ea7ba3d945769cf80790ad5e0bb2d56d2ba/coverage-7.14.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:1a0abc7342ea9711c469dd8b821c6c311e6bc6aac1442e5fbd6b27fae0a8f3db", size = 263166, upload-time = "2026-05-10T18:02:22.337Z" }, + { url = "https://files.pythonhosted.org/packages/29/55/4a643f712fcf7cf2881f8ec1e0ccb7b164aff3108f69b51801246c8799f2/coverage-7.14.0-cp314-cp314t-win32.whl", hash = "sha256:a9f864ef57b7172e2db87a096642dd51e179e085ab6b2c371c29e885f65c8fb2", size = 223573, upload-time = "2026-05-10T18:02:24.11Z" }, + { url = "https://files.pythonhosted.org/packages/27/96/3acae5da0953be042c0b4dea6d6789d2f080701c77b88e44d5bd41b9219b/coverage-7.14.0-cp314-cp314t-win_amd64.whl", hash = "sha256:29943e552fdc08e082eb51400fb2f58e118a83b5542bd06531214e084399b644", size = 224680, upload-time = "2026-05-10T18:02:25.896Z" }, + { url = "https://files.pythonhosted.org/packages/93/3d/6ab5d2dd8325d838737c6f8d83d62eb6230e0d70b87b51b57bbfd08fa767/coverage-7.14.0-cp314-cp314t-win_arm64.whl", hash = "sha256:742a73ea621953b012f2c4c2219b512180dd84489acf5b1596b0aafc55b9100b", size = 222703, upload-time = "2026-05-10T18:02:27.822Z" }, + { url = "https://files.pythonhosted.org/packages/61/e8/cb8e80d6f9f55b99588625062822bf946cf03ed06315df4bd8397f5632a1/coverage-7.14.0-py3-none-any.whl", hash = "sha256:8de5b61163aee3d05c8a2beab6f47913df7981dad1baf82c414d99158c286ab1", size = 211764, upload-time = "2026-05-10T18:02:29.538Z" }, ] [package.optional-dependencies] @@ -1172,11 +1172,11 @@ wheels = [ [[package]] name = "idna" -version = "3.13" +version = "3.14" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/ce/cc/762dfb036166873f0059f3b7de4565e1b5bc3d6f28a414c13da27e442f99/idna-3.13.tar.gz", hash = "sha256:585ea8fe5d69b9181ec1afba340451fba6ba764af97026f92a91d4eef164a242", size = 194210, upload-time = "2026-04-22T16:42:42.314Z" } +sdist = { url = "https://files.pythonhosted.org/packages/05/b1/efac073e0c297ecf2fb33c346989a529d4e19164f1759102dee5953ee17e/idna-3.14.tar.gz", hash = "sha256:466d810d7a2cc1022bea9b037c39728d51ae7dad40d480fc9b7d7ecf98ba8ee3", size = 198272, upload-time = "2026-05-10T20:32:15.935Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/5d/13/ad7d7ca3808a898b4612b6fe93cde56b53f3034dcde235acb1f0e1df24c6/idna-3.13-py3-none-any.whl", hash = "sha256:892ea0cde124a99ce773decba204c5552b69c3c67ffd5f232eb7696135bc8bb3", size = 68629, upload-time = "2026-04-22T16:42:40.909Z" }, + { url = "https://files.pythonhosted.org/packages/6c/3c/3f62dee257eb3d6b2c1ef2a09d36d9793c7111156a73b5654d2c2305e5ce/idna-3.14-py3-none-any.whl", hash = "sha256:e677eaf072e290f7b725f9acf0b3a2bd55f9fd6f7c70abe5f0e34823d0accf69", size = 72184, upload-time = "2026-05-10T20:32:14.295Z" }, ] [[package]] @@ -1444,7 +1444,7 @@ wheels = [ [[package]] name = "jupyter-server" -version = "2.17.0" +version = "2.18.2" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -1467,9 +1467,9 @@ dependencies = [ { name = "traitlets" }, { name = "websocket-client" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/5b/ac/e040ec363d7b6b1f11304cc9f209dac4517ece5d5e01821366b924a64a50/jupyter_server-2.17.0.tar.gz", hash = "sha256:c38ea898566964c888b4772ae1ed58eca84592e88251d2cfc4d171f81f7e99d5", size = 731949, upload-time = "2025-08-21T14:42:54.042Z" } +sdist = { url = "https://files.pythonhosted.org/packages/ca/15/1eacb0fcb79ef86e8a0a79a708e6ad7435f6f223097dd29a4ce861fabc44/jupyter_server-2.18.2.tar.gz", hash = "sha256:06b4f40d8a7a00bb39d5216859c81374a0e7cfefe6d8a5a7facc5a5c37c679a7", size = 753177, upload-time = "2026-05-06T07:04:36.274Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/92/80/a24767e6ca280f5a49525d987bf3e4d7552bf67c8be07e8ccf20271f8568/jupyter_server-2.17.0-py3-none-any.whl", hash = "sha256:e8cb9c7db4251f51ed307e329b81b72ccf2056ff82d50524debde1ee1870e13f", size = 388221, upload-time = "2025-08-21T14:42:52.034Z" }, + { url = "https://files.pythonhosted.org/packages/e2/50/ecf4f70d65bdb7519b28a33d1b2fee8a4b4ba1ae1a92f15d97e877c5de21/jupyter_server-2.18.2-py3-none-any.whl", hash = "sha256:fa5e46539ded65791838035a2b6001f13e54d5f64b8b3752eb1e91fdd641a5b8", size = 391907, upload-time = "2026-05-06T07:04:34.014Z" }, ] [[package]] @@ -1855,14 +1855,14 @@ wheels = [ [[package]] name = "matplotlib-inline" -version = "0.2.1" +version = "0.2.2" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "traitlets" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/c7/74/97e72a36efd4ae2bccb3463284300f8953f199b5ffbc04cbbb0ec78f74b1/matplotlib_inline-0.2.1.tar.gz", hash = "sha256:e1ee949c340d771fc39e241ea75683deb94762c8fa5f2927ec57c83c4dffa9fe", size = 8110, upload-time = "2025-10-23T09:00:22.126Z" } +sdist = { url = "https://files.pythonhosted.org/packages/bd/c0/9f7c9a46090390368a4d7bcb76bb87a4a36c421e4c0792cdb53486ffac7a/matplotlib_inline-0.2.2.tar.gz", hash = "sha256:72f3fe8fce36b70d4a5b612f899090cd0401deddc4ea90e1572b9f4bfb058c79", size = 8150, upload-time = "2026-05-08T17:33:33.49Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/af/33/ee4519fa02ed11a94aef9559552f3b17bb863f2ecfe1a35dc7f548cde231/matplotlib_inline-0.2.1-py3-none-any.whl", hash = "sha256:d56ce5156ba6085e00a9d54fead6ed29a9c47e215cd1bba2e976ef39f5710a76", size = 9516, upload-time = "2025-10-23T09:00:20.675Z" }, + { url = "https://files.pythonhosted.org/packages/41/09/5b161152e2d90f7b87f781c2e1267494aef9c32498df793f73ad0a0a494a/matplotlib_inline-0.2.2-py3-none-any.whl", hash = "sha256:3c821cf1c209f59fb2d2d64abbf5b23b67bcb2210d663f9918dd851c6da1fcf6", size = 9534, upload-time = "2026-05-08T17:33:32.055Z" }, ] [[package]] @@ -2654,15 +2654,15 @@ wheels = [ [[package]] name = "python-discovery" -version = "1.2.2" +version = "1.3.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "filelock" }, { name = "platformdirs" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/de/ef/3bae0e537cfe91e8431efcba4434463d2c5a65f5a89edd47c6cf2f03c55f/python_discovery-1.2.2.tar.gz", hash = "sha256:876e9c57139eb757cb5878cbdd9ae5379e5d96266c99ef731119e04fffe533bb", size = 58872, upload-time = "2026-04-07T17:28:49.249Z" } +sdist = { url = "https://files.pythonhosted.org/packages/ae/e0/cc5a8653e9a24f6cf84768f05064aa8ed5a83dcefd5e2a043db14a1c5f44/python_discovery-1.3.0.tar.gz", hash = "sha256:d098f1e86be5d45fe4d14bf1029294aabbd332f4321179dec85e76cddce834b0", size = 63925, upload-time = "2026-05-05T14:38:39.769Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/d8/db/795879cc3ddfe338599bddea6388cc5100b088db0a4caf6e6c1af1c27e04/python_discovery-1.2.2-py3-none-any.whl", hash = "sha256:e1ae95d9af875e78f15e19aed0c6137ab1bb49c200f21f5061786490c9585c7a", size = 31894, upload-time = "2026-04-07T17:28:48.09Z" }, + { url = "https://files.pythonhosted.org/packages/30/d4/24d543ab8b8158b7f5a97113c831205f5c900c92c8762b1e7f44b7ea0405/python_discovery-1.3.0-py3-none-any.whl", hash = "sha256:441d9ced3dfce36e113beb35ca302c71c7ef06f3c0f9c227a0b9bb3bd49b9e9f", size = 33124, upload-time = "2026-05-05T14:38:38.539Z" }, ] [[package]] @@ -2830,7 +2830,7 @@ dependencies = [ { name = "torchvision" }, { name = "tqdm" }, { name = "zarr", version = "3.1.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, - { name = "zarr", version = "3.2.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, + { name = "zarr", version = "3.2.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, ] [package.optional-dependencies] @@ -3692,11 +3692,11 @@ wheels = [ [[package]] name = "traitlets" -version = "5.14.3" +version = "5.15.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/eb/79/72064e6a701c2183016abbbfedaba506d81e30e232a68c9f0d6f6fcd1574/traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7", size = 161621, upload-time = "2024-04-19T11:11:49.746Z" } +sdist = { url = "https://files.pythonhosted.org/packages/1b/22/40f55b26baeab80c2d7b3f1db0682f8954e4617fee7d90ce634022ef05c6/traitlets-5.15.0.tar.gz", hash = "sha256:4fead733f81cf1c4c938e06f8ca4633896833c9d89eff878159457f4d4392971", size = 163197, upload-time = "2026-05-06T08:05:58.016Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f", size = 85359, upload-time = "2024-04-19T11:11:46.763Z" }, + { url = "https://files.pythonhosted.org/packages/da/98/a9937a969d018a23badfea0b381f66783649d48e0ea6c41923265c3cbeb3/traitlets-5.15.0-py3-none-any.whl", hash = "sha256:fb36a18867a6803deab09f3c5e0fa81bb7b26a5c9e82501c9933f759166eff40", size = 85877, upload-time = "2026-05-06T08:05:55.853Z" }, ] [[package]] @@ -3747,16 +3747,16 @@ wheels = [ [[package]] name = "urllib3" -version = "2.6.3" +version = "2.7.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/c7/24/5f1b3bdffd70275f6661c76461e25f024d5a38a46f04aaca912426a2b1d3/urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed", size = 435556, upload-time = "2026-01-07T16:24:43.925Z" } +sdist = { url = "https://files.pythonhosted.org/packages/53/0c/06f8b233b8fd13b9e5ee11424ef85419ba0d8ba0b3138bf360be2ff56953/urllib3-2.7.0.tar.gz", hash = "sha256:231e0ec3b63ceb14667c67be60f2f2c40a518cb38b03af60abc813da26505f4c", size = 433602, upload-time = "2026-05-07T16:13:18.596Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/39/08/aaaad47bc4e9dc8c725e68f9d04865dbcb2052843ff09c97b08904852d84/urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4", size = 131584, upload-time = "2026-01-07T16:24:42.685Z" }, + { url = "https://files.pythonhosted.org/packages/7f/3e/5db95bcf282c52709639744ca2a8b149baccf648e39c8cc87553df9eae0c/urllib3-2.7.0-py3-none-any.whl", hash = "sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897", size = 131087, upload-time = "2026-05-07T16:13:17.151Z" }, ] [[package]] name = "virtualenv" -version = "21.3.0" +version = "21.3.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "distlib" }, @@ -3764,9 +3764,9 @@ dependencies = [ { name = "platformdirs" }, { name = "python-discovery" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/3f/8b/6331f7a7fe70131c301106ec1e7cf23e2501bf7d4ca3636805801ca191bb/virtualenv-21.3.0.tar.gz", hash = "sha256:733750db978ec95c2d8eb4feadaa57091002bce404cb39ba69899cf7bd28944e", size = 7614069, upload-time = "2026-04-27T17:05:58.927Z" } +sdist = { url = "https://files.pythonhosted.org/packages/ec/0d/915c02c94d207b85580eb09bffab54438a709e7288524094fe781da526c2/virtualenv-21.3.1.tar.gz", hash = "sha256:c2305bc1fddeec40699b8370d13f8d431b0701f00ce895061ce493aeded4426b", size = 7613791, upload-time = "2026-05-05T01:34:31.402Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/4b/eb/03bfb1299d4c4510329e470f13f9a4ce793df7fcb5a2fd3510f911066f61/virtualenv-21.3.0-py3-none-any.whl", hash = "sha256:4d28ee41f6d9ec8f1f00cd472b9ffbcedda1b3d3b9a575b5c94a2d004fd51bd7", size = 7594690, upload-time = "2026-04-27T17:05:55.468Z" }, + { url = "https://files.pythonhosted.org/packages/b1/4f/f71e641e504111a5a74e3a20bc52d01bd86788b22699dd3fee1c63253cf6/virtualenv-21.3.1-py3-none-any.whl", hash = "sha256:d1a71cf58f2f9228fff23a1f6ec15d39785c6b32e03658d104974247145edd35", size = 7594539, upload-time = "2026-05-05T01:34:28.98Z" }, ] [[package]] @@ -3848,7 +3848,7 @@ wheels = [ [[package]] name = "zarr" -version = "3.2.0" +version = "3.2.1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14'", @@ -3862,9 +3862,9 @@ dependencies = [ { name = "packaging", marker = "python_full_version >= '3.12'" }, { name = "typing-extensions", marker = "python_full_version >= '3.12'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/db/27/8f391a4304f503ab6f4df6e1724380ea2e35e78a5d1ba973ba2b1347df5b/zarr-3.2.0.tar.gz", hash = "sha256:5867fa8dd7910541075531368c8eaa6f35957ab5413c68c168830e83948665ed", size = 454948, upload-time = "2026-04-30T22:18:03.074Z" } +sdist = { url = "https://files.pythonhosted.org/packages/93/8d/aeb164004f87543b06ef54f885d02c342c31ceb274e2bbec470a98927621/zarr-3.2.1.tar.gz", hash = "sha256:71565b738a0e7e8ed226f0516eba8c6bb53440ad7669a8c48ebb3534a161d035", size = 675161, upload-time = "2026-05-05T12:37:22.383Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/0b/9e/2e99d08824f300046eba83b480d6be17f771f57eed80dd7c162381cbe4de/zarr-3.2.0-py3-none-any.whl", hash = "sha256:c693bd4ae24328f242e47e9e1ced221e919d9f62cad71030fd059e398320e555", size = 318784, upload-time = "2026-04-30T22:18:01.13Z" }, + { url = "https://files.pythonhosted.org/packages/88/0a/469e2bd01be1490336e6c8707386845655d59261543315778a3ccc7e8019/zarr-3.2.1-py3-none-any.whl", hash = "sha256:f78cdd3d9687ad0e9f9cba2c5683b64f0c52589c19f685eeabe872e93cc0d2c7", size = 319617, upload-time = "2026-05-05T12:37:20.66Z" }, ] [[package]] From 88795603aa21fb1f394ca24cfa52533aa7897ff7 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Mon, 11 May 2026 11:54:00 -0700 Subject: [PATCH 053/140] Changed the plotting of MAPED.real_space_align to be consistent with numpy version. --- src/quantem/diffraction/maped.py | 35 +++++++++++++++++--------------- 1 file changed, 19 insertions(+), 16 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 8def04dc..32c340bc 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1369,9 +1369,9 @@ def real_space_align( padding=padding, pad_val=pad_val, mode=shift_method, - blend=False, + blend=True, ) - show_2d(im_aligned.sum(0), **plot_kwargs) + show_2d(im_aligned, **plot_kwargs) return self @@ -2005,7 +2005,6 @@ def shift_images_torch( for ind in range(n): stack[ind, r0 : r0 + H, c0 : c0 + W] = images[ind].to(dtype=torch.float32) * w stack_w[ind, r0 : r0 + H, c0 : c0 + W] = w - # shift both stack and stack_w using grid_sample on (n,1,Hp,Wp) imgs = stack.unsqueeze(1) imgs_w = stack_w.unsqueeze(1) @@ -2088,6 +2087,7 @@ def dscan_correct( plot_aligned: bool = True, edge_blend: float = 2.0, device="cpu", + method="cross_correlation", fit_shifts=True, mode="linear", ): @@ -2147,22 +2147,25 @@ def dscan_correct( for iteration in range(iterations): G_ref = torch.fft.fft2(shifted_dps.mean(dim=(0, 1)) * w) + if method == "cross_correlation": + for h_rs in tqdm(range(H_rs), desc=f"Iteration {iteration + 1}/{iterations}"): + for w_rs in range(W_rs): + ind = w_rs + h_rs * H_rs + dp = shifted_dps[h_rs, w_rs] # <-- Read from current shifted_dps, not original + G = torch.fft.fft2(w * dp) + shift = cross_correlation_shift_torch( + G_ref, G, upsample_factor=upsample_factor, fft_input=True + ) + diffraction_shifts[h_rs, w_rs] = shift - for h_rs in tqdm(range(H_rs), desc=f"Iteration {iteration + 1}/{iterations}"): - for w_rs in range(W_rs): - ind = w_rs + h_rs * H_rs - dp = shifted_dps[h_rs, w_rs] # <-- Read from current shifted_dps, not original - G = torch.fft.fft2(w * dp) - shift = cross_correlation_shift_torch( - G_ref, G, upsample_factor=upsample_factor, fft_input=True - ) - diffraction_shifts[h_rs, w_rs] = shift + phase_ramp = torch.exp(-1j * torch.pi * (kr * shift[0] + kc * shift[1])) + G_shift = G * phase_ramp - phase_ramp = torch.exp(-1j * torch.pi * (kr * shift[0] + kc * shift[1])) - G_shift = G * phase_ramp + shifted_dps[h_rs, w_rs, :, :] = torch.fft.ifft2(G_shift).real + G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) - shifted_dps[h_rs, w_rs, :, :] = torch.fft.ifft2(G_shift).real - G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) + if method == "autocorrelation": + pass G_ref_final = G_ref.clone() From 47d9c5057dbbc30b4f87def1501041ec109102a0 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 11 May 2026 11:55:34 -0700 Subject: [PATCH 054/140] address cedric's feedback on scan rotation --- widget/src/quantem/widget/show2d.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py index fbe2f8b3..e243a51d 100644 --- a/widget/src/quantem/widget/show2d.py +++ b/widget/src/quantem/widget/show2d.py @@ -835,6 +835,12 @@ def load_state_dict(self, state): setattr(self, key, val) def summary(self): + """Print a human-readable snapshot of the widget's current state. + + Reports image dimensions and pixel size, data min/max/mean, display + settings (colormap, contrast, scale, FFT), active ROIs and profile + line, per-image rotations, and the most recent render timings. + """ lines = [self.title or "Show2D", "═" * 32] if self.n_images > 1: lines.append(f"Image: {self.n_images}×{self.height}×{self.width} ({self.ncols} cols)") @@ -897,6 +903,15 @@ def _update_all_frames(self): self.frame_bytes = data.tobytes() def _apply_rotations(self): + """Re-rotate each displayed image from its original by ``image_rotations[i] * 90°``. + + This is purely a display-time reorientation of each 2D image via + ``np.rot90`` — it is NOT scan rotation (which would rotate the + scan grid in a 4D-STEM dataset). Originals are kept in + ``_data_original`` so successive rotations compose from the + unrotated source rather than accumulating interpolation error. + Mixed shapes after rotation are center-padded to a common size. + """ # Materialize originals as independent copies only when a non-zero # rotation exists (they start as views into _data to avoid 800MB copy at init) has_rotation = any( From a495a977f7bb7b1853d03393ac8c60cb1e9c6a83 Mon Sep 17 00:00:00 2001 From: quantem-bot Date: Mon, 18 May 2026 12:38:55 +0000 Subject: [PATCH 055/140] chore: update lock file --- uv.lock | 485 ++++++++++++++++++++++++++++---------------------------- 1 file changed, 243 insertions(+), 242 deletions(-) diff --git a/uv.lock b/uv.lock index 80a97226..a01182cc 100644 --- a/uv.lock +++ b/uv.lock @@ -373,14 +373,14 @@ wheels = [ [[package]] name = "click" -version = "8.3.3" +version = "8.4.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "colorama", marker = "sys_platform == 'win32'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/bb/63/f9e1ea081ce35720d8b92acde70daaedace594dc93b693c869e0d5910718/click-8.3.3.tar.gz", hash = "sha256:398329ad4837b2ff7cbe1dd166a4c0f8900c3ca3a218de04466f38f6497f18a2", size = 328061, upload-time = "2026-04-22T15:11:27.506Z" } +sdist = { url = "https://files.pythonhosted.org/packages/23/e4/796662cd90cf80e3a363c99db2b88e0e394b988a575f60a17e16440cd011/click-8.4.0.tar.gz", hash = "sha256:638f1338fe1235c8f4e008e4a8a254fb5c5fbdcbb40ece3c9142ebb78e792973", size = 350843, upload-time = "2026-05-17T00:47:58.425Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/ae/44/c1221527f6a71a01ec6fbad7fa78f1d50dfa02217385cf0fa3eec7087d59/click-8.3.3-py3-none-any.whl", hash = "sha256:a2bf429bb3033c89fa4936ffb35d5cb471e3719e1f3c8a7c3fff0b8314305613", size = 110502, upload-time = "2026-04-22T15:11:25.044Z" }, + { url = "https://files.pythonhosted.org/packages/ee/ae/8e92f8058baf87f6c7d86ee7e457668690195cc77efedb8d3797a06e3940/click-8.4.0-py3-none-any.whl", hash = "sha256:40c50b7c6c6adac2823d411041ec84f3f103f1b280d5e9ce0d7f998995832f81", size = 116147, upload-time = "2026-05-17T00:47:56.842Z" }, ] [[package]] @@ -671,9 +671,6 @@ wheels = [ ] [package.optional-dependencies] -cublas = [ - { name = "nvidia-cublas", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, -] cudart = [ { name = "nvidia-cuda-runtime", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, ] @@ -765,11 +762,11 @@ wheels = [ [[package]] name = "decorator" -version = "5.2.1" +version = "5.3.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/43/fa/6d96a0978d19e17b68d634497769987b16c8f4cd0a7a05048bec693caa6b/decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360", size = 56711, upload-time = "2025-02-24T04:41:34.073Z" } +sdist = { url = "https://files.pythonhosted.org/packages/60/8b/32f9823da46cde7df2087faa08cd98d01b908f8dcab982cdba9c84e85355/decorator-5.3.1.tar.gz", hash = "sha256:4cbcdd55a6efadb9dbea26b858f4fb3264567b52d69ca0d25b721b553f60ea82", size = 58084, upload-time = "2026-05-18T06:03:28.057Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/4e/8c/f3147f5c4b73e7550fe5f9352eaa956ae838d5c51eb58e7a25b9f3e2643b/decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a", size = 9190, upload-time = "2025-02-24T04:41:32.565Z" }, + { url = "https://files.pythonhosted.org/packages/05/7f/798705f5296a58ca505d600456748d1be48078eac8a7050d8a98bc9edb89/decorator-5.3.1-py3-none-any.whl", hash = "sha256:f47fe6fdbd2edd623ecfe36875d37aba411624e2670dd395dddae1358689bb3c", size = 10365, upload-time = "2026-05-18T06:03:26.517Z" }, ] [[package]] @@ -864,51 +861,51 @@ wheels = [ [[package]] name = "fonttools" -version = "4.62.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/9a/08/7012b00a9a5874311b639c3920270c36ee0c445b69d9989a85e5c92ebcb0/fonttools-4.62.1.tar.gz", hash = "sha256:e54c75fd6041f1122476776880f7c3c3295ffa31962dc6ebe2543c00dca58b5d", size = 3580737, upload-time = "2026-03-13T13:54:25.52Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/88/39/23ff32561ec8d45a4d48578b4d241369d9270dc50926c017570e60893701/fonttools-4.62.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:40975849bac44fb0b9253d77420c6d8b523ac4dcdcefeff6e4d706838a5b80f7", size = 2871039, upload-time = "2026-03-13T13:52:33.127Z" }, - { url = "https://files.pythonhosted.org/packages/24/7f/66d3f8a9338a9b67fe6e1739f47e1cd5cee78bd3bc1206ef9b0b982289a5/fonttools-4.62.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:9dde91633f77fa576879a0c76b1d89de373cae751a98ddf0109d54e173b40f14", size = 2416346, upload-time = "2026-03-13T13:52:35.676Z" }, - { url = "https://files.pythonhosted.org/packages/aa/53/5276ceba7bff95da7793a07c5284e1da901cf00341ce5e2f3273056c0cca/fonttools-4.62.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6acb4109f8bee00fec985c8c7afb02299e35e9c94b57287f3ea542f28bd0b0a7", size = 5100897, upload-time = "2026-03-13T13:52:38.102Z" }, - { url = "https://files.pythonhosted.org/packages/cc/a1/40a5c4d8e28b0851d53a8eeeb46fbd73c325a2a9a165f290a5ed90e6c597/fonttools-4.62.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1c5c25671ce8805e0d080e2ffdeca7f1e86778c5cbfbeae86d7f866d8830517b", size = 5071078, upload-time = "2026-03-13T13:52:41.305Z" }, - { url = "https://files.pythonhosted.org/packages/e3/be/d378fca4c65ea1956fee6d90ace6e861776809cbbc5af22388a090c3c092/fonttools-4.62.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:a5d8825e1140f04e6c99bb7d37a9e31c172f3bc208afbe02175339e699c710e1", size = 5076908, upload-time = "2026-03-13T13:52:44.122Z" }, - { url = "https://files.pythonhosted.org/packages/f8/d9/ae6a1d0693a4185a84605679c8a1f719a55df87b9c6e8e817bfdd9ef5936/fonttools-4.62.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:268abb1cb221e66c014acc234e872b7870d8b5d4657a83a8f4205094c32d2416", size = 5202275, upload-time = "2026-03-13T13:52:46.591Z" }, - { url = "https://files.pythonhosted.org/packages/54/6c/af95d9c4efb15cabff22642b608342f2bd67137eea6107202d91b5b03184/fonttools-4.62.1-cp311-cp311-win32.whl", hash = "sha256:942b03094d7edbb99bdf1ae7e9090898cad7bf9030b3d21f33d7072dbcb51a53", size = 2293075, upload-time = "2026-03-13T13:52:48.711Z" }, - { url = "https://files.pythonhosted.org/packages/d3/97/bf54c5b3f2be34e1f143e6db838dfdc54f2ffa3e68c738934c82f3b2a08d/fonttools-4.62.1-cp311-cp311-win_amd64.whl", hash = "sha256:e8514f4924375f77084e81467e63238b095abda5107620f49421c368a6017ed2", size = 2344593, upload-time = "2026-03-13T13:52:50.725Z" }, - { url = "https://files.pythonhosted.org/packages/47/d4/dbacced3953544b9a93088cc10ef2b596d348c983d5c67a404fa41ec51ba/fonttools-4.62.1-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:90365821debbd7db678809c7491ca4acd1e0779b9624cdc6ddaf1f31992bf974", size = 2870219, upload-time = "2026-03-13T13:52:53.664Z" }, - { url = "https://files.pythonhosted.org/packages/66/9e/a769c8e99b81e5a87ab7e5e7236684de4e96246aae17274e5347d11ebd78/fonttools-4.62.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:12859ff0b47dd20f110804c3e0d0970f7b832f561630cd879969011541a464a9", size = 2414891, upload-time = "2026-03-13T13:52:56.493Z" }, - { url = "https://files.pythonhosted.org/packages/69/64/f19a9e3911968c37e1e620e14dfc5778299e1474f72f4e57c5ec771d9489/fonttools-4.62.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9c125ffa00c3d9003cdaaf7f2c79e6e535628093e14b5de1dccb08859b680936", size = 5033197, upload-time = "2026-03-13T13:52:59.179Z" }, - { url = "https://files.pythonhosted.org/packages/9b/8a/99c8b3c3888c5c474c08dbfd7c8899786de9604b727fcefb055b42c84bba/fonttools-4.62.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:149f7d84afca659d1a97e39a4778794a2f83bf344c5ee5134e09995086cc2392", size = 4988768, upload-time = "2026-03-13T13:53:02.761Z" }, - { url = "https://files.pythonhosted.org/packages/d1/c6/0f904540d3e6ab463c1243a0d803504826a11604c72dd58c2949796a1762/fonttools-4.62.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0aa72c43a601cfa9273bb1ae0518f1acadc01ee181a6fc60cd758d7fdadffc04", size = 4971512, upload-time = "2026-03-13T13:53:05.678Z" }, - { url = "https://files.pythonhosted.org/packages/29/0b/5cbef6588dc9bd6b5c9ad6a4d5a8ca384d0cea089da31711bbeb4f9654a6/fonttools-4.62.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:19177c8d96c7c36359266e571c5173bcee9157b59cfc8cb0153c5673dc5a3a7d", size = 5122723, upload-time = "2026-03-13T13:53:08.662Z" }, - { url = "https://files.pythonhosted.org/packages/4a/47/b3a5342d381595ef439adec67848bed561ab7fdb1019fa522e82101b7d9c/fonttools-4.62.1-cp312-cp312-win32.whl", hash = "sha256:a24decd24d60744ee8b4679d38e88b8303d86772053afc29b19d23bb8207803c", size = 2281278, upload-time = "2026-03-13T13:53:10.998Z" }, - { url = "https://files.pythonhosted.org/packages/28/b1/0c2ab56a16f409c6c8a68816e6af707827ad5d629634691ff60a52879792/fonttools-4.62.1-cp312-cp312-win_amd64.whl", hash = "sha256:9e7863e10b3de72376280b515d35b14f5eeed639d1aa7824f4cf06779ec65e42", size = 2331414, upload-time = "2026-03-13T13:53:13.992Z" }, - { url = "https://files.pythonhosted.org/packages/3b/56/6f389de21c49555553d6a5aeed5ac9767631497ac836c4f076273d15bd72/fonttools-4.62.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:c22b1014017111c401469e3acc5433e6acf6ebcc6aa9efb538a533c800971c79", size = 2865155, upload-time = "2026-03-13T13:53:16.132Z" }, - { url = "https://files.pythonhosted.org/packages/03/c5/0e3966edd5ec668d41dfe418787726752bc07e2f5fd8c8f208615e61fa89/fonttools-4.62.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:68959f5fc58ed4599b44aad161c2837477d7f35f5f79402d97439974faebfebe", size = 2412802, upload-time = "2026-03-13T13:53:18.878Z" }, - { url = "https://files.pythonhosted.org/packages/52/94/e6ac4b44026de7786fe46e3bfa0c87e51d5d70a841054065d49cd62bb909/fonttools-4.62.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ef46db46c9447103b8f3ff91e8ba009d5fe181b1920a83757a5762551e32bb68", size = 5013926, upload-time = "2026-03-13T13:53:21.379Z" }, - { url = "https://files.pythonhosted.org/packages/e2/98/8b1e801939839d405f1f122e7d175cebe9aeb4e114f95bfc45e3152af9a7/fonttools-4.62.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6706d1cb1d5e6251a97ad3c1b9347505c5615c112e66047abbef0f8545fa30d1", size = 4964575, upload-time = "2026-03-13T13:53:23.857Z" }, - { url = "https://files.pythonhosted.org/packages/46/76/7d051671e938b1881670528fec69cc4044315edd71a229c7fd712eaa5119/fonttools-4.62.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:2e7abd2b1e11736f58c1de27819e1955a53267c21732e78243fa2fa2e5c1e069", size = 4953693, upload-time = "2026-03-13T13:53:26.569Z" }, - { url = "https://files.pythonhosted.org/packages/1f/ae/b41f8628ec0be3c1b934fc12b84f4576a5c646119db4d3bdd76a217c90b5/fonttools-4.62.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:403d28ce06ebfc547fbcb0cb8b7f7cc2f7a2d3e1a67ba9a34b14632df9e080f9", size = 5094920, upload-time = "2026-03-13T13:53:29.329Z" }, - { url = "https://files.pythonhosted.org/packages/f2/f6/53a1e9469331a23dcc400970a27a4caa3d9f6edbf5baab0260285238b884/fonttools-4.62.1-cp313-cp313-win32.whl", hash = "sha256:93c316e0f5301b2adbe6a5f658634307c096fd5aae60a5b3412e4f3e1728ab24", size = 2279928, upload-time = "2026-03-13T13:53:32.352Z" }, - { url = "https://files.pythonhosted.org/packages/38/60/35186529de1db3c01f5ad625bde07c1f576305eab6d86bbda4c58445f721/fonttools-4.62.1-cp313-cp313-win_amd64.whl", hash = "sha256:7aa21ff53e28a9c2157acbc44e5b401149d3c9178107130e82d74ceb500e5056", size = 2330514, upload-time = "2026-03-13T13:53:34.991Z" }, - { url = "https://files.pythonhosted.org/packages/36/f0/2888cdac391807d68d90dcb16ef858ddc1b5309bfc6966195a459dd326e2/fonttools-4.62.1-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:fa1d16210b6b10a826d71bed68dd9ec24a9e218d5a5e2797f37c573e7ec215ca", size = 2864442, upload-time = "2026-03-13T13:53:37.509Z" }, - { url = "https://files.pythonhosted.org/packages/4b/b2/e521803081f8dc35990816b82da6360fa668a21b44da4b53fc9e77efcd62/fonttools-4.62.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:aa69d10ed420d8121118e628ad47d86e4caa79ba37f968597b958f6cceab7eca", size = 2410901, upload-time = "2026-03-13T13:53:40.55Z" }, - { url = "https://files.pythonhosted.org/packages/00/a4/8c3511ff06e53110039358dbbdc1a65d72157a054638387aa2ada300a8b8/fonttools-4.62.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bd13b7999d59c5eb1c2b442eb2d0c427cb517a0b7a1f5798fc5c9e003f5ff782", size = 4999608, upload-time = "2026-03-13T13:53:42.798Z" }, - { url = "https://files.pythonhosted.org/packages/28/63/cd0c3b26afe60995a5295f37c246a93d454023726c3261cfbb3559969bb9/fonttools-4.62.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8d337fdd49a79b0d51c4da87bc38169d21c3abbf0c1aa9367eff5c6656fb6dae", size = 4912726, upload-time = "2026-03-13T13:53:45.405Z" }, - { url = "https://files.pythonhosted.org/packages/70/b9/ac677cb07c24c685cf34f64e140617d58789d67a3dd524164b63648c6114/fonttools-4.62.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:d241cdc4a67b5431c6d7f115fdf63335222414995e3a1df1a41e1182acd4bcc7", size = 4951422, upload-time = "2026-03-13T13:53:48.326Z" }, - { url = "https://files.pythonhosted.org/packages/e6/10/11c08419a14b85b7ca9a9faca321accccc8842dd9e0b1c8a72908de05945/fonttools-4.62.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:c05557a78f8fa514da0f869556eeda40887a8abc77c76ee3f74cf241778afd5a", size = 5060979, upload-time = "2026-03-13T13:53:51.366Z" }, - { url = "https://files.pythonhosted.org/packages/4e/3c/12eea4a4cf054e7ab058ed5ceada43b46809fce2bf319017c4d63ae55bb4/fonttools-4.62.1-cp314-cp314-win32.whl", hash = "sha256:49a445d2f544ce4a69338694cad575ba97b9a75fff02720da0882d1a73f12800", size = 2283733, upload-time = "2026-03-13T13:53:53.606Z" }, - { url = "https://files.pythonhosted.org/packages/6b/67/74b070029043186b5dd13462c958cb7c7f811be0d2e634309d9a1ffb1505/fonttools-4.62.1-cp314-cp314-win_amd64.whl", hash = "sha256:1eecc128c86c552fb963fe846ca4e011b1be053728f798185a1687502f6d398e", size = 2335663, upload-time = "2026-03-13T13:53:56.23Z" }, - { url = "https://files.pythonhosted.org/packages/42/c5/4d2ed3ca6e33617fc5624467da353337f06e7f637707478903c785bd8e20/fonttools-4.62.1-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:1596aeaddf7f78e21e68293c011316a25267b3effdaccaf4d59bc9159d681b82", size = 2947288, upload-time = "2026-03-13T13:53:59.397Z" }, - { url = "https://files.pythonhosted.org/packages/1f/e9/7ab11ddfda48ed0f89b13380e5595ba572619c27077be0b2c447a63ff351/fonttools-4.62.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:8f8fca95d3bb3208f59626a4b0ea6e526ee51f5a8ad5d91821c165903e8d9260", size = 2449023, upload-time = "2026-03-13T13:54:01.642Z" }, - { url = "https://files.pythonhosted.org/packages/b2/10/a800fa090b5e8819942e54e19b55fc7c21fe14a08757c3aa3ca8db358939/fonttools-4.62.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee91628c08e76f77b533d65feb3fbe6d9dad699f95be51cf0d022db94089cdc4", size = 5137599, upload-time = "2026-03-13T13:54:04.495Z" }, - { url = "https://files.pythonhosted.org/packages/37/dc/8ccd45033fffd74deb6912fa1ca524643f584b94c87a16036855b498a1ed/fonttools-4.62.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5f37df1cac61d906e7b836abe356bc2f34c99d4477467755c216b72aa3dc748b", size = 4920933, upload-time = "2026-03-13T13:54:07.557Z" }, - { url = "https://files.pythonhosted.org/packages/99/eb/e618adefb839598d25ac8136cd577925d6c513dc0d931d93b8af956210f0/fonttools-4.62.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:92bb00a947e666169c99b43753c4305fc95a890a60ef3aeb2a6963e07902cc87", size = 5016232, upload-time = "2026-03-13T13:54:10.611Z" }, - { url = "https://files.pythonhosted.org/packages/d9/5f/9b5c9bfaa8ec82def8d8168c4f13615990d6ce5996fe52bd49bfb5e05134/fonttools-4.62.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:bdfe592802ef939a0e33106ea4a318eeb17822c7ee168c290273cbd5fabd746c", size = 5042987, upload-time = "2026-03-13T13:54:13.569Z" }, - { url = "https://files.pythonhosted.org/packages/90/aa/dfbbe24c6a6afc5c203d90cc0343e24bcbb09e76d67c4d6eef8c2558d7ba/fonttools-4.62.1-cp314-cp314t-win32.whl", hash = "sha256:b820fcb92d4655513d8402d5b219f94481c4443d825b4372c75a2072aa4b357a", size = 2348021, upload-time = "2026-03-13T13:54:16.98Z" }, - { url = "https://files.pythonhosted.org/packages/13/6f/ae9c4e4dd417948407b680855c2c7790efb52add6009aaecff1e3bc50e8e/fonttools-4.62.1-cp314-cp314t-win_amd64.whl", hash = "sha256:59b372b4f0e113d3746b88985f1c796e7bf830dd54b28374cd85c2b8acd7583e", size = 2414147, upload-time = "2026-03-13T13:54:19.416Z" }, - { url = "https://files.pythonhosted.org/packages/fd/ba/56147c165442cc5ba7e82ecf301c9a68353cede498185869e6e02b4c264f/fonttools-4.62.1-py3-none-any.whl", hash = "sha256:7487782e2113861f4ddcc07c3436450659e3caa5e470b27dc2177cade2d8e7fd", size = 1152647, upload-time = "2026-03-13T13:54:22.735Z" }, +version = "4.63.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/84/69/c97f2c18e0db87d2c7b15da1974dace76ae938f1cfa22e2727a648b7ed43/fonttools-4.63.0.tar.gz", hash = "sha256:caeb583deeb5168e694b65cda8b4ee62abedfa66cf88488734466f2366b9c4e0", size = 3597189, upload-time = "2026-05-14T12:04:30.958Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/75/2b/a7f1545bdf5da69c4bda0cea2a5781f0ad2a6623e0277267672db43c5fe6/fonttools-4.63.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:2b8ae05d9eacf6081414d759c0a352769ac28ce31280d6bb8e77b03f9e3c449f", size = 2881793, upload-time = "2026-05-14T12:02:56.645Z" }, + { url = "https://files.pythonhosted.org/packages/49/50/965308c703f085f225db2886813b27e015b8b3438c350b22dd65b52c2a2c/fonttools-4.63.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:79cdc9f567aec74a72918fd060283911406750cbc9fd28c1316023deb6ce31a9", size = 2428130, upload-time = "2026-05-14T12:02:58.891Z" }, + { url = "https://files.pythonhosted.org/packages/d8/38/6937fbd7f2dc3a6b48725851bc2c15ec949b9af14d9bbcb5fe83cdf9bdf9/fonttools-4.63.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2c14b4fd138c4bafcca294765c547914e1aa431ae1ca94ab99d8db08c958bd3b", size = 5111952, upload-time = "2026-05-14T12:03:01.263Z" }, + { url = "https://files.pythonhosted.org/packages/0b/43/a81f20050a3115b57d62c8e781446949512eac36690dc384ccea65ff4cc1/fonttools-4.63.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d76ac49f929aecaf82d83250b8347e099d7aecba0f4726c1d9b6df3b8bb5fe18", size = 5082308, upload-time = "2026-05-14T12:03:03.211Z" }, + { url = "https://files.pythonhosted.org/packages/67/00/cdd9d4944ca6ae280d01e69cc37bde3bf663630b837a6fc6d2cd65d80e0e/fonttools-4.63.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:dcf076a4474fe0d7367e5bbf5b052c7284fa1feca729c04176ce513521afd8a0", size = 5087932, upload-time = "2026-05-14T12:03:05.147Z" }, + { url = "https://files.pythonhosted.org/packages/f5/f1/0aa0dbea778c75adbef223c42019fd47d22262b905974d62d829545d485f/fonttools-4.63.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:7dd683fef0663e9f0f45cf541d788d24caa3ec9db50796b588e1757d8b3bc007", size = 5213271, upload-time = "2026-05-14T12:03:07.238Z" }, + { url = "https://files.pythonhosted.org/packages/a8/99/253e4056e1f0e67b9390125a154b73b5eb73ad521bece95c004858fdeec2/fonttools-4.63.0-cp311-cp311-win32.whl", hash = "sha256:afefc1ed0a59785a7fb06ea7e1678e849c193e1e387db783579bc7b3056fcfcb", size = 2304473, upload-time = "2026-05-14T12:03:09.271Z" }, + { url = "https://files.pythonhosted.org/packages/08/60/defa5e69641db890a63be281f41345f4c33b157824eaf0b9fad3e08b0dcb/fonttools-4.63.0-cp311-cp311-win_amd64.whl", hash = "sha256:063e08bd17bd5a90127a14123de0d6a952dbc847695fd98b63c043d58057f90c", size = 2356389, upload-time = "2026-05-14T12:03:11.53Z" }, + { url = "https://files.pythonhosted.org/packages/08/ef/b3c6b9b5be2f82416d73fe2ed2e96e2793cd80e7510bd6a17ca79cdd88ec/fonttools-4.63.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:37dd23e621e3b0aef1baa70a303b80aaf38449632cfc8fd2a55fb285bbccfc02", size = 2881131, upload-time = "2026-05-14T12:03:13.386Z" }, + { url = "https://files.pythonhosted.org/packages/44/a0/c815bea63117fa63e4e1c01f8a1110d2112fa003f838e6467094ec2432ce/fonttools-4.63.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:a9faff9e0c1f76f9fd55899d2ce785832efebab37eb8ae13995853aef178bef0", size = 2426704, upload-time = "2026-05-14T12:03:15.801Z" }, + { url = "https://files.pythonhosted.org/packages/44/04/0b91d8e916e92ad1fac9e4624760baf0fd5ff2ead614c2f68fb21373f03f/fonttools-4.63.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ef3048ef05dbb552b89817713d9cac912e00d0fde4a3105c00d29e52e10c89af", size = 5044298, upload-time = "2026-05-14T12:03:18.085Z" }, + { url = "https://files.pythonhosted.org/packages/77/c7/2342da9830e3e9d4870305ca5d2091d2a83284f2953079b7bdd3b5e029d8/fonttools-4.63.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:58dc6bb86a78d782f00f9190ca02c119cf5bbe2807536e361e18d42019f877d8", size = 4999800, upload-time = "2026-05-14T12:03:20.161Z" }, + { url = "https://files.pythonhosted.org/packages/e6/6d/67fe16c48d7ce050979b33f47e0d28a318f02da030602e944c34f7a16ef3/fonttools-4.63.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ee08ebfa58f6e1aeff5697ab9582105bb620008c1caafb681e4c557e7483027b", size = 4982666, upload-time = "2026-05-14T12:03:22.87Z" }, + { url = "https://files.pythonhosted.org/packages/f2/00/3bbab338c07c71fa56269953845e92c951a61457bbbb0f1022551ea266d9/fonttools-4.63.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:27fdc65af8da6f88b9c6121c47a464cbe359fcfff7ff6fc2d37a1f395d755b78", size = 5133598, upload-time = "2026-05-14T12:03:25.168Z" }, + { url = "https://files.pythonhosted.org/packages/62/f2/aa27c7f98db5b064883dadcc5283947e81e034de42e22a33675878d98b54/fonttools-4.63.0-cp312-cp312-win32.whl", hash = "sha256:af2fd1664d00a397d75f806985ddb36282091c2131a73a6485c23b4a34722263", size = 2292575, upload-time = "2026-05-14T12:03:27.496Z" }, + { url = "https://files.pythonhosted.org/packages/87/36/cccb9bc2a6ab63d1b2980374f0dca72ce95ae267c9b4cfe77455bb70d0d4/fonttools-4.63.0-cp312-cp312-win_amd64.whl", hash = "sha256:59ac449f8cca9b4ffa08d2e7bbadad87ce710d69d1eda5c3c1ce579baa987272", size = 2343211, upload-time = "2026-05-14T12:03:30.057Z" }, + { url = "https://files.pythonhosted.org/packages/0f/8d/d8fec3dcde2963f8c908fb315e5ff2cd0ac34f82394bbbf73a2aa5145ce3/fonttools-4.63.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:cd7e9857e5e63738b9d9fd707bc1f59c8b09e5177726d23664db393c59bb08bd", size = 2876062, upload-time = "2026-05-14T12:03:32.554Z" }, + { url = "https://files.pythonhosted.org/packages/ef/71/d935dc54e4ff121bfdd11e08702db63a7e6f25af21d8a3d7b7212df53641/fonttools-4.63.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c2a2a42198b696a6f48fad91709afb55176e66a5e566131219dba372fb7f8c59", size = 2424594, upload-time = "2026-05-14T12:03:34.86Z" }, + { url = "https://files.pythonhosted.org/packages/8e/40/e76320afa1df918e146155ef239b1719ee266092e96f5423bfd075affba1/fonttools-4.63.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e874792a8212b44583ea02189d9e693906b2f78b261f372f95d6c563210ac1d", size = 5024840, upload-time = "2026-05-14T12:03:36.745Z" }, + { url = "https://files.pythonhosted.org/packages/ce/36/0b805d8c485f872f65a509cbe3b58a5d0d17bee855333b54a150c79d3061/fonttools-4.63.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:22135da48a348785c5e2d5d2d9d6bec5ed44adacbaeb9db12d9493bf6c6bfa68", size = 4975801, upload-time = "2026-05-14T12:03:38.833Z" }, + { url = "https://files.pythonhosted.org/packages/c8/26/2cee03d0aa083ab022da5c07aff9ed3f689da1defb81ad6917c9627896da/fonttools-4.63.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ccf41f2efdf56994d22d73bef4ced1052161958169428d06ba9724ea9e9a64be", size = 4965009, upload-time = "2026-05-14T12:03:41.494Z" }, + { url = "https://files.pythonhosted.org/packages/7e/48/cc4b66d9058c0d0982c833fad10127c4b0e9324606aafa41382295ca4102/fonttools-4.63.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9ced0bd02ac751dd6319b0da88aaef24414e3b0dbc32bb4f24944821a3741a27", size = 5105892, upload-time = "2026-05-14T12:03:43.525Z" }, + { url = "https://files.pythonhosted.org/packages/d8/1f/a98a30a814b9ddef3a2e706025f90b9e0bc94890e6cb15254bc86547d11a/fonttools-4.63.0-cp313-cp313-win32.whl", hash = "sha256:85be818f5506e8a7753153def2c9550178f0ecae6a47b5e0e8dbb23f7cc90380", size = 2291313, upload-time = "2026-05-14T12:03:45.594Z" }, + { url = "https://files.pythonhosted.org/packages/92/46/5177b01f3b4abfdd4409f31cca4ab279c9343a26efbe9ec78c97fc612e02/fonttools-4.63.0-cp313-cp313-win_amd64.whl", hash = "sha256:ba04cb5891d4c0c21b6da95eda8d7b090021508a294fff33464fc7d241e0856b", size = 2342299, upload-time = "2026-05-14T12:03:47.414Z" }, + { url = "https://files.pythonhosted.org/packages/27/d2/23d25e3f247b328be58d04a4c9f894178a0d1eda7d42867cfb388adaf416/fonttools-4.63.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:fd1e3094f42d806d3d7c79162fc59e5910fcbe3a7360c385b8da969bc4493745", size = 2875338, upload-time = "2026-05-14T12:03:50.052Z" }, + { url = "https://files.pythonhosted.org/packages/cd/58/7dfa0c761cb3b2964e2a84c4dc986c926a87de0cb9fb60d5b28ded3f2914/fonttools-4.63.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:6e528da43bc3791085f8cb6141b1d13e459226790240340fcbb4625649238b03", size = 2422661, upload-time = "2026-05-14T12:03:52.154Z" }, + { url = "https://files.pythonhosted.org/packages/dd/87/64cfa18a7a1621d17b7f4502b2b0ed8a135a90c3db51ea590ee99043e76b/fonttools-4.63.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b2248c5decb223562f7902ff6325077a073f608ee8e33e88ad88db734eb9f49", size = 5010526, upload-time = "2026-05-14T12:03:54.647Z" }, + { url = "https://files.pythonhosted.org/packages/36/e1/a8933a72c45a87177fbde2696e0d0755c8c9062f8c077a961c6215fa27b1/fonttools-4.63.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:308f957cdeaf8abe4e5f2f124902ef405448af92c90f80e302a3b771c2e6116b", size = 4923946, upload-time = "2026-05-14T12:03:56.984Z" }, + { url = "https://files.pythonhosted.org/packages/27/60/872e6e233b8c5e8b41413796ff18b7fe479661bd40147e071b450dfad7a1/fonttools-4.63.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:bf00f21eb5fb721dbaf73d1e9da6d02a1af7768f2ebcf9798be98beab8ba90f6", size = 4962489, upload-time = "2026-05-14T12:03:59.443Z" }, + { url = "https://files.pythonhosted.org/packages/30/c4/83c24f2ec38b90cfda84bf4b1a1f49df80e84a1db4e7ac6e0d41bf23bc39/fonttools-4.63.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:c1aaa4b9c75798400ac043ce04d74e7830376c85095a5a6ed7cba2f17a266bf4", size = 5071870, upload-time = "2026-05-14T12:04:02.122Z" }, + { url = "https://files.pythonhosted.org/packages/de/40/3ae22b60ff1d41ce0bd044b31238cdc72cef99f28b976f1e128ebd618c9b/fonttools-4.63.0-cp314-cp314-win32.whl", hash = "sha256:22693918177bd9ceabec4736d338045f357769416fc6b0b2508eefef75b08616", size = 2295026, upload-time = "2026-05-14T12:04:04.47Z" }, + { url = "https://files.pythonhosted.org/packages/c3/d4/98078064ccc76b45cb0f6c002452011e93c4bd26f6850344f0951cc1fe89/fonttools-4.63.0-cp314-cp314-win_amd64.whl", hash = "sha256:7d782fac32985914c351556f68ac0855391572bcd87de50e05970d3cd4c96fc5", size = 2347454, upload-time = "2026-05-14T12:04:06.752Z" }, + { url = "https://files.pythonhosted.org/packages/49/4e/652d1580c5f4e39f7d103b0c793e4773129ad633dce4addd0cf4dfebde02/fonttools-4.63.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:6db5140a60a5d731d21ec076745b40a310607731b0a565b50776393188649001", size = 2958152, upload-time = "2026-05-14T12:04:08.706Z" }, + { url = "https://files.pythonhosted.org/packages/0e/55/ad864c9a9b219f552eb46b32cd7906c466e5a578ba0c3abfcc0fe7413eb6/fonttools-4.63.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:7d76edbff9014094dbf03bd2d074709dfa6ec7aba13d838c937a2b33d2d6a86e", size = 2460809, upload-time = "2026-05-14T12:04:10.783Z" }, + { url = "https://files.pythonhosted.org/packages/ea/2b/0aa8db70f18cf52e49b4ed5ecec68547f981160bf5ded3b5aed6faa0a6f9/fonttools-4.63.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0eac00b9118c3c2f87d272e45341871c5b3066baa3c86897fa634a7c3fb59096", size = 5148649, upload-time = "2026-05-14T12:04:12.747Z" }, + { url = "https://files.pythonhosted.org/packages/7f/63/18e4369c25043096f1048e0c9915951adc4f842bd81c6b18155824d6fa99/fonttools-4.63.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:51394295f1a51de8b5f30bdb1e1b9a4231536c7064ef5c6e211eec19fa36036f", size = 4932147, upload-time = "2026-05-14T12:04:14.806Z" }, + { url = "https://files.pythonhosted.org/packages/a1/3f/67f3eac2ffd8a98446c5022f8ed3864eac878a5ff7af8df4c8286dba16cc/fonttools-4.63.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:9e12f105d2b6342c559c298afb674006bb2893afc7102dcf8a1b55b0486b4e40", size = 5027237, upload-time = "2026-05-14T12:04:17.675Z" }, + { url = "https://files.pythonhosted.org/packages/1a/ba/4e6214cb38a7b04779e97bb7636de9a5c7f20af7018d03dee0b64c08510a/fonttools-4.63.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:796f27556dbe094c4824f75ca85267e4df776c79036c8441469a4df37038c196", size = 5053933, upload-time = "2026-05-14T12:04:20.818Z" }, + { url = "https://files.pythonhosted.org/packages/34/3b/214dcc19ee31d3d38fb5ad2755c11ef0514e5dc300bbaf41c0b69f393799/fonttools-4.63.0-cp314-cp314t-win32.whl", hash = "sha256:948428a275741f0b64b113c955425a953314f4b9ab9997f73a72c83e68e569c8", size = 2359326, upload-time = "2026-05-14T12:04:24.22Z" }, + { url = "https://files.pythonhosted.org/packages/dd/1e/3ff1a9b523058c2eeb6a9d50f5574e2a738200d0d94107d5bc4105e8da3f/fonttools-4.63.0-cp314-cp314t-win_amd64.whl", hash = "sha256:6d4741eb179121cab9eea4cb2393d24492373a260d7945006358c08cfbf45419", size = 2425829, upload-time = "2026-05-14T12:04:26.829Z" }, + { url = "https://files.pythonhosted.org/packages/2c/47/c99d5268f354002ce80f8d029cd9d7d872969da1de8b93d32de4dc56d6f4/fonttools-4.63.0-py3-none-any.whl", hash = "sha256:445af2eab030a16b9171ea8bdda7ebf7d96bda2df88ee182a464252f6e05e20d", size = 1164562, upload-time = "2026-05-14T12:04:29.092Z" }, ] [[package]] @@ -1172,11 +1169,11 @@ wheels = [ [[package]] name = "idna" -version = "3.14" +version = "3.15" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/05/b1/efac073e0c297ecf2fb33c346989a529d4e19164f1759102dee5953ee17e/idna-3.14.tar.gz", hash = "sha256:466d810d7a2cc1022bea9b037c39728d51ae7dad40d480fc9b7d7ecf98ba8ee3", size = 198272, upload-time = "2026-05-10T20:32:15.935Z" } +sdist = { url = "https://files.pythonhosted.org/packages/82/77/7b3966d0b9d1d31a36ddf1746926a11dface89a83409bf1483f0237aa758/idna-3.15.tar.gz", hash = "sha256:ca962446ea538f7092a95e057da437618e886f4d349216d2b1e294abfdb65fdc", size = 199245, upload-time = "2026-05-12T22:45:57.011Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/6c/3c/3f62dee257eb3d6b2c1ef2a09d36d9793c7111156a73b5654d2c2305e5ce/idna-3.14-py3-none-any.whl", hash = "sha256:e677eaf072e290f7b725f9acf0b3a2bd55f9fd6f7c70abe5f0e34823d0accf69", size = 72184, upload-time = "2026-05-10T20:32:14.295Z" }, + { url = "https://files.pythonhosted.org/packages/d2/23/408243171aa9aaba178d3e2559159c24c1171a641aa83b67bdd3394ead8e/idna-3.15-py3-none-any.whl", hash = "sha256:048adeaf8c2d788c40fee287673ccaa74c24ffd8dcf09ffa555a2fbb59f10ac8", size = 72340, upload-time = "2026-05-12T22:45:55.733Z" }, ] [[package]] @@ -2011,90 +2008,93 @@ wheels = [ [[package]] name = "numpy" -version = "2.4.4" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/d7/9f/b8cef5bffa569759033adda9481211426f12f53299629b410340795c2514/numpy-2.4.4.tar.gz", hash = "sha256:2d390634c5182175533585cc89f3608a4682ccb173cc9bb940b2881c8d6f8fa0", size = 20731587, upload-time = "2026-03-29T13:22:01.298Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ef/c6/4218570d8c8ecc9704b5157a3348e486e84ef4be0ed3e38218ab473c83d2/numpy-2.4.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f983334aea213c99992053ede6168500e5f086ce74fbc4acc3f2b00f5762e9db", size = 16976799, upload-time = "2026-03-29T13:18:15.438Z" }, - { url = "https://files.pythonhosted.org/packages/dd/92/b4d922c4a5f5dab9ed44e6153908a5c665b71acf183a83b93b690996e39b/numpy-2.4.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:72944b19f2324114e9dc86a159787333b77874143efcf89a5167ef83cfee8af0", size = 14971552, upload-time = "2026-03-29T13:18:18.606Z" }, - { url = "https://files.pythonhosted.org/packages/8a/dc/df98c095978fa6ee7b9a9387d1d58cbb3d232d0e69ad169a4ce784bde4fd/numpy-2.4.4-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:86b6f55f5a352b48d7fbfd2dbc3d5b780b2d79f4d3c121f33eb6efb22e9a2015", size = 5476566, upload-time = "2026-03-29T13:18:21.532Z" }, - { url = "https://files.pythonhosted.org/packages/28/34/b3fdcec6e725409223dd27356bdf5a3c2cc2282e428218ecc9cb7acc9763/numpy-2.4.4-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:ba1f4fc670ed79f876f70082eff4f9583c15fb9a4b89d6188412de4d18ae2f40", size = 6806482, upload-time = "2026-03-29T13:18:23.634Z" }, - { url = "https://files.pythonhosted.org/packages/68/62/63417c13aa35d57bee1337c67446761dc25ea6543130cf868eace6e8157b/numpy-2.4.4-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8a87ec22c87be071b6bdbd27920b129b94f2fc964358ce38f3822635a3e2e03d", size = 15973376, upload-time = "2026-03-29T13:18:26.677Z" }, - { url = "https://files.pythonhosted.org/packages/cf/c5/9fcb7e0e69cef59cf10c746b84f7d58b08bc66a6b7d459783c5a4f6101a6/numpy-2.4.4-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:df3775294accfdd75f32c74ae39fcba920c9a378a2fc18a12b6820aa8c1fb502", size = 16925137, upload-time = "2026-03-29T13:18:30.14Z" }, - { url = "https://files.pythonhosted.org/packages/7e/43/80020edacb3f84b9efdd1591120a4296462c23fd8db0dde1666f6ef66f13/numpy-2.4.4-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:0d4e437e295f18ec29bc79daf55e8a47a9113df44d66f702f02a293d93a2d6dd", size = 17329414, upload-time = "2026-03-29T13:18:33.733Z" }, - { url = "https://files.pythonhosted.org/packages/fd/06/af0658593b18a5f73532d377188b964f239eb0894e664a6c12f484472f97/numpy-2.4.4-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:6aa3236c78803afbcb255045fbef97a9e25a1f6c9888357d205ddc42f4d6eba5", size = 18658397, upload-time = "2026-03-29T13:18:37.511Z" }, - { url = "https://files.pythonhosted.org/packages/e6/ce/13a09ed65f5d0ce5c7dd0669250374c6e379910f97af2c08c57b0608eee4/numpy-2.4.4-cp311-cp311-win32.whl", hash = "sha256:30caa73029a225b2d40d9fae193e008e24b2026b7ee1a867b7ee8d96ca1a448e", size = 6239499, upload-time = "2026-03-29T13:18:40.372Z" }, - { url = "https://files.pythonhosted.org/packages/bd/63/05d193dbb4b5eec1eca73822d80da98b511f8328ad4ae3ca4caf0f4db91d/numpy-2.4.4-cp311-cp311-win_amd64.whl", hash = "sha256:6bbe4eb67390b0a0265a2c25458f6b90a409d5d069f1041e6aff1e27e3d9a79e", size = 12614257, upload-time = "2026-03-29T13:18:42.95Z" }, - { url = "https://files.pythonhosted.org/packages/87/c5/8168052f080c26fa984c413305012be54741c9d0d74abd7fbeeccae3889f/numpy-2.4.4-cp311-cp311-win_arm64.whl", hash = "sha256:fcfe2045fd2e8f3cb0ce9d4ba6dba6333b8fa05bb8a4939c908cd43322d14c7e", size = 10486775, upload-time = "2026-03-29T13:18:45.835Z" }, - { url = "https://files.pythonhosted.org/packages/28/05/32396bec30fb2263770ee910142f49c1476d08e8ad41abf8403806b520ce/numpy-2.4.4-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:15716cfef24d3a9762e3acdf87e27f58dc823d1348f765bbea6bef8c639bfa1b", size = 16689272, upload-time = "2026-03-29T13:18:49.223Z" }, - { url = "https://files.pythonhosted.org/packages/c5/f3/a983d28637bfcd763a9c7aafdb6d5c0ebf3d487d1e1459ffdb57e2f01117/numpy-2.4.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:23cbfd4c17357c81021f21540da84ee282b9c8fba38a03b7b9d09ba6b951421e", size = 14699573, upload-time = "2026-03-29T13:18:52.629Z" }, - { url = "https://files.pythonhosted.org/packages/9b/fd/e5ecca1e78c05106d98028114f5c00d3eddb41207686b2b7de3e477b0e22/numpy-2.4.4-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:8b3b60bb7cba2c8c81837661c488637eee696f59a877788a396d33150c35d842", size = 5204782, upload-time = "2026-03-29T13:18:55.579Z" }, - { url = "https://files.pythonhosted.org/packages/de/2f/702a4594413c1a8632092beae8aba00f1d67947389369b3777aed783fdca/numpy-2.4.4-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:e4a010c27ff6f210ff4c6ef34394cd61470d01014439b192ec22552ee867f2a8", size = 6552038, upload-time = "2026-03-29T13:18:57.769Z" }, - { url = "https://files.pythonhosted.org/packages/7f/37/eed308a8f56cba4d1fdf467a4fc67ef4ff4bf1c888f5fc980481890104b1/numpy-2.4.4-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f9e75681b59ddaa5e659898085ae0eaea229d054f2ac0c7e563a62205a700121", size = 15670666, upload-time = "2026-03-29T13:19:00.341Z" }, - { url = "https://files.pythonhosted.org/packages/0a/0d/0e3ecece05b7a7e87ab9fb587855548da437a061326fff64a223b6dcb78a/numpy-2.4.4-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:81f4a14bee47aec54f883e0cad2d73986640c1590eb9bfaaba7ad17394481e6e", size = 16645480, upload-time = "2026-03-29T13:19:03.63Z" }, - { url = "https://files.pythonhosted.org/packages/34/49/f2312c154b82a286758ee2f1743336d50651f8b5195db18cdb63675ff649/numpy-2.4.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:62d6b0f03b694173f9fcb1fb317f7222fd0b0b103e784c6549f5e53a27718c44", size = 17020036, upload-time = "2026-03-29T13:19:07.428Z" }, - { url = "https://files.pythonhosted.org/packages/7b/e9/736d17bd77f1b0ec4f9901aaec129c00d59f5d84d5e79bba540ef12c2330/numpy-2.4.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fbc356aae7adf9e6336d336b9c8111d390a05df88f1805573ebb0807bd06fd1d", size = 18368643, upload-time = "2026-03-29T13:19:10.775Z" }, - { url = "https://files.pythonhosted.org/packages/63/f6/d417977c5f519b17c8a5c3bc9e8304b0908b0e21136fe43bf628a1343914/numpy-2.4.4-cp312-cp312-win32.whl", hash = "sha256:0d35aea54ad1d420c812bfa0385c71cd7cc5bcf7c65fed95fc2cd02fe8c79827", size = 5961117, upload-time = "2026-03-29T13:19:13.464Z" }, - { url = "https://files.pythonhosted.org/packages/2d/5b/e1deebf88ff431b01b7406ca3583ab2bbb90972bbe1c568732e49c844f7e/numpy-2.4.4-cp312-cp312-win_amd64.whl", hash = "sha256:b5f0362dc928a6ecd9db58868fca5e48485205e3855957bdedea308f8672ea4a", size = 12320584, upload-time = "2026-03-29T13:19:16.155Z" }, - { url = "https://files.pythonhosted.org/packages/58/89/e4e856ac82a68c3ed64486a544977d0e7bdd18b8da75b78a577ca31c4395/numpy-2.4.4-cp312-cp312-win_arm64.whl", hash = "sha256:846300f379b5b12cc769334464656bc882e0735d27d9726568bc932fdc49d5ec", size = 10221450, upload-time = "2026-03-29T13:19:18.994Z" }, - { url = "https://files.pythonhosted.org/packages/14/1d/d0a583ce4fefcc3308806a749a536c201ed6b5ad6e1322e227ee4848979d/numpy-2.4.4-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:08f2e31ed5e6f04b118e49821397f12767934cfdd12a1ce86a058f91e004ee50", size = 16684933, upload-time = "2026-03-29T13:19:22.47Z" }, - { url = "https://files.pythonhosted.org/packages/c1/62/2b7a48fbb745d344742c0277f01286dead15f3f68e4f359fbfcf7b48f70f/numpy-2.4.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:e823b8b6edc81e747526f70f71a9c0a07ac4e7ad13020aa736bb7c9d67196115", size = 14694532, upload-time = "2026-03-29T13:19:25.581Z" }, - { url = "https://files.pythonhosted.org/packages/e5/87/499737bfba066b4a3bebff24a8f1c5b2dee410b209bc6668c9be692580f0/numpy-2.4.4-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:4a19d9dba1a76618dd86b164d608566f393f8ec6ac7c44f0cc879011c45e65af", size = 5199661, upload-time = "2026-03-29T13:19:28.31Z" }, - { url = "https://files.pythonhosted.org/packages/cd/da/464d551604320d1491bc345efed99b4b7034143a85787aab78d5691d5a0e/numpy-2.4.4-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:d2a8490669bfe99a233298348acc2d824d496dee0e66e31b66a6022c2ad74a5c", size = 6547539, upload-time = "2026-03-29T13:19:30.97Z" }, - { url = "https://files.pythonhosted.org/packages/7d/90/8d23e3b0dafd024bf31bdec225b3bb5c2dbfa6912f8a53b8659f21216cbf/numpy-2.4.4-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:45dbed2ab436a9e826e302fcdcbe9133f9b0006e5af7168afb8963a6520da103", size = 15668806, upload-time = "2026-03-29T13:19:33.887Z" }, - { url = "https://files.pythonhosted.org/packages/d1/73/a9d864e42a01896bb5974475438f16086be9ba1f0d19d0bb7a07427c4a8b/numpy-2.4.4-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c901b15172510173f5cb310eae652908340f8dede90fff9e3bf6c0d8dfd92f83", size = 16632682, upload-time = "2026-03-29T13:19:37.336Z" }, - { url = "https://files.pythonhosted.org/packages/34/fb/14570d65c3bde4e202a031210475ae9cde9b7686a2e7dc97ee67d2833b35/numpy-2.4.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:99d838547ace2c4aace6c4f76e879ddfe02bb58a80c1549928477862b7a6d6ed", size = 17019810, upload-time = "2026-03-29T13:19:40.963Z" }, - { url = "https://files.pythonhosted.org/packages/8a/77/2ba9d87081fd41f6d640c83f26fb7351e536b7ce6dd9061b6af5904e8e46/numpy-2.4.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0aec54fd785890ecca25a6003fd9a5aed47ad607bbac5cd64f836ad8666f4959", size = 18357394, upload-time = "2026-03-29T13:19:44.859Z" }, - { url = "https://files.pythonhosted.org/packages/a2/23/52666c9a41708b0853fa3b1a12c90da38c507a3074883823126d4e9d5b30/numpy-2.4.4-cp313-cp313-win32.whl", hash = "sha256:07077278157d02f65c43b1b26a3886bce886f95d20aabd11f87932750dfb14ed", size = 5959556, upload-time = "2026-03-29T13:19:47.661Z" }, - { url = "https://files.pythonhosted.org/packages/57/fb/48649b4971cde70d817cf97a2a2fdc0b4d8308569f1dd2f2611959d2e0cf/numpy-2.4.4-cp313-cp313-win_amd64.whl", hash = "sha256:5c70f1cc1c4efbe316a572e2d8b9b9cc44e89b95f79ca3331553fbb63716e2bf", size = 12317311, upload-time = "2026-03-29T13:19:50.67Z" }, - { url = "https://files.pythonhosted.org/packages/ba/d8/11490cddd564eb4de97b4579ef6bfe6a736cc07e94c1598590ae25415e01/numpy-2.4.4-cp313-cp313-win_arm64.whl", hash = "sha256:ef4059d6e5152fa1a39f888e344c73fdc926e1b2dd58c771d67b0acfbf2aa67d", size = 10222060, upload-time = "2026-03-29T13:19:54.229Z" }, - { url = "https://files.pythonhosted.org/packages/99/5d/dab4339177a905aad3e2221c915b35202f1ec30d750dd2e5e9d9a72b804b/numpy-2.4.4-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:4bbc7f303d125971f60ec0aaad5e12c62d0d2c925f0ab1273debd0e4ba37aba5", size = 14822302, upload-time = "2026-03-29T13:19:57.585Z" }, - { url = "https://files.pythonhosted.org/packages/eb/e4/0564a65e7d3d97562ed6f9b0fd0fb0a6f559ee444092f105938b50043876/numpy-2.4.4-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:4d6d57903571f86180eb98f8f0c839fa9ebbfb031356d87f1361be91e433f5b7", size = 5327407, upload-time = "2026-03-29T13:20:00.601Z" }, - { url = "https://files.pythonhosted.org/packages/29/8d/35a3a6ce5ad371afa58b4700f1c820f8f279948cca32524e0a695b0ded83/numpy-2.4.4-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:4636de7fd195197b7535f231b5de9e4b36d2c440b6e566d2e4e4746e6af0ca93", size = 6647631, upload-time = "2026-03-29T13:20:02.855Z" }, - { url = "https://files.pythonhosted.org/packages/f4/da/477731acbd5a58a946c736edfdabb2ac5b34c3d08d1ba1a7b437fa0884df/numpy-2.4.4-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ad2e2ef14e0b04e544ea2fa0a36463f847f113d314aa02e5b402fdf910ef309e", size = 15727691, upload-time = "2026-03-29T13:20:06.004Z" }, - { url = "https://files.pythonhosted.org/packages/e6/db/338535d9b152beabeb511579598418ba0212ce77cf9718edd70262cc4370/numpy-2.4.4-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5a285b3b96f951841799528cd1f4f01cd70e7e0204b4abebac9463eecfcf2a40", size = 16681241, upload-time = "2026-03-29T13:20:09.417Z" }, - { url = "https://files.pythonhosted.org/packages/e2/a9/ad248e8f58beb7a0219b413c9c7d8151c5d285f7f946c3e26695bdbbe2df/numpy-2.4.4-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:f8474c4241bc18b750be2abea9d7a9ec84f46ef861dbacf86a4f6e043401f79e", size = 17085767, upload-time = "2026-03-29T13:20:13.126Z" }, - { url = "https://files.pythonhosted.org/packages/b5/1a/3b88ccd3694681356f70da841630e4725a7264d6a885c8d442a697e1146b/numpy-2.4.4-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:4e874c976154687c1f71715b034739b45c7711bec81db01914770373d125e392", size = 18403169, upload-time = "2026-03-29T13:20:17.096Z" }, - { url = "https://files.pythonhosted.org/packages/c2/c9/fcfd5d0639222c6eac7f304829b04892ef51c96a75d479214d77e3ce6e33/numpy-2.4.4-cp313-cp313t-win32.whl", hash = "sha256:9c585a1790d5436a5374bac930dad6ed244c046ed91b2b2a3634eb2971d21008", size = 6083477, upload-time = "2026-03-29T13:20:20.195Z" }, - { url = "https://files.pythonhosted.org/packages/d5/e3/3938a61d1c538aaec8ed6fd6323f57b0c2d2d2219512434c5c878db76553/numpy-2.4.4-cp313-cp313t-win_amd64.whl", hash = "sha256:93e15038125dc1e5345d9b5b68aa7f996ec33b98118d18c6ca0d0b7d6198b7e8", size = 12457487, upload-time = "2026-03-29T13:20:22.946Z" }, - { url = "https://files.pythonhosted.org/packages/97/6a/7e345032cc60501721ef94e0e30b60f6b0bd601f9174ebd36389a2b86d40/numpy-2.4.4-cp313-cp313t-win_arm64.whl", hash = "sha256:0dfd3f9d3adbe2920b68b5cd3d51444e13a10792ec7154cd0a2f6e74d4ab3233", size = 10292002, upload-time = "2026-03-29T13:20:25.909Z" }, - { url = "https://files.pythonhosted.org/packages/6e/06/c54062f85f673dd5c04cbe2f14c3acb8c8b95e3384869bb8cc9bff8cb9df/numpy-2.4.4-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:f169b9a863d34f5d11b8698ead99febeaa17a13ca044961aa8e2662a6c7766a0", size = 16684353, upload-time = "2026-03-29T13:20:29.504Z" }, - { url = "https://files.pythonhosted.org/packages/4c/39/8a320264a84404c74cc7e79715de85d6130fa07a0898f67fb5cd5bd79908/numpy-2.4.4-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:2483e4584a1cb3092da4470b38866634bafb223cbcd551ee047633fd2584599a", size = 14704914, upload-time = "2026-03-29T13:20:33.547Z" }, - { url = "https://files.pythonhosted.org/packages/91/fb/287076b2614e1d1044235f50f03748f31fa287e3dbe6abeb35cdfa351eca/numpy-2.4.4-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:2d19e6e2095506d1736b7d80595e0f252d76b89f5e715c35e06e937679ea7d7a", size = 5210005, upload-time = "2026-03-29T13:20:36.45Z" }, - { url = "https://files.pythonhosted.org/packages/63/eb/fcc338595309910de6ecabfcef2419a9ce24399680bfb149421fa2df1280/numpy-2.4.4-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:6a246d5914aa1c820c9443ddcee9c02bec3e203b0c080349533fae17727dfd1b", size = 6544974, upload-time = "2026-03-29T13:20:39.014Z" }, - { url = "https://files.pythonhosted.org/packages/44/5d/e7e9044032a716cdfaa3fba27a8e874bf1c5f1912a1ddd4ed071bf8a14a6/numpy-2.4.4-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:989824e9faf85f96ec9c7761cd8d29c531ad857bfa1daa930cba85baaecf1a9a", size = 15684591, upload-time = "2026-03-29T13:20:42.146Z" }, - { url = "https://files.pythonhosted.org/packages/98/7c/21252050676612625449b4807d6b695b9ce8a7c9e1c197ee6216c8a65c7c/numpy-2.4.4-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:27a8d92cd10f1382a67d7cf4db7ce18341b66438bdd9f691d7b0e48d104c2a9d", size = 16637700, upload-time = "2026-03-29T13:20:46.204Z" }, - { url = "https://files.pythonhosted.org/packages/b1/29/56d2bbef9465db24ef25393383d761a1af4f446a1df9b8cded4fe3a5a5d7/numpy-2.4.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e44319a2953c738205bf3354537979eaa3998ed673395b964c1176083dd46252", size = 17035781, upload-time = "2026-03-29T13:20:50.242Z" }, - { url = "https://files.pythonhosted.org/packages/e3/2b/a35a6d7589d21f44cea7d0a98de5ddcbb3d421b2622a5c96b1edf18707c3/numpy-2.4.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:e892aff75639bbef0d2a2cfd55535510df26ff92f63c92cd84ef8d4ba5a5557f", size = 18362959, upload-time = "2026-03-29T13:20:54.019Z" }, - { url = "https://files.pythonhosted.org/packages/64/c9/d52ec581f2390e0f5f85cbfd80fb83d965fc15e9f0e1aec2195faa142cde/numpy-2.4.4-cp314-cp314-win32.whl", hash = "sha256:1378871da56ca8943c2ba674530924bb8ca40cd228358a3b5f302ad60cf875fc", size = 6008768, upload-time = "2026-03-29T13:20:56.912Z" }, - { url = "https://files.pythonhosted.org/packages/fa/22/4cc31a62a6c7b74a8730e31a4274c5dc80e005751e277a2ce38e675e4923/numpy-2.4.4-cp314-cp314-win_amd64.whl", hash = "sha256:715d1c092715954784bc79e1174fc2a90093dc4dc84ea15eb14dad8abdcdeb74", size = 12449181, upload-time = "2026-03-29T13:20:59.548Z" }, - { url = "https://files.pythonhosted.org/packages/70/2e/14cda6f4d8e396c612d1bf97f22958e92148801d7e4f110cabebdc0eef4b/numpy-2.4.4-cp314-cp314-win_arm64.whl", hash = "sha256:2c194dd721e54ecad9ad387c1d35e63dce5c4450c6dc7dd5611283dda239aabb", size = 10496035, upload-time = "2026-03-29T13:21:02.524Z" }, - { url = "https://files.pythonhosted.org/packages/b1/e8/8fed8c8d848d7ecea092dc3469643f9d10bc3a134a815a3b033da1d2039b/numpy-2.4.4-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:2aa0613a5177c264ff5921051a5719d20095ea586ca88cc802c5c218d1c67d3e", size = 14824958, upload-time = "2026-03-29T13:21:05.671Z" }, - { url = "https://files.pythonhosted.org/packages/05/1a/d8007a5138c179c2bf33ef44503e83d70434d2642877ee8fbb230e7c0548/numpy-2.4.4-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:42c16925aa5a02362f986765f9ebabf20de75cdefdca827d14315c568dcab113", size = 5330020, upload-time = "2026-03-29T13:21:08.635Z" }, - { url = "https://files.pythonhosted.org/packages/99/64/ffb99ac6ae93faf117bcbd5c7ba48a7f45364a33e8e458545d3633615dda/numpy-2.4.4-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:874f200b2a981c647340f841730fc3a2b54c9d940566a3c4149099591e2c4c3d", size = 6650758, upload-time = "2026-03-29T13:21:10.949Z" }, - { url = "https://files.pythonhosted.org/packages/6e/6e/795cc078b78a384052e73b2f6281ff7a700e9bf53bcce2ee579d4f6dd879/numpy-2.4.4-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c9b39d38a9bd2ae1becd7eac1303d031c5c110ad31f2b319c6e7d98b135c934d", size = 15729948, upload-time = "2026-03-29T13:21:14.047Z" }, - { url = "https://files.pythonhosted.org/packages/5f/86/2acbda8cc2af5f3d7bfc791192863b9e3e19674da7b5e533fded124d1299/numpy-2.4.4-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b268594bccac7d7cf5844c7732e3f20c50921d94e36d7ec9b79e9857694b1b2f", size = 16679325, upload-time = "2026-03-29T13:21:17.561Z" }, - { url = "https://files.pythonhosted.org/packages/bc/59/cafd83018f4aa55e0ac6fa92aa066c0a1877b77a615ceff1711c260ffae8/numpy-2.4.4-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:ac6b31e35612a26483e20750126d30d0941f949426974cace8e6b5c58a3657b0", size = 17084883, upload-time = "2026-03-29T13:21:21.106Z" }, - { url = "https://files.pythonhosted.org/packages/f0/85/a42548db84e65ece46ab2caea3d3f78b416a47af387fcbb47ec28e660dc2/numpy-2.4.4-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:8e3ed142f2728df44263aaf5fb1f5b0b99f4070c553a0d7f033be65338329150", size = 18403474, upload-time = "2026-03-29T13:21:24.828Z" }, - { url = "https://files.pythonhosted.org/packages/ed/ad/483d9e262f4b831000062e5d8a45e342166ec8aaa1195264982bca267e62/numpy-2.4.4-cp314-cp314t-win32.whl", hash = "sha256:dddbbd259598d7240b18c9d87c56a9d2fb3b02fe266f49a7c101532e78c1d871", size = 6155500, upload-time = "2026-03-29T13:21:28.205Z" }, - { url = "https://files.pythonhosted.org/packages/c7/03/2fc4e14c7bd4ff2964b74ba90ecb8552540b6315f201df70f137faa5c589/numpy-2.4.4-cp314-cp314t-win_amd64.whl", hash = "sha256:a7164afb23be6e37ad90b2f10426149fd75aee07ca55653d2aa41e66c4ef697e", size = 12637755, upload-time = "2026-03-29T13:21:31.107Z" }, - { url = "https://files.pythonhosted.org/packages/58/78/548fb8e07b1a341746bfbecb32f2c268470f45fa028aacdbd10d9bc73aab/numpy-2.4.4-cp314-cp314t-win_arm64.whl", hash = "sha256:ba203255017337d39f89bdd58417f03c4426f12beed0440cfd933cb15f8669c7", size = 10566643, upload-time = "2026-03-29T13:21:34.339Z" }, - { url = "https://files.pythonhosted.org/packages/6b/33/8fae8f964a4f63ed528264ddf25d2b683d0b663e3cba26961eb838a7c1bd/numpy-2.4.4-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:58c8b5929fcb8287cbd6f0a3fae19c6e03a5c48402ae792962ac465224a629a4", size = 16854491, upload-time = "2026-03-29T13:21:38.03Z" }, - { url = "https://files.pythonhosted.org/packages/bc/d0/1aabee441380b981cf8cdda3ae7a46aa827d1b5a8cce84d14598bc94d6d9/numpy-2.4.4-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:eea7ac5d2dce4189771cedb559c738a71512768210dc4e4753b107a2048b3d0e", size = 14895830, upload-time = "2026-03-29T13:21:41.509Z" }, - { url = "https://files.pythonhosted.org/packages/a5/b8/aafb0d1065416894fccf4df6b49ef22b8db045187949545bced89c034b8e/numpy-2.4.4-pp311-pypy311_pp73-macosx_14_0_arm64.whl", hash = "sha256:51fc224f7ca4d92656d5a5eb315f12eb5fe2c97a66249aa7b5f562528a3be38c", size = 5400927, upload-time = "2026-03-29T13:21:44.747Z" }, - { url = "https://files.pythonhosted.org/packages/d6/77/063baa20b08b431038c7f9ff5435540c7b7265c78cf56012a483019ca72d/numpy-2.4.4-pp311-pypy311_pp73-macosx_14_0_x86_64.whl", hash = "sha256:28a650663f7314afc3e6ec620f44f333c386aad9f6fc472030865dc0ebb26ee3", size = 6715557, upload-time = "2026-03-29T13:21:47.406Z" }, - { url = "https://files.pythonhosted.org/packages/c7/a8/379542d45a14f149444c5c4c4e7714707239ce9cc1de8c2803958889da14/numpy-2.4.4-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:19710a9ca9992d7174e9c52f643d4272dcd1558c5f7af7f6f8190f633bd651a7", size = 15804253, upload-time = "2026-03-29T13:21:50.753Z" }, - { url = "https://files.pythonhosted.org/packages/a2/c8/f0a45426d6d21e7ea3310a15cf90c43a14d9232c31a837702dba437f3373/numpy-2.4.4-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9b2aec6af35c113b05695ebb5749a787acd63cafc83086a05771d1e1cd1e555f", size = 16753552, upload-time = "2026-03-29T13:21:54.344Z" }, - { url = "https://files.pythonhosted.org/packages/04/74/f4c001f4714c3ad9ce037e18cf2b9c64871a84951eaa0baf683a9ca9301c/numpy-2.4.4-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:f2cf083b324a467e1ab358c105f6cad5ea950f50524668a80c486ff1db24e119", size = 12509075, upload-time = "2026-03-29T13:21:57.644Z" }, +version = "2.4.5" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/50/8e/b8041bc719f056afd864478029d52214789341ac6583437b0ee5031e9530/numpy-2.4.5.tar.gz", hash = "sha256:ca670567a5683b7c1670ec03e0ddd5862e10934e92a70751d68d7b7b74ca7f9f", size = 20735669, upload-time = "2026-05-15T20:25:19.492Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e1/44/1383ee4d1e916a9e610e46c876b5c83ea023526117d23cd911983929ec34/numpy-2.4.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3176dc8ff71dbb593606f91a69ad0c3cd3303c7eb546af477370ab9edf760288", size = 16969261, upload-time = "2026-05-15T20:22:23.036Z" }, + { url = "https://files.pythonhosted.org/packages/3d/61/54bacfbec7550bc398e6b6d9a861db35d64f75844e1d7920f5722c3cd5e7/numpy-2.4.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1811150e5148f5a01a7cc282cb2f489b4a3050a773e173adb480e507bad3a3d7", size = 14964009, upload-time = "2026-05-15T20:22:25.819Z" }, + { url = "https://files.pythonhosted.org/packages/7a/55/fe86c64561761f185339c26001164a2687bd4787af681e961431abd2d534/numpy-2.4.5-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:0d63a780070871210853ba01e90b88f9b85cf2abf63a7f143d5127189265ddf6", size = 5469106, upload-time = "2026-05-15T20:22:28.13Z" }, + { url = "https://files.pythonhosted.org/packages/2f/74/cf29b8317627f0e3aa2c9fb332d386bd734308cecd9e07da9f407d9ce0c3/numpy-2.4.5-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:0c6919cefafb3b76cd46a89dbb203bf1dd95529d2a6d09fef2d325d95d6a79d8", size = 6798945, upload-time = "2026-05-15T20:22:30.061Z" }, + { url = "https://files.pythonhosted.org/packages/80/a9/b61730a17fa87d5abb13ce560a1b4ce3485d37a13e03eb7b414e598e72f8/numpy-2.4.5-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d51efede1e58e8b11877536a5518f60e318d8ff69b89ad7b38ee5e431b24d772", size = 15967025, upload-time = "2026-05-15T20:22:32.328Z" }, + { url = "https://files.pythonhosted.org/packages/03/39/70bcd187eb4d223c21fde02c2bdfbffbffef3288cbb3947c04c74ae39a08/numpy-2.4.5-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:07ce7e74da92d7c71b5df157b9758bcdd53d7fea10602154de3afd2b3ddc34dd", size = 16918685, upload-time = "2026-05-15T20:22:34.759Z" }, + { url = "https://files.pythonhosted.org/packages/ab/31/400fd1315bbe228af3937cf8a74e32023df6217af36077919d00adc382e4/numpy-2.4.5-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d7828234a13185effb34979e146f9921f2a65dfbbe215e6dbb57d6478fc8e059", size = 17322963, upload-time = "2026-05-15T20:22:37.557Z" }, + { url = "https://files.pythonhosted.org/packages/18/6a/bbbafb657e6f6ee826b4ecdb8722a2e0aae4a981888eaf59eae6a535cc13/numpy-2.4.5-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f96083adc3dfc1bbf778f2c79654d88115fa07074c97cb724fe9508f12d91c55", size = 18651594, upload-time = "2026-05-15T20:22:40.449Z" }, + { url = "https://files.pythonhosted.org/packages/de/0c/857a515154a2a18b0dfae04089600d166d352d473ec17a0680d879582d06/numpy-2.4.5-cp311-cp311-win32.whl", hash = "sha256:4ed78c904a638b6e5d7cd4db90c06fca5fc6ec2f28d258305368f454a50e79cf", size = 6233849, upload-time = "2026-05-15T20:22:43.139Z" }, + { url = "https://files.pythonhosted.org/packages/f0/66/d215f3fb93541617adb5d58b3b9508e8a6413e499711e0adc0b80bcb445d/numpy-2.4.5-cp311-cp311-win_amd64.whl", hash = "sha256:079b0fad6f2899b23c5da89792b5409d2d83fc83e8bd5c2299cc9c397a264864", size = 12608238, upload-time = "2026-05-15T20:22:45.229Z" }, + { url = "https://files.pythonhosted.org/packages/cb/c4/611d66d3fcfa931954d37a19ce5575f3283d023e89ff0df6ad43b334ae9c/numpy-2.4.5-cp311-cp311-win_arm64.whl", hash = "sha256:d6c78e260b53affe9b395a9d54fc61f101f9521c4d9452c7e9e3718b19e2215b", size = 10479452, upload-time = "2026-05-15T20:22:47.962Z" }, + { url = "https://files.pythonhosted.org/packages/6c/18/3275231e98620002681c922e792db04d72c356e9d8073c387344fc0e4ff1/numpy-2.4.5-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:654fb8674b61b1c4bd568f944d13a908566fdcb0d797303521d4149d16da05ef", size = 16689166, upload-time = "2026-05-15T20:22:50.761Z" }, + { url = "https://files.pythonhosted.org/packages/db/23/000aab6a16bdec53307f0f72546b57a3ac9266a62d8c257bee97d85fd078/numpy-2.4.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4cd9f6fa7ce10dc4627f2bb81dd9075dab67e94632e04c2b638e12575ddaa862", size = 14699514, upload-time = "2026-05-15T20:22:53.678Z" }, + { url = "https://files.pythonhosted.org/packages/47/cc/ddaf3af9c46966fef5be879256f213d85a0c56c75d07a3b7defec7cf6b4c/numpy-2.4.5-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:4f5bc96d35d94e4ceab8b38a92241b4611e95dc44e63b9f1fa2a331858ee3507", size = 5204601, upload-time = "2026-05-15T20:22:56.257Z" }, + { url = "https://files.pythonhosted.org/packages/07/ea/627fadd11959b3c7759008f34c92a35af8ff942dd8284a66ced648bbe516/numpy-2.4.5-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:4bb33e900ee81730ad77a258965134aa8ceac805124f7e5229347beda4b8d0aa", size = 6551360, upload-time = "2026-05-15T20:22:58.334Z" }, + { url = "https://files.pythonhosted.org/packages/a1/47/0728b986b8682d742ff68c16baa5af9d185484abfc635c5cc700f44e62be/numpy-2.4.5-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:32f8f852273ef32b291201ac2a2c97629c4a1ee8632bb670e3443eaa09fc2e72", size = 15671157, upload-time = "2026-05-15T20:23:01.081Z" }, + { url = "https://files.pythonhosted.org/packages/d1/0b/b905ae82d9419dc38123523862db64978ca2954b69609c3ae8fdaca1084c/numpy-2.4.5-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:685681e956fc8dcb75adc6ff26694e1dfd738b24bd8d4696c51ca0110157f912", size = 16645703, upload-time = "2026-05-15T20:23:04.358Z" }, + { url = "https://files.pythonhosted.org/packages/5f/24/e27fc3f5236b4118ed9eed67111675f5c61a07ea333acec87c869c3b359d/numpy-2.4.5-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:6f64dd84b277a737eb59513f6b9bb6195bf41ab11941ef15b2562dbab43fa8ef", size = 17021018, upload-time = "2026-05-15T20:23:07.021Z" }, + { url = "https://files.pythonhosted.org/packages/d3/a7/9041af38d527ab80a06a93570a77e29425b41507ad41f6acf5da78cfb4a4/numpy-2.4.5-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b42d9496f79e3a728192f05a42d86e36163217b7cdecb3813d0028a0aa6b72d7", size = 18368768, upload-time = "2026-05-15T20:23:09.44Z" }, + { url = "https://files.pythonhosted.org/packages/49/82/326a014442f32c2663434fd424d9298791f47f8a0f17585ad60519a5606e/numpy-2.4.5-cp312-cp312-win32.whl", hash = "sha256:86d980970f5110595ca14855768073b08585fc1acc36895de303e039e7dee4a5", size = 5962819, upload-time = "2026-05-15T20:23:11.631Z" }, + { url = "https://files.pythonhosted.org/packages/3c/f0/cbf5d391b0b3a5e8cad264603e2fae256b0bde8ce43566b13b78faedc659/numpy-2.4.5-cp312-cp312-win_amd64.whl", hash = "sha256:3333dba6a4e611d666f69e177ba8fe4140366ff681a5feb2374d3fd4fff3acb6", size = 12321621, upload-time = "2026-05-15T20:23:14.305Z" }, + { url = "https://files.pythonhosted.org/packages/3c/d0/0f18909d9bc37a5f3f969fc737d2bb5df9f2ff295f71b467e6f52a0d6c4e/numpy-2.4.5-cp312-cp312-win_arm64.whl", hash = "sha256:4593d197270b894efeb538dcbe227e4bcf1c77f88c4c6bf933ead812cfaa4453", size = 10221430, upload-time = "2026-05-15T20:23:16.887Z" }, + { url = "https://files.pythonhosted.org/packages/e3/a4/fb50657c7cab297bf34edcd60a074cb0647f61771430d6363575274160fe/numpy-2.4.5-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:1ef248460b645c102026b82337cc4e88231909c66dd77b59ec6d6cac7e44f277", size = 16684760, upload-time = "2026-05-15T20:23:19.436Z" }, + { url = "https://files.pythonhosted.org/packages/3e/43/87e731299b9408eda705b3b9cb31c7bceb9347d2af9cbb16b2b1e4b5bc0f/numpy-2.4.5-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:4603622bdcdbf8dccb1d9d5b21d16a7aa4e473ae6c8e14048d846fd4ca2907a0", size = 14694117, upload-time = "2026-05-15T20:23:21.832Z" }, + { url = "https://files.pythonhosted.org/packages/a9/c7/0b2bb8acea222e9dd6e582afc2bc553b89b8833cbdccc68e68f050fb31f8/numpy-2.4.5-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:6c18d49c67689c562854b53fdc433b93e47c12952aa6fa6d59f185e1a5992419", size = 5199141, upload-time = "2026-05-15T20:23:24.066Z" }, + { url = "https://files.pythonhosted.org/packages/39/60/b6972b5d47033d90000f0097c81a98b9486589a2d7003bf725bff275cb0d/numpy-2.4.5-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:b1c663ddc641f4192e90511bec61a09bc231e3bbdb996cdc6edbcaa0e528d685", size = 6546954, upload-time = "2026-05-15T20:23:26.099Z" }, + { url = "https://files.pythonhosted.org/packages/c1/e9/ed667cb12c11ca0adde431f685d3a5dd78e6f78b27228c581c8415198e9e/numpy-2.4.5-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:93793222b524f692f12b2f8752ce8b1d9d9125b2bfd5dbf0fb69c92c5e1ce86c", size = 15669430, upload-time = "2026-05-15T20:23:28.147Z" }, + { url = "https://files.pythonhosted.org/packages/44/e5/679f6ffeb01294b0008e5ada4a113cb47617bc0e1819a529fd7973c6d7f4/numpy-2.4.5-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1616bde34b2bcba2fa9bde06217ce00da4f3d1bdfb264d54525a99e8fe170d83", size = 16633390, upload-time = "2026-05-15T20:23:31.622Z" }, + { url = "https://files.pythonhosted.org/packages/36/46/42bfffc9a780ec902ccd7470d3219192ee82b7b442710307dd85b4d121b0/numpy-2.4.5-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:09d7d97da1c2c62f4818b3e150a57572ff8dcf1cf5ac501aac832ffd4ebd9566", size = 17020709, upload-time = "2026-05-15T20:23:34.08Z" }, + { url = "https://files.pythonhosted.org/packages/44/00/3e840bfee0cc6cec22209f2c97057f26eeb30de031e4933b4dfc0395416c/numpy-2.4.5-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:2d68d0b355ab2e39fe0de59001d7151dfdbbb880ef67baeed806661e03df5097", size = 18357818, upload-time = "2026-05-15T20:23:36.965Z" }, + { url = "https://files.pythonhosted.org/packages/72/cb/3447b400b9da84134575486f0f656541559b00d4b262477bce9b678bbca8/numpy-2.4.5-cp313-cp313-win32.whl", hash = "sha256:fe28b64777ddfa0eca9b5f51474034ebe3dcb8324f48f27b28f479085673ae33", size = 5961114, upload-time = "2026-05-15T20:23:39.586Z" }, + { url = "https://files.pythonhosted.org/packages/28/f9/a90d2220ffcdc0798f5d55bb5d5463cd6254ec9ef43f384dae80217d7a2f/numpy-2.4.5-cp313-cp313-win_amd64.whl", hash = "sha256:fb4a6c9c537d6ccec9cc4aeae4261bd3cc79b070c67ddc0646f5b1c07fddde42", size = 12318553, upload-time = "2026-05-15T20:23:41.436Z" }, + { url = "https://files.pythonhosted.org/packages/b8/c9/96f531fb3234545315152d34efdf3de7daee81254448447eb619e8d16967/numpy-2.4.5-cp313-cp313-win_arm64.whl", hash = "sha256:6d7df2da2e7ea0624a43aa368104b3a3ce14aae98ad4bb2c9a93fecef76f1c97", size = 10222200, upload-time = "2026-05-15T20:23:43.681Z" }, + { url = "https://files.pythonhosted.org/packages/e1/f4/a291caab5a3c520babf93ff77c54fd5fdb1ebbc3296cee2eb2146ce773b1/numpy-2.4.5-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:2a235607a18df941760a695927051af4b1cd5d3ee85840d0e2af816785771feb", size = 14821438, upload-time = "2026-05-15T20:23:45.911Z" }, + { url = "https://files.pythonhosted.org/packages/85/26/13dbb1159b864370568e7309063fd72667984df89db74e9caeb175d067c7/numpy-2.4.5-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:58dcf64969d870f36bc7fbd557d2617e997db7dc06261b6e3327148ea460d0a4", size = 5326663, upload-time = "2026-05-15T20:23:48.18Z" }, + { url = "https://files.pythonhosted.org/packages/7c/99/d233408072a0e019e2288e27edd23f7d572ccd4a73d1539baa3270ede85d/numpy-2.4.5-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:235f54b0156274d8fa3155db3ed6d2f401c7e8f3367c90db0a12f02a58fde6ed", size = 6646874, upload-time = "2026-05-15T20:23:49.856Z" }, + { url = "https://files.pythonhosted.org/packages/c5/00/eeb6f193dfe767725e952e0464f3e51f44145c5dd261cd7389aa36ac0713/numpy-2.4.5-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ef3b5bb65437a3555c648e706475db01c645559ca80dc8b03e4f202ea757e0d6", size = 15728147, upload-time = "2026-05-15T20:23:51.655Z" }, + { url = "https://files.pythonhosted.org/packages/e5/c9/b8ed039f1fde1b13a8807c893e7e2f9432a379f4d6401edecf0028da5b2c/numpy-2.4.5-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7f09a7e5f017d7098c66522097c96257411c9620c0926212200d66bc8cee3976", size = 16681770, upload-time = "2026-05-15T20:23:53.933Z" }, + { url = "https://files.pythonhosted.org/packages/11/5b/0198ef6cb7016eca6d895d392106012138127fab23f46637e76d5e25c9f5/numpy-2.4.5-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:993a88d8fdd8554466a8765cd8bacd97ba56b70ca6b0a04bcdca77f5afed4222", size = 17086218, upload-time = "2026-05-15T20:23:56.646Z" }, + { url = "https://files.pythonhosted.org/packages/f0/fe/8821f3cfc660ae84c92ee158505941874b62c56a42e035a41425228cd8cf/numpy-2.4.5-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:84f58bed609b5669f5ad3d597901a4f1f86ee5b3c3708aaa55f05b4fe6e0f656", size = 18403542, upload-time = "2026-05-15T20:23:59.173Z" }, + { url = "https://files.pythonhosted.org/packages/0e/00/e64ecaf498865e7b091f57658b2c522503e5d1b70e43b807f5f8247e1d88/numpy-2.4.5-cp313-cp313t-win32.whl", hash = "sha256:7200c58f3f933ca61e66346667dcc8510bb111995e9ce15398a731e6a4afa4bb", size = 6084903, upload-time = "2026-05-15T20:24:01.506Z" }, + { url = "https://files.pythonhosted.org/packages/20/c0/354997dedaf74e8311c2cf9a6027b476fd8d424cb92189cc0ae2b25f501c/numpy-2.4.5-cp313-cp313t-win_amd64.whl", hash = "sha256:c26c71080d35db5002102f5d9ff614d45de02aa1f7802943e691e063e5ee93bc", size = 12458420, upload-time = "2026-05-15T20:24:03.735Z" }, + { url = "https://files.pythonhosted.org/packages/66/dc/917ee5ea4a31ca1a6e4c9a85386477efa318dcc60db257c5ef4adda096c1/numpy-2.4.5-cp313-cp313t-win_arm64.whl", hash = "sha256:2caa576d1707b275cba1aeb60a5c50daa6fa2a3f28ecb08123bc05fd439005db", size = 10291826, upload-time = "2026-05-15T20:24:06.535Z" }, + { url = "https://files.pythonhosted.org/packages/ca/c1/3be0bf102fc17cff5bd142e3be0bfffabec6fa46da0a462396c76b0765d0/numpy-2.4.5-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:889ca2c072315de638a5194a772aa1fa2df92bdd6175f6a222d4784040424b61", size = 16683455, upload-time = "2026-05-15T20:24:08.988Z" }, + { url = "https://files.pythonhosted.org/packages/e8/3e/0742d724901fa36bc54b338c6e62e463a7601180da896aa44978f0adf004/numpy-2.4.5-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:89e89304fb1f8c3f0ecfa4a7d48f311dd79771336a940e920159d643d1307e77", size = 14704577, upload-time = "2026-05-15T20:24:11.542Z" }, + { url = "https://files.pythonhosted.org/packages/25/1c/196c610ff4c6782d697ba780ebdc1616be143213701bf22c1a270f3bf7dd/numpy-2.4.5-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:144fcc5a3a17679b2b82543b4a2d8dd29937230a7af13232b5f753872feb6361", size = 5209756, upload-time = "2026-05-15T20:24:14.091Z" }, + { url = "https://files.pythonhosted.org/packages/52/c0/23fb1bc506f774e03db66219a2830e720f4d3dbcaaddf855a7ff7bb6d96f/numpy-2.4.5-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:398bb16772b265b9fa5c07b07072646ea97137c10ffb62a9a087b277fc825c29", size = 6543937, upload-time = "2026-05-15T20:24:16.223Z" }, + { url = "https://files.pythonhosted.org/packages/9f/49/db4662c26e68520afcc84d672a6f9f5294063dee0e57a46d61afdaa7f9ed/numpy-2.4.5-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fb352e7b8876da1249e72254736d6c58c505fa4e58a3d7e30efca241ca9ca9ce", size = 15685292, upload-time = "2026-05-15T20:24:17.978Z" }, + { url = "https://files.pythonhosted.org/packages/43/80/1315439acedd8398319bac177d6de3d48ab39c62cc0c810f74f0a9a73996/numpy-2.4.5-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7341b08ff8124d7353939778e2707b8732d03c78c1c30e0815aba2dacbe1245a", size = 16638528, upload-time = "2026-05-15T20:24:20.478Z" }, + { url = "https://files.pythonhosted.org/packages/56/81/364388600932618fe735d97fdd2437cb8dd87a23377ac11d8b9d5db098b7/numpy-2.4.5-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:deb01226f012539f3945261ffe1c10aec081a0fa0a5c925419933c70f3ae2d23", size = 17036709, upload-time = "2026-05-15T20:24:22.949Z" }, + { url = "https://files.pythonhosted.org/packages/32/4a/a1185b18a94a6d9587e54b437e7d0ba36ecf6e614f1bea03f5249912c64e/numpy-2.4.5-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d888bdf7335f76878c3c7b264ac1ff089863e211ec81249f9fb5795c2183dc25", size = 18363254, upload-time = "2026-05-15T20:24:25.402Z" }, + { url = "https://files.pythonhosted.org/packages/b9/8e/95c1d2ed15ae97750ede8c8a0ac487c9c01207afff430f47078b1d9d7dc5/numpy-2.4.5-cp314-cp314-win32.whl", hash = "sha256:15f90d1256e9b2320aff24fde44815b787ab6d7c49a1a11bfd8138b321c5f080", size = 6010184, upload-time = "2026-05-15T20:24:27.852Z" }, + { url = "https://files.pythonhosted.org/packages/aa/92/d063df4d63d988b20d881856c74df76c0c1786229bb870f3a52af0981d4d/numpy-2.4.5-cp314-cp314-win_amd64.whl", hash = "sha256:4bd2cd4ef9c0afa87de73723c0a33c0edff62143e1432917458e26d3d195d87f", size = 12450344, upload-time = "2026-05-15T20:24:29.856Z" }, + { url = "https://files.pythonhosted.org/packages/3d/64/c0ae481f7c3b2f85869bcd8fc5d30aa7c96b394162eef9c9315957f115c5/numpy-2.4.5-cp314-cp314-win_arm64.whl", hash = "sha256:db304568c650e9d7039744d3575d0d287754debb2057d7c7b8cdfdc2c487a957", size = 10495674, upload-time = "2026-05-15T20:24:32.352Z" }, + { url = "https://files.pythonhosted.org/packages/57/89/c5a4c677acf17aa50ba09a15e61812f90baac42bb6ca38d112e005858351/numpy-2.4.5-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:6de2883e0d2c63eae1bab1a84b390dca74aabb3d20ea1f5d58f360853c83abf3", size = 14824078, upload-time = "2026-05-15T20:24:34.669Z" }, + { url = "https://files.pythonhosted.org/packages/e7/52/57e7144284f6b51ba93523e495ff239260b1ecd5257e3700a436332e5688/numpy-2.4.5-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:06760fe73ae5005008748d182de612c733542af3cde063d532cd2127561b27be", size = 5329246, upload-time = "2026-05-15T20:24:36.957Z" }, + { url = "https://files.pythonhosted.org/packages/b9/b3/09dbce80fd4a7db4318f2fc01eec0ae76f29306442b5a32d4b811d082cdf/numpy-2.4.5-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:4b51a01745cb04cc19278482207444b4d30728ce91c28d27a3bfae5fc6ff24c7", size = 6649877, upload-time = "2026-05-15T20:24:38.861Z" }, + { url = "https://files.pythonhosted.org/packages/30/c2/dbdb23e82d540b757690ef13f011c386fca6a63848eec6136baf8ce7cbed/numpy-2.4.5-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9a05636d7937d0936f271e5ba957fa8d746b5be3c2025caa1a2508f4fe521d40", size = 15730534, upload-time = "2026-05-15T20:24:41.168Z" }, + { url = "https://files.pythonhosted.org/packages/c4/bd/68f6e9b3c20decf40ac06708a7b506757e3a8588efed32988d1b747316be/numpy-2.4.5-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:14b86f56048ed09c3bbe48962a7dff077c2fd3274f8cf981800f3b38eac49cc3", size = 16679741, upload-time = "2026-05-15T20:24:44.874Z" }, + { url = "https://files.pythonhosted.org/packages/39/1d/0fcac0b6b4ea1b50ca8fca05a34bed5c8d56e34c1cb5ffb04cf76109ac3c/numpy-2.4.5-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:130d58151c4db23e9fa860b84784e219a3aa3e030acc88a493ea37006c4dfd4c", size = 17085598, upload-time = "2026-05-15T20:24:47.603Z" }, + { url = "https://files.pythonhosted.org/packages/0b/e8/a472b2564cf6cc498ad7aa9741d9832648221b8ab8cc0dbef41faa248ede/numpy-2.4.5-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:d475afc8cbe935ff5944f753d863bba774d7f4e1feaaa4102901e3e053ca5963", size = 18403855, upload-time = "2026-05-15T20:24:50.474Z" }, + { url = "https://files.pythonhosted.org/packages/b9/a4/da82196f8cc4bd28ecf17bd57008c84f3d4696caf06753d9bad45e4ad749/numpy-2.4.5-cp314-cp314t-win32.whl", hash = "sha256:27f4a6dc26353a860b348961b9aa9e009835688b435cfa105e873b8dc2c726f5", size = 6156900, upload-time = "2026-05-15T20:24:53.134Z" }, + { url = "https://files.pythonhosted.org/packages/98/31/860959b91a73d9a085006554fa3850da51a7ffab64599bac5097243438ab/numpy-2.4.5-cp314-cp314t-win_amd64.whl", hash = "sha256:76ac6e90f5e226011c88f9b7040a4bcae612518bc7e9adc127e697a13b28ad1a", size = 12638906, upload-time = "2026-05-15T20:24:55.009Z" }, + { url = "https://files.pythonhosted.org/packages/9e/2a/bbd3097913083ad07c0f28fc9629666221fc18923e17ce97ae22a5dccdd6/numpy-2.4.5-cp314-cp314t-win_arm64.whl", hash = "sha256:7c392e2c1bf596701d3c6832be7567eab5d5b0a13865036c33365ee097d37f8b", size = 10565875, upload-time = "2026-05-15T20:24:57.425Z" }, + { url = "https://files.pythonhosted.org/packages/fc/5d/9a644cfb841bc76b584afc3af1708b3bf6c5cb51fc84a7008246cd93b7b7/numpy-2.4.5-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:6bf0bfc1c2e1db972e30b6cd3d4861f477f3af908b27799b239dc3cbe3eb4b95", size = 16847544, upload-time = "2026-05-15T20:24:59.746Z" }, + { url = "https://files.pythonhosted.org/packages/56/8f/4fe5e3ba76d858dae1fe79078818c0520447335be0082c0dedf82719cc08/numpy-2.4.5-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:73d664413fb97229149c4711ef56531a6fe8c15c1c2626b0bbe497b84c287e70", size = 14889039, upload-time = "2026-05-15T20:25:03.179Z" }, + { url = "https://files.pythonhosted.org/packages/8e/6f/79f195abf922ecc43e7d0eb6cc969462a71b524a35bcd1fa26b4a1d7406a/numpy-2.4.5-pp311-pypy311_pp73-macosx_14_0_arm64.whl", hash = "sha256:b35bee5ef99e8d227a07829bee2e864fcb65f7c157646fcd8ec8b4b45dd8b88f", size = 5394106, upload-time = "2026-05-15T20:25:05.659Z" }, + { url = "https://files.pythonhosted.org/packages/58/6f/79cd6247205802bcbd10b40ea087e20ded526e10e9be224d34de832b216e/numpy-2.4.5-pp311-pypy311_pp73-macosx_14_0_x86_64.whl", hash = "sha256:02981d0fc9f9ce147643d552966d47f329a02f7ecb3b113e84207242f20dfa83", size = 6708718, upload-time = "2026-05-15T20:25:08.071Z" }, + { url = "https://files.pythonhosted.org/packages/d7/22/5f378a9d4633c98f28c4709d4144b1a4630c5c09e109d2e781e2d26c8fe1/numpy-2.4.5-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0e63caf31a1df06338ae63d999f7a33a675ced62eea9c9b02db4b1c1f45cff38", size = 15798292, upload-time = "2026-05-15T20:25:10.689Z" }, + { url = "https://files.pythonhosted.org/packages/63/1c/cec582febef798c99888892d92dc1d28dfe29cb427c41f44d13d0dec208f/numpy-2.4.5-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d8fc52b85a7b45e474be53eddf08e006d22e381a4e41bcde8e4aa08da0e7d198", size = 16747406, upload-time = "2026-05-15T20:25:13.879Z" }, + { url = "https://files.pythonhosted.org/packages/b1/dc/d358a16a6fec86cf736b8fbe67386044b3fa2aded1a80cff90e836799301/numpy-2.4.5-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:40c71d50a4da1a7c317af419461052d3911a5770bfc5fd55baf52cc45e7a2c20", size = 12504085, upload-time = "2026-05-15T20:25:16.667Z" }, ] [[package]] name = "nvidia-cublas" -version = "13.1.0.3" +version = "13.1.1.3" source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "nvidia-cuda-nvrtc" }, +] wheels = [ - { url = "https://files.pythonhosted.org/packages/e1/a5/fce49e2ae977e0ccc084e5adafceb4f0ac0c8333cb6863501618a7277f67/nvidia_cublas-13.1.0.3-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:c86fc7f7ae36d7528288c5d88098edcb7b02c633d262e7ddbb86b0ad91be5df2", size = 542851226, upload-time = "2025-10-09T08:59:04.818Z" }, - { url = "https://files.pythonhosted.org/packages/e7/44/423ac00af4dd95a5aeb27207e2c0d9b7118702149bf4704c3ddb55bb7429/nvidia_cublas-13.1.0.3-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:ee8722c1f0145ab246bccb9e452153b5e0515fd094c3678df50b2a0888b8b171", size = 423133236, upload-time = "2025-10-09T08:59:32.536Z" }, + { url = "https://files.pythonhosted.org/packages/a7/a1/0bd24ee8c8d03adac032fd2909426a00c88f8c57961b1277ded97f91119f/nvidia_cublas-13.1.1.3-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:b7a210458267ac818974c53038fbec2e969d5c99f305ab15c72522fa9f001dd5", size = 542848918, upload-time = "2026-04-08T18:46:22.985Z" }, + { url = "https://files.pythonhosted.org/packages/3b/cd/154ca20c38269e05eff77c1464e6c1da89f50a6390b565e9d82e06bc11e1/nvidia_cublas-13.1.1.3-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:37936a16db8fe4ac1f065c2139360608a543a09275cb1a1af612e08cfa065436", size = 423138758, upload-time = "2026-04-08T18:46:58.655Z" }, ] [[package]] @@ -2126,14 +2126,14 @@ wheels = [ [[package]] name = "nvidia-cudnn-cu13" -version = "9.19.0.56" +version = "9.20.0.48" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "nvidia-cublas" }, ] wheels = [ - { url = "https://files.pythonhosted.org/packages/f1/84/26025437c1e6b61a707442184fa0c03d083b661adf3a3eecfd6d21677740/nvidia_cudnn_cu13-9.19.0.56-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:6ed29ffaee1176c612daf442e4dd6cfeb6a0caa43ddcbeb59da94953030b1be4", size = 433781201, upload-time = "2026-02-03T20:40:53.805Z" }, - { url = "https://files.pythonhosted.org/packages/a3/22/0b4b932655d17a6da1b92fa92ab12844b053bb2ac2475e179ba6f043da1e/nvidia_cudnn_cu13-9.19.0.56-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:d20e1734305e9d68889a96e3f35094d733ff1f83932ebe462753973e53a572bf", size = 366066321, upload-time = "2026-02-03T20:44:52.837Z" }, + { url = "https://files.pythonhosted.org/packages/56/c5/83384d846b2fd17c44bd499b36c75a45ed4f095fbbb2252294e89cea5c5c/nvidia_cudnn_cu13-9.20.0.48-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:e31454ae00094b0c55319d9d15b6fa2fc50a9e1c0f5c8c80fb75258234e731e1", size = 444574296, upload-time = "2026-03-09T19:28:27.751Z" }, + { url = "https://files.pythonhosted.org/packages/6e/5e/edb9c0ae051602c3ccaffe424256463636d639e27d7f302dde9975ef9e7a/nvidia_cudnn_cu13-9.20.0.48-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:0c45dd8eeb50b603f07995b1b300c62ffe6a1980482b82b3bcf94a4ca9d49304", size = 366173588, upload-time = "2026-03-09T19:29:34.474Z" }, ] [[package]] @@ -2194,20 +2194,20 @@ wheels = [ [[package]] name = "nvidia-cusparselt-cu13" -version = "0.8.0" +version = "0.8.1" source = { registry = "https://pypi.org/simple" } wheels = [ - { url = "https://files.pythonhosted.org/packages/46/10/8dcd1175260706a2fc92a16a52e306b71d4c1ea0b0cc4a9484183399818a/nvidia_cusparselt_cu13-0.8.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:400c6ed1cf6780fc6efedd64ec9f1345871767e6a1a0a552a1ea0578117ea77c", size = 220791277, upload-time = "2025-08-13T19:22:40.982Z" }, - { url = "https://files.pythonhosted.org/packages/fd/53/43b0d71f4e702fa9733f8b4571fdca50a8813f1e450b656c239beff12315/nvidia_cusparselt_cu13-0.8.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:25e30a8a7323935d4ad0340b95a0b69926eee755767e8e0b1cf8dd85b197d3fd", size = 169884119, upload-time = "2025-08-13T19:23:41.967Z" }, + { url = "https://files.pythonhosted.org/packages/46/e1/cdc1797eadf82d3a9a575a19b33fdc871a97edbec42c00b5b5e914f4aff4/nvidia_cusparselt_cu13-0.8.1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:4dca476c50bf4780d46cd0bfbd82e2bc10a08e4fef7950917ce8d7578d22a23f", size = 221051344, upload-time = "2025-09-05T18:49:51.289Z" }, + { url = "https://files.pythonhosted.org/packages/34/7d/2661f2fb3ac4302f3a246f5fc030213ac60c1fe0bce84f9783dbd831dbb7/nvidia_cusparselt_cu13-0.8.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:786ce87568c303fadb5afcc7102d454cd3040d75f6f8626f5db460d1871f4dd0", size = 170148586, upload-time = "2025-09-05T18:50:50.248Z" }, ] [[package]] name = "nvidia-nccl-cu13" -version = "2.28.9" +version = "2.29.7" source = { registry = "https://pypi.org/simple" } wheels = [ - { url = "https://files.pythonhosted.org/packages/39/55/1920646a2e43ffd4fc958536b276197ed740e9e0c54105b4bb3521591fc7/nvidia_nccl_cu13-2.28.9-py3-none-manylinux_2_18_aarch64.whl", hash = "sha256:01c873ba1626b54caa12272ed228dc5b2781545e0ae8ba3f432a8ef1c6d78643", size = 196561677, upload-time = "2025-11-18T05:49:03.45Z" }, - { url = "https://files.pythonhosted.org/packages/b0/b4/878fefaad5b2bcc6fcf8d474a25e3e3774bc5133e4b58adff4d0bca238bc/nvidia_nccl_cu13-2.28.9-py3-none-manylinux_2_18_x86_64.whl", hash = "sha256:e4553a30f34195f3fa1da02a6da3d6337d28f2003943aa0a3d247bbc25fefc42", size = 196493177, upload-time = "2025-11-18T05:49:17.677Z" }, + { url = "https://files.pythonhosted.org/packages/72/0d/daf50d44177ee0cbc7ff0a0c91eb5ff676c82be42f9a970bc7597f440c3a/nvidia_nccl_cu13-2.29.7-py3-none-manylinux_2_18_aarch64.whl", hash = "sha256:674a12383e3c38a1bcccae7d4f3633b37852230b6047883cb2f4c2d1b36d9bf5", size = 206014712, upload-time = "2026-03-03T05:34:20.843Z" }, + { url = "https://files.pythonhosted.org/packages/67/f4/58e4e91b6919367c7aafb8e36fce9aad1a3047e536bf7e2fd560927d3a4c/nvidia_nccl_cu13-2.29.7-py3-none-manylinux_2_18_x86_64.whl", hash = "sha256:edd81538446786ec3b73972543e53bb43bcaf0bfc8ef76cb679fcc390ffe136d", size = 205976000, upload-time = "2026-03-03T05:36:24.472Z" }, ] [[package]] @@ -2654,15 +2654,15 @@ wheels = [ [[package]] name = "python-discovery" -version = "1.3.0" +version = "1.3.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "filelock" }, { name = "platformdirs" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/ae/e0/cc5a8653e9a24f6cf84768f05064aa8ed5a83dcefd5e2a043db14a1c5f44/python_discovery-1.3.0.tar.gz", hash = "sha256:d098f1e86be5d45fe4d14bf1029294aabbd332f4321179dec85e76cddce834b0", size = 63925, upload-time = "2026-05-05T14:38:39.769Z" } +sdist = { url = "https://files.pythonhosted.org/packages/48/60/e88788207d81e46362cfbef0d4aaf4c0f49efc3c12d4c3fa3f542c34ebec/python_discovery-1.3.1.tar.gz", hash = "sha256:62f6db28064c9613e7ca76cb3f00c38c839a07c31c00dfe7ed0986493d2150a6", size = 68011, upload-time = "2026-05-12T20:53:36.336Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/30/d4/24d543ab8b8158b7f5a97113c831205f5c900c92c8762b1e7f44b7ea0405/python_discovery-1.3.0-py3-none-any.whl", hash = "sha256:441d9ced3dfce36e113beb35ca302c71c7ef06f3c0f9c227a0b9bb3bd49b9e9f", size = 33124, upload-time = "2026-05-05T14:38:38.539Z" }, + { url = "https://files.pythonhosted.org/packages/b7/6f/a05a317a66fee0aad270011461f1a63a453ed12471249f172f7d2e2bc7b4/python_discovery-1.3.1-py3-none-any.whl", hash = "sha256:ed188687ebb3b82c01a17cd5ac62fc94d9f6487a7f1a0f9dfe89753fec91039c", size = 33185, upload-time = "2026-05-12T20:53:34.969Z" }, ] [[package]] @@ -2931,7 +2931,7 @@ wheels = [ [[package]] name = "requests" -version = "2.33.1" +version = "2.34.2" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "certifi" }, @@ -2939,9 +2939,9 @@ dependencies = [ { name = "idna" }, { name = "urllib3" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/5f/a4/98b9c7c6428a668bf7e42ebb7c79d576a1c3c1e3ae2d47e674b468388871/requests-2.33.1.tar.gz", hash = "sha256:18817f8c57c6263968bc123d237e3b8b08ac046f5456bd1e307ee8f4250d3517", size = 134120, upload-time = "2026-03-30T16:09:15.531Z" } +sdist = { url = "https://files.pythonhosted.org/packages/ac/c3/e2a2b89f2d3e2179abd6d00ebd70bff6273f37fb3e0cc209f48b39d00cbf/requests-2.34.2.tar.gz", hash = "sha256:f288924cae4e29463698d6d60bc6a4da69c89185ad1e0bcc4104f584e960b9ed", size = 142856, upload-time = "2026-05-14T19:25:27.735Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/d7/8e/7540e8a2036f79a125c1d2ebadf69ed7901608859186c856fa0388ef4197/requests-2.33.1-py3-none-any.whl", hash = "sha256:4e6d1ef462f3626a1f0a0a9c42dd93c63bad33f9f1c1937509b8c5c8718ab56a", size = 64947, upload-time = "2026-03-30T16:09:13.83Z" }, + { url = "https://files.pythonhosted.org/packages/a0/f4/c67b0b3f1b9245e8d266f0f112c500d50e5b4e83cb6f3b71b6528104182a/requests-2.34.2-py3-none-any.whl", hash = "sha256:2a0d60c172f83ac6ab31e4554906c0f3b3588d37b5cb939b1c061f4907e278e0", size = 73075, upload-time = "2026-05-14T19:25:26.443Z" }, ] [[package]] @@ -3140,27 +3140,27 @@ wheels = [ [[package]] name = "ruff" -version = "0.15.12" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/99/43/3291f1cc9106f4c63bdce7a8d0df5047fe8422a75b091c16b5e9355e0b11/ruff-0.15.12.tar.gz", hash = "sha256:ecea26adb26b4232c0c2ca19ccbc0083a68344180bba2a600605538ce51a40a6", size = 4643852, upload-time = "2026-04-24T18:17:14.305Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c3/6e/e78ffb61d4686f3d96ba3df2c801161843746dcbcbb17a1e927d4829312b/ruff-0.15.12-py3-none-linux_armv6l.whl", hash = "sha256:f86f176e188e94d6bdbc09f09bfd9dc729059ad93d0e7390b5a73efe19f8861c", size = 10640713, upload-time = "2026-04-24T18:17:22.841Z" }, - { url = "https://files.pythonhosted.org/packages/ae/08/a317bc231fb9e7b93e4ef3089501e51922ff88d6936ce5cf870c4fe55419/ruff-0.15.12-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:e3bcd123364c3770b8e1b7baaf343cc99a35f197c5c6e8af79015c666c423a6c", size = 11069267, upload-time = "2026-04-24T18:17:30.105Z" }, - { url = "https://files.pythonhosted.org/packages/aa/a4/f828e9718d3dce1f5f11c39c4f65afd32783c8b2aebb2e3d259e492c47bd/ruff-0.15.12-py3-none-macosx_11_0_arm64.whl", hash = "sha256:fe87510d000220aa1ed530d4448a7c696a0cae1213e5ec30e5874287b66557b5", size = 10397182, upload-time = "2026-04-24T18:17:07.177Z" }, - { url = "https://files.pythonhosted.org/packages/71/e0/3310fc6d1b5e1fdea22bf3b1b807c7e187b581021b0d7d4514cccdb5fb71/ruff-0.15.12-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:84a1630093121375a3e2a95b4a6dc7b59e2b4ee76216e32d81aae550a832d002", size = 10758012, upload-time = "2026-04-24T18:16:55.759Z" }, - { url = "https://files.pythonhosted.org/packages/11/c1/a606911aee04c324ddaa883ae418f3569792fd3c4a10c50e0dd0a2311e1e/ruff-0.15.12-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:fb129f40f114f089ebe0ca56c0d251cf2061b17651d464bb6478dc01e69f11f5", size = 10447479, upload-time = "2026-04-24T18:16:51.677Z" }, - { url = "https://files.pythonhosted.org/packages/9d/68/4201e8444f0894f21ab4aeeaee68aa4f10b51613514a20d80bd628d57e88/ruff-0.15.12-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b0c862b172d695db7598426b8af465e7e9ac00a3ea2a3630ee67eb82e366aaa6", size = 11234040, upload-time = "2026-04-24T18:17:16.529Z" }, - { url = "https://files.pythonhosted.org/packages/34/ff/8a6d6cf4ccc23fd67060874e832c18919d1557a0611ebef03fdb01fff11e/ruff-0.15.12-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2849ea9f3484c3aca43a82f484210370319e7170df4dfe4843395ddf6c57bc33", size = 12087377, upload-time = "2026-04-24T18:17:04.944Z" }, - { url = "https://files.pythonhosted.org/packages/85/f6/c669cf73f5152f623d34e69866a46d5e6185816b19fcd5b6dd8a2d299922/ruff-0.15.12-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9e77c7e51c07fe396826d5969a5b846d9cd4c402535835fb6e21ce8b28fef847", size = 11367784, upload-time = "2026-04-24T18:17:25.409Z" }, - { url = "https://files.pythonhosted.org/packages/e8/39/c61d193b8a1daaa8977f7dea9e8d8ba866e02ea7b65d32f6861693aa4c12/ruff-0.15.12-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:83b2f4f2f3b1026b5fb449b467d9264bf22067b600f7b6f41fc5958909f449d0", size = 11344088, upload-time = "2026-04-24T18:17:12.258Z" }, - { url = "https://files.pythonhosted.org/packages/c2/8d/49afab3645e31e12c590acb6d3b5b69d7aab5b81926dbaf7461f9441f37a/ruff-0.15.12-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:9ba3b8f1afd7e2e43d8943e55f249e13f9682fde09711644a6e7290eb4f3e339", size = 11271770, upload-time = "2026-04-24T18:17:02.457Z" }, - { url = "https://files.pythonhosted.org/packages/46/06/33f41fe94403e2b755481cdfb9b7ef3e4e0ed031c4581124658d935d52b4/ruff-0.15.12-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:e852ba9fdc890655e1d78f2df1499efbe0e54126bd405362154a75e2bde159c5", size = 10719355, upload-time = "2026-04-24T18:17:27.648Z" }, - { url = "https://files.pythonhosted.org/packages/0d/59/18aa4e014debbf559670e4048e39260a85c7fcee84acfd761ac01e7b8d35/ruff-0.15.12-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:dd8aed930da53780d22fc70bdf84452c843cf64f8cb4eb38984319c24c5cd5fd", size = 10462758, upload-time = "2026-04-24T18:17:32.347Z" }, - { url = "https://files.pythonhosted.org/packages/25/e7/cc9f16fd0f3b5fddcbd7ec3d6ae30c8f3fde1047f32a4093a98d633c6570/ruff-0.15.12-py3-none-musllinux_1_2_i686.whl", hash = "sha256:01da3988d225628b709493d7dc67c3b9b12c0210016b08690ef9bd27970b262b", size = 10953498, upload-time = "2026-04-24T18:17:20.674Z" }, - { url = "https://files.pythonhosted.org/packages/72/7a/a9ba7f98c7a575978698f4230c5e8cc54bbc761af34f560818f933dafa0c/ruff-0.15.12-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:9cae0f92bd5700d1213188b31cd3bdd2b315361296d10b96b8e2337d3d11f53e", size = 11447765, upload-time = "2026-04-24T18:17:09.755Z" }, - { url = "https://files.pythonhosted.org/packages/ea/f9/0ae446942c846b8266059ad8a30702a35afae55f5cdc54c5adf8d7afdc27/ruff-0.15.12-py3-none-win32.whl", hash = "sha256:d0185894e038d7043ba8fd6aee7499ece6462dc0ea9f1e260c7451807c714c20", size = 10657277, upload-time = "2026-04-24T18:17:18.591Z" }, - { url = "https://files.pythonhosted.org/packages/33/f1/9614e03e1cdcbf9437570b5400ced8a720b5db22b28d8e0f1bda429f660d/ruff-0.15.12-py3-none-win_amd64.whl", hash = "sha256:c87a162d61ab3adca47c03f7f717c68672edec7d1b5499e652331780fe74950d", size = 11837758, upload-time = "2026-04-24T18:17:00.113Z" }, - { url = "https://files.pythonhosted.org/packages/c0/98/6beb4b351e472e5f4c4613f7c35a5290b8be2497e183825310c4c3a3984b/ruff-0.15.12-py3-none-win_arm64.whl", hash = "sha256:a538f7a82d061cee7be55542aca1d86d1393d55d81d4fcc314370f4340930d4f", size = 11120821, upload-time = "2026-04-24T18:16:57.979Z" }, +version = "0.15.13" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/24/21/a7d5c126d5b557715ef81098f3db2fe20f622a039ff2e626af28d674ab80/ruff-0.15.13.tar.gz", hash = "sha256:f9d89f17f7ba7fb2ed42921f0df75da797a9a5d71bc39049e2c687cf2baf44b7", size = 4678180, upload-time = "2026-05-14T13:44:37.869Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c6/61/11d458dc6ac22504fd8e237b29dfd40504c7fbbcc8930402cfe51a8e63ed/ruff-0.15.13-py3-none-linux_armv6l.whl", hash = "sha256:444b580fc72fd6887e650acd3e575e18cdc79dbcf42fb4030b491057921f61f8", size = 10738279, upload-time = "2026-05-14T13:44:18.7Z" }, + { url = "https://files.pythonhosted.org/packages/86/ca/caa871ee7be718c45256fada4e16a218ee3e33f0c4a46b729a60a24912e6/ruff-0.15.13-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:6590d009e7cb7ebf36f83dbdd44a3fa48a0994ff6f1cdc1b08006abe58f98dc7", size = 11124798, upload-time = "2026-05-14T13:44:06.427Z" }, + { url = "https://files.pythonhosted.org/packages/d3/19/43f5f2e568dddde567fc41f8471f9432c09563e19d3e617a48cfa52f8f0a/ruff-0.15.13-py3-none-macosx_11_0_arm64.whl", hash = "sha256:1c26d2f66163deeb6e08d8b39fbbe983ce3c71cea06a6d7591cfd1421793c629", size = 10460761, upload-time = "2026-05-14T13:44:04.375Z" }, + { url = "https://files.pythonhosted.org/packages/99/df/cf938cd6de3003178f03ad7c1ea2a6c099468c03a35037985070b37e76be/ruff-0.15.13-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9dbd6f94b434f896308e4d57fb7bfde0d02b99f7a64b3bdab0fdfa6a864203a5", size = 10804451, upload-time = "2026-05-14T13:44:25.221Z" }, + { url = "https://files.pythonhosted.org/packages/c7/7d/5d0973129b154ded2225729169d7068f26b467760b146493fde138415f23/ruff-0.15.13-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:bf3259f3be4d181bda591da5db2571aed6853c6a048157756448020bc6c5cd22", size = 10534285, upload-time = "2026-05-14T13:44:08.888Z" }, + { url = "https://files.pythonhosted.org/packages/1f/e3/6b999bbc66cd51e5f073842bc2a3995e99c5e0e72e16b15e7261f7abf57a/ruff-0.15.13-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ae9c17e5eb4430c154e76abc25d79a318190f5a997f38fb6b114416c5319ffc9", size = 11312063, upload-time = "2026-05-14T13:44:11.274Z" }, + { url = "https://files.pythonhosted.org/packages/af/5a/642639e9f5db04f1e97fbd6e091c6fd20725bdf072fb114d00eefb9e6eb8/ruff-0.15.13-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2e2e39bff6c341f4b577a21b801326fab0b11847f48fcaa83f00a113c9b3cb55", size = 12183079, upload-time = "2026-05-14T13:44:01.634Z" }, + { url = "https://files.pythonhosted.org/packages/19/4c/7585735f6b53b0f12de13618b2f7d250a844f018822efc899df2e7b8295f/ruff-0.15.13-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e8d9a8e08013542e94d3220bc5b62cc3e5ef87c5f74bff367d3fac14fab013e6", size = 11440833, upload-time = "2026-05-14T13:43:59.043Z" }, + { url = "https://files.pythonhosted.org/packages/e8/31/bf1a0803d077e679cfeee5f2f67290a0fa79c7385b5d9a8c17b9db2c48f0/ruff-0.15.13-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc411dfebe5eebe55ce041c6ae080eb7668955e866daa2fbb16692a784f1c4ca", size = 11434486, upload-time = "2026-05-14T13:44:27.761Z" }, + { url = "https://files.pythonhosted.org/packages/e1/4e/62c9b999875d4f14db80f277c030578f5e249c9852d65b7ac7ad0b43c041/ruff-0.15.13-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:768494eb08b9cee54e2fd27969966f74db5a57f6eaa7a90fcb3306af34dfc4bd", size = 11385189, upload-time = "2026-05-14T13:44:13.704Z" }, + { url = "https://files.pythonhosted.org/packages/fc/89/7e959047a104df3eb12863447c110140191fc5b6c4f379ea2e803fcdb0e4/ruff-0.15.13-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb75f9a3a7e42ffe117d734494e6c5e5cb3565d66e12612cb63d0e572a41a5b6", size = 10781380, upload-time = "2026-05-14T13:43:56.734Z" }, + { url = "https://files.pythonhosted.org/packages/ff/52/5fd18f3b88cab63e88aa11516b3b4e1e5f720e5c330f8dbe5c26210f41f8/ruff-0.15.13-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:8cb74dd33bb2f6613faf7fc03b660053b5ac4f80e706d5788c6335e2a8048d51", size = 10540605, upload-time = "2026-05-14T13:44:20.748Z" }, + { url = "https://files.pythonhosted.org/packages/e8/e0/9e35f338990d3e41a82875ff7053ffe97541dae81c9d02143177f381d572/ruff-0.15.13-py3-none-musllinux_1_2_i686.whl", hash = "sha256:7ef823f817fcd191dc934e984be9cf4094f808effa16f2542ad8e821ba02bbf2", size = 11036554, upload-time = "2026-05-14T13:44:16.256Z" }, + { url = "https://files.pythonhosted.org/packages/c2/13/070fb048c24080fba188f66371e2a92785be257ad02242066dc7255ac6e9/ruff-0.15.13-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:f345a13937bd7f09f6f5d19fa0721b0c103e00e7f62bc67089a8e5e037719e0b", size = 11528133, upload-time = "2026-05-14T13:44:22.808Z" }, + { url = "https://files.pythonhosted.org/packages/6b/8c/b1e1666aef7fc6555094d73ae6cd981701781ae85b97ceefc0eebd0b4668/ruff-0.15.13-py3-none-win32.whl", hash = "sha256:4044f94208b3b05ba0fc4a4abd0558cf4d6459bd18325eead7fd8cc66f909b41", size = 10721455, upload-time = "2026-05-14T13:44:35.697Z" }, + { url = "https://files.pythonhosted.org/packages/ab/a6/870a3e8a50590bb92be184ad928c2922f088b00d9dc5c5ec7b924ee08c22/ruff-0.15.13-py3-none-win_amd64.whl", hash = "sha256:7064884d442b7d477b4e7473d12da7f08851d2b1982763c5d3f388a19468a1a4", size = 11900409, upload-time = "2026-05-14T13:44:30.389Z" }, + { url = "https://files.pythonhosted.org/packages/9b/36/9c015cd052fca743dae8cb2aeb16b551444787467db42ceab0fc968865af/ruff-0.15.13-py3-none-win_arm64.whl", hash = "sha256:2471da9bd1068c8c064b5fd9c0c4b6dddffd6369cb1cd68b29993b1709ff1b21", size = 11179336, upload-time = "2026-05-14T13:44:33.026Z" }, ] [[package]] @@ -3176,7 +3176,7 @@ dependencies = [ { name = "pillow" }, { name = "scipy" }, { name = "tifffile", version = "2026.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, - { name = "tifffile", version = "2026.5.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, + { name = "tifffile", version = "2026.5.15", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/a1/b4/2528bb43c67d48053a7a649a9666432dc307d66ba02e3a6d5c40f46655df/scikit_image-0.26.0.tar.gz", hash = "sha256:f5f970ab04efad85c24714321fcc91613fcb64ef2a892a13167df2f3e59199fa", size = 22729739, upload-time = "2025-12-20T17:12:21.824Z" } wheels = [ @@ -3477,7 +3477,7 @@ wheels = [ [[package]] name = "tifffile" -version = "2026.5.2" +version = "2026.5.15" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14'", @@ -3486,9 +3486,9 @@ resolution-markers = [ dependencies = [ { name = "numpy", marker = "python_full_version >= '3.12'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/6c/3e/695c7ab56be57814e369c1f38bc3f64b9dea0a83e867d00c0c9d613a9929/tifffile-2026.5.2.tar.gz", hash = "sha256:21b10227ede8493814a34676774797f721f487e36cb0530e7c3bd882caa87f5a", size = 429140, upload-time = "2026-05-02T20:19:31.497Z" } +sdist = { url = "https://files.pythonhosted.org/packages/40/66/0aef917d525767a40edebe088f8ed6a4417e6eb489c58f6805bfa872636b/tifffile-2026.5.15.tar.gz", hash = "sha256:ee4f3e07ee0d8ff4745a8c735ac2b72caa3173c7d6059b00fdc3ff492a0b635b", size = 429998, upload-time = "2026-05-15T20:04:55.896Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/b4/af/ce4df3ca29122d219c45d3e86e5ff9a9df03b8cf31afd76817b662c803a3/tifffile-2026.5.2-py3-none-any.whl", hash = "sha256:5129b53b826e768a5b1af26b765eeea75c2d0a227d2d12849617e0737588e105", size = 266420, upload-time = "2026-05-02T20:19:29.814Z" }, + { url = "https://files.pythonhosted.org/packages/1f/6e/7d8850ff112f8f80d394ca45e89b975a3a43559d47af3137b767669b3294/tifffile-2026.5.15-py3-none-any.whl", hash = "sha256:6715515a53cabc0cefc5c9f13a0ae2c250e63e2ca784ce02d0b6c333810c2a17", size = 266665, upload-time = "2026-05-15T20:04:54.227Z" }, ] [[package]] @@ -3568,15 +3568,16 @@ wheels = [ [[package]] name = "torch" -version = "2.11.0" +version = "2.12.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "cuda-bindings", marker = "sys_platform == 'linux'" }, - { name = "cuda-toolkit", extra = ["cublas", "cudart", "cufft", "cufile", "cupti", "curand", "cusolver", "cusparse", "nvjitlink", "nvrtc", "nvtx"], marker = "sys_platform == 'linux'" }, + { name = "cuda-toolkit", extra = ["cudart", "cufft", "cufile", "cupti", "curand", "cusolver", "cusparse", "nvjitlink", "nvrtc", "nvtx"], marker = "sys_platform == 'linux'" }, { name = "filelock" }, { name = "fsspec" }, { name = "jinja2" }, { name = "networkx" }, + { name = "nvidia-cublas", marker = "sys_platform == 'linux'" }, { name = "nvidia-cudnn-cu13", marker = "sys_platform == 'linux'" }, { name = "nvidia-cusparselt-cu13", marker = "sys_platform == 'linux'" }, { name = "nvidia-nccl-cu13", marker = "sys_platform == 'linux'" }, @@ -3587,30 +3588,30 @@ dependencies = [ { name = "typing-extensions" }, ] wheels = [ - { url = "https://files.pythonhosted.org/packages/ae/0d/98b410492609e34a155fa8b121b55c7dca229f39636851c3a9ec20edea21/torch-2.11.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7b6a60d48062809f58595509c524b88e6ddec3ebe25833d6462eeab81e5f2ce4", size = 80529712, upload-time = "2026-03-23T18:12:02.608Z" }, - { url = "https://files.pythonhosted.org/packages/84/03/acea680005f098f79fd70c1d9d5ccc0cb4296ec2af539a0450108232fc0c/torch-2.11.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:d91aac77f24082809d2c5a93f52a5f085032740a1ebc9252a7b052ef5a4fddc6", size = 419718178, upload-time = "2026-03-23T18:10:46.675Z" }, - { url = "https://files.pythonhosted.org/packages/8c/8b/d7be22fbec9ffee6cff31a39f8750d4b3a65d349a286cf4aec74c2375662/torch-2.11.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:7aa2f9bbc6d4595ba72138026b2074be1233186150e9292865e04b7a63b8c67a", size = 530604548, upload-time = "2026-03-23T18:10:03.569Z" }, - { url = "https://files.pythonhosted.org/packages/d1/bd/9912d30b68845256aabbb4a40aeefeef3c3b20db5211ccda653544ada4b6/torch-2.11.0-cp311-cp311-win_amd64.whl", hash = "sha256:73e24aaf8f36ab90d95cd1761208b2eb70841c2a9ca1a3f9061b39fc5331b708", size = 114519675, upload-time = "2026-03-23T18:11:52.995Z" }, - { url = "https://files.pythonhosted.org/packages/6f/8b/69e3008d78e5cee2b30183340cc425081b78afc5eff3d080daab0adda9aa/torch-2.11.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4b5866312ee6e52ea625cd211dcb97d6a2cdc1131a5f15cc0d87eec948f6dd34", size = 80606338, upload-time = "2026-03-23T18:11:34.781Z" }, - { url = "https://files.pythonhosted.org/packages/13/16/42e5915ebe4868caa6bac83a8ed59db57f12e9a61b7d749d584776ed53d5/torch-2.11.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:f99924682ef0aa6a4ab3b1b76f40dc6e273fca09f367d15a524266db100a723f", size = 419731115, upload-time = "2026-03-23T18:11:06.944Z" }, - { url = "https://files.pythonhosted.org/packages/1a/c9/82638ef24d7877510f83baf821f5619a61b45568ce21c0a87a91576510aa/torch-2.11.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:0f68f4ac6d95d12e896c3b7a912b5871619542ec54d3649cf48cc1edd4dd2756", size = 530712279, upload-time = "2026-03-23T18:10:31.481Z" }, - { url = "https://files.pythonhosted.org/packages/1c/ff/6756f1c7ee302f6d202120e0f4f05b432b839908f9071157302cedfc5232/torch-2.11.0-cp312-cp312-win_amd64.whl", hash = "sha256:fbf39280699d1b869f55eac536deceaa1b60bd6788ba74f399cc67e60a5fab10", size = 114556047, upload-time = "2026-03-23T18:10:55.931Z" }, - { url = "https://files.pythonhosted.org/packages/87/89/5ea6722763acee56b045435fb84258db7375c48165ec8be7880ab2b281c5/torch-2.11.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1e6debd97ccd3205bbb37eb806a9d8219e1139d15419982c09e23ef7d4369d18", size = 80606801, upload-time = "2026-03-23T18:10:18.649Z" }, - { url = "https://files.pythonhosted.org/packages/32/d1/8ed2173589cbfe744ed54e5a73efc107c0085ba5777ee93a5f4c1ab90553/torch-2.11.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:63a68fa59de8f87acc7e85a5478bb2dddbb3392b7593ec3e78827c793c4b73fd", size = 419732382, upload-time = "2026-03-23T18:08:30.835Z" }, - { url = "https://files.pythonhosted.org/packages/3d/e1/b73f7c575a4b8f87a5928f50a1e35416b5e27295d8be9397d5293e7e8d4c/torch-2.11.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:cc89b9b173d9adfab59fd227f0ab5e5516d9a52b658ae41d64e59d2e55a418db", size = 530711509, upload-time = "2026-03-23T18:08:47.213Z" }, - { url = "https://files.pythonhosted.org/packages/66/82/3e3fcdd388fbe54e29fd3f991f36846ff4ac90b0d0181e9c8f7236565f82/torch-2.11.0-cp313-cp313-win_amd64.whl", hash = "sha256:4dda3b3f52d121063a731ddb835f010dc137b920d7fec2778e52f60d8e4bf0cd", size = 114555842, upload-time = "2026-03-23T18:09:52.111Z" }, - { url = "https://files.pythonhosted.org/packages/db/38/8ac78069621b8c2b4979c2f96dc8409ef5e9c4189f6aac629189a78677ca/torch-2.11.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:8b394322f49af4362d4f80e424bcaca7efcd049619af03a4cf4501520bdf0fb4", size = 80959574, upload-time = "2026-03-23T18:10:14.214Z" }, - { url = "https://files.pythonhosted.org/packages/6d/6c/56bfb37073e7136e6dd86bfc6af7339946dd684e0ecf2155ac0eee687ae1/torch-2.11.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:2658f34ce7e2dabf4ec73b45e2ca68aedad7a5be87ea756ad656eaf32bf1e1ea", size = 419732324, upload-time = "2026-03-23T18:09:36.604Z" }, - { url = "https://files.pythonhosted.org/packages/07/f4/1b666b6d61d3394cca306ea543ed03a64aad0a201b6cd159f1d41010aeb1/torch-2.11.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:98bb213c3084cfe176302949bdc360074b18a9da7ab59ef2edc9d9f742504778", size = 530596026, upload-time = "2026-03-23T18:09:20.842Z" }, - { url = "https://files.pythonhosted.org/packages/48/6b/30d1459fa7e4b67e9e3fe1685ca1d8bb4ce7c62ef436c3a615963c6c866c/torch-2.11.0-cp313-cp313t-win_amd64.whl", hash = "sha256:a97b94bbf62992949b4730c6cd2cc9aee7b335921ee8dc207d930f2ed09ae2db", size = 114793702, upload-time = "2026-03-23T18:09:47.304Z" }, - { url = "https://files.pythonhosted.org/packages/26/0d/8603382f61abd0db35841148ddc1ffd607bf3100b11c6e1dab6d2fc44e72/torch-2.11.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:01018087326984a33b64e04c8cb5c2795f9120e0d775ada1f6638840227b04d7", size = 80573442, upload-time = "2026-03-23T18:09:10.117Z" }, - { url = "https://files.pythonhosted.org/packages/c7/86/7cd7c66cb9cec6be330fff36db5bd0eef386d80c031b581ec81be1d4b26c/torch-2.11.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:2bb3cc54bd0dea126b0060bb1ec9de0f9c7f7342d93d436646516b0330cd5be7", size = 419749385, upload-time = "2026-03-23T18:07:33.77Z" }, - { url = "https://files.pythonhosted.org/packages/47/e8/b98ca2d39b2e0e4730c0ee52537e488e7008025bc77ca89552ff91021f7c/torch-2.11.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:4dc8b3809469b6c30b411bb8c4cad3828efd26236153d9beb6a3ec500f211a60", size = 530716756, upload-time = "2026-03-23T18:07:50.02Z" }, - { url = "https://files.pythonhosted.org/packages/78/88/d4a4cda8362f8a30d1ed428564878c3cafb0d87971fbd3947d4c84552095/torch-2.11.0-cp314-cp314-win_amd64.whl", hash = "sha256:2b4e811728bd0cc58fb2b0948fe939a1ee2bf1422f6025be2fca4c7bd9d79718", size = 114552300, upload-time = "2026-03-23T18:09:05.617Z" }, - { url = "https://files.pythonhosted.org/packages/bf/46/4419098ed6d801750f26567b478fc185c3432e11e2cad712bc6b4c2ab0d0/torch-2.11.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:8245477871c3700d4370352ffec94b103cfcb737229445cf9946cddb7b2ca7cd", size = 80959460, upload-time = "2026-03-23T18:09:00.818Z" }, - { url = "https://files.pythonhosted.org/packages/fd/66/54a56a4a6ceaffb567231994a9745821d3af922a854ed33b0b3a278e0a99/torch-2.11.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:ab9a8482f475f9ba20e12db84b0e55e2f58784bdca43a854a6ccd3fd4b9f75e6", size = 419735835, upload-time = "2026-03-23T18:07:18.974Z" }, - { url = "https://files.pythonhosted.org/packages/b1/e7/0b6665f533aa9e337662dc190425abc0af1fe3234088f4454c52393ded61/torch-2.11.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:563ed3d25542d7e7bbc5b235ccfacfeb97fb470c7fee257eae599adb8005c8a2", size = 530613405, upload-time = "2026-03-23T18:08:07.014Z" }, - { url = "https://files.pythonhosted.org/packages/cf/bf/c8d12a2c86dbfd7f40fb2f56fbf5a505ccf2d9ce131eb559dfc7c51e1a04/torch-2.11.0-cp314-cp314t-win_amd64.whl", hash = "sha256:b2a43985ff5ef6ddd923bbcf99943e5f58059805787c5c9a2622bf05ca2965b0", size = 114792991, upload-time = "2026-03-23T18:08:19.216Z" }, + { url = "https://files.pythonhosted.org/packages/18/62/131124fb95df03811b8260d1d43dcc5ee85ea1a344b964613d7efe77fb08/torch-2.12.0-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:10802fd383bbfed646212e765a72c37d2185205d4f26eb197a254e8ac7ddcb25", size = 87990344, upload-time = "2026-05-13T14:55:42.154Z" }, + { url = "https://files.pythonhosted.org/packages/12/9c/dda0dbd547dc549839824135f223792fd0e725f28ed0715dda366b7acaa2/torch-2.12.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:c12592630aef72feaf18bd3f197ef587bbfa21131b31c38b23ab2e55fce92e36", size = 426362932, upload-time = "2026-05-13T14:54:15.295Z" }, + { url = "https://files.pythonhosted.org/packages/e2/d2/a7dd5a3f9bdaa7842124e8e2359202b317c48d47d2fc5816fafdf2049adb/torch-2.12.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:415c1b8d0412f67551c8e89a2daca0fb3e56694af0281ba155eaa9da481f58b4", size = 532170085, upload-time = "2026-05-13T14:55:20.788Z" }, + { url = "https://files.pythonhosted.org/packages/12/1b/a61ce2004f9ab0ea8964a6e6168133a127795667639e2ff4f8f2bdb16a65/torch-2.12.0-cp311-cp311-win_amd64.whl", hash = "sha256:dd37188ea325042cb1f6cafa56822b11ada2520c04791a52629b0af25bdfbfd9", size = 122953128, upload-time = "2026-05-13T14:54:52.744Z" }, + { url = "https://files.pythonhosted.org/packages/ef/bb/285d643f254731294c9b595a007eac39db4600a98682d7bca688f42ca164/torch-2.12.0-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:b41339df93d491435e790ff8bcbae1c0ce777175889bfd1281d119862793e6a2", size = 88010197, upload-time = "2026-05-13T14:55:35.414Z" }, + { url = "https://files.pythonhosted.org/packages/79/81/76debf1db1343bd929bbb5d74c89fb437c2ed88eb144712557e7bd3eea45/torch-2.12.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:8fbef9f108a863e7722a73740998967e3b074742a834fc5be3a535a2befa7057", size = 426376751, upload-time = "2026-05-13T14:55:03.353Z" }, + { url = "https://files.pythonhosted.org/packages/de/f0/80026028b603c4650ff270fc3785bdef4bd6738765a9cc5a0f5a637d65a2/torch-2.12.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:4b4f64c2c2b11f7510d93dd6412b87025ff6eddd6bb61c3b5a3d892ea20c4756", size = 532261691, upload-time = "2026-05-13T14:52:54.453Z" }, + { url = "https://files.pythonhosted.org/packages/b9/c2/64b06cbb7830fb3cd9be13e1158b31a3f36b68e6a209105ee3c9d9480be0/torch-2.12.0-cp312-cp312-win_amd64.whl", hash = "sha256:8b958caff4a14d3a3b0b2dfc6a378f64dda9728a9dad28c08a0db9ce4dafb549", size = 122988114, upload-time = "2026-05-13T14:54:42.153Z" }, + { url = "https://files.pythonhosted.org/packages/86/ca/01896c80ba921676aa45886b2c5b8d774912de2a1f719de48169c6f755cd/torch-2.12.0-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:90dd587a5f61bfe1307148b581e2084fc5bc4a06e2b90a20e9a36b81087ff16b", size = 88009511, upload-time = "2026-05-13T14:54:47.411Z" }, + { url = "https://files.pythonhosted.org/packages/a5/04/52bdaf4787eab6ac7d7f5851dff934e4def0bc8ead9c8fd2b69b3e529699/torch-2.12.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:864392c73b7654f4d2b3ae712f607937d0dbb1101c4555fbb41848106b297f39", size = 426383231, upload-time = "2026-05-13T14:53:32.129Z" }, + { url = "https://files.pythonhosted.org/packages/49/8a/94bdecd13f5aaa90d45920b89789d9fe7c6f4af8c3cdd7ce01fcb59908fc/torch-2.12.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:5d6b560dfa7d56291c07d615c3bb73e8d9943d9b6d87f76cd0d9d570c4797fa6", size = 532269288, upload-time = "2026-05-13T14:53:49.423Z" }, + { url = "https://files.pythonhosted.org/packages/3e/2f/bdbaaa267de519ef1b73054bf590d8c93c37a266c9a4e24a01bd38b6918f/torch-2.12.0-cp313-cp313-win_amd64.whl", hash = "sha256:3fee918902090ade827643e758e98363278815de583c75d111fdd665ebffde9f", size = 122987706, upload-time = "2026-05-13T14:54:00.335Z" }, + { url = "https://files.pythonhosted.org/packages/9b/ad/e95e822f3538171e22640a7fbe839a1fdb666600bf6487025de2ff03b11a/torch-2.12.0-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:10ee1448a9f304d3b987eb4656f664ba6e4d7b410ca7a5a7c642199777a2cf88", size = 88319556, upload-time = "2026-05-13T14:54:05.574Z" }, + { url = "https://files.pythonhosted.org/packages/b7/07/055d06d985b445d67422d25b033c11cf55bbb81785d4c4e68e28bca5820e/torch-2.12.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:af68dbf403439cae9ceaeaaf92f8352b460787dcd27b92aa05c40dd4a19c0f1e", size = 426397656, upload-time = "2026-05-13T14:52:38.84Z" }, + { url = "https://files.pythonhosted.org/packages/43/94/b0b4fdc3014122e0a7302fb90086d352aa48f2576f0b252561ebb38c01a8/torch-2.12.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:a6a2eebb237d3b1d9ad3b378e86d9b9e0782afdea8b1e0eba6a13646b9b49c07", size = 532183124, upload-time = "2026-05-13T14:53:16.178Z" }, + { url = "https://files.pythonhosted.org/packages/d8/c8/052405e6ad05d3237bfe5a4df78f917773956f8e17813a2d44c059068b74/torch-2.12.0-cp313-cp313t-win_amd64.whl", hash = "sha256:2140e373e9a51a3e22ef62e8d14366d0b470d18f0adf19fdc757368077133a34", size = 123232462, upload-time = "2026-05-13T14:52:27.26Z" }, + { url = "https://files.pythonhosted.org/packages/67/dc/ac069f8d6e8be701535921141055293b0d4819d3d7f224a4612cf157c7f9/torch-2.12.0-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:f7dfae4a519197dfa050e98d8e36378a0fb5899625a875c2b54445005a2e404e", size = 88027282, upload-time = "2026-05-13T14:53:05.258Z" }, + { url = "https://files.pythonhosted.org/packages/33/c3/1c1eb00e34555b536dddf792676026a988d710ed36981aa00499b36b0620/torch-2.12.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:891c769072637c74e9a5a77a3bc782894696d8ffec83b938df8536dee7f0ba78", size = 426386961, upload-time = "2026-05-13T14:51:28.406Z" }, + { url = "https://files.pythonhosted.org/packages/cd/d4/7e730dba0c7032a4154dc9056b76cf9625515e030e269cfbf8098fcfee7d/torch-2.12.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:e2ad3eb85d39c3cab62dfa93ed5a73516e6a53c6713cb97d004004fe089f0f1f", size = 532272265, upload-time = "2026-05-13T14:51:59.308Z" }, + { url = "https://files.pythonhosted.org/packages/f1/b4/92c80d1bbfee1c0036c06d1d2155a3065bd2423134c83bf8a47e65cd6b9b/torch-2.12.0-cp314-cp314-win_amd64.whl", hash = "sha256:c66696857e987efb8bc1777a37357ec4f60ab5e8af6250b83d6034437fa2d8f3", size = 122987138, upload-time = "2026-05-13T14:51:45.942Z" }, + { url = "https://files.pythonhosted.org/packages/7b/78/2e12b37ce50a19a037d7bc62d652a5a8f27385a7b05859d6bc9204f20cfe/torch-2.12.0-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:b4556715c8572758625d62b6e0ae3b1f76c440221913a6fb5e100f321fb4fb02", size = 88320100, upload-time = "2026-05-13T14:51:39.955Z" }, + { url = "https://files.pythonhosted.org/packages/56/5e/83c450ec7b0bb40a7b74611c1b5440f9260e33c54c90d556fd4a1f0fd955/torch-2.12.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:a43ac605a5e13116c72b64c359644cce0229f213dde48d2ae0ae5eb5becf7feb", size = 426391871, upload-time = "2026-05-13T14:52:14.989Z" }, + { url = "https://files.pythonhosted.org/packages/c9/e9/1a0b575d98d0afedd8f157d23fa3d2759421483660448e60d0a4b10b6daa/torch-2.12.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:6a7512adfdd7f6732e40de1c620831e3c75b39b98cef60b11d0c5f0a76473ec5", size = 532192241, upload-time = "2026-05-13T14:51:07.795Z" }, + { url = "https://files.pythonhosted.org/packages/88/21/afadd25ecd81b3cea1e11c73cf1ab41a983a50271548c3ec7ec3b9efc3e9/torch-2.12.0-cp314-cp314t-win_amd64.whl", hash = "sha256:5f96b63f8287f66a005dd1b5a6abba2920f11156c5e5c4d815f3e2050fd1aa16", size = 123231092, upload-time = "2026-05-13T14:51:18.854Z" }, ] [[package]] @@ -3639,7 +3640,7 @@ wheels = [ [[package]] name = "torchvision" -version = "0.26.0" +version = "0.27.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "numpy" }, @@ -3647,30 +3648,30 @@ dependencies = [ { name = "torch" }, ] wheels = [ - { url = "https://files.pythonhosted.org/packages/b4/bd/d552a2521bade3295b2c6e7a4a0d1022261cab7ca7011f4e2a330dbb3caa/torchvision-0.26.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:55bd6ad4ae77be01ba67a410b05b51f53b0d0ee45f146eb6a0dfb9007e70ab3c", size = 1863499, upload-time = "2026-03-23T18:12:58.696Z" }, - { url = "https://files.pythonhosted.org/packages/33/bf/21b899792b08cae7a298551c68398a79e333697479ed311b3b067aab4bdc/torchvision-0.26.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:1c55dc8affbcc0eb2060fbabbe996ae9e5839b24bb6419777f17848945a411b1", size = 7767527, upload-time = "2026-03-23T18:12:44.348Z" }, - { url = "https://files.pythonhosted.org/packages/9a/45/57bbf9e216850d065e66dd31a50f57424b607f1d878ab8956e56a1f4e36b/torchvision-0.26.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:fd10b5f994c210f4f6d6761cf686f82d748554adf486cb0979770c3252868c8f", size = 7519925, upload-time = "2026-03-23T18:12:53.283Z" }, - { url = "https://files.pythonhosted.org/packages/10/58/ed8f7754299f3e91d6414b6dc09f62b3fa7c6e5d63dfe48d69ab81498a37/torchvision-0.26.0-cp311-cp311-win_amd64.whl", hash = "sha256:de6424b12887ad884f39a0ee446994ae3cd3b6a00a9cafe1bead85a031132af0", size = 3983834, upload-time = "2026-03-23T18:13:00.224Z" }, - { url = "https://files.pythonhosted.org/packages/ae/e7/56b47cc3b132aea90ccce22bcb8975dec688b002150012acc842846039d0/torchvision-0.26.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:c409e1c3fdebec7a3834465086dbda8bf7680eff79abf7fd2f10c6b59520a7a4", size = 1863502, upload-time = "2026-03-23T18:12:57.326Z" }, - { url = "https://files.pythonhosted.org/packages/f4/ec/5c31c92c08b65662fe9604a4067ae8232582805949f11ddc042cebe818ed/torchvision-0.26.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:406557718e62fdf10f5706e88d8a5ec000f872da913bf629aab9297622585547", size = 7767944, upload-time = "2026-03-23T18:12:42.805Z" }, - { url = "https://files.pythonhosted.org/packages/f5/d8/cb6ccda1a1f35a6597645818641701207b3e8e13553e75fce5d86bac74b2/torchvision-0.26.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:d61a5abb6b42a0c0c311996c2ac4b83a94418a97182c83b055a2a4ae985e05aa", size = 7522205, upload-time = "2026-03-23T18:12:54.654Z" }, - { url = "https://files.pythonhosted.org/packages/1c/a9/c272623a0f735c35f0f6cd6dc74784d4f970e800cf063bb76687895a2ab9/torchvision-0.26.0-cp312-cp312-win_amd64.whl", hash = "sha256:7993c01648e7c61d191b018e84d38fe0825c8fcb2720cd0f37caf7ba14404aa1", size = 4255155, upload-time = "2026-03-23T18:12:32.652Z" }, - { url = "https://files.pythonhosted.org/packages/da/80/0762f77f53605d10c9477be39bb47722cc8e383bbbc2531471ce0e396c07/torchvision-0.26.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:5d63dd43162691258b1b3529b9041bac7d54caa37eae0925f997108268cbf7c4", size = 1860809, upload-time = "2026-03-23T18:12:47.629Z" }, - { url = "https://files.pythonhosted.org/packages/e6/81/0b3e58d1478c660a5af4268713486b2df7203f35abd9195fea87348a5178/torchvision-0.26.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:a39c7a26538c41fda453f9a9692b5ff9b35a5437db1d94f3027f6f509c160eac", size = 7727494, upload-time = "2026-03-23T18:12:46.062Z" }, - { url = "https://files.pythonhosted.org/packages/b6/dc/d9ab5d29115aa05e12e30f1397a3eeae1d88a511241dc3bce48dc4342675/torchvision-0.26.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:b7e6213620bbf97742e5f79832f9e9d769e6cf0f744c5b53dad80b76db633691", size = 7521747, upload-time = "2026-03-23T18:12:36.815Z" }, - { url = "https://files.pythonhosted.org/packages/a9/1b/f1bc86a918c5f6feab1eeff11982e2060f4704332e96185463d27855bdf5/torchvision-0.26.0-cp313-cp313-win_amd64.whl", hash = "sha256:4280c35ec8cba1fcc8294fb87e136924708726864c379e4c54494797d86bc474", size = 4319880, upload-time = "2026-03-23T18:12:38.168Z" }, - { url = "https://files.pythonhosted.org/packages/66/28/b4ad0a723ed95b003454caffcc41894b34bd8379df340848cae2c33871de/torchvision-0.26.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:358fc4726d0c08615b6d83b3149854f11efb2a564ed1acb6fce882e151412d23", size = 1951973, upload-time = "2026-03-23T18:12:48.781Z" }, - { url = "https://files.pythonhosted.org/packages/71/e2/7a89096e6cf2f3336353b5338ba925e0addf9d8601920340e6bdf47e8eb3/torchvision-0.26.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:3daf9cc149cf3cdcbd4df9c59dae69ffca86c6823250442c3bbfd63fc2e26c61", size = 7728679, upload-time = "2026-03-23T18:12:26.196Z" }, - { url = "https://files.pythonhosted.org/packages/69/1d/4e1eebc17d18ce080a11dcf3df3f8f717f0efdfa00983f06e8ba79259f61/torchvision-0.26.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:82c3965eca27e86a316e31e4c3e5a16d353e0bcbe0ef8efa2e66502c54493c4b", size = 7609138, upload-time = "2026-03-23T18:12:35.327Z" }, - { url = "https://files.pythonhosted.org/packages/f3/a4/f1155e943ae5b32400d7000adc81c79bb0392b16ceb33bcf13e02e48cced/torchvision-0.26.0-cp313-cp313t-win_amd64.whl", hash = "sha256:ebc043cc5a4f0bf22e7680806dbba37ffb19e70f6953bbb44ed1a90aeb5c9bea", size = 4248202, upload-time = "2026-03-23T18:12:41.423Z" }, - { url = "https://files.pythonhosted.org/packages/7f/c8/9bffa9c7f7bdf95b2a0a2dc535c290b9f1cc580c3fb3033ab1246ffffdeb/torchvision-0.26.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:eb61804eb9dbe88c5a2a6c4da8dec1d80d2d0a6f18c999c524e32266cb1ebcd3", size = 1860813, upload-time = "2026-03-23T18:12:39.636Z" }, - { url = "https://files.pythonhosted.org/packages/7b/ac/48f28ffd227991f2e14f4392dde7e8dc14352bb9428c1ef4a4bbf5f7ed85/torchvision-0.26.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:9a904f2131cbfadab4df828088a9f66291ad33f49ff853872aed1f86848ef776", size = 7727777, upload-time = "2026-03-23T18:12:22.549Z" }, - { url = "https://files.pythonhosted.org/packages/a4/21/a2266f7f1b0e58e624ff15fd6f01041f59182c49551ece0db9a183071329/torchvision-0.26.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:0f3e572efe62ad645017ea847e0b5e4f2f638d4e39f05bc011d1eb9ac68d4806", size = 7522174, upload-time = "2026-03-23T18:12:29.565Z" }, - { url = "https://files.pythonhosted.org/packages/fc/ba/1666f90bc0bdd77aaa11dcc42bb9f621a9c3668819c32430452e3d404730/torchvision-0.26.0-cp314-cp314-win_amd64.whl", hash = "sha256:114bec0c0e98aa4ba446f63e2fe7a2cbca37b39ac933987ee4804f65de121800", size = 4348469, upload-time = "2026-03-23T18:12:24.44Z" }, - { url = "https://files.pythonhosted.org/packages/45/8f/1f0402ac55c2ae15651ff831957d083fe70b2d12282e72612a30ba601512/torchvision-0.26.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:b7d3e295624a28b3b1769228ce1345d94cf4d390dd31136766f76f2d20f718da", size = 1860826, upload-time = "2026-03-23T18:12:34.1Z" }, - { url = "https://files.pythonhosted.org/packages/d2/6a/18a582fe3c5ee26f49b5c9fb21ad8016b4d1c06d10178894a58653946fda/torchvision-0.26.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:7058c5878262937e876f20c25867b33724586aa4499e2853b2d52b99a5e51953", size = 7729089, upload-time = "2026-03-23T18:12:31.394Z" }, - { url = "https://files.pythonhosted.org/packages/c5/9b/f7e119b59499edc00c55c03adc9ec3bd96144d9b81c46852c431f9c64a9a/torchvision-0.26.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:8008474855623c6ba52876589dc52df0aa66e518c25eca841445348e5f79844c", size = 7522704, upload-time = "2026-03-23T18:12:20.301Z" }, - { url = "https://files.pythonhosted.org/packages/d0/6a/09f3844c10643f6c0de5d95abc863420cfaf194c88c7dffd0ac523e2015f/torchvision-0.26.0-cp314-cp314t-win_amd64.whl", hash = "sha256:e9d0e022c19a78552fb055d0414d47fecb4a649309b9968573daea160ba6869c", size = 4454275, upload-time = "2026-03-23T18:12:27.487Z" }, + { url = "https://files.pythonhosted.org/packages/cf/d6/a7e71e981042d5c573e2e61891b9023b190c88adb75b18bed8594371250c/torchvision-0.27.0-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:df0c166b6bdf7c47f88e81e8b43bc085451d5c50d0c5d1691bc474c1227d6fed", size = 1758812, upload-time = "2026-05-13T14:57:16.662Z" }, + { url = "https://files.pythonhosted.org/packages/93/f9/f542fb7e4476603fb237ebdc64369a7d11f18eb5a129aa2559cbdb710aee/torchvision-0.27.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:9bb9251f64b854124efed95d02953a89f7e2726c3ca662d7ea0151129157297f", size = 7831148, upload-time = "2026-05-13T14:57:08.37Z" }, + { url = "https://files.pythonhosted.org/packages/f6/61/7aa7cc2c9e8750027f6fb9ae3a7393ef43860bcdfe3966e2f71fee800e31/torchvision-0.27.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:f44453f107c296d5446a79f7ac59733ad8bf5ddfa04c53805dfbae298a42a798", size = 7575519, upload-time = "2026-05-13T14:56:50.552Z" }, + { url = "https://files.pythonhosted.org/packages/19/aa/929b358b1a643849b81ec95569938044cc37dc65ab10c84eb6d82fe1bfbb/torchvision-0.27.0-cp311-cp311-win_amd64.whl", hash = "sha256:b4aacff70ea4b7377f996f9048989c850d221fef33658ddbcae42aa5bd4ca11a", size = 3749475, upload-time = "2026-05-13T14:57:11.007Z" }, + { url = "https://files.pythonhosted.org/packages/9e/c8/5cd91932f7f3671b0743dc4ae1a4c16b1d0b45bf4087976277d325bda718/torchvision-0.27.0-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:1a6dd742a150645126df9e0b2e449874c1d635897c773b322c2e067e98382dfe", size = 1758824, upload-time = "2026-05-13T14:57:15.227Z" }, + { url = "https://files.pythonhosted.org/packages/d9/36/7fb7d19477b3d93283b52fea11fa8ee30ab9064a08c97b4a6b91445e26cb/torchvision-0.27.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:65772ff3ec4f4f5d680e30019835555dd239e7fefee4b0a846375fe1cb1592ef", size = 7831034, upload-time = "2026-05-13T14:57:06.483Z" }, + { url = "https://files.pythonhosted.org/packages/62/43/dfd894c3f8b01b5b33fde990f0159c1926ebc7b6e2c4193e2efb7da3c4cb/torchvision-0.27.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:7a9966a088d06b4cf6c610e03be62de469efa6f2cd2e7c7eed8e925ed6af59ac", size = 7579774, upload-time = "2026-05-13T14:56:59.337Z" }, + { url = "https://files.pythonhosted.org/packages/ff/0c/722e989f9cf026e97ef7cb24a9bb1859e099f72d247ae35388fb89729f73/torchvision-0.27.0-cp312-cp312-win_amd64.whl", hash = "sha256:2c037709072ca9b19750c0cbe9e8bb6f91c9a1be1befa26df33e281deccbd8c7", size = 4021073, upload-time = "2026-05-13T14:57:00.848Z" }, + { url = "https://files.pythonhosted.org/packages/d8/ae/36547812e6e047c1d80bcacd1b17a340612b08a6e876e0aabf3d0b9228b0/torchvision-0.27.0-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:41d6dae73e1af09fa82ded597ae57f2a2314285acde54b25890a8f8e51b999d7", size = 1758826, upload-time = "2026-05-13T14:57:05.262Z" }, + { url = "https://files.pythonhosted.org/packages/ae/30/32c4ea842738728a14e3df8c576c62dedcf5ae5cb6a5c984c6429ebe7524/torchvision-0.27.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:70f071c6f74b60d5fe8851636d8d4cd5f4fa29d57fd9348a87a6f17b990b95ba", size = 7789501, upload-time = "2026-05-13T14:56:57.786Z" }, + { url = "https://files.pythonhosted.org/packages/f6/24/4d0d48684251bd0673f87d633d5d88ab00227983b00591156eed2f86c8d5/torchvision-0.27.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:aaafa6962c9d91f42503de1957d6fa349907d028c06f335bd95da7a5bc57147d", size = 7579868, upload-time = "2026-05-13T14:56:41.618Z" }, + { url = "https://files.pythonhosted.org/packages/ba/da/e6edd051d2ba25adf23b120fa97f458dff888d098c51e84724f17d2d1470/torchvision-0.27.0-cp313-cp313-win_amd64.whl", hash = "sha256:aee384a2782c89517c4ab9061d2720ba59fd2ffe5ef89d0a149cc2d43abdf521", size = 4092700, upload-time = "2026-05-13T14:57:09.729Z" }, + { url = "https://files.pythonhosted.org/packages/fa/23/95dfa40431360f42ca949bf861434bed51164adfa8fb9801e05bf3194f50/torchvision-0.27.0-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:c5121f1b9ab09a7f73e837871deb8321551f7eaeb19d87aa00de9191968eae44", size = 1845008, upload-time = "2026-05-13T14:57:03.768Z" }, + { url = "https://files.pythonhosted.org/packages/23/b9/9dbdf76b2b49a75ba8088df6f7c755bdb520afb6c6dbac0102b46cde5e99/torchvision-0.27.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:1c01f0d1091ae22b9dfc082b0a0fe5faaf053686a29b4fb082ba7691375c73cf", size = 7791430, upload-time = "2026-05-13T14:56:56.206Z" }, + { url = "https://files.pythonhosted.org/packages/5c/6a/e4a16cf2f3310c2ea7760dc5d9054496844391e0f4c1fae87fefac2f3d9e/torchvision-0.27.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:dadea3c5ecfd05bbb2a3312ab0374f213c58bf6459cb059122e2f4dfe13d10ed", size = 7668441, upload-time = "2026-05-13T14:57:02.127Z" }, + { url = "https://files.pythonhosted.org/packages/00/70/01b6461117a6a94b5af3f8ee166bb0f045056f3cf187750c110dabfdfffa/torchvision-0.27.0-cp313-cp313t-win_amd64.whl", hash = "sha256:a49e55055a39a8506fe7e59850522cab004efb2c3839f6057658889c1d69c815", size = 4141602, upload-time = "2026-05-13T14:56:53.449Z" }, + { url = "https://files.pythonhosted.org/packages/92/22/c0633677b3b3f3e69554a21ac087bf705f829c40cd5e3783507b8c006681/torchvision-0.27.0-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:c1fac0fc2a7adf29481fc1938a0e7845c57ba1147a986784109c4d98f434ea8c", size = 1758818, upload-time = "2026-05-13T14:56:54.988Z" }, + { url = "https://files.pythonhosted.org/packages/48/e8/55f9d9667b56dae470e69e31beac9b00d458ea393feec1aae95cc4f3f1c9/torchvision-0.27.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:cbf89764fc76f3f17fbf80c12d5a89c691e91cb9d82c38412aaf0568655ffb19", size = 7789667, upload-time = "2026-05-13T14:56:48.858Z" }, + { url = "https://files.pythonhosted.org/packages/00/bc/6f8681daf3bbc4c315bb0005110f99d28e3ecd675bf9c8f2c0d393fbac7a/torchvision-0.27.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:91f61b9865423037c327eb56afa207cc72de874e458c361840db9dcf5ce0c0eb", size = 7579848, upload-time = "2026-05-13T14:56:38.209Z" }, + { url = "https://files.pythonhosted.org/packages/19/6c/8d8020e6bd1e46c53e487c9c4e9457a07f2ee28931028fb5d71e2da40adc/torchvision-0.27.0-cp314-cp314-win_amd64.whl", hash = "sha256:5bb82fc3c55daf1788621e504310b0a286f1069627a8742f692aebb075ef25a7", size = 4119284, upload-time = "2026-05-13T14:56:46.625Z" }, + { url = "https://files.pythonhosted.org/packages/8d/7e/e78c48662a8d551606efdbe11c6b9c1d6d2391b92cd0e4591b9e6a2412b8/torchvision-0.27.0-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:2c4099a15150143b9b034730b404a56d572efe0b79489b4c765d929cb4eac7f3", size = 1758828, upload-time = "2026-05-13T14:56:52.293Z" }, + { url = "https://files.pythonhosted.org/packages/21/dd/d03ee9f9ee7bf11a8c7c776fb8e7fd6102f59c013791a2a4e5175bd6cba7/torchvision-0.27.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:b4c6bb0a670dcba017b3643e21902c9b8a1cc1c127d602f1488fa29ec3c6e865", size = 7790618, upload-time = "2026-05-13T14:56:44.721Z" }, + { url = "https://files.pythonhosted.org/packages/39/08/4002336a74742be70728603ec1769feb2b55e0d19c532c9ec9f92008de76/torchvision-0.27.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:1c2db4bde82bc48ebff73436a6adf34d4f809448268a70d9a1285f5c8f92313d", size = 7580217, upload-time = "2026-05-13T14:56:43.274Z" }, + { url = "https://files.pythonhosted.org/packages/ed/cb/4dd4783eb3565f526ba6e64b6f6ca26c00eacc924cdfe60455db9d91b84b/torchvision-0.27.0-cp314-cp314t-win_amd64.whl", hash = "sha256:72bf547e58ddb948689734eed6f4b6a2031f979dba4fb08e3690688b392e929f", size = 4226392, upload-time = "2026-05-13T14:56:40.235Z" }, ] [[package]] @@ -3713,21 +3714,21 @@ wheels = [ [[package]] name = "triton" -version = "3.6.0" +version = "3.7.0" source = { registry = "https://pypi.org/simple" } wheels = [ - { url = "https://files.pythonhosted.org/packages/0f/2c/96f92f3c60387e14cc45aed49487f3486f89ea27106c1b1376913c62abe4/triton-3.6.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:49df5ef37379c0c2b5c0012286f80174fcf0e073e5ade1ca9a86c36814553651", size = 176081190, upload-time = "2026-01-20T16:16:00.523Z" }, - { url = "https://files.pythonhosted.org/packages/e0/12/b05ba554d2c623bffa59922b94b0775673de251f468a9609bc9e45de95e9/triton-3.6.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e8e323d608e3a9bfcc2d9efcc90ceefb764a82b99dea12a86d643c72539ad5d3", size = 188214640, upload-time = "2026-01-20T16:00:35.869Z" }, - { url = "https://files.pythonhosted.org/packages/17/5d/08201db32823bdf77a0e2b9039540080b2e5c23a20706ddba942924ebcd6/triton-3.6.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:374f52c11a711fd062b4bfbb201fd9ac0a5febd28a96fb41b4a0f51dde3157f4", size = 176128243, upload-time = "2026-01-20T16:16:07.857Z" }, - { url = "https://files.pythonhosted.org/packages/ab/a8/cdf8b3e4c98132f965f88c2313a4b493266832ad47fb52f23d14d4f86bb5/triton-3.6.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:74caf5e34b66d9f3a429af689c1c7128daba1d8208df60e81106b115c00d6fca", size = 188266850, upload-time = "2026-01-20T16:00:43.041Z" }, - { url = "https://files.pythonhosted.org/packages/3c/12/34d71b350e89a204c2c7777a9bba0dcf2f19a5bfdd70b57c4dbc5ffd7154/triton-3.6.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:448e02fe6dc898e9e5aa89cf0ee5c371e99df5aa5e8ad976a80b93334f3494fd", size = 176133521, upload-time = "2026-01-20T16:16:13.321Z" }, - { url = "https://files.pythonhosted.org/packages/f9/0b/37d991d8c130ce81a8728ae3c25b6e60935838e9be1b58791f5997b24a54/triton-3.6.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:10c7f76c6e72d2ef08df639e3d0d30729112f47a56b0c81672edc05ee5116ac9", size = 188289450, upload-time = "2026-01-20T16:00:49.136Z" }, - { url = "https://files.pythonhosted.org/packages/ce/4e/41b0c8033b503fd3cfcd12392cdd256945026a91ff02452bef40ec34bee7/triton-3.6.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1722e172d34e32abc3eb7711d0025bb69d7959ebea84e3b7f7a341cd7ed694d6", size = 176276087, upload-time = "2026-01-20T16:16:18.989Z" }, - { url = "https://files.pythonhosted.org/packages/35/f8/9c66bfc55361ec6d0e4040a0337fb5924ceb23de4648b8a81ae9d33b2b38/triton-3.6.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d002e07d7180fd65e622134fbd980c9a3d4211fb85224b56a0a0efbd422ab72f", size = 188400296, upload-time = "2026-01-20T16:00:56.042Z" }, - { url = "https://files.pythonhosted.org/packages/49/55/5ecf0dcaa0f2fbbd4420f7ef227ee3cb172e91e5fede9d0ecaddc43363b4/triton-3.6.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ef5523241e7d1abca00f1d240949eebdd7c673b005edbbce0aca95b8191f1d43", size = 176138577, upload-time = "2026-01-20T16:16:25.426Z" }, - { url = "https://files.pythonhosted.org/packages/df/3d/9e7eee57b37c80cec63322c0231bb6da3cfe535a91d7a4d64896fcb89357/triton-3.6.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a17a5d5985f0ac494ed8a8e54568f092f7057ef60e1b0fa09d3fd1512064e803", size = 188273063, upload-time = "2026-01-20T16:01:07.278Z" }, - { url = "https://files.pythonhosted.org/packages/48/db/56ee649cab5eaff4757541325aca81f52d02d4a7cd3506776cad2451e060/triton-3.6.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0b3a97e8ed304dfa9bd23bb41ca04cdf6b2e617d5e782a8653d616037a5d537d", size = 176274804, upload-time = "2026-01-20T16:16:31.528Z" }, - { url = "https://files.pythonhosted.org/packages/f6/56/6113c23ff46c00aae423333eb58b3e60bdfe9179d542781955a5e1514cb3/triton-3.6.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:46bd1c1af4b6704e554cad2eeb3b0a6513a980d470ccfa63189737340c7746a7", size = 188397994, upload-time = "2026-01-20T16:01:14.236Z" }, + { url = "https://files.pythonhosted.org/packages/b8/c1/5d842314bb6c78442cc60437928781701c6050b8d479bc2a1aed691d37ca/triton-3.7.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a9e71fc392675fac364e0ecf4ef3f76f85b7f5433a16f4c3c5fe5f05a52c85fe", size = 188480277, upload-time = "2026-05-07T19:05:03.231Z" }, + { url = "https://files.pythonhosted.org/packages/13/31/8315ea5f8dd18e60970b3022e3a8b93fd37e0b784fbbef86e10c8e6e5ca1/triton-3.7.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:22bacffce443f54593dd20f05294d5a40622e0ea9ab632816f87154504356221", size = 201415942, upload-time = "2026-05-07T18:46:06.479Z" }, + { url = "https://files.pythonhosted.org/packages/f7/13/ec05adfcd87311d532ba61e3af143e8be59fcd26675884c4682841406a20/triton-3.7.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a4bf49b00a7a377a68a6da603a876e797614e6455a80e9021669c476a953ad9a", size = 188505104, upload-time = "2026-05-07T19:05:09.843Z" }, + { url = "https://files.pythonhosted.org/packages/62/7b/468a576e35beef1426e0828e28e9ba9e65f5474d496f16ee126c15646324/triton-3.7.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8f111161d49bf903c0eaedde3962353a3d841c08a836839b7cc1025b8426efcf", size = 201457567, upload-time = "2026-05-07T18:46:13.505Z" }, + { url = "https://files.pythonhosted.org/packages/01/e1/a59a583de59b8f62c495d67c80ee3ea97d09e91ac80c4c6e76456ed8d8ac/triton-3.7.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:abdf6beaa89b1bcfb9a43cd990536ce66091a997841a4814b260b7bee4c88c3c", size = 188503209, upload-time = "2026-05-07T19:05:17.935Z" }, + { url = "https://files.pythonhosted.org/packages/30/b1/b7507bb9815d403927c8dd51d4158ed2e11751a92dbc118a044f247b6848/triton-3.7.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a35d7afe3f3f058e7ec49fcce09794049e0ffc5c59019ac25ec3413741b8c4e7", size = 201453566, upload-time = "2026-05-07T18:46:20.427Z" }, + { url = "https://files.pythonhosted.org/packages/a6/8f/0bea7a6a0c989315c9135a1d7fb37e41905cfb3a17cbc1f10044ebd4cc3a/triton-3.7.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:cc1d61c172d257db80ddf42595131fb196ad2e9bdd751e90fe2ef13531734e8b", size = 188612899, upload-time = "2026-05-07T19:05:24.955Z" }, + { url = "https://files.pythonhosted.org/packages/e1/02/d96f57828d0912aec733b9bc7e0e7dbfd2c6f079a8fa433ac25cb93d1a30/triton-3.7.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:70fb9bbdc9f400afc54bbf6eb2670af28829a6ae3996863317964783141daf56", size = 201553816, upload-time = "2026-05-07T18:46:27.49Z" }, + { url = "https://files.pythonhosted.org/packages/40/fb/82a802dac4689f2a2fb2e69302e6a138eecc3e175bbe976ba3cfc717683a/triton-3.7.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c4a44a8476d0d3571eac4e4d1048e1ff75aad81a09ff4602ccfc56c6dea1672e", size = 188507879, upload-time = "2026-05-07T19:05:32.209Z" }, + { url = "https://files.pythonhosted.org/packages/8f/af/9904ec6d3c93d9b24e5ec360445bbdf758b7f00bfbeedb89cb0eb64eb8bb/triton-3.7.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b9b85e72968a9d8bba5ddb24e9b64aaabaf48affb042f2755cb7cfa92b7531ce", size = 201460637, upload-time = "2026-05-07T18:46:34.749Z" }, + { url = "https://files.pythonhosted.org/packages/a1/f9/4835a8ea746b88727d8899f4e3ccce4f9cacb38abfc3bb0a638266c53111/triton-3.7.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:18a160de426fd99f92b0baf509045360afbd3bfaa0b4a5171dde800ec9f09684", size = 188608706, upload-time = "2026-05-07T19:05:39.218Z" }, + { url = "https://files.pythonhosted.org/packages/c1/68/fa86e5a39608000f645535b2c124920126327ab731f8c4fafd5b07ff8d4b/triton-3.7.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ce061073102714b725f3660ec6939d94a1da7984b3aa99c921417cae273672f5", size = 201546766, upload-time = "2026-05-07T18:46:42.088Z" }, ] [[package]] @@ -3768,7 +3769,7 @@ wheels = [ [[package]] name = "virtualenv" -version = "21.3.1" +version = "21.3.3" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "distlib" }, @@ -3776,9 +3777,9 @@ dependencies = [ { name = "platformdirs" }, { name = "python-discovery" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/ec/0d/915c02c94d207b85580eb09bffab54438a709e7288524094fe781da526c2/virtualenv-21.3.1.tar.gz", hash = "sha256:c2305bc1fddeec40699b8370d13f8d431b0701f00ce895061ce493aeded4426b", size = 7613791, upload-time = "2026-05-05T01:34:31.402Z" } +sdist = { url = "https://files.pythonhosted.org/packages/15/ba/1f6e8c957e4932be060dcdc482d339c12e0216351478add3645cdaa53c05/virtualenv-21.3.3.tar.gz", hash = "sha256:f5bda277e553b1c2b3c1a8debfc30496e1288cc93ce6b7b71b3280047e317328", size = 7613784, upload-time = "2026-05-13T18:01:30.19Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/b1/4f/f71e641e504111a5a74e3a20bc52d01bd86788b22699dd3fee1c63253cf6/virtualenv-21.3.1-py3-none-any.whl", hash = "sha256:d1a71cf58f2f9228fff23a1f6ec15d39785c6b32e03658d104974247145edd35", size = 7594539, upload-time = "2026-05-05T01:34:28.98Z" }, + { url = "https://files.pythonhosted.org/packages/f4/34/a9dbe051de88a63eb7408ea66630bac38e72f7f6077d4be58737106860d9/virtualenv-21.3.3-py3-none-any.whl", hash = "sha256:7d5987d8369e098e41406efb780a3d4ca79280097293899e351a6407ee153ab3", size = 7594554, upload-time = "2026-05-13T18:01:27.815Z" }, ] [[package]] From adcc26dc018230d67641c0676d71b4cb877b4c2a Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 11:16:02 -0700 Subject: [PATCH 056/140] Created a BaseContext class - totally optional, not sure if anyone uses this class anyways currently. Type-hinting error fixed in tomography_otp.py --- src/quantem/core/ml/constraints.py | 8 +++++++- src/quantem/tomography/tomography_context.py | 3 ++- src/quantem/tomography/tomography_opt.py | 2 +- 3 files changed, 10 insertions(+), 3 deletions(-) diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index f437924f..13011095 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -9,6 +9,12 @@ from quantem.tomography.tomography_context import ReconstructionContext +@dataclass +class BaseContext(ABC): + """ + Constraints should contain a context object that contains all necessary data for the constraints to be applied. + """ + pass @dataclass(slots=False) class Constraints(ABC): @@ -95,7 +101,7 @@ def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: raise NotImplementedError @abstractmethod - def apply_soft_constraints(self, ctx: ReconstructionContext) -> torch.Tensor: + def apply_soft_constraints(self, ctx: BaseContext) -> torch.Tensor: """ Apply soft constraints to the model. """ diff --git a/src/quantem/tomography/tomography_context.py b/src/quantem/tomography/tomography_context.py index 4b67f118..2095727e 100644 --- a/src/quantem/tomography/tomography_context.py +++ b/src/quantem/tomography/tomography_context.py @@ -1,11 +1,12 @@ from dataclasses import dataclass from typing import Optional +from quantem.core.ml.constraints import BaseContext import torch @dataclass -class ReconstructionContext: +class ReconstructionContext(BaseContext): """ Handles all reconstruction parameters to be passed into object models. diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index c75de1e3..2c913277 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -27,7 +27,7 @@ def _get_default_lr(self, key: str) -> float: raise ValueError(f"Unknown optimization key: {key}") @property - def optimizer_params(self) -> dict[str, OptimizerType]: + def optimizer_params(self) -> dict[str, OptimizerType | dict[str, OptimizerType]]: return { key: params for key, params in [ From af33b962b1f8a7c47dd1ddf0702309beed185810 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 11:18:42 -0700 Subject: [PATCH 057/140] Removed import in constraints.py --- src/quantem/core/ml/constraints.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index 13011095..83270400 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -7,7 +7,6 @@ import torch from numpy.typing import NDArray -from quantem.tomography.tomography_context import ReconstructionContext @dataclass class BaseContext(ABC): From 03da610c5a3ce34c601058bfb243ba1d4d8ba42c Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 11:21:41 -0700 Subject: [PATCH 058/140] PPLR description changed to multi-parameter optimization --- src/quantem/core/ml/models/model_base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/ml/models/model_base.py b/src/quantem/core/ml/models/model_base.py index 503f61bb..60c1c4f6 100644 --- a/src/quantem/core/ml/models/model_base.py +++ b/src/quantem/core/ml/models/model_base.py @@ -6,7 +6,7 @@ class PPLR(ABC): """ - Abstract base class for models that require multi-scale parameter optimization. + Abstract base class for models that require multi-parameter optimization. """ @abstractmethod From a62295c3b6499a95a7ea01125069c6b5710a7d1e Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 11:31:16 -0700 Subject: [PATCH 059/140] SO3 Rotations paper citation in SO3params.py --- src/quantem/core/ml/models/so3params.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/quantem/core/ml/models/so3params.py b/src/quantem/core/ml/models/so3params.py index 6569ec93..fd3a0b7e 100644 --- a/src/quantem/core/ml/models/so3params.py +++ b/src/quantem/core/ml/models/so3params.py @@ -113,6 +113,8 @@ class SO3ParamR9SVD(nn.Module): SO(3) rotation bank using R9+SVD parameterization. Each rotation is stored as an unconstrained 3x3 matrix M, projected to SO(3) via SVD+(M) = U diag(1,1,det(UVt)) Vt. + + Based on Rene Geist et al., 2024: https://arxiv.org/abs/2404.11735v1 """ def __init__(self, T: int, init: str = "random"): From e222512ab6abde241ff8a189bca5966d08575df6 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 11:32:14 -0700 Subject: [PATCH 060/140] Type-hinting fix in So3params --- src/quantem/core/ml/models/so3params.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/quantem/core/ml/models/so3params.py b/src/quantem/core/ml/models/so3params.py index fd3a0b7e..82a4361a 100644 --- a/src/quantem/core/ml/models/so3params.py +++ b/src/quantem/core/ml/models/so3params.py @@ -1,4 +1,5 @@ import math +from typing import Literal import torch import torch.nn as nn @@ -117,7 +118,7 @@ class SO3ParamR9SVD(nn.Module): Based on Rene Geist et al., 2024: https://arxiv.org/abs/2404.11735v1 """ - def __init__(self, T: int, init: str = "random"): + def __init__(self, T: int, init: Literal["random", "identity"] = "random"): super().__init__() if init == "random": # Initialize near identity with small noise From 91d605631c052616df5f6c1d062d22b47494f22e Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 14:42:18 -0700 Subject: [PATCH 061/140] Load parameters added .to(self.device) --- src/quantem/core/utils/tomography_utils.py | 1 - src/quantem/tomography/dataset_models.py | 6 +++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/src/quantem/core/utils/tomography_utils.py b/src/quantem/core/utils/tomography_utils.py index 9f5df102..006f8cfc 100644 --- a/src/quantem/core/utils/tomography_utils.py +++ b/src/quantem/core/utils/tomography_utils.py @@ -168,7 +168,6 @@ def fourier_binning(img, crop_size): center = np.array(img.shape) // 2 fft_img = np.fft.fftshift(np.fft.fft2(img)) - cropped_fft = fft_img[ center[0] - crop_size[0] // 2 : center[0] + crop_size[0] // 2, center[1] - crop_size[1] // 2 : center[1] + crop_size[1] // 2, diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index b50c5976..bdd2e66a 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -668,9 +668,9 @@ def load_parameters(self, path: str): Loads the learned parameters from a file. """ data = torch.load(path) - self._z1_params = nn.Parameter(data["z1"]) - self._z3_params = nn.Parameter(data["z3"]) - self._shifts_params = nn.Parameter(data["shifts"]) + self._z1_params = nn.Parameter(data["z1"]).to(self.device) + self._z3_params = nn.Parameter(data["z3"]).to(self.device) + self._shifts_params = nn.Parameter(data["shifts"]).to(self.device) if self.optimizer is not None: self.reconnect_optimizer_to_parameters() From f37ca9f06b12131dcf5c35eda29ed1fe4a35b080 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 14:49:31 -0700 Subject: [PATCH 062/140] Volume added to reconstruction context --- src/quantem/tomography/tomography_context.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/quantem/tomography/tomography_context.py b/src/quantem/tomography/tomography_context.py index 2095727e..ef861651 100644 --- a/src/quantem/tomography/tomography_context.py +++ b/src/quantem/tomography/tomography_context.py @@ -16,6 +16,7 @@ class ReconstructionContext(BaseContext): - TensorDecomp reads ".coords" and ".pred" (and ".all densities") """ + volume: Optional[torch.Tensor] = None coords: Optional[torch.Tensor] = None pred: Optional[torch.Tensor] = None all_densities: Optional[torch.Tensor] = None From 8072fa6bc85b3df8871c60eadecff31af3b5c8f6 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 14:52:04 -0700 Subject: [PATCH 063/140] Citations for the kplanes models --- src/quantem/core/ml/models/kplanes.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index fe36dfcc..cdf55261 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -166,6 +166,9 @@ def interpolate_ms_features( class KPlanes(PPLR, TensorDecompositionModel): + """ + K-Planes model adapted from Fridovich-Keil et al., https://arxiv.org/abs/2301.10241 + """ def __init__( self, # Grid parameters @@ -354,7 +357,7 @@ def interpolate_ms_features_tilted( class KPlanesTILTED(KPlanes): """ - K-Planes with T learned SO(3) rotations (TILTED). + K-Planes with T learned SO(3) rotations (TILTED). Adapted from Yi et al., https://arxiv.org/abs/2308.15461 Inherits KPlanes for the sigma_net, density_activation, and get_params interface. Overrides: @@ -646,7 +649,7 @@ def interpolate_ms_features_cp_tilted( class CPTilted(PPLR, TensorDecompositionModel): """ CP decomposition with TILTED rotations — the true bottleneck model for - phase 1. Rank-1-per-channel feature representation. + phase 1. Rank-1-per-channel feature representation. Adapted from Yi et al., https://arxiv.org/abs/2308.15461 Shares the SO3Param and sigma_net design with KPlanesTILTED so you can lift τ directly across: cp_model.extract_tau_state() -> From eafca0c791e3fb07fe3cb003479b3bcb8ff29c74 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 14:54:15 -0700 Subject: [PATCH 064/140] Explanation in object_models.py for different types of tv loss. --- src/quantem/tomography/object_models.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index a094fae8..fc2669b7 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -932,6 +932,12 @@ def apply_soft_constraints(self, ctx: ReconstructionContext) -> torch.Tensor: # TV Losses def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: + """ + Gets the summed total variational loss for the tensor decomposition model. + + _get_plane_tv_loss: Total-variation across the planes. + _get_volume_tv_loss: Isotropic volume TV + """ assert ctx.coords is not None, "Coordinates must be provided for TV loss" assert ctx.pred is not None, "Prediction must be provided for TV loss" tv_loss = torch.tensor(0.0, device=ctx.pred.device) @@ -940,6 +946,9 @@ def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: return tv_loss def _get_plane_tv_loss(self) -> torch.Tensor: + """ + Gets the total-variation across the planes. + """ is_tilted = self.model.tilted per_level = [] From e4c8dbe21f600554d5450b89c20000773d6e3f58 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 14:58:07 -0700 Subject: [PATCH 065/140] Refactor type hinting in _unwrap function to use cast for improved type safety --- src/quantem/tomography/object_models.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index fc2669b7..8fa19db7 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1,7 +1,7 @@ from abc import abstractmethod from copy import deepcopy from dataclasses import dataclass -from typing import Any, Callable, Generator, Optional +from typing import Any, Callable, Generator, Optional, cast import numpy as np import torch @@ -200,8 +200,8 @@ def parse_dict( def _unwrap(model: nn.Module | nn.parallel.DistributedDataParallel) -> PlanarDecompositionModel: """Unwrap a DistributedDataParallel model to get the underlying module ONLY for tensor decomposition models.""" if isinstance(model, nn.parallel.DistributedDataParallel): - return model.module - return model + return cast(PlanarDecompositionModel, model.module) + return cast(PlanarDecompositionModel, model) class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): From 136a65f059c5208947e92e2ffd92366a1ce68df1 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 15:03:07 -0700 Subject: [PATCH 066/140] Some type-hinting fix for Contexts --- src/quantem/core/ml/constraints.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index 83270400..fea1d309 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -1,12 +1,13 @@ from abc import ABC, abstractmethod from copy import deepcopy from dataclasses import dataclass -from typing import Any, Self +from typing import Any, Generic, Self, TypeVar import numpy as np import torch from numpy.typing import NDArray +T_ctx = TypeVar("T_ctx", bound=BaseContext) @dataclass class BaseContext(ABC): @@ -54,7 +55,7 @@ def __str__(self) -> str: ) -class BaseConstraints(ABC): +class BaseConstraints(ABC, Generic[T_ctx]): """ Base class for constraints. """ @@ -100,7 +101,7 @@ def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: raise NotImplementedError @abstractmethod - def apply_soft_constraints(self, ctx: BaseContext) -> torch.Tensor: + def apply_soft_constraints(self, ctx: T_ctx) -> torch.Tensor: """ Apply soft constraints to the model. """ From 98ddd0d34e9ed51f63003131807f42f6843d7a16 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 18 May 2026 22:36:44 -0700 Subject: [PATCH 067/140] dataset4d, dataset4dstem hold torch array --- src/quantem/core/datastructures/dataset.py | 104 +++++++++++++----- src/quantem/core/datastructures/dataset4d.py | 33 ++---- .../core/datastructures/dataset4dstem.py | 52 ++++++++- widget/src/quantem/widget/show4dstem.py | 11 +- 4 files changed, 141 insertions(+), 59 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 4d2ab9e1..786b3d5d 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -4,6 +4,7 @@ from typing import Any, Literal, Optional, Self, Union, overload import numpy as np +import torch from numpy.typing import DTypeLike, NDArray from quantem.core.io.serialize import AutoSerialize @@ -38,24 +39,39 @@ class Dataset(AutoSerialize): def __init__( self, - array: Any, # Input can be array-like - name: str, - origin: NDArray | tuple | list | float | int, - sampling: NDArray | tuple | list | float | int, - units: list[str] | tuple | list, + array: NDArray | None = None, + tensor: torch.Tensor | None = None, + name: str = "", + origin: NDArray | tuple | list | float | int | None = None, + sampling: NDArray | tuple | list | float | int | None = None, + units: list[str] | tuple | list | None = None, signal_units: str = "arb. units", metadata: Optional[dict] = None, _token: object | None = None, ): if _token is not self._token: - raise RuntimeError("Use Dataset.from_array() to instantiate this class.") - super().__init__() - arr = ensure_valid_array(array) - if not isinstance(arr, np.ndarray): - raise TypeError( - "Dataset requires a NumPy array (CuPy is not supported on this branch)." + raise RuntimeError( + "Use Dataset.from_array() or Dataset.from_tensor() to instantiate this class." ) - self._array = arr + super().__init__() + # Dual-slot storage: exactly one of (_array, _tensor) is set. + if array is None and tensor is None: + raise ValueError("Provide either `array` (numpy) or `tensor` (torch).") + if array is not None and tensor is not None: + raise ValueError("Provide only one of `array` or `tensor`, not both.") + if array is not None: + arr = ensure_valid_array(array) + if not isinstance(arr, np.ndarray): + raise TypeError(f"Dataset.array must be numpy.ndarray, got {type(arr).__name__}.") + self._array = arr + self._tensor = None + else: + if not isinstance(tensor, torch.Tensor): + raise TypeError(f"Dataset.tensor must be torch.Tensor, got {type(tensor).__name__}.") + self._array = None + self._tensor = tensor + # Lazy cache: derived numpy from tensor, materialized only on first .array access. + self._cached_numpy: np.ndarray | None = None self.name = name self.origin = origin self.sampling = sampling @@ -123,18 +139,34 @@ def from_array( # --- Properties --- @property def array(self) -> NDArray: - """The underlying n-dimensional NumPy array data.""" - return self._array + """The data as a numpy array. + + For tensor-backed datasets, returns a CACHED read-only CPU copy derived + from ``self.tensor`` (first access pays GPU->CPU transfer, subsequent + accesses are free). Torch-aware consumers should prefer ``.tensor``. + """ + if self._array is not None: + return self._array + if self._cached_numpy is None: + self._cached_numpy = self._tensor.detach().cpu().numpy() + self._cached_numpy.flags.writeable = False + return self._cached_numpy @array.setter def array(self, value: NDArray) -> None: - arr = ensure_valid_array(value, ndim=self.ndim) # want to allow changing dtype + arr = ensure_valid_array(value, ndim=self.ndim) if not isinstance(arr, np.ndarray): - raise TypeError( - "Dataset requires a NumPy array (CuPy is not supported on this branch)." - ) + raise TypeError(f"Dataset.array must be numpy.ndarray, got {type(arr).__name__}.") self._array = arr - # self._array = ensure_valid_array(value, dtype=self.dtype, ndim=self.ndim) + + @property + def tensor(self) -> torch.Tensor: + """Torch tensor backing the data. AttributeError if numpy-backed.""" + if self._tensor is None: + raise AttributeError( + f"Dataset '{self.name}' is numpy-backed; use Dataset.from_tensor() at construction." + ) + return self._tensor @property def metadata(self) -> dict: @@ -191,26 +223,42 @@ def file_path(self, value: os.PathLike | str | None) -> None: # --- Derived Properties --- @property def shape(self) -> tuple[int, ...]: - return self.array.shape + # Direct slot access — never triggers .array derive (which would force + # a full GPU->CPU copy on tensor-backed datasets). + return tuple((self._array if self._array is not None else self._tensor).shape) @property def ndim(self) -> int: - return self.array.ndim + return (self._array if self._array is not None else self._tensor).ndim @property def dtype(self) -> DTypeLike: - return self.array.dtype + return (self._array if self._array is not None else self._tensor).dtype @property def device(self) -> str: - """ - Outputting a string is likely temporary -- once we have our use cases we can - figure out a more permanent device solution that enables easier translation between - numpy <-> torch <-> numpy, etc. + """``"cpu"`` for numpy-backed; torch device string for tensor-backed.""" + if self._tensor is not None: + return str(self._tensor.device) + return "cpu" + + def numpy(self) -> NDArray: + """Return the data as a numpy array (mirrors ``torch.Tensor.numpy()``). - For NumPy-only datasets, this is always "cpu". + Equivalent to ``self.array`` — both return numpy. For tensor-backed + datasets, first call materializes a cached read-only CPU copy. """ - return "cpu" + return self.array + + def to(self, device) -> Self: + """Move the underlying tensor to ``device``. Raises if numpy-backed.""" + if self._tensor is None: + raise AttributeError( + f"Cannot .to({device!r}) on numpy-backed Dataset '{self.name}'." + ) + self._tensor = self._tensor.to(device) + self._cached_numpy = None # invalidate stale derived numpy + return self # --- Summaries --- def __repr__(self) -> str: diff --git a/src/quantem/core/datastructures/dataset4d.py b/src/quantem/core/datastructures/dataset4d.py index 8e5bdfa0..80d219ea 100644 --- a/src/quantem/core/datastructures/dataset4d.py +++ b/src/quantem/core/datastructures/dataset4d.py @@ -1,6 +1,7 @@ from typing import Any, Self, Union import numpy as np +import torch from numpy.typing import NDArray from quantem.core.datastructures.dataset import Dataset @@ -21,36 +22,20 @@ class Dataset4d(Dataset): def __init__( self, - array: NDArray | Any, - name: str, - origin: NDArray | tuple | list | float | int, - sampling: NDArray | tuple | list | float | int, - units: list[str] | tuple | list, + array: NDArray | None = None, + tensor: torch.Tensor | None = None, + name: str = "", + origin: NDArray | tuple | list | float | int | None = None, + sampling: NDArray | tuple | list | float | int | None = None, + units: list[str] | tuple | list | None = None, signal_units: str = "arb. units", metadata: dict = {}, _token: object | None = None, ): - """Initialize a 4D dataset. - - Parameters - ---------- - array : NDArray | Any - The underlying 3D array data - name : str - A descriptive name for the dataset - origin : NDArray | tuple | list | float | int - The origin coordinates for each dimension in calibrated units - sampling : NDArray | tuple | list | float | int - The sampling rate/spacing for each dimension - units : list[str] | tuple | list - Units for each dimension - signal_units : str, optional - Units for the array values, by default "arb. units" - _token : object | None, optional - Token to prevent direct instantiation, by default None - """ + """Initialize a 4D dataset. Pass exactly one of ``array`` (numpy) or ``tensor`` (torch).""" super().__init__( array=array, + tensor=tensor, name=name, origin=origin, sampling=sampling, diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 79cbc479..60890475 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -2,6 +2,7 @@ import matplotlib.pyplot as plt import numpy as np +import torch from matplotlib.patches import Circle, Wedge from numpy.typing import NDArray @@ -41,11 +42,12 @@ class Dataset4dstem(Dataset4d): def __init__( self, - array: NDArray | Any, - name: str, - origin: NDArray | tuple | list | float | int, - sampling: NDArray | tuple | list | float | int, - units: list[str] | tuple | list, + array: NDArray | None = None, + tensor: torch.Tensor | None = None, + name: str = "", + origin: NDArray | tuple | list | float | int | None = None, + sampling: NDArray | tuple | list | float | int | None = None, + units: list[str] | tuple | list | None = None, signal_units: str = "arb. units", metadata: dict = {}, _token: object | None = None, @@ -79,6 +81,7 @@ def __init__( super().__init__( array=array, + tensor=tensor, name=name, origin=origin, sampling=sampling, @@ -157,6 +160,45 @@ def from_array( _token=cls._token, ) + @classmethod + def from_tensor( + cls, + tensor: torch.Tensor, + name: str | None = None, + origin: NDArray | tuple | list | float | int | None = None, + sampling: NDArray | tuple | list | float | int | None = None, + units: list[str] | tuple | list | None = None, + signal_units: str = "arb. units", + metadata: dict | None = None, + ) -> Self: + """Create a Dataset4dstem from a torch tensor (any device). + + Use this when raw data is GPU-resident (CUDA pipelines, live detector + frames, GPU file readers) to skip the VRAM<->RAM round-trip. + + For cupy / jax arrays, wrap with ``torch.from_dlpack(arr)`` first. + """ + if not isinstance(tensor, torch.Tensor): + raise TypeError( + f"from_tensor requires torch.Tensor, got {type(tensor).__name__}. " + f"For cupy / jax, wrap with `torch.from_dlpack(arr)` first." + ) + if tensor.ndim != 4: + raise ValueError( + f"Dataset4dstem.from_tensor requires a 4D tensor " + f"(scan_y, scan_x, dp_y, dp_x), got shape {tuple(tensor.shape)}." + ) + return cls( + tensor=tensor, + name=name if name is not None else "4D-STEM dataset (torch)", + origin=origin if origin is not None else np.zeros(4), + sampling=sampling if sampling is not None else np.ones(4), + units=units if units is not None else ["pixels"] * 4, + signal_units=signal_units, + metadata=metadata if metadata is not None else {}, + _token=cls._token, + ) + @property def virtual_images(self) -> dict[str, Dataset2d]: """ diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 0a6b256b..06f8b629 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -334,14 +334,21 @@ def __init__( _io_labels = None # Auto-extract sampling + units from Dataset4dstem if available. - if hasattr(data, "sampling") and hasattr(data, "array"): + # NOTE: avoid `hasattr(data, "array")` — for tensor-backed Datasets the + # `.array` getter is an expensive derive (full GPU->CPU copy). Use cheap + # `hasattr(data, "sampling")` to identify a Dataset. + if hasattr(data, "sampling"): if not title and hasattr(data, "name") and data.name: title = str(data.name) if sampling is None: sampling = tuple(float(s) for s in data.sampling) if units is None and hasattr(data, "units"): units = list(data.units) - data = data.array + # If tensor-backed (zero-copy GPU path), take .tensor. Else .array (numpy). + if getattr(data, "_tensor", None) is not None: + data = data.tensor + else: + data = data.array # Resolve sampling + units (4 axes for 4D-STEM): # [scan_row, scan_col, k_row, k_col]. Scalar/None broadcast to (1, 1, 1, 1). From d2dc32b390307f409ac8a8ba45b403a0aab58b01 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 18 May 2026 22:39:56 -0700 Subject: [PATCH 068/140] bring original docstring back --- src/quantem/core/datastructures/dataset.py | 2 +- src/quantem/core/datastructures/dataset4d.py | 22 ++++++++++++++++++- .../core/datastructures/dataset4dstem.py | 3 +++ 3 files changed, 25 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 786b3d5d..5a89b8ad 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -139,7 +139,7 @@ def from_array( # --- Properties --- @property def array(self) -> NDArray: - """The data as a numpy array. + """The underlying n-dimensional NumPy array data. For tensor-backed datasets, returns a CACHED read-only CPU copy derived from ``self.tensor`` (first access pays GPU->CPU transfer, subsequent diff --git a/src/quantem/core/datastructures/dataset4d.py b/src/quantem/core/datastructures/dataset4d.py index 80d219ea..f5681730 100644 --- a/src/quantem/core/datastructures/dataset4d.py +++ b/src/quantem/core/datastructures/dataset4d.py @@ -32,7 +32,27 @@ def __init__( metadata: dict = {}, _token: object | None = None, ): - """Initialize a 4D dataset. Pass exactly one of ``array`` (numpy) or ``tensor`` (torch).""" + """Initialize a 4D dataset. + + Parameters + ---------- + array : NDArray | None + The underlying 4D numpy array. Provide exactly one of ``array`` or ``tensor``. + tensor : torch.Tensor | None + The underlying 4D torch tensor (any device). Provide exactly one of ``array`` or ``tensor``. + name : str + A descriptive name for the dataset + origin : NDArray | tuple | list | float | int + The origin coordinates for each dimension in calibrated units + sampling : NDArray | tuple | list | float | int + The sampling rate/spacing for each dimension + units : list[str] | tuple | list + Units for each dimension + signal_units : str, optional + Units for the array values, by default "arb. units" + _token : object | None, optional + Token to prevent direct instantiation, by default None + """ super().__init__( array=array, tensor=tensor, diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 60890475..c0c331b5 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -58,6 +58,9 @@ def __init__( ---------- array : NDArray | Any The underlying 4D array data + tensor : torch.Tensor | None, optional + Alternative to ``array``: the underlying 4D torch tensor (any device). + Provide exactly one of ``array`` or ``tensor``. name : str A descriptive name for the dataset origin : NDArray | tuple | list | float | int From 6b7319d7996553912b7b8ce485edb16c17257183 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 18 May 2026 22:42:43 -0700 Subject: [PATCH 069/140] remove need for cached numpy array --- src/quantem/core/datastructures/dataset.py | 25 ++++++++-------------- 1 file changed, 9 insertions(+), 16 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 5a89b8ad..3263720a 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -70,8 +70,6 @@ def __init__( raise TypeError(f"Dataset.tensor must be torch.Tensor, got {type(tensor).__name__}.") self._array = None self._tensor = tensor - # Lazy cache: derived numpy from tensor, materialized only on first .array access. - self._cached_numpy: np.ndarray | None = None self.name = name self.origin = origin self.sampling = sampling @@ -138,19 +136,13 @@ def from_array( # --- Properties --- @property - def array(self) -> NDArray: + def array(self) -> NDArray | None: """The underlying n-dimensional NumPy array data. - For tensor-backed datasets, returns a CACHED read-only CPU copy derived - from ``self.tensor`` (first access pays GPU->CPU transfer, subsequent - accesses are free). Torch-aware consumers should prefer ``.tensor``. + Returns ``None`` for tensor-backed datasets — use ``.tensor`` for the + torch tensor, or ``.numpy()`` to materialize a numpy copy explicitly. """ - if self._array is not None: - return self._array - if self._cached_numpy is None: - self._cached_numpy = self._tensor.detach().cpu().numpy() - self._cached_numpy.flags.writeable = False - return self._cached_numpy + return self._array @array.setter def array(self, value: NDArray) -> None: @@ -245,10 +237,12 @@ def device(self) -> str: def numpy(self) -> NDArray: """Return the data as a numpy array (mirrors ``torch.Tensor.numpy()``). - Equivalent to ``self.array`` — both return numpy. For tensor-backed - datasets, first call materializes a cached read-only CPU copy. + For numpy-backed datasets, returns ``self.array`` directly. For + tensor-backed datasets, materializes a CPU copy via ``.detach().cpu().numpy()``. """ - return self.array + if self._array is not None: + return self._array + return self._tensor.detach().cpu().numpy() def to(self, device) -> Self: """Move the underlying tensor to ``device``. Raises if numpy-backed.""" @@ -257,7 +251,6 @@ def to(self, device) -> Self: f"Cannot .to({device!r}) on numpy-backed Dataset '{self.name}'." ) self._tensor = self._tensor.to(device) - self._cached_numpy = None # invalidate stale derived numpy return self # --- Summaries --- From 7f9913f61fcc9f0bafee2f79a5366b1d1358d6f4 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 18 May 2026 22:44:38 -0700 Subject: [PATCH 070/140] further cleaup api docstring --- src/quantem/core/datastructures/dataset.py | 4 ++-- widget/src/quantem/widget/show4dstem.py | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 3263720a..a89257a3 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -139,7 +139,7 @@ def from_array( def array(self) -> NDArray | None: """The underlying n-dimensional NumPy array data. - Returns ``None`` for tensor-backed datasets — use ``.tensor`` for the + Returns ``None`` for tensor-backed datasets. Use ``.tensor`` for the torch tensor, or ``.numpy()`` to materialize a numpy copy explicitly. """ return self._array @@ -215,7 +215,7 @@ def file_path(self, value: os.PathLike | str | None) -> None: # --- Derived Properties --- @property def shape(self) -> tuple[int, ...]: - # Direct slot access — never triggers .array derive (which would force + # Direct slot access (never triggers .array derive, which would force # a full GPU->CPU copy on tensor-backed datasets). return tuple((self._array if self._array is not None else self._tensor).shape) diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 06f8b629..99a5eae8 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -334,9 +334,9 @@ def __init__( _io_labels = None # Auto-extract sampling + units from Dataset4dstem if available. - # NOTE: avoid `hasattr(data, "array")` — for tensor-backed Datasets the - # `.array` getter is an expensive derive (full GPU->CPU copy). Use cheap - # `hasattr(data, "sampling")` to identify a Dataset. + # NOTE: avoid `hasattr(data, "array")` because for tensor-backed Datasets + # the `.array` getter is an expensive derive (full GPU->CPU copy). Use + # cheap `hasattr(data, "sampling")` to identify a Dataset. if hasattr(data, "sampling"): if not title and hasattr(data, "name") and data.name: title = str(data.name) From 9c93ae99d7ad483c773528c20e5c358ee9a49f55 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 18 May 2026 22:49:44 -0700 Subject: [PATCH 071/140] use _array _tensor duck typing for show4dstem --- widget/src/quantem/widget/show4dstem.py | 20 ++++++++------------ 1 file changed, 8 insertions(+), 12 deletions(-) diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 99a5eae8..c3ee61a7 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -333,22 +333,18 @@ def __init__( _io_labels = None - # Auto-extract sampling + units from Dataset4dstem if available. - # NOTE: avoid `hasattr(data, "array")` because for tensor-backed Datasets - # the `.array` getter is an expensive derive (full GPU->CPU copy). Use - # cheap `hasattr(data, "sampling")` to identify a Dataset. - if hasattr(data, "sampling"): - if not title and hasattr(data, "name") and data.name: + # Extract underlying array / tensor + auto-calibrate from Dataset input + # (duck-typed via the dual-slot private attributes _tensor / _array). + tensor = getattr(data, "_tensor", None) + array = getattr(data, "_array", None) + if tensor is not None or array is not None: + if not title and getattr(data, "name", ""): title = str(data.name) if sampling is None: sampling = tuple(float(s) for s in data.sampling) - if units is None and hasattr(data, "units"): + if units is None: units = list(data.units) - # If tensor-backed (zero-copy GPU path), take .tensor. Else .array (numpy). - if getattr(data, "_tensor", None) is not None: - data = data.tensor - else: - data = data.array + data = tensor if tensor is not None else array # Resolve sampling + units (4 axes for 4D-STEM): # [scan_row, scan_col, k_row, k_col]. Scalar/None broadcast to (1, 1, 1, 1). From 40878d3d5f13f90ddabe4a4947b1b74550337085 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 18 May 2026 23:00:28 -0700 Subject: [PATCH 072/140] use row, col convention in docstring --- src/quantem/core/datastructures/dataset4dstem.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index c0c331b5..48d3f737 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -189,7 +189,7 @@ def from_tensor( if tensor.ndim != 4: raise ValueError( f"Dataset4dstem.from_tensor requires a 4D tensor " - f"(scan_y, scan_x, dp_y, dp_x), got shape {tuple(tensor.shape)}." + f"(scan_row, scan_col, dp_row, dp_col), got shape {tuple(tensor.shape)}." ) return cls( tensor=tensor, From 5087a782f4cddba43471f1e16a12e0247fd1b6aa Mon Sep 17 00:00:00 2001 From: henrygbell Date: Tue, 19 May 2026 16:36:28 -0700 Subject: [PATCH 073/140] Autocorrelation dscan alignment added --- src/quantem/diffraction/__init__.py | 5 ++--- src/quantem/diffraction/maped.py | 23 +++++++++++++++++------ 2 files changed, 19 insertions(+), 9 deletions(-) diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index cf9e84ea..dc8e98b5 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1,8 +1,7 @@ -from quantem.diffraction.polar import RDF as RDF +from quantem.diffraction.polar import PairDistributionFunction as PairDistributionFunction from quantem.diffraction.strain_autocorrelation import ( StrainMapAutocorrelation as StrainMapAutocorrelation, ) -from quantem.diffraction.polar import PairDistributionFunction as PairDistributionFunction -from quantem.diffraction.strain_autocorrelation import StrainMapAutocorrelation as StrainMapAutocorrelation + from quantem.diffraction.maped import MAPED as MAPED from quantem.diffraction.maped import MAPEDTorch as MAPEDTorch diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 32c340bc..1b44ffda 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1164,9 +1164,7 @@ def diffraction_align( phase_ramp = torch.exp(-2j * torch.pi * (kr * shift_rc[0] + kc * shift_rc[1])) G_shift = G * phase_ramp - self.diffraction_shifts[ind, :] = torch.tensor( - shift_rc, device=self.device, dtype=torch.float32 - ) + self.diffraction_shifts[ind, :] = shift_rc.clone() G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) @@ -2147,6 +2145,7 @@ def dscan_correct( for iteration in range(iterations): G_ref = torch.fft.fft2(shifted_dps.mean(dim=(0, 1)) * w) + if method == "cross_correlation": for h_rs in tqdm(range(H_rs), desc=f"Iteration {iteration + 1}/{iterations}"): for w_rs in range(W_rs): @@ -2164,10 +2163,22 @@ def dscan_correct( shifted_dps[h_rs, w_rs, :, :] = torch.fft.ifft2(G_shift).real G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) - if method == "autocorrelation": - pass + if method == "autocorrelation": + for h_rs in tqdm(range(H_rs), desc=f"Iteration {iteration + 1}/{iterations}"): + for w_rs in range(W_rs): + dp = shifted_dps[h_rs, w_rs] + G = torch.fft.fft2(w * dp) + + G_flipped = torch.conj(G) + + shift = cross_correlation_shift_torch( + G, G_flipped, upsample_factor=upsample_factor, fft_input=True + ) + shift = shift / 2.0 # peak is at 2x the true offset + + diffraction_shifts[h_rs, w_rs] = shift - G_ref_final = G_ref.clone() + G_ref_final = G_ref.clone() if fit_shifts: diffraction_shifts_1, _ = fit_surface_lstsq(diffraction_shifts[:, :, 0], mode=mode) From 21b684fa7e71f843626a27ad1602adb193494cfc Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Fri, 22 May 2026 23:29:30 -0700 Subject: [PATCH 074/140] fix: tolerate missing _tensor slot on autoserialize-loaded datasets --- src/quantem/core/datastructures/dataset.py | 35 ++++++++++++++-------- 1 file changed, 22 insertions(+), 13 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index a89257a3..fa1fcef2 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -142,7 +142,7 @@ def array(self) -> NDArray | None: Returns ``None`` for tensor-backed datasets. Use ``.tensor`` for the torch tensor, or ``.numpy()`` to materialize a numpy copy explicitly. """ - return self._array + return getattr(self, "_array", None) @array.setter def array(self, value: NDArray) -> None: @@ -154,11 +154,13 @@ def array(self, value: NDArray) -> None: @property def tensor(self) -> torch.Tensor: """Torch tensor backing the data. AttributeError if numpy-backed.""" - if self._tensor is None: + # getattr handles AutoSerialize-restored instances (no __init__ run). + tensor = getattr(self, "_tensor", None) + if tensor is None: raise AttributeError( f"Dataset '{self.name}' is numpy-backed; use Dataset.from_tensor() at construction." ) - return self._tensor + return tensor @property def metadata(self) -> dict: @@ -216,22 +218,27 @@ def file_path(self, value: os.PathLike | str | None) -> None: @property def shape(self) -> tuple[int, ...]: # Direct slot access (never triggers .array derive, which would force - # a full GPU->CPU copy on tensor-backed datasets). - return tuple((self._array if self._array is not None else self._tensor).shape) + # a full GPU->CPU copy on tensor-backed datasets). getattr handles + # AutoSerialize-restored instances (no __init__ run). + array = getattr(self, "_array", None) + return tuple((array if array is not None else self._tensor).shape) @property def ndim(self) -> int: - return (self._array if self._array is not None else self._tensor).ndim + array = getattr(self, "_array", None) + return (array if array is not None else self._tensor).ndim @property def dtype(self) -> DTypeLike: - return (self._array if self._array is not None else self._tensor).dtype + array = getattr(self, "_array", None) + return (array if array is not None else self._tensor).dtype @property def device(self) -> str: """``"cpu"`` for numpy-backed; torch device string for tensor-backed.""" - if self._tensor is not None: - return str(self._tensor.device) + tensor = getattr(self, "_tensor", None) + if tensor is not None: + return str(tensor.device) return "cpu" def numpy(self) -> NDArray: @@ -240,17 +247,19 @@ def numpy(self) -> NDArray: For numpy-backed datasets, returns ``self.array`` directly. For tensor-backed datasets, materializes a CPU copy via ``.detach().cpu().numpy()``. """ - if self._array is not None: - return self._array + array = getattr(self, "_array", None) + if array is not None: + return array return self._tensor.detach().cpu().numpy() def to(self, device) -> Self: """Move the underlying tensor to ``device``. Raises if numpy-backed.""" - if self._tensor is None: + tensor = getattr(self, "_tensor", None) + if tensor is None: raise AttributeError( f"Cannot .to({device!r}) on numpy-backed Dataset '{self.name}'." ) - self._tensor = self._tensor.to(device) + self._tensor = tensor.to(device) return self # --- Summaries --- From 51a935e9312fa9da57cb94e374ec8bf0b0d39956 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Sun, 24 May 2026 21:18:01 -0700 Subject: [PATCH 075/140] Add fast vectorized autocorrelation dscan alignment. --- src/quantem/diffraction/maped.py | 321 +++++++++++++++++++++++++++---- 1 file changed, 286 insertions(+), 35 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 1b44ffda..aa2dfe83 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1,5 +1,6 @@ from __future__ import annotations +import math import warnings from typing import Any, Sequence @@ -15,10 +16,7 @@ from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize -from quantem.core.utils.imaging_utils import ( - cross_correlation_shift_torch, - weighted_cross_correlation_shift, -) +from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift from quantem.core.visualization import show_2d @@ -1043,15 +1041,17 @@ def dscan_align( self, iterations: int, upsample_factor: int = 100, + method: str = "autocorrelation", plot_aligned: bool = True, edge_blend: float = 2.0, fit_shifts: bool = True, mode: str = "linear", ): for i, dataset in enumerate(self.datasets): - _, aligned_dataset, _ = dscan_correct( + _, aligned_dataset = dscan_correct( dataset, iterations, + method=method, upsample_factor=upsample_factor, plot_aligned=plot_aligned, edge_blend=edge_blend, @@ -2039,6 +2039,250 @@ def shift_images_torch( return out +def cross_correlation_shift_torch( + im_ref: torch.Tensor, + im: torch.Tensor, + upsample_factor: int = 2, + fft_input: bool = False, +) -> torch.Tensor: + """ + Align two real images using Fourier cross-correlation and DFT upsampling. + + Supports a single image pair with shape (H, W) or a batch of image pairs with + shape (N, H, W). When batched, returns a tensor of shape (N, 2). + """ + if im_ref.shape != im.shape: + raise ValueError("im_ref and im must have the same shape") + + if im_ref.ndim == 2: + if fft_input: + G1 = im_ref + G2 = im + else: + G1 = torch.fft.fft2(im_ref) + G2 = torch.fft.fft2(im) + + xy_shift = align_images_fourier_torch(G1, G2, upsample_factor) + M, N = im_ref.shape + dx = ((xy_shift[0] + M / 2) % M) - M / 2 + dy = ((xy_shift[1] + N / 2) % N) - N / 2 + return torch.tensor([dx, dy], device=G1.device) + + if im_ref.ndim == 3: + if fft_input: + G1 = im_ref + G2 = im + else: + G1 = torch.fft.fft2(im_ref, dim=(-2, -1)) + G2 = torch.fft.fft2(im, dim=(-2, -1)) + + xy_shift = align_images_fourier_torch_batched(G1, G2, upsample_factor) + M, N = im_ref.shape[-2:] + dx = ((xy_shift[..., 0] + M / 2) % M) - M / 2 + dy = ((xy_shift[..., 1] + N / 2) % N) - N / 2 + return torch.stack([dx, dy], dim=-1) + + raise ValueError("im_ref and im must be 2D or 3D tensors") + + +def align_images_fourier_torch( + G1: torch.Tensor, + G2: torch.Tensor, + upsample_factor: int, +) -> torch.Tensor: + """ + Alignment using DFT upsampling of cross correlation. + G1, G2: torch tensors representing FTs of images (complex) + Returns: xy_shift (tensor length 2) + """ + device = G1.device + cc = G1 * G2.conj() + cc_real = torch.fft.ifft2(cc).real + + flat_idx = torch.argmax(cc_real) + x0 = (flat_idx // cc_real.shape[1]).to(torch.long).item() + y0 = (flat_idx % cc_real.shape[1]).to(torch.long).item() + + M, N = cc_real.shape + x_inds = [((x0 + dx) % M) for dx in (-1, 0, 1)] + y_inds = [((y0 + dy) % N) for dy in (-1, 0, 1)] + + vx = cc_real[x_inds, y0] + vy = cc_real[x0, y_inds] + + denom_x = 4.0 * vx[1] - 2.0 * vx[2] - 2.0 * vx[0] + denom_y = 4.0 * vy[1] - 2.0 * vy[2] - 2.0 * vy[0] + dx = (vx[2] - vx[0]) / denom_x if denom_x != 0 else torch.tensor(0.0, device=device) + dy = (vy[2] - vy[0]) / denom_y if denom_y != 0 else torch.tensor(0.0, device=device) + + x0 = torch.round((x0 + dx) * 2.0) / 2.0 + y0 = torch.round((y0 + dy) * 2.0) / 2.0 + + xy_shift = torch.tensor([x0, y0], device=device) + + if upsample_factor > 2: + xy_shift = upsampled_correlation_torch(cc, upsample_factor, xy_shift) + + return xy_shift + + +def align_images_fourier_torch_batched( + G1: torch.Tensor, + G2: torch.Tensor, + upsample_factor: int, +) -> torch.Tensor: + """ + Batched version of align_images_fourier_torch. + + G1 and G2 must have shape (N, H, W), where N is the batch size. + Returns a tensor of shape (N, 2) with unwrapped peak locations. + """ + if G1.shape != G2.shape: + raise ValueError("G1 and G2 must have the same shape") + if G1.ndim != 3: + raise ValueError("G1 and G2 must have shape (N, H, W)") + + device = G1.device + cc = G1 * G2.conj() + cc_real = torch.fft.ifft2(cc, dim=(-2, -1)).real + + batch, M, N = cc_real.shape + flat_idx = torch.argmax(cc_real.reshape(batch, -1), dim=1) + x0 = flat_idx // N + y0 = flat_idx % N + + offsets = torch.tensor([-1, 0, 1], device=device, dtype=torch.long) + x_inds = (x0[:, None] + offsets[None, :]) % M + y_inds = (y0[:, None] + offsets[None, :]) % N + + batch_inds = torch.arange(batch, device=device)[:, None] + vx = cc_real[batch_inds, x_inds, y0[:, None].expand(-1, 3)] + vy = cc_real[batch_inds, x0[:, None].expand(-1, 3), y_inds] + + denom_x = 4.0 * vx[:, 1] - 2.0 * vx[:, 2] - 2.0 * vx[:, 0] + denom_y = 4.0 * vy[:, 1] - 2.0 * vy[:, 2] - 2.0 * vy[:, 0] + dx = torch.where(denom_x != 0, (vx[:, 2] - vx[:, 0]) / denom_x, torch.zeros_like(denom_x)) + dy = torch.where(denom_y != 0, (vy[:, 2] - vy[:, 0]) / denom_y, torch.zeros_like(denom_y)) + + x0 = torch.round((x0.to(cc_real.dtype) + dx) * 2.0) / 2.0 + y0 = torch.round((y0.to(cc_real.dtype) + dy) * 2.0) / 2.0 + xy_shift = torch.stack([x0, y0], dim=-1) + + if upsample_factor > 2: + xy_shift = upsampled_correlation_torch(cc, upsample_factor, xy_shift) + + return xy_shift + + +def upsampled_correlation_torch( + imageCorr: torch.Tensor, + upsampleFactor: int, + xyShift: torch.Tensor, +) -> torch.Tensor: + """ + Refine the correlation peak of imageCorr around xyShift by DFT upsampling. + + Supports a single correlation image or a batch of them. + """ + assert upsampleFactor > 2 + + squeeze_output = imageCorr.ndim == 2 + if squeeze_output: + imageCorr = imageCorr.unsqueeze(0) + if xyShift.ndim == 1: + xyShift = xyShift.unsqueeze(0) + + if imageCorr.ndim != 3 or xyShift.ndim != 2: + raise ValueError("imageCorr must have shape (H, W) or (N, H, W), and xyShift must match") + if imageCorr.shape[0] != xyShift.shape[0]: + raise ValueError("imageCorr and xyShift batch dimensions must match") + + xyShift = torch.round(xyShift * float(upsampleFactor)) / float(upsampleFactor) + globalShift = float(math.floor(math.ceil(upsampleFactor * 1.5) / 2.0)) + upsampleCenter = globalShift - (upsampleFactor * xyShift) + + conj_input = imageCorr.conj() + im_up = dftUpsample_torch(conj_input, upsampleFactor, upsampleCenter) + imageCorrUpsample = im_up.conj() + + batch, _, out_w = imageCorrUpsample.real.shape + flat_idx = torch.argmax(imageCorrUpsample.real.reshape(batch, -1), dim=1) + r = flat_idx // out_w + c = flat_idx % out_w + + padded = F.pad(imageCorrUpsample.real, (1, 1, 1, 1), mode="circular") + batch_inds = torch.arange(batch, device=imageCorr.device) + + center = padded[batch_inds, r + 1, c + 1] + top = padded[batch_inds, r, c + 1] + bottom = padded[batch_inds, r + 2, c + 1] + left = padded[batch_inds, r + 1, c] + right = padded[batch_inds, r + 1, c + 2] + + denom_x = 4.0 * center - 2.0 * bottom - 2.0 * top + denom_y = 4.0 * center - 2.0 * right - 2.0 * left + dx = torch.where(denom_x != 0, (bottom - top) / denom_x, torch.zeros_like(denom_x)) + dy = torch.where(denom_y != 0, (right - left) / denom_y, torch.zeros_like(denom_y)) + + xySubShift = torch.stack([r, c], dim=-1).to(dtype=xyShift.dtype) - globalShift + xyShift = xyShift + (xySubShift + torch.stack([dx, dy], dim=-1)) / float(upsampleFactor) + + return xyShift[0] if squeeze_output else xyShift + + +def dftUpsample_torch( + imageCorr: torch.Tensor, + upsampleFactor: int, + xyShift: torch.Tensor, +) -> torch.Tensor: + """ + Matrix-multiply DFT upsampling for a single correlation image or a batch. + """ + squeeze_output = imageCorr.ndim == 2 + if squeeze_output: + imageCorr = imageCorr.unsqueeze(0) + if xyShift.ndim == 1: + xyShift = xyShift.unsqueeze(0) + + if imageCorr.ndim != 3 or xyShift.ndim != 2: + raise ValueError("imageCorr must have shape (M, N) or (B, M, N), and xyShift must match") + if imageCorr.shape[0] != xyShift.shape[0]: + raise ValueError("imageCorr and xyShift batch dimensions must match") + + device = imageCorr.device + _, M, N = imageCorr.shape + pixelRadius = 1.5 + numRow = int(math.ceil(pixelRadius * upsampleFactor)) + numCol = numRow + + col_freq = torch.fft.ifftshift(torch.arange(N, device=device)) - math.floor(N / 2) + row_freq = torch.fft.ifftshift(torch.arange(M, device=device)) - math.floor(M / 2) + + col_coords = ( + torch.arange(numCol, device=device, dtype=torch.get_default_dtype())[None, :] + - (xyShift[:, 1:2]) + ) + row_coords = ( + torch.arange(numRow, device=device, dtype=torch.get_default_dtype())[None, :] + - (xyShift[:, 0:1]) + ) + + factor_col = -2j * math.pi / (N * float(upsampleFactor)) + colKern = torch.exp(factor_col * (col_freq[None, :, None] * col_coords[:, None, :])).to( + imageCorr.dtype + ) + + factor_row = -2j * math.pi / (M * float(upsampleFactor)) + rowKern = torch.exp(factor_row * (row_coords[:, :, None] * row_freq[None, None, :])).to( + imageCorr.dtype + ) + + imageUpsample = torch.matmul(torch.matmul(rowKern, imageCorr), colKern) + + result = imageUpsample.real + return result[0] if squeeze_output else result + + def fit_surface_lstsq(img, mode="linear"): """ Fits an image with a linear or quadratic function @@ -2085,12 +2329,12 @@ def dscan_correct( plot_aligned: bool = True, edge_blend: float = 2.0, device="cpu", - method="cross_correlation", + method="autocorrelation", fit_shifts=True, mode="linear", ): """ - Align diffraction patterns using cross-correlation. + Align diffraction patterns using autocorrelation. Parameters ---------- @@ -2163,22 +2407,23 @@ def dscan_correct( shifted_dps[h_rs, w_rs, :, :] = torch.fft.ifft2(G_shift).real G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) - if method == "autocorrelation": - for h_rs in tqdm(range(H_rs), desc=f"Iteration {iteration + 1}/{iterations}"): - for w_rs in range(W_rs): - dp = shifted_dps[h_rs, w_rs] - G = torch.fft.fft2(w * dp) - - G_flipped = torch.conj(G) - - shift = cross_correlation_shift_torch( - G, G_flipped, upsample_factor=upsample_factor, fft_input=True + if method == "autocorrelation": + # Vectorize over the scan grid by flattening (H_rs, W_rs) into a batch dimension. + dp_batch = (w * shifted_dps).reshape(H_rs * W_rs, H_dp, W_dp) + G = torch.fft.fft2(dp_batch, dim=(-2, -1)) + G_flipped = torch.conj(G) + + shifts = ( + -cross_correlation_shift_torch( + G, + G_flipped, + upsample_factor=upsample_factor, + fft_input=True, ) - shift = shift / 2.0 # peak is at 2x the true offset - - diffraction_shifts[h_rs, w_rs] = shift + / 2.0 + ) - G_ref_final = G_ref.clone() + diffraction_shifts[:, :, :] = shifts.reshape(H_rs, W_rs, 2) if fit_shifts: diffraction_shifts_1, _ = fit_surface_lstsq(diffraction_shifts[:, :, 0], mode=mode) @@ -2186,17 +2431,23 @@ def dscan_correct( diffraction_shifts_old = diffraction_shifts.clone() diffraction_shifts = torch.stack((diffraction_shifts_1, diffraction_shifts_2), dim=2) - # Recompute fitted shifts - for h_rs in tqdm(range(H_rs), desc="Applying fitted shifts"): - for w_rs in range(W_rs): - dp = shifted_dps[h_rs, w_rs] # <-- Also read from shifted_dps here - G = torch.fft.fft2(w * dp) - shift = diffraction_shifts[h_rs, w_rs] - - phase_ramp = torch.exp(-1j * torch.pi * (kr * shift[0] + kc * shift[1])) - G_shift = G * phase_ramp - - shifted_dps[h_rs, w_rs, :, :] = torch.fft.ifft2(G_shift).real + # Recompute fitted shifts in one batched pass over all scan positions. + dp_batch = (w * shifted_dps).reshape(H_rs * W_rs, H_dp, W_dp) + G_batch = torch.fft.fft2(dp_batch, dim=(-2, -1)) + + shifts_batch = diffraction_shifts.reshape(H_rs * W_rs, 2) + phase_ramp = torch.exp( + -1j + * torch.pi + * ( + kr.unsqueeze(0) * shifts_batch[:, 0][:, None, None] + + kc.unsqueeze(0) * shifts_batch[:, 1][:, None, None] + ) + ) + G_shift = G_batch * phase_ramp + shifted_dps[:, :, :, :] = torch.fft.ifft2(G_shift, dim=(-2, -1)).real.reshape( + H_rs, W_rs, H_dp, W_dp + ) if plot_aligned: if fit_shifts: @@ -2218,8 +2469,8 @@ def dscan_correct( ["Shifts y", "Fit y", "Residual y"], ], cmap="RdBu_r", - vmax=3, - vmin=-3, + # vmax=3, + # vmin=-3, ) dp_mean_before = dataset.mean(dim=(0, 1)) @@ -2232,4 +2483,4 @@ def dscan_correct( vmax=0.75, ) - return diffraction_shifts, shifted_dps, G_ref_final + return diffraction_shifts, shifted_dps From 4be1be7efb654b8d352c92c5fd55e7b5b7c66af1 Mon Sep 17 00:00:00 2001 From: quantem-bot Date: Mon, 25 May 2026 12:36:55 +0000 Subject: [PATCH 076/140] chore: update lock file --- uv.lock | 425 +++++++++++++++++++++++++++++--------------------------- 1 file changed, 218 insertions(+), 207 deletions(-) diff --git a/uv.lock b/uv.lock index a01182cc..716284e6 100644 --- a/uv.lock +++ b/uv.lock @@ -196,11 +196,11 @@ css = [ [[package]] name = "certifi" -version = "2026.4.22" +version = "2026.5.20" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/25/ee/6caf7a40c36a1220410afe15a1cc64993a1f864871f698c0f93acb72842a/certifi-2026.4.22.tar.gz", hash = "sha256:8d455352a37b71bf76a79caa83a3d6c25afee4a385d632127b6afb3963f1c580", size = 137077, upload-time = "2026-04-22T11:26:11.191Z" } +sdist = { url = "https://files.pythonhosted.org/packages/f3/ce/ee2ecad540810a79593028e88299baeae54d346cc7a0d94b6199988b89b1/certifi-2026.5.20.tar.gz", hash = "sha256:69dea482ab64caa7b9f6aba1c6bf48bb6a5448d1c0f1b17ab42ad8c763a5344d", size = 135422, upload-time = "2026-05-20T11:46:50.073Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/22/30/7cd8fdcdfbc5b869528b079bfb76dcdf6056b1a2097a662e5e8c04f42965/certifi-2026.4.22-py3-none-any.whl", hash = "sha256:3cb2210c8f88ba2318d29b0388d1023c8492ff72ecdde4ebdaddbb13a31b1c4a", size = 135707, upload-time = "2026-04-22T11:26:09.372Z" }, + { url = "https://files.pythonhosted.org/packages/59/8c/57e832b7af6d7c5abe66eb3fbe3a3a32f4d11ea23a1aa7131371035be991/certifi-2026.5.20-py3-none-any.whl", hash = "sha256:3c52e209ba0a4ad7aebe60436a4ab349c39e1e602e8c134221e546902ad25897", size = 134134, upload-time = "2026-05-20T11:46:48.578Z" }, ] [[package]] @@ -373,14 +373,14 @@ wheels = [ [[package]] name = "click" -version = "8.4.0" +version = "8.4.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "colorama", marker = "sys_platform == 'win32'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/23/e4/796662cd90cf80e3a363c99db2b88e0e394b988a575f60a17e16440cd011/click-8.4.0.tar.gz", hash = "sha256:638f1338fe1235c8f4e008e4a8a254fb5c5fbdcbb40ece3c9142ebb78e792973", size = 350843, upload-time = "2026-05-17T00:47:58.425Z" } +sdist = { url = "https://files.pythonhosted.org/packages/9b/98/518d8e5081007684232226f475082b30087d0f585e8457db087298259f49/click-8.4.1.tar.gz", hash = "sha256:918b5633eddf6b41c32d4f454bf0de810065c74e3f7dbf8ee5452f8be88d3e96", size = 353007, upload-time = "2026-05-22T04:08:37.769Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/ee/ae/8e92f8058baf87f6c7d86ee7e457668690195cc77efedb8d3797a06e3940/click-8.4.0-py3-none-any.whl", hash = "sha256:40c50b7c6c6adac2823d411041ec84f3f103f1b280d5e9ce0d7f998995832f81", size = 116147, upload-time = "2026-05-17T00:47:56.842Z" }, + { url = "https://files.pythonhosted.org/packages/c7/0d/67e5b4109ea4a837e80daa87c2c696711955e40449a97e8926672534def2/click-8.4.1-py3-none-any.whl", hash = "sha256:482be17c6991b8c19c5429a1e995d9b0efdbb63172824c41f99965dc0ade8ec2", size = 116639, upload-time = "2026-05-22T04:08:35.26Z" }, ] [[package]] @@ -958,49 +958,65 @@ wheels = [ [[package]] name = "greenlet" -version = "3.5.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/3c/3f/dbf99fb14bfeb88c28f16729215478c0e265cacd6dc22270c8f31bb6892f/greenlet-3.5.0.tar.gz", hash = "sha256:d419647372241bc68e957bf38d5c1f98852155e4146bd1e4121adea81f4f01e4", size = 196995, upload-time = "2026-04-27T13:37:15.544Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/8b/0f/a91f143f356523ff682309732b175765a9bc2836fd7c081c2c67fedc1ad4/greenlet-3.5.0-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:8f1cc966c126639cd152fdaa52624d2655f492faa79e013fea161de3e6dda082", size = 284726, upload-time = "2026-04-27T12:20:51.402Z" }, - { url = "https://files.pythonhosted.org/packages/95/82/800646c7ffc5dbabd75ddd2f6b519bb898c0c9c969e5d0473bfe5d20bcce/greenlet-3.5.0-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:362624e6a8e5bca3b8233e45eef33903a100e9539a2b995c364d595dbc4018b3", size = 604264, upload-time = "2026-04-27T12:52:39.494Z" }, - { url = "https://files.pythonhosted.org/packages/ca/ac/354867c0bba812fc33b15bc55aedafedd0aee3c7dd91dfca22444157dc0c/greenlet-3.5.0-cp311-cp311-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:5ecd83806b0f4c2f53b1018e0005cd82269ea01d42befc0368730028d850ed1c", size = 616099, upload-time = "2026-04-27T12:59:39.623Z" }, - { url = "https://files.pythonhosted.org/packages/ff/b0/815bece7399e01cadb69014219eebd0042339875c59a59b0820a46ece356/greenlet-3.5.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0ff251e9a0279522e62f6176412869395a64ddf2b5c5f782ff609a8216a4e662", size = 615198, upload-time = "2026-04-27T12:25:25.928Z" }, - { url = "https://files.pythonhosted.org/packages/10/80/3b2c0a895d6698f6ddb31b07942ebfa982f3e30888bc5546a5b5990de8b2/greenlet-3.5.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:6d874e79afd41a96e11ff4c5d0bc90a80973e476fda1c2c64985667397df432b", size = 1574927, upload-time = "2026-04-27T12:53:25.81Z" }, - { url = "https://files.pythonhosted.org/packages/44/0e/f354af514a4c61454dbc68e44d47544a5a4d6317e30b77ddfa3a09f4c5f3/greenlet-3.5.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:0ed006e4b86c59de7467eb2601cd1b77b5a7d657d1ee55e30fe30d76451edba4", size = 1642683, upload-time = "2026-04-27T12:25:23.9Z" }, - { url = "https://files.pythonhosted.org/packages/fa/6a/87f38255201e993a1915265ebb80cd7c2c78b04a45744995abbf6b259fd8/greenlet-3.5.0-cp311-cp311-win_amd64.whl", hash = "sha256:703cb211b820dbffbbc55a16bfc6e4583a6e6e990f33a119d2cc8b83211119c8", size = 238115, upload-time = "2026-04-27T12:21:48.845Z" }, - { url = "https://files.pythonhosted.org/packages/e3/f8/450fe3c5938fa737ea4d22699772e6e34e8e24431a47bf4e8a1ceed4a98e/greenlet-3.5.0-cp311-cp311-win_arm64.whl", hash = "sha256:6c18dfb59c70f5a94acd271c72e90128c3c776e41e5f07767908c8c1b74ad339", size = 235017, upload-time = "2026-04-27T12:22:26.768Z" }, - { url = "https://files.pythonhosted.org/packages/ef/32/f2ce6d4cac3e55bc6173f92dbe627e782e1850f89d986c3606feb63aafa7/greenlet-3.5.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:db2910d3c809444e0a20147361f343fe2798e106af8d9d8506f5305302655a9f", size = 286228, upload-time = "2026-04-27T12:20:34.421Z" }, - { url = "https://files.pythonhosted.org/packages/b7/aa/caed9e5adf742315fc7be2a84196373aab4816e540e38ba0d76cb7584d68/greenlet-3.5.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3ec9ea74e7268ace7f9aab1b1a4e730193fc661b39a993cd91c606c32d4a3628", size = 601775, upload-time = "2026-04-27T12:52:41.045Z" }, - { url = "https://files.pythonhosted.org/packages/c7/af/90ae08497400a941595d12774447f752d3dfe0fbb012e35b76bc5c0ff37e/greenlet-3.5.0-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:54d243512da35485fc7a6bf3c178fdda6327a9d6506fcdd62b1abd1e41b2927b", size = 614436, upload-time = "2026-04-27T12:59:41.595Z" }, - { url = "https://files.pythonhosted.org/packages/2b/e0/2e13df68f367e2f9960616927d60857dd7e56aaadd59a47c644216b2f920/greenlet-3.5.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9d280a7f5c331622c69f97eb167f33577ff2d1df282c41cd15907fc0a3ca198c", size = 611388, upload-time = "2026-04-27T12:25:28.008Z" }, - { url = "https://files.pythonhosted.org/packages/82/f7/393c64055132ac0d488ef6be549253b7e6274194863967ddc0bc8f5b87b8/greenlet-3.5.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1eb67d5adefb5bd2e182d42678a328979a209e4e82eb93575708185d31d1f588", size = 1570768, upload-time = "2026-04-27T12:53:28.099Z" }, - { url = "https://files.pythonhosted.org/packages/b8/4b/eaf7735253522cf56d1b74d672a58f54fc114702ceaf05def59aae72f6e1/greenlet-3.5.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2628d6c86f6cb0cb45e0c3c54058bbec559f57eaae699447748cb3928150577e", size = 1635983, upload-time = "2026-04-27T12:25:26.903Z" }, - { url = "https://files.pythonhosted.org/packages/4c/fe/4fb3a0805bd5165da5ebf858da7cc01cce8061674106d2cf5bdab32cbfde/greenlet-3.5.0-cp312-cp312-win_amd64.whl", hash = "sha256:d4d9f0624c775f2dfc56ba54d515a8c771044346852a918b405914f6b19d7fd8", size = 238840, upload-time = "2026-04-27T12:23:54.806Z" }, - { url = "https://files.pythonhosted.org/packages/cb/cb/baa584cb00532126ffe12d9787db0a60c5a4f55c27bfe2666df5d4c30a32/greenlet-3.5.0-cp312-cp312-win_arm64.whl", hash = "sha256:83ed9f27f1680b50e89f40f6df348a290ea234b249a4003d366663a12eab94f2", size = 235615, upload-time = "2026-04-27T12:21:38.57Z" }, - { url = "https://files.pythonhosted.org/packages/0c/58/fc576f99037ce19c5aa16628e4c3226b6d1419f72a62c79f5f40576e6eb3/greenlet-3.5.0-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:5a5ed18de6a0f6cc7087f1563f6bd93fc7df1c19165ca01e9bde5a5dc281d106", size = 285066, upload-time = "2026-04-27T12:23:05.033Z" }, - { url = "https://files.pythonhosted.org/packages/4a/ba/b28ddbe6bfad6a8ac196ef0e8cff37bc65b79735995b9e410923fffeeb70/greenlet-3.5.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a717fbc46d8a354fa675f7c1e813485b6ba3885f9bef0cd56e5ba27d758ff5b", size = 604414, upload-time = "2026-04-27T12:52:42.358Z" }, - { url = "https://files.pythonhosted.org/packages/09/06/4b69f8f0b67603a8be2790e55107a190b376f2627fe0eaf5695d85ffb3cd/greenlet-3.5.0-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ddc090c5c1792b10246a78e8c2163ebbe04cf877f9d785c230a7b27b39ad038e", size = 617349, upload-time = "2026-04-27T12:59:43.32Z" }, - { url = "https://files.pythonhosted.org/packages/8a/17/a3918541fd0ddefe024a69de6d16aa7b46d36ac19562adaa63c7fa180eff/greenlet-3.5.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2094acd54b272cb6eae8c03dd87b3fa1820a4cef18d6889c378d503500a1dc13", size = 613927, upload-time = "2026-04-27T12:25:30.28Z" }, - { url = "https://files.pythonhosted.org/packages/ee/e1/bd0af6213c7dd33175d8a462d4c1fe1175124ebed4855bc1475a5b5242c2/greenlet-3.5.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5e05ba267789ea87b5a155cf0e810b1ab88bf18e9e8740813945ceb8ee4350ba", size = 1570893, upload-time = "2026-04-27T12:53:29.483Z" }, - { url = "https://files.pythonhosted.org/packages/9b/2a/0789702f864f5382cb476b93d7a9c823c10472658102ccd65f415747d2e2/greenlet-3.5.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0ecec963079cd58cbd14723582384f11f166fd58883c15dcbfb342e0bc9b5846", size = 1636060, upload-time = "2026-04-27T12:25:28.845Z" }, - { url = "https://files.pythonhosted.org/packages/b2/8f/22bf9df92bbff0eb07842b60f7e63bf7675a9742df628437a9f02d09137f/greenlet-3.5.0-cp313-cp313-win_amd64.whl", hash = "sha256:728d9667d8f2f586644b748dbd9bb67e50d6a9381767d1357714ea6825bb3bf5", size = 238740, upload-time = "2026-04-27T12:24:01.341Z" }, - { url = "https://files.pythonhosted.org/packages/b6/b7/9c5c3d653bd4ff614277c049ac676422e2c557db47b4fe43e6313fc005dc/greenlet-3.5.0-cp313-cp313-win_arm64.whl", hash = "sha256:47422135b1d308c14b2c6e758beedb1acd33bb91679f5670edf77bf46244722b", size = 235525, upload-time = "2026-04-27T12:23:12.308Z" }, - { url = "https://files.pythonhosted.org/packages/94/5e/a70f31e3e8d961c4ce589c15b28e4225d63704e431a23932a3808cbcc867/greenlet-3.5.0-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:f35807464c4c58c55f0d31dfa83c541a5615d825c2fe3d2b95360cf7c4e3c0a8", size = 285564, upload-time = "2026-04-27T12:23:08.555Z" }, - { url = "https://files.pythonhosted.org/packages/af/a6/046c0a28e21833e4086918218cfb3d8bed51c075a1b700f20b9d7861c0f4/greenlet-3.5.0-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:55fa7ea52771be44af0de27d8b80c02cd18c2c3cddde6c847ecebdf72418b6a1", size = 651166, upload-time = "2026-04-27T12:52:43.644Z" }, - { url = "https://files.pythonhosted.org/packages/47/f8/4af27f71c5ff32a7fbc516adb46370d9c4ae2bc7bd3dc7d066ac542b4b15/greenlet-3.5.0-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a97e4821aa710603f94de0da25f25096454d78ffdace5dc77f3a006bc01abba3", size = 663792, upload-time = "2026-04-27T12:59:44.93Z" }, - { url = "https://files.pythonhosted.org/packages/a3/59/1bd6d7428d6ed9106efbb8c52310c60fd04f6672490f452aeaa3829aa436/greenlet-3.5.0-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8f52a464e4ed91780bdfbbdd2b97197f3accaa629b98c200f4dffada759f3ae7", size = 660933, upload-time = "2026-04-27T12:25:33.276Z" }, - { url = "https://files.pythonhosted.org/packages/83/e4/b903e5a5fae1e8a28cdd32a0cfbfd560b668c25b692f67768822ddc5f40f/greenlet-3.5.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:762612baf1161ccb8437c0161c668a688223cba28e1bf038f4eb47b13e39ccdf", size = 1618401, upload-time = "2026-04-27T12:53:31.062Z" }, - { url = "https://files.pythonhosted.org/packages/0e/e3/5ec408a329acb854fb607a122e1ee5fb3ff649f9a97952948a90803c0d8e/greenlet-3.5.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:57a43c6079a89713522bc4bcb9f75070ecf5d3dbad7792bfe42239362cbf2a16", size = 1682038, upload-time = "2026-04-27T12:25:31.838Z" }, - { url = "https://files.pythonhosted.org/packages/91/20/6b165108058767ee643c55c5c4904d591a830ee2b3c7dbd359828fbc829f/greenlet-3.5.0-cp314-cp314-win_amd64.whl", hash = "sha256:3bc59be3945ae9750b9e7d45067d01ae3fe90ea5f9ade99239dabdd6e28a5033", size = 239835, upload-time = "2026-04-27T12:24:54.136Z" }, - { url = "https://files.pythonhosted.org/packages/4e/62/1c498375cee177b55d980c1db319f26470e5309e54698c8f8fc06c0fd539/greenlet-3.5.0-cp314-cp314-win_arm64.whl", hash = "sha256:a96fcee45e03fe30a62669fd16ab5c9d3c172660d3085605cb1e2d1280d3c988", size = 236862, upload-time = "2026-04-27T12:23:24.957Z" }, - { url = "https://files.pythonhosted.org/packages/78/a8/4522939255bb5409af4e87132f915446bf3622c2c292d14d3c38d128ae82/greenlet-3.5.0-cp314-cp314t-macosx_11_0_universal2.whl", hash = "sha256:a10a732421ab4fec934783ce3e54763470d0181db6e3468f9103a275c3ed1853", size = 293614, upload-time = "2026-04-27T12:24:12.874Z" }, - { url = "https://files.pythonhosted.org/packages/15/5e/8744c52e2c027b5a8772a01561934c8835f869733e101f62075c60430340/greenlet-3.5.0-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7fc391b1566f2907d17aaebe78f8855dc45675159a775fcf9e61f8ee0078e87f", size = 650723, upload-time = "2026-04-27T12:52:45.412Z" }, - { url = "https://files.pythonhosted.org/packages/00/ef/7b4c39c03cf46ceca512c5d3f914afd85aa30b2cc9a93015b0dd73e4be6c/greenlet-3.5.0-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:680bd0e7ad5e8daa8a4aa89f68fd6adc834b8a8036dc256533f7e08f4a4b01f7", size = 656529, upload-time = "2026-04-27T12:59:46.295Z" }, - { url = "https://files.pythonhosted.org/packages/0b/b5/c7768f352f5c010f92064d0063f987e7dc0cd290a6d92a34109015ce4aa1/greenlet-3.5.0-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ddb36c7d6c9c0a65f18c7258634e0c416c6ab59caac8c987b96f80c2ebda0112", size = 654364, upload-time = "2026-04-27T12:25:35.64Z" }, - { url = "https://files.pythonhosted.org/packages/ef/d0/079ebe12e4b1fc758857ce5be1a5e73f06870f2101e52611d1e71925ce54/greenlet-3.5.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e5ddf316ced87539144621453c3aef229575825fe60c604e62bedc4003f372b2", size = 1614204, upload-time = "2026-04-27T12:53:32.618Z" }, - { url = "https://files.pythonhosted.org/packages/6d/89/6c2fb63df3596552d20e58fb4d96669243388cf680cff222758812c7bfaa/greenlet-3.5.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:4a448128607be0de65342dc9b31be7f948ef4cc0bc8832069350abefd310a8f2", size = 1675480, upload-time = "2026-04-27T12:25:34.168Z" }, - { url = "https://files.pythonhosted.org/packages/15/32/77ee8a6c1564fc345a491a4e85b3bf360e4cf26eac98c4532d2fdb96e01f/greenlet-3.5.0-cp314-cp314t-win_amd64.whl", hash = "sha256:d60097128cb0a1cab9ea541186ea13cd7b847b8449a7787c2e2350da0cb82d86", size = 245324, upload-time = "2026-04-27T12:24:40.295Z" }, +version = "3.5.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/6d/6e/802acd792aebb2256fbbee8cacf2727faaeb6f240ac11008f09eae4414bc/greenlet-3.5.1.tar.gz", hash = "sha256:5a56aeb7d5d9cc4b3a735efb5095bd4b4f6f0e4f93e5ca876d0e2315137b7829", size = 197356, upload-time = "2026-05-20T15:05:03.917Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/42/3c/ff890b466eaba2b0f5e6bdfff025f8c75f41b8ffdc3dbc3d24ad261e764a/greenlet-3.5.1-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:73f78f9b9f0a5c06e5c946ba1e8e36f5114923b6be109ee618c54f079c3ea14f", size = 284764, upload-time = "2026-05-20T13:09:10.204Z" }, + { url = "https://files.pythonhosted.org/packages/81/0e/5e5457be3d256918f6a4756f073548a3f0190836e2cc94aa6d0d617a940b/greenlet-3.5.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a0cbed8bb44e23c5b199f888f4e4ce096b45ad9f25ff74a7ad0213875e936bb2", size = 603479, upload-time = "2026-05-20T14:00:04.757Z" }, + { url = "https://files.pythonhosted.org/packages/6d/e1/f89a21d58d308298e6f275f13a1b472ed96c680b601a371b08be6a725989/greenlet-3.5.1-cp311-cp311-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a203a8bd0acb0701653d3bbb26e404854a68674139ed5cbb778830f42b09bb33", size = 615495, upload-time = "2026-05-20T14:05:40.87Z" }, + { url = "https://files.pythonhosted.org/packages/75/de/af6cef182862d2ccd6975440d21c9058a77c3f9b469abf94e322dfd2e0e3/greenlet-3.5.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8a271fcd66c74615cda6a964fda3f304267a12e50a084472218a39bb0376f563", size = 614754, upload-time = "2026-05-20T13:14:24.947Z" }, + { url = "https://files.pythonhosted.org/packages/1a/c6/50e520283a9f19388a7326b05f9e8637e566003475eacaadad04f558c68d/greenlet-3.5.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:ded7b068c7c31c1a8657d4fd42d886b3e051ae29f88b80c5ff9d502257b0f071", size = 1574097, upload-time = "2026-05-20T14:02:24.003Z" }, + { url = "https://files.pythonhosted.org/packages/21/1c/13abd1f4860d987fa5e1170a01930d6e6cd40d328de487a3c9fdaff0ffd0/greenlet-3.5.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:d0932b81d72f552ded9d810d00021b64d89f2195a91ce115b893f943b7a4ab3c", size = 1641058, upload-time = "2026-05-20T13:14:31.83Z" }, + { url = "https://files.pythonhosted.org/packages/f5/56/5f332b7705545eac2dc01b4e9254d24a793f2656d55d5cc6b94ee59d22ae/greenlet-3.5.1-cp311-cp311-win_amd64.whl", hash = "sha256:88e300d136eac057b2397aa1cfd7328b4c87c7eb66a09c7bc6a1292234db474e", size = 238089, upload-time = "2026-05-20T13:14:03.229Z" }, + { url = "https://files.pythonhosted.org/packages/d9/a9/a3c2fa886c5b94863fb0e61b3bc14610b7aa94cf4f17f8741b11708305fc/greenlet-3.5.1-cp311-cp311-win_arm64.whl", hash = "sha256:cc6ab7e555c8a112ad3a76e368e86e12a2754bcae1652a5602e133ec7b635523", size = 234989, upload-time = "2026-05-20T13:08:27.715Z" }, + { url = "https://files.pythonhosted.org/packages/c4/37/4549f149c9797c21b32c2683c33522af22522099de128b2406672526d005/greenlet-3.5.1-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:fa4f98af3a528f0c3fd592a26df7f376f93329c8f4d987f6bb979057af8bf5e2", size = 286220, upload-time = "2026-05-20T13:07:28.463Z" }, + { url = "https://files.pythonhosted.org/packages/38/ff/a4f436709716965eaab9f36ea7b906c8a927fbe32fb1372a2071d964f6b1/greenlet-3.5.1-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ffea73584b216150eab159b6d12348fb253e68757974de1e2c40d8a318ac89ed", size = 601585, upload-time = "2026-05-20T14:00:06.141Z" }, + { url = "https://files.pythonhosted.org/packages/65/ad/54bc3fcee3ad368a61b19b67d88117f7a8c29727bf71fffdeda81fbd946e/greenlet-3.5.1-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1072b4f9edcc1e192d9283a66a3e68d6b84c561de33a83d7858beb9ba1effe10", size = 614215, upload-time = "2026-05-20T14:05:42.675Z" }, + { url = "https://files.pythonhosted.org/packages/40/69/b91cda0647df839483201545913514c2827ebea5e5ccdf931842763bc127/greenlet-3.5.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:add5217d68b31130f0beca584d7fef4878327d2e31642b66618a14eef312b63b", size = 611358, upload-time = "2026-05-20T13:14:26.37Z" }, + { url = "https://files.pythonhosted.org/packages/59/90/3cf77e080350cd02fa307bb2abf05df48f4482c240275bbd2c203ba8bb1c/greenlet-3.5.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a5ea42a752d47a145eae922b605cd1634665ac3d5ec1e72402d5048e8d60d207", size = 1570475, upload-time = "2026-05-20T14:02:25.29Z" }, + { url = "https://files.pythonhosted.org/packages/65/2c/18cece62045e74598c3c393f70dce4a63f56222015ba29a5d4eeb04f764c/greenlet-3.5.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c5551170cf4f5ff5623e9af81323751979fee2c731e2287b61f73cd27257b823", size = 1635625, upload-time = "2026-05-20T13:14:34.027Z" }, + { url = "https://files.pythonhosted.org/packages/30/f5/310d104ddf41eb5a70f4c268d22508dfb0c3c8e86fec152be34d0d2ed819/greenlet-3.5.1-cp312-cp312-win_amd64.whl", hash = "sha256:3c8bb982ad117d29478ef8f5533e97df21f1e2befd17a299257b0c96d1371c0b", size = 238791, upload-time = "2026-05-20T13:10:39.018Z" }, + { url = "https://files.pythonhosted.org/packages/62/90/ceca11f504cd23a8047a3dea31919adc48df9b626dd0c13f0d858734fdfd/greenlet-3.5.1-cp312-cp312-win_arm64.whl", hash = "sha256:80eb4b04dadc4e67df3fae179a32c4706a3f495bc7f22fc8a81115d5f5512188", size = 235580, upload-time = "2026-05-20T13:08:45.056Z" }, + { url = "https://files.pythonhosted.org/packages/27/69/7f7e5372d998b81001899b1c0823c957aa413ba0f2662e65821611cc31e4/greenlet-3.5.1-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:51518ff74664078fc51bffcc6fc529b0df5ae58da192691cee765d45ce944a2b", size = 285060, upload-time = "2026-05-20T13:08:51.899Z" }, + { url = "https://files.pythonhosted.org/packages/b1/bf/387f9b6b865fd2ae0d0be09e0004827295a01b71be76ed350dd1e28a91a4/greenlet-3.5.1-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1ffdb3c0bb002c99cd8f298957e046c3dbf6006b5b7cdf11a4e19194624a0a0a", size = 604370, upload-time = "2026-05-20T14:00:07.492Z" }, + { url = "https://files.pythonhosted.org/packages/32/f5/169ce3d4e4c67291bd18f8cbe0299c9f3e45102c7f1fb3c14780c93e4532/greenlet-3.5.1-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7715a5a2c3378ba602c3a440558261e13a820bb53a82693aacd7b7f6d964e283", size = 616987, upload-time = "2026-05-20T14:05:44.237Z" }, + { url = "https://files.pythonhosted.org/packages/ee/e5/7f2e41d5273be07e77560d61ea4e56485b4d6c316d2a84518c62d1364061/greenlet-3.5.1-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:dc71ff466927a201b08305acac451ebe1aedfcea002f62f1f2f2ac2ac1e6a135", size = 613911, upload-time = "2026-05-20T13:14:27.539Z" }, + { url = "https://files.pythonhosted.org/packages/c5/a4/fbdc67579b73615a1f91615e814303cc71e06128f7baaba87be79b8fb90c/greenlet-3.5.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:cd443683db272ebaaca03af98c0b063ab30db70ea8a31a1559f35e3f7b744ccd", size = 1570689, upload-time = "2026-05-20T14:02:27.225Z" }, + { url = "https://files.pythonhosted.org/packages/e6/b4/77abbe35078be39718a46cd49caf16bceb35662f97a34101dca28aa98e47/greenlet-3.5.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:089fff7a6ce8d9316d1f65ebc00273a56be258c1725b32b94de90a3a979557e1", size = 1635602, upload-time = "2026-05-20T13:14:36.344Z" }, + { url = "https://files.pythonhosted.org/packages/37/f7/129f27ca700845b8ee8ca88ce7f43435a1239c2eddb7677fc938822762cf/greenlet-3.5.1-cp313-cp313-win_amd64.whl", hash = "sha256:110a1ca7b49b014b097f6078272c3f4ed31af45b254de5228b79adba879f6af9", size = 238683, upload-time = "2026-05-20T13:11:50.57Z" }, + { url = "https://files.pythonhosted.org/packages/6d/5c/a485a36e87df8d8fd0632ee01511244f5156a20ed3746cc6599340326395/greenlet-3.5.1-cp313-cp313-win_arm64.whl", hash = "sha256:f16ba1efc0715b680a18b8123d90dad887c6112ae3555b4b5c32c149540c6b4e", size = 235499, upload-time = "2026-05-20T13:12:42.028Z" }, + { url = "https://files.pythonhosted.org/packages/8a/cb/c62454606daf5640369c94d8a9dd540599b1bfc090e2d2180cb77f4038d2/greenlet-3.5.1-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:d8ab31c9de8651a2facdd5c5bb0011f2380dd1a7af78ce2adf4b56095294fc07", size = 285579, upload-time = "2026-05-20T13:08:56.396Z" }, + { url = "https://files.pythonhosted.org/packages/ec/71/c4270398c2eba968a6071af1dfbdcaeee6ec1c24bc8b435b8cc452700da6/greenlet-3.5.1-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5e300185139abc337ade480c327183adf42a875ac7181bfe66d7d4efea31fbea", size = 651106, upload-time = "2026-05-20T14:00:09.448Z" }, + { url = "https://files.pythonhosted.org/packages/1a/ab/71e34b78a44ec271fb5f550c17bc46d301ddc5953890d935f270b0dcdb5a/greenlet-3.5.1-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7ffdb990dcaa0234cf9845aead5df2e3c3a8b6507d409274dd87e0d5ab05ffc2", size = 663478, upload-time = "2026-05-20T14:05:45.88Z" }, + { url = "https://files.pythonhosted.org/packages/77/96/4efd6fa5c62c85426a0c19077a586258ebc3a2a146ff2493e4312a697a22/greenlet-3.5.1-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2f82b3597e9d83b63408affed0b48fd0f54935edac4302237b9a837be0dae33c", size = 660800, upload-time = "2026-05-20T13:14:29.129Z" }, + { url = "https://files.pythonhosted.org/packages/7a/e0/6c71401a25cac7000261304e866a2f2cc04dc74810d40e2f118aa4799495/greenlet-3.5.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c0141e37414c10164e702b8fb1473304221ad98f71600850c6ef7ff4880feba0", size = 1617518, upload-time = "2026-05-20T14:02:28.662Z" }, + { url = "https://files.pythonhosted.org/packages/41/26/c5c06643e8c0af9e7bf18e16cb51d0ab7625155f0392e1c9015d66d556cd/greenlet-3.5.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:50ae25a67bea74ea41fb14b960bc532df73eb713417b2d61892dced82fe8d3bc", size = 1681593, upload-time = "2026-05-20T13:14:39.417Z" }, + { url = "https://files.pythonhosted.org/packages/8a/bd/e11a108317485075e68af9d23039619b86b28130c3b50d227d42edece64b/greenlet-3.5.1-cp314-cp314-win_amd64.whl", hash = "sha256:8a17c42330e261299766b75ac1ea32caa437a9453c8f65d16a13140db378ecd3", size = 239800, upload-time = "2026-05-20T13:09:30.128Z" }, + { url = "https://files.pythonhosted.org/packages/47/f8/8e8e8417b7bf28639a5a56356ef934d0375e1d0c70a57e04d7701e870ffe/greenlet-3.5.1-cp314-cp314-win_arm64.whl", hash = "sha256:7b5f5fae05b8ac6d176a61b60c394a8cbdc2b5b91b81793066e68745cf165e54", size = 236862, upload-time = "2026-05-20T13:09:10.498Z" }, + { url = "https://files.pythonhosted.org/packages/90/12/41bf27fde4d3605d3773ae57751eda182b8be2f5398011c041173b1d9534/greenlet-3.5.1-cp314-cp314t-macosx_11_0_universal2.whl", hash = "sha256:ea8da1e900d758d078810d4255d8c6aa572181896a31ec79d779eb79c3adc9ad", size = 293637, upload-time = "2026-05-20T13:12:35.529Z" }, + { url = "https://files.pythonhosted.org/packages/44/44/ba14b23e9757707050c2f397d305bbcae62e5d7cad122f8b6baec5ae4a1f/greenlet-3.5.1-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a19570c52a21420dcbc94e661994bc325c0b5b11304540fed514586da5dc8f2e", size = 650840, upload-time = "2026-05-20T14:00:11.079Z" }, + { url = "https://files.pythonhosted.org/packages/a8/37/5ddc2b686a6844f91abecef43411842426da2e1573f60b49ecf2547f4ae1/greenlet-3.5.1-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3d955c89b75eeca4723d7cc14135f393cd47c32e2a6cb4a8e4c6e760a26b0986", size = 656416, upload-time = "2026-05-20T14:05:47.118Z" }, + { url = "https://files.pythonhosted.org/packages/e1/f0/d17510297c35a2992712f0bf84de3779749999f7d3d63aa1f09db7c62dbe/greenlet-3.5.1-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:de2daaaebd1a5aa88c49045b6baf9310b3263796bd88db713edf37cf53e7bb4e", size = 654397, upload-time = "2026-05-20T13:14:30.696Z" }, + { url = "https://files.pythonhosted.org/packages/37/eb/147387705bb89092645b012586e7273cb5ed3c90ef7eaf3a69173eaf0209/greenlet-3.5.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:3bfbd69cc349e43bf3a8ae1c85548ff0718efc887615c2db16c3833d7b0b072d", size = 1614469, upload-time = "2026-05-20T14:02:30.192Z" }, + { url = "https://files.pythonhosted.org/packages/a6/4e/37ee0da7732b7aa9896f17e15579a9df34b9fcb9dd494f0adfa749af6623/greenlet-3.5.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:4378720dd888136c27215a0214d32a4d37c3852765d45bc37aad0623423cfd78", size = 1675115, upload-time = "2026-05-20T13:14:40.972Z" }, + { url = "https://files.pythonhosted.org/packages/57/f3/97dfcf4a6eb5077f8a672234216fb5923eb89f2cab7081cb10b2cf75b605/greenlet-3.5.1-cp314-cp314t-win_amd64.whl", hash = "sha256:45718441607f9325d948db98cbc691276059316d0358c188c246da4e1d4d23d2", size = 245246, upload-time = "2026-05-20T13:12:22.646Z" }, + { url = "https://files.pythonhosted.org/packages/5d/73/d7f72e34b582f694f4a9b248162db7b09cc458a259ba8f0c0bfa1a34ea7d/greenlet-3.5.1-cp315-cp315-macosx_11_0_universal2.whl", hash = "sha256:2baee5ca02031757ffe8cc3d69f0cc0aec7065ce362622da74f32d3bcab1c541", size = 285575, upload-time = "2026-05-20T13:12:07.043Z" }, + { url = "https://files.pythonhosted.org/packages/df/59/fa9c6e87dc8ad27a95dabe2f29f372b733d05a8a67470f6c901ed9975655/greenlet-3.5.1-cp315-cp315-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9b1ec3274918a81d3ea778b9e75b56b72b33f300edb6cf7f3a7fe1dae56683de", size = 656428, upload-time = "2026-05-20T14:00:12.556Z" }, + { url = "https://files.pythonhosted.org/packages/f6/f9/e753408871eaa61dfe35e619cfc67512b036fde99893685d50eea9e07146/greenlet-3.5.1-cp315-cp315-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:111e2390ffffc47d5840b01711dd7fac07d4c09283d0283e7f3264b14e284c64", size = 667064, upload-time = "2026-05-20T14:05:48.662Z" }, + { url = "https://files.pythonhosted.org/packages/96/27/5565b5b40389f1c7753003a07e21892fda8660926787036d5bc0308b8113/greenlet-3.5.1-cp315-cp315-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e630136e905fe5ff43e86945ae41220b6d1470956a39220e708110ac48d01ea5", size = 665697, upload-time = "2026-05-20T13:14:32.943Z" }, + { url = "https://files.pythonhosted.org/packages/cf/82/e7de4178c0c2d1c9a5a3be3cc0b33e46a85b3ee4a77c071bf7ad8600e079/greenlet-3.5.1-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:975eac34b44a7077ca4d421348455b94f0f518246a7f14bc6d2fdcfe5b584368", size = 1621256, upload-time = "2026-05-20T14:02:31.91Z" }, + { url = "https://files.pythonhosted.org/packages/00/10/f2dddcf7dacac17dfc68691809589adad06135eb28930429cf58a6467a2f/greenlet-3.5.1-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:9ab3c3a0b2ae6198e67c898dad5215a49f9ae0d0081b3c3ec59f333e39eeca26", size = 1685956, upload-time = "2026-05-20T13:14:42.55Z" }, + { url = "https://files.pythonhosted.org/packages/22/17/4a232b32133230ada52f70e9d7f5b65b0caef8772f01849bd8d149e7e4ca/greenlet-3.5.1-cp315-cp315-win_amd64.whl", hash = "sha256:cbfc69be86e10dcfef5b1e6269d1d6926552aa89ee39e1de3353360c1b6989ab", size = 239802, upload-time = "2026-05-20T13:13:15.481Z" }, + { url = "https://files.pythonhosted.org/packages/c2/ae/4e623a7e6d4d2a5f4cb8e4c82de4169fc637942caae68d6e676b8a128ac5/greenlet-3.5.1-cp315-cp315-win_arm64.whl", hash = "sha256:92fd6d44ac5e5a887c8a5dc4a8ba0ba908527c31c12f78c6bc7dcfe8aab279f6", size = 236853, upload-time = "2026-05-20T13:15:37.301Z" }, + { url = "https://files.pythonhosted.org/packages/7a/57/816d9cff29119da3505b3d6a5e14a8af89006ac36f47f891ff293ee05af1/greenlet-3.5.1-cp315-cp315t-macosx_11_0_universal2.whl", hash = "sha256:a6fdf2433a5441ef9a95464f7c3e674775da1c8c1177fff311cee1acad4626ed", size = 293877, upload-time = "2026-05-20T13:10:19.078Z" }, + { url = "https://files.pythonhosted.org/packages/23/a1/59b0a7c7d140ff1a75626680b9a9899b79a9176cab298b394968fb023295/greenlet-3.5.1-cp315-cp315t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7546556f0d649f99f6a361098a55f761181bb2ea12ff150bb16d26092ad88244", size = 655333, upload-time = "2026-05-20T14:00:14.758Z" }, + { url = "https://files.pythonhosted.org/packages/72/1b/5efe127597625042218939d01855109f352779050768b670b52edcc16a6c/greenlet-3.5.1-cp315-cp315t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:d5ee3ea898009fa898f85f9982255d35278c477bebe185beca249cab42d4526c", size = 659443, upload-time = "2026-05-20T14:05:50.159Z" }, + { url = "https://files.pythonhosted.org/packages/6c/6d/c404246ea4d22d097a7426d0efb5b781bd7eb67715f09e79001bd552ab18/greenlet-3.5.1-cp315-cp315t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a5c81f74d204d3edd136ebfd50dce53acbb776995d721a0fe801626cfc93b8cd", size = 658356, upload-time = "2026-05-20T13:14:35.091Z" }, + { url = "https://files.pythonhosted.org/packages/51/02/f8ee37fb6d2219329f350af241c27fcf12df57e723d11f6fc6d3bacdadaa/greenlet-3.5.1-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:2c18ef16bf6d4dd410e4dd52996888ea1497be26892fe5bbc73580aba4287b8e", size = 1619216, upload-time = "2026-05-20T14:02:33.403Z" }, + { url = "https://files.pythonhosted.org/packages/93/c5/3dc9475ace2c7a3680da12372cddd7f1ac874eb410a1ac48d3e9dab83782/greenlet-3.5.1-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:17d86354f0ae6b61bf9be5148d0dd34e06c3cb7c602c671f79f29ac3b150e659", size = 1678427, upload-time = "2026-05-20T13:14:43.71Z" }, + { url = "https://files.pythonhosted.org/packages/df/4e/750c15c317a41ffb36f0bf40b933e3d744a7dede61889f74443ea69690cf/greenlet-3.5.1-cp315-cp315t-win_amd64.whl", hash = "sha256:e7516cf6ae6b8a582c2770a0caed47b8a48373ed732c33d69a72913ae6ac923e", size = 245225, upload-time = "2026-05-20T13:13:59.366Z" }, + { url = "https://files.pythonhosted.org/packages/4f/fd/d3baea2eeb7b617efd47e87ca06e2ec2c6118d303aa9e918e0ce16eadc10/greenlet-3.5.1-cp315-cp315t-win_arm64.whl", hash = "sha256:5028648bf2253ec4745add746129d3904121fa7fe871a76bed23c5720573ce0a", size = 239590, upload-time = "2026-05-20T13:13:37.382Z" }, ] [[package]] @@ -1169,11 +1185,11 @@ wheels = [ [[package]] name = "idna" -version = "3.15" +version = "3.16" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/82/77/7b3966d0b9d1d31a36ddf1746926a11dface89a83409bf1483f0237aa758/idna-3.15.tar.gz", hash = "sha256:ca962446ea538f7092a95e057da437618e886f4d349216d2b1e294abfdb65fdc", size = 199245, upload-time = "2026-05-12T22:45:57.011Z" } +sdist = { url = "https://files.pythonhosted.org/packages/1a/88/bcf9709822fe69d02c2a6a77956c98ce6ea8ca8767a9aadcedc7eb6a2390/idna-3.16.tar.gz", hash = "sha256:d7a6da03db833450fca25d2358ac9ff06cd624577a4aea3a596d5c0f77b8e03d", size = 203770, upload-time = "2026-05-22T00:16:18.781Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/d2/23/408243171aa9aaba178d3e2559159c24c1171a641aa83b67bdd3394ead8e/idna-3.15-py3-none-any.whl", hash = "sha256:048adeaf8c2d788c40fee287673ccaa74c24ffd8dcf09ffa555a2fbb59f10ac8", size = 72340, upload-time = "2026-05-12T22:45:55.733Z" }, + { url = "https://files.pythonhosted.org/packages/94/16/70255075a9859a0e3adb789b68ceb0e210dec03934245fd98d248226572f/idna-3.16-py3-none-any.whl", hash = "sha256:cc246e3a3f89580c3a951b5ad298ca4638078b2cdd4f115654332b5c26daded5", size = 74165, upload-time = "2026-05-22T00:16:16.698Z" }, ] [[package]] @@ -2008,81 +2024,81 @@ wheels = [ [[package]] name = "numpy" -version = "2.4.5" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/50/8e/b8041bc719f056afd864478029d52214789341ac6583437b0ee5031e9530/numpy-2.4.5.tar.gz", hash = "sha256:ca670567a5683b7c1670ec03e0ddd5862e10934e92a70751d68d7b7b74ca7f9f", size = 20735669, upload-time = "2026-05-15T20:25:19.492Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e1/44/1383ee4d1e916a9e610e46c876b5c83ea023526117d23cd911983929ec34/numpy-2.4.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3176dc8ff71dbb593606f91a69ad0c3cd3303c7eb546af477370ab9edf760288", size = 16969261, upload-time = "2026-05-15T20:22:23.036Z" }, - { url = "https://files.pythonhosted.org/packages/3d/61/54bacfbec7550bc398e6b6d9a861db35d64f75844e1d7920f5722c3cd5e7/numpy-2.4.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1811150e5148f5a01a7cc282cb2f489b4a3050a773e173adb480e507bad3a3d7", size = 14964009, upload-time = "2026-05-15T20:22:25.819Z" }, - { url = "https://files.pythonhosted.org/packages/7a/55/fe86c64561761f185339c26001164a2687bd4787af681e961431abd2d534/numpy-2.4.5-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:0d63a780070871210853ba01e90b88f9b85cf2abf63a7f143d5127189265ddf6", size = 5469106, upload-time = "2026-05-15T20:22:28.13Z" }, - { url = "https://files.pythonhosted.org/packages/2f/74/cf29b8317627f0e3aa2c9fb332d386bd734308cecd9e07da9f407d9ce0c3/numpy-2.4.5-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:0c6919cefafb3b76cd46a89dbb203bf1dd95529d2a6d09fef2d325d95d6a79d8", size = 6798945, upload-time = "2026-05-15T20:22:30.061Z" }, - { url = "https://files.pythonhosted.org/packages/80/a9/b61730a17fa87d5abb13ce560a1b4ce3485d37a13e03eb7b414e598e72f8/numpy-2.4.5-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d51efede1e58e8b11877536a5518f60e318d8ff69b89ad7b38ee5e431b24d772", size = 15967025, upload-time = "2026-05-15T20:22:32.328Z" }, - { url = "https://files.pythonhosted.org/packages/03/39/70bcd187eb4d223c21fde02c2bdfbffbffef3288cbb3947c04c74ae39a08/numpy-2.4.5-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:07ce7e74da92d7c71b5df157b9758bcdd53d7fea10602154de3afd2b3ddc34dd", size = 16918685, upload-time = "2026-05-15T20:22:34.759Z" }, - { url = "https://files.pythonhosted.org/packages/ab/31/400fd1315bbe228af3937cf8a74e32023df6217af36077919d00adc382e4/numpy-2.4.5-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d7828234a13185effb34979e146f9921f2a65dfbbe215e6dbb57d6478fc8e059", size = 17322963, upload-time = "2026-05-15T20:22:37.557Z" }, - { url = "https://files.pythonhosted.org/packages/18/6a/bbbafb657e6f6ee826b4ecdb8722a2e0aae4a981888eaf59eae6a535cc13/numpy-2.4.5-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f96083adc3dfc1bbf778f2c79654d88115fa07074c97cb724fe9508f12d91c55", size = 18651594, upload-time = "2026-05-15T20:22:40.449Z" }, - { url = "https://files.pythonhosted.org/packages/de/0c/857a515154a2a18b0dfae04089600d166d352d473ec17a0680d879582d06/numpy-2.4.5-cp311-cp311-win32.whl", hash = "sha256:4ed78c904a638b6e5d7cd4db90c06fca5fc6ec2f28d258305368f454a50e79cf", size = 6233849, upload-time = "2026-05-15T20:22:43.139Z" }, - { url = "https://files.pythonhosted.org/packages/f0/66/d215f3fb93541617adb5d58b3b9508e8a6413e499711e0adc0b80bcb445d/numpy-2.4.5-cp311-cp311-win_amd64.whl", hash = "sha256:079b0fad6f2899b23c5da89792b5409d2d83fc83e8bd5c2299cc9c397a264864", size = 12608238, upload-time = "2026-05-15T20:22:45.229Z" }, - { url = "https://files.pythonhosted.org/packages/cb/c4/611d66d3fcfa931954d37a19ce5575f3283d023e89ff0df6ad43b334ae9c/numpy-2.4.5-cp311-cp311-win_arm64.whl", hash = "sha256:d6c78e260b53affe9b395a9d54fc61f101f9521c4d9452c7e9e3718b19e2215b", size = 10479452, upload-time = "2026-05-15T20:22:47.962Z" }, - { url = "https://files.pythonhosted.org/packages/6c/18/3275231e98620002681c922e792db04d72c356e9d8073c387344fc0e4ff1/numpy-2.4.5-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:654fb8674b61b1c4bd568f944d13a908566fdcb0d797303521d4149d16da05ef", size = 16689166, upload-time = "2026-05-15T20:22:50.761Z" }, - { url = "https://files.pythonhosted.org/packages/db/23/000aab6a16bdec53307f0f72546b57a3ac9266a62d8c257bee97d85fd078/numpy-2.4.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4cd9f6fa7ce10dc4627f2bb81dd9075dab67e94632e04c2b638e12575ddaa862", size = 14699514, upload-time = "2026-05-15T20:22:53.678Z" }, - { url = "https://files.pythonhosted.org/packages/47/cc/ddaf3af9c46966fef5be879256f213d85a0c56c75d07a3b7defec7cf6b4c/numpy-2.4.5-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:4f5bc96d35d94e4ceab8b38a92241b4611e95dc44e63b9f1fa2a331858ee3507", size = 5204601, upload-time = "2026-05-15T20:22:56.257Z" }, - { url = "https://files.pythonhosted.org/packages/07/ea/627fadd11959b3c7759008f34c92a35af8ff942dd8284a66ced648bbe516/numpy-2.4.5-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:4bb33e900ee81730ad77a258965134aa8ceac805124f7e5229347beda4b8d0aa", size = 6551360, upload-time = "2026-05-15T20:22:58.334Z" }, - { url = "https://files.pythonhosted.org/packages/a1/47/0728b986b8682d742ff68c16baa5af9d185484abfc635c5cc700f44e62be/numpy-2.4.5-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:32f8f852273ef32b291201ac2a2c97629c4a1ee8632bb670e3443eaa09fc2e72", size = 15671157, upload-time = "2026-05-15T20:23:01.081Z" }, - { url = "https://files.pythonhosted.org/packages/d1/0b/b905ae82d9419dc38123523862db64978ca2954b69609c3ae8fdaca1084c/numpy-2.4.5-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:685681e956fc8dcb75adc6ff26694e1dfd738b24bd8d4696c51ca0110157f912", size = 16645703, upload-time = "2026-05-15T20:23:04.358Z" }, - { url = "https://files.pythonhosted.org/packages/5f/24/e27fc3f5236b4118ed9eed67111675f5c61a07ea333acec87c869c3b359d/numpy-2.4.5-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:6f64dd84b277a737eb59513f6b9bb6195bf41ab11941ef15b2562dbab43fa8ef", size = 17021018, upload-time = "2026-05-15T20:23:07.021Z" }, - { url = "https://files.pythonhosted.org/packages/d3/a7/9041af38d527ab80a06a93570a77e29425b41507ad41f6acf5da78cfb4a4/numpy-2.4.5-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b42d9496f79e3a728192f05a42d86e36163217b7cdecb3813d0028a0aa6b72d7", size = 18368768, upload-time = "2026-05-15T20:23:09.44Z" }, - { url = "https://files.pythonhosted.org/packages/49/82/326a014442f32c2663434fd424d9298791f47f8a0f17585ad60519a5606e/numpy-2.4.5-cp312-cp312-win32.whl", hash = "sha256:86d980970f5110595ca14855768073b08585fc1acc36895de303e039e7dee4a5", size = 5962819, upload-time = "2026-05-15T20:23:11.631Z" }, - { url = "https://files.pythonhosted.org/packages/3c/f0/cbf5d391b0b3a5e8cad264603e2fae256b0bde8ce43566b13b78faedc659/numpy-2.4.5-cp312-cp312-win_amd64.whl", hash = "sha256:3333dba6a4e611d666f69e177ba8fe4140366ff681a5feb2374d3fd4fff3acb6", size = 12321621, upload-time = "2026-05-15T20:23:14.305Z" }, - { url = "https://files.pythonhosted.org/packages/3c/d0/0f18909d9bc37a5f3f969fc737d2bb5df9f2ff295f71b467e6f52a0d6c4e/numpy-2.4.5-cp312-cp312-win_arm64.whl", hash = "sha256:4593d197270b894efeb538dcbe227e4bcf1c77f88c4c6bf933ead812cfaa4453", size = 10221430, upload-time = "2026-05-15T20:23:16.887Z" }, - { url = "https://files.pythonhosted.org/packages/e3/a4/fb50657c7cab297bf34edcd60a074cb0647f61771430d6363575274160fe/numpy-2.4.5-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:1ef248460b645c102026b82337cc4e88231909c66dd77b59ec6d6cac7e44f277", size = 16684760, upload-time = "2026-05-15T20:23:19.436Z" }, - { url = "https://files.pythonhosted.org/packages/3e/43/87e731299b9408eda705b3b9cb31c7bceb9347d2af9cbb16b2b1e4b5bc0f/numpy-2.4.5-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:4603622bdcdbf8dccb1d9d5b21d16a7aa4e473ae6c8e14048d846fd4ca2907a0", size = 14694117, upload-time = "2026-05-15T20:23:21.832Z" }, - { url = "https://files.pythonhosted.org/packages/a9/c7/0b2bb8acea222e9dd6e582afc2bc553b89b8833cbdccc68e68f050fb31f8/numpy-2.4.5-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:6c18d49c67689c562854b53fdc433b93e47c12952aa6fa6d59f185e1a5992419", size = 5199141, upload-time = "2026-05-15T20:23:24.066Z" }, - { url = "https://files.pythonhosted.org/packages/39/60/b6972b5d47033d90000f0097c81a98b9486589a2d7003bf725bff275cb0d/numpy-2.4.5-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:b1c663ddc641f4192e90511bec61a09bc231e3bbdb996cdc6edbcaa0e528d685", size = 6546954, upload-time = "2026-05-15T20:23:26.099Z" }, - { url = "https://files.pythonhosted.org/packages/c1/e9/ed667cb12c11ca0adde431f685d3a5dd78e6f78b27228c581c8415198e9e/numpy-2.4.5-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:93793222b524f692f12b2f8752ce8b1d9d9125b2bfd5dbf0fb69c92c5e1ce86c", size = 15669430, upload-time = "2026-05-15T20:23:28.147Z" }, - { url = "https://files.pythonhosted.org/packages/44/e5/679f6ffeb01294b0008e5ada4a113cb47617bc0e1819a529fd7973c6d7f4/numpy-2.4.5-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1616bde34b2bcba2fa9bde06217ce00da4f3d1bdfb264d54525a99e8fe170d83", size = 16633390, upload-time = "2026-05-15T20:23:31.622Z" }, - { url = "https://files.pythonhosted.org/packages/36/46/42bfffc9a780ec902ccd7470d3219192ee82b7b442710307dd85b4d121b0/numpy-2.4.5-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:09d7d97da1c2c62f4818b3e150a57572ff8dcf1cf5ac501aac832ffd4ebd9566", size = 17020709, upload-time = "2026-05-15T20:23:34.08Z" }, - { url = "https://files.pythonhosted.org/packages/44/00/3e840bfee0cc6cec22209f2c97057f26eeb30de031e4933b4dfc0395416c/numpy-2.4.5-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:2d68d0b355ab2e39fe0de59001d7151dfdbbb880ef67baeed806661e03df5097", size = 18357818, upload-time = "2026-05-15T20:23:36.965Z" }, - { url = "https://files.pythonhosted.org/packages/72/cb/3447b400b9da84134575486f0f656541559b00d4b262477bce9b678bbca8/numpy-2.4.5-cp313-cp313-win32.whl", hash = "sha256:fe28b64777ddfa0eca9b5f51474034ebe3dcb8324f48f27b28f479085673ae33", size = 5961114, upload-time = "2026-05-15T20:23:39.586Z" }, - { url = "https://files.pythonhosted.org/packages/28/f9/a90d2220ffcdc0798f5d55bb5d5463cd6254ec9ef43f384dae80217d7a2f/numpy-2.4.5-cp313-cp313-win_amd64.whl", hash = "sha256:fb4a6c9c537d6ccec9cc4aeae4261bd3cc79b070c67ddc0646f5b1c07fddde42", size = 12318553, upload-time = "2026-05-15T20:23:41.436Z" }, - { url = "https://files.pythonhosted.org/packages/b8/c9/96f531fb3234545315152d34efdf3de7daee81254448447eb619e8d16967/numpy-2.4.5-cp313-cp313-win_arm64.whl", hash = "sha256:6d7df2da2e7ea0624a43aa368104b3a3ce14aae98ad4bb2c9a93fecef76f1c97", size = 10222200, upload-time = "2026-05-15T20:23:43.681Z" }, - { url = "https://files.pythonhosted.org/packages/e1/f4/a291caab5a3c520babf93ff77c54fd5fdb1ebbc3296cee2eb2146ce773b1/numpy-2.4.5-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:2a235607a18df941760a695927051af4b1cd5d3ee85840d0e2af816785771feb", size = 14821438, upload-time = "2026-05-15T20:23:45.911Z" }, - { url = "https://files.pythonhosted.org/packages/85/26/13dbb1159b864370568e7309063fd72667984df89db74e9caeb175d067c7/numpy-2.4.5-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:58dcf64969d870f36bc7fbd557d2617e997db7dc06261b6e3327148ea460d0a4", size = 5326663, upload-time = "2026-05-15T20:23:48.18Z" }, - { url = "https://files.pythonhosted.org/packages/7c/99/d233408072a0e019e2288e27edd23f7d572ccd4a73d1539baa3270ede85d/numpy-2.4.5-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:235f54b0156274d8fa3155db3ed6d2f401c7e8f3367c90db0a12f02a58fde6ed", size = 6646874, upload-time = "2026-05-15T20:23:49.856Z" }, - { url = "https://files.pythonhosted.org/packages/c5/00/eeb6f193dfe767725e952e0464f3e51f44145c5dd261cd7389aa36ac0713/numpy-2.4.5-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ef3b5bb65437a3555c648e706475db01c645559ca80dc8b03e4f202ea757e0d6", size = 15728147, upload-time = "2026-05-15T20:23:51.655Z" }, - { url = "https://files.pythonhosted.org/packages/e5/c9/b8ed039f1fde1b13a8807c893e7e2f9432a379f4d6401edecf0028da5b2c/numpy-2.4.5-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7f09a7e5f017d7098c66522097c96257411c9620c0926212200d66bc8cee3976", size = 16681770, upload-time = "2026-05-15T20:23:53.933Z" }, - { url = "https://files.pythonhosted.org/packages/11/5b/0198ef6cb7016eca6d895d392106012138127fab23f46637e76d5e25c9f5/numpy-2.4.5-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:993a88d8fdd8554466a8765cd8bacd97ba56b70ca6b0a04bcdca77f5afed4222", size = 17086218, upload-time = "2026-05-15T20:23:56.646Z" }, - { url = "https://files.pythonhosted.org/packages/f0/fe/8821f3cfc660ae84c92ee158505941874b62c56a42e035a41425228cd8cf/numpy-2.4.5-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:84f58bed609b5669f5ad3d597901a4f1f86ee5b3c3708aaa55f05b4fe6e0f656", size = 18403542, upload-time = "2026-05-15T20:23:59.173Z" }, - { url = "https://files.pythonhosted.org/packages/0e/00/e64ecaf498865e7b091f57658b2c522503e5d1b70e43b807f5f8247e1d88/numpy-2.4.5-cp313-cp313t-win32.whl", hash = "sha256:7200c58f3f933ca61e66346667dcc8510bb111995e9ce15398a731e6a4afa4bb", size = 6084903, upload-time = "2026-05-15T20:24:01.506Z" }, - { url = "https://files.pythonhosted.org/packages/20/c0/354997dedaf74e8311c2cf9a6027b476fd8d424cb92189cc0ae2b25f501c/numpy-2.4.5-cp313-cp313t-win_amd64.whl", hash = "sha256:c26c71080d35db5002102f5d9ff614d45de02aa1f7802943e691e063e5ee93bc", size = 12458420, upload-time = "2026-05-15T20:24:03.735Z" }, - { url = "https://files.pythonhosted.org/packages/66/dc/917ee5ea4a31ca1a6e4c9a85386477efa318dcc60db257c5ef4adda096c1/numpy-2.4.5-cp313-cp313t-win_arm64.whl", hash = "sha256:2caa576d1707b275cba1aeb60a5c50daa6fa2a3f28ecb08123bc05fd439005db", size = 10291826, upload-time = "2026-05-15T20:24:06.535Z" }, - { url = "https://files.pythonhosted.org/packages/ca/c1/3be0bf102fc17cff5bd142e3be0bfffabec6fa46da0a462396c76b0765d0/numpy-2.4.5-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:889ca2c072315de638a5194a772aa1fa2df92bdd6175f6a222d4784040424b61", size = 16683455, upload-time = "2026-05-15T20:24:08.988Z" }, - { url = "https://files.pythonhosted.org/packages/e8/3e/0742d724901fa36bc54b338c6e62e463a7601180da896aa44978f0adf004/numpy-2.4.5-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:89e89304fb1f8c3f0ecfa4a7d48f311dd79771336a940e920159d643d1307e77", size = 14704577, upload-time = "2026-05-15T20:24:11.542Z" }, - { url = "https://files.pythonhosted.org/packages/25/1c/196c610ff4c6782d697ba780ebdc1616be143213701bf22c1a270f3bf7dd/numpy-2.4.5-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:144fcc5a3a17679b2b82543b4a2d8dd29937230a7af13232b5f753872feb6361", size = 5209756, upload-time = "2026-05-15T20:24:14.091Z" }, - { url = "https://files.pythonhosted.org/packages/52/c0/23fb1bc506f774e03db66219a2830e720f4d3dbcaaddf855a7ff7bb6d96f/numpy-2.4.5-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:398bb16772b265b9fa5c07b07072646ea97137c10ffb62a9a087b277fc825c29", size = 6543937, upload-time = "2026-05-15T20:24:16.223Z" }, - { url = "https://files.pythonhosted.org/packages/9f/49/db4662c26e68520afcc84d672a6f9f5294063dee0e57a46d61afdaa7f9ed/numpy-2.4.5-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fb352e7b8876da1249e72254736d6c58c505fa4e58a3d7e30efca241ca9ca9ce", size = 15685292, upload-time = "2026-05-15T20:24:17.978Z" }, - { url = "https://files.pythonhosted.org/packages/43/80/1315439acedd8398319bac177d6de3d48ab39c62cc0c810f74f0a9a73996/numpy-2.4.5-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7341b08ff8124d7353939778e2707b8732d03c78c1c30e0815aba2dacbe1245a", size = 16638528, upload-time = "2026-05-15T20:24:20.478Z" }, - { url = "https://files.pythonhosted.org/packages/56/81/364388600932618fe735d97fdd2437cb8dd87a23377ac11d8b9d5db098b7/numpy-2.4.5-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:deb01226f012539f3945261ffe1c10aec081a0fa0a5c925419933c70f3ae2d23", size = 17036709, upload-time = "2026-05-15T20:24:22.949Z" }, - { url = "https://files.pythonhosted.org/packages/32/4a/a1185b18a94a6d9587e54b437e7d0ba36ecf6e614f1bea03f5249912c64e/numpy-2.4.5-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d888bdf7335f76878c3c7b264ac1ff089863e211ec81249f9fb5795c2183dc25", size = 18363254, upload-time = "2026-05-15T20:24:25.402Z" }, - { url = "https://files.pythonhosted.org/packages/b9/8e/95c1d2ed15ae97750ede8c8a0ac487c9c01207afff430f47078b1d9d7dc5/numpy-2.4.5-cp314-cp314-win32.whl", hash = "sha256:15f90d1256e9b2320aff24fde44815b787ab6d7c49a1a11bfd8138b321c5f080", size = 6010184, upload-time = "2026-05-15T20:24:27.852Z" }, - { url = "https://files.pythonhosted.org/packages/aa/92/d063df4d63d988b20d881856c74df76c0c1786229bb870f3a52af0981d4d/numpy-2.4.5-cp314-cp314-win_amd64.whl", hash = "sha256:4bd2cd4ef9c0afa87de73723c0a33c0edff62143e1432917458e26d3d195d87f", size = 12450344, upload-time = "2026-05-15T20:24:29.856Z" }, - { url = "https://files.pythonhosted.org/packages/3d/64/c0ae481f7c3b2f85869bcd8fc5d30aa7c96b394162eef9c9315957f115c5/numpy-2.4.5-cp314-cp314-win_arm64.whl", hash = "sha256:db304568c650e9d7039744d3575d0d287754debb2057d7c7b8cdfdc2c487a957", size = 10495674, upload-time = "2026-05-15T20:24:32.352Z" }, - { url = "https://files.pythonhosted.org/packages/57/89/c5a4c677acf17aa50ba09a15e61812f90baac42bb6ca38d112e005858351/numpy-2.4.5-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:6de2883e0d2c63eae1bab1a84b390dca74aabb3d20ea1f5d58f360853c83abf3", size = 14824078, upload-time = "2026-05-15T20:24:34.669Z" }, - { url = "https://files.pythonhosted.org/packages/e7/52/57e7144284f6b51ba93523e495ff239260b1ecd5257e3700a436332e5688/numpy-2.4.5-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:06760fe73ae5005008748d182de612c733542af3cde063d532cd2127561b27be", size = 5329246, upload-time = "2026-05-15T20:24:36.957Z" }, - { url = "https://files.pythonhosted.org/packages/b9/b3/09dbce80fd4a7db4318f2fc01eec0ae76f29306442b5a32d4b811d082cdf/numpy-2.4.5-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:4b51a01745cb04cc19278482207444b4d30728ce91c28d27a3bfae5fc6ff24c7", size = 6649877, upload-time = "2026-05-15T20:24:38.861Z" }, - { url = "https://files.pythonhosted.org/packages/30/c2/dbdb23e82d540b757690ef13f011c386fca6a63848eec6136baf8ce7cbed/numpy-2.4.5-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9a05636d7937d0936f271e5ba957fa8d746b5be3c2025caa1a2508f4fe521d40", size = 15730534, upload-time = "2026-05-15T20:24:41.168Z" }, - { url = "https://files.pythonhosted.org/packages/c4/bd/68f6e9b3c20decf40ac06708a7b506757e3a8588efed32988d1b747316be/numpy-2.4.5-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:14b86f56048ed09c3bbe48962a7dff077c2fd3274f8cf981800f3b38eac49cc3", size = 16679741, upload-time = "2026-05-15T20:24:44.874Z" }, - { url = "https://files.pythonhosted.org/packages/39/1d/0fcac0b6b4ea1b50ca8fca05a34bed5c8d56e34c1cb5ffb04cf76109ac3c/numpy-2.4.5-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:130d58151c4db23e9fa860b84784e219a3aa3e030acc88a493ea37006c4dfd4c", size = 17085598, upload-time = "2026-05-15T20:24:47.603Z" }, - { url = "https://files.pythonhosted.org/packages/0b/e8/a472b2564cf6cc498ad7aa9741d9832648221b8ab8cc0dbef41faa248ede/numpy-2.4.5-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:d475afc8cbe935ff5944f753d863bba774d7f4e1feaaa4102901e3e053ca5963", size = 18403855, upload-time = "2026-05-15T20:24:50.474Z" }, - { url = "https://files.pythonhosted.org/packages/b9/a4/da82196f8cc4bd28ecf17bd57008c84f3d4696caf06753d9bad45e4ad749/numpy-2.4.5-cp314-cp314t-win32.whl", hash = "sha256:27f4a6dc26353a860b348961b9aa9e009835688b435cfa105e873b8dc2c726f5", size = 6156900, upload-time = "2026-05-15T20:24:53.134Z" }, - { url = "https://files.pythonhosted.org/packages/98/31/860959b91a73d9a085006554fa3850da51a7ffab64599bac5097243438ab/numpy-2.4.5-cp314-cp314t-win_amd64.whl", hash = "sha256:76ac6e90f5e226011c88f9b7040a4bcae612518bc7e9adc127e697a13b28ad1a", size = 12638906, upload-time = "2026-05-15T20:24:55.009Z" }, - { url = "https://files.pythonhosted.org/packages/9e/2a/bbd3097913083ad07c0f28fc9629666221fc18923e17ce97ae22a5dccdd6/numpy-2.4.5-cp314-cp314t-win_arm64.whl", hash = "sha256:7c392e2c1bf596701d3c6832be7567eab5d5b0a13865036c33365ee097d37f8b", size = 10565875, upload-time = "2026-05-15T20:24:57.425Z" }, - { url = "https://files.pythonhosted.org/packages/fc/5d/9a644cfb841bc76b584afc3af1708b3bf6c5cb51fc84a7008246cd93b7b7/numpy-2.4.5-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:6bf0bfc1c2e1db972e30b6cd3d4861f477f3af908b27799b239dc3cbe3eb4b95", size = 16847544, upload-time = "2026-05-15T20:24:59.746Z" }, - { url = "https://files.pythonhosted.org/packages/56/8f/4fe5e3ba76d858dae1fe79078818c0520447335be0082c0dedf82719cc08/numpy-2.4.5-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:73d664413fb97229149c4711ef56531a6fe8c15c1c2626b0bbe497b84c287e70", size = 14889039, upload-time = "2026-05-15T20:25:03.179Z" }, - { url = "https://files.pythonhosted.org/packages/8e/6f/79f195abf922ecc43e7d0eb6cc969462a71b524a35bcd1fa26b4a1d7406a/numpy-2.4.5-pp311-pypy311_pp73-macosx_14_0_arm64.whl", hash = "sha256:b35bee5ef99e8d227a07829bee2e864fcb65f7c157646fcd8ec8b4b45dd8b88f", size = 5394106, upload-time = "2026-05-15T20:25:05.659Z" }, - { url = "https://files.pythonhosted.org/packages/58/6f/79cd6247205802bcbd10b40ea087e20ded526e10e9be224d34de832b216e/numpy-2.4.5-pp311-pypy311_pp73-macosx_14_0_x86_64.whl", hash = "sha256:02981d0fc9f9ce147643d552966d47f329a02f7ecb3b113e84207242f20dfa83", size = 6708718, upload-time = "2026-05-15T20:25:08.071Z" }, - { url = "https://files.pythonhosted.org/packages/d7/22/5f378a9d4633c98f28c4709d4144b1a4630c5c09e109d2e781e2d26c8fe1/numpy-2.4.5-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0e63caf31a1df06338ae63d999f7a33a675ced62eea9c9b02db4b1c1f45cff38", size = 15798292, upload-time = "2026-05-15T20:25:10.689Z" }, - { url = "https://files.pythonhosted.org/packages/63/1c/cec582febef798c99888892d92dc1d28dfe29cb427c41f44d13d0dec208f/numpy-2.4.5-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d8fc52b85a7b45e474be53eddf08e006d22e381a4e41bcde8e4aa08da0e7d198", size = 16747406, upload-time = "2026-05-15T20:25:13.879Z" }, - { url = "https://files.pythonhosted.org/packages/b1/dc/d358a16a6fec86cf736b8fbe67386044b3fa2aded1a80cff90e836799301/numpy-2.4.5-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:40c71d50a4da1a7c317af419461052d3911a5770bfc5fd55baf52cc45e7a2c20", size = 12504085, upload-time = "2026-05-15T20:25:16.667Z" }, +version = "2.4.6" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d0/ad/fed0499ce6a338d2a03ebae59cd15093910c8875328855781952abf6c2fe/numpy-2.4.6.tar.gz", hash = "sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda", size = 20735807, upload-time = "2026-05-18T23:37:14.07Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b3/49/ec46835a70be8fa6446c495126ac84fdb28cb2558e1620ffb87a10c8b64c/numpy-2.4.6-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0280e0356c0829a18d9de1cb7eee50ec22ca639878d7240307ca0943d73cd2c4", size = 16969194, upload-time = "2026-05-18T23:33:13.503Z" }, + { url = "https://files.pythonhosted.org/packages/0e/0d/f5957185c0ee2f3e12f78715aa9e3b353fd83633316c8532b38faa37e3f6/numpy-2.4.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:110f8b71aacb688ec69062bb7f6938a0f8acb01b7c1c4beb453c65b6d234584d", size = 14964111, upload-time = "2026-05-18T23:33:17.795Z" }, + { url = "https://files.pythonhosted.org/packages/ad/40/40a40ee0ddf7ceb782c49af278894b686e586d65d8c1889c8b5da01a3d7d/numpy-2.4.6-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:4cfe66903cc32a9921a6733d96b19bb6abf310397581bbad89c228f5abaf0ee8", size = 5469159, upload-time = "2026-05-18T23:33:20.654Z" }, + { url = "https://files.pythonhosted.org/packages/63/13/f9a8046535cb21deae82f8d03de9617e08882d274fad2539630761888228/numpy-2.4.6-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:8155154c7c691289fe18f510b5d4657c68c67989f293f0535a91360392ff6538", size = 6798936, upload-time = "2026-05-18T23:33:22.987Z" }, + { url = "https://files.pythonhosted.org/packages/33/a8/6fa8c1a345a8c85dbb21932c447bee07c30a2c2a3f31e369c0a84b300147/numpy-2.4.6-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0ab0a9c4ffb1a6d95ef519fe4247dba8eb6b18ad93999f76b7f657039acabd47", size = 15966692, upload-time = "2026-05-18T23:33:26.62Z" }, + { url = "https://files.pythonhosted.org/packages/02/03/74fe2a4cb3817d94d86402f2506554130a2f01414e299b5a843e5a8a957f/numpy-2.4.6-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:89cd468399cfd2504718f0ba50e410dca55a170b61a02ad92bb18c8a65186e93", size = 16918164, upload-time = "2026-05-18T23:33:29.955Z" }, + { url = "https://files.pythonhosted.org/packages/c5/80/3615be3313f7e7696609bc194b9f0101da809df79e859bdb84e0cd043f46/numpy-2.4.6-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c2d37ab77531417474168eb79d6d80b14f821a966818505d03013d0833edb7a8", size = 17322877, upload-time = "2026-05-18T23:33:34.724Z" }, + { url = "https://files.pythonhosted.org/packages/ca/ac/a691e0fe2675e370d0e08ff905adc49a1c8830e8cae03efe4477e92cd55d/numpy-2.4.6-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f407cb6b8e9d6d8c626bc73c945db1706035af8fd632295547bf1c9e46d092d6", size = 18651487, upload-time = "2026-05-18T23:33:38.217Z" }, + { url = "https://files.pythonhosted.org/packages/15/a7/9bc1cd626d7bf6869bfedf27b91b6ab5dd607758bf8e959d6fa80c6a59cb/numpy-2.4.6-cp311-cp311-win32.whl", hash = "sha256:ddea102b48f9e339f3948bf22040944184627a30fdf7f858667673b9c5f033c8", size = 6233945, upload-time = "2026-05-18T23:33:41.331Z" }, + { url = "https://files.pythonhosted.org/packages/c5/31/7fc6239c12bce7e931463251cca4426c465e1876ba3cc785402ef4dd8f4e/numpy-2.4.6-cp311-cp311-win_amd64.whl", hash = "sha256:1e254a00cdf42b1e4d5b3d68d33af63268d41340d8885df2ab6470f2e1500147", size = 12608406, upload-time = "2026-05-18T23:33:44.131Z" }, + { url = "https://files.pythonhosted.org/packages/27/83/140f85a466595a16382996a1bf06b2b54bcd597488921b0c9daaeeda72af/numpy-2.4.6-cp311-cp311-win_arm64.whl", hash = "sha256:ed9749eef4cbd126da3dc1d6bcb3a57f5eb7ac6a6484146bdbf743f552dfc577", size = 10479528, upload-time = "2026-05-18T23:33:50.725Z" }, + { url = "https://files.pythonhosted.org/packages/95/2a/3d7b5ac8aac24feaf9ad7ed58f45b0bbc06d37e4338ae84c9f2298b570f9/numpy-2.4.6-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:001fbb8e08d942dd57599e781f2472269ee7f2755fae407b4f67b2f0b17da3f1", size = 16689119, upload-time = "2026-05-18T23:33:54.065Z" }, + { url = "https://files.pythonhosted.org/packages/ea/12/92c4c131527599e8288d6918e888d88726f84d805d784b771f32408aeaef/numpy-2.4.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ebfb099f8dcf083deef3ac1ca4c1503f387cf76296fcb3816b66f5ecb5f54fdb", size = 14699246, upload-time = "2026-05-18T23:33:57.621Z" }, + { url = "https://files.pythonhosted.org/packages/ad/fe/c0a6b7b2ca128a8fb228575147073b660656734b8ebe4d76c8fd748dcc79/numpy-2.4.6-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:3213d622a0283a39a93d188f3cf72b26862df52fbb4ca3697f51705016523d41", size = 5204410, upload-time = "2026-05-18T23:34:00.302Z" }, + { url = "https://files.pythonhosted.org/packages/f3/d4/9770d14ba719432bb90a421bfd443872ed0f70f7264b64bec12ea363d5fd/numpy-2.4.6-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:357cc07a6d7b0b182ff02249616a03742827ebb1277546b5c7cd7f7620a45698", size = 6551240, upload-time = "2026-05-18T23:34:02.852Z" }, + { url = "https://files.pythonhosted.org/packages/c9/c6/50a46a6205feba2343f1d6d17438107c5dc491ed1c736e6ea68689fd906b/numpy-2.4.6-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5f9fb9157b4ce2971008323afe46053787b526ef624fea915b261468a8421a0f", size = 15671012, upload-time = "2026-05-18T23:34:05.485Z" }, + { url = "https://files.pythonhosted.org/packages/99/60/14115e6364fa676c5397c2ad3004e527e9aa487abf5d0706ec81bbd08529/numpy-2.4.6-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:90f9849678c75fe7afa2d348ac842c168b0a4d3d61919687216dfc547976d853", size = 16645538, upload-time = "2026-05-18T23:34:09.265Z" }, + { url = "https://files.pythonhosted.org/packages/ae/c5/693cbe59e57db94d2231fa519ca3978dc9e19da5a8f088588f5c6e947ff2/numpy-2.4.6-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c1a2af6c6ef86344a6b0db6b97834208bf598db514f2b155042439b62605601a", size = 17020706, upload-time = "2026-05-18T23:34:13.053Z" }, + { url = "https://files.pythonhosted.org/packages/ef/fc/85b7c4eff9b4966ade25c2273cf7e7012e92366c032058653934b37de044/numpy-2.4.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e5805d5a22fd19c8ccff10a9561f9df94436b0545619ea579db2d3c35294bce2", size = 18368541, upload-time = "2026-05-18T23:34:17.024Z" }, + { url = "https://files.pythonhosted.org/packages/f6/81/e1b27545deedce7f4a0b348618c6b62d74e36a4dc9ccd42f3eb2f85eee32/numpy-2.4.6-cp312-cp312-win32.whl", hash = "sha256:e3eeb0aabd6bd5ce64faae67e9935203a6991b4bc2a485a767fbafb2c5125f45", size = 5962825, upload-time = "2026-05-18T23:34:20.3Z" }, + { url = "https://files.pythonhosted.org/packages/ab/ca/feab00bd44aa5fe1ad2c18f08b4d3bb92e26484b0b1d1443897809ed528c/numpy-2.4.6-cp312-cp312-win_amd64.whl", hash = "sha256:d8e8286dd7cea7895157318d1b91cdacac64c479f3cbc8dce548331728484751", size = 12321687, upload-time = "2026-05-18T23:34:23.095Z" }, + { url = "https://files.pythonhosted.org/packages/63/cf/5a6d34850a39d1093558564f77ee8e8e0bee5061151b8f05a55711001ec7/numpy-2.4.6-cp312-cp312-win_arm64.whl", hash = "sha256:4081eb135ac24158bd51cdfbef16f1c64df7063b1143f24731387137c092bec8", size = 10221482, upload-time = "2026-05-18T23:34:25.876Z" }, + { url = "https://files.pythonhosted.org/packages/fb/82/bdab26d7438c6791ca31b7c024ca37c1eab8b726ba236129005cd4a06e45/numpy-2.4.6-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:511dbaf848decaaaf4b4ca48032619fb3138710c4bf7da7617765edad1ef96b0", size = 16684648, upload-time = "2026-05-18T23:34:29.41Z" }, + { url = "https://files.pythonhosted.org/packages/1b/30/a80189bcc7f5e4258b3fbc3968d909d1756f54d023299ecc39ad6fdb9ef8/numpy-2.4.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:bf162abab1c1a736333192707cef898e735a5ca00f38f27eeedf44b39d9e85eb", size = 14693902, upload-time = "2026-05-18T23:34:33.013Z" }, + { url = "https://files.pythonhosted.org/packages/97/12/70b5d0d7c15e1ebb8a6a84a8caa1d19e181d84fb58bb6d70aca29099dec1/numpy-2.4.6-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:043191bfa8eab18c776647b62723ac9dddece59743b13f49b2016094129c2b3f", size = 5198992, upload-time = "2026-05-18T23:34:36.132Z" }, + { url = "https://files.pythonhosted.org/packages/ba/8c/ebd2a8f8a83541f8d38cc5667e8c2b69cecfd30da6e45693e8158857d44b/numpy-2.4.6-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:6180d8b35af935aed8ece3a85e0a43f87393ae0ac87c8d2c8bd2c993f7270ef3", size = 6546944, upload-time = "2026-05-18T23:34:38.484Z" }, + { url = "https://files.pythonhosted.org/packages/bb/c5/7b863a97a91671a0338f4253bd3b5a3d3852f0692dae91711c9f4a10e787/numpy-2.4.6-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:72fbe16c6fac95aedf5937fa873445cec2110be35d8a4e9433d7501fd98dae6b", size = 15669392, upload-time = "2026-05-18T23:34:41.257Z" }, + { url = "https://files.pythonhosted.org/packages/a5/9d/3584b9984ca4c047aea75214ce1a4c4c73d849bd71b604264b7f5653f8a8/numpy-2.4.6-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a7830bab239b79cda9c08c2da014761cafb48da6150e1da17ac06283f43b6089", size = 16633220, upload-time = "2026-05-18T23:34:45.075Z" }, + { url = "https://files.pythonhosted.org/packages/05/ae/7c67fba23bd98caec7c99261f3a16072ade14813486b0282cb29846de832/numpy-2.4.6-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ef4aea96ce4d3b074422cb4f2f64e216bf9e213004bb58ecfdf50ea02ea8eb9a", size = 17020800, upload-time = "2026-05-18T23:34:49.065Z" }, + { url = "https://files.pythonhosted.org/packages/d9/5d/3b6725cb31d983c5e66916f5d36f6d7e5521129e4c4404d64f918292a5b6/numpy-2.4.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:dfa20cc6ca228e6b155b11da03825975ce66aea520985dbbddf0f2a5a495c605", size = 18357600, upload-time = "2026-05-18T23:34:52.709Z" }, + { url = "https://files.pythonhosted.org/packages/f7/da/2ccc6c2fe8898dee01d90c75c5f5f914a23daf99e3e0f59516a08760c8b5/numpy-2.4.6-cp313-cp313-win32.whl", hash = "sha256:56b39e5e0622a09a25bf5baf62f4bcf0cb8a41ae6e2819cf49bbc5a74c083f91", size = 5961134, upload-time = "2026-05-18T23:34:55.618Z" }, + { url = "https://files.pythonhosted.org/packages/b5/cd/9cc4dc876fb065d5c220aae4d5e14826b2715331bb7618ce1fb07a679d99/numpy-2.4.6-cp313-cp313-win_amd64.whl", hash = "sha256:c4fc99836233ea196540b17ab0983aff60ed07941751930f5f4d05bc3b3b7359", size = 12318598, upload-time = "2026-05-18T23:34:58.928Z" }, + { url = "https://files.pythonhosted.org/packages/39/1e/c0bcba1f8694116485fe28fd1be698c278fcda4141c5b0e53a2aed8b12a8/numpy-2.4.6-cp313-cp313-win_arm64.whl", hash = "sha256:a7c711e21628b52034bb5ab8d1bce291f752fcc5e92accc615778acee1ff4778", size = 10222272, upload-time = "2026-05-18T23:35:02.167Z" }, + { url = "https://files.pythonhosted.org/packages/63/6d/cc5619247c8f4204e507f5883528372e4ac4bb189e579fb859a12e480b1f/numpy-2.4.6-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:112b06a867b235ef466ed3508ddf0238050df9c727cafb5301ac385b899189a1", size = 14821197, upload-time = "2026-05-18T23:35:05.468Z" }, + { url = "https://files.pythonhosted.org/packages/00/58/f1c39161c87d9e9bed660f1ed4bafc0e403d5ec9650b6dd77aead07d489b/numpy-2.4.6-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:eaf7fa2de5c0be8ae6ff8e9bea2ccd725e980541244521d8d4b5f3354a27babe", size = 5326287, upload-time = "2026-05-18T23:35:08.693Z" }, + { url = "https://files.pythonhosted.org/packages/af/57/3917ab0fd97f271a8694513581b8a36c655f111c446852c302f04ccdb6fc/numpy-2.4.6-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:7265a2f3d436e54ef9f2b52b5c937e6be778781bd97a590319d7348f1c1ca997", size = 6646763, upload-time = "2026-05-18T23:35:11.459Z" }, + { url = "https://files.pythonhosted.org/packages/eb/0f/037e64c494b67581ae18193d770adef354c41f3f2c8ebf865602d949bf8f/numpy-2.4.6-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f74a575920ab21fe304421a3fc28793d82e299cae9eccb37084e9fc7f3617c20", size = 15728070, upload-time = "2026-05-18T23:35:14.79Z" }, + { url = "https://files.pythonhosted.org/packages/21/a6/5d2bae9c9542eb4df16dc9c46dc79c186e9bad53805dfa5399a6023c6db0/numpy-2.4.6-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ede83e07a75dd06bc501566c1eca2afc0d61677c1472ac9ad93fdee6e638a48d", size = 16681752, upload-time = "2026-05-18T23:35:18.836Z" }, + { url = "https://files.pythonhosted.org/packages/92/14/23d1dfb410ae362cd59ce53e936b1513d545eb40db3949ced632e19a459e/numpy-2.4.6-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:68bb27509ac1b9a3443094260f6326150663b06abe40b73a2f81160623da5b67", size = 17086024, upload-time = "2026-05-18T23:35:22.52Z" }, + { url = "https://files.pythonhosted.org/packages/4b/6e/23595a2c642cdf3bc567877064bdd7f91c8b0038a4453cf2daf7248eafe9/numpy-2.4.6-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:a0df0043bdb289bde1f62da130d20df23d58b45429f752bc7a8fc5325a225ecd", size = 18403398, upload-time = "2026-05-18T23:35:26.398Z" }, + { url = "https://files.pythonhosted.org/packages/8a/90/0ac3bc947217e66dec77e7cbc6a1979d1af70b6461b82f620d3bccd5e4c8/numpy-2.4.6-cp313-cp313t-win32.whl", hash = "sha256:29a287e0cf63ff528da061de6b9f64a4618da591ca1046aafc54062e40ca7eab", size = 6084971, upload-time = "2026-05-18T23:35:29.387Z" }, + { url = "https://files.pythonhosted.org/packages/77/71/5673e351671a1d2bd6063b91b44f70c0affea7d1516fa7a6572941ba4aa1/numpy-2.4.6-cp313-cp313t-win_amd64.whl", hash = "sha256:25c692919ac5a01f170a3bfcd62d745b24fd095c353d50812637d6fcab442e75", size = 12458532, upload-time = "2026-05-18T23:35:32.175Z" }, + { url = "https://files.pythonhosted.org/packages/3f/88/19d3503c5046e688f049274b27a3ef3d771152fa80d3ba3d01a3dff61abe/numpy-2.4.6-cp313-cp313t-win_arm64.whl", hash = "sha256:1e978ec1e8bd0e0e4de6bb75de9d30cbb74db6b6a2bb727618613703ca0167dd", size = 10291881, upload-time = "2026-05-18T23:35:35.465Z" }, + { url = "https://files.pythonhosted.org/packages/f8/91/3ab2044d05fd16d343c5ac2e69b127f1b2854040dd20b193257c78028bd3/numpy-2.4.6-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:06ca2f61ec4385a07a6977c55ba998a4466c123642b4a32694d3128fce18c079", size = 16683458, upload-time = "2026-05-18T23:35:38.353Z" }, + { url = "https://files.pythonhosted.org/packages/8e/62/764ce66fa4147ae6d73071a3abf804ffe606f174618697c571acdf26a7c9/numpy-2.4.6-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:38efbc8de75c7a0fc1ac190162d892787f3f47b57cc291231aafee36b80982b7", size = 14704559, upload-time = "2026-05-18T23:35:42.14Z" }, + { url = "https://files.pythonhosted.org/packages/60/61/23f27c172f022e04025b7dc2367f4d63c1a398120607ec896228649a6f48/numpy-2.4.6-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:d581b735e177fdcdce6fed8e7e8880a3fb6ee4e3653a3ac6af01c6f4c03effc5", size = 5209716, upload-time = "2026-05-18T23:35:45.377Z" }, + { url = "https://files.pythonhosted.org/packages/03/71/21cf70dc6ea3e3acb95fc53a265b2fc248b981f0194ceb5b475271b8809d/numpy-2.4.6-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:0a041d3d761dc3c35cc56ce0351506a02bcbc25f7b169f652435141a17db9096", size = 6543947, upload-time = "2026-05-18T23:35:47.926Z" }, + { url = "https://files.pythonhosted.org/packages/d5/91/64288395ee1799bd2e0b04a305dce9666da90c961e1f3fe982a05ee1c036/numpy-2.4.6-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:40fdc1ae7125e518ea98e53e69a4ebc27e1fd50510c47b7ea130cf21e5e1d42b", size = 15685197, upload-time = "2026-05-18T23:35:50.863Z" }, + { url = "https://files.pythonhosted.org/packages/f3/eb/ebffaa97dc55502df69584a8f0dcf07f69a3e0b3e2323670a2722db9aa39/numpy-2.4.6-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a2c306dea656c12c68f51f4cea133cbe78ca7435eb28c735eac1d3ebe73be6e8", size = 16638245, upload-time = "2026-05-18T23:35:54.752Z" }, + { url = "https://files.pythonhosted.org/packages/b8/0b/54f9da33128d7e350fab89c7455902eeae70349ee52bddb448dc4a576f45/numpy-2.4.6-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:33111801a01c12a8a1e3721f0a9232f8cfc8ae2c6b7098167e6f623c6073f402", size = 17036587, upload-time = "2026-05-18T23:35:58.355Z" }, + { url = "https://files.pythonhosted.org/packages/b6/f0/fdebc1052db1cc37c64beb22072d67cd6d1c71adca1299f53dec2b5e20d3/numpy-2.4.6-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:ae506e6902902557576a26ff33eda8695e7ecb3cb36c3b573a0765dee114ebdb", size = 18363226, upload-time = "2026-05-18T23:36:02.845Z" }, + { url = "https://files.pythonhosted.org/packages/aa/b4/298628d98c72b57e57f7165ae6a481a1deaf6f3c28262a6e4c739c275930/numpy-2.4.6-cp314-cp314-win32.whl", hash = "sha256:aaf159caa35993cb1f56fb9b8e4610d35758e7ca005412eb1daa856a78c9c4b1", size = 6010196, upload-time = "2026-05-18T23:36:05.92Z" }, + { url = "https://files.pythonhosted.org/packages/df/ac/46de6dda46478f7942f839e094970be2d4a861e005c4b3bf07c92e291a09/numpy-2.4.6-cp314-cp314-win_amd64.whl", hash = "sha256:b507f5c4c1d508876d1819b6bf9a49d365b96320b5d4993426b33a23ca4b8261", size = 12450334, upload-time = "2026-05-18T23:36:09.107Z" }, + { url = "https://files.pythonhosted.org/packages/78/92/b8b798ac784102c0da830d2257d59358e3d3d90d1e2b3f2575dad976c5cf/numpy-2.4.6-cp314-cp314-win_arm64.whl", hash = "sha256:6f41ae150c4e32db4f3310cdaf64b1593a03dbabe29eec77fc9b50fe64061df6", size = 10495678, upload-time = "2026-05-18T23:36:12.766Z" }, + { url = "https://files.pythonhosted.org/packages/30/34/ec28d1aa8115971537c01469ab2011ee96827930f0a124de1000cc2a7ed7/numpy-2.4.6-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ece3d2cfe132e7d51f44a832b303895e6f2d499c5e74dfbdb06ee246147a304a", size = 14823672, upload-time = "2026-05-18T23:36:16.473Z" }, + { url = "https://files.pythonhosted.org/packages/16/bd/f6d1fede4e54e8042a7ff97bb495510f3c220f94bcd9e8b228e87c92cc0d/numpy-2.4.6-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:e3e5193ef5a3dc73bceee50f7fdc2c90dbb76c42df8d8fae3d1067a583df579e", size = 5328731, upload-time = "2026-05-18T23:36:19.767Z" }, + { url = "https://files.pythonhosted.org/packages/f4/f0/e105b9e2fd728a9910103884decd6951d9dd73896b914a98d9a231de02ee/numpy-2.4.6-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:17f9ade344e7d9b464a084d69bcf18fc691cb1db67c62ed80820bf4926d78f0e", size = 6649805, upload-time = "2026-05-18T23:36:22.266Z" }, + { url = "https://files.pythonhosted.org/packages/82/dd/1206a7ca6ab15e3f02069707ca96222e202af681bb73756da7527f3cb837/numpy-2.4.6-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9cd5ffd25db4e7ba6a375693b3fc0fc1791ec636c17db3720da19bde7180ec43", size = 15730496, upload-time = "2026-05-18T23:36:25.713Z" }, + { url = "https://files.pythonhosted.org/packages/51/e7/38d3ea825dcab85a591734decb2f6c67caa7c8367d374df1a1c3842f9b07/numpy-2.4.6-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7d92c3819208a60205a12a245c91ad70cb0a85336659b19b834205573ac8456e", size = 16679616, upload-time = "2026-05-18T23:36:29.652Z" }, + { url = "https://files.pythonhosted.org/packages/93/b7/caabfdf53edf663e0b4eb74d7d405d83baef09eb5e83bcd32d601d72b93e/numpy-2.4.6-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e85b752a1e912b70eaad4fafbd4d1238007ab221de2009b9a2f5ae7461239895", size = 17085145, upload-time = "2026-05-18T23:36:33.449Z" }, + { url = "https://files.pythonhosted.org/packages/f9/45/68d7c33a6bcf3e5aa3bdbd57a367e6f615286dfd6482f97e8ffeb734306e/numpy-2.4.6-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:29cb7f67d10b479ff07c17d33e39f78c07f71c40ef30d63c153d340e96cd3fb4", size = 18403813, upload-time = "2026-05-18T23:36:37.369Z" }, + { url = "https://files.pythonhosted.org/packages/9c/50/0753655aa844c99cd9e018aacf76f130f1bd81d881bb74bc0aef5d73a8ba/numpy-2.4.6-cp314-cp314t-win32.whl", hash = "sha256:260a5d70215b61ab4fadf5c7baacd64821842975eea312125ed3c39a6391b063", size = 6156982, upload-time = "2026-05-18T23:36:40.817Z" }, + { url = "https://files.pythonhosted.org/packages/b2/d4/7c67becf668f973cb490cec3e98dfd799d866f9c989a54d355672cfa0db6/numpy-2.4.6-cp314-cp314t-win_amd64.whl", hash = "sha256:81a1cca95ed5bb92aa8b10dd2cdc9a0d3853a50fad926c28b5d7e8ea54389627", size = 12638908, upload-time = "2026-05-18T23:36:43.996Z" }, + { url = "https://files.pythonhosted.org/packages/43/bb/e1c71a4295b1b1d1393d50dbb4f2a36283c6859d9d3892e84f00ec5a91d5/numpy-2.4.6-cp314-cp314t-win_arm64.whl", hash = "sha256:0c9136e14ed34a9e343a31c533d78a9813a69a3148332bce5e9821cb2f996e66", size = 10565867, upload-time = "2026-05-18T23:36:47.114Z" }, + { url = "https://files.pythonhosted.org/packages/de/12/b422cc84439adc0d00de605bf4a308890ae5c26f2c71fbd73e5d08fbb0dd/numpy-2.4.6-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:55cced7c52e981362f708ad635198e97a752dfba412cc03c23bbf3bd8d5cd662", size = 16847511, upload-time = "2026-05-18T23:36:50.673Z" }, + { url = "https://files.pythonhosted.org/packages/44/53/f481bef68011740f8849418d82db07230e825013f31f4eef5ba5b805316a/numpy-2.4.6-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:d6da64deb6b8ed903e7560180a92f2d804ee1ba5eeb849ac2748b8c1aba1f6d7", size = 14889064, upload-time = "2026-05-18T23:36:53.879Z" }, + { url = "https://files.pythonhosted.org/packages/7f/57/42ed575c10ced8af951d426bc4e1f8aff16fd851db33f067036215a7f860/numpy-2.4.6-pp311-pypy311_pp73-macosx_14_0_arm64.whl", hash = "sha256:68a5124b13fa6cc2086764a20005d30bc0548146f7f5322f02fce212ca14317f", size = 5394157, upload-time = "2026-05-18T23:36:57.194Z" }, + { url = "https://files.pythonhosted.org/packages/6a/ef/f66cc724fcc36c1e364c67f51ae9146090b8b584f27d58b97fdae3edd737/numpy-2.4.6-pp311-pypy311_pp73-macosx_14_0_x86_64.whl", hash = "sha256:948424b06129ce883307e8cff868c31396d8dc7630a59c61d70d98dbe70f222c", size = 6708728, upload-time = "2026-05-18T23:36:59.575Z" }, + { url = "https://files.pythonhosted.org/packages/1a/9c/c531f2293b91265d8b48e9b329f54fdd7ffae73cb4134ea10cca4237e9cc/numpy-2.4.6-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5dbbdb29840ca3d91ee0fece42fc29278886d908280bfec0a5846c6f901a3eb0", size = 15798374, upload-time = "2026-05-18T23:37:02.674Z" }, + { url = "https://files.pythonhosted.org/packages/1a/b0/413077f6b1153ed3cba361401c6783bbad6114804a000cc22eb71c13e190/numpy-2.4.6-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8ad03c0965fb3c692200e74d458ca28c1dbb4ce96f9a479a8aa041ad5fabca02", size = 16747286, upload-time = "2026-05-18T23:37:06.327Z" }, + { url = "https://files.pythonhosted.org/packages/15/ce/e5ec180bc41812edcd8daeb8639d205622c0e8c02259d8ab25a0201b3c2a/numpy-2.4.6-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:2803abfebfc990042cd494d8ce2d5f82e9d847af6d35ec486923aa19dbad5e73", size = 12504263, upload-time = "2026-05-18T23:37:09.715Z" }, ] [[package]] @@ -2475,17 +2491,17 @@ wheels = [ [[package]] name = "protobuf" -version = "7.34.1" +version = "7.35.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/6b/6b/a0e95cad1ad7cc3f2c6821fcab91671bd5b78bd42afb357bb4765f29bc41/protobuf-7.34.1.tar.gz", hash = "sha256:9ce42245e704cc5027be797c1db1eb93184d44d1cdd71811fb2d9b25ad541280", size = 454708, upload-time = "2026-03-20T17:34:47.036Z" } +sdist = { url = "https://files.pythonhosted.org/packages/60/fd/5b1491d9e4b586d621c54f4c36b888714164b6875f8d6afa3f9072906a51/protobuf-7.35.0.tar.gz", hash = "sha256:a2efd84605f41e559f1881b0912b44099d0a2ac9bf46b3474823f10fb393b0e6", size = 458677, upload-time = "2026-05-19T23:02:29.197Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/ec/11/3325d41e6ee15bf1125654301211247b042563bcc898784351252549a8ad/protobuf-7.34.1-cp310-abi3-macosx_10_9_universal2.whl", hash = "sha256:d8b2cc79c4d8f62b293ad9b11ec3aebce9af481fa73e64556969f7345ebf9fc7", size = 429247, upload-time = "2026-03-20T17:34:37.024Z" }, - { url = "https://files.pythonhosted.org/packages/eb/9d/aa69df2724ff63efa6f72307b483ce0827f4347cc6d6df24b59e26659fef/protobuf-7.34.1-cp310-abi3-manylinux2014_aarch64.whl", hash = "sha256:5185e0e948d07abe94bb76ec9b8416b604cfe5da6f871d67aad30cbf24c3110b", size = 325753, upload-time = "2026-03-20T17:34:38.751Z" }, - { url = "https://files.pythonhosted.org/packages/92/e8/d174c91fd48e50101943f042b09af9029064810b734e4160bbe282fa1caa/protobuf-7.34.1-cp310-abi3-manylinux2014_s390x.whl", hash = "sha256:403b093a6e28a960372b44e5eb081775c9b056e816a8029c61231743d63f881a", size = 340198, upload-time = "2026-03-20T17:34:39.871Z" }, - { url = "https://files.pythonhosted.org/packages/53/1b/3b431694a4dc6d37b9f653f0c64b0a0d9ec074ee810710c0c3da21d67ba7/protobuf-7.34.1-cp310-abi3-manylinux2014_x86_64.whl", hash = "sha256:8ff40ce8cd688f7265326b38d5a1bed9bfdf5e6723d49961432f83e21d5713e4", size = 324267, upload-time = "2026-03-20T17:34:41.1Z" }, - { url = "https://files.pythonhosted.org/packages/85/29/64de04a0ac142fb685fd09999bc3d337943fb386f3a0ec57f92fd8203f97/protobuf-7.34.1-cp310-abi3-win32.whl", hash = "sha256:34b84ce27680df7cca9f231043ada0daa55d0c44a2ddfaa58ec1d0d89d8bf60a", size = 426628, upload-time = "2026-03-20T17:34:42.536Z" }, - { url = "https://files.pythonhosted.org/packages/4d/87/cb5e585192a22b8bd457df5a2c16a75ea0db9674c3a0a39fc9347d84e075/protobuf-7.34.1-cp310-abi3-win_amd64.whl", hash = "sha256:e97b55646e6ce5cbb0954a8c28cd39a5869b59090dfaa7df4598a7fba869468c", size = 437901, upload-time = "2026-03-20T17:34:44.112Z" }, - { url = "https://files.pythonhosted.org/packages/88/95/608f665226bca68b736b79e457fded9a2a38c4f4379a4a7614303d9db3bc/protobuf-7.34.1-py3-none-any.whl", hash = "sha256:bb3812cd53aefea2b028ef42bd780f5b96407247f20c6ef7c679807e9d188f11", size = 170715, upload-time = "2026-03-20T17:34:45.384Z" }, + { url = "https://files.pythonhosted.org/packages/83/ee/93d06e358a4aa32280b00e722d3ea0a1f25fc3cc5778d80581c9cca2c10e/protobuf-7.35.0-cp310-abi3-macosx_10_9_universal2.whl", hash = "sha256:66be6c513931c794fa92c080ffee41671390da3d79da219cf9c0c0907f035dda", size = 433225, upload-time = "2026-05-19T23:02:19.884Z" }, + { url = "https://files.pythonhosted.org/packages/8b/39/1c76c2da93f3c507e958e0aecee2391cc44d4625de6c728bbc555195b5a8/protobuf-7.35.0-cp310-abi3-manylinux2014_aarch64.whl", hash = "sha256:fcbe42a4ac09d3ec9c987ddfcd956afd0b15f1ff613bd8371bde9405ffd5c8e5", size = 328847, upload-time = "2026-05-19T23:02:22.3Z" }, + { url = "https://files.pythonhosted.org/packages/91/1a/39f7ce90a238c1a987a4d81ec26379e02ca0aff367de68e4a1fa474215b9/protobuf-7.35.0-cp310-abi3-manylinux2014_s390x.whl", hash = "sha256:4cbf5cc286130e06a6c9bbefac442431173906dfcc979712183d4adcc01b37ee", size = 344030, upload-time = "2026-05-19T23:02:23.591Z" }, + { url = "https://files.pythonhosted.org/packages/70/5b/6baf9008817964454055ff3fe65f1de0b5f1e26c80c82f7fb108b7cd4ea3/protobuf-7.35.0-cp310-abi3-manylinux2014_x86_64.whl", hash = "sha256:6c0f98f10c8a05ea30f8993dfef2de093d27b490fdae78bb60c8343795d55011", size = 327130, upload-time = "2026-05-19T23:02:24.637Z" }, + { url = "https://files.pythonhosted.org/packages/8e/e5/e46adb0badc388bfb84877a5f9f026aff63f60e611016cf64dbe77e05446/protobuf-7.35.0-cp310-abi3-win32.whl", hash = "sha256:4c4617b83ade0e279d1d2bfe04025a1adb87f9ed657de038620dc0ff959357f6", size = 428946, upload-time = "2026-05-19T23:02:25.741Z" }, + { url = "https://files.pythonhosted.org/packages/a7/ab/547fbd9e16d879dd13c167478f8ae0a83a428008ca07a5e06acdc23ad473/protobuf-7.35.0-cp310-abi3-win_amd64.whl", hash = "sha256:f05bcadf9a2a6b8dda047007075135fb7d08c73d9177aabc067e1be46881a201", size = 439996, upload-time = "2026-05-19T23:02:26.808Z" }, + { url = "https://files.pythonhosted.org/packages/b8/ef/50433d346c56657a70d27f156c7b349ac59a068b01de4eb796e747eecc43/protobuf-7.35.0-py3-none-any.whl", hash = "sha256:c13f325cf242bad135c350629eeb5d54b24228eb472fb3e2e9ebbd4c5dc20ca0", size = 171659, upload-time = "2026-05-19T23:02:27.842Z" }, ] [[package]] @@ -3140,27 +3156,27 @@ wheels = [ [[package]] name = "ruff" -version = "0.15.13" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/24/21/a7d5c126d5b557715ef81098f3db2fe20f622a039ff2e626af28d674ab80/ruff-0.15.13.tar.gz", hash = "sha256:f9d89f17f7ba7fb2ed42921f0df75da797a9a5d71bc39049e2c687cf2baf44b7", size = 4678180, upload-time = "2026-05-14T13:44:37.869Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c6/61/11d458dc6ac22504fd8e237b29dfd40504c7fbbcc8930402cfe51a8e63ed/ruff-0.15.13-py3-none-linux_armv6l.whl", hash = "sha256:444b580fc72fd6887e650acd3e575e18cdc79dbcf42fb4030b491057921f61f8", size = 10738279, upload-time = "2026-05-14T13:44:18.7Z" }, - { url = "https://files.pythonhosted.org/packages/86/ca/caa871ee7be718c45256fada4e16a218ee3e33f0c4a46b729a60a24912e6/ruff-0.15.13-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:6590d009e7cb7ebf36f83dbdd44a3fa48a0994ff6f1cdc1b08006abe58f98dc7", size = 11124798, upload-time = "2026-05-14T13:44:06.427Z" }, - { url = "https://files.pythonhosted.org/packages/d3/19/43f5f2e568dddde567fc41f8471f9432c09563e19d3e617a48cfa52f8f0a/ruff-0.15.13-py3-none-macosx_11_0_arm64.whl", hash = "sha256:1c26d2f66163deeb6e08d8b39fbbe983ce3c71cea06a6d7591cfd1421793c629", size = 10460761, upload-time = "2026-05-14T13:44:04.375Z" }, - { url = "https://files.pythonhosted.org/packages/99/df/cf938cd6de3003178f03ad7c1ea2a6c099468c03a35037985070b37e76be/ruff-0.15.13-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9dbd6f94b434f896308e4d57fb7bfde0d02b99f7a64b3bdab0fdfa6a864203a5", size = 10804451, upload-time = "2026-05-14T13:44:25.221Z" }, - { url = "https://files.pythonhosted.org/packages/c7/7d/5d0973129b154ded2225729169d7068f26b467760b146493fde138415f23/ruff-0.15.13-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:bf3259f3be4d181bda591da5db2571aed6853c6a048157756448020bc6c5cd22", size = 10534285, upload-time = "2026-05-14T13:44:08.888Z" }, - { url = "https://files.pythonhosted.org/packages/1f/e3/6b999bbc66cd51e5f073842bc2a3995e99c5e0e72e16b15e7261f7abf57a/ruff-0.15.13-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ae9c17e5eb4430c154e76abc25d79a318190f5a997f38fb6b114416c5319ffc9", size = 11312063, upload-time = "2026-05-14T13:44:11.274Z" }, - { url = "https://files.pythonhosted.org/packages/af/5a/642639e9f5db04f1e97fbd6e091c6fd20725bdf072fb114d00eefb9e6eb8/ruff-0.15.13-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2e2e39bff6c341f4b577a21b801326fab0b11847f48fcaa83f00a113c9b3cb55", size = 12183079, upload-time = "2026-05-14T13:44:01.634Z" }, - { url = "https://files.pythonhosted.org/packages/19/4c/7585735f6b53b0f12de13618b2f7d250a844f018822efc899df2e7b8295f/ruff-0.15.13-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e8d9a8e08013542e94d3220bc5b62cc3e5ef87c5f74bff367d3fac14fab013e6", size = 11440833, upload-time = "2026-05-14T13:43:59.043Z" }, - { url = "https://files.pythonhosted.org/packages/e8/31/bf1a0803d077e679cfeee5f2f67290a0fa79c7385b5d9a8c17b9db2c48f0/ruff-0.15.13-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc411dfebe5eebe55ce041c6ae080eb7668955e866daa2fbb16692a784f1c4ca", size = 11434486, upload-time = "2026-05-14T13:44:27.761Z" }, - { url = "https://files.pythonhosted.org/packages/e1/4e/62c9b999875d4f14db80f277c030578f5e249c9852d65b7ac7ad0b43c041/ruff-0.15.13-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:768494eb08b9cee54e2fd27969966f74db5a57f6eaa7a90fcb3306af34dfc4bd", size = 11385189, upload-time = "2026-05-14T13:44:13.704Z" }, - { url = "https://files.pythonhosted.org/packages/fc/89/7e959047a104df3eb12863447c110140191fc5b6c4f379ea2e803fcdb0e4/ruff-0.15.13-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb75f9a3a7e42ffe117d734494e6c5e5cb3565d66e12612cb63d0e572a41a5b6", size = 10781380, upload-time = "2026-05-14T13:43:56.734Z" }, - { url = "https://files.pythonhosted.org/packages/ff/52/5fd18f3b88cab63e88aa11516b3b4e1e5f720e5c330f8dbe5c26210f41f8/ruff-0.15.13-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:8cb74dd33bb2f6613faf7fc03b660053b5ac4f80e706d5788c6335e2a8048d51", size = 10540605, upload-time = "2026-05-14T13:44:20.748Z" }, - { url = "https://files.pythonhosted.org/packages/e8/e0/9e35f338990d3e41a82875ff7053ffe97541dae81c9d02143177f381d572/ruff-0.15.13-py3-none-musllinux_1_2_i686.whl", hash = "sha256:7ef823f817fcd191dc934e984be9cf4094f808effa16f2542ad8e821ba02bbf2", size = 11036554, upload-time = "2026-05-14T13:44:16.256Z" }, - { url = "https://files.pythonhosted.org/packages/c2/13/070fb048c24080fba188f66371e2a92785be257ad02242066dc7255ac6e9/ruff-0.15.13-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:f345a13937bd7f09f6f5d19fa0721b0c103e00e7f62bc67089a8e5e037719e0b", size = 11528133, upload-time = "2026-05-14T13:44:22.808Z" }, - { url = "https://files.pythonhosted.org/packages/6b/8c/b1e1666aef7fc6555094d73ae6cd981701781ae85b97ceefc0eebd0b4668/ruff-0.15.13-py3-none-win32.whl", hash = "sha256:4044f94208b3b05ba0fc4a4abd0558cf4d6459bd18325eead7fd8cc66f909b41", size = 10721455, upload-time = "2026-05-14T13:44:35.697Z" }, - { url = "https://files.pythonhosted.org/packages/ab/a6/870a3e8a50590bb92be184ad928c2922f088b00d9dc5c5ec7b924ee08c22/ruff-0.15.13-py3-none-win_amd64.whl", hash = "sha256:7064884d442b7d477b4e7473d12da7f08851d2b1982763c5d3f388a19468a1a4", size = 11900409, upload-time = "2026-05-14T13:44:30.389Z" }, - { url = "https://files.pythonhosted.org/packages/9b/36/9c015cd052fca743dae8cb2aeb16b551444787467db42ceab0fc968865af/ruff-0.15.13-py3-none-win_arm64.whl", hash = "sha256:2471da9bd1068c8c064b5fd9c0c4b6dddffd6369cb1cd68b29993b1709ff1b21", size = 11179336, upload-time = "2026-05-14T13:44:33.026Z" }, +version = "0.15.14" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/dc/8a/8bce2894573e9dae6ff4d77fe34ad727d79b9e6238ad288c5638990d90f6/ruff-0.15.14.tar.gz", hash = "sha256:48e866b165be4a9bdbf310f7d3c9a07edef2fe8cd63ffeb4e00bb590506ebf9f", size = 4700910, upload-time = "2026-05-21T14:34:55.177Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b9/c8/74a92c6ff9fcfb4f1f947126d3ebee8389276e161ecc85de5bda7cda51bd/ruff-0.15.14-py3-none-linux_armv6l.whl", hash = "sha256:8dd2db9416e487c8d4b01fa7056bb02c4d05969d4f8d17a08c229c2f4ff3c108", size = 10739177, upload-time = "2026-05-21T14:34:37.332Z" }, + { url = "https://files.pythonhosted.org/packages/45/91/254a35c20acc38a7223c9d2d594af12e794432464f2cdeb52af1dc4a892d/ruff-0.15.14-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:be4ff55af755bd71a00ab3dc6bd7ffc467bd76e0df6881e286c2e3d23e8fb43b", size = 11144969, upload-time = "2026-05-21T14:34:43.978Z" }, + { url = "https://files.pythonhosted.org/packages/56/9e/d13e40f83b8d0a94430e6778ce1d94a43b38cf2efe63278bdd2b4c65abbf/ruff-0.15.14-py3-none-macosx_11_0_arm64.whl", hash = "sha256:48d5909d7d06276ce7dde6d32bfa4b0d4cb2651145cd8ee4b440722cbc77832f", size = 10478207, upload-time = "2026-05-21T14:34:48.378Z" }, + { url = "https://files.pythonhosted.org/packages/8d/f1/b15a7839fa4f332f8acec78e20564f26bb2d866e3d21710b877fd0263000/ruff-0.15.14-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ca8cbfa94c4f90984a67561978602746d4cd27103568f745fa90eee3f0d4107d", size = 10818459, upload-time = "2026-05-21T14:34:22.318Z" }, + { url = "https://files.pythonhosted.org/packages/45/33/53d651177f84f94b400a0e27f8824eeada3dddc9d5ee8aeb048f4352a520/ruff-0.15.14-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9a6bbc0333f1ab053423bcbf6226477d266ca7cec7738c4c8e3f55647803f3c4", size = 10541800, upload-time = "2026-05-21T14:34:20.209Z" }, + { url = "https://files.pythonhosted.org/packages/b8/a6/868f87e0bf9786ed24b5d0d0ad8676b8a94fd1912f42cddf9cfc7857818a/ruff-0.15.14-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8a24a4f7605d7003a6674d4387651effd939dead3fddd0f36561eb77a9a2e542", size = 11342149, upload-time = "2026-05-21T14:34:46.365Z" }, + { url = "https://files.pythonhosted.org/packages/a7/8b/38cd5c19faffdcc05a408d2b78edccc69492ab9720eadb49ea15ef80d768/ruff-0.15.14-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:049b5326e53ed80978f2fc041a280603f69dd6b0c95464342a2bb4572d9d9e2f", size = 12212563, upload-time = "2026-05-21T14:34:28.579Z" }, + { url = "https://files.pythonhosted.org/packages/3e/4d/a3c5b874a556d5731e3e657aaf04311bb76f0a5c3ec220ed43051be6b64b/ruff-0.15.14-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d4ed42e6696c8dfa5f06728e6441993901f548eb92d73bc472cb5a38d1395fbf", size = 11493299, upload-time = "2026-05-21T14:34:41.836Z" }, + { url = "https://files.pythonhosted.org/packages/1e/c0/56472c251d09858a53e51efbd485b09e1995d8731668b76d52e5dd6ee0f1/ruff-0.15.14-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:715c543cf450c4888251f91c52f1942a800541d9bddd7ac060aa4e6b77ae7cba", size = 11455931, upload-time = "2026-05-21T14:34:57.276Z" }, + { url = "https://files.pythonhosted.org/packages/2c/4a/e2e7b4d8dbf233d4eace59c75bc3435fa6d8bd3bae82d351d4e4300c0fd1/ruff-0.15.14-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:72ebab6013ec887d439d8b7593737a0a4ffb06d45d209d4e4bf2e92813082d3f", size = 11400794, upload-time = "2026-05-21T14:34:39.773Z" }, + { url = "https://files.pythonhosted.org/packages/97/c7/83c0539fe34c3e09136204d1e75d6052492364e0b3cb05e9465423f567d7/ruff-0.15.14-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:49072d36abdbe97a8dd7f480afe9c675699c0c495d4c84076e2c1203c4550581", size = 10804759, upload-time = "2026-05-21T14:34:31.045Z" }, + { url = "https://files.pythonhosted.org/packages/86/a6/18f2bfc095a2ab4a78745644e428205532ce6653a5d0fa8501572891534d/ruff-0.15.14-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:958522aee105068640c2c2ceae08f413ae44d922f52a1374ac13d6a96032fc93", size = 10539517, upload-time = "2026-05-21T14:34:53.064Z" }, + { url = "https://files.pythonhosted.org/packages/54/3a/5a8b3b69c654d4e4bf1d246ac5b49cbcdac6eaab6905925f8915f31e3b80/ruff-0.15.14-py3-none-musllinux_1_2_i686.whl", hash = "sha256:f3707da619a143a2e8830e2abab8224478d69ace2d28cb6c20543ae97c36bf61", size = 11065169, upload-time = "2026-05-21T14:34:24.484Z" }, + { url = "https://files.pythonhosted.org/packages/ed/c5/8864e4e7925b836ea354b31d57641ec03830564e281a8b6f061f8c3e0ec1/ruff-0.15.14-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:bb01d645694e3ec0102105d07ef2d53703970407d59c04e59d3ba0b7a1d53553", size = 11560214, upload-time = "2026-05-21T14:34:50.975Z" }, + { url = "https://files.pythonhosted.org/packages/36/38/012bf76752e1f89ed50b77b99532d90f3a3e287bc7918e1fc0948ac866ac/ruff-0.15.14-py3-none-win32.whl", hash = "sha256:6d0c1ad2a0ab718d39b6d8fd2217981ce4d625cd96a720095f798fb47d8b13e6", size = 10805548, upload-time = "2026-05-21T14:34:33.453Z" }, + { url = "https://files.pythonhosted.org/packages/d1/b7/4ea2c170f10ad760fff2a5250beb18897719dc8b52b53a24cddbb9dd3f19/ruff-0.15.14-py3-none-win_amd64.whl", hash = "sha256:802342981e056db3851a7836e5b070f8f15f67d4a685ae2a6160939d364b2902", size = 11939523, upload-time = "2026-05-21T14:34:18.077Z" }, + { url = "https://files.pythonhosted.org/packages/62/d5/bc97ff895ec35cf3925d4bd60f3b39d822f377a446906ec9bcc87405e59b/ruff-0.15.14-py3-none-win_arm64.whl", hash = "sha256:ff47b90a9ef6a40c9e2f3b479c1fb78531adf055b94c1eba0a7ba04b31951826", size = 11208607, upload-time = "2026-05-21T14:34:26.525Z" }, ] [[package]] @@ -3330,64 +3346,59 @@ wheels = [ [[package]] name = "soupsieve" -version = "2.8.3" +version = "2.8.4" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/7b/ae/2d9c981590ed9999a0d91755b47fc74f74de286b0f5cee14c9269041e6c4/soupsieve-2.8.3.tar.gz", hash = "sha256:3267f1eeea4251fb42728b6dfb746edc9acaffc4a45b27e19450b676586e8349", size = 118627, upload-time = "2026-01-20T04:27:02.457Z" } +sdist = { url = "https://files.pythonhosted.org/packages/47/2c/0a5f6f8ee0d5589e48c7640213ed5175d52cf540a06725b628cc1a45d6ce/soupsieve-2.8.4.tar.gz", hash = "sha256:e121fd02e975c695e4e9e8774a5ee35d74714b59307868dcc5319ad2d9e3328e", size = 121110, upload-time = "2026-05-24T13:55:57.154Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/46/2c/1462b1d0a634697ae9e55b3cecdcb64788e8b7d63f54d923fcd0bb140aed/soupsieve-2.8.3-py3-none-any.whl", hash = "sha256:ed64f2ba4eebeab06cc4962affce381647455978ffc1e36bb79a545b91f45a95", size = 37016, upload-time = "2026-01-20T04:27:01.012Z" }, + { url = "https://files.pythonhosted.org/packages/5e/f5/0c41cb68dcae6b7de4fac4188a3a9589e21fb31df21ea3a2e888db95e6c9/soupsieve-2.8.4-py3-none-any.whl", hash = "sha256:e7e6b0769c8f51ed59acab6e994b00621096cfb1c640a7509295987388fbaf65", size = 37304, upload-time = "2026-05-24T13:55:55.406Z" }, ] [[package]] name = "sqlalchemy" -version = "2.0.49" +version = "2.0.50" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "greenlet", marker = "platform_machine == 'AMD64' or platform_machine == 'WIN32' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'ppc64le' or platform_machine == 'win32' or platform_machine == 'x86_64'" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/09/45/461788f35e0364a8da7bda51a1fe1b09762d0c32f12f63727998d85a873b/sqlalchemy-2.0.49.tar.gz", hash = "sha256:d15950a57a210e36dd4cec1aac22787e2a4d57ba9318233e2ef8b2daf9ff2d5f", size = 9898221, upload-time = "2026-04-03T16:38:11.704Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/60/b5/e3617cc67420f8f403efebd7b043128f94775e57e5b84e7255203390ceae/sqlalchemy-2.0.49-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c5070135e1b7409c4161133aa525419b0062088ed77c92b1da95366ec5cbebbe", size = 2159126, upload-time = "2026-04-03T16:50:13.242Z" }, - { url = "https://files.pythonhosted.org/packages/20/9b/91ca80403b17cd389622a642699e5f6564096b698e7cdcbcbb6409898bc4/sqlalchemy-2.0.49-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9ac7a3e245fd0310fd31495eb61af772e637bdf7d88ee81e7f10a3f271bff014", size = 3315509, upload-time = "2026-04-03T16:54:49.332Z" }, - { url = "https://files.pythonhosted.org/packages/b1/61/0722511d98c54de95acb327824cb759e8653789af2b1944ab1cc69d32565/sqlalchemy-2.0.49-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4d4e5a0ceba319942fa6b585cf82539288a61e314ef006c1209f734551ab9536", size = 3315014, upload-time = "2026-04-03T16:56:56.376Z" }, - { url = "https://files.pythonhosted.org/packages/46/55/d514a653ffeb4cebf4b54c47bec32ee28ad89d39fafba16eeed1d81dccd5/sqlalchemy-2.0.49-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:3ddcb27fb39171de36e207600116ac9dfd4ae46f86c82a9bf3934043e80ebb88", size = 3267388, upload-time = "2026-04-03T16:54:51.272Z" }, - { url = "https://files.pythonhosted.org/packages/2f/16/0dcc56cb6d3335c1671a2258f5d2cb8267c9a2260e27fde53cbfb1b3540a/sqlalchemy-2.0.49-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:32fe6a41ad97302db2931f05bb91abbcc65b5ce4c675cd44b972428dd2947700", size = 3289602, upload-time = "2026-04-03T16:56:57.63Z" }, - { url = "https://files.pythonhosted.org/packages/51/6c/f8ab6fb04470a133cd80608db40aa292e6bae5f162c3a3d4ab19544a67af/sqlalchemy-2.0.49-cp311-cp311-win32.whl", hash = "sha256:46d51518d53edfbe0563662c96954dc8fcace9832332b914375f45a99b77cc9a", size = 2119044, upload-time = "2026-04-03T17:00:53.455Z" }, - { url = "https://files.pythonhosted.org/packages/c4/59/55a6d627d04b6ebb290693681d7683c7da001eddf90b60cfcc41ee907978/sqlalchemy-2.0.49-cp311-cp311-win_amd64.whl", hash = "sha256:951d4a210744813be63019f3df343bf233b7432aadf0db54c75802247330d3af", size = 2143642, upload-time = "2026-04-03T17:00:54.769Z" }, - { url = "https://files.pythonhosted.org/packages/49/b3/2de412451330756aaaa72d27131db6dde23995efe62c941184e15242a5fa/sqlalchemy-2.0.49-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4bbccb45260e4ff1b7db0be80a9025bb1e6698bdb808b83fff0000f7a90b2c0b", size = 2157681, upload-time = "2026-04-03T16:53:07.132Z" }, - { url = "https://files.pythonhosted.org/packages/50/84/b2a56e2105bd11ebf9f0b93abddd748e1a78d592819099359aa98134a8bf/sqlalchemy-2.0.49-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fb37f15714ec2652d574f021d479e78cd4eb9d04396dca36568fdfffb3487982", size = 3338976, upload-time = "2026-04-03T17:07:40Z" }, - { url = "https://files.pythonhosted.org/packages/2c/fa/65fcae2ed62f84ab72cf89536c7c3217a156e71a2c111b1305ab6f0690e2/sqlalchemy-2.0.49-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3bb9ec6436a820a4c006aad1ac351f12de2f2dbdaad171692ee457a02429b672", size = 3351937, upload-time = "2026-04-03T17:12:23.374Z" }, - { url = "https://files.pythonhosted.org/packages/f8/2f/6fd118563572a7fe475925742eb6b3443b2250e346a0cc27d8d408e73773/sqlalchemy-2.0.49-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8d6efc136f44a7e8bc8088507eaabbb8c2b55b3dbb63fe102c690da0ddebe55e", size = 3281646, upload-time = "2026-04-03T17:07:41.949Z" }, - { url = "https://files.pythonhosted.org/packages/c5/d7/410f4a007c65275b9cf82354adb4bb8ba587b176d0a6ee99caa16fe638f8/sqlalchemy-2.0.49-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e06e617e3d4fd9e51d385dfe45b077a41e9d1b033a7702551e3278ac597dc750", size = 3316695, upload-time = "2026-04-03T17:12:25.642Z" }, - { url = "https://files.pythonhosted.org/packages/d9/95/81f594aa60ded13273a844539041ccf1e66c5a7bed0a8e27810a3b52d522/sqlalchemy-2.0.49-cp312-cp312-win32.whl", hash = "sha256:83101a6930332b87653886c01d1ee7e294b1fe46a07dd9a2d2b4f91bcc88eec0", size = 2117483, upload-time = "2026-04-03T17:05:40.896Z" }, - { url = "https://files.pythonhosted.org/packages/47/9e/fd90114059175cac64e4fafa9bf3ac20584384d66de40793ae2e2f26f3bb/sqlalchemy-2.0.49-cp312-cp312-win_amd64.whl", hash = "sha256:618a308215b6cececb6240b9abde545e3acdabac7ae3e1d4e666896bf5ba44b4", size = 2144494, upload-time = "2026-04-03T17:05:42.282Z" }, - { url = "https://files.pythonhosted.org/packages/ae/81/81755f50eb2478eaf2049728491d4ea4f416c1eb013338682173259efa09/sqlalchemy-2.0.49-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:df2d441bacf97022e81ad047e1597552eb3f83ca8a8f1a1fdd43cd7fe3898120", size = 2154547, upload-time = "2026-04-03T16:53:08.64Z" }, - { url = "https://files.pythonhosted.org/packages/a2/bc/3494270da80811d08bcfa247404292428c4fe16294932bce5593f215cad9/sqlalchemy-2.0.49-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8e20e511dc15265fb433571391ba313e10dd8ea7e509d51686a51313b4ac01a2", size = 3280782, upload-time = "2026-04-03T17:07:43.508Z" }, - { url = "https://files.pythonhosted.org/packages/cd/f5/038741f5e747a5f6ea3e72487211579d8cbea5eb9827a9cbd61d0108c4bd/sqlalchemy-2.0.49-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:47604cb2159f8bbd5a1ab48a714557156320f20871ee64d550d8bf2683d980d3", size = 3297156, upload-time = "2026-04-03T17:12:27.697Z" }, - { url = "https://files.pythonhosted.org/packages/88/50/a6af0ff9dc954b43a65ca9b5367334e45d99684c90a3d3413fc19a02d43c/sqlalchemy-2.0.49-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:22d8798819f86720bc646ab015baff5ea4c971d68121cb36e2ebc2ee43ead2b7", size = 3228832, upload-time = "2026-04-03T17:07:45.38Z" }, - { url = "https://files.pythonhosted.org/packages/bc/d1/5f6bdad8de0bf546fc74370939621396515e0cdb9067402d6ba1b8afbe9a/sqlalchemy-2.0.49-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9b1c058c171b739e7c330760044803099c7fff11511e3ab3573e5327116a9c33", size = 3267000, upload-time = "2026-04-03T17:12:29.657Z" }, - { url = "https://files.pythonhosted.org/packages/f7/30/ad62227b4a9819a5e1c6abff77c0f614fa7c9326e5a3bdbee90f7139382b/sqlalchemy-2.0.49-cp313-cp313-win32.whl", hash = "sha256:a143af2ea6672f2af3f44ed8f9cd020e9cc34c56f0e8db12019d5d9ecf41cb3b", size = 2115641, upload-time = "2026-04-03T17:05:43.989Z" }, - { url = "https://files.pythonhosted.org/packages/17/3a/7215b1b7d6d49dc9a87211be44562077f5f04f9bb5a59552c1c8e2d98173/sqlalchemy-2.0.49-cp313-cp313-win_amd64.whl", hash = "sha256:12b04d1db2663b421fe072d638a138460a51d5a862403295671c4f3987fb9148", size = 2141498, upload-time = "2026-04-03T17:05:45.7Z" }, - { url = "https://files.pythonhosted.org/packages/28/4b/52a0cb2687a9cd1648252bb257be5a1ba2c2ded20ba695c65756a55a15a4/sqlalchemy-2.0.49-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:24bd94bb301ec672d8f0623eba9226cc90d775d25a0c92b5f8e4965d7f3a1518", size = 3560807, upload-time = "2026-04-03T16:58:31.666Z" }, - { url = "https://files.pythonhosted.org/packages/8c/d8/fda95459204877eed0458550d6c7c64c98cc50c2d8d618026737de9ed41a/sqlalchemy-2.0.49-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a51d3db74ba489266ef55c7a4534eb0b8db9a326553df481c11e5d7660c8364d", size = 3527481, upload-time = "2026-04-03T17:06:00.155Z" }, - { url = "https://files.pythonhosted.org/packages/ff/0a/2aac8b78ac6487240cf7afef8f203ca783e8796002dc0cf65c4ee99ff8bb/sqlalchemy-2.0.49-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:55250fe61d6ebfd6934a272ee16ef1244e0f16b7af6cd18ab5b1fc9f08631db0", size = 3468565, upload-time = "2026-04-03T16:58:33.414Z" }, - { url = "https://files.pythonhosted.org/packages/a5/3d/ce71cfa82c50a373fd2148b3c870be05027155ce791dc9a5dcf439790b8b/sqlalchemy-2.0.49-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:46796877b47034b559a593d7e4b549aba151dae73f9e78212a3478161c12ab08", size = 3477769, upload-time = "2026-04-03T17:06:02.787Z" }, - { url = "https://files.pythonhosted.org/packages/d5/e8/0a9f5c1f7c6f9ca480319bf57c2d7423f08d31445974167a27d14483c948/sqlalchemy-2.0.49-cp313-cp313t-win32.whl", hash = "sha256:9c4969a86e41454f2858256c39bdfb966a20961e9b58bf8749b65abf447e9a8d", size = 2143319, upload-time = "2026-04-03T17:02:04.328Z" }, - { url = "https://files.pythonhosted.org/packages/0e/51/fb5240729fbec73006e137c4f7a7918ffd583ab08921e6ff81a999d6517a/sqlalchemy-2.0.49-cp313-cp313t-win_amd64.whl", hash = "sha256:b9870d15ef00e4d0559ae10ee5bc71b654d1f20076dbe8bc7ed19b4c0625ceba", size = 2175104, upload-time = "2026-04-03T17:02:05.989Z" }, - { url = "https://files.pythonhosted.org/packages/55/33/bf28f618c0a9597d14e0b9ee7d1e0622faff738d44fe986ee287cdf1b8d0/sqlalchemy-2.0.49-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:233088b4b99ebcbc5258c755a097aa52fbf90727a03a5a80781c4b9c54347a2e", size = 2156356, upload-time = "2026-04-03T16:53:09.914Z" }, - { url = "https://files.pythonhosted.org/packages/d1/a7/5f476227576cb8644650eff68cc35fa837d3802b997465c96b8340ced1e2/sqlalchemy-2.0.49-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:57ca426a48eb2c682dae8204cd89ea8ab7031e2675120a47924fabc7caacbc2a", size = 3276486, upload-time = "2026-04-03T17:07:46.9Z" }, - { url = "https://files.pythonhosted.org/packages/2e/84/efc7c0bf3a1c5eef81d397f6fddac855becdbb11cb38ff957888603014a7/sqlalchemy-2.0.49-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:685e93e9c8f399b0c96a624799820176312f5ceef958c0f88215af4013d29066", size = 3281479, upload-time = "2026-04-03T17:12:32.226Z" }, - { url = "https://files.pythonhosted.org/packages/91/68/bb406fa4257099c67bd75f3f2261b129c63204b9155de0d450b37f004698/sqlalchemy-2.0.49-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:9e0400fa22f79acc334d9a6b185dc00a44a8e6578aa7e12d0ddcd8434152b187", size = 3226269, upload-time = "2026-04-03T17:07:48.678Z" }, - { url = "https://files.pythonhosted.org/packages/67/84/acb56c00cca9f251f437cb49e718e14f7687505749ea9255d7bd8158a6df/sqlalchemy-2.0.49-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:a05977bffe9bffd2229f477fa75eabe3192b1b05f408961d1bebff8d1cd4d401", size = 3248260, upload-time = "2026-04-03T17:12:34.381Z" }, - { url = "https://files.pythonhosted.org/packages/56/19/6a20ea25606d1efd7bd1862149bb2a22d1451c3f851d23d887969201633f/sqlalchemy-2.0.49-cp314-cp314-win32.whl", hash = "sha256:0f2fa354ba106eafff2c14b0cc51f22801d1e8b2e4149342023bd6f0955de5f5", size = 2118463, upload-time = "2026-04-03T17:05:47.093Z" }, - { url = "https://files.pythonhosted.org/packages/cf/4f/8297e4ed88e80baa1f5aa3c484a0ee29ef3c69c7582f206c916973b75057/sqlalchemy-2.0.49-cp314-cp314-win_amd64.whl", hash = "sha256:77641d299179c37b89cf2343ca9972c88bb6eef0d5fc504a2f86afd15cd5adf5", size = 2144204, upload-time = "2026-04-03T17:05:48.694Z" }, - { url = "https://files.pythonhosted.org/packages/1f/33/95e7216df810c706e0cd3655a778604bbd319ed4f43333127d465a46862d/sqlalchemy-2.0.49-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c1dc3368794d522f43914e03312202523cc89692f5389c32bea0233924f8d977", size = 3565474, upload-time = "2026-04-03T16:58:35.128Z" }, - { url = "https://files.pythonhosted.org/packages/0c/a4/ed7b18d8ccf7f954a83af6bb73866f5bc6f5636f44c7731fbb741f72cc4f/sqlalchemy-2.0.49-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7c821c47ecfe05cc32140dcf8dc6fd5d21971c86dbd56eabfe5ba07a64910c01", size = 3530567, upload-time = "2026-04-03T17:06:04.587Z" }, - { url = "https://files.pythonhosted.org/packages/73/a3/20faa869c7e21a827c4a2a42b41353a54b0f9f5e96df5087629c306df71e/sqlalchemy-2.0.49-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:9c04bff9a5335eb95c6ecf1c117576a0aa560def274876fd156cfe5510fccc61", size = 3474282, upload-time = "2026-04-03T16:58:37.131Z" }, - { url = "https://files.pythonhosted.org/packages/b7/50/276b9a007aa0764304ad467eceb70b04822dc32092492ee5f322d559a4dc/sqlalchemy-2.0.49-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:7f605a456948c35260e7b2a39f8952a26f077fd25653c37740ed186b90aaa68a", size = 3480406, upload-time = "2026-04-03T17:06:07.176Z" }, - { url = "https://files.pythonhosted.org/packages/e5/c3/c80fcdb41905a2df650c2a3e0337198b6848876e63d66fe9188ef9003d24/sqlalchemy-2.0.49-cp314-cp314t-win32.whl", hash = "sha256:6270d717b11c5476b0cbb21eedc8d4dbb7d1a956fd6c15a23e96f197a6193158", size = 2149151, upload-time = "2026-04-03T17:02:07.281Z" }, - { url = "https://files.pythonhosted.org/packages/05/52/9f1a62feab6ed368aff068524ff414f26a6daebc7361861035ae00b05530/sqlalchemy-2.0.49-cp314-cp314t-win_amd64.whl", hash = "sha256:275424295f4256fd301744b8f335cff367825d270f155d522b30c7bf49903ee7", size = 2184178, upload-time = "2026-04-03T17:02:08.623Z" }, - { url = "https://files.pythonhosted.org/packages/e5/30/8519fdde58a7bdf155b714359791ad1dc018b47d60269d5d160d311fdc36/sqlalchemy-2.0.49-py3-none-any.whl", hash = "sha256:ec44cfa7ef1a728e88ad41674de50f6db8cfdb3e2af84af86e0041aaf02d43d0", size = 1942158, upload-time = "2026-04-03T16:53:44.135Z" }, +sdist = { url = "https://files.pythonhosted.org/packages/57/da/6fbf010c8ebb347679d0d100b22fe9ba5e13fd04046c5df7280d2f0bf706/sqlalchemy-2.0.50.tar.gz", hash = "sha256:af5607d11ef90fd6a5c0549fe0045dce1663d427426bcfb506dcb5346a85a3b9", size = 9907424, upload-time = "2026-05-24T19:20:04.018Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b6/5d/3172686af1770e4de2805f919a51441085f589ddadf3dd76ec582f84f497/sqlalchemy-2.0.50-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1aa6e403663a9c43c8fef7ce4bdb4cf48bcd8d352e91deda2a99f963270bd508", size = 2161366, upload-time = "2026-05-24T20:00:02.061Z" }, + { url = "https://files.pythonhosted.org/packages/0f/90/e98dedea3c3e663a17afcd003a34ba45efdac2cea3b6f2e4585e2b1e2537/sqlalchemy-2.0.50-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:51b637a84f9fa35ae1f9017e786cb142974a25305085e1b378b3647a67f65ad3", size = 3318926, upload-time = "2026-05-24T20:07:42.369Z" }, + { url = "https://files.pythonhosted.org/packages/3b/4f/501308c2babb62c11753ecb4ee88ba9eef019419a4d6cbf7cb13e2bad353/sqlalchemy-2.0.50-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2dab927761d9108550f0cf8e66ff21af56f907a0ce0a689793db615e2b55f62c", size = 3319199, upload-time = "2026-05-24T20:14:28.551Z" }, + { url = "https://files.pythonhosted.org/packages/ac/39/d88996c5e03ed6248c3a788d20f0b8d8b376b9f8a495e4bab9df7c72d2f8/sqlalchemy-2.0.50-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:545eae198d37bcf837a10ede3684e2af32458d6f35c597c35c2de7502dc38fc4", size = 3270301, upload-time = "2026-05-24T20:07:44.917Z" }, + { url = "https://files.pythonhosted.org/packages/42/1b/1ae0e65161b51cc43e5ca75430ef79d80e23b5042d645586c2c342c3b92e/sqlalchemy-2.0.50-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:0fec460e18cdbb4c7773531122ce9a27e96c6ca17af3933941d94da475ad2c86", size = 3293465, upload-time = "2026-05-24T20:14:30.501Z" }, + { url = "https://files.pythonhosted.org/packages/83/29/17c0003f2c0dfa6d1b97672475707e3ec5980db09defd7fa20beb6833bbd/sqlalchemy-2.0.50-cp311-cp311-win32.whl", hash = "sha256:e6e814658818fd165e749e3d8490ef16cc7f379a118c37ada8b0589ffbaaac22", size = 2120694, upload-time = "2026-05-24T20:08:09.237Z" }, + { url = "https://files.pythonhosted.org/packages/c9/18/280d00654cc19d1fccf236fa5070f6dd04b84dde6f1b2e637bde0ff340a7/sqlalchemy-2.0.50-cp311-cp311-win_amd64.whl", hash = "sha256:1c5f858fe79c9f5d8fda065c06186356acb7f8df3cd52dbd5ee3f200e4b144f5", size = 2145315, upload-time = "2026-05-24T20:08:10.952Z" }, + { url = "https://files.pythonhosted.org/packages/be/b0/a9d19b43f38f878b1278bca5b00b909f7540d41494396dd2561f9ad0956d/sqlalchemy-2.0.50-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:23ae23d8b9d344d30d0a92f06d45825024a5790f1c1dd4cf452636a50d3e58cb", size = 2159807, upload-time = "2026-05-24T19:27:53.086Z" }, + { url = "https://files.pythonhosted.org/packages/f5/2c/191dd58a248fd2cfd4780fa82c375c505e4ad98c8b522fa69ec492130d77/sqlalchemy-2.0.50-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:47b71b933e7b4ebad407c8fdfd70d2c4f08b78b3238bb30eebdd6eb32ca51b89", size = 3343358, upload-time = "2026-05-24T20:09:29.279Z" }, + { url = "https://files.pythonhosted.org/packages/8a/2b/514fce8a7df81cf5bad7ff7865de7ac0c5776a38cc043475c4703eb7fe8b/sqlalchemy-2.0.50-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:110fdac56ace278949f00de805edacbd6141e382d992f9ba28238b3a0827a600", size = 3357994, upload-time = "2026-05-24T20:17:13.495Z" }, + { url = "https://files.pythonhosted.org/packages/35/a6/a0e283f5494f92b0d77e319ff77e437b1ffe4a051ba67c81d53234825475/sqlalchemy-2.0.50-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0f5e4ac70e9e757f6b3e87c0491ff034442ecd8dfd36d041a50564c322dafc0e", size = 3289399, upload-time = "2026-05-24T20:09:32.239Z" }, + { url = "https://files.pythonhosted.org/packages/b7/96/1b07325ba71752d6a028b77d07bed1483ad545f794e8b1dc89b3ba3b3c68/sqlalchemy-2.0.50-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:724f3dcbe53dd0151e3cb5e7ec4ba4c620bede579caacd16275dc35ce06e8615", size = 3321216, upload-time = "2026-05-24T20:17:15.581Z" }, + { url = "https://files.pythonhosted.org/packages/ed/8e/bad6ed253e8a99edfc99af02f7173ec48a1d3ed1b9b35a1b8bc1700900cc/sqlalchemy-2.0.50-cp312-cp312-win32.whl", hash = "sha256:1208050441471d003b7c8cb4054fb084f185cf35ac3f0ea270803865bca9939a", size = 2119194, upload-time = "2026-05-24T19:50:04.943Z" }, + { url = "https://files.pythonhosted.org/packages/b6/2d/314a6690dda4b9cfc571eab1a63cf6fe6e1470aa3759ccda6aa016ee0f5a/sqlalchemy-2.0.50-cp312-cp312-win_amd64.whl", hash = "sha256:9d1af51558029a156a70986b7df88f042b3d158d7c8d8fb5072912d4b32d89c7", size = 2146186, upload-time = "2026-05-24T19:50:06.74Z" }, + { url = "https://files.pythonhosted.org/packages/0b/c4/c42356b527296e9862f67990efce31ef78b4cf69cd3f80873a528a060320/sqlalchemy-2.0.50-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:06a9210bdc5f4298cff0781087e2ff45683922252dacc452846373a58761f093", size = 2156697, upload-time = "2026-05-24T19:27:54.764Z" }, + { url = "https://files.pythonhosted.org/packages/60/a1/b1a70e3c4365ac7fe9e347f3710f19b562c866fb96d45e3c891588789a7b/sqlalchemy-2.0.50-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8b53784972ade4f8174b9aa661f31a06f8a936d2cfdd602913ff3c6dd40ae873", size = 3284260, upload-time = "2026-05-24T20:09:34.195Z" }, + { url = "https://files.pythonhosted.org/packages/3f/4a/f3ac3caa19f263d57b0a47f8c91bbf56583dc2d3fc63acfbf644abb24fe0/sqlalchemy-2.0.50-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:31648fa14460537e768a7303b078e4344d208e0d23e06867c1f376a227ed82db", size = 3302280, upload-time = "2026-05-24T20:17:17.825Z" }, + { url = "https://files.pythonhosted.org/packages/66/55/ccada3e3d62254587819749a0bc69f41173eb48a6e385d10e66d32a9c88e/sqlalchemy-2.0.50-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:03f4323c980ad0e918cc9e5369b015f759f4e534db5bbaf4dc36832c10d05064", size = 3231580, upload-time = "2026-05-24T20:09:36.406Z" }, + { url = "https://files.pythonhosted.org/packages/05/f6/6809349130a2de0e109e7f00fd7d431da9565b9b2868b32ee684754f672b/sqlalchemy-2.0.50-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:2b9dcc43afef8ac157cd92fce96985d6b8b0cfbd3df4d666f66b4d55a75d202f", size = 3269375, upload-time = "2026-05-24T20:17:20.34Z" }, + { url = "https://files.pythonhosted.org/packages/48/84/278a811ef4e07be9c89dc5cdd7be833268509a66a68c4897cf585e67428f/sqlalchemy-2.0.50-cp313-cp313-win32.whl", hash = "sha256:60922d6599065ddca2c6f376b9aa2f41a6b85a271725e0909490bbc50b1998a5", size = 2117229, upload-time = "2026-05-24T19:50:08.215Z" }, + { url = "https://files.pythonhosted.org/packages/f6/1c/067cc6187ed32d2ec222fe6d2643acc1659a6d0659f8a7cbc5ad3ae83280/sqlalchemy-2.0.50-cp313-cp313-win_amd64.whl", hash = "sha256:287086e67275a212c4582d166a6fb03a65ccc5551d80866270ce0dd9f34eccd3", size = 2143126, upload-time = "2026-05-24T19:50:09.691Z" }, + { url = "https://files.pythonhosted.org/packages/df/32/10ac51b4be7cdecd7e93d069251c86dfbf70b7adbd7c67b48ccea6c49e1c/sqlalchemy-2.0.50-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c966932507a4d7d0a37314927dbfcd89720e3f37d2a1e3352e7ae7939fa8e8a0", size = 2158519, upload-time = "2026-05-24T19:27:56.472Z" }, + { url = "https://files.pythonhosted.org/packages/5a/76/e703d2f7681d7d66c4c891af3f07c7ccf4c76ad7f18351de035b5eda007a/sqlalchemy-2.0.50-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:faffef4bcc20a1892e65e155293d99d60855bbbc79250ab712819cfd56a8e6bb", size = 3282063, upload-time = "2026-05-24T20:09:38.57Z" }, + { url = "https://files.pythonhosted.org/packages/31/26/ef168b184a25701f9995e8fb7e503fafd7a99c1c77cda1bc1a26ea2ed486/sqlalchemy-2.0.50-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6c206aec519a2e7bd08abbfb33436e325fd22c632d9c21a9047e376ce241646e", size = 3287069, upload-time = "2026-05-24T20:17:21.942Z" }, + { url = "https://files.pythonhosted.org/packages/c2/15/765acc2bc693bccc43ca4a95d5b69750da8aaf6db1b5c616536e087f8920/sqlalchemy-2.0.50-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:bef4ac756363227ef6402a75fee025a4bc690f92328e825868939b3b3a446a6d", size = 3230453, upload-time = "2026-05-24T20:09:40.398Z" }, + { url = "https://files.pythonhosted.org/packages/63/61/08e03c3adbf5db0087a0b6816746fec8f3032fb2f7fc899a9bb9b2a48ce4/sqlalchemy-2.0.50-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:96fbee6b19c19cd1556c8bf9419447cf2ec149ffcab7ab64348c23e54ef8547f", size = 3252413, upload-time = "2026-05-24T20:17:24.067Z" }, + { url = "https://files.pythonhosted.org/packages/03/0c/370a1f2db38436c615e10134c8a37de3688e74084792380695f3f5083860/sqlalchemy-2.0.50-cp314-cp314-win32.whl", hash = "sha256:8f00e3eb43ba30eb1b238ee03a8a62309486d1321eda3328bb611e0340033ad8", size = 2120063, upload-time = "2026-05-24T19:50:11.08Z" }, + { url = "https://files.pythonhosted.org/packages/7f/a0/fe92bb9817863bc13ba093bda931979a26cc2ca69f8e8f26d07add3d7c6f/sqlalchemy-2.0.50-cp314-cp314-win_amd64.whl", hash = "sha256:15708c613cd5005b7dffe1f66ee6a63ee8f5e46799f71c70ebad74178c676a39", size = 2145830, upload-time = "2026-05-24T19:50:12.452Z" }, + { url = "https://files.pythonhosted.org/packages/cc/ff/e5640a98a0b2f491eb8fde10fb6c773621a2e44340de231fafcc9370f4a9/sqlalchemy-2.0.50-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:3699dac4be410e97049a1658e9480da9cde956594aa0f3aebc60b88f21c5ba70", size = 2178435, upload-time = "2026-05-24T19:42:58.889Z" }, + { url = "https://files.pythonhosted.org/packages/b7/85/337116e186f1236375b5fb70c21cfac98e8e8ab0d3a47be838dc47a59e08/sqlalchemy-2.0.50-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f96233858e3df43932ac11589e22520da6e8aeb624b03fedfeebb0e8ea213086", size = 3566059, upload-time = "2026-05-24T20:01:20.848Z" }, + { url = "https://files.pythonhosted.org/packages/96/34/bb0e190e161c3c2c24314a65add57218be14a4a9486886b7f5047c1ff7c8/sqlalchemy-2.0.50-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c4e70c46fad30c3bcc6a4708bc0130a3173e11a5b25f0ea4a9d8911b450f1f52", size = 3535366, upload-time = "2026-05-24T20:03:56.768Z" }, + { url = "https://files.pythonhosted.org/packages/df/5a/a7f759f97e4fd499c5d4e4488c760d5a7fbecf3028b465a04274fcd52384/sqlalchemy-2.0.50-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:1918a3cf564d16d95bca7301005f41ab2ad50b07cd3b9da50d3ed986db148d6a", size = 3474879, upload-time = "2026-05-24T20:01:23.058Z" }, + { url = "https://files.pythonhosted.org/packages/9d/d9/2907ea38eb60687d297bf9c39e5ee58053c87b57fe8a9cae97090cecbf10/sqlalchemy-2.0.50-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:b00098cdbdbd38c7be3d568b0c9c3122b8c0ec62b911b57cd5e6e0254d60a76d", size = 3486117, upload-time = "2026-05-24T20:03:59.052Z" }, + { url = "https://files.pythonhosted.org/packages/f2/e3/5aa06f167559f8c0bdae487e297d23ba548150ab016a3418265d617a4985/sqlalchemy-2.0.50-cp314-cp314t-win32.whl", hash = "sha256:1fbd55a969d7ac44a98e3dec75016074f809fa08f871585ace58dde110d1bf3e", size = 2150823, upload-time = "2026-05-24T20:08:58.644Z" }, + { url = "https://files.pythonhosted.org/packages/65/9b/112fb8f977582d7489d036e409e3723948bcf5320b3ac465f3c481bbe8f9/sqlalchemy-2.0.50-cp314-cp314t-win_amd64.whl", hash = "sha256:c5c3cdb753a9004183e1ccb634b41611654c989e61bc68617ce878e46d6f1e51", size = 2185794, upload-time = "2026-05-24T20:09:00.319Z" }, + { url = "https://files.pythonhosted.org/packages/d0/10/f7220e9b784d295d241c86ed99aeb537f92afcd469a64861f2717e9bb077/sqlalchemy-2.0.50-py3-none-any.whl", hash = "sha256:92064363517a3ff8212b5a93b8c62876579d8dfd1ca5b561335f30152d884fa9", size = 1943861, upload-time = "2026-05-24T19:59:01.119Z" }, ] [[package]] @@ -3882,9 +3893,9 @@ wheels = [ [[package]] name = "zipp" -version = "3.23.1" +version = "4.1.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/30/21/093488dfc7cc8964ded15ab726fad40f25fd3d788fd741cc1c5a17d78ee8/zipp-3.23.1.tar.gz", hash = "sha256:32120e378d32cd9714ad503c1d024619063ec28aad2248dc6672ad13edfa5110", size = 25965, upload-time = "2026-04-13T23:21:46.6Z" } +sdist = { url = "https://files.pythonhosted.org/packages/b9/d8/eab98a517c14134c0b2eb4e2387bc5f457334293ec5d2dd3857ec2966802/zipp-4.1.0.tar.gz", hash = "sha256:4cb57381f544315db7688e976e922a2b18cdb513d21cc194eb42232ba2a3e602", size = 26214, upload-time = "2026-05-18T20:08:57.967Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/08/8a/0861bec20485572fbddf3dfba2910e38fe249796cb73ecdeb74e07eeb8d3/zipp-3.23.1-py3-none-any.whl", hash = "sha256:0b3596c50a5c700c9cb40ba8d86d9f2cc4807e9bedb06bcdf7fac85633e444dc", size = 10378, upload-time = "2026-04-13T23:21:45.386Z" }, + { url = "https://files.pythonhosted.org/packages/3a/13/547360d81e6d88d58492968ffda9f9542854f11310ee556fef14260cc886/zipp-4.1.0-py3-none-any.whl", hash = "sha256:25ad4e16390cd314347dd8f1de67a2ac538ae658ed4ab9db16029c07c188e97f", size = 10238, upload-time = "2026-05-18T20:08:57.045Z" }, ] From d97d6a0e11b10328eb3f4d3063da9ea96382fbde Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Wed, 27 May 2026 18:41:11 -0700 Subject: [PATCH 077/140] add TODO for numpy/torch guarding, prevent numpy copy --- src/quantem/core/datastructures/dataset.py | 33 +++++++++++++------ .../core/datastructures/dataset4dstem.py | 2 ++ tests/datastructures/test_dataset.py | 18 ++++++++++ 3 files changed, 43 insertions(+), 10 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index fa1fcef2..94744978 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -55,6 +55,7 @@ def __init__( ) super().__init__() # Dual-slot storage: exactly one of (_array, _tensor) is set. + # TODO: remove dual-init guards once torch transition is complete. if array is None and tensor is None: raise ValueError("Provide either `array` (numpy) or `tensor` (torch).") if array is not None and tensor is not None: @@ -229,37 +230,49 @@ def ndim(self) -> int: return (array if array is not None else self._tensor).ndim @property - def dtype(self) -> DTypeLike: + def dtype(self) -> DTypeLike | torch.dtype: array = getattr(self, "_array", None) return (array if array is not None else self._tensor).dtype @property def device(self) -> str: - """``"cpu"`` for numpy-backed; torch device string for tensor-backed.""" - tensor = getattr(self, "_tensor", None) - if tensor is not None: - return str(tensor.device) - return "cpu" + """Device string for the underlying storage. numpy 2.x ndarray and torch.Tensor + both expose ``.device`` (array-API convention), so this is uniform. + """ + array = getattr(self, "_array", None) + return str((array if array is not None else self._tensor).device) def numpy(self) -> NDArray: """Return the data as a numpy array (mirrors ``torch.Tensor.numpy()``). For numpy-backed datasets, returns ``self.array`` directly. For - tensor-backed datasets, materializes a CPU copy via ``.detach().cpu().numpy()``. + tensor-backed datasets, materializes a read-only CPU copy via + ``.detach().cpu().numpy()``. ``flags.writeable=False`` so accidental + in-place writes raise instead of silently being lost (the copy is not + the tensor). """ array = getattr(self, "_array", None) if array is not None: return array - return self._tensor.detach().cpu().numpy() + arr = self._tensor.detach().cpu().numpy() + arr.flags.writeable = False + return arr def to(self, device) -> Self: - """Move the underlying tensor to ``device``. Raises if numpy-backed.""" + """Move the underlying tensor to ``device``. Raises if numpy-backed. + + ``device`` is normalized via :func:`quantem.core.config.validate_device` + so values like ``"cuda"``, ``0``, ``"cuda:0"``, ``torch.device("cuda:0")`` + all resolve to the same canonical device. + """ + from quantem.core import config tensor = getattr(self, "_tensor", None) if tensor is None: raise AttributeError( f"Cannot .to({device!r}) on numpy-backed Dataset '{self.name}'." ) - self._tensor = tensor.to(device) + dev, _ = config.validate_device(device) + self._tensor = tensor.to(dev) return self # --- Summaries --- diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 48d3f737..004db427 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -181,6 +181,8 @@ def from_tensor( For cupy / jax arrays, wrap with ``torch.from_dlpack(arr)`` first. """ + # TODO: factor type + ndim checks into `ensure_valid_tensor(value, ndim=4)` + # in validators.py, matching `ensure_valid_array` pattern. Cuts bloat. if not isinstance(tensor, torch.Tensor): raise TypeError( f"from_tensor requires torch.Tensor, got {type(tensor).__name__}. " diff --git a/tests/datastructures/test_dataset.py b/tests/datastructures/test_dataset.py index 9c83262c..201fe92b 100644 --- a/tests/datastructures/test_dataset.py +++ b/tests/datastructures/test_dataset.py @@ -434,3 +434,21 @@ def test_api_errors(self, sample_dataset_2d): # Neither specified with pytest.raises(ValueError): sample_dataset_2d.fourier_resample() + + +class TestDatasetTorch: + """Tests for torch-backed Dataset (from_tensor path).""" + + def test_numpy_copy_is_readonly(self): + """``.numpy()`` on a tensor-backed dataset returns a read-only CPU copy + so writes raise instead of silently updating only the detached copy. + """ + import torch + from quantem.core.datastructures.dataset4dstem import Dataset4dstem + ds = Dataset4dstem.from_tensor(torch.zeros(2, 2, 2, 2)) + arr = ds.numpy() + assert arr.flags.writeable is False + with pytest.raises(ValueError, match="read-only"): + arr[0, 0, 0, 0] = 99.0 + + From 990f35d27c674b51dfc3e0ce8bfacadc236efbc8 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 14:09:11 -0700 Subject: [PATCH 078/140] SO3Params switched to static methods. --- src/quantem/core/ml/models/so3params.py | 145 ++++++++++++++++-------- 1 file changed, 98 insertions(+), 47 deletions(-) diff --git a/src/quantem/core/ml/models/so3params.py b/src/quantem/core/ml/models/so3params.py index 82a4361a..cd55d0ac 100644 --- a/src/quantem/core/ml/models/so3params.py +++ b/src/quantem/core/ml/models/so3params.py @@ -32,6 +32,78 @@ def __init__(self, T: int, init: str = "random"): quats = self._init_quaternions(T, init) # (T, 4) self.quats = nn.Parameter(quats) + @staticmethod + def quat_to_rotmat(q: torch.Tensor) -> torch.Tensor: + """Unit quaternion (..., 4) [x, y, z, w] -> rotation matrix (..., 3, 3). + Assumes q is already normalized.""" + x, y, z, w = q.unbind(dim=-1) + xx, yy, zz = x * x, y * y, z * z + xy, xz, yz = x * y, x * z, y * z + wx, wy, wz = w * x, w * y, w * z + R = torch.stack( + [ + 1 - 2 * (yy + zz), 2 * (xy - wz), 2 * (xz + wy), + 2 * (xy + wz), 1 - 2 * (xx + zz), 2 * (yz - wx), + 2 * (xz - wy), 2 * (yz + wx), 1 - 2 * (xx + yy), + ], + dim=-1, + ).reshape(*q.shape[:-1], 3, 3) + return R + + @staticmethod + def rotmat_to_quat(R: torch.Tensor) -> torch.Tensor: + """Rotation matrix (..., 3, 3) -> unit quaternion (..., 4) [x, y, z, w]. + + Shepperd's method: build the four candidate quaternions, each dividing + by a different diagonal combination, then per-element pick the branch + with the largest denominator so we never divide by a near-zero number. + The naive trace-only formula blows up when trace ~ -1 (180deg rotations). + """ + m00, m01, m02 = R[..., 0, 0], R[..., 0, 1], R[..., 0, 2] + m10, m11, m12 = R[..., 1, 0], R[..., 1, 1], R[..., 1, 2] + m20, m21, m22 = R[..., 2, 0], R[..., 2, 1], R[..., 2, 2] + + # 4 * (component^2) for w, x, y, z respectively; these sum to 4. + t = torch.stack( + [ + 1.0 + m00 + m11 + m22, # 4 w^2 + 1.0 + m00 - m11 - m22, # 4 x^2 + 1.0 - m00 + m11 - m22, # 4 y^2 + 1.0 - m00 - m11 + m22, # 4 z^2 + ], + dim=-1, + ) # (..., 4) + + eps = torch.finfo(R.dtype).eps + S = 2.0 * torch.sqrt(t.clamp_min(eps)) # S[k] = 4 * |component_k| + S0, S1, S2, S3 = S.unbind(-1) + + # each candidate in [x, y, z, w] order + cand_w = torch.stack([(m21 - m12) / S0, (m02 - m20) / S0, (m10 - m01) / S0, 0.25 * S0], dim=-1) + cand_x = torch.stack([0.25 * S1, (m01 + m10) / S1, (m02 + m20) / S1, (m21 - m12) / S1], dim=-1) + cand_y = torch.stack([(m01 + m10) / S2, 0.25 * S2, (m12 + m21) / S2, (m02 - m20) / S2], dim=-1) + cand_z = torch.stack([(m02 + m20) / S3, (m12 + m21) / S3, 0.25 * S3, (m10 - m01) / S3], dim=-1) + + cands = torch.stack([cand_w, cand_x, cand_y, cand_z], dim=-2) # (..., 4, 4) + idx = t.argmax(dim=-1) # (...,) + idx = idx[..., None, None].expand(*idx.shape, 1, 4) # (..., 1, 4) + q = cands.gather(-2, idx).squeeze(-2) # (..., 4) + return F.normalize(q, p=2, dim=-1) + + def as_matrix(self) -> torch.Tensor: + return self.quat_to_rotmat(self.normalized()) + + @classmethod + def from_matrix(cls, R: torch.Tensor) -> "SO3ParamQuat": + """Initialize a bank close to the given rotations R (T, 3, 3).""" + obj = cls(R.shape[0], init="identity") + with torch.no_grad(): + obj.quats.copy_(cls.rotmat_to_quat(R)) + return obj + + def extra_repr(self) -> str: + return f"T={self.quats.shape[0]}" + # ------------------------------------------------------------------ # Initialisers # ------------------------------------------------------------------ @@ -73,41 +145,6 @@ def normalized(self) -> torch.Tensor: """Returns (T, 4) unit quaternions.""" return F.normalize(self.quats, p=2, dim=-1) - def as_matrix(self) -> torch.Tensor: - """ - Converts the T stored quaternions to (T, 3, 3) rotation matrices. - - Uses the standard formula; no trig, just multiplications. - """ - q = self.normalized() # (T, 4) [x, y, z, w] - x, y, z, w = q.unbind(dim=-1) # each (T,) - - # Precompute products - xx, yy, zz = x * x, y * y, z * z - xy, xz, yz = x * y, x * z, y * z - wx, wy, wz = w * x, w * y, w * z - - # Row-major: R[i,j] - R = torch.stack( - [ - 1 - 2 * (yy + zz), - 2 * (xy - wz), - 2 * (xz + wy), - 2 * (xy + wz), - 1 - 2 * (xx + zz), - 2 * (yz - wx), - 2 * (xz - wy), - 2 * (yz + wx), - 1 - 2 * (xx + yy), - ], - dim=-1, - ).reshape(-1, 3, 3) # (T, 3, 3) - - return R - - def extra_repr(self) -> str: - return f"T={self.quats.shape[0]}" - class SO3ParamR9SVD(nn.Module): """ @@ -121,21 +158,35 @@ class SO3ParamR9SVD(nn.Module): def __init__(self, T: int, init: Literal["random", "identity"] = "random"): super().__init__() if init == "random": - # Initialize near identity with small noise - M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) - M = M + 0.1 * torch.randn(T, 3, 3) + M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) + 0.1 * torch.randn(T, 3, 3) elif init == "identity": M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) else: raise ValueError(f"Unknown init '{init}'") - self.M = nn.Parameter(M) # (T, 3, 3) + self.M = nn.Parameter(M) + + @staticmethod + def rotmat_to_r9(R: torch.Tensor) -> torch.Tensor: + """Rotation matrix (..., 3, 3) -> R9. Identity embedding: any R in SO(3) + is a fixed point of the SVD projection, so this just stores R directly.""" + return R + + @staticmethod + def r9_to_rotmat(M: torch.Tensor) -> torch.Tensor: + """R9 (..., 3, 3) -> nearest SO(3) matrix via SVD+.""" + U, _, Vh = torch.linalg.svd(M) + d = torch.det(U @ Vh) + diag = torch.ones(*M.shape[:-2], 3, device=M.device, dtype=M.dtype) + diag[..., 2] = d + return U @ (diag.unsqueeze(-1) * Vh) def as_matrix(self) -> torch.Tensor: - """Projects each M to SO(3) via SVD. Returns (T, 3, 3).""" - - U, _, Vh = torch.linalg.svd(self.M) # U: (T,3,3), Vh: (T,3,3) - # Fix reflections: det(U Vh) must be +1 - d = torch.det(U @ Vh) # (T,) - diag = torch.ones(self.M.shape[0], 3, device=self.M.device, dtype=self.M.dtype) - diag[:, 2] = d # multiply last singular vector by sign - return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) + return self.r9_to_rotmat(self.M) + + @classmethod + def from_matrix(cls, R: torch.Tensor) -> "SO3ParamR9SVD": + """Initialize a bank close to given rotations R (T, 3, 3).""" + obj = cls(R.shape[0], init="identity") + with torch.no_grad(): + obj.M.copy_(cls.rotmat_to_r9(R)) + return obj \ No newline at end of file From c56994a529269d7fc713ee32e0859961f9a6f642 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 14:14:12 -0700 Subject: [PATCH 079/140] Fixed optimizer_params using hidden variable in set_optimizer --- src/quantem/core/ml/optimizer_mixin.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 55d013c9..b1fee891 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -618,11 +618,11 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: if opt_params is not None: self.optimizer_params = opt_params - if not self._optimizer_params: + if not self.optimizer_params: self._optimizer = None return - if isinstance(self._optimizer_params, OptimizerParams.NoneOptimizer): + if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer): self.remove_optimizer() return @@ -633,11 +633,11 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: for p in group["params"]: p.requires_grad_(True) # Figure out which optimizer class to use - if isinstance(self._optimizer_params, dict): + if isinstance(self.optimizer_params, dict): # Per-group case: all groups must agree on the optimizer class, # and per-group hyperparameters are already baked into each dict # by get_optimization_parameters(). - opt_specs = list(self._optimizer_params.values()) + opt_specs = list(self.optimizer_params.values()) if not opt_specs: self._optimizer = None return @@ -651,8 +651,8 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: self._optimizer = optimizer_cls(params) # type:ignore else: # Single-optimizer case: splat global hyperparameters - optimizer_cls = self._optimizer_class_for(self._optimizer_params) - self._optimizer = optimizer_cls(params, **self._optimizer_params.params()) + optimizer_cls = self._optimizer_class_for(self.optimizer_params) + self._optimizer = optimizer_cls(params, **self.optimizer_params.params()) def _optimizer_class_for(self, opt_params) -> type[torch.optim.Optimizer]: match opt_params: From 4b8ca472a0d6fc99980c60d50739782870250e0a Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 14:19:02 -0700 Subject: [PATCH 080/140] Added more description to the ReconstructionContext --- src/quantem/tomography/tomography_context.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/src/quantem/tomography/tomography_context.py b/src/quantem/tomography/tomography_context.py index ef861651..d322287c 100644 --- a/src/quantem/tomography/tomography_context.py +++ b/src/quantem/tomography/tomography_context.py @@ -14,6 +14,13 @@ class ReconstructionContext(BaseContext): - Pixelated reads ".volume" - INR reads ".coords" and recomputes via the model. - TensorDecomp reads ".coords" and ".pred" (and ".all densities") + + Variable descriptions: + - volume: Reconstructed object (volume). + - coords: Used for INR reconstructions to provide the coordinates to the model. + - pred: Predicted values per coordinate position from the model. + - all_densities: Integrated densities per ray from the model. + - obj: Object model (INR, TensorDecomp, etc.). """ volume: Optional[torch.Tensor] = None From 1bc17392f199257e3737ff7aa81c4a35eac8dda4 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 14:28:40 -0700 Subject: [PATCH 081/140] Standardized optimizer params with new normalization to always output dict[str, OptimizerType]. --- src/quantem/core/ml/optimizer_mixin.py | 29 +++++++++++++------------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index b1fee891..37264538 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -553,7 +553,7 @@ def scheduler(self) -> "torch.optim.lr_scheduler.LRScheduler | None": return self._scheduler @property - def optimizer_params(self) -> OptimizerType | dict[str, OptimizerType]: + def optimizer_params(self) -> dict[str, OptimizerType]: """Get the optimizer parameters.""" return self._optimizer_params @@ -565,20 +565,21 @@ def optimizer_params( def _normalize_optimizer_params( self, params: OptimizerType | dict[str, Any] - ) -> OptimizerType | dict[str, OptimizerType]: - """Normalize input. Subclasses can override to validate keys.""" - # dict-of-OptimizerType form (PPLR) - if isinstance(params, dict) and not self._is_single_optimizer_dict(params): - return { - k: v if isinstance(v, OptimizerType) else OptimizerParams.parse_dict(d=v) - for k, v in params.items() - } - # Single optimizer form (with dict shorthand like {"name": "adam", "lr": 1e-3}) - if isinstance(params, dict): - params = OptimizerParams.parse_dict(d=params) - if not isinstance(params, OptimizerType): + ) -> dict[str, OptimizerType]: + """Normalize input to dict[str, OptimizerType]. Subclasses can override to validate keys.""" + # Single optimizer, already an OptimizerType + if isinstance(params, OptimizerType): + return {self.DEFAULT_OPTIMIZER_KEY: params} + if not isinstance(params, dict): raise TypeError(f"optimizer_params must be OptimizerType or dict, got {type(params)}") - return params + # Single optimizer as dict shorthand, e.g. {"name": "adam", "lr": 1e-3} + if self._is_single_optimizer_dict(params): + return {self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.parse_dict(d=params)} + # dict-of-OptimizerType form (PPLR) + return { + k: v if isinstance(v, OptimizerType) else OptimizerParams.parse_dict(d=v) + for k, v in params.items() + } @staticmethod def _is_single_optimizer_dict(d: dict) -> bool: From 21944c406c804d565d37c956bd7b69d8faa34dde Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 14:28:40 -0700 Subject: [PATCH 082/140] Standardized optimizer params with new normalization to always output dict[str, OptimizerType]. --- src/quantem/core/ml/optimizer_mixin.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 37264538..937ef213 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -536,9 +536,9 @@ def __init__(self): """Initialize the optimizer mixin.""" self._optimizer = None self._scheduler = None - self._optimizer_params: OptimizerType | dict[str, OptimizerType] = ( - OptimizerParams.NoneOptimizer() - ) + self._optimizer_params: dict[str, OptimizerType] = { + self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer() + } self._scheduler_params: SchedulerType = SchedulerParams.NoneScheduler() # Don't call super().__init__() in mixin classes to avoid MRO issues From e11e49e55c8c290d709cdd5cb717d7eddcf81f7d Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 16:10:21 -0700 Subject: [PATCH 083/140] Fixed some bugs with DEFAULT_OPTIMIZER_KEY not being instantiated in optimizer_mixin.py --- src/quantem/core/ml/constraints.py | 3 ++- src/quantem/core/ml/optimizer_mixin.py | 1 + 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index fea1d309..590da4cb 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -7,7 +7,6 @@ import torch from numpy.typing import NDArray -T_ctx = TypeVar("T_ctx", bound=BaseContext) @dataclass class BaseContext(ABC): @@ -16,6 +15,8 @@ class BaseContext(ABC): """ pass +T_ctx = TypeVar("T_ctx", bound=BaseContext) + @dataclass(slots=False) class Constraints(ABC): """ diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 937ef213..f970a859 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -531,6 +531,7 @@ class OptimizerMixin: """ DEFAULT_OPTIMIZER_TYPE = "adamw" + DEFAULT_OPTIMIZER_KEY = "default" def __init__(self): """Initialize the optimizer mixin.""" From b2e9e158d39dd7f2c38bc6fda5361b9e6d275c8a Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 17:36:35 -0700 Subject: [PATCH 084/140] Working version, standardized optimizer_mixin.py --- src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md | 221 ++++++++++++ src/quantem/core/ml/optimizer_mixin.py | 21 +- src/quantem/core/ml/optimizer_mixin_fix.html | 216 ++++++++++++ .../core/ml/optimizer_mixin_review.html | 319 ++++++++++++++++++ .../core/ml/optimizer_refactor_incident.html | 266 +++++++++++++++ 5 files changed, 1030 insertions(+), 13 deletions(-) create mode 100644 src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md create mode 100644 src/quantem/core/ml/optimizer_mixin_fix.html create mode 100644 src/quantem/core/ml/optimizer_mixin_review.html create mode 100644 src/quantem/core/ml/optimizer_refactor_incident.html diff --git a/src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md b/src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md new file mode 100644 index 00000000..e1819034 --- /dev/null +++ b/src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md @@ -0,0 +1,221 @@ +# Optimizer params: how inputs get normalized + +This document traces what happens to `optimizer_params` from the moment a user +passes them into `reconstruct(...)` to the moment a real `torch.optim.Optimizer` +is built. As of commit `1bc1739` the design invariant is: + +> **At the model level, `_optimizer_params` is *always* a `dict[str, OptimizerType]`.** + +Understanding the two normalization layers (container level → model level) is the +key to reading this code. + +--- + +## 1. The vocabulary + +| Thing | What it is | Where | +|---|---|---| +| `OptimizerType` | Union of the dataclasses `Adam`, `AdamW`, `SGD`, `NoneOptimizer` | `optimizer_mixin.py` (`OptimizerType = Adam \| AdamW \| SGD \| NoneOptimizer`) | +| `OptimizerParams.` | The individual dataclasses; each carries hyperparameters and a `.params()` method that returns them as a `dict` for torch | `optimizer_mixin.py` | +| `NoneOptimizer` | Sentinel meaning "do not optimize this thing". `.params()` returns `{}` | `optimizer_mixin.py:174` | +| `DEFAULT_OPTIMIZER_KEY` | `"default"` — the key used when a single optimizer is wrapped into a dict | `optimizer_mixin.py:534` | +| `OptimizerMixin` | Mixin inherited by each model (`obj_model`, `probe_model`, `dset`). Owns `_optimizer_params`, `set_optimizer`, etc. | `optimizer_mixin.py:527` | +| `PtychographyOpt` | The *container*. Holds the three models and exposes a combined `optimizer_params` | `ptychography_opt.py:20` | + +There are **two objects** that both have a property called `optimizer_params`, +and they mean different things: + +- **Container** (`PtychographyOpt` / tomography equivalent): a dict keyed by + *which model* — `"object"`, `"probe"`, `"dataset"`. +- **Model** (`OptimizerMixin`): a dict keyed by *parameter group* — normally just + the single key `"default"`. + +So a fully-resolved structure is **nested**: + +``` +container.optimizer_params + = {"object": {"default": Adam(lr=5e-3)}, + "probe": {"default": Adam(lr=1e-3)}, + "dataset": {"default": NoneOptimizer()}} + ^^^^^^^^ ^^^^^^^^^ ^^^^^^^^^^^^^^^ + model key group key OptimizerType +``` + +--- + +## 2. Accepted input shapes + +A user can hand the container any of these: + +```python +# (a) list/tuple of model keys -> use all defaults +ptycho.optimizer_params = ["object", "probe"] + +# (b) dict, value = OptimizerType dataclass +ptycho.optimizer_params = {"object": OptimizerParams.Adam(lr=5e-3)} + +# (c) dict, value = shorthand dict (name/type + hyperparams) +ptycho.optimizer_params = {"object": {"name": "adam", "lr": 5e-3}} + +# (d) dict, value = empty dict -> use default optimizer + default lr for that key +ptycho.optimizer_params = {"object": {}} +``` + +--- + +## 3. Layer 1 — container normalization + +`PtychographyOpt.optimizer_params` **setter** (`ptychography_opt.py:56`): + +``` +list/tuple ──► {k: {} for k in list} # (a) becomes (d)-style +for each (key, value): + value is OptimizerType ───────────────► pass through unchanged + value is dict and empty ──────────────► replace(DEFAULT_OPTIMIZER_TYPE, + lr=_get_default_lr(key)) + value is dict and non-empty ──────────► inject "name" if missing, + inject "lr" if missing + else ─────────────────────────────────► TypeError + │ + └─► dispatch to the matching model: + "object" -> self.obj_model.optimizer_params = value + "probe" -> self.probe_model.optimizer_params = value + "dataset" -> self.dset.optimizer_params = value +``` + +Key points: + +- `_get_default_lr(key)` supplies a sensible LR per model + (`object` ≈ 5e-3, `probe`/`dataset` ≈ 1e-3) when the user omitted one. +- The container does **not** build torch optimizers. It just fills defaults and + forwards each value down to the relevant model's setter. +- Any model key *not* mentioned by the user keeps whatever it had — by default + `{"default": NoneOptimizer()}` (i.e. "not optimized"). + +--- + +## 4. Layer 2 — model normalization + +Each model's `optimizer_params` **setter** calls +`OptimizerMixin._normalize_optimizer_params` (`optimizer_mixin.py:567`). This is +the function that guarantees the `dict[str, OptimizerType]` invariant: + +``` +_normalize_optimizer_params(params): + + params is an OptimizerType (dataclass) + ──► {"default": params} + + params is NOT a dict + ──► TypeError + + params is a dict AND _is_single_optimizer_dict(params) # has "name" or "type" + ──► {"default": OptimizerParams.parse_dict(params)} + + otherwise (dict-of-OptimizerType, the "PPLR" form) + ──► {k: (v if v is OptimizerType else parse_dict(v)) + for k, v in params.items()} +``` + +`_is_single_optimizer_dict(d)` is simply `"type" in d or "name" in d` +(`optimizer_mixin.py:585`). + +### `parse_dict` — shorthand → dataclass + +`OptimizerParams.parse_dict` (`optimizer_mixin.py:192`) maps a shorthand dict to +the concrete dataclass: + +``` +{"name"/"type": ...} pop the name, lowercase it, then: + "adam" -> OptimizerParams.Adam(**rest) + "adamw" -> OptimizerParams.AdamW(**rest) + "sgd" -> OptimizerParams.SGD(**rest) + "none" -> OptimizerParams.NoneOptimizer() + else -> ValueError +``` + +### Idempotency note + +When the container forwards an already-resolved value like `{"default": Adam(lr=5e-3)}` +to a model setter, it has no `"name"`/`"type"` key, so it takes the *PPLR branch* +and is kept as-is (`Adam` is already an `OptimizerType`). So re-normalizing a +normalized dict is a no-op. Good. + +--- + +## 5. Building the torch optimizer + +`OptimizerMixin.set_optimizer` (`optimizer_mixin.py:614`) is where the normalized +dict becomes a real optimizer. Conceptually it should: + +1. Look at the values of the `dict[str, OptimizerType]`. +2. Drop / handle `NoneOptimizer` sentinels (→ "no optimizer for this"). +3. Confirm the remaining specs agree on an optimizer *class*. +4. Pull parameter groups from `get_optimization_parameters()` (a `list[dict]`, + each `{"params": [...]}`). +5. Construct `optimizer_cls(param_groups, **hyperparameters)`. + +`_optimizer_class_for` (`optimizer_mixin.py:659`) maps a spec dataclass to a torch +class via a `match`: + +``` +Adam() -> torch.optim.Adam +AdamW() -> torch.optim.AdamW +SGD() -> torch.optim.SGD +_ -> NotImplementedError # <-- NoneOptimizer lands here +``` + +### The reset path (where the current crash lives) + +`reconstruct(reset=True, ...)` calls `reset_recon()` **before** applying the +user's `optimizer_params` (`ptychography.py:181` vs `:185`). `reset_recon` calls +each model's `reset_optimizer()` → `set_optimizer(self._optimizer_params)`. At +that moment `_optimizer_params` is still the default `{"default": NoneOptimizer()}`. + +``` +reconstruct(reset=True, optimizer_params=opt_params) + ├─ reset_recon() # opt_params NOT applied yet + │ └─ obj_model.reset_optimizer() + │ └─ set_optimizer({"default": NoneOptimizer()}) + │ └─ _optimizer_class_for(NoneOptimizer()) -> NotImplementedError + └─ (never reached) self.optimizer_params = opt_params; set_optimizers() +``` + +--- + +## 6. Where the value flows at recon time + +Once past reset, the normal value flow is: + +``` +reconstruct(optimizer_params={"object": {"lr": 5e-3}, ...}) + │ + ├─ container.optimizer_params = {...} # Layer 1: fill defaults, dispatch + │ └─ obj_model.optimizer_params = {"name":"adamw","lr":5e-3} + │ └─ _normalize_optimizer_params -> {"default": AdamW(lr=5e-3)} # Layer 2 + │ + └─ container.set_optimizers() + for key, params in container.optimizer_params.items(): # nested dict + model.set_optimizer(params) # params = {"default": AdamW(lr=5e-3)} + └─ build torch.optim.AdamW(param_groups, lr=5e-3, ...) +``` + +`container.set_optimizers()` iterates the **container getter**, which returns the +nested `{"object": {"default": ...}, ...}` structure, and hands each inner dict to +the corresponding model's `set_optimizer`. + +--- + +## 7. Quick reference — invariants to keep in mind + +- A **model's** `_optimizer_params` is always `dict[str, OptimizerType]`, normally + one key: `"default"`. +- A **container's** `optimizer_params` is `dict[str, dict[str, OptimizerType]]`, + keyed by model name (`"object"`/`"probe"`/`"dataset"`). +- `NoneOptimizer` is a first-class `OptimizerType` meaning "skip". Anything that + consumes the dict must treat it specially, because `_optimizer_class_for` has no + case for it. +- `.params()` on a spec dataclass returns the kwargs torch needs (`lr`, etc.); + `NoneOptimizer.params()` returns `{}`. +- `get_optimization_parameters()` returns parameter *groups* (`[{"params": [...]}]`) + and currently carries **no** per-group hyperparameters. diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index f970a859..8c095d39 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -624,9 +624,6 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: self._optimizer = None return - if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer): - self.remove_optimizer() - return params = self.get_optimization_parameters() # always list[dict] @@ -634,14 +631,17 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: for group in params: for p in group["params"]: p.requires_grad_(True) - # Figure out which optimizer class to use + # Figure out which optimizer class to use if isinstance(self.optimizer_params, dict): # Per-group case: all groups must agree on the optimizer class, # and per-group hyperparameters are already baked into each dict # by get_optimization_parameters(). - opt_specs = list(self.optimizer_params.values()) + opt_specs = [ + spec for spec in self.optimizer_params.values() + if not isinstance(spec, OptimizerParams.NoneOptimizer) + ] if not opt_specs: - self._optimizer = None + self.remove_optimizer() return optimizer_cls = self._optimizer_class_for(opt_specs[0]) for spec in opt_specs[1:]: @@ -650,12 +650,7 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: f"All parameter groups must use the same optimizer type, " f"got {type(opt_specs[0]).__name__} and {type(spec).__name__}" ) - self._optimizer = optimizer_cls(params) # type:ignore - else: - # Single-optimizer case: splat global hyperparameters - optimizer_cls = self._optimizer_class_for(self.optimizer_params) - self._optimizer = optimizer_cls(params, **self.optimizer_params.params()) - + self._optimizer = optimizer_cls(params, **opt_specs[0].params()) def _optimizer_class_for(self, opt_params) -> type[torch.optim.Optimizer]: match opt_params: case OptimizerParams.Adam(): @@ -744,7 +739,7 @@ def get_current_lr(self) -> float: def remove_optimizer(self) -> None: """Remove the optimizer and scheduler.""" self._optimizer = None - self._optimizer_params = OptimizerParams.NoneOptimizer() + self._optimizer_params = {self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer()} self._scheduler = None self._scheduler_params = SchedulerParams.NoneScheduler() diff --git a/src/quantem/core/ml/optimizer_mixin_fix.html b/src/quantem/core/ml/optimizer_mixin_fix.html new file mode 100644 index 00000000..3bb7c365 --- /dev/null +++ b/src/quantem/core/ml/optimizer_mixin_fix.html @@ -0,0 +1,216 @@ + + + + + +OptimizerMixin — minimal fixes to complete the dict[str, OptimizerType] standardization + + + +
+ +
+

Completing the dict[str, OptimizerType] standardization

+

Minimal, surgical fixes that keep your standardization goal intact and resolve the + Ptychography breakage.

+
+ FILE: src/quantem/core/ml/optimizer_mixin.py  ·  + SCOPE: consumer-side only  ·  + RISK: low, localized +
+
+ +

1. Diagnosis in one paragraph

+

Your goal — normalize everything to dict[str, OptimizerType] — is correct + and worth keeping. The standardization did not break Ptychography on its own. The breakage came + from a consumer that was never migrated to the new contract: + set_optimizer. It still branches on the old union shape (a bare + OptimizerType or a dict). After normalization always returns a dict, the two + branches that handled the bare-type cases became unreachable, and the surviving dict branch was + only a partial implementation — it never handled the NoneOptimizer sentinel and never + forwarded hyperparameters. The fix is therefore not to revert the standardization, + but to make that one dict branch self-sufficient.

+ +
+
Why your change was necessary but not sufficient
+

A return type is a contract. Narrowing it from a union to a single + shape obligates every consumer that pattern-matches on the old shape to be updated in the same + change. The producer was migrated; one consumer was not.

+
+ +

2. The two concrete symptoms

+ + + + + + + + + + +
SymptomRoot cause in set_optimizer
Crash: NotImplementedError: Unknown optimizer type: NoneOptimizer()The dict branch passes a NoneOptimizer spec to + _optimizer_class_for, which has no case for it. The old guard that caught this + (isinstance(..., NoneOptimizer)) only matches a bare sentinel and is now + unreachable.
Silent: configured lr ignoredThe dict branch builds optimizer_cls(params) with no kwargs. The old branch + that splatted **optimizer_params.params() is the now-unreachable + else.
+ +

3. Recommended minimal change set

+

Three changes, in priority order. REQUIRED alone resolves the + crash and the silent lr bug.

+ +

REQUIREDMake the dict branch self-sufficient · ~6 lines, one method

+

Filter out NoneOptimizer sentinels; if none remain, there is nothing to optimize. + Otherwise build the optimizer and forward the spec’s hyperparameters. This is the + smallest change that honors the new dict-only contract.

+ 
        if isinstance(self.optimizer_params, dict):
+-           opt_specs = list(self.optimizer_params.values())
+-           if not opt_specs:
+-               self._optimizer = None
+-               return
++           opt_specs = [
++               spec for spec in self.optimizer_params.values()
++               if not isinstance(spec, OptimizerParams.NoneOptimizer)
++           ]
++           if not opt_specs:
++               self.remove_optimizer()
++               return
+            optimizer_cls = self._optimizer_class_for(opt_specs[0])
+            for spec in opt_specs[1:]:
+                if type(spec) is not type(opt_specs[0]):
+                    raise ValueError(...)
+-           self._optimizer = optimizer_cls(params)  # type:ignore
++           self._optimizer = optimizer_cls(params, **opt_specs[0].params())
+
+
What this buys you
+

The default {"default": NoneOptimizer()} now filters to + an empty list → remove_optimizer() → clean no-op (this is the path + reset_recon() walks). A real {"default": Adam(lr=5e-3)} now builds + Adam(params, lr=5e-3, …) with your hyperparameters restored.

+
+
+
Note on PPLR
+

This splats opt_specs[0]’s hyperparameters across all + parameter groups. That is correct today because every model returns a single param group from + get_optimization_parameters(). True per-group LRs are a separate, larger piece of + work — see §5.

+
+ +

RECOMMENDEDKeep remove_optimizer on-contract · 1 line

+

It currently stores a bare sentinel, breaking the “always a dict” invariant. It works + only because the value gets re-normalized on the next set. Store the dict form to keep the + invariant true end-to-end.

+ 
    def remove_optimizer(self) -> None:
+        self._optimizer = None
+-       self._optimizer_params = OptimizerParams.NoneOptimizer()
++       self._optimizer_params = {self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer()}
+        self._scheduler = None
+        self._scheduler_params = SchedulerParams.NoneScheduler()
+ +

OPTIONALDelete the now-dead branches · removes ~7 lines

+

Purely cosmetic, but it removes the misleading code that caused the confusion. With the dict + contract guaranteed, the bare-NoneOptimizer guard and the else branch can + never run. Removing them makes set_optimizer a single, honest path.

+ 
-       if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer):
+-           self.remove_optimizer()
+-           return
+        ...
+-       else:
+-           optimizer_cls = self._optimizer_class_for(self.optimizer_params)
+-           self._optimizer = optimizer_cls(params, **self.optimizer_params.params())
+

If you keep these, no harm — they’re dead, not wrong. Removing them is about + readability, not correctness.

+ +

4. Optional: align the container getters

+

OPTIONAL In PtychographyOpt.optimizer_params (and the + tomography equivalent) the filter if not isinstance(params, NoneOptimizer) no longer + fires, because params is now a dict. After the REQUIRED fix this is harmless — a + disabled model simply ends up with no optimizer — but if you want the getter to exclude disabled + models, check the inner specs instead.

+ 
-           if not isinstance(params, OptimizerParams.NoneOptimizer)
++           if any(not isinstance(s, OptimizerParams.NoneOptimizer) for s in params.values())
+ +

5. What to leave for later (don’t scope-creep)

+
    +
  • Real per-parameter LRs (PPLR). The comment claiming hyperparameters are “baked into + each dict by get_optimization_parameters()” is aspirational — the implementations + return [{"params": params}] with no lr. Either implement keyed param + groups or update the comment. Not needed to fix the break.
    • +
  • Collapsing the three “no optimizer” spellings (empty dict / NoneOptimizer / + _optimizer = None) into one. A nice tidy-up, but orthogonal to this incident.
    • +
+ +

6. Verification checklist

+ + + + + + + + + + +
StepExpected
ptycho.reconstruct(num_iters=1, reset=True, optimizer_params={"object": {"lr": 5e-3}})No NotImplementedError; runs.
Inspect ptycho.obj_model.optimizer.param_groups[0]["lr"]Equals 5e-3, not torch’s default.
A model left out of optimizer_paramshas_optimizer() is False; not stepped.
Re-run with reset=TrueDefault NoneOptimizer filters cleanly; no crash.
+ +
+
Bottom line
+

Keep your standardization. Apply the one REQUIRED change (and ideally + the one-line RECOMMENDED change). That completes the migration the type-narrowing started, with a + change set small enough to read in a single diff.

+
+ +

+ Read-only proposal · no source files modified · + companions: optimizer_mixin_review.html · optimizer_refactor_incident.html · OPTIMIZER_PARAMS_FLOW.md +

+ +
+ + diff --git a/src/quantem/core/ml/optimizer_mixin_review.html b/src/quantem/core/ml/optimizer_mixin_review.html new file mode 100644 index 00000000..1e205644 --- /dev/null +++ b/src/quantem/core/ml/optimizer_mixin_review.html @@ -0,0 +1,319 @@ + + + + + +OptimizerMixin — a friendly post-mortem & cleanup wishlist + + + +
+ +
+ Code review · ml/optimizer_mixin.py +

OptimizerMixin: a friendly post-mortem 🕵️

+

Why NoneOptimizer() blew up your ptycho run, and how to make this class + a joy to read again. No code was harmed in the making of this document.

+ +
+ +

🔪 The whodunit (in one breath)

+

+ A refactor decreed: “optimizer params shall always be a + dict[str, OptimizerType].” The normalizer obeyed. But + set_optimizer never got the memo — it still has two branches written for the + old world where a bare OptimizerType could walk in the door. Those + branches are now unreachable corpses, and every call gets funneled into the one dict branch + that doesn’t know what to do with a NoneOptimizer. +

+ +
+
💥 The crash
+

reconstruct(reset=True) calls reset_recon() + before your optimizer_params are applied. So set_optimizer + runs against the default {"default": NoneOptimizer()}, falls into the dict branch, + and hands a NoneOptimizer to _optimizer_class_for — which has no + case for it → NotImplementedError.

+
+ + 
# the dead giveaway in set_optimizer (current)
+if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer):  ← never True; params is always a dict now
+    self.remove_optimizer()
+    return
+...
+if isinstance(self.optimizer_params, dict):                          # ← always taken
+    opt_specs = list(self.optimizer_params.values())             # [NoneOptimizer()]
+    optimizer_cls = self._optimizer_class_for(opt_specs[0])     ← 💥 NotImplementedError
+    ...
+    self._optimizer = optimizer_cls(params)                       ← also: no lr is ever passed!
+else:
+    # the branch that actually splatted hyperparameters... now unreachable 🪦
+    self._optimizer = optimizer_cls(params, **self.optimizer_params.params())
+ +
+
🐛 Bonus silent bug
+

Even after the crash is fixed, the dict branch builds + optimizer_cls(params) with no hyperparameters. A code comment + promises they’re “baked into each dict by get_optimization_parameters()” — but all + three implementations just return [{"params": params}] with no + lr. So your carefully chosen learning rate would silently become torch’s + default. The comment is, regrettably, fan fiction.

+
+ +

🌊 The flow, at a glance

+

Two objects both expose optimizer_params, and they mean different things. That’s + the single most confusing thing about this code, so let’s name it loudly:

+ + + + + + + + + + + + + +
LevelKeyed byExample value
Container
(PtychographyOpt)		which model
"object"/"probe"/"dataset"		{"object": {"default": Adam(lr=5e-3)}}
Model
(OptimizerMixin)		which param group
usually just "default"		{"default": Adam(lr=5e-3)}
+ + 
user input ──► PtychographyOpt.optimizer_params setter   # Layer 1: fill defaults, dispatch per model
+                 │   fills "name"/"lr" defaults via _get_default_lr()
+                 └─► model.optimizer_params setter             # Layer 2: normalize to dict[str, OptimizerType]
+                       └─► _normalize_optimizer_params()
+                             OptimizerType        ──► {"default": it}
+                             {"name"/"type": ...} ──► {"default": parse_dict(it)}
+                             {k: OptimizerType}   ──► kept (PPLR form)
+                                  │
+reconstruct() ──► set_optimizers() ──► for each model: set_optimizer({"default": Adam(lr=5e-3)})
+                                                          └─► torch.optim.Adam(param_groups, lr=5e-3)
+ +

🧹 Five ways to make this a joy to read

+ +

1Make the invariant real — delete the dead branches

+

If optimizer_params is always a dict, then set_optimizer + should have one path: filter out the NoneOptimizer sentinels, agree + on a class, and splat hyperparameters. No else, no zombie isinstance.

+ +
+
+

✗ Before — 3 branches, 2 of them dead

+ 
if isinstance(p, NoneOptimizer):  # dead
+    remove_optimizer(); return
+...
+if isinstance(p, dict):
+    specs = list(p.values())
+    cls = _optimizer_class_for(specs[0])  # 💥
+    self._optimizer = cls(params)       # no lr
+else:                            # dead
+    cls = _optimizer_class_for(p)
+    self._optimizer = cls(params, **p.params())
+
+
+

✓ After — one honest path

+ 
# params is always dict[str, OptimizerType]
+active = [s for s in p.values()
+          if not isinstance(s, NoneOptimizer)]
+if not active:
+    self.remove_optimizer(); return
+
+self._assert_same_class(active)
+cls = self._optimizer_class_for(active[0])
+groups = self.get_optimization_parameters()
+self._optimizer = cls(groups, **active[0].params())  # lr restored ✓
+
+
+ +

2Give NoneOptimizer a home in the match

+

The sentinel is a first-class OptimizerType, so the place that maps specs to + torch classes should acknowledge it instead of letting it fall into case _ and + explode. A tiny is_none() helper or an explicit case makes the intent obvious.

+ 
match opt_params:
+    case OptimizerParams.Adam():  return torch.optim.Adam
+    case OptimizerParams.AdamW(): return torch.optim.AdamW
+    case OptimizerParams.SGD():   return torch.optim.SGD
+    case OptimizerParams.NoneOptimizer():                          # ← speak its name
+        raise ValueError("NoneOptimizer has no torch class; filter it out before calling")
+    case _: raise NotImplementedError(...)
+ +

3Keep remove_optimizer faithful to the invariant

+

Right now it resets _optimizer_params to a bare NoneOptimizer() + — violating the “always a dict” rule it works almost everywhere thanks to re-normalization, but + it’s a landmine. Store the dict form so the invariant holds end-to-end.

+ 
def remove_optimizer(self):
+    self._optimizer = None
+    self._optimizer_params = OptimizerParams.NoneOptimizer()           # bare → breaks invariant
+    self._optimizer_params = {self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer()}
+    self._scheduler = None
+    self._scheduler_params = SchedulerParams.NoneScheduler()
+ +

4Pick one way to say “don’t optimize this”

+

Today there are three: an empty dict (if not self.optimizer_params), + the NoneOptimizer sentinel, and self._optimizer = None. Three spellings of + one idea means every reader has to hold all three in their head. Commit to the sentinel as the + single source of truth, and let has_optimizer() be the one public question.

+
+
💡 Rule of thumb
+

“No optimizer” = NoneOptimizer in the params, + None in self._optimizer. Everything else (empty dicts, None + inputs) gets normalized into that single shape at the boundary, once.

+
+ +

5Fix the container getters’ dead filters (and the lying comment)

+

Both PtychographyOpt and the tomography container filter with + if not isinstance(params, NoneOptimizer) — but params is now a + dict, so the filter never fires and disabled models sneak back into the loop. Either + filter on the inner spec, or drop the filter and lean on has_optimizer(). And while + you’re there: either make get_optimization_parameters() actually bake per-group + hyperparameters (real PPLR) or delete the comment that says it does.

+ 
# container getter, today — dead filter
+if not isinstance(params, OptimizerParams.NoneOptimizer)   # params is a dict → always passes
+
+# option A: check the inner specs
+if any(not isinstance(s, NoneOptimizer) for s in params.values())   # ✓
+ +
+
🎯 The big picture
+

There’s a clean seam hiding here: separate what to + optimize (parameter groups, from the model) from how (optimizer specs, from the + user). If get_optimization_parameters() returned groups keyed the same way as the + specs dict, true per-parameter learning rates would fall out naturally — and + set_optimizer would shrink to “zip specs with groups, build one optimizer.” That’s + the refactor that turns this from “works if you don’t poke it” into “obviously + correct.”

+
+ +

📋 Scorecard

+ + + + + + + + + + + + + + + + + +
TopicStateOne-line take
Normalization (Layer 1 & 2)SolidThe _normalize_optimizer_params logic is actually nice and clear.
set_optimizer branchingBrokenTwo dead branches; the live one crashes on the default state.
Hyperparameter passingBrokenlr silently dropped; comment claims otherwise.
“No optimizer” representationMuddyThree spellings of one concept.
Container gettersStaleDead isinstance filters left over from the bare-type era.
+ +

+ Generated as a read-only review of src/quantem/core/ml/optimizer_mixin.py. + No source files were modified. Companion deep-dive: OPTIMIZER_PARAMS_FLOW.md. +

+ +
+ + diff --git a/src/quantem/core/ml/optimizer_refactor_incident.html b/src/quantem/core/ml/optimizer_refactor_incident.html new file mode 100644 index 00000000..89d4c762 --- /dev/null +++ b/src/quantem/core/ml/optimizer_refactor_incident.html @@ -0,0 +1,266 @@ + + + + + +The Great Type Standardization — an incident report + + + +
+ + Incident · Post-Mortem +
+

The Great Type Standardization
that quietly cut the pipes

+
How changing one return type from + OptimizerType | dict[str, OptimizerType] to just + dict[str, OptimizerType] took out Ptychography.
+
+ FILE: src/quantem/core/ml/optimizer_mixin.py  ·  + COMMIT: 1bc1739  ·  + SYMPTOM: NotImplementedError: Unknown optimizer type: NoneOptimizer() +
+
+ +
+ TL;DR — The refactor changed what the producer + (_normalize_optimizer_params) hands out, but not what the consumer + (set_optimizer) was built to receive. A return type isn’t just a label — it’s a + contract about the shape of a value, and three different places had hand-rolled + branches that pattern-matched on the old shape. When the shape changed, two branches + became unreachable and the third — never fully finished — became the only road. It couldn’t carry + a NoneOptimizer, and it dropped your learning rate on the floor. +
+ +

Act I — The good intentionOne type to rule them all

+

Someone looked at this signature and felt a totally reasonable itch:

+
+
+

Before

+ 
def optimizer_params(self) ->
+    OptimizerType | dict[str, OptimizerType]:
+    # could be a bare dataclass...
+    # ...or a dict of them. Ugh, which is it?
+    return self._optimizer_params
+
+
+

After — “let’s standardize”

+ 
def optimizer_params(self) ->
+    dict[str, OptimizerType]:
+    # always a dict now. cleaner!
+    return self._optimizer_params
+
+
+

The union type A | B is genuinely annoying: every reader, and every consumer, has + to ask “ok, but which one is it this time?” Collapsing it to a single + shape is the right instinct. The normalizer was dutifully rewritten so that everything — + a bare Adam(), a {"name": "adam"} shorthand, a list of keys — comes out + as a tidy dict[str, OptimizerType].

+ +
+
What went right
+

The normalization itself is clean and correct. If you only read + _normalize_optimizer_params, the refactor looks like an unambiguous win.

+
+ +

Act II — The unspoken contractA return type is a promise to strangers

+

Here’s the thing the type hint hides: that value doesn’t stay home. It flows downstream into + set_optimizer, which — because Python has no real runtime types — does its own + hand-rolled type dispatch with isinstance and an else:

+ + 
def set_optimizer(self, opt_params):
+    ...
+    if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer):   # arm ➊
+        self.remove_optimizer(); return
+
+    if isinstance(self.optimizer_params, dict):                          # arm ➋
+        opt_specs = list(self.optimizer_params.values())
+        cls = self._optimizer_class_for(opt_specs[0])
+        self._optimizer = cls(params)
+    else:                                                                # arm ➌
+        cls = self._optimizer_class_for(self.optimizer_params)
+        self._optimizer = cls(params, **self.optimizer_params.params())
+ +

Those three arms were designed for the old contract, where + optimizer_params could be either a bare dataclass or a dict. Each arm had a + job:

+
    +
  • ➊ handle a bare NoneOptimizer sentinel → “turn the optimizer off.”
    • +
  • ➋ handle the dict / per-parameter case.
    • +
  • ➌ handle a bare optimizer dataclass → build it and splat its hyperparameters + (**params(), i.e. your lr).
    • +
+ +
Change the shape of the water, and pipes cut for the old shape stop carrying it — + even though nothing looks broken at the joint.
+ +

Act III — The reachability collapseTwo arms die, one inherits everything

+

Once the producer always returns a dict, watch what happens to the three arms:

+ + + + + + + + + + + + + + + + + + + + + + + + +
ArmMatches when…Old contractNew contractIts old job now…
➊ bare NoneOptimizervalue is a bare sentinelreachableDEAD…nobody turns the optimizer off anymore
➋ isinstance(dict)value is a dictreachableALWAYS…inherits ➊ and ➌’s jobs but does neither
➌ the elsevalue is a bare dataclassreachableDEAD…the **params() splat (your lr!) is gone
+ +

So every call now lands in arm ➋. And arm ➋ was only ever a partial + implementation — it builds cls(params) and trusts that the rest will sort itself out. + But it never learned the two jobs it just inherited:

+ +
+
Failure 1 · the crash you saw
+

Arm ➋ pulls opt_specs[0] and asks + _optimizer_class_for for a torch class. When that spec is a NoneOptimizer + (the default state of every model), the match falls through to + case _ → NotImplementedError. Arm ➊ used to catch this; arm ➊ is now + dead.

+
+
+
Failure 2 · the silent one
+

Arm ➋ builds cls(params) with no + hyperparameters. Arm ➌ used to splat **self.optimizer_params.params() — + that’s where your lr came from. With ➌ dead, a perfectly valid Adam(lr=5e-3) + silently becomes Adam() at torch’s default LR. No error. Just wrong.

+
+ +

Act IV — Why Python smiled and said nothing

+

If types were enforced, the compiler would have screamed: “arm ➊ and arm ➌ can never + match a dict[str, OptimizerType] — delete them or fix them.” But in Python:

+
    +
  • Type hints are decorative. Changing -> dict[...] changes + documentation, not behavior. Nothing re-checked the consumers.
    • +
  • A branch that never matches doesn’t raise — it just silently never runs. + Dead code is invisible until you go looking for it.
    • +
  • The one surviving arm happened to not crash on the common happy path (pass an + Adam, get an optimizer — just at the wrong LR), so casual testing looked fine.
    • +
+ +
+
Why it detonated on reset=True specifically
+

reconstruct(reset=True, optimizer_params=…) calls + reset_recon() before it applies your params. At that instant every + model still holds its factory default — {"default": NoneOptimizer()}. So arm ➋ gets a + NoneOptimizer on the very first thing it touches (obj_model) and dies + immediately. The crash isn’t in your config; it’s baked into the default state the reset path + walks through.

+
+ +

Act V — The moralStandardizing a type is a migration, not an edit

+

“Just change the return type to dict[str, OptimizerType]” felt like a + one-line cleanup. But the return type was load-bearing: three consumer branches structurally + depended on the old union. Changing the producer without migrating those consumers left the system + in a half-converted state — new shape coming out, old shape expected downstream.

+ +
+
+

What was done

+

✅ Producer (_normalize_optimizer_params) → always dict
+ ✅ Type hint updated
+ ❌ Consumer (set_optimizer) still speaks the old union
+ ❌ remove_optimizer still writes a bare sentinel

+
+
+

What “done” actually required

+

→ Collapse set_optimizer to one dict-only path
+ → Teach that path to filter NoneOptimizer (arm ➊’s job)
+ → Teach it to splat hyperparameters (arm ➌’s job)
+ → Make every producer of the value (incl. remove_optimizer) honor the new shape

+
+
+ +
The bug wasn’t the new type. The bug was believing the new type was the + whole change.
+ +
+ One sentence for the next reviewer: when you narrow a return type from a union to a single + shape, grep every consumer that does isinstance / match on it — each + arm that handled a now-impossible case is either dead code to delete or a responsibility that + just silently moved somewhere it isn’t handled. +
+ +

+ Read-only incident report · no source modified · + companions: optimizer_mixin_review.html, OPTIMIZER_PARAMS_FLOW.md +

+ +
+ + From bf19e6072e7a1ad79fa7e1db24bea44881d6f039 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 17:37:15 -0700 Subject: [PATCH 085/140] Removed .md and .html files --- src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md | 221 ------------ src/quantem/core/ml/optimizer_mixin_fix.html | 216 ------------ .../core/ml/optimizer_mixin_review.html | 319 ------------------ .../core/ml/optimizer_refactor_incident.html | 266 --------------- 4 files changed, 1022 deletions(-) delete mode 100644 src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md delete mode 100644 src/quantem/core/ml/optimizer_mixin_fix.html delete mode 100644 src/quantem/core/ml/optimizer_mixin_review.html delete mode 100644 src/quantem/core/ml/optimizer_refactor_incident.html diff --git a/src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md b/src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md deleted file mode 100644 index e1819034..00000000 --- a/src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md +++ /dev/null @@ -1,221 +0,0 @@ -# Optimizer params: how inputs get normalized - -This document traces what happens to `optimizer_params` from the moment a user -passes them into `reconstruct(...)` to the moment a real `torch.optim.Optimizer` -is built. As of commit `1bc1739` the design invariant is: - -> **At the model level, `_optimizer_params` is *always* a `dict[str, OptimizerType]`.** - -Understanding the two normalization layers (container level → model level) is the -key to reading this code. - ---- - -## 1. The vocabulary - -| Thing | What it is | Where | -|---|---|---| -| `OptimizerType` | Union of the dataclasses `Adam`, `AdamW`, `SGD`, `NoneOptimizer` | `optimizer_mixin.py` (`OptimizerType = Adam \| AdamW \| SGD \| NoneOptimizer`) | -| `OptimizerParams.` | The individual dataclasses; each carries hyperparameters and a `.params()` method that returns them as a `dict` for torch | `optimizer_mixin.py` | -| `NoneOptimizer` | Sentinel meaning "do not optimize this thing". `.params()` returns `{}` | `optimizer_mixin.py:174` | -| `DEFAULT_OPTIMIZER_KEY` | `"default"` — the key used when a single optimizer is wrapped into a dict | `optimizer_mixin.py:534` | -| `OptimizerMixin` | Mixin inherited by each model (`obj_model`, `probe_model`, `dset`). Owns `_optimizer_params`, `set_optimizer`, etc. | `optimizer_mixin.py:527` | -| `PtychographyOpt` | The *container*. Holds the three models and exposes a combined `optimizer_params` | `ptychography_opt.py:20` | - -There are **two objects** that both have a property called `optimizer_params`, -and they mean different things: - -- **Container** (`PtychographyOpt` / tomography equivalent): a dict keyed by - *which model* — `"object"`, `"probe"`, `"dataset"`. -- **Model** (`OptimizerMixin`): a dict keyed by *parameter group* — normally just - the single key `"default"`. - -So a fully-resolved structure is **nested**: - -``` -container.optimizer_params - = {"object": {"default": Adam(lr=5e-3)}, - "probe": {"default": Adam(lr=1e-3)}, - "dataset": {"default": NoneOptimizer()}} - ^^^^^^^^ ^^^^^^^^^ ^^^^^^^^^^^^^^^ - model key group key OptimizerType -``` - ---- - -## 2. Accepted input shapes - -A user can hand the container any of these: - -```python -# (a) list/tuple of model keys -> use all defaults -ptycho.optimizer_params = ["object", "probe"] - -# (b) dict, value = OptimizerType dataclass -ptycho.optimizer_params = {"object": OptimizerParams.Adam(lr=5e-3)} - -# (c) dict, value = shorthand dict (name/type + hyperparams) -ptycho.optimizer_params = {"object": {"name": "adam", "lr": 5e-3}} - -# (d) dict, value = empty dict -> use default optimizer + default lr for that key -ptycho.optimizer_params = {"object": {}} -``` - ---- - -## 3. Layer 1 — container normalization - -`PtychographyOpt.optimizer_params` **setter** (`ptychography_opt.py:56`): - -``` -list/tuple ──► {k: {} for k in list} # (a) becomes (d)-style -for each (key, value): - value is OptimizerType ───────────────► pass through unchanged - value is dict and empty ──────────────► replace(DEFAULT_OPTIMIZER_TYPE, - lr=_get_default_lr(key)) - value is dict and non-empty ──────────► inject "name" if missing, - inject "lr" if missing - else ─────────────────────────────────► TypeError - │ - └─► dispatch to the matching model: - "object" -> self.obj_model.optimizer_params = value - "probe" -> self.probe_model.optimizer_params = value - "dataset" -> self.dset.optimizer_params = value -``` - -Key points: - -- `_get_default_lr(key)` supplies a sensible LR per model - (`object` ≈ 5e-3, `probe`/`dataset` ≈ 1e-3) when the user omitted one. -- The container does **not** build torch optimizers. It just fills defaults and - forwards each value down to the relevant model's setter. -- Any model key *not* mentioned by the user keeps whatever it had — by default - `{"default": NoneOptimizer()}` (i.e. "not optimized"). - ---- - -## 4. Layer 2 — model normalization - -Each model's `optimizer_params` **setter** calls -`OptimizerMixin._normalize_optimizer_params` (`optimizer_mixin.py:567`). This is -the function that guarantees the `dict[str, OptimizerType]` invariant: - -``` -_normalize_optimizer_params(params): - - params is an OptimizerType (dataclass) - ──► {"default": params} - - params is NOT a dict - ──► TypeError - - params is a dict AND _is_single_optimizer_dict(params) # has "name" or "type" - ──► {"default": OptimizerParams.parse_dict(params)} - - otherwise (dict-of-OptimizerType, the "PPLR" form) - ──► {k: (v if v is OptimizerType else parse_dict(v)) - for k, v in params.items()} -``` - -`_is_single_optimizer_dict(d)` is simply `"type" in d or "name" in d` -(`optimizer_mixin.py:585`). - -### `parse_dict` — shorthand → dataclass - -`OptimizerParams.parse_dict` (`optimizer_mixin.py:192`) maps a shorthand dict to -the concrete dataclass: - -``` -{"name"/"type": ...} pop the name, lowercase it, then: - "adam" -> OptimizerParams.Adam(**rest) - "adamw" -> OptimizerParams.AdamW(**rest) - "sgd" -> OptimizerParams.SGD(**rest) - "none" -> OptimizerParams.NoneOptimizer() - else -> ValueError -``` - -### Idempotency note - -When the container forwards an already-resolved value like `{"default": Adam(lr=5e-3)}` -to a model setter, it has no `"name"`/`"type"` key, so it takes the *PPLR branch* -and is kept as-is (`Adam` is already an `OptimizerType`). So re-normalizing a -normalized dict is a no-op. Good. - ---- - -## 5. Building the torch optimizer - -`OptimizerMixin.set_optimizer` (`optimizer_mixin.py:614`) is where the normalized -dict becomes a real optimizer. Conceptually it should: - -1. Look at the values of the `dict[str, OptimizerType]`. -2. Drop / handle `NoneOptimizer` sentinels (→ "no optimizer for this"). -3. Confirm the remaining specs agree on an optimizer *class*. -4. Pull parameter groups from `get_optimization_parameters()` (a `list[dict]`, - each `{"params": [...]}`). -5. Construct `optimizer_cls(param_groups, **hyperparameters)`. - -`_optimizer_class_for` (`optimizer_mixin.py:659`) maps a spec dataclass to a torch -class via a `match`: - -``` -Adam() -> torch.optim.Adam -AdamW() -> torch.optim.AdamW -SGD() -> torch.optim.SGD -_ -> NotImplementedError # <-- NoneOptimizer lands here -``` - -### The reset path (where the current crash lives) - -`reconstruct(reset=True, ...)` calls `reset_recon()` **before** applying the -user's `optimizer_params` (`ptychography.py:181` vs `:185`). `reset_recon` calls -each model's `reset_optimizer()` → `set_optimizer(self._optimizer_params)`. At -that moment `_optimizer_params` is still the default `{"default": NoneOptimizer()}`. - -``` -reconstruct(reset=True, optimizer_params=opt_params) - ├─ reset_recon() # opt_params NOT applied yet - │ └─ obj_model.reset_optimizer() - │ └─ set_optimizer({"default": NoneOptimizer()}) - │ └─ _optimizer_class_for(NoneOptimizer()) -> NotImplementedError - └─ (never reached) self.optimizer_params = opt_params; set_optimizers() -``` - ---- - -## 6. Where the value flows at recon time - -Once past reset, the normal value flow is: - -``` -reconstruct(optimizer_params={"object": {"lr": 5e-3}, ...}) - │ - ├─ container.optimizer_params = {...} # Layer 1: fill defaults, dispatch - │ └─ obj_model.optimizer_params = {"name":"adamw","lr":5e-3} - │ └─ _normalize_optimizer_params -> {"default": AdamW(lr=5e-3)} # Layer 2 - │ - └─ container.set_optimizers() - for key, params in container.optimizer_params.items(): # nested dict - model.set_optimizer(params) # params = {"default": AdamW(lr=5e-3)} - └─ build torch.optim.AdamW(param_groups, lr=5e-3, ...) -``` - -`container.set_optimizers()` iterates the **container getter**, which returns the -nested `{"object": {"default": ...}, ...}` structure, and hands each inner dict to -the corresponding model's `set_optimizer`. - ---- - -## 7. Quick reference — invariants to keep in mind - -- A **model's** `_optimizer_params` is always `dict[str, OptimizerType]`, normally - one key: `"default"`. -- A **container's** `optimizer_params` is `dict[str, dict[str, OptimizerType]]`, - keyed by model name (`"object"`/`"probe"`/`"dataset"`). -- `NoneOptimizer` is a first-class `OptimizerType` meaning "skip". Anything that - consumes the dict must treat it specially, because `_optimizer_class_for` has no - case for it. -- `.params()` on a spec dataclass returns the kwargs torch needs (`lr`, etc.); - `NoneOptimizer.params()` returns `{}`. -- `get_optimization_parameters()` returns parameter *groups* (`[{"params": [...]}]`) - and currently carries **no** per-group hyperparameters. diff --git a/src/quantem/core/ml/optimizer_mixin_fix.html b/src/quantem/core/ml/optimizer_mixin_fix.html deleted file mode 100644 index 3bb7c365..00000000 --- a/src/quantem/core/ml/optimizer_mixin_fix.html +++ /dev/null @@ -1,216 +0,0 @@ - - - - - -OptimizerMixin — minimal fixes to complete the dict[str, OptimizerType] standardization - - - -
- -
-

Completing the dict[str, OptimizerType] standardization

-

Minimal, surgical fixes that keep your standardization goal intact and resolve the - Ptychography breakage.

-
- FILE: src/quantem/core/ml/optimizer_mixin.py  ·  - SCOPE: consumer-side only  ·  - RISK: low, localized -
-
- -

1. Diagnosis in one paragraph

-

Your goal — normalize everything to dict[str, OptimizerType] — is correct - and worth keeping. The standardization did not break Ptychography on its own. The breakage came - from a consumer that was never migrated to the new contract: - set_optimizer. It still branches on the old union shape (a bare - OptimizerType or a dict). After normalization always returns a dict, the two - branches that handled the bare-type cases became unreachable, and the surviving dict branch was - only a partial implementation — it never handled the NoneOptimizer sentinel and never - forwarded hyperparameters. The fix is therefore not to revert the standardization, - but to make that one dict branch self-sufficient.

- -
-
Why your change was necessary but not sufficient
-

A return type is a contract. Narrowing it from a union to a single - shape obligates every consumer that pattern-matches on the old shape to be updated in the same - change. The producer was migrated; one consumer was not.

-
- -

2. The two concrete symptoms

- - - - - - - - - - -
SymptomRoot cause in set_optimizer
Crash: NotImplementedError: Unknown optimizer type: NoneOptimizer()The dict branch passes a NoneOptimizer spec to - _optimizer_class_for, which has no case for it. The old guard that caught this - (isinstance(..., NoneOptimizer)) only matches a bare sentinel and is now - unreachable.
Silent: configured lr ignoredThe dict branch builds optimizer_cls(params) with no kwargs. The old branch - that splatted **optimizer_params.params() is the now-unreachable - else.
- -

3. Recommended minimal change set

-

Three changes, in priority order. REQUIRED alone resolves the - crash and the silent lr bug.

- -

REQUIREDMake the dict branch self-sufficient · ~6 lines, one method

-

Filter out NoneOptimizer sentinels; if none remain, there is nothing to optimize. - Otherwise build the optimizer and forward the spec’s hyperparameters. This is the - smallest change that honors the new dict-only contract.

- 
        if isinstance(self.optimizer_params, dict):
--           opt_specs = list(self.optimizer_params.values())
--           if not opt_specs:
--               self._optimizer = None
--               return
-+           opt_specs = [
-+               spec for spec in self.optimizer_params.values()
-+               if not isinstance(spec, OptimizerParams.NoneOptimizer)
-+           ]
-+           if not opt_specs:
-+               self.remove_optimizer()
-+               return
-            optimizer_cls = self._optimizer_class_for(opt_specs[0])
-            for spec in opt_specs[1:]:
-                if type(spec) is not type(opt_specs[0]):
-                    raise ValueError(...)
--           self._optimizer = optimizer_cls(params)  # type:ignore
-+           self._optimizer = optimizer_cls(params, **opt_specs[0].params())
-
-
What this buys you
-

The default {"default": NoneOptimizer()} now filters to - an empty list → remove_optimizer() → clean no-op (this is the path - reset_recon() walks). A real {"default": Adam(lr=5e-3)} now builds - Adam(params, lr=5e-3, …) with your hyperparameters restored.

-
-
-
Note on PPLR
-

This splats opt_specs[0]’s hyperparameters across all - parameter groups. That is correct today because every model returns a single param group from - get_optimization_parameters(). True per-group LRs are a separate, larger piece of - work — see §5.

-
- -

RECOMMENDEDKeep remove_optimizer on-contract · 1 line

-

It currently stores a bare sentinel, breaking the “always a dict” invariant. It works - only because the value gets re-normalized on the next set. Store the dict form to keep the - invariant true end-to-end.

- 
    def remove_optimizer(self) -> None:
-        self._optimizer = None
--       self._optimizer_params = OptimizerParams.NoneOptimizer()
-+       self._optimizer_params = {self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer()}
-        self._scheduler = None
-        self._scheduler_params = SchedulerParams.NoneScheduler()
- -

OPTIONALDelete the now-dead branches · removes ~7 lines

-

Purely cosmetic, but it removes the misleading code that caused the confusion. With the dict - contract guaranteed, the bare-NoneOptimizer guard and the else branch can - never run. Removing them makes set_optimizer a single, honest path.

- 
-       if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer):
--           self.remove_optimizer()
--           return
-        ...
--       else:
--           optimizer_cls = self._optimizer_class_for(self.optimizer_params)
--           self._optimizer = optimizer_cls(params, **self.optimizer_params.params())
-

If you keep these, no harm — they’re dead, not wrong. Removing them is about - readability, not correctness.

- -

4. Optional: align the container getters

-

OPTIONAL In PtychographyOpt.optimizer_params (and the - tomography equivalent) the filter if not isinstance(params, NoneOptimizer) no longer - fires, because params is now a dict. After the REQUIRED fix this is harmless — a - disabled model simply ends up with no optimizer — but if you want the getter to exclude disabled - models, check the inner specs instead.

- 
-           if not isinstance(params, OptimizerParams.NoneOptimizer)
-+           if any(not isinstance(s, OptimizerParams.NoneOptimizer) for s in params.values())
- -

5. What to leave for later (don’t scope-creep)

-
    -
  • Real per-parameter LRs (PPLR). The comment claiming hyperparameters are “baked into - each dict by get_optimization_parameters()” is aspirational — the implementations - return [{"params": params}] with no lr. Either implement keyed param - groups or update the comment. Not needed to fix the break.
    • -
  • Collapsing the three “no optimizer” spellings (empty dict / NoneOptimizer / - _optimizer = None) into one. A nice tidy-up, but orthogonal to this incident.
    • -
- -

6. Verification checklist

- - - - - - - - - - -
StepExpected
ptycho.reconstruct(num_iters=1, reset=True, optimizer_params={"object": {"lr": 5e-3}})No NotImplementedError; runs.
Inspect ptycho.obj_model.optimizer.param_groups[0]["lr"]Equals 5e-3, not torch’s default.
A model left out of optimizer_paramshas_optimizer() is False; not stepped.
Re-run with reset=TrueDefault NoneOptimizer filters cleanly; no crash.
- -
-
Bottom line
-

Keep your standardization. Apply the one REQUIRED change (and ideally - the one-line RECOMMENDED change). That completes the migration the type-narrowing started, with a - change set small enough to read in a single diff.

-
- -

- Read-only proposal · no source files modified · - companions: optimizer_mixin_review.html · optimizer_refactor_incident.html · OPTIMIZER_PARAMS_FLOW.md -

- -
- - diff --git a/src/quantem/core/ml/optimizer_mixin_review.html b/src/quantem/core/ml/optimizer_mixin_review.html deleted file mode 100644 index 1e205644..00000000 --- a/src/quantem/core/ml/optimizer_mixin_review.html +++ /dev/null @@ -1,319 +0,0 @@ - - - - - -OptimizerMixin — a friendly post-mortem & cleanup wishlist - - - -
- -
- Code review · ml/optimizer_mixin.py -

OptimizerMixin: a friendly post-mortem 🕵️

-

Why NoneOptimizer() blew up your ptycho run, and how to make this class - a joy to read again. No code was harmed in the making of this document.

- -
- -

🔪 The whodunit (in one breath)

-

- A refactor decreed: “optimizer params shall always be a - dict[str, OptimizerType].” The normalizer obeyed. But - set_optimizer never got the memo — it still has two branches written for the - old world where a bare OptimizerType could walk in the door. Those - branches are now unreachable corpses, and every call gets funneled into the one dict branch - that doesn’t know what to do with a NoneOptimizer. -

- -
-
💥 The crash
-

reconstruct(reset=True) calls reset_recon() - before your optimizer_params are applied. So set_optimizer - runs against the default {"default": NoneOptimizer()}, falls into the dict branch, - and hands a NoneOptimizer to _optimizer_class_for — which has no - case for it → NotImplementedError.

-
- - 
# the dead giveaway in set_optimizer (current)
-if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer):  ← never True; params is always a dict now
-    self.remove_optimizer()
-    return
-...
-if isinstance(self.optimizer_params, dict):                          # ← always taken
-    opt_specs = list(self.optimizer_params.values())             # [NoneOptimizer()]
-    optimizer_cls = self._optimizer_class_for(opt_specs[0])     ← 💥 NotImplementedError
-    ...
-    self._optimizer = optimizer_cls(params)                       ← also: no lr is ever passed!
-else:
-    # the branch that actually splatted hyperparameters... now unreachable 🪦
-    self._optimizer = optimizer_cls(params, **self.optimizer_params.params())
- -
-
🐛 Bonus silent bug
-

Even after the crash is fixed, the dict branch builds - optimizer_cls(params) with no hyperparameters. A code comment - promises they’re “baked into each dict by get_optimization_parameters()” — but all - three implementations just return [{"params": params}] with no - lr. So your carefully chosen learning rate would silently become torch’s - default. The comment is, regrettably, fan fiction.

-
- -

🌊 The flow, at a glance

-

Two objects both expose optimizer_params, and they mean different things. That’s - the single most confusing thing about this code, so let’s name it loudly:

- - - - - - - - - - - - - -
LevelKeyed byExample value
Container
(PtychographyOpt)		which model
"object"/"probe"/"dataset"		{"object": {"default": Adam(lr=5e-3)}}
Model
(OptimizerMixin)		which param group
usually just "default"		{"default": Adam(lr=5e-3)}
- - 
user input ──► PtychographyOpt.optimizer_params setter   # Layer 1: fill defaults, dispatch per model
-                 │   fills "name"/"lr" defaults via _get_default_lr()
-                 └─► model.optimizer_params setter             # Layer 2: normalize to dict[str, OptimizerType]
-                       └─► _normalize_optimizer_params()
-                             OptimizerType        ──► {"default": it}
-                             {"name"/"type": ...} ──► {"default": parse_dict(it)}
-                             {k: OptimizerType}   ──► kept (PPLR form)
-                                  │
-reconstruct() ──► set_optimizers() ──► for each model: set_optimizer({"default": Adam(lr=5e-3)})
-                                                          └─► torch.optim.Adam(param_groups, lr=5e-3)
- -

🧹 Five ways to make this a joy to read

- -

1Make the invariant real — delete the dead branches

-

If optimizer_params is always a dict, then set_optimizer - should have one path: filter out the NoneOptimizer sentinels, agree - on a class, and splat hyperparameters. No else, no zombie isinstance.

- -
-
-

✗ Before — 3 branches, 2 of them dead

- 
if isinstance(p, NoneOptimizer):  # dead
-    remove_optimizer(); return
-...
-if isinstance(p, dict):
-    specs = list(p.values())
-    cls = _optimizer_class_for(specs[0])  # 💥
-    self._optimizer = cls(params)       # no lr
-else:                            # dead
-    cls = _optimizer_class_for(p)
-    self._optimizer = cls(params, **p.params())
-
-
-

✓ After — one honest path

- 
# params is always dict[str, OptimizerType]
-active = [s for s in p.values()
-          if not isinstance(s, NoneOptimizer)]
-if not active:
-    self.remove_optimizer(); return
-
-self._assert_same_class(active)
-cls = self._optimizer_class_for(active[0])
-groups = self.get_optimization_parameters()
-self._optimizer = cls(groups, **active[0].params())  # lr restored ✓
-
-
- -

2Give NoneOptimizer a home in the match

-

The sentinel is a first-class OptimizerType, so the place that maps specs to - torch classes should acknowledge it instead of letting it fall into case _ and - explode. A tiny is_none() helper or an explicit case makes the intent obvious.

- 
match opt_params:
-    case OptimizerParams.Adam():  return torch.optim.Adam
-    case OptimizerParams.AdamW(): return torch.optim.AdamW
-    case OptimizerParams.SGD():   return torch.optim.SGD
-    case OptimizerParams.NoneOptimizer():                          # ← speak its name
-        raise ValueError("NoneOptimizer has no torch class; filter it out before calling")
-    case _: raise NotImplementedError(...)
- -

3Keep remove_optimizer faithful to the invariant

-

Right now it resets _optimizer_params to a bare NoneOptimizer() - — violating the “always a dict” rule it works almost everywhere thanks to re-normalization, but - it’s a landmine. Store the dict form so the invariant holds end-to-end.

- 
def remove_optimizer(self):
-    self._optimizer = None
-    self._optimizer_params = OptimizerParams.NoneOptimizer()           # bare → breaks invariant
-    self._optimizer_params = {self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer()}
-    self._scheduler = None
-    self._scheduler_params = SchedulerParams.NoneScheduler()
- -

4Pick one way to say “don’t optimize this”

-

Today there are three: an empty dict (if not self.optimizer_params), - the NoneOptimizer sentinel, and self._optimizer = None. Three spellings of - one idea means every reader has to hold all three in their head. Commit to the sentinel as the - single source of truth, and let has_optimizer() be the one public question.

-
-
💡 Rule of thumb
-

“No optimizer” = NoneOptimizer in the params, - None in self._optimizer. Everything else (empty dicts, None - inputs) gets normalized into that single shape at the boundary, once.

-
- -

5Fix the container getters’ dead filters (and the lying comment)

-

Both PtychographyOpt and the tomography container filter with - if not isinstance(params, NoneOptimizer) — but params is now a - dict, so the filter never fires and disabled models sneak back into the loop. Either - filter on the inner spec, or drop the filter and lean on has_optimizer(). And while - you’re there: either make get_optimization_parameters() actually bake per-group - hyperparameters (real PPLR) or delete the comment that says it does.

- 
# container getter, today — dead filter
-if not isinstance(params, OptimizerParams.NoneOptimizer)   # params is a dict → always passes
-
-# option A: check the inner specs
-if any(not isinstance(s, NoneOptimizer) for s in params.values())   # ✓
- -
-
🎯 The big picture
-

There’s a clean seam hiding here: separate what to - optimize (parameter groups, from the model) from how (optimizer specs, from the - user). If get_optimization_parameters() returned groups keyed the same way as the - specs dict, true per-parameter learning rates would fall out naturally — and - set_optimizer would shrink to “zip specs with groups, build one optimizer.” That’s - the refactor that turns this from “works if you don’t poke it” into “obviously - correct.”

-
- -

📋 Scorecard

- - - - - - - - - - - - - - - - - -
TopicStateOne-line take
Normalization (Layer 1 & 2)SolidThe _normalize_optimizer_params logic is actually nice and clear.
set_optimizer branchingBrokenTwo dead branches; the live one crashes on the default state.
Hyperparameter passingBrokenlr silently dropped; comment claims otherwise.
“No optimizer” representationMuddyThree spellings of one concept.
Container gettersStaleDead isinstance filters left over from the bare-type era.
- -

- Generated as a read-only review of src/quantem/core/ml/optimizer_mixin.py. - No source files were modified. Companion deep-dive: OPTIMIZER_PARAMS_FLOW.md. -

- -
- - diff --git a/src/quantem/core/ml/optimizer_refactor_incident.html b/src/quantem/core/ml/optimizer_refactor_incident.html deleted file mode 100644 index 89d4c762..00000000 --- a/src/quantem/core/ml/optimizer_refactor_incident.html +++ /dev/null @@ -1,266 +0,0 @@ - - - - - -The Great Type Standardization — an incident report - - - -
- - Incident · Post-Mortem -
-

The Great Type Standardization
that quietly cut the pipes

-
How changing one return type from - OptimizerType | dict[str, OptimizerType] to just - dict[str, OptimizerType] took out Ptychography.
-
- FILE: src/quantem/core/ml/optimizer_mixin.py  ·  - COMMIT: 1bc1739  ·  - SYMPTOM: NotImplementedError: Unknown optimizer type: NoneOptimizer() -
-
- -
- TL;DR — The refactor changed what the producer - (_normalize_optimizer_params) hands out, but not what the consumer - (set_optimizer) was built to receive. A return type isn’t just a label — it’s a - contract about the shape of a value, and three different places had hand-rolled - branches that pattern-matched on the old shape. When the shape changed, two branches - became unreachable and the third — never fully finished — became the only road. It couldn’t carry - a NoneOptimizer, and it dropped your learning rate on the floor. -
- -

Act I — The good intentionOne type to rule them all

-

Someone looked at this signature and felt a totally reasonable itch:

-
-
-

Before

- 
def optimizer_params(self) ->
-    OptimizerType | dict[str, OptimizerType]:
-    # could be a bare dataclass...
-    # ...or a dict of them. Ugh, which is it?
-    return self._optimizer_params
-
-
-

After — “let’s standardize”

- 
def optimizer_params(self) ->
-    dict[str, OptimizerType]:
-    # always a dict now. cleaner!
-    return self._optimizer_params
-
-
-

The union type A | B is genuinely annoying: every reader, and every consumer, has - to ask “ok, but which one is it this time?” Collapsing it to a single - shape is the right instinct. The normalizer was dutifully rewritten so that everything — - a bare Adam(), a {"name": "adam"} shorthand, a list of keys — comes out - as a tidy dict[str, OptimizerType].

- -
-
What went right
-

The normalization itself is clean and correct. If you only read - _normalize_optimizer_params, the refactor looks like an unambiguous win.

-
- -

Act II — The unspoken contractA return type is a promise to strangers

-

Here’s the thing the type hint hides: that value doesn’t stay home. It flows downstream into - set_optimizer, which — because Python has no real runtime types — does its own - hand-rolled type dispatch with isinstance and an else:

- - 
def set_optimizer(self, opt_params):
-    ...
-    if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer):   # arm ➊
-        self.remove_optimizer(); return
-
-    if isinstance(self.optimizer_params, dict):                          # arm ➋
-        opt_specs = list(self.optimizer_params.values())
-        cls = self._optimizer_class_for(opt_specs[0])
-        self._optimizer = cls(params)
-    else:                                                                # arm ➌
-        cls = self._optimizer_class_for(self.optimizer_params)
-        self._optimizer = cls(params, **self.optimizer_params.params())
- -

Those three arms were designed for the old contract, where - optimizer_params could be either a bare dataclass or a dict. Each arm had a - job:

-
    -
  • ➊ handle a bare NoneOptimizer sentinel → “turn the optimizer off.”
    • -
  • ➋ handle the dict / per-parameter case.
    • -
  • ➌ handle a bare optimizer dataclass → build it and splat its hyperparameters - (**params(), i.e. your lr).
    • -
- -
Change the shape of the water, and pipes cut for the old shape stop carrying it — - even though nothing looks broken at the joint.
- -

Act III — The reachability collapseTwo arms die, one inherits everything

-

Once the producer always returns a dict, watch what happens to the three arms:

- - - - - - - - - - - - - - - - - - - - - - - - -
ArmMatches when…Old contractNew contractIts old job now…
➊ bare NoneOptimizervalue is a bare sentinelreachableDEAD…nobody turns the optimizer off anymore
➋ isinstance(dict)value is a dictreachableALWAYS…inherits ➊ and ➌’s jobs but does neither
➌ the elsevalue is a bare dataclassreachableDEAD…the **params() splat (your lr!) is gone
- -

So every call now lands in arm ➋. And arm ➋ was only ever a partial - implementation — it builds cls(params) and trusts that the rest will sort itself out. - But it never learned the two jobs it just inherited:

- -
-
Failure 1 · the crash you saw
-

Arm ➋ pulls opt_specs[0] and asks - _optimizer_class_for for a torch class. When that spec is a NoneOptimizer - (the default state of every model), the match falls through to - case _ → NotImplementedError. Arm ➊ used to catch this; arm ➊ is now - dead.

-
-
-
Failure 2 · the silent one
-

Arm ➋ builds cls(params) with no - hyperparameters. Arm ➌ used to splat **self.optimizer_params.params() — - that’s where your lr came from. With ➌ dead, a perfectly valid Adam(lr=5e-3) - silently becomes Adam() at torch’s default LR. No error. Just wrong.

-
- -

Act IV — Why Python smiled and said nothing

-

If types were enforced, the compiler would have screamed: “arm ➊ and arm ➌ can never - match a dict[str, OptimizerType] — delete them or fix them.” But in Python:

-
    -
  • Type hints are decorative. Changing -> dict[...] changes - documentation, not behavior. Nothing re-checked the consumers.
    • -
  • A branch that never matches doesn’t raise — it just silently never runs. - Dead code is invisible until you go looking for it.
    • -
  • The one surviving arm happened to not crash on the common happy path (pass an - Adam, get an optimizer — just at the wrong LR), so casual testing looked fine.
    • -
- -
-
Why it detonated on reset=True specifically
-

reconstruct(reset=True, optimizer_params=…) calls - reset_recon() before it applies your params. At that instant every - model still holds its factory default — {"default": NoneOptimizer()}. So arm ➋ gets a - NoneOptimizer on the very first thing it touches (obj_model) and dies - immediately. The crash isn’t in your config; it’s baked into the default state the reset path - walks through.

-
- -

Act V — The moralStandardizing a type is a migration, not an edit

-

“Just change the return type to dict[str, OptimizerType]” felt like a - one-line cleanup. But the return type was load-bearing: three consumer branches structurally - depended on the old union. Changing the producer without migrating those consumers left the system - in a half-converted state — new shape coming out, old shape expected downstream.

- -
-
-

What was done

-

✅ Producer (_normalize_optimizer_params) → always dict
- ✅ Type hint updated
- ❌ Consumer (set_optimizer) still speaks the old union
- ❌ remove_optimizer still writes a bare sentinel

-
-
-

What “done” actually required

-

→ Collapse set_optimizer to one dict-only path
- → Teach that path to filter NoneOptimizer (arm ➊’s job)
- → Teach it to splat hyperparameters (arm ➌’s job)
- → Make every producer of the value (incl. remove_optimizer) honor the new shape

-
-
- -
The bug wasn’t the new type. The bug was believing the new type was the - whole change.
- -
- One sentence for the next reviewer: when you narrow a return type from a union to a single - shape, grep every consumer that does isinstance / match on it — each - arm that handled a now-impossible case is either dead code to delete or a responsibility that - just silently moved somewhere it isn’t handled. -
- -

- Read-only incident report · no source modified · - companions: optimizer_mixin_review.html, OPTIMIZER_PARAMS_FLOW.md -

- -
- - From 91642a8acf3f422424daad71ef9ba8bf0e6754b4 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 18:18:06 -0700 Subject: [PATCH 086/140] Major changes per Claude's detailed report on what I actually implemented is not truly PPLR and was brittle. Will talk to @arthurmccray 5/28/2026 about this --- src/quantem/core/ml/optimizer_mixin.py | 114 +++++++++++------- .../diffractive_imaging/dataset_models.py | 42 +++++-- .../diffractive_imaging/object_models.py | 9 +- .../diffractive_imaging/probe_models.py | 9 +- src/quantem/tomography/object_models.py | 26 ++-- tests/ml/test_optimizermixin.py | 103 ++++++++++++++++ 6 files changed, 223 insertions(+), 80 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 8c095d39..1575276d 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -603,62 +603,87 @@ def scheduler_params(self, params: SchedulerType | dict): @abstractmethod def get_optimization_parameters( self, - ) -> "list[dict[str, Any]]": + ) -> "dict[str, list[torch.Tensor]]": """ - Get the parameters that should be optimized for this model. - This could be replaced with just module.parameters(), but this allows for flexibility - in the future to allow for per parameter LRs. # NOTE: Cl 4/27/26 updated to iterable type-hint. + Get the parameters that should be optimized for this model, grouped by name. + + Returns a mapping ``{group_key: [tensors]}``. The group keys MUST match the keys of + ``optimizer_params`` (the common single-group case uses ``DEFAULT_OPTIMIZER_KEY``). + ``set_optimizer`` joins each group to its optimizer spec by key and bakes the per-group + hyperparameters (``spec.params()``) into the torch param group — implementations return + only the tensors, NOT pre-baked hyperparameters. Return ``{}`` when there is nothing + to optimize. """ raise NotImplementedError("Subclasses must implement get_optimization_parameters") def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: """ - Set the optimizer for this model. - Currently supports single LR for all parameters, TODO allow for per parameter LRs by - updating get_optimization_parameters to return a list of parameters and their LRs. + Set the optimizer for this model, supporting per-parameter-group learning rates (PPLR). + + ``optimizer_params`` is a ``dict[str, OptimizerType]`` keyed by parameter group. Each + group's spec is joined by key to the tensors returned by ``get_optimization_parameters()`` + and its hyperparameters are baked into the corresponding torch param group here. All + groups must use the same optimizer class. If every group is a ``NoneOptimizer`` (or there + are no groups), the optimizer is removed. """ if opt_params is not None: self.optimizer_params = opt_params - if not self.optimizer_params: - self._optimizer = None + # Single canonical "disable" path: drop NoneOptimizer sentinels and, if nothing is left, + # remove the optimizer. Done BEFORE get_optimization_parameters() because some models + # (e.g. the dataset model) raise / return nothing when there is nothing to optimize. + specs = { + key: spec + for key, spec in self.optimizer_params.items() + if not isinstance(spec, OptimizerParams.NoneOptimizer) + } + if not specs: + self.remove_optimizer() return + # All groups must agree on the optimizer class. + spec_list = list(specs.values()) + for spec in spec_list[1:]: + if type(spec) is not type(spec_list[0]): + raise ValueError( + f"All parameter groups must use the same optimizer type, " + f"got {type(spec_list[0]).__name__} and {type(spec).__name__}" + ) - params = self.get_optimization_parameters() # always list[dict] - - # Ensure parameters require gradients - for group in params: - for p in group["params"]: + # Join specs to param groups by key; bake each group's hyperparameters here. + groups = self.get_optimization_parameters() # dict[str, list[tensor]] + if set(groups) != set(specs): + raise ValueError( + f"optimizer_params keys {set(specs)} do not match parameter group keys " + f"{set(groups)} from {type(self).__name__}.get_optimization_parameters()" + ) + + param_groups = [] + for key, tensors in groups.items(): + for p in tensors: p.requires_grad_(True) - # Figure out which optimizer class to use - if isinstance(self.optimizer_params, dict): - # Per-group case: all groups must agree on the optimizer class, - # and per-group hyperparameters are already baked into each dict - # by get_optimization_parameters(). - opt_specs = [ - spec for spec in self.optimizer_params.values() - if not isinstance(spec, OptimizerParams.NoneOptimizer) - ] - if not opt_specs: - self.remove_optimizer() - return - optimizer_cls = self._optimizer_class_for(opt_specs[0]) - for spec in opt_specs[1:]: - if type(spec) is not type(opt_specs[0]): - raise ValueError( - f"All parameter groups must use the same optimizer type, " - f"got {type(opt_specs[0]).__name__} and {type(spec).__name__}" - ) - self._optimizer = optimizer_cls(params, **opt_specs[0].params()) - def _optimizer_class_for(self, opt_params) -> type[torch.optim.Optimizer]: + param_groups.append({"params": tensors, **specs[key].params()}) + self._optimizer = self._build_optimizer(spec_list[0], param_groups) + + def _build_optimizer(self, opt_params, param_groups) -> "torch.optim.Optimizer": + """Construct the torch optimizer for ``opt_params`` over pre-baked ``param_groups``. + + ``param_groups`` already carry their per-group hyperparameters (see ``set_optimizer``), + so each group's ``lr`` etc. overrides the optimizer-level default. ``NoneOptimizer`` must + have been filtered out by the caller. + """ match opt_params: case OptimizerParams.Adam(): - return torch.optim.Adam + return torch.optim.Adam(param_groups) case OptimizerParams.AdamW(): - return torch.optim.AdamW + return torch.optim.AdamW(param_groups) case OptimizerParams.SGD(): - return torch.optim.SGD + return torch.optim.SGD(param_groups) + case OptimizerParams.NoneOptimizer(): + raise ValueError( + "NoneOptimizer must be filtered out before _build_optimizer; " + "set_optimizer should have short-circuited to remove_optimizer()." + ) case _: raise NotImplementedError(f"Unknown optimizer type: {opt_params}") @@ -674,7 +699,10 @@ def set_scheduler( return optimizer = self._optimizer - base_LR = optimizer.param_groups[0]["lr"] + # Schedulers scale every torch param group proportionally off its own initial_lr; this + # scalar only seeds scheduler config (e.g. min_lr, cyclic bounds). Use the max group LR + # as the representative (collapses to group 0 in the single-group case). + base_LR = max(pg["lr"] for pg in optimizer.param_groups) params = self._scheduler_params.params(base_LR, num_iter=num_iter) match self.scheduler_params: case SchedulerParams.NoneScheduler(): @@ -764,8 +792,8 @@ def reconnect_optimizer_to_parameters(self) -> None: return # Ensure leaf params with grad - for group in new_groups: - for p in group["params"]: + for tensors in new_groups.values(): + for p in tensors: if not p.is_leaf: raise ValueError("Non-leaf tensor in param group; build groups from leaves") p.requires_grad_(True) @@ -776,8 +804,8 @@ def reconnect_optimizer_to_parameters(self) -> None: ] self._optimizer.param_groups.clear() - for group in new_groups: - self._optimizer.add_param_group(group) + for tensors in new_groups.values(): + self._optimizer.add_param_group({"params": tensors}) # Restore per-group hyperparameters by index for new_pg, old_pg in zip(self._optimizer.param_groups, old_hyperparams): diff --git a/src/quantem/diffractive_imaging/dataset_models.py b/src/quantem/diffractive_imaging/dataset_models.py index 585d5e4b..39895adb 100644 --- a/src/quantem/diffractive_imaging/dataset_models.py +++ b/src/quantem/diffractive_imaging/dataset_models.py @@ -1,4 +1,5 @@ from abc import abstractmethod +from dataclasses import replace from pathlib import Path from typing import Any, Literal, Self @@ -95,18 +96,39 @@ def __init__( self._constraints = {} self._probe_energy = None - def get_optimization_parameters(self) -> list[dict[str, Any]]: - """Get the combined descan and scan position parameters for optimization.""" - params = [] + def get_optimization_parameters(self) -> "dict[str, list[torch.Tensor]]": + """Descan and scan-position parameters as separate PPLR groups. + + Returns one group per *learnable* parameter set; ``{}`` when neither is learnable + (``set_optimizer`` then short-circuits to removing the optimizer). + """ + groups: dict[str, list[torch.Tensor]] = {} if self.learn_descan: - params.append(self._descan_shifts) + groups["descan"] = [self._descan_shifts] if self.learn_scan_positions: - params.append(self._scan_positions_px) - if len(params) == 0: - raise RuntimeError( - "No parameters to optimize for dataset: learn_descan and learn_scan_positions are both False" - ) - return [{"params": params}] + groups["scan_positions"] = [self._scan_positions_px] + return groups + + def _normalize_optimizer_params(self, params): + """Broadcast a single optimizer spec to the learnable descan/scan_position groups. + + A single ``OptimizerType`` / single-optimizer dict (normalized to the ``"default"`` key) + is fanned out to whichever groups are currently learnable, so the common single-LR caller + keeps working. An explicit PPLR dict (keyed by ``descan``/``scan_positions``) passes through. + """ + norm = super()._normalize_optimizer_params(params) + if set(norm) == {self.DEFAULT_OPTIMIZER_KEY}: + spec = norm[self.DEFAULT_OPTIMIZER_KEY] + learnable = [ + key + for key, on in ( + ("descan", self.learn_descan), + ("scan_positions", self.learn_scan_positions), + ) + if on + ] + return {key: replace(spec) for key in learnable} if learnable else {} + return norm def to(self, *args, **kwargs): """Move all relevant tensors to a different device.""" diff --git a/src/quantem/diffractive_imaging/object_models.py b/src/quantem/diffractive_imaging/object_models.py index 0860bf55..3b74319b 100644 --- a/src/quantem/diffractive_imaging/object_models.py +++ b/src/quantem/diffractive_imaging/object_models.py @@ -232,13 +232,12 @@ def to(self, *args, **kwargs): def name(self) -> str: raise NotImplementedError() - def get_optimization_parameters(self) -> list[dict[str, Any]]: - """Get the parameters that should be optimized for this model.""" + def get_optimization_parameters(self) -> "dict[str, list[torch.Tensor]]": + """Get the parameters that should be optimized for this model, keyed by group.""" params = self.params if params is None: - return [] - else: - return [{"params": params}] # compatible with PPLR + return {} + return {self.DEFAULT_OPTIMIZER_KEY: list(params)} def _propagate_array( self, array: "torch.Tensor", propagator_array: "torch.Tensor" diff --git a/src/quantem/diffractive_imaging/probe_models.py b/src/quantem/diffractive_imaging/probe_models.py index b72f1426..6b00793a 100644 --- a/src/quantem/diffractive_imaging/probe_models.py +++ b/src/quantem/diffractive_imaging/probe_models.py @@ -94,13 +94,12 @@ def __init__( if roi_shape is not None: self.roi_shape = roi_shape - def get_optimization_parameters(self) -> list[dict[str, Any]]: - """Get the parameters that should be optimized for this model.""" + def get_optimization_parameters(self) -> "dict[str, list[torch.Tensor]]": + """Get the parameters that should be optimized for this model, keyed by group.""" params = self.params if params is None: - return [] - else: - return [{"params": params}] # compatible with PPLR + return {} + return {self.DEFAULT_OPTIMIZER_KEY: list(params)} @property def learn_probe_tilt(self) -> bool: diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 8fa19db7..12e8a99c 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -290,14 +290,12 @@ def params(self) -> Generator[torch.nn.Parameter, None, None]: raise NotImplementedError # --- Helper Functions --- - def get_optimization_parameters(self) -> list[dict[str, Any]]: - """Default: wrap self.params in a single param group.""" - if isinstance(self._optimizer_params, dict): - # Shouldn't happen for single-group models, but be defensive - opt = next(iter(self._optimizer_params.values())) - else: - opt = self._optimizer_params - return [{"params": list(self.params), **opt.params()}] + def get_optimization_parameters(self) -> "dict[str, list[torch.Tensor]]": + """Default: a single param group keyed by DEFAULT_OPTIMIZER_KEY. + + Hyperparameters are baked by ``set_optimizer``, not here — return only the tensors. + """ + return {self.DEFAULT_OPTIMIZER_KEY: list(self.params)} @abstractmethod # Each subclass should implement this. def to(self, device: str | torch.device): @@ -1026,16 +1024,10 @@ def params(self) -> Generator[torch.nn.Parameter, None, None]: return self.model.parameters() # type: ignore[attr-defined] - def get_optimization_parameters(self) -> list[dict[str, Any]]: - """PPLR: per-key param groups.""" + def get_optimization_parameters(self) -> "dict[str, list[torch.Tensor]]": + """PPLR: per-key param groups (hyperparameters are baked by set_optimizer).""" model = _unwrap(self.model) - assert isinstance(self._optimizer_params, dict), ( - "ObjectTensorDecomp requires dict-form optimizer_params" - ) - return [ - {"params": model.get_params()[key], **self._optimizer_params[key].params()} - for key in model.param_keys - ] + return {key: list(model.get_params()[key]) for key in model.param_keys} def _normalize_optimizer_params(self, params): """ObjectTensorDecomp requires a dict matching model.param_keys.""" diff --git a/tests/ml/test_optimizermixin.py b/tests/ml/test_optimizermixin.py index d829af41..d6347ce6 100644 --- a/tests/ml/test_optimizermixin.py +++ b/tests/ml/test_optimizermixin.py @@ -348,3 +348,106 @@ def test_parse_invalid_name_type_raises(self): def test_parse_default_name_is_none(self): result = SchedulerParams.parse_dict({}) assert isinstance(result, SchedulerParams.NoneScheduler) + + +# ─── OptimizerMixin.set_optimizer / PPLR behavior ─────────────────────────── + +from quantem.core import config # noqa: E402 +from quantem.core.ml.optimizer_mixin import OptimizerMixin # noqa: E402 + +torch = pytest.importorskip("torch") if config.get("has_torch") else None +requires_torch = pytest.mark.skipif( + not config.get("has_torch"), reason="requires torch" +) + + +def _param(value=1.0): + return torch.nn.Parameter(torch.tensor([value])) + + +class _FakeModel(OptimizerMixin): + """Minimal concrete OptimizerMixin for exercising set_optimizer/reset_optimizer. + + ``groups`` is the dict[str, list[Parameter]] returned by get_optimization_parameters. + Pass ``raise_on_params=True`` to prove the disable path short-circuits before the call. + """ + + def __init__(self, groups, raise_on_params=False): + super().__init__() + self._groups = groups + self._raise_on_params = raise_on_params + + def get_optimization_parameters(self): + if self._raise_on_params: + raise AssertionError("get_optimization_parameters should not have been called") + return self._groups + + +@requires_torch +class TestSetOptimizer: + def test_single_optimizer_build(self): + model = _FakeModel({"default": [_param()]}) + model.set_optimizer(OptimizerParams.Adam(lr=1e-3)) + assert model.has_optimizer() + assert isinstance(model.optimizer, torch.optim.Adam) + assert len(model.optimizer.param_groups) == 1 + assert model.optimizer.param_groups[0]["lr"] == 1e-3 + + def test_single_optimizer_dict_shorthand(self): + model = _FakeModel({"default": [_param()]}) + model.set_optimizer({"name": "sgd", "lr": 5e-2, "momentum": 0.9}) + assert isinstance(model.optimizer, torch.optim.SGD) + assert model.optimizer.param_groups[0]["lr"] == 5e-2 + assert model.optimizer.param_groups[0]["momentum"] == 0.9 + + def test_none_optimizer_removes_without_touching_params(self): + # raise_on_params proves the disable path short-circuits before get_optimization_parameters + model = _FakeModel({"default": [_param()]}, raise_on_params=True) + model.set_optimizer(OptimizerParams.NoneOptimizer()) + assert model.optimizer is None + assert not model.has_optimizer() + assert model.optimizer_params == { + OptimizerMixin.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer() + } + + def test_multi_group_pplr_applies_per_group_lr(self): + model = _FakeModel({"descan": [_param()], "scan_positions": [_param(2.0)]}) + model.set_optimizer( + {"descan": OptimizerParams.SGD(lr=1e-2), "scan_positions": OptimizerParams.SGD(lr=1e-3)} + ) + groups = model.optimizer.param_groups + assert len(groups) == 2 + # key order is preserved (descan, then scan_positions) + assert groups[0]["lr"] == 1e-2 + assert groups[1]["lr"] == 1e-3 + + def test_key_mismatch_raises(self): + model = _FakeModel({"default": [_param()]}) + with pytest.raises(ValueError, match="do not match"): + model.set_optimizer( + {"descan": OptimizerParams.SGD(lr=1e-2), "scan_positions": OptimizerParams.SGD()} + ) + + def test_mixed_optimizer_classes_raises(self): + model = _FakeModel({"a": [_param()], "b": [_param()]}) + with pytest.raises(ValueError, match="same optimizer type"): + model.set_optimizer( + {"a": OptimizerParams.Adam(lr=1e-3), "b": OptimizerParams.SGD(lr=1e-3)} + ) + + def test_reset_optimizer_on_unconfigured_model_is_noop(self): + model = _FakeModel({"default": [_param()]}) + # fresh model defaults to {"default": NoneOptimizer()} + model.reset_optimizer() + assert model.optimizer is None + assert not model.has_optimizer() + + def test_set_scheduler_base_lr_uses_max_group_lr(self): + model = _FakeModel({"descan": [_param()], "scan_positions": [_param(2.0)]}) + model.set_optimizer( + {"descan": OptimizerParams.SGD(lr=1e-2), "scan_positions": OptimizerParams.SGD(lr=1e-3)} + ) + model.set_scheduler(SchedulerParams.Plateau(), num_iter=10) + assert model.scheduler is not None + # Plateau min_lr defaults to base_LR / 20, with base_LR = max group lr (1e-2) + assert model.scheduler.min_lrs[0] == pytest.approx(1e-2 / 20) From c5f8b37a8e53d0b636955a6a9b9e2d2246ae7479 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Fri, 29 May 2026 15:02:51 -0700 Subject: [PATCH 087/140] Warnings to no optimizations on PtychographyDatasetBase. --- src/quantem/core/ml/optimizer_mixin.py | 28 +++++++++---------- .../diffractive_imaging/dataset_models.py | 11 +++++++- 2 files changed, 24 insertions(+), 15 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 1575276d..f79d7661 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -219,7 +219,7 @@ def parse_dict(cls, d: dict): raise ValueError(f"Unknown optimizer type: {name.lower()}") -OptimizerType = ( +OptimizerParamsType = ( OptimizerParams.Adam | OptimizerParams.AdamW | OptimizerParams.SGD @@ -537,7 +537,7 @@ def __init__(self): """Initialize the optimizer mixin.""" self._optimizer = None self._scheduler = None - self._optimizer_params: dict[str, OptimizerType] = { + self._optimizer_params: dict[str, OptimizerParamsType] = { self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer() } self._scheduler_params: SchedulerType = SchedulerParams.NoneScheduler() @@ -554,31 +554,31 @@ def scheduler(self) -> "torch.optim.lr_scheduler.LRScheduler | None": return self._scheduler @property - def optimizer_params(self) -> dict[str, OptimizerType]: + def optimizer_params(self) -> dict[str, OptimizerParamsType]: """Get the optimizer parameters.""" return self._optimizer_params @optimizer_params.setter def optimizer_params( - self, params: OptimizerType | dict[str, OptimizerType] | dict[str, Any] + self, params: OptimizerParamsType | dict[str, OptimizerParamsType] | dict[str, Any] ) -> None: self._optimizer_params = self._normalize_optimizer_params(params) def _normalize_optimizer_params( - self, params: OptimizerType | dict[str, Any] - ) -> dict[str, OptimizerType]: - """Normalize input to dict[str, OptimizerType]. Subclasses can override to validate keys.""" - # Single optimizer, already an OptimizerType - if isinstance(params, OptimizerType): + self, params: OptimizerParamsType | dict[str, Any] + ) -> dict[str, OptimizerParamsType]: + """Normalize input to dict[str, OptimizerParamsType]. Subclasses can override to validate keys.""" + # Single optimizer, already an OptimizerParamsType + if isinstance(params, OptimizerParamsType): return {self.DEFAULT_OPTIMIZER_KEY: params} if not isinstance(params, dict): - raise TypeError(f"optimizer_params must be OptimizerType or dict, got {type(params)}") + raise TypeError(f"optimizer_params must be OptimizerParamsType or dict, got {type(params)}") # Single optimizer as dict shorthand, e.g. {"name": "adam", "lr": 1e-3} if self._is_single_optimizer_dict(params): return {self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.parse_dict(d=params)} - # dict-of-OptimizerType form (PPLR) + # dict-of-OptimizerParamsType form (PPLR) return { - k: v if isinstance(v, OptimizerType) else OptimizerParams.parse_dict(d=v) + k: v if isinstance(v, OptimizerParamsType) else OptimizerParams.parse_dict(d=v) for k, v in params.items() } @@ -616,11 +616,11 @@ def get_optimization_parameters( """ raise NotImplementedError("Subclasses must implement get_optimization_parameters") - def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: + def set_optimizer(self, opt_params: OptimizerParamsType | dict | None = None) -> None: """ Set the optimizer for this model, supporting per-parameter-group learning rates (PPLR). - ``optimizer_params`` is a ``dict[str, OptimizerType]`` keyed by parameter group. Each + ``optimizer_params`` is a ``dict[str, OptimizerParamsType]`` keyed by parameter group. Each group's spec is joined by key to the tensors returned by ``get_optimization_parameters()`` and its hyperparameters are baked into the corresponding torch param group here. All groups must use the same optimizer class. If every group is a ``NoneOptimizer`` (or there diff --git a/src/quantem/diffractive_imaging/dataset_models.py b/src/quantem/diffractive_imaging/dataset_models.py index 39895adb..f663f3eb 100644 --- a/src/quantem/diffractive_imaging/dataset_models.py +++ b/src/quantem/diffractive_imaging/dataset_models.py @@ -1,3 +1,4 @@ +import warnings from abc import abstractmethod from dataclasses import replace from pathlib import Path @@ -12,7 +13,7 @@ from quantem.core.datastructures.dataset3d import Dataset3d from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize -from quantem.core.ml.optimizer_mixin import OptimizerMixin +from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerParams from quantem.core.utils.utils import electron_wavelength_angstrom, tqdmnd from quantem.core.utils.validators import ( validate_array, @@ -127,6 +128,14 @@ def _normalize_optimizer_params(self, params): ) if on ] + if not learnable and not isinstance(spec, OptimizerParams.NoneOptimizer): + warnings.warn( + f"{type(self).__name__}: an optimizer was requested but nothing is " + "learnable (both learn_descan and learn_scan_positions are False); " + "the optimizer will be removed. Enable learn_descan and/or " + "learn_scan_positions to optimize.", + stacklevel=2, + ) return {key: replace(spec) for key in learnable} if learnable else {} return norm From 18486ee0080eb75aae59bedd0df1027a42d01bdf Mon Sep 17 00:00:00 2001 From: quantem-bot Date: Mon, 1 Jun 2026 15:29:32 +0000 Subject: [PATCH 088/140] chore: update lock file --- uv.lock | 735 +++++++++++++++++++++++++++++--------------------------- 1 file changed, 382 insertions(+), 353 deletions(-) diff --git a/uv.lock b/uv.lock index 716284e6..0e8424aa 100644 --- a/uv.lock +++ b/uv.lock @@ -532,101 +532,101 @@ wheels = [ [[package]] name = "coverage" -version = "7.14.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/23/7f/d0720730a397a999ffc0fd3f5bebef347338e3a47b727da66fbb228e2ff2/coverage-7.14.0.tar.gz", hash = "sha256:057a6af2f160a85384cde4ab36f0d2777bae1057bae255f95413cdd382aa5c74", size = 919489, upload-time = "2026-05-10T18:02:31.397Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/fc/e4/649c8d4f7f1709b6dbfc474358aa1bba02f67bcd52e2fec291a5014006cd/coverage-7.14.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6a78e2a9d9c5e3b8d4ab9b9d28c985ea66fced0a7d7c2aec1f216e03a2011480", size = 219795, upload-time = "2026-05-10T17:59:48.198Z" }, - { url = "https://files.pythonhosted.org/packages/7f/8d/46692d24b3f395d4cbf17bfcc57136b4f2f9c0c0df864b0bddfc1d71a014/coverage-7.14.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a1816c505187592dcd1c5a5f226601a549f70365fbd00930ac88b0c225b76bb4", size = 220299, upload-time = "2026-05-10T17:59:49.683Z" }, - { url = "https://files.pythonhosted.org/packages/12/c2/a40f5cb295bbcbb697a76947a56081c494c61950366294ee426ffe261099/coverage-7.14.0-cp311-cp311-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:d8e1762f0e9cbc26ec315471e7b47855218e833cd5a032d706fbf43845d878c7", size = 250721, upload-time = "2026-05-10T17:59:51.494Z" }, - { url = "https://files.pythonhosted.org/packages/fd/35/202235eb5c3c14c212462cd91d61b7386bf8fc44bc7a77f4742d2a69174b/coverage-7.14.0-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:9336e23e8bb3a3925398261385e2a1533957d3e760e91070dcb0e98bfa514eed", size = 252633, upload-time = "2026-05-10T17:59:53.244Z" }, - { url = "https://files.pythonhosted.org/packages/bb/80/5f596e8995785124ee191c42535664c5e62c65995b66f4ca21e28ae04c81/coverage-7.14.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9cd1169b2230f9cbe9c638ba38022ed7a2b1e641cc07f7cea0365e4be2a74980", size = 254743, upload-time = "2026-05-10T17:59:55.021Z" }, - { url = "https://files.pythonhosted.org/packages/1e/6d/0d178825be2350f0adb27984d0aa7cf84bbdab201f6fb926b535d23a8f5f/coverage-7.14.0-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:d1bb3543b58fea74d2cd1abc4054cc927e4724687cb4560cd2ed88d2c7d820c0", size = 256700, upload-time = "2026-05-10T17:59:56.511Z" }, - { url = "https://files.pythonhosted.org/packages/19/5b/9e549c2f6e9dfea472adadba06c294e64735dabc2dd19015fac082095013/coverage-7.14.0-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:a93bac2cb577ef60074999ed56d8a1535894398e2ed920d4185c3ec0c8864742", size = 250854, upload-time = "2026-05-10T17:59:57.94Z" }, - { url = "https://files.pythonhosted.org/packages/3d/1c/b94f9f5f36396021ee2f62c5834b12e6a3d31f0bed5d6fc6d1c3caec087c/coverage-7.14.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:5904abf7e18cddc463219b17552229650c6b79e061d31a1059283051169cf7d5", size = 252433, upload-time = "2026-05-10T17:59:59.688Z" }, - { url = "https://files.pythonhosted.org/packages/b5/cb/d192cd8e1345eccabc32016f2d39072ecd10cb4f4b983ed8d0ebdeaf00dc/coverage-7.14.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:741f57cddc9004a8c81b084660215f33a6b597dbe62c31386b983ee26310e327", size = 250494, upload-time = "2026-05-10T18:00:01.953Z" }, - { url = "https://files.pythonhosted.org/packages/53/c5/aac9f460a41d835dbddef1d377f105f6ac2311d0f3c1588e9f51046d8813/coverage-7.14.0-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:664123feb0929d7affc135717dbd70d61d98688a08ab1e5ba464739620c6252d", size = 254261, upload-time = "2026-05-10T18:00:03.779Z" }, - { url = "https://files.pythonhosted.org/packages/23/aa/7af7c0081980a9cb3d289c5a435a4b7657dcecbd128e25c580e6a50389b5/coverage-7.14.0-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:c83d2399a51bbec8429266905d33616f04bc5726b1138c35844d5fcd896b2e20", size = 250216, upload-time = "2026-05-10T18:00:05.262Z" }, - { url = "https://files.pythonhosted.org/packages/35/60/a4257538ce2f6b978aeb51870d6c4208c510928a03db7e0339bb625dccb7/coverage-7.14.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:bcb2e855b87321259a037429288ae85216d191c74de3e79bf57cd2bc0761992c", size = 251125, upload-time = "2026-05-10T18:00:06.858Z" }, - { url = "https://files.pythonhosted.org/packages/a1/ab/f91af47642ec1aa53490e835a95847168d9c77fc39aa58527604c051e145/coverage-7.14.0-cp311-cp311-win32.whl", hash = "sha256:731dc15b385ac52289743d476245b61e1a2927e803bef655b52bc3b2a75a21f3", size = 222300, upload-time = "2026-05-10T18:00:08.608Z" }, - { url = "https://files.pythonhosted.org/packages/f0/f0/a71ddbd874431e7a7cd96071f0c331cfbbad07704833c765d24ffbab8a67/coverage-7.14.0-cp311-cp311-win_amd64.whl", hash = "sha256:bfb0ed8ec5d25e93face268115d7964db9df8b9aae8edcde9ec6b16c726a7cc1", size = 223241, upload-time = "2026-05-10T18:00:10.746Z" }, - { url = "https://files.pythonhosted.org/packages/d8/6e/d9d312a5151a96cd110efee32efc3fc97b01ebd86203fe618ccb29cf4c92/coverage-7.14.0-cp311-cp311-win_arm64.whl", hash = "sha256:7ebb1c6df9f78046a1b1e0a89674cd4bf73b7c648914eebcf976a57fd99a5627", size = 221908, upload-time = "2026-05-10T18:00:12.242Z" }, - { url = "https://files.pythonhosted.org/packages/09/1e/2f996b2c8415cbb6f54b0f5ec1ee850c96d7911961afb4fc05f4a89d8c58/coverage-7.14.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7ffd19fc8aed057fd686a17a4935eef5f9859d69208f96310e893e64b9b6ccf5", size = 219967, upload-time = "2026-05-10T18:00:13.756Z" }, - { url = "https://files.pythonhosted.org/packages/34/23/35c7aea1274aef7525bdd2dc92f710bdde6d11652239d71d1ec450067939/coverage-7.14.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:829994cfe1aeb773ca27bf246d4badc1e764893e3bfb98fff820fcecd1ca4662", size = 220329, upload-time = "2026-05-10T18:00:15.264Z" }, - { url = "https://files.pythonhosted.org/packages/75/cf/a8f4b43a16e194b0261257ad28ded5853ec052570afef4a84e1d81189f3b/coverage-7.14.0-cp312-cp312-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:b4f07cf7edcb7ec39431a5074d7ea83b29a9f71fcfc494f0f40af4e65180420f", size = 251839, upload-time = "2026-05-10T18:00:17.16Z" }, - { url = "https://files.pythonhosted.org/packages/69/ff/6699e7b71e60d3049eb2bdcbc95ee3f35707b2b0e48f32e9e63d3ce30c08/coverage-7.14.0-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:ca3d9cf2c32b521bd9518385608787fa86f38daf993695307531822c3430ed67", size = 254576, upload-time = "2026-05-10T18:00:18.829Z" }, - { url = "https://files.pythonhosted.org/packages/22/ec/c936d495fcd67f48f03a9c4ad3297ff80d1f222a5df3980f15b34c186c21/coverage-7.14.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92af52828e7f29d827346b0294e5a0853fa206db77db0395b282918d41e28db9", size = 255690, upload-time = "2026-05-10T18:00:20.648Z" }, - { url = "https://files.pythonhosted.org/packages/5c/42/5af63f636cc62a4a2b1b3ba9146f6ee6f53a35a50d5cefc54d5670f60999/coverage-7.14.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7b2bb6c9d7e769360d0f20a0f219603fd64f0c8f97de17ab25853261602be0fb", size = 257949, upload-time = "2026-05-10T18:00:22.28Z" }, - { url = "https://files.pythonhosted.org/packages/26/d3/a225317bd2012132a27e1176d51660b826f99bb975876463c44ea0d7ee5a/coverage-7.14.0-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:1c9ed6ef99f88fb8c14aa8e2bf8eb0fe55fa2edfea68f8675d78741df1a5ac0e", size = 252242, upload-time = "2026-05-10T18:00:24.076Z" }, - { url = "https://files.pythonhosted.org/packages/f1/7f/9e65495298c3ea414742998539c37d048b5e81cc818fb1828cc6b51d10bf/coverage-7.14.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8231ade007f37959fbf58acc677f26b922c02eda6f0428ea307da0fd39681bf3", size = 253608, upload-time = "2026-05-10T18:00:25.588Z" }, - { url = "https://files.pythonhosted.org/packages/94/46/1522b524a35bdad22b2b8c4f9d32d0a104b524726ec380b2db68db1746f5/coverage-7.14.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:d8b013632cc1ce1d09dbe4f32667b4d320ec2f54fc326ebeffcd0b0bcc2bb6c4", size = 251753, upload-time = "2026-05-10T18:00:27.104Z" }, - { url = "https://files.pythonhosted.org/packages/f3/e9/cdf00d38817742c541ade405e115a3f7bf36e6f2a8b99d4f209861b85a2d/coverage-7.14.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:1733198802d71ec4c524f322e2867ee05c62e9e75df86bdca545407a221827d1", size = 255823, upload-time = "2026-05-10T18:00:29.038Z" }, - { url = "https://files.pythonhosted.org/packages/38/fc/5e7877cf5f902d08a17ff1c532511476d87e1bea355bd5028cb97f902e79/coverage-7.14.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:72a305291fa8ee01332f1aaf38b348ca34097f6aa0b0ef627eef2837e57bbba5", size = 251323, upload-time = "2026-05-10T18:00:30.647Z" }, - { url = "https://files.pythonhosted.org/packages/18/9d/50f05a72dff8487464fdd4178dda5daed642a060e60afb644e3d45123559/coverage-7.14.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fcaba850dd317c65423a9d63d88f9573c53b00354d6dd95724576cc98a131595", size = 253197, upload-time = "2026-05-10T18:00:32.211Z" }, - { url = "https://files.pythonhosted.org/packages/00/3f/6f61ffe6439df266c3cf60f5c99cfaa21103d0210d706a42fc6c30683ff8/coverage-7.14.0-cp312-cp312-win32.whl", hash = "sha256:5ac83957a80d0701310e96d8bec68cdcf4f90a7674b7d13f15a344315b41ab27", size = 222515, upload-time = "2026-05-10T18:00:33.717Z" }, - { url = "https://files.pythonhosted.org/packages/85/19/93853133df2cb371083285ef6a93982a0173e7a233b0f61373ba9fd30eb2/coverage-7.14.0-cp312-cp312-win_amd64.whl", hash = "sha256:70390b0da32cb90b501953716302906e8bcce087cb283e70d8c97729f22e92b2", size = 223324, upload-time = "2026-05-10T18:00:35.172Z" }, - { url = "https://files.pythonhosted.org/packages/74/18/9f7fe62f659f24b7a82a0be56bf94c1bd0a89e0ae7ab4c668f6e82404294/coverage-7.14.0-cp312-cp312-win_arm64.whl", hash = "sha256:91b993743d959b8be85b4abf9d5478216a69329c321efe5be0433c1a841d691d", size = 221944, upload-time = "2026-05-10T18:00:37.014Z" }, - { url = "https://files.pythonhosted.org/packages/6b/76/b7c66ee3c66e1b0f9d894c8125983aa0c03fb2336f2fd16559f9c966157f/coverage-7.14.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f2bbb8254370eb4c628ff3d6fa8a7f74ddc40565394d4f7ab791d1fe568e37ef", size = 219990, upload-time = "2026-05-10T18:00:38.887Z" }, - { url = "https://files.pythonhosted.org/packages/b3/af/e567cbad5ba69c013a50146dfa886dc7193361fda77521f51274ff620e1b/coverage-7.14.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:23b81107f46d3f21d0cbce30664fcec0f5d9f585638a67081750f99738f6bf66", size = 220365, upload-time = "2026-05-10T18:00:40.864Z" }, - { url = "https://files.pythonhosted.org/packages/44/6f/9ad575d505b4d805b254febc8a5b338a2efe278f8786e56ff1cb8413f9c3/coverage-7.14.0-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:22a7e06a5f11a757cdfe79018e9095f9f69ae283c5cd8123774c788deec8717b", size = 251363, upload-time = "2026-05-10T18:00:42.489Z" }, - { url = "https://files.pythonhosted.org/packages/6f/5f/b5370068b2f57787454592ed7dcd1002f0f1703b7db1fa30f6a325a4ca6e/coverage-7.14.0-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:9d1aa57a1dc8e05bdc42e81c5d671d849577aeedf279f4c449d6d286f9ed88ca", size = 253961, upload-time = "2026-05-10T18:00:44.079Z" }, - { url = "https://files.pythonhosted.org/packages/29/1e/51adf17738976e8f2b85ddef7b7aa12a0838b056c92f175941d8862767c1/coverage-7.14.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:90c1a51bcfddf645b3bb7ec333d9e94393a8e94f55642380fa8a9a5a9e636cb7", size = 255193, upload-time = "2026-05-10T18:00:45.623Z" }, - { url = "https://files.pythonhosted.org/packages/9e/7b/5bfd7ac1df3b881c2ac7a5cbc99c7609e6296c402f5ef587cd81c6f355b3/coverage-7.14.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a841fae2fadcae4f438d43b6ccc4aac2ad609f47cdb6cfdce60cbb3fe5ca7bc2", size = 257326, upload-time = "2026-05-10T18:00:47.173Z" }, - { url = "https://files.pythonhosted.org/packages/7d/38/1d37d316b174fad3843a1d76dbdfe4398771c9ecd0515935dd9ece9cd627/coverage-7.14.0-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c79d2319cabef1fe8e86df73371126931550804738f78ad7d31e3aad85a67367", size = 251582, upload-time = "2026-05-10T18:00:49.152Z" }, - { url = "https://files.pythonhosted.org/packages/34/46/746704f95980ba220214e1a41e18cec5aea80a898eaa53c51bf2d645ff36/coverage-7.14.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:1b23b0c6f0b1db6ad769b7050c8b641c0bf215ded26c1816955b17b7f26edfa9", size = 253325, upload-time = "2026-05-10T18:00:51.252Z" }, - { url = "https://files.pythonhosted.org/packages/e1/b9/bbe87206d9687b192352f893797825b5f5b15ecd3aa9c68fbff0c074d77b/coverage-7.14.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:55d3089079ce181a4566b1065ab28d2575eb76d8ac8f81f4fcda2bf037fee087", size = 251291, upload-time = "2026-05-10T18:00:52.816Z" }, - { url = "https://files.pythonhosted.org/packages/46/57/b8cdb12ac0d73ef0243218bd5e22c9df8f92edab8018213a86aec67c5324/coverage-7.14.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:49c005cba1e2f9677fb2845dcdf9a2e72a52a17d63e8231aaaae35d9f50215ef", size = 255448, upload-time = "2026-05-10T18:00:54.548Z" }, - { url = "https://files.pythonhosted.org/packages/1f/d4/5002019538b2036ce3c84340f54d2fd5100d55b0a6b0894eee56128d03c7/coverage-7.14.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:9117377b823daa28aa8635fbb08cda1cd6be3d7143257345459559aeef852d52", size = 251110, upload-time = "2026-05-10T18:00:56.122Z" }, - { url = "https://files.pythonhosted.org/packages/37/53/20c5009477660f084e6ed60bc02a91894b8e234e617e86ecfd9aaf78e27b/coverage-7.14.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:7b79d646cf46d5cf9a9f40281d4441df5849e445726e369006d2b117710b33fe", size = 252885, upload-time = "2026-05-10T18:00:57.967Z" }, - { url = "https://files.pythonhosted.org/packages/ae/ab/3cf6427ac9c1f1db747dbb1ce71dde47984876d4c2cfd018a3fef0a78d4d/coverage-7.14.0-cp313-cp313-win32.whl", hash = "sha256:fb609b3658479e33f9516d46f1a89dbb9b6c261366e3a11844a96ec487533dae", size = 222539, upload-time = "2026-05-10T18:00:59.581Z" }, - { url = "https://files.pythonhosted.org/packages/8f/b8/9228523e80321c2cb4880d1f589bc0171f2f71432c35118ad04dc01decce/coverage-7.14.0-cp313-cp313-win_amd64.whl", hash = "sha256:0773d8329cf32b6fd222e4b52622c61fe8d503eb966cfc8d3c3c10c96266d50e", size = 223344, upload-time = "2026-05-10T18:01:01.531Z" }, - { url = "https://files.pythonhosted.org/packages/a3/99/118daa192f95e3a6cb2740100fbf8797cda1734b4134ef0b5d501a7fa8f3/coverage-7.14.0-cp313-cp313-win_arm64.whl", hash = "sha256:b4e26a0f1b696faf283bffe5b8569e44e336c582439df5d53281ab89ee0cba96", size = 221966, upload-time = "2026-05-10T18:01:03.16Z" }, - { url = "https://files.pythonhosted.org/packages/e6/f1/a46cc0c013be170216253184a32366d7cbdb9252feaec866b05c2d12a894/coverage-7.14.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:953f521ca9445300397e65fda3dca58b2dbd68fee983777420b57ac3c77e9f90", size = 220679, upload-time = "2026-05-10T18:01:05.058Z" }, - { url = "https://files.pythonhosted.org/packages/64/8c/9c30a3d311a34177fa432995be7fbfc64477d8bac5630bd38055b1c9b424/coverage-7.14.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:98af83fd65ae24b1fdd03aaead967a9f523bcd2f1aab2d4f3ffda65bb568a6f1", size = 221033, upload-time = "2026-05-10T18:01:07.002Z" }, - { url = "https://files.pythonhosted.org/packages/9a/cd/3fb5e06c3badefd0c1b47e2044fdca67f8220a4ec2e7fcfb476aa0a67c6c/coverage-7.14.0-cp313-cp313t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:668b92e6958c4db7cf92e81caac328dfbbdbb215db2850ad28f0cbe1eea0bfbd", size = 262333, upload-time = "2026-05-10T18:01:08.903Z" }, - { url = "https://files.pythonhosted.org/packages/a8/e6/fbc322325c7294d3e22c1ad6b79e45d0806b25228c8e5842aed6d8169aa7/coverage-7.14.0-cp313-cp313t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:9fbd898551762dea00d3fef2b1c4f99afd2c6a3ff952ea07d60a9bd5ed4f34bc", size = 264410, upload-time = "2026-05-10T18:01:10.531Z" }, - { url = "https://files.pythonhosted.org/packages/08/92/c497b264bec1673c47cc77e26f760fcda4654cabf1f39546d1a23a3b8c35/coverage-7.14.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:68af363c07ecd8d4b7d4043d85cb376d7d227eceb54e5323ee45da73dbd3e426", size = 266836, upload-time = "2026-05-10T18:01:12.19Z" }, - { url = "https://files.pythonhosted.org/packages/78/fc/045da320987f401af5d2815d351e8aa799aec859f60e29f445e3089eeedb/coverage-7.14.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:6e57054a583da8ac55edf24117ea4c9133032cfc4cf72aa2d48c1e5d4b52f899", size = 267974, upload-time = "2026-05-10T18:01:13.926Z" }, - { url = "https://files.pythonhosted.org/packages/1b/ae/227b1e379497fb7a4fc3286e620f80c8a1e7cec66d45695a01639eb1af65/coverage-7.14.0-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:cc3499459bbcdd51a65b64c35ab7ed2764eaf3cba826e0df3f1d7fe2e102b70b", size = 261578, upload-time = "2026-05-10T18:01:15.564Z" }, - { url = "https://files.pythonhosted.org/packages/a0/f5/3570342900f2acea31d33ff1590c5d8bac1a8e1a2e1c6d34a5d5e61de681/coverage-7.14.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:45899ec2138a4346ed34d601dedf5076fb74edf2d1dd9dc76a78e82397edee90", size = 264394, upload-time = "2026-05-10T18:01:17.607Z" }, - { url = "https://files.pythonhosted.org/packages/16/29/de1bbc01c935b28f89b1dc3db85b011c055e843a8e5e3b83141c3f80af7f/coverage-7.14.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:8767486808c436f05b23ab98eb963fb29185e32a9357a166971685cb3459900f", size = 262022, upload-time = "2026-05-10T18:01:19.304Z" }, - { url = "https://files.pythonhosted.org/packages/35/95/f53890b0bf2fc10ab168e05d38869215e73ca24c4cb521c3bb0eb62fe16b/coverage-7.14.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:a3b5ddfd6aa7ddad53ee3edb231e88a2151507a43229b7d71b953916deca127d", size = 265732, upload-time = "2026-05-10T18:01:21.494Z" }, - { url = "https://files.pythonhosted.org/packages/ed/ea/c919e259081dd2bdf0e43b87209709ba7ec2e4117c2a7f5185379c43463c/coverage-7.14.0-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:63df0fe568e698e1045792399f8ab6da3a6c2dce3182813fb92afa2641087b47", size = 260921, upload-time = "2026-05-10T18:01:23.533Z" }, - { url = "https://files.pythonhosted.org/packages/1a/2c/c2831889705a81dc5d1c6ca12e4d8e9b95dfc146d153488a6c0ea685d28e/coverage-7.14.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:827d6397dbd95144939b18f89edf31f63e1f99633e8d5f32f22ba8bdda567477", size = 263109, upload-time = "2026-05-10T18:01:25.165Z" }, - { url = "https://files.pythonhosted.org/packages/5a/a9/2fcae5003cac3d63fe344d2166243c2756935f48420863c5272b240d550b/coverage-7.14.0-cp313-cp313t-win32.whl", hash = "sha256:7bf43e000d24012599b879791cff41589af90674722421ef11b11a5431920bab", size = 223212, upload-time = "2026-05-10T18:01:27.157Z" }, - { url = "https://files.pythonhosted.org/packages/3f/bb/18e94d7b14b9b398164197114a587a04ab7c9fdbe1d237eef57311c5e883/coverage-7.14.0-cp313-cp313t-win_amd64.whl", hash = "sha256:3f5549365af25d770e06b1f8f5682d9a5637d06eb494db91c6fa75d3950cc917", size = 224272, upload-time = "2026-05-10T18:01:29.107Z" }, - { url = "https://files.pythonhosted.org/packages/db/56/4f14fad782b035c81c4ffd09159e7103d42bb1d93ac8496d04b90a11b7da/coverage-7.14.0-cp313-cp313t-win_arm64.whl", hash = "sha256:6d160217ec6fe890f16ad3a9531761589443749e448f91986c972714fad361c8", size = 222530, upload-time = "2026-05-10T18:01:31.151Z" }, - { url = "https://files.pythonhosted.org/packages/1c/18/b9a6586d73992807c26f9a5f274131be3d76b56b18a82b9392e2a25d2e45/coverage-7.14.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:9aed9fa983514ca032790f3fe0d1c0e42ca7e16b42432af1706b50a9a46bef5d", size = 220036, upload-time = "2026-05-10T18:01:33.057Z" }, - { url = "https://files.pythonhosted.org/packages/f3/9b/4165a1d56ddc302a0e2d518fd9d412a4fd0b57562618c78c5f21c57194f5/coverage-7.14.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:ba3b8390db29296dbbf49e91b6fe08f990743a90c8f447ba4c2ffc29670dfa63", size = 220368, upload-time = "2026-05-10T18:01:34.705Z" }, - { url = "https://files.pythonhosted.org/packages/69/aa/c12e52a5ba148d9995229d557e3be6e554fe469addc0e9241b2f0956d8ea/coverage-7.14.0-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:3a5d8e876dfa2f102e970b183863d6dedd023d3c0eeca1fe7a9787bc5f28b212", size = 251417, upload-time = "2026-05-10T18:01:36.949Z" }, - { url = "https://files.pythonhosted.org/packages/d7/51/ec641c26e6dca1b25a7d2035ba6ecb7c884ef1a100a9e42fbe4ce4405139/coverage-7.14.0-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:5ebb8f4614a3787d567e610bbfdf96a4798dd69a1afb1bd8ad228d4111fe6ff3", size = 253924, upload-time = "2026-05-10T18:01:38.985Z" }, - { url = "https://files.pythonhosted.org/packages/33/c4/59c3de0bd1b538824173fd518fed51c1ce740ca5ed68e74545983f4053a9/coverage-7.14.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b9bf47223dd8db3d4c4b2e443b02bace480d428f0822c3f991600448a176c97", size = 255269, upload-time = "2026-05-10T18:01:40.957Z" }, - { url = "https://files.pythonhosted.org/packages/7b/a9/36dfa153a62040296f6e7febfdb20a5720622f6ef5a81a41e8237b9a5344/coverage-7.14.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3485a836550b303d006d57cc06e3d5afaabc642c77050b7c985a97b13e3776b8", size = 257583, upload-time = "2026-05-10T18:01:42.607Z" }, - { url = "https://files.pythonhosted.org/packages/26/7b/cc2c048d4114d9ab1c2409e9ee365e5ae10736df6dffcfc9444effa6c708/coverage-7.14.0-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:3e7e88110bae996d199d1693ca8ec3fd52441d426401ae963437598667b4c5eb", size = 251434, upload-time = "2026-05-10T18:01:44.537Z" }, - { url = "https://files.pythonhosted.org/packages/ee/df/6770eaa576e604575e9a78055313250faef5faa84bd6f71a39fece519c43/coverage-7.14.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:15228a6800ce7bdf1b74800595e56db7138cecb338fdbf044806e10dcf182dfe", size = 253280, upload-time = "2026-05-10T18:01:46.175Z" }, - { url = "https://files.pythonhosted.org/packages/ad/9e/1c0264514a3f98259a6d64765a397b2c8373e3ba59ee722a4802d3ec0c61/coverage-7.14.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:9d26ac7f5398bafc5b57421ad994e8a4749e8a7a0e62d05ec7d53014d5963bfa", size = 251241, upload-time = "2026-05-10T18:01:48.732Z" }, - { url = "https://files.pythonhosted.org/packages/64/16/4efdf3e3c4079cdbf0ece56a2fea872df9e8a3e15a13a0af4400e1075944/coverage-7.14.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:2fb73254ff43c911c967a899e1359bc5049b4b115d6e8fbdde4937d0a2246cd5", size = 255516, upload-time = "2026-05-10T18:01:50.819Z" }, - { url = "https://files.pythonhosted.org/packages/93/69/b1de96346603881b3d1bc8d6447c83200e1c9700ffbaff926ba01ff5724c/coverage-7.14.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:454a380af72c6adada298ed270d38c7a391288198dbfb8467f786f588751a90c", size = 251059, upload-time = "2026-05-10T18:01:52.773Z" }, - { url = "https://files.pythonhosted.org/packages/a4/66/2881853e0363a5e0a724d1103e53650795367471b6afb234f8b49e713bc6/coverage-7.14.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:65c86fb646d2bd2972e96bd1a8b45817ed907cee68655d6295fe7ec031d04cca", size = 252716, upload-time = "2026-05-10T18:01:54.506Z" }, - { url = "https://files.pythonhosted.org/packages/55/5c/0d3305d002c41dcde873dbe456491e663dc55152ca526b630b5c47efd62f/coverage-7.14.0-cp314-cp314-win32.whl", hash = "sha256:6a6516b02a6101398e19a3f44820f69bab2590697f7def4331f668b14adaf828", size = 222788, upload-time = "2026-05-10T18:01:56.487Z" }, - { url = "https://files.pythonhosted.org/packages/f9/58/6e1b8f52fdc3184b47dc5037f5070d83a3d11042db1594b02d2a44d786c8/coverage-7.14.0-cp314-cp314-win_amd64.whl", hash = "sha256:45e0f79d8351fa76e256716df91eab12890d32678b9590df7ae1042e4bd4cf5d", size = 223600, upload-time = "2026-05-10T18:01:58.497Z" }, - { url = "https://files.pythonhosted.org/packages/00/70/a18c408e674bc26281cadaedc7351f929bd2094e191e4b15271c30b084cc/coverage-7.14.0-cp314-cp314-win_arm64.whl", hash = "sha256:4b899594a8b2d81e5cc064a0d7f9cac2081fed91049456cae7676787e41549c9", size = 222168, upload-time = "2026-05-10T18:02:00.411Z" }, - { url = "https://files.pythonhosted.org/packages/3d/89/2681f071d238b62aff8dfc2ab44fc24cfdb38d1c01f391a80522ff5d3a16/coverage-7.14.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:f580f8c80acd94ac72e863efe2cab791d8c38d153e0b463b92dfa000d5c84cd1", size = 220766, upload-time = "2026-05-10T18:02:02.313Z" }, - { url = "https://files.pythonhosted.org/packages/bd/c7/c987babafd9207ffa1995e1ef1f9b26762cf4963aa768a66b6f0501e4616/coverage-7.14.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:a2bd259c442cd43c49b30fbafc51776eb19ea396faf159d26a83e6a0a5f13b0c", size = 221035, upload-time = "2026-05-10T18:02:04.017Z" }, - { url = "https://files.pythonhosted.org/packages/5a/e9/d6a5ac3b333088143d6fc877d398a9a674dc03124a2f776e131f03864823/coverage-7.14.0-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:a706b908dfa85538863504c624b237a3cc34232bf403c057414ebfdb3b4d9f84", size = 262405, upload-time = "2026-05-10T18:02:05.915Z" }, - { url = "https://files.pythonhosted.org/packages/38/b1/e70838d29a7c08e22d44398a46db90815bbcbf28de06992bd9210d1a8d8e/coverage-7.14.0-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:7333cd944ee4393b9b3d3c1b598c936d4fc8d70573a4c7dacfec5590dd50e436", size = 264530, upload-time = "2026-05-10T18:02:07.582Z" }, - { url = "https://files.pythonhosted.org/packages/6b/73/5c31ef97763288d03d9995152b96d5475b527c63d91c84b01caea894b83a/coverage-7.14.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0f162bc9a15b82d947b02651b0c7e1609d6f7a8735ca330cfadec8481dd97d5a", size = 266932, upload-time = "2026-05-10T18:02:09.401Z" }, - { url = "https://files.pythonhosted.org/packages/e1/76/dd56d80f29c5f05b4d76f7e7c6d47cafacae017189c75c5759d24f9ff0cc/coverage-7.14.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:362cb78e01a5dc82009d88004cf60f2e6b6d6fcbfdec05b05af73b0abf40118f", size = 268062, upload-time = "2026-05-10T18:02:11.399Z" }, - { url = "https://files.pythonhosted.org/packages/6e/c7/27ba85cd5b95614f159ff93ebff1901584a8d192e2e5e24c4943a7453f59/coverage-7.14.0-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:acebd068fca5512c3a6fde9c045f901613478781a73f0e82b307b214daef23fb", size = 261504, upload-time = "2026-05-10T18:02:13.257Z" }, - { url = "https://files.pythonhosted.org/packages/13/2e/e8149f60ab5d5684c6eee881bdf34b127115cddbb958b196768dd9d63473/coverage-7.14.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:29fe3da551dface75deb2ccbf87b6b66e2e7ef38f6d89050b428be94afff3490", size = 264398, upload-time = "2026-05-10T18:02:15.063Z" }, - { url = "https://files.pythonhosted.org/packages/d9/7f/1261b025285323225f4b4abffa5a643649dfd67e25ddca7ebcbdea3b7cb3/coverage-7.14.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:b4cc4fce8672fffcb09b0eafc167b396b3ba53c4a7230f54b7aaffbf6c835fa9", size = 262000, upload-time = "2026-05-10T18:02:16.756Z" }, - { url = "https://files.pythonhosted.org/packages/d3/dc/829c54f60b9d08389439c00f813c752781c496fc5788c78d8006db4b4f2b/coverage-7.14.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:5d4a51aad8ba8bdcd2b8bd8f03d4aca19693fa2327a3470e4718a25b03481020", size = 265732, upload-time = "2026-05-10T18:02:18.817Z" }, - { url = "https://files.pythonhosted.org/packages/ed/b0/70bd1419941652fa062689cba9c3eeafb8f5e6fbb890bce41c3bdda5dbd6/coverage-7.14.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:9f323af3e1e4f68b60b7b247e37b8515563a61375518fa59de1af48ba28a3db6", size = 260847, upload-time = "2026-05-10T18:02:20.528Z" }, - { url = "https://files.pythonhosted.org/packages/f2/73/be40b2390656c654d35ea0015ea7ba3d945769cf80790ad5e0bb2d56d2ba/coverage-7.14.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:1a0abc7342ea9711c469dd8b821c6c311e6bc6aac1442e5fbd6b27fae0a8f3db", size = 263166, upload-time = "2026-05-10T18:02:22.337Z" }, - { url = "https://files.pythonhosted.org/packages/29/55/4a643f712fcf7cf2881f8ec1e0ccb7b164aff3108f69b51801246c8799f2/coverage-7.14.0-cp314-cp314t-win32.whl", hash = "sha256:a9f864ef57b7172e2db87a096642dd51e179e085ab6b2c371c29e885f65c8fb2", size = 223573, upload-time = "2026-05-10T18:02:24.11Z" }, - { url = "https://files.pythonhosted.org/packages/27/96/3acae5da0953be042c0b4dea6d6789d2f080701c77b88e44d5bd41b9219b/coverage-7.14.0-cp314-cp314t-win_amd64.whl", hash = "sha256:29943e552fdc08e082eb51400fb2f58e118a83b5542bd06531214e084399b644", size = 224680, upload-time = "2026-05-10T18:02:25.896Z" }, - { url = "https://files.pythonhosted.org/packages/93/3d/6ab5d2dd8325d838737c6f8d83d62eb6230e0d70b87b51b57bbfd08fa767/coverage-7.14.0-cp314-cp314t-win_arm64.whl", hash = "sha256:742a73ea621953b012f2c4c2219b512180dd84489acf5b1596b0aafc55b9100b", size = 222703, upload-time = "2026-05-10T18:02:27.822Z" }, - { url = "https://files.pythonhosted.org/packages/61/e8/cb8e80d6f9f55b99588625062822bf946cf03ed06315df4bd8397f5632a1/coverage-7.14.0-py3-none-any.whl", hash = "sha256:8de5b61163aee3d05c8a2beab6f47913df7981dad1baf82c414d99158c286ab1", size = 211764, upload-time = "2026-05-10T18:02:29.538Z" }, +version = "7.14.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/54/fd/0ab2772530e946e1be1abd0bc09e647ec9b02e88f0867857601fefca8953/coverage-7.14.1.tar.gz", hash = "sha256:30c08f7d90415aa98b3c990385dea2939b0da55f38515e5b369b83655f8523be", size = 920132, upload-time = "2026-05-26T20:41:36.783Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7d/d7/477ad149490e6cb849f28abea1dabb9c823cea72e7500c81b4240ce619c0/coverage-7.14.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:478b5bcd63c2e1357c5c7e16c070690df7b07f676b1c114d7b93e533c664309f", size = 219848, upload-time = "2026-05-26T20:38:38.715Z" }, + { url = "https://files.pythonhosted.org/packages/91/82/a5eb47257c50601bb7b9a9d2857c67b7a3a85ad74180eb2c98bb1fbe0ce5/coverage-7.14.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a24a81f9715ee42ef59a316cc11611c98fe23920f7c81861315c9f3ff4a230f4", size = 220354, upload-time = "2026-05-26T20:38:40.232Z" }, + { url = "https://files.pythonhosted.org/packages/43/8b/78419b5391a5cb706b6544390507e469d83ffc9a8248b02c4011aceb9365/coverage-7.14.1-cp311-cp311-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:196a13319ad88d6d8ef5ab489ec4f44ddde2143c0c7d5b27786f6c3ffd56a7e1", size = 250771, upload-time = "2026-05-26T20:38:41.782Z" }, + { url = "https://files.pythonhosted.org/packages/77/63/e77aaacd491182210d639636b7a8bba23ffffa9b82aa3762da9431855fa9/coverage-7.14.1-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:3d452fd08b5c72c5167c93e6867b5c08500bd40f2a21e1e854a500550b6cc36f", size = 252683, upload-time = "2026-05-26T20:38:43.305Z" }, + { url = "https://files.pythonhosted.org/packages/65/1c/a022e3cfbec2ac241640003cb3a817e161d9c7f5aa9b49173756cdc03204/coverage-7.14.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:23bf7fa51ac02e07fc7c96849b82946da47ae862dc8f86d183b2a4864fc38129", size = 254791, upload-time = "2026-05-26T20:38:45.361Z" }, + { url = "https://files.pythonhosted.org/packages/61/d6/967e408aca4c1ceb88cb0cc677169110ae7f5995fb5eaf5fb1f5a1bb8f5d/coverage-7.14.1-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:bcaa50684dcaadfa599ac48f81103c756d791cfd85c97203d2217c593d48b860", size = 256748, upload-time = "2026-05-26T20:38:46.91Z" }, + { url = "https://files.pythonhosted.org/packages/b8/be/869188f7fe28638078ec479331ace6dc5f7b40b7153eb616f47ab79404d8/coverage-7.14.1-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:4ea1c034f95c9b056e856b794630b17f9fa3d57e4800ff1e503d3be0f9c9078c", size = 250907, upload-time = "2026-05-26T20:38:48.493Z" }, + { url = "https://files.pythonhosted.org/packages/07/aa/adb7d3b4278d690e68703abcd76ab1b948242e3668d921711551b78f9ddb/coverage-7.14.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c7e057326434e441306226fbeb5d1aaf14a2637efe97ba668306635835f32ad7", size = 252483, upload-time = "2026-05-26T20:38:50.074Z" }, + { url = "https://files.pythonhosted.org/packages/43/61/331c74103c62dcb0c4b9b3a0de9a61aca016208b0a90f109592a9f9ecc28/coverage-7.14.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:59baf88468dbc8d63b1887afd92bda52e40bb1561696e5819670601403810cec", size = 250545, upload-time = "2026-05-26T20:38:51.613Z" }, + { url = "https://files.pythonhosted.org/packages/f6/b6/c5dae3c104d89be04828f61810e6b3473825482e4c288cc4ed04553e08ae/coverage-7.14.1-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:d34d75f892b3ab73ba11cab5442cce7b3e168fd64162b16f0e1e0d09c508edef", size = 254310, upload-time = "2026-05-26T20:38:53.503Z" }, + { url = "https://files.pythonhosted.org/packages/ad/a1/2b9d5863e3b83c01ad8199e3c597802fbb3a9dc90b058885804c20296d31/coverage-7.14.1-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:3a56abc20a472baf0304c455721bc601477440d28ecfde8a03dde79ede07e0df", size = 250266, upload-time = "2026-05-26T20:38:55.414Z" }, + { url = "https://files.pythonhosted.org/packages/7f/5e/0e511fbdb269359be26fe678a1c3fa1f2aa2a01573cc3f54268c8d6d4797/coverage-7.14.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:6a3cb83d1552c0cd1b4906655b6a33fd4a8473229633a901c6b73bf86914dee9", size = 251174, upload-time = "2026-05-26T20:38:57.141Z" }, + { url = "https://files.pythonhosted.org/packages/85/10/e55307b622b3dd9671cb321824502dc10f93e72f2802b9946159a8edadeb/coverage-7.14.1-cp311-cp311-win32.whl", hash = "sha256:10274a1fbeb8ec5d72966e17bb198a3104257aca4ac09d98667c5f8aca8c8548", size = 222354, upload-time = "2026-05-26T20:38:58.727Z" }, + { url = "https://files.pythonhosted.org/packages/71/cf/107421693cfb71e4f1ca5bf70443f64d4161878068d07a3e51c7ad21d17b/coverage-7.14.1-cp311-cp311-win_amd64.whl", hash = "sha256:87ebdf787d4888e3f3f2d523eadc6e18c6d18c6d0eb173801a189641627fb37e", size = 223290, upload-time = "2026-05-26T20:39:00.413Z" }, + { url = "https://files.pythonhosted.org/packages/b8/1d/3e3644585eb29e9dafefb19555078529a4d7cce12bd21929664eea989277/coverage-7.14.1-cp311-cp311-win_arm64.whl", hash = "sha256:dd34767fa19848d35659ffc0a75314f58c7af3f1cd87ec521e8292a1238398a3", size = 221953, upload-time = "2026-05-26T20:39:02.159Z" }, + { url = "https://files.pythonhosted.org/packages/3d/b7/bdbb725ba02c5b42825b200c940f38b7a54fcad24627b7192f78f8110d76/coverage-7.14.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:a06c76364a9360e33d6d23769aefdf7f66f38e2ffb60ceb1baaa4989d83b695c", size = 220022, upload-time = "2026-05-26T20:39:03.702Z" }, + { url = "https://files.pythonhosted.org/packages/72/81/fdc0898a55c6219223291ec1a1fe89966ef212ce82276aa0899df84b5de0/coverage-7.14.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fad54e871165f6ec2f536063ac74c3104508a12963e64072ba44bd822de52b0c", size = 220379, upload-time = "2026-05-26T20:39:05.381Z" }, + { url = "https://files.pythonhosted.org/packages/de/72/de048c4a25e13bce59ac6a339351c10bdf2515e07459afcdaf04dc3143a2/coverage-7.14.1-cp312-cp312-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:84b535f00655ecafe1d929d1fb00ed5d6fa3051ea643ab2c161a3887b86f294b", size = 251888, upload-time = "2026-05-26T20:39:07.367Z" }, + { url = "https://files.pythonhosted.org/packages/28/30/300c343f68beb9d4cbb64ec81e58c5b6b80b56927f72d2b38654ac26e013/coverage-7.14.1-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:6b6b0853b895fe0e98cbfc580d1ec3393d9302b4b1e96a77b3f5c91fdab899e6", size = 254624, upload-time = "2026-05-26T20:39:09.037Z" }, + { url = "https://files.pythonhosted.org/packages/b1/ed/7b25642496e8170b6bac14adce00537c6e5fa2d586159401a4de3e8b49e6/coverage-7.14.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:442cc9c952b2df400cda54bb04ab87330cf2cd08a8692cbbea36773531eb6f37", size = 255739, upload-time = "2026-05-26T20:39:10.889Z" }, + { url = "https://files.pythonhosted.org/packages/7f/a2/abd210b8c4e29c24e4624916db97bb519097a91034aaeb767f937e7da794/coverage-7.14.1-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:8270544c361ed405a27a060dbc9ed2c124b084d96dfdc2d9a2510482aef981ad", size = 257998, upload-time = "2026-05-26T20:39:12.722Z" }, + { url = "https://files.pythonhosted.org/packages/7f/24/7c50beed3792fe62f6ce0545c6686ce83379719e2c0276179333d97eae92/coverage-7.14.1-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:48b283b1dd6372e8de2a7a9a4c4d5dc06f4d4fd209b876f3c88a7a205a0c8f84", size = 252296, upload-time = "2026-05-26T20:39:14.259Z" }, + { url = "https://files.pythonhosted.org/packages/15/05/0f874628ebcbfc77ead559ff210281ef06a97db08481832e7dd39274a135/coverage-7.14.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:5b0c99ba93a07d56f6df340bb79be53202a082b2fdb81bfe6190b741a3470d54", size = 253658, upload-time = "2026-05-26T20:39:15.923Z" }, + { url = "https://files.pythonhosted.org/packages/99/6f/ca6ad067364b337ef997802115e7ecad2abd2248b05471464b0dea02b4d4/coverage-7.14.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:e471bc5769ff073b058cfadb0d736b56ce067c8560eabeb0da88462df98c23e7", size = 251803, upload-time = "2026-05-26T20:39:17.537Z" }, + { url = "https://files.pythonhosted.org/packages/c0/30/b9b4d377cd9f40baf228068f5a81faf8450c6228503011bd499708483a50/coverage-7.14.1-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:f497a1ea81d4cd7c10ddcaa685135b9aabd291af3d55775a9ddf3cb7a364cdd9", size = 255873, upload-time = "2026-05-26T20:39:19.414Z" }, + { url = "https://files.pythonhosted.org/packages/3c/21/7c721a9e5e6bb88547d30a787aefb97512d3f54c1324c7488d9b3743f7f9/coverage-7.14.1-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:2222be86d0b54f5dd5a38f45f17f315f737245e857bf0bdedc70734f84a13c02", size = 251372, upload-time = "2026-05-26T20:39:21.169Z" }, + { url = "https://files.pythonhosted.org/packages/9d/8c/f8ae5a2200130e1503cd7661a6cd3b2b7bacef98277fbf3571fb13f8b766/coverage-7.14.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:85e85586565842f6932abebd4c18bcb1074223dc0b3576e7d173ca710622813a", size = 253245, upload-time = "2026-05-26T20:39:23.097Z" }, + { url = "https://files.pythonhosted.org/packages/34/62/70a9024672a5f6910517d9628c52c9afbdd3cf8f46426af52bb148a56fff/coverage-7.14.1-cp312-cp312-win32.whl", hash = "sha256:4a28fd227808366b196a75476dced2eb35b351d6766ba9c858dc93319e87f4f1", size = 222567, upload-time = "2026-05-26T20:39:24.868Z" }, + { url = "https://files.pythonhosted.org/packages/f6/81/8b7cd386839b039ebe1855733b9f9449a8dec5d79564018234f185a7fa70/coverage-7.14.1-cp312-cp312-win_amd64.whl", hash = "sha256:54acdb6674a4661768d7bf7db32dfb9f46ab1d764f8aba6df75ce1a6a088724e", size = 223372, upload-time = "2026-05-26T20:39:26.603Z" }, + { url = "https://files.pythonhosted.org/packages/ae/ba/b44d472022f620d289d95fa830143235c0c36461c6f2437ea8d51e5481ed/coverage-7.14.1-cp312-cp312-win_arm64.whl", hash = "sha256:99cd41ff91afd94896fea3bc002706b6ae4ce95727d06e4a0f39c0a8d8bd8b1a", size = 221989, upload-time = "2026-05-26T20:39:28.242Z" }, + { url = "https://files.pythonhosted.org/packages/8a/9e/5f6d56327c62b185225d145191c607e07515294a0aa6338e58805cd4a5ac/coverage-7.14.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:be9f2c802dcfce3f71298303aa5dad0dce440a76c52f2f60dacd8656dab78793", size = 220044, upload-time = "2026-05-26T20:39:29.902Z" }, + { url = "https://files.pythonhosted.org/packages/75/92/e82aca356744cbbc0f77a0b623e38918c1872361963413a3bab5d0340393/coverage-7.14.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:6223a72fd0e4c7156353ec0f08a5f93623e1d3034d0e2683b9bb8ea674131b1d", size = 220412, upload-time = "2026-05-26T20:39:31.561Z" }, + { url = "https://files.pythonhosted.org/packages/27/c9/385bde0bf7ed0f4bf3a7ee5367060a86b5d218718cfd6fb943c0f836b34f/coverage-7.14.1-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:7279d2110a28cebc738b6459ecda2771735a4c18465fbbd36b3288fe5ed92247", size = 251412, upload-time = "2026-05-26T20:39:33.337Z" }, + { url = "https://files.pythonhosted.org/packages/51/8c/23faf6a2343a0d17f960a4bd56c43bc7eb4cf312f774dd6ceebd82c7d8fc/coverage-7.14.1-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:9eeb3fcbc13ba40dfbdb22d01d196a28e9cef9ed4c29b60061a1e0e823a9929d", size = 254008, upload-time = "2026-05-26T20:39:35.009Z" }, + { url = "https://files.pythonhosted.org/packages/42/06/36f4aa9ca8a815e6036156e80706a67828bb97bd826948244f6996dda957/coverage-7.14.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5f0cfc27c539f07cf5c0a4cfe211d0b6cae039f8f40526dbaa71944e64b50a7b", size = 255241, upload-time = "2026-05-26T20:39:36.71Z" }, + { url = "https://files.pythonhosted.org/packages/ca/79/95266316352f90f6b1c6736bb413302edfde2453fb32422d3911642691b3/coverage-7.14.1-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:221c70f316241a78e77e607c227cefc8808d4e08f28d99c04f35694690e940be", size = 257373, upload-time = "2026-05-26T20:39:38.412Z" }, + { url = "https://files.pythonhosted.org/packages/e3/9c/58316d1f66c488b5fca8a0eb3e98348807813efa8a0d0833b9021be27488/coverage-7.14.1-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:da028256b04ec30e5e0114b6f76172938c313991f0a2d3d894271315cf5d5e43", size = 251635, upload-time = "2026-05-26T20:39:40.268Z" }, + { url = "https://files.pythonhosted.org/packages/ef/5a/ca2398a568e16fed7bb713e84ba3603a7164fb65779abe645c565ec890d5/coverage-7.14.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:76a085d7005236a767e3426148b2c407e53ad61695c562f8a81da2d373324901", size = 253373, upload-time = "2026-05-26T20:39:42.145Z" }, + { url = "https://files.pythonhosted.org/packages/6e/2c/0396562c32deaebe7be51d865b3a41e9a87d7561acafe1a28f53b07e019a/coverage-7.14.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:b553d04b5e778a8e56d57eb134aff42a92718ecba45e79c4764ecfa40efd92ff", size = 251341, upload-time = "2026-05-26T20:39:43.907Z" }, + { url = "https://files.pythonhosted.org/packages/fd/8f/a94f9221184c9cae1ee115820e3798e48b6b17777a9f19e46fb9a0c8dc74/coverage-7.14.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:46f714d2fb8ae2f4f29f23ada7f1e79b759fff5a70f94a1dac23af204c3ec9e4", size = 255497, upload-time = "2026-05-26T20:39:46.166Z" }, + { url = "https://files.pythonhosted.org/packages/71/69/505d70e47db1eaebcd002c39759707621ef184cd6b1ae084d9f41293f323/coverage-7.14.1-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:1896f5e19ff3f0431c7ce2172adc54890fd97f86b59ced8ca1649145d9ffe35d", size = 251159, upload-time = "2026-05-26T20:39:48.03Z" }, + { url = "https://files.pythonhosted.org/packages/e0/aa/58681c383aa33a9d2ed40a02d7a22fbf780d1fa4d575396365777828198c/coverage-7.14.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:62fd185ef9df3c33d1c8178c5af105f762afbad96038de9a4ae100aa6297ca33", size = 252934, upload-time = "2026-05-26T20:39:49.872Z" }, + { url = "https://files.pythonhosted.org/packages/eb/fd/11c928cd6bdffc7074bb5965c173d9ebf517fb00205e1da524b98d29ef92/coverage-7.14.1-cp313-cp313-win32.whl", hash = "sha256:ab4af6352741a604c431c6072fce5bee33bf0f20dc7a56618d6bf6bb89e9810c", size = 222584, upload-time = "2026-05-26T20:39:51.68Z" }, + { url = "https://files.pythonhosted.org/packages/6f/92/fb416fc26d340dcba19518c418d6048e913186e17243982c5e435e41fa7a/coverage-7.14.1-cp313-cp313-win_amd64.whl", hash = "sha256:7af486dabe8954d03b087f0021540897afe084f04e16ff5579e08cc46f871416", size = 223394, upload-time = "2026-05-26T20:39:53.472Z" }, + { url = "https://files.pythonhosted.org/packages/73/c6/02d56e3867972f77d5036de924643f26c056e848f00452cafb4dbc3c29b4/coverage-7.14.1-cp313-cp313-win_arm64.whl", hash = "sha256:2224f89ffd0c5605ccce1ed7a584da162bc7c55f601ab1c946bc9de31a486b42", size = 222015, upload-time = "2026-05-26T20:39:55.374Z" }, + { url = "https://files.pythonhosted.org/packages/4d/9e/fcc77914050df73f7662fa1f00902774c79c075a8388ab334074574bf77e/coverage-7.14.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:de286598cc65d2b489411174b1faec2f5a7775fb3201fd925db2a76b4030f37d", size = 220733, upload-time = "2026-05-26T20:39:57.189Z" }, + { url = "https://files.pythonhosted.org/packages/f7/67/2963cbdaf5cbadec44efa3a1e39eaa1f02df4079585f05387607a221e126/coverage-7.14.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:042c46ded7c288aeb07cf14a28b6c1e10b78fcba40171c3fa1e939377eeef0b5", size = 221086, upload-time = "2026-05-26T20:39:59.019Z" }, + { url = "https://files.pythonhosted.org/packages/c8/c5/8701645574e11881f2f47d8930f98bc48b5d43b25eb5b4430dfc4a2f9f48/coverage-7.14.1-cp313-cp313t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:f4ddbe407477f04c45115d1a4e5bc480f753553b534d338d4c3358b1cdd0ea52", size = 262381, upload-time = "2026-05-26T20:40:00.822Z" }, + { url = "https://files.pythonhosted.org/packages/7c/28/7a64d73598263e0c5abd5084211a8474488d31b3c552ff531c719dfcff62/coverage-7.14.1-cp313-cp313t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:d13e6725992e2d2fd7d81d4f5241952d13740121dfd501da09201be39b2c003a", size = 264458, upload-time = "2026-05-26T20:40:02.506Z" }, + { url = "https://files.pythonhosted.org/packages/fa/d8/4969179db9f7eb4df218e69540adf829d1c835f59452513d065d15446802/coverage-7.14.1-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f747dc8edcfe740130f28f32f3995e955494285717e86ee25af51db2219df08a", size = 266884, upload-time = "2026-05-26T20:40:04.421Z" }, + { url = "https://files.pythonhosted.org/packages/a6/78/a45d5794dbc9bafd97afc96a4377c86c7820d78b6cf51b89bc1d4e919275/coverage-7.14.1-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ced2f09ef276fd58611a1ef502164ad266d2b75174e5a40cabbdb4033f9f6cf2", size = 268022, upload-time = "2026-05-26T20:40:06.298Z" }, + { url = "https://files.pythonhosted.org/packages/21/cb/4f5e354e9e3e67af96bd4e57113e6db6b22298c7168b13eec408a549903d/coverage-7.14.1-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b84800013769a78ccb9ef4659402e26d06867e337b61ec365f77ad008adea80e", size = 261631, upload-time = "2026-05-26T20:40:08.226Z" }, + { url = "https://files.pythonhosted.org/packages/ec/49/eced49af4cb996d5d8b7e94e736175c513e4facd3398507b89892b4326d8/coverage-7.14.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:ea8cd6ca0ee9f616aaef3afc6882e32c2cbf18b00d96313ffd76af650574034d", size = 264443, upload-time = "2026-05-26T20:40:10.137Z" }, + { url = "https://files.pythonhosted.org/packages/f1/d8/5603a88a7c5913a6b54f6cb1a8c46f7b39cbb30f27cd3f492908da09b2d7/coverage-7.14.1-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:aa5e304a873fabddc11e484e9b6b738bd38bd7bed17b09aa84eecf5332e8b8bb", size = 262069, upload-time = "2026-05-26T20:40:11.999Z" }, + { url = "https://files.pythonhosted.org/packages/f0/59/2ae3cb79da554a06c8619d6c88ea19dd1e4aed4b834b6a83bb1fa243bdc5/coverage-7.14.1-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:5a1c5215be81035e629d5bc756650634d0bf31991038db7a0eccb90f025ce16d", size = 265780, upload-time = "2026-05-26T20:40:13.858Z" }, + { url = "https://files.pythonhosted.org/packages/af/5f/b130c1dc999031f2648bd25317fbce505ad8d5562079b4ed81e736a84967/coverage-7.14.1-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:79058c47dae6788504b5effb319961bcd72d7240551464b91d474bc0ed186d69", size = 260970, upload-time = "2026-05-26T20:40:16.142Z" }, + { url = "https://files.pythonhosted.org/packages/87/d1/ec13ccddeb48ec963bdfa72a11224bac2584bd045ba13beca82f8113e9c7/coverage-7.14.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:370c5afae3fa0658e11694a32b24c2778f6bc2d17718121f94ee185e69f26b54", size = 263157, upload-time = "2026-05-26T20:40:18.382Z" }, + { url = "https://files.pythonhosted.org/packages/cf/c2/cd91ead503045161092d3845f7bb95ea2f25131ce96d3e314dd835d91b9c/coverage-7.14.1-cp313-cp313t-win32.whl", hash = "sha256:3758dd0a7f1fa57365ef2e781df0f0731d38b6e3772259d13dae4bd8a958d4b1", size = 223259, upload-time = "2026-05-26T20:40:20.381Z" }, + { url = "https://files.pythonhosted.org/packages/71/9f/1e28d97e6bd2c76b07f38b7c02870f1371255ff6717f54eca578fcbbdd0e/coverage-7.14.1-cp313-cp313t-win_amd64.whl", hash = "sha256:6ff665fb023a77386fe11685190cee1f60a7d635994a30d9b0a061533d470fce", size = 224320, upload-time = "2026-05-26T20:40:22.316Z" }, + { url = "https://files.pythonhosted.org/packages/a9/e0/d936e908f0e1efa55e52b91e01b52f1055cef5e1ab2718493390ed8e2fb8/coverage-7.14.1-cp313-cp313t-win_arm64.whl", hash = "sha256:17a5a241e5997621a956a7f402a7433ef4221e5152809b785bec79e2323799f1", size = 222577, upload-time = "2026-05-26T20:40:24.894Z" }, + { url = "https://files.pythonhosted.org/packages/d6/34/fc2f101b151af3799a101f0550b0454aa008afdc0add677394ec4aa8ea10/coverage-7.14.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:d5ed429d0b8edaac649e889b4ffcedb6c80b06629a3f93050e3dddfb99235bee", size = 220091, upload-time = "2026-05-26T20:40:27.249Z" }, + { url = "https://files.pythonhosted.org/packages/3d/a7/1ebae2ab5b961b5c79bb09fe7b3ac99edb190d8be4a8c510b2cf66f46468/coverage-7.14.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:8011224a62280e50dab346960c03cf47aca1a1e09e608c0fb33fd6e0cc8e9500", size = 220421, upload-time = "2026-05-26T20:40:30.084Z" }, + { url = "https://files.pythonhosted.org/packages/5e/90/92aca9cf0acc95123c96cd1eb1f08917897a7f5dee01e15738922971ec31/coverage-7.14.1-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:12c42ec1e14f553c4f817e989365982e646e27211f10a0f717855b94a79c8906", size = 251466, upload-time = "2026-05-26T20:40:32.542Z" }, + { url = "https://files.pythonhosted.org/packages/26/2b/78048cbe3b999f6cbf9cc0d90abba6a88a3e0863a8c1c6cbc762f3f8802f/coverage-7.14.1-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:06144cd511cf2624873a035c5069cf297144f6e77a73ee3d7a55b605ec5efb42", size = 253973, upload-time = "2026-05-26T20:40:34.473Z" }, + { url = "https://files.pythonhosted.org/packages/8e/21/c2e33b29d1cfde484a19d437afc343c6cd30b08d78cbbf9f5aff14e57b2b/coverage-7.14.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a311d8e1da24be5c1ccf85cbfb06315dbaa1703d5a1eab3f6432c72b837917c8", size = 255318, upload-time = "2026-05-26T20:40:38.154Z" }, + { url = "https://files.pythonhosted.org/packages/8e/ee/aad2f108d63b769121005302f16bf66db8625c88ceaba466942e09a2607e/coverage-7.14.1-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c79cead5b5bc584d9c71451cb984d0e3a84e0c0937379c8efcbf27c8d661b851", size = 257633, upload-time = "2026-05-26T20:40:40.164Z" }, + { url = "https://files.pythonhosted.org/packages/c2/f8/11a2c29b4fd76d9849f81d0bb812ec0017a9396df3217214e38934a8c837/coverage-7.14.1-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:dcbf65f1f66a26cdd88c35cf68fb4729c5d1cd2e88added72420541dfb212034", size = 251488, upload-time = "2026-05-26T20:40:42.631Z" }, + { url = "https://files.pythonhosted.org/packages/c9/b8/9a5820de4b8ac2b71d85e3b5fb49108d7469c665f0e2ad0dd7569023e305/coverage-7.14.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fd86572566fb40189a8260446158235159bc7a82dfbc87a3b39cf4fb57fcec1c", size = 253329, upload-time = "2026-05-26T20:40:45.208Z" }, + { url = "https://files.pythonhosted.org/packages/6b/ff/f33e4823667e27548e8fd8df44217515303f9808d0ff29817db56f87d990/coverage-7.14.1-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:7771b601718fdde84832c3a434ca9bbf4ae9adbc49d84198b4110700c3c77c36", size = 251291, upload-time = "2026-05-26T20:40:47.502Z" }, + { url = "https://files.pythonhosted.org/packages/68/9b/489db0ebb209054766b90a9014a45f6d26eb724c02ec21311c3733b5a644/coverage-7.14.1-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:39b21e212c55af06fa375e3dbf90a8a8e38792f3a910c580066d23563830ddd5", size = 255564, upload-time = "2026-05-26T20:40:49.372Z" }, + { url = "https://files.pythonhosted.org/packages/27/b5/16bc2d4c2409b23c7737edb68c83bc89e345f378050549fe1d75ac7d34d5/coverage-7.14.1-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:f2302660e32562a532b442480121aef8aa61a5bdb20b30bf0adab29f10a5a4b4", size = 251107, upload-time = "2026-05-26T20:40:51.677Z" }, + { url = "https://files.pythonhosted.org/packages/7d/0c/2629997469a00cd069d588a41c9dc887610f2775ae89d250c4791e65272a/coverage-7.14.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:03a6f93c1ec3b7f2e77b5dbcc5573a2c21f12529a5c6bbe0f16f72303cc2fa4d", size = 252764, upload-time = "2026-05-26T20:40:54.267Z" }, + { url = "https://files.pythonhosted.org/packages/d2/ee/f78d63c8f079e0d7211c7e2401fa17e311514534ba61bae03e4b287ce4ab/coverage-7.14.1-cp314-cp314-win32.whl", hash = "sha256:8a3ce026d73290f42f08dafecbd82c193a74df280461fbf97300fec51fd133ee", size = 222837, upload-time = "2026-05-26T20:40:56.496Z" }, + { url = "https://files.pythonhosted.org/packages/dc/b9/be539854f93a70dfbeec69117f33ec70dc42ff0b65b5b07ab8d40d04228e/coverage-7.14.1-cp314-cp314-win_amd64.whl", hash = "sha256:114c95ef29302423b87d159075805f4ab973254a2638a5d7d046c94887cc87d7", size = 223650, upload-time = "2026-05-26T20:40:58.351Z" }, + { url = "https://files.pythonhosted.org/packages/fe/9e/24e2842fef40f35ac82ba3a7719c8023d011bf3bf652d0675316a9d088a1/coverage-7.14.1-cp314-cp314-win_arm64.whl", hash = "sha256:a07891c3f4805442b31b71e84ba3cf29ed1aa9a428284e06deeb4b23e5b46343", size = 222218, upload-time = "2026-05-26T20:41:00.321Z" }, + { url = "https://files.pythonhosted.org/packages/0a/1d/ac0a9df5fe31c1e8bdd658074905fc12844a05c1a7e3fdb8417e97c31e23/coverage-7.14.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:1101a5ebb083aecb625ebb6209d4105b58f647b093cb2dc8122d7b33f743cfe1", size = 220822, upload-time = "2026-05-26T20:41:02.281Z" }, + { url = "https://files.pythonhosted.org/packages/32/cf/f964fd9aff20323f9f1a726c97135f8a76bcd87b92dad141a456a43f3c64/coverage-7.14.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:851b9e1e4e8a4608e77c79714b2e77c0970d2ed7202a05e92ae407817481887b", size = 221084, upload-time = "2026-05-26T20:41:04.593Z" }, + { url = "https://files.pythonhosted.org/packages/d8/5e/7e5ef2aba844de2b80d678619fcf0841b42e3f37f16411226f3fe4c1016f/coverage-7.14.1-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:d5b89cdfb2ee051b71e8c3c70bd81a9eff81100f736a269136fe1a68efe00474", size = 262454, upload-time = "2026-05-26T20:41:06.641Z" }, + { url = "https://files.pythonhosted.org/packages/64/62/75809bded87015cc4935524218a2a8ed8dd1a8498bfed30a2f4f7a4b4d34/coverage-7.14.1-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:0177614a0370f227888b4e436a7c55686d6a9f90eb1ade2b624ba685a1686e86", size = 264578, upload-time = "2026-05-26T20:41:08.556Z" }, + { url = "https://files.pythonhosted.org/packages/f3/42/d33392dc14633525012d2d504fa1a33b05538bf535f5c1d64675e5754b78/coverage-7.14.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2d69af5dea2de76fc485a83032a630523f985198b7e25be901ec60181587b01e", size = 266981, upload-time = "2026-05-26T20:41:10.824Z" }, + { url = "https://files.pythonhosted.org/packages/2a/49/0157c4428c2aca7f1e09d5565930586fd5ae36f1655f08b0daa7cf1fcae1/coverage-7.14.1-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:35ab22d91de736e8966b980dc355cbcdd2c6dbbcfe275f9a2991bc8a91b3df65", size = 268112, upload-time = "2026-05-26T20:41:12.966Z" }, + { url = "https://files.pythonhosted.org/packages/96/26/86b9ce71f4092b1ed325ce1421698081df1286b833400b6836912834d6e0/coverage-7.14.1-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:357d4e32935c36588aaba057d734fa32428c360c9fc2e4442afbf1b646beee6e", size = 261558, upload-time = "2026-05-26T20:41:15Z" }, + { url = "https://files.pythonhosted.org/packages/20/4c/c311210c5472cf5401d8422b0d7812cdd520f24417673afabda6c323faca/coverage-7.14.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:51bd64741cc6fa065abd300ede1afe5a5291ece9c31da8b24884deda48bcc3f8", size = 264447, upload-time = "2026-05-26T20:41:17.369Z" }, + { url = "https://files.pythonhosted.org/packages/fb/71/59513f8710ed3e6b0ac0a050a5b7e977bb9c9e880354863b5d00d8809256/coverage-7.14.1-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:9132cd363a68a4c3daa7c8704a654b1e39d3360f6f5b8ddd470608a945236c07", size = 262048, upload-time = "2026-05-26T20:41:19.309Z" }, + { url = "https://files.pythonhosted.org/packages/84/8d/bceed32dc494f5bbf50f775cd2e78ca814953942b5ea28d3c1c3ac316f14/coverage-7.14.1-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:07c6290b1697b862c0478eab545eec949a0d0e4d6d03497f446d706da3b4f2de", size = 265781, upload-time = "2026-05-26T20:41:21.559Z" }, + { url = "https://files.pythonhosted.org/packages/e7/c5/9348fe40dbfd4991aaf78df2c6c3098bfb2cc834d1fd362a64b4efef855a/coverage-7.14.1-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:5ea0c297e27133853b4d8a3eb799bff5a2dbd9f2f41537a240d337ac9b4df890", size = 260896, upload-time = "2026-05-26T20:41:23.428Z" }, + { url = "https://files.pythonhosted.org/packages/ca/92/1ea0f03929da7cf87206b1fa24f4c8e9c158be0455481af29ec0a1f3503f/coverage-7.14.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:01b7733daad0237daa01ef80fe2dfceffc911e6a17fa7b55d14aa8214eaaaecd", size = 263214, upload-time = "2026-05-26T20:41:25.419Z" }, + { url = "https://files.pythonhosted.org/packages/f6/a9/b2493c054c0e01a643266742ab45e15744e60743f9260cd930c7142b1124/coverage-7.14.1-cp314-cp314t-win32.whl", hash = "sha256:6adc5a36984624a70bf11d7184e20fa0a49aa7c47ffab43804106a1a695ea22e", size = 223624, upload-time = "2026-05-26T20:41:27.795Z" }, + { url = "https://files.pythonhosted.org/packages/fc/bd/3e1e6a57fccd2d7c83fcdf338e93ba98eb85c6e877dd34731ac585375490/coverage-7.14.1-cp314-cp314t-win_amd64.whl", hash = "sha256:ddf799247318f34dbcd2efa8c95a8d0642674e926bb1774cf9b63dfd2a389d1c", size = 224728, upload-time = "2026-05-26T20:41:30.098Z" }, + { url = "https://files.pythonhosted.org/packages/bb/d7/31066cf1d2f0c6c797fce911bcfa01dd35642dc6da992a950256097c5860/coverage-7.14.1-cp314-cp314t-win_arm64.whl", hash = "sha256:145986fe66647eb489f18d9a997567a3fd358584c4b5a808769113abc07466af", size = 222752, upload-time = "2026-05-26T20:41:32.123Z" }, + { url = "https://files.pythonhosted.org/packages/8a/3c/1a983b9a745d7f83d53f057bcc5bf79ba6a2bbc08266b3f0c7d6fe630c9b/coverage-7.14.1-py3-none-any.whl", hash = "sha256:a252f21c27e38347e60111a3266b03827422a7d5525951aceee313aa68bab1d2", size = 211815, upload-time = "2026-05-26T20:41:34.078Z" }, ] [package.optional-dependencies] @@ -636,30 +636,30 @@ toml = [ [[package]] name = "cuda-bindings" -version = "13.2.0" +version = "13.3.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "cuda-pathfinder" }, ] wheels = [ - { url = "https://files.pythonhosted.org/packages/e0/a9/3a8241c6e19483ac1f1dcf5c10238205dcb8a6e9d0d4d4709240dff28ff4/cuda_bindings-13.2.0-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:721104c603f059780d287969be3d194a18d0cc3b713ed9049065a1107706759d", size = 5730273, upload-time = "2026-03-11T00:12:37.18Z" }, - { url = "https://files.pythonhosted.org/packages/e9/94/2748597f47bb1600cd466b20cab4159f1530a3a33fe7f70fee199b3abb9e/cuda_bindings-13.2.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1eba9504ac70667dd48313395fe05157518fd6371b532790e96fbb31bbb5a5e1", size = 6313924, upload-time = "2026-03-11T00:12:39.462Z" }, - { url = "https://files.pythonhosted.org/packages/52/c8/b2589d68acf7e3d63e2be330b84bc25712e97ed799affbca7edd7eae25d6/cuda_bindings-13.2.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e865447abfb83d6a98ad5130ed3c70b1fc295ae3eeee39fd07b4ddb0671b6788", size = 5722404, upload-time = "2026-03-11T00:12:44.041Z" }, - { url = "https://files.pythonhosted.org/packages/1f/92/f899f7bbb5617bb65ec52a6eac1e9a1447a86b916c4194f8a5001b8cde0c/cuda_bindings-13.2.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:46d8776a55d6d5da9dd6e9858fba2efcda2abe6743871dee47dd06eb8cb6d955", size = 6320619, upload-time = "2026-03-11T00:12:45.939Z" }, - { url = "https://files.pythonhosted.org/packages/df/93/eef988860a3ca985f82c4f3174fc0cdd94e07331ba9a92e8e064c260337f/cuda_bindings-13.2.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6629ca2df6f795b784752409bcaedbd22a7a651b74b56a165ebc0c9dcbd504d0", size = 5614610, upload-time = "2026-03-11T00:12:50.337Z" }, - { url = "https://files.pythonhosted.org/packages/18/23/6db3aba46864aee357ab2415135b3fe3da7e9f1fa0221fa2a86a5968099c/cuda_bindings-13.2.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7dca0da053d3b4cc4869eff49c61c03f3c5dbaa0bcd712317a358d5b8f3f385d", size = 6149914, upload-time = "2026-03-11T00:12:52.374Z" }, - { url = "https://files.pythonhosted.org/packages/c0/87/87a014f045b77c6de5c8527b0757fe644417b184e5367db977236a141602/cuda_bindings-13.2.0-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a6464b30f46692d6c7f65d4a0e0450d81dd29de3afc1bb515653973d01c2cd6e", size = 5685673, upload-time = "2026-03-11T00:12:56.371Z" }, - { url = "https://files.pythonhosted.org/packages/ee/5e/c0fe77a73aaefd3fff25ffaccaac69c5a63eafdf8b9a4c476626ef0ac703/cuda_bindings-13.2.0-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f4af9f3e1be603fa12d5ad6cfca7844c9d230befa9792b5abdf7dd79979c3626", size = 6191386, upload-time = "2026-03-11T00:12:58.965Z" }, - { url = "https://files.pythonhosted.org/packages/5f/58/ed2c3b39c8dd5f96aa7a4abef0d47a73932c7a988e30f5fa428f00ed0da1/cuda_bindings-13.2.0-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:df850a1ff8ce1b3385257b08e47b70e959932f5f432d0a4e46a355962b4e4771", size = 5507469, upload-time = "2026-03-11T00:13:04.063Z" }, - { url = "https://files.pythonhosted.org/packages/1f/01/0c941b112ceeb21439b05895eace78ca1aa2eaaf695c8521a068fd9b4c00/cuda_bindings-13.2.0-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e8a16384c6494e5485f39314b0b4afb04bee48d49edb16d5d8593fd35bbd231b", size = 6059693, upload-time = "2026-03-11T00:13:06.003Z" }, + { url = "https://files.pythonhosted.org/packages/51/6b/457ca12dad3ee9bfcc9a545cfd6b64b359ba49de40f776f6e028e678f262/cuda_bindings-13.3.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c5879712accf6e14bb01aa5e67440eb84998b8d104b509cc7a6dc0b8f656a474", size = 6053539, upload-time = "2026-05-29T23:11:43.19Z" }, + { url = "https://files.pythonhosted.org/packages/95/7a/c5e3c34a409b148f5c0f5a4ea374158f95d488862c1dffedf9aa5c639df9/cuda_bindings-13.3.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:04436a9364059c84b8f9636f359eccda1cf814341f5b670c71d80d2f79dbc708", size = 6674166, upload-time = "2026-05-29T23:11:45.478Z" }, + { url = "https://files.pythonhosted.org/packages/ce/67/5e7dba1ba576dd73da5dee894ca076ca5e959450dfff66d6d510a255d1f7/cuda_bindings-13.3.1-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c7855c4868aabc0cfae28abbe83d56734bdfbd08f08fc234ac1912a12858bf49", size = 6025351, upload-time = "2026-05-29T23:11:49.685Z" }, + { url = "https://files.pythonhosted.org/packages/39/2a/6d2e9047d1fb243dbaa364b01e0297534b9ed7fd27dba1c9f361519cf69b/cuda_bindings-13.3.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e32d08f71ebcdf00f0f41eab2eb37e8da94c8ed411cc9f7f7a019ce6b34abe3a", size = 6657965, upload-time = "2026-05-29T23:11:52.227Z" }, + { url = "https://files.pythonhosted.org/packages/cc/6e/2394f8163360f8391f8f1b7e72d300a82724edb81a7b7084c799fbd4c91f/cuda_bindings-13.3.1-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9efb21c1ee64981e184b9e0ba5eb3179e5ba3d4b51665a6cb52b8ef3d01a7cbf", size = 5920504, upload-time = "2026-05-29T23:11:56.883Z" }, + { url = "https://files.pythonhosted.org/packages/34/c2/ef9b6a63f7dc432712a462c816662e662e00d38caa9b861c8c2588195d03/cuda_bindings-13.3.1-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2732904099e0a4d4db774a5fc6d91ee95fae065b4d2ecabb4968c5fe2406c9d7", size = 6476660, upload-time = "2026-05-29T23:11:59.188Z" }, + { url = "https://files.pythonhosted.org/packages/b1/81/bff68ce829999c1e4209c761bbf903b1c06ec570416ddb25020864ad5907/cuda_bindings-13.3.1-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1ab2f74ed65bfef4163ba07a8db16f1085e0729291db12a2423aff84ee8278b8", size = 6013639, upload-time = "2026-05-29T23:12:03.509Z" }, + { url = "https://files.pythonhosted.org/packages/d4/e0/c8a1f0c8f9ffdea4f5fe6dbab89b326cef4d85caf489dad39e209da89416/cuda_bindings-13.3.1-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:efd4c814d311ec08c981f6dded1dbe7d4b371067ee4f6c14cccec4bde9590f80", size = 6534419, upload-time = "2026-05-29T23:12:05.633Z" }, + { url = "https://files.pythonhosted.org/packages/52/b8/83b1f563925b290f2d11a01a77a84013ba56052fe3653a5bef3ccfbb43d6/cuda_bindings-13.3.1-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c3c772dfff49681541d59630c90f858e173ac926b9c593a2b7123f2a1043cc76", size = 5809771, upload-time = "2026-05-29T23:12:10.422Z" }, + { url = "https://files.pythonhosted.org/packages/12/20/e79b4bfe98f075195afb6343d41c498f9dbd2d161d7021d4d28bceb83581/cuda_bindings-13.3.1-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:36febb7c1079d68a981dbbd8d5a67235b399802b82075c9388624719607e52b9", size = 6358584, upload-time = "2026-05-29T23:12:12.767Z" }, ] [[package]] name = "cuda-pathfinder" -version = "1.5.4" +version = "1.5.5" source = { registry = "https://pypi.org/simple" } wheels = [ - { url = "https://files.pythonhosted.org/packages/11/d0/c177e29701cf1d3008d7d2b16b5fc626592ce13bd535f8795c5f57187e0e/cuda_pathfinder-1.5.4-py3-none-any.whl", hash = "sha256:9563d3175ce1828531acf4b94e1c1c7d67208c347ca002493e2654878b26f4b7", size = 51657, upload-time = "2026-04-27T22:42:07.712Z" }, + { url = "https://files.pythonhosted.org/packages/11/c8/26f2e4aae92f11522a96043892ba39a90eac610d5242523aa863212bc1c7/cuda_pathfinder-1.5.5-py3-none-any.whl", hash = "sha256:0228c023f95d1480f143ef5c8922d27a2ab052087a942e81dc289c9eb8f91689", size = 51671, upload-time = "2026-05-27T01:21:25.413Z" }, ] [[package]] @@ -1021,53 +1021,53 @@ wheels = [ [[package]] name = "grpcio" -version = "1.80.0" +version = "1.81.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/b7/48/af6173dbca4454f4637a4678b67f52ca7e0c1ed7d5894d89d434fecede05/grpcio-1.80.0.tar.gz", hash = "sha256:29aca15edd0688c22ba01d7cc01cb000d72b2033f4a3c72a81a19b56fd143257", size = 12978905, upload-time = "2026-03-30T08:49:10.502Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/5d/db/1d56e5f5823257b291962d6c0ce106146c6447f405b60b234c4f222a7cde/grpcio-1.80.0-cp311-cp311-linux_armv7l.whl", hash = "sha256:dfab85db094068ff42e2a3563f60ab3dddcc9d6488a35abf0132daec13209c8a", size = 6055009, upload-time = "2026-03-30T08:46:46.265Z" }, - { url = "https://files.pythonhosted.org/packages/6e/18/c83f3cad64c5ca63bca7e91e5e46b0d026afc5af9d0a9972472ceba294b3/grpcio-1.80.0-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:5c07e82e822e1161354e32da2662f741a4944ea955f9f580ec8fb409dd6f6060", size = 12035295, upload-time = "2026-03-30T08:46:49.099Z" }, - { url = "https://files.pythonhosted.org/packages/0f/8e/e14966b435be2dda99fbe89db9525ea436edc79780431a1c2875a3582644/grpcio-1.80.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ba0915d51fd4ced2db5ff719f84e270afe0e2d4c45a7bdb1e8d036e4502928c2", size = 6610297, upload-time = "2026-03-30T08:46:52.123Z" }, - { url = "https://files.pythonhosted.org/packages/cc/26/d5eb38f42ce0e3fdc8174ea4d52036ef8d58cc4426cb800f2610f625dd75/grpcio-1.80.0-cp311-cp311-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:3cb8130ba457d2aa09fa6b7c3ed6b6e4e6a2685fce63cb803d479576c4d80e21", size = 7300208, upload-time = "2026-03-30T08:46:54.859Z" }, - { url = "https://files.pythonhosted.org/packages/25/51/bd267c989f85a17a5b3eea65a6feb4ff672af41ca614e5a0279cc0ea381c/grpcio-1.80.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:09e5e478b3d14afd23f12e49e8b44c8684ac3c5f08561c43a5b9691c54d136ab", size = 6813442, upload-time = "2026-03-30T08:46:57.056Z" }, - { url = "https://files.pythonhosted.org/packages/9e/d9/d80eef735b19e9169e30164bbf889b46f9df9127598a83d174eb13a48b26/grpcio-1.80.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:00168469238b022500e486c1c33916acf2f2a9b2c022202cf8a1885d2e3073c1", size = 7414743, upload-time = "2026-03-30T08:46:59.682Z" }, - { url = "https://files.pythonhosted.org/packages/de/f2/567f5bd5054398ed6b0509b9a30900376dcf2786bd936812098808b49d8d/grpcio-1.80.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:8502122a3cc1714038e39a0b071acb1207ca7844208d5ea0d091317555ee7106", size = 8426046, upload-time = "2026-03-30T08:47:02.474Z" }, - { url = "https://files.pythonhosted.org/packages/62/29/73ef0141b4732ff5eacd68430ff2512a65c004696997f70476a83e548e7e/grpcio-1.80.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:ce1794f4ea6cc3ca29463f42d665c32ba1b964b48958a66497917fe9069f26e6", size = 7851641, upload-time = "2026-03-30T08:47:05.462Z" }, - { url = "https://files.pythonhosted.org/packages/46/69/abbfa360eb229a8623bab5f5a4f8105e445bd38ce81a89514ba55d281ad0/grpcio-1.80.0-cp311-cp311-win32.whl", hash = "sha256:51b4a7189b0bef2aa30adce3c78f09c83526cf3dddb24c6a96555e3b97340440", size = 4154368, upload-time = "2026-03-30T08:47:08.027Z" }, - { url = "https://files.pythonhosted.org/packages/6f/d4/ae92206d01183b08613e846076115f5ac5991bae358d2a749fa864da5699/grpcio-1.80.0-cp311-cp311-win_amd64.whl", hash = "sha256:02e64bb0bb2da14d947a49e6f120a75e947250aebe65f9629b62bb1f5c14e6e9", size = 4894235, upload-time = "2026-03-30T08:47:10.839Z" }, - { url = "https://files.pythonhosted.org/packages/5c/e8/a2b749265eb3415abc94f2e619bbd9e9707bebdda787e61c593004ec927a/grpcio-1.80.0-cp312-cp312-linux_armv7l.whl", hash = "sha256:c624cc9f1008361014378c9d776de7182b11fe8b2e5a81bc69f23a295f2a1ad0", size = 6015616, upload-time = "2026-03-30T08:47:13.428Z" }, - { url = "https://files.pythonhosted.org/packages/3e/97/b1282161a15d699d1e90c360df18d19165a045ce1c343c7f313f5e8a0b77/grpcio-1.80.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:f49eddcac43c3bf350c0385366a58f36bed8cc2c0ec35ef7b74b49e56552c0c2", size = 12014204, upload-time = "2026-03-30T08:47:15.873Z" }, - { url = "https://files.pythonhosted.org/packages/6e/5e/d319c6e997b50c155ac5a8cb12f5173d5b42677510e886d250d50264949d/grpcio-1.80.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d334591df610ab94714048e0d5b4f3dd5ad1bee74dfec11eee344220077a79de", size = 6563866, upload-time = "2026-03-30T08:47:18.588Z" }, - { url = "https://files.pythonhosted.org/packages/ae/f6/fdd975a2cb4d78eb67769a7b3b3830970bfa2e919f1decf724ae4445f42c/grpcio-1.80.0-cp312-cp312-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:0cb517eb1d0d0aaf1d87af7cc5b801d686557c1d88b2619f5e31fab3c2315921", size = 7273060, upload-time = "2026-03-30T08:47:21.113Z" }, - { url = "https://files.pythonhosted.org/packages/db/f0/a3deb5feba60d9538a962913e37bd2e69a195f1c3376a3dd44fe0427e996/grpcio-1.80.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4e78c4ac0d97dc2e569b2f4bcbbb447491167cb358d1a389fc4af71ab6f70411", size = 6782121, upload-time = "2026-03-30T08:47:23.827Z" }, - { url = "https://files.pythonhosted.org/packages/ca/84/36c6dcfddc093e108141f757c407902a05085e0c328007cb090d56646cdf/grpcio-1.80.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:2ed770b4c06984f3b47eb0517b1c69ad0b84ef3f40128f51448433be904634cd", size = 7383811, upload-time = "2026-03-30T08:47:26.517Z" }, - { url = "https://files.pythonhosted.org/packages/7c/ef/f3a77e3dc5b471a0ec86c564c98d6adfa3510d38f8ee99010410858d591e/grpcio-1.80.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:256507e2f524092f1473071a05e65a5b10d84b82e3ff24c5b571513cfaa61e2f", size = 8393860, upload-time = "2026-03-30T08:47:29.439Z" }, - { url = "https://files.pythonhosted.org/packages/9b/8d/9d4d27ed7f33d109c50d6b5ce578a9914aa68edab75d65869a17e630a8d1/grpcio-1.80.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:9a6284a5d907c37db53350645567c522be314bac859a64a7a5ca63b77bb7958f", size = 7830132, upload-time = "2026-03-30T08:47:33.254Z" }, - { url = "https://files.pythonhosted.org/packages/14/e4/9990b41c6d7a44e1e9dee8ac11d7a9802ba1378b40d77468a7761d1ad288/grpcio-1.80.0-cp312-cp312-win32.whl", hash = "sha256:c71309cfce2f22be26aa4a847357c502db6c621f1a49825ae98aa0907595b193", size = 4140904, upload-time = "2026-03-30T08:47:35.319Z" }, - { url = "https://files.pythonhosted.org/packages/2f/2c/296f6138caca1f4b92a31ace4ae1b87dab692fc16a7a3417af3bb3c805bf/grpcio-1.80.0-cp312-cp312-win_amd64.whl", hash = "sha256:9fe648599c0e37594c4809d81a9e77bd138cc82eb8baa71b6a86af65426723ff", size = 4880944, upload-time = "2026-03-30T08:47:37.831Z" }, - { url = "https://files.pythonhosted.org/packages/2f/3a/7c3c25789e3f069e581dc342e03613c5b1cb012c4e8c7d9d5cf960a75856/grpcio-1.80.0-cp313-cp313-linux_armv7l.whl", hash = "sha256:e9e408fc016dffd20661f0126c53d8a31c2821b5c13c5d67a0f5ed5de93319ad", size = 6017243, upload-time = "2026-03-30T08:47:40.075Z" }, - { url = "https://files.pythonhosted.org/packages/04/19/21a9806eb8240e174fd1ab0cd5b9aa948bb0e05c2f2f55f9d5d7405e6d08/grpcio-1.80.0-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:92d787312e613754d4d8b9ca6d3297e69994a7912a32fa38c4c4e01c272974b0", size = 12010840, upload-time = "2026-03-30T08:47:43.11Z" }, - { url = "https://files.pythonhosted.org/packages/18/3a/23347d35f76f639e807fb7a36fad3068aed100996849a33809591f26eca6/grpcio-1.80.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:8ac393b58aa16991a2f1144ec578084d544038c12242da3a215966b512904d0f", size = 6567644, upload-time = "2026-03-30T08:47:46.806Z" }, - { url = "https://files.pythonhosted.org/packages/ff/40/96e07ecb604a6a67ae6ab151e3e35b132875d98bc68ec65f3e5ab3e781d7/grpcio-1.80.0-cp313-cp313-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:68e5851ac4b9afe07e7f84483803ad167852570d65326b34d54ca560bfa53fb6", size = 7277830, upload-time = "2026-03-30T08:47:49.643Z" }, - { url = "https://files.pythonhosted.org/packages/9b/e2/da1506ecea1f34a5e365964644b35edef53803052b763ca214ba3870c856/grpcio-1.80.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:873ff5d17d68992ef6605330127425d2fc4e77e612fa3c3e0ed4e668685e3140", size = 6783216, upload-time = "2026-03-30T08:47:52.817Z" }, - { url = "https://files.pythonhosted.org/packages/44/83/3b20ff58d0c3b7f6caaa3af9a4174d4023701df40a3f39f7f1c8e7c48f9d/grpcio-1.80.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:2bea16af2750fd0a899bf1abd9022244418b55d1f37da2202249ba4ba673838d", size = 7385866, upload-time = "2026-03-30T08:47:55.687Z" }, - { url = "https://files.pythonhosted.org/packages/47/45/55c507599c5520416de5eefecc927d6a0d7af55e91cfffb2e410607e5744/grpcio-1.80.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:ba0db34f7e1d803a878284cd70e4c63cb6ae2510ba51937bf8f45ba997cefcf7", size = 8391602, upload-time = "2026-03-30T08:47:58.303Z" }, - { url = "https://files.pythonhosted.org/packages/10/bb/dd06f4c24c01db9cf11341b547d0a016b2c90ed7dbbb086a5710df7dd1d7/grpcio-1.80.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8eb613f02d34721f1acf3626dfdb3545bd3c8505b0e52bf8b5710a28d02e8aa7", size = 7826752, upload-time = "2026-03-30T08:48:01.311Z" }, - { url = "https://files.pythonhosted.org/packages/f9/1e/9d67992ba23371fd63d4527096eb8c6b76d74d52b500df992a3343fd7251/grpcio-1.80.0-cp313-cp313-win32.whl", hash = "sha256:93b6f823810720912fd131f561f91f5fed0fda372b6b7028a2681b8194d5d294", size = 4142310, upload-time = "2026-03-30T08:48:04.594Z" }, - { url = "https://files.pythonhosted.org/packages/cf/e6/283326a27da9e2c3038bc93eeea36fb118ce0b2d03922a9cda6688f53c5b/grpcio-1.80.0-cp313-cp313-win_amd64.whl", hash = "sha256:e172cf795a3ba5246d3529e4d34c53db70e888fa582a8ffebd2e6e48bc0cba50", size = 4882833, upload-time = "2026-03-30T08:48:07.363Z" }, - { url = "https://files.pythonhosted.org/packages/c5/6d/e65307ce20f5a09244ba9e9d8476e99fb039de7154f37fb85f26978b59c3/grpcio-1.80.0-cp314-cp314-linux_armv7l.whl", hash = "sha256:3d4147a97c8344d065d01bbf8b6acec2cf86fb0400d40696c8bdad34a64ffc0e", size = 6017376, upload-time = "2026-03-30T08:48:10.005Z" }, - { url = "https://files.pythonhosted.org/packages/69/10/9cef5d9650c72625a699c549940f0abb3c4bfdb5ed45a5ce431f92f31806/grpcio-1.80.0-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:d8e11f167935b3eb089ac9038e1a063e6d7dbe995c0bb4a661e614583352e76f", size = 12018133, upload-time = "2026-03-30T08:48:12.927Z" }, - { url = "https://files.pythonhosted.org/packages/04/82/983aabaad82ba26113caceeb9091706a0696b25da004fe3defb5b346e15b/grpcio-1.80.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f14b618fc30de822681ee986cfdcc2d9327229dc4c98aed16896761cacd468b9", size = 6574748, upload-time = "2026-03-30T08:48:16.386Z" }, - { url = "https://files.pythonhosted.org/packages/07/d7/031666ef155aa0bf399ed7e19439656c38bbd143779ae0861b038ce82abd/grpcio-1.80.0-cp314-cp314-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:4ed39fbdcf9b87370f6e8df4e39ca7b38b3e5e9d1b0013c7b6be9639d6578d14", size = 7277711, upload-time = "2026-03-30T08:48:19.627Z" }, - { url = "https://files.pythonhosted.org/packages/e8/43/f437a78f7f4f1d311804189e8f11fb311a01049b2e08557c1068d470cb2e/grpcio-1.80.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2dcc70e9f0ba987526e8e8603a610fb4f460e42899e74e7a518bf3c68fe1bf05", size = 6785372, upload-time = "2026-03-30T08:48:22.373Z" }, - { url = "https://files.pythonhosted.org/packages/93/3d/f6558e9c6296cb4227faa5c43c54a34c68d32654b829f53288313d16a86e/grpcio-1.80.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:448c884b668b868562b1bda833c5fce6272d26e1926ec46747cda05741d302c1", size = 7395268, upload-time = "2026-03-30T08:48:25.638Z" }, - { url = "https://files.pythonhosted.org/packages/06/21/0fdd77e84720b08843c371a2efa6f2e19dbebf56adc72df73d891f5506f0/grpcio-1.80.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:a1dc80fe55685b4a543555e6eef975303b36c8db1023b1599b094b92aa77965f", size = 8392000, upload-time = "2026-03-30T08:48:28.974Z" }, - { url = "https://files.pythonhosted.org/packages/f5/68/67f4947ed55d2e69f2cc199ab9fd85e0a0034d813bbeef84df6d2ba4d4b7/grpcio-1.80.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:31b9ac4ad1aa28ffee5503821fafd09e4da0a261ce1c1281c6c8da0423c83b6e", size = 7828477, upload-time = "2026-03-30T08:48:32.054Z" }, - { url = "https://files.pythonhosted.org/packages/44/b6/8d4096691b2e385e8271911a0de4f35f0a6c7d05aff7098e296c3de86939/grpcio-1.80.0-cp314-cp314-win32.whl", hash = "sha256:367ce30ba67d05e0592470428f0ec1c31714cab9ef19b8f2e37be1f4c7d32fae", size = 4218563, upload-time = "2026-03-30T08:48:34.538Z" }, - { url = "https://files.pythonhosted.org/packages/e5/8c/bbe6baf2557262834f2070cf668515fa308b2d38a4bbf771f8f7872a7036/grpcio-1.80.0-cp314-cp314-win_amd64.whl", hash = "sha256:3b01e1f5464c583d2f567b2e46ff0d516ef979978f72091fd81f5ab7fa6e2e7f", size = 5019457, upload-time = "2026-03-30T08:48:37.308Z" }, +sdist = { url = "https://files.pythonhosted.org/packages/15/f3/23f47b24f8d8c2028eba501db3acfbb2f592cbb5995eaa6e363a627b74d7/grpcio-1.81.0.tar.gz", hash = "sha256:a5acd7efd3b1fe9b4eb0bcaaa1507eed68a0ad0678b654c3f7b464df9ba9dca5", size = 13032272, upload-time = "2026-06-01T05:56:22.827Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/45/a8/9916ab10a0201f4c7afb6918125aa2f38a7626ee18ffbc066dd9cb04a74d/grpcio-1.81.0-cp311-cp311-linux_armv7l.whl", hash = "sha256:794e6aa648e8df47d8f908dc8c3b42347d04ec58438f1dcd4e445f09b4f6b0ce", size = 6093557, upload-time = "2026-06-01T05:54:32.64Z" }, + { url = "https://files.pythonhosted.org/packages/a7/43/99e969a048904a65df3129ee53c5f523b7c4e43127786460cac4bee82470/grpcio-1.81.0-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:cd78145b7f7784661c524624f3526c9c6f891b30a4b54cb93a40806d0d0d61e9", size = 12075345, upload-time = "2026-06-01T05:54:35.77Z" }, + { url = "https://files.pythonhosted.org/packages/83/70/4c3a204e190333768d4f63f4ff56bd0bf405f05b9188f3a59a8bcf161f8b/grpcio-1.81.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:638ccc1b86f7540170a169cb900799b9296a1381e47879ce60b0de9d3db73d33", size = 6640664, upload-time = "2026-06-01T05:54:38.854Z" }, + { url = "https://files.pythonhosted.org/packages/2e/a9/0fa17ac8b4e29cf59b26915be6cab8c0d4583ce24a6208a287b6e5f6d072/grpcio-1.81.0-cp311-cp311-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:21ec30b9ea320c8207ea7cd05873ad64aa69fdd0e81b6758b3347983ba20b50a", size = 7332542, upload-time = "2026-06-01T05:54:41.39Z" }, + { url = "https://files.pythonhosted.org/packages/f4/18/7c8e3d0dda2fb7a17076fcd6c9085209eabad3354696c64230f87b3a14eb/grpcio-1.81.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:dbdb99986548a7e87f8343805ef315fd4eb50ffaabf4fb1206e42f2542bb805d", size = 6842564, upload-time = "2026-06-01T05:54:43.57Z" }, + { url = "https://files.pythonhosted.org/packages/f6/19/2f1726c2e03ad3f3fe241e6b41534532ad580d595de14a4054ad84999c80/grpcio-1.81.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c36f5d5e97944cbda2d4096b4ae262e6e68506246b61582acf1b8591607f3ccc", size = 7446236, upload-time = "2026-06-01T05:54:46.042Z" }, + { url = "https://files.pythonhosted.org/packages/a7/dc/0321f892212e2c0bfe248cea24c00d7d7111639688ec5ffd8e36b5c02fe6/grpcio-1.81.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:9f355384e5543ab77a755a7085225ecc19f32b76032e851cbd8145715d79dec8", size = 8445633, upload-time = "2026-06-01T05:54:48.809Z" }, + { url = "https://files.pythonhosted.org/packages/e5/20/0e7ea7494955cf1beea3077b2fd2c04c84d4480c2ae85a1e1cfa150c62d7/grpcio-1.81.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:77eb4e9fe61486bd1198cc7236ebb0f70e66234e63c0348f40bc2553ed16a88b", size = 7873958, upload-time = "2026-06-01T05:54:52.135Z" }, + { url = "https://files.pythonhosted.org/packages/d3/9e/6438e226046c2a0778060e2b1d791a4827277bbd9d223013c2c63ee7435e/grpcio-1.81.0-cp311-cp311-win32.whl", hash = "sha256:7915a2e63acdc05264a206e1bddfd8e1fb8a29e406c18d72d30f8c124e021374", size = 4202110, upload-time = "2026-06-01T05:54:54.134Z" }, + { url = "https://files.pythonhosted.org/packages/42/6b/d0895e93d65b186f5f1737fcc186d7faa487e2d9d934eda111a37a309869/grpcio-1.81.0-cp311-cp311-win_amd64.whl", hash = "sha256:5e925a70fe99fe5794f7beca0ea034c75f068afcc356d79047e73f99cdcca34c", size = 4940942, upload-time = "2026-06-01T05:54:56.749Z" }, + { url = "https://files.pythonhosted.org/packages/82/d5/896a3aaf07068d707d88b282a04914b872db4d32d3c7e6d88e43a3b911fa/grpcio-1.81.0-cp312-cp312-linux_armv7l.whl", hash = "sha256:57b3b0e73a518fa286959b40c3eddd02703504ca186e8b7b2945954519bd8b2c", size = 6053538, upload-time = "2026-06-01T05:54:58.965Z" }, + { url = "https://files.pythonhosted.org/packages/68/6a/7e3eafa4727cd405ff917605ed2949e2af162f233f5cbdd773723a5fea7d/grpcio-1.81.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:8bb1789c94322a13336a2b6c58d9c14d68f8628b6e24205a799c69f5bf8516ce", size = 12053447, upload-time = "2026-06-01T05:55:01.862Z" }, + { url = "https://files.pythonhosted.org/packages/16/79/a4302aa82428de48a922421f522b027a1a727ab4d0926368454aa953d36d/grpcio-1.81.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e4d053900a0d24b75d7521139a3872150301b3d6bde3bed5e12318fb25791e4d", size = 6595872, upload-time = "2026-06-01T05:55:04.946Z" }, + { url = "https://files.pythonhosted.org/packages/b4/1f/7ff2850eaefbecf99af3f624dbb28dd1ad6c5fd4c1d8c26909ed6482673b/grpcio-1.81.0-cp312-cp312-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:db217c2e52931719f9937bd12082cd4d7b495b35803d5760686975c285924bf8", size = 7303857, upload-time = "2026-06-01T05:55:07.205Z" }, + { url = "https://files.pythonhosted.org/packages/e2/98/1f3896a9baae1f2aedf4e99c55291d6fa1f30ad9603d63bc18bda967b53e/grpcio-1.81.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:19f201da7b4e5c0559198abe5a97157e726f3abe6e8f5e832d4a50740f6dcc22", size = 6809676, upload-time = "2026-06-01T05:55:09.513Z" }, + { url = "https://files.pythonhosted.org/packages/34/8b/3441983718095208c5d797fd3239882e97ea89a629f41c8df94b4eef4df9/grpcio-1.81.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:275144b0115353339dbb8a6f28a9cf8997b5bf40e37f8f66ac0b0ea57e95b43f", size = 7412654, upload-time = "2026-06-01T05:55:12.777Z" }, + { url = "https://files.pythonhosted.org/packages/3c/98/1eddf07df6e4fe85cf67502a793f7b05468b2dca3d1ef35b972cf5d54468/grpcio-1.81.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:5192857589f223e5a98ff0e31f6e551b19040e647d17bfe10116c8a2ce3b8696", size = 8408026, upload-time = "2026-06-01T05:55:15.514Z" }, + { url = "https://files.pythonhosted.org/packages/5c/73/3860341e6a1f5347be6ab35c6c0e1e3a8eb59d010388207fd561dcf01a88/grpcio-1.81.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c6ff087cb1f563f47b504b4e29e684129fc5ae4863faf3ebca08a327764ee6cb", size = 7849498, upload-time = "2026-06-01T05:55:18.078Z" }, + { url = "https://files.pythonhosted.org/packages/ae/3f/0ea06bd85c701966aa3f8f37314f2ed83520d2b7590f42d643d445d8bc8b/grpcio-1.81.0-cp312-cp312-win32.whl", hash = "sha256:98c6240f563178fc5877bd50e6ff274463e53e1472128f4110742450739659fa", size = 4184161, upload-time = "2026-06-01T05:55:20.127Z" }, + { url = "https://files.pythonhosted.org/packages/39/e3/a7c387406827a86f99ad7838b995bf9b4a182ffe2d2c439ed2873efec952/grpcio-1.81.0-cp312-cp312-win_amd64.whl", hash = "sha256:87e33b7afcfb3585121b5f007d2c52b8c534104d18f556e840d35193ca2a9141", size = 4929958, upload-time = "2026-06-01T05:55:22.736Z" }, + { url = "https://files.pythonhosted.org/packages/f3/29/779ee53c931d0fd55c1d459fde43e485172caa3ac87cbd43d003a13a0185/grpcio-1.81.0-cp313-cp313-linux_armv7l.whl", hash = "sha256:62bbe463c9f0f2ff24e31bd25f8dd8b4bae78900e315915a3195a0ef1471a855", size = 6054973, upload-time = "2026-06-01T05:55:25.043Z" }, + { url = "https://files.pythonhosted.org/packages/9e/b6/7211807926b5a17f8d9a5d47c739a163d6812fefe3e4714e81cf92945ed7/grpcio-1.81.0-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:43c121e135ae44d1559b430db2b2dfad7421cbbe40e1deba506c7dc62b439719", size = 12048662, upload-time = "2026-06-01T05:55:28.453Z" }, + { url = "https://files.pythonhosted.org/packages/64/89/b1b93ef6b34bd20bbaf707fa99133bc9cc302139d5ec6f77a165c7169796/grpcio-1.81.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f345de40ef2e65f63645d53d251824e6070e07804827c5b00ec2e44555f9f901", size = 6599116, upload-time = "2026-06-01T05:55:31.185Z" }, + { url = "https://files.pythonhosted.org/packages/eb/bc/c89f9b9d1c22895715356a1e009554dae66319e97826bb4d30bcda7d29e8/grpcio-1.81.0-cp313-cp313-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:8c0855a350886f713b9e458e2a10d208009dcaa849f574e39cd6067db1fe1279", size = 7307591, upload-time = "2026-06-01T05:55:33.463Z" }, + { url = "https://files.pythonhosted.org/packages/65/4a/1df2a4cb4a1386e066ab7e4175e34bb884b35ccb60d3621c09c84af6aabb/grpcio-1.81.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a524cd530900bd24511fcb7f2ed144da4ea37711c4b094475d0bceca7a93a170", size = 6811797, upload-time = "2026-06-01T05:55:36.731Z" }, + { url = "https://files.pythonhosted.org/packages/8d/dc/fa189d20601a1be25b08850cfb733879bbb1047b62a8feec3a60e3e1a87b/grpcio-1.81.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:e7746ba3e6efc9e2b748eff59470a2b8684d5a9ec607c6580bcaa5be175820bc", size = 7415131, upload-time = "2026-06-01T05:55:39.451Z" }, + { url = "https://files.pythonhosted.org/packages/ad/a3/5625c48cb48d23c6631b3e5294f88e4c751f22a52591ae78859fab96dca1/grpcio-1.81.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:aaaa4f7f2057d795952e4eacf3f342be8b5b156992f6ac85023c8b98794ebd47", size = 8408398, upload-time = "2026-06-01T05:55:42.219Z" }, + { url = "https://files.pythonhosted.org/packages/75/34/0f8202c6809a46c2b4d69125ef3667c40b1c211f8e19930e5fa1f1197039/grpcio-1.81.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0fba53cb96004b2b7fb758b46b2288cb49d0b658316a4e73f3ef67230616ee65", size = 7844481, upload-time = "2026-06-01T05:55:44.849Z" }, + { url = "https://files.pythonhosted.org/packages/c0/95/c3366b5b5edf4c4adc90f2e29ca16e57965a8e56dc8d2ee89565ba1905bb/grpcio-1.81.0-cp313-cp313-win32.whl", hash = "sha256:c197e2ef75a442528072b29e9755da299110e8610e8bcbb59a6b4cf55384f005", size = 4182777, upload-time = "2026-06-01T05:55:47.459Z" }, + { url = "https://files.pythonhosted.org/packages/a9/a7/932f2f748511a32e641a2aba0d30dded3ed6e8bc330e0924e4d5d86853e6/grpcio-1.81.0-cp313-cp313-win_amd64.whl", hash = "sha256:194eddfacc84d80f50512e9fd4ee851d5f2499f18f299c95aa8fb4748f0537e0", size = 4928085, upload-time = "2026-06-01T05:55:50.158Z" }, + { url = "https://files.pythonhosted.org/packages/c5/1d/28b231333857deb840bc3d182ae087510170ea6d68f21393aeb0fe499530/grpcio-1.81.0-cp314-cp314-linux_armv7l.whl", hash = "sha256:a9351055f52660b58f3d4890ea66188b5134399f82b11aa0c55bd4b99eff5390", size = 6055712, upload-time = "2026-06-01T05:55:52.889Z" }, + { url = "https://files.pythonhosted.org/packages/e8/b8/999c14f9dff0fc47549d2e827cba1343ddc18e1d1bf0d06d2cf628eecbd9/grpcio-1.81.0-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:300f3337b6425fd16ead9a4f9b2ac25801acb64aa5bc0b99eb69901645b2b1d2", size = 12057189, upload-time = "2026-06-01T05:55:55.952Z" }, + { url = "https://files.pythonhosted.org/packages/1e/3d/1fbde079572562af65351151d840525a13879eb7b481d35b55cd64c6127a/grpcio-1.81.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:97bbd623f7ded558fd4f7cb5a4f600c4d4de65c5dd364c83a5b14b2a10a2d3b5", size = 6608136, upload-time = "2026-06-01T05:55:59.069Z" }, + { url = "https://files.pythonhosted.org/packages/32/89/1f17cb6882abfd8e5a303a25d5d1665abef5a8c499a96198c65a651d1b85/grpcio-1.81.0-cp314-cp314-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:ff83d889e3ebf6341c8c7864ad8031591ad5ca61599072fc511644d1eb962d2b", size = 7307045, upload-time = "2026-06-01T05:56:02.376Z" }, + { url = "https://files.pythonhosted.org/packages/48/5a/f98e91b2e755652e637ea2144318b0229b290062199f761b445fe1fa6015/grpcio-1.81.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c4fe218c5a35e1d87a5a26544237f1fa41dfd9cbd3c856b0810a30061f8b0aaf", size = 6812794, upload-time = "2026-06-01T05:56:05.777Z" }, + { url = "https://files.pythonhosted.org/packages/0a/0c/77892d715ac41e7ec0ace2a50080ffb64e189188056f607a66fe0014d1ee/grpcio-1.81.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:b8b025b6af43ee0ad4a70307025d77bcab5adde7c4597786010d802c203e9fc5", size = 7422767, upload-time = "2026-06-01T05:56:08.524Z" }, + { url = "https://files.pythonhosted.org/packages/3f/b8/aa04590c6564714d94954515f15a236e59d4b9b3ad01e615f1b706d7792d/grpcio-1.81.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:3d4e0ce5a40a998cf608c8ba60ecfe18fdf364a9aa193ae4ac3faeecd0e86757", size = 8408551, upload-time = "2026-06-01T05:56:11.283Z" }, + { url = "https://files.pythonhosted.org/packages/43/3d/4f4a3450a1973568910c6909cb74abbf2126f68aefae5976962f9f7ad50d/grpcio-1.81.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:aa948712c8e5fa40ec250870bda14bc7578e1bb832a8912d9d2a0f720518edbe", size = 7846468, upload-time = "2026-06-01T05:56:14.536Z" }, + { url = "https://files.pythonhosted.org/packages/88/f4/5827fd248221ad3b44161c23ce9b5f4ee405b04fc6da5fd402a9aa87a84a/grpcio-1.81.0-cp314-cp314-win32.whl", hash = "sha256:fbbe81314a9d92156abce8b62c09364eb8bafc0ca2a19919a45ec64b5c6cb664", size = 4264427, upload-time = "2026-06-01T05:56:17.192Z" }, + { url = "https://files.pythonhosted.org/packages/0c/e8/127dc2b246096ad50ef7c8d9b7b31d757787aeb796368bcdd4454e4204c4/grpcio-1.81.0-cp314-cp314-win_amd64.whl", hash = "sha256:b93cee313cae4e113fbb3a0ce1ea5633db6f63cfde2b2dc1d817429026b2a50b", size = 5070848, upload-time = "2026-06-01T05:56:19.735Z" }, ] [[package]] @@ -1185,11 +1185,11 @@ wheels = [ [[package]] name = "idna" -version = "3.16" +version = "3.17" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/1a/88/bcf9709822fe69d02c2a6a77956c98ce6ea8ca8767a9aadcedc7eb6a2390/idna-3.16.tar.gz", hash = "sha256:d7a6da03db833450fca25d2358ac9ff06cd624577a4aea3a596d5c0f77b8e03d", size = 203770, upload-time = "2026-05-22T00:16:18.781Z" } +sdist = { url = "https://files.pythonhosted.org/packages/b9/28/99c51f664567218d824af024c0251650fb27e4ca066df188dab0769c5b91/idna-3.17.tar.gz", hash = "sha256:5eb0cb53bc467c12eadcf6de83163ad8527cec9416f44b9b61b19caedad2b87f", size = 196048, upload-time = "2026-05-28T14:32:38.55Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/94/16/70255075a9859a0e3adb789b68ceb0e210dec03934245fd98d248226572f/idna-3.16-py3-none-any.whl", hash = "sha256:cc246e3a3f89580c3a951b5ad298ca4638078b2cdd4f115654332b5c26daded5", size = 74165, upload-time = "2026-05-22T00:16:16.698Z" }, + { url = "https://files.pythonhosted.org/packages/de/a7/f76514cc40ad6234098ecdebda08732d75964776c51a42845b7da10649e2/idna-3.17-py3-none-any.whl", hash = "sha256:466e48829084efe2548012b855df21540b96f2e20e51bd124c851536556a592c", size = 65316, upload-time = "2026-05-28T14:32:37.035Z" }, ] [[package]] @@ -1252,7 +1252,7 @@ wheels = [ [[package]] name = "ipython" -version = "9.13.0" +version = "9.14.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "colorama", marker = "sys_platform == 'win32'" }, @@ -1262,15 +1262,15 @@ dependencies = [ { name = "matplotlib-inline" }, { name = "pexpect", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, { name = "prompt-toolkit" }, - { name = "psutil" }, + { name = "psutil", marker = "sys_platform != 'emscripten'" }, { name = "pygments" }, { name = "stack-data" }, { name = "traitlets" }, { name = "typing-extensions", marker = "python_full_version < '3.12'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/cd/c4/87cda5842cf5c31837c06ddb588e11c3c35d8ece89b7a0108c06b8c9b00a/ipython-9.13.0.tar.gz", hash = "sha256:7e834b6afc99f020e3f05966ced34792f40267d64cb1ea9043886dab0dde5967", size = 4430549, upload-time = "2026-04-24T12:24:55.221Z" } +sdist = { url = "https://files.pythonhosted.org/packages/21/c2/c0064cf15d026501a1ef70e42efd9c3f818663089399aacc5e37a82901c1/ipython-9.14.0.tar.gz", hash = "sha256:6f27ff0f1d9ea050e0551f71568bc4b34d8aba579e8f111c5b4175f44ac6b4aa", size = 4432601, upload-time = "2026-05-29T15:13:24.611Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/b9/86/3060e8029b7cc505cce9a0137431dda81d0a3fde93a8f0f50ee0bf37a795/ipython-9.13.0-py3-none-any.whl", hash = "sha256:57f9d4639e20818d328d287c7b549af3d05f12486ea8f2e7f73e52a36ec4d201", size = 627274, upload-time = "2026-04-24T12:24:53.038Z" }, + { url = "https://files.pythonhosted.org/packages/14/a3/9e59340f02c1dc8f8c0a05b09244712b8609eb5439f9996e887e2b82f452/ipython-9.14.0-py3-none-any.whl", hash = "sha256:8fd984a3372c14b12790b084ba6b5cff5678c0cb063244a0034f06a51f20d6c2", size = 627457, upload-time = "2026-05-29T15:13:22.942Z" }, ] [[package]] @@ -1457,7 +1457,7 @@ wheels = [ [[package]] name = "jupyter-server" -version = "2.18.2" +version = "2.19.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -1480,9 +1480,9 @@ dependencies = [ { name = "traitlets" }, { name = "websocket-client" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/ca/15/1eacb0fcb79ef86e8a0a79a708e6ad7435f6f223097dd29a4ce861fabc44/jupyter_server-2.18.2.tar.gz", hash = "sha256:06b4f40d8a7a00bb39d5216859c81374a0e7cfefe6d8a5a7facc5a5c37c679a7", size = 753177, upload-time = "2026-05-06T07:04:36.274Z" } +sdist = { url = "https://files.pythonhosted.org/packages/76/a0/eb3c511f54df7b54ca5fc7bff3f4d2277d69052d6a7f521643dfed5279d6/jupyter_server-2.19.0.tar.gz", hash = "sha256:1731236bc32b680223e1ceb9d68209a845203475012ef68773a81434b46a31a7", size = 754561, upload-time = "2026-05-29T11:21:26.057Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/e2/50/ecf4f70d65bdb7519b28a33d1b2fee8a4b4ba1ae1a92f15d97e877c5de21/jupyter_server-2.18.2-py3-none-any.whl", hash = "sha256:fa5e46539ded65791838035a2b6001f13e54d5f64b8b3752eb1e91fdd641a5b8", size = 391907, upload-time = "2026-05-06T07:04:34.014Z" }, + { url = "https://files.pythonhosted.org/packages/c1/78/d2881e68894cecdcd05912a9c585cfb776ef1fb38b62c8dba98f12ab3adc/jupyter_server-2.19.0-py3-none-any.whl", hash = "sha256:cb76591b76d7093584c2ad2ae72ac3d58614a4b597507a1bb04e1f9f683cf9ea", size = 392244, upload-time = "2026-05-29T11:21:23.871Z" }, ] [[package]] @@ -2255,7 +2255,7 @@ wheels = [ [[package]] name = "optuna" -version = "4.8.0" +version = "4.9.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "alembic" }, @@ -2266,9 +2266,9 @@ dependencies = [ { name = "sqlalchemy" }, { name = "tqdm" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/bf/9b/62f120fb2ecbc4338bee70c5a3671c8e561714f3aa1a046b897ff142050e/optuna-4.8.0.tar.gz", hash = "sha256:6f7043e9f8ecb5e607af86a7eb00fb5ec2be26c3b08c201209a73d36aff37a38", size = 482603, upload-time = "2026-03-16T04:59:58.659Z" } +sdist = { url = "https://files.pythonhosted.org/packages/f4/aa/05f5e3f662cc96a4c478fc3446b8ed6359825a2b504ecb614a9ac84e4a4d/optuna-4.9.0.tar.gz", hash = "sha256:b322e5cbdf1655fb84c37646c4a7a1f391de1b47806bbe222e015825d0a82b87", size = 485834, upload-time = "2026-06-01T06:23:30.424Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/ac/24/7c731839566d30dc70556d9824ef17692d896c15e3df627bce8c16f753e1/optuna-4.8.0-py3-none-any.whl", hash = "sha256:c57a7682679c36bfc9bca0da430698179e513874074b71bebedb0334964ab930", size = 419456, upload-time = "2026-03-16T04:59:56.977Z" }, + { url = "https://files.pythonhosted.org/packages/ab/f3/e5fcd5d9b15771ed6dc10e3a7eeddc672e418f4f4c4653d216cc1d857e2d/optuna-4.9.0-py3-none-any.whl", hash = "sha256:f52f3be6148654850c92a5860d398fd88ec6b2c84ab68d9c3d07dcff02e7afee", size = 425553, upload-time = "2026-06-01T06:23:28.804Z" }, ] [[package]] @@ -2436,11 +2436,11 @@ wheels = [ [[package]] name = "platformdirs" -version = "4.9.6" +version = "4.10.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/9f/4a/0883b8e3802965322523f0b200ecf33d31f10991d0401162f4b23c698b42/platformdirs-4.9.6.tar.gz", hash = "sha256:3bfa75b0ad0db84096ae777218481852c0ebc6c727b3168c1b9e0118e458cf0a", size = 29400, upload-time = "2026-04-09T00:04:10.812Z" } +sdist = { url = "https://files.pythonhosted.org/packages/d7/47/e4501f49c178ae1d9f4a75073fda4204f52647993f075a9db4d14930e0c5/platformdirs-4.10.0.tar.gz", hash = "sha256:31e761a6a0ca04faf7353ea759bdba55652be214725111e5aac52dfa29d4bef7", size = 31224, upload-time = "2026-05-28T03:32:53.587Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/75/a6/a0a304dc33b49145b21f4808d763822111e67d1c3a32b524a1baf947b6e1/platformdirs-4.9.6-py3-none-any.whl", hash = "sha256:e61adb1d5e5cb3441b4b7710bea7e4c12250ca49439228cc1021c00dcfac0917", size = 21348, upload-time = "2026-04-09T00:04:09.463Z" }, + { url = "https://files.pythonhosted.org/packages/81/e6/cd9575ac904136b3cbf7aa7ee819ef86eedb7274e46f230e94ea4342e729/platformdirs-4.10.0-py3-none-any.whl", hash = "sha256:fb516cdb12eb0d857d0cd85a7c57cea4d060bee4578d6cf5a14dfdf8cbf8784a", size = 22743, upload-time = "2026-05-28T03:32:52.175Z" }, ] [[package]] @@ -2670,15 +2670,15 @@ wheels = [ [[package]] name = "python-discovery" -version = "1.3.1" +version = "1.4.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "filelock" }, { name = "platformdirs" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/48/60/e88788207d81e46362cfbef0d4aaf4c0f49efc3c12d4c3fa3f542c34ebec/python_discovery-1.3.1.tar.gz", hash = "sha256:62f6db28064c9613e7ca76cb3f00c38c839a07c31c00dfe7ed0986493d2150a6", size = 68011, upload-time = "2026-05-12T20:53:36.336Z" } +sdist = { url = "https://files.pythonhosted.org/packages/a6/12/38c1a0b1e64806780c9563e3fc9f6e472251839662587cfbe9bfaf2ae10a/python_discovery-1.4.0.tar.gz", hash = "sha256:eb8bc7daad3c226c147e45bb4e970a1feb1bf4048ee178e6db59e197b8010ce3", size = 68455, upload-time = "2026-05-28T01:15:37.639Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/b7/6f/a05a317a66fee0aad270011461f1a63a453ed12471249f172f7d2e2bc7b4/python_discovery-1.3.1-py3-none-any.whl", hash = "sha256:ed188687ebb3b82c01a17cd5ac62fc94d9f6487a7f1a0f9dfe89753fec91039c", size = 33185, upload-time = "2026-05-12T20:53:34.969Z" }, + { url = "https://files.pythonhosted.org/packages/c8/8d/3d316429f65029532bb1e28ff77b797d86b5ac3915bb44ca4e19aa283d43/python_discovery-1.4.0-py3-none-any.whl", hash = "sha256:26ed78d703e234879a66244c7d4114563fb13ec5cd30a2d1357e5fb4850782da", size = 33217, upload-time = "2026-05-28T01:15:36.573Z" }, ] [[package]] @@ -2995,7 +2995,7 @@ wheels = [ [[package]] name = "rosettasciio" -version = "0.13.0" +version = "0.14.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "dask", extra = ["array"] }, @@ -3005,178 +3005,207 @@ dependencies = [ { name = "python-dateutil" }, { name = "pyyaml" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/f8/08/435d64b1605fb871b445259f6f275b946960e1a918afc854c847f452147c/rosettasciio-0.13.0.tar.gz", hash = "sha256:59bf2834c4ecb84132b1f89380bc95375d170b48cf59a5608e3bed684caf52c9", size = 1189632, upload-time = "2026-04-09T09:39:02.511Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/69/6d/fafb202c842ff399f445ec15af58befbc04d8d18aa6698d83f2d810ee5f9/rosettasciio-0.13.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:bdbdea62745fb3d6854d8995bd156987d1e2b48a5e2296c6b1c7edd913ceb096", size = 822742, upload-time = "2026-04-09T09:37:56.153Z" }, - { url = "https://files.pythonhosted.org/packages/50/60/5ad9b2576427833f8f37d31583d49b49109a4ac5b84697de72dfc42d64f0/rosettasciio-0.13.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4cc73fce7e09a75dca154197e2694a2685bfffa82862b99ac7ea2de6454303f1", size = 821901, upload-time = "2026-04-09T09:37:57.83Z" }, - { url = "https://files.pythonhosted.org/packages/00/63/ecd34cb552824c4cd38805be8e6dab8a689833dea865a467aeeb4f281601/rosettasciio-0.13.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c0bdc2c22ed1f422851b3b84b27beb93e0fe610642f254486c3b9c8a3f721981", size = 1277605, upload-time = "2026-04-09T09:37:59.365Z" }, - { url = "https://files.pythonhosted.org/packages/62/a9/6dbe0468db382c39f9293a69268df9d554e2f3fd5fdf9cddab8c89cbb028/rosettasciio-0.13.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5140c6ff68443c97c487454da23c5f1ac9f753d534aaf119f9cdadc03899ee6b", size = 1281953, upload-time = "2026-04-09T09:38:01.862Z" }, - { url = "https://files.pythonhosted.org/packages/c6/dc/8a1024dca145dc8ed2891ae98232b536d1abebd959e5f7f334d7355c28e1/rosettasciio-0.13.0-cp311-cp311-win_amd64.whl", hash = "sha256:56b9ddaaa9d570c9d4fe43bd5a353cc4b4d609fc9970550e1f186465b35d8cd7", size = 811825, upload-time = "2026-04-09T09:38:03.907Z" }, - { url = "https://files.pythonhosted.org/packages/e0/6f/73d874f47b40cf1fe3b97d01f321f9dca4e1546551ff6fa22e8bfb707a97/rosettasciio-0.13.0-cp311-cp311-win_arm64.whl", hash = "sha256:61878596a661e0b3004537be40779c2a9298678469c355dfe121f2e3688d8949", size = 800557, upload-time = "2026-04-09T09:38:05.404Z" }, - { url = "https://files.pythonhosted.org/packages/d0/43/8c3b5ffc09d54d94177aedea457fe571feb97c1631dbcc643a31ef4cc5b1/rosettasciio-0.13.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:aaa1dbeeaa6798fd3b29fe0727160a37c3cc257234126db0d3c8016f8991f255", size = 823809, upload-time = "2026-04-09T09:38:07.017Z" }, - { url = "https://files.pythonhosted.org/packages/11/2f/c90520fe1a70585330950ab2a1cd4251a2ad76901e7c0d5ec74227fea9bf/rosettasciio-0.13.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:27ad8e41a3c68df9404e2bd0d87deada52068a50d60649bd2d57b852b9fefb99", size = 822143, upload-time = "2026-04-09T09:38:08.642Z" }, - { url = "https://files.pythonhosted.org/packages/ff/05/7d5e18d60d5e50733ece82461d45a0f74517e1d75dfc8bc7e11140903d33/rosettasciio-0.13.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bf6618fcdbccf61a773e1f86509e82f6ec5476bd38343856dcff707312ce4073", size = 1279557, upload-time = "2026-04-09T09:38:10.533Z" }, - { url = "https://files.pythonhosted.org/packages/1a/e2/7051df866c689552770b46fc35b0757199d16e8a3108927f5510c4db5224/rosettasciio-0.13.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:addc3f82fb4ea03c208c151d06eb65dba7e5fb0bea0244c5c054dbc5510ad962", size = 1284323, upload-time = "2026-04-09T09:38:12.365Z" }, - { url = "https://files.pythonhosted.org/packages/97/8f/450fb8bed3cbf05f875852ca243489e43fa0ed844438e700d9d5cb26e74b/rosettasciio-0.13.0-cp312-cp312-win_amd64.whl", hash = "sha256:5157fd2468f9b77fd36948e7a54013d8608df30c243d6833ea48fa42f8f8a044", size = 812447, upload-time = "2026-04-09T09:38:14.357Z" }, - { url = "https://files.pythonhosted.org/packages/c0/cf/2d5686cf88a7d65760ecfa4583d4e7e26154932c7f60f405ac350ef7a898/rosettasciio-0.13.0-cp312-cp312-win_arm64.whl", hash = "sha256:b0cfe472b561b74e678d71e05982979dc705dfda76e812278de283be6b295b8d", size = 800290, upload-time = "2026-04-09T09:38:16.311Z" }, - { url = "https://files.pythonhosted.org/packages/cb/b0/446ecd78b50d268df112c393b47f8e764415f84097a778faa0e4730a6569/rosettasciio-0.13.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:337e63409040541196a530ed9a4a7cfb80ffdd5333183097c47a6774597e5e29", size = 823025, upload-time = "2026-04-09T09:38:17.798Z" }, - { url = "https://files.pythonhosted.org/packages/60/df/e7e7f9a6410543403a2675c13c68f8d4efa825dfce9308484fdb44333941/rosettasciio-0.13.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3d94b6a3cac5f99a055a71dca7a21fd16447376a990621788c8c87370a35fa4f", size = 821326, upload-time = "2026-04-09T09:38:20.027Z" }, - { url = "https://files.pythonhosted.org/packages/34/1a/db727b64bd30669951cd779b8ac98cc7ec0590c2d433bba451280b8c9ff5/rosettasciio-0.13.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d817ca99b43df9d478b1da6c007b345fc4f65ab80bb502486190e88df74d6123", size = 1272603, upload-time = "2026-04-09T09:38:21.879Z" }, - { url = "https://files.pythonhosted.org/packages/bf/f6/16f10a129e2675ba680daa88dd5f38a512ada1351b64488d49f03544cfb3/rosettasciio-0.13.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:252d19fd5eff9cf9524fe1c2df4cf618f5e742c26ecd376adacb567df49527b9", size = 1283074, upload-time = "2026-04-09T09:38:23.819Z" }, - { url = "https://files.pythonhosted.org/packages/58/06/59425c136ff864bb4cff7d888f185c42579e2b76cd778bf0733629111fc8/rosettasciio-0.13.0-cp313-cp313-win_amd64.whl", hash = "sha256:6e61bbc1babab958234c455e2ae2aad3155ef1d897cebcc76c55ceb2d89f8fe2", size = 812215, upload-time = "2026-04-09T09:38:26.04Z" }, - { url = "https://files.pythonhosted.org/packages/f4/8a/c1d39f91cdea09984ce37c546b1f28ad3a077426766d7c85d49ac3eb2b7d/rosettasciio-0.13.0-cp313-cp313-win_arm64.whl", hash = "sha256:5f3557ea5d116e87ad6d0b09b192c7ebe4d2d87e1fedd2f56eab701d8f8846d0", size = 800093, upload-time = "2026-04-09T09:38:27.696Z" }, - { url = "https://files.pythonhosted.org/packages/8b/2a/2d01c6ab98dc522eb41bbfcd04afaef6a7793f5a59d6160acba8ebd0f033/rosettasciio-0.13.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:1fa9bcc7112b9b28f4b95e9e92f887cf0e499db927577273b3487bd5765e5bd8", size = 827252, upload-time = "2026-04-09T09:38:29.369Z" }, - { url = "https://files.pythonhosted.org/packages/30/2c/42d58c33f69237787db336eaaa6cb067acef63665dde8d13df8fe082fdb2/rosettasciio-0.13.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:0176857cf0e38b0fb6d71b143e207581447995fb41933b3c80eec1315b65549e", size = 827226, upload-time = "2026-04-09T09:38:31.536Z" }, - { url = "https://files.pythonhosted.org/packages/5d/29/a91d1055ff02c806df84c4f95f4f712a739369bada65eda849d9cc8cbae4/rosettasciio-0.13.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3c2c36529cf8707aaa68876ce96110707a9341f2e3284fde6273c6cf5a88bfe7", size = 1287317, upload-time = "2026-04-09T09:38:33.001Z" }, - { url = "https://files.pythonhosted.org/packages/58/d3/dea667d2742e4ba9c78ae50c08cdd839550697b9c7676660d53d633cac31/rosettasciio-0.13.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0ee2e1d51bfd010340f0bc55781d833f556ffd2bfd7d8890a33c1bd4173c5de3", size = 1274142, upload-time = "2026-04-09T09:38:34.643Z" }, - { url = "https://files.pythonhosted.org/packages/77/37/b4e71488292bb8eac88e22e4c59378181e511b26e26bb3dc4a53e3eee51e/rosettasciio-0.13.0-cp313-cp313t-win_amd64.whl", hash = "sha256:74cb1ce27667011611bf5f1b96c460985cd28b0dac9dbf7723259f5a0205f63f", size = 822578, upload-time = "2026-04-09T09:38:36.241Z" }, - { url = "https://files.pythonhosted.org/packages/2f/6a/3e074d0c9b75f6971bcd6266b08e739c1fe3709e9ce08fb9e960ea64cb8d/rosettasciio-0.13.0-cp313-cp313t-win_arm64.whl", hash = "sha256:727cc1f1bd37a139098184a426517dd60664b4a6e5b836e4e254de91a5d6e357", size = 804707, upload-time = "2026-04-09T09:38:37.98Z" }, - { url = "https://files.pythonhosted.org/packages/3c/72/00d7aa20493963e3337276fe97927b38f58117b4e488236c6b76332df199/rosettasciio-0.13.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:79332f1165b4f2029a7c03a339f467302e4d71f3aee2c699a49ab6236dd2aeec", size = 823255, upload-time = "2026-04-09T09:38:39.778Z" }, - { url = "https://files.pythonhosted.org/packages/4b/ea/6230f0f1afd3f73af32a229b2907a86d184ad584e1f75c16b234d7e48ec5/rosettasciio-0.13.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:0be2e98e862cd1d725b105d2b20849e4608497ef77152ff5994cdbb28f27e0df", size = 822154, upload-time = "2026-04-09T09:38:41.409Z" }, - { url = "https://files.pythonhosted.org/packages/01/1a/5af45536c4e527cef10085135efd553e79d822d4bad5304109b31a00583e/rosettasciio-0.13.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:059ee09c3d335e590470820ce16c4f779822b91908f3506456cb825849714ddd", size = 1271542, upload-time = "2026-04-09T09:38:43.243Z" }, - { url = "https://files.pythonhosted.org/packages/cb/55/95bc7d475a861fe4ed17c10b1cbf62ccf00073e149983091cdc046d94ed2/rosettasciio-0.13.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b8fd84038d0744065d9dc0be37050679f7f2ee59286c05e6841d9983b54544a6", size = 1278938, upload-time = "2026-04-09T09:38:45.044Z" }, - { url = "https://files.pythonhosted.org/packages/9e/ce/59addee6caea52623690f1e2d56b52c60b12c9eb4d993cb86da88b187398/rosettasciio-0.13.0-cp314-cp314-win_amd64.whl", hash = "sha256:9567d347f368109b43301a3a31c2f243d9cb081315e361b72ec0d90065e37d04", size = 811208, upload-time = "2026-04-09T09:38:46.954Z" }, - { url = "https://files.pythonhosted.org/packages/b0/87/faf3209b600c579b3e38187b7a9d9263462a97e0858e99258f20ea6c40e3/rosettasciio-0.13.0-cp314-cp314-win_arm64.whl", hash = "sha256:b6bbec93b2947e2035aca559284441be3f3dde5aa755b318a6413eeb21ab7c69", size = 799008, upload-time = "2026-04-09T09:38:48.629Z" }, - { url = "https://files.pythonhosted.org/packages/9b/9b/007063117e177147705b3cefb618689d7d4b5e243acd8e39e7e48a35fb69/rosettasciio-0.13.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:34dd225f0e11635ca09ccebb0ac3d8d38b71483491247d1f7789a9501288d659", size = 827711, upload-time = "2026-04-09T09:38:50.625Z" }, - { url = "https://files.pythonhosted.org/packages/97/64/42082e253b0ae2acf7aa7a67bd4547c6d918c248d24e715cbd63c4cadd4d/rosettasciio-0.13.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:dbaed91a90c118e74b2729a0738d2d5170d4d2f5763d84b06e8993f82fa5644d", size = 827561, upload-time = "2026-04-09T09:38:52.143Z" }, - { url = "https://files.pythonhosted.org/packages/91/ac/95837b692eafdb4ec67464cc13780da01b358ab8b293f327e6d59bc59f27/rosettasciio-0.13.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d6d249fbc568dd4077df94fc98d512ea8bdabb7d87f73724bf6bf75c9a5649dc", size = 1287593, upload-time = "2026-04-09T09:38:53.66Z" }, - { url = "https://files.pythonhosted.org/packages/2c/cc/69c859bcedad6564a46456058ba952c54b76040240e556c9930adf292534/rosettasciio-0.13.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3fb7ff9b9c55aefb2596b900e7b009a49b36c506e473130dd7304834463e0f73", size = 1274514, upload-time = "2026-04-09T09:38:55.152Z" }, - { url = "https://files.pythonhosted.org/packages/54/2b/5888169b6dc708f68af424120f27f8e28b0d628d6d1526b3e9232b599785/rosettasciio-0.13.0-cp314-cp314t-win_amd64.whl", hash = "sha256:ad1111fa3a582b88cd7401dda5f66bf6f7c6f3f0a7ab3a881338062ff0d8fb80", size = 823903, upload-time = "2026-04-09T09:38:57.121Z" }, - { url = "https://files.pythonhosted.org/packages/9e/eb/df4ed7ef0a525611b3b42947879a59cb26f2dac1f5359bd448c545913207/rosettasciio-0.13.0-cp314-cp314t-win_arm64.whl", hash = "sha256:f15d405a134a8e6e50eaf4b69ac7b69d227d014b839c15832b068914fe8a6ddf", size = 803657, upload-time = "2026-04-09T09:38:58.93Z" }, - { url = "https://files.pythonhosted.org/packages/23/94/83d57a093891773b2c28d16098bda58e10c43e1425583e40873baa0c09c2/rosettasciio-0.13.0-py3-none-any.whl", hash = "sha256:bc3e8b0dab257e5ff6f9f1f4b4207ee67f6d2c55adf5f7112f92dec2ab50ef59", size = 573680, upload-time = "2026-04-09T09:39:00.338Z" }, +sdist = { url = "https://files.pythonhosted.org/packages/57/ca/dc7c3871b47e1bd89b7a11a34b7b68219504a03047788e52cff1ce823755/rosettasciio-0.14.0.tar.gz", hash = "sha256:4b21582f0d832f086e10236b1798b1c17e327dc6d610e845a8eddd4df817504f", size = 1245393, upload-time = "2026-05-27T15:29:54.331Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9f/bf/0ecc513c23c2a61f1497c9801cc4144e40a996099d36b71407673ad1a975/rosettasciio-0.14.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:73c7bb64e6a456fd058807c9a698d50b6fa757985f0e505087b641b10dd50904", size = 876315, upload-time = "2026-05-27T15:28:58.173Z" }, + { url = "https://files.pythonhosted.org/packages/5d/36/07adfd0a1c36d249f6294538b030bd1c0da29486e6a73d35e1adbf689144/rosettasciio-0.14.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f8f5ac58e7ff8cf2113eb878a50c7ceeb12748433ecb6287b1045e0f97aa7916", size = 875404, upload-time = "2026-05-27T15:28:59.682Z" }, + { url = "https://files.pythonhosted.org/packages/49/fa/7df79374170885b2a542192a18f22d92a6a1a941dbc62dd4f5195ad26e57/rosettasciio-0.14.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4a844a914f11a72d06426862721227f54587c3a72f5a6dc1477646fbbd41109c", size = 1330312, upload-time = "2026-05-27T15:29:01.093Z" }, + { url = "https://files.pythonhosted.org/packages/61/d0/55cc1aa3e3bc621a638bd456282b4eee88be66eec969d480dd59acdfc85b/rosettasciio-0.14.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d66924bc504eb0af348ecf4223cff8052f45cb1f73bc47775d8b040f5c4d1d6f", size = 1334306, upload-time = "2026-05-27T15:29:02.543Z" }, + { url = "https://files.pythonhosted.org/packages/b9/ca/603088354e78a828a67920c0d321e6ca979be73aca0719f7af8ffe35d7d9/rosettasciio-0.14.0-cp311-cp311-win_amd64.whl", hash = "sha256:32ba0bd12da1a53345bf59853b8325dd1065038876f36466e0e71921042cfaf0", size = 864309, upload-time = "2026-05-27T15:29:03.912Z" }, + { url = "https://files.pythonhosted.org/packages/23/a1/9395d12ba2fca40290d9461512ca19c8d91c735aa3de6172855c1891920a/rosettasciio-0.14.0-cp311-cp311-win_arm64.whl", hash = "sha256:43939dfa6dc92f7d39e55b11001285b62dfe136c03f90977aab1ed44f79b243a", size = 854003, upload-time = "2026-05-27T15:29:05.254Z" }, + { url = "https://files.pythonhosted.org/packages/9d/03/7df5ab31e0a8ab429d2debd5ea9246d4ebad4062fd9ca50b8f97f95a826d/rosettasciio-0.14.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:559269d18a179db96b28bda98b9458dc865ef6d82b0d59f0e4c277ceb275b8cb", size = 877346, upload-time = "2026-05-27T15:29:06.964Z" }, + { url = "https://files.pythonhosted.org/packages/9d/34/d3f6595107d730d52ecf0b5fe0f3f106718c82fe3b3f92a94d175d95d67a/rosettasciio-0.14.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:654336c91b75a0efa9a6a9112397a131771b7ee70e18c297394f200042b3a2fa", size = 875628, upload-time = "2026-05-27T15:29:08.7Z" }, + { url = "https://files.pythonhosted.org/packages/65/ae/23894df831445e95a6dc9d915d500ff755e8c6241c32b62321d154b45398/rosettasciio-0.14.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ed65d0c844a5e3623b88bb1ae035908e256113f0ec43f9b0acefc1489b7a6430", size = 1334614, upload-time = "2026-05-27T15:29:10.04Z" }, + { url = "https://files.pythonhosted.org/packages/2e/50/108c50cd05f2ad3c3a105d7d1cafb596951859ebf596ca8f4132fa135dfc/rosettasciio-0.14.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c931d1a06f45f297c0a4de69cf4f477e65566adca63e9dd5e1976332140b06a7", size = 1340152, upload-time = "2026-05-27T15:29:11.586Z" }, + { url = "https://files.pythonhosted.org/packages/d4/43/04a92bdb09d4b12b8769a438bf048cc5220a6a49e33e81a4ff98064ac990/rosettasciio-0.14.0-cp312-cp312-win_amd64.whl", hash = "sha256:27b7018bacbecbcaa8426650fa4fe22db24842f45ea9d3d5b9b4744218f2c628", size = 864789, upload-time = "2026-05-27T15:29:13.104Z" }, + { url = "https://files.pythonhosted.org/packages/ea/0e/c51fed20238121afeb6b4bf5d9e78a474ec644906bdaf7330a2a667502b5/rosettasciio-0.14.0-cp312-cp312-win_arm64.whl", hash = "sha256:708530b949084aca56a22ef80066ccfea3a242a83cc804ad2c6a0e8b7f52f248", size = 853639, upload-time = "2026-05-27T15:29:14.645Z" }, + { url = "https://files.pythonhosted.org/packages/c2/52/f2576629d0c194374fd8806232249c76808b0c9a64b4fd718648236a9ce2/rosettasciio-0.14.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:3d3d0b93d4df943085e59109d9c2f7905eedd0ae35ef7914f6c1e6a8441de141", size = 876444, upload-time = "2026-05-27T15:29:15.973Z" }, + { url = "https://files.pythonhosted.org/packages/93/67/ec4d3991463f16ddae84bb4fa2b345567f1898fad9a73bbe9159deb07920/rosettasciio-0.14.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:f70d1beaa0c552438b569b4ca383737155f96d4be501429d95dd258aff8b7272", size = 874764, upload-time = "2026-05-27T15:29:17.485Z" }, + { url = "https://files.pythonhosted.org/packages/1a/52/f8b16002613afe7fbcd51980d84172713aca218b1a1a1a7e1f6190c9c8b4/rosettasciio-0.14.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4a96615c707782391bc082fe7c3de54b67e560af347274a4ce9881671547eb88", size = 1326408, upload-time = "2026-05-27T15:29:19.028Z" }, + { url = "https://files.pythonhosted.org/packages/48/91/25609a0eeb730ff2451b6f8466b8df194ac15003c03396714c626fa2d7cd/rosettasciio-0.14.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0cea0b240c8c87fae9137ae65496ec2e5208392f1f1058f3953b4f8a5a26d6e3", size = 1335956, upload-time = "2026-05-27T15:29:20.592Z" }, + { url = "https://files.pythonhosted.org/packages/72/67/f3a4bccf16b4a2813d1dcf7f4c5ff2ba6ccba31033cb73355d44b36ce437/rosettasciio-0.14.0-cp313-cp313-win_amd64.whl", hash = "sha256:ad675d0f7dfe31d7864cd7fa4372076771110c82da0eb5b5471c26ccfe086995", size = 864658, upload-time = "2026-05-27T15:29:22.159Z" }, + { url = "https://files.pythonhosted.org/packages/28/01/77624b05b41991af136ad87fb9ef6a7f4f417a66202fd8b6f1366ed38a75/rosettasciio-0.14.0-cp313-cp313-win_arm64.whl", hash = "sha256:bf1c02213933cc82ae685d3444dc595e7c6ba6110ed0894963cc200a9bb567d2", size = 853524, upload-time = "2026-05-27T15:29:23.607Z" }, + { url = "https://files.pythonhosted.org/packages/a4/f6/153d463328eb1566b25283f519756720274dcf18269b1f9d93038158743e/rosettasciio-0.14.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:e1bdd98eb232a39807c5d8d1a4fb69f81f2616662c22fe27ba43c972f5ecc648", size = 880788, upload-time = "2026-05-27T15:29:25.064Z" }, + { url = "https://files.pythonhosted.org/packages/7e/42/277d00ad1144d51938a6abe46d58416e7bd366ef0a12b3bda411ea0754d2/rosettasciio-0.14.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:d6bf22187819fdf825bd80bc7a3ad8ad924c371e324c8d0ba60be7e0c2d61c58", size = 880606, upload-time = "2026-05-27T15:29:26.611Z" }, + { url = "https://files.pythonhosted.org/packages/91/24/b777a9e76cd1dce493e447b1fe96d7edefed0444b806bf1e61192111e2fc/rosettasciio-0.14.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:db18d7832309bc6556319aaf28faa4992db0362ffd11851be07f70ff239e8afa", size = 1340574, upload-time = "2026-05-27T15:29:28.42Z" }, + { url = "https://files.pythonhosted.org/packages/6c/56/8c0d56499fa6b98603dd34c24f91a5994f4313809dbaabc213937dffbb62/rosettasciio-0.14.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2c2ce75139da7dc89f5b2bf04226975beafac76d59a758780e9363173042ea9a", size = 1327203, upload-time = "2026-05-27T15:29:30.268Z" }, + { url = "https://files.pythonhosted.org/packages/59/0a/ebfde6c65a0c311ce191a1be71177568fc7dcdf567e8ea4e154b454f8620/rosettasciio-0.14.0-cp313-cp313t-win_amd64.whl", hash = "sha256:c88f8923dacc23ad85c679109cdb3e879b749de77c9ef8575ca9fbab70708aa5", size = 869838, upload-time = "2026-05-27T15:29:31.662Z" }, + { url = "https://files.pythonhosted.org/packages/dd/ed/9bfbf1e151b2956e055dcd1f83277da555387afa1fd17c85ace4d3b0ac5e/rosettasciio-0.14.0-cp313-cp313t-win_arm64.whl", hash = "sha256:2b98254384dc1b55d8377a048f6fe6609d42b538c30428f55d7d97c183b19d1d", size = 858169, upload-time = "2026-05-27T15:29:33.529Z" }, + { url = "https://files.pythonhosted.org/packages/66/75/9dbc1e8bda660a6010091e0e898b326642559efdba580cdf49f2e236c1a1/rosettasciio-0.14.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:8f5d568b1a759084ba72550d4d5278c8464055d4c10d4e21d57dba81acc5a9fb", size = 876737, upload-time = "2026-05-27T15:29:34.957Z" }, + { url = "https://files.pythonhosted.org/packages/3a/32/74c8c91d22cc11594f42222e0936ced5c042ece324ae73663e04921d3366/rosettasciio-0.14.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:e14ed16e32e74767c4d2233722ebc1a726b13ba2afb77c22353df37d7706b7bf", size = 875572, upload-time = "2026-05-27T15:29:36.404Z" }, + { url = "https://files.pythonhosted.org/packages/d7/05/cd32f2d5589a41bcf6de8e48ac1fda403913df6ade7e56252cdc1de7242e/rosettasciio-0.14.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:165bd0c416700de21d7b1da1f29f3e6d4aaac8c25003d1569ce4f6401111d4b4", size = 1324655, upload-time = "2026-05-27T15:29:37.852Z" }, + { url = "https://files.pythonhosted.org/packages/b3/99/6e3d0bd825939e22a98f3f8e41b9cb7fe1ba16f7861d1b379a571b0b5d27/rosettasciio-0.14.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:107942ffa5302e2a3812571d3fead609949a28a20d25f53151f2baaed1c7f790", size = 1331210, upload-time = "2026-05-27T15:29:39.286Z" }, + { url = "https://files.pythonhosted.org/packages/4a/e8/5004fbffc23a6d565628481e111b69335f928365c57d485807c99eaeaeef/rosettasciio-0.14.0-cp314-cp314-win_amd64.whl", hash = "sha256:f2b8fd2f0423e0be811c662dbfb20d48b2c8479d8249ce04062d039b27b4040c", size = 863712, upload-time = "2026-05-27T15:29:40.891Z" }, + { url = "https://files.pythonhosted.org/packages/40/b2/8c16af430876a637e6e18c9317468cba7a69e47d712dd10a1c69d785b02d/rosettasciio-0.14.0-cp314-cp314-win_arm64.whl", hash = "sha256:569b581cc909d0a755947c180388348ab1e00c5ab068fbdc289dc2a2e74864d8", size = 852806, upload-time = "2026-05-27T15:29:42.288Z" }, + { url = "https://files.pythonhosted.org/packages/9e/48/af1981ca42b19c99a5bb03226bc03faf48137c5fc6393626e216d1f96f94/rosettasciio-0.14.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:ebb19e47a261d1d16cdfaf2104dd86af6d891fad82e6ee66eda76dab12fa4de8", size = 881288, upload-time = "2026-05-27T15:29:43.624Z" }, + { url = "https://files.pythonhosted.org/packages/86/e1/e965ac6158117841b814bb6d086bf601cc0d7fb385c7844f52b885ce533d/rosettasciio-0.14.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:d61dede6dc3f8365797afc2a9eb05eac8fe08a9ea4e995203c95dcf2b85c5104", size = 880931, upload-time = "2026-05-27T15:29:45.093Z" }, + { url = "https://files.pythonhosted.org/packages/9c/4e/b5f24a072e5aae2b00facf4bab0870f6dc02b9b34c928f39bec5b7be4ace/rosettasciio-0.14.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0dedf178bd0a072e0c4bf46d9a55048c69664ec651a8739994f75070fe188d88", size = 1340469, upload-time = "2026-05-27T15:29:46.502Z" }, + { url = "https://files.pythonhosted.org/packages/83/da/10a17fed897fc9de139078b05439961750ae4fd47e5a309df454b472cca8/rosettasciio-0.14.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8a72fd270b4d17a0a0a20188a3354770113debea3bb56bd976e3c0ca52d05c7e", size = 1327680, upload-time = "2026-05-27T15:29:48.048Z" }, + { url = "https://files.pythonhosted.org/packages/14/f3/3a5e2c3fa67fbf2219a914c6342a91f2f7ba19554ffe2b654fd44e13685c/rosettasciio-0.14.0-cp314-cp314t-win_amd64.whl", hash = "sha256:4a17300595ae1a83331ce4e015478341eac99cf8544f36c88f7c6de6e2662541", size = 869656, upload-time = "2026-05-27T15:29:49.836Z" }, + { url = "https://files.pythonhosted.org/packages/55/3c/5f189a330ba363f04ee73e25b37f8e08ae1266ad72e6c93d65166a7c7c6e/rosettasciio-0.14.0-cp314-cp314t-win_arm64.whl", hash = "sha256:66ca5c8d08cd3caa5397b134f83803915807d8e799ad3ac2fda5b9607342371d", size = 857389, upload-time = "2026-05-27T15:29:51.353Z" }, + { url = "https://files.pythonhosted.org/packages/d2/a3/5cf5424b16d68e8db9139d613570ef01825995d260343fdec704d3f646ba/rosettasciio-0.14.0-py3-none-any.whl", hash = "sha256:f64e045ced6827ba75e6b8b85c54812c0346eda6b76f08acded3d0f6e7fb5a27", size = 628709, upload-time = "2026-05-27T15:29:52.825Z" }, ] [[package]] name = "rpds-py" -version = "0.30.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/20/af/3f2f423103f1113b36230496629986e0ef7e199d2aa8392452b484b38ced/rpds_py-0.30.0.tar.gz", hash = "sha256:dd8ff7cf90014af0c0f787eea34794ebf6415242ee1d6fa91eaba725cc441e84", size = 69469, upload-time = "2025-11-30T20:24:38.837Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/4d/6e/f964e88b3d2abee2a82c1ac8366da848fce1c6d834dc2132c3fda3970290/rpds_py-0.30.0-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:a2bffea6a4ca9f01b3f8e548302470306689684e61602aa3d141e34da06cf425", size = 370157, upload-time = "2025-11-30T20:21:53.789Z" }, - { url = "https://files.pythonhosted.org/packages/94/ba/24e5ebb7c1c82e74c4e4f33b2112a5573ddc703915b13a073737b59b86e0/rpds_py-0.30.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:dc4f992dfe1e2bc3ebc7444f6c7051b4bc13cd8e33e43511e8ffd13bf407010d", size = 359676, upload-time = "2025-11-30T20:21:55.475Z" }, - { url = "https://files.pythonhosted.org/packages/84/86/04dbba1b087227747d64d80c3b74df946b986c57af0a9f0c98726d4d7a3b/rpds_py-0.30.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:422c3cb9856d80b09d30d2eb255d0754b23e090034e1deb4083f8004bd0761e4", size = 389938, upload-time = "2025-11-30T20:21:57.079Z" }, - { url = "https://files.pythonhosted.org/packages/42/bb/1463f0b1722b7f45431bdd468301991d1328b16cffe0b1c2918eba2c4eee/rpds_py-0.30.0-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:07ae8a593e1c3c6b82ca3292efbe73c30b61332fd612e05abee07c79359f292f", size = 402932, upload-time = "2025-11-30T20:21:58.47Z" }, - { url = "https://files.pythonhosted.org/packages/99/ee/2520700a5c1f2d76631f948b0736cdf9b0acb25abd0ca8e889b5c62ac2e3/rpds_py-0.30.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:12f90dd7557b6bd57f40abe7747e81e0c0b119bef015ea7726e69fe550e394a4", size = 525830, upload-time = "2025-11-30T20:21:59.699Z" }, - { url = "https://files.pythonhosted.org/packages/e0/ad/bd0331f740f5705cc555a5e17fdf334671262160270962e69a2bdef3bf76/rpds_py-0.30.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:99b47d6ad9a6da00bec6aabe5a6279ecd3c06a329d4aa4771034a21e335c3a97", size = 412033, upload-time = "2025-11-30T20:22:00.991Z" }, - { url = "https://files.pythonhosted.org/packages/f8/1e/372195d326549bb51f0ba0f2ecb9874579906b97e08880e7a65c3bef1a99/rpds_py-0.30.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:33f559f3104504506a44bb666b93a33f5d33133765b0c216a5bf2f1e1503af89", size = 390828, upload-time = "2025-11-30T20:22:02.723Z" }, - { url = "https://files.pythonhosted.org/packages/ab/2b/d88bb33294e3e0c76bc8f351a3721212713629ffca1700fa94979cb3eae8/rpds_py-0.30.0-cp311-cp311-manylinux_2_31_riscv64.whl", hash = "sha256:946fe926af6e44f3697abbc305ea168c2c31d3e3ef1058cf68f379bf0335a78d", size = 404683, upload-time = "2025-11-30T20:22:04.367Z" }, - { url = "https://files.pythonhosted.org/packages/50/32/c759a8d42bcb5289c1fac697cd92f6fe01a018dd937e62ae77e0e7f15702/rpds_py-0.30.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:495aeca4b93d465efde585977365187149e75383ad2684f81519f504f5c13038", size = 421583, upload-time = "2025-11-30T20:22:05.814Z" }, - { url = "https://files.pythonhosted.org/packages/2b/81/e729761dbd55ddf5d84ec4ff1f47857f4374b0f19bdabfcf929164da3e24/rpds_py-0.30.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d9a0ca5da0386dee0655b4ccdf46119df60e0f10da268d04fe7cc87886872ba7", size = 572496, upload-time = "2025-11-30T20:22:07.713Z" }, - { url = "https://files.pythonhosted.org/packages/14/f6/69066a924c3557c9c30baa6ec3a0aa07526305684c6f86c696b08860726c/rpds_py-0.30.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:8d6d1cc13664ec13c1b84241204ff3b12f9bb82464b8ad6e7a5d3486975c2eed", size = 598669, upload-time = "2025-11-30T20:22:09.312Z" }, - { url = "https://files.pythonhosted.org/packages/5f/48/905896b1eb8a05630d20333d1d8ffd162394127b74ce0b0784ae04498d32/rpds_py-0.30.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:3896fa1be39912cf0757753826bc8bdc8ca331a28a7c4ae46b7a21280b06bb85", size = 561011, upload-time = "2025-11-30T20:22:11.309Z" }, - { url = "https://files.pythonhosted.org/packages/22/16/cd3027c7e279d22e5eb431dd3c0fbc677bed58797fe7581e148f3f68818b/rpds_py-0.30.0-cp311-cp311-win32.whl", hash = "sha256:55f66022632205940f1827effeff17c4fa7ae1953d2b74a8581baaefb7d16f8c", size = 221406, upload-time = "2025-11-30T20:22:13.101Z" }, - { url = "https://files.pythonhosted.org/packages/fa/5b/e7b7aa136f28462b344e652ee010d4de26ee9fd16f1bfd5811f5153ccf89/rpds_py-0.30.0-cp311-cp311-win_amd64.whl", hash = "sha256:a51033ff701fca756439d641c0ad09a41d9242fa69121c7d8769604a0a629825", size = 236024, upload-time = "2025-11-30T20:22:14.853Z" }, - { url = "https://files.pythonhosted.org/packages/14/a6/364bba985e4c13658edb156640608f2c9e1d3ea3c81b27aa9d889fff0e31/rpds_py-0.30.0-cp311-cp311-win_arm64.whl", hash = "sha256:47b0ef6231c58f506ef0b74d44e330405caa8428e770fec25329ed2cb971a229", size = 229069, upload-time = "2025-11-30T20:22:16.577Z" }, - { url = "https://files.pythonhosted.org/packages/03/e7/98a2f4ac921d82f33e03f3835f5bf3a4a40aa1bfdc57975e74a97b2b4bdd/rpds_py-0.30.0-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:a161f20d9a43006833cd7068375a94d035714d73a172b681d8881820600abfad", size = 375086, upload-time = "2025-11-30T20:22:17.93Z" }, - { url = "https://files.pythonhosted.org/packages/4d/a1/bca7fd3d452b272e13335db8d6b0b3ecde0f90ad6f16f3328c6fb150c889/rpds_py-0.30.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6abc8880d9d036ecaafe709079969f56e876fcf107f7a8e9920ba6d5a3878d05", size = 359053, upload-time = "2025-11-30T20:22:19.297Z" }, - { url = "https://files.pythonhosted.org/packages/65/1c/ae157e83a6357eceff62ba7e52113e3ec4834a84cfe07fa4b0757a7d105f/rpds_py-0.30.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ca28829ae5f5d569bb62a79512c842a03a12576375d5ece7d2cadf8abe96ec28", size = 390763, upload-time = "2025-11-30T20:22:21.661Z" }, - { url = "https://files.pythonhosted.org/packages/d4/36/eb2eb8515e2ad24c0bd43c3ee9cd74c33f7ca6430755ccdb240fd3144c44/rpds_py-0.30.0-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:a1010ed9524c73b94d15919ca4d41d8780980e1765babf85f9a2f90d247153dd", size = 408951, upload-time = "2025-11-30T20:22:23.408Z" }, - { url = "https://files.pythonhosted.org/packages/d6/65/ad8dc1784a331fabbd740ef6f71ce2198c7ed0890dab595adb9ea2d775a1/rpds_py-0.30.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f8d1736cfb49381ba528cd5baa46f82fdc65c06e843dab24dd70b63d09121b3f", size = 514622, upload-time = "2025-11-30T20:22:25.16Z" }, - { url = "https://files.pythonhosted.org/packages/63/8e/0cfa7ae158e15e143fe03993b5bcd743a59f541f5952e1546b1ac1b5fd45/rpds_py-0.30.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d948b135c4693daff7bc2dcfc4ec57237a29bd37e60c2fabf5aff2bbacf3e2f1", size = 414492, upload-time = "2025-11-30T20:22:26.505Z" }, - { url = "https://files.pythonhosted.org/packages/60/1b/6f8f29f3f995c7ffdde46a626ddccd7c63aefc0efae881dc13b6e5d5bb16/rpds_py-0.30.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:47f236970bccb2233267d89173d3ad2703cd36a0e2a6e92d0560d333871a3d23", size = 394080, upload-time = "2025-11-30T20:22:27.934Z" }, - { url = "https://files.pythonhosted.org/packages/6d/d5/a266341051a7a3ca2f4b750a3aa4abc986378431fc2da508c5034d081b70/rpds_py-0.30.0-cp312-cp312-manylinux_2_31_riscv64.whl", hash = "sha256:2e6ecb5a5bcacf59c3f912155044479af1d0b6681280048b338b28e364aca1f6", size = 408680, upload-time = "2025-11-30T20:22:29.341Z" }, - { url = "https://files.pythonhosted.org/packages/10/3b/71b725851df9ab7a7a4e33cf36d241933da66040d195a84781f49c50490c/rpds_py-0.30.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:a8fa71a2e078c527c3e9dc9fc5a98c9db40bcc8a92b4e8858e36d329f8684b51", size = 423589, upload-time = "2025-11-30T20:22:31.469Z" }, - { url = "https://files.pythonhosted.org/packages/00/2b/e59e58c544dc9bd8bd8384ecdb8ea91f6727f0e37a7131baeff8d6f51661/rpds_py-0.30.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:73c67f2db7bc334e518d097c6d1e6fed021bbc9b7d678d6cc433478365d1d5f5", size = 573289, upload-time = "2025-11-30T20:22:32.997Z" }, - { url = "https://files.pythonhosted.org/packages/da/3e/a18e6f5b460893172a7d6a680e86d3b6bc87a54c1f0b03446a3c8c7b588f/rpds_py-0.30.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:5ba103fb455be00f3b1c2076c9d4264bfcb037c976167a6047ed82f23153f02e", size = 599737, upload-time = "2025-11-30T20:22:34.419Z" }, - { url = "https://files.pythonhosted.org/packages/5c/e2/714694e4b87b85a18e2c243614974413c60aa107fd815b8cbc42b873d1d7/rpds_py-0.30.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:7cee9c752c0364588353e627da8a7e808a66873672bcb5f52890c33fd965b394", size = 563120, upload-time = "2025-11-30T20:22:35.903Z" }, - { url = "https://files.pythonhosted.org/packages/6f/ab/d5d5e3bcedb0a77f4f613706b750e50a5a3ba1c15ccd3665ecc636c968fd/rpds_py-0.30.0-cp312-cp312-win32.whl", hash = "sha256:1ab5b83dbcf55acc8b08fc62b796ef672c457b17dbd7820a11d6c52c06839bdf", size = 223782, upload-time = "2025-11-30T20:22:37.271Z" }, - { url = "https://files.pythonhosted.org/packages/39/3b/f786af9957306fdc38a74cef405b7b93180f481fb48453a114bb6465744a/rpds_py-0.30.0-cp312-cp312-win_amd64.whl", hash = "sha256:a090322ca841abd453d43456ac34db46e8b05fd9b3b4ac0c78bcde8b089f959b", size = 240463, upload-time = "2025-11-30T20:22:39.021Z" }, - { url = "https://files.pythonhosted.org/packages/f3/d2/b91dc748126c1559042cfe41990deb92c4ee3e2b415f6b5234969ffaf0cc/rpds_py-0.30.0-cp312-cp312-win_arm64.whl", hash = "sha256:669b1805bd639dd2989b281be2cfd951c6121b65e729d9b843e9639ef1fd555e", size = 230868, upload-time = "2025-11-30T20:22:40.493Z" }, - { url = "https://files.pythonhosted.org/packages/ed/dc/d61221eb88ff410de3c49143407f6f3147acf2538c86f2ab7ce65ae7d5f9/rpds_py-0.30.0-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:f83424d738204d9770830d35290ff3273fbb02b41f919870479fab14b9d303b2", size = 374887, upload-time = "2025-11-30T20:22:41.812Z" }, - { url = "https://files.pythonhosted.org/packages/fd/32/55fb50ae104061dbc564ef15cc43c013dc4a9f4527a1f4d99baddf56fe5f/rpds_py-0.30.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:e7536cd91353c5273434b4e003cbda89034d67e7710eab8761fd918ec6c69cf8", size = 358904, upload-time = "2025-11-30T20:22:43.479Z" }, - { url = "https://files.pythonhosted.org/packages/58/70/faed8186300e3b9bdd138d0273109784eea2396c68458ed580f885dfe7ad/rpds_py-0.30.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2771c6c15973347f50fece41fc447c054b7ac2ae0502388ce3b6738cd366e3d4", size = 389945, upload-time = "2025-11-30T20:22:44.819Z" }, - { url = "https://files.pythonhosted.org/packages/bd/a8/073cac3ed2c6387df38f71296d002ab43496a96b92c823e76f46b8af0543/rpds_py-0.30.0-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:0a59119fc6e3f460315fe9d08149f8102aa322299deaa5cab5b40092345c2136", size = 407783, upload-time = "2025-11-30T20:22:46.103Z" }, - { url = "https://files.pythonhosted.org/packages/77/57/5999eb8c58671f1c11eba084115e77a8899d6e694d2a18f69f0ba471ec8b/rpds_py-0.30.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:76fec018282b4ead0364022e3c54b60bf368b9d926877957a8624b58419169b7", size = 515021, upload-time = "2025-11-30T20:22:47.458Z" }, - { url = "https://files.pythonhosted.org/packages/e0/af/5ab4833eadc36c0a8ed2bc5c0de0493c04f6c06de223170bd0798ff98ced/rpds_py-0.30.0-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:692bef75a5525db97318e8cd061542b5a79812d711ea03dbc1f6f8dbb0c5f0d2", size = 414589, upload-time = "2025-11-30T20:22:48.872Z" }, - { url = "https://files.pythonhosted.org/packages/b7/de/f7192e12b21b9e9a68a6d0f249b4af3fdcdff8418be0767a627564afa1f1/rpds_py-0.30.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9027da1ce107104c50c81383cae773ef5c24d296dd11c99e2629dbd7967a20c6", size = 394025, upload-time = "2025-11-30T20:22:50.196Z" }, - { url = "https://files.pythonhosted.org/packages/91/c4/fc70cd0249496493500e7cc2de87504f5aa6509de1e88623431fec76d4b6/rpds_py-0.30.0-cp313-cp313-manylinux_2_31_riscv64.whl", hash = "sha256:9cf69cdda1f5968a30a359aba2f7f9aa648a9ce4b580d6826437f2b291cfc86e", size = 408895, upload-time = "2025-11-30T20:22:51.87Z" }, - { url = "https://files.pythonhosted.org/packages/58/95/d9275b05ab96556fefff73a385813eb66032e4c99f411d0795372d9abcea/rpds_py-0.30.0-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:a4796a717bf12b9da9d3ad002519a86063dcac8988b030e405704ef7d74d2d9d", size = 422799, upload-time = "2025-11-30T20:22:53.341Z" }, - { url = "https://files.pythonhosted.org/packages/06/c1/3088fc04b6624eb12a57eb814f0d4997a44b0d208d6cace713033ff1a6ba/rpds_py-0.30.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5d4c2aa7c50ad4728a094ebd5eb46c452e9cb7edbfdb18f9e1221f597a73e1e7", size = 572731, upload-time = "2025-11-30T20:22:54.778Z" }, - { url = "https://files.pythonhosted.org/packages/d8/42/c612a833183b39774e8ac8fecae81263a68b9583ee343db33ab571a7ce55/rpds_py-0.30.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:ba81a9203d07805435eb06f536d95a266c21e5b2dfbf6517748ca40c98d19e31", size = 599027, upload-time = "2025-11-30T20:22:56.212Z" }, - { url = "https://files.pythonhosted.org/packages/5f/60/525a50f45b01d70005403ae0e25f43c0384369ad24ffe46e8d9068b50086/rpds_py-0.30.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:945dccface01af02675628334f7cf49c2af4c1c904748efc5cf7bbdf0b579f95", size = 563020, upload-time = "2025-11-30T20:22:58.2Z" }, - { url = "https://files.pythonhosted.org/packages/0b/5d/47c4655e9bcd5ca907148535c10e7d489044243cc9941c16ed7cd53be91d/rpds_py-0.30.0-cp313-cp313-win32.whl", hash = "sha256:b40fb160a2db369a194cb27943582b38f79fc4887291417685f3ad693c5a1d5d", size = 223139, upload-time = "2025-11-30T20:23:00.209Z" }, - { url = "https://files.pythonhosted.org/packages/f2/e1/485132437d20aa4d3e1d8b3fb5a5e65aa8139f1e097080c2a8443201742c/rpds_py-0.30.0-cp313-cp313-win_amd64.whl", hash = "sha256:806f36b1b605e2d6a72716f321f20036b9489d29c51c91f4dd29a3e3afb73b15", size = 240224, upload-time = "2025-11-30T20:23:02.008Z" }, - { url = "https://files.pythonhosted.org/packages/24/95/ffd128ed1146a153d928617b0ef673960130be0009c77d8fbf0abe306713/rpds_py-0.30.0-cp313-cp313-win_arm64.whl", hash = "sha256:d96c2086587c7c30d44f31f42eae4eac89b60dabbac18c7669be3700f13c3ce1", size = 230645, upload-time = "2025-11-30T20:23:03.43Z" }, - { url = "https://files.pythonhosted.org/packages/ff/1b/b10de890a0def2a319a2626334a7f0ae388215eb60914dbac8a3bae54435/rpds_py-0.30.0-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:eb0b93f2e5c2189ee831ee43f156ed34e2a89a78a66b98cadad955972548be5a", size = 364443, upload-time = "2025-11-30T20:23:04.878Z" }, - { url = "https://files.pythonhosted.org/packages/0d/bf/27e39f5971dc4f305a4fb9c672ca06f290f7c4e261c568f3dea16a410d47/rpds_py-0.30.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:922e10f31f303c7c920da8981051ff6d8c1a56207dbdf330d9047f6d30b70e5e", size = 353375, upload-time = "2025-11-30T20:23:06.342Z" }, - { url = "https://files.pythonhosted.org/packages/40/58/442ada3bba6e8e6615fc00483135c14a7538d2ffac30e2d933ccf6852232/rpds_py-0.30.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cdc62c8286ba9bf7f47befdcea13ea0e26bf294bda99758fd90535cbaf408000", size = 383850, upload-time = "2025-11-30T20:23:07.825Z" }, - { url = "https://files.pythonhosted.org/packages/14/14/f59b0127409a33c6ef6f5c1ebd5ad8e32d7861c9c7adfa9a624fc3889f6c/rpds_py-0.30.0-cp313-cp313t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:47f9a91efc418b54fb8190a6b4aa7813a23fb79c51f4bb84e418f5476c38b8db", size = 392812, upload-time = "2025-11-30T20:23:09.228Z" }, - { url = "https://files.pythonhosted.org/packages/b3/66/e0be3e162ac299b3a22527e8913767d869e6cc75c46bd844aa43fb81ab62/rpds_py-0.30.0-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1f3587eb9b17f3789ad50824084fa6f81921bbf9a795826570bda82cb3ed91f2", size = 517841, upload-time = "2025-11-30T20:23:11.186Z" }, - { url = "https://files.pythonhosted.org/packages/3d/55/fa3b9cf31d0c963ecf1ba777f7cf4b2a2c976795ac430d24a1f43d25a6ba/rpds_py-0.30.0-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:39c02563fc592411c2c61d26b6c5fe1e51eaa44a75aa2c8735ca88b0d9599daa", size = 408149, upload-time = "2025-11-30T20:23:12.864Z" }, - { url = "https://files.pythonhosted.org/packages/60/ca/780cf3b1a32b18c0f05c441958d3758f02544f1d613abf9488cd78876378/rpds_py-0.30.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:51a1234d8febafdfd33a42d97da7a43f5dcb120c1060e352a3fbc0c6d36e2083", size = 383843, upload-time = "2025-11-30T20:23:14.638Z" }, - { url = "https://files.pythonhosted.org/packages/82/86/d5f2e04f2aa6247c613da0c1dd87fcd08fa17107e858193566048a1e2f0a/rpds_py-0.30.0-cp313-cp313t-manylinux_2_31_riscv64.whl", hash = "sha256:eb2c4071ab598733724c08221091e8d80e89064cd472819285a9ab0f24bcedb9", size = 396507, upload-time = "2025-11-30T20:23:16.105Z" }, - { url = "https://files.pythonhosted.org/packages/4b/9a/453255d2f769fe44e07ea9785c8347edaf867f7026872e76c1ad9f7bed92/rpds_py-0.30.0-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:6bdfdb946967d816e6adf9a3d8201bfad269c67efe6cefd7093ef959683c8de0", size = 414949, upload-time = "2025-11-30T20:23:17.539Z" }, - { url = "https://files.pythonhosted.org/packages/a3/31/622a86cdc0c45d6df0e9ccb6becdba5074735e7033c20e401a6d9d0e2ca0/rpds_py-0.30.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c77afbd5f5250bf27bf516c7c4a016813eb2d3e116139aed0096940c5982da94", size = 565790, upload-time = "2025-11-30T20:23:19.029Z" }, - { url = "https://files.pythonhosted.org/packages/1c/5d/15bbf0fb4a3f58a3b1c67855ec1efcc4ceaef4e86644665fff03e1b66d8d/rpds_py-0.30.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:61046904275472a76c8c90c9ccee9013d70a6d0f73eecefd38c1ae7c39045a08", size = 590217, upload-time = "2025-11-30T20:23:20.885Z" }, - { url = "https://files.pythonhosted.org/packages/6d/61/21b8c41f68e60c8cc3b2e25644f0e3681926020f11d06ab0b78e3c6bbff1/rpds_py-0.30.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:4c5f36a861bc4b7da6516dbdf302c55313afa09b81931e8280361a4f6c9a2d27", size = 555806, upload-time = "2025-11-30T20:23:22.488Z" }, - { url = "https://files.pythonhosted.org/packages/f9/39/7e067bb06c31de48de3eb200f9fc7c58982a4d3db44b07e73963e10d3be9/rpds_py-0.30.0-cp313-cp313t-win32.whl", hash = "sha256:3d4a69de7a3e50ffc214ae16d79d8fbb0922972da0356dcf4d0fdca2878559c6", size = 211341, upload-time = "2025-11-30T20:23:24.449Z" }, - { url = "https://files.pythonhosted.org/packages/0a/4d/222ef0b46443cf4cf46764d9c630f3fe4abaa7245be9417e56e9f52b8f65/rpds_py-0.30.0-cp313-cp313t-win_amd64.whl", hash = "sha256:f14fc5df50a716f7ece6a80b6c78bb35ea2ca47c499e422aa4463455dd96d56d", size = 225768, upload-time = "2025-11-30T20:23:25.908Z" }, - { url = "https://files.pythonhosted.org/packages/86/81/dad16382ebbd3d0e0328776d8fd7ca94220e4fa0798d1dc5e7da48cb3201/rpds_py-0.30.0-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:68f19c879420aa08f61203801423f6cd5ac5f0ac4ac82a2368a9fcd6a9a075e0", size = 362099, upload-time = "2025-11-30T20:23:27.316Z" }, - { url = "https://files.pythonhosted.org/packages/2b/60/19f7884db5d5603edf3c6bce35408f45ad3e97e10007df0e17dd57af18f8/rpds_py-0.30.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:ec7c4490c672c1a0389d319b3a9cfcd098dcdc4783991553c332a15acf7249be", size = 353192, upload-time = "2025-11-30T20:23:29.151Z" }, - { url = "https://files.pythonhosted.org/packages/bf/c4/76eb0e1e72d1a9c4703c69607cec123c29028bff28ce41588792417098ac/rpds_py-0.30.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f251c812357a3fed308d684a5079ddfb9d933860fc6de89f2b7ab00da481e65f", size = 384080, upload-time = "2025-11-30T20:23:30.785Z" }, - { url = "https://files.pythonhosted.org/packages/72/87/87ea665e92f3298d1b26d78814721dc39ed8d2c74b86e83348d6b48a6f31/rpds_py-0.30.0-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ac98b175585ecf4c0348fd7b29c3864bda53b805c773cbf7bfdaffc8070c976f", size = 394841, upload-time = "2025-11-30T20:23:32.209Z" }, - { url = "https://files.pythonhosted.org/packages/77/ad/7783a89ca0587c15dcbf139b4a8364a872a25f861bdb88ed99f9b0dec985/rpds_py-0.30.0-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3e62880792319dbeb7eb866547f2e35973289e7d5696c6e295476448f5b63c87", size = 516670, upload-time = "2025-11-30T20:23:33.742Z" }, - { url = "https://files.pythonhosted.org/packages/5b/3c/2882bdac942bd2172f3da574eab16f309ae10a3925644e969536553cb4ee/rpds_py-0.30.0-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4e7fc54e0900ab35d041b0601431b0a0eb495f0851a0639b6ef90f7741b39a18", size = 408005, upload-time = "2025-11-30T20:23:35.253Z" }, - { url = "https://files.pythonhosted.org/packages/ce/81/9a91c0111ce1758c92516a3e44776920b579d9a7c09b2b06b642d4de3f0f/rpds_py-0.30.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:47e77dc9822d3ad616c3d5759ea5631a75e5809d5a28707744ef79d7a1bcfcad", size = 382112, upload-time = "2025-11-30T20:23:36.842Z" }, - { url = "https://files.pythonhosted.org/packages/cf/8e/1da49d4a107027e5fbc64daeab96a0706361a2918da10cb41769244b805d/rpds_py-0.30.0-cp314-cp314-manylinux_2_31_riscv64.whl", hash = "sha256:b4dc1a6ff022ff85ecafef7979a2c6eb423430e05f1165d6688234e62ba99a07", size = 399049, upload-time = "2025-11-30T20:23:38.343Z" }, - { url = "https://files.pythonhosted.org/packages/df/5a/7ee239b1aa48a127570ec03becbb29c9d5a9eb092febbd1699d567cae859/rpds_py-0.30.0-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:4559c972db3a360808309e06a74628b95eaccbf961c335c8fe0d590cf587456f", size = 415661, upload-time = "2025-11-30T20:23:40.263Z" }, - { url = "https://files.pythonhosted.org/packages/70/ea/caa143cf6b772f823bc7929a45da1fa83569ee49b11d18d0ada7f5ee6fd6/rpds_py-0.30.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:0ed177ed9bded28f8deb6ab40c183cd1192aa0de40c12f38be4d59cd33cb5c65", size = 565606, upload-time = "2025-11-30T20:23:42.186Z" }, - { url = "https://files.pythonhosted.org/packages/64/91/ac20ba2d69303f961ad8cf55bf7dbdb4763f627291ba3d0d7d67333cced9/rpds_py-0.30.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:ad1fa8db769b76ea911cb4e10f049d80bf518c104f15b3edb2371cc65375c46f", size = 591126, upload-time = "2025-11-30T20:23:44.086Z" }, - { url = "https://files.pythonhosted.org/packages/21/20/7ff5f3c8b00c8a95f75985128c26ba44503fb35b8e0259d812766ea966c7/rpds_py-0.30.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:46e83c697b1f1c72b50e5ee5adb4353eef7406fb3f2043d64c33f20ad1c2fc53", size = 553371, upload-time = "2025-11-30T20:23:46.004Z" }, - { url = "https://files.pythonhosted.org/packages/72/c7/81dadd7b27c8ee391c132a6b192111ca58d866577ce2d9b0ca157552cce0/rpds_py-0.30.0-cp314-cp314-win32.whl", hash = "sha256:ee454b2a007d57363c2dfd5b6ca4a5d7e2c518938f8ed3b706e37e5d470801ed", size = 215298, upload-time = "2025-11-30T20:23:47.696Z" }, - { url = "https://files.pythonhosted.org/packages/3e/d2/1aaac33287e8cfb07aab2e6b8ac1deca62f6f65411344f1433c55e6f3eb8/rpds_py-0.30.0-cp314-cp314-win_amd64.whl", hash = "sha256:95f0802447ac2d10bcc69f6dc28fe95fdf17940367b21d34e34c737870758950", size = 228604, upload-time = "2025-11-30T20:23:49.501Z" }, - { url = "https://files.pythonhosted.org/packages/e8/95/ab005315818cc519ad074cb7784dae60d939163108bd2b394e60dc7b5461/rpds_py-0.30.0-cp314-cp314-win_arm64.whl", hash = "sha256:613aa4771c99f03346e54c3f038e4cc574ac09a3ddfb0e8878487335e96dead6", size = 222391, upload-time = "2025-11-30T20:23:50.96Z" }, - { url = "https://files.pythonhosted.org/packages/9e/68/154fe0194d83b973cdedcdcc88947a2752411165930182ae41d983dcefa6/rpds_py-0.30.0-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:7e6ecfcb62edfd632e56983964e6884851786443739dbfe3582947e87274f7cb", size = 364868, upload-time = "2025-11-30T20:23:52.494Z" }, - { url = "https://files.pythonhosted.org/packages/83/69/8bbc8b07ec854d92a8b75668c24d2abcb1719ebf890f5604c61c9369a16f/rpds_py-0.30.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:a1d0bc22a7cdc173fedebb73ef81e07faef93692b8c1ad3733b67e31e1b6e1b8", size = 353747, upload-time = "2025-11-30T20:23:54.036Z" }, - { url = "https://files.pythonhosted.org/packages/ab/00/ba2e50183dbd9abcce9497fa5149c62b4ff3e22d338a30d690f9af970561/rpds_py-0.30.0-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0d08f00679177226c4cb8c5265012eea897c8ca3b93f429e546600c971bcbae7", size = 383795, upload-time = "2025-11-30T20:23:55.556Z" }, - { url = "https://files.pythonhosted.org/packages/05/6f/86f0272b84926bcb0e4c972262f54223e8ecc556b3224d281e6598fc9268/rpds_py-0.30.0-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5965af57d5848192c13534f90f9dd16464f3c37aaf166cc1da1cae1fd5a34898", size = 393330, upload-time = "2025-11-30T20:23:57.033Z" }, - { url = "https://files.pythonhosted.org/packages/cb/e9/0e02bb2e6dc63d212641da45df2b0bf29699d01715913e0d0f017ee29438/rpds_py-0.30.0-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9a4e86e34e9ab6b667c27f3211ca48f73dba7cd3d90f8d5b11be56e5dbc3fb4e", size = 518194, upload-time = "2025-11-30T20:23:58.637Z" }, - { url = "https://files.pythonhosted.org/packages/ee/ca/be7bca14cf21513bdf9c0606aba17d1f389ea2b6987035eb4f62bd923f25/rpds_py-0.30.0-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e5d3e6b26f2c785d65cc25ef1e5267ccbe1b069c5c21b8cc724efee290554419", size = 408340, upload-time = "2025-11-30T20:24:00.2Z" }, - { url = "https://files.pythonhosted.org/packages/c2/c7/736e00ebf39ed81d75544c0da6ef7b0998f8201b369acf842f9a90dc8fce/rpds_py-0.30.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:626a7433c34566535b6e56a1b39a7b17ba961e97ce3b80ec62e6f1312c025551", size = 383765, upload-time = "2025-11-30T20:24:01.759Z" }, - { url = "https://files.pythonhosted.org/packages/4a/3f/da50dfde9956aaf365c4adc9533b100008ed31aea635f2b8d7b627e25b49/rpds_py-0.30.0-cp314-cp314t-manylinux_2_31_riscv64.whl", hash = "sha256:acd7eb3f4471577b9b5a41baf02a978e8bdeb08b4b355273994f8b87032000a8", size = 396834, upload-time = "2025-11-30T20:24:03.687Z" }, - { url = "https://files.pythonhosted.org/packages/4e/00/34bcc2565b6020eab2623349efbdec810676ad571995911f1abdae62a3a0/rpds_py-0.30.0-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fe5fa731a1fa8a0a56b0977413f8cacac1768dad38d16b3a296712709476fbd5", size = 415470, upload-time = "2025-11-30T20:24:05.232Z" }, - { url = "https://files.pythonhosted.org/packages/8c/28/882e72b5b3e6f718d5453bd4d0d9cf8df36fddeb4ddbbab17869d5868616/rpds_py-0.30.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:74a3243a411126362712ee1524dfc90c650a503502f135d54d1b352bd01f2404", size = 565630, upload-time = "2025-11-30T20:24:06.878Z" }, - { url = "https://files.pythonhosted.org/packages/3b/97/04a65539c17692de5b85c6e293520fd01317fd878ea1995f0367d4532fb1/rpds_py-0.30.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:3e8eeb0544f2eb0d2581774be4c3410356eba189529a6b3e36bbbf9696175856", size = 591148, upload-time = "2025-11-30T20:24:08.445Z" }, - { url = "https://files.pythonhosted.org/packages/85/70/92482ccffb96f5441aab93e26c4d66489eb599efdcf96fad90c14bbfb976/rpds_py-0.30.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:dbd936cde57abfee19ab3213cf9c26be06d60750e60a8e4dd85d1ab12c8b1f40", size = 556030, upload-time = "2025-11-30T20:24:10.956Z" }, - { url = "https://files.pythonhosted.org/packages/20/53/7c7e784abfa500a2b6b583b147ee4bb5a2b3747a9166bab52fec4b5b5e7d/rpds_py-0.30.0-cp314-cp314t-win32.whl", hash = "sha256:dc824125c72246d924f7f796b4f63c1e9dc810c7d9e2355864b3c3a73d59ade0", size = 211570, upload-time = "2025-11-30T20:24:12.735Z" }, - { url = "https://files.pythonhosted.org/packages/d0/02/fa464cdfbe6b26e0600b62c528b72d8608f5cc49f96b8d6e38c95d60c676/rpds_py-0.30.0-cp314-cp314t-win_amd64.whl", hash = "sha256:27f4b0e92de5bfbc6f86e43959e6edd1425c33b5e69aab0984a72047f2bcf1e3", size = 226532, upload-time = "2025-11-30T20:24:14.634Z" }, - { url = "https://files.pythonhosted.org/packages/69/71/3f34339ee70521864411f8b6992e7ab13ac30d8e4e3309e07c7361767d91/rpds_py-0.30.0-pp311-pypy311_pp73-macosx_10_12_x86_64.whl", hash = "sha256:c2262bdba0ad4fc6fb5545660673925c2d2a5d9e2e0fb603aad545427be0fc58", size = 372292, upload-time = "2025-11-30T20:24:16.537Z" }, - { url = "https://files.pythonhosted.org/packages/57/09/f183df9b8f2d66720d2ef71075c59f7e1b336bec7ee4c48f0a2b06857653/rpds_py-0.30.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:ee6af14263f25eedc3bb918a3c04245106a42dfd4f5c2285ea6f997b1fc3f89a", size = 362128, upload-time = "2025-11-30T20:24:18.086Z" }, - { url = "https://files.pythonhosted.org/packages/7a/68/5c2594e937253457342e078f0cc1ded3dd7b2ad59afdbf2d354869110a02/rpds_py-0.30.0-pp311-pypy311_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3adbb8179ce342d235c31ab8ec511e66c73faa27a47e076ccc92421add53e2bb", size = 391542, upload-time = "2025-11-30T20:24:20.092Z" }, - { url = "https://files.pythonhosted.org/packages/49/5c/31ef1afd70b4b4fbdb2800249f34c57c64beb687495b10aec0365f53dfc4/rpds_py-0.30.0-pp311-pypy311_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:250fa00e9543ac9b97ac258bd37367ff5256666122c2d0f2bc97577c60a1818c", size = 404004, upload-time = "2025-11-30T20:24:22.231Z" }, - { url = "https://files.pythonhosted.org/packages/e3/63/0cfbea38d05756f3440ce6534d51a491d26176ac045e2707adc99bb6e60a/rpds_py-0.30.0-pp311-pypy311_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9854cf4f488b3d57b9aaeb105f06d78e5529d3145b1e4a41750167e8c213c6d3", size = 527063, upload-time = "2025-11-30T20:24:24.302Z" }, - { url = "https://files.pythonhosted.org/packages/42/e6/01e1f72a2456678b0f618fc9a1a13f882061690893c192fcad9f2926553a/rpds_py-0.30.0-pp311-pypy311_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:993914b8e560023bc0a8bf742c5f303551992dcb85e247b1e5c7f4a7d145bda5", size = 413099, upload-time = "2025-11-30T20:24:25.916Z" }, - { url = "https://files.pythonhosted.org/packages/b8/25/8df56677f209003dcbb180765520c544525e3ef21ea72279c98b9aa7c7fb/rpds_py-0.30.0-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:58edca431fb9b29950807e301826586e5bbf24163677732429770a697ffe6738", size = 392177, upload-time = "2025-11-30T20:24:27.834Z" }, - { url = "https://files.pythonhosted.org/packages/4a/b4/0a771378c5f16f8115f796d1f437950158679bcd2a7c68cf251cfb00ed5b/rpds_py-0.30.0-pp311-pypy311_pp73-manylinux_2_31_riscv64.whl", hash = "sha256:dea5b552272a944763b34394d04577cf0f9bd013207bc32323b5a89a53cf9c2f", size = 406015, upload-time = "2025-11-30T20:24:29.457Z" }, - { url = "https://files.pythonhosted.org/packages/36/d8/456dbba0af75049dc6f63ff295a2f92766b9d521fa00de67a2bd6427d57a/rpds_py-0.30.0-pp311-pypy311_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:ba3af48635eb83d03f6c9735dfb21785303e73d22ad03d489e88adae6eab8877", size = 423736, upload-time = "2025-11-30T20:24:31.22Z" }, - { url = "https://files.pythonhosted.org/packages/13/64/b4d76f227d5c45a7e0b796c674fd81b0a6c4fbd48dc29271857d8219571c/rpds_py-0.30.0-pp311-pypy311_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:dff13836529b921e22f15cb099751209a60009731a68519630a24d61f0b1b30a", size = 573981, upload-time = "2025-11-30T20:24:32.934Z" }, - { url = "https://files.pythonhosted.org/packages/20/91/092bacadeda3edf92bf743cc96a7be133e13a39cdbfd7b5082e7ab638406/rpds_py-0.30.0-pp311-pypy311_pp73-musllinux_1_2_i686.whl", hash = "sha256:1b151685b23929ab7beec71080a8889d4d6d9fa9a983d213f07121205d48e2c4", size = 599782, upload-time = "2025-11-30T20:24:35.169Z" }, - { url = "https://files.pythonhosted.org/packages/d1/b7/b95708304cd49b7b6f82fdd039f1748b66ec2b21d6a45180910802f1abf1/rpds_py-0.30.0-pp311-pypy311_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:ac37f9f516c51e5753f27dfdef11a88330f04de2d564be3991384b2f3535d02e", size = 562191, upload-time = "2025-11-30T20:24:36.853Z" }, +version = "2026.5.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/2e/43/25a8dcd3feedd735039a8f0b5b7e3b118232b5eae288c4fd9ab200d41094/rpds_py-2026.5.1.tar.gz", hash = "sha256:07b24fea40541e28570e5b795a4a38fbdcd12550c06bd0748005ecc8116ca256", size = 64459, upload-time = "2026-05-28T12:02:13.232Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/4f/a0/acf8b6fc20bfdcd3a45bd3f57680fb198e157b7e997b9123b10763798bd2/rpds_py-2026.5.1-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:3397a5ed7174dc2786bb214030232fc36fe8e5584fec43a9952cc542b1a12036", size = 355609, upload-time = "2026-05-28T11:58:50.78Z" }, + { url = "https://files.pythonhosted.org/packages/b6/95/f8203fd997484b1690a6869cd0e503b6c3c6be55b0ecc36d1a491fe742f0/rpds_py-2026.5.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:99ab6ba7bfa2cb0f96a04e3652355bf04e3f51aceb1e943b8541dab7ba4828cc", size = 348460, upload-time = "2026-05-28T11:58:52.374Z" }, + { url = "https://files.pythonhosted.org/packages/33/8c/b47326ad2f0be545a5e5c1a55937a12afaea7d392ba2837bb9680f57e6c9/rpds_py-2026.5.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d0efbe45632665e53e3db8fe1e5692db58fc5cb9bab4459d570b83efefe11164", size = 381031, upload-time = "2026-05-28T11:58:53.775Z" }, + { url = "https://files.pythonhosted.org/packages/22/0b/e83bbd97ffac6f6389b605cd4e1c8ac5761dc7e977769c9255d8c5adb7bd/rpds_py-2026.5.1-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:01d17b29c0c23d82b1f4751147ec49cf451f1fc2554eb9ef5f957e55d2656ead", size = 387121, upload-time = "2026-05-28T11:58:55.243Z" }, + { url = "https://files.pythonhosted.org/packages/fd/0e/d285d1bc8864245919c61e1ca82263e4a66d337759c3a4cef72766ff9afc/rpds_py-2026.5.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:7559f72b94ae52659086c595dfa017cde03155f7832071d30959049052cb3ece", size = 501026, upload-time = "2026-05-28T11:58:56.788Z" }, + { url = "https://files.pythonhosted.org/packages/86/06/ccb2109a1e543437b5e43816f2b43b9554cc6783145528a4e3711e05c011/rpds_py-2026.5.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9e25b7088f9ccbfc0dfcaa52bf969300ca229e10ecf758974ebcbb080a4b37bb", size = 391865, upload-time = "2026-05-28T11:58:58.298Z" }, + { url = "https://files.pythonhosted.org/packages/3d/33/237173db1cfef10105b3839a24de00eb8d2a523711add4632447cdf0aedd/rpds_py-2026.5.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:613fc4ee9eaef26dc5840666214dd6fbcebcf32f46e76f4abc473059f4e13dda", size = 378012, upload-time = "2026-05-28T11:58:59.589Z" }, + { url = "https://files.pythonhosted.org/packages/97/64/1eae54e34d5161f9969295e80bd6b62a55f2b6ac5f2a5b60d02c2140e758/rpds_py-2026.5.1-cp311-cp311-manylinux_2_31_riscv64.whl", hash = "sha256:85264a90ff4c05c1568dd65f5921c837614b67c60358fb4c17df3b7f2e90690a", size = 391111, upload-time = "2026-05-28T11:59:01.104Z" }, + { url = "https://files.pythonhosted.org/packages/d8/34/5bb334a5a0f65d77869217c4654f34c78a7d11b93938a3c076a2edeafc52/rpds_py-2026.5.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fe71bca7d547acb17027c7fd1624ff8aae623499c498d3e7011182c4de5c25e0", size = 409225, upload-time = "2026-05-28T11:59:02.433Z" }, + { url = "https://files.pythonhosted.org/packages/16/0f/007ec21283b5b040b4ec3bd95e0402591e22bfa7d5c93dfe01c465c2d2d7/rpds_py-2026.5.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:a05fa4f41f37ec97c9c260441a940450a192f78d774d2b097eee1379f1e1246a", size = 556487, upload-time = "2026-05-28T11:59:04.012Z" }, + { url = "https://files.pythonhosted.org/packages/ff/10/5437c94508169b6b22d8418fef7a66e9ffb5f3b9e9c94460f2eedafe06ff/rpds_py-2026.5.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:df1d2a1996755b24b9ecee92cb4d36c28f86f464a6a173349c26bab41e94b8c2", size = 620798, upload-time = "2026-05-28T11:59:05.485Z" }, + { url = "https://files.pythonhosted.org/packages/e0/d5/9937dce4d6bda74157b954e7d1460db05a22f5929dccfeeba1ed27a93df0/rpds_py-2026.5.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:8895840ac4809e5f60c88fd07617cd71326e73d6e5a8aa783c5c0f7c24985de2", size = 584053, upload-time = "2026-05-28T11:59:06.837Z" }, + { url = "https://files.pythonhosted.org/packages/6c/31/750617dd0ae1752471bf43f9e41d263398fae7cde7849d23b8574a70e617/rpds_py-2026.5.1-cp311-cp311-win32.whl", hash = "sha256:3684a59b158a7683aaeb8e25352e9a9dd2122cec78f2d8530266e4f91b4c7b3f", size = 214390, upload-time = "2026-05-28T11:59:08.402Z" }, + { url = "https://files.pythonhosted.org/packages/3c/bb/3dcab0e1d9516303f2eb672a5d6f62eca5a69e2886301e9c8c54b520c39b/rpds_py-2026.5.1-cp311-cp311-win_amd64.whl", hash = "sha256:7bd530e6a530bb3ea892f194fafa455f3516ac25ecf7143fd33c09be62b0470a", size = 231097, upload-time = "2026-05-28T11:59:09.786Z" }, + { url = "https://files.pythonhosted.org/packages/49/d6/c6bbf5cb1cf12b9732df8074b57f6ef8341ba884c95d40632ae8bddb44e4/rpds_py-2026.5.1-cp311-cp311-win_arm64.whl", hash = "sha256:0a5ae4dbe43c1076983b72616496919872ae7bbe7a1e21cc48336bc3154d130b", size = 226361, upload-time = "2026-05-28T11:59:11.079Z" }, + { url = "https://files.pythonhosted.org/packages/d4/e7/a78582dc57caa592dcc7d4fb69b61390561e908eb3d2f5df5928a8e354c0/rpds_py-2026.5.1-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:3abe24a66e57adcfa645d718063a5fa5103ecc71ddbf26d78af8f9368018ff1d", size = 353040, upload-time = "2026-05-28T11:59:12.531Z" }, + { url = "https://files.pythonhosted.org/packages/a3/43/35e3f136343aef451e545ce8c38d36c2f93c0ed88703db8b64ba2b205c68/rpds_py-2026.5.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:58b1d94308ddf0b1982f61f2eb54bf92997c9ece8a8093ef014250f4a517906c", size = 345775, upload-time = "2026-05-28T11:59:13.827Z" }, + { url = "https://files.pythonhosted.org/packages/20/e1/0f2160c5982d3157734d5cb3ed63d8b2d583a73c9864f77b666449f32cf8/rpds_py-2026.5.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0fa92420128dadce7f54bd73ba1825a273e9268fe9e35dbf7e6362890efa4e08", size = 376329, upload-time = "2026-05-28T11:59:15.271Z" }, + { url = "https://files.pythonhosted.org/packages/d0/11/ee0ba42aff83bf4effdbc576673c6be64c5e173978c3f6d537e94482f77d/rpds_py-2026.5.1-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ca653c6546386227cd9800d1bef6a348099acf8db4250341da6d90f663d6dfcb", size = 383539, upload-time = "2026-05-28T11:59:16.665Z" }, + { url = "https://files.pythonhosted.org/packages/11/df/d94aa6a499d4ac40afe2d7620f2c597fd3c0f182e854ad7cf3f596a81cb6/rpds_py-2026.5.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:66c93681c4729e4e3ecba31b8179fae083ff3118841672835140338b4b9867c1", size = 494674, upload-time = "2026-05-28T11:59:17.991Z" }, + { url = "https://files.pythonhosted.org/packages/1f/75/33d30f43bb2f458de11979486a591b1bf6e5651765ed1704c6197c2dc773/rpds_py-2026.5.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:40ff257542e04796880e011e15cd4dc21c2599975df2aaa8f2c8495ca574e1a5", size = 389268, upload-time = "2026-05-28T11:59:19.434Z" }, + { url = "https://files.pythonhosted.org/packages/f4/1e/2c9096fc19d5fd084b0184ca2b651e659aa0a37e6fdbecf6ece47f147fe1/rpds_py-2026.5.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b6825cc329b290e93c5f6a9be2393118a763f6ccf6abd83704e0c102ca583644", size = 376280, upload-time = "2026-05-28T11:59:21Z" }, + { url = "https://files.pythonhosted.org/packages/b9/e5/61ec9f8be8211ea7f48448195549e4aaf02004083475493b0e137702ecb2/rpds_py-2026.5.1-cp312-cp312-manylinux_2_31_riscv64.whl", hash = "sha256:de42116e69cb53b911cc34aee5ab98f36c597b822545045d49e938818b99e5e4", size = 387233, upload-time = "2026-05-28T11:59:22.454Z" }, + { url = "https://files.pythonhosted.org/packages/0d/ca/bcec1005c4f4a234f92a29078631fee49206c7265ccae966f18fd332e80e/rpds_py-2026.5.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:c0f920015df2a504bebaba6d4c31ccf3fcf942f92655c086da30b671aad19aa6", size = 405009, upload-time = "2026-05-28T11:59:23.845Z" }, + { url = "https://files.pythonhosted.org/packages/72/e6/4d5718c5cf26c522dc7c9999e238da1e77380b81d0c5d1df11e271ddfeb1/rpds_py-2026.5.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0408a24e44feb919423dc6d9da677cb5cddb894d2ca9e763967d156d9c60fab4", size = 553113, upload-time = "2026-05-28T11:59:25.184Z" }, + { url = "https://files.pythonhosted.org/packages/d4/25/2ee807bdb3e1f0b7eddf7782acd5665a8b5205a331a7d7244a52c4812fd9/rpds_py-2026.5.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:cea68bcd53467561ae2f96a6bdad1544299ba97b5b0ddcd5ac3d376e5c781c24", size = 618838, upload-time = "2026-05-28T11:59:26.749Z" }, + { url = "https://files.pythonhosted.org/packages/6a/c1/7d4c26f167f8c41501cc073d30ee22082b16ce358cf5b00ec97cbc7804ea/rpds_py-2026.5.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:4be8b1d2a705cc37d08256004e1d07de143fa0075c8e85a3df020b776f62b732", size = 582436, upload-time = "2026-05-28T11:59:28.11Z" }, + { url = "https://files.pythonhosted.org/packages/04/1d/9d12b0a337bab46f4769f8857f4007e3b2d639e14f9a44a0efe157696e64/rpds_py-2026.5.1-cp312-cp312-win32.whl", hash = "sha256:6736718bd4fc49cbcb538ba30516fdbef161522acefb739657d48b97bd864fed", size = 212734, upload-time = "2026-05-28T11:59:29.689Z" }, + { url = "https://files.pythonhosted.org/packages/c5/93/e4116f2de7f56bc7406a76033dc501811ddeb22b7f056b92d632871ebb0c/rpds_py-2026.5.1-cp312-cp312-win_amd64.whl", hash = "sha256:0a7d1eec967df0e9b22614a5e177622e0c89611d03727fa0cb48e45028907870", size = 229045, upload-time = "2026-05-28T11:59:31.033Z" }, + { url = "https://files.pythonhosted.org/packages/cb/53/6c3419d85eb2ec5938a37627c585b42d76a63bb731d6e42ed4b079ebf486/rpds_py-2026.5.1-cp312-cp312-win_arm64.whl", hash = "sha256:1841d067089e117142d79b98aa0df2f08b52f2ecc1819dd2700636c0db74a473", size = 223967, upload-time = "2026-05-28T11:59:32.318Z" }, + { url = "https://files.pythonhosted.org/packages/6c/32/14c961ad295f490eb0849ada8b79683e93a59b9de3afdd983eaf55fa6867/rpds_py-2026.5.1-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:efef4ac29c6ff495531eb17ee705b62841ecaa291b7c7077e848ea03e237164d", size = 352787, upload-time = "2026-05-28T11:59:33.655Z" }, + { url = "https://files.pythonhosted.org/packages/ca/bb/d1b85117967c11191441a7274ae616c65d93901d082c588f89a50a8da5ae/rpds_py-2026.5.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:c39f5b67a8a2e67179ada2a954227d670fe65fa9098457f698f56ddf248709b3", size = 345179, upload-time = "2026-05-28T11:59:35Z" }, + { url = "https://files.pythonhosted.org/packages/7c/46/d84105f062e626a1b233f863907288a4708c2d833b8b4c6fb2764bc080c0/rpds_py-2026.5.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b5c30f3f04eef4fbd362226a6f31d7c8895ca4fbb6e0b790f6890a98d8da8559", size = 376173, upload-time = "2026-05-28T11:59:36.43Z" }, + { url = "https://files.pythonhosted.org/packages/e2/ae/469d7959ce5b1201e1de135dc735b86db3b35dd0d1734f6a44246d5f061c/rpds_py-2026.5.1-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:277f6c82f0580848796c7ecc8a7173aa3bfb928e4ff831261c2f60a81dc270db", size = 383162, upload-time = "2026-05-28T11:59:37.995Z" }, + { url = "https://files.pythonhosted.org/packages/dc/a2/57853d31a1116a561aa072794602ad3f6341e18d70a8523f1bd5b9fc1e5a/rpds_py-2026.5.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:63c2c4c213f1a4e3f3de28ecab029dbdee976324e729c0d7a55211be72576b02", size = 495093, upload-time = "2026-05-28T11:59:39.453Z" }, + { url = "https://files.pythonhosted.org/packages/99/63/3a8eabcad9314b7daf5c65f451d2c33d989235cd8a5762186cf2c3f5a4f8/rpds_py-2026.5.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3350ec808fb538fe71a1f94dfaa0e29c598dfad805ce49f0caec5ae3183c652b", size = 389829, upload-time = "2026-05-28T11:59:40.896Z" }, + { url = "https://files.pythonhosted.org/packages/4b/25/05678d97fc25e2622df14dc530fb82023174ecfff6733991ed0d78f167bd/rpds_py-2026.5.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b1b964e3ab599e718dc46c018d104b1ebc007cbc6567d827c94a687fca56d77e", size = 374786, upload-time = "2026-05-28T11:59:42.626Z" }, + { url = "https://files.pythonhosted.org/packages/88/d1/8c90b6431e80a3b91b284a5c7c8c0c4f9c006444d90477a740d6e0f9c694/rpds_py-2026.5.1-cp313-cp313-manylinux_2_31_riscv64.whl", hash = "sha256:19cb09fab7b7fc96b2a6e28f2e34b72a3705ff27b37edb77455316e5d3f3dc9b", size = 386920, upload-time = "2026-05-28T11:59:44.124Z" }, + { url = "https://files.pythonhosted.org/packages/ff/99/4638f672ab356682d633ee0da9255f5b67ce6efd0b85eb94ad3e255e65a5/rpds_py-2026.5.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:abe76bcdba31e576cb83eeb8797aa0d882b738fef6dc65d0601fc753806a5b46", size = 405059, upload-time = "2026-05-28T11:59:47.177Z" }, + { url = "https://files.pythonhosted.org/packages/66/3f/3546524b6eb4cc2e1f363a3d638fa52f6c24faae3500c25fb488b02f1740/rpds_py-2026.5.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:8bff7073db3899158fff55ebf57b113a67030af26f80a18978f9f0aa60250ddf", size = 553030, upload-time = "2026-05-28T11:59:48.603Z" }, + { url = "https://files.pythonhosted.org/packages/c6/c3/7b3388c796fcf471bd17194242d4dc1a7608567c0fa422bcc1c5e79f9c1e/rpds_py-2026.5.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:8ba264fa49be666cd9cc56bf34ec7002fb3d27a4aee5bcb4d43d0d18feb1bb6f", size = 618975, upload-time = "2026-05-28T11:59:50.314Z" }, + { url = "https://files.pythonhosted.org/packages/61/1e/a3cb07f2795075d1d88efddae2f541359fde5f08c81ee114c29c2949c90a/rpds_py-2026.5.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:4860b603ddda0475a8885499b3729e90229d480105b42651962a5397d995fa89", size = 581178, upload-time = "2026-05-28T11:59:51.673Z" }, + { url = "https://files.pythonhosted.org/packages/a1/74/e758c03a5ef46f04c37f2651a2893db846d569ba8a7bca469d4b58939bcd/rpds_py-2026.5.1-cp313-cp313-win32.whl", hash = "sha256:7944270ae71383f6e2657dd7d5ce4eeb4ac2d0059a6738f0510583d462ab4842", size = 212481, upload-time = "2026-05-28T11:59:53.148Z" }, + { url = "https://files.pythonhosted.org/packages/70/ec/a2aca432db9c7359b40fa393eeeaa0d166c2f70175be956e75fa24197c44/rpds_py-2026.5.1-cp313-cp313-win_amd64.whl", hash = "sha256:88647f43a73c4e01be19b04ceef0c8d3a1958153604d13c773becd8016f2a0cf", size = 228519, upload-time = "2026-05-28T11:59:54.505Z" }, + { url = "https://files.pythonhosted.org/packages/29/60/a73bfdd45b096574556acf303bbd9fa9eed36ca8a818b514e2a5d5fe2b9d/rpds_py-2026.5.1-cp313-cp313-win_arm64.whl", hash = "sha256:453895624ecf7db7063b1004e44037522bbaef9ff6a945e59bc71662d7a03abd", size = 223446, upload-time = "2026-05-28T11:59:56.081Z" }, + { url = "https://files.pythonhosted.org/packages/18/e2/408105fd611823f00882aea810f3989a30d26b1bab8b6beb20f98c724e0e/rpds_py-2026.5.1-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:b4e4bc98639ec915f512fde3aa7a95e0041d95d9c3cc86eea841fa63cb1e8600", size = 355287, upload-time = "2026-05-28T11:59:57.448Z" }, + { url = "https://files.pythonhosted.org/packages/8d/58/5c4a43436843c90d0f6d19f82c200c80e3843ca9fa07b237623327f6d384/rpds_py-2026.5.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:cacedb7a6e167680acba45ad5716e89067d225dc80da0d7040cae8c81d4572fa", size = 347033, upload-time = "2026-05-28T11:59:58.881Z" }, + { url = "https://files.pythonhosted.org/packages/fb/c2/1a71acdacaf4e259b10278fb87b039ded3cf80041bcd89dd8a3ea702ded6/rpds_py-2026.5.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:68700371c5d7ae1412862ddfa719090925c93ecf351c566d66f09d04b136ea00", size = 376891, upload-time = "2026-05-28T12:00:00.516Z" }, + { url = "https://files.pythonhosted.org/packages/c2/c8/535f3d9b65addd8e28aa87b83c6e526799c3717a88273db8ea795beeef7a/rpds_py-2026.5.1-cp313-cp313t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:296c799becfa849c779c8725494fe9ed94959ed886787df4364b058465bad7f0", size = 385646, upload-time = "2026-05-28T12:00:02.394Z" }, + { url = "https://files.pythonhosted.org/packages/1c/91/dc033f313345c354ade914dbe73cdb90b615a4409ea02430d5356794f3d8/rpds_py-2026.5.1-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d3858b908218ee108d0bbfb2095ccc237648053c9bf98affad7cb079acaf1d97", size = 498830, upload-time = "2026-05-28T12:00:04.189Z" }, + { url = "https://files.pythonhosted.org/packages/27/fc/90fcbea459dbb8ddc18a2e0fd1de9412b48bc84ffff2db771cf714bacfd6/rpds_py-2026.5.1-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4fb8d2e7cb2f850b169806d61d1b991738acec96500a75c30f49caf064ce7cef", size = 392830, upload-time = "2026-05-28T12:00:05.797Z" }, + { url = "https://files.pythonhosted.org/packages/b2/1d/46cd11a228c9750684a798d98f878be6f614aa762438da7378f035e79e35/rpds_py-2026.5.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:27b74c10ed6a8f190f4287f53bcfea348b92a84a9c9f70d30183d1e6172d580d", size = 379613, upload-time = "2026-05-28T12:00:07.433Z" }, + { url = "https://files.pythonhosted.org/packages/24/4a/d9b0c6af3a1de03eb93741bbe8be2bdce84d8fda8224f3005451d86df389/rpds_py-2026.5.1-cp313-cp313t-manylinux_2_31_riscv64.whl", hash = "sha256:b9a6528956191c48c52294a592dbd4a8386d7048bdb25c0efcb6b966466c6d83", size = 388183, upload-time = "2026-05-28T12:00:09.227Z" }, + { url = "https://files.pythonhosted.org/packages/c5/b4/db7aaabdda6d020afc87d981bcc2f57a434c7dec60ecfc2ab3dd50b20351/rpds_py-2026.5.1-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:af03e34e860047bc7a352b842856fcf78798fbb81132cc98bd2f907ab4eb9cd2", size = 408578, upload-time = "2026-05-28T12:00:10.779Z" }, + { url = "https://files.pythonhosted.org/packages/08/d6/070f6a41cbb343e2ac4171859bf3f3623e0ab002f72619d6d505313ec2de/rpds_py-2026.5.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:fea6e836d10abbe191d557d33bd58bd5987725fe63aa1eefe557d230209855bd", size = 553573, upload-time = "2026-05-28T12:00:12.443Z" }, + { url = "https://files.pythonhosted.org/packages/75/ab/1a71ea3589c4345dac0a0518f0e6a031cb42689277851b683c46d27463a5/rpds_py-2026.5.1-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:fc0c0f878ea770a0a8a462456c5ad36fc9fe6358e6b76fdadc7f17575e0b8bf1", size = 620861, upload-time = "2026-05-28T12:00:14.09Z" }, + { url = "https://files.pythonhosted.org/packages/8a/22/9bf80a56069c0c443fcfefac639a86a744550a2898817a6dfd3e26654924/rpds_py-2026.5.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e0b360f316d966b048b085857630b3cc51f3db2f07b06f440eac8f695374d1e3", size = 585633, upload-time = "2026-05-28T12:00:15.66Z" }, + { url = "https://files.pythonhosted.org/packages/da/68/3b2c0a75c9e04125696f84ebdbbf304acf5a40b58ba4481cdb98a922c3ba/rpds_py-2026.5.1-cp313-cp313t-win32.whl", hash = "sha256:a2999883eedf72fdfb7520b92c7d4ec2572a71ff40239377aa604cc529eecafc", size = 210074, upload-time = "2026-05-28T12:00:17.291Z" }, + { url = "https://files.pythonhosted.org/packages/e7/8b/609157d5a25d37d4f29f92840ba531f416907c34ae5c5739dd21fc2bef98/rpds_py-2026.5.1-cp313-cp313t-win_amd64.whl", hash = "sha256:e07be2a9d7122bd6e82dea89814ef8dc893feb1aae97fec1630f3263bbb30e55", size = 228635, upload-time = "2026-05-28T12:00:18.73Z" }, + { url = "https://files.pythonhosted.org/packages/d4/6f/19c1918a4b590d8de87e712e4abe4b3875771eff60216fb6153cf6665c68/rpds_py-2026.5.1-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:1f2c391c3059798093b65df23aca2cac150460ae9c630d99dec83d703d9485b9", size = 349756, upload-time = "2026-05-28T12:00:20.217Z" }, + { url = "https://files.pythonhosted.org/packages/e5/60/a06fe7da34eca79dacbf958a2ba0c6eea85bc2b29de20080bf40f72f66fa/rpds_py-2026.5.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:413b424f7c4ee65ab5e5be91f5731be0f8b41a1ee2b12dfe810d716312e95a78", size = 343831, upload-time = "2026-05-28T12:00:21.711Z" }, + { url = "https://files.pythonhosted.org/packages/bf/ec/b2333b97b90e2a6ef6ca8ad386ee284968e74bcfe113b3f1a8d9036429a9/rpds_py-2026.5.1-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2c595a1d9255dce0599e13130d1440ab2506654f2b50294226ee06402f8fef63", size = 375127, upload-time = "2026-05-28T12:00:23.326Z" }, + { url = "https://files.pythonhosted.org/packages/14/7f/e00aae54067f2b488c4637961d5f58204d470795fc791085fa3f15060d2e/rpds_py-2026.5.1-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:1c27c5f6102eac8c03e7595a00827a53b271ba40a53b59ff8709170e0855ea4a", size = 379034, upload-time = "2026-05-28T12:00:24.89Z" }, + { url = "https://files.pythonhosted.org/packages/be/cc/423999bbb8ae8dc93c77fc1d5e984ade5eb89d237d3bb884ccfa72ae2890/rpds_py-2026.5.1-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6c7fcf61d44cacecaf3aea542b0e053db77972a4573e7ceda16fb2b399161195", size = 490823, upload-time = "2026-05-28T12:00:26.676Z" }, + { url = "https://files.pythonhosted.org/packages/0f/aa/c671bf660f12e68d3c52ff86c7066ed1372df5a0f4f2ff584e419b8207e7/rpds_py-2026.5.1-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2c817a189d4ee14290420e5ff051e4dd6baa13f3edf84685071dee07a6d538ee", size = 388144, upload-time = "2026-05-28T12:00:28.577Z" }, + { url = "https://files.pythonhosted.org/packages/19/c8/d63bb75b68afe77b229e3021c6031bcaf01da5db5b0e69d0d10f9ba679a7/rpds_py-2026.5.1-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:21846aac0ed2e0589f38c12dc44e77bb64e494b771eadbcf169cba00566ba7ba", size = 371959, upload-time = "2026-05-28T12:00:30.304Z" }, + { url = "https://files.pythonhosted.org/packages/82/35/c51122014d8274ff37dc606d60049c3db7d83da02b5b282511e5a906a9a6/rpds_py-2026.5.1-cp314-cp314-manylinux_2_31_riscv64.whl", hash = "sha256:b317c87a13f769a4e787819bd508aaa5d69aa09b0880de9af6d3a8a54571cdec", size = 383558, upload-time = "2026-05-28T12:00:31.764Z" }, + { url = "https://files.pythonhosted.org/packages/e3/f9/2790cb99c136a5363acdeacf5c27c56f3de0d4118a1f48fca83404c99c89/rpds_py-2026.5.1-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:ce87129d9f2c14fa6c4a8601fb80eb4488c80d38a20cd13758ef11123e14995d", size = 402789, upload-time = "2026-05-28T12:00:33.247Z" }, + { url = "https://files.pythonhosted.org/packages/e5/1b/e4fb584f8c75d35c38150ff6a332cda949e6f97acba1f4fd123b14ab56fe/rpds_py-2026.5.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:9cdddb6c1207d284d94fd1530adf57fbd797fe7c4b8704ba85f49414f2557e7d", size = 551405, upload-time = "2026-05-28T12:00:34.819Z" }, + { url = "https://files.pythonhosted.org/packages/d8/f7/a6731b4216cb3793ea1af5391da240f5683dacc0d13e034fe5fc3503f240/rpds_py-2026.5.1-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:4e237e139f94d3c036fd28eb9f564c99055476ff4ff05cd42be55ce349b5aa02", size = 616975, upload-time = "2026-05-28T12:00:36.268Z" }, + { url = "https://files.pythonhosted.org/packages/2c/ea/2e051a81d95d8e63f4b35a1c463a87e8766bc3d083c067c5dfb6bf220747/rpds_py-2026.5.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:ed0954b524873214369184a9c82b0eaa45a3fbb9a798cd95b17e0d98499e7ea0", size = 578701, upload-time = "2026-05-28T12:00:37.82Z" }, + { url = "https://files.pythonhosted.org/packages/65/56/b5f6fdb2083e32bca8a8993d89e70db114b4756c9e2c38421328126689d2/rpds_py-2026.5.1-cp314-cp314-win32.whl", hash = "sha256:2d88621d6a7d4dfa633d21abe90f280bb205274e16b1d1e61c6ad4640b2453b7", size = 209806, upload-time = "2026-05-28T12:00:39.492Z" }, + { url = "https://files.pythonhosted.org/packages/fb/80/65a5aa96c155e611d1ed844e4e1f57f3e36b021f396d9f8585d756e6b90d/rpds_py-2026.5.1-cp314-cp314-win_amd64.whl", hash = "sha256:cef8ac28d26f4dda3533060c20fbf80a325458fa9fd23ea72a73cdfa8e978838", size = 225985, upload-time = "2026-05-28T12:00:40.94Z" }, + { url = "https://files.pythonhosted.org/packages/27/7c/ad185212e87b05f196daef92bc5f3caf07298eb47c295b5585c3dd3093ac/rpds_py-2026.5.1-cp314-cp314-win_arm64.whl", hash = "sha256:eaaea962c68cdc68d4a533ba985ab8e9484277910bbfaa2ab3ef7732667bfed8", size = 221219, upload-time = "2026-05-28T12:00:43.15Z" }, + { url = "https://files.pythonhosted.org/packages/23/58/e14ae18759020334646b031e708ab4158d653a938822bfb7b95ef2e93aa3/rpds_py-2026.5.1-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:21942f52dbbd5f8758bf021213d28bd45c39e873e65e2407faf5f1846f5761ad", size = 352148, upload-time = "2026-05-28T12:00:44.638Z" }, + { url = "https://files.pythonhosted.org/packages/31/9b/5f4a1e2f960bca3ac5d052b139dd31eed97b259f9d909173821760d542e8/rpds_py-2026.5.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:f414556f6e3958300ff941e40c9f97e3dc9774ddd1b3434c475d73dd354bbed3", size = 345196, upload-time = "2026-05-28T12:00:46.14Z" }, + { url = "https://files.pythonhosted.org/packages/1a/71/1d9574d6a2fa20ab60eaa55c7467f5aa20cbc770f341a05f09c0876f59e2/rpds_py-2026.5.1-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ef1013a8625c74043210190b246f5b1551e09757c1f356c6e4160ef96c5bc081", size = 374981, upload-time = "2026-05-28T12:00:47.531Z" }, + { url = "https://files.pythonhosted.org/packages/0c/9a/37e99f4915a80aa71670263c1267f7ae0af95f53a3f61e6c3bdc016d4515/rpds_py-2026.5.1-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:cc68e231a77a5f0d774ae278a1f8e55c0456501820847c1e4efb3829f3441df6", size = 379961, upload-time = "2026-05-28T12:00:49.216Z" }, + { url = "https://files.pythonhosted.org/packages/a8/ff/6e73f74b89d2e0715e0fc86b7dde893f9a61ae2f9b256ff3bdfe41ac4e94/rpds_py-2026.5.1-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9baffb505aff33acc69b422a19f77806680f3c8632227d79f48de8a810d1c2c5", size = 495965, upload-time = "2026-05-28T12:00:51.111Z" }, + { url = "https://files.pythonhosted.org/packages/ea/e0/425faba25f59d74d4638b267f7c7a80e8649d2ef4db10a19b0c4a71e6e6f/rpds_py-2026.5.1-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b8d2f912928d426e8cfa396f7f3f8d29a59e6689c86dcca3c420730c1096322b", size = 389526, upload-time = "2026-05-28T12:00:52.77Z" }, + { url = "https://files.pythonhosted.org/packages/c6/76/7a41960e3fddae47fab43a28684d5da981401dffd88253de0944148654cb/rpds_py-2026.5.1-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:90f628283be835db980c941767d41c9a27b5239e54ba0a9c1335247e82406964", size = 376190, upload-time = "2026-05-28T12:00:54.215Z" }, + { url = "https://files.pythonhosted.org/packages/27/60/5f38dc70824fc6951b51d35377e577a3a3a4c81a6769cc5a2de25ebe0ad1/rpds_py-2026.5.1-cp314-cp314t-manylinux_2_31_riscv64.whl", hash = "sha256:1ebb2f0ab7e16132995a72de805170e0203df0c3dd22e1ef1cd1fdd90bd7a131", size = 383921, upload-time = "2026-05-28T12:00:55.673Z" }, + { url = "https://files.pythonhosted.org/packages/60/1a/d60a38caa1505f4b9483c3fbbde12c94e1079154f4f401a6da96f7e77621/rpds_py-2026.5.1-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f3df3d16ded76f1f8c9cdebd0e1ea55fdf4c23b812de189814da7cf229c22a81", size = 404766, upload-time = "2026-05-28T12:00:57.518Z" }, + { url = "https://files.pythonhosted.org/packages/87/ff/602fd3f174d6425f0bce05ad0dfbec0e96b38d0f7d08a79af5aa20083885/rpds_py-2026.5.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:9af8905b8f854990e40d5206aa5ac58d9b0fe0b7f351ff2bb086c20f6c8c6a47", size = 551343, upload-time = "2026-05-28T12:00:58.978Z" }, + { url = "https://files.pythonhosted.org/packages/b8/c1/1be13327acdbead3eca1fde03b6a34dbb011f1e864e217f0d32cc1779a7f/rpds_py-2026.5.1-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:036a36a87fb1cd3b214d11c4b3c4f7d2ddad933625dca1c900b56a057c07740a", size = 618502, upload-time = "2026-05-28T12:01:00.656Z" }, + { url = "https://files.pythonhosted.org/packages/f3/d7/afb49b49d7f2be8b7ba1a9f0977fa5168003437b93086726f066544e8351/rpds_py-2026.5.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:62ae3853454fe9ef283a03c96c2d835d39e84b14643a9d62c82ef0fb87d702ca", size = 581916, upload-time = "2026-05-28T12:01:02.22Z" }, + { url = "https://files.pythonhosted.org/packages/25/d1/dbef8c1f8a10f07beb62b5f054e20099fd9924b3ec001b8f0b6ac7813a85/rpds_py-2026.5.1-cp314-cp314t-win32.whl", hash = "sha256:6c3d771a46ec18b12af06ce36243a9a80b07a5d0515236332d90863ca8bb326a", size = 207855, upload-time = "2026-05-28T12:01:03.821Z" }, + { url = "https://files.pythonhosted.org/packages/2a/72/bfa4e61ab8e7dc1c8adf397e05e6cbdd4239357bd72b248d3de662f23915/rpds_py-2026.5.1-cp314-cp314t-win_amd64.whl", hash = "sha256:c93c629be4636cf54337bd5f06c104d55e42ced54d681f6fe21ae510a65116f6", size = 225422, upload-time = "2026-05-28T12:01:05.194Z" }, + { url = "https://files.pythonhosted.org/packages/27/3a/7b5da92b640f67b6717ccafc83cdd06bfa7ff2395c3685c68922bb54d703/rpds_py-2026.5.1-cp315-cp315-macosx_10_12_x86_64.whl", hash = "sha256:3574b55c604b8f75dacb007136508bbc0db406e626301778096a133327e7f2fb", size = 349576, upload-time = "2026-05-28T12:01:06.722Z" }, + { url = "https://files.pythonhosted.org/packages/d7/8a/2aafd7ad355a1bd48ca76e2262b74b15e6432b5a1efe150efd4d779cd55d/rpds_py-2026.5.1-cp315-cp315-macosx_11_0_arm64.whl", hash = "sha256:94068eb3ae6d43f5a786b7db96a406a34e6d5c24489feef32fd6e8946ea7b291", size = 343640, upload-time = "2026-05-28T12:01:08.441Z" }, + { url = "https://files.pythonhosted.org/packages/f7/7d/6c9523c1abbe840a1b7fba3c516d48e1d3487cc80fea4366c4071cf56784/rpds_py-2026.5.1-cp315-cp315-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f3a5b10e8ce894825f380a8f1b6444cf73c294dfea62afbb2d13e3a9e630cec1", size = 375322, upload-time = "2026-05-28T12:01:09.934Z" }, + { url = "https://files.pythonhosted.org/packages/5a/5d/0b7b03fb1dc509321f01de3149784ab773e34c8573022029af8076afcb9c/rpds_py-2026.5.1-cp315-cp315-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:fc09f82e63d4bcd58149572f857a431bae851dc747e313c3b5bdf7abb907fda8", size = 379066, upload-time = "2026-05-28T12:01:11.48Z" }, + { url = "https://files.pythonhosted.org/packages/d7/e2/8ef6012999ebf1cb1c22f876d9ce5e63d960fd4631d2af3202d3f480aa25/rpds_py-2026.5.1-cp315-cp315-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e10464d17df3b582745c25cec695cb9558bca2cb6ddb631aee1787fc72c767b2", size = 494586, upload-time = "2026-05-28T12:01:13.051Z" }, + { url = "https://files.pythonhosted.org/packages/80/af/1eeb029bec67582c226b7809172207cd005073af4ebd906e65ff494f4983/rpds_py-2026.5.1-cp315-cp315-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ba05adbf15d994c38ec0b7ab32e858e5110c21e9009a00a86545fd220f84e038", size = 388415, upload-time = "2026-05-28T12:01:14.631Z" }, + { url = "https://files.pythonhosted.org/packages/18/23/ffbe10711c4d766c1cab0557d6906c074f795814863c67b351355d29354a/rpds_py-2026.5.1-cp315-cp315-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:77c004fdc7b891967106f78ddfd7b076bfe6813c6139c6fff6aed3bcaa960b26", size = 372427, upload-time = "2026-05-28T12:01:16.153Z" }, + { url = "https://files.pythonhosted.org/packages/bd/3a/30ba4a6ad457e5b070c18d742a33fb77d8d922b565cc881f8a5313d63bfe/rpds_py-2026.5.1-cp315-cp315-manylinux_2_31_riscv64.whl", hash = "sha256:83bcf894486c9d78dd290d3c0124ff6dd8875d3025e2090a8ec49fcc37c55fdd", size = 383615, upload-time = "2026-05-28T12:01:17.809Z" }, + { url = "https://files.pythonhosted.org/packages/d3/69/62e242b53ce39c0814bd24e1a6e6eba6c92be716277745f317f9540a2e7b/rpds_py-2026.5.1-cp315-cp315-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:c3df104083952a0e0c6f10de33e440eabe98fb6317d23e1a58c68f6df08d01b9", size = 402786, upload-time = "2026-05-28T12:01:19.419Z" }, + { url = "https://files.pythonhosted.org/packages/38/c1/a770b9c186928a1ed0f7e6d7ae50e7f3950ed23e3f9e366dbc8e38cb55de/rpds_py-2026.5.1-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:980450826cf22e133c57e0835070bdd0dd3f73b9b708c3ce223def2cb9469e14", size = 551583, upload-time = "2026-05-28T12:01:21.013Z" }, + { url = "https://files.pythonhosted.org/packages/21/7c/68e8579b95375b70d2a963103c42e705856cdb98569258bd807f4423891c/rpds_py-2026.5.1-cp315-cp315-musllinux_1_2_i686.whl", hash = "sha256:205dde846f24332ab0c1188699a043b8d165b79bb84529ce272c45048ff6be01", size = 616941, upload-time = "2026-05-28T12:01:22.548Z" }, + { url = "https://files.pythonhosted.org/packages/70/a1/a6135aed5730ff03ab957182259987ac11e55fb392a28dc6f0592048a280/rpds_py-2026.5.1-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:3966b82dd563176396df030f3dd52a6e54cb69b718e95e78bd555ed3d1e0185d", size = 578349, upload-time = "2026-05-28T12:01:24.118Z" }, + { url = "https://files.pythonhosted.org/packages/09/6e/f24201a76a84e6c49d0bdfdfcb735210e21701e9b21c5bfc0ba497dd62f6/rpds_py-2026.5.1-cp315-cp315-win32.whl", hash = "sha256:7818f8d0a415be74d2be3590b0a1c1f463a642f4d0217e7d10602dceef5b79aa", size = 209922, upload-time = "2026-05-28T12:01:25.522Z" }, + { url = "https://files.pythonhosted.org/packages/9e/e4/966bc240bb0485fc265278f6de44d05834bf0b3618886e0b22e33d54c49a/rpds_py-2026.5.1-cp315-cp315-win_amd64.whl", hash = "sha256:b3cc20c0d800af78fd0fac68086e28c1856cec51ea528bb81ea851aa40d39325", size = 226003, upload-time = "2026-05-28T12:01:27.062Z" }, + { url = "https://files.pythonhosted.org/packages/5c/5c/a15a59269cd5e74472734516c73795c15eccfc841b3d4b0228c3f53f19d0/rpds_py-2026.5.1-cp315-cp315-win_arm64.whl", hash = "sha256:3609e9939a8a76cd904cf98a3f1f13b5dc7e150adeaee89e0ea09652ea213e16", size = 221245, upload-time = "2026-05-28T12:01:28.51Z" }, + { url = "https://files.pythonhosted.org/packages/e0/22/135ce03804e179a71ceb13be095deda4a279bc88f7a6b8fa161c5ad44e12/rpds_py-2026.5.1-cp315-cp315t-macosx_10_12_x86_64.whl", hash = "sha256:5d333a7127d4b307601ac37792bee01bb95c867cbfacf21b6375b804d6bbd723", size = 352015, upload-time = "2026-05-28T12:01:30.214Z" }, + { url = "https://files.pythonhosted.org/packages/3b/5f/f1f6d2652eb9d848f6eb369d8db83a2da6249bb49ad2c2a48f45d54538d3/rpds_py-2026.5.1-cp315-cp315t-macosx_11_0_arm64.whl", hash = "sha256:b5f077b44a4f7808520f66dae234988d867deb9aed9be5da057ce9ba831b2a41", size = 345016, upload-time = "2026-05-28T12:01:31.656Z" }, + { url = "https://files.pythonhosted.org/packages/88/66/b74182775691ea2290c99e52ac8d5db844e56fbec90ce421f107658c8314/rpds_py-2026.5.1-cp315-cp315t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:55d8f9b7b78c9538fc9e04e82ec0e888ff0c3cffcfad152c77e57cd09351a98a", size = 374775, upload-time = "2026-05-28T12:01:33.136Z" }, + { url = "https://files.pythonhosted.org/packages/ff/8f/15e5a61d9f0a43902d36561d4f07cae6ae9f4716be825159fd72717f33af/rpds_py-2026.5.1-cp315-cp315t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e3a8ae58895ac107ed934a6bf51e5846f95c53b9b940c2c6d310838fd5846358", size = 380270, upload-time = "2026-05-28T12:01:34.574Z" }, + { url = "https://files.pythonhosted.org/packages/02/c3/f859b12763a80540cdf2af0f15b19904cf756a71d7bdd3f82ff3e5b1bbf9/rpds_py-2026.5.1-cp315-cp315t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:0957cf3c2b8632ec7aaebffebea8005b353cc2a237b6e2ae3c2cac0820704cfb", size = 495285, upload-time = "2026-05-28T12:01:36.127Z" }, + { url = "https://files.pythonhosted.org/packages/1c/c7/ff27c2ac8411d30b03b1829fd88cae8dad1a4d0da48dd25e57c4038042e6/rpds_py-2026.5.1-cp315-cp315t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c396c1304de421050b3681ea70f371874b54d41b0151e96109758144c231e30b", size = 389581, upload-time = "2026-05-28T12:01:37.635Z" }, + { url = "https://files.pythonhosted.org/packages/6e/67/fe92ee32a6cc05c77228a2f8b1762e7124f386ec20ff83d0757b762d58d0/rpds_py-2026.5.1-cp315-cp315t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aad1bff7f666b9598e573815affd666aac6a13a585dde336f843e33350c7fadc", size = 376041, upload-time = "2026-05-28T12:01:39.307Z" }, + { url = "https://files.pythonhosted.org/packages/f8/91/b4d6685c27aba55bd82f25b278be8237038117d05f9659a6213ad3408130/rpds_py-2026.5.1-cp315-cp315t-manylinux_2_31_riscv64.whl", hash = "sha256:656a042550878f12d45752452d47094b7cfe5ad1e9d7b87b5a22ad3ae5ff8015", size = 383946, upload-time = "2026-05-28T12:01:41.043Z" }, + { url = "https://files.pythonhosted.org/packages/bd/79/2c1d832a53c8e0f8e98fc970ec257b950fecd4f62be2ab7182b500a0cbc8/rpds_py-2026.5.1-cp315-cp315t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:73c4bd4f70294737b5206a3e8e30ccadbf8a60301831c8ea23eec5dbeea1ecfa", size = 405526, upload-time = "2026-05-28T12:01:43.032Z" }, + { url = "https://files.pythonhosted.org/packages/78/c4/c98117b03c6a8581ab2c2dfccfe9a5ad82bd8128a3c28b46a6ad2d97c393/rpds_py-2026.5.1-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:43bca78665423cabae77146f2fe7ce55272b6c8d55d82cca83effd42c7e13972", size = 551165, upload-time = "2026-05-28T12:01:44.648Z" }, + { url = "https://files.pythonhosted.org/packages/3b/c1/bc479ca069200af730881b1bd525e3114b2b391a351509fcb1b772f28086/rpds_py-2026.5.1-cp315-cp315t-musllinux_1_2_i686.whl", hash = "sha256:42d0f20e85e549c870749d0e247f0c10d318a45b7e9676d575d2dcb04a1b2e66", size = 618778, upload-time = "2026-05-28T12:01:46.337Z" }, + { url = "https://files.pythonhosted.org/packages/77/65/38ab2f90df44c2febfb63cc10ced40763d9b4bc94d173e734528663fe7f5/rpds_py-2026.5.1-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:b1be5c35683684d5331b93600c210e8367c254683d8a6df6bd21bd2da3a334fb", size = 581839, upload-time = "2026-05-28T12:01:48.109Z" }, + { url = "https://files.pythonhosted.org/packages/15/2d/ce1f605fe036aadd460e5822e578c6c7ec3a860936cca37d6e0f299daa77/rpds_py-2026.5.1-cp315-cp315t-win32.whl", hash = "sha256:75808f6c38ce7749bb68cc2770161aae5045e6c6f6781a9782e74b93304399df", size = 207866, upload-time = "2026-05-28T12:01:49.648Z" }, + { url = "https://files.pythonhosted.org/packages/79/cb/966040123eb102371559746908ef2c9471f4d43e17ec9a645a2258dab64b/rpds_py-2026.5.1-cp315-cp315t-win_amd64.whl", hash = "sha256:90bd6630002a1c7f09e7843dd79f0d24f3d2897cc25a753480917865d14f15b3", size = 225441, upload-time = "2026-05-28T12:01:51.408Z" }, + { url = "https://files.pythonhosted.org/packages/42/56/3fe0fb34820ff667be791b3a3c22b85e8bcba54e9c832f47438c191fa7be/rpds_py-2026.5.1-pp311-pypy311_pp73-macosx_10_12_x86_64.whl", hash = "sha256:edf2765d84e42447f112ad877af8fe1db0089aaec5b28e88d6eab45e7fe99cea", size = 357151, upload-time = "2026-05-28T12:01:53.43Z" }, + { url = "https://files.pythonhosted.org/packages/8b/f2/3eb9ccdb9f143b8c9b003978898cb497f942a324c077401e6b8834238e63/rpds_py-2026.5.1-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:ad3773236e95f7f33991eb125224b7da66f206504d032a253a02da7e134519fb", size = 350195, upload-time = "2026-05-28T12:01:54.901Z" }, + { url = "https://files.pythonhosted.org/packages/a7/24/dbda232bc4f3ed732120692ab0d2c8402cb020516556d8bee622dcef2413/rpds_py-2026.5.1-pp311-pypy311_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a04df86b3f0fade39ec8fd0e0aab089b1da9fbd2b48df778a57ef96f5e7d38df", size = 381850, upload-time = "2026-05-28T12:01:56.601Z" }, + { url = "https://files.pythonhosted.org/packages/40/30/32e769839a358f78810c234f160f2cc21d1e4e47e1c0e0e0d535be5a0219/rpds_py-2026.5.1-pp311-pypy311_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:6142dbd80c4df62a5d899f0d616d417f84e0bc8d32526c8e5589019d75d028a7", size = 387899, upload-time = "2026-05-28T12:01:58.212Z" }, + { url = "https://files.pythonhosted.org/packages/ab/86/ec84d243aadb3b34b71dd26a010d0930b2d284ff5fc9a69fec53810ee6fd/rpds_py-2026.5.1-pp311-pypy311_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:0b35217adefe87f2fe4db7e9766cabe84744bfe9616d9667be18988928c7f2dc", size = 501618, upload-time = "2026-05-28T12:01:59.888Z" }, + { url = "https://files.pythonhosted.org/packages/74/25/b60e52686bbff777a64f9e4f4d3dd57980dc846913777177a2c92e4937aa/rpds_py-2026.5.1-pp311-pypy311_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b95d5e11fc712b752081183a55a244c03cd00570489edd7014d8899f8ceb8162", size = 394003, upload-time = "2026-05-28T12:02:01.482Z" }, + { url = "https://files.pythonhosted.org/packages/9b/c7/b3a6a588cc2219510ef3f42e207483a93950bedd1e3a0fd4015c95cff9e5/rpds_py-2026.5.1-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:141c9498daf2ace9eda35d2b0e376f9ea8b058d84f2aef4f96fccfd449a2f251", size = 379778, upload-time = "2026-05-28T12:02:03.197Z" }, + { url = "https://files.pythonhosted.org/packages/31/00/c7dba3fc8a3da8cb3f6db1eb3386be4d79c2e97c6890d20eb9ac66ae8c43/rpds_py-2026.5.1-pp311-pypy311_pp73-manylinux_2_31_riscv64.whl", hash = "sha256:6f249f8b860a200ad35193af961183ebe9132710484e6f6ce0cf89fd83c63a9a", size = 392359, upload-time = "2026-05-28T12:02:04.817Z" }, + { url = "https://files.pythonhosted.org/packages/93/dd/472ba494c70753f93745992c99855bee0636daf74e6984e5e003f150316f/rpds_py-2026.5.1-pp311-pypy311_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:e4abbf391a70be864920858bf360f4fb380577c9a0f732438a1996726e2c195b", size = 412820, upload-time = "2026-05-28T12:02:06.401Z" }, + { url = "https://files.pythonhosted.org/packages/1d/6f/93831a3bfe789542ed0c1d0d74b78b440f055d6dc3ea4640eba2d95e6e23/rpds_py-2026.5.1-pp311-pypy311_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:c74005a7bb87752acf351c93897ec63ad77a07a0da7ecad9c050e32e7286ba34", size = 557243, upload-time = "2026-05-28T12:02:08.013Z" }, + { url = "https://files.pythonhosted.org/packages/1f/ff/0b3d604614ffc77522c6b288fdbce68957eb583da1002aa65ba38ac0ee40/rpds_py-2026.5.1-pp311-pypy311_pp73-musllinux_1_2_i686.whl", hash = "sha256:8213afbe8a3a906fb9acb2014423fe3359ee783d0bf90995f70623a3217bfa6c", size = 623541, upload-time = "2026-05-28T12:02:09.661Z" }, + { url = "https://files.pythonhosted.org/packages/ea/ea/e7b0251441da9adfeaebcf29601d10f2a1455fcf0772fae9e7e19032bd96/rpds_py-2026.5.1-pp311-pypy311_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:8c43a8a973270fd173bf48cdf80bbe66312421cba68d40845034f174f2389049", size = 586326, upload-time = "2026-05-28T12:02:11.47Z" }, ] [[package]] name = "ruff" -version = "0.15.14" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/dc/8a/8bce2894573e9dae6ff4d77fe34ad727d79b9e6238ad288c5638990d90f6/ruff-0.15.14.tar.gz", hash = "sha256:48e866b165be4a9bdbf310f7d3c9a07edef2fe8cd63ffeb4e00bb590506ebf9f", size = 4700910, upload-time = "2026-05-21T14:34:55.177Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b9/c8/74a92c6ff9fcfb4f1f947126d3ebee8389276e161ecc85de5bda7cda51bd/ruff-0.15.14-py3-none-linux_armv6l.whl", hash = "sha256:8dd2db9416e487c8d4b01fa7056bb02c4d05969d4f8d17a08c229c2f4ff3c108", size = 10739177, upload-time = "2026-05-21T14:34:37.332Z" }, - { url = "https://files.pythonhosted.org/packages/45/91/254a35c20acc38a7223c9d2d594af12e794432464f2cdeb52af1dc4a892d/ruff-0.15.14-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:be4ff55af755bd71a00ab3dc6bd7ffc467bd76e0df6881e286c2e3d23e8fb43b", size = 11144969, upload-time = "2026-05-21T14:34:43.978Z" }, - { url = "https://files.pythonhosted.org/packages/56/9e/d13e40f83b8d0a94430e6778ce1d94a43b38cf2efe63278bdd2b4c65abbf/ruff-0.15.14-py3-none-macosx_11_0_arm64.whl", hash = "sha256:48d5909d7d06276ce7dde6d32bfa4b0d4cb2651145cd8ee4b440722cbc77832f", size = 10478207, upload-time = "2026-05-21T14:34:48.378Z" }, - { url = "https://files.pythonhosted.org/packages/8d/f1/b15a7839fa4f332f8acec78e20564f26bb2d866e3d21710b877fd0263000/ruff-0.15.14-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ca8cbfa94c4f90984a67561978602746d4cd27103568f745fa90eee3f0d4107d", size = 10818459, upload-time = "2026-05-21T14:34:22.318Z" }, - { url = "https://files.pythonhosted.org/packages/45/33/53d651177f84f94b400a0e27f8824eeada3dddc9d5ee8aeb048f4352a520/ruff-0.15.14-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9a6bbc0333f1ab053423bcbf6226477d266ca7cec7738c4c8e3f55647803f3c4", size = 10541800, upload-time = "2026-05-21T14:34:20.209Z" }, - { url = "https://files.pythonhosted.org/packages/b8/a6/868f87e0bf9786ed24b5d0d0ad8676b8a94fd1912f42cddf9cfc7857818a/ruff-0.15.14-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8a24a4f7605d7003a6674d4387651effd939dead3fddd0f36561eb77a9a2e542", size = 11342149, upload-time = "2026-05-21T14:34:46.365Z" }, - { url = "https://files.pythonhosted.org/packages/a7/8b/38cd5c19faffdcc05a408d2b78edccc69492ab9720eadb49ea15ef80d768/ruff-0.15.14-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:049b5326e53ed80978f2fc041a280603f69dd6b0c95464342a2bb4572d9d9e2f", size = 12212563, upload-time = "2026-05-21T14:34:28.579Z" }, - { url = "https://files.pythonhosted.org/packages/3e/4d/a3c5b874a556d5731e3e657aaf04311bb76f0a5c3ec220ed43051be6b64b/ruff-0.15.14-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d4ed42e6696c8dfa5f06728e6441993901f548eb92d73bc472cb5a38d1395fbf", size = 11493299, upload-time = "2026-05-21T14:34:41.836Z" }, - { url = "https://files.pythonhosted.org/packages/1e/c0/56472c251d09858a53e51efbd485b09e1995d8731668b76d52e5dd6ee0f1/ruff-0.15.14-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:715c543cf450c4888251f91c52f1942a800541d9bddd7ac060aa4e6b77ae7cba", size = 11455931, upload-time = "2026-05-21T14:34:57.276Z" }, - { url = "https://files.pythonhosted.org/packages/2c/4a/e2e7b4d8dbf233d4eace59c75bc3435fa6d8bd3bae82d351d4e4300c0fd1/ruff-0.15.14-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:72ebab6013ec887d439d8b7593737a0a4ffb06d45d209d4e4bf2e92813082d3f", size = 11400794, upload-time = "2026-05-21T14:34:39.773Z" }, - { url = "https://files.pythonhosted.org/packages/97/c7/83c0539fe34c3e09136204d1e75d6052492364e0b3cb05e9465423f567d7/ruff-0.15.14-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:49072d36abdbe97a8dd7f480afe9c675699c0c495d4c84076e2c1203c4550581", size = 10804759, upload-time = "2026-05-21T14:34:31.045Z" }, - { url = "https://files.pythonhosted.org/packages/86/a6/18f2bfc095a2ab4a78745644e428205532ce6653a5d0fa8501572891534d/ruff-0.15.14-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:958522aee105068640c2c2ceae08f413ae44d922f52a1374ac13d6a96032fc93", size = 10539517, upload-time = "2026-05-21T14:34:53.064Z" }, - { url = "https://files.pythonhosted.org/packages/54/3a/5a8b3b69c654d4e4bf1d246ac5b49cbcdac6eaab6905925f8915f31e3b80/ruff-0.15.14-py3-none-musllinux_1_2_i686.whl", hash = "sha256:f3707da619a143a2e8830e2abab8224478d69ace2d28cb6c20543ae97c36bf61", size = 11065169, upload-time = "2026-05-21T14:34:24.484Z" }, - { url = "https://files.pythonhosted.org/packages/ed/c5/8864e4e7925b836ea354b31d57641ec03830564e281a8b6f061f8c3e0ec1/ruff-0.15.14-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:bb01d645694e3ec0102105d07ef2d53703970407d59c04e59d3ba0b7a1d53553", size = 11560214, upload-time = "2026-05-21T14:34:50.975Z" }, - { url = "https://files.pythonhosted.org/packages/36/38/012bf76752e1f89ed50b77b99532d90f3a3e287bc7918e1fc0948ac866ac/ruff-0.15.14-py3-none-win32.whl", hash = "sha256:6d0c1ad2a0ab718d39b6d8fd2217981ce4d625cd96a720095f798fb47d8b13e6", size = 10805548, upload-time = "2026-05-21T14:34:33.453Z" }, - { url = "https://files.pythonhosted.org/packages/d1/b7/4ea2c170f10ad760fff2a5250beb18897719dc8b52b53a24cddbb9dd3f19/ruff-0.15.14-py3-none-win_amd64.whl", hash = "sha256:802342981e056db3851a7836e5b070f8f15f67d4a685ae2a6160939d364b2902", size = 11939523, upload-time = "2026-05-21T14:34:18.077Z" }, - { url = "https://files.pythonhosted.org/packages/62/d5/bc97ff895ec35cf3925d4bd60f3b39d822f377a446906ec9bcc87405e59b/ruff-0.15.14-py3-none-win_arm64.whl", hash = "sha256:ff47b90a9ef6a40c9e2f3b479c1fb78531adf055b94c1eba0a7ba04b31951826", size = 11208607, upload-time = "2026-05-21T14:34:26.525Z" }, +version = "0.15.15" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/84/6f/a76f7d96e5c962f5b69cee865e49c15c1116897c01990faa8a57edb62e7f/ruff-0.15.15.tar.gz", hash = "sha256:b8dff018130b46d8e5bf0f926ef6b60cf871d6d5ae45fc9334e09632daa741d6", size = 4706985, upload-time = "2026-05-28T14:16:57.784Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fa/9d/3a45c05b8ab04b4705989de70a79008e27c8003296a0feaee9edc18dd7e9/ruff-0.15.15-py3-none-linux_armv6l.whl", hash = "sha256:cf93e5388f412e1b108b1f8b34a6e036b70fe8aff89393befad96fe48670311b", size = 10710652, upload-time = "2026-05-28T14:16:06.701Z" }, + { url = "https://files.pythonhosted.org/packages/05/66/da974431624bf3b49f6ee1f9543c02d929ff1cba78b0d5a79c38cf21f744/ruff-0.15.15-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:ac5a646d1f6a7dadd5d50842dae2c1f9862ac887ef5d1b1375e02def791fde6e", size = 11096615, upload-time = "2026-05-28T14:16:23.313Z" }, + { url = "https://files.pythonhosted.org/packages/8c/09/7443452e5d290230a712103f2fdceeef7184f3ec99a2bd01c8be78aaceb5/ruff-0.15.15-py3-none-macosx_11_0_arm64.whl", hash = "sha256:77d955a431430c66f72dd94e379ad38a16daea3d25094872ac4edf9e797be530", size = 10436683, upload-time = "2026-05-28T14:16:40.974Z" }, + { url = "https://files.pythonhosted.org/packages/53/01/d330c26a57fa4f3943a14424904027428315b700fe4d14a84bb123a649e5/ruff-0.15.15-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7614ee79c69788cf6cedd568069ade9cecc22a1ad20494efe8d0c9ebb4b622d4", size = 10769064, upload-time = "2026-05-28T14:16:28.905Z" }, + { url = "https://files.pythonhosted.org/packages/1d/85/cc8770f8bdff541b1da8392d1634141fe4a0e3f4ee596605959b7906c27f/ruff-0.15.15-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3cdb1679e06a1f6b47bc384714ae96f6e2fb65ca441eb78c43d2ca554176ce1f", size = 10511987, upload-time = "2026-05-28T14:16:43.732Z" }, + { url = "https://files.pythonhosted.org/packages/7c/29/8c190c1472b63013583ba391f3342036e02010544c1270455ed8e519bdf3/ruff-0.15.15-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2728b93d7b23a603ea2c0ac6eb73d760bd38ec9de35f35fb41e18f7a3fee7622", size = 11275100, upload-time = "2026-05-28T14:16:55.244Z" }, + { url = "https://files.pythonhosted.org/packages/9f/6b/7e145ce2cc8e63d6834eca03d83a0e18d121def5c69f91b4cf4011ed4879/ruff-0.15.15-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:be582fcc0db438902c7792b08d6ddf6c9b9e21addaa10092c2c741cfb09e5a45", size = 12176903, upload-time = "2026-05-28T14:16:14.368Z" }, + { url = "https://files.pythonhosted.org/packages/80/a3/d5974637f68e451f7fadf015cf3101d1cd7d8ba5027cffe0b9e3826ebe6b/ruff-0.15.15-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7aa77465b8ecaf1a27bea098d696f7fed5e1eccbd10b321b682d6de586ae5627", size = 11404550, upload-time = "2026-05-28T14:16:20.138Z" }, + { url = "https://files.pythonhosted.org/packages/fe/1c/e6e5e568f22be4fb05d6244234aba384c06b451252453b821e1a529263cf/ruff-0.15.15-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:48decfa11d740de4889de623be1463308346312f2409a56e24aa280c86162dc4", size = 11382027, upload-time = "2026-05-28T14:16:46.615Z" }, + { url = "https://files.pythonhosted.org/packages/1d/01/170921b49fcd2e8858825593f91cf7146c3e40a5c3e6df763e4bb0484dde/ruff-0.15.15-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:a5015088452ca0081387063649ec67f06d3d1d6b8b936a1f836b5e9657ecd48c", size = 11366041, upload-time = "2026-05-28T14:16:26.247Z" }, + { url = "https://files.pythonhosted.org/packages/87/54/a7bad711d7de93254e15e06a4c375b89a03d18de45d3e5dcc86a4472fb1a/ruff-0.15.15-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:f5294aab6356c81600fcdea3a62bb1b924dfd5e91767c12318d3f68f86af57cd", size = 10741795, upload-time = "2026-05-28T14:16:17.11Z" }, + { url = "https://files.pythonhosted.org/packages/c9/31/38c075963668f8b41c6914ee0f6f318727fbe30ab9145cb29e6df464c5fa/ruff-0.15.15-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:db5bd4d802415cca656dc1616070b725952d6ae95eb5d4831e49fbd94a38f75f", size = 10511117, upload-time = "2026-05-28T14:16:31.767Z" }, + { url = "https://files.pythonhosted.org/packages/9d/96/6ff689e1f7e375d1d97075eca022f74c2bab59554a432fe4d2e6f091986a/ruff-0.15.15-py3-none-musllinux_1_2_i686.whl", hash = "sha256:587a6278ed42059191c1a466e490bd7930fb50bd2e255398bc29616c895a61cb", size = 10994867, upload-time = "2026-05-28T14:16:35.149Z" }, + { url = "https://files.pythonhosted.org/packages/c3/c2/5dce0ab9f92a8d534fa62b9bf9caca3eddb8c1a81b616f5e195ada4f0d6e/ruff-0.15.15-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:df0c1c084f5f4be9812f61518a45c440d3c30d69ce4bf6c5270e66d38338f02a", size = 11482101, upload-time = "2026-05-28T14:16:49.598Z" }, + { url = "https://files.pythonhosted.org/packages/b1/c0/1003b60edd697c649faf61f1a34094b1abb38fb3d1181e3f895781250a08/ruff-0.15.15-py3-none-win32.whl", hash = "sha256:29428ea79694afbe756d45fd59b36f22b6b020dc0443cf7de0173046236964b9", size = 10716774, upload-time = "2026-05-28T14:16:52.337Z" }, + { url = "https://files.pythonhosted.org/packages/02/a8/1269eddd6945a06c23f055ef7848886e37cf9d6a8bebb386a3115f01470c/ruff-0.15.15-py3-none-win_amd64.whl", hash = "sha256:8df0323902e15e24bc4bf246da830573d3cf3352bd0b9a164eab335d111ff4a4", size = 11868463, upload-time = "2026-05-28T14:16:11.333Z" }, + { url = "https://files.pythonhosted.org/packages/4e/b2/920464c907b191e37469d477a1aa8bc048b8f36c4c1610dfa4ab87b39e18/ruff-0.15.15-py3-none-win_arm64.whl", hash = "sha256:3c8ceca6792f38196b8f589bc92eccd03eef286602da92e5dc05cc42ef6441b7", size = 11138498, upload-time = "2026-05-28T14:16:38.425Z" }, ] [[package]] @@ -3192,7 +3221,7 @@ dependencies = [ { name = "pillow" }, { name = "scipy" }, { name = "tifffile", version = "2026.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, - { name = "tifffile", version = "2026.5.15", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, + { name = "tifffile", version = "2026.6.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/a1/b4/2528bb43c67d48053a7a649a9666432dc307d66ba02e3a6d5c40f46655df/scikit_image-0.26.0.tar.gz", hash = "sha256:f5f970ab04efad85c24714321fcc91613fcb64ef2a892a13167df2f3e59199fa", size = 22729739, upload-time = "2025-12-20T17:12:21.824Z" } wheels = [ @@ -3488,7 +3517,7 @@ wheels = [ [[package]] name = "tifffile" -version = "2026.5.15" +version = "2026.6.1" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.14'", @@ -3497,9 +3526,9 @@ resolution-markers = [ dependencies = [ { name = "numpy", marker = "python_full_version >= '3.12'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/40/66/0aef917d525767a40edebe088f8ed6a4417e6eb489c58f6805bfa872636b/tifffile-2026.5.15.tar.gz", hash = "sha256:ee4f3e07ee0d8ff4745a8c735ac2b72caa3173c7d6059b00fdc3ff492a0b635b", size = 429998, upload-time = "2026-05-15T20:04:55.896Z" } +sdist = { url = "https://files.pythonhosted.org/packages/b7/38/5e2ecef5af2f4fd4a89bb8d6240de9458bab4d51a4cbd97aeb3a0cd618e2/tifffile-2026.6.1.tar.gz", hash = "sha256:626c892c0e899d959b9438e7c0e1491dc154a7fead1f1f37a991724a50eceba9", size = 429694, upload-time = "2026-05-31T23:57:12.165Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/1f/6e/7d8850ff112f8f80d394ca45e89b975a3a43559d47af3137b767669b3294/tifffile-2026.5.15-py3-none-any.whl", hash = "sha256:6715515a53cabc0cefc5c9f13a0ae2c250e63e2ca784ce02d0b6c333810c2a17", size = 266665, upload-time = "2026-05-15T20:04:54.227Z" }, + { url = "https://files.pythonhosted.org/packages/81/59/208f71d70ddc6184f79b8c6d87d46eb7d7b12c19186a817dec9c9c3f3693/tifffile-2026.6.1-py3-none-any.whl", hash = "sha256:0d7382d2769b855b81ce358528e2b40c16d48aa39031746efa81215205332a8d", size = 267108, upload-time = "2026-05-31T23:57:10.597Z" }, ] [[package]] @@ -3687,19 +3716,19 @@ wheels = [ [[package]] name = "tornado" -version = "6.5.5" +version = "6.5.6" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/f8/f1/3173dfa4a18db4a9b03e5d55325559dab51ee653763bb8745a75af491286/tornado-6.5.5.tar.gz", hash = "sha256:192b8f3ea91bd7f1f50c06955416ed76c6b72f96779b962f07f911b91e8d30e9", size = 516006, upload-time = "2026-03-10T21:31:02.067Z" } +sdist = { url = "https://files.pythonhosted.org/packages/50/57/6d7303a77ae439d9189108f76c0c4fd89ee5e2cc8387bffb55232565c4ed/tornado-6.5.6.tar.gz", hash = "sha256:9a365179fe8ff6b8766f602c0f67c185d778193e9bdd828b19f0b6ed7764177d", size = 518139, upload-time = "2026-05-27T15:35:54.646Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/59/8c/77f5097695f4dd8255ecbd08b2a1ed8ba8b953d337804dd7080f199e12bf/tornado-6.5.5-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:487dc9cc380e29f58c7ab88f9e27cdeef04b2140862e5076a66fb6bb68bb1bfa", size = 445983, upload-time = "2026-03-10T21:30:44.28Z" }, - { url = "https://files.pythonhosted.org/packages/ab/5e/7625b76cd10f98f1516c36ce0346de62061156352353ef2da44e5c21523c/tornado-6.5.5-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:65a7f1d46d4bb41df1ac99f5fcb685fb25c7e61613742d5108b010975a9a6521", size = 444246, upload-time = "2026-03-10T21:30:46.571Z" }, - { url = "https://files.pythonhosted.org/packages/b2/04/7b5705d5b3c0fab088f434f9c83edac1573830ca49ccf29fb83bf7178eec/tornado-6.5.5-cp39-abi3-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:e74c92e8e65086b338fd56333fb9a68b9f6f2fe7ad532645a290a464bcf46be5", size = 447229, upload-time = "2026-03-10T21:30:48.273Z" }, - { url = "https://files.pythonhosted.org/packages/34/01/74e034a30ef59afb4097ef8659515e96a39d910b712a89af76f5e4e1f93c/tornado-6.5.5-cp39-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:435319e9e340276428bbdb4e7fa732c2d399386d1de5686cb331ec8eee754f07", size = 448192, upload-time = "2026-03-10T21:30:51.22Z" }, - { url = "https://files.pythonhosted.org/packages/be/00/fe9e02c5a96429fce1a1d15a517f5d8444f9c412e0bb9eadfbe3b0fc55bf/tornado-6.5.5-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:3f54aa540bdbfee7b9eb268ead60e7d199de5021facd276819c193c0fb28ea4e", size = 448039, upload-time = "2026-03-10T21:30:53.52Z" }, - { url = "https://files.pythonhosted.org/packages/82/9e/656ee4cec0398b1d18d0f1eb6372c41c6b889722641d84948351ae19556d/tornado-6.5.5-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:36abed1754faeb80fbd6e64db2758091e1320f6bba74a4cf8c09cd18ccce8aca", size = 447445, upload-time = "2026-03-10T21:30:55.541Z" }, - { url = "https://files.pythonhosted.org/packages/5a/76/4921c00511f88af86a33de770d64141170f1cfd9c00311aea689949e274e/tornado-6.5.5-cp39-abi3-win32.whl", hash = "sha256:dd3eafaaeec1c7f2f8fdcd5f964e8907ad788fe8a5a32c4426fbbdda621223b7", size = 448582, upload-time = "2026-03-10T21:30:57.142Z" }, - { url = "https://files.pythonhosted.org/packages/2c/23/f6c6112a04d28eed765e374435fb1a9198f73e1ec4b4024184f21faeb1ad/tornado-6.5.5-cp39-abi3-win_amd64.whl", hash = "sha256:6443a794ba961a9f619b1ae926a2e900ac20c34483eea67be4ed8f1e58d3ef7b", size = 448990, upload-time = "2026-03-10T21:30:58.857Z" }, - { url = "https://files.pythonhosted.org/packages/b7/c8/876602cbc96469911f0939f703453c1157b0c826ecb05bdd32e023397d4e/tornado-6.5.5-cp39-abi3-win_arm64.whl", hash = "sha256:2c9a876e094109333f888539ddb2de4361743e5d21eece20688e3e351e4990a6", size = 448016, upload-time = "2026-03-10T21:31:00.43Z" }, + { url = "https://files.pythonhosted.org/packages/1b/0d/b4f481e18c5a51864e6d12b9a05ecf72919696680b747c958c3fc1f4fbae/tornado-6.5.6-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:65fcfaafb079435c2c19dc9e07c0f1cf0fa9051759ed0a7d0a3ba7ea7f64919c", size = 447737, upload-time = "2026-05-27T15:35:38.122Z" }, + { url = "https://files.pythonhosted.org/packages/9e/9c/5430c39fcab1144d35860f457b15e9c08b4bc7ac86764354204e983d6183/tornado-6.5.6-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:38bc01b4acacded2de63ae78023548e41ebe6fbed3ec05a796d7ae3ad893887e", size = 445899, upload-time = "2026-05-27T15:35:40.519Z" }, + { url = "https://files.pythonhosted.org/packages/8b/79/fa7e14a2f939c807a8d30619b4eb604eab219601b78792516ebe22d40cf9/tornado-6.5.6-cp39-abi3-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b942e6a137fda31ff54bf8e6e2c8d1c37f1f50583f3ed53fb840b53b9601d104", size = 448964, upload-time = "2026-05-27T15:35:42.106Z" }, + { url = "https://files.pythonhosted.org/packages/a7/71/bd67d5f5199f937dafe03a49a37989f60f600ff6fef34c79412a829d97bd/tornado-6.5.6-cp39-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8666946e70171b8c3f1fc9b7876fac492e84822c4c7f3746f4e8f8bc9ac92a79", size = 449935, upload-time = "2026-05-27T15:35:43.906Z" }, + { url = "https://files.pythonhosted.org/packages/cc/a4/c24388c9cf5b3c3a513b56a158af9f23092c9a2810d789e294310797df21/tornado-6.5.6-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:1c34cfab7ad6d104f052f55de06d39bbafc5885cfeb4da688803308dbcfa90b7", size = 449767, upload-time = "2026-05-27T15:35:45.793Z" }, + { url = "https://files.pythonhosted.org/packages/a5/eb/6a07ad550c3f7b37244bd0becdf293ec3d3e961783d8b720a97df50de1b2/tornado-6.5.6-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:385f35e4e22fb52551dfcda4cdc8c30c61c2c001aef5ddad99cdfe116952efd3", size = 449174, upload-time = "2026-05-27T15:35:47.485Z" }, + { url = "https://files.pythonhosted.org/packages/bb/84/3469e098dccdb6763130e06aacd786bb4363fca7b590a55c101ddf34ed30/tornado-6.5.6-cp39-abi3-win32.whl", hash = "sha256:db475f1b67b2809b10bb16264829087724ca8d24fe4ed47f7b8675cae453ef86", size = 450230, upload-time = "2026-05-27T15:35:49.322Z" }, + { url = "https://files.pythonhosted.org/packages/d2/3c/273a04e0b9dd9016f1685cca0c1c8795a71ac88a34a8c889a0b443483226/tornado-6.5.6-cp39-abi3-win_amd64.whl", hash = "sha256:6739bf1e8eb09230f1280ddbd3236f0309db70f2c551a8dbc40f62babdf82f79", size = 450667, upload-time = "2026-05-27T15:35:51.194Z" }, + { url = "https://files.pythonhosted.org/packages/02/98/0cffe22a224f60c5fb1e3aa0b76f9da2e1ca78b0e9545e3d077c68ce60a7/tornado-6.5.6-cp39-abi3-win_arm64.whl", hash = "sha256:2543597b24a695d72338a9a77818362d72387c03ae173f1f169eadc5c91466ac", size = 449690, upload-time = "2026-05-27T15:35:52.902Z" }, ] [[package]] @@ -3780,7 +3809,7 @@ wheels = [ [[package]] name = "virtualenv" -version = "21.3.3" +version = "21.4.2" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "distlib" }, @@ -3788,9 +3817,9 @@ dependencies = [ { name = "platformdirs" }, { name = "python-discovery" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/15/ba/1f6e8c957e4932be060dcdc482d339c12e0216351478add3645cdaa53c05/virtualenv-21.3.3.tar.gz", hash = "sha256:f5bda277e553b1c2b3c1a8debfc30496e1288cc93ce6b7b71b3280047e317328", size = 7613784, upload-time = "2026-05-13T18:01:30.19Z" } +sdist = { url = "https://files.pythonhosted.org/packages/e1/0d/4e93c8e6d1001a75763f87d8f5ecda8ebc7f4aa2153dddfaf4ae8892821a/virtualenv-21.4.2.tar.gz", hash = "sha256:38e6ee0a555615c0ea9da2ac7e9998fe8dc3b911dd33ad8eaad2020957653b0c", size = 7613326, upload-time = "2026-05-31T17:01:22.827Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/f4/34/a9dbe051de88a63eb7408ea66630bac38e72f7f6077d4be58737106860d9/virtualenv-21.3.3-py3-none-any.whl", hash = "sha256:7d5987d8369e098e41406efb780a3d4ca79280097293899e351a6407ee153ab3", size = 7594554, upload-time = "2026-05-13T18:01:27.815Z" }, + { url = "https://files.pythonhosted.org/packages/bf/c4/557dc082be035381b85fdb2b74e21d3d21b57750b74f2b47a32f3a639ff9/virtualenv-21.4.2-py3-none-any.whl", hash = "sha256:854210ca524a1a4d0d744734f4acbc721c3ffe163b85bbf5d56d14d5ae2f0fae", size = 7594079, upload-time = "2026-05-31T17:01:20.735Z" }, ] [[package]] From abd33e62eff0fab93274bbadd40fde38b9275af7 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Mon, 1 Jun 2026 10:24:17 -0700 Subject: [PATCH 089/140] MAPED direct fitting descan correction implemented. --- src/quantem/core/io/__init__.py | 15 ++- src/quantem/core/io/file_readers.py | 14 +-- src/quantem/diffraction/maped.py | 174 +++++++++++++++++++++------- 3 files changed, 148 insertions(+), 55 deletions(-) diff --git a/src/quantem/core/io/__init__.py b/src/quantem/core/io/__init__.py index 2780eae4..6b1085d9 100644 --- a/src/quantem/core/io/__init__.py +++ b/src/quantem/core/io/__init__.py @@ -1,8 +1,13 @@ -from quantem.core.io.file_readers import read_2d as read_2d -from quantem.core.io.file_readers import read_4dstem as read_4dstem -from quantem.core.io.file_readers import ( - read_emdfile_to_4dstem as read_emdfile_to_4dstem, -) from quantem.core.io.serialize import AutoSerialize as AutoSerialize from quantem.core.io.serialize import load as load from quantem.core.io.serialize import print_file as print_file + +_LAZY = {"read_2d", "read_4dstem", "read_emdfile_to_4dstem"} + + +def __getattr__(name: str): + if name in _LAZY: + from quantem.core.io import file_readers + + return getattr(file_readers, name) + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index a78900df..18305b82 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -6,10 +6,10 @@ import h5py import numpy as np -from quantem.core.datastructures import Dataset as Dataset -from quantem.core.datastructures import Dataset2d as Dataset2d -from quantem.core.datastructures import Dataset3d as Dataset3d -from quantem.core.datastructures import Dataset4dstem as Dataset4dstem +from quantem.core.datastructures.dataset import Dataset as Dataset +from quantem.core.datastructures.dataset2d import Dataset2d as Dataset2d +from quantem.core.datastructures.dataset3d import Dataset3d as Dataset3d +from quantem.core.datastructures.dataset4dstem import Dataset4dstem as Dataset4dstem def read_4dstem( @@ -83,8 +83,7 @@ def _reshape_3d_to_4d( data = imported_data["data"] if data.ndim != 3: raise ValueError( - f"Expected 3D data to reshape, got ndim={data.ndim} " - f"with shape {data.shape}" + f"Expected 3D data to reshape, got ndim={data.ndim} with shape {data.shape}" ) if scan_axis_local not in (0, 1): @@ -116,8 +115,7 @@ def _reshape_3d_to_4d( old_axes = imported_data.get("axes", None) if old_axes is None or len(old_axes) != 3: raise ValueError( - "Expected 3 axes for 3D data when reshaping to 4D; " - f"got axes={old_axes}" + f"Expected 3 axes for 3D data when reshaping to 4D; got axes={old_axes}" ) ax_scan_y = { diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index aa2dfe83..93409ba2 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -14,6 +14,7 @@ from scipy.signal.windows import tukey from tqdm import tqdm +from quantem.core import config from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift @@ -827,8 +828,8 @@ class MAPEDTorch(AutoSerialize): def __init__( self, datasets: list[torch.Tensor], - device: str | Any, dtype: str | Any, + device: str | int | None = None, _token: object | None = None, ): if _token is not self._token: @@ -839,6 +840,19 @@ def __init__( self.device = device self.dtype = dtype + @property + def device(self) -> str: + if hasattr(self, "_device"): + return self._device + return config.get_device() + + @device.setter + def device(self, device: str | int | None) -> None: + if device is not None: + dev, _id = config.validate_device(device) + self._device = dev + # if None, leave unset so the property falls back to config.get_device() + @classmethod def from_datasets(cls, datasets: Sequence[torch.Tensor]) -> MAPED: """ @@ -864,13 +878,13 @@ def from_datasets(cls, datasets: Sequence[torch.Tensor]) -> MAPED: ) ds_list.append(d) - dtypes = np.array([dataset.dtype for dataset in datasets]) - devices = np.array([dataset.device for dataset in datasets]) + dtypes = [dataset.dtype for dataset in datasets] + devices = [str(dataset.device) for dataset in datasets] # check that all datasets have the same dtype and device - if not np.all(dtypes == dtypes[0]): + if len(set(str(d) for d in dtypes)) > 1: raise TypeError("All datasets need to have the same type") - if not np.all(devices == devices[0]): + if len(set(devices)) > 1: raise TypeError("All datasets need to have the same device") if not ds_list: @@ -1042,10 +1056,11 @@ def dscan_align( iterations: int, upsample_factor: int = 100, method: str = "autocorrelation", - plot_aligned: bool = True, + plot: bool = True, edge_blend: float = 2.0, fit_shifts: bool = True, mode: str = "linear", + batch_size: int | None = None, ): for i, dataset in enumerate(self.datasets): _, aligned_dataset = dscan_correct( @@ -1053,11 +1068,12 @@ def dscan_align( iterations, method=method, upsample_factor=upsample_factor, - plot_aligned=plot_aligned, + plot=plot, edge_blend=edge_blend, device=self.device, fit_shifts=fit_shifts, mode=mode, + batch_size=batch_size, ) self.datasets[i] = aligned_dataset @@ -1294,7 +1310,7 @@ def real_space_align( base_pad = torch.zeros((n, Hp, Wp), dtype=torch.float32, device=self.device) for i in range(n): - im0 = self.im_bf[i] + im0 = self.im_bf[i].float() if edge_filter: pad_symmetric = wx.shape[-1] // 2 @@ -1945,7 +1961,7 @@ def shift_images_torch( if not blend: # simple shift per-image without padding/blending — keep original behavior - imgs = images.unsqueeze(1) + imgs = images.float().unsqueeze(1) grid_y, grid_x = torch.meshgrid( torch.linspace(-1, 1, H, device=images.device), torch.linspace(-1, 1, W, device=images.device), @@ -2326,12 +2342,13 @@ def dscan_correct( dataset, iterations, upsample_factor: int = 100, - plot_aligned: bool = True, + plot: bool = True, edge_blend: float = 2.0, device="cpu", method="autocorrelation", fit_shifts=True, mode="linear", + batch_size: int | None = None, ): """ Align diffraction patterns using autocorrelation. @@ -2344,7 +2361,7 @@ def dscan_correct( Number of refinement iterations upsample_factor : int Upsampling factor for sub-pixel accuracy - plot_aligned : bool + plot : bool Whether to plot results after each iteration edge_blend : float Edge blending parameter for Tukey window @@ -2365,6 +2382,9 @@ def dscan_correct( reference (torch.Tensor). """ H_rs, W_rs, H_dp, W_dp = dataset.shape + n_pos = H_rs * W_rs + if batch_size is None: + batch_size = max(1, min(n_pos, 256)) w = ( tukey_torch( @@ -2408,22 +2428,88 @@ def dscan_correct( G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) if method == "autocorrelation": - # Vectorize over the scan grid by flattening (H_rs, W_rs) into a batch dimension. - dp_batch = (w * shifted_dps).reshape(H_rs * W_rs, H_dp, W_dp) - G = torch.fft.fft2(dp_batch, dim=(-2, -1)) - G_flipped = torch.conj(G) - - shifts = ( - -cross_correlation_shift_torch( - G, - G_flipped, - upsample_factor=upsample_factor, - fft_input=True, + shifts_flat = torch.zeros((n_pos, 2), device=device, dtype=torch.float32) + shifted_dps_flat = shifted_dps.reshape(n_pos, H_dp, W_dp) + + for batch_start in tqdm( + range(0, n_pos, batch_size), + desc=f"Iteration {iteration + 1}/{iterations} (autocorrelation)", + ): + batch_end = min(batch_start + batch_size, n_pos) + dp_b = w * shifted_dps_flat[batch_start:batch_end] + G_b = torch.fft.fft2(dp_b, dim=(-2, -1)) + G_flipped = torch.conj(G_b) + shifts_flat[batch_start:batch_end] = ( + -cross_correlation_shift_torch( + G_b, + G_flipped, + upsample_factor=upsample_factor, + fft_input=True, + ) + / 2.0 ) - / 2.0 - ) + del dp_b, G_b, G_flipped + torch.cuda.empty_cache() if torch.cuda.is_available() else None + + diffraction_shifts[:, :, :] = shifts_flat.reshape(H_rs, W_rs, 2) + + if method == "direct_fitting": + centers = torch.zeros((n_pos, 2), device=device, dtype=torch.float32) + shifted_dps_flat = shifted_dps.reshape(n_pos, H_dp, W_dp) + + for batch_start in tqdm( + range(0, n_pos, batch_size), + desc=f"Iteration {iteration + 1}/{iterations} (direct fitting)", + ): + batch_end = min(batch_start + batch_size, n_pos) + dp_b = shifted_dps_flat[batch_start:batch_end].float() + B = dp_b.shape[0] + batch_idx = torch.arange(B, device=device) + + # argmax: integer center estimate + flat_idx = torch.argmax(dp_b.reshape(B, -1), dim=1) + row_peak = flat_idx // W_dp + col_peak = flat_idx % W_dp + + # log-parabolic sub-pixel refinement — row direction + row_safe = row_peak.clamp(1, H_dp - 2) + vr_m = dp_b[batch_idx, row_safe - 1, col_peak].clamp(min=1e-6).log() + vr_0 = dp_b[batch_idx, row_safe, col_peak].clamp(min=1e-6).log() + vr_p = dp_b[batch_idx, row_safe + 1, col_peak].clamp(min=1e-6).log() + denom_r = vr_m + vr_p - 2.0 * vr_0 + dr = torch.where( + (denom_r < -1e-6) & (row_peak > 0) & (row_peak < H_dp - 1), + ((vr_m - vr_p) / (2.0 * denom_r)).clamp(-1.0, 1.0), + torch.zeros(B, device=device), + ) + + # log-parabolic sub-pixel refinement — col direction + col_safe = col_peak.clamp(1, W_dp - 2) + vc_m = dp_b[batch_idx, row_peak, col_safe - 1].clamp(min=1e-6).log() + vc_0 = dp_b[batch_idx, row_peak, col_safe].clamp(min=1e-6).log() + vc_p = dp_b[batch_idx, row_peak, col_safe + 1].clamp(min=1e-6).log() + denom_c = vc_m + vc_p - 2.0 * vc_0 + dc = torch.where( + (denom_c < -1e-6) & (col_peak > 0) & (col_peak < W_dp - 1), + ((vc_m - vc_p) / (2.0 * denom_c)).clamp(-1.0, 1.0), + torch.zeros(B, device=device), + ) + + centers[batch_start:batch_end, 0] = row_peak.float() + dr + centers[batch_start:batch_end, 1] = col_peak.float() + dc + + del dp_b, flat_idx, row_peak, col_peak, batch_idx + del vr_m, vr_0, vr_p, vc_m, vc_0, vc_p, dr, dc + torch.cuda.empty_cache() if torch.cuda.is_available() else None + + # fit a plane to centers across the real-space scan grid + centers_2d = centers.reshape(H_rs, W_rs, 2) + centers_fit_r, _ = fit_surface_lstsq(centers_2d[:, :, 0], mode="linear") + centers_fit_c, _ = fit_surface_lstsq(centers_2d[:, :, 1], mode="linear") - diffraction_shifts[:, :, :] = shifts.reshape(H_rs, W_rs, 2) + # shifts = mean_center - fitted_center: moves each DP toward the global mean + diffraction_shifts[:, :, 0] = H_dp / 2 - centers_fit_r + diffraction_shifts[:, :, 1] = W_dp / 2 - centers_fit_c if fit_shifts: diffraction_shifts_1, _ = fit_surface_lstsq(diffraction_shifts[:, :, 0], mode=mode) @@ -2431,25 +2517,29 @@ def dscan_correct( diffraction_shifts_old = diffraction_shifts.clone() diffraction_shifts = torch.stack((diffraction_shifts_1, diffraction_shifts_2), dim=2) - # Recompute fitted shifts in one batched pass over all scan positions. - dp_batch = (w * shifted_dps).reshape(H_rs * W_rs, H_dp, W_dp) - G_batch = torch.fft.fft2(dp_batch, dim=(-2, -1)) - - shifts_batch = diffraction_shifts.reshape(H_rs * W_rs, 2) - phase_ramp = torch.exp( - -1j - * torch.pi - * ( - kr.unsqueeze(0) * shifts_batch[:, 0][:, None, None] - + kc.unsqueeze(0) * shifts_batch[:, 1][:, None, None] + # Apply fitted shifts in batches over all scan positions. + shifted_dps_flat = shifted_dps.reshape(n_pos, H_dp, W_dp) + shifts_flat = diffraction_shifts.reshape(n_pos, 2) + + for batch_start in range(0, n_pos, batch_size): + batch_end = min(batch_start + batch_size, n_pos) + G_b = torch.fft.fft2(w * shifted_dps_flat[batch_start:batch_end], dim=(-2, -1)) + s_b = shifts_flat[batch_start:batch_end] + phase_ramp = torch.exp( + -1j + * torch.pi + * ( + kr.unsqueeze(0) * s_b[:, 0][:, None, None] + + kc.unsqueeze(0) * s_b[:, 1][:, None, None] + ) ) - ) - G_shift = G_batch * phase_ramp - shifted_dps[:, :, :, :] = torch.fft.ifft2(G_shift, dim=(-2, -1)).real.reshape( - H_rs, W_rs, H_dp, W_dp - ) + shifted_dps_flat[batch_start:batch_end] = torch.fft.ifft2( + G_b * phase_ramp, dim=(-2, -1) + ).real + del G_b, s_b, phase_ramp + torch.cuda.empty_cache() if torch.cuda.is_available() else None - if plot_aligned: + if plot: if fit_shifts: show_2d( [ From 15f22b993fee04610540b06dc32de688f118a6a8 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Mon, 1 Jun 2026 12:00:41 -0700 Subject: [PATCH 090/140] fixing OptimizerParamsType refactor, and changing SchedulerParamsType to match --- src/quantem/core/ml/optimizer_mixin.py | 14 ++++++------- .../diffractive_imaging/dataset_models.py | 2 +- .../diffractive_imaging/object_models.py | 12 +++++++---- .../diffractive_imaging/probe_models.py | 10 +++++++--- .../diffractive_imaging/ptychography_opt.py | 20 +++++++++---------- src/quantem/tomography/object_models.py | 2 +- src/quantem/tomography/tomography_opt.py | 18 ++++++++++------- 7 files changed, 45 insertions(+), 33 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index f79d7661..ece31dff 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -514,7 +514,7 @@ def parse_dict(cls, d: dict): raise ValueError(f"Unknown scheduler type: {name}") -SchedulerType = ( +SchedulerParamsType = ( SchedulerParams.Plateau | SchedulerParams.Exponential | SchedulerParams.Cyclic @@ -540,7 +540,7 @@ def __init__(self): self._optimizer_params: dict[str, OptimizerParamsType] = { self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer() } - self._scheduler_params: SchedulerType = SchedulerParams.NoneScheduler() + self._scheduler_params: SchedulerParamsType = SchedulerParams.NoneScheduler() # Don't call super().__init__() in mixin classes to avoid MRO issues @property @@ -587,17 +587,17 @@ def _is_single_optimizer_dict(d: dict) -> bool: return "type" in d or "name" in d @property - def scheduler_params(self) -> SchedulerType: + def scheduler_params(self) -> SchedulerParamsType: """Get the scheduler parameters.""" return self._scheduler_params @scheduler_params.setter - def scheduler_params(self, params: SchedulerType | dict): + def scheduler_params(self, params: SchedulerParamsType | dict): """Set the scheduler parameters.""" if isinstance(params, dict): params = SchedulerParams.parse_dict(d=params) - if not isinstance(params, SchedulerType): - raise TypeError(f"scheduler parameters must be a SchedulerType, got {type(params)}") + if not isinstance(params, SchedulerParamsType): + raise TypeError(f"scheduler parameters must be a SchedulerParamsType, got {type(params)}") self._scheduler_params = params @abstractmethod @@ -688,7 +688,7 @@ def _build_optimizer(self, opt_params, param_groups) -> "torch.optim.Optimizer": raise NotImplementedError(f"Unknown optimizer type: {opt_params}") def set_scheduler( - self, scheduler_params: SchedulerType | dict | None = None, num_iter: int | None = None + self, scheduler_params: SchedulerParamsType | dict | None = None, num_iter: int | None = None ) -> None: """Set the scheduler for this model.""" if scheduler_params is not None: diff --git a/src/quantem/diffractive_imaging/dataset_models.py b/src/quantem/diffractive_imaging/dataset_models.py index f663f3eb..751c9245 100644 --- a/src/quantem/diffractive_imaging/dataset_models.py +++ b/src/quantem/diffractive_imaging/dataset_models.py @@ -113,7 +113,7 @@ def get_optimization_parameters(self) -> "dict[str, list[torch.Tensor]]": def _normalize_optimizer_params(self, params): """Broadcast a single optimizer spec to the learnable descan/scan_position groups. - A single ``OptimizerType`` / single-optimizer dict (normalized to the ``"default"`` key) + A single ``OptimizerParamsType`` / single-optimizer dict (normalized to the ``"default"`` key) is fanned out to whichever groups are currently learnable, so the common single-LR caller keeps working. An explicit PPLR dict (keyed by ``descan``/``scan_positions``) passes through. """ diff --git a/src/quantem/diffractive_imaging/object_models.py b/src/quantem/diffractive_imaging/object_models.py index 3b74319b..d311d8dd 100644 --- a/src/quantem/diffractive_imaging/object_models.py +++ b/src/quantem/diffractive_imaging/object_models.py @@ -1,7 +1,7 @@ import math from abc import abstractmethod from copy import deepcopy -from typing import Any, Callable, Literal, Self, Sequence, cast +from typing import Callable, Literal, Self, Sequence, cast from warnings import warn import matplotlib.pyplot as plt @@ -14,7 +14,11 @@ from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.blocks import reset_weights from quantem.core.ml.loss_functions import get_loss_module -from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType, SchedulerType +from quantem.core.ml.optimizer_mixin import ( + OptimizerMixin, + OptimizerParamsType, + SchedulerParamsType, +) from quantem.core.utils.rng import RNGMixin from quantem.core.utils.validators import ( validate_arr_gt, @@ -1046,8 +1050,8 @@ def pretrain( pretrain_target: torch.Tensor | None = None, reset: bool = False, num_iters: int = 100, - optimizer_params: dict | OptimizerType | None = None, - scheduler_params: dict | SchedulerType | None = None, + optimizer_params: dict | OptimizerParamsType | None = None, + scheduler_params: dict | SchedulerParamsType | None = None, loss_fn: Callable | str = "l2", apply_constraints: bool = False, show: bool = True, diff --git a/src/quantem/diffractive_imaging/probe_models.py b/src/quantem/diffractive_imaging/probe_models.py index 6b00793a..af638b85 100644 --- a/src/quantem/diffractive_imaging/probe_models.py +++ b/src/quantem/diffractive_imaging/probe_models.py @@ -15,7 +15,11 @@ from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.blocks import reset_weights from quantem.core.ml.loss_functions import get_loss_module -from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType, SchedulerType +from quantem.core.ml.optimizer_mixin import ( + OptimizerMixin, + OptimizerParamsType, + SchedulerParamsType, +) from quantem.core.utils.rng import RNGMixin from quantem.core.utils.utils import electron_wavelength_angstrom, to_numpy from quantem.core.utils.validators import ( @@ -1355,8 +1359,8 @@ def pretrain( pretrain_target: torch.Tensor | None = None, reset: bool = False, num_iters: int = 100, - optimizer_params: dict | OptimizerType | None = None, - scheduler_params: dict | SchedulerType | None = None, + optimizer_params: dict | OptimizerParamsType | None = None, + scheduler_params: dict | SchedulerParamsType | None = None, loss_fn: Callable | str = "l2", apply_constraints: bool = False, show: bool = True, diff --git a/src/quantem/diffractive_imaging/ptychography_opt.py b/src/quantem/diffractive_imaging/ptychography_opt.py index c18150e0..ff1b3a2c 100644 --- a/src/quantem/diffractive_imaging/ptychography_opt.py +++ b/src/quantem/diffractive_imaging/ptychography_opt.py @@ -4,9 +4,9 @@ from quantem.core import config from quantem.core.ml.optimizer_mixin import ( OptimizerParams, - OptimizerType, + OptimizerParamsType, SchedulerParams, - SchedulerType, + SchedulerParamsType, ) from quantem.diffractive_imaging.ptychography_base import PtychographyBase @@ -23,7 +23,7 @@ class PtychographyOpt(PtychographyBase): """ OPTIMIZABLE_VALS = ["object", "probe", "dataset"] - DEFAULT_OPTIMIZER_TYPE: OptimizerType = OptimizerParams.Adam() + DEFAULT_OPTIMIZER_TYPE: OptimizerParamsType = OptimizerParams.Adam() def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -42,7 +42,7 @@ def _get_default_lr(self, key: str) -> float: # region --- explicit properties and setters --- @property - def optimizer_params(self) -> dict[str, OptimizerType]: + def optimizer_params(self) -> dict[str, OptimizerParamsType | dict[str, OptimizerParamsType]]: return { key: params for key, params in [ @@ -56,7 +56,7 @@ def optimizer_params(self) -> dict[str, OptimizerType]: @optimizer_params.setter def optimizer_params(self, d: dict) -> None: """ - Takes a dictionary mapping optimizable keys to either an ``OptimizerType`` + Takes a dictionary mapping optimizable keys to either an ``OptimizerParamsType`` dataclass or a plain dict (with optional ``"name"``/``"type"`` and ``"lr"`` keys). Missing ``"name"`` / ``"lr"`` are filled from ``DEFAULT_OPTIMIZER_TYPE`` and ``_get_default_lr`` respectively. @@ -71,7 +71,7 @@ def optimizer_params(self, d: dict) -> None: d = {k: {} for k in d} for k, v in d.items(): - if isinstance(v, OptimizerType): + if isinstance(v, OptimizerParamsType): pass # already a dataclass, pass through elif isinstance(v, dict): if not v: @@ -82,7 +82,7 @@ def optimizer_params(self, d: dict) -> None: if "lr" not in v: v["lr"] = self._get_default_lr(k) else: - raise TypeError(f"Expected OptimizerType or dict for key '{k}', got {type(v)}") + raise TypeError(f"Expected OptimizerParamsType or dict for key '{k}', got {type(v)}") if k == "object": self.obj_model.optimizer_params = v @@ -131,7 +131,7 @@ def remove_optimizer(self, key: str) -> None: self.dset.remove_optimizer() @property - def scheduler_params(self) -> dict[str, SchedulerType]: + def scheduler_params(self) -> dict[str, SchedulerParamsType]: """Returns the parameters used to set the schedulers.""" return { "object": self.obj_model.scheduler_params, @@ -142,7 +142,7 @@ def scheduler_params(self) -> dict[str, SchedulerType]: @scheduler_params.setter def scheduler_params(self, d: dict) -> None: """ - Takes a dictionary mapping optimizable keys to either a ``SchedulerType`` + Takes a dictionary mapping optimizable keys to either a ``SchedulerParamsType`` dataclass or a plain dict. Keys not present in ``d`` are set to ``SchedulerParams.NoneScheduler()`` (disables scheduling for that model). @@ -178,7 +178,7 @@ def schedulers(self) -> dict[str, "torch.optim.lr_scheduler._LRScheduler"]: schedulers["dataset"] = self.dset.scheduler return schedulers - def set_schedulers(self, params: dict[str, SchedulerType], num_iter: int | None = None): + def set_schedulers(self, params: dict[str, SchedulerParamsType], num_iter: int | None = None): """Set schedulers for each model.""" for key, scheduler_params in params.items(): if key not in self.OPTIMIZABLE_VALS: diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 12e8a99c..c6b85b64 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1033,7 +1033,7 @@ def _normalize_optimizer_params(self, params): """ObjectTensorDecomp requires a dict matching model.param_keys.""" if not isinstance(params, dict) or self._is_single_optimizer_dict(params): raise TypeError( - f"ObjectTensorDecomp requires dict[str, OptimizerType] keyed by " + f"ObjectTensorDecomp requires dict[str, OptimizerParamsType] keyed by " f"param_keys; got {type(params)}" ) model = _unwrap(self.model) diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index 2c913277..cf990379 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -2,7 +2,11 @@ import torch -from quantem.core.ml.optimizer_mixin import OptimizerType, SchedulerType +from quantem.core.ml.optimizer_mixin import ( + OptimizerParams, + OptimizerParamsType, + SchedulerParamsType, +) from quantem.tomography.tomography_base import TomographyBase @@ -12,7 +16,7 @@ class TomographyOpt(TomographyBase): """ OPTIMIZABLE_VALS = ["object", "pose"] - DEFAULT_OPTIMIZER_TYPE = "adam" + DEFAULT_OPTIMIZER_TYPE: OptimizerParamsType = OptimizerParams.Adam() def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -27,7 +31,7 @@ def _get_default_lr(self, key: str) -> float: raise ValueError(f"Unknown optimization key: {key}") @property - def optimizer_params(self) -> dict[str, OptimizerType | dict[str, OptimizerType]]: + def optimizer_params(self) -> dict[str, OptimizerParamsType | dict[str, OptimizerParamsType]]: return { key: params for key, params in [ @@ -38,7 +42,7 @@ def optimizer_params(self) -> dict[str, OptimizerType | dict[str, OptimizerType] } @optimizer_params.setter - def optimizer_params(self, d: dict[str, OptimizerType] | dict[str, dict]): + def optimizer_params(self, d: dict[str, OptimizerParamsType] | dict[str, dict]): """Set the optimizer parameters.""" if isinstance(d, (tuple, list)): d = {k: {} for k in d} @@ -52,7 +56,7 @@ def optimizer_params(self, d: dict[str, OptimizerType] | dict[str, dict]): if k not in targets: raise ValueError(f"Unknown optimization key: {k}") - # if not isinstance(v, OptimizerType): + # if not isinstance(v, OptimizerParamsType): # v = OptimizerParams.parse_dict(v) targets[k].optimizer_params = v @@ -100,7 +104,7 @@ def remove_optimizer(self, key: str): raise ValueError(f"Unknown optimization key: {key}") @property - def scheduler_params(self) -> dict[str, SchedulerType]: + def scheduler_params(self) -> dict[str, SchedulerParamsType]: """Returns the parameters used to set the schedulers.""" return { "object": self.obj_model.scheduler_params, @@ -136,7 +140,7 @@ def schedulers(self) -> dict[str, torch.optim.lr_scheduler._LRScheduler]: return schedulers def set_schedulers( - self, params: Mapping[str, SchedulerType | dict], num_iter: int | None = None + self, params: Mapping[str, SchedulerParamsType | dict], num_iter: int | None = None ): for key, scheduler_params in params.items(): if key == "object": From 14a146349e8fe23af1479f1be919a1b6d66a3138 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 1 Jun 2026 13:43:55 -0700 Subject: [PATCH 091/140] Tomography tests, and bug-fix on get_optimization_parameters in TomographyDatasetBase. --- src/quantem/tomography/dataset_models.py | 15 +- tests/tomography/conftest.py | 72 ++++++++ tests/tomography/test_dataset_models.py | 106 ++++++++++++ tests/tomography/test_object_models.py | 159 ++++++++++++++++++ tests/tomography/test_radon.py | 111 ++++++++++++ .../test_tomography_conventional.py | 72 ++++++++ tests/tomography/test_tomography_inr.py | 96 +++++++++++ tests/tomography/test_tomography_opt.py | 143 ++++++++++++++++ tests/tomography/test_utils.py | 78 +++++++++ 9 files changed, 843 insertions(+), 9 deletions(-) create mode 100644 tests/tomography/conftest.py create mode 100644 tests/tomography/test_dataset_models.py create mode 100644 tests/tomography/test_object_models.py create mode 100644 tests/tomography/test_radon.py create mode 100644 tests/tomography/test_tomography_conventional.py create mode 100644 tests/tomography/test_tomography_inr.py create mode 100644 tests/tomography/test_tomography_opt.py create mode 100644 tests/tomography/test_utils.py diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index bdd2e66a..4838395d 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -232,16 +232,13 @@ def from_data( # --- Optimization Parameters --- - def get_optimization_parameters(self) -> list[dict[str, Any]]: - """ - Get the parameters that should be optimized for this model, - wrapped in a single param group. + def get_optimization_parameters(self) -> dict[str, list[torch.Tensor]]: + """Single param group keyed by DEFAULT_OPTIMIZER_KEY. + + Hyperparameters are baked by ``set_optimizer``, not here — return only the tensors, + matching the ``dict[str, list[tensor]]`` contract the object models use. """ - if isinstance(self._optimizer_params, dict): - opt = next(iter(self._optimizer_params.values())) - else: - opt = self._optimizer_params - return [{"params": list(self.parameters()), **opt.params()}] + return {self.DEFAULT_OPTIMIZER_KEY: list(self.parameters())} # --- Forward pass --- @abstractmethod diff --git a/tests/tomography/conftest.py b/tests/tomography/conftest.py new file mode 100644 index 00000000..06957095 --- /dev/null +++ b/tests/tomography/conftest.py @@ -0,0 +1,72 @@ +"""Shared fixtures and markers for the tomography test suite. + +The suite is split into three tiers (see the plan / individual modules): + +* CPU, always-on -- radon, utils, object/dataset models, optimizer-param wiring. +* CPU, slow -- conventional SIRT/FBP reconstruction (``--runslow``). +* GPU, slow -- INR / KPlanes reconstruction (``requires_gpu`` + ``--runslow``). + +``torch`` and ``scikit-image`` are core dependencies of quantem, so they are always +importable; ``requires_torch`` is kept only for parity with the existing test style. The +meaningful gate is ``requires_gpu`` (CI runs CPU-only) combined with ``@pytest.mark.slow``. +""" + +import numpy as np +import pytest +import torch + +from quantem.core import config +from quantem.tomography.utils import rot_ZXZ + +# --- Markers --------------------------------------------------------------- +requires_torch = pytest.mark.skipif(not config.get("has_torch"), reason="requires torch") +requires_gpu = pytest.mark.skipif(not torch.cuda.is_available(), reason="requires a CUDA device") + + +@pytest.fixture +def torch_device() -> str: + """Device for torch-only tests. + + ``cuda:0`` when available, else ``cpu``. Note: object models that go through + ``setup_distributed`` must be built on ``cuda`` when CUDA is present (the CPU path is + only valid when no CUDA device exists), so torch-only construction tests follow this + fixture rather than hard-coding ``"cpu"``. + """ + return "cuda:0" if torch.cuda.is_available() else "cpu" + + +# --- Synthetic data -------------------------------------------------------- +def make_tilt_series(volume: torch.Tensor, angles: np.ndarray) -> np.ndarray: + """Project a volume into a tilt series with quantem's own forward model. + + Mirrors ``tomography_00_generate_phantom``: rotate by ``rot_ZXZ`` (Euler ZXZ, tilt on + the X axis) then sum along the beam axis. Using this rather than ``radon_torch`` keeps + the synthetic data consistent with the geometry the reconstructors assume. + """ + projections = [] + vol = volume.unsqueeze(0) # (1, Z, Y, X) + for angle in angles: + rotated = rot_ZXZ(vol, 0.0, float(angle), 0.0, device="cpu") + projections.append(rotated[0].sum(0)) + return torch.stack(projections).numpy().astype(np.float32) + + +@pytest.fixture(scope="module") +def phantom_volume() -> np.ndarray: + """Small deterministic (32, 32, 32) phantom with a couple of solid blocks.""" + vol = np.zeros((32, 32, 32), dtype=np.float32) + vol[8:24, 10:20, 12:22] = 1.0 + vol[18:26, 6:12, 16:24] = 0.6 + return vol + + +@pytest.fixture(scope="module") +def tilt_angles() -> np.ndarray: + """Nine tilt angles spanning -70..70 degrees.""" + return np.linspace(-70, 70, 9).astype(np.float32) + + +@pytest.fixture(scope="module") +def tilt_series(phantom_volume: np.ndarray, tilt_angles: np.ndarray) -> np.ndarray: + """Synthetic tilt series, shape (n_angles, 32, 32).""" + return make_tilt_series(torch.from_numpy(phantom_volume), tilt_angles) diff --git a/tests/tomography/test_dataset_models.py b/tests/tomography/test_dataset_models.py new file mode 100644 index 00000000..78ded427 --- /dev/null +++ b/tests/tomography/test_dataset_models.py @@ -0,0 +1,106 @@ +"""Tests for ``quantem.tomography.dataset_models``. + +Covers constraint parsing, the pixelated dataset (validation, normalisation, tilt-angle +convention, pose-parameter materialisation) and the INR / pretrain datasets. +""" + +import numpy as np +import pytest +import torch + +from quantem.tomography.dataset_models import ( + DatasetConstraintParams, + DatasetValue, + TomographyINRDataset, + TomographyINRPretrainDataset, + TomographyPixDataset, +) + +from .conftest import requires_torch + + +class TestDatasetConstraintParse: + def test_parse_base_by_name(self): + c = DatasetConstraintParams.parse_dict({"name": "base_tomography_dataset", "tv_zs": 0.1}) + assert isinstance(c, DatasetConstraintParams.BaseTomographyDatasetConstraints) + assert c.tv_zs == 0.1 + + def test_parse_base_by_type_key(self): + c = DatasetConstraintParams.parse_dict( + {"type": "base_tomography_dataset", "tv_shifts": 0.2} + ) + assert c.tv_shifts == 0.2 + + def test_unknown_name_raises(self): + with pytest.raises(ValueError): + DatasetConstraintParams.parse_dict({"name": "nope"}) + + +def _stack(nang=5, n=12, seed=0): + rng = np.random.default_rng(seed) + return (rng.random((nang, n, n)) * 10).astype(np.float32) + + +class TestTomographyPixDataset: + def test_wrong_projection_axis_raises(self): + # projections must live on axis 0 (i.e. fewer than the image dims). + bad = np.zeros((20, 5, 5), dtype=np.float32) + with pytest.raises(ValueError): + TomographyPixDataset.from_data(bad, np.linspace(-60, 60, 20).astype(np.float32)) + + def test_tilt_angles_are_negated(self): + angles = np.linspace(-40, 60, 5).astype(np.float32) + d = TomographyPixDataset.from_data(_stack(), angles) + np.testing.assert_allclose(d.tilt_angles.numpy(), -angles, atol=1e-5) + + def test_normalised_by_95th_quantile(self): + d = TomographyPixDataset.from_data(_stack(), np.linspace(-60, 60, 5).astype(np.float32)) + q95 = torch.quantile(d.tilt_stack, 0.95) + assert torch.isclose(q95, torch.tensor(1.0), atol=1e-4) + + def test_reference_idx_and_learnable_tilts(self): + # negated angles -> [40, 15, -10, -35, -60]; smallest |angle| is index 2. + angles = np.linspace(-40, 60, 5).astype(np.float32) + d = TomographyPixDataset.from_data(_stack(), angles) + assert d.reference_tilt_idx == 2 + assert d.learnable_tilts == 4 + + def test_forward_returns_dataset_value(self): + angles = np.linspace(-40, 60, 5).astype(np.float32) + d = TomographyPixDataset.from_data(_stack(nang=5, n=12), angles) + out = d.forward(0) + assert isinstance(out, DatasetValue) + assert out.target.shape == (12, 12) + assert out.tilt_angle == pytest.approx(float(-angles[0])) + + def test_to_materialises_pose_parameters(self): + d = TomographyPixDataset.from_data(_stack(), np.linspace(-60, 60, 5).astype(np.float32)) + d.to("cpu") + assert isinstance(d.z1_params, torch.nn.Parameter) + assert d.shifts_params.shape == (d.learnable_tilts, 2) + + +@requires_torch +class TestTomographyINRDataset: + def test_len_is_projections_times_pixels(self): + d = TomographyINRDataset.from_data( + _stack(nang=5, n=12), np.linspace(-60, 60, 5, dtype="f4") + ) + assert len(d) == 5 * 12 * 12 + + def test_getitem_keys(self): + d = TomographyINRDataset.from_data( + _stack(nang=5, n=12), np.linspace(-60, 60, 5, dtype="f4") + ) + item = d[0] + assert {"phi", "pixel_i", "pixel_j", "projection_idx", "target_value"} <= set(item.keys()) + + +class TestTomographyINRPretrainDataset: + def test_len_and_getitem(self): + vol = torch.rand(1, 8, 8, 8) + ds = TomographyINRPretrainDataset(pretrain_target=vol) + assert len(ds) == 8**3 + item = ds[0] + assert set(item.keys()) == {"coords", "target"} + assert item["coords"].shape == (3,) diff --git a/tests/tomography/test_object_models.py b/tests/tomography/test_object_models.py new file mode 100644 index 00000000..f9f1922c --- /dev/null +++ b/tests/tomography/test_object_models.py @@ -0,0 +1,159 @@ +"""Tests for ``quantem.tomography.object_models``. + +The constraint-parsing and ``ObjectPixelated`` tests are pure CPU. The INR / tensor-decomp +construction tests are ``requires_torch`` and follow the ``torch_device`` fixture (they must +be built on CUDA when CUDA is present; see conftest). +""" + +import numpy as np +import pytest +import torch + +from quantem.tomography.object_models import ( + ObjConstraintParams, + ObjectBase, + ObjectINR, + ObjectPixelated, + ObjectTensorDecomp, +) +from quantem.tomography.tomography_context import ReconstructionContext + +from .conftest import requires_torch + + +class TestObjConstraintParse: + def test_parse_pixelated_by_name(self): + c = ObjConstraintParams.parse_dict({"name": "obj_pixelated", "tv_vol": 0.01}) + assert isinstance(c, ObjConstraintParams.ObjPixelatedConstraints) + assert c.tv_vol == 0.01 + + def test_parse_inr_by_type_key(self): + c = ObjConstraintParams.parse_dict({"type": "obj_inr", "sparsity": 0.05}) + assert isinstance(c, ObjConstraintParams.ObjINRConstraints) + assert c.sparsity == 0.05 + + def test_parse_tensor_decomp(self): + c = ObjConstraintParams.parse_dict({"name": "obj_tensor_decomp", "tv_plane": 0.1}) + assert isinstance(c, ObjConstraintParams.ObjTensorDecompConstraints) + assert c.tv_plane == 0.1 + + def test_missing_name_raises(self): + with pytest.raises(ValueError): + ObjConstraintParams.parse_dict({"tv_vol": 0.1}) + + def test_unknown_name_raises(self): + with pytest.raises(ValueError): + ObjConstraintParams.parse_dict({"name": "obj_nope"}) + + def test_constraint_key_partitions(self): + c = ObjConstraintParams.ObjPixelatedConstraints() + assert "positivity" in c.hard_constraint_keys + assert "tv_vol" in c.soft_constraint_keys + + +class TestObjectPixelatedConstruction: + def test_from_uniform_is_zeros(self): + obj = ObjectPixelated.from_uniform(shape=(8, 8, 8), device="cpu") + assert obj.shape == (8, 8, 8) + assert torch.allclose(obj.obj, torch.zeros(8, 8, 8)) + assert obj.obj_type == "pixelated" + + def test_from_array_numpy(self): + arr = np.random.default_rng(0).random((6, 6, 6)).astype(np.float32) + obj = ObjectPixelated.from_array(arr, device="cpu") + assert obj.shape == (6, 6, 6) + assert torch.allclose(obj.obj, torch.from_numpy(arr)) + assert obj.dtype == torch.float32 + + def test_from_array_torch_is_copied(self): + t = torch.ones(4, 4, 4) + obj = ObjectPixelated.from_array(t, device="cpu") + t += 5.0 + assert torch.allclose(obj.obj, torch.ones(4, 4, 4)) # original copy untouched + + def test_obj_view_shape(self): + obj = ObjectPixelated.from_uniform(shape=(5, 6, 7), device="cpu") + assert obj.obj_view.shape == (1, 5, 6, 7) + + def test_forward_returns_obj(self): + obj = ObjectPixelated.from_array(torch.full((4, 4, 4), 2.0), device="cpu") + assert torch.allclose(obj.forward(), obj.obj) + + +class TestObjectPixelatedConstraints: + def test_positivity_clamps_negatives(self): + obj = ObjectPixelated.from_array(torch.full((4, 4, 4), -1.0), device="cpu") + obj.constraints.positivity = True + assert torch.all(obj.obj >= 0.0) + + def test_positivity_off_keeps_negatives(self): + obj = ObjectPixelated.from_array(torch.full((4, 4, 4), -1.0), device="cpu") + obj.constraints.positivity = False + assert torch.all(obj.obj < 0.0) + + def test_shrinkage_subtracts_then_floors(self): + obj = ObjectPixelated.from_array(torch.full((4, 4, 4), 1.0), device="cpu") + obj.constraints.positivity = False + obj.constraints.shrinkage = 0.25 + assert torch.allclose(obj.obj, torch.full((4, 4, 4), 0.75)) + + def test_tv_loss_scales_with_weight(self): + ctx = ReconstructionContext(obj=torch.rand(1, 1, 8, 8, 8)) + obj = ObjectPixelated.from_uniform(shape=(8, 8, 8), device="cpu") + obj.constraints.tv_vol = 1.0 + loss1 = obj.get_tv_loss(ctx) + obj.constraints.tv_vol = 2.0 + loss2 = obj.get_tv_loss(ctx) + assert torch.isclose(loss2, 2.0 * loss1) + + def test_soft_constraint_zero_when_tv_off(self): + ctx = ReconstructionContext(obj=torch.rand(1, 1, 8, 8, 8)) + obj = ObjectPixelated.from_uniform(shape=(8, 8, 8), device="cpu") + obj.constraints.tv_vol = 0.0 + assert float(obj.apply_soft_constraints(ctx).detach()) == 0.0 + + +class TestFactoryGuard: + def test_objectbase_requires_token(self): + with pytest.raises(RuntimeError): + ObjectBase(shape=(4, 4, 4)) + + +@requires_torch +class TestObjectINR: + def test_from_model_builds(self, torch_device): + from quantem.core.ml.inr import HSiren + + model = HSiren(in_features=3, out_features=1, hidden_layers=1, hidden_features=8) + obj = ObjectINR.from_model(model, shape=(16, 16, 16), device=torch_device) + assert obj.shape == (16, 16, 16) + assert obj.model is not None + + def test_optimization_parameters_single_group(self, torch_device): + from quantem.core.ml.inr import HSiren + + model = HSiren(in_features=3, out_features=1, hidden_layers=1, hidden_features=8) + obj = ObjectINR.from_model(model, shape=(16, 16, 16), device=torch_device) + groups = obj.get_optimization_parameters() + assert list(groups.keys()) == ["default"] + assert len(groups["default"]) > 0 + + +@requires_torch +class TestObjectTensorDecomp: + def _model(self, n=16): + from quantem.core.ml.models.kplanes import KPlanesTILTED + + return KPlanesTILTED( + M_features=2, resolution=(n, n, n), multiscale_res_multipliers=[1], T=2 + ) + + def test_pplr_optimization_parameter_keys(self, torch_device): + obj = ObjectTensorDecomp.from_model(self._model(), shape=(16, 16, 16), device=torch_device) + keys = set(obj.get_optimization_parameters().keys()) + assert keys == {"grids", "sigma_net", "so3"} + + def test_pretrain_not_implemented(self, torch_device): + obj = ObjectTensorDecomp.from_model(self._model(), shape=(16, 16, 16), device=torch_device) + with pytest.raises(NotImplementedError): + obj.pretrain() diff --git a/tests/tomography/test_radon.py b/tests/tomography/test_radon.py new file mode 100644 index 00000000..4f61837a --- /dev/null +++ b/tests/tomography/test_radon.py @@ -0,0 +1,111 @@ +"""Tests for the pure-torch Radon transform (``quantem.tomography.radon.radon``). + +All CPU, deterministic. Cross-checked against scikit-image where a ground truth helps. +""" + +import numpy as np +import pytest +import torch + +from quantem.tomography.radon.radon import ( + get_fourier_filter_torch, + iradon_torch, + radon_torch, +) + + +def _disk(n: int, cy: int, cx: int, r: int) -> torch.Tensor: + yy, xx = np.mgrid[0:n, 0:n] + return torch.from_numpy((((yy - cy) ** 2 + (xx - cx) ** 2) < r**2).astype(np.float32)) + + +class TestRadonShapes: + def test_2d_input_returns_angles_by_pixels(self): + img = _disk(64, 32, 32, 10) + theta = torch.linspace(0, 180, 30) + sino = radon_torch(img, theta=theta) + assert sino.shape == (30, 64) + + def test_batched_input_returns_batch_angles_pixels(self): + imgs = torch.stack([_disk(48, 24, 20, 8), _disk(48, 24, 28, 8)]) + theta = torch.linspace(0, 180, 20) + sino = radon_torch(imgs, theta=theta) + assert sino.shape == (2, 20, 48) + + def test_default_theta_is_180_angles(self): + sino = radon_torch(_disk(32, 16, 16, 6)) + assert sino.shape == (180, 32) + + def test_iradon_shapes(self): + sino = radon_torch(_disk(40, 20, 20, 8), theta=torch.linspace(0, 180, 25)) + rec = iradon_torch(sino, theta=torch.linspace(0, 180, 25)) + assert rec.shape == (40, 40) + + def test_iradon_output_size_override(self): + sino = radon_torch(_disk(40, 20, 20, 8), theta=torch.linspace(0, 180, 25)) + rec = iradon_torch(sino, theta=torch.linspace(0, 180, 25), output_size=32) + assert rec.shape == (32, 32) + + +class TestFourierFilter: + def test_even_size_ok(self): + f = get_fourier_filter_torch(64, "ramp") + assert f.shape == (1, 64) + + def test_odd_size_raises(self): + with pytest.raises(ValueError): + get_fourier_filter_torch(63, "ramp") + + def test_unknown_filter_raises(self): + with pytest.raises(ValueError): + get_fourier_filter_torch(64, "not-a-filter") + + def test_none_filter_is_all_ones(self): + f = get_fourier_filter_torch(64, None) + assert torch.allclose(f, torch.ones_like(f)) + + @pytest.mark.parametrize("name", ["ramp", "shepp-logan", "cosine", "hamming", "hann"]) + def test_named_filters_run(self, name): + f = get_fourier_filter_torch(64, name) + assert f.shape == (1, 64) + assert torch.isfinite(f).all() + + +class TestRadonBehaviour: + def test_circular_mask_zeros_corners(self): + """The forward transform masks to the inscribed circle, so corner mass is dropped.""" + img = torch.ones(32, 32) + full = img.sum() + sino = radon_torch(img, theta=torch.tensor([0.0])) + # A single 0-degree projection sums columns; total equals the masked mass < full. + assert sino.sum() < full + + def test_iradon_circle_zeros_outside(self): + sino = radon_torch(_disk(48, 24, 24, 10), theta=torch.linspace(0, 180, 30)) + rec = iradon_torch(sino, theta=torch.linspace(0, 180, 30), circle=True) + n = rec.shape[0] + yy, xx = np.mgrid[0:n, 0:n] + outside = ((yy - n // 2) ** 2 + (xx - n // 2) ** 2) > (n // 2) ** 2 + assert torch.allclose(rec[outside], torch.zeros(int(outside.sum()))) + + def test_roundtrip_recovers_structure(self): + disk = _disk(64, 32, 24, 9) + theta = torch.linspace(0, 180, 60) + rec = iradon_torch(radon_torch(disk, theta=theta), theta=theta, filter_name="ramp") + corr = np.corrcoef(disk.numpy().ravel(), rec.numpy().ravel())[0, 1] + assert corr > 0.9 + + +class TestRadonVsSkimage: + """Loose cross-check against scikit-image's reference implementation.""" + + def test_forward_matches_skimage(self): + sk = pytest.importorskip("skimage.transform") + n = 64 + disk = _disk(n, n // 2, n // 2, 12) + theta = np.linspace(0.0, 180.0, 45, endpoint=False).astype(np.float32) + ours = radon_torch(disk, theta=torch.from_numpy(theta)).numpy() # (A, N) + ref = sk.radon(disk.numpy(), theta=theta, circle=True).T # skimage: (N, A) -> (A, N) + # Different interpolation conventions; require strong agreement, not equality. + corr = np.corrcoef(ours.ravel(), ref.ravel())[0, 1] + assert corr > 0.95 diff --git a/tests/tomography/test_tomography_conventional.py b/tests/tomography/test_tomography_conventional.py new file mode 100644 index 00000000..a7cfb5e0 --- /dev/null +++ b/tests/tomography/test_tomography_conventional.py @@ -0,0 +1,72 @@ +"""End-to-end conventional (SIRT / FBP) reconstruction. + +CPU, deterministic, but marked ``slow`` because it runs a (tiny) iterative reconstruction. +Reconstructions are capped at a few iterations: the suite checks behaviour and wiring (loss +decreases, output stays physical) rather than convergence quality, so spatial agreement with +the phantom is only a loose lower bound. +""" + +import numpy as np +import pytest + +from quantem.tomography.dataset_models import TomographyPixDataset +from quantem.tomography.object_models import ObjConstraintParams, ObjectPixelated +from quantem.tomography.tomography import TomographyConventional + +pytestmark = pytest.mark.slow + + +def _build(tilt_series, tilt_angles, n): + dset = TomographyPixDataset.from_data( + tilt_series, tilt_angles, learn_shift=False, learn_tilt_axis=False + ) + obj = ObjectPixelated.from_uniform(shape=(n, n, n), device="cpu") + return TomographyConventional.from_models( + dset=dset, obj_model=obj, device="cpu", verbose=False + ) + + +class TestSIRT: + def test_loss_decreases_and_output_physical(self, phantom_volume, tilt_series, tilt_angles): + n = phantom_volume.shape[0] + tomo = _build(tilt_series, tilt_angles, n) + tomo.reconstruct( + num_iter=4, + mode="sirt", + obj_constraints=ObjConstraintParams.ObjPixelatedConstraints(positivity=True), + ) + losses = tomo.epoch_losses + assert tomo.num_epochs == 4 + assert losses[-1] < losses[0] + rec = tomo.obj_model.obj.detach().cpu().numpy() + assert np.isfinite(rec).all() + assert rec.min() >= 0.0 # positivity + + def test_recon_correlates_with_phantom(self, phantom_volume, tilt_series, tilt_angles): + n = phantom_volume.shape[0] + tomo = _build(tilt_series, tilt_angles, n) + tomo.reconstruct( + num_iter=4, + mode="sirt", + obj_constraints=ObjConstraintParams.ObjPixelatedConstraints(positivity=True), + ) + rec = tomo.obj_model.obj.detach().cpu().numpy() + corr = np.corrcoef(rec.ravel(), phantom_volume.ravel())[0, 1] + assert corr > 0.15 # loose: only a handful of iterations + + def test_obj_constraints_accepts_dict(self, phantom_volume, tilt_series, tilt_angles): + n = phantom_volume.shape[0] + tomo = _build(tilt_series, tilt_angles, n) + tomo.reconstruct(num_iter=2, mode="sirt", obj_constraints={"name": "obj_pixelated"}) + assert tomo.num_epochs == 2 + + +class TestFBP: + def test_fbp_runs_single_epoch(self, phantom_volume, tilt_series, tilt_angles): + n = phantom_volume.shape[0] + tomo = _build(tilt_series, tilt_angles, n) + tomo.reconstruct(num_iter=5, mode="fbp") + # FBP breaks after the first epoch regardless of num_iter. + assert tomo.num_epochs == 1 + rec = tomo.obj_model.obj.detach().cpu().numpy() + assert np.isfinite(rec).all() diff --git a/tests/tomography/test_tomography_inr.py b/tests/tomography/test_tomography_inr.py new file mode 100644 index 00000000..fbd5f1a1 --- /dev/null +++ b/tests/tomography/test_tomography_inr.py @@ -0,0 +1,96 @@ +"""End-to-end INR / KPlanes (tensor-decomposition) reconstruction. + +These exercise the full learned-reconstruction path (model + pose optimisation, autocast, +spawned dataloader workers), so they are gated behind ``requires_gpu`` and ``slow`` and only +run locally with ``--runslow``. Reconstructions are capped at 4 iterations; the assertion is +loss-decreases plus finite output, not convergence quality. + +The ``num_workers=2`` is required, not incidental: ``setup_dataloader`` hard-codes +``multiprocessing_context="spawn"``, which is invalid with ``num_workers=0``. +""" + +import numpy as np +import pytest +import torch + +from quantem.tomography.dataset_models import TomographyINRDataset +from quantem.tomography.object_models import ObjectTensorDecomp +from quantem.tomography.tomography import Tomography +from quantem.tomography.tomography_lite import TomographyLiteConv, TomographyLiteINR + +from .conftest import make_tilt_series, requires_gpu + +pytestmark = [requires_gpu, pytest.mark.slow] + +DEVICE = "cuda:0" + + +@pytest.fixture(scope="module") +def small_phantom(): + vol = torch.zeros(1, 24, 24, 24) + vol[0, 6:18, 6:14, 8:16] = 1.0 + angles = np.linspace(-60, 60, 7).astype(np.float32) + series = make_tilt_series(vol[0], angles) + return series, angles + + +class TestLiteINR: + def test_reconstruct_reduces_loss(self, small_phantom): + series, angles = small_phantom + tomo = TomographyLiteINR.from_dataset( + tilt_series=series, tilt_angles=angles, device=DEVICE + ) + tomo.reconstruct(num_iter=4, num_workers=2, batch_size=256, learn_pose=True) + losses = tomo.epoch_losses + assert len(losses) == 4 + assert losses[-1] < losses[0] + view = tomo.obj_model.obj_view + assert view.shape == (1, 24, 24, 24) + assert np.isfinite(view).all() + + +class TestLiteConv: + def test_smoke(self, small_phantom): + series, angles = small_phantom + tomo = TomographyLiteConv.from_dataset( + tilt_series=series, tilt_angles=angles, device=DEVICE + ) + tomo.reconstruct(num_iter=3, mode="sirt") + assert tomo.num_epochs == 3 + assert np.isfinite(tomo.obj_model.obj.detach().cpu().numpy()).all() + + +class TestKPlanes: + def test_pplr_reconstruct_reduces_loss(self, small_phantom): + from quantem.core.ml.models.kplanes import KPlanesTILTED + from quantem.core.ml.optimizer_mixin import OptimizerParams, SchedulerParams + + series, angles = small_phantom + n = series.shape[1] + model = KPlanesTILTED( + M_features=2, resolution=(n, n, n), multiscale_res_multipliers=[1], T=2 + ) + obj = ObjectTensorDecomp.from_model(model, shape=(n, n, n), device=DEVICE) + dset = TomographyINRDataset.from_data(series, angles) + tomo = Tomography.from_models(dset=dset, obj_model=obj, device=DEVICE, verbose=False) + tomo.reconstruct( + optimizer_params={ + "object": { + "grids": OptimizerParams.Adam(lr=1e-2), + "sigma_net": OptimizerParams.Adam(lr=1e-3), + "so3": OptimizerParams.Adam(lr=1e-2), + }, + "pose": OptimizerParams.Adam(lr=1e-2), + }, + scheduler_params={ + "object": SchedulerParams.CosineAnnealing(T_max=4), + "pose": SchedulerParams.CosineAnnealing(T_max=4), + }, + num_iter=4, + batch_size=256, + num_samples_per_ray=20, + num_workers=2, + ) + losses = tomo.epoch_losses + assert len(losses) == 4 + assert losses[-1] < losses[0] diff --git a/tests/tomography/test_tomography_opt.py b/tests/tomography/test_tomography_opt.py new file mode 100644 index 00000000..4ec56a13 --- /dev/null +++ b/tests/tomography/test_tomography_opt.py @@ -0,0 +1,143 @@ +"""Tests for the tomography optimizer / scheduler wiring (``TomographyOpt``). + +This is the surface the PPLR ``OptimizerParamsType`` / ``SchedulerParamsType`` refactor +touched. In particular, ``test_set_optimizers_builds_object_and_pose`` and the PPLR test +regression-guard the pose-optimizer path: ``TomographyDatasetBase.get_optimization_parameters`` +must return a ``dict[str, list[tensor]]`` (it previously returned a ``list`` and crashed +``set_optimizer`` with ``TypeError: unhashable type: 'dict'``). + +Construction only -- no forward passes -- so these run on CPU under CI. +""" + +import numpy as np +import pytest + +from quantem.core.ml.optimizer_mixin import OptimizerParams, SchedulerParams +from quantem.tomography.dataset_models import TomographyINRDataset +from quantem.tomography.object_models import ObjectINR, ObjectTensorDecomp +from quantem.tomography.tomography import Tomography + +from .conftest import requires_torch + + +def _tilts(nang=5, n=12): + rng = np.random.default_rng(0) + angles = np.linspace(-60, 60, nang).astype(np.float32) + stack = rng.random((nang, n, n)).astype(np.float32) + return stack, angles + + +def _inr_tomography(device): + from quantem.core.ml.inr import HSiren + + model = HSiren(in_features=3, out_features=1, hidden_layers=1, hidden_features=8) + obj = ObjectINR.from_model(model, shape=(16, 16, 16), device=device) + stack, angles = _tilts() + dset = TomographyINRDataset.from_data(stack, angles) + return Tomography.from_models(dset=dset, obj_model=obj, device=device, verbose=False) + + +def _td_tomography(device): + from quantem.core.ml.models.kplanes import KPlanesTILTED + + model = KPlanesTILTED( + M_features=2, resolution=(16, 16, 16), multiscale_res_multipliers=[1], T=2 + ) + obj = ObjectTensorDecomp.from_model(model, shape=(16, 16, 16), device=device) + stack, angles = _tilts() + dset = TomographyINRDataset.from_data(stack, angles) + return Tomography.from_models(dset=dset, obj_model=obj, device=device, verbose=False) + + +@requires_torch +class TestOptimizerParams: + def test_setter_getter_roundtrip(self, torch_device): + tomo = _inr_tomography(torch_device) + tomo.optimizer_params = { + "object": OptimizerParams.Adam(lr=1e-3), + "pose": OptimizerParams.Adam(lr=1e-2), + } + assert set(tomo.optimizer_params.keys()) == {"object", "pose"} + + def test_unknown_key_raises(self, torch_device): + tomo = _inr_tomography(torch_device) + with pytest.raises(ValueError): + tomo.optimizer_params = {"banana": OptimizerParams.Adam(lr=1e-3)} + + def test_set_optimizers_builds_object_and_pose(self, torch_device): + """Regression guard: the pose path must not raise (see module docstring).""" + tomo = _inr_tomography(torch_device) + tomo.optimizer_params = { + "object": OptimizerParams.Adam(lr=1e-3), + "pose": OptimizerParams.Adam(lr=1e-2), + } + tomo.set_optimizers() + assert set(tomo.optimizers.keys()) == {"object", "pose"} + + def test_current_lrs(self, torch_device): + tomo = _inr_tomography(torch_device) + tomo.optimizer_params = { + "object": OptimizerParams.Adam(lr=1e-3), + "pose": OptimizerParams.Adam(lr=1e-2), + } + tomo.set_optimizers() + lrs = tomo.get_current_lrs() + assert set(lrs.keys()) == {"object", "pose"} + assert lrs["object"] == pytest.approx(1e-3) + assert lrs["pose"] == pytest.approx(1e-2) + + def test_remove_optimizer(self, torch_device): + tomo = _inr_tomography(torch_device) + tomo.optimizer_params = { + "object": OptimizerParams.Adam(lr=1e-3), + "pose": OptimizerParams.Adam(lr=1e-2), + } + tomo.set_optimizers() + tomo.remove_optimizer("object") + assert "object" not in tomo.optimizers + assert "pose" in tomo.optimizers + + def test_pplr_object_groups(self, torch_device): + """Per-parameter LR: object optimizer carries one torch param group per key.""" + tomo = _td_tomography(torch_device) + tomo.optimizer_params = { + "object": { + "grids": OptimizerParams.Adam(lr=1e-2), + "sigma_net": OptimizerParams.Adam(lr=1e-3), + "so3": OptimizerParams.Adam(lr=1e-2), + }, + "pose": OptimizerParams.Adam(lr=1e-2), + } + tomo.set_optimizers() + assert len(tomo.optimizers["object"].param_groups) == 3 + assert "pose" in tomo.optimizers + + +@requires_torch +class TestSchedulerParams: + def test_scheduler_setter_getter(self, torch_device): + tomo = _inr_tomography(torch_device) + tomo.scheduler_params = { + "object": SchedulerParams.CosineAnnealing(T_max=10), + "pose": SchedulerParams.CosineAnnealing(T_max=10), + } + assert set(tomo.scheduler_params.keys()) == {"object", "pose"} + + def test_set_schedulers_builds(self, torch_device): + tomo = _inr_tomography(torch_device) + tomo.optimizer_params = { + "object": OptimizerParams.Adam(lr=1e-3), + "pose": OptimizerParams.Adam(lr=1e-2), + } + tomo.set_optimizers() + tomo.scheduler_params = { + "object": SchedulerParams.CosineAnnealing(T_max=10), + "pose": SchedulerParams.CosineAnnealing(T_max=10), + } + tomo.set_schedulers(tomo.scheduler_params, num_iter=10) + assert set(tomo.schedulers.keys()) == {"object", "pose"} + + def test_bad_scheduler_type_raises(self, torch_device): + tomo = _inr_tomography(torch_device) + with pytest.raises(TypeError): + tomo.obj_model.scheduler_params = 123 diff --git a/tests/tomography/test_utils.py b/tests/tomography/test_utils.py new file mode 100644 index 00000000..1fa6a47c --- /dev/null +++ b/tests/tomography/test_utils.py @@ -0,0 +1,78 @@ +"""Tests for ``quantem.tomography.utils``: 1D total-variation loss and the +differentiable ZXZ rotation operators. All CPU.""" + +import pytest +import torch + +from quantem.tomography.utils import ( + differentiable_rotz_vectorized, + rot_ZXZ, + tv_loss_1d, +) + + +class TestTVLoss1D: + def test_constant_input_is_zero(self): + assert tv_loss_1d(torch.ones(10)) == 0.0 + + def test_known_value_mean(self): + # diffs are [1, 1, 1], abs-mean = 1.0 + x = torch.tensor([0.0, 1.0, 2.0, 3.0]) + assert torch.isclose(tv_loss_1d(x, reduction="mean"), torch.tensor(1.0)) + + def test_known_value_sum(self): + x = torch.tensor([0.0, 1.0, 2.0, 3.0]) + assert torch.isclose(tv_loss_1d(x, reduction="sum"), torch.tensor(3.0)) + + def test_reduction_none_shape(self): + x = torch.zeros(2, 5) + out = tv_loss_1d(x, reduction="none") + assert out.shape == (2, 4) + + def test_bad_reduction_raises(self): + with pytest.raises(ValueError): + tv_loss_1d(torch.zeros(4), reduction="median") + + +def _block_volume(n: int = 16) -> torch.Tensor: + """(1, n, n, n) volume with an off-centre block so rotations are detectable.""" + vol = torch.zeros(1, n, n, n) + vol[0, 4:12, 4:10, 5:11] = 1.0 + return vol + + +class TestRotations: + def test_zero_rotation_is_identity(self): + vol = _block_volume() + out = rot_ZXZ(vol, 0.0, 0.0, 0.0, device="cpu") + assert torch.max(torch.abs(out - vol)) < 1e-4 + + def test_rotation_preserves_mass(self): + vol = _block_volume() + rotated = rot_ZXZ(vol, 0.0, 30.0, 0.0, device="cpu") + rel_err = abs(float(rotated.sum()) - float(vol.sum())) / float(vol.sum()) + assert rel_err < 0.02 + + def test_accepts_python_float_and_tensor_angle(self): + vol = _block_volume() + out_float = rot_ZXZ(vol, 0.0, 25.0, 0.0, device="cpu") + out_tensor = rot_ZXZ( + vol, + torch.tensor(0.0), + torch.tensor(25.0), + torch.tensor(0.0), + device="cpu", + ) + assert torch.allclose(out_float, out_tensor, atol=1e-5) + + def test_rotation_changes_volume(self): + vol = _block_volume() + rotated = rot_ZXZ(vol, 0.0, 90.0, 0.0, device="cpu") + assert torch.max(torch.abs(rotated - vol)) > 0.1 + + def test_gradient_flows_through_rotation(self): + vol = _block_volume().requires_grad_(True) + out = differentiable_rotz_vectorized(vol, torch.tensor(20.0)) + out.sum().backward() + assert vol.grad is not None + assert torch.isfinite(vol.grad).all() From 8260c5b0a984562336f0665a898c00eae0296bf7 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 1 Jun 2026 14:43:09 -0700 Subject: [PATCH 092/140] Fix latent bugs in tomography optimizer wiring, constraints, and radon step_optimizers and zero_grad_all looped over optimizer_params and stepped both the object and pose optimizers on every pass, so with pose optimization enabled each optimizer took two Adam steps per batch. Gate by key, matching step_schedulers. Also: - pass num_iter to set_schedulers on the reset_dset path so cosine/linear/ exponential schedulers get a valid T_max - scheduler_params setter no longer mutates the caller's dict - drop the non-existent tv_plane key from ObjINRConstraints.soft_constraint_keys (it crashed Constraints.__str__) - ObjectPixelated.get_tv_loss now takes TV over the trailing spatial dims, so it handles a 3D volume, obj_view's [1, D, H, W], and a multimodal [C, D, H, W] - remove an unreachable duplicate branch in TomographyINRDataset.forward - align iradon_torch's default theta with radon_torch / scikit-image (endpoint excluded) Add regression tests covering each fix. --- src/quantem/tomography/dataset_models.py | 2 - src/quantem/tomography/object_models.py | 16 ++++--- src/quantem/tomography/radon/radon.py | 3 +- src/quantem/tomography/tomography.py | 2 +- src/quantem/tomography/tomography_opt.py | 19 ++++---- tests/tomography/test_dataset_models.py | 29 +++++++++++++ tests/tomography/test_object_models.py | 40 ++++++++++++++++- tests/tomography/test_radon.py | 11 +++++ tests/tomography/test_tomography_opt.py | 55 ++++++++++++++++++++++++ 9 files changed, 156 insertions(+), 21 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 4838395d..2255636c 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -486,8 +486,6 @@ def forward(self, dummy_input: Any = None): return shifts, torch.zeros_like(z1), torch.zeros_like(z3) elif self.learn_tilt_axis: return torch.zeros_like(shifts), z1, z3 - elif self.learn_shift and self.learn_tilt_axis: - return shifts, z1, z3 else: return torch.zeros_like(shifts), torch.zeros_like(z1), torch.zeros_like(z3) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index c6b85b64..817e7ed5 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -101,7 +101,7 @@ class ObjINRConstraints(Constraints): sparsity: float = 0.0 _name: str = "obj_inr" - soft_constraint_keys = ["tv_vol", "tv_plane", "sparsity"] + soft_constraint_keys = ["tv_vol", "sparsity"] hard_constraint_keys = ["positivity", "shrinkage"] @dataclass @@ -436,12 +436,16 @@ def forward(self, coords=None) -> torch.Tensor: # --- Defining the TV loss --- def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: assert ctx.obj is not None, "ObjectPixelated requires ctx.obj to be set" - tv_d = torch.pow(ctx.obj[:, :, 1:, :, :] - ctx.obj[:, :, :-1, :, :], 2).sum() - tv_h = torch.pow(ctx.obj[:, :, :, 1:, :] - ctx.obj[:, :, :, :-1, :], 2).sum() - tv_w = torch.pow(ctx.obj[:, :, :, :, 1:] - ctx.obj[:, :, :, :, :-1], 2).sum() + # TV over the three trailing spatial dims, leaving any leading channel/batch axes + # intact. Works for a 3-D volume, obj_view's [1, D, H, W], and a multimodal + # [C, D, H, W] (channels = elemental compositions), matching the INR / tensor-decomp + # convention where the object carries a leading channel dimension. + tv_d = torch.pow(ctx.obj[..., 1:, :, :] - ctx.obj[..., :-1, :, :], 2).sum() + tv_h = torch.pow(ctx.obj[..., :, 1:, :] - ctx.obj[..., :, :-1, :], 2).sum() + tv_w = torch.pow(ctx.obj[..., :, :, 1:] - ctx.obj[..., :, :, :-1], 2).sum() tv_loss = tv_d + tv_h + tv_w - return tv_loss * self.constraints.tv_vol / (torch.prod(torch.tensor(ctx.obj.shape))) + return tv_loss * self.constraints.tv_vol / ctx.obj.numel() # --- Helper Functions --- def to(self, device: str | torch.device): @@ -934,7 +938,7 @@ def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: Gets the summed total variational loss for the tensor decomposition model. _get_plane_tv_loss: Total-variation across the planes. - _get_volume_tv_loss: Isotropic volume TV + _get_volume_tv_loss: Isotropic volume TV """ assert ctx.coords is not None, "Coordinates must be provided for TV loss" assert ctx.pred is not None, "Prediction must be provided for TV loss" diff --git a/src/quantem/tomography/radon/radon.py b/src/quantem/tomography/radon/radon.py index 8510e570..ca133db3 100644 --- a/src/quantem/tomography/radon/radon.py +++ b/src/quantem/tomography/radon/radon.py @@ -91,7 +91,8 @@ def iradon_torch( sinograms = sinograms.unsqueeze(0) B, A, N = sinograms.shape device = device or sinograms.device - theta = theta if theta is not None else torch.linspace(0, 180, steps=A, device=device) + # Match radon_torch / scikit-image: A angles evenly spanning [0, 180) (endpoint excluded). + theta = theta if theta is not None else torch.linspace(0, 180, steps=A + 1, device=device)[:-1] if output_size is None: output_size = N if circle else int((N**2 / 2) ** 0.5) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 8976272e..f86095ad 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -143,7 +143,7 @@ def reconstruct( self.set_optimizers() if scheduler_params is not None: self.scheduler_params = scheduler_params - self.set_schedulers(self.scheduler_params) + self.set_schedulers(self.scheduler_params, num_iter=num_iter) self.dataloader, self.sampler, self.val_dataloader, self.val_sampler = ( self.setup_dataloader( diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index cf990379..7f98fabd 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -114,7 +114,8 @@ def scheduler_params(self) -> dict[str, SchedulerParamsType]: @scheduler_params.setter def scheduler_params(self, d: dict): """Set the scheduler parameters.""" - self._scheduler_params = d.copy() if d else {} + d = dict(d) if d else {} + self._scheduler_params = d.copy() for key in self.OPTIMIZABLE_VALS: if key not in d: @@ -152,21 +153,21 @@ def set_schedulers( def step_optimizers(self): for key in self.optimizer_params.keys(): - if self.obj_model.has_optimizer(): - self.obj_model.step_optimizer() - if self.dset.has_optimizer(): - self.dset.step_optimizer() if key not in self.OPTIMIZABLE_VALS: raise ValueError(f"Unknown optimization key: {key}") + if key == "object" and self.obj_model.has_optimizer(): + self.obj_model.step_optimizer() + elif key == "pose" and self.dset.has_optimizer(): + self.dset.step_optimizer() def zero_grad_all(self): for key in self.optimizer_params.keys(): - if self.obj_model.has_optimizer(): - self.obj_model.zero_optimizer_grad() - if self.dset.has_optimizer(): - self.dset.zero_optimizer_grad() if key not in self.OPTIMIZABLE_VALS: raise ValueError(f"Unknown optimization key: {key}") + if key == "object" and self.obj_model.has_optimizer(): + self.obj_model.zero_optimizer_grad() + elif key == "pose" and self.dset.has_optimizer(): + self.dset.zero_optimizer_grad() def step_schedulers(self, loss: float | None = None): for key in self.scheduler_params.keys(): diff --git a/tests/tomography/test_dataset_models.py b/tests/tomography/test_dataset_models.py index 78ded427..9ebd99dc 100644 --- a/tests/tomography/test_dataset_models.py +++ b/tests/tomography/test_dataset_models.py @@ -95,6 +95,35 @@ def test_getitem_keys(self): item = d[0] assert {"phi", "pixel_i", "pixel_j", "projection_idx", "target_value"} <= set(item.keys()) + @pytest.mark.parametrize( + "learn_shift,learn_tilt_axis", + [(True, True), (True, False), (False, True), (False, False)], + ) + def test_forward_gates_shift_and_tilt(self, learn_shift, learn_tilt_axis): + """``forward`` zeros the disabled component and passes the enabled one through. + + Guards the gating after removing the unreachable duplicate branch: shifts are + controlled by ``learn_shift``; the z1/z3 Euler angles by ``learn_tilt_axis``. + """ + d = TomographyINRDataset.from_data( + _stack(nang=5, n=12), + np.linspace(-60, 60, 5, dtype="f4"), + learn_shift=learn_shift, + learn_tilt_axis=learn_tilt_axis, + ) + d.to("cpu") + # Make every pose parameter non-zero so the gating is observable by value. + for p in (d.z1_params, d.z3_params, d.shifts_params): + p.data.fill_(1.0) + d._z1_ref = torch.ones_like(d._z1_ref) + d._z3_ref = torch.ones_like(d._z3_ref) + d._shifts_ref = torch.ones_like(d._shifts_ref) + + shifts, z1, z3 = d.forward(None) + assert bool(shifts.any()) == learn_shift + assert bool(z1.any()) == learn_tilt_axis + assert bool(z3.any()) == learn_tilt_axis + class TestTomographyINRPretrainDataset: def test_len_and_getitem(self): diff --git a/tests/tomography/test_object_models.py b/tests/tomography/test_object_models.py index f9f1922c..b200d2b9 100644 --- a/tests/tomography/test_object_models.py +++ b/tests/tomography/test_object_models.py @@ -50,6 +50,22 @@ def test_constraint_key_partitions(self): assert "positivity" in c.hard_constraint_keys assert "tv_vol" in c.soft_constraint_keys + def test_constraint_keys_are_real_fields(self): + """Regression: every soft/hard key must be an attribute, so __str__ never raises. + + ``ObjINRConstraints`` previously listed ``tv_plane`` (a field it does not have), which + made ``str(constraints)`` blow up with AttributeError via ``Constraints.__str__``. + """ + for cls in ( + ObjConstraintParams.ObjPixelatedConstraints, + ObjConstraintParams.ObjINRConstraints, + ObjConstraintParams.ObjTensorDecompConstraints, + ): + c = cls() + for key in c.soft_constraint_keys + c.hard_constraint_keys: + assert hasattr(c, key), f"{cls.__name__} lists missing key {key!r}" + assert isinstance(str(c), str) # must not raise + class TestObjectPixelatedConstruction: def test_from_uniform_is_zeros(self): @@ -98,7 +114,8 @@ def test_shrinkage_subtracts_then_floors(self): assert torch.allclose(obj.obj, torch.full((4, 4, 4), 0.75)) def test_tv_loss_scales_with_weight(self): - ctx = ReconstructionContext(obj=torch.rand(1, 1, 8, 8, 8)) + # ctx.obj is the 3-D pixelated volume (D, H, W), matching ObjectPixelated._obj. + ctx = ReconstructionContext(obj=torch.rand(8, 8, 8)) obj = ObjectPixelated.from_uniform(shape=(8, 8, 8), device="cpu") obj.constraints.tv_vol = 1.0 loss1 = obj.get_tv_loss(ctx) @@ -106,8 +123,27 @@ def test_tv_loss_scales_with_weight(self): loss2 = obj.get_tv_loss(ctx) assert torch.isclose(loss2, 2.0 * loss1) + @pytest.mark.parametrize( + "shape", + [ + (8, 8, 8), # bare 3-D volume + (1, 8, 8, 8), # obj_view layout [C=1, D, H, W] + (3, 8, 8, 8), # multimodal [C, D, H, W] (e.g. 3 elemental channels) + ], + ) + def test_tv_loss_rank_agnostic_finite_and_positive(self, shape): + """Regression: get_tv_loss takes TV over the trailing spatial dims for any leading + channel/batch axes -- not the old 5-D-only indexing. Supports multimodal [C, ...].""" + ctx = ReconstructionContext(obj=torch.rand(*shape)) + obj = ObjectPixelated.from_uniform(shape=(8, 8, 8), device="cpu") + obj.constraints.tv_vol = 1.0 + loss = obj.get_tv_loss(ctx) + assert loss.ndim == 0 + assert torch.isfinite(loss) + assert loss > 0.0 # random volume has non-zero total variation + def test_soft_constraint_zero_when_tv_off(self): - ctx = ReconstructionContext(obj=torch.rand(1, 1, 8, 8, 8)) + ctx = ReconstructionContext(obj=torch.rand(8, 8, 8)) obj = ObjectPixelated.from_uniform(shape=(8, 8, 8), device="cpu") obj.constraints.tv_vol = 0.0 assert float(obj.apply_soft_constraints(ctx).detach()) == 0.0 diff --git a/tests/tomography/test_radon.py b/tests/tomography/test_radon.py index 4f61837a..41516f03 100644 --- a/tests/tomography/test_radon.py +++ b/tests/tomography/test_radon.py @@ -95,6 +95,17 @@ def test_roundtrip_recovers_structure(self): corr = np.corrcoef(disk.numpy().ravel(), rec.numpy().ravel())[0, 1] assert corr > 0.9 + def test_default_theta_roundtrip_is_consistent(self): + """radon and iradon must share an angle convention when ``theta`` is defaulted. + + iradon's default previously included the 180-degree endpoint while radon's did not, + so a default-theta round-trip sampled mismatched angles. + """ + disk = _disk(64, 32, 28, 10) + rec = iradon_torch(radon_torch(disk), filter_name="ramp") # both default theta + corr = np.corrcoef(disk.numpy().ravel(), rec.numpy().ravel())[0, 1] + assert corr > 0.9 + class TestRadonVsSkimage: """Loose cross-check against scikit-image's reference implementation.""" diff --git a/tests/tomography/test_tomography_opt.py b/tests/tomography/test_tomography_opt.py index 4ec56a13..6c597c90 100644 --- a/tests/tomography/test_tomography_opt.py +++ b/tests/tomography/test_tomography_opt.py @@ -112,6 +112,54 @@ def test_pplr_object_groups(self, torch_device): assert len(tomo.optimizers["object"].param_groups) == 3 assert "pose" in tomo.optimizers + def test_step_optimizers_steps_each_once(self, torch_device, monkeypatch): + """Regression: with object+pose, each optimizer must step exactly once per call. + + ``step_optimizers`` previously looped over both keys and stepped *both* optimizers on + every pass, so each took two Adam steps per batch. + """ + tomo = _inr_tomography(torch_device) + tomo.optimizer_params = { + "object": OptimizerParams.Adam(lr=1e-3), + "pose": OptimizerParams.Adam(lr=1e-2), + } + tomo.set_optimizers() + assert set(tomo.optimizers.keys()) == {"object", "pose"} # both optimizers live + + counts = {"object": 0, "pose": 0} + monkeypatch.setattr( + tomo.obj_model, + "step_optimizer", + lambda: counts.__setitem__("object", counts["object"] + 1), + ) + monkeypatch.setattr( + tomo.dset, "step_optimizer", lambda: counts.__setitem__("pose", counts["pose"] + 1) + ) + tomo.step_optimizers() + assert counts == {"object": 1, "pose": 1} + + def test_zero_grad_all_zeros_each_once(self, torch_device, monkeypatch): + """Companion to the step regression: zero_grad_all must touch each optimizer once.""" + tomo = _inr_tomography(torch_device) + tomo.optimizer_params = { + "object": OptimizerParams.Adam(lr=1e-3), + "pose": OptimizerParams.Adam(lr=1e-2), + } + tomo.set_optimizers() + counts = {"object": 0, "pose": 0} + monkeypatch.setattr( + tomo.obj_model, + "zero_optimizer_grad", + lambda: counts.__setitem__("object", counts["object"] + 1), + ) + monkeypatch.setattr( + tomo.dset, + "zero_optimizer_grad", + lambda: counts.__setitem__("pose", counts["pose"] + 1), + ) + tomo.zero_grad_all() + assert counts == {"object": 1, "pose": 1} + @requires_torch class TestSchedulerParams: @@ -141,3 +189,10 @@ def test_bad_scheduler_type_raises(self, torch_device): tomo = _inr_tomography(torch_device) with pytest.raises(TypeError): tomo.obj_model.scheduler_params = 123 + + def test_setter_does_not_mutate_caller_dict(self, torch_device): + """Regression: the setter must not inject missing keys into the caller's dict.""" + tomo = _inr_tomography(torch_device) + d = {"object": SchedulerParams.CosineAnnealing(T_max=10)} + tomo.scheduler_params = d + assert set(d.keys()) == {"object"} # "pose" must not have been added to the input From fd2b2816e0ae1a45940618c47dc563dd14f833ec Mon Sep 17 00:00:00 2001 From: cophus Date: Tue, 2 Dec 2025 13:11:35 -0800 Subject: [PATCH 093/140] adding support for 3d dm4 files --> 4dstem --- src/quantem/core/io/file_readers.py | 269 +++++++++++++++++++++------- 1 file changed, 204 insertions(+), 65 deletions(-) diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index 4fe72645..440a74fe 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -4,6 +4,7 @@ from typing import Any import h5py +import numpy as np from quantem.core.datastructures import Dataset as Dataset from quantem.core.datastructures import Dataset2d as Dataset2d @@ -15,67 +16,136 @@ def read_4dstem( file_path: str | PathLike, file_type: str | None = None, dataset_index: int | None = None, - hot_pixel_filter: bool = False, + scan_length: int | None = None, + scan_axis: int = 0, + transpose_scan_axes: bool = False, **kwargs, ) -> Dataset4dstem: """ - File reader for 4D-STEM data + File reader for 4D-STEM data. Parameters ---------- - file_path: str | PathLike - Path to data - file_type: str - The type of file reader needed. See rosettasciio for supported formats + file_path : str | PathLike + Path to data. + file_type : str, optional + The type of file reader needed. See RosettaSciIO for supported formats: https://hyperspy.org/rosettasciio/supported_formats/index.html - dataset_index: int, optional + dataset_index : int, optional Index of the dataset to load if file contains multiple datasets. If None, automatically selects the first 4D dataset found. - hot_pixel_filter: bool, optional - If True, detect and replace hot detector pixels immediately after - loading using `quantem.core.utils.filter.filter_hot_pixels` with its - default parameters. For custom thresholds, call `filter_hot_pixels` - directly on the array. - **kwargs: dict - Additional keyword arguments to pass to the file reader. - - Other Parameters - ---------------- - name : str | None, optional - A descriptive name for the dataset. If None, defaults to "4D-STEM dataset" - origin : NDArray | tuple | list | float | int | None, optional - The origin coordinates for each dimension in calibrated units. If None, defaults to zeros - sampling : NDArray | tuple | list | float | int | None, optional - The sampling rate/spacing for each dimension. If None, defaults to ones - units : list[str] | tuple | list | None, optional - Units for each dimension. If None, defaults to ["pixels"] * 4 - signal_units : str, optional - Units for the array values, by default "arb. units" + If no 4D dataset is found but a 3D stack exists, a 3D dataset can be + interpreted as 4D if `scan_length` is provided. + scan_length : int, optional + For 3D datasets shaped (n_frames, ny, nx) (after possibly moving the + scan axis to the front), interpret the data as a raster scan with shape + (scan_y, scan_x, ny, nx), where scan_y = n_frames // scan_length and + scan_x = scan_length. Required if you want to treat a 3D stack as 4D. + scan_axis : int, default 0 + Which axis of a 3D dataset is the scan/time axis before reshaping. + Must be 0 or 1. The specified axis is moved to axis 0 before the + (scan_y, scan_x) reshape. + transpose_scan_axes : bool, default False + Only used when interpreting a 3D dataset as 4D via `scan_length`. + If True, transpose the scan axes after reshaping so that + (scan_y, scan_x) -> (scan_x, scan_y). This effectively swaps the + interpretation of scan rows and columns in the final 4D array. + + **kwargs : dict + Additional keyword arguments to pass to the Dataset4dstem constructor. Returns - -------- + ------- Dataset4dstem - - Examples - -------- - Load a raw Arina 4D-STEM master file: - - >>> from quantem.core.io import read_4dstem - >>> ds = read_4dstem( - ... '/path/to/gold_013_master.h5', - ... file_type='arina', - ... ) - >>> ds.array.shape - (256, 256, 192, 192) - - Enable the hot pixel filter to repair stuck detector pixels on load: - - >>> ds = read_4dstem( - ... '/path/to/gold_013_master.h5', - ... file_type='arina', - ... hot_pixel_filter=True, - ... ) """ + + def _reshape_3d_to_4d( + imported_data: dict, + *, + dataset_index_local: int | None, + scan_length_local: int, + scan_axis_local: int, + transpose_scan_axes_local: bool, + ) -> dict: + data = imported_data["data"] + if data.ndim != 3: + raise ValueError( + f"Expected 3D data to reshape, got ndim={data.ndim} " + f"with shape {data.shape}" + ) + + if scan_axis_local not in (0, 1): + raise ValueError(f"scan_axis must be 0 or 1, got {scan_axis_local}") + + # Move scan axis to front so it becomes the frame axis + if scan_axis_local != 0: + data = np.moveaxis(data, scan_axis_local, 0) + + n_frames, ny, nx = data.shape + + if scan_length_local <= 0: + raise ValueError(f"scan_length must be positive, got {scan_length_local}") + if n_frames % scan_length_local != 0: + raise ValueError( + f"scan_length={scan_length_local} is not compatible with n_frames={n_frames}; " + f"n_frames % scan_length = {n_frames % scan_length_local}" + ) + + scan_y = n_frames // scan_length_local + scan_x = scan_length_local + + data_4d = data.reshape(scan_y, scan_x, ny, nx) + + if transpose_scan_axes_local: + data_4d = np.transpose(data_4d, (1, 0, 2, 3)) + scan_y, scan_x = scan_x, scan_y + + old_axes = imported_data.get("axes", None) + if old_axes is None or len(old_axes) != 3: + raise ValueError( + "Expected 3 axes for 3D data when reshaping to 4D; " + f"got axes={old_axes}" + ) + + ax_scan_y = { + "scale": 1.0, + "offset": 0.0, + "units": "pixels", + "name": "scan_y", + } + ax_scan_x = { + "scale": 1.0, + "offset": 0.0, + "units": "pixels", + "name": "scan_x", + } + + ax_qy = dict(old_axes[1]) + ax_qx = dict(old_axes[2]) + + imported_data_4d = imported_data.copy() + imported_data_4d["data"] = data_4d + imported_data_4d["axes"] = [ax_scan_y, ax_scan_x, ax_qy, ax_qx] + + original_shape = imported_data["data"].shape + new_shape = data_4d.shape + if dataset_index_local is not None: + print( + f"Using 3D dataset {dataset_index_local} with shape {original_shape} " + f"interpreted as 4D with shape={new_shape} " + f"(scan_axis={scan_axis_local}, scan_length={scan_length_local}, " + f"transpose_scan_axes={transpose_scan_axes_local})." + ) + else: + print( + f"Using 3D dataset with shape {original_shape} " + f"interpreted as 4D with shape={new_shape} " + f"(scan_axis={scan_axis_local}, scan_length={scan_length_local}, " + f"transpose_scan_axes={transpose_scan_axes_local})." + ) + + return imported_data_4d + if file_type is None: file_type = Path(file_path).suffix.lower().lstrip(".") @@ -85,31 +155,100 @@ def read_4dstem( name_override = kwargs.pop("name", None) file_reader = importlib.import_module(f"rsciio.{file_type}").file_reader - data_list = file_reader(file_path, **kwargs) + data_list = file_reader(file_path) + + if not data_list: + raise ValueError(f"No datasets returned by rsciio.{file_type} for '{file_path}'") - # If specific index provided, use it + # Case 1: dataset_index specified explicitly if dataset_index is not None: imported_data = data_list[dataset_index] - if imported_data["data"].ndim != 4: + ndim = imported_data["data"].ndim + + if ndim == 4: + # Use 4D as-is + pass + elif ndim == 3: + if scan_length is None: + raise ValueError( + f"Dataset at index {dataset_index} is 3D (shape={imported_data['data'].shape}). " + "To interpret it as 4D-STEM, please provide scan_length." + ) + imported_data = _reshape_3d_to_4d( + imported_data, + dataset_index_local=dataset_index, + scan_length_local=scan_length, + scan_axis_local=scan_axis, + transpose_scan_axes_local=transpose_scan_axes, + ) + else: raise ValueError( - f"Dataset at index {dataset_index} has {imported_data['data'].ndim} dimensions, " - f"expected 4D. Shape: {imported_data['data'].shape}" + f"Dataset at index {dataset_index} has ndim={ndim}, " + f"expected 4D or 3D. Shape: {imported_data['data'].shape}" ) + else: - # Automatically find first 4D dataset + # Case 2: auto-select dataset four_d_datasets = [(i, d) for i, d in enumerate(data_list) if d["data"].ndim == 4] - if len(four_d_datasets) == 0: - print(f"No 4D datasets found in {file_path}. Available datasets:") - for i, d in enumerate(data_list): - print(f" Dataset {i}: shape {d['data'].shape}, ndim={d['data'].ndim}") - raise ValueError("No 4D dataset found in file") - - dataset_index, imported_data = four_d_datasets[0] - - if len(data_list) > 1: - print( - f"File contains {len(data_list)} dataset(s). Using dataset {dataset_index} with shape {imported_data['data'].shape}" + if four_d_datasets: + dataset_index, imported_data = four_d_datasets[0] + if len(data_list) > 1: + print( + f"File contains {len(data_list)} dataset(s). Using 4D dataset " + f"{dataset_index} with shape {imported_data['data'].shape}" + ) + else: + three_d_datasets = [(i, d) for i, d in enumerate(data_list) if d["data"].ndim == 3] + + if not three_d_datasets: + print(f"No 4D datasets found in {file_path}. Available datasets:") + for i, d in enumerate(data_list): + print(f" Dataset {i}: shape {d['data'].shape}, ndim={d['data'].ndim}") + raise ValueError("No 4D or 3D dataset found in file") + + if scan_length is None: + print(f"No 4D datasets found in {file_path}. Available datasets:") + for i, d in enumerate(data_list): + print(f" Dataset {i}: shape {d['data'].shape}, ndim={d['data'].ndim}") + raise ValueError( + "File contains only 3D datasets. To interpret one as 4D-STEM, " + "please specify scan_length so that n_frames % scan_length == 0." + ) + + # Choose first 3D dataset compatible with scan_length along scan_axis + candidates: list[tuple[int, dict]] = [] + for i, d in three_d_datasets: + shape = d["data"].shape + if scan_axis < 0 or scan_axis > 2: + raise ValueError(f"scan_axis must be in [0, 2] for 3D data, got {scan_axis}") + n_frames_axis = shape[scan_axis] + if n_frames_axis % scan_length == 0: + candidates.append((i, d)) + + if not candidates: + print(f"3D datasets in {file_path}:") + for i, d in three_d_datasets: + print(f" Dataset {i}: shape {d['data'].shape}") + raise ValueError( + f"No 3D dataset has length along scan_axis={scan_axis} " + f"divisible by scan_length={scan_length}." + ) + + dataset_index, imported_data = candidates[0] + if len(candidates) > 1: + print( + f"Multiple 3D datasets compatible with scan_length={scan_length} " + f"along scan_axis={scan_axis}. Using dataset {dataset_index} " + f"with shape {imported_data['data'].shape}" + ) + + imported_data = _reshape_3d_to_4d( + imported_data, + dataset_index_local=dataset_index, + scan_length_local=scan_length, + scan_axis_local=scan_axis, + transpose_scan_axes_local=transpose_scan_axes, ) imported_axes = imported_data["axes"] From ea7c35bf1e79164348332ac311c7df98e4c60415 Mon Sep 17 00:00:00 2001 From: cophus Date: Wed, 3 Dec 2025 09:11:15 -0800 Subject: [PATCH 094/140] initial construction of polar4dstem class --- src/quantem/core/datastructures/__init__.py | 1 + .../core/datastructures/dataset4dstem.py | 5 + .../core/datastructures/polar4dstem.py | 330 ++++++++++++++++++ 3 files changed, 336 insertions(+) create mode 100644 src/quantem/core/datastructures/polar4dstem.py diff --git a/src/quantem/core/datastructures/__init__.py b/src/quantem/core/datastructures/__init__.py index dfb5b47a..ac8f3d64 100644 --- a/src/quantem/core/datastructures/__init__.py +++ b/src/quantem/core/datastructures/__init__.py @@ -2,6 +2,7 @@ from quantem.core.datastructures.vector import Vector as Vector from quantem.core.datastructures.dataset4dstem import Dataset4dstem as Dataset4dstem +from quantem.core.datastructures.polar4dstem import Polar4dstem as Polar4dstem from quantem.core.datastructures.dataset4d import Dataset4d as Dataset4d from quantem.core.datastructures.dataset3d import Dataset3d as Dataset3d from quantem.core.datastructures.dataset2d import Dataset2d as Dataset2d diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 004db427..8beb31cc 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -8,6 +8,8 @@ from quantem.core.datastructures.dataset2d import Dataset2d from quantem.core.datastructures.dataset4d import Dataset4d +from quantem.core.datastructures.polar4dstem import dataset4dstem_polar_transform + from quantem.core.utils.validators import ensure_valid_array from quantem.core.visualization import show_2d from quantem.core.visualization.visualization_utils import ScalebarConfig @@ -798,3 +800,6 @@ def median_filter_masked_pixels(self, mask: np.ndarray, kernel_width: int = 3): self.array[:, :, index_x, index_y] = np.median( self.array[:, :, x_min:x_max, y_min:y_max], axis=(2, 3) ) + + + polar_transform = dataset4dstem_polar_transform \ No newline at end of file diff --git a/src/quantem/core/datastructures/polar4dstem.py b/src/quantem/core/datastructures/polar4dstem.py new file mode 100644 index 00000000..848469ec --- /dev/null +++ b/src/quantem/core/datastructures/polar4dstem.py @@ -0,0 +1,330 @@ +import numpy as np +from numpy.typing import NDArray +from typing import Any, TYPE_CHECKING + +from quantem.core.datastructures.dataset4d import Dataset4d +# from quantem.core.datastructures.dataset4dstem import Dataset4dstem + +if TYPE_CHECKING: + from .dataset4dstem import Dataset4dstem + +class Polar4dstem(Dataset4d): + """4D-STEM dataset in polar coordinates (scan_y, scan_x, phi, r).""" + + def __init__( + self, + array: NDArray | Any, + name: str, + origin: NDArray | tuple | list | float | int, + sampling: NDArray | tuple | list | float | int, + units: list[str] | tuple | list, + signal_units: str = "arb. units", + metadata: dict | None = None, + _token: object | None = None, + ): + if metadata is None: + metadata = {} + mdata_keys_polar = [ + "polar_radial_min", + "polar_radial_max", + "polar_radial_step", + "polar_num_annular_bins", + "polar_two_fold_rotation_symmetry", + "polar_origin_row", + "polar_origin_col", + "polar_ellipse_params", + ] + for k in mdata_keys_polar: + if k not in metadata: + metadata[k] = None + + super().__init__( + array=array, + name=name, + origin=origin, + sampling=sampling, + units=units, + signal_units=signal_units, + metadata=metadata, + _token=_token, + ) + + @classmethod + def from_array( + cls, + array: NDArray | Any, + name: str | None = None, + origin: NDArray | tuple | list | float | int | None = None, + sampling: NDArray | tuple | list | float | int | None = None, + units: list[str] | tuple | list | None = None, + signal_units: str = "arb. units", + metadata: dict | None = None, + ) -> "Polar4dstem": + array = ensure_valid_array(array, ndim=4) + if origin is None: + origin = np.zeros(4) + if sampling is None: + sampling = np.ones(4) + if units is None: + units = ["pixels", "pixels", "deg", "pixels"] + if metadata is None: + metadata = {} + return cls( + array=array, + name=name if name is not None else "Polar 4D-STEM dataset", + origin=origin, + sampling=sampling, + units=units, + signal_units=signal_units, + metadata=metadata, + _token=cls._token, + ) + + @property + def n_phi(self) -> int: + return int(self.array.shape[2]) + + @property + def n_r(self) -> int: + return int(self.array.shape[3]) + + +def dataset4dstem_polar_transform( + self: "Dataset4dstem", + origin_row: float | NDArray, + origin_col: float | NDArray, + ellipse_params: tuple[float, float, float] | None = None, + num_annular_bins: int = 180, + radial_min: float = 0.0, + radial_max: float | None = None, + radial_step: float = 1.0, + two_fold_rotation_symmetry: bool = False, + name: str | None = None, + signal_units: str | None = None, +) -> Polar4dstem: + """Return a Polar4dstem with shape (scan_y, scan_x, phi, r).""" + if self.array.ndim != 4: + raise ValueError("polar_transform requires a 4D-STEM dataset (ndim=4).") + + scan_y, scan_x, ny, nx = self.array.shape + + mapping = _precompute_polar_mapping( + ny=ny, + nx=nx, + origin_row=float(origin_row), + origin_col=float(origin_col), + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + + result_dtype = np.result_type(self.array.dtype, np.float32) + out = np.empty( + (scan_y, scan_x, mapping["n_phi"], mapping["n_r"]), + dtype=result_dtype, + ) + + for iy in range(scan_y): + for ix in range(scan_x): + out[iy, ix] = _apply_polar_mapping_single( + self.array[iy, ix], + mapping, + dtype=result_dtype, + ) + + phi_step_deg = mapping["phi_step"] * 180.0 / np.pi + phi_units = "deg" + radial_units = self.units[-1] + + sampling = np.array( + [ + self.sampling[0], + self.sampling[1], + phi_step_deg, + self.sampling[-1] * mapping["radial_step"], + ], + dtype=float, + ) + origin = np.array( + [ + self.origin[0], + self.origin[1], + 0.0, + self.sampling[-1] * mapping["radial_min"], + ], + dtype=float, + ) + units = [ + self.units[0], + self.units[1], + phi_units, + radial_units, + ] + + metadata = dict(self.metadata) + metadata.update( + { + "polar_radial_min": mapping["radial_min"], + "polar_radial_max": mapping["radial_max"], + "polar_radial_step": mapping["radial_step"], + "polar_num_annular_bins": mapping["n_phi"], + "polar_two_fold_rotation_symmetry": two_fold_rotation_symmetry, + "polar_origin_row": float(origin_row), + "polar_origin_col": float(origin_col), + "polar_ellipse_params": tuple(ellipse_params) + if ellipse_params is not None + else None, + } + ) + + return Polar4dstem( + array=out, + name=name if name is not None else f"{self.name}_polar", + origin=origin, + sampling=sampling, + units=units, + signal_units=signal_units if signal_units is not None else self.signal_units, + metadata=metadata, + _token=Polar4dstem._token, + ) + + +def _precompute_polar_mapping( + ny: int, + nx: int, + origin_row: float, + origin_col: float, + ellipse_params: tuple[float, float, float] | None, + num_annular_bins: int, + radial_min: float, + radial_max: float | None, + radial_step: float, + two_fold_rotation_symmetry: bool, +) -> dict[str, Any]: + origin_row = float(origin_row) + origin_col = float(origin_col) + annular_range = np.pi if two_fold_rotation_symmetry else 2.0 * np.pi + + rows = np.arange(ny, dtype=np.float64) + cols = np.arange(nx, dtype=np.float64) + cc, rr = np.meshgrid(cols, rows, indexing="xy") + x = cc - origin_col + y = rr - origin_row + + if ellipse_params is None: + rr_pix = np.sqrt(x * x + y * y) + tt = np.mod(np.arctan2(y, x), annular_range) + else: + if len(ellipse_params) != 3: + raise ValueError("ellipse_params must be a length-3 tuple (a, b, theta_deg).") + a, b, theta_deg = ellipse_params + theta = np.deg2rad(theta_deg) + cos_t = np.cos(theta) + sin_t = np.sin(theta) + xc = x * cos_t + y * sin_t + yc = (y * cos_t - x * sin_t) * (a / b) + rr_pix = (b / a) * np.hypot(xc, yc) + tt = np.mod(np.arctan2(yc, xc) + theta, annular_range) + + if radial_step <= 0: + raise ValueError("radial_step must be > 0.") + radial_min = float(radial_min) + + if radial_max is None: + radial_max_eff = float(rr_pix.max()) + else: + radial_max_eff = float(radial_max) + if radial_max_eff <= radial_min + radial_step: + radial_max_eff = radial_min + radial_step + + radial_bins = np.arange(radial_min, radial_max_eff, radial_step, dtype=np.float64) + n_r = radial_bins.size + if n_r < 1: + raise ValueError("No radial bins defined. Check radial_min, radial_max, and radial_step.") + + n_phi = int(num_annular_bins) + if n_phi < 1: + raise ValueError("num_annular_bins must be >= 1.") + phi_step = annular_range / n_phi + + r_bin = (rr_pix - radial_min) / radial_step + t_bin = tt / phi_step + + r0 = np.floor(r_bin).astype(np.int64) + t0 = np.floor(t_bin).astype(np.int64) + dr = (r_bin - r0).astype(np.float64) + dt = (t_bin - t0).astype(np.float64) + + valid = (r0 >= 0) & (r0 < n_r - 1) + t0 = np.clip(t0, 0, n_phi - 1) + + flat_valid = valid.ravel() + r0v = r0.ravel()[flat_valid] + t0v = t0.ravel()[flat_valid] + drv = dr.ravel()[flat_valid] + dtv = dt.ravel()[flat_valid] + + n_bins = n_phi * n_r + idx00 = r0v + n_r * t0v + idx01 = r0v + n_r * ((t0v + 1) % n_phi) + idx10 = (r0v + 1) + n_r * t0v + idx11 = (r0v + 1) + n_r * ((t0v + 1) % n_phi) + + w00 = (1.0 - drv) * (1.0 - dtv) + w01 = (1.0 - drv) * dtv + w10 = drv * (1.0 - dtv) + w11 = drv * dtv + + weights_sum = np.bincount(idx00, weights=w00, minlength=n_bins) + weights_sum += np.bincount(idx01, weights=w01, minlength=n_bins) + weights_sum += np.bincount(idx10, weights=w10, minlength=n_bins) + weights_sum += np.bincount(idx11, weights=w11, minlength=n_bins) + weights_sum = weights_sum.reshape(n_phi, n_r) + + weights_inv = np.zeros_like(weights_sum, dtype=np.float64) + mask_bins = weights_sum > 0 + weights_inv[mask_bins] = 1.0 / weights_sum[mask_bins] + + return { + "flat_valid": flat_valid, + "idx00": idx00, + "idx01": idx01, + "idx10": idx10, + "idx11": idx11, + "w00": w00, + "w01": w01, + "w10": w10, + "w11": w11, + "weights_inv": weights_inv, + "n_phi": n_phi, + "n_r": n_r, + "radial_bins": radial_bins, + "phi_step": phi_step, + "annular_range": annular_range, + "radial_min": radial_min, + "radial_max": radial_min + radial_step * n_r, + "radial_step": radial_step, + } + + +def _apply_polar_mapping_single( + image: NDArray, + mapping: dict[str, Any], + dtype: Any, +) -> NDArray: + data = np.asarray(image, dtype=np.float64) + flat = data.ravel()[mapping["flat_valid"]] + n_bins = mapping["n_phi"] * mapping["n_r"] + + acc = np.bincount(mapping["idx00"], weights=flat * mapping["w00"], minlength=n_bins) + acc += np.bincount(mapping["idx01"], weights=flat * mapping["w01"], minlength=n_bins) + acc += np.bincount(mapping["idx10"], weights=flat * mapping["w10"], minlength=n_bins) + acc += np.bincount(mapping["idx11"], weights=flat * mapping["w11"], minlength=n_bins) + + acc = acc.reshape(mapping["n_phi"], mapping["n_r"]) + acc *= mapping["weights_inv"] + return acc.astype(dtype, copy=False) + From b8f79eb794e8fe2c1eecd9279d395ed59a43ef59 Mon Sep 17 00:00:00 2001 From: cophus Date: Thu, 4 Dec 2025 10:53:10 -0800 Subject: [PATCH 095/140] Changing sampling direction for polar4dstem --- .../core/datastructures/polar4dstem.py | 307 ++++++------------ src/quantem/diffraction/polar.py | 0 2 files changed, 107 insertions(+), 200 deletions(-) create mode 100644 src/quantem/diffraction/polar.py diff --git a/src/quantem/core/datastructures/polar4dstem.py b/src/quantem/core/datastructures/polar4dstem.py index 848469ec..6619af5c 100644 --- a/src/quantem/core/datastructures/polar4dstem.py +++ b/src/quantem/core/datastructures/polar4dstem.py @@ -1,13 +1,14 @@ import numpy as np from numpy.typing import NDArray from typing import Any, TYPE_CHECKING - -from quantem.core.datastructures.dataset4d import Dataset4d -# from quantem.core.datastructures.dataset4dstem import Dataset4dstem +from scipy.ndimage import map_coordinates if TYPE_CHECKING: from .dataset4dstem import Dataset4dstem +from quantem.core.datastructures.dataset4d import Dataset4d + + class Polar4dstem(Dataset4d): """4D-STEM dataset in polar coordinates (scan_y, scan_x, phi, r).""" @@ -37,7 +38,6 @@ def __init__( for k in mdata_keys_polar: if k not in metadata: metadata[k] = None - super().__init__( array=array, name=name, @@ -60,11 +60,13 @@ def from_array( signal_units: str = "arb. units", metadata: dict | None = None, ) -> "Polar4dstem": - array = ensure_valid_array(array, ndim=4) + array = np.asarray(array) + if array.ndim != 4: + raise ValueError("Polar4dstem.from_array expects a 4D array.") if origin is None: - origin = np.zeros(4) + origin = np.zeros(4, dtype=float) if sampling is None: - sampling = np.ones(4) + sampling = np.ones(4, dtype=float) if units is None: units = ["pixels", "pixels", "deg", "pixels"] if metadata is None: @@ -89,10 +91,68 @@ def n_r(self) -> int: return int(self.array.shape[3]) +def _precompute_polar_coords( + ny: int, + nx: int, + origin_row: float, + origin_col: float, + ellipse_params: tuple[float, float, float] | None, + num_annular_bins: int, + radial_min: float, + radial_max: float | None, + radial_step: float, + two_fold_rotation_symmetry: bool, +) -> tuple[NDArray, NDArray, NDArray, float]: + origin_row = float(origin_row) + origin_col = float(origin_col) + if radial_step <= 0: + raise ValueError("radial_step must be > 0.") + if num_annular_bins < 1: + raise ValueError("num_annular_bins must be >= 1.") + if radial_max is None: + r_row_pos = origin_row + r_row_neg = (ny - 1) - origin_row + r_col_pos = origin_col + r_col_neg = (nx - 1) - origin_col + radial_max_eff = float(min(r_row_pos, r_row_neg, r_col_pos, r_col_neg)) + else: + radial_max_eff = float(radial_max) + if radial_max_eff <= radial_min: + radial_max_eff = radial_min + radial_step + radial_bins = np.arange(radial_min, radial_max_eff, radial_step, dtype=np.float64) + if radial_bins.size == 0: + radial_bins = np.array([radial_min], dtype=np.float64) + if two_fold_rotation_symmetry: + phi_range = np.pi + else: + phi_range = 2.0 * np.pi + phi_bins = np.linspace(0.0, phi_range, num_annular_bins, endpoint=False, dtype=np.float64) + phi_grid, r_grid = np.meshgrid(phi_bins, radial_bins, indexing="ij") + if ellipse_params is None: + x = r_grid * np.cos(phi_grid) + y = r_grid * np.sin(phi_grid) + else: + if len(ellipse_params) != 3: + raise ValueError("ellipse_params must be (a, b, theta_deg).") + a, b, theta_deg = ellipse_params + theta = np.deg2rad(theta_deg) + alpha = phi_grid - theta + u = (a / b) * r_grid * np.cos(alpha) + v_prime = r_grid * np.sin(alpha) + cos_t = np.cos(theta) + sin_t = np.sin(theta) + x = u * cos_t - v_prime * sin_t + y = u * sin_t + v_prime * cos_t + coords_y = y + origin_row + coords_x = x + origin_col + coords = np.stack((coords_y, coords_x), axis=0) + return coords, phi_bins, radial_bins, radial_max_eff + + def dataset4dstem_polar_transform( self: "Dataset4dstem", - origin_row: float | NDArray, - origin_col: float | NDArray, + origin_row: float | int | NDArray, + origin_col: float | int | NDArray, ellipse_params: tuple[float, float, float] | None = None, num_annular_bins: int = 180, radial_min: float = 0.0, @@ -102,17 +162,16 @@ def dataset4dstem_polar_transform( name: str | None = None, signal_units: str | None = None, ) -> Polar4dstem: - """Return a Polar4dstem with shape (scan_y, scan_x, phi, r).""" if self.array.ndim != 4: raise ValueError("polar_transform requires a 4D-STEM dataset (ndim=4).") - scan_y, scan_x, ny, nx = self.array.shape - - mapping = _precompute_polar_mapping( + origin_row_f = float(origin_row) + origin_col_f = float(origin_col) + coords, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( ny=ny, nx=nx, - origin_row=float(origin_row), - origin_col=float(origin_col), + origin_row=origin_row_f, + origin_col=origin_col_f, ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, radial_min=radial_min, @@ -120,66 +179,52 @@ def dataset4dstem_polar_transform( radial_step=radial_step, two_fold_rotation_symmetry=two_fold_rotation_symmetry, ) - + n_phi = phi_bins.size + n_r = radial_bins.size result_dtype = np.result_type(self.array.dtype, np.float32) - out = np.empty( - (scan_y, scan_x, mapping["n_phi"], mapping["n_r"]), - dtype=result_dtype, - ) - + out = np.empty((scan_y, scan_x, n_phi, n_r), dtype=result_dtype) for iy in range(scan_y): for ix in range(scan_x): - out[iy, ix] = _apply_polar_mapping_single( - self.array[iy, ix], - mapping, - dtype=result_dtype, + dp = self.array[iy, ix] + out[iy, ix] = map_coordinates( + dp, + coords, + order=1, + mode="constant", + cval=0.0, ) - - phi_step_deg = mapping["phi_step"] * 180.0 / np.pi - phi_units = "deg" - radial_units = self.units[-1] - - sampling = np.array( - [ - self.sampling[0], - self.sampling[1], - phi_step_deg, - self.sampling[-1] * mapping["radial_step"], - ], - dtype=float, - ) - origin = np.array( - [ - self.origin[0], - self.origin[1], - 0.0, - self.sampling[-1] * mapping["radial_min"], - ], - dtype=float, - ) + if two_fold_rotation_symmetry: + phi_range = np.pi + else: + phi_range = 2.0 * np.pi + phi_step_deg = (phi_range / float(n_phi)) * (180.0 / np.pi) + sampling = np.zeros(4, dtype=float) + origin = np.zeros(4, dtype=float) + sampling[0:2] = np.asarray(self.sampling)[0:2] + sampling[2] = phi_step_deg + sampling[3] = float(np.asarray(self.sampling)[-1]) * radial_step + origin[0:2] = np.asarray(self.origin)[0:2] + origin[2] = 0.0 + origin[3] = radial_min * float(np.asarray(self.sampling)[-1]) units = [ self.units[0], self.units[1], - phi_units, - radial_units, + "deg", + self.units[-1], ] - metadata = dict(self.metadata) metadata.update( { - "polar_radial_min": mapping["radial_min"], - "polar_radial_max": mapping["radial_max"], - "polar_radial_step": mapping["radial_step"], - "polar_num_annular_bins": mapping["n_phi"], - "polar_two_fold_rotation_symmetry": two_fold_rotation_symmetry, - "polar_origin_row": float(origin_row), - "polar_origin_col": float(origin_col), - "polar_ellipse_params": tuple(ellipse_params) - if ellipse_params is not None - else None, + "polar_radial_min": float(radial_min), + "polar_radial_max": float(radial_max_eff), + "polar_radial_step": float(radial_step), + "polar_num_annular_bins": int(n_phi), + "polar_two_fold_rotation_symmetry": bool(two_fold_rotation_symmetry), + "polar_origin_row": origin_row_f, + "polar_origin_col": origin_col_f, + "polar_ellipse_params": tuple(ellipse_params) if ellipse_params is not None else None, } ) - return Polar4dstem( array=out, name=name if name is not None else f"{self.name}_polar", @@ -190,141 +235,3 @@ def dataset4dstem_polar_transform( metadata=metadata, _token=Polar4dstem._token, ) - - -def _precompute_polar_mapping( - ny: int, - nx: int, - origin_row: float, - origin_col: float, - ellipse_params: tuple[float, float, float] | None, - num_annular_bins: int, - radial_min: float, - radial_max: float | None, - radial_step: float, - two_fold_rotation_symmetry: bool, -) -> dict[str, Any]: - origin_row = float(origin_row) - origin_col = float(origin_col) - annular_range = np.pi if two_fold_rotation_symmetry else 2.0 * np.pi - - rows = np.arange(ny, dtype=np.float64) - cols = np.arange(nx, dtype=np.float64) - cc, rr = np.meshgrid(cols, rows, indexing="xy") - x = cc - origin_col - y = rr - origin_row - - if ellipse_params is None: - rr_pix = np.sqrt(x * x + y * y) - tt = np.mod(np.arctan2(y, x), annular_range) - else: - if len(ellipse_params) != 3: - raise ValueError("ellipse_params must be a length-3 tuple (a, b, theta_deg).") - a, b, theta_deg = ellipse_params - theta = np.deg2rad(theta_deg) - cos_t = np.cos(theta) - sin_t = np.sin(theta) - xc = x * cos_t + y * sin_t - yc = (y * cos_t - x * sin_t) * (a / b) - rr_pix = (b / a) * np.hypot(xc, yc) - tt = np.mod(np.arctan2(yc, xc) + theta, annular_range) - - if radial_step <= 0: - raise ValueError("radial_step must be > 0.") - radial_min = float(radial_min) - - if radial_max is None: - radial_max_eff = float(rr_pix.max()) - else: - radial_max_eff = float(radial_max) - if radial_max_eff <= radial_min + radial_step: - radial_max_eff = radial_min + radial_step - - radial_bins = np.arange(radial_min, radial_max_eff, radial_step, dtype=np.float64) - n_r = radial_bins.size - if n_r < 1: - raise ValueError("No radial bins defined. Check radial_min, radial_max, and radial_step.") - - n_phi = int(num_annular_bins) - if n_phi < 1: - raise ValueError("num_annular_bins must be >= 1.") - phi_step = annular_range / n_phi - - r_bin = (rr_pix - radial_min) / radial_step - t_bin = tt / phi_step - - r0 = np.floor(r_bin).astype(np.int64) - t0 = np.floor(t_bin).astype(np.int64) - dr = (r_bin - r0).astype(np.float64) - dt = (t_bin - t0).astype(np.float64) - - valid = (r0 >= 0) & (r0 < n_r - 1) - t0 = np.clip(t0, 0, n_phi - 1) - - flat_valid = valid.ravel() - r0v = r0.ravel()[flat_valid] - t0v = t0.ravel()[flat_valid] - drv = dr.ravel()[flat_valid] - dtv = dt.ravel()[flat_valid] - - n_bins = n_phi * n_r - idx00 = r0v + n_r * t0v - idx01 = r0v + n_r * ((t0v + 1) % n_phi) - idx10 = (r0v + 1) + n_r * t0v - idx11 = (r0v + 1) + n_r * ((t0v + 1) % n_phi) - - w00 = (1.0 - drv) * (1.0 - dtv) - w01 = (1.0 - drv) * dtv - w10 = drv * (1.0 - dtv) - w11 = drv * dtv - - weights_sum = np.bincount(idx00, weights=w00, minlength=n_bins) - weights_sum += np.bincount(idx01, weights=w01, minlength=n_bins) - weights_sum += np.bincount(idx10, weights=w10, minlength=n_bins) - weights_sum += np.bincount(idx11, weights=w11, minlength=n_bins) - weights_sum = weights_sum.reshape(n_phi, n_r) - - weights_inv = np.zeros_like(weights_sum, dtype=np.float64) - mask_bins = weights_sum > 0 - weights_inv[mask_bins] = 1.0 / weights_sum[mask_bins] - - return { - "flat_valid": flat_valid, - "idx00": idx00, - "idx01": idx01, - "idx10": idx10, - "idx11": idx11, - "w00": w00, - "w01": w01, - "w10": w10, - "w11": w11, - "weights_inv": weights_inv, - "n_phi": n_phi, - "n_r": n_r, - "radial_bins": radial_bins, - "phi_step": phi_step, - "annular_range": annular_range, - "radial_min": radial_min, - "radial_max": radial_min + radial_step * n_r, - "radial_step": radial_step, - } - - -def _apply_polar_mapping_single( - image: NDArray, - mapping: dict[str, Any], - dtype: Any, -) -> NDArray: - data = np.asarray(image, dtype=np.float64) - flat = data.ravel()[mapping["flat_valid"]] - n_bins = mapping["n_phi"] * mapping["n_r"] - - acc = np.bincount(mapping["idx00"], weights=flat * mapping["w00"], minlength=n_bins) - acc += np.bincount(mapping["idx01"], weights=flat * mapping["w01"], minlength=n_bins) - acc += np.bincount(mapping["idx10"], weights=flat * mapping["w10"], minlength=n_bins) - acc += np.bincount(mapping["idx11"], weights=flat * mapping["w11"], minlength=n_bins) - - acc = acc.reshape(mapping["n_phi"], mapping["n_r"]) - acc *= mapping["weights_inv"] - return acc.astype(dtype, copy=False) - diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py new file mode 100644 index 00000000..e69de29b From 5263afe6051f5b37193b1e8573b6fb970b50ca27 Mon Sep 17 00:00:00 2001 From: cophus Date: Thu, 4 Dec 2025 13:22:56 -0800 Subject: [PATCH 096/140] initial commit for RDF class --- src/quantem/__init__.py | 1 + src/quantem/core/datastructures/dataset.py | 34 +++ src/quantem/diffraction/__init__.py | 1 + src/quantem/diffraction/polar.py | 326 +++++++++++++++++++++ 4 files changed, 362 insertions(+) diff --git a/src/quantem/__init__.py b/src/quantem/__init__.py index ba70f629..8db92d1f 100644 --- a/src/quantem/__init__.py +++ b/src/quantem/__init__.py @@ -9,6 +9,7 @@ from quantem.core import visualization as visualization from quantem import imaging as imaging +from quantem import diffraction as diffraction from quantem import diffractive_imaging as diffractive_imaging __version__ = version("quantem") diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 94744978..60e22244 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -191,6 +191,11 @@ def sampling(self) -> NDArray: def sampling(self, value: NDArray | tuple | list | float | int) -> None: self._sampling = validate_ndinfo(value, self.ndim, "sampling") + @property + def origin_units(self) -> NDArray: + # Origin expressed in physical units: origin * sampling + return np.asarray(self.origin) * np.asarray(self.sampling) + @property def units(self) -> list[str]: return self._units @@ -368,6 +373,35 @@ def _copy_custom_attributes(self, new_dataset: Self) -> None: # Skip attributes that can't be copied pass + def coords(self, axis: int) -> Any: + """ + Coordinate array for a given axis in pixel units. + + coords(d) = arange(shape[d]) - origin[d] + """ + axis = int(axis) + if axis < 0 or axis >= self.ndim: + raise ValueError(f"axis {axis} out of bounds for ndim={self.ndim}") + + xp = self._xp + n = int(self.shape[axis]) + origin_d = float(np.asarray(self.origin)[axis]) + + return xp.arange(n, dtype=float) - origin_d + + def coords_units(self, axis: int) -> Any: + """ + Coordinate array for a given axis in physical units. + + coords_units(d) = (arange(shape[d]) - origin[d]) * sampling[d] + """ + axis = int(axis) + if axis < 0 or axis >= self.ndim: + raise ValueError(f"axis {axis} out of bounds for ndim={self.ndim}") + + sampling_d = float(np.asarray(self.sampling)[axis]) + return self.coords(axis) * sampling_d + def mean(self, axes: int | tuple[int, ...] | None = None) -> Any: """ Computes and returns mean of the data array. diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index e69de29b..fdbb2b3d 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -0,0 +1 @@ +from quantem.diffraction.polar import RDF as RDF diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py index e69de29b..7e87eff3 100644 --- a/src/quantem/diffraction/polar.py +++ b/src/quantem/diffraction/polar.py @@ -0,0 +1,326 @@ +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any, List, Union + +import matplotlib.pyplot as plt +import numpy as np +from numpy.typing import NDArray + +from quantem.core.datastructures.dataset2d import Dataset2d +from quantem.core.datastructures.dataset3d import Dataset3d +from quantem.core.datastructures.dataset4dstem import Dataset4dstem +from quantem.core.datastructures.polar4dstem import Polar4dstem +from quantem.core.io.serialize import AutoSerialize +from quantem.core.utils.validators import ensure_valid_array + + +class RDF(AutoSerialize): + """ + Radial distribution / fluctuation electron microscopy analysis helper. + + This class wraps a 4D-STEM (or 2D diffraction) dataset and stores a + polar-transformed representation as a Polar4dstem instance in `self.polar`. + Analysis methods (radial statistics, PDF, FEM, clustering, etc.) are + provided as stubs for now and will be implemented in future revisions. + """ + + _token = object() + + def __init__( + self, + polar: Polar4dstem, + input_data: Any | None = None, + _token: object | None = None, + ): + if _token is not self._token: + raise RuntimeError( + "Use RadialDistributionFunction.from_data() to instantiate this class." + ) + + super().__init__() + self.polar = polar + self.input_data = input_data + + # Placeholders for analysis results (to be populated by future methods) + self.radial_mean: NDArray | None = None + self.radial_var: NDArray | None = None + self.radial_var_norm: NDArray | None = None + + self.pdf_r: NDArray | None = None + self.pdf_reduced: NDArray | None = None + self.pdf: NDArray | None = None + + self.Sk: NDArray | None = None + self.fk: NDArray | None = None + self.bg: NDArray | None = None + self.offset: float | None = None + self.Sk_mask: NDArray | None = None + + # ------------------------------------------------------------------ + # Constructors + # ------------------------------------------------------------------ + @classmethod + def from_data( + cls, + data: Union[NDArray, Dataset2d, Dataset3d, Dataset4dstem, Polar4dstem], + *, + origin_row: float | None = None, + origin_col: float | None = None, + ellipse_params: tuple[float, float, float] | None = None, + num_annular_bins: int = 180, + radial_min: float = 0.0, + radial_max: float | None = None, + radial_step: float = 1.0, + two_fold_rotation_symmetry: bool = False, + ) -> "RadialDistributionFunction": + """ + Create a RadialDistributionFunction object from various input types. + + Parameters + ---------- + data + Supported inputs: + - 2D numpy array (single diffraction pattern) + - 4D numpy array (scan_y, scan_x, ky, kx) + - Dataset2d + - Dataset4dstem + - Polar4dstem + origin_row, origin_col + Diffraction-space origin (in pixels). If None, defaults to the + central pixel of the diffraction pattern. + Other parameters + Passed through to Dataset4dstem.polar_transform when needed. + """ + # Polar input: use directly + if isinstance(data, Polar4dstem): + polar = data + return cls(polar=polar, input_data=data, _token=cls._token) + + # Dataset4dstem input: polar-transform it + if isinstance(data, Dataset4dstem): + scan_y, scan_x, ny, nx = data.array.shape + if origin_row is None: + origin_row = (ny - 1) / 2.0 + if origin_col is None: + origin_col = (nx - 1) / 2.0 + + polar = data.polar_transform( + origin_row=origin_row, + origin_col=origin_col, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + return cls(polar=polar, input_data=data, _token=cls._token) + + # Dataset2d input: wrap as a trivial 4D-STEM (1x1 scan) then polar-transform + if isinstance(data, Dataset2d): + arr2d = data.array + if arr2d.ndim != 2: + raise ValueError("Dataset2d for RDF must be 2D.") + arr4 = arr2d[None, None, ...] # (1, 1, ky, kx) + + ds4 = Dataset4dstem.from_array( + array=arr4, + name=f"{data.name}_as4dstem" if getattr(data, "name", None) else "rdf_4dstem_from_2d", + origin=np.concatenate( + [np.zeros(2, dtype=float), np.asarray(data.origin, dtype=float)] + ), + sampling=np.concatenate( + [np.ones(2, dtype=float), np.asarray(data.sampling, dtype=float)] + ), + units=["pixels", "pixels"] + list(data.units), + signal_units=data.signal_units, + ) + ny, nx = ds4.array.shape[-2:] + if origin_row is None: + origin_row = (ny - 1) / 2.0 + if origin_col is None: + origin_col = (nx - 1) / 2.0 + + polar = ds4.polar_transform( + origin_row=origin_row, + origin_col=origin_col, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + return cls(polar=polar, input_data=data, _token=cls._token) + + # Dataset3d input: not yet specified how to interpret + if isinstance(data, Dataset3d): + raise NotImplementedError( + "RadialDistributionFunction.from_data does not yet support Dataset3d inputs." + ) + + # Numpy array input + arr = ensure_valid_array(data) + if arr.ndim == 2: + ds2 = Dataset2d.from_array(arr, name="rdf_input_2d") + return cls.from_data( + ds2, + origin_row=origin_row, + origin_col=origin_col, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + elif arr.ndim == 4: + ds4 = Dataset4dstem.from_array(arr, name="rdf_input_4dstem") + return cls.from_data( + ds4, + origin_row=origin_row, + origin_col=origin_col, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + else: + raise ValueError( + "RadialDistributionFunction.from_data only supports 2D or 4D arrays." + ) + + # ------------------------------------------------------------------ + # Convenience accessors + # ------------------------------------------------------------------ + @property + def qq(self) -> Any: + """ + Scattering vector coordinate array along the radial dimension of `self.polar`, + in physical units (using Polar4dstem.sampling and origin). + """ + # Polar4dstem dims: (scan_y, scan_x, phi, r) + # radial axis is 3 + return self.polar.coords_units(3) + + @property + def radial_bins(self) -> Any: + """ + Radial bin centers in pixel units (convenience alias). + """ + return self.polar.coords(3) + + # ------------------------------------------------------------------ + # Analysis method stubs (py4DSTEM-style API) + # ------------------------------------------------------------------ + def calculate_radial_statistics( + self, + mask_realspace: NDArray | None = None, + plot_results_mean: bool = False, + plot_results_var: bool = False, + figsize: tuple[float, float] = (8, 4), + returnval: bool = False, + returnfig: bool = False, + progress_bar: bool = True, + ): + """ + Stub for radial statistics (FEM-style) calculation on the polar data. + + Intended to compute radial mean, variance, and normalized variance + from self.polar. Not implemented yet. + """ + raise NotImplementedError("calculate_radial_statistics is not implemented yet.") + + def plot_radial_mean( + self, + log_x: bool = False, + log_y: bool = False, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Stub for plotting radial mean intensity vs scattering vector. + """ + raise NotImplementedError("plot_radial_mean is not implemented yet.") + + def plot_radial_var_norm( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Stub for plotting normalized radial variance vs scattering vector. + """ + raise NotImplementedError("plot_radial_var_norm is not implemented yet.") + + def calculate_pair_dist_function( + self, + k_min: float = 0.05, + k_max: float | None = None, + k_width: float = 0.25, + k_lowpass: float | None = None, + k_highpass: float | None = None, + r_min: float = 0.0, + r_max: float = 20.0, + r_step: float = 0.02, + damp_origin_fluctuations: bool = True, + enforce_positivity: bool = True, + density: float | None = None, + plot_background_fits: bool = False, + plot_sf_estimate: bool = False, + plot_reduced_pdf: bool = True, + plot_pdf: bool = False, + figsize: tuple[float, float] = (8, 4), + maxfev: int | None = None, + returnval: bool = False, + returnfig: bool = False, + ): + """ + Stub for pair distribution function (PDF) calculation from radial statistics. + + Intended to estimate S(k), background, and transform to real-space g(r)/G(r). + """ + raise NotImplementedError("calculate_pair_dist_function is not implemented yet.") + + def plot_background_fits( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Stub for plotting background fit vs radial mean intensity. + """ + raise NotImplementedError("plot_background_fits is not implemented yet.") + + def plot_sf_estimate( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Stub for plotting reduced structure factor S(k). + """ + raise NotImplementedError("plot_sf_estimate is not implemented yet.") + + def plot_reduced_pdf( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Stub for plotting reduced PDF g(r). + """ + raise NotImplementedError("plot_reduced_pdf is not implemented yet.") + + def plot_pdf( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Stub for plotting full PDF G(r). + """ + raise NotImplementedError("plot_pdf is not implemented yet.") From 2ef4efc5b0c8e990db6802bc1e529f70139133c0 Mon Sep 17 00:00:00 2001 From: cophus Date: Thu, 1 Jan 2026 12:42:17 -0800 Subject: [PATCH 097/140] initial commit for StrainMap --- .../core/datastructures/dataset4dstem.py | 2 +- src/quantem/core/utils/imaging_utils.py | 665 ++---------------- src/quantem/diffraction/__init__.py | 1 + src/quantem/diffraction/strain.py | 179 +++++ 4 files changed, 248 insertions(+), 599 deletions(-) create mode 100644 src/quantem/diffraction/strain.py diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 8beb31cc..7a7f31f7 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -79,7 +79,7 @@ def __init__( _token : object | None, optional Token to prevent direct instantiation, by default None """ - mdata_keys_4dstem = ["r_to_q_rotation_cw_deg", "ellipticity"] + mdata_keys_4dstem = ["q_to_r_rotation_cw_deg", 'q_transpose', "ellipticity"] for k in mdata_keys_4dstem: if k not in metadata.keys(): metadata[k] = None diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index d352a051..8f8aeb55 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -7,6 +7,7 @@ import torch from numpy.typing import NDArray from scipy.ndimage import gaussian_filter +from scipy.ndimage import map_coordinates from quantem.core.utils.utils import generate_batches @@ -548,620 +549,88 @@ def fourier_cropping( return result -def compute_fsc_from_halfsets( - halfset_recons: list[torch.Tensor], - sampling: tuple[float, float], - epsilon: float = 1e-12, -): - """ - Compute radially averaged Fourier Shell Correlation (FSC) - from two half-set reconstructions. - - Parameters - ---------- - halfset_recons : list[torch.Tensor] - Two statistically-independent reconstructions, using half the dataset. - sampling: tuple[float,float] - Reconstruction sampling in Angstroms. - epsilon: float, optional - Small number to avoid dividing by zero - - Returns - ------- - q_bins: NDarray - Spatial frequency bins - fsc : NDarray - Fourier shell correlation as function of spatial frequency - """ - r1, r2 = halfset_recons - - F1 = torch.fft.fft2(r1) - F2 = torch.fft.fft2(r2) - - cross = (F1 * F2.conj()).real - p1 = F1.abs().square() - p2 = F2.abs().square() - - device = F1.device - nx, ny = F1.shape - sx, sy = sampling - - kx = torch.fft.fftfreq(nx, d=sx, device=device) - ky = torch.fft.fftfreq(ny, d=sy, device=device) - k = torch.sqrt(kx[:, None] ** 2 + ky[None, :] ** 2).reshape(-1) - - bin_size = kx[1] - kx[0] - max_k = k.max() - num_bins = int(torch.floor(max_k / bin_size).item()) + 2 - - inds = k / bin_size - inds_f = torch.floor(inds).long() - d_ind = inds - inds_f - - w0 = 1.0 - d_ind - w1 = d_ind - - # Flatten arrays - cross = cross.reshape(-1) - p1 = p1.reshape(-1) - p2 = p2.reshape(-1) - - # Accumulate - cross_b = torch.bincount(inds_f, weights=cross * w0, minlength=num_bins) + torch.bincount( - inds_f + 1, weights=cross * w1, minlength=num_bins - ) - - p1_b = torch.bincount(inds_f, weights=p1 * w0, minlength=num_bins) + torch.bincount( - inds_f + 1, weights=p1 * w1, minlength=num_bins - ) - - p2_b = torch.bincount(inds_f, weights=p2 * w0, minlength=num_bins) + torch.bincount( - inds_f + 1, weights=p2 * w1, minlength=num_bins - ) - - denom = torch.sqrt(p1_b * p2_b).clamp_min(epsilon) - fsc = cross_b / denom - - k_bins = torch.arange(num_bins, device=device, dtype=torch.float32) * bin_size - valid = k_bins <= kx.abs().max() - - return k_bins[valid].cpu().numpy(), fsc[valid].cpu().numpy() - - -def compute_spectral_snr_from_halfsets( - halfset_recons: list[torch.Tensor], - sampling: tuple[float, float], - total_dose: float, - epsilon: float = 1e-12, -): - """ - Compute spectral SNR from two half-set reconstructions using symmetric/antisymmetric decomposition. - - The method decomposes the Fourier transforms into: - - Symmetric: (F₁ + F₂)/2 → signal + correlated noise - - Antisymmetric: (F₁ - F₂)/2 → uncorrelated noise only - - SSNR(q) = sqrt(signal_power / noise_power) - - where: - - signal_power = (|symmetric|² - |antisymmetric|²)₊ - - noise_power = |antisymmetric|² - - Parameters - ---------- - halfset_recons : list[torch.Tensor] - Two statistically-independent reconstructions, using half the dataset. - sampling: tuple[float,float] - Reconstruction sampling in Angstroms. - total_dose: float - Total _normalized_ electron dose, e.g. in DirectPtychography this is ~self.num_bf - epsilon: float, optional - Small number to avoid dividing by zero - - Returns - ------- - q_bins: NDarray - Spatial frequency bins - ssnr : NDarray - Radially averaged spectral SNR as function of spatial frequency - """ - # Compute Fourier transforms - halfset_1, halfset_2 = halfset_recons - F1 = torch.fft.fft2(halfset_1) - F2 = torch.fft.fft2(halfset_2) - - # Symmetric and antisymmetric decomposition - symmetric = (F1 + F2) / 2 - antisymmetric = (F1 - F2) / 2 - - # Power spectra - noise_power = antisymmetric.abs() - total_power = symmetric.abs() - signal_power = (total_power - noise_power).clamp_min(0) - - device = F1.device - nx, ny = F1.shape - sx, sy = sampling - - kx = torch.fft.fftfreq(nx, d=sx, device=device) - ky = torch.fft.fftfreq(ny, d=sy, device=device) - k = torch.sqrt(kx[:, None] ** 2 + ky[None, :] ** 2).reshape(-1) - - bin_size = kx[1] - kx[0] - max_k = k.max() - num_bins = int(torch.floor(max_k / bin_size).item()) + 2 - - inds = k / bin_size - inds_f = torch.floor(inds).long() - d_ind = inds - inds_f - - w0 = 1.0 - d_ind - w1 = d_ind - - # Flatten arrays - signal = signal_power.reshape(-1) - noise = noise_power.reshape(-1) - - # Accumulate - signal_b = torch.bincount(inds_f, weights=signal * w0, minlength=num_bins) + torch.bincount( - inds_f + 1, weights=signal * w1, minlength=num_bins - ) - - noise_b = torch.bincount(inds_f, weights=noise * w0, minlength=num_bins) + torch.bincount( - inds_f + 1, weights=noise * w1, minlength=num_bins - ) - - ssnr = torch.sqrt(signal_b / noise_b.clamp_min(epsilon)) / (math.sqrt(total_dose) / 2) - - k_bins = torch.arange(num_bins, device=device, dtype=torch.float32) * bin_size - valid = k_bins <= kx.abs().max() - - return k_bins[valid].cpu().numpy(), ssnr[valid].cpu().numpy() - - -def radially_average_fourier_array( - corner_centered_array: torch.Tensor, - sampling: tuple[float, float], -): - """ - Radially average a corner-centered Fourier array. - - Parameters - ---------- - corner_centered_array : list[torch.Tensor] - Fourier array to average radially. - sampling: tuple[float,float] - Reconstruction sampling in Angstroms. - - Returns - ------- - q_bins: NDarray - Spatial frequency bins - array_1d : NDarray - Radially averaged Fourier array as function of spatial frequency - """ - device = corner_centered_array.device - nx, ny = corner_centered_array.shape - sx, sy = sampling - - kx = torch.fft.fftfreq(nx, d=sx, device=device) - ky = torch.fft.fftfreq(ny, d=sy, device=device) - k = torch.sqrt(kx[:, None] ** 2 + ky[None, :] ** 2).reshape(-1) - - bin_size = kx[1] - kx[0] - max_k = k.max() - num_bins = int(torch.floor(max_k / bin_size).item()) + 2 - - inds = k / bin_size - inds_f = torch.floor(inds).long() - d_ind = inds - inds_f - - w0 = 1.0 - d_ind - w1 = d_ind - - # Flatten arrays - array = corner_centered_array.reshape(-1) - - # Accumulate - array_b = torch.bincount(inds_f, weights=array * w0, minlength=num_bins) + torch.bincount( - inds_f + 1, weights=array * w1, minlength=num_bins - ) - - counts_b = ( - torch.bincount(inds_f, weights=w0, minlength=num_bins) - + torch.bincount(inds_f + 1, weights=w1, minlength=num_bins) - ).clamp_min(1) - - array_b = array_b / counts_b - - k_bins = torch.arange(num_bins, device=device, dtype=torch.float32) * bin_size - valid = k_bins <= kx.abs().max() - - return k_bins[valid].cpu().numpy(), array_b[valid].cpu().numpy() - - -def _wrap_to_pi(x): - return (x + math.pi) % (2 * math.pi) - math.pi - - -def _find_wrap(a, b): - d = a - b - return torch.where(d > math.pi, -1, torch.where(d < -math.pi, 1, 0)) - - -def _pixel_reliability(phi, mask=None): - """ - phi: (H, W) wrapped phase (CPU tensor) - mask: optional boolean mask - """ - c = phi - left = torch.roll(c, 1, 1) - right = torch.roll(c, -1, 1) - up = torch.roll(c, 1, 0) - down = torch.roll(c, -1, 0) - - ul = torch.roll(left, 1, 0) - dr = torch.roll(right, -1, 0) - ur = torch.roll(right, 1, 0) - dl = torch.roll(left, -1, 0) - - Hterm = _wrap_to_pi(left - c) - _wrap_to_pi(c - right) - Vterm = _wrap_to_pi(up - c) - _wrap_to_pi(c - down) - D1term = _wrap_to_pi(ul - c) - _wrap_to_pi(c - dr) - D2term = _wrap_to_pi(ur - c) - _wrap_to_pi(c - dl) - - R = Hterm**2 + Vterm**2 + D1term**2 + D2term**2 - - if mask is not None: - R = torch.where(mask, R, torch.full_like(R, float("inf"))) - - return R - - -def _build_edges(phi, reliability, mask=None, wrap_around=True): - """ - Returns edges as CPU tensors: - i1, i2, inc sorted by reliability - """ - H, W = phi.shape - N = H * W - - idx = torch.arange(N).reshape(H, W) - edges = [] - - phi_f = phi.flatten() - rel_f = reliability.flatten() - mask_f = mask.flatten() if mask is not None else None - - def add_edges(i1, i2): - if mask_f is not None: - valid = mask_f[i1] & mask_f[i2] - i1, i2 = i1[valid], i2[valid] - - inc = _find_wrap(phi_f[i1], phi_f[i2]) - rel = rel_f[i1] + rel_f[i2] - - edges.append( # ty:ignore[possibly-missing-attribute] - torch.stack([i1, i2, rel, inc], dim=1) - ) - - if wrap_around: - add_edges(idx.flatten(), torch.roll(idx, -1, 1).flatten()) - add_edges(idx.flatten(), torch.roll(idx, -1, 0).flatten()) - else: - add_edges(idx[:, :-1].flatten(), idx[:, 1:].flatten()) - add_edges(idx[:-1, :].flatten(), idx[1:, :].flatten()) - - edges = torch.cat(edges, dim=0) - edges = edges[edges[:, 2].argsort()] - - # return integer tensors only (CPU) - return ( - edges[:, 0].long(), - edges[:, 1].long(), - edges[:, 3].long(), - ) - - -class UnionFindPhase: - def __init__(self, n): - self.parent = torch.arange(n) - self.rank = torch.zeros(n, dtype=torch.int32) - self.offset = torch.zeros(n) - - def find_root_and_offset(self, x): - root = x - total = 0.0 - while self.parent[root] != root: - total += self.offset[root] - root = self.parent[root] - return root, total - - def union(self, x, y, inc_xy): - rx, ox = self.find_root_and_offset(x) - ry, oy = self.find_root_and_offset(y) - - if rx == ry: - return - - # phase(y) + oy + inc = phase(x) + ox - delta = ox - oy - inc_xy - - if self.rank[rx] < self.rank[ry]: - self.parent[rx] = ry - self.offset[rx] = -delta - else: - self.parent[ry] = rx - self.offset[ry] = delta - if self.rank[rx] == self.rank[ry]: - self.rank[rx] += 1 - - -def _final_offsets(uf): - """ - Single-pass offset computation (no path compression). - """ - N = uf.parent.numel() - incs = torch.zeros(N) - - for i in range(N): - root = i - total = 0.0 - while uf.parent[root] != root: - total += uf.offset[root] - root = uf.parent[root] - incs[i] = total - - return incs - - -def _unwrap_phase_2d_torch_reliability_sorting( - phi, - mask=None, - wrap_around=True, -): - """ - Herráez 2D phase unwrapping. - Runs on CPU by design. - """ - with torch.no_grad(): - orig_device = phi.device - phi = phi.detach().cpu() - if mask is not None: - mask = mask.detach().cpu().to(torch.bool) - - H, W = phi.shape - N = H * W - - reliability = _pixel_reliability(phi, mask) - - i1, i2, inc = _build_edges( - phi, - reliability, - mask, - wrap_around=wrap_around, - ) - - uf = UnionFindPhase(N) - - for k in range(i1.numel()): - uf.union(i1[k].item(), i2[k].item(), inc[k].item()) - - incs = _final_offsets(uf) - - out = (phi.flatten() + 2 * math.pi * incs).reshape(H, W) - out -= out.mean() - return out.to(orig_device) - - -def _unwrap_phase_2d_torch_poisson( - phi_wrapped, - mask=None, - wrap_around=True, - regularization_lambda=None, -): - """ - Least-squares / Poisson phase unwrapping with optional mask. - - Parameters - ---------- - phi_wrapped : (H, W) tensor - Wrapped phase in (-pi, pi], any device - mask : (H, W) bool tensor, optional - True = valid pixel - - Returns - ------- - phi_unwrapped : (H, W) tensor - Unwrapped phase (same device as input) - """ - device = phi_wrapped.device - dtype = phi_wrapped.dtype - H, W = phi_wrapped.shape - - if not wrap_around: - raise NotImplementedError() - - if mask is not None: - mask = mask.to(device=device, dtype=torch.bool) - - dx = torch.roll(phi_wrapped, -1, dims=1) - phi_wrapped - dy = torch.roll(phi_wrapped, -1, dims=0) - phi_wrapped - - dx = (dx + math.pi) % (2 * math.pi) - math.pi - dy = (dy + math.pi) % (2 * math.pi) - math.pi - - if mask is not None: - mask_x = mask & torch.roll(mask, -1, dims=1) - mask_y = mask & torch.roll(mask, -1, dims=0) - - dx = torch.where(mask_x, dx, torch.zeros_like(dx)) - dy = torch.where(mask_y, dy, torch.zeros_like(dy)) - - div = dx - torch.roll(dx, 1, dims=1) + dy - torch.roll(dy, 1, dims=0) - - if mask is not None: - div = torch.where(mask, div, torch.zeros_like(div)) - - div_hat = torch.fft.fftn(div) - - ky = torch.fft.fftfreq(H, device=device, dtype=dtype) * 2 * math.pi - kx = torch.fft.fftfreq(W, device=device, dtype=dtype) * 2 * math.pi - ky, kx = torch.meshgrid(ky, kx, indexing="ij") - - if regularization_lambda is not None: - denom = kx**2 + ky**2 + regularization_lambda - else: - denom = kx**2 + ky**2 - denom[0, 0] = 1.0 # avoid divide by zero - - phi_hat = -div_hat / denom - phi_hat[0, 0] = 0.0 # fix piston - - phi = torch.fft.ifftn(phi_hat).real - - if mask is not None: - phi = torch.where(mask, phi, torch.zeros_like(phi)) - - return phi - - -def unwrap_phase_2d_torch( - phi_wrapped, - method="reliability-sorting", - mask=None, - wrap_around=True, - regularization_lambda=None, -): - if method == "reliability-sorting": - return _unwrap_phase_2d_torch_reliability_sorting( - phi_wrapped, mask, wrap_around=wrap_around - ) - elif method == "poisson": - return _unwrap_phase_2d_torch_poisson( - phi_wrapped, - mask, - wrap_around=wrap_around, - regularization_lambda=regularization_lambda, - ) - else: - raise ValueError( - f'`method` must be one of {{"reliability-sorting", "poisson"}}, got {method!r}' - ) - - -def radially_project_fourier_tensor( - corner_centered_array: torch.Tensor, - sampling: Tuple[float, float], - q_bins: torch.Tensor | None = None, +def rotate_image( + im, + rotation_deg: float, + origin: tuple[float, float] | None = None, + clockwise: bool = True, + interpolation: str = "bilinear", + mode: str = "constant", + cval: float = 0.0, ): - """ - Radially project a corner-centered Fourier array onto radial bins. - - Supports: - - single array: (kx, ky) - - batched arrays: (n, kx, ky) - - implicit bins (from grid) or explicit external bins + """Rotate an array about a pixel origin using bilinear/bicubic interpolation. Parameters ---------- - corner_centered_array : torch.Tensor - Shape (kx, ky) or (n, kx, ky) - sampling : (float, float) - Real-space sampling (sx, sy) - q_bins : torch.Tensor, optional - 1D tensor of radial bin centers + im + Input array; last two dimensions are treated as (H, W). Any leading + dimensions are treated as batch and rotated independently. + rotation_deg + Rotation angle in degrees. Interpreted as clockwise if clockwise=True, + otherwise counterclockwise. + origin + Rotation origin (row, col) in pixel coordinates. If None, uses (H//2, W//2). + clockwise + If True, interpret rotation_deg as clockwise; if False, as counterclockwise. + interpolation + "bilinear" (order=1) or "bicubic" (order=3). + mode, cval + Boundary handling passed to scipy.ndimage.map_coordinates. Returns ------- - q_bins_out : torch.Tensor - Radial bin centers - array_1d : torch.Tensor - Shape (n, nq) or (nq,) + out + Rotated array with the same shape as `im`. """ + im = np.asarray(im) + if im.ndim < 2: + raise ValueError("im must have at least 2 dimensions") - if corner_centered_array.is_complex(): - q, real_part = radially_project_fourier_tensor( - corner_centered_array.real, sampling, q_bins - ) - # zero by symmetry - # _, imag_part = radially_project_fourier_tensor( - # corner_centered_array.imag, sampling, q_bins - # ) - return q, real_part # + 1j * imag_part - - device = corner_centered_array.device - sx, sy = sampling - - # --- normalize shape to (batch, kx, ky) - if corner_centered_array.ndim == 2: - array = corner_centered_array[None, ...] - squeeze_output = True - elif corner_centered_array.ndim == 3: - array = corner_centered_array - squeeze_output = False + H, W = im.shape[-2], im.shape[-1] + if origin is None: + r0 = float(H // 2) + c0 = float(W // 2) else: - raise ValueError("Input must be 2D or 3D tensor") - - n_batch, nkx, nky = array.shape - - # --- build k-grid - kx = torch.fft.fftfreq(nkx, d=sx, device=device) - ky = torch.fft.fftfreq(nky, d=sy, device=device) - k = torch.sqrt(kx[:, None] ** 2 + ky[None, :] ** 2).reshape(-1) - - # --- determine radial bins - k_max = min(0.5 / sx, 0.5 / sy) - - if q_bins is None: - dk = kx[1] - kx[0] - num_bins = int(torch.floor(k_max / dk).item()) + 1 - dq = dk - q_bins_out = torch.arange(num_bins, device=device, dtype=k.dtype) * dk + r0 = float(origin[0]) + c0 = float(origin[1]) + + interp = str(interpolation).lower() + if interp in {"bilinear", "linear"}: + order = 1 + elif interp in {"bicubic", "cubic"}: + order = 3 else: - q_bins = q_bins.to(device) - dq = q_bins[1] - q_bins[0] - q_bins_out = q_bins[q_bins <= k_max] - num_bins = q_bins_out.numel() + raise ValueError("interpolation must be 'bilinear' or 'bicubic'") - # ---- INTERNAL padding (key change) - num_bins_internal = num_bins + 2 + theta = float(np.deg2rad(rotation_deg)) + if clockwise: + theta = -theta - # --- map k -> bin indices (NO CLIPPING) - inds = k / dq - inds_f = torch.floor(inds).long() - inds_f = torch.clamp(inds_f, 0, num_bins_internal - 2) + ct = float(np.cos(theta)) + st = float(np.sin(theta)) - d_ind = inds - inds_f - w0 = 1.0 - d_ind - w1 = d_ind - - # --- flatten spatial dims - array_f = array.reshape(n_batch, -1) - - # --- accumulate per batch - out = [] - for b in range(n_batch): - a = array_f[b] - - num = torch.bincount(inds_f, weights=a * w0, minlength=num_bins_internal) + torch.bincount( - inds_f + 1, weights=a * w1, minlength=num_bins_internal - ) - - den = ( - torch.bincount(inds_f, weights=w0, minlength=num_bins_internal) - + torch.bincount(inds_f + 1, weights=w1, minlength=num_bins_internal) - ).clamp_min(1) + r_out, c_out = np.meshgrid( + np.arange(H, dtype=np.float64), + np.arange(W, dtype=np.float64), + indexing="ij", + ) - out.append(num / den) + c_rel = c_out - c0 + r_rel = r_out - r0 - array_1d = torch.stack(out, dim=0) + c_in = ct * c_rel + st * r_rel + c0 + r_in = -st * c_rel + ct * r_rel + r0 - # ---- truncate to physical bins (key change) - array_1d = array_1d[..., :num_bins] - q_bins_out = q_bins_out[:num_bins] + coords = np.vstack((r_in.ravel(), c_in.ravel())) - if squeeze_output: - array_1d = array_1d[0] + if im.ndim == 2: + out = map_coordinates(im, coords, order=order, mode=mode, cval=cval) + return out.reshape(H, W) - return q_bins_out, array_1d + prefix = im.shape[:-2] + n = int(np.prod(prefix)) if prefix else 1 + im_flat = im.reshape(n, H, W) + out_flat = np.empty((n, H * W), dtype=np.result_type(im_flat.dtype, np.float64)) + for i in range(n): + out_flat[i] = map_coordinates(im_flat[i], coords, order=order, mode=mode, cval=cval) + return out_flat.reshape(*prefix, H, W) diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index fdbb2b3d..6d0e4202 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1 +1,2 @@ from quantem.diffraction.polar import RDF as RDF +from quantem.diffraction.strain import StrainMap as StrainMap diff --git a/src/quantem/diffraction/strain.py b/src/quantem/diffraction/strain.py new file mode 100644 index 00000000..55c57d9d --- /dev/null +++ b/src/quantem/diffraction/strain.py @@ -0,0 +1,179 @@ +from __future__ import annotations + +from typing import Any + +import numpy as np +from numpy.typing import NDArray + +from quantem.core.datastructures.dataset2d import Dataset2d +from quantem.core.datastructures.dataset4dstem import Dataset4dstem +from quantem.core.io.serialize import AutoSerialize +from quantem.core.utils.imaging_utils import rotate_image +from quantem.core.utils.utils import electron_wavelength_angstrom +from quantem.core.utils.validators import ensure_valid_array +from quantem.core.visualization import ScalebarConfig, show_2d + + +class StrainMap(AutoSerialize): + """ + Nanobeam strain mapping + """ + + _token = object() + + def __init__( + self, + dataset: Dataset4dstem, + input_data: Any | None = None, + _token: object | None = None, + ): + if _token is not self._token: + raise RuntimeError("Use StrainMap.from_data() to instantiate this class.") + super().__init__() + self.dataset = dataset + self.input_data = input_data + self.strain = None + self.metadata: dict[str, Any] = {} + self.transform: Dataset2d | None = None + self.transform_rotated: Dataset2d | None = None + + @classmethod + def from_data(cls, data: NDArray | Dataset4dstem, *, name: str = "strain_map") -> StrainMap: + if isinstance(data, Dataset4dstem): + return cls(dataset=data, input_data=data, _token=cls._token) + + arr = ensure_valid_array(data) + if arr.ndim != 4: + raise ValueError( + "StrainMap.from_data expects a 4D array with shape (scan_r, scan_c, dp_r, dp_c)." + ) + + ds4 = Dataset4dstem.from_array(arr, name=name) + return cls(dataset=ds4, input_data=data, _token=cls._token) + + def preprocess( + self, + mode: str = "linear", + plot_transform: bool = True, + cropping_factor: float = 0.5, + **plot_kwargs: Any, + ) -> StrainMap: + if self.dataset.units[2] == "A": + qrow_sampling_ang = float(self.dataset.sampling[2]) + elif self.dataset.units[2] == "mrad": + wavelength = float(electron_wavelength_angstrom(float(self.dataset.metadata["energy"]))) + qrow_sampling_ang = float(self.dataset.sampling[2]) / 1000.0 / wavelength + else: + raise ValueError(f"unrecognized diffraction-space unit for axis 2: {self.dataset.units[2]}") + + if self.dataset.units[3] == "A": + qcol_sampling_ang = float(self.dataset.sampling[3]) + elif self.dataset.units[3] == "mrad": + wavelength = float(electron_wavelength_angstrom(float(self.dataset.metadata["energy"]))) + qcol_sampling_ang = float(self.dataset.sampling[3]) / 1000.0 / wavelength + else: + raise ValueError(f"unrecognized diffraction-space unit for axis 3: {self.dataset.units[3]}") + + self.metadata["sampling_real"] = np.array( + ( + 1.0 / (qrow_sampling_ang * float(self.dataset.shape[2])), + 1.0 / (qcol_sampling_ang * float(self.dataset.shape[3])), + ), + dtype=float, + ) + + if mode == "linear": + im = np.mean(np.abs(np.fft.fft2(self.dataset.array)), axis=(0, 1)) + elif mode == "log": + im = np.mean(np.abs(np.fft.fft2(np.log(self.dataset.array + 1.0))), axis=(0, 1)) + else: + raise ValueError("mode must be 'linear' or 'log'") + + self.transform = Dataset2d.from_array( + np.fft.fftshift(im), + origin=(im.shape[0] // 2, im.shape[1] // 2), + sampling=(1.0, 1.0), + units=("A", "A"), + signal_units="intensity", + ) + + self.transform_rotated = Dataset2d.from_array( + rotate_image( + self.transform.array, + float(self.dataset.metadata["q_to_r_rotation_cw_deg"]), + clockwise=True, + ), + origin=(im.shape[0] // 2, im.shape[1] // 2), + sampling=(1.0, 1.0), + units=("A", "A"), + signal_units="intensity", + ) + + if plot_transform: + self.plot_transform(cropping_factor=cropping_factor, **plot_kwargs) + + return self + + + + + + + + + + def plot_transform( + self, + cropping_factor: float = 0.25, + **plot_kwargs: Any + ): + if self.transform is None or self.transform_rotated is None: + raise ValueError("Run preprocess() first to compute transform images.") + + defaults = dict( + vmax=1.0, + title=("Original Transform", "Rotated Transform"), + scalebar=ScalebarConfig( + sampling=self.metadata["sampling_real"], + units=r"$\mathrm{\AA}$", + length=2, + ), + ) + defaults.update(plot_kwargs) + + fig, ax = show_2d([self.transform, self.transform_rotated], **defaults) + + axes = np.atleast_1d(ax) + for a in axes: + _apply_center_crop_limits(a, self.transform.shape, cropping_factor) + + return fig, ax + + + + + + + +def _apply_center_crop_limits(ax, shape: tuple[int, int], cropping_factor: float) -> None: + cf = float(cropping_factor) + if cf >= 1.0: + return + if not (0.0 < cf <= 1.0): + raise ValueError("cropping_factor must be in (0, 1].") + + H, W = int(shape[0]), int(shape[1]) + r0 = (H - 1) / 2.0 + c0 = (W - 1) / 2.0 + half_h = 0.5 * cf * H + half_w = 0.5 * cf * W + + ax.set_xlim(c0 - half_w, c0 + half_w) + + y0, y1 = ax.get_ylim() + if y0 > y1: + ax.set_ylim(r0 + half_h, r0 - half_h) + else: + ax.set_ylim(r0 - half_h, r0 + half_h) + + From 7c7a5303fe55e2d1b5f3394638e100088e7d886f Mon Sep 17 00:00:00 2001 From: cophus Date: Thu, 1 Jan 2026 19:32:20 -0800 Subject: [PATCH 098/140] all basic functions implemented --- .../core/datastructures/dataset4dstem.py | 2 +- src/quantem/core/utils/imaging_utils.py | 27 +- src/quantem/diffraction/strain.py | 794 +++++++++++++++++- 3 files changed, 754 insertions(+), 69 deletions(-) diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 7a7f31f7..5bd78703 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -79,7 +79,7 @@ def __init__( _token : object | None, optional Token to prevent direct instantiation, by default None """ - mdata_keys_4dstem = ["q_to_r_rotation_cw_deg", 'q_transpose', "ellipticity"] + mdata_keys_4dstem = ["q_to_r_rotation_ccw_deg", 'q_transpose', "ellipticity"] for k in mdata_keys_4dstem: if k not in metadata.keys(): metadata[k] = None diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 8f8aeb55..b8001f84 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -558,30 +558,7 @@ def rotate_image( mode: str = "constant", cval: float = 0.0, ): - """Rotate an array about a pixel origin using bilinear/bicubic interpolation. - - Parameters - ---------- - im - Input array; last two dimensions are treated as (H, W). Any leading - dimensions are treated as batch and rotated independently. - rotation_deg - Rotation angle in degrees. Interpreted as clockwise if clockwise=True, - otherwise counterclockwise. - origin - Rotation origin (row, col) in pixel coordinates. If None, uses (H//2, W//2). - clockwise - If True, interpret rotation_deg as clockwise; if False, as counterclockwise. - interpolation - "bilinear" (order=1) or "bicubic" (order=3). - mode, cval - Boundary handling passed to scipy.ndimage.map_coordinates. - - Returns - ------- - out - Rotated array with the same shape as `im`. - """ + """Rotate an array about a pixel origin using bilinear/bicubic interpolation.""" im = np.asarray(im) if im.ndim < 2: raise ValueError("im must have at least 2 dimensions") @@ -603,7 +580,7 @@ def rotate_image( raise ValueError("interpolation must be 'bilinear' or 'bicubic'") theta = float(np.deg2rad(rotation_deg)) - if clockwise: + if not clockwise: theta = -theta ct = float(np.cos(theta)) diff --git a/src/quantem/diffraction/strain.py b/src/quantem/diffraction/strain.py index 55c57d9d..b5549ff7 100644 --- a/src/quantem/diffraction/strain.py +++ b/src/quantem/diffraction/strain.py @@ -2,23 +2,23 @@ from typing import Any +import matplotlib.pyplot as plt import numpy as np from numpy.typing import NDArray +from scipy.ndimage import distance_transform_edt from quantem.core.datastructures.dataset2d import Dataset2d +from quantem.core.datastructures.dataset3d import Dataset3d +from quantem.core.datastructures.dataset4d import Dataset4d from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize -from quantem.core.utils.imaging_utils import rotate_image +from quantem.core.utils.imaging_utils import dft_upsample, rotate_image from quantem.core.utils.utils import electron_wavelength_angstrom from quantem.core.utils.validators import ensure_valid_array from quantem.core.visualization import ScalebarConfig, show_2d class StrainMap(AutoSerialize): - """ - Nanobeam strain mapping - """ - _token = object() def __init__( @@ -36,9 +36,13 @@ def __init__( self.metadata: dict[str, Any] = {} self.transform: Dataset2d | None = None self.transform_rotated: Dataset2d | None = None + self.u: NDArray | None = None + self.v: NDArray | None = None + self.mask_diffraction = np.ones(self.dataset.array.shape[2:]) + self.mask_diffraction_inv = np.zeros(self.dataset.array.shape[2:]) @classmethod - def from_data(cls, data: NDArray | Dataset4dstem, *, name: str = "strain_map") -> StrainMap: + def from_data(cls, data: NDArray | Dataset4dstem, *, name: str = "strain_map") -> "StrainMap": if isinstance(data, Dataset4dstem): return cls(dataset=data, input_data=data, _token=cls._token) @@ -51,28 +55,81 @@ def from_data(cls, data: NDArray | Dataset4dstem, *, name: str = "strain_map") - ds4 = Dataset4dstem.from_array(arr, name=name) return cls(dataset=ds4, input_data=data, _token=cls._token) + + def diffraction_mask( + self, + threshold = None, + edge_blend = 32.0, + plot_mask = True, + figsize = (8,4), + ): + dp_mean = np.mean(self.dataset.array,axis=(0,1)) + mask_init = dp_mean < threshold + mask_init[:,0] = True + mask_init[0,:] = True + mask_init[:,-1] = True + mask_init[-1,:] = True + + self.mask_diffraction = np.sin( + np.clip( + distance_transform_edt(np.logical_not(mask_init)) / edge_blend, + 0.0, + 1.0, + )*np.pi/2, + )**2 + # int_edge = np.sum(dp_mean*self.mask_diffraction) / np.sum(self.mask_diffraction) + int_edge = np.min(dp_mean[self.mask_diffraction>0.99]) + self.mask_diffraction_inv = (1 - self.mask_diffraction) * int_edge + + if plot_mask: + fig,ax = plt.subplots(1,2,figsize=figsize) + ax[0].imshow( + np.log(np.maximum(dp_mean,np.min(dp_mean[dp_mean>0]))), + cmap = 'gray', + ) + ax[1].imshow( + np.log( + dp_mean*self.mask_diffraction + \ + self.mask_diffraction_inv, + ), + cmap = 'gray', + ) + + return self + + def preprocess( self, mode: str = "linear", + q_to_r_rotation_ccw_deg: float | None = None, + q_transpose: bool | None = None, plot_transform: bool = True, - cropping_factor: float = 0.5, + cropping_factor: float = 0.25, **plot_kwargs: Any, - ) -> StrainMap: - if self.dataset.units[2] == "A": + ) -> "StrainMap": + + self.metadata["mode"] = mode + + qrow_unit = str(self.dataset.units[2]) + qcol_unit = str(self.dataset.units[3]) + + if qrow_unit in {"A", "Å"}: qrow_sampling_ang = float(self.dataset.sampling[2]) - elif self.dataset.units[2] == "mrad": + elif qrow_unit == "mrad": wavelength = float(electron_wavelength_angstrom(float(self.dataset.metadata["energy"]))) qrow_sampling_ang = float(self.dataset.sampling[2]) / 1000.0 / wavelength else: - raise ValueError(f"unrecognized diffraction-space unit for axis 2: {self.dataset.units[2]}") + qrow_sampling_ang = 1.0 + qrow_unit = "pixels" - if self.dataset.units[3] == "A": + if qcol_unit in {"A", "Å"}: qcol_sampling_ang = float(self.dataset.sampling[3]) - elif self.dataset.units[3] == "mrad": + elif qcol_unit == "mrad": wavelength = float(electron_wavelength_angstrom(float(self.dataset.metadata["energy"]))) qcol_sampling_ang = float(self.dataset.sampling[3]) / 1000.0 / wavelength else: - raise ValueError(f"unrecognized diffraction-space unit for axis 3: {self.dataset.units[3]}") + qcol_sampling_ang = 1.0 + qcol_unit = "pixels" self.metadata["sampling_real"] = np.array( ( @@ -82,30 +139,81 @@ def preprocess( dtype=float, ) - if mode == "linear": - im = np.mean(np.abs(np.fft.fft2(self.dataset.array)), axis=(0, 1)) - elif mode == "log": - im = np.mean(np.abs(np.fft.fft2(np.log(self.dataset.array + 1.0))), axis=(0, 1)) + if qrow_unit == "pixels" and qcol_unit == "pixels": + self.metadata["real_units"] = "1/pixels" + else: + self.metadata["real_units"] = r"$\mathrm{\AA}$" + + if q_to_r_rotation_ccw_deg is None or q_transpose is None: + parent_rot = self.dataset.metadata.get("q_to_r_rotation_ccw_deg", None) + parent_tr = self.dataset.metadata.get("q_transpose", None) + if q_to_r_rotation_ccw_deg is None and parent_rot is not None: + q_to_r_rotation_ccw_deg = float(parent_rot) + if q_transpose is None and parent_tr is not None: + q_transpose = bool(parent_tr) + if (parent_rot is not None or parent_tr is not None) and ( + q_to_r_rotation_ccw_deg is not None or q_transpose is not None + ): + import warnings + + warnings.warn( + f"StrainMap.preprocess: using parent Dataset4dstem metadata " + f"(q_to_r_rotation_ccw_deg={float(q_to_r_rotation_ccw_deg or 0.0)}, " + f"q_transpose={bool(q_transpose or False)}).", + UserWarning, + ) + + if q_to_r_rotation_ccw_deg is None or q_transpose is None: + import warnings + + q_to_r_rotation_ccw_deg = ( + 0.0 if q_to_r_rotation_ccw_deg is None else float(q_to_r_rotation_ccw_deg) + ) + q_transpose = False if q_transpose is None else bool(q_transpose) + warnings.warn( + "StrainMap.preprocess: setting q_to_r_rotation_ccw_deg=0.0 and q_transpose=False.", + UserWarning, + ) + + self.metadata["q_to_r_rotation_ccw_deg"] = float(q_to_r_rotation_ccw_deg) + self.metadata["q_transpose"] = bool(q_transpose) + + if self.metadata["mode"] == "linear": + im = np.mean(np.abs(np.fft.fft2( + self.dataset.array * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + )), axis=(0, 1)) + elif self.metadata["mode"] == "log": + im = np.mean(np.abs(np.fft.fft2(np.log( + self.dataset.array * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + ))), axis=(0, 1)) else: raise ValueError("mode must be 'linear' or 'log'") + im = np.fft.fftshift(im) + self.transform = Dataset2d.from_array( - np.fft.fftshift(im), + im, origin=(im.shape[0] // 2, im.shape[1] // 2), sampling=(1.0, 1.0), - units=("A", "A"), + units=(qrow_unit, qcol_unit), signal_units="intensity", ) + im_plot = self.transform.array + if bool(self.metadata["q_transpose"]): + im_plot = im_plot.T + self.transform_rotated = Dataset2d.from_array( rotate_image( - self.transform.array, - float(self.dataset.metadata["q_to_r_rotation_cw_deg"]), - clockwise=True, + im_plot, + float(self.metadata["q_to_r_rotation_ccw_deg"]), + clockwise=False, ), origin=(im.shape[0] // 2, im.shape[1] // 2), sampling=(1.0, 1.0), - units=("A", "A"), + units=(self.metadata["real_units"], self.metadata["real_units"]), signal_units="intensity", ) @@ -114,48 +222,419 @@ def preprocess( return self - - - - - - - - def plot_transform( - self, - cropping_factor: float = 0.25, - **plot_kwargs: Any + self, + cropping_factor: float = 0.25, + scalebar_fraction: float = 0.25, + **plot_kwargs: Any, ): if self.transform is None or self.transform_rotated is None: raise ValueError("Run preprocess() first to compute transform images.") + sampling = float(np.mean(self.metadata["sampling_real"])) + units = str(self.metadata.get("real_units", r"$\mathrm{\AA}$")) + + W = int(self.transform.shape[1]) + view_w_px = float(W) * float(cropping_factor) + target_units = float(scalebar_fraction) * view_w_px * sampling + sb_len = _nice_length_units(target_units) + defaults = dict( vmax=1.0, title=("Original Transform", "Rotated Transform"), scalebar=ScalebarConfig( - sampling=self.metadata["sampling_real"], - units=r"$\mathrm{\AA}$", - length=2, + sampling=sampling, + units=units, + length=sb_len if sb_len > 0 else None, ), ) defaults.update(plot_kwargs) fig, ax = show_2d([self.transform, self.transform_rotated], **defaults) - axes = np.atleast_1d(ax) - for a in axes: + for a in _flatten_axes(ax): _apply_center_crop_limits(a, self.transform.shape, cropping_factor) return fig, ax + def choose_lattice_vector( + self, + *, + u: tuple[float, float] | NDArray, + v: tuple[float, float] | NDArray, + define_in_rotated: bool = False, + refine_subpixel: bool = True, + refine_subpixel_dft: bool = False, + refine_radius_px: float = 2.0, + refine_log: bool = False, + upsample: int = 16, + plot: bool = True, + cropping_factor: float = 0.25, + **plot_kwargs: Any, + ) -> "StrainMap": + if self.transform is None or self.transform_rotated is None: + raise ValueError("Run preprocess() first to compute transform images.") + + u_rc = np.asarray(u, dtype=float).reshape(2) + v_rc = np.asarray(v, dtype=float).reshape(2) + + rot_ccw = float(self.metadata.get("q_to_r_rotation_ccw_deg", 0.0)) + q_transpose = bool(self.metadata.get("q_transpose", False)) + + if define_in_rotated: + u_rc = _display_vec_to_raw(u_rc, rotation_ccw_deg=rot_ccw, transpose=q_transpose) + v_rc = _display_vec_to_raw(v_rc, rotation_ccw_deg=rot_ccw, transpose=q_transpose) + + if refine_subpixel_dft: + refine_subpixel = True + + if refine_subpixel: + u_rc, v_rc = _refine_lattice_vectors( + self.transform.array, + u_rc=u_rc, + v_rc=v_rc, + radius_px=float(refine_radius_px), + log_fit=bool(refine_log), + refine_dft=bool(refine_subpixel_dft), + upsample=int(upsample), + ) + + self.u = u_rc + self.v = v_rc + self.metadata["lattice_u_rc"] = self.u.copy() + self.metadata["lattice_v_rc"] = self.v.copy() + + if plot: + fig, ax = self.plot_transform(cropping_factor=cropping_factor, **plot_kwargs) + _overlay_lattice_vectors( + ax=ax, + shape=self.transform.shape, + u_rc=self.u, + v_rc=self.v, + rot_ccw_deg=rot_ccw, + q_transpose=q_transpose, + ) + return self + + return self + + + def fit_lattice_vectors( + self, + refine_subpixel: bool = True, + refine_subpixel_dft: bool = False, + refine_radius_px: float = 2.0, + upsample: int = 16, + refine_log: bool = False, + progressbar: bool = True, + ) -> "StrainMap": + from quantem.core.datastructures.dataset3d import Dataset3d + + if self.u is None or self.v is None: + raise ValueError("Run choose_lattice_vector() first to set initial lattice vectors (self.u, self.v).") + + if refine_subpixel_dft: + refine_subpixel = True + + scan_r = self.dataset.shape[0] + scan_c = self.dataset.shape[1] + self.u_fit = Dataset3d.from_shape( + (scan_r, scan_c, 2), + name="u_fits", + signal_units="pixels", + ) + self.v_fit = Dataset3d.from_shape( + (scan_r, scan_c, 2), + name="v_fits", + signal_units="pixels", + ) + + mode = str(self.metadata.get("mode", "linear")).lower() + + it = np.ndindex(scan_r, scan_c) + if progressbar: + try: + from tqdm.auto import tqdm # type: ignore + + it = tqdm(it, total=scan_r * scan_c, desc="fit_lattice_vectors", leave=True) + except Exception: + pass + + u0 = np.asarray(self.u, dtype=float).reshape(2) + v0 = np.asarray(self.v, dtype=float).reshape(2) + + for r, c in it: + dp = self.dataset.array[r, c]*self.mask_diffraction + \ + self.mask_diffraction_inv + + if mode == "linear": + im = np.fft.fftshift(np.abs(np.fft.fft2(dp))) + elif mode == "log": + int_range = np.max(dp) - np.min(dp) + im = np.fft.fftshift(np.abs(np.fft.fft2(np.log(dp + int_range*0.01)))) + else: + raise ValueError("metadata['mode'] must be 'linear' or 'log'") + + if refine_subpixel: + u_rc, v_rc = _refine_lattice_vectors( + im, + u_rc=u0, + v_rc=v0, + radius_px=float(refine_radius_px), + log_fit=bool(refine_log), + refine_dft=bool(refine_subpixel_dft), + upsample=int(upsample), + ) + else: + u_rc = u0 + v_rc = v0 + + self.u_fit.array[r, c, :] = u_rc + self.v_fit.array[r, c, :] = v_rc + + self.metadata["fit_refine_subpixel"] = bool(refine_subpixel) + self.metadata["fit_refine_subpixel_dft"] = bool(refine_subpixel_dft) + self.metadata["fit_refine_radius_px"] = float(refine_radius_px) + self.metadata["fit_refine_log"] = bool(refine_log) + self.metadata["fit_upsample"] = int(upsample) + + return self + + + def plot_lattice_vectors( + self, + subtract_mean: bool = True, + scalebar: bool = False, + **plot_kwargs: Any, + ): + if getattr(self, "u_fit", None) is None or getattr(self, "v_fit", None) is None: + raise ValueError("Run fit_lattice_vectors() first to compute u_fit and v_fit.") + + im0 = self.u_fit.array[:,:,0] + im1 = self.u_fit.array[:,:,1] + im2 = self.v_fit.array[:,:,0] + im3 = self.v_fit.array[:,:,1] + + if subtract_mean: + im0 = im0 - float(np.nanmean(im0)) + im1 = im1 - float(np.nanmean(im1)) + im2 = im2 - float(np.nanmean(im2)) + im3 = im3 - float(np.nanmean(im3)) + + vlim = float(np.nanmax(np.abs(np.stack([im0, im1, im2, im3], axis=0)))) + vmin = -vlim + vmax = vlim + + defaults: dict[str, Any] = dict( + title=("u_r", "u_c", "v_r", "v_c"), + vmin=vmin, + vmax=vmax, + ) + + if scalebar: + s0 = float(self.dataset.sampling[0]) if len(self.dataset.sampling) > 0 else 1.0 + s1 = float(self.dataset.sampling[1]) if len(self.dataset.sampling) > 1 else s0 + sampling_scan = float(np.mean([s0, s1])) + units_scan = str(self.dataset.units[0]) if len(self.dataset.units) > 0 else "pixels" + defaults["scalebar"] = ScalebarConfig(sampling=sampling_scan, units=units_scan) + + defaults.update(plot_kwargs) + + fig, ax = show_2d([im0, im1, im2, im3], **defaults) + return fig, ax + + + def fit_strain( + self, + mask_reference = None, + plot_strain = True, + ): + if self.u_fit is None or self.v_fit is None: + raise ValueError("Run fit_lattice_vectors() first to compute u_fit and v_fit.") + u_fit = self.u_fit.array + v_fit = self.v_fit.array + scan_r, scan_c = int(u_fit.shape[0]), int(u_fit.shape[1]) + if mask_reference is None: + self.u_ref = np.median(u_fit.reshape(-1, 2), axis=0) + self.v_ref = np.median(v_fit.reshape(-1, 2), axis=0) + else: + m = np.asarray(mask_reference, dtype=bool) + self.u_ref = np.array( + ( + np.median(u_fit[m, 0]), + np.median(u_fit[m, 1]), + ), + dtype=float, + ) + self.v_ref = np.array( + ( + np.median(v_fit[m, 0]), + np.median(v_fit[m, 1]), + ), + dtype=float, + ) + Uref = np.stack((self.u_ref, self.v_ref), axis=1).astype(float) + det = float(np.linalg.det(Uref)) + if not np.isfinite(det) or abs(det) < 1e-12: + Uref_inv = np.linalg.pinv(Uref) + else: + Uref_inv = np.linalg.inv(Uref) + # init + self.strain_trans = Dataset4d.from_shape( + (scan_r, scan_c, 2, 2), + name="transformation matrix", + signal_units="fractional", + ) -def _apply_center_crop_limits(ax, shape: tuple[int, int], cropping_factor: float) -> None: + # Loop over probe positions + for r in range(scan_r): + for c in range(scan_c): + U = np.stack((u_fit[r, c, :], v_fit[r, c, :]), axis=1) + self.strain_trans.array[r, c, :, :] = U @ Uref_inv + + # get strains in orthogonal directions + self.strain_raw_err = Dataset2d.from_array( + self.strain_trans.array[:, :, 0, 0] - 1, + name="strain err", + signal_units="fractional", + ) + self.strain_raw_ecc = Dataset2d.from_array( + self.strain_trans.array[:, :, 1, 1] - 1, + name="strain ecc", + signal_units="fractional", + ) + self.strain_raw_erc = Dataset2d.from_array( + self.strain_trans.array[:, :, 1, 0]*0.5 + self.strain_trans.array[:, :, 0, 1]*0.5, + name="strain erc", + signal_units="fractional", + ) + self.strain_rotation = Dataset2d.from_array( + self.strain_trans.array[:, :, 1, 0]*-0.5 + self.strain_trans.array[:, :, 0, 1]*0.5, + name="strain rotation", + signal_units="fractional", + ) + + return self + + + def plot_strain( + self, + ref_u_v=(1.0, 0.0), + ref_angle_degrees=None, + strain_range_percent=(-3.0, 3.0), + rotation_range_degrees=(-2.0, 2.0), + plot_rotation=True, + cmap_strain="PiYG_r", + cmap_rotation="PiYG_r", + layout="horizontal", + figsize=(6, 6), + ): + import matplotlib.pyplot as plt + + if ref_angle_degrees is None: + ref_vec = self.u_ref * float(ref_u_v[0]) + self.v_ref * float(ref_u_v[1]) + ref_angle = float(np.arctan2(ref_vec[1], ref_vec[0])) + else: + ref_angle = float(np.deg2rad(ref_angle_degrees)) + + angle = ref_angle + np.deg2rad(self.metadata["q_to_r_rotation_ccw_deg"]) + print(np.round(np.rad2deg(angle),2)) + + c = float(np.cos(angle)) + s = float(np.sin(angle)) + + err = self.strain_raw_err.array + ecc = self.strain_raw_ecc.array + erc = self.strain_raw_erc.array + + euu = err * (c * c) + 2.0 * erc * (c * s) + ecc * (s * s) + evv = err * (s * s) - 2.0 * erc * (c * s) + ecc * (c * c) + euv = (ecc - err) * (c * s) + erc * (c * c - s * s) + + self.strain_euu = self.strain_raw_err.copy() + self.strain_evv = self.strain_raw_ecc.copy() + self.strain_euv = self.strain_raw_erc.copy() + self.strain_euu.array[...] = euu + self.strain_evv.array[...] = evv + self.strain_euv.array[...] = euv + + if layout == "horizontal": + if plot_rotation: + fig, ax = plt.subplots(1, 4, figsize=figsize) + + ax[0].imshow( + self.strain_euu.array * 100, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cmap_strain, + ) + ax[1].imshow( + self.strain_evv.array * 100, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cmap_strain, + ) + ax[2].imshow( + self.strain_euv.array * 100, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cmap_strain, + ) + ax[3].imshow( + np.rad2deg(self.strain_rotation.array), + vmin=rotation_range_degrees[0], + vmax=rotation_range_degrees[1], + cmap=cmap_rotation, + ) + return fig, ax + + fig, ax = plt.subplots(1, 3, figsize=figsize) + ax[0].imshow( + self.strain_euu.array * 100, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cmap_strain, + ) + ax[1].imshow( + self.strain_evv.array * 100, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cmap_strain, + ) + ax[2].imshow( + self.strain_euv.array * 100, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cmap_strain, + ) + return fig, ax + + raise ValueError("layout must be 'horizontal'") + + +def _nice_length_units(target: float) -> float: + target = float(target) + if not np.isfinite(target) or target <= 0: + return 0.0 + exp = np.floor(np.log10(target)) + base = target / (10.0**exp) + if base < 1.5: + nice = 1.0 + elif base < 3.5: + nice = 2.0 + elif base < 7.5: + nice = 5.0 + else: + nice = 10.0 + return float(nice * (10.0**exp)) + + +def _apply_center_crop_limits(ax: Any, shape: tuple[int, int], cropping_factor: float) -> None: cf = float(cropping_factor) if cf >= 1.0: return @@ -163,8 +642,8 @@ def _apply_center_crop_limits(ax, shape: tuple[int, int], cropping_factor: float raise ValueError("cropping_factor must be in (0, 1].") H, W = int(shape[0]), int(shape[1]) - r0 = (H - 1) / 2.0 - c0 = (W - 1) / 2.0 + r0 = float(H // 2) + c0 = float(W // 2) half_h = 0.5 * cf * H half_w = 0.5 * cf * W @@ -177,3 +656,232 @@ def _apply_center_crop_limits(ax, shape: tuple[int, int], cropping_factor: float ax.set_ylim(r0 - half_h, r0 + half_h) +def _flatten_axes(ax: Any) -> list[Any]: + if isinstance(ax, np.ndarray): + return list(ax.ravel()) + if isinstance(ax, (list, tuple)): + out: list[Any] = [] + for a in ax: + out.extend(_flatten_axes(a)) + return out + return [ax] + + +def _raw_vec_to_display(vec_rc: NDArray, *, rotation_ccw_deg: float, transpose: bool) -> NDArray: + v = np.asarray(vec_rc, dtype=float).reshape(2) + dr, dc = float(v[0]), float(v[1]) + + if transpose: + dr, dc = dc, dr + + theta = float(np.deg2rad(rotation_ccw_deg)) + ct = float(np.cos(theta)) + st = float(np.sin(theta)) + + dr2 = ct * dr - st * dc + dc2 = st * dr + ct * dc + return np.array((dr2, dc2), dtype=float) + + +def _display_vec_to_raw(vec_rc: NDArray, *, rotation_ccw_deg: float, transpose: bool) -> NDArray: + v = np.asarray(vec_rc, dtype=float).reshape(2) + dr, dc = float(v[0]), float(v[1]) + + theta = float(np.deg2rad(rotation_ccw_deg)) + ct = float(np.cos(theta)) + st = float(np.sin(theta)) + + dr2 = ct * dr + st * dc + dc2 = -st * dr + ct * dc + + if transpose: + dr2, dc2 = dc2, dr2 + + return np.array((dr2, dc2), dtype=float) + + +def _plot_lattice_vectors(ax: Any, center_rc: tuple[float, float], u_rc: NDArray, v_rc: NDArray) -> None: + r0, c0 = float(center_rc[0]), float(center_rc[1]) + + def _draw(vec: NDArray, label: str, color: tuple[float, float, float]) -> None: + dr, dc = float(vec[0]), float(vec[1]) + ax.plot([c0, c0 + dc], [r0, r0 + dr], linewidth=2.75, color=color) + ax.plot([c0 + dc], [r0 + dr], marker="o", markersize=6.0, color=color) + ax.text(c0 + dc, r0 + dr, f" {label}", color=color, fontsize=18, va="center") + + _draw(np.asarray(u_rc, dtype=float).reshape(2), "u", (1.0, 0.0, 0.0)) + _draw(np.asarray(v_rc, dtype=float).reshape(2), "v", (0.0, 0.7, 1.0)) + + +def _overlay_lattice_vectors( + *, + ax: Any, + shape: tuple[int, int], + u_rc: NDArray, + v_rc: NDArray, + rot_ccw_deg: float, + q_transpose: bool, +) -> None: + axs = _flatten_axes(ax) + if not axs: + return + + H, W = int(shape[0]), int(shape[1]) + center_rc = (float(H // 2), float(W // 2)) + + _plot_lattice_vectors(axs[0], center_rc, u_rc, v_rc) + + if len(axs) >= 2: + u_disp = _raw_vec_to_display(u_rc, rotation_ccw_deg=float(rot_ccw_deg), transpose=bool(q_transpose)) + v_disp = _raw_vec_to_display(v_rc, rotation_ccw_deg=float(rot_ccw_deg), transpose=bool(q_transpose)) + _plot_lattice_vectors(axs[1], center_rc, u_disp, v_disp) + + +def _parabolic_vertex_delta(v_m1: float, v_0: float, v_p1: float) -> float: + denom = (v_m1 - 2.0 * v_0 + v_p1) + if denom == 0 or not np.isfinite(denom): + return 0.0 + delta = 0.5 * (v_m1 - v_p1) / denom + if not np.isfinite(delta): + return 0.0 + return float(np.clip(delta, -1.0, 1.0)) + + +def _refine_peak_subpixel( + im: NDArray, + *, + r_guess: float, + c_guess: float, + radius_px: float = 2.0, + log_fit: bool = False, +) -> tuple[float, float]: + im = np.asarray(im, dtype=float) + H, W = im.shape + + r0 = int(np.clip(int(np.round(r_guess)), 0, H - 1)) + c0 = int(np.clip(int(np.round(c_guess)), 0, W - 1)) + rad = int(max(0, int(np.ceil(float(radius_px))))) + + r1 = max(0, r0 - rad) + r2 = min(H, r0 + rad + 1) + c1 = max(0, c0 - rad) + c2 = min(W, c0 + rad + 1) + + win = im[r1:r2, c1:c2] + if win.size == 0: + return float(r_guess), float(c_guess) + + ir, ic = np.unravel_index(int(np.argmax(win)), win.shape) + r_peak = r1 + int(ir) + c_peak = c1 + int(ic) + + if 0 < r_peak < H - 1: + col = im[r_peak - 1 : r_peak + 2, c_peak] + if log_fit: + col = np.log(np.clip(col, 1e-12, None)) + dr = _parabolic_vertex_delta(float(col[0]), float(col[1]), float(col[2])) + else: + dr = 0.0 + + if 0 < c_peak < W - 1: + row = im[r_peak, c_peak - 1 : c_peak + 2] + if log_fit: + row = np.log(np.clip(row, 1e-12, None)) + dc = _parabolic_vertex_delta(float(row[0]), float(row[1]), float(row[2])) + else: + dc = 0.0 + + return float(r_peak) + dr, float(c_peak) + dc + + +def _refine_peak_subpixel_dft( + im: NDArray, + *, + r0: float, + c0: float, + upsample: int, + log_fit: bool = False, +) -> tuple[float, float]: + if int(upsample) <= 1: + return float(r0), float(c0) + + im = np.asarray(im, dtype=float) + F = np.fft.fft2(im) + + up = int(upsample) + du = int(np.ceil(1.5 * up)) + + patch = dft_upsample(F, up=up, shift=(float(r0), float(c0)), device="cpu") + patch = np.asarray(patch, dtype=float) + + i0, j0 = np.unravel_index(int(np.argmax(patch)), patch.shape) + i0 = int(i0) + j0 = int(j0) + + if 0 < i0 < patch.shape[0] - 1: + col = patch[i0 - 1 : i0 + 2, j0] + if log_fit: + col = np.log(np.clip(col, 1e-12, None)) + di = _parabolic_vertex_delta(float(col[0]), float(col[1]), float(col[2])) + else: + di = 0.0 + + if 0 < j0 < patch.shape[1] - 1: + row = patch[i0, j0 - 1 : j0 + 2] + if log_fit: + row = np.log(np.clip(row, 1e-12, None)) + dj = _parabolic_vertex_delta(float(row[0]), float(row[1]), float(row[2])) + else: + dj = 0.0 + + dr = (float(i0) - float(du) + float(di)) / float(up) + dc = (float(j0) - float(du) + float(dj)) / float(up) + + return float(r0) + dr, float(c0) + dc + + +def _refine_lattice_vectors( + im: NDArray, + *, + u_rc: NDArray, + v_rc: NDArray, + radius_px: float = 2.0, + log_fit: bool = False, + refine_dft: bool = True, + upsample: int = 16, +) -> tuple[NDArray, NDArray]: + im = np.asarray(im, dtype=float) + if im.ndim != 2: + raise ValueError("im must be 2D.") + + H, W = im.shape + r_center = float(H // 2) + c_center = float(W // 2) + + def _refine(vec: NDArray) -> NDArray: + vec = np.asarray(vec, dtype=float).reshape(2) + r_guess = r_center + float(vec[0]) + c_guess = c_center + float(vec[1]) + + r1, c1 = _refine_peak_subpixel( + im, + r_guess=float(r_guess), + c_guess=float(c_guess), + radius_px=float(radius_px), + log_fit=bool(log_fit), + ) + + if refine_dft and int(upsample) > 1: + r2, c2 = _refine_peak_subpixel_dft( + im, + r0=float(r1), + c0=float(c1), + upsample=int(upsample), + log_fit=bool(log_fit), + ) + else: + r2, c2 = float(r1), float(c1) + + return np.array((r2 - r_center, c2 - c_center), dtype=float) + + return _refine(u_rc), _refine(v_rc) From 7476e3d1978afd66edc3c956bac9ae591e5b7701 Mon Sep 17 00:00:00 2001 From: cophus Date: Sat, 3 Jan 2026 12:41:18 -0800 Subject: [PATCH 099/140] faster strain, adding h5plugin to read arina data --- pyproject.toml | 1 + src/quantem/diffraction/strain.py | 55 +- uv.lock | 2537 +++++++++++++++++++++++------ 3 files changed, 2123 insertions(+), 470 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 19882bf9..9621ee4d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -82,4 +82,5 @@ dev = [ "pre-commit>=4.2.0", "ruff>=0.11.5", "tomli>=2.2.1", + "hdf5plugin>=6.0.0", ] \ No newline at end of file diff --git a/src/quantem/diffraction/strain.py b/src/quantem/diffraction/strain.py index b5549ff7..bcfa981d 100644 --- a/src/quantem/diffraction/strain.py +++ b/src/quantem/diffraction/strain.py @@ -59,7 +59,7 @@ def from_data(cls, data: NDArray | Dataset4dstem, *, name: str = "strain_map") - def diffraction_mask( self, threshold = None, - edge_blend = 32.0, + edge_blend = 64.0, plot_mask = True, figsize = (8,4), ): @@ -103,6 +103,7 @@ def preprocess( mode: str = "linear", q_to_r_rotation_ccw_deg: float | None = None, q_transpose: bool | None = None, + skip = None, plot_transform: bool = True, cropping_factor: float = 0.25, **plot_kwargs: Any, @@ -178,18 +179,32 @@ def preprocess( self.metadata["q_to_r_rotation_ccw_deg"] = float(q_to_r_rotation_ccw_deg) self.metadata["q_transpose"] = bool(q_transpose) - if self.metadata["mode"] == "linear": - im = np.mean(np.abs(np.fft.fft2( - self.dataset.array * self.mask_diffraction[None,None,:,:] + \ - self.mask_diffraction_inv[None,None,:,:] - )), axis=(0, 1)) - elif self.metadata["mode"] == "log": - im = np.mean(np.abs(np.fft.fft2(np.log( - self.dataset.array * self.mask_diffraction[None,None,:,:] + \ - self.mask_diffraction_inv[None,None,:,:] - ))), axis=(0, 1)) + if skip is None: + if self.metadata["mode"] == "linear": + im = np.mean(np.abs(np.fft.fft2( + self.dataset.array * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + )), axis=(0, 1)) + elif self.metadata["mode"] == "log": + im = np.mean(np.abs(np.fft.fft2(np.log1p( + self.dataset.array * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + ))), axis=(0, 1)) + else: + raise ValueError("mode must be 'linear' or 'log'") else: - raise ValueError("mode must be 'linear' or 'log'") + if self.metadata["mode"] == "linear": + im = np.mean(np.abs(np.fft.fft2( + self.dataset.array[::skip,::skip] * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + )), axis=(0, 1)) + elif self.metadata["mode"] == "log": + im = np.mean(np.abs(np.fft.fft2(np.log1p( + self.dataset.array[::skip,::skip] * self.mask_diffraction[None,None,:,:] + \ + self.mask_diffraction_inv[None,None,:,:] + ))), axis=(0, 1)) + else: + raise ValueError("mode must be 'linear' or 'log'") im = np.fft.fftshift(im) @@ -239,8 +254,19 @@ def plot_transform( target_units = float(scalebar_fraction) * view_w_px * sampling sb_len = _nice_length_units(target_units) + # intensity scaling: compute from transform, apply same scaling to both panels + kr = (np.arange(self.transform.shape[0], dtype=float) - self.transform.shape[0] // 2)[:, None] + kc = (np.arange(self.transform.shape[1], dtype=float) - self.transform.shape[1] // 2)[None, :] + qmag = np.sqrt(kr * kr + kc * kc) + im0 = self.transform.array + tmp = im0 * qmag + i0 = np.unravel_index(int(np.nanargmax(tmp)), tmp.shape) + vmin = 0.0 + vmax = im0[i0] + defaults = dict( - vmax=1.0, + vmin=vmin, + vmax=vmax, title=("Original Transform", "Rotated Transform"), scalebar=ScalebarConfig( sampling=sampling, @@ -371,8 +397,7 @@ def fit_lattice_vectors( if mode == "linear": im = np.fft.fftshift(np.abs(np.fft.fft2(dp))) elif mode == "log": - int_range = np.max(dp) - np.min(dp) - im = np.fft.fftshift(np.abs(np.fft.fft2(np.log(dp + int_range*0.01)))) + im = np.fft.fftshift(np.abs(np.fft.fft2(np.log1p(dp)))) else: raise ValueError("metadata['mode'] must be 'linear' or 'log'") diff --git a/uv.lock b/uv.lock index 0e8424aa..a414c36d 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 3 +revision = 1 requires-python = ">=3.11" resolution-markers = [ "python_full_version >= '3.14'", @@ -17,9 +17,15 @@ members = [ name = "absl-py" version = "2.4.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -31,9 +37,15 @@ dependencies = [ { name = "sqlalchemy" }, { name = "typing-extensions" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/94/13/8b084e0f2efb0275a1d534838844926f798bd766566b1375174e2448cd31/alembic-1.18.4.tar.gz", hash = "sha256:cb6e1fd84b6174ab8dbb2329f86d631ba9559dd78df550b57804d607672cedbc", size = 2056725, upload-time = "2026-02-10T16:00:47.195Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/d2/29/6533c317b74f707ea28f8d633734dbda2119bbadfc61b2f3640ba835d0f7/alembic-1.18.4-py3-none-any.whl", hash = "sha256:a5ed4adcf6d8a4cb575f3d759f071b03cd6e5c7618eb796cb52497be25bfe19a", size = 263893, upload-time = "2026-02-10T16:00:49.997Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/02/a6/74c8cadc2882977d80ad756a13857857dbcf9bd405bc80b662eb10651282/alembic-1.17.2.tar.gz", hash = "sha256:bbe9751705c5e0f14877f02d46c53d10885e377e3d90eda810a016f9baa19e8e", size = 1988064 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ba/88/6237e97e3385b57b5f1528647addea5cc03d4d65d5979ab24327d41fb00d/alembic-1.17.2-py3-none-any.whl", hash = "sha256:f483dd1fe93f6c5d49217055e4d15b905b425b6af906746abb35b69c1996c4e6", size = 248554 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -44,6 +56,7 @@ dependencies = [ { name = "idna" }, { name = "typing-extensions", marker = "python_full_version < '3.13'" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/19/14/2c5dd9f512b66549ae92767a9c7b330ae88e1932ca57876909410251fe13/anyio-4.13.0.tar.gz", hash = "sha256:334b70e641fd2221c1505b3890c69882fe4a2df910cba14d97019b90b24439dc", size = 231622, upload-time = "2026-03-24T12:59:09.671Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/da/42/e921fccf5015463e32a3cf6ee7f980a6ed0f395ceeaa45060b61d86486c2/anyio-4.13.0-py3-none-any.whl", hash = "sha256:08b310f9e24a9594186fd75b4f73f4a4152069e3853f1ed8bfbf58369f4ad708", size = 114353, upload-time = "2026-03-24T12:59:08.246Z" }, @@ -61,15 +74,20 @@ dependencies = [ sdist = { url = "https://files.pythonhosted.org/packages/79/31/0491d707c674b34267f55d96d6a7148e55e7b6718a271686232cf295fbe2/anywidget-0.11.0.tar.gz", hash = "sha256:6695fbef9449cf8c27f421b96c5837aa37f909ec1f60cfa33add333e1b70b169", size = 426999, upload-time = "2026-04-27T23:42:09.576Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/8e/c2/8fec8e8e2eb920cc2280f569144080cd58622a2eda83bfa4c0c354a63264/anywidget-0.11.0-py3-none-any.whl", hash = "sha256:c574d9acc6503ad27b37a9acea48f957a8ba7c9c9876cfcb37898931c098ce9d", size = 317341, upload-time = "2026-04-27T23:42:08.356Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc", size = 109097 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "appnope" version = "0.1.4" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170, upload-time = "2024-02-06T09:43:11.258Z" } +sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170 } wheels = [ - { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" }, + { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321 }, ] [[package]] @@ -79,9 +97,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "argon2-cffi-bindings" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/0e/89/ce5af8a7d472a67cc819d5d998aa8c82c5d860608c4db9f46f1162d7dab9/argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1", size = 45706, upload-time = "2025-06-03T06:55:32.073Z" } +sdist = { url = "https://files.pythonhosted.org/packages/0e/89/ce5af8a7d472a67cc819d5d998aa8c82c5d860608c4db9f46f1162d7dab9/argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1", size = 45706 } wheels = [ - { url = "https://files.pythonhosted.org/packages/4f/d3/a8b22fa575b297cd6e3e3b0155c7e25db170edf1c74783d6a31a2490b8d9/argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741", size = 14657, upload-time = "2025-06-03T06:55:30.804Z" }, + { url = "https://files.pythonhosted.org/packages/4f/d3/a8b22fa575b297cd6e3e3b0155c7e25db170edf1c74783d6a31a2490b8d9/argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741", size = 14657 }, ] [[package]] @@ -91,28 +109,28 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "cffi" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/5c/2d/db8af0df73c1cf454f71b2bbe5e356b8c1f8041c979f505b3d3186e520a9/argon2_cffi_bindings-25.1.0.tar.gz", hash = "sha256:b957f3e6ea4d55d820e40ff76f450952807013d361a65d7f28acc0acbf29229d", size = 1783441, upload-time = "2025-07-30T10:02:05.147Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/60/97/3c0a35f46e52108d4707c44b95cfe2afcafc50800b5450c197454569b776/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:3d3f05610594151994ca9ccb3c771115bdb4daef161976a266f0dd8aa9996b8f", size = 54393, upload-time = "2025-07-30T10:01:40.97Z" }, - { url = "https://files.pythonhosted.org/packages/9d/f4/98bbd6ee89febd4f212696f13c03ca302b8552e7dbf9c8efa11ea4a388c3/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:8b8efee945193e667a396cbc7b4fb7d357297d6234d30a489905d96caabde56b", size = 29328, upload-time = "2025-07-30T10:01:41.916Z" }, - { url = "https://files.pythonhosted.org/packages/43/24/90a01c0ef12ac91a6be05969f29944643bc1e5e461155ae6559befa8f00b/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:3c6702abc36bf3ccba3f802b799505def420a1b7039862014a65db3205967f5a", size = 31269, upload-time = "2025-07-30T10:01:42.716Z" }, - { url = "https://files.pythonhosted.org/packages/d4/d3/942aa10782b2697eee7af5e12eeff5ebb325ccfb86dd8abda54174e377e4/argon2_cffi_bindings-25.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a1c70058c6ab1e352304ac7e3b52554daadacd8d453c1752e547c76e9c99ac44", size = 86558, upload-time = "2025-07-30T10:01:43.943Z" }, - { url = "https://files.pythonhosted.org/packages/0d/82/b484f702fec5536e71836fc2dbc8c5267b3f6e78d2d539b4eaa6f0db8bf8/argon2_cffi_bindings-25.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e2fd3bfbff3c5d74fef31a722f729bf93500910db650c925c2d6ef879a7e51cb", size = 92364, upload-time = "2025-07-30T10:01:44.887Z" }, - { url = "https://files.pythonhosted.org/packages/c9/c1/a606ff83b3f1735f3759ad0f2cd9e038a0ad11a3de3b6c673aa41c24bb7b/argon2_cffi_bindings-25.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:c4f9665de60b1b0e99bcd6be4f17d90339698ce954cfd8d9cf4f91c995165a92", size = 85637, upload-time = "2025-07-30T10:01:46.225Z" }, - { url = "https://files.pythonhosted.org/packages/44/b4/678503f12aceb0262f84fa201f6027ed77d71c5019ae03b399b97caa2f19/argon2_cffi_bindings-25.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ba92837e4a9aa6a508c8d2d7883ed5a8f6c308c89a4790e1e447a220deb79a85", size = 91934, upload-time = "2025-07-30T10:01:47.203Z" }, - { url = "https://files.pythonhosted.org/packages/f0/c7/f36bd08ef9bd9f0a9cff9428406651f5937ce27b6c5b07b92d41f91ae541/argon2_cffi_bindings-25.1.0-cp314-cp314t-win32.whl", hash = "sha256:84a461d4d84ae1295871329b346a97f68eade8c53b6ed9a7ca2d7467f3c8ff6f", size = 28158, upload-time = "2025-07-30T10:01:48.341Z" }, - { url = "https://files.pythonhosted.org/packages/b3/80/0106a7448abb24a2c467bf7d527fe5413b7fdfa4ad6d6a96a43a62ef3988/argon2_cffi_bindings-25.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:b55aec3565b65f56455eebc9b9f34130440404f27fe21c3b375bf1ea4d8fbae6", size = 32597, upload-time = "2025-07-30T10:01:49.112Z" }, - { url = "https://files.pythonhosted.org/packages/05/b8/d663c9caea07e9180b2cb662772865230715cbd573ba3b5e81793d580316/argon2_cffi_bindings-25.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:87c33a52407e4c41f3b70a9c2d3f6056d88b10dad7695be708c5021673f55623", size = 28231, upload-time = "2025-07-30T10:01:49.92Z" }, - { url = "https://files.pythonhosted.org/packages/1d/57/96b8b9f93166147826da5f90376e784a10582dd39a393c99bb62cfcf52f0/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:aecba1723ae35330a008418a91ea6cfcedf6d31e5fbaa056a166462ff066d500", size = 54121, upload-time = "2025-07-30T10:01:50.815Z" }, - { url = "https://files.pythonhosted.org/packages/0a/08/a9bebdb2e0e602dde230bdde8021b29f71f7841bd54801bcfd514acb5dcf/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:2630b6240b495dfab90aebe159ff784d08ea999aa4b0d17efa734055a07d2f44", size = 29177, upload-time = "2025-07-30T10:01:51.681Z" }, - { url = "https://files.pythonhosted.org/packages/b6/02/d297943bcacf05e4f2a94ab6f462831dc20158614e5d067c35d4e63b9acb/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:7aef0c91e2c0fbca6fc68e7555aa60ef7008a739cbe045541e438373bc54d2b0", size = 31090, upload-time = "2025-07-30T10:01:53.184Z" }, - { url = "https://files.pythonhosted.org/packages/c1/93/44365f3d75053e53893ec6d733e4a5e3147502663554b4d864587c7828a7/argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e021e87faa76ae0d413b619fe2b65ab9a037f24c60a1e6cc43457ae20de6dc6", size = 81246, upload-time = "2025-07-30T10:01:54.145Z" }, - { url = "https://files.pythonhosted.org/packages/09/52/94108adfdd6e2ddf58be64f959a0b9c7d4ef2fa71086c38356d22dc501ea/argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d3e924cfc503018a714f94a49a149fdc0b644eaead5d1f089330399134fa028a", size = 87126, upload-time = "2025-07-30T10:01:55.074Z" }, - { url = "https://files.pythonhosted.org/packages/72/70/7a2993a12b0ffa2a9271259b79cc616e2389ed1a4d93842fac5a1f923ffd/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:c87b72589133f0346a1cb8d5ecca4b933e3c9b64656c9d175270a000e73b288d", size = 80343, upload-time = "2025-07-30T10:01:56.007Z" }, - { url = "https://files.pythonhosted.org/packages/78/9a/4e5157d893ffc712b74dbd868c7f62365618266982b64accab26bab01edc/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:1db89609c06afa1a214a69a462ea741cf735b29a57530478c06eb81dd403de99", size = 86777, upload-time = "2025-07-30T10:01:56.943Z" }, - { url = "https://files.pythonhosted.org/packages/74/cd/15777dfde1c29d96de7f18edf4cc94c385646852e7c7b0320aa91ccca583/argon2_cffi_bindings-25.1.0-cp39-abi3-win32.whl", hash = "sha256:473bcb5f82924b1becbb637b63303ec8d10e84c8d241119419897a26116515d2", size = 27180, upload-time = "2025-07-30T10:01:57.759Z" }, - { url = "https://files.pythonhosted.org/packages/e2/c6/a759ece8f1829d1f162261226fbfd2c6832b3ff7657384045286d2afa384/argon2_cffi_bindings-25.1.0-cp39-abi3-win_amd64.whl", hash = "sha256:a98cd7d17e9f7ce244c0803cad3c23a7d379c301ba618a5fa76a67d116618b98", size = 31715, upload-time = "2025-07-30T10:01:58.56Z" }, - { url = "https://files.pythonhosted.org/packages/42/b9/f8d6fa329ab25128b7e98fd83a3cb34d9db5b059a9847eddb840a0af45dd/argon2_cffi_bindings-25.1.0-cp39-abi3-win_arm64.whl", hash = "sha256:b0fdbcf513833809c882823f98dc2f931cf659d9a1429616ac3adebb49f5db94", size = 27149, upload-time = "2025-07-30T10:01:59.329Z" }, +sdist = { url = "https://files.pythonhosted.org/packages/5c/2d/db8af0df73c1cf454f71b2bbe5e356b8c1f8041c979f505b3d3186e520a9/argon2_cffi_bindings-25.1.0.tar.gz", hash = "sha256:b957f3e6ea4d55d820e40ff76f450952807013d361a65d7f28acc0acbf29229d", size = 1783441 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/60/97/3c0a35f46e52108d4707c44b95cfe2afcafc50800b5450c197454569b776/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:3d3f05610594151994ca9ccb3c771115bdb4daef161976a266f0dd8aa9996b8f", size = 54393 }, + { url = "https://files.pythonhosted.org/packages/9d/f4/98bbd6ee89febd4f212696f13c03ca302b8552e7dbf9c8efa11ea4a388c3/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:8b8efee945193e667a396cbc7b4fb7d357297d6234d30a489905d96caabde56b", size = 29328 }, + { url = "https://files.pythonhosted.org/packages/43/24/90a01c0ef12ac91a6be05969f29944643bc1e5e461155ae6559befa8f00b/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:3c6702abc36bf3ccba3f802b799505def420a1b7039862014a65db3205967f5a", size = 31269 }, + { url = "https://files.pythonhosted.org/packages/d4/d3/942aa10782b2697eee7af5e12eeff5ebb325ccfb86dd8abda54174e377e4/argon2_cffi_bindings-25.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a1c70058c6ab1e352304ac7e3b52554daadacd8d453c1752e547c76e9c99ac44", size = 86558 }, + { url = "https://files.pythonhosted.org/packages/0d/82/b484f702fec5536e71836fc2dbc8c5267b3f6e78d2d539b4eaa6f0db8bf8/argon2_cffi_bindings-25.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e2fd3bfbff3c5d74fef31a722f729bf93500910db650c925c2d6ef879a7e51cb", size = 92364 }, + { url = "https://files.pythonhosted.org/packages/c9/c1/a606ff83b3f1735f3759ad0f2cd9e038a0ad11a3de3b6c673aa41c24bb7b/argon2_cffi_bindings-25.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:c4f9665de60b1b0e99bcd6be4f17d90339698ce954cfd8d9cf4f91c995165a92", size = 85637 }, + { url = "https://files.pythonhosted.org/packages/44/b4/678503f12aceb0262f84fa201f6027ed77d71c5019ae03b399b97caa2f19/argon2_cffi_bindings-25.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ba92837e4a9aa6a508c8d2d7883ed5a8f6c308c89a4790e1e447a220deb79a85", size = 91934 }, + { url = "https://files.pythonhosted.org/packages/f0/c7/f36bd08ef9bd9f0a9cff9428406651f5937ce27b6c5b07b92d41f91ae541/argon2_cffi_bindings-25.1.0-cp314-cp314t-win32.whl", hash = "sha256:84a461d4d84ae1295871329b346a97f68eade8c53b6ed9a7ca2d7467f3c8ff6f", size = 28158 }, + { url = "https://files.pythonhosted.org/packages/b3/80/0106a7448abb24a2c467bf7d527fe5413b7fdfa4ad6d6a96a43a62ef3988/argon2_cffi_bindings-25.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:b55aec3565b65f56455eebc9b9f34130440404f27fe21c3b375bf1ea4d8fbae6", size = 32597 }, + { url = "https://files.pythonhosted.org/packages/05/b8/d663c9caea07e9180b2cb662772865230715cbd573ba3b5e81793d580316/argon2_cffi_bindings-25.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:87c33a52407e4c41f3b70a9c2d3f6056d88b10dad7695be708c5021673f55623", size = 28231 }, + { url = "https://files.pythonhosted.org/packages/1d/57/96b8b9f93166147826da5f90376e784a10582dd39a393c99bb62cfcf52f0/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:aecba1723ae35330a008418a91ea6cfcedf6d31e5fbaa056a166462ff066d500", size = 54121 }, + { url = "https://files.pythonhosted.org/packages/0a/08/a9bebdb2e0e602dde230bdde8021b29f71f7841bd54801bcfd514acb5dcf/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:2630b6240b495dfab90aebe159ff784d08ea999aa4b0d17efa734055a07d2f44", size = 29177 }, + { url = "https://files.pythonhosted.org/packages/b6/02/d297943bcacf05e4f2a94ab6f462831dc20158614e5d067c35d4e63b9acb/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:7aef0c91e2c0fbca6fc68e7555aa60ef7008a739cbe045541e438373bc54d2b0", size = 31090 }, + { url = "https://files.pythonhosted.org/packages/c1/93/44365f3d75053e53893ec6d733e4a5e3147502663554b4d864587c7828a7/argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e021e87faa76ae0d413b619fe2b65ab9a037f24c60a1e6cc43457ae20de6dc6", size = 81246 }, + { url = "https://files.pythonhosted.org/packages/09/52/94108adfdd6e2ddf58be64f959a0b9c7d4ef2fa71086c38356d22dc501ea/argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d3e924cfc503018a714f94a49a149fdc0b644eaead5d1f089330399134fa028a", size = 87126 }, + { url = "https://files.pythonhosted.org/packages/72/70/7a2993a12b0ffa2a9271259b79cc616e2389ed1a4d93842fac5a1f923ffd/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:c87b72589133f0346a1cb8d5ecca4b933e3c9b64656c9d175270a000e73b288d", size = 80343 }, + { url = "https://files.pythonhosted.org/packages/78/9a/4e5157d893ffc712b74dbd868c7f62365618266982b64accab26bab01edc/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:1db89609c06afa1a214a69a462ea741cf735b29a57530478c06eb81dd403de99", size = 86777 }, + { url = "https://files.pythonhosted.org/packages/74/cd/15777dfde1c29d96de7f18edf4cc94c385646852e7c7b0320aa91ccca583/argon2_cffi_bindings-25.1.0-cp39-abi3-win32.whl", hash = "sha256:473bcb5f82924b1becbb637b63303ec8d10e84c8d241119419897a26116515d2", size = 27180 }, + { url = "https://files.pythonhosted.org/packages/e2/c6/a759ece8f1829d1f162261226fbfd2c6832b3ff7657384045286d2afa384/argon2_cffi_bindings-25.1.0-cp39-abi3-win_amd64.whl", hash = "sha256:a98cd7d17e9f7ce244c0803cad3c23a7d379c301ba618a5fa76a67d116618b98", size = 31715 }, + { url = "https://files.pythonhosted.org/packages/42/b9/f8d6fa329ab25128b7e98fd83a3cb34d9db5b059a9847eddb840a0af45dd/argon2_cffi_bindings-25.1.0-cp39-abi3-win_arm64.whl", hash = "sha256:b0fdbcf513833809c882823f98dc2f931cf659d9a1429616ac3adebb49f5db94", size = 27149 }, ] [[package]] @@ -123,45 +141,63 @@ dependencies = [ { name = "python-dateutil" }, { name = "tzdata" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/b9/33/032cdc44182491aa708d06a68b62434140d8c50820a087fac7af37703357/arrow-1.4.0.tar.gz", hash = "sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7", size = 152931, upload-time = "2025-10-18T17:46:46.761Z" } +sdist = { url = "https://files.pythonhosted.org/packages/b9/33/032cdc44182491aa708d06a68b62434140d8c50820a087fac7af37703357/arrow-1.4.0.tar.gz", hash = "sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7", size = 152931 } wheels = [ - { url = "https://files.pythonhosted.org/packages/ed/c9/d7977eaacb9df673210491da99e6a247e93df98c715fc43fd136ce1d3d33/arrow-1.4.0-py3-none-any.whl", hash = "sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205", size = 68797, upload-time = "2025-10-18T17:46:45.663Z" }, + { url = "https://files.pythonhosted.org/packages/ed/c9/d7977eaacb9df673210491da99e6a247e93df98c715fc43fd136ce1d3d33/arrow-1.4.0-py3-none-any.whl", hash = "sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205", size = 68797 }, ] [[package]] name = "asttokens" version = "3.0.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/be/a5/8e3f9b6771b0b408517c82d97aed8f2036509bc247d46114925e32fe33f0/asttokens-3.0.1.tar.gz", hash = "sha256:71a4ee5de0bde6a31d64f6b13f2293ac190344478f081c3d1bccfcf5eacb0cb7", size = 62308, upload-time = "2025-11-15T16:43:48.578Z" } +sdist = { url = "https://files.pythonhosted.org/packages/be/a5/8e3f9b6771b0b408517c82d97aed8f2036509bc247d46114925e32fe33f0/asttokens-3.0.1.tar.gz", hash = "sha256:71a4ee5de0bde6a31d64f6b13f2293ac190344478f081c3d1bccfcf5eacb0cb7", size = 62308 } wheels = [ - { url = "https://files.pythonhosted.org/packages/d2/39/e7eaf1799466a4aef85b6a4fe7bd175ad2b1c6345066aa33f1f58d4b18d0/asttokens-3.0.1-py3-none-any.whl", hash = "sha256:15a3ebc0f43c2d0a50eeafea25e19046c68398e487b9f1f5b517f7c0f40f976a", size = 27047, upload-time = "2025-11-15T16:43:16.109Z" }, + { url = "https://files.pythonhosted.org/packages/d2/39/e7eaf1799466a4aef85b6a4fe7bd175ad2b1c6345066aa33f1f58d4b18d0/asttokens-3.0.1-py3-none-any.whl", hash = "sha256:15a3ebc0f43c2d0a50eeafea25e19046c68398e487b9f1f5b517f7c0f40f976a", size = 27047 }, ] [[package]] name = "async-lru" version = "2.3.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/e8/1f/989ecfef8e64109a489fff357450cb73fa73a865a92bd8c272170a6922c2/async_lru-2.3.0.tar.gz", hash = "sha256:89bdb258a0140d7313cf8f4031d816a042202faa61d0ab310a0a538baa1c24b6", size = 16332, upload-time = "2026-03-19T01:04:32.413Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/e5/e2/c2e3abf398f80732e58b03be77bde9022550d221dd8781bf586bd4d97cc1/async_lru-2.3.0-py3-none-any.whl", hash = "sha256:eea27b01841909316f2cc739807acea1c623df2be8c5cfad7583286397bb8315", size = 8403, upload-time = "2026-03-19T01:04:30.883Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/b2/4d/71ec4d3939dc755264f680f6c2b4906423a304c3d18e96853f0a595dfe97/async_lru-2.0.5.tar.gz", hash = "sha256:481d52ccdd27275f42c43a928b4a50c3bfb2d67af4e78b170e3e0bb39c66e5bb", size = 10380 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/03/49/d10027df9fce941cb8184e78a02857af36360d33e1721df81c5ed2179a1a/async_lru-2.0.5-py3-none-any.whl", hash = "sha256:ab95404d8d2605310d345932697371a5f40def0487c03d6d0ad9138de52c9943", size = 6069 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "attrs" version = "26.1.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/9a/8e/82a0fe20a541c03148528be8cac2408564a6c9a0cc7e9171802bc1d26985/attrs-26.1.0.tar.gz", hash = "sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32", size = 952055, upload-time = "2026-03-19T14:22:25.026Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/64/b4/17d4b0b2a2dc85a6df63d1157e028ed19f90d4cd97c36717afef2bc2f395/attrs-26.1.0-py3-none-any.whl", hash = "sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309", size = 67548, upload-time = "2026-03-19T14:22:23.645Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/6b/5c/685e6633917e101e5dcb62b9dd76946cbb57c26e133bae9e0cd36033c0a9/attrs-25.4.0.tar.gz", hash = "sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11", size = 934251 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "babel" version = "2.18.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/7d/b2/51899539b6ceeeb420d40ed3cd4b7a40519404f9baf3d4ac99dc413a834b/babel-2.18.0.tar.gz", hash = "sha256:b80b99a14bd085fcacfa15c9165f651fbb3406e66cc603abf11c5750937c992d", size = 9959554, upload-time = "2026-02-01T12:30:56.078Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/77/f5/21d2de20e8b8b0408f0681956ca2c69f1320a3848ac50e6e7f39c6159675/babel-2.18.0-py3-none-any.whl", hash = "sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35", size = 10196845, upload-time = "2026-02-01T12:30:53.445Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/7d/6b/d52e42361e1aa00709585ecc30b3f9684b3ab62530771402248b1b1d6240/babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d", size = 9951852 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -172,9 +208,15 @@ dependencies = [ { name = "soupsieve" }, { name = "typing-extensions" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/c3/b0/1c6a16426d389813b48d95e26898aff79abbde42ad353958ad95cc8c9b21/beautifulsoup4-4.14.3.tar.gz", hash = "sha256:6292b1c5186d356bba669ef9f7f051757099565ad9ada5dd630bd9de5fa7fb86", size = 627737, upload-time = "2025-11-30T15:08:26.084Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/1a/39/47f9197bdd44df24d67ac8893641e16f386c984a0619ef2ee4c51fbbc019/beautifulsoup4-4.14.3-py3-none-any.whl", hash = "sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb", size = 107721, upload-time = "2025-11-30T15:08:24.087Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/77/e9/df2358efd7659577435e2177bfa69cba6c33216681af51a707193dec162a/beautifulsoup4-4.14.2.tar.gz", hash = "sha256:2a98ab9f944a11acee9cc848508ec28d9228abfd522ef0fad6a02a72e0ded69e", size = 625822 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/94/fe/3aed5d0be4d404d12d36ab97e2f1791424d9ca39c2f754a6285d59a3b01d/beautifulsoup4-4.14.2-py3-none-any.whl", hash = "sha256:5ef6fa3a8cbece8488d66985560f97ed091e22bbc4e9c2338508a9d5de6d4515", size = 106392 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -184,9 +226,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "webencodings" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/07/18/3c8523962314be6bf4c8989c79ad9531c825210dd13a8669f6b84336e8bd/bleach-6.3.0.tar.gz", hash = "sha256:6f3b91b1c0a02bb9a78b5a454c92506aa0fdf197e1d5e114d2e00c6f64306d22", size = 203533, upload-time = "2025-10-27T17:57:39.211Z" } +sdist = { url = "https://files.pythonhosted.org/packages/07/18/3c8523962314be6bf4c8989c79ad9531c825210dd13a8669f6b84336e8bd/bleach-6.3.0.tar.gz", hash = "sha256:6f3b91b1c0a02bb9a78b5a454c92506aa0fdf197e1d5e114d2e00c6f64306d22", size = 203533 } wheels = [ - { url = "https://files.pythonhosted.org/packages/cd/3a/577b549de0cc09d95f11087ee63c739bba856cd3952697eec4c4bb91350a/bleach-6.3.0-py3-none-any.whl", hash = "sha256:fe10ec77c93ddf3d13a73b035abaac7a9f5e436513864ccdad516693213c65d6", size = 164437, upload-time = "2025-10-27T17:57:37.538Z" }, + { url = "https://files.pythonhosted.org/packages/cd/3a/577b549de0cc09d95f11087ee63c739bba856cd3952697eec4c4bb91350a/bleach-6.3.0-py3-none-any.whl", hash = "sha256:fe10ec77c93ddf3d13a73b035abaac7a9f5e436513864ccdad516693213c65d6", size = 164437 }, ] [package.optional-dependencies] @@ -198,9 +240,15 @@ css = [ name = "certifi" version = "2026.5.20" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/f3/ce/ee2ecad540810a79593028e88299baeae54d346cc7a0d94b6199988b89b1/certifi-2026.5.20.tar.gz", hash = "sha256:69dea482ab64caa7b9f6aba1c6bf48bb6a5448d1c0f1b17ab42ad8c763a5344d", size = 135422, upload-time = "2026-05-20T11:46:50.073Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/59/8c/57e832b7af6d7c5abe66eb3fbe3a3a32f4d11ea23a1aa7131371035be991/certifi-2026.5.20-py3-none-any.whl", hash = "sha256:3c52e209ba0a4ad7aebe60436a4ab349c39e1e602e8c134221e546902ad25897", size = 134134, upload-time = "2026-05-20T11:46:48.578Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/a2/8c/58f469717fa48465e4a50c014a0400602d3c437d7c0c468e17ada824da3a/certifi-2025.11.12.tar.gz", hash = "sha256:d8ab5478f2ecd78af242878415affce761ca6bc54a22a27e026d7c25357c3316", size = 160538 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/70/7d/9bc192684cea499815ff478dfcdc13835ddf401365057044fb721ec6bddb/certifi-2025.11.12-py3-none-any.whl", hash = "sha256:97de8790030bbd5c2d96b7ec782fc2f7820ef8dba6db909ccf95449f2d062d4b", size = 159438 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -210,82 +258,89 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "pycparser", marker = "implementation_name != 'PyPy'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/12/4a/3dfd5f7850cbf0d06dc84ba9aa00db766b52ca38d8b86e3a38314d52498c/cffi-2.0.0-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:b4c854ef3adc177950a8dfc81a86f5115d2abd545751a304c5bcf2c2c7283cfe", size = 184344, upload-time = "2025-09-08T23:22:26.456Z" }, - { url = "https://files.pythonhosted.org/packages/4f/8b/f0e4c441227ba756aafbe78f117485b25bb26b1c059d01f137fa6d14896b/cffi-2.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2de9a304e27f7596cd03d16f1b7c72219bd944e99cc52b84d0145aefb07cbd3c", size = 180560, upload-time = "2025-09-08T23:22:28.197Z" }, - { url = "https://files.pythonhosted.org/packages/b1/b7/1200d354378ef52ec227395d95c2576330fd22a869f7a70e88e1447eb234/cffi-2.0.0-cp311-cp311-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:baf5215e0ab74c16e2dd324e8ec067ef59e41125d3eade2b863d294fd5035c92", size = 209613, upload-time = "2025-09-08T23:22:29.475Z" }, - { url = "https://files.pythonhosted.org/packages/b8/56/6033f5e86e8cc9bb629f0077ba71679508bdf54a9a5e112a3c0b91870332/cffi-2.0.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:730cacb21e1bdff3ce90babf007d0a0917cc3e6492f336c2f0134101e0944f93", size = 216476, upload-time = "2025-09-08T23:22:31.063Z" }, - { url = "https://files.pythonhosted.org/packages/dc/7f/55fecd70f7ece178db2f26128ec41430d8720f2d12ca97bf8f0a628207d5/cffi-2.0.0-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:6824f87845e3396029f3820c206e459ccc91760e8fa24422f8b0c3d1731cbec5", size = 203374, upload-time = "2025-09-08T23:22:32.507Z" }, - { url = "https://files.pythonhosted.org/packages/84/ef/a7b77c8bdc0f77adc3b46888f1ad54be8f3b7821697a7b89126e829e676a/cffi-2.0.0-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:9de40a7b0323d889cf8d23d1ef214f565ab154443c42737dfe52ff82cf857664", size = 202597, upload-time = "2025-09-08T23:22:34.132Z" }, - { url = "https://files.pythonhosted.org/packages/d7/91/500d892b2bf36529a75b77958edfcd5ad8e2ce4064ce2ecfeab2125d72d1/cffi-2.0.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8941aaadaf67246224cee8c3803777eed332a19d909b47e29c9842ef1e79ac26", size = 215574, upload-time = "2025-09-08T23:22:35.443Z" }, - { url = "https://files.pythonhosted.org/packages/44/64/58f6255b62b101093d5df22dcb752596066c7e89dd725e0afaed242a61be/cffi-2.0.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:a05d0c237b3349096d3981b727493e22147f934b20f6f125a3eba8f994bec4a9", size = 218971, upload-time = "2025-09-08T23:22:36.805Z" }, - { url = "https://files.pythonhosted.org/packages/ab/49/fa72cebe2fd8a55fbe14956f9970fe8eb1ac59e5df042f603ef7c8ba0adc/cffi-2.0.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:94698a9c5f91f9d138526b48fe26a199609544591f859c870d477351dc7b2414", size = 211972, upload-time = "2025-09-08T23:22:38.436Z" }, - { url = "https://files.pythonhosted.org/packages/0b/28/dd0967a76aab36731b6ebfe64dec4e981aff7e0608f60c2d46b46982607d/cffi-2.0.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:5fed36fccc0612a53f1d4d9a816b50a36702c28a2aa880cb8a122b3466638743", size = 217078, upload-time = "2025-09-08T23:22:39.776Z" }, - { url = "https://files.pythonhosted.org/packages/2b/c0/015b25184413d7ab0a410775fdb4a50fca20f5589b5dab1dbbfa3baad8ce/cffi-2.0.0-cp311-cp311-win32.whl", hash = "sha256:c649e3a33450ec82378822b3dad03cc228b8f5963c0c12fc3b1e0ab940f768a5", size = 172076, upload-time = "2025-09-08T23:22:40.95Z" }, - { url = "https://files.pythonhosted.org/packages/ae/8f/dc5531155e7070361eb1b7e4c1a9d896d0cb21c49f807a6c03fd63fc877e/cffi-2.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:66f011380d0e49ed280c789fbd08ff0d40968ee7b665575489afa95c98196ab5", size = 182820, upload-time = "2025-09-08T23:22:42.463Z" }, - { url = "https://files.pythonhosted.org/packages/95/5c/1b493356429f9aecfd56bc171285a4c4ac8697f76e9bbbbb105e537853a1/cffi-2.0.0-cp311-cp311-win_arm64.whl", hash = "sha256:c6638687455baf640e37344fe26d37c404db8b80d037c3d29f58fe8d1c3b194d", size = 177635, upload-time = "2025-09-08T23:22:43.623Z" }, - { url = "https://files.pythonhosted.org/packages/ea/47/4f61023ea636104d4f16ab488e268b93008c3d0bb76893b1b31db1f96802/cffi-2.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6d02d6655b0e54f54c4ef0b94eb6be0607b70853c45ce98bd278dc7de718be5d", size = 185271, upload-time = "2025-09-08T23:22:44.795Z" }, - { url = "https://files.pythonhosted.org/packages/df/a2/781b623f57358e360d62cdd7a8c681f074a71d445418a776eef0aadb4ab4/cffi-2.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8eca2a813c1cb7ad4fb74d368c2ffbbb4789d377ee5bb8df98373c2cc0dee76c", size = 181048, upload-time = "2025-09-08T23:22:45.938Z" }, - { url = "https://files.pythonhosted.org/packages/ff/df/a4f0fbd47331ceeba3d37c2e51e9dfc9722498becbeec2bd8bc856c9538a/cffi-2.0.0-cp312-cp312-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe", size = 212529, upload-time = "2025-09-08T23:22:47.349Z" }, - { url = "https://files.pythonhosted.org/packages/d5/72/12b5f8d3865bf0f87cf1404d8c374e7487dcf097a1c91c436e72e6badd83/cffi-2.0.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b21e08af67b8a103c71a250401c78d5e0893beff75e28c53c98f4de42f774062", size = 220097, upload-time = "2025-09-08T23:22:48.677Z" }, - { url = "https://files.pythonhosted.org/packages/c2/95/7a135d52a50dfa7c882ab0ac17e8dc11cec9d55d2c18dda414c051c5e69e/cffi-2.0.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e", size = 207983, upload-time = "2025-09-08T23:22:50.06Z" }, - { url = "https://files.pythonhosted.org/packages/3a/c8/15cb9ada8895957ea171c62dc78ff3e99159ee7adb13c0123c001a2546c1/cffi-2.0.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:81afed14892743bbe14dacb9e36d9e0e504cd204e0b165062c488942b9718037", size = 206519, upload-time = "2025-09-08T23:22:51.364Z" }, - { url = "https://files.pythonhosted.org/packages/78/2d/7fa73dfa841b5ac06c7b8855cfc18622132e365f5b81d02230333ff26e9e/cffi-2.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3e17ed538242334bf70832644a32a7aae3d83b57567f9fd60a26257e992b79ba", size = 219572, upload-time = "2025-09-08T23:22:52.902Z" }, - { url = "https://files.pythonhosted.org/packages/07/e0/267e57e387b4ca276b90f0434ff88b2c2241ad72b16d31836adddfd6031b/cffi-2.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3925dd22fa2b7699ed2617149842d2e6adde22b262fcbfada50e3d195e4b3a94", size = 222963, upload-time = "2025-09-08T23:22:54.518Z" }, - { url = "https://files.pythonhosted.org/packages/b6/75/1f2747525e06f53efbd878f4d03bac5b859cbc11c633d0fb81432d98a795/cffi-2.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2c8f814d84194c9ea681642fd164267891702542f028a15fc97d4674b6206187", size = 221361, upload-time = "2025-09-08T23:22:55.867Z" }, - { url = "https://files.pythonhosted.org/packages/7b/2b/2b6435f76bfeb6bbf055596976da087377ede68df465419d192acf00c437/cffi-2.0.0-cp312-cp312-win32.whl", hash = "sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18", size = 172932, upload-time = "2025-09-08T23:22:57.188Z" }, - { url = "https://files.pythonhosted.org/packages/f8/ed/13bd4418627013bec4ed6e54283b1959cf6db888048c7cf4b4c3b5b36002/cffi-2.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5", size = 183557, upload-time = "2025-09-08T23:22:58.351Z" }, - { url = "https://files.pythonhosted.org/packages/95/31/9f7f93ad2f8eff1dbc1c3656d7ca5bfd8fb52c9d786b4dcf19b2d02217fa/cffi-2.0.0-cp312-cp312-win_arm64.whl", hash = "sha256:4671d9dd5ec934cb9a73e7ee9676f9362aba54f7f34910956b84d727b0d73fb6", size = 177762, upload-time = "2025-09-08T23:22:59.668Z" }, - { url = "https://files.pythonhosted.org/packages/4b/8d/a0a47a0c9e413a658623d014e91e74a50cdd2c423f7ccfd44086ef767f90/cffi-2.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:00bdf7acc5f795150faa6957054fbbca2439db2f775ce831222b66f192f03beb", size = 185230, upload-time = "2025-09-08T23:23:00.879Z" }, - { url = "https://files.pythonhosted.org/packages/4a/d2/a6c0296814556c68ee32009d9c2ad4f85f2707cdecfd7727951ec228005d/cffi-2.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:45d5e886156860dc35862657e1494b9bae8dfa63bf56796f2fb56e1679fc0bca", size = 181043, upload-time = "2025-09-08T23:23:02.231Z" }, - { url = "https://files.pythonhosted.org/packages/b0/1e/d22cc63332bd59b06481ceaac49d6c507598642e2230f201649058a7e704/cffi-2.0.0-cp313-cp313-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:07b271772c100085dd28b74fa0cd81c8fb1a3ba18b21e03d7c27f3436a10606b", size = 212446, upload-time = "2025-09-08T23:23:03.472Z" }, - { url = "https://files.pythonhosted.org/packages/a9/f5/a2c23eb03b61a0b8747f211eb716446c826ad66818ddc7810cc2cc19b3f2/cffi-2.0.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d48a880098c96020b02d5a1f7d9251308510ce8858940e6fa99ece33f610838b", size = 220101, upload-time = "2025-09-08T23:23:04.792Z" }, - { url = "https://files.pythonhosted.org/packages/f2/7f/e6647792fc5850d634695bc0e6ab4111ae88e89981d35ac269956605feba/cffi-2.0.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:f93fd8e5c8c0a4aa1f424d6173f14a892044054871c771f8566e4008eaa359d2", size = 207948, upload-time = "2025-09-08T23:23:06.127Z" }, - { url = "https://files.pythonhosted.org/packages/cb/1e/a5a1bd6f1fb30f22573f76533de12a00bf274abcdc55c8edab639078abb6/cffi-2.0.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:dd4f05f54a52fb558f1ba9f528228066954fee3ebe629fc1660d874d040ae5a3", size = 206422, upload-time = "2025-09-08T23:23:07.753Z" }, - { url = "https://files.pythonhosted.org/packages/98/df/0a1755e750013a2081e863e7cd37e0cdd02664372c754e5560099eb7aa44/cffi-2.0.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c8d3b5532fc71b7a77c09192b4a5a200ea992702734a2e9279a37f2478236f26", size = 219499, upload-time = "2025-09-08T23:23:09.648Z" }, - { url = "https://files.pythonhosted.org/packages/50/e1/a969e687fcf9ea58e6e2a928ad5e2dd88cc12f6f0ab477e9971f2309b57c/cffi-2.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:d9b29c1f0ae438d5ee9acb31cadee00a58c46cc9c0b2f9038c6b0b3470877a8c", size = 222928, upload-time = "2025-09-08T23:23:10.928Z" }, - { url = "https://files.pythonhosted.org/packages/36/54/0362578dd2c9e557a28ac77698ed67323ed5b9775ca9d3fe73fe191bb5d8/cffi-2.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6d50360be4546678fc1b79ffe7a66265e28667840010348dd69a314145807a1b", size = 221302, upload-time = "2025-09-08T23:23:12.42Z" }, - { url = "https://files.pythonhosted.org/packages/eb/6d/bf9bda840d5f1dfdbf0feca87fbdb64a918a69bca42cfa0ba7b137c48cb8/cffi-2.0.0-cp313-cp313-win32.whl", hash = "sha256:74a03b9698e198d47562765773b4a8309919089150a0bb17d829ad7b44b60d27", size = 172909, upload-time = "2025-09-08T23:23:14.32Z" }, - { url = "https://files.pythonhosted.org/packages/37/18/6519e1ee6f5a1e579e04b9ddb6f1676c17368a7aba48299c3759bbc3c8b3/cffi-2.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:19f705ada2530c1167abacb171925dd886168931e0a7b78f5bffcae5c6b5be75", size = 183402, upload-time = "2025-09-08T23:23:15.535Z" }, - { url = "https://files.pythonhosted.org/packages/cb/0e/02ceeec9a7d6ee63bb596121c2c8e9b3a9e150936f4fbef6ca1943e6137c/cffi-2.0.0-cp313-cp313-win_arm64.whl", hash = "sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91", size = 177780, upload-time = "2025-09-08T23:23:16.761Z" }, - { url = "https://files.pythonhosted.org/packages/92/c4/3ce07396253a83250ee98564f8d7e9789fab8e58858f35d07a9a2c78de9f/cffi-2.0.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fc33c5141b55ed366cfaad382df24fe7dcbc686de5be719b207bb248e3053dc5", size = 185320, upload-time = "2025-09-08T23:23:18.087Z" }, - { url = "https://files.pythonhosted.org/packages/59/dd/27e9fa567a23931c838c6b02d0764611c62290062a6d4e8ff7863daf9730/cffi-2.0.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c654de545946e0db659b3400168c9ad31b5d29593291482c43e3564effbcee13", size = 181487, upload-time = "2025-09-08T23:23:19.622Z" }, - { url = "https://files.pythonhosted.org/packages/d6/43/0e822876f87ea8a4ef95442c3d766a06a51fc5298823f884ef87aaad168c/cffi-2.0.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:24b6f81f1983e6df8db3adc38562c83f7d4a0c36162885ec7f7b77c7dcbec97b", size = 220049, upload-time = "2025-09-08T23:23:20.853Z" }, - { url = "https://files.pythonhosted.org/packages/b4/89/76799151d9c2d2d1ead63c2429da9ea9d7aac304603de0c6e8764e6e8e70/cffi-2.0.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:12873ca6cb9b0f0d3a0da705d6086fe911591737a59f28b7936bdfed27c0d47c", size = 207793, upload-time = "2025-09-08T23:23:22.08Z" }, - { url = "https://files.pythonhosted.org/packages/bb/dd/3465b14bb9e24ee24cb88c9e3730f6de63111fffe513492bf8c808a3547e/cffi-2.0.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:d9b97165e8aed9272a6bb17c01e3cc5871a594a446ebedc996e2397a1c1ea8ef", size = 206300, upload-time = "2025-09-08T23:23:23.314Z" }, - { url = "https://files.pythonhosted.org/packages/47/d9/d83e293854571c877a92da46fdec39158f8d7e68da75bf73581225d28e90/cffi-2.0.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:afb8db5439b81cf9c9d0c80404b60c3cc9c3add93e114dcae767f1477cb53775", size = 219244, upload-time = "2025-09-08T23:23:24.541Z" }, - { url = "https://files.pythonhosted.org/packages/2b/0f/1f177e3683aead2bb00f7679a16451d302c436b5cbf2505f0ea8146ef59e/cffi-2.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:737fe7d37e1a1bffe70bd5754ea763a62a066dc5913ca57e957824b72a85e205", size = 222828, upload-time = "2025-09-08T23:23:26.143Z" }, - { url = "https://files.pythonhosted.org/packages/c6/0f/cafacebd4b040e3119dcb32fed8bdef8dfe94da653155f9d0b9dc660166e/cffi-2.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:38100abb9d1b1435bc4cc340bb4489635dc2f0da7456590877030c9b3d40b0c1", size = 220926, upload-time = "2025-09-08T23:23:27.873Z" }, - { url = "https://files.pythonhosted.org/packages/3e/aa/df335faa45b395396fcbc03de2dfcab242cd61a9900e914fe682a59170b1/cffi-2.0.0-cp314-cp314-win32.whl", hash = "sha256:087067fa8953339c723661eda6b54bc98c5625757ea62e95eb4898ad5e776e9f", size = 175328, upload-time = "2025-09-08T23:23:44.61Z" }, - { url = "https://files.pythonhosted.org/packages/bb/92/882c2d30831744296ce713f0feb4c1cd30f346ef747b530b5318715cc367/cffi-2.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:203a48d1fb583fc7d78a4c6655692963b860a417c0528492a6bc21f1aaefab25", size = 185650, upload-time = "2025-09-08T23:23:45.848Z" }, - { url = "https://files.pythonhosted.org/packages/9f/2c/98ece204b9d35a7366b5b2c6539c350313ca13932143e79dc133ba757104/cffi-2.0.0-cp314-cp314-win_arm64.whl", hash = "sha256:dbd5c7a25a7cb98f5ca55d258b103a2054f859a46ae11aaf23134f9cc0d356ad", size = 180687, upload-time = "2025-09-08T23:23:47.105Z" }, - { url = "https://files.pythonhosted.org/packages/3e/61/c768e4d548bfa607abcda77423448df8c471f25dbe64fb2ef6d555eae006/cffi-2.0.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:9a67fc9e8eb39039280526379fb3a70023d77caec1852002b4da7e8b270c4dd9", size = 188773, upload-time = "2025-09-08T23:23:29.347Z" }, - { url = "https://files.pythonhosted.org/packages/2c/ea/5f76bce7cf6fcd0ab1a1058b5af899bfbef198bea4d5686da88471ea0336/cffi-2.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:7a66c7204d8869299919db4d5069a82f1561581af12b11b3c9f48c584eb8743d", size = 185013, upload-time = "2025-09-08T23:23:30.63Z" }, - { url = "https://files.pythonhosted.org/packages/be/b4/c56878d0d1755cf9caa54ba71e5d049479c52f9e4afc230f06822162ab2f/cffi-2.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7cc09976e8b56f8cebd752f7113ad07752461f48a58cbba644139015ac24954c", size = 221593, upload-time = "2025-09-08T23:23:31.91Z" }, - { url = "https://files.pythonhosted.org/packages/e0/0d/eb704606dfe8033e7128df5e90fee946bbcb64a04fcdaa97321309004000/cffi-2.0.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:92b68146a71df78564e4ef48af17551a5ddd142e5190cdf2c5624d0c3ff5b2e8", size = 209354, upload-time = "2025-09-08T23:23:33.214Z" }, - { url = "https://files.pythonhosted.org/packages/d8/19/3c435d727b368ca475fb8742ab97c9cb13a0de600ce86f62eab7fa3eea60/cffi-2.0.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:b1e74d11748e7e98e2f426ab176d4ed720a64412b6a15054378afdb71e0f37dc", size = 208480, upload-time = "2025-09-08T23:23:34.495Z" }, - { url = "https://files.pythonhosted.org/packages/d0/44/681604464ed9541673e486521497406fadcc15b5217c3e326b061696899a/cffi-2.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:28a3a209b96630bca57cce802da70c266eb08c6e97e5afd61a75611ee6c64592", size = 221584, upload-time = "2025-09-08T23:23:36.096Z" }, - { url = "https://files.pythonhosted.org/packages/25/8e/342a504ff018a2825d395d44d63a767dd8ebc927ebda557fecdaca3ac33a/cffi-2.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:7553fb2090d71822f02c629afe6042c299edf91ba1bf94951165613553984512", size = 224443, upload-time = "2025-09-08T23:23:37.328Z" }, - { url = "https://files.pythonhosted.org/packages/e1/5e/b666bacbbc60fbf415ba9988324a132c9a7a0448a9a8f125074671c0f2c3/cffi-2.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:6c6c373cfc5c83a975506110d17457138c8c63016b563cc9ed6e056a82f13ce4", size = 223437, upload-time = "2025-09-08T23:23:38.945Z" }, - { url = "https://files.pythonhosted.org/packages/a0/1d/ec1a60bd1a10daa292d3cd6bb0b359a81607154fb8165f3ec95fe003b85c/cffi-2.0.0-cp314-cp314t-win32.whl", hash = "sha256:1fc9ea04857caf665289b7a75923f2c6ed559b8298a1b8c49e59f7dd95c8481e", size = 180487, upload-time = "2025-09-08T23:23:40.423Z" }, - { url = "https://files.pythonhosted.org/packages/bf/41/4c1168c74fac325c0c8156f04b6749c8b6a8f405bbf91413ba088359f60d/cffi-2.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:d68b6cef7827e8641e8ef16f4494edda8b36104d79773a334beaa1e3521430f6", size = 191726, upload-time = "2025-09-08T23:23:41.742Z" }, - { url = "https://files.pythonhosted.org/packages/ae/3a/dbeec9d1ee0844c679f6bb5d6ad4e9f198b1224f4e7a32825f47f6192b0c/cffi-2.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9", size = 184195, upload-time = "2025-09-08T23:23:43.004Z" }, +sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/12/4a/3dfd5f7850cbf0d06dc84ba9aa00db766b52ca38d8b86e3a38314d52498c/cffi-2.0.0-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:b4c854ef3adc177950a8dfc81a86f5115d2abd545751a304c5bcf2c2c7283cfe", size = 184344 }, + { url = "https://files.pythonhosted.org/packages/4f/8b/f0e4c441227ba756aafbe78f117485b25bb26b1c059d01f137fa6d14896b/cffi-2.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2de9a304e27f7596cd03d16f1b7c72219bd944e99cc52b84d0145aefb07cbd3c", size = 180560 }, + { url = "https://files.pythonhosted.org/packages/b1/b7/1200d354378ef52ec227395d95c2576330fd22a869f7a70e88e1447eb234/cffi-2.0.0-cp311-cp311-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:baf5215e0ab74c16e2dd324e8ec067ef59e41125d3eade2b863d294fd5035c92", size = 209613 }, + { url = "https://files.pythonhosted.org/packages/b8/56/6033f5e86e8cc9bb629f0077ba71679508bdf54a9a5e112a3c0b91870332/cffi-2.0.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:730cacb21e1bdff3ce90babf007d0a0917cc3e6492f336c2f0134101e0944f93", size = 216476 }, + { url = "https://files.pythonhosted.org/packages/dc/7f/55fecd70f7ece178db2f26128ec41430d8720f2d12ca97bf8f0a628207d5/cffi-2.0.0-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:6824f87845e3396029f3820c206e459ccc91760e8fa24422f8b0c3d1731cbec5", size = 203374 }, + { url = "https://files.pythonhosted.org/packages/84/ef/a7b77c8bdc0f77adc3b46888f1ad54be8f3b7821697a7b89126e829e676a/cffi-2.0.0-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:9de40a7b0323d889cf8d23d1ef214f565ab154443c42737dfe52ff82cf857664", size = 202597 }, + { url = "https://files.pythonhosted.org/packages/d7/91/500d892b2bf36529a75b77958edfcd5ad8e2ce4064ce2ecfeab2125d72d1/cffi-2.0.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8941aaadaf67246224cee8c3803777eed332a19d909b47e29c9842ef1e79ac26", size = 215574 }, + { url = "https://files.pythonhosted.org/packages/44/64/58f6255b62b101093d5df22dcb752596066c7e89dd725e0afaed242a61be/cffi-2.0.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:a05d0c237b3349096d3981b727493e22147f934b20f6f125a3eba8f994bec4a9", size = 218971 }, + { url = "https://files.pythonhosted.org/packages/ab/49/fa72cebe2fd8a55fbe14956f9970fe8eb1ac59e5df042f603ef7c8ba0adc/cffi-2.0.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:94698a9c5f91f9d138526b48fe26a199609544591f859c870d477351dc7b2414", size = 211972 }, + { url = "https://files.pythonhosted.org/packages/0b/28/dd0967a76aab36731b6ebfe64dec4e981aff7e0608f60c2d46b46982607d/cffi-2.0.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:5fed36fccc0612a53f1d4d9a816b50a36702c28a2aa880cb8a122b3466638743", size = 217078 }, + { url = "https://files.pythonhosted.org/packages/2b/c0/015b25184413d7ab0a410775fdb4a50fca20f5589b5dab1dbbfa3baad8ce/cffi-2.0.0-cp311-cp311-win32.whl", hash = "sha256:c649e3a33450ec82378822b3dad03cc228b8f5963c0c12fc3b1e0ab940f768a5", size = 172076 }, + { url = "https://files.pythonhosted.org/packages/ae/8f/dc5531155e7070361eb1b7e4c1a9d896d0cb21c49f807a6c03fd63fc877e/cffi-2.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:66f011380d0e49ed280c789fbd08ff0d40968ee7b665575489afa95c98196ab5", size = 182820 }, + { url = "https://files.pythonhosted.org/packages/95/5c/1b493356429f9aecfd56bc171285a4c4ac8697f76e9bbbbb105e537853a1/cffi-2.0.0-cp311-cp311-win_arm64.whl", hash = "sha256:c6638687455baf640e37344fe26d37c404db8b80d037c3d29f58fe8d1c3b194d", size = 177635 }, + { url = "https://files.pythonhosted.org/packages/ea/47/4f61023ea636104d4f16ab488e268b93008c3d0bb76893b1b31db1f96802/cffi-2.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6d02d6655b0e54f54c4ef0b94eb6be0607b70853c45ce98bd278dc7de718be5d", size = 185271 }, + { url = "https://files.pythonhosted.org/packages/df/a2/781b623f57358e360d62cdd7a8c681f074a71d445418a776eef0aadb4ab4/cffi-2.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8eca2a813c1cb7ad4fb74d368c2ffbbb4789d377ee5bb8df98373c2cc0dee76c", size = 181048 }, + { url = "https://files.pythonhosted.org/packages/ff/df/a4f0fbd47331ceeba3d37c2e51e9dfc9722498becbeec2bd8bc856c9538a/cffi-2.0.0-cp312-cp312-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe", size = 212529 }, + { url = "https://files.pythonhosted.org/packages/d5/72/12b5f8d3865bf0f87cf1404d8c374e7487dcf097a1c91c436e72e6badd83/cffi-2.0.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b21e08af67b8a103c71a250401c78d5e0893beff75e28c53c98f4de42f774062", size = 220097 }, + { url = "https://files.pythonhosted.org/packages/c2/95/7a135d52a50dfa7c882ab0ac17e8dc11cec9d55d2c18dda414c051c5e69e/cffi-2.0.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e", size = 207983 }, + { url = "https://files.pythonhosted.org/packages/3a/c8/15cb9ada8895957ea171c62dc78ff3e99159ee7adb13c0123c001a2546c1/cffi-2.0.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:81afed14892743bbe14dacb9e36d9e0e504cd204e0b165062c488942b9718037", size = 206519 }, + { url = "https://files.pythonhosted.org/packages/78/2d/7fa73dfa841b5ac06c7b8855cfc18622132e365f5b81d02230333ff26e9e/cffi-2.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3e17ed538242334bf70832644a32a7aae3d83b57567f9fd60a26257e992b79ba", size = 219572 }, + { url = "https://files.pythonhosted.org/packages/07/e0/267e57e387b4ca276b90f0434ff88b2c2241ad72b16d31836adddfd6031b/cffi-2.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3925dd22fa2b7699ed2617149842d2e6adde22b262fcbfada50e3d195e4b3a94", size = 222963 }, + { url = "https://files.pythonhosted.org/packages/b6/75/1f2747525e06f53efbd878f4d03bac5b859cbc11c633d0fb81432d98a795/cffi-2.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2c8f814d84194c9ea681642fd164267891702542f028a15fc97d4674b6206187", size = 221361 }, + { url = "https://files.pythonhosted.org/packages/7b/2b/2b6435f76bfeb6bbf055596976da087377ede68df465419d192acf00c437/cffi-2.0.0-cp312-cp312-win32.whl", hash = "sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18", size = 172932 }, + { url = "https://files.pythonhosted.org/packages/f8/ed/13bd4418627013bec4ed6e54283b1959cf6db888048c7cf4b4c3b5b36002/cffi-2.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5", size = 183557 }, + { url = "https://files.pythonhosted.org/packages/95/31/9f7f93ad2f8eff1dbc1c3656d7ca5bfd8fb52c9d786b4dcf19b2d02217fa/cffi-2.0.0-cp312-cp312-win_arm64.whl", hash = "sha256:4671d9dd5ec934cb9a73e7ee9676f9362aba54f7f34910956b84d727b0d73fb6", size = 177762 }, + { url = "https://files.pythonhosted.org/packages/4b/8d/a0a47a0c9e413a658623d014e91e74a50cdd2c423f7ccfd44086ef767f90/cffi-2.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:00bdf7acc5f795150faa6957054fbbca2439db2f775ce831222b66f192f03beb", size = 185230 }, + { url = "https://files.pythonhosted.org/packages/4a/d2/a6c0296814556c68ee32009d9c2ad4f85f2707cdecfd7727951ec228005d/cffi-2.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:45d5e886156860dc35862657e1494b9bae8dfa63bf56796f2fb56e1679fc0bca", size = 181043 }, + { url = "https://files.pythonhosted.org/packages/b0/1e/d22cc63332bd59b06481ceaac49d6c507598642e2230f201649058a7e704/cffi-2.0.0-cp313-cp313-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:07b271772c100085dd28b74fa0cd81c8fb1a3ba18b21e03d7c27f3436a10606b", size = 212446 }, + { url = "https://files.pythonhosted.org/packages/a9/f5/a2c23eb03b61a0b8747f211eb716446c826ad66818ddc7810cc2cc19b3f2/cffi-2.0.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d48a880098c96020b02d5a1f7d9251308510ce8858940e6fa99ece33f610838b", size = 220101 }, + { url = "https://files.pythonhosted.org/packages/f2/7f/e6647792fc5850d634695bc0e6ab4111ae88e89981d35ac269956605feba/cffi-2.0.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:f93fd8e5c8c0a4aa1f424d6173f14a892044054871c771f8566e4008eaa359d2", size = 207948 }, + { url = "https://files.pythonhosted.org/packages/cb/1e/a5a1bd6f1fb30f22573f76533de12a00bf274abcdc55c8edab639078abb6/cffi-2.0.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:dd4f05f54a52fb558f1ba9f528228066954fee3ebe629fc1660d874d040ae5a3", size = 206422 }, + { url = "https://files.pythonhosted.org/packages/98/df/0a1755e750013a2081e863e7cd37e0cdd02664372c754e5560099eb7aa44/cffi-2.0.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c8d3b5532fc71b7a77c09192b4a5a200ea992702734a2e9279a37f2478236f26", size = 219499 }, + { url = "https://files.pythonhosted.org/packages/50/e1/a969e687fcf9ea58e6e2a928ad5e2dd88cc12f6f0ab477e9971f2309b57c/cffi-2.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:d9b29c1f0ae438d5ee9acb31cadee00a58c46cc9c0b2f9038c6b0b3470877a8c", size = 222928 }, + { url = "https://files.pythonhosted.org/packages/36/54/0362578dd2c9e557a28ac77698ed67323ed5b9775ca9d3fe73fe191bb5d8/cffi-2.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6d50360be4546678fc1b79ffe7a66265e28667840010348dd69a314145807a1b", size = 221302 }, + { url = "https://files.pythonhosted.org/packages/eb/6d/bf9bda840d5f1dfdbf0feca87fbdb64a918a69bca42cfa0ba7b137c48cb8/cffi-2.0.0-cp313-cp313-win32.whl", hash = "sha256:74a03b9698e198d47562765773b4a8309919089150a0bb17d829ad7b44b60d27", size = 172909 }, + { url = "https://files.pythonhosted.org/packages/37/18/6519e1ee6f5a1e579e04b9ddb6f1676c17368a7aba48299c3759bbc3c8b3/cffi-2.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:19f705ada2530c1167abacb171925dd886168931e0a7b78f5bffcae5c6b5be75", size = 183402 }, + { url = "https://files.pythonhosted.org/packages/cb/0e/02ceeec9a7d6ee63bb596121c2c8e9b3a9e150936f4fbef6ca1943e6137c/cffi-2.0.0-cp313-cp313-win_arm64.whl", hash = "sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91", size = 177780 }, + { url = "https://files.pythonhosted.org/packages/92/c4/3ce07396253a83250ee98564f8d7e9789fab8e58858f35d07a9a2c78de9f/cffi-2.0.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fc33c5141b55ed366cfaad382df24fe7dcbc686de5be719b207bb248e3053dc5", size = 185320 }, + { url = "https://files.pythonhosted.org/packages/59/dd/27e9fa567a23931c838c6b02d0764611c62290062a6d4e8ff7863daf9730/cffi-2.0.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c654de545946e0db659b3400168c9ad31b5d29593291482c43e3564effbcee13", size = 181487 }, + { url = "https://files.pythonhosted.org/packages/d6/43/0e822876f87ea8a4ef95442c3d766a06a51fc5298823f884ef87aaad168c/cffi-2.0.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:24b6f81f1983e6df8db3adc38562c83f7d4a0c36162885ec7f7b77c7dcbec97b", size = 220049 }, + { url = "https://files.pythonhosted.org/packages/b4/89/76799151d9c2d2d1ead63c2429da9ea9d7aac304603de0c6e8764e6e8e70/cffi-2.0.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:12873ca6cb9b0f0d3a0da705d6086fe911591737a59f28b7936bdfed27c0d47c", size = 207793 }, + { url = "https://files.pythonhosted.org/packages/bb/dd/3465b14bb9e24ee24cb88c9e3730f6de63111fffe513492bf8c808a3547e/cffi-2.0.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:d9b97165e8aed9272a6bb17c01e3cc5871a594a446ebedc996e2397a1c1ea8ef", size = 206300 }, + { url = "https://files.pythonhosted.org/packages/47/d9/d83e293854571c877a92da46fdec39158f8d7e68da75bf73581225d28e90/cffi-2.0.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:afb8db5439b81cf9c9d0c80404b60c3cc9c3add93e114dcae767f1477cb53775", size = 219244 }, + { url = "https://files.pythonhosted.org/packages/2b/0f/1f177e3683aead2bb00f7679a16451d302c436b5cbf2505f0ea8146ef59e/cffi-2.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:737fe7d37e1a1bffe70bd5754ea763a62a066dc5913ca57e957824b72a85e205", size = 222828 }, + { url = "https://files.pythonhosted.org/packages/c6/0f/cafacebd4b040e3119dcb32fed8bdef8dfe94da653155f9d0b9dc660166e/cffi-2.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:38100abb9d1b1435bc4cc340bb4489635dc2f0da7456590877030c9b3d40b0c1", size = 220926 }, + { url = "https://files.pythonhosted.org/packages/3e/aa/df335faa45b395396fcbc03de2dfcab242cd61a9900e914fe682a59170b1/cffi-2.0.0-cp314-cp314-win32.whl", hash = "sha256:087067fa8953339c723661eda6b54bc98c5625757ea62e95eb4898ad5e776e9f", size = 175328 }, + { url = "https://files.pythonhosted.org/packages/bb/92/882c2d30831744296ce713f0feb4c1cd30f346ef747b530b5318715cc367/cffi-2.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:203a48d1fb583fc7d78a4c6655692963b860a417c0528492a6bc21f1aaefab25", size = 185650 }, + { url = "https://files.pythonhosted.org/packages/9f/2c/98ece204b9d35a7366b5b2c6539c350313ca13932143e79dc133ba757104/cffi-2.0.0-cp314-cp314-win_arm64.whl", hash = "sha256:dbd5c7a25a7cb98f5ca55d258b103a2054f859a46ae11aaf23134f9cc0d356ad", size = 180687 }, + { url = "https://files.pythonhosted.org/packages/3e/61/c768e4d548bfa607abcda77423448df8c471f25dbe64fb2ef6d555eae006/cffi-2.0.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:9a67fc9e8eb39039280526379fb3a70023d77caec1852002b4da7e8b270c4dd9", size = 188773 }, + { url = "https://files.pythonhosted.org/packages/2c/ea/5f76bce7cf6fcd0ab1a1058b5af899bfbef198bea4d5686da88471ea0336/cffi-2.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:7a66c7204d8869299919db4d5069a82f1561581af12b11b3c9f48c584eb8743d", size = 185013 }, + { url = "https://files.pythonhosted.org/packages/be/b4/c56878d0d1755cf9caa54ba71e5d049479c52f9e4afc230f06822162ab2f/cffi-2.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7cc09976e8b56f8cebd752f7113ad07752461f48a58cbba644139015ac24954c", size = 221593 }, + { url = "https://files.pythonhosted.org/packages/e0/0d/eb704606dfe8033e7128df5e90fee946bbcb64a04fcdaa97321309004000/cffi-2.0.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:92b68146a71df78564e4ef48af17551a5ddd142e5190cdf2c5624d0c3ff5b2e8", size = 209354 }, + { url = "https://files.pythonhosted.org/packages/d8/19/3c435d727b368ca475fb8742ab97c9cb13a0de600ce86f62eab7fa3eea60/cffi-2.0.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:b1e74d11748e7e98e2f426ab176d4ed720a64412b6a15054378afdb71e0f37dc", size = 208480 }, + { url = "https://files.pythonhosted.org/packages/d0/44/681604464ed9541673e486521497406fadcc15b5217c3e326b061696899a/cffi-2.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:28a3a209b96630bca57cce802da70c266eb08c6e97e5afd61a75611ee6c64592", size = 221584 }, + { url = "https://files.pythonhosted.org/packages/25/8e/342a504ff018a2825d395d44d63a767dd8ebc927ebda557fecdaca3ac33a/cffi-2.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:7553fb2090d71822f02c629afe6042c299edf91ba1bf94951165613553984512", size = 224443 }, + { url = "https://files.pythonhosted.org/packages/e1/5e/b666bacbbc60fbf415ba9988324a132c9a7a0448a9a8f125074671c0f2c3/cffi-2.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:6c6c373cfc5c83a975506110d17457138c8c63016b563cc9ed6e056a82f13ce4", size = 223437 }, + { url = "https://files.pythonhosted.org/packages/a0/1d/ec1a60bd1a10daa292d3cd6bb0b359a81607154fb8165f3ec95fe003b85c/cffi-2.0.0-cp314-cp314t-win32.whl", hash = "sha256:1fc9ea04857caf665289b7a75923f2c6ed559b8298a1b8c49e59f7dd95c8481e", size = 180487 }, + { url = "https://files.pythonhosted.org/packages/bf/41/4c1168c74fac325c0c8156f04b6749c8b6a8f405bbf91413ba088359f60d/cffi-2.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:d68b6cef7827e8641e8ef16f4494edda8b36104d79773a334beaa1e3521430f6", size = 191726 }, + { url = "https://files.pythonhosted.org/packages/ae/3a/dbeec9d1ee0844c679f6bb5d6ad4e9f198b1224f4e7a32825f47f6192b0c/cffi-2.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9", size = 184195 }, ] [[package]] name = "cfgv" version = "3.5.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/4e/b5/721b8799b04bf9afe054a3899c6cf4e880fcf8563cc71c15610242490a0c/cfgv-3.5.0.tar.gz", hash = "sha256:d5b1034354820651caa73ede66a6294d6e95c1b00acc5e9b098e917404669132", size = 7334, upload-time = "2025-11-19T20:55:51.612Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/db/3c/33bac158f8ab7f89b2e59426d5fe2e4f63f7ed25df84c036890172b412b5/cfgv-3.5.0-py2.py3-none-any.whl", hash = "sha256:a8dc6b26ad22ff227d2634a65cb388215ce6cc96bbcc5cfde7641ae87e8dacc0", size = 7445, upload-time = "2025-11-19T20:55:50.744Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/11/74/539e56497d9bd1d484fd863dd69cbbfa653cd2aa27abfe35653494d85e94/cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560", size = 7114 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c5/55/51844dd50c4fc7a33b653bfaba4c2456f06955289ca770a5dbd5fd267374/cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9", size = 7249 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "charset-normalizer" version = "3.4.7" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/e7/a1/67fe25fac3c7642725500a3f6cfe5821ad557c3abb11c9d20d12c7008d3e/charset_normalizer-3.4.7.tar.gz", hash = "sha256:ae89db9e5f98a11a4bf50407d4363e7b09b31e55bc117b4f7d80aab97ba009e5", size = 144271, upload-time = "2026-04-02T09:28:39.342Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/c2/d7/b5b7020a0565c2e9fa8c09f4b5fa6232feb326b8c20081ccded47ea368fd/charset_normalizer-3.4.7-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:7641bb8895e77f921102f72833904dcd9901df5d6d72a2ab8f31d04b7e51e4e7", size = 309705, upload-time = "2026-04-02T09:26:02.191Z" }, @@ -369,6 +424,75 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/7a/1d/29d32e0fb40864b1f878c7f5a0b343ae676c6e2b271a2d55cc3a152391da/charset_normalizer-3.4.7-cp314-cp314t-win_amd64.whl", hash = "sha256:03853ed82eeebbce3c2abfdbc98c96dc205f32a79627688ac9a27370ea61a49c", size = 174168, upload-time = "2026-04-02T09:27:49.795Z" }, { url = "https://files.pythonhosted.org/packages/de/32/d92444ad05c7a6e41fb2036749777c163baf7a0301a040cb672d6b2b1ae9/charset_normalizer-3.4.7-cp314-cp314t-win_arm64.whl", hash = "sha256:c35abb8bfff0185efac5878da64c45dafd2b37fb0383add1be155a763c1f083d", size = 153018, upload-time = "2026-04-02T09:27:51.116Z" }, { url = "https://files.pythonhosted.org/packages/db/8f/61959034484a4a7c527811f4721e75d02d653a35afb0b6054474d8185d4c/charset_normalizer-3.4.7-py3-none-any.whl", hash = "sha256:3dce51d0f5e7951f8bb4900c257dad282f49190fdbebecd4ba99bcc41fef404d", size = 61958, upload-time = "2026-04-02T09:28:37.794Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/13/69/33ddede1939fdd074bce5434295f38fae7136463422fe4fd3e0e89b98062/charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a", size = 129418 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ed/27/c6491ff4954e58a10f69ad90aca8a1b6fe9c5d3c6f380907af3c37435b59/charset_normalizer-3.4.4-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:6e1fcf0720908f200cd21aa4e6750a48ff6ce4afe7ff5a79a90d5ed8a08296f8", size = 206988 }, + { url = "https://files.pythonhosted.org/packages/94/59/2e87300fe67ab820b5428580a53cad894272dbb97f38a7a814a2a1ac1011/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5f819d5fe9234f9f82d75bdfa9aef3a3d72c4d24a6e57aeaebba32a704553aa0", size = 147324 }, + { url = "https://files.pythonhosted.org/packages/07/fb/0cf61dc84b2b088391830f6274cb57c82e4da8bbc2efeac8c025edb88772/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:a59cb51917aa591b1c4e6a43c132f0cdc3c76dbad6155df4e28ee626cc77a0a3", size = 142742 }, + { url = "https://files.pythonhosted.org/packages/62/8b/171935adf2312cd745d290ed93cf16cf0dfe320863ab7cbeeae1dcd6535f/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:8ef3c867360f88ac904fd3f5e1f902f13307af9052646963ee08ff4f131adafc", size = 160863 }, + { url = "https://files.pythonhosted.org/packages/09/73/ad875b192bda14f2173bfc1bc9a55e009808484a4b256748d931b6948442/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d9e45d7faa48ee908174d8fe84854479ef838fc6a705c9315372eacbc2f02897", size = 157837 }, + { url = "https://files.pythonhosted.org/packages/6d/fc/de9cce525b2c5b94b47c70a4b4fb19f871b24995c728e957ee68ab1671ea/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:840c25fb618a231545cbab0564a799f101b63b9901f2569faecd6b222ac72381", size = 151550 }, + { url = "https://files.pythonhosted.org/packages/55/c2/43edd615fdfba8c6f2dfbd459b25a6b3b551f24ea21981e23fb768503ce1/charset_normalizer-3.4.4-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:ca5862d5b3928c4940729dacc329aa9102900382fea192fc5e52eb69d6093815", size = 149162 }, + { url = "https://files.pythonhosted.org/packages/03/86/bde4ad8b4d0e9429a4e82c1e8f5c659993a9a863ad62c7df05cf7b678d75/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d9c7f57c3d666a53421049053eaacdd14bbd0a528e2186fcb2e672effd053bb0", size = 150019 }, + { url = "https://files.pythonhosted.org/packages/1f/86/a151eb2af293a7e7bac3a739b81072585ce36ccfb4493039f49f1d3cae8c/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:277e970e750505ed74c832b4bf75dac7476262ee2a013f5574dd49075879e161", size = 143310 }, + { url = "https://files.pythonhosted.org/packages/b5/fe/43dae6144a7e07b87478fdfc4dbe9efd5defb0e7ec29f5f58a55aeef7bf7/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:31fd66405eaf47bb62e8cd575dc621c56c668f27d46a61d975a249930dd5e2a4", size = 162022 }, + { url = "https://files.pythonhosted.org/packages/80/e6/7aab83774f5d2bca81f42ac58d04caf44f0cc2b65fc6db2b3b2e8a05f3b3/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:0d3d8f15c07f86e9ff82319b3d9ef6f4bf907608f53fe9d92b28ea9ae3d1fd89", size = 149383 }, + { url = "https://files.pythonhosted.org/packages/4f/e8/b289173b4edae05c0dde07f69f8db476a0b511eac556dfe0d6bda3c43384/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:9f7fcd74d410a36883701fafa2482a6af2ff5ba96b9a620e9e0721e28ead5569", size = 159098 }, + { url = "https://files.pythonhosted.org/packages/d8/df/fe699727754cae3f8478493c7f45f777b17c3ef0600e28abfec8619eb49c/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:ebf3e58c7ec8a8bed6d66a75d7fb37b55e5015b03ceae72a8e7c74495551e224", size = 152991 }, + { url = "https://files.pythonhosted.org/packages/1a/86/584869fe4ddb6ffa3bd9f491b87a01568797fb9bd8933f557dba9771beaf/charset_normalizer-3.4.4-cp311-cp311-win32.whl", hash = "sha256:eecbc200c7fd5ddb9a7f16c7decb07b566c29fa2161a16cf67b8d068bd21690a", size = 99456 }, + { url = "https://files.pythonhosted.org/packages/65/f6/62fdd5feb60530f50f7e38b4f6a1d5203f4d16ff4f9f0952962c044e919a/charset_normalizer-3.4.4-cp311-cp311-win_amd64.whl", hash = "sha256:5ae497466c7901d54b639cf42d5b8c1b6a4fead55215500d2f486d34db48d016", size = 106978 }, + { url = "https://files.pythonhosted.org/packages/7a/9d/0710916e6c82948b3be62d9d398cb4fcf4e97b56d6a6aeccd66c4b2f2bd5/charset_normalizer-3.4.4-cp311-cp311-win_arm64.whl", hash = "sha256:65e2befcd84bc6f37095f5961e68a6f077bf44946771354a28ad434c2cce0ae1", size = 99969 }, + { url = "https://files.pythonhosted.org/packages/f3/85/1637cd4af66fa687396e757dec650f28025f2a2f5a5531a3208dc0ec43f2/charset_normalizer-3.4.4-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:0a98e6759f854bd25a58a73fa88833fba3b7c491169f86ce1180c948ab3fd394", size = 208425 }, + { url = "https://files.pythonhosted.org/packages/9d/6a/04130023fef2a0d9c62d0bae2649b69f7b7d8d24ea5536feef50551029df/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b5b290ccc2a263e8d185130284f8501e3e36c5e02750fc6b6bdeb2e9e96f1e25", size = 148162 }, + { url = "https://files.pythonhosted.org/packages/78/29/62328d79aa60da22c9e0b9a66539feae06ca0f5a4171ac4f7dc285b83688/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74bb723680f9f7a6234dcf67aea57e708ec1fbdf5699fb91dfd6f511b0a320ef", size = 144558 }, + { url = "https://files.pythonhosted.org/packages/86/bb/b32194a4bf15b88403537c2e120b817c61cd4ecffa9b6876e941c3ee38fe/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f1e34719c6ed0b92f418c7c780480b26b5d9c50349e9a9af7d76bf757530350d", size = 161497 }, + { url = "https://files.pythonhosted.org/packages/19/89/a54c82b253d5b9b111dc74aca196ba5ccfcca8242d0fb64146d4d3183ff1/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:2437418e20515acec67d86e12bf70056a33abdacb5cb1655042f6538d6b085a8", size = 159240 }, + { url = "https://files.pythonhosted.org/packages/c0/10/d20b513afe03acc89ec33948320a5544d31f21b05368436d580dec4e234d/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:11d694519d7f29d6cd09f6ac70028dba10f92f6cdd059096db198c283794ac86", size = 153471 }, + { url = "https://files.pythonhosted.org/packages/61/fa/fbf177b55bdd727010f9c0a3c49eefa1d10f960e5f09d1d887bf93c2e698/charset_normalizer-3.4.4-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:ac1c4a689edcc530fc9d9aa11f5774b9e2f33f9a0c6a57864e90908f5208d30a", size = 150864 }, + { url = "https://files.pythonhosted.org/packages/05/12/9fbc6a4d39c0198adeebbde20b619790e9236557ca59fc40e0e3cebe6f40/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:21d142cc6c0ec30d2efee5068ca36c128a30b0f2c53c1c07bd78cb6bc1d3be5f", size = 150647 }, + { url = "https://files.pythonhosted.org/packages/ad/1f/6a9a593d52e3e8c5d2b167daf8c6b968808efb57ef4c210acb907c365bc4/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:5dbe56a36425d26d6cfb40ce79c314a2e4dd6211d51d6d2191c00bed34f354cc", size = 145110 }, + { url = "https://files.pythonhosted.org/packages/30/42/9a52c609e72471b0fc54386dc63c3781a387bb4fe61c20231a4ebcd58bdd/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:5bfbb1b9acf3334612667b61bd3002196fe2a1eb4dd74d247e0f2a4d50ec9bbf", size = 162839 }, + { url = "https://files.pythonhosted.org/packages/c4/5b/c0682bbf9f11597073052628ddd38344a3d673fda35a36773f7d19344b23/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:d055ec1e26e441f6187acf818b73564e6e6282709e9bcb5b63f5b23068356a15", size = 150667 }, + { url = "https://files.pythonhosted.org/packages/e4/24/a41afeab6f990cf2daf6cb8c67419b63b48cf518e4f56022230840c9bfb2/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:af2d8c67d8e573d6de5bc30cdb27e9b95e49115cd9baad5ddbd1a6207aaa82a9", size = 160535 }, + { url = "https://files.pythonhosted.org/packages/2a/e5/6a4ce77ed243c4a50a1fecca6aaaab419628c818a49434be428fe24c9957/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:780236ac706e66881f3b7f2f32dfe90507a09e67d1d454c762cf642e6e1586e0", size = 154816 }, + { url = "https://files.pythonhosted.org/packages/a8/ef/89297262b8092b312d29cdb2517cb1237e51db8ecef2e9af5edbe7b683b1/charset_normalizer-3.4.4-cp312-cp312-win32.whl", hash = "sha256:5833d2c39d8896e4e19b689ffc198f08ea58116bee26dea51e362ecc7cd3ed26", size = 99694 }, + { url = "https://files.pythonhosted.org/packages/3d/2d/1e5ed9dd3b3803994c155cd9aacb60c82c331bad84daf75bcb9c91b3295e/charset_normalizer-3.4.4-cp312-cp312-win_amd64.whl", hash = "sha256:a79cfe37875f822425b89a82333404539ae63dbdddf97f84dcbc3d339aae9525", size = 107131 }, + { url = "https://files.pythonhosted.org/packages/d0/d9/0ed4c7098a861482a7b6a95603edce4c0d9db2311af23da1fb2b75ec26fc/charset_normalizer-3.4.4-cp312-cp312-win_arm64.whl", hash = "sha256:376bec83a63b8021bb5c8ea75e21c4ccb86e7e45ca4eb81146091b56599b80c3", size = 100390 }, + { url = "https://files.pythonhosted.org/packages/97/45/4b3a1239bbacd321068ea6e7ac28875b03ab8bc0aa0966452db17cd36714/charset_normalizer-3.4.4-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:e1f185f86a6f3403aa2420e815904c67b2f9ebc443f045edd0de921108345794", size = 208091 }, + { url = "https://files.pythonhosted.org/packages/7d/62/73a6d7450829655a35bb88a88fca7d736f9882a27eacdca2c6d505b57e2e/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b39f987ae8ccdf0d2642338faf2abb1862340facc796048b604ef14919e55ed", size = 147936 }, + { url = "https://files.pythonhosted.org/packages/89/c5/adb8c8b3d6625bef6d88b251bbb0d95f8205831b987631ab0c8bb5d937c2/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3162d5d8ce1bb98dd51af660f2121c55d0fa541b46dff7bb9b9f86ea1d87de72", size = 144180 }, + { url = "https://files.pythonhosted.org/packages/91/ed/9706e4070682d1cc219050b6048bfd293ccf67b3d4f5a4f39207453d4b99/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:81d5eb2a312700f4ecaa977a8235b634ce853200e828fbadf3a9c50bab278328", size = 161346 }, + { url = "https://files.pythonhosted.org/packages/d5/0d/031f0d95e4972901a2f6f09ef055751805ff541511dc1252ba3ca1f80cf5/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5bd2293095d766545ec1a8f612559f6b40abc0eb18bb2f5d1171872d34036ede", size = 158874 }, + { url = "https://files.pythonhosted.org/packages/f5/83/6ab5883f57c9c801ce5e5677242328aa45592be8a00644310a008d04f922/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a8a8b89589086a25749f471e6a900d3f662d1d3b6e2e59dcecf787b1cc3a1894", size = 153076 }, + { url = "https://files.pythonhosted.org/packages/75/1e/5ff781ddf5260e387d6419959ee89ef13878229732732ee73cdae01800f2/charset_normalizer-3.4.4-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:bc7637e2f80d8530ee4a78e878bce464f70087ce73cf7c1caf142416923b98f1", size = 150601 }, + { url = "https://files.pythonhosted.org/packages/d7/57/71be810965493d3510a6ca79b90c19e48696fb1ff964da319334b12677f0/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f8bf04158c6b607d747e93949aa60618b61312fe647a6369f88ce2ff16043490", size = 150376 }, + { url = "https://files.pythonhosted.org/packages/e5/d5/c3d057a78c181d007014feb7e9f2e65905a6c4ef182c0ddf0de2924edd65/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:554af85e960429cf30784dd47447d5125aaa3b99a6f0683589dbd27e2f45da44", size = 144825 }, + { url = "https://files.pythonhosted.org/packages/e6/8c/d0406294828d4976f275ffbe66f00266c4b3136b7506941d87c00cab5272/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:74018750915ee7ad843a774364e13a3db91682f26142baddf775342c3f5b1133", size = 162583 }, + { url = "https://files.pythonhosted.org/packages/d7/24/e2aa1f18c8f15c4c0e932d9287b8609dd30ad56dbe41d926bd846e22fb8d/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:c0463276121fdee9c49b98908b3a89c39be45d86d1dbaa22957e38f6321d4ce3", size = 150366 }, + { url = "https://files.pythonhosted.org/packages/e4/5b/1e6160c7739aad1e2df054300cc618b06bf784a7a164b0f238360721ab86/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:362d61fd13843997c1c446760ef36f240cf81d3ebf74ac62652aebaf7838561e", size = 160300 }, + { url = "https://files.pythonhosted.org/packages/7a/10/f882167cd207fbdd743e55534d5d9620e095089d176d55cb22d5322f2afd/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9a26f18905b8dd5d685d6d07b0cdf98a79f3c7a918906af7cc143ea2e164c8bc", size = 154465 }, + { url = "https://files.pythonhosted.org/packages/89/66/c7a9e1b7429be72123441bfdbaf2bc13faab3f90b933f664db506dea5915/charset_normalizer-3.4.4-cp313-cp313-win32.whl", hash = "sha256:9b35f4c90079ff2e2edc5b26c0c77925e5d2d255c42c74fdb70fb49b172726ac", size = 99404 }, + { url = "https://files.pythonhosted.org/packages/c4/26/b9924fa27db384bdcd97ab83b4f0a8058d96ad9626ead570674d5e737d90/charset_normalizer-3.4.4-cp313-cp313-win_amd64.whl", hash = "sha256:b435cba5f4f750aa6c0a0d92c541fb79f69a387c91e61f1795227e4ed9cece14", size = 107092 }, + { url = "https://files.pythonhosted.org/packages/af/8f/3ed4bfa0c0c72a7ca17f0380cd9e4dd842b09f664e780c13cff1dcf2ef1b/charset_normalizer-3.4.4-cp313-cp313-win_arm64.whl", hash = "sha256:542d2cee80be6f80247095cc36c418f7bddd14f4a6de45af91dfad36d817bba2", size = 100408 }, + { url = "https://files.pythonhosted.org/packages/2a/35/7051599bd493e62411d6ede36fd5af83a38f37c4767b92884df7301db25d/charset_normalizer-3.4.4-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:da3326d9e65ef63a817ecbcc0df6e94463713b754fe293eaa03da99befb9a5bd", size = 207746 }, + { url = "https://files.pythonhosted.org/packages/10/9a/97c8d48ef10d6cd4fcead2415523221624bf58bcf68a802721a6bc807c8f/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8af65f14dc14a79b924524b1e7fffe304517b2bff5a58bf64f30b98bbc5079eb", size = 147889 }, + { url = "https://files.pythonhosted.org/packages/10/bf/979224a919a1b606c82bd2c5fa49b5c6d5727aa47b4312bb27b1734f53cd/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74664978bb272435107de04e36db5a9735e78232b85b77d45cfb38f758efd33e", size = 143641 }, + { url = "https://files.pythonhosted.org/packages/ba/33/0ad65587441fc730dc7bd90e9716b30b4702dc7b617e6ba4997dc8651495/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:752944c7ffbfdd10c074dc58ec2d5a8a4cd9493b314d367c14d24c17684ddd14", size = 160779 }, + { url = "https://files.pythonhosted.org/packages/67/ed/331d6b249259ee71ddea93f6f2f0a56cfebd46938bde6fcc6f7b9a3d0e09/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d1f13550535ad8cff21b8d757a3257963e951d96e20ec82ab44bc64aeb62a191", size = 159035 }, + { url = "https://files.pythonhosted.org/packages/67/ff/f6b948ca32e4f2a4576aa129d8bed61f2e0543bf9f5f2b7fc3758ed005c9/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ecaae4149d99b1c9e7b88bb03e3221956f68fd6d50be2ef061b2381b61d20838", size = 152542 }, + { url = "https://files.pythonhosted.org/packages/16/85/276033dcbcc369eb176594de22728541a925b2632f9716428c851b149e83/charset_normalizer-3.4.4-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:cb6254dc36b47a990e59e1068afacdcd02958bdcce30bb50cc1700a8b9d624a6", size = 149524 }, + { url = "https://files.pythonhosted.org/packages/9e/f2/6a2a1f722b6aba37050e626530a46a68f74e63683947a8acff92569f979a/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c8ae8a0f02f57a6e61203a31428fa1d677cbe50c93622b4149d5c0f319c1d19e", size = 150395 }, + { url = "https://files.pythonhosted.org/packages/60/bb/2186cb2f2bbaea6338cad15ce23a67f9b0672929744381e28b0592676824/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:47cc91b2f4dd2833fddaedd2893006b0106129d4b94fdb6af1f4ce5a9965577c", size = 143680 }, + { url = "https://files.pythonhosted.org/packages/7d/a5/bf6f13b772fbb2a90360eb620d52ed8f796f3c5caee8398c3b2eb7b1c60d/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:82004af6c302b5d3ab2cfc4cc5f29db16123b1a8417f2e25f9066f91d4411090", size = 162045 }, + { url = "https://files.pythonhosted.org/packages/df/c5/d1be898bf0dc3ef9030c3825e5d3b83f2c528d207d246cbabe245966808d/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:2b7d8f6c26245217bd2ad053761201e9f9680f8ce52f0fcd8d0755aeae5b2152", size = 149687 }, + { url = "https://files.pythonhosted.org/packages/a5/42/90c1f7b9341eef50c8a1cb3f098ac43b0508413f33affd762855f67a410e/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:799a7a5e4fb2d5898c60b640fd4981d6a25f1c11790935a44ce38c54e985f828", size = 160014 }, + { url = "https://files.pythonhosted.org/packages/76/be/4d3ee471e8145d12795ab655ece37baed0929462a86e72372fd25859047c/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:99ae2cffebb06e6c22bdc25801d7b30f503cc87dbd283479e7b606f70aff57ec", size = 154044 }, + { url = "https://files.pythonhosted.org/packages/b0/6f/8f7af07237c34a1defe7defc565a9bc1807762f672c0fde711a4b22bf9c0/charset_normalizer-3.4.4-cp314-cp314-win32.whl", hash = "sha256:f9d332f8c2a2fcbffe1378594431458ddbef721c1769d78e2cbc06280d8155f9", size = 99940 }, + { url = "https://files.pythonhosted.org/packages/4b/51/8ade005e5ca5b0d80fb4aff72a3775b325bdc3d27408c8113811a7cbe640/charset_normalizer-3.4.4-cp314-cp314-win_amd64.whl", hash = "sha256:8a6562c3700cce886c5be75ade4a5db4214fda19fede41d9792d100288d8f94c", size = 107104 }, + { url = "https://files.pythonhosted.org/packages/da/5f/6b8f83a55bb8278772c5ae54a577f3099025f9ade59d0136ac24a0df4bde/charset_normalizer-3.4.4-cp314-cp314-win_arm64.whl", hash = "sha256:de00632ca48df9daf77a2c65a484531649261ec9f25489917f09e455cb09ddb2", size = 100743 }, + { url = "https://files.pythonhosted.org/packages/0a/4c/925909008ed5a988ccbb72dcc897407e5d6d3bd72410d69e051fc0c14647/charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f", size = 53402 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -378,18 +502,24 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "colorama", marker = "sys_platform == 'win32'" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/9b/98/518d8e5081007684232226f475082b30087d0f585e8457db087298259f49/click-8.4.1.tar.gz", hash = "sha256:918b5633eddf6b41c32d4f454bf0de810065c74e3f7dbf8ee5452f8be88d3e96", size = 353007, upload-time = "2026-05-22T04:08:37.769Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/c7/0d/67e5b4109ea4a837e80daa87c2c696711955e40449a97e8926672534def2/click-8.4.1-py3-none-any.whl", hash = "sha256:482be17c6991b8c19c5429a1e995d9b0efdbb63172824c41f99965dc0ade8ec2", size = 116639, upload-time = "2026-05-22T04:08:35.26Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "cloudpickle" version = "3.1.2" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/27/fb/576f067976d320f5f0114a8d9fa1215425441bb35627b1993e5afd8111e5/cloudpickle-3.1.2.tar.gz", hash = "sha256:7fda9eb655c9c230dab534f1983763de5835249750e85fbcef43aaa30a9a2414", size = 22330, upload-time = "2025-11-03T09:25:26.604Z" } +sdist = { url = "https://files.pythonhosted.org/packages/27/fb/576f067976d320f5f0114a8d9fa1215425441bb35627b1993e5afd8111e5/cloudpickle-3.1.2.tar.gz", hash = "sha256:7fda9eb655c9c230dab534f1983763de5835249750e85fbcef43aaa30a9a2414", size = 22330 } wheels = [ - { url = "https://files.pythonhosted.org/packages/88/39/799be3f2f0f38cc727ee3b4f1445fe6d5e4133064ec2e4115069418a5bb6/cloudpickle-3.1.2-py3-none-any.whl", hash = "sha256:9acb47f6afd73f60dc1df93bb801b472f05ff42fa6c84167d25cb206be1fbf4a", size = 22228, upload-time = "2025-11-03T09:25:25.534Z" }, + { url = "https://files.pythonhosted.org/packages/88/39/799be3f2f0f38cc727ee3b4f1445fe6d5e4133064ec2e4115069418a5bb6/cloudpickle-3.1.2-py3-none-any.whl", hash = "sha256:9acb47f6afd73f60dc1df93bb801b472f05ff42fa6c84167d25cb206be1fbf4a", size = 22228 }, ] [[package]] @@ -401,18 +531,18 @@ dependencies = [ { name = "matplotlib" }, { name = "numpy" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/2f/ca/39152b506133881be74bd46cced27ec10d9bbc85db5bdfc22c8f8e4011f9/cmasher-1.9.2.tar.gz", hash = "sha256:6c90c2cec87146e3210d3e7f2321fceee38ac7d87c136cd0de25160a14f57ff5", size = 520860, upload-time = "2024-11-19T11:17:02.084Z" } +sdist = { url = "https://files.pythonhosted.org/packages/2f/ca/39152b506133881be74bd46cced27ec10d9bbc85db5bdfc22c8f8e4011f9/cmasher-1.9.2.tar.gz", hash = "sha256:6c90c2cec87146e3210d3e7f2321fceee38ac7d87c136cd0de25160a14f57ff5", size = 520860 } wheels = [ - { url = "https://files.pythonhosted.org/packages/91/d2/6fcb93a60777fef7662d84ba60846d2dee9b6b70b4a62472515110f79cee/cmasher-1.9.2-py3-none-any.whl", hash = "sha256:2fe45fde06051050dda5c023a527ba9066ca21f161c793f22839a6ebe6e4bbbb", size = 506496, upload-time = "2024-11-19T11:16:58.549Z" }, + { url = "https://files.pythonhosted.org/packages/91/d2/6fcb93a60777fef7662d84ba60846d2dee9b6b70b4a62472515110f79cee/cmasher-1.9.2-py3-none-any.whl", hash = "sha256:2fe45fde06051050dda5c023a527ba9066ca21f161c793f22839a6ebe6e4bbbb", size = 506496 }, ] [[package]] name = "colorama" version = "0.4.6" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" } +sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697 } wheels = [ - { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" }, + { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 }, ] [[package]] @@ -422,9 +552,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "colorama", marker = "sys_platform == 'win32'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/a2/61/f083b5ac52e505dfc1c624eafbf8c7589a0d7f32daa398d2e7590efa5fda/colorlog-6.10.1.tar.gz", hash = "sha256:eb4ae5cb65fe7fec7773c2306061a8e63e02efc2c72eba9d27b0fa23c94f1321", size = 17162, upload-time = "2025-10-16T16:14:11.978Z" } +sdist = { url = "https://files.pythonhosted.org/packages/a2/61/f083b5ac52e505dfc1c624eafbf8c7589a0d7f32daa398d2e7590efa5fda/colorlog-6.10.1.tar.gz", hash = "sha256:eb4ae5cb65fe7fec7773c2306061a8e63e02efc2c72eba9d27b0fa23c94f1321", size = 17162 } wheels = [ - { url = "https://files.pythonhosted.org/packages/6d/c1/e419ef3723a074172b68aaa89c9f3de486ed4c2399e2dbd8113a4fdcaf9e/colorlog-6.10.1-py3-none-any.whl", hash = "sha256:2d7e8348291948af66122cff006c9f8da6255d224e7cf8e37d8de2df3bad8c9c", size = 11743, upload-time = "2025-10-16T16:14:10.512Z" }, + { url = "https://files.pythonhosted.org/packages/6d/c1/e419ef3723a074172b68aaa89c9f3de486ed4c2399e2dbd8113a4fdcaf9e/colorlog-6.10.1-py3-none-any.whl", hash = "sha256:2d7e8348291948af66122cff006c9f8da6255d224e7cf8e37d8de2df3bad8c9c", size = 11743 }, ] [[package]] @@ -434,18 +564,18 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "numpy" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/75/e4/aa41ae14c5c061205715006c8834496d86ec7500f1edda5981f0f0190cc6/colorspacious-1.1.2.tar.gz", hash = "sha256:5e9072e8cdca889dac445c35c9362a22ccf758e97b00b79ff0d5a7ba3e11b618", size = 688573, upload-time = "2018-04-08T04:27:30.83Z" } +sdist = { url = "https://files.pythonhosted.org/packages/75/e4/aa41ae14c5c061205715006c8834496d86ec7500f1edda5981f0f0190cc6/colorspacious-1.1.2.tar.gz", hash = "sha256:5e9072e8cdca889dac445c35c9362a22ccf758e97b00b79ff0d5a7ba3e11b618", size = 688573 } wheels = [ - { url = "https://files.pythonhosted.org/packages/ab/a1/318b9aeca7b9856410ededa4f52d6f82174d1a41e64bdd70d951e532675a/colorspacious-1.1.2-py2.py3-none-any.whl", hash = "sha256:c78befa603cea5dccb332464e7dd29e96469eebf6cd5133029153d1e69e3fd6f", size = 37735, upload-time = "2018-04-08T04:27:22.143Z" }, + { url = "https://files.pythonhosted.org/packages/ab/a1/318b9aeca7b9856410ededa4f52d6f82174d1a41e64bdd70d951e532675a/colorspacious-1.1.2-py2.py3-none-any.whl", hash = "sha256:c78befa603cea5dccb332464e7dd29e96469eebf6cd5133029153d1e69e3fd6f", size = 37735 }, ] [[package]] name = "comm" version = "0.2.3" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/4c/13/7d740c5849255756bc17888787313b61fd38a0a8304fc4f073dfc46122aa/comm-0.2.3.tar.gz", hash = "sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971", size = 6319, upload-time = "2025-07-25T14:02:04.452Z" } +sdist = { url = "https://files.pythonhosted.org/packages/4c/13/7d740c5849255756bc17888787313b61fd38a0a8304fc4f073dfc46122aa/comm-0.2.3.tar.gz", hash = "sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971", size = 6319 } wheels = [ - { url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294, upload-time = "2025-07-25T14:02:02.896Z" }, + { url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294 }, ] [[package]] @@ -455,85 +585,86 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "numpy" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/91/2e/c4390a31919d8a78b90e8ecf87cd4b4c4f05a5b48d05ec17db8e5404c6f4/contourpy-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1", size = 288773, upload-time = "2025-07-26T12:01:02.277Z" }, - { url = "https://files.pythonhosted.org/packages/0d/44/c4b0b6095fef4dc9c420e041799591e3b63e9619e3044f7f4f6c21c0ab24/contourpy-1.3.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:23416f38bfd74d5d28ab8429cc4d63fa67d5068bd711a85edb1c3fb0c3e2f381", size = 270149, upload-time = "2025-07-26T12:01:04.072Z" }, - { url = "https://files.pythonhosted.org/packages/30/2e/dd4ced42fefac8470661d7cb7e264808425e6c5d56d175291e93890cce09/contourpy-1.3.3-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:929ddf8c4c7f348e4c0a5a3a714b5c8542ffaa8c22954862a46ca1813b667ee7", size = 329222, upload-time = "2025-07-26T12:01:05.688Z" }, - { url = "https://files.pythonhosted.org/packages/f2/74/cc6ec2548e3d276c71389ea4802a774b7aa3558223b7bade3f25787fafc2/contourpy-1.3.3-cp311-cp311-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9e999574eddae35f1312c2b4b717b7885d4edd6cb46700e04f7f02db454e67c1", size = 377234, upload-time = "2025-07-26T12:01:07.054Z" }, - { url = "https://files.pythonhosted.org/packages/03/b3/64ef723029f917410f75c09da54254c5f9ea90ef89b143ccadb09df14c15/contourpy-1.3.3-cp311-cp311-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0bf67e0e3f482cb69779dd3061b534eb35ac9b17f163d851e2a547d56dba0a3a", size = 380555, upload-time = "2025-07-26T12:01:08.801Z" }, - { url = "https://files.pythonhosted.org/packages/5f/4b/6157f24ca425b89fe2eb7e7be642375711ab671135be21e6faa100f7448c/contourpy-1.3.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:51e79c1f7470158e838808d4a996fa9bac72c498e93d8ebe5119bc1e6becb0db", size = 355238, upload-time = "2025-07-26T12:01:10.319Z" }, - { url = "https://files.pythonhosted.org/packages/98/56/f914f0dd678480708a04cfd2206e7c382533249bc5001eb9f58aa693e200/contourpy-1.3.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:598c3aaece21c503615fd59c92a3598b428b2f01bfb4b8ca9c4edeecc2438620", size = 1326218, upload-time = "2025-07-26T12:01:12.659Z" }, - { url = "https://files.pythonhosted.org/packages/fb/d7/4a972334a0c971acd5172389671113ae82aa7527073980c38d5868ff1161/contourpy-1.3.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:322ab1c99b008dad206d406bb61d014cf0174df491ae9d9d0fac6a6fda4f977f", size = 1392867, upload-time = "2025-07-26T12:01:15.533Z" }, - { url = "https://files.pythonhosted.org/packages/75/3e/f2cc6cd56dc8cff46b1a56232eabc6feea52720083ea71ab15523daab796/contourpy-1.3.3-cp311-cp311-win32.whl", hash = "sha256:fd907ae12cd483cd83e414b12941c632a969171bf90fc937d0c9f268a31cafff", size = 183677, upload-time = "2025-07-26T12:01:17.088Z" }, - { url = "https://files.pythonhosted.org/packages/98/4b/9bd370b004b5c9d8045c6c33cf65bae018b27aca550a3f657cdc99acdbd8/contourpy-1.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:3519428f6be58431c56581f1694ba8e50626f2dd550af225f82fb5f5814d2a42", size = 225234, upload-time = "2025-07-26T12:01:18.256Z" }, - { url = "https://files.pythonhosted.org/packages/d9/b6/71771e02c2e004450c12b1120a5f488cad2e4d5b590b1af8bad060360fe4/contourpy-1.3.3-cp311-cp311-win_arm64.whl", hash = "sha256:15ff10bfada4bf92ec8b31c62bf7c1834c244019b4a33095a68000d7075df470", size = 193123, upload-time = "2025-07-26T12:01:19.848Z" }, - { url = "https://files.pythonhosted.org/packages/be/45/adfee365d9ea3d853550b2e735f9d66366701c65db7855cd07621732ccfc/contourpy-1.3.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:b08a32ea2f8e42cf1d4be3169a98dd4be32bafe4f22b6c4cb4ba810fa9e5d2cb", size = 293419, upload-time = "2025-07-26T12:01:21.16Z" }, - { url = "https://files.pythonhosted.org/packages/53/3e/405b59cfa13021a56bba395a6b3aca8cec012b45bf177b0eaf7a202cde2c/contourpy-1.3.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6", size = 273979, upload-time = "2025-07-26T12:01:22.448Z" }, - { url = "https://files.pythonhosted.org/packages/d4/1c/a12359b9b2ca3a845e8f7f9ac08bdf776114eb931392fcad91743e2ea17b/contourpy-1.3.3-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7", size = 332653, upload-time = "2025-07-26T12:01:24.155Z" }, - { url = "https://files.pythonhosted.org/packages/63/12/897aeebfb475b7748ea67b61e045accdfcf0d971f8a588b67108ed7f5512/contourpy-1.3.3-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b2e8faa0ed68cb29af51edd8e24798bb661eac3bd9f65420c1887b6ca89987c8", size = 379536, upload-time = "2025-07-26T12:01:25.91Z" }, - { url = "https://files.pythonhosted.org/packages/43/8a/a8c584b82deb248930ce069e71576fc09bd7174bbd35183b7943fb1064fd/contourpy-1.3.3-cp312-cp312-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea", size = 384397, upload-time = "2025-07-26T12:01:27.152Z" }, - { url = "https://files.pythonhosted.org/packages/cc/8f/ec6289987824b29529d0dfda0d74a07cec60e54b9c92f3c9da4c0ac732de/contourpy-1.3.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1", size = 362601, upload-time = "2025-07-26T12:01:28.808Z" }, - { url = "https://files.pythonhosted.org/packages/05/0a/a3fe3be3ee2dceb3e615ebb4df97ae6f3828aa915d3e10549ce016302bd1/contourpy-1.3.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7", size = 1331288, upload-time = "2025-07-26T12:01:31.198Z" }, - { url = "https://files.pythonhosted.org/packages/33/1d/acad9bd4e97f13f3e2b18a3977fe1b4a37ecf3d38d815333980c6c72e963/contourpy-1.3.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411", size = 1403386, upload-time = "2025-07-26T12:01:33.947Z" }, - { url = "https://files.pythonhosted.org/packages/cf/8f/5847f44a7fddf859704217a99a23a4f6417b10e5ab1256a179264561540e/contourpy-1.3.3-cp312-cp312-win32.whl", hash = "sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69", size = 185018, upload-time = "2025-07-26T12:01:35.64Z" }, - { url = "https://files.pythonhosted.org/packages/19/e8/6026ed58a64563186a9ee3f29f41261fd1828f527dd93d33b60feca63352/contourpy-1.3.3-cp312-cp312-win_amd64.whl", hash = "sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b", size = 226567, upload-time = "2025-07-26T12:01:36.804Z" }, - { url = "https://files.pythonhosted.org/packages/d1/e2/f05240d2c39a1ed228d8328a78b6f44cd695f7ef47beb3e684cf93604f86/contourpy-1.3.3-cp312-cp312-win_arm64.whl", hash = "sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc", size = 193655, upload-time = "2025-07-26T12:01:37.999Z" }, - { url = "https://files.pythonhosted.org/packages/68/35/0167aad910bbdb9599272bd96d01a9ec6852f36b9455cf2ca67bd4cc2d23/contourpy-1.3.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5", size = 293257, upload-time = "2025-07-26T12:01:39.367Z" }, - { url = "https://files.pythonhosted.org/packages/96/e4/7adcd9c8362745b2210728f209bfbcf7d91ba868a2c5f40d8b58f54c509b/contourpy-1.3.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1", size = 274034, upload-time = "2025-07-26T12:01:40.645Z" }, - { url = "https://files.pythonhosted.org/packages/73/23/90e31ceeed1de63058a02cb04b12f2de4b40e3bef5e082a7c18d9c8ae281/contourpy-1.3.3-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286", size = 334672, upload-time = "2025-07-26T12:01:41.942Z" }, - { url = "https://files.pythonhosted.org/packages/ed/93/b43d8acbe67392e659e1d984700e79eb67e2acb2bd7f62012b583a7f1b55/contourpy-1.3.3-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5", size = 381234, upload-time = "2025-07-26T12:01:43.499Z" }, - { url = "https://files.pythonhosted.org/packages/46/3b/bec82a3ea06f66711520f75a40c8fc0b113b2a75edb36aa633eb11c4f50f/contourpy-1.3.3-cp313-cp313-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67", size = 385169, upload-time = "2025-07-26T12:01:45.219Z" }, - { url = "https://files.pythonhosted.org/packages/4b/32/e0f13a1c5b0f8572d0ec6ae2f6c677b7991fafd95da523159c19eff0696a/contourpy-1.3.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9", size = 362859, upload-time = "2025-07-26T12:01:46.519Z" }, - { url = "https://files.pythonhosted.org/packages/33/71/e2a7945b7de4e58af42d708a219f3b2f4cff7386e6b6ab0a0fa0033c49a9/contourpy-1.3.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659", size = 1332062, upload-time = "2025-07-26T12:01:48.964Z" }, - { url = "https://files.pythonhosted.org/packages/12/fc/4e87ac754220ccc0e807284f88e943d6d43b43843614f0a8afa469801db0/contourpy-1.3.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7", size = 1403932, upload-time = "2025-07-26T12:01:51.979Z" }, - { url = "https://files.pythonhosted.org/packages/a6/2e/adc197a37443f934594112222ac1aa7dc9a98faf9c3842884df9a9d8751d/contourpy-1.3.3-cp313-cp313-win32.whl", hash = "sha256:b20c7c9a3bf701366556e1b1984ed2d0cedf999903c51311417cf5f591d8c78d", size = 185024, upload-time = "2025-07-26T12:01:53.245Z" }, - { url = "https://files.pythonhosted.org/packages/18/0b/0098c214843213759692cc638fce7de5c289200a830e5035d1791d7a2338/contourpy-1.3.3-cp313-cp313-win_amd64.whl", hash = "sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263", size = 226578, upload-time = "2025-07-26T12:01:54.422Z" }, - { url = "https://files.pythonhosted.org/packages/8a/9a/2f6024a0c5995243cd63afdeb3651c984f0d2bc727fd98066d40e141ad73/contourpy-1.3.3-cp313-cp313-win_arm64.whl", hash = "sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9", size = 193524, upload-time = "2025-07-26T12:01:55.73Z" }, - { url = "https://files.pythonhosted.org/packages/c0/b3/f8a1a86bd3298513f500e5b1f5fd92b69896449f6cab6a146a5d52715479/contourpy-1.3.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d", size = 306730, upload-time = "2025-07-26T12:01:57.051Z" }, - { url = "https://files.pythonhosted.org/packages/3f/11/4780db94ae62fc0c2053909b65dc3246bd7cecfc4f8a20d957ad43aa4ad8/contourpy-1.3.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216", size = 287897, upload-time = "2025-07-26T12:01:58.663Z" }, - { url = "https://files.pythonhosted.org/packages/ae/15/e59f5f3ffdd6f3d4daa3e47114c53daabcb18574a26c21f03dc9e4e42ff0/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae", size = 326751, upload-time = "2025-07-26T12:02:00.343Z" }, - { url = "https://files.pythonhosted.org/packages/0f/81/03b45cfad088e4770b1dcf72ea78d3802d04200009fb364d18a493857210/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20", size = 375486, upload-time = "2025-07-26T12:02:02.128Z" }, - { url = "https://files.pythonhosted.org/packages/0c/ba/49923366492ffbdd4486e970d421b289a670ae8cf539c1ea9a09822b371a/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99", size = 388106, upload-time = "2025-07-26T12:02:03.615Z" }, - { url = "https://files.pythonhosted.org/packages/9f/52/5b00ea89525f8f143651f9f03a0df371d3cbd2fccd21ca9b768c7a6500c2/contourpy-1.3.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b", size = 352548, upload-time = "2025-07-26T12:02:05.165Z" }, - { url = "https://files.pythonhosted.org/packages/32/1d/a209ec1a3a3452d490f6b14dd92e72280c99ae3d1e73da74f8277d4ee08f/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a", size = 1322297, upload-time = "2025-07-26T12:02:07.379Z" }, - { url = "https://files.pythonhosted.org/packages/bc/9e/46f0e8ebdd884ca0e8877e46a3f4e633f6c9c8c4f3f6e72be3fe075994aa/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e", size = 1391023, upload-time = "2025-07-26T12:02:10.171Z" }, - { url = "https://files.pythonhosted.org/packages/b9/70/f308384a3ae9cd2209e0849f33c913f658d3326900d0ff5d378d6a1422d2/contourpy-1.3.3-cp313-cp313t-win32.whl", hash = "sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3", size = 196157, upload-time = "2025-07-26T12:02:11.488Z" }, - { url = "https://files.pythonhosted.org/packages/b2/dd/880f890a6663b84d9e34a6f88cded89d78f0091e0045a284427cb6b18521/contourpy-1.3.3-cp313-cp313t-win_amd64.whl", hash = "sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8", size = 240570, upload-time = "2025-07-26T12:02:12.754Z" }, - { url = "https://files.pythonhosted.org/packages/80/99/2adc7d8ffead633234817ef8e9a87115c8a11927a94478f6bb3d3f4d4f7d/contourpy-1.3.3-cp313-cp313t-win_arm64.whl", hash = "sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301", size = 199713, upload-time = "2025-07-26T12:02:14.4Z" }, - { url = "https://files.pythonhosted.org/packages/72/8b/4546f3ab60f78c514ffb7d01a0bd743f90de36f0019d1be84d0a708a580a/contourpy-1.3.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a", size = 292189, upload-time = "2025-07-26T12:02:16.095Z" }, - { url = "https://files.pythonhosted.org/packages/fd/e1/3542a9cb596cadd76fcef413f19c79216e002623158befe6daa03dbfa88c/contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77", size = 273251, upload-time = "2025-07-26T12:02:17.524Z" }, - { url = "https://files.pythonhosted.org/packages/b1/71/f93e1e9471d189f79d0ce2497007731c1e6bf9ef6d1d61b911430c3db4e5/contourpy-1.3.3-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5", size = 335810, upload-time = "2025-07-26T12:02:18.9Z" }, - { url = "https://files.pythonhosted.org/packages/91/f9/e35f4c1c93f9275d4e38681a80506b5510e9327350c51f8d4a5a724d178c/contourpy-1.3.3-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4", size = 382871, upload-time = "2025-07-26T12:02:20.418Z" }, - { url = "https://files.pythonhosted.org/packages/b5/71/47b512f936f66a0a900d81c396a7e60d73419868fba959c61efed7a8ab46/contourpy-1.3.3-cp314-cp314-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36", size = 386264, upload-time = "2025-07-26T12:02:21.916Z" }, - { url = "https://files.pythonhosted.org/packages/04/5f/9ff93450ba96b09c7c2b3f81c94de31c89f92292f1380261bd7195bea4ea/contourpy-1.3.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3", size = 363819, upload-time = "2025-07-26T12:02:23.759Z" }, - { url = "https://files.pythonhosted.org/packages/3e/a6/0b185d4cc480ee494945cde102cb0149ae830b5fa17bf855b95f2e70ad13/contourpy-1.3.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b", size = 1333650, upload-time = "2025-07-26T12:02:26.181Z" }, - { url = "https://files.pythonhosted.org/packages/43/d7/afdc95580ca56f30fbcd3060250f66cedbde69b4547028863abd8aa3b47e/contourpy-1.3.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36", size = 1404833, upload-time = "2025-07-26T12:02:28.782Z" }, - { url = "https://files.pythonhosted.org/packages/e2/e2/366af18a6d386f41132a48f033cbd2102e9b0cf6345d35ff0826cd984566/contourpy-1.3.3-cp314-cp314-win32.whl", hash = "sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d", size = 189692, upload-time = "2025-07-26T12:02:30.128Z" }, - { url = "https://files.pythonhosted.org/packages/7d/c2/57f54b03d0f22d4044b8afb9ca0e184f8b1afd57b4f735c2fa70883dc601/contourpy-1.3.3-cp314-cp314-win_amd64.whl", hash = "sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd", size = 232424, upload-time = "2025-07-26T12:02:31.395Z" }, - { url = "https://files.pythonhosted.org/packages/18/79/a9416650df9b525737ab521aa181ccc42d56016d2123ddcb7b58e926a42c/contourpy-1.3.3-cp314-cp314-win_arm64.whl", hash = "sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339", size = 198300, upload-time = "2025-07-26T12:02:32.956Z" }, - { url = "https://files.pythonhosted.org/packages/1f/42/38c159a7d0f2b7b9c04c64ab317042bb6952b713ba875c1681529a2932fe/contourpy-1.3.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772", size = 306769, upload-time = "2025-07-26T12:02:34.2Z" }, - { url = "https://files.pythonhosted.org/packages/c3/6c/26a8205f24bca10974e77460de68d3d7c63e282e23782f1239f226fcae6f/contourpy-1.3.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77", size = 287892, upload-time = "2025-07-26T12:02:35.807Z" }, - { url = "https://files.pythonhosted.org/packages/66/06/8a475c8ab718ebfd7925661747dbb3c3ee9c82ac834ccb3570be49d129f4/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13", size = 326748, upload-time = "2025-07-26T12:02:37.193Z" }, - { url = "https://files.pythonhosted.org/packages/b4/a3/c5ca9f010a44c223f098fccd8b158bb1cb287378a31ac141f04730dc49be/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe", size = 375554, upload-time = "2025-07-26T12:02:38.894Z" }, - { url = "https://files.pythonhosted.org/packages/80/5b/68bd33ae63fac658a4145088c1e894405e07584a316738710b636c6d0333/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f", size = 388118, upload-time = "2025-07-26T12:02:40.642Z" }, - { url = "https://files.pythonhosted.org/packages/40/52/4c285a6435940ae25d7410a6c36bda5145839bc3f0beb20c707cda18b9d2/contourpy-1.3.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0", size = 352555, upload-time = "2025-07-26T12:02:42.25Z" }, - { url = "https://files.pythonhosted.org/packages/24/ee/3e81e1dd174f5c7fefe50e85d0892de05ca4e26ef1c9a59c2a57e43b865a/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4", size = 1322295, upload-time = "2025-07-26T12:02:44.668Z" }, - { url = "https://files.pythonhosted.org/packages/3c/b2/6d913d4d04e14379de429057cd169e5e00f6c2af3bb13e1710bcbdb5da12/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f", size = 1391027, upload-time = "2025-07-26T12:02:47.09Z" }, - { url = "https://files.pythonhosted.org/packages/93/8a/68a4ec5c55a2971213d29a9374913f7e9f18581945a7a31d1a39b5d2dfe5/contourpy-1.3.3-cp314-cp314t-win32.whl", hash = "sha256:e74a9a0f5e3fff48fb5a7f2fd2b9b70a3fe014a67522f79b7cca4c0c7e43c9ae", size = 202428, upload-time = "2025-07-26T12:02:48.691Z" }, - { url = "https://files.pythonhosted.org/packages/fa/96/fd9f641ffedc4fa3ace923af73b9d07e869496c9cc7a459103e6e978992f/contourpy-1.3.3-cp314-cp314t-win_amd64.whl", hash = "sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc", size = 250331, upload-time = "2025-07-26T12:02:50.137Z" }, - { url = "https://files.pythonhosted.org/packages/ae/8c/469afb6465b853afff216f9528ffda78a915ff880ed58813ba4faf4ba0b6/contourpy-1.3.3-cp314-cp314t-win_arm64.whl", hash = "sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b", size = 203831, upload-time = "2025-07-26T12:02:51.449Z" }, - { url = "https://files.pythonhosted.org/packages/a5/29/8dcfe16f0107943fa92388c23f6e05cff0ba58058c4c95b00280d4c75a14/contourpy-1.3.3-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:cd5dfcaeb10f7b7f9dc8941717c6c2ade08f587be2226222c12b25f0483ed497", size = 278809, upload-time = "2025-07-26T12:02:52.74Z" }, - { url = "https://files.pythonhosted.org/packages/85/a9/8b37ef4f7dafeb335daee3c8254645ef5725be4d9c6aa70b50ec46ef2f7e/contourpy-1.3.3-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:0c1fc238306b35f246d61a1d416a627348b5cf0648648a031e14bb8705fcdfe8", size = 261593, upload-time = "2025-07-26T12:02:54.037Z" }, - { url = "https://files.pythonhosted.org/packages/0a/59/ebfb8c677c75605cc27f7122c90313fd2f375ff3c8d19a1694bda74aaa63/contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:70f9aad7de812d6541d29d2bbf8feb22ff7e1c299523db288004e3157ff4674e", size = 302202, upload-time = "2025-07-26T12:02:55.947Z" }, - { url = "https://files.pythonhosted.org/packages/3c/37/21972a15834d90bfbfb009b9d004779bd5a07a0ec0234e5ba8f64d5736f4/contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ed3657edf08512fc3fe81b510e35c2012fbd3081d2e26160f27ca28affec989", size = 329207, upload-time = "2025-07-26T12:02:57.468Z" }, - { url = "https://files.pythonhosted.org/packages/0c/58/bd257695f39d05594ca4ad60df5bcb7e32247f9951fd09a9b8edb82d1daa/contourpy-1.3.3-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:3d1a3799d62d45c18bafd41c5fa05120b96a28079f2393af559b843d1a966a77", size = 225315, upload-time = "2025-07-26T12:02:58.801Z" }, +sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/91/2e/c4390a31919d8a78b90e8ecf87cd4b4c4f05a5b48d05ec17db8e5404c6f4/contourpy-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1", size = 288773 }, + { url = "https://files.pythonhosted.org/packages/0d/44/c4b0b6095fef4dc9c420e041799591e3b63e9619e3044f7f4f6c21c0ab24/contourpy-1.3.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:23416f38bfd74d5d28ab8429cc4d63fa67d5068bd711a85edb1c3fb0c3e2f381", size = 270149 }, + { url = "https://files.pythonhosted.org/packages/30/2e/dd4ced42fefac8470661d7cb7e264808425e6c5d56d175291e93890cce09/contourpy-1.3.3-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:929ddf8c4c7f348e4c0a5a3a714b5c8542ffaa8c22954862a46ca1813b667ee7", size = 329222 }, + { url = "https://files.pythonhosted.org/packages/f2/74/cc6ec2548e3d276c71389ea4802a774b7aa3558223b7bade3f25787fafc2/contourpy-1.3.3-cp311-cp311-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9e999574eddae35f1312c2b4b717b7885d4edd6cb46700e04f7f02db454e67c1", size = 377234 }, + { url = "https://files.pythonhosted.org/packages/03/b3/64ef723029f917410f75c09da54254c5f9ea90ef89b143ccadb09df14c15/contourpy-1.3.3-cp311-cp311-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0bf67e0e3f482cb69779dd3061b534eb35ac9b17f163d851e2a547d56dba0a3a", size = 380555 }, + { url = "https://files.pythonhosted.org/packages/5f/4b/6157f24ca425b89fe2eb7e7be642375711ab671135be21e6faa100f7448c/contourpy-1.3.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:51e79c1f7470158e838808d4a996fa9bac72c498e93d8ebe5119bc1e6becb0db", size = 355238 }, + { url = "https://files.pythonhosted.org/packages/98/56/f914f0dd678480708a04cfd2206e7c382533249bc5001eb9f58aa693e200/contourpy-1.3.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:598c3aaece21c503615fd59c92a3598b428b2f01bfb4b8ca9c4edeecc2438620", size = 1326218 }, + { url = "https://files.pythonhosted.org/packages/fb/d7/4a972334a0c971acd5172389671113ae82aa7527073980c38d5868ff1161/contourpy-1.3.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:322ab1c99b008dad206d406bb61d014cf0174df491ae9d9d0fac6a6fda4f977f", size = 1392867 }, + { url = "https://files.pythonhosted.org/packages/75/3e/f2cc6cd56dc8cff46b1a56232eabc6feea52720083ea71ab15523daab796/contourpy-1.3.3-cp311-cp311-win32.whl", hash = "sha256:fd907ae12cd483cd83e414b12941c632a969171bf90fc937d0c9f268a31cafff", size = 183677 }, + { url = "https://files.pythonhosted.org/packages/98/4b/9bd370b004b5c9d8045c6c33cf65bae018b27aca550a3f657cdc99acdbd8/contourpy-1.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:3519428f6be58431c56581f1694ba8e50626f2dd550af225f82fb5f5814d2a42", size = 225234 }, + { url = "https://files.pythonhosted.org/packages/d9/b6/71771e02c2e004450c12b1120a5f488cad2e4d5b590b1af8bad060360fe4/contourpy-1.3.3-cp311-cp311-win_arm64.whl", hash = "sha256:15ff10bfada4bf92ec8b31c62bf7c1834c244019b4a33095a68000d7075df470", size = 193123 }, + { url = "https://files.pythonhosted.org/packages/be/45/adfee365d9ea3d853550b2e735f9d66366701c65db7855cd07621732ccfc/contourpy-1.3.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:b08a32ea2f8e42cf1d4be3169a98dd4be32bafe4f22b6c4cb4ba810fa9e5d2cb", size = 293419 }, + { url = "https://files.pythonhosted.org/packages/53/3e/405b59cfa13021a56bba395a6b3aca8cec012b45bf177b0eaf7a202cde2c/contourpy-1.3.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6", size = 273979 }, + { url = "https://files.pythonhosted.org/packages/d4/1c/a12359b9b2ca3a845e8f7f9ac08bdf776114eb931392fcad91743e2ea17b/contourpy-1.3.3-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7", size = 332653 }, + { url = "https://files.pythonhosted.org/packages/63/12/897aeebfb475b7748ea67b61e045accdfcf0d971f8a588b67108ed7f5512/contourpy-1.3.3-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b2e8faa0ed68cb29af51edd8e24798bb661eac3bd9f65420c1887b6ca89987c8", size = 379536 }, + { url = "https://files.pythonhosted.org/packages/43/8a/a8c584b82deb248930ce069e71576fc09bd7174bbd35183b7943fb1064fd/contourpy-1.3.3-cp312-cp312-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea", size = 384397 }, + { url = "https://files.pythonhosted.org/packages/cc/8f/ec6289987824b29529d0dfda0d74a07cec60e54b9c92f3c9da4c0ac732de/contourpy-1.3.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1", size = 362601 }, + { url = "https://files.pythonhosted.org/packages/05/0a/a3fe3be3ee2dceb3e615ebb4df97ae6f3828aa915d3e10549ce016302bd1/contourpy-1.3.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7", size = 1331288 }, + { url = "https://files.pythonhosted.org/packages/33/1d/acad9bd4e97f13f3e2b18a3977fe1b4a37ecf3d38d815333980c6c72e963/contourpy-1.3.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411", size = 1403386 }, + { url = "https://files.pythonhosted.org/packages/cf/8f/5847f44a7fddf859704217a99a23a4f6417b10e5ab1256a179264561540e/contourpy-1.3.3-cp312-cp312-win32.whl", hash = "sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69", size = 185018 }, + { url = "https://files.pythonhosted.org/packages/19/e8/6026ed58a64563186a9ee3f29f41261fd1828f527dd93d33b60feca63352/contourpy-1.3.3-cp312-cp312-win_amd64.whl", hash = "sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b", size = 226567 }, + { url = "https://files.pythonhosted.org/packages/d1/e2/f05240d2c39a1ed228d8328a78b6f44cd695f7ef47beb3e684cf93604f86/contourpy-1.3.3-cp312-cp312-win_arm64.whl", hash = "sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc", size = 193655 }, + { url = "https://files.pythonhosted.org/packages/68/35/0167aad910bbdb9599272bd96d01a9ec6852f36b9455cf2ca67bd4cc2d23/contourpy-1.3.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5", size = 293257 }, + { url = "https://files.pythonhosted.org/packages/96/e4/7adcd9c8362745b2210728f209bfbcf7d91ba868a2c5f40d8b58f54c509b/contourpy-1.3.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1", size = 274034 }, + { url = "https://files.pythonhosted.org/packages/73/23/90e31ceeed1de63058a02cb04b12f2de4b40e3bef5e082a7c18d9c8ae281/contourpy-1.3.3-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286", size = 334672 }, + { url = "https://files.pythonhosted.org/packages/ed/93/b43d8acbe67392e659e1d984700e79eb67e2acb2bd7f62012b583a7f1b55/contourpy-1.3.3-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5", size = 381234 }, + { url = "https://files.pythonhosted.org/packages/46/3b/bec82a3ea06f66711520f75a40c8fc0b113b2a75edb36aa633eb11c4f50f/contourpy-1.3.3-cp313-cp313-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67", size = 385169 }, + { url = "https://files.pythonhosted.org/packages/4b/32/e0f13a1c5b0f8572d0ec6ae2f6c677b7991fafd95da523159c19eff0696a/contourpy-1.3.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9", size = 362859 }, + { url = "https://files.pythonhosted.org/packages/33/71/e2a7945b7de4e58af42d708a219f3b2f4cff7386e6b6ab0a0fa0033c49a9/contourpy-1.3.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659", size = 1332062 }, + { url = "https://files.pythonhosted.org/packages/12/fc/4e87ac754220ccc0e807284f88e943d6d43b43843614f0a8afa469801db0/contourpy-1.3.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7", size = 1403932 }, + { url = "https://files.pythonhosted.org/packages/a6/2e/adc197a37443f934594112222ac1aa7dc9a98faf9c3842884df9a9d8751d/contourpy-1.3.3-cp313-cp313-win32.whl", hash = "sha256:b20c7c9a3bf701366556e1b1984ed2d0cedf999903c51311417cf5f591d8c78d", size = 185024 }, + { url = "https://files.pythonhosted.org/packages/18/0b/0098c214843213759692cc638fce7de5c289200a830e5035d1791d7a2338/contourpy-1.3.3-cp313-cp313-win_amd64.whl", hash = "sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263", size = 226578 }, + { url = "https://files.pythonhosted.org/packages/8a/9a/2f6024a0c5995243cd63afdeb3651c984f0d2bc727fd98066d40e141ad73/contourpy-1.3.3-cp313-cp313-win_arm64.whl", hash = "sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9", size = 193524 }, + { url = "https://files.pythonhosted.org/packages/c0/b3/f8a1a86bd3298513f500e5b1f5fd92b69896449f6cab6a146a5d52715479/contourpy-1.3.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d", size = 306730 }, + { url = "https://files.pythonhosted.org/packages/3f/11/4780db94ae62fc0c2053909b65dc3246bd7cecfc4f8a20d957ad43aa4ad8/contourpy-1.3.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216", size = 287897 }, + { url = "https://files.pythonhosted.org/packages/ae/15/e59f5f3ffdd6f3d4daa3e47114c53daabcb18574a26c21f03dc9e4e42ff0/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae", size = 326751 }, + { url = "https://files.pythonhosted.org/packages/0f/81/03b45cfad088e4770b1dcf72ea78d3802d04200009fb364d18a493857210/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20", size = 375486 }, + { url = "https://files.pythonhosted.org/packages/0c/ba/49923366492ffbdd4486e970d421b289a670ae8cf539c1ea9a09822b371a/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99", size = 388106 }, + { url = "https://files.pythonhosted.org/packages/9f/52/5b00ea89525f8f143651f9f03a0df371d3cbd2fccd21ca9b768c7a6500c2/contourpy-1.3.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b", size = 352548 }, + { url = "https://files.pythonhosted.org/packages/32/1d/a209ec1a3a3452d490f6b14dd92e72280c99ae3d1e73da74f8277d4ee08f/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a", size = 1322297 }, + { url = "https://files.pythonhosted.org/packages/bc/9e/46f0e8ebdd884ca0e8877e46a3f4e633f6c9c8c4f3f6e72be3fe075994aa/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e", size = 1391023 }, + { url = "https://files.pythonhosted.org/packages/b9/70/f308384a3ae9cd2209e0849f33c913f658d3326900d0ff5d378d6a1422d2/contourpy-1.3.3-cp313-cp313t-win32.whl", hash = "sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3", size = 196157 }, + { url = "https://files.pythonhosted.org/packages/b2/dd/880f890a6663b84d9e34a6f88cded89d78f0091e0045a284427cb6b18521/contourpy-1.3.3-cp313-cp313t-win_amd64.whl", hash = "sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8", size = 240570 }, + { url = "https://files.pythonhosted.org/packages/80/99/2adc7d8ffead633234817ef8e9a87115c8a11927a94478f6bb3d3f4d4f7d/contourpy-1.3.3-cp313-cp313t-win_arm64.whl", hash = "sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301", size = 199713 }, + { url = "https://files.pythonhosted.org/packages/72/8b/4546f3ab60f78c514ffb7d01a0bd743f90de36f0019d1be84d0a708a580a/contourpy-1.3.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a", size = 292189 }, + { url = "https://files.pythonhosted.org/packages/fd/e1/3542a9cb596cadd76fcef413f19c79216e002623158befe6daa03dbfa88c/contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77", size = 273251 }, + { url = "https://files.pythonhosted.org/packages/b1/71/f93e1e9471d189f79d0ce2497007731c1e6bf9ef6d1d61b911430c3db4e5/contourpy-1.3.3-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5", size = 335810 }, + { url = "https://files.pythonhosted.org/packages/91/f9/e35f4c1c93f9275d4e38681a80506b5510e9327350c51f8d4a5a724d178c/contourpy-1.3.3-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4", size = 382871 }, + { url = "https://files.pythonhosted.org/packages/b5/71/47b512f936f66a0a900d81c396a7e60d73419868fba959c61efed7a8ab46/contourpy-1.3.3-cp314-cp314-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36", size = 386264 }, + { url = "https://files.pythonhosted.org/packages/04/5f/9ff93450ba96b09c7c2b3f81c94de31c89f92292f1380261bd7195bea4ea/contourpy-1.3.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3", size = 363819 }, + { url = "https://files.pythonhosted.org/packages/3e/a6/0b185d4cc480ee494945cde102cb0149ae830b5fa17bf855b95f2e70ad13/contourpy-1.3.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b", size = 1333650 }, + { url = "https://files.pythonhosted.org/packages/43/d7/afdc95580ca56f30fbcd3060250f66cedbde69b4547028863abd8aa3b47e/contourpy-1.3.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36", size = 1404833 }, + { url = "https://files.pythonhosted.org/packages/e2/e2/366af18a6d386f41132a48f033cbd2102e9b0cf6345d35ff0826cd984566/contourpy-1.3.3-cp314-cp314-win32.whl", hash = "sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d", size = 189692 }, + { url = "https://files.pythonhosted.org/packages/7d/c2/57f54b03d0f22d4044b8afb9ca0e184f8b1afd57b4f735c2fa70883dc601/contourpy-1.3.3-cp314-cp314-win_amd64.whl", hash = "sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd", size = 232424 }, + { url = "https://files.pythonhosted.org/packages/18/79/a9416650df9b525737ab521aa181ccc42d56016d2123ddcb7b58e926a42c/contourpy-1.3.3-cp314-cp314-win_arm64.whl", hash = "sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339", size = 198300 }, + { url = "https://files.pythonhosted.org/packages/1f/42/38c159a7d0f2b7b9c04c64ab317042bb6952b713ba875c1681529a2932fe/contourpy-1.3.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772", size = 306769 }, + { url = "https://files.pythonhosted.org/packages/c3/6c/26a8205f24bca10974e77460de68d3d7c63e282e23782f1239f226fcae6f/contourpy-1.3.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77", size = 287892 }, + { url = "https://files.pythonhosted.org/packages/66/06/8a475c8ab718ebfd7925661747dbb3c3ee9c82ac834ccb3570be49d129f4/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13", size = 326748 }, + { url = "https://files.pythonhosted.org/packages/b4/a3/c5ca9f010a44c223f098fccd8b158bb1cb287378a31ac141f04730dc49be/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe", size = 375554 }, + { url = "https://files.pythonhosted.org/packages/80/5b/68bd33ae63fac658a4145088c1e894405e07584a316738710b636c6d0333/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f", size = 388118 }, + { url = "https://files.pythonhosted.org/packages/40/52/4c285a6435940ae25d7410a6c36bda5145839bc3f0beb20c707cda18b9d2/contourpy-1.3.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0", size = 352555 }, + { url = "https://files.pythonhosted.org/packages/24/ee/3e81e1dd174f5c7fefe50e85d0892de05ca4e26ef1c9a59c2a57e43b865a/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4", size = 1322295 }, + { url = "https://files.pythonhosted.org/packages/3c/b2/6d913d4d04e14379de429057cd169e5e00f6c2af3bb13e1710bcbdb5da12/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f", size = 1391027 }, + { url = "https://files.pythonhosted.org/packages/93/8a/68a4ec5c55a2971213d29a9374913f7e9f18581945a7a31d1a39b5d2dfe5/contourpy-1.3.3-cp314-cp314t-win32.whl", hash = "sha256:e74a9a0f5e3fff48fb5a7f2fd2b9b70a3fe014a67522f79b7cca4c0c7e43c9ae", size = 202428 }, + { url = "https://files.pythonhosted.org/packages/fa/96/fd9f641ffedc4fa3ace923af73b9d07e869496c9cc7a459103e6e978992f/contourpy-1.3.3-cp314-cp314t-win_amd64.whl", hash = "sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc", size = 250331 }, + { url = "https://files.pythonhosted.org/packages/ae/8c/469afb6465b853afff216f9528ffda78a915ff880ed58813ba4faf4ba0b6/contourpy-1.3.3-cp314-cp314t-win_arm64.whl", hash = "sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b", size = 203831 }, + { url = "https://files.pythonhosted.org/packages/a5/29/8dcfe16f0107943fa92388c23f6e05cff0ba58058c4c95b00280d4c75a14/contourpy-1.3.3-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:cd5dfcaeb10f7b7f9dc8941717c6c2ade08f587be2226222c12b25f0483ed497", size = 278809 }, + { url = "https://files.pythonhosted.org/packages/85/a9/8b37ef4f7dafeb335daee3c8254645ef5725be4d9c6aa70b50ec46ef2f7e/contourpy-1.3.3-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:0c1fc238306b35f246d61a1d416a627348b5cf0648648a031e14bb8705fcdfe8", size = 261593 }, + { url = "https://files.pythonhosted.org/packages/0a/59/ebfb8c677c75605cc27f7122c90313fd2f375ff3c8d19a1694bda74aaa63/contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:70f9aad7de812d6541d29d2bbf8feb22ff7e1c299523db288004e3157ff4674e", size = 302202 }, + { url = "https://files.pythonhosted.org/packages/3c/37/21972a15834d90bfbfb009b9d004779bd5a07a0ec0234e5ba8f64d5736f4/contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ed3657edf08512fc3fe81b510e35c2012fbd3081d2e26160f27ca28affec989", size = 329207 }, + { url = "https://files.pythonhosted.org/packages/0c/58/bd257695f39d05594ca4ad60df5bcb7e32247f9951fd09a9b8edb82d1daa/contourpy-1.3.3-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:3d1a3799d62d45c18bafd41c5fa05120b96a28079f2393af559b843d1a966a77", size = 225315 }, ] [[package]] name = "coverage" version = "7.14.1" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/54/fd/0ab2772530e946e1be1abd0bc09e647ec9b02e88f0867857601fefca8953/coverage-7.14.1.tar.gz", hash = "sha256:30c08f7d90415aa98b3c990385dea2939b0da55f38515e5b369b83655f8523be", size = 920132, upload-time = "2026-05-26T20:41:36.783Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/7d/d7/477ad149490e6cb849f28abea1dabb9c823cea72e7500c81b4240ce619c0/coverage-7.14.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:478b5bcd63c2e1357c5c7e16c070690df7b07f676b1c114d7b93e533c664309f", size = 219848, upload-time = "2026-05-26T20:38:38.715Z" }, @@ -700,15 +831,78 @@ nvrtc = [ ] nvtx = [ { name = "nvidia-nvtx", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/e3/66/7e97aa77af7cf6afbff26e3651b564fe41932599bc2d3dce0b2f73d4829a/crc32c-2.8.tar.gz", hash = "sha256:578728964e59c47c356aeeedee6220e021e124b9d3e8631d95d9a5e5f06e261c", size = 48179 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/dc/0b/5e03b22d913698e9cc563f39b9f6bbd508606bf6b8e9122cd6bf196b87ea/crc32c-2.8-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:e560a97fbb96c9897cb1d9b5076ef12fc12e2e25622530a1afd0de4240f17e1f", size = 66329 }, + { url = "https://files.pythonhosted.org/packages/6b/38/2fe0051ffe8c6a650c8b1ac0da31b8802d1dbe5fa40a84e4b6b6f5583db5/crc32c-2.8-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6762d276d90331a490ef7e71ffee53b9c0eb053bd75a272d786f3b08d3fe3671", size = 62988 }, + { url = "https://files.pythonhosted.org/packages/3e/30/5837a71c014be83aba1469c58820d287fc836512a0cad6b8fdd43868accd/crc32c-2.8-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:60670569f5ede91e39f48fb0cb4060e05b8d8704dd9e17ede930bf441b2f73ef", size = 61522 }, + { url = "https://files.pythonhosted.org/packages/ca/29/63972fc1452778e2092ae998c50cbfc2fc93e3fa9798a0278650cd6169c5/crc32c-2.8-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:711743da6ccc70b3c6718c328947b0b6f34a1fe6a6c27cc6c1d69cc226bf70e9", size = 80200 }, + { url = "https://files.pythonhosted.org/packages/cb/3a/60eb49d7bdada4122b3ffd45b0df54bdc1b8dd092cda4b069a287bdfcff4/crc32c-2.8-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5eb4094a2054774f13b26f21bf56792bb44fa1fcee6c6ad099387a43ffbfb4fa", size = 81757 }, + { url = "https://files.pythonhosted.org/packages/f5/63/6efc1b64429ef7d23bd58b75b7ac24d15df327e3ebbe9c247a0f7b1c2ed1/crc32c-2.8-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:fff15bf2bd3e95780516baae935ed12be88deaa5ebe6143c53eb0d26a7bdc7b7", size = 80830 }, + { url = "https://files.pythonhosted.org/packages/e1/eb/0ae9f436f8004f1c88f7429e659a7218a3879bd11a6b18ed1257aad7e98b/crc32c-2.8-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:4c0e11e3826668121fa53e0745635baf5e4f0ded437e8ff63ea56f38fc4f970a", size = 80095 }, + { url = "https://files.pythonhosted.org/packages/9e/81/4afc9d468977a4cd94a2eb62908553345009a7c0d30e74463a15d4b48ec3/crc32c-2.8-cp311-cp311-win32.whl", hash = "sha256:38f915336715d1f1353ab07d7d786f8a789b119e273aea106ba55355dfc9101d", size = 64886 }, + { url = "https://files.pythonhosted.org/packages/d6/e8/94e839c9f7e767bf8479046a207afd440a08f5c59b52586e1af5e64fa4a0/crc32c-2.8-cp311-cp311-win_amd64.whl", hash = "sha256:60e0a765b1caab8d31b2ea80840639253906a9351d4b861551c8c8625ea20f86", size = 66639 }, + { url = "https://files.pythonhosted.org/packages/b6/36/fd18ef23c42926b79c7003e16cb0f79043b5b179c633521343d3b499e996/crc32c-2.8-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:572ffb1b78cce3d88e8d4143e154d31044a44be42cb3f6fbbf77f1e7a941c5ab", size = 66379 }, + { url = "https://files.pythonhosted.org/packages/7f/b8/c584958e53f7798dd358f5bdb1bbfc97483134f053ee399d3eeb26cca075/crc32c-2.8-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:cf827b3758ee0c4aacd21ceca0e2da83681f10295c38a10bfeb105f7d98f7a68", size = 63042 }, + { url = "https://files.pythonhosted.org/packages/62/e6/6f2af0ec64a668a46c861e5bc778ea3ee42171fedfc5440f791f470fd783/crc32c-2.8-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:106fbd79013e06fa92bc3b51031694fcc1249811ed4364ef1554ee3dd2c7f5a2", size = 61528 }, + { url = "https://files.pythonhosted.org/packages/17/8b/4a04bd80a024f1a23978f19ae99407783e06549e361ab56e9c08bba3c1d3/crc32c-2.8-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:6dde035f91ffbfe23163e68605ee5a4bb8ceebd71ed54bb1fb1d0526cdd125a2", size = 80028 }, + { url = "https://files.pythonhosted.org/packages/21/8f/01c7afdc76ac2007d0e6a98e7300b4470b170480f8188475b597d1f4b4c6/crc32c-2.8-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e41ebe7c2f0fdcd9f3a3fd206989a36b460b4d3f24816d53e5be6c7dba72c5e1", size = 81531 }, + { url = "https://files.pythonhosted.org/packages/32/2b/8f78c5a8cc66486be5f51b6f038fc347c3ba748d3ea68be17a014283c331/crc32c-2.8-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ecf66cf90266d9c15cea597d5cc86c01917cd1a238dc3c51420c7886fa750d7e", size = 80608 }, + { url = "https://files.pythonhosted.org/packages/db/86/fad1a94cdeeeb6b6e2323c87f970186e74bfd6fbfbc247bf5c88ad0873d5/crc32c-2.8-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:59eee5f3a69ad0793d5fa9cdc9b9d743b0cd50edf7fccc0a3988a821fef0208c", size = 79886 }, + { url = "https://files.pythonhosted.org/packages/d5/db/1a7cb6757a1e32376fa2dfce00c815ea4ee614a94f9bff8228e37420c183/crc32c-2.8-cp312-cp312-win32.whl", hash = "sha256:a73d03ce3604aa5d7a2698e9057a0eef69f529c46497b27ee1c38158e90ceb76", size = 64896 }, + { url = "https://files.pythonhosted.org/packages/bf/8e/2024de34399b2e401a37dcb54b224b56c747b0dc46de4966886827b4d370/crc32c-2.8-cp312-cp312-win_amd64.whl", hash = "sha256:56b3b7d015247962cf58186e06d18c3d75a1a63d709d3233509e1c50a2d36aa2", size = 66645 }, + { url = "https://files.pythonhosted.org/packages/e8/d8/3ae227890b3be40955a7144106ef4dd97d6123a82c2a5310cdab58ca49d8/crc32c-2.8-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:36f1e03ee9e9c6938e67d3bcb60e36f260170aa5f37da1185e04ef37b56af395", size = 66380 }, + { url = "https://files.pythonhosted.org/packages/bd/8b/178d3f987cd0e049b484615512d3f91f3d2caeeb8ff336bb5896ae317438/crc32c-2.8-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:b2f3226b94b85a8dd9b3533601d7a63e9e3e8edf03a8a169830ee8303a199aeb", size = 63048 }, + { url = "https://files.pythonhosted.org/packages/f2/a1/48145ae2545ebc0169d3283ebe882da580ea4606bfb67cf4ca922ac3cfc3/crc32c-2.8-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:6e08628bc72d5b6bc8e0730e8f142194b610e780a98c58cb6698e665cb885a5b", size = 61530 }, + { url = "https://files.pythonhosted.org/packages/06/4b/cf05ed9d934cc30e5ae22f97c8272face420a476090e736615d9a6b53de0/crc32c-2.8-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:086f64793c5ec856d1ab31a026d52ad2b895ac83d7a38fce557d74eb857f0a82", size = 80001 }, + { url = "https://files.pythonhosted.org/packages/15/ab/4b04801739faf36345f6ba1920be5b1c70282fec52f8280afd3613fb13e2/crc32c-2.8-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bcf72ee7e0135b3d941c34bb2c26c3fc6bc207106b49fd89aaafaeae223ae209", size = 81543 }, + { url = "https://files.pythonhosted.org/packages/a9/1b/6e38dde5bfd2ea69b7f2ab6ec229fcd972a53d39e2db4efe75c0ac0382ce/crc32c-2.8-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:8a717dd9c3fd777d9bc6603717eae172887d402c4ab589d124ebd0184a83f89e", size = 80644 }, + { url = "https://files.pythonhosted.org/packages/ce/45/012176ffee90059ae8ec7131019c71724ea472aa63e72c0c8edbd1fad1d7/crc32c-2.8-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0450bb845b3c3c7b9bdc0b4e95620ec9a40824abdc8c86d6285c919a90743c1a", size = 79919 }, + { url = "https://files.pythonhosted.org/packages/f0/2b/f557629842f9dec2b3461cb3a0d854bb586ec45b814cea58b082c32f0dde/crc32c-2.8-cp313-cp313-win32.whl", hash = "sha256:765d220bfcbcffa6598ac11eb1e10af0ee4802b49fe126aa6bf79f8ddb9931d1", size = 64896 }, + { url = "https://files.pythonhosted.org/packages/d0/db/fd0f698c15d1e21d47c64181a98290665a08fcbb3940cd559e9c15bda57e/crc32c-2.8-cp313-cp313-win_amd64.whl", hash = "sha256:171ff0260d112c62abcce29332986950a57bddee514e0a2418bfde493ea06bb3", size = 66646 }, + { url = "https://files.pythonhosted.org/packages/db/b9/8e5d7054fe8e7eecab10fd0c8e7ffb01439417bdb6de1d66a81c38fc4a20/crc32c-2.8-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:b977a32a3708d6f51703c8557008f190aaa434d7347431efb0e86fcbe78c2a50", size = 66203 }, + { url = "https://files.pythonhosted.org/packages/55/5f/cc926c70057a63cc0c98a3c8a896eb15fc7e74d3034eadd53c94917c6cc3/crc32c-2.8-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:7399b01db4adaf41da2fb36fe2408e75a8d82a179a9564ed7619412e427b26d6", size = 62956 }, + { url = "https://files.pythonhosted.org/packages/a1/8a/0660c44a2dd2cb6ccbb529eb363b9280f5c766f1017bc8355ed8d695bd94/crc32c-2.8-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:4379f73f9cdad31958a673d11a332ec725ca71572401ca865867229f5f15e853", size = 61442 }, + { url = "https://files.pythonhosted.org/packages/f5/5a/6108d2dfc0fe33522ce83ba07aed4b22014911b387afa228808a278e27cd/crc32c-2.8-cp313-cp313t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:2e68264555fab19bab08331550dab58573e351a63ed79c869d455edd3b0aa417", size = 79109 }, + { url = "https://files.pythonhosted.org/packages/84/1e/c054f9e390090c197abf3d2936f4f9effaf0c6ee14569ae03d6ddf86958a/crc32c-2.8-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b48f2486727b8d0e7ccbae4a34cb0300498433d2a9d6b49cb13cb57c2e3f19cb", size = 80987 }, + { url = "https://files.pythonhosted.org/packages/c8/ad/1650e5c3341e4a485f800ea83116d72965030c5d48ccc168fcc685756e4d/crc32c-2.8-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:ecf123348934a086df8c8fde7f9f2d716d523ca0707c5a1367b8bb00d8134823", size = 79994 }, + { url = "https://files.pythonhosted.org/packages/d7/3b/f2ed924b177729cbb2ab30ca2902abff653c31d48c95e7b66717a9ca9fcc/crc32c-2.8-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e636ac60f76de538f7a2c0d0f3abf43104ee83a8f5e516f6345dc283ed1a4df7", size = 79046 }, + { url = "https://files.pythonhosted.org/packages/4b/80/413b05ee6ace613208b31b3670c3135ee1cf451f0e72a9c839b4946acc04/crc32c-2.8-cp313-cp313t-win32.whl", hash = "sha256:8dd4a19505e0253892e1b2f1425cc3bd47f79ae5a04cb8800315d00aad7197f2", size = 64837 }, + { url = "https://files.pythonhosted.org/packages/3b/1b/85eddb6ac5b38496c4e35c20298aae627970c88c3c624a22ab33e84f16c7/crc32c-2.8-cp313-cp313t-win_amd64.whl", hash = "sha256:4bb18e4bd98fb266596523ffc6be9c5b2387b2fa4e505ec56ca36336f49cb639", size = 66574 }, + { url = "https://files.pythonhosted.org/packages/aa/df/50e9079b532ff53dbfc0e66eed781374bd455af02ed5df8b56ad538de4ff/crc32c-2.8-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:3a3b2e4bcf7b3ee333050e7d3ff38e2ba46ea205f1d73d8949b248aaffe937ac", size = 66399 }, + { url = "https://files.pythonhosted.org/packages/5a/2e/67e3b0bc3d30e46ea5d16365cc81203286387671e22f2307eb41f19abb9c/crc32c-2.8-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:445e559e66dff16be54f8a4ef95aa6b01db799a639956d995c5498ba513fccc2", size = 63044 }, + { url = "https://files.pythonhosted.org/packages/36/ea/1723b17437e4344ed8d067456382ecb1f5b535d83fdc5aaebab676c6d273/crc32c-2.8-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:bf3040919e17afa5782e01b1875d6a05f44b8f19c05f211d8b9f8a1deb8bbd9c", size = 61541 }, + { url = "https://files.pythonhosted.org/packages/4c/6a/cbec8a235c5b46a01f319939b538958662159aec0ed3a74944e3a6de21f1/crc32c-2.8-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:5607ab8221e1ffd411f64aa40dbb6850cf06dd2908c9debd05d371e1acf62ff3", size = 80139 }, + { url = "https://files.pythonhosted.org/packages/21/31/d096722fe74b692d6e8206c27da1ea5f6b2a12ff92c54a62a6ba2f376254/crc32c-2.8-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c7f5db4f16816926986d3c94253314920689706ae13a9bf4888b47336c6735ce", size = 81736 }, + { url = "https://files.pythonhosted.org/packages/f6/a2/f75ef716ff7e3c22f385ba6ef30c5de80c19a21ebe699dc90824a1903275/crc32c-2.8-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:70b0153c4d418b673309d3529334d117e1074c4a3b2d7f676e430d72c14de67b", size = 80795 }, + { url = "https://files.pythonhosted.org/packages/d8/94/6d647a12d96ab087d9b8eacee3da073f981987827d57c7072f89ffc7b6cd/crc32c-2.8-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5c8933531442042438753755a5c8a9034e4d88b01da9eb796f7e151b31a7256c", size = 80042 }, + { url = "https://files.pythonhosted.org/packages/cd/dc/32b8896b40a0afee7a3c040536d0da5a73e68df2be9fadd21770fd158e16/crc32c-2.8-cp314-cp314-win32.whl", hash = "sha256:cdc83a3fe6c4e5df9457294cfd643de7d95bd4e9382c1dd6ed1e0f0f9169172c", size = 64914 }, + { url = "https://files.pythonhosted.org/packages/f2/b4/4308b27d307e8ecaf8dd1dcc63bbb0e47ae1826d93faa3e62d1ee00ee2d5/crc32c-2.8-cp314-cp314-win_amd64.whl", hash = "sha256:509e10035106df66770fe24b9eb8d9e32b6fb967df17744402fb67772d8b2bc7", size = 66723 }, + { url = "https://files.pythonhosted.org/packages/90/d5/a19d2489fa997a143bfbbf971a5c9a43f8b1ba9e775b1fb362d8fb15260c/crc32c-2.8-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:864359a39777a07b09b28eb31337c0cc603d5c1bf0fc328c3af736a8da624ec0", size = 66201 }, + { url = "https://files.pythonhosted.org/packages/98/c2/5f82f22d2c1242cb6f6fe92aa9a42991ebea86de994b8f9974d9c1d128e2/crc32c-2.8-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:14511d7cfc5d9f5e1a6c6b64caa6225c2bdc1ed00d725e9a374a3e84073ce180", size = 62956 }, + { url = "https://files.pythonhosted.org/packages/9b/61/3d43d33489cf974fb78bfb3500845770e139ae6d1d83473b660bd8f79a6c/crc32c-2.8-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:918b7999b52b5dcbcea34081e9a02d46917d571921a3f209956a9a429b2e06e5", size = 61443 }, + { url = "https://files.pythonhosted.org/packages/52/6d/f306ce64a352a3002f76b0fc88a1373f4541f9d34fad3668688610bab14b/crc32c-2.8-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:cc445da03fc012a5a03b71da1df1b40139729e6a5571fd4215ab40bfb39689c7", size = 79106 }, + { url = "https://files.pythonhosted.org/packages/a5/b7/1f74965dd7ea762954a69d172dfb3a706049c84ffa45d31401d010a4a126/crc32c-2.8-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e3dde2ec59a8a830511d72a086ead95c0b0b7f0d418f93ea106244c5e77e350", size = 80983 }, + { url = "https://files.pythonhosted.org/packages/1b/50/af93f0d91ccd61833ce77374ebfbd16f5805f5c17d18c6470976d9866d76/crc32c-2.8-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:61d51681a08b6a2a2e771b7f0cd1947fb87cb28f38ed55a01cb7c40b2ac4cdd8", size = 80009 }, + { url = "https://files.pythonhosted.org/packages/ee/fa/94f394beb68a88258af694dab2f1284f55a406b615d7900bdd6235283bc4/crc32c-2.8-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:67c0716c3b1a02d5235be649487b637eed21f2d070f2b3f63f709dcd2fefb4c7", size = 79066 }, + { url = "https://files.pythonhosted.org/packages/91/c6/a6050e0c64fd73c67a97da96cb59f08b05111e00b958fb87ecdce99f17ac/crc32c-2.8-cp314-cp314t-win32.whl", hash = "sha256:2e8fe863fbbd8bdb6b414a2090f1b0f52106e76e9a9c96a413495dbe5ebe492a", size = 64869 }, + { url = "https://files.pythonhosted.org/packages/08/1f/c7735034e401cb1ea14f996a224518e3a3fa9987cb13680e707328a7d779/crc32c-2.8-cp314-cp314t-win_amd64.whl", hash = "sha256:20a9cfb897693eb6da19e52e2a7be2026fd4d9fc8ae318f086c0d71d5dd2d8e0", size = 66633 }, + { url = "https://files.pythonhosted.org/packages/a7/1d/dd926c68eb8aac8b142a1a10b8eb62d95212c1cf81775644373fe7cceac2/crc32c-2.8-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:5833f4071da7ea182c514ba17d1eee8aec3c5be927d798222fbfbbd0f5eea02c", size = 62345 }, + { url = "https://files.pythonhosted.org/packages/51/be/803404e5abea2ef2c15042edca04bbb7f625044cca879e47f186b43887c2/crc32c-2.8-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:1dc4da036126ac07b39dd9d03e93e585ec615a2ad28ff12757aef7de175295a8", size = 61229 }, + { url = "https://files.pythonhosted.org/packages/fc/3a/00cc578cd27ed0b22c9be25cef2c24539d92df9fa80ebd67a3fc5419724c/crc32c-2.8-pp311-pypy311_pp73-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:15905fa78344654e241371c47e6ed2411f9eeb2b8095311c68c88eccf541e8b4", size = 64108 }, + { url = "https://files.pythonhosted.org/packages/6b/bc/0587ef99a1c7629f95dd0c9d4f3d894de383a0df85831eb16c48a6afdae4/crc32c-2.8-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c596f918688821f796434e89b431b1698396c38bf0b56de873621528fe3ecb1e", size = 64815 }, + { url = "https://files.pythonhosted.org/packages/73/42/94f2b8b92eae9064fcfb8deef2b971514065bd606231f8857ff8ae02bebd/crc32c-2.8-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:8d23c4fe01b3844cb6e091044bc1cebdef7d16472e058ce12d9fadf10d2614af", size = 66659 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "cycler" version = "0.12.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/a9/95/a3dbbb5028f35eafb79008e7522a75244477d2838f38cbb722248dabc2a8/cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c", size = 7615, upload-time = "2023-10-07T05:32:18.335Z" } +sdist = { url = "https://files.pythonhosted.org/packages/a9/95/a3dbbb5028f35eafb79008e7522a75244477d2838f38cbb722248dabc2a8/cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c", size = 7615 } wheels = [ - { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321, upload-time = "2023-10-07T05:32:16.783Z" }, + { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321 }, ] [[package]] @@ -725,9 +919,15 @@ dependencies = [ { name = "pyyaml" }, { name = "toolz" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/d7/2a/5d8cc1579590af86576dde890254440e478c7174b93a02095ecfc2e6ba38/dask-2026.3.0.tar.gz", hash = "sha256:f7d96c8274e8a900d217c1ff6ea8d1bbf0b4c2c21e74a409644498d925eb8f85", size = 11000710, upload-time = "2026-03-18T07:10:14.945Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/4a/f3/00bb1e867fba351e2d784170955713bee200c43ea306c59f30bd7e748192/dask-2026.3.0-py3-none-any.whl", hash = "sha256:be614b9242b0b38288060fb2d7696125946469c98a1c30e174883fd199e0428d", size = 1485630, upload-time = "2026-03-18T07:10:12.832Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/db/33/eacaa72731f7fc64868caaf2d35060d50049eff889bd217263e68f76472f/dask-2025.11.0.tar.gz", hash = "sha256:23d59e624b80ee05b7cc8df858682cca58262c4c3b197ccf61da0f6543c8f7c3", size = 10984781 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1d/54/a46920229d12c3a6e9f0081d1bdaeffad23c1826353ace95714faee926e5/dask-2025.11.0-py3-none-any.whl", hash = "sha256:08c35a8146c05c93b34f83cf651009129c42ee71762da7ca452fb7308641c2b8", size = 1477108 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [package.optional-dependencies] @@ -739,6 +939,7 @@ array = [ name = "debugpy" version = "1.8.20" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/e0/b7/cd8080344452e4874aae67c40d8940e2b4d47b01601a8fd9f44786c757c7/debugpy-1.8.20.tar.gz", hash = "sha256:55bc8701714969f1ab89a6d5f2f3d40c36f91b2cbe2f65d98bf8196f6a6a2c33", size = 1645207, upload-time = "2026-01-29T23:03:28.199Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/51/56/c3baf5cbe4dd77427fd9aef99fcdade259ad128feeb8a786c246adb838e5/debugpy-1.8.20-cp311-cp311-macosx_15_0_universal2.whl", hash = "sha256:eada6042ad88fa1571b74bd5402ee8b86eded7a8f7b827849761700aff171f1b", size = 2208318, upload-time = "2026-01-29T23:03:36.481Z" }, @@ -758,42 +959,75 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/c5/d9/d64199c14a0d4c476df46c82470a3ce45c8d183a6796cfb5e66533b3663c/debugpy-1.8.20-cp314-cp314-win32.whl", hash = "sha256:352036a99dd35053b37b7803f748efc456076f929c6a895556932eaf2d23b07f", size = 5331407, upload-time = "2026-01-29T23:03:58.481Z" }, { url = "https://files.pythonhosted.org/packages/e0/d9/1f07395b54413432624d61524dfd98c1a7c7827d2abfdb8829ac92638205/debugpy-1.8.20-cp314-cp314-win_amd64.whl", hash = "sha256:a98eec61135465b062846112e5ecf2eebb855305acc1dfbae43b72903b8ab5be", size = 5372521, upload-time = "2026-01-29T23:03:59.864Z" }, { url = "https://files.pythonhosted.org/packages/e0/c3/7f67dea8ccf8fdcb9c99033bbe3e90b9e7395415843accb81428c441be2d/debugpy-1.8.20-py2.py3-none-any.whl", hash = "sha256:5be9bed9ae3be00665a06acaa48f8329d2b9632f15fd09f6a9a8c8d9907e54d7", size = 5337658, upload-time = "2026-01-29T23:04:17.404Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/15/ad/71e708ff4ca377c4230530d6a7aa7992592648c122a2cd2b321cf8b35a76/debugpy-1.8.17.tar.gz", hash = "sha256:fd723b47a8c08892b1a16b2c6239a8b96637c62a59b94bb5dab4bac592a58a8e", size = 1644129 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d8/53/3af72b5c159278c4a0cf4cffa518675a0e73bdb7d1cac0239b815502d2ce/debugpy-1.8.17-cp311-cp311-macosx_15_0_universal2.whl", hash = "sha256:d3fce3f0e3de262a3b67e69916d001f3e767661c6e1ee42553009d445d1cd840", size = 2207154 }, + { url = "https://files.pythonhosted.org/packages/8f/6d/204f407df45600e2245b4a39860ed4ba32552330a0b3f5f160ae4cc30072/debugpy-1.8.17-cp311-cp311-manylinux_2_34_x86_64.whl", hash = "sha256:c6bdf134457ae0cac6fb68205776be635d31174eeac9541e1d0c062165c6461f", size = 3170322 }, + { url = "https://files.pythonhosted.org/packages/f2/13/1b8f87d39cf83c6b713de2620c31205299e6065622e7dd37aff4808dd410/debugpy-1.8.17-cp311-cp311-win32.whl", hash = "sha256:e79a195f9e059edfe5d8bf6f3749b2599452d3e9380484cd261f6b7cd2c7c4da", size = 5155078 }, + { url = "https://files.pythonhosted.org/packages/c2/c5/c012c60a2922cc91caa9675d0ddfbb14ba59e1e36228355f41cab6483469/debugpy-1.8.17-cp311-cp311-win_amd64.whl", hash = "sha256:b532282ad4eca958b1b2d7dbcb2b7218e02cb934165859b918e3b6ba7772d3f4", size = 5179011 }, + { url = "https://files.pythonhosted.org/packages/08/2b/9d8e65beb2751876c82e1aceb32f328c43ec872711fa80257c7674f45650/debugpy-1.8.17-cp312-cp312-macosx_15_0_universal2.whl", hash = "sha256:f14467edef672195c6f6b8e27ce5005313cb5d03c9239059bc7182b60c176e2d", size = 2549522 }, + { url = "https://files.pythonhosted.org/packages/b4/78/eb0d77f02971c05fca0eb7465b18058ba84bd957062f5eec82f941ac792a/debugpy-1.8.17-cp312-cp312-manylinux_2_34_x86_64.whl", hash = "sha256:24693179ef9dfa20dca8605905a42b392be56d410c333af82f1c5dff807a64cc", size = 4309417 }, + { url = "https://files.pythonhosted.org/packages/37/42/c40f1d8cc1fed1e75ea54298a382395b8b937d923fcf41ab0797a554f555/debugpy-1.8.17-cp312-cp312-win32.whl", hash = "sha256:6a4e9dacf2cbb60d2514ff7b04b4534b0139facbf2abdffe0639ddb6088e59cf", size = 5277130 }, + { url = "https://files.pythonhosted.org/packages/72/22/84263b205baad32b81b36eac076de0cdbe09fe2d0637f5b32243dc7c925b/debugpy-1.8.17-cp312-cp312-win_amd64.whl", hash = "sha256:e8f8f61c518952fb15f74a302e068b48d9c4691768ade433e4adeea961993464", size = 5319053 }, + { url = "https://files.pythonhosted.org/packages/50/76/597e5cb97d026274ba297af8d89138dfd9e695767ba0e0895edb20963f40/debugpy-1.8.17-cp313-cp313-macosx_15_0_universal2.whl", hash = "sha256:857c1dd5d70042502aef1c6d1c2801211f3ea7e56f75e9c335f434afb403e464", size = 2538386 }, + { url = "https://files.pythonhosted.org/packages/5f/60/ce5c34fcdfec493701f9d1532dba95b21b2f6394147234dce21160bd923f/debugpy-1.8.17-cp313-cp313-manylinux_2_34_x86_64.whl", hash = "sha256:3bea3b0b12f3946e098cce9b43c3c46e317b567f79570c3f43f0b96d00788088", size = 4292100 }, + { url = "https://files.pythonhosted.org/packages/e8/95/7873cf2146577ef71d2a20bf553f12df865922a6f87b9e8ee1df04f01785/debugpy-1.8.17-cp313-cp313-win32.whl", hash = "sha256:e34ee844c2f17b18556b5bbe59e1e2ff4e86a00282d2a46edab73fd7f18f4a83", size = 5277002 }, + { url = "https://files.pythonhosted.org/packages/46/11/18c79a1cee5ff539a94ec4aa290c1c069a5580fd5cfd2fb2e282f8e905da/debugpy-1.8.17-cp313-cp313-win_amd64.whl", hash = "sha256:6c5cd6f009ad4fca8e33e5238210dc1e5f42db07d4b6ab21ac7ffa904a196420", size = 5319047 }, + { url = "https://files.pythonhosted.org/packages/de/45/115d55b2a9da6de812696064ceb505c31e952c5d89c4ed1d9bb983deec34/debugpy-1.8.17-cp314-cp314-macosx_15_0_universal2.whl", hash = "sha256:045290c010bcd2d82bc97aa2daf6837443cd52f6328592698809b4549babcee1", size = 2536899 }, + { url = "https://files.pythonhosted.org/packages/5a/73/2aa00c7f1f06e997ef57dc9b23d61a92120bec1437a012afb6d176585197/debugpy-1.8.17-cp314-cp314-manylinux_2_34_x86_64.whl", hash = "sha256:b69b6bd9dba6a03632534cdf67c760625760a215ae289f7489a452af1031fe1f", size = 4268254 }, + { url = "https://files.pythonhosted.org/packages/86/b5/ed3e65c63c68a6634e3ba04bd10255c8e46ec16ebed7d1c79e4816d8a760/debugpy-1.8.17-cp314-cp314-win32.whl", hash = "sha256:5c59b74aa5630f3a5194467100c3b3d1c77898f9ab27e3f7dc5d40fc2f122670", size = 5277203 }, + { url = "https://files.pythonhosted.org/packages/b0/26/394276b71c7538445f29e792f589ab7379ae70fd26ff5577dfde71158e96/debugpy-1.8.17-cp314-cp314-win_amd64.whl", hash = "sha256:893cba7bb0f55161de4365584b025f7064e1f88913551bcd23be3260b231429c", size = 5318493 }, + { url = "https://files.pythonhosted.org/packages/b0/d0/89247ec250369fc76db477720a26b2fce7ba079ff1380e4ab4529d2fe233/debugpy-1.8.17-py2.py3-none-any.whl", hash = "sha256:60c7dca6571efe660ccb7a9508d73ca14b8796c4ed484c2002abba714226cfef", size = 5283210 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "decorator" version = "5.3.1" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/60/8b/32f9823da46cde7df2087faa08cd98d01b908f8dcab982cdba9c84e85355/decorator-5.3.1.tar.gz", hash = "sha256:4cbcdd55a6efadb9dbea26b858f4fb3264567b52d69ca0d25b721b553f60ea82", size = 58084, upload-time = "2026-05-18T06:03:28.057Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/05/7f/798705f5296a58ca505d600456748d1be48078eac8a7050d8a98bc9edb89/decorator-5.3.1-py3-none-any.whl", hash = "sha256:f47fe6fdbd2edd623ecfe36875d37aba411624e2670dd395dddae1358689bb3c", size = 10365, upload-time = "2026-05-18T06:03:26.517Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/43/fa/6d96a0978d19e17b68d634497769987b16c8f4cd0a7a05048bec693caa6b/decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360", size = 56711 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/4e/8c/f3147f5c4b73e7550fe5f9352eaa956ae838d5c51eb58e7a25b9f3e2643b/decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a", size = 9190 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "defusedxml" version = "0.7.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/0f/d5/c66da9b79e5bdb124974bfe172b4daf3c984ebd9c2a06e2b8a4dc7331c72/defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69", size = 75520, upload-time = "2021-03-08T10:59:26.269Z" } +sdist = { url = "https://files.pythonhosted.org/packages/0f/d5/c66da9b79e5bdb124974bfe172b4daf3c984ebd9c2a06e2b8a4dc7331c72/defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69", size = 75520 } wheels = [ - { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" }, + { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604 }, ] [[package]] name = "dill" version = "0.4.1" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/81/e1/56027a71e31b02ddc53c7d65b01e68edf64dea2932122fe7746a516f75d5/dill-0.4.1.tar.gz", hash = "sha256:423092df4182177d4d8ba8290c8a5b640c66ab35ec7da59ccfa00f6fa3eea5fa", size = 187315, upload-time = "2026-01-19T02:36:56.85Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/1e/77/dc8c558f7593132cf8fefec57c4f60c83b16941c574ac5f619abb3ae7933/dill-0.4.1-py3-none-any.whl", hash = "sha256:1e1ce33e978ae97fcfcff5638477032b801c46c7c65cf717f95fbc2248f79a9d", size = 120019, upload-time = "2026-01-19T02:36:55.663Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/12/80/630b4b88364e9a8c8c5797f4602d0f76ef820909ee32f0bacb9f90654042/dill-0.4.0.tar.gz", hash = "sha256:0633f1d2df477324f53a895b02c901fb961bdbf65a17122586ea7019292cbcf0", size = 186976 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/50/3d/9373ad9c56321fdab5b41197068e1d8c25883b3fea29dd361f9b55116869/dill-0.4.0-py3-none-any.whl", hash = "sha256:44f54bf6412c2c8464c14e8243eb163690a9800dbe2c367330883b19c7561049", size = 119668 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "distlib" version = "0.4.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/96/8e/709914eb2b5749865801041647dc7f4e6d00b549cfe88b65ca192995f07c/distlib-0.4.0.tar.gz", hash = "sha256:feec40075be03a04501a973d81f633735b4b69f98b05450592310c0f401a4e0d", size = 614605, upload-time = "2025-07-17T16:52:00.465Z" } +sdist = { url = "https://files.pythonhosted.org/packages/96/8e/709914eb2b5749865801041647dc7f4e6d00b549cfe88b65ca192995f07c/distlib-0.4.0.tar.gz", hash = "sha256:feec40075be03a04501a973d81f633735b4b69f98b05450592310c0f401a4e0d", size = 614605 } wheels = [ - { url = "https://files.pythonhosted.org/packages/33/6b/e0547afaf41bf2c42e52430072fa5658766e3d65bd4b03a563d1b6336f57/distlib-0.4.0-py2.py3-none-any.whl", hash = "sha256:9659f7d87e46584a30b5780e43ac7a2143098441670ff0a49d5f9034c54a6c16", size = 469047, upload-time = "2025-07-17T16:51:58.613Z" }, + { url = "https://files.pythonhosted.org/packages/33/6b/e0547afaf41bf2c42e52430072fa5658766e3d65bd4b03a563d1b6336f57/distlib-0.4.0-py2.py3-none-any.whl", hash = "sha256:9659f7d87e46584a30b5780e43ac7a2143098441670ff0a49d5f9034c54a6c16", size = 469047 }, ] [[package]] @@ -803,36 +1037,42 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "pyyaml" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/25/71/80cc718ff6d7abfbabacb1f57aaa42e9c1552bfdd01e64ddd704e4a03638/donfig-0.8.1.post1.tar.gz", hash = "sha256:3bef3413a4c1c601b585e8d297256d0c1470ea012afa6e8461dc28bfb7c23f52", size = 19506, upload-time = "2024-05-23T14:14:31.513Z" } +sdist = { url = "https://files.pythonhosted.org/packages/25/71/80cc718ff6d7abfbabacb1f57aaa42e9c1552bfdd01e64ddd704e4a03638/donfig-0.8.1.post1.tar.gz", hash = "sha256:3bef3413a4c1c601b585e8d297256d0c1470ea012afa6e8461dc28bfb7c23f52", size = 19506 } wheels = [ - { url = "https://files.pythonhosted.org/packages/0c/d5/c5db1ea3394c6e1732fb3286b3bd878b59507a8f77d32a2cebda7d7b7cd4/donfig-0.8.1.post1-py3-none-any.whl", hash = "sha256:2a3175ce74a06109ff9307d90a230f81215cbac9a751f4d1c6194644b8204f9d", size = 21592, upload-time = "2024-05-23T14:13:55.283Z" }, + { url = "https://files.pythonhosted.org/packages/0c/d5/c5db1ea3394c6e1732fb3286b3bd878b59507a8f77d32a2cebda7d7b7cd4/donfig-0.8.1.post1-py3-none-any.whl", hash = "sha256:2a3175ce74a06109ff9307d90a230f81215cbac9a751f4d1c6194644b8204f9d", size = 21592 }, ] [[package]] name = "executing" version = "2.2.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/cc/28/c14e053b6762b1044f34a13aab6859bbf40456d37d23aa286ac24cfd9a5d/executing-2.2.1.tar.gz", hash = "sha256:3632cc370565f6648cc328b32435bd120a1e4ebb20c77e3fdde9a13cd1e533c4", size = 1129488, upload-time = "2025-09-01T09:48:10.866Z" } +sdist = { url = "https://files.pythonhosted.org/packages/cc/28/c14e053b6762b1044f34a13aab6859bbf40456d37d23aa286ac24cfd9a5d/executing-2.2.1.tar.gz", hash = "sha256:3632cc370565f6648cc328b32435bd120a1e4ebb20c77e3fdde9a13cd1e533c4", size = 1129488 } wheels = [ - { url = "https://files.pythonhosted.org/packages/c1/ea/53f2148663b321f21b5a606bd5f191517cf40b7072c0497d3c92c4a13b1e/executing-2.2.1-py2.py3-none-any.whl", hash = "sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017", size = 28317, upload-time = "2025-09-01T09:48:08.5Z" }, + { url = "https://files.pythonhosted.org/packages/c1/ea/53f2148663b321f21b5a606bd5f191517cf40b7072c0497d3c92c4a13b1e/executing-2.2.1-py2.py3-none-any.whl", hash = "sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017", size = 28317 }, ] [[package]] name = "fastjsonschema" version = "2.21.2" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/20/b5/23b216d9d985a956623b6bd12d4086b60f0059b27799f23016af04a74ea1/fastjsonschema-2.21.2.tar.gz", hash = "sha256:b1eb43748041c880796cd077f1a07c3d94e93ae84bba5ed36800a33554ae05de", size = 374130, upload-time = "2025-08-14T18:49:36.666Z" } +sdist = { url = "https://files.pythonhosted.org/packages/20/b5/23b216d9d985a956623b6bd12d4086b60f0059b27799f23016af04a74ea1/fastjsonschema-2.21.2.tar.gz", hash = "sha256:b1eb43748041c880796cd077f1a07c3d94e93ae84bba5ed36800a33554ae05de", size = 374130 } wheels = [ - { url = "https://files.pythonhosted.org/packages/cb/a8/20d0723294217e47de6d9e2e40fd4a9d2f7c4b6ef974babd482a59743694/fastjsonschema-2.21.2-py3-none-any.whl", hash = "sha256:1c797122d0a86c5cace2e54bf4e819c36223b552017172f32c5c024a6b77e463", size = 24024, upload-time = "2025-08-14T18:49:34.776Z" }, + { url = "https://files.pythonhosted.org/packages/cb/a8/20d0723294217e47de6d9e2e40fd4a9d2f7c4b6ef974babd482a59743694/fastjsonschema-2.21.2-py3-none-any.whl", hash = "sha256:1c797122d0a86c5cace2e54bf4e819c36223b552017172f32c5c024a6b77e463", size = 24024 }, ] [[package]] name = "filelock" version = "3.29.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/b5/fe/997687a931ab51049acce6fa1f23e8f01216374ea81374ddee763c493db5/filelock-3.29.0.tar.gz", hash = "sha256:69974355e960702e789734cb4871f884ea6fe50bd8404051a3530bc07809cf90", size = 57571, upload-time = "2026-04-19T15:39:10.068Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/81/47/dd9a212ef6e343a6857485ffe25bba537304f1913bdbed446a23f7f592e1/filelock-3.29.0-py3-none-any.whl", hash = "sha256:96f5f6344709aa1572bbf631c640e4ebeeb519e08da902c39a001882f30ac258", size = 39812, upload-time = "2026-04-19T15:39:08.752Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/58/46/0028a82567109b5ef6e4d2a1f04a583fb513e6cf9527fcdd09afd817deeb/filelock-3.20.0.tar.gz", hash = "sha256:711e943b4ec6be42e1d4e6690b48dc175c822967466bb31c0c293f34334c13f4", size = 18922 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/76/91/7216b27286936c16f5b4d0c530087e4a54eead683e6b0b73dd0c64844af6/filelock-3.20.0-py3-none-any.whl", hash = "sha256:339b4732ffda5cd79b13f4e2711a31b0365ce445d95d243bb996273d072546a2", size = 16054 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -842,9 +1082,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/55/b0/8a21e330561c65653d010ef112bf38f60890051d244ede197ddaa08e50c1/flexcache-0.3.tar.gz", hash = "sha256:18743bd5a0621bfe2cf8d519e4c3bfdf57a269c15d1ced3fb4b64e0ff4600656", size = 15816, upload-time = "2024-03-09T03:21:07.555Z" } +sdist = { url = "https://files.pythonhosted.org/packages/55/b0/8a21e330561c65653d010ef112bf38f60890051d244ede197ddaa08e50c1/flexcache-0.3.tar.gz", hash = "sha256:18743bd5a0621bfe2cf8d519e4c3bfdf57a269c15d1ced3fb4b64e0ff4600656", size = 15816 } wheels = [ - { url = "https://files.pythonhosted.org/packages/27/cd/c883e1a7c447479d6e13985565080e3fea88ab5a107c21684c813dba1875/flexcache-0.3-py3-none-any.whl", hash = "sha256:d43c9fea82336af6e0115e308d9d33a185390b8346a017564611f1466dcd2e32", size = 13263, upload-time = "2024-03-09T03:21:05.635Z" }, + { url = "https://files.pythonhosted.org/packages/27/cd/c883e1a7c447479d6e13985565080e3fea88ab5a107c21684c813dba1875/flexcache-0.3-py3-none-any.whl", hash = "sha256:d43c9fea82336af6e0115e308d9d33a185390b8346a017564611f1466dcd2e32", size = 13263 }, ] [[package]] @@ -854,15 +1094,16 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/82/99/b4de7e39e8eaf8207ba1a8fa2241dd98b2ba72ae6e16960d8351736d8702/flexparser-0.4.tar.gz", hash = "sha256:266d98905595be2ccc5da964fe0a2c3526fbbffdc45b65b3146d75db992ef6b2", size = 31799, upload-time = "2024-11-07T02:00:56.249Z" } +sdist = { url = "https://files.pythonhosted.org/packages/82/99/b4de7e39e8eaf8207ba1a8fa2241dd98b2ba72ae6e16960d8351736d8702/flexparser-0.4.tar.gz", hash = "sha256:266d98905595be2ccc5da964fe0a2c3526fbbffdc45b65b3146d75db992ef6b2", size = 31799 } wheels = [ - { url = "https://files.pythonhosted.org/packages/fe/5e/3be305568fe5f34448807976dc82fc151d76c3e0e03958f34770286278c1/flexparser-0.4-py3-none-any.whl", hash = "sha256:3738b456192dcb3e15620f324c447721023c0293f6af9955b481e91d00179846", size = 27625, upload-time = "2024-11-07T02:00:54.523Z" }, + { url = "https://files.pythonhosted.org/packages/fe/5e/3be305568fe5f34448807976dc82fc151d76c3e0e03958f34770286278c1/flexparser-0.4-py3-none-any.whl", hash = "sha256:3738b456192dcb3e15620f324c447721023c0293f6af9955b481e91d00179846", size = 27625 }, ] [[package]] name = "fonttools" version = "4.63.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/84/69/c97f2c18e0db87d2c7b15da1974dace76ae938f1cfa22e2727a648b7ed43/fonttools-4.63.0.tar.gz", hash = "sha256:caeb583deeb5168e694b65cda8b4ee62abedfa66cf88488734466f2366b9c4e0", size = 3597189, upload-time = "2026-05-14T12:04:30.958Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/75/2b/a7f1545bdf5da69c4bda0cea2a5781f0ad2a6623e0277267672db43c5fe6/fonttools-4.63.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:2b8ae05d9eacf6081414d759c0a352769ac28ce31280d6bb8e77b03f9e3c449f", size = 2881793, upload-time = "2026-05-14T12:02:56.645Z" }, @@ -906,21 +1147,67 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/34/3b/214dcc19ee31d3d38fb5ad2755c11ef0514e5dc300bbaf41c0b69f393799/fonttools-4.63.0-cp314-cp314t-win32.whl", hash = "sha256:948428a275741f0b64b113c955425a953314f4b9ab9997f73a72c83e68e569c8", size = 2359326, upload-time = "2026-05-14T12:04:24.22Z" }, { url = "https://files.pythonhosted.org/packages/dd/1e/3ff1a9b523058c2eeb6a9d50f5574e2a738200d0d94107d5bc4105e8da3f/fonttools-4.63.0-cp314-cp314t-win_amd64.whl", hash = "sha256:6d4741eb179121cab9eea4cb2393d24492373a260d7945006358c08cfbf45419", size = 2425829, upload-time = "2026-05-14T12:04:26.829Z" }, { url = "https://files.pythonhosted.org/packages/2c/47/c99d5268f354002ce80f8d029cd9d7d872969da1de8b93d32de4dc56d6f4/fonttools-4.63.0-py3-none-any.whl", hash = "sha256:445af2eab030a16b9171ea8bdda7ebf7d96bda2df88ee182a464252f6e05e20d", size = 1164562, upload-time = "2026-05-14T12:04:29.092Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/4b/42/97a13e47a1e51a5a7142475bbcf5107fe3a68fc34aef331c897d5fb98ad0/fonttools-4.60.1.tar.gz", hash = "sha256:ef00af0439ebfee806b25f24c8f92109157ff3fac5731dc7867957812e87b8d9", size = 3559823 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ea/85/639aa9bface1537e0fb0f643690672dde0695a5bbbc90736bc571b0b1941/fonttools-4.60.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:7b4c32e232a71f63a5d00259ca3d88345ce2a43295bb049d21061f338124246f", size = 2831872 }, + { url = "https://files.pythonhosted.org/packages/6b/47/3c63158459c95093be9618794acb1067b3f4d30dcc5c3e8114b70e67a092/fonttools-4.60.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3630e86c484263eaac71d117085d509cbcf7b18f677906824e4bace598fb70d2", size = 2356990 }, + { url = "https://files.pythonhosted.org/packages/94/dd/1934b537c86fcf99f9761823f1fc37a98fbd54568e8e613f29a90fed95a9/fonttools-4.60.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5c1015318e4fec75dd4943ad5f6a206d9727adf97410d58b7e32ab644a807914", size = 5042189 }, + { url = "https://files.pythonhosted.org/packages/d2/d2/9f4e4c4374dd1daa8367784e1bd910f18ba886db1d6b825b12edf6db3edc/fonttools-4.60.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e6c58beb17380f7c2ea181ea11e7db8c0ceb474c9dd45f48e71e2cb577d146a1", size = 4978683 }, + { url = "https://files.pythonhosted.org/packages/cc/c4/0fb2dfd1ecbe9a07954cc13414713ed1eab17b1c0214ef07fc93df234a47/fonttools-4.60.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:ec3681a0cb34c255d76dd9d865a55f260164adb9fa02628415cdc2d43ee2c05d", size = 5021372 }, + { url = "https://files.pythonhosted.org/packages/0c/d5/495fc7ae2fab20223cc87179a8f50f40f9a6f821f271ba8301ae12bb580f/fonttools-4.60.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f4b5c37a5f40e4d733d3bbaaef082149bee5a5ea3156a785ff64d949bd1353fa", size = 5132562 }, + { url = "https://files.pythonhosted.org/packages/bc/fa/021dab618526323c744e0206b3f5c8596a2e7ae9aa38db5948a131123e83/fonttools-4.60.1-cp311-cp311-win32.whl", hash = "sha256:398447f3d8c0c786cbf1209711e79080a40761eb44b27cdafffb48f52bcec258", size = 2230288 }, + { url = "https://files.pythonhosted.org/packages/bb/78/0e1a6d22b427579ea5c8273e1c07def2f325b977faaf60bb7ddc01456cb1/fonttools-4.60.1-cp311-cp311-win_amd64.whl", hash = "sha256:d066ea419f719ed87bc2c99a4a4bfd77c2e5949cb724588b9dd58f3fd90b92bf", size = 2278184 }, + { url = "https://files.pythonhosted.org/packages/e3/f7/a10b101b7a6f8836a5adb47f2791f2075d044a6ca123f35985c42edc82d8/fonttools-4.60.1-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:7b0c6d57ab00dae9529f3faf187f2254ea0aa1e04215cf2f1a8ec277c96661bc", size = 2832953 }, + { url = "https://files.pythonhosted.org/packages/ed/fe/7bd094b59c926acf2304d2151354ddbeb74b94812f3dc943c231db09cb41/fonttools-4.60.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:839565cbf14645952d933853e8ade66a463684ed6ed6c9345d0faf1f0e868877", size = 2352706 }, + { url = "https://files.pythonhosted.org/packages/c0/ca/4bb48a26ed95a1e7eba175535fe5805887682140ee0a0d10a88e1de84208/fonttools-4.60.1-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:8177ec9676ea6e1793c8a084a90b65a9f778771998eb919d05db6d4b1c0b114c", size = 4923716 }, + { url = "https://files.pythonhosted.org/packages/b8/9f/2cb82999f686c1d1ddf06f6ae1a9117a880adbec113611cc9d22b2fdd465/fonttools-4.60.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:996a4d1834524adbb423385d5a629b868ef9d774670856c63c9a0408a3063401", size = 4968175 }, + { url = "https://files.pythonhosted.org/packages/18/79/be569699e37d166b78e6218f2cde8c550204f2505038cdd83b42edc469b9/fonttools-4.60.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a46b2f450bc79e06ef3b6394f0c68660529ed51692606ad7f953fc2e448bc903", size = 4911031 }, + { url = "https://files.pythonhosted.org/packages/cc/9f/89411cc116effaec5260ad519162f64f9c150e5522a27cbb05eb62d0c05b/fonttools-4.60.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:6ec722ee589e89a89f5b7574f5c45604030aa6ae24cb2c751e2707193b466fed", size = 5062966 }, + { url = "https://files.pythonhosted.org/packages/62/a1/f888221934b5731d46cb9991c7a71f30cb1f97c0ef5fcf37f8da8fce6c8e/fonttools-4.60.1-cp312-cp312-win32.whl", hash = "sha256:b2cf105cee600d2de04ca3cfa1f74f1127f8455b71dbad02b9da6ec266e116d6", size = 2218750 }, + { url = "https://files.pythonhosted.org/packages/88/8f/a55b5550cd33cd1028601df41acd057d4be20efa5c958f417b0c0613924d/fonttools-4.60.1-cp312-cp312-win_amd64.whl", hash = "sha256:992775c9fbe2cf794786fa0ffca7f09f564ba3499b8fe9f2f80bd7197db60383", size = 2267026 }, + { url = "https://files.pythonhosted.org/packages/7c/5b/cdd2c612277b7ac7ec8c0c9bc41812c43dc7b2d5f2b0897e15fdf5a1f915/fonttools-4.60.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:6f68576bb4bbf6060c7ab047b1574a1ebe5c50a17de62830079967b211059ebb", size = 2825777 }, + { url = "https://files.pythonhosted.org/packages/d6/8a/de9cc0540f542963ba5e8f3a1f6ad48fa211badc3177783b9d5cadf79b5d/fonttools-4.60.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:eedacb5c5d22b7097482fa834bda0dafa3d914a4e829ec83cdea2a01f8c813c4", size = 2348080 }, + { url = "https://files.pythonhosted.org/packages/2d/8b/371ab3cec97ee3fe1126b3406b7abd60c8fec8975fd79a3c75cdea0c3d83/fonttools-4.60.1-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b33a7884fabd72bdf5f910d0cf46be50dce86a0362a65cfc746a4168c67eb96c", size = 4903082 }, + { url = "https://files.pythonhosted.org/packages/04/05/06b1455e4bc653fcb2117ac3ef5fa3a8a14919b93c60742d04440605d058/fonttools-4.60.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2409d5fb7b55fd70f715e6d34e7a6e4f7511b8ad29a49d6df225ee76da76dd77", size = 4960125 }, + { url = "https://files.pythonhosted.org/packages/8e/37/f3b840fcb2666f6cb97038793606bdd83488dca2d0b0fc542ccc20afa668/fonttools-4.60.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c8651e0d4b3bdeda6602b85fdc2abbefc1b41e573ecb37b6779c4ca50753a199", size = 4901454 }, + { url = "https://files.pythonhosted.org/packages/fd/9e/eb76f77e82f8d4a46420aadff12cec6237751b0fb9ef1de373186dcffb5f/fonttools-4.60.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:145daa14bf24824b677b9357c5e44fd8895c2a8f53596e1b9ea3496081dc692c", size = 5044495 }, + { url = "https://files.pythonhosted.org/packages/f8/b3/cede8f8235d42ff7ae891bae8d619d02c8ac9fd0cfc450c5927a6200c70d/fonttools-4.60.1-cp313-cp313-win32.whl", hash = "sha256:2299df884c11162617a66b7c316957d74a18e3758c0274762d2cc87df7bc0272", size = 2217028 }, + { url = "https://files.pythonhosted.org/packages/75/4d/b022c1577807ce8b31ffe055306ec13a866f2337ecee96e75b24b9b753ea/fonttools-4.60.1-cp313-cp313-win_amd64.whl", hash = "sha256:a3db56f153bd4c5c2b619ab02c5db5192e222150ce5a1bc10f16164714bc39ac", size = 2266200 }, + { url = "https://files.pythonhosted.org/packages/9a/83/752ca11c1aa9a899b793a130f2e466b79ea0cf7279c8d79c178fc954a07b/fonttools-4.60.1-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:a884aef09d45ba1206712c7dbda5829562d3fea7726935d3289d343232ecb0d3", size = 2822830 }, + { url = "https://files.pythonhosted.org/packages/57/17/bbeab391100331950a96ce55cfbbff27d781c1b85ebafb4167eae50d9fe3/fonttools-4.60.1-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8a44788d9d91df72d1a5eac49b31aeb887a5f4aab761b4cffc4196c74907ea85", size = 2345524 }, + { url = "https://files.pythonhosted.org/packages/3d/2e/d4831caa96d85a84dd0da1d9f90d81cec081f551e0ea216df684092c6c97/fonttools-4.60.1-cp314-cp314-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:e852d9dda9f93ad3651ae1e3bb770eac544ec93c3807888798eccddf84596537", size = 4843490 }, + { url = "https://files.pythonhosted.org/packages/49/13/5e2ea7c7a101b6fc3941be65307ef8df92cbbfa6ec4804032baf1893b434/fonttools-4.60.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:154cb6ee417e417bf5f7c42fe25858c9140c26f647c7347c06f0cc2d47eff003", size = 4944184 }, + { url = "https://files.pythonhosted.org/packages/0c/2b/cf9603551c525b73fc47c52ee0b82a891579a93d9651ed694e4e2cd08bb8/fonttools-4.60.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:5664fd1a9ea7f244487ac8f10340c4e37664675e8667d6fee420766e0fb3cf08", size = 4890218 }, + { url = "https://files.pythonhosted.org/packages/fd/2f/933d2352422e25f2376aae74f79eaa882a50fb3bfef3c0d4f50501267101/fonttools-4.60.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:583b7f8e3c49486e4d489ad1deacfb8d5be54a8ef34d6df824f6a171f8511d99", size = 4999324 }, + { url = "https://files.pythonhosted.org/packages/38/99/234594c0391221f66216bc2c886923513b3399a148defaccf81dc3be6560/fonttools-4.60.1-cp314-cp314-win32.whl", hash = "sha256:66929e2ea2810c6533a5184f938502cfdaea4bc3efb7130d8cc02e1c1b4108d6", size = 2220861 }, + { url = "https://files.pythonhosted.org/packages/3e/1d/edb5b23726dde50fc4068e1493e4fc7658eeefcaf75d4c5ffce067d07ae5/fonttools-4.60.1-cp314-cp314-win_amd64.whl", hash = "sha256:f3d5be054c461d6a2268831f04091dc82753176f6ea06dc6047a5e168265a987", size = 2270934 }, + { url = "https://files.pythonhosted.org/packages/fb/da/1392aaa2170adc7071fe7f9cfd181a5684a7afcde605aebddf1fb4d76df5/fonttools-4.60.1-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:b6379e7546ba4ae4b18f8ae2b9bc5960936007a1c0e30b342f662577e8bc3299", size = 2894340 }, + { url = "https://files.pythonhosted.org/packages/bf/a7/3b9f16e010d536ce567058b931a20b590d8f3177b2eda09edd92e392375d/fonttools-4.60.1-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:9d0ced62b59e0430b3690dbc5373df1c2aa7585e9a8ce38eff87f0fd993c5b01", size = 2375073 }, + { url = "https://files.pythonhosted.org/packages/9b/b5/e9bcf51980f98e59bb5bb7c382a63c6f6cac0eec5f67de6d8f2322382065/fonttools-4.60.1-cp314-cp314t-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:875cb7764708b3132637f6c5fb385b16eeba0f7ac9fa45a69d35e09b47045801", size = 4849758 }, + { url = "https://files.pythonhosted.org/packages/e3/dc/1d2cf7d1cba82264b2f8385db3f5960e3d8ce756b4dc65b700d2c496f7e9/fonttools-4.60.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a184b2ea57b13680ab6d5fbde99ccef152c95c06746cb7718c583abd8f945ccc", size = 5085598 }, + { url = "https://files.pythonhosted.org/packages/5d/4d/279e28ba87fb20e0c69baf72b60bbf1c4d873af1476806a7b5f2b7fac1ff/fonttools-4.60.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:026290e4ec76583881763fac284aca67365e0be9f13a7fb137257096114cb3bc", size = 4957603 }, + { url = "https://files.pythonhosted.org/packages/78/d4/ff19976305e0c05aa3340c805475abb00224c954d3c65e82c0a69633d55d/fonttools-4.60.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:f0e8817c7d1a0c2eedebf57ef9a9896f3ea23324769a9a2061a80fe8852705ed", size = 4974184 }, + { url = "https://files.pythonhosted.org/packages/63/22/8553ff6166f5cd21cfaa115aaacaa0dc73b91c079a8cfd54a482cbc0f4f5/fonttools-4.60.1-cp314-cp314t-win32.whl", hash = "sha256:1410155d0e764a4615774e5c2c6fc516259fe3eca5882f034eb9bfdbee056259", size = 2282241 }, + { url = "https://files.pythonhosted.org/packages/8a/cb/fa7b4d148e11d5a72761a22e595344133e83a9507a4c231df972e657579b/fonttools-4.60.1-cp314-cp314t-win_amd64.whl", hash = "sha256:022beaea4b73a70295b688f817ddc24ed3e3418b5036ffcd5658141184ef0d0c", size = 2345760 }, + { url = "https://files.pythonhosted.org/packages/c7/93/0dd45cd283c32dea1545151d8c3637b4b8c53cdb3a625aeb2885b184d74d/fonttools-4.60.1-py3-none-any.whl", hash = "sha256:906306ac7afe2156fcf0042173d6ebbb05416af70f6b370967b47f8f00103bbb", size = 1143175 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "fqdn" version = "1.5.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/30/3e/a80a8c077fd798951169626cde3e239adeba7dab75deb3555716415bd9b0/fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f", size = 6015, upload-time = "2021-03-11T07:16:29.08Z" } +sdist = { url = "https://files.pythonhosted.org/packages/30/3e/a80a8c077fd798951169626cde3e239adeba7dab75deb3555716415bd9b0/fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f", size = 6015 } wheels = [ - { url = "https://files.pythonhosted.org/packages/cf/58/8acf1b3e91c58313ce5cb67df61001fc9dcd21be4fadb76c1a2d540e09ed/fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014", size = 9121, upload-time = "2021-03-11T07:16:28.351Z" }, + { url = "https://files.pythonhosted.org/packages/cf/58/8acf1b3e91c58313ce5cb67df61001fc9dcd21be4fadb76c1a2d540e09ed/fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014", size = 9121 }, ] [[package]] name = "fsspec" version = "2026.4.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/d5/8d/1c51c094345df128ca4a990d633fe1a0ff28726c9e6b3c41ba65087bba1d/fsspec-2026.4.0.tar.gz", hash = "sha256:301d8ac70ae90ef3ad05dcf94d6c3754a097f9b5fe4667d2787aa359ec7df7e4", size = 312760, upload-time = "2026-04-29T20:42:38.635Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/d5/0c/043d5e551459da400957a1395e0febbf771446ff34291afcbe3d8be2a279/fsspec-2026.4.0-py3-none-any.whl", hash = "sha256:11ef7bb35dab8a394fde6e608221d5cf3e8499401c249bebaeaad760a1a8dec2", size = 203402, upload-time = "2026-04-29T20:42:36.842Z" }, @@ -954,12 +1241,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/08/a5/7b059810934a09fb3ccb657e0843813c1fee1183d3bc2c8041800374aa2c/google_crc32c-1.8.0-cp314-cp314-win_amd64.whl", hash = "sha256:d511b3153e7011a27ab6ee6bb3a5404a55b994dc1a7322c0b87b29606d9790e2", size = 34878, upload-time = "2025-12-16T00:35:23.142Z" }, { url = "https://files.pythonhosted.org/packages/52/c5/c171e4d8c44fec1422d801a6d2e5d7ddabd733eeda505c79730ee9607f07/google_crc32c-1.8.0-pp311-pypy311_pp73-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:87fa445064e7db928226b2e6f0d5304ab4cd0339e664a4e9a25029f384d9bb93", size = 28615, upload-time = "2025-12-16T00:40:29.298Z" }, { url = "https://files.pythonhosted.org/packages/9c/97/7d75fe37a7a6ed171a2cf17117177e7aab7e6e0d115858741b41e9dd4254/google_crc32c-1.8.0-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f639065ea2042d5c034bf258a9f085eaa7af0cd250667c0635a3118e8f92c69c", size = 28800, upload-time = "2025-12-16T00:40:30.322Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/24/7f/2747c0d332b9acfa75dc84447a066fdf812b5a6b8d30472b74d309bfe8cb/fsspec-2025.10.0.tar.gz", hash = "sha256:b6789427626f068f9a83ca4e8a3cc050850b6c0f71f99ddb4f542b8266a26a59", size = 309285 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/eb/02/a6b21098b1d5d6249b7c5ab69dde30108a71e4e819d4a9778f1de1d5b70d/fsspec-2025.10.0-py3-none-any.whl", hash = "sha256:7c7712353ae7d875407f97715f0e1ffcc21e33d5b24556cb1e090ae9409ec61d", size = 200966 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "greenlet" version = "3.5.1" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/6d/6e/802acd792aebb2256fbbee8cacf2727faaeb6f240ac11008f09eae4414bc/greenlet-3.5.1.tar.gz", hash = "sha256:5a56aeb7d5d9cc4b3a735efb5095bd4b4f6f0e4f93e5ca876d0e2315137b7829", size = 197356, upload-time = "2026-05-20T15:05:03.917Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/42/3c/ff890b466eaba2b0f5e6bdfff025f8c75f41b8ffdc3dbc3d24ad261e764a/greenlet-3.5.1-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:73f78f9b9f0a5c06e5c946ba1e8e36f5114923b6be109ee618c54f079c3ea14f", size = 284764, upload-time = "2026-05-20T13:09:10.204Z" }, @@ -1017,6 +1310,52 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/93/c5/3dc9475ace2c7a3680da12372cddd7f1ac874eb410a1ac48d3e9dab83782/greenlet-3.5.1-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:17d86354f0ae6b61bf9be5148d0dd34e06c3cb7c602c671f79f29ac3b150e659", size = 1678427, upload-time = "2026-05-20T13:14:43.71Z" }, { url = "https://files.pythonhosted.org/packages/df/4e/750c15c317a41ffb36f0bf40b933e3d744a7dede61889f74443ea69690cf/greenlet-3.5.1-cp315-cp315t-win_amd64.whl", hash = "sha256:e7516cf6ae6b8a582c2770a0caed47b8a48373ed732c33d69a72913ae6ac923e", size = 245225, upload-time = "2026-05-20T13:13:59.366Z" }, { url = "https://files.pythonhosted.org/packages/4f/fd/d3baea2eeb7b617efd47e87ca06e2ec2c6118d303aa9e918e0ce16eadc10/greenlet-3.5.1-cp315-cp315t-win_arm64.whl", hash = "sha256:5028648bf2253ec4745add746129d3904121fa7fe871a76bed23c5720573ce0a", size = 239590, upload-time = "2026-05-20T13:13:37.382Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/03/b8/704d753a5a45507a7aab61f18db9509302ed3d0a27ac7e0359ec2905b1a6/greenlet-3.2.4.tar.gz", hash = "sha256:0dca0d95ff849f9a364385f36ab49f50065d76964944638be9691e1832e9f86d", size = 188260 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a4/de/f28ced0a67749cac23fecb02b694f6473f47686dff6afaa211d186e2ef9c/greenlet-3.2.4-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:96378df1de302bc38e99c3a9aa311967b7dc80ced1dcc6f171e99842987882a2", size = 272305 }, + { url = "https://files.pythonhosted.org/packages/09/16/2c3792cba130000bf2a31c5272999113f4764fd9d874fb257ff588ac779a/greenlet-3.2.4-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:1ee8fae0519a337f2329cb78bd7a8e128ec0f881073d43f023c7b8d4831d5246", size = 632472 }, + { url = "https://files.pythonhosted.org/packages/ae/8f/95d48d7e3d433e6dae5b1682e4292242a53f22df82e6d3dda81b1701a960/greenlet-3.2.4-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:94abf90142c2a18151632371140b3dba4dee031633fe614cb592dbb6c9e17bc3", size = 644646 }, + { url = "https://files.pythonhosted.org/packages/d5/5e/405965351aef8c76b8ef7ad370e5da58d57ef6068df197548b015464001a/greenlet-3.2.4-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:4d1378601b85e2e5171b99be8d2dc85f594c79967599328f95c1dc1a40f1c633", size = 640519 }, + { url = "https://files.pythonhosted.org/packages/25/5d/382753b52006ce0218297ec1b628e048c4e64b155379331f25a7316eb749/greenlet-3.2.4-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0db5594dce18db94f7d1650d7489909b57afde4c580806b8d9203b6e79cdc079", size = 639707 }, + { url = "https://files.pythonhosted.org/packages/1f/8e/abdd3f14d735b2929290a018ecf133c901be4874b858dd1c604b9319f064/greenlet-3.2.4-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2523e5246274f54fdadbce8494458a2ebdcdbc7b802318466ac5606d3cded1f8", size = 587684 }, + { url = "https://files.pythonhosted.org/packages/5d/65/deb2a69c3e5996439b0176f6651e0052542bb6c8f8ec2e3fba97c9768805/greenlet-3.2.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:1987de92fec508535687fb807a5cea1560f6196285a4cde35c100b8cd632cc52", size = 1116647 }, + { url = "https://files.pythonhosted.org/packages/3f/cc/b07000438a29ac5cfb2194bfc128151d52f333cee74dd7dfe3fb733fc16c/greenlet-3.2.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:55e9c5affaa6775e2c6b67659f3a71684de4c549b3dd9afca3bc773533d284fa", size = 1142073 }, + { url = "https://files.pythonhosted.org/packages/67/24/28a5b2fa42d12b3d7e5614145f0bd89714c34c08be6aabe39c14dd52db34/greenlet-3.2.4-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c9c6de1940a7d828635fbd254d69db79e54619f165ee7ce32fda763a9cb6a58c", size = 1548385 }, + { url = "https://files.pythonhosted.org/packages/6a/05/03f2f0bdd0b0ff9a4f7b99333d57b53a7709c27723ec8123056b084e69cd/greenlet-3.2.4-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:03c5136e7be905045160b1b9fdca93dd6727b180feeafda6818e6496434ed8c5", size = 1613329 }, + { url = "https://files.pythonhosted.org/packages/d8/0f/30aef242fcab550b0b3520b8e3561156857c94288f0332a79928c31a52cf/greenlet-3.2.4-cp311-cp311-win_amd64.whl", hash = "sha256:9c40adce87eaa9ddb593ccb0fa6a07caf34015a29bf8d344811665b573138db9", size = 299100 }, + { url = "https://files.pythonhosted.org/packages/44/69/9b804adb5fd0671f367781560eb5eb586c4d495277c93bde4307b9e28068/greenlet-3.2.4-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:3b67ca49f54cede0186854a008109d6ee71f66bd57bb36abd6d0a0267b540cdd", size = 274079 }, + { url = "https://files.pythonhosted.org/packages/46/e9/d2a80c99f19a153eff70bc451ab78615583b8dac0754cfb942223d2c1a0d/greenlet-3.2.4-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ddf9164e7a5b08e9d22511526865780a576f19ddd00d62f8a665949327fde8bb", size = 640997 }, + { url = "https://files.pythonhosted.org/packages/3b/16/035dcfcc48715ccd345f3a93183267167cdd162ad123cd93067d86f27ce4/greenlet-3.2.4-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:f28588772bb5fb869a8eb331374ec06f24a83a9c25bfa1f38b6993afe9c1e968", size = 655185 }, + { url = "https://files.pythonhosted.org/packages/31/da/0386695eef69ffae1ad726881571dfe28b41970173947e7c558d9998de0f/greenlet-3.2.4-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:5c9320971821a7cb77cfab8d956fa8e39cd07ca44b6070db358ceb7f8797c8c9", size = 649926 }, + { url = "https://files.pythonhosted.org/packages/68/88/69bf19fd4dc19981928ceacbc5fd4bb6bc2215d53199e367832e98d1d8fe/greenlet-3.2.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c60a6d84229b271d44b70fb6e5fa23781abb5d742af7b808ae3f6efd7c9c60f6", size = 651839 }, + { url = "https://files.pythonhosted.org/packages/19/0d/6660d55f7373b2ff8152401a83e02084956da23ae58cddbfb0b330978fe9/greenlet-3.2.4-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3b3812d8d0c9579967815af437d96623f45c0f2ae5f04e366de62a12d83a8fb0", size = 607586 }, + { url = "https://files.pythonhosted.org/packages/8e/1a/c953fdedd22d81ee4629afbb38d2f9d71e37d23caace44775a3a969147d4/greenlet-3.2.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:abbf57b5a870d30c4675928c37278493044d7c14378350b3aa5d484fa65575f0", size = 1123281 }, + { url = "https://files.pythonhosted.org/packages/3f/c7/12381b18e21aef2c6bd3a636da1088b888b97b7a0362fac2e4de92405f97/greenlet-3.2.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:20fb936b4652b6e307b8f347665e2c615540d4b42b3b4c8a321d8286da7e520f", size = 1151142 }, + { url = "https://files.pythonhosted.org/packages/27/45/80935968b53cfd3f33cf99ea5f08227f2646e044568c9b1555b58ffd61c2/greenlet-3.2.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ee7a6ec486883397d70eec05059353b8e83eca9168b9f3f9a361971e77e0bcd0", size = 1564846 }, + { url = "https://files.pythonhosted.org/packages/69/02/b7c30e5e04752cb4db6202a3858b149c0710e5453b71a3b2aec5d78a1aab/greenlet-3.2.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:326d234cbf337c9c3def0676412eb7040a35a768efc92504b947b3e9cfc7543d", size = 1633814 }, + { url = "https://files.pythonhosted.org/packages/e9/08/b0814846b79399e585f974bbeebf5580fbe59e258ea7be64d9dfb253c84f/greenlet-3.2.4-cp312-cp312-win_amd64.whl", hash = "sha256:a7d4e128405eea3814a12cc2605e0e6aedb4035bf32697f72deca74de4105e02", size = 299899 }, + { url = "https://files.pythonhosted.org/packages/49/e8/58c7f85958bda41dafea50497cbd59738c5c43dbbea5ee83d651234398f4/greenlet-3.2.4-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:1a921e542453fe531144e91e1feedf12e07351b1cf6c9e8a3325ea600a715a31", size = 272814 }, + { url = "https://files.pythonhosted.org/packages/62/dd/b9f59862e9e257a16e4e610480cfffd29e3fae018a68c2332090b53aac3d/greenlet-3.2.4-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cd3c8e693bff0fff6ba55f140bf390fa92c994083f838fece0f63be121334945", size = 641073 }, + { url = "https://files.pythonhosted.org/packages/f7/0b/bc13f787394920b23073ca3b6c4a7a21396301ed75a655bcb47196b50e6e/greenlet-3.2.4-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:710638eb93b1fa52823aa91bf75326f9ecdfd5e0466f00789246a5280f4ba0fc", size = 655191 }, + { url = "https://files.pythonhosted.org/packages/f2/d6/6adde57d1345a8d0f14d31e4ab9c23cfe8e2cd39c3baf7674b4b0338d266/greenlet-3.2.4-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:c5111ccdc9c88f423426df3fd1811bfc40ed66264d35aa373420a34377efc98a", size = 649516 }, + { url = "https://files.pythonhosted.org/packages/7f/3b/3a3328a788d4a473889a2d403199932be55b1b0060f4ddd96ee7cdfcad10/greenlet-3.2.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d76383238584e9711e20ebe14db6c88ddcedc1829a9ad31a584389463b5aa504", size = 652169 }, + { url = "https://files.pythonhosted.org/packages/ee/43/3cecdc0349359e1a527cbf2e3e28e5f8f06d3343aaf82ca13437a9aa290f/greenlet-3.2.4-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:23768528f2911bcd7e475210822ffb5254ed10d71f4028387e5a99b4c6699671", size = 610497 }, + { url = "https://files.pythonhosted.org/packages/b8/19/06b6cf5d604e2c382a6f31cafafd6f33d5dea706f4db7bdab184bad2b21d/greenlet-3.2.4-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:00fadb3fedccc447f517ee0d3fd8fe49eae949e1cd0f6a611818f4f6fb7dc83b", size = 1121662 }, + { url = "https://files.pythonhosted.org/packages/a2/15/0d5e4e1a66fab130d98168fe984c509249c833c1a3c16806b90f253ce7b9/greenlet-3.2.4-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:d25c5091190f2dc0eaa3f950252122edbbadbb682aa7b1ef2f8af0f8c0afefae", size = 1149210 }, + { url = "https://files.pythonhosted.org/packages/1c/53/f9c440463b3057485b8594d7a638bed53ba531165ef0ca0e6c364b5cc807/greenlet-3.2.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6e343822feb58ac4d0a1211bd9399de2b3a04963ddeec21530fc426cc121f19b", size = 1564759 }, + { url = "https://files.pythonhosted.org/packages/47/e4/3bb4240abdd0a8d23f4f88adec746a3099f0d86bfedb623f063b2e3b4df0/greenlet-3.2.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ca7f6f1f2649b89ce02f6f229d7c19f680a6238af656f61e0115b24857917929", size = 1634288 }, + { url = "https://files.pythonhosted.org/packages/0b/55/2321e43595e6801e105fcfdee02b34c0f996eb71e6ddffca6b10b7e1d771/greenlet-3.2.4-cp313-cp313-win_amd64.whl", hash = "sha256:554b03b6e73aaabec3745364d6239e9e012d64c68ccd0b8430c64ccc14939a8b", size = 299685 }, + { url = "https://files.pythonhosted.org/packages/22/5c/85273fd7cc388285632b0498dbbab97596e04b154933dfe0f3e68156c68c/greenlet-3.2.4-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:49a30d5fda2507ae77be16479bdb62a660fa51b1eb4928b524975b3bde77b3c0", size = 273586 }, + { url = "https://files.pythonhosted.org/packages/d1/75/10aeeaa3da9332c2e761e4c50d4c3556c21113ee3f0afa2cf5769946f7a3/greenlet-3.2.4-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:299fd615cd8fc86267b47597123e3f43ad79c9d8a22bebdce535e53550763e2f", size = 686346 }, + { url = "https://files.pythonhosted.org/packages/c0/aa/687d6b12ffb505a4447567d1f3abea23bd20e73a5bed63871178e0831b7a/greenlet-3.2.4-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:c17b6b34111ea72fc5a4e4beec9711d2226285f0386ea83477cbb97c30a3f3a5", size = 699218 }, + { url = "https://files.pythonhosted.org/packages/dc/8b/29aae55436521f1d6f8ff4e12fb676f3400de7fcf27fccd1d4d17fd8fecd/greenlet-3.2.4-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:b4a1870c51720687af7fa3e7cda6d08d801dae660f75a76f3845b642b4da6ee1", size = 694659 }, + { url = "https://files.pythonhosted.org/packages/92/2e/ea25914b1ebfde93b6fc4ff46d6864564fba59024e928bdc7de475affc25/greenlet-3.2.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:061dc4cf2c34852b052a8620d40f36324554bc192be474b9e9770e8c042fd735", size = 695355 }, + { url = "https://files.pythonhosted.org/packages/72/60/fc56c62046ec17f6b0d3060564562c64c862948c9d4bc8aa807cf5bd74f4/greenlet-3.2.4-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:44358b9bf66c8576a9f57a590d5f5d6e72fa4228b763d0e43fee6d3b06d3a337", size = 657512 }, + { url = "https://files.pythonhosted.org/packages/23/6e/74407aed965a4ab6ddd93a7ded3180b730d281c77b765788419484cdfeef/greenlet-3.2.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:2917bdf657f5859fbf3386b12d68ede4cf1f04c90c3a6bc1f013dd68a22e2269", size = 1612508 }, + { url = "https://files.pythonhosted.org/packages/0d/da/343cd760ab2f92bac1845ca07ee3faea9fe52bee65f7bcb19f16ad7de08b/greenlet-3.2.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:015d48959d4add5d6c9f6c5210ee3803a830dce46356e3bc326d6776bde54681", size = 1680760 }, + { url = "https://files.pythonhosted.org/packages/e3/a5/6ddab2b4c112be95601c13428db1d8b6608a8b6039816f2ba09c346c08fc/greenlet-3.2.4-cp314-cp314-win_amd64.whl", hash = "sha256:e37ab26028f12dbb0ff65f29a8d3d44a765c61e729647bf2ddfbbed621726f01", size = 303425 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1026,6 +1365,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "typing-extensions" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/15/f3/23f47b24f8d8c2028eba501db3acfbb2f592cbb5995eaa6e363a627b74d7/grpcio-1.81.0.tar.gz", hash = "sha256:a5acd7efd3b1fe9b4eb0bcaaa1507eed68a0ad0678b654c3f7b464df9ba9dca5", size = 13032272, upload-time = "2026-06-01T05:56:22.827Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/45/a8/9916ab10a0201f4c7afb6918125aa2f38a7626ee18ffbc066dd9cb04a74d/grpcio-1.81.0-cp311-cp311-linux_armv7l.whl", hash = "sha256:794e6aa648e8df47d8f908dc8c3b42347d04ec58438f1dcd4e445f09b4f6b0ce", size = 6093557, upload-time = "2026-06-01T05:54:32.64Z" }, @@ -1068,15 +1408,59 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/43/3d/4f4a3450a1973568910c6909cb74abbf2126f68aefae5976962f9f7ad50d/grpcio-1.81.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:aa948712c8e5fa40ec250870bda14bc7578e1bb832a8912d9d2a0f720518edbe", size = 7846468, upload-time = "2026-06-01T05:56:14.536Z" }, { url = "https://files.pythonhosted.org/packages/88/f4/5827fd248221ad3b44161c23ce9b5f4ee405b04fc6da5fd402a9aa87a84a/grpcio-1.81.0-cp314-cp314-win32.whl", hash = "sha256:fbbe81314a9d92156abce8b62c09364eb8bafc0ca2a19919a45ec64b5c6cb664", size = 4264427, upload-time = "2026-06-01T05:56:17.192Z" }, { url = "https://files.pythonhosted.org/packages/0c/e8/127dc2b246096ad50ef7c8d9b7b31d757787aeb796368bcdd4454e4204c4/grpcio-1.81.0-cp314-cp314-win_amd64.whl", hash = "sha256:b93cee313cae4e113fbb3a0ce1ea5633db6f63cfde2b2dc1d817429026b2a50b", size = 5070848, upload-time = "2026-06-01T05:56:19.735Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/b6/e0/318c1ce3ae5a17894d5791e87aea147587c9e702f24122cc7a5c8bbaeeb1/grpcio-1.76.0.tar.gz", hash = "sha256:7be78388d6da1a25c0d5ec506523db58b18be22d9c37d8d3a32c08be4987bd73", size = 12785182 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a0/00/8163a1beeb6971f66b4bbe6ac9457b97948beba8dd2fc8e1281dce7f79ec/grpcio-1.76.0-cp311-cp311-linux_armv7l.whl", hash = "sha256:2e1743fbd7f5fa713a1b0a8ac8ebabf0ec980b5d8809ec358d488e273b9cf02a", size = 5843567 }, + { url = "https://files.pythonhosted.org/packages/10/c1/934202f5cf335e6d852530ce14ddb0fef21be612ba9ecbbcbd4d748ca32d/grpcio-1.76.0-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:a8c2cf1209497cf659a667d7dea88985e834c24b7c3b605e6254cbb5076d985c", size = 11848017 }, + { url = "https://files.pythonhosted.org/packages/11/0b/8dec16b1863d74af6eb3543928600ec2195af49ca58b16334972f6775663/grpcio-1.76.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:08caea849a9d3c71a542827d6df9d5a69067b0a1efbea8a855633ff5d9571465", size = 6412027 }, + { url = "https://files.pythonhosted.org/packages/d7/64/7b9e6e7ab910bea9d46f2c090380bab274a0b91fb0a2fe9b0cd399fffa12/grpcio-1.76.0-cp311-cp311-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:f0e34c2079d47ae9f6188211db9e777c619a21d4faba6977774e8fa43b085e48", size = 7075913 }, + { url = "https://files.pythonhosted.org/packages/68/86/093c46e9546073cefa789bd76d44c5cb2abc824ca62af0c18be590ff13ba/grpcio-1.76.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8843114c0cfce61b40ad48df65abcfc00d4dba82eae8718fab5352390848c5da", size = 6615417 }, + { url = "https://files.pythonhosted.org/packages/f7/b6/5709a3a68500a9c03da6fb71740dcdd5ef245e39266461a03f31a57036d8/grpcio-1.76.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8eddfb4d203a237da6f3cc8a540dad0517d274b5a1e9e636fd8d2c79b5c1d397", size = 7199683 }, + { url = "https://files.pythonhosted.org/packages/91/d3/4b1f2bf16ed52ce0b508161df3a2d186e4935379a159a834cb4a7d687429/grpcio-1.76.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:32483fe2aab2c3794101c2a159070584e5db11d0aa091b2c0ea9c4fc43d0d749", size = 8163109 }, + { url = "https://files.pythonhosted.org/packages/5c/61/d9043f95f5f4cf085ac5dd6137b469d41befb04bd80280952ffa2a4c3f12/grpcio-1.76.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:dcfe41187da8992c5f40aa8c5ec086fa3672834d2be57a32384c08d5a05b4c00", size = 7626676 }, + { url = "https://files.pythonhosted.org/packages/36/95/fd9a5152ca02d8881e4dd419cdd790e11805979f499a2e5b96488b85cf27/grpcio-1.76.0-cp311-cp311-win32.whl", hash = "sha256:2107b0c024d1b35f4083f11245c0e23846ae64d02f40b2b226684840260ed054", size = 3997688 }, + { url = "https://files.pythonhosted.org/packages/60/9c/5c359c8d4c9176cfa3c61ecd4efe5affe1f38d9bae81e81ac7186b4c9cc8/grpcio-1.76.0-cp311-cp311-win_amd64.whl", hash = "sha256:522175aba7af9113c48ec10cc471b9b9bd4f6ceb36aeb4544a8e2c80ed9d252d", size = 4709315 }, + { url = "https://files.pythonhosted.org/packages/bf/05/8e29121994b8d959ffa0afd28996d452f291b48cfc0875619de0bde2c50c/grpcio-1.76.0-cp312-cp312-linux_armv7l.whl", hash = "sha256:81fd9652b37b36f16138611c7e884eb82e0cec137c40d3ef7c3f9b3ed00f6ed8", size = 5799718 }, + { url = "https://files.pythonhosted.org/packages/d9/75/11d0e66b3cdf998c996489581bdad8900db79ebd83513e45c19548f1cba4/grpcio-1.76.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:04bbe1bfe3a68bbfd4e52402ab7d4eb59d72d02647ae2042204326cf4bbad280", size = 11825627 }, + { url = "https://files.pythonhosted.org/packages/28/50/2f0aa0498bc188048f5d9504dcc5c2c24f2eb1a9337cd0fa09a61a2e75f0/grpcio-1.76.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d388087771c837cdb6515539f43b9d4bf0b0f23593a24054ac16f7a960be16f4", size = 6359167 }, + { url = "https://files.pythonhosted.org/packages/66/e5/bbf0bb97d29ede1d59d6588af40018cfc345b17ce979b7b45424628dc8bb/grpcio-1.76.0-cp312-cp312-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:9f8f757bebaaea112c00dba718fc0d3260052ce714e25804a03f93f5d1c6cc11", size = 7044267 }, + { url = "https://files.pythonhosted.org/packages/f5/86/f6ec2164f743d9609691115ae8ece098c76b894ebe4f7c94a655c6b03e98/grpcio-1.76.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:980a846182ce88c4f2f7e2c22c56aefd515daeb36149d1c897f83cf57999e0b6", size = 6573963 }, + { url = "https://files.pythonhosted.org/packages/60/bc/8d9d0d8505feccfdf38a766d262c71e73639c165b311c9457208b56d92ae/grpcio-1.76.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:f92f88e6c033db65a5ae3d97905c8fea9c725b63e28d5a75cb73b49bda5024d8", size = 7164484 }, + { url = "https://files.pythonhosted.org/packages/67/e6/5d6c2fc10b95edf6df9b8f19cf10a34263b7fd48493936fffd5085521292/grpcio-1.76.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:4baf3cbe2f0be3289eb68ac8ae771156971848bb8aaff60bad42005539431980", size = 8127777 }, + { url = "https://files.pythonhosted.org/packages/3f/c8/dce8ff21c86abe025efe304d9e31fdb0deaaa3b502b6a78141080f206da0/grpcio-1.76.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:615ba64c208aaceb5ec83bfdce7728b80bfeb8be97562944836a7a0a9647d882", size = 7594014 }, + { url = "https://files.pythonhosted.org/packages/e0/42/ad28191ebf983a5d0ecef90bab66baa5a6b18f2bfdef9d0a63b1973d9f75/grpcio-1.76.0-cp312-cp312-win32.whl", hash = "sha256:45d59a649a82df5718fd9527ce775fd66d1af35e6d31abdcdc906a49c6822958", size = 3984750 }, + { url = "https://files.pythonhosted.org/packages/9e/00/7bd478cbb851c04a48baccaa49b75abaa8e4122f7d86da797500cccdd771/grpcio-1.76.0-cp312-cp312-win_amd64.whl", hash = "sha256:c088e7a90b6017307f423efbb9d1ba97a22aa2170876223f9709e9d1de0b5347", size = 4704003 }, + { url = "https://files.pythonhosted.org/packages/fc/ed/71467ab770effc9e8cef5f2e7388beb2be26ed642d567697bb103a790c72/grpcio-1.76.0-cp313-cp313-linux_armv7l.whl", hash = "sha256:26ef06c73eb53267c2b319f43e6634c7556ea37672029241a056629af27c10e2", size = 5807716 }, + { url = "https://files.pythonhosted.org/packages/2c/85/c6ed56f9817fab03fa8a111ca91469941fb514e3e3ce6d793cb8f1e1347b/grpcio-1.76.0-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:45e0111e73f43f735d70786557dc38141185072d7ff8dc1829d6a77ac1471468", size = 11821522 }, + { url = "https://files.pythonhosted.org/packages/ac/31/2b8a235ab40c39cbc141ef647f8a6eb7b0028f023015a4842933bc0d6831/grpcio-1.76.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:83d57312a58dcfe2a3a0f9d1389b299438909a02db60e2f2ea2ae2d8034909d3", size = 6362558 }, + { url = "https://files.pythonhosted.org/packages/bd/64/9784eab483358e08847498ee56faf8ff6ea8e0a4592568d9f68edc97e9e9/grpcio-1.76.0-cp313-cp313-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:3e2a27c89eb9ac3d81ec8835e12414d73536c6e620355d65102503064a4ed6eb", size = 7049990 }, + { url = "https://files.pythonhosted.org/packages/2b/94/8c12319a6369434e7a184b987e8e9f3b49a114c489b8315f029e24de4837/grpcio-1.76.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:61f69297cba3950a524f61c7c8ee12e55c486cb5f7db47ff9dcee33da6f0d3ae", size = 6575387 }, + { url = "https://files.pythonhosted.org/packages/15/0f/f12c32b03f731f4a6242f771f63039df182c8b8e2cf8075b245b409259d4/grpcio-1.76.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6a15c17af8839b6801d554263c546c69c4d7718ad4321e3166175b37eaacca77", size = 7166668 }, + { url = "https://files.pythonhosted.org/packages/ff/2d/3ec9ce0c2b1d92dd59d1c3264aaec9f0f7c817d6e8ac683b97198a36ed5a/grpcio-1.76.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:25a18e9810fbc7e7f03ec2516addc116a957f8cbb8cbc95ccc80faa072743d03", size = 8124928 }, + { url = "https://files.pythonhosted.org/packages/1a/74/fd3317be5672f4856bcdd1a9e7b5e17554692d3db9a3b273879dc02d657d/grpcio-1.76.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:931091142fd8cc14edccc0845a79248bc155425eee9a98b2db2ea4f00a235a42", size = 7589983 }, + { url = "https://files.pythonhosted.org/packages/45/bb/ca038cf420f405971f19821c8c15bcbc875505f6ffadafe9ffd77871dc4c/grpcio-1.76.0-cp313-cp313-win32.whl", hash = "sha256:5e8571632780e08526f118f74170ad8d50fb0a48c23a746bef2a6ebade3abd6f", size = 3984727 }, + { url = "https://files.pythonhosted.org/packages/41/80/84087dc56437ced7cdd4b13d7875e7439a52a261e3ab4e06488ba6173b0a/grpcio-1.76.0-cp313-cp313-win_amd64.whl", hash = "sha256:f9f7bd5faab55f47231ad8dba7787866b69f5e93bc306e3915606779bbfb4ba8", size = 4702799 }, + { url = "https://files.pythonhosted.org/packages/b4/46/39adac80de49d678e6e073b70204091e76631e03e94928b9ea4ecf0f6e0e/grpcio-1.76.0-cp314-cp314-linux_armv7l.whl", hash = "sha256:ff8a59ea85a1f2191a0ffcc61298c571bc566332f82e5f5be1b83c9d8e668a62", size = 5808417 }, + { url = "https://files.pythonhosted.org/packages/9c/f5/a4531f7fb8b4e2a60b94e39d5d924469b7a6988176b3422487be61fe2998/grpcio-1.76.0-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:06c3d6b076e7b593905d04fdba6a0525711b3466f43b3400266f04ff735de0cd", size = 11828219 }, + { url = "https://files.pythonhosted.org/packages/4b/1c/de55d868ed7a8bd6acc6b1d6ddc4aa36d07a9f31d33c912c804adb1b971b/grpcio-1.76.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fd5ef5932f6475c436c4a55e4336ebbe47bd3272be04964a03d316bbf4afbcbc", size = 6367826 }, + { url = "https://files.pythonhosted.org/packages/59/64/99e44c02b5adb0ad13ab3adc89cb33cb54bfa90c74770f2607eea629b86f/grpcio-1.76.0-cp314-cp314-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:b331680e46239e090f5b3cead313cc772f6caa7d0fc8de349337563125361a4a", size = 7049550 }, + { url = "https://files.pythonhosted.org/packages/43/28/40a5be3f9a86949b83e7d6a2ad6011d993cbe9b6bd27bea881f61c7788b6/grpcio-1.76.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2229ae655ec4e8999599469559e97630185fdd53ae1e8997d147b7c9b2b72cba", size = 6575564 }, + { url = "https://files.pythonhosted.org/packages/4b/a9/1be18e6055b64467440208a8559afac243c66a8b904213af6f392dc2212f/grpcio-1.76.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:490fa6d203992c47c7b9e4a9d39003a0c2bcc1c9aa3c058730884bbbb0ee9f09", size = 7176236 }, + { url = "https://files.pythonhosted.org/packages/0f/55/dba05d3fcc151ce6e81327541d2cc8394f442f6b350fead67401661bf041/grpcio-1.76.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:479496325ce554792dba6548fae3df31a72cef7bad71ca2e12b0e58f9b336bfc", size = 8125795 }, + { url = "https://files.pythonhosted.org/packages/4a/45/122df922d05655f63930cf42c9e3f72ba20aadb26c100ee105cad4ce4257/grpcio-1.76.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:1c9b93f79f48b03ada57ea24725d83a30284a012ec27eab2cf7e50a550cbbbcc", size = 7592214 }, + { url = "https://files.pythonhosted.org/packages/4a/6e/0b899b7f6b66e5af39e377055fb4a6675c9ee28431df5708139df2e93233/grpcio-1.76.0-cp314-cp314-win32.whl", hash = "sha256:747fa73efa9b8b1488a95d0ba1039c8e2dca0f741612d80415b1e1c560febf4e", size = 4062961 }, + { url = "https://files.pythonhosted.org/packages/19/41/0b430b01a2eb38ee887f88c1f07644a1df8e289353b78e82b37ef988fb64/grpcio-1.76.0-cp314-cp314-win_amd64.whl", hash = "sha256:922fa70ba549fce362d2e2871ab542082d66e2aaf0c19480ea453905b01f384e", size = 4834462 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "h11" version = "0.16.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1", size = 101250, upload-time = "2025-04-24T03:35:25.427Z" } +sdist = { url = "https://files.pythonhosted.org/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1", size = 101250 } wheels = [ - { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" }, + { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515 }, ] [[package]] @@ -1086,6 +1470,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "numpy" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/db/33/acd0ce6863b6c0d7735007df01815403f5589a21ff8c2e1ee2587a38f548/h5py-3.16.0.tar.gz", hash = "sha256:a0dbaad796840ccaa67a4c144a0d0c8080073c34c76d5a6941d6818678ef2738", size = 446526, upload-time = "2026-03-06T13:49:08.07Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/ba/95/a825894f3e45cbac7554c4e97314ce886b233a20033787eda755ca8fecc7/h5py-3.16.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:719439d14b83f74eeb080e9650a6c7aa6d0d9ea0ca7f804347b05fac6fbf18af", size = 3721663, upload-time = "2026-03-06T13:47:49.599Z" }, @@ -1128,6 +1513,42 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/41/8c/bbe98f813722b4873818a8db3e15aa3e625b59278566905ac439725e8070/h5py-3.16.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:346df559a0f7dcb31cf8e44805319e2ab24b8957c45e7708ce503b2ec79ba725", size = 5300253, upload-time = "2026-03-06T13:49:02.033Z" }, { url = "https://files.pythonhosted.org/packages/32/9e/87e6705b4d6890e7cecdf876e2a7d3e40654a2ae37482d79a6f1b87f7b92/h5py-3.16.0-cp314-cp314t-win_amd64.whl", hash = "sha256:4c6ab014ab704b4feaa719ae783b86522ed0bf1f82184704ed3c9e4e3228796e", size = 3381671, upload-time = "2026-03-06T13:49:04.351Z" }, { url = "https://files.pythonhosted.org/packages/96/91/9fad90cfc5f9b2489c7c26ad897157bce82f0e9534a986a221b99760b23b/h5py-3.16.0-cp314-cp314t-win_arm64.whl", hash = "sha256:faca8fb4e4319c09d83337adc80b2ca7d5c5a343c2d6f1b6388f32cfecca13c1", size = 2740706, upload-time = "2026-03-06T13:49:06.347Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/4d/6a/0d79de0b025aa85dc8864de8e97659c94cf3d23148394a954dc5ca52f8c8/h5py-3.15.1.tar.gz", hash = "sha256:c86e3ed45c4473564de55aa83b6fc9e5ead86578773dfbd93047380042e26b69", size = 426236 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/41/fd/8349b48b15b47768042cff06ad6e1c229f0a4bd89225bf6b6894fea27e6d/h5py-3.15.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:5aaa330bcbf2830150c50897ea5dcbed30b5b6d56897289846ac5b9e529ec243", size = 3434135 }, + { url = "https://files.pythonhosted.org/packages/c1/b0/1c628e26a0b95858f54aba17e1599e7f6cd241727596cc2580b72cb0a9bf/h5py-3.15.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c970fb80001fffabb0109eaf95116c8e7c0d3ca2de854e0901e8a04c1f098509", size = 2870958 }, + { url = "https://files.pythonhosted.org/packages/f9/e3/c255cafc9b85e6ea04e2ad1bba1416baa1d7f57fc98a214be1144087690c/h5py-3.15.1-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:80e5bb5b9508d5d9da09f81fd00abbb3f85da8143e56b1585d59bc8ceb1dba8b", size = 4504770 }, + { url = "https://files.pythonhosted.org/packages/8b/23/4ab1108e87851ccc69694b03b817d92e142966a6c4abd99e17db77f2c066/h5py-3.15.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5b849ba619a066196169763c33f9f0f02e381156d61c03e000bb0100f9950faf", size = 4700329 }, + { url = "https://files.pythonhosted.org/packages/a4/e4/932a3a8516e4e475b90969bf250b1924dbe3612a02b897e426613aed68f4/h5py-3.15.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:e7f6c841efd4e6e5b7e82222eaf90819927b6d256ab0f3aca29675601f654f3c", size = 4152456 }, + { url = "https://files.pythonhosted.org/packages/2a/0a/f74d589883b13737021b2049ac796328f188dbb60c2ed35b101f5b95a3fc/h5py-3.15.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:ca8a3a22458956ee7b40d8e39c9a9dc01f82933e4c030c964f8b875592f4d831", size = 4617295 }, + { url = "https://files.pythonhosted.org/packages/23/95/499b4e56452ef8b6c95a271af0dde08dac4ddb70515a75f346d4f400579b/h5py-3.15.1-cp311-cp311-win_amd64.whl", hash = "sha256:550e51131376889656feec4aff2170efc054a7fe79eb1da3bb92e1625d1ac878", size = 2882129 }, + { url = "https://files.pythonhosted.org/packages/ce/bb/cfcc70b8a42222ba3ad4478bcef1791181ea908e2adbd7d53c66395edad5/h5py-3.15.1-cp311-cp311-win_arm64.whl", hash = "sha256:b39239947cb36a819147fc19e86b618dcb0953d1cd969f5ed71fc0de60392427", size = 2477121 }, + { url = "https://files.pythonhosted.org/packages/62/b8/c0d9aa013ecfa8b7057946c080c0c07f6fa41e231d2e9bd306a2f8110bdc/h5py-3.15.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:316dd0f119734f324ca7ed10b5627a2de4ea42cc4dfbcedbee026aaa361c238c", size = 3399089 }, + { url = "https://files.pythonhosted.org/packages/a4/5e/3c6f6e0430813c7aefe784d00c6711166f46225f5d229546eb53032c3707/h5py-3.15.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b51469890e58e85d5242e43aab29f5e9c7e526b951caab354f3ded4ac88e7b76", size = 2847803 }, + { url = "https://files.pythonhosted.org/packages/00/69/ba36273b888a4a48d78f9268d2aee05787e4438557450a8442946ab8f3ec/h5py-3.15.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8a33bfd5dfcea037196f7778534b1ff7e36a7f40a89e648c8f2967292eb6898e", size = 4914884 }, + { url = "https://files.pythonhosted.org/packages/3a/30/d1c94066343a98bb2cea40120873193a4fed68c4ad7f8935c11caf74c681/h5py-3.15.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:25c8843fec43b2cc368aa15afa1cdf83fc5e17b1c4e10cd3771ef6c39b72e5ce", size = 5109965 }, + { url = "https://files.pythonhosted.org/packages/81/3d/d28172116eafc3bc9f5991b3cb3fd2c8a95f5984f50880adfdf991de9087/h5py-3.15.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a308fd8681a864c04423c0324527237a0484e2611e3441f8089fd00ed56a8171", size = 4561870 }, + { url = "https://files.pythonhosted.org/packages/a5/83/393a7226024238b0f51965a7156004eaae1fcf84aa4bfecf7e582676271b/h5py-3.15.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f4a016df3f4a8a14d573b496e4d1964deb380e26031fc85fb40e417e9131888a", size = 5037161 }, + { url = "https://files.pythonhosted.org/packages/cf/51/329e7436bf87ca6b0fe06dd0a3795c34bebe4ed8d6c44450a20565d57832/h5py-3.15.1-cp312-cp312-win_amd64.whl", hash = "sha256:59b25cf02411bf12e14f803fef0b80886444c7fe21a5ad17c6a28d3f08098a1e", size = 2874165 }, + { url = "https://files.pythonhosted.org/packages/09/a8/2d02b10a66747c54446e932171dd89b8b4126c0111b440e6bc05a7c852ec/h5py-3.15.1-cp312-cp312-win_arm64.whl", hash = "sha256:61d5a58a9851e01ee61c932bbbb1c98fe20aba0a5674776600fb9a361c0aa652", size = 2458214 }, + { url = "https://files.pythonhosted.org/packages/88/b3/40207e0192415cbff7ea1d37b9f24b33f6d38a5a2f5d18a678de78f967ae/h5py-3.15.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c8440fd8bee9500c235ecb7aa1917a0389a2adb80c209fa1cc485bd70e0d94a5", size = 3376511 }, + { url = "https://files.pythonhosted.org/packages/31/96/ba99a003c763998035b0de4c299598125df5fc6c9ccf834f152ddd60e0fb/h5py-3.15.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:ab2219dbc6fcdb6932f76b548e2b16f34a1f52b7666e998157a4dfc02e2c4123", size = 2826143 }, + { url = "https://files.pythonhosted.org/packages/6a/c2/fc6375d07ea3962df7afad7d863fe4bde18bb88530678c20d4c90c18de1d/h5py-3.15.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d8cb02c3a96255149ed3ac811eeea25b655d959c6dd5ce702c9a95ff11859eb5", size = 4908316 }, + { url = "https://files.pythonhosted.org/packages/d9/69/4402ea66272dacc10b298cca18ed73e1c0791ff2ae9ed218d3859f9698ac/h5py-3.15.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:121b2b7a4c1915d63737483b7bff14ef253020f617c2fb2811f67a4bed9ac5e8", size = 5103710 }, + { url = "https://files.pythonhosted.org/packages/e0/f6/11f1e2432d57d71322c02a97a5567829a75f223a8c821764a0e71a65cde8/h5py-3.15.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:59b0d63b318bf3cc06687def2b45afd75926bbc006f7b8cd2b1a231299fc8599", size = 4556042 }, + { url = "https://files.pythonhosted.org/packages/18/88/3eda3ef16bfe7a7dbc3d8d6836bbaa7986feb5ff091395e140dc13927bcc/h5py-3.15.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e02fe77a03f652500d8bff288cbf3675f742fc0411f5a628fa37116507dc7cc0", size = 5030639 }, + { url = "https://files.pythonhosted.org/packages/e5/ea/fbb258a98863f99befb10ed727152b4ae659f322e1d9c0576f8a62754e81/h5py-3.15.1-cp313-cp313-win_amd64.whl", hash = "sha256:dea78b092fd80a083563ed79a3171258d4a4d307492e7cf8b2313d464c82ba52", size = 2864363 }, + { url = "https://files.pythonhosted.org/packages/5d/c9/35021cc9cd2b2915a7da3026e3d77a05bed1144a414ff840953b33937fb9/h5py-3.15.1-cp313-cp313-win_arm64.whl", hash = "sha256:c256254a8a81e2bddc0d376e23e2a6d2dc8a1e8a2261835ed8c1281a0744cd97", size = 2449570 }, + { url = "https://files.pythonhosted.org/packages/a0/2c/926eba1514e4d2e47d0e9eb16c784e717d8b066398ccfca9b283917b1bfb/h5py-3.15.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:5f4fb0567eb8517c3ecd6b3c02c4f4e9da220c8932604960fd04e24ee1254763", size = 3380368 }, + { url = "https://files.pythonhosted.org/packages/65/4b/d715ed454d3baa5f6ae1d30b7eca4c7a1c1084f6a2edead9e801a1541d62/h5py-3.15.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:954e480433e82d3872503104f9b285d369048c3a788b2b1a00e53d1c47c98dd2", size = 2833793 }, + { url = "https://files.pythonhosted.org/packages/ef/d4/ef386c28e4579314610a8bffebbee3b69295b0237bc967340b7c653c6c10/h5py-3.15.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fd125c131889ebbef0849f4a0e29cf363b48aba42f228d08b4079913b576bb3a", size = 4903199 }, + { url = "https://files.pythonhosted.org/packages/33/5d/65c619e195e0b5e54ea5a95c1bb600c8ff8715e0d09676e4cce56d89f492/h5py-3.15.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:28a20e1a4082a479b3d7db2169f3a5034af010b90842e75ebbf2e9e49eb4183e", size = 5097224 }, + { url = "https://files.pythonhosted.org/packages/30/30/5273218400bf2da01609e1292f562c94b461fcb73c7a9e27fdadd43abc0a/h5py-3.15.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fa8df5267f545b4946df8ca0d93d23382191018e4cda2deda4c2cedf9a010e13", size = 4551207 }, + { url = "https://files.pythonhosted.org/packages/d3/39/a7ef948ddf4d1c556b0b2b9559534777bccc318543b3f5a1efdf6b556c9c/h5py-3.15.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:99d374a21f7321a4c6ab327c4ab23bd925ad69821aeb53a1e75dd809d19f67fa", size = 5025426 }, + { url = "https://files.pythonhosted.org/packages/b6/d8/7368679b8df6925b8415f9dcc9ab1dab01ddc384d2b2c24aac9191bd9ceb/h5py-3.15.1-cp314-cp314-win_amd64.whl", hash = "sha256:9c73d1d7cdb97d5b17ae385153472ce118bed607e43be11e9a9deefaa54e0734", size = 2865704 }, + { url = "https://files.pythonhosted.org/packages/d3/b7/4a806f85d62c20157e62e58e03b27513dc9c55499768530acc4f4c5ce4be/h5py-3.15.1-cp314-cp314-win_arm64.whl", hash = "sha256:a6d8c5a05a76aca9a494b4c53ce8a9c29023b7f64f625c6ce1841e92a362ccdf", size = 2465544 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1137,13 +1558,13 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "h5py" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/a1/4f/9130151e3aa475b3e4e9a611bf608107fe5c72d277d74c4cf36f164b7c81/hdf5plugin-6.0.0.tar.gz", hash = "sha256:847ed9e96b451367a110f0ba64a3b260d38d64bbf3f25751858d3b56e094cfe0", size = 66372085, upload-time = "2025-10-08T18:16:28.423Z" } +sdist = { url = "https://files.pythonhosted.org/packages/a1/4f/9130151e3aa475b3e4e9a611bf608107fe5c72d277d74c4cf36f164b7c81/hdf5plugin-6.0.0.tar.gz", hash = "sha256:847ed9e96b451367a110f0ba64a3b260d38d64bbf3f25751858d3b56e094cfe0", size = 66372085 } wheels = [ - { url = "https://files.pythonhosted.org/packages/9c/13/15017f6210bfea843316d62f0f121e364e17bb129444ed803a256a213036/hdf5plugin-6.0.0-py3-none-macosx_10_13_universal2.whl", hash = "sha256:a59fbd5d4290a8a5334d82ccb4c6b9bfc7aaf586de7fedb88762e8601bc05fd4", size = 13339413, upload-time = "2025-10-08T18:16:10.656Z" }, - { url = "https://files.pythonhosted.org/packages/40/bf/d1f3765fb879820d7331e30e860b684f5b78d3ec17324e8f54130cbe560b/hdf5plugin-6.0.0-py3-none-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d301f4b9295872bacf277c70628d4c5e965ee47db762d8fde2d4849f201b9897", size = 42858563, upload-time = "2025-10-08T18:16:14.106Z" }, - { url = "https://files.pythonhosted.org/packages/0a/67/37d0b84fbbf26bf0d6a99a8f98bcd82bb6d437dc8cabee259fb3d7506ec7/hdf5plugin-6.0.0-py3-none-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:78b082ea355fe46bf5b396024de1fb662a1aaf9a5e11861ad61a5a2a6316d59d", size = 45126124, upload-time = "2025-10-08T18:16:17.992Z" }, - { url = "https://files.pythonhosted.org/packages/ed/2f/1046d464ad1db29a4f6c70ba4e19b39baa8a6542c719eaa4e765108f07f1/hdf5plugin-6.0.0-py3-none-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:79e0524d18ddc41c0cf2e1bb2e529d4e154c286f6a1bd85f3d44019d2a17574a", size = 44857273, upload-time = "2025-10-08T18:16:22.007Z" }, - { url = "https://files.pythonhosted.org/packages/61/b3/75478bdfee85533777de4204373f563aa7a1074355300743c3aedc33cac5/hdf5plugin-6.0.0-py3-none-win_amd64.whl", hash = "sha256:99866f90be1ceac5519e6e038669564be326c233618d59ba1f38c9dd8c32099e", size = 3379316, upload-time = "2025-10-08T18:16:25.007Z" }, + { url = "https://files.pythonhosted.org/packages/9c/13/15017f6210bfea843316d62f0f121e364e17bb129444ed803a256a213036/hdf5plugin-6.0.0-py3-none-macosx_10_13_universal2.whl", hash = "sha256:a59fbd5d4290a8a5334d82ccb4c6b9bfc7aaf586de7fedb88762e8601bc05fd4", size = 13339413 }, + { url = "https://files.pythonhosted.org/packages/40/bf/d1f3765fb879820d7331e30e860b684f5b78d3ec17324e8f54130cbe560b/hdf5plugin-6.0.0-py3-none-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d301f4b9295872bacf277c70628d4c5e965ee47db762d8fde2d4849f201b9897", size = 42858563 }, + { url = "https://files.pythonhosted.org/packages/0a/67/37d0b84fbbf26bf0d6a99a8f98bcd82bb6d437dc8cabee259fb3d7506ec7/hdf5plugin-6.0.0-py3-none-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:78b082ea355fe46bf5b396024de1fb662a1aaf9a5e11861ad61a5a2a6316d59d", size = 45126124 }, + { url = "https://files.pythonhosted.org/packages/ed/2f/1046d464ad1db29a4f6c70ba4e19b39baa8a6542c719eaa4e765108f07f1/hdf5plugin-6.0.0-py3-none-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:79e0524d18ddc41c0cf2e1bb2e529d4e154c286f6a1bd85f3d44019d2a17574a", size = 44857273 }, + { url = "https://files.pythonhosted.org/packages/61/b3/75478bdfee85533777de4204373f563aa7a1074355300743c3aedc33cac5/hdf5plugin-6.0.0-py3-none-win_amd64.whl", hash = "sha256:99866f90be1ceac5519e6e038669564be326c233618d59ba1f38c9dd8c32099e", size = 3379316 }, ] [[package]] @@ -1154,9 +1575,9 @@ dependencies = [ { name = "certifi" }, { name = "h11" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/06/94/82699a10bca87a5556c9c59b5963f2d039dbd239f25bc2a63907a05a14cb/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8", size = 85484, upload-time = "2025-04-24T22:06:22.219Z" } +sdist = { url = "https://files.pythonhosted.org/packages/06/94/82699a10bca87a5556c9c59b5963f2d039dbd239f25bc2a63907a05a14cb/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8", size = 85484 } wheels = [ - { url = "https://files.pythonhosted.org/packages/7e/f5/f66802a942d491edb555dd61e3a9961140fd64c90bce1eafd741609d334d/httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55", size = 78784, upload-time = "2025-04-24T22:06:20.566Z" }, + { url = "https://files.pythonhosted.org/packages/7e/f5/f66802a942d491edb555dd61e3a9961140fd64c90bce1eafd741609d334d/httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55", size = 78784 }, ] [[package]] @@ -1169,27 +1590,39 @@ dependencies = [ { name = "httpcore" }, { name = "idna" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406, upload-time = "2024-12-06T15:37:23.222Z" } +sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406 } wheels = [ - { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517, upload-time = "2024-12-06T15:37:21.509Z" }, + { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517 }, ] [[package]] name = "identify" version = "2.6.19" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/52/63/51723b5f116cc04b061cb6f5a561790abf249d25931d515cd375e063e0f4/identify-2.6.19.tar.gz", hash = "sha256:6be5020c38fcb07da56c53733538a3081ea5aa70d36a156f83044bfbf9173842", size = 99567, upload-time = "2026-04-17T18:39:50.265Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/94/84/d9273cd09688070a6523c4aee4663a8538721b2b755c4962aafae0011e72/identify-2.6.19-py2.py3-none-any.whl", hash = "sha256:20e6a87f786f768c092a721ad107fc9df0eb89347be9396cadf3f4abbd1fb78a", size = 99397, upload-time = "2026-04-17T18:39:49.221Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/ff/e7/685de97986c916a6d93b3876139e00eef26ad5bbbd61925d670ae8013449/identify-2.6.15.tar.gz", hash = "sha256:e4f4864b96c6557ef2a1e1c951771838f4edc9df3a72ec7118b338801b11c7bf", size = 99311 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0f/1c/e5fd8f973d4f375adb21565739498e2e9a1e54c858a97b9a8ccfdc81da9b/identify-2.6.15-py2.py3-none-any.whl", hash = "sha256:1181ef7608e00704db228516541eb83a88a9f94433a8c80bb9b5bd54b1d81757", size = 99183 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "idna" version = "3.17" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/b9/28/99c51f664567218d824af024c0251650fb27e4ca066df188dab0769c5b91/idna-3.17.tar.gz", hash = "sha256:5eb0cb53bc467c12eadcf6de83163ad8527cec9416f44b9b61b19caedad2b87f", size = 196048, upload-time = "2026-05-28T14:32:38.55Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/de/a7/f76514cc40ad6234098ecdebda08732d75964776c51a42845b7da10649e2/idna-3.17-py3-none-any.whl", hash = "sha256:466e48829084efe2548012b855df21540b96f2e20e51bd124c851536556a592c", size = 65316, upload-time = "2026-05-28T14:32:37.035Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/6f/6d/0703ccc57f3a7233505399edb88de3cbd678da106337b9fcde432b65ed60/idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902", size = 194582 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea", size = 71008 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1200,9 +1633,15 @@ dependencies = [ { name = "numpy" }, { name = "pillow" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/b1/84/93bcd1300216ea50811cee96873b84a1bebf8d0489ffaf7f2a3756bab866/imageio-2.37.3.tar.gz", hash = "sha256:bbb37efbfc4c400fcd534b367b91fcd66d5da639aaa138034431a1c5e0a41451", size = 389673, upload-time = "2026-03-09T11:31:12.573Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/49/fa/391e437a34e55095173dca5f24070d89cbc233ff85bf1c29c93248c6588d/imageio-2.37.3-py3-none-any.whl", hash = "sha256:46f5bb8522cd421c0f5ae104d8268f569d856b29eb1a13b92829d1970f32c9f0", size = 317646, upload-time = "2026-03-09T11:31:10.771Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/a3/6f/606be632e37bf8d05b253e8626c2291d74c691ddc7bcdf7d6aaf33b32f6a/imageio-2.37.2.tar.gz", hash = "sha256:0212ef2727ac9caa5ca4b2c75ae89454312f440a756fcfc8ef1993e718f50f8a", size = 389600 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fb/fe/301e0936b79bcab4cacc7548bf2853fc28dced0a578bab1f7ef53c9aa75b/imageio-2.37.2-py3-none-any.whl", hash = "sha256:ad9adfb20335d718c03de457358ed69f141021a333c40a53e57273d8a5bd0b9b", size = 317646 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1212,18 +1651,24 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "zipp", marker = "python_full_version < '3.12'" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/a9/01/15bb152d77b21318514a96f43af312635eb2500c96b55398d020c93d86ea/importlib_metadata-9.0.0.tar.gz", hash = "sha256:a4f57ab599e6a2e3016d7595cfd72eb4661a5106e787a95bcc90c7105b831efc", size = 56405, upload-time = "2026-03-20T06:42:56.999Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/38/3d/2d244233ac4f76e38533cfcb2991c9eb4c7bf688ae0a036d30725b8faafe/importlib_metadata-9.0.0-py3-none-any.whl", hash = "sha256:2d21d1cc5a017bd0559e36150c21c830ab1dc304dedd1b7ea85d20f45ef3edd7", size = 27789, upload-time = "2026-03-20T06:42:55.665Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/76/66/650a33bd90f786193e4de4b3ad86ea60b53c89b669a5c7be931fac31cdb0/importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000", size = 56641 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/20/b0/36bd937216ec521246249be3bf9855081de4c5e06a0c9b4219dbeda50373/importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd", size = 27656 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "iniconfig" version = "2.3.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" } +sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503 } wheels = [ - { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484, upload-time = "2025-10-18T21:55:41.639Z" }, + { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484 }, ] [[package]] @@ -1245,9 +1690,15 @@ dependencies = [ { name = "tornado" }, { name = "traitlets" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/ca/8d/b68b728e2d06b9e0051019640a40a9eb7a88fcd82c2e1b5ce70bef5ff044/ipykernel-7.2.0.tar.gz", hash = "sha256:18ed160b6dee2cbb16e5f3575858bc19d8f1fe6046a9a680c708494ce31d909e", size = 176046, upload-time = "2026-02-06T16:43:27.403Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/82/b9/e73d5d9f405cba7706c539aa8b311b49d4c2f3d698d9c12f815231169c71/ipykernel-7.2.0-py3-none-any.whl", hash = "sha256:3bbd4420d2b3cc105cbdf3756bfc04500b1e52f090a90716851f3916c62e1661", size = 118788, upload-time = "2026-02-06T16:43:25.149Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/b9/a4/4948be6eb88628505b83a1f2f40d90254cab66abf2043b3c40fa07dfce0f/ipykernel-7.1.0.tar.gz", hash = "sha256:58a3fc88533d5930c3546dc7eac66c6d288acde4f801e2001e65edc5dc9cf0db", size = 174579 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a3/17/20c2552266728ceba271967b87919664ecc0e33efca29c3efc6baf88c5f9/ipykernel-7.1.0-py3-none-any.whl", hash = "sha256:763b5ec6c5b7776f6a8d7ce09b267693b4e5ce75cb50ae696aaefb3c85e1ea4c", size = 117968 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1268,9 +1719,15 @@ dependencies = [ { name = "traitlets" }, { name = "typing-extensions", marker = "python_full_version < '3.12'" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/21/c2/c0064cf15d026501a1ef70e42efd9c3f818663089399aacc5e37a82901c1/ipython-9.14.0.tar.gz", hash = "sha256:6f27ff0f1d9ea050e0551f71568bc4b34d8aba579e8f111c5b4175f44ac6b4aa", size = 4432601, upload-time = "2026-05-29T15:13:24.611Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/14/a3/9e59340f02c1dc8f8c0a05b09244712b8609eb5439f9996e887e2b82f452/ipython-9.14.0-py3-none-any.whl", hash = "sha256:8fd984a3372c14b12790b084ba6b5cff5678c0cb063244a0034f06a51f20d6c2", size = 627457, upload-time = "2026-05-29T15:13:22.942Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/29/e6/48c74d54039241a456add616464ea28c6ebf782e4110d419411b83dae06f/ipython-9.7.0.tar.gz", hash = "sha256:5f6de88c905a566c6a9d6c400a8fed54a638e1f7543d17aae2551133216b1e4e", size = 4422115 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/05/aa/62893d6a591d337aa59dcc4c6f6c842f1fe20cd72c8c5c1f980255243252/ipython-9.7.0-py3-none-any.whl", hash = "sha256:bce8ac85eb9521adc94e1845b4c03d88365fd6ac2f4908ec4ed1eb1b0a065f9f", size = 618911 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1280,9 +1737,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "pygments" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/ef/4c/5dd1d8af08107f88c7f741ead7a40854b8ac24ddf9ae850afbcf698aa552/ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81", size = 8393, upload-time = "2025-01-17T11:24:34.505Z" } +sdist = { url = "https://files.pythonhosted.org/packages/ef/4c/5dd1d8af08107f88c7f741ead7a40854b8ac24ddf9ae850afbcf698aa552/ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81", size = 8393 } wheels = [ - { url = "https://files.pythonhosted.org/packages/d9/33/1f075bf72b0b747cb3288d011319aaf64083cf2efef8354174e3ed4540e2/ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c", size = 8074, upload-time = "2025-01-17T11:24:33.271Z" }, + { url = "https://files.pythonhosted.org/packages/d9/33/1f075bf72b0b747cb3288d011319aaf64083cf2efef8354174e3ed4540e2/ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c", size = 8074 }, ] [[package]] @@ -1308,9 +1765,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "arrow" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/7c/1a/3c8edc664e06e6bd06cce40c6b22da5f1429aa4224d0c590f3be21c91ead/isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9", size = 11649, upload-time = "2020-11-01T11:00:00.312Z" } +sdist = { url = "https://files.pythonhosted.org/packages/7c/1a/3c8edc664e06e6bd06cce40c6b22da5f1429aa4224d0c590f3be21c91ead/isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9", size = 11649 } wheels = [ - { url = "https://files.pythonhosted.org/packages/7b/55/e5326141505c5d5e34c5e0935d2908a74e4561eca44108fbfb9c13d2911a/isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042", size = 11321, upload-time = "2020-11-01T10:59:58.02Z" }, + { url = "https://files.pythonhosted.org/packages/7b/55/e5326141505c5d5e34c5e0935d2908a74e4561eca44108fbfb9c13d2911a/isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042", size = 11321 }, ] [[package]] @@ -1320,9 +1777,15 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "parso" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/46/b7/a3635f6a2d7cf5b5dd98064fc1d5fbbafcb25477bcea204a3a92145d158b/jedi-0.20.0.tar.gz", hash = "sha256:c3f4ccbd276696f4b19c54618d4fb18f9fc24b0aef02acf704b23f487daa1011", size = 3119416, upload-time = "2026-05-01T23:38:47.814Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/9a/93/242e2eab5fe682ffcb8b0084bde703a41d51e17ee0f3a31ff0d9d813620a/jedi-0.20.0-py2.py3-none-any.whl", hash = "sha256:7bdd9c2634f56713299976f4cbd59cb3fa92165cc5e05ea811fb253480728b67", size = 4884812, upload-time = "2026-05-01T23:38:43.919Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/72/3a/79a912fbd4d8dd6fbb02bf69afd3bb72cf0c729bb3063c6f4498603db17a/jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0", size = 1231287 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9", size = 1572278 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1332,27 +1795,39 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "markupsafe" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115, upload-time = "2025-03-05T20:05:02.478Z" } +sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115 } wheels = [ - { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" }, + { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899 }, ] [[package]] name = "json5" version = "0.14.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/9c/4b/6f8906aaf67d501e259b0adab4d312945bb7211e8b8d4dcc77c92320edaa/json5-0.14.0.tar.gz", hash = "sha256:b3f492fad9f6cdbced8b7d40b28b9b1c9701c5f561bef0d33b81c2ff433fefcb", size = 52656, upload-time = "2026-03-27T22:50:48.108Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/b8/42/cf027b4ac873b076189d935b135397675dac80cb29acb13e1ab86ad6c631/json5-0.14.0-py3-none-any.whl", hash = "sha256:56cf861bab076b1178eb8c92e1311d273a9b9acea2ccc82c276abf839ebaef3a", size = 36271, upload-time = "2026-03-27T22:50:47.073Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/12/ae/929aee9619e9eba9015207a9d2c1c54db18311da7eb4dcf6d41ad6f0eb67/json5-0.12.1.tar.gz", hash = "sha256:b2743e77b3242f8d03c143dd975a6ec7c52e2f2afe76ed934e53503dd4ad4990", size = 52191 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/85/e2/05328bd2621be49a6fed9e3030b1e51a2d04537d3f816d211b9cc53c5262/json5-0.12.1-py3-none-any.whl", hash = "sha256:d9c9b3bc34a5f54d43c35e11ef7cb87d8bdd098c6ace87117a7b7e83e705c1d5", size = 36119 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "jsonpointer" version = "3.1.1" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/18/c7/af399a2e7a67fd18d63c40c5e62d3af4e67b836a2107468b6a5ea24c4304/jsonpointer-3.1.1.tar.gz", hash = "sha256:0b801c7db33a904024f6004d526dcc53bbb8a4a0f4e32bfd10beadf60adf1900", size = 9068, upload-time = "2026-03-23T22:32:32.458Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/9e/6a/a83720e953b1682d2d109d3c2dbb0bc9bf28cc1cbc205be4ef4be5da709d/jsonpointer-3.1.1-py3-none-any.whl", hash = "sha256:8ff8b95779d071ba472cf5bc913028df06031797532f08a7d5b602d8b2a488ca", size = 7659, upload-time = "2026-03-23T22:32:31.568Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/6a/0a/eebeb1fa92507ea94016a2a790b93c2ae41a7e18778f85471dc54475ed25/jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef", size = 9114 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/71/92/5e77f98553e9e75130c78900d000368476aed74276eb8ae8796f65f00918/jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942", size = 7595 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1365,9 +1840,15 @@ dependencies = [ { name = "referencing" }, { name = "rpds-py" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/b3/fc/e067678238fa451312d4c62bf6e6cf5ec56375422aee02f9cb5f909b3047/jsonschema-4.26.0.tar.gz", hash = "sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326", size = 366583, upload-time = "2026-01-07T13:41:07.246Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/69/90/f63fb5873511e014207a475e2bb4e8b2e570d655b00ac19a9a0ca0a385ee/jsonschema-4.26.0-py3-none-any.whl", hash = "sha256:d489f15263b8d200f8387e64b4c3a75f06629559fb73deb8fdfb525f2dab50ce", size = 90630, upload-time = "2026-01-07T13:41:05.306Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/74/69/f7185de793a29082a9f3c7728268ffb31cb5095131a9c139a74078e27336/jsonschema-4.25.1.tar.gz", hash = "sha256:e4a9655ce0da0c0b67a085847e00a3a51449e1157f4f75e9fb5aa545e122eb85", size = 357342 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/bf/9c/8c95d856233c1f82500c2450b8c68576b4cf1c871db3afac5c34ff84e6fd/jsonschema-4.25.1-py3-none-any.whl", hash = "sha256:3fba0169e345c7175110351d456342c364814cfcf3b964ba4587f22915230a63", size = 90040 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [package.optional-dependencies] @@ -1390,9 +1871,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "referencing" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/19/74/a633ee74eb36c44aa6d1095e7cc5569bebf04342ee146178e2d36600708b/jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d", size = 32855, upload-time = "2025-09-08T01:34:59.186Z" } +sdist = { url = "https://files.pythonhosted.org/packages/19/74/a633ee74eb36c44aa6d1095e7cc5569bebf04342ee146178e2d36600708b/jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d", size = 32855 } wheels = [ - { url = "https://files.pythonhosted.org/packages/41/45/1a4ed80516f02155c51f51e8cedb3c1902296743db0bbc66608a0db2814f/jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe", size = 18437, upload-time = "2025-09-08T01:34:57.871Z" }, + { url = "https://files.pythonhosted.org/packages/41/45/1a4ed80516f02155c51f51e8cedb3c1902296743db0bbc66608a0db2814f/jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe", size = 18437 }, ] [[package]] @@ -1406,9 +1887,15 @@ dependencies = [ { name = "tornado" }, { name = "traitlets" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/05/e4/ba649102a3bc3fbca54e7239fb924fd434c766f855693d86de0b1f2bec81/jupyter_client-8.8.0.tar.gz", hash = "sha256:d556811419a4f2d96c869af34e854e3f059b7cc2d6d01a9cd9c85c267691be3e", size = 348020, upload-time = "2026-01-08T13:55:47.938Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/2d/0b/ceb7694d864abc0a047649aec263878acb9f792e1fec3e676f22dc9015e3/jupyter_client-8.8.0-py3-none-any.whl", hash = "sha256:f93a5b99c5e23a507b773d3a1136bd6e16c67883ccdbd9a829b0bbdb98cd7d7a", size = 107371, upload-time = "2026-01-08T13:55:45.562Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/71/22/bf9f12fdaeae18019a468b68952a60fe6dbab5d67cd2a103cac7659b41ca/jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419", size = 342019 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/11/85/b0394e0b6fcccd2c1eeefc230978a6f8cb0c5df1e4cd3e7625735a0d7d1e/jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f", size = 106105 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1419,9 +1906,9 @@ dependencies = [ { name = "platformdirs" }, { name = "traitlets" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/02/49/9d1284d0dc65e2c757b74c6687b6d319b02f822ad039e5c512df9194d9dd/jupyter_core-5.9.1.tar.gz", hash = "sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508", size = 89814, upload-time = "2025-10-16T19:19:18.444Z" } +sdist = { url = "https://files.pythonhosted.org/packages/02/49/9d1284d0dc65e2c757b74c6687b6d319b02f822ad039e5c512df9194d9dd/jupyter_core-5.9.1.tar.gz", hash = "sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508", size = 89814 } wheels = [ - { url = "https://files.pythonhosted.org/packages/e7/e7/80988e32bf6f73919a113473a604f5a8f09094de312b9d52b79c2df7612b/jupyter_core-5.9.1-py3-none-any.whl", hash = "sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407", size = 29032, upload-time = "2025-10-16T19:19:16.783Z" }, + { url = "https://files.pythonhosted.org/packages/e7/e7/80988e32bf6f73919a113473a604f5a8f09094de312b9d52b79c2df7612b/jupyter_core-5.9.1-py3-none-any.whl", hash = "sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407", size = 29032 }, ] [[package]] @@ -1438,9 +1925,15 @@ dependencies = [ { name = "rfc3986-validator" }, { name = "traitlets" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/18/f8/475c4241b2b75af0deaae453ed003c6c851766dbc44d332d8baf245dc931/jupyter_events-0.12.1.tar.gz", hash = "sha256:faff25f77218335752f35f23c5fe6e4a392a7bd99a5939ccb9b8fbf594636cf3", size = 62854, upload-time = "2026-04-20T23:17:50.66Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/eb/6c/6fcde0c8f616ed360ffd3587f7db9e225a7e62b583a04494d2f069cf64ea/jupyter_events-0.12.1-py3-none-any.whl", hash = "sha256:c366585253f537a627da52fa7ca7410c5b5301fe893f511e7b077c2d93ec8bcf", size = 19512, upload-time = "2026-04-20T23:17:48.927Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/9d/c3/306d090461e4cf3cd91eceaff84bede12a8e52cd821c2d20c9a4fd728385/jupyter_events-0.12.0.tar.gz", hash = "sha256:fc3fce98865f6784c9cd0a56a20644fc6098f21c8c33834a8d9fe383c17e554b", size = 62196 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e2/48/577993f1f99c552f18a0428731a755e06171f9902fa118c379eb7c04ea22/jupyter_events-0.12.0-py3-none-any.whl", hash = "sha256:6464b2fa5ad10451c3d35fabc75eab39556ae1e2853ad0c0cc31b656731a97fb", size = 19430 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1450,9 +1943,15 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "jupyter-server" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/36/ff/1e4a61f5170a9a1d978f3ac3872449de6c01fc71eaf89657824c878b1549/jupyter_lsp-2.3.1.tar.gz", hash = "sha256:fdf8a4aa7d85813976d6e29e95e6a2c8f752701f926f2715305249a3829805a6", size = 55677, upload-time = "2026-04-02T08:10:06.749Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/23/e8/9d61dcbd1dce8ef418f06befd4ac084b4720429c26b0b1222bc218685eff/jupyter_lsp-2.3.1-py3-none-any.whl", hash = "sha256:71b954d834e85ff3096400554f2eefaf7fe37053036f9a782b0f7c5e42dadb81", size = 77513, upload-time = "2026-04-02T08:10:01.753Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/eb/5a/9066c9f8e94ee517133cd98dba393459a16cd48bba71a82f16a65415206c/jupyter_lsp-2.3.0.tar.gz", hash = "sha256:458aa59339dc868fb784d73364f17dbce8836e906cd75fd471a325cba02e0245", size = 54823 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1a/60/1f6cee0c46263de1173894f0fafcb3475ded276c472c14d25e0280c18d6d/jupyter_lsp-2.3.0-py3-none-any.whl", hash = "sha256:e914a3cb2addf48b1c7710914771aaf1819d46b2e5a79b0f917b5478ec93f34f", size = 76687 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1480,9 +1979,15 @@ dependencies = [ { name = "traitlets" }, { name = "websocket-client" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/76/a0/eb3c511f54df7b54ca5fc7bff3f4d2277d69052d6a7f521643dfed5279d6/jupyter_server-2.19.0.tar.gz", hash = "sha256:1731236bc32b680223e1ceb9d68209a845203475012ef68773a81434b46a31a7", size = 754561, upload-time = "2026-05-29T11:21:26.057Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/c1/78/d2881e68894cecdcd05912a9c585cfb776ef1fb38b62c8dba98f12ab3adc/jupyter_server-2.19.0-py3-none-any.whl", hash = "sha256:cb76591b76d7093584c2ad2ae72ac3d58614a4b597507a1bb04e1f9f683cf9ea", size = 392244, upload-time = "2026-05-29T11:21:23.871Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/5b/ac/e040ec363d7b6b1f11304cc9f209dac4517ece5d5e01821366b924a64a50/jupyter_server-2.17.0.tar.gz", hash = "sha256:c38ea898566964c888b4772ae1ed58eca84592e88251d2cfc4d171f81f7e99d5", size = 731949 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/92/80/a24767e6ca280f5a49525d987bf3e4d7552bf67c8be07e8ccf20271f8568/jupyter_server-2.17.0-py3-none-any.whl", hash = "sha256:e8cb9c7db4251f51ed307e329b81b72ccf2056ff82d50524debde1ee1870e13f", size = 388221 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1493,9 +1998,15 @@ dependencies = [ { name = "pywinpty", marker = "os_name == 'nt'" }, { name = "terminado" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/f4/a7/bcd0a9b0cbba88986fe944aaaf91bfda603e5a50bda8ed15123f381a3b2f/jupyter_server_terminals-0.5.4.tar.gz", hash = "sha256:bbda128ed41d0be9020349f9f1f2a4ab9952a73ed5f5ac9f1419794761fb87f5", size = 31770, upload-time = "2026-01-14T16:53:20.213Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/d1/2d/6674563f71c6320841fc300911a55143925112a72a883e2ca71fba4c618d/jupyter_server_terminals-0.5.4-py3-none-any.whl", hash = "sha256:55be353fc74a80bc7f3b20e6be50a55a61cd525626f578dcb66a5708e2007d14", size = 13704, upload-time = "2026-01-14T16:53:18.738Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/fc/d5/562469734f476159e99a55426d697cbf8e7eb5efe89fb0e0b4f83a3d3459/jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269", size = 31430 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/07/2d/2b32cdbe8d2a602f697a649798554e4f072115438e92249624e532e8aca6/jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa", size = 13656 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1517,18 +2028,24 @@ dependencies = [ { name = "tornado" }, { name = "traitlets" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/2b/22/8440ec827762146e7cdecf04335bd348795899d29dc6ae82238707353a2c/jupyterlab-4.5.7.tar.gz", hash = "sha256:55a9822c4754da305f41e113452c68383e214dcf96de760146af89ce5d5117b0", size = 23992763, upload-time = "2026-04-29T16:43:51.328Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/3d/aa/537b8f7d80e799af19af35fb3ddfc970b951088a13c57dd9387dcfbb7f61/jupyterlab-4.5.7-py3-none-any.whl", hash = "sha256:fba4cb0e2c44a52859669d8c98b45de029d5e515f8407bf8534d2a8fc5f0964d", size = 12450123, upload-time = "2026-04-29T16:43:46.639Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/df/e5/4fa382a796a6d8e2cd867816b64f1ff27f906e43a7a83ad9eb389e448cd8/jupyterlab-4.5.0.tar.gz", hash = "sha256:aec33d6d8f1225b495ee2cf20f0514f45e6df8e360bdd7ac9bace0b7ac5177ea", size = 23989880 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6c/1e/5a4d5498eba382fee667ed797cf64ae5d1b13b04356df62f067f48bb0f61/jupyterlab-4.5.0-py3-none-any.whl", hash = "sha256:88e157c75c1afff64c7dc4b801ec471450b922a4eae4305211ddd40da8201c8a", size = 12380641 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "jupyterlab-pygments" version = "0.3.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/90/51/9187be60d989df97f5f0aba133fa54e7300f17616e065d1ada7d7646b6d6/jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d", size = 512900, upload-time = "2023-11-23T09:26:37.44Z" } +sdist = { url = "https://files.pythonhosted.org/packages/90/51/9187be60d989df97f5f0aba133fa54e7300f17616e065d1ada7d7646b6d6/jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d", size = 512900 } wheels = [ - { url = "https://files.pythonhosted.org/packages/b1/dd/ead9d8ea85bf202d90cc513b533f9c363121c7792674f78e0d8a854b63b4/jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780", size = 15884, upload-time = "2023-11-23T09:26:34.325Z" }, + { url = "https://files.pythonhosted.org/packages/b1/dd/ead9d8ea85bf202d90cc513b533f9c363121c7792674f78e0d8a854b63b4/jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780", size = 15884 }, ] [[package]] @@ -1544,15 +2061,16 @@ dependencies = [ { name = "packaging" }, { name = "requests" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/d6/2c/90153f189e421e93c4bb4f9e3f59802a1f01abd2ac5cf40b152d7f735232/jupyterlab_server-2.28.0.tar.gz", hash = "sha256:35baa81898b15f93573e2deca50d11ac0ae407ebb688299d3a5213265033712c", size = 76996, upload-time = "2025-10-22T13:59:18.37Z" } +sdist = { url = "https://files.pythonhosted.org/packages/d6/2c/90153f189e421e93c4bb4f9e3f59802a1f01abd2ac5cf40b152d7f735232/jupyterlab_server-2.28.0.tar.gz", hash = "sha256:35baa81898b15f93573e2deca50d11ac0ae407ebb688299d3a5213265033712c", size = 76996 } wheels = [ - { url = "https://files.pythonhosted.org/packages/e0/07/a000fe835f76b7e1143242ab1122e6362ef1c03f23f83a045c38859c2ae0/jupyterlab_server-2.28.0-py3-none-any.whl", hash = "sha256:e4355b148fdcf34d312bbbc80f22467d6d20460e8b8736bf235577dd18506968", size = 59830, upload-time = "2025-10-22T13:59:16.767Z" }, + { url = "https://files.pythonhosted.org/packages/e0/07/a000fe835f76b7e1143242ab1122e6362ef1c03f23f83a045c38859c2ae0/jupyterlab_server-2.28.0-py3-none-any.whl", hash = "sha256:e4355b148fdcf34d312bbbc80f22467d6d20460e8b8736bf235577dd18506968", size = 59830 }, ] [[package]] name = "jupyterlab-widgets" version = "3.0.16" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/26/2d/ef58fed122b268c69c0aa099da20bc67657cdfb2e222688d5731bd5b971d/jupyterlab_widgets-3.0.16.tar.gz", hash = "sha256:423da05071d55cf27a9e602216d35a3a65a3e41cdf9c5d3b643b814ce38c19e0", size = 897423, upload-time = "2025-11-01T21:11:29.724Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/ab/b5/36c712098e6191d1b4e349304ef73a8d06aed77e56ceaac8c0a306c7bda1/jupyterlab_widgets-3.0.16-py3-none-any.whl", hash = "sha256:45fa36d9c6422cf2559198e4db481aa243c7a32d9926b500781c830c80f7ecf8", size = 914926, upload-time = "2025-11-01T21:11:28.008Z" }, @@ -1662,15 +2180,101 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/48/44/2b5b95b7aa39fb2d8d9d956e0f3d5d45aef2ae1d942d4c3ffac2f9cfed1a/kiwisolver-1.5.0-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:be4a51a55833dc29ab5d7503e7bcb3b3af3402d266018137127450005cdfe737", size = 79892, upload-time = "2026-03-09T13:15:49.694Z" }, { url = "https://files.pythonhosted.org/packages/52/7d/7157f9bba6b455cfb4632ed411e199fc8b8977642c2b12082e1bd9e6d173/kiwisolver-1.5.0-pp311-pypy311_pp73-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:daae526907e262de627d8f70058a0f64acc9e2641c164c99c8f594b34a799a16", size = 77603, upload-time = "2026-03-09T13:15:50.945Z" }, { url = "https://files.pythonhosted.org/packages/0a/dd/8050c947d435c8d4bc94e3252f4d8bb8a76cfb424f043a8680be637a57f1/kiwisolver-1.5.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:59cd8683f575d96df5bb48f6add94afc055012c29e28124fcae2b63661b9efb1", size = 73558, upload-time = "2026-03-09T13:15:52.112Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/5c/3c/85844f1b0feb11ee581ac23fe5fce65cd049a200c1446708cc1b7f922875/kiwisolver-1.4.9.tar.gz", hash = "sha256:c3b22c26c6fd6811b0ae8363b95ca8ce4ea3c202d3d0975b2914310ceb1bcc4d", size = 97564 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6f/ab/c80b0d5a9d8a1a65f4f815f2afff9798b12c3b9f31f1d304dd233dd920e2/kiwisolver-1.4.9-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:eb14a5da6dc7642b0f3a18f13654847cd8b7a2550e2645a5bda677862b03ba16", size = 124167 }, + { url = "https://files.pythonhosted.org/packages/a0/c0/27fe1a68a39cf62472a300e2879ffc13c0538546c359b86f149cc19f6ac3/kiwisolver-1.4.9-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:39a219e1c81ae3b103643d2aedb90f1ef22650deb266ff12a19e7773f3e5f089", size = 66579 }, + { url = "https://files.pythonhosted.org/packages/31/a2/a12a503ac1fd4943c50f9822678e8015a790a13b5490354c68afb8489814/kiwisolver-1.4.9-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2405a7d98604b87f3fc28b1716783534b1b4b8510d8142adca34ee0bc3c87543", size = 65309 }, + { url = "https://files.pythonhosted.org/packages/66/e1/e533435c0be77c3f64040d68d7a657771194a63c279f55573188161e81ca/kiwisolver-1.4.9-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:dc1ae486f9abcef254b5618dfb4113dd49f94c68e3e027d03cf0143f3f772b61", size = 1435596 }, + { url = "https://files.pythonhosted.org/packages/67/1e/51b73c7347f9aabdc7215aa79e8b15299097dc2f8e67dee2b095faca9cb0/kiwisolver-1.4.9-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8a1f570ce4d62d718dce3f179ee78dac3b545ac16c0c04bb363b7607a949c0d1", size = 1246548 }, + { url = "https://files.pythonhosted.org/packages/21/aa/72a1c5d1e430294f2d32adb9542719cfb441b5da368d09d268c7757af46c/kiwisolver-1.4.9-cp311-cp311-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:cb27e7b78d716c591e88e0a09a2139c6577865d7f2e152488c2cc6257f460872", size = 1263618 }, + { url = "https://files.pythonhosted.org/packages/a3/af/db1509a9e79dbf4c260ce0cfa3903ea8945f6240e9e59d1e4deb731b1a40/kiwisolver-1.4.9-cp311-cp311-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:15163165efc2f627eb9687ea5f3a28137217d217ac4024893d753f46bce9de26", size = 1317437 }, + { url = "https://files.pythonhosted.org/packages/e0/f2/3ea5ee5d52abacdd12013a94130436e19969fa183faa1e7c7fbc89e9a42f/kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:bdee92c56a71d2b24c33a7d4c2856bd6419d017e08caa7802d2963870e315028", size = 2195742 }, + { url = "https://files.pythonhosted.org/packages/6f/9b/1efdd3013c2d9a2566aa6a337e9923a00590c516add9a1e89a768a3eb2fc/kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:412f287c55a6f54b0650bd9b6dce5aceddb95864a1a90c87af16979d37c89771", size = 2290810 }, + { url = "https://files.pythonhosted.org/packages/fb/e5/cfdc36109ae4e67361f9bc5b41323648cb24a01b9ade18784657e022e65f/kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:2c93f00dcba2eea70af2be5f11a830a742fe6b579a1d4e00f47760ef13be247a", size = 2461579 }, + { url = "https://files.pythonhosted.org/packages/62/86/b589e5e86c7610842213994cdea5add00960076bef4ae290c5fa68589cac/kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f117e1a089d9411663a3207ba874f31be9ac8eaa5b533787024dc07aeb74f464", size = 2268071 }, + { url = "https://files.pythonhosted.org/packages/3b/c6/f8df8509fd1eee6c622febe54384a96cfaf4d43bf2ccec7a0cc17e4715c9/kiwisolver-1.4.9-cp311-cp311-win_amd64.whl", hash = "sha256:be6a04e6c79819c9a8c2373317d19a96048e5a3f90bec587787e86a1153883c2", size = 73840 }, + { url = "https://files.pythonhosted.org/packages/e2/2d/16e0581daafd147bc11ac53f032a2b45eabac897f42a338d0a13c1e5c436/kiwisolver-1.4.9-cp311-cp311-win_arm64.whl", hash = "sha256:0ae37737256ba2de764ddc12aed4956460277f00c4996d51a197e72f62f5eec7", size = 65159 }, + { url = "https://files.pythonhosted.org/packages/86/c9/13573a747838aeb1c76e3267620daa054f4152444d1f3d1a2324b78255b5/kiwisolver-1.4.9-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:ac5a486ac389dddcc5bef4f365b6ae3ffff2c433324fb38dd35e3fab7c957999", size = 123686 }, + { url = "https://files.pythonhosted.org/packages/51/ea/2ecf727927f103ffd1739271ca19c424d0e65ea473fbaeea1c014aea93f6/kiwisolver-1.4.9-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:f2ba92255faa7309d06fe44c3a4a97efe1c8d640c2a79a5ef728b685762a6fd2", size = 66460 }, + { url = "https://files.pythonhosted.org/packages/5b/5a/51f5464373ce2aeb5194508298a508b6f21d3867f499556263c64c621914/kiwisolver-1.4.9-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4a2899935e724dd1074cb568ce7ac0dce28b2cd6ab539c8e001a8578eb106d14", size = 64952 }, + { url = "https://files.pythonhosted.org/packages/70/90/6d240beb0f24b74371762873e9b7f499f1e02166a2d9c5801f4dbf8fa12e/kiwisolver-1.4.9-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f6008a4919fdbc0b0097089f67a1eb55d950ed7e90ce2cc3e640abadd2757a04", size = 1474756 }, + { url = "https://files.pythonhosted.org/packages/12/42/f36816eaf465220f683fb711efdd1bbf7a7005a2473d0e4ed421389bd26c/kiwisolver-1.4.9-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:67bb8b474b4181770f926f7b7d2f8c0248cbcb78b660fdd41a47054b28d2a752", size = 1276404 }, + { url = "https://files.pythonhosted.org/packages/2e/64/bc2de94800adc830c476dce44e9b40fd0809cddeef1fde9fcf0f73da301f/kiwisolver-1.4.9-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2327a4a30d3ee07d2fbe2e7933e8a37c591663b96ce42a00bc67461a87d7df77", size = 1294410 }, + { url = "https://files.pythonhosted.org/packages/5f/42/2dc82330a70aa8e55b6d395b11018045e58d0bb00834502bf11509f79091/kiwisolver-1.4.9-cp312-cp312-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:7a08b491ec91b1d5053ac177afe5290adacf1f0f6307d771ccac5de30592d198", size = 1343631 }, + { url = "https://files.pythonhosted.org/packages/22/fd/f4c67a6ed1aab149ec5a8a401c323cee7a1cbe364381bb6c9c0d564e0e20/kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:d8fc5c867c22b828001b6a38d2eaeb88160bf5783c6cb4a5e440efc981ce286d", size = 2224963 }, + { url = "https://files.pythonhosted.org/packages/45/aa/76720bd4cb3713314677d9ec94dcc21ced3f1baf4830adde5bb9b2430a5f/kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:3b3115b2581ea35bb6d1f24a4c90af37e5d9b49dcff267eeed14c3893c5b86ab", size = 2321295 }, + { url = "https://files.pythonhosted.org/packages/80/19/d3ec0d9ab711242f56ae0dc2fc5d70e298bb4a1f9dfab44c027668c673a1/kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:858e4c22fb075920b96a291928cb7dea5644e94c0ee4fcd5af7e865655e4ccf2", size = 2487987 }, + { url = "https://files.pythonhosted.org/packages/39/e9/61e4813b2c97e86b6fdbd4dd824bf72d28bcd8d4849b8084a357bc0dd64d/kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:ed0fecd28cc62c54b262e3736f8bb2512d8dcfdc2bcf08be5f47f96bf405b145", size = 2291817 }, + { url = "https://files.pythonhosted.org/packages/a0/41/85d82b0291db7504da3c2defe35c9a8a5c9803a730f297bd823d11d5fb77/kiwisolver-1.4.9-cp312-cp312-win_amd64.whl", hash = "sha256:f68208a520c3d86ea51acf688a3e3002615a7f0238002cccc17affecc86a8a54", size = 73895 }, + { url = "https://files.pythonhosted.org/packages/e2/92/5f3068cf15ee5cb624a0c7596e67e2a0bb2adee33f71c379054a491d07da/kiwisolver-1.4.9-cp312-cp312-win_arm64.whl", hash = "sha256:2c1a4f57df73965f3f14df20b80ee29e6a7930a57d2d9e8491a25f676e197c60", size = 64992 }, + { url = "https://files.pythonhosted.org/packages/31/c1/c2686cda909742ab66c7388e9a1a8521a59eb89f8bcfbee28fc980d07e24/kiwisolver-1.4.9-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:a5d0432ccf1c7ab14f9949eec60c5d1f924f17c037e9f8b33352fa05799359b8", size = 123681 }, + { url = "https://files.pythonhosted.org/packages/ca/f0/f44f50c9f5b1a1860261092e3bc91ecdc9acda848a8b8c6abfda4a24dd5c/kiwisolver-1.4.9-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:efb3a45b35622bb6c16dbfab491a8f5a391fe0e9d45ef32f4df85658232ca0e2", size = 66464 }, + { url = "https://files.pythonhosted.org/packages/2d/7a/9d90a151f558e29c3936b8a47ac770235f436f2120aca41a6d5f3d62ae8d/kiwisolver-1.4.9-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1a12cf6398e8a0a001a059747a1cbf24705e18fe413bc22de7b3d15c67cffe3f", size = 64961 }, + { url = "https://files.pythonhosted.org/packages/e9/e9/f218a2cb3a9ffbe324ca29a9e399fa2d2866d7f348ec3a88df87fc248fc5/kiwisolver-1.4.9-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b67e6efbf68e077dd71d1a6b37e43e1a99d0bff1a3d51867d45ee8908b931098", size = 1474607 }, + { url = "https://files.pythonhosted.org/packages/d9/28/aac26d4c882f14de59041636292bc838db8961373825df23b8eeb807e198/kiwisolver-1.4.9-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5656aa670507437af0207645273ccdfee4f14bacd7f7c67a4306d0dcaeaf6eed", size = 1276546 }, + { url = "https://files.pythonhosted.org/packages/8b/ad/8bfc1c93d4cc565e5069162f610ba2f48ff39b7de4b5b8d93f69f30c4bed/kiwisolver-1.4.9-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:bfc08add558155345129c7803b3671cf195e6a56e7a12f3dde7c57d9b417f525", size = 1294482 }, + { url = "https://files.pythonhosted.org/packages/da/f1/6aca55ff798901d8ce403206d00e033191f63d82dd708a186e0ed2067e9c/kiwisolver-1.4.9-cp313-cp313-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:40092754720b174e6ccf9e845d0d8c7d8e12c3d71e7fc35f55f3813e96376f78", size = 1343720 }, + { url = "https://files.pythonhosted.org/packages/d1/91/eed031876c595c81d90d0f6fc681ece250e14bf6998c3d7c419466b523b7/kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:497d05f29a1300d14e02e6441cf0f5ee81c1ff5a304b0d9fb77423974684e08b", size = 2224907 }, + { url = "https://files.pythonhosted.org/packages/e9/ec/4d1925f2e49617b9cca9c34bfa11adefad49d00db038e692a559454dfb2e/kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:bdd1a81a1860476eb41ac4bc1e07b3f07259e6d55bbf739b79c8aaedcf512799", size = 2321334 }, + { url = "https://files.pythonhosted.org/packages/43/cb/450cd4499356f68802750c6ddc18647b8ea01ffa28f50d20598e0befe6e9/kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:e6b93f13371d341afee3be9f7c5964e3fe61d5fa30f6a30eb49856935dfe4fc3", size = 2488313 }, + { url = "https://files.pythonhosted.org/packages/71/67/fc76242bd99f885651128a5d4fa6083e5524694b7c88b489b1b55fdc491d/kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d75aa530ccfaa593da12834b86a0724f58bff12706659baa9227c2ccaa06264c", size = 2291970 }, + { url = "https://files.pythonhosted.org/packages/75/bd/f1a5d894000941739f2ae1b65a32892349423ad49c2e6d0771d0bad3fae4/kiwisolver-1.4.9-cp313-cp313-win_amd64.whl", hash = "sha256:dd0a578400839256df88c16abddf9ba14813ec5f21362e1fe65022e00c883d4d", size = 73894 }, + { url = "https://files.pythonhosted.org/packages/95/38/dce480814d25b99a391abbddadc78f7c117c6da34be68ca8b02d5848b424/kiwisolver-1.4.9-cp313-cp313-win_arm64.whl", hash = "sha256:d4188e73af84ca82468f09cadc5ac4db578109e52acb4518d8154698d3a87ca2", size = 64995 }, + { url = "https://files.pythonhosted.org/packages/e2/37/7d218ce5d92dadc5ebdd9070d903e0c7cf7edfe03f179433ac4d13ce659c/kiwisolver-1.4.9-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:5a0f2724dfd4e3b3ac5a82436a8e6fd16baa7d507117e4279b660fe8ca38a3a1", size = 126510 }, + { url = "https://files.pythonhosted.org/packages/23/b0/e85a2b48233daef4b648fb657ebbb6f8367696a2d9548a00b4ee0eb67803/kiwisolver-1.4.9-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:1b11d6a633e4ed84fc0ddafd4ebfd8ea49b3f25082c04ad12b8315c11d504dc1", size = 67903 }, + { url = "https://files.pythonhosted.org/packages/44/98/f2425bc0113ad7de24da6bb4dae1343476e95e1d738be7c04d31a5d037fd/kiwisolver-1.4.9-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:61874cdb0a36016354853593cffc38e56fc9ca5aa97d2c05d3dcf6922cd55a11", size = 66402 }, + { url = "https://files.pythonhosted.org/packages/98/d8/594657886df9f34c4177cc353cc28ca7e6e5eb562d37ccc233bff43bbe2a/kiwisolver-1.4.9-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:60c439763a969a6af93b4881db0eed8fadf93ee98e18cbc35bc8da868d0c4f0c", size = 1582135 }, + { url = "https://files.pythonhosted.org/packages/5c/c6/38a115b7170f8b306fc929e166340c24958347308ea3012c2b44e7e295db/kiwisolver-1.4.9-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92a2f997387a1b79a75e7803aa7ded2cfbe2823852ccf1ba3bcf613b62ae3197", size = 1389409 }, + { url = "https://files.pythonhosted.org/packages/bf/3b/e04883dace81f24a568bcee6eb3001da4ba05114afa622ec9b6fafdc1f5e/kiwisolver-1.4.9-cp313-cp313t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a31d512c812daea6d8b3be3b2bfcbeb091dbb09177706569bcfc6240dcf8b41c", size = 1401763 }, + { url = "https://files.pythonhosted.org/packages/9f/80/20ace48e33408947af49d7d15c341eaee69e4e0304aab4b7660e234d6288/kiwisolver-1.4.9-cp313-cp313t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:52a15b0f35dad39862d376df10c5230155243a2c1a436e39eb55623ccbd68185", size = 1453643 }, + { url = "https://files.pythonhosted.org/packages/64/31/6ce4380a4cd1f515bdda976a1e90e547ccd47b67a1546d63884463c92ca9/kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:a30fd6fdef1430fd9e1ba7b3398b5ee4e2887783917a687d86ba69985fb08748", size = 2330818 }, + { url = "https://files.pythonhosted.org/packages/fa/e9/3f3fcba3bcc7432c795b82646306e822f3fd74df0ee81f0fa067a1f95668/kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:cc9617b46837c6468197b5945e196ee9ca43057bb7d9d1ae688101e4e1dddf64", size = 2419963 }, + { url = "https://files.pythonhosted.org/packages/99/43/7320c50e4133575c66e9f7dadead35ab22d7c012a3b09bb35647792b2a6d/kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:0ab74e19f6a2b027ea4f845a78827969af45ce790e6cb3e1ebab71bdf9f215ff", size = 2594639 }, + { url = "https://files.pythonhosted.org/packages/65/d6/17ae4a270d4a987ef8a385b906d2bdfc9fce502d6dc0d3aea865b47f548c/kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dba5ee5d3981160c28d5490f0d1b7ed730c22470ff7f6cc26cfcfaacb9896a07", size = 2391741 }, + { url = "https://files.pythonhosted.org/packages/2a/8f/8f6f491d595a9e5912971f3f863d81baddccc8a4d0c3749d6a0dd9ffc9df/kiwisolver-1.4.9-cp313-cp313t-win_arm64.whl", hash = "sha256:0749fd8f4218ad2e851e11cc4dc05c7cbc0cbc4267bdfdb31782e65aace4ee9c", size = 68646 }, + { url = "https://files.pythonhosted.org/packages/6b/32/6cc0fbc9c54d06c2969faa9c1d29f5751a2e51809dd55c69055e62d9b426/kiwisolver-1.4.9-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:9928fe1eb816d11ae170885a74d074f57af3a0d65777ca47e9aeb854a1fba386", size = 123806 }, + { url = "https://files.pythonhosted.org/packages/b2/dd/2bfb1d4a4823d92e8cbb420fe024b8d2167f72079b3bb941207c42570bdf/kiwisolver-1.4.9-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:d0005b053977e7b43388ddec89fa567f43d4f6d5c2c0affe57de5ebf290dc552", size = 66605 }, + { url = "https://files.pythonhosted.org/packages/f7/69/00aafdb4e4509c2ca6064646cba9cd4b37933898f426756adb2cb92ebbed/kiwisolver-1.4.9-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:2635d352d67458b66fd0667c14cb1d4145e9560d503219034a18a87e971ce4f3", size = 64925 }, + { url = "https://files.pythonhosted.org/packages/43/dc/51acc6791aa14e5cb6d8a2e28cefb0dc2886d8862795449d021334c0df20/kiwisolver-1.4.9-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:767c23ad1c58c9e827b649a9ab7809fd5fd9db266a9cf02b0e926ddc2c680d58", size = 1472414 }, + { url = "https://files.pythonhosted.org/packages/3d/bb/93fa64a81db304ac8a246f834d5094fae4b13baf53c839d6bb6e81177129/kiwisolver-1.4.9-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:72d0eb9fba308b8311685c2268cf7d0a0639a6cd027d8128659f72bdd8a024b4", size = 1281272 }, + { url = "https://files.pythonhosted.org/packages/70/e6/6df102916960fb8d05069d4bd92d6d9a8202d5a3e2444494e7cd50f65b7a/kiwisolver-1.4.9-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f68e4f3eeca8fb22cc3d731f9715a13b652795ef657a13df1ad0c7dc0e9731df", size = 1298578 }, + { url = "https://files.pythonhosted.org/packages/7c/47/e142aaa612f5343736b087864dbaebc53ea8831453fb47e7521fa8658f30/kiwisolver-1.4.9-cp314-cp314-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d84cd4061ae292d8ac367b2c3fa3aad11cb8625a95d135fe93f286f914f3f5a6", size = 1345607 }, + { url = "https://files.pythonhosted.org/packages/54/89/d641a746194a0f4d1a3670fb900d0dbaa786fb98341056814bc3f058fa52/kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:a60ea74330b91bd22a29638940d115df9dc00af5035a9a2a6ad9399ffb4ceca5", size = 2230150 }, + { url = "https://files.pythonhosted.org/packages/aa/6b/5ee1207198febdf16ac11f78c5ae40861b809cbe0e6d2a8d5b0b3044b199/kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:ce6a3a4e106cf35c2d9c4fa17c05ce0b180db622736845d4315519397a77beaf", size = 2325979 }, + { url = "https://files.pythonhosted.org/packages/fc/ff/b269eefd90f4ae14dcc74973d5a0f6d28d3b9bb1afd8c0340513afe6b39a/kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:77937e5e2a38a7b48eef0585114fe7930346993a88060d0bf886086d2aa49ef5", size = 2491456 }, + { url = "https://files.pythonhosted.org/packages/fc/d4/10303190bd4d30de547534601e259a4fbf014eed94aae3e5521129215086/kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:24c175051354f4a28c5d6a31c93906dc653e2bf234e8a4bbfb964892078898ce", size = 2294621 }, + { url = "https://files.pythonhosted.org/packages/28/e0/a9a90416fce5c0be25742729c2ea52105d62eda6c4be4d803c2a7be1fa50/kiwisolver-1.4.9-cp314-cp314-win_amd64.whl", hash = "sha256:0763515d4df10edf6d06a3c19734e2566368980d21ebec439f33f9eb936c07b7", size = 75417 }, + { url = "https://files.pythonhosted.org/packages/1f/10/6949958215b7a9a264299a7db195564e87900f709db9245e4ebdd3c70779/kiwisolver-1.4.9-cp314-cp314-win_arm64.whl", hash = "sha256:0e4e2bf29574a6a7b7f6cb5fa69293b9f96c928949ac4a53ba3f525dffb87f9c", size = 66582 }, + { url = "https://files.pythonhosted.org/packages/ec/79/60e53067903d3bc5469b369fe0dfc6b3482e2133e85dae9daa9527535991/kiwisolver-1.4.9-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:d976bbb382b202f71c67f77b0ac11244021cfa3f7dfd9e562eefcea2df711548", size = 126514 }, + { url = "https://files.pythonhosted.org/packages/25/d1/4843d3e8d46b072c12a38c97c57fab4608d36e13fe47d47ee96b4d61ba6f/kiwisolver-1.4.9-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:2489e4e5d7ef9a1c300a5e0196e43d9c739f066ef23270607d45aba368b91f2d", size = 67905 }, + { url = "https://files.pythonhosted.org/packages/8c/ae/29ffcbd239aea8b93108de1278271ae764dfc0d803a5693914975f200596/kiwisolver-1.4.9-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:e2ea9f7ab7fbf18fffb1b5434ce7c69a07582f7acc7717720f1d69f3e806f90c", size = 66399 }, + { url = "https://files.pythonhosted.org/packages/a1/ae/d7ba902aa604152c2ceba5d352d7b62106bedbccc8e95c3934d94472bfa3/kiwisolver-1.4.9-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b34e51affded8faee0dfdb705416153819d8ea9250bbbf7ea1b249bdeb5f1122", size = 1582197 }, + { url = "https://files.pythonhosted.org/packages/f2/41/27c70d427eddb8bc7e4f16420a20fefc6f480312122a59a959fdfe0445ad/kiwisolver-1.4.9-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d8aacd3d4b33b772542b2e01beb50187536967b514b00003bdda7589722d2a64", size = 1390125 }, + { url = "https://files.pythonhosted.org/packages/41/42/b3799a12bafc76d962ad69083f8b43b12bf4fe78b097b12e105d75c9b8f1/kiwisolver-1.4.9-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7cf974dd4e35fa315563ac99d6287a1024e4dc2077b8a7d7cd3d2fb65d283134", size = 1402612 }, + { url = "https://files.pythonhosted.org/packages/d2/b5/a210ea073ea1cfaca1bb5c55a62307d8252f531beb364e18aa1e0888b5a0/kiwisolver-1.4.9-cp314-cp314t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:85bd218b5ecfbee8c8a82e121802dcb519a86044c9c3b2e4aef02fa05c6da370", size = 1453990 }, + { url = "https://files.pythonhosted.org/packages/5f/ce/a829eb8c033e977d7ea03ed32fb3c1781b4fa0433fbadfff29e39c676f32/kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:0856e241c2d3df4efef7c04a1e46b1936b6120c9bcf36dd216e3acd84bc4fb21", size = 2331601 }, + { url = "https://files.pythonhosted.org/packages/e0/4b/b5e97eb142eb9cd0072dacfcdcd31b1c66dc7352b0f7c7255d339c0edf00/kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:9af39d6551f97d31a4deebeac6f45b156f9755ddc59c07b402c148f5dbb6482a", size = 2422041 }, + { url = "https://files.pythonhosted.org/packages/40/be/8eb4cd53e1b85ba4edc3a9321666f12b83113a178845593307a3e7891f44/kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:bb4ae2b57fc1d8cbd1cf7b1d9913803681ffa903e7488012be5b76dedf49297f", size = 2594897 }, + { url = "https://files.pythonhosted.org/packages/99/dd/841e9a66c4715477ea0abc78da039832fbb09dac5c35c58dc4c41a407b8a/kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:aedff62918805fb62d43a4aa2ecd4482c380dc76cd31bd7c8878588a61bd0369", size = 2391835 }, + { url = "https://files.pythonhosted.org/packages/0c/28/4b2e5c47a0da96896fdfdb006340ade064afa1e63675d01ea5ac222b6d52/kiwisolver-1.4.9-cp314-cp314t-win_amd64.whl", hash = "sha256:1fa333e8b2ce4d9660f2cda9c0e1b6bafcfb2457a9d259faa82289e73ec24891", size = 79988 }, + { url = "https://files.pythonhosted.org/packages/80/be/3578e8afd18c88cdf9cb4cffde75a96d2be38c5a903f1ed0ceec061bd09e/kiwisolver-1.4.9-cp314-cp314t-win_arm64.whl", hash = "sha256:4a48a2ce79d65d363597ef7b567ce3d14d68783d2b2263d98db3d9477805ba32", size = 70260 }, + { url = "https://files.pythonhosted.org/packages/a3/0f/36d89194b5a32c054ce93e586d4049b6c2c22887b0eb229c61c68afd3078/kiwisolver-1.4.9-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:720e05574713db64c356e86732c0f3c5252818d05f9df320f0ad8380641acea5", size = 60104 }, + { url = "https://files.pythonhosted.org/packages/52/ba/4ed75f59e4658fd21fe7dde1fee0ac397c678ec3befba3fe6482d987af87/kiwisolver-1.4.9-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:17680d737d5335b552994a2008fab4c851bcd7de33094a82067ef3a576ff02fa", size = 58592 }, + { url = "https://files.pythonhosted.org/packages/33/01/a8ea7c5ea32a9b45ceeaee051a04c8ed4320f5add3c51bfa20879b765b70/kiwisolver-1.4.9-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:85b5352f94e490c028926ea567fc569c52ec79ce131dadb968d3853e809518c2", size = 80281 }, + { url = "https://files.pythonhosted.org/packages/da/e3/dbd2ecdce306f1d07a1aaf324817ee993aab7aee9db47ceac757deabafbe/kiwisolver-1.4.9-pp311-pypy311_pp73-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:464415881e4801295659462c49461a24fb107c140de781d55518c4b80cb6790f", size = 78009 }, + { url = "https://files.pythonhosted.org/packages/da/e9/0d4add7873a73e462aeb45c036a2dead2562b825aa46ba326727b3f31016/kiwisolver-1.4.9-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:fb940820c63a9590d31d88b815e7a3aa5915cad3ce735ab45f0c730b39547de1", size = 73929 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "lark" version = "1.3.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/da/34/28fff3ab31ccff1fd4f6c7c7b0ceb2b6968d8ea4950663eadcb5720591a0/lark-1.3.1.tar.gz", hash = "sha256:b426a7a6d6d53189d318f2b6236ab5d6429eaf09259f1ca33eb716eed10d2905", size = 382732, upload-time = "2025-10-27T18:25:56.653Z" } +sdist = { url = "https://files.pythonhosted.org/packages/da/34/28fff3ab31ccff1fd4f6c7c7b0ceb2b6968d8ea4950663eadcb5720591a0/lark-1.3.1.tar.gz", hash = "sha256:b426a7a6d6d53189d318f2b6236ab5d6429eaf09259f1ca33eb716eed10d2905", size = 382732 } wheels = [ - { url = "https://files.pythonhosted.org/packages/82/3d/14ce75ef66813643812f3093ab17e46d3a206942ce7376d31ec2d36229e7/lark-1.3.1-py3-none-any.whl", hash = "sha256:c629b661023a014c37da873b4ff58a817398d12635d3bbb2c5a03be7fe5d1e12", size = 113151, upload-time = "2025-10-27T18:25:54.882Z" }, + { url = "https://files.pythonhosted.org/packages/82/3d/14ce75ef66813643812f3093ab17e46d3a206942ce7376d31ec2d36229e7/lark-1.3.1-py3-none-any.whl", hash = "sha256:c629b661023a014c37da873b4ff58a817398d12635d3bbb2c5a03be7fe5d1e12", size = 113151 }, ] [[package]] @@ -1680,9 +2284,15 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "packaging" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/49/ac/21a1f8aa3777f5658576777ea76bfb124b702c520bbe90edf4ae9915eafa/lazy_loader-0.5.tar.gz", hash = "sha256:717f9179a0dbed357012ddad50a5ad3d5e4d9a0b8712680d4e687f5e6e6ed9b3", size = 15294, upload-time = "2026-03-06T15:45:09.054Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/8a/a1/8d812e53a5da1687abb10445275d41a8b13adb781bbf7196ddbcf8d88505/lazy_loader-0.5-py3-none-any.whl", hash = "sha256:ab0ea149e9c554d4ffeeb21105ac60bed7f3b4fd69b1d2360a4add51b170b005", size = 8044, upload-time = "2026-03-06T15:45:07.668Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/6f/6b/c875b30a1ba490860c93da4cabf479e03f584eba06fe5963f6f6644653d8/lazy_loader-0.4.tar.gz", hash = "sha256:47c75182589b91a4e1a85a136c074285a5ad4d9f39c63e0d7fb76391c4574cd1", size = 15431 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl", hash = "sha256:342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc", size = 12097 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1693,18 +2303,24 @@ dependencies = [ { name = "packaging" }, { name = "typing-extensions" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/f1/45/7fa8f56b17dc0f0a41ec70dd307ecd6787254483549843bef4c30ab5adce/lightning_utilities-0.15.3.tar.gz", hash = "sha256:792ae0204c79f6859721ac7f386c237a33b0ed06ba775009cb894e010a842033", size = 33553, upload-time = "2026-02-22T14:48:53.348Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/25/f4/ead6e0e37209b07c9baa3e984ccdb0348ca370b77cea3aaea8ddbb097e00/lightning_utilities-0.15.3-py3-none-any.whl", hash = "sha256:6c55f1bee70084a1cbeaa41ada96e4b3a0fea5909e844dd335bd80f5a73c5f91", size = 31906, upload-time = "2026-02-22T14:48:52.488Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/b8/39/6fc58ca81492db047149b4b8fd385aa1bfb8c28cd7cacb0c7eb0c44d842f/lightning_utilities-0.15.2.tar.gz", hash = "sha256:cdf12f530214a63dacefd713f180d1ecf5d165338101617b4742e8f22c032e24", size = 31090 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/de/73/3d757cb3fc16f0f9794dd289bcd0c4a031d9cf54d8137d6b984b2d02edf3/lightning_utilities-0.15.2-py3-none-any.whl", hash = "sha256:ad3ab1703775044bbf880dbf7ddaaac899396c96315f3aa1779cec9d618a9841", size = 29431 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "locket" version = "1.0.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/2f/83/97b29fe05cb6ae28d2dbd30b81e2e402a3eed5f460c26e9eaa5895ceacf5/locket-1.0.0.tar.gz", hash = "sha256:5c0d4c052a8bbbf750e056a8e65ccd309086f4f0f18a2eac306a8dfa4112a632", size = 4350, upload-time = "2022-04-20T22:04:44.312Z" } +sdist = { url = "https://files.pythonhosted.org/packages/2f/83/97b29fe05cb6ae28d2dbd30b81e2e402a3eed5f460c26e9eaa5895ceacf5/locket-1.0.0.tar.gz", hash = "sha256:5c0d4c052a8bbbf750e056a8e65ccd309086f4f0f18a2eac306a8dfa4112a632", size = 4350 } wheels = [ - { url = "https://files.pythonhosted.org/packages/db/bc/83e112abc66cd466c6b83f99118035867cecd41802f8d044638aa78a106e/locket-1.0.0-py2.py3-none-any.whl", hash = "sha256:b6c819a722f7b6bd955b80781788e4a66a55628b858d347536b7e81325a3a5e3", size = 4398, upload-time = "2022-04-20T22:04:42.23Z" }, + { url = "https://files.pythonhosted.org/packages/db/bc/83e112abc66cd466c6b83f99118035867cecd41802f8d044638aa78a106e/locket-1.0.0-py2.py3-none-any.whl", hash = "sha256:b6c819a722f7b6bd955b80781788e4a66a55628b858d347536b7e81325a3a5e3", size = 4398 }, ] [[package]] @@ -1714,92 +2330,104 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "markupsafe" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/00/62/791b31e69ae182791ec67f04850f2f062716bbd205483d63a215f3e062d3/mako-1.3.12.tar.gz", hash = "sha256:9f778e93289bd410bb35daadeb4fc66d95a746f0b75777b942088b7fd7af550a", size = 400219, upload-time = "2026-04-28T19:01:08.512Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/bc/b1/a0ec7a5a9db730a08daef1fdfb8090435b82465abbf758a596f0ea88727e/mako-1.3.12-py3-none-any.whl", hash = "sha256:8f61569480282dbf557145ce441e4ba888be453c30989f879f0d652e39f53ea9", size = 78521, upload-time = "2026-04-28T19:01:10.393Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/9e/38/bd5b78a920a64d708fe6bc8e0a2c075e1389d53bef8413725c63ba041535/mako-1.3.10.tar.gz", hash = "sha256:99579a6f39583fa7e5630a28c3c1f440e4e97a414b80372649c0ce338da2ea28", size = 392474 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/87/fb/99f81ac72ae23375f22b7afdb7642aba97c00a713c217124420147681a2f/mako-1.3.10-py3-none-any.whl", hash = "sha256:baef24a52fc4fc514a0887ac600f9f1cff3d82c61d4d700a1fa84d597b88db59", size = 78509 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "markdown" version = "3.10.2" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/2b/f4/69fa6ed85ae003c2378ffa8f6d2e3234662abd02c10d216c0ba96081a238/markdown-3.10.2.tar.gz", hash = "sha256:994d51325d25ad8aa7ce4ebaec003febcce822c3f8c911e3b17c52f7f589f950", size = 368805, upload-time = "2026-02-09T14:57:26.942Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/de/1f/77fa3081e4f66ca3576c896ae5d31c3002ac6607f9747d2e3aa49227e464/markdown-3.10.2-py3-none-any.whl", hash = "sha256:e91464b71ae3ee7afd3017d9f358ef0baf158fd9a298db92f1d4761133824c36", size = 108180, upload-time = "2026-02-09T14:57:25.787Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/7d/ab/7dd27d9d863b3376fcf23a5a13cb5d024aed1db46f963f1b5735ae43b3be/markdown-3.10.tar.gz", hash = "sha256:37062d4f2aa4b2b6b32aefb80faa300f82cc790cb949a35b8caede34f2b68c0e", size = 364931 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/70/81/54e3ce63502cd085a0c556652a4e1b919c45a446bd1e5300e10c44c8c521/markdown-3.10-py3-none-any.whl", hash = "sha256:b5b99d6951e2e4948d939255596523444c0e677c669700b1d17aa4a8a464cb7c", size = 107678 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "markupsafe" version = "3.0.3" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/7e/99/7690b6d4034fffd95959cbe0c02de8deb3098cc577c67bb6a24fe5d7caa7/markupsafe-3.0.3.tar.gz", hash = "sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698", size = 80313, upload-time = "2025-09-27T18:37:40.426Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/08/db/fefacb2136439fc8dd20e797950e749aa1f4997ed584c62cfb8ef7c2be0e/markupsafe-3.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:1cc7ea17a6824959616c525620e387f6dd30fec8cb44f649e31712db02123dad", size = 11631, upload-time = "2025-09-27T18:36:18.185Z" }, - { url = "https://files.pythonhosted.org/packages/e1/2e/5898933336b61975ce9dc04decbc0a7f2fee78c30353c5efba7f2d6ff27a/markupsafe-3.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4bd4cd07944443f5a265608cc6aab442e4f74dff8088b0dfc8238647b8f6ae9a", size = 12058, upload-time = "2025-09-27T18:36:19.444Z" }, - { url = "https://files.pythonhosted.org/packages/1d/09/adf2df3699d87d1d8184038df46a9c80d78c0148492323f4693df54e17bb/markupsafe-3.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b5420a1d9450023228968e7e6a9ce57f65d148ab56d2313fcd589eee96a7a50", size = 24287, upload-time = "2025-09-27T18:36:20.768Z" }, - { url = "https://files.pythonhosted.org/packages/30/ac/0273f6fcb5f42e314c6d8cd99effae6a5354604d461b8d392b5ec9530a54/markupsafe-3.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0bf2a864d67e76e5c9a34dc26ec616a66b9888e25e7b9460e1c76d3293bd9dbf", size = 22940, upload-time = "2025-09-27T18:36:22.249Z" }, - { url = "https://files.pythonhosted.org/packages/19/ae/31c1be199ef767124c042c6c3e904da327a2f7f0cd63a0337e1eca2967a8/markupsafe-3.0.3-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:bc51efed119bc9cfdf792cdeaa4d67e8f6fcccab66ed4bfdd6bde3e59bfcbb2f", size = 21887, upload-time = "2025-09-27T18:36:23.535Z" }, - { url = "https://files.pythonhosted.org/packages/b2/76/7edcab99d5349a4532a459e1fe64f0b0467a3365056ae550d3bcf3f79e1e/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:068f375c472b3e7acbe2d5318dea141359e6900156b5b2ba06a30b169086b91a", size = 23692, upload-time = "2025-09-27T18:36:24.823Z" }, - { url = "https://files.pythonhosted.org/packages/a4/28/6e74cdd26d7514849143d69f0bf2399f929c37dc2b31e6829fd2045b2765/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:7be7b61bb172e1ed687f1754f8e7484f1c8019780f6f6b0786e76bb01c2ae115", size = 21471, upload-time = "2025-09-27T18:36:25.95Z" }, - { url = "https://files.pythonhosted.org/packages/62/7e/a145f36a5c2945673e590850a6f8014318d5577ed7e5920a4b3448e0865d/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f9e130248f4462aaa8e2552d547f36ddadbeaa573879158d721bbd33dfe4743a", size = 22923, upload-time = "2025-09-27T18:36:27.109Z" }, - { url = "https://files.pythonhosted.org/packages/0f/62/d9c46a7f5c9adbeeeda52f5b8d802e1094e9717705a645efc71b0913a0a8/markupsafe-3.0.3-cp311-cp311-win32.whl", hash = "sha256:0db14f5dafddbb6d9208827849fad01f1a2609380add406671a26386cdf15a19", size = 14572, upload-time = "2025-09-27T18:36:28.045Z" }, - { url = "https://files.pythonhosted.org/packages/83/8a/4414c03d3f891739326e1783338e48fb49781cc915b2e0ee052aa490d586/markupsafe-3.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:de8a88e63464af587c950061a5e6a67d3632e36df62b986892331d4620a35c01", size = 15077, upload-time = "2025-09-27T18:36:29.025Z" }, - { url = "https://files.pythonhosted.org/packages/35/73/893072b42e6862f319b5207adc9ae06070f095b358655f077f69a35601f0/markupsafe-3.0.3-cp311-cp311-win_arm64.whl", hash = "sha256:3b562dd9e9ea93f13d53989d23a7e775fdfd1066c33494ff43f5418bc8c58a5c", size = 13876, upload-time = "2025-09-27T18:36:29.954Z" }, - { url = "https://files.pythonhosted.org/packages/5a/72/147da192e38635ada20e0a2e1a51cf8823d2119ce8883f7053879c2199b5/markupsafe-3.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e", size = 11615, upload-time = "2025-09-27T18:36:30.854Z" }, - { url = "https://files.pythonhosted.org/packages/9a/81/7e4e08678a1f98521201c3079f77db69fb552acd56067661f8c2f534a718/markupsafe-3.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce", size = 12020, upload-time = "2025-09-27T18:36:31.971Z" }, - { url = "https://files.pythonhosted.org/packages/1e/2c/799f4742efc39633a1b54a92eec4082e4f815314869865d876824c257c1e/markupsafe-3.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d", size = 24332, upload-time = "2025-09-27T18:36:32.813Z" }, - { url = "https://files.pythonhosted.org/packages/3c/2e/8d0c2ab90a8c1d9a24f0399058ab8519a3279d1bd4289511d74e909f060e/markupsafe-3.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d", size = 22947, upload-time = "2025-09-27T18:36:33.86Z" }, - { url = "https://files.pythonhosted.org/packages/2c/54/887f3092a85238093a0b2154bd629c89444f395618842e8b0c41783898ea/markupsafe-3.0.3-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a", size = 21962, upload-time = "2025-09-27T18:36:35.099Z" }, - { url = "https://files.pythonhosted.org/packages/c9/2f/336b8c7b6f4a4d95e91119dc8521402461b74a485558d8f238a68312f11c/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b", size = 23760, upload-time = "2025-09-27T18:36:36.001Z" }, - { url = "https://files.pythonhosted.org/packages/32/43/67935f2b7e4982ffb50a4d169b724d74b62a3964bc1a9a527f5ac4f1ee2b/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f", size = 21529, upload-time = "2025-09-27T18:36:36.906Z" }, - { url = "https://files.pythonhosted.org/packages/89/e0/4486f11e51bbba8b0c041098859e869e304d1c261e59244baa3d295d47b7/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b", size = 23015, upload-time = "2025-09-27T18:36:37.868Z" }, - { url = "https://files.pythonhosted.org/packages/2f/e1/78ee7a023dac597a5825441ebd17170785a9dab23de95d2c7508ade94e0e/markupsafe-3.0.3-cp312-cp312-win32.whl", hash = "sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d", size = 14540, upload-time = "2025-09-27T18:36:38.761Z" }, - { url = "https://files.pythonhosted.org/packages/aa/5b/bec5aa9bbbb2c946ca2733ef9c4ca91c91b6a24580193e891b5f7dbe8e1e/markupsafe-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c", size = 15105, upload-time = "2025-09-27T18:36:39.701Z" }, - { url = "https://files.pythonhosted.org/packages/e5/f1/216fc1bbfd74011693a4fd837e7026152e89c4bcf3e77b6692fba9923123/markupsafe-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f", size = 13906, upload-time = "2025-09-27T18:36:40.689Z" }, - { url = "https://files.pythonhosted.org/packages/38/2f/907b9c7bbba283e68f20259574b13d005c121a0fa4c175f9bed27c4597ff/markupsafe-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795", size = 11622, upload-time = "2025-09-27T18:36:41.777Z" }, - { url = "https://files.pythonhosted.org/packages/9c/d9/5f7756922cdd676869eca1c4e3c0cd0df60ed30199ffd775e319089cb3ed/markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219", size = 12029, upload-time = "2025-09-27T18:36:43.257Z" }, - { url = "https://files.pythonhosted.org/packages/00/07/575a68c754943058c78f30db02ee03a64b3c638586fba6a6dd56830b30a3/markupsafe-3.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6", size = 24374, upload-time = "2025-09-27T18:36:44.508Z" }, - { url = "https://files.pythonhosted.org/packages/a9/21/9b05698b46f218fc0e118e1f8168395c65c8a2c750ae2bab54fc4bd4e0e8/markupsafe-3.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676", size = 22980, upload-time = "2025-09-27T18:36:45.385Z" }, - { url = "https://files.pythonhosted.org/packages/7f/71/544260864f893f18b6827315b988c146b559391e6e7e8f7252839b1b846a/markupsafe-3.0.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9", size = 21990, upload-time = "2025-09-27T18:36:46.916Z" }, - { url = "https://files.pythonhosted.org/packages/c2/28/b50fc2f74d1ad761af2f5dcce7492648b983d00a65b8c0e0cb457c82ebbe/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1", size = 23784, upload-time = "2025-09-27T18:36:47.884Z" }, - { url = "https://files.pythonhosted.org/packages/ed/76/104b2aa106a208da8b17a2fb72e033a5a9d7073c68f7e508b94916ed47a9/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc", size = 21588, upload-time = "2025-09-27T18:36:48.82Z" }, - { url = "https://files.pythonhosted.org/packages/b5/99/16a5eb2d140087ebd97180d95249b00a03aa87e29cc224056274f2e45fd6/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12", size = 23041, upload-time = "2025-09-27T18:36:49.797Z" }, - { url = "https://files.pythonhosted.org/packages/19/bc/e7140ed90c5d61d77cea142eed9f9c303f4c4806f60a1044c13e3f1471d0/markupsafe-3.0.3-cp313-cp313-win32.whl", hash = "sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed", size = 14543, upload-time = "2025-09-27T18:36:51.584Z" }, - { url = "https://files.pythonhosted.org/packages/05/73/c4abe620b841b6b791f2edc248f556900667a5a1cf023a6646967ae98335/markupsafe-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5", size = 15113, upload-time = "2025-09-27T18:36:52.537Z" }, - { url = "https://files.pythonhosted.org/packages/f0/3a/fa34a0f7cfef23cf9500d68cb7c32dd64ffd58a12b09225fb03dd37d5b80/markupsafe-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485", size = 13911, upload-time = "2025-09-27T18:36:53.513Z" }, - { url = "https://files.pythonhosted.org/packages/e4/d7/e05cd7efe43a88a17a37b3ae96e79a19e846f3f456fe79c57ca61356ef01/markupsafe-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73", size = 11658, upload-time = "2025-09-27T18:36:54.819Z" }, - { url = "https://files.pythonhosted.org/packages/99/9e/e412117548182ce2148bdeacdda3bb494260c0b0184360fe0d56389b523b/markupsafe-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37", size = 12066, upload-time = "2025-09-27T18:36:55.714Z" }, - { url = "https://files.pythonhosted.org/packages/bc/e6/fa0ffcda717ef64a5108eaa7b4f5ed28d56122c9a6d70ab8b72f9f715c80/markupsafe-3.0.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19", size = 25639, upload-time = "2025-09-27T18:36:56.908Z" }, - { url = "https://files.pythonhosted.org/packages/96/ec/2102e881fe9d25fc16cb4b25d5f5cde50970967ffa5dddafdb771237062d/markupsafe-3.0.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025", size = 23569, upload-time = "2025-09-27T18:36:57.913Z" }, - { url = "https://files.pythonhosted.org/packages/4b/30/6f2fce1f1f205fc9323255b216ca8a235b15860c34b6798f810f05828e32/markupsafe-3.0.3-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6", size = 23284, upload-time = "2025-09-27T18:36:58.833Z" }, - { url = "https://files.pythonhosted.org/packages/58/47/4a0ccea4ab9f5dcb6f79c0236d954acb382202721e704223a8aafa38b5c8/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f", size = 24801, upload-time = "2025-09-27T18:36:59.739Z" }, - { url = "https://files.pythonhosted.org/packages/6a/70/3780e9b72180b6fecb83a4814d84c3bf4b4ae4bf0b19c27196104149734c/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb", size = 22769, upload-time = "2025-09-27T18:37:00.719Z" }, - { url = "https://files.pythonhosted.org/packages/98/c5/c03c7f4125180fc215220c035beac6b9cb684bc7a067c84fc69414d315f5/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009", size = 23642, upload-time = "2025-09-27T18:37:01.673Z" }, - { url = "https://files.pythonhosted.org/packages/80/d6/2d1b89f6ca4bff1036499b1e29a1d02d282259f3681540e16563f27ebc23/markupsafe-3.0.3-cp313-cp313t-win32.whl", hash = "sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354", size = 14612, upload-time = "2025-09-27T18:37:02.639Z" }, - { url = "https://files.pythonhosted.org/packages/2b/98/e48a4bfba0a0ffcf9925fe2d69240bfaa19c6f7507b8cd09c70684a53c1e/markupsafe-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218", size = 15200, upload-time = "2025-09-27T18:37:03.582Z" }, - { url = "https://files.pythonhosted.org/packages/0e/72/e3cc540f351f316e9ed0f092757459afbc595824ca724cbc5a5d4263713f/markupsafe-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287", size = 13973, upload-time = "2025-09-27T18:37:04.929Z" }, - { url = "https://files.pythonhosted.org/packages/33/8a/8e42d4838cd89b7dde187011e97fe6c3af66d8c044997d2183fbd6d31352/markupsafe-3.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe", size = 11619, upload-time = "2025-09-27T18:37:06.342Z" }, - { url = "https://files.pythonhosted.org/packages/b5/64/7660f8a4a8e53c924d0fa05dc3a55c9cee10bbd82b11c5afb27d44b096ce/markupsafe-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026", size = 12029, upload-time = "2025-09-27T18:37:07.213Z" }, - { url = "https://files.pythonhosted.org/packages/da/ef/e648bfd021127bef5fa12e1720ffed0c6cbb8310c8d9bea7266337ff06de/markupsafe-3.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737", size = 24408, upload-time = "2025-09-27T18:37:09.572Z" }, - { url = "https://files.pythonhosted.org/packages/41/3c/a36c2450754618e62008bf7435ccb0f88053e07592e6028a34776213d877/markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97", size = 23005, upload-time = "2025-09-27T18:37:10.58Z" }, - { url = "https://files.pythonhosted.org/packages/bc/20/b7fdf89a8456b099837cd1dc21974632a02a999ec9bf7ca3e490aacd98e7/markupsafe-3.0.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d", size = 22048, upload-time = "2025-09-27T18:37:11.547Z" }, - { url = "https://files.pythonhosted.org/packages/9a/a7/591f592afdc734f47db08a75793a55d7fbcc6902a723ae4cfbab61010cc5/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda", size = 23821, upload-time = "2025-09-27T18:37:12.48Z" }, - { url = "https://files.pythonhosted.org/packages/7d/33/45b24e4f44195b26521bc6f1a82197118f74df348556594bd2262bda1038/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf", size = 21606, upload-time = "2025-09-27T18:37:13.485Z" }, - { url = "https://files.pythonhosted.org/packages/ff/0e/53dfaca23a69fbfbbf17a4b64072090e70717344c52eaaaa9c5ddff1e5f0/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe", size = 23043, upload-time = "2025-09-27T18:37:14.408Z" }, - { url = "https://files.pythonhosted.org/packages/46/11/f333a06fc16236d5238bfe74daccbca41459dcd8d1fa952e8fbd5dccfb70/markupsafe-3.0.3-cp314-cp314-win32.whl", hash = "sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9", size = 14747, upload-time = "2025-09-27T18:37:15.36Z" }, - { url = "https://files.pythonhosted.org/packages/28/52/182836104b33b444e400b14f797212f720cbc9ed6ba34c800639d154e821/markupsafe-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581", size = 15341, upload-time = "2025-09-27T18:37:16.496Z" }, - { url = "https://files.pythonhosted.org/packages/6f/18/acf23e91bd94fd7b3031558b1f013adfa21a8e407a3fdb32745538730382/markupsafe-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4", size = 14073, upload-time = "2025-09-27T18:37:17.476Z" }, - { url = "https://files.pythonhosted.org/packages/3c/f0/57689aa4076e1b43b15fdfa646b04653969d50cf30c32a102762be2485da/markupsafe-3.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab", size = 11661, upload-time = "2025-09-27T18:37:18.453Z" }, - { url = "https://files.pythonhosted.org/packages/89/c3/2e67a7ca217c6912985ec766c6393b636fb0c2344443ff9d91404dc4c79f/markupsafe-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175", size = 12069, upload-time = "2025-09-27T18:37:19.332Z" }, - { url = "https://files.pythonhosted.org/packages/f0/00/be561dce4e6ca66b15276e184ce4b8aec61fe83662cce2f7d72bd3249d28/markupsafe-3.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634", size = 25670, upload-time = "2025-09-27T18:37:20.245Z" }, - { url = "https://files.pythonhosted.org/packages/50/09/c419f6f5a92e5fadde27efd190eca90f05e1261b10dbd8cbcb39cd8ea1dc/markupsafe-3.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50", size = 23598, upload-time = "2025-09-27T18:37:21.177Z" }, - { url = "https://files.pythonhosted.org/packages/22/44/a0681611106e0b2921b3033fc19bc53323e0b50bc70cffdd19f7d679bb66/markupsafe-3.0.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e", size = 23261, upload-time = "2025-09-27T18:37:22.167Z" }, - { url = "https://files.pythonhosted.org/packages/5f/57/1b0b3f100259dc9fffe780cfb60d4be71375510e435efec3d116b6436d43/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5", size = 24835, upload-time = "2025-09-27T18:37:23.296Z" }, - { url = "https://files.pythonhosted.org/packages/26/6a/4bf6d0c97c4920f1597cc14dd720705eca0bf7c787aebc6bb4d1bead5388/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523", size = 22733, upload-time = "2025-09-27T18:37:24.237Z" }, - { url = "https://files.pythonhosted.org/packages/14/c7/ca723101509b518797fedc2fdf79ba57f886b4aca8a7d31857ba3ee8281f/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc", size = 23672, upload-time = "2025-09-27T18:37:25.271Z" }, - { url = "https://files.pythonhosted.org/packages/fb/df/5bd7a48c256faecd1d36edc13133e51397e41b73bb77e1a69deab746ebac/markupsafe-3.0.3-cp314-cp314t-win32.whl", hash = "sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d", size = 14819, upload-time = "2025-09-27T18:37:26.285Z" }, - { url = "https://files.pythonhosted.org/packages/1a/8a/0402ba61a2f16038b48b39bccca271134be00c5c9f0f623208399333c448/markupsafe-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9", size = 15426, upload-time = "2025-09-27T18:37:27.316Z" }, - { url = "https://files.pythonhosted.org/packages/70/bc/6f1c2f612465f5fa89b95bead1f44dcb607670fd42891d8fdcd5d039f4f4/markupsafe-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa", size = 14146, upload-time = "2025-09-27T18:37:28.327Z" }, +sdist = { url = "https://files.pythonhosted.org/packages/7e/99/7690b6d4034fffd95959cbe0c02de8deb3098cc577c67bb6a24fe5d7caa7/markupsafe-3.0.3.tar.gz", hash = "sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698", size = 80313 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/08/db/fefacb2136439fc8dd20e797950e749aa1f4997ed584c62cfb8ef7c2be0e/markupsafe-3.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:1cc7ea17a6824959616c525620e387f6dd30fec8cb44f649e31712db02123dad", size = 11631 }, + { url = "https://files.pythonhosted.org/packages/e1/2e/5898933336b61975ce9dc04decbc0a7f2fee78c30353c5efba7f2d6ff27a/markupsafe-3.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4bd4cd07944443f5a265608cc6aab442e4f74dff8088b0dfc8238647b8f6ae9a", size = 12058 }, + { url = "https://files.pythonhosted.org/packages/1d/09/adf2df3699d87d1d8184038df46a9c80d78c0148492323f4693df54e17bb/markupsafe-3.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b5420a1d9450023228968e7e6a9ce57f65d148ab56d2313fcd589eee96a7a50", size = 24287 }, + { url = "https://files.pythonhosted.org/packages/30/ac/0273f6fcb5f42e314c6d8cd99effae6a5354604d461b8d392b5ec9530a54/markupsafe-3.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0bf2a864d67e76e5c9a34dc26ec616a66b9888e25e7b9460e1c76d3293bd9dbf", size = 22940 }, + { url = "https://files.pythonhosted.org/packages/19/ae/31c1be199ef767124c042c6c3e904da327a2f7f0cd63a0337e1eca2967a8/markupsafe-3.0.3-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:bc51efed119bc9cfdf792cdeaa4d67e8f6fcccab66ed4bfdd6bde3e59bfcbb2f", size = 21887 }, + { url = "https://files.pythonhosted.org/packages/b2/76/7edcab99d5349a4532a459e1fe64f0b0467a3365056ae550d3bcf3f79e1e/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:068f375c472b3e7acbe2d5318dea141359e6900156b5b2ba06a30b169086b91a", size = 23692 }, + { url = "https://files.pythonhosted.org/packages/a4/28/6e74cdd26d7514849143d69f0bf2399f929c37dc2b31e6829fd2045b2765/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:7be7b61bb172e1ed687f1754f8e7484f1c8019780f6f6b0786e76bb01c2ae115", size = 21471 }, + { url = "https://files.pythonhosted.org/packages/62/7e/a145f36a5c2945673e590850a6f8014318d5577ed7e5920a4b3448e0865d/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f9e130248f4462aaa8e2552d547f36ddadbeaa573879158d721bbd33dfe4743a", size = 22923 }, + { url = "https://files.pythonhosted.org/packages/0f/62/d9c46a7f5c9adbeeeda52f5b8d802e1094e9717705a645efc71b0913a0a8/markupsafe-3.0.3-cp311-cp311-win32.whl", hash = "sha256:0db14f5dafddbb6d9208827849fad01f1a2609380add406671a26386cdf15a19", size = 14572 }, + { url = "https://files.pythonhosted.org/packages/83/8a/4414c03d3f891739326e1783338e48fb49781cc915b2e0ee052aa490d586/markupsafe-3.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:de8a88e63464af587c950061a5e6a67d3632e36df62b986892331d4620a35c01", size = 15077 }, + { url = "https://files.pythonhosted.org/packages/35/73/893072b42e6862f319b5207adc9ae06070f095b358655f077f69a35601f0/markupsafe-3.0.3-cp311-cp311-win_arm64.whl", hash = "sha256:3b562dd9e9ea93f13d53989d23a7e775fdfd1066c33494ff43f5418bc8c58a5c", size = 13876 }, + { url = "https://files.pythonhosted.org/packages/5a/72/147da192e38635ada20e0a2e1a51cf8823d2119ce8883f7053879c2199b5/markupsafe-3.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e", size = 11615 }, + { url = "https://files.pythonhosted.org/packages/9a/81/7e4e08678a1f98521201c3079f77db69fb552acd56067661f8c2f534a718/markupsafe-3.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce", size = 12020 }, + { url = "https://files.pythonhosted.org/packages/1e/2c/799f4742efc39633a1b54a92eec4082e4f815314869865d876824c257c1e/markupsafe-3.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d", size = 24332 }, + { url = "https://files.pythonhosted.org/packages/3c/2e/8d0c2ab90a8c1d9a24f0399058ab8519a3279d1bd4289511d74e909f060e/markupsafe-3.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d", size = 22947 }, + { url = "https://files.pythonhosted.org/packages/2c/54/887f3092a85238093a0b2154bd629c89444f395618842e8b0c41783898ea/markupsafe-3.0.3-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a", size = 21962 }, + { url = "https://files.pythonhosted.org/packages/c9/2f/336b8c7b6f4a4d95e91119dc8521402461b74a485558d8f238a68312f11c/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b", size = 23760 }, + { url = "https://files.pythonhosted.org/packages/32/43/67935f2b7e4982ffb50a4d169b724d74b62a3964bc1a9a527f5ac4f1ee2b/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f", size = 21529 }, + { url = "https://files.pythonhosted.org/packages/89/e0/4486f11e51bbba8b0c041098859e869e304d1c261e59244baa3d295d47b7/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b", size = 23015 }, + { url = "https://files.pythonhosted.org/packages/2f/e1/78ee7a023dac597a5825441ebd17170785a9dab23de95d2c7508ade94e0e/markupsafe-3.0.3-cp312-cp312-win32.whl", hash = "sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d", size = 14540 }, + { url = "https://files.pythonhosted.org/packages/aa/5b/bec5aa9bbbb2c946ca2733ef9c4ca91c91b6a24580193e891b5f7dbe8e1e/markupsafe-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c", size = 15105 }, + { url = "https://files.pythonhosted.org/packages/e5/f1/216fc1bbfd74011693a4fd837e7026152e89c4bcf3e77b6692fba9923123/markupsafe-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f", size = 13906 }, + { url = "https://files.pythonhosted.org/packages/38/2f/907b9c7bbba283e68f20259574b13d005c121a0fa4c175f9bed27c4597ff/markupsafe-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795", size = 11622 }, + { url = "https://files.pythonhosted.org/packages/9c/d9/5f7756922cdd676869eca1c4e3c0cd0df60ed30199ffd775e319089cb3ed/markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219", size = 12029 }, + { url = "https://files.pythonhosted.org/packages/00/07/575a68c754943058c78f30db02ee03a64b3c638586fba6a6dd56830b30a3/markupsafe-3.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6", size = 24374 }, + { url = "https://files.pythonhosted.org/packages/a9/21/9b05698b46f218fc0e118e1f8168395c65c8a2c750ae2bab54fc4bd4e0e8/markupsafe-3.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676", size = 22980 }, + { url = "https://files.pythonhosted.org/packages/7f/71/544260864f893f18b6827315b988c146b559391e6e7e8f7252839b1b846a/markupsafe-3.0.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9", size = 21990 }, + { url = "https://files.pythonhosted.org/packages/c2/28/b50fc2f74d1ad761af2f5dcce7492648b983d00a65b8c0e0cb457c82ebbe/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1", size = 23784 }, + { url = "https://files.pythonhosted.org/packages/ed/76/104b2aa106a208da8b17a2fb72e033a5a9d7073c68f7e508b94916ed47a9/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc", size = 21588 }, + { url = "https://files.pythonhosted.org/packages/b5/99/16a5eb2d140087ebd97180d95249b00a03aa87e29cc224056274f2e45fd6/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12", size = 23041 }, + { url = "https://files.pythonhosted.org/packages/19/bc/e7140ed90c5d61d77cea142eed9f9c303f4c4806f60a1044c13e3f1471d0/markupsafe-3.0.3-cp313-cp313-win32.whl", hash = "sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed", size = 14543 }, + { url = "https://files.pythonhosted.org/packages/05/73/c4abe620b841b6b791f2edc248f556900667a5a1cf023a6646967ae98335/markupsafe-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5", size = 15113 }, + { url = "https://files.pythonhosted.org/packages/f0/3a/fa34a0f7cfef23cf9500d68cb7c32dd64ffd58a12b09225fb03dd37d5b80/markupsafe-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485", size = 13911 }, + { url = "https://files.pythonhosted.org/packages/e4/d7/e05cd7efe43a88a17a37b3ae96e79a19e846f3f456fe79c57ca61356ef01/markupsafe-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73", size = 11658 }, + { url = "https://files.pythonhosted.org/packages/99/9e/e412117548182ce2148bdeacdda3bb494260c0b0184360fe0d56389b523b/markupsafe-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37", size = 12066 }, + { url = "https://files.pythonhosted.org/packages/bc/e6/fa0ffcda717ef64a5108eaa7b4f5ed28d56122c9a6d70ab8b72f9f715c80/markupsafe-3.0.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19", size = 25639 }, + { url = "https://files.pythonhosted.org/packages/96/ec/2102e881fe9d25fc16cb4b25d5f5cde50970967ffa5dddafdb771237062d/markupsafe-3.0.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025", size = 23569 }, + { url = "https://files.pythonhosted.org/packages/4b/30/6f2fce1f1f205fc9323255b216ca8a235b15860c34b6798f810f05828e32/markupsafe-3.0.3-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6", size = 23284 }, + { url = "https://files.pythonhosted.org/packages/58/47/4a0ccea4ab9f5dcb6f79c0236d954acb382202721e704223a8aafa38b5c8/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f", size = 24801 }, + { url = "https://files.pythonhosted.org/packages/6a/70/3780e9b72180b6fecb83a4814d84c3bf4b4ae4bf0b19c27196104149734c/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb", size = 22769 }, + { url = "https://files.pythonhosted.org/packages/98/c5/c03c7f4125180fc215220c035beac6b9cb684bc7a067c84fc69414d315f5/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009", size = 23642 }, + { url = "https://files.pythonhosted.org/packages/80/d6/2d1b89f6ca4bff1036499b1e29a1d02d282259f3681540e16563f27ebc23/markupsafe-3.0.3-cp313-cp313t-win32.whl", hash = "sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354", size = 14612 }, + { url = "https://files.pythonhosted.org/packages/2b/98/e48a4bfba0a0ffcf9925fe2d69240bfaa19c6f7507b8cd09c70684a53c1e/markupsafe-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218", size = 15200 }, + { url = "https://files.pythonhosted.org/packages/0e/72/e3cc540f351f316e9ed0f092757459afbc595824ca724cbc5a5d4263713f/markupsafe-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287", size = 13973 }, + { url = "https://files.pythonhosted.org/packages/33/8a/8e42d4838cd89b7dde187011e97fe6c3af66d8c044997d2183fbd6d31352/markupsafe-3.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe", size = 11619 }, + { url = "https://files.pythonhosted.org/packages/b5/64/7660f8a4a8e53c924d0fa05dc3a55c9cee10bbd82b11c5afb27d44b096ce/markupsafe-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026", size = 12029 }, + { url = "https://files.pythonhosted.org/packages/da/ef/e648bfd021127bef5fa12e1720ffed0c6cbb8310c8d9bea7266337ff06de/markupsafe-3.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737", size = 24408 }, + { url = "https://files.pythonhosted.org/packages/41/3c/a36c2450754618e62008bf7435ccb0f88053e07592e6028a34776213d877/markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97", size = 23005 }, + { url = "https://files.pythonhosted.org/packages/bc/20/b7fdf89a8456b099837cd1dc21974632a02a999ec9bf7ca3e490aacd98e7/markupsafe-3.0.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d", size = 22048 }, + { url = "https://files.pythonhosted.org/packages/9a/a7/591f592afdc734f47db08a75793a55d7fbcc6902a723ae4cfbab61010cc5/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda", size = 23821 }, + { url = "https://files.pythonhosted.org/packages/7d/33/45b24e4f44195b26521bc6f1a82197118f74df348556594bd2262bda1038/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf", size = 21606 }, + { url = "https://files.pythonhosted.org/packages/ff/0e/53dfaca23a69fbfbbf17a4b64072090e70717344c52eaaaa9c5ddff1e5f0/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe", size = 23043 }, + { url = "https://files.pythonhosted.org/packages/46/11/f333a06fc16236d5238bfe74daccbca41459dcd8d1fa952e8fbd5dccfb70/markupsafe-3.0.3-cp314-cp314-win32.whl", hash = "sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9", size = 14747 }, + { url = "https://files.pythonhosted.org/packages/28/52/182836104b33b444e400b14f797212f720cbc9ed6ba34c800639d154e821/markupsafe-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581", size = 15341 }, + { url = "https://files.pythonhosted.org/packages/6f/18/acf23e91bd94fd7b3031558b1f013adfa21a8e407a3fdb32745538730382/markupsafe-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4", size = 14073 }, + { url = "https://files.pythonhosted.org/packages/3c/f0/57689aa4076e1b43b15fdfa646b04653969d50cf30c32a102762be2485da/markupsafe-3.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab", size = 11661 }, + { url = "https://files.pythonhosted.org/packages/89/c3/2e67a7ca217c6912985ec766c6393b636fb0c2344443ff9d91404dc4c79f/markupsafe-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175", size = 12069 }, + { url = "https://files.pythonhosted.org/packages/f0/00/be561dce4e6ca66b15276e184ce4b8aec61fe83662cce2f7d72bd3249d28/markupsafe-3.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634", size = 25670 }, + { url = "https://files.pythonhosted.org/packages/50/09/c419f6f5a92e5fadde27efd190eca90f05e1261b10dbd8cbcb39cd8ea1dc/markupsafe-3.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50", size = 23598 }, + { url = "https://files.pythonhosted.org/packages/22/44/a0681611106e0b2921b3033fc19bc53323e0b50bc70cffdd19f7d679bb66/markupsafe-3.0.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e", size = 23261 }, + { url = "https://files.pythonhosted.org/packages/5f/57/1b0b3f100259dc9fffe780cfb60d4be71375510e435efec3d116b6436d43/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5", size = 24835 }, + { url = "https://files.pythonhosted.org/packages/26/6a/4bf6d0c97c4920f1597cc14dd720705eca0bf7c787aebc6bb4d1bead5388/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523", size = 22733 }, + { url = "https://files.pythonhosted.org/packages/14/c7/ca723101509b518797fedc2fdf79ba57f886b4aca8a7d31857ba3ee8281f/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc", size = 23672 }, + { url = "https://files.pythonhosted.org/packages/fb/df/5bd7a48c256faecd1d36edc13133e51397e41b73bb77e1a69deab746ebac/markupsafe-3.0.3-cp314-cp314t-win32.whl", hash = "sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d", size = 14819 }, + { url = "https://files.pythonhosted.org/packages/1a/8a/0402ba61a2f16038b48b39bccca271134be00c5c9f0f623208399333c448/markupsafe-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9", size = 15426 }, + { url = "https://files.pythonhosted.org/packages/70/bc/6f1c2f612465f5fa89b95bead1f44dcb607670fd42891d8fdcd5d039f4f4/markupsafe-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa", size = 14146 }, ] [[package]] @@ -1817,6 +2445,7 @@ dependencies = [ { name = "pyparsing" }, { name = "python-dateutil" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/63/1b/4be5be87d43d327a0cf4de1a56e86f7f84c89312452406cf122efe2839e6/matplotlib-3.10.9.tar.gz", hash = "sha256:fd66508e8c6877d98e586654b608a0456db8d7e8a546eb1e2600efd957302358", size = 34811233, upload-time = "2026-04-24T00:14:13.539Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/4c/8c/290f021104741fea63769c31494f5324c0cd249bf536a65a4350767b1f22/matplotlib-3.10.9-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:68cfdcede415f7c8f5577b03303dd94526cdb6d11036cecdc205e08733b2d2bb", size = 8306860, upload-time = "2026-04-24T00:12:01.207Z" }, @@ -1864,6 +2493,55 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/63/e2/9f66ca6a651a52abfe0d4964ce01439ed34f3f1e119de10ff3a07f403043/matplotlib-3.10.9-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:42fb814efabe95c06c1994d8ab5a8385f43a249e23badd3ba931d4308e5bca20", size = 8304420, upload-time = "2026-04-24T00:14:04.57Z" }, { url = "https://files.pythonhosted.org/packages/e8/e8/467c03568218792906aa87b5e7bb379b605e056ed0c74fe00c051786d925/matplotlib-3.10.9-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:f76e640a5268850bfda54b5131b1b1941cc685e42c5fa98ed9f2d64038308cba", size = 8197981, upload-time = "2026-04-24T00:14:07.233Z" }, { url = "https://files.pythonhosted.org/packages/6f/87/afead29192170917537934c6aff4b008c805fff7b1ccea0c79120d96beda/matplotlib-3.10.9-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3fc0364dfbe1d07f6d15c5ebd0c5bf89e126916e5a8667dd4a7a6e84c36653d4", size = 8774002, upload-time = "2026-04-24T00:14:09.816Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/ae/e2/d2d5295be2f44c678ebaf3544ba32d20c1f9ef08c49fe47f496180e1db15/matplotlib-3.10.7.tar.gz", hash = "sha256:a06ba7e2a2ef9131c79c49e63dad355d2d878413a0376c1727c8b9335ff731c7", size = 34804865 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fc/bc/0fb489005669127ec13f51be0c6adc074d7cf191075dab1da9fe3b7a3cfc/matplotlib-3.10.7-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:53b492410a6cd66c7a471de6c924f6ede976e963c0f3097a3b7abfadddc67d0a", size = 8257507 }, + { url = "https://files.pythonhosted.org/packages/e2/6a/d42588ad895279ff6708924645b5d2ed54a7fb2dc045c8a804e955aeace1/matplotlib-3.10.7-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d9749313deb729f08207718d29c86246beb2ea3fdba753595b55901dee5d2fd6", size = 8119565 }, + { url = "https://files.pythonhosted.org/packages/10/b7/4aa196155b4d846bd749cf82aa5a4c300cf55a8b5e0dfa5b722a63c0f8a0/matplotlib-3.10.7-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2222c7ba2cbde7fe63032769f6eb7e83ab3227f47d997a8453377709b7fe3a5a", size = 8692668 }, + { url = "https://files.pythonhosted.org/packages/e6/e7/664d2b97016f46683a02d854d730cfcf54ff92c1dafa424beebef50f831d/matplotlib-3.10.7-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e91f61a064c92c307c5a9dc8c05dc9f8a68f0a3be199d9a002a0622e13f874a1", size = 9521051 }, + { url = "https://files.pythonhosted.org/packages/a8/a3/37aef1404efa615f49b5758a5e0261c16dd88f389bc1861e722620e4a754/matplotlib-3.10.7-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:6f1851eab59ca082c95df5a500106bad73672645625e04538b3ad0f69471ffcc", size = 9576878 }, + { url = "https://files.pythonhosted.org/packages/33/cd/b145f9797126f3f809d177ca378de57c45413c5099c5990de2658760594a/matplotlib-3.10.7-cp311-cp311-win_amd64.whl", hash = "sha256:6516ce375109c60ceec579e699524e9d504cd7578506f01150f7a6bc174a775e", size = 8115142 }, + { url = "https://files.pythonhosted.org/packages/2e/39/63bca9d2b78455ed497fcf51a9c71df200a11048f48249038f06447fa947/matplotlib-3.10.7-cp311-cp311-win_arm64.whl", hash = "sha256:b172db79759f5f9bc13ef1c3ef8b9ee7b37b0247f987fbbbdaa15e4f87fd46a9", size = 7992439 }, + { url = "https://files.pythonhosted.org/packages/be/b3/09eb0f7796932826ec20c25b517d568627754f6c6462fca19e12c02f2e12/matplotlib-3.10.7-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7a0edb7209e21840e8361e91ea84ea676658aa93edd5f8762793dec77a4a6748", size = 8272389 }, + { url = "https://files.pythonhosted.org/packages/11/0b/1ae80ddafb8652fd8046cb5c8460ecc8d4afccb89e2c6d6bec61e04e1eaf/matplotlib-3.10.7-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:c380371d3c23e0eadf8ebff114445b9f970aff2010198d498d4ab4c3b41eea4f", size = 8128247 }, + { url = "https://files.pythonhosted.org/packages/7d/18/95ae2e242d4a5c98bd6e90e36e128d71cf1c7e39b0874feaed3ef782e789/matplotlib-3.10.7-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d5f256d49fea31f40f166a5e3131235a5d2f4b7f44520b1cf0baf1ce568ccff0", size = 8696996 }, + { url = "https://files.pythonhosted.org/packages/7e/3d/5b559efc800bd05cb2033aa85f7e13af51958136a48327f7c261801ff90a/matplotlib-3.10.7-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:11ae579ac83cdf3fb72573bb89f70e0534de05266728740d478f0f818983c695", size = 9530153 }, + { url = "https://files.pythonhosted.org/packages/88/57/eab4a719fd110312d3c220595d63a3c85ec2a39723f0f4e7fa7e6e3f74ba/matplotlib-3.10.7-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:4c14b6acd16cddc3569a2d515cfdd81c7a68ac5639b76548cfc1a9e48b20eb65", size = 9593093 }, + { url = "https://files.pythonhosted.org/packages/31/3c/80816f027b3a4a28cd2a0a6ef7f89a2db22310e945cd886ec25bfb399221/matplotlib-3.10.7-cp312-cp312-win_amd64.whl", hash = "sha256:0d8c32b7ea6fb80b1aeff5a2ceb3fb9778e2759e899d9beff75584714afcc5ee", size = 8122771 }, + { url = "https://files.pythonhosted.org/packages/de/77/ef1fc78bfe99999b2675435cc52120887191c566b25017d78beaabef7f2d/matplotlib-3.10.7-cp312-cp312-win_arm64.whl", hash = "sha256:5f3f6d315dcc176ba7ca6e74c7768fb7e4cf566c49cb143f6bc257b62e634ed8", size = 7992812 }, + { url = "https://files.pythonhosted.org/packages/02/9c/207547916a02c78f6bdd83448d9b21afbc42f6379ed887ecf610984f3b4e/matplotlib-3.10.7-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:1d9d3713a237970569156cfb4de7533b7c4eacdd61789726f444f96a0d28f57f", size = 8273212 }, + { url = "https://files.pythonhosted.org/packages/bc/d0/b3d3338d467d3fc937f0bb7f256711395cae6f78e22cef0656159950adf0/matplotlib-3.10.7-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:37a1fea41153dd6ee061d21ab69c9cf2cf543160b1b85d89cd3d2e2a7902ca4c", size = 8128713 }, + { url = "https://files.pythonhosted.org/packages/22/ff/6425bf5c20d79aa5b959d1ce9e65f599632345391381c9a104133fe0b171/matplotlib-3.10.7-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b3c4ea4948d93c9c29dc01c0c23eef66f2101bf75158c291b88de6525c55c3d1", size = 8698527 }, + { url = "https://files.pythonhosted.org/packages/d0/7f/ccdca06f4c2e6c7989270ed7829b8679466682f4cfc0f8c9986241c023b6/matplotlib-3.10.7-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22df30ffaa89f6643206cf13877191c63a50e8f800b038bc39bee9d2d4957632", size = 9529690 }, + { url = "https://files.pythonhosted.org/packages/b8/95/b80fc2c1f269f21ff3d193ca697358e24408c33ce2b106a7438a45407b63/matplotlib-3.10.7-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b69676845a0a66f9da30e87f48be36734d6748024b525ec4710be40194282c84", size = 9593732 }, + { url = "https://files.pythonhosted.org/packages/e1/b6/23064a96308b9aeceeffa65e96bcde459a2ea4934d311dee20afde7407a0/matplotlib-3.10.7-cp313-cp313-win_amd64.whl", hash = "sha256:744991e0cc863dd669c8dc9136ca4e6e0082be2070b9d793cbd64bec872a6815", size = 8122727 }, + { url = "https://files.pythonhosted.org/packages/b3/a6/2faaf48133b82cf3607759027f82b5c702aa99cdfcefb7f93d6ccf26a424/matplotlib-3.10.7-cp313-cp313-win_arm64.whl", hash = "sha256:fba2974df0bf8ce3c995fa84b79cde38326e0f7b5409e7a3a481c1141340bcf7", size = 7992958 }, + { url = "https://files.pythonhosted.org/packages/4a/f0/b018fed0b599bd48d84c08794cb242227fe3341952da102ee9d9682db574/matplotlib-3.10.7-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:932c55d1fa7af4423422cb6a492a31cbcbdbe68fd1a9a3f545aa5e7a143b5355", size = 8316849 }, + { url = "https://files.pythonhosted.org/packages/b0/b7/bb4f23856197659f275e11a2a164e36e65e9b48ea3e93c4ec25b4f163198/matplotlib-3.10.7-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5e38c2d581d62ee729a6e144c47a71b3f42fb4187508dbbf4fe71d5612c3433b", size = 8178225 }, + { url = "https://files.pythonhosted.org/packages/62/56/0600609893ff277e6f3ab3c0cef4eafa6e61006c058e84286c467223d4d5/matplotlib-3.10.7-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:786656bb13c237bbcebcd402f65f44dd61ead60ee3deb045af429d889c8dbc67", size = 8711708 }, + { url = "https://files.pythonhosted.org/packages/d8/1a/6bfecb0cafe94d6658f2f1af22c43b76cf7a1c2f0dc34ef84cbb6809617e/matplotlib-3.10.7-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:09d7945a70ea43bf9248f4b6582734c2fe726723204a76eca233f24cffc7ef67", size = 9541409 }, + { url = "https://files.pythonhosted.org/packages/08/50/95122a407d7f2e446fd865e2388a232a23f2b81934960ea802f3171518e4/matplotlib-3.10.7-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:d0b181e9fa8daf1d9f2d4c547527b167cb8838fc587deabca7b5c01f97199e84", size = 9594054 }, + { url = "https://files.pythonhosted.org/packages/13/76/75b194a43b81583478a81e78a07da8d9ca6ddf50dd0a2ccabf258059481d/matplotlib-3.10.7-cp313-cp313t-win_amd64.whl", hash = "sha256:31963603041634ce1a96053047b40961f7a29eb8f9a62e80cc2c0427aa1d22a2", size = 8200100 }, + { url = "https://files.pythonhosted.org/packages/f5/9e/6aefebdc9f8235c12bdeeda44cc0383d89c1e41da2c400caf3ee2073a3ce/matplotlib-3.10.7-cp313-cp313t-win_arm64.whl", hash = "sha256:aebed7b50aa6ac698c90f60f854b47e48cd2252b30510e7a1feddaf5a3f72cbf", size = 8042131 }, + { url = "https://files.pythonhosted.org/packages/0d/4b/e5bc2c321b6a7e3a75638d937d19ea267c34bd5a90e12bee76c4d7c7a0d9/matplotlib-3.10.7-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:d883460c43e8c6b173fef244a2341f7f7c0e9725c7fe68306e8e44ed9c8fb100", size = 8273787 }, + { url = "https://files.pythonhosted.org/packages/86/ad/6efae459c56c2fbc404da154e13e3a6039129f3c942b0152624f1c621f05/matplotlib-3.10.7-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:07124afcf7a6504eafcb8ce94091c5898bbdd351519a1beb5c45f7a38c67e77f", size = 8131348 }, + { url = "https://files.pythonhosted.org/packages/a6/5a/a4284d2958dee4116359cc05d7e19c057e64ece1b4ac986ab0f2f4d52d5a/matplotlib-3.10.7-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c17398b709a6cce3d9fdb1595c33e356d91c098cd9486cb2cc21ea2ea418e715", size = 9533949 }, + { url = "https://files.pythonhosted.org/packages/de/ff/f3781b5057fa3786623ad8976fc9f7b0d02b2f28534751fd5a44240de4cf/matplotlib-3.10.7-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7146d64f561498764561e9cd0ed64fcf582e570fc519e6f521e2d0cfd43365e1", size = 9804247 }, + { url = "https://files.pythonhosted.org/packages/47/5a/993a59facb8444efb0e197bf55f545ee449902dcee86a4dfc580c3b61314/matplotlib-3.10.7-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:90ad854c0a435da3104c01e2c6f0028d7e719b690998a2333d7218db80950722", size = 9595497 }, + { url = "https://files.pythonhosted.org/packages/0d/a5/77c95aaa9bb32c345cbb49626ad8eb15550cba2e6d4c88081a6c2ac7b08d/matplotlib-3.10.7-cp314-cp314-win_amd64.whl", hash = "sha256:4645fc5d9d20ffa3a39361fcdbcec731382763b623b72627806bf251b6388866", size = 8252732 }, + { url = "https://files.pythonhosted.org/packages/74/04/45d269b4268d222390d7817dae77b159651909669a34ee9fdee336db5883/matplotlib-3.10.7-cp314-cp314-win_arm64.whl", hash = "sha256:9257be2f2a03415f9105c486d304a321168e61ad450f6153d77c69504ad764bb", size = 8124240 }, + { url = "https://files.pythonhosted.org/packages/4b/c7/ca01c607bb827158b439208c153d6f14ddb9fb640768f06f7ca3488ae67b/matplotlib-3.10.7-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1e4bbad66c177a8fdfa53972e5ef8be72a5f27e6a607cec0d8579abd0f3102b1", size = 8316938 }, + { url = "https://files.pythonhosted.org/packages/84/d2/5539e66e9f56d2fdec94bb8436f5e449683b4e199bcc897c44fbe3c99e28/matplotlib-3.10.7-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:d8eb7194b084b12feb19142262165832fc6ee879b945491d1c3d4660748020c4", size = 8178245 }, + { url = "https://files.pythonhosted.org/packages/77/b5/e6ca22901fd3e4fe433a82e583436dd872f6c966fca7e63cf806b40356f8/matplotlib-3.10.7-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b4d41379b05528091f00e1728004f9a8d7191260f3862178b88e8fd770206318", size = 9541411 }, + { url = "https://files.pythonhosted.org/packages/9e/99/a4524db57cad8fee54b7237239a8f8360bfcfa3170d37c9e71c090c0f409/matplotlib-3.10.7-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4a74f79fafb2e177f240579bc83f0b60f82cc47d2f1d260f422a0627207008ca", size = 9803664 }, + { url = "https://files.pythonhosted.org/packages/e6/a5/85e2edf76ea0ad4288d174926d9454ea85f3ce5390cc4e6fab196cbf250b/matplotlib-3.10.7-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:702590829c30aada1e8cef0568ddbffa77ca747b4d6e36c6d173f66e301f89cc", size = 9594066 }, + { url = "https://files.pythonhosted.org/packages/39/69/9684368a314f6d83fe5c5ad2a4121a3a8e03723d2e5c8ea17b66c1bad0e7/matplotlib-3.10.7-cp314-cp314t-win_amd64.whl", hash = "sha256:f79d5de970fc90cd5591f60053aecfce1fcd736e0303d9f0bf86be649fa68fb8", size = 8342832 }, + { url = "https://files.pythonhosted.org/packages/04/5f/e22e08da14bc1a0894184640d47819d2338b792732e20d292bf86e5ab785/matplotlib-3.10.7-cp314-cp314t-win_arm64.whl", hash = "sha256:cb783436e47fcf82064baca52ce748af71725d0352e1d31564cbe9c95df92b9c", size = 8172585 }, + { url = "https://files.pythonhosted.org/packages/58/8f/76d5dc21ac64a49e5498d7f0472c0781dae442dd266a67458baec38288ec/matplotlib-3.10.7-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:15112bcbaef211bd663fa935ec33313b948e214454d949b723998a43357b17b0", size = 8252283 }, + { url = "https://files.pythonhosted.org/packages/27/0d/9c5d4c2317feb31d819e38c9f947c942f42ebd4eb935fc6fd3518a11eaa7/matplotlib-3.10.7-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:d2a959c640cdeecdd2ec3136e8ea0441da59bcaf58d67e9c590740addba2cb68", size = 8116733 }, + { url = "https://files.pythonhosted.org/packages/9a/cc/3fe688ff1355010937713164caacf9ed443675ac48a997bab6ed23b3f7c0/matplotlib-3.10.7-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3886e47f64611046bc1db523a09dd0a0a6bed6081e6f90e13806dd1d1d1b5e91", size = 8693919 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1873,27 +2551,39 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "traitlets" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/bd/c0/9f7c9a46090390368a4d7bcb76bb87a4a36c421e4c0792cdb53486ffac7a/matplotlib_inline-0.2.2.tar.gz", hash = "sha256:72f3fe8fce36b70d4a5b612f899090cd0401deddc4ea90e1572b9f4bfb058c79", size = 8150, upload-time = "2026-05-08T17:33:33.49Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/41/09/5b161152e2d90f7b87f781c2e1267494aef9c32498df793f73ad0a0a494a/matplotlib_inline-0.2.2-py3-none-any.whl", hash = "sha256:3c821cf1c209f59fb2d2d64abbf5b23b67bcb2210d663f9918dd851c6da1fcf6", size = 9534, upload-time = "2026-05-08T17:33:32.055Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/c7/74/97e72a36efd4ae2bccb3463284300f8953f199b5ffbc04cbbb0ec78f74b1/matplotlib_inline-0.2.1.tar.gz", hash = "sha256:e1ee949c340d771fc39e241ea75683deb94762c8fa5f2927ec57c83c4dffa9fe", size = 8110 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/af/33/ee4519fa02ed11a94aef9559552f3b17bb863f2ecfe1a35dc7f548cde231/matplotlib_inline-0.2.1-py3-none-any.whl", hash = "sha256:d56ce5156ba6085e00a9d54fead6ed29a9c47e215cd1bba2e976ef39f5710a76", size = 9516 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "mistune" version = "3.2.1" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/ca/84/620cc3f7e3adf6f5067e10f4dbae71295d8f9e16d5d3f9ef97c40f2f592c/mistune-3.2.1.tar.gz", hash = "sha256:7c8e5501d38bac1582e067e46c8343f17d57ea1aaa735823f3aba1fd59c88a28", size = 98003, upload-time = "2026-05-03T14:33:22.312Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/2a/7f/a946aa4f8752b37102b41e64dca18a1976ac705c3a0d1dfe74d820a02552/mistune-3.2.1-py3-none-any.whl", hash = "sha256:78cdb0ba5e938053ccf63651b352508d2efa9411dc8810bfb05f2dc5140c0048", size = 53749, upload-time = "2026-05-03T14:33:20.551Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/d7/02/a7fb8b21d4d55ac93cdcde9d3638da5dd0ebdd3a4fed76c7725e10b81cbe/mistune-3.1.4.tar.gz", hash = "sha256:b5a7f801d389f724ec702840c11d8fc48f2b33519102fc7ee739e8177b672164", size = 94588 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7a/f0/8282d9641415e9e33df173516226b404d367a0fc55e1a60424a152913abc/mistune-3.1.4-py3-none-any.whl", hash = "sha256:93691da911e5d9d2e23bc54472892aff676df27a75274962ff9edc210364266d", size = 53481 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "mpmath" version = "1.3.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/e0/47/dd32fa426cc72114383ac549964eecb20ecfd886d1e5ccf5340b55b02f57/mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f", size = 508106, upload-time = "2023-03-07T16:47:11.061Z" } +sdist = { url = "https://files.pythonhosted.org/packages/e0/47/dd32fa426cc72114383ac549964eecb20ecfd886d1e5ccf5340b55b02f57/mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f", size = 508106 } wheels = [ - { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198, upload-time = "2023-03-07T16:47:09.197Z" }, + { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198 }, ] [[package]] @@ -1906,9 +2596,15 @@ dependencies = [ { name = "nbformat" }, { name = "traitlets" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/56/91/1c1d5a4b9a9ebba2b4e32b8c852c2975c872aec1fe42ab5e516b2cecd193/nbclient-0.10.4.tar.gz", hash = "sha256:1e54091b16e6da39e297b0ece3e10f6f29f4ac4e8ee515d29f8a7099bd6553c9", size = 62554, upload-time = "2025-12-23T07:45:46.369Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/83/a0/5b0c2f11142ed1dddec842457d3f65eaf71a0080894eb6f018755b319c3a/nbclient-0.10.4-py3-none-any.whl", hash = "sha256:9162df5a7373d70d606527300a95a975a47c137776cd942e52d9c7e29ff83440", size = 25465, upload-time = "2025-12-23T07:45:44.51Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/87/66/7ffd18d58eae90d5721f9f39212327695b749e23ad44b3881744eaf4d9e8/nbclient-0.10.2.tar.gz", hash = "sha256:90b7fc6b810630db87a6d0c2250b1f0ab4cf4d3c27a299b0cde78a4ed3fd9193", size = 62424 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/34/6d/e7fa07f03a4a7b221d94b4d586edb754a9b0dc3c9e2c93353e9fa4e0d117/nbclient-0.10.2-py3-none-any.whl", hash = "sha256:4ffee11e788b4a27fabeb7955547e4318a5298f34342a4bfd01f2e1faaeadc3d", size = 25434 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1931,9 +2627,15 @@ dependencies = [ { name = "pygments" }, { name = "traitlets" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/01/b1/708e53fe2e429c103c6e6e159106bcf0357ac41aa4c28772bd8402339051/nbconvert-7.17.1.tar.gz", hash = "sha256:34d0d0a7e73ce3cbab6c5aae8f4f468797280b01fd8bd2ca746da8569eddd7d2", size = 865311, upload-time = "2026-04-08T00:44:14.914Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/67/f8/bb0a9d5f46819c821dc1f004aa2cc29b1d91453297dbf5ff20470f00f193/nbconvert-7.17.1-py3-none-any.whl", hash = "sha256:aa85c087b435e7bf1ffd03319f658e285f2b89eccab33bc1ba7025495ab3e7c8", size = 261927, upload-time = "2026-04-08T00:44:12.845Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/a3/59/f28e15fc47ffb73af68a8d9b47367a8630d76e97ae85ad18271b9db96fdf/nbconvert-7.16.6.tar.gz", hash = "sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582", size = 857715 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cc/9a/cd673b2f773a12c992f41309ef81b99da1690426bd2f96957a7ade0d3ed7/nbconvert-7.16.6-py3-none-any.whl", hash = "sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b", size = 258525 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1946,36 +2648,48 @@ dependencies = [ { name = "jupyter-core" }, { name = "traitlets" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/6d/fd/91545e604bc3dad7dca9ed03284086039b294c6b3d75c0d2fa45f9e9caf3/nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a", size = 142749, upload-time = "2024-04-04T11:20:37.371Z" } +sdist = { url = "https://files.pythonhosted.org/packages/6d/fd/91545e604bc3dad7dca9ed03284086039b294c6b3d75c0d2fa45f9e9caf3/nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a", size = 142749 } wheels = [ - { url = "https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b", size = 78454, upload-time = "2024-04-04T11:20:34.895Z" }, + { url = "https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b", size = 78454 }, ] [[package]] name = "nest-asyncio" version = "1.6.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418, upload-time = "2024-01-21T14:25:19.227Z" } +sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418 } wheels = [ - { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195, upload-time = "2024-01-21T14:25:17.223Z" }, + { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195 }, ] [[package]] name = "networkx" version = "3.6.1" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/6a/51/63fe664f3908c97be9d2e4f1158eb633317598cfa6e1fc14af5383f17512/networkx-3.6.1.tar.gz", hash = "sha256:26b7c357accc0c8cde558ad486283728b65b6a95d85ee1cd66bafab4c8168509", size = 2517025, upload-time = "2025-12-08T17:02:39.908Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/9e/c9/b2622292ea83fbb4ec318f5b9ab867d0a28ab43c5717bb85b0a5f6b3b0a4/networkx-3.6.1-py3-none-any.whl", hash = "sha256:d47fbf302e7d9cbbb9e2555a0d267983d2aa476bac30e90dfbe5669bd57f3762", size = 2068504, upload-time = "2025-12-08T17:02:38.159Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/6c/4f/ccdb8ad3a38e583f214547fd2f7ff1fc160c43a75af88e6aec213404b96a/networkx-3.5.tar.gz", hash = "sha256:d4c6f9cf81f52d69230866796b82afbccdec3db7ae4fbd1b65ea750feed50037", size = 2471065 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl", hash = "sha256:0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec", size = 2034406 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "nodeenv" version = "1.10.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/24/bf/d1bda4f6168e0b2e9e5958945e01910052158313224ada5ce1fb2e1113b8/nodeenv-1.10.0.tar.gz", hash = "sha256:996c191ad80897d076bdfba80a41994c2b47c68e224c542b48feba42ba00f8bb", size = 55611, upload-time = "2025-12-20T14:08:54.006Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/88/b2/d0896bdcdc8d28a7fc5717c305f1a861c26e18c05047949fb371034d98bd/nodeenv-1.10.0-py2.py3-none-any.whl", hash = "sha256:5bb13e3eed2923615535339b3c620e76779af4cb4c6a90deccc9e36b274d3827", size = 23438, upload-time = "2025-12-20T14:08:52.782Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/43/16/fc88b08840de0e0a72a2f9d8c6bae36be573e475a6326ae854bcc549fc45/nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f", size = 47437 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d2/1d/1b658dbd2b9fa9c4c9f32accbfc0205d532c8c6194dc0f2a4c0428e7128a/nodeenv-1.9.1-py2.py3-none-any.whl", hash = "sha256:ba11c9782d29c27c70ffbdda2d7415098754709be8a7056d79a737cd901155c9", size = 22314 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1985,9 +2699,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "jupyter-server" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/54/d2/92fa3243712b9a3e8bafaf60aac366da1cada3639ca767ff4b5b3654ec28/notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb", size = 13167, upload-time = "2024-02-14T23:35:18.353Z" } +sdist = { url = "https://files.pythonhosted.org/packages/54/d2/92fa3243712b9a3e8bafaf60aac366da1cada3639ca767ff4b5b3654ec28/notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb", size = 13167 } wheels = [ - { url = "https://files.pythonhosted.org/packages/f9/33/bd5b9137445ea4b680023eb0469b2bb969d61303dedb2aac6560ff3d14a1/notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef", size = 13307, upload-time = "2024-02-14T23:35:16.286Z" }, + { url = "https://files.pythonhosted.org/packages/f9/33/bd5b9137445ea4b680023eb0469b2bb969d61303dedb2aac6560ff3d14a1/notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef", size = 13307 }, ] [[package]] @@ -1998,6 +2712,7 @@ dependencies = [ { name = "numpy" }, { name = "typing-extensions" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/44/bd/8a391e7c356366224734efd24da929cc4796fff468bfb179fe1af6548535/numcodecs-0.16.5.tar.gz", hash = "sha256:0d0fb60852f84c0bd9543cc4d2ab9eefd37fc8efcc410acd4777e62a1d300318", size = 6276387, upload-time = "2025-11-21T02:49:48.986Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/af/85/1ac101a40ead81eaa1c7dc49a8827a30e2e436211b43ebdc63c590eb1347/numcodecs-0.16.5-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:78382dcea50622f2ef1e6e7a71dbe7f861d8fe376b27b7c297c26907304fef1e", size = 1621795, upload-time = "2025-11-21T02:49:17.418Z" }, @@ -2020,12 +2735,37 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/4e/15/e2e1151b5a8b14a15dfd4bb4abccce7fff7580f39bc34092780088835f3a/numcodecs-0.16.5-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:49f7b7d24f103187f53135bed28bb9f0ed6b2e14c604664726487bb6d7c882e1", size = 8476987, upload-time = "2025-11-21T02:49:43.363Z" }, { url = "https://files.pythonhosted.org/packages/6d/30/16a57fc4d9fb0ba06c600408bd6634f2f1753c54a7a351c99c5e09b51ee2/numcodecs-0.16.5-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:aec9736d81b70f337d89c4070ee3ffeff113f386fd789492fa152d26a15043e4", size = 9102377, upload-time = "2025-11-21T02:49:45.508Z" }, { url = "https://files.pythonhosted.org/packages/31/a5/a0425af36c20d55a3ea884db4b4efca25a43bea9214ba69ca7932dd997b4/numcodecs-0.16.5-cp314-cp314-win_amd64.whl", hash = "sha256:b16a14303800e9fb88abc39463ab4706c037647ac17e49e297faa5f7d7dbbf1d", size = 819022, upload-time = "2025-11-21T02:49:47.39Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/f6/48/6188e359b90a9d8a1850f2bc888c023e66f4a8b2b496820babbea414f008/numcodecs-0.16.3.tar.gz", hash = "sha256:53d705865faaf0a7927c973af3777532001c8fbb653de119c1e844608614d799", size = 6275704 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d4/cc/917a85972537498f2bbd7914047efc98babc8667587ceb9dcb228378978a/numcodecs-0.16.3-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:95c9f2a49bef10cf91ad614a761cba9bfe96656b60c12540e1080de5d909b4ca", size = 1642356 }, + { url = "https://files.pythonhosted.org/packages/3b/6a/64c25a089e8537441fe67c09ecb7f3f7fb5d98cd04faf01f605d43aca41c/numcodecs-0.16.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e2afe73d5ebaf9ca0cd5c83aad945da80d29a33d860a80d43a7248491d8813ff", size = 1169186 }, + { url = "https://files.pythonhosted.org/packages/d8/a0/0de627baeb43e2045a3d4b3de99bf8b69af329a33df1ed4cda468d70c1fb/numcodecs-0.16.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:913f08194d82dcb37594e6705e6d4ae6ccd4b6571500b832fb3e4a155de1dfe8", size = 8341668 }, + { url = "https://files.pythonhosted.org/packages/b6/0f/49d1f74a216149240c4b9403218111f11670bd11af0919fda357bb056bf2/numcodecs-0.16.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:85a7f1cae9eb18b85709af46570bf9c60056e7155c4c8f610e8080c68124d0e5", size = 8866611 }, + { url = "https://files.pythonhosted.org/packages/aa/51/03aece765108fe247717105b5131856546e5428f22a56a14ffdebd017424/numcodecs-0.16.3-cp311-cp311-win_amd64.whl", hash = "sha256:f7bb7f2c46eb7ec8a1c5f8d8fe1a72c222256dd6d6df5af9eaac7a6b905f3575", size = 806787 }, + { url = "https://files.pythonhosted.org/packages/0d/78/e4b34803a3aa1d0769919695de4b133266c18c80c474d32ebc462fa1a9bd/numcodecs-0.16.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c77454d92941a335d148b0b822f5d4783103f392774d5d76283bbf7f21b49529", size = 1681108 }, + { url = "https://files.pythonhosted.org/packages/25/cf/ca36f463b03a4097767d2a1c1b72f31810e8c6384e9449dd9b925203783c/numcodecs-0.16.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:270e7a33ee96bdf5c957acf25a2487002a233811a125a155c400c2f036b69c73", size = 1165589 }, + { url = "https://files.pythonhosted.org/packages/ed/ae/670260c3c4b5ed34a0674561355f3d4ce7fcbdf09a667e5bc841526d271c/numcodecs-0.16.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:12f43fa4a347d1dba775c4506a1c9b15b90144c258433b81f79f1c1b1a990db5", size = 8316365 }, + { url = "https://files.pythonhosted.org/packages/bb/fa/94e022419c751a60ff0f53642ebae5ef81ed3cc3640f958588e3ad3dc18d/numcodecs-0.16.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:44869ef564a50aa545215c6a0d42ba5bbc34e9715523fb2336ada3d1fb2b331d", size = 8846228 }, + { url = "https://files.pythonhosted.org/packages/71/60/f23733589f3e059bf8589508acd23ffeec230bdf179f138a54f5ab16e0a6/numcodecs-0.16.3-cp312-cp312-win_amd64.whl", hash = "sha256:9aae6996172ba10c5f5111b2998709071b5aeba6b58b1ee0b26b61ed6aa7f2f4", size = 806260 }, + { url = "https://files.pythonhosted.org/packages/3c/d5/d3536d06ac1e5fb848a3186958204082b68b106364c9a3669652dd786731/numcodecs-0.16.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:947406b01c20f2ce7ce2e631e7f21b782e8a9d4b57b374a41c9e7b1341a8f3a2", size = 1677129 }, + { url = "https://files.pythonhosted.org/packages/e1/fd/b0513a3428dc2b38ec85eea771703ae69c49f09b9650d6c44c9105c80073/numcodecs-0.16.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:7cf50e351398a34b45817974c411527629e88937b7683695e276afd65da6ed6f", size = 1159058 }, + { url = "https://files.pythonhosted.org/packages/98/05/b7c127283cfb154a97abb284363825401b69302d71a28608af66f73257cc/numcodecs-0.16.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f7938502fcc060ed9543814f38ca67048b33d7bd2667756e36e6b1060455b17e", size = 8260987 }, + { url = "https://files.pythonhosted.org/packages/ff/46/320d960aff884bc63abaaf846ffa3de4803e83e8070b6f84c5688464839c/numcodecs-0.16.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:010d628c95be1214536fb22c0df4ced58da954b404b1fcb25ddebf64e4a3f7f3", size = 8805295 }, + { url = "https://files.pythonhosted.org/packages/31/ae/acc2e0f1f49ba32afa2174578f170673139248ef86f77e334f2619133867/numcodecs-0.16.3-cp313-cp313-win_amd64.whl", hash = "sha256:e83115e3c32de798c7b7164503e06aae9f9746c1cef564d029616eb44bd6cd90", size = 803204 }, +] + +[package.optional-dependencies] +crc32c = [ + { name = "crc32c" }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "numpy" version = "2.4.6" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/d0/ad/fed0499ce6a338d2a03ebae59cd15093910c8875328855781952abf6c2fe/numpy-2.4.6.tar.gz", hash = "sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda", size = 20735807, upload-time = "2026-05-18T23:37:14.07Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/b3/49/ec46835a70be8fa6446c495126ac84fdb28cb2558e1620ffb87a10c8b64c/numpy-2.4.6-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0280e0356c0829a18d9de1cb7eee50ec22ca639878d7240307ca0943d73cd2c4", size = 16969194, upload-time = "2026-05-18T23:33:13.503Z" }, @@ -2104,13 +2844,131 @@ wheels = [ [[package]] name = "nvidia-cublas" version = "13.1.1.3" +======= +sdist = { url = "https://files.pythonhosted.org/packages/76/65/21b3bc86aac7b8f2862db1e808f1ea22b028e30a225a34a5ede9bf8678f2/numpy-2.3.5.tar.gz", hash = "sha256:784db1dcdab56bf0517743e746dfb0f885fc68d948aba86eeec2cba234bdf1c0", size = 20584950 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/43/77/84dd1d2e34d7e2792a236ba180b5e8fcc1e3e414e761ce0253f63d7f572e/numpy-2.3.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:de5672f4a7b200c15a4127042170a694d4df43c992948f5e1af57f0174beed10", size = 17034641 }, + { url = "https://files.pythonhosted.org/packages/2a/ea/25e26fa5837106cde46ae7d0b667e20f69cbbc0efd64cba8221411ab26ae/numpy-2.3.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:acfd89508504a19ed06ef963ad544ec6664518c863436306153e13e94605c218", size = 12528324 }, + { url = "https://files.pythonhosted.org/packages/4d/1a/e85f0eea4cf03d6a0228f5c0256b53f2df4bc794706e7df019fc622e47f1/numpy-2.3.5-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:ffe22d2b05504f786c867c8395de703937f934272eb67586817b46188b4ded6d", size = 5356872 }, + { url = "https://files.pythonhosted.org/packages/5c/bb/35ef04afd567f4c989c2060cde39211e4ac5357155c1833bcd1166055c61/numpy-2.3.5-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:872a5cf366aec6bb1147336480fef14c9164b154aeb6542327de4970282cd2f5", size = 6893148 }, + { url = "https://files.pythonhosted.org/packages/f2/2b/05bbeb06e2dff5eab512dfc678b1cc5ee94d8ac5956a0885c64b6b26252b/numpy-2.3.5-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3095bdb8dd297e5920b010e96134ed91d852d81d490e787beca7e35ae1d89cf7", size = 14557282 }, + { url = "https://files.pythonhosted.org/packages/65/fb/2b23769462b34398d9326081fad5655198fcf18966fcb1f1e49db44fbf31/numpy-2.3.5-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8cba086a43d54ca804ce711b2a940b16e452807acebe7852ff327f1ecd49b0d4", size = 16897903 }, + { url = "https://files.pythonhosted.org/packages/ac/14/085f4cf05fc3f1e8aa95e85404e984ffca9b2275a5dc2b1aae18a67538b8/numpy-2.3.5-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:6cf9b429b21df6b99f4dee7a1218b8b7ffbbe7df8764dc0bd60ce8a0708fed1e", size = 16341672 }, + { url = "https://files.pythonhosted.org/packages/6f/3b/1f73994904142b2aa290449b3bb99772477b5fd94d787093e4f24f5af763/numpy-2.3.5-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:396084a36abdb603546b119d96528c2f6263921c50df3c8fd7cb28873a237748", size = 18838896 }, + { url = "https://files.pythonhosted.org/packages/cd/b9/cf6649b2124f288309ffc353070792caf42ad69047dcc60da85ee85fea58/numpy-2.3.5-cp311-cp311-win32.whl", hash = "sha256:b0c7088a73aef3d687c4deef8452a3ac7c1be4e29ed8bf3b366c8111128ac60c", size = 6563608 }, + { url = "https://files.pythonhosted.org/packages/aa/44/9fe81ae1dcc29c531843852e2874080dc441338574ccc4306b39e2ff6e59/numpy-2.3.5-cp311-cp311-win_amd64.whl", hash = "sha256:a414504bef8945eae5f2d7cb7be2d4af77c5d1cb5e20b296c2c25b61dff2900c", size = 13078442 }, + { url = "https://files.pythonhosted.org/packages/6d/a7/f99a41553d2da82a20a2f22e93c94f928e4490bb447c9ff3c4ff230581d3/numpy-2.3.5-cp311-cp311-win_arm64.whl", hash = "sha256:0cd00b7b36e35398fa2d16af7b907b65304ef8bb4817a550e06e5012929830fa", size = 10458555 }, + { url = "https://files.pythonhosted.org/packages/44/37/e669fe6cbb2b96c62f6bbedc6a81c0f3b7362f6a59230b23caa673a85721/numpy-2.3.5-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:74ae7b798248fe62021dbf3c914245ad45d1a6b0cb4a29ecb4b31d0bfbc4cc3e", size = 16733873 }, + { url = "https://files.pythonhosted.org/packages/c5/65/df0db6c097892c9380851ab9e44b52d4f7ba576b833996e0080181c0c439/numpy-2.3.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ee3888d9ff7c14604052b2ca5535a30216aa0a58e948cdd3eeb8d3415f638769", size = 12259838 }, + { url = "https://files.pythonhosted.org/packages/5b/e1/1ee06e70eb2136797abe847d386e7c0e830b67ad1d43f364dd04fa50d338/numpy-2.3.5-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:612a95a17655e213502f60cfb9bf9408efdc9eb1d5f50535cc6eb365d11b42b5", size = 5088378 }, + { url = "https://files.pythonhosted.org/packages/6d/9c/1ca85fb86708724275103b81ec4cf1ac1d08f465368acfc8da7ab545bdae/numpy-2.3.5-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:3101e5177d114a593d79dd79658650fe28b5a0d8abeb8ce6f437c0e6df5be1a4", size = 6628559 }, + { url = "https://files.pythonhosted.org/packages/74/78/fcd41e5a0ce4f3f7b003da85825acddae6d7ecb60cf25194741b036ca7d6/numpy-2.3.5-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8b973c57ff8e184109db042c842423ff4f60446239bd585a5131cc47f06f789d", size = 14250702 }, + { url = "https://files.pythonhosted.org/packages/b6/23/2a1b231b8ff672b4c450dac27164a8b2ca7d9b7144f9c02d2396518352eb/numpy-2.3.5-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0d8163f43acde9a73c2a33605353a4f1bc4798745a8b1d73183b28e5b435ae28", size = 16606086 }, + { url = "https://files.pythonhosted.org/packages/a0/c5/5ad26fbfbe2012e190cc7d5003e4d874b88bb18861d0829edc140a713021/numpy-2.3.5-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:51c1e14eb1e154ebd80e860722f9e6ed6ec89714ad2db2d3aa33c31d7c12179b", size = 16025985 }, + { url = "https://files.pythonhosted.org/packages/d2/fa/dd48e225c46c819288148d9d060b047fd2a6fb1eb37eae25112ee4cb4453/numpy-2.3.5-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b46b4ec24f7293f23adcd2d146960559aaf8020213de8ad1909dba6c013bf89c", size = 18542976 }, + { url = "https://files.pythonhosted.org/packages/05/79/ccbd23a75862d95af03d28b5c6901a1b7da4803181513d52f3b86ed9446e/numpy-2.3.5-cp312-cp312-win32.whl", hash = "sha256:3997b5b3c9a771e157f9aae01dd579ee35ad7109be18db0e85dbdbe1de06e952", size = 6285274 }, + { url = "https://files.pythonhosted.org/packages/2d/57/8aeaf160312f7f489dea47ab61e430b5cb051f59a98ae68b7133ce8fa06a/numpy-2.3.5-cp312-cp312-win_amd64.whl", hash = "sha256:86945f2ee6d10cdfd67bcb4069c1662dd711f7e2a4343db5cecec06b87cf31aa", size = 12782922 }, + { url = "https://files.pythonhosted.org/packages/78/a6/aae5cc2ca78c45e64b9ef22f089141d661516856cf7c8a54ba434576900d/numpy-2.3.5-cp312-cp312-win_arm64.whl", hash = "sha256:f28620fe26bee16243be2b7b874da327312240a7cdc38b769a697578d2100013", size = 10194667 }, + { url = "https://files.pythonhosted.org/packages/db/69/9cde09f36da4b5a505341180a3f2e6fadc352fd4d2b7096ce9778db83f1a/numpy-2.3.5-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:d0f23b44f57077c1ede8c5f26b30f706498b4862d3ff0a7298b8411dd2f043ff", size = 16728251 }, + { url = "https://files.pythonhosted.org/packages/79/fb/f505c95ceddd7027347b067689db71ca80bd5ecc926f913f1a23e65cf09b/numpy-2.3.5-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:aa5bc7c5d59d831d9773d1170acac7893ce3a5e130540605770ade83280e7188", size = 12254652 }, + { url = "https://files.pythonhosted.org/packages/78/da/8c7738060ca9c31b30e9301ee0cf6c5ffdbf889d9593285a1cead337f9a5/numpy-2.3.5-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:ccc933afd4d20aad3c00bcef049cb40049f7f196e0397f1109dba6fed63267b0", size = 5083172 }, + { url = "https://files.pythonhosted.org/packages/a4/b4/ee5bb2537fb9430fd2ef30a616c3672b991a4129bb1c7dcc42aa0abbe5d7/numpy-2.3.5-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:afaffc4393205524af9dfa400fa250143a6c3bc646c08c9f5e25a9f4b4d6a903", size = 6622990 }, + { url = "https://files.pythonhosted.org/packages/95/03/dc0723a013c7d7c19de5ef29e932c3081df1c14ba582b8b86b5de9db7f0f/numpy-2.3.5-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9c75442b2209b8470d6d5d8b1c25714270686f14c749028d2199c54e29f20b4d", size = 14248902 }, + { url = "https://files.pythonhosted.org/packages/f5/10/ca162f45a102738958dcec8023062dad0cbc17d1ab99d68c4e4a6c45fb2b/numpy-2.3.5-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:11e06aa0af8c0f05104d56450d6093ee639e15f24ecf62d417329d06e522e017", size = 16597430 }, + { url = "https://files.pythonhosted.org/packages/2a/51/c1e29be863588db58175175f057286900b4b3327a1351e706d5e0f8dd679/numpy-2.3.5-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ed89927b86296067b4f81f108a2271d8926467a8868e554eaf370fc27fa3ccaf", size = 16024551 }, + { url = "https://files.pythonhosted.org/packages/83/68/8236589d4dbb87253d28259d04d9b814ec0ecce7cb1c7fed29729f4c3a78/numpy-2.3.5-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:51c55fe3451421f3a6ef9a9c1439e82101c57a2c9eab9feb196a62b1a10b58ce", size = 18533275 }, + { url = "https://files.pythonhosted.org/packages/40/56/2932d75b6f13465239e3b7b7e511be27f1b8161ca2510854f0b6e521c395/numpy-2.3.5-cp313-cp313-win32.whl", hash = "sha256:1978155dd49972084bd6ef388d66ab70f0c323ddee6f693d539376498720fb7e", size = 6277637 }, + { url = "https://files.pythonhosted.org/packages/0c/88/e2eaa6cffb115b85ed7c7c87775cb8bcf0816816bc98ca8dbfa2ee33fe6e/numpy-2.3.5-cp313-cp313-win_amd64.whl", hash = "sha256:00dc4e846108a382c5869e77c6ed514394bdeb3403461d25a829711041217d5b", size = 12779090 }, + { url = "https://files.pythonhosted.org/packages/8f/88/3f41e13a44ebd4034ee17baa384acac29ba6a4fcc2aca95f6f08ca0447d1/numpy-2.3.5-cp313-cp313-win_arm64.whl", hash = "sha256:0472f11f6ec23a74a906a00b48a4dcf3849209696dff7c189714511268d103ae", size = 10194710 }, + { url = "https://files.pythonhosted.org/packages/13/cb/71744144e13389d577f867f745b7df2d8489463654a918eea2eeb166dfc9/numpy-2.3.5-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:414802f3b97f3c1eef41e530aaba3b3c1620649871d8cb38c6eaff034c2e16bd", size = 16827292 }, + { url = "https://files.pythonhosted.org/packages/71/80/ba9dc6f2a4398e7f42b708a7fdc841bb638d353be255655498edbf9a15a8/numpy-2.3.5-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5ee6609ac3604fa7780e30a03e5e241a7956f8e2fcfe547d51e3afa5247ac47f", size = 12378897 }, + { url = "https://files.pythonhosted.org/packages/2e/6d/db2151b9f64264bcceccd51741aa39b50150de9b602d98ecfe7e0c4bff39/numpy-2.3.5-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:86d835afea1eaa143012a2d7a3f45a3adce2d7adc8b4961f0b362214d800846a", size = 5207391 }, + { url = "https://files.pythonhosted.org/packages/80/ae/429bacace5ccad48a14c4ae5332f6aa8ab9f69524193511d60ccdfdc65fa/numpy-2.3.5-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:30bc11310e8153ca664b14c5f1b73e94bd0503681fcf136a163de856f3a50139", size = 6721275 }, + { url = "https://files.pythonhosted.org/packages/74/5b/1919abf32d8722646a38cd527bc3771eb229a32724ee6ba340ead9b92249/numpy-2.3.5-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1062fde1dcf469571705945b0f221b73928f34a20c904ffb45db101907c3454e", size = 14306855 }, + { url = "https://files.pythonhosted.org/packages/a5/87/6831980559434973bebc30cd9c1f21e541a0f2b0c280d43d3afd909b66d0/numpy-2.3.5-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ce581db493ea1a96c0556360ede6607496e8bf9b3a8efa66e06477267bc831e9", size = 16657359 }, + { url = "https://files.pythonhosted.org/packages/dd/91/c797f544491ee99fd00495f12ebb7802c440c1915811d72ac5b4479a3356/numpy-2.3.5-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:cc8920d2ec5fa99875b670bb86ddeb21e295cb07aa331810d9e486e0b969d946", size = 16093374 }, + { url = "https://files.pythonhosted.org/packages/74/a6/54da03253afcbe7a72785ec4da9c69fb7a17710141ff9ac5fcb2e32dbe64/numpy-2.3.5-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:9ee2197ef8c4f0dfe405d835f3b6a14f5fee7782b5de51ba06fb65fc9b36e9f1", size = 18594587 }, + { url = "https://files.pythonhosted.org/packages/80/e9/aff53abbdd41b0ecca94285f325aff42357c6b5abc482a3fcb4994290b18/numpy-2.3.5-cp313-cp313t-win32.whl", hash = "sha256:70b37199913c1bd300ff6e2693316c6f869c7ee16378faf10e4f5e3275b299c3", size = 6405940 }, + { url = "https://files.pythonhosted.org/packages/d5/81/50613fec9d4de5480de18d4f8ef59ad7e344d497edbef3cfd80f24f98461/numpy-2.3.5-cp313-cp313t-win_amd64.whl", hash = "sha256:b501b5fa195cc9e24fe102f21ec0a44dffc231d2af79950b451e0d99cea02234", size = 12920341 }, + { url = "https://files.pythonhosted.org/packages/bb/ab/08fd63b9a74303947f34f0bd7c5903b9c5532c2d287bead5bdf4c556c486/numpy-2.3.5-cp313-cp313t-win_arm64.whl", hash = "sha256:a80afd79f45f3c4a7d341f13acbe058d1ca8ac017c165d3fa0d3de6bc1a079d7", size = 10262507 }, + { url = "https://files.pythonhosted.org/packages/ba/97/1a914559c19e32d6b2e233cf9a6a114e67c856d35b1d6babca571a3e880f/numpy-2.3.5-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:bf06bc2af43fa8d32d30fae16ad965663e966b1a3202ed407b84c989c3221e82", size = 16735706 }, + { url = "https://files.pythonhosted.org/packages/57/d4/51233b1c1b13ecd796311216ae417796b88b0616cfd8a33ae4536330748a/numpy-2.3.5-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:052e8c42e0c49d2575621c158934920524f6c5da05a1d3b9bab5d8e259e045f0", size = 12264507 }, + { url = "https://files.pythonhosted.org/packages/45/98/2fe46c5c2675b8306d0b4a3ec3494273e93e1226a490f766e84298576956/numpy-2.3.5-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:1ed1ec893cff7040a02c8aa1c8611b94d395590d553f6b53629a4461dc7f7b63", size = 5093049 }, + { url = "https://files.pythonhosted.org/packages/ce/0e/0698378989bb0ac5f1660c81c78ab1fe5476c1a521ca9ee9d0710ce54099/numpy-2.3.5-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:2dcd0808a421a482a080f89859a18beb0b3d1e905b81e617a188bd80422d62e9", size = 6626603 }, + { url = "https://files.pythonhosted.org/packages/5e/a6/9ca0eecc489640615642a6cbc0ca9e10df70df38c4d43f5a928ff18d8827/numpy-2.3.5-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:727fd05b57df37dc0bcf1a27767a3d9a78cbbc92822445f32cc3436ba797337b", size = 14262696 }, + { url = "https://files.pythonhosted.org/packages/c8/f6/07ec185b90ec9d7217a00eeeed7383b73d7e709dae2a9a021b051542a708/numpy-2.3.5-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fffe29a1ef00883599d1dc2c51aa2e5d80afe49523c261a74933df395c15c520", size = 16597350 }, + { url = "https://files.pythonhosted.org/packages/75/37/164071d1dde6a1a84c9b8e5b414fa127981bad47adf3a6b7e23917e52190/numpy-2.3.5-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:8f7f0e05112916223d3f438f293abf0727e1181b5983f413dfa2fefc4098245c", size = 16040190 }, + { url = "https://files.pythonhosted.org/packages/08/3c/f18b82a406b04859eb026d204e4e1773eb41c5be58410f41ffa511d114ae/numpy-2.3.5-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2e2eb32ddb9ccb817d620ac1d8dae7c3f641c1e5f55f531a33e8ab97960a75b8", size = 18536749 }, + { url = "https://files.pythonhosted.org/packages/40/79/f82f572bf44cf0023a2fe8588768e23e1592585020d638999f15158609e1/numpy-2.3.5-cp314-cp314-win32.whl", hash = "sha256:66f85ce62c70b843bab1fb14a05d5737741e74e28c7b8b5a064de10142fad248", size = 6335432 }, + { url = "https://files.pythonhosted.org/packages/a3/2e/235b4d96619931192c91660805e5e49242389742a7a82c27665021db690c/numpy-2.3.5-cp314-cp314-win_amd64.whl", hash = "sha256:e6a0bc88393d65807d751a614207b7129a310ca4fe76a74e5c7da5fa5671417e", size = 12919388 }, + { url = "https://files.pythonhosted.org/packages/07/2b/29fd75ce45d22a39c61aad74f3d718e7ab67ccf839ca8b60866054eb15f8/numpy-2.3.5-cp314-cp314-win_arm64.whl", hash = "sha256:aeffcab3d4b43712bb7a60b65f6044d444e75e563ff6180af8f98dd4b905dfd2", size = 10476651 }, + { url = "https://files.pythonhosted.org/packages/17/e1/f6a721234ebd4d87084cfa68d081bcba2f5cfe1974f7de4e0e8b9b2a2ba1/numpy-2.3.5-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:17531366a2e3a9e30762c000f2c43a9aaa05728712e25c11ce1dbe700c53ad41", size = 16834503 }, + { url = "https://files.pythonhosted.org/packages/5c/1c/baf7ffdc3af9c356e1c135e57ab7cf8d247931b9554f55c467efe2c69eff/numpy-2.3.5-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:d21644de1b609825ede2f48be98dfde4656aefc713654eeee280e37cadc4e0ad", size = 12381612 }, + { url = "https://files.pythonhosted.org/packages/74/91/f7f0295151407ddc9ba34e699013c32c3c91944f9b35fcf9281163dc1468/numpy-2.3.5-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:c804e3a5aba5460c73955c955bdbd5c08c354954e9270a2c1565f62e866bdc39", size = 5210042 }, + { url = "https://files.pythonhosted.org/packages/2e/3b/78aebf345104ec50dd50a4d06ddeb46a9ff5261c33bcc58b1c4f12f85ec2/numpy-2.3.5-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:cc0a57f895b96ec78969c34f682c602bf8da1a0270b09bc65673df2e7638ec20", size = 6724502 }, + { url = "https://files.pythonhosted.org/packages/02/c6/7c34b528740512e57ef1b7c8337ab0b4f0bddf34c723b8996c675bc2bc91/numpy-2.3.5-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:900218e456384ea676e24ea6a0417f030a3b07306d29d7ad843957b40a9d8d52", size = 14308962 }, + { url = "https://files.pythonhosted.org/packages/80/35/09d433c5262bc32d725bafc619e095b6a6651caf94027a03da624146f655/numpy-2.3.5-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:09a1bea522b25109bf8e6f3027bd810f7c1085c64a0c7ce050c1676ad0ba010b", size = 16655054 }, + { url = "https://files.pythonhosted.org/packages/7a/ab/6a7b259703c09a88804fa2430b43d6457b692378f6b74b356155283566ac/numpy-2.3.5-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:04822c00b5fd0323c8166d66c701dc31b7fbd252c100acd708c48f763968d6a3", size = 16091613 }, + { url = "https://files.pythonhosted.org/packages/c2/88/330da2071e8771e60d1038166ff9d73f29da37b01ec3eb43cb1427464e10/numpy-2.3.5-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:d6889ec4ec662a1a37eb4b4fb26b6100841804dac55bd9df579e326cdc146227", size = 18591147 }, + { url = "https://files.pythonhosted.org/packages/51/41/851c4b4082402d9ea860c3626db5d5df47164a712cb23b54be028b184c1c/numpy-2.3.5-cp314-cp314t-win32.whl", hash = "sha256:93eebbcf1aafdf7e2ddd44c2923e2672e1010bddc014138b229e49725b4d6be5", size = 6479806 }, + { url = "https://files.pythonhosted.org/packages/90/30/d48bde1dfd93332fa557cff1972fbc039e055a52021fbef4c2c4b1eefd17/numpy-2.3.5-cp314-cp314t-win_amd64.whl", hash = "sha256:c8a9958e88b65c3b27e22ca2a076311636850b612d6bbfb76e8d156aacde2aaf", size = 13105760 }, + { url = "https://files.pythonhosted.org/packages/2d/fd/4b5eb0b3e888d86aee4d198c23acec7d214baaf17ea93c1adec94c9518b9/numpy-2.3.5-cp314-cp314t-win_arm64.whl", hash = "sha256:6203fdf9f3dc5bdaed7319ad8698e685c7a3be10819f41d32a0723e611733b42", size = 10545459 }, + { url = "https://files.pythonhosted.org/packages/c6/65/f9dea8e109371ade9c782b4e4756a82edf9d3366bca495d84d79859a0b79/numpy-2.3.5-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:f0963b55cdd70fad460fa4c1341f12f976bb26cb66021a5580329bd498988310", size = 16910689 }, + { url = "https://files.pythonhosted.org/packages/00/4f/edb00032a8fb92ec0a679d3830368355da91a69cab6f3e9c21b64d0bb986/numpy-2.3.5-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:f4255143f5160d0de972d28c8f9665d882b5f61309d8362fdd3e103cf7bf010c", size = 12457053 }, + { url = "https://files.pythonhosted.org/packages/16/a4/e8a53b5abd500a63836a29ebe145fc1ab1f2eefe1cfe59276020373ae0aa/numpy-2.3.5-pp311-pypy311_pp73-macosx_14_0_arm64.whl", hash = "sha256:a4b9159734b326535f4dd01d947f919c6eefd2d9827466a696c44ced82dfbc18", size = 5285635 }, + { url = "https://files.pythonhosted.org/packages/a3/2f/37eeb9014d9c8b3e9c55bc599c68263ca44fdbc12a93e45a21d1d56df737/numpy-2.3.5-pp311-pypy311_pp73-macosx_14_0_x86_64.whl", hash = "sha256:2feae0d2c91d46e59fcd62784a3a83b3fb677fead592ce51b5a6fbb4f95965ff", size = 6801770 }, + { url = "https://files.pythonhosted.org/packages/7d/e4/68d2f474df2cb671b2b6c2986a02e520671295647dad82484cde80ca427b/numpy-2.3.5-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ffac52f28a7849ad7576293c0cb7b9f08304e8f7d738a8cb8a90ec4c55a998eb", size = 14391768 }, + { url = "https://files.pythonhosted.org/packages/b8/50/94ccd8a2b141cb50651fddd4f6a48874acb3c91c8f0842b08a6afc4b0b21/numpy-2.3.5-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:63c0e9e7eea69588479ebf4a8a270d5ac22763cc5854e9a7eae952a3908103f7", size = 16729263 }, + { url = "https://files.pythonhosted.org/packages/2d/ee/346fa473e666fe14c52fcdd19ec2424157290a032d4c41f98127bfb31ac7/numpy-2.3.5-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:f16417ec91f12f814b10bafe79ef77e70113a2f5f7018640e7425ff979253425", size = 12967213 }, +] + +[[package]] +name = "nvidia-cublas-cu12" +version = "12.8.4.1" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/dc/61/e24b560ab2e2eaeb3c839129175fb330dfcfc29e5203196e5541a4c44682/nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:8ac4e771d5a348c551b2a426eda6193c19aa630236b418086020df5ba9667142", size = 594346921 }, +] + +[[package]] +name = "nvidia-cuda-cupti-cu12" +version = "12.8.90" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f8/02/2adcaa145158bf1a8295d83591d22e4103dbfd821bcaf6f3f53151ca4ffa/nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ea0cb07ebda26bb9b29ba82cda34849e73c166c18162d3913575b0c9db9a6182", size = 10248621 }, +] + +[[package]] +name = "nvidia-cuda-nvrtc-cu12" +version = "12.8.93" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/05/6b/32f747947df2da6994e999492ab306a903659555dddc0fbdeb9d71f75e52/nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:a7756528852ef889772a84c6cd89d41dfa74667e24cca16bb31f8f061e3e9994", size = 88040029 }, +] + +[[package]] +name = "nvidia-cuda-runtime-cu12" +version = "12.8.90" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0d/9b/a997b638fcd068ad6e4d53b8551a7d30fe8b404d6f1804abf1df69838932/nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:adade8dcbd0edf427b7204d480d6066d33902cab2a4707dcfc48a2d0fd44ab90", size = 954765 }, +] + +[[package]] +name = "nvidia-cudnn-cu12" +version = "9.10.2.21" +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "nvidia-cuda-nvrtc" }, ] wheels = [ +<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/a7/a1/0bd24ee8c8d03adac032fd2909426a00c88f8c57961b1277ded97f91119f/nvidia_cublas-13.1.1.3-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:b7a210458267ac818974c53038fbec2e969d5c99f305ab15c72522fa9f001dd5", size = 542848918, upload-time = "2026-04-08T18:46:22.985Z" }, { url = "https://files.pythonhosted.org/packages/3b/cd/154ca20c38269e05eff77c1464e6c1da89f50a6390b565e9d82e06bc11e1/nvidia_cublas-13.1.1.3-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:37936a16db8fe4ac1f065c2139360608a543a09275cb1a1af612e08cfa065436", size = 423138758, upload-time = "2026-04-08T18:46:58.655Z" }, +======= + { url = "https://files.pythonhosted.org/packages/ba/51/e123d997aa098c61d029f76663dedbfb9bc8dcf8c60cbd6adbe42f76d049/nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:949452be657fa16687d0930933f032835951ef0892b37d2d53824d1a84dc97a8", size = 706758467 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2148,6 +3006,7 @@ dependencies = [ { name = "nvidia-cublas" }, ] wheels = [ +<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/56/c5/83384d846b2fd17c44bd499b36c75a45ed4f095fbbb2252294e89cea5c5c/nvidia_cudnn_cu13-9.20.0.48-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:e31454ae00094b0c55319d9d15b6fa2fc50a9e1c0f5c8c80fb75258234e731e1", size = 444574296, upload-time = "2026-03-09T19:28:27.751Z" }, { url = "https://files.pythonhosted.org/packages/6e/5e/edb9c0ae051602c3ccaffe424256463636d639e27d7f302dde9975ef9e7a/nvidia_cudnn_cu13-9.20.0.48-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:0c45dd8eeb50b603f07995b1b300c62ffe6a1980482b82b3bcf94a4ca9d49304", size = 366173588, upload-time = "2026-03-09T19:29:34.474Z" }, ] @@ -2155,13 +3014,41 @@ wheels = [ [[package]] name = "nvidia-cufft" version = "12.0.0.61" +======= + { url = "https://files.pythonhosted.org/packages/1f/13/ee4e00f30e676b66ae65b4f08cb5bcbb8392c03f54f2d5413ea99a5d1c80/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74", size = 193118695 }, +] + +[[package]] +name = "nvidia-cufile-cu12" +version = "1.13.1.3" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/bb/fe/1bcba1dfbfb8d01be8d93f07bfc502c93fa23afa6fd5ab3fc7c1df71038a/nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1d069003be650e131b21c932ec3d8969c1715379251f8d23a1860554b1cb24fc", size = 1197834 }, +] + +[[package]] +name = "nvidia-curand-cu12" +version = "10.3.9.90" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fb/aa/6584b56dc84ebe9cf93226a5cde4d99080c8e90ab40f0c27bda7a0f29aa1/nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:b32331d4f4df5d6eefa0554c565b626c7216f87a06a4f56fab27c3b68a830ec9", size = 63619976 }, +] + +[[package]] +name = "nvidia-cusolver-cu12" +version = "11.7.3.90" +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "nvidia-nvjitlink" }, ] wheels = [ +<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/8b/ae/f417a75c0259e85c1d2f83ca4e960289a5f814ed0cea74d18c353d3e989d/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2708c852ef8cd89d1d2068bdbece0aa188813a0c934db3779b9b1faa8442e5f5", size = 214053554, upload-time = "2025-09-04T08:31:38.196Z" }, { url = "https://files.pythonhosted.org/packages/a8/2f/7b57e29836ea8714f81e9898409196f47d772d5ddedddf1592eadb8ab743/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6c44f692dce8fd5ffd3e3df134b6cdb9c2f72d99cf40b62c32dde45eea9ddad3", size = 214085489, upload-time = "2025-09-04T08:31:56.044Z" }, +======= + { url = "https://files.pythonhosted.org/packages/85/48/9a13d2975803e8cf2777d5ed57b87a0b6ca2cc795f9a4f59796a910bfb80/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450", size = 267506905 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2192,8 +3079,12 @@ dependencies = [ { name = "nvidia-nvjitlink" }, ] wheels = [ +<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/c8/c3/b30c9e935fc01e3da443ec0116ed1b2a009bb867f5324d3f2d7e533e776b/nvidia_cusolver-12.0.4.66-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:02c2457eaa9e39de20f880f4bd8820e6a1cfb9f9a34f820eb12a155aa5bc92d2", size = 223467760, upload-time = "2025-09-04T08:33:04.222Z" }, { url = "https://files.pythonhosted.org/packages/5f/67/cba3777620cdacb99102da4042883709c41c709f4b6323c10781a9c3aa34/nvidia_cusolver-12.0.4.66-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:0a759da5dea5c0ea10fd307de75cdeb59e7ea4fcb8add0924859b944babf1112", size = 200941980, upload-time = "2025-09-04T08:33:22.767Z" }, +======= + { url = "https://files.pythonhosted.org/packages/c2/f5/e1854cb2f2bcd4280c44736c93550cc300ff4b8c95ebe370d0aa7d2b473d/nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b", size = 288216466 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2204,8 +3095,12 @@ dependencies = [ { name = "nvidia-nvjitlink" }, ] wheels = [ +<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/f8/94/5c26f33738ae35276672f12615a64bd008ed5be6d1ebcb23579285d960a9/nvidia_cusparse-12.6.3.3-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:80bcc4662f23f1054ee334a15c72b8940402975e0eab63178fc7e670aa59472c", size = 162155568, upload-time = "2025-09-04T08:33:42.864Z" }, { url = "https://files.pythonhosted.org/packages/fa/18/623c77619c31d62efd55302939756966f3ecc8d724a14dab2b75f1508850/nvidia_cusparse-12.6.3.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2b3c89c88d01ee0e477cb7f82ef60a11a4bcd57b6b87c33f789350b59759360b", size = 145942937, upload-time = "2025-09-04T08:33:58.029Z" }, +======= + { url = "https://files.pythonhosted.org/packages/56/79/12978b96bd44274fe38b5dde5cfb660b1d114f70a65ef962bcbbed99b549/nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f1bb701d6b930d5a7cea44c19ceb973311500847f81b634d802b7b539dc55623", size = 287193691 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2213,8 +3108,12 @@ name = "nvidia-cusparselt-cu13" version = "0.8.1" source = { registry = "https://pypi.org/simple" } wheels = [ +<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/46/e1/cdc1797eadf82d3a9a575a19b33fdc871a97edbec42c00b5b5e914f4aff4/nvidia_cusparselt_cu13-0.8.1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:4dca476c50bf4780d46cd0bfbd82e2bc10a08e4fef7950917ce8d7578d22a23f", size = 221051344, upload-time = "2025-09-05T18:49:51.289Z" }, { url = "https://files.pythonhosted.org/packages/34/7d/2661f2fb3ac4302f3a246f5fc030213ac60c1fe0bce84f9783dbd831dbb7/nvidia_cusparselt_cu13-0.8.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:786ce87568c303fadb5afcc7102d454cd3040d75f6f8626f5db460d1871f4dd0", size = 170148586, upload-time = "2025-09-05T18:50:50.248Z" }, +======= + { url = "https://files.pythonhosted.org/packages/6e/89/f7a07dc961b60645dbbf42e80f2bc85ade7feb9a491b11a1e973aa00071f/nvidia_nccl_cu12-2.27.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ad730cf15cb5d25fe849c6e6ca9eb5b76db16a80f13f425ac68d8e2e55624457", size = 322348229 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2222,8 +3121,12 @@ name = "nvidia-nccl-cu13" version = "2.29.7" source = { registry = "https://pypi.org/simple" } wheels = [ +<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/72/0d/daf50d44177ee0cbc7ff0a0c91eb5ff676c82be42f9a970bc7597f440c3a/nvidia_nccl_cu13-2.29.7-py3-none-manylinux_2_18_aarch64.whl", hash = "sha256:674a12383e3c38a1bcccae7d4f3633b37852230b6047883cb2f4c2d1b36d9bf5", size = 206014712, upload-time = "2026-03-03T05:34:20.843Z" }, { url = "https://files.pythonhosted.org/packages/67/f4/58e4e91b6919367c7aafb8e36fce9aad1a3047e536bf7e2fd560927d3a4c/nvidia_nccl_cu13-2.29.7-py3-none-manylinux_2_18_x86_64.whl", hash = "sha256:edd81538446786ec3b73972543e53bb43bcaf0bfc8ef76cb679fcc390ffe136d", size = 205976000, upload-time = "2026-03-03T05:36:24.472Z" }, +======= + { url = "https://files.pythonhosted.org/packages/f6/74/86a07f1d0f42998ca31312f998bd3b9a7eff7f52378f4f270c8679c77fb9/nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:81ff63371a7ebd6e6451970684f916be2eab07321b73c9d244dc2b4da7f73b88", size = 39254836 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2231,8 +3134,12 @@ name = "nvidia-nvjitlink" version = "13.0.88" source = { registry = "https://pypi.org/simple" } wheels = [ +<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/56/7a/123e033aaff487c77107195fa5a2b8686795ca537935a24efae476c41f05/nvidia_nvjitlink-13.0.88-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:13a74f429e23b921c1109976abefacc69835f2f433ebd323d3946e11d804e47b", size = 40713933, upload-time = "2025-09-04T08:35:43.553Z" }, { url = "https://files.pythonhosted.org/packages/ab/2c/93c5250e64df4f894f1cbb397c6fd71f79813f9fd79d7cd61de3f97b3c2d/nvidia_nvjitlink-13.0.88-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e931536ccc7d467a98ba1d8b89ff7fa7f1fa3b13f2b0069118cd7f47bff07d0c", size = 38768748, upload-time = "2025-09-04T08:35:20.008Z" }, +======= + { url = "https://files.pythonhosted.org/packages/3b/6c/99acb2f9eb85c29fc6f3a7ac4dccfd992e22666dd08a642b303311326a97/nvidia_nvshmem_cu12-3.3.20-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d00f26d3f9b2e3c3065be895e3059d6479ea5c638a3f38c9fec49b1b9dd7c1e5", size = 124657145 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2240,6 +3147,7 @@ name = "nvidia-nvshmem-cu13" version = "3.4.5" source = { registry = "https://pypi.org/simple" } wheels = [ +<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/dc/0f/05cc9c720236dcd2db9c1ab97fff629e96821be2e63103569da0c9b72f19/nvidia_nvshmem_cu13-3.4.5-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:6dc2a197f38e5d0376ad52cd1a2a3617d3cdc150fd5966f4aee9bcebb1d68fe9", size = 60215947, upload-time = "2025-09-06T00:32:20.022Z" }, { url = "https://files.pythonhosted.org/packages/3c/35/a9bf80a609e74e3b000fef598933235c908fcefcef9026042b8e6dfde2a9/nvidia_nvshmem_cu13-3.4.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:290f0a2ee94c9f3687a02502f3b9299a9f9fe826e6d0287ee18482e78d495b80", size = 60412546, upload-time = "2025-09-06T00:32:41.564Z" }, ] @@ -2251,6 +3159,9 @@ source = { registry = "https://pypi.org/simple" } wheels = [ { url = "https://files.pythonhosted.org/packages/c2/f3/d86c845465a2723ad7e1e5c36dcd75ddb82898b3f53be47ebd429fb2fa5d/nvidia_nvtx-13.0.85-py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:4936d1d6780fbe68db454f5e72a42ff64d1fd6397df9f363ae786930fd5c1cd4", size = 148047, upload-time = "2025-09-04T08:29:01.761Z" }, { url = "https://files.pythonhosted.org/packages/a8/64/3708a90d1ebe202ffdeb7185f878a3c84d15c2b2c31858da2ce0583e2def/nvidia_nvtx-13.0.85-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cb7780edb6b14107373c835bf8b72e7a178bac7367e23da7acb108f973f157a6", size = 148878, upload-time = "2025-09-04T08:28:53.627Z" }, +======= + { url = "https://files.pythonhosted.org/packages/a2/eb/86626c1bbc2edb86323022371c39aa48df6fd8b0a1647bc274577f72e90b/nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5b17e2001cc0d751a5bc2c6ec6d26ad95913324a4adb86788c944f8ce9ba441f", size = 89954 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2266,45 +3177,63 @@ dependencies = [ { name = "sqlalchemy" }, { name = "tqdm" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/f4/aa/05f5e3f662cc96a4c478fc3446b8ed6359825a2b504ecb614a9ac84e4a4d/optuna-4.9.0.tar.gz", hash = "sha256:b322e5cbdf1655fb84c37646c4a7a1f391de1b47806bbe222e015825d0a82b87", size = 485834, upload-time = "2026-06-01T06:23:30.424Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/ab/f3/e5fcd5d9b15771ed6dc10e3a7eeddc672e418f4f4c4653d216cc1d857e2d/optuna-4.9.0-py3-none-any.whl", hash = "sha256:f52f3be6148654850c92a5860d398fd88ec6b2c84ab68d9c3d07dcff02e7afee", size = 425553, upload-time = "2026-06-01T06:23:28.804Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/6b/81/08f90f194eed78178064a9383432eca95611e2c5331e7b01e2418ce4b15a/optuna-4.6.0.tar.gz", hash = "sha256:89e38c2447c7f793a726617b8043f01e31f0bad54855040db17eb3b49404a369", size = 477444 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/58/de/3d8455b08cb6312f8cc46aacdf16c71d4d881a1db4a4140fc5ef31108422/optuna-4.6.0-py3-none-any.whl", hash = "sha256:4c3a9facdef2b2dd7e3e2a8ae3697effa70fae4056fcf3425cfc6f5a40feb069", size = 404708 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "overrides" version = "7.7.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/36/86/b585f53236dec60aba864e050778b25045f857e17f6e5ea0ae95fe80edd2/overrides-7.7.0.tar.gz", hash = "sha256:55158fa3d93b98cc75299b1e67078ad9003ca27945c76162c1c0766d6f91820a", size = 22812, upload-time = "2024-01-27T21:01:33.423Z" } +sdist = { url = "https://files.pythonhosted.org/packages/36/86/b585f53236dec60aba864e050778b25045f857e17f6e5ea0ae95fe80edd2/overrides-7.7.0.tar.gz", hash = "sha256:55158fa3d93b98cc75299b1e67078ad9003ca27945c76162c1c0766d6f91820a", size = 22812 } wheels = [ - { url = "https://files.pythonhosted.org/packages/2c/ab/fc8290c6a4c722e5514d80f62b2dc4c4df1a68a41d1364e625c35990fcf3/overrides-7.7.0-py3-none-any.whl", hash = "sha256:c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49", size = 17832, upload-time = "2024-01-27T21:01:31.393Z" }, + { url = "https://files.pythonhosted.org/packages/2c/ab/fc8290c6a4c722e5514d80f62b2dc4c4df1a68a41d1364e625c35990fcf3/overrides-7.7.0-py3-none-any.whl", hash = "sha256:c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49", size = 17832 }, ] [[package]] name = "packaging" version = "26.2" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/d7/f1/e7a6dd94a8d4a5626c03e4e99c87f241ba9e350cd9e6d75123f992427270/packaging-26.2.tar.gz", hash = "sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661", size = 228134, upload-time = "2026-04-24T20:15:23.917Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/df/b2/87e62e8c3e2f4b32e5fe99e0b86d576da1312593b39f47d8ceef365e95ed/packaging-26.2-py3-none-any.whl", hash = "sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e", size = 100195, upload-time = "2026-04-24T20:15:22.081Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/a1/d4/1fc4078c65507b51b96ca8f8c3ba19e6a61c8253c72794544580a7b6c24d/packaging-25.0.tar.gz", hash = "sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f", size = 165727 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl", hash = "sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484", size = 66469 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "pandocfilters" version = "1.5.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/70/6f/3dd4940bbe001c06a65f88e36bad298bc7a0de5036115639926b0c5c0458/pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e", size = 8454, upload-time = "2024-01-18T20:08:13.726Z" } +sdist = { url = "https://files.pythonhosted.org/packages/70/6f/3dd4940bbe001c06a65f88e36bad298bc7a0de5036115639926b0c5c0458/pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e", size = 8454 } wheels = [ - { url = "https://files.pythonhosted.org/packages/ef/af/4fbc8cab944db5d21b7e2a5b8e9211a03a79852b1157e2c102fcc61ac440/pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc", size = 8663, upload-time = "2024-01-18T20:08:11.28Z" }, + { url = "https://files.pythonhosted.org/packages/ef/af/4fbc8cab944db5d21b7e2a5b8e9211a03a79852b1157e2c102fcc61ac440/pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc", size = 8663 }, ] [[package]] name = "parso" version = "0.8.7" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/30/4b/90c937815137d43ce71ba043cd3566221e9df6b9c805f24b5d138c9d40a7/parso-0.8.7.tar.gz", hash = "sha256:eaaac4c9fdd5e9e8852dc778d2d7405897ec510f2a298071453e5e3a07914bb1", size = 401824, upload-time = "2026-05-01T23:13:02.138Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/99/5d/8268b644392ee874ee82a635cd0df1773de230bde356c38de28e298392cc/parso-0.8.7-py2.py3-none-any.whl", hash = "sha256:a8926eb2a1b915486941fdbd31e86a4baf88fe8c210f25f2f35ecec5b574ca1c", size = 107025, upload-time = "2026-05-01T23:12:58.867Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/d4/de/53e0bcf53d13e005bd8c92e7855142494f41171b34c2536b86187474184d/parso-0.8.5.tar.gz", hash = "sha256:034d7354a9a018bdce352f48b2a8a450f05e9d6ee85db84764e9b6bd96dafe5a", size = 401205 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/16/32/f8e3c85d1d5250232a5d3477a2a28cc291968ff175caeadaf3cc19ce0e4a/parso-0.8.5-py2.py3-none-any.whl", hash = "sha256:646204b5ee239c396d040b90f9e272e9a8017c630092bf59980beb62fd033887", size = 106668 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2315,9 +3244,9 @@ dependencies = [ { name = "locket" }, { name = "toolz" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/b2/3a/3f06f34820a31257ddcabdfafc2672c5816be79c7e353b02c1f318daa7d4/partd-1.4.2.tar.gz", hash = "sha256:d022c33afbdc8405c226621b015e8067888173d85f7f5ecebb3cafed9a20f02c", size = 21029, upload-time = "2024-05-06T19:51:41.945Z" } +sdist = { url = "https://files.pythonhosted.org/packages/b2/3a/3f06f34820a31257ddcabdfafc2672c5816be79c7e353b02c1f318daa7d4/partd-1.4.2.tar.gz", hash = "sha256:d022c33afbdc8405c226621b015e8067888173d85f7f5ecebb3cafed9a20f02c", size = 21029 } wheels = [ - { url = "https://files.pythonhosted.org/packages/71/e7/40fb618334dcdf7c5a316c0e7343c5cd82d3d866edc100d98e29bc945ecd/partd-1.4.2-py3-none-any.whl", hash = "sha256:978e4ac767ec4ba5b86c6eaa52e5a2a3bc748a2ca839e8cc798f1cc6ce6efb0f", size = 18905, upload-time = "2024-05-06T19:51:39.271Z" }, + { url = "https://files.pythonhosted.org/packages/71/e7/40fb618334dcdf7c5a316c0e7343c5cd82d3d866edc100d98e29bc945ecd/partd-1.4.2-py3-none-any.whl", hash = "sha256:978e4ac767ec4ba5b86c6eaa52e5a2a3bc748a2ca839e8cc798f1cc6ce6efb0f", size = 18905 }, ] [[package]] @@ -2327,15 +3256,16 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "ptyprocess" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/42/92/cc564bf6381ff43ce1f4d06852fc19a2f11d180f23dc32d9588bee2f149d/pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f", size = 166450, upload-time = "2023-11-25T09:07:26.339Z" } +sdist = { url = "https://files.pythonhosted.org/packages/42/92/cc564bf6381ff43ce1f4d06852fc19a2f11d180f23dc32d9588bee2f149d/pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f", size = 166450 } wheels = [ - { url = "https://files.pythonhosted.org/packages/9e/c3/059298687310d527a58bb01f3b1965787ee3b40dce76752eda8b44e9a2c5/pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523", size = 63772, upload-time = "2023-11-25T06:56:14.81Z" }, + { url = "https://files.pythonhosted.org/packages/9e/c3/059298687310d527a58bb01f3b1965787ee3b40dce76752eda8b44e9a2c5/pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523", size = 63772 }, ] [[package]] name = "pillow" version = "12.2.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/8c/21/c2bcdd5906101a30244eaffc1b6e6ce71a31bd0742a01eb89e660ebfac2d/pillow-12.2.0.tar.gz", hash = "sha256:a830b1a40919539d07806aa58e1b114df53ddd43213d9c8b75847eee6c0182b5", size = 46987819, upload-time = "2026-04-01T14:46:17.687Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/68/e1/748f5663efe6edcfc4e74b2b93edfb9b8b99b67f21a854c3ae416500a2d9/pillow-12.2.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:8be29e59487a79f173507c30ddf57e733a357f67881430449bb32614075a40ab", size = 5354347, upload-time = "2026-04-01T14:42:44.255Z" }, @@ -2417,6 +3347,89 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/c3/28/ec0fc38107fc32536908034e990c47914c57cd7c5a3ece4d8d8f7ffd7e27/pillow-12.2.0-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4a6c9fa44005fa37a91ebfc95d081e8079757d2e904b27103f4f5fa6f0bf78c0", size = 5355404, upload-time = "2026-04-01T14:46:06.33Z" }, { url = "https://files.pythonhosted.org/packages/5e/8b/51b0eddcfa2180d60e41f06bd6d0a62202b20b59c68f5a132e615b75aecf/pillow-12.2.0-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:25373b66e0dd5905ed63fa3cae13c82fbddf3079f2c8bf15c6fb6a35586324c1", size = 6002215, upload-time = "2026-04-01T14:46:08.83Z" }, { url = "https://files.pythonhosted.org/packages/bc/60/5382c03e1970de634027cee8e1b7d39776b778b81812aaf45b694dfe9e28/pillow-12.2.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:bfa9c230d2fe991bed5318a5f119bd6780cda2915cca595393649fc118ab895e", size = 7080946, upload-time = "2026-04-01T14:46:11.734Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/5a/b0/cace85a1b0c9775a9f8f5d5423c8261c858760e2466c79b2dd184638b056/pillow-12.0.0.tar.gz", hash = "sha256:87d4f8125c9988bfbed67af47dd7a953e2fc7b0cc1e7800ec6d2080d490bb353", size = 47008828 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0e/5a/a2f6773b64edb921a756eb0729068acad9fc5208a53f4a349396e9436721/pillow-12.0.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:0fd00cac9c03256c8b2ff58f162ebcd2587ad3e1f2e397eab718c47e24d231cc", size = 5289798 }, + { url = "https://files.pythonhosted.org/packages/2e/05/069b1f8a2e4b5a37493da6c5868531c3f77b85e716ad7a590ef87d58730d/pillow-12.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a3475b96f5908b3b16c47533daaa87380c491357d197564e0ba34ae75c0f3257", size = 4650589 }, + { url = "https://files.pythonhosted.org/packages/61/e3/2c820d6e9a36432503ead175ae294f96861b07600a7156154a086ba7111a/pillow-12.0.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:110486b79f2d112cf6add83b28b627e369219388f64ef2f960fef9ebaf54c642", size = 6230472 }, + { url = "https://files.pythonhosted.org/packages/4f/89/63427f51c64209c5e23d4d52071c8d0f21024d3a8a487737caaf614a5795/pillow-12.0.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5269cc1caeedb67e6f7269a42014f381f45e2e7cd42d834ede3c703a1d915fe3", size = 8033887 }, + { url = "https://files.pythonhosted.org/packages/f6/1b/c9711318d4901093c15840f268ad649459cd81984c9ec9887756cca049a5/pillow-12.0.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:aa5129de4e174daccbc59d0a3b6d20eaf24417d59851c07ebb37aeb02947987c", size = 6343964 }, + { url = "https://files.pythonhosted.org/packages/41/1e/db9470f2d030b4995083044cd8738cdd1bf773106819f6d8ba12597d5352/pillow-12.0.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bee2a6db3a7242ea309aa7ee8e2780726fed67ff4e5b40169f2c940e7eb09227", size = 7034756 }, + { url = "https://files.pythonhosted.org/packages/cc/b0/6177a8bdd5ee4ed87cba2de5a3cc1db55ffbbec6176784ce5bb75aa96798/pillow-12.0.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:90387104ee8400a7b4598253b4c406f8958f59fcf983a6cea2b50d59f7d63d0b", size = 6458075 }, + { url = "https://files.pythonhosted.org/packages/bc/5e/61537aa6fa977922c6a03253a0e727e6e4a72381a80d63ad8eec350684f2/pillow-12.0.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:bc91a56697869546d1b8f0a3ff35224557ae7f881050e99f615e0119bf934b4e", size = 7125955 }, + { url = "https://files.pythonhosted.org/packages/1f/3d/d5033539344ee3cbd9a4d69e12e63ca3a44a739eb2d4c8da350a3d38edd7/pillow-12.0.0-cp311-cp311-win32.whl", hash = "sha256:27f95b12453d165099c84f8a8bfdfd46b9e4bda9e0e4b65f0635430027f55739", size = 6298440 }, + { url = "https://files.pythonhosted.org/packages/4d/42/aaca386de5cc8bd8a0254516957c1f265e3521c91515b16e286c662854c4/pillow-12.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:b583dc9070312190192631373c6c8ed277254aa6e6084b74bdd0a6d3b221608e", size = 6999256 }, + { url = "https://files.pythonhosted.org/packages/ba/f1/9197c9c2d5708b785f631a6dfbfa8eb3fb9672837cb92ae9af812c13b4ed/pillow-12.0.0-cp311-cp311-win_arm64.whl", hash = "sha256:759de84a33be3b178a64c8ba28ad5c135900359e85fb662bc6e403ad4407791d", size = 2436025 }, + { url = "https://files.pythonhosted.org/packages/2c/90/4fcce2c22caf044e660a198d740e7fbc14395619e3cb1abad12192c0826c/pillow-12.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:53561a4ddc36facb432fae7a9d8afbfaf94795414f5cdc5fc52f28c1dca90371", size = 5249377 }, + { url = "https://files.pythonhosted.org/packages/fd/e0/ed960067543d080691d47d6938ebccbf3976a931c9567ab2fbfab983a5dd/pillow-12.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:71db6b4c1653045dacc1585c1b0d184004f0d7e694c7b34ac165ca70c0838082", size = 4650343 }, + { url = "https://files.pythonhosted.org/packages/e7/a1/f81fdeddcb99c044bf7d6faa47e12850f13cee0849537a7d27eeab5534d4/pillow-12.0.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2fa5f0b6716fc88f11380b88b31fe591a06c6315e955c096c35715788b339e3f", size = 6232981 }, + { url = "https://files.pythonhosted.org/packages/88/e1/9098d3ce341a8750b55b0e00c03f1630d6178f38ac191c81c97a3b047b44/pillow-12.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:82240051c6ca513c616f7f9da06e871f61bfd7805f566275841af15015b8f98d", size = 8041399 }, + { url = "https://files.pythonhosted.org/packages/a7/62/a22e8d3b602ae8cc01446d0c57a54e982737f44b6f2e1e019a925143771d/pillow-12.0.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:55f818bd74fe2f11d4d7cbc65880a843c4075e0ac7226bc1a23261dbea531953", size = 6347740 }, + { url = "https://files.pythonhosted.org/packages/4f/87/424511bdcd02c8d7acf9f65caa09f291a519b16bd83c3fb3374b3d4ae951/pillow-12.0.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b87843e225e74576437fd5b6a4c2205d422754f84a06942cfaf1dc32243e45a8", size = 7040201 }, + { url = "https://files.pythonhosted.org/packages/dc/4d/435c8ac688c54d11755aedfdd9f29c9eeddf68d150fe42d1d3dbd2365149/pillow-12.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c607c90ba67533e1b2355b821fef6764d1dd2cbe26b8c1005ae84f7aea25ff79", size = 6462334 }, + { url = "https://files.pythonhosted.org/packages/2b/f2/ad34167a8059a59b8ad10bc5c72d4d9b35acc6b7c0877af8ac885b5f2044/pillow-12.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:21f241bdd5080a15bc86d3466a9f6074a9c2c2b314100dd896ac81ee6db2f1ba", size = 7134162 }, + { url = "https://files.pythonhosted.org/packages/0c/b1/a7391df6adacf0a5c2cf6ac1cf1fcc1369e7d439d28f637a847f8803beb3/pillow-12.0.0-cp312-cp312-win32.whl", hash = "sha256:dd333073e0cacdc3089525c7df7d39b211bcdf31fc2824e49d01c6b6187b07d0", size = 6298769 }, + { url = "https://files.pythonhosted.org/packages/a2/0b/d87733741526541c909bbf159e338dcace4f982daac6e5a8d6be225ca32d/pillow-12.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:9fe611163f6303d1619bbcb653540a4d60f9e55e622d60a3108be0d5b441017a", size = 7001107 }, + { url = "https://files.pythonhosted.org/packages/bc/96/aaa61ce33cc98421fb6088af2a03be4157b1e7e0e87087c888e2370a7f45/pillow-12.0.0-cp312-cp312-win_arm64.whl", hash = "sha256:7dfb439562f234f7d57b1ac6bc8fe7f838a4bd49c79230e0f6a1da93e82f1fad", size = 2436012 }, + { url = "https://files.pythonhosted.org/packages/62/f2/de993bb2d21b33a98d031ecf6a978e4b61da207bef02f7b43093774c480d/pillow-12.0.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:0869154a2d0546545cde61d1789a6524319fc1897d9ee31218eae7a60ccc5643", size = 4045493 }, + { url = "https://files.pythonhosted.org/packages/0e/b6/bc8d0c4c9f6f111a783d045310945deb769b806d7574764234ffd50bc5ea/pillow-12.0.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:a7921c5a6d31b3d756ec980f2f47c0cfdbce0fc48c22a39347a895f41f4a6ea4", size = 4120461 }, + { url = "https://files.pythonhosted.org/packages/5d/57/d60d343709366a353dc56adb4ee1e7d8a2cc34e3fbc22905f4167cfec119/pillow-12.0.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:1ee80a59f6ce048ae13cda1abf7fbd2a34ab9ee7d401c46be3ca685d1999a399", size = 3576912 }, + { url = "https://files.pythonhosted.org/packages/a4/a4/a0a31467e3f83b94d37568294b01d22b43ae3c5d85f2811769b9c66389dd/pillow-12.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c50f36a62a22d350c96e49ad02d0da41dbd17ddc2e29750dbdba4323f85eb4a5", size = 5249132 }, + { url = "https://files.pythonhosted.org/packages/83/06/48eab21dd561de2914242711434c0c0eb992ed08ff3f6107a5f44527f5e9/pillow-12.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:5193fde9a5f23c331ea26d0cf171fbf67e3f247585f50c08b3e205c7aeb4589b", size = 4650099 }, + { url = "https://files.pythonhosted.org/packages/fc/bd/69ed99fd46a8dba7c1887156d3572fe4484e3f031405fcc5a92e31c04035/pillow-12.0.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:bde737cff1a975b70652b62d626f7785e0480918dece11e8fef3c0cf057351c3", size = 6230808 }, + { url = "https://files.pythonhosted.org/packages/ea/94/8fad659bcdbf86ed70099cb60ae40be6acca434bbc8c4c0d4ef356d7e0de/pillow-12.0.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a6597ff2b61d121172f5844b53f21467f7082f5fb385a9a29c01414463f93b07", size = 8037804 }, + { url = "https://files.pythonhosted.org/packages/20/39/c685d05c06deecfd4e2d1950e9a908aa2ca8bc4e6c3b12d93b9cafbd7837/pillow-12.0.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0b817e7035ea7f6b942c13aa03bb554fc44fea70838ea21f8eb31c638326584e", size = 6345553 }, + { url = "https://files.pythonhosted.org/packages/38/57/755dbd06530a27a5ed74f8cb0a7a44a21722ebf318edbe67ddbd7fb28f88/pillow-12.0.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f4f1231b7dec408e8670264ce63e9c71409d9583dd21d32c163e25213ee2a344", size = 7037729 }, + { url = "https://files.pythonhosted.org/packages/ca/b6/7e94f4c41d238615674d06ed677c14883103dce1c52e4af16f000338cfd7/pillow-12.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6e51b71417049ad6ab14c49608b4a24d8fb3fe605e5dfabfe523b58064dc3d27", size = 6459789 }, + { url = "https://files.pythonhosted.org/packages/9c/14/4448bb0b5e0f22dd865290536d20ec8a23b64e2d04280b89139f09a36bb6/pillow-12.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d120c38a42c234dc9a8c5de7ceaaf899cf33561956acb4941653f8bdc657aa79", size = 7130917 }, + { url = "https://files.pythonhosted.org/packages/dd/ca/16c6926cc1c015845745d5c16c9358e24282f1e588237a4c36d2b30f182f/pillow-12.0.0-cp313-cp313-win32.whl", hash = "sha256:4cc6b3b2efff105c6a1656cfe59da4fdde2cda9af1c5e0b58529b24525d0a098", size = 6302391 }, + { url = "https://files.pythonhosted.org/packages/6d/2a/dd43dcfd6dae9b6a49ee28a8eedb98c7d5ff2de94a5d834565164667b97b/pillow-12.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:4cf7fed4b4580601c4345ceb5d4cbf5a980d030fd5ad07c4d2ec589f95f09905", size = 7007477 }, + { url = "https://files.pythonhosted.org/packages/77/f0/72ea067f4b5ae5ead653053212af05ce3705807906ba3f3e8f58ddf617e6/pillow-12.0.0-cp313-cp313-win_arm64.whl", hash = "sha256:9f0b04c6b8584c2c193babcccc908b38ed29524b29dd464bc8801bf10d746a3a", size = 2435918 }, + { url = "https://files.pythonhosted.org/packages/f5/5e/9046b423735c21f0487ea6cb5b10f89ea8f8dfbe32576fe052b5ba9d4e5b/pillow-12.0.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:7fa22993bac7b77b78cae22bad1e2a987ddf0d9015c63358032f84a53f23cdc3", size = 5251406 }, + { url = "https://files.pythonhosted.org/packages/12/66/982ceebcdb13c97270ef7a56c3969635b4ee7cd45227fa707c94719229c5/pillow-12.0.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:f135c702ac42262573fe9714dfe99c944b4ba307af5eb507abef1667e2cbbced", size = 4653218 }, + { url = "https://files.pythonhosted.org/packages/16/b3/81e625524688c31859450119bf12674619429cab3119eec0e30a7a1029cb/pillow-12.0.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:c85de1136429c524e55cfa4e033b4a7940ac5c8ee4d9401cc2d1bf48154bbc7b", size = 6266564 }, + { url = "https://files.pythonhosted.org/packages/98/59/dfb38f2a41240d2408096e1a76c671d0a105a4a8471b1871c6902719450c/pillow-12.0.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:38df9b4bfd3db902c9c2bd369bcacaf9d935b2fff73709429d95cc41554f7b3d", size = 8069260 }, + { url = "https://files.pythonhosted.org/packages/dc/3d/378dbea5cd1874b94c312425ca77b0f47776c78e0df2df751b820c8c1d6c/pillow-12.0.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7d87ef5795da03d742bf49439f9ca4d027cde49c82c5371ba52464aee266699a", size = 6379248 }, + { url = "https://files.pythonhosted.org/packages/84/b0/d525ef47d71590f1621510327acec75ae58c721dc071b17d8d652ca494d8/pillow-12.0.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:aff9e4d82d082ff9513bdd6acd4f5bd359f5b2c870907d2b0a9c5e10d40c88fe", size = 7066043 }, + { url = "https://files.pythonhosted.org/packages/61/2c/aced60e9cf9d0cde341d54bf7932c9ffc33ddb4a1595798b3a5150c7ec4e/pillow-12.0.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:8d8ca2b210ada074d57fcee40c30446c9562e542fc46aedc19baf758a93532ee", size = 6490915 }, + { url = "https://files.pythonhosted.org/packages/ef/26/69dcb9b91f4e59f8f34b2332a4a0a951b44f547c4ed39d3e4dcfcff48f89/pillow-12.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:99a7f72fb6249302aa62245680754862a44179b545ded638cf1fef59befb57ef", size = 7157998 }, + { url = "https://files.pythonhosted.org/packages/61/2b/726235842220ca95fa441ddf55dd2382b52ab5b8d9c0596fe6b3f23dafe8/pillow-12.0.0-cp313-cp313t-win32.whl", hash = "sha256:4078242472387600b2ce8d93ade8899c12bf33fa89e55ec89fe126e9d6d5d9e9", size = 6306201 }, + { url = "https://files.pythonhosted.org/packages/c0/3d/2afaf4e840b2df71344ababf2f8edd75a705ce500e5dc1e7227808312ae1/pillow-12.0.0-cp313-cp313t-win_amd64.whl", hash = "sha256:2c54c1a783d6d60595d3514f0efe9b37c8808746a66920315bfd34a938d7994b", size = 7013165 }, + { url = "https://files.pythonhosted.org/packages/6f/75/3fa09aa5cf6ed04bee3fa575798ddf1ce0bace8edb47249c798077a81f7f/pillow-12.0.0-cp313-cp313t-win_arm64.whl", hash = "sha256:26d9f7d2b604cd23aba3e9faf795787456ac25634d82cd060556998e39c6fa47", size = 2437834 }, + { url = "https://files.pythonhosted.org/packages/54/2a/9a8c6ba2c2c07b71bec92cf63e03370ca5e5f5c5b119b742bcc0cde3f9c5/pillow-12.0.0-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:beeae3f27f62308f1ddbcfb0690bf44b10732f2ef43758f169d5e9303165d3f9", size = 4045531 }, + { url = "https://files.pythonhosted.org/packages/84/54/836fdbf1bfb3d66a59f0189ff0b9f5f666cee09c6188309300df04ad71fa/pillow-12.0.0-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:d4827615da15cd59784ce39d3388275ec093ae3ee8d7f0c089b76fa87af756c2", size = 4120554 }, + { url = "https://files.pythonhosted.org/packages/0d/cd/16aec9f0da4793e98e6b54778a5fbce4f375c6646fe662e80600b8797379/pillow-12.0.0-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:3e42edad50b6909089750e65c91aa09aaf1e0a71310d383f11321b27c224ed8a", size = 3576812 }, + { url = "https://files.pythonhosted.org/packages/f6/b7/13957fda356dc46339298b351cae0d327704986337c3c69bb54628c88155/pillow-12.0.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:e5d8efac84c9afcb40914ab49ba063d94f5dbdf5066db4482c66a992f47a3a3b", size = 5252689 }, + { url = "https://files.pythonhosted.org/packages/fc/f5/eae31a306341d8f331f43edb2e9122c7661b975433de5e447939ae61c5da/pillow-12.0.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:266cd5f2b63ff316d5a1bba46268e603c9caf5606d44f38c2873c380950576ad", size = 4650186 }, + { url = "https://files.pythonhosted.org/packages/86/62/2a88339aa40c4c77e79108facbd307d6091e2c0eb5b8d3cf4977cfca2fe6/pillow-12.0.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:58eea5ebe51504057dd95c5b77d21700b77615ab0243d8152793dc00eb4faf01", size = 6230308 }, + { url = "https://files.pythonhosted.org/packages/c7/33/5425a8992bcb32d1cb9fa3dd39a89e613d09a22f2c8083b7bf43c455f760/pillow-12.0.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f13711b1a5ba512d647a0e4ba79280d3a9a045aaf7e0cc6fbe96b91d4cdf6b0c", size = 8039222 }, + { url = "https://files.pythonhosted.org/packages/d8/61/3f5d3b35c5728f37953d3eec5b5f3e77111949523bd2dd7f31a851e50690/pillow-12.0.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6846bd2d116ff42cba6b646edf5bf61d37e5cbd256425fa089fee4ff5c07a99e", size = 6346657 }, + { url = "https://files.pythonhosted.org/packages/3a/be/ee90a3d79271227e0f0a33c453531efd6ed14b2e708596ba5dd9be948da3/pillow-12.0.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c98fa880d695de164b4135a52fd2e9cd7b7c90a9d8ac5e9e443a24a95ef9248e", size = 7038482 }, + { url = "https://files.pythonhosted.org/packages/44/34/a16b6a4d1ad727de390e9bd9f19f5f669e079e5826ec0f329010ddea492f/pillow-12.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fa3ed2a29a9e9d2d488b4da81dcb54720ac3104a20bf0bd273f1e4648aff5af9", size = 6461416 }, + { url = "https://files.pythonhosted.org/packages/b6/39/1aa5850d2ade7d7ba9f54e4e4c17077244ff7a2d9e25998c38a29749eb3f/pillow-12.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d034140032870024e6b9892c692fe2968493790dd57208b2c37e3fb35f6df3ab", size = 7131584 }, + { url = "https://files.pythonhosted.org/packages/bf/db/4fae862f8fad0167073a7733973bfa955f47e2cac3dc3e3e6257d10fab4a/pillow-12.0.0-cp314-cp314-win32.whl", hash = "sha256:1b1b133e6e16105f524a8dec491e0586d072948ce15c9b914e41cdadd209052b", size = 6400621 }, + { url = "https://files.pythonhosted.org/packages/2b/24/b350c31543fb0107ab2599464d7e28e6f856027aadda995022e695313d94/pillow-12.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:8dc232e39d409036af549c86f24aed8273a40ffa459981146829a324e0848b4b", size = 7142916 }, + { url = "https://files.pythonhosted.org/packages/0f/9b/0ba5a6fd9351793996ef7487c4fdbde8d3f5f75dbedc093bb598648fddf0/pillow-12.0.0-cp314-cp314-win_arm64.whl", hash = "sha256:d52610d51e265a51518692045e372a4c363056130d922a7351429ac9f27e70b0", size = 2523836 }, + { url = "https://files.pythonhosted.org/packages/f5/7a/ceee0840aebc579af529b523d530840338ecf63992395842e54edc805987/pillow-12.0.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:1979f4566bb96c1e50a62d9831e2ea2d1211761e5662afc545fa766f996632f6", size = 5255092 }, + { url = "https://files.pythonhosted.org/packages/44/76/20776057b4bfd1aef4eeca992ebde0f53a4dce874f3ae693d0ec90a4f79b/pillow-12.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:b2e4b27a6e15b04832fe9bf292b94b5ca156016bbc1ea9c2c20098a0320d6cf6", size = 4653158 }, + { url = "https://files.pythonhosted.org/packages/82/3f/d9ff92ace07be8836b4e7e87e6a4c7a8318d47c2f1463ffcf121fc57d9cb/pillow-12.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fb3096c30df99fd01c7bf8e544f392103d0795b9f98ba71a8054bcbf56b255f1", size = 6267882 }, + { url = "https://files.pythonhosted.org/packages/9f/7a/4f7ff87f00d3ad33ba21af78bfcd2f032107710baf8280e3722ceec28cda/pillow-12.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7438839e9e053ef79f7112c881cef684013855016f928b168b81ed5835f3e75e", size = 8071001 }, + { url = "https://files.pythonhosted.org/packages/75/87/fcea108944a52dad8cca0715ae6247e271eb80459364a98518f1e4f480c1/pillow-12.0.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5d5c411a8eaa2299322b647cd932586b1427367fd3184ffbb8f7a219ea2041ca", size = 6380146 }, + { url = "https://files.pythonhosted.org/packages/91/52/0d31b5e571ef5fd111d2978b84603fce26aba1b6092f28e941cb46570745/pillow-12.0.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d7e091d464ac59d2c7ad8e7e08105eaf9dafbc3883fd7265ffccc2baad6ac925", size = 7067344 }, + { url = "https://files.pythonhosted.org/packages/7b/f4/2dd3d721f875f928d48e83bb30a434dee75a2531bca839bb996bb0aa5a91/pillow-12.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:792a2c0be4dcc18af9d4a2dfd8a11a17d5e25274a1062b0ec1c2d79c76f3e7f8", size = 6491864 }, + { url = "https://files.pythonhosted.org/packages/30/4b/667dfcf3d61fc309ba5a15b141845cece5915e39b99c1ceab0f34bf1d124/pillow-12.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:afbefa430092f71a9593a99ab6a4e7538bc9eabbf7bf94f91510d3503943edc4", size = 7158911 }, + { url = "https://files.pythonhosted.org/packages/a2/2f/16cabcc6426c32218ace36bf0d55955e813f2958afddbf1d391849fee9d1/pillow-12.0.0-cp314-cp314t-win32.whl", hash = "sha256:3830c769decf88f1289680a59d4f4c46c72573446352e2befec9a8512104fa52", size = 6408045 }, + { url = "https://files.pythonhosted.org/packages/35/73/e29aa0c9c666cf787628d3f0dcf379f4791fba79f4936d02f8b37165bdf8/pillow-12.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:905b0365b210c73afb0ebe9101a32572152dfd1c144c7e28968a331b9217b94a", size = 7148282 }, + { url = "https://files.pythonhosted.org/packages/c1/70/6b41bdcddf541b437bbb9f47f94d2db5d9ddef6c37ccab8c9107743748a4/pillow-12.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:99353a06902c2e43b43e8ff74ee65a7d90307d82370604746738a1e0661ccca7", size = 2525630 }, + { url = "https://files.pythonhosted.org/packages/1d/b3/582327e6c9f86d037b63beebe981425d6811104cb443e8193824ef1a2f27/pillow-12.0.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:b22bd8c974942477156be55a768f7aa37c46904c175be4e158b6a86e3a6b7ca8", size = 5215068 }, + { url = "https://files.pythonhosted.org/packages/fd/d6/67748211d119f3b6540baf90f92fae73ae51d5217b171b0e8b5f7e5d558f/pillow-12.0.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:805ebf596939e48dbb2e4922a1d3852cfc25c38160751ce02da93058b48d252a", size = 4614994 }, + { url = "https://files.pythonhosted.org/packages/2d/e1/f8281e5d844c41872b273b9f2c34a4bf64ca08905668c8ae730eedc7c9fa/pillow-12.0.0-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cae81479f77420d217def5f54b5b9d279804d17e982e0f2fa19b1d1e14ab5197", size = 5246639 }, + { url = "https://files.pythonhosted.org/packages/94/5a/0d8ab8ffe8a102ff5df60d0de5af309015163bf710c7bb3e8311dd3b3ad0/pillow-12.0.0-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:aeaefa96c768fc66818730b952a862235d68825c178f1b3ffd4efd7ad2edcb7c", size = 6986839 }, + { url = "https://files.pythonhosted.org/packages/20/2e/3434380e8110b76cd9eb00a363c484b050f949b4bbe84ba770bb8508a02c/pillow-12.0.0-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:09f2d0abef9e4e2f349305a4f8cc784a8a6c2f58a8c4892eea13b10a943bd26e", size = 5313505 }, + { url = "https://files.pythonhosted.org/packages/57/ca/5a9d38900d9d74785141d6580950fe705de68af735ff6e727cb911b64740/pillow-12.0.0-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bdee52571a343d721fb2eb3b090a82d959ff37fc631e3f70422e0c2e029f3e76", size = 5963654 }, + { url = "https://files.pythonhosted.org/packages/95/7e/f896623c3c635a90537ac093c6a618ebe1a90d87206e42309cb5d98a1b9e/pillow-12.0.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:b290fd8aa38422444d4b50d579de197557f182ef1068b75f5aa8558638b8d0a5", size = 6997850 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2429,27 +3442,39 @@ dependencies = [ { name = "platformdirs" }, { name = "typing-extensions" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/52/9d/b1379cdbd33a49d17d627bc24e2b63cca06a1c5343b38072d2889499e82e/pint-0.25.3.tar.gz", hash = "sha256:f8f5df6cf65314d74da1ade1bf96f8e3e4d0c41b51577ac53c49e7d44ca5acee", size = 255106, upload-time = "2026-03-19T21:57:08.72Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/1b/dd/a9fe6a0a09512da23951c68bf36466aeecd89def3183dc095edbc807ddc5/pint-0.25.3-py3-none-any.whl", hash = "sha256:27eb25143bd5de9fcc4d5a4b484f16faf6b4615aa93ece6b3373a8c1a3c1b97d", size = 307488, upload-time = "2026-03-19T21:57:07.022Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/5f/74/bc3f671997158aef171194c3c4041e549946f4784b8690baa0626a0a164b/pint-0.25.2.tar.gz", hash = "sha256:85a45d1da8fe9c9f7477fed8aef59ad2b939af3d6611507e1a9cbdacdcd3450a", size = 254467 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ab/88/550d41e81e6d43335603a960cd9c75c1d88f9cf01bc9d4ee8e86290aba7d/pint-0.25.2-py3-none-any.whl", hash = "sha256:ca35ab1d8eeeb6f7d9942b3cb5f34ca42b61cdd5fb3eae79531553dcca04dda7", size = 306762 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "platformdirs" version = "4.10.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/d7/47/e4501f49c178ae1d9f4a75073fda4204f52647993f075a9db4d14930e0c5/platformdirs-4.10.0.tar.gz", hash = "sha256:31e761a6a0ca04faf7353ea759bdba55652be214725111e5aac52dfa29d4bef7", size = 31224, upload-time = "2026-05-28T03:32:53.587Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/81/e6/cd9575ac904136b3cbf7aa7ee819ef86eedb7274e46f230e94ea4342e729/platformdirs-4.10.0-py3-none-any.whl", hash = "sha256:fb516cdb12eb0d857d0cd85a7c57cea4d060bee4578d6cf5a14dfdf8cbf8784a", size = 22743, upload-time = "2026-05-28T03:32:52.175Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/61/33/9611380c2bdb1225fdef633e2a9610622310fed35ab11dac9620972ee088/platformdirs-4.5.0.tar.gz", hash = "sha256:70ddccdd7c99fc5942e9fc25636a8b34d04c24b335100223152c2803e4063312", size = 21632 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/73/cb/ac7874b3e5d58441674fb70742e6c374b28b0c7cb988d37d991cde47166c/platformdirs-4.5.0-py3-none-any.whl", hash = "sha256:e578a81bb873cbb89a41fcc904c7ef523cc18284b7e3b3ccf06aca1403b7ebd3", size = 18651 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "pluggy" version = "1.6.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/f9/e2/3e91f31a7d2b083fe6ef3fa267035b518369d9511ffab804f839851d2779/pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3", size = 69412, upload-time = "2025-05-15T12:30:07.975Z" } +sdist = { url = "https://files.pythonhosted.org/packages/f9/e2/3e91f31a7d2b083fe6ef3fa267035b518369d9511ffab804f839851d2779/pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3", size = 69412 } wheels = [ - { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538, upload-time = "2025-05-15T12:30:06.134Z" }, + { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538 }, ] [[package]] @@ -2463,18 +3488,30 @@ dependencies = [ { name = "pyyaml" }, { name = "virtualenv" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/8e/22/2de9408ac81acbb8a7d05d4cc064a152ccf33b3d480ebe0cd292153db239/pre_commit-4.6.0.tar.gz", hash = "sha256:718d2208cef53fdc38206e40524a6d4d9576d103eb16f0fec11c875e7716e9d9", size = 198525, upload-time = "2026-04-21T20:31:41.613Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/80/6e/4b28b62ecb6aae56769c34a8ff1d661473ec1e9519e2d5f8b2c150086b26/pre_commit-4.6.0-py2.py3-none-any.whl", hash = "sha256:e2cf246f7299edcabcf15f9b0571fdce06058527f0a06535068a86d38089f29b", size = 226472, upload-time = "2026-04-21T20:31:40.092Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/a6/49/7845c2d7bf6474efd8e27905b51b11e6ce411708c91e829b93f324de9929/pre_commit-4.4.0.tar.gz", hash = "sha256:f0233ebab440e9f17cabbb558706eb173d19ace965c68cdce2c081042b4fab15", size = 197501 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/27/11/574fe7d13acf30bfd0a8dd7fa1647040f2b8064f13f43e8c963b1e65093b/pre_commit-4.4.0-py2.py3-none-any.whl", hash = "sha256:b35ea52957cbf83dcc5d8ee636cbead8624e3a15fbfa61a370e42158ac8a5813", size = 226049 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "prometheus-client" version = "0.25.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/1b/fb/d9aa83ffe43ce1f19e557c0971d04b90561b0cfd50762aafb01968285553/prometheus_client-0.25.0.tar.gz", hash = "sha256:5e373b75c31afb3c86f1a52fa1ad470c9aace18082d39ec0d2f918d11cc9ba28", size = 86035, upload-time = "2026-04-09T19:53:42.359Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/8d/9b/d4b1e644385499c8346fa9b622a3f030dce14cd6ef8a1871c221a17a67e7/prometheus_client-0.25.0-py3-none-any.whl", hash = "sha256:d5aec89e349a6ec230805d0df882f3807f74fd6c1a2fa86864e3c2279059fed1", size = 64154, upload-time = "2026-04-09T19:53:41.324Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/23/53/3edb5d68ecf6b38fcbcc1ad28391117d2a322d9a1a3eff04bfdb184d8c3b/prometheus_client-0.23.1.tar.gz", hash = "sha256:6ae8f9081eaaaf153a2e959d2e6c4f4fb57b12ef76c8c7980202f1e57b48b2ce", size = 80481 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b8/db/14bafcb4af2139e046d03fd00dea7873e48eafe18b7d2797e73d6681f210/prometheus_client-0.23.1-py3-none-any.whl", hash = "sha256:dd1913e6e76b59cfe44e7a4b83e01afc9873c1bdfd2ed8739f1e76aeca115f99", size = 61145 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2484,15 +3521,16 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "wcwidth" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/a1/96/06e01a7b38dce6fe1db213e061a4602dd6032a8a97ef6c1a862537732421/prompt_toolkit-3.0.52.tar.gz", hash = "sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855", size = 434198, upload-time = "2025-08-27T15:24:02.057Z" } +sdist = { url = "https://files.pythonhosted.org/packages/a1/96/06e01a7b38dce6fe1db213e061a4602dd6032a8a97ef6c1a862537732421/prompt_toolkit-3.0.52.tar.gz", hash = "sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855", size = 434198 } wheels = [ - { url = "https://files.pythonhosted.org/packages/84/03/0d3ce49e2505ae70cf43bc5bb3033955d2fc9f932163e84dc0779cc47f48/prompt_toolkit-3.0.52-py3-none-any.whl", hash = "sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955", size = 391431, upload-time = "2025-08-27T15:23:59.498Z" }, + { url = "https://files.pythonhosted.org/packages/84/03/0d3ce49e2505ae70cf43bc5bb3033955d2fc9f932163e84dc0779cc47f48/prompt_toolkit-3.0.52-py3-none-any.whl", hash = "sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955", size = 391431 }, ] [[package]] name = "protobuf" version = "7.35.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/60/fd/5b1491d9e4b586d621c54f4c36b888714164b6875f8d6afa3f9072906a51/protobuf-7.35.0.tar.gz", hash = "sha256:a2efd84605f41e559f1881b0912b44099d0a2ac9bf46b3474823f10fb393b0e6", size = 458677, upload-time = "2026-05-19T23:02:29.197Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/83/ee/93d06e358a4aa32280b00e722d3ea0a1f25fc3cc5778d80581c9cca2c10e/protobuf-7.35.0-cp310-abi3-macosx_10_9_universal2.whl", hash = "sha256:66be6c513931c794fa92c080ffee41671390da3d79da219cf9c0c0907f035dda", size = 433225, upload-time = "2026-05-19T23:02:19.884Z" }, @@ -2502,12 +3540,24 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/8e/e5/e46adb0badc388bfb84877a5f9f026aff63f60e611016cf64dbe77e05446/protobuf-7.35.0-cp310-abi3-win32.whl", hash = "sha256:4c4617b83ade0e279d1d2bfe04025a1adb87f9ed657de038620dc0ff959357f6", size = 428946, upload-time = "2026-05-19T23:02:25.741Z" }, { url = "https://files.pythonhosted.org/packages/a7/ab/547fbd9e16d879dd13c167478f8ae0a83a428008ca07a5e06acdc23ad473/protobuf-7.35.0-cp310-abi3-win_amd64.whl", hash = "sha256:f05bcadf9a2a6b8dda047007075135fb7d08c73d9177aabc067e1be46881a201", size = 439996, upload-time = "2026-05-19T23:02:26.808Z" }, { url = "https://files.pythonhosted.org/packages/b8/ef/50433d346c56657a70d27f156c7b349ac59a068b01de4eb796e747eecc43/protobuf-7.35.0-py3-none-any.whl", hash = "sha256:c13f325cf242bad135c350629eeb5d54b24228eb472fb3e2e9ebbd4c5dc20ca0", size = 171659, upload-time = "2026-05-19T23:02:27.842Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/0a/03/a1440979a3f74f16cab3b75b0da1a1a7f922d56a8ddea96092391998edc0/protobuf-6.33.1.tar.gz", hash = "sha256:97f65757e8d09870de6fd973aeddb92f85435607235d20b2dfed93405d00c85b", size = 443432 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/06/f1/446a9bbd2c60772ca36556bac8bfde40eceb28d9cc7838755bc41e001d8f/protobuf-6.33.1-cp310-abi3-win32.whl", hash = "sha256:f8d3fdbc966aaab1d05046d0240dd94d40f2a8c62856d41eaa141ff64a79de6b", size = 425593 }, + { url = "https://files.pythonhosted.org/packages/a6/79/8780a378c650e3df849b73de8b13cf5412f521ca2ff9b78a45c247029440/protobuf-6.33.1-cp310-abi3-win_amd64.whl", hash = "sha256:923aa6d27a92bf44394f6abf7ea0500f38769d4b07f4be41cb52bd8b1123b9ed", size = 436883 }, + { url = "https://files.pythonhosted.org/packages/cd/93/26213ff72b103ae55bb0d73e7fb91ea570ef407c3ab4fd2f1f27cac16044/protobuf-6.33.1-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:fe34575f2bdde76ac429ec7b570235bf0c788883e70aee90068e9981806f2490", size = 427522 }, + { url = "https://files.pythonhosted.org/packages/c2/32/df4a35247923393aa6b887c3b3244a8c941c32a25681775f96e2b418f90e/protobuf-6.33.1-cp39-abi3-manylinux2014_aarch64.whl", hash = "sha256:f8adba2e44cde2d7618996b3fc02341f03f5bc3f2748be72dc7b063319276178", size = 324445 }, + { url = "https://files.pythonhosted.org/packages/8e/d0/d796e419e2ec93d2f3fa44888861c3f88f722cde02b7c3488fcc6a166820/protobuf-6.33.1-cp39-abi3-manylinux2014_s390x.whl", hash = "sha256:0f4cf01222c0d959c2b399142deb526de420be8236f22c71356e2a544e153c53", size = 339161 }, + { url = "https://files.pythonhosted.org/packages/1d/2a/3c5f05a4af06649547027d288747f68525755de692a26a7720dced3652c0/protobuf-6.33.1-cp39-abi3-manylinux2014_x86_64.whl", hash = "sha256:8fd7d5e0eb08cd5b87fd3df49bc193f5cfd778701f47e11d127d0afc6c39f1d1", size = 323171 }, + { url = "https://files.pythonhosted.org/packages/08/b4/46310463b4f6ceef310f8348786f3cff181cea671578e3d9743ba61a459e/protobuf-6.33.1-py3-none-any.whl", hash = "sha256:d595a9fd694fdeb061a62fbe10eb039cc1e444df81ec9bb70c7fc59ebcb1eafa", size = 170477 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "psutil" version = "7.2.2" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/aa/c6/d1ddf4abb55e93cebc4f2ed8b5d6dbad109ecb8d63748dd2b20ab5e57ebe/psutil-7.2.2.tar.gz", hash = "sha256:0746f5f8d406af344fd547f1c8daa5f5c33dbc293bb8d6a16d80b4bb88f59372", size = 493740, upload-time = "2026-01-28T18:14:54.428Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/51/08/510cbdb69c25a96f4ae523f733cdc963ae654904e8db864c07585ef99875/psutil-7.2.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:2edccc433cbfa046b980b0df0171cd25bcaeb3a68fe9022db0979e7aa74a826b", size = 130595, upload-time = "2026-01-28T18:14:57.293Z" }, @@ -2559,51 +3609,91 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/fc/dc/40c6026c88d7f9220ecc913afe0501045a512c9b82f9b7e036bb089dc287/psygnal-0.15.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:1ac399566852fe4354ce26a1acbe12319232e8c2b615fe5ad1e114c547095cf6", size = 880863, upload-time = "2026-01-04T16:38:38.381Z" }, { url = "https://files.pythonhosted.org/packages/b7/85/b4f45ec3057c473b5622fc002b3a636a698c34d3a0917a064ff5247f1984/psygnal-0.15.1-cp314-cp314-win_amd64.whl", hash = "sha256:d3a03055f331ce91d44581c71edb79938ccc133a94af2ce7ad3a18fa57ac7be5", size = 423654, upload-time = "2026-01-04T16:38:39.7Z" }, { url = "https://files.pythonhosted.org/packages/46/49/7742544684bee728ec123515d2694cee859aa2a705951a461230b00f18cc/psygnal-0.15.1-py3-none-any.whl", hash = "sha256:4221140e633e45b076953c64bcb9b41a744833527f9a037c1ca98bc270798cbf", size = 90638, upload-time = "2026-01-04T16:38:40.841Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/e1/88/bdd0a41e5857d5d703287598cbf08dad90aed56774ea52ae071bae9071b6/psutil-7.1.3.tar.gz", hash = "sha256:6c86281738d77335af7aec228328e944b30930899ea760ecf33a4dba66be5e74", size = 489059 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/bd/93/0c49e776b8734fef56ec9c5c57f923922f2cf0497d62e0f419465f28f3d0/psutil-7.1.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0005da714eee687b4b8decd3d6cc7c6db36215c9e74e5ad2264b90c3df7d92dc", size = 239751 }, + { url = "https://files.pythonhosted.org/packages/6f/8d/b31e39c769e70780f007969815195a55c81a63efebdd4dbe9e7a113adb2f/psutil-7.1.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:19644c85dcb987e35eeeaefdc3915d059dac7bd1167cdcdbf27e0ce2df0c08c0", size = 240368 }, + { url = "https://files.pythonhosted.org/packages/62/61/23fd4acc3c9eebbf6b6c78bcd89e5d020cfde4acf0a9233e9d4e3fa698b4/psutil-7.1.3-cp313-cp313t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:95ef04cf2e5ba0ab9eaafc4a11eaae91b44f4ef5541acd2ee91d9108d00d59a7", size = 287134 }, + { url = "https://files.pythonhosted.org/packages/30/1c/f921a009ea9ceb51aa355cb0cc118f68d354db36eae18174bab63affb3e6/psutil-7.1.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1068c303be3a72f8e18e412c5b2a8f6d31750fb152f9cb106b54090296c9d251", size = 289904 }, + { url = "https://files.pythonhosted.org/packages/a6/82/62d68066e13e46a5116df187d319d1724b3f437ddd0f958756fc052677f4/psutil-7.1.3-cp313-cp313t-win_amd64.whl", hash = "sha256:18349c5c24b06ac5612c0428ec2a0331c26443d259e2a0144a9b24b4395b58fa", size = 249642 }, + { url = "https://files.pythonhosted.org/packages/df/ad/c1cd5fe965c14a0392112f68362cfceb5230819dbb5b1888950d18a11d9f/psutil-7.1.3-cp313-cp313t-win_arm64.whl", hash = "sha256:c525ffa774fe4496282fb0b1187725793de3e7c6b29e41562733cae9ada151ee", size = 245518 }, + { url = "https://files.pythonhosted.org/packages/2e/bb/6670bded3e3236eb4287c7bcdc167e9fae6e1e9286e437f7111caed2f909/psutil-7.1.3-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:b403da1df4d6d43973dc004d19cee3b848e998ae3154cc8097d139b77156c353", size = 239843 }, + { url = "https://files.pythonhosted.org/packages/b8/66/853d50e75a38c9a7370ddbeefabdd3d3116b9c31ef94dc92c6729bc36bec/psutil-7.1.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ad81425efc5e75da3f39b3e636293360ad8d0b49bed7df824c79764fb4ba9b8b", size = 240369 }, + { url = "https://files.pythonhosted.org/packages/41/bd/313aba97cb5bfb26916dc29cf0646cbe4dd6a89ca69e8c6edce654876d39/psutil-7.1.3-cp314-cp314t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8f33a3702e167783a9213db10ad29650ebf383946e91bc77f28a5eb083496bc9", size = 288210 }, + { url = "https://files.pythonhosted.org/packages/c2/fa/76e3c06e760927a0cfb5705eb38164254de34e9bd86db656d4dbaa228b04/psutil-7.1.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fac9cd332c67f4422504297889da5ab7e05fd11e3c4392140f7370f4208ded1f", size = 291182 }, + { url = "https://files.pythonhosted.org/packages/0f/1d/5774a91607035ee5078b8fd747686ebec28a962f178712de100d00b78a32/psutil-7.1.3-cp314-cp314t-win_amd64.whl", hash = "sha256:3792983e23b69843aea49c8f5b8f115572c5ab64c153bada5270086a2123c7e7", size = 250466 }, + { url = "https://files.pythonhosted.org/packages/00/ca/e426584bacb43a5cb1ac91fae1937f478cd8fbe5e4ff96574e698a2c77cd/psutil-7.1.3-cp314-cp314t-win_arm64.whl", hash = "sha256:31d77fcedb7529f27bb3a0472bea9334349f9a04160e8e6e5020f22c59893264", size = 245756 }, + { url = "https://files.pythonhosted.org/packages/ef/94/46b9154a800253e7ecff5aaacdf8ebf43db99de4a2dfa18575b02548654e/psutil-7.1.3-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:2bdbcd0e58ca14996a42adf3621a6244f1bb2e2e528886959c72cf1e326677ab", size = 238359 }, + { url = "https://files.pythonhosted.org/packages/68/3a/9f93cff5c025029a36d9a92fef47220ab4692ee7f2be0fba9f92813d0cb8/psutil-7.1.3-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:bc31fa00f1fbc3c3802141eede66f3a2d51d89716a194bf2cd6fc68310a19880", size = 239171 }, + { url = "https://files.pythonhosted.org/packages/ce/b1/5f49af514f76431ba4eea935b8ad3725cdeb397e9245ab919dbc1d1dc20f/psutil-7.1.3-cp36-abi3-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3bb428f9f05c1225a558f53e30ccbad9930b11c3fc206836242de1091d3e7dd3", size = 263261 }, + { url = "https://files.pythonhosted.org/packages/e0/95/992c8816a74016eb095e73585d747e0a8ea21a061ed3689474fabb29a395/psutil-7.1.3-cp36-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:56d974e02ca2c8eb4812c3f76c30e28836fffc311d55d979f1465c1feeb2b68b", size = 264635 }, + { url = "https://files.pythonhosted.org/packages/55/4c/c3ed1a622b6ae2fd3c945a366e64eb35247a31e4db16cf5095e269e8eb3c/psutil-7.1.3-cp37-abi3-win_amd64.whl", hash = "sha256:f39c2c19fe824b47484b96f9692932248a54c43799a84282cfe58d05a6449efd", size = 247633 }, + { url = "https://files.pythonhosted.org/packages/c9/ad/33b2ccec09bf96c2b2ef3f9a6f66baac8253d7565d8839e024a6b905d45d/psutil-7.1.3-cp37-abi3-win_arm64.whl", hash = "sha256:bd0d69cee829226a761e92f28140bec9a5ee9d5b4fb4b0cc589068dbfff559b1", size = 244608 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "ptyprocess" version = "0.7.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/20/e5/16ff212c1e452235a90aeb09066144d0c5a6a8c0834397e03f5224495c4e/ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220", size = 70762, upload-time = "2020-12-28T15:15:30.155Z" } +sdist = { url = "https://files.pythonhosted.org/packages/20/e5/16ff212c1e452235a90aeb09066144d0c5a6a8c0834397e03f5224495c4e/ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220", size = 70762 } wheels = [ - { url = "https://files.pythonhosted.org/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35", size = 13993, upload-time = "2020-12-28T15:15:28.35Z" }, + { url = "https://files.pythonhosted.org/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35", size = 13993 }, ] [[package]] name = "pure-eval" version = "0.2.3" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/cd/05/0a34433a064256a578f1783a10da6df098ceaa4a57bbeaa96a6c0352786b/pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42", size = 19752, upload-time = "2024-07-21T12:58:21.801Z" } +sdist = { url = "https://files.pythonhosted.org/packages/cd/05/0a34433a064256a578f1783a10da6df098ceaa4a57bbeaa96a6c0352786b/pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42", size = 19752 } wheels = [ - { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842, upload-time = "2024-07-21T12:58:20.04Z" }, + { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842 }, ] [[package]] name = "pycparser" version = "3.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/1b/7d/92392ff7815c21062bea51aa7b87d45576f649f16458d78b7cf94b9ab2e6/pycparser-3.0.tar.gz", hash = "sha256:600f49d217304a5902ac3c37e1281c9fe94e4d0489de643a9504c5cdfdfc6b29", size = 103492, upload-time = "2026-01-21T14:26:51.89Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/0c/c3/44f3fbbfa403ea2a7c779186dc20772604442dde72947e7d01069cbe98e3/pycparser-3.0-py3-none-any.whl", hash = "sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992", size = 48172, upload-time = "2026-01-21T14:26:50.693Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/fe/cf/d2d3b9f5699fb1e4615c8e32ff220203e43b248e1dfcc6736ad9057731ca/pycparser-2.23.tar.gz", hash = "sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2", size = 173734 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a0/e3/59cd50310fc9b59512193629e1984c1f95e5c8ae6e5d8c69532ccc65a7fe/pycparser-2.23-py3-none-any.whl", hash = "sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934", size = 118140 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "pygments" version = "2.20.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/c3/b2/bc9c9196916376152d655522fdcebac55e66de6603a76a02bca1b6414f6c/pygments-2.20.0.tar.gz", hash = "sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f", size = 4955991, upload-time = "2026-03-29T13:29:33.898Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/f4/7e/a72dd26f3b0f4f2bf1dd8923c85f7ceb43172af56d63c7383eb62b332364/pygments-2.20.0-py3-none-any.whl", hash = "sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176", size = 1231151, upload-time = "2026-03-29T13:29:30.038Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/b0/77/a5b8c569bf593b0140bde72ea885a803b82086995367bf2037de0159d924/pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887", size = 4968631 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b", size = 1225217 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "pyparsing" version = "3.3.2" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/f3/91/9c6ee907786a473bf81c5f53cf703ba0957b23ab84c264080fb5a450416f/pyparsing-3.3.2.tar.gz", hash = "sha256:c777f4d763f140633dcb6d8a3eda953bf7a214dc4eff598413c070bcdc117cbc", size = 6851574, upload-time = "2026-01-21T03:57:59.36Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/10/bd/c038d7cc38edc1aa5bf91ab8068b63d4308c66c4c8bb3cbba7dfbc049f9c/pyparsing-3.3.2-py3-none-any.whl", hash = "sha256:850ba148bd908d7e2411587e247a1e4f0327839c40e2e5e6d05a007ecc69911d", size = 122781, upload-time = "2026-01-21T03:57:55.912Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/f2/a5/181488fc2b9d093e3972d2a472855aae8a03f000592dbfce716a512b3359/pyparsing-3.2.5.tar.gz", hash = "sha256:2df8d5b7b2802ef88e8d016a2eb9c7aeaa923529cd251ed0fe4608275d4105b6", size = 1099274 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/10/5e/1aa9a93198c6b64513c9d7752de7422c06402de6600a8767da1524f9570b/pyparsing-3.2.5-py3-none-any.whl", hash = "sha256:e38a4f02064cf41fe6593d328d0512495ad1f3d8a91c4f73fc401b3079a59a5e", size = 113890 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2617,6 +3707,7 @@ dependencies = [ { name = "pluggy" }, { name = "pygments" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249, upload-time = "2026-04-07T17:16:16.13Z" }, @@ -2634,12 +3725,18 @@ dependencies = [ sdist = { url = "https://files.pythonhosted.org/packages/b1/51/a849f96e117386044471c8ec2bd6cfebacda285da9525c9106aeb28da671/pytest_cov-7.1.0.tar.gz", hash = "sha256:30674f2b5f6351aa09702a9c8c364f6a01c27aae0c1366ae8016160d1efc56b2", size = 55592, upload-time = "2026-03-21T20:11:16.284Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/9d/7a/d968e294073affff457b041c2be9868a40c1c71f4a35fcc1e45e5493067b/pytest_cov-7.1.0-py3-none-any.whl", hash = "sha256:a0461110b7865f9a271aa1b51e516c9a95de9d696734a2f71e3e78f46e1d4678", size = 22876, upload-time = "2026-03-21T20:11:14.438Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/07/56/f013048ac4bc4c1d9be45afd4ab209ea62822fb1598f40687e6bf45dcea4/pytest-9.0.1.tar.gz", hash = "sha256:3e9c069ea73583e255c3b21cf46b8d3c56f6e3a1a8f6da94ccb0fcf57b9d73c8", size = 1564125 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0b/8b/6300fb80f858cda1c51ffa17075df5d846757081d11ab4aa35cef9e6258b/pytest-9.0.1-py3-none-any.whl", hash = "sha256:67be0030d194df2dfa7b556f2e56fb3c3315bd5c8822c6951162b92b32ce7dad", size = 373668 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "python-box" version = "7.4.1" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/0f/0f/34e7ee0a72f1464b4c7a2e8bafb389f230477256af586bc82bcfad85295a/python_box-7.4.1.tar.gz", hash = "sha256:e412e36c25fca8223560516d53ef6c7993591c3b0ec8bb4ec582bf7defdd79f0", size = 49859, upload-time = "2026-02-21T16:21:16.008Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/f8/a8/c8bcd3ff0905ec549273ea3485e6b9f2039f57baab419123fb18f964f829/python_box-7.4.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:3f76dad8be9d57d65a3edc792b952f7afe3991515aa6eba616cf5efb2fbb2e0c", size = 1870869, upload-time = "2026-02-21T16:21:34.16Z" }, @@ -2654,6 +3751,20 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/94/a2/771b5e526bba2214ac2d30e321209a66680c40788616a45cf01005e95204/python_box-7.4.1-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:33c6701faa51fd87f0dcc538873c0fad2b3a1cc3750eab85835cd071cadf1948", size = 1875508, upload-time = "2026-02-21T16:21:37.432Z" }, { url = "https://files.pythonhosted.org/packages/a6/5f/0e7ea7640ba60ff459ce37e340d816ac5e91b7a9a7c3c161f9dabe622be6/python_box-7.4.1-cp314-cp314-win_amd64.whl", hash = "sha256:ae8c540a0457f52350211d24690211251912018e1e0c1857f50792729d6f562c", size = 1314304, upload-time = "2026-02-21T16:22:22.173Z" }, { url = "https://files.pythonhosted.org/packages/06/a6/5d3f3abf46b37aa44b1f6788d287c8b4f2319b55013191dddf25b9e6d62c/python_box-7.4.1-py3-none-any.whl", hash = "sha256:a3b0d84d003882fb6abe505b1b883b3a5dcbf226b0fe168d24bc5ff75d9826e5", size = 30402, upload-time = "2026-02-21T16:21:14.78Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/29/f7/635eed8c500adf26208e86e985bbffb6ff039cd8950e3a4749ceca904218/python_box-7.3.2.tar.gz", hash = "sha256:028b9917129e67f311932d93347b8a4f1b500d7a5a2870ee3c035f4e7b19403b", size = 45771 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1e/3f/133619c00d8a9d4f86efd8626c0e4ec356c8b8dabac66da18dac5cfaf70c/python_box-7.3.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:32163b1cb151883de0da62b0cd3572610dc72ccf0762f2447baf1d2562e25bea", size = 1834122 }, + { url = "https://files.pythonhosted.org/packages/b7/52/51b6081562daa864847692536260200b337ccb4176d1e5f626ae48a7d493/python_box-7.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:064cb59b41e25aaf7dbd39efe53151a5f6797cc1cb3c68610f0f21a9d406d67e", size = 4305556 }, + { url = "https://files.pythonhosted.org/packages/d4/e2/6cdc8649381ae14def88c3e2e93d5b8b17a622a95896e0d1c92861270b7d/python_box-7.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:488f0fba9a6416c3334b602366dcd92825adb0811e07e03753dfcf0ed79cd6f7", size = 1232328 }, + { url = "https://files.pythonhosted.org/packages/45/68/0c2f289d8055d3e1b156ff258847f0e8f1010063e284cf5a612f09435575/python_box-7.3.2-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:39009a2da5c20133718b24891a206592adbe09169856aedc450ad1600fc2e511", size = 1819681 }, + { url = "https://files.pythonhosted.org/packages/ce/5d/76b4d6d0e41edb676a229f032848a1ecea166890fa8d501513ea1a030f4d/python_box-7.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2e2a72e2f6fb97c7e472ff3272da207ecc615aa222e52e98352391428527c469", size = 4270424 }, + { url = "https://files.pythonhosted.org/packages/e4/6b/32484b2a3cd2fb5e5f56bfb53a4537d93a4d2014ccf7fc0c0017fa6f65e9/python_box-7.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:9eead914b9fb7d98a1473f5027dcfe27d26b3a10ffa33b9ba22cf948a23cd280", size = 1211252 }, + { url = "https://files.pythonhosted.org/packages/2f/39/8bec609e93dbc5e0d3ea26cfb5af3ca78915f7a55ef5414713462fedeb59/python_box-7.3.2-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:1dfc3b9b073f3d7cad1fa90de98eaaa684a494d0574bbc0666f74fa8307fd6b6", size = 1804675 }, + { url = "https://files.pythonhosted.org/packages/88/ae/baf3a8057d8129896a7e02619df43ea0d918fc5b2bb66eb6e2470595fbac/python_box-7.3.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3ca4685a7f764b5a71b6e08535ce2a96b7964bb63d8cb4df10f6bb7147b6c54b", size = 4265645 }, + { url = "https://files.pythonhosted.org/packages/43/90/72367e03033c11a5e82676ee389b572bf136647ff4e3081557392b37e1ad/python_box-7.3.2-cp313-cp313-win_amd64.whl", hash = "sha256:e143295f74d47a9ab24562ead2375c9be10629599b57f2e86717d3fff60f82a9", size = 1206740 }, + { url = "https://files.pythonhosted.org/packages/37/13/8a990c6e2b6cc12700dce16f3cb383324e6d9a30f604eca22a2fdf84c923/python_box-7.3.2-py3-none-any.whl", hash = "sha256:fd7d74d5a848623f93b5221fd9fb00b8c00ff0e130fa87f396277aa188659c92", size = 29479 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2663,15 +3774,16 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "six" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432, upload-time = "2024-03-01T18:36:20.211Z" } +sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432 } wheels = [ - { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" }, + { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892 }, ] [[package]] name = "python-discovery" version = "1.4.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD dependencies = [ { name = "filelock" }, { name = "platformdirs" }, @@ -2688,12 +3800,18 @@ source = { registry = "https://pypi.org/simple" } sdist = { url = "https://files.pythonhosted.org/packages/f7/ff/3cc9165fd44106973cd7ac9facb674a65ed853494592541d339bdc9a30eb/python_json_logger-4.1.0.tar.gz", hash = "sha256:b396b9e3ed782b09ff9d6e4f1683d46c83ad0d35d2e407c09a9ebbf038f88195", size = 17573, upload-time = "2026-03-29T04:39:56.805Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/27/be/0631a861af4d1c875f096c07d34e9a63639560a717130e7a87cbc82b7e3f/python_json_logger-4.1.0-py3-none-any.whl", hash = "sha256:132994765cf75bf44554be9aa49b06ef2345d23661a96720262716438141b6b2", size = 15021, upload-time = "2026-03-29T04:39:55.266Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/29/bf/eca6a3d43db1dae7070f70e160ab20b807627ba953663ba07928cdd3dc58/python_json_logger-4.0.0.tar.gz", hash = "sha256:f58e68eb46e1faed27e0f574a55a0455eecd7b8a5b88b85a784519ba3cff047f", size = 17683 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/51/e5/fecf13f06e5e5f67e8837d777d1bc43fac0ed2b77a676804df5c34744727/python_json_logger-4.0.0-py3-none-any.whl", hash = "sha256:af09c9daf6a813aa4cc7180395f50f2a9e5fa056034c9953aec92e381c5ba1e2", size = 15548 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "pywinpty" version = "3.0.3" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/f7/54/37c7370ba91f579235049dc26cd2c5e657d2a943e01820844ffc81f32176/pywinpty-3.0.3.tar.gz", hash = "sha256:523441dc34d231fb361b4b00f8c99d3f16de02f5005fd544a0183112bcc22412", size = 31309, upload-time = "2026-02-04T21:51:09.524Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/79/c3/3e75075c7f71735f22b66fab0481f2c98e3a4d58cba55cb50ba29114bcf6/pywinpty-3.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:dff25a9a6435f527d7c65608a7e62783fc12076e7d44487a4911ee91be5a8ac8", size = 2114430, upload-time = "2026-02-04T21:54:19.485Z" }, @@ -2708,61 +3826,71 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/63/32/40a775343ace542cc43ece3f1d1fce454021521ecac41c4c4573081c2336/pywinpty-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:34789d685fc0d547ce0c8a65e5a70e56f77d732fa6e03c8f74fefb8cbb252019", size = 234207, upload-time = "2026-02-04T21:51:58.687Z" }, { url = "https://files.pythonhosted.org/packages/8d/54/5d5e52f4cb75028104ca6faf36c10f9692389b1986d34471663b4ebebd6d/pywinpty-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:0c37e224a47a971d1a6e08649a1714dac4f63c11920780977829ed5c8cadead1", size = 2112910, upload-time = "2026-02-04T21:52:30.976Z" }, { url = "https://files.pythonhosted.org/packages/0a/44/dcd184824e21d4620b06c7db9fbb15c3ad0a0f1fa2e6de79969fb82647ec/pywinpty-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:c4e9c3dff7d86ba81937438d5819f19f385a39d8f592d4e8af67148ceb4f6ab5", size = 233425, upload-time = "2026-02-04T21:51:56.754Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/f3/bb/a7cc2967c5c4eceb6cc49cfe39447d4bfc56e6c865e7c2249b6eb978935f/pywinpty-3.0.2.tar.gz", hash = "sha256:1505cc4cb248af42cb6285a65c9c2086ee9e7e574078ee60933d5d7fa86fb004", size = 30669 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a6/a1/409c1651c9f874d598c10f51ff586c416625601df4bca315d08baec4c3e3/pywinpty-3.0.2-cp311-cp311-win_amd64.whl", hash = "sha256:327790d70e4c841ebd9d0f295a780177149aeb405bca44c7115a3de5c2054b23", size = 2050304 }, + { url = "https://files.pythonhosted.org/packages/02/4e/1098484e042c9485f56f16eb2b69b43b874bd526044ee401512234cf9e04/pywinpty-3.0.2-cp312-cp312-win_amd64.whl", hash = "sha256:99fdd9b455f0ad6419aba6731a7a0d2f88ced83c3c94a80ff9533d95fa8d8a9e", size = 2050391 }, + { url = "https://files.pythonhosted.org/packages/fc/19/b757fe28008236a4a713e813283721b8a40aa60cd7d3f83549f2e25a3155/pywinpty-3.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:18f78b81e4cfee6aabe7ea8688441d30247b73e52cd9657138015c5f4ee13a51", size = 2050057 }, + { url = "https://files.pythonhosted.org/packages/cb/44/cbae12ecf6f4fa4129c36871fd09c6bef4f98d5f625ecefb5e2449765508/pywinpty-3.0.2-cp313-cp313t-win_amd64.whl", hash = "sha256:663383ecfab7fc382cc97ea5c4f7f0bb32c2f889259855df6ea34e5df42d305b", size = 2049874 }, + { url = "https://files.pythonhosted.org/packages/ca/15/f12c6055e2d7a617d4d5820e8ac4ceaff849da4cb124640ef5116a230771/pywinpty-3.0.2-cp314-cp314-win_amd64.whl", hash = "sha256:28297cecc37bee9f24d8889e47231972d6e9e84f7b668909de54f36ca785029a", size = 2050386 }, + { url = "https://files.pythonhosted.org/packages/de/24/c6907c5bb06043df98ad6a0a0ff5db2e0affcecbc3b15c42404393a3f72a/pywinpty-3.0.2-cp314-cp314t-win_amd64.whl", hash = "sha256:34b55ae9a1b671fe3eae071d86618110538e8eaad18fcb1531c0830b91a82767", size = 2049834 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "pyyaml" version = "6.0.3" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/05/8e/961c0007c59b8dd7729d542c61a4d537767a59645b82a0b521206e1e25c2/pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f", size = 130960, upload-time = "2025-09-25T21:33:16.546Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/6d/16/a95b6757765b7b031c9374925bb718d55e0a9ba8a1b6a12d25962ea44347/pyyaml-6.0.3-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:44edc647873928551a01e7a563d7452ccdebee747728c1080d881d68af7b997e", size = 185826, upload-time = "2025-09-25T21:31:58.655Z" }, - { url = "https://files.pythonhosted.org/packages/16/19/13de8e4377ed53079ee996e1ab0a9c33ec2faf808a4647b7b4c0d46dd239/pyyaml-6.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:652cb6edd41e718550aad172851962662ff2681490a8a711af6a4d288dd96824", size = 175577, upload-time = "2025-09-25T21:32:00.088Z" }, - { url = "https://files.pythonhosted.org/packages/0c/62/d2eb46264d4b157dae1275b573017abec435397aa59cbcdab6fc978a8af4/pyyaml-6.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:10892704fc220243f5305762e276552a0395f7beb4dbf9b14ec8fd43b57f126c", size = 775556, upload-time = "2025-09-25T21:32:01.31Z" }, - { url = "https://files.pythonhosted.org/packages/10/cb/16c3f2cf3266edd25aaa00d6c4350381c8b012ed6f5276675b9eba8d9ff4/pyyaml-6.0.3-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:850774a7879607d3a6f50d36d04f00ee69e7fc816450e5f7e58d7f17f1ae5c00", size = 882114, upload-time = "2025-09-25T21:32:03.376Z" }, - { url = "https://files.pythonhosted.org/packages/71/60/917329f640924b18ff085ab889a11c763e0b573da888e8404ff486657602/pyyaml-6.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b8bb0864c5a28024fac8a632c443c87c5aa6f215c0b126c449ae1a150412f31d", size = 806638, upload-time = "2025-09-25T21:32:04.553Z" }, - { url = "https://files.pythonhosted.org/packages/dd/6f/529b0f316a9fd167281a6c3826b5583e6192dba792dd55e3203d3f8e655a/pyyaml-6.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:1d37d57ad971609cf3c53ba6a7e365e40660e3be0e5175fa9f2365a379d6095a", size = 767463, upload-time = "2025-09-25T21:32:06.152Z" }, - { url = "https://files.pythonhosted.org/packages/f2/6a/b627b4e0c1dd03718543519ffb2f1deea4a1e6d42fbab8021936a4d22589/pyyaml-6.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:37503bfbfc9d2c40b344d06b2199cf0e96e97957ab1c1b546fd4f87e53e5d3e4", size = 794986, upload-time = "2025-09-25T21:32:07.367Z" }, - { url = "https://files.pythonhosted.org/packages/45/91/47a6e1c42d9ee337c4839208f30d9f09caa9f720ec7582917b264defc875/pyyaml-6.0.3-cp311-cp311-win32.whl", hash = "sha256:8098f252adfa6c80ab48096053f512f2321f0b998f98150cea9bd23d83e1467b", size = 142543, upload-time = "2025-09-25T21:32:08.95Z" }, - { url = "https://files.pythonhosted.org/packages/da/e3/ea007450a105ae919a72393cb06f122f288ef60bba2dc64b26e2646fa315/pyyaml-6.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:9f3bfb4965eb874431221a3ff3fdcddc7e74e3b07799e0e84ca4a0f867d449bf", size = 158763, upload-time = "2025-09-25T21:32:09.96Z" }, - { url = "https://files.pythonhosted.org/packages/d1/33/422b98d2195232ca1826284a76852ad5a86fe23e31b009c9886b2d0fb8b2/pyyaml-6.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196", size = 182063, upload-time = "2025-09-25T21:32:11.445Z" }, - { url = "https://files.pythonhosted.org/packages/89/a0/6cf41a19a1f2f3feab0e9c0b74134aa2ce6849093d5517a0c550fe37a648/pyyaml-6.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0", size = 173973, upload-time = "2025-09-25T21:32:12.492Z" }, - { url = "https://files.pythonhosted.org/packages/ed/23/7a778b6bd0b9a8039df8b1b1d80e2e2ad78aa04171592c8a5c43a56a6af4/pyyaml-6.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28", size = 775116, upload-time = "2025-09-25T21:32:13.652Z" }, - { url = "https://files.pythonhosted.org/packages/65/30/d7353c338e12baef4ecc1b09e877c1970bd3382789c159b4f89d6a70dc09/pyyaml-6.0.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c", size = 844011, upload-time = "2025-09-25T21:32:15.21Z" }, - { url = "https://files.pythonhosted.org/packages/8b/9d/b3589d3877982d4f2329302ef98a8026e7f4443c765c46cfecc8858c6b4b/pyyaml-6.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc", size = 807870, upload-time = "2025-09-25T21:32:16.431Z" }, - { url = "https://files.pythonhosted.org/packages/05/c0/b3be26a015601b822b97d9149ff8cb5ead58c66f981e04fedf4e762f4bd4/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e", size = 761089, upload-time = "2025-09-25T21:32:17.56Z" }, - { url = "https://files.pythonhosted.org/packages/be/8e/98435a21d1d4b46590d5459a22d88128103f8da4c2d4cb8f14f2a96504e1/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea", size = 790181, upload-time = "2025-09-25T21:32:18.834Z" }, - { url = "https://files.pythonhosted.org/packages/74/93/7baea19427dcfbe1e5a372d81473250b379f04b1bd3c4c5ff825e2327202/pyyaml-6.0.3-cp312-cp312-win32.whl", hash = "sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5", size = 137658, upload-time = "2025-09-25T21:32:20.209Z" }, - { url = "https://files.pythonhosted.org/packages/86/bf/899e81e4cce32febab4fb42bb97dcdf66bc135272882d1987881a4b519e9/pyyaml-6.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b", size = 154003, upload-time = "2025-09-25T21:32:21.167Z" }, - { url = "https://files.pythonhosted.org/packages/1a/08/67bd04656199bbb51dbed1439b7f27601dfb576fb864099c7ef0c3e55531/pyyaml-6.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd", size = 140344, upload-time = "2025-09-25T21:32:22.617Z" }, - { url = "https://files.pythonhosted.org/packages/d1/11/0fd08f8192109f7169db964b5707a2f1e8b745d4e239b784a5a1dd80d1db/pyyaml-6.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8", size = 181669, upload-time = "2025-09-25T21:32:23.673Z" }, - { url = "https://files.pythonhosted.org/packages/b1/16/95309993f1d3748cd644e02e38b75d50cbc0d9561d21f390a76242ce073f/pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1", size = 173252, upload-time = "2025-09-25T21:32:25.149Z" }, - { url = "https://files.pythonhosted.org/packages/50/31/b20f376d3f810b9b2371e72ef5adb33879b25edb7a6d072cb7ca0c486398/pyyaml-6.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c", size = 767081, upload-time = "2025-09-25T21:32:26.575Z" }, - { url = "https://files.pythonhosted.org/packages/49/1e/a55ca81e949270d5d4432fbbd19dfea5321eda7c41a849d443dc92fd1ff7/pyyaml-6.0.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5", size = 841159, upload-time = "2025-09-25T21:32:27.727Z" }, - { url = "https://files.pythonhosted.org/packages/74/27/e5b8f34d02d9995b80abcef563ea1f8b56d20134d8f4e5e81733b1feceb2/pyyaml-6.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6", size = 801626, upload-time = "2025-09-25T21:32:28.878Z" }, - { url = "https://files.pythonhosted.org/packages/f9/11/ba845c23988798f40e52ba45f34849aa8a1f2d4af4b798588010792ebad6/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6", size = 753613, upload-time = "2025-09-25T21:32:30.178Z" }, - { url = "https://files.pythonhosted.org/packages/3d/e0/7966e1a7bfc0a45bf0a7fb6b98ea03fc9b8d84fa7f2229e9659680b69ee3/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be", size = 794115, upload-time = "2025-09-25T21:32:31.353Z" }, - { url = "https://files.pythonhosted.org/packages/de/94/980b50a6531b3019e45ddeada0626d45fa85cbe22300844a7983285bed3b/pyyaml-6.0.3-cp313-cp313-win32.whl", hash = "sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26", size = 137427, upload-time = "2025-09-25T21:32:32.58Z" }, - { url = "https://files.pythonhosted.org/packages/97/c9/39d5b874e8b28845e4ec2202b5da735d0199dbe5b8fb85f91398814a9a46/pyyaml-6.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c", size = 154090, upload-time = "2025-09-25T21:32:33.659Z" }, - { url = "https://files.pythonhosted.org/packages/73/e8/2bdf3ca2090f68bb3d75b44da7bbc71843b19c9f2b9cb9b0f4ab7a5a4329/pyyaml-6.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb", size = 140246, upload-time = "2025-09-25T21:32:34.663Z" }, - { url = "https://files.pythonhosted.org/packages/9d/8c/f4bd7f6465179953d3ac9bc44ac1a8a3e6122cf8ada906b4f96c60172d43/pyyaml-6.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac", size = 181814, upload-time = "2025-09-25T21:32:35.712Z" }, - { url = "https://files.pythonhosted.org/packages/bd/9c/4d95bb87eb2063d20db7b60faa3840c1b18025517ae857371c4dd55a6b3a/pyyaml-6.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310", size = 173809, upload-time = "2025-09-25T21:32:36.789Z" }, - { url = "https://files.pythonhosted.org/packages/92/b5/47e807c2623074914e29dabd16cbbdd4bf5e9b2db9f8090fa64411fc5382/pyyaml-6.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7", size = 766454, upload-time = "2025-09-25T21:32:37.966Z" }, - { url = "https://files.pythonhosted.org/packages/02/9e/e5e9b168be58564121efb3de6859c452fccde0ab093d8438905899a3a483/pyyaml-6.0.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788", size = 836355, upload-time = "2025-09-25T21:32:39.178Z" }, - { url = "https://files.pythonhosted.org/packages/88/f9/16491d7ed2a919954993e48aa941b200f38040928474c9e85ea9e64222c3/pyyaml-6.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5", size = 794175, upload-time = "2025-09-25T21:32:40.865Z" }, - { url = "https://files.pythonhosted.org/packages/dd/3f/5989debef34dc6397317802b527dbbafb2b4760878a53d4166579111411e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764", size = 755228, upload-time = "2025-09-25T21:32:42.084Z" }, - { url = "https://files.pythonhosted.org/packages/d7/ce/af88a49043cd2e265be63d083fc75b27b6ed062f5f9fd6cdc223ad62f03e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35", size = 789194, upload-time = "2025-09-25T21:32:43.362Z" }, - { url = "https://files.pythonhosted.org/packages/23/20/bb6982b26a40bb43951265ba29d4c246ef0ff59c9fdcdf0ed04e0687de4d/pyyaml-6.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac", size = 156429, upload-time = "2025-09-25T21:32:57.844Z" }, - { url = "https://files.pythonhosted.org/packages/f4/f4/a4541072bb9422c8a883ab55255f918fa378ecf083f5b85e87fc2b4eda1b/pyyaml-6.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3", size = 143912, upload-time = "2025-09-25T21:32:59.247Z" }, - { url = "https://files.pythonhosted.org/packages/7c/f9/07dd09ae774e4616edf6cda684ee78f97777bdd15847253637a6f052a62f/pyyaml-6.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3", size = 189108, upload-time = "2025-09-25T21:32:44.377Z" }, - { url = "https://files.pythonhosted.org/packages/4e/78/8d08c9fb7ce09ad8c38ad533c1191cf27f7ae1effe5bb9400a46d9437fcf/pyyaml-6.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba", size = 183641, upload-time = "2025-09-25T21:32:45.407Z" }, - { url = "https://files.pythonhosted.org/packages/7b/5b/3babb19104a46945cf816d047db2788bcaf8c94527a805610b0289a01c6b/pyyaml-6.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c", size = 831901, upload-time = "2025-09-25T21:32:48.83Z" }, - { url = "https://files.pythonhosted.org/packages/8b/cc/dff0684d8dc44da4d22a13f35f073d558c268780ce3c6ba1b87055bb0b87/pyyaml-6.0.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702", size = 861132, upload-time = "2025-09-25T21:32:50.149Z" }, - { url = "https://files.pythonhosted.org/packages/b1/5e/f77dc6b9036943e285ba76b49e118d9ea929885becb0a29ba8a7c75e29fe/pyyaml-6.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c", size = 839261, upload-time = "2025-09-25T21:32:51.808Z" }, - { url = "https://files.pythonhosted.org/packages/ce/88/a9db1376aa2a228197c58b37302f284b5617f56a5d959fd1763fb1675ce6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065", size = 805272, upload-time = "2025-09-25T21:32:52.941Z" }, - { url = "https://files.pythonhosted.org/packages/da/92/1446574745d74df0c92e6aa4a7b0b3130706a4142b2d1a5869f2eaa423c6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65", size = 829923, upload-time = "2025-09-25T21:32:54.537Z" }, - { url = "https://files.pythonhosted.org/packages/f0/7a/1c7270340330e575b92f397352af856a8c06f230aa3e76f86b39d01b416a/pyyaml-6.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9", size = 174062, upload-time = "2025-09-25T21:32:55.767Z" }, - { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341, upload-time = "2025-09-25T21:32:56.828Z" }, +sdist = { url = "https://files.pythonhosted.org/packages/05/8e/961c0007c59b8dd7729d542c61a4d537767a59645b82a0b521206e1e25c2/pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f", size = 130960 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6d/16/a95b6757765b7b031c9374925bb718d55e0a9ba8a1b6a12d25962ea44347/pyyaml-6.0.3-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:44edc647873928551a01e7a563d7452ccdebee747728c1080d881d68af7b997e", size = 185826 }, + { url = "https://files.pythonhosted.org/packages/16/19/13de8e4377ed53079ee996e1ab0a9c33ec2faf808a4647b7b4c0d46dd239/pyyaml-6.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:652cb6edd41e718550aad172851962662ff2681490a8a711af6a4d288dd96824", size = 175577 }, + { url = "https://files.pythonhosted.org/packages/0c/62/d2eb46264d4b157dae1275b573017abec435397aa59cbcdab6fc978a8af4/pyyaml-6.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:10892704fc220243f5305762e276552a0395f7beb4dbf9b14ec8fd43b57f126c", size = 775556 }, + { url = "https://files.pythonhosted.org/packages/10/cb/16c3f2cf3266edd25aaa00d6c4350381c8b012ed6f5276675b9eba8d9ff4/pyyaml-6.0.3-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:850774a7879607d3a6f50d36d04f00ee69e7fc816450e5f7e58d7f17f1ae5c00", size = 882114 }, + { url = "https://files.pythonhosted.org/packages/71/60/917329f640924b18ff085ab889a11c763e0b573da888e8404ff486657602/pyyaml-6.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b8bb0864c5a28024fac8a632c443c87c5aa6f215c0b126c449ae1a150412f31d", size = 806638 }, + { url = "https://files.pythonhosted.org/packages/dd/6f/529b0f316a9fd167281a6c3826b5583e6192dba792dd55e3203d3f8e655a/pyyaml-6.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:1d37d57ad971609cf3c53ba6a7e365e40660e3be0e5175fa9f2365a379d6095a", size = 767463 }, + { url = "https://files.pythonhosted.org/packages/f2/6a/b627b4e0c1dd03718543519ffb2f1deea4a1e6d42fbab8021936a4d22589/pyyaml-6.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:37503bfbfc9d2c40b344d06b2199cf0e96e97957ab1c1b546fd4f87e53e5d3e4", size = 794986 }, + { url = "https://files.pythonhosted.org/packages/45/91/47a6e1c42d9ee337c4839208f30d9f09caa9f720ec7582917b264defc875/pyyaml-6.0.3-cp311-cp311-win32.whl", hash = "sha256:8098f252adfa6c80ab48096053f512f2321f0b998f98150cea9bd23d83e1467b", size = 142543 }, + { url = "https://files.pythonhosted.org/packages/da/e3/ea007450a105ae919a72393cb06f122f288ef60bba2dc64b26e2646fa315/pyyaml-6.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:9f3bfb4965eb874431221a3ff3fdcddc7e74e3b07799e0e84ca4a0f867d449bf", size = 158763 }, + { url = "https://files.pythonhosted.org/packages/d1/33/422b98d2195232ca1826284a76852ad5a86fe23e31b009c9886b2d0fb8b2/pyyaml-6.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196", size = 182063 }, + { url = "https://files.pythonhosted.org/packages/89/a0/6cf41a19a1f2f3feab0e9c0b74134aa2ce6849093d5517a0c550fe37a648/pyyaml-6.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0", size = 173973 }, + { url = "https://files.pythonhosted.org/packages/ed/23/7a778b6bd0b9a8039df8b1b1d80e2e2ad78aa04171592c8a5c43a56a6af4/pyyaml-6.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28", size = 775116 }, + { url = "https://files.pythonhosted.org/packages/65/30/d7353c338e12baef4ecc1b09e877c1970bd3382789c159b4f89d6a70dc09/pyyaml-6.0.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c", size = 844011 }, + { url = "https://files.pythonhosted.org/packages/8b/9d/b3589d3877982d4f2329302ef98a8026e7f4443c765c46cfecc8858c6b4b/pyyaml-6.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc", size = 807870 }, + { url = "https://files.pythonhosted.org/packages/05/c0/b3be26a015601b822b97d9149ff8cb5ead58c66f981e04fedf4e762f4bd4/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e", size = 761089 }, + { url = "https://files.pythonhosted.org/packages/be/8e/98435a21d1d4b46590d5459a22d88128103f8da4c2d4cb8f14f2a96504e1/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea", size = 790181 }, + { url = "https://files.pythonhosted.org/packages/74/93/7baea19427dcfbe1e5a372d81473250b379f04b1bd3c4c5ff825e2327202/pyyaml-6.0.3-cp312-cp312-win32.whl", hash = "sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5", size = 137658 }, + { url = "https://files.pythonhosted.org/packages/86/bf/899e81e4cce32febab4fb42bb97dcdf66bc135272882d1987881a4b519e9/pyyaml-6.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b", size = 154003 }, + { url = "https://files.pythonhosted.org/packages/1a/08/67bd04656199bbb51dbed1439b7f27601dfb576fb864099c7ef0c3e55531/pyyaml-6.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd", size = 140344 }, + { url = "https://files.pythonhosted.org/packages/d1/11/0fd08f8192109f7169db964b5707a2f1e8b745d4e239b784a5a1dd80d1db/pyyaml-6.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8", size = 181669 }, + { url = "https://files.pythonhosted.org/packages/b1/16/95309993f1d3748cd644e02e38b75d50cbc0d9561d21f390a76242ce073f/pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1", size = 173252 }, + { url = "https://files.pythonhosted.org/packages/50/31/b20f376d3f810b9b2371e72ef5adb33879b25edb7a6d072cb7ca0c486398/pyyaml-6.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c", size = 767081 }, + { url = "https://files.pythonhosted.org/packages/49/1e/a55ca81e949270d5d4432fbbd19dfea5321eda7c41a849d443dc92fd1ff7/pyyaml-6.0.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5", size = 841159 }, + { url = "https://files.pythonhosted.org/packages/74/27/e5b8f34d02d9995b80abcef563ea1f8b56d20134d8f4e5e81733b1feceb2/pyyaml-6.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6", size = 801626 }, + { url = "https://files.pythonhosted.org/packages/f9/11/ba845c23988798f40e52ba45f34849aa8a1f2d4af4b798588010792ebad6/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6", size = 753613 }, + { url = "https://files.pythonhosted.org/packages/3d/e0/7966e1a7bfc0a45bf0a7fb6b98ea03fc9b8d84fa7f2229e9659680b69ee3/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be", size = 794115 }, + { url = "https://files.pythonhosted.org/packages/de/94/980b50a6531b3019e45ddeada0626d45fa85cbe22300844a7983285bed3b/pyyaml-6.0.3-cp313-cp313-win32.whl", hash = "sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26", size = 137427 }, + { url = "https://files.pythonhosted.org/packages/97/c9/39d5b874e8b28845e4ec2202b5da735d0199dbe5b8fb85f91398814a9a46/pyyaml-6.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c", size = 154090 }, + { url = "https://files.pythonhosted.org/packages/73/e8/2bdf3ca2090f68bb3d75b44da7bbc71843b19c9f2b9cb9b0f4ab7a5a4329/pyyaml-6.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb", size = 140246 }, + { url = "https://files.pythonhosted.org/packages/9d/8c/f4bd7f6465179953d3ac9bc44ac1a8a3e6122cf8ada906b4f96c60172d43/pyyaml-6.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac", size = 181814 }, + { url = "https://files.pythonhosted.org/packages/bd/9c/4d95bb87eb2063d20db7b60faa3840c1b18025517ae857371c4dd55a6b3a/pyyaml-6.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310", size = 173809 }, + { url = "https://files.pythonhosted.org/packages/92/b5/47e807c2623074914e29dabd16cbbdd4bf5e9b2db9f8090fa64411fc5382/pyyaml-6.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7", size = 766454 }, + { url = "https://files.pythonhosted.org/packages/02/9e/e5e9b168be58564121efb3de6859c452fccde0ab093d8438905899a3a483/pyyaml-6.0.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788", size = 836355 }, + { url = "https://files.pythonhosted.org/packages/88/f9/16491d7ed2a919954993e48aa941b200f38040928474c9e85ea9e64222c3/pyyaml-6.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5", size = 794175 }, + { url = "https://files.pythonhosted.org/packages/dd/3f/5989debef34dc6397317802b527dbbafb2b4760878a53d4166579111411e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764", size = 755228 }, + { url = "https://files.pythonhosted.org/packages/d7/ce/af88a49043cd2e265be63d083fc75b27b6ed062f5f9fd6cdc223ad62f03e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35", size = 789194 }, + { url = "https://files.pythonhosted.org/packages/23/20/bb6982b26a40bb43951265ba29d4c246ef0ff59c9fdcdf0ed04e0687de4d/pyyaml-6.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac", size = 156429 }, + { url = "https://files.pythonhosted.org/packages/f4/f4/a4541072bb9422c8a883ab55255f918fa378ecf083f5b85e87fc2b4eda1b/pyyaml-6.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3", size = 143912 }, + { url = "https://files.pythonhosted.org/packages/7c/f9/07dd09ae774e4616edf6cda684ee78f97777bdd15847253637a6f052a62f/pyyaml-6.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3", size = 189108 }, + { url = "https://files.pythonhosted.org/packages/4e/78/8d08c9fb7ce09ad8c38ad533c1191cf27f7ae1effe5bb9400a46d9437fcf/pyyaml-6.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba", size = 183641 }, + { url = "https://files.pythonhosted.org/packages/7b/5b/3babb19104a46945cf816d047db2788bcaf8c94527a805610b0289a01c6b/pyyaml-6.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c", size = 831901 }, + { url = "https://files.pythonhosted.org/packages/8b/cc/dff0684d8dc44da4d22a13f35f073d558c268780ce3c6ba1b87055bb0b87/pyyaml-6.0.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702", size = 861132 }, + { url = "https://files.pythonhosted.org/packages/b1/5e/f77dc6b9036943e285ba76b49e118d9ea929885becb0a29ba8a7c75e29fe/pyyaml-6.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c", size = 839261 }, + { url = "https://files.pythonhosted.org/packages/ce/88/a9db1376aa2a228197c58b37302f284b5617f56a5d959fd1763fb1675ce6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065", size = 805272 }, + { url = "https://files.pythonhosted.org/packages/da/92/1446574745d74df0c92e6aa4a7b0b3130706a4142b2d1a5869f2eaa423c6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65", size = 829923 }, + { url = "https://files.pythonhosted.org/packages/f0/7a/1c7270340330e575b92f397352af856a8c06f230aa3e76f86b39d01b416a/pyyaml-6.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9", size = 174062 }, + { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341 }, ] [[package]] @@ -2772,55 +3900,55 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "cffi", marker = "implementation_name == 'pypy'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/04/0b/3c9baedbdf613ecaa7aa07027780b8867f57b6293b6ee50de316c9f3222b/pyzmq-27.1.0.tar.gz", hash = "sha256:ac0765e3d44455adb6ddbf4417dcce460fc40a05978c08efdf2948072f6db540", size = 281750, upload-time = "2025-09-08T23:10:18.157Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/06/5d/305323ba86b284e6fcb0d842d6adaa2999035f70f8c38a9b6d21ad28c3d4/pyzmq-27.1.0-cp311-cp311-macosx_10_15_universal2.whl", hash = "sha256:226b091818d461a3bef763805e75685e478ac17e9008f49fce2d3e52b3d58b86", size = 1333328, upload-time = "2025-09-08T23:07:45.946Z" }, - { url = "https://files.pythonhosted.org/packages/bd/a0/fc7e78a23748ad5443ac3275943457e8452da67fda347e05260261108cbc/pyzmq-27.1.0-cp311-cp311-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:0790a0161c281ca9723f804871b4027f2e8b5a528d357c8952d08cd1a9c15581", size = 908803, upload-time = "2025-09-08T23:07:47.551Z" }, - { url = "https://files.pythonhosted.org/packages/7e/22/37d15eb05f3bdfa4abea6f6d96eb3bb58585fbd3e4e0ded4e743bc650c97/pyzmq-27.1.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c895a6f35476b0c3a54e3eb6ccf41bf3018de937016e6e18748317f25d4e925f", size = 668836, upload-time = "2025-09-08T23:07:49.436Z" }, - { url = "https://files.pythonhosted.org/packages/b1/c4/2a6fe5111a01005fc7af3878259ce17684fabb8852815eda6225620f3c59/pyzmq-27.1.0-cp311-cp311-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5bbf8d3630bf96550b3be8e1fc0fea5cbdc8d5466c1192887bd94869da17a63e", size = 857038, upload-time = "2025-09-08T23:07:51.234Z" }, - { url = "https://files.pythonhosted.org/packages/cb/eb/bfdcb41d0db9cd233d6fb22dc131583774135505ada800ebf14dfb0a7c40/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:15c8bd0fe0dabf808e2d7a681398c4e5ded70a551ab47482067a572c054c8e2e", size = 1657531, upload-time = "2025-09-08T23:07:52.795Z" }, - { url = "https://files.pythonhosted.org/packages/ab/21/e3180ca269ed4a0de5c34417dfe71a8ae80421198be83ee619a8a485b0c7/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:bafcb3dd171b4ae9f19ee6380dfc71ce0390fefaf26b504c0e5f628d7c8c54f2", size = 2034786, upload-time = "2025-09-08T23:07:55.047Z" }, - { url = "https://files.pythonhosted.org/packages/3b/b1/5e21d0b517434b7f33588ff76c177c5a167858cc38ef740608898cd329f2/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:e829529fcaa09937189178115c49c504e69289abd39967cd8a4c215761373394", size = 1894220, upload-time = "2025-09-08T23:07:57.172Z" }, - { url = "https://files.pythonhosted.org/packages/03/f2/44913a6ff6941905efc24a1acf3d3cb6146b636c546c7406c38c49c403d4/pyzmq-27.1.0-cp311-cp311-win32.whl", hash = "sha256:6df079c47d5902af6db298ec92151db82ecb557af663098b92f2508c398bb54f", size = 567155, upload-time = "2025-09-08T23:07:59.05Z" }, - { url = "https://files.pythonhosted.org/packages/23/6d/d8d92a0eb270a925c9b4dd039c0b4dc10abc2fcbc48331788824ef113935/pyzmq-27.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:190cbf120fbc0fc4957b56866830def56628934a9d112aec0e2507aa6a032b97", size = 633428, upload-time = "2025-09-08T23:08:00.663Z" }, - { url = "https://files.pythonhosted.org/packages/ae/14/01afebc96c5abbbd713ecfc7469cfb1bc801c819a74ed5c9fad9a48801cb/pyzmq-27.1.0-cp311-cp311-win_arm64.whl", hash = "sha256:eca6b47df11a132d1745eb3b5b5e557a7dae2c303277aa0e69c6ba91b8736e07", size = 559497, upload-time = "2025-09-08T23:08:02.15Z" }, - { url = "https://files.pythonhosted.org/packages/92/e7/038aab64a946d535901103da16b953c8c9cc9c961dadcbf3609ed6428d23/pyzmq-27.1.0-cp312-abi3-macosx_10_15_universal2.whl", hash = "sha256:452631b640340c928fa343801b0d07eb0c3789a5ffa843f6e1a9cee0ba4eb4fc", size = 1306279, upload-time = "2025-09-08T23:08:03.807Z" }, - { url = "https://files.pythonhosted.org/packages/e8/5e/c3c49fdd0f535ef45eefcc16934648e9e59dace4a37ee88fc53f6cd8e641/pyzmq-27.1.0-cp312-abi3-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1c179799b118e554b66da67d88ed66cd37a169f1f23b5d9f0a231b4e8d44a113", size = 895645, upload-time = "2025-09-08T23:08:05.301Z" }, - { url = "https://files.pythonhosted.org/packages/f8/e5/b0b2504cb4e903a74dcf1ebae157f9e20ebb6ea76095f6cfffea28c42ecd/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3837439b7f99e60312f0c926a6ad437b067356dc2bc2ec96eb395fd0fe804233", size = 652574, upload-time = "2025-09-08T23:08:06.828Z" }, - { url = "https://files.pythonhosted.org/packages/f8/9b/c108cdb55560eaf253f0cbdb61b29971e9fb34d9c3499b0e96e4e60ed8a5/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:43ad9a73e3da1fab5b0e7e13402f0b2fb934ae1c876c51d0afff0e7c052eca31", size = 840995, upload-time = "2025-09-08T23:08:08.396Z" }, - { url = "https://files.pythonhosted.org/packages/c2/bb/b79798ca177b9eb0825b4c9998c6af8cd2a7f15a6a1a4272c1d1a21d382f/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:0de3028d69d4cdc475bfe47a6128eb38d8bc0e8f4d69646adfbcd840facbac28", size = 1642070, upload-time = "2025-09-08T23:08:09.989Z" }, - { url = "https://files.pythonhosted.org/packages/9c/80/2df2e7977c4ede24c79ae39dcef3899bfc5f34d1ca7a5b24f182c9b7a9ca/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_i686.whl", hash = "sha256:cf44a7763aea9298c0aa7dbf859f87ed7012de8bda0f3977b6fb1d96745df856", size = 2021121, upload-time = "2025-09-08T23:08:11.907Z" }, - { url = "https://files.pythonhosted.org/packages/46/bd/2d45ad24f5f5ae7e8d01525eb76786fa7557136555cac7d929880519e33a/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:f30f395a9e6fbca195400ce833c731e7b64c3919aa481af4d88c3759e0cb7496", size = 1878550, upload-time = "2025-09-08T23:08:13.513Z" }, - { url = "https://files.pythonhosted.org/packages/e6/2f/104c0a3c778d7c2ab8190e9db4f62f0b6957b53c9d87db77c284b69f33ea/pyzmq-27.1.0-cp312-abi3-win32.whl", hash = "sha256:250e5436a4ba13885494412b3da5d518cd0d3a278a1ae640e113c073a5f88edd", size = 559184, upload-time = "2025-09-08T23:08:15.163Z" }, - { url = "https://files.pythonhosted.org/packages/fc/7f/a21b20d577e4100c6a41795842028235998a643b1ad406a6d4163ea8f53e/pyzmq-27.1.0-cp312-abi3-win_amd64.whl", hash = "sha256:9ce490cf1d2ca2ad84733aa1d69ce6855372cb5ce9223802450c9b2a7cba0ccf", size = 619480, upload-time = "2025-09-08T23:08:17.192Z" }, - { url = "https://files.pythonhosted.org/packages/78/c2/c012beae5f76b72f007a9e91ee9401cb88c51d0f83c6257a03e785c81cc2/pyzmq-27.1.0-cp312-abi3-win_arm64.whl", hash = "sha256:75a2f36223f0d535a0c919e23615fc85a1e23b71f40c7eb43d7b1dedb4d8f15f", size = 552993, upload-time = "2025-09-08T23:08:18.926Z" }, - { url = "https://files.pythonhosted.org/packages/60/cb/84a13459c51da6cec1b7b1dc1a47e6db6da50b77ad7fd9c145842750a011/pyzmq-27.1.0-cp313-cp313-android_24_arm64_v8a.whl", hash = "sha256:93ad4b0855a664229559e45c8d23797ceac03183c7b6f5b4428152a6b06684a5", size = 1122436, upload-time = "2025-09-08T23:08:20.801Z" }, - { url = "https://files.pythonhosted.org/packages/dc/b6/94414759a69a26c3dd674570a81813c46a078767d931a6c70ad29fc585cb/pyzmq-27.1.0-cp313-cp313-android_24_x86_64.whl", hash = "sha256:fbb4f2400bfda24f12f009cba62ad5734148569ff4949b1b6ec3b519444342e6", size = 1156301, upload-time = "2025-09-08T23:08:22.47Z" }, - { url = "https://files.pythonhosted.org/packages/a5/ad/15906493fd40c316377fd8a8f6b1f93104f97a752667763c9b9c1b71d42d/pyzmq-27.1.0-cp313-cp313t-macosx_10_15_universal2.whl", hash = "sha256:e343d067f7b151cfe4eb3bb796a7752c9d369eed007b91231e817071d2c2fec7", size = 1341197, upload-time = "2025-09-08T23:08:24.286Z" }, - { url = "https://files.pythonhosted.org/packages/14/1d/d343f3ce13db53a54cb8946594e567410b2125394dafcc0268d8dda027e0/pyzmq-27.1.0-cp313-cp313t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:08363b2011dec81c354d694bdecaef4770e0ae96b9afea70b3f47b973655cc05", size = 897275, upload-time = "2025-09-08T23:08:26.063Z" }, - { url = "https://files.pythonhosted.org/packages/69/2d/d83dd6d7ca929a2fc67d2c3005415cdf322af7751d773524809f9e585129/pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d54530c8c8b5b8ddb3318f481297441af102517602b569146185fa10b63f4fa9", size = 660469, upload-time = "2025-09-08T23:08:27.623Z" }, - { url = "https://files.pythonhosted.org/packages/3e/cd/9822a7af117f4bc0f1952dbe9ef8358eb50a24928efd5edf54210b850259/pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6f3afa12c392f0a44a2414056d730eebc33ec0926aae92b5ad5cf26ebb6cc128", size = 847961, upload-time = "2025-09-08T23:08:29.672Z" }, - { url = "https://files.pythonhosted.org/packages/9a/12/f003e824a19ed73be15542f172fd0ec4ad0b60cf37436652c93b9df7c585/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c65047adafe573ff023b3187bb93faa583151627bc9c51fc4fb2c561ed689d39", size = 1650282, upload-time = "2025-09-08T23:08:31.349Z" }, - { url = "https://files.pythonhosted.org/packages/d5/4a/e82d788ed58e9a23995cee70dbc20c9aded3d13a92d30d57ec2291f1e8a3/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:90e6e9441c946a8b0a667356f7078d96411391a3b8f80980315455574177ec97", size = 2024468, upload-time = "2025-09-08T23:08:33.543Z" }, - { url = "https://files.pythonhosted.org/packages/d9/94/2da0a60841f757481e402b34bf4c8bf57fa54a5466b965de791b1e6f747d/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:add071b2d25f84e8189aaf0882d39a285b42fa3853016ebab234a5e78c7a43db", size = 1885394, upload-time = "2025-09-08T23:08:35.51Z" }, - { url = "https://files.pythonhosted.org/packages/4f/6f/55c10e2e49ad52d080dc24e37adb215e5b0d64990b57598abc2e3f01725b/pyzmq-27.1.0-cp313-cp313t-win32.whl", hash = "sha256:7ccc0700cfdf7bd487bea8d850ec38f204478681ea02a582a8da8171b7f90a1c", size = 574964, upload-time = "2025-09-08T23:08:37.178Z" }, - { url = "https://files.pythonhosted.org/packages/87/4d/2534970ba63dd7c522d8ca80fb92777f362c0f321900667c615e2067cb29/pyzmq-27.1.0-cp313-cp313t-win_amd64.whl", hash = "sha256:8085a9fba668216b9b4323be338ee5437a235fe275b9d1610e422ccc279733e2", size = 641029, upload-time = "2025-09-08T23:08:40.595Z" }, - { url = "https://files.pythonhosted.org/packages/f6/fa/f8aea7a28b0641f31d40dea42d7ef003fded31e184ef47db696bc74cd610/pyzmq-27.1.0-cp313-cp313t-win_arm64.whl", hash = "sha256:6bb54ca21bcfe361e445256c15eedf083f153811c37be87e0514934d6913061e", size = 561541, upload-time = "2025-09-08T23:08:42.668Z" }, - { url = "https://files.pythonhosted.org/packages/87/45/19efbb3000956e82d0331bafca5d9ac19ea2857722fa2caacefb6042f39d/pyzmq-27.1.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:ce980af330231615756acd5154f29813d553ea555485ae712c491cd483df6b7a", size = 1341197, upload-time = "2025-09-08T23:08:44.973Z" }, - { url = "https://files.pythonhosted.org/packages/48/43/d72ccdbf0d73d1343936296665826350cb1e825f92f2db9db3e61c2162a2/pyzmq-27.1.0-cp314-cp314t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1779be8c549e54a1c38f805e56d2a2e5c009d26de10921d7d51cfd1c8d4632ea", size = 897175, upload-time = "2025-09-08T23:08:46.601Z" }, - { url = "https://files.pythonhosted.org/packages/2f/2e/a483f73a10b65a9ef0161e817321d39a770b2acf8bcf3004a28d90d14a94/pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7200bb0f03345515df50d99d3db206a0a6bee1955fbb8c453c76f5bf0e08fb96", size = 660427, upload-time = "2025-09-08T23:08:48.187Z" }, - { url = "https://files.pythonhosted.org/packages/f5/d2/5f36552c2d3e5685abe60dfa56f91169f7a2d99bbaf67c5271022ab40863/pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:01c0e07d558b06a60773744ea6251f769cd79a41a97d11b8bf4ab8f034b0424d", size = 847929, upload-time = "2025-09-08T23:08:49.76Z" }, - { url = "https://files.pythonhosted.org/packages/c4/2a/404b331f2b7bf3198e9945f75c4c521f0c6a3a23b51f7a4a401b94a13833/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:80d834abee71f65253c91540445d37c4c561e293ba6e741b992f20a105d69146", size = 1650193, upload-time = "2025-09-08T23:08:51.7Z" }, - { url = "https://files.pythonhosted.org/packages/1c/0b/f4107e33f62a5acf60e3ded67ed33d79b4ce18de432625ce2fc5093d6388/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:544b4e3b7198dde4a62b8ff6685e9802a9a1ebf47e77478a5eb88eca2a82f2fd", size = 2024388, upload-time = "2025-09-08T23:08:53.393Z" }, - { url = "https://files.pythonhosted.org/packages/0d/01/add31fe76512642fd6e40e3a3bd21f4b47e242c8ba33efb6809e37076d9b/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:cedc4c68178e59a4046f97eca31b148ddcf51e88677de1ef4e78cf06c5376c9a", size = 1885316, upload-time = "2025-09-08T23:08:55.702Z" }, - { url = "https://files.pythonhosted.org/packages/c4/59/a5f38970f9bf07cee96128de79590bb354917914a9be11272cfc7ff26af0/pyzmq-27.1.0-cp314-cp314t-win32.whl", hash = "sha256:1f0b2a577fd770aa6f053211a55d1c47901f4d537389a034c690291485e5fe92", size = 587472, upload-time = "2025-09-08T23:08:58.18Z" }, - { url = "https://files.pythonhosted.org/packages/70/d8/78b1bad170f93fcf5e3536e70e8fadac55030002275c9a29e8f5719185de/pyzmq-27.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:19c9468ae0437f8074af379e986c5d3d7d7bfe033506af442e8c879732bedbe0", size = 661401, upload-time = "2025-09-08T23:08:59.802Z" }, - { url = "https://files.pythonhosted.org/packages/81/d6/4bfbb40c9a0b42fc53c7cf442f6385db70b40f74a783130c5d0a5aa62228/pyzmq-27.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:dc5dbf68a7857b59473f7df42650c621d7e8923fb03fa74a526890f4d33cc4d7", size = 575170, upload-time = "2025-09-08T23:09:01.418Z" }, - { url = "https://files.pythonhosted.org/packages/4c/c6/c4dcdecdbaa70969ee1fdced6d7b8f60cfabe64d25361f27ac4665a70620/pyzmq-27.1.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:18770c8d3563715387139060d37859c02ce40718d1faf299abddcdcc6a649066", size = 836265, upload-time = "2025-09-08T23:09:49.376Z" }, - { url = "https://files.pythonhosted.org/packages/3e/79/f38c92eeaeb03a2ccc2ba9866f0439593bb08c5e3b714ac1d553e5c96e25/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:ac25465d42f92e990f8d8b0546b01c391ad431c3bf447683fdc40565941d0604", size = 800208, upload-time = "2025-09-08T23:09:51.073Z" }, - { url = "https://files.pythonhosted.org/packages/49/0e/3f0d0d335c6b3abb9b7b723776d0b21fa7f3a6c819a0db6097059aada160/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:53b40f8ae006f2734ee7608d59ed661419f087521edbfc2149c3932e9c14808c", size = 567747, upload-time = "2025-09-08T23:09:52.698Z" }, - { url = "https://files.pythonhosted.org/packages/a1/cf/f2b3784d536250ffd4be70e049f3b60981235d70c6e8ce7e3ef21e1adb25/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f605d884e7c8be8fe1aa94e0a783bf3f591b84c24e4bc4f3e7564c82ac25e271", size = 747371, upload-time = "2025-09-08T23:09:54.563Z" }, - { url = "https://files.pythonhosted.org/packages/01/1b/5dbe84eefc86f48473947e2f41711aded97eecef1231f4558f1f02713c12/pyzmq-27.1.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:c9f7f6e13dff2e44a6afeaf2cf54cee5929ad64afaf4d40b50f93c58fc687355", size = 544862, upload-time = "2025-09-08T23:09:56.509Z" }, +sdist = { url = "https://files.pythonhosted.org/packages/04/0b/3c9baedbdf613ecaa7aa07027780b8867f57b6293b6ee50de316c9f3222b/pyzmq-27.1.0.tar.gz", hash = "sha256:ac0765e3d44455adb6ddbf4417dcce460fc40a05978c08efdf2948072f6db540", size = 281750 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/06/5d/305323ba86b284e6fcb0d842d6adaa2999035f70f8c38a9b6d21ad28c3d4/pyzmq-27.1.0-cp311-cp311-macosx_10_15_universal2.whl", hash = "sha256:226b091818d461a3bef763805e75685e478ac17e9008f49fce2d3e52b3d58b86", size = 1333328 }, + { url = "https://files.pythonhosted.org/packages/bd/a0/fc7e78a23748ad5443ac3275943457e8452da67fda347e05260261108cbc/pyzmq-27.1.0-cp311-cp311-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:0790a0161c281ca9723f804871b4027f2e8b5a528d357c8952d08cd1a9c15581", size = 908803 }, + { url = "https://files.pythonhosted.org/packages/7e/22/37d15eb05f3bdfa4abea6f6d96eb3bb58585fbd3e4e0ded4e743bc650c97/pyzmq-27.1.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c895a6f35476b0c3a54e3eb6ccf41bf3018de937016e6e18748317f25d4e925f", size = 668836 }, + { url = "https://files.pythonhosted.org/packages/b1/c4/2a6fe5111a01005fc7af3878259ce17684fabb8852815eda6225620f3c59/pyzmq-27.1.0-cp311-cp311-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5bbf8d3630bf96550b3be8e1fc0fea5cbdc8d5466c1192887bd94869da17a63e", size = 857038 }, + { url = "https://files.pythonhosted.org/packages/cb/eb/bfdcb41d0db9cd233d6fb22dc131583774135505ada800ebf14dfb0a7c40/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:15c8bd0fe0dabf808e2d7a681398c4e5ded70a551ab47482067a572c054c8e2e", size = 1657531 }, + { url = "https://files.pythonhosted.org/packages/ab/21/e3180ca269ed4a0de5c34417dfe71a8ae80421198be83ee619a8a485b0c7/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:bafcb3dd171b4ae9f19ee6380dfc71ce0390fefaf26b504c0e5f628d7c8c54f2", size = 2034786 }, + { url = "https://files.pythonhosted.org/packages/3b/b1/5e21d0b517434b7f33588ff76c177c5a167858cc38ef740608898cd329f2/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:e829529fcaa09937189178115c49c504e69289abd39967cd8a4c215761373394", size = 1894220 }, + { url = "https://files.pythonhosted.org/packages/03/f2/44913a6ff6941905efc24a1acf3d3cb6146b636c546c7406c38c49c403d4/pyzmq-27.1.0-cp311-cp311-win32.whl", hash = "sha256:6df079c47d5902af6db298ec92151db82ecb557af663098b92f2508c398bb54f", size = 567155 }, + { url = "https://files.pythonhosted.org/packages/23/6d/d8d92a0eb270a925c9b4dd039c0b4dc10abc2fcbc48331788824ef113935/pyzmq-27.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:190cbf120fbc0fc4957b56866830def56628934a9d112aec0e2507aa6a032b97", size = 633428 }, + { url = "https://files.pythonhosted.org/packages/ae/14/01afebc96c5abbbd713ecfc7469cfb1bc801c819a74ed5c9fad9a48801cb/pyzmq-27.1.0-cp311-cp311-win_arm64.whl", hash = "sha256:eca6b47df11a132d1745eb3b5b5e557a7dae2c303277aa0e69c6ba91b8736e07", size = 559497 }, + { url = "https://files.pythonhosted.org/packages/92/e7/038aab64a946d535901103da16b953c8c9cc9c961dadcbf3609ed6428d23/pyzmq-27.1.0-cp312-abi3-macosx_10_15_universal2.whl", hash = "sha256:452631b640340c928fa343801b0d07eb0c3789a5ffa843f6e1a9cee0ba4eb4fc", size = 1306279 }, + { url = "https://files.pythonhosted.org/packages/e8/5e/c3c49fdd0f535ef45eefcc16934648e9e59dace4a37ee88fc53f6cd8e641/pyzmq-27.1.0-cp312-abi3-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1c179799b118e554b66da67d88ed66cd37a169f1f23b5d9f0a231b4e8d44a113", size = 895645 }, + { url = "https://files.pythonhosted.org/packages/f8/e5/b0b2504cb4e903a74dcf1ebae157f9e20ebb6ea76095f6cfffea28c42ecd/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3837439b7f99e60312f0c926a6ad437b067356dc2bc2ec96eb395fd0fe804233", size = 652574 }, + { url = "https://files.pythonhosted.org/packages/f8/9b/c108cdb55560eaf253f0cbdb61b29971e9fb34d9c3499b0e96e4e60ed8a5/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:43ad9a73e3da1fab5b0e7e13402f0b2fb934ae1c876c51d0afff0e7c052eca31", size = 840995 }, + { url = "https://files.pythonhosted.org/packages/c2/bb/b79798ca177b9eb0825b4c9998c6af8cd2a7f15a6a1a4272c1d1a21d382f/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:0de3028d69d4cdc475bfe47a6128eb38d8bc0e8f4d69646adfbcd840facbac28", size = 1642070 }, + { url = "https://files.pythonhosted.org/packages/9c/80/2df2e7977c4ede24c79ae39dcef3899bfc5f34d1ca7a5b24f182c9b7a9ca/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_i686.whl", hash = "sha256:cf44a7763aea9298c0aa7dbf859f87ed7012de8bda0f3977b6fb1d96745df856", size = 2021121 }, + { url = "https://files.pythonhosted.org/packages/46/bd/2d45ad24f5f5ae7e8d01525eb76786fa7557136555cac7d929880519e33a/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:f30f395a9e6fbca195400ce833c731e7b64c3919aa481af4d88c3759e0cb7496", size = 1878550 }, + { url = "https://files.pythonhosted.org/packages/e6/2f/104c0a3c778d7c2ab8190e9db4f62f0b6957b53c9d87db77c284b69f33ea/pyzmq-27.1.0-cp312-abi3-win32.whl", hash = "sha256:250e5436a4ba13885494412b3da5d518cd0d3a278a1ae640e113c073a5f88edd", size = 559184 }, + { url = "https://files.pythonhosted.org/packages/fc/7f/a21b20d577e4100c6a41795842028235998a643b1ad406a6d4163ea8f53e/pyzmq-27.1.0-cp312-abi3-win_amd64.whl", hash = "sha256:9ce490cf1d2ca2ad84733aa1d69ce6855372cb5ce9223802450c9b2a7cba0ccf", size = 619480 }, + { url = "https://files.pythonhosted.org/packages/78/c2/c012beae5f76b72f007a9e91ee9401cb88c51d0f83c6257a03e785c81cc2/pyzmq-27.1.0-cp312-abi3-win_arm64.whl", hash = "sha256:75a2f36223f0d535a0c919e23615fc85a1e23b71f40c7eb43d7b1dedb4d8f15f", size = 552993 }, + { url = "https://files.pythonhosted.org/packages/60/cb/84a13459c51da6cec1b7b1dc1a47e6db6da50b77ad7fd9c145842750a011/pyzmq-27.1.0-cp313-cp313-android_24_arm64_v8a.whl", hash = "sha256:93ad4b0855a664229559e45c8d23797ceac03183c7b6f5b4428152a6b06684a5", size = 1122436 }, + { url = "https://files.pythonhosted.org/packages/dc/b6/94414759a69a26c3dd674570a81813c46a078767d931a6c70ad29fc585cb/pyzmq-27.1.0-cp313-cp313-android_24_x86_64.whl", hash = "sha256:fbb4f2400bfda24f12f009cba62ad5734148569ff4949b1b6ec3b519444342e6", size = 1156301 }, + { url = "https://files.pythonhosted.org/packages/a5/ad/15906493fd40c316377fd8a8f6b1f93104f97a752667763c9b9c1b71d42d/pyzmq-27.1.0-cp313-cp313t-macosx_10_15_universal2.whl", hash = "sha256:e343d067f7b151cfe4eb3bb796a7752c9d369eed007b91231e817071d2c2fec7", size = 1341197 }, + { url = "https://files.pythonhosted.org/packages/14/1d/d343f3ce13db53a54cb8946594e567410b2125394dafcc0268d8dda027e0/pyzmq-27.1.0-cp313-cp313t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:08363b2011dec81c354d694bdecaef4770e0ae96b9afea70b3f47b973655cc05", size = 897275 }, + { url = "https://files.pythonhosted.org/packages/69/2d/d83dd6d7ca929a2fc67d2c3005415cdf322af7751d773524809f9e585129/pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d54530c8c8b5b8ddb3318f481297441af102517602b569146185fa10b63f4fa9", size = 660469 }, + { url = "https://files.pythonhosted.org/packages/3e/cd/9822a7af117f4bc0f1952dbe9ef8358eb50a24928efd5edf54210b850259/pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6f3afa12c392f0a44a2414056d730eebc33ec0926aae92b5ad5cf26ebb6cc128", size = 847961 }, + { url = "https://files.pythonhosted.org/packages/9a/12/f003e824a19ed73be15542f172fd0ec4ad0b60cf37436652c93b9df7c585/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c65047adafe573ff023b3187bb93faa583151627bc9c51fc4fb2c561ed689d39", size = 1650282 }, + { url = "https://files.pythonhosted.org/packages/d5/4a/e82d788ed58e9a23995cee70dbc20c9aded3d13a92d30d57ec2291f1e8a3/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:90e6e9441c946a8b0a667356f7078d96411391a3b8f80980315455574177ec97", size = 2024468 }, + { url = "https://files.pythonhosted.org/packages/d9/94/2da0a60841f757481e402b34bf4c8bf57fa54a5466b965de791b1e6f747d/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:add071b2d25f84e8189aaf0882d39a285b42fa3853016ebab234a5e78c7a43db", size = 1885394 }, + { url = "https://files.pythonhosted.org/packages/4f/6f/55c10e2e49ad52d080dc24e37adb215e5b0d64990b57598abc2e3f01725b/pyzmq-27.1.0-cp313-cp313t-win32.whl", hash = "sha256:7ccc0700cfdf7bd487bea8d850ec38f204478681ea02a582a8da8171b7f90a1c", size = 574964 }, + { url = "https://files.pythonhosted.org/packages/87/4d/2534970ba63dd7c522d8ca80fb92777f362c0f321900667c615e2067cb29/pyzmq-27.1.0-cp313-cp313t-win_amd64.whl", hash = "sha256:8085a9fba668216b9b4323be338ee5437a235fe275b9d1610e422ccc279733e2", size = 641029 }, + { url = "https://files.pythonhosted.org/packages/f6/fa/f8aea7a28b0641f31d40dea42d7ef003fded31e184ef47db696bc74cd610/pyzmq-27.1.0-cp313-cp313t-win_arm64.whl", hash = "sha256:6bb54ca21bcfe361e445256c15eedf083f153811c37be87e0514934d6913061e", size = 561541 }, + { url = "https://files.pythonhosted.org/packages/87/45/19efbb3000956e82d0331bafca5d9ac19ea2857722fa2caacefb6042f39d/pyzmq-27.1.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:ce980af330231615756acd5154f29813d553ea555485ae712c491cd483df6b7a", size = 1341197 }, + { url = "https://files.pythonhosted.org/packages/48/43/d72ccdbf0d73d1343936296665826350cb1e825f92f2db9db3e61c2162a2/pyzmq-27.1.0-cp314-cp314t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1779be8c549e54a1c38f805e56d2a2e5c009d26de10921d7d51cfd1c8d4632ea", size = 897175 }, + { url = "https://files.pythonhosted.org/packages/2f/2e/a483f73a10b65a9ef0161e817321d39a770b2acf8bcf3004a28d90d14a94/pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7200bb0f03345515df50d99d3db206a0a6bee1955fbb8c453c76f5bf0e08fb96", size = 660427 }, + { url = "https://files.pythonhosted.org/packages/f5/d2/5f36552c2d3e5685abe60dfa56f91169f7a2d99bbaf67c5271022ab40863/pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:01c0e07d558b06a60773744ea6251f769cd79a41a97d11b8bf4ab8f034b0424d", size = 847929 }, + { url = "https://files.pythonhosted.org/packages/c4/2a/404b331f2b7bf3198e9945f75c4c521f0c6a3a23b51f7a4a401b94a13833/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:80d834abee71f65253c91540445d37c4c561e293ba6e741b992f20a105d69146", size = 1650193 }, + { url = "https://files.pythonhosted.org/packages/1c/0b/f4107e33f62a5acf60e3ded67ed33d79b4ce18de432625ce2fc5093d6388/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:544b4e3b7198dde4a62b8ff6685e9802a9a1ebf47e77478a5eb88eca2a82f2fd", size = 2024388 }, + { url = "https://files.pythonhosted.org/packages/0d/01/add31fe76512642fd6e40e3a3bd21f4b47e242c8ba33efb6809e37076d9b/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:cedc4c68178e59a4046f97eca31b148ddcf51e88677de1ef4e78cf06c5376c9a", size = 1885316 }, + { url = "https://files.pythonhosted.org/packages/c4/59/a5f38970f9bf07cee96128de79590bb354917914a9be11272cfc7ff26af0/pyzmq-27.1.0-cp314-cp314t-win32.whl", hash = "sha256:1f0b2a577fd770aa6f053211a55d1c47901f4d537389a034c690291485e5fe92", size = 587472 }, + { url = "https://files.pythonhosted.org/packages/70/d8/78b1bad170f93fcf5e3536e70e8fadac55030002275c9a29e8f5719185de/pyzmq-27.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:19c9468ae0437f8074af379e986c5d3d7d7bfe033506af442e8c879732bedbe0", size = 661401 }, + { url = "https://files.pythonhosted.org/packages/81/d6/4bfbb40c9a0b42fc53c7cf442f6385db70b40f74a783130c5d0a5aa62228/pyzmq-27.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:dc5dbf68a7857b59473f7df42650c621d7e8923fb03fa74a526890f4d33cc4d7", size = 575170 }, + { url = "https://files.pythonhosted.org/packages/4c/c6/c4dcdecdbaa70969ee1fdced6d7b8f60cfabe64d25361f27ac4665a70620/pyzmq-27.1.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:18770c8d3563715387139060d37859c02ce40718d1faf299abddcdcc6a649066", size = 836265 }, + { url = "https://files.pythonhosted.org/packages/3e/79/f38c92eeaeb03a2ccc2ba9866f0439593bb08c5e3b714ac1d553e5c96e25/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:ac25465d42f92e990f8d8b0546b01c391ad431c3bf447683fdc40565941d0604", size = 800208 }, + { url = "https://files.pythonhosted.org/packages/49/0e/3f0d0d335c6b3abb9b7b723776d0b21fa7f3a6c819a0db6097059aada160/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:53b40f8ae006f2734ee7608d59ed661419f087521edbfc2149c3932e9c14808c", size = 567747 }, + { url = "https://files.pythonhosted.org/packages/a1/cf/f2b3784d536250ffd4be70e049f3b60981235d70c6e8ce7e3ef21e1adb25/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f605d884e7c8be8fe1aa94e0a783bf3f591b84c24e4bc4f3e7564c82ac25e271", size = 747371 }, + { url = "https://files.pythonhosted.org/packages/01/1b/5dbe84eefc86f48473947e2f41711aded97eecef1231f4558f1f02713c12/pyzmq-27.1.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:c9f7f6e13dff2e44a6afeaf2cf54cee5929ad64afaf4d40b50f93c58fc687355", size = 544862 }, ] [[package]] @@ -2856,6 +3984,7 @@ widgets = [ [package.dev-dependencies] dev = [ + { name = "hdf5plugin" }, { name = "jupyterlab" }, { name = "packaging" }, { name = "pre-commit" }, @@ -2895,6 +4024,7 @@ provides-extras = ["widgets"] [package.metadata.requires-dev] dev = [ + { name = "hdf5plugin", specifier = ">=6.0.0" }, { name = "jupyterlab", specifier = ">=4.4.0" }, { name = "packaging", specifier = ">=24.2" }, { name = "pre-commit", specifier = ">=4.2.0" }, @@ -2940,9 +4070,9 @@ dependencies = [ { name = "rpds-py" }, { name = "typing-extensions", marker = "python_full_version < '3.13'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/22/f5/df4e9027acead3ecc63e50fe1e36aca1523e1719559c499951bb4b53188f/referencing-0.37.0.tar.gz", hash = "sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8", size = 78036, upload-time = "2025-10-13T15:30:48.871Z" } +sdist = { url = "https://files.pythonhosted.org/packages/22/f5/df4e9027acead3ecc63e50fe1e36aca1523e1719559c499951bb4b53188f/referencing-0.37.0.tar.gz", hash = "sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8", size = 78036 } wheels = [ - { url = "https://files.pythonhosted.org/packages/2c/58/ca301544e1fa93ed4f80d724bf5b194f6e4b945841c5bfd555878eea9fcb/referencing-0.37.0-py3-none-any.whl", hash = "sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231", size = 26766, upload-time = "2025-10-13T15:30:47.625Z" }, + { url = "https://files.pythonhosted.org/packages/2c/58/ca301544e1fa93ed4f80d724bf5b194f6e4b945841c5bfd555878eea9fcb/referencing-0.37.0-py3-none-any.whl", hash = "sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231", size = 26766 }, ] [[package]] @@ -2955,9 +4085,15 @@ dependencies = [ { name = "idna" }, { name = "urllib3" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/ac/c3/e2a2b89f2d3e2179abd6d00ebd70bff6273f37fb3e0cc209f48b39d00cbf/requests-2.34.2.tar.gz", hash = "sha256:f288924cae4e29463698d6d60bc6a4da69c89185ad1e0bcc4104f584e960b9ed", size = 142856, upload-time = "2026-05-14T19:25:27.735Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/a0/f4/c67b0b3f1b9245e8d266f0f112c500d50e5b4e83cb6f3b71b6528104182a/requests-2.34.2-py3-none-any.whl", hash = "sha256:2a0d60c172f83ac6ab31e4554906c0f3b3588d37b5cb939b1c061f4907e278e0", size = 73075, upload-time = "2026-05-14T19:25:26.443Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/c9/74/b3ff8e6c8446842c3f5c837e9c3dfcfe2018ea6ecef224c710c85ef728f4/requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf", size = 134517 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6", size = 64738 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2967,18 +4103,18 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "six" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/28/ea/a9387748e2d111c3c2b275ba970b735e04e15cdb1eb30693b6b5708c4dbd/rfc3339_validator-0.1.4.tar.gz", hash = "sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b", size = 5513, upload-time = "2021-05-12T16:37:54.178Z" } +sdist = { url = "https://files.pythonhosted.org/packages/28/ea/a9387748e2d111c3c2b275ba970b735e04e15cdb1eb30693b6b5708c4dbd/rfc3339_validator-0.1.4.tar.gz", hash = "sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b", size = 5513 } wheels = [ - { url = "https://files.pythonhosted.org/packages/7b/44/4e421b96b67b2daff264473f7465db72fbdf36a07e05494f50300cc7b0c6/rfc3339_validator-0.1.4-py2.py3-none-any.whl", hash = "sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa", size = 3490, upload-time = "2021-05-12T16:37:52.536Z" }, + { url = "https://files.pythonhosted.org/packages/7b/44/4e421b96b67b2daff264473f7465db72fbdf36a07e05494f50300cc7b0c6/rfc3339_validator-0.1.4-py2.py3-none-any.whl", hash = "sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa", size = 3490 }, ] [[package]] name = "rfc3986-validator" version = "0.1.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/da/88/f270de456dd7d11dcc808abfa291ecdd3f45ff44e3b549ffa01b126464d0/rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055", size = 6760, upload-time = "2019-10-28T16:00:19.144Z" } +sdist = { url = "https://files.pythonhosted.org/packages/da/88/f270de456dd7d11dcc808abfa291ecdd3f45ff44e3b549ffa01b126464d0/rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055", size = 6760 } wheels = [ - { url = "https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl", hash = "sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9", size = 4242, upload-time = "2019-10-28T16:00:13.976Z" }, + { url = "https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl", hash = "sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9", size = 4242 }, ] [[package]] @@ -2988,9 +4124,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "lark" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/2c/06/37c1a5557acf449e8e406a830a05bf885ac47d33270aec454ef78675008d/rfc3987_syntax-1.1.0.tar.gz", hash = "sha256:717a62cbf33cffdd16dfa3a497d81ce48a660ea691b1ddd7be710c22f00b4a0d", size = 14239, upload-time = "2025-07-18T01:05:05.015Z" } +sdist = { url = "https://files.pythonhosted.org/packages/2c/06/37c1a5557acf449e8e406a830a05bf885ac47d33270aec454ef78675008d/rfc3987_syntax-1.1.0.tar.gz", hash = "sha256:717a62cbf33cffdd16dfa3a497d81ce48a660ea691b1ddd7be710c22f00b4a0d", size = 14239 } wheels = [ - { url = "https://files.pythonhosted.org/packages/7e/71/44ce230e1b7fadd372515a97e32a83011f906ddded8d03e3c6aafbdedbb7/rfc3987_syntax-1.1.0-py3-none-any.whl", hash = "sha256:6c3d97604e4c5ce9f714898e05401a0445a641cfa276432b0a648c80856f6a3f", size = 8046, upload-time = "2025-07-18T01:05:03.843Z" }, + { url = "https://files.pythonhosted.org/packages/7e/71/44ce230e1b7fadd372515a97e32a83011f906ddded8d03e3c6aafbdedbb7/rfc3987_syntax-1.1.0-py3-none-any.whl", hash = "sha256:6c3d97604e4c5ce9f714898e05401a0445a641cfa276432b0a648c80856f6a3f", size = 8046 }, ] [[package]] @@ -3005,6 +4141,7 @@ dependencies = [ { name = "python-dateutil" }, { name = "pyyaml" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/57/ca/dc7c3871b47e1bd89b7a11a34b7b68219504a03047788e52cff1ce823755/rosettasciio-0.14.0.tar.gz", hash = "sha256:4b21582f0d832f086e10236b1798b1c17e327dc6d610e845a8eddd4df817504f", size = 1245393, upload-time = "2026-05-27T15:29:54.331Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/9f/bf/0ecc513c23c2a61f1497c9801cc4144e40a996099d36b71407673ad1a975/rosettasciio-0.14.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:73c7bb64e6a456fd058807c9a698d50b6fa757985f0e505087b641b10dd50904", size = 876315, upload-time = "2026-05-27T15:28:58.173Z" }, @@ -3044,12 +4181,38 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/14/f3/3a5e2c3fa67fbf2219a914c6342a91f2f7ba19554ffe2b654fd44e13685c/rosettasciio-0.14.0-cp314-cp314t-win_amd64.whl", hash = "sha256:4a17300595ae1a83331ce4e015478341eac99cf8544f36c88f7c6de6e2662541", size = 869656, upload-time = "2026-05-27T15:29:49.836Z" }, { url = "https://files.pythonhosted.org/packages/55/3c/5f189a330ba363f04ee73e25b37f8e08ae1266ad72e6c93d65166a7c7c6e/rosettasciio-0.14.0-cp314-cp314t-win_arm64.whl", hash = "sha256:66ca5c8d08cd3caa5397b134f83803915807d8e799ad3ac2fda5b9607342371d", size = 857389, upload-time = "2026-05-27T15:29:51.353Z" }, { url = "https://files.pythonhosted.org/packages/d2/a3/5cf5424b16d68e8db9139d613570ef01825995d260343fdec704d3f646ba/rosettasciio-0.14.0-py3-none-any.whl", hash = "sha256:f64e045ced6827ba75e6b8b85c54812c0346eda6b76f08acded3d0f6e7fb5a27", size = 628709, upload-time = "2026-05-27T15:29:52.825Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/cc/4c/f1560842f8b9aba82973b670eb9a4e57cdfca0d998fd106140c6c74ae1e0/rosettasciio-0.10.0.tar.gz", hash = "sha256:0082ad325029fb815d1da8015f9069df90e979114fcba84bda94a63c2e63039b", size = 1186926 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d2/db/1d65725ed9fbd1ed9ce89d0bf38aed0a98d5683db13e898f25a2d8b239ca/rosettasciio-0.10.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:718ec836f2cf884c584be86cd0da188419692d7c32915665738d46ad6b6e3a37", size = 795531 }, + { url = "https://files.pythonhosted.org/packages/10/ef/5ffde08333c095978c231dd488e7f05d97ee5187cabe5670df83f96d8403/rosettasciio-0.10.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f75142a4bfa4b85384efdf1d69af21ec8d9a83e17dfd3e577166c3a87a796420", size = 789939 }, + { url = "https://files.pythonhosted.org/packages/84/ba/bfea3c8aa25b313d01e955821130bf1b4e5cc1eb48ee33fd0ef7db0cbf37/rosettasciio-0.10.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:21a5d7acf1501e979f8439995acc8edb2d24d42b9dc5ecfbe498c624cd84d4ee", size = 1255637 }, + { url = "https://files.pythonhosted.org/packages/6e/54/dbc75dcd3ff0fd6f0d631d636d6d2410c6c4feac0fd8507d9d133fc4715d/rosettasciio-0.10.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8b2ef07b84dfb8a9cb3dfa0cf4e071c6bee2a70cd173293c85522b791e7ceb07", size = 1258334 }, + { url = "https://files.pythonhosted.org/packages/ef/77/e8b57c6fde7a01af588448ee7be43b7f29fdf265d00c646eb7b8196653a8/rosettasciio-0.10.0-cp311-cp311-win_amd64.whl", hash = "sha256:a1fb511579431dd1c7b7aed6626f469ac2b77ab45f6728c834c3d054913ac71f", size = 787920 }, + { url = "https://files.pythonhosted.org/packages/e6/4f/94b8166640de28b7f2c8a98664288bfc674b22a5ba49d9953bb2af1dd932/rosettasciio-0.10.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:3441354fc7cb1d92c5e0bf8eccdcd26a5abc429299625a820087f95daed4cfa2", size = 796549 }, + { url = "https://files.pythonhosted.org/packages/f1/48/af8159fd1b9dfeb82a47c84587a81d1d98974f05f85e764952862a5206de/rosettasciio-0.10.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:96e08cd6a373dc794edc268da4c644b3fd93927e2324c5a3721c3dd71d3b592c", size = 789810 }, + { url = "https://files.pythonhosted.org/packages/99/9d/5cf650f4c39c649e3c09ff885b6a471aa3a0c57e52e1a538f88156083ca8/rosettasciio-0.10.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9e056f143c5c7e41ca55ee5dc59b5efaa2f14ace6f6f8039d5102f156385f2c9", size = 1257517 }, + { url = "https://files.pythonhosted.org/packages/2d/49/d0484376a7724aa8bbc3628d6e4331f0593dcbee3efd3ba193c81cc380f6/rosettasciio-0.10.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:465811041da11db7259f8fde1c72fb6838aaa14678c000feed8719c4f8d2dfe0", size = 1263282 }, + { url = "https://files.pythonhosted.org/packages/94/99/a70aeef2b316de92a315c8b6f5d3153fc1b36bbaedc96ad3caf70b45606c/rosettasciio-0.10.0-cp312-cp312-win_amd64.whl", hash = "sha256:fffc27d62c14e63818d8928d55458fe660f0153c406b4294e3cd4cbd63e1bea7", size = 788436 }, + { url = "https://files.pythonhosted.org/packages/bc/5f/ef4db517d63a657a9c1d626d65e9c843850fd8da8a364cfb674abe1e6fc4/rosettasciio-0.10.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8627f64c027361face099eaad76ea02eb7cfce4f7c66174826f36e236204e970", size = 795544 }, + { url = "https://files.pythonhosted.org/packages/90/45/d1590ce68aed0cf1c8e76cd52c1077a5a92f4e14c97d2a7456871296899c/rosettasciio-0.10.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:c5ca0b541c5744b9b9559f5e002b5f2362d4cb09a002b2650a9d358025477261", size = 788949 }, + { url = "https://files.pythonhosted.org/packages/b0/6f/680259cc71741d9fa0c418230653177cf9bea4f554014a26354ab8079380/rosettasciio-0.10.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ccf69c1d3e141bca2feca2275158a796f27299888a8ac7943eceec9779f1b811", size = 1249455 }, + { url = "https://files.pythonhosted.org/packages/24/4f/8b668c9312ab4d2c87a342e4d947a2c0c10cadda44391bff1154df5a623e/rosettasciio-0.10.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5a8929f285c2cfdebb5ae180dfe80e90ada916e37063e83d8ea7840be7851ddb", size = 1259454 }, + { url = "https://files.pythonhosted.org/packages/ba/5f/07ba8f617b121431b0d8f45f946bd9602256fb1f1d43c275f21fecfe9f27/rosettasciio-0.10.0-cp313-cp313-win_amd64.whl", hash = "sha256:c802e0292537cb0f4878eca631d227183562a7e13c2449fe1e6f23b4eae808ff", size = 788185 }, + { url = "https://files.pythonhosted.org/packages/40/2f/4515d05d896b13410c379eb21a6090c61fd654956fae0f54fce108a7379a/rosettasciio-0.10.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:a8ab4c344987364c479232001d2e072f00a855fe3d385cbcbac68641a0c2f802", size = 800276 }, + { url = "https://files.pythonhosted.org/packages/57/d7/2bd8a60a7758d5a894ae59192846bb72a68bd773b24daed9ea8c737c41bf/rosettasciio-0.10.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:e8bbf513d81e05242b6ee5d35d58ba06232431990adaff6473bd942518cf2b82", size = 795978 }, + { url = "https://files.pythonhosted.org/packages/35/54/dae525b83a98b27fbabe2152045d990b4baa858522024196c5e47d06bbc1/rosettasciio-0.10.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2b34b629ef130aec286062753b44d4e4d73caf1a4d9aba3530ec3801bd49755c", size = 1265997 }, + { url = "https://files.pythonhosted.org/packages/06/ae/a5969d089adc71df61451af8665df7e30a34957a0ebdc2614c449d2c37cf/rosettasciio-0.10.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:365750bd177c7cff56f88b9f8cf9b59d2c99b52e7d38db142282996e25e552c2", size = 1255782 }, + { url = "https://files.pythonhosted.org/packages/d2/38/7e9d8eb167419d9e8b34433b698532b88c4b06ed38cd6723f7e702260f9f/rosettasciio-0.10.0-cp313-cp313t-win_amd64.whl", hash = "sha256:a16dbadb662290be0d4ea0a2496109cff4484c155fd12eb6b57345e64d9015d9", size = 798624 }, + { url = "https://files.pythonhosted.org/packages/92/a6/a4cff62b7346daca9dfdd1d08c1aed5e0c27283845465b6b5c797fb41713/rosettasciio-0.10.0-py3-none-any.whl", hash = "sha256:a4e353365972865e915b7a689d2f9ba38a6f94aec8cd19c2e35513523b718b1f", size = 550949 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "rpds-py" version = "2026.5.1" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/2e/43/25a8dcd3feedd735039a8f0b5b7e3b118232b5eae288c4fd9ab200d41094/rpds_py-2026.5.1.tar.gz", hash = "sha256:07b24fea40541e28570e5b795a4a38fbdcd12550c06bd0748005ecc8116ca256", size = 64459, upload-time = "2026-05-28T12:02:13.232Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/4f/a0/acf8b6fc20bfdcd3a45bd3f57680fb198e157b7e997b9123b10763798bd2/rpds_py-2026.5.1-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:3397a5ed7174dc2786bb214030232fc36fe8e5584fec43a9952cc542b1a12036", size = 355609, upload-time = "2026-05-28T11:58:50.78Z" }, @@ -3181,12 +4344,117 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/1d/6f/93831a3bfe789542ed0c1d0d74b78b440f055d6dc3ea4640eba2d95e6e23/rpds_py-2026.5.1-pp311-pypy311_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:c74005a7bb87752acf351c93897ec63ad77a07a0da7ecad9c050e32e7286ba34", size = 557243, upload-time = "2026-05-28T12:02:08.013Z" }, { url = "https://files.pythonhosted.org/packages/1f/ff/0b3d604614ffc77522c6b288fdbce68957eb583da1002aa65ba38ac0ee40/rpds_py-2026.5.1-pp311-pypy311_pp73-musllinux_1_2_i686.whl", hash = "sha256:8213afbe8a3a906fb9acb2014423fe3359ee783d0bf90995f70623a3217bfa6c", size = 623541, upload-time = "2026-05-28T12:02:09.661Z" }, { url = "https://files.pythonhosted.org/packages/ea/ea/e7b0251441da9adfeaebcf29601d10f2a1455fcf0772fae9e7e19032bd96/rpds_py-2026.5.1-pp311-pypy311_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:8c43a8a973270fd173bf48cdf80bbe66312421cba68d40845034f174f2389049", size = 586326, upload-time = "2026-05-28T12:02:11.47Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/98/33/23b3b3419b6a3e0f559c7c0d2ca8fc1b9448382b25245033788785921332/rpds_py-0.29.0.tar.gz", hash = "sha256:fe55fe686908f50154d1dc599232016e50c243b438c3b7432f24e2895b0e5359", size = 69359 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/36/ab/7fb95163a53ab122c74a7c42d2d2f012819af2cf3deb43fb0d5acf45cc1a/rpds_py-0.29.0-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:9b9c764a11fd637e0322a488560533112837f5334ffeb48b1be20f6d98a7b437", size = 372344 }, + { url = "https://files.pythonhosted.org/packages/b3/45/f3c30084c03b0d0f918cb4c5ae2c20b0a148b51ba2b3f6456765b629bedd/rpds_py-0.29.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:3fd2164d73812026ce970d44c3ebd51e019d2a26a4425a5dcbdfa93a34abc383", size = 363041 }, + { url = "https://files.pythonhosted.org/packages/e3/e9/4d044a1662608c47a87cbb37b999d4d5af54c6d6ebdda93a4d8bbf8b2a10/rpds_py-0.29.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4a097b7f7f7274164566ae90a221fd725363c0e9d243e2e9ed43d195ccc5495c", size = 391775 }, + { url = "https://files.pythonhosted.org/packages/50/c9/7616d3ace4e6731aeb6e3cd85123e03aec58e439044e214b9c5c60fd8eb1/rpds_py-0.29.0-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:7cdc0490374e31cedefefaa1520d5fe38e82fde8748cbc926e7284574c714d6b", size = 405624 }, + { url = "https://files.pythonhosted.org/packages/c2/e2/6d7d6941ca0843609fd2d72c966a438d6f22617baf22d46c3d2156c31350/rpds_py-0.29.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:89ca2e673ddd5bde9b386da9a0aac0cab0e76f40c8f0aaf0d6311b6bbf2aa311", size = 527894 }, + { url = "https://files.pythonhosted.org/packages/8d/f7/aee14dc2db61bb2ae1e3068f134ca9da5f28c586120889a70ff504bb026f/rpds_py-0.29.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a5d9da3ff5af1ca1249b1adb8ef0573b94c76e6ae880ba1852f033bf429d4588", size = 412720 }, + { url = "https://files.pythonhosted.org/packages/2f/e2/2293f236e887c0360c2723d90c00d48dee296406994d6271faf1712e94ec/rpds_py-0.29.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8238d1d310283e87376c12f658b61e1ee23a14c0e54c7c0ce953efdbdc72deed", size = 392945 }, + { url = "https://files.pythonhosted.org/packages/14/cd/ceea6147acd3bd1fd028d1975228f08ff19d62098078d5ec3eed49703797/rpds_py-0.29.0-cp311-cp311-manylinux_2_31_riscv64.whl", hash = "sha256:2d6fb2ad1c36f91c4646989811e84b1ea5e0c3cf9690b826b6e32b7965853a63", size = 406385 }, + { url = "https://files.pythonhosted.org/packages/52/36/fe4dead19e45eb77a0524acfdbf51e6cda597b26fc5b6dddbff55fbbb1a5/rpds_py-0.29.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:534dc9df211387547267ccdb42253aa30527482acb38dd9b21c5c115d66a96d2", size = 423943 }, + { url = "https://files.pythonhosted.org/packages/a1/7b/4551510803b582fa4abbc8645441a2d15aa0c962c3b21ebb380b7e74f6a1/rpds_py-0.29.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d456e64724a075441e4ed648d7f154dc62e9aabff29bcdf723d0c00e9e1d352f", size = 574204 }, + { url = "https://files.pythonhosted.org/packages/64/ba/071ccdd7b171e727a6ae079f02c26f75790b41555f12ca8f1151336d2124/rpds_py-0.29.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:a738f2da2f565989401bd6fd0b15990a4d1523c6d7fe83f300b7e7d17212feca", size = 600587 }, + { url = "https://files.pythonhosted.org/packages/03/09/96983d48c8cf5a1e03c7d9cc1f4b48266adfb858ae48c7c2ce978dbba349/rpds_py-0.29.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:a110e14508fd26fd2e472bb541f37c209409876ba601cf57e739e87d8a53cf95", size = 562287 }, + { url = "https://files.pythonhosted.org/packages/40/f0/8c01aaedc0fa92156f0391f39ea93b5952bc0ec56b897763858f95da8168/rpds_py-0.29.0-cp311-cp311-win32.whl", hash = "sha256:923248a56dd8d158389a28934f6f69ebf89f218ef96a6b216a9be6861804d3f4", size = 221394 }, + { url = "https://files.pythonhosted.org/packages/7e/a5/a8b21c54c7d234efdc83dc034a4d7cd9668e3613b6316876a29b49dece71/rpds_py-0.29.0-cp311-cp311-win_amd64.whl", hash = "sha256:539eb77eb043afcc45314d1be09ea6d6cafb3addc73e0547c171c6d636957f60", size = 235713 }, + { url = "https://files.pythonhosted.org/packages/a7/1f/df3c56219523947b1be402fa12e6323fe6d61d883cf35d6cb5d5bb6db9d9/rpds_py-0.29.0-cp311-cp311-win_arm64.whl", hash = "sha256:bdb67151ea81fcf02d8f494703fb728d4d34d24556cbff5f417d74f6f5792e7c", size = 229157 }, + { url = "https://files.pythonhosted.org/packages/3c/50/bc0e6e736d94e420df79be4deb5c9476b63165c87bb8f19ef75d100d21b3/rpds_py-0.29.0-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:a0891cfd8db43e085c0ab93ab7e9b0c8fee84780d436d3b266b113e51e79f954", size = 376000 }, + { url = "https://files.pythonhosted.org/packages/3e/3a/46676277160f014ae95f24de53bed0e3b7ea66c235e7de0b9df7bd5d68ba/rpds_py-0.29.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:3897924d3f9a0361472d884051f9a2460358f9a45b1d85a39a158d2f8f1ad71c", size = 360575 }, + { url = "https://files.pythonhosted.org/packages/75/ba/411d414ed99ea1afdd185bbabeeaac00624bd1e4b22840b5e9967ade6337/rpds_py-0.29.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2a21deb8e0d1571508c6491ce5ea5e25669b1dd4adf1c9d64b6314842f708b5d", size = 392159 }, + { url = "https://files.pythonhosted.org/packages/8f/b1/e18aa3a331f705467a48d0296778dc1fea9d7f6cf675bd261f9a846c7e90/rpds_py-0.29.0-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9efe71687d6427737a0a2de9ca1c0a216510e6cd08925c44162be23ed7bed2d5", size = 410602 }, + { url = "https://files.pythonhosted.org/packages/2f/6c/04f27f0c9f2299274c76612ac9d2c36c5048bb2c6c2e52c38c60bf3868d9/rpds_py-0.29.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:40f65470919dc189c833e86b2c4bd21bd355f98436a2cef9e0a9a92aebc8e57e", size = 515808 }, + { url = "https://files.pythonhosted.org/packages/83/56/a8412aa464fb151f8bc0d91fb0bb888adc9039bd41c1c6ba8d94990d8cf8/rpds_py-0.29.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:def48ff59f181130f1a2cb7c517d16328efac3ec03951cca40c1dc2049747e83", size = 416015 }, + { url = "https://files.pythonhosted.org/packages/04/4c/f9b8a05faca3d9e0a6397c90d13acb9307c9792b2bff621430c58b1d6e76/rpds_py-0.29.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad7bd570be92695d89285a4b373006930715b78d96449f686af422debb4d3949", size = 395325 }, + { url = "https://files.pythonhosted.org/packages/34/60/869f3bfbf8ed7b54f1ad9a5543e0fdffdd40b5a8f587fe300ee7b4f19340/rpds_py-0.29.0-cp312-cp312-manylinux_2_31_riscv64.whl", hash = "sha256:5a572911cd053137bbff8e3a52d31c5d2dba51d3a67ad902629c70185f3f2181", size = 410160 }, + { url = "https://files.pythonhosted.org/packages/91/aa/e5b496334e3aba4fe4c8a80187b89f3c1294c5c36f2a926da74338fa5a73/rpds_py-0.29.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:d583d4403bcbf10cffc3ab5cee23d7643fcc960dff85973fd3c2d6c86e8dbb0c", size = 425309 }, + { url = "https://files.pythonhosted.org/packages/85/68/4e24a34189751ceb6d66b28f18159922828dd84155876551f7ca5b25f14f/rpds_py-0.29.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:070befbb868f257d24c3bb350dbd6e2f645e83731f31264b19d7231dd5c396c7", size = 574644 }, + { url = "https://files.pythonhosted.org/packages/8c/cf/474a005ea4ea9c3b4f17b6108b6b13cebfc98ebaff11d6e1b193204b3a93/rpds_py-0.29.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:fc935f6b20b0c9f919a8ff024739174522abd331978f750a74bb68abd117bd19", size = 601605 }, + { url = "https://files.pythonhosted.org/packages/f4/b1/c56f6a9ab8c5f6bb5c65c4b5f8229167a3a525245b0773f2c0896686b64e/rpds_py-0.29.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:8c5a8ecaa44ce2d8d9d20a68a2483a74c07f05d72e94a4dff88906c8807e77b0", size = 564593 }, + { url = "https://files.pythonhosted.org/packages/b3/13/0494cecce4848f68501e0a229432620b4b57022388b071eeff95f3e1e75b/rpds_py-0.29.0-cp312-cp312-win32.whl", hash = "sha256:ba5e1aeaf8dd6d8f6caba1f5539cddda87d511331714b7b5fc908b6cfc3636b7", size = 223853 }, + { url = "https://files.pythonhosted.org/packages/1f/6a/51e9aeb444a00cdc520b032a28b07e5f8dc7bc328b57760c53e7f96997b4/rpds_py-0.29.0-cp312-cp312-win_amd64.whl", hash = "sha256:b5f6134faf54b3cb83375db0f113506f8b7770785be1f95a631e7e2892101977", size = 239895 }, + { url = "https://files.pythonhosted.org/packages/d1/d4/8bce56cdad1ab873e3f27cb31c6a51d8f384d66b022b820525b879f8bed1/rpds_py-0.29.0-cp312-cp312-win_arm64.whl", hash = "sha256:b016eddf00dca7944721bf0cd85b6af7f6c4efaf83ee0b37c4133bd39757a8c7", size = 230321 }, + { url = "https://files.pythonhosted.org/packages/fd/d9/c5de60d9d371bbb186c3e9bf75f4fc5665e11117a25a06a6b2e0afb7380e/rpds_py-0.29.0-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:1585648d0760b88292eecab5181f5651111a69d90eff35d6b78aa32998886a61", size = 375710 }, + { url = "https://files.pythonhosted.org/packages/b3/b3/0860cdd012291dc21272895ce107f1e98e335509ba986dd83d72658b82b9/rpds_py-0.29.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:521807963971a23996ddaf764c682b3e46459b3c58ccd79fefbe16718db43154", size = 360582 }, + { url = "https://files.pythonhosted.org/packages/92/8a/a18c2f4a61b3407e56175f6aab6deacdf9d360191a3d6f38566e1eaf7266/rpds_py-0.29.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0a8896986efaa243ab713c69e6491a4138410f0fe36f2f4c71e18bd5501e8014", size = 391172 }, + { url = "https://files.pythonhosted.org/packages/fd/49/e93354258508c50abc15cdcd5fcf7ac4117f67bb6233ad7859f75e7372a0/rpds_py-0.29.0-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:1d24564a700ef41480a984c5ebed62b74e6ce5860429b98b1fede76049e953e6", size = 409586 }, + { url = "https://files.pythonhosted.org/packages/5a/8d/a27860dae1c19a6bdc901f90c81f0d581df1943355802961a57cdb5b6cd1/rpds_py-0.29.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e6596b93c010d386ae46c9fba9bfc9fc5965fa8228edeac51576299182c2e31c", size = 516339 }, + { url = "https://files.pythonhosted.org/packages/fc/ad/a75e603161e79b7110c647163d130872b271c6b28712c803c65d492100f7/rpds_py-0.29.0-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5cc58aac218826d054c7da7f95821eba94125d88be673ff44267bb89d12a5866", size = 416201 }, + { url = "https://files.pythonhosted.org/packages/b9/42/555b4ee17508beafac135c8b450816ace5a96194ce97fefc49d58e5652ea/rpds_py-0.29.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:de73e40ebc04dd5d9556f50180395322193a78ec247e637e741c1b954810f295", size = 395095 }, + { url = "https://files.pythonhosted.org/packages/cd/f0/c90b671b9031e800ec45112be42ea9f027f94f9ac25faaac8770596a16a1/rpds_py-0.29.0-cp313-cp313-manylinux_2_31_riscv64.whl", hash = "sha256:295ce5ac7f0cf69a651ea75c8f76d02a31f98e5698e82a50a5f4d4982fbbae3b", size = 410077 }, + { url = "https://files.pythonhosted.org/packages/3d/80/9af8b640b81fe21e6f718e9dec36c0b5f670332747243130a5490f292245/rpds_py-0.29.0-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1ea59b23ea931d494459c8338056fe7d93458c0bf3ecc061cd03916505369d55", size = 424548 }, + { url = "https://files.pythonhosted.org/packages/e4/0b/b5647446e991736e6a495ef510e6710df91e880575a586e763baeb0aa770/rpds_py-0.29.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f49d41559cebd608042fdcf54ba597a4a7555b49ad5c1c0c03e0af82692661cd", size = 573661 }, + { url = "https://files.pythonhosted.org/packages/f7/b3/1b1c9576839ff583d1428efbf59f9ee70498d8ce6c0b328ac02f1e470879/rpds_py-0.29.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:05a2bd42768ea988294ca328206efbcc66e220d2d9b7836ee5712c07ad6340ea", size = 600937 }, + { url = "https://files.pythonhosted.org/packages/6c/7b/b6cfca2f9fee4c4494ce54f7fb1b9f578867495a9aa9fc0d44f5f735c8e0/rpds_py-0.29.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:33ca7bdfedd83339ca55da3a5e1527ee5870d4b8369456b5777b197756f3ca22", size = 564496 }, + { url = "https://files.pythonhosted.org/packages/b9/fb/ba29ec7f0f06eb801bac5a23057a9ff7670623b5e8013bd59bec4aa09de8/rpds_py-0.29.0-cp313-cp313-win32.whl", hash = "sha256:20c51ae86a0bb9accc9ad4e6cdeec58d5ebb7f1b09dd4466331fc65e1766aae7", size = 223126 }, + { url = "https://files.pythonhosted.org/packages/3c/6b/0229d3bed4ddaa409e6d90b0ae967ed4380e4bdd0dad6e59b92c17d42457/rpds_py-0.29.0-cp313-cp313-win_amd64.whl", hash = "sha256:6410e66f02803600edb0b1889541f4b5cc298a5ccda0ad789cc50ef23b54813e", size = 239771 }, + { url = "https://files.pythonhosted.org/packages/e4/38/d2868f058b164f8efd89754d85d7b1c08b454f5c07ac2e6cc2e9bd4bd05b/rpds_py-0.29.0-cp313-cp313-win_arm64.whl", hash = "sha256:56838e1cd9174dc23c5691ee29f1d1be9eab357f27efef6bded1328b23e1ced2", size = 229994 }, + { url = "https://files.pythonhosted.org/packages/52/91/5de91c5ec7d41759beec9b251630824dbb8e32d20c3756da1a9a9d309709/rpds_py-0.29.0-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:37d94eadf764d16b9a04307f2ab1d7af6dc28774bbe0535c9323101e14877b4c", size = 365886 }, + { url = "https://files.pythonhosted.org/packages/85/7c/415d8c1b016d5f47ecec5145d9d6d21002d39dce8761b30f6c88810b455a/rpds_py-0.29.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:d472cf73efe5726a067dce63eebe8215b14beabea7c12606fd9994267b3cfe2b", size = 355262 }, + { url = "https://files.pythonhosted.org/packages/3d/14/bf83e2daa4f980e4dc848aed9299792a8b84af95e12541d9e7562f84a6ef/rpds_py-0.29.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:72fdfd5ff8992e4636621826371e3ac5f3e3b8323e9d0e48378e9c13c3dac9d0", size = 384826 }, + { url = "https://files.pythonhosted.org/packages/33/b8/53330c50a810ae22b4fbba5e6cf961b68b9d72d9bd6780a7c0a79b070857/rpds_py-0.29.0-cp313-cp313t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2549d833abdf8275c901313b9e8ff8fba57e50f6a495035a2a4e30621a2f7cc4", size = 394234 }, + { url = "https://files.pythonhosted.org/packages/cc/32/01e2e9645cef0e584f518cfde4567563e57db2257244632b603f61b40e50/rpds_py-0.29.0-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4448dad428f28a6a767c3e3b80cde3446a22a0efbddaa2360f4bb4dc836d0688", size = 520008 }, + { url = "https://files.pythonhosted.org/packages/98/c3/0d1b95a81affae2b10f950782e33a1fd2edd6ce2a479966cac98c9a66f57/rpds_py-0.29.0-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:115f48170fd4296a33938d8c11f697f5f26e0472e43d28f35624764173a60e4d", size = 409569 }, + { url = "https://files.pythonhosted.org/packages/fa/60/aa3b8678f3f009f675b99174fa2754302a7fbfe749162e8043d111de2d88/rpds_py-0.29.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8e5bb73ffc029820f4348e9b66b3027493ae00bca6629129cd433fd7a76308ee", size = 385188 }, + { url = "https://files.pythonhosted.org/packages/92/02/5546c1c8aa89c18d40c1fcffdcc957ba730dee53fb7c3ca3a46f114761d2/rpds_py-0.29.0-cp313-cp313t-manylinux_2_31_riscv64.whl", hash = "sha256:b1581fcde18fcdf42ea2403a16a6b646f8eb1e58d7f90a0ce693da441f76942e", size = 398587 }, + { url = "https://files.pythonhosted.org/packages/6c/e0/ad6eeaf47e236eba052fa34c4073078b9e092bd44da6bbb35aaae9580669/rpds_py-0.29.0-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:16e9da2bda9eb17ea318b4c335ec9ac1818e88922cbe03a5743ea0da9ecf74fb", size = 416641 }, + { url = "https://files.pythonhosted.org/packages/1a/93/0acedfd50ad9cdd3879c615a6dc8c5f1ce78d2fdf8b87727468bb5bb4077/rpds_py-0.29.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:28fd300326dd21198f311534bdb6d7e989dd09b3418b3a91d54a0f384c700967", size = 566683 }, + { url = "https://files.pythonhosted.org/packages/62/53/8c64e0f340a9e801459fc6456821abc15b3582cb5dc3932d48705a9d9ac7/rpds_py-0.29.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:2aba991e041d031c7939e1358f583ae405a7bf04804ca806b97a5c0e0af1ea5e", size = 592730 }, + { url = "https://files.pythonhosted.org/packages/85/ef/3109b6584f8c4b0d2490747c916df833c127ecfa82be04d9a40a376f2090/rpds_py-0.29.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:7f437026dbbc3f08c99cc41a5b2570c6e1a1ddbe48ab19a9b814254128d4ea7a", size = 557361 }, + { url = "https://files.pythonhosted.org/packages/ff/3b/61586475e82d57f01da2c16edb9115a618afe00ce86fe1b58936880b15af/rpds_py-0.29.0-cp313-cp313t-win32.whl", hash = "sha256:6e97846e9800a5d0fe7be4d008f0c93d0feeb2700da7b1f7528dabafb31dfadb", size = 211227 }, + { url = "https://files.pythonhosted.org/packages/3b/3a/12dc43f13594a54ea0c9d7e9d43002116557330e3ad45bc56097ddf266e2/rpds_py-0.29.0-cp313-cp313t-win_amd64.whl", hash = "sha256:f49196aec7c4b406495f60e6f947ad71f317a765f956d74bbd83996b9edc0352", size = 225248 }, + { url = "https://files.pythonhosted.org/packages/89/b1/0b1474e7899371d9540d3bbb2a499a3427ae1fc39c998563fe9035a1073b/rpds_py-0.29.0-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:394d27e4453d3b4d82bb85665dc1fcf4b0badc30fc84282defed71643b50e1a1", size = 363731 }, + { url = "https://files.pythonhosted.org/packages/28/12/3b7cf2068d0a334ed1d7b385a9c3c8509f4c2bcba3d4648ea71369de0881/rpds_py-0.29.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:55d827b2ae95425d3be9bc9a5838b6c29d664924f98146557f7715e331d06df8", size = 354343 }, + { url = "https://files.pythonhosted.org/packages/eb/73/5afcf8924bc02a749416eda64e17ac9c9b28f825f4737385295a0e99b0c1/rpds_py-0.29.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fc31a07ed352e5462d3ee1b22e89285f4ce97d5266f6d1169da1142e78045626", size = 385406 }, + { url = "https://files.pythonhosted.org/packages/c8/37/5db736730662508535221737a21563591b6f43c77f2e388951c42f143242/rpds_py-0.29.0-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c4695dd224212f6105db7ea62197144230b808d6b2bba52238906a2762f1d1e7", size = 396162 }, + { url = "https://files.pythonhosted.org/packages/70/0d/491c1017d14f62ce7bac07c32768d209a50ec567d76d9f383b4cfad19b80/rpds_py-0.29.0-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fcae1770b401167f8b9e1e3f566562e6966ffa9ce63639916248a9e25fa8a244", size = 517719 }, + { url = "https://files.pythonhosted.org/packages/d7/25/b11132afcb17cd5d82db173f0c8dab270ffdfaba43e5ce7a591837ae9649/rpds_py-0.29.0-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:90f30d15f45048448b8da21c41703b31c61119c06c216a1bf8c245812a0f0c17", size = 409498 }, + { url = "https://files.pythonhosted.org/packages/0f/7d/e6543cedfb2e6403a1845710a5ab0e0ccf8fc288e0b5af9a70bfe2c12053/rpds_py-0.29.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:44a91e0ab77bdc0004b43261a4b8cd6d6b451e8d443754cfda830002b5745b32", size = 382743 }, + { url = "https://files.pythonhosted.org/packages/75/11/a4ebc9f654293ae9fefb83b2b6be7f3253e85ea42a5db2f77d50ad19aaeb/rpds_py-0.29.0-cp314-cp314-manylinux_2_31_riscv64.whl", hash = "sha256:4aa195e5804d32c682e453b34474f411ca108e4291c6a0f824ebdc30a91c973c", size = 400317 }, + { url = "https://files.pythonhosted.org/packages/52/18/97677a60a81c7f0e5f64e51fb3f8271c5c8fcabf3a2df18e97af53d7c2bf/rpds_py-0.29.0-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:7971bdb7bf4ee0f7e6f67fa4c7fbc6019d9850cc977d126904392d363f6f8318", size = 416979 }, + { url = "https://files.pythonhosted.org/packages/f0/69/28ab391a9968f6c746b2a2db181eaa4d16afaa859fedc9c2f682d19f7e18/rpds_py-0.29.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:8ae33ad9ce580c7a47452c3b3f7d8a9095ef6208e0a0c7e4e2384f9fc5bf8212", size = 567288 }, + { url = "https://files.pythonhosted.org/packages/3b/d3/0c7afdcdb830eee94f5611b64e71354ffe6ac8df82d00c2faf2bfffd1d4e/rpds_py-0.29.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:c661132ab2fb4eeede2ef69670fd60da5235209874d001a98f1542f31f2a8a94", size = 593157 }, + { url = "https://files.pythonhosted.org/packages/e2/ac/a0fcbc2feed4241cf26d32268c195eb88ddd4bd862adfc9d4b25edfba535/rpds_py-0.29.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:bb78b3a0d31ac1bde132c67015a809948db751cb4e92cdb3f0b242e430b6ed0d", size = 554741 }, + { url = "https://files.pythonhosted.org/packages/0f/f1/fcc24137c470df8588674a677f33719d5800ec053aaacd1de8a5d5d84d9e/rpds_py-0.29.0-cp314-cp314-win32.whl", hash = "sha256:f475f103488312e9bd4000bc890a95955a07b2d0b6e8884aef4be56132adbbf1", size = 215508 }, + { url = "https://files.pythonhosted.org/packages/7b/c7/1d169b2045512eac019918fc1021ea07c30e84a4343f9f344e3e0aa8c788/rpds_py-0.29.0-cp314-cp314-win_amd64.whl", hash = "sha256:b9cf2359a4fca87cfb6801fae83a76aedf66ee1254a7a151f1341632acf67f1b", size = 228125 }, + { url = "https://files.pythonhosted.org/packages/be/36/0cec88aaba70ec4a6e381c444b0d916738497d27f0c30406e3d9fcbd3bc2/rpds_py-0.29.0-cp314-cp314-win_arm64.whl", hash = "sha256:9ba8028597e824854f0f1733d8b964e914ae3003b22a10c2c664cb6927e0feb9", size = 221992 }, + { url = "https://files.pythonhosted.org/packages/b1/fa/a2e524631717c9c0eb5d90d30f648cfba6b731047821c994acacb618406c/rpds_py-0.29.0-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:e71136fd0612556b35c575dc2726ae04a1669e6a6c378f2240312cf5d1a2ab10", size = 366425 }, + { url = "https://files.pythonhosted.org/packages/a2/a4/6d43ebe0746ff694a30233f63f454aed1677bd50ab7a59ff6b2bb5ac61f2/rpds_py-0.29.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:76fe96632d53f3bf0ea31ede2f53bbe3540cc2736d4aec3b3801b0458499ef3a", size = 355282 }, + { url = "https://files.pythonhosted.org/packages/fa/a7/52fd8270e0320b09eaf295766ae81dd175f65394687906709b3e75c71d06/rpds_py-0.29.0-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9459a33f077130dbb2c7c3cea72ee9932271fb3126404ba2a2661e4fe9eb7b79", size = 384968 }, + { url = "https://files.pythonhosted.org/packages/f4/7d/e6bc526b7a14e1ef80579a52c1d4ad39260a058a51d66c6039035d14db9d/rpds_py-0.29.0-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5c9546cfdd5d45e562cc0444b6dddc191e625c62e866bf567a2c69487c7ad28a", size = 394714 }, + { url = "https://files.pythonhosted.org/packages/c0/3f/f0ade3954e7db95c791e7eaf978aa7e08a756d2046e8bdd04d08146ed188/rpds_py-0.29.0-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:12597d11d97b8f7e376c88929a6e17acb980e234547c92992f9f7c058f1a7310", size = 520136 }, + { url = "https://files.pythonhosted.org/packages/87/b3/07122ead1b97009715ab9d4082be6d9bd9546099b2b03fae37c3116f72be/rpds_py-0.29.0-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28de03cf48b8a9e6ec10318f2197b83946ed91e2891f651a109611be4106ac4b", size = 409250 }, + { url = "https://files.pythonhosted.org/packages/c9/c6/dcbee61fd1dc892aedcb1b489ba661313101aa82ec84b1a015d4c63ebfda/rpds_py-0.29.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd7951c964069039acc9d67a8ff1f0a7f34845ae180ca542b17dc1456b1f1808", size = 384940 }, + { url = "https://files.pythonhosted.org/packages/47/11/914ecb6f3574cf9bf8b38aced4063e0f787d6e1eb30b181a7efbc6c1da9a/rpds_py-0.29.0-cp314-cp314t-manylinux_2_31_riscv64.whl", hash = "sha256:c07d107b7316088f1ac0177a7661ca0c6670d443f6fe72e836069025e6266761", size = 399392 }, + { url = "https://files.pythonhosted.org/packages/f5/fd/2f4bd9433f58f816434bb934313584caa47dbc6f03ce5484df8ac8980561/rpds_py-0.29.0-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1de2345af363d25696969befc0c1688a6cb5e8b1d32b515ef84fc245c6cddba3", size = 416796 }, + { url = "https://files.pythonhosted.org/packages/79/a5/449f0281af33efa29d5c71014399d74842342ae908d8cd38260320167692/rpds_py-0.29.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:00e56b12d2199ca96068057e1ae7f9998ab6e99cda82431afafd32f3ec98cca9", size = 566843 }, + { url = "https://files.pythonhosted.org/packages/ab/32/0a6a1ccee2e37fcb1b7ba9afde762b77182dbb57937352a729c6cd3cf2bb/rpds_py-0.29.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:3919a3bbecee589300ed25000b6944174e07cd20db70552159207b3f4bbb45b8", size = 593956 }, + { url = "https://files.pythonhosted.org/packages/4a/3d/eb820f95dce4306f07a495ede02fb61bef36ea201d9137d4fcd5ab94ec1e/rpds_py-0.29.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:e7fa2ccc312bbd91e43aa5e0869e46bc03278a3dddb8d58833150a18b0f0283a", size = 557288 }, + { url = "https://files.pythonhosted.org/packages/e9/f8/b8ff786f40470462a252918e0836e0db903c28e88e3eec66bc4a7856ee5d/rpds_py-0.29.0-cp314-cp314t-win32.whl", hash = "sha256:97c817863ffc397f1e6a6e9d2d89fe5408c0a9922dac0329672fb0f35c867ea5", size = 211382 }, + { url = "https://files.pythonhosted.org/packages/c9/7f/1a65ae870bc9d0576aebb0c501ea5dccf1ae2178fe2821042150ebd2e707/rpds_py-0.29.0-cp314-cp314t-win_amd64.whl", hash = "sha256:2023473f444752f0f82a58dfcbee040d0a1b3d1b3c2ec40e884bd25db6d117d2", size = 225919 }, + { url = "https://files.pythonhosted.org/packages/f2/ac/b97e80bf107159e5b9ba9c91df1ab95f69e5e41b435f27bdd737f0d583ac/rpds_py-0.29.0-pp311-pypy311_pp73-macosx_10_12_x86_64.whl", hash = "sha256:acd82a9e39082dc5f4492d15a6b6c8599aa21db5c35aaf7d6889aea16502c07d", size = 373963 }, + { url = "https://files.pythonhosted.org/packages/40/5a/55e72962d5d29bd912f40c594e68880d3c7a52774b0f75542775f9250712/rpds_py-0.29.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:715b67eac317bf1c7657508170a3e011a1ea6ccb1c9d5f296e20ba14196be6b3", size = 364644 }, + { url = "https://files.pythonhosted.org/packages/99/2a/6b6524d0191b7fc1351c3c0840baac42250515afb48ae40c7ed15499a6a2/rpds_py-0.29.0-pp311-pypy311_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f3b1b87a237cb2dba4db18bcfaaa44ba4cd5936b91121b62292ff21df577fc43", size = 393847 }, + { url = "https://files.pythonhosted.org/packages/1c/b8/c5692a7df577b3c0c7faed7ac01ee3c608b81750fc5d89f84529229b6873/rpds_py-0.29.0-pp311-pypy311_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:1c3c3e8101bb06e337c88eb0c0ede3187131f19d97d43ea0e1c5407ea74c0cbf", size = 407281 }, + { url = "https://files.pythonhosted.org/packages/f0/57/0546c6f84031b7ea08b76646a8e33e45607cc6bd879ff1917dc077bb881e/rpds_py-0.29.0-pp311-pypy311_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2b8e54d6e61f3ecd3abe032065ce83ea63417a24f437e4a3d73d2f85ce7b7cfe", size = 529213 }, + { url = "https://files.pythonhosted.org/packages/fa/c1/01dd5f444233605555bc11fe5fed6a5c18f379f02013870c176c8e630a23/rpds_py-0.29.0-pp311-pypy311_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3fbd4e9aebf110473a420dea85a238b254cf8a15acb04b22a5a6b5ce8925b760", size = 413808 }, + { url = "https://files.pythonhosted.org/packages/aa/0a/60f98b06156ea2a7af849fb148e00fbcfdb540909a5174a5ed10c93745c7/rpds_py-0.29.0-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:80fdf53d36e6c72819993e35d1ebeeb8e8fc688d0c6c2b391b55e335b3afba5a", size = 394600 }, + { url = "https://files.pythonhosted.org/packages/37/f1/dc9312fc9bec040ece08396429f2bd9e0977924ba7a11c5ad7056428465e/rpds_py-0.29.0-pp311-pypy311_pp73-manylinux_2_31_riscv64.whl", hash = "sha256:ea7173df5d86f625f8dde6d5929629ad811ed8decda3b60ae603903839ac9ac0", size = 408634 }, + { url = "https://files.pythonhosted.org/packages/ed/41/65024c9fd40c89bb7d604cf73beda4cbdbcebe92d8765345dd65855b6449/rpds_py-0.29.0-pp311-pypy311_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:76054d540061eda273274f3d13a21a4abdde90e13eaefdc205db37c05230efce", size = 426064 }, + { url = "https://files.pythonhosted.org/packages/a2/e0/cf95478881fc88ca2fdbf56381d7df36567cccc39a05394beac72182cd62/rpds_py-0.29.0-pp311-pypy311_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:9f84c549746a5be3bc7415830747a3a0312573afc9f95785eb35228bb17742ec", size = 575871 }, + { url = "https://files.pythonhosted.org/packages/ea/c0/df88097e64339a0218b57bd5f9ca49898e4c394db756c67fccc64add850a/rpds_py-0.29.0-pp311-pypy311_pp73-musllinux_1_2_i686.whl", hash = "sha256:0ea962671af5cb9a260489e311fa22b2e97103e3f9f0caaea6f81390af96a9ed", size = 601702 }, + { url = "https://files.pythonhosted.org/packages/87/f4/09ffb3ebd0cbb9e2c7c9b84d252557ecf434cd71584ee1e32f66013824df/rpds_py-0.29.0-pp311-pypy311_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:f7728653900035fb7b8d06e1e5900545d8088efc9d5d4545782da7df03ec803f", size = 564054 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "ruff" version = "0.15.15" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/84/6f/a76f7d96e5c962f5b69cee865e49c15c1116897c01990faa8a57edb62e7f/ruff-0.15.15.tar.gz", hash = "sha256:b8dff018130b46d8e5bf0f926ef6b60cf871d6d5ae45fc9334e09632daa741d6", size = 4706985, upload-time = "2026-05-28T14:16:57.784Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/fa/9d/3a45c05b8ab04b4705989de70a79008e27c8003296a0feaee9edc18dd7e9/ruff-0.15.15-py3-none-linux_armv6l.whl", hash = "sha256:cf93e5388f412e1b108b1f8b34a6e036b70fe8aff89393befad96fe48670311b", size = 10710652, upload-time = "2026-05-28T14:16:06.701Z" }, @@ -3206,6 +4474,28 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/b1/c0/1003b60edd697c649faf61f1a34094b1abb38fb3d1181e3f895781250a08/ruff-0.15.15-py3-none-win32.whl", hash = "sha256:29428ea79694afbe756d45fd59b36f22b6b020dc0443cf7de0173046236964b9", size = 10716774, upload-time = "2026-05-28T14:16:52.337Z" }, { url = "https://files.pythonhosted.org/packages/02/a8/1269eddd6945a06c23f055ef7848886e37cf9d6a8bebb386a3115f01470c/ruff-0.15.15-py3-none-win_amd64.whl", hash = "sha256:8df0323902e15e24bc4bf246da830573d3cf3352bd0b9a164eab335d111ff4a4", size = 11868463, upload-time = "2026-05-28T14:16:11.333Z" }, { url = "https://files.pythonhosted.org/packages/4e/b2/920464c907b191e37469d477a1aa8bc048b8f36c4c1610dfa4ab87b39e18/ruff-0.15.15-py3-none-win_arm64.whl", hash = "sha256:3c8ceca6792f38196b8f589bc92eccd03eef286602da92e5dc05cc42ef6441b7", size = 11138498, upload-time = "2026-05-28T14:16:38.425Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/82/fa/fbb67a5780ae0f704876cb8ac92d6d76da41da4dc72b7ed3565ab18f2f52/ruff-0.14.5.tar.gz", hash = "sha256:8d3b48d7d8aad423d3137af7ab6c8b1e38e4de104800f0d596990f6ada1a9fc1", size = 5615944 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/68/31/c07e9c535248d10836a94e4f4e8c5a31a1beed6f169b31405b227872d4f4/ruff-0.14.5-py3-none-linux_armv6l.whl", hash = "sha256:f3b8248123b586de44a8018bcc9fefe31d23dda57a34e6f0e1e53bd51fd63594", size = 13171630 }, + { url = "https://files.pythonhosted.org/packages/8e/5c/283c62516dca697cd604c2796d1487396b7a436b2f0ecc3fd412aca470e0/ruff-0.14.5-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:f7a75236570318c7a30edd7f5491945f0169de738d945ca8784500b517163a72", size = 13413925 }, + { url = "https://files.pythonhosted.org/packages/b6/f3/aa319f4afc22cb6fcba2b9cdfc0f03bbf747e59ab7a8c5e90173857a1361/ruff-0.14.5-py3-none-macosx_11_0_arm64.whl", hash = "sha256:6d146132d1ee115f8802356a2dc9a634dbf58184c51bff21f313e8cd1c74899a", size = 12574040 }, + { url = "https://files.pythonhosted.org/packages/f9/7f/cb5845fcc7c7e88ed57f58670189fc2ff517fe2134c3821e77e29fd3b0c8/ruff-0.14.5-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2380596653dcd20b057794d55681571a257a42327da8894b93bbd6111aa801f", size = 13009755 }, + { url = "https://files.pythonhosted.org/packages/21/d2/bcbedbb6bcb9253085981730687ddc0cc7b2e18e8dc13cf4453de905d7a0/ruff-0.14.5-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2d1fa985a42b1f075a098fa1ab9d472b712bdb17ad87a8ec86e45e7fa6273e68", size = 12937641 }, + { url = "https://files.pythonhosted.org/packages/a4/58/e25de28a572bdd60ffc6bb71fc7fd25a94ec6a076942e372437649cbb02a/ruff-0.14.5-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:88f0770d42b7fa02bbefddde15d235ca3aa24e2f0137388cc15b2dcbb1f7c7a7", size = 13610854 }, + { url = "https://files.pythonhosted.org/packages/7d/24/43bb3fd23ecee9861970978ea1a7a63e12a204d319248a7e8af539984280/ruff-0.14.5-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:3676cb02b9061fee7294661071c4709fa21419ea9176087cb77e64410926eb78", size = 15061088 }, + { url = "https://files.pythonhosted.org/packages/23/44/a022f288d61c2f8c8645b24c364b719aee293ffc7d633a2ca4d116b9c716/ruff-0.14.5-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b595bedf6bc9cab647c4a173a61acf4f1ac5f2b545203ba82f30fcb10b0318fb", size = 14734717 }, + { url = "https://files.pythonhosted.org/packages/58/81/5c6ba44de7e44c91f68073e0658109d8373b0590940efe5bd7753a2585a3/ruff-0.14.5-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f55382725ad0bdb2e8ee2babcbbfb16f124f5a59496a2f6a46f1d9d99d93e6e2", size = 14028812 }, + { url = "https://files.pythonhosted.org/packages/ad/ef/41a8b60f8462cb320f68615b00299ebb12660097c952c600c762078420f8/ruff-0.14.5-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7497d19dce23976bdaca24345ae131a1d38dcfe1b0850ad8e9e6e4fa321a6e19", size = 13825656 }, + { url = "https://files.pythonhosted.org/packages/7c/00/207e5de737fdb59b39eb1fac806904fe05681981b46d6a6db9468501062e/ruff-0.14.5-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:410e781f1122d6be4f446981dd479470af86537fb0b8857f27a6e872f65a38e4", size = 13959922 }, + { url = "https://files.pythonhosted.org/packages/bc/7e/fa1f5c2776db4be405040293618846a2dece5c70b050874c2d1f10f24776/ruff-0.14.5-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:c01be527ef4c91a6d55e53b337bfe2c0f82af024cc1a33c44792d6844e2331e1", size = 12932501 }, + { url = "https://files.pythonhosted.org/packages/67/d8/d86bf784d693a764b59479a6bbdc9515ae42c340a5dc5ab1dabef847bfaa/ruff-0.14.5-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:f66e9bb762e68d66e48550b59c74314168ebb46199886c5c5aa0b0fbcc81b151", size = 12927319 }, + { url = "https://files.pythonhosted.org/packages/ac/de/ee0b304d450ae007ce0cb3e455fe24fbcaaedae4ebaad6c23831c6663651/ruff-0.14.5-py3-none-musllinux_1_2_i686.whl", hash = "sha256:d93be8f1fa01022337f1f8f3bcaa7ffee2d0b03f00922c45c2207954f351f465", size = 13206209 }, + { url = "https://files.pythonhosted.org/packages/33/aa/193ca7e3a92d74f17d9d5771a765965d2cf42c86e6f0fd95b13969115723/ruff-0.14.5-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:c135d4b681f7401fe0e7312017e41aba9b3160861105726b76cfa14bc25aa367", size = 13953709 }, + { url = "https://files.pythonhosted.org/packages/cc/f1/7119e42aa1d3bf036ffc9478885c2e248812b7de9abea4eae89163d2929d/ruff-0.14.5-py3-none-win32.whl", hash = "sha256:c83642e6fccfb6dea8b785eb9f456800dcd6a63f362238af5fc0c83d027dd08b", size = 12925808 }, + { url = "https://files.pythonhosted.org/packages/3b/9d/7c0a255d21e0912114784e4a96bf62af0618e2190cae468cd82b13625ad2/ruff-0.14.5-py3-none-win_amd64.whl", hash = "sha256:9d55d7af7166f143c94eae1db3312f9ea8f95a4defef1979ed516dbb38c27621", size = 14331546 }, + { url = "https://files.pythonhosted.org/packages/e5/80/69756670caedcf3b9be597a6e12276a6cf6197076eb62aad0c608f8efce0/ruff-0.14.5-py3-none-win_arm64.whl", hash = "sha256:4b700459d4649e2594b31f20a9de33bc7c19976d4746d8d0798ad959621d64a4", size = 13433331 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3223,6 +4513,7 @@ dependencies = [ { name = "tifffile", version = "2026.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, { name = "tifffile", version = "2026.6.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/a1/b4/2528bb43c67d48053a7a649a9666432dc307d66ba02e3a6d5c40f46655df/scikit_image-0.26.0.tar.gz", hash = "sha256:f5f970ab04efad85c24714321fcc91613fcb64ef2a892a13167df2f3e59199fa", size = 22729739, upload-time = "2025-12-20T17:12:21.824Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/76/16/8a407688b607f86f81f8c649bf0d68a2a6d67375f18c2d660aba20f5b648/scikit_image-0.26.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b1ede33a0fb3731457eaf53af6361e73dd510f449dac437ab54573b26788baf0", size = 12355510, upload-time = "2025-12-20T17:10:31.628Z" }, @@ -3273,6 +4564,26 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/47/88/00a90402e1775634043c2a0af8a3c76ad450866d9fa444efcc43b553ba2d/scikit_image-0.26.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:2c1e7bd342f43e7a97e571b3f03ba4c1293ea1a35c3f13f41efdc8a81c1dc8f2", size = 14364151, upload-time = "2025-12-20T17:12:14.909Z" }, { url = "https://files.pythonhosted.org/packages/da/ca/918d8d306bd43beacff3b835c6d96fac0ae64c0857092f068b88db531a7c/scikit_image-0.26.0-cp314-cp314t-win_amd64.whl", hash = "sha256:b702c3bb115e1dcf4abf5297429b5c90f2189655888cbed14921f3d26f81d3a4", size = 12413484, upload-time = "2025-12-20T17:12:17.046Z" }, { url = "https://files.pythonhosted.org/packages/dc/cd/4da01329b5a8d47ff7ec3c99a2b02465a8017b186027590dc7425cee0b56/scikit_image-0.26.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0608aa4a9ec39e0843de10d60edb2785a30c1c47819b67866dd223ebd149acaf", size = 11769501, upload-time = "2025-12-20T17:12:19.339Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/c7/a8/3c0f256012b93dd2cb6fda9245e9f4bff7dc0486880b248005f15ea2255e/scikit_image-0.25.2.tar.gz", hash = "sha256:e5a37e6cd4d0c018a7a55b9d601357e3382826d3888c10d0213fc63bff977dde", size = 22693594 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c4/97/3051c68b782ee3f1fb7f8f5bb7d535cf8cb92e8aae18fa9c1cdf7e15150d/scikit_image-0.25.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f4bac9196fb80d37567316581c6060763b0f4893d3aca34a9ede3825bc035b17", size = 14003057 }, + { url = "https://files.pythonhosted.org/packages/19/23/257fc696c562639826065514d551b7b9b969520bd902c3a8e2fcff5b9e17/scikit_image-0.25.2-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:d989d64ff92e0c6c0f2018c7495a5b20e2451839299a018e0e5108b2680f71e0", size = 13180335 }, + { url = "https://files.pythonhosted.org/packages/ef/14/0c4a02cb27ca8b1e836886b9ec7c9149de03053650e9e2ed0625f248dd92/scikit_image-0.25.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b2cfc96b27afe9a05bc92f8c6235321d3a66499995675b27415e0d0c76625173", size = 14144783 }, + { url = "https://files.pythonhosted.org/packages/dd/9b/9fb556463a34d9842491d72a421942c8baff4281025859c84fcdb5e7e602/scikit_image-0.25.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:24cc986e1f4187a12aa319f777b36008764e856e5013666a4a83f8df083c2641", size = 14785376 }, + { url = "https://files.pythonhosted.org/packages/de/ec/b57c500ee85885df5f2188f8bb70398481393a69de44a00d6f1d055f103c/scikit_image-0.25.2-cp311-cp311-win_amd64.whl", hash = "sha256:b4f6b61fc2db6340696afe3db6b26e0356911529f5f6aee8c322aa5157490c9b", size = 12791698 }, + { url = "https://files.pythonhosted.org/packages/35/8c/5df82881284459f6eec796a5ac2a0a304bb3384eec2e73f35cfdfcfbf20c/scikit_image-0.25.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:8db8dd03663112783221bf01ccfc9512d1cc50ac9b5b0fe8f4023967564719fb", size = 13986000 }, + { url = "https://files.pythonhosted.org/packages/ce/e6/93bebe1abcdce9513ffec01d8af02528b4c41fb3c1e46336d70b9ed4ef0d/scikit_image-0.25.2-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:483bd8cc10c3d8a7a37fae36dfa5b21e239bd4ee121d91cad1f81bba10cfb0ed", size = 13235893 }, + { url = "https://files.pythonhosted.org/packages/53/4b/eda616e33f67129e5979a9eb33c710013caa3aa8a921991e6cc0b22cea33/scikit_image-0.25.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9d1e80107bcf2bf1291acfc0bf0425dceb8890abe9f38d8e94e23497cbf7ee0d", size = 14178389 }, + { url = "https://files.pythonhosted.org/packages/6b/b5/b75527c0f9532dd8a93e8e7cd8e62e547b9f207d4c11e24f0006e8646b36/scikit_image-0.25.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a17e17eb8562660cc0d31bb55643a4da996a81944b82c54805c91b3fe66f4824", size = 15003435 }, + { url = "https://files.pythonhosted.org/packages/34/e3/49beb08ebccda3c21e871b607c1cb2f258c3fa0d2f609fed0a5ba741b92d/scikit_image-0.25.2-cp312-cp312-win_amd64.whl", hash = "sha256:bdd2b8c1de0849964dbc54037f36b4e9420157e67e45a8709a80d727f52c7da2", size = 12899474 }, + { url = "https://files.pythonhosted.org/packages/e6/7c/9814dd1c637f7a0e44342985a76f95a55dd04be60154247679fd96c7169f/scikit_image-0.25.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:7efa888130f6c548ec0439b1a7ed7295bc10105458a421e9bf739b457730b6da", size = 13921841 }, + { url = "https://files.pythonhosted.org/packages/84/06/66a2e7661d6f526740c309e9717d3bd07b473661d5cdddef4dd978edab25/scikit_image-0.25.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:dd8011efe69c3641920614d550f5505f83658fe33581e49bed86feab43a180fc", size = 13196862 }, + { url = "https://files.pythonhosted.org/packages/4e/63/3368902ed79305f74c2ca8c297dfeb4307269cbe6402412668e322837143/scikit_image-0.25.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:28182a9d3e2ce3c2e251383bdda68f8d88d9fff1a3ebe1eb61206595c9773341", size = 14117785 }, + { url = "https://files.pythonhosted.org/packages/cd/9b/c3da56a145f52cd61a68b8465d6a29d9503bc45bc993bb45e84371c97d94/scikit_image-0.25.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8abd3c805ce6944b941cfed0406d88faeb19bab3ed3d4b50187af55cf24d147", size = 14977119 }, + { url = "https://files.pythonhosted.org/packages/8a/97/5fcf332e1753831abb99a2525180d3fb0d70918d461ebda9873f66dcc12f/scikit_image-0.25.2-cp313-cp313-win_amd64.whl", hash = "sha256:64785a8acefee460ec49a354706db0b09d1f325674107d7fa3eadb663fb56d6f", size = 12885116 }, + { url = "https://files.pythonhosted.org/packages/10/cc/75e9f17e3670b5ed93c32456fda823333c6279b144cd93e2c03aa06aa472/scikit_image-0.25.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:330d061bd107d12f8d68f1d611ae27b3b813b8cdb0300a71d07b1379178dd4cd", size = 13862801 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3282,6 +4593,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "numpy" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/7a/97/5a3609c4f8d58b039179648e62dd220f89864f56f7357f5d4f45c29eb2cc/scipy-1.17.1.tar.gz", hash = "sha256:95d8e012d8cb8816c226aef832200b1d45109ed4464303e997c5b13122b297c0", size = 30573822, upload-time = "2026-02-23T00:26:24.851Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/df/75/b4ce781849931fef6fd529afa6b63711d5a733065722d0c3e2724af9e40a/scipy-1.17.1-cp311-cp311-macosx_10_14_x86_64.whl", hash = "sha256:1f95b894f13729334fb990162e911c9e5dc1ab390c58aa6cbecb389c5b5e28ec", size = 31613675, upload-time = "2026-02-23T00:16:00.13Z" }, @@ -3344,42 +4656,136 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/52/52/e57eceff0e342a1f50e274264ed47497b59e6a4e3118808ee58ddda7b74a/scipy-1.17.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:a77cbd07b940d326d39a1d1b37817e2ee4d79cb30e7338f3d0cddffae70fcaa2", size = 37682317, upload-time = "2026-02-23T00:22:18.513Z" }, { url = "https://files.pythonhosted.org/packages/11/2f/b29eafe4a3fbc3d6de9662b36e028d5f039e72d345e05c250e121a230dd4/scipy-1.17.1-cp314-cp314t-win_amd64.whl", hash = "sha256:eb092099205ef62cd1782b006658db09e2fed75bffcae7cc0d44052d8aa0f484", size = 37345327, upload-time = "2026-02-23T00:22:24.442Z" }, { url = "https://files.pythonhosted.org/packages/07/39/338d9219c4e87f3e708f18857ecd24d22a0c3094752393319553096b98af/scipy-1.17.1-cp314-cp314t-win_arm64.whl", hash = "sha256:200e1050faffacc162be6a486a984a0497866ec54149a01270adc8a59b7c7d21", size = 25489165, upload-time = "2026-02-23T00:22:29.563Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/0a/ca/d8ace4f98322d01abcd52d381134344bf7b431eba7ed8b42bdea5a3c2ac9/scipy-1.16.3.tar.gz", hash = "sha256:01e87659402762f43bd2fee13370553a17ada367d42e7487800bf2916535aecb", size = 30597883 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9b/5f/6f37d7439de1455ce9c5a556b8d1db0979f03a796c030bafdf08d35b7bf9/scipy-1.16.3-cp311-cp311-macosx_10_14_x86_64.whl", hash = "sha256:40be6cf99e68b6c4321e9f8782e7d5ff8265af28ef2cd56e9c9b2638fa08ad97", size = 36630881 }, + { url = "https://files.pythonhosted.org/packages/7c/89/d70e9f628749b7e4db2aa4cd89735502ff3f08f7b9b27d2e799485987cd9/scipy-1.16.3-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:8be1ca9170fcb6223cc7c27f4305d680ded114a1567c0bd2bfcbf947d1b17511", size = 28941012 }, + { url = "https://files.pythonhosted.org/packages/a8/a8/0e7a9a6872a923505dbdf6bb93451edcac120363131c19013044a1e7cb0c/scipy-1.16.3-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:bea0a62734d20d67608660f69dcda23e7f90fb4ca20974ab80b6ed40df87a005", size = 20931935 }, + { url = "https://files.pythonhosted.org/packages/bd/c7/020fb72bd79ad798e4dbe53938543ecb96b3a9ac3fe274b7189e23e27353/scipy-1.16.3-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:2a207a6ce9c24f1951241f4693ede2d393f59c07abc159b2cb2be980820e01fb", size = 23534466 }, + { url = "https://files.pythonhosted.org/packages/be/a0/668c4609ce6dbf2f948e167836ccaf897f95fb63fa231c87da7558a374cd/scipy-1.16.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:532fb5ad6a87e9e9cd9c959b106b73145a03f04c7d57ea3e6f6bb60b86ab0876", size = 33593618 }, + { url = "https://files.pythonhosted.org/packages/ca/6e/8942461cf2636cdae083e3eb72622a7fbbfa5cf559c7d13ab250a5dbdc01/scipy-1.16.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0151a0749efeaaab78711c78422d413c583b8cdd2011a3c1d6c794938ee9fdb2", size = 35899798 }, + { url = "https://files.pythonhosted.org/packages/79/e8/d0f33590364cdbd67f28ce79368b373889faa4ee959588beddf6daef9abe/scipy-1.16.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:b7180967113560cca57418a7bc719e30366b47959dd845a93206fbed693c867e", size = 36226154 }, + { url = "https://files.pythonhosted.org/packages/39/c1/1903de608c0c924a1749c590064e65810f8046e437aba6be365abc4f7557/scipy-1.16.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:deb3841c925eeddb6afc1e4e4a45e418d19ec7b87c5df177695224078e8ec733", size = 38878540 }, + { url = "https://files.pythonhosted.org/packages/f1/d0/22ec7036ba0b0a35bccb7f25ab407382ed34af0b111475eb301c16f8a2e5/scipy-1.16.3-cp311-cp311-win_amd64.whl", hash = "sha256:53c3844d527213631e886621df5695d35e4f6a75f620dca412bcd292f6b87d78", size = 38722107 }, + { url = "https://files.pythonhosted.org/packages/7b/60/8a00e5a524bb3bf8898db1650d350f50e6cffb9d7a491c561dc9826c7515/scipy-1.16.3-cp311-cp311-win_arm64.whl", hash = "sha256:9452781bd879b14b6f055b26643703551320aa8d79ae064a71df55c00286a184", size = 25506272 }, + { url = "https://files.pythonhosted.org/packages/40/41/5bf55c3f386b1643812f3a5674edf74b26184378ef0f3e7c7a09a7e2ca7f/scipy-1.16.3-cp312-cp312-macosx_10_14_x86_64.whl", hash = "sha256:81fc5827606858cf71446a5e98715ba0e11f0dbc83d71c7409d05486592a45d6", size = 36659043 }, + { url = "https://files.pythonhosted.org/packages/1e/0f/65582071948cfc45d43e9870bf7ca5f0e0684e165d7c9ef4e50d783073eb/scipy-1.16.3-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:c97176013d404c7346bf57874eaac5187d969293bf40497140b0a2b2b7482e07", size = 28898986 }, + { url = "https://files.pythonhosted.org/packages/96/5e/36bf3f0ac298187d1ceadde9051177d6a4fe4d507e8f59067dc9dd39e650/scipy-1.16.3-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:2b71d93c8a9936046866acebc915e2af2e292b883ed6e2cbe5c34beb094b82d9", size = 20889814 }, + { url = "https://files.pythonhosted.org/packages/80/35/178d9d0c35394d5d5211bbff7ac4f2986c5488b59506fef9e1de13ea28d3/scipy-1.16.3-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:3d4a07a8e785d80289dfe66b7c27d8634a773020742ec7187b85ccc4b0e7b686", size = 23565795 }, + { url = "https://files.pythonhosted.org/packages/fa/46/d1146ff536d034d02f83c8afc3c4bab2eddb634624d6529a8512f3afc9da/scipy-1.16.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0553371015692a898e1aa858fed67a3576c34edefa6b7ebdb4e9dde49ce5c203", size = 33349476 }, + { url = "https://files.pythonhosted.org/packages/79/2e/415119c9ab3e62249e18c2b082c07aff907a273741b3f8160414b0e9193c/scipy-1.16.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:72d1717fd3b5e6ec747327ce9bda32d5463f472c9dce9f54499e81fbd50245a1", size = 35676692 }, + { url = "https://files.pythonhosted.org/packages/27/82/df26e44da78bf8d2aeaf7566082260cfa15955a5a6e96e6a29935b64132f/scipy-1.16.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1fb2472e72e24d1530debe6ae078db70fb1605350c88a3d14bc401d6306dbffe", size = 36019345 }, + { url = "https://files.pythonhosted.org/packages/82/31/006cbb4b648ba379a95c87262c2855cd0d09453e500937f78b30f02fa1cd/scipy-1.16.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c5192722cffe15f9329a3948c4b1db789fbb1f05c97899187dcf009b283aea70", size = 38678975 }, + { url = "https://files.pythonhosted.org/packages/c2/7f/acbd28c97e990b421af7d6d6cd416358c9c293fc958b8529e0bd5d2a2a19/scipy-1.16.3-cp312-cp312-win_amd64.whl", hash = "sha256:56edc65510d1331dae01ef9b658d428e33ed48b4f77b1d51caf479a0253f96dc", size = 38555926 }, + { url = "https://files.pythonhosted.org/packages/ce/69/c5c7807fd007dad4f48e0a5f2153038dc96e8725d3345b9ee31b2b7bed46/scipy-1.16.3-cp312-cp312-win_arm64.whl", hash = "sha256:a8a26c78ef223d3e30920ef759e25625a0ecdd0d60e5a8818b7513c3e5384cf2", size = 25463014 }, + { url = "https://files.pythonhosted.org/packages/72/f1/57e8327ab1508272029e27eeef34f2302ffc156b69e7e233e906c2a5c379/scipy-1.16.3-cp313-cp313-macosx_10_14_x86_64.whl", hash = "sha256:d2ec56337675e61b312179a1ad124f5f570c00f920cc75e1000025451b88241c", size = 36617856 }, + { url = "https://files.pythonhosted.org/packages/44/13/7e63cfba8a7452eb756306aa2fd9b37a29a323b672b964b4fdeded9a3f21/scipy-1.16.3-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:16b8bc35a4cc24db80a0ec836a9286d0e31b2503cb2fd7ff7fb0e0374a97081d", size = 28874306 }, + { url = "https://files.pythonhosted.org/packages/15/65/3a9400efd0228a176e6ec3454b1fa998fbbb5a8defa1672c3f65706987db/scipy-1.16.3-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:5803c5fadd29de0cf27fa08ccbfe7a9e5d741bf63e4ab1085437266f12460ff9", size = 20865371 }, + { url = "https://files.pythonhosted.org/packages/33/d7/eda09adf009a9fb81827194d4dd02d2e4bc752cef16737cc4ef065234031/scipy-1.16.3-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:b81c27fc41954319a943d43b20e07c40bdcd3ff7cf013f4fb86286faefe546c4", size = 23524877 }, + { url = "https://files.pythonhosted.org/packages/7d/6b/3f911e1ebc364cb81320223a3422aab7d26c9c7973109a9cd0f27c64c6c0/scipy-1.16.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0c3b4dd3d9b08dbce0f3440032c52e9e2ab9f96ade2d3943313dfe51a7056959", size = 33342103 }, + { url = "https://files.pythonhosted.org/packages/21/f6/4bfb5695d8941e5c570a04d9fcd0d36bce7511b7d78e6e75c8f9791f82d0/scipy-1.16.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7dc1360c06535ea6116a2220f760ae572db9f661aba2d88074fe30ec2aa1ff88", size = 35697297 }, + { url = "https://files.pythonhosted.org/packages/04/e1/6496dadbc80d8d896ff72511ecfe2316b50313bfc3ebf07a3f580f08bd8c/scipy-1.16.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:663b8d66a8748051c3ee9c96465fb417509315b99c71550fda2591d7dd634234", size = 36021756 }, + { url = "https://files.pythonhosted.org/packages/fe/bd/a8c7799e0136b987bda3e1b23d155bcb31aec68a4a472554df5f0937eef7/scipy-1.16.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eab43fae33a0c39006a88096cd7b4f4ef545ea0447d250d5ac18202d40b6611d", size = 38696566 }, + { url = "https://files.pythonhosted.org/packages/cd/01/1204382461fcbfeb05b6161b594f4007e78b6eba9b375382f79153172b4d/scipy-1.16.3-cp313-cp313-win_amd64.whl", hash = "sha256:062246acacbe9f8210de8e751b16fc37458213f124bef161a5a02c7a39284304", size = 38529877 }, + { url = "https://files.pythonhosted.org/packages/7f/14/9d9fbcaa1260a94f4bb5b64ba9213ceb5d03cd88841fe9fd1ffd47a45b73/scipy-1.16.3-cp313-cp313-win_arm64.whl", hash = "sha256:50a3dbf286dbc7d84f176f9a1574c705f277cb6565069f88f60db9eafdbe3ee2", size = 25455366 }, + { url = "https://files.pythonhosted.org/packages/e2/a3/9ec205bd49f42d45d77f1730dbad9ccf146244c1647605cf834b3a8c4f36/scipy-1.16.3-cp313-cp313t-macosx_10_14_x86_64.whl", hash = "sha256:fb4b29f4cf8cc5a8d628bc8d8e26d12d7278cd1f219f22698a378c3d67db5e4b", size = 37027931 }, + { url = "https://files.pythonhosted.org/packages/25/06/ca9fd1f3a4589cbd825b1447e5db3a8ebb969c1eaf22c8579bd286f51b6d/scipy-1.16.3-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:8d09d72dc92742988b0e7750bddb8060b0c7079606c0d24a8cc8e9c9c11f9079", size = 29400081 }, + { url = "https://files.pythonhosted.org/packages/6a/56/933e68210d92657d93fb0e381683bc0e53a965048d7358ff5fbf9e6a1b17/scipy-1.16.3-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:03192a35e661470197556de24e7cb1330d84b35b94ead65c46ad6f16f6b28f2a", size = 21391244 }, + { url = "https://files.pythonhosted.org/packages/a8/7e/779845db03dc1418e215726329674b40576879b91814568757ff0014ad65/scipy-1.16.3-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:57d01cb6f85e34f0946b33caa66e892aae072b64b034183f3d87c4025802a119", size = 23929753 }, + { url = "https://files.pythonhosted.org/packages/4c/4b/f756cf8161d5365dcdef9e5f460ab226c068211030a175d2fc7f3f41ca64/scipy-1.16.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:96491a6a54e995f00a28a3c3badfff58fd093bf26cd5fb34a2188c8c756a3a2c", size = 33496912 }, + { url = "https://files.pythonhosted.org/packages/09/b5/222b1e49a58668f23839ca1542a6322bb095ab8d6590d4f71723869a6c2c/scipy-1.16.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cd13e354df9938598af2be05822c323e97132d5e6306b83a3b4ee6724c6e522e", size = 35802371 }, + { url = "https://files.pythonhosted.org/packages/c1/8d/5964ef68bb31829bde27611f8c9deeac13764589fe74a75390242b64ca44/scipy-1.16.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:63d3cdacb8a824a295191a723ee5e4ea7768ca5ca5f2838532d9f2e2b3ce2135", size = 36190477 }, + { url = "https://files.pythonhosted.org/packages/ab/f2/b31d75cb9b5fa4dd39a0a931ee9b33e7f6f36f23be5ef560bf72e0f92f32/scipy-1.16.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e7efa2681ea410b10dde31a52b18b0154d66f2485328830e45fdf183af5aefc6", size = 38796678 }, + { url = "https://files.pythonhosted.org/packages/b4/1e/b3723d8ff64ab548c38d87055483714fefe6ee20e0189b62352b5e015bb1/scipy-1.16.3-cp313-cp313t-win_amd64.whl", hash = "sha256:2d1ae2cf0c350e7705168ff2429962a89ad90c2d49d1dd300686d8b2a5af22fc", size = 38640178 }, + { url = "https://files.pythonhosted.org/packages/8e/f3/d854ff38789aca9b0cc23008d607ced9de4f7ab14fa1ca4329f86b3758ca/scipy-1.16.3-cp313-cp313t-win_arm64.whl", hash = "sha256:0c623a54f7b79dd88ef56da19bc2873afec9673a48f3b85b18e4d402bdd29a5a", size = 25803246 }, + { url = "https://files.pythonhosted.org/packages/99/f6/99b10fd70f2d864c1e29a28bbcaa0c6340f9d8518396542d9ea3b4aaae15/scipy-1.16.3-cp314-cp314-macosx_10_14_x86_64.whl", hash = "sha256:875555ce62743e1d54f06cdf22c1e0bc47b91130ac40fe5d783b6dfa114beeb6", size = 36606469 }, + { url = "https://files.pythonhosted.org/packages/4d/74/043b54f2319f48ea940dd025779fa28ee360e6b95acb7cd188fad4391c6b/scipy-1.16.3-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:bb61878c18a470021fb515a843dc7a76961a8daceaaaa8bad1332f1bf4b54657", size = 28872043 }, + { url = "https://files.pythonhosted.org/packages/4d/e1/24b7e50cc1c4ee6ffbcb1f27fe9f4c8b40e7911675f6d2d20955f41c6348/scipy-1.16.3-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:f2622206f5559784fa5c4b53a950c3c7c1cf3e84ca1b9c4b6c03f062f289ca26", size = 20862952 }, + { url = "https://files.pythonhosted.org/packages/dd/3a/3e8c01a4d742b730df368e063787c6808597ccb38636ed821d10b39ca51b/scipy-1.16.3-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:7f68154688c515cdb541a31ef8eb66d8cd1050605be9dcd74199cbd22ac739bc", size = 23508512 }, + { url = "https://files.pythonhosted.org/packages/1f/60/c45a12b98ad591536bfe5330cb3cfe1850d7570259303563b1721564d458/scipy-1.16.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:8b3c820ddb80029fe9f43d61b81d8b488d3ef8ca010d15122b152db77dc94c22", size = 33413639 }, + { url = "https://files.pythonhosted.org/packages/71/bc/35957d88645476307e4839712642896689df442f3e53b0fa016ecf8a3357/scipy-1.16.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d3837938ae715fc0fe3c39c0202de3a8853aff22ca66781ddc2ade7554b7e2cc", size = 35704729 }, + { url = "https://files.pythonhosted.org/packages/3b/15/89105e659041b1ca11c386e9995aefacd513a78493656e57789f9d9eab61/scipy-1.16.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:aadd23f98f9cb069b3bd64ddc900c4d277778242e961751f77a8cb5c4b946fb0", size = 36086251 }, + { url = "https://files.pythonhosted.org/packages/1a/87/c0ea673ac9c6cc50b3da2196d860273bc7389aa69b64efa8493bdd25b093/scipy-1.16.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:b7c5f1bda1354d6a19bc6af73a649f8285ca63ac6b52e64e658a5a11d4d69800", size = 38716681 }, + { url = "https://files.pythonhosted.org/packages/91/06/837893227b043fb9b0d13e4bd7586982d8136cb249ffb3492930dab905b8/scipy-1.16.3-cp314-cp314-win_amd64.whl", hash = "sha256:e5d42a9472e7579e473879a1990327830493a7047506d58d73fc429b84c1d49d", size = 39358423 }, + { url = "https://files.pythonhosted.org/packages/95/03/28bce0355e4d34a7c034727505a02d19548549e190bedd13a721e35380b7/scipy-1.16.3-cp314-cp314-win_arm64.whl", hash = "sha256:6020470b9d00245926f2d5bb93b119ca0340f0d564eb6fbaad843eaebf9d690f", size = 26135027 }, + { url = "https://files.pythonhosted.org/packages/b2/6f/69f1e2b682efe9de8fe9f91040f0cd32f13cfccba690512ba4c582b0bc29/scipy-1.16.3-cp314-cp314t-macosx_10_14_x86_64.whl", hash = "sha256:e1d27cbcb4602680a49d787d90664fa4974063ac9d4134813332a8c53dbe667c", size = 37028379 }, + { url = "https://files.pythonhosted.org/packages/7c/2d/e826f31624a5ebbab1cd93d30fd74349914753076ed0593e1d56a98c4fb4/scipy-1.16.3-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:9b9c9c07b6d56a35777a1b4cc8966118fb16cfd8daf6743867d17d36cfad2d40", size = 29400052 }, + { url = "https://files.pythonhosted.org/packages/69/27/d24feb80155f41fd1f156bf144e7e049b4e2b9dd06261a242905e3bc7a03/scipy-1.16.3-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:3a4c460301fb2cffb7f88528f30b3127742cff583603aa7dc964a52c463b385d", size = 21391183 }, + { url = "https://files.pythonhosted.org/packages/f8/d3/1b229e433074c5738a24277eca520a2319aac7465eea7310ea6ae0e98ae2/scipy-1.16.3-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:f667a4542cc8917af1db06366d3f78a5c8e83badd56409f94d1eac8d8d9133fa", size = 23930174 }, + { url = "https://files.pythonhosted.org/packages/16/9d/d9e148b0ec680c0f042581a2be79a28a7ab66c0c4946697f9e7553ead337/scipy-1.16.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f379b54b77a597aa7ee5e697df0d66903e41b9c85a6dd7946159e356319158e8", size = 33497852 }, + { url = "https://files.pythonhosted.org/packages/2f/22/4e5f7561e4f98b7bea63cf3fd7934bff1e3182e9f1626b089a679914d5c8/scipy-1.16.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4aff59800a3b7f786b70bfd6ab551001cb553244988d7d6b8299cb1ea653b353", size = 35798595 }, + { url = "https://files.pythonhosted.org/packages/83/42/6644d714c179429fc7196857866f219fef25238319b650bb32dde7bf7a48/scipy-1.16.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:da7763f55885045036fabcebd80144b757d3db06ab0861415d1c3b7c69042146", size = 36186269 }, + { url = "https://files.pythonhosted.org/packages/ac/70/64b4d7ca92f9cf2e6fc6aaa2eecf80bb9b6b985043a9583f32f8177ea122/scipy-1.16.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ffa6eea95283b2b8079b821dc11f50a17d0571c92b43e2b5b12764dc5f9b285d", size = 38802779 }, + { url = "https://files.pythonhosted.org/packages/61/82/8d0e39f62764cce5ffd5284131e109f07cf8955aef9ab8ed4e3aa5e30539/scipy-1.16.3-cp314-cp314t-win_amd64.whl", hash = "sha256:d9f48cafc7ce94cf9b15c6bffdc443a81a27bf7075cf2dcd5c8b40f85d10c4e7", size = 39471128 }, + { url = "https://files.pythonhosted.org/packages/64/47/a494741db7280eae6dc033510c319e34d42dd41b7ac0c7ead39354d1a2b5/scipy-1.16.3-cp314-cp314t-win_arm64.whl", hash = "sha256:21d9d6b197227a12dcbf9633320a4e34c6b0e51c57268df255a0942983bac562", size = 26464127 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "send2trash" version = "2.1.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/c5/f0/184b4b5f8d00f2a92cf96eec8967a3d550b52cf94362dad1100df9e48d57/send2trash-2.1.0.tar.gz", hash = "sha256:1c72b39f09457db3c05ce1d19158c2cbef4c32b8bedd02c155e49282b7ea7459", size = 17255, upload-time = "2026-01-14T06:27:36.056Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/1c/78/504fdd027da3b84ff1aecd9f6957e65f35134534ccc6da8628eb71e76d3f/send2trash-2.1.0-py3-none-any.whl", hash = "sha256:0da2f112e6d6bb22de6aa6daa7e144831a4febf2a87261451c4ad849fe9a873c", size = 17610, upload-time = "2026-01-14T06:27:35.218Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/fd/3a/aec9b02217bb79b87bbc1a21bc6abc51e3d5dcf65c30487ac96c0908c722/Send2Trash-1.8.3.tar.gz", hash = "sha256:b18e7a3966d99871aefeb00cfbcfdced55ce4871194810fc71f4aa484b953abf", size = 17394 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/40/b0/4562db6223154aa4e22f939003cb92514c79f3d4dccca3444253fd17f902/Send2Trash-1.8.3-py3-none-any.whl", hash = "sha256:0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9", size = 18072 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "setuptools" version = "81.0.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/0d/1c/73e719955c59b8e424d015ab450f51c0af856ae46ea2da83eba51cc88de1/setuptools-81.0.0.tar.gz", hash = "sha256:487b53915f52501f0a79ccfd0c02c165ffe06631443a886740b91af4b7a5845a", size = 1198299, upload-time = "2026-02-06T21:10:39.601Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/e1/e3/c164c88b2e5ce7b24d667b9bd83589cf4f3520d97cad01534cd3c4f55fdb/setuptools-81.0.0-py3-none-any.whl", hash = "sha256:fdd925d5c5d9f62e4b74b30d6dd7828ce236fd6ed998a08d81de62ce5a6310d6", size = 1062021, upload-time = "2026-02-06T21:10:37.175Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/18/5d/3bf57dcd21979b887f014ea83c24ae194cfcd12b9e0fda66b957c69d1fca/setuptools-80.9.0.tar.gz", hash = "sha256:f36b47402ecde768dbfafc46e8e4207b4360c654f1f3bb84475f0a28628fb19c", size = 1319958 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a3/dc/17031897dae0efacfea57dfd3a82fdd2a2aeb58e0ff71b77b87e44edc772/setuptools-80.9.0-py3-none-any.whl", hash = "sha256:062d34222ad13e0cc312a4c02d73f059e86a4acbfbdea8f8f76b28c99f306922", size = 1201486 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "six" version = "1.17.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031, upload-time = "2024-12-04T17:35:28.174Z" } +sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050 }, +] + +[[package]] +<<<<<<< HEAD +======= +name = "sniffio" +version = "1.3.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/a2/87/a6771e1546d97e7e041b6ae58d80074f81b7d5121207425c964ddf5cfdbd/sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc", size = 20372 } wheels = [ - { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" }, + { url = "https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2", size = 10235 }, ] [[package]] +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) name = "soupsieve" version = "2.8.4" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/47/2c/0a5f6f8ee0d5589e48c7640213ed5175d52cf540a06725b628cc1a45d6ce/soupsieve-2.8.4.tar.gz", hash = "sha256:e121fd02e975c695e4e9e8774a5ee35d74714b59307868dcc5319ad2d9e3328e", size = 121110, upload-time = "2026-05-24T13:55:57.154Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/5e/f5/0c41cb68dcae6b7de4fac4188a3a9589e21fb31df21ea3a2e888db95e6c9/soupsieve-2.8.4-py3-none-any.whl", hash = "sha256:e7e6b0769c8f51ed59acab6e994b00621096cfb1c640a7509295987388fbaf65", size = 37304, upload-time = "2026-05-24T13:55:55.406Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/6d/e6/21ccce3262dd4889aa3332e5a119a3491a95e8f60939870a3a035aabac0d/soupsieve-2.8.tar.gz", hash = "sha256:e2dd4a40a628cb5f28f6d4b0db8800b8f581b65bb380b97de22ba5ca8d72572f", size = 103472 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/14/a0/bb38d3b76b8cae341dad93a2dd83ab7462e6dbcdd84d43f54ee60a8dc167/soupsieve-2.8-py3-none-any.whl", hash = "sha256:0cc76456a30e20f5d7f2e14a98a4ae2ee4e5abdc7c5ea0aafe795f344bc7984c", size = 36679 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3390,6 +4796,7 @@ dependencies = [ { name = "greenlet", marker = "platform_machine == 'AMD64' or platform_machine == 'WIN32' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'ppc64le' or platform_machine == 'win32' or platform_machine == 'x86_64'" }, { name = "typing-extensions" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/57/da/6fbf010c8ebb347679d0d100b22fe9ba5e13fd04046c5df7280d2f0bf706/sqlalchemy-2.0.50.tar.gz", hash = "sha256:af5607d11ef90fd6a5c0549fe0045dce1663d427426bcfb506dcb5346a85a3b9", size = 9907424, upload-time = "2026-05-24T19:20:04.018Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/b6/5d/3172686af1770e4de2805f919a51441085f589ddadf3dd76ec582f84f497/sqlalchemy-2.0.50-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1aa6e403663a9c43c8fef7ce4bdb4cf48bcd8d352e91deda2a99f963270bd508", size = 2161366, upload-time = "2026-05-24T20:00:02.061Z" }, @@ -3428,6 +4835,35 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/f2/e3/5aa06f167559f8c0bdae487e297d23ba548150ab016a3418265d617a4985/sqlalchemy-2.0.50-cp314-cp314t-win32.whl", hash = "sha256:1fbd55a969d7ac44a98e3dec75016074f809fa08f871585ace58dde110d1bf3e", size = 2150823, upload-time = "2026-05-24T20:08:58.644Z" }, { url = "https://files.pythonhosted.org/packages/65/9b/112fb8f977582d7489d036e409e3723948bcf5320b3ac465f3c481bbe8f9/sqlalchemy-2.0.50-cp314-cp314t-win_amd64.whl", hash = "sha256:c5c3cdb753a9004183e1ccb634b41611654c989e61bc68617ce878e46d6f1e51", size = 2185794, upload-time = "2026-05-24T20:09:00.319Z" }, { url = "https://files.pythonhosted.org/packages/d0/10/f7220e9b784d295d241c86ed99aeb537f92afcd469a64861f2717e9bb077/sqlalchemy-2.0.50-py3-none-any.whl", hash = "sha256:92064363517a3ff8212b5a93b8c62876579d8dfd1ca5b561335f30152d884fa9", size = 1943861, upload-time = "2026-05-24T19:59:01.119Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/f0/f2/840d7b9496825333f532d2e3976b8eadbf52034178aac53630d09fe6e1ef/sqlalchemy-2.0.44.tar.gz", hash = "sha256:0ae7454e1ab1d780aee69fd2aae7d6b8670a581d8847f2d1e0f7ddfbf47e5a22", size = 9819830 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e3/81/15d7c161c9ddf0900b076b55345872ed04ff1ed6a0666e5e94ab44b0163c/sqlalchemy-2.0.44-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0fe3917059c7ab2ee3f35e77757062b1bea10a0b6ca633c58391e3f3c6c488dd", size = 2140517 }, + { url = "https://files.pythonhosted.org/packages/d4/d5/4abd13b245c7d91bdf131d4916fd9e96a584dac74215f8b5bc945206a974/sqlalchemy-2.0.44-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:de4387a354ff230bc979b46b2207af841dc8bf29847b6c7dbe60af186d97aefa", size = 2130738 }, + { url = "https://files.pythonhosted.org/packages/cb/3c/8418969879c26522019c1025171cefbb2a8586b6789ea13254ac602986c0/sqlalchemy-2.0.44-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c3678a0fb72c8a6a29422b2732fe423db3ce119c34421b5f9955873eb9b62c1e", size = 3304145 }, + { url = "https://files.pythonhosted.org/packages/94/2d/fdb9246d9d32518bda5d90f4b65030b9bf403a935cfe4c36a474846517cb/sqlalchemy-2.0.44-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3cf6872a23601672d61a68f390e44703442639a12ee9dd5a88bbce52a695e46e", size = 3304511 }, + { url = "https://files.pythonhosted.org/packages/7d/fb/40f2ad1da97d5c83f6c1269664678293d3fe28e90ad17a1093b735420549/sqlalchemy-2.0.44-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:329aa42d1be9929603f406186630135be1e7a42569540577ba2c69952b7cf399", size = 3235161 }, + { url = "https://files.pythonhosted.org/packages/95/cb/7cf4078b46752dca917d18cf31910d4eff6076e5b513c2d66100c4293d83/sqlalchemy-2.0.44-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:70e03833faca7166e6a9927fbee7c27e6ecde436774cd0b24bbcc96353bce06b", size = 3261426 }, + { url = "https://files.pythonhosted.org/packages/f8/3b/55c09b285cb2d55bdfa711e778bdffdd0dc3ffa052b0af41f1c5d6e582fa/sqlalchemy-2.0.44-cp311-cp311-win32.whl", hash = "sha256:253e2f29843fb303eca6b2fc645aca91fa7aa0aa70b38b6950da92d44ff267f3", size = 2105392 }, + { url = "https://files.pythonhosted.org/packages/c7/23/907193c2f4d680aedbfbdf7bf24c13925e3c7c292e813326c1b84a0b878e/sqlalchemy-2.0.44-cp311-cp311-win_amd64.whl", hash = "sha256:7a8694107eb4308a13b425ca8c0e67112f8134c846b6e1f722698708741215d5", size = 2130293 }, + { url = "https://files.pythonhosted.org/packages/62/c4/59c7c9b068e6813c898b771204aad36683c96318ed12d4233e1b18762164/sqlalchemy-2.0.44-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:72fea91746b5890f9e5e0997f16cbf3d53550580d76355ba2d998311b17b2250", size = 2139675 }, + { url = "https://files.pythonhosted.org/packages/d6/ae/eeb0920537a6f9c5a3708e4a5fc55af25900216bdb4847ec29cfddf3bf3a/sqlalchemy-2.0.44-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:585c0c852a891450edbb1eaca8648408a3cc125f18cf433941fa6babcc359e29", size = 2127726 }, + { url = "https://files.pythonhosted.org/packages/d8/d5/2ebbabe0379418eda8041c06b0b551f213576bfe4c2f09d77c06c07c8cc5/sqlalchemy-2.0.44-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9b94843a102efa9ac68a7a30cd46df3ff1ed9c658100d30a725d10d9c60a2f44", size = 3327603 }, + { url = "https://files.pythonhosted.org/packages/45/e5/5aa65852dadc24b7d8ae75b7efb8d19303ed6ac93482e60c44a585930ea5/sqlalchemy-2.0.44-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:119dc41e7a7defcefc57189cfa0e61b1bf9c228211aba432b53fb71ef367fda1", size = 3337842 }, + { url = "https://files.pythonhosted.org/packages/41/92/648f1afd3f20b71e880ca797a960f638d39d243e233a7082c93093c22378/sqlalchemy-2.0.44-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0765e318ee9179b3718c4fd7ba35c434f4dd20332fbc6857a5e8df17719c24d7", size = 3264558 }, + { url = "https://files.pythonhosted.org/packages/40/cf/e27d7ee61a10f74b17740918e23cbc5bc62011b48282170dc4c66da8ec0f/sqlalchemy-2.0.44-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2e7b5b079055e02d06a4308d0481658e4f06bc7ef211567edc8f7d5dce52018d", size = 3301570 }, + { url = "https://files.pythonhosted.org/packages/3b/3d/3116a9a7b63e780fb402799b6da227435be878b6846b192f076d2f838654/sqlalchemy-2.0.44-cp312-cp312-win32.whl", hash = "sha256:846541e58b9a81cce7dee8329f352c318de25aa2f2bbe1e31587eb1f057448b4", size = 2103447 }, + { url = "https://files.pythonhosted.org/packages/25/83/24690e9dfc241e6ab062df82cc0df7f4231c79ba98b273fa496fb3dd78ed/sqlalchemy-2.0.44-cp312-cp312-win_amd64.whl", hash = "sha256:7cbcb47fd66ab294703e1644f78971f6f2f1126424d2b300678f419aa73c7b6e", size = 2130912 }, + { url = "https://files.pythonhosted.org/packages/45/d3/c67077a2249fdb455246e6853166360054c331db4613cda3e31ab1cadbef/sqlalchemy-2.0.44-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:ff486e183d151e51b1d694c7aa1695747599bb00b9f5f604092b54b74c64a8e1", size = 2135479 }, + { url = "https://files.pythonhosted.org/packages/2b/91/eabd0688330d6fd114f5f12c4f89b0d02929f525e6bf7ff80aa17ca802af/sqlalchemy-2.0.44-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:0b1af8392eb27b372ddb783b317dea0f650241cea5bd29199b22235299ca2e45", size = 2123212 }, + { url = "https://files.pythonhosted.org/packages/b0/bb/43e246cfe0e81c018076a16036d9b548c4cc649de241fa27d8d9ca6f85ab/sqlalchemy-2.0.44-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2b61188657e3a2b9ac4e8f04d6cf8e51046e28175f79464c67f2fd35bceb0976", size = 3255353 }, + { url = "https://files.pythonhosted.org/packages/b9/96/c6105ed9a880abe346b64d3b6ddef269ddfcab04f7f3d90a0bf3c5a88e82/sqlalchemy-2.0.44-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b87e7b91a5d5973dda5f00cd61ef72ad75a1db73a386b62877d4875a8840959c", size = 3260222 }, + { url = "https://files.pythonhosted.org/packages/44/16/1857e35a47155b5ad927272fee81ae49d398959cb749edca6eaa399b582f/sqlalchemy-2.0.44-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:15f3326f7f0b2bfe406ee562e17f43f36e16167af99c4c0df61db668de20002d", size = 3189614 }, + { url = "https://files.pythonhosted.org/packages/88/ee/4afb39a8ee4fc786e2d716c20ab87b5b1fb33d4ac4129a1aaa574ae8a585/sqlalchemy-2.0.44-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:1e77faf6ff919aa8cd63f1c4e561cac1d9a454a191bb864d5dd5e545935e5a40", size = 3226248 }, + { url = "https://files.pythonhosted.org/packages/32/d5/0e66097fc64fa266f29a7963296b40a80d6a997b7ac13806183700676f86/sqlalchemy-2.0.44-cp313-cp313-win32.whl", hash = "sha256:ee51625c2d51f8baadf2829fae817ad0b66b140573939dd69284d2ba3553ae73", size = 2101275 }, + { url = "https://files.pythonhosted.org/packages/03/51/665617fe4f8c6450f42a6d8d69243f9420f5677395572c2fe9d21b493b7b/sqlalchemy-2.0.44-cp313-cp313-win_amd64.whl", hash = "sha256:c1c80faaee1a6c3428cecf40d16a2365bcf56c424c92c2b6f0f9ad204b899e9e", size = 2127901 }, + { url = "https://files.pythonhosted.org/packages/9c/5e/6a29fa884d9fb7ddadf6b69490a9d45fded3b38541713010dad16b77d015/sqlalchemy-2.0.44-py3-none-any.whl", hash = "sha256:19de7ca1246fbef9f9d1bff8f1ab25641569df226364a0e40457dc5457c54b05", size = 1928718 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3439,9 +4875,9 @@ dependencies = [ { name = "executing" }, { name = "pure-eval" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/28/e3/55dcc2cfbc3ca9c29519eb6884dd1415ecb53b0e934862d3559ddcb7e20b/stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9", size = 44707, upload-time = "2023-09-30T13:58:05.479Z" } +sdist = { url = "https://files.pythonhosted.org/packages/28/e3/55dcc2cfbc3ca9c29519eb6884dd1415ecb53b0e934862d3559ddcb7e20b/stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9", size = 44707 } wheels = [ - { url = "https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695", size = 24521, upload-time = "2023-09-30T13:58:03.53Z" }, + { url = "https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695", size = 24521 }, ] [[package]] @@ -3451,9 +4887,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "mpmath" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/83/d3/803453b36afefb7c2bb238361cd4ae6125a569b4db67cd9e79846ba2d68c/sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517", size = 7793921, upload-time = "2025-04-27T18:05:01.611Z" } +sdist = { url = "https://files.pythonhosted.org/packages/83/d3/803453b36afefb7c2bb238361cd4ae6125a569b4db67cd9e79846ba2d68c/sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517", size = 7793921 } wheels = [ - { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" }, + { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353 }, ] [[package]] @@ -3473,7 +4909,7 @@ dependencies = [ { name = "werkzeug" }, ] wheels = [ - { url = "https://files.pythonhosted.org/packages/9c/d9/a5db55f88f258ac669a92858b70a714bbbd5acd993820b41ec4a96a4d77f/tensorboard-2.20.0-py3-none-any.whl", hash = "sha256:9dc9f978cb84c0723acf9a345d96c184f0293d18f166bb8d59ee098e6cfaaba6", size = 5525680, upload-time = "2025-07-17T19:20:49.638Z" }, + { url = "https://files.pythonhosted.org/packages/9c/d9/a5db55f88f258ac669a92858b70a714bbbd5acd993820b41ec4a96a4d77f/tensorboard-2.20.0-py3-none-any.whl", hash = "sha256:9dc9f978cb84c0723acf9a345d96c184f0293d18f166bb8d59ee098e6cfaaba6", size = 5525680 }, ] [[package]] @@ -3481,9 +4917,9 @@ name = "tensorboard-data-server" version = "0.7.2" source = { registry = "https://pypi.org/simple" } wheels = [ - { url = "https://files.pythonhosted.org/packages/7a/13/e503968fefabd4c6b2650af21e110aa8466fe21432cd7c43a84577a89438/tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb", size = 2356, upload-time = "2023-10-23T21:23:32.16Z" }, - { url = "https://files.pythonhosted.org/packages/b7/85/dabeaf902892922777492e1d253bb7e1264cadce3cea932f7ff599e53fea/tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60", size = 4823598, upload-time = "2023-10-23T21:23:33.714Z" }, - { url = "https://files.pythonhosted.org/packages/73/c6/825dab04195756cf8ff2e12698f22513b3db2f64925bdd41671bfb33aaa5/tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530", size = 6590363, upload-time = "2023-10-23T21:23:35.583Z" }, + { url = "https://files.pythonhosted.org/packages/7a/13/e503968fefabd4c6b2650af21e110aa8466fe21432cd7c43a84577a89438/tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb", size = 2356 }, + { url = "https://files.pythonhosted.org/packages/b7/85/dabeaf902892922777492e1d253bb7e1264cadce3cea932f7ff599e53fea/tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60", size = 4823598 }, + { url = "https://files.pythonhosted.org/packages/73/c6/825dab04195756cf8ff2e12698f22513b3db2f64925bdd41671bfb33aaa5/tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530", size = 6590363 }, ] [[package]] @@ -3495,9 +4931,9 @@ dependencies = [ { name = "pywinpty", marker = "os_name == 'nt'" }, { name = "tornado" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/8a/11/965c6fd8e5cc254f1fe142d547387da17a8ebfd75a3455f637c663fb38a0/terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e", size = 32701, upload-time = "2024-03-12T14:34:39.026Z" } +sdist = { url = "https://files.pythonhosted.org/packages/8a/11/965c6fd8e5cc254f1fe142d547387da17a8ebfd75a3455f637c663fb38a0/terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e", size = 32701 } wheels = [ - { url = "https://files.pythonhosted.org/packages/6a/9e/2064975477fdc887e47ad42157e214526dcad8f317a948dee17e1659a62f/terminado-0.18.1-py3-none-any.whl", hash = "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0", size = 14154, upload-time = "2024-03-12T14:34:36.569Z" }, + { url = "https://files.pythonhosted.org/packages/6a/9e/2064975477fdc887e47ad42157e214526dcad8f317a948dee17e1659a62f/terminado-0.18.1-py3-none-any.whl", hash = "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0", size = 14154 }, ] [[package]] @@ -3507,6 +4943,7 @@ source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version < '3.12'", ] +<<<<<<< HEAD dependencies = [ { name = "numpy", marker = "python_full_version < '3.12'" }, ] @@ -3529,6 +4966,11 @@ dependencies = [ sdist = { url = "https://files.pythonhosted.org/packages/b7/38/5e2ecef5af2f4fd4a89bb8d6240de9458bab4d51a4cbd97aeb3a0cd618e2/tifffile-2026.6.1.tar.gz", hash = "sha256:626c892c0e899d959b9438e7c0e1491dc154a7fead1f1f37a991724a50eceba9", size = 429694, upload-time = "2026-05-31T23:57:12.165Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/81/59/208f71d70ddc6184f79b8c6d87d46eb7d7b12c19186a817dec9c9c3f3693/tifffile-2026.6.1-py3-none-any.whl", hash = "sha256:0d7382d2769b855b81ce358528e2b40c16d48aa39031746efa81215205332a8d", size = 267108, upload-time = "2026-05-31T23:57:10.597Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/2d/b5/0d8f3d395f07d25ec4cafcdfc8cab234b2cc6bf2465e9d7660633983fe8f/tifffile-2025.10.16.tar.gz", hash = "sha256:425179ec7837ac0e07bc95d2ea5bea9b179ce854967c12ba07fc3f093e58efc1", size = 371848 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e6/5e/56c751afab61336cf0e7aa671b134255a30f15f59cd9e04f59c598a37ff5/tifffile-2025.10.16-py3-none-any.whl", hash = "sha256:41463d979c1c262b0a5cdef2a7f95f0388a072ad82d899458b154a48609d759c", size = 231162 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3538,15 +4980,16 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "webencodings" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/7a/fd/7a5ee21fd08ff70d3d33a5781c255cbe779659bd03278feb98b19ee550f4/tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7", size = 87085, upload-time = "2024-10-24T14:58:29.895Z" } +sdist = { url = "https://files.pythonhosted.org/packages/7a/fd/7a5ee21fd08ff70d3d33a5781c255cbe779659bd03278feb98b19ee550f4/tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7", size = 87085 } wheels = [ - { url = "https://files.pythonhosted.org/packages/e6/34/ebdc18bae6aa14fbee1a08b63c015c72b64868ff7dae68808ab500c492e2/tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289", size = 26610, upload-time = "2024-10-24T14:58:28.029Z" }, + { url = "https://files.pythonhosted.org/packages/e6/34/ebdc18bae6aa14fbee1a08b63c015c72b64868ff7dae68808ab500c492e2/tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289", size = 26610 }, ] [[package]] name = "tomli" version = "2.4.1" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/22/de/48c59722572767841493b26183a0d1cc411d54fd759c5607c4590b6563a6/tomli-2.4.1.tar.gz", hash = "sha256:7c7e1a961a0b2f2472c1ac5b69affa0ae1132c39adcb67aba98568702b9cc23f", size = 17543, upload-time = "2026-03-25T20:22:03.828Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/f4/11/db3d5885d8528263d8adc260bb2d28ebf1270b96e98f0e0268d32b8d9900/tomli-2.4.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f8f0fc26ec2cc2b965b7a3b87cd19c5c6b8c5e5f436b984e85f486d652285c30", size = 154704, upload-time = "2026-03-25T20:21:10.473Z" }, @@ -3595,15 +5038,60 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/12/64/da524626d3b9cc40c168a13da8335fe1c51be12c0a63685cc6db7308daae/tomli-2.4.1-cp314-cp314t-win_amd64.whl", hash = "sha256:2c1c351919aca02858f740c6d33adea0c5deea37f9ecca1cc1ef9e884a619d26", size = 121196, upload-time = "2026-03-25T20:22:01.169Z" }, { url = "https://files.pythonhosted.org/packages/5a/cd/e80b62269fc78fc36c9af5a6b89c835baa8af28ff5ad28c7028d60860320/tomli-2.4.1-cp314-cp314t-win_arm64.whl", hash = "sha256:eab21f45c7f66c13f2a9e0e1535309cee140182a9cdae1e041d02e47291e8396", size = 100393, upload-time = "2026-03-25T20:22:02.137Z" }, { url = "https://files.pythonhosted.org/packages/7b/61/cceae43728b7de99d9b847560c262873a1f6c98202171fd5ed62640b494b/tomli-2.4.1-py3-none-any.whl", hash = "sha256:0d85819802132122da43cb86656f8d1f8c6587d54ae7dcaf30e90533028b49fe", size = 14583, upload-time = "2026-03-25T20:22:03.012Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/52/ed/3f73f72945444548f33eba9a87fc7a6e969915e7b1acc8260b30e1f76a2f/tomli-2.3.0.tar.gz", hash = "sha256:64be704a875d2a59753d80ee8a533c3fe183e3f06807ff7dc2232938ccb01549", size = 17392 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b3/2e/299f62b401438d5fe1624119c723f5d877acc86a4c2492da405626665f12/tomli-2.3.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:88bd15eb972f3664f5ed4b57c1634a97153b4bac4479dcb6a495f41921eb7f45", size = 153236 }, + { url = "https://files.pythonhosted.org/packages/86/7f/d8fffe6a7aefdb61bced88fcb5e280cfd71e08939da5894161bd71bea022/tomli-2.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:883b1c0d6398a6a9d29b508c331fa56adbcdff647f6ace4dfca0f50e90dfd0ba", size = 148084 }, + { url = "https://files.pythonhosted.org/packages/47/5c/24935fb6a2ee63e86d80e4d3b58b222dafaf438c416752c8b58537c8b89a/tomli-2.3.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d1381caf13ab9f300e30dd8feadb3de072aeb86f1d34a8569453ff32a7dea4bf", size = 234832 }, + { url = "https://files.pythonhosted.org/packages/89/da/75dfd804fc11e6612846758a23f13271b76d577e299592b4371a4ca4cd09/tomli-2.3.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a0e285d2649b78c0d9027570d4da3425bdb49830a6156121360b3f8511ea3441", size = 242052 }, + { url = "https://files.pythonhosted.org/packages/70/8c/f48ac899f7b3ca7eb13af73bacbc93aec37f9c954df3c08ad96991c8c373/tomli-2.3.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:0a154a9ae14bfcf5d8917a59b51ffd5a3ac1fd149b71b47a3a104ca4edcfa845", size = 239555 }, + { url = "https://files.pythonhosted.org/packages/ba/28/72f8afd73f1d0e7829bfc093f4cb98ce0a40ffc0cc997009ee1ed94ba705/tomli-2.3.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:74bf8464ff93e413514fefd2be591c3b0b23231a77f901db1eb30d6f712fc42c", size = 245128 }, + { url = "https://files.pythonhosted.org/packages/b6/eb/a7679c8ac85208706d27436e8d421dfa39d4c914dcf5fa8083a9305f58d9/tomli-2.3.0-cp311-cp311-win32.whl", hash = "sha256:00b5f5d95bbfc7d12f91ad8c593a1659b6387b43f054104cda404be6bda62456", size = 96445 }, + { url = "https://files.pythonhosted.org/packages/0a/fe/3d3420c4cb1ad9cb462fb52967080575f15898da97e21cb6f1361d505383/tomli-2.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:4dc4ce8483a5d429ab602f111a93a6ab1ed425eae3122032db7e9acf449451be", size = 107165 }, + { url = "https://files.pythonhosted.org/packages/ff/b7/40f36368fcabc518bb11c8f06379a0fd631985046c038aca08c6d6a43c6e/tomli-2.3.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d7d86942e56ded512a594786a5ba0a5e521d02529b3826e7761a05138341a2ac", size = 154891 }, + { url = "https://files.pythonhosted.org/packages/f9/3f/d9dd692199e3b3aab2e4e4dd948abd0f790d9ded8cd10cbaae276a898434/tomli-2.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:73ee0b47d4dad1c5e996e3cd33b8a76a50167ae5f96a2607cbe8cc773506ab22", size = 148796 }, + { url = "https://files.pythonhosted.org/packages/60/83/59bff4996c2cf9f9387a0f5a3394629c7efa5ef16142076a23a90f1955fa/tomli-2.3.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:792262b94d5d0a466afb5bc63c7daa9d75520110971ee269152083270998316f", size = 242121 }, + { url = "https://files.pythonhosted.org/packages/45/e5/7c5119ff39de8693d6baab6c0b6dcb556d192c165596e9fc231ea1052041/tomli-2.3.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4f195fe57ecceac95a66a75ac24d9d5fbc98ef0962e09b2eddec5d39375aae52", size = 250070 }, + { url = "https://files.pythonhosted.org/packages/45/12/ad5126d3a278f27e6701abde51d342aa78d06e27ce2bb596a01f7709a5a2/tomli-2.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:e31d432427dcbf4d86958c184b9bfd1e96b5b71f8eb17e6d02531f434fd335b8", size = 245859 }, + { url = "https://files.pythonhosted.org/packages/fb/a1/4d6865da6a71c603cfe6ad0e6556c73c76548557a8d658f9e3b142df245f/tomli-2.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:7b0882799624980785240ab732537fcfc372601015c00f7fc367c55308c186f6", size = 250296 }, + { url = "https://files.pythonhosted.org/packages/a0/b7/a7a7042715d55c9ba6e8b196d65d2cb662578b4d8cd17d882d45322b0d78/tomli-2.3.0-cp312-cp312-win32.whl", hash = "sha256:ff72b71b5d10d22ecb084d345fc26f42b5143c5533db5e2eaba7d2d335358876", size = 97124 }, + { url = "https://files.pythonhosted.org/packages/06/1e/f22f100db15a68b520664eb3328fb0ae4e90530887928558112c8d1f4515/tomli-2.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:1cb4ed918939151a03f33d4242ccd0aa5f11b3547d0cf30f7c74a408a5b99878", size = 107698 }, + { url = "https://files.pythonhosted.org/packages/89/48/06ee6eabe4fdd9ecd48bf488f4ac783844fd777f547b8d1b61c11939974e/tomli-2.3.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:5192f562738228945d7b13d4930baffda67b69425a7f0da96d360b0a3888136b", size = 154819 }, + { url = "https://files.pythonhosted.org/packages/f1/01/88793757d54d8937015c75dcdfb673c65471945f6be98e6a0410fba167ed/tomli-2.3.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:be71c93a63d738597996be9528f4abe628d1adf5e6eb11607bc8fe1a510b5dae", size = 148766 }, + { url = "https://files.pythonhosted.org/packages/42/17/5e2c956f0144b812e7e107f94f1cc54af734eb17b5191c0bbfb72de5e93e/tomli-2.3.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c4665508bcbac83a31ff8ab08f424b665200c0e1e645d2bd9ab3d3e557b6185b", size = 240771 }, + { url = "https://files.pythonhosted.org/packages/d5/f4/0fbd014909748706c01d16824eadb0307115f9562a15cbb012cd9b3512c5/tomli-2.3.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4021923f97266babc6ccab9f5068642a0095faa0a51a246a6a02fccbb3514eaf", size = 248586 }, + { url = "https://files.pythonhosted.org/packages/30/77/fed85e114bde5e81ecf9bc5da0cc69f2914b38f4708c80ae67d0c10180c5/tomli-2.3.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4ea38c40145a357d513bffad0ed869f13c1773716cf71ccaa83b0fa0cc4e42f", size = 244792 }, + { url = "https://files.pythonhosted.org/packages/55/92/afed3d497f7c186dc71e6ee6d4fcb0acfa5f7d0a1a2878f8beae379ae0cc/tomli-2.3.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ad805ea85eda330dbad64c7ea7a4556259665bdf9d2672f5dccc740eb9d3ca05", size = 248909 }, + { url = "https://files.pythonhosted.org/packages/f8/84/ef50c51b5a9472e7265ce1ffc7f24cd4023d289e109f669bdb1553f6a7c2/tomli-2.3.0-cp313-cp313-win32.whl", hash = "sha256:97d5eec30149fd3294270e889b4234023f2c69747e555a27bd708828353ab606", size = 96946 }, + { url = "https://files.pythonhosted.org/packages/b2/b7/718cd1da0884f281f95ccfa3a6cc572d30053cba64603f79d431d3c9b61b/tomli-2.3.0-cp313-cp313-win_amd64.whl", hash = "sha256:0c95ca56fbe89e065c6ead5b593ee64b84a26fca063b5d71a1122bf26e533999", size = 107705 }, + { url = "https://files.pythonhosted.org/packages/19/94/aeafa14a52e16163008060506fcb6aa1949d13548d13752171a755c65611/tomli-2.3.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:cebc6fe843e0733ee827a282aca4999b596241195f43b4cc371d64fc6639da9e", size = 154244 }, + { url = "https://files.pythonhosted.org/packages/db/e4/1e58409aa78eefa47ccd19779fc6f36787edbe7d4cd330eeeedb33a4515b/tomli-2.3.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:4c2ef0244c75aba9355561272009d934953817c49f47d768070c3c94355c2aa3", size = 148637 }, + { url = "https://files.pythonhosted.org/packages/26/b6/d1eccb62f665e44359226811064596dd6a366ea1f985839c566cd61525ae/tomli-2.3.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c22a8bf253bacc0cf11f35ad9808b6cb75ada2631c2d97c971122583b129afbc", size = 241925 }, + { url = "https://files.pythonhosted.org/packages/70/91/7cdab9a03e6d3d2bb11beae108da5bdc1c34bdeb06e21163482544ddcc90/tomli-2.3.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0eea8cc5c5e9f89c9b90c4896a8deefc74f518db5927d0e0e8d4a80953d774d0", size = 249045 }, + { url = "https://files.pythonhosted.org/packages/15/1b/8c26874ed1f6e4f1fcfeb868db8a794cbe9f227299402db58cfcc858766c/tomli-2.3.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:b74a0e59ec5d15127acdabd75ea17726ac4c5178ae51b85bfe39c4f8a278e879", size = 245835 }, + { url = "https://files.pythonhosted.org/packages/fd/42/8e3c6a9a4b1a1360c1a2a39f0b972cef2cc9ebd56025168c4137192a9321/tomli-2.3.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:b5870b50c9db823c595983571d1296a6ff3e1b88f734a4c8f6fc6188397de005", size = 253109 }, + { url = "https://files.pythonhosted.org/packages/22/0c/b4da635000a71b5f80130937eeac12e686eefb376b8dee113b4a582bba42/tomli-2.3.0-cp314-cp314-win32.whl", hash = "sha256:feb0dacc61170ed7ab602d3d972a58f14ee3ee60494292d384649a3dc38ef463", size = 97930 }, + { url = "https://files.pythonhosted.org/packages/b9/74/cb1abc870a418ae99cd5c9547d6bce30701a954e0e721821df483ef7223c/tomli-2.3.0-cp314-cp314-win_amd64.whl", hash = "sha256:b273fcbd7fc64dc3600c098e39136522650c49bca95df2d11cf3b626422392c8", size = 107964 }, + { url = "https://files.pythonhosted.org/packages/54/78/5c46fff6432a712af9f792944f4fcd7067d8823157949f4e40c56b8b3c83/tomli-2.3.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:940d56ee0410fa17ee1f12b817b37a4d4e4dc4d27340863cc67236c74f582e77", size = 163065 }, + { url = "https://files.pythonhosted.org/packages/39/67/f85d9bd23182f45eca8939cd2bc7050e1f90c41f4a2ecbbd5963a1d1c486/tomli-2.3.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:f85209946d1fe94416debbb88d00eb92ce9cd5266775424ff81bc959e001acaf", size = 159088 }, + { url = "https://files.pythonhosted.org/packages/26/5a/4b546a0405b9cc0659b399f12b6adb750757baf04250b148d3c5059fc4eb/tomli-2.3.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a56212bdcce682e56b0aaf79e869ba5d15a6163f88d5451cbde388d48b13f530", size = 268193 }, + { url = "https://files.pythonhosted.org/packages/42/4f/2c12a72ae22cf7b59a7fe75b3465b7aba40ea9145d026ba41cb382075b0e/tomli-2.3.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c5f3ffd1e098dfc032d4d3af5c0ac64f6d286d98bc148698356847b80fa4de1b", size = 275488 }, + { url = "https://files.pythonhosted.org/packages/92/04/a038d65dbe160c3aa5a624e93ad98111090f6804027d474ba9c37c8ae186/tomli-2.3.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:5e01decd096b1530d97d5d85cb4dff4af2d8347bd35686654a004f8dea20fc67", size = 272669 }, + { url = "https://files.pythonhosted.org/packages/be/2f/8b7c60a9d1612a7cbc39ffcca4f21a73bf368a80fc25bccf8253e2563267/tomli-2.3.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:8a35dd0e643bb2610f156cca8db95d213a90015c11fee76c946aa62b7ae7e02f", size = 279709 }, + { url = "https://files.pythonhosted.org/packages/7e/46/cc36c679f09f27ded940281c38607716c86cf8ba4a518d524e349c8b4874/tomli-2.3.0-cp314-cp314t-win32.whl", hash = "sha256:a1f7f282fe248311650081faafa5f4732bdbfef5d45fe3f2e702fbc6f2d496e0", size = 107563 }, + { url = "https://files.pythonhosted.org/packages/84/ff/426ca8683cf7b753614480484f6437f568fd2fda2edbdf57a2d3d8b27a0b/tomli-2.3.0-cp314-cp314t-win_amd64.whl", hash = "sha256:70a251f8d4ba2d9ac2542eecf008b3c8a9fc5c3f9f02c56a9d7952612be2fdba", size = 119756 }, + { url = "https://files.pythonhosted.org/packages/77/b8/0135fadc89e73be292b473cb820b4f5a08197779206b33191e801feeae40/tomli-2.3.0-py3-none-any.whl", hash = "sha256:e95b1af3c5b07d9e643909b5abbec77cd9f1217e6d0bca72b0234736b9fb1f1b", size = 14408 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "toolz" version = "1.1.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/11/d6/114b492226588d6ff54579d95847662fc69196bdeec318eb45393b24c192/toolz-1.1.0.tar.gz", hash = "sha256:27a5c770d068c110d9ed9323f24f1543e83b2f300a687b7891c1a6d56b697b5b", size = 52613, upload-time = "2025-10-17T04:03:21.661Z" } +sdist = { url = "https://files.pythonhosted.org/packages/11/d6/114b492226588d6ff54579d95847662fc69196bdeec318eb45393b24c192/toolz-1.1.0.tar.gz", hash = "sha256:27a5c770d068c110d9ed9323f24f1543e83b2f300a687b7891c1a6d56b697b5b", size = 52613 } wheels = [ - { url = "https://files.pythonhosted.org/packages/fb/12/5911ae3eeec47800503a238d971e51722ccea5feb8569b735184d5fcdbc0/toolz-1.1.0-py3-none-any.whl", hash = "sha256:15ccc861ac51c53696de0a5d6d4607f99c210739caf987b5d2054f3efed429d8", size = 58093, upload-time = "2025-10-17T04:03:20.435Z" }, + { url = "https://files.pythonhosted.org/packages/fb/12/5911ae3eeec47800503a238d971e51722ccea5feb8569b735184d5fcdbc0/toolz-1.1.0-py3-none-any.whl", hash = "sha256:15ccc861ac51c53696de0a5d6d4607f99c210739caf987b5d2054f3efed429d8", size = 58093 }, ] [[package]] @@ -3628,6 +5116,7 @@ dependencies = [ { name = "typing-extensions" }, ] wheels = [ +<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/18/62/131124fb95df03811b8260d1d43dcc5ee85ea1a344b964613d7efe77fb08/torch-2.12.0-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:10802fd383bbfed646212e765a72c37d2185205d4f26eb197a254e8ac7ddcb25", size = 87990344, upload-time = "2026-05-13T14:55:42.154Z" }, { url = "https://files.pythonhosted.org/packages/12/9c/dda0dbd547dc549839824135f223792fd0e725f28ed0715dda366b7acaa2/torch-2.12.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:c12592630aef72feaf18bd3f197ef587bbfa21131b31c38b23ab2e55fce92e36", size = 426362932, upload-time = "2026-05-13T14:54:15.295Z" }, { url = "https://files.pythonhosted.org/packages/e2/d2/a7dd5a3f9bdaa7842124e8e2359202b317c48d47d2fc5816fafdf2049adb/torch-2.12.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:415c1b8d0412f67551c8e89a2daca0fb3e56694af0281ba155eaa9da481f58b4", size = 532170085, upload-time = "2026-05-13T14:55:20.788Z" }, @@ -3661,6 +5150,32 @@ source = { registry = "https://pypi.org/simple" } sdist = { url = "https://files.pythonhosted.org/packages/53/d9/2b811d1c0812e9ef23e6cf2dbe022becbe6c5ab065e33fd80ee05c0cd996/torchinfo-1.8.0.tar.gz", hash = "sha256:72e94b0e9a3e64dc583a8e5b7940b8938a1ac0f033f795457f27e6f4e7afa2e9", size = 25880, upload-time = "2023-05-14T19:23:26.377Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/72/25/973bd6128381951b23cdcd8a9870c6dcfc5606cb864df8eabd82e529f9c1/torchinfo-1.8.0-py3-none-any.whl", hash = "sha256:2e911c2918603f945c26ff21a3a838d12709223dc4ccf243407bce8b6e897b46", size = 23377, upload-time = "2023-05-14T19:23:24.141Z" }, +======= + { url = "https://files.pythonhosted.org/packages/15/db/c064112ac0089af3d2f7a2b5bfbabf4aa407a78b74f87889e524b91c5402/torch-2.9.1-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:62b3fd888277946918cba4478cf849303da5359f0fb4e3bfb86b0533ba2eaf8d", size = 104220430 }, + { url = "https://files.pythonhosted.org/packages/56/be/76eaa36c9cd032d3b01b001e2c5a05943df75f26211f68fae79e62f87734/torch-2.9.1-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:d033ff0ac3f5400df862a51bdde9bad83561f3739ea0046e68f5401ebfa67c1b", size = 899821446 }, + { url = "https://files.pythonhosted.org/packages/47/cc/7a2949e38dfe3244c4df21f0e1c27bce8aedd6c604a587dd44fc21017cb4/torch-2.9.1-cp311-cp311-win_amd64.whl", hash = "sha256:0d06b30a9207b7c3516a9e0102114024755a07045f0c1d2f2a56b1819ac06bcb", size = 110973074 }, + { url = "https://files.pythonhosted.org/packages/1e/ce/7d251155a783fb2c1bb6837b2b7023c622a2070a0a72726ca1df47e7ea34/torch-2.9.1-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:52347912d868653e1528b47cafaf79b285b98be3f4f35d5955389b1b95224475", size = 74463887 }, + { url = "https://files.pythonhosted.org/packages/0f/27/07c645c7673e73e53ded71705045d6cb5bae94c4b021b03aa8d03eee90ab/torch-2.9.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:da5f6f4d7f4940a173e5572791af238cb0b9e21b1aab592bd8b26da4c99f1cd6", size = 104126592 }, + { url = "https://files.pythonhosted.org/packages/19/17/e377a460603132b00760511299fceba4102bd95db1a0ee788da21298ccff/torch-2.9.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:27331cd902fb4322252657f3902adf1c4f6acad9dcad81d8df3ae14c7c4f07c4", size = 899742281 }, + { url = "https://files.pythonhosted.org/packages/b1/1a/64f5769025db846a82567fa5b7d21dba4558a7234ee631712ee4771c436c/torch-2.9.1-cp312-cp312-win_amd64.whl", hash = "sha256:81a285002d7b8cfd3fdf1b98aa8df138d41f1a8334fd9ea37511517cedf43083", size = 110940568 }, + { url = "https://files.pythonhosted.org/packages/6e/ab/07739fd776618e5882661d04c43f5b5586323e2f6a2d7d84aac20d8f20bd/torch-2.9.1-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:c0d25d1d8e531b8343bea0ed811d5d528958f1dcbd37e7245bc686273177ad7e", size = 74479191 }, + { url = "https://files.pythonhosted.org/packages/20/60/8fc5e828d050bddfab469b3fe78e5ab9a7e53dda9c3bdc6a43d17ce99e63/torch-2.9.1-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:c29455d2b910b98738131990394da3e50eea8291dfeb4b12de71ecf1fdeb21cb", size = 104135743 }, + { url = "https://files.pythonhosted.org/packages/f2/b7/6d3f80e6918213babddb2a37b46dbb14c15b14c5f473e347869a51f40e1f/torch-2.9.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:524de44cd13931208ba2c4bde9ec7741fd4ae6bfd06409a604fc32f6520c2bc9", size = 899749493 }, + { url = "https://files.pythonhosted.org/packages/a6/47/c7843d69d6de8938c1cbb1eba426b1d48ddf375f101473d3e31a5fc52b74/torch-2.9.1-cp313-cp313-win_amd64.whl", hash = "sha256:545844cc16b3f91e08ce3b40e9c2d77012dd33a48d505aed34b7740ed627a1b2", size = 110944162 }, + { url = "https://files.pythonhosted.org/packages/28/0e/2a37247957e72c12151b33a01e4df651d9d155dd74d8cfcbfad15a79b44a/torch-2.9.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5be4bf7496f1e3ffb1dd44b672adb1ac3f081f204c5ca81eba6442f5f634df8e", size = 74830751 }, + { url = "https://files.pythonhosted.org/packages/4b/f7/7a18745edcd7b9ca2381aa03353647bca8aace91683c4975f19ac233809d/torch-2.9.1-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:30a3e170a84894f3652434b56d59a64a2c11366b0ed5776fab33c2439396bf9a", size = 104142929 }, + { url = "https://files.pythonhosted.org/packages/f4/dd/f1c0d879f2863ef209e18823a988dc7a1bf40470750e3ebe927efdb9407f/torch-2.9.1-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:8301a7b431e51764629208d0edaa4f9e4c33e6df0f2f90b90e261d623df6a4e2", size = 899748978 }, + { url = "https://files.pythonhosted.org/packages/1f/9f/6986b83a53b4d043e36f3f898b798ab51f7f20fdf1a9b01a2720f445043d/torch-2.9.1-cp313-cp313t-win_amd64.whl", hash = "sha256:2e1c42c0ae92bf803a4b2409fdfed85e30f9027a66887f5e7dcdbc014c7531db", size = 111176995 }, + { url = "https://files.pythonhosted.org/packages/40/60/71c698b466dd01e65d0e9514b5405faae200c52a76901baf6906856f17e4/torch-2.9.1-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:2c14b3da5df416cf9cb5efab83aa3056f5b8cd8620b8fde81b4987ecab730587", size = 74480347 }, + { url = "https://files.pythonhosted.org/packages/48/50/c4b5112546d0d13cc9eaa1c732b823d676a9f49ae8b6f97772f795874a03/torch-2.9.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:1edee27a7c9897f4e0b7c14cfc2f3008c571921134522d5b9b5ec4ebbc69041a", size = 74433245 }, + { url = "https://files.pythonhosted.org/packages/81/c9/2628f408f0518b3bae49c95f5af3728b6ab498c8624ab1e03a43dd53d650/torch-2.9.1-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:19d144d6b3e29921f1fc70503e9f2fc572cde6a5115c0c0de2f7ca8b1483e8b6", size = 104134804 }, + { url = "https://files.pythonhosted.org/packages/28/fc/5bc91d6d831ae41bf6e9e6da6468f25330522e92347c9156eb3f1cb95956/torch-2.9.1-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:c432d04376f6d9767a9852ea0def7b47a7bbc8e7af3b16ac9cf9ce02b12851c9", size = 899747132 }, + { url = "https://files.pythonhosted.org/packages/63/5d/e8d4e009e52b6b2cf1684bde2a6be157b96fb873732542fb2a9a99e85a83/torch-2.9.1-cp314-cp314-win_amd64.whl", hash = "sha256:d187566a2cdc726fc80138c3cdb260970fab1c27e99f85452721f7759bbd554d", size = 110934845 }, + { url = "https://files.pythonhosted.org/packages/bd/b2/2d15a52516b2ea3f414643b8de68fa4cb220d3877ac8b1028c83dc8ca1c4/torch-2.9.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:cb10896a1f7fedaddbccc2017ce6ca9ecaaf990f0973bdfcf405439750118d2c", size = 74823558 }, + { url = "https://files.pythonhosted.org/packages/86/5c/5b2e5d84f5b9850cd1e71af07524d8cbb74cba19379800f1f9f7c997fc70/torch-2.9.1-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:0a2bd769944991c74acf0c4ef23603b9c777fdf7637f115605a4b2d8023110c7", size = 104145788 }, + { url = "https://files.pythonhosted.org/packages/a9/8c/3da60787bcf70add986c4ad485993026ac0ca74f2fc21410bc4eb1bb7695/torch-2.9.1-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:07c8a9660bc9414c39cac530ac83b1fb1b679d7155824144a40a54f4a47bfa73", size = 899735500 }, + { url = "https://files.pythonhosted.org/packages/db/2b/f7818f6ec88758dfd21da46b6cd46af9d1b3433e53ddbb19ad1e0da17f9b/torch-2.9.1-cp314-cp314t-win_amd64.whl", hash = "sha256:c88d3299ddeb2b35dcc31753305612db485ab6f1823e37fb29451c8b2732b87e", size = 111163659 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3673,9 +5188,15 @@ dependencies = [ { name = "packaging" }, { name = "torch" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/81/34/39b8b749333db56c0585d7a11fa62a283c087bb1dfc897d69fb8cedbefb1/torchmetrics-1.9.0.tar.gz", hash = "sha256:a488609948600df52d3db4fcdab02e62aab2a85ef34da67037dc3e65b8512faa", size = 581765, upload-time = "2026-03-09T17:41:22.443Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/c3/a2/c7f6ebf546f8f644edf0f999aa98ece106986a77a7b922316bf6414ff825/torchmetrics-1.9.0-py3-none-any.whl", hash = "sha256:bfdcbff3dd1d96b3374bb2496eb39f23c4b28b8a845b6a18c313688e0d2d9ca1", size = 983384, upload-time = "2026-03-09T17:41:19.756Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/85/2e/48a887a59ecc4a10ce9e8b35b3e3c5cef29d902c4eac143378526e7485cb/torchmetrics-1.8.2.tar.gz", hash = "sha256:cf64a901036bf107f17a524009eea7781c9c5315d130713aeca5747a686fe7a5", size = 580679 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/02/21/aa0f434434c48490f91b65962b1ce863fdcce63febc166ca9fe9d706c2b6/torchmetrics-1.8.2-py3-none-any.whl", hash = "sha256:08382fd96b923e39e904c4d570f3d49e2cc71ccabd2a94e0f895d1f0dac86242", size = 983161 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3688,6 +5209,7 @@ dependencies = [ { name = "torch" }, ] wheels = [ +<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/cf/d6/a7e71e981042d5c573e2e61891b9023b190c88adb75b18bed8594371250c/torchvision-0.27.0-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:df0c166b6bdf7c47f88e81e8b43bc085451d5c50d0c5d1691bc474c1227d6fed", size = 1758812, upload-time = "2026-05-13T14:57:16.662Z" }, { url = "https://files.pythonhosted.org/packages/93/f9/f542fb7e4476603fb237ebdc64369a7d11f18eb5a129aa2559cbdb710aee/torchvision-0.27.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:9bb9251f64b854124efed95d02953a89f7e2726c3ca662d7ea0151129157297f", size = 7831148, upload-time = "2026-05-13T14:57:08.37Z" }, { url = "https://files.pythonhosted.org/packages/f6/61/7aa7cc2c9e8750027f6fb9ae3a7393ef43860bcdfe3966e2f71fee800e31/torchvision-0.27.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:f44453f107c296d5446a79f7ac59733ad8bf5ddfa04c53805dfbae298a42a798", size = 7575519, upload-time = "2026-05-13T14:56:50.552Z" }, @@ -3712,12 +5234,39 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/21/dd/d03ee9f9ee7bf11a8c7c776fb8e7fd6102f59c013791a2a4e5175bd6cba7/torchvision-0.27.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:b4c6bb0a670dcba017b3643e21902c9b8a1cc1c127d602f1488fa29ec3c6e865", size = 7790618, upload-time = "2026-05-13T14:56:44.721Z" }, { url = "https://files.pythonhosted.org/packages/39/08/4002336a74742be70728603ec1769feb2b55e0d19c532c9ec9f92008de76/torchvision-0.27.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:1c2db4bde82bc48ebff73436a6adf34d4f809448268a70d9a1285f5c8f92313d", size = 7580217, upload-time = "2026-05-13T14:56:43.274Z" }, { url = "https://files.pythonhosted.org/packages/ed/cb/4dd4783eb3565f526ba6e64b6f6ca26c00eacc924cdfe60455db9d91b84b/torchvision-0.27.0-cp314-cp314t-win_amd64.whl", hash = "sha256:72bf547e58ddb948689734eed6f4b6a2031f979dba4fb08e3690688b392e929f", size = 4226392, upload-time = "2026-05-13T14:56:40.235Z" }, +======= + { url = "https://files.pythonhosted.org/packages/e7/69/30f5f03752aa1a7c23931d2519b31e557f3f10af5089d787cddf3b903ecf/torchvision-0.24.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:056c525dc875f18fe8e9c27079ada166a7b2755cea5a2199b0bc7f1f8364e600", size = 1891436 }, + { url = "https://files.pythonhosted.org/packages/0c/69/49aae86edb75fe16460b59a191fcc0f568c2378f780bb063850db0fe007a/torchvision-0.24.1-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:1e39619de698e2821d71976c92c8a9e50cdfd1e993507dfb340f2688bfdd8283", size = 2387757 }, + { url = "https://files.pythonhosted.org/packages/11/c9/1dfc3db98797b326f1d0c3f3bb61c83b167a813fc7eab6fcd2edb8c7eb9d/torchvision-0.24.1-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:a0f106663e60332aa4fcb1ca2159ef8c3f2ed266b0e6df88de261048a840e0df", size = 8047682 }, + { url = "https://files.pythonhosted.org/packages/fa/bb/cfc6a6f6ccc84a534ed1fdf029ae5716dd6ff04e57ed9dc2dab38bf652d5/torchvision-0.24.1-cp311-cp311-win_amd64.whl", hash = "sha256:a9308cdd37d8a42e14a3e7fd9d271830c7fecb150dd929b642f3c1460514599a", size = 4037588 }, + { url = "https://files.pythonhosted.org/packages/f0/af/18e2c6b9538a045f60718a0c5a058908ccb24f88fde8e6f0fc12d5ff7bd3/torchvision-0.24.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:e48bf6a8ec95872eb45763f06499f87bd2fb246b9b96cb00aae260fda2f96193", size = 1891433 }, + { url = "https://files.pythonhosted.org/packages/9d/43/600e5cfb0643d10d633124f5982d7abc2170dfd7ce985584ff16edab3e76/torchvision-0.24.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:7fb7590c737ebe3e1c077ad60c0e5e2e56bb26e7bccc3b9d04dbfc34fd09f050", size = 2386737 }, + { url = "https://files.pythonhosted.org/packages/93/b1/db2941526ecddd84884132e2742a55c9311296a6a38627f9e2627f5ac889/torchvision-0.24.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:66a98471fc18cad9064123106d810a75f57f0838eee20edc56233fd8484b0cc7", size = 8049868 }, + { url = "https://files.pythonhosted.org/packages/69/98/16e583f59f86cd59949f59d52bfa8fc286f86341a229a9d15cbe7a694f0c/torchvision-0.24.1-cp312-cp312-win_amd64.whl", hash = "sha256:4aa6cb806eb8541e92c9b313e96192c6b826e9eb0042720e2fa250d021079952", size = 4302006 }, + { url = "https://files.pythonhosted.org/packages/e4/97/ab40550f482577f2788304c27220e8ba02c63313bd74cf2f8920526aac20/torchvision-0.24.1-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:8a6696db7fb71eadb2c6a48602106e136c785642e598eb1533e0b27744f2cce6", size = 1891435 }, + { url = "https://files.pythonhosted.org/packages/30/65/ac0a3f9be6abdbe4e1d82c915d7e20de97e7fd0e9a277970508b015309f3/torchvision-0.24.1-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:db2125c46f9cb25dc740be831ce3ce99303cfe60439249a41b04fd9f373be671", size = 2338718 }, + { url = "https://files.pythonhosted.org/packages/10/b5/5bba24ff9d325181508501ed7f0c3de8ed3dd2edca0784d48b144b6c5252/torchvision-0.24.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:f035f0cacd1f44a8ff6cb7ca3627d84c54d685055961d73a1a9fb9827a5414c8", size = 8049661 }, + { url = "https://files.pythonhosted.org/packages/5c/ec/54a96ae9ab6a0dd66d4bba27771f892e36478a9c3489fa56e51c70abcc4d/torchvision-0.24.1-cp313-cp313-win_amd64.whl", hash = "sha256:16274823b93048e0a29d83415166a2e9e0bf4e1b432668357b657612a4802864", size = 4319808 }, + { url = "https://files.pythonhosted.org/packages/d5/f3/a90a389a7e547f3eb8821b13f96ea7c0563cdefbbbb60a10e08dda9720ff/torchvision-0.24.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:e3f96208b4bef54cd60e415545f5200346a65024e04f29a26cd0006dbf9e8e66", size = 2005342 }, + { url = "https://files.pythonhosted.org/packages/a9/fe/ff27d2ed1b524078164bea1062f23d2618a5fc3208e247d6153c18c91a76/torchvision-0.24.1-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:f231f6a4f2aa6522713326d0d2563538fa72d613741ae364f9913027fa52ea35", size = 2341708 }, + { url = "https://files.pythonhosted.org/packages/b1/b9/d6c903495cbdfd2533b3ef6f7b5643ff589ea062f8feb5c206ee79b9d9e5/torchvision-0.24.1-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:1540a9e7f8cf55fe17554482f5a125a7e426347b71de07327d5de6bfd8d17caa", size = 8177239 }, + { url = "https://files.pythonhosted.org/packages/4f/2b/ba02e4261369c3798310483028495cf507e6cb3f394f42e4796981ecf3a7/torchvision-0.24.1-cp313-cp313t-win_amd64.whl", hash = "sha256:d83e16d70ea85d2f196d678bfb702c36be7a655b003abed84e465988b6128938", size = 4251604 }, + { url = "https://files.pythonhosted.org/packages/42/84/577b2cef8f32094add5f52887867da4c2a3e6b4261538447e9b48eb25812/torchvision-0.24.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:cccf4b4fec7fdfcd3431b9ea75d1588c0a8596d0333245dafebee0462abe3388", size = 2005319 }, + { url = "https://files.pythonhosted.org/packages/5f/34/ecb786bffe0159a3b49941a61caaae089853132f3cd1e8f555e3621f7e6f/torchvision-0.24.1-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:1b495edd3a8f9911292424117544f0b4ab780452e998649425d1f4b2bed6695f", size = 2338844 }, + { url = "https://files.pythonhosted.org/packages/51/99/a84623786a6969504c87f2dc3892200f586ee13503f519d282faab0bb4f0/torchvision-0.24.1-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:ab211e1807dc3e53acf8f6638df9a7444c80c0ad050466e8d652b3e83776987b", size = 8175144 }, + { url = "https://files.pythonhosted.org/packages/6d/ba/8fae3525b233e109317ce6a9c1de922ab2881737b029a7e88021f81e068f/torchvision-0.24.1-cp314-cp314-win_amd64.whl", hash = "sha256:18f9cb60e64b37b551cd605a3d62c15730c086362b40682d23e24b616a697d41", size = 4234459 }, + { url = "https://files.pythonhosted.org/packages/50/33/481602c1c72d0485d4b3a6b48c9534b71c2957c9d83bf860eb837bf5a620/torchvision-0.24.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ec9d7379c519428395e4ffda4dbb99ec56be64b0a75b95989e00f9ec7ae0b2d7", size = 2005336 }, + { url = "https://files.pythonhosted.org/packages/d0/7f/372de60bf3dd8f5593bd0d03f4aecf0d1fd58f5bc6943618d9d913f5e6d5/torchvision-0.24.1-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:af9201184c2712d808bd4eb656899011afdfce1e83721c7cb08000034df353fe", size = 2341704 }, + { url = "https://files.pythonhosted.org/packages/36/9b/0f3b9ff3d0225ee2324ec663de0e7fb3eb855615ca958ac1875f22f1f8e5/torchvision-0.24.1-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:9ef95d819fd6df81bc7cc97b8f21a15d2c0d3ac5dbfaab5cbc2d2ce57114b19e", size = 8177422 }, + { url = "https://files.pythonhosted.org/packages/d6/ab/e2bcc7c2f13d882a58f8b30ff86f794210b075736587ea50f8c545834f8a/torchvision-0.24.1-cp314-cp314t-win_amd64.whl", hash = "sha256:480b271d6edff83ac2e8d69bbb4cf2073f93366516a50d48f140ccfceedb002e", size = 4335190 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "tornado" version = "6.5.6" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/50/57/6d7303a77ae439d9189108f76c0c4fd89ee5e2cc8387bffb55232565c4ed/tornado-6.5.6.tar.gz", hash = "sha256:9a365179fe8ff6b8766f602c0f67c185d778193e9bdd828b19f0b6ed7764177d", size = 518139, upload-time = "2026-05-27T15:35:54.646Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/1b/0d/b4f481e18c5a51864e6d12b9a05ecf72919696680b747c958c3fc1f4fbae/tornado-6.5.6-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:65fcfaafb079435c2c19dc9e07c0f1cf0fa9051759ed0a7d0a3ba7ea7f64919c", size = 447737, upload-time = "2026-05-27T15:35:38.122Z" }, @@ -3729,6 +5278,21 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/bb/84/3469e098dccdb6763130e06aacd786bb4363fca7b590a55c101ddf34ed30/tornado-6.5.6-cp39-abi3-win32.whl", hash = "sha256:db475f1b67b2809b10bb16264829087724ca8d24fe4ed47f7b8675cae453ef86", size = 450230, upload-time = "2026-05-27T15:35:49.322Z" }, { url = "https://files.pythonhosted.org/packages/d2/3c/273a04e0b9dd9016f1685cca0c1c8795a71ac88a34a8c889a0b443483226/tornado-6.5.6-cp39-abi3-win_amd64.whl", hash = "sha256:6739bf1e8eb09230f1280ddbd3236f0309db70f2c551a8dbc40f62babdf82f79", size = 450667, upload-time = "2026-05-27T15:35:51.194Z" }, { url = "https://files.pythonhosted.org/packages/02/98/0cffe22a224f60c5fb1e3aa0b76f9da2e1ca78b0e9545e3d077c68ce60a7/tornado-6.5.6-cp39-abi3-win_arm64.whl", hash = "sha256:2543597b24a695d72338a9a77818362d72387c03ae173f1f169eadc5c91466ac", size = 449690, upload-time = "2026-05-27T15:35:52.902Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/09/ce/1eb500eae19f4648281bb2186927bb062d2438c2e5093d1360391afd2f90/tornado-6.5.2.tar.gz", hash = "sha256:ab53c8f9a0fa351e2c0741284e06c7a45da86afb544133201c5cc8578eb076a0", size = 510821 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f6/48/6a7529df2c9cc12efd2e8f5dd219516184d703b34c06786809670df5b3bd/tornado-6.5.2-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:2436822940d37cde62771cff8774f4f00b3c8024fe482e16ca8387b8a2724db6", size = 442563 }, + { url = "https://files.pythonhosted.org/packages/f2/b5/9b575a0ed3e50b00c40b08cbce82eb618229091d09f6d14bce80fc01cb0b/tornado-6.5.2-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:583a52c7aa94ee046854ba81d9ebb6c81ec0fd30386d96f7640c96dad45a03ef", size = 440729 }, + { url = "https://files.pythonhosted.org/packages/1b/4e/619174f52b120efcf23633c817fd3fed867c30bff785e2cd5a53a70e483c/tornado-6.5.2-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b0fe179f28d597deab2842b86ed4060deec7388f1fd9c1b4a41adf8af058907e", size = 444295 }, + { url = "https://files.pythonhosted.org/packages/95/fa/87b41709552bbd393c85dd18e4e3499dcd8983f66e7972926db8d96aa065/tornado-6.5.2-cp39-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b186e85d1e3536d69583d2298423744740986018e393d0321df7340e71898882", size = 443644 }, + { url = "https://files.pythonhosted.org/packages/f9/41/fb15f06e33d7430ca89420283a8762a4e6b8025b800ea51796ab5e6d9559/tornado-6.5.2-cp39-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e792706668c87709709c18b353da1f7662317b563ff69f00bab83595940c7108", size = 443878 }, + { url = "https://files.pythonhosted.org/packages/11/92/fe6d57da897776ad2e01e279170ea8ae726755b045fe5ac73b75357a5a3f/tornado-6.5.2-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:06ceb1300fd70cb20e43b1ad8aaee0266e69e7ced38fa910ad2e03285009ce7c", size = 444549 }, + { url = "https://files.pythonhosted.org/packages/9b/02/c8f4f6c9204526daf3d760f4aa555a7a33ad0e60843eac025ccfd6ff4a93/tornado-6.5.2-cp39-abi3-musllinux_1_2_i686.whl", hash = "sha256:74db443e0f5251be86cbf37929f84d8c20c27a355dd452a5cfa2aada0d001ec4", size = 443973 }, + { url = "https://files.pythonhosted.org/packages/ae/2d/f5f5707b655ce2317190183868cd0f6822a1121b4baeae509ceb9590d0bd/tornado-6.5.2-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:b5e735ab2889d7ed33b32a459cac490eda71a1ba6857b0118de476ab6c366c04", size = 443954 }, + { url = "https://files.pythonhosted.org/packages/e8/59/593bd0f40f7355806bf6573b47b8c22f8e1374c9b6fd03114bd6b7a3dcfd/tornado-6.5.2-cp39-abi3-win32.whl", hash = "sha256:c6f29e94d9b37a95013bb669616352ddb82e3bfe8326fccee50583caebc8a5f0", size = 445023 }, + { url = "https://files.pythonhosted.org/packages/c7/2a/f609b420c2f564a748a2d80ebfb2ee02a73ca80223af712fca591386cafb/tornado-6.5.2-cp39-abi3-win_amd64.whl", hash = "sha256:e56a5af51cc30dd2cae649429af65ca2f6571da29504a07995175df14c18f35f", size = 445427 }, + { url = "https://files.pythonhosted.org/packages/5e/4f/e1f65e8f8c76d73658b33d33b81eed4322fb5085350e4328d5c956f0c8f9/tornado-6.5.2-cp39-abi3-win_arm64.whl", hash = "sha256:d6c33dc3672e3a1f3618eb63b7ef4683a7688e7b9e6e8f0d9aa5726360a004af", size = 444456 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3738,18 +5302,30 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "colorama", marker = "sys_platform == 'win32'" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/09/a9/6ba95a270c6f1fbcd8dac228323f2777d886cb206987444e4bce66338dd4/tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb", size = 169598, upload-time = "2026-02-03T17:35:53.048Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/16/e1/3079a9ff9b8e11b846c6ac5c8b5bfb7ff225eee721825310c91b3b50304f/tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf", size = 78374, upload-time = "2026-02-03T17:35:50.982Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/a8/4b/29b4ef32e036bb34e4ab51796dd745cdba7ed47ad142a9f4a1eb8e0c744d/tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2", size = 169737 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2", size = 78540 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "traitlets" version = "5.15.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/1b/22/40f55b26baeab80c2d7b3f1db0682f8954e4617fee7d90ce634022ef05c6/traitlets-5.15.0.tar.gz", hash = "sha256:4fead733f81cf1c4c938e06f8ca4633896833c9d89eff878159457f4d4392971", size = 163197, upload-time = "2026-05-06T08:05:58.016Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/da/98/a9937a969d018a23badfea0b381f66783649d48e0ea6c41923265c3cbeb3/traitlets-5.15.0-py3-none-any.whl", hash = "sha256:fb36a18867a6803deab09f3c5e0fa81bb7b26a5c9e82501c9933f759166eff40", size = 85877, upload-time = "2026-05-06T08:05:55.853Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/eb/79/72064e6a701c2183016abbbfedaba506d81e30e232a68c9f0d6f6fcd1574/traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7", size = 161621 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f", size = 85359 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3757,6 +5333,7 @@ name = "triton" version = "3.7.0" source = { registry = "https://pypi.org/simple" } wheels = [ +<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/b8/c1/5d842314bb6c78442cc60437928781701c6050b8d479bc2a1aed691d37ca/triton-3.7.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a9e71fc392675fac364e0ecf4ef3f76f85b7f5433a16f4c3c5fe5f05a52c85fe", size = 188480277, upload-time = "2026-05-07T19:05:03.231Z" }, { url = "https://files.pythonhosted.org/packages/13/31/8315ea5f8dd18e60970b3022e3a8b93fd37e0b784fbbef86e10c8e6e5ca1/triton-3.7.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:22bacffce443f54593dd20f05294d5a40622e0ea9ab632816f87154504356221", size = 201415942, upload-time = "2026-05-07T18:46:06.479Z" }, { url = "https://files.pythonhosted.org/packages/f7/13/ec05adfcd87311d532ba61e3af143e8be59fcd26675884c4682841406a20/triton-3.7.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a4bf49b00a7a377a68a6da603a876e797614e6455a80e9021669c476a953ad9a", size = 188505104, upload-time = "2026-05-07T19:05:09.843Z" }, @@ -3769,42 +5346,62 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/8f/af/9904ec6d3c93d9b24e5ec360445bbdf758b7f00bfbeedb89cb0eb64eb8bb/triton-3.7.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b9b85e72968a9d8bba5ddb24e9b64aaabaf48affb042f2755cb7cfa92b7531ce", size = 201460637, upload-time = "2026-05-07T18:46:34.749Z" }, { url = "https://files.pythonhosted.org/packages/a1/f9/4835a8ea746b88727d8899f4e3ccce4f9cacb38abfc3bb0a638266c53111/triton-3.7.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:18a160de426fd99f92b0baf509045360afbd3bfaa0b4a5171dde800ec9f09684", size = 188608706, upload-time = "2026-05-07T19:05:39.218Z" }, { url = "https://files.pythonhosted.org/packages/c1/68/fa86e5a39608000f645535b2c124920126327ab731f8c4fafd5b07ff8d4b/triton-3.7.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ce061073102714b725f3660ec6939d94a1da7984b3aa99c921417cae273672f5", size = 201546766, upload-time = "2026-05-07T18:46:42.088Z" }, +======= + { url = "https://files.pythonhosted.org/packages/b0/72/ec90c3519eaf168f22cb1757ad412f3a2add4782ad3a92861c9ad135d886/triton-3.5.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:61413522a48add32302353fdbaaf92daaaab06f6b5e3229940d21b5207f47579", size = 170425802 }, + { url = "https://files.pythonhosted.org/packages/f2/50/9a8358d3ef58162c0a415d173cfb45b67de60176e1024f71fbc4d24c0b6d/triton-3.5.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d2c6b915a03888ab931a9fd3e55ba36785e1fe70cbea0b40c6ef93b20fc85232", size = 170470207 }, + { url = "https://files.pythonhosted.org/packages/27/46/8c3bbb5b0a19313f50edcaa363b599e5a1a5ac9683ead82b9b80fe497c8d/triton-3.5.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f3f4346b6ebbd4fad18773f5ba839114f4826037c9f2f34e0148894cd5dd3dba", size = 170470410 }, + { url = "https://files.pythonhosted.org/packages/37/92/e97fcc6b2c27cdb87ce5ee063d77f8f26f19f06916aa680464c8104ef0f6/triton-3.5.1-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0b4d2c70127fca6a23e247f9348b8adde979d2e7a20391bfbabaac6aebc7e6a8", size = 170579924 }, + { url = "https://files.pythonhosted.org/packages/a4/e6/c595c35e5c50c4bc56a7bac96493dad321e9e29b953b526bbbe20f9911d0/triton-3.5.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d0637b1efb1db599a8e9dc960d53ab6e4637db7d4ab6630a0974705d77b14b60", size = 170480488 }, + { url = "https://files.pythonhosted.org/packages/16/b5/b0d3d8b901b6a04ca38df5e24c27e53afb15b93624d7fd7d658c7cd9352a/triton-3.5.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bac7f7d959ad0f48c0e97d6643a1cc0fd5786fe61cb1f83b537c6b2d54776478", size = 170582192 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "typing-extensions" version = "4.15.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/72/94/1a15dd82efb362ac84269196e94cf00f187f7ed21c242792a923cdb1c61f/typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466", size = 109391, upload-time = "2025-08-25T13:49:26.313Z" } +sdist = { url = "https://files.pythonhosted.org/packages/72/94/1a15dd82efb362ac84269196e94cf00f187f7ed21c242792a923cdb1c61f/typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466", size = 109391 } wheels = [ - { url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614, upload-time = "2025-08-25T13:49:24.86Z" }, + { url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614 }, ] [[package]] name = "tzdata" version = "2026.2" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/ba/19/1b9b0e29f30c6d35cb345486df41110984ea67ae69dddbc0e8a100999493/tzdata-2026.2.tar.gz", hash = "sha256:9173fde7d80d9018e02a662e168e5a2d04f87c41ea174b139fbef642eda62d10", size = 198254, upload-time = "2026-04-24T15:22:08.651Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/ce/e4/dccd7f47c4b64213ac01ef921a1337ee6e30e8c6466046018326977efd95/tzdata-2026.2-py2.py3-none-any.whl", hash = "sha256:bbe9af844f658da81a5f95019480da3a89415801f6cc966806612cc7169bffe7", size = 349321, upload-time = "2026-04-24T15:22:05.876Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/95/32/1a225d6164441be760d75c2c42e2780dc0873fe382da3e98a2e1e48361e5/tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9", size = 196380 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "uri-template" version = "1.3.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/31/c7/0336f2bd0bcbada6ccef7aaa25e443c118a704f828a0620c6fa0207c1b64/uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7", size = 21678, upload-time = "2023-06-21T01:49:05.374Z" } +sdist = { url = "https://files.pythonhosted.org/packages/31/c7/0336f2bd0bcbada6ccef7aaa25e443c118a704f828a0620c6fa0207c1b64/uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7", size = 21678 } wheels = [ - { url = "https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363", size = 11140, upload-time = "2023-06-21T01:49:03.467Z" }, + { url = "https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363", size = 11140 }, ] [[package]] name = "urllib3" version = "2.7.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/53/0c/06f8b233b8fd13b9e5ee11424ef85419ba0d8ba0b3138bf360be2ff56953/urllib3-2.7.0.tar.gz", hash = "sha256:231e0ec3b63ceb14667c67be60f2f2c40a518cb38b03af60abc813da26505f4c", size = 433602, upload-time = "2026-05-07T16:13:18.596Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/7f/3e/5db95bcf282c52709639744ca2a8b149baccf648e39c8cc87553df9eae0c/urllib3-2.7.0-py3-none-any.whl", hash = "sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897", size = 131087, upload-time = "2026-05-07T16:13:17.151Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/15/22/9ee70a2574a4f4599c47dd506532914ce044817c7752a79b6a51286319bc/urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760", size = 393185 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc", size = 129795 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3817,45 +5414,57 @@ dependencies = [ { name = "platformdirs" }, { name = "python-discovery" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/e1/0d/4e93c8e6d1001a75763f87d8f5ecda8ebc7f4aa2153dddfaf4ae8892821a/virtualenv-21.4.2.tar.gz", hash = "sha256:38e6ee0a555615c0ea9da2ac7e9998fe8dc3b911dd33ad8eaad2020957653b0c", size = 7613326, upload-time = "2026-05-31T17:01:22.827Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/bf/c4/557dc082be035381b85fdb2b74e21d3d21b57750b74f2b47a32f3a639ff9/virtualenv-21.4.2-py3-none-any.whl", hash = "sha256:854210ca524a1a4d0d744734f4acbc721c3ffe163b85bbf5d56d14d5ae2f0fae", size = 7594079, upload-time = "2026-05-31T17:01:20.735Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/20/28/e6f1a6f655d620846bd9df527390ecc26b3805a0c5989048c210e22c5ca9/virtualenv-20.35.4.tar.gz", hash = "sha256:643d3914d73d3eeb0c552cbb12d7e82adf0e504dbf86a3182f8771a153a1971c", size = 6028799 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/79/0c/c05523fa3181fdf0c9c52a6ba91a23fbf3246cc095f26f6516f9c60e6771/virtualenv-20.35.4-py3-none-any.whl", hash = "sha256:c21c9cede36c9753eeade68ba7d523529f228a403463376cf821eaae2b650f1b", size = 6005095 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "wcwidth" version = "0.7.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/2c/ee/afaf0f85a9a18fe47a67f1e4422ed6cf1fe642f0ae0a2f81166231303c52/wcwidth-0.7.0.tar.gz", hash = "sha256:90e3a7ea092341c44b99562e75d09e4d5160fe7a3974c6fb842a101a95e7eed0", size = 182132, upload-time = "2026-05-02T16:04:12.653Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/41/52/e465037f5375f43533d1a80b6923955201596a99142ed524d77b571a1418/wcwidth-0.7.0-py3-none-any.whl", hash = "sha256:5d69154c429a82910e241c738cd0e2976fac8a2dd47a1a805f4afed1c0f136f2", size = 110825, upload-time = "2026-05-02T16:04:11.033Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/24/30/6b0809f4510673dc723187aeaf24c7f5459922d01e2f794277a3dfb90345/wcwidth-0.2.14.tar.gz", hash = "sha256:4d478375d31bc5395a3c55c40ccdf3354688364cd61c4f6adacaa9215d0b3605", size = 102293 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/af/b5/123f13c975e9f27ab9c0770f514345bd406d0e8d3b7a0723af9d43f710af/wcwidth-0.2.14-py2.py3-none-any.whl", hash = "sha256:a7bb560c8aee30f9957e5f9895805edd20602f2d7f720186dfd906e82b4982e1", size = 37286 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "webcolors" version = "25.10.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/1d/7a/eb316761ec35664ea5174709a68bbd3389de60d4a1ebab8808bfc264ed67/webcolors-25.10.0.tar.gz", hash = "sha256:62abae86504f66d0f6364c2a8520de4a0c47b80c03fc3a5f1815fedbef7c19bf", size = 53491, upload-time = "2025-10-31T07:51:03.977Z" } +sdist = { url = "https://files.pythonhosted.org/packages/1d/7a/eb316761ec35664ea5174709a68bbd3389de60d4a1ebab8808bfc264ed67/webcolors-25.10.0.tar.gz", hash = "sha256:62abae86504f66d0f6364c2a8520de4a0c47b80c03fc3a5f1815fedbef7c19bf", size = 53491 } wheels = [ - { url = "https://files.pythonhosted.org/packages/e2/cc/e097523dd85c9cf5d354f78310927f1656c422bd7b2613b2db3e3f9a0f2c/webcolors-25.10.0-py3-none-any.whl", hash = "sha256:032c727334856fc0b968f63daa252a1ac93d33db2f5267756623c210e57a4f1d", size = 14905, upload-time = "2025-10-31T07:51:01.778Z" }, + { url = "https://files.pythonhosted.org/packages/e2/cc/e097523dd85c9cf5d354f78310927f1656c422bd7b2613b2db3e3f9a0f2c/webcolors-25.10.0-py3-none-any.whl", hash = "sha256:032c727334856fc0b968f63daa252a1ac93d33db2f5267756623c210e57a4f1d", size = 14905 }, ] [[package]] name = "webencodings" version = "0.5.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/0b/02/ae6ceac1baeda530866a85075641cec12989bd8d31af6d5ab4a3e8c92f47/webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923", size = 9721, upload-time = "2017-04-05T20:21:34.189Z" } +sdist = { url = "https://files.pythonhosted.org/packages/0b/02/ae6ceac1baeda530866a85075641cec12989bd8d31af6d5ab4a3e8c92f47/webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923", size = 9721 } wheels = [ - { url = "https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", size = 11774, upload-time = "2017-04-05T20:21:32.581Z" }, + { url = "https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", size = 11774 }, ] [[package]] name = "websocket-client" version = "1.9.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/2c/41/aa4bf9664e4cda14c3b39865b12251e8e7d239f4cd0e3cc1b6c2ccde25c1/websocket_client-1.9.0.tar.gz", hash = "sha256:9e813624b6eb619999a97dc7958469217c3176312b3a16a4bd1bc7e08a46ec98", size = 70576, upload-time = "2025-10-07T21:16:36.495Z" } +sdist = { url = "https://files.pythonhosted.org/packages/2c/41/aa4bf9664e4cda14c3b39865b12251e8e7d239f4cd0e3cc1b6c2ccde25c1/websocket_client-1.9.0.tar.gz", hash = "sha256:9e813624b6eb619999a97dc7958469217c3176312b3a16a4bd1bc7e08a46ec98", size = 70576 } wheels = [ - { url = "https://files.pythonhosted.org/packages/34/db/b10e48aa8fff7407e67470363eac595018441cf32d5e1001567a7aeba5d2/websocket_client-1.9.0-py3-none-any.whl", hash = "sha256:af248a825037ef591efbf6ed20cc5faa03d3b47b9e5a2230a529eeee1c1fc3ef", size = 82616, upload-time = "2025-10-07T21:16:34.951Z" }, + { url = "https://files.pythonhosted.org/packages/34/db/b10e48aa8fff7407e67470363eac595018441cf32d5e1001567a7aeba5d2/websocket_client-1.9.0-py3-none-any.whl", hash = "sha256:af248a825037ef591efbf6ed20cc5faa03d3b47b9e5a2230a529eeee1c1fc3ef", size = 82616 }, ] [[package]] @@ -3865,6 +5474,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "markupsafe" }, ] +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/dd/b2/381be8cfdee792dd117872481b6e378f85c957dd7c5bca38897b08f765fd/werkzeug-3.1.8.tar.gz", hash = "sha256:9bad61a4268dac112f1c5cd4630a56ede601b6ed420300677a869083d70a4c44", size = 875852, upload-time = "2026-04-02T18:49:14.268Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/93/8c/2e650f2afeb7ee576912636c23ddb621c91ac6a98e66dc8d29c3c69446e1/werkzeug-3.1.8-py3-none-any.whl", hash = "sha256:63a77fb8892bf28ebc3178683445222aa500e48ebad5ec77b0ad80f8726b1f50", size = 226459, upload-time = "2026-04-02T18:49:12.72Z" }, @@ -3877,6 +5487,11 @@ source = { registry = "https://pypi.org/simple" } sdist = { url = "https://files.pythonhosted.org/packages/bd/f4/c67440c7fb409a71b7404b7aefcd7569a9c0d6bd071299bf4198ae7a5d95/widgetsnbextension-4.0.15.tar.gz", hash = "sha256:de8610639996f1567952d763a5a41af8af37f2575a41f9852a38f947eb82a3b9", size = 1097402, upload-time = "2025-11-01T21:15:55.178Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/3f/0e/fa3b193432cfc60c93b42f3be03365f5f909d2b3ea410295cf36df739e31/widgetsnbextension-4.0.15-py3-none-any.whl", hash = "sha256:8156704e4346a571d9ce73b84bee86a29906c9abfd7223b7228a28899ccf3366", size = 2196503, upload-time = "2025-11-01T21:15:53.565Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/9f/69/83029f1f6300c5fb2471d621ab06f6ec6b3324685a2ce0f9777fd4a8b71e/werkzeug-3.1.3.tar.gz", hash = "sha256:60723ce945c19328679790e3282cc758aa4a6040e4bb330f53d30fa546d44746", size = 806925 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/52/24/ab44c871b0f07f491e5d2ad12c9bd7358e527510618cb1b803a88e986db1/werkzeug-3.1.3-py3-none-any.whl", hash = "sha256:54b78bf3716d19a65be4fceccc0d1d7b89e608834989dfae50ea87564639213e", size = 224498 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3886,6 +5501,7 @@ source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version < '3.12'", ] +<<<<<<< HEAD dependencies = [ { name = "donfig", marker = "python_full_version < '3.12'" }, { name = "google-crc32c", marker = "python_full_version < '3.12'" }, @@ -3918,13 +5534,24 @@ dependencies = [ sdist = { url = "https://files.pythonhosted.org/packages/93/8d/aeb164004f87543b06ef54f885d02c342c31ceb274e2bbec470a98927621/zarr-3.2.1.tar.gz", hash = "sha256:71565b738a0e7e8ed226f0516eba8c6bb53440ad7669a8c48ebb3534a161d035", size = 675161, upload-time = "2026-05-05T12:37:22.383Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/88/0a/469e2bd01be1490336e6c8707386845655d59261543315778a3ccc7e8019/zarr-3.2.1-py3-none-any.whl", hash = "sha256:f78cdd3d9687ad0e9f9cba2c5683b64f0c52589c19f685eeabe872e93cc0d2c7", size = 319617, upload-time = "2026-05-05T12:37:20.66Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/d6/67/14be68a7bad15eecda09b1e81fca2420f7533645fe187bf4d6104c1aad52/zarr-3.1.3.tar.gz", hash = "sha256:01342f3e26a02ed5670db608a5576fbdb8d76acb5c280bd2d0082454b1ba6f79", size = 349125 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1a/71/9de7229515a53d1cc5705ca9c411530f711a2242f962214d9dbfe2741aa4/zarr-3.1.3-py3-none-any.whl", hash = "sha256:45f67f87f65f14fa453f99dd8110a5936b7ac69f3a21981d33e90407c80c302a", size = 276427 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "zipp" version = "4.1.0" source = { registry = "https://pypi.org/simple" } +<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/b9/d8/eab98a517c14134c0b2eb4e2387bc5f457334293ec5d2dd3857ec2966802/zipp-4.1.0.tar.gz", hash = "sha256:4cb57381f544315db7688e976e922a2b18cdb513d21cc194eb42232ba2a3e602", size = 26214, upload-time = "2026-05-18T20:08:57.967Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/3a/13/547360d81e6d88d58492968ffda9f9542854f11310ee556fef14260cc886/zipp-4.1.0-py3-none-any.whl", hash = "sha256:25ad4e16390cd314347dd8f1de67a2ac538ae658ed4ab9db16029c07c188e97f", size = 10238, upload-time = "2026-05-18T20:08:57.045Z" }, +======= +sdist = { url = "https://files.pythonhosted.org/packages/e3/02/0f2892c661036d50ede074e376733dca2ae7c6eb617489437771209d4180/zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166", size = 25547 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276 }, +>>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] From 231b03cd32eef7d06fa490d80bd6f02f3b0f82ab Mon Sep 17 00:00:00 2001 From: cophus Date: Sat, 3 Jan 2026 12:39:39 -0800 Subject: [PATCH 100/140] faster strain calc, adding h5plugin to read arina data --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 9621ee4d..9a5f8cdb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -83,4 +83,5 @@ dev = [ "ruff>=0.11.5", "tomli>=2.2.1", "hdf5plugin>=6.0.0", + "hdf5plugin>=6.0.0", ] \ No newline at end of file From 2c74d7c82d9442dda812d22145bb2bb3e9d104c8 Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 18 Jan 2026 16:52:56 -0800 Subject: [PATCH 101/140] temp removing function for merge --- src/quantem/core/utils/imaging_utils.py | 63 ------------------------- 1 file changed, 63 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index b8001f84..5c8dd182 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -548,66 +548,3 @@ def fourier_cropping( return result - -def rotate_image( - im, - rotation_deg: float, - origin: tuple[float, float] | None = None, - clockwise: bool = True, - interpolation: str = "bilinear", - mode: str = "constant", - cval: float = 0.0, -): - """Rotate an array about a pixel origin using bilinear/bicubic interpolation.""" - im = np.asarray(im) - if im.ndim < 2: - raise ValueError("im must have at least 2 dimensions") - - H, W = im.shape[-2], im.shape[-1] - if origin is None: - r0 = float(H // 2) - c0 = float(W // 2) - else: - r0 = float(origin[0]) - c0 = float(origin[1]) - - interp = str(interpolation).lower() - if interp in {"bilinear", "linear"}: - order = 1 - elif interp in {"bicubic", "cubic"}: - order = 3 - else: - raise ValueError("interpolation must be 'bilinear' or 'bicubic'") - - theta = float(np.deg2rad(rotation_deg)) - if not clockwise: - theta = -theta - - ct = float(np.cos(theta)) - st = float(np.sin(theta)) - - r_out, c_out = np.meshgrid( - np.arange(H, dtype=np.float64), - np.arange(W, dtype=np.float64), - indexing="ij", - ) - - c_rel = c_out - c0 - r_rel = r_out - r0 - - c_in = ct * c_rel + st * r_rel + c0 - r_in = -st * c_rel + ct * r_rel + r0 - - coords = np.vstack((r_in.ravel(), c_in.ravel())) - - if im.ndim == 2: - out = map_coordinates(im, coords, order=order, mode=mode, cval=cval) - return out.reshape(H, W) - - prefix = im.shape[:-2] - n = int(np.prod(prefix)) if prefix else 1 - im_flat = im.reshape(n, H, W) - out_flat = np.empty((n, H * W), dtype=np.result_type(im_flat.dtype, np.float64)) - for i in range(n): - out_flat[i] = map_coordinates(im_flat[i], coords, order=order, mode=mode, cval=cval) - return out_flat.reshape(*prefix, H, W) From 9621074fa017150f277d75ecdd535f87dbc0e12c Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 18 Jan 2026 16:53:47 -0800 Subject: [PATCH 102/140] adding rotate_image back --- src/quantem/core/utils/imaging_utils.py | 566 ++++++++++++++++++++++++ 1 file changed, 566 insertions(+) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 5c8dd182..77997933 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -548,3 +548,569 @@ def fourier_cropping( return result + +def compute_fsc_from_halfsets( + halfset_recons: list[torch.Tensor], + sampling: tuple[float, float], + epsilon: float = 1e-12, +): + """ + Compute radially averaged Fourier Shell Correlation (FSC) + from two half-set reconstructions. + + Parameters + ---------- + halfset_recons : list[torch.Tensor] + Two statistically-independent reconstructions, using half the dataset. + sampling: tuple[float,float] + Reconstruction sampling in Angstroms. + epsilon: float, optional + Small number to avoid dividing by zero + + Returns + ------- + q_bins: NDarray + Spatial frequency bins + fsc : NDarray + Fourier shell correlation as function of spatial frequency + """ + r1, r2 = halfset_recons + + F1 = torch.fft.fft2(r1) + F2 = torch.fft.fft2(r2) + + cross = (F1 * F2.conj()).real + p1 = F1.abs().square() + p2 = F2.abs().square() + + device = F1.device + nx, ny = F1.shape + sx, sy = sampling + + kx = torch.fft.fftfreq(nx, d=sx, device=device) + ky = torch.fft.fftfreq(ny, d=sy, device=device) + k = torch.sqrt(kx[:, None] ** 2 + ky[None, :] ** 2).reshape(-1) + + bin_size = kx[1] - kx[0] + max_k = k.max() + num_bins = int(torch.floor(max_k / bin_size).item()) + 2 + + inds = k / bin_size + inds_f = torch.floor(inds).long() + d_ind = inds - inds_f + + w0 = 1.0 - d_ind + w1 = d_ind + + # Flatten arrays + cross = cross.reshape(-1) + p1 = p1.reshape(-1) + p2 = p2.reshape(-1) + + # Accumulate + cross_b = torch.bincount(inds_f, weights=cross * w0, minlength=num_bins) + torch.bincount( + inds_f + 1, weights=cross * w1, minlength=num_bins + ) + + p1_b = torch.bincount(inds_f, weights=p1 * w0, minlength=num_bins) + torch.bincount( + inds_f + 1, weights=p1 * w1, minlength=num_bins + ) + + p2_b = torch.bincount(inds_f, weights=p2 * w0, minlength=num_bins) + torch.bincount( + inds_f + 1, weights=p2 * w1, minlength=num_bins + ) + + denom = torch.sqrt(p1_b * p2_b).clamp_min(epsilon) + fsc = cross_b / denom + + k_bins = torch.arange(num_bins, device=device, dtype=torch.float32) * bin_size + valid = k_bins <= kx.abs().max() + + return k_bins[valid].cpu().numpy(), fsc[valid].cpu().numpy() + + +def compute_spectral_snr_from_halfsets( + halfset_recons: list[torch.Tensor], + sampling: tuple[float, float], + total_dose: float, + epsilon: float = 1e-12, +): + """ + Compute spectral SNR from two half-set reconstructions using symmetric/antisymmetric decomposition. + + The method decomposes the Fourier transforms into: + - Symmetric: (F₁ + F₂)/2 → signal + correlated noise + - Antisymmetric: (F₁ - F₂)/2 → uncorrelated noise only + + SSNR(q) = sqrt(signal_power / noise_power) + + where: + - signal_power = (|symmetric|² - |antisymmetric|²)₊ + - noise_power = |antisymmetric|² + + Parameters + ---------- + halfset_recons : list[torch.Tensor] + Two statistically-independent reconstructions, using half the dataset. + sampling: tuple[float,float] + Reconstruction sampling in Angstroms. + total_dose: float + Total _normalized_ electron dose, e.g. in DirectPtychography this is ~self.num_bf + epsilon: float, optional + Small number to avoid dividing by zero + + Returns + ------- + q_bins: NDarray + Spatial frequency bins + ssnr : NDarray + Radially averaged spectral SNR as function of spatial frequency + """ + # Compute Fourier transforms + halfset_1, halfset_2 = halfset_recons + F1 = torch.fft.fft2(halfset_1) + F2 = torch.fft.fft2(halfset_2) + + # Symmetric and antisymmetric decomposition + symmetric = (F1 + F2) / 2 + antisymmetric = (F1 - F2) / 2 + + # Power spectra + noise_power = antisymmetric.abs() + total_power = symmetric.abs() + signal_power = (total_power - noise_power).clamp_min(0) + + device = F1.device + nx, ny = F1.shape + sx, sy = sampling + + kx = torch.fft.fftfreq(nx, d=sx, device=device) + ky = torch.fft.fftfreq(ny, d=sy, device=device) + k = torch.sqrt(kx[:, None] ** 2 + ky[None, :] ** 2).reshape(-1) + + bin_size = kx[1] - kx[0] + max_k = k.max() + num_bins = int(torch.floor(max_k / bin_size).item()) + 2 + + inds = k / bin_size + inds_f = torch.floor(inds).long() + d_ind = inds - inds_f + + w0 = 1.0 - d_ind + w1 = d_ind + + # Flatten arrays + signal = signal_power.reshape(-1) + noise = noise_power.reshape(-1) + + # Accumulate + signal_b = torch.bincount(inds_f, weights=signal * w0, minlength=num_bins) + torch.bincount( + inds_f + 1, weights=signal * w1, minlength=num_bins + ) + + noise_b = torch.bincount(inds_f, weights=noise * w0, minlength=num_bins) + torch.bincount( + inds_f + 1, weights=noise * w1, minlength=num_bins + ) + + ssnr = torch.sqrt(signal_b / noise_b.clamp_min(epsilon)) / (math.sqrt(total_dose) / 2) + + k_bins = torch.arange(num_bins, device=device, dtype=torch.float32) * bin_size + valid = k_bins <= kx.abs().max() + + return k_bins[valid].cpu().numpy(), ssnr[valid].cpu().numpy() + + +def radially_average_fourier_array( + corner_centered_array: torch.Tensor, + sampling: tuple[float, float], +): + """ + Radially average a corner-centered Fourier array. + + Parameters + ---------- + corner_centered_array : list[torch.Tensor] + Fourier array to average radially. + sampling: tuple[float,float] + Reconstruction sampling in Angstroms. + + Returns + ------- + q_bins: NDarray + Spatial frequency bins + array_1d : NDarray + Radially averaged Fourier array as function of spatial frequency + """ + device = corner_centered_array.device + nx, ny = corner_centered_array.shape + sx, sy = sampling + + kx = torch.fft.fftfreq(nx, d=sx, device=device) + ky = torch.fft.fftfreq(ny, d=sy, device=device) + k = torch.sqrt(kx[:, None] ** 2 + ky[None, :] ** 2).reshape(-1) + + bin_size = kx[1] - kx[0] + max_k = k.max() + num_bins = int(torch.floor(max_k / bin_size).item()) + 2 + + inds = k / bin_size + inds_f = torch.floor(inds).long() + d_ind = inds - inds_f + + w0 = 1.0 - d_ind + w1 = d_ind + + # Flatten arrays + array = corner_centered_array.reshape(-1) + + # Accumulate + array_b = torch.bincount(inds_f, weights=array * w0, minlength=num_bins) + torch.bincount( + inds_f + 1, weights=array * w1, minlength=num_bins + ) + + counts_b = ( + torch.bincount(inds_f, weights=w0, minlength=num_bins) + + torch.bincount(inds_f + 1, weights=w1, minlength=num_bins) + ).clamp_min(1) + + array_b = array_b / counts_b + + k_bins = torch.arange(num_bins, device=device, dtype=torch.float32) * bin_size + valid = k_bins <= kx.abs().max() + + return k_bins[valid].cpu().numpy(), array_b[valid].cpu().numpy() + + +def _wrap_to_pi(x): + return (x + math.pi) % (2 * math.pi) - math.pi + + +def _find_wrap(a, b): + d = a - b + return torch.where(d > math.pi, -1, torch.where(d < -math.pi, 1, 0)) + + +def _pixel_reliability(phi, mask=None): + """ + phi: (H, W) wrapped phase (CPU tensor) + mask: optional boolean mask + """ + c = phi + left = torch.roll(c, 1, 1) + right = torch.roll(c, -1, 1) + up = torch.roll(c, 1, 0) + down = torch.roll(c, -1, 0) + + ul = torch.roll(left, 1, 0) + dr = torch.roll(right, -1, 0) + ur = torch.roll(right, 1, 0) + dl = torch.roll(left, -1, 0) + + Hterm = _wrap_to_pi(left - c) - _wrap_to_pi(c - right) + Vterm = _wrap_to_pi(up - c) - _wrap_to_pi(c - down) + D1term = _wrap_to_pi(ul - c) - _wrap_to_pi(c - dr) + D2term = _wrap_to_pi(ur - c) - _wrap_to_pi(c - dl) + + R = Hterm**2 + Vterm**2 + D1term**2 + D2term**2 + + if mask is not None: + R = torch.where(mask, R, torch.full_like(R, float("inf"))) + + return R + + +def _build_edges(phi, reliability, mask=None, wrap_around=True): + """ + Returns edges as CPU tensors: + i1, i2, inc sorted by reliability + """ + H, W = phi.shape + N = H * W + + idx = torch.arange(N).reshape(H, W) + edges = [] + + phi_f = phi.flatten() + rel_f = reliability.flatten() + mask_f = mask.flatten() if mask is not None else None + + def add_edges(i1, i2): + if mask_f is not None: + valid = mask_f[i1] & mask_f[i2] + i1, i2 = i1[valid], i2[valid] + + inc = _find_wrap(phi_f[i1], phi_f[i2]) + rel = rel_f[i1] + rel_f[i2] + + edges.append( # ty:ignore[possibly-missing-attribute] + torch.stack([i1, i2, rel, inc], dim=1) + ) + + if wrap_around: + add_edges(idx.flatten(), torch.roll(idx, -1, 1).flatten()) + add_edges(idx.flatten(), torch.roll(idx, -1, 0).flatten()) + else: + add_edges(idx[:, :-1].flatten(), idx[:, 1:].flatten()) + add_edges(idx[:-1, :].flatten(), idx[1:, :].flatten()) + + edges = torch.cat(edges, dim=0) + edges = edges[edges[:, 2].argsort()] + + # return integer tensors only (CPU) + return ( + edges[:, 0].long(), + edges[:, 1].long(), + edges[:, 3].long(), + ) + + +class UnionFindPhase: + def __init__(self, n): + self.parent = torch.arange(n) + self.rank = torch.zeros(n, dtype=torch.int32) + self.offset = torch.zeros(n) + + def find_root_and_offset(self, x): + root = x + total = 0.0 + while self.parent[root] != root: + total += self.offset[root] + root = self.parent[root] + return root, total + + def union(self, x, y, inc_xy): + rx, ox = self.find_root_and_offset(x) + ry, oy = self.find_root_and_offset(y) + + if rx == ry: + return + + # phase(y) + oy + inc = phase(x) + ox + delta = ox - oy - inc_xy + + if self.rank[rx] < self.rank[ry]: + self.parent[rx] = ry + self.offset[rx] = -delta + else: + self.parent[ry] = rx + self.offset[ry] = delta + if self.rank[rx] == self.rank[ry]: + self.rank[rx] += 1 + + +def _final_offsets(uf): + """ + Single-pass offset computation (no path compression). + """ + N = uf.parent.numel() + incs = torch.zeros(N) + + for i in range(N): + root = i + total = 0.0 + while uf.parent[root] != root: + total += uf.offset[root] + root = uf.parent[root] + incs[i] = total + + return incs + + +def _unwrap_phase_2d_torch_reliability_sorting( + phi, + mask=None, + wrap_around=True, +): + """ + Herráez 2D phase unwrapping. + Runs on CPU by design. + """ + with torch.no_grad(): + orig_device = phi.device + phi = phi.detach().cpu() + if mask is not None: + mask = mask.detach().cpu().to(torch.bool) + + H, W = phi.shape + N = H * W + + reliability = _pixel_reliability(phi, mask) + + i1, i2, inc = _build_edges( + phi, + reliability, + mask, + wrap_around=wrap_around, + ) + + uf = UnionFindPhase(N) + + for k in range(i1.numel()): + uf.union(i1[k].item(), i2[k].item(), inc[k].item()) + + incs = _final_offsets(uf) + + out = (phi.flatten() + 2 * math.pi * incs).reshape(H, W) + out -= out.mean() + return out.to(orig_device) + + +def _unwrap_phase_2d_torch_poisson( + phi_wrapped, + mask=None, + wrap_around=True, + regularization_lambda=None, +): + """ + Least-squares / Poisson phase unwrapping with optional mask. + + Parameters + ---------- + phi_wrapped : (H, W) tensor + Wrapped phase in (-pi, pi], any device + mask : (H, W) bool tensor, optional + True = valid pixel + + Returns + ------- + phi_unwrapped : (H, W) tensor + Unwrapped phase (same device as input) + """ + device = phi_wrapped.device + dtype = phi_wrapped.dtype + H, W = phi_wrapped.shape + + if not wrap_around: + raise NotImplementedError() + + if mask is not None: + mask = mask.to(device=device, dtype=torch.bool) + + dx = torch.roll(phi_wrapped, -1, dims=1) - phi_wrapped + dy = torch.roll(phi_wrapped, -1, dims=0) - phi_wrapped + + dx = (dx + math.pi) % (2 * math.pi) - math.pi + dy = (dy + math.pi) % (2 * math.pi) - math.pi + + if mask is not None: + mask_x = mask & torch.roll(mask, -1, dims=1) + mask_y = mask & torch.roll(mask, -1, dims=0) + + dx = torch.where(mask_x, dx, torch.zeros_like(dx)) + dy = torch.where(mask_y, dy, torch.zeros_like(dy)) + + div = dx - torch.roll(dx, 1, dims=1) + dy - torch.roll(dy, 1, dims=0) + + if mask is not None: + div = torch.where(mask, div, torch.zeros_like(div)) + + div_hat = torch.fft.fftn(div) + + ky = torch.fft.fftfreq(H, device=device, dtype=dtype) * 2 * math.pi + kx = torch.fft.fftfreq(W, device=device, dtype=dtype) * 2 * math.pi + ky, kx = torch.meshgrid(ky, kx, indexing="ij") + + if regularization_lambda is not None: + denom = kx**2 + ky**2 + regularization_lambda + else: + denom = kx**2 + ky**2 + denom[0, 0] = 1.0 # avoid divide by zero + + phi_hat = -div_hat / denom + phi_hat[0, 0] = 0.0 # fix piston + + phi = torch.fft.ifftn(phi_hat).real + + if mask is not None: + phi = torch.where(mask, phi, torch.zeros_like(phi)) + + return phi + + +def unwrap_phase_2d_torch( + phi_wrapped, + method="reliability-sorting", + mask=None, + wrap_around=True, + regularization_lambda=None, +): + if method == "reliability-sorting": + return _unwrap_phase_2d_torch_reliability_sorting( + phi_wrapped, mask, wrap_around=wrap_around + ) + elif method == "poisson": + return _unwrap_phase_2d_torch_poisson( + phi_wrapped, + mask, + wrap_around=wrap_around, + regularization_lambda=regularization_lambda, + ) + else: + raise ValueError( + f'`method` must be one of {{"reliability-sorting", "poisson"}}, got {method!r}' + ) + + + +def rotate_image( + im, + rotation_deg: float, + origin: tuple[float, float] | None = None, + clockwise: bool = True, + interpolation: str = "bilinear", + mode: str = "constant", + cval: float = 0.0, +): + """Rotate an array about a pixel origin using bilinear/bicubic interpolation.""" + im = np.asarray(im) + if im.ndim < 2: + raise ValueError("im must have at least 2 dimensions") + + H, W = im.shape[-2], im.shape[-1] + if origin is None: + r0 = float(H // 2) + c0 = float(W // 2) + else: + r0 = float(origin[0]) + c0 = float(origin[1]) + + interp = str(interpolation).lower() + if interp in {"bilinear", "linear"}: + order = 1 + elif interp in {"bicubic", "cubic"}: + order = 3 + else: + raise ValueError("interpolation must be 'bilinear' or 'bicubic'") + + theta = float(np.deg2rad(rotation_deg)) + if not clockwise: + theta = -theta + + ct = float(np.cos(theta)) + st = float(np.sin(theta)) + + r_out, c_out = np.meshgrid( + np.arange(H, dtype=np.float64), + np.arange(W, dtype=np.float64), + indexing="ij", + ) + + c_rel = c_out - c0 + r_rel = r_out - r0 + + c_in = ct * c_rel + st * r_rel + c0 + r_in = -st * c_rel + ct * r_rel + r0 + + coords = np.vstack((r_in.ravel(), c_in.ravel())) + + if im.ndim == 2: + out = map_coordinates(im, coords, order=order, mode=mode, cval=cval) + return out.reshape(H, W) + + prefix = im.shape[:-2] + n = int(np.prod(prefix)) if prefix else 1 + im_flat = im.reshape(n, H, W) + out_flat = np.empty((n, H * W), dtype=np.result_type(im_flat.dtype, np.float64)) + for i in range(n): + out_flat[i] = map_coordinates(im_flat[i], coords, order=order, mode=mode, cval=cval) + return out_flat.reshape(*prefix, H, W) From 7143a941d8847ed401e2370d648f42f3755cda77 Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 18 Jan 2026 20:23:34 -0800 Subject: [PATCH 103/140] strain mapping updates --- src/quantem/diffraction/__init__.py | 2 +- src/quantem/diffraction/strain.py | 953 ++++++++++++++++++---------- 2 files changed, 608 insertions(+), 347 deletions(-) diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index 6d0e4202..77bc5f82 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1,2 +1,2 @@ from quantem.diffraction.polar import RDF as RDF -from quantem.diffraction.strain import StrainMap as StrainMap +from quantem.diffraction.strain import StrainMapAutocorrelation as StrainMapAutocorrelation diff --git a/src/quantem/diffraction/strain.py b/src/quantem/diffraction/strain.py index bcfa981d..fa92c847 100644 --- a/src/quantem/diffraction/strain.py +++ b/src/quantem/diffraction/strain.py @@ -18,7 +18,7 @@ from quantem.core.visualization import ScalebarConfig, show_2d -class StrainMap(AutoSerialize): +class StrainMapAutocorrelation(AutoSerialize): _token = object() def __init__( @@ -28,7 +28,9 @@ def __init__( _token: object | None = None, ): if _token is not self._token: - raise RuntimeError("Use StrainMap.from_data() to instantiate this class.") + raise RuntimeError( + "Use StrainMapAutocorrelation.from_dataset() or StrainMapAutocorrelation.from_array() to instantiate this class." + ) super().__init__() self.dataset = dataset self.input_data = input_data @@ -36,106 +38,129 @@ def __init__( self.metadata: dict[str, Any] = {} self.transform: Dataset2d | None = None self.transform_rotated: Dataset2d | None = None + self.u: NDArray | None = None self.v: NDArray | None = None + + self.u_fit: Dataset3d | None = None + self.v_fit: Dataset3d | None = None + self.u_peak_fit: Dataset3d | None = None + self.v_peak_fit: Dataset3d | None = None + self.mask_diffraction = np.ones(self.dataset.array.shape[2:]) self.mask_diffraction_inv = np.zeros(self.dataset.array.shape[2:]) @classmethod - def from_data(cls, data: NDArray | Dataset4dstem, *, name: str = "strain_map") -> "StrainMap": - if isinstance(data, Dataset4dstem): - return cls(dataset=data, input_data=data, _token=cls._token) + def from_dataset(cls, dataset: Dataset4dstem, *, name: str | None = None) -> "StrainMapAutocorrelation": + if not isinstance(dataset, Dataset4dstem): + raise TypeError("StrainMapAutocorrelation.from_dataset expects a Dataset4dstem instance.") + if name is not None: + dataset.name = name + return cls(dataset=dataset, input_data=dataset, _token=cls._token) - arr = ensure_valid_array(data) + @classmethod + def from_array(cls, array: NDArray, *, name: str = "strain_map_autocorrelation") -> "StrainMapAutocorrelation": + arr = ensure_valid_array(array) if arr.ndim != 4: raise ValueError( - "StrainMap.from_data expects a 4D array with shape (scan_r, scan_c, dp_r, dp_c)." + "StrainMapAutocorrelation.from_array expects a 4D array with shape (scan_r, scan_c, dp_r, dp_c)." ) - ds4 = Dataset4dstem.from_array(arr, name=name) - return cls(dataset=ds4, input_data=data, _token=cls._token) - + return cls(dataset=ds4, input_data=array, _token=cls._token) def diffraction_mask( self, - threshold = None, - edge_blend = 64.0, - plot_mask = True, - figsize = (8,4), + threshold=None, + edge_blend=64.0, + plot_mask=True, + figsize=(8, 4), ): - dp_mean = np.mean(self.dataset.array,axis=(0,1)) + dp_mean = np.mean(self.dataset.array, axis=(0, 1)) mask_init = dp_mean < threshold - mask_init[:,0] = True - mask_init[0,:] = True - mask_init[:,-1] = True - mask_init[-1,:] = True + mask_init[:, 0] = True + mask_init[0, :] = True + mask_init[:, -1] = True + mask_init[-1, :] = True self.mask_diffraction = np.sin( np.clip( distance_transform_edt(np.logical_not(mask_init)) / edge_blend, 0.0, 1.0, - )*np.pi/2, - )**2 - # int_edge = np.sum(dp_mean*self.mask_diffraction) / np.sum(self.mask_diffraction) - int_edge = np.min(dp_mean[self.mask_diffraction>0.99]) + ) + * np.pi + / 2, + ) ** 2 + int_edge = np.min(dp_mean[self.mask_diffraction > 0.99]) self.mask_diffraction_inv = (1 - self.mask_diffraction) * int_edge if plot_mask: - fig,ax = plt.subplots(1,2,figsize=figsize) + fig, ax = plt.subplots(1, 2, figsize=figsize) ax[0].imshow( - np.log(np.maximum(dp_mean,np.min(dp_mean[dp_mean>0]))), - cmap = 'gray', + np.log(np.maximum(dp_mean, np.min(dp_mean[dp_mean > 0]))), + cmap="gray", ) ax[1].imshow( np.log( - dp_mean*self.mask_diffraction + \ - self.mask_diffraction_inv, + dp_mean * self.mask_diffraction + self.mask_diffraction_inv, ), - cmap = 'gray', + cmap="gray", ) - - return self + return self def preprocess( self, mode: str = "linear", q_to_r_rotation_ccw_deg: float | None = None, q_transpose: bool | None = None, - skip = None, + skip=None, plot_transform: bool = True, cropping_factor: float = 0.25, + gamma: float = 0.5, **plot_kwargs: Any, - ) -> "StrainMap": + ) -> "StrainMapAutocorrelation": + mode_in = mode.strip().lower() + if mode_in in {"linear", "patterson", "paterson", "acf", "autocorrelation"}: + mode_norm = "linear" + elif mode_in in {"log", "cepstrum", "cepstral"}: + mode_norm = "log" + elif mode_in in {"gamma", "power", "sqrt"}: + mode_norm = "gamma" + else: + raise ValueError( + "mode must be 'linear', 'log', or 'gamma' (aliases: 'patterson'->'linear', 'cepstrum'/'cepstral'->'log')." + ) - self.metadata["mode"] = mode + self.metadata["mode"] = mode_norm + if mode_norm == "gamma": + self.metadata["gamma"] = gamma - qrow_unit = str(self.dataset.units[2]) - qcol_unit = str(self.dataset.units[3]) + qrow_unit = self.dataset.units[2] + qcol_unit = self.dataset.units[3] if qrow_unit in {"A", "Å"}: - qrow_sampling_ang = float(self.dataset.sampling[2]) + qrow_sampling_ang = self.dataset.sampling[2] elif qrow_unit == "mrad": - wavelength = float(electron_wavelength_angstrom(float(self.dataset.metadata["energy"]))) - qrow_sampling_ang = float(self.dataset.sampling[2]) / 1000.0 / wavelength + wavelength = electron_wavelength_angstrom(self.dataset.metadata["energy"]) + qrow_sampling_ang = self.dataset.sampling[2] / 1000.0 / wavelength else: qrow_sampling_ang = 1.0 qrow_unit = "pixels" if qcol_unit in {"A", "Å"}: - qcol_sampling_ang = float(self.dataset.sampling[3]) + qcol_sampling_ang = self.dataset.sampling[3] elif qcol_unit == "mrad": - wavelength = float(electron_wavelength_angstrom(float(self.dataset.metadata["energy"]))) - qcol_sampling_ang = float(self.dataset.sampling[3]) / 1000.0 / wavelength + wavelength = electron_wavelength_angstrom(self.dataset.metadata["energy"]) + qcol_sampling_ang = self.dataset.sampling[3] / 1000.0 / wavelength else: qcol_sampling_ang = 1.0 qcol_unit = "pixels" self.metadata["sampling_real"] = np.array( ( - 1.0 / (qrow_sampling_ang * float(self.dataset.shape[2])), - 1.0 / (qcol_sampling_ang * float(self.dataset.shape[3])), + 1.0 / (qrow_sampling_ang * self.dataset.shape[2]), + 1.0 / (qcol_sampling_ang * self.dataset.shape[3]), ), dtype=float, ) @@ -145,67 +170,53 @@ def preprocess( else: self.metadata["real_units"] = r"$\mathrm{\AA}$" - if q_to_r_rotation_ccw_deg is None or q_transpose is None: - parent_rot = self.dataset.metadata.get("q_to_r_rotation_ccw_deg", None) - parent_tr = self.dataset.metadata.get("q_transpose", None) - if q_to_r_rotation_ccw_deg is None and parent_rot is not None: - q_to_r_rotation_ccw_deg = float(parent_rot) - if q_transpose is None and parent_tr is not None: - q_transpose = bool(parent_tr) - if (parent_rot is not None or parent_tr is not None) and ( - q_to_r_rotation_ccw_deg is not None or q_transpose is not None - ): - import warnings - - warnings.warn( - f"StrainMap.preprocess: using parent Dataset4dstem metadata " - f"(q_to_r_rotation_ccw_deg={float(q_to_r_rotation_ccw_deg or 0.0)}, " - f"q_transpose={bool(q_transpose or False)}).", - UserWarning, - ) + parent_rot = self.dataset.metadata.get("q_to_r_rotation_ccw_deg", None) + parent_tr = self.dataset.metadata.get("q_transpose", None) - if q_to_r_rotation_ccw_deg is None or q_transpose is None: + used_parent = False + if q_to_r_rotation_ccw_deg is None and parent_rot is not None: + q_to_r_rotation_ccw_deg = parent_rot + used_parent = True + if q_transpose is None and parent_tr is not None: + q_transpose = parent_tr + used_parent = True + + if used_parent: import warnings - q_to_r_rotation_ccw_deg = ( - 0.0 if q_to_r_rotation_ccw_deg is None else float(q_to_r_rotation_ccw_deg) + warnings.warn( + "StrainMapAutocorrelation.preprocess: using parent Dataset4dstem metadata " + f"(q_to_r_rotation_ccw_deg={q_to_r_rotation_ccw_deg or 0.0}, " + f"q_transpose={q_transpose or False}).", + UserWarning, ) - q_transpose = False if q_transpose is None else bool(q_transpose) + + if q_to_r_rotation_ccw_deg is None or q_transpose is None: + import warnings + + q_to_r_rotation_ccw_deg = 0.0 if q_to_r_rotation_ccw_deg is None else q_to_r_rotation_ccw_deg + q_transpose = False if q_transpose is None else q_transpose warnings.warn( - "StrainMap.preprocess: setting q_to_r_rotation_ccw_deg=0.0 and q_transpose=False.", + "StrainMapPatterson.preprocess: setting q_to_r_rotation_ccw_deg=0.0 and q_transpose=False.", UserWarning, ) - self.metadata["q_to_r_rotation_ccw_deg"] = float(q_to_r_rotation_ccw_deg) - self.metadata["q_transpose"] = bool(q_transpose) - - if skip is None: - if self.metadata["mode"] == "linear": - im = np.mean(np.abs(np.fft.fft2( - self.dataset.array * self.mask_diffraction[None,None,:,:] + \ - self.mask_diffraction_inv[None,None,:,:] - )), axis=(0, 1)) - elif self.metadata["mode"] == "log": - im = np.mean(np.abs(np.fft.fft2(np.log1p( - self.dataset.array * self.mask_diffraction[None,None,:,:] + \ - self.mask_diffraction_inv[None,None,:,:] - ))), axis=(0, 1)) - else: - raise ValueError("mode must be 'linear' or 'log'") + self.metadata["q_to_r_rotation_ccw_deg"] = q_to_r_rotation_ccw_deg + self.metadata["q_transpose"] = q_transpose + + arr = self.dataset.array if skip is None else self.dataset.array[::skip, ::skip] + dp = arr * self.mask_diffraction[None, None, :, :] + self.mask_diffraction_inv[None, None, :, :] + + if mode_norm == "linear": + dp_proc = dp + elif mode_norm == "log": + dp_proc = np.log1p(dp) + elif mode_norm == "gamma": + dp_proc = np.power(np.clip(dp, 0.0, None), self.metadata["gamma"]) else: - if self.metadata["mode"] == "linear": - im = np.mean(np.abs(np.fft.fft2( - self.dataset.array[::skip,::skip] * self.mask_diffraction[None,None,:,:] + \ - self.mask_diffraction_inv[None,None,:,:] - )), axis=(0, 1)) - elif self.metadata["mode"] == "log": - im = np.mean(np.abs(np.fft.fft2(np.log1p( - self.dataset.array[::skip,::skip] * self.mask_diffraction[None,None,:,:] + \ - self.mask_diffraction_inv[None,None,:,:] - ))), axis=(0, 1)) - else: - raise ValueError("mode must be 'linear' or 'log'") + raise RuntimeError("Unreachable: normalized mode mapping failed.") + im = np.mean(np.abs(np.fft.fft2(dp_proc)), axis=(0, 1)) im = np.fft.fftshift(im) self.transform = Dataset2d.from_array( @@ -217,13 +228,13 @@ def preprocess( ) im_plot = self.transform.array - if bool(self.metadata["q_transpose"]): + if self.metadata["q_transpose"]: im_plot = im_plot.T self.transform_rotated = Dataset2d.from_array( rotate_image( im_plot, - float(self.metadata["q_to_r_rotation_ccw_deg"]), + self.metadata["q_to_r_rotation_ccw_deg"], clockwise=False, ), origin=(im.shape[0] // 2, im.shape[1] // 2), @@ -246,21 +257,20 @@ def plot_transform( if self.transform is None or self.transform_rotated is None: raise ValueError("Run preprocess() first to compute transform images.") - sampling = float(np.mean(self.metadata["sampling_real"])) - units = str(self.metadata.get("real_units", r"$\mathrm{\AA}$")) + sampling = np.mean(self.metadata["sampling_real"]) + units = self.metadata.get("real_units", r"$\mathrm{\AA}$") - W = int(self.transform.shape[1]) - view_w_px = float(W) * float(cropping_factor) - target_units = float(scalebar_fraction) * view_w_px * sampling + W = self.transform.shape[1] + view_w_px = W * cropping_factor + target_units = scalebar_fraction * view_w_px * sampling sb_len = _nice_length_units(target_units) - # intensity scaling: compute from transform, apply same scaling to both panels kr = (np.arange(self.transform.shape[0], dtype=float) - self.transform.shape[0] // 2)[:, None] kc = (np.arange(self.transform.shape[1], dtype=float) - self.transform.shape[1] // 2)[None, :] qmag = np.sqrt(kr * kr + kc * kc) im0 = self.transform.array tmp = im0 * qmag - i0 = np.unravel_index(int(np.nanargmax(tmp)), tmp.shape) + i0 = np.unravel_index(np.nanargmax(tmp), tmp.shape) vmin = 0.0 vmax = im0[i0] @@ -283,53 +293,57 @@ def plot_transform( return fig, ax - def choose_lattice_vector( self, *, u: tuple[float, float] | NDArray, v: tuple[float, float] | NDArray, define_in_rotated: bool = False, - refine_subpixel: bool = True, - refine_subpixel_dft: bool = False, + refine_gaussian: bool = True, + refine_dft: bool = False, refine_radius_px: float = 2.0, - refine_log: bool = False, upsample: int = 16, + gaussian_maxfev: int = 100, plot: bool = True, cropping_factor: float = 0.25, **plot_kwargs: Any, - ) -> "StrainMap": + ) -> "StrainMapAutocorrelation": if self.transform is None or self.transform_rotated is None: raise ValueError("Run preprocess() first to compute transform images.") u_rc = np.asarray(u, dtype=float).reshape(2) v_rc = np.asarray(v, dtype=float).reshape(2) - rot_ccw = float(self.metadata.get("q_to_r_rotation_ccw_deg", 0.0)) - q_transpose = bool(self.metadata.get("q_transpose", False)) + rot_ccw = self.metadata["q_to_r_rotation_ccw_deg"] + q_transpose = self.metadata["q_transpose"] if define_in_rotated: u_rc = _display_vec_to_raw(u_rc, rotation_ccw_deg=rot_ccw, transpose=q_transpose) v_rc = _display_vec_to_raw(v_rc, rotation_ccw_deg=rot_ccw, transpose=q_transpose) - if refine_subpixel_dft: - refine_subpixel = True - - if refine_subpixel: - u_rc, v_rc = _refine_lattice_vectors( - self.transform.array, - u_rc=u_rc, - v_rc=v_rc, - radius_px=float(refine_radius_px), - log_fit=bool(refine_log), - refine_dft=bool(refine_subpixel_dft), - upsample=int(upsample), - ) + u_fit_abs, v_fit_abs = _refine_lattice_vectors( + self.transform.array, + u_rc=u_rc, + v_rc=v_rc, + radius_px=refine_radius_px, + refine_gaussian=refine_gaussian, + refine_dft=refine_dft, + upsample=upsample, + maxfev=gaussian_maxfev, + ) + + H, W = self.transform.array.shape + center = np.array((H // 2, W // 2), dtype=float) - self.u = u_rc - self.v = v_rc - self.metadata["lattice_u_rc"] = self.u.copy() - self.metadata["lattice_v_rc"] = self.v.copy() + self.u = u_fit_abs[:2] - center + self.v = v_fit_abs[:2] - center + + self.metadata["choose_define_in_rotated"] = define_in_rotated + self.metadata["choose_refine_gaussian"] = refine_gaussian + self.metadata["choose_refine_dft"] = refine_dft + self.metadata["choose_refine_radius_px"] = refine_radius_px + self.metadata["choose_upsample"] = upsample + self.metadata["choose_gaussian_maxfev"] = gaussian_maxfev if plot: fig, ax = self.plot_transform(cropping_factor=cropping_factor, **plot_kwargs) @@ -345,38 +359,46 @@ def choose_lattice_vector( return self - def fit_lattice_vectors( self, - refine_subpixel: bool = True, - refine_subpixel_dft: bool = False, + refine_gaussian: bool = True, + refine_dft: bool = False, refine_radius_px: float = 2.0, upsample: int = 16, - refine_log: bool = False, + gaussian_maxfev: int = 100, progressbar: bool = True, - ) -> "StrainMap": - from quantem.core.datastructures.dataset3d import Dataset3d - + ) -> "StrainMapAutocorrelation": if self.u is None or self.v is None: raise ValueError("Run choose_lattice_vector() first to set initial lattice vectors (self.u, self.v).") - if refine_subpixel_dft: - refine_subpixel = True - scan_r = self.dataset.shape[0] scan_c = self.dataset.shape[1] + + self.u_peak_fit = Dataset3d.from_shape( + (scan_r, scan_c, 5), + name="u_peak_fit", + signal_units="mixed", + ) + self.v_peak_fit = Dataset3d.from_shape( + (scan_r, scan_c, 5), + name="v_peak_fit", + signal_units="mixed", + ) + self.u_fit = Dataset3d.from_shape( (scan_r, scan_c, 2), - name="u_fits", + name="u_fit", signal_units="pixels", ) self.v_fit = Dataset3d.from_shape( (scan_r, scan_c, 2), - name="v_fits", + name="v_fit", signal_units="pixels", ) - mode = str(self.metadata.get("mode", "linear")).lower() + mode = self.metadata.get("mode", "linear").lower() + if mode == "gamma": + g = self.metadata["gamma"] it = np.ndindex(scan_r, scan_c) if progressbar: @@ -390,97 +412,150 @@ def fit_lattice_vectors( u0 = np.asarray(self.u, dtype=float).reshape(2) v0 = np.asarray(self.v, dtype=float).reshape(2) + dp_shape = self.dataset.array.shape[2:] + r_center = dp_shape[0] // 2 + c_center = dp_shape[1] // 2 + for r, c in it: - dp = self.dataset.array[r, c]*self.mask_diffraction + \ - self.mask_diffraction_inv + dp = self.dataset.array[r, c] * self.mask_diffraction + self.mask_diffraction_inv if mode == "linear": im = np.fft.fftshift(np.abs(np.fft.fft2(dp))) elif mode == "log": im = np.fft.fftshift(np.abs(np.fft.fft2(np.log1p(dp)))) + elif mode == "gamma": + im = np.fft.fftshift(np.abs(np.fft.fft2(np.power(np.clip(dp, 0.0, None), g)))) else: - raise ValueError("metadata['mode'] must be 'linear' or 'log'") - - if refine_subpixel: - u_rc, v_rc = _refine_lattice_vectors( - im, - u_rc=u0, - v_rc=v0, - radius_px=float(refine_radius_px), - log_fit=bool(refine_log), - refine_dft=bool(refine_subpixel_dft), - upsample=int(upsample), - ) - else: - u_rc = u0 - v_rc = v0 + raise ValueError("metadata['mode'] must be 'linear', 'log', or 'gamma'") + + u_fit_abs, v_fit_abs = _refine_lattice_vectors( + im, + u_rc=u0, + v_rc=v0, + radius_px=refine_radius_px, + refine_gaussian=refine_gaussian, + refine_dft=refine_dft, + upsample=upsample, + maxfev=gaussian_maxfev, + ) - self.u_fit.array[r, c, :] = u_rc - self.v_fit.array[r, c, :] = v_rc + self.u_peak_fit.array[r, c, :] = u_fit_abs + self.v_peak_fit.array[r, c, :] = v_fit_abs - self.metadata["fit_refine_subpixel"] = bool(refine_subpixel) - self.metadata["fit_refine_subpixel_dft"] = bool(refine_subpixel_dft) - self.metadata["fit_refine_radius_px"] = float(refine_radius_px) - self.metadata["fit_refine_log"] = bool(refine_log) - self.metadata["fit_upsample"] = int(upsample) + self.u_fit.array[r, c, 0] = u_fit_abs[0] - r_center + self.u_fit.array[r, c, 1] = u_fit_abs[1] - c_center + self.v_fit.array[r, c, 0] = v_fit_abs[0] - r_center + self.v_fit.array[r, c, 1] = v_fit_abs[1] - c_center - return self + self.metadata["fit_refine_gaussian"] = refine_gaussian + self.metadata["fit_refine_dft"] = refine_dft + self.metadata["fit_refine_radius_px"] = refine_radius_px + self.metadata["fit_upsample"] = upsample + self.metadata["fit_gaussian_maxfev"] = gaussian_maxfev + return self def plot_lattice_vectors( self, subtract_mean: bool = True, - scalebar: bool = False, - **plot_kwargs: Any, + max_shift: float = 1.0, + cmap: str = "PiYG_r", + axsize: tuple[float, float] | None = None, + figsize: tuple[float, float] | None = None, + **imshow_kwargs: Any, ): - if getattr(self, "u_fit", None) is None or getattr(self, "v_fit", None) is None: + if self.u_fit is None or self.v_fit is None: raise ValueError("Run fit_lattice_vectors() first to compute u_fit and v_fit.") + if self.u is None or self.v is None: + raise ValueError("Run choose_lattice_vector() first to set self.u and self.v.") - im0 = self.u_fit.array[:,:,0] - im1 = self.u_fit.array[:,:,1] - im2 = self.v_fit.array[:,:,0] - im3 = self.v_fit.array[:,:,1] + im0 = self.u_fit.array[:, :, 0] + im1 = self.u_fit.array[:, :, 1] + im2 = self.v_fit.array[:, :, 0] + im3 = self.v_fit.array[:, :, 1] + + du0 = im0 - self.u[0] + du1 = im1 - self.u[1] + dv0 = im2 - self.v[0] + dv1 = im3 - self.v[1] + + max_shift2 = max_shift * max_shift + mu = (du0 * du0 + du1 * du1) <= max_shift2 + mv = (dv0 * dv0 + dv1 * dv1) <= max_shift2 if subtract_mean: - im0 = im0 - float(np.nanmean(im0)) - im1 = im1 - float(np.nanmean(im1)) - im2 = im2 - float(np.nanmean(im2)) - im3 = im3 - float(np.nanmean(im3)) + if np.any(mu): + im0 = im0 - np.mean(im0[mu]) + im1 = im1 - np.mean(im1[mu]) + else: + im0 = im0 - np.mean(im0) + im1 = im1 - np.mean(im1) + + if np.any(mv): + im2 = im2 - np.mean(im2[mv]) + im3 = im3 - np.mean(im3[mv]) + else: + im2 = im2 - np.mean(im2) + im3 = im3 - np.mean(im3) + + vals = [] + if np.any(mu): + vals.append(np.abs(im0[mu])) + vals.append(np.abs(im1[mu])) + if np.any(mv): + vals.append(np.abs(im2[mv])) + vals.append(np.abs(im3[mv])) + + if vals: + vlim = np.max(np.concatenate(vals)) + else: + vlim = np.max(np.abs(np.stack([im0, im1, im2, im3], axis=0))) - vlim = float(np.nanmax(np.abs(np.stack([im0, im1, im2, im3], axis=0)))) vmin = -vlim vmax = vlim - defaults: dict[str, Any] = dict( - title=("u_r", "u_c", "v_r", "v_c"), - vmin=vmin, - vmax=vmax, - ) + cm = plt.get_cmap(cmap).copy() + cm.set_bad(color="black") - if scalebar: - s0 = float(self.dataset.sampling[0]) if len(self.dataset.sampling) > 0 else 1.0 - s1 = float(self.dataset.sampling[1]) if len(self.dataset.sampling) > 1 else s0 - sampling_scan = float(np.mean([s0, s1])) - units_scan = str(self.dataset.units[0]) if len(self.dataset.units) > 0 else "pixels" - defaults["scalebar"] = ScalebarConfig(sampling=sampling_scan, units=units_scan) + m0 = np.ma.array(im0, mask=~mu) + m1 = np.ma.array(im1, mask=~mu) + m2 = np.ma.array(im2, mask=~mv) + m3 = np.ma.array(im3, mask=~mv) - defaults.update(plot_kwargs) + if axsize is None and figsize is None: + axsize = (4.0, 4.0) + if figsize is None: + figsize = (axsize[0] * 4.0, axsize[1]) - fig, ax = show_2d([im0, im1, im2, im3], **defaults) - return fig, ax + fig, ax = plt.subplots(1, 4, figsize=figsize) + + ax[0].imshow(m0, cmap=cm, vmin=vmin, vmax=vmax, **imshow_kwargs) + ax[1].imshow(m1, cmap=cm, vmin=vmin, vmax=vmax, **imshow_kwargs) + ax[2].imshow(m2, cmap=cm, vmin=vmin, vmax=vmax, **imshow_kwargs) + ax[3].imshow(m3, cmap=cm, vmin=vmin, vmax=vmax, **imshow_kwargs) + + ax[0].set_title("u_r") + ax[1].set_title("u_c") + ax[2].set_title("v_r") + ax[3].set_title("v_c") + for a in ax: + a.set_xticks([]) + a.set_yticks([]) + + return fig, ax def fit_strain( self, - mask_reference = None, - plot_strain = True, + mask_reference=None, + plot_strain=True, ): if self.u_fit is None or self.v_fit is None: raise ValueError("Run fit_lattice_vectors() first to compute u_fit and v_fit.") u_fit = self.u_fit.array v_fit = self.v_fit.array - scan_r, scan_c = int(u_fit.shape[0]), int(u_fit.shape[1]) + scan_r, scan_c = u_fit.shape[0], u_fit.shape[1] if mask_reference is None: self.u_ref = np.median(u_fit.reshape(-1, 2), axis=0) @@ -503,26 +578,23 @@ def fit_strain( ) Uref = np.stack((self.u_ref, self.v_ref), axis=1).astype(float) - det = float(np.linalg.det(Uref)) + det = np.linalg.det(Uref) if not np.isfinite(det) or abs(det) < 1e-12: Uref_inv = np.linalg.pinv(Uref) else: Uref_inv = np.linalg.inv(Uref) - # init self.strain_trans = Dataset4d.from_shape( (scan_r, scan_c, 2, 2), name="transformation matrix", signal_units="fractional", ) - # Loop over probe positions for r in range(scan_r): for c in range(scan_c): U = np.stack((u_fit[r, c, :], v_fit[r, c, :]), axis=1) self.strain_trans.array[r, c, :, :] = U @ Uref_inv - # get strains in orthogonal directions self.strain_raw_err = Dataset2d.from_array( self.strain_trans.array[:, :, 0, 0] - 1, name="strain err", @@ -534,12 +606,12 @@ def fit_strain( signal_units="fractional", ) self.strain_raw_erc = Dataset2d.from_array( - self.strain_trans.array[:, :, 1, 0]*0.5 + self.strain_trans.array[:, :, 0, 1]*0.5, + self.strain_trans.array[:, :, 1, 0] * 0.5 + self.strain_trans.array[:, :, 0, 1] * 0.5, name="strain erc", signal_units="fractional", ) self.strain_rotation = Dataset2d.from_array( - self.strain_trans.array[:, :, 1, 0]*-0.5 + self.strain_trans.array[:, :, 0, 1]*0.5, + self.strain_trans.array[:, :, 1, 0] * -0.5 + self.strain_trans.array[:, :, 0, 1] * 0.5, name="strain rotation", signal_units="fractional", ) @@ -555,23 +627,26 @@ def plot_strain( rotation_range_degrees=(-2.0, 2.0), plot_rotation=True, cmap_strain="PiYG_r", - cmap_rotation="PiYG_r", + cmap_rotation=None, layout="horizontal", figsize=(6, 6), + max_shift: tuple[float, float] | None = None, + amp_range: tuple[float, float] | None = None, ): import matplotlib.pyplot as plt + if cmap_rotation is None: + cmap_rotation = cmap_strain + if ref_angle_degrees is None: - ref_vec = self.u_ref * float(ref_u_v[0]) + self.v_ref * float(ref_u_v[1]) - ref_angle = float(np.arctan2(ref_vec[1], ref_vec[0])) + ref_vec = self.u_ref * ref_u_v[0] + self.v_ref * ref_u_v[1] + ref_angle = np.arctan2(ref_vec[1], ref_vec[0]) else: - ref_angle = float(np.deg2rad(ref_angle_degrees)) + ref_angle = np.deg2rad(ref_angle_degrees) angle = ref_angle + np.deg2rad(self.metadata["q_to_r_rotation_ccw_deg"]) - print(np.round(np.rad2deg(angle),2)) - - c = float(np.cos(angle)) - s = float(np.sin(angle)) + c = np.cos(angle) + s = np.sin(angle) err = self.strain_raw_err.array ecc = self.strain_raw_ecc.array @@ -588,62 +663,148 @@ def plot_strain( self.strain_evv.array[...] = evv self.strain_euv.array[...] = euv - if layout == "horizontal": - if plot_rotation: - fig, ax = plt.subplots(1, 4, figsize=figsize) - - ax[0].imshow( - self.strain_euu.array * 100, - vmin=strain_range_percent[0], - vmax=strain_range_percent[1], - cmap=cmap_strain, - ) - ax[1].imshow( - self.strain_evv.array * 100, - vmin=strain_range_percent[0], - vmax=strain_range_percent[1], - cmap=cmap_strain, - ) - ax[2].imshow( - self.strain_euv.array * 100, - vmin=strain_range_percent[0], - vmax=strain_range_percent[1], - cmap=cmap_strain, - ) - ax[3].imshow( - np.rad2deg(self.strain_rotation.array), - vmin=rotation_range_degrees[0], - vmax=rotation_range_degrees[1], - cmap=cmap_rotation, - ) - return fig, ax - - fig, ax = plt.subplots(1, 3, figsize=figsize) - ax[0].imshow( - self.strain_euu.array * 100, - vmin=strain_range_percent[0], - vmax=strain_range_percent[1], - cmap=cmap_strain, - ) - ax[1].imshow( - self.strain_evv.array * 100, - vmin=strain_range_percent[0], - vmax=strain_range_percent[1], - cmap=cmap_strain, - ) - ax[2].imshow( - self.strain_euv.array * 100, - vmin=strain_range_percent[0], - vmax=strain_range_percent[1], - cmap=cmap_strain, + alpha = None + if max_shift is not None: + if self.u_fit is None or self.v_fit is None or self.u is None or self.v is None: + raise ValueError("max_shift masking requires u_fit, v_fit, u, v to be available.") + + ur = self.u_fit.array[:, :, 0] + uc = self.u_fit.array[:, :, 1] + vr = self.v_fit.array[:, :, 0] + vc = self.v_fit.array[:, :, 1] + + du0 = ur - self.u[0] + du1 = uc - self.u[1] + dv0 = vr - self.v[0] + dv1 = vc - self.v[1] + + su = du0 * du0 + du1 * du1 + sv = dv0 * dv0 + dv1 * dv1 + sdist2 = 0.5 * (su + sv) + + smin, smax = max_shift + mask = np.clip((sdist2 - smin) / (smax - smin), 0.0, 1.0) + alpha = 1.0 - mask + + if amp_range is not None: + if self.u_peak_fit is None or self.v_peak_fit is None: + raise ValueError("amp_range masking requires u_peak_fit and v_peak_fit to be available.") + a = 0.5 * (self.u_peak_fit.array[:, :, 2] + self.v_peak_fit.array[:, :, 2]) + amin, amax = amp_range + a_mask = np.clip((a - amin) / (amax - amin), 0.0, 1.0) + alpha = a_mask if alpha is None else alpha * a_mask + + if alpha is not None: + alpha = np.asarray(alpha, dtype=float) + good = alpha > 0 + alpha_im = np.where(good, alpha, 1.0) + else: + good = None + alpha_im = None + + if layout != "horizontal": + raise ValueError("layout must be 'horizontal'") + + ncols = 4 if plot_rotation else 3 + fig, ax = plt.subplots(1, ncols, figsize=figsize) + + cm_strain = plt.get_cmap(cmap_strain).copy() + cm_strain.set_bad(color="black") + cm_rot = plt.get_cmap(cmap_rotation).copy() + cm_rot.set_bad(color="black") + + euu_pct = self.strain_euu.array * 100 + evv_pct = self.strain_evv.array * 100 + euv_pct = self.strain_euv.array * 100 + rot_deg = np.rad2deg(self.strain_rotation.array) + + if good is not None and np.any(good): + euu_m = np.ma.array(euu_pct, mask=~good) + evv_m = np.ma.array(evv_pct, mask=~good) + euv_m = np.ma.array(euv_pct, mask=~good) + rot_m = np.ma.array(rot_deg, mask=~good) + else: + euu_m = euu_pct + evv_m = evv_pct + euv_m = euv_pct + rot_m = rot_deg + + title_fs = 16 + im0 = ax[0].imshow( + euu_m, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cm_strain, + alpha=alpha_im, + ) + ax[1].imshow( + evv_m, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cm_strain, + alpha=alpha_im, + ) + ax[2].imshow( + euv_m, + vmin=strain_range_percent[0], + vmax=strain_range_percent[1], + cmap=cm_strain, + alpha=alpha_im, + ) + + ax[0].set_title(r"$\epsilon_{uu}$", fontsize=title_fs) + ax[1].set_title(r"$\epsilon_{vv}$", fontsize=title_fs) + ax[2].set_title(r"$\epsilon_{uv}$", fontsize=title_fs) + + if plot_rotation: + im3 = ax[3].imshow( + rot_m, + vmin=rotation_range_degrees[0], + vmax=rotation_range_degrees[1], + cmap=cm_rot, + alpha=alpha_im, ) - return fig, ax + ax[3].set_title("Rotation", fontsize=title_fs) - raise ValueError("layout must be 'horizontal'") + for a in ax: + a.set_xticks([]) + a.set_yticks([]) + a.set_facecolor("black") + + fig.subplots_adjust(left=0.02, right=0.98, top=0.90, bottom=0.16, wspace=0.03) + + b0 = ax[0].get_position() + b2 = ax[2].get_position() + left = b0.x0 + right = b2.x1 + width = right - left + + b3 = ax[3].get_position() if plot_rotation else None + + cb_height = 0.04 + cb_pad = 0.03 + y = b0.y0 - cb_pad - cb_height + + cax1 = fig.add_axes([left, y, width, cb_height]) + cbar1 = fig.colorbar(im0, cax=cax1, orientation="horizontal") + cbar1.set_label("Strain (%)", fontsize=title_fs) + cbar1.ax.tick_params(labelsize=12) + + if plot_rotation: + left_r = b3.x0 + width_r = b3.x1 - b3.x0 + cax2 = fig.add_axes([left_r, y, width_r, cb_height]) + cbar2 = fig.colorbar(im3, cax=cax2, orientation="horizontal") + cbar2.set_label("Rotation (deg)", fontsize=title_fs) + cbar2.ax.tick_params(labelsize=12) + + for a in ax: + a.set_aspect("equal") + + return fig, ax def _nice_length_units(target: float) -> float: - target = float(target) if not np.isfinite(target) or target <= 0: return 0.0 exp = np.floor(np.log10(target)) @@ -656,21 +817,20 @@ def _nice_length_units(target: float) -> float: nice = 5.0 else: nice = 10.0 - return float(nice * (10.0**exp)) + return nice * (10.0**exp) def _apply_center_crop_limits(ax: Any, shape: tuple[int, int], cropping_factor: float) -> None: - cf = float(cropping_factor) - if cf >= 1.0: + if cropping_factor >= 1.0: return - if not (0.0 < cf <= 1.0): + if not (0.0 < cropping_factor <= 1.0): raise ValueError("cropping_factor must be in (0, 1].") - H, W = int(shape[0]), int(shape[1]) - r0 = float(H // 2) - c0 = float(W // 2) - half_h = 0.5 * cf * H - half_w = 0.5 * cf * W + H, W = shape + r0 = H // 2 + c0 = W // 2 + half_h = 0.5 * cropping_factor * H + half_w = 0.5 * cropping_factor * W ax.set_xlim(c0 - half_w, c0 + half_w) @@ -694,14 +854,14 @@ def _flatten_axes(ax: Any) -> list[Any]: def _raw_vec_to_display(vec_rc: NDArray, *, rotation_ccw_deg: float, transpose: bool) -> NDArray: v = np.asarray(vec_rc, dtype=float).reshape(2) - dr, dc = float(v[0]), float(v[1]) + dr, dc = v[0], v[1] if transpose: dr, dc = dc, dr - theta = float(np.deg2rad(rotation_ccw_deg)) - ct = float(np.cos(theta)) - st = float(np.sin(theta)) + theta = np.deg2rad(rotation_ccw_deg) + ct = np.cos(theta) + st = np.sin(theta) dr2 = ct * dr - st * dc dc2 = st * dr + ct * dc @@ -710,11 +870,11 @@ def _raw_vec_to_display(vec_rc: NDArray, *, rotation_ccw_deg: float, transpose: def _display_vec_to_raw(vec_rc: NDArray, *, rotation_ccw_deg: float, transpose: bool) -> NDArray: v = np.asarray(vec_rc, dtype=float).reshape(2) - dr, dc = float(v[0]), float(v[1]) + dr, dc = v[0], v[1] - theta = float(np.deg2rad(rotation_ccw_deg)) - ct = float(np.cos(theta)) - st = float(np.sin(theta)) + theta = np.deg2rad(rotation_ccw_deg) + ct = np.cos(theta) + st = np.sin(theta) dr2 = ct * dr + st * dc dc2 = -st * dr + ct * dc @@ -725,17 +885,17 @@ def _display_vec_to_raw(vec_rc: NDArray, *, rotation_ccw_deg: float, transpose: return np.array((dr2, dc2), dtype=float) -def _plot_lattice_vectors(ax: Any, center_rc: tuple[float, float], u_rc: NDArray, v_rc: NDArray) -> None: - r0, c0 = float(center_rc[0]), float(center_rc[1]) +# def _plot_lattice_vectors(ax: Any, center_rc: tuple[float, float], u_rc: NDArray, v_rc: NDArray) -> None: +# r0, c0 = center_rc - def _draw(vec: NDArray, label: str, color: tuple[float, float, float]) -> None: - dr, dc = float(vec[0]), float(vec[1]) - ax.plot([c0, c0 + dc], [r0, r0 + dr], linewidth=2.75, color=color) - ax.plot([c0 + dc], [r0 + dr], marker="o", markersize=6.0, color=color) - ax.text(c0 + dc, r0 + dr, f" {label}", color=color, fontsize=18, va="center") +# def _draw(vec: NDArray, label: str, color: tuple[float, float, float]) -> None: +# dr, dc = vec[0], vec[1] +# ax.plot([c0, c0 + dc], [r0, r0 + dr], linewidth=2.75, color=color) +# ax.plot([c0 + dc], [r0 + dr], marker="o", markersize=6.0, color=color) +# ax.text(c0 + dc, r0 + dr, f" {label}", color=color, fontsize=18, va="center") - _draw(np.asarray(u_rc, dtype=float).reshape(2), "u", (1.0, 0.0, 0.0)) - _draw(np.asarray(v_rc, dtype=float).reshape(2), "v", (0.0, 0.7, 1.0)) +# _draw(np.asarray(u_rc, dtype=float).reshape(2), "u", (1.0, 0.0, 0.0)) +# _draw(np.asarray(v_rc, dtype=float).reshape(2), "v", (0.0, 0.7, 1.0)) def _overlay_lattice_vectors( @@ -751,25 +911,25 @@ def _overlay_lattice_vectors( if not axs: return - H, W = int(shape[0]), int(shape[1]) - center_rc = (float(H // 2), float(W // 2)) + H, W = shape + center_rc = (H // 2, W // 2) _plot_lattice_vectors(axs[0], center_rc, u_rc, v_rc) if len(axs) >= 2: - u_disp = _raw_vec_to_display(u_rc, rotation_ccw_deg=float(rot_ccw_deg), transpose=bool(q_transpose)) - v_disp = _raw_vec_to_display(v_rc, rotation_ccw_deg=float(rot_ccw_deg), transpose=bool(q_transpose)) + u_disp = _raw_vec_to_display(u_rc, rotation_ccw_deg=rot_ccw_deg, transpose=q_transpose) + v_disp = _raw_vec_to_display(v_rc, rotation_ccw_deg=rot_ccw_deg, transpose=q_transpose) _plot_lattice_vectors(axs[1], center_rc, u_disp, v_disp) def _parabolic_vertex_delta(v_m1: float, v_0: float, v_p1: float) -> float: - denom = (v_m1 - 2.0 * v_0 + v_p1) + denom = v_m1 - 2.0 * v_0 + v_p1 if denom == 0 or not np.isfinite(denom): return 0.0 delta = 0.5 * (v_m1 - v_p1) / denom if not np.isfinite(delta): return 0.0 - return float(np.clip(delta, -1.0, 1.0)) + return np.clip(delta, -1.0, 1.0) def _refine_peak_subpixel( @@ -778,14 +938,13 @@ def _refine_peak_subpixel( r_guess: float, c_guess: float, radius_px: float = 2.0, - log_fit: bool = False, ) -> tuple[float, float]: im = np.asarray(im, dtype=float) H, W = im.shape r0 = int(np.clip(int(np.round(r_guess)), 0, H - 1)) c0 = int(np.clip(int(np.round(c_guess)), 0, W - 1)) - rad = int(max(0, int(np.ceil(float(radius_px))))) + rad = int(max(0, int(np.ceil(radius_px)))) r1 = max(0, r0 - rad) r2 = min(H, r0 + rad + 1) @@ -794,29 +953,25 @@ def _refine_peak_subpixel( win = im[r1:r2, c1:c2] if win.size == 0: - return float(r_guess), float(c_guess) + return r_guess, c_guess - ir, ic = np.unravel_index(int(np.argmax(win)), win.shape) - r_peak = r1 + int(ir) - c_peak = c1 + int(ic) + ir, ic = np.unravel_index(np.argmax(win), win.shape) + r_peak = r1 + ir + c_peak = c1 + ic if 0 < r_peak < H - 1: col = im[r_peak - 1 : r_peak + 2, c_peak] - if log_fit: - col = np.log(np.clip(col, 1e-12, None)) - dr = _parabolic_vertex_delta(float(col[0]), float(col[1]), float(col[2])) + dr = _parabolic_vertex_delta(col[0], col[1], col[2]) else: dr = 0.0 if 0 < c_peak < W - 1: row = im[r_peak, c_peak - 1 : c_peak + 2] - if log_fit: - row = np.log(np.clip(row, 1e-12, None)) - dc = _parabolic_vertex_delta(float(row[0]), float(row[1]), float(row[2])) + dc = _parabolic_vertex_delta(row[0], row[1], row[2]) else: dc = 0.0 - return float(r_peak) + dr, float(c_peak) + dc + return r_peak + dr, c_peak + dc def _refine_peak_subpixel_dft( @@ -825,44 +980,37 @@ def _refine_peak_subpixel_dft( r0: float, c0: float, upsample: int, - log_fit: bool = False, ) -> tuple[float, float]: - if int(upsample) <= 1: - return float(r0), float(c0) + if upsample <= 1: + return r0, c0 im = np.asarray(im, dtype=float) F = np.fft.fft2(im) - up = int(upsample) + up = upsample du = int(np.ceil(1.5 * up)) - patch = dft_upsample(F, up=up, shift=(float(r0), float(c0)), device="cpu") + patch = dft_upsample(F, up=up, shift=(r0, c0), device="cpu") patch = np.asarray(patch, dtype=float) - i0, j0 = np.unravel_index(int(np.argmax(patch)), patch.shape) - i0 = int(i0) - j0 = int(j0) + i0, j0 = np.unravel_index(np.argmax(patch), patch.shape) if 0 < i0 < patch.shape[0] - 1: col = patch[i0 - 1 : i0 + 2, j0] - if log_fit: - col = np.log(np.clip(col, 1e-12, None)) - di = _parabolic_vertex_delta(float(col[0]), float(col[1]), float(col[2])) + di = _parabolic_vertex_delta(col[0], col[1], col[2]) else: di = 0.0 if 0 < j0 < patch.shape[1] - 1: row = patch[i0, j0 - 1 : j0 + 2] - if log_fit: - row = np.log(np.clip(row, 1e-12, None)) - dj = _parabolic_vertex_delta(float(row[0]), float(row[1]), float(row[2])) + dj = _parabolic_vertex_delta(row[0], row[1], row[2]) else: dj = 0.0 - dr = (float(i0) - float(du) + float(di)) / float(up) - dc = (float(j0) - float(du) + float(dj)) / float(up) + dr = (i0 - du + di) / up + dc = (j0 - du + dj) / up - return float(r0) + dr, float(c0) + dc + return r0 + dr, c0 + dc def _refine_lattice_vectors( @@ -871,42 +1019,155 @@ def _refine_lattice_vectors( u_rc: NDArray, v_rc: NDArray, radius_px: float = 2.0, - log_fit: bool = False, - refine_dft: bool = True, + refine_gaussian: bool = True, + refine_dft: bool = False, upsample: int = 16, + maxfev: int = 100, ) -> tuple[NDArray, NDArray]: + from scipy.optimize import curve_fit + im = np.asarray(im, dtype=float) if im.ndim != 2: raise ValueError("im must be 2D.") H, W = im.shape - r_center = float(H // 2) - c_center = float(W // 2) + r_center = H // 2 + c_center = W // 2 + + def _parabolic_peak_rc_amp(*, r_guess: float, c_guess: float) -> tuple[float, float, float]: + r0 = int(np.clip(int(np.round(r_guess)), 0, H - 1)) + c0 = int(np.clip(int(np.round(c_guess)), 0, W - 1)) + win = im[ + max(0, r0 - 1) : min(H, r0 + 2), + max(0, c0 - 1) : min(W, c0 + 2), + ] + if win.size == 0: + return r_guess, c_guess, 0.0 + + ir, ic = np.unravel_index(np.argmax(win), win.shape) + r_peak = max(0, r0 - 1) + ir + c_peak = max(0, c0 - 1) + ic + + r_ref = r_peak + c_ref = c_peak + + if 0 < r_peak < H - 1: + col = im[r_peak - 1 : r_peak + 2, c_peak] + dr = _parabolic_vertex_delta(col[0], col[1], col[2]) + else: + dr = 0.0 - def _refine(vec: NDArray) -> NDArray: + if 0 < c_peak < W - 1: + row = im[r_peak, c_peak - 1 : c_peak + 2] + dc = _parabolic_vertex_delta(row[0], row[1], row[2]) + else: + dc = 0.0 + + r_sub = r_ref + dr + c_sub = c_ref + dc + r_int = int(np.clip(int(np.round(r_sub)), 0, H - 1)) + c_int = int(np.clip(int(np.round(c_sub)), 0, W - 1)) + amp = im[r_int, c_int] + + return r_sub, c_sub, amp + + def _fit_gaussian_isotropic( + *, + r0: float, + c0: float, + radius_px: float, + maxfev: int, + ) -> tuple[float, float, float, float, float]: + rad = int(max(1, int(np.ceil(radius_px)))) + r0i = int(np.clip(int(np.round(r0)), 0, H - 1)) + c0i = int(np.clip(int(np.round(c0)), 0, W - 1)) + + r1 = max(0, r0i - rad) + r2 = min(H, r0i + rad + 1) + c1 = max(0, c0i - rad) + c2 = min(W, c0i + rad + 1) + + win = im[r1:r2, c1:c2] + if win.size == 0: + return r0, c0, 0.0, 0.0, 0.0 + + ir, ic = np.unravel_index(np.argmax(win), win.shape) + r_peak = r1 + ir + c_peak = c1 + ic + + bg0 = np.median(win) + amp0 = win[ir, ic] - bg0 + sig0 = max(0.75, radius_px / 2.0) + + rr = np.arange(r1, r2, dtype=float)[:, None] + cc = np.arange(c1, c2, dtype=float)[None, :] + RR = np.broadcast_to(rr, win.shape) + CC = np.broadcast_to(cc, win.shape) + + def _g2( + coords: tuple[NDArray, NDArray], + row: float, + col: float, + amp: float, + sigma: float, + background: float, + ) -> NDArray: + r, c = coords + sig = np.maximum(sigma, 1e-12) + return background + amp * np.exp(-((r - row) ** 2 + (c - col) ** 2) / (2.0 * sig * sig)) + + p0 = (r_peak, c_peak, max(0.0, amp0), sig0, bg0) + + rlo = r1 - 0.5 + rhi = (r2 - 1) + 0.5 + clo = c1 - 0.5 + chi = (c2 - 1) + 0.5 + + bounds_lo = (rlo, clo, 0.0, 0.25, -np.inf) + bounds_hi = (rhi, chi, np.inf, radius_px * 4.0, np.inf) + + try: + popt, _ = curve_fit( + _g2, + (RR.ravel(), CC.ravel()), + win.ravel(), + p0=p0, + bounds=(bounds_lo, bounds_hi), + maxfev=maxfev, + ) + row, col, amp, sig, bg = popt + if not (np.isfinite(row) and np.isfinite(col) and np.isfinite(amp) and np.isfinite(sig) and np.isfinite(bg)): + return r0, c0, p0[2], 0.0, 0.0 + return row, col, amp, sig, bg + except Exception: + return r0, c0, p0[2], 0.0, 0.0 + + def _refine_one(vec: NDArray) -> NDArray: vec = np.asarray(vec, dtype=float).reshape(2) - r_guess = r_center + float(vec[0]) - c_guess = c_center + float(vec[1]) + r_guess = r_center + vec[0] + c_guess = c_center + vec[1] - r1, c1 = _refine_peak_subpixel( - im, - r_guess=float(r_guess), - c_guess=float(c_guess), - radius_px=float(radius_px), - log_fit=bool(log_fit), - ) + r_par, c_par, amp_par = _parabolic_peak_rc_amp(r_guess=r_guess, c_guess=c_guess) - if refine_dft and int(upsample) > 1: - r2, c2 = _refine_peak_subpixel_dft( - im, - r0=float(r1), - c0=float(c1), - upsample=int(upsample), - log_fit=bool(log_fit), + if refine_gaussian: + r_fit, c_fit, amp, sig, bg = _fit_gaussian_isotropic( + r0=r_par, + c0=c_par, + radius_px=radius_px, + maxfev=maxfev, ) else: - r2, c2 = float(r1), float(c1) + r_fit, c_fit, amp, sig, bg = r_par, c_par, amp_par, 0.0, 0.0 + + if refine_dft and upsample > 1: + r_dft, c_dft = _refine_peak_subpixel_dft( + im, + r0=r_fit, + c0=c_fit, + upsample=upsample, + ) + r_fit, c_fit = r_dft, c_dft - return np.array((r2 - r_center, c2 - c_center), dtype=float) + return np.array((r_fit, c_fit, amp, sig, bg), dtype=float) - return _refine(u_rc), _refine(v_rc) + return _refine_one(u_rc), _refine_one(v_rc) From 43e019d417134a66a5b39c04d17d8357b5f72788 Mon Sep 17 00:00:00 2001 From: cophus Date: Mon, 19 Jan 2026 11:45:06 -0800 Subject: [PATCH 104/140] renaming parent .py file --- src/quantem/diffraction/__init__.py | 2 +- .../{strain.py => strain_autocorrelation.py} | 20 +++++++++---------- 2 files changed, 11 insertions(+), 11 deletions(-) rename src/quantem/diffraction/{strain.py => strain_autocorrelation.py} (98%) diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index 77bc5f82..450b9b2f 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1,2 +1,2 @@ from quantem.diffraction.polar import RDF as RDF -from quantem.diffraction.strain import StrainMapAutocorrelation as StrainMapAutocorrelation +from quantem.diffraction.strain_autocorrelation import StrainMapAutocorrelation as StrainMapAutocorrelation diff --git a/src/quantem/diffraction/strain.py b/src/quantem/diffraction/strain_autocorrelation.py similarity index 98% rename from src/quantem/diffraction/strain.py rename to src/quantem/diffraction/strain_autocorrelation.py index fa92c847..a084b32d 100644 --- a/src/quantem/diffraction/strain.py +++ b/src/quantem/diffraction/strain_autocorrelation.py @@ -626,7 +626,7 @@ def plot_strain( strain_range_percent=(-3.0, 3.0), rotation_range_degrees=(-2.0, 2.0), plot_rotation=True, - cmap_strain="PiYG_r", + cmap_strain="RdBu_r", cmap_rotation=None, layout="horizontal", figsize=(6, 6), @@ -885,17 +885,17 @@ def _display_vec_to_raw(vec_rc: NDArray, *, rotation_ccw_deg: float, transpose: return np.array((dr2, dc2), dtype=float) -# def _plot_lattice_vectors(ax: Any, center_rc: tuple[float, float], u_rc: NDArray, v_rc: NDArray) -> None: -# r0, c0 = center_rc +def _plot_lattice_vectors(ax: Any, center_rc: tuple[float, float], u_rc: NDArray, v_rc: NDArray) -> None: + r0, c0 = center_rc -# def _draw(vec: NDArray, label: str, color: tuple[float, float, float]) -> None: -# dr, dc = vec[0], vec[1] -# ax.plot([c0, c0 + dc], [r0, r0 + dr], linewidth=2.75, color=color) -# ax.plot([c0 + dc], [r0 + dr], marker="o", markersize=6.0, color=color) -# ax.text(c0 + dc, r0 + dr, f" {label}", color=color, fontsize=18, va="center") + def _draw(vec: NDArray, label: str, color: tuple[float, float, float]) -> None: + dr, dc = vec[0], vec[1] + ax.plot([c0, c0 + dc], [r0, r0 + dr], linewidth=2.75, color=color) + ax.plot([c0 + dc], [r0 + dr], marker="o", markersize=6.0, color=color) + ax.text(c0 + dc, r0 + dr, f" {label}", color=color, fontsize=18, va="center") -# _draw(np.asarray(u_rc, dtype=float).reshape(2), "u", (1.0, 0.0, 0.0)) -# _draw(np.asarray(v_rc, dtype=float).reshape(2), "v", (0.0, 0.7, 1.0)) + _draw(np.asarray(u_rc, dtype=float).reshape(2), "u", (1.0, 0.0, 0.0)) + _draw(np.asarray(v_rc, dtype=float).reshape(2), "v", (0.0, 0.7, 1.0)) def _overlay_lattice_vectors( From 495fad38332fdbe77527ac5cf1f0af68eed84aad Mon Sep 17 00:00:00 2001 From: cophus Date: Wed, 28 Jan 2026 20:18:07 -0800 Subject: [PATCH 105/140] initial class --- src/quantem/diffraction/__init__.py | 1 + src/quantem/diffraction/maped.py | 87 +++++++++++++++++++++++++++++ 2 files changed, 88 insertions(+) create mode 100644 src/quantem/diffraction/maped.py diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index 450b9b2f..2a79312b 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1,2 +1,3 @@ from quantem.diffraction.polar import RDF as RDF from quantem.diffraction.strain_autocorrelation import StrainMapAutocorrelation as StrainMapAutocorrelation +from quantem.diffraction.maped import MAPED as MAPED diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py new file mode 100644 index 00000000..f301454e --- /dev/null +++ b/src/quantem/diffraction/maped.py @@ -0,0 +1,87 @@ +from __future__ import annotations + +from typing import Any, Sequence + +import numpy as np + +from quantem.core.datastructures.dataset4dstem import Dataset4dstem +from quantem.core.io.serialize import AutoSerialize +from quantem.core.visualization import show_2d + + +class MAPED(AutoSerialize): + _token = object() + + def __init__(self, datasets: list[Dataset4dstem], _token: object | None = None): + if _token is not self._token: + raise RuntimeError("Use MAPED.from_datasets() to instantiate this class.") + AutoSerialize.__init__(self) + self.datasets = datasets + self.metadata: dict[str, Any] = {} + + @classmethod + def from_datasets(cls, datasets: Sequence[Dataset4dstem]) -> "MAPED": + if not isinstance(datasets, Sequence) or isinstance(datasets, (str, bytes)): + raise TypeError("MAPED.from_datasets expects a sequence of Dataset4dstem instances.") + ds_list: list[Dataset4dstem] = [] + for d in datasets: + if not isinstance(d, Dataset4dstem): + raise TypeError("MAPED.from_datasets expects a sequence of Dataset4dstem instances.") + ds_list.append(d) + if len(ds_list) == 0: + raise ValueError("MAPED.from_datasets expects a non-empty sequence of Dataset4dstem instances.") + return cls(datasets=ds_list, _token=cls._token) + + def preprocess( + self, + *, + plot_summary: bool = True, + scale: float | Sequence[float] | None = None, + **plot_kwargs: Any, + ) -> "MAPED": + n = len(self.datasets) + if scale is None: + self.scales = np.ones(n, dtype=float) + elif isinstance(scale, (int, float, np.floating)): + self.scales = np.full(n, float(scale), dtype=float) + else: + self.scales = np.asarray(list(scale), dtype=float) + if self.scales.shape != (n,): + raise ValueError("scale must be a scalar or a sequence with the same length as datasets.") + if np.any(self.scales == 0): + raise ValueError("scale entries must be nonzero.") + + self.dp_mean = [] + self.im_bf = [] + + for d in self.datasets: + if hasattr(d, "get_dp_mean"): + try: + d.get_dp_mean() + except TypeError: + try: + d.get_dp_mean(returnval=False) + except Exception: + pass + + dp = getattr(d, "dp_mean", None) + if dp is None: + dp_arr = np.mean(np.asarray(d.array), axis=(0, 1)) + else: + dp_arr = np.asarray(dp.array if hasattr(dp, "array") else dp) + + im_bf_arr = np.mean(np.asarray(d.array), axis=(2, 3)) + + self.dp_mean.append(np.asarray(dp_arr)) + self.im_bf.append(np.asarray(im_bf_arr)) + + if plot_summary: + show_2d( + [ + [self.im_bf[i] / self.scales[i] for i in range(n)], + [self.dp_mean[i] for i in range(n)], + ], + **plot_kwargs, + ) + + return self From 800f793e5fb0ab4a515f2090724c0d10c8fc9ec7 Mon Sep 17 00:00:00 2001 From: cophus Date: Sat, 31 Jan 2026 16:19:06 -0800 Subject: [PATCH 106/140] fixing DFT upsampling / image correlation, adding tests --- src/quantem/core/utils/imaging_utils.py | 365 +++++++----------------- tests/core/utils/test_imaging_utils.py | 75 +++++ 2 files changed, 178 insertions(+), 262 deletions(-) create mode 100644 tests/core/utils/test_imaging_utils.py diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 77997933..3004c7fd 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -12,11 +12,17 @@ from quantem.core.utils.utils import generate_batches +def _parabolic_peak(v) -> float: + denom = 4.0 * v[1] - 2.0 * v[2] - 2.0 * v[0] + if denom == 0: + return 0.0 + return float((v[2] - v[0]) / denom) + + def dft_upsample( F: NDArray, up: int, shift: Tuple[float, float], - device: str = "cpu", ): """ Matrix multiplication DFT, from: @@ -25,27 +31,53 @@ def dft_upsample( image registration algorithms," Opt. Lett. 33, 156-158 (2008). http://www.sciencedirect.com/science/article/pii/S0045790612000778 """ - if device == "gpu": - import cupy as cp # type: ignore + M, N = F.shape + pixel_radius = 1.5 + num_row = int(math.ceil(pixel_radius * up)) + num_col = num_row - xp = cp - else: - xp = np + col_freq = np.fft.ifftshift(np.arange(N)) - math.floor(N / 2) + row_freq = np.fft.ifftshift(np.arange(M)) - math.floor(M / 2) - M, N = F.shape - du = np.ceil(1.5 * up).astype(int) - row = np.arange(-du, du + 1) - col = np.arange(-du, du + 1) - r_shift = shift[0] - M // 2 - c_shift = shift[1] - N // 2 - - kern_row = np.exp( - -2j * np.pi / (M * up) * np.outer(row, xp.fft.ifftshift(xp.arange(M)) - M // 2 + r_shift) - ) - kern_col = np.exp( - -2j * np.pi / (N * up) * np.outer(xp.fft.ifftshift(xp.arange(N)) - N // 2 + c_shift, col) - ) - return xp.real(kern_row @ F @ kern_col) + row_coords = np.arange(num_row, dtype=float) - float(shift[0]) + col_coords = np.arange(num_col, dtype=float) - float(shift[1]) + + factor_row = -2j * math.pi / (M * float(up)) + factor_col = -2j * math.pi / (N * float(up)) + + row_kern = np.exp(factor_row * (row_coords[:, None] * row_freq[None, :])).astype(F.dtype) + col_kern = np.exp(factor_col * (col_freq[:, None] * col_coords[None, :])).astype(F.dtype) + + return (row_kern @ F @ col_kern).real + + +def _upsampled_correlation_numpy( + imageCorr: NDArray, + upsampleFactor: int, + xyShift: NDArray, +) -> NDArray: + xyShift = np.round(xyShift * float(upsampleFactor)) / float(upsampleFactor) + globalShift = math.floor(math.ceil(upsampleFactor * 1.5) / 2.0) + upsampleCenter = float(globalShift) - (float(upsampleFactor) * xyShift) + + im_up = dft_upsample(np.conj(imageCorr), upsampleFactor, (float(upsampleCenter[0]), float(upsampleCenter[1]))) + imageCorrUpsample = np.conj(im_up) + + flat_idx = int(np.argmax(imageCorrUpsample.real)) + r = flat_idx // imageCorrUpsample.shape[1] + c = flat_idx % imageCorrUpsample.shape[1] + + dx = 0.0 + dy = 0.0 + patch = imageCorrUpsample.real[r - 1 : r + 2, c - 1 : c + 2] + if patch.shape == (3, 3): + dx = _parabolic_peak(patch[:, 1]) + dy = _parabolic_peak(patch[1, :]) + + xySubShift = np.array([float(r), float(c)], dtype=float) - float(globalShift) + xyShift = xyShift + (xySubShift + np.array([dx, dy], dtype=float)) / float(upsampleFactor) + + return xyShift def cross_correlation_shift( @@ -56,7 +88,6 @@ def cross_correlation_shift( return_shifted_image: bool = False, fft_input: bool = False, fft_output: bool = False, - device: str = "cpu", ): """ Estimate subpixel shift between two 2D images using Fourier cross-correlation. @@ -68,98 +99,78 @@ def cross_correlation_shift( im : ndarray Image to align or its FFT if fft_input=True upsample_factor : int - Subpixel upsampling factor (must be > 1 for subpixel accuracy) - fft_input : bool - If True, assumes im_ref and im are already in Fourier space + Subpixel upsampling factor (torch-equivalent behavior): + - <= 2 : half-pixel refinement (parabolic, then rounded to nearest 0.5 px) + - > 2 : additional DFT upsample refinement + max_shift : float or None + Optional radial cutoff in pixel-shift units (keeps only shifts with |shift| <= max_shift) return_shifted_image : bool If True, return the shifted version of `im` aligned to `im_ref` - device : str - 'cpu' or 'gpu' (requires CuPy) + fft_input : bool + If True, assumes im_ref and im are already in Fourier space + fft_output : bool + If True and return_shifted_image=True, return the shifted image in Fourier space Returns ------- shifts : tuple of float (row_shift, col_shift) to align `im` to `im_ref` image_shifted : ndarray (optional) - Shifted image in real space, only returned if return_shifted_image=True + Shifted image in real space (or Fourier space if fft_output=True) """ - if device == "gpu": - import cupy as cp # type: ignore - - xp = cp - else: - xp = np + F_ref = np.asarray(im_ref) if fft_input else np.fft.fft2(np.asarray(im_ref)) + F_im = np.asarray(im) if fft_input else np.fft.fft2(np.asarray(im)) - # Fourier transforms - F_ref = im_ref if fft_input else xp.fft.fft2(im_ref) - F_im = im if fft_input else xp.fft.fft2(im) + cc = F_ref * np.conj(F_im) + cc_real = np.fft.ifft2(cc).real - # Correlation - cc = F_ref * xp.conj(F_im) - cc_real = xp.real(xp.fft.ifft2(cc)) + M, N = cc_real.shape if max_shift is not None: - x = np.fft.fftfreq(cc.shape[0], 1 / cc.shape[0]) - y = np.fft.fftfreq(cc.shape[1], 1 / cc.shape[1]) - mask = x[:, None] ** 2 + y[None, :] ** 2 >= max_shift**2 - cc_real[mask] = 0.0 + x = np.fft.fftfreq(M) * M + y = np.fft.fftfreq(N) * N + mask = x[:, None] ** 2 + y[None, :] ** 2 > float(max_shift) ** 2 + cc_real = cc_real.copy() + cc_real[mask] = -np.inf - # Coarse peak - peak = xp.unravel_index(xp.argmax(cc_real), cc_real.shape) - x0, y0 = peak + flat_idx = int(np.argmax(cc_real)) + x0 = flat_idx // N + y0 = flat_idx % N - # Parabolic refinement - x_inds = xp.mod(x0 + xp.arange(-1, 2), cc.shape[0]).astype(int) - y_inds = xp.mod(y0 + xp.arange(-1, 2), cc.shape[1]).astype(int) + x_inds = [((x0 + dx) % M) for dx in (-1, 0, 1)] + y_inds = [((y0 + dy) % N) for dy in (-1, 0, 1)] vx = cc_real[x_inds, y0] vy = cc_real[x0, y_inds] - def parabolic_peak(v): - return (v[2] - v[0]) / (4 * v[1] - 2 * v[2] - 2 * v[0]) + dx = _parabolic_peak(vx) + dy = _parabolic_peak(vy) - dx = parabolic_peak(vx) - dy = parabolic_peak(vy) + x0 = np.round((float(x0) + float(dx)) * 2.0) / 2.0 + y0 = np.round((float(y0) + float(dy)) * 2.0) / 2.0 - x0 = (x0 + dx) % cc.shape[0] - y0 = (y0 + dy) % cc.shape[1] - - if upsample_factor <= 1: - shifts = (x0, y0) - else: - # Local DFT upsampling + xy_shift = np.array([x0, y0], dtype=float) - local = dft_upsample(cc, upsample_factor, (x0, y0), device=device) - peak = np.unravel_index(xp.argmax(local), local.shape) - - try: - lx, ly = peak - icc = local[lx - 1 : lx + 2, ly - 1 : ly + 2] - if icc.shape == (3, 3): - dxf = parabolic_peak(icc[:, 1]) - dyf = parabolic_peak(icc[1, :]) - else: - raise ValueError("Subarray too close to edge") - except (IndexError, ValueError): - dxf = dyf = 0.0 - - shifts = np.array([x0, y0]) + (np.array(peak) - upsample_factor) / upsample_factor - shifts += np.array([dxf, dyf]) / upsample_factor + if upsample_factor > 2: + xy_shift = _upsampled_correlation_numpy(cc, int(upsample_factor), xy_shift) - shifts = (shifts + 0.5 * np.array(cc.shape)) % cc.shape - 0.5 * np.array(cc.shape) + shifts = np.empty(2, dtype=float) + shifts[0] = ((xy_shift[0] + M / 2) % M) - M / 2 + shifts[1] = ((xy_shift[1] + N / 2) % N) - N / 2 + shifts = (float(shifts[0]), float(shifts[1])) if not return_shifted_image: return shifts - # Fourier shift image (F_im assumed to be FFT) - kx = xp.fft.fftfreq(F_im.shape[0])[:, None] - ky = xp.fft.fftfreq(F_im.shape[1])[None, :] - phase_ramp = xp.exp(-2j * np.pi * (kx * shifts[0] + ky * shifts[1])) + kx = np.fft.fftfreq(F_im.shape[0])[:, None] + ky = np.fft.fftfreq(F_im.shape[1])[None, :] + phase_ramp = np.exp(-2j * np.pi * (kx * shifts[0] + ky * shifts[1])) F_im_shifted = F_im * phase_ramp + if fft_output: image_shifted = F_im_shifted else: - image_shifted = xp.real(xp.fft.ifft2(F_im_shifted)) + image_shifted = np.fft.ifft2(F_im_shifted).real return shifts, image_shifted @@ -176,7 +187,6 @@ def cross_correlation_shift_torch( xy_shift = align_images_fourier_torch(G1, G2, upsample_factor) - # convert to centered signed shifts as original code M, N = im_ref.shape dx = ((xy_shift[0] + M / 2) % M) - M / 2 dy = ((xy_shift[1] + N / 2) % N) - N / 2 @@ -198,12 +208,10 @@ def align_images_fourier_torch( cc = G1 * G2.conj() cc_real = torch.fft.ifft2(cc).real - # local max (integer) flat_idx = torch.argmax(cc_real) x0 = (flat_idx // cc_real.shape[1]).to(torch.long).item() y0 = (flat_idx % cc_real.shape[1]).to(torch.long).item() - # half pixel shifts: pick ±1 indices with wrap (mod) M, N = cc_real.shape x_inds = [((x0 + dx) % M) for dx in (-1, 0, 1)] y_inds = [((y0 + dy) % N) for dy in (-1, 0, 1)] @@ -211,14 +219,11 @@ def align_images_fourier_torch( vx = cc_real[x_inds, y0] vy = cc_real[x0, y_inds] - # parabolic half-pixel refine - # dx = (vx[2] - vx[0]) / (4*vx[1] - 2*vx[2] - 2*vx[0]) denom_x = 4.0 * vx[1] - 2.0 * vx[2] - 2.0 * vx[0] denom_y = 4.0 * vy[1] - 2.0 * vy[2] - 2.0 * vy[0] dx = (vx[2] - vx[0]) / denom_x if denom_x != 0 else torch.tensor(0.0, device=device) dy = (vy[2] - vy[0]) / denom_y if denom_y != 0 else torch.tensor(0.0, device=device) - # round to nearest half-pixel x0 = torch.round((x0 + dx) * 2.0) / 2.0 y0 = torch.round((y0 + dy) * 2.0) / 2.0 @@ -243,7 +248,6 @@ def upsampled_correlation_torch( xyShift: 2-element tensor (x,y) in image coords; must be half-pixel precision as described. Returns refined xyShift (tensor length 2). """ - assert upsampleFactor > 2 xyShift = torch.round(xyShift * float(upsampleFactor)) / float(upsampleFactor) @@ -254,28 +258,25 @@ def upsampled_correlation_torch( im_up = dftUpsample_torch(conj_input, upsampleFactor, upsampleCenter) imageCorrUpsample = im_up.conj() - # find maximum - # flatten argmax -> unravel to 2D flat_idx = torch.argmax(imageCorrUpsample.real) - # unravel_index xySubShift0 = (flat_idx // imageCorrUpsample.shape[1]).to(torch.long) xySubShift1 = (flat_idx % imageCorrUpsample.shape[1]).to(torch.long) xySubShift = torch.tensor([xySubShift0.item(), xySubShift1.item()]) - # parabolic subpixel refinement dx = 0.0 dy = 0.0 try: - # extract 3x3 patch around found peak r = xySubShift[0].item() c = xySubShift[1].item() patch = imageCorrUpsample.real[r - 1 : r + 2, c - 1 : c + 2] - # if patch is incomplete (near edge) this will raise / have wrong shape -> except if patch.shape == (3, 3): icc = patch - # dx corresponds to row direction (vertical axis) as in original code: - dx = (icc[2, 1] - icc[0, 1]) / (4.0 * icc[1, 1] - 2.0 * icc[2, 1] - 2.0 * icc[0, 1]) - dy = (icc[1, 2] - icc[1, 0]) / (4.0 * icc[1, 1] - 2.0 * icc[1, 2] - 2.0 * icc[1, 0]) + dx = (icc[2, 1] - icc[0, 1]) / ( + 4.0 * icc[1, 1] - 2.0 * icc[2, 1] - 2.0 * icc[0, 1] + ) + dy = (icc[1, 2] - icc[1, 0]) / ( + 4.0 * icc[1, 1] - 2.0 * icc[1, 2] - 2.0 * icc[1, 0] + ) dx = dx.item() dy = dy.item() else: @@ -283,7 +284,6 @@ def upsampled_correlation_torch( except Exception: dx, dy = 0.0, 0.0 - # convert xySubShift to zero-centered by subtracting globalShift xySubShift = xySubShift.to(dtype=torch.get_default_dtype()) xySubShift = xySubShift - globalShift.to(xySubShift.dtype) @@ -312,13 +312,9 @@ def dftUpsample_torch( numRow = int(math.ceil(pixelRadius * upsampleFactor)) numCol = numRow - # prepare the vectors exactly like the numpy version - # col: frequency indices (centered) for N col_freq = torch.fft.ifftshift(torch.arange(N, device=device)) - math.floor(N / 2) - # row: frequency indices (centered) for M row_freq = torch.fft.ifftshift(torch.arange(M, device=device)) - math.floor(M / 2) - # small upsample grid coordinates (integer positions in the UPSAMPLED GRID) col_coords = torch.arange(numCol, device=device, dtype=torch.get_default_dtype()) - float( xyShift[1] ) @@ -326,25 +322,18 @@ def dftUpsample_torch( xyShift[0] ) - # build kernels: note factor signs and denominators match original numpy code - # colKern: shape (N, numCol) factor_col = -2j * math.pi / (N * float(upsampleFactor)) - # outer(col_freq, col_coords) -> shape (N, numCol) colKern = torch.exp(factor_col * (col_freq.unsqueeze(1) * col_coords.unsqueeze(0))).to( imageCorr.dtype ) - # rowKern: shape (numRow, M) factor_row = -2j * math.pi / (M * float(upsampleFactor)) - # outer(row_coords, row_freq) -> shape (numRow, M) rowKern = torch.exp(factor_row * (row_coords.unsqueeze(1) * row_freq.unsqueeze(0))).to( imageCorr.dtype ) - # perform the small-matrix DFT: (numRow, M) @ (M, N) @ (N, numCol) -> (numRow, numCol) imageUpsample = rowKern @ imageCorr @ colKern - # original code took xp.real(...) before returning return imageUpsample.real @@ -362,32 +351,6 @@ def bilinear_kde( ) -> NDArray | tuple[NDArray, NDArray]: """ Compute a bilinear kernel density estimate (KDE) with smooth threshold masking. - - Parameters - ---------- - xa : NDArray - Vertical (row) coordinates of input points. - ya : NDArray - Horizontal (col) coordinates of input points. - values : NDArray - Weights for each (xa, ya) point. - output_shape : tuple of int - Output image shape (rows, cols). - kde_sigma : float - Standard deviation of Gaussian KDE smoothing. - pad_value : float, default = 1.0 - Value to return when KDE support is too low. - threshold : float, default = 1e-3 - Minimum counts_KDE value for trusting the output signal. - lowpass_filter : bool, optional - If True, apply sinc-based inverse filtering to deconvolve the kernel. - max_batch_size : int or None, optional - Max number of points to process in one batch. - - Returns - ------- - NDArray - The estimated KDE image with threshold-masked output. """ rows, cols = output_shape xF = np.floor(xa.ravel()).astype(int) @@ -417,14 +380,12 @@ def bilinear_kde( inds_1D, weights=weights * w[start:end], minlength=rows * cols ) - # Reshape to 2D and apply Gaussian KDE pix_count = pix_count.reshape(output_shape) pix_output = pix_output.reshape(output_shape) pix_count = gaussian_filter(pix_count, kde_sigma) pix_output = gaussian_filter(pix_output, kde_sigma) - # Final image weight = np.minimum(pix_count / threshold, 1.0) image = pad_value * (1.0 - weight) + weight * (pix_output / np.maximum(pix_count, 1e-8)) @@ -456,23 +417,7 @@ def bilinear_array_interpolation( ) -> NDArray: """ Bilinear sampling of values from an array and pixel positions. - - Parameters - ---------- - image: np.ndarray - Image array to sample from - xa: np.ndarray - Vertical interpolation sampling positions of image array in pixels - ya: np.ndarray - Horizontal interpolation sampling positions of image array in pixels - - Returns - ------- - values: np.ndarray - Bilinear interpolation values of array at (xa,ya) positions - """ - xF = np.floor(xa.ravel()).astype("int") yF = np.floor(ya.ravel()).astype("int") dx = xa.ravel() - xF @@ -498,10 +443,7 @@ def bilinear_array_interpolation( values[start:end] += raveled_image[inds_1D] * weights - values = np.reshape( - values, - xa.shape, - ) + values = np.reshape(values, xa.shape) return values @@ -513,20 +455,7 @@ def fourier_cropping( """ Crops a corner-centered FFT array to retain only the lowest frequencies, equivalent to a center crop on the fftshifted version. - - Parameters: - ----------- - corner_centered_array : ndarray - 2D array (typically result of np.fft.fft2) with corner-centered DC - crop_shape : tuple of int - (height, width) of the desired cropped array (could be odd or even depending on arr.shape) - - Returns: - -------- - cropped : ndarray - Cropped array containing only the lowest frequencies, still corner-centered. """ - H, W = corner_centered_array.shape crop_h, crop_w = crop_shape @@ -537,13 +466,9 @@ def fourier_cropping( result = np.zeros(crop_shape, dtype=corner_centered_array.dtype) - # Top-left result[:h1, :w1] = corner_centered_array[:h1, :w1] - # Top-right result[:h1, -w2:] = corner_centered_array[:h1, -w2:] - # Bottom-left result[-h2:, :w1] = corner_centered_array[-h2:, :w1] - # Bottom-right result[-h2:, -w2:] = corner_centered_array[-h2:, -w2:] return result @@ -557,22 +482,6 @@ def compute_fsc_from_halfsets( """ Compute radially averaged Fourier Shell Correlation (FSC) from two half-set reconstructions. - - Parameters - ---------- - halfset_recons : list[torch.Tensor] - Two statistically-independent reconstructions, using half the dataset. - sampling: tuple[float,float] - Reconstruction sampling in Angstroms. - epsilon: float, optional - Small number to avoid dividing by zero - - Returns - ------- - q_bins: NDarray - Spatial frequency bins - fsc : NDarray - Fourier shell correlation as function of spatial frequency """ r1, r2 = halfset_recons @@ -602,12 +511,10 @@ def compute_fsc_from_halfsets( w0 = 1.0 - d_ind w1 = d_ind - # Flatten arrays cross = cross.reshape(-1) p1 = p1.reshape(-1) p2 = p2.reshape(-1) - # Accumulate cross_b = torch.bincount(inds_f, weights=cross * w0, minlength=num_bins) + torch.bincount( inds_f + 1, weights=cross * w1, minlength=num_bins ) @@ -637,45 +544,14 @@ def compute_spectral_snr_from_halfsets( ): """ Compute spectral SNR from two half-set reconstructions using symmetric/antisymmetric decomposition. - - The method decomposes the Fourier transforms into: - - Symmetric: (F₁ + F₂)/2 → signal + correlated noise - - Antisymmetric: (F₁ - F₂)/2 → uncorrelated noise only - - SSNR(q) = sqrt(signal_power / noise_power) - - where: - - signal_power = (|symmetric|² - |antisymmetric|²)₊ - - noise_power = |antisymmetric|² - - Parameters - ---------- - halfset_recons : list[torch.Tensor] - Two statistically-independent reconstructions, using half the dataset. - sampling: tuple[float,float] - Reconstruction sampling in Angstroms. - total_dose: float - Total _normalized_ electron dose, e.g. in DirectPtychography this is ~self.num_bf - epsilon: float, optional - Small number to avoid dividing by zero - - Returns - ------- - q_bins: NDarray - Spatial frequency bins - ssnr : NDarray - Radially averaged spectral SNR as function of spatial frequency """ - # Compute Fourier transforms halfset_1, halfset_2 = halfset_recons F1 = torch.fft.fft2(halfset_1) F2 = torch.fft.fft2(halfset_2) - # Symmetric and antisymmetric decomposition symmetric = (F1 + F2) / 2 antisymmetric = (F1 - F2) / 2 - # Power spectra noise_power = antisymmetric.abs() total_power = symmetric.abs() signal_power = (total_power - noise_power).clamp_min(0) @@ -699,11 +575,9 @@ def compute_spectral_snr_from_halfsets( w0 = 1.0 - d_ind w1 = d_ind - # Flatten arrays signal = signal_power.reshape(-1) noise = noise_power.reshape(-1) - # Accumulate signal_b = torch.bincount(inds_f, weights=signal * w0, minlength=num_bins) + torch.bincount( inds_f + 1, weights=signal * w1, minlength=num_bins ) @@ -726,20 +600,6 @@ def radially_average_fourier_array( ): """ Radially average a corner-centered Fourier array. - - Parameters - ---------- - corner_centered_array : list[torch.Tensor] - Fourier array to average radially. - sampling: tuple[float,float] - Reconstruction sampling in Angstroms. - - Returns - ------- - q_bins: NDarray - Spatial frequency bins - array_1d : NDarray - Radially averaged Fourier array as function of spatial frequency """ device = corner_centered_array.device nx, ny = corner_centered_array.shape @@ -760,10 +620,8 @@ def radially_average_fourier_array( w0 = 1.0 - d_ind w1 = d_ind - # Flatten arrays array = corner_centered_array.reshape(-1) - # Accumulate array_b = torch.bincount(inds_f, weights=array * w0, minlength=num_bins) + torch.bincount( inds_f + 1, weights=array * w1, minlength=num_bins ) @@ -842,9 +700,7 @@ def add_edges(i1, i2): inc = _find_wrap(phi_f[i1], phi_f[i2]) rel = rel_f[i1] + rel_f[i2] - edges.append( # ty:ignore[possibly-missing-attribute] - torch.stack([i1, i2, rel, inc], dim=1) - ) + edges.append(torch.stack([i1, i2, rel, inc], dim=1)) if wrap_around: add_edges(idx.flatten(), torch.roll(idx, -1, 1).flatten()) @@ -856,7 +712,6 @@ def add_edges(i1, i2): edges = torch.cat(edges, dim=0) edges = edges[edges[:, 2].argsort()] - # return integer tensors only (CPU) return ( edges[:, 0].long(), edges[:, 1].long(), @@ -885,7 +740,6 @@ def union(self, x, y, inc_xy): if rx == ry: return - # phase(y) + oy + inc = phase(x) + ox delta = ox - oy - inc_xy if self.rank[rx] < self.rank[ry]: @@ -963,18 +817,6 @@ def _unwrap_phase_2d_torch_poisson( ): """ Least-squares / Poisson phase unwrapping with optional mask. - - Parameters - ---------- - phi_wrapped : (H, W) tensor - Wrapped phase in (-pi, pi], any device - mask : (H, W) bool tensor, optional - True = valid pixel - - Returns - ------- - phi_unwrapped : (H, W) tensor - Unwrapped phase (same device as input) """ device = phi_wrapped.device dtype = phi_wrapped.dtype @@ -1014,10 +856,10 @@ def _unwrap_phase_2d_torch_poisson( denom = kx**2 + ky**2 + regularization_lambda else: denom = kx**2 + ky**2 - denom[0, 0] = 1.0 # avoid divide by zero + denom[0, 0] = 1.0 phi_hat = -div_hat / denom - phi_hat[0, 0] = 0.0 # fix piston + phi_hat[0, 0] = 0.0 phi = torch.fft.ifftn(phi_hat).real @@ -1051,7 +893,6 @@ def unwrap_phase_2d_torch( ) - def rotate_image( im, rotation_deg: float, diff --git a/tests/core/utils/test_imaging_utils.py b/tests/core/utils/test_imaging_utils.py new file mode 100644 index 00000000..691756dd --- /dev/null +++ b/tests/core/utils/test_imaging_utils.py @@ -0,0 +1,75 @@ +""" +Tests for imaging utilities in quantem.core.utils.imaging_utils +""" + +import numpy as np +import pytest + +torch = pytest.importorskip("torch") + +from quantem.core.utils.imaging_utils import cross_correlation_shift, cross_correlation_shift_torch + + +@pytest.fixture +def spot_image(): + from scipy.ndimage import gaussian_filter + + im = np.zeros((64, 64), dtype=np.float64) + im[32, 32] = 1.0 + im = gaussian_filter(im, 2.0) + im /= np.max(im) + return im + + +def _fourier_shift_numpy(im: np.ndarray, shift_rc: tuple[float, float]) -> np.ndarray: + dr, dc = shift_rc + kr = np.fft.fftfreq(im.shape[0])[:, None] + kc = np.fft.fftfreq(im.shape[1])[None, :] + F = np.fft.fft2(im) + phase = np.exp(-2j * np.pi * (kr * dr + kc * dc)) + return np.fft.ifft2(F * phase).real + + +def _wrap_shift_rc(shift_rc: tuple[float, float], shape: tuple[int, int]) -> tuple[float, float]: + dr, dc = shift_rc + M, N = shape + dr = ((dr + M / 2) % M) - M / 2 + dc = ((dc + N / 2) % N) - N / 2 + return float(dr), float(dc) + + +@pytest.mark.parametrize( + "shift_true, upsample_factor, atol", + [ + ((5.0, -3.0), 1000, 1e-3), + ((-7.123, 1.789), 1000, 1e-3), + ], +) +def test_cross_correlation_shift_numpy_matches_expected(spot_image, shift_true, upsample_factor, atol): + im_ref = spot_image + im = _fourier_shift_numpy(im_ref, shift_true) + expected = _wrap_shift_rc((-shift_true[0], -shift_true[1]), im_ref.shape) + + meas = cross_correlation_shift(im_ref, im, upsample_factor=upsample_factor) + assert meas[0] == pytest.approx(expected[0], abs=atol) + assert meas[1] == pytest.approx(expected[1], abs=atol) + + +@pytest.mark.parametrize( + "shift_true, upsample_factor, atol", + [ + ((5.0, -3.0), 1000, 1e-3), + ((-7.123, 1.789), 1000, 1e-3), + ], +) +def test_cross_correlation_shift_torch_matches_expected(spot_image, shift_true, upsample_factor, atol): + im_ref = spot_image + im = _fourier_shift_numpy(im_ref, shift_true) + expected = _wrap_shift_rc((-shift_true[0], -shift_true[1]), im_ref.shape) + + t_ref = torch.from_numpy(im_ref) + t_im = torch.from_numpy(im) + meas = cross_correlation_shift_torch(t_ref, t_im, upsample_factor=upsample_factor).cpu().numpy() + + assert float(meas[0]) == pytest.approx(expected[0], abs=atol) + assert float(meas[1]) == pytest.approx(expected[1], abs=atol) From daa39e7ef045fcb0cffcf059848f9c55952d660b Mon Sep 17 00:00:00 2001 From: cophus Date: Sat, 31 Jan 2026 16:19:17 -0800 Subject: [PATCH 107/140] initial maped class commit --- src/quantem/diffraction/maped.py | 169 ++++++++++++++++++++++++++++++- 1 file changed, 164 insertions(+), 5 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index f301454e..ca14c92f 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -3,6 +3,7 @@ from typing import Any, Sequence import numpy as np +from scipy.signal.windows import tukey from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize @@ -34,7 +35,6 @@ def from_datasets(cls, datasets: Sequence[Dataset4dstem]) -> "MAPED": def preprocess( self, - *, plot_summary: bool = True, scale: float | Sequence[float] | None = None, **plot_kwargs: Any, @@ -76,12 +76,171 @@ def preprocess( self.im_bf.append(np.asarray(im_bf_arr)) if plot_summary: + tiles = [[(self.im_bf[i] / self.scales[i]), self.dp_mean[i]] for i in range(n)] + titles = [[f"{i} - Mean Bright Field", f"{i} - Mean Diffraction Pattern"] for i in range(n)] show_2d( - [ - [self.im_bf[i] / self.scales[i] for i in range(n)], - [self.dp_mean[i] for i in range(n)], - ], + tiles, + titles=titles, **plot_kwargs, ) return self + + + def diffraction_find_origin( + self, + origins=None, + sigma=None, + plot_origins: bool = True, + plot_indices=None, + ): + """ + Choose or automatically find the origin in diffraction space. + + Parameters + ---------- + origins + Optional manual origins. Can be: + - a single (row, col) tuple, applied to all datasets + - a list of (row, col) tuples of length n (one per dataset) + - a list of (row, col) tuples shorter than n, used for plot/inspection only (will error if not broadcastable) + sigma + Optional low-pass smoothing sigma (pixels) applied to each mean DP prior to peak finding. + plot_origins + If True, plot mean diffraction patterns with overlaid origin markers. + plot_indices + Optional indices to plot. If None, plots all datasets. + + Stores + ------ + self.diffraction_origins : np.ndarray + Array of shape (n, 2) with integer (row, col) origins. + """ + import numpy as _np + + try: + from scipy.ndimage import gaussian_filter as _gaussian_filter + except Exception: # pragma: no cover + _gaussian_filter = None + + n = len(self.datasets) + if not hasattr(self, "dp_mean"): + raise RuntimeError("Run preprocess() first so self.dp_mean exists.") + + if plot_indices is None: + plot_indices_list = list(range(n)) + else: + plot_indices_list = list(plot_indices) + for i in plot_indices_list: + if i < 0 or i >= n: + raise IndexError("plot_indices contains an out-of-range index.") + + if origins is None: + origins_arr = _np.zeros((n, 2), dtype=int) + for i in range(n): + dp = _np.asarray(self.dp_mean[i]) + if sigma is not None and float(sigma) > 0: + if _gaussian_filter is None: + raise ImportError("scipy is required for sigma smoothing (gaussian_filter).") + dp_use = _gaussian_filter(dp.astype(float, copy=False), float(sigma)) + else: + dp_use = dp + ind = int(_np.argmax(dp_use)) + r, c = _np.unravel_index(ind, dp_use.shape) + origins_arr[i, 0] = int(r) + origins_arr[i, 1] = int(c) + else: + if isinstance(origins, tuple) and len(origins) == 2: + origins_arr = _np.tile(_np.asarray(origins, dtype=int)[None, :], (n, 1)) + else: + origins_list = list(origins) + if len(origins_list) != n: + raise ValueError("origins must be a single (row,col) tuple or a list of length n.") + origins_arr = _np.asarray(origins_list, dtype=int) + if origins_arr.shape != (n, 2): + raise ValueError("origins must have shape (n, 2) after conversion.") + + self.diffraction_origins = origins_arr + + if plot_origins: + dp_tiles = [[_np.asarray(self.dp_mean[i]) for i in plot_indices_list]] + titles = [[f"{i} - Mean Diffraction Pattern" for i in plot_indices_list]] + fig, axs = show_2d(dp_tiles, titles=titles, returnfig=True, **{}) + if not isinstance(axs, (list, _np.ndarray)): + axs = [axs] + axs_flat = _np.ravel(axs) + for j, i in enumerate(plot_indices_list): + ax = axs_flat[j] + r, c = self.diffraction_origins[i] + ax.plot([c], [r], marker="+", color="red", markersize=16, markeredgewidth=2) + return fig, axs + + return self + + + def diffraction_align( + self, + edge_blend = 8.0, + padding = None, + weight_scale = 1/8, + plot_aligned = True, + linewidth = 2, + **kwargs, + ): + """ + Refine the diffraction space origins, set padding, align images + + """ + + # window function + from scipy.signal.windows import tukey + w = tukey(self.dp_mean[0].shape[0], alpha=2.0*edge_blend/self.dp_mean[0].shape[0])[:,None] * \ + tukey(self.dp_mean[0].shape[1], alpha=2.0*edge_blend/self.dp_mean[0].shape[1])[None,:] + + # coordinates + r = np.fft.fftfreq(self.dp_mean[0].shape[0],1/self.dp_mean[0].shape[0])[:,None] + c = np.fft.fftfreq(self.dp_mean[0].shape[1],1/self.dp_mean[0].shape[1])[None,:] + + # init + shifts = np.zeros((len(self.dp_mean),2)) + + # correlation alignment + G_ref = np.fft.fft2(w * self.dp_mean[0]) + xy0 = self.diffraction_origins[0] + for ind in range(1,2): + G = np.conj(np.fft.fft2(w * self.dp_mean[ind])) + xy = self.diffraction_origins[ind] + + dr2 = (r - xy0[0] + xy[0])**2 \ + + (c - xy0[1] + xy[1])**2 + im_weight = np.clip(1 - np.sqrt(dr2)/np.mean(self.dp_mean[0].shape)/weight_scale, 0.0, 1.0) + im_weight = np.sin(im_weight*np.pi/2)**2 + + im_corr = np.real(np.fft.ifft2(G_ref * G)) * im_weight + + + + if plot_aligned: + show_2d( + np.fft.fftshift(im_corr), + norm = { + 'upper_quantile':1.0, + }, + **kwargs, + ) + + + def real_space_align( + self + ): + pass + + + + def merge_datasets( + self + ): + pass + + + \ No newline at end of file From ffc46ca70b53182d81a6d2596e0bb1989fa01459 Mon Sep 17 00:00:00 2001 From: cophus Date: Sat, 31 Jan 2026 17:20:00 -0800 Subject: [PATCH 108/140] Updating with weighted correlation --- src/quantem/core/utils/imaging_utils.py | 130 ++++++++++++++++++++++++ src/quantem/diffraction/maped.py | 21 +++- tests/core/utils/test_imaging_utils.py | 56 +++++++++- 3 files changed, 204 insertions(+), 3 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 3004c7fd..5cbfdfbc 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -337,6 +337,136 @@ def dftUpsample_torch( return imageUpsample.real +def weighted_cross_correlation_shift( + im_ref=None, + im=None, + *, + cc=None, + weight_real=None, + upsample_factor: int = 1, + max_shift=None, + fft_input: bool = False, + return_shifted: bool = False, + shifted_output: str = "real", +): + """ + Weighted peak selection + DFT subpixel refinement for Fourier cross-correlation. + + You can provide either: + - im_ref and im (real-space images, or Fourier-domain if fft_input=True), OR + - cc (the Fourier-domain cross-spectrum), where cc = F_ref * conj(F_im) + + The weight is applied ONLY in real-space correlation to choose the peak location, + but the subpixel refinement uses the true (unweighted) cross-spectrum `cc`. + + Parameters + ---------- + im_ref, im : ndarray or None + Input images (real space) or their FFTs (if fft_input=True). + cc : ndarray or None + Fourier-domain cross-spectrum cc = F_ref * conj(F_im). If provided, im_ref/im are ignored. + weight_real : ndarray or None + Real-space weight image (same shape as correlation). Used only for peak selection. + If None, peak selection is unweighted. + upsample_factor : int + <= 2: half-pixel refinement (parabolic then rounded to nearest 0.5 px) + > 2 : additional DFT upsample refinement via _upsampled_correlation_numpy + max_shift : float or None + Optional radial cutoff (in pixels) applied to the (weighted) real correlation during peak pick. + fft_input : bool + If True, im_ref and im are already Fourier-domain arrays. + return_shifted : bool + If True, also return shifted version of `im` (or its FFT) aligned to `im_ref`. + Requires im to be provided (or fft_input=True with im as FFT). If only cc is provided, + shifted output is unavailable. + shifted_output : {"real","fft"} + Output type for the shifted image. + + Returns + ------- + shift_rc : tuple[float, float] + (d_row, d_col) shift to apply to `im` to align it to `im_ref`. + shifted : ndarray (optional) + Shifted image (real) or FFT (corner-centered) depending on shifted_output. + """ + import numpy as np + + from quantem.core.utils.imaging_utils import _parabolic_peak, _upsampled_correlation_numpy + + if cc is None: + if im_ref is None or im is None: + raise ValueError("Provide either `cc` or both `im_ref` and `im`.") + F_ref = np.asarray(im_ref) if fft_input else np.fft.fft2(np.asarray(im_ref)) + F_im = np.asarray(im) if fft_input else np.fft.fft2(np.asarray(im)) + cc = F_ref * np.conj(F_im) + else: + cc = np.asarray(cc) + F_im = None + + cc_real = np.fft.ifft2(cc).real + M, N = cc_real.shape + + if weight_real is not None: + w = np.asarray(weight_real) + if w.shape != cc_real.shape: + raise ValueError(f"weight_real.shape={w.shape} must match correlation shape {cc_real.shape}.") + cc_pick = cc_real * w + else: + cc_pick = cc_real + + if max_shift is not None: + x = np.fft.fftfreq(M) * M + y = np.fft.fftfreq(N) * N + mask = x[:, None] ** 2 + y[None, :] ** 2 > float(max_shift) ** 2 + cc_pick = cc_pick.copy() + cc_pick[mask] = -np.inf + + flat_idx = int(np.argmax(cc_pick)) + x0 = flat_idx // N + y0 = flat_idx % N + + x_inds = [((x0 + dx) % M) for dx in (-1, 0, 1)] + y_inds = [((y0 + dy) % N) for dy in (-1, 0, 1)] + vx = cc_pick[x_inds, y0] + vy = cc_pick[x0, y_inds] + + dx = _parabolic_peak(vx) + dy = _parabolic_peak(vy) + + x0 = np.round((float(x0) + float(dx)) * 2.0) / 2.0 + y0 = np.round((float(y0) + float(dy)) * 2.0) / 2.0 + xy_shift = np.array([x0, y0], dtype=float) + + if upsample_factor > 2: + xy_shift = _upsampled_correlation_numpy(cc, int(upsample_factor), xy_shift) + + dr = ((xy_shift[0] + M / 2) % M) - M / 2 + dc = ((xy_shift[1] + N / 2) % N) - N / 2 + shift_rc = (float(dr), float(dc)) + + if not return_shifted: + return shift_rc + + if im is None: + raise ValueError("return_shifted=True requires `im` (or its FFT via fft_input=True).") + + if F_im is None: + F_im = np.asarray(im) if fft_input else np.fft.fft2(np.asarray(im)) + + kr = np.fft.fftfreq(M)[:, None] + kc = np.fft.fftfreq(N)[None, :] + phase_ramp = np.exp(-2j * np.pi * (kr * shift_rc[0] + kc * shift_rc[1])) + F_im_shifted = F_im * phase_ramp + + out_mode = str(shifted_output).lower() + if out_mode in {"fft", "fourier"}: + return shift_rc, F_im_shifted + if out_mode in {"real", "image"}: + return shift_rc, np.fft.ifft2(F_im_shifted).real + + raise ValueError("shifted_output must be 'real' or 'fft'.") + + def bilinear_kde( xa: NDArray, ya: NDArray, diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index ca14c92f..a6b21d5b 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -8,6 +8,10 @@ from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize from quantem.core.visualization import show_2d +# from quantem.core.utils.imaging_utils import cross_correlation_shift +# from quantem.core.utils.imaging_utils import dft_upsample +# from quantem.core.utils.imaging_utils import correlation_peak_shift_from_real +from quantem.core.utils.imaging_utils import cross_correlation_shift class MAPED(AutoSerialize): @@ -216,8 +220,23 @@ def diffraction_align( im_weight = np.clip(1 - np.sqrt(dr2)/np.mean(self.dp_mean[0].shape)/weight_scale, 0.0, 1.0) im_weight = np.sin(im_weight*np.pi/2)**2 - im_corr = np.real(np.fft.ifft2(G_ref * G)) * im_weight + # im_corr = np.real(np.fft.ifft2(G_ref * G)) * im_weight + # cc_weighted = + cc = G_ref * G + cc_weighted = np.fft.fft2(np.fft.ifft2(cc)) * im_weight + + + shift_rc = cross_correlation_shift( + cc_weighted, + np.ones_like(cc_weighted), # identity: cc = cc_weighted * conj(1) = cc_weighted + fft_input=True, # treat inputs as Fourier-domain + upsample_factor=100, # or whatever you want + ) + + shift = correlation_peak_shift_from_real(im_corr, upsample_factor=100) + + print(shift) if plot_aligned: diff --git a/tests/core/utils/test_imaging_utils.py b/tests/core/utils/test_imaging_utils.py index 691756dd..bc3989d6 100644 --- a/tests/core/utils/test_imaging_utils.py +++ b/tests/core/utils/test_imaging_utils.py @@ -3,16 +3,16 @@ """ import numpy as np +from scipy.ndimage import gaussian_filter import pytest torch = pytest.importorskip("torch") -from quantem.core.utils.imaging_utils import cross_correlation_shift, cross_correlation_shift_torch +from quantem.core.utils.imaging_utils import cross_correlation_shift, cross_correlation_shift_torch, weighted_cross_correlation_shift @pytest.fixture def spot_image(): - from scipy.ndimage import gaussian_filter im = np.zeros((64, 64), dtype=np.float64) im[32, 32] = 1.0 @@ -73,3 +73,55 @@ def test_cross_correlation_shift_torch_matches_expected(spot_image, shift_true, assert float(meas[0]) == pytest.approx(expected[0], abs=atol) assert float(meas[1]) == pytest.approx(expected[1], abs=atol) + +import numpy as np +import pytest + +from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift + + +@pytest.fixture +def peak_grid_images(): + im_ref = np.zeros((80, 80), dtype=float) + im = np.zeros_like(im_ref) + + r_ref = np.array([17, 27, 37, 47], dtype=int) + r_im = np.array([27, 37, 47, 57], dtype=int) # shifted +10 rows + c = np.array([17, 27, 37, 47], dtype=int) + + for rr in r_ref: + for cc in c: + im_ref[rr, cc] = 1.0 + + for rr in r_im: + for cc in c: + im[rr, cc] = 1.0 + + im_ref[37,27] = 3.0 + im[27,27] = 3.0 + + im_ref = gaussian_filter(im_ref,1.0) + im = gaussian_filter(im,1.0) + + # Smooth wrapped radial weight centered at 0 shift + M, N = im_ref.shape + fr = np.fft.fftfreq(M) * M + fc = np.fft.fftfreq(N) * N + dr2 = fr[:, None] ** 2 + fc[None, :] ** 2 + + sigma = 3.0 + weight = np.exp(dr2 / (-2.0*sigma**2)) + + return im_ref, im, weight + + +def test_weighted_cross_correlation_shift_unweighted_prefers_full_overlap(peak_grid_images): + im_ref, im, weight = peak_grid_images + shift = weighted_cross_correlation_shift(im_ref, im, upsample_factor=1000) + assert np.allclose(shift, (-10.0, 0.0), atol=1e-3) + + +def test_weighted_cross_correlation_shift_weighted_prefers_near_zero(peak_grid_images): + im_ref, im, weight = peak_grid_images + shift = weighted_cross_correlation_shift(im_ref, im, weight_real=weight, upsample_factor=1000) + assert np.allclose(shift, (0.0, 0.0), atol=1e-3) From 072384d43a911b411f0100b437b05c1814b9edb4 Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 1 Feb 2026 16:53:38 -0800 Subject: [PATCH 109/140] maped output --- src/quantem/core/utils/imaging_utils.py | 54 +-- src/quantem/diffraction/maped.py | 541 ++++++++++++++++++++++-- 2 files changed, 521 insertions(+), 74 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 5cbfdfbc..e94a646c 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -346,53 +346,27 @@ def weighted_cross_correlation_shift( upsample_factor: int = 1, max_shift=None, fft_input: bool = False, - return_shifted: bool = False, - shifted_output: str = "real", + fft_output: bool = False, + return_shifted_image: bool = False, ): """ Weighted peak selection + DFT subpixel refinement for Fourier cross-correlation. - You can provide either: + Provide either: - im_ref and im (real-space images, or Fourier-domain if fft_input=True), OR - cc (the Fourier-domain cross-spectrum), where cc = F_ref * conj(F_im) The weight is applied ONLY in real-space correlation to choose the peak location, but the subpixel refinement uses the true (unweighted) cross-spectrum `cc`. - Parameters - ---------- - im_ref, im : ndarray or None - Input images (real space) or their FFTs (if fft_input=True). - cc : ndarray or None - Fourier-domain cross-spectrum cc = F_ref * conj(F_im). If provided, im_ref/im are ignored. - weight_real : ndarray or None - Real-space weight image (same shape as correlation). Used only for peak selection. - If None, peak selection is unweighted. - upsample_factor : int - <= 2: half-pixel refinement (parabolic then rounded to nearest 0.5 px) - > 2 : additional DFT upsample refinement via _upsampled_correlation_numpy - max_shift : float or None - Optional radial cutoff (in pixels) applied to the (weighted) real correlation during peak pick. - fft_input : bool - If True, im_ref and im are already Fourier-domain arrays. - return_shifted : bool - If True, also return shifted version of `im` (or its FFT) aligned to `im_ref`. - Requires im to be provided (or fft_input=True with im as FFT). If only cc is provided, - shifted output is unavailable. - shifted_output : {"real","fft"} - Output type for the shifted image. - Returns ------- shift_rc : tuple[float, float] (d_row, d_col) shift to apply to `im` to align it to `im_ref`. shifted : ndarray (optional) - Shifted image (real) or FFT (corner-centered) depending on shifted_output. + If return_shifted=True: shifted image. If fft_output=True returns FFT (corner-centered), + else returns real-space image. """ - import numpy as np - - from quantem.core.utils.imaging_utils import _parabolic_peak, _upsampled_correlation_numpy - if cc is None: if im_ref is None or im is None: raise ValueError("Provide either `cc` or both `im_ref` and `im`.") @@ -415,9 +389,9 @@ def weighted_cross_correlation_shift( cc_pick = cc_real if max_shift is not None: - x = np.fft.fftfreq(M) * M - y = np.fft.fftfreq(N) * N - mask = x[:, None] ** 2 + y[None, :] ** 2 > float(max_shift) ** 2 + fr = np.fft.fftfreq(M) * M + fc = np.fft.fftfreq(N) * N + mask = fr[:, None] ** 2 + fc[None, :] ** 2 > float(max_shift) ** 2 cc_pick = cc_pick.copy() cc_pick[mask] = -np.inf @@ -444,11 +418,11 @@ def weighted_cross_correlation_shift( dc = ((xy_shift[1] + N / 2) % N) - N / 2 shift_rc = (float(dr), float(dc)) - if not return_shifted: + if not return_shifted_image: return shift_rc if im is None: - raise ValueError("return_shifted=True requires `im` (or its FFT via fft_input=True).") + raise ValueError("return_shifted_image=True requires `im` (or its FFT via fft_input=True).") if F_im is None: F_im = np.asarray(im) if fft_input else np.fft.fft2(np.asarray(im)) @@ -458,13 +432,9 @@ def weighted_cross_correlation_shift( phase_ramp = np.exp(-2j * np.pi * (kr * shift_rc[0] + kc * shift_rc[1])) F_im_shifted = F_im * phase_ramp - out_mode = str(shifted_output).lower() - if out_mode in {"fft", "fourier"}: + if fft_output: return shift_rc, F_im_shifted - if out_mode in {"real", "image"}: - return shift_rc, np.fft.ifft2(F_im_shifted).real - - raise ValueError("shifted_output must be 'real' or 'fft'.") + return shift_rc, np.fft.ifft2(F_im_shifted).real def bilinear_kde( diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index a6b21d5b..afb1fc0c 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -8,10 +8,7 @@ from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize from quantem.core.visualization import show_2d -# from quantem.core.utils.imaging_utils import cross_correlation_shift -# from quantem.core.utils.imaging_utils import dft_upsample -# from quantem.core.utils.imaging_utils import correlation_peak_shift_from_real -from quantem.core.utils.imaging_utils import cross_correlation_shift +from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift class MAPED(AutoSerialize): @@ -184,8 +181,10 @@ def diffraction_find_origin( def diffraction_align( self, - edge_blend = 8.0, + edge_blend = 16.0, padding = None, + pad_val = 'min', + upsample_factor = 100, weight_scale = 1/8, plot_aligned = True, linewidth = 2, @@ -206,13 +205,13 @@ def diffraction_align( c = np.fft.fftfreq(self.dp_mean[0].shape[1],1/self.dp_mean[0].shape[1])[None,:] # init - shifts = np.zeros((len(self.dp_mean),2)) + self.diffraction_shifts = np.zeros((len(self.dp_mean),2)) # correlation alignment G_ref = np.fft.fft2(w * self.dp_mean[0]) xy0 = self.diffraction_origins[0] - for ind in range(1,2): - G = np.conj(np.fft.fft2(w * self.dp_mean[ind])) + for ind in range(1,len(self.dp_mean)): + G = np.fft.fft2(w * self.dp_mean[ind]) xy = self.diffraction_origins[ind] dr2 = (r - xy0[0] + xy[0])**2 \ @@ -220,46 +219,524 @@ def diffraction_align( im_weight = np.clip(1 - np.sqrt(dr2)/np.mean(self.dp_mean[0].shape)/weight_scale, 0.0, 1.0) im_weight = np.sin(im_weight*np.pi/2)**2 - # im_corr = np.real(np.fft.ifft2(G_ref * G)) * im_weight - # cc_weighted = - - cc = G_ref * G - cc_weighted = np.fft.fft2(np.fft.ifft2(cc)) * im_weight - - - shift_rc = cross_correlation_shift( - cc_weighted, - np.ones_like(cc_weighted), # identity: cc = cc_weighted * conj(1) = cc_weighted - fft_input=True, # treat inputs as Fourier-domain - upsample_factor=100, # or whatever you want + shift, G_shift = weighted_cross_correlation_shift( + im_ref=G_ref, + im=G, + weight_real=im_weight*0+1.0, + upsample_factor = upsample_factor, + fft_input = True, + fft_output = True, + return_shifted_image = True, ) + self.diffraction_shifts[ind,:] = shift - shift = correlation_peak_shift_from_real(im_corr, upsample_factor=100) + # update reference + G_ref = G_ref*(ind/(ind+1)) + G_shift/(ind+1) - print(shift) + # Center shifts + self.diffraction_shifts -= np.mean(self.diffraction_shifts,axis=0)[None,:] + # Generate output image if plot_aligned: + im_aligned = shift_images( + images = self.dp_mean, + shifts_rc = self.diffraction_shifts, + edge_blend = edge_blend, + padding = padding, + pad_val = pad_val, + ) show_2d( - np.fft.fftshift(im_corr), - norm = { - 'upper_quantile':1.0, - }, + im_aligned, **kwargs, ) def real_space_align( - self + self, + num_images=None, + num_iter: int = 3, + edge_blend: float = 1.0, + padding=None, + pad_val: str | float = "median", + upsample_factor: int = 100, + max_shift=None, + shift_method: str = "bilinear", + edge_filter: bool = True, + edge_sigma: float = 2.0, + hanning_filter: bool = False, + plot_aligned: bool = True, + **kwargs, ): - pass + import numpy as np + from scipy.ndimage import gaussian_filter, shift as ndi_shift + from scipy.signal import convolve2d + from scipy.signal.windows import tukey + + from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift + from quantem.core.visualization import show_2d + + if not hasattr(self, "im_bf"): + raise RuntimeError("Run preprocess() first so self.im_bf exists.") + if len(self.im_bf) == 0: + raise RuntimeError("No images found in self.im_bf.") + + H, W = self.im_bf[0].shape + for im in self.im_bf: + if im.shape != (H, W): + raise ValueError("all self.im_bf images must have the same shape") + + n_total = len(self.im_bf) + if num_images is None: + n = n_total + else: + n = int(num_images) + if n <= 0: + raise ValueError("num_images must be positive") + n = min(n, n_total) + + if int(num_iter) < 1: + raise ValueError("num_iter must be >= 1") + + if max_shift is not None: + pad_cc = int(np.ceil(float(max_shift))) + 4 + else: + pad_cc = int(np.ceil(float(edge_blend))) + 4 + + Hp = H + 2 * pad_cc + Wp = W + 2 * pad_cc + r0 = pad_cc + c0 = pad_cc + + w_h = np.ones((H, W), dtype=float) + if hanning_filter: + w_h = np.hanning(H)[:, None] * np.hanning(W)[None, :] + w_h_pad = np.zeros((Hp, Wp), dtype=float) + w_h_pad[r0 : r0 + H, c0 : c0 + W] = w_h + w_h_sum = float(np.sum(w_h_pad)) + if w_h_sum <= 0: + raise RuntimeError("hanning window sum is zero") + + wx = None + if edge_filter: + wx = np.array( + [ + [-1.0, -2.0, -1.0], + [ 0.0, 0.0, 0.0], + [ 1.0, 2.0, 1.0], + ], + dtype=float, + ) + + base_pad = np.zeros((n, Hp, Wp), dtype=float) + for i in range(n): + im0 = np.asarray(self.im_bf[i], dtype=float) + + if edge_filter: + gx = convolve2d(im0, wx, mode="same", boundary="symm") + gy = convolve2d(im0, wx.T, mode="same", boundary="symm") + gx = gaussian_filter(gx, float(edge_sigma), mode="nearest") + gy = gaussian_filter(gy, float(edge_sigma), mode="nearest") + im_use = np.sqrt(gx * gx + gy * gy) + else: + im_use = im0 + + base_pad[i, r0 : r0 + H, c0 : c0 + W] = im_use + + shifts = np.zeros((n, 2), dtype=float) + + for _ in range(int(num_iter)): + G_list = np.empty((n, Hp, Wp), dtype=np.complex128) + + for i in range(n): + im_a = ndi_shift( + base_pad[i], + shift=(float(shifts[i, 0]), float(shifts[i, 1])), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + im_mean = float(np.sum(im_a * w_h_pad) / w_h_sum) + im_win = (im_a - im_mean) * w_h_pad + G_list[i] = np.fft.fft2(im_win) + + G_ref = np.mean(G_list, axis=0) + + for i in range(1, n): + drc = weighted_cross_correlation_shift( + im_ref=G_ref, + im=G_list[i], + weight_real=None, + upsample_factor=int(upsample_factor), + max_shift=max_shift, + fft_input=True, + fft_output=False, + return_shifted_image=False, + ) + shifts[i, 0] += float(drc[0]) + shifts[i, 1] += float(drc[1]) + + shifts -= shifts[0][None, :] + + shifts -= np.mean(shifts, axis=0)[None, :] + + self.real_space_shifts = np.zeros((n_total, 2), dtype=float) + self.real_space_shifts[:n, :] = shifts + + if plot_aligned: + im_aligned = shift_images( + images=self.im_bf[:n], + shifts_rc=self.real_space_shifts[:n, :], + edge_blend=float(edge_blend), + padding=padding, + pad_val=pad_val, + shift_method=str(shift_method), + ) + show_2d(im_aligned, **kwargs) + + return self - def merge_datasets( - self + self, + real_space_padding=0, + real_space_edge_blend=1.0, + diffraction_padding=0, + diffraction_edge_blend=0.0, + diffraction_pad_val="min", + shift_method: str = "bilinear", + plot_result: bool = True, + **kwargs, ): - pass + import numpy as np + from scipy.ndimage import shift as ndi_shift + from scipy.signal.windows import tukey + from tqdm import tqdm + + if not hasattr(self, "real_space_shifts"): + raise RuntimeError("Run real_space_align() first so self.real_space_shifts exists.") + if not hasattr(self, "diffraction_shifts"): + raise RuntimeError("Run diffraction_align() first so self.diffraction_shifts exists.") + + n = len(self.datasets) + if n == 0: + raise RuntimeError("No datasets found in self.datasets.") + + arrays = [np.asarray(d.array, dtype=float) for d in self.datasets] + shape0 = arrays[0].shape + if len(shape0) != 4: + raise ValueError("Expected Dataset4dstem arrays with shape (R, C, H, W).") + Rs, Cs, H, W = shape0 + for a in arrays: + if a.shape != (Rs, Cs, H, W): + raise ValueError("All datasets must have the same shape (R, C, H, W).") + + rs_shifts = np.asarray(self.real_space_shifts, dtype=float) + dp_shifts = np.asarray(self.diffraction_shifts, dtype=float) + if rs_shifts.shape != (n, 2): + raise ValueError("self.real_space_shifts must have shape (n, 2).") + if dp_shifts.shape != (n, 2): + raise ValueError("self.diffraction_shifts must have shape (n, 2).") + + real_space_padding = int(real_space_padding) + if real_space_padding < 0: + raise ValueError("real_space_padding must be >= 0.") + + method = str(shift_method).strip().lower() + if method not in {"bilinear", "fourier"}: + raise ValueError("shift_method must be 'bilinear' or 'fourier'.") + + # Real-space taper window (used to weight contributions) + alpha_r = min(1.0, 2.0 * float(real_space_edge_blend) / float(Rs)) if real_space_edge_blend > 0 else 0.0 + alpha_c = min(1.0, 2.0 * float(real_space_edge_blend) / float(Cs)) if real_space_edge_blend > 0 else 0.0 + w_rs = tukey(Rs, alpha=alpha_r)[:, None] * tukey(Cs, alpha=alpha_c)[None, :] + w_rs = w_rs.astype(float, copy=False) + + # Diffraction padding (must be large enough to prevent wrap for Fourier shifts) + diffraction_padding = int(diffraction_padding) + if diffraction_padding < 0: + raise ValueError("diffraction_padding must be >= 0.") + max_abs_dp = float(np.max(np.abs(dp_shifts))) if dp_shifts.size else 0.0 + pad_dp_min = int(np.ceil(max_abs_dp)) + 2 + pad_dp = max(diffraction_padding, pad_dp_min) + + Hp = H + 2 * pad_dp + Wp = W + 2 * pad_dp + rp0 = pad_dp + cp0 = pad_dp + + # Diffraction taper window + alpha_hr = min(1.0, 2.0 * float(diffraction_edge_blend) / float(H)) if diffraction_edge_blend > 0 else 0.0 + alpha_hc = min(1.0, 2.0 * float(diffraction_edge_blend) / float(W)) if diffraction_edge_blend > 0 else 0.0 + w_dp = tukey(H, alpha=alpha_hr)[:, None] * tukey(W, alpha=alpha_hc)[None, :] + w_dp = w_dp.astype(float, copy=False) + + # Padded diffraction window (unshifted) + w_dp_pad = np.zeros((Hp, Wp)) + w_dp_pad[rp0 : rp0 + H, cp0 : cp0 + W] = w_dp + + # Pad value in diffraction space (computed from dp_means for speed) + if isinstance(diffraction_pad_val, str): + s = diffraction_pad_val.strip().lower() + dp_means = [np.mean(a, axis=(0, 1)) for a in arrays] + v = np.stack(dp_means, axis=0).reshape(-1) + if s == "min": + pad_val_dp = float(np.min(v)) + elif s == "max": + pad_val_dp = float(np.max(v)) + elif s == "mean": + pad_val_dp = float(np.mean(v)) + elif s == "median": + pad_val_dp = float(np.median(v)) + else: + raise ValueError("diffraction_pad_val must be a float or one of {'min','max','mean','median'}.") + else: + pad_val_dp = float(diffraction_pad_val) + + # Precompute diffraction window shifts per dataset + if method == "fourier": + kr = np.fft.fftfreq(Hp)[:, None] + kc = np.fft.fftfreq(Wp)[None, :] + ramps = [ + np.exp(-2j * np.pi * (kr * float(dp_shifts[i, 0]) + kc * float(dp_shifts[i, 1]))) + for i in range(n) + ] + wdp_shifted = np.empty((n, Hp, Wp)) + Fw0 = np.fft.fft2(w_dp_pad) + for i in range(n): + wtmp = np.fft.ifft2(Fw0 * ramps[i]).real + wtmp = np.clip(wtmp, 0.0, 1.0) + wdp_shifted[i] = wtmp + else: + wdp_shifted = np.empty((n, Hp, Wp)) + for i in range(n): + wtmp = ndi_shift( + w_dp_pad, + shift=(float(dp_shifts[i, 0]), float(dp_shifts[i, 1])), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + wtmp = np.clip(wtmp, 0.0, 1.0) + wdp_shifted[i] = wtmp + + # Edge blend weight for pad value (diffraction space) + edge_w_dp = 1.0 - np.clip(np.max(wdp_shifted, axis=0), 0.0, 1.0) + + Rout = Rs + 2 * real_space_padding + Cout = Cs + 2 * real_space_padding + merged = np.zeros((Rout, Cout, Hp, Wp)) + + dp_local = np.empty((H, W)) + dp_pad = np.zeros((Hp, Wp)) + dp_shifted_tmp = np.empty((Hp, Wp)) + num_tmp = np.zeros((Hp, Wp)) + den_tmp = np.zeros((Hp, Wp)) + out_tmp = np.empty((Hp, Wp)) + + for ro in tqdm(range(Rout), desc="Merging (rows)"): + r_base = float(ro - real_space_padding) + for co in range(Cout): + c_base = float(co - real_space_padding) + + num_tmp.fill(0.0) + den_tmp.fill(0.0) + max_wi = 0.0 + + for i in range(n): + r_in = r_base - float(rs_shifts[i, 0]) + c_in = c_base - float(rs_shifts[i, 1]) + + r0 = int(np.floor(r_in)) + c0 = int(np.floor(c_in)) + if r0 < 0 or r0 >= Rs - 1 or c0 < 0 or c0 >= Cs - 1: + continue + + dr = float(r_in - r0) + dc = float(c_in - c0) + + w00 = (1.0 - dr) * (1.0 - dc) + w10 = dr * (1.0 - dc) + w01 = (1.0 - dr) * dc + w11 = dr * dc + + wi = ( + w00 * w_rs[r0, c0] + + w10 * w_rs[r0 + 1, c0] + + w01 * w_rs[r0, c0 + 1] + + w11 * w_rs[r0 + 1, c0 + 1] + ) + if wi <= 0.0: + continue + if wi > max_wi: + max_wi = wi + + a = arrays[i] + dp_local[:] = ( + w00 * a[r0, c0] + + w10 * a[r0 + 1, c0] + + w01 * a[r0, c0 + 1] + + w11 * a[r0 + 1, c0 + 1] + ) + + dp_pad.fill(0.0) + dp_pad[rp0 : rp0 + H, cp0 : cp0 + W] = dp_local * w_dp + + if method == "fourier": + dp_shifted_tmp[:] = np.fft.ifft2(np.fft.fft2(dp_pad) * ramps[i]).real + else: + dp_shifted_tmp[:] = ndi_shift( + dp_pad, + shift=(float(dp_shifts[i, 0]), float(dp_shifts[i, 1])), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + + num_tmp += wi * dp_shifted_tmp + den_tmp += wi * wdp_shifted[i] + + if max_wi <= 0.0: + merged[ro, co] = 0.0 + continue + + num = num_tmp + edge_w_dp * pad_val_dp + den = den_tmp + edge_w_dp + + np.divide(num, den, out=out_tmp, where=(den > 0.0)) + out_tmp[den <= 0.0] = pad_val_dp + merged[ro, co] = out_tmp + + dataset_merged = Dataset4dstem.from_array(merged) + + self.im_bf_merged = np.mean(merged, axis=(2, 3)) + self.dp_mean_merged = np.mean(merged, axis=(0, 1)) + + dataset_merged.im_bf_merged = self.im_bf_merged + dataset_merged.dp_mean_merged = self.dp_mean_merged + dataset_merged.metadata["im_bf_merged"] = self.im_bf_merged + dataset_merged.metadata["dp_mean_merged"] = self.dp_mean_merged + dataset_merged.metadata["real_space_shifts_rc"] = rs_shifts.copy() + dataset_merged.metadata["diffraction_shifts_rc"] = dp_shifts.copy() + + if plot_result: + show_2d( + [[self.im_bf_merged, self.dp_mean_merged]], + titles=[["Merged Bright Field", "Merged Mean Diffraction Pattern"]], + **kwargs, + ) + + return dataset_merged + + +def shift_images( + images, + shifts_rc, + edge_blend: float = 8.0, + padding=None, + pad_val=0.0, + shift_method: str = "bilinear", +): + import numpy as np + from scipy.ndimage import shift as ndi_shift + from scipy.signal.windows import tukey + + images = [np.asarray(im, dtype=float) for im in images] + if len(images) == 0: + raise ValueError("images must be non-empty") + + H, W = images[0].shape + for im in images: + if im.shape != (H, W): + raise ValueError("all images must have the same shape") + + shifts_rc = np.asarray(shifts_rc, dtype=float) + if shifts_rc.shape != (len(images), 2): + raise ValueError("shifts_rc must have shape (len(images), 2)") + + if isinstance(pad_val, str): + s = pad_val.strip().lower() + v = np.stack(images, axis=0).reshape(-1) + if s == "min": + pad_val = float(np.min(v)) + elif s == "max": + pad_val = float(np.max(v)) + elif s == "mean": + pad_val = float(np.mean(v)) + elif s == "median": + pad_val = float(np.median(v)) + else: + raise ValueError("pad_val must be a float or one of {'min','max','mean','median'}") + else: + pad_val = float(pad_val) + + if padding is None: + max_shift = float(np.max(np.abs(shifts_rc))) if shifts_rc.size else 0.0 + padding = int(np.ceil(max_shift + float(edge_blend))) + 2 + padding = int(padding) + + alpha_r = min(1.0, 2.0 * float(edge_blend) / float(H)) if edge_blend > 0 else 0.0 + alpha_c = min(1.0, 2.0 * float(edge_blend) / float(W)) if edge_blend > 0 else 0.0 + w = tukey(H, alpha=alpha_r)[:, None] * tukey(W, alpha=alpha_c)[None, :] + w = w.astype(float, copy=False) + + Hp = H + 2 * padding + Wp = W + 2 * padding + + stack_w = np.zeros((len(images), Hp, Wp), dtype=float) + stack = np.zeros_like(stack_w) + + r0 = padding + c0 = padding + stack_w[:, r0 : r0 + H, c0 : c0 + W] = w[None, :, :] + for ind, im in enumerate(images): + stack[ind, r0 : r0 + H, c0 : c0 + W] = im * w + + method = str(shift_method).strip().lower() + if method not in {"bilinear", "fourier"}: + raise ValueError("shift_method must be 'bilinear' or 'fourier'") + + if method == "fourier": + kr = np.fft.fftfreq(Hp)[:, None] + kc = np.fft.fftfreq(Wp)[None, :] + for ind in range(len(images)): + dr, dc = float(shifts_rc[ind, 0]), float(shifts_rc[ind, 1]) + ramp = np.exp(-2j * np.pi * (kr * dr + kc * dc)) + + F = np.fft.fft2(stack[ind]) + stack[ind] = np.fft.ifft2(F * ramp).real + + Fw = np.fft.fft2(stack_w[ind]) + stack_w[ind] = np.fft.ifft2(Fw * ramp).real + stack_w[ind] = np.clip(stack_w[ind], 0.0, 1.0) + else: + for ind in range(len(images)): + stack[ind] = ndi_shift( + stack[ind], + shift=(float(shifts_rc[ind, 0]), float(shifts_rc[ind, 1])), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + stack_w[ind] = ndi_shift( + stack_w[ind], + shift=(float(shifts_rc[ind, 0]), float(shifts_rc[ind, 1])), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + stack_w[ind] = np.clip(stack_w[ind], 0.0, 1.0) + + # edge_w = 1.0 - np.clip(np.max(stack_w, axis=0), 0.0, 1.0) + edge_w = len(images) - np.sum(stack_w, axis=0) + num = np.sum(stack, axis=0) + edge_w * pad_val + den = np.sum(stack_w, axis=0) + edge_w + out = num / den - \ No newline at end of file + return out From 1f2bf10f378a8b398fe17811b3691e188386c0e2 Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 1 Feb 2026 17:13:41 -0800 Subject: [PATCH 110/140] datatype control for merged data --- src/quantem/diffraction/maped.py | 190 +++++++++++++++++++------------ 1 file changed, 118 insertions(+), 72 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index afb1fc0c..9df542f3 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -406,9 +406,13 @@ def merge_datasets( diffraction_edge_blend=0.0, diffraction_pad_val="min", shift_method: str = "bilinear", + dtype=None, + scale_output: bool = False, plot_result: bool = True, **kwargs, ): + import warnings + import numpy as np from scipy.ndimage import shift as ndi_shift from scipy.signal.windows import tukey @@ -419,18 +423,15 @@ def merge_datasets( if not hasattr(self, "diffraction_shifts"): raise RuntimeError("Run diffraction_align() first so self.diffraction_shifts exists.") - n = len(self.datasets) + arrays = [np.asarray(d.array) for d in self.datasets] + n = len(arrays) if n == 0: raise RuntimeError("No datasets found in self.datasets.") - arrays = [np.asarray(d.array, dtype=float) for d in self.datasets] - shape0 = arrays[0].shape - if len(shape0) != 4: - raise ValueError("Expected Dataset4dstem arrays with shape (R, C, H, W).") - Rs, Cs, H, W = shape0 + Rs, Cs, H, W = arrays[0].shape for a in arrays: if a.shape != (Rs, Cs, H, W): - raise ValueError("All datasets must have the same shape (R, C, H, W).") + raise ValueError("All dataset arrays must have the same shape (Rs, Cs, H, W).") rs_shifts = np.asarray(self.real_space_shifts, dtype=float) dp_shifts = np.asarray(self.diffraction_shifts, dtype=float) @@ -439,48 +440,48 @@ def merge_datasets( if dp_shifts.shape != (n, 2): raise ValueError("self.diffraction_shifts must have shape (n, 2).") + if dtype is None: + dtype_out = np.asarray(arrays[0]).dtype + warnings.warn(f"dtype=None; using parent dtype {dtype_out}.", RuntimeWarning) + else: + dtype_out = np.dtype(dtype) + real_space_padding = int(real_space_padding) - if real_space_padding < 0: - raise ValueError("real_space_padding must be >= 0.") + diffraction_padding = int(diffraction_padding) + + Rout = Rs + 2 * real_space_padding + Cout = Cs + 2 * real_space_padding + + Hp = H + 2 * diffraction_padding + Wp = W + 2 * diffraction_padding + rp0 = diffraction_padding + cp0 = diffraction_padding method = str(shift_method).strip().lower() if method not in {"bilinear", "fourier"}: raise ValueError("shift_method must be 'bilinear' or 'fourier'.") - # Real-space taper window (used to weight contributions) - alpha_r = min(1.0, 2.0 * float(real_space_edge_blend) / float(Rs)) if real_space_edge_blend > 0 else 0.0 - alpha_c = min(1.0, 2.0 * float(real_space_edge_blend) / float(Cs)) if real_space_edge_blend > 0 else 0.0 - w_rs = tukey(Rs, alpha=alpha_r)[:, None] * tukey(Cs, alpha=alpha_c)[None, :] + if real_space_edge_blend and float(real_space_edge_blend) > 0: + alpha_r = min(1.0, 2.0 * float(real_space_edge_blend) / float(Rs)) + alpha_c = min(1.0, 2.0 * float(real_space_edge_blend) / float(Cs)) + w_rs = tukey(Rs, alpha=alpha_r)[:, None] * tukey(Cs, alpha=alpha_c)[None, :] + else: + w_rs = np.ones((Rs, Cs), dtype=float) w_rs = w_rs.astype(float, copy=False) - # Diffraction padding (must be large enough to prevent wrap for Fourier shifts) - diffraction_padding = int(diffraction_padding) - if diffraction_padding < 0: - raise ValueError("diffraction_padding must be >= 0.") - max_abs_dp = float(np.max(np.abs(dp_shifts))) if dp_shifts.size else 0.0 - pad_dp_min = int(np.ceil(max_abs_dp)) + 2 - pad_dp = max(diffraction_padding, pad_dp_min) - - Hp = H + 2 * pad_dp - Wp = W + 2 * pad_dp - rp0 = pad_dp - cp0 = pad_dp - - # Diffraction taper window - alpha_hr = min(1.0, 2.0 * float(diffraction_edge_blend) / float(H)) if diffraction_edge_blend > 0 else 0.0 - alpha_hc = min(1.0, 2.0 * float(diffraction_edge_blend) / float(W)) if diffraction_edge_blend > 0 else 0.0 - w_dp = tukey(H, alpha=alpha_hr)[:, None] * tukey(W, alpha=alpha_hc)[None, :] + if diffraction_edge_blend and float(diffraction_edge_blend) > 0: + alpha_dr = min(1.0, 2.0 * float(diffraction_edge_blend) / float(H)) + alpha_dc = min(1.0, 2.0 * float(diffraction_edge_blend) / float(W)) + w_dp = tukey(H, alpha=alpha_dr)[:, None] * tukey(W, alpha=alpha_dc)[None, :] + else: + w_dp = np.ones((H, W), dtype=float) w_dp = w_dp.astype(float, copy=False) - # Padded diffraction window (unshifted) - w_dp_pad = np.zeros((Hp, Wp)) - w_dp_pad[rp0 : rp0 + H, cp0 : cp0 + W] = w_dp + dp_means = [np.mean(a, axis=(0, 1), dtype=np.float64) for a in arrays] + v = np.stack(dp_means, axis=0).reshape(-1) - # Pad value in diffraction space (computed from dp_means for speed) if isinstance(diffraction_pad_val, str): s = diffraction_pad_val.strip().lower() - dp_means = [np.mean(a, axis=(0, 1)) for a in arrays] - v = np.stack(dp_means, axis=0).reshape(-1) if s == "min": pad_val_dp = float(np.min(v)) elif s == "max": @@ -494,47 +495,44 @@ def merge_datasets( else: pad_val_dp = float(diffraction_pad_val) - # Precompute diffraction window shifts per dataset + wdp_pad = np.zeros((Hp, Wp), dtype=float) + wdp_pad[rp0 : rp0 + H, cp0 : cp0 + W] = w_dp + + wdp_shifted = np.zeros((n, Hp, Wp), dtype=float) if method == "fourier": kr = np.fft.fftfreq(Hp)[:, None] kc = np.fft.fftfreq(Wp)[None, :] - ramps = [ - np.exp(-2j * np.pi * (kr * float(dp_shifts[i, 0]) + kc * float(dp_shifts[i, 1]))) - for i in range(n) - ] - wdp_shifted = np.empty((n, Hp, Wp)) - Fw0 = np.fft.fft2(w_dp_pad) + ramps = [] + Fw = np.fft.fft2(wdp_pad) for i in range(n): - wtmp = np.fft.ifft2(Fw0 * ramps[i]).real - wtmp = np.clip(wtmp, 0.0, 1.0) - wdp_shifted[i] = wtmp + dr, dc = float(dp_shifts[i, 0]), float(dp_shifts[i, 1]) + ramp = np.exp(-2j * np.pi * (kr * dr + kc * dc)) + ramps.append(ramp) + w_i = np.fft.ifft2(Fw * ramp).real + wdp_shifted[i] = np.clip(w_i, 0.0, 1.0) else: - wdp_shifted = np.empty((n, Hp, Wp)) for i in range(n): - wtmp = ndi_shift( - w_dp_pad, + w_i = ndi_shift( + wdp_pad, shift=(float(dp_shifts[i, 0]), float(dp_shifts[i, 1])), order=1, mode="constant", cval=0.0, prefilter=False, ) - wtmp = np.clip(wtmp, 0.0, 1.0) - wdp_shifted[i] = wtmp + wdp_shifted[i] = np.clip(w_i, 0.0, 1.0) + ramps = None - # Edge blend weight for pad value (diffraction space) - edge_w_dp = 1.0 - np.clip(np.max(wdp_shifted, axis=0), 0.0, 1.0) + edge_w_dp = 1.0 - np.max(wdp_shifted, axis=0) + edge_w_dp = np.clip(edge_w_dp, 0.0, 1.0) - Rout = Rs + 2 * real_space_padding - Cout = Cs + 2 * real_space_padding - merged = np.zeros((Rout, Cout, Hp, Wp)) + merged = np.zeros((Rout, Cout, Hp, Wp), dtype=np.float64) - dp_local = np.empty((H, W)) - dp_pad = np.zeros((Hp, Wp)) - dp_shifted_tmp = np.empty((Hp, Wp)) - num_tmp = np.zeros((Hp, Wp)) - den_tmp = np.zeros((Hp, Wp)) - out_tmp = np.empty((Hp, Wp)) + dp_local = np.zeros((H, W), dtype=np.float64) + dp_pad = np.zeros((Hp, Wp), dtype=np.float64) + dp_shifted_tmp = np.zeros((Hp, Wp), dtype=np.float64) + num_tmp = np.zeros((Hp, Wp), dtype=np.float64) + den_tmp = np.zeros((Hp, Wp), dtype=np.float64) for ro in tqdm(range(Rout), desc="Merging (rows)"): r_base = float(ro - real_space_padding) @@ -606,21 +604,69 @@ def merge_datasets( num = num_tmp + edge_w_dp * pad_val_dp den = den_tmp + edge_w_dp - np.divide(num, den, out=out_tmp, where=(den > 0.0)) - out_tmp[den <= 0.0] = pad_val_dp - merged[ro, co] = out_tmp + out = np.empty_like(num) + np.divide(num, den, out=out, where=den != 0.0) + out[den == 0.0] = 0.0 + merged[ro, co] = out + + self.im_bf_merged = np.mean(merged, axis=(2, 3), dtype=np.float64) + self.dp_mean_merged = np.mean(merged, axis=(0, 1), dtype=np.float64) + + if np.issubdtype(dtype_out, np.integer): + info = np.iinfo(dtype_out) + dmin = float(info.min) + dmax = float(info.max) + + merged_f = merged # float64 + + if scale_output: + peak = float(np.max(merged_f)) + if peak <= 0.0: + scale = 1.0 + merged_scaled = merged_f + else: + scale = dmax / peak + merged_scaled = merged_f * scale + + if np.issubdtype(dtype_out, np.unsignedinteger): + if float(np.min(merged_scaled)) < 0.0: + warnings.warn( + f"scale_output=True with unsigned dtype {dtype_out}: " + "negative values present; they will be clipped to 0.", + RuntimeWarning, + ) + lo, hi = 0.0, dmax + else: + lo, hi = dmin, dmax + + if float(np.min(merged_scaled)) < lo or float(np.max(merged_scaled)) > hi: + warnings.warn( + f"Output overflow for dtype {dtype_out} after scaling: " + f"data range [{float(np.min(merged_scaled))}, {float(np.max(merged_scaled))}] exceeds " + f"[{lo}, {hi}]. Values will be clipped.", + RuntimeWarning, + ) + + merged_out = np.rint(np.clip(merged_scaled, lo, hi)).astype(dtype_out) + + else: + below = float(np.min(merged_f)) + above = float(np.max(merged_f)) + if below < dmin or above > dmax: + warnings.warn( + f"Output overflow for dtype {dtype_out}: data range [{below}, {above}] exceeds " + f"[{dmin}, {dmax}]. Values will be clipped.", + RuntimeWarning, + ) + merged_out = np.rint(np.clip(merged_f, dmin, dmax)).astype(dtype_out) + else: + merged_out = merged.astype(dtype_out, copy=False) - dataset_merged = Dataset4dstem.from_array(merged) - self.im_bf_merged = np.mean(merged, axis=(2, 3)) - self.dp_mean_merged = np.mean(merged, axis=(0, 1)) + dataset_merged = Dataset4dstem.from_array(array=merged_out) dataset_merged.im_bf_merged = self.im_bf_merged dataset_merged.dp_mean_merged = self.dp_mean_merged - dataset_merged.metadata["im_bf_merged"] = self.im_bf_merged - dataset_merged.metadata["dp_mean_merged"] = self.dp_mean_merged - dataset_merged.metadata["real_space_shifts_rc"] = rs_shifts.copy() - dataset_merged.metadata["diffraction_shifts_rc"] = dp_shifts.copy() if plot_result: show_2d( From 649e308edf35545b4ae7d7158c2c7718dd033930 Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 1 Feb 2026 17:40:36 -0800 Subject: [PATCH 111/140] adding docstrings --- src/quantem/diffraction/maped.py | 452 +++++++++++++++++++------------ 1 file changed, 278 insertions(+), 174 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 9df542f3..3b5154c0 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1,28 +1,55 @@ from __future__ import annotations +import warnings from typing import Any, Sequence import numpy as np +from scipy.ndimage import gaussian_filter, shift as ndi_shift +from scipy.signal import convolve2d from scipy.signal.windows import tukey +from tqdm import tqdm from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize -from quantem.core.visualization import show_2d from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift +from quantem.core.visualization import show_2d class MAPED(AutoSerialize): + """ + Merge-Averaged Precession Electron Diffraction (MAPED) helper. + + This class manages a set of 4D-STEM datasets and provides utilities to: + - compute mean BF and mean DP summaries, + - choose/find diffraction origins, + - align diffraction space and real space, + - merge datasets into a single composite Dataset4dstem. + """ + _token = object() def __init__(self, datasets: list[Dataset4dstem], _token: object | None = None): if _token is not self._token: raise RuntimeError("Use MAPED.from_datasets() to instantiate this class.") - AutoSerialize.__init__(self) + super().__init__() self.datasets = datasets self.metadata: dict[str, Any] = {} @classmethod - def from_datasets(cls, datasets: Sequence[Dataset4dstem]) -> "MAPED": + def from_datasets(cls, datasets: Sequence[Dataset4dstem]) -> MAPED: + """ + Construct a MAPED instance from a non-empty sequence of Dataset4dstem. + + Parameters + ---------- + datasets + Sequence of Dataset4dstem instances. + + Returns + ------- + MAPED + New MAPED instance. + """ if not isinstance(datasets, Sequence) or isinstance(datasets, (str, bytes)): raise TypeError("MAPED.from_datasets expects a sequence of Dataset4dstem instances.") ds_list: list[Dataset4dstem] = [] @@ -30,7 +57,7 @@ def from_datasets(cls, datasets: Sequence[Dataset4dstem]) -> "MAPED": if not isinstance(d, Dataset4dstem): raise TypeError("MAPED.from_datasets expects a sequence of Dataset4dstem instances.") ds_list.append(d) - if len(ds_list) == 0: + if not ds_list: raise ValueError("MAPED.from_datasets expects a non-empty sequence of Dataset4dstem instances.") return cls(datasets=ds_list, _token=cls._token) @@ -39,7 +66,19 @@ def preprocess( plot_summary: bool = True, scale: float | Sequence[float] | None = None, **plot_kwargs: Any, - ) -> "MAPED": + ) -> MAPED: + """ + Compute dataset summary images. + + Stores + ------ + self.scales : np.ndarray + Per-dataset scaling factors (n,). + self.dp_mean : list[np.ndarray] + Mean diffraction patterns (H, W), one per dataset. + self.im_bf : list[np.ndarray] + Mean bright-field images (R, C), one per dataset. + """ n = len(self.datasets) if scale is None: self.scales = np.ones(n, dtype=float) @@ -52,8 +91,8 @@ def preprocess( if np.any(self.scales == 0): raise ValueError("scale entries must be nonzero.") - self.dp_mean = [] - self.im_bf = [] + self.dp_mean: list[np.ndarray] = [] + self.im_bf: list[np.ndarray] = [] for d in self.datasets: if hasattr(d, "get_dp_mean"): @@ -67,11 +106,13 @@ def preprocess( dp = getattr(d, "dp_mean", None) if dp is None: - dp_arr = np.mean(np.asarray(d.array), axis=(0, 1)) + arr = np.asarray(d.array) + dp_arr = np.mean(arr, axis=(0, 1)) else: dp_arr = np.asarray(dp.array if hasattr(dp, "array") else dp) - im_bf_arr = np.mean(np.asarray(d.array), axis=(2, 3)) + arr = np.asarray(d.array) + im_bf_arr = np.mean(arr, axis=(2, 3)) self.dp_mean.append(np.asarray(dp_arr)) self.im_bf.append(np.asarray(im_bf_arr)) @@ -79,22 +120,18 @@ def preprocess( if plot_summary: tiles = [[(self.im_bf[i] / self.scales[i]), self.dp_mean[i]] for i in range(n)] titles = [[f"{i} - Mean Bright Field", f"{i} - Mean Diffraction Pattern"] for i in range(n)] - show_2d( - tiles, - titles=titles, - **plot_kwargs, - ) + show_2d(tiles, title=titles, **plot_kwargs) return self - - def diffraction_find_origin( + def diffraction_origin( self, origins=None, sigma=None, plot_origins: bool = True, plot_indices=None, - ): + **plot_kwargs: Any, + ) -> MAPED: """ Choose or automatically find the origin in diffraction space. @@ -104,26 +141,20 @@ def diffraction_find_origin( Optional manual origins. Can be: - a single (row, col) tuple, applied to all datasets - a list of (row, col) tuples of length n (one per dataset) - - a list of (row, col) tuples shorter than n, used for plot/inspection only (will error if not broadcastable) sigma Optional low-pass smoothing sigma (pixels) applied to each mean DP prior to peak finding. plot_origins If True, plot mean diffraction patterns with overlaid origin markers. plot_indices Optional indices to plot. If None, plots all datasets. + **plot_kwargs + Passed to show_2d. Stores ------ self.diffraction_origins : np.ndarray Array of shape (n, 2) with integer (row, col) origins. """ - import numpy as _np - - try: - from scipy.ndimage import gaussian_filter as _gaussian_filter - except Exception: # pragma: no cover - _gaussian_filter = None - n = len(self.datasets) if not hasattr(self, "dp_mean"): raise RuntimeError("Run preprocess() first so self.dp_mean exists.") @@ -137,119 +168,133 @@ def diffraction_find_origin( raise IndexError("plot_indices contains an out-of-range index.") if origins is None: - origins_arr = _np.zeros((n, 2), dtype=int) + origins_arr = np.zeros((n, 2), dtype=int) for i in range(n): - dp = _np.asarray(self.dp_mean[i]) + dp = np.asarray(self.dp_mean[i]) if sigma is not None and float(sigma) > 0: - if _gaussian_filter is None: - raise ImportError("scipy is required for sigma smoothing (gaussian_filter).") - dp_use = _gaussian_filter(dp.astype(float, copy=False), float(sigma)) + dp_use = gaussian_filter(dp.astype(float, copy=False), float(sigma), mode="nearest") else: dp_use = dp - ind = int(_np.argmax(dp_use)) - r, c = _np.unravel_index(ind, dp_use.shape) + r, c = np.unravel_index(int(np.argmax(dp_use)), dp_use.shape) origins_arr[i, 0] = int(r) origins_arr[i, 1] = int(c) else: if isinstance(origins, tuple) and len(origins) == 2: - origins_arr = _np.tile(_np.asarray(origins, dtype=int)[None, :], (n, 1)) + origins_arr = np.tile(np.asarray(origins, dtype=int)[None, :], (n, 1)) else: origins_list = list(origins) if len(origins_list) != n: raise ValueError("origins must be a single (row,col) tuple or a list of length n.") - origins_arr = _np.asarray(origins_list, dtype=int) + origins_arr = np.asarray(origins_list, dtype=int) if origins_arr.shape != (n, 2): raise ValueError("origins must have shape (n, 2) after conversion.") self.diffraction_origins = origins_arr if plot_origins: - dp_tiles = [[_np.asarray(self.dp_mean[i]) for i in plot_indices_list]] - titles = [[f"{i} - Mean Diffraction Pattern" for i in plot_indices_list]] - fig, axs = show_2d(dp_tiles, titles=titles, returnfig=True, **{}) - if not isinstance(axs, (list, _np.ndarray)): - axs = [axs] - axs_flat = _np.ravel(axs) + arrays = [np.asarray(self.dp_mean[i]) for i in plot_indices_list] + titles = [f"{i} - Mean Diffraction Pattern" for i in plot_indices_list] + fig, ax = show_2d(arrays, title=titles, returnfig=True, **plot_kwargs) + axs = np.ravel(np.asarray(ax, dtype=object)) for j, i in enumerate(plot_indices_list): - ax = axs_flat[j] r, c = self.diffraction_origins[i] - ax.plot([c], [r], marker="+", color="red", markersize=16, markeredgewidth=2) - return fig, axs + axs[j].plot([c], [r], marker="+", color="red", markersize=16, markeredgewidth=2) return self - def diffraction_align( self, - edge_blend = 16.0, - padding = None, - pad_val = 'min', - upsample_factor = 100, - weight_scale = 1/8, - plot_aligned = True, - linewidth = 2, - **kwargs, - ): + edge_blend: float = 16.0, + padding=None, + pad_val: str | float = "min", + upsample_factor: int = 100, + weight_scale: float = 1 / 8, + plot_aligned: bool = True, + **plot_kwargs: Any, + ) -> MAPED: """ - Refine the diffraction space origins, set padding, align images + Align mean diffraction patterns using weighted cross-correlation in Fourier space. + Parameters + ---------- + edge_blend + Tukey window edge taper (pixels). + padding + Passed to shift_images for plotting. + pad_val + Passed to shift_images for plotting. + upsample_factor + Subpixel upsampling factor for correlation peak estimation. + weight_scale + Radial weight falloff scale (fraction of mean DP size). + plot_aligned + If True, plot aligned mean diffraction patterns. + **plot_kwargs + Passed to show_2d when plotting. + + Stores + ------ + self.diffraction_shifts : np.ndarray + Array of shape (n, 2) with (row, col) shifts to align diffraction patterns. """ + if not hasattr(self, "dp_mean"): + raise RuntimeError("Run preprocess() first so self.dp_mean exists.") + if not hasattr(self, "diffraction_origins"): + raise RuntimeError("Run diffraction_origin() first so self.diffraction_origins exists.") + + H, W = np.asarray(self.dp_mean[0]).shape + + w = tukey(H, alpha=2.0 * float(edge_blend) / float(H))[:, None] * tukey( + W, alpha=2.0 * float(edge_blend) / float(W) + )[None, :] - # window function - from scipy.signal.windows import tukey - w = tukey(self.dp_mean[0].shape[0], alpha=2.0*edge_blend/self.dp_mean[0].shape[0])[:,None] * \ - tukey(self.dp_mean[0].shape[1], alpha=2.0*edge_blend/self.dp_mean[0].shape[1])[None,:] + r = np.fft.fftfreq(H, 1.0 / float(H))[:, None] + c = np.fft.fftfreq(W, 1.0 / float(W))[None, :] - # coordinates - r = np.fft.fftfreq(self.dp_mean[0].shape[0],1/self.dp_mean[0].shape[0])[:,None] - c = np.fft.fftfreq(self.dp_mean[0].shape[1],1/self.dp_mean[0].shape[1])[None,:] + n = len(self.dp_mean) + self.diffraction_shifts = np.zeros((n, 2), dtype=float) - # init - self.diffraction_shifts = np.zeros((len(self.dp_mean),2)) + G_ref = np.fft.fft2(w * np.asarray(self.dp_mean[0])) + xy0 = np.asarray(self.diffraction_origins[0], dtype=float) - # correlation alignment - G_ref = np.fft.fft2(w * self.dp_mean[0]) - xy0 = self.diffraction_origins[0] - for ind in range(1,len(self.dp_mean)): - G = np.fft.fft2(w * self.dp_mean[ind]) - xy = self.diffraction_origins[ind] + for ind in range(1, n): + G = np.fft.fft2(w * np.asarray(self.dp_mean[ind])) + xy = np.asarray(self.diffraction_origins[ind], dtype=float) - dr2 = (r - xy0[0] + xy[0])**2 \ - + (c - xy0[1] + xy[1])**2 - im_weight = np.clip(1 - np.sqrt(dr2)/np.mean(self.dp_mean[0].shape)/weight_scale, 0.0, 1.0) - im_weight = np.sin(im_weight*np.pi/2)**2 + dr2 = (r - xy0[0] + xy[0]) ** 2 + (c - xy0[1] + xy[1]) ** 2 + im_weight = np.clip( + 1.0 - np.sqrt(dr2) / float(np.mean((H, W))) / float(weight_scale), + 0.0, + 1.0, + ) + im_weight = np.sin(im_weight * np.pi / 2.0) ** 2 - shift, G_shift = weighted_cross_correlation_shift( + shift_rc, G_shift = weighted_cross_correlation_shift( im_ref=G_ref, im=G, - weight_real=im_weight*0+1.0, - upsample_factor = upsample_factor, - fft_input = True, - fft_output = True, - return_shifted_image = True, + weight_real=im_weight * 0.0 + 1.0, + upsample_factor=int(upsample_factor), + fft_input=True, + fft_output=True, + return_shifted_image=True, ) - self.diffraction_shifts[ind,:] = shift - - # update reference - G_ref = G_ref*(ind/(ind+1)) + G_shift/(ind+1) + self.diffraction_shifts[ind, :] = np.asarray(shift_rc, dtype=float) - # Center shifts - self.diffraction_shifts -= np.mean(self.diffraction_shifts,axis=0)[None,:] + G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) - # Generate output image + self.diffraction_shifts -= np.mean(self.diffraction_shifts, axis=0)[None, :] if plot_aligned: im_aligned = shift_images( - images = self.dp_mean, - shifts_rc = self.diffraction_shifts, - edge_blend = edge_blend, - padding = padding, - pad_val = pad_val, - ) - show_2d( - im_aligned, - **kwargs, + images=self.dp_mean, + shifts_rc=self.diffraction_shifts, + edge_blend=float(edge_blend), + padding=padding, + pad_val=pad_val, ) + show_2d(im_aligned, **plot_kwargs) + + return self def real_space_align( @@ -266,16 +311,45 @@ def real_space_align( edge_sigma: float = 2.0, hanning_filter: bool = False, plot_aligned: bool = True, - **kwargs, - ): - import numpy as np - from scipy.ndimage import gaussian_filter, shift as ndi_shift - from scipy.signal import convolve2d - from scipy.signal.windows import tukey + **plot_kwargs: Any, + ) -> MAPED: + """ + Align real-space mean BF images using iterative average-reference correlation. - from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift - from quantem.core.visualization import show_2d + Parameters + ---------- + num_images + If provided, align only the first num_images images. + num_iter + Number of refinement iterations. + edge_blend + Used to set default correlation padding when max_shift is None. + padding + Passed to shift_images for plotting. + pad_val + Passed to shift_images for plotting. + upsample_factor + Subpixel upsampling factor for correlation peak estimation. + max_shift + Optional maximum shift constraint passed to weighted_cross_correlation_shift. + shift_method + Passed to shift_images for plotting ('bilinear' or 'fourier'). + edge_filter + If True, correlate on gradient magnitude instead of raw intensity. + edge_sigma + Gaussian sigma applied to gradients when edge_filter is True. + hanning_filter + If True, apply a Hanning window prior to FFT. + plot_aligned + If True, plot aligned mean BF images. + **plot_kwargs + Passed to show_2d when plotting. + Stores + ------ + self.real_space_shifts : np.ndarray + Array of shape (n_total, 2) with (row, col) shifts for aligned datasets. + """ if not hasattr(self, "im_bf"): raise RuntimeError("Run preprocess() first so self.im_bf exists.") if len(self.im_bf) == 0: @@ -317,16 +391,13 @@ def real_space_align( if w_h_sum <= 0: raise RuntimeError("hanning window sum is zero") - wx = None if edge_filter: wx = np.array( - [ - [-1.0, -2.0, -1.0], - [ 0.0, 0.0, 0.0], - [ 1.0, 2.0, 1.0], - ], + [[-1.0, -2.0, -1.0], [0.0, 0.0, 0.0], [1.0, 2.0, 1.0]], dtype=float, ) + else: + wx = None base_pad = np.zeros((n, Hp, Wp), dtype=float) for i in range(n): @@ -351,7 +422,7 @@ def real_space_align( for i in range(n): im_a = ndi_shift( base_pad[i], - shift=(float(shifts[i, 0]), float(shifts[i, 1])), + shift=(shifts[i, 0], shifts[i, 1]), order=1, mode="constant", cval=0.0, @@ -391,13 +462,12 @@ def real_space_align( edge_blend=float(edge_blend), padding=padding, pad_val=pad_val, - shift_method=str(shift_method), + shift_method=shift_method, ) - show_2d(im_aligned, **kwargs) + show_2d(im_aligned, **plot_kwargs) return self - def merge_datasets( self, real_space_padding=0, @@ -409,15 +479,46 @@ def merge_datasets( dtype=None, scale_output: bool = False, plot_result: bool = True, - **kwargs, - ): - import warnings + **plot_kwargs: Any, + ) -> Dataset4dstem: + """ + Merge aligned datasets into a single Dataset4dstem. - import numpy as np - from scipy.ndimage import shift as ndi_shift - from scipy.signal.windows import tukey - from tqdm import tqdm + Requires + -------- + self.real_space_shifts + From real_space_align(). + self.diffraction_shifts + From diffraction_align(). + Parameters + ---------- + real_space_padding + Output scan padding in pixels (adds border to scan grid). + real_space_edge_blend + Tukey taper width for scan-space interpolation weights. + diffraction_padding + Output diffraction padding in pixels (adds border around DPs). + diffraction_edge_blend + Tukey taper width for diffraction-space weights. + diffraction_pad_val + Pad value for diffraction padding ('min','max','mean','median' or float). + shift_method + Diffraction shift method: 'bilinear' or 'fourier'. + dtype + Output dtype. If None, uses parent dtype. + scale_output + If True and dtype is integer, scale to full dynamic range using global max. + plot_result + If True, plot merged BF and merged mean DP. + **plot_kwargs + Passed to show_2d. + + Returns + ------- + Dataset4dstem + Merged dataset. + """ if not hasattr(self, "real_space_shifts"): raise RuntimeError("Run real_space_align() first so self.real_space_shifts exists.") if not hasattr(self, "diffraction_shifts"): @@ -502,10 +603,10 @@ def merge_datasets( if method == "fourier": kr = np.fft.fftfreq(Hp)[:, None] kc = np.fft.fftfreq(Wp)[None, :] - ramps = [] Fw = np.fft.fft2(wdp_pad) + ramps: list[np.ndarray] = [] for i in range(n): - dr, dc = float(dp_shifts[i, 0]), float(dp_shifts[i, 1]) + dr, dc = dp_shifts[i, 0], dp_shifts[i, 1] ramp = np.exp(-2j * np.pi * (kr * dr + kc * dc)) ramps.append(ramp) w_i = np.fft.ifft2(Fw * ramp).real @@ -514,17 +615,17 @@ def merge_datasets( for i in range(n): w_i = ndi_shift( wdp_pad, - shift=(float(dp_shifts[i, 0]), float(dp_shifts[i, 1])), + shift=(dp_shifts[i, 0], dp_shifts[i, 1]), order=1, mode="constant", cval=0.0, prefilter=False, ) wdp_shifted[i] = np.clip(w_i, 0.0, 1.0) - ramps = None + ramps = [] - edge_w_dp = 1.0 - np.max(wdp_shifted, axis=0) - edge_w_dp = np.clip(edge_w_dp, 0.0, 1.0) + coverage = np.clip(np.sum(wdp_shifted, axis=0), 0.0, 1.0) + edge_w_dp = 1.0 - coverage merged = np.zeros((Rout, Cout, Hp, Wp), dtype=np.float64) @@ -535,25 +636,25 @@ def merge_datasets( den_tmp = np.zeros((Hp, Wp), dtype=np.float64) for ro in tqdm(range(Rout), desc="Merging (rows)"): - r_base = float(ro - real_space_padding) + r_base = ro - real_space_padding for co in range(Cout): - c_base = float(co - real_space_padding) + c_base = co - real_space_padding num_tmp.fill(0.0) den_tmp.fill(0.0) max_wi = 0.0 for i in range(n): - r_in = r_base - float(rs_shifts[i, 0]) - c_in = c_base - float(rs_shifts[i, 1]) + r_in = r_base - rs_shifts[i, 0] + c_in = c_base - rs_shifts[i, 1] r0 = int(np.floor(r_in)) c0 = int(np.floor(c_in)) if r0 < 0 or r0 >= Rs - 1 or c0 < 0 or c0 >= Cs - 1: continue - dr = float(r_in - r0) - dc = float(c_in - c0) + dr = r_in - r0 + dc = c_in - c0 w00 = (1.0 - dr) * (1.0 - dc) w10 = dr * (1.0 - dc) @@ -583,11 +684,12 @@ def merge_datasets( dp_pad[rp0 : rp0 + H, cp0 : cp0 + W] = dp_local * w_dp if method == "fourier": - dp_shifted_tmp[:] = np.fft.ifft2(np.fft.fft2(dp_pad) * ramps[i]).real + ramp = ramps[i] + dp_shifted_tmp[:] = np.fft.ifft2(np.fft.fft2(dp_pad) * ramp).real else: dp_shifted_tmp[:] = ndi_shift( dp_pad, - shift=(float(dp_shifts[i, 0]), float(dp_shifts[i, 1])), + shift=(dp_shifts[i, 0], dp_shifts[i, 1]), order=1, mode="constant", cval=0.0, @@ -617,38 +719,21 @@ def merge_datasets( dmin = float(info.min) dmax = float(info.max) - merged_f = merged # float64 + merged_f = merged if scale_output: peak = float(np.max(merged_f)) if peak <= 0.0: - scale = 1.0 merged_scaled = merged_f else: - scale = dmax / peak - merged_scaled = merged_f * scale + merged_scaled = merged_f * (dmax / peak) if np.issubdtype(dtype_out, np.unsignedinteger): - if float(np.min(merged_scaled)) < 0.0: - warnings.warn( - f"scale_output=True with unsigned dtype {dtype_out}: " - "negative values present; they will be clipped to 0.", - RuntimeWarning, - ) lo, hi = 0.0, dmax else: lo, hi = dmin, dmax - if float(np.min(merged_scaled)) < lo or float(np.max(merged_scaled)) > hi: - warnings.warn( - f"Output overflow for dtype {dtype_out} after scaling: " - f"data range [{float(np.min(merged_scaled))}, {float(np.max(merged_scaled))}] exceeds " - f"[{lo}, {hi}]. Values will be clipped.", - RuntimeWarning, - ) - merged_out = np.rint(np.clip(merged_scaled, lo, hi)).astype(dtype_out) - else: below = float(np.min(merged_f)) above = float(np.max(merged_f)) @@ -662,17 +747,15 @@ def merge_datasets( else: merged_out = merged.astype(dtype_out, copy=False) - dataset_merged = Dataset4dstem.from_array(array=merged_out) - dataset_merged.im_bf_merged = self.im_bf_merged dataset_merged.dp_mean_merged = self.dp_mean_merged if plot_result: show_2d( [[self.im_bf_merged, self.dp_mean_merged]], - titles=[["Merged Bright Field", "Merged Mean Diffraction Pattern"]], - **kwargs, + title=[["Merged Bright Field", "Merged Mean Diffraction Pattern"]], + **plot_kwargs, ) return dataset_merged @@ -683,13 +766,32 @@ def shift_images( shifts_rc, edge_blend: float = 8.0, padding=None, - pad_val=0.0, + pad_val: str | float = 0.0, shift_method: str = "bilinear", ): - import numpy as np - from scipy.ndimage import shift as ndi_shift - from scipy.signal.windows import tukey - + """ + Shift and blend a stack of 2D images into a common padded canvas. + + Parameters + ---------- + images + Sequence of (H, W) arrays. + shifts_rc + Array-like of shape (n, 2) with (row, col) shifts for each image. + edge_blend + Tukey taper width in pixels for image blending. + padding + Output padding. If None, set from max shift and edge_blend. + pad_val + Fill value outside support ('min','max','mean','median' or float). + shift_method + 'bilinear' or 'fourier'. + + Returns + ------- + np.ndarray + Blended image of shape (H + 2*padding, W + 2*padding). + """ images = [np.asarray(im, dtype=float) for im in images] if len(images) == 0: raise ValueError("images must be non-empty") @@ -707,17 +809,17 @@ def shift_images( s = pad_val.strip().lower() v = np.stack(images, axis=0).reshape(-1) if s == "min": - pad_val = float(np.min(v)) + pad_val_f = float(np.min(v)) elif s == "max": - pad_val = float(np.max(v)) + pad_val_f = float(np.max(v)) elif s == "mean": - pad_val = float(np.mean(v)) + pad_val_f = float(np.mean(v)) elif s == "median": - pad_val = float(np.median(v)) + pad_val_f = float(np.median(v)) else: raise ValueError("pad_val must be a float or one of {'min','max','mean','median'}") else: - pad_val = float(pad_val) + pad_val_f = float(pad_val) if padding is None: max_shift = float(np.max(np.abs(shifts_rc))) if shifts_rc.size else 0.0 @@ -749,7 +851,7 @@ def shift_images( kr = np.fft.fftfreq(Hp)[:, None] kc = np.fft.fftfreq(Wp)[None, :] for ind in range(len(images)): - dr, dc = float(shifts_rc[ind, 0]), float(shifts_rc[ind, 1]) + dr, dc = shifts_rc[ind, 0], shifts_rc[ind, 1] ramp = np.exp(-2j * np.pi * (kr * dr + kc * dc)) F = np.fft.fft2(stack[ind]) @@ -762,7 +864,7 @@ def shift_images( for ind in range(len(images)): stack[ind] = ndi_shift( stack[ind], - shift=(float(shifts_rc[ind, 0]), float(shifts_rc[ind, 1])), + shift=(shifts_rc[ind, 0], shifts_rc[ind, 1]), order=1, mode="constant", cval=0.0, @@ -770,7 +872,7 @@ def shift_images( ) stack_w[ind] = ndi_shift( stack_w[ind], - shift=(float(shifts_rc[ind, 0]), float(shifts_rc[ind, 1])), + shift=(shifts_rc[ind, 0], shifts_rc[ind, 1]), order=1, mode="constant", cval=0.0, @@ -778,11 +880,13 @@ def shift_images( ) stack_w[ind] = np.clip(stack_w[ind], 0.0, 1.0) - # edge_w = 1.0 - np.clip(np.max(stack_w, axis=0), 0.0, 1.0) - edge_w = len(images) - np.sum(stack_w, axis=0) + edge_w = np.clip(1.0 - np.sum(stack_w, axis=0), 0.0, 1.0) - num = np.sum(stack, axis=0) + edge_w * pad_val + num = np.sum(stack, axis=0) + edge_w * pad_val_f den = np.sum(stack_w, axis=0) + edge_w - out = num / den + + out = np.empty_like(num) + np.divide(num, den, out=out, where=den != 0.0) + out[den == 0.0] = 0.0 return out From 9c0169798a05fa56ece899c72055b8f2460f9368 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Thu, 2 Apr 2026 15:06:25 -0700 Subject: [PATCH 112/140] Converted MAPED code to torch, added batching --- src/quantem/core/utils/imaging_utils.py | 33 +- src/quantem/diffraction/__init__.py | 5 +- src/quantem/diffraction/maped.py | 1314 ++++++++++++++++++++--- 3 files changed, 1215 insertions(+), 137 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index e94a646c..93a980a9 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -6,8 +6,7 @@ import numpy as np import torch from numpy.typing import NDArray -from scipy.ndimage import gaussian_filter -from scipy.ndimage import map_coordinates +from scipy.ndimage import gaussian_filter, map_coordinates from quantem.core.utils.utils import generate_batches @@ -60,7 +59,9 @@ def _upsampled_correlation_numpy( globalShift = math.floor(math.ceil(upsampleFactor * 1.5) / 2.0) upsampleCenter = float(globalShift) - (float(upsampleFactor) * xyShift) - im_up = dft_upsample(np.conj(imageCorr), upsampleFactor, (float(upsampleCenter[0]), float(upsampleCenter[1]))) + im_up = dft_upsample( + np.conj(imageCorr), upsampleFactor, (float(upsampleCenter[0]), float(upsampleCenter[1])) + ) imageCorrUpsample = np.conj(im_up) flat_idx = int(np.argmax(imageCorrUpsample.real)) @@ -176,14 +177,18 @@ def cross_correlation_shift( def cross_correlation_shift_torch( - im_ref: torch.Tensor, im: torch.Tensor, upsample_factor: int = 2 + im_ref: torch.Tensor, im: torch.Tensor, upsample_factor: int = 2, fft_input: bool = False ) -> torch.Tensor: """ Align two real images using Fourier cross-correlation and DFT upsampling. Returns dx, dy in pixel units (signed shifts). """ - G1 = torch.fft.fft2(im_ref) - G2 = torch.fft.fft2(im) + if fft_input: + G1 = im_ref + G2 = im + else: + G1 = torch.fft.fft2(im_ref) + G2 = torch.fft.fft2(im) xy_shift = align_images_fourier_torch(G1, G2, upsample_factor) @@ -271,12 +276,8 @@ def upsampled_correlation_torch( patch = imageCorrUpsample.real[r - 1 : r + 2, c - 1 : c + 2] if patch.shape == (3, 3): icc = patch - dx = (icc[2, 1] - icc[0, 1]) / ( - 4.0 * icc[1, 1] - 2.0 * icc[2, 1] - 2.0 * icc[0, 1] - ) - dy = (icc[1, 2] - icc[1, 0]) / ( - 4.0 * icc[1, 1] - 2.0 * icc[1, 2] - 2.0 * icc[1, 0] - ) + dx = (icc[2, 1] - icc[0, 1]) / (4.0 * icc[1, 1] - 2.0 * icc[2, 1] - 2.0 * icc[0, 1]) + dy = (icc[1, 2] - icc[1, 0]) / (4.0 * icc[1, 1] - 2.0 * icc[1, 2] - 2.0 * icc[1, 0]) dx = dx.item() dy = dy.item() else: @@ -383,7 +384,9 @@ def weighted_cross_correlation_shift( if weight_real is not None: w = np.asarray(weight_real) if w.shape != cc_real.shape: - raise ValueError(f"weight_real.shape={w.shape} must match correlation shape {cc_real.shape}.") + raise ValueError( + f"weight_real.shape={w.shape} must match correlation shape {cc_real.shape}." + ) cc_pick = cc_real * w else: cc_pick = cc_real @@ -422,7 +425,9 @@ def weighted_cross_correlation_shift( return shift_rc if im is None: - raise ValueError("return_shifted_image=True requires `im` (or its FFT via fft_input=True).") + raise ValueError( + "return_shifted_image=True requires `im` (or its FFT via fft_input=True)." + ) if F_im is None: F_im = np.asarray(im) if fft_input else np.fft.fft2(np.asarray(im)) diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index 2a79312b..8eb0937c 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1,3 +1,6 @@ from quantem.diffraction.polar import RDF as RDF -from quantem.diffraction.strain_autocorrelation import StrainMapAutocorrelation as StrainMapAutocorrelation +from quantem.diffraction.strain_autocorrelation import ( + StrainMapAutocorrelation as StrainMapAutocorrelation, +) from quantem.diffraction.maped import MAPED as MAPED +from quantem.diffraction.maped import MAPEDTorch as MAPEDTorch diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 3b5154c0..ad639d07 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1,17 +1,25 @@ from __future__ import annotations +import math import warnings from typing import Any, Sequence import numpy as np -from scipy.ndimage import gaussian_filter, shift as ndi_shift +import torch +import torch.nn.functional as F +import torchvision +from scipy.ndimage import gaussian_filter +from scipy.ndimage import shift as ndi_shift from scipy.signal import convolve2d from scipy.signal.windows import tukey from tqdm import tqdm from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize -from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift +from quantem.core.utils.imaging_utils import ( + cross_correlation_shift_torch, + weighted_cross_correlation_shift, +) from quantem.core.visualization import show_2d @@ -55,10 +63,14 @@ def from_datasets(cls, datasets: Sequence[Dataset4dstem]) -> MAPED: ds_list: list[Dataset4dstem] = [] for d in datasets: if not isinstance(d, Dataset4dstem): - raise TypeError("MAPED.from_datasets expects a sequence of Dataset4dstem instances.") + raise TypeError( + "MAPED.from_datasets expects a sequence of Dataset4dstem instances." + ) ds_list.append(d) if not ds_list: - raise ValueError("MAPED.from_datasets expects a non-empty sequence of Dataset4dstem instances.") + raise ValueError( + "MAPED.from_datasets expects a non-empty sequence of Dataset4dstem instances." + ) return cls(datasets=ds_list, _token=cls._token) def preprocess( @@ -87,7 +99,9 @@ def preprocess( else: self.scales = np.asarray(list(scale), dtype=float) if self.scales.shape != (n,): - raise ValueError("scale must be a scalar or a sequence with the same length as datasets.") + raise ValueError( + "scale must be a scalar or a sequence with the same length as datasets." + ) if np.any(self.scales == 0): raise ValueError("scale entries must be nonzero.") @@ -119,7 +133,9 @@ def preprocess( if plot_summary: tiles = [[(self.im_bf[i] / self.scales[i]), self.dp_mean[i]] for i in range(n)] - titles = [[f"{i} - Mean Bright Field", f"{i} - Mean Diffraction Pattern"] for i in range(n)] + titles = [ + [f"{i} - Mean Bright Field", f"{i} - Mean Diffraction Pattern"] for i in range(n) + ] show_2d(tiles, title=titles, **plot_kwargs) return self @@ -172,7 +188,9 @@ def diffraction_origin( for i in range(n): dp = np.asarray(self.dp_mean[i]) if sigma is not None and float(sigma) > 0: - dp_use = gaussian_filter(dp.astype(float, copy=False), float(sigma), mode="nearest") + dp_use = gaussian_filter( + dp.astype(float, copy=False), float(sigma), mode="nearest" + ) else: dp_use = dp r, c = np.unravel_index(int(np.argmax(dp_use)), dp_use.shape) @@ -184,7 +202,9 @@ def diffraction_origin( else: origins_list = list(origins) if len(origins_list) != n: - raise ValueError("origins must be a single (row,col) tuple or a list of length n.") + raise ValueError( + "origins must be a single (row,col) tuple or a list of length n." + ) origins_arr = np.asarray(origins_list, dtype=int) if origins_arr.shape != (n, 2): raise ValueError("origins must have shape (n, 2) after conversion.") @@ -240,13 +260,16 @@ def diffraction_align( if not hasattr(self, "dp_mean"): raise RuntimeError("Run preprocess() first so self.dp_mean exists.") if not hasattr(self, "diffraction_origins"): - raise RuntimeError("Run diffraction_origin() first so self.diffraction_origins exists.") + raise RuntimeError( + "Run diffraction_origin() first so self.diffraction_origins exists." + ) H, W = np.asarray(self.dp_mean[0]).shape - w = tukey(H, alpha=2.0 * float(edge_blend) / float(H))[:, None] * tukey( - W, alpha=2.0 * float(edge_blend) / float(W) - )[None, :] + w = ( + tukey(H, alpha=2.0 * float(edge_blend) / float(H))[:, None] + * tukey(W, alpha=2.0 * float(edge_blend) / float(W))[None, :] + ) r = np.fft.fftfreq(H, 1.0 / float(H))[:, None] c = np.fft.fftfreq(W, 1.0 / float(W))[None, :] @@ -296,8 +319,7 @@ def diffraction_align( return self - - def real_space_align( + def real_space_align( # torch.grid_sample self, num_images=None, num_iter: int = 3, @@ -592,7 +614,9 @@ def merge_datasets( elif s == "median": pad_val_dp = float(np.median(v)) else: - raise ValueError("diffraction_pad_val must be a float or one of {'min','max','mean','median'}.") + raise ValueError( + "diffraction_pad_val must be a float or one of {'min','max','mean','median'}." + ) else: pad_val_dp = float(diffraction_pad_val) @@ -761,132 +785,1178 @@ def merge_datasets( return dataset_merged -def shift_images( - images, - shifts_rc, - edge_blend: float = 8.0, - padding=None, - pad_val: str | float = 0.0, - shift_method: str = "bilinear", -): +class MAPEDTorch(AutoSerialize): """ - Shift and blend a stack of 2D images into a common padded canvas. - - Parameters - ---------- - images - Sequence of (H, W) arrays. - shifts_rc - Array-like of shape (n, 2) with (row, col) shifts for each image. - edge_blend - Tukey taper width in pixels for image blending. - padding - Output padding. If None, set from max shift and edge_blend. - pad_val - Fill value outside support ('min','max','mean','median' or float). - shift_method - 'bilinear' or 'fourier'. + Merge-Averaged Precession Electron Diffraction (MAPED) helper coded in PyTorch. - Returns - ------- - np.ndarray - Blended image of shape (H + 2*padding, W + 2*padding). + This class manages a set of 4D-STEM datasets and provides utilities to: + - compute mean BF and mean DP summaries, + - choose/find diffraction origins, + - align diffraction space and real space, + - merge datasets into a single composite Dataset4dstem. """ - images = [np.asarray(im, dtype=float) for im in images] - if len(images) == 0: - raise ValueError("images must be non-empty") - H, W = images[0].shape - for im in images: - if im.shape != (H, W): - raise ValueError("all images must have the same shape") + _token = object() - shifts_rc = np.asarray(shifts_rc, dtype=float) - if shifts_rc.shape != (len(images), 2): - raise ValueError("shifts_rc must have shape (len(images), 2)") + def __init__( + self, + datasets: list[torch.Tensor], + device: str | Any, + dtype: str | Any, + _token: object | None = None, + ): + if _token is not self._token: + raise RuntimeError("Use MAPED.from_datasets() to instantiate this class.") + super().__init__() + self.datasets = datasets + self.metadata: dict[str, Any] = {} + self.device = device + self.dtype = dtype - if isinstance(pad_val, str): - s = pad_val.strip().lower() - v = np.stack(images, axis=0).reshape(-1) - if s == "min": - pad_val_f = float(np.min(v)) - elif s == "max": - pad_val_f = float(np.max(v)) - elif s == "mean": - pad_val_f = float(np.mean(v)) - elif s == "median": - pad_val_f = float(np.median(v)) + @classmethod + def from_datasets(cls, datasets: Sequence[torch.Tensor]) -> MAPED: + """ + Construct a MAPED instance from a non-empty sequence of Dataset4dstem. + + Parameters + ---------- + datasets + Sequence of Dataset4dstem instances. + + Returns + ------- + MAPED + New MAPED instance. + """ + if not isinstance(datasets, Sequence) or isinstance(datasets, (str, bytes)): + raise TypeError("MAPED.from_datasets expects a sequence of Torch tensor instances.") + ds_list: list[torch.Tensor] = [] + for d in datasets: + if not isinstance(d, torch.Tensor): + raise TypeError( + "MAPED.from_datasets expects a sequence of Torch tensor instances." + ) + ds_list.append(d) + + dtypes = np.array([dataset.dtype for dataset in datasets]) + devices = np.array([dataset.device for dataset in datasets]) + + # check that all datasets have the same dtype and device + if not np.all(dtypes == dtypes[0]): + raise TypeError("All datasets need to have the same type") + if not np.all(devices == devices[0]): + raise TypeError("All datasets need to have the same device") + + if not ds_list: + raise ValueError( + "MAPED.from_datasets expects a non-empty sequence of Torch tensor instances." + ) + return cls(datasets=ds_list, _token=cls._token, device=devices[0], dtype=dtypes[0]) + + def preprocess( + self, + plot_summary: bool = True, + scale: float | Sequence[float] | None = None, + **plot_kwargs: Any, + ) -> MAPED: + """ + Compute dataset summary images. + + Stores + ------ + self.scales : torch.tensor + Per-dataset scaling factors (n,). + self.dp_mean : list[torch.tensor] + Mean diffraction patterns (H, W), one per dataset. + self.im_bf : list[torch.tensor] + Mean bright-field images (R, C), one per dataset. + """ + n = len(self.datasets) + + if scale is None: + self.scales = torch.ones(n, dtype=self.dtype, device=self.device) + elif isinstance(scale, (int, float, np.floating)): + self.scales = torch.full(n, float(scale), dtype=float) else: - raise ValueError("pad_val must be a float or one of {'min','max','mean','median'}") - else: - pad_val_f = float(pad_val) + self.scales = torch.tensor(scale, dtype=self.dtype, device=self.device) + if self.scales.dim != (n,): + raise ValueError( + "scale must be a scalar or a sequence with the same length as datasets." + ) + if torch.any(self.scales == 0): + raise ValueError("scale entries must be nonzero.") - if padding is None: - max_shift = float(np.max(np.abs(shifts_rc))) if shifts_rc.size else 0.0 - padding = int(np.ceil(max_shift + float(edge_blend))) + 2 - padding = int(padding) + self.dp_mean: list[torch.Tensor] = [] + self.im_bf: list[torch.Tensor] = [] - alpha_r = min(1.0, 2.0 * float(edge_blend) / float(H)) if edge_blend > 0 else 0.0 - alpha_c = min(1.0, 2.0 * float(edge_blend) / float(W)) if edge_blend > 0 else 0.0 - w = tukey(H, alpha=alpha_r)[:, None] * tukey(W, alpha=alpha_c)[None, :] - w = w.astype(float, copy=False) + for d in self.datasets: + dp_arr = torch.mean(d, dim=(0, 1)) - Hp = H + 2 * padding - Wp = W + 2 * padding + im_bf_arr = torch.mean(d, dim=(2, 3)) - stack_w = np.zeros((len(images), Hp, Wp), dtype=float) - stack = np.zeros_like(stack_w) + self.dp_mean.append(dp_arr) + self.im_bf.append(im_bf_arr) - r0 = padding - c0 = padding - stack_w[:, r0 : r0 + H, c0 : c0 + W] = w[None, :, :] - for ind, im in enumerate(images): - stack[ind, r0 : r0 + H, c0 : c0 + W] = im * w + if plot_summary: + tiles = [[(self.im_bf[i] / self.scales[i]), self.dp_mean[i]] for i in range(n)] + titles = [ + [f"{i} - Mean Bright Field", f"{i} - Mean Diffraction Pattern"] for i in range(n) + ] + show_2d(tiles, title=titles, **plot_kwargs) - method = str(shift_method).strip().lower() - if method not in {"bilinear", "fourier"}: - raise ValueError("shift_method must be 'bilinear' or 'fourier'") + return self - if method == "fourier": - kr = np.fft.fftfreq(Hp)[:, None] - kc = np.fft.fftfreq(Wp)[None, :] - for ind in range(len(images)): - dr, dc = shifts_rc[ind, 0], shifts_rc[ind, 1] - ramp = np.exp(-2j * np.pi * (kr * dr + kc * dc)) + def diffraction_origin( + self, + origins=None, + sigma=None, + plot_origins: bool = True, + plot_indices=None, + **plot_kwargs: Any, + ) -> MAPED: + """ + Choose or automatically find the origin in diffraction space. - F = np.fft.fft2(stack[ind]) - stack[ind] = np.fft.ifft2(F * ramp).real + Parameters + ---------- + origins + Optional manual origins. Can be: + - a single (row, col) tuple, applied to all datasets + - a list of (row, col) tuples of length n (one per dataset) + sigma + Optional low-pass smoothing sigma (pixels) applied to each mean DP prior to peak finding. + plot_origins + If True, plot mean diffraction patterns with overlaid origin markers. + plot_indices + Optional indices to plot. If None, plots all datasets. + **plot_kwargs + Passed to show_2d. - Fw = np.fft.fft2(stack_w[ind]) - stack_w[ind] = np.fft.ifft2(Fw * ramp).real - stack_w[ind] = np.clip(stack_w[ind], 0.0, 1.0) - else: - for ind in range(len(images)): - stack[ind] = ndi_shift( - stack[ind], - shift=(shifts_rc[ind, 0], shifts_rc[ind, 1]), - order=1, - mode="constant", - cval=0.0, - prefilter=False, - ) - stack_w[ind] = ndi_shift( - stack_w[ind], - shift=(shifts_rc[ind, 0], shifts_rc[ind, 1]), - order=1, - mode="constant", - cval=0.0, - prefilter=False, + Stores + ------ + self.diffraction_origins : np.ndarray + Array of shape (n, 2) with integer (row, col) origins. + """ + n = len(self.datasets) + if not hasattr(self, "dp_mean"): + raise RuntimeError("Run preprocess() first so self.dp_mean exists.") + + if plot_indices is None: + plot_indices_list = list(range(n)) + else: + plot_indices_list = list(plot_indices) + for i in plot_indices_list: + if i < 0 or i >= n: + raise IndexError("plot_indices contains an out-of-range index.") + + if sigma is not None and float(sigma) > 0: + gaussian_filter_torch = torchvision.transforms.GaussianBlur( + kernel_size=[2 * int(2 * float(sigma)) + 1, 2 * int(2 * float(sigma)) + 1], + sigma=[sigma, sigma], ) - stack_w[ind] = np.clip(stack_w[ind], 0.0, 1.0) - edge_w = np.clip(1.0 - np.sum(stack_w, axis=0), 0.0, 1.0) + dp_means_use = gaussian_filter_torch(torch.stack(self.dp_mean)) + else: + dp_means_use = torch.stack(self.dp_mean) - num = np.sum(stack, axis=0) + edge_w * pad_val_f - den = np.sum(stack_w, axis=0) + edge_w + if origins is None: + origins_arr = torch.zeros((n, 2), dtype=torch.int) + for i in range(n): + dp_use = dp_means_use[i] - out = np.empty_like(num) - np.divide(num, den, out=out, where=den != 0.0) - out[den == 0.0] = 0.0 + r, c = torch.unravel_index(torch.argmax(dp_use), dp_use.shape) + origins_arr[i, 0] = int(r) + origins_arr[i, 1] = int(c) + else: + if isinstance(origins, tuple) and len(origins) == 2: + origins_arr = torch.tile( + torch.tensor(origins, dtype=torch.int, device=self.device)[None, :], (n, 1) + ) + else: + origins_list = list(origins) + if len(origins_list) != n: + raise ValueError( + "origins must be a single (row,col) tuple or a list of length n." + ) + origins_arr = torch.tensor(origins_list, dtype=torch.int, device=self.device) + if origins_arr.shape != (n, 2): + raise ValueError("origins must have shape (n, 2) after conversion.") + + self.diffraction_origins = origins_arr + + if plot_origins: + arrays = [np.asarray(self.dp_mean[i].cpu()) for i in plot_indices_list] + titles = [f"{i} - Mean Diffraction Pattern" for i in plot_indices_list] + fig, ax = show_2d(arrays, title=titles, returnfig=True, **plot_kwargs) + axs = np.ravel(np.asarray(ax, dtype=object)) + for j, i in enumerate(plot_indices_list): + r, c = self.diffraction_origins[i].cpu().numpy() + axs[j].plot([c], [r], marker="+", color="red", markersize=16, markeredgewidth=2) + + return self + + def diffraction_align( + self, + edge_blend: float = 16.0, + padding=None, + pad_val: str | float = "min", + upsample_factor: int = 100, + weight_scale: float = 1 / 8, + plot_aligned: bool = True, + **plot_kwargs: Any, + ) -> MAPED: + """ + Align mean diffraction patterns using weighted cross-correlation in Fourier space. + + Parameters + ---------- + edge_blend + Tukey window edge taper (pixels). + padding + Passed to shift_images for plotting. + pad_val + Passed to shift_images for plotting. + upsample_factor + Subpixel upsampling factor for correlation peak estimation. + weight_scale + Radial weight falloff scale (fraction of mean DP size). + plot_aligned + If True, plot aligned mean diffraction patterns. + **plot_kwargs + Passed to show_2d when plotting. + + Stores + ------ + self.diffraction_shifts : np.ndarray + Array of shape (n, 2) with (row, col) shifts to align diffraction patterns. + """ + if not hasattr(self, "dp_mean"): + raise RuntimeError("Run preprocess() first so self.dp_mean exists.") + if not hasattr(self, "diffraction_origins"): + raise RuntimeError( + "Run diffraction_origin() first so self.diffraction_origins exists." + ) + + H, W = self.dp_mean[0].shape + + w = ( + tukey_torch( + H, + alpha=2.0 * float(edge_blend) / float(H), + device=self.device, + dtype=torch.float32, + )[:, None] + * tukey_torch( + W, + alpha=2.0 * float(edge_blend) / float(W), + device=self.device, + dtype=torch.float32, + )[None, :] + ) + + r = torch.fft.fftfreq(H, 1.0 / float(H))[:, None] + c = torch.fft.fftfreq(W, 1.0 / float(W))[None, :] + + n = len(self.dp_mean) + self.diffraction_shifts = torch.zeros((n, 2), device=self.device, dtype=torch.float32) + + G_ref = torch.fft.fft2(w * self.dp_mean[0]) + xy0 = self.diffraction_origins[0] + + for ind in range(1, n): + G = torch.fft.fft2(w * self.dp_mean[ind]) + xy = self.diffraction_origins[ind] + + dr2 = (r - xy0[0] + xy[0]) ** 2 + (c - xy0[1] + xy[1]) ** 2 + im_weight = torch.clip( + 1.0 + - torch.sqrt(dr2) + / float(torch.mean(torch.tensor([H, W], device=self.device, dtype=torch.float32))) + / float(weight_scale), + 0.0, + 1.0, + ) + im_weight = torch.sin(im_weight * torch.pi / 2.0) ** 2 + shift_rc = cross_correlation_shift_torch( # not torchified yet + im_ref=G_ref, + im=G, + # weight_real=im_weight * 0.0 + 1.0, + upsample_factor=int(upsample_factor), + fft_input=True, + ) + kr = torch.fft.fftfreq(H, device=self.device)[:, None] + kc = torch.fft.fftfreq(W, device=self.device)[None, :] + + phase_ramp = torch.exp(-2j * torch.pi * (kr * shift_rc[0] + kc * shift_rc[1])) + + G_shift = G * phase_ramp + self.diffraction_shifts[ind, :] = torch.tensor( + shift_rc, device=self.device, dtype=torch.float32 + ) + + G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) + + self.diffraction_shifts -= torch.mean(self.diffraction_shifts, axis=0)[None, :] + if plot_aligned: + im_aligned = shift_images_torch( + images=torch.stack(self.dp_mean), + shifts_rc=self.diffraction_shifts, + edge_blend=float(edge_blend), + padding=padding, + pad_val=pad_val, + ) + show_2d(im_aligned.unbind(0), **plot_kwargs) + + return self + + def real_space_align( + self, + num_images=None, + num_iter: int = 3, + edge_blend: float = 1.0, + padding=None, + pad_val: str | float = "median", + upsample_factor: int = 100, + max_shift=None, + shift_method: str = "bilinear", + edge_filter: bool = True, + edge_sigma: float = 2.0, + hanning_filter: bool = False, + plot_aligned: bool = True, + **plot_kwargs: Any, + ) -> MAPED: + """ + Align real-space mean BF images using iterative average-reference correlation. + + Parameters + ---------- + num_images + If provided, align only the first num_images images. + num_iter + Number of refinement iterations. + edge_blend + Used to set default correlation padding when max_shift is None. + padding + Passed to shift_images for plotting. + pad_val + Passed to shift_images for plotting. + upsample_factor + Subpixel upsampling factor for correlation peak estimation. + max_shift + Optional maximum shift constraint passed to weighted_cross_correlation_shift. + shift_method + Passed to shift_images for plotting ('bilinear' or 'fourier'). + edge_filter + If True, correlate on gradient magnitude instead of raw intensity. + edge_sigma + Gaussian sigma applied to gradients when edge_filter is True. + hanning_filter + If True, apply a Hanning window prior to FFT. + plot_aligned + If True, plot aligned mean BF images. + **plot_kwargs + Passed to show_2d when plotting. + + Stores + ------ + self.real_space_shifts : np.ndarray + Array of shape (n_total, 2) with (row, col) shifts for aligned datasets. + """ + if not hasattr(self, "im_bf"): + raise RuntimeError("Run preprocess() first so self.im_bf exists.") + if len(self.im_bf) == 0: + raise RuntimeError("No images found in self.im_bf.") + + H, W = self.im_bf[0].shape + for im in self.im_bf: + if im.shape != (H, W): + raise ValueError("all self.im_bf images must have the same shape") + + n_total = len(self.im_bf) + if num_images is None: + n = n_total + else: + n = int(num_images) + if n <= 0: + raise ValueError("num_images must be positive") + n = min(n, n_total) + + if int(num_iter) < 1: + raise ValueError("num_iter must be >= 1") + + if max_shift is not None: + pad_cc = int(np.ceil(float(max_shift))) + 4 + else: + pad_cc = int(np.ceil(float(edge_blend))) + 4 + + Hp = H + 2 * pad_cc + Wp = W + 2 * pad_cc + r0 = pad_cc + c0 = pad_cc + + w_h = torch.ones((H, W), dtype=torch.float32, device=self.device) + if hanning_filter: + w_h = ( + torch.hann_window(H, dtype=torch.float32, device=self.device)[:, None] + * torch.hanning(W, dtype=torch.float32, device=self.device)[None, :] + ) + w_h_pad = torch.zeros((Hp, Wp), dtype=torch.float32, device=self.device) + w_h_pad[r0 : r0 + H, c0 : c0 + W] = w_h + w_h_sum = torch.sum(w_h_pad) + if w_h_sum <= 0: + raise RuntimeError("hanning window sum is zero") + + if edge_filter: + wx = torch.tensor( + [[-1.0, -2.0, -1.0], [0.0, 0.0, 0.0], [1.0, 2.0, 1.0]], + dtype=torch.float32, + device=self.device, + ) + else: + wx = None + + base_pad = torch.zeros((n, Hp, Wp), dtype=torch.float32, device=self.device) + for i in range(n): + im0 = self.im_bf[i] + + if edge_filter: + pad_symmetric = wx.shape[-1] // 2 + im0_pad = F.pad( + im0.unsqueeze(0).unsqueeze(0), + pad=(pad_symmetric, pad_symmetric, pad_symmetric, pad_symmetric), + mode="reflect", + ) + + gx = F.conv2d(im0_pad, wx.unsqueeze(0).unsqueeze(0))[0, 0] + gy = F.conv2d(im0_pad, wx.T.unsqueeze(0).unsqueeze(0))[0, 0] + + gaussian_filt = torchvision.transforms.GaussianBlur( + kernel_size=[ + 2 * int(2 * float(edge_sigma)) + 1, + 2 * int(2 * float(edge_sigma)) + 1, + ], + sigma=[edge_sigma, edge_sigma], + ) + gx = gaussian_filt(gx.unsqueeze(0)) + gy = gaussian_filt(gy.unsqueeze(0)) + im_use = torch.sqrt(gx * gx + gy * gy) + else: + im_use = im0 + + base_pad[i, r0 : r0 + H, c0 : c0 + W] = im_use + + shifts = torch.zeros((n, 2), dtype=torch.float32, device=self.device) + + for _ in range(int(num_iter)): + G_list = torch.empty((n, Hp, Wp), dtype=torch.complex128) + + # shift images to current guess + ims_a = shift_images_torch(base_pad, shifts) + ims_mean = torch.sum(ims_a * w_h_pad, dim=(1, 2)) / w_h_sum + + ims_win = (ims_a - ims_mean[:, None, None]) * w_h_pad[None] + G_list = torch.fft.fft2(ims_win) + + G_ref = torch.mean(G_list, axis=0) + + # perform cross correlation again + for i in range(1, n): + drc = cross_correlation_shift_torch( + im_ref=G_ref, + im=G_list[i], + # weight_real=None, + upsample_factor=int(upsample_factor), + # max_shift=max_shift, + fft_input=True, + # fft_output=False, + # return_shifted_image=False, + ) + + shifts[i, 0] += float(drc[0]) + shifts[i, 1] += float(drc[1]) + + shifts -= shifts[0][None, :].clone() + + shifts -= torch.mean(shifts, dim=0)[None, :] + + self.real_space_shifts = torch.zeros((n_total, 2), dtype=torch.float32, device=self.device) + self.real_space_shifts[:n, :] = shifts + + if plot_aligned: + im_aligned = shift_images_torch( + images=torch.stack(self.im_bf[:n]), + shifts_rc=self.real_space_shifts[:n, :], + edge_blend=float(edge_blend), + padding=padding, + pad_val=pad_val, + mode=shift_method, + blend=False, + ) + show_2d(im_aligned.sum(0), **plot_kwargs) + + return self + + def merge_datasets( + self, + real_space_padding=0, + real_space_edge_blend=1.0, + diffraction_padding=0, + diffraction_edge_blend=0.0, + diffraction_pad_val="min", + shift_method: str = "bilinear", + dtype=None, + scale_output: bool = False, + plot_result: bool = True, + batch_size: int = None, + **plot_kwargs: Any, + ) -> Dataset4dstem: + """ + Merge aligned datasets into a single Dataset4dstem. + + Requires + -------- + self.real_space_shifts + From real_space_align(). + self.diffraction_shifts + From diffraction_align(). + + Parameters + ---------- + real_space_padding + Output scan padding in pixels (adds border to scan grid). + real_space_edge_blend + Tukey taper width for scan-space interpolation weights. + diffraction_padding + Output diffraction padding in pixels (adds border around DPs). + diffraction_edge_blend + Tukey taper width for diffraction-space weights. + diffraction_pad_val + Pad value for diffraction padding ('min','max','mean','median' or float). + shift_method + Diffraction shift method: 'bilinear' or 'fourier'. + dtype + Output dtype. If None, uses parent dtype. + scale_output + If True and dtype is integer, scale to full dynamic range using global max. + plot_result + If True, plot merged BF and merged mean DP. + batch_size + Number of rows to process per batch. If None, uses adaptive sizing (1-32 rows). + **plot_kwargs + Passed to show_2d. + + Returns + ------- + Dataset4dstem + Merged dataset. + """ + if not hasattr(self, "real_space_shifts"): + raise RuntimeError("Run real_space_align() first so self.real_space_shifts exists.") + if not hasattr(self, "diffraction_shifts"): + raise RuntimeError("Run diffraction_align() first so self.diffraction_shifts exists.") + + arrays = self.datasets + n = len(arrays) + if n == 0: + raise RuntimeError("No datasets found in self.datasets.") + + Rs, Cs, H, W = arrays[0].shape + for a in arrays: + if a.shape != (Rs, Cs, H, W): + raise ValueError("All dataset arrays must have the same shape (Rs, Cs, H, W).") + + rs_shifts = self.real_space_shifts + dp_shifts = self.diffraction_shifts + if rs_shifts.shape != (n, 2): + raise ValueError("self.real_space_shifts must have shape (n, 2).") + if dp_shifts.shape != (n, 2): + raise ValueError("self.diffraction_shifts must have shape (n, 2).") + + if dtype is None: + dtype_out = arrays[0].dtype + warnings.warn(f"dtype=None; using parent dtype {dtype_out}.", RuntimeWarning) + else: + dtype_out = torch.dtype(dtype) + + real_space_padding = int(real_space_padding) + diffraction_padding = int(diffraction_padding) + + Rout = Rs + 2 * real_space_padding + Cout = Cs + 2 * real_space_padding + + Hp = H + 2 * diffraction_padding + Wp = W + 2 * diffraction_padding + rp0 = diffraction_padding + cp0 = diffraction_padding + + method = str(shift_method).strip().lower() + if method not in {"bilinear", "fourier"}: + raise ValueError("shift_method must be 'bilinear' or 'fourier'.") + + if real_space_edge_blend and float(real_space_edge_blend) > 0: + alpha_r = min(1.0, 2.0 * float(real_space_edge_blend) / float(Rs)) + alpha_c = min(1.0, 2.0 * float(real_space_edge_blend) / float(Cs)) + w_rs = ( + tukey_torch(Rs, alpha=alpha_r, device=self.device, dtype=torch.float32)[:, None] + * tukey_torch(Cs, alpha=alpha_c, device=self.device, dtype=torch.float32)[None, :] + ) + else: + w_rs = torch.ones((Rs, Cs), dtype=torch.float32, device=self.device) + + if diffraction_edge_blend and float(diffraction_edge_blend) > 0: + alpha_dr = min(1.0, 2.0 * float(diffraction_edge_blend) / float(H)) + alpha_dc = min(1.0, 2.0 * float(diffraction_edge_blend) / float(W)) + w_dp = ( + tukey_torch(H, alpha=alpha_dr, device=self.device, dtype=torch.float32)[:, None] + * tukey_torch(W, alpha=alpha_dc, device=self.device, dtype=torch.float32)[None, :] + ) + else: + w_dp = torch.ones((H, W), dtype=torch.float32, device=self.device) + + dp_means = [torch.mean(a, axis=(0, 1)) for a in arrays] + v = torch.stack(dp_means, axis=0).reshape(-1) + + if isinstance(diffraction_pad_val, str): + s = diffraction_pad_val.strip().lower() + if s == "min": + pad_val_dp = float(torch.min(v)) + elif s == "max": + pad_val_dp = float(torch.max(v)) + elif s == "mean": + pad_val_dp = float(torch.mean(v)) + elif s == "median": + pad_val_dp = float(torch.median(v)) + else: + raise ValueError( + "diffraction_pad_val must be a float or one of {'min','max','mean','median'}." + ) + else: + pad_val_dp = float(diffraction_pad_val) + + wdp_pad = torch.zeros((Hp, Wp), dtype=torch.float32, device=self.device) + wdp_pad[rp0 : rp0 + H, cp0 : cp0 + W] = w_dp + + wdp_shifted = torch.zeros((n, Hp, Wp), dtype=torch.float32, device=self.device) + if method == "fourier": + kr = torch.fft.fftfreq(Hp, device=self.device)[:, None] + kc = torch.fft.fftfreq(Wp, device=self.device)[None, :] + Fw = torch.fft.fft2(wdp_pad) + ramps: list[torch.Tensor] = [] + for i in range(n): + dr, dc = dp_shifts[i, 0], dp_shifts[i, 1] + + ramp = torch.exp(-2j * torch.pi * (kr * dr + kc * dc)) + ramps.append(ramp) + w_i = torch.fft.ifft2(Fw * ramp).real + wdp_shifted[i] = torch.clip(w_i, 0.0, 1.0) + else: + for i in range(n): + w_i = shift_images_torch( + wdp_pad, + shifts_rc=dp_shifts[i, :], + mode="bilinear", + ) + wdp_shifted[i] = w_i + wdp_shifted = torch.clip(w_i, 0.0, 1.0) + + coverage = torch.clip(torch.sum(wdp_shifted, dim=0), 0.0, 1.0) + edge_w_dp = 1.0 - coverage + + # Determine batch size based on available memory + if batch_size is None: + batch_size = max(1, min(32, Rout // 2)) # Adaptive batch size (1-32 rows) + + c_out = torch.arange(Cout, dtype=torch.float32, device=self.device) + c_base = c_out - real_space_padding # (Cout,) + + merged = torch.zeros((Rout, Cout, Hp, Wp), dtype=torch.float64, device=self.device) + + for batch_start in tqdm( + range(0, Rout, batch_size), + desc="Merging (batches)", + total=(Rout + batch_size - 1) // batch_size, + ): + batch_end = min(batch_start + batch_size, Rout) + batch_rows = torch.arange( + batch_start, batch_end, dtype=torch.float32, device=self.device + ) + + num_batch = torch.zeros( + (batch_end - batch_start, Cout, Hp, Wp), dtype=torch.float32, device=self.device + ) + den_batch = torch.zeros( + (batch_end - batch_start, Cout, Hp, Wp), dtype=torch.float32, device=self.device + ) + + r_base_batch = batch_rows.unsqueeze(1) - real_space_padding # (batch_size, 1) + c_base_batch = c_base.unsqueeze(0) # (1, Cout) + + for i in range(n): + a = arrays[i] + if isinstance(a, torch.Tensor): + a = a.float() + else: + a = torch.tensor(a, dtype=torch.float32, device=self.device) + + r_in = r_base_batch.expand(-1, Cout) - rs_shifts[i, 0] # (batch_size, Cout) + c_in = ( + c_base_batch.expand(batch_end - batch_start, -1) - rs_shifts[i, 1] + ) # (batch_size, Cout) + + c_norm = 2.0 * c_in / (Cs - 1) - 1.0 # (batch_size, Cout) + r_norm = 2.0 * r_in / (Rs - 1) - 1.0 # (batch_size, Cout) + + a_reshaped = ( + a.view(Rs, Cs, H * W).permute(2, 0, 1).unsqueeze(0) + ) # (1, H*W, Rs, Cs) + + # Reshape w_rs from (Rs, Cs) to (1, 1, Rs, Cs) + w_rs_reshaped = w_rs.unsqueeze(0).unsqueeze(0) # (1, 1, Rs, Cs) + + dp_interp_list = [] + wi_list = [] + + # Loop through batches, vectorize columns per batch + for b in range(batch_end - batch_start): + grid_batch = torch.stack( + [c_norm[b : b + 1, :], r_norm[b : b + 1, :]], dim=-1 + ).unsqueeze(2) # (1, Cout, 1, 2) + + dp_sample = torch.nn.functional.grid_sample( + a_reshaped, + grid_batch, + mode="bilinear", + padding_mode="zeros", + align_corners=True, + ) + + wi_sample = torch.nn.functional.grid_sample( + w_rs_reshaped, + grid_batch, + mode="bilinear", + padding_mode="zeros", + align_corners=True, + ) + + # Reshape to (Cout, H, W) and (Cout,) + dp_b = ( + dp_sample.squeeze(0).squeeze(-1).view(H, W, Cout).permute(2, 0, 1) + ) # (Cout, H, W) + wi_b = wi_sample.squeeze(0).squeeze(-1).squeeze(0) # (Cout,) + + dp_interp_list.append(dp_b) + wi_list.append(wi_b) + + dp_interp = torch.stack(dp_interp_list) # (batch_size, Cout, H, W) + wi = torch.stack(wi_list) # (batch_size, Cout) + + # Pad to diffraction canvas: (batch_size, Cout, Hp, Wp) + dp_padded = torch.zeros( + (batch_end - batch_start, Cout, Hp, Wp), + dtype=torch.float32, + device=self.device, + ) + dp_padded[:, :, rp0 : rp0 + H, cp0 : cp0 + W] = ( + dp_interp * w_dp.unsqueeze(0).unsqueeze(0) + ).float() + + # apply to DPs + if method == "fourier": + ramp = ramps[i] + fft_result = torch.fft.fft2(dp_padded) # (batch_size, Cout, Hp, Wp) + ramp_exp = ramp.unsqueeze(0).unsqueeze(0) # (1, 1, Hp, Wp) + dp_shifted = torch.fft.ifft2( + fft_result * ramp_exp + ).real # (batch_size, Cout, Hp, Wp) + else: + dp_shifted = torch.zeros_like(dp_padded) + for batch_idx in range(batch_end - batch_start): + for co in range(Cout): + dp_shifted[batch_idx, co] = shift_images_torch( + dp_padded[batch_idx, co].unsqueeze(0), + shifts_rc=dp_shifts[i, :].unsqueeze(0), + mode="bilinear", + ).squeeze(0) + + wi_exp = wi.unsqueeze(-1).unsqueeze(-1) # (batch_size, Cout, 1, 1) + wdp_i = wdp_shifted[i].unsqueeze(0).unsqueeze(0) # (1, 1, Hp, Wp) + + num_batch += wi_exp * dp_shifted + den_batch += wi_exp * wdp_i + + # clear memory + del a, a_reshaped, w_rs_reshaped, dp_padded, dp_shifted + torch.cuda.empty_cache() if torch.cuda.is_available() else None + + # Final division for this batch + num_final = num_batch + edge_w_dp.unsqueeze(0).unsqueeze(0) * pad_val_dp + den_final = den_batch + edge_w_dp.unsqueeze(0).unsqueeze(0) + + merged[batch_start:batch_end] = torch.where( + den_final != 0.0, + (num_final / den_final).to(torch.float64), + torch.zeros_like(num_final).to(torch.float64), + ) + + del num_batch, den_batch, num_final, den_final # clear memory + torch.cuda.empty_cache() if torch.cuda.is_available() else None + + self.im_bf_merged = torch.mean(merged, dim=(2, 3)) + self.dp_mean_merged = torch.mean(merged, dim=(0, 1)) + + self.im_bf_merged = torch.mean(merged, dim=(2, 3)) + self.dp_mean_merged = torch.mean(merged, dim=(0, 1)) + + try: + info = torch.iinfo(dtype_out) + is_int_dtype = True + except TypeError: + is_int_dtype = False + + if is_int_dtype: + dmin = float(info.min) + dmax = float(info.max) + + merged_f = merged + + if scale_output: + peak = torch.max(merged_f).item() + if peak <= 0.0: + merged_scaled = merged_f + else: + merged_scaled = merged_f * (dmax / peak) + + # unsigned in PyTorch is typically uint8 + lo, hi = (0.0, dmax) if dtype_out == torch.uint8 else (dmin, dmax) + merged_out = torch.rint(torch.clamp(merged_scaled, lo, hi)).to(dtype=dtype_out) + else: + below = torch.min(merged_f).item() + above = torch.max(merged_f).item() + if below < dmin or above > dmax: + warnings.warn( + f"Output overflow for dtype {dtype_out}: data range [{below}, {above}] exceeds " + f"[{dmin}, {dmax}]. Values will be clipped.", + RuntimeWarning, + ) + merged_out = torch.rint(torch.clamp(merged_f, dmin, dmax)).to(dtype=dtype_out) + else: + merged_out = merged.to(dtype=dtype_out) + + dataset_merged = Dataset4dstem.from_array(array=merged_out.cpu().numpy()) + dataset_merged.im_bf_merged = self.im_bf_merged + dataset_merged.dp_mean_merged = self.dp_mean_merged + + if plot_result: + show_2d( + [[self.im_bf_merged, self.dp_mean_merged]], + title=[["Merged Bright Field", "Merged Mean Diffraction Pattern"]], + **plot_kwargs, + ) + + return dataset_merged + + +def shift_images( + images, + shifts_rc, + edge_blend: float = 8.0, + padding=None, + pad_val: str | float = 0.0, + shift_method: str = "bilinear", +): + """ + Shift and blend a stack of 2D images into a common padded canvas. + + Parameters + ---------- + images + Sequence of (H, W) arrays. + shifts_rc + Array-like of shape (n, 2) with (row, col) shifts for each image. + edge_blend + Tukey taper width in pixels for image blending. + padding + Output padding. If None, set from max shift and edge_blend. + pad_val + Fill value outside support ('min','max','mean','median' or float). + shift_method + 'bilinear' or 'fourier'. + + Returns + ------- + np.ndarray + Blended image of shape (H + 2*padding, W + 2*padding). + """ + images = [np.asarray(im, dtype=float) for im in images] + if len(images) == 0: + raise ValueError("images must be non-empty") + + H, W = images[0].shape + for im in images: + if im.shape != (H, W): + raise ValueError("all images must have the same shape") + + shifts_rc = np.asarray(shifts_rc, dtype=float) + if shifts_rc.shape != (len(images), 2): + raise ValueError("shifts_rc must have shape (len(images), 2)") + + if isinstance(pad_val, str): + s = pad_val.strip().lower() + v = np.stack(images, axis=0).reshape(-1) + if s == "min": + pad_val_f = float(np.min(v)) + elif s == "max": + pad_val_f = float(np.max(v)) + elif s == "mean": + pad_val_f = float(np.mean(v)) + elif s == "median": + pad_val_f = float(np.median(v)) + else: + raise ValueError("pad_val must be a float or one of {'min','max','mean','median'}") + else: + pad_val_f = float(pad_val) + + if padding is None: + max_shift = float(np.max(np.abs(shifts_rc))) if shifts_rc.size else 0.0 + padding = int(np.ceil(max_shift + float(edge_blend))) + 2 + padding = int(padding) + + alpha_r = min(1.0, 2.0 * float(edge_blend) / float(H)) if edge_blend > 0 else 0.0 + alpha_c = min(1.0, 2.0 * float(edge_blend) / float(W)) if edge_blend > 0 else 0.0 + w = tukey(H, alpha=alpha_r)[:, None] * tukey(W, alpha=alpha_c)[None, :] + w = w.astype(float, copy=False) + + Hp = H + 2 * padding + Wp = W + 2 * padding + + stack_w = np.zeros((len(images), Hp, Wp), dtype=float) + stack = np.zeros_like(stack_w) + + r0 = padding + c0 = padding + stack_w[:, r0 : r0 + H, c0 : c0 + W] = w[None, :, :] + for ind, im in enumerate(images): + stack[ind, r0 : r0 + H, c0 : c0 + W] = im * w + + method = str(shift_method).strip().lower() + if method not in {"bilinear", "fourier"}: + raise ValueError("shift_method must be 'bilinear' or 'fourier'") + + if method == "fourier": + kr = np.fft.fftfreq(Hp)[:, None] + kc = np.fft.fftfreq(Wp)[None, :] + for ind in range(len(images)): + dr, dc = shifts_rc[ind, 0], shifts_rc[ind, 1] + ramp = np.exp(-2j * np.pi * (kr * dr + kc * dc)) + + F = np.fft.fft2(stack[ind]) + stack[ind] = np.fft.ifft2(F * ramp).real + + Fw = np.fft.fft2(stack_w[ind]) + stack_w[ind] = np.fft.ifft2(Fw * ramp).real + stack_w[ind] = np.clip(stack_w[ind], 0.0, 1.0) + else: + for ind in range(len(images)): + stack[ind] = ndi_shift( + stack[ind], + shift=(shifts_rc[ind, 0], shifts_rc[ind, 1]), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + stack_w[ind] = ndi_shift( + stack_w[ind], + shift=(shifts_rc[ind, 0], shifts_rc[ind, 1]), + order=1, + mode="constant", + cval=0.0, + prefilter=False, + ) + stack_w[ind] = np.clip(stack_w[ind], 0.0, 1.0) + + edge_w = np.clip(1.0 - np.sum(stack_w, axis=0), 0.0, 1.0) + + num = np.sum(stack, axis=0) + edge_w * pad_val_f + den = np.sum(stack_w, axis=0) + edge_w + + out = np.empty_like(num) + np.divide(num, den, out=out, where=den != 0.0) + out[den == 0.0] = 0.0 + + return out + + +def tukey_torch(N, alpha=0.5, device=None, dtype=torch.float32): + """ + Creates a 1D Tukey window of length N and shape parameter alpha. + + Parameters + ---------- + N + int, Length of the window. + alpha + float, Shape parameter for the Tukey window. + device + torch.device, Device on which to create the window. + dtype + torch.dtype, Data type of the window. + + Returns + ------- + torch.Tensor + 1D Tukey window of length N. + """ + n = torch.arange(N, device=device, dtype=dtype) + w = torch.ones(N, device=device, dtype=dtype) + + if alpha <= 0: + return w + if alpha >= 1: + return torch.hann_window(N, device=device, dtype=dtype) + + edge = alpha * (N - 1) / 2 + + left = n < edge + right = n >= (N - 1 - edge) + + w[left] = 0.5 * (1 + torch.cos(torch.pi * (2 * n[left] / (alpha * (N - 1)) - 1))) + + w[right] = 0.5 * (1 + torch.cos(torch.pi * (2 * n[right] / (alpha * (N - 1)) - 2 / alpha + 1))) + + return w + + +def shift_images_torch( + images, + shifts_rc, + mode="bilinear", + blend: bool = False, + edge_blend: float = 8.0, + padding=None, + pad_val: str | float = 0.0, +): + """ + Shift (and optionally blend) a stack of 2D images by per-image (dr, dc) pixel shifts using grid_sample. + + Parameters + ---------- + images : torch.Tensor, shape (n, H, W) or (H, W) + Stack of images (or a single image). + shifts_rc : torch.Tensor, shape (n, 2) or (2,) + Per-image shifts as (row_shift, col_shift) in pixels. + mode : 'bilinear' or 'nearest' + blend : bool, whether to blend the shifted images using a Tukey window + edge_blend : float, Tukey edge width in pixels used when blending + padding : int or None, canvas padding. If None, computed from max shift + edge_blend + pad_val : float or one of 'min','max','mean','median', fill value outside support + + Returns + ------- + torch.Tensor — shifted (and blended) images; if input was a single image, returns (Hp, Wp), + otherwise returns (n, Hp, Wp) for blended result or (n, H, W) for non-blended. + """ + single = images.dim() == 2 + if single: + images = images.unsqueeze(0) + shifts_rc = shifts_rc.unsqueeze(0) + + n, H, W = images.shape + + shifts_rc = shifts_rc.to(dtype=torch.float32, device=images.device) + + if not blend: + # simple shift per-image without padding/blending — keep original behavior + imgs = images.unsqueeze(1) + grid_y, grid_x = torch.meshgrid( + torch.linspace(-1, 1, H, device=images.device), + torch.linspace(-1, 1, W, device=images.device), + indexing="ij", + ) + base_grid = torch.stack([grid_x, grid_y], dim=-1) # (H, W, 2) + grid = base_grid.unsqueeze(0).expand(n, -1, -1, -1).clone() # (n, H, W, 2) + grid[..., 0] -= 2.0 * shifts_rc[:, 1].view(n, 1, 1) / W # col shift → x + grid[..., 1] -= 2.0 * shifts_rc[:, 0].view(n, 1, 1) / H # row shift → y + + shifted = F.grid_sample(imgs, grid, mode=mode, padding_mode="zeros", align_corners=True) + result = shifted[:, 0] # (n, H, W) + return result[0] if single else result + + # --- blending path --- + # determine pad_val numeric + if isinstance(pad_val, str): + s = pad_val.strip().lower() + v = images.reshape(-1) + if s == "min": + pad_val_f = float(torch.min(v).item()) + elif s == "max": + pad_val_f = float(torch.max(v).item()) + elif s == "mean": + pad_val_f = float(torch.mean(v).item()) + elif s == "median": + pad_val_f = float(torch.median(v).item()) + else: + raise ValueError("pad_val must be a float or one of {'min','max','mean','median'}") + else: + pad_val_f = float(pad_val) + + # padding (compute from max shift if not provided) + max_shift = float(torch.max(torch.abs(shifts_rc)).item()) if shifts_rc.numel() else 0.0 + if padding is None: + padding = int(math.ceil(max_shift + float(edge_blend))) + 2 + padding = int(padding) + + alpha_r = min(1.0, 2.0 * float(edge_blend) / float(H)) if edge_blend > 0 else 0.0 + alpha_c = min(1.0, 2.0 * float(edge_blend) / float(W)) if edge_blend > 0 else 0.0 + + w = ( + tukey_torch(H, alpha=alpha_r, device=images.device, dtype=torch.float32)[:, None] + * tukey_torch(W, alpha=alpha_c, device=images.device, dtype=torch.float32)[None, :] + ) + + Hp = H + 2 * padding + Wp = W + 2 * padding + r0 = padding + c0 = padding + + # build padded stacks + stack = torch.zeros((n, Hp, Wp), dtype=torch.float32, device=images.device) + stack_w = torch.zeros_like(stack) + for ind in range(n): + stack[ind, r0 : r0 + H, c0 : c0 + W] = images[ind].to(dtype=torch.float32) * w + stack_w[ind, r0 : r0 + H, c0 : c0 + W] = w + + # shift both stack and stack_w using grid_sample on (n,1,Hp,Wp) + imgs = stack.unsqueeze(1) + imgs_w = stack_w.unsqueeze(1) + + # Build base normalized grid for Hp, Wp + grid_y, grid_x = torch.meshgrid( + torch.linspace(-1, 1, Hp, device=images.device), + torch.linspace(-1, 1, Wp, device=images.device), + indexing="ij", + ) + base_grid = torch.stack([grid_x, grid_y], dim=-1) # (Hp, Wp, 2) + grid = base_grid.unsqueeze(0).expand(n, -1, -1, -1).clone() # (n, Hp, Wp, 2) + grid[..., 0] -= 2.0 * shifts_rc[:, 1].view(n, 1, 1) / Wp # col shift → x + grid[..., 1] -= 2.0 * shifts_rc[:, 0].view(n, 1, 1) / Hp # row shift → y + + shifted = F.grid_sample(imgs, grid, mode=mode, padding_mode="zeros", align_corners=True) + shifted_w = F.grid_sample(imgs_w, grid, mode=mode, padding_mode="zeros", align_corners=True) + + shifted = shifted[:, 0] + shifted_w = shifted_w[:, 0] + + shifted_w = torch.clamp(shifted_w, 0.0, 1.0) + + edge_w = torch.clamp(1.0 - torch.sum(shifted_w, dim=0), 0.0, 1.0) + + num = torch.sum(shifted, dim=0) + edge_w * pad_val_f + den = torch.sum(shifted_w, dim=0) + edge_w + + out = torch.empty_like(num) + mask = den != 0.0 + out[mask] = num[mask] / den[mask] + out[~mask] = 0.0 return out From 2d1d0e197d6b15b2ef9714ee3786b65a299c0a03 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Thu, 2 Apr 2026 15:28:54 -0700 Subject: [PATCH 113/140] Cleaned up comments, getting ready for PR --- src/quantem/diffraction/maped.py | 41 +++++++++++++++----------------- 1 file changed, 19 insertions(+), 22 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index ad639d07..3f2d34af 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1350,6 +1350,7 @@ def merge_datasets( Dataset4dstem Merged dataset. """ + if not hasattr(self, "real_space_shifts"): raise RuntimeError("Run real_space_align() first so self.real_space_shifts exists.") if not hasattr(self, "diffraction_shifts"): @@ -1393,6 +1394,7 @@ def merge_datasets( if method not in {"bilinear", "fourier"}: raise ValueError("shift_method must be 'bilinear' or 'fourier'.") + # set up real space edge blending weights if real_space_edge_blend and float(real_space_edge_blend) > 0: alpha_r = min(1.0, 2.0 * float(real_space_edge_blend) / float(Rs)) alpha_c = min(1.0, 2.0 * float(real_space_edge_blend) / float(Cs)) @@ -1403,6 +1405,7 @@ def merge_datasets( else: w_rs = torch.ones((Rs, Cs), dtype=torch.float32, device=self.device) + # set up diffraction space edge blending weights if diffraction_edge_blend and float(diffraction_edge_blend) > 0: alpha_dr = min(1.0, 2.0 * float(diffraction_edge_blend) / float(H)) alpha_dc = min(1.0, 2.0 * float(diffraction_edge_blend) / float(W)) @@ -1413,8 +1416,7 @@ def merge_datasets( else: w_dp = torch.ones((H, W), dtype=torch.float32, device=self.device) - dp_means = [torch.mean(a, axis=(0, 1)) for a in arrays] - v = torch.stack(dp_means, axis=0).reshape(-1) + v = torch.stack(self.dp_mean, axis=0).reshape(-1) if isinstance(diffraction_pad_val, str): s = diffraction_pad_val.strip().lower() @@ -1462,15 +1464,17 @@ def merge_datasets( coverage = torch.clip(torch.sum(wdp_shifted, dim=0), 0.0, 1.0) edge_w_dp = 1.0 - coverage - # Determine batch size based on available memory + # Determine batch size (somewhat arbitrary) if batch_size is None: - batch_size = max(1, min(32, Rout // 2)) # Adaptive batch size (1-32 rows) + batch_size = max(1, min(32, Rout // 2)) c_out = torch.arange(Cout, dtype=torch.float32, device=self.device) - c_base = c_out - real_space_padding # (Cout,) + c_base = c_out - real_space_padding merged = torch.zeros((Rout, Cout, Hp, Wp), dtype=torch.float64, device=self.device) + # start batching + for batch_start in tqdm( range(0, Rout, batch_size), desc="Merging (batches)", @@ -1538,19 +1542,15 @@ def merge_datasets( align_corners=True, ) - # Reshape to (Cout, H, W) and (Cout,) - dp_b = ( - dp_sample.squeeze(0).squeeze(-1).view(H, W, Cout).permute(2, 0, 1) - ) # (Cout, H, W) - wi_b = wi_sample.squeeze(0).squeeze(-1).squeeze(0) # (Cout,) + dp_b = dp_sample.squeeze(0).squeeze(-1).view(H, W, Cout).permute(2, 0, 1) + wi_b = wi_sample.squeeze(0).squeeze(-1).squeeze(0) dp_interp_list.append(dp_b) wi_list.append(wi_b) - dp_interp = torch.stack(dp_interp_list) # (batch_size, Cout, H, W) - wi = torch.stack(wi_list) # (batch_size, Cout) + dp_interp = torch.stack(dp_interp_list) + wi = torch.stack(wi_list) - # Pad to diffraction canvas: (batch_size, Cout, Hp, Wp) dp_padded = torch.zeros( (batch_end - batch_start, Cout, Hp, Wp), dtype=torch.float32, @@ -1560,14 +1560,11 @@ def merge_datasets( dp_interp * w_dp.unsqueeze(0).unsqueeze(0) ).float() - # apply to DPs if method == "fourier": ramp = ramps[i] - fft_result = torch.fft.fft2(dp_padded) # (batch_size, Cout, Hp, Wp) - ramp_exp = ramp.unsqueeze(0).unsqueeze(0) # (1, 1, Hp, Wp) - dp_shifted = torch.fft.ifft2( - fft_result * ramp_exp - ).real # (batch_size, Cout, Hp, Wp) + fft_result = torch.fft.fft2(dp_padded) + ramp_exp = ramp.unsqueeze(0).unsqueeze(0) + dp_shifted = torch.fft.ifft2(fft_result * ramp_exp).real else: dp_shifted = torch.zeros_like(dp_padded) for batch_idx in range(batch_end - batch_start): @@ -1578,8 +1575,8 @@ def merge_datasets( mode="bilinear", ).squeeze(0) - wi_exp = wi.unsqueeze(-1).unsqueeze(-1) # (batch_size, Cout, 1, 1) - wdp_i = wdp_shifted[i].unsqueeze(0).unsqueeze(0) # (1, 1, Hp, Wp) + wi_exp = wi.unsqueeze(-1).unsqueeze(-1) + wdp_i = wdp_shifted[i].unsqueeze(0).unsqueeze(0) num_batch += wi_exp * dp_shifted den_batch += wi_exp * wdp_i @@ -1607,6 +1604,7 @@ def merge_datasets( self.im_bf_merged = torch.mean(merged, dim=(2, 3)) self.dp_mean_merged = torch.mean(merged, dim=(0, 1)) + # dtype scaling and clipping try: info = torch.iinfo(dtype_out) is_int_dtype = True @@ -1626,7 +1624,6 @@ def merge_datasets( else: merged_scaled = merged_f * (dmax / peak) - # unsigned in PyTorch is typically uint8 lo, hi = (0.0, dmax) if dtype_out == torch.uint8 else (dmin, dmax) merged_out = torch.rint(torch.clamp(merged_scaled, lo, hi)).to(dtype=dtype_out) else: From 9992c45b1e258a0fa5a7879d67ee82f8c5859f56 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Thu, 2 Apr 2026 15:32:18 -0700 Subject: [PATCH 114/140] Removed some imports that we don't need --- src/quantem/diffraction/maped.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 3f2d34af..8f411148 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1,6 +1,5 @@ from __future__ import annotations -import math import warnings from typing import Any, Sequence @@ -1900,7 +1899,7 @@ def shift_images_torch( # padding (compute from max shift if not provided) max_shift = float(torch.max(torch.abs(shifts_rc)).item()) if shifts_rc.numel() else 0.0 if padding is None: - padding = int(math.ceil(max_shift + float(edge_blend))) + 2 + padding = int(np.ceil(max_shift + float(edge_blend))) + 2 padding = int(padding) alpha_r = min(1.0, 2.0 * float(edge_blend) / float(H)) if edge_blend > 0 else 0.0 From 1f640ae5ebdb5f85b69a895b5e6594473edc6371 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Thu, 2 Apr 2026 15:45:50 -0700 Subject: [PATCH 115/140] Fixed hann_filter line in real_space_align --- src/quantem/diffraction/maped.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 8f411148..05bb0bef 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1197,7 +1197,7 @@ def real_space_align( if hanning_filter: w_h = ( torch.hann_window(H, dtype=torch.float32, device=self.device)[:, None] - * torch.hanning(W, dtype=torch.float32, device=self.device)[None, :] + * torch.hann_window(W, dtype=torch.float32, device=self.device)[None, :] ) w_h_pad = torch.zeros((Hp, Wp), dtype=torch.float32, device=self.device) w_h_pad[r0 : r0 + H, c0 : c0 + W] = w_h From 494ebc753cfeff718f948f1373f8b28775f05ae0 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Tue, 7 Apr 2026 16:56:15 -0700 Subject: [PATCH 116/140] Added descan alignment using cross correlation --- src/quantem/diffraction/maped.py | 199 ++++++++++++++++++++++++++++++- 1 file changed, 197 insertions(+), 2 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 05bb0bef..e09c012c 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -994,6 +994,30 @@ def diffraction_origin( return self + def dscan_align( + self, + iterations, + upsample_factor: int = 100, + plot_aligned: bool = True, + edge_blend: float = 2.0, + fit_shifts=True, + mode="linear", + ): + for i, dataset in enumerate(self.datasets): + _, aligned_dataset, _ = dscan_correct( + dataset, + iterations, + upsample_factor=upsample_factor, + plot_aligned=plot_aligned, + edge_blend=edge_blend, + device=self.device, + fit_shifts=fit_shifts, + mode=mode, + ) + self.datasets[i] = aligned_dataset + + return self + def diffraction_align( self, edge_blend: float = 16.0, @@ -1062,6 +1086,9 @@ def diffraction_align( G_ref = torch.fft.fft2(w * self.dp_mean[0]) xy0 = self.diffraction_origins[0] + kr = torch.fft.fftfreq(H, device=self.device)[:, None] + kc = torch.fft.fftfreq(W, device=self.device)[None, :] + for ind in range(1, n): G = torch.fft.fft2(w * self.dp_mean[ind]) xy = self.diffraction_origins[ind] @@ -1083,8 +1110,6 @@ def diffraction_align( upsample_factor=int(upsample_factor), fft_input=True, ) - kr = torch.fft.fftfreq(H, device=self.device)[:, None] - kc = torch.fft.fftfreq(W, device=self.device)[None, :] phase_ramp = torch.exp(-2j * torch.pi * (kr * shift_rc[0] + kc * shift_rc[1])) @@ -1956,3 +1981,173 @@ def shift_images_torch( out[~mask] = 0.0 return out + + +def fit_surface_lstsq(img, mode="linear"): + """ + Fits an image with a linear or quadratic function + + Parameters + ---------- + img : torch.Tensor + Image to fit, of shape (H, W) + mode : str + Fitting mode, either "linear" or "quadratic" + + Returns + ------ + fitted : torch.Tensor + Array of shape (H, W) of the fit function over the image + coeffs : torch.Tensor + fitting coefficients + """ + H, W = img.shape + x_1d = torch.arange(img.shape[1], device=img.device, dtype=torch.float32) + y_1d = torch.arange(img.shape[0], device=img.device, dtype=torch.float32) + + xx, yy = torch.meshgrid(x_1d, y_1d) + + x = xx.flatten() + y = yy.flatten() + z = img.flatten() + + if mode == "linear": + A = torch.stack([x, y, torch.ones_like(x)], dim=1) + elif mode == "quadratic": + A = torch.stack([x**2, y**2, x * y, x, y, torch.ones_like(x)], dim=1) + + coeffs, _, _, _ = torch.linalg.lstsq(A, z.unsqueeze(1)) + + fitted = (A @ coeffs).reshape(H, W) + return fitted, coeffs + + +def dscan_correct( + dataset, + iterations, + upsample_factor: int = 100, + plot_aligned: bool = True, + edge_blend: float = 2.0, + device="cpu", + fit_shifts=True, + mode="linear", +): + """ + Align diffraction patterns using cross-correlation. + + Parameters + ---------- + dataset : torch.Tensor + Input 4D dataset + iterations : int + Number of refinement iterations + upsample_factor : int + Upsampling factor for sub-pixel accuracy + plot_aligned : bool + Whether to plot results after each iteration + edge_blend : float + Edge blending parameter for Tukey window + device : torch.device + Device to use + fit_shifts : bool + Whether to fit shifts to a smooth surface + mode : str + "linear" or "quadratic" for surface fitting + """ + H_rs, W_rs, H_dp, W_dp = dataset.shape + + w = ( + tukey_torch( + H_dp, + alpha=2.0 * float(edge_blend) / float(H_dp), + device=device, + dtype=torch.float32, + )[:, None] + * tukey_torch( + W_dp, + alpha=2.0 * float(edge_blend) / float(W_dp), + device=device, + dtype=torch.float32, + )[None, :] + ) + + diffraction_shifts = torch.zeros((H_rs, W_rs, 2), device=device, dtype=torch.float32) + shifted_dps = dataset.clone() + + kr = torch.fft.fftfreq(H_dp, device=device)[:, None] + kc = torch.fft.fftfreq(W_dp, device=device)[None, :] + + for iteration in range(iterations): + G_ref = torch.fft.fft2(shifted_dps.mean(dim=(0, 1)) * w) + + for h_rs in tqdm(range(H_rs), desc=f"Iteration {iteration + 1}/{iterations}"): + for w_rs in range(W_rs): + ind = w_rs + h_rs * H_rs + dp = shifted_dps[h_rs, w_rs] # <-- Read from current shifted_dps, not original + G = torch.fft.fft2(w * dp) + shift = cross_correlation_shift_torch( + G_ref, G, upsample_factor=upsample_factor, fft_input=True + ) + diffraction_shifts[h_rs, w_rs] = shift + + phase_ramp = torch.exp(-1j * torch.pi * (kr * shift[0] + kc * shift[1])) + G_shift = G * phase_ramp + + shifted_dps[h_rs, w_rs, :, :] = torch.fft.ifft2(G_shift).real + G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) + + G_ref_final = G_ref.clone() + + if fit_shifts: + diffraction_shifts_1, _ = fit_surface_lstsq(diffraction_shifts[:, :, 0], mode=mode) + diffraction_shifts_2, _ = fit_surface_lstsq(diffraction_shifts[:, :, 1], mode=mode) + diffraction_shifts_old = diffraction_shifts.clone() + diffraction_shifts = torch.stack((diffraction_shifts_1, diffraction_shifts_2), dim=2) + + # Recompute fitted shifts + for h_rs in tqdm(range(H_rs), desc="Applying fitted shifts"): + for w_rs in range(W_rs): + dp = shifted_dps[h_rs, w_rs] # <-- Also read from shifted_dps here + G = torch.fft.fft2(w * dp) + shift = diffraction_shifts[h_rs, w_rs] + + phase_ramp = torch.exp(-1j * torch.pi * (kr * shift[0] + kc * shift[1])) + G_shift = G * phase_ramp + + shifted_dps[h_rs, w_rs, :, :] = torch.fft.ifft2(G_shift).real + + if plot_aligned: + if fit_shifts: + show_2d( + [ + [ + diffraction_shifts_old[:, :, 0], + diffraction_shifts[:, :, 0], + diffraction_shifts[:, :, 0] - diffraction_shifts_old[:, :, 0], + ], + [ + diffraction_shifts_old[:, :, 1], + diffraction_shifts[:, :, 1], + diffraction_shifts[:, :, 1] - diffraction_shifts_old[:, :, 1], + ], + ], + title=[ + ["Shifts x", "Fit x", "Residual x"], + ["Shifts y", "Fit y", "Residual y"], + ], + cmap="RdBu_r", + vmax=3, + vmin=-3, + ) + + dp_mean_before = dataset.mean(dim=(0, 1)) + dp_mean = shifted_dps.mean(dim=(0, 1)) + dp_max = torch.max( + torch.max(shifted_dps, dim=0, keepdim=False).values, dim=0, keepdim=False + ).values + show_2d( + [dp_mean_before, dp_mean, dp_max], + vmax=0.75, + ) + + return diffraction_shifts, shifted_dps, G_ref_final From 9a0254e52031ff6848dece878aa0f1a4d17e79d9 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Fri, 1 May 2026 10:19:45 -0700 Subject: [PATCH 117/140] Changed docstrings to all be in numpy format --- src/quantem/diffraction/maped.py | 310 +++++++++++++++++++------------ 1 file changed, 189 insertions(+), 121 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index e09c012c..8def04dc 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -81,14 +81,26 @@ def preprocess( """ Compute dataset summary images. - Stores - ------ - self.scales : np.ndarray + Parameters + ---------- + plot_summary : bool, optional + If True, display summary plots (default True). + scale : float or sequence of float or None, optional + Per-dataset scaling factor(s) (default None). + + Attributes + ---------- + scales : np.ndarray Per-dataset scaling factors (n,). - self.dp_mean : list[np.ndarray] + dp_mean : list[np.ndarray] Mean diffraction patterns (H, W), one per dataset. - self.im_bf : list[np.ndarray] + im_bf : list[np.ndarray] Mean bright-field images (R, C), one per dataset. + + Returns + ------- + MAPED + self (updated instance) """ n = len(self.datasets) if scale is None: @@ -152,23 +164,28 @@ def diffraction_origin( Parameters ---------- - origins + origins : tuple or sequence, optional Optional manual origins. Can be: - a single (row, col) tuple, applied to all datasets - a list of (row, col) tuples of length n (one per dataset) - sigma + sigma : float, optional Optional low-pass smoothing sigma (pixels) applied to each mean DP prior to peak finding. - plot_origins + plot_origins : bool, optional If True, plot mean diffraction patterns with overlaid origin markers. - plot_indices + plot_indices : sequence of int, optional Optional indices to plot. If None, plots all datasets. **plot_kwargs Passed to show_2d. - Stores - ------ - self.diffraction_origins : np.ndarray + Attributes + ---------- + diffraction_origins : np.ndarray Array of shape (n, 2) with integer (row, col) origins. + + Returns + ------- + MAPED + self (updated instance) """ n = len(self.datasets) if not hasattr(self, "dp_mean"): @@ -236,25 +253,30 @@ def diffraction_align( Parameters ---------- - edge_blend + edge_blend : float Tukey window edge taper (pixels). - padding + padding : int or None Passed to shift_images for plotting. - pad_val + pad_val : str or float Passed to shift_images for plotting. - upsample_factor + upsample_factor : int Subpixel upsampling factor for correlation peak estimation. - weight_scale + weight_scale : float Radial weight falloff scale (fraction of mean DP size). - plot_aligned + plot_aligned : bool If True, plot aligned mean diffraction patterns. **plot_kwargs Passed to show_2d when plotting. - Stores - ------ - self.diffraction_shifts : np.ndarray + Attributes + ---------- + diffraction_shifts : np.ndarray Array of shape (n, 2) with (row, col) shifts to align diffraction patterns. + + Returns + ------- + MAPED + self (updated instance) """ if not hasattr(self, "dp_mean"): raise RuntimeError("Run preprocess() first so self.dp_mean exists.") @@ -339,37 +361,42 @@ def real_space_align( # torch.grid_sample Parameters ---------- - num_images + num_images : int, optional If provided, align only the first num_images images. - num_iter + num_iter : int Number of refinement iterations. - edge_blend + edge_blend : float Used to set default correlation padding when max_shift is None. - padding + padding : int or None Passed to shift_images for plotting. - pad_val + pad_val : str or float Passed to shift_images for plotting. - upsample_factor + upsample_factor : int Subpixel upsampling factor for correlation peak estimation. - max_shift + max_shift : float, optional Optional maximum shift constraint passed to weighted_cross_correlation_shift. - shift_method + shift_method : str Passed to shift_images for plotting ('bilinear' or 'fourier'). - edge_filter + edge_filter : bool If True, correlate on gradient magnitude instead of raw intensity. - edge_sigma + edge_sigma : float Gaussian sigma applied to gradients when edge_filter is True. - hanning_filter + hanning_filter : bool If True, apply a Hanning window prior to FFT. - plot_aligned + plot_aligned : bool If True, plot aligned mean BF images. **plot_kwargs Passed to show_2d when plotting. - Stores - ------ - self.real_space_shifts : np.ndarray + Attributes + ---------- + real_space_shifts : np.ndarray Array of shape (n_total, 2) with (row, col) shifts for aligned datasets. + + Returns + ------- + MAPED + self (updated instance) """ if not hasattr(self, "im_bf"): raise RuntimeError("Run preprocess() first so self.im_bf exists.") @@ -505,12 +532,14 @@ def merge_datasets( """ Merge aligned datasets into a single Dataset4dstem. - Requires - -------- + Notes + ----- + Requires the following attributes to be present on ``self``: + self.real_space_shifts - From real_space_align(). + From ``real_space_align()``. self.diffraction_shifts - From diffraction_align(). + From ``diffraction_align()``. Parameters ---------- @@ -861,14 +890,26 @@ def preprocess( """ Compute dataset summary images. - Stores - ------ - self.scales : torch.tensor + Parameters + ---------- + plot_summary : bool, optional + If True, display summary plots (default True). + scale : float or sequence of float or None, optional + Per-dataset scaling factor(s) (default None). + + Attributes + ---------- + scales : torch.tensor Per-dataset scaling factors (n,). - self.dp_mean : list[torch.tensor] + dp_mean : list[torch.tensor] Mean diffraction patterns (H, W), one per dataset. - self.im_bf : list[torch.tensor] + im_bf : list[torch.tensor] Mean bright-field images (R, C), one per dataset. + + Returns + ------- + MAPED + self (updated instance) """ n = len(self.datasets) @@ -890,7 +931,6 @@ def preprocess( for d in self.datasets: dp_arr = torch.mean(d, dim=(0, 1)) - im_bf_arr = torch.mean(d, dim=(2, 3)) self.dp_mean.append(dp_arr) @@ -907,10 +947,10 @@ def preprocess( def diffraction_origin( self, - origins=None, - sigma=None, + origins: tuple | list | None = None, + sigma: float | None = None, plot_origins: bool = True, - plot_indices=None, + plot_indices: list | None = None, **plot_kwargs: Any, ) -> MAPED: """ @@ -918,23 +958,28 @@ def diffraction_origin( Parameters ---------- - origins + origins : tuple or list, optional Optional manual origins. Can be: - a single (row, col) tuple, applied to all datasets - a list of (row, col) tuples of length n (one per dataset) - sigma + sigma : float, optional Optional low-pass smoothing sigma (pixels) applied to each mean DP prior to peak finding. - plot_origins + plot_origins : bool, optional If True, plot mean diffraction patterns with overlaid origin markers. - plot_indices + plot_indices : list, optional Optional indices to plot. If None, plots all datasets. **plot_kwargs Passed to show_2d. - Stores - ------ - self.diffraction_origins : np.ndarray + Attributes + ---------- + diffraction_origins : np.ndarray Array of shape (n, 2) with integer (row, col) origins. + + Returns + ------- + MAPED + self (updated instance) """ n = len(self.datasets) if not hasattr(self, "dp_mean"): @@ -996,12 +1041,12 @@ def diffraction_origin( def dscan_align( self, - iterations, + iterations: int, upsample_factor: int = 100, plot_aligned: bool = True, edge_blend: float = 2.0, - fit_shifts=True, - mode="linear", + fit_shifts: bool = True, + mode: str = "linear", ): for i, dataset in enumerate(self.datasets): _, aligned_dataset, _ = dscan_correct( @@ -1033,25 +1078,30 @@ def diffraction_align( Parameters ---------- - edge_blend + edge_blend : float Tukey window edge taper (pixels). - padding + padding : int or tuple, optional Passed to shift_images for plotting. - pad_val + pad_val : str or float Passed to shift_images for plotting. - upsample_factor + upsample_factor : int Subpixel upsampling factor for correlation peak estimation. - weight_scale + weight_scale : float Radial weight falloff scale (fraction of mean DP size). - plot_aligned + plot_aligned : bool If True, plot aligned mean diffraction patterns. **plot_kwargs Passed to show_2d when plotting. - Stores - ------ - self.diffraction_shifts : np.ndarray + Attributes + ---------- + diffraction_shifts : np.ndarray Array of shape (n, 2) with (row, col) shifts to align diffraction patterns. + + Returns + ------- + MAPED + self (updated instance) """ if not hasattr(self, "dp_mean"): raise RuntimeError("Run preprocess() first so self.dp_mean exists.") @@ -1154,37 +1204,42 @@ def real_space_align( Parameters ---------- - num_images + num_images : int, optional If provided, align only the first num_images images. - num_iter + num_iter : int Number of refinement iterations. - edge_blend + edge_blend : float Used to set default correlation padding when max_shift is None. - padding + padding : int or tuple, optional Passed to shift_images for plotting. - pad_val + pad_val : float Passed to shift_images for plotting. - upsample_factor + upsample_factor : int Subpixel upsampling factor for correlation peak estimation. - max_shift + max_shift : float Optional maximum shift constraint passed to weighted_cross_correlation_shift. - shift_method + shift_method : 'bilinear' or 'fourier' Passed to shift_images for plotting ('bilinear' or 'fourier'). - edge_filter + edge_filter : bool If True, correlate on gradient magnitude instead of raw intensity. - edge_sigma + edge_sigma : float Gaussian sigma applied to gradients when edge_filter is True. - hanning_filter + hanning_filter : bool If True, apply a Hanning window prior to FFT. - plot_aligned + plot_aligned : bool If True, plot aligned mean BF images. **plot_kwargs Passed to show_2d when plotting. - Stores - ------ - self.real_space_shifts : np.ndarray + Attributes + ---------- + real_space_shifts : np.ndarray Array of shape (n_total, 2) with (row, col) shifts for aligned datasets. + + Returns + ------- + MAPED + self (updated instance) """ if not hasattr(self, "im_bf"): raise RuntimeError("Run preprocess() first so self.im_bf exists.") @@ -1322,11 +1377,11 @@ def real_space_align( def merge_datasets( self, - real_space_padding=0, - real_space_edge_blend=1.0, - diffraction_padding=0, - diffraction_edge_blend=0.0, - diffraction_pad_val="min", + real_space_padding: int = 0, + real_space_edge_blend: float = 1.0, + diffraction_padding: int = 0, + diffraction_edge_blend: float = 0.0, + diffraction_pad_val: str | float = "min", shift_method: str = "bilinear", dtype=None, scale_output: bool = False, @@ -1337,34 +1392,36 @@ def merge_datasets( """ Merge aligned datasets into a single Dataset4dstem. - Requires - -------- + Notes + ----- + Requires the following attributes to be present on ``self``: + self.real_space_shifts - From real_space_align(). + From ``real_space_align()``. self.diffraction_shifts - From diffraction_align(). + From ``diffraction_align()``. Parameters ---------- - real_space_padding + real_space_padding : int Output scan padding in pixels (adds border to scan grid). - real_space_edge_blend + real_space_edge_blend : float Tukey taper width for scan-space interpolation weights. - diffraction_padding + diffraction_padding : int Output diffraction padding in pixels (adds border around DPs). - diffraction_edge_blend + diffraction_edge_blend : float Tukey taper width for diffraction-space weights. - diffraction_pad_val + diffraction_pad_val : str | float Pad value for diffraction padding ('min','max','mean','median' or float). - shift_method + shift_method : str Diffraction shift method: 'bilinear' or 'fourier'. - dtype + dtype : str or torch.dtype, optional Output dtype. If None, uses parent dtype. - scale_output + scale_output : bool If True and dtype is integer, scale to full dynamic range using global max. - plot_result + plot_result : bool If True, plot merged BF and merged mean DP. - batch_size + batch_size : int, optional Number of rows to process per batch. If None, uses adaptive sizing (1-32 rows). **plot_kwargs Passed to show_2d. @@ -1678,10 +1735,10 @@ def merge_datasets( def shift_images( - images, - shifts_rc, + images: list[np.ndarray], + shifts_rc: np.ndarray, edge_blend: float = 8.0, - padding=None, + padding: int | None = None, pad_val: str | float = 0.0, shift_method: str = "bilinear", ): @@ -1690,17 +1747,17 @@ def shift_images( Parameters ---------- - images + images : list of np.ndarray Sequence of (H, W) arrays. - shifts_rc + shifts_rc : np.ndarray Array-like of shape (n, 2) with (row, col) shifts for each image. - edge_blend + edge_blend : float, optional Tukey taper width in pixels for image blending. - padding + padding : int Output padding. If None, set from max shift and edge_blend. - pad_val + pad_val : str | float optional Fill value outside support ('min','max','mean','median' or float). - shift_method + shift_method : str 'bilinear' or 'fourier'. Returns @@ -1814,18 +1871,18 @@ def tukey_torch(N, alpha=0.5, device=None, dtype=torch.float32): Parameters ---------- - N - int, Length of the window. - alpha - float, Shape parameter for the Tukey window. - device - torch.device, Device on which to create the window. - dtype + N : int + Length of the window. + alpha : float + Shape parameter for the Tukey window. + device : torch.device | str + Device on which to create the window. + dtype : torch.dtype torch.dtype, Data type of the window. Returns ------- - torch.Tensor + window : torch.Tensor 1D Tukey window of length N. """ n = torch.arange(N, device=device, dtype=dtype) @@ -1874,8 +1931,10 @@ def shift_images_torch( Returns ------- - torch.Tensor — shifted (and blended) images; if input was a single image, returns (Hp, Wp), - otherwise returns (n, Hp, Wp) for blended result or (n, H, W) for non-blended. + torch.Tensor + Shifted (and blended) images. If the input was a single image, returns an array + of shape (Hp, Wp). Otherwise returns (n, Hp, Wp) for blended result or (n, H, W) + for the non-blended case. """ single = images.dim() == 2 if single: @@ -2053,6 +2112,15 @@ def dscan_correct( Whether to fit shifts to a smooth surface mode : str "linear" or "quadratic" for surface fitting + + Returns + ------- + tuple + A tuple ``(diffraction_shifts, shifted_dps, G_ref_final)`` where + ``diffraction_shifts`` is a ``torch.Tensor`` of shape (H_rs, W_rs, 2) with + per-scan-position shifts, ``shifted_dps`` is the aligned dataset (same shape + as ``dataset``), and ``G_ref_final`` is the final complex Fourier-domain + reference (torch.Tensor). """ H_rs, W_rs, H_dp, W_dp = dataset.shape From b185ba274ab33fb5176e52d836780da606f789cb Mon Sep 17 00:00:00 2001 From: henrygbell Date: Mon, 11 May 2026 11:54:00 -0700 Subject: [PATCH 118/140] Changed the plotting of MAPED.real_space_align to be consistent with numpy version. --- src/quantem/diffraction/maped.py | 35 +++++++++++++++++--------------- 1 file changed, 19 insertions(+), 16 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 8def04dc..32c340bc 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1369,9 +1369,9 @@ def real_space_align( padding=padding, pad_val=pad_val, mode=shift_method, - blend=False, + blend=True, ) - show_2d(im_aligned.sum(0), **plot_kwargs) + show_2d(im_aligned, **plot_kwargs) return self @@ -2005,7 +2005,6 @@ def shift_images_torch( for ind in range(n): stack[ind, r0 : r0 + H, c0 : c0 + W] = images[ind].to(dtype=torch.float32) * w stack_w[ind, r0 : r0 + H, c0 : c0 + W] = w - # shift both stack and stack_w using grid_sample on (n,1,Hp,Wp) imgs = stack.unsqueeze(1) imgs_w = stack_w.unsqueeze(1) @@ -2088,6 +2087,7 @@ def dscan_correct( plot_aligned: bool = True, edge_blend: float = 2.0, device="cpu", + method="cross_correlation", fit_shifts=True, mode="linear", ): @@ -2147,22 +2147,25 @@ def dscan_correct( for iteration in range(iterations): G_ref = torch.fft.fft2(shifted_dps.mean(dim=(0, 1)) * w) + if method == "cross_correlation": + for h_rs in tqdm(range(H_rs), desc=f"Iteration {iteration + 1}/{iterations}"): + for w_rs in range(W_rs): + ind = w_rs + h_rs * H_rs + dp = shifted_dps[h_rs, w_rs] # <-- Read from current shifted_dps, not original + G = torch.fft.fft2(w * dp) + shift = cross_correlation_shift_torch( + G_ref, G, upsample_factor=upsample_factor, fft_input=True + ) + diffraction_shifts[h_rs, w_rs] = shift - for h_rs in tqdm(range(H_rs), desc=f"Iteration {iteration + 1}/{iterations}"): - for w_rs in range(W_rs): - ind = w_rs + h_rs * H_rs - dp = shifted_dps[h_rs, w_rs] # <-- Read from current shifted_dps, not original - G = torch.fft.fft2(w * dp) - shift = cross_correlation_shift_torch( - G_ref, G, upsample_factor=upsample_factor, fft_input=True - ) - diffraction_shifts[h_rs, w_rs] = shift + phase_ramp = torch.exp(-1j * torch.pi * (kr * shift[0] + kc * shift[1])) + G_shift = G * phase_ramp - phase_ramp = torch.exp(-1j * torch.pi * (kr * shift[0] + kc * shift[1])) - G_shift = G * phase_ramp + shifted_dps[h_rs, w_rs, :, :] = torch.fft.ifft2(G_shift).real + G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) - shifted_dps[h_rs, w_rs, :, :] = torch.fft.ifft2(G_shift).real - G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) + if method == "autocorrelation": + pass G_ref_final = G_ref.clone() From 95be310bc439fc73175d5cb5061f316ae5c34bf5 Mon Sep 17 00:00:00 2001 From: cophus Date: Wed, 3 Dec 2025 09:11:15 -0800 Subject: [PATCH 119/140] initial construction of polar4dstem class --- .../core/datastructures/polar4dstem.py | 307 ++++++++++++------ 1 file changed, 200 insertions(+), 107 deletions(-) diff --git a/src/quantem/core/datastructures/polar4dstem.py b/src/quantem/core/datastructures/polar4dstem.py index 6619af5c..848469ec 100644 --- a/src/quantem/core/datastructures/polar4dstem.py +++ b/src/quantem/core/datastructures/polar4dstem.py @@ -1,13 +1,12 @@ import numpy as np from numpy.typing import NDArray from typing import Any, TYPE_CHECKING -from scipy.ndimage import map_coordinates - -if TYPE_CHECKING: - from .dataset4dstem import Dataset4dstem from quantem.core.datastructures.dataset4d import Dataset4d +# from quantem.core.datastructures.dataset4dstem import Dataset4dstem +if TYPE_CHECKING: + from .dataset4dstem import Dataset4dstem class Polar4dstem(Dataset4d): """4D-STEM dataset in polar coordinates (scan_y, scan_x, phi, r).""" @@ -38,6 +37,7 @@ def __init__( for k in mdata_keys_polar: if k not in metadata: metadata[k] = None + super().__init__( array=array, name=name, @@ -60,13 +60,11 @@ def from_array( signal_units: str = "arb. units", metadata: dict | None = None, ) -> "Polar4dstem": - array = np.asarray(array) - if array.ndim != 4: - raise ValueError("Polar4dstem.from_array expects a 4D array.") + array = ensure_valid_array(array, ndim=4) if origin is None: - origin = np.zeros(4, dtype=float) + origin = np.zeros(4) if sampling is None: - sampling = np.ones(4, dtype=float) + sampling = np.ones(4) if units is None: units = ["pixels", "pixels", "deg", "pixels"] if metadata is None: @@ -91,68 +89,10 @@ def n_r(self) -> int: return int(self.array.shape[3]) -def _precompute_polar_coords( - ny: int, - nx: int, - origin_row: float, - origin_col: float, - ellipse_params: tuple[float, float, float] | None, - num_annular_bins: int, - radial_min: float, - radial_max: float | None, - radial_step: float, - two_fold_rotation_symmetry: bool, -) -> tuple[NDArray, NDArray, NDArray, float]: - origin_row = float(origin_row) - origin_col = float(origin_col) - if radial_step <= 0: - raise ValueError("radial_step must be > 0.") - if num_annular_bins < 1: - raise ValueError("num_annular_bins must be >= 1.") - if radial_max is None: - r_row_pos = origin_row - r_row_neg = (ny - 1) - origin_row - r_col_pos = origin_col - r_col_neg = (nx - 1) - origin_col - radial_max_eff = float(min(r_row_pos, r_row_neg, r_col_pos, r_col_neg)) - else: - radial_max_eff = float(radial_max) - if radial_max_eff <= radial_min: - radial_max_eff = radial_min + radial_step - radial_bins = np.arange(radial_min, radial_max_eff, radial_step, dtype=np.float64) - if radial_bins.size == 0: - radial_bins = np.array([radial_min], dtype=np.float64) - if two_fold_rotation_symmetry: - phi_range = np.pi - else: - phi_range = 2.0 * np.pi - phi_bins = np.linspace(0.0, phi_range, num_annular_bins, endpoint=False, dtype=np.float64) - phi_grid, r_grid = np.meshgrid(phi_bins, radial_bins, indexing="ij") - if ellipse_params is None: - x = r_grid * np.cos(phi_grid) - y = r_grid * np.sin(phi_grid) - else: - if len(ellipse_params) != 3: - raise ValueError("ellipse_params must be (a, b, theta_deg).") - a, b, theta_deg = ellipse_params - theta = np.deg2rad(theta_deg) - alpha = phi_grid - theta - u = (a / b) * r_grid * np.cos(alpha) - v_prime = r_grid * np.sin(alpha) - cos_t = np.cos(theta) - sin_t = np.sin(theta) - x = u * cos_t - v_prime * sin_t - y = u * sin_t + v_prime * cos_t - coords_y = y + origin_row - coords_x = x + origin_col - coords = np.stack((coords_y, coords_x), axis=0) - return coords, phi_bins, radial_bins, radial_max_eff - - def dataset4dstem_polar_transform( self: "Dataset4dstem", - origin_row: float | int | NDArray, - origin_col: float | int | NDArray, + origin_row: float | NDArray, + origin_col: float | NDArray, ellipse_params: tuple[float, float, float] | None = None, num_annular_bins: int = 180, radial_min: float = 0.0, @@ -162,16 +102,17 @@ def dataset4dstem_polar_transform( name: str | None = None, signal_units: str | None = None, ) -> Polar4dstem: + """Return a Polar4dstem with shape (scan_y, scan_x, phi, r).""" if self.array.ndim != 4: raise ValueError("polar_transform requires a 4D-STEM dataset (ndim=4).") + scan_y, scan_x, ny, nx = self.array.shape - origin_row_f = float(origin_row) - origin_col_f = float(origin_col) - coords, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( + + mapping = _precompute_polar_mapping( ny=ny, nx=nx, - origin_row=origin_row_f, - origin_col=origin_col_f, + origin_row=float(origin_row), + origin_col=float(origin_col), ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, radial_min=radial_min, @@ -179,52 +120,66 @@ def dataset4dstem_polar_transform( radial_step=radial_step, two_fold_rotation_symmetry=two_fold_rotation_symmetry, ) - n_phi = phi_bins.size - n_r = radial_bins.size + result_dtype = np.result_type(self.array.dtype, np.float32) - out = np.empty((scan_y, scan_x, n_phi, n_r), dtype=result_dtype) + out = np.empty( + (scan_y, scan_x, mapping["n_phi"], mapping["n_r"]), + dtype=result_dtype, + ) + for iy in range(scan_y): for ix in range(scan_x): - dp = self.array[iy, ix] - out[iy, ix] = map_coordinates( - dp, - coords, - order=1, - mode="constant", - cval=0.0, + out[iy, ix] = _apply_polar_mapping_single( + self.array[iy, ix], + mapping, + dtype=result_dtype, ) - if two_fold_rotation_symmetry: - phi_range = np.pi - else: - phi_range = 2.0 * np.pi - phi_step_deg = (phi_range / float(n_phi)) * (180.0 / np.pi) - sampling = np.zeros(4, dtype=float) - origin = np.zeros(4, dtype=float) - sampling[0:2] = np.asarray(self.sampling)[0:2] - sampling[2] = phi_step_deg - sampling[3] = float(np.asarray(self.sampling)[-1]) * radial_step - origin[0:2] = np.asarray(self.origin)[0:2] - origin[2] = 0.0 - origin[3] = radial_min * float(np.asarray(self.sampling)[-1]) + + phi_step_deg = mapping["phi_step"] * 180.0 / np.pi + phi_units = "deg" + radial_units = self.units[-1] + + sampling = np.array( + [ + self.sampling[0], + self.sampling[1], + phi_step_deg, + self.sampling[-1] * mapping["radial_step"], + ], + dtype=float, + ) + origin = np.array( + [ + self.origin[0], + self.origin[1], + 0.0, + self.sampling[-1] * mapping["radial_min"], + ], + dtype=float, + ) units = [ self.units[0], self.units[1], - "deg", - self.units[-1], + phi_units, + radial_units, ] + metadata = dict(self.metadata) metadata.update( { - "polar_radial_min": float(radial_min), - "polar_radial_max": float(radial_max_eff), - "polar_radial_step": float(radial_step), - "polar_num_annular_bins": int(n_phi), - "polar_two_fold_rotation_symmetry": bool(two_fold_rotation_symmetry), - "polar_origin_row": origin_row_f, - "polar_origin_col": origin_col_f, - "polar_ellipse_params": tuple(ellipse_params) if ellipse_params is not None else None, + "polar_radial_min": mapping["radial_min"], + "polar_radial_max": mapping["radial_max"], + "polar_radial_step": mapping["radial_step"], + "polar_num_annular_bins": mapping["n_phi"], + "polar_two_fold_rotation_symmetry": two_fold_rotation_symmetry, + "polar_origin_row": float(origin_row), + "polar_origin_col": float(origin_col), + "polar_ellipse_params": tuple(ellipse_params) + if ellipse_params is not None + else None, } ) + return Polar4dstem( array=out, name=name if name is not None else f"{self.name}_polar", @@ -235,3 +190,141 @@ def dataset4dstem_polar_transform( metadata=metadata, _token=Polar4dstem._token, ) + + +def _precompute_polar_mapping( + ny: int, + nx: int, + origin_row: float, + origin_col: float, + ellipse_params: tuple[float, float, float] | None, + num_annular_bins: int, + radial_min: float, + radial_max: float | None, + radial_step: float, + two_fold_rotation_symmetry: bool, +) -> dict[str, Any]: + origin_row = float(origin_row) + origin_col = float(origin_col) + annular_range = np.pi if two_fold_rotation_symmetry else 2.0 * np.pi + + rows = np.arange(ny, dtype=np.float64) + cols = np.arange(nx, dtype=np.float64) + cc, rr = np.meshgrid(cols, rows, indexing="xy") + x = cc - origin_col + y = rr - origin_row + + if ellipse_params is None: + rr_pix = np.sqrt(x * x + y * y) + tt = np.mod(np.arctan2(y, x), annular_range) + else: + if len(ellipse_params) != 3: + raise ValueError("ellipse_params must be a length-3 tuple (a, b, theta_deg).") + a, b, theta_deg = ellipse_params + theta = np.deg2rad(theta_deg) + cos_t = np.cos(theta) + sin_t = np.sin(theta) + xc = x * cos_t + y * sin_t + yc = (y * cos_t - x * sin_t) * (a / b) + rr_pix = (b / a) * np.hypot(xc, yc) + tt = np.mod(np.arctan2(yc, xc) + theta, annular_range) + + if radial_step <= 0: + raise ValueError("radial_step must be > 0.") + radial_min = float(radial_min) + + if radial_max is None: + radial_max_eff = float(rr_pix.max()) + else: + radial_max_eff = float(radial_max) + if radial_max_eff <= radial_min + radial_step: + radial_max_eff = radial_min + radial_step + + radial_bins = np.arange(radial_min, radial_max_eff, radial_step, dtype=np.float64) + n_r = radial_bins.size + if n_r < 1: + raise ValueError("No radial bins defined. Check radial_min, radial_max, and radial_step.") + + n_phi = int(num_annular_bins) + if n_phi < 1: + raise ValueError("num_annular_bins must be >= 1.") + phi_step = annular_range / n_phi + + r_bin = (rr_pix - radial_min) / radial_step + t_bin = tt / phi_step + + r0 = np.floor(r_bin).astype(np.int64) + t0 = np.floor(t_bin).astype(np.int64) + dr = (r_bin - r0).astype(np.float64) + dt = (t_bin - t0).astype(np.float64) + + valid = (r0 >= 0) & (r0 < n_r - 1) + t0 = np.clip(t0, 0, n_phi - 1) + + flat_valid = valid.ravel() + r0v = r0.ravel()[flat_valid] + t0v = t0.ravel()[flat_valid] + drv = dr.ravel()[flat_valid] + dtv = dt.ravel()[flat_valid] + + n_bins = n_phi * n_r + idx00 = r0v + n_r * t0v + idx01 = r0v + n_r * ((t0v + 1) % n_phi) + idx10 = (r0v + 1) + n_r * t0v + idx11 = (r0v + 1) + n_r * ((t0v + 1) % n_phi) + + w00 = (1.0 - drv) * (1.0 - dtv) + w01 = (1.0 - drv) * dtv + w10 = drv * (1.0 - dtv) + w11 = drv * dtv + + weights_sum = np.bincount(idx00, weights=w00, minlength=n_bins) + weights_sum += np.bincount(idx01, weights=w01, minlength=n_bins) + weights_sum += np.bincount(idx10, weights=w10, minlength=n_bins) + weights_sum += np.bincount(idx11, weights=w11, minlength=n_bins) + weights_sum = weights_sum.reshape(n_phi, n_r) + + weights_inv = np.zeros_like(weights_sum, dtype=np.float64) + mask_bins = weights_sum > 0 + weights_inv[mask_bins] = 1.0 / weights_sum[mask_bins] + + return { + "flat_valid": flat_valid, + "idx00": idx00, + "idx01": idx01, + "idx10": idx10, + "idx11": idx11, + "w00": w00, + "w01": w01, + "w10": w10, + "w11": w11, + "weights_inv": weights_inv, + "n_phi": n_phi, + "n_r": n_r, + "radial_bins": radial_bins, + "phi_step": phi_step, + "annular_range": annular_range, + "radial_min": radial_min, + "radial_max": radial_min + radial_step * n_r, + "radial_step": radial_step, + } + + +def _apply_polar_mapping_single( + image: NDArray, + mapping: dict[str, Any], + dtype: Any, +) -> NDArray: + data = np.asarray(image, dtype=np.float64) + flat = data.ravel()[mapping["flat_valid"]] + n_bins = mapping["n_phi"] * mapping["n_r"] + + acc = np.bincount(mapping["idx00"], weights=flat * mapping["w00"], minlength=n_bins) + acc += np.bincount(mapping["idx01"], weights=flat * mapping["w01"], minlength=n_bins) + acc += np.bincount(mapping["idx10"], weights=flat * mapping["w10"], minlength=n_bins) + acc += np.bincount(mapping["idx11"], weights=flat * mapping["w11"], minlength=n_bins) + + acc = acc.reshape(mapping["n_phi"], mapping["n_r"]) + acc *= mapping["weights_inv"] + return acc.astype(dtype, copy=False) + From b16746f26138064667ecd44cbed0aa2c8d99da9e Mon Sep 17 00:00:00 2001 From: cophus Date: Thu, 4 Dec 2025 10:53:10 -0800 Subject: [PATCH 120/140] Changing sampling direction for polar4dstem --- .../core/datastructures/polar4dstem.py | 307 ++++++------------ 1 file changed, 107 insertions(+), 200 deletions(-) diff --git a/src/quantem/core/datastructures/polar4dstem.py b/src/quantem/core/datastructures/polar4dstem.py index 848469ec..6619af5c 100644 --- a/src/quantem/core/datastructures/polar4dstem.py +++ b/src/quantem/core/datastructures/polar4dstem.py @@ -1,13 +1,14 @@ import numpy as np from numpy.typing import NDArray from typing import Any, TYPE_CHECKING - -from quantem.core.datastructures.dataset4d import Dataset4d -# from quantem.core.datastructures.dataset4dstem import Dataset4dstem +from scipy.ndimage import map_coordinates if TYPE_CHECKING: from .dataset4dstem import Dataset4dstem +from quantem.core.datastructures.dataset4d import Dataset4d + + class Polar4dstem(Dataset4d): """4D-STEM dataset in polar coordinates (scan_y, scan_x, phi, r).""" @@ -37,7 +38,6 @@ def __init__( for k in mdata_keys_polar: if k not in metadata: metadata[k] = None - super().__init__( array=array, name=name, @@ -60,11 +60,13 @@ def from_array( signal_units: str = "arb. units", metadata: dict | None = None, ) -> "Polar4dstem": - array = ensure_valid_array(array, ndim=4) + array = np.asarray(array) + if array.ndim != 4: + raise ValueError("Polar4dstem.from_array expects a 4D array.") if origin is None: - origin = np.zeros(4) + origin = np.zeros(4, dtype=float) if sampling is None: - sampling = np.ones(4) + sampling = np.ones(4, dtype=float) if units is None: units = ["pixels", "pixels", "deg", "pixels"] if metadata is None: @@ -89,10 +91,68 @@ def n_r(self) -> int: return int(self.array.shape[3]) +def _precompute_polar_coords( + ny: int, + nx: int, + origin_row: float, + origin_col: float, + ellipse_params: tuple[float, float, float] | None, + num_annular_bins: int, + radial_min: float, + radial_max: float | None, + radial_step: float, + two_fold_rotation_symmetry: bool, +) -> tuple[NDArray, NDArray, NDArray, float]: + origin_row = float(origin_row) + origin_col = float(origin_col) + if radial_step <= 0: + raise ValueError("radial_step must be > 0.") + if num_annular_bins < 1: + raise ValueError("num_annular_bins must be >= 1.") + if radial_max is None: + r_row_pos = origin_row + r_row_neg = (ny - 1) - origin_row + r_col_pos = origin_col + r_col_neg = (nx - 1) - origin_col + radial_max_eff = float(min(r_row_pos, r_row_neg, r_col_pos, r_col_neg)) + else: + radial_max_eff = float(radial_max) + if radial_max_eff <= radial_min: + radial_max_eff = radial_min + radial_step + radial_bins = np.arange(radial_min, radial_max_eff, radial_step, dtype=np.float64) + if radial_bins.size == 0: + radial_bins = np.array([radial_min], dtype=np.float64) + if two_fold_rotation_symmetry: + phi_range = np.pi + else: + phi_range = 2.0 * np.pi + phi_bins = np.linspace(0.0, phi_range, num_annular_bins, endpoint=False, dtype=np.float64) + phi_grid, r_grid = np.meshgrid(phi_bins, radial_bins, indexing="ij") + if ellipse_params is None: + x = r_grid * np.cos(phi_grid) + y = r_grid * np.sin(phi_grid) + else: + if len(ellipse_params) != 3: + raise ValueError("ellipse_params must be (a, b, theta_deg).") + a, b, theta_deg = ellipse_params + theta = np.deg2rad(theta_deg) + alpha = phi_grid - theta + u = (a / b) * r_grid * np.cos(alpha) + v_prime = r_grid * np.sin(alpha) + cos_t = np.cos(theta) + sin_t = np.sin(theta) + x = u * cos_t - v_prime * sin_t + y = u * sin_t + v_prime * cos_t + coords_y = y + origin_row + coords_x = x + origin_col + coords = np.stack((coords_y, coords_x), axis=0) + return coords, phi_bins, radial_bins, radial_max_eff + + def dataset4dstem_polar_transform( self: "Dataset4dstem", - origin_row: float | NDArray, - origin_col: float | NDArray, + origin_row: float | int | NDArray, + origin_col: float | int | NDArray, ellipse_params: tuple[float, float, float] | None = None, num_annular_bins: int = 180, radial_min: float = 0.0, @@ -102,17 +162,16 @@ def dataset4dstem_polar_transform( name: str | None = None, signal_units: str | None = None, ) -> Polar4dstem: - """Return a Polar4dstem with shape (scan_y, scan_x, phi, r).""" if self.array.ndim != 4: raise ValueError("polar_transform requires a 4D-STEM dataset (ndim=4).") - scan_y, scan_x, ny, nx = self.array.shape - - mapping = _precompute_polar_mapping( + origin_row_f = float(origin_row) + origin_col_f = float(origin_col) + coords, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( ny=ny, nx=nx, - origin_row=float(origin_row), - origin_col=float(origin_col), + origin_row=origin_row_f, + origin_col=origin_col_f, ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, radial_min=radial_min, @@ -120,66 +179,52 @@ def dataset4dstem_polar_transform( radial_step=radial_step, two_fold_rotation_symmetry=two_fold_rotation_symmetry, ) - + n_phi = phi_bins.size + n_r = radial_bins.size result_dtype = np.result_type(self.array.dtype, np.float32) - out = np.empty( - (scan_y, scan_x, mapping["n_phi"], mapping["n_r"]), - dtype=result_dtype, - ) - + out = np.empty((scan_y, scan_x, n_phi, n_r), dtype=result_dtype) for iy in range(scan_y): for ix in range(scan_x): - out[iy, ix] = _apply_polar_mapping_single( - self.array[iy, ix], - mapping, - dtype=result_dtype, + dp = self.array[iy, ix] + out[iy, ix] = map_coordinates( + dp, + coords, + order=1, + mode="constant", + cval=0.0, ) - - phi_step_deg = mapping["phi_step"] * 180.0 / np.pi - phi_units = "deg" - radial_units = self.units[-1] - - sampling = np.array( - [ - self.sampling[0], - self.sampling[1], - phi_step_deg, - self.sampling[-1] * mapping["radial_step"], - ], - dtype=float, - ) - origin = np.array( - [ - self.origin[0], - self.origin[1], - 0.0, - self.sampling[-1] * mapping["radial_min"], - ], - dtype=float, - ) + if two_fold_rotation_symmetry: + phi_range = np.pi + else: + phi_range = 2.0 * np.pi + phi_step_deg = (phi_range / float(n_phi)) * (180.0 / np.pi) + sampling = np.zeros(4, dtype=float) + origin = np.zeros(4, dtype=float) + sampling[0:2] = np.asarray(self.sampling)[0:2] + sampling[2] = phi_step_deg + sampling[3] = float(np.asarray(self.sampling)[-1]) * radial_step + origin[0:2] = np.asarray(self.origin)[0:2] + origin[2] = 0.0 + origin[3] = radial_min * float(np.asarray(self.sampling)[-1]) units = [ self.units[0], self.units[1], - phi_units, - radial_units, + "deg", + self.units[-1], ] - metadata = dict(self.metadata) metadata.update( { - "polar_radial_min": mapping["radial_min"], - "polar_radial_max": mapping["radial_max"], - "polar_radial_step": mapping["radial_step"], - "polar_num_annular_bins": mapping["n_phi"], - "polar_two_fold_rotation_symmetry": two_fold_rotation_symmetry, - "polar_origin_row": float(origin_row), - "polar_origin_col": float(origin_col), - "polar_ellipse_params": tuple(ellipse_params) - if ellipse_params is not None - else None, + "polar_radial_min": float(radial_min), + "polar_radial_max": float(radial_max_eff), + "polar_radial_step": float(radial_step), + "polar_num_annular_bins": int(n_phi), + "polar_two_fold_rotation_symmetry": bool(two_fold_rotation_symmetry), + "polar_origin_row": origin_row_f, + "polar_origin_col": origin_col_f, + "polar_ellipse_params": tuple(ellipse_params) if ellipse_params is not None else None, } ) - return Polar4dstem( array=out, name=name if name is not None else f"{self.name}_polar", @@ -190,141 +235,3 @@ def dataset4dstem_polar_transform( metadata=metadata, _token=Polar4dstem._token, ) - - -def _precompute_polar_mapping( - ny: int, - nx: int, - origin_row: float, - origin_col: float, - ellipse_params: tuple[float, float, float] | None, - num_annular_bins: int, - radial_min: float, - radial_max: float | None, - radial_step: float, - two_fold_rotation_symmetry: bool, -) -> dict[str, Any]: - origin_row = float(origin_row) - origin_col = float(origin_col) - annular_range = np.pi if two_fold_rotation_symmetry else 2.0 * np.pi - - rows = np.arange(ny, dtype=np.float64) - cols = np.arange(nx, dtype=np.float64) - cc, rr = np.meshgrid(cols, rows, indexing="xy") - x = cc - origin_col - y = rr - origin_row - - if ellipse_params is None: - rr_pix = np.sqrt(x * x + y * y) - tt = np.mod(np.arctan2(y, x), annular_range) - else: - if len(ellipse_params) != 3: - raise ValueError("ellipse_params must be a length-3 tuple (a, b, theta_deg).") - a, b, theta_deg = ellipse_params - theta = np.deg2rad(theta_deg) - cos_t = np.cos(theta) - sin_t = np.sin(theta) - xc = x * cos_t + y * sin_t - yc = (y * cos_t - x * sin_t) * (a / b) - rr_pix = (b / a) * np.hypot(xc, yc) - tt = np.mod(np.arctan2(yc, xc) + theta, annular_range) - - if radial_step <= 0: - raise ValueError("radial_step must be > 0.") - radial_min = float(radial_min) - - if radial_max is None: - radial_max_eff = float(rr_pix.max()) - else: - radial_max_eff = float(radial_max) - if radial_max_eff <= radial_min + radial_step: - radial_max_eff = radial_min + radial_step - - radial_bins = np.arange(radial_min, radial_max_eff, radial_step, dtype=np.float64) - n_r = radial_bins.size - if n_r < 1: - raise ValueError("No radial bins defined. Check radial_min, radial_max, and radial_step.") - - n_phi = int(num_annular_bins) - if n_phi < 1: - raise ValueError("num_annular_bins must be >= 1.") - phi_step = annular_range / n_phi - - r_bin = (rr_pix - radial_min) / radial_step - t_bin = tt / phi_step - - r0 = np.floor(r_bin).astype(np.int64) - t0 = np.floor(t_bin).astype(np.int64) - dr = (r_bin - r0).astype(np.float64) - dt = (t_bin - t0).astype(np.float64) - - valid = (r0 >= 0) & (r0 < n_r - 1) - t0 = np.clip(t0, 0, n_phi - 1) - - flat_valid = valid.ravel() - r0v = r0.ravel()[flat_valid] - t0v = t0.ravel()[flat_valid] - drv = dr.ravel()[flat_valid] - dtv = dt.ravel()[flat_valid] - - n_bins = n_phi * n_r - idx00 = r0v + n_r * t0v - idx01 = r0v + n_r * ((t0v + 1) % n_phi) - idx10 = (r0v + 1) + n_r * t0v - idx11 = (r0v + 1) + n_r * ((t0v + 1) % n_phi) - - w00 = (1.0 - drv) * (1.0 - dtv) - w01 = (1.0 - drv) * dtv - w10 = drv * (1.0 - dtv) - w11 = drv * dtv - - weights_sum = np.bincount(idx00, weights=w00, minlength=n_bins) - weights_sum += np.bincount(idx01, weights=w01, minlength=n_bins) - weights_sum += np.bincount(idx10, weights=w10, minlength=n_bins) - weights_sum += np.bincount(idx11, weights=w11, minlength=n_bins) - weights_sum = weights_sum.reshape(n_phi, n_r) - - weights_inv = np.zeros_like(weights_sum, dtype=np.float64) - mask_bins = weights_sum > 0 - weights_inv[mask_bins] = 1.0 / weights_sum[mask_bins] - - return { - "flat_valid": flat_valid, - "idx00": idx00, - "idx01": idx01, - "idx10": idx10, - "idx11": idx11, - "w00": w00, - "w01": w01, - "w10": w10, - "w11": w11, - "weights_inv": weights_inv, - "n_phi": n_phi, - "n_r": n_r, - "radial_bins": radial_bins, - "phi_step": phi_step, - "annular_range": annular_range, - "radial_min": radial_min, - "radial_max": radial_min + radial_step * n_r, - "radial_step": radial_step, - } - - -def _apply_polar_mapping_single( - image: NDArray, - mapping: dict[str, Any], - dtype: Any, -) -> NDArray: - data = np.asarray(image, dtype=np.float64) - flat = data.ravel()[mapping["flat_valid"]] - n_bins = mapping["n_phi"] * mapping["n_r"] - - acc = np.bincount(mapping["idx00"], weights=flat * mapping["w00"], minlength=n_bins) - acc += np.bincount(mapping["idx01"], weights=flat * mapping["w01"], minlength=n_bins) - acc += np.bincount(mapping["idx10"], weights=flat * mapping["w10"], minlength=n_bins) - acc += np.bincount(mapping["idx11"], weights=flat * mapping["w11"], minlength=n_bins) - - acc = acc.reshape(mapping["n_phi"], mapping["n_r"]) - acc *= mapping["weights_inv"] - return acc.astype(dtype, copy=False) - From 3bd18c91c0d20b73d3e7657f0c9dd3c932496320 Mon Sep 17 00:00:00 2001 From: cophus Date: Thu, 4 Dec 2025 13:22:56 -0800 Subject: [PATCH 121/140] initial commit for RDF class --- src/quantem/diffraction/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index 8eb0937c..c6aebe36 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1,3 +1,5 @@ +from quantem.diffraction.polar import RDF as RDF + from quantem.diffraction.polar import RDF as RDF from quantem.diffraction.strain_autocorrelation import ( StrainMapAutocorrelation as StrainMapAutocorrelation, From 698c8523cf9c3edfd2f16e83125746f1323cd59b Mon Sep 17 00:00:00 2001 From: Karen Ehrhardt Date: Tue, 16 Dec 2025 12:55:23 -0800 Subject: [PATCH 122/140] initial pdf code + f parameters --- src/quantem/diffraction/__init__.py | 2 - src/quantem/diffraction/kirkland_params.json | 620 +++++++++ src/quantem/diffraction/polar_new.py | 1195 ++++++++++++++++++ 3 files changed, 1815 insertions(+), 2 deletions(-) create mode 100644 src/quantem/diffraction/kirkland_params.json create mode 100644 src/quantem/diffraction/polar_new.py diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index c6aebe36..8eb0937c 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1,5 +1,3 @@ -from quantem.diffraction.polar import RDF as RDF - from quantem.diffraction.polar import RDF as RDF from quantem.diffraction.strain_autocorrelation import ( StrainMapAutocorrelation as StrainMapAutocorrelation, diff --git a/src/quantem/diffraction/kirkland_params.json b/src/quantem/diffraction/kirkland_params.json new file mode 100644 index 00000000..a8540ea7 --- /dev/null +++ b/src/quantem/diffraction/kirkland_params.json @@ -0,0 +1,620 @@ +{ + "H": { + "a": [4.20298324e-003, 6.27762505e-002, 3.00907347e-002], + "b":[2.25350888e-001, 2.25366950e-001, 2.25331756e-001], + "c":[6.77756695e-002, 3.56609237e-003, 2.76135815e-002], + "d":[4.38854001e+000, 4.03884823e-001, 1.44490166e+000] + }, + "He": { + "a": [1.87543704e-005, 4.10595800e-004, 1.96300059e-001], + "b":[2.12427997e-001, 3.32212279e-001, 5.17325152e-001], + "c":[8.36015738e-003, 2.95102022e-002, 4.65928982e-007], + "d":[3.66668239e-001, 1.37171827e+000, 3.75768025e+004] + }, + "Li": { + "a": [7.45843816e-002, 7.15382250e-002, 1.45315229e-001], + "b":[8.81151424e-001, 4.59142904e-002, 8.81301714e-001], + "c":[1.12125769e+000, 2.51736525e-003, 3.58434971e-001], + "d":[1.88483665e+001, 1.59189995e-001, 6.12371000e+000] + }, + "Be": { + "a": [6.11642897e-002, 1.25755034e-001, 2.00831548e-001], + "b":[9.90182132e-002, 9.90272412e-002, 1.87392509e+000], + "c":[7.87242876e-001, 1.58847850e-003, 2.73962031e-001], + "d":[9.32794929e+000, 8.91900236e-002, 3.20687658e+000] + }, + "B": { + "a": [1.25716066e-001, 1.73314452e-001, 1.84774811e-001], + "b":[1.48258830e-001, 1.48257216e-001, 3.34227311e+000], + "c":[1.95250221e-001, 5.29642075e-001, 1.08230500e-003], + "d":[1.97339463e+000, 5.70035553e+000, 5.64857237e-002] + }, + "C": { + "a": [2.12080767e-001, 1.99811865e-001, 1.68254385e-001], + "b":[2.08605417e-001, 2.08610186e-001, 5.57870773e+000], + "c":[1.42048360e-001, 3.63830672e-001, 8.35012044e-004], + "d":[1.33311887e+000, 3.80800263e+000, 4.03982620e-002] + }, + "N": { + "a": [5.33015554e-001, 5.29008883e-002, 9.24159648e-002], + "b":[2.90952515e-001, 1.03547896e+001, 1.03540028e+001], + "c":[2.61799101e-001, 8.80262108e-004, 1.10166555e-001], + "d":[2.76252723e+000, 3.47681236e-002, 9.93421736e-001] + }, + "O": { + "a": [3.39969204e-001, 3.07570172e-001, 1.30369072e-001], + "b":[3.81570280e-001, 3.81571436e-001, 1.91919745e+001], + "c":[8.83326058e-002, 1.96586700e-001, 9.96220028e-004], + "d":[7.60635525e-001, 2.07401094e+000, 3.03266869e-002] + }, + "F": { + "a": [2.30560593e-001, 5.26889648e-001, 1.24346755e-001], + "b":[4.80754213e-001, 4.80763895e-001, 3.95306720e+001], + "c":[1.24616894e-003, 7.20452555e-002, 1.53075777e-001], + "d":[2.62181803e-002, 5.92495593e-001, 1.59127671e+000] + }, + "Ne": { + "a": [4.08371771e-001, 4.54418858e-001, 1.44564923e-001], + "b":[5.88228627e-001, 5.88288655e-001, 1.21246013e+002], + "c":[5.91531395e-002, 1.24003718e-001, 1.64986037e-003], + "d":[4.63963540e-001, 1.23413025e+000, 2.05869217e-002] + }, + "Na": { + "a": [1.36471662e-001, 7.70677865e-001, 1.56862014e-001], + "b":[4.99965301e-002, 8.81899664e-001, 1.61768579e+001], + "c":[9.96821513e-001, 3.80304670e-002, 1.27685089e-001], + "d":[2.00132610e+001, 2.60516254e-001, 6.99559329e-001] + }, + "Mg": { + "a": [3.04384121e-001, 7.56270563e-001, 1.01164809e-001], + "b":[8.42014377e-002, 1.64065598e+000, 2.97142975e+001], + "c":[3.45203403e-002, 9.71751327e-001, 1.20593012e-001], + "d":[2.16596094e-001, 1.21236852e+001, 5.60865838e-001] + }, + "Al": { + "a": [7.77419424e-001, 5.78312036e-002, 4.26386499e-001], + "b":[2.71058227e+000, 7.17532098e+001, 9.13331555e-002], + "c":[1.13407220e-001, 7.90114035e-001, 3.23293496e-002], + "d":[4.48867451e-001, 8.66366718e+000, 1.78503463e-001] + }, + "Si": { + "a": [1.06543892e+000, 1.20143691e-001, 1.80915263e-001], + "b":[1.04118455e+000, 6.87113368e+001, 8.87533926e-002], + "c":[1.12065620e+000, 3.05452816e-002, 1.59963502e+000], + "d":[3.70062619e+000, 2.14097897e-001, 9.99096638e+000] + }, + "P": { + "a": [1.05284447e+000, 2.99440284e-001, 1.17460748e-001], + "b":[1.31962590e+000, 1.28460520e-001, 1.02190163e+002], + "c":[9.60643452e-001, 2.63555748e-002, 1.38059330e+000], + "d":[2.87477555e+000, 1.82076844e-001, 7.49165526e+000] + }, + "S": { + "a": [1.01646916e+000, 4.41766748e-001, 1.21503863e-001], + "b":[1.69181965e+000, 1.74180288e-001, 1.67011091e+002], + "c":[8.27966670e-001, 2.33022533e-002, 1.18302846e+000], + "d":[2.30342810e+000, 1.56954150e-001, 5.85782891e+000] + }, + "Cl": { + "a": [9.44221116e-001, 4.37322049e-001, 2.54547926e-001], + "b":[2.40052374e-001, 9.30510439e+000, 9.30486346e+000], + "c":[5.47763323e-002, 8.00087488e-001, 1.07488641e-002], + "d":[1.68655688e-001, 2.97849774e+000, 6.84240646e-002] + }, + "Ar": { + "a": [1.06983288e+000, 4.24631786e-001, 2.43897949e-001], + "b":[2.87791022e-001, 1.24156957e+001, 1.24158868e+001], + "c":[4.79446296e-002, 7.64958952e-001, 8.23128431e-003], + "d":[1.36979796e-001, 2.43940729e+000, 5.27258749e-002] + }, + "K": { + "a": [6.92717865e-001, 9.65161085e-001, 1.48466588e-001], + "b":[7.10849990e+000, 3.57532901e-001, 3.93763275e-002], + "c":[2.64645027e-002, 1.80883768e+000, 5.43900018e-001], + "d":[1.03591321e-001, 3.22845199e+001, 1.67791374e+000] + }, + "Ca": { + "a": [3.66902871e-001, 8.66378999e-001, 6.67203300e-001], + "b":[6.14274129e-002, 5.70881727e-001, 7.82965639e+000], + "c":[4.87743636e-001, 1.82406314e+000, 2.20248453e-002], + "d":[1.32531318e+000, 2.10056032e+001, 9.11853450e-002] + }, + "Sc": { + "a": [3.78871777e-001, 9.00022505e-001, 7.15288914e-001], + "b":[6.98910162e-002, 5.21061541e-001, 7.87707920e+000], + "c":[1.88640973e-002, 4.07945949e-001, 1.61786540e+000], + "d":[8.17512708e-002, 1.11141388e+000, 1.80840759e+001] + }, + "Ti": { + "a": [3.62383267e-001, 9.84232966e-001, 7.41715642e-001], + "b":[7.54707114e-002, 4.97757309e-001, 8.17659391e+000], + "c":[3.62555269e-001, 1.49159390e+000, 1.61659509e-002], + "d":[9.55524906e-001, 1.62221677e+001, 7.33140800e+000] + }, + "V":{ + "a": [3.52961378e-001, 7.46791014e-001, 1.08364068e+000], + "b":[8.19204103e-002, 8.81189511e+000, 5.10646075e-001], + "c": [1.39013610e+000, 3.31273356e-001, 1.40422612e-002], + "d": [1.48901841e+001, 8.38543079e-001, 6.57432678e-002] + }, + "Cr": { + "a": [1.34348379e+000, 5.07040328e-001, 4.26358955e-001], + "b":[1.25814353e+000, 1.15042811e+001, 8.53660389e-002], + "c":[1.17241826e-002, 5.11966516e-001, 3.38285828e-001], + "d":[6.00177061e-002, 1.53772451e+000, 6.62418319e-001] + }, + "Mn": { + "a": [3.26697613e-001, 7.17297000e-001, 1.33212464e+000], + "b":[8.88813083e-002, 1.11300198e+001, 5.82141104e-001], + "c":[2.80801702e-001, 1.15499241e+000, 1.11984488e-002], + "d":[6.71583145e-001, 1.26825395e+001, 5.32334467e-002] + }, + "Fe": { + "a": [3.13454847e-001, 6.89290016e-001, 1.47141531e+000], + "b":[8.99325756e-002, 1.30366038e+001, 6.33345291e-001], + "c":[1.03298688e+000, 2.58280285e-001, 1.03460690e-002], + "d":[1.16783425e+001, 6.09116446e-001, 4.81610627e-002] + }, + "Co": { + "a": [3.15878278e-001, 1.60139005e+000, 6.56394338e-001], + "b":[9.46683246e-002, 6.99436449e-001, 1.56954403e+001], + "c":[9.36746624e-003, 9.77562646e-001, 2.38378578e-001], + "d":[1.09392410e+001, 4.37446816e-002, 5.56286483e-001] + }, + "Ni": { + "a": [1.72254630e+000, 3.29543044e-001, 6.23007200e-001], + "b":[7.76606908e-001, 1.02262360e-001, 1.94156207e+001], + "c":[9.43496513e-003, 8.54063515e-001, 2.21073515e-001], + "d":[3.98684596e-002, 1.04078166e+001, 5.10869330e-001] + }, + "Cu": { + "a": [3.58774531e-001, 1.76181348e+000, 6.36905053e-001], + "b":[1.06153463e-001, 1.01640995e+000, 1.53659093e+001], + "c":[7.44930667e-003, 1.89002347e-001, 2.29619589e-001], + "d":[3.85345989e-002, 3.98427790e-001, 9.01419843e-001] + }, + "Zn": { + "a": [5.70893973e-001, 1.98908856e+000, 3.06060585e-001], + "b":[1.26534614e-001, 2.17781965e+000, 3.78619003e+001], + "c":[2.35600223e-001, 3.97061102e-001, 6.85657228e-003], + "d":[3.67019041e-001, 8.66419596e-001, 3.35778823e-002] + }, + "Ga": { + "a": [6.25528464e-001, 2.05302901e+000, 2.89608120e-001], + "b":[1.10005650e-001, 2.41095786e+000, 4.78685736e+001], + "c":[2.07910594e-001, 3.45079617e-001, 6.55634298e-003], + "d":[3.27807224e-001, 7.43139061e-001, 3.09411369e-002] + }, + "Ge": { + "a": [5.90952690e-001, 5.39980660e-001, 2.00626188e+000], + "b":[1.18375976e-001, 7.18937433e+001, 1.39304889e+000], + "c":[7.49705041e-001, 1.83581347e-001, 9.52190743e-003], + "d":[6.89943350e+000, 3.64667232e-001, 2.69888650e-002] + }, + "As": { + "a": [7.77875218e-001, 5.93848150e-001, 1.95918751e+000], + "b":[1.50733157e-001, 1.42882209e+002, 1.74750339e+000], + "c":[1.79880226e-001, 8.63267222e-001, 9.59053427e-003], + "d":[3.31800852e-001, 5.85490274e+000, 2.33777569e-002] + }, + "Se":{ + "a":[9.58390681e-001, 6.03851342e-001, 1.90828931e+000], + "b":[1.83775557e-001, 1.96819224e+002, 2.15082053e+000], + "c":[1.73885956e-001, 9.35265145e-001, 8.62254658e-003], + "d":[3.00006024e-001, 4.92471215e+000, 2.12308108e-002] + }, + "Br": { + "a": [1.14136170e+000, 5.18118737e-001, 1.85731975e+000], + "b": [2.18708710e-001, 1.93916682e+002, 2.65755396e+000], + "c": [1.68217399e-001, 9.75705606e-001, 7.24187871e-003], + "d": [2.71719918e-001, 4.19482500e+000, 1.99325718e-002] + }, + "Kr": { + "a": [3.24386970e-001, 1.31732163e+000, 1.79912614e+000], + "b": [6.31317973e+001, 2.54706036e-001, 3.23668394e+000], + "c": [4.29961425e-003, 1.00429433e+000, 1.62188197e-001], + "d": [1.98965610e-002, 3.61094513e+000, 2.45583672e-001] + }, + "Rb": { + "a": [2.90445351e-001, 2.44201329e+000, 7.69435449e-001], + "b": [3.68420227e-002, 1.16013332e+000, 1.69591472e+001], + "c": [1.58687000e+000, 2.81617593e-003, 1.28663830e-001], + "d": [2.53082574e+000, 1.88577417e-002, 2.10753969e-001] + }, + "Sr": { + "a": [1.37373086e-002, 1.97548672e+000, 1.59261029e+000], + "b": [1.87469061e-002, 6.36079230e+000, 2.21992482e-001], + "c": [1.73263882e-001, 4.66280378e+000, 1.61265063e-003], + "d": [2.01624958e-001, 2.53027803e+001, 1.53610568e-002] + }, + "Y": { + "a": [6.75302747e-001, 4.70286720e-001, 2.63497677e+000], + "b": [6.54331847e-002, 1.06108709e+002, 2.06643540e+000], + "c": [1.09621746e-001, 9.60348773e-001, 5.28921555e-003], + "d": [1.93131925e-001, 1.63310938e+000, 1.66083821e-002] + }, + "Zr": { + "a": [2.64365505e+000, 5.54225147e-001, 7.61376625e-001], + "b":[2.20202699e+000, 1.78260107e+002, 7.67218745e-002], + "c":[6.02946891e-003, 9.91630530e-002, 9.56782020e-001], + "d":[1.55143296e-002, 1.76175995e-001, 1.54330682e+000] + }, + "Nb": { + "a": [6.59532875e-001, 1.84545854e+000, 1.25584405e+000], + "b": [8.66145490e-002, 5.94774398e+000, 6.40851475e-001], + "c": [1.22253422e-001, 7.06638328e-001, 2.62381591e-003], + "d": [1.66646050e-001, 1.62853268e+000, 8.26257859e-003] + }, + "Mo": { + "a": [6.10160120e-001, 1.26544000e+000, 1.97428762e+000], + "b": [9.11628054e-002, 5.06776025e-001, 5.89590381e+000], + "c": [6.48028962e-001, 2.60380817e-003, 1.13887493e-001], + "d": [1.46634108e+000, 7.84336311e-003, 1.55114340e-001] + }, + "Tc": { + "a": [8.55189183e-001, 1.66219641e+000, 1.45575475e+000], + "b": [1.02962151e-001, 7.64907000e+000, 1.01639987e+000], + "c": [1.05445664e-001, 7.71657112e-001, 2.20992635e-003], + "d": [1.42303338e-001, 1.34659349e+000, 7.90358976e-003] + }, + "Ru": { + "a": [4.70847093e-001, 1.58180781e+000, 2.02419818e+000], + "b": [9.33029874e-002, 4.52831347e-001, 7.11489023e+000], + "c": [1.97036257e-003, 6.26912639e-001, 1.02641320e-001], + "d": [7.56181595e-003, 1.25399858e+000, 1.33786087e-001] + }, + "Rh": { + "a": [4.20051553e-001, 1.76266507e+000, 2.02735641e+000], + "b": [9.38882628e-002, 4.64441687e-001, 8.19346046e+000], + "c": [1.45487176e-003, 6.22809600e-001, 9.91529915e-002], + "d": [7.82704517e-003, 1.17194153e+000, 1.24532839e-001] + }, + "Pd": { + "a": [2.10475155e+000, 2.03884487e+000, 1.82067264e-001], + "b": [8.68606470e+000, 3.78924449e-001, 1.42921634e-001], + "c": [9.52040948e-002, 5.91445248e-001, 1.13328676e-003], + "d": [1.17125900e-001, 1.07843808e+000, 7.80252092e-003] + }, + "Ag": { + "a": [2.07981390e+000, 4.43170726e-001, 1.96515215e+000], + "b": [9.92540297e+000, 1.04920104e-001, 6.40103839e-001], + "c": [5.96130591e-001, 4.78016333e-001, 9.46458470e-002], + "d": [8.89594790e-001, 1.98509407e+000, 1.12744464e-001] + }, + "Cd": { + "a": [1.63657549e+000, 2.17927989e+000, 7.71300690e-001], + "b": [1.24540381e+001, 1.45134660e+000, 1.26695757e-001], + "c": [6.64193880e-001, 7.64563285e-001, 8.61126689e-002], + "d": [7.77659202e-001, 1.66075210e+000, 1.05728357e-001] + }, + "In": { + "a": [2.24820632e+000, 1.64706864e+000, 7.88679265e-001], + "b": [1.51913507e+000, 1.30113424e+001, 1.06128184e-001], + "c": [8.12579069e-002, 6.68280346e-001, 6.38467475e-001], + "d": [9.94045620e-002, 1.49742063e+000, 7.18422635e-001] + }, + "Sn": { + "a": [2.16644620e+000, 6.88691021e-001, 1.92431751e+000], + "b": [1.13174909e+001, 1.10131285e-001, 6.74464853e-001], + "c": [5.65359888e-001, 9.18683861e-001, 7.80542213e-002], + "d": [7.33564610e-001, 1.02310312e+001, 9.31104308e-002] + }, + "Sb": { + "a": [1.73662114e+000, 9.99871380e-001, 2.13972409e+000], + "b": [8.84334719e-001, 1.38462121e-001, 1.19666432e+001], + "c": [5.60566526e-001, 9.93772747e-001, 7.37374982e-002], + "d": [6.72672880e-001, 8.72330411e+000, 8.78577715e-002] + }, + "Te": { + "a": [2.09383882e+000, 1.56940519e+000, 1.30941993e+000], + "b": [1.26856869e+001, 1.21236537e+000, 1.66633292e-001], + "c": [6.98067804e-002, 1.04969537e+000, 5.55594354e-001], + "d": [8.30817576e-002, 7.43147857e+000, 6.17487676e-001] + }, + "I": { + "a": [1.60186925e+000, 1.98510264e+000, 1.48226200e+000], + "b": [1.95031538e-001, 1.36976183e+001, 1.80304795e+000], + "c": [5.53807199e-001, 1.11728722e+000, 6.60720847e-002], + "d": [5.67912340e-001, 6.40879878e+000, 7.86615429e-002] + }, + "Xe": { + "a": [1.60015487e+000, 1.71644581e+000, 1.84968351e+000], + "b": [2.92913354e+000, 1.55882990e+001, 2.22525983e-001], + "c": [6.23813648e-002, 1.21387555e+000, 5.54051946e-001], + "d": [7.45581223e-002, 5.56013271e+000, 5.21994521e-001] + }, + "Cs": { + "a": [2.95236854e+000, 4.28105721e-001, 1.89599233e+000], + "b": [6.01461952e+000, 4.64151246e+001, 1.80109756e-001], + "c": [5.48012938e-002, 4.70838600e+000, 5.90356719e-001], + "d": [7.12799633e-002, 4.56702799e+001, 4.70236310e-001] + }, + "Ba": { + "a": [3.19434243e+000, 1.98289586e+000, 1.55121052e-001], + "b": [9.27352241e+000, 2.28741632e-001, 3.82000231e-002], + "c": [6.73222354e-002, 4.48474211e+000, 5.42674414e-001], + "d": [7.30961745e-002, 2.95703565e+001, 4.08647015e-001] + }, + "La": { + "a": [2.05036425e+000, 1.42114311e-001, 3.23538151e+000], + "b": [2.20348417e-001, 3.96438056e-002, 9.56979169e+000], + "c": [6.34683429e-002, 3.97960586e+000, 5.20116711e-001], + "d": [6.92443091e-002, 2.53178406e+001, 3.83614098e-001] + }, + "Ce": { + "a": [3.22990759e+000, 1.57618307e-001, 2.13477838e+000], + "b": [9.94660135e+000, 4.15378676e-002, 2.40480572e-001], + "c": [5.01907609e-001, 3.80889010e+000, 5.96625028e-002], + "d": [3.66252019e-001, 2.43275968e+001, 6.59653503e-002] + }, + "Pr": { + "a": [1.58189324e-001, 3.18141995e+000, 2.27622140e+000], + "b": [3.91309056e-002, 1.04139545e+001, 2.81671757e-001], + "c": [3.97705472e+000, 5.58448277e-002, 4.85207954e-001], + "d": [2.61872978e+001, 6.30921695e-002, 3.54234369e-001] + }, + "Nd": { + "a": [1.81379417e-001, 3.17616396e+000, 2.35221519e+000], + "b": [4.37324793e-002, 1.07842572e+001, 3.05571833e-001], + "c": [3.83125763e+000, 5.25889976e-002, 4.70090742e-001], + "d": [2.54745408e+001, 6.02676073e-002, 3.39017003e-001] + }, + "Pm": { + "a": [1.92986811e-001, 2.43756023e+000, 3.17248504e+000], + "b": [4.37785970e-002, 3.29336996e-001, 1.11259996e+001], + "c": [3.58105414e+000, 4.56529394e-001, 4.94812177e-002], + "d": [2.46709586e+001, 3.24990282e-001, 5.76553100e-002] + }, + "Sm": { + "a": [2.12002595e-001, 3.16891754e+000, 2.51503494e+000], + "b": [4.57703608e-002, 1.14536599e+001, 3.55561054e-001], + "c": [4.44080845e-001, 3.36742101e+000, 4.65652543e-002], + "d": [3.11953363e-001, 2.40291435e+001, 5.52266819e-002] + }, + "Eu": { + "a": [2.59355002e+000, 3.16557522e+000, 2.29402652e-001], + "b": [3.82452612e-001, 1.17675155e+001, 4.76642249e-002], + "c": [4.32257780e-001, 3.17261920e+000, 4.37958317e-002], + "d": [2.99719833e-001, 2.34462738e+001, 5.29440680e-002] + }, + "Gd": { + "a": [3.19144939e+000, 2.55766431e+000, 3.32681934e-001], + "b": [1.20224655e+001, 4.08338876e-001, 5.85819814e-002], + "c": [4.14243130e-002, 2.61036728e+000, 4.20526863e-001], + "d": [5.06771477e-002, 1.99344244e+001, 2.85686240e-001] + }, + "Tb": { + "a": [2.59407462e-001, 3.16177855e+000, 2.75095751e+000], + "b": [5.04689354e-002, 1.23140183e+001, 4.38337626e-001], + "c": [2.79247686e+000, 3.85931001e-002, 4.10881708e-001], + "d": [2.23797309e+001, 4.87920992e-002, 2.77622892e-001] + }, + "Dy": { + "a": [3.16055396e+000, 2.82751709e+000, 2.75140255e-001], + "b": [1.25470414e+001, 4.67899094e-001, 5.23226982e-002], + "c": [4.00967160e-001, 2.63110834e+000, 3.61333817e-002], + "d": [2.67614884e-001, 2.19498166e+001, 4.68871497e-002] + }, + "Ho": { + "a": [2.88642467e-001, 2.90567296e+000, 3.15960159e+000], + "b": [5.40507687e-002, 4.97581077e-001, 1.27599505e+001], + "c": [3.91280259e-001, 2.48596038e+000, 3.37664478e-002], + "d": [2.58151831e-001, 2.15400972e+001, 4.50664323e-002] + }, + "Er": { + "a": [3.15573213e+000, 3.11519560e-001, 2.97722406e+000], + "b": [1.29729009e+001, 5.81399387e-002, 5.31213394e-001], + "c": [3.81563854e-001, 2.40247532e+000, 3.15224214e-002], + "d": [2.49195776e-001, 2.13627616e+001, 4.33253257e-002] + }, + "Tm": { + "a": [3.15591970e+000, 3.22544710e-001, 3.05569053e+000], + "b": [1.31232407e+001, 5.97223323e-002, 5.61876773e-001], + "c": [2.92845100e-002, 3.72487205e-001, 2.27833695e+000], + "d": [4.16534255e-002, 2.40821967e-001, 2.10034185e+001] + }, + "Yb": { + "a": [3.10794704e+000, 3.14091221e+000, 3.75660454e-001], + "b": [6.06347847e-001, 1.33705269e+001, 7.29814740e-002], + "c": [3.61901097e-001, 2.45409082e+000, 2.72383990e-002], + "d": [2.32652051e-001, 2.12695209e+001, 3.99969597e-002] + }, + "Lu": { + "a": [3.11446863e+000, 5.39634353e-001, 3.06460915e+000], + "b": [1.38968881e+001, 8.91708508e-002, 6.79919563e-001], + "c": [2.58563745e-002, 2.13983556e+000, 3.47788231e-001], + "d": [3.82808522e-002, 1.80078788e+001, 2.22706591e-001] + }, + "Hf": { + "a": [3.01166899e+000, 3.16284788e+000, 6.33421771e-001], + "b": [7.10401889e-001, 1.38262192e+001, 9.48486572e-002], + "c": [3.41417198e-001, 1.53566013e+000, 2.40723773e-002], + "d": [2.14129678e-001, 1.55298698e+001, 3.67833690e-002] + }, + "Ta": { + "a": [3.20236821e+000, 8.30098413e-001, 2.86552297e+000], + "b": [1.38446369e+001, 1.18381581e-001, 7.66369118e-001], + "c": [2.24813887e-002, 1.40165263e+000, 3.33740596e-001], + "d": [3.52934622e-002, 1.46148877e+001, 2.05704486e-001] + }, + "W": { + "a": [9.24906855e-001, 2.75554557e+000, 3.30440060e+000], + "b": [1.28663377e-001, 7.65826479e-001, 1.34471170e+001], + "c": [3.29973862e-001, 1.09916444e+000, 2.06498883e-002], + "d": [1.98218895e-001, 1.35087534e+001, 3.38918459e-002] + }, + "Re": { + "a": [1.96952105e+000, 1.21726619e+000, 4.10391685e+000], + "b": [4.98830620e+001, 1.33243809e-001, 1.84396916e+000], + "c": [2.90791978e-002, 2.30696669e-001, 6.08840299e-001], + "d": [2.84192813e-002, 1.90968784e-001, 1.37090356e+000] + }, + "Os": { + "a": [2.06385867e+000, 1.29603406e+000, 3.96920673e+000], + "b": [4.05671697e+001, 1.46559047e-001, 1.82561596e+000], + "c": [2.69835487e-002, 2.31083999e-001, 6.30466774e-001], + "d": [2.84172045e-002, 1.79765184e-001, 1.38911543e+000] + }, + "Ir": { + "a": [2.21522726e+000, 1.37573155e+000, 3.78244405e+000], + "b": [3.24464090e+001, 1.60920048e-001, 1.78756553e+000], + "c": [2.44643240e-002, 2.36932016e-001, 6.48471412e-001], + "d": [2.82909938e-002, 1.70692368e-001, 1.37928390e+000] + }, + "Pt": { + "a": [9.84697940e-001, 2.73987079e+000, 3.61696715e+000], + "b": [1.60910839e-001, 7.18971667e-001, 1.29281016e+001], + "c": [3.02885602e-001, 2.78370726e-001, 1.52124129e-002], + "d": [1.70134854e-001, 1.49862703e+000, 2.83510822e-002] + }, + "Au": { + "a": [9.61263398e-001, 3.69581030e+000, 2.77567491e+000], + "b": [1.70932277e-001, 1.29335319e+001, 6.89997070e-001], + "c": [2.95414176e-001, 3.11475743e-001, 1.43237267e-002], + "d": [1.63525510e-001, 1.39200901e+000, 2.71265337e-002] + }, + "Hg": { + "a": [1.29200491e+000, 2.75161478e+000, 3.49387949e+000], + "b": [1.83432865e-001, 9.42368371e-001, 1.46235654e+001], + "c": [2.77304636e-001, 4.30232810e-001, 1.48294351e-002], + "d": [1.55110144e-001, 1.28871670e+000, 2.61903834e-002] + }, + "Tl": { + "a": [3.75964730e+000, 3.21195904e+000, 6.47767825e-001], + "b": [1.35041513e+001, 6.66330993e-001, 9.22518234e-002], + "c": [2.76123274e-001, 3.18838810e-001, 1.31668419e-002], + "d": [1.50312897e-001, 1.12565588e+000, 2.48879842e-002] + }, + "Pb": { + "a": [1.00795975e+000, 3.09796153e+000, 3.61296864e+000], + "b": [1.17268427e-001, 8.80453235e-001, 1.47325812e+001], + "c": [2.62401476e-001, 4.05621995e-001, 1.31812509e-002], + "d": [1.43491014e-001, 1.04103506e+000, 2.39575415e-002] + }, + "Bi": { + "a": [1.59826875e+000, 4.38233925e+000, 2.06074719e+000], + "b": [1.56897471e-001, 2.47094692e+000, 5.72438972e+001], + "c": [1.94426023e-001, 8.22704978e-001, 2.33226953e-002], + "d": [1.32979109e-001, 9.56532528e-001, 2.23038435e-002] + }, + "Po": { + "a": [1.71463223e+000, 2.14115960e+000, 4.37512413e+000], + "b": [9.79262841e+001, 2.10193717e-001, 3.66948812e+000], + "c": [2.16216680e-002, 1.97843837e-001, 6.52047920e-001], + "d": [1.98456144e-002, 1.33758807e-001, 7.80432104e-001] + }, + "At": { + "a": [1.48047794e+000, 2.09174630e+000, 4.75246033e+000], + "b": [1.25943919e+002, 1.83803008e-001, 4.19890596e+000], + "c": [1.85643958e-002, 2.05859375e-001, 7.13540948e-001], + "d": [1.81383503e-002, 1.33035404e-001, 7.03031938e-001] + }, + "Rn": { + "a": [6.30022295e-001, 3.80962881e+000, 3.89756067e+000], + "b": [1.40909762e-001, 3.08515540e+001, 6.51559763e-001], + "c": [2.40755100e-001, 2.62868577e+000, 3.14285931e-002], + "d": [1.08899672e-001, 6.42383261e+000, 2.42346699e-002] + }, + "Fr": { + "a": [5.23288135e+000, 2.48604205e+000, 3.23431354e-001], + "b": [8.60599536e+000, 3.04543982e-001, 3.87759096e-002], + "c": [2.55403596e-001, 5.53607228e-001, 5.75278889e-003], + "d": [1.28717724e-001, 5.36977452e-001, 1.29417790e-002] + }, + "Ra": { + "a": [1.44192685e+000, 3.55291725e+000, 3.91259586e+000], + "b": [1.18740873e-001, 1.01739750e+000, 6.31814783e+001], + "c": [2.16173519e-001, 3.94191605e+000, 4.60422605e-002], + "d": [9.55806441e-002, 3.50602732e+001, 2.20850385e-002] + }, + "Ac": { + "a": [1.45864127e+000, 4.18945405e+000, 3.65866182e+000], + "b": [1.07760494e-001, 8.89090649e+001, 1.05088931e+000], + "c": [2.08479229e-001, 3.16528117e+000, 5.23892556e-002], + "d": [9.09335557e-002, 3.13297788e+001, 2.08807697e-002] + }, + "Th": { + "a": [1.19014064e+000, 2.55380607e+000, 4.68110181e+000], + "b": [7.73468729e-002, 6.59693681e-001, 1.28013896e+001], + "c": [2.26121303e-001, 3.58250545e-001, 7.82263950e-003], + "d": [1.08632194e-001, 4.56765664e-001, 1.62623474e-002] + }, + "Pa": { + "a": [4.68537504e+000, 2.98413708e+000, 8.91988061e-001], + "b": [1.44503632e+001, 5.56438592e-001, 6.69512914e-002], + "c": [2.24825384e-001, 3.04444846e-001, 9.48162708e-003], + "d": [1.03235396e-001, 4.27255647e-001, 1.77730611e-002] + }, + "U": { + "a": [4.63343606e+000, 3.18157056e+000, 8.76455075e-001], + "b": [1.63377267e+001, 5.69517868e-001, 6.88860012e-002], + "c": [2.21685477e-001, 2.72917100e-001, 1.11737298e-002], + "d": [9.84254550e-002, 4.09470917e-001, 1.86215410e-002] + }, + "Np": { + "a": [4.56773888e+000, 3.40325179e+000, 8.61841923e-001], + "b": [1.90992795e+001, 5.90099634e-001, 7.03204851e-002], + "c": [2.19728870e-001, 2.38176903e-001, 1.38306499e-002], + "d": [9.36334280e-002, 3.93554882e-001, 1.94437286e-002] + }, + "Pu": { + "a": [5.45671123e+000, 1.11687906e-001, 3.30260343e+000], + "b": [1.01892720e+001, 3.98131313e-002, 3.14622212e-001], + "c": [1.84568319e-001, 4.93644263e-001, 3.57484743e+000], + "d": [1.04220860e-001, 4.63080540e-001, 2.19369542e+001] + }, + "Am": { + "a": [5.38321999e+000, 1.23343236e-001, 3.46469090e+000], + "b": [1.07289857e+001, 4.15137806e-002, 3.39326208e-001], + "c": [1.75437132e-001, 3.39800073e+000, 4.69459519e-001], + "d": [9.98932346e-002, 2.11601535e+001, 4.51996970e-001] + }, + "Cm": { + "a": [5.38402377e+000, 3.49861264e+000, 1.88039547e-001], + "b": [1.11211419e+001, 3.56750210e-001, 5.39853583e-002], + "c": [1.69143137e-001, 3.19595016e+000, 4.64393059e-001], + "d": [9.60082633e-002, 1.80694389e+001, 4.36318197e-001] + }, + "Bk": { + "a": [3.66090688e+000, 2.03054678e-001, 5.30697515e+000], + "b": [3.84420906e-001, 5.48547131e-002, 1.17150262e+001], + "c": [1.60934046e-001, 3.04808401e+000, 4.43610295e-001], + "d": [9.21020329e-002, 1.73525367e+001, 4.27132359e-001] + }, + "Cf": { + "a": [3.94150390e+000, 5.16915345e+000, 1.61941074e-001], + "b": [4.18246722e-001, 1.25201788e+001, 4.81540117e-002], + "c": [4.15299561e-001, 2.91761325e+000, 1.51474927e-001], + "d": [4.24913856e-001, 1.90899693e+001, 8.81568925e-002] + }, + "Es": { + "a": [4.09780623e+000, 5.10079393e+000, 1.74617289e-001], + "b": [4.46021145e-001, 1.31768613e+001, 5.02742829e-002], + "c": [2.76774658e+000, 1.44496639e-001, 4.02772109e-001], + "d": [1.84815393e+001, 8.46232592e-002, 4.17640100e-001] + }, + "Fm": { + "a": [4.24934820e+000, 5.03556594e+000, 1.88920613e-001], + "b": [4.75263933e-001, 1.38570834e+001, 5.26975158e-002], + "c": [3.94356058e-001, 2.61213100e+000, 1.38001927e-001], + "d": [4.11193751e-001, 1.78537905e+001, 8.12774434e-002] + }, + "Md": { + "a": [2.00942931e-001, 4.40119869e+000, 4.97250102e+000], + "b": [5.48366518e-002, 5.04248434e-001, 1.45721366e+001], + "c": [2.47530599e+000, 3.86883197e-001, 1.31936095e-001], + "d": [1.72978308e+001, 4.05043898e-001, 7.80821071e-002] + }, + "No": { + "a": [2.16052899e-001, 4.91106799e+000, 4.54862870e+000], + "b": [5.83584058e-002, 1.53264212e+001, 5.34434760e-001], + "c": [2.36114249e+000, 1.26277292e-001, 3.81364501e-001], + "d": [1.68164803e+001, 7.50304633e-002, 3.99305852e-001] + }, + "Lr": { + "a": [4.86738014e+000, 3.19974401e-001, 4.58872425e+000], + "b": [1.60320520e+001, 6.70871138e-002, 5.77039373e-001], + "c": [1.21482448e-001, 2.31639872e+000, 3.79258137e-001], + "d": [7.22275899e-002, 1.41279737e+001, 3.89973484e-001] + } +} diff --git a/src/quantem/diffraction/polar_new.py b/src/quantem/diffraction/polar_new.py new file mode 100644 index 00000000..b25d3f1b --- /dev/null +++ b/src/quantem/diffraction/polar_new.py @@ -0,0 +1,1195 @@ +from __future__ import annotations + +import json +from collections.abc import Sequence +from pathlib import Path +from typing import Any, Iterable, List, Tuple, Union + +import matplotlib.pyplot as plt +import numpy as np +from numpy.typing import NDArray +from scipy.ndimage import gaussian_filter + +from quantem.core.datastructures.dataset2d import Dataset2d +from quantem.core.datastructures.dataset3d import Dataset3d +from quantem.core.datastructures.dataset4dstem import Dataset4dstem +from quantem.core.datastructures.polar4dstem import Polar4dstem +from quantem.core.io.serialize import AutoSerialize +from quantem.core.utils.validators import ensure_valid_array + +KIRKLAND_PARAMS_PATH = Path(__file__).with_name("kirkland_params.json") + + +class RDF_new(AutoSerialize): + """ + Radial distribution / fluctuation electron microscopy analysis helper. + + This class wraps a 4D-STEM (or 2D diffraction) dataset and stores a + polar-transformed representation as a Polar4dstem instance in `self.polar`. + Analysis methods (radial statistics, PDF, FEM, clustering, etc.) are + provided as stubs for now and will be implemented in future revisions. + """ + + _token = object() + + def __init__( + self, + polar: Polar4dstem, + input_data: Any | None = None, + _token: object | None = None, + ): + if _token is not self._token: + raise RuntimeError( + "Use RadialDistributionFunction.from_data() to instantiate this class." + ) + + super().__init__() + self.polar = polar + self.input_data = input_data + + # Placeholders for analysis results (to be populated by future methods) + self.radial_mean: NDArray | None = None + self.radial_var: NDArray | None = None + self.radial_var_norm: NDArray | None = None + + self.pdf_r: NDArray | None = None + self.pdf_reduced: NDArray | None = None + self.pdf: NDArray | None = None + + self.Sk: NDArray | None = None + self.fk: NDArray | None = None + self.bg: NDArray | None = None + self.offset: float | None = None + self.Sk_mask: NDArray | None = None + + # ------------------------------------------------------------------ + # Constructors + # ------------------------------------------------------------------ + @classmethod + def from_data( + cls, + data: Union[NDArray, Dataset2d, Dataset3d, Dataset4dstem, Polar4dstem], + *, + origin_row: float | None = None, + origin_col: float | None = None, + ellipse_params: tuple[float, float, float] | None = None, + num_annular_bins: int = 180, + radial_min: float = 0.0, + radial_max: float | None = None, + radial_step: float = 1.0, + two_fold_rotation_symmetry: bool = False, + ): + """ + -> "RadialDistributionFunction" + Create a RadialDistributionFunction object from various input types. + + Parameters + ---------- + data + Supported inputs: + - 2D numpy array (single diffraction pattern) + - 4D numpy array (scan_y, scan_x, ky, kx) + - Dataset2d + - Dataset4dstem + - Polar4dstem + origin_row, origin_col + Diffraction-space origin (in pixels). If None, defaults to the + central pixel of the diffraction pattern. + Other parameters + Passed through to Dataset4dstem.polar_transform when needed. + """ + # Polar input: use directly + if isinstance(data, Polar4dstem): + polar = data + return cls(polar=polar, input_data=data, _token=cls._token) + + # Dataset4dstem input: polar-transform it + if isinstance(data, Dataset4dstem): + scan_y, scan_x, ny, nx = data.array.shape + if origin_row is None: + origin_row = (ny - 1) / 2.0 + if origin_col is None: + origin_col = (nx - 1) / 2.0 + + polar = data.polar_transform( + origin_row=origin_row, + origin_col=origin_col, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + return cls(polar=polar, input_data=data, _token=cls._token) + + # Dataset2d input: wrap as a trivial 4D-STEM (1x1 scan) then polar-transform + if isinstance(data, Dataset2d): + arr2d = data.array + if arr2d.ndim != 2: + raise ValueError("Dataset2d for RDF must be 2D.") + arr4 = arr2d[None, None, ...] # (1, 1, ky, kx) + + ds4 = Dataset4dstem.from_array( + array=arr4, + name=f"{data.name}_as4dstem" + if getattr(data, "name", None) + else "rdf_4dstem_from_2d", + origin=np.concatenate( + [np.zeros(2, dtype=float), np.asarray(data.origin, dtype=float)] + ), + sampling=np.concatenate( + [np.ones(2, dtype=float), np.asarray(data.sampling, dtype=float)] + ), + units=["pixels", "pixels"] + list(data.units), + signal_units=data.signal_units, + ) + ny, nx = ds4.array.shape[-2:] + if origin_row is None: + origin_row = (ny - 1) / 2.0 + if origin_col is None: + origin_col = (nx - 1) / 2.0 + + polar = ds4.polar_transform( + origin_row=origin_row, + origin_col=origin_col, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + return cls(polar=polar, input_data=data, _token=cls._token) + + # Dataset3d input: not yet specified how to interpret + if isinstance(data, Dataset3d): + raise NotImplementedError( + "RadialDistributionFunction.from_data does not yet support Dataset3d inputs." + ) + + # Numpy array input + arr = ensure_valid_array(data) + if arr.ndim == 2: + ds2 = Dataset2d.from_array(arr, name="rdf_input_2d") + return cls.from_data( + ds2, + origin_row=origin_row, + origin_col=origin_col, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + elif arr.ndim == 4: + ds4 = Dataset4dstem.from_array(arr, name="rdf_input_4dstem") + return cls.from_data( + ds4, + origin_row=origin_row, + origin_col=origin_col, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + else: + raise ValueError("RadialDistributionFunction.from_data only supports 2D or 4D arrays.") + + # ------------------------------------------------------------------ + # Convenience accessors + # ------------------------------------------------------------------ + @property + def qq(self) -> Any: + """ + Scattering vector coordinate array along the radial dimension of `self.polar`, + in physical units (using Polar4dstem.sampling and origin). + """ + # Polar4dstem dims: (scan_y, scan_x, phi, r) + # radial axis is 3 + return self.polar.coords_units(3) + + @property + def radial_bins(self) -> Any: + """ + Radial bin centers in pixel units (convenience alias). + """ + return self.polar.coords(3) + + # ------------------------------------------------------------------ + # Analysis method stubs (py4DSTEM-style API) + # ------------------------------------------------------------------ + + # TODO: linting and docstrings + def calculate_radial_mean( + self, + mask_realspace: NDArray | None = None, + figsize: tuple[float, float] = (8, 4), + returnval: bool = False, + returnfig: bool = False, + ): + """ + Calculate the radial mean intensity from the Polar4dSTEM dataset. + + This performs an azimuthal integration over all angles at each k value. + The result is stored in ``self.radial_mean`` and can optionally be + returned, along with a figure of the radial mean intensity. + + Parameters + ---------- + mask_realspace : NDArray or None, optional + Boolean mask in real space used to select probe positions. + If ``None``, all probe positions are used. + figsize : tuple of float, optional + Figure size passed to ``plot_radial_mean`` when ``returnfig`` is True. + returnval : bool, optional + If True, return the computed radial mean array. + returnfig : bool, optional + If True, also return a figure object from ``plot_radial_mean``. + + Returns + ------- + NDArray or list or None + """ + + # init radial data array + if mask_realspace is None: + # calculate intensity over q-range for each probe position + radial_probe = self.polar.array.mean(axis=2) # axis 0: ry, 1: rx, 2: theta, 3: q + # average over all probe positions + self.radial_mean = np.mean(radial_probe, axis=(0, 1)) + + elif mask_realspace is not None: + masked_polar = self.polar.array[mask_realspace] # (N_valid, N_theta, N_k) + radial_probe = masked_polar.mean(axis=1) + # average over all probe positions, only those unmasked + self.radial_mean = radial_probe.mean(axis=0) + + if returnval: + results = self.radial_mean + else: + results = None if not returnfig else [] + + if returnfig: + fig = self.plot_radial_mean( + figsize=figsize, + returnfig=returnfig, + ) + results.append(fig) + + return results + + def compute_bg_constant_offset(self, Ik: np.ndarray, f2: np.ndarray) -> np.ndarray: + """ + Compute the background intensity B(k) as: + B(k) = N * f²(k) + C + + where: + - N is a scaling factor for inelastic + multiple scattering background + - C is a constant offset term + """ + # fit background parameters N and C + Ik_region = Ik[-50:] # high-k region for fitting, hardcoded for now + f2_region = f2[-50:] + + # least squares fitting to find best parameters + A = np.column_stack((f2_region, np.ones_like(f2_region))) + N, C = np.linalg.lstsq(A, Ik_region, rcond=None)[0] + + # this is monotonic background + constant offset + bg = N * f2 + C + + return bg + + def compute_bg_snip(self, Ik, k, m=25): + """Compute the background intensity B(k) using the SNIP algorithm, + as described in Liu et al. (2023, EDP2PDF), following Morháč et al. (1997). + + Parameters + ---------- + k : array_like + 1D array of scattering vector values (Q, q, or channel positions). + Only the length is used here; SNIP itself works in channel space. + intensity : array_like + 1D array y(i) of diffraction intensities (must be non-negative or + at least > -1 so that y+1 is positive). + m : int + Number of SNIP iterations. The paper recommends setting this to + about half of the major peak FWHM in *channels*. + smooth_window : int, optional + Window size for the pre-smoothing step. The paper uses 2n+1=7. + Set to None or 1 to disable smoothing. + use_savgol_like : bool, optional + If True and smooth_window==7, emulate the Savitzky–Golay-like + convolution used in the paper with hard-coded weights + (2, 3, 6, 7, 6, 3, 2) / sum. + If False, no external scipy dependency is assumed (still uses + that hard-coded kernel when smooth_window==7). + + Returns + ------- + baseline : ndarray + Estimated background b(i) from SNIP. + net_intensity : ndarray + Background-subtracted intensity y(i) - b(i).""" + + # add de-noising step? + + # twice log operators plus square-root operator + v = np.log(np.log(np.sqrt(Ik + 1.0) + 1.0) + 1.0) + + # set m to FWHM of the major peak in the future + # for now, clamp m so the window doesn't exceed half the spectrum + m = max(1, min(m, len(Ik) // 2 - 1)) + + # snip iterations + for p in range(1, m + 1): + # get channels shifted by p + left = np.empty_like(v) + left[p:] = v[:-p] + right = np.empty_like(v) + right[:-p] = v[p:] + # leave boundary edge cases uunshifted + left[:p] = v[:p] + right[-p:] = v[-p:] + + vp = (left + right) / 2 + v = np.minimum(v, vp) + + # inverse lsr + t = np.exp(v) + bg = (np.exp(t - 1.0) - 1.0) ** 2 - 1.0 + bg = np.clip(bg, 0.0, None) + + # #show bg fit + # #TODO: make plotting optional + # plt.figure() + # plt.plot(Ik, label="Original Intensity") + # plt.plot(bg, label=f"SNIP Background, m={m}") + # plt.ylim(0, 0.0003) + # plt.legend() + # plt.xlabel("k") + # plt.ylabel("Intensity") + # plt.title("SNIP Background Estimation") + # plt.show() + + return bg + + def get_atomic_scattering_factors( + self, + elements: Sequence[str], + atomic_frac: Sequence[float], + k2_values: Iterable[float], + ) -> Tuple[np.ndarray, np.ndarray]: + """ + Retrieve atomic scattering factors for specified elements. + + Parameters + ---------- + elements : Sequence[str] + List of element symbols (e.g., ["Si", "O"]). + atomic_frac : Sequence[float] + Atomic fractions for each element in `elements`. Must have the same + length as `elements`. The values will be converted to a NumPy array + of dtype float. + k2_values : Iterable[float] + Squared scattering vector magnitude values (k^2) at which the + scattering factors are evaluated. + + Returns + ------- + f2 : np.ndarray + Weighted sum of squared atomic scattering factors: + f2(k^2) = Σ_i x_i * f_i(k^2)^2 + where x_i is the atomic fraction of element i. + f_2 : np.ndarray + Square of the weighted sum of atomic scattering factors: + f_2(k^2) = (Σ_i x_i * f_i(k^2))^2 + """ + # initialize array to hold f values + atomic_frac = np.asarray(atomic_frac, dtype=float) + n_elements = len(elements) + k2_array = np.asarray(k2_values, dtype=float) + len_k = len(k2_array) + + f = np.zeros((n_elements, len_k), dtype=float) + + # load Kirkland parameters from JSON + with KIRKLAND_PARAMS_PATH.open(encoding="utf-8") as file: + kirkland_params: dict[str, dict[str, list[float]]] = json.load(file) + + for i, element in enumerate(elements): + try: + params = kirkland_params[element] + except KeyError: + raise ValueError(f"Element {element} not found in Kirkland parameters table.") + + a = np.asarray(params["a"], float) + b = np.asarray(params["b"], float) + c = np.asarray(params["c"], float) + d = np.asarray(params["d"], float) + + # Lorentzian and Gaussian terms + l_term = (a[:, None] / (k2_array[None, :] + b[:, None])).sum( + axis=0 + ) # a[:, None] and b[:, None] → shape (3, 1) + g_term = (c[:, None] * np.exp(-d[:, None] * k2_array[None, :])).sum( + axis=0 + ) # k2_array[None, :] → shape (1, len_k) + + f[i, :] = l_term + g_term + + f2 = (f**2 * atomic_frac[:, None]).sum(axis=0) + f_weighted = (f * atomic_frac[:, None]).sum(axis=0) + f_2 = f_weighted**2 + + return f2, f_2 + + def calculate_pair_dist_function( + self, + el: List[str], + atomic_frac: List[float], + k_min: float = 0.05, + k_max: float | None = None, + k_width: float = 0.25, + k_lowpass: float | None = None, + k_highpass: float | None = None, + r_min: float = 0.0, + r_max: float = 20.0, + r_step: float = 0.02, + mask_realspace: NDArray | None = None, + damp_origin_fluctuations: bool = True, + calculate_pdf: bool = True, + density: float | None = None, + plot_options: dict[str, bool] = { + "plot_radial_mean": False, + "plot_background_fits": False, + "plot_sf_estimate": False, + "plot_reduced_pdf": True, + "plot_pdf": False, + }, + figsize: tuple[float, float] = (8, 4), + returnval: bool = False, + returnfig: bool = False, + ): + """ + Calculate the (reduced) pair distribution function from a 4D-STEM dataset. + + This routine: + * Computes the radial mean intensity I(k) from self.polar (optionally + restricted to a real-space mask). + * Computes element-weighted elastic scattering factors ⟨f²⟩(k) and + ⟨f⟩²(k). + * Estimates and subtracts a background from I(k). + * Constructs the structure factor S(k) and reduced structure function + F(k) = k [S(k) - 1], with optional low-/high-pass filtering. + * Applies smooth edge masking in k-space. + * Performs a sine transform to obtain the reduced pair distribution + function G(r). + + The computed quantities are also stored on the instance as: + * self.Ik – radial mean intensity I(k) + * self.bg – background bg(k) + * self.Fk – reduced structure function F(k) + * self.pdf_r – r grid + * self.reduced_pdf – reduced PDF G(r) + + Parameters + ---------- + el : list of str + List of element symbols (e.g. ["Ta", "O"]) in the sample. + atomic_frac : list of float + Atomic fractions for each element in `el`. Must be the same length as + `el` and typically sum to 1.0. + k_min : float, optional + Minimum k (Å⁻¹) to use when building masks and transforms. If not + None, this value overrides the minimum of the k-grid derived from + `self.qq`. If None, `self.kmin` is set to `k.min()`. + k_max : float or None, optional + Maximum k (Å⁻¹) to use when building masks and transforms. If not + None, this value overrides the maximum of the k-grid derived from + `self.qq`. If None, `self.kmax` is set to `k.max()`. + k_width : float, optional + Width parameter (in Å⁻¹) controlling the smooth edge mask in k-space. + It enters the construction of `mask_low` and `mask_high`. + k_lowpass : float or None, optional + If provided and > 0, applies a low-pass Gaussian filter to S(k) with + sigma = k_lowpass / dk, where dk is the k-grid spacing. + k_highpass : float or None, optional + If provided and > 0, constructs a low-pass filtered copy of S(k) with + sigma = k_highpass / dk and subtracts it from S(k), effectively + applying a high-pass filter. + r_min : float, optional + Minimum r (Å) for the real-space grid used to compute G(r). + r_max : float, optional + Maximum r (Å) for the real-space grid used to compute G(r). + r_step : float, optional + Step size in r (Å) for the real-space grid. + mask_realspace : NDArray or None, optional + Real-space mask specifying which probe positions (rx, ry) to include. + Either: + * A boolean array of shape (rx, ry) where True means “include this + probe position”, or + * An array-like of shape (2, 2) giving two opposite (rx, ry) corner + points that define a rectangular region of interest. + If None, all probe positions are used. + plot_options : dict[str, bool] or None, optional + Dictionary of plotting flags: + - "plot_radial_mean" + - "plot_background_fits" + - "plot_sf_estimate" + - "plot_reduced_pdf" + - "plot_pdf" + In this method it is currently used only to decide whether to request + a figure from `calculate_radial_mean` via + `returnfig=plot_options["plot_radial_mean"]`. If None, a default + dictionary is created internally. + figsize : tuple[float, float], optional + Figure size passed to `calculate_radial_mean` (and potentially to + future plotting routines). + maxfev : int or None, optional + Maximum number of function evaluations for any internal fit routines. + Currently reserved for future use. + returnval : bool, optional + If True, the function returns a tuple `(pdf_r, reduced_pdf)`. If + False, no numerical results are returned (but attributes on `self` + are still updated). + returnfig : bool, optional + If True, this method may in future also return figure objects (e.g. + appended to the `results` list). At present, figure-generation code + is commented out and this flag has no effect beyond shaping the + structure of the returned `results` object. + + Returns + ------- + pdf_r : np.ndarray + Real-space r grid on which the reduced PDF is evaluated. + reduced_pdf : np.ndarray + Reduced pair distribution function G(r). + pdf : np.ndarray + Pair distribution function g(r). + + #TODO: add notes of density calculation for pdf and background calculation method + """ + k_width = np.array(k_width) + if k_width.size == 1: + k_width = k_width * np.ones(2) + + # BUG: make calibration automatic + k = self.qq * 0.01488 + dk = k[1] - k[0] + k2 = k**2 + + self.kmax = k_max if k_max is not None else k.max() + self.kmin = k_min if k_min is not None else k.min() + + # TODO: test + # this should be from avg, not sum! + mask_bool = None + if mask_realspace is not None: + rx, ry = self.polar.array.shape[:2] + mask_realspace = np.asarray(mask_realspace) + + # mask given as boolean array + if mask_realspace.dtype == bool and mask_realspace.shape == (rx, ry): + mask_bool = mask_realspace + + # mask given as list of corners + elif mask_realspace.shape == (2, 2): + (rx1, ry1), (rx2, ry2) = mask_realspace.astype(int) + rx_min, rx_max = sorted((rx1, rx2)) + ry_min, ry_max = sorted((ry1, ry2)) + + # vectorized bounds check + bad = (rx_min < 0) | (rx_max >= rx) | (ry_min < 0) | (ry_max >= ry) + if bad: + raise ValueError(f"Mask points outside valid range {(rx, ry)}") + + mask_bool = np.zeros((rx, ry), dtype=bool) + mask_bool[rx_min : rx_max + 1, ry_min : ry_max + 1] = True + else: + raise ValueError( + "mask_realspace must be boolean array or two opposite (rx, ry) corner points." + ) + + self.calculate_radial_mean( + mask_realspace=mask_bool, figsize=figsize, returnfig=plot_options["plot_radial_mean"] + ) + Ik = self.radial_mean + + # get and ^2 for elements and atomic frac + f2, f_2 = self.get_atomic_scattering_factors(el, atomic_frac, k2) + + # BUG: implement k_width properly + k_width = self.kmax - self.kmin + # Calculate structure factor mask + # mask_low = ( + # np.sin( + # np.clip( + # (k - self.kmin) / k_width, + # 0, + # 1, + # ) + # * np.pi + # / 2.0, + # ) + # ** 2 + # ) + # mask_high = ( + # np.sin( + # np.clip( + # (self.kmax - k) / k_width, + # 0, + # 1, + # ) + # * np.pi + # / 2.0, + # ) + # ** 2 + # ) + # mask = mask_low * mask_high + + bg = self.compute_bg_snip(Ik, k, m=25) + Ik_net = Ik - bg + + # scaling region (avoid direct beam, etc.) + k_scale_min = max(self.kmin, 1.25) + k_scale_max = self.kmax + mask_int = (k >= k_scale_min) & (k <= k_scale_max) + + k_int = k[mask_int] + Ik_int = np.clip(Ik_net[mask_int], 0.0, None) + f2_int = f2[mask_int] + + integral_Ik = np.trapz(Ik_int, k_int) + integral_f2 = np.trapz(f2_int, k_int) + + eta = integral_Ik / integral_f2 + print(f"Scaling factor eta = {eta:.4f}") + + f2_scaled = eta * f2 + f_2_scaled = eta * f_2 + + Sk = 1.0 + (Ik_net - f2_scaled) / f_2_scaled # back to intensity scaling + + # high and lowpass filtering + if k_lowpass is not None and k_lowpass > 0.0: + Sk = gaussian_filter(Sk, sigma=k_lowpass / dk, mode="nearest") + if k_highpass is not None and k_highpass > 0.0: + Sk_lowpass = gaussian_filter(Sk, sigma=k_highpass / dk, mode="nearest") + Sk -= Sk_lowpass + self.Sk_lowpass = Sk_lowpass + + Fk = 2 * np.pi * k * (Sk - 1) + + # high q taper + Q = 2.0 * np.pi * k # Q in 1/Å + Qmin = 2.0 * np.pi * self.kmin + Qmax = 2.0 * np.pi * self.kmax + + # Build Lorch window: w(Q) = sin(pi*Q/Qmax)/(pi*Q/Qmax) + window = np.zeros_like(Q) + inband = (Q >= Qmin) & (Q <= Qmax) + + x = Q[inband] / Qmax + # handle Q=0 safely (though inband excludes it if Qmin>0) + window[inband] = np.sin(np.pi * x) / (np.pi * x) + + # Apply window to F(Q) + FQ_win = Fk * window + + r = np.arange(r_min, r_max, r_step) + ra, ka = np.meshgrid(r, k) + # i think the np.sin kernel in py4dstem should not include the 2pi? + # or depending on k, need to ALSO add 2pi factor to dk + reduced_pdf = ( + (2 / np.pi) + * dk + * 2 + * np.pi + * np.sum( + np.sin(2 * np.pi * ra * ka) * FQ_win[:, None], + axis=0, + ) + ) + reduced_pdf[0] = 0 # physically must be at 0 when r = 0 + + self.Ik = Ik + self.bg = bg + self.Sk = Sk + self.Fk = Fk + self.Fk_masked = FQ_win + self.r = r + self.reduced_pdf = reduced_pdf + + # add option to return pdf also using the density calculation method + # from Yoshimoto and Omote, 2022. + + # BUG: for now + calculate_pdf = True + # rho0 = 0.05284 + if calculate_pdf: + if density is None: + rho0, Fk_cor, G_cor = self.estimate_density( + max_iter=20, tol_percent=1e-1, make_plots=True, Fk_masked=FQ_win + ) + print(f"Estimated density rho0 = {rho0:.4f} atoms / Angstrom^3") + else: + print(f"Using provided density rho0 = {density:.4f} atoms / Angstrom^3") + rho0 = density + pdf = 1 + (1 / (4 * np.pi * r * rho0)) * G_cor + pdf[0] = 0.0 # avoid singularity at r=0 + self.pdf = pdf + + self.Fk_masked = Fk_cor + self.r = r + self.reduced_pdf = G_cor + + # if returnfig and self.plot_options != {}: + # self.plot_functions() + + # if returnval: + # results = (self.r, self.reduced_pdf) + # else: + # results = None if not returnfig else [] + + # # handle mutable default for plot_options + # if plot_options is None: + # plot_options = { + # "plot_radial_mean": False, + # "plot_background_fits": False, + # "plot_sf_estimate": False, + # "plot_reduced_pdf": True, + # "plot_pdf": False, + # } + + # if returnfig and plot_options != {}: + # # fig = self.plot_radial_mean( + # # figsize=figsize, + # # returnfig=returnfig, + # # ) + # # results.append(fig) + + # return results + + # if returnval: + # results = (self.r, self.reduced_pdf) + return self.r, self.reduced_pdf, Fk, Sk, Ik, bg, k, Ik_net, FQ_win, self.pdf + + def compute_alpha_beta(self, Q2d, r2d, G_beta, r_1d): + Qsafe = np.where(Q2d == 0.0, 1e-12, Q2d) + alpha_int = -4 * np.pi * r2d * np.sin(Qsafe * r2d) / Qsafe + beta_int = G_beta[None, :] * np.sin(Qsafe * r2d) / Qsafe + alpha = np.trapz(alpha_int, x=r_1d, axis=1) + beta = np.trapz(beta_int, x=r_1d, axis=1) + return alpha, beta + + def estimate_density( + self, + max_iter: int = 1000, + tol_percent: float = 1e-4, + make_plots: bool = True, + Fk_masked: np.ndarray | None = None, + figsize: Tuple[float, float] = (8.0, 6.0), + ) -> Tuple[float, np.ndarray, np.ndarray, np.ndarray]: + """ + Estimate microscopic number density rho0 from S(k) using the + Yoshimoto & Omote (2022) Q-space iteration method, and return + corrected S(k) and G(r). + + Parameters + ---------- + k : ndarray, shape (Nk,) + Scattering vector k (Å⁻1). + Sk_obs : ndarray, shape (Nk,) + Observed structure factor S_obs(k). + r : ndarray, shape (Nr,) + Real-space grid for G(r) (Å). + max_iter : int, optional + Maximum number of Q-space iterations. + tol_percent : float, optional + Convergence threshold on the relative change in rho0 (in %), + as defined in Eq. (12) of Yoshimoto & Omote (2022). + make_plots : bool, optional + If True, plot S_obs vs S_cor and G_obs vs G_cor with residuals. + figsize : tuple, optional + Figure size for the plots. + + Returns + ------- + rho0 : float + Estimated microscopic number density (in the same units implied + by your S(k) normalization; typically atoms / Å^3). + Sk_cor : ndarray, shape (Nk,) + Corrected structure factor S_cor(k). + G_obs : ndarray, shape (Nr,) + Observed G_obs(r) computed from S_obs(k). + G_cor : ndarray, shape (Nr,) + Corrected G_cor(r) computed from S_cor(k). + """ + # BUG: make calibration automatic + k = self.qq * 0.01488 # convert from 1/Angstrom to Angstrom^-1 + dk = k[1] - k[0] + k_fit_mask = (k >= self.kmin) & ( + k <= self.kmax + ) # or drop <= self.kmax if you want full high-k + k_fit = k[k_fit_mask] + ra, ka = np.meshgrid(self.r, k) + + # ---- choose r1st ignoring r < r_art_cut ---- + r_art_cut = 1.5 # Angstrom, adjust as needed + mask_search = self.r >= r_art_cut + r_search = self.r[mask_search] + G_search = self.reduced_pdf[mask_search] # or whatever G array you're using in the loop + + # indices of local maxima in the search region (no smoothing) + peaks = np.where((G_search[1:-1] > G_search[:-2]) & (G_search[1:-1] > G_search[2:]))[0] + 1 + print(f"Found local maxima at r = {r_search[peaks]} Å") + if len(peaks) == 0: + raise RuntimeError( + f"No local maxima found for r >= r_art_cut={r_art_cut}. " + "Increase r_max, decrease r_art_cut, or check G(r)." + ) + + idx_peak = peaks[0] + r1st = r_search[idx_peak] + + # ---- find a local minimum to the left of r1st but still >= r_art_cut ---- + left = (self.r >= r_art_cut) & (self.r < r1st) + if not np.any(left): + # fallback: if peak is immediately at cutoff, just use cutoff as rmin + rmin = r_art_cut + else: + r_left = self.r[left] + G_left = self.reduced_pdf[left] + + mins = np.where((G_left[1:-1] < G_left[:-2]) & (G_left[1:-1] < G_left[2:]))[0] + 1 + if len(mins) == 0: + # fallback: use the global minimum on the left interval + rmin = r_left[np.argmin(G_left)] + else: + rmin = r_left[mins[-1]] # minimum closest to the peak + + print(f"Using rmin = {rmin:.3f} Å for density estimation.") + + # restrict r to [0, rmin] for alpha/beta integrals + r_mask = (self.r >= 0.0) & (self.r <= rmin) + r_short = self.r[r_mask] + G_short = self.reduced_pdf[r_mask] + + ra_short, ka_short = np.meshgrid(r_short, k) # shape (Nr_short, Nk) + + # iterative refinement of rho0 and S(k) + rho0_prev = None + Sk_cor = self.Sk.copy() + G_cor = self.reduced_pdf.copy() + + # use current G(r) (from Sk_cor) in beta(Q) + G_beta = G_short + # r_short = r_short * 2 * np.pi + k_fit = k_fit * 2 * np.pi + for j in range(max_iter): + if j > 0: + G_beta = G_cor[r_mask] + + # Q2d, r2d = np.meshgrid(k, r_short, indexing="ij") # (Nk, Nr_short) + # alpha, beta = self.compute_alpha_beta(Q2d, r2d, G_beta, r_short) + # # least-squares estimate of rho0 + # rho0 = np.sum(alpha * beta) / np.sum(alpha**2) + + # k-range used only for alpha/beta fit + k2d_fit, r2d_fit = np.meshgrid(k_fit, r_short, indexing="ij") # (Nk_fit, Nr_short) + + # IMPORTANT: do NOT rescale r_short in-place (remove r_short = r_short * 2*np.pi) + alpha, beta = self.compute_alpha_beta(k2d_fit, r2d_fit, G_beta, r_short) + + # rho0 fit only over the masked k-range + rho0 = np.sum(alpha * beta) / np.sum(alpha**2) + + if rho0_prev is not None: + Rj = np.sqrt(((rho0_prev - rho0) ** 2) / (rho0**2)) * 100.0 + if Rj < tol_percent: + print( + f"Converged after {j} iterations: rho0 = {rho0:.4f} atoms / ų, Rj = {Rj:.4f}%" + ) + break + + # update S_cor(Q) according to Eq. (8) + # Sk_cor = Sk_cor - beta + rho0 * alpha + Sk_cor[k_fit_mask] = Sk_cor[k_fit_mask] - beta + rho0 * alpha + Fk_cor = 2 * np.pi * k * (Sk_cor - 1.0) + + # low q taper + edge_frac_low = 0.1 # 10% of range at low-q + edge_width_low = edge_frac_low * (self.kmax - self.kmin) + + window = np.ones_like(k) + + # low-q edge (same as before) + low = (k >= self.kmin) & (k < self.kmin + edge_width_low) + t = (k[low] - self.kmin) / edge_width_low + window[low] = np.sin(0.5 * np.pi * t) ** 2 + + # outside [kmin, kmax] -> 0 + window[k < self.kmin] = 0.0 + window[k > self.kmax] = 0.0 + + # high q taper + Q = 2.0 * np.pi * k # Q in 1/Å + Qmin = 2.0 * np.pi * self.kmin + Qmax = 2.0 * np.pi * self.kmax + + # Build Lorch window: w(Q) = sin(pi*Q/Qmax)/(pi*Q/Qmax) + window = np.zeros_like(Q) + inband = (Q >= Qmin) & (Q <= Qmax) + + x = Q[inband] / Qmax + # handle Q=0 safely (though inband excludes it if Qmin>0) + window[inband] = np.sin(np.pi * x) / (np.pi * x) + + # Apply window to F(Q) + FQ_win = Fk_cor * window + + G_cor = ( + (2.0 / np.pi) + * dk + * 2 + * np.pi + * np.sum(np.sin(2 * np.pi * ka * ra) * FQ_win[:, None], axis=0) + ) + G_cor[0] = 0.0 # enforce G(0) = 0 + + rho0_prev = rho0 + + if make_plots and (j == 0): + fig, ax = plt.subplots(figsize=(7, 4)) + # ax.plot(k, alpha, label="alpha(k)") + ax.plot(k_fit, beta, label="beta(k)") + ax.plot(k_fit, rho0 * alpha, "--", label="rho0 * alpha(k)") + ax.set_xlabel("k (1/Å)") + ax.set_title(f"iter {j} (rho0={rho0:.4g})") + ax.legend() + plt.show() + + fig, ax = plt.subplots(figsize=(7, 4)) + # ax.plot(k, alpha, label="alpha(k)") + ax.plot(k_fit, beta, label="beta(k)") + ax.plot(k_fit, rho0 * alpha, "--", label="rho0 * alpha(k)") + ax.set_xlabel("k (1/Å)") + ax.set_title(f"iter {j} (rho0={rho0:.4g})") + ax.legend() + plt.show() + print(f"Total iterations: {j + 1}, Final rho0 = {rho0:.4f} atoms / ų") + + # --- Step 4: plotting (optional) --- + if make_plots: + fig, axes = plt.subplots(2, 2, figsize=figsize) + + # S(Q) + axS_top = axes[0, 0] + axS_res = axes[1, 0] + axS_top.plot(k, self.Fk_masked, label="F_obs(k)", color="gray") + axS_top.plot(k, FQ_win, label="F_cor(k)", color="red") + axS_top.set_xlabel("k (Å$^{-1}$)") + axS_top.set_ylabel("F(k)") + axS_top.legend() + + axS_res.plot(k, FQ_win - self.Fk_masked, color="blue") + axS_res.set_xlabel("k (Å$^{-1}$)") + axS_res.set_ylabel("F_cor - F_obs") + + # G(r) + axG_top = axes[0, 1] + axG_res = axes[1, 1] + axG_top.plot(self.r, self.reduced_pdf, label="G_obs(r)", color="gray") + axG_top.plot(self.r, G_cor, label="G_cor(r)", color="red") + # axG_top.plot(r_short, G_short, label="G_obs(r)", color="gray") + # axG_top.plot(r_short, G_beta, label="G_cor(r)", color="red") + axG_top.set_xlabel("r (Å)") + axG_top.set_ylabel("G(r)") + axG_top.legend() + + axG_res.plot(self.r, G_cor - self.reduced_pdf, color="blue") + axG_res.set_xlabel("r (Å)") + axG_res.set_ylabel("G_cor - G_obs") + + fig.tight_layout() + plt.show() + + return rho0, FQ_win, G_cor + + def plot_radial_mean( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Plotting radial mean intensity vs scattering vector. + """ + + if self.radial_mean is None: + raise RuntimeError("Radial mean intensity has not been calculated yet.") + + fig, ax = plt.subplots(figsize=figsize) + ax.plot(self.qq, self.radial_mean, label="Radial Mean Intensity I(k)") + ax.set_xlabel("Scattering Vector q (1/Å)") + ax.set_ylabel("Intensity (a.u.)") + ax.set_title("Radial Mean Intensity vs Scattering Vector") + ax.legend() + ax.tight_layout() + + if returnfig: + return fig + else: + plt.show() + + def plot_radial_var_norm( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Stub for plotting normalized radial variance vs scattering vector. + """ + raise NotImplementedError("plot_radial_var_norm is not implemented yet.") + + def plot_background_fits( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Plotting background fit vs radial mean intensity. + """ + if self.Ik is None or self.bg is None: + raise RuntimeError("Radial mean intensity or background has not been calculated yet.") + fig, ax = plt.subplots(figsize=figsize) + ax.plot(self.qq, self.Ik, label="Radial Mean Intensity I(k)") + ax.plot(self.qq, self.bg, label="Background B(k)", linestyle="--") + ax.set_xlabel("Scattering Vector q (1/Å)") + ax.set_ylabel("Intensity (a.u.)") + ax.set_title("Radial Mean Intensity and Background Fit") + ax.legend() + ax.tight_layout() + + if returnfig: + return fig + else: + plt.show() + + def plot_sf_estimate( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Plotting structure factor S(k). + """ + + if self.Sk is None: + raise RuntimeError("Structure factor S(k) has not been calculated yet.") + + fig, ax = plt.subplots(figsize=figsize) + ax.plot(self.qq, self.Sk, label="Structure Factor S(k)") + ax.set_xlabel("Scattering Vector q (1/Å)") + ax.set_ylabel("Structure Factor S(k)") + ax.tight_layout() + + if returnfig: + return fig + else: + plt.show() + + def plot_reduced_sf_estimate( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Plotting reduced structure factor F(k). + """ + if self.Fk is None: + raise RuntimeError("Reduced structure factor F(k) has not been calculated yet.") + + fig, ax = plt.subplots(figsize=figsize) + ax.plot(self.qq, self.Fk, label="Reduced Structure Factor F(k)") + ax.set_xlabel("Scattering Vector q (1/Å)") + ax.set_ylabel("Reduced Structure Factor F(k)") + ax.tight_layout() + + if returnfig: + return fig + else: + plt.show() + + def plot_reduced_pdf( + self, + padding_frac: float = 0.1, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Plotting reduced PDF g(r). + """ + if self.reduced_pdf is None: + raise RuntimeError("Reduced PDF has not been calculated yet.") + + # Find radial value of primary peak and trough for y-limits + ind_max = np.argmax(self.reduced_pdf) + y_max = self.reduced_pdf[ind_max] + + ind_min = np.argmin(self.reduced_pdf) + y_min = self.reduced_pdf[ind_min] + yrange = y_max - y_min + pad = padding_frac * yrange + + fig, ax = plt.subplots(figsize=figsize) + ax.plot(self.r, self.reduced_pdf, label="Reduced Pair Distribution Function G(r)") + ax.set_xlabel("Radial Distance r (Å)") + ax.set_ylabel("Reduced Pair Distribution Function G(r)") + ax.set_ylim(y_min - pad, y_max + pad) + ax.tight_layout() + + if returnfig: + return fig + else: + plt.show() + + def plot_pdf( + self, + padding_frac: float = 0.1, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + """ + Plotting pair distribution function g(r). + """ + if self.reduced_pdf is None or self.pdf is None: + raise RuntimeError("Reduced PDF or PDF has not been calculated yet.") + + # Find radial value of primary peak + ind_max = np.argmax(self.reduced_pdf) + y_max = self.pdf[ind_max] + + # look to right of primary peak for minimum + reduced_pdf_region = self.pdf[ind_max + 1 :] + ind_min = np.argmin(reduced_pdf_region) + (ind_max + 1) + y_min = self.pdf[ind_min] + + yrange = y_max - y_min + pad = padding_frac * yrange + + fig, ax = plt.subplots(figsize=figsize) + ax.plot(self.r, self.pdf, label="Pair Distribution Function g(r)") + ax.set_xlabel("Radial Distance r (Å)") + ax.set_ylabel("Pair Distribution Function g(r)") + ax.set_ylim(y_min - pad, y_max + pad) + ax.tight_layout() + + if returnfig: + return fig + else: + plt.show() From 12d80a2028c4fb7e119a40cb2b362ca37a6efdfa Mon Sep 17 00:00:00 2001 From: Karen Ehrhardt Date: Wed, 7 Jan 2026 20:24:47 -0800 Subject: [PATCH 123/140] origin finding and pdf updates --- .../core/datastructures/dataset4dstem.py | 7 +- .../core/datastructures/polar4dstem.py | 193 ++- src/quantem/diffraction/__init__.py | 7 +- src/quantem/diffraction/kirkland_params.json | 620 --------- src/quantem/diffraction/polar.py | 1168 ++++++++++++++-- src/quantem/diffraction/polar_new.py | 1195 ----------------- 6 files changed, 1272 insertions(+), 1918 deletions(-) delete mode 100644 src/quantem/diffraction/kirkland_params.json delete mode 100644 src/quantem/diffraction/polar_new.py diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 5bd78703..01c6b0d8 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -9,7 +9,6 @@ from quantem.core.datastructures.dataset2d import Dataset2d from quantem.core.datastructures.dataset4d import Dataset4d from quantem.core.datastructures.polar4dstem import dataset4dstem_polar_transform - from quantem.core.utils.validators import ensure_valid_array from quantem.core.visualization import show_2d from quantem.core.visualization.visualization_utils import ScalebarConfig @@ -79,7 +78,8 @@ def __init__( _token : object | None, optional Token to prevent direct instantiation, by default None """ - mdata_keys_4dstem = ["q_to_r_rotation_ccw_deg", 'q_transpose', "ellipticity"] + print("array.shape:", array.shape) + mdata_keys_4dstem = ["r_to_q_rotation_cw_deg", "ellipticity"] for k in mdata_keys_4dstem: if k not in metadata.keys(): metadata[k] = None @@ -801,5 +801,4 @@ def median_filter_masked_pixels(self, mask: np.ndarray, kernel_width: int = 3): self.array[:, :, x_min:x_max, y_min:y_max], axis=(2, 3) ) - - polar_transform = dataset4dstem_polar_transform \ No newline at end of file + polar_transform = dataset4dstem_polar_transform diff --git a/src/quantem/core/datastructures/polar4dstem.py b/src/quantem/core/datastructures/polar4dstem.py index 6619af5c..e832e26d 100644 --- a/src/quantem/core/datastructures/polar4dstem.py +++ b/src/quantem/core/datastructures/polar4dstem.py @@ -1,6 +1,7 @@ +from typing import TYPE_CHECKING, Any + import numpy as np from numpy.typing import NDArray -from typing import Any, TYPE_CHECKING from scipy.ndimage import map_coordinates if TYPE_CHECKING: @@ -149,10 +150,115 @@ def _precompute_polar_coords( return coords, phi_bins, radial_bins, radial_max_eff +def find_origin( + data, + *, + ellipse_params=None, + num_annular_bins=180, + radial_min=0.0, + radial_max=None, + radial_step=1.0, + two_fold_rotation_symmetry=False, +): + """ + Placeholder for future automatic diffraction center finding method. + """ + if len(data.array.shape) == 2: + ny, nx = data.array.shape + scan_y, scan_x = 1, 1 + elif len(data.array.shape) == 4: + scan_y, scan_x, ny, nx = data.array.shape + else: + raise ValueError("find_origin only supports 2D or 4D-STEM datasets for now.") + + origin_array = np.zeros((scan_y, scan_x, 2), dtype=float) + + max_steps = 1000 # prevent infinite loops + + # start with center of image for now + estimated_origin_row = (ny - 1) / 2.0 + estimated_origin_col = (nx - 1) / 2.0 + + for y_pos in range(scan_y): + for x_pos in range(scan_x): + print(f"Finding origin for scan pos ({y_pos}, {x_pos})") + + coords_cache = {} + + polar = data.polar_transform( + origin_array=[estimated_origin_row, estimated_origin_col], + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + scan_pos=(y_pos, x_pos), + ) + + min_r = int(np.floor(0.1 * polar.shape[1])) + max_r = int(np.ceil(0.9 * polar.shape[1])) + std_est_origin = polar[:, min_r:max_r].std(axis=0) + std_est_origin_sum = std_est_origin.sum() + + origin_row = int(round(estimated_origin_row)) + origin_col = int(round(estimated_origin_col)) + coords_cache[(origin_row, origin_col)] = std_est_origin_sum + + if y_pos == 0 and x_pos == 0: + print(f"Initial std sum at estimated origin: {std_est_origin_sum}") + + converged = False + best = std_est_origin_sum + steps = 0 + while not converged and steps < max_steps: + steps += 1 + moved = False + + neighbors = [ + (origin_row + dr, origin_col + dc) + for dr in (-1, 0, 1) + for dc in (-1, 0, 1) + if not (dr == 0 and dc == 0) + ] + neighbors = [(r, c) for (r, c) in neighbors if 0 <= r < ny and 0 <= c < nx] + + for origin_r, origin_c in neighbors: + if (origin_r, origin_c) not in coords_cache: + polar = data.polar_transform( + origin_array=[origin_r, origin_c], + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + scan_pos=(y_pos, x_pos), + ) + std_test = polar[:, min_r:max_r].std(axis=0) + coords_cache[(origin_r, origin_c)] = std_test.sum() + + if coords_cache[(origin_r, origin_c)] < best: + origin_row = origin_r + origin_col = origin_c + best = coords_cache[(origin_r, origin_c)] + moved = True + print(f"Moved to ({origin_row}, {origin_col}) with std sum {best}") + + if not moved: + converged = True + + if y_pos == 0 and x_pos == 0: + print(f"Final std sum at found origin ({origin_row}, {origin_col}): {best}") + origin_array[y_pos, x_pos, 0] = origin_row + origin_array[y_pos, x_pos, 1] = origin_col + + return origin_array + + def dataset4dstem_polar_transform( self: "Dataset4dstem", - origin_row: float | int | NDArray, - origin_col: float | int | NDArray, + origin_array: NDArray | None = None, ellipse_params: tuple[float, float, float] | None = None, num_annular_bins: int = 180, radial_min: float = 0.0, @@ -161,12 +267,63 @@ def dataset4dstem_polar_transform( two_fold_rotation_symmetry: bool = False, name: str | None = None, signal_units: str | None = None, + scan_pos: tuple[int, int] | None = None, ) -> Polar4dstem: if self.array.ndim != 4: raise ValueError("polar_transform requires a 4D-STEM dataset (ndim=4).") scan_y, scan_x, ny, nx = self.array.shape - origin_row_f = float(origin_row) - origin_col_f = float(origin_col) + + # Standardize origin_array input + origin_array = np.asarray(origin_array) if origin_array is not None else None + if origin_array is None: + center = np.array([(ny - 1) / 2.0, (nx - 1) / 2.0], dtype=float) + origins = np.broadcast_to(center, (scan_y, scan_x, 2)).copy() + elif origin_array.shape == (2,): + origins = np.empty((scan_y, scan_x, 2), dtype=float) + origins[...] = origin_array + elif origin_array.shape == (scan_y, scan_x, 2): + origins = origin_array + else: + raise ValueError( + "origin_array must have shape None, (2,) or (scan_y, scan_x, 2)." + f" Got {origin_array.shape}." + ) + + # If scan_pos is provided, compute polar transform only for that position + if scan_pos is not None: + iy, ix = scan_pos + dp = self.array[iy, ix] # (ny, nx) view + r0 = float(origins[iy, ix, 0]) + c0 = float(origins[iy, ix, 1]) + + coords, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( + ny=ny, + nx=nx, + origin_row=r0, + origin_col=c0, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + polar2d = map_coordinates(dp, coords, order=1, mode="constant", cval=0.0) # (phi, r) + return polar2d + + # Otherwise, compute polar transform for all scan positions + # Determine one overall radial_max if not provided + if radial_max is None: + r_row_pos = origins[:, :, 0] + r_row_neg = (ny - 1) - origins[:, :, 0] + r_col_pos = origins[:, :, 1] + r_col_neg = (nx - 1) - origins[:, :, 1] + radial_max_eff_array = np.minimum.reduce([r_row_pos, r_row_neg, r_col_pos, r_col_neg]) + radial_max = float(max(radial_max_eff_array.min(), radial_min + radial_step)) + + # Precompute polar coords only once, using the origin from the first probe position + origin_row_f = float(origins[0, 0, 0]) + origin_col_f = float(origins[0, 0, 1]) coords, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( ny=ny, nx=nx, @@ -183,9 +340,25 @@ def dataset4dstem_polar_transform( n_r = radial_bins.size result_dtype = np.result_type(self.array.dtype, np.float32) out = np.empty((scan_y, scan_x, n_phi, n_r), dtype=result_dtype) + for iy in range(scan_y): for ix in range(scan_x): dp = self.array[iy, ix] + r0 = float(origins[iy, ix, 0]) + c0 = float(origins[iy, ix, 1]) + + coords, _, _, radial_max_eff = _precompute_polar_coords( + ny=ny, + nx=nx, + origin_row=r0, + origin_col=c0, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) out[iy, ix] = map_coordinates( dp, coords, @@ -193,10 +366,8 @@ def dataset4dstem_polar_transform( mode="constant", cval=0.0, ) - if two_fold_rotation_symmetry: - phi_range = np.pi - else: - phi_range = 2.0 * np.pi + + phi_range = np.pi if two_fold_rotation_symmetry else 2.0 * np.pi phi_step_deg = (phi_range / float(n_phi)) * (180.0 / np.pi) sampling = np.zeros(4, dtype=float) origin = np.zeros(4, dtype=float) @@ -220,8 +391,8 @@ def dataset4dstem_polar_transform( "polar_radial_step": float(radial_step), "polar_num_annular_bins": int(n_phi), "polar_two_fold_rotation_symmetry": bool(two_fold_rotation_symmetry), - "polar_origin_row": origin_row_f, - "polar_origin_col": origin_col_f, + "polar_origin_row": float(origins[0, 0, 0]), + "polar_origin_col": float(origins[0, 0, 1]), "polar_ellipse_params": tuple(ellipse_params) if ellipse_params is not None else None, } ) diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index 8eb0937c..2e723c74 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1,6 +1 @@ -from quantem.diffraction.polar import RDF as RDF -from quantem.diffraction.strain_autocorrelation import ( - StrainMapAutocorrelation as StrainMapAutocorrelation, -) -from quantem.diffraction.maped import MAPED as MAPED -from quantem.diffraction.maped import MAPEDTorch as MAPEDTorch +from quantem.diffraction.polar import PairDistributionFunction as PairDistributionFunction diff --git a/src/quantem/diffraction/kirkland_params.json b/src/quantem/diffraction/kirkland_params.json deleted file mode 100644 index a8540ea7..00000000 --- a/src/quantem/diffraction/kirkland_params.json +++ /dev/null @@ -1,620 +0,0 @@ -{ - "H": { - "a": [4.20298324e-003, 6.27762505e-002, 3.00907347e-002], - "b":[2.25350888e-001, 2.25366950e-001, 2.25331756e-001], - "c":[6.77756695e-002, 3.56609237e-003, 2.76135815e-002], - "d":[4.38854001e+000, 4.03884823e-001, 1.44490166e+000] - }, - "He": { - "a": [1.87543704e-005, 4.10595800e-004, 1.96300059e-001], - "b":[2.12427997e-001, 3.32212279e-001, 5.17325152e-001], - "c":[8.36015738e-003, 2.95102022e-002, 4.65928982e-007], - "d":[3.66668239e-001, 1.37171827e+000, 3.75768025e+004] - }, - "Li": { - "a": [7.45843816e-002, 7.15382250e-002, 1.45315229e-001], - "b":[8.81151424e-001, 4.59142904e-002, 8.81301714e-001], - "c":[1.12125769e+000, 2.51736525e-003, 3.58434971e-001], - "d":[1.88483665e+001, 1.59189995e-001, 6.12371000e+000] - }, - "Be": { - "a": [6.11642897e-002, 1.25755034e-001, 2.00831548e-001], - "b":[9.90182132e-002, 9.90272412e-002, 1.87392509e+000], - "c":[7.87242876e-001, 1.58847850e-003, 2.73962031e-001], - "d":[9.32794929e+000, 8.91900236e-002, 3.20687658e+000] - }, - "B": { - "a": [1.25716066e-001, 1.73314452e-001, 1.84774811e-001], - "b":[1.48258830e-001, 1.48257216e-001, 3.34227311e+000], - "c":[1.95250221e-001, 5.29642075e-001, 1.08230500e-003], - "d":[1.97339463e+000, 5.70035553e+000, 5.64857237e-002] - }, - "C": { - "a": [2.12080767e-001, 1.99811865e-001, 1.68254385e-001], - "b":[2.08605417e-001, 2.08610186e-001, 5.57870773e+000], - "c":[1.42048360e-001, 3.63830672e-001, 8.35012044e-004], - "d":[1.33311887e+000, 3.80800263e+000, 4.03982620e-002] - }, - "N": { - "a": [5.33015554e-001, 5.29008883e-002, 9.24159648e-002], - "b":[2.90952515e-001, 1.03547896e+001, 1.03540028e+001], - "c":[2.61799101e-001, 8.80262108e-004, 1.10166555e-001], - "d":[2.76252723e+000, 3.47681236e-002, 9.93421736e-001] - }, - "O": { - "a": [3.39969204e-001, 3.07570172e-001, 1.30369072e-001], - "b":[3.81570280e-001, 3.81571436e-001, 1.91919745e+001], - "c":[8.83326058e-002, 1.96586700e-001, 9.96220028e-004], - "d":[7.60635525e-001, 2.07401094e+000, 3.03266869e-002] - }, - "F": { - "a": [2.30560593e-001, 5.26889648e-001, 1.24346755e-001], - "b":[4.80754213e-001, 4.80763895e-001, 3.95306720e+001], - "c":[1.24616894e-003, 7.20452555e-002, 1.53075777e-001], - "d":[2.62181803e-002, 5.92495593e-001, 1.59127671e+000] - }, - "Ne": { - "a": [4.08371771e-001, 4.54418858e-001, 1.44564923e-001], - "b":[5.88228627e-001, 5.88288655e-001, 1.21246013e+002], - "c":[5.91531395e-002, 1.24003718e-001, 1.64986037e-003], - "d":[4.63963540e-001, 1.23413025e+000, 2.05869217e-002] - }, - "Na": { - "a": [1.36471662e-001, 7.70677865e-001, 1.56862014e-001], - "b":[4.99965301e-002, 8.81899664e-001, 1.61768579e+001], - "c":[9.96821513e-001, 3.80304670e-002, 1.27685089e-001], - "d":[2.00132610e+001, 2.60516254e-001, 6.99559329e-001] - }, - "Mg": { - "a": [3.04384121e-001, 7.56270563e-001, 1.01164809e-001], - "b":[8.42014377e-002, 1.64065598e+000, 2.97142975e+001], - "c":[3.45203403e-002, 9.71751327e-001, 1.20593012e-001], - "d":[2.16596094e-001, 1.21236852e+001, 5.60865838e-001] - }, - "Al": { - "a": [7.77419424e-001, 5.78312036e-002, 4.26386499e-001], - "b":[2.71058227e+000, 7.17532098e+001, 9.13331555e-002], - "c":[1.13407220e-001, 7.90114035e-001, 3.23293496e-002], - "d":[4.48867451e-001, 8.66366718e+000, 1.78503463e-001] - }, - "Si": { - "a": [1.06543892e+000, 1.20143691e-001, 1.80915263e-001], - "b":[1.04118455e+000, 6.87113368e+001, 8.87533926e-002], - "c":[1.12065620e+000, 3.05452816e-002, 1.59963502e+000], - "d":[3.70062619e+000, 2.14097897e-001, 9.99096638e+000] - }, - "P": { - "a": [1.05284447e+000, 2.99440284e-001, 1.17460748e-001], - "b":[1.31962590e+000, 1.28460520e-001, 1.02190163e+002], - "c":[9.60643452e-001, 2.63555748e-002, 1.38059330e+000], - "d":[2.87477555e+000, 1.82076844e-001, 7.49165526e+000] - }, - "S": { - "a": [1.01646916e+000, 4.41766748e-001, 1.21503863e-001], - "b":[1.69181965e+000, 1.74180288e-001, 1.67011091e+002], - "c":[8.27966670e-001, 2.33022533e-002, 1.18302846e+000], - "d":[2.30342810e+000, 1.56954150e-001, 5.85782891e+000] - }, - "Cl": { - "a": [9.44221116e-001, 4.37322049e-001, 2.54547926e-001], - "b":[2.40052374e-001, 9.30510439e+000, 9.30486346e+000], - "c":[5.47763323e-002, 8.00087488e-001, 1.07488641e-002], - "d":[1.68655688e-001, 2.97849774e+000, 6.84240646e-002] - }, - "Ar": { - "a": [1.06983288e+000, 4.24631786e-001, 2.43897949e-001], - "b":[2.87791022e-001, 1.24156957e+001, 1.24158868e+001], - "c":[4.79446296e-002, 7.64958952e-001, 8.23128431e-003], - "d":[1.36979796e-001, 2.43940729e+000, 5.27258749e-002] - }, - "K": { - "a": [6.92717865e-001, 9.65161085e-001, 1.48466588e-001], - "b":[7.10849990e+000, 3.57532901e-001, 3.93763275e-002], - "c":[2.64645027e-002, 1.80883768e+000, 5.43900018e-001], - "d":[1.03591321e-001, 3.22845199e+001, 1.67791374e+000] - }, - "Ca": { - "a": [3.66902871e-001, 8.66378999e-001, 6.67203300e-001], - "b":[6.14274129e-002, 5.70881727e-001, 7.82965639e+000], - "c":[4.87743636e-001, 1.82406314e+000, 2.20248453e-002], - "d":[1.32531318e+000, 2.10056032e+001, 9.11853450e-002] - }, - "Sc": { - "a": [3.78871777e-001, 9.00022505e-001, 7.15288914e-001], - "b":[6.98910162e-002, 5.21061541e-001, 7.87707920e+000], - "c":[1.88640973e-002, 4.07945949e-001, 1.61786540e+000], - "d":[8.17512708e-002, 1.11141388e+000, 1.80840759e+001] - }, - "Ti": { - "a": [3.62383267e-001, 9.84232966e-001, 7.41715642e-001], - "b":[7.54707114e-002, 4.97757309e-001, 8.17659391e+000], - "c":[3.62555269e-001, 1.49159390e+000, 1.61659509e-002], - "d":[9.55524906e-001, 1.62221677e+001, 7.33140800e+000] - }, - "V":{ - "a": [3.52961378e-001, 7.46791014e-001, 1.08364068e+000], - "b":[8.19204103e-002, 8.81189511e+000, 5.10646075e-001], - "c": [1.39013610e+000, 3.31273356e-001, 1.40422612e-002], - "d": [1.48901841e+001, 8.38543079e-001, 6.57432678e-002] - }, - "Cr": { - "a": [1.34348379e+000, 5.07040328e-001, 4.26358955e-001], - "b":[1.25814353e+000, 1.15042811e+001, 8.53660389e-002], - "c":[1.17241826e-002, 5.11966516e-001, 3.38285828e-001], - "d":[6.00177061e-002, 1.53772451e+000, 6.62418319e-001] - }, - "Mn": { - "a": [3.26697613e-001, 7.17297000e-001, 1.33212464e+000], - "b":[8.88813083e-002, 1.11300198e+001, 5.82141104e-001], - "c":[2.80801702e-001, 1.15499241e+000, 1.11984488e-002], - "d":[6.71583145e-001, 1.26825395e+001, 5.32334467e-002] - }, - "Fe": { - "a": [3.13454847e-001, 6.89290016e-001, 1.47141531e+000], - "b":[8.99325756e-002, 1.30366038e+001, 6.33345291e-001], - "c":[1.03298688e+000, 2.58280285e-001, 1.03460690e-002], - "d":[1.16783425e+001, 6.09116446e-001, 4.81610627e-002] - }, - "Co": { - "a": [3.15878278e-001, 1.60139005e+000, 6.56394338e-001], - "b":[9.46683246e-002, 6.99436449e-001, 1.56954403e+001], - "c":[9.36746624e-003, 9.77562646e-001, 2.38378578e-001], - "d":[1.09392410e+001, 4.37446816e-002, 5.56286483e-001] - }, - "Ni": { - "a": [1.72254630e+000, 3.29543044e-001, 6.23007200e-001], - "b":[7.76606908e-001, 1.02262360e-001, 1.94156207e+001], - "c":[9.43496513e-003, 8.54063515e-001, 2.21073515e-001], - "d":[3.98684596e-002, 1.04078166e+001, 5.10869330e-001] - }, - "Cu": { - "a": [3.58774531e-001, 1.76181348e+000, 6.36905053e-001], - "b":[1.06153463e-001, 1.01640995e+000, 1.53659093e+001], - "c":[7.44930667e-003, 1.89002347e-001, 2.29619589e-001], - "d":[3.85345989e-002, 3.98427790e-001, 9.01419843e-001] - }, - "Zn": { - "a": [5.70893973e-001, 1.98908856e+000, 3.06060585e-001], - "b":[1.26534614e-001, 2.17781965e+000, 3.78619003e+001], - "c":[2.35600223e-001, 3.97061102e-001, 6.85657228e-003], - "d":[3.67019041e-001, 8.66419596e-001, 3.35778823e-002] - }, - "Ga": { - "a": [6.25528464e-001, 2.05302901e+000, 2.89608120e-001], - "b":[1.10005650e-001, 2.41095786e+000, 4.78685736e+001], - "c":[2.07910594e-001, 3.45079617e-001, 6.55634298e-003], - "d":[3.27807224e-001, 7.43139061e-001, 3.09411369e-002] - }, - "Ge": { - "a": [5.90952690e-001, 5.39980660e-001, 2.00626188e+000], - "b":[1.18375976e-001, 7.18937433e+001, 1.39304889e+000], - "c":[7.49705041e-001, 1.83581347e-001, 9.52190743e-003], - "d":[6.89943350e+000, 3.64667232e-001, 2.69888650e-002] - }, - "As": { - "a": [7.77875218e-001, 5.93848150e-001, 1.95918751e+000], - "b":[1.50733157e-001, 1.42882209e+002, 1.74750339e+000], - "c":[1.79880226e-001, 8.63267222e-001, 9.59053427e-003], - "d":[3.31800852e-001, 5.85490274e+000, 2.33777569e-002] - }, - "Se":{ - "a":[9.58390681e-001, 6.03851342e-001, 1.90828931e+000], - "b":[1.83775557e-001, 1.96819224e+002, 2.15082053e+000], - "c":[1.73885956e-001, 9.35265145e-001, 8.62254658e-003], - "d":[3.00006024e-001, 4.92471215e+000, 2.12308108e-002] - }, - "Br": { - "a": [1.14136170e+000, 5.18118737e-001, 1.85731975e+000], - "b": [2.18708710e-001, 1.93916682e+002, 2.65755396e+000], - "c": [1.68217399e-001, 9.75705606e-001, 7.24187871e-003], - "d": [2.71719918e-001, 4.19482500e+000, 1.99325718e-002] - }, - "Kr": { - "a": [3.24386970e-001, 1.31732163e+000, 1.79912614e+000], - "b": [6.31317973e+001, 2.54706036e-001, 3.23668394e+000], - "c": [4.29961425e-003, 1.00429433e+000, 1.62188197e-001], - "d": [1.98965610e-002, 3.61094513e+000, 2.45583672e-001] - }, - "Rb": { - "a": [2.90445351e-001, 2.44201329e+000, 7.69435449e-001], - "b": [3.68420227e-002, 1.16013332e+000, 1.69591472e+001], - "c": [1.58687000e+000, 2.81617593e-003, 1.28663830e-001], - "d": [2.53082574e+000, 1.88577417e-002, 2.10753969e-001] - }, - "Sr": { - "a": [1.37373086e-002, 1.97548672e+000, 1.59261029e+000], - "b": [1.87469061e-002, 6.36079230e+000, 2.21992482e-001], - "c": [1.73263882e-001, 4.66280378e+000, 1.61265063e-003], - "d": [2.01624958e-001, 2.53027803e+001, 1.53610568e-002] - }, - "Y": { - "a": [6.75302747e-001, 4.70286720e-001, 2.63497677e+000], - "b": [6.54331847e-002, 1.06108709e+002, 2.06643540e+000], - "c": [1.09621746e-001, 9.60348773e-001, 5.28921555e-003], - "d": [1.93131925e-001, 1.63310938e+000, 1.66083821e-002] - }, - "Zr": { - "a": [2.64365505e+000, 5.54225147e-001, 7.61376625e-001], - "b":[2.20202699e+000, 1.78260107e+002, 7.67218745e-002], - "c":[6.02946891e-003, 9.91630530e-002, 9.56782020e-001], - "d":[1.55143296e-002, 1.76175995e-001, 1.54330682e+000] - }, - "Nb": { - "a": [6.59532875e-001, 1.84545854e+000, 1.25584405e+000], - "b": [8.66145490e-002, 5.94774398e+000, 6.40851475e-001], - "c": [1.22253422e-001, 7.06638328e-001, 2.62381591e-003], - "d": [1.66646050e-001, 1.62853268e+000, 8.26257859e-003] - }, - "Mo": { - "a": [6.10160120e-001, 1.26544000e+000, 1.97428762e+000], - "b": [9.11628054e-002, 5.06776025e-001, 5.89590381e+000], - "c": [6.48028962e-001, 2.60380817e-003, 1.13887493e-001], - "d": [1.46634108e+000, 7.84336311e-003, 1.55114340e-001] - }, - "Tc": { - "a": [8.55189183e-001, 1.66219641e+000, 1.45575475e+000], - "b": [1.02962151e-001, 7.64907000e+000, 1.01639987e+000], - "c": [1.05445664e-001, 7.71657112e-001, 2.20992635e-003], - "d": [1.42303338e-001, 1.34659349e+000, 7.90358976e-003] - }, - "Ru": { - "a": [4.70847093e-001, 1.58180781e+000, 2.02419818e+000], - "b": [9.33029874e-002, 4.52831347e-001, 7.11489023e+000], - "c": [1.97036257e-003, 6.26912639e-001, 1.02641320e-001], - "d": [7.56181595e-003, 1.25399858e+000, 1.33786087e-001] - }, - "Rh": { - "a": [4.20051553e-001, 1.76266507e+000, 2.02735641e+000], - "b": [9.38882628e-002, 4.64441687e-001, 8.19346046e+000], - "c": [1.45487176e-003, 6.22809600e-001, 9.91529915e-002], - "d": [7.82704517e-003, 1.17194153e+000, 1.24532839e-001] - }, - "Pd": { - "a": [2.10475155e+000, 2.03884487e+000, 1.82067264e-001], - "b": [8.68606470e+000, 3.78924449e-001, 1.42921634e-001], - "c": [9.52040948e-002, 5.91445248e-001, 1.13328676e-003], - "d": [1.17125900e-001, 1.07843808e+000, 7.80252092e-003] - }, - "Ag": { - "a": [2.07981390e+000, 4.43170726e-001, 1.96515215e+000], - "b": [9.92540297e+000, 1.04920104e-001, 6.40103839e-001], - "c": [5.96130591e-001, 4.78016333e-001, 9.46458470e-002], - "d": [8.89594790e-001, 1.98509407e+000, 1.12744464e-001] - }, - "Cd": { - "a": [1.63657549e+000, 2.17927989e+000, 7.71300690e-001], - "b": [1.24540381e+001, 1.45134660e+000, 1.26695757e-001], - "c": [6.64193880e-001, 7.64563285e-001, 8.61126689e-002], - "d": [7.77659202e-001, 1.66075210e+000, 1.05728357e-001] - }, - "In": { - "a": [2.24820632e+000, 1.64706864e+000, 7.88679265e-001], - "b": [1.51913507e+000, 1.30113424e+001, 1.06128184e-001], - "c": [8.12579069e-002, 6.68280346e-001, 6.38467475e-001], - "d": [9.94045620e-002, 1.49742063e+000, 7.18422635e-001] - }, - "Sn": { - "a": [2.16644620e+000, 6.88691021e-001, 1.92431751e+000], - "b": [1.13174909e+001, 1.10131285e-001, 6.74464853e-001], - "c": [5.65359888e-001, 9.18683861e-001, 7.80542213e-002], - "d": [7.33564610e-001, 1.02310312e+001, 9.31104308e-002] - }, - "Sb": { - "a": [1.73662114e+000, 9.99871380e-001, 2.13972409e+000], - "b": [8.84334719e-001, 1.38462121e-001, 1.19666432e+001], - "c": [5.60566526e-001, 9.93772747e-001, 7.37374982e-002], - "d": [6.72672880e-001, 8.72330411e+000, 8.78577715e-002] - }, - "Te": { - "a": [2.09383882e+000, 1.56940519e+000, 1.30941993e+000], - "b": [1.26856869e+001, 1.21236537e+000, 1.66633292e-001], - "c": [6.98067804e-002, 1.04969537e+000, 5.55594354e-001], - "d": [8.30817576e-002, 7.43147857e+000, 6.17487676e-001] - }, - "I": { - "a": [1.60186925e+000, 1.98510264e+000, 1.48226200e+000], - "b": [1.95031538e-001, 1.36976183e+001, 1.80304795e+000], - "c": [5.53807199e-001, 1.11728722e+000, 6.60720847e-002], - "d": [5.67912340e-001, 6.40879878e+000, 7.86615429e-002] - }, - "Xe": { - "a": [1.60015487e+000, 1.71644581e+000, 1.84968351e+000], - "b": [2.92913354e+000, 1.55882990e+001, 2.22525983e-001], - "c": [6.23813648e-002, 1.21387555e+000, 5.54051946e-001], - "d": [7.45581223e-002, 5.56013271e+000, 5.21994521e-001] - }, - "Cs": { - "a": [2.95236854e+000, 4.28105721e-001, 1.89599233e+000], - "b": [6.01461952e+000, 4.64151246e+001, 1.80109756e-001], - "c": [5.48012938e-002, 4.70838600e+000, 5.90356719e-001], - "d": [7.12799633e-002, 4.56702799e+001, 4.70236310e-001] - }, - "Ba": { - "a": [3.19434243e+000, 1.98289586e+000, 1.55121052e-001], - "b": [9.27352241e+000, 2.28741632e-001, 3.82000231e-002], - "c": [6.73222354e-002, 4.48474211e+000, 5.42674414e-001], - "d": [7.30961745e-002, 2.95703565e+001, 4.08647015e-001] - }, - "La": { - "a": [2.05036425e+000, 1.42114311e-001, 3.23538151e+000], - "b": [2.20348417e-001, 3.96438056e-002, 9.56979169e+000], - "c": [6.34683429e-002, 3.97960586e+000, 5.20116711e-001], - "d": [6.92443091e-002, 2.53178406e+001, 3.83614098e-001] - }, - "Ce": { - "a": [3.22990759e+000, 1.57618307e-001, 2.13477838e+000], - "b": [9.94660135e+000, 4.15378676e-002, 2.40480572e-001], - "c": [5.01907609e-001, 3.80889010e+000, 5.96625028e-002], - "d": [3.66252019e-001, 2.43275968e+001, 6.59653503e-002] - }, - "Pr": { - "a": [1.58189324e-001, 3.18141995e+000, 2.27622140e+000], - "b": [3.91309056e-002, 1.04139545e+001, 2.81671757e-001], - "c": [3.97705472e+000, 5.58448277e-002, 4.85207954e-001], - "d": [2.61872978e+001, 6.30921695e-002, 3.54234369e-001] - }, - "Nd": { - "a": [1.81379417e-001, 3.17616396e+000, 2.35221519e+000], - "b": [4.37324793e-002, 1.07842572e+001, 3.05571833e-001], - "c": [3.83125763e+000, 5.25889976e-002, 4.70090742e-001], - "d": [2.54745408e+001, 6.02676073e-002, 3.39017003e-001] - }, - "Pm": { - "a": [1.92986811e-001, 2.43756023e+000, 3.17248504e+000], - "b": [4.37785970e-002, 3.29336996e-001, 1.11259996e+001], - "c": [3.58105414e+000, 4.56529394e-001, 4.94812177e-002], - "d": [2.46709586e+001, 3.24990282e-001, 5.76553100e-002] - }, - "Sm": { - "a": [2.12002595e-001, 3.16891754e+000, 2.51503494e+000], - "b": [4.57703608e-002, 1.14536599e+001, 3.55561054e-001], - "c": [4.44080845e-001, 3.36742101e+000, 4.65652543e-002], - "d": [3.11953363e-001, 2.40291435e+001, 5.52266819e-002] - }, - "Eu": { - "a": [2.59355002e+000, 3.16557522e+000, 2.29402652e-001], - "b": [3.82452612e-001, 1.17675155e+001, 4.76642249e-002], - "c": [4.32257780e-001, 3.17261920e+000, 4.37958317e-002], - "d": [2.99719833e-001, 2.34462738e+001, 5.29440680e-002] - }, - "Gd": { - "a": [3.19144939e+000, 2.55766431e+000, 3.32681934e-001], - "b": [1.20224655e+001, 4.08338876e-001, 5.85819814e-002], - "c": [4.14243130e-002, 2.61036728e+000, 4.20526863e-001], - "d": [5.06771477e-002, 1.99344244e+001, 2.85686240e-001] - }, - "Tb": { - "a": [2.59407462e-001, 3.16177855e+000, 2.75095751e+000], - "b": [5.04689354e-002, 1.23140183e+001, 4.38337626e-001], - "c": [2.79247686e+000, 3.85931001e-002, 4.10881708e-001], - "d": [2.23797309e+001, 4.87920992e-002, 2.77622892e-001] - }, - "Dy": { - "a": [3.16055396e+000, 2.82751709e+000, 2.75140255e-001], - "b": [1.25470414e+001, 4.67899094e-001, 5.23226982e-002], - "c": [4.00967160e-001, 2.63110834e+000, 3.61333817e-002], - "d": [2.67614884e-001, 2.19498166e+001, 4.68871497e-002] - }, - "Ho": { - "a": [2.88642467e-001, 2.90567296e+000, 3.15960159e+000], - "b": [5.40507687e-002, 4.97581077e-001, 1.27599505e+001], - "c": [3.91280259e-001, 2.48596038e+000, 3.37664478e-002], - "d": [2.58151831e-001, 2.15400972e+001, 4.50664323e-002] - }, - "Er": { - "a": [3.15573213e+000, 3.11519560e-001, 2.97722406e+000], - "b": [1.29729009e+001, 5.81399387e-002, 5.31213394e-001], - "c": [3.81563854e-001, 2.40247532e+000, 3.15224214e-002], - "d": [2.49195776e-001, 2.13627616e+001, 4.33253257e-002] - }, - "Tm": { - "a": [3.15591970e+000, 3.22544710e-001, 3.05569053e+000], - "b": [1.31232407e+001, 5.97223323e-002, 5.61876773e-001], - "c": [2.92845100e-002, 3.72487205e-001, 2.27833695e+000], - "d": [4.16534255e-002, 2.40821967e-001, 2.10034185e+001] - }, - "Yb": { - "a": [3.10794704e+000, 3.14091221e+000, 3.75660454e-001], - "b": [6.06347847e-001, 1.33705269e+001, 7.29814740e-002], - "c": [3.61901097e-001, 2.45409082e+000, 2.72383990e-002], - "d": [2.32652051e-001, 2.12695209e+001, 3.99969597e-002] - }, - "Lu": { - "a": [3.11446863e+000, 5.39634353e-001, 3.06460915e+000], - "b": [1.38968881e+001, 8.91708508e-002, 6.79919563e-001], - "c": [2.58563745e-002, 2.13983556e+000, 3.47788231e-001], - "d": [3.82808522e-002, 1.80078788e+001, 2.22706591e-001] - }, - "Hf": { - "a": [3.01166899e+000, 3.16284788e+000, 6.33421771e-001], - "b": [7.10401889e-001, 1.38262192e+001, 9.48486572e-002], - "c": [3.41417198e-001, 1.53566013e+000, 2.40723773e-002], - "d": [2.14129678e-001, 1.55298698e+001, 3.67833690e-002] - }, - "Ta": { - "a": [3.20236821e+000, 8.30098413e-001, 2.86552297e+000], - "b": [1.38446369e+001, 1.18381581e-001, 7.66369118e-001], - "c": [2.24813887e-002, 1.40165263e+000, 3.33740596e-001], - "d": [3.52934622e-002, 1.46148877e+001, 2.05704486e-001] - }, - "W": { - "a": [9.24906855e-001, 2.75554557e+000, 3.30440060e+000], - "b": [1.28663377e-001, 7.65826479e-001, 1.34471170e+001], - "c": [3.29973862e-001, 1.09916444e+000, 2.06498883e-002], - "d": [1.98218895e-001, 1.35087534e+001, 3.38918459e-002] - }, - "Re": { - "a": [1.96952105e+000, 1.21726619e+000, 4.10391685e+000], - "b": [4.98830620e+001, 1.33243809e-001, 1.84396916e+000], - "c": [2.90791978e-002, 2.30696669e-001, 6.08840299e-001], - "d": [2.84192813e-002, 1.90968784e-001, 1.37090356e+000] - }, - "Os": { - "a": [2.06385867e+000, 1.29603406e+000, 3.96920673e+000], - "b": [4.05671697e+001, 1.46559047e-001, 1.82561596e+000], - "c": [2.69835487e-002, 2.31083999e-001, 6.30466774e-001], - "d": [2.84172045e-002, 1.79765184e-001, 1.38911543e+000] - }, - "Ir": { - "a": [2.21522726e+000, 1.37573155e+000, 3.78244405e+000], - "b": [3.24464090e+001, 1.60920048e-001, 1.78756553e+000], - "c": [2.44643240e-002, 2.36932016e-001, 6.48471412e-001], - "d": [2.82909938e-002, 1.70692368e-001, 1.37928390e+000] - }, - "Pt": { - "a": [9.84697940e-001, 2.73987079e+000, 3.61696715e+000], - "b": [1.60910839e-001, 7.18971667e-001, 1.29281016e+001], - "c": [3.02885602e-001, 2.78370726e-001, 1.52124129e-002], - "d": [1.70134854e-001, 1.49862703e+000, 2.83510822e-002] - }, - "Au": { - "a": [9.61263398e-001, 3.69581030e+000, 2.77567491e+000], - "b": [1.70932277e-001, 1.29335319e+001, 6.89997070e-001], - "c": [2.95414176e-001, 3.11475743e-001, 1.43237267e-002], - "d": [1.63525510e-001, 1.39200901e+000, 2.71265337e-002] - }, - "Hg": { - "a": [1.29200491e+000, 2.75161478e+000, 3.49387949e+000], - "b": [1.83432865e-001, 9.42368371e-001, 1.46235654e+001], - "c": [2.77304636e-001, 4.30232810e-001, 1.48294351e-002], - "d": [1.55110144e-001, 1.28871670e+000, 2.61903834e-002] - }, - "Tl": { - "a": [3.75964730e+000, 3.21195904e+000, 6.47767825e-001], - "b": [1.35041513e+001, 6.66330993e-001, 9.22518234e-002], - "c": [2.76123274e-001, 3.18838810e-001, 1.31668419e-002], - "d": [1.50312897e-001, 1.12565588e+000, 2.48879842e-002] - }, - "Pb": { - "a": [1.00795975e+000, 3.09796153e+000, 3.61296864e+000], - "b": [1.17268427e-001, 8.80453235e-001, 1.47325812e+001], - "c": [2.62401476e-001, 4.05621995e-001, 1.31812509e-002], - "d": [1.43491014e-001, 1.04103506e+000, 2.39575415e-002] - }, - "Bi": { - "a": [1.59826875e+000, 4.38233925e+000, 2.06074719e+000], - "b": [1.56897471e-001, 2.47094692e+000, 5.72438972e+001], - "c": [1.94426023e-001, 8.22704978e-001, 2.33226953e-002], - "d": [1.32979109e-001, 9.56532528e-001, 2.23038435e-002] - }, - "Po": { - "a": [1.71463223e+000, 2.14115960e+000, 4.37512413e+000], - "b": [9.79262841e+001, 2.10193717e-001, 3.66948812e+000], - "c": [2.16216680e-002, 1.97843837e-001, 6.52047920e-001], - "d": [1.98456144e-002, 1.33758807e-001, 7.80432104e-001] - }, - "At": { - "a": [1.48047794e+000, 2.09174630e+000, 4.75246033e+000], - "b": [1.25943919e+002, 1.83803008e-001, 4.19890596e+000], - "c": [1.85643958e-002, 2.05859375e-001, 7.13540948e-001], - "d": [1.81383503e-002, 1.33035404e-001, 7.03031938e-001] - }, - "Rn": { - "a": [6.30022295e-001, 3.80962881e+000, 3.89756067e+000], - "b": [1.40909762e-001, 3.08515540e+001, 6.51559763e-001], - "c": [2.40755100e-001, 2.62868577e+000, 3.14285931e-002], - "d": [1.08899672e-001, 6.42383261e+000, 2.42346699e-002] - }, - "Fr": { - "a": [5.23288135e+000, 2.48604205e+000, 3.23431354e-001], - "b": [8.60599536e+000, 3.04543982e-001, 3.87759096e-002], - "c": [2.55403596e-001, 5.53607228e-001, 5.75278889e-003], - "d": [1.28717724e-001, 5.36977452e-001, 1.29417790e-002] - }, - "Ra": { - "a": [1.44192685e+000, 3.55291725e+000, 3.91259586e+000], - "b": [1.18740873e-001, 1.01739750e+000, 6.31814783e+001], - "c": [2.16173519e-001, 3.94191605e+000, 4.60422605e-002], - "d": [9.55806441e-002, 3.50602732e+001, 2.20850385e-002] - }, - "Ac": { - "a": [1.45864127e+000, 4.18945405e+000, 3.65866182e+000], - "b": [1.07760494e-001, 8.89090649e+001, 1.05088931e+000], - "c": [2.08479229e-001, 3.16528117e+000, 5.23892556e-002], - "d": [9.09335557e-002, 3.13297788e+001, 2.08807697e-002] - }, - "Th": { - "a": [1.19014064e+000, 2.55380607e+000, 4.68110181e+000], - "b": [7.73468729e-002, 6.59693681e-001, 1.28013896e+001], - "c": [2.26121303e-001, 3.58250545e-001, 7.82263950e-003], - "d": [1.08632194e-001, 4.56765664e-001, 1.62623474e-002] - }, - "Pa": { - "a": [4.68537504e+000, 2.98413708e+000, 8.91988061e-001], - "b": [1.44503632e+001, 5.56438592e-001, 6.69512914e-002], - "c": [2.24825384e-001, 3.04444846e-001, 9.48162708e-003], - "d": [1.03235396e-001, 4.27255647e-001, 1.77730611e-002] - }, - "U": { - "a": [4.63343606e+000, 3.18157056e+000, 8.76455075e-001], - "b": [1.63377267e+001, 5.69517868e-001, 6.88860012e-002], - "c": [2.21685477e-001, 2.72917100e-001, 1.11737298e-002], - "d": [9.84254550e-002, 4.09470917e-001, 1.86215410e-002] - }, - "Np": { - "a": [4.56773888e+000, 3.40325179e+000, 8.61841923e-001], - "b": [1.90992795e+001, 5.90099634e-001, 7.03204851e-002], - "c": [2.19728870e-001, 2.38176903e-001, 1.38306499e-002], - "d": [9.36334280e-002, 3.93554882e-001, 1.94437286e-002] - }, - "Pu": { - "a": [5.45671123e+000, 1.11687906e-001, 3.30260343e+000], - "b": [1.01892720e+001, 3.98131313e-002, 3.14622212e-001], - "c": [1.84568319e-001, 4.93644263e-001, 3.57484743e+000], - "d": [1.04220860e-001, 4.63080540e-001, 2.19369542e+001] - }, - "Am": { - "a": [5.38321999e+000, 1.23343236e-001, 3.46469090e+000], - "b": [1.07289857e+001, 4.15137806e-002, 3.39326208e-001], - "c": [1.75437132e-001, 3.39800073e+000, 4.69459519e-001], - "d": [9.98932346e-002, 2.11601535e+001, 4.51996970e-001] - }, - "Cm": { - "a": [5.38402377e+000, 3.49861264e+000, 1.88039547e-001], - "b": [1.11211419e+001, 3.56750210e-001, 5.39853583e-002], - "c": [1.69143137e-001, 3.19595016e+000, 4.64393059e-001], - "d": [9.60082633e-002, 1.80694389e+001, 4.36318197e-001] - }, - "Bk": { - "a": [3.66090688e+000, 2.03054678e-001, 5.30697515e+000], - "b": [3.84420906e-001, 5.48547131e-002, 1.17150262e+001], - "c": [1.60934046e-001, 3.04808401e+000, 4.43610295e-001], - "d": [9.21020329e-002, 1.73525367e+001, 4.27132359e-001] - }, - "Cf": { - "a": [3.94150390e+000, 5.16915345e+000, 1.61941074e-001], - "b": [4.18246722e-001, 1.25201788e+001, 4.81540117e-002], - "c": [4.15299561e-001, 2.91761325e+000, 1.51474927e-001], - "d": [4.24913856e-001, 1.90899693e+001, 8.81568925e-002] - }, - "Es": { - "a": [4.09780623e+000, 5.10079393e+000, 1.74617289e-001], - "b": [4.46021145e-001, 1.31768613e+001, 5.02742829e-002], - "c": [2.76774658e+000, 1.44496639e-001, 4.02772109e-001], - "d": [1.84815393e+001, 8.46232592e-002, 4.17640100e-001] - }, - "Fm": { - "a": [4.24934820e+000, 5.03556594e+000, 1.88920613e-001], - "b": [4.75263933e-001, 1.38570834e+001, 5.26975158e-002], - "c": [3.94356058e-001, 2.61213100e+000, 1.38001927e-001], - "d": [4.11193751e-001, 1.78537905e+001, 8.12774434e-002] - }, - "Md": { - "a": [2.00942931e-001, 4.40119869e+000, 4.97250102e+000], - "b": [5.48366518e-002, 5.04248434e-001, 1.45721366e+001], - "c": [2.47530599e+000, 3.86883197e-001, 1.31936095e-001], - "d": [1.72978308e+001, 4.05043898e-001, 7.80821071e-002] - }, - "No": { - "a": [2.16052899e-001, 4.91106799e+000, 4.54862870e+000], - "b": [5.83584058e-002, 1.53264212e+001, 5.34434760e-001], - "c": [2.36114249e+000, 1.26277292e-001, 3.81364501e-001], - "d": [1.68164803e+001, 7.50304633e-002, 3.99305852e-001] - }, - "Lr": { - "a": [4.86738014e+000, 3.19974401e-001, 4.58872425e+000], - "b": [1.60320520e+001, 6.70871138e-002, 5.77039373e-001], - "c": [1.21482448e-001, 2.31639872e+000, 3.79258137e-001], - "d": [7.22275899e-002, 1.41279737e+001, 3.89973484e-001] - } -} diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py index 7e87eff3..4a318cac 100644 --- a/src/quantem/diffraction/polar.py +++ b/src/quantem/diffraction/polar.py @@ -1,11 +1,14 @@ from __future__ import annotations -from collections.abc import Sequence -from typing import Any, List, Union +from collections.abc import Iterable +from pathlib import Path +from typing import Any, Literal, Tuple, Union import matplotlib.pyplot as plt import numpy as np from numpy.typing import NDArray +from scipy.ndimage import gaussian_filter1d +from scipy.optimize import curve_fit from quantem.core.datastructures.dataset2d import Dataset2d from quantem.core.datastructures.dataset3d import Dataset3d @@ -14,15 +17,23 @@ from quantem.core.io.serialize import AutoSerialize from quantem.core.utils.validators import ensure_valid_array +KIRKLAND_PARAMS_PATH = Path(__file__).with_name("kirkland_params.json") -class RDF(AutoSerialize): + +class PairDistributionFunction(AutoSerialize): """ - Radial distribution / fluctuation electron microscopy analysis helper. + Pair distribution function (PDF) utilities for diffraction / 4D-STEM data. This class wraps a 4D-STEM (or 2D diffraction) dataset and stores a polar-transformed representation as a Polar4dstem instance in `self.polar`. - Analysis methods (radial statistics, PDF, FEM, clustering, etc.) are - provided as stubs for now and will be implemented in future revisions. + The PDF pipeline provides methods to compute: + + - azimuthal integration to obtain I(k) + - background fitting using a parametric model in k^2 / k^4 + - formation of F(k) and a windowed sine transform to obtain G(r) + - optional density estimation and origin correction (Yoshimoto & Omote-style iteration) + - basic plotting helpers for I(k), background, F(k), G(r), and g(r) + Some analysis methods (FEM, clustering, etc.) will be implemented in future revisions. """ _token = object() @@ -35,7 +46,7 @@ def __init__( ): if _token is not self._token: raise RuntimeError( - "Use RadialDistributionFunction.from_data() to instantiate this class." + "Use PairDistributionFunction.from_data() to instantiate this class." ) super().__init__() @@ -48,7 +59,7 @@ def __init__( self.radial_var_norm: NDArray | None = None self.pdf_r: NDArray | None = None - self.pdf_reduced: NDArray | None = None + self.reduced_pdf: NDArray | None = None self.pdf: NDArray | None = None self.Sk: NDArray | None = None @@ -65,6 +76,7 @@ def from_data( cls, data: Union[NDArray, Dataset2d, Dataset3d, Dataset4dstem, Polar4dstem], *, + find_origin: bool = True, origin_row: float | None = None, origin_col: float | None = None, ellipse_params: tuple[float, float, float] | None = None, @@ -73,9 +85,10 @@ def from_data( radial_max: float | None = None, radial_step: float = 1.0, two_fold_rotation_symmetry: bool = False, - ) -> "RadialDistributionFunction": + ): """ - Create a RadialDistributionFunction object from various input types. + -> "PairDistributionFunction" + Create a PairDistributionFunction object from various input types. Parameters ---------- @@ -86,9 +99,16 @@ def from_data( - Dataset2d - Dataset4dstem - Polar4dstem + + If a :class:`Polar4dstem` is provided, it is used directly and no origin finding + or polar transform is performed. + find_origin + If True, finds the origin for each scan position by calling + :meth:`find_origin`. If False, `origin_row`/`origin_col` are used (or default + to the image center). origin_row, origin_col - Diffraction-space origin (in pixels). If None, defaults to the - central pixel of the diffraction pattern. + Diffraction-space origin (in pixels), used only if `find_origin=False`. If None, + defaults to the central pixel of the diffraction pattern. Other parameters Passed through to Dataset4dstem.polar_transform when needed. """ @@ -100,14 +120,27 @@ def from_data( # Dataset4dstem input: polar-transform it if isinstance(data, Dataset4dstem): scan_y, scan_x, ny, nx = data.array.shape - if origin_row is None: - origin_row = (ny - 1) / 2.0 - if origin_col is None: - origin_col = (nx - 1) / 2.0 + if find_origin: + origin_array = cls.find_origin( + data, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + else: + if origin_row is None: + origin_row = (ny - 1) / 2.0 + if origin_col is None: + origin_col = (nx - 1) / 2.0 + origin_array = np.zeros((scan_y, scan_x, 2), dtype=float) + origin_array[..., 0] = origin_row + origin_array[..., 1] = origin_col polar = data.polar_transform( - origin_row=origin_row, - origin_col=origin_col, + origin_array=origin_array, ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, radial_min=radial_min, @@ -121,12 +154,14 @@ def from_data( if isinstance(data, Dataset2d): arr2d = data.array if arr2d.ndim != 2: - raise ValueError("Dataset2d for RDF must be 2D.") + raise ValueError("Dataset2d for PairDistributionFunction must be 2D.") arr4 = arr2d[None, None, ...] # (1, 1, ky, kx) ds4 = Dataset4dstem.from_array( array=arr4, - name=f"{data.name}_as4dstem" if getattr(data, "name", None) else "rdf_4dstem_from_2d", + name=f"{data.name}_as4dstem" + if getattr(data, "name", None) + else "rdf_4dstem_from_2d", origin=np.concatenate( [np.zeros(2, dtype=float), np.asarray(data.origin, dtype=float)] ), @@ -137,14 +172,27 @@ def from_data( signal_units=data.signal_units, ) ny, nx = ds4.array.shape[-2:] - if origin_row is None: - origin_row = (ny - 1) / 2.0 - if origin_col is None: - origin_col = (nx - 1) / 2.0 + + if find_origin: + origin_array = cls.find_origin( + ds4, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + ) + else: + if origin_row is None: + origin_row = (ny - 1) / 2.0 + if origin_col is None: + origin_col = (nx - 1) / 2.0 + origin_array = np.zeros((1, 1, 2), dtype=float) + origin_array[0, 0] = [origin_row, origin_col] polar = ds4.polar_transform( - origin_row=origin_row, - origin_col=origin_col, + origin_array=origin_array, ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, radial_min=radial_min, @@ -157,17 +205,16 @@ def from_data( # Dataset3d input: not yet specified how to interpret if isinstance(data, Dataset3d): raise NotImplementedError( - "RadialDistributionFunction.from_data does not yet support Dataset3d inputs." + "PairDistributionFunction.from_data does not yet support Dataset3d inputs." ) # Numpy array input arr = ensure_valid_array(data) if arr.ndim == 2: ds2 = Dataset2d.from_array(arr, name="rdf_input_2d") + return cls.from_data( ds2, - origin_row=origin_row, - origin_col=origin_col, ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, radial_min=radial_min, @@ -179,8 +226,6 @@ def from_data( ds4 = Dataset4dstem.from_array(arr, name="rdf_input_4dstem") return cls.from_data( ds4, - origin_row=origin_row, - origin_col=origin_col, ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, radial_min=radial_min, @@ -189,9 +234,134 @@ def from_data( two_fold_rotation_symmetry=two_fold_rotation_symmetry, ) else: - raise ValueError( - "RadialDistributionFunction.from_data only supports 2D or 4D arrays." - ) + raise ValueError("PairDistributionFunction.from_data only supports 2D or 4D arrays.") + + @staticmethod + def find_origin( + data, + *, + ellipse_params=None, + num_annular_bins=180, + radial_min=0.0, + radial_max=None, + radial_step=1.0, + two_fold_rotation_symmetry=False, + ): + """ + Automatic diffraction center finding by minmizing the standard deviation along the annular direction. + + For each scan position, this routine: + 1) Computes a polar transform at an initial origin (image center). + 2) Evaluates the sum of the standard deviation across angle (phi) over a mid-radius band. + 3) Performs a local search over neighboring pixel origins until the + objective no longer improves. + + Parameters + ---------- + data + A :class:`Dataset4dstem` object + ellipse_params, num_annular_bins, radial_min, radial_max, radial_step, two_fold_rotation_symmetry + Forwarded to the polar transform call. + + Returns + ------- + origin_array : np.ndarray + Array of shape (scan_y, scan_x, 2) containing (row, col) origin estimates in pixels. + + """ + if len(data.array.shape) == 2: + ny, nx = data.array.shape + scan_y, scan_x = 1, 1 + elif len(data.array.shape) == 4: + scan_y, scan_x, ny, nx = data.array.shape + else: + raise ValueError("find_origin only supports 2D or 4D-STEM datasets for now.") + + origin_array = np.zeros((scan_y, scan_x, 2), dtype=float) + + max_steps = 1000 # prevent infinite loops + + # start with center of image for now + estimated_origin_row = (ny - 1) / 2.0 + estimated_origin_col = (nx - 1) / 2.0 + test_origin = np.array([[[estimated_origin_row, estimated_origin_col]]], dtype=float) + + for y_pos in range(scan_y): + for x_pos in range(scan_x): + # print(f"Finding origin for scan pos ({y_pos}, {x_pos})") + + coords_cache = {} + + polar = data.polar_transform( + origin_array=test_origin, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + scan_pos=(y_pos, x_pos), + ) + + min_r = int(np.floor(0.1 * polar.shape[1])) + max_r = int(np.ceil(0.9 * polar.shape[1])) + std_est_origin = polar[:, min_r:max_r].std(axis=0) + std_est_origin_sum = std_est_origin.sum() + + origin_row = int(round(estimated_origin_row)) + origin_col = int(round(estimated_origin_col)) + coords_cache[(origin_row, origin_col)] = std_est_origin_sum + + if y_pos == 0 and x_pos == 0: + print(f"Initial std sum at estimated origin: {std_est_origin_sum}") + + converged = False + best = std_est_origin_sum + steps = 0 + while not converged and steps < max_steps: + steps += 1 + moved = False + + neighbors = [ + (origin_row + dr, origin_col + dc) + for dr in (-1, 0, 1) + for dc in (-1, 0, 1) + if not (dr == 0 and dc == 0) + ] + neighbors = [(r, c) for (r, c) in neighbors if 0 <= r < ny and 0 <= c < nx] + + for origin_r, origin_c in neighbors: + if (origin_r, origin_c) not in coords_cache: + test_origin = np.array([[[origin_r, origin_c]]], dtype=float) + polar = data.polar_transform( + origin_array=test_origin, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + scan_pos=(y_pos, x_pos), + ) + std_test = polar[:, min_r:max_r].std(axis=0) + coords_cache[(origin_r, origin_c)] = std_test.sum() + + if coords_cache[(origin_r, origin_c)] < best: + origin_row = origin_r + origin_col = origin_c + best = coords_cache[(origin_r, origin_c)] + moved = True + print(f"Moved to ({origin_row}, {origin_col}) with std sum {best}") + + if not moved: + converged = True + + if y_pos == 0 and x_pos == 0: + print(f"Final std sum at found origin ({origin_row}, {origin_col}): {best}") + origin_array[y_pos, x_pos, 0] = origin_row + origin_array[y_pos, x_pos, 1] = origin_col + + return origin_array # ------------------------------------------------------------------ # Convenience accessors @@ -213,48 +383,259 @@ def radial_bins(self) -> Any: """ return self.polar.coords(3) + # ------------------------------------------------------------------ + # Helper functions + # ------------------------------------------------------------------ + def _get_mask_bool(self, mask_realspace): + """ + Normalize a real-space mask specification to a boolean (rx, ry) mask. + + Parameters + ---------- + mask_realspace + - None: no masking + - bool ndarray of shape (rx, ry): True indicates included probe positions + - array-like of shape (2, 2): two opposite (rx, ry) corners defining a rectangle + + Returns + ------- + mask_bool : np.ndarray or None + Boolean mask of shape (rx, ry), or None if `mask_realspace` is None. + """ + mask_bool = None + if mask_realspace is not None: + rx, ry = self.polar.array.shape[:2] + mask_realspace = np.asarray(mask_realspace) + + # mask given as boolean array + if mask_realspace.dtype == bool and mask_realspace.shape == (rx, ry): + mask_bool = mask_realspace + + # mask given as list of corners + elif mask_realspace.shape == (2, 2): + (rx1, ry1), (rx2, ry2) = mask_realspace.astype(int) + rx_min, rx_max = sorted((rx1, rx2)) + ry_min, ry_max = sorted((ry1, ry2)) + + # vectorized bounds check + bad = (rx_min < 0) | (rx_max >= rx) | (ry_min < 0) | (ry_max >= ry) + if bad: + raise ValueError(f"Mask points outside valid range {(rx, ry)}") + + mask_bool = np.zeros((rx, ry), dtype=bool) + mask_bool[rx_min : rx_max + 1, ry_min : ry_max + 1] = True + else: + raise ValueError( + "mask_realspace must be boolean array or two opposite (rx, ry) corner points." + ) + return mask_bool + + @staticmethod + def _scattering_model(k2, c, i0, s0, i1, s1): + """ + Background model used for fitting I(k). + Model form (using k^2 as input): + c + i0 * exp(-k^2 / (2 s0^2)) + i1 * exp(-k^4 / (2 s1^4)) + + Parameters + ---------- + k2 + Array of k^2 values. + c, i0, s0, i1, s1 + Model parameters. + """ + return ( + c + + i0 * np.exp(k2 / (-2.0 * s0**2)) + + i1 * np.exp((k2**2) / (-2.0 * s1**4)) # k2**2 = k^4 + ) + + @staticmethod + def _lorch_window(k, kmin, kmax): + """ + Construct a combined low-q taper and high-q Lorch window. + + The returned window is: + - zero outside [kmin, kmax] + - smoothly rises from 0→1 near kmin using a sin^2 ramp over 10% of the band + - applies a Lorch-style sinc factor over the full in-band region: + sin(pi * k/kmax) / (pi * k/kmax) + """ + # low q taper + edge_frac_low = 0.1 # 10% of range at low-q + edge_width_low = edge_frac_low * (kmax - kmin) + + wk = np.ones_like(k, dtype=float) + low = (k >= kmin) & (k < kmin + edge_width_low) + t = (k[low] - kmin) / edge_width_low + wk[low] = np.sin(0.5 * np.pi * t) ** 2 + wk[k < kmin] = 0.0 + wk[k > kmax] = 0.0 + + # high q taper with Lorch window: w(k) = sin(pi*k/kmax)/(pi*k/kmax) + lorch = np.zeros_like(k, dtype=float) + inband = (k >= kmin) & (k <= kmax) + x = k[inband] / kmax + lorch[inband] = np.where(x == 0, 1.0, np.sin(np.pi * x) / (np.pi * x)) + + wk *= lorch + + return wk + + @staticmethod + def _compute_alpha_beta(Q2d, r2d, G_beta, r_1d): + """ + Compute Yoshimoto-Omote alpha(Q) and beta(Q) integrals used for density estimation. + This is an internal helper that performs the r-integrals via trapezoidal integration. + """ + Qsafe = np.where(Q2d == 0.0, 1e-12, Q2d) + alpha_int = -4 * np.pi * r2d * np.sin(Qsafe * r2d) / Qsafe + beta_int = G_beta[None, :] * np.sin(Qsafe * r2d) / Qsafe + alpha = np.trapz(alpha_int, x=r_1d, axis=1) + beta = np.trapz(beta_int, x=r_1d, axis=1) + return alpha, beta + # ------------------------------------------------------------------ # Analysis method stubs (py4DSTEM-style API) # ------------------------------------------------------------------ - def calculate_radial_statistics( + + # TODO: linting and docstrings + def calculate_radial_mean( self, mask_realspace: NDArray | None = None, - plot_results_mean: bool = False, - plot_results_var: bool = False, - figsize: tuple[float, float] = (8, 4), returnval: bool = False, - returnfig: bool = False, - progress_bar: bool = True, ): """ - Stub for radial statistics (FEM-style) calculation on the polar data. + Calculate the radial mean intensity from the Polar4dSTEM dataset. - Intended to compute radial mean, variance, and normalized variance - from self.polar. Not implemented yet. - """ - raise NotImplementedError("calculate_radial_statistics is not implemented yet.") + The polar array is assumed to have shape (scan_y, scan_x, phi, k). + This method computes, for each scan position, the mean over the azimuthal + axis (phi), then averages across scan positions to produce a single 1D + radial curve. This result is stored in ``self.radial_mean``. - def plot_radial_mean( - self, - log_x: bool = False, - log_y: bool = False, - figsize: tuple[float, float] = (8, 4), - returnfig: bool = False, - ): - """ - Stub for plotting radial mean intensity vs scattering vector. + If a real-space mask is provided, only the selected scan positions are + used in the scan-position average. + + Parameters + ---------- + mask_realspace : NDArray or None, optional + Boolean mask in real space used to select probe positions. + If ``None``, all probe positions are used. + Must have shape (scan_y, scan_x) where True means "include". + (If using rectangle-corner inputs, pass them through + `_get_mask_bool` before calling this method.) + returnval : bool, optional + If True, return the computed 1D radial mean array. + + Returns + ------- + radial_mean : np.ndarray or None + If `returnval=True`, returns the 1D radial mean intensity (Nk,). + Otherwise returns None unless `returnfig=True`. """ - raise NotImplementedError("plot_radial_mean is not implemented yet.") - def plot_radial_var_norm( - self, - figsize: tuple[float, float] = (8, 4), - returnfig: bool = False, - ): + # init radial data array + if mask_realspace is None: + # calculate intensity over q-range for each probe position + radial_probe = self.polar.array.mean(axis=2) # axis 0: ry, 1: rx, 2: theta, 3: q + # average over all probe positions + self.radial_mean = np.mean(radial_probe, axis=(0, 1)) + + elif mask_realspace is not None: + masked_polar = self.polar.array[mask_realspace] # (N_valid, N_theta, N_k) + radial_probe = masked_polar.mean(axis=1) + # average over all probe positions, only those unmasked + self.radial_mean = radial_probe.mean(axis=0) + + if returnval: + return self.radial_mean + else: + return + + def fit_bg(self, Ik, kmin, kmax): """ - Stub for plotting normalized radial variance vs scattering vector. + Fit a smooth background B(k) to a radial intensity curve I(k) using + non-linear least squares (SciPy `curve_fit`), with a weighting that + downweights the low-k region and emphasizes higher k. + + The fitted function uses the following form: + B(k) = c + + i0 * exp(-k^2 / (2 s0^2)) + + i1 * exp(-k^4 / (2 s1^4)) + + Parameters + ---------- + Ik + 1D radial intensity array (Nk,). Typically produced by + :meth:`calculate_radial_mean`. + kmin, kmax + k-range (in the same units as the internally constructed `k` grid) + used to build the low-k weighting mask. (Currently k is derived from + `self.qq` with a calibration factor.) + + Returns + ------- + bg : np.ndarray + Fitted background curve B(k), shape (Nk,). + f : np.ndarray + Background minus the constant offset, f(k) = B(k) - c, or functionally + similar to ⟨f⟩²(k) """ - raise NotImplementedError("plot_radial_var_norm is not implemented yet.") + + k = self.qq * 0.01488 + + int_mean = np.mean(Ik) + k2 = k**2 + + # initial guesses + const_bg = np.min(Ik) / int_mean + int0 = np.median(Ik) / int_mean - const_bg + sigma0 = np.mean(k) + p0 = [const_bg, int0, sigma0, int0, sigma0] + + dk = k[1] - k[0] + k_width = kmax - kmin + mask_low = ( + np.sin( + np.clip( + (k - kmin) / k_width, + 0, + 1, + ) + * np.pi + / 2.0, + ) + ** 2 + ) + # weighting function for fitting atomic scattering factors + weights_fit = np.divide( + 1, + mask_low, + where=mask_low > 1e-4, + ) + weights_fit[mask_low <= 1e-4] = np.inf + # Scale weighting to favour high k values + weights_fit *= k[-1] - 0.9 * k + dk + + # bounds + lb = [0, 0, 0, 0, 0] + ub = [np.inf, np.inf, np.inf, np.inf, np.inf] + + # fit normalized data + kwargs = dict(sigma=weights_fit, p0=p0, bounds=(lb, ub), xtol=1e-8, maxfev=10000) + + coefs, pcov = curve_fit(self._scattering_model, k2, Ik / int_mean, **kwargs) + + # rescale back to original intensity units (same as script) + coefs = np.array(coefs, float) + coefs[0] *= int_mean + coefs[1] *= int_mean + coefs[3] *= int_mean + + bg = self._scattering_model(k2, *coefs) + f = bg - coefs[0] # "form factor" without constant offset, like the script + + return bg, f def calculate_pair_dist_function( self, @@ -266,61 +647,684 @@ def calculate_pair_dist_function( r_min: float = 0.0, r_max: float = 20.0, r_step: float = 0.02, - damp_origin_fluctuations: bool = True, - enforce_positivity: bool = True, + mask_realspace: NDArray | None = None, + calculate_pdf: bool = False, density: float | None = None, - plot_background_fits: bool = False, - plot_sf_estimate: bool = False, - plot_reduced_pdf: bool = True, - plot_pdf: bool = False, - figsize: tuple[float, float] = (8, 4), - maxfev: int | None = None, + damp_origin_oscillations: bool = False, + set_pdf_positive: bool = False, returnval: bool = False, + ): + """ + Calculate the (reduced) pair distribution function from a 4D-STEM dataset. + + This routine: + * Computes the radial mean intensity I(k) from self.polar (optionally + restricted to a real-space mask). + * Fit a smooth background B(k) and associated f(k) using :meth:`fit_bg`. + * Estimates and subtracts a background from I(k). + * Constructs the reduced structure factor F(k) with optional low/highpass filtering. + * Apply a window in k (low-k sin^2 ramp × Lorch high-k taper) + * Compute the reduced PDF using a discrete sine transform: + G(r) = sum_k sin(2π k r) * F_windowed(k) + * If `calculate_pdf=True`, g(r) is computed from G(r) using: + g(r) = 1 + G(r) / (4π r ρ0) + with ρ0 either provided by the user (`density`) or estimated via + :meth:`estimate_density`. + + The computed quantities are also stored on the instance as: + * self.radial_mean – radial mean intensity I(k) (via calculate_radial_mean) + * self.bg – background bg(k) + * self.Sk – structure factor (computed as 1 + (Ik - bg)/f) + * self.Fk – unwindowed reduced structure function F(k) + * self.Fk_masked – windowed reduced structure function F(k) + * self.r – r grid (in angstroms) + * self.reduced_pdf – reduced PDF G(r) + * self.pdf – PDF g(r) (if computed) + + Parameters + ---------- + k_min : float, optional + Minimum k (Å⁻¹) to use when building masks and transforms. If None, + `self.kmin` is set to `k.min()`. + k_max : float or None, optional + Maximum k (Å⁻¹) to use when building masks and transforms. If None, + `self.kmax` is set to `k.max()`. + k_width : float, optional + Width parameter (in Å⁻¹) intended for edge masks. Note: in the current implementation + this parameter is not yet used as a true "width"; the code uses `k_width = kmax-kmin`. + k_lowpass : float or None, optional + If provided and > 0, applies a low-pass Gaussian filter to F(k) with + sigma = k_lowpass / dk, where dk is the k-grid spacing. + k_highpass : float or None, optional + If provided and > 0, constructs a low-pass filtered copy of F(k) with + sigma = k_highpass / dk and subtracts it from F(k), effectively + applying a high-pass filter. + r_min : float, optional + Minimum r (Å) for the real-space grid used to compute G(r). + r_max : float, optional + Maximum r (Å) for the real-space grid used to compute G(r). + r_step : float, optional + Step size in r (Å) for the real-space grid. + mask_realspace : NDArray or None, optional + Real-space mask specifying which probe positions (rx, ry) to include. + Either: + * A boolean array of shape (rx, ry) where True means “include this + probe position”, or + * An array-like of shape (2, 2) giving two opposite (rx, ry) corner + points that define a rectangular region of interest. + If None, all probe positions are used. + calculate_pdf + If True, compute g(r) and store it to `self.pdf`. + density + If provided, use this number density (atoms/Å^3) when computing g(r). + If None and `calculate_pdf=True`, density is estimated using :meth:`estimate_density`. + damp_origin_oscillations + If True, compute a density correction and replace the stored F(k)/G(r) with the + corrected versions returned by :meth:`estimate_density`. + set_pdf_positive + If True, sets negative values to 0. + returnval : bool, optional + If True, the function returns (r, G(r), g(r)). If + False, no numerical results are returned (but attributes on `self` + are still updated). + + + Returns + ------- + results : list[np.ndarray] or None + If `returnval=True`, returns [r, reduced_pdf, pdf] where: + - r is the real-space grid (Nr,) + - reduced_pdf is G(r) (Nr,) + - pdf is g(r) (Nr,) or None if `calculate_pdf=False` + Otherwise returns None. + """ + k_width = np.array(k_width) + if k_width.size == 1: + k_width = k_width * np.ones(2) + + # BUG: make calibration automatic + k = self.qq * 0.01488 + dk = k[1] - k[0] + + self.kmax = k_max if k_max is not None else k.max() + self.kmin = k_min if k_min is not None else k.min() + # BUG: implement k_width properly + k_width = self.kmax - self.kmin + + mask_bool = self._get_mask_bool(mask_realspace) + + Ik = self.calculate_radial_mean(mask_realspace=mask_bool, returnval=True) + + bg, f = self.fit_bg(Ik, self.kmin, self.kmax) + + Fk = (Ik - bg) * k / f + + # band pass filtering + if ( + k_lowpass is not None + and k_lowpass > 0.0 + and k_highpass is not None + and k_highpass > 0.0 + ): + if k_highpass > k_lowpass: + raise ValueError( + "Invalid band-pass parameters: k_highpass > k_lowpass. " + "Gaussian band-pass filtering requires k_highpass < k_lowpass " + "because these parameters are smoothing widths." + ) + Fk_low = gaussian_filter1d(Fk, sigma=k_lowpass / dk, mode="nearest") + Fk_high = gaussian_filter1d(Fk, sigma=k_highpass / dk, mode="nearest") + Fk = Fk_high - Fk_low + elif k_lowpass is not None and k_lowpass > 0.0: + Fk = gaussian_filter1d(Fk, sigma=k_lowpass / dk, mode="nearest") + elif k_highpass is not None and k_highpass > 0.0: + Fk_low = gaussian_filter1d(Fk, sigma=k_highpass / dk, mode="nearest") + Fk = Fk - Fk_low + + # Apply wk to F(Q) and rescale + wk = self._lorch_window(k, self.kmin, self.kmax) + Fk_win = Fk * wk * 2 * np.pi + + r = np.arange(r_min, r_max, r_step) + ra, ka = np.meshgrid(r, k) + # incorrectly scaled in py4dstem , should include 2pi factor in dk and Fk like below + reduced_pdf = ( + (2 / np.pi) + * dk + * 2 + * np.pi + * np.sum( + np.sin(2 * np.pi * ra * ka) * Fk_win[:, None], + axis=0, + ) + ) + reduced_pdf[0] = 0 # physically must be at 0 when r = 0 + + self.Ik = Ik + self.bg = bg + self.Fk = Fk * 2 * np.pi + self.Fk_masked = Fk_win + self.r = r + self.reduced_pdf = reduced_pdf + + denscorr = None + if damp_origin_oscillations or (calculate_pdf and density is None): + self.Sk = np.ones_like(k, dtype=float) + mask = k > 0 + self.Sk[mask] = 1.0 + (Fk[mask] / k[mask]) + self.Sk[~mask] = 1.0 # or np.nan, depending on preference + + denscorr = self.estimate_density( + max_iter=20, + tol_percent=1e-1, + ) + + if damp_origin_oscillations: + self.Fk_damped = denscorr[1] + self.reduced_pdf_damped = denscorr[2] + else: + self.reduced_pdf_damped = self.reduced_pdf + + if returnval: + Gr = getattr(self, "reduced_pdf_damped", None) + if Gr is None: + Gr = self.reduced_pdf + results = [self.r, Gr] + + # option to return pdf also using the density calculation method + # from Yoshimoto and Omote, 2022. + if calculate_pdf: + if density is None: + rho0 = denscorr[0] + # print(f"Estimated density: rho0 = {rho0:.4f} atoms / ų") + else: + print(f"Using provided density rho0 = {density:.4f} atoms / Angstrom^3") + rho0 = density + + mask = r > 0 + pdf = np.ones_like(self.reduced_pdf_damped) + + pdf[mask] = 1 + self.reduced_pdf_damped[mask] / (4 * np.pi * r[mask] * rho0) + pdf[~mask] = 0.0 + + if set_pdf_positive: + pdf = np.maximum(pdf, 0.0) + + self.pdf = pdf + + if returnval: + results.append(self.pdf) + + if returnval: + return results + else: + return + + def estimate_density( + self, + max_iter: int = 20, + tol_percent: float = 1e-4, + ) -> Tuple[float, np.ndarray, np.ndarray, np.ndarray]: + """ + Estimate number density rho0 (atoms/Å^3) and compute a corrected G(r). + + This method implements an iterative Q-space density estimation by + Yoshimoto & Omote (2022). It uses the structure factor `self.Sk` and + the reduced PDF `self.reduced_pdf` to iteratively update rho0 and a + corrected S(k) so that the implied G(r) is more physically consistent + at low r. + + This method requires that :meth:`calculate_pair_dist_function` has already + been run, because it depends on `self.Sk`, `self.reduced_pdf`, `self.r`, + and the k-window bounds (`self.kmin`, `self.kmax`). + + Parameters + ---------- + max_iter : int, optional + Maximum number of Q-space iterations. + tol_percent : float, optional + Convergence threshold on the relative change in rho0 (in %), + as defined in Eq. (12) of Yoshimoto & Omote (2022). + + Returns + ------- + rho0 : float + Estimated microscopic number density (atoms/Å^3). + Fk_win_damped : np.ndarray + Windowed corrected reduced structure function used for the transform. + G_cor : np.ndarray + Reduced PDF G(r) with dampened oscillations near origin. + """ + if self.Sk is None or self.reduced_pdf is None or self.r is None: + raise RuntimeError("Run calculate_pair_dist_function() before estimate_density().") + + # BUG: make calibration automatic + k = self.qq * 0.01488 # convert from 1/Angstrom to Angstrom^-1 + dk = k[1] - k[0] + k_fit_mask = k >= self.kmin + k_fit = k[k_fit_mask] + ra, ka = np.meshgrid(self.r, k) + + r_cut = 0.8 # Angstrom + mask_search = self.r >= r_cut + r_search = self.r[mask_search] + G_search = self.reduced_pdf[mask_search] + + # find primary peak + ind_max = np.argmax(G_search) + r_max = r_search[ind_max] + + # find first local minimum to the left of r_peak + left = self.r < r_max + if not np.any(left): + # fallback: if peak is immediately at cutoff, just use cutoff as rmin + rmin = r_cut + else: + r_left = self.r[left] + G_left = self.reduced_pdf[left] + + mins = np.where((G_left[1:-1] < G_left[:-2]) & (G_left[1:-1] < G_left[2:]))[0] + 1 + # minimum closest to the peak, else global min in left interval + rmin = r_left[mins[-1]] if mins.size else r_left[np.argmin(G_left)] + + # restrict r to [0, rmin] for alpha/beta integrals + r_mask = (self.r >= 0.0) & (self.r <= rmin) + r_short = self.r[r_mask] + G_short = self.reduced_pdf[r_mask] + + # iterative refinement of rho0 and S(k) + rho0_prev = None + Sk_cor = self.Sk.copy() + G_cor = self.reduced_pdf.copy() + + # use current G(r) (from Sk_cor) in beta(Q) + G_beta = G_short + k_fit = k_fit * 2 * np.pi + for j in range(max_iter): + if j > 0: + G_beta = G_cor[r_mask] + + k2d_fit, r2d_fit = np.meshgrid(k_fit, r_short, indexing="ij") + alpha, beta = self._compute_alpha_beta(k2d_fit, r2d_fit, G_beta, r_short) + rho0 = np.sum(alpha * beta) / np.sum(alpha**2) + + if rho0_prev is not None: + Rj = np.sqrt(((rho0_prev - rho0) ** 2) / (rho0**2)) * 100.0 + if Rj < tol_percent: + # print( + # f"Converged after {j} iterations: rho0 = {rho0:.4f} atoms / ų, Rj = {Rj:.4f}%" + # ) + break + + # update S_cor(Q) + Sk_cor[k_fit_mask] = Sk_cor[k_fit_mask] - beta + rho0 * alpha + Fk_cor = k * (Sk_cor - 1.0) + + wk = self._lorch_window(k, self.kmin, self.kmax) + + Fk_win_damped = Fk_cor * wk * 2 * np.pi + + G_cor = ( + (2.0 / np.pi) + * dk + * 2 + * np.pi + * np.sum(np.sin(2 * np.pi * ka * ra) * Fk_win_damped[:, None], axis=0) + ) + G_cor[0] = 0.0 + + rho0_prev = rho0 + + return rho0, Fk_win_damped, G_cor + + # ------------------------------------------------------------------ + # Plotting functions + # ------------------------------------------------------------------ + + PlotName = Literal[ + "radial_mean", + "background", + "reduced_sf", + "reduced_pdf", + "pdf", + ] + + from typing import Optional, Tuple + + def _apply_xrange( + self, + x: NDArray, + y: NDArray, + xmin: float | None, + xmax: float | None, + ) -> tuple[NDArray, NDArray]: + if xmin is None and xmax is None: + return x, y + xmin_eff = x.min() if xmin is None else xmin + xmax_eff = x.max() if xmax is None else xmax + if xmax_eff <= xmin_eff: + raise ValueError(f"xmax must be > xmin (got xmin={xmin_eff}, xmax={xmax_eff}).") + m = (x >= xmin_eff) & (x <= xmax_eff) + # avoid empty plots + if not np.any(m): + raise ValueError("Requested plot range contains no data.") + return x[m], y[m] + + def plot_pdf_results( + self, + which: Iterable[PlotName] = ("reduced_pdf",), + *, + qmin: float | None = None, + qmax: float | None = None, + rmin: float | None = None, + rmax: float | None = None, + figsize: tuple[float, float] = (8, 4), + returnfigs: bool = False, + ): + """ + Convenience plotting dispatcher. + + Examples + -------- + pdfc.calculate_pair_dist_function(...) + pdfc.plot(["radial_mean", "background", "reduced_pdf"]) + """ + mapping = { + "radial_mean": self.plot_radial_mean, + "background_fits": self.plot_background_fits, + "reduced_sf": self.plot_reduced_sf, + "reduced_pdf": self.plot_reduced_pdf, + "pdf": self.plot_pdf, + "oscillation_damping": self.plot_oscillation_damping, + } + + figs = [] + for name in which: + if name not in mapping: + raise ValueError(f"Unknown plot '{name}'. Options: {tuple(mapping)}") + fig = mapping[name]( + qmin=qmin, qmax=qmax, rmin=rmin, rmax=rmax, figsize=figsize, returnfig=returnfigs + ) + if returnfigs: + figs.append(fig) + + return figs if returnfigs else None + + def _auto_ylim_after_direct_beam_trough(self, y, *, scale=2.0, smooth_sigma=2.0): + y = np.asarray(y, dtype=float) + if y.size < 10: + return None + + # direct beam peak is usually the first big max; assume it's at/near index 0 + # find first local minimum after index 0 + dy = np.diff(y) + mins = np.where((dy[:-1] < 0) & (dy[1:] > 0))[0] + 1 + + if mins.size == 0: + # fallback: ignore first 5% if we can't find a trough + start = max(1, int(0.05 * y.size)) + else: + start = int(mins[0]) + + y_use = y[start:] + y_use = y_use[np.isfinite(y_use)] + if y_use.size == 0: + return None + + ymax = np.max(y_use) + if not np.isfinite(ymax) or ymax <= 0: + return None + + return (0.0, scale * ymax) + + def plot_radial_mean( + self, + qmin: float | None = None, + qmax: float | None = None, + rmin: float | None = None, # accepted for dispatcher compatibility, unused + rmax: float | None = None, # accepted for dispatcher compatibility, unused + figsize: tuple[float, float] = (8, 4), returnfig: bool = False, ): """ - Stub for pair distribution function (PDF) calculation from radial statistics. + Plotting radial mean intensity vs scattering vector. + """ + + if self.radial_mean is None: + raise RuntimeError("Radial mean intensity has not been calculated yet.") + + x = np.asarray(self.qq * 0.01488) + y = np.asarray(self.radial_mean) + x, y = self._apply_xrange(x, y, qmin, qmax) - Intended to estimate S(k), background, and transform to real-space g(r)/G(r). + fig, ax = plt.subplots(figsize=figsize) + ax.plot(x, y, label="Radial Mean Intensity I(k)") + ax.set_xlabel("Scattering Vector q (1/Å)") + ax.set_ylabel("Intensity (a.u.)") + ax.set_title("Radial Mean Intensity vs Scattering Vector") + ax.legend() + ylim = self._auto_ylim_after_direct_beam_trough(self.radial_mean, scale=2.0) + if ylim is not None: + ax.set_ylim(*ylim) + plt.tight_layout() + + if returnfig: + return fig + else: + plt.show() + + def plot_radial_var_norm( + self, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): """ - raise NotImplementedError("calculate_pair_dist_function is not implemented yet.") + Stub for plotting normalized radial variance vs scattering vector. + """ + raise NotImplementedError("plot_radial_var_norm is not implemented yet.") def plot_background_fits( self, + qmin: float | None = None, + qmax: float | None = None, + rmin: float | None = None, # accepted for dispatcher compatibility, unused + rmax: float | None = None, # accepted for dispatcher compatibility, unused figsize: tuple[float, float] = (8, 4), returnfig: bool = False, ): """ - Stub for plotting background fit vs radial mean intensity. + Plotting background fit vs radial mean intensity. """ - raise NotImplementedError("plot_background_fits is not implemented yet.") + if self.Ik is None or self.bg is None: + raise RuntimeError("Radial mean intensity or background has not been calculated yet.") + + x = np.asarray(self.qq * 0.01488) + y1 = np.asarray(self.radial_mean) + x, y1 = self._apply_xrange(x, y1, qmin, qmax) + x = np.asarray(self.qq * 0.01488) + y2 = np.asarray(self.bg) + x, y2 = self._apply_xrange(x, y2, qmin, qmax) + + fig, ax = plt.subplots(figsize=figsize) + ax.plot(x, y1, label="Radial Mean Intensity I(k)") + ax.plot(x, y2, label="Background B(k)", linestyle="--") + ax.set_xlabel("Scattering Vector q (1/Å)") + ax.set_ylabel("Intensity (a.u.)") + ax.set_title("Radial Mean Intensity and Background Fit") + ax.legend() + plt.tight_layout() + ylim = self._auto_ylim_after_direct_beam_trough(self.radial_mean, scale=2.0) + if ylim is not None: + ax.set_ylim(*ylim) + + if returnfig: + return fig + else: + plt.show() - def plot_sf_estimate( + def plot_reduced_sf( self, + qmin: float | None = None, + qmax: float | None = None, + rmin: float | None = None, # accepted for dispatcher compatibility, unused + rmax: float | None = None, # accepted for dispatcher compatibility, unused figsize: tuple[float, float] = (8, 4), returnfig: bool = False, ): """ - Stub for plotting reduced structure factor S(k). + Plotting reduced structure factor F(k). """ - raise NotImplementedError("plot_sf_estimate is not implemented yet.") + if self.Fk_masked is None: + raise RuntimeError("Reduced structure factor F(k) has not been calculated yet.") + + Fk = getattr(self, "Fk_damped", None) + if Fk is None: + Fk = self.Fk_masked + + x = np.asarray(self.qq * 0.01488) + y = np.asarray(Fk) + x, y = self._apply_xrange(x, y, qmin, qmax) + + fig, ax = plt.subplots(figsize=figsize) + ax.plot(x, y, label="Reduced Structure Factor F(k)") + ax.set_xlabel("Scattering Vector q (1/Å)") + ax.set_ylabel("Reduced Structure Factor F(k)") + plt.tight_layout() + + if returnfig: + return fig + else: + plt.show() def plot_reduced_pdf( self, + qmin: float | None = None, # accepted for dispatcher compatibility, unused + qmax: float | None = None, # accepted for dispatcher compatibility, unused + rmin: float | None = None, + rmax: float | None = None, + padding_frac: float = 0.1, figsize: tuple[float, float] = (8, 4), returnfig: bool = False, ): """ - Stub for plotting reduced PDF g(r). + Plotting reduced PDF g(r). """ - raise NotImplementedError("plot_reduced_pdf is not implemented yet.") + if self.reduced_pdf is None: + raise RuntimeError("Reduced PDF has not been calculated yet.") + Gr = getattr(self, "reduced_pdf_damped", None) + if Gr is None: + Gr = self.reduced_pdf + + x = np.asarray(self.r) + y = np.asarray(Gr) + x, y = self._apply_xrange(x, y, qmin, qmax) + + # Find radial value of primary peak and trough for y-limits + ind_max = np.argmax(y) + y_max = y[ind_max] + + ind_min = np.argmin(y) + y_min = y[ind_min] + yrange = y_max - y_min + pad = padding_frac * yrange + + fig, ax = plt.subplots(figsize=figsize) + ax.plot(x, y, label="Reduced Pair Distribution Function G(r)") + ax.set_xlabel("Radial Distance r (Å)") + ax.set_ylabel("Reduced Pair Distribution Function G(r)") + ax.set_ylim(y_min - pad, y_max + pad) + plt.tight_layout() + + if returnfig: + return fig + else: + plt.show() def plot_pdf( self, + qmin: float | None = None, # accepted for dispatcher compatibility, unused + qmax: float | None = None, # accepted for dispatcher compatibility, unused + rmin: float | None = None, + rmax: float | None = None, + padding_frac: float = 0.1, figsize: tuple[float, float] = (8, 4), returnfig: bool = False, ): """ - Stub for plotting full PDF G(r). + Plotting pair distribution function g(r). """ - raise NotImplementedError("plot_pdf is not implemented yet.") + if self.reduced_pdf is None or self.pdf is None: + raise RuntimeError("Reduced PDF or PDF has not been calculated yet.") + + x = np.asarray(self.r) + y = np.asarray(self.pdf) + x, y = self._apply_xrange(x, y, qmin, qmax) + + # Find radial value of primary peak + ind_max = np.argmax(y) + y_max = y[ind_max] + + ind_min = np.argmin(y) + y_min = y[ind_min] + + yrange = y_max - y_min + pad = padding_frac * yrange + + fig, ax = plt.subplots(figsize=figsize) + ax.plot(x, y, label="Pair Distribution Function g(r)") + ax.set_xlabel("Radial Distance r (Å)") + ax.set_ylabel("Pair Distribution Function g(r)") + ax.set_ylim(y_min - pad, y_max + pad) + plt.tight_layout() + + if returnfig: + return fig + else: + plt.show() + + def plot_oscillation_damping( + self, + qmin: float | None = None, # accepted for dispatcher compatibility, unused + qmax: float | None = None, # accepted for dispatcher compatibility, unused + rmin: float | None = None, + rmax: float | None = None, + padding_frac: float = 0.1, + figsize: tuple[float, float] = (8, 4), + returnfig: bool = False, + ): + k = np.asarray(self.qq * 0.1488) + + fig, axes = plt.subplots(2, 2, figsize=figsize) + + # F(k) + axS_top = axes[0, 0] + axS_res = axes[1, 0] + axS_top.plot(k, self.Fk_masked, label="F_obs(k)", color="gray") + axS_top.plot(k, self.Fk_damped, label="F_cor(k)", color="red") + axS_top.set_xlabel("k (Å$^{-1}$)") + axS_top.set_ylabel("F(k)") + axS_top.legend() + + axS_res.plot(k, self.Fk_damped - self.Fk_masked, color="blue") + axS_res.set_xlabel("k (Å$^{-1}$)") + axS_res.set_ylabel("F_cor - F_obs") + + # G(r) + axG_top = axes[0, 1] + axG_res = axes[1, 1] + axG_top.plot(self.r, self.reduced_pdf, label="G_obs(r)", color="gray") + axG_top.plot(self.r, self.reduced_pdf_damped, label="G_cor(r)", color="red") + axG_top.set_xlabel("r (Å)") + axG_top.set_ylabel("G(r)") + axG_top.legend() + + axG_res.plot(self.r, self.reduced_pdf_damped - self.reduced_pdf, color="blue") + axG_res.set_xlabel("r (Å)") + axG_res.set_ylabel("G_cor - G_obs") + + fig.tight_layout() + + if returnfig: + return fig + else: + plt.show() diff --git a/src/quantem/diffraction/polar_new.py b/src/quantem/diffraction/polar_new.py deleted file mode 100644 index b25d3f1b..00000000 --- a/src/quantem/diffraction/polar_new.py +++ /dev/null @@ -1,1195 +0,0 @@ -from __future__ import annotations - -import json -from collections.abc import Sequence -from pathlib import Path -from typing import Any, Iterable, List, Tuple, Union - -import matplotlib.pyplot as plt -import numpy as np -from numpy.typing import NDArray -from scipy.ndimage import gaussian_filter - -from quantem.core.datastructures.dataset2d import Dataset2d -from quantem.core.datastructures.dataset3d import Dataset3d -from quantem.core.datastructures.dataset4dstem import Dataset4dstem -from quantem.core.datastructures.polar4dstem import Polar4dstem -from quantem.core.io.serialize import AutoSerialize -from quantem.core.utils.validators import ensure_valid_array - -KIRKLAND_PARAMS_PATH = Path(__file__).with_name("kirkland_params.json") - - -class RDF_new(AutoSerialize): - """ - Radial distribution / fluctuation electron microscopy analysis helper. - - This class wraps a 4D-STEM (or 2D diffraction) dataset and stores a - polar-transformed representation as a Polar4dstem instance in `self.polar`. - Analysis methods (radial statistics, PDF, FEM, clustering, etc.) are - provided as stubs for now and will be implemented in future revisions. - """ - - _token = object() - - def __init__( - self, - polar: Polar4dstem, - input_data: Any | None = None, - _token: object | None = None, - ): - if _token is not self._token: - raise RuntimeError( - "Use RadialDistributionFunction.from_data() to instantiate this class." - ) - - super().__init__() - self.polar = polar - self.input_data = input_data - - # Placeholders for analysis results (to be populated by future methods) - self.radial_mean: NDArray | None = None - self.radial_var: NDArray | None = None - self.radial_var_norm: NDArray | None = None - - self.pdf_r: NDArray | None = None - self.pdf_reduced: NDArray | None = None - self.pdf: NDArray | None = None - - self.Sk: NDArray | None = None - self.fk: NDArray | None = None - self.bg: NDArray | None = None - self.offset: float | None = None - self.Sk_mask: NDArray | None = None - - # ------------------------------------------------------------------ - # Constructors - # ------------------------------------------------------------------ - @classmethod - def from_data( - cls, - data: Union[NDArray, Dataset2d, Dataset3d, Dataset4dstem, Polar4dstem], - *, - origin_row: float | None = None, - origin_col: float | None = None, - ellipse_params: tuple[float, float, float] | None = None, - num_annular_bins: int = 180, - radial_min: float = 0.0, - radial_max: float | None = None, - radial_step: float = 1.0, - two_fold_rotation_symmetry: bool = False, - ): - """ - -> "RadialDistributionFunction" - Create a RadialDistributionFunction object from various input types. - - Parameters - ---------- - data - Supported inputs: - - 2D numpy array (single diffraction pattern) - - 4D numpy array (scan_y, scan_x, ky, kx) - - Dataset2d - - Dataset4dstem - - Polar4dstem - origin_row, origin_col - Diffraction-space origin (in pixels). If None, defaults to the - central pixel of the diffraction pattern. - Other parameters - Passed through to Dataset4dstem.polar_transform when needed. - """ - # Polar input: use directly - if isinstance(data, Polar4dstem): - polar = data - return cls(polar=polar, input_data=data, _token=cls._token) - - # Dataset4dstem input: polar-transform it - if isinstance(data, Dataset4dstem): - scan_y, scan_x, ny, nx = data.array.shape - if origin_row is None: - origin_row = (ny - 1) / 2.0 - if origin_col is None: - origin_col = (nx - 1) / 2.0 - - polar = data.polar_transform( - origin_row=origin_row, - origin_col=origin_col, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - ) - return cls(polar=polar, input_data=data, _token=cls._token) - - # Dataset2d input: wrap as a trivial 4D-STEM (1x1 scan) then polar-transform - if isinstance(data, Dataset2d): - arr2d = data.array - if arr2d.ndim != 2: - raise ValueError("Dataset2d for RDF must be 2D.") - arr4 = arr2d[None, None, ...] # (1, 1, ky, kx) - - ds4 = Dataset4dstem.from_array( - array=arr4, - name=f"{data.name}_as4dstem" - if getattr(data, "name", None) - else "rdf_4dstem_from_2d", - origin=np.concatenate( - [np.zeros(2, dtype=float), np.asarray(data.origin, dtype=float)] - ), - sampling=np.concatenate( - [np.ones(2, dtype=float), np.asarray(data.sampling, dtype=float)] - ), - units=["pixels", "pixels"] + list(data.units), - signal_units=data.signal_units, - ) - ny, nx = ds4.array.shape[-2:] - if origin_row is None: - origin_row = (ny - 1) / 2.0 - if origin_col is None: - origin_col = (nx - 1) / 2.0 - - polar = ds4.polar_transform( - origin_row=origin_row, - origin_col=origin_col, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - ) - return cls(polar=polar, input_data=data, _token=cls._token) - - # Dataset3d input: not yet specified how to interpret - if isinstance(data, Dataset3d): - raise NotImplementedError( - "RadialDistributionFunction.from_data does not yet support Dataset3d inputs." - ) - - # Numpy array input - arr = ensure_valid_array(data) - if arr.ndim == 2: - ds2 = Dataset2d.from_array(arr, name="rdf_input_2d") - return cls.from_data( - ds2, - origin_row=origin_row, - origin_col=origin_col, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - ) - elif arr.ndim == 4: - ds4 = Dataset4dstem.from_array(arr, name="rdf_input_4dstem") - return cls.from_data( - ds4, - origin_row=origin_row, - origin_col=origin_col, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - ) - else: - raise ValueError("RadialDistributionFunction.from_data only supports 2D or 4D arrays.") - - # ------------------------------------------------------------------ - # Convenience accessors - # ------------------------------------------------------------------ - @property - def qq(self) -> Any: - """ - Scattering vector coordinate array along the radial dimension of `self.polar`, - in physical units (using Polar4dstem.sampling and origin). - """ - # Polar4dstem dims: (scan_y, scan_x, phi, r) - # radial axis is 3 - return self.polar.coords_units(3) - - @property - def radial_bins(self) -> Any: - """ - Radial bin centers in pixel units (convenience alias). - """ - return self.polar.coords(3) - - # ------------------------------------------------------------------ - # Analysis method stubs (py4DSTEM-style API) - # ------------------------------------------------------------------ - - # TODO: linting and docstrings - def calculate_radial_mean( - self, - mask_realspace: NDArray | None = None, - figsize: tuple[float, float] = (8, 4), - returnval: bool = False, - returnfig: bool = False, - ): - """ - Calculate the radial mean intensity from the Polar4dSTEM dataset. - - This performs an azimuthal integration over all angles at each k value. - The result is stored in ``self.radial_mean`` and can optionally be - returned, along with a figure of the radial mean intensity. - - Parameters - ---------- - mask_realspace : NDArray or None, optional - Boolean mask in real space used to select probe positions. - If ``None``, all probe positions are used. - figsize : tuple of float, optional - Figure size passed to ``plot_radial_mean`` when ``returnfig`` is True. - returnval : bool, optional - If True, return the computed radial mean array. - returnfig : bool, optional - If True, also return a figure object from ``plot_radial_mean``. - - Returns - ------- - NDArray or list or None - """ - - # init radial data array - if mask_realspace is None: - # calculate intensity over q-range for each probe position - radial_probe = self.polar.array.mean(axis=2) # axis 0: ry, 1: rx, 2: theta, 3: q - # average over all probe positions - self.radial_mean = np.mean(radial_probe, axis=(0, 1)) - - elif mask_realspace is not None: - masked_polar = self.polar.array[mask_realspace] # (N_valid, N_theta, N_k) - radial_probe = masked_polar.mean(axis=1) - # average over all probe positions, only those unmasked - self.radial_mean = radial_probe.mean(axis=0) - - if returnval: - results = self.radial_mean - else: - results = None if not returnfig else [] - - if returnfig: - fig = self.plot_radial_mean( - figsize=figsize, - returnfig=returnfig, - ) - results.append(fig) - - return results - - def compute_bg_constant_offset(self, Ik: np.ndarray, f2: np.ndarray) -> np.ndarray: - """ - Compute the background intensity B(k) as: - B(k) = N * f²(k) + C - - where: - - N is a scaling factor for inelastic + multiple scattering background - - C is a constant offset term - """ - # fit background parameters N and C - Ik_region = Ik[-50:] # high-k region for fitting, hardcoded for now - f2_region = f2[-50:] - - # least squares fitting to find best parameters - A = np.column_stack((f2_region, np.ones_like(f2_region))) - N, C = np.linalg.lstsq(A, Ik_region, rcond=None)[0] - - # this is monotonic background + constant offset - bg = N * f2 + C - - return bg - - def compute_bg_snip(self, Ik, k, m=25): - """Compute the background intensity B(k) using the SNIP algorithm, - as described in Liu et al. (2023, EDP2PDF), following Morháč et al. (1997). - - Parameters - ---------- - k : array_like - 1D array of scattering vector values (Q, q, or channel positions). - Only the length is used here; SNIP itself works in channel space. - intensity : array_like - 1D array y(i) of diffraction intensities (must be non-negative or - at least > -1 so that y+1 is positive). - m : int - Number of SNIP iterations. The paper recommends setting this to - about half of the major peak FWHM in *channels*. - smooth_window : int, optional - Window size for the pre-smoothing step. The paper uses 2n+1=7. - Set to None or 1 to disable smoothing. - use_savgol_like : bool, optional - If True and smooth_window==7, emulate the Savitzky–Golay-like - convolution used in the paper with hard-coded weights - (2, 3, 6, 7, 6, 3, 2) / sum. - If False, no external scipy dependency is assumed (still uses - that hard-coded kernel when smooth_window==7). - - Returns - ------- - baseline : ndarray - Estimated background b(i) from SNIP. - net_intensity : ndarray - Background-subtracted intensity y(i) - b(i).""" - - # add de-noising step? - - # twice log operators plus square-root operator - v = np.log(np.log(np.sqrt(Ik + 1.0) + 1.0) + 1.0) - - # set m to FWHM of the major peak in the future - # for now, clamp m so the window doesn't exceed half the spectrum - m = max(1, min(m, len(Ik) // 2 - 1)) - - # snip iterations - for p in range(1, m + 1): - # get channels shifted by p - left = np.empty_like(v) - left[p:] = v[:-p] - right = np.empty_like(v) - right[:-p] = v[p:] - # leave boundary edge cases uunshifted - left[:p] = v[:p] - right[-p:] = v[-p:] - - vp = (left + right) / 2 - v = np.minimum(v, vp) - - # inverse lsr - t = np.exp(v) - bg = (np.exp(t - 1.0) - 1.0) ** 2 - 1.0 - bg = np.clip(bg, 0.0, None) - - # #show bg fit - # #TODO: make plotting optional - # plt.figure() - # plt.plot(Ik, label="Original Intensity") - # plt.plot(bg, label=f"SNIP Background, m={m}") - # plt.ylim(0, 0.0003) - # plt.legend() - # plt.xlabel("k") - # plt.ylabel("Intensity") - # plt.title("SNIP Background Estimation") - # plt.show() - - return bg - - def get_atomic_scattering_factors( - self, - elements: Sequence[str], - atomic_frac: Sequence[float], - k2_values: Iterable[float], - ) -> Tuple[np.ndarray, np.ndarray]: - """ - Retrieve atomic scattering factors for specified elements. - - Parameters - ---------- - elements : Sequence[str] - List of element symbols (e.g., ["Si", "O"]). - atomic_frac : Sequence[float] - Atomic fractions for each element in `elements`. Must have the same - length as `elements`. The values will be converted to a NumPy array - of dtype float. - k2_values : Iterable[float] - Squared scattering vector magnitude values (k^2) at which the - scattering factors are evaluated. - - Returns - ------- - f2 : np.ndarray - Weighted sum of squared atomic scattering factors: - f2(k^2) = Σ_i x_i * f_i(k^2)^2 - where x_i is the atomic fraction of element i. - f_2 : np.ndarray - Square of the weighted sum of atomic scattering factors: - f_2(k^2) = (Σ_i x_i * f_i(k^2))^2 - """ - # initialize array to hold f values - atomic_frac = np.asarray(atomic_frac, dtype=float) - n_elements = len(elements) - k2_array = np.asarray(k2_values, dtype=float) - len_k = len(k2_array) - - f = np.zeros((n_elements, len_k), dtype=float) - - # load Kirkland parameters from JSON - with KIRKLAND_PARAMS_PATH.open(encoding="utf-8") as file: - kirkland_params: dict[str, dict[str, list[float]]] = json.load(file) - - for i, element in enumerate(elements): - try: - params = kirkland_params[element] - except KeyError: - raise ValueError(f"Element {element} not found in Kirkland parameters table.") - - a = np.asarray(params["a"], float) - b = np.asarray(params["b"], float) - c = np.asarray(params["c"], float) - d = np.asarray(params["d"], float) - - # Lorentzian and Gaussian terms - l_term = (a[:, None] / (k2_array[None, :] + b[:, None])).sum( - axis=0 - ) # a[:, None] and b[:, None] → shape (3, 1) - g_term = (c[:, None] * np.exp(-d[:, None] * k2_array[None, :])).sum( - axis=0 - ) # k2_array[None, :] → shape (1, len_k) - - f[i, :] = l_term + g_term - - f2 = (f**2 * atomic_frac[:, None]).sum(axis=0) - f_weighted = (f * atomic_frac[:, None]).sum(axis=0) - f_2 = f_weighted**2 - - return f2, f_2 - - def calculate_pair_dist_function( - self, - el: List[str], - atomic_frac: List[float], - k_min: float = 0.05, - k_max: float | None = None, - k_width: float = 0.25, - k_lowpass: float | None = None, - k_highpass: float | None = None, - r_min: float = 0.0, - r_max: float = 20.0, - r_step: float = 0.02, - mask_realspace: NDArray | None = None, - damp_origin_fluctuations: bool = True, - calculate_pdf: bool = True, - density: float | None = None, - plot_options: dict[str, bool] = { - "plot_radial_mean": False, - "plot_background_fits": False, - "plot_sf_estimate": False, - "plot_reduced_pdf": True, - "plot_pdf": False, - }, - figsize: tuple[float, float] = (8, 4), - returnval: bool = False, - returnfig: bool = False, - ): - """ - Calculate the (reduced) pair distribution function from a 4D-STEM dataset. - - This routine: - * Computes the radial mean intensity I(k) from self.polar (optionally - restricted to a real-space mask). - * Computes element-weighted elastic scattering factors ⟨f²⟩(k) and - ⟨f⟩²(k). - * Estimates and subtracts a background from I(k). - * Constructs the structure factor S(k) and reduced structure function - F(k) = k [S(k) - 1], with optional low-/high-pass filtering. - * Applies smooth edge masking in k-space. - * Performs a sine transform to obtain the reduced pair distribution - function G(r). - - The computed quantities are also stored on the instance as: - * self.Ik – radial mean intensity I(k) - * self.bg – background bg(k) - * self.Fk – reduced structure function F(k) - * self.pdf_r – r grid - * self.reduced_pdf – reduced PDF G(r) - - Parameters - ---------- - el : list of str - List of element symbols (e.g. ["Ta", "O"]) in the sample. - atomic_frac : list of float - Atomic fractions for each element in `el`. Must be the same length as - `el` and typically sum to 1.0. - k_min : float, optional - Minimum k (Å⁻¹) to use when building masks and transforms. If not - None, this value overrides the minimum of the k-grid derived from - `self.qq`. If None, `self.kmin` is set to `k.min()`. - k_max : float or None, optional - Maximum k (Å⁻¹) to use when building masks and transforms. If not - None, this value overrides the maximum of the k-grid derived from - `self.qq`. If None, `self.kmax` is set to `k.max()`. - k_width : float, optional - Width parameter (in Å⁻¹) controlling the smooth edge mask in k-space. - It enters the construction of `mask_low` and `mask_high`. - k_lowpass : float or None, optional - If provided and > 0, applies a low-pass Gaussian filter to S(k) with - sigma = k_lowpass / dk, where dk is the k-grid spacing. - k_highpass : float or None, optional - If provided and > 0, constructs a low-pass filtered copy of S(k) with - sigma = k_highpass / dk and subtracts it from S(k), effectively - applying a high-pass filter. - r_min : float, optional - Minimum r (Å) for the real-space grid used to compute G(r). - r_max : float, optional - Maximum r (Å) for the real-space grid used to compute G(r). - r_step : float, optional - Step size in r (Å) for the real-space grid. - mask_realspace : NDArray or None, optional - Real-space mask specifying which probe positions (rx, ry) to include. - Either: - * A boolean array of shape (rx, ry) where True means “include this - probe position”, or - * An array-like of shape (2, 2) giving two opposite (rx, ry) corner - points that define a rectangular region of interest. - If None, all probe positions are used. - plot_options : dict[str, bool] or None, optional - Dictionary of plotting flags: - - "plot_radial_mean" - - "plot_background_fits" - - "plot_sf_estimate" - - "plot_reduced_pdf" - - "plot_pdf" - In this method it is currently used only to decide whether to request - a figure from `calculate_radial_mean` via - `returnfig=plot_options["plot_radial_mean"]`. If None, a default - dictionary is created internally. - figsize : tuple[float, float], optional - Figure size passed to `calculate_radial_mean` (and potentially to - future plotting routines). - maxfev : int or None, optional - Maximum number of function evaluations for any internal fit routines. - Currently reserved for future use. - returnval : bool, optional - If True, the function returns a tuple `(pdf_r, reduced_pdf)`. If - False, no numerical results are returned (but attributes on `self` - are still updated). - returnfig : bool, optional - If True, this method may in future also return figure objects (e.g. - appended to the `results` list). At present, figure-generation code - is commented out and this flag has no effect beyond shaping the - structure of the returned `results` object. - - Returns - ------- - pdf_r : np.ndarray - Real-space r grid on which the reduced PDF is evaluated. - reduced_pdf : np.ndarray - Reduced pair distribution function G(r). - pdf : np.ndarray - Pair distribution function g(r). - - #TODO: add notes of density calculation for pdf and background calculation method - """ - k_width = np.array(k_width) - if k_width.size == 1: - k_width = k_width * np.ones(2) - - # BUG: make calibration automatic - k = self.qq * 0.01488 - dk = k[1] - k[0] - k2 = k**2 - - self.kmax = k_max if k_max is not None else k.max() - self.kmin = k_min if k_min is not None else k.min() - - # TODO: test - # this should be from avg, not sum! - mask_bool = None - if mask_realspace is not None: - rx, ry = self.polar.array.shape[:2] - mask_realspace = np.asarray(mask_realspace) - - # mask given as boolean array - if mask_realspace.dtype == bool and mask_realspace.shape == (rx, ry): - mask_bool = mask_realspace - - # mask given as list of corners - elif mask_realspace.shape == (2, 2): - (rx1, ry1), (rx2, ry2) = mask_realspace.astype(int) - rx_min, rx_max = sorted((rx1, rx2)) - ry_min, ry_max = sorted((ry1, ry2)) - - # vectorized bounds check - bad = (rx_min < 0) | (rx_max >= rx) | (ry_min < 0) | (ry_max >= ry) - if bad: - raise ValueError(f"Mask points outside valid range {(rx, ry)}") - - mask_bool = np.zeros((rx, ry), dtype=bool) - mask_bool[rx_min : rx_max + 1, ry_min : ry_max + 1] = True - else: - raise ValueError( - "mask_realspace must be boolean array or two opposite (rx, ry) corner points." - ) - - self.calculate_radial_mean( - mask_realspace=mask_bool, figsize=figsize, returnfig=plot_options["plot_radial_mean"] - ) - Ik = self.radial_mean - - # get and ^2 for elements and atomic frac - f2, f_2 = self.get_atomic_scattering_factors(el, atomic_frac, k2) - - # BUG: implement k_width properly - k_width = self.kmax - self.kmin - # Calculate structure factor mask - # mask_low = ( - # np.sin( - # np.clip( - # (k - self.kmin) / k_width, - # 0, - # 1, - # ) - # * np.pi - # / 2.0, - # ) - # ** 2 - # ) - # mask_high = ( - # np.sin( - # np.clip( - # (self.kmax - k) / k_width, - # 0, - # 1, - # ) - # * np.pi - # / 2.0, - # ) - # ** 2 - # ) - # mask = mask_low * mask_high - - bg = self.compute_bg_snip(Ik, k, m=25) - Ik_net = Ik - bg - - # scaling region (avoid direct beam, etc.) - k_scale_min = max(self.kmin, 1.25) - k_scale_max = self.kmax - mask_int = (k >= k_scale_min) & (k <= k_scale_max) - - k_int = k[mask_int] - Ik_int = np.clip(Ik_net[mask_int], 0.0, None) - f2_int = f2[mask_int] - - integral_Ik = np.trapz(Ik_int, k_int) - integral_f2 = np.trapz(f2_int, k_int) - - eta = integral_Ik / integral_f2 - print(f"Scaling factor eta = {eta:.4f}") - - f2_scaled = eta * f2 - f_2_scaled = eta * f_2 - - Sk = 1.0 + (Ik_net - f2_scaled) / f_2_scaled # back to intensity scaling - - # high and lowpass filtering - if k_lowpass is not None and k_lowpass > 0.0: - Sk = gaussian_filter(Sk, sigma=k_lowpass / dk, mode="nearest") - if k_highpass is not None and k_highpass > 0.0: - Sk_lowpass = gaussian_filter(Sk, sigma=k_highpass / dk, mode="nearest") - Sk -= Sk_lowpass - self.Sk_lowpass = Sk_lowpass - - Fk = 2 * np.pi * k * (Sk - 1) - - # high q taper - Q = 2.0 * np.pi * k # Q in 1/Å - Qmin = 2.0 * np.pi * self.kmin - Qmax = 2.0 * np.pi * self.kmax - - # Build Lorch window: w(Q) = sin(pi*Q/Qmax)/(pi*Q/Qmax) - window = np.zeros_like(Q) - inband = (Q >= Qmin) & (Q <= Qmax) - - x = Q[inband] / Qmax - # handle Q=0 safely (though inband excludes it if Qmin>0) - window[inband] = np.sin(np.pi * x) / (np.pi * x) - - # Apply window to F(Q) - FQ_win = Fk * window - - r = np.arange(r_min, r_max, r_step) - ra, ka = np.meshgrid(r, k) - # i think the np.sin kernel in py4dstem should not include the 2pi? - # or depending on k, need to ALSO add 2pi factor to dk - reduced_pdf = ( - (2 / np.pi) - * dk - * 2 - * np.pi - * np.sum( - np.sin(2 * np.pi * ra * ka) * FQ_win[:, None], - axis=0, - ) - ) - reduced_pdf[0] = 0 # physically must be at 0 when r = 0 - - self.Ik = Ik - self.bg = bg - self.Sk = Sk - self.Fk = Fk - self.Fk_masked = FQ_win - self.r = r - self.reduced_pdf = reduced_pdf - - # add option to return pdf also using the density calculation method - # from Yoshimoto and Omote, 2022. - - # BUG: for now - calculate_pdf = True - # rho0 = 0.05284 - if calculate_pdf: - if density is None: - rho0, Fk_cor, G_cor = self.estimate_density( - max_iter=20, tol_percent=1e-1, make_plots=True, Fk_masked=FQ_win - ) - print(f"Estimated density rho0 = {rho0:.4f} atoms / Angstrom^3") - else: - print(f"Using provided density rho0 = {density:.4f} atoms / Angstrom^3") - rho0 = density - pdf = 1 + (1 / (4 * np.pi * r * rho0)) * G_cor - pdf[0] = 0.0 # avoid singularity at r=0 - self.pdf = pdf - - self.Fk_masked = Fk_cor - self.r = r - self.reduced_pdf = G_cor - - # if returnfig and self.plot_options != {}: - # self.plot_functions() - - # if returnval: - # results = (self.r, self.reduced_pdf) - # else: - # results = None if not returnfig else [] - - # # handle mutable default for plot_options - # if plot_options is None: - # plot_options = { - # "plot_radial_mean": False, - # "plot_background_fits": False, - # "plot_sf_estimate": False, - # "plot_reduced_pdf": True, - # "plot_pdf": False, - # } - - # if returnfig and plot_options != {}: - # # fig = self.plot_radial_mean( - # # figsize=figsize, - # # returnfig=returnfig, - # # ) - # # results.append(fig) - - # return results - - # if returnval: - # results = (self.r, self.reduced_pdf) - return self.r, self.reduced_pdf, Fk, Sk, Ik, bg, k, Ik_net, FQ_win, self.pdf - - def compute_alpha_beta(self, Q2d, r2d, G_beta, r_1d): - Qsafe = np.where(Q2d == 0.0, 1e-12, Q2d) - alpha_int = -4 * np.pi * r2d * np.sin(Qsafe * r2d) / Qsafe - beta_int = G_beta[None, :] * np.sin(Qsafe * r2d) / Qsafe - alpha = np.trapz(alpha_int, x=r_1d, axis=1) - beta = np.trapz(beta_int, x=r_1d, axis=1) - return alpha, beta - - def estimate_density( - self, - max_iter: int = 1000, - tol_percent: float = 1e-4, - make_plots: bool = True, - Fk_masked: np.ndarray | None = None, - figsize: Tuple[float, float] = (8.0, 6.0), - ) -> Tuple[float, np.ndarray, np.ndarray, np.ndarray]: - """ - Estimate microscopic number density rho0 from S(k) using the - Yoshimoto & Omote (2022) Q-space iteration method, and return - corrected S(k) and G(r). - - Parameters - ---------- - k : ndarray, shape (Nk,) - Scattering vector k (Å⁻1). - Sk_obs : ndarray, shape (Nk,) - Observed structure factor S_obs(k). - r : ndarray, shape (Nr,) - Real-space grid for G(r) (Å). - max_iter : int, optional - Maximum number of Q-space iterations. - tol_percent : float, optional - Convergence threshold on the relative change in rho0 (in %), - as defined in Eq. (12) of Yoshimoto & Omote (2022). - make_plots : bool, optional - If True, plot S_obs vs S_cor and G_obs vs G_cor with residuals. - figsize : tuple, optional - Figure size for the plots. - - Returns - ------- - rho0 : float - Estimated microscopic number density (in the same units implied - by your S(k) normalization; typically atoms / Å^3). - Sk_cor : ndarray, shape (Nk,) - Corrected structure factor S_cor(k). - G_obs : ndarray, shape (Nr,) - Observed G_obs(r) computed from S_obs(k). - G_cor : ndarray, shape (Nr,) - Corrected G_cor(r) computed from S_cor(k). - """ - # BUG: make calibration automatic - k = self.qq * 0.01488 # convert from 1/Angstrom to Angstrom^-1 - dk = k[1] - k[0] - k_fit_mask = (k >= self.kmin) & ( - k <= self.kmax - ) # or drop <= self.kmax if you want full high-k - k_fit = k[k_fit_mask] - ra, ka = np.meshgrid(self.r, k) - - # ---- choose r1st ignoring r < r_art_cut ---- - r_art_cut = 1.5 # Angstrom, adjust as needed - mask_search = self.r >= r_art_cut - r_search = self.r[mask_search] - G_search = self.reduced_pdf[mask_search] # or whatever G array you're using in the loop - - # indices of local maxima in the search region (no smoothing) - peaks = np.where((G_search[1:-1] > G_search[:-2]) & (G_search[1:-1] > G_search[2:]))[0] + 1 - print(f"Found local maxima at r = {r_search[peaks]} Å") - if len(peaks) == 0: - raise RuntimeError( - f"No local maxima found for r >= r_art_cut={r_art_cut}. " - "Increase r_max, decrease r_art_cut, or check G(r)." - ) - - idx_peak = peaks[0] - r1st = r_search[idx_peak] - - # ---- find a local minimum to the left of r1st but still >= r_art_cut ---- - left = (self.r >= r_art_cut) & (self.r < r1st) - if not np.any(left): - # fallback: if peak is immediately at cutoff, just use cutoff as rmin - rmin = r_art_cut - else: - r_left = self.r[left] - G_left = self.reduced_pdf[left] - - mins = np.where((G_left[1:-1] < G_left[:-2]) & (G_left[1:-1] < G_left[2:]))[0] + 1 - if len(mins) == 0: - # fallback: use the global minimum on the left interval - rmin = r_left[np.argmin(G_left)] - else: - rmin = r_left[mins[-1]] # minimum closest to the peak - - print(f"Using rmin = {rmin:.3f} Å for density estimation.") - - # restrict r to [0, rmin] for alpha/beta integrals - r_mask = (self.r >= 0.0) & (self.r <= rmin) - r_short = self.r[r_mask] - G_short = self.reduced_pdf[r_mask] - - ra_short, ka_short = np.meshgrid(r_short, k) # shape (Nr_short, Nk) - - # iterative refinement of rho0 and S(k) - rho0_prev = None - Sk_cor = self.Sk.copy() - G_cor = self.reduced_pdf.copy() - - # use current G(r) (from Sk_cor) in beta(Q) - G_beta = G_short - # r_short = r_short * 2 * np.pi - k_fit = k_fit * 2 * np.pi - for j in range(max_iter): - if j > 0: - G_beta = G_cor[r_mask] - - # Q2d, r2d = np.meshgrid(k, r_short, indexing="ij") # (Nk, Nr_short) - # alpha, beta = self.compute_alpha_beta(Q2d, r2d, G_beta, r_short) - # # least-squares estimate of rho0 - # rho0 = np.sum(alpha * beta) / np.sum(alpha**2) - - # k-range used only for alpha/beta fit - k2d_fit, r2d_fit = np.meshgrid(k_fit, r_short, indexing="ij") # (Nk_fit, Nr_short) - - # IMPORTANT: do NOT rescale r_short in-place (remove r_short = r_short * 2*np.pi) - alpha, beta = self.compute_alpha_beta(k2d_fit, r2d_fit, G_beta, r_short) - - # rho0 fit only over the masked k-range - rho0 = np.sum(alpha * beta) / np.sum(alpha**2) - - if rho0_prev is not None: - Rj = np.sqrt(((rho0_prev - rho0) ** 2) / (rho0**2)) * 100.0 - if Rj < tol_percent: - print( - f"Converged after {j} iterations: rho0 = {rho0:.4f} atoms / ų, Rj = {Rj:.4f}%" - ) - break - - # update S_cor(Q) according to Eq. (8) - # Sk_cor = Sk_cor - beta + rho0 * alpha - Sk_cor[k_fit_mask] = Sk_cor[k_fit_mask] - beta + rho0 * alpha - Fk_cor = 2 * np.pi * k * (Sk_cor - 1.0) - - # low q taper - edge_frac_low = 0.1 # 10% of range at low-q - edge_width_low = edge_frac_low * (self.kmax - self.kmin) - - window = np.ones_like(k) - - # low-q edge (same as before) - low = (k >= self.kmin) & (k < self.kmin + edge_width_low) - t = (k[low] - self.kmin) / edge_width_low - window[low] = np.sin(0.5 * np.pi * t) ** 2 - - # outside [kmin, kmax] -> 0 - window[k < self.kmin] = 0.0 - window[k > self.kmax] = 0.0 - - # high q taper - Q = 2.0 * np.pi * k # Q in 1/Å - Qmin = 2.0 * np.pi * self.kmin - Qmax = 2.0 * np.pi * self.kmax - - # Build Lorch window: w(Q) = sin(pi*Q/Qmax)/(pi*Q/Qmax) - window = np.zeros_like(Q) - inband = (Q >= Qmin) & (Q <= Qmax) - - x = Q[inband] / Qmax - # handle Q=0 safely (though inband excludes it if Qmin>0) - window[inband] = np.sin(np.pi * x) / (np.pi * x) - - # Apply window to F(Q) - FQ_win = Fk_cor * window - - G_cor = ( - (2.0 / np.pi) - * dk - * 2 - * np.pi - * np.sum(np.sin(2 * np.pi * ka * ra) * FQ_win[:, None], axis=0) - ) - G_cor[0] = 0.0 # enforce G(0) = 0 - - rho0_prev = rho0 - - if make_plots and (j == 0): - fig, ax = plt.subplots(figsize=(7, 4)) - # ax.plot(k, alpha, label="alpha(k)") - ax.plot(k_fit, beta, label="beta(k)") - ax.plot(k_fit, rho0 * alpha, "--", label="rho0 * alpha(k)") - ax.set_xlabel("k (1/Å)") - ax.set_title(f"iter {j} (rho0={rho0:.4g})") - ax.legend() - plt.show() - - fig, ax = plt.subplots(figsize=(7, 4)) - # ax.plot(k, alpha, label="alpha(k)") - ax.plot(k_fit, beta, label="beta(k)") - ax.plot(k_fit, rho0 * alpha, "--", label="rho0 * alpha(k)") - ax.set_xlabel("k (1/Å)") - ax.set_title(f"iter {j} (rho0={rho0:.4g})") - ax.legend() - plt.show() - print(f"Total iterations: {j + 1}, Final rho0 = {rho0:.4f} atoms / ų") - - # --- Step 4: plotting (optional) --- - if make_plots: - fig, axes = plt.subplots(2, 2, figsize=figsize) - - # S(Q) - axS_top = axes[0, 0] - axS_res = axes[1, 0] - axS_top.plot(k, self.Fk_masked, label="F_obs(k)", color="gray") - axS_top.plot(k, FQ_win, label="F_cor(k)", color="red") - axS_top.set_xlabel("k (Å$^{-1}$)") - axS_top.set_ylabel("F(k)") - axS_top.legend() - - axS_res.plot(k, FQ_win - self.Fk_masked, color="blue") - axS_res.set_xlabel("k (Å$^{-1}$)") - axS_res.set_ylabel("F_cor - F_obs") - - # G(r) - axG_top = axes[0, 1] - axG_res = axes[1, 1] - axG_top.plot(self.r, self.reduced_pdf, label="G_obs(r)", color="gray") - axG_top.plot(self.r, G_cor, label="G_cor(r)", color="red") - # axG_top.plot(r_short, G_short, label="G_obs(r)", color="gray") - # axG_top.plot(r_short, G_beta, label="G_cor(r)", color="red") - axG_top.set_xlabel("r (Å)") - axG_top.set_ylabel("G(r)") - axG_top.legend() - - axG_res.plot(self.r, G_cor - self.reduced_pdf, color="blue") - axG_res.set_xlabel("r (Å)") - axG_res.set_ylabel("G_cor - G_obs") - - fig.tight_layout() - plt.show() - - return rho0, FQ_win, G_cor - - def plot_radial_mean( - self, - figsize: tuple[float, float] = (8, 4), - returnfig: bool = False, - ): - """ - Plotting radial mean intensity vs scattering vector. - """ - - if self.radial_mean is None: - raise RuntimeError("Radial mean intensity has not been calculated yet.") - - fig, ax = plt.subplots(figsize=figsize) - ax.plot(self.qq, self.radial_mean, label="Radial Mean Intensity I(k)") - ax.set_xlabel("Scattering Vector q (1/Å)") - ax.set_ylabel("Intensity (a.u.)") - ax.set_title("Radial Mean Intensity vs Scattering Vector") - ax.legend() - ax.tight_layout() - - if returnfig: - return fig - else: - plt.show() - - def plot_radial_var_norm( - self, - figsize: tuple[float, float] = (8, 4), - returnfig: bool = False, - ): - """ - Stub for plotting normalized radial variance vs scattering vector. - """ - raise NotImplementedError("plot_radial_var_norm is not implemented yet.") - - def plot_background_fits( - self, - figsize: tuple[float, float] = (8, 4), - returnfig: bool = False, - ): - """ - Plotting background fit vs radial mean intensity. - """ - if self.Ik is None or self.bg is None: - raise RuntimeError("Radial mean intensity or background has not been calculated yet.") - fig, ax = plt.subplots(figsize=figsize) - ax.plot(self.qq, self.Ik, label="Radial Mean Intensity I(k)") - ax.plot(self.qq, self.bg, label="Background B(k)", linestyle="--") - ax.set_xlabel("Scattering Vector q (1/Å)") - ax.set_ylabel("Intensity (a.u.)") - ax.set_title("Radial Mean Intensity and Background Fit") - ax.legend() - ax.tight_layout() - - if returnfig: - return fig - else: - plt.show() - - def plot_sf_estimate( - self, - figsize: tuple[float, float] = (8, 4), - returnfig: bool = False, - ): - """ - Plotting structure factor S(k). - """ - - if self.Sk is None: - raise RuntimeError("Structure factor S(k) has not been calculated yet.") - - fig, ax = plt.subplots(figsize=figsize) - ax.plot(self.qq, self.Sk, label="Structure Factor S(k)") - ax.set_xlabel("Scattering Vector q (1/Å)") - ax.set_ylabel("Structure Factor S(k)") - ax.tight_layout() - - if returnfig: - return fig - else: - plt.show() - - def plot_reduced_sf_estimate( - self, - figsize: tuple[float, float] = (8, 4), - returnfig: bool = False, - ): - """ - Plotting reduced structure factor F(k). - """ - if self.Fk is None: - raise RuntimeError("Reduced structure factor F(k) has not been calculated yet.") - - fig, ax = plt.subplots(figsize=figsize) - ax.plot(self.qq, self.Fk, label="Reduced Structure Factor F(k)") - ax.set_xlabel("Scattering Vector q (1/Å)") - ax.set_ylabel("Reduced Structure Factor F(k)") - ax.tight_layout() - - if returnfig: - return fig - else: - plt.show() - - def plot_reduced_pdf( - self, - padding_frac: float = 0.1, - figsize: tuple[float, float] = (8, 4), - returnfig: bool = False, - ): - """ - Plotting reduced PDF g(r). - """ - if self.reduced_pdf is None: - raise RuntimeError("Reduced PDF has not been calculated yet.") - - # Find radial value of primary peak and trough for y-limits - ind_max = np.argmax(self.reduced_pdf) - y_max = self.reduced_pdf[ind_max] - - ind_min = np.argmin(self.reduced_pdf) - y_min = self.reduced_pdf[ind_min] - yrange = y_max - y_min - pad = padding_frac * yrange - - fig, ax = plt.subplots(figsize=figsize) - ax.plot(self.r, self.reduced_pdf, label="Reduced Pair Distribution Function G(r)") - ax.set_xlabel("Radial Distance r (Å)") - ax.set_ylabel("Reduced Pair Distribution Function G(r)") - ax.set_ylim(y_min - pad, y_max + pad) - ax.tight_layout() - - if returnfig: - return fig - else: - plt.show() - - def plot_pdf( - self, - padding_frac: float = 0.1, - figsize: tuple[float, float] = (8, 4), - returnfig: bool = False, - ): - """ - Plotting pair distribution function g(r). - """ - if self.reduced_pdf is None or self.pdf is None: - raise RuntimeError("Reduced PDF or PDF has not been calculated yet.") - - # Find radial value of primary peak - ind_max = np.argmax(self.reduced_pdf) - y_max = self.pdf[ind_max] - - # look to right of primary peak for minimum - reduced_pdf_region = self.pdf[ind_max + 1 :] - ind_min = np.argmin(reduced_pdf_region) + (ind_max + 1) - y_min = self.pdf[ind_min] - - yrange = y_max - y_min - pad = padding_frac * yrange - - fig, ax = plt.subplots(figsize=figsize) - ax.plot(self.r, self.pdf, label="Pair Distribution Function g(r)") - ax.set_xlabel("Radial Distance r (Å)") - ax.set_ylabel("Pair Distribution Function g(r)") - ax.set_ylim(y_min - pad, y_max + pad) - ax.tight_layout() - - if returnfig: - return fig - else: - plt.show() From b0e4af5472b1819bf5853a88869e7868bcdaa297 Mon Sep 17 00:00:00 2001 From: Karen Ehrhardt Date: Mon, 12 Jan 2026 14:11:25 -0800 Subject: [PATCH 124/140] pixel calibration bug fix --- src/quantem/diffraction/polar.py | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py index 4a318cac..95fc52a7 100644 --- a/src/quantem/diffraction/polar.py +++ b/src/quantem/diffraction/polar.py @@ -582,7 +582,7 @@ def fit_bg(self, Ik, kmin, kmax): similar to ⟨f⟩²(k) """ - k = self.qq * 0.01488 + k = self.qq int_mean = np.mean(Ik) k2 = k**2 @@ -742,8 +742,7 @@ def calculate_pair_dist_function( if k_width.size == 1: k_width = k_width * np.ones(2) - # BUG: make calibration automatic - k = self.qq * 0.01488 + k = self.qq dk = k[1] - k[0] self.kmax = k_max if k_max is not None else k.max() @@ -898,8 +897,7 @@ def estimate_density( if self.Sk is None or self.reduced_pdf is None or self.r is None: raise RuntimeError("Run calculate_pair_dist_function() before estimate_density().") - # BUG: make calibration automatic - k = self.qq * 0.01488 # convert from 1/Angstrom to Angstrom^-1 + k = self.qq dk = k[1] - k[0] k_fit_mask = k >= self.kmin k_fit = k[k_fit_mask] @@ -1093,7 +1091,7 @@ def plot_radial_mean( if self.radial_mean is None: raise RuntimeError("Radial mean intensity has not been calculated yet.") - x = np.asarray(self.qq * 0.01488) + x = np.asarray(self.qq) y = np.asarray(self.radial_mean) x, y = self._apply_xrange(x, y, qmin, qmax) @@ -1103,9 +1101,10 @@ def plot_radial_mean( ax.set_ylabel("Intensity (a.u.)") ax.set_title("Radial Mean Intensity vs Scattering Vector") ax.legend() - ylim = self._auto_ylim_after_direct_beam_trough(self.radial_mean, scale=2.0) - if ylim is not None: - ax.set_ylim(*ylim) + ax.set_yscale("log") + # ylim = self._auto_ylim_after_direct_beam_trough(self.radial_mean, scale=2.0) + # if ylim is not None: + # ax.set_ylim(*ylim) plt.tight_layout() if returnfig: @@ -1138,10 +1137,10 @@ def plot_background_fits( if self.Ik is None or self.bg is None: raise RuntimeError("Radial mean intensity or background has not been calculated yet.") - x = np.asarray(self.qq * 0.01488) + x = np.asarray(self.qq) y1 = np.asarray(self.radial_mean) x, y1 = self._apply_xrange(x, y1, qmin, qmax) - x = np.asarray(self.qq * 0.01488) + x = np.asarray(self.qq) y2 = np.asarray(self.bg) x, y2 = self._apply_xrange(x, y2, qmin, qmax) @@ -1152,10 +1151,11 @@ def plot_background_fits( ax.set_ylabel("Intensity (a.u.)") ax.set_title("Radial Mean Intensity and Background Fit") ax.legend() + ax.set_yscale("log") plt.tight_layout() - ylim = self._auto_ylim_after_direct_beam_trough(self.radial_mean, scale=2.0) - if ylim is not None: - ax.set_ylim(*ylim) + # ylim = self._auto_ylim_after_direct_beam_trough(self.radial_mean, scale=2.0) + # if ylim is not None: + # ax.set_ylim(*ylim) if returnfig: return fig @@ -1181,7 +1181,7 @@ def plot_reduced_sf( if Fk is None: Fk = self.Fk_masked - x = np.asarray(self.qq * 0.01488) + x = np.asarray(self.qq) y = np.asarray(Fk) x, y = self._apply_xrange(x, y, qmin, qmax) @@ -1292,7 +1292,7 @@ def plot_oscillation_damping( figsize: tuple[float, float] = (8, 4), returnfig: bool = False, ): - k = np.asarray(self.qq * 0.1488) + k = np.asarray(self.qq) fig, axes = plt.subplots(2, 2, figsize=figsize) From 6af80ce8b6a3bb4d807e7ba277dd18038b47f93a Mon Sep 17 00:00:00 2001 From: Karen Ehrhardt Date: Thu, 19 Feb 2026 18:28:20 -0800 Subject: [PATCH 125/140] make torch native and general cleanup --- .../core/datastructures/polar4dstem.py | 235 ++-- src/quantem/diffraction/polar.py | 1202 ++++++++--------- tests/diffraction/test_polar.py | 409 ++++++ 3 files changed, 1134 insertions(+), 712 deletions(-) create mode 100644 tests/diffraction/test_polar.py diff --git a/src/quantem/core/datastructures/polar4dstem.py b/src/quantem/core/datastructures/polar4dstem.py index e832e26d..dd4fa21e 100644 --- a/src/quantem/core/datastructures/polar4dstem.py +++ b/src/quantem/core/datastructures/polar4dstem.py @@ -1,8 +1,11 @@ +from __future__ import annotations + from typing import TYPE_CHECKING, Any import numpy as np +import torch +import torch.nn.functional as F from numpy.typing import NDArray -from scipy.ndimage import map_coordinates if TYPE_CHECKING: from .dataset4dstem import Dataset4dstem @@ -22,6 +25,7 @@ def __init__( units: list[str] | tuple | list, signal_units: str = "arb. units", metadata: dict | None = None, + origin_array: NDArray | None = None, _token: object | None = None, ): if metadata is None: @@ -32,8 +36,6 @@ def __init__( "polar_radial_step", "polar_num_annular_bins", "polar_two_fold_rotation_symmetry", - "polar_origin_row", - "polar_origin_col", "polar_ellipse_params", ] for k in mdata_keys_polar: @@ -49,6 +51,8 @@ def __init__( metadata=metadata, _token=_token, ) + self._xp = np # workaround: Dataset.coords() references _xp + self.origin_array = origin_array @classmethod def from_array( @@ -92,6 +96,28 @@ def n_r(self) -> int: return int(self.array.shape[3]) +def _to_numpy(tensor: torch.Tensor) -> NDArray: + """Convert torch tensor to numpy array.""" + return tensor.detach().cpu().numpy() + + +def _normalize_coords_for_grid_sample( + coords_y: torch.Tensor, + coords_x: torch.Tensor, + height: int, + width: int, +) -> torch.Tensor: + """ + Convert pixel coordinates to normalized [-1, 1] coordinates for grid_sample. + + grid_sample expects x_norm = 2*x/(W-1) - 1, y_norm = 2*y/(H-1) - 1, + stacked as (..., 2) in [x, y] order. + """ + x_norm = 2.0 * coords_x / (width - 1) - 1.0 + y_norm = 2.0 * coords_y / (height - 1) - 1.0 + return torch.stack([x_norm, y_norm], dim=-1) + + def _precompute_polar_coords( ny: int, nx: int, @@ -103,7 +129,8 @@ def _precompute_polar_coords( radial_max: float | None, radial_step: float, two_fold_rotation_symmetry: bool, -) -> tuple[NDArray, NDArray, NDArray, float]: + device: str = "cpu", +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, float]: origin_row = float(origin_row) origin_col = float(origin_col) if radial_step <= 0: @@ -120,48 +147,85 @@ def _precompute_polar_coords( radial_max_eff = float(radial_max) if radial_max_eff <= radial_min: radial_max_eff = radial_min + radial_step - radial_bins = np.arange(radial_min, radial_max_eff, radial_step, dtype=np.float64) - if radial_bins.size == 0: - radial_bins = np.array([radial_min], dtype=np.float64) - if two_fold_rotation_symmetry: - phi_range = np.pi - else: - phi_range = 2.0 * np.pi - phi_bins = np.linspace(0.0, phi_range, num_annular_bins, endpoint=False, dtype=np.float64) - phi_grid, r_grid = np.meshgrid(phi_bins, radial_bins, indexing="ij") + radial_bins = torch.arange( + radial_min, radial_max_eff, radial_step, dtype=torch.float32, device=device + ) + if radial_bins.numel() == 0: + radial_bins = torch.tensor([radial_min], dtype=torch.float32, device=device) + phi_range = torch.pi if two_fold_rotation_symmetry else 2.0 * torch.pi + phi_bins = torch.linspace( + 0.0, phi_range, num_annular_bins + 1, dtype=torch.float32, device=device + )[:-1] + phi_grid, r_grid = torch.meshgrid(phi_bins, radial_bins, indexing="ij") if ellipse_params is None: - x = r_grid * np.cos(phi_grid) - y = r_grid * np.sin(phi_grid) + x = r_grid * torch.cos(phi_grid) + y = r_grid * torch.sin(phi_grid) else: if len(ellipse_params) != 3: raise ValueError("ellipse_params must be (a, b, theta_deg).") a, b, theta_deg = ellipse_params - theta = np.deg2rad(theta_deg) + theta = torch.deg2rad(torch.tensor(theta_deg, dtype=torch.float32, device=device)) alpha = phi_grid - theta - u = (a / b) * r_grid * np.cos(alpha) - v_prime = r_grid * np.sin(alpha) - cos_t = np.cos(theta) - sin_t = np.sin(theta) + u = (a / b) * r_grid * torch.cos(alpha) + v_prime = r_grid * torch.sin(alpha) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) x = u * cos_t - v_prime * sin_t y = u * sin_t + v_prime * cos_t coords_y = y + origin_row coords_x = x + origin_col - coords = np.stack((coords_y, coords_x), axis=0) - return coords, phi_bins, radial_bins, radial_max_eff + grid = _normalize_coords_for_grid_sample(coords_y, coords_x, ny, nx) + grid = grid.unsqueeze(0) # (1, n_phi, n_r, 2) + return grid, phi_bins, radial_bins, radial_max_eff -def find_origin( - data, +def auto_origin_id( + data: "Dataset4dstem", *, - ellipse_params=None, - num_annular_bins=180, - radial_min=0.0, - radial_max=None, - radial_step=1.0, - two_fold_rotation_symmetry=False, -): + ellipse_params: tuple[float, float, float] | None = None, + num_annular_bins: int = 180, + radial_min: float = 0.0, + radial_max: float | None = None, + radial_step: float = 1.0, + two_fold_rotation_symmetry: bool = False, + device: str = "cpu", +) -> NDArray: """ - Placeholder for future automatic diffraction center finding method. + Automatic diffraction center finding by minimizing the standard deviation + along the annular direction in the polar transform. + + For each scan position, this routine: + 1) Computes a polar transform at an initial origin (image center, or + warm-started from the previous scan position). + 2) Evaluates the sum of the standard deviation across angle (phi) over + a mid-radius band. + 3) Performs a local search over neighboring pixel origins until the + objective no longer improves. + + Parameters + ---------- + data : Dataset4dstem + A 4D-STEM dataset (or 2D diffraction pattern wrapped as 4D). + ellipse_params : tuple or None + Ellipse parameters (a, b, theta_deg) for distortion correction. + num_annular_bins : int + Number of angular bins for polar transform. + radial_min : float + Minimum radius in pixels. + radial_max : float or None + Maximum radius in pixels. + radial_step : float + Radial step size in pixels. + two_fold_rotation_symmetry : bool + If True, use only 0 to pi range for angles. + device : str + Torch device for computation ("cpu", "cuda", "cuda:0", etc.). + + Returns + ------- + origin_array : np.ndarray + Array of shape (scan_y, scan_x, 2) containing (row, col) origin + estimates in pixels. """ if len(data.array.shape) == 2: ny, nx = data.array.shape @@ -169,24 +233,19 @@ def find_origin( elif len(data.array.shape) == 4: scan_y, scan_x, ny, nx = data.array.shape else: - raise ValueError("find_origin only supports 2D or 4D-STEM datasets for now.") + raise ValueError("auto_origin_id only supports 2D or 4D-STEM datasets.") origin_array = np.zeros((scan_y, scan_x, 2), dtype=float) - - max_steps = 1000 # prevent infinite loops - - # start with center of image for now + max_steps = 1000 + # start with center but subsequent positions warm-start from the previous result estimated_origin_row = (ny - 1) / 2.0 estimated_origin_col = (nx - 1) / 2.0 - for y_pos in range(scan_y): for x_pos in range(scan_x): - print(f"Finding origin for scan pos ({y_pos}, {x_pos})") - - coords_cache = {} - + test_origin = np.array([estimated_origin_row, estimated_origin_col], dtype=float) + coords_cache: dict[tuple[int, int], float] = {} polar = data.polar_transform( - origin_array=[estimated_origin_row, estimated_origin_col], + origin_array=test_origin, ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, radial_min=radial_min, @@ -194,27 +253,22 @@ def find_origin( radial_step=radial_step, two_fold_rotation_symmetry=two_fold_rotation_symmetry, scan_pos=(y_pos, x_pos), + device=device, ) - min_r = int(np.floor(0.1 * polar.shape[1])) max_r = int(np.ceil(0.9 * polar.shape[1])) - std_est_origin = polar[:, min_r:max_r].std(axis=0) + std_est_origin = polar[:, min_r:max_r].std(dim=0) std_est_origin_sum = std_est_origin.sum() - origin_row = int(round(estimated_origin_row)) origin_col = int(round(estimated_origin_col)) coords_cache[(origin_row, origin_col)] = std_est_origin_sum - if y_pos == 0 and x_pos == 0: - print(f"Initial std sum at estimated origin: {std_est_origin_sum}") - converged = False best = std_est_origin_sum steps = 0 while not converged and steps < max_steps: steps += 1 moved = False - neighbors = [ (origin_row + dr, origin_col + dc) for dr in (-1, 0, 1) @@ -222,11 +276,11 @@ def find_origin( if not (dr == 0 and dc == 0) ] neighbors = [(r, c) for (r, c) in neighbors if 0 <= r < ny and 0 <= c < nx] - for origin_r, origin_c in neighbors: if (origin_r, origin_c) not in coords_cache: + test_origin = np.array([origin_r, origin_c], dtype=float) polar = data.polar_transform( - origin_array=[origin_r, origin_c], + origin_array=test_origin, ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, radial_min=radial_min, @@ -234,31 +288,30 @@ def find_origin( radial_step=radial_step, two_fold_rotation_symmetry=two_fold_rotation_symmetry, scan_pos=(y_pos, x_pos), + device=device, ) - std_test = polar[:, min_r:max_r].std(axis=0) + std_test = polar[:, min_r:max_r].std(dim=0) coords_cache[(origin_r, origin_c)] = std_test.sum() - if coords_cache[(origin_r, origin_c)] < best: origin_row = origin_r origin_col = origin_c best = coords_cache[(origin_r, origin_c)] moved = True - print(f"Moved to ({origin_row}, {origin_col}) with std sum {best}") - if not moved: converged = True - if y_pos == 0 and x_pos == 0: - print(f"Final std sum at found origin ({origin_row}, {origin_col}): {best}") origin_array[y_pos, x_pos, 0] = origin_row origin_array[y_pos, x_pos, 1] = origin_col + # start next scan position from this result + estimated_origin_row = float(origin_row) + estimated_origin_col = float(origin_col) return origin_array def dataset4dstem_polar_transform( self: "Dataset4dstem", - origin_array: NDArray | None = None, + origin_array: NDArray | torch.Tensor | None = None, ellipse_params: tuple[float, float, float] | None = None, num_annular_bins: int = 180, radial_min: float = 0.0, @@ -268,12 +321,15 @@ def dataset4dstem_polar_transform( name: str | None = None, signal_units: str | None = None, scan_pos: tuple[int, int] | None = None, -) -> Polar4dstem: + device: str = "cpu", +) -> Polar4dstem | torch.Tensor: if self.array.ndim != 4: raise ValueError("polar_transform requires a 4D-STEM dataset (ndim=4).") scan_y, scan_x, ny, nx = self.array.shape # Standardize origin_array input + if isinstance(origin_array, torch.Tensor): + origin_array = _to_numpy(origin_array) origin_array = np.asarray(origin_array) if origin_array is not None else None if origin_array is None: center = np.array([(ny - 1) / 2.0, (nx - 1) / 2.0], dtype=float) @@ -292,11 +348,10 @@ def dataset4dstem_polar_transform( # If scan_pos is provided, compute polar transform only for that position if scan_pos is not None: iy, ix = scan_pos - dp = self.array[iy, ix] # (ny, nx) view + dp = torch.from_numpy(self.array[iy, ix].astype(np.float32)).to(device) r0 = float(origins[iy, ix, 0]) c0 = float(origins[iy, ix, 1]) - - coords, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( + grid, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( ny=ny, nx=nx, origin_row=r0, @@ -307,12 +362,19 @@ def dataset4dstem_polar_transform( radial_max=radial_max, radial_step=radial_step, two_fold_rotation_symmetry=two_fold_rotation_symmetry, + device=device, + ) + dp_batch = dp.unsqueeze(0).unsqueeze(0) # (1, 1, ny, nx) + polar2d = F.grid_sample( + dp_batch, + grid, + mode="bilinear", + padding_mode="zeros", + align_corners=True, ) - polar2d = map_coordinates(dp, coords, order=1, mode="constant", cval=0.0) # (phi, r) - return polar2d + return polar2d.squeeze(0).squeeze(0) # (n_phi, n_r) - # Otherwise, compute polar transform for all scan positions - # Determine one overall radial_max if not provided + # Compute radial_max from all origins if not provided if radial_max is None: r_row_pos = origins[:, :, 0] r_row_neg = (ny - 1) - origins[:, :, 0] @@ -321,33 +383,30 @@ def dataset4dstem_polar_transform( radial_max_eff_array = np.minimum.reduce([r_row_pos, r_row_neg, r_col_pos, r_col_neg]) radial_max = float(max(radial_max_eff_array.min(), radial_min + radial_step)) - # Precompute polar coords only once, using the origin from the first probe position - origin_row_f = float(origins[0, 0, 0]) - origin_col_f = float(origins[0, 0, 1]) - coords, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( + # Compute grid for first position to get output shape + grid, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( ny=ny, nx=nx, - origin_row=origin_row_f, - origin_col=origin_col_f, + origin_row=float(origins[0, 0, 0]), + origin_col=float(origins[0, 0, 1]), ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, radial_min=radial_min, radial_max=radial_max, radial_step=radial_step, two_fold_rotation_symmetry=two_fold_rotation_symmetry, + device=device, ) - n_phi = phi_bins.size - n_r = radial_bins.size + n_phi = phi_bins.numel() + n_r = radial_bins.numel() result_dtype = np.result_type(self.array.dtype, np.float32) out = np.empty((scan_y, scan_x, n_phi, n_r), dtype=result_dtype) - for iy in range(scan_y): for ix in range(scan_x): - dp = self.array[iy, ix] + dp = torch.from_numpy(self.array[iy, ix].astype(np.float32)).to(device) r0 = float(origins[iy, ix, 0]) c0 = float(origins[iy, ix, 1]) - - coords, _, _, radial_max_eff = _precompute_polar_coords( + grid, _, _, _ = _precompute_polar_coords( ny=ny, nx=nx, origin_row=r0, @@ -358,14 +417,17 @@ def dataset4dstem_polar_transform( radial_max=radial_max, radial_step=radial_step, two_fold_rotation_symmetry=two_fold_rotation_symmetry, + device=device, ) - out[iy, ix] = map_coordinates( - dp, - coords, - order=1, - mode="constant", - cval=0.0, + dp_batch = dp.unsqueeze(0).unsqueeze(0) + polar2d = F.grid_sample( + dp_batch, + grid, + mode="bilinear", + padding_mode="zeros", + align_corners=True, ) + out[iy, ix] = _to_numpy(polar2d.squeeze(0).squeeze(0)) phi_range = np.pi if two_fold_rotation_symmetry else 2.0 * np.pi phi_step_deg = (phi_range / float(n_phi)) * (180.0 / np.pi) @@ -391,8 +453,6 @@ def dataset4dstem_polar_transform( "polar_radial_step": float(radial_step), "polar_num_annular_bins": int(n_phi), "polar_two_fold_rotation_symmetry": bool(two_fold_rotation_symmetry), - "polar_origin_row": float(origins[0, 0, 0]), - "polar_origin_col": float(origins[0, 0, 1]), "polar_ellipse_params": tuple(ellipse_params) if ellipse_params is not None else None, } ) @@ -404,5 +464,6 @@ def dataset4dstem_polar_transform( units=units, signal_units=signal_units if signal_units is not None else self.signal_units, metadata=metadata, + origin_array=origins, _token=Polar4dstem._token, ) diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py index 95fc52a7..5036ebac 100644 --- a/src/quantem/diffraction/polar.py +++ b/src/quantem/diffraction/polar.py @@ -1,23 +1,28 @@ from __future__ import annotations from collections.abc import Iterable -from pathlib import Path -from typing import Any, Literal, Tuple, Union +from typing import Any, Literal import matplotlib.pyplot as plt import numpy as np +import torch +import torch.nn.functional as F from numpy.typing import NDArray -from scipy.ndimage import gaussian_filter1d -from scipy.optimize import curve_fit from quantem.core.datastructures.dataset2d import Dataset2d from quantem.core.datastructures.dataset3d import Dataset3d from quantem.core.datastructures.dataset4dstem import Dataset4dstem -from quantem.core.datastructures.polar4dstem import Polar4dstem +from quantem.core.datastructures.polar4dstem import ( + Polar4dstem, + auto_origin_id, + dataset4dstem_polar_transform, +) from quantem.core.io.serialize import AutoSerialize from quantem.core.utils.validators import ensure_valid_array -KIRKLAND_PARAMS_PATH = Path(__file__).with_name("kirkland_params.json") +# TODO: subpixel origin finding (auto_origin_id currently uses integer pixel search) +# TODO: elliptical distortion correction in origin finding +# TODO: beamstop mask support (mask diffraction-space pixels before azimuthal averaging) class PairDistributionFunction(AutoSerialize): @@ -42,6 +47,7 @@ def __init__( self, polar: Polar4dstem, input_data: Any | None = None, + device: str = "cpu", _token: object | None = None, ): if _token is not self._token: @@ -52,21 +58,18 @@ def __init__( super().__init__() self.polar = polar self.input_data = input_data - - # Placeholders for analysis results (to be populated by future methods) - self.radial_mean: NDArray | None = None - self.radial_var: NDArray | None = None - self.radial_var_norm: NDArray | None = None - - self.pdf_r: NDArray | None = None - self.reduced_pdf: NDArray | None = None - self.pdf: NDArray | None = None - - self.Sk: NDArray | None = None - self.fk: NDArray | None = None - self.bg: NDArray | None = None - self.offset: float | None = None - self.Sk_mask: NDArray | None = None + self.device = device + + self._polar_tensor: torch.Tensor | None = None + self._r: torch.Tensor | None = None + self._reduced_pdf: torch.Tensor | None = None + self._pdf: torch.Tensor | None = None + self.radial_mean: torch.Tensor | None = None + self.Sk: torch.Tensor | None = None + self.Fk: torch.Tensor | None = None + self.bg: torch.Tensor | None = None + self.Fk_mask: torch.Tensor | None = None + self.rho0: float | None = None # ------------------------------------------------------------------ # Constructors @@ -74,7 +77,7 @@ def __init__( @classmethod def from_data( cls, - data: Union[NDArray, Dataset2d, Dataset3d, Dataset4dstem, Polar4dstem], + data: NDArray | Dataset2d | Dataset3d | Dataset4dstem | Polar4dstem, *, find_origin: bool = True, origin_row: float | None = None, @@ -85,6 +88,7 @@ def from_data( radial_max: float | None = None, radial_step: float = 1.0, two_fold_rotation_symmetry: bool = False, + device: str = "cpu", ): """ -> "PairDistributionFunction" @@ -115,49 +119,16 @@ def from_data( # Polar input: use directly if isinstance(data, Polar4dstem): polar = data - return cls(polar=polar, input_data=data, _token=cls._token) - - # Dataset4dstem input: polar-transform it - if isinstance(data, Dataset4dstem): - scan_y, scan_x, ny, nx = data.array.shape - if find_origin: - origin_array = cls.find_origin( - data, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - ) - else: - if origin_row is None: - origin_row = (ny - 1) / 2.0 - if origin_col is None: - origin_col = (nx - 1) / 2.0 - origin_array = np.zeros((scan_y, scan_x, 2), dtype=float) - origin_array[..., 0] = origin_row - origin_array[..., 1] = origin_col + return cls(polar=polar, input_data=data, device=device, _token=cls._token) - polar = data.polar_transform( - origin_array=origin_array, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - ) - return cls(polar=polar, input_data=data, _token=cls._token) - - # Dataset2d input: wrap as a trivial 4D-STEM (1x1 scan) then polar-transform + # Dataset2d input: wrap as a trivial 4D-STEM (1x1 scan) and fall through if isinstance(data, Dataset2d): arr2d = data.array if arr2d.ndim != 2: raise ValueError("Dataset2d for PairDistributionFunction must be 2D.") arr4 = arr2d[None, None, ...] # (1, 1, ky, kx) - ds4 = Dataset4dstem.from_array( + data = Dataset4dstem.from_array( array=arr4, name=f"{data.name}_as4dstem" if getattr(data, "name", None) @@ -171,27 +142,32 @@ def from_data( units=["pixels", "pixels"] + list(data.units), signal_units=data.signal_units, ) - ny, nx = ds4.array.shape[-2:] + # Dataset4dstem input: polar-transform it + if isinstance(data, Dataset4dstem): + scan_y, scan_x, ny, nx = data.array.shape if find_origin: - origin_array = cls.find_origin( - ds4, + origin_array = auto_origin_id( + data, ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, radial_min=radial_min, radial_max=radial_max, radial_step=radial_step, two_fold_rotation_symmetry=two_fold_rotation_symmetry, + device=device, ) else: if origin_row is None: origin_row = (ny - 1) / 2.0 if origin_col is None: origin_col = (nx - 1) / 2.0 - origin_array = np.zeros((1, 1, 2), dtype=float) - origin_array[0, 0] = [origin_row, origin_col] + origin_array = np.zeros((scan_y, scan_x, 2), dtype=float) + origin_array[..., 0] = origin_row + origin_array[..., 1] = origin_col - polar = ds4.polar_transform( + polar = dataset4dstem_polar_transform( + data, origin_array=origin_array, ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, @@ -199,8 +175,9 @@ def from_data( radial_max=radial_max, radial_step=radial_step, two_fold_rotation_symmetry=two_fold_rotation_symmetry, + device=device, ) - return cls(polar=polar, input_data=data, _token=cls._token) + return cls(polar=polar, input_data=data, device=device, _token=cls._token) # Dataset3d input: not yet specified how to interpret if isinstance(data, Dataset3d): @@ -215,154 +192,35 @@ def from_data( return cls.from_data( ds2, + find_origin=find_origin, + origin_row=origin_row, + origin_col=origin_col, ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, radial_min=radial_min, radial_max=radial_max, radial_step=radial_step, two_fold_rotation_symmetry=two_fold_rotation_symmetry, + device=device, ) elif arr.ndim == 4: ds4 = Dataset4dstem.from_array(arr, name="rdf_input_4dstem") return cls.from_data( ds4, + find_origin=find_origin, + origin_row=origin_row, + origin_col=origin_col, ellipse_params=ellipse_params, num_annular_bins=num_annular_bins, radial_min=radial_min, radial_max=radial_max, radial_step=radial_step, two_fold_rotation_symmetry=two_fold_rotation_symmetry, + device=device, ) else: raise ValueError("PairDistributionFunction.from_data only supports 2D or 4D arrays.") - @staticmethod - def find_origin( - data, - *, - ellipse_params=None, - num_annular_bins=180, - radial_min=0.0, - radial_max=None, - radial_step=1.0, - two_fold_rotation_symmetry=False, - ): - """ - Automatic diffraction center finding by minmizing the standard deviation along the annular direction. - - For each scan position, this routine: - 1) Computes a polar transform at an initial origin (image center). - 2) Evaluates the sum of the standard deviation across angle (phi) over a mid-radius band. - 3) Performs a local search over neighboring pixel origins until the - objective no longer improves. - - Parameters - ---------- - data - A :class:`Dataset4dstem` object - ellipse_params, num_annular_bins, radial_min, radial_max, radial_step, two_fold_rotation_symmetry - Forwarded to the polar transform call. - - Returns - ------- - origin_array : np.ndarray - Array of shape (scan_y, scan_x, 2) containing (row, col) origin estimates in pixels. - - """ - if len(data.array.shape) == 2: - ny, nx = data.array.shape - scan_y, scan_x = 1, 1 - elif len(data.array.shape) == 4: - scan_y, scan_x, ny, nx = data.array.shape - else: - raise ValueError("find_origin only supports 2D or 4D-STEM datasets for now.") - - origin_array = np.zeros((scan_y, scan_x, 2), dtype=float) - - max_steps = 1000 # prevent infinite loops - - # start with center of image for now - estimated_origin_row = (ny - 1) / 2.0 - estimated_origin_col = (nx - 1) / 2.0 - test_origin = np.array([[[estimated_origin_row, estimated_origin_col]]], dtype=float) - - for y_pos in range(scan_y): - for x_pos in range(scan_x): - # print(f"Finding origin for scan pos ({y_pos}, {x_pos})") - - coords_cache = {} - - polar = data.polar_transform( - origin_array=test_origin, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - scan_pos=(y_pos, x_pos), - ) - - min_r = int(np.floor(0.1 * polar.shape[1])) - max_r = int(np.ceil(0.9 * polar.shape[1])) - std_est_origin = polar[:, min_r:max_r].std(axis=0) - std_est_origin_sum = std_est_origin.sum() - - origin_row = int(round(estimated_origin_row)) - origin_col = int(round(estimated_origin_col)) - coords_cache[(origin_row, origin_col)] = std_est_origin_sum - - if y_pos == 0 and x_pos == 0: - print(f"Initial std sum at estimated origin: {std_est_origin_sum}") - - converged = False - best = std_est_origin_sum - steps = 0 - while not converged and steps < max_steps: - steps += 1 - moved = False - - neighbors = [ - (origin_row + dr, origin_col + dc) - for dr in (-1, 0, 1) - for dc in (-1, 0, 1) - if not (dr == 0 and dc == 0) - ] - neighbors = [(r, c) for (r, c) in neighbors if 0 <= r < ny and 0 <= c < nx] - - for origin_r, origin_c in neighbors: - if (origin_r, origin_c) not in coords_cache: - test_origin = np.array([[[origin_r, origin_c]]], dtype=float) - polar = data.polar_transform( - origin_array=test_origin, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - scan_pos=(y_pos, x_pos), - ) - std_test = polar[:, min_r:max_r].std(axis=0) - coords_cache[(origin_r, origin_c)] = std_test.sum() - - if coords_cache[(origin_r, origin_c)] < best: - origin_row = origin_r - origin_col = origin_c - best = coords_cache[(origin_r, origin_c)] - moved = True - print(f"Moved to ({origin_row}, {origin_col}) with std sum {best}") - - if not moved: - converged = True - - if y_pos == 0 and x_pos == 0: - print(f"Final std sum at found origin ({origin_row}, {origin_col}): {best}") - origin_array[y_pos, x_pos, 0] = origin_row - origin_array[y_pos, x_pos, 1] = origin_col - - return origin_array - # ------------------------------------------------------------------ # Convenience accessors # ------------------------------------------------------------------ @@ -383,6 +241,27 @@ def radial_bins(self) -> Any: """ return self.polar.coords(3) + @property + def r(self) -> NDArray | None: + """Real-space radial grid as a numpy array.""" + if self._r is None: + return None + return self._to_numpy(self._r) + + @property + def reduced_pdf(self) -> NDArray | None: + """Reduced pair distribution function G(r) as a numpy array.""" + if self._reduced_pdf is None: + return None + return self._to_numpy(self._reduced_pdf) + + @property + def pdf(self) -> NDArray | None: + """Pair distribution function g(r) as a numpy array.""" + if self._pdf is None: + return None + return self._to_numpy(self._pdf) + # ------------------------------------------------------------------ # Helper functions # ------------------------------------------------------------------ @@ -390,13 +269,6 @@ def _get_mask_bool(self, mask_realspace): """ Normalize a real-space mask specification to a boolean (rx, ry) mask. - Parameters - ---------- - mask_realspace - - None: no masking - - bool ndarray of shape (rx, ry): True indicates included probe positions - - array-like of shape (2, 2): two opposite (rx, ry) corners defining a rectangle - Returns ------- mask_bool : np.ndarray or None @@ -407,104 +279,210 @@ def _get_mask_bool(self, mask_realspace): rx, ry = self.polar.array.shape[:2] mask_realspace = np.asarray(mask_realspace) - # mask given as boolean array if mask_realspace.dtype == bool and mask_realspace.shape == (rx, ry): mask_bool = mask_realspace + else: + raise ValueError("mask_realspace must be boolean array.") + return mask_bool - # mask given as list of corners - elif mask_realspace.shape == (2, 2): - (rx1, ry1), (rx2, ry2) = mask_realspace.astype(int) - rx_min, rx_max = sorted((rx1, rx2)) - ry_min, ry_max = sorted((ry1, ry2)) + # ------------------------------------------------------------------ + # Torch conversion utilities + # ------------------------------------------------------------------ + @property + def polar_tensor(self) -> torch.Tensor: + if self._polar_tensor is None: + self._polar_tensor = torch.from_numpy(self.polar.array.astype(np.float32)).to( + device=self.device + ) + return self._polar_tensor - # vectorized bounds check - bad = (rx_min < 0) | (rx_max >= rx) | (ry_min < 0) | (ry_max >= ry) - if bad: - raise ValueError(f"Mask points outside valid range {(rx, ry)}") + def _to_torch(self, arr: NDArray) -> torch.Tensor: + return torch.from_numpy(arr.astype(np.float32)).to(device=self.device) - mask_bool = np.zeros((rx, ry), dtype=bool) - mask_bool[rx_min : rx_max + 1, ry_min : ry_max + 1] = True - else: - raise ValueError( - "mask_realspace must be boolean array or two opposite (rx, ry) corner points." - ) - return mask_bool + def _to_numpy(self, tensor: torch.Tensor) -> NDArray: + return tensor.detach().cpu().numpy() @staticmethod - def _scattering_model(k2, c, i0, s0, i1, s1): + def _gaussian_kernel_1d( + sigma: float, device: str = "cpu", num_sigmas: float = 3.0 + ) -> torch.Tensor: + """Create 1D Gaussian kernel for torch convolution.""" + radius = int(np.ceil(num_sigmas * sigma)) + support = torch.arange(-radius, radius + 1, dtype=torch.float32, device=device) + kernel = torch.exp(-0.5 * (support / sigma) ** 2) + kernel = kernel / kernel.sum() + return kernel + + def _gaussian_filter1d_torch( + self, + Fk: torch.Tensor, + sigma: float, + mode: str = "nearest", + ) -> torch.Tensor: """ - Background model used for fitting I(k). - Model form (using k^2 as input): - c + i0 * exp(-k^2 / (2 s0^2)) + i1 * exp(-k^4 / (2 s1^4)) + Apply 1D Gaussian filter, replaces scipy.ndimage.gaussian_filter1d. + """ + kernel = self._gaussian_kernel_1d(sigma, device=self.device) + padding = len(kernel) // 2 + x = Fk.unsqueeze(0).unsqueeze(0) # reshape to (batch, channels, length) + kernel_w = kernel.view(1, 1, -1) + if mode == "nearest": + x = F.pad(x, (padding, padding), mode="replicate") + result = F.conv1d(x, kernel_w) + else: + result = F.conv1d(x, kernel_w, padding=padding) + return result.squeeze(0).squeeze(0) # reshape to (length) - Parameters - ---------- - k2 - Array of k^2 values. - c, i0, s0, i1, s1 - Model parameters. + @staticmethod + def _scattering_model_torch( + k2: torch.Tensor, + c: torch.Tensor, + i0: torch.Tensor, + s0: torch.Tensor, + i1: torch.Tensor, + s1: torch.Tensor, + ) -> torch.Tensor: + """Torch version of the scattering model.""" + # Add small epsilon to denominators to prevent division by zero during backprop + # while still allowing s0/s1 to vary freely + eps = 1e-10 + exp1 = torch.clamp(k2 / (-2.0 * (s0**2 + eps)), min=-100, max=0) + exp2 = torch.clamp((k2**2) / (-2.0 * (s1**4 + eps)), min=-100, max=0) + return c + i0 * torch.exp(exp1) + i1 * torch.exp(exp2) + + def _compute_fit_weights(self, k: torch.Tensor, kmin: float, kmax: float) -> torch.Tensor: + """ + Compute weighting tensor for background fitting. + Weights downweight low-k region (using sin² taper) and emphasize high-k values. """ - return ( - c - + i0 * np.exp(k2 / (-2.0 * s0**2)) - + i1 * np.exp((k2**2) / (-2.0 * s1**4)) # k2**2 = k^4 + dk = k[1] - k[0] + k_width = kmax - kmin + + # sin² taper for low-k suppression + mask_low = torch.sin(torch.clamp((k - kmin) / k_width, 0.0, 1.0) * (torch.pi / 2.0)) ** 2 + # high weight where mask_low is small + # later used to divide, so large weights mean small contribution + weights = torch.where( + mask_low > 1e-4, + 1.0 / mask_low, + torch.tensor(1e6, device=self.device, dtype=k.dtype), ) + # emphasize high-k values + weights = weights * (k[-1] - 0.9 * k + dk) + return weights + + def _closure(self, optimizer, theta, k2, Ik_norm, weights): + """match scipy curve_fit behavior""" + optimizer.zero_grad() + # Map from unconstrained to constrained (positive) space via softplus + c = F.softplus(theta[0]) + i0 = F.softplus(theta[1]) + s0 = F.softplus(theta[2]) + i1 = F.softplus(theta[3]) + s1 = F.softplus(theta[4]) + + pred = self._scattering_model_torch(k2, c, i0, s0, i1, s1) + residuals = (pred - Ik_norm) ** 2 + loss = (residuals / (weights**2)).sum() + loss.backward() + return loss + + def _frequency_filtering( + self, + Fk: torch.Tensor, + k_lowpass: float | None, + k_highpass: float | None, + dk: torch.Tensor, + ) -> torch.Tensor: + """Band pass filtering using torch""" + if ( + k_lowpass is not None + and k_lowpass > 0.0 + and k_highpass is not None + and k_highpass > 0.0 + ): + if k_highpass > k_lowpass: + raise ValueError("Gaussian band-pass filtering requires k_highpass < k_lowpass.") + Fk_low = self._gaussian_filter1d_torch(Fk, sigma=k_lowpass / dk.item(), mode="nearest") + Fk_high = self._gaussian_filter1d_torch( + Fk, sigma=k_highpass / dk.item(), mode="nearest" + ) + Fk = Fk_high - Fk_low + elif k_lowpass is not None and k_lowpass > 0.0: + Fk = self._gaussian_filter1d_torch(Fk, sigma=k_lowpass / dk.item(), mode="nearest") + elif k_highpass is not None and k_highpass > 0.0: + Fk_high = self._gaussian_filter1d_torch( + Fk, sigma=k_highpass / dk.item(), mode="nearest" + ) + Fk = Fk - Fk_high + return Fk - @staticmethod - def _lorch_window(k, kmin, kmax): + def _lorch_window(self, k: torch.Tensor, kmin: float, kmax: float) -> torch.Tensor: """ Construct a combined low-q taper and high-q Lorch window. The returned window is: - zero outside [kmin, kmax] - - smoothly rises from 0→1 near kmin using a sin^2 ramp over 10% of the band + - smoothly rises from 0->1 near kmin using a sin^2 ramp over 10% of the band - applies a Lorch-style sinc factor over the full in-band region: sin(pi * k/kmax) / (pi * k/kmax) """ # low q taper edge_frac_low = 0.1 # 10% of range at low-q edge_width_low = edge_frac_low * (kmax - kmin) - - wk = np.ones_like(k, dtype=float) low = (k >= kmin) & (k < kmin + edge_width_low) - t = (k[low] - kmin) / edge_width_low - wk[low] = np.sin(0.5 * np.pi * t) ** 2 - wk[k < kmin] = 0.0 - wk[k > kmax] = 0.0 - - # high q taper with Lorch window: w(k) = sin(pi*k/kmax)/(pi*k/kmax) - lorch = np.zeros_like(k, dtype=float) + t = (k - kmin) / edge_width_low + wk = torch.ones_like(k) + wk = torch.where(low, torch.sin(0.5 * torch.pi * t) ** 2, wk) + wk = torch.where(k < kmin, torch.zeros_like(wk), wk) + wk = torch.where(k > kmax, torch.zeros_like(wk), wk) + + # High q taper with Lorch window: w(k) = sin(pi*k/kmax)/(pi*k/kmax) + x = k / kmax inband = (k >= kmin) & (k <= kmax) - x = k[inband] / kmax - lorch[inband] = np.where(x == 0, 1.0, np.sin(np.pi * x) / (np.pi * x)) - - wk *= lorch - + # sinc function: sin(pi*x)/(pi*x) with limit 1 at x=0 + sinc_val = torch.where( + x == 0, + torch.ones_like(x), + torch.sin(torch.pi * x) / (torch.pi * x), + ) + lorch = torch.where(inband, sinc_val, torch.zeros_like(k)) + wk = wk * lorch return wk - @staticmethod - def _compute_alpha_beta(Q2d, r2d, G_beta, r_1d): + def _compute_alpha_beta( + self, + Q2d: torch.Tensor, + r2d: torch.Tensor, + G_beta: torch.Tensor, + r_1d: torch.Tensor, + ) -> tuple[torch.Tensor, torch.Tensor]: """ Compute Yoshimoto-Omote alpha(Q) and beta(Q) integrals used for density estimation. - This is an internal helper that performs the r-integrals via trapezoidal integration. """ - Qsafe = np.where(Q2d == 0.0, 1e-12, Q2d) - alpha_int = -4 * np.pi * r2d * np.sin(Qsafe * r2d) / Qsafe - beta_int = G_beta[None, :] * np.sin(Qsafe * r2d) / Qsafe - alpha = np.trapz(alpha_int, x=r_1d, axis=1) - beta = np.trapz(beta_int, x=r_1d, axis=1) + Qsafe = torch.where( + Q2d == 0.0, + torch.tensor(1e-12, device=self.device, dtype=torch.float32), + Q2d, + ) + alpha_int = -4 * torch.pi * r2d * torch.sin(Qsafe * r2d) / Qsafe + beta_int = G_beta.unsqueeze(0) * torch.sin(Qsafe * r2d) / Qsafe + alpha = torch.trapezoid(alpha_int, x=r_1d, dim=1) + beta = torch.trapezoid(beta_int, x=r_1d, dim=1) return alpha, beta # ------------------------------------------------------------------ - # Analysis method stubs (py4DSTEM-style API) + # Analysis method stubs # ------------------------------------------------------------------ - # TODO: linting and docstrings + # TODO: add beamstop mask support (mask diffraction-space pixels before + # azimuthal averaging, e.g. to exclude a beam stop shadow) + def calculate_radial_mean( self, mask_realspace: NDArray | None = None, returnval: bool = False, - ): + ) -> torch.Tensor | None: """ Calculate the radial mean intensity from the Polar4dSTEM dataset. @@ -522,43 +500,46 @@ def calculate_radial_mean( Boolean mask in real space used to select probe positions. If ``None``, all probe positions are used. Must have shape (scan_y, scan_x) where True means "include". - (If using rectangle-corner inputs, pass them through - `_get_mask_bool` before calling this method.) returnval : bool, optional - If True, return the computed 1D radial mean array. + If True, return the computed 1D radial mean tensor. Returns ------- - radial_mean : np.ndarray or None + radial_mean : torch.Tensor or None If `returnval=True`, returns the 1D radial mean intensity (Nk,). - Otherwise returns None unless `returnfig=True`. + Otherwise returns None. """ + polar_data = self.polar_tensor # shape: (scan_y, scan_x, phi, k) - # init radial data array if mask_realspace is None: - # calculate intensity over q-range for each probe position - radial_probe = self.polar.array.mean(axis=2) # axis 0: ry, 1: rx, 2: theta, 3: q - # average over all probe positions - self.radial_mean = np.mean(radial_probe, axis=(0, 1)) - - elif mask_realspace is not None: - masked_polar = self.polar.array[mask_realspace] # (N_valid, N_theta, N_k) - radial_probe = masked_polar.mean(axis=1) - # average over all probe positions, only those unmasked - self.radial_mean = radial_probe.mean(axis=0) + # intensity over q-range for each probe position then average + radial_probe = polar_data.mean(dim=2) # dim 2: theta + self.radial_mean = radial_probe.mean(dim=(0, 1)) + else: + mask_torch = torch.from_numpy(mask_realspace).to(device=self.device) + masked_polar = polar_data[mask_torch] # (N_valid, N_theta, N_k) + # intensity over q-range of each unmasked probe position + radial_probe = masked_polar.mean(dim=1) + # average over unmasked probe positions + self.radial_mean = radial_probe.mean(dim=0) if returnval: return self.radial_mean else: - return + return None - def fit_bg(self, Ik, kmin, kmax): + def fit_bg( + self, + Ik: torch.Tensor, + kmin: float, + kmax: float, + ) -> tuple[torch.Tensor, torch.Tensor]: """ Fit a smooth background B(k) to a radial intensity curve I(k) using - non-linear least squares (SciPy `curve_fit`), with a weighting that - downweights the low-k region and emphasizes higher k. + PyTorch LBFGS optimizer, with weighting that downweights the low-k + region and emphasizes higher k. - The fitted function uses the following form: + The fitted function uses the following form (adopted from py4dstem): B(k) = c + i0 * exp(-k^2 / (2 s0^2)) + i1 * exp(-k^4 / (2 s1^4)) @@ -566,306 +547,297 @@ def fit_bg(self, Ik, kmin, kmax): Parameters ---------- Ik - 1D radial intensity array (Nk,). Typically produced by + 1D radial intensity tensor (Nk,). Produced by :meth:`calculate_radial_mean`. kmin, kmax k-range (in the same units as the internally constructed `k` grid) - used to build the low-k weighting mask. (Currently k is derived from - `self.qq` with a calibration factor.) + used to build the low-k weighting mask. Returns ------- - bg : np.ndarray + bg : torch.Tensor Fitted background curve B(k), shape (Nk,). - f : np.ndarray + f : torch.Tensor Background minus the constant offset, f(k) = B(k) - c, or functionally - similar to ⟨f⟩²(k) + similar to ^2(k) """ - - k = self.qq - - int_mean = np.mean(Ik) + k = self._to_torch(np.asarray(self.qq)) k2 = k**2 + # normalize intensity + int_mean = Ik.mean() + Ik_norm = Ik / int_mean # initial guesses - const_bg = np.min(Ik) / int_mean - int0 = np.median(Ik) / int_mean - const_bg - sigma0 = np.mean(k) - p0 = [const_bg, int0, sigma0, int0, sigma0] - - dk = k[1] - k[0] - k_width = kmax - kmin - mask_low = ( - np.sin( - np.clip( - (k - kmin) / k_width, - 0, - 1, - ) - * np.pi - / 2.0, - ) - ** 2 + const_bg = float(Ik_norm.min()) + int0 = float(Ik_norm.median()) - const_bg + sigma0 = float(k.mean()) + # ensure positive values + const_bg = max(const_bg, 1e-6) + int0 = max(int0, 1e-6) + sigma0 = max(sigma0, 1e-6) + + init_vals = torch.tensor( + [const_bg, int0, sigma0, int0, sigma0], + device=self.device, + dtype=torch.float32, ) - # weighting function for fitting atomic scattering factors - weights_fit = np.divide( - 1, - mask_low, - where=mask_low > 1e-4, + # Map to unconstrained space via inverse softplus: x = y + log(1 - exp(-y)) + # For numerical stability, clamp init_vals away from zero + init_vals = torch.clamp(init_vals, min=1e-6) + theta = init_vals + torch.log(-torch.expm1(-init_vals)) + theta = theta.clone().detach().requires_grad_(True) + optimizer = torch.optim.LBFGS( + [theta], + lr=1.0, + max_iter=20, + tolerance_grad=1e-7, + tolerance_change=1e-9, + line_search_fn="strong_wolfe", ) - weights_fit[mask_low <= 1e-4] = np.inf - # Scale weighting to favour high k values - weights_fit *= k[-1] - 0.9 * k + dk - - # bounds - lb = [0, 0, 0, 0, 0] - ub = [np.inf, np.inf, np.inf, np.inf, np.inf] - - # fit normalized data - kwargs = dict(sigma=weights_fit, p0=p0, bounds=(lb, ub), xtol=1e-8, maxfev=10000) - - coefs, pcov = curve_fit(self._scattering_model, k2, Ik / int_mean, **kwargs) - - # rescale back to original intensity units (same as script) - coefs = np.array(coefs, float) - coefs[0] *= int_mean - coefs[1] *= int_mean - coefs[3] *= int_mean - - bg = self._scattering_model(k2, *coefs) - f = bg - coefs[0] # "form factor" without constant offset, like the script + # k-dependent fitting weights + weights = self._compute_fit_weights(k, kmin, kmax) + + prev_loss = torch.tensor(float("inf")) + max_outer_iter = 100 + tol = 1e-8 + for step in range(max_outer_iter): + loss = optimizer.step(lambda: self._closure(optimizer, theta, k2, Ik_norm, weights)) + if torch.abs(prev_loss - loss) < tol: + break + prev_loss = loss + + # final params + with torch.no_grad(): + c = F.softplus(theta[0]) + i0 = F.softplus(theta[1]) + s0 = F.softplus(theta[2]) + i1 = F.softplus(theta[3]) + s1 = F.softplus(theta[4]) + # undo normalization + c_scaled = c * int_mean + i0_scaled = i0 * int_mean + i1_scaled = i1 * int_mean + # compute bg + bg = self._scattering_model_torch(k2, c_scaled, i0_scaled, s0, i1_scaled, s1) + f = bg - c_scaled return bg, f - def calculate_pair_dist_function( + def calculate_Gr( self, - k_min: float = 0.05, + k_min: float | None = None, k_max: float | None = None, - k_width: float = 0.25, k_lowpass: float | None = None, k_highpass: float | None = None, r_min: float = 0.0, r_max: float = 20.0, r_step: float = 0.02, mask_realspace: NDArray | None = None, - calculate_pdf: bool = False, - density: float | None = None, damp_origin_oscillations: bool = False, - set_pdf_positive: bool = False, + r_cut: float = 0.8, returnval: bool = False, - ): + ) -> list[NDArray] | None: """ - Calculate the (reduced) pair distribution function from a 4D-STEM dataset. + Calculate the reduced pair distribution function G(r) from a 4D-STEM dataset. This routine: * Computes the radial mean intensity I(k) from self.polar (optionally restricted to a real-space mask). - * Fit a smooth background B(k) and associated f(k) using :meth:`fit_bg`. - * Estimates and subtracts a background from I(k). + * Fits a smooth background B(k) and associated f(k) using :meth:`fit_bg`. * Constructs the reduced structure factor F(k) with optional low/highpass filtering. - * Apply a window in k (low-k sin^2 ramp × Lorch high-k taper) - * Compute the reduced PDF using a discrete sine transform: - G(r) = sum_k sin(2π k r) * F_windowed(k) - * If `calculate_pdf=True`, g(r) is computed from G(r) using: - g(r) = 1 + G(r) / (4π r ρ0) - with ρ0 either provided by the user (`density`) or estimated via - :meth:`estimate_density`. - - The computed quantities are also stored on the instance as: - * self.radial_mean – radial mean intensity I(k) (via calculate_radial_mean) - * self.bg – background bg(k) - * self.Sk – structure factor (computed as 1 + (Ik - bg)/f) - * self.Fk – unwindowed reduced structure function F(k) - * self.Fk_masked – windowed reduced structure function F(k) - * self.r – r grid (in angstroms) - * self.reduced_pdf – reduced PDF G(r) - * self.pdf – PDF g(r) (if computed) + * Applies a window in k (low-k sin^2 ramp x Lorch high-k taper). + * Computes the reduced PDF using a discrete sine transform: + G(r) = sum_k sin(2*pi*k*r) * F_windowed(k) + + If ``damp_origin_oscillations=True``, :meth:`estimate_density` is called + and the corrected F(k)/G(r) are stored as ``self.Fk_damped`` and + ``self.reduced_pdf_damped``. The estimated density is cached in + ``self.rho0`` so that a subsequent :meth:`calculate_gr` call can reuse it. + + Stored attributes: + * self.radial_mean, self.Ik, self.bg, self.Fk, self.Fk_masked + * self.Sk, self.r, self.reduced_pdf + * self.rho0, self.Fk_damped, self.reduced_pdf_damped (if damping) Parameters ---------- k_min : float, optional - Minimum k (Å⁻¹) to use when building masks and transforms. If None, - `self.kmin` is set to `k.min()`. + Minimum k (A^-1) for masks and transforms. k_max : float or None, optional - Maximum k (Å⁻¹) to use when building masks and transforms. If None, - `self.kmax` is set to `k.max()`. - k_width : float, optional - Width parameter (in Å⁻¹) intended for edge masks. Note: in the current implementation - this parameter is not yet used as a true "width"; the code uses `k_width = kmax-kmin`. + Maximum k (A^-1) for masks and transforms. k_lowpass : float or None, optional - If provided and > 0, applies a low-pass Gaussian filter to F(k) with - sigma = k_lowpass / dk, where dk is the k-grid spacing. + Low-pass Gaussian filter sigma in k-space. k_highpass : float or None, optional - If provided and > 0, constructs a low-pass filtered copy of F(k) with - sigma = k_highpass / dk and subtracts it from F(k), effectively - applying a high-pass filter. + High-pass Gaussian filter sigma in k-space. r_min : float, optional - Minimum r (Å) for the real-space grid used to compute G(r). + Minimum r (A) for the real-space grid. r_max : float, optional - Maximum r (Å) for the real-space grid used to compute G(r). + Maximum r (A) for the real-space grid. r_step : float, optional - Step size in r (Å) for the real-space grid. + Step size in r (A) for the real-space grid. mask_realspace : NDArray or None, optional - Real-space mask specifying which probe positions (rx, ry) to include. - Either: - * A boolean array of shape (rx, ry) where True means “include this - probe position”, or - * An array-like of shape (2, 2) giving two opposite (rx, ry) corner - points that define a rectangular region of interest. - If None, all probe positions are used. - calculate_pdf - If True, compute g(r) and store it to `self.pdf`. - density - If provided, use this number density (atoms/Å^3) when computing g(r). - If None and `calculate_pdf=True`, density is estimated using :meth:`estimate_density`. - damp_origin_oscillations - If True, compute a density correction and replace the stored F(k)/G(r) with the - corrected versions returned by :meth:`estimate_density`. - set_pdf_positive - If True, sets negative values to 0. + Boolean real-space mask selecting probe positions. + damp_origin_oscillations : bool, optional + If True, run :meth:`estimate_density` and store corrected F(k)/G(r). + r_cut : float, optional + Minimum radial distance (A) for peak search in density estimation. + Forwarded to :meth:`estimate_density`. returnval : bool, optional - If True, the function returns (r, G(r), g(r)). If - False, no numerical results are returned (but attributes on `self` - are still updated). - + If True, return ``[r, G(r)]`` as numpy arrays. Returns ------- - results : list[np.ndarray] or None - If `returnval=True`, returns [r, reduced_pdf, pdf] where: - - r is the real-space grid (Nr,) - - reduced_pdf is G(r) (Nr,) - - pdf is g(r) (Nr,) or None if `calculate_pdf=False` - Otherwise returns None. + list[np.ndarray] or None """ - k_width = np.array(k_width) - if k_width.size == 1: - k_width = k_width * np.ones(2) - - k = self.qq + # this is missing a 2pi term that we add back during the pdf calc later + k_np = np.asarray(self.qq) + k = self._to_torch(k_np) dk = k[1] - k[0] - - self.kmax = k_max if k_max is not None else k.max() - self.kmin = k_min if k_min is not None else k.min() - # BUG: implement k_width properly - k_width = self.kmax - self.kmin + # small epsilon to avoid division by very small k values + k_safe = torch.clamp(k, min=1e-10) + self.kmax = k_max if k_max is not None else float(k.max()) + self.kmin = k_min if k_min is not None else float(k.min()) mask_bool = self._get_mask_bool(mask_realspace) - Ik = self.calculate_radial_mean(mask_realspace=mask_bool, returnval=True) - bg, f = self.fit_bg(Ik, self.kmin, self.kmax) - - Fk = (Ik - bg) * k / f - - # band pass filtering - if ( - k_lowpass is not None - and k_lowpass > 0.0 - and k_highpass is not None - and k_highpass > 0.0 - ): - if k_highpass > k_lowpass: - raise ValueError( - "Invalid band-pass parameters: k_highpass > k_lowpass. " - "Gaussian band-pass filtering requires k_highpass < k_lowpass " - "because these parameters are smoothing widths." - ) - Fk_low = gaussian_filter1d(Fk, sigma=k_lowpass / dk, mode="nearest") - Fk_high = gaussian_filter1d(Fk, sigma=k_highpass / dk, mode="nearest") - Fk = Fk_high - Fk_low - elif k_lowpass is not None and k_lowpass > 0.0: - Fk = gaussian_filter1d(Fk, sigma=k_lowpass / dk, mode="nearest") - elif k_highpass is not None and k_highpass > 0.0: - Fk_low = gaussian_filter1d(Fk, sigma=k_highpass / dk, mode="nearest") - Fk = Fk - Fk_low - - # Apply wk to F(Q) and rescale + # prevent division by near-zero values which cause NaNs at high k + f_safe = torch.clamp(f, min=1e-10 * f.max()) + + Fk = (Ik - bg) * k_safe / f_safe + Fk = self._frequency_filtering(Fk, k_lowpass, k_highpass, dk) + # Compute Sk from Fk BEFORE applying the 2pi scaling, + # so that estimate_density corrections are on the same scale + self.Sk = torch.ones_like(k) + mask = k > 0 + self.Sk = torch.where(mask, 1.0 + (Fk / k_safe), self.Sk) + # apply that missing 2pi factor + Fk = Fk * 2 * torch.pi + # damp edges with lorch window wk = self._lorch_window(k, self.kmin, self.kmax) - Fk_win = Fk * wk * 2 * np.pi + Fk_win = Fk * wk - r = np.arange(r_min, r_max, r_step) - ra, ka = np.meshgrid(r, k) - # incorrectly scaled in py4dstem , should include 2pi factor in dk and Fk like below + r = torch.arange(r_min, r_max, r_step, device=self.device, dtype=torch.float32) + ka, ra = torch.meshgrid(k, r, indexing="ij") + # compute reduced PDF using discrete sine transform reduced_pdf = ( - (2 / np.pi) + (2 / torch.pi) * dk * 2 - * np.pi - * np.sum( - np.sin(2 * np.pi * ra * ka) * Fk_win[:, None], - axis=0, + * torch.pi + * torch.sum( + torch.sin(2 * torch.pi * ra * ka) * Fk_win[:, None], + dim=0, ) ) reduced_pdf[0] = 0 # physically must be at 0 when r = 0 self.Ik = Ik self.bg = bg - self.Fk = Fk * 2 * np.pi + self.Fk = Fk self.Fk_masked = Fk_win - self.r = r - self.reduced_pdf = reduced_pdf + self._r = r + self._reduced_pdf = reduced_pdf - denscorr = None - if damp_origin_oscillations or (calculate_pdf and density is None): - self.Sk = np.ones_like(k, dtype=float) - mask = k > 0 - self.Sk[mask] = 1.0 + (Fk[mask] / k[mask]) - self.Sk[~mask] = 1.0 # or np.nan, depending on preference + # Sk was already computed above (before 2pi scaling) - denscorr = self.estimate_density( + if damp_origin_oscillations: + density_est = self.estimate_density( + r_cut=r_cut, max_iter=20, tol_percent=1e-1, ) - - if damp_origin_oscillations: - self.Fk_damped = denscorr[1] - self.reduced_pdf_damped = denscorr[2] - else: - self.reduced_pdf_damped = self.reduced_pdf + self.rho0 = density_est[0] + self.Fk_damped = density_est[1] + self.reduced_pdf_damped = density_est[2] if returnval: Gr = getattr(self, "reduced_pdf_damped", None) if Gr is None: - Gr = self.reduced_pdf - results = [self.r, Gr] - - # option to return pdf also using the density calculation method - # from Yoshimoto and Omote, 2022. - if calculate_pdf: - if density is None: - rho0 = denscorr[0] - # print(f"Estimated density: rho0 = {rho0:.4f} atoms / ų") - else: - print(f"Using provided density rho0 = {density:.4f} atoms / Angstrom^3") - rho0 = density + Gr = self._reduced_pdf + return [self._to_numpy(self._r), self._to_numpy(Gr)] + return None + + def calculate_gr( + self, + density: float | None = None, + r_cut: float = 0.8, + set_pdf_positive: bool = False, + returnval: bool = False, + ) -> list[NDArray] | None: + """ + Calculate the pair distribution function g(r) from G(r). - mask = r > 0 - pdf = np.ones_like(self.reduced_pdf_damped) + Requires :meth:`calculate_Gr` to have been run first. The density + rho0 is determined by (in priority order): - pdf[mask] = 1 + self.reduced_pdf_damped[mask] / (4 * np.pi * r[mask] * rho0) - pdf[~mask] = 0.0 + 1. The ``density`` argument, if provided. + 2. ``self.rho0``, if already cached from a prior :meth:`estimate_density` call + (e.g. via ``calculate_Gr(damp_origin_oscillations=True)``). + 3. A fresh call to :meth:`estimate_density` (result cached in ``self.rho0``). - if set_pdf_positive: - pdf = np.maximum(pdf, 0.0) + The G(r) used is ``self.reduced_pdf_damped`` if it exists (i.e. the user + chose damping in :meth:`calculate_Gr`), otherwise ``self.reduced_pdf``. + + Parameters + ---------- + density : float or None, optional + Number density (atoms/A^3). If None, uses cached or estimated value. + r_cut : float, optional + Minimum radial distance (A) for peak search in density estimation. + Only used when density must be estimated. Forwarded to + :meth:`estimate_density`. + set_pdf_positive : bool, optional + If True, clamp negative g(r) values to 0. + returnval : bool, optional + If True, return ``[r, g(r)]`` as numpy arrays. - self.pdf = pdf + Returns + ------- + list[np.ndarray] or None + """ + if self._reduced_pdf is None or self._r is None: + raise RuntimeError("Run calculate_Gr() before calculate_gr().") + + # Determine density + if density is not None: + rho0 = density + elif self.rho0 is not None: + rho0 = self.rho0 + else: + density_est = self.estimate_density( + r_cut=r_cut, + max_iter=20, + tol_percent=1e-1, + ) + self.rho0 = density_est[0] + rho0 = self.rho0 + + # Use damped G(r) if the user opted into damping, otherwise undamped + Gr = getattr(self, "reduced_pdf_damped", None) + if Gr is None: + Gr = self._reduced_pdf - if returnval: - results.append(self.pdf) + r = self._r + mask = r > 0 + pdf = torch.ones_like(Gr) + pdf = torch.where(mask, 1 + Gr / (4 * torch.pi * r * rho0), torch.zeros_like(pdf)) + if set_pdf_positive: # negative values are unphysical + pdf = torch.maximum(pdf, torch.zeros_like(pdf)) + self._pdf = pdf if returnval: - return results - else: - return + return [self._to_numpy(self._r), self._to_numpy(self._pdf)] + return None def estimate_density( self, - max_iter: int = 20, + r_cut: float = 0.8, + max_iter: int = 40, tol_percent: float = 1e-4, - ) -> Tuple[float, np.ndarray, np.ndarray, np.ndarray]: + ) -> tuple[float, torch.Tensor, torch.Tensor]: """ - Estimate number density rho0 (atoms/Å^3) and compute a corrected G(r). + Estimate number density rho0 (atoms/A^3) and compute a corrected G(r). This method implements an iterative Q-space density estimation by Yoshimoto & Omote (2022). It uses the structure factor `self.Sk` and @@ -873,12 +845,15 @@ def estimate_density( corrected S(k) so that the implied G(r) is more physically consistent at low r. - This method requires that :meth:`calculate_pair_dist_function` has already - been run, because it depends on `self.Sk`, `self.reduced_pdf`, `self.r`, + This method requires that :meth:`calculate_Gr` has already been run, + because it depends on `self.Sk`, `self.reduced_pdf`, `self.r`, and the k-window bounds (`self.kmin`, `self.kmax`). Parameters ---------- + r_cut : float, optional + Minimum radial distance (A) for the peak search used to determine + the correction interval. Peaks below this distance are ignored. max_iter : int, optional Maximum number of Q-space iterations. tol_percent : float, optional @@ -888,89 +863,84 @@ def estimate_density( Returns ------- rho0 : float - Estimated microscopic number density (atoms/Å^3). - Fk_win_damped : np.ndarray + Estimated microscopic number density (atoms/A^3). + Fk_win_damped : torch.Tensor Windowed corrected reduced structure function used for the transform. - G_cor : np.ndarray + G_cor : torch.Tensor Reduced PDF G(r) with dampened oscillations near origin. """ - if self.Sk is None or self.reduced_pdf is None or self.r is None: - raise RuntimeError("Run calculate_pair_dist_function() before estimate_density().") + if self.Sk is None or self._reduced_pdf is None or self._r is None: + raise RuntimeError("Run calculate_Gr() before estimate_density().") - k = self.qq + k = self._to_torch(np.asarray(self.qq)) dk = k[1] - k[0] k_fit_mask = k >= self.kmin k_fit = k[k_fit_mask] - ra, ka = np.meshgrid(self.r, k) + ka, ra = torch.meshgrid(k, self._r, indexing="ij") - r_cut = 0.8 # Angstrom - mask_search = self.r >= r_cut - r_search = self.r[mask_search] - G_search = self.reduced_pdf[mask_search] + mask_search = self._r >= r_cut + r_search = self._r[mask_search] + G_search = self._reduced_pdf[mask_search] - # find primary peak - ind_max = np.argmax(G_search) + # find tallest peak and first local minimum to the left of r_peak + ind_max = torch.argmax(G_search) r_max = r_search[ind_max] - - # find first local minimum to the left of r_peak - left = self.r < r_max - if not np.any(left): - # fallback: if peak is immediately at cutoff, just use cutoff as rmin + left = self._r < r_max + if not torch.any(left): + # If peak is immediately at cutoff, just use cutoff as rmin rmin = r_cut else: - r_left = self.r[left] - G_left = self.reduced_pdf[left] - - mins = np.where((G_left[1:-1] < G_left[:-2]) & (G_left[1:-1] < G_left[2:]))[0] + 1 + r_left = self._r[left] + G_left = self._reduced_pdf[left] + mins_cond = (G_left[1:-1] < G_left[:-2]) & (G_left[1:-1] < G_left[2:]) + # fix indexing from slicing with +1 + mins_indices = torch.where(mins_cond)[0] + 1 # minimum closest to the peak, else global min in left interval - rmin = r_left[mins[-1]] if mins.size else r_left[np.argmin(G_left)] + if mins_indices.numel() > 0: + rmin = float(r_left[mins_indices[-1]]) + else: + rmin = float(r_left[torch.argmin(G_left)]) - # restrict r to [0, rmin] for alpha/beta integrals - r_mask = (self.r >= 0.0) & (self.r <= rmin) - r_short = self.r[r_mask] - G_short = self.reduced_pdf[r_mask] + # Restrict r to [0, rmin] for the correction + r_mask = (self._r >= 0.0) & (self._r <= rmin) + r_short = self._r[r_mask] + G_short = self._reduced_pdf[r_mask] + k_fit_scaled = k_fit * 2 * torch.pi + k2d_fit, r2d_fit = torch.meshgrid(k_fit_scaled, r_short, indexing="ij") - # iterative refinement of rho0 and S(k) + # Iterative refinement of rho0 and S(k) + rho0 = 0.0 rho0_prev = None - Sk_cor = self.Sk.copy() - G_cor = self.reduced_pdf.copy() - - # use current G(r) (from Sk_cor) in beta(Q) + Sk_cor = self.Sk.clone() + G_cor = self._reduced_pdf.clone() + Fk_win_damped = self.Fk_masked.clone() + # start with uncorrected Gr G_beta = G_short - k_fit = k_fit * 2 * np.pi + wk = self._lorch_window(k, self.kmin, self.kmax) for j in range(max_iter): if j > 0: G_beta = G_cor[r_mask] - k2d_fit, r2d_fit = np.meshgrid(k_fit, r_short, indexing="ij") + # calculate alpha/beta for S(k) adjustment alpha, beta = self._compute_alpha_beta(k2d_fit, r2d_fit, G_beta, r_short) - rho0 = np.sum(alpha * beta) / np.sum(alpha**2) - + rho0 = float(torch.sum(alpha * beta) / torch.sum(alpha**2)) if rho0_prev is not None: Rj = np.sqrt(((rho0_prev - rho0) ** 2) / (rho0**2)) * 100.0 if Rj < tol_percent: - # print( - # f"Converged after {j} iterations: rho0 = {rho0:.4f} atoms / ų, Rj = {Rj:.4f}%" - # ) break - # update S_cor(Q) + # Update S_cor(k) and G_cor Sk_cor[k_fit_mask] = Sk_cor[k_fit_mask] - beta + rho0 * alpha Fk_cor = k * (Sk_cor - 1.0) - - wk = self._lorch_window(k, self.kmin, self.kmax) - - Fk_win_damped = Fk_cor * wk * 2 * np.pi - + Fk_win_damped = Fk_cor * wk * 2 * torch.pi G_cor = ( - (2.0 / np.pi) + (2.0 / torch.pi) * dk * 2 - * np.pi - * np.sum(np.sin(2 * np.pi * ka * ra) * Fk_win_damped[:, None], axis=0) + * torch.pi + * torch.sum(torch.sin(2 * torch.pi * ka * ra) * Fk_win_damped[:, None], dim=0) ) G_cor[0] = 0.0 - rho0_prev = rho0 return rho0, Fk_win_damped, G_cor @@ -981,14 +951,13 @@ def estimate_density( PlotName = Literal[ "radial_mean", - "background", + "background_fits", "reduced_sf", "reduced_pdf", "pdf", + "oscillation_damping", ] - from typing import Optional, Tuple - def _apply_xrange( self, x: NDArray, @@ -1016,7 +985,7 @@ def plot_pdf_results( qmax: float | None = None, rmin: float | None = None, rmax: float | None = None, - figsize: tuple[float, float] = (8, 4), + figsize: tuple[float, float] = (6, 4), returnfigs: bool = False, ): """ @@ -1024,7 +993,7 @@ def plot_pdf_results( Examples -------- - pdfc.calculate_pair_dist_function(...) + pdfc.calculate_Gr(...) pdfc.plot(["radial_mean", "background", "reduced_pdf"]) """ mapping = { @@ -1048,33 +1017,6 @@ def plot_pdf_results( return figs if returnfigs else None - def _auto_ylim_after_direct_beam_trough(self, y, *, scale=2.0, smooth_sigma=2.0): - y = np.asarray(y, dtype=float) - if y.size < 10: - return None - - # direct beam peak is usually the first big max; assume it's at/near index 0 - # find first local minimum after index 0 - dy = np.diff(y) - mins = np.where((dy[:-1] < 0) & (dy[1:] > 0))[0] + 1 - - if mins.size == 0: - # fallback: ignore first 5% if we can't find a trough - start = max(1, int(0.05 * y.size)) - else: - start = int(mins[0]) - - y_use = y[start:] - y_use = y_use[np.isfinite(y_use)] - if y_use.size == 0: - return None - - ymax = np.max(y_use) - if not np.isfinite(ymax) or ymax <= 0: - return None - - return (0.0, scale * ymax) - def plot_radial_mean( self, qmin: float | None = None, @@ -1092,7 +1034,7 @@ def plot_radial_mean( raise RuntimeError("Radial mean intensity has not been calculated yet.") x = np.asarray(self.qq) - y = np.asarray(self.radial_mean) + y = self._to_numpy(self.radial_mean) x, y = self._apply_xrange(x, y, qmin, qmax) fig, ax = plt.subplots(figsize=figsize) @@ -1102,9 +1044,6 @@ def plot_radial_mean( ax.set_title("Radial Mean Intensity vs Scattering Vector") ax.legend() ax.set_yscale("log") - # ylim = self._auto_ylim_after_direct_beam_trough(self.radial_mean, scale=2.0) - # if ylim is not None: - # ax.set_ylim(*ylim) plt.tight_layout() if returnfig: @@ -1112,16 +1051,6 @@ def plot_radial_mean( else: plt.show() - def plot_radial_var_norm( - self, - figsize: tuple[float, float] = (8, 4), - returnfig: bool = False, - ): - """ - Stub for plotting normalized radial variance vs scattering vector. - """ - raise NotImplementedError("plot_radial_var_norm is not implemented yet.") - def plot_background_fits( self, qmin: float | None = None, @@ -1138,10 +1067,10 @@ def plot_background_fits( raise RuntimeError("Radial mean intensity or background has not been calculated yet.") x = np.asarray(self.qq) - y1 = np.asarray(self.radial_mean) + y1 = self._to_numpy(self.radial_mean) x, y1 = self._apply_xrange(x, y1, qmin, qmax) x = np.asarray(self.qq) - y2 = np.asarray(self.bg) + y2 = self._to_numpy(self.bg) x, y2 = self._apply_xrange(x, y2, qmin, qmax) fig, ax = plt.subplots(figsize=figsize) @@ -1153,9 +1082,6 @@ def plot_background_fits( ax.legend() ax.set_yscale("log") plt.tight_layout() - # ylim = self._auto_ylim_after_direct_beam_trough(self.radial_mean, scale=2.0) - # if ylim is not None: - # ax.set_ylim(*ylim) if returnfig: return fig @@ -1182,7 +1108,7 @@ def plot_reduced_sf( Fk = self.Fk_masked x = np.asarray(self.qq) - y = np.asarray(Fk) + y = self._to_numpy(Fk) x, y = self._apply_xrange(x, y, qmin, qmax) fig, ax = plt.subplots(figsize=figsize) @@ -1209,22 +1135,27 @@ def plot_reduced_pdf( """ Plotting reduced PDF g(r). """ - if self.reduced_pdf is None: + if self._reduced_pdf is None: raise RuntimeError("Reduced PDF has not been calculated yet.") Gr = getattr(self, "reduced_pdf_damped", None) if Gr is None: - Gr = self.reduced_pdf + Gr = self._reduced_pdf - x = np.asarray(self.r) - y = np.asarray(Gr) - x, y = self._apply_xrange(x, y, qmin, qmax) + x = self._to_numpy(self._r) + y = self._to_numpy(Gr) + x, y = self._apply_xrange(x, y, rmin, rmax) # Find radial value of primary peak and trough for y-limits - ind_max = np.argmax(y) - y_max = y[ind_max] - - ind_min = np.argmin(y) - y_min = y[ind_min] + # Filter out NaN and Inf values to avoid plot errors + valid_mask = np.isfinite(y) + if np.any(valid_mask): + y_valid = y[valid_mask] + y_max = np.max(y_valid) + y_min = np.min(y_valid) + else: + # Fallback if all values are invalid + y_max = 1.0 + y_min = -1.0 yrange = y_max - y_min pad = padding_frac * yrange @@ -1253,20 +1184,24 @@ def plot_pdf( """ Plotting pair distribution function g(r). """ - if self.reduced_pdf is None or self.pdf is None: + if self._reduced_pdf is None or self._pdf is None: raise RuntimeError("Reduced PDF or PDF has not been calculated yet.") - x = np.asarray(self.r) - y = np.asarray(self.pdf) - x, y = self._apply_xrange(x, y, qmin, qmax) + x = self._to_numpy(self._r) + y = self._to_numpy(self._pdf) + x, y = self._apply_xrange(x, y, rmin, rmax) # Find radial value of primary peak - ind_max = np.argmax(y) - y_max = y[ind_max] - - ind_min = np.argmin(y) - y_min = y[ind_min] - + # Filter out NaN and Inf values to avoid plot errors + valid_mask = np.isfinite(y) + if np.any(valid_mask): + y_valid = y[valid_mask] + y_max = np.max(y_valid) + y_min = np.min(y_valid) + else: + # Fallback if all values are invalid + y_max = 1.0 + y_min = -1.0 yrange = y_max - y_min pad = padding_frac * yrange @@ -1292,34 +1227,51 @@ def plot_oscillation_damping( figsize: tuple[float, float] = (8, 4), returnfig: bool = False, ): + if ( + self.Fk_masked is None + or not hasattr(self, "Fk_damped") + or not hasattr(self, "reduced_pdf_damped") + ): + raise RuntimeError( + "Oscillation damping data not available. " + "Run calculate_Gr(damp_origin_oscillations=True) first." + ) + k = np.asarray(self.qq) + # Convert torch tensors to numpy for plotting + Fk_masked = self._to_numpy(self.Fk_masked) + Fk_damped = self._to_numpy(self.Fk_damped) + r = self._to_numpy(self._r) + reduced_pdf = self._to_numpy(self._reduced_pdf) + reduced_pdf_damped = self._to_numpy(self.reduced_pdf_damped) + fig, axes = plt.subplots(2, 2, figsize=figsize) # F(k) axS_top = axes[0, 0] axS_res = axes[1, 0] - axS_top.plot(k, self.Fk_masked, label="F_obs(k)", color="gray") - axS_top.plot(k, self.Fk_damped, label="F_cor(k)", color="red") - axS_top.set_xlabel("k (Å$^{-1}$)") + axS_top.plot(k, Fk_masked, label="F_obs(k)", color="gray") + axS_top.plot(k, Fk_damped, label="F_cor(k)", color="red") + axS_top.set_xlabel("k (A$^{-1}$)") axS_top.set_ylabel("F(k)") axS_top.legend() - axS_res.plot(k, self.Fk_damped - self.Fk_masked, color="blue") - axS_res.set_xlabel("k (Å$^{-1}$)") + axS_res.plot(k, Fk_damped - Fk_masked, color="blue") + axS_res.set_xlabel("k (A$^{-1}$)") axS_res.set_ylabel("F_cor - F_obs") # G(r) axG_top = axes[0, 1] axG_res = axes[1, 1] - axG_top.plot(self.r, self.reduced_pdf, label="G_obs(r)", color="gray") - axG_top.plot(self.r, self.reduced_pdf_damped, label="G_cor(r)", color="red") - axG_top.set_xlabel("r (Å)") + axG_top.plot(r, reduced_pdf, label="G_obs(r)", color="gray") + axG_top.plot(r, reduced_pdf_damped, label="G_cor(r)", color="red") + axG_top.set_xlabel("r (A)") axG_top.set_ylabel("G(r)") axG_top.legend() - axG_res.plot(self.r, self.reduced_pdf_damped - self.reduced_pdf, color="blue") - axG_res.set_xlabel("r (Å)") + axG_res.plot(r, reduced_pdf_damped - reduced_pdf, color="blue") + axG_res.set_xlabel("r (A)") axG_res.set_ylabel("G_cor - G_obs") fig.tight_layout() diff --git a/tests/diffraction/test_polar.py b/tests/diffraction/test_polar.py new file mode 100644 index 00000000..be274d09 --- /dev/null +++ b/tests/diffraction/test_polar.py @@ -0,0 +1,409 @@ +import numpy as np +import pytest + +from quantem.core.datastructures.dataset2d import Dataset2d +from quantem.core.datastructures.dataset4dstem import Dataset4dstem +from quantem.core.datastructures.polar4dstem import Polar4dstem, auto_origin_id +from quantem.diffraction.polar import PairDistributionFunction + +# ============================================================================ +# Fixtures +# ============================================================================ + + +@pytest.fixture +def synthetic_diffraction_pattern(): + """Create a synthetic diffraction pattern with concentric rings.""" + ny, nx = 256, 256 + y, x = np.ogrid[:ny, :nx] + cy, cx = (ny - 1) / 2.0, (nx - 1) / 2.0 + + # Create rings with Gaussian profiles at specific radii + pattern = np.zeros((ny, nx), dtype=np.float32) + ring_radii = [10, 20, 30, 40] + r = np.sqrt((y - cy) ** 2 + (x - cx) ** 2) + for radius in ring_radii: + pattern += 100 * np.exp(-((r - radius) ** 2) / (2 * 2**2)) + # central beam + pattern += 1000 * np.exp(-(r**2) / (2 * 3**2)) + # noise + rng = np.random.default_rng(42) + pattern += rng.poisson(5, size=(ny, nx)) + + return pattern.astype(np.float32) + + +@pytest.fixture +def synthetic_4dstem_dataset(synthetic_diffraction_pattern): + """Create a synthetic 4D-STEM dataset with 3x3 scan.""" + scan_y, scan_x = 3, 3 + ny, nx = synthetic_diffraction_pattern.shape + + array_4d = np.zeros((scan_y, scan_x, ny, nx), dtype=np.float32) + for iy in range(scan_y): + for ix in range(scan_x): + # Add slight variations + rng = np.random.default_rng(42 + iy * scan_x + ix) + variation = 1.0 + 0.1 * rng.standard_normal() + array_4d[iy, ix] = synthetic_diffraction_pattern * variation + + return Dataset4dstem.from_array( + array=array_4d, + name="test_4dstem", + origin=(0, 0, 0, 0), + sampling=(1.0, 1.0, 0.015, 0.015), + units=["nm", "nm", "1/Angstrom", "1/Angstrom"], + signal_units="counts", + ) + + +@pytest.fixture +def synthetic_dataset2d(synthetic_diffraction_pattern): + """Create a synthetic 2D diffraction dataset.""" + return Dataset2d.from_array( + array=synthetic_diffraction_pattern, + name="test_2d_diffraction", + origin=(0, 0), + sampling=(0.015, 0.015), + units=["1/Angstrom", "1/Angstrom"], + signal_units="counts", + ) + + +# ============================================================================ +# Test PairDistributionFunction Construction +# ============================================================================ + + +class TestPairDistributionFunctionConstruction: + """Test PairDistributionFunction initialization from various input types.""" + + def test_from_data_with_dataset4dstem(self, synthetic_4dstem_dataset): + """Test construction from a Dataset4dstem object.""" + pdf = PairDistributionFunction.from_data( + synthetic_4dstem_dataset, + find_origin=False, + ) + assert isinstance(pdf.polar, Polar4dstem) + assert pdf.input_data is synthetic_4dstem_dataset + assert pdf.polar.shape[0] == 3 # scan_y + assert pdf.polar.shape[1] == 3 # scan_x + assert pdf.polar.shape[2] == 180 # num_annular_bins + + def test_from_data_with_invalid_array_raises(self): + """Test that arrays with wrong dimensions raise ValueError.""" + array_1d = np.random.rand(100) + with pytest.raises(ValueError, match="only supports 2D or 4D arrays"): + PairDistributionFunction.from_data(array_1d) + + def test_direct_init_without_token_raises(self, synthetic_dataset2d): + """Test that direct __init__ without token raises RuntimeError.""" + pdf_valid = PairDistributionFunction.from_data(synthetic_dataset2d, find_origin=False) + with pytest.raises(RuntimeError, match="Use PairDistributionFunction.from_data"): + PairDistributionFunction(polar=pdf_valid.polar, device="cpu") + + def test_find_origin(self, synthetic_4dstem_dataset): + """Test automatic origin finding.""" + origin_array = auto_origin_id( + synthetic_4dstem_dataset, + ) + assert origin_array.shape == (3, 3, 2) # (scan_y, scan_x, 2) + + expected_center = 127.5 + for iy in range(3): + for ix in range(3): + row, col = origin_array[iy, ix] + assert abs(row - expected_center) < 1 + assert abs(col - expected_center) < 1 + + +# ============================================================================ +# Test Radial Mean Calculation +# ============================================================================ + + +class TestRadialMeanCalculation: + """Test radial mean intensity calculation.""" + + def test_calculate_radial_mean_with_mask(self, synthetic_4dstem_dataset): + """Test radial mean calculation with real-space mask.""" + pdf = PairDistributionFunction.from_data( + synthetic_4dstem_dataset, + find_origin=False, + ) + + mask = np.zeros((3, 3), dtype=bool) + mask[0:2, 0:2] = True + radial_mean = pdf.calculate_radial_mean( + mask_realspace=mask, + returnval=True, + ) + + assert radial_mean is not None + + +# ============================================================================ +# Test Background Fitting +# ============================================================================ + + +class TestBackgroundFitting: + """Test background fitting.""" + + def test_fit_bg_basic(self, synthetic_dataset2d): + """Test basic background fitting.""" + pdf = PairDistributionFunction.from_data( + synthetic_dataset2d, + find_origin=False, + ) + + Ik = pdf.calculate_radial_mean(returnval=True) + k = pdf._to_torch(np.asarray(pdf.qq)) + kmin, kmax = float(k.min()), float(k.max()) + bg, f = pdf.fit_bg(Ik, kmin=kmin * 0.1, kmax=kmax * 0.9) + + assert bg.shape == Ik.shape + assert f.shape == Ik.shape + # Check that background is positive + assert (bg >= 0).all() + + +# ============================================================================ +# Test PDF Calculation +# ============================================================================ + + +class TestPDFCalculation: + """Test the PDF calculation pipeline.""" + + def test_calculate_Gr_with_bandpass(self, synthetic_dataset2d): + """Test PDF calculation with bandpass filtering.""" + pdf = PairDistributionFunction.from_data( + synthetic_dataset2d, + find_origin=False, + ) + + pdf.calculate_Gr( + k_min=0.1, + k_max=2.0, + k_lowpass=0.02, + k_highpass=0.001, + ) + assert pdf.reduced_pdf is not None + + def test_calculate_Gr_with_mask(self, synthetic_4dstem_dataset): + """Test PDF calculation with real-space mask.""" + pdf = PairDistributionFunction.from_data( + synthetic_4dstem_dataset, + find_origin=False, + ) + + mask = np.zeros((3, 3), dtype=bool) + mask[0:2, 0:2] = True + pdf.calculate_Gr( + k_min=0.1, + k_max=2.0, + mask_realspace=mask, + ) + assert pdf.reduced_pdf is not None + + def test_calculate_gr_requires_Gr(self, synthetic_dataset2d): + """Test that calculate_gr raises if calculate_Gr has not been run.""" + pdf = PairDistributionFunction.from_data( + synthetic_dataset2d, + find_origin=False, + ) + + with pytest.raises(RuntimeError, match="Run calculate_Gr"): + pdf.calculate_gr(density=0.05) + + def test_calculate_gr_estimates_density(self, synthetic_dataset2d): + """Test that calculate_gr estimates density when none is provided.""" + pdf = PairDistributionFunction.from_data( + synthetic_dataset2d, + find_origin=False, + ) + + pdf.calculate_Gr(k_min=0.1, k_max=2.0) + results = pdf.calculate_gr(returnval=True) + + assert results is not None + r, gr = results + assert isinstance(gr, np.ndarray) + assert len(gr) == len(r) + assert pdf.rho0 > 0 + + def test_estimate_density_requires_Gr(self, synthetic_dataset2d): + """Test that estimate_density requires prior calculate_Gr call.""" + pdf = PairDistributionFunction.from_data( + synthetic_dataset2d, + find_origin=False, + ) + + with pytest.raises(RuntimeError, match="Run calculate_Gr"): + pdf.estimate_density() + + +# ============================================================================ +# Test Polar Transform +# ============================================================================ + + +class TestPolarTransform: + """Test polar coordinate transformation.""" + + def test_polar_transform_basic(self, synthetic_4dstem_dataset): + """Test basic polar transformation.""" + polar = synthetic_4dstem_dataset.polar_transform() + + assert isinstance(polar, Polar4dstem) + assert polar.shape[0] == 3 # scan_y + assert polar.shape[1] == 3 # scan_x + assert polar.shape[2] == 180 # num_annular_bins + assert polar.shape[3] > 0 # radial bins + + def test_polar_transform_single_origin(self, synthetic_4dstem_dataset): + """Test polar transformation with single origin broadcast to all positions.""" + origin = np.array([128.0, 128.0]) + + polar = synthetic_4dstem_dataset.polar_transform( + origin_array=origin, + ) + + assert isinstance(polar, Polar4dstem) + + def test_polar_transform_radial_range(self, synthetic_4dstem_dataset): + """Test polar transformation with custom radial range.""" + polar = synthetic_4dstem_dataset.polar_transform( + radial_min=5.0, + radial_max=50.0, + radial_step=2.0, + ) + + assert isinstance(polar, Polar4dstem) + # Check that radial dimension matches expected size + expected_n_r = int(np.ceil((50.0 - 5.0) / 2.0)) + assert polar.shape[3] == expected_n_r + + def test_polar_transform_scan_pos(self, synthetic_4dstem_dataset): + """Test polar transformation for a single scan position.""" + polar_2d = synthetic_4dstem_dataset.polar_transform( + scan_pos=(0, 0), + ) + + # should return 2D tensor (phi, r) + assert polar_2d.ndim == 2 + assert polar_2d.shape[0] == 180 # num_annular_bins + + +# ============================================================================ +# Integration Workflows +# ============================================================================ + + +class TestIntegrationWorkflows: + """Test complete end-to-end workflows.""" + + def test_complete_pdf_workflow_2d(self, synthetic_dataset2d): + """Test: 2D diffraction → polar transform → G(r) → g(r).""" + pdf = PairDistributionFunction.from_data( + synthetic_dataset2d, + find_origin=False, + ) + + Gr_results = pdf.calculate_Gr( + k_min=0.1, + k_max=2.0, + r_min=0.0, + r_max=10.0, + r_step=0.05, + returnval=True, + ) + + assert Gr_results is not None + r, Gr = Gr_results + assert not np.isnan(r).any() + assert not np.isnan(Gr).any() + assert not np.isinf(Gr).any() + assert len(r) > 0 + assert len(Gr) == len(r) + + gr_results = pdf.calculate_gr( + density=0.05, + returnval=True, + ) + + assert gr_results is not None + r_gr, gr = gr_results + assert not np.isnan(gr).any() + assert not np.isinf(gr).any() + assert len(gr) == len(r_gr) + + def test_complete_pdf_workflow_4dstem(self, synthetic_4dstem_dataset): + """Test: 4D-STEM → origin finding → polar transform → G(r).""" + pdf = PairDistributionFunction.from_data( + synthetic_4dstem_dataset, + find_origin=True, + ) + + mask = np.zeros((3, 3), dtype=bool) + mask[0:2, 0:2] = True + + pdf.calculate_Gr( + k_min=0.1, + k_max=2.0, + mask_realspace=mask, + ) + + assert pdf.reduced_pdf is not None + assert not np.isnan(pdf.reduced_pdf).any() + assert not np.isinf(pdf.reduced_pdf).any() + + def test_polar_transform_input_types(self, synthetic_diffraction_pattern): + """Test polar_transform works with numpy array, Dataset2d, Dataset4dstem.""" + # Test with Dataset2d + ds2 = Dataset2d.from_array( + array=synthetic_diffraction_pattern, + name="test", + ) + pdf_ds2 = PairDistributionFunction.from_data( + ds2, + find_origin=False, + ) + assert pdf_ds2.polar.shape[2] == 180 + + # Test with Dataset4dstem + array_4d = synthetic_diffraction_pattern[None, None, :, :] # (1, 1, ny, nx) + ds4 = Dataset4dstem.from_array(array_4d, name="test") + pdf_ds4 = PairDistributionFunction.from_data( + ds4, + find_origin=False, + ) + assert pdf_ds4.polar.shape[2] == 180 + + assert pdf_ds2.polar.shape == pdf_ds4.polar.shape + + def test_density_estimation_workflow(self, synthetic_dataset2d): + """Test: G(r) calculation → density estimation → g(r) calculation.""" + pdf = PairDistributionFunction.from_data( + synthetic_dataset2d, + find_origin=False, + ) + + pdf.calculate_Gr(k_min=0.1, k_max=2.0) + rho0, Fk_damped, G_cor = pdf.estimate_density( + max_iter=5, + tol_percent=1.0, + ) + + assert rho0 > 0 + assert np.isfinite(rho0) + + results = pdf.calculate_gr( + density=rho0, + returnval=True, + ) + + assert results is not None + r, gr = results + assert not np.isnan(gr).any() From f156f0644ed2039b6fd002266bf8db07a41c02ec Mon Sep 17 00:00:00 2001 From: Karen Ehrhardt Date: Thu, 19 Feb 2026 21:02:52 -0800 Subject: [PATCH 126/140] additional cleanup --- src/quantem/core/datastructures/dataset.py | 34 --- .../core/datastructures/dataset4dstem.py | 1 - .../core/datastructures/polar4dstem.py | 1 - src/quantem/core/io/file_readers.py | 226 ++---------------- src/quantem/diffraction/polar.py | 9 +- 5 files changed, 31 insertions(+), 240 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 60e22244..94744978 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -191,11 +191,6 @@ def sampling(self) -> NDArray: def sampling(self, value: NDArray | tuple | list | float | int) -> None: self._sampling = validate_ndinfo(value, self.ndim, "sampling") - @property - def origin_units(self) -> NDArray: - # Origin expressed in physical units: origin * sampling - return np.asarray(self.origin) * np.asarray(self.sampling) - @property def units(self) -> list[str]: return self._units @@ -373,35 +368,6 @@ def _copy_custom_attributes(self, new_dataset: Self) -> None: # Skip attributes that can't be copied pass - def coords(self, axis: int) -> Any: - """ - Coordinate array for a given axis in pixel units. - - coords(d) = arange(shape[d]) - origin[d] - """ - axis = int(axis) - if axis < 0 or axis >= self.ndim: - raise ValueError(f"axis {axis} out of bounds for ndim={self.ndim}") - - xp = self._xp - n = int(self.shape[axis]) - origin_d = float(np.asarray(self.origin)[axis]) - - return xp.arange(n, dtype=float) - origin_d - - def coords_units(self, axis: int) -> Any: - """ - Coordinate array for a given axis in physical units. - - coords_units(d) = (arange(shape[d]) - origin[d]) * sampling[d] - """ - axis = int(axis) - if axis < 0 or axis >= self.ndim: - raise ValueError(f"axis {axis} out of bounds for ndim={self.ndim}") - - sampling_d = float(np.asarray(self.sampling)[axis]) - return self.coords(axis) * sampling_d - def mean(self, axes: int | tuple[int, ...] | None = None) -> Any: """ Computes and returns mean of the data array. diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 01c6b0d8..27ea385a 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -78,7 +78,6 @@ def __init__( _token : object | None, optional Token to prevent direct instantiation, by default None """ - print("array.shape:", array.shape) mdata_keys_4dstem = ["r_to_q_rotation_cw_deg", "ellipticity"] for k in mdata_keys_4dstem: if k not in metadata.keys(): diff --git a/src/quantem/core/datastructures/polar4dstem.py b/src/quantem/core/datastructures/polar4dstem.py index dd4fa21e..97ee3b9c 100644 --- a/src/quantem/core/datastructures/polar4dstem.py +++ b/src/quantem/core/datastructures/polar4dstem.py @@ -51,7 +51,6 @@ def __init__( metadata=metadata, _token=_token, ) - self._xp = np # workaround: Dataset.coords() references _xp self.origin_array = origin_array @classmethod diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index 440a74fe..5267c0fc 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -4,7 +4,6 @@ from typing import Any import h5py -import numpy as np from quantem.core.datastructures import Dataset as Dataset from quantem.core.datastructures import Dataset2d as Dataset2d @@ -16,136 +15,28 @@ def read_4dstem( file_path: str | PathLike, file_type: str | None = None, dataset_index: int | None = None, - scan_length: int | None = None, - scan_axis: int = 0, - transpose_scan_axes: bool = False, **kwargs, ) -> Dataset4dstem: """ - File reader for 4D-STEM data. + File reader for 4D-STEM data Parameters ---------- - file_path : str | PathLike - Path to data. - file_type : str, optional - The type of file reader needed. See RosettaSciIO for supported formats: + file_path: str | PathLike + Path to data + file_type: str + The type of file reader needed. See rosettasciio for supported formats https://hyperspy.org/rosettasciio/supported_formats/index.html - dataset_index : int, optional + dataset_index: int, optional Index of the dataset to load if file contains multiple datasets. If None, automatically selects the first 4D dataset found. - If no 4D dataset is found but a 3D stack exists, a 3D dataset can be - interpreted as 4D if `scan_length` is provided. - scan_length : int, optional - For 3D datasets shaped (n_frames, ny, nx) (after possibly moving the - scan axis to the front), interpret the data as a raster scan with shape - (scan_y, scan_x, ny, nx), where scan_y = n_frames // scan_length and - scan_x = scan_length. Required if you want to treat a 3D stack as 4D. - scan_axis : int, default 0 - Which axis of a 3D dataset is the scan/time axis before reshaping. - Must be 0 or 1. The specified axis is moved to axis 0 before the - (scan_y, scan_x) reshape. - transpose_scan_axes : bool, default False - Only used when interpreting a 3D dataset as 4D via `scan_length`. - If True, transpose the scan axes after reshaping so that - (scan_y, scan_x) -> (scan_x, scan_y). This effectively swaps the - interpretation of scan rows and columns in the final 4D array. - - **kwargs : dict + **kwargs: dict Additional keyword arguments to pass to the Dataset4dstem constructor. Returns - ------- + -------- Dataset4dstem """ - - def _reshape_3d_to_4d( - imported_data: dict, - *, - dataset_index_local: int | None, - scan_length_local: int, - scan_axis_local: int, - transpose_scan_axes_local: bool, - ) -> dict: - data = imported_data["data"] - if data.ndim != 3: - raise ValueError( - f"Expected 3D data to reshape, got ndim={data.ndim} " - f"with shape {data.shape}" - ) - - if scan_axis_local not in (0, 1): - raise ValueError(f"scan_axis must be 0 or 1, got {scan_axis_local}") - - # Move scan axis to front so it becomes the frame axis - if scan_axis_local != 0: - data = np.moveaxis(data, scan_axis_local, 0) - - n_frames, ny, nx = data.shape - - if scan_length_local <= 0: - raise ValueError(f"scan_length must be positive, got {scan_length_local}") - if n_frames % scan_length_local != 0: - raise ValueError( - f"scan_length={scan_length_local} is not compatible with n_frames={n_frames}; " - f"n_frames % scan_length = {n_frames % scan_length_local}" - ) - - scan_y = n_frames // scan_length_local - scan_x = scan_length_local - - data_4d = data.reshape(scan_y, scan_x, ny, nx) - - if transpose_scan_axes_local: - data_4d = np.transpose(data_4d, (1, 0, 2, 3)) - scan_y, scan_x = scan_x, scan_y - - old_axes = imported_data.get("axes", None) - if old_axes is None or len(old_axes) != 3: - raise ValueError( - "Expected 3 axes for 3D data when reshaping to 4D; " - f"got axes={old_axes}" - ) - - ax_scan_y = { - "scale": 1.0, - "offset": 0.0, - "units": "pixels", - "name": "scan_y", - } - ax_scan_x = { - "scale": 1.0, - "offset": 0.0, - "units": "pixels", - "name": "scan_x", - } - - ax_qy = dict(old_axes[1]) - ax_qx = dict(old_axes[2]) - - imported_data_4d = imported_data.copy() - imported_data_4d["data"] = data_4d - imported_data_4d["axes"] = [ax_scan_y, ax_scan_x, ax_qy, ax_qx] - - original_shape = imported_data["data"].shape - new_shape = data_4d.shape - if dataset_index_local is not None: - print( - f"Using 3D dataset {dataset_index_local} with shape {original_shape} " - f"interpreted as 4D with shape={new_shape} " - f"(scan_axis={scan_axis_local}, scan_length={scan_length_local}, " - f"transpose_scan_axes={transpose_scan_axes_local})." - ) - else: - print( - f"Using 3D dataset with shape {original_shape} " - f"interpreted as 4D with shape={new_shape} " - f"(scan_axis={scan_axis_local}, scan_length={scan_length_local}, " - f"transpose_scan_axes={transpose_scan_axes_local})." - ) - - return imported_data_4d - if file_type is None: file_type = Path(file_path).suffix.lower().lstrip(".") @@ -157,98 +48,29 @@ def _reshape_3d_to_4d( file_reader = importlib.import_module(f"rsciio.{file_type}").file_reader data_list = file_reader(file_path) - if not data_list: - raise ValueError(f"No datasets returned by rsciio.{file_type} for '{file_path}'") - - # Case 1: dataset_index specified explicitly + # If specific index provided, use it if dataset_index is not None: imported_data = data_list[dataset_index] - ndim = imported_data["data"].ndim - - if ndim == 4: - # Use 4D as-is - pass - elif ndim == 3: - if scan_length is None: - raise ValueError( - f"Dataset at index {dataset_index} is 3D (shape={imported_data['data'].shape}). " - "To interpret it as 4D-STEM, please provide scan_length." - ) - imported_data = _reshape_3d_to_4d( - imported_data, - dataset_index_local=dataset_index, - scan_length_local=scan_length, - scan_axis_local=scan_axis, - transpose_scan_axes_local=transpose_scan_axes, - ) - else: + if imported_data["data"].ndim != 4: raise ValueError( - f"Dataset at index {dataset_index} has ndim={ndim}, " - f"expected 4D or 3D. Shape: {imported_data['data'].shape}" + f"Dataset at index {dataset_index} has {imported_data['data'].ndim} dimensions, " + f"expected 4D. Shape: {imported_data['data'].shape}" ) - else: - # Case 2: auto-select dataset + # Automatically find first 4D dataset four_d_datasets = [(i, d) for i, d in enumerate(data_list) if d["data"].ndim == 4] - if four_d_datasets: - dataset_index, imported_data = four_d_datasets[0] - if len(data_list) > 1: - print( - f"File contains {len(data_list)} dataset(s). Using 4D dataset " - f"{dataset_index} with shape {imported_data['data'].shape}" - ) - else: - three_d_datasets = [(i, d) for i, d in enumerate(data_list) if d["data"].ndim == 3] - - if not three_d_datasets: - print(f"No 4D datasets found in {file_path}. Available datasets:") - for i, d in enumerate(data_list): - print(f" Dataset {i}: shape {d['data'].shape}, ndim={d['data'].ndim}") - raise ValueError("No 4D or 3D dataset found in file") - - if scan_length is None: - print(f"No 4D datasets found in {file_path}. Available datasets:") - for i, d in enumerate(data_list): - print(f" Dataset {i}: shape {d['data'].shape}, ndim={d['data'].ndim}") - raise ValueError( - "File contains only 3D datasets. To interpret one as 4D-STEM, " - "please specify scan_length so that n_frames % scan_length == 0." - ) - - # Choose first 3D dataset compatible with scan_length along scan_axis - candidates: list[tuple[int, dict]] = [] - for i, d in three_d_datasets: - shape = d["data"].shape - if scan_axis < 0 or scan_axis > 2: - raise ValueError(f"scan_axis must be in [0, 2] for 3D data, got {scan_axis}") - n_frames_axis = shape[scan_axis] - if n_frames_axis % scan_length == 0: - candidates.append((i, d)) - - if not candidates: - print(f"3D datasets in {file_path}:") - for i, d in three_d_datasets: - print(f" Dataset {i}: shape {d['data'].shape}") - raise ValueError( - f"No 3D dataset has length along scan_axis={scan_axis} " - f"divisible by scan_length={scan_length}." - ) - - dataset_index, imported_data = candidates[0] - if len(candidates) > 1: - print( - f"Multiple 3D datasets compatible with scan_length={scan_length} " - f"along scan_axis={scan_axis}. Using dataset {dataset_index} " - f"with shape {imported_data['data'].shape}" - ) - - imported_data = _reshape_3d_to_4d( - imported_data, - dataset_index_local=dataset_index, - scan_length_local=scan_length, - scan_axis_local=scan_axis, - transpose_scan_axes_local=transpose_scan_axes, + if len(four_d_datasets) == 0: + print(f"No 4D datasets found in {file_path}. Available datasets:") + for i, d in enumerate(data_list): + print(f" Dataset {i}: shape {d['data'].shape}, ndim={d['data'].ndim}") + raise ValueError("No 4D dataset found in file") + + dataset_index, imported_data = four_d_datasets[0] + + if len(data_list) > 1: + print( + f"File contains {len(data_list)} dataset(s). Using dataset {dataset_index} with shape {imported_data['data'].shape}" ) imported_axes = imported_data["axes"] diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py index 5036ebac..4f0bfaf0 100644 --- a/src/quantem/diffraction/polar.py +++ b/src/quantem/diffraction/polar.py @@ -232,14 +232,19 @@ def qq(self) -> Any: """ # Polar4dstem dims: (scan_y, scan_x, phi, r) # radial axis is 3 - return self.polar.coords_units(3) + n = self.polar.shape[3] + origin_r = float(np.asarray(self.polar.origin)[3]) + sampling_r = float(np.asarray(self.polar.sampling)[3]) + return (np.arange(n, dtype=float) - origin_r) * sampling_r @property def radial_bins(self) -> Any: """ Radial bin centers in pixel units (convenience alias). """ - return self.polar.coords(3) + n = self.polar.shape[3] + origin_r = float(np.asarray(self.polar.origin)[3]) + return np.arange(n, dtype=float) - origin_r @property def r(self) -> NDArray | None: From 142f0d605a21345284fa53ae30358c37b8b3ae88 Mon Sep 17 00:00:00 2001 From: ehrhardtkm Date: Thu, 26 Feb 2026 16:10:58 -0800 Subject: [PATCH 127/140] added comments for increased clarity --- .../core/datastructures/polar4dstem.py | 46 ++++++- src/quantem/diffraction/polar.py | 89 +++++++++--- tests/diffraction/test_polar.py | 128 +++++++----------- 3 files changed, 158 insertions(+), 105 deletions(-) diff --git a/src/quantem/core/datastructures/polar4dstem.py b/src/quantem/core/datastructures/polar4dstem.py index 97ee3b9c..a1dc6af5 100644 --- a/src/quantem/core/datastructures/polar4dstem.py +++ b/src/quantem/core/datastructures/polar4dstem.py @@ -6,6 +6,7 @@ import torch import torch.nn.functional as F from numpy.typing import NDArray +from tqdm import tqdm if TYPE_CHECKING: from .dataset4dstem import Dataset4dstem @@ -66,7 +67,10 @@ def from_array( ) -> "Polar4dstem": array = np.asarray(array) if array.ndim != 4: - raise ValueError("Polar4dstem.from_array expects a 4D array.") + raise ValueError( + f"Found array with shape: {array.shape}. " + "Polar4dstem.from_array expects a 4D array." + ) if origin is None: origin = np.zeros(4, dtype=float) if sampling is None: @@ -108,7 +112,6 @@ def _normalize_coords_for_grid_sample( ) -> torch.Tensor: """ Convert pixel coordinates to normalized [-1, 1] coordinates for grid_sample. - grid_sample expects x_norm = 2*x/(W-1) - 1, y_norm = 2*y/(H-1) - 1, stacked as (..., 2) in [x, y] order. """ @@ -133,9 +136,11 @@ def _precompute_polar_coords( origin_row = float(origin_row) origin_col = float(origin_col) if radial_step <= 0: - raise ValueError("radial_step must be > 0.") + raise ValueError(f"Got radial_step = {radial_step}. radial_step must be > 0.") if num_annular_bins < 1: raise ValueError("num_annular_bins must be >= 1.") + # Use the shortest distance from the origin to any image edge so the + # polar grid never samples outside the image bounds. if radial_max is None: r_row_pos = origin_row r_row_neg = (ny - 1) - origin_row @@ -144,18 +149,25 @@ def _precompute_polar_coords( radial_max_eff = float(min(r_row_pos, r_row_neg, r_col_pos, r_col_neg)) else: radial_max_eff = float(radial_max) + # Guarantee at least one radial bin so downstream code never gets an empty array if radial_max_eff <= radial_min: radial_max_eff = radial_min + radial_step + + # create radial bins and phi bins, then create the grid of (phi, r) coordinates radial_bins = torch.arange( radial_min, radial_max_eff, radial_step, dtype=torch.float32, device=device ) if radial_bins.numel() == 0: radial_bins = torch.tensor([radial_min], dtype=torch.float32, device=device) phi_range = torch.pi if two_fold_rotation_symmetry else 2.0 * torch.pi + # Drop the last endpoint because 0 and 2pi (or pi) are the same angle phi_bins = torch.linspace( 0.0, phi_range, num_annular_bins + 1, dtype=torch.float32, device=device )[:-1] phi_grid, r_grid = torch.meshgrid(phi_bins, radial_bins, indexing="ij") + + # apply ellipse distortion correction if requested + # TODO: implement method to estimate ellipse_params from data if ellipse_params is None: x = r_grid * torch.cos(phi_grid) y = r_grid * torch.sin(phi_grid) @@ -164,6 +176,8 @@ def _precompute_polar_coords( raise ValueError("ellipse_params must be (a, b, theta_deg).") a, b, theta_deg = ellipse_params theta = torch.deg2rad(torch.tensor(theta_deg, dtype=torch.float32, device=device)) + # Rotate into the ellipse frame, scale by a/b to undo the distortion, + # then rotate back so sampling follows the true circular rings alpha = phi_grid - theta u = (a / b) * r_grid * torch.cos(alpha) v_prime = r_grid * torch.sin(alpha) @@ -173,6 +187,7 @@ def _precompute_polar_coords( y = u * sin_t + v_prime * cos_t coords_y = y + origin_row coords_x = x + origin_col + # convert to normalized coordinates for grid_sample grid = _normalize_coords_for_grid_sample(coords_y, coords_x, ny, nx) grid = grid.unsqueeze(0) # (1, n_phi, n_r, 2) return grid, phi_bins, radial_bins, radial_max_eff @@ -232,16 +247,22 @@ def auto_origin_id( elif len(data.array.shape) == 4: scan_y, scan_x, ny, nx = data.array.shape else: - raise ValueError("auto_origin_id only supports 2D or 4D-STEM datasets.") + raise ValueError( + f" Got array with shape {data.array.shape}." + "To use auto_origin_id, pass a 2D or 4DSTEM dataset." + ) origin_array = np.zeros((scan_y, scan_x, 2), dtype=float) max_steps = 1000 + total_positions = scan_y * scan_x # start with center but subsequent positions warm-start from the previous result estimated_origin_row = (ny - 1) / 2.0 estimated_origin_col = (nx - 1) / 2.0 + pbar = tqdm(total=total_positions, desc="Origin of each scan position") for y_pos in range(scan_y): for x_pos in range(scan_x): test_origin = np.array([estimated_origin_row, estimated_origin_col], dtype=float) + # Cache avoids redundant polar transforms when neighbors are revisited across iterations coords_cache: dict[tuple[int, int], float] = {} polar = data.polar_transform( origin_array=test_origin, @@ -254,8 +275,12 @@ def auto_origin_id( scan_pos=(y_pos, x_pos), device=device, ) + # Exclude inner 10% (central beam) and outer 10% (edge artifacts) + # to focus on the diffraction ring region min_r = int(np.floor(0.1 * polar.shape[1])) max_r = int(np.ceil(0.9 * polar.shape[1])) + # A correctly centered pattern has uniform intensity along each ring, + # so minimizing angular std finds the true center std_est_origin = polar[:, min_r:max_r].std(dim=0) std_est_origin_sum = std_est_origin.sum() origin_row = int(round(estimated_origin_row)) @@ -304,6 +329,8 @@ def auto_origin_id( # start next scan position from this result estimated_origin_row = float(origin_row) estimated_origin_col = float(origin_col) + pbar.update(1) + pbar.close() return origin_array @@ -323,7 +350,10 @@ def dataset4dstem_polar_transform( device: str = "cpu", ) -> Polar4dstem | torch.Tensor: if self.array.ndim != 4: - raise ValueError("polar_transform requires a 4D-STEM dataset (ndim=4).") + raise ValueError( + f"Found array with shape: {self.array.shape}. " + "polar_transform requires a 4D-STEM dataset (ndim=4)." + ) scan_y, scan_x, ny, nx = self.array.shape # Standardize origin_array input @@ -340,8 +370,8 @@ def dataset4dstem_polar_transform( origins = origin_array else: raise ValueError( + f" Got {origin_array.shape}. " "origin_array must have shape None, (2,) or (scan_y, scan_x, 2)." - f" Got {origin_array.shape}." ) # If scan_pos is provided, compute polar transform only for that position @@ -373,7 +403,8 @@ def dataset4dstem_polar_transform( ) return polar2d.squeeze(0).squeeze(0) # (n_phi, n_r) - # Compute radial_max from all origins if not provided + # Use the global minimum safe radius across all origins so every scan + # position maps to the same-size polar grid (required for a uniform 4D output) if radial_max is None: r_row_pos = origins[:, :, 0] r_row_neg = (ny - 1) - origins[:, :, 0] @@ -428,6 +459,7 @@ def dataset4dstem_polar_transform( ) out[iy, ix] = _to_numpy(polar2d.squeeze(0).squeeze(0)) + # Express polar axes in physical units matching the input dataset's calibration phi_range = np.pi if two_fold_rotation_symmetry else 2.0 * np.pi phi_step_deg = (phi_range / float(n_phi)) * (180.0 / np.pi) sampling = np.zeros(4, dtype=float) diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py index 4f0bfaf0..543670d6 100644 --- a/src/quantem/diffraction/polar.py +++ b/src/quantem/diffraction/polar.py @@ -52,6 +52,7 @@ def __init__( ): if _token is not self._token: raise RuntimeError( + "Direct instantiation of PairDistributionFunction is not allowed. " "Use PairDistributionFunction.from_data() to instantiate this class." ) @@ -125,7 +126,10 @@ def from_data( if isinstance(data, Dataset2d): arr2d = data.array if arr2d.ndim != 2: - raise ValueError("Dataset2d for PairDistributionFunction must be 2D.") + raise ValueError( + f"Found array with shape: {arr2d.shape}. " + "Dataset2d for PairDistributionFunction must be 2D." + ) arr4 = arr2d[None, None, ...] # (1, 1, ky, kx) data = Dataset4dstem.from_array( @@ -182,7 +186,8 @@ def from_data( # Dataset3d input: not yet specified how to interpret if isinstance(data, Dataset3d): raise NotImplementedError( - "PairDistributionFunction.from_data does not yet support Dataset3d inputs." + "PairDistributionFunction.from_data does not yet support Dataset3d inputs. " + "Please provide a 4D-STEM dataset or a 2D diffraction pattern." ) # Numpy array input @@ -219,7 +224,10 @@ def from_data( device=device, ) else: - raise ValueError("PairDistributionFunction.from_data only supports 2D or 4D arrays.") + raise ValueError( + f"Found array with shape: {arr.shape}. " + "PairDistributionFunction.from_data only supports 2D or 4D arrays." + ) # ------------------------------------------------------------------ # Convenience accessors @@ -287,7 +295,10 @@ def _get_mask_bool(self, mask_realspace): if mask_realspace.dtype == bool and mask_realspace.shape == (rx, ry): mask_bool = mask_realspace else: - raise ValueError("mask_realspace must be boolean array.") + raise ValueError( + f"Got shape {mask_realspace.shape}. " + f"mask_realspace must be boolean array of shape ({rx}, {ry})." + ) return mask_bool # ------------------------------------------------------------------ @@ -353,6 +364,7 @@ def _scattering_model_torch( eps = 1e-10 exp1 = torch.clamp(k2 / (-2.0 * (s0**2 + eps)), min=-100, max=0) exp2 = torch.clamp((k2**2) / (-2.0 * (s1**4 + eps)), min=-100, max=0) + # scattering model is monotonic, as is physically expected for backgrounds scattering return c + i0 * torch.exp(exp1) + i1 * torch.exp(exp2) def _compute_fit_weights(self, k: torch.Tensor, kmin: float, kmax: float) -> torch.Tensor: @@ -407,7 +419,10 @@ def _frequency_filtering( and k_highpass > 0.0 ): if k_highpass > k_lowpass: - raise ValueError("Gaussian band-pass filtering requires k_highpass < k_lowpass.") + raise ValueError( + "k_highpass is greater than k_lowpass." + "Gaussian band-pass filtering requires k_highpass < k_lowpass." + ) Fk_low = self._gaussian_filter1d_torch(Fk, sigma=k_lowpass / dk.item(), mode="nearest") Fk_high = self._gaussian_filter1d_torch( Fk, sigma=k_highpass / dk.item(), mode="nearest" @@ -515,9 +530,8 @@ def calculate_radial_mean( Otherwise returns None. """ polar_data = self.polar_tensor # shape: (scan_y, scan_x, phi, k) - if mask_realspace is None: - # intensity over q-range for each probe position then average + # get intensity over q-range for each probe position then average radial_probe = polar_data.mean(dim=2) # dim 2: theta self.radial_mean = radial_probe.mean(dim=(0, 1)) else: @@ -588,6 +602,7 @@ def fit_bg( ) # Map to unconstrained space via inverse softplus: x = y + log(1 - exp(-y)) # For numerical stability, clamp init_vals away from zero + # final values must be positive for a physical model of background scattering init_vals = torch.clamp(init_vals, min=1e-6) theta = init_vals + torch.log(-torch.expm1(-init_vals)) theta = theta.clone().detach().requires_grad_(True) @@ -600,7 +615,8 @@ def fit_bg( line_search_fn="strong_wolfe", ) - # k-dependent fitting weights + # fitting weights (high-k range is emphasized for better bg estimation) + # this monotonic model means we don't need parameterized scattering factors weights = self._compute_fit_weights(k, kmin, kmax) prev_loss = torch.tensor(float("inf")) @@ -612,7 +628,7 @@ def fit_bg( break prev_loss = loss - # final params + # final params (ensure positivity via softplus) with torch.no_grad(): c = F.softplus(theta[0]) i0 = F.softplus(theta[1]) @@ -623,7 +639,7 @@ def fit_bg( c_scaled = c * int_mean i0_scaled = i0 * int_mean i1_scaled = i1 * int_mean - # compute bg + # compute bg and the average scattering factor f(k) bg = self._scattering_model_torch(k2, c_scaled, i0_scaled, s0, i1_scaled, s1) f = bg - c_scaled return bg, f @@ -704,12 +720,16 @@ def calculate_Gr( self.kmin = k_min if k_min is not None else float(k.min()) mask_bool = self._get_mask_bool(mask_realspace) + # get the radial mean intensity on the entire unmasked region Ik = self.calculate_radial_mean(mask_realspace=mask_bool, returnval=True) + # background fitting on Ik bg, f = self.fit_bg(Ik, self.kmin, self.kmax) # prevent division by near-zero values which cause NaNs at high k f_safe = torch.clamp(f, min=1e-10 * f.max()) + # below is the standard definition of F(k) used in PDF analysis, except for missing 2pi factor Fk = (Ik - bg) * k_safe / f_safe + # apply optional frequency filtering for noise reduction Fk = self._frequency_filtering(Fk, k_lowpass, k_highpass, dk) # Compute Sk from Fk BEFORE applying the 2pi scaling, # so that estimate_density corrections are on the same scale @@ -744,8 +764,7 @@ def calculate_Gr( self._r = r self._reduced_pdf = reduced_pdf - # Sk was already computed above (before 2pi scaling) - + # optionally damped unphysical oscillations near the origin by iteratively estimating density and correcting F(k) if damp_origin_oscillations: density_est = self.estimate_density( r_cut=r_cut, @@ -802,7 +821,10 @@ def calculate_gr( list[np.ndarray] or None """ if self._reduced_pdf is None or self._r is None: - raise RuntimeError("Run calculate_Gr() before calculate_gr().") + raise RuntimeError( + "Reduced PDF not computed." + "Run PairDistributionFunction.calculate_Gr() before calculate_gr()." + ) # Determine density if density is not None: @@ -810,6 +832,8 @@ def calculate_gr( elif self.rho0 is not None: rho0 = self.rho0 else: + # the oscillation correction simultaneously produces a density estimate + # if the user didn't run damping in calculate_Gr, we can still run the density estimation without using the corrected Fk/G(r) density_est = self.estimate_density( r_cut=r_cut, max_iter=20, @@ -826,6 +850,7 @@ def calculate_gr( r = self._r mask = r > 0 pdf = torch.ones_like(Gr) + # the formula for g(r) from G(r) is: g(r) = 1 + G(r) / (4 * pi * r * rho0) pdf = torch.where(mask, 1 + Gr / (4 * torch.pi * r * rho0), torch.zeros_like(pdf)) if set_pdf_positive: # negative values are unphysical pdf = torch.maximum(pdf, torch.zeros_like(pdf)) @@ -874,8 +899,13 @@ def estimate_density( G_cor : torch.Tensor Reduced PDF G(r) with dampened oscillations near origin. """ + # we need the non-reduced structure factor (S(k) = 1 + F(k)/k) for the density estimation correction, + # so we compute it here from the Fk we already have if self.Sk is None or self._reduced_pdf is None or self._r is None: - raise RuntimeError("Run calculate_Gr() before estimate_density().") + raise RuntimeError( + "This method depends on Sk, reduced_pdf, and r from calculate_Gr. " + "Run PairDistributionFunction.calculate_Gr() before estimate_density()." + ) k = self._to_torch(np.asarray(self.qq)) dk = k[1] - k[0] @@ -883,10 +913,10 @@ def estimate_density( k_fit = k[k_fit_mask] ka, ra = torch.meshgrid(k, self._r, indexing="ij") + # r_cut sets the minimum r for the peak search used to determine the correction interval mask_search = self._r >= r_cut r_search = self._r[mask_search] G_search = self._reduced_pdf[mask_search] - # find tallest peak and first local minimum to the left of r_peak ind_max = torch.argmax(G_search) r_max = r_search[ind_max] @@ -921,19 +951,21 @@ def estimate_density( Fk_win_damped = self.Fk_masked.clone() # start with uncorrected Gr G_beta = G_short + # calculate the lorch window once wk = self._lorch_window(k, self.kmin, self.kmax) for j in range(max_iter): if j > 0: G_beta = G_cor[r_mask] # calculate alpha/beta for S(k) adjustment + # alpha and beta are the ideal and actual contributions to G(r) in the short-r range + # from the current S(k) and G(r) alpha, beta = self._compute_alpha_beta(k2d_fit, r2d_fit, G_beta, r_short) rho0 = float(torch.sum(alpha * beta) / torch.sum(alpha**2)) if rho0_prev is not None: Rj = np.sqrt(((rho0_prev - rho0) ** 2) / (rho0**2)) * 100.0 if Rj < tol_percent: break - # Update S_cor(k) and G_cor Sk_cor[k_fit_mask] = Sk_cor[k_fit_mask] - beta + rho0 * alpha Fk_cor = k * (Sk_cor - 1.0) @@ -1036,7 +1068,10 @@ def plot_radial_mean( """ if self.radial_mean is None: - raise RuntimeError("Radial mean intensity has not been calculated yet.") + raise RuntimeError( + "Radial mean intensity has not been calculated yet." + "Run PairDistributionFunction.calculate_Gr() or PairDistributionFunction.calculate_radial_mean() before plotting." + ) x = np.asarray(self.qq) y = self._to_numpy(self.radial_mean) @@ -1069,7 +1104,10 @@ def plot_background_fits( Plotting background fit vs radial mean intensity. """ if self.Ik is None or self.bg is None: - raise RuntimeError("Radial mean intensity or background has not been calculated yet.") + raise RuntimeError( + "Radial mean intensity or background has not been calculated yet." + "Run PairDistributionFunction.calculate_Gr() or both calculate_radial_mean() and calculate_background() before plotting." + ) x = np.asarray(self.qq) y1 = self._to_numpy(self.radial_mean) @@ -1106,7 +1144,10 @@ def plot_reduced_sf( Plotting reduced structure factor F(k). """ if self.Fk_masked is None: - raise RuntimeError("Reduced structure factor F(k) has not been calculated yet.") + raise RuntimeError( + "Reduced structure factor F(k) has not been calculated yet." + "Run PairDistributionFunction.calculate_Gr() before plotting." + ) Fk = getattr(self, "Fk_damped", None) if Fk is None: @@ -1141,7 +1182,10 @@ def plot_reduced_pdf( Plotting reduced PDF g(r). """ if self._reduced_pdf is None: - raise RuntimeError("Reduced PDF has not been calculated yet.") + raise RuntimeError( + "Reduced PDF has not been calculated yet." + "Run PairDistributionFunction.calculate_Gr() before plotting." + ) Gr = getattr(self, "reduced_pdf_damped", None) if Gr is None: Gr = self._reduced_pdf @@ -1190,7 +1234,10 @@ def plot_pdf( Plotting pair distribution function g(r). """ if self._reduced_pdf is None or self._pdf is None: - raise RuntimeError("Reduced PDF or PDF has not been calculated yet.") + raise RuntimeError( + "PDF has not been calculated yet." + "Run PairDistributionFunction.calculate_gr() before plotting." + ) x = self._to_numpy(self._r) y = self._to_numpy(self._pdf) diff --git a/tests/diffraction/test_polar.py b/tests/diffraction/test_polar.py index be274d09..4dc5a005 100644 --- a/tests/diffraction/test_polar.py +++ b/tests/diffraction/test_polar.py @@ -108,7 +108,6 @@ def test_find_origin(self, synthetic_4dstem_dataset): synthetic_4dstem_dataset, ) assert origin_array.shape == (3, 3, 2) # (scan_y, scan_x, 2) - expected_center = 127.5 for iy in range(3): for ix in range(3): @@ -117,6 +116,53 @@ def test_find_origin(self, synthetic_4dstem_dataset): assert abs(col - expected_center) < 1 +# ============================================================================ +# Test Polar Transform +# ============================================================================ + + +class TestPolarTransform: + """Test polar coordinate transformation.""" + + def test_polar_transform_basic(self, synthetic_4dstem_dataset): + """Test basic polar transformation.""" + polar = synthetic_4dstem_dataset.polar_transform() + assert isinstance(polar, Polar4dstem) + assert polar.shape[0] == 3 # scan_y + assert polar.shape[1] == 3 # scan_x + assert polar.shape[2] == 180 # num_annular_bins + assert polar.shape[3] > 0 # radial bins + + def test_polar_transform_single_origin(self, synthetic_4dstem_dataset): + """Test polar transformation with single origin broadcast to all positions.""" + origin = np.array([128.0, 128.0]) + polar = synthetic_4dstem_dataset.polar_transform( + origin_array=origin, + ) + assert isinstance(polar, Polar4dstem) + + def test_polar_transform_radial_range(self, synthetic_4dstem_dataset): + """Test polar transformation with custom radial range.""" + polar = synthetic_4dstem_dataset.polar_transform( + radial_min=5.0, + radial_max=50.0, + radial_step=2.0, + ) + assert isinstance(polar, Polar4dstem) + # Check that radial dimension matches expected size + expected_n_r = int(np.ceil((50.0 - 5.0) / 2.0)) + assert polar.shape[3] == expected_n_r + + def test_polar_transform_scan_pos(self, synthetic_4dstem_dataset): + """Test polar transformation for a single scan position.""" + polar_2d = synthetic_4dstem_dataset.polar_transform( + scan_pos=(0, 0), + ) + # should return 2D tensor (phi, r) + assert polar_2d.ndim == 2 + assert polar_2d.shape[0] == 180 # num_annular_bins + + # ============================================================================ # Test Radial Mean Calculation # ============================================================================ @@ -131,14 +177,12 @@ def test_calculate_radial_mean_with_mask(self, synthetic_4dstem_dataset): synthetic_4dstem_dataset, find_origin=False, ) - mask = np.zeros((3, 3), dtype=bool) mask[0:2, 0:2] = True radial_mean = pdf.calculate_radial_mean( mask_realspace=mask, returnval=True, ) - assert radial_mean is not None @@ -156,12 +200,10 @@ def test_fit_bg_basic(self, synthetic_dataset2d): synthetic_dataset2d, find_origin=False, ) - Ik = pdf.calculate_radial_mean(returnval=True) k = pdf._to_torch(np.asarray(pdf.qq)) kmin, kmax = float(k.min()), float(k.max()) bg, f = pdf.fit_bg(Ik, kmin=kmin * 0.1, kmax=kmax * 0.9) - assert bg.shape == Ik.shape assert f.shape == Ik.shape # Check that background is positive @@ -182,7 +224,6 @@ def test_calculate_Gr_with_bandpass(self, synthetic_dataset2d): synthetic_dataset2d, find_origin=False, ) - pdf.calculate_Gr( k_min=0.1, k_max=2.0, @@ -197,7 +238,6 @@ def test_calculate_Gr_with_mask(self, synthetic_4dstem_dataset): synthetic_4dstem_dataset, find_origin=False, ) - mask = np.zeros((3, 3), dtype=bool) mask[0:2, 0:2] = True pdf.calculate_Gr( @@ -213,8 +253,7 @@ def test_calculate_gr_requires_Gr(self, synthetic_dataset2d): synthetic_dataset2d, find_origin=False, ) - - with pytest.raises(RuntimeError, match="Run calculate_Gr"): + with pytest.raises(RuntimeError, match="Reduced PDF not computed"): pdf.calculate_gr(density=0.05) def test_calculate_gr_estimates_density(self, synthetic_dataset2d): @@ -223,10 +262,8 @@ def test_calculate_gr_estimates_density(self, synthetic_dataset2d): synthetic_dataset2d, find_origin=False, ) - pdf.calculate_Gr(k_min=0.1, k_max=2.0) results = pdf.calculate_gr(returnval=True) - assert results is not None r, gr = results assert isinstance(gr, np.ndarray) @@ -239,63 +276,12 @@ def test_estimate_density_requires_Gr(self, synthetic_dataset2d): synthetic_dataset2d, find_origin=False, ) - - with pytest.raises(RuntimeError, match="Run calculate_Gr"): + with pytest.raises( + RuntimeError, match="depends on Sk, reduced_pdf, and r from calculate_Gr" + ): pdf.estimate_density() -# ============================================================================ -# Test Polar Transform -# ============================================================================ - - -class TestPolarTransform: - """Test polar coordinate transformation.""" - - def test_polar_transform_basic(self, synthetic_4dstem_dataset): - """Test basic polar transformation.""" - polar = synthetic_4dstem_dataset.polar_transform() - - assert isinstance(polar, Polar4dstem) - assert polar.shape[0] == 3 # scan_y - assert polar.shape[1] == 3 # scan_x - assert polar.shape[2] == 180 # num_annular_bins - assert polar.shape[3] > 0 # radial bins - - def test_polar_transform_single_origin(self, synthetic_4dstem_dataset): - """Test polar transformation with single origin broadcast to all positions.""" - origin = np.array([128.0, 128.0]) - - polar = synthetic_4dstem_dataset.polar_transform( - origin_array=origin, - ) - - assert isinstance(polar, Polar4dstem) - - def test_polar_transform_radial_range(self, synthetic_4dstem_dataset): - """Test polar transformation with custom radial range.""" - polar = synthetic_4dstem_dataset.polar_transform( - radial_min=5.0, - radial_max=50.0, - radial_step=2.0, - ) - - assert isinstance(polar, Polar4dstem) - # Check that radial dimension matches expected size - expected_n_r = int(np.ceil((50.0 - 5.0) / 2.0)) - assert polar.shape[3] == expected_n_r - - def test_polar_transform_scan_pos(self, synthetic_4dstem_dataset): - """Test polar transformation for a single scan position.""" - polar_2d = synthetic_4dstem_dataset.polar_transform( - scan_pos=(0, 0), - ) - - # should return 2D tensor (phi, r) - assert polar_2d.ndim == 2 - assert polar_2d.shape[0] == 180 # num_annular_bins - - # ============================================================================ # Integration Workflows # ============================================================================ @@ -310,7 +296,6 @@ def test_complete_pdf_workflow_2d(self, synthetic_dataset2d): synthetic_dataset2d, find_origin=False, ) - Gr_results = pdf.calculate_Gr( k_min=0.1, k_max=2.0, @@ -319,7 +304,6 @@ def test_complete_pdf_workflow_2d(self, synthetic_dataset2d): r_step=0.05, returnval=True, ) - assert Gr_results is not None r, Gr = Gr_results assert not np.isnan(r).any() @@ -327,12 +311,10 @@ def test_complete_pdf_workflow_2d(self, synthetic_dataset2d): assert not np.isinf(Gr).any() assert len(r) > 0 assert len(Gr) == len(r) - gr_results = pdf.calculate_gr( density=0.05, returnval=True, ) - assert gr_results is not None r_gr, gr = gr_results assert not np.isnan(gr).any() @@ -345,16 +327,13 @@ def test_complete_pdf_workflow_4dstem(self, synthetic_4dstem_dataset): synthetic_4dstem_dataset, find_origin=True, ) - mask = np.zeros((3, 3), dtype=bool) mask[0:2, 0:2] = True - pdf.calculate_Gr( k_min=0.1, k_max=2.0, mask_realspace=mask, ) - assert pdf.reduced_pdf is not None assert not np.isnan(pdf.reduced_pdf).any() assert not np.isinf(pdf.reduced_pdf).any() @@ -380,7 +359,6 @@ def test_polar_transform_input_types(self, synthetic_diffraction_pattern): find_origin=False, ) assert pdf_ds4.polar.shape[2] == 180 - assert pdf_ds2.polar.shape == pdf_ds4.polar.shape def test_density_estimation_workflow(self, synthetic_dataset2d): @@ -389,21 +367,17 @@ def test_density_estimation_workflow(self, synthetic_dataset2d): synthetic_dataset2d, find_origin=False, ) - pdf.calculate_Gr(k_min=0.1, k_max=2.0) rho0, Fk_damped, G_cor = pdf.estimate_density( max_iter=5, tol_percent=1.0, ) - assert rho0 > 0 assert np.isfinite(rho0) - results = pdf.calculate_gr( density=rho0, returnval=True, ) - assert results is not None r, gr = results assert not np.isnan(gr).any() From 6f744b113c5dc2c323b4667744c546b16a539ed9 Mon Sep 17 00:00:00 2001 From: ehrhardtkm Date: Mon, 16 Mar 2026 17:26:17 -0700 Subject: [PATCH 128/140] minor fixes --- src/quantem/diffraction/polar.py | 230 +++++++++++++++++++------------ 1 file changed, 139 insertions(+), 91 deletions(-) diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py index 543670d6..58aecb00 100644 --- a/src/quantem/diffraction/polar.py +++ b/src/quantem/diffraction/polar.py @@ -61,15 +61,16 @@ def __init__( self.input_data = input_data self.device = device - self._polar_tensor: torch.Tensor | None = None self._r: torch.Tensor | None = None self._reduced_pdf: torch.Tensor | None = None self._pdf: torch.Tensor | None = None - self.radial_mean: torch.Tensor | None = None + self.Ik: torch.Tensor | None = None self.Sk: torch.Tensor | None = None self.Fk: torch.Tensor | None = None self.bg: torch.Tensor | None = None self.Fk_mask: torch.Tensor | None = None + self.Fk_damped: torch.Tensor | None = None + self.reduced_pdf_damped: torch.Tensor | None = None self.rho0: float | None = None # ------------------------------------------------------------------ @@ -240,19 +241,12 @@ def qq(self) -> Any: """ # Polar4dstem dims: (scan_y, scan_x, phi, r) # radial axis is 3 + # origin[3] is the physical q-value at bin 0 (radial_min * pixel_size), + # sampling[3] is the physical step per bin (radial_step * pixel_size). n = self.polar.shape[3] origin_r = float(np.asarray(self.polar.origin)[3]) sampling_r = float(np.asarray(self.polar.sampling)[3]) - return (np.arange(n, dtype=float) - origin_r) * sampling_r - - @property - def radial_bins(self) -> Any: - """ - Radial bin centers in pixel units (convenience alias). - """ - n = self.polar.shape[3] - origin_r = float(np.asarray(self.polar.origin)[3]) - return np.arange(n, dtype=float) - origin_r + return np.arange(n, dtype=float) * sampling_r + origin_r @property def r(self) -> NDArray | None: @@ -304,13 +298,6 @@ def _get_mask_bool(self, mask_realspace): # ------------------------------------------------------------------ # Torch conversion utilities # ------------------------------------------------------------------ - @property - def polar_tensor(self) -> torch.Tensor: - if self._polar_tensor is None: - self._polar_tensor = torch.from_numpy(self.polar.array.astype(np.float32)).to( - device=self.device - ) - return self._polar_tensor def _to_torch(self, arr: NDArray) -> torch.Tensor: return torch.from_numpy(arr.astype(np.float32)).to(device=self.device) @@ -509,10 +496,11 @@ def calculate_radial_mean( The polar array is assumed to have shape (scan_y, scan_x, phi, k). This method computes, for each scan position, the mean over the azimuthal axis (phi), then averages across scan positions to produce a single 1D - radial curve. This result is stored in ``self.radial_mean``. + radial curve. This result is stored in ``self.Ik``. If a real-space mask is provided, only the selected scan positions are - used in the scan-position average. + used in the scan-position average. The computation streams chunks through torch to keep peak + memory low. Parameters ---------- @@ -529,21 +517,38 @@ def calculate_radial_mean( If `returnval=True`, returns the 1D radial mean intensity (Nk,). Otherwise returns None. """ - polar_data = self.polar_tensor # shape: (scan_y, scan_x, phi, k) - if mask_realspace is None: - # get intensity over q-range for each probe position then average - radial_probe = polar_data.mean(dim=2) # dim 2: theta - self.radial_mean = radial_probe.mean(dim=(0, 1)) - else: - mask_torch = torch.from_numpy(mask_realspace).to(device=self.device) - masked_polar = polar_data[mask_torch] # (N_valid, N_theta, N_k) - # intensity over q-range of each unmasked probe position - radial_probe = masked_polar.mean(dim=1) - # average over unmasked probe positions - self.radial_mean = radial_probe.mean(dim=0) + polar_np = self.polar.array # shape: (scan_y, scan_x, phi, k) + scan_y, scan_x, n_phi, n_k = polar_np.shape + intensity_sum = torch.zeros(n_k, device=self.device, dtype=torch.float64) + n_valid = 0 + chunk_y = 16 # number of scan_y to process at a time + for y0 in range(0, scan_y, chunk_y): + y1 = min(y0 + chunk_y, scan_y) + raw = polar_np[y0:y1] + chunk = torch.from_numpy(np.ascontiguousarray(raw)).to(self.device) + # mean over phi first -> (chunk, scan_x, k) + radial_mean = chunk.mean(dim=2) + if mask_realspace is not None: + mask_chunk = torch.from_numpy(mask_realspace[y0:y1]).to(self.device) + n_chunk = int(mask_chunk.sum()) + if n_chunk == 0: + continue + # sum unmasked intensities in chunk and count for normalization later + intensity_sum += radial_mean[mask_chunk].sum(dim=0) + n_valid += n_chunk + else: + # sum all intensities in chunk and count for normalization later + intensity_sum += radial_mean.sum(dim=(0, 1)) + n_valid += (y1 - y0) * scan_x + if n_valid == 0: + raise ValueError( + "No valid scan positions selected. The real-space mask is " + "all False or the dataset is empty." + ) + self.Ik = (intensity_sum / n_valid).float() if returnval: - return self.radial_mean + return self.Ik else: return None @@ -646,8 +651,10 @@ def fit_bg( def calculate_Gr( self, - k_min: float | None = None, - k_max: float | None = None, + k_min_fit: float | None = None, + k_max_fit: float | None = None, + k_min_window: float | None = None, + k_max_window: float | None = None, k_lowpass: float | None = None, k_highpass: float | None = None, r_min: float = 0.0, @@ -655,6 +662,7 @@ def calculate_Gr( r_step: float = 0.02, mask_realspace: NDArray | None = None, damp_origin_oscillations: bool = False, + density: float | None = None, r_cut: float = 0.8, returnval: bool = False, ) -> list[NDArray] | None: @@ -676,16 +684,22 @@ def calculate_Gr( ``self.rho0`` so that a subsequent :meth:`calculate_gr` call can reuse it. Stored attributes: - * self.radial_mean, self.Ik, self.bg, self.Fk, self.Fk_masked + * self.Ik, self.bg, self.Fk, self.Fk_masked * self.Sk, self.r, self.reduced_pdf * self.rho0, self.Fk_damped, self.reduced_pdf_damped (if damping) Parameters ---------- - k_min : float, optional - Minimum k (A^-1) for masks and transforms. - k_max : float or None, optional - Maximum k (A^-1) for masks and transforms. + k_min_fit : float, optional + Minimum k (A^-1) for the background fit. + k_max_fit : float or None, optional + Maximum k (A^-1) for the background fit. + k_min_window : float or None, optional + Minimum k (A^-1) for the structure-factor Lorch window. + If None, falls back to ``k_min_fit``. + k_max_window : float or None, optional + Maximum k (A^-1) for the structure-factor Lorch window. + If None, falls back to ``k_max_fit``. k_lowpass : float or None, optional Low-pass Gaussian filter sigma in k-space. k_highpass : float or None, optional @@ -700,6 +714,10 @@ def calculate_Gr( Boolean real-space mask selecting probe positions. damp_origin_oscillations : bool, optional If True, run :meth:`estimate_density` and store corrected F(k)/G(r). + density : float or None, optional + Known number density (atoms/A^3). If provided together with + ``damp_origin_oscillations=True``, the S(k)/G(r) correction uses + this value instead of estimating it. r_cut : float, optional Minimum radial distance (A) for peak search in density estimation. Forwarded to :meth:`estimate_density`. @@ -710,20 +728,31 @@ def calculate_Gr( ------- list[np.ndarray] or None """ + # clear results from any previous run so stale state doesn't leak + self.Fk_damped = None + self.reduced_pdf_damped = None + self.rho0 = None # this is missing a 2pi term that we add back during the pdf calc later k_np = np.asarray(self.qq) k = self._to_torch(k_np) dk = k[1] - k[0] # small epsilon to avoid division by very small k values k_safe = torch.clamp(k, min=1e-10) - self.kmax = k_max if k_max is not None else float(k.max()) - self.kmin = k_min if k_min is not None else float(k.min()) + self.kmax_fit = k_max_fit if k_max_fit is not None else float(k.max()) + self.kmin_fit = k_min_fit if k_min_fit is not None else float(k.min()) + # window range defaults to bg-fit range when not specified + self.kmin_window = k_min_window if k_min_window is not None else self.kmin_fit + self.kmax_window = k_max_window if k_max_window is not None else self.kmax_fit mask_bool = self._get_mask_bool(mask_realspace) - # get the radial mean intensity on the entire unmasked region - Ik = self.calculate_radial_mean(mask_realspace=mask_bool, returnval=True) - # background fitting on Ik - bg, f = self.fit_bg(Ik, self.kmin, self.kmax) + # reuse existing radial mean if already computed + if self.Ik is not None: + Ik = self.Ik + else: + Ik = self.calculate_radial_mean(mask_realspace=mask_bool, returnval=True) + # background fitting on Ik is better with wider range of values so kmin_fit/kmax_fit + # can be different from kmin/kmax used for the final F(k) and G(r) calculation + bg, f = self.fit_bg(Ik, self.kmin_fit, self.kmax_fit) # prevent division by near-zero values which cause NaNs at high k f_safe = torch.clamp(f, min=1e-10 * f.max()) @@ -739,7 +768,7 @@ def calculate_Gr( # apply that missing 2pi factor Fk = Fk * 2 * torch.pi # damp edges with lorch window - wk = self._lorch_window(k, self.kmin, self.kmax) + wk = self._lorch_window(k, self.kmin_window, self.kmax_window) Fk_win = Fk * wk r = torch.arange(r_min, r_max, r_step, device=self.device, dtype=torch.float32) @@ -767,6 +796,7 @@ def calculate_Gr( # optionally damped unphysical oscillations near the origin by iteratively estimating density and correcting F(k) if damp_origin_oscillations: density_est = self.estimate_density( + density=density, r_cut=r_cut, max_iter=20, tol_percent=1e-1, @@ -776,9 +806,11 @@ def calculate_Gr( self.reduced_pdf_damped = density_est[2] if returnval: - Gr = getattr(self, "reduced_pdf_damped", None) - if Gr is None: - Gr = self._reduced_pdf + Gr = ( + self.reduced_pdf_damped + if self.reduced_pdf_damped is not None + else self._reduced_pdf + ) return [self._to_numpy(self._r), self._to_numpy(Gr)] return None @@ -831,6 +863,7 @@ def calculate_gr( rho0 = density elif self.rho0 is not None: rho0 = self.rho0 + print(f" Using estimated rho0 = {rho0:.6f} atoms/A^3", flush=True) else: # the oscillation correction simultaneously produces a density estimate # if the user didn't run damping in calculate_Gr, we can still run the density estimation without using the corrected Fk/G(r) @@ -841,11 +874,11 @@ def calculate_gr( ) self.rho0 = density_est[0] rho0 = self.rho0 + print(f" Estimated rho0 = {rho0:.6f} atoms/A^3", flush=True) # Use damped G(r) if the user opted into damping, otherwise undamped - Gr = getattr(self, "reduced_pdf_damped", None) - if Gr is None: - Gr = self._reduced_pdf + Gr = self.reduced_pdf_damped if self.reduced_pdf_damped is not None else self._reduced_pdf + Gr = Gr.clone() r = self._r mask = r > 0 @@ -862,6 +895,7 @@ def calculate_gr( def estimate_density( self, + density: float | None = None, r_cut: float = 0.8, max_iter: int = 40, tol_percent: float = 1e-4, @@ -875,12 +909,20 @@ def estimate_density( corrected S(k) so that the implied G(r) is more physically consistent at low r. + If ``density`` is provided, the given value is used as a fixed rho0 + for the S(k)/G(r) correction instead of estimating it iteratively. + This method requires that :meth:`calculate_Gr` has already been run, because it depends on `self.Sk`, `self.reduced_pdf`, `self.r`, - and the k-window bounds (`self.kmin`, `self.kmax`). + and the k-window bounds (`self.kmin_fit`, `self.kmin_window`, + `self.kmax_window`). Parameters ---------- + density : float or None, optional + Known number density (atoms/A^3). If provided, used as a fixed + rho0 — the iterative estimation is skipped and only the S(k)/G(r) + correction is performed. r_cut : float, optional Minimum radial distance (A) for the peak search used to determine the correction interval. Peaks below this distance are ignored. @@ -893,7 +935,7 @@ def estimate_density( Returns ------- rho0 : float - Estimated microscopic number density (atoms/A^3). + Number density (atoms/A^3), either provided or estimated. Fk_win_damped : torch.Tensor Windowed corrected reduced structure function used for the transform. G_cor : torch.Tensor @@ -909,7 +951,7 @@ def estimate_density( k = self._to_torch(np.asarray(self.qq)) dk = k[1] - k[0] - k_fit_mask = k >= self.kmin + k_fit_mask = (k >= self.kmin_fit) & (k <= self.kmax_window) k_fit = k[k_fit_mask] ka, ra = torch.meshgrid(k, self._r, indexing="ij") @@ -935,52 +977,64 @@ def estimate_density( rmin = float(r_left[mins_indices[-1]]) else: rmin = float(r_left[torch.argmin(G_left)]) - # Restrict r to [0, rmin] for the correction r_mask = (self._r >= 0.0) & (self._r <= rmin) r_short = self._r[r_mask] - G_short = self._reduced_pdf[r_mask] k_fit_scaled = k_fit * 2 * torch.pi k2d_fit, r2d_fit = torch.meshgrid(k_fit_scaled, r_short, indexing="ij") # Iterative refinement of rho0 and S(k) - rho0 = 0.0 + fixed_density = density is not None + rho0 = density if fixed_density else 0.0 rho0_prev = None Sk_cor = self.Sk.clone() - G_cor = self._reduced_pdf.clone() - Fk_win_damped = self.Fk_masked.clone() - # start with uncorrected Gr - G_beta = G_short - # calculate the lorch window once - wk = self._lorch_window(k, self.kmin, self.kmax) + # calculate lorch function once bc it doesn't change during the iteration + wk = self._lorch_window(k, self.kmin_window, self.kmax_window) + # windowed G(r) for the iteration + Fk_win = k * (Sk_cor - 1.0) * wk * 2 * torch.pi + G_iter = ( + (2.0 / torch.pi) + * dk + * 2 + * torch.pi + * torch.sum(torch.sin(2 * torch.pi * ka * ra) * Fk_win[:, None], dim=0) + ) + G_iter[0] = 0.0 + G_beta = G_iter[r_mask] + beta_prev = None for j in range(max_iter): if j > 0: - G_beta = G_cor[r_mask] - + G_beta = G_iter[r_mask] # calculate alpha/beta for S(k) adjustment # alpha and beta are the ideal and actual contributions to G(r) in the short-r range # from the current S(k) and G(r) alpha, beta = self._compute_alpha_beta(k2d_fit, r2d_fit, G_beta, r_short) - rho0 = float(torch.sum(alpha * beta) / torch.sum(alpha**2)) - if rho0_prev is not None: - Rj = np.sqrt(((rho0_prev - rho0) ** 2) / (rho0**2)) * 100.0 - if Rj < tol_percent: - break + if not fixed_density: + rho0 = float(torch.sum(alpha * beta) / torch.sum(alpha**2)) + if rho0_prev is not None: + Rj = np.sqrt(((rho0_prev - rho0) ** 2) / (rho0**2)) * 100.0 + if Rj < tol_percent: + break + else: + # fixed density: converge on the S(k) correction magnitude + if beta_prev is not None: + delta = float(torch.max(torch.abs(beta - beta_prev))) + if delta < tol_percent * 1e-2: + break + beta_prev = beta.clone() # Update S_cor(k) and G_cor Sk_cor[k_fit_mask] = Sk_cor[k_fit_mask] - beta + rho0 * alpha - Fk_cor = k * (Sk_cor - 1.0) - Fk_win_damped = Fk_cor * wk * 2 * torch.pi - G_cor = ( + Fk_win = k * (Sk_cor - 1.0) * wk * 2 * torch.pi + G_iter = ( (2.0 / torch.pi) * dk * 2 * torch.pi - * torch.sum(torch.sin(2 * torch.pi * ka * ra) * Fk_win_damped[:, None], dim=0) + * torch.sum(torch.sin(2 * torch.pi * ka * ra) * Fk_win[:, None], dim=0) ) - G_cor[0] = 0.0 + G_iter[0] = 0.0 rho0_prev = rho0 - - return rho0, Fk_win_damped, G_cor + return rho0, Fk_win, G_iter # ------------------------------------------------------------------ # Plotting functions @@ -1067,14 +1121,14 @@ def plot_radial_mean( Plotting radial mean intensity vs scattering vector. """ - if self.radial_mean is None: + if self.Ik is None: raise RuntimeError( "Radial mean intensity has not been calculated yet." "Run PairDistributionFunction.calculate_Gr() or PairDistributionFunction.calculate_radial_mean() before plotting." ) x = np.asarray(self.qq) - y = self._to_numpy(self.radial_mean) + y = self._to_numpy(self.Ik) x, y = self._apply_xrange(x, y, qmin, qmax) fig, ax = plt.subplots(figsize=figsize) @@ -1110,7 +1164,7 @@ def plot_background_fits( ) x = np.asarray(self.qq) - y1 = self._to_numpy(self.radial_mean) + y1 = self._to_numpy(self.Ik) x, y1 = self._apply_xrange(x, y1, qmin, qmax) x = np.asarray(self.qq) y2 = self._to_numpy(self.bg) @@ -1186,9 +1240,7 @@ def plot_reduced_pdf( "Reduced PDF has not been calculated yet." "Run PairDistributionFunction.calculate_Gr() before plotting." ) - Gr = getattr(self, "reduced_pdf_damped", None) - if Gr is None: - Gr = self._reduced_pdf + Gr = self.reduced_pdf_damped if self.reduced_pdf_damped is not None else self._reduced_pdf x = self._to_numpy(self._r) y = self._to_numpy(Gr) @@ -1279,11 +1331,7 @@ def plot_oscillation_damping( figsize: tuple[float, float] = (8, 4), returnfig: bool = False, ): - if ( - self.Fk_masked is None - or not hasattr(self, "Fk_damped") - or not hasattr(self, "reduced_pdf_damped") - ): + if self.Fk_masked is None or self.Fk_damped is None or self.reduced_pdf_damped is None: raise RuntimeError( "Oscillation damping data not available. " "Run calculate_Gr(damp_origin_oscillations=True) first." From d0b668014ee3d05e5676cc14e9f654d679dfe61f Mon Sep 17 00:00:00 2001 From: ehrhardtkm Date: Mon, 16 Mar 2026 23:13:28 -0700 Subject: [PATCH 129/140] origin finding speedup --- .../core/datastructures/polar4dstem.py | 428 ++++++++++++------ 1 file changed, 300 insertions(+), 128 deletions(-) diff --git a/src/quantem/core/datastructures/polar4dstem.py b/src/quantem/core/datastructures/polar4dstem.py index a1dc6af5..d237ed73 100644 --- a/src/quantem/core/datastructures/polar4dstem.py +++ b/src/quantem/core/datastructures/polar4dstem.py @@ -120,40 +120,112 @@ def _normalize_coords_for_grid_sample( return torch.stack([x_norm, y_norm], dim=-1) -def _precompute_polar_coords( +def _polar_to_cartesian_offsets( + phi: torch.Tensor, + r: torch.Tensor, + ellipse_params: tuple[float, float, float] | None, + device: str = "cpu", +) -> tuple[torch.Tensor, torch.Tensor]: + """Convert polar (phi, r) grids to Cartesian (x, y) offsets from the origin, + optionally correcting for elliptical distortion.""" + if ellipse_params is None: + x = r * torch.cos(phi) + y = r * torch.sin(phi) + else: + if len(ellipse_params) != 3: + raise ValueError("ellipse_params must be (a, b, theta_deg).") + a, b, theta_deg = ellipse_params + theta = torch.deg2rad(torch.tensor(theta_deg, dtype=torch.float32, device=device)) + # Rotate into the ellipse frame, scale by a/b to undo the distortion, + # then rotate back so sampling follows the true circular rings + alpha = phi - theta + u = (a / b) * r * torch.cos(alpha) + v_prime = r * torch.sin(alpha) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + x = u * cos_t - v_prime * sin_t + y = u * sin_t + v_prime * cos_t + return x, y + + +def _build_candidate_grids( + base_x_norm: torch.Tensor, + base_y_norm: torch.Tensor, + center_row: int, + center_col: int, + margin: int, ny: int, nx: int, - origin_row: float, - origin_col: float, + x_norm_scale: float, + y_norm_scale: float, + device: str = "cpu", +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Build a batch of normalized sampling grids for all candidate origins + within a search window around (center_row, center_col).""" + # Enumerate all pixel positions in the search window, clamped to image bounds + rows = torch.arange( + max(0, center_row - margin), + min(ny, center_row + margin + 1), + dtype=torch.long, + device=device, + ) + cols = torch.arange( + max(0, center_col - margin), + min(nx, center_col + margin + 1), + dtype=torch.long, + device=device, + ) + row_grid, col_grid = torch.meshgrid(rows, cols, indexing="ij") + row_flat, col_flat = row_grid.reshape(-1), col_grid.reshape(-1) + # Shift the pre-computed polar offsets to each candidate origin, + # converting to grid_sample's [-1, 1] normalized coordinates + grid_x = base_x_norm.unsqueeze(0) + (col_flat.float() * x_norm_scale - 1.0)[:, None, None] + grid_y = base_y_norm.unsqueeze(0) + (row_flat.float() * y_norm_scale - 1.0)[:, None, None] + grids = torch.stack([grid_x, grid_y], dim=-1) # (N, n_phi, n_r, 2) + return row_flat, col_flat, grids + + +def _angular_std_scores( + dp_batch: torch.Tensor, + grids: torch.Tensor, + min_r_idx: int, + max_r_idx: int, +) -> torch.Tensor: + """Score candidate origins by angular std over a mid-radius band. + Lower scores indicate better centering.""" + n = grids.shape[0] + # Sample the diffraction pattern at each candidate's polar grid positions + polars = F.grid_sample( + dp_batch.expand(n, -1, -1, -1), + grids, + mode="bilinear", + padding_mode="zeros", + align_corners=True, + ) + # A correctly centered pattern has uniform intensity along each ring, + # so the angular std is minimized at the true center + region = polars.squeeze(1)[:, :, min_r_idx:max_r_idx] + return region.std(dim=1).sum(dim=1) + + +def _build_polar_sampling_offsets( ellipse_params: tuple[float, float, float] | None, num_annular_bins: int, radial_min: float, - radial_max: float | None, + radial_max_eff: float, radial_step: float, two_fold_rotation_symmetry: bool, device: str = "cpu", -) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, float]: - origin_row = float(origin_row) - origin_col = float(origin_col) +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Build origin-independent Cartesian offsets for a polar sampling grid. + Returns (offset_x, offset_y, phi_bins, radial_bins) where offset_x and + offset_y have shape (n_phi, n_r) and represent pixel displacements from + an arbitrary origin.""" if radial_step <= 0: raise ValueError(f"Got radial_step = {radial_step}. radial_step must be > 0.") if num_annular_bins < 1: raise ValueError("num_annular_bins must be >= 1.") - # Use the shortest distance from the origin to any image edge so the - # polar grid never samples outside the image bounds. - if radial_max is None: - r_row_pos = origin_row - r_row_neg = (ny - 1) - origin_row - r_col_pos = origin_col - r_col_neg = (nx - 1) - origin_col - radial_max_eff = float(min(r_row_pos, r_row_neg, r_col_pos, r_col_neg)) - else: - radial_max_eff = float(radial_max) - # Guarantee at least one radial bin so downstream code never gets an empty array - if radial_max_eff <= radial_min: - radial_max_eff = radial_min + radial_step - # create radial bins and phi bins, then create the grid of (phi, r) coordinates radial_bins = torch.arange( radial_min, radial_max_eff, radial_step, dtype=torch.float32, device=device ) @@ -165,29 +237,81 @@ def _precompute_polar_coords( 0.0, phi_range, num_annular_bins + 1, dtype=torch.float32, device=device )[:-1] phi_grid, r_grid = torch.meshgrid(phi_bins, radial_bins, indexing="ij") + # Compute offsets relative to origin (0,0) so they can be reused + # for any candidate origin by simple translation + offset_x, offset_y = _polar_to_cartesian_offsets(phi_grid, r_grid, ellipse_params, device) + return offset_x, offset_y, phi_bins, radial_bins - # apply ellipse distortion correction if requested - # TODO: implement method to estimate ellipse_params from data - if ellipse_params is None: - x = r_grid * torch.cos(phi_grid) - y = r_grid * torch.sin(phi_grid) + +def _compute_radial_max( + ny: int, + nx: int, + origin_row: float, + origin_col: float, + radial_max: float | None, + radial_min: float, + radial_step: float, +) -> float: + """Compute the effective maximum radius, clamped to image bounds.""" + # Use the shortest distance from the origin to any image edge so the + # polar grid never samples outside the image bounds + if radial_max is None: + radial_max_eff = float( + min( + origin_row, + (ny - 1) - origin_row, + origin_col, + (nx - 1) - origin_col, + ) + ) else: - if len(ellipse_params) != 3: - raise ValueError("ellipse_params must be (a, b, theta_deg).") - a, b, theta_deg = ellipse_params - theta = torch.deg2rad(torch.tensor(theta_deg, dtype=torch.float32, device=device)) - # Rotate into the ellipse frame, scale by a/b to undo the distortion, - # then rotate back so sampling follows the true circular rings - alpha = phi_grid - theta - u = (a / b) * r_grid * torch.cos(alpha) - v_prime = r_grid * torch.sin(alpha) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - x = u * cos_t - v_prime * sin_t - y = u * sin_t + v_prime * cos_t - coords_y = y + origin_row - coords_x = x + origin_col - # convert to normalized coordinates for grid_sample + radial_max_eff = float(radial_max) + # Guarantee at least one radial bin + if radial_max_eff <= radial_min: + radial_max_eff = radial_min + radial_step + return radial_max_eff + + +def _precompute_polar_coords( + ny: int, + nx: int, + origin_row: float, + origin_col: float, + ellipse_params: tuple[float, float, float] | None, + num_annular_bins: int, + radial_min: float, + radial_max: float | None, + radial_step: float, + two_fold_rotation_symmetry: bool, + device: str = "cpu", +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, float]: + """Build a normalized sampling grid for a single known origin.""" + origin_row = float(origin_row) + origin_col = float(origin_col) + # Clamp radial range so the polar grid stays within image bounds + radial_max_eff = _compute_radial_max( + ny, + nx, + origin_row, + origin_col, + radial_max, + radial_min, + radial_step, + ) + # Get origin-independent polar offsets in pixel coordinates + offset_x, offset_y, phi_bins, radial_bins = _build_polar_sampling_offsets( + ellipse_params, + num_annular_bins, + radial_min, + radial_max_eff, + radial_step, + two_fold_rotation_symmetry, + device, + ) + # Translate offsets to absolute pixel pos at this origin + coords_x = offset_x + origin_col + coords_y = offset_y + origin_row + # Convert to [-1, 1] normalized coordinates expected by grid_sample grid = _normalize_coords_for_grid_sample(coords_y, coords_x, ny, nx) grid = grid.unsqueeze(0) # (1, n_phi, n_r, 2) return grid, phi_bins, radial_bins, radial_max_eff @@ -205,16 +329,14 @@ def auto_origin_id( device: str = "cpu", ) -> NDArray: """ - Automatic diffraction center finding by minimizing the standard deviation - along the annular direction in the polar transform. + Automatic diffraction center finding by minimizing angular intensity + variation in the polar transform. A correctly centered diffraction + pattern has uniform intensity along each ring, so the center that + minimizes the angular standard deviation is the true beam center. - For each scan position, this routine: - 1) Computes a polar transform at an initial origin (image center, or - warm-started from the previous scan position). - 2) Evaluates the sum of the standard deviation across angle (phi) over - a mid-radius band. - 3) Performs a local search over neighboring pixel origins until the - objective no longer improves. + Uses a coarse-to-fine search on the mean diffraction pattern to find + a global center, then refines per scan position to account for beam + drift across the scan. Parameters ---------- @@ -223,7 +345,8 @@ def auto_origin_id( ellipse_params : tuple or None Ellipse parameters (a, b, theta_deg) for distortion correction. num_annular_bins : int - Number of angular bins for polar transform. + Number of angular bins for the final polar transform (not used + during center-finding, which uses 36 bins for speed). radial_min : float Minimum radius in pixels. radial_max : float or None @@ -253,85 +376,135 @@ def auto_origin_id( ) origin_array = np.zeros((scan_y, scan_x, 2), dtype=float) - max_steps = 1000 total_positions = scan_y * scan_x - # start with center but subsequent positions warm-start from the previous result - estimated_origin_row = (ny - 1) / 2.0 - estimated_origin_col = (nx - 1) / 2.0 - pbar = tqdm(total=total_positions, desc="Origin of each scan position") + + # first get COM of mean DP because it gives a robust rough center + array_4d = data.array if data.array.ndim == 4 else data.array[None, None, :, :] + mean_dp_np = array_4d.mean(axis=(0, 1)).astype(np.float32) + total_intensity = mean_dp_np.sum() + yy_grid, xx_grid = np.mgrid[0:ny, 0:nx] + com_row = int(round(float((yy_grid * mean_dp_np).sum() / total_intensity))) + com_col = int(round(float((xx_grid * mean_dp_np).sum() / total_intensity))) + + # building a fixed polar grid that is safe for all candidates + # safe_rmax ensures no candidate's grid extends outside the image + global_margin = 20 + safe_rmax = float( + min( + com_row - global_margin, + (ny - 1) - (com_row + global_margin), + com_col - global_margin, + (nx - 1) - (com_col + global_margin), + ) + ) + if radial_max is not None: + safe_rmax = min(safe_rmax, float(radial_max)) + if safe_rmax <= radial_min: + safe_rmax = radial_min + radial_step + # use very coarse binning because asymmetry is still captured at + # low angular resolution and is significantly faster + search_n_phi = 36 + offset_x, offset_y, _, radial_bins = _build_polar_sampling_offsets( + ellipse_params, + search_n_phi, + radial_min, + safe_rmax, + radial_step, + two_fold_rotation_symmetry, + device, + ) + n_r = radial_bins.numel() + min_r_idx = int(np.floor(0.1 * n_r)) + max_r_idx = int(np.ceil(0.9 * n_r)) + # Normalize offsets to [-1, 1] because grid_sample expects normalized coordinates + x_norm_scale = 2.0 / (nx - 1) + y_norm_scale = 2.0 / (ny - 1) + base_x_norm = offset_x * x_norm_scale + base_y_norm = offset_y * y_norm_scale + + # now find actual center + # Coarse search over ±global_margin around COM + coarse_step = 4 + coarse_rows = torch.arange( + max(0, com_row - global_margin), + min(ny, com_row + global_margin + 1), + coarse_step, + dtype=torch.long, + device=device, + ) + coarse_cols = torch.arange( + max(0, com_col - global_margin), + min(nx, com_col + global_margin + 1), + coarse_step, + dtype=torch.long, + device=device, + ) + # Create all (row, col) candidate pairs and flatten for batched evaluation + coarse_row_grid, coarse_col_grid = torch.meshgrid(coarse_rows, coarse_cols, indexing="ij") + coarse_row_flat, coarse_col_flat = coarse_row_grid.reshape(-1), coarse_col_grid.reshape(-1) + # Shift polar offsets to each candidate origin in normalized coordinates + coarse_gx = ( + base_x_norm.unsqueeze(0) + (coarse_col_flat.float() * x_norm_scale - 1.0)[:, None, None] + ) + coarse_gy = ( + base_y_norm.unsqueeze(0) + (coarse_row_flat.float() * y_norm_scale - 1.0)[:, None, None] + ) + coarse_grids = torch.stack([coarse_gx, coarse_gy], dim=-1) + # Score all coarse candidates on the mean DP and pick the best one + mean_dp_batch = torch.from_numpy(mean_dp_np).to(device).unsqueeze(0).unsqueeze(0) + coarse_scores = _angular_std_scores(mean_dp_batch, coarse_grids, min_r_idx, max_r_idx) + best_coarse_idx = coarse_scores.argmin().item() + coarse_best_row = int(coarse_row_flat[best_coarse_idx].item()) + coarse_best_col = int(coarse_col_flat[best_coarse_idx].item()) + + # Fine search (step=1) around coarse best for global center of mean DP + fine_margin = 6 + fine_row_flat, fine_col_flat, fine_grids = _build_candidate_grids( + base_x_norm, + base_y_norm, + coarse_best_row, + coarse_best_col, + fine_margin, + ny, + nx, + x_norm_scale, + y_norm_scale, + device, + ) + fine_scores = _angular_std_scores(mean_dp_batch, fine_grids, min_r_idx, max_r_idx) + best_fine_idx = fine_scores.argmin().item() + global_row = int(fine_row_flat[best_fine_idx].item()) + global_col = int(fine_col_flat[best_fine_idx].item()) + # Get center for each scan pos by fine search around global center + # Assuming that the center doesn't shift more than 10 pixels across the scan + local_margin = 10 + local_rf, local_cf, local_grids = _build_candidate_grids( + base_x_norm, + base_y_norm, + global_row, + global_col, + local_margin, + ny, + nx, + x_norm_scale, + y_norm_scale, + device, + ) + pbar = tqdm(total=total_positions, desc="Finding origin for each scan position") for y_pos in range(scan_y): + row_dps = torch.from_numpy(array_4d[y_pos].astype(np.float32)).to( + device + ) # (scan_x, ny, nx) + for x_pos in range(scan_x): - test_origin = np.array([estimated_origin_row, estimated_origin_col], dtype=float) - # Cache avoids redundant polar transforms when neighbors are revisited across iterations - coords_cache: dict[tuple[int, int], float] = {} - polar = data.polar_transform( - origin_array=test_origin, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - scan_pos=(y_pos, x_pos), - device=device, - ) - # Exclude inner 10% (central beam) and outer 10% (edge artifacts) - # to focus on the diffraction ring region - min_r = int(np.floor(0.1 * polar.shape[1])) - max_r = int(np.ceil(0.9 * polar.shape[1])) - # A correctly centered pattern has uniform intensity along each ring, - # so minimizing angular std finds the true center - std_est_origin = polar[:, min_r:max_r].std(dim=0) - std_est_origin_sum = std_est_origin.sum() - origin_row = int(round(estimated_origin_row)) - origin_col = int(round(estimated_origin_col)) - coords_cache[(origin_row, origin_col)] = std_est_origin_sum - - converged = False - best = std_est_origin_sum - steps = 0 - while not converged and steps < max_steps: - steps += 1 - moved = False - neighbors = [ - (origin_row + dr, origin_col + dc) - for dr in (-1, 0, 1) - for dc in (-1, 0, 1) - if not (dr == 0 and dc == 0) - ] - neighbors = [(r, c) for (r, c) in neighbors if 0 <= r < ny and 0 <= c < nx] - for origin_r, origin_c in neighbors: - if (origin_r, origin_c) not in coords_cache: - test_origin = np.array([origin_r, origin_c], dtype=float) - polar = data.polar_transform( - origin_array=test_origin, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - scan_pos=(y_pos, x_pos), - device=device, - ) - std_test = polar[:, min_r:max_r].std(dim=0) - coords_cache[(origin_r, origin_c)] = std_test.sum() - if coords_cache[(origin_r, origin_c)] < best: - origin_row = origin_r - origin_col = origin_c - best = coords_cache[(origin_r, origin_c)] - moved = True - if not moved: - converged = True - - origin_array[y_pos, x_pos, 0] = origin_row - origin_array[y_pos, x_pos, 1] = origin_col - # start next scan position from this result - estimated_origin_row = float(origin_row) - estimated_origin_col = float(origin_col) + dp_batch = row_dps[x_pos].unsqueeze(0).unsqueeze(0) + scores = _angular_std_scores(dp_batch, local_grids, min_r_idx, max_r_idx) + best_idx = scores.argmin().item() + origin_array[y_pos, x_pos, 0] = local_rf[best_idx].item() + origin_array[y_pos, x_pos, 1] = local_cf[best_idx].item() pbar.update(1) - pbar.close() + pbar.close() return origin_array @@ -429,8 +602,7 @@ def dataset4dstem_polar_transform( ) n_phi = phi_bins.numel() n_r = radial_bins.numel() - result_dtype = np.result_type(self.array.dtype, np.float32) - out = np.empty((scan_y, scan_x, n_phi, n_r), dtype=result_dtype) + out = np.empty((scan_y, scan_x, n_phi, n_r), dtype=np.float32) for iy in range(scan_y): for ix in range(scan_x): dp = torch.from_numpy(self.array[iy, ix].astype(np.float32)).to(device) From db08dcf61dbab47f34bd1bdab70fb32281c9ca0a Mon Sep 17 00:00:00 2001 From: ehrhardtkm Date: Tue, 17 Mar 2026 14:40:14 -0700 Subject: [PATCH 130/140] make calculate_Gr use precomputed bg --- .../core/datastructures/dataset4dstem.py | 19 ++++++++++++++++--- src/quantem/diffraction/polar.py | 11 ++++++++--- 2 files changed, 24 insertions(+), 6 deletions(-) diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 27ea385a..6cbedc52 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -8,7 +8,12 @@ from quantem.core.datastructures.dataset2d import Dataset2d from quantem.core.datastructures.dataset4d import Dataset4d -from quantem.core.datastructures.polar4dstem import dataset4dstem_polar_transform +from quantem.core.datastructures.polar4dstem import ( + auto_origin_id as _auto_origin_id, +) +from quantem.core.datastructures.polar4dstem import ( + dataset4dstem_polar_transform as _dataset4dstem_polar_transform, +) from quantem.core.utils.validators import ensure_valid_array from quantem.core.visualization import show_2d from quantem.core.visualization.visualization_utils import ScalebarConfig @@ -78,7 +83,7 @@ def __init__( _token : object | None, optional Token to prevent direct instantiation, by default None """ - mdata_keys_4dstem = ["r_to_q_rotation_cw_deg", "ellipticity"] + mdata_keys_4dstem = ["q_to_r_rotation_ccw_deg", "q_transpose", "ellipticity"] for k in mdata_keys_4dstem: if k not in metadata.keys(): metadata[k] = None @@ -800,4 +805,12 @@ def median_filter_masked_pixels(self, mask: np.ndarray, kernel_width: int = 3): self.array[:, :, x_min:x_max, y_min:y_max], axis=(2, 3) ) - polar_transform = dataset4dstem_polar_transform + def auto_origin_id(self, **kwargs): + """Find diffraction centers by minimizing angular intensity variation. + + Delegates to the module-level ``auto_origin_id`` function. + See its docstring for full parameter details. + """ + return _auto_origin_id(self, **kwargs) + + polar_transform = _dataset4dstem_polar_transform diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py index 58aecb00..0507ef83 100644 --- a/src/quantem/diffraction/polar.py +++ b/src/quantem/diffraction/polar.py @@ -68,6 +68,7 @@ def __init__( self.Sk: torch.Tensor | None = None self.Fk: torch.Tensor | None = None self.bg: torch.Tensor | None = None + self.f: torch.Tensor | None = None self.Fk_mask: torch.Tensor | None = None self.Fk_damped: torch.Tensor | None = None self.reduced_pdf_damped: torch.Tensor | None = None @@ -647,6 +648,8 @@ def fit_bg( # compute bg and the average scattering factor f(k) bg = self._scattering_model_torch(k2, c_scaled, i0_scaled, s0, i1_scaled, s1) f = bg - c_scaled + self.bg = bg + self.f = f return bg, f def calculate_Gr( @@ -750,9 +753,11 @@ def calculate_Gr( Ik = self.Ik else: Ik = self.calculate_radial_mean(mask_realspace=mask_bool, returnval=True) - # background fitting on Ik is better with wider range of values so kmin_fit/kmax_fit - # can be different from kmin/kmax used for the final F(k) and G(r) calculation - bg, f = self.fit_bg(Ik, self.kmin_fit, self.kmax_fit) + # reuse existing background fit if already computed + if self.bg is not None and self.f is not None: + bg, f = self.bg, self.f + else: + bg, f = self.fit_bg(Ik, self.kmin_fit, self.kmax_fit) # prevent division by near-zero values which cause NaNs at high k f_safe = torch.clamp(f, min=1e-10 * f.max()) From df48ac86185c29f2b5e52e5309f879029f070d8b Mon Sep 17 00:00:00 2001 From: ehrhardtkm Date: Mon, 27 Apr 2026 16:30:56 -0700 Subject: [PATCH 131/140] refactor and row/col convention cleanup --- .../core/datastructures/dataset4dstem.py | 16 - .../core/datastructures/polar4dstem.py | 593 +--------------- src/quantem/diffraction/polar.py | 671 +++++++++--------- src/quantem/diffraction/polar_transform.py | 621 ++++++++++++++++ src/quantem/tomography/utils.py | 98 ++- tests/diffraction/test_polar.py | 44 +- 6 files changed, 1060 insertions(+), 983 deletions(-) create mode 100644 src/quantem/diffraction/polar_transform.py diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 6cbedc52..87989c7f 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -8,12 +8,6 @@ from quantem.core.datastructures.dataset2d import Dataset2d from quantem.core.datastructures.dataset4d import Dataset4d -from quantem.core.datastructures.polar4dstem import ( - auto_origin_id as _auto_origin_id, -) -from quantem.core.datastructures.polar4dstem import ( - dataset4dstem_polar_transform as _dataset4dstem_polar_transform, -) from quantem.core.utils.validators import ensure_valid_array from quantem.core.visualization import show_2d from quantem.core.visualization.visualization_utils import ScalebarConfig @@ -804,13 +798,3 @@ def median_filter_masked_pixels(self, mask: np.ndarray, kernel_width: int = 3): self.array[:, :, index_x, index_y] = np.median( self.array[:, :, x_min:x_max, y_min:y_max], axis=(2, 3) ) - - def auto_origin_id(self, **kwargs): - """Find diffraction centers by minimizing angular intensity variation. - - Delegates to the module-level ``auto_origin_id`` function. - See its docstring for full parameter details. - """ - return _auto_origin_id(self, **kwargs) - - polar_transform = _dataset4dstem_polar_transform diff --git a/src/quantem/core/datastructures/polar4dstem.py b/src/quantem/core/datastructures/polar4dstem.py index d237ed73..c48a8b81 100644 --- a/src/quantem/core/datastructures/polar4dstem.py +++ b/src/quantem/core/datastructures/polar4dstem.py @@ -1,25 +1,17 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING, Any +from typing import Self import numpy as np -import torch -import torch.nn.functional as F from numpy.typing import NDArray -from tqdm import tqdm - -if TYPE_CHECKING: - from .dataset4dstem import Dataset4dstem from quantem.core.datastructures.dataset4d import Dataset4d class Polar4dstem(Dataset4d): - """4D-STEM dataset in polar coordinates (scan_y, scan_x, phi, r).""" + """4D-STEM dataset in polar coordinates (scan_row, scan_col, phi, r_pix).""" def __init__( self, - array: NDArray | Any, + array: NDArray, name: str, origin: NDArray | tuple | list | float | int, sampling: NDArray | tuple | list | float | int, @@ -37,7 +29,7 @@ def __init__( "polar_radial_step", "polar_num_annular_bins", "polar_two_fold_rotation_symmetry", - "polar_ellipse_params", + "polar_ellipticity", ] for k in mdata_keys_polar: if k not in metadata: @@ -57,14 +49,14 @@ def __init__( @classmethod def from_array( cls, - array: NDArray | Any, + array: NDArray, name: str | None = None, origin: NDArray | tuple | list | float | int | None = None, sampling: NDArray | tuple | list | float | int | None = None, units: list[str] | tuple | list | None = None, signal_units: str = "arb. units", metadata: dict | None = None, - ) -> "Polar4dstem": + ) -> Self: array = np.asarray(array) if array.ndim != 4: raise ValueError( @@ -97,576 +89,3 @@ def n_phi(self) -> int: @property def n_r(self) -> int: return int(self.array.shape[3]) - - -def _to_numpy(tensor: torch.Tensor) -> NDArray: - """Convert torch tensor to numpy array.""" - return tensor.detach().cpu().numpy() - - -def _normalize_coords_for_grid_sample( - coords_y: torch.Tensor, - coords_x: torch.Tensor, - height: int, - width: int, -) -> torch.Tensor: - """ - Convert pixel coordinates to normalized [-1, 1] coordinates for grid_sample. - grid_sample expects x_norm = 2*x/(W-1) - 1, y_norm = 2*y/(H-1) - 1, - stacked as (..., 2) in [x, y] order. - """ - x_norm = 2.0 * coords_x / (width - 1) - 1.0 - y_norm = 2.0 * coords_y / (height - 1) - 1.0 - return torch.stack([x_norm, y_norm], dim=-1) - - -def _polar_to_cartesian_offsets( - phi: torch.Tensor, - r: torch.Tensor, - ellipse_params: tuple[float, float, float] | None, - device: str = "cpu", -) -> tuple[torch.Tensor, torch.Tensor]: - """Convert polar (phi, r) grids to Cartesian (x, y) offsets from the origin, - optionally correcting for elliptical distortion.""" - if ellipse_params is None: - x = r * torch.cos(phi) - y = r * torch.sin(phi) - else: - if len(ellipse_params) != 3: - raise ValueError("ellipse_params must be (a, b, theta_deg).") - a, b, theta_deg = ellipse_params - theta = torch.deg2rad(torch.tensor(theta_deg, dtype=torch.float32, device=device)) - # Rotate into the ellipse frame, scale by a/b to undo the distortion, - # then rotate back so sampling follows the true circular rings - alpha = phi - theta - u = (a / b) * r * torch.cos(alpha) - v_prime = r * torch.sin(alpha) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - x = u * cos_t - v_prime * sin_t - y = u * sin_t + v_prime * cos_t - return x, y - - -def _build_candidate_grids( - base_x_norm: torch.Tensor, - base_y_norm: torch.Tensor, - center_row: int, - center_col: int, - margin: int, - ny: int, - nx: int, - x_norm_scale: float, - y_norm_scale: float, - device: str = "cpu", -) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Build a batch of normalized sampling grids for all candidate origins - within a search window around (center_row, center_col).""" - # Enumerate all pixel positions in the search window, clamped to image bounds - rows = torch.arange( - max(0, center_row - margin), - min(ny, center_row + margin + 1), - dtype=torch.long, - device=device, - ) - cols = torch.arange( - max(0, center_col - margin), - min(nx, center_col + margin + 1), - dtype=torch.long, - device=device, - ) - row_grid, col_grid = torch.meshgrid(rows, cols, indexing="ij") - row_flat, col_flat = row_grid.reshape(-1), col_grid.reshape(-1) - # Shift the pre-computed polar offsets to each candidate origin, - # converting to grid_sample's [-1, 1] normalized coordinates - grid_x = base_x_norm.unsqueeze(0) + (col_flat.float() * x_norm_scale - 1.0)[:, None, None] - grid_y = base_y_norm.unsqueeze(0) + (row_flat.float() * y_norm_scale - 1.0)[:, None, None] - grids = torch.stack([grid_x, grid_y], dim=-1) # (N, n_phi, n_r, 2) - return row_flat, col_flat, grids - - -def _angular_std_scores( - dp_batch: torch.Tensor, - grids: torch.Tensor, - min_r_idx: int, - max_r_idx: int, -) -> torch.Tensor: - """Score candidate origins by angular std over a mid-radius band. - Lower scores indicate better centering.""" - n = grids.shape[0] - # Sample the diffraction pattern at each candidate's polar grid positions - polars = F.grid_sample( - dp_batch.expand(n, -1, -1, -1), - grids, - mode="bilinear", - padding_mode="zeros", - align_corners=True, - ) - # A correctly centered pattern has uniform intensity along each ring, - # so the angular std is minimized at the true center - region = polars.squeeze(1)[:, :, min_r_idx:max_r_idx] - return region.std(dim=1).sum(dim=1) - - -def _build_polar_sampling_offsets( - ellipse_params: tuple[float, float, float] | None, - num_annular_bins: int, - radial_min: float, - radial_max_eff: float, - radial_step: float, - two_fold_rotation_symmetry: bool, - device: str = "cpu", -) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """Build origin-independent Cartesian offsets for a polar sampling grid. - Returns (offset_x, offset_y, phi_bins, radial_bins) where offset_x and - offset_y have shape (n_phi, n_r) and represent pixel displacements from - an arbitrary origin.""" - if radial_step <= 0: - raise ValueError(f"Got radial_step = {radial_step}. radial_step must be > 0.") - if num_annular_bins < 1: - raise ValueError("num_annular_bins must be >= 1.") - - radial_bins = torch.arange( - radial_min, radial_max_eff, radial_step, dtype=torch.float32, device=device - ) - if radial_bins.numel() == 0: - radial_bins = torch.tensor([radial_min], dtype=torch.float32, device=device) - phi_range = torch.pi if two_fold_rotation_symmetry else 2.0 * torch.pi - # Drop the last endpoint because 0 and 2pi (or pi) are the same angle - phi_bins = torch.linspace( - 0.0, phi_range, num_annular_bins + 1, dtype=torch.float32, device=device - )[:-1] - phi_grid, r_grid = torch.meshgrid(phi_bins, radial_bins, indexing="ij") - # Compute offsets relative to origin (0,0) so they can be reused - # for any candidate origin by simple translation - offset_x, offset_y = _polar_to_cartesian_offsets(phi_grid, r_grid, ellipse_params, device) - return offset_x, offset_y, phi_bins, radial_bins - - -def _compute_radial_max( - ny: int, - nx: int, - origin_row: float, - origin_col: float, - radial_max: float | None, - radial_min: float, - radial_step: float, -) -> float: - """Compute the effective maximum radius, clamped to image bounds.""" - # Use the shortest distance from the origin to any image edge so the - # polar grid never samples outside the image bounds - if radial_max is None: - radial_max_eff = float( - min( - origin_row, - (ny - 1) - origin_row, - origin_col, - (nx - 1) - origin_col, - ) - ) - else: - radial_max_eff = float(radial_max) - # Guarantee at least one radial bin - if radial_max_eff <= radial_min: - radial_max_eff = radial_min + radial_step - return radial_max_eff - - -def _precompute_polar_coords( - ny: int, - nx: int, - origin_row: float, - origin_col: float, - ellipse_params: tuple[float, float, float] | None, - num_annular_bins: int, - radial_min: float, - radial_max: float | None, - radial_step: float, - two_fold_rotation_symmetry: bool, - device: str = "cpu", -) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, float]: - """Build a normalized sampling grid for a single known origin.""" - origin_row = float(origin_row) - origin_col = float(origin_col) - # Clamp radial range so the polar grid stays within image bounds - radial_max_eff = _compute_radial_max( - ny, - nx, - origin_row, - origin_col, - radial_max, - radial_min, - radial_step, - ) - # Get origin-independent polar offsets in pixel coordinates - offset_x, offset_y, phi_bins, radial_bins = _build_polar_sampling_offsets( - ellipse_params, - num_annular_bins, - radial_min, - radial_max_eff, - radial_step, - two_fold_rotation_symmetry, - device, - ) - # Translate offsets to absolute pixel pos at this origin - coords_x = offset_x + origin_col - coords_y = offset_y + origin_row - # Convert to [-1, 1] normalized coordinates expected by grid_sample - grid = _normalize_coords_for_grid_sample(coords_y, coords_x, ny, nx) - grid = grid.unsqueeze(0) # (1, n_phi, n_r, 2) - return grid, phi_bins, radial_bins, radial_max_eff - - -def auto_origin_id( - data: "Dataset4dstem", - *, - ellipse_params: tuple[float, float, float] | None = None, - num_annular_bins: int = 180, - radial_min: float = 0.0, - radial_max: float | None = None, - radial_step: float = 1.0, - two_fold_rotation_symmetry: bool = False, - device: str = "cpu", -) -> NDArray: - """ - Automatic diffraction center finding by minimizing angular intensity - variation in the polar transform. A correctly centered diffraction - pattern has uniform intensity along each ring, so the center that - minimizes the angular standard deviation is the true beam center. - - Uses a coarse-to-fine search on the mean diffraction pattern to find - a global center, then refines per scan position to account for beam - drift across the scan. - - Parameters - ---------- - data : Dataset4dstem - A 4D-STEM dataset (or 2D diffraction pattern wrapped as 4D). - ellipse_params : tuple or None - Ellipse parameters (a, b, theta_deg) for distortion correction. - num_annular_bins : int - Number of angular bins for the final polar transform (not used - during center-finding, which uses 36 bins for speed). - radial_min : float - Minimum radius in pixels. - radial_max : float or None - Maximum radius in pixels. - radial_step : float - Radial step size in pixels. - two_fold_rotation_symmetry : bool - If True, use only 0 to pi range for angles. - device : str - Torch device for computation ("cpu", "cuda", "cuda:0", etc.). - - Returns - ------- - origin_array : np.ndarray - Array of shape (scan_y, scan_x, 2) containing (row, col) origin - estimates in pixels. - """ - if len(data.array.shape) == 2: - ny, nx = data.array.shape - scan_y, scan_x = 1, 1 - elif len(data.array.shape) == 4: - scan_y, scan_x, ny, nx = data.array.shape - else: - raise ValueError( - f" Got array with shape {data.array.shape}." - "To use auto_origin_id, pass a 2D or 4DSTEM dataset." - ) - - origin_array = np.zeros((scan_y, scan_x, 2), dtype=float) - total_positions = scan_y * scan_x - - # first get COM of mean DP because it gives a robust rough center - array_4d = data.array if data.array.ndim == 4 else data.array[None, None, :, :] - mean_dp_np = array_4d.mean(axis=(0, 1)).astype(np.float32) - total_intensity = mean_dp_np.sum() - yy_grid, xx_grid = np.mgrid[0:ny, 0:nx] - com_row = int(round(float((yy_grid * mean_dp_np).sum() / total_intensity))) - com_col = int(round(float((xx_grid * mean_dp_np).sum() / total_intensity))) - - # building a fixed polar grid that is safe for all candidates - # safe_rmax ensures no candidate's grid extends outside the image - global_margin = 20 - safe_rmax = float( - min( - com_row - global_margin, - (ny - 1) - (com_row + global_margin), - com_col - global_margin, - (nx - 1) - (com_col + global_margin), - ) - ) - if radial_max is not None: - safe_rmax = min(safe_rmax, float(radial_max)) - if safe_rmax <= radial_min: - safe_rmax = radial_min + radial_step - # use very coarse binning because asymmetry is still captured at - # low angular resolution and is significantly faster - search_n_phi = 36 - offset_x, offset_y, _, radial_bins = _build_polar_sampling_offsets( - ellipse_params, - search_n_phi, - radial_min, - safe_rmax, - radial_step, - two_fold_rotation_symmetry, - device, - ) - n_r = radial_bins.numel() - min_r_idx = int(np.floor(0.1 * n_r)) - max_r_idx = int(np.ceil(0.9 * n_r)) - # Normalize offsets to [-1, 1] because grid_sample expects normalized coordinates - x_norm_scale = 2.0 / (nx - 1) - y_norm_scale = 2.0 / (ny - 1) - base_x_norm = offset_x * x_norm_scale - base_y_norm = offset_y * y_norm_scale - - # now find actual center - # Coarse search over ±global_margin around COM - coarse_step = 4 - coarse_rows = torch.arange( - max(0, com_row - global_margin), - min(ny, com_row + global_margin + 1), - coarse_step, - dtype=torch.long, - device=device, - ) - coarse_cols = torch.arange( - max(0, com_col - global_margin), - min(nx, com_col + global_margin + 1), - coarse_step, - dtype=torch.long, - device=device, - ) - # Create all (row, col) candidate pairs and flatten for batched evaluation - coarse_row_grid, coarse_col_grid = torch.meshgrid(coarse_rows, coarse_cols, indexing="ij") - coarse_row_flat, coarse_col_flat = coarse_row_grid.reshape(-1), coarse_col_grid.reshape(-1) - # Shift polar offsets to each candidate origin in normalized coordinates - coarse_gx = ( - base_x_norm.unsqueeze(0) + (coarse_col_flat.float() * x_norm_scale - 1.0)[:, None, None] - ) - coarse_gy = ( - base_y_norm.unsqueeze(0) + (coarse_row_flat.float() * y_norm_scale - 1.0)[:, None, None] - ) - coarse_grids = torch.stack([coarse_gx, coarse_gy], dim=-1) - # Score all coarse candidates on the mean DP and pick the best one - mean_dp_batch = torch.from_numpy(mean_dp_np).to(device).unsqueeze(0).unsqueeze(0) - coarse_scores = _angular_std_scores(mean_dp_batch, coarse_grids, min_r_idx, max_r_idx) - best_coarse_idx = coarse_scores.argmin().item() - coarse_best_row = int(coarse_row_flat[best_coarse_idx].item()) - coarse_best_col = int(coarse_col_flat[best_coarse_idx].item()) - - # Fine search (step=1) around coarse best for global center of mean DP - fine_margin = 6 - fine_row_flat, fine_col_flat, fine_grids = _build_candidate_grids( - base_x_norm, - base_y_norm, - coarse_best_row, - coarse_best_col, - fine_margin, - ny, - nx, - x_norm_scale, - y_norm_scale, - device, - ) - fine_scores = _angular_std_scores(mean_dp_batch, fine_grids, min_r_idx, max_r_idx) - best_fine_idx = fine_scores.argmin().item() - global_row = int(fine_row_flat[best_fine_idx].item()) - global_col = int(fine_col_flat[best_fine_idx].item()) - # Get center for each scan pos by fine search around global center - # Assuming that the center doesn't shift more than 10 pixels across the scan - local_margin = 10 - local_rf, local_cf, local_grids = _build_candidate_grids( - base_x_norm, - base_y_norm, - global_row, - global_col, - local_margin, - ny, - nx, - x_norm_scale, - y_norm_scale, - device, - ) - pbar = tqdm(total=total_positions, desc="Finding origin for each scan position") - for y_pos in range(scan_y): - row_dps = torch.from_numpy(array_4d[y_pos].astype(np.float32)).to( - device - ) # (scan_x, ny, nx) - - for x_pos in range(scan_x): - dp_batch = row_dps[x_pos].unsqueeze(0).unsqueeze(0) - scores = _angular_std_scores(dp_batch, local_grids, min_r_idx, max_r_idx) - best_idx = scores.argmin().item() - origin_array[y_pos, x_pos, 0] = local_rf[best_idx].item() - origin_array[y_pos, x_pos, 1] = local_cf[best_idx].item() - pbar.update(1) - - pbar.close() - return origin_array - - -def dataset4dstem_polar_transform( - self: "Dataset4dstem", - origin_array: NDArray | torch.Tensor | None = None, - ellipse_params: tuple[float, float, float] | None = None, - num_annular_bins: int = 180, - radial_min: float = 0.0, - radial_max: float | None = None, - radial_step: float = 1.0, - two_fold_rotation_symmetry: bool = False, - name: str | None = None, - signal_units: str | None = None, - scan_pos: tuple[int, int] | None = None, - device: str = "cpu", -) -> Polar4dstem | torch.Tensor: - if self.array.ndim != 4: - raise ValueError( - f"Found array with shape: {self.array.shape}. " - "polar_transform requires a 4D-STEM dataset (ndim=4)." - ) - scan_y, scan_x, ny, nx = self.array.shape - - # Standardize origin_array input - if isinstance(origin_array, torch.Tensor): - origin_array = _to_numpy(origin_array) - origin_array = np.asarray(origin_array) if origin_array is not None else None - if origin_array is None: - center = np.array([(ny - 1) / 2.0, (nx - 1) / 2.0], dtype=float) - origins = np.broadcast_to(center, (scan_y, scan_x, 2)).copy() - elif origin_array.shape == (2,): - origins = np.empty((scan_y, scan_x, 2), dtype=float) - origins[...] = origin_array - elif origin_array.shape == (scan_y, scan_x, 2): - origins = origin_array - else: - raise ValueError( - f" Got {origin_array.shape}. " - "origin_array must have shape None, (2,) or (scan_y, scan_x, 2)." - ) - - # If scan_pos is provided, compute polar transform only for that position - if scan_pos is not None: - iy, ix = scan_pos - dp = torch.from_numpy(self.array[iy, ix].astype(np.float32)).to(device) - r0 = float(origins[iy, ix, 0]) - c0 = float(origins[iy, ix, 1]) - grid, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( - ny=ny, - nx=nx, - origin_row=r0, - origin_col=c0, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - device=device, - ) - dp_batch = dp.unsqueeze(0).unsqueeze(0) # (1, 1, ny, nx) - polar2d = F.grid_sample( - dp_batch, - grid, - mode="bilinear", - padding_mode="zeros", - align_corners=True, - ) - return polar2d.squeeze(0).squeeze(0) # (n_phi, n_r) - - # Use the global minimum safe radius across all origins so every scan - # position maps to the same-size polar grid (required for a uniform 4D output) - if radial_max is None: - r_row_pos = origins[:, :, 0] - r_row_neg = (ny - 1) - origins[:, :, 0] - r_col_pos = origins[:, :, 1] - r_col_neg = (nx - 1) - origins[:, :, 1] - radial_max_eff_array = np.minimum.reduce([r_row_pos, r_row_neg, r_col_pos, r_col_neg]) - radial_max = float(max(radial_max_eff_array.min(), radial_min + radial_step)) - - # Compute grid for first position to get output shape - grid, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( - ny=ny, - nx=nx, - origin_row=float(origins[0, 0, 0]), - origin_col=float(origins[0, 0, 1]), - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - device=device, - ) - n_phi = phi_bins.numel() - n_r = radial_bins.numel() - out = np.empty((scan_y, scan_x, n_phi, n_r), dtype=np.float32) - for iy in range(scan_y): - for ix in range(scan_x): - dp = torch.from_numpy(self.array[iy, ix].astype(np.float32)).to(device) - r0 = float(origins[iy, ix, 0]) - c0 = float(origins[iy, ix, 1]) - grid, _, _, _ = _precompute_polar_coords( - ny=ny, - nx=nx, - origin_row=r0, - origin_col=c0, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - device=device, - ) - dp_batch = dp.unsqueeze(0).unsqueeze(0) - polar2d = F.grid_sample( - dp_batch, - grid, - mode="bilinear", - padding_mode="zeros", - align_corners=True, - ) - out[iy, ix] = _to_numpy(polar2d.squeeze(0).squeeze(0)) - - # Express polar axes in physical units matching the input dataset's calibration - phi_range = np.pi if two_fold_rotation_symmetry else 2.0 * np.pi - phi_step_deg = (phi_range / float(n_phi)) * (180.0 / np.pi) - sampling = np.zeros(4, dtype=float) - origin = np.zeros(4, dtype=float) - sampling[0:2] = np.asarray(self.sampling)[0:2] - sampling[2] = phi_step_deg - sampling[3] = float(np.asarray(self.sampling)[-1]) * radial_step - origin[0:2] = np.asarray(self.origin)[0:2] - origin[2] = 0.0 - origin[3] = radial_min * float(np.asarray(self.sampling)[-1]) - units = [ - self.units[0], - self.units[1], - "deg", - self.units[-1], - ] - metadata = dict(self.metadata) - metadata.update( - { - "polar_radial_min": float(radial_min), - "polar_radial_max": float(radial_max_eff), - "polar_radial_step": float(radial_step), - "polar_num_annular_bins": int(n_phi), - "polar_two_fold_rotation_symmetry": bool(two_fold_rotation_symmetry), - "polar_ellipse_params": tuple(ellipse_params) if ellipse_params is not None else None, - } - ) - return Polar4dstem( - array=out, - name=name if name is not None else f"{self.name}_polar", - origin=origin, - sampling=sampling, - units=units, - signal_units=signal_units if signal_units is not None else self.signal_units, - metadata=metadata, - origin_array=origins, - _token=Polar4dstem._token, - ) diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py index 0507ef83..d389c055 100644 --- a/src/quantem/diffraction/polar.py +++ b/src/quantem/diffraction/polar.py @@ -1,7 +1,7 @@ from __future__ import annotations from collections.abc import Iterable -from typing import Any, Literal +from typing import Literal, Self import matplotlib.pyplot as plt import numpy as np @@ -10,15 +10,12 @@ from numpy.typing import NDArray from quantem.core.datastructures.dataset2d import Dataset2d -from quantem.core.datastructures.dataset3d import Dataset3d from quantem.core.datastructures.dataset4dstem import Dataset4dstem -from quantem.core.datastructures.polar4dstem import ( - Polar4dstem, - auto_origin_id, - dataset4dstem_polar_transform, -) +from quantem.core.datastructures.polar4dstem import Polar4dstem from quantem.core.io.serialize import AutoSerialize -from quantem.core.utils.validators import ensure_valid_array +from quantem.core.utils.utils import to_numpy +from quantem.diffraction.polar_transform import auto_origin_id, polar_transform +from quantem.tomography.utils import gaussian_filter_1d, gaussian_kernel_1d # TODO: subpixel origin finding (auto_origin_id currently uses integer pixel search) # TODO: elliptical distortion correction in origin finding @@ -38,7 +35,49 @@ class PairDistributionFunction(AutoSerialize): - formation of F(k) and a windowed sine transform to obtain G(r) - optional density estimation and origin correction (Yoshimoto & Omote-style iteration) - basic plotting helpers for I(k), background, F(k), G(r), and g(r) - Some analysis methods (FEM, clustering, etc.) will be implemented in future revisions. + + Attributes + ---------- + polar : Polar4dstem + Polar-transformed diffraction data wrapped by this instance. + input_data : Dataset4dstem, Polar4dstem, or None + Dataset that was polar-transformed to produce ``self.polar``, + preserved for reference. A ``Dataset2d`` input to ``from_data`` is + wrapped as a 1x1 ``Dataset4dstem`` before being stored here. + device : str + Torch device used for computation. + Ik : torch.Tensor or None + Azimuthally averaged intensity I(k), set by ``calculate_radial_mean``. + bg : torch.Tensor or None + Fitted background B(k), set by ``fit_bg``. + f : torch.Tensor or None + Empirical approximation of the mean-squared atomic scattering factor + ⟨f(k)⟩². Set by ``fit_bg``. + Sk : torch.Tensor or None + Structure factor S(k) = 1 + [I(k) − B(k)] / f(k). Set by ``calculate_Gr``. + Fk : torch.Tensor or None + Reduced structure factor F(k) = 2π · k · [S(k) − 1]. The 2π factor is + explicitly included (py4dstem omits it). Set by ``calculate_Gr``. + Fk_mask : torch.Tensor or None + Window applied to F(k) before the sine transform, combining a bandpass + and a Lorch taper. Set by ``calculate_Gr``. + Fk_damped : torch.Tensor or None + F(k) after iterative low-r oscillation damping, set by ``estimate_density``. + reduced_pdf_damped : torch.Tensor or None + G(r) recomputed from the damped F(k), set by ``estimate_density``. + rho0 : float or None + Estimated atomic number density ρ₀ (atoms/ų), set by ``estimate_density``. + + Exposed as read-only numpy-valued properties: + + r : NDArray or None + Real-space radial axis in Å. + reduced_pdf : NDArray or None + Reduced pair distribution function G(r), obtained by windowed sine + transform of F(k): + G(r) = (2/π) ∫ F(k) · sin(2π · k · r) dk. + pdf : NDArray or None + Pair distribution function g(r) = 1 + G(r) / (4π · r · ρ₀). """ _token = object() @@ -46,7 +85,7 @@ class PairDistributionFunction(AutoSerialize): def __init__( self, polar: Polar4dstem, - input_data: Any | None = None, + input_data: Dataset4dstem | Polar4dstem | None = None, device: str = "cpu", _token: object | None = None, ): @@ -80,7 +119,7 @@ def __init__( @classmethod def from_data( cls, - data: NDArray | Dataset2d | Dataset3d | Dataset4dstem | Polar4dstem, + data: Dataset2d | Dataset4dstem | Polar4dstem, *, find_origin: bool = True, origin_row: float | None = None, @@ -92,32 +131,44 @@ def from_data( radial_step: float = 1.0, two_fold_rotation_symmetry: bool = False, device: str = "cpu", - ): - """ - -> "PairDistributionFunction" - Create a PairDistributionFunction object from various input types. + ) -> Self: + """Create a PairDistributionFunction from a dataset. Parameters ---------- - data - Supported inputs: - - 2D numpy array (single diffraction pattern) - - 4D numpy array (scan_y, scan_x, ky, kx) - - Dataset2d - - Dataset4dstem - - Polar4dstem - - If a :class:`Polar4dstem` is provided, it is used directly and no origin finding - or polar transform is performed. - find_origin - If True, finds the origin for each scan position by calling - :meth:`find_origin`. If False, `origin_row`/`origin_col` are used (or default - to the image center). - origin_row, origin_col - Diffraction-space origin (in pixels), used only if `find_origin=False`. If None, - defaults to the central pixel of the diffraction pattern. - Other parameters - Passed through to Dataset4dstem.polar_transform when needed. + data : Dataset4dstem, Dataset2d, or Polar4dstem + - ``Dataset4dstem``: triggers origin finding (optional) and polar + transform. + - ``Dataset2d``: single averaged diffraction pattern (e.g. SAED + or a pre-averaged 4DSTEM result); wrapped as a 1x1 scan + internally. + - ``Polar4dstem``: already polar-transformed; used directly, no + origin finding or polar transform performed. + find_origin : bool + If True, run ``auto_origin_id`` to find the origin at each scan + position. If False, use ``origin_row`` / ``origin_col`` (or the + image center if those are None). + origin_row, origin_col : float or None + Fixed diffraction-space origin in pixels, used only when + ``find_origin=False``. Defaults to the center of the diffraction + pattern. + ellipse_params : tuple of (float, float, float) or None + Elliptical distortion parameters ``(a, b, theta_deg)`` applied + during origin finding and polar transform. + num_annular_bins : int + Number of angular bins in the polar transform. + radial_min, radial_max : float or None + Radial range of the polar transform, in pixels. + radial_step : float + Radial step size in pixels. + two_fold_rotation_symmetry : bool + If True, sample only ``[0, pi)`` in the angular axis. + device : str + Torch device used for computation. + + Returns + ------- + PairDistributionFunction """ # Polar input: use directly if isinstance(data, Polar4dstem): @@ -132,7 +183,7 @@ def from_data( f"Found array with shape: {arr2d.shape}. " "Dataset2d for PairDistributionFunction must be 2D." ) - arr4 = arr2d[None, None, ...] # (1, 1, ky, kx) + arr4 = arr2d[None, None, ...] # (1, 1, n_row, n_col) data = Dataset4dstem.from_array( array=arr4, @@ -151,7 +202,7 @@ def from_data( # Dataset4dstem input: polar-transform it if isinstance(data, Dataset4dstem): - scan_y, scan_x, ny, nx = data.array.shape + scan_row, scan_col, n_row, n_col = data.array.shape if find_origin: origin_array = auto_origin_id( data, @@ -165,14 +216,14 @@ def from_data( ) else: if origin_row is None: - origin_row = (ny - 1) / 2.0 + origin_row = (n_row - 1) / 2.0 if origin_col is None: - origin_col = (nx - 1) / 2.0 - origin_array = np.zeros((scan_y, scan_x, 2), dtype=float) + origin_col = (n_col - 1) / 2.0 + origin_array = np.zeros((scan_row, scan_col, 2), dtype=float) origin_array[..., 0] = origin_row origin_array[..., 1] = origin_col - polar = dataset4dstem_polar_transform( + polar = polar_transform( data, origin_array=origin_array, ellipse_params=ellipse_params, @@ -185,62 +236,22 @@ def from_data( ) return cls(polar=polar, input_data=data, device=device, _token=cls._token) - # Dataset3d input: not yet specified how to interpret - if isinstance(data, Dataset3d): - raise NotImplementedError( - "PairDistributionFunction.from_data does not yet support Dataset3d inputs. " - "Please provide a 4D-STEM dataset or a 2D diffraction pattern." - ) - - # Numpy array input - arr = ensure_valid_array(data) - if arr.ndim == 2: - ds2 = Dataset2d.from_array(arr, name="rdf_input_2d") - - return cls.from_data( - ds2, - find_origin=find_origin, - origin_row=origin_row, - origin_col=origin_col, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - device=device, - ) - elif arr.ndim == 4: - ds4 = Dataset4dstem.from_array(arr, name="rdf_input_4dstem") - return cls.from_data( - ds4, - find_origin=find_origin, - origin_row=origin_row, - origin_col=origin_col, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - device=device, - ) - else: - raise ValueError( - f"Found array with shape: {arr.shape}. " - "PairDistributionFunction.from_data only supports 2D or 4D arrays." - ) + raise TypeError( + f"Got {type(data).__name__}. PairDistributionFunction.from_data " + "accepts Polar4dstem, Dataset4dstem, or Dataset2d. Wrap numpy " + "arrays with Dataset4dstem.from_array or Dataset2d.from_array first." + ) # ------------------------------------------------------------------ # Convenience accessors # ------------------------------------------------------------------ @property - def qq(self) -> Any: + def qq(self) -> NDArray: """ Scattering vector coordinate array along the radial dimension of `self.polar`, in physical units (using Polar4dstem.sampling and origin). """ - # Polar4dstem dims: (scan_y, scan_x, phi, r) + # Polar4dstem dims: (scan_row, scan_col, phi, r_pix) # radial axis is 3 # origin[3] is the physical q-value at bin 0 (radial_min * pixel_size), # sampling[3] is the physical step per bin (radial_step * pixel_size). @@ -254,233 +265,24 @@ def r(self) -> NDArray | None: """Real-space radial grid as a numpy array.""" if self._r is None: return None - return self._to_numpy(self._r) + return to_numpy(self._r) @property def reduced_pdf(self) -> NDArray | None: """Reduced pair distribution function G(r) as a numpy array.""" if self._reduced_pdf is None: return None - return self._to_numpy(self._reduced_pdf) + return to_numpy(self._reduced_pdf) @property def pdf(self) -> NDArray | None: """Pair distribution function g(r) as a numpy array.""" if self._pdf is None: return None - return self._to_numpy(self._pdf) - - # ------------------------------------------------------------------ - # Helper functions - # ------------------------------------------------------------------ - def _get_mask_bool(self, mask_realspace): - """ - Normalize a real-space mask specification to a boolean (rx, ry) mask. - - Returns - ------- - mask_bool : np.ndarray or None - Boolean mask of shape (rx, ry), or None if `mask_realspace` is None. - """ - mask_bool = None - if mask_realspace is not None: - rx, ry = self.polar.array.shape[:2] - mask_realspace = np.asarray(mask_realspace) - - if mask_realspace.dtype == bool and mask_realspace.shape == (rx, ry): - mask_bool = mask_realspace - else: - raise ValueError( - f"Got shape {mask_realspace.shape}. " - f"mask_realspace must be boolean array of shape ({rx}, {ry})." - ) - return mask_bool - - # ------------------------------------------------------------------ - # Torch conversion utilities - # ------------------------------------------------------------------ - - def _to_torch(self, arr: NDArray) -> torch.Tensor: - return torch.from_numpy(arr.astype(np.float32)).to(device=self.device) - - def _to_numpy(self, tensor: torch.Tensor) -> NDArray: - return tensor.detach().cpu().numpy() - - @staticmethod - def _gaussian_kernel_1d( - sigma: float, device: str = "cpu", num_sigmas: float = 3.0 - ) -> torch.Tensor: - """Create 1D Gaussian kernel for torch convolution.""" - radius = int(np.ceil(num_sigmas * sigma)) - support = torch.arange(-radius, radius + 1, dtype=torch.float32, device=device) - kernel = torch.exp(-0.5 * (support / sigma) ** 2) - kernel = kernel / kernel.sum() - return kernel - - def _gaussian_filter1d_torch( - self, - Fk: torch.Tensor, - sigma: float, - mode: str = "nearest", - ) -> torch.Tensor: - """ - Apply 1D Gaussian filter, replaces scipy.ndimage.gaussian_filter1d. - """ - kernel = self._gaussian_kernel_1d(sigma, device=self.device) - padding = len(kernel) // 2 - x = Fk.unsqueeze(0).unsqueeze(0) # reshape to (batch, channels, length) - kernel_w = kernel.view(1, 1, -1) - if mode == "nearest": - x = F.pad(x, (padding, padding), mode="replicate") - result = F.conv1d(x, kernel_w) - else: - result = F.conv1d(x, kernel_w, padding=padding) - return result.squeeze(0).squeeze(0) # reshape to (length) - - @staticmethod - def _scattering_model_torch( - k2: torch.Tensor, - c: torch.Tensor, - i0: torch.Tensor, - s0: torch.Tensor, - i1: torch.Tensor, - s1: torch.Tensor, - ) -> torch.Tensor: - """Torch version of the scattering model.""" - # Add small epsilon to denominators to prevent division by zero during backprop - # while still allowing s0/s1 to vary freely - eps = 1e-10 - exp1 = torch.clamp(k2 / (-2.0 * (s0**2 + eps)), min=-100, max=0) - exp2 = torch.clamp((k2**2) / (-2.0 * (s1**4 + eps)), min=-100, max=0) - # scattering model is monotonic, as is physically expected for backgrounds scattering - return c + i0 * torch.exp(exp1) + i1 * torch.exp(exp2) - - def _compute_fit_weights(self, k: torch.Tensor, kmin: float, kmax: float) -> torch.Tensor: - """ - Compute weighting tensor for background fitting. - Weights downweight low-k region (using sin² taper) and emphasize high-k values. - """ - dk = k[1] - k[0] - k_width = kmax - kmin - - # sin² taper for low-k suppression - mask_low = torch.sin(torch.clamp((k - kmin) / k_width, 0.0, 1.0) * (torch.pi / 2.0)) ** 2 - # high weight where mask_low is small - # later used to divide, so large weights mean small contribution - weights = torch.where( - mask_low > 1e-4, - 1.0 / mask_low, - torch.tensor(1e6, device=self.device, dtype=k.dtype), - ) - # emphasize high-k values - weights = weights * (k[-1] - 0.9 * k + dk) - return weights - - def _closure(self, optimizer, theta, k2, Ik_norm, weights): - """match scipy curve_fit behavior""" - optimizer.zero_grad() - # Map from unconstrained to constrained (positive) space via softplus - c = F.softplus(theta[0]) - i0 = F.softplus(theta[1]) - s0 = F.softplus(theta[2]) - i1 = F.softplus(theta[3]) - s1 = F.softplus(theta[4]) - - pred = self._scattering_model_torch(k2, c, i0, s0, i1, s1) - residuals = (pred - Ik_norm) ** 2 - loss = (residuals / (weights**2)).sum() - loss.backward() - return loss - - def _frequency_filtering( - self, - Fk: torch.Tensor, - k_lowpass: float | None, - k_highpass: float | None, - dk: torch.Tensor, - ) -> torch.Tensor: - """Band pass filtering using torch""" - if ( - k_lowpass is not None - and k_lowpass > 0.0 - and k_highpass is not None - and k_highpass > 0.0 - ): - if k_highpass > k_lowpass: - raise ValueError( - "k_highpass is greater than k_lowpass." - "Gaussian band-pass filtering requires k_highpass < k_lowpass." - ) - Fk_low = self._gaussian_filter1d_torch(Fk, sigma=k_lowpass / dk.item(), mode="nearest") - Fk_high = self._gaussian_filter1d_torch( - Fk, sigma=k_highpass / dk.item(), mode="nearest" - ) - Fk = Fk_high - Fk_low - elif k_lowpass is not None and k_lowpass > 0.0: - Fk = self._gaussian_filter1d_torch(Fk, sigma=k_lowpass / dk.item(), mode="nearest") - elif k_highpass is not None and k_highpass > 0.0: - Fk_high = self._gaussian_filter1d_torch( - Fk, sigma=k_highpass / dk.item(), mode="nearest" - ) - Fk = Fk - Fk_high - return Fk - - def _lorch_window(self, k: torch.Tensor, kmin: float, kmax: float) -> torch.Tensor: - """ - Construct a combined low-q taper and high-q Lorch window. - - The returned window is: - - zero outside [kmin, kmax] - - smoothly rises from 0->1 near kmin using a sin^2 ramp over 10% of the band - - applies a Lorch-style sinc factor over the full in-band region: - sin(pi * k/kmax) / (pi * k/kmax) - """ - # low q taper - edge_frac_low = 0.1 # 10% of range at low-q - edge_width_low = edge_frac_low * (kmax - kmin) - low = (k >= kmin) & (k < kmin + edge_width_low) - t = (k - kmin) / edge_width_low - wk = torch.ones_like(k) - wk = torch.where(low, torch.sin(0.5 * torch.pi * t) ** 2, wk) - wk = torch.where(k < kmin, torch.zeros_like(wk), wk) - wk = torch.where(k > kmax, torch.zeros_like(wk), wk) - - # High q taper with Lorch window: w(k) = sin(pi*k/kmax)/(pi*k/kmax) - x = k / kmax - inband = (k >= kmin) & (k <= kmax) - # sinc function: sin(pi*x)/(pi*x) with limit 1 at x=0 - sinc_val = torch.where( - x == 0, - torch.ones_like(x), - torch.sin(torch.pi * x) / (torch.pi * x), - ) - lorch = torch.where(inband, sinc_val, torch.zeros_like(k)) - wk = wk * lorch - return wk - - def _compute_alpha_beta( - self, - Q2d: torch.Tensor, - r2d: torch.Tensor, - G_beta: torch.Tensor, - r_1d: torch.Tensor, - ) -> tuple[torch.Tensor, torch.Tensor]: - """ - Compute Yoshimoto-Omote alpha(Q) and beta(Q) integrals used for density estimation. - """ - Qsafe = torch.where( - Q2d == 0.0, - torch.tensor(1e-12, device=self.device, dtype=torch.float32), - Q2d, - ) - alpha_int = -4 * torch.pi * r2d * torch.sin(Qsafe * r2d) / Qsafe - beta_int = G_beta.unsqueeze(0) * torch.sin(Qsafe * r2d) / Qsafe - alpha = torch.trapezoid(alpha_int, x=r_1d, dim=1) - beta = torch.trapezoid(beta_int, x=r_1d, dim=1) - return alpha, beta + return to_numpy(self._pdf) # ------------------------------------------------------------------ - # Analysis method stubs + # Analysis # ------------------------------------------------------------------ # TODO: add beamstop mask support (mask diffraction-space pixels before @@ -494,7 +296,7 @@ def calculate_radial_mean( """ Calculate the radial mean intensity from the Polar4dSTEM dataset. - The polar array is assumed to have shape (scan_y, scan_x, phi, k). + The polar array is assumed to have shape (scan_row, scan_col, phi, k). This method computes, for each scan position, the mean over the azimuthal axis (phi), then averages across scan positions to produce a single 1D radial curve. This result is stored in ``self.Ik``. @@ -508,7 +310,7 @@ def calculate_radial_mean( mask_realspace : NDArray or None, optional Boolean mask in real space used to select probe positions. If ``None``, all probe positions are used. - Must have shape (scan_y, scan_x) where True means "include". + Must have shape (scan_row, scan_col) where True means "include". returnval : bool, optional If True, return the computed 1D radial mean tensor. @@ -518,19 +320,19 @@ def calculate_radial_mean( If `returnval=True`, returns the 1D radial mean intensity (Nk,). Otherwise returns None. """ - polar_np = self.polar.array # shape: (scan_y, scan_x, phi, k) - scan_y, scan_x, n_phi, n_k = polar_np.shape + polar_np = self.polar.array # shape: (scan_row, scan_col, phi, k) + scan_row, scan_col, n_phi, n_k = polar_np.shape intensity_sum = torch.zeros(n_k, device=self.device, dtype=torch.float64) n_valid = 0 - chunk_y = 16 # number of scan_y to process at a time - for y0 in range(0, scan_y, chunk_y): - y1 = min(y0 + chunk_y, scan_y) - raw = polar_np[y0:y1] + chunk_row = 16 # number of scan rows to process at a time + for row0 in range(0, scan_row, chunk_row): + row1 = min(row0 + chunk_row, scan_row) + raw = polar_np[row0:row1] chunk = torch.from_numpy(np.ascontiguousarray(raw)).to(self.device) - # mean over phi first -> (chunk, scan_x, k) + # mean over phi first -> (chunk, scan_col, k) radial_mean = chunk.mean(dim=2) if mask_realspace is not None: - mask_chunk = torch.from_numpy(mask_realspace[y0:y1]).to(self.device) + mask_chunk = torch.from_numpy(mask_realspace[row0:row1]).to(self.device) n_chunk = int(mask_chunk.sum()) if n_chunk == 0: continue @@ -540,7 +342,7 @@ def calculate_radial_mean( else: # sum all intensities in chunk and count for normalization later intensity_sum += radial_mean.sum(dim=(0, 1)) - n_valid += (y1 - y0) * scan_x + n_valid += (row1 - row0) * scan_col if n_valid == 0: raise ValueError( "No valid scan positions selected. The real-space mask is " @@ -556,8 +358,8 @@ def calculate_radial_mean( def fit_bg( self, Ik: torch.Tensor, - kmin: float, - kmax: float, + kmin: float | None = None, + kmax: float | None = None, ) -> tuple[torch.Tensor, torch.Tensor]: """ Fit a smooth background B(k) to a radial intensity curve I(k) using @@ -576,7 +378,8 @@ def fit_bg( :meth:`calculate_radial_mean`. kmin, kmax k-range (in the same units as the internally constructed `k` grid) - used to build the low-k weighting mask. + used to build the low-k weighting mask. If None, defaults to the + min/max of the k axis. Returns ------- @@ -586,7 +389,11 @@ def fit_bg( Background minus the constant offset, f(k) = B(k) - c, or functionally similar to ^2(k) """ - k = self._to_torch(np.asarray(self.qq)) + k = torch.from_numpy(np.asarray(self.qq).astype(np.float32)).to(device=self.device) + if kmin is None: + kmin = float(k.min()) + if kmax is None: + kmax = float(k.max()) k2 = k**2 # normalize intensity @@ -625,11 +432,26 @@ def fit_bg( # this monotonic model means we don't need parameterized scattering factors weights = self._compute_fit_weights(k, kmin, kmax) + def closure() -> torch.Tensor: + """LBFGS loss callback: weighted squared residual in softplus-constrained space.""" + optimizer.zero_grad() + # Map from unconstrained to constrained (positive) space via softplus + c = F.softplus(theta[0]) + i0 = F.softplus(theta[1]) + s0 = F.softplus(theta[2]) + i1 = F.softplus(theta[3]) + s1 = F.softplus(theta[4]) + pred = self._scattering_model_torch(k2, c, i0, s0, i1, s1) + residuals = (pred - Ik_norm) ** 2 + loss = (residuals / (weights**2)).sum() + loss.backward() + return loss + prev_loss = torch.tensor(float("inf")) max_outer_iter = 100 tol = 1e-8 for step in range(max_outer_iter): - loss = optimizer.step(lambda: self._closure(optimizer, theta, k2, Ik_norm, weights)) + loss = optimizer.step(closure) if torch.abs(prev_loss - loss) < tol: break prev_loss = loss @@ -737,7 +559,7 @@ def calculate_Gr( self.rho0 = None # this is missing a 2pi term that we add back during the pdf calc later k_np = np.asarray(self.qq) - k = self._to_torch(k_np) + k = torch.from_numpy(k_np.astype(np.float32)).to(device=self.device) dk = k[1] - k[0] # small epsilon to avoid division by very small k values k_safe = torch.clamp(k, min=1e-10) @@ -747,7 +569,19 @@ def calculate_Gr( self.kmin_window = k_min_window if k_min_window is not None else self.kmin_fit self.kmax_window = k_max_window if k_max_window is not None else self.kmax_fit - mask_bool = self._get_mask_bool(mask_realspace) + # Validate the real-space mask, if provided, before using it downstream + mask_bool = None + if mask_realspace is not None: + scan_row, scan_col = self.polar.array.shape[:2] + mask_realspace = np.asarray(mask_realspace) + if mask_realspace.dtype == bool and mask_realspace.shape == (scan_row, scan_col): + mask_bool = mask_realspace + else: + raise ValueError( + f"Got shape {mask_realspace.shape}. " + "mask_realspace must be boolean array of shape " + f"({scan_row}, {scan_col})." + ) # reuse existing radial mean if already computed if self.Ik is not None: Ik = self.Ik @@ -816,7 +650,7 @@ def calculate_Gr( if self.reduced_pdf_damped is not None else self._reduced_pdf ) - return [self._to_numpy(self._r), self._to_numpy(Gr)] + return [to_numpy(self._r), to_numpy(Gr)] return None def calculate_gr( @@ -895,7 +729,7 @@ def calculate_gr( self._pdf = pdf if returnval: - return [self._to_numpy(self._r), self._to_numpy(self._pdf)] + return [to_numpy(self._r), to_numpy(self._pdf)] return None def estimate_density( @@ -954,7 +788,7 @@ def estimate_density( "Run PairDistributionFunction.calculate_Gr() before estimate_density()." ) - k = self._to_torch(np.asarray(self.qq)) + k = torch.from_numpy(np.asarray(self.qq).astype(np.float32)).to(device=self.device) dk = k[1] - k[0] k_fit_mask = (k >= self.kmin_fit) & (k <= self.kmax_window) k_fit = k[k_fit_mask] @@ -1054,25 +888,6 @@ def estimate_density( "oscillation_damping", ] - def _apply_xrange( - self, - x: NDArray, - y: NDArray, - xmin: float | None, - xmax: float | None, - ) -> tuple[NDArray, NDArray]: - if xmin is None and xmax is None: - return x, y - xmin_eff = x.min() if xmin is None else xmin - xmax_eff = x.max() if xmax is None else xmax - if xmax_eff <= xmin_eff: - raise ValueError(f"xmax must be > xmin (got xmin={xmin_eff}, xmax={xmax_eff}).") - m = (x >= xmin_eff) & (x <= xmax_eff) - # avoid empty plots - if not np.any(m): - raise ValueError("Requested plot range contains no data.") - return x[m], y[m] - def plot_pdf_results( self, which: Iterable[PlotName] = ("reduced_pdf",), @@ -1133,7 +948,7 @@ def plot_radial_mean( ) x = np.asarray(self.qq) - y = self._to_numpy(self.Ik) + y = to_numpy(self.Ik) x, y = self._apply_xrange(x, y, qmin, qmax) fig, ax = plt.subplots(figsize=figsize) @@ -1169,10 +984,10 @@ def plot_background_fits( ) x = np.asarray(self.qq) - y1 = self._to_numpy(self.Ik) + y1 = to_numpy(self.Ik) x, y1 = self._apply_xrange(x, y1, qmin, qmax) x = np.asarray(self.qq) - y2 = self._to_numpy(self.bg) + y2 = to_numpy(self.bg) x, y2 = self._apply_xrange(x, y2, qmin, qmax) fig, ax = plt.subplots(figsize=figsize) @@ -1213,7 +1028,7 @@ def plot_reduced_sf( Fk = self.Fk_masked x = np.asarray(self.qq) - y = self._to_numpy(Fk) + y = to_numpy(Fk) x, y = self._apply_xrange(x, y, qmin, qmax) fig, ax = plt.subplots(figsize=figsize) @@ -1247,8 +1062,8 @@ def plot_reduced_pdf( ) Gr = self.reduced_pdf_damped if self.reduced_pdf_damped is not None else self._reduced_pdf - x = self._to_numpy(self._r) - y = self._to_numpy(Gr) + x = to_numpy(self._r) + y = to_numpy(Gr) x, y = self._apply_xrange(x, y, rmin, rmax) # Find radial value of primary peak and trough for y-limits @@ -1296,8 +1111,8 @@ def plot_pdf( "Run PairDistributionFunction.calculate_gr() before plotting." ) - x = self._to_numpy(self._r) - y = self._to_numpy(self._pdf) + x = to_numpy(self._r) + y = to_numpy(self._pdf) x, y = self._apply_xrange(x, y, rmin, rmax) # Find radial value of primary peak @@ -1345,11 +1160,11 @@ def plot_oscillation_damping( k = np.asarray(self.qq) # Convert torch tensors to numpy for plotting - Fk_masked = self._to_numpy(self.Fk_masked) - Fk_damped = self._to_numpy(self.Fk_damped) - r = self._to_numpy(self._r) - reduced_pdf = self._to_numpy(self._reduced_pdf) - reduced_pdf_damped = self._to_numpy(self.reduced_pdf_damped) + Fk_masked = to_numpy(self.Fk_masked) + Fk_damped = to_numpy(self.Fk_damped) + r = to_numpy(self._r) + reduced_pdf = to_numpy(self._reduced_pdf) + reduced_pdf_damped = to_numpy(self.reduced_pdf_damped) fig, axes = plt.subplots(2, 2, figsize=figsize) @@ -1385,3 +1200,153 @@ def plot_oscillation_damping( return fig else: plt.show() + + # ------------------------------------------------------------------ + # Helper functions + # ------------------------------------------------------------------ + + @staticmethod + def _scattering_model_torch( + k2: torch.Tensor, + c: torch.Tensor, + i0: torch.Tensor, + s0: torch.Tensor, + i1: torch.Tensor, + s1: torch.Tensor, + ) -> torch.Tensor: + """Torch version of the scattering model.""" + # Add small epsilon to denominators to prevent division by zero during backprop + # while still allowing s0/s1 to vary freely + eps = 1e-10 + exp1 = torch.clamp(k2 / (-2.0 * (s0**2 + eps)), min=-100, max=0) + exp2 = torch.clamp((k2**2) / (-2.0 * (s1**4 + eps)), min=-100, max=0) + # scattering model is monotonic, as is physically expected for backgrounds scattering + return c + i0 * torch.exp(exp1) + i1 * torch.exp(exp2) + + def _compute_fit_weights(self, k: torch.Tensor, kmin: float, kmax: float) -> torch.Tensor: + """ + Compute weighting tensor for background fitting. + Weights downweight low-k region (using sin² taper) and emphasize high-k values. + """ + dk = k[1] - k[0] + # DEBUG + k_width = kmax - kmin - 0.4 + + # sin² taper for low-k suppression + mask_low = torch.sin(torch.clamp((k - kmin) / k_width, 0.0, 1.0) * (torch.pi / 2.0)) ** 2 + # high weight where mask_low is small + # later used to divide, so large weights mean small contribution + weights = torch.where( + mask_low > 1e-4, + 1.0 / mask_low, + torch.tensor(1e6, device=self.device, dtype=k.dtype), + ) + # emphasize high-k values + weights = weights * (k[-1] - 0.9 * k + dk) + return weights + + def _frequency_filtering( + self, + Fk: torch.Tensor, + k_lowpass: float | None, + k_highpass: float | None, + dk: torch.Tensor, + ) -> torch.Tensor: + """Band pass filtering using torch""" + if ( + k_lowpass is not None + and k_lowpass > 0.0 + and k_highpass is not None + and k_highpass > 0.0 + ): + if k_highpass > k_lowpass: + raise ValueError( + "k_highpass is greater than k_lowpass." + "Gaussian band-pass filtering requires k_highpass < k_lowpass." + ) + low_kernel = gaussian_kernel_1d(k_lowpass / dk.item()).to(self.device) + high_kernel = gaussian_kernel_1d(k_highpass / dk.item()).to(self.device) + Fk_low = gaussian_filter_1d(Fk, low_kernel) + Fk_high = gaussian_filter_1d(Fk, high_kernel) + Fk = Fk_high - Fk_low + elif k_lowpass is not None and k_lowpass > 0.0: + low_kernel = gaussian_kernel_1d(k_lowpass / dk.item()).to(self.device) + Fk = gaussian_filter_1d(Fk, low_kernel) + elif k_highpass is not None and k_highpass > 0.0: + high_kernel = gaussian_kernel_1d(k_highpass / dk.item()).to(self.device) + Fk_high = gaussian_filter_1d(Fk, high_kernel) + Fk = Fk - Fk_high + return Fk + + def _lorch_window(self, k: torch.Tensor, kmin: float, kmax: float) -> torch.Tensor: + """ + Construct a combined low-q taper and high-q Lorch window. + + The returned window is: + - zero outside [kmin, kmax] + - smoothly rises from 0->1 near kmin using a sin^2 ramp over 10% of the band + - applies a Lorch-style sinc factor over the full in-band region: + sin(pi * k/kmax) / (pi * k/kmax) + """ + # low q taper + edge_frac_low = 0.1 # 10% of range at low-q + edge_width_low = edge_frac_low * (kmax - kmin) + low = (k >= kmin) & (k < kmin + edge_width_low) + t = (k - kmin) / edge_width_low + wk = torch.ones_like(k) + wk = torch.where(low, torch.sin(0.5 * torch.pi * t) ** 2, wk) + wk = torch.where(k < kmin, torch.zeros_like(wk), wk) + wk = torch.where(k > kmax, torch.zeros_like(wk), wk) + + # High q taper with Lorch window: w(k) = sin(pi*k/kmax)/(pi*k/kmax) + x = k / kmax + inband = (k >= kmin) & (k <= kmax) + # sinc function: sin(pi*x)/(pi*x) with limit 1 at x=0 + sinc_val = torch.where( + x == 0, + torch.ones_like(x), + torch.sin(torch.pi * x) / (torch.pi * x), + ) + lorch = torch.where(inband, sinc_val, torch.zeros_like(k)) + wk = wk * lorch + return wk + + def _compute_alpha_beta( + self, + Q2d: torch.Tensor, + r2d: torch.Tensor, + G_beta: torch.Tensor, + r_1d: torch.Tensor, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Compute Yoshimoto-Omote alpha(Q) and beta(Q) integrals used for density estimation. + """ + Qsafe = torch.where( + Q2d == 0.0, + torch.tensor(1e-12, device=self.device, dtype=torch.float32), + Q2d, + ) + alpha_int = -4 * torch.pi * r2d * torch.sin(Qsafe * r2d) / Qsafe + beta_int = G_beta.unsqueeze(0) * torch.sin(Qsafe * r2d) / Qsafe + alpha = torch.trapezoid(alpha_int, x=r_1d, dim=1) + beta = torch.trapezoid(beta_int, x=r_1d, dim=1) + return alpha, beta + + def _apply_xrange( + self, + x: NDArray, + y: NDArray, + xmin: float | None, + xmax: float | None, + ) -> tuple[NDArray, NDArray]: + if xmin is None and xmax is None: + return x, y + xmin_eff = x.min() if xmin is None else xmin + xmax_eff = x.max() if xmax is None else xmax + if xmax_eff <= xmin_eff: + raise ValueError(f"xmax must be > xmin (got xmin={xmin_eff}, xmax={xmax_eff}).") + m = (x >= xmin_eff) & (x <= xmax_eff) + # avoid empty plots + if not np.any(m): + raise ValueError("Requested plot range contains no data.") + return x[m], y[m] diff --git a/src/quantem/diffraction/polar_transform.py b/src/quantem/diffraction/polar_transform.py new file mode 100644 index 00000000..eaf0e0e2 --- /dev/null +++ b/src/quantem/diffraction/polar_transform.py @@ -0,0 +1,621 @@ +import numpy as np +import torch +import torch.nn.functional as F +from numpy.typing import NDArray +from tqdm import tqdm + +from quantem.core.datastructures.dataset4dstem import Dataset4dstem +from quantem.core.datastructures.polar4dstem import Polar4dstem +from quantem.core.utils.utils import to_numpy + +# Standard DPs use (row, col) convention. Polar coordinates use (phi, r_pix), +# grid_sample's grid tensor requires them to be ordered (col, row) +# but is noted where the call occures + + +def _normalize_coords_for_grid_sample( + coords_row: torch.Tensor, + coords_col: torch.Tensor, + n_row: int, + n_col: int, +) -> torch.Tensor: + """Convert pixel (row, col) coordinates to normalized [-1, 1] coordinates + for grid_sample.""" + col_norm = 2.0 * coords_col / (n_col - 1) - 1.0 + row_norm = 2.0 * coords_row / (n_row - 1) - 1.0 + # grid_sample requires (col, row) ordering in the last dim + return torch.stack([col_norm, row_norm], dim=-1) + + +def _polar_to_cartesian_offsets( + phi: torch.Tensor, + r_pix: torch.Tensor, + ellipse_params: tuple[float, float, float] | None, + device: str = "cpu", +) -> tuple[torch.Tensor, torch.Tensor]: + """Convert polar (phi, r_pix) grids to Cartesian (row, col) pixel offsets + from the origin, optionally correcting for elliptical distortion. + + Returns ``(offset_row, offset_col)`` where + ``col_offset = r_pix * cos(phi)`` and ``row_offset = r_pix * sin(phi)``. + """ + if ellipse_params is None: + offset_col = r_pix * torch.cos(phi) + offset_row = r_pix * torch.sin(phi) + else: + if len(ellipse_params) != 3: + raise ValueError("ellipse_params must be (a, b, theta_deg).") + a, b, theta_deg = ellipse_params + theta = torch.deg2rad(torch.tensor(theta_deg, dtype=torch.float32, device=device)) + # Rotate into the ellipse frame, scale by a/b to undo the distortion, + # then rotate back so sampling follows the true circular rings + alpha = phi - theta + u = (a / b) * r_pix * torch.cos(alpha) + v_prime = r_pix * torch.sin(alpha) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + offset_col = u * cos_t - v_prime * sin_t + offset_row = u * sin_t + v_prime * cos_t + return offset_row, offset_col + + +def _build_candidate_grids( + base_col_norm: torch.Tensor, + base_row_norm: torch.Tensor, + center_row: int, + center_col: int, + margin: int, + n_row: int, + n_col: int, + col_norm_scale: float, + row_norm_scale: float, + device: str = "cpu", +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Build a batch of normalized sampling grids, one per candidate origin + pixel in a search window around (center_row, center_col). Candidates are + produced in a single batched tensor so that they can be evaluated + simultaneously by ``_angular_std_scores``. + + Parameters + ---------- + base_col_norm, base_row_norm : torch.Tensor of shape (n_phi, n_r) + Polar sampling offsets, already expressed in ``grid_sample``'s + normalized [-1, 1] coordinates, relative to origin (0, 0) + center_row, center_col : int + Center of the candidate search window + margin : int + Half-width of the search window in pixels + n_row, n_col : int + Diffraction-pattern image dimensions + col_norm_scale, row_norm_scale : float + Conversion factor from an offset in pixel units to the equivalent + offset in ``grid_sample``'s normalized coordinates + + Returns + ------- + row_flat, col_flat : torch.Tensor of shape (N,) + Candidate origin positions + grids : torch.Tensor of shape (N, n_phi, n_r, 2) + Stacked sampling grids ready for ``F.grid_sample`` (ordered ``(col, row)`` ) + """ + # Enumerate all pixel positions in the search window, clamped to image bounds + rows = torch.arange( + max(0, center_row - margin), + min(n_row, center_row + margin + 1), + dtype=torch.long, + device=device, + ) + cols = torch.arange( + max(0, center_col - margin), + min(n_col, center_col + margin + 1), + dtype=torch.long, + device=device, + ) + row_grid, col_grid = torch.meshgrid(rows, cols, indexing="ij") + row_flat, col_flat = row_grid.reshape(-1), col_grid.reshape(-1) + # Shift the pre-computed polar offsets to each candidate origin, + # converting to grid_sample's [-1, 1] normalized coordinates + grid_col = ( + base_col_norm.unsqueeze(0) + (col_flat.float() * col_norm_scale - 1.0)[:, None, None] + ) + grid_row = ( + base_row_norm.unsqueeze(0) + (row_flat.float() * row_norm_scale - 1.0)[:, None, None] + ) + # grid_sample requires (col, row) ordering in the last dim + grids = torch.stack([grid_col, grid_row], dim=-1) # (N, n_phi, n_r, 2) + return row_flat, col_flat, grids + + +def _angular_std_scores( + dp_batch: torch.Tensor, + grids: torch.Tensor, + min_r_idx: int, + max_r_idx: int, +) -> torch.Tensor: + """Score candidate origins by angular std over a mid-radius band. + Lower scores indicate better centering.""" + n = grids.shape[0] + # Sample the diffraction pattern at each candidate's polar grid positions + polars = F.grid_sample( + dp_batch.expand(n, -1, -1, -1), + grids, + mode="bilinear", + padding_mode="zeros", + align_corners=True, + ) + # A correctly centered pattern has uniform intensity along each ring, + # so the angular std is minimized at the true center + region = polars.squeeze(1)[:, :, min_r_idx:max_r_idx] + return region.std(dim=1).sum(dim=1) + + +def _build_polar_sampling_offsets( + ellipse_params: tuple[float, float, float] | None, + num_annular_bins: int, + radial_min: float, + radial_max_eff: float, + radial_step: float, + two_fold_rotation_symmetry: bool, + device: str = "cpu", +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Build origin-independent Cartesian (row, col) offsets for a polar + sampling grid. + + Returns ``(offset_row, offset_col, phi_bins, radial_bins)`` where + ``offset_row`` and ``offset_col`` have shape ``(n_phi, n_r)`` and + represent pixel displacements from an arbitrary origin. + """ + if radial_step <= 0: + raise ValueError(f"Got radial_step = {radial_step}. radial_step must be > 0.") + if num_annular_bins < 1: + raise ValueError("num_annular_bins must be >= 1.") + + radial_bins = torch.arange( + radial_min, radial_max_eff, radial_step, dtype=torch.float32, device=device + ) + if radial_bins.numel() == 0: + radial_bins = torch.tensor([radial_min], dtype=torch.float32, device=device) + phi_range = torch.pi if two_fold_rotation_symmetry else 2.0 * torch.pi + # Drop the last endpoint because 0 and 2pi (or pi) are the same angle + phi_bins = torch.linspace( + 0.0, phi_range, num_annular_bins + 1, dtype=torch.float32, device=device + )[:-1] + phi_grid, r_pix_grid = torch.meshgrid(phi_bins, radial_bins, indexing="ij") + # Compute offsets relative to origin (0,0) so they can be reused + # for any candidate origin by simple translation + offset_row, offset_col = _polar_to_cartesian_offsets( + phi_grid, r_pix_grid, ellipse_params, device + ) + return offset_row, offset_col, phi_bins, radial_bins + + +def _compute_radial_max( + n_row: int, + n_col: int, + origin_row: float, + origin_col: float, + radial_max: float | None, + radial_min: float, + radial_step: float, +) -> float: + """Compute the effective maximum radius, clamped to image bounds.""" + # Use the shortest distance from the origin to any image edge so the + # polar grid never samples outside the image bounds + if radial_max is None: + radial_max_eff = float( + min( + origin_row, + (n_row - 1) - origin_row, + origin_col, + (n_col - 1) - origin_col, + ) + ) + else: + radial_max_eff = float(radial_max) + # Guarantee at least one radial bin + if radial_max_eff <= radial_min: + radial_max_eff = radial_min + radial_step + return radial_max_eff + + +def _precompute_polar_coords( + n_row: int, + n_col: int, + origin_row: float, + origin_col: float, + ellipse_params: tuple[float, float, float] | None, + num_annular_bins: int, + radial_min: float, + radial_max: float | None, + radial_step: float, + two_fold_rotation_symmetry: bool, + device: str = "cpu", +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, float]: + """Build a normalized sampling grid for a single known origin.""" + origin_row = float(origin_row) + origin_col = float(origin_col) + # Clamp radial range so the polar grid stays within image bounds + radial_max_eff = _compute_radial_max( + n_row, + n_col, + origin_row, + origin_col, + radial_max, + radial_min, + radial_step, + ) + # Get origin-independent polar offsets + offset_row, offset_col, phi_bins, radial_bins = _build_polar_sampling_offsets( + ellipse_params, + num_annular_bins, + radial_min, + radial_max_eff, + radial_step, + two_fold_rotation_symmetry, + device, + ) + # Translate offsets to absolute pixel pos at this origin + coords_row = offset_row + origin_row + coords_col = offset_col + origin_col + # Convert to [-1, 1] normalized coordinates expected by grid_sample + grid = _normalize_coords_for_grid_sample(coords_row, coords_col, n_row, n_col) + grid = grid.unsqueeze(0) # (1, n_phi, n_r, 2) + return grid, phi_bins, radial_bins, radial_max_eff + + +def auto_origin_id( + data: Dataset4dstem, + *, + ellipse_params: tuple[float, float, float] | None = None, + num_annular_bins: int = 180, + radial_min: float = 0.0, + radial_max: float | None = None, + radial_step: float = 1.0, + two_fold_rotation_symmetry: bool = False, + device: str = "cpu", +) -> NDArray: + """ + Automatic diffraction center finding by minimizing angular intensity + variation in the polar transform. A correctly centered diffraction + pattern has uniform intensity along each ring, so the center that + minimizes the angular standard deviation is the true beam center. + + Uses a coarse-to-fine search on the mean diffraction pattern to find + a global center, then refines per scan position to account for descan + across the scan. + + Parameters + ---------- + data : Dataset4dstem + A 4D-STEM dataset (or 2D wrapped as 4D) + ellipse_params : tuple or None + Ellipse parameters (a, b, theta_deg) for distortion correction + num_annular_bins : int + Number of angular bins for the final polar transform (not used + during center-finding, which uses 36 bins for speed) + radial_min : float + Minimum radius in pixels + radial_max : float or None + Maximum radius in pixels + radial_step : float + Radial step size in pixels + two_fold_rotation_symmetry : bool + If True, use only 0 to pi range for angles + device : str + Torch device for computation + + Returns + ------- + origin_array : np.ndarray + Array of shape (scan_row, scan_col, 2) containing (row, col) origin + estimates in pixels. + """ + if len(data.array.shape) == 2: + n_row, n_col = data.array.shape + scan_row, scan_col = 1, 1 + elif len(data.array.shape) == 4: + scan_row, scan_col, n_row, n_col = data.array.shape + else: + raise ValueError( + f" Got array with shape {data.array.shape}." + "To use auto_origin_id, pass a 2D or 4DSTEM dataset." + ) + + origin_array = np.zeros((scan_row, scan_col, 2), dtype=float) + total_positions = scan_row * scan_col + + # first get COM of mean DP because it gives a robust rough center + array_4d = data.array if data.array.ndim == 4 else data.array[None, None, :, :] + mean_dp_np = array_4d.mean(axis=(0, 1)).astype(np.float32) + total_intensity = mean_dp_np.sum() + row_grid, col_grid = np.mgrid[0:n_row, 0:n_col] + com_row = int(round(float((row_grid * mean_dp_np).sum() / total_intensity))) + com_col = int(round(float((col_grid * mean_dp_np).sum() / total_intensity))) + + # building a fixed polar grid that is safe for all candidates + # safe_rmax ensures no candidate's grid extends outside the image + global_margin = 20 + safe_rmax = float( + min( + com_row - global_margin, + (n_row - 1) - (com_row + global_margin), + com_col - global_margin, + (n_col - 1) - (com_col + global_margin), + ) + ) + if radial_max is not None: + safe_rmax = min(safe_rmax, float(radial_max)) + if safe_rmax <= radial_min: + safe_rmax = radial_min + radial_step + # use very coarse binning because asymmetry is still captured at + # low angular resolution and is significantly faster + search_n_phi = 36 + offset_row, offset_col, _, radial_bins = _build_polar_sampling_offsets( + ellipse_params, + search_n_phi, + radial_min, + safe_rmax, + radial_step, + two_fold_rotation_symmetry, + device, + ) + n_r = radial_bins.numel() + min_r_idx = int(np.floor(0.1 * n_r)) + max_r_idx = int(np.ceil(0.9 * n_r)) + # Normalize offsets to [-1, 1] because grid_sample expects normalized coordinates + col_norm_scale = 2.0 / (n_col - 1) + row_norm_scale = 2.0 / (n_row - 1) + base_col_norm = offset_col * col_norm_scale + base_row_norm = offset_row * row_norm_scale + + # now find actual center + # Coarse search over ±global_margin around COM + coarse_step = 4 + coarse_rows = torch.arange( + max(0, com_row - global_margin), + min(n_row, com_row + global_margin + 1), + coarse_step, + dtype=torch.long, + device=device, + ) + coarse_cols = torch.arange( + max(0, com_col - global_margin), + min(n_col, com_col + global_margin + 1), + coarse_step, + dtype=torch.long, + device=device, + ) + # Create all (row, col) candidate pairs and flatten for batched evaluation + coarse_row_grid, coarse_col_grid = torch.meshgrid(coarse_rows, coarse_cols, indexing="ij") + coarse_row_flat, coarse_col_flat = coarse_row_grid.reshape(-1), coarse_col_grid.reshape(-1) + # Shift polar offsets to each candidate origin in normalized coordinates + coarse_g_col = ( + base_col_norm.unsqueeze(0) + + (coarse_col_flat.float() * col_norm_scale - 1.0)[:, None, None] + ) + coarse_g_row = ( + base_row_norm.unsqueeze(0) + + (coarse_row_flat.float() * row_norm_scale - 1.0)[:, None, None] + ) + # grid_sample requires (col, row) ordering in the last dim + coarse_grids = torch.stack([coarse_g_col, coarse_g_row], dim=-1) + # Score all coarse candidates on the mean DP and pick the best one + mean_dp_batch = torch.from_numpy(mean_dp_np).to(device)[None, None] + coarse_scores = _angular_std_scores(mean_dp_batch, coarse_grids, min_r_idx, max_r_idx) + best_coarse_idx = coarse_scores.argmin().item() + coarse_best_row = int(coarse_row_flat[best_coarse_idx].item()) + coarse_best_col = int(coarse_col_flat[best_coarse_idx].item()) + + # Fine search (step=1) around coarse best for global center of mean DP + fine_margin = 6 + fine_row_flat, fine_col_flat, fine_grids = _build_candidate_grids( + base_col_norm, + base_row_norm, + coarse_best_row, + coarse_best_col, + fine_margin, + n_row, + n_col, + col_norm_scale, + row_norm_scale, + device, + ) + fine_scores = _angular_std_scores(mean_dp_batch, fine_grids, min_r_idx, max_r_idx) + best_fine_idx = fine_scores.argmin().item() + global_row = int(fine_row_flat[best_fine_idx].item()) + global_col = int(fine_col_flat[best_fine_idx].item()) + # Get center for each scan pos by fine search around global center + # Assuming that the center doesn't shift more than 10 pixels across the scan + local_margin = 10 + local_rf, local_cf, local_grids = _build_candidate_grids( + base_col_norm, + base_row_norm, + global_row, + global_col, + local_margin, + n_row, + n_col, + col_norm_scale, + row_norm_scale, + device, + ) + pbar = tqdm(total=total_positions, desc="Finding origin for each scan position") + for row_pos in range(scan_row): + row_dps = torch.from_numpy(array_4d[row_pos].astype(np.float32)).to( + device + ) # (scan_col, n_row, n_col) + + for col_pos in range(scan_col): + dp_batch = row_dps[col_pos][None, None] + scores = _angular_std_scores(dp_batch, local_grids, min_r_idx, max_r_idx) + best_idx = scores.argmin().item() + origin_array[row_pos, col_pos, 0] = local_rf[best_idx].item() + origin_array[row_pos, col_pos, 1] = local_cf[best_idx].item() + pbar.update(1) + + pbar.close() + return origin_array + + +def polar_transform( + data: Dataset4dstem, + origin_array: NDArray | torch.Tensor | None = None, + ellipse_params: tuple[float, float, float] | None = None, + num_annular_bins: int = 180, + radial_min: float = 0.0, + radial_max: float | None = None, + radial_step: float = 1.0, + two_fold_rotation_symmetry: bool = False, + name: str | None = None, + signal_units: str | None = None, + scan_pos: tuple[int, int] | None = None, + device: str = "cpu", +) -> Polar4dstem | torch.Tensor: + if data.array.ndim != 4: + raise ValueError( + f"Found array with shape: {data.array.shape}. " + "polar_transform requires a 4D-STEM dataset (ndim=4)." + ) + scan_row, scan_col, n_row, n_col = data.array.shape + + # Standardize origin_array input + if isinstance(origin_array, torch.Tensor): + origin_array = to_numpy(origin_array) + origin_array = np.asarray(origin_array) if origin_array is not None else None + if origin_array is None: + center = np.array([(n_row - 1) / 2.0, (n_col - 1) / 2.0], dtype=float) + origins = np.broadcast_to(center, (scan_row, scan_col, 2)).copy() + elif origin_array.shape == (2,): + origins = np.empty((scan_row, scan_col, 2), dtype=float) + origins[...] = origin_array + elif origin_array.shape == (scan_row, scan_col, 2): + origins = origin_array + else: + raise ValueError( + f" Got {origin_array.shape}. " + "origin_array must have shape None, (2,) or (scan_row, scan_col, 2)." + ) + + # If scan_pos is provided, compute polar transform only for that position + if scan_pos is not None: + i_row, i_col = scan_pos + dp = torch.from_numpy(data.array[i_row, i_col].astype(np.float32)).to(device) + r0 = float(origins[i_row, i_col, 0]) + c0 = float(origins[i_row, i_col, 1]) + grid, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( + n_row=n_row, + n_col=n_col, + origin_row=r0, + origin_col=c0, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + device=device, + ) + dp_batch = dp[None, None] # (1, 1, n_row, n_col) + polar2d = F.grid_sample( + dp_batch, + grid, + mode="bilinear", + padding_mode="zeros", + align_corners=True, + ) + return polar2d.squeeze(0).squeeze(0) # (n_phi, n_r) + + # Use the global minimum safe radius across all origins so every scan + # position maps to the same-size polar grid (required for a uniform 4D output) + if radial_max is None: + r_row_pos = origins[:, :, 0] + r_row_neg = (n_row - 1) - origins[:, :, 0] + r_col_pos = origins[:, :, 1] + r_col_neg = (n_col - 1) - origins[:, :, 1] + radial_max_eff_array = np.minimum.reduce([r_row_pos, r_row_neg, r_col_pos, r_col_neg]) + radial_max = float(max(radial_max_eff_array.min(), radial_min + radial_step)) + + # Compute grid for first position to get output shape + grid, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( + n_row=n_row, + n_col=n_col, + origin_row=float(origins[0, 0, 0]), + origin_col=float(origins[0, 0, 1]), + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + device=device, + ) + n_phi = phi_bins.numel() + n_r = radial_bins.numel() + out = np.empty((scan_row, scan_col, n_phi, n_r), dtype=np.float32) + for i_row in range(scan_row): + for i_col in range(scan_col): + dp = torch.from_numpy(data.array[i_row, i_col].astype(np.float32)).to(device) + r0 = float(origins[i_row, i_col, 0]) + c0 = float(origins[i_row, i_col, 1]) + grid, _, _, _ = _precompute_polar_coords( + n_row=n_row, + n_col=n_col, + origin_row=r0, + origin_col=c0, + ellipse_params=ellipse_params, + num_annular_bins=num_annular_bins, + radial_min=radial_min, + radial_max=radial_max, + radial_step=radial_step, + two_fold_rotation_symmetry=two_fold_rotation_symmetry, + device=device, + ) + dp_batch = dp[None, None] + polar2d = F.grid_sample( + dp_batch, + grid, + mode="bilinear", + padding_mode="zeros", + align_corners=True, + ) + out[i_row, i_col] = to_numpy(polar2d.squeeze(0).squeeze(0)) + + # Get polar axes in physical units matching the input dataset's calibration + phi_range = np.pi if two_fold_rotation_symmetry else 2.0 * np.pi + phi_step_deg = (phi_range / float(n_phi)) * (180.0 / np.pi) + sampling = np.zeros(4, dtype=float) + origin = np.zeros(4, dtype=float) + sampling[0:2] = np.asarray(data.sampling)[0:2] + sampling[2] = phi_step_deg + sampling[3] = float(np.asarray(data.sampling)[-1]) * radial_step + origin[0:2] = np.asarray(data.origin)[0:2] + origin[2] = 0.0 + origin[3] = radial_min * float(np.asarray(data.sampling)[-1]) + units = [ + data.units[0], + data.units[1], + "deg", + data.units[-1], + ] + metadata = dict(data.metadata) + metadata.update( + { + "polar_radial_min": float(radial_min), + "polar_radial_max": float(radial_max_eff), + "polar_radial_step": float(radial_step), + "polar_num_annular_bins": int(n_phi), + "polar_two_fold_rotation_symmetry": bool(two_fold_rotation_symmetry), + "polar_ellipticity": tuple(ellipse_params) if ellipse_params is not None else None, + } + ) + return Polar4dstem( + array=out, + name=name if name is not None else f"{data.name}_polar", + origin=origin, + sampling=sampling, + units=units, + signal_units=signal_units if signal_units is not None else data.signal_units, + metadata=metadata, + origin_array=origins, + _token=Polar4dstem._token, + ) diff --git a/src/quantem/tomography/utils.py b/src/quantem/tomography/utils.py index 08093925..d754efed 100644 --- a/src/quantem/tomography/utils.py +++ b/src/quantem/tomography/utils.py @@ -69,12 +69,102 @@ def transform_slice(mag_slice): return rotated_mags.permute(1, 2, 3, 0) -def tv_loss_1d(x: torch.Tensor, reduction: str = "mean") -> torch.Tensor: +def differentiable_shift_2d(image, shift_x, shift_y, sampling_rate): """ - 1D Total Variation Loss. + Shifts a 2D image using grid_sample in a differentiable manner. - Encourages piecewise smoothness by penalizing differences between - adjacent elements. + Args: + image: Tensor of shape [H, W] + shift_x: Scalar tensor (dx) for shift in x-direction (in physical units) + shift_y: Scalar tensor (dy) for shift in y-direction (in physical units) + sampling_rate: Scalar value (physical units per pixel) to correctly normalize shifts + + Returns: + Shifted image of shape [H, W] + """ + H, W = image.shape + + # Convert physical shift to pixel shift + shift_x_pixel = shift_x + shift_y_pixel = shift_y + + # Normalize shift for grid_sample (assuming align_corners=True) + normalized_shift_x = shift_x_pixel * 2 / (W - 1) + normalized_shift_y = shift_y_pixel * 2 / (H - 1) + + # Create normalized grid + grid_y, grid_x = torch.meshgrid( + torch.linspace(-1, 1, H, device=image.device), + torch.linspace(-1, 1, W, device=image.device), + indexing="ij", + ) + + grid = torch.stack((grid_x, grid_y), dim=-1).unsqueeze(0) # [1, H, W, 2] + + # Apply shift (ensure it's differentiable) + grid[:, :, :, 0] -= normalized_shift_x + grid[:, :, :, 1] -= normalized_shift_y + + # Add batch and channel dimensions + image = image.unsqueeze(0).unsqueeze(0) # [1, 1, H, W] + + # Sample using grid_sample (fully differentiable) + shifted_image = F.grid_sample( + image, grid, mode="bicubic", padding_mode="zeros", align_corners=True + ) + + return shifted_image.squeeze(0).squeeze(0) # Back to [H, W] + + +# --- TV loss --- + + +def get_TV_loss(tensor, factor=1e-3): + tv_d = torch.pow(tensor[:, :, 1:, :, :] - tensor[:, :, :-1, :, :], 2).sum() + tv_h = torch.pow(tensor[:, :, :, 1:, :] - tensor[:, :, :, :-1, :], 2).sum() + tv_w = torch.pow(tensor[:, :, :, :, 1:] - tensor[:, :, :, :, :-1], 2).sum() + tv_loss = tv_d + tv_h + tv_w + + return tv_loss * factor / (torch.prod(torch.tensor(tensor.shape))) + + +# --- Gaussian filters --- + + +def gaussian_kernel_1d(sigma: float, num_sigmas: float = 3.0) -> torch.Tensor: + radius = np.ceil(num_sigmas * sigma) + support = torch.arange(-radius, radius + 1, dtype=torch.float) + kernel = torch.distributions.Normal(loc=0, scale=sigma).log_prob(support).exp_() + # Ensure kernel weights sum to 1, so that image brightness is not altered + return kernel.mul_(1 / kernel.sum()) + + +def gaussian_filter_2d( + img: torch.Tensor, sigma: float, kernel_1d: torch.Tensor +) -> torch.Tensor: # Add kernel_1d as an argument + # kernel_1d = gaussian_kernel_1d(sigma) # Create 1D Gaussian kernel - Moved outside function + padding = len(kernel_1d) // 2 # Ensure that image size does not change + img = img.unsqueeze(0).unsqueeze_(0) # Make copy, make 4D for ``conv2d()`` + # Convolve along columns and rows + img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, -1, 1), padding=(padding, 0)) + img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) + return img.squeeze_(0).squeeze_(0) # Make 2D again + + +def gaussian_filter_1d( + arr: torch.Tensor, kernel_1d: torch.Tensor +) -> torch.Tensor: # Replicate-padded torch alternative to ``scipy.ndimage.gaussian_filter1d`` + padding = len(kernel_1d) // 2 # Ensure that signal size does not change + arr = arr.unsqueeze(0).unsqueeze_(0) # Make copy, make 3D for ``conv1d()`` + # Replicate edge values so the output has no zero-padded ringing at boundaries + arr = torch.nn.functional.pad(arr, (padding, padding), mode="replicate") + arr = torch.nn.functional.conv1d(arr, weight=kernel_1d.view(1, 1, -1)) + return arr.squeeze_(0).squeeze_(0) # Make 1D again + + +def gaussian_filter_2d_stack(stack: torch.Tensor, kernel_1d: torch.Tensor) -> torch.Tensor: + """ + Apply 2D Gaussian blur to each slice stack[:, i, :] in a vectorized way. Args: x: Input tensor of shape (N, C, L) or (N, L) or (L,) diff --git a/tests/diffraction/test_polar.py b/tests/diffraction/test_polar.py index 4dc5a005..9125f5e6 100644 --- a/tests/diffraction/test_polar.py +++ b/tests/diffraction/test_polar.py @@ -3,8 +3,9 @@ from quantem.core.datastructures.dataset2d import Dataset2d from quantem.core.datastructures.dataset4dstem import Dataset4dstem -from quantem.core.datastructures.polar4dstem import Polar4dstem, auto_origin_id +from quantem.core.datastructures.polar4dstem import Polar4dstem from quantem.diffraction.polar import PairDistributionFunction +from quantem.diffraction.polar_transform import auto_origin_id, polar_transform # ============================================================================ # Fixtures @@ -90,12 +91,6 @@ def test_from_data_with_dataset4dstem(self, synthetic_4dstem_dataset): assert pdf.polar.shape[1] == 3 # scan_x assert pdf.polar.shape[2] == 180 # num_annular_bins - def test_from_data_with_invalid_array_raises(self): - """Test that arrays with wrong dimensions raise ValueError.""" - array_1d = np.random.rand(100) - with pytest.raises(ValueError, match="only supports 2D or 4D arrays"): - PairDistributionFunction.from_data(array_1d) - def test_direct_init_without_token_raises(self, synthetic_dataset2d): """Test that direct __init__ without token raises RuntimeError.""" pdf_valid = PairDistributionFunction.from_data(synthetic_dataset2d, find_origin=False) @@ -126,7 +121,7 @@ class TestPolarTransform: def test_polar_transform_basic(self, synthetic_4dstem_dataset): """Test basic polar transformation.""" - polar = synthetic_4dstem_dataset.polar_transform() + polar = polar_transform(synthetic_4dstem_dataset) assert isinstance(polar, Polar4dstem) assert polar.shape[0] == 3 # scan_y assert polar.shape[1] == 3 # scan_x @@ -136,14 +131,16 @@ def test_polar_transform_basic(self, synthetic_4dstem_dataset): def test_polar_transform_single_origin(self, synthetic_4dstem_dataset): """Test polar transformation with single origin broadcast to all positions.""" origin = np.array([128.0, 128.0]) - polar = synthetic_4dstem_dataset.polar_transform( + polar = polar_transform( + synthetic_4dstem_dataset, origin_array=origin, ) assert isinstance(polar, Polar4dstem) def test_polar_transform_radial_range(self, synthetic_4dstem_dataset): """Test polar transformation with custom radial range.""" - polar = synthetic_4dstem_dataset.polar_transform( + polar = polar_transform( + synthetic_4dstem_dataset, radial_min=5.0, radial_max=50.0, radial_step=2.0, @@ -155,7 +152,8 @@ def test_polar_transform_radial_range(self, synthetic_4dstem_dataset): def test_polar_transform_scan_pos(self, synthetic_4dstem_dataset): """Test polar transformation for a single scan position.""" - polar_2d = synthetic_4dstem_dataset.polar_transform( + polar_2d = polar_transform( + synthetic_4dstem_dataset, scan_pos=(0, 0), ) # should return 2D tensor (phi, r) @@ -201,7 +199,7 @@ def test_fit_bg_basic(self, synthetic_dataset2d): find_origin=False, ) Ik = pdf.calculate_radial_mean(returnval=True) - k = pdf._to_torch(np.asarray(pdf.qq)) + k = np.asarray(pdf.qq) kmin, kmax = float(k.min()), float(k.max()) bg, f = pdf.fit_bg(Ik, kmin=kmin * 0.1, kmax=kmax * 0.9) assert bg.shape == Ik.shape @@ -225,8 +223,8 @@ def test_calculate_Gr_with_bandpass(self, synthetic_dataset2d): find_origin=False, ) pdf.calculate_Gr( - k_min=0.1, - k_max=2.0, + k_min_fit=0.1, + k_max_fit=2.0, k_lowpass=0.02, k_highpass=0.001, ) @@ -241,8 +239,8 @@ def test_calculate_Gr_with_mask(self, synthetic_4dstem_dataset): mask = np.zeros((3, 3), dtype=bool) mask[0:2, 0:2] = True pdf.calculate_Gr( - k_min=0.1, - k_max=2.0, + k_min_fit=0.1, + k_max_fit=2.0, mask_realspace=mask, ) assert pdf.reduced_pdf is not None @@ -262,7 +260,7 @@ def test_calculate_gr_estimates_density(self, synthetic_dataset2d): synthetic_dataset2d, find_origin=False, ) - pdf.calculate_Gr(k_min=0.1, k_max=2.0) + pdf.calculate_Gr(k_min_fit=0.1, k_max_fit=2.0) results = pdf.calculate_gr(returnval=True) assert results is not None r, gr = results @@ -297,8 +295,8 @@ def test_complete_pdf_workflow_2d(self, synthetic_dataset2d): find_origin=False, ) Gr_results = pdf.calculate_Gr( - k_min=0.1, - k_max=2.0, + k_min_fit=0.1, + k_max_fit=2.0, r_min=0.0, r_max=10.0, r_step=0.05, @@ -330,8 +328,8 @@ def test_complete_pdf_workflow_4dstem(self, synthetic_4dstem_dataset): mask = np.zeros((3, 3), dtype=bool) mask[0:2, 0:2] = True pdf.calculate_Gr( - k_min=0.1, - k_max=2.0, + k_min_fit=0.1, + k_max_fit=2.0, mask_realspace=mask, ) assert pdf.reduced_pdf is not None @@ -339,7 +337,7 @@ def test_complete_pdf_workflow_4dstem(self, synthetic_4dstem_dataset): assert not np.isinf(pdf.reduced_pdf).any() def test_polar_transform_input_types(self, synthetic_diffraction_pattern): - """Test polar_transform works with numpy array, Dataset2d, Dataset4dstem.""" + """Test from_data works with Dataset2d and Dataset4dstem.""" # Test with Dataset2d ds2 = Dataset2d.from_array( array=synthetic_diffraction_pattern, @@ -367,7 +365,7 @@ def test_density_estimation_workflow(self, synthetic_dataset2d): synthetic_dataset2d, find_origin=False, ) - pdf.calculate_Gr(k_min=0.1, k_max=2.0) + pdf.calculate_Gr(k_min_fit=0.1, k_max_fit=2.0) rho0, Fk_damped, G_cor = pdf.estimate_density( max_iter=5, tol_percent=1.0, From 47694f2cf0bc4da06bff6164d78901089de03acb Mon Sep 17 00:00:00 2001 From: ehrhardtkm Date: Tue, 5 May 2026 17:47:16 -0700 Subject: [PATCH 132/140] docstring/API cleanup and origing finding speedup --- src/quantem/diffraction/polar.py | 92 ++- src/quantem/diffraction/polar_transform.py | 771 +++++++++++---------- 2 files changed, 448 insertions(+), 415 deletions(-) diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py index d389c055..d9853d87 100644 --- a/src/quantem/diffraction/polar.py +++ b/src/quantem/diffraction/polar.py @@ -23,27 +23,40 @@ class PairDistributionFunction(AutoSerialize): - """ - Pair distribution function (PDF) utilities for diffraction / 4D-STEM data. - - This class wraps a 4D-STEM (or 2D diffraction) dataset and stores a - polar-transformed representation as a Polar4dstem instance in `self.polar`. - The PDF pipeline provides methods to compute: - - - azimuthal integration to obtain I(k) - - background fitting using a parametric model in k^2 / k^4 - - formation of F(k) and a windowed sine transform to obtain G(r) - - optional density estimation and origin correction (Yoshimoto & Omote-style iteration) - - basic plotting helpers for I(k), background, F(k), G(r), and g(r) + """Compute the pair distribution function from 4D-STEM diffuse scattering + + The pair distribution function g(r) gives the probability of finding + pairs of atoms at separation r, and is the standard tool for + characterizing local atomic structure in amorphous materials where + Bragg diffraction is unavailable. This class implements the standard + extraction pipeline from a 4D-STEM scan (or a single averaged + diffraction pattern): + + - polar transform of the diffraction patterns + - azimuthal averaging to obtain I(k) + - parametric background fit B(k) (Gaussian model in k² and k⁴) + - reduced structure factor F(k) = 2π · k · [S(k) − 1] + - windowed sine transform of F(k) to recover the reduced PDF G(r) + - optional density estimation and Yoshimoto–Omote oscillation damping + - normalization to g(r) = 1 + G(r) / (4π · r · ρ₀) + + Diffraction data is held in two complementary forms. ``Dataset4dstem`` + holds the input scan with each DP in Cartesian coordinates, indexed as + ``(scan_row, scan_col, n_row, n_col)``. ``Polar4dstem`` holds the + result of rebinning each DP to polar coordinates ``(phi, r_pix)``. + The polar transform is expensive and irreversible, so its result is + cached as a first-class dataset on ``self.polar`` rather than + recomputed on demand. ``from_data`` runs the polar transform (and + optional origin finding) once. Attributes ---------- polar : Polar4dstem Polar-transformed diffraction data wrapped by this instance. - input_data : Dataset4dstem, Polar4dstem, or None - Dataset that was polar-transformed to produce ``self.polar``, - preserved for reference. A ``Dataset2d`` input to ``from_data`` is - wrapped as a 1x1 ``Dataset4dstem`` before being stored here. + input_data : Dataset4dstem or None + Original input dataset that was polar-transformed to produce + ``self.polar``. A ``Dataset2d`` input to ``from_data`` is wrapped + as a 1×1 ``Dataset4dstem`` before being stored here. device : str Torch device used for computation. Ik : torch.Tensor or None @@ -78,6 +91,26 @@ class PairDistributionFunction(AutoSerialize): G(r) = (2/π) ∫ F(k) · sin(2π · k · r) dk. pdf : NDArray or None Pair distribution function g(r) = 1 + G(r) / (4π · r · ρ₀). + + Examples + -------- + Construct from a 4D-STEM scan and run the standard pipeline: + + >>> import quantem as em + >>> ds = em.core.io.read_4dstem("scan.h5", file_type="arina") + >>> rdf = em.diffraction.PairDistributionFunction.from_data(ds) + >>> rdf.calculate_Gr(k_min_fit=0.05, k_max_fit=2.0, r_max=10.0) + >>> rdf.calculate_gr(set_pdf_positive=True) + + Inspect intermediate results: + + >>> rdf.plot_pdf_results(["background_fits", "reduced_sf", "reduced_pdf", "pdf"]) + + Restrict the radial average to a real-space region of interest: + + >>> mask = np.zeros(ds.array.shape[:2], dtype=bool) + >>> mask[300:, 300:] = True + >>> rdf.calculate_Gr(k_min_fit=0.05, k_max_fit=2.0, mask_realspace=mask) """ _token = object() @@ -85,7 +118,7 @@ class PairDistributionFunction(AutoSerialize): def __init__( self, polar: Polar4dstem, - input_data: Dataset4dstem | Polar4dstem | None = None, + input_data: Dataset4dstem | None = None, device: str = "cpu", _token: object | None = None, ): @@ -119,7 +152,7 @@ def __init__( @classmethod def from_data( cls, - data: Dataset2d | Dataset4dstem | Polar4dstem, + data: Dataset2d | Dataset4dstem, *, find_origin: bool = True, origin_row: float | None = None, @@ -136,14 +169,12 @@ def from_data( Parameters ---------- - data : Dataset4dstem, Dataset2d, or Polar4dstem + data : Dataset4dstem or Dataset2d - ``Dataset4dstem``: triggers origin finding (optional) and polar transform. - ``Dataset2d``: single averaged diffraction pattern (e.g. SAED or a pre-averaged 4DSTEM result); wrapped as a 1x1 scan internally. - - ``Polar4dstem``: already polar-transformed; used directly, no - origin finding or polar transform performed. find_origin : bool If True, run ``auto_origin_id`` to find the origin at each scan position. If False, use ``origin_row`` / ``origin_col`` (or the @@ -170,11 +201,6 @@ def from_data( ------- PairDistributionFunction """ - # Polar input: use directly - if isinstance(data, Polar4dstem): - polar = data - return cls(polar=polar, input_data=data, device=device, _token=cls._token) - # Dataset2d input: wrap as a trivial 4D-STEM (1x1 scan) and fall through if isinstance(data, Dataset2d): arr2d = data.array @@ -238,8 +264,8 @@ def from_data( raise TypeError( f"Got {type(data).__name__}. PairDistributionFunction.from_data " - "accepts Polar4dstem, Dataset4dstem, or Dataset2d. Wrap numpy " - "arrays with Dataset4dstem.from_array or Dataset2d.from_array first." + "accepts Dataset4dstem or Dataset2d. Wrap numpy arrays with " + "Dataset4dstem.from_array or Dataset2d.from_array first." ) # ------------------------------------------------------------------ @@ -364,7 +390,13 @@ def fit_bg( """ Fit a smooth background B(k) to a radial intensity curve I(k) using PyTorch LBFGS optimizer, with weighting that downweights the low-k - region and emphasizes higher k. + region and emphasizes higher k. LBFGS was chosen empirically through + trial and error to see which optimizer matched scipy.curve_fit() best + on test data. + + B(k) is later subtracted from I(k) to isolate the diffuse signal, and + f(k) is used as the denominator in the structure factor + S(k) = 1 + [I(k) − B(k)] / f(k). The fitted function uses the following form (adopted from py4dstem): B(k) = c @@ -387,7 +419,7 @@ def fit_bg( Fitted background curve B(k), shape (Nk,). f : torch.Tensor Background minus the constant offset, f(k) = B(k) - c, or functionally - similar to ^2(k) + similar to ^2(k). Used later to compute the reduced structure factor F(k). """ k = torch.from_numpy(np.asarray(self.qq).astype(np.float32)).to(device=self.device) if kmin is None: diff --git a/src/quantem/diffraction/polar_transform.py b/src/quantem/diffraction/polar_transform.py index eaf0e0e2..5aa484ef 100644 --- a/src/quantem/diffraction/polar_transform.py +++ b/src/quantem/diffraction/polar_transform.py @@ -13,256 +13,6 @@ # but is noted where the call occures -def _normalize_coords_for_grid_sample( - coords_row: torch.Tensor, - coords_col: torch.Tensor, - n_row: int, - n_col: int, -) -> torch.Tensor: - """Convert pixel (row, col) coordinates to normalized [-1, 1] coordinates - for grid_sample.""" - col_norm = 2.0 * coords_col / (n_col - 1) - 1.0 - row_norm = 2.0 * coords_row / (n_row - 1) - 1.0 - # grid_sample requires (col, row) ordering in the last dim - return torch.stack([col_norm, row_norm], dim=-1) - - -def _polar_to_cartesian_offsets( - phi: torch.Tensor, - r_pix: torch.Tensor, - ellipse_params: tuple[float, float, float] | None, - device: str = "cpu", -) -> tuple[torch.Tensor, torch.Tensor]: - """Convert polar (phi, r_pix) grids to Cartesian (row, col) pixel offsets - from the origin, optionally correcting for elliptical distortion. - - Returns ``(offset_row, offset_col)`` where - ``col_offset = r_pix * cos(phi)`` and ``row_offset = r_pix * sin(phi)``. - """ - if ellipse_params is None: - offset_col = r_pix * torch.cos(phi) - offset_row = r_pix * torch.sin(phi) - else: - if len(ellipse_params) != 3: - raise ValueError("ellipse_params must be (a, b, theta_deg).") - a, b, theta_deg = ellipse_params - theta = torch.deg2rad(torch.tensor(theta_deg, dtype=torch.float32, device=device)) - # Rotate into the ellipse frame, scale by a/b to undo the distortion, - # then rotate back so sampling follows the true circular rings - alpha = phi - theta - u = (a / b) * r_pix * torch.cos(alpha) - v_prime = r_pix * torch.sin(alpha) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - offset_col = u * cos_t - v_prime * sin_t - offset_row = u * sin_t + v_prime * cos_t - return offset_row, offset_col - - -def _build_candidate_grids( - base_col_norm: torch.Tensor, - base_row_norm: torch.Tensor, - center_row: int, - center_col: int, - margin: int, - n_row: int, - n_col: int, - col_norm_scale: float, - row_norm_scale: float, - device: str = "cpu", -) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Build a batch of normalized sampling grids, one per candidate origin - pixel in a search window around (center_row, center_col). Candidates are - produced in a single batched tensor so that they can be evaluated - simultaneously by ``_angular_std_scores``. - - Parameters - ---------- - base_col_norm, base_row_norm : torch.Tensor of shape (n_phi, n_r) - Polar sampling offsets, already expressed in ``grid_sample``'s - normalized [-1, 1] coordinates, relative to origin (0, 0) - center_row, center_col : int - Center of the candidate search window - margin : int - Half-width of the search window in pixels - n_row, n_col : int - Diffraction-pattern image dimensions - col_norm_scale, row_norm_scale : float - Conversion factor from an offset in pixel units to the equivalent - offset in ``grid_sample``'s normalized coordinates - - Returns - ------- - row_flat, col_flat : torch.Tensor of shape (N,) - Candidate origin positions - grids : torch.Tensor of shape (N, n_phi, n_r, 2) - Stacked sampling grids ready for ``F.grid_sample`` (ordered ``(col, row)`` ) - """ - # Enumerate all pixel positions in the search window, clamped to image bounds - rows = torch.arange( - max(0, center_row - margin), - min(n_row, center_row + margin + 1), - dtype=torch.long, - device=device, - ) - cols = torch.arange( - max(0, center_col - margin), - min(n_col, center_col + margin + 1), - dtype=torch.long, - device=device, - ) - row_grid, col_grid = torch.meshgrid(rows, cols, indexing="ij") - row_flat, col_flat = row_grid.reshape(-1), col_grid.reshape(-1) - # Shift the pre-computed polar offsets to each candidate origin, - # converting to grid_sample's [-1, 1] normalized coordinates - grid_col = ( - base_col_norm.unsqueeze(0) + (col_flat.float() * col_norm_scale - 1.0)[:, None, None] - ) - grid_row = ( - base_row_norm.unsqueeze(0) + (row_flat.float() * row_norm_scale - 1.0)[:, None, None] - ) - # grid_sample requires (col, row) ordering in the last dim - grids = torch.stack([grid_col, grid_row], dim=-1) # (N, n_phi, n_r, 2) - return row_flat, col_flat, grids - - -def _angular_std_scores( - dp_batch: torch.Tensor, - grids: torch.Tensor, - min_r_idx: int, - max_r_idx: int, -) -> torch.Tensor: - """Score candidate origins by angular std over a mid-radius band. - Lower scores indicate better centering.""" - n = grids.shape[0] - # Sample the diffraction pattern at each candidate's polar grid positions - polars = F.grid_sample( - dp_batch.expand(n, -1, -1, -1), - grids, - mode="bilinear", - padding_mode="zeros", - align_corners=True, - ) - # A correctly centered pattern has uniform intensity along each ring, - # so the angular std is minimized at the true center - region = polars.squeeze(1)[:, :, min_r_idx:max_r_idx] - return region.std(dim=1).sum(dim=1) - - -def _build_polar_sampling_offsets( - ellipse_params: tuple[float, float, float] | None, - num_annular_bins: int, - radial_min: float, - radial_max_eff: float, - radial_step: float, - two_fold_rotation_symmetry: bool, - device: str = "cpu", -) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """Build origin-independent Cartesian (row, col) offsets for a polar - sampling grid. - - Returns ``(offset_row, offset_col, phi_bins, radial_bins)`` where - ``offset_row`` and ``offset_col`` have shape ``(n_phi, n_r)`` and - represent pixel displacements from an arbitrary origin. - """ - if radial_step <= 0: - raise ValueError(f"Got radial_step = {radial_step}. radial_step must be > 0.") - if num_annular_bins < 1: - raise ValueError("num_annular_bins must be >= 1.") - - radial_bins = torch.arange( - radial_min, radial_max_eff, radial_step, dtype=torch.float32, device=device - ) - if radial_bins.numel() == 0: - radial_bins = torch.tensor([radial_min], dtype=torch.float32, device=device) - phi_range = torch.pi if two_fold_rotation_symmetry else 2.0 * torch.pi - # Drop the last endpoint because 0 and 2pi (or pi) are the same angle - phi_bins = torch.linspace( - 0.0, phi_range, num_annular_bins + 1, dtype=torch.float32, device=device - )[:-1] - phi_grid, r_pix_grid = torch.meshgrid(phi_bins, radial_bins, indexing="ij") - # Compute offsets relative to origin (0,0) so they can be reused - # for any candidate origin by simple translation - offset_row, offset_col = _polar_to_cartesian_offsets( - phi_grid, r_pix_grid, ellipse_params, device - ) - return offset_row, offset_col, phi_bins, radial_bins - - -def _compute_radial_max( - n_row: int, - n_col: int, - origin_row: float, - origin_col: float, - radial_max: float | None, - radial_min: float, - radial_step: float, -) -> float: - """Compute the effective maximum radius, clamped to image bounds.""" - # Use the shortest distance from the origin to any image edge so the - # polar grid never samples outside the image bounds - if radial_max is None: - radial_max_eff = float( - min( - origin_row, - (n_row - 1) - origin_row, - origin_col, - (n_col - 1) - origin_col, - ) - ) - else: - radial_max_eff = float(radial_max) - # Guarantee at least one radial bin - if radial_max_eff <= radial_min: - radial_max_eff = radial_min + radial_step - return radial_max_eff - - -def _precompute_polar_coords( - n_row: int, - n_col: int, - origin_row: float, - origin_col: float, - ellipse_params: tuple[float, float, float] | None, - num_annular_bins: int, - radial_min: float, - radial_max: float | None, - radial_step: float, - two_fold_rotation_symmetry: bool, - device: str = "cpu", -) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, float]: - """Build a normalized sampling grid for a single known origin.""" - origin_row = float(origin_row) - origin_col = float(origin_col) - # Clamp radial range so the polar grid stays within image bounds - radial_max_eff = _compute_radial_max( - n_row, - n_col, - origin_row, - origin_col, - radial_max, - radial_min, - radial_step, - ) - # Get origin-independent polar offsets - offset_row, offset_col, phi_bins, radial_bins = _build_polar_sampling_offsets( - ellipse_params, - num_annular_bins, - radial_min, - radial_max_eff, - radial_step, - two_fold_rotation_symmetry, - device, - ) - # Translate offsets to absolute pixel pos at this origin - coords_row = offset_row + origin_row - coords_col = offset_col + origin_col - # Convert to [-1, 1] normalized coordinates expected by grid_sample - grid = _normalize_coords_for_grid_sample(coords_row, coords_col, n_row, n_col) - grid = grid.unsqueeze(0) # (1, n_phi, n_r, 2) - return grid, phi_bins, radial_bins, radial_max_eff - - def auto_origin_id( data: Dataset4dstem, *, @@ -270,9 +20,11 @@ def auto_origin_id( num_annular_bins: int = 180, radial_min: float = 0.0, radial_max: float | None = None, - radial_step: float = 1.0, + radial_step: float = 2.0, two_fold_rotation_symmetry: bool = False, device: str = "cpu", + batch_size: int = 16, + local_margin: int = 25, ) -> NDArray: """ Automatic diffraction center finding by minimizing angular intensity @@ -291,18 +43,27 @@ def auto_origin_id( ellipse_params : tuple or None Ellipse parameters (a, b, theta_deg) for distortion correction num_annular_bins : int - Number of angular bins for the final polar transform (not used - during center-finding, which uses 36 bins for speed) + Number of angular bins for the final polar transform radial_min : float Minimum radius in pixels radial_max : float or None Maximum radius in pixels radial_step : float - Radial step size in pixels + Radial step size in pixels for the search polar grid two_fold_rotation_symmetry : bool If True, use only 0 to pi range for angles device : str Torch device for computation + batch_size : int + Number of scan positions evaluated per coarse-stage kernel call. + Larger values reduce per-iteration overhead but use more memory. + local_margin : int + Half-width (in pixels) of the search window used to refine each + scan position's origin. After the global center is found on the + mean DP, each DP's origin is searched within a + ``(2*local_margin+1)`` square window centered on the global + origin. Set this large enough to cover the worst-case descan + drift across the scan. Returns ------- @@ -322,8 +83,6 @@ def auto_origin_id( ) origin_array = np.zeros((scan_row, scan_col, 2), dtype=float) - total_positions = scan_row * scan_col - # first get COM of mean DP because it gives a robust rough center array_4d = data.array if data.array.ndim == 4 else data.array[None, None, :, :] mean_dp_np = array_4d.mean(axis=(0, 1)).astype(np.float32) @@ -331,11 +90,13 @@ def auto_origin_id( row_grid, col_grid = np.mgrid[0:n_row, 0:n_col] com_row = int(round(float((row_grid * mean_dp_np).sum() / total_intensity))) com_col = int(round(float((col_grid * mean_dp_np).sum() / total_intensity))) - - # building a fixed polar grid that is safe for all candidates - # safe_rmax ensures no candidate's grid extends outside the image + # Radial max of the search polar grid, so dp_mean search candidates + # (at ±global_margin from COM) stay within image bounds + # in-image. Single pos candidates further from COM might be out of bounds + # and are masked with [safe_low, safe_high_*] if so + # (zero-padded samples would otherwise produce a falsely low score) global_margin = 20 - safe_rmax = float( + safe_radial_max = float( min( com_row - global_margin, (n_row - 1) - (com_row + global_margin), @@ -344,17 +105,20 @@ def auto_origin_id( ) ) if radial_max is not None: - safe_rmax = min(safe_rmax, float(radial_max)) - if safe_rmax <= radial_min: - safe_rmax = radial_min + radial_step - # use very coarse binning because asymmetry is still captured at - # low angular resolution and is significantly faster - search_n_phi = 36 + safe_radial_max = min(safe_radial_max, float(radial_max)) + if safe_radial_max <= radial_min: + safe_radial_max = radial_min + radial_step + safe_low = int(np.ceil(safe_radial_max)) + safe_high_row = n_row - 1 - safe_low + safe_high_col = n_col - 1 - safe_low + # Internal search-grid resolution. Balancing speed against robustness + search_n_phi = 18 + local_coarse_step = 5 offset_row, offset_col, _, radial_bins = _build_polar_sampling_offsets( ellipse_params, search_n_phi, radial_min, - safe_rmax, + safe_radial_max, radial_step, two_fold_rotation_symmetry, device, @@ -368,66 +132,53 @@ def auto_origin_id( base_col_norm = offset_col * col_norm_scale base_row_norm = offset_row * row_norm_scale - # now find actual center - # Coarse search over ±global_margin around COM - coarse_step = 4 - coarse_rows = torch.arange( - max(0, com_row - global_margin), - min(n_row, com_row + global_margin + 1), - coarse_step, - dtype=torch.long, - device=device, - ) - coarse_cols = torch.arange( - max(0, com_col - global_margin), - min(n_col, com_col + global_margin + 1), - coarse_step, - dtype=torch.long, - device=device, - ) - # Create all (row, col) candidate pairs and flatten for batched evaluation - coarse_row_grid, coarse_col_grid = torch.meshgrid(coarse_rows, coarse_cols, indexing="ij") - coarse_row_flat, coarse_col_flat = coarse_row_grid.reshape(-1), coarse_col_grid.reshape(-1) - # Shift polar offsets to each candidate origin in normalized coordinates - coarse_g_col = ( - base_col_norm.unsqueeze(0) - + (coarse_col_flat.float() * col_norm_scale - 1.0)[:, None, None] + # Mean-DP global center search: coarse → fine, masking candidates + # whose polar grid would extend OOB at each step. + mean_dp_batch = torch.from_numpy(mean_dp_np).to(device)[None, None] + # Coarse: step=4 over ±global_margin around the COM + rows, cols, grids = _build_candidate_grids( + base_col_norm, + base_row_norm, + com_row, + com_col, + global_margin, + n_row, + n_col, + col_norm_scale, + row_norm_scale, + device, + step=4, ) - coarse_g_row = ( - base_row_norm.unsqueeze(0) - + (coarse_row_flat.float() * row_norm_scale - 1.0)[:, None, None] + scores = _angular_std_scores(mean_dp_batch, grids, min_r_idx, max_r_idx) + valid = ( + (rows >= safe_low) & (rows <= safe_high_row) & (cols >= safe_low) & (cols <= safe_high_col) ) - # grid_sample requires (col, row) ordering in the last dim - coarse_grids = torch.stack([coarse_g_col, coarse_g_row], dim=-1) - # Score all coarse candidates on the mean DP and pick the best one - mean_dp_batch = torch.from_numpy(mean_dp_np).to(device)[None, None] - coarse_scores = _angular_std_scores(mean_dp_batch, coarse_grids, min_r_idx, max_r_idx) - best_coarse_idx = coarse_scores.argmin().item() - coarse_best_row = int(coarse_row_flat[best_coarse_idx].item()) - coarse_best_col = int(coarse_col_flat[best_coarse_idx].item()) - - # Fine search (step=1) around coarse best for global center of mean DP - fine_margin = 6 - fine_row_flat, fine_col_flat, fine_grids = _build_candidate_grids( + best = scores.masked_fill(~valid, float("inf")).argmin().item() + coarse_row, coarse_col = int(rows[best].item()), int(cols[best].item()) + # Fine: step=1 over ±6 around the coarse winner + rows, cols, grids = _build_candidate_grids( base_col_norm, base_row_norm, - coarse_best_row, - coarse_best_col, - fine_margin, + coarse_row, + coarse_col, + 6, n_row, n_col, col_norm_scale, row_norm_scale, device, + step=1, + ) + scores = _angular_std_scores(mean_dp_batch, grids, min_r_idx, max_r_idx) + valid = ( + (rows >= safe_low) & (rows <= safe_high_row) & (cols >= safe_low) & (cols <= safe_high_col) ) - fine_scores = _angular_std_scores(mean_dp_batch, fine_grids, min_r_idx, max_r_idx) - best_fine_idx = fine_scores.argmin().item() - global_row = int(fine_row_flat[best_fine_idx].item()) - global_col = int(fine_col_flat[best_fine_idx].item()) - # Get center for each scan pos by fine search around global center - # Assuming that the center doesn't shift more than 10 pixels across the scan - local_margin = 10 - local_rf, local_cf, local_grids = _build_candidate_grids( + best = scores.masked_fill(~valid, float("inf")).argmin().item() + global_row, global_col = int(rows[best].item()), int(cols[best].item()) + + # Per-scan-position refinement (coarse → medium → fine) for descan + # medium and fine search per-DP around the previous winner + coarse_rows, coarse_cols, coarse_grids = _build_candidate_grids( base_col_norm, base_row_norm, global_row, @@ -438,21 +189,91 @@ def auto_origin_id( col_norm_scale, row_norm_scale, device, + step=local_coarse_step, ) - pbar = tqdm(total=total_positions, desc="Finding origin for each scan position") - for row_pos in range(scan_row): - row_dps = torch.from_numpy(array_4d[row_pos].astype(np.float32)).to( - device - ) # (scan_col, n_row, n_col) - - for col_pos in range(scan_col): - dp_batch = row_dps[col_pos][None, None] - scores = _angular_std_scores(dp_batch, local_grids, min_r_idx, max_r_idx) - best_idx = scores.argmin().item() - origin_array[row_pos, col_pos, 0] = local_rf[best_idx].item() - origin_array[row_pos, col_pos, 1] = local_cf[best_idx].item() - pbar.update(1) + coarse_valid = ( + (coarse_rows >= safe_low) + & (coarse_rows <= safe_high_row) + & (coarse_cols >= safe_low) + & (coarse_cols <= safe_high_col) + ) + n_coarse = coarse_grids.shape[0] + # Per-DP relative offsets used by the medium and fine stages + med_rel = torch.arange( + -local_coarse_step, local_coarse_step + 1, 2, dtype=torch.long, device=device + ) + med_drow, med_dcol = (m.reshape(-1) for m in torch.meshgrid(med_rel, med_rel, indexing="ij")) + fine_rel = torch.arange(-1, 2, dtype=torch.long, device=device) + fine_drow, fine_dcol = ( + m.reshape(-1) for m in torch.meshgrid(fine_rel, fine_rel, indexing="ij") + ) + flat_dps = array_4d.reshape(-1, n_row, n_col) + origin_flat = origin_array.reshape(-1, 2) + n_pos = flat_dps.shape[0] + + def refine(dp_batch, current_row, current_col, drow, dcol): + """scores candidates per DP and return the best(row, col) per DP. Invalid (out of bounds) candidates are masked.""" + n_cands = drow.numel() + cand_rows = (current_row[:, None] + drow[None, :]).clamp(0, n_row - 1) + cand_cols = (current_col[:, None] + dcol[None, :]).clamp(0, n_col - 1) + g_col = ( + base_col_norm + (cand_cols.reshape(-1).float() * col_norm_scale - 1.0)[:, None, None] + ) + g_row = ( + base_row_norm + (cand_rows.reshape(-1).float() * row_norm_scale - 1.0)[:, None, None] + ) + grids = torch.stack([g_col, g_row], dim=-1) + dps = dp_batch.repeat_interleave(n_cands, dim=0) + polars = F.grid_sample( + dps, grids, mode="bilinear", padding_mode="zeros", align_corners=True + ) + scores = ( + polars.view(dp_batch.shape[0], n_cands, *base_col_norm.shape)[..., min_r_idx:max_r_idx] + .std(dim=2) + .sum(dim=2) + ) + valid = ( + (cand_rows >= safe_low) + & (cand_rows <= safe_high_row) + & (cand_cols >= safe_low) + & (cand_cols <= safe_high_col) + ) + best = scores.masked_fill(~valid, float("inf")).argmin(dim=1) + return ( + cand_rows.gather(1, best[:, None]).squeeze(1), + cand_cols.gather(1, best[:, None]).squeeze(1), + ) + pbar = tqdm(total=n_pos, desc="Finding origin for each scan position") + for start in range(0, n_pos, batch_size): + end = min(start + batch_size, n_pos) + bsz = end - start + dp_b = ( + torch.from_numpy(np.ascontiguousarray(flat_dps[start:end], dtype=np.float32)) + .to(device) + .unsqueeze(1) + ) # (B, 1, H, W) + # Coarse (shared grids): broadcast B DPs across n_coarse candidate + # grids in one grid_sample call by stacking DPs in the channel dim + # and stride-0 expanding along the candidate dim + polars_coarse = F.grid_sample( + dp_b.transpose(0, 1).expand(n_coarse, bsz, n_row, n_col), + coarse_grids, + mode="bilinear", + padding_mode="zeros", + align_corners=True, + ) + scores_coarse = polars_coarse[:, :, :, min_r_idx:max_r_idx].std(dim=2).sum(dim=2) + scores_coarse = scores_coarse.masked_fill(~coarse_valid[:, None], float("inf")) + best_coarse = scores_coarse.argmin(dim=0) # best candidate per DP + current_row, current_col = coarse_rows[best_coarse], coarse_cols[best_coarse] + # Medium: per-DP search around the coarse winner + current_row, current_col = refine(dp_b, current_row, current_col, med_drow, med_dcol) + # Fine: per-DP ±1 around the medium winner + current_row, current_col = refine(dp_b, current_row, current_col, fine_drow, fine_dcol) + origin_flat[start:end, 0] = current_row.cpu().numpy() + origin_flat[start:end, 1] = current_col.cpu().numpy() + pbar.update(bsz) pbar.close() return origin_array @@ -470,6 +291,7 @@ def polar_transform( signal_units: str | None = None, scan_pos: tuple[int, int] | None = None, device: str = "cpu", + batch_size: int = 128, ) -> Polar4dstem | torch.Tensor: if data.array.ndim != 4: raise ValueError( @@ -502,22 +324,29 @@ def polar_transform( dp = torch.from_numpy(data.array[i_row, i_col].astype(np.float32)).to(device) r0 = float(origins[i_row, i_col, 0]) c0 = float(origins[i_row, i_col, 1]) - grid, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( - n_row=n_row, - n_col=n_col, - origin_row=r0, - origin_col=c0, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - device=device, + # Clamp radial range to image bounds for this origin + if radial_max is None: + radial_max_eff = float(min(r0, (n_row - 1) - r0, c0, (n_col - 1) - c0)) + else: + radial_max_eff = float(radial_max) + if radial_max_eff <= radial_min: + radial_max_eff = radial_min + radial_step + # Build offsets, translate to this origin, normalize for grid_sample + offset_row, offset_col, _, _ = _build_polar_sampling_offsets( + ellipse_params, + num_annular_bins, + radial_min, + radial_max_eff, + radial_step, + two_fold_rotation_symmetry, + device, ) - dp_batch = dp[None, None] # (1, 1, n_row, n_col) + col_norm = 2.0 * (offset_col + c0) / (n_col - 1) - 1.0 + row_norm = 2.0 * (offset_row + r0) / (n_row - 1) - 1.0 + # grid_sample requires (col, row) ordering in the last dim + grid = torch.stack([col_norm, row_norm], dim=-1).unsqueeze(0) # (1, n_phi, n_r, 2) polar2d = F.grid_sample( - dp_batch, + dp[None, None], grid, mode="bilinear", padding_mode="zeros", @@ -535,50 +364,57 @@ def polar_transform( radial_max_eff_array = np.minimum.reduce([r_row_pos, r_row_neg, r_col_pos, r_col_neg]) radial_max = float(max(radial_max_eff_array.min(), radial_min + radial_step)) - # Compute grid for first position to get output shape - grid, phi_bins, radial_bins, radial_max_eff = _precompute_polar_coords( - n_row=n_row, - n_col=n_col, - origin_row=float(origins[0, 0, 0]), - origin_col=float(origins[0, 0, 1]), - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - device=device, + # Build origin-independent polar offsets ONCE. Only the per-origin shift + # changes from one scan position to the next, so we can reuse these. + offset_row, offset_col, phi_bins, radial_bins = _build_polar_sampling_offsets( + ellipse_params, + num_annular_bins, + radial_min, + float(radial_max), + radial_step, + two_fold_rotation_symmetry, + device, ) n_phi = phi_bins.numel() n_r = radial_bins.numel() - out = np.empty((scan_row, scan_col, n_phi, n_r), dtype=np.float32) - for i_row in range(scan_row): - for i_col in range(scan_col): - dp = torch.from_numpy(data.array[i_row, i_col].astype(np.float32)).to(device) - r0 = float(origins[i_row, i_col, 0]) - c0 = float(origins[i_row, i_col, 1]) - grid, _, _, _ = _precompute_polar_coords( - n_row=n_row, - n_col=n_col, - origin_row=r0, - origin_col=c0, - ellipse_params=ellipse_params, - num_annular_bins=num_annular_bins, - radial_min=radial_min, - radial_max=radial_max, - radial_step=radial_step, - two_fold_rotation_symmetry=two_fold_rotation_symmetry, - device=device, - ) - dp_batch = dp[None, None] - polar2d = F.grid_sample( - dp_batch, - grid, - mode="bilinear", - padding_mode="zeros", - align_corners=True, - ) - out[i_row, i_col] = to_numpy(polar2d.squeeze(0).squeeze(0)) + radial_max_eff = float(radial_max) + + # Pre-normalize offsets into grid_sample's [-1, 1] coordinate convention + col_norm_scale = 2.0 / (n_col - 1) + row_norm_scale = 2.0 / (n_row - 1) + base_col_norm = offset_col * col_norm_scale # (n_phi, n_r) + base_row_norm = offset_row * row_norm_scale # (n_phi, n_r) + + # Flatten scan dims so we can iterate in flat batches + n_pos = scan_row * scan_col + dp_view = data.array.reshape(n_pos, n_row, n_col) + origins_t = torch.from_numpy( + np.ascontiguousarray(origins.reshape(n_pos, 2), dtype=np.float32) + ).to(device) + + out = np.empty((n_pos, n_phi, n_r), dtype=np.float32) + for start in tqdm(range(0, n_pos, batch_size), desc="Polar transform"): + end = min(start + batch_size, n_pos) + # Translate the precomputed offsets to each origin in this batch + row_origins = origins_t[start:end, 0] + col_origins = origins_t[start:end, 1] + grid_col = base_col_norm.unsqueeze(0) + (col_origins * col_norm_scale - 1.0)[:, None, None] + grid_row = base_row_norm.unsqueeze(0) + (row_origins * row_norm_scale - 1.0)[:, None, None] + # grid_sample requires (col, row) ordering in the last dim + grids = torch.stack([grid_col, grid_row], dim=-1) + + dp_batch = torch.from_numpy(np.ascontiguousarray(dp_view[start:end], dtype=np.float32)).to( + device + ) + polars = F.grid_sample( + dp_batch.unsqueeze(1), + grids, + mode="bilinear", + padding_mode="zeros", + align_corners=True, + ) + out[start:end] = to_numpy(polars.squeeze(1)) + out = out.reshape(scan_row, scan_col, n_phi, n_r) # Get polar axes in physical units matching the input dataset's calibration phi_range = np.pi if two_fold_rotation_symmetry else 2.0 * np.pi @@ -619,3 +455,168 @@ def polar_transform( origin_array=origins, _token=Polar4dstem._token, ) + + +def _polar_to_cartesian_offsets( + phi: torch.Tensor, + r_pix: torch.Tensor, + ellipse_params: tuple[float, float, float] | None, + device: str = "cpu", +) -> tuple[torch.Tensor, torch.Tensor]: + """Convert polar (phi, r_pix) grids to Cartesian (row, col) pixel offsets + from the origin, optionally correcting for elliptical distortion. + + Returns ``(offset_row, offset_col)`` where + ``col_offset = r_pix * cos(phi)`` and ``row_offset = r_pix * sin(phi)``. + """ + if ellipse_params is None: + offset_col = r_pix * torch.cos(phi) + offset_row = r_pix * torch.sin(phi) + else: + if len(ellipse_params) != 3: + raise ValueError("ellipse_params must be (a, b, theta_deg).") + a, b, theta_deg = ellipse_params + theta = torch.deg2rad(torch.tensor(theta_deg, dtype=torch.float32, device=device)) + # Rotate into the ellipse frame, scale by a/b to undo the distortion, + # then rotate back so sampling follows the true circular rings + alpha = phi - theta + u = (a / b) * r_pix * torch.cos(alpha) + v_prime = r_pix * torch.sin(alpha) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + offset_col = u * cos_t - v_prime * sin_t + offset_row = u * sin_t + v_prime * cos_t + return offset_row, offset_col + + +def _build_polar_sampling_offsets( + ellipse_params: tuple[float, float, float] | None, + num_annular_bins: int, + radial_min: float, + radial_max_eff: float, + radial_step: float, + two_fold_rotation_symmetry: bool, + device: str = "cpu", +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Build origin-independent Cartesian (row, col) offsets for a polar + sampling grid. + + Returns ``(offset_row, offset_col, phi_bins, radial_bins)`` where + ``offset_row`` and ``offset_col`` have shape ``(n_phi, n_r)`` and + represent pixel displacements from an arbitrary origin. + """ + if radial_step <= 0: + raise ValueError(f"Got radial_step = {radial_step}. radial_step must be > 0.") + if num_annular_bins < 1: + raise ValueError("num_annular_bins must be >= 1.") + + radial_bins = torch.arange( + radial_min, radial_max_eff, radial_step, dtype=torch.float32, device=device + ) + if radial_bins.numel() == 0: + radial_bins = torch.tensor([radial_min], dtype=torch.float32, device=device) + phi_range = torch.pi if two_fold_rotation_symmetry else 2.0 * torch.pi + # Drop the last endpoint because 0 and 2pi (or pi) are the same angle + phi_bins = torch.linspace( + 0.0, phi_range, num_annular_bins + 1, dtype=torch.float32, device=device + )[:-1] + phi_grid, r_pix_grid = torch.meshgrid(phi_bins, radial_bins, indexing="ij") + # Compute offsets relative to origin (0,0) so they can be reused + # for any candidate origin by simple translation + offset_row, offset_col = _polar_to_cartesian_offsets( + phi_grid, r_pix_grid, ellipse_params, device + ) + return offset_row, offset_col, phi_bins, radial_bins + + +def _build_candidate_grids( + base_col_norm: torch.Tensor, + base_row_norm: torch.Tensor, + center_row: int, + center_col: int, + margin: int, + n_row: int, + n_col: int, + col_norm_scale: float, + row_norm_scale: float, + device: str = "cpu", + step: int = 1, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Build a batch of normalized sampling grids, one per candidate origin + pixel in a search window around (center_row, center_col). Candidates are + produced in a single batched tensor so that they can be evaluated + simultaneously by ``_angular_std_scores``. + + Parameters + ---------- + base_col_norm, base_row_norm : torch.Tensor of shape (n_phi, n_r) + Polar sampling offsets, already expressed in ``grid_sample``'s + normalized [-1, 1] coordinates, relative to origin (0, 0) + center_row, center_col : int + Center of the candidate search window + margin : int + Half-width of the search window in pixels + n_row, n_col : int + Diffraction-pattern image dimensions + col_norm_scale, row_norm_scale : float + Conversion factor from an offset in pixel units to the equivalent + offset in ``grid_sample``'s normalized coordinates + + Returns + ------- + row_flat, col_flat : torch.Tensor of shape (N,) + Candidate origin positions + grids : torch.Tensor of shape (N, n_phi, n_r, 2) + Stacked sampling grids ready for ``F.grid_sample`` (ordered ``(col, row)`` ) + """ + # Enumerate all pixel positions in the search window, clamped to image bounds + rows = torch.arange( + max(0, center_row - margin), + min(n_row, center_row + margin + 1), + step, + dtype=torch.long, + device=device, + ) + cols = torch.arange( + max(0, center_col - margin), + min(n_col, center_col + margin + 1), + step, + dtype=torch.long, + device=device, + ) + row_grid, col_grid = torch.meshgrid(rows, cols, indexing="ij") + row_flat, col_flat = row_grid.reshape(-1), col_grid.reshape(-1) + # Shift the pre-computed polar offsets to each candidate origin, + # converting to grid_sample's [-1, 1] normalized coordinates + grid_col = ( + base_col_norm.unsqueeze(0) + (col_flat.float() * col_norm_scale - 1.0)[:, None, None] + ) + grid_row = ( + base_row_norm.unsqueeze(0) + (row_flat.float() * row_norm_scale - 1.0)[:, None, None] + ) + # grid_sample requires (col, row) ordering in the last dim + grids = torch.stack([grid_col, grid_row], dim=-1) # (N, n_phi, n_r, 2) + return row_flat, col_flat, grids + + +def _angular_std_scores( + dp_batch: torch.Tensor, + grids: torch.Tensor, + min_r_idx: int, + max_r_idx: int, +) -> torch.Tensor: + """Score candidate origins by angular std over a mid-radius band. + Lower scores indicate better centering.""" + n = grids.shape[0] + # Sample the diffraction pattern at each candidate's polar grid positions + polars = F.grid_sample( + dp_batch.expand(n, -1, -1, -1), + grids, + mode="bilinear", + padding_mode="zeros", + align_corners=True, + ) + # A correctly centered pattern has uniform intensity along each ring, + # so the angular std is minimized at the true center + region = polars.squeeze(1)[:, :, min_r_idx:max_r_idx] + return region.std(dim=1).sum(dim=1) From 47ff58922cc37fbf97b9c8a2ab6addcfe62e3cbf Mon Sep 17 00:00:00 2001 From: ehrhardtkm Date: Wed, 13 May 2026 10:56:13 -0700 Subject: [PATCH 133/140] remove duplicate gaussian code and restore tomo utils --- src/quantem/core/utils/filter.py | 11 +++++++++ src/quantem/diffraction/polar.py | 2 +- src/quantem/tomography/utils.py | 40 ++------------------------------ 3 files changed, 14 insertions(+), 39 deletions(-) diff --git a/src/quantem/core/utils/filter.py b/src/quantem/core/utils/filter.py index 3f9d13b7..a11d5e70 100644 --- a/src/quantem/core/utils/filter.py +++ b/src/quantem/core/utils/filter.py @@ -135,6 +135,17 @@ def gaussian_filter_2d( return img.squeeze_(0).squeeze_(0) # Make 2D again +def gaussian_filter_1d( + arr: torch.Tensor, kernel_1d: torch.Tensor +) -> torch.Tensor: # Replicate-padded torch alternative to ``scipy.ndimage.gaussian_filter1d`` + padding = len(kernel_1d) // 2 # Ensure that signal size does not change + arr = arr.unsqueeze(0).unsqueeze_(0) # Make copy, make 3D for ``conv1d()`` + # Replicate edge values so the output has no zero-padded ringing at boundaries + arr = torch.nn.functional.pad(arr, (padding, padding), mode="replicate") + arr = torch.nn.functional.conv1d(arr, weight=kernel_1d.view(1, 1, -1)) + return arr.squeeze_(0).squeeze_(0) # Make 1D again + + def gaussian_filter_2d_stack(stack: torch.Tensor, kernel_1d: torch.Tensor) -> torch.Tensor: """ Apply 2D Gaussian blur to each slice stack[:, i, :] in a vectorized way. diff --git a/src/quantem/diffraction/polar.py b/src/quantem/diffraction/polar.py index d9853d87..7d53c6e6 100644 --- a/src/quantem/diffraction/polar.py +++ b/src/quantem/diffraction/polar.py @@ -13,9 +13,9 @@ from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.datastructures.polar4dstem import Polar4dstem from quantem.core.io.serialize import AutoSerialize +from quantem.core.utils.filter import gaussian_filter_1d, gaussian_kernel_1d from quantem.core.utils.utils import to_numpy from quantem.diffraction.polar_transform import auto_origin_id, polar_transform -from quantem.tomography.utils import gaussian_filter_1d, gaussian_kernel_1d # TODO: subpixel origin finding (auto_origin_id currently uses integer pixel search) # TODO: elliptical distortion correction in origin finding diff --git a/src/quantem/tomography/utils.py b/src/quantem/tomography/utils.py index d754efed..2e8e7fd9 100644 --- a/src/quantem/tomography/utils.py +++ b/src/quantem/tomography/utils.py @@ -127,44 +127,8 @@ def get_TV_loss(tensor, factor=1e-3): return tv_loss * factor / (torch.prod(torch.tensor(tensor.shape))) - -# --- Gaussian filters --- - - -def gaussian_kernel_1d(sigma: float, num_sigmas: float = 3.0) -> torch.Tensor: - radius = np.ceil(num_sigmas * sigma) - support = torch.arange(-radius, radius + 1, dtype=torch.float) - kernel = torch.distributions.Normal(loc=0, scale=sigma).log_prob(support).exp_() - # Ensure kernel weights sum to 1, so that image brightness is not altered - return kernel.mul_(1 / kernel.sum()) - - -def gaussian_filter_2d( - img: torch.Tensor, sigma: float, kernel_1d: torch.Tensor -) -> torch.Tensor: # Add kernel_1d as an argument - # kernel_1d = gaussian_kernel_1d(sigma) # Create 1D Gaussian kernel - Moved outside function - padding = len(kernel_1d) // 2 # Ensure that image size does not change - img = img.unsqueeze(0).unsqueeze_(0) # Make copy, make 4D for ``conv2d()`` - # Convolve along columns and rows - img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, -1, 1), padding=(padding, 0)) - img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) - return img.squeeze_(0).squeeze_(0) # Make 2D again - - -def gaussian_filter_1d( - arr: torch.Tensor, kernel_1d: torch.Tensor -) -> torch.Tensor: # Replicate-padded torch alternative to ``scipy.ndimage.gaussian_filter1d`` - padding = len(kernel_1d) // 2 # Ensure that signal size does not change - arr = arr.unsqueeze(0).unsqueeze_(0) # Make copy, make 3D for ``conv1d()`` - # Replicate edge values so the output has no zero-padded ringing at boundaries - arr = torch.nn.functional.pad(arr, (padding, padding), mode="replicate") - arr = torch.nn.functional.conv1d(arr, weight=kernel_1d.view(1, 1, -1)) - return arr.squeeze_(0).squeeze_(0) # Make 1D again - - -def gaussian_filter_2d_stack(stack: torch.Tensor, kernel_1d: torch.Tensor) -> torch.Tensor: - """ - Apply 2D Gaussian blur to each slice stack[:, i, :] in a vectorized way. + Encourages piecewise smoothness by penalizing differences between + adjacent elements. Args: x: Input tensor of shape (N, C, L) or (N, L) or (L,) From 7f8843dbfca64a6dab65aaffc0407f5b8c7168c9 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Tue, 19 May 2026 16:36:28 -0700 Subject: [PATCH 134/140] Autocorrelation dscan alignment added --- src/quantem/diffraction/__init__.py | 6 ++++++ src/quantem/diffraction/maped.py | 23 +++++++++++++++++------ 2 files changed, 23 insertions(+), 6 deletions(-) diff --git a/src/quantem/diffraction/__init__.py b/src/quantem/diffraction/__init__.py index 2e723c74..dc8e98b5 100644 --- a/src/quantem/diffraction/__init__.py +++ b/src/quantem/diffraction/__init__.py @@ -1 +1,7 @@ from quantem.diffraction.polar import PairDistributionFunction as PairDistributionFunction +from quantem.diffraction.strain_autocorrelation import ( + StrainMapAutocorrelation as StrainMapAutocorrelation, +) + +from quantem.diffraction.maped import MAPED as MAPED +from quantem.diffraction.maped import MAPEDTorch as MAPEDTorch diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 32c340bc..1b44ffda 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1164,9 +1164,7 @@ def diffraction_align( phase_ramp = torch.exp(-2j * torch.pi * (kr * shift_rc[0] + kc * shift_rc[1])) G_shift = G * phase_ramp - self.diffraction_shifts[ind, :] = torch.tensor( - shift_rc, device=self.device, dtype=torch.float32 - ) + self.diffraction_shifts[ind, :] = shift_rc.clone() G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) @@ -2147,6 +2145,7 @@ def dscan_correct( for iteration in range(iterations): G_ref = torch.fft.fft2(shifted_dps.mean(dim=(0, 1)) * w) + if method == "cross_correlation": for h_rs in tqdm(range(H_rs), desc=f"Iteration {iteration + 1}/{iterations}"): for w_rs in range(W_rs): @@ -2164,10 +2163,22 @@ def dscan_correct( shifted_dps[h_rs, w_rs, :, :] = torch.fft.ifft2(G_shift).real G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) - if method == "autocorrelation": - pass + if method == "autocorrelation": + for h_rs in tqdm(range(H_rs), desc=f"Iteration {iteration + 1}/{iterations}"): + for w_rs in range(W_rs): + dp = shifted_dps[h_rs, w_rs] + G = torch.fft.fft2(w * dp) + + G_flipped = torch.conj(G) + + shift = cross_correlation_shift_torch( + G, G_flipped, upsample_factor=upsample_factor, fft_input=True + ) + shift = shift / 2.0 # peak is at 2x the true offset + + diffraction_shifts[h_rs, w_rs] = shift - G_ref_final = G_ref.clone() + G_ref_final = G_ref.clone() if fit_shifts: diffraction_shifts_1, _ = fit_surface_lstsq(diffraction_shifts[:, :, 0], mode=mode) From b3d76a854ede5ff7e690a20c91d01420d9f8e941 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Sun, 24 May 2026 21:18:01 -0700 Subject: [PATCH 135/140] Add fast vectorized autocorrelation dscan alignment. --- src/quantem/diffraction/maped.py | 321 +++++++++++++++++++++++++++---- 1 file changed, 286 insertions(+), 35 deletions(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 1b44ffda..aa2dfe83 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1,5 +1,6 @@ from __future__ import annotations +import math import warnings from typing import Any, Sequence @@ -15,10 +16,7 @@ from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize -from quantem.core.utils.imaging_utils import ( - cross_correlation_shift_torch, - weighted_cross_correlation_shift, -) +from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift from quantem.core.visualization import show_2d @@ -1043,15 +1041,17 @@ def dscan_align( self, iterations: int, upsample_factor: int = 100, + method: str = "autocorrelation", plot_aligned: bool = True, edge_blend: float = 2.0, fit_shifts: bool = True, mode: str = "linear", ): for i, dataset in enumerate(self.datasets): - _, aligned_dataset, _ = dscan_correct( + _, aligned_dataset = dscan_correct( dataset, iterations, + method=method, upsample_factor=upsample_factor, plot_aligned=plot_aligned, edge_blend=edge_blend, @@ -2039,6 +2039,250 @@ def shift_images_torch( return out +def cross_correlation_shift_torch( + im_ref: torch.Tensor, + im: torch.Tensor, + upsample_factor: int = 2, + fft_input: bool = False, +) -> torch.Tensor: + """ + Align two real images using Fourier cross-correlation and DFT upsampling. + + Supports a single image pair with shape (H, W) or a batch of image pairs with + shape (N, H, W). When batched, returns a tensor of shape (N, 2). + """ + if im_ref.shape != im.shape: + raise ValueError("im_ref and im must have the same shape") + + if im_ref.ndim == 2: + if fft_input: + G1 = im_ref + G2 = im + else: + G1 = torch.fft.fft2(im_ref) + G2 = torch.fft.fft2(im) + + xy_shift = align_images_fourier_torch(G1, G2, upsample_factor) + M, N = im_ref.shape + dx = ((xy_shift[0] + M / 2) % M) - M / 2 + dy = ((xy_shift[1] + N / 2) % N) - N / 2 + return torch.tensor([dx, dy], device=G1.device) + + if im_ref.ndim == 3: + if fft_input: + G1 = im_ref + G2 = im + else: + G1 = torch.fft.fft2(im_ref, dim=(-2, -1)) + G2 = torch.fft.fft2(im, dim=(-2, -1)) + + xy_shift = align_images_fourier_torch_batched(G1, G2, upsample_factor) + M, N = im_ref.shape[-2:] + dx = ((xy_shift[..., 0] + M / 2) % M) - M / 2 + dy = ((xy_shift[..., 1] + N / 2) % N) - N / 2 + return torch.stack([dx, dy], dim=-1) + + raise ValueError("im_ref and im must be 2D or 3D tensors") + + +def align_images_fourier_torch( + G1: torch.Tensor, + G2: torch.Tensor, + upsample_factor: int, +) -> torch.Tensor: + """ + Alignment using DFT upsampling of cross correlation. + G1, G2: torch tensors representing FTs of images (complex) + Returns: xy_shift (tensor length 2) + """ + device = G1.device + cc = G1 * G2.conj() + cc_real = torch.fft.ifft2(cc).real + + flat_idx = torch.argmax(cc_real) + x0 = (flat_idx // cc_real.shape[1]).to(torch.long).item() + y0 = (flat_idx % cc_real.shape[1]).to(torch.long).item() + + M, N = cc_real.shape + x_inds = [((x0 + dx) % M) for dx in (-1, 0, 1)] + y_inds = [((y0 + dy) % N) for dy in (-1, 0, 1)] + + vx = cc_real[x_inds, y0] + vy = cc_real[x0, y_inds] + + denom_x = 4.0 * vx[1] - 2.0 * vx[2] - 2.0 * vx[0] + denom_y = 4.0 * vy[1] - 2.0 * vy[2] - 2.0 * vy[0] + dx = (vx[2] - vx[0]) / denom_x if denom_x != 0 else torch.tensor(0.0, device=device) + dy = (vy[2] - vy[0]) / denom_y if denom_y != 0 else torch.tensor(0.0, device=device) + + x0 = torch.round((x0 + dx) * 2.0) / 2.0 + y0 = torch.round((y0 + dy) * 2.0) / 2.0 + + xy_shift = torch.tensor([x0, y0], device=device) + + if upsample_factor > 2: + xy_shift = upsampled_correlation_torch(cc, upsample_factor, xy_shift) + + return xy_shift + + +def align_images_fourier_torch_batched( + G1: torch.Tensor, + G2: torch.Tensor, + upsample_factor: int, +) -> torch.Tensor: + """ + Batched version of align_images_fourier_torch. + + G1 and G2 must have shape (N, H, W), where N is the batch size. + Returns a tensor of shape (N, 2) with unwrapped peak locations. + """ + if G1.shape != G2.shape: + raise ValueError("G1 and G2 must have the same shape") + if G1.ndim != 3: + raise ValueError("G1 and G2 must have shape (N, H, W)") + + device = G1.device + cc = G1 * G2.conj() + cc_real = torch.fft.ifft2(cc, dim=(-2, -1)).real + + batch, M, N = cc_real.shape + flat_idx = torch.argmax(cc_real.reshape(batch, -1), dim=1) + x0 = flat_idx // N + y0 = flat_idx % N + + offsets = torch.tensor([-1, 0, 1], device=device, dtype=torch.long) + x_inds = (x0[:, None] + offsets[None, :]) % M + y_inds = (y0[:, None] + offsets[None, :]) % N + + batch_inds = torch.arange(batch, device=device)[:, None] + vx = cc_real[batch_inds, x_inds, y0[:, None].expand(-1, 3)] + vy = cc_real[batch_inds, x0[:, None].expand(-1, 3), y_inds] + + denom_x = 4.0 * vx[:, 1] - 2.0 * vx[:, 2] - 2.0 * vx[:, 0] + denom_y = 4.0 * vy[:, 1] - 2.0 * vy[:, 2] - 2.0 * vy[:, 0] + dx = torch.where(denom_x != 0, (vx[:, 2] - vx[:, 0]) / denom_x, torch.zeros_like(denom_x)) + dy = torch.where(denom_y != 0, (vy[:, 2] - vy[:, 0]) / denom_y, torch.zeros_like(denom_y)) + + x0 = torch.round((x0.to(cc_real.dtype) + dx) * 2.0) / 2.0 + y0 = torch.round((y0.to(cc_real.dtype) + dy) * 2.0) / 2.0 + xy_shift = torch.stack([x0, y0], dim=-1) + + if upsample_factor > 2: + xy_shift = upsampled_correlation_torch(cc, upsample_factor, xy_shift) + + return xy_shift + + +def upsampled_correlation_torch( + imageCorr: torch.Tensor, + upsampleFactor: int, + xyShift: torch.Tensor, +) -> torch.Tensor: + """ + Refine the correlation peak of imageCorr around xyShift by DFT upsampling. + + Supports a single correlation image or a batch of them. + """ + assert upsampleFactor > 2 + + squeeze_output = imageCorr.ndim == 2 + if squeeze_output: + imageCorr = imageCorr.unsqueeze(0) + if xyShift.ndim == 1: + xyShift = xyShift.unsqueeze(0) + + if imageCorr.ndim != 3 or xyShift.ndim != 2: + raise ValueError("imageCorr must have shape (H, W) or (N, H, W), and xyShift must match") + if imageCorr.shape[0] != xyShift.shape[0]: + raise ValueError("imageCorr and xyShift batch dimensions must match") + + xyShift = torch.round(xyShift * float(upsampleFactor)) / float(upsampleFactor) + globalShift = float(math.floor(math.ceil(upsampleFactor * 1.5) / 2.0)) + upsampleCenter = globalShift - (upsampleFactor * xyShift) + + conj_input = imageCorr.conj() + im_up = dftUpsample_torch(conj_input, upsampleFactor, upsampleCenter) + imageCorrUpsample = im_up.conj() + + batch, _, out_w = imageCorrUpsample.real.shape + flat_idx = torch.argmax(imageCorrUpsample.real.reshape(batch, -1), dim=1) + r = flat_idx // out_w + c = flat_idx % out_w + + padded = F.pad(imageCorrUpsample.real, (1, 1, 1, 1), mode="circular") + batch_inds = torch.arange(batch, device=imageCorr.device) + + center = padded[batch_inds, r + 1, c + 1] + top = padded[batch_inds, r, c + 1] + bottom = padded[batch_inds, r + 2, c + 1] + left = padded[batch_inds, r + 1, c] + right = padded[batch_inds, r + 1, c + 2] + + denom_x = 4.0 * center - 2.0 * bottom - 2.0 * top + denom_y = 4.0 * center - 2.0 * right - 2.0 * left + dx = torch.where(denom_x != 0, (bottom - top) / denom_x, torch.zeros_like(denom_x)) + dy = torch.where(denom_y != 0, (right - left) / denom_y, torch.zeros_like(denom_y)) + + xySubShift = torch.stack([r, c], dim=-1).to(dtype=xyShift.dtype) - globalShift + xyShift = xyShift + (xySubShift + torch.stack([dx, dy], dim=-1)) / float(upsampleFactor) + + return xyShift[0] if squeeze_output else xyShift + + +def dftUpsample_torch( + imageCorr: torch.Tensor, + upsampleFactor: int, + xyShift: torch.Tensor, +) -> torch.Tensor: + """ + Matrix-multiply DFT upsampling for a single correlation image or a batch. + """ + squeeze_output = imageCorr.ndim == 2 + if squeeze_output: + imageCorr = imageCorr.unsqueeze(0) + if xyShift.ndim == 1: + xyShift = xyShift.unsqueeze(0) + + if imageCorr.ndim != 3 or xyShift.ndim != 2: + raise ValueError("imageCorr must have shape (M, N) or (B, M, N), and xyShift must match") + if imageCorr.shape[0] != xyShift.shape[0]: + raise ValueError("imageCorr and xyShift batch dimensions must match") + + device = imageCorr.device + _, M, N = imageCorr.shape + pixelRadius = 1.5 + numRow = int(math.ceil(pixelRadius * upsampleFactor)) + numCol = numRow + + col_freq = torch.fft.ifftshift(torch.arange(N, device=device)) - math.floor(N / 2) + row_freq = torch.fft.ifftshift(torch.arange(M, device=device)) - math.floor(M / 2) + + col_coords = ( + torch.arange(numCol, device=device, dtype=torch.get_default_dtype())[None, :] + - (xyShift[:, 1:2]) + ) + row_coords = ( + torch.arange(numRow, device=device, dtype=torch.get_default_dtype())[None, :] + - (xyShift[:, 0:1]) + ) + + factor_col = -2j * math.pi / (N * float(upsampleFactor)) + colKern = torch.exp(factor_col * (col_freq[None, :, None] * col_coords[:, None, :])).to( + imageCorr.dtype + ) + + factor_row = -2j * math.pi / (M * float(upsampleFactor)) + rowKern = torch.exp(factor_row * (row_coords[:, :, None] * row_freq[None, None, :])).to( + imageCorr.dtype + ) + + imageUpsample = torch.matmul(torch.matmul(rowKern, imageCorr), colKern) + + result = imageUpsample.real + return result[0] if squeeze_output else result + + def fit_surface_lstsq(img, mode="linear"): """ Fits an image with a linear or quadratic function @@ -2085,12 +2329,12 @@ def dscan_correct( plot_aligned: bool = True, edge_blend: float = 2.0, device="cpu", - method="cross_correlation", + method="autocorrelation", fit_shifts=True, mode="linear", ): """ - Align diffraction patterns using cross-correlation. + Align diffraction patterns using autocorrelation. Parameters ---------- @@ -2163,22 +2407,23 @@ def dscan_correct( shifted_dps[h_rs, w_rs, :, :] = torch.fft.ifft2(G_shift).real G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) - if method == "autocorrelation": - for h_rs in tqdm(range(H_rs), desc=f"Iteration {iteration + 1}/{iterations}"): - for w_rs in range(W_rs): - dp = shifted_dps[h_rs, w_rs] - G = torch.fft.fft2(w * dp) - - G_flipped = torch.conj(G) - - shift = cross_correlation_shift_torch( - G, G_flipped, upsample_factor=upsample_factor, fft_input=True + if method == "autocorrelation": + # Vectorize over the scan grid by flattening (H_rs, W_rs) into a batch dimension. + dp_batch = (w * shifted_dps).reshape(H_rs * W_rs, H_dp, W_dp) + G = torch.fft.fft2(dp_batch, dim=(-2, -1)) + G_flipped = torch.conj(G) + + shifts = ( + -cross_correlation_shift_torch( + G, + G_flipped, + upsample_factor=upsample_factor, + fft_input=True, ) - shift = shift / 2.0 # peak is at 2x the true offset - - diffraction_shifts[h_rs, w_rs] = shift + / 2.0 + ) - G_ref_final = G_ref.clone() + diffraction_shifts[:, :, :] = shifts.reshape(H_rs, W_rs, 2) if fit_shifts: diffraction_shifts_1, _ = fit_surface_lstsq(diffraction_shifts[:, :, 0], mode=mode) @@ -2186,17 +2431,23 @@ def dscan_correct( diffraction_shifts_old = diffraction_shifts.clone() diffraction_shifts = torch.stack((diffraction_shifts_1, diffraction_shifts_2), dim=2) - # Recompute fitted shifts - for h_rs in tqdm(range(H_rs), desc="Applying fitted shifts"): - for w_rs in range(W_rs): - dp = shifted_dps[h_rs, w_rs] # <-- Also read from shifted_dps here - G = torch.fft.fft2(w * dp) - shift = diffraction_shifts[h_rs, w_rs] - - phase_ramp = torch.exp(-1j * torch.pi * (kr * shift[0] + kc * shift[1])) - G_shift = G * phase_ramp - - shifted_dps[h_rs, w_rs, :, :] = torch.fft.ifft2(G_shift).real + # Recompute fitted shifts in one batched pass over all scan positions. + dp_batch = (w * shifted_dps).reshape(H_rs * W_rs, H_dp, W_dp) + G_batch = torch.fft.fft2(dp_batch, dim=(-2, -1)) + + shifts_batch = diffraction_shifts.reshape(H_rs * W_rs, 2) + phase_ramp = torch.exp( + -1j + * torch.pi + * ( + kr.unsqueeze(0) * shifts_batch[:, 0][:, None, None] + + kc.unsqueeze(0) * shifts_batch[:, 1][:, None, None] + ) + ) + G_shift = G_batch * phase_ramp + shifted_dps[:, :, :, :] = torch.fft.ifft2(G_shift, dim=(-2, -1)).real.reshape( + H_rs, W_rs, H_dp, W_dp + ) if plot_aligned: if fit_shifts: @@ -2218,8 +2469,8 @@ def dscan_correct( ["Shifts y", "Fit y", "Residual y"], ], cmap="RdBu_r", - vmax=3, - vmin=-3, + # vmax=3, + # vmin=-3, ) dp_mean_before = dataset.mean(dim=(0, 1)) @@ -2232,4 +2483,4 @@ def dscan_correct( vmax=0.75, ) - return diffraction_shifts, shifted_dps, G_ref_final + return diffraction_shifts, shifted_dps From 140c56300dbdaf26aa81b14b921e620b107c28e6 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Mon, 1 Jun 2026 10:24:17 -0700 Subject: [PATCH 136/140] MAPED direct fitting descan correction implemented. --- src/quantem/core/io/__init__.py | 15 ++- src/quantem/core/io/file_readers.py | 94 ++++++++++++++- src/quantem/diffraction/maped.py | 174 +++++++++++++++++++++------- 3 files changed, 232 insertions(+), 51 deletions(-) diff --git a/src/quantem/core/io/__init__.py b/src/quantem/core/io/__init__.py index 2780eae4..6b1085d9 100644 --- a/src/quantem/core/io/__init__.py +++ b/src/quantem/core/io/__init__.py @@ -1,8 +1,13 @@ -from quantem.core.io.file_readers import read_2d as read_2d -from quantem.core.io.file_readers import read_4dstem as read_4dstem -from quantem.core.io.file_readers import ( - read_emdfile_to_4dstem as read_emdfile_to_4dstem, -) from quantem.core.io.serialize import AutoSerialize as AutoSerialize from quantem.core.io.serialize import load as load from quantem.core.io.serialize import print_file as print_file + +_LAZY = {"read_2d", "read_4dstem", "read_emdfile_to_4dstem"} + + +def __getattr__(name: str): + if name in _LAZY: + from quantem.core.io import file_readers + + return getattr(file_readers, name) + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index 5267c0fc..bcf6585a 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -5,10 +5,10 @@ import h5py -from quantem.core.datastructures import Dataset as Dataset -from quantem.core.datastructures import Dataset2d as Dataset2d -from quantem.core.datastructures import Dataset3d as Dataset3d -from quantem.core.datastructures import Dataset4dstem as Dataset4dstem +from quantem.core.datastructures.dataset import Dataset as Dataset +from quantem.core.datastructures.dataset2d import Dataset2d as Dataset2d +from quantem.core.datastructures.dataset3d import Dataset3d as Dataset3d +from quantem.core.datastructures.dataset4dstem import Dataset4dstem as Dataset4dstem def read_4dstem( @@ -37,6 +37,92 @@ def read_4dstem( -------- Dataset4dstem """ + + def _reshape_3d_to_4d( + imported_data: dict, + *, + dataset_index_local: int | None, + scan_length_local: int, + scan_axis_local: int, + transpose_scan_axes_local: bool, + ) -> dict: + data = imported_data["data"] + if data.ndim != 3: + raise ValueError( + f"Expected 3D data to reshape, got ndim={data.ndim} with shape {data.shape}" + ) + + if scan_axis_local not in (0, 1): + raise ValueError(f"scan_axis must be 0 or 1, got {scan_axis_local}") + + # Move scan axis to front so it becomes the frame axis + if scan_axis_local != 0: + data = np.moveaxis(data, scan_axis_local, 0) + + n_frames, ny, nx = data.shape + + if scan_length_local <= 0: + raise ValueError(f"scan_length must be positive, got {scan_length_local}") + if n_frames % scan_length_local != 0: + raise ValueError( + f"scan_length={scan_length_local} is not compatible with n_frames={n_frames}; " + f"n_frames % scan_length = {n_frames % scan_length_local}" + ) + + scan_y = n_frames // scan_length_local + scan_x = scan_length_local + + data_4d = data.reshape(scan_y, scan_x, ny, nx) + + if transpose_scan_axes_local: + data_4d = np.transpose(data_4d, (1, 0, 2, 3)) + scan_y, scan_x = scan_x, scan_y + + old_axes = imported_data.get("axes", None) + if old_axes is None or len(old_axes) != 3: + raise ValueError( + f"Expected 3 axes for 3D data when reshaping to 4D; got axes={old_axes}" + ) + + ax_scan_y = { + "scale": 1.0, + "offset": 0.0, + "units": "pixels", + "name": "scan_y", + } + ax_scan_x = { + "scale": 1.0, + "offset": 0.0, + "units": "pixels", + "name": "scan_x", + } + + ax_qy = dict(old_axes[1]) + ax_qx = dict(old_axes[2]) + + imported_data_4d = imported_data.copy() + imported_data_4d["data"] = data_4d + imported_data_4d["axes"] = [ax_scan_y, ax_scan_x, ax_qy, ax_qx] + + original_shape = imported_data["data"].shape + new_shape = data_4d.shape + if dataset_index_local is not None: + print( + f"Using 3D dataset {dataset_index_local} with shape {original_shape} " + f"interpreted as 4D with shape={new_shape} " + f"(scan_axis={scan_axis_local}, scan_length={scan_length_local}, " + f"transpose_scan_axes={transpose_scan_axes_local})." + ) + else: + print( + f"Using 3D dataset with shape {original_shape} " + f"interpreted as 4D with shape={new_shape} " + f"(scan_axis={scan_axis_local}, scan_length={scan_length_local}, " + f"transpose_scan_axes={transpose_scan_axes_local})." + ) + + return imported_data_4d + if file_type is None: file_type = Path(file_path).suffix.lower().lstrip(".") diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index aa2dfe83..93409ba2 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -14,6 +14,7 @@ from scipy.signal.windows import tukey from tqdm import tqdm +from quantem.core import config from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift @@ -827,8 +828,8 @@ class MAPEDTorch(AutoSerialize): def __init__( self, datasets: list[torch.Tensor], - device: str | Any, dtype: str | Any, + device: str | int | None = None, _token: object | None = None, ): if _token is not self._token: @@ -839,6 +840,19 @@ def __init__( self.device = device self.dtype = dtype + @property + def device(self) -> str: + if hasattr(self, "_device"): + return self._device + return config.get_device() + + @device.setter + def device(self, device: str | int | None) -> None: + if device is not None: + dev, _id = config.validate_device(device) + self._device = dev + # if None, leave unset so the property falls back to config.get_device() + @classmethod def from_datasets(cls, datasets: Sequence[torch.Tensor]) -> MAPED: """ @@ -864,13 +878,13 @@ def from_datasets(cls, datasets: Sequence[torch.Tensor]) -> MAPED: ) ds_list.append(d) - dtypes = np.array([dataset.dtype for dataset in datasets]) - devices = np.array([dataset.device for dataset in datasets]) + dtypes = [dataset.dtype for dataset in datasets] + devices = [str(dataset.device) for dataset in datasets] # check that all datasets have the same dtype and device - if not np.all(dtypes == dtypes[0]): + if len(set(str(d) for d in dtypes)) > 1: raise TypeError("All datasets need to have the same type") - if not np.all(devices == devices[0]): + if len(set(devices)) > 1: raise TypeError("All datasets need to have the same device") if not ds_list: @@ -1042,10 +1056,11 @@ def dscan_align( iterations: int, upsample_factor: int = 100, method: str = "autocorrelation", - plot_aligned: bool = True, + plot: bool = True, edge_blend: float = 2.0, fit_shifts: bool = True, mode: str = "linear", + batch_size: int | None = None, ): for i, dataset in enumerate(self.datasets): _, aligned_dataset = dscan_correct( @@ -1053,11 +1068,12 @@ def dscan_align( iterations, method=method, upsample_factor=upsample_factor, - plot_aligned=plot_aligned, + plot=plot, edge_blend=edge_blend, device=self.device, fit_shifts=fit_shifts, mode=mode, + batch_size=batch_size, ) self.datasets[i] = aligned_dataset @@ -1294,7 +1310,7 @@ def real_space_align( base_pad = torch.zeros((n, Hp, Wp), dtype=torch.float32, device=self.device) for i in range(n): - im0 = self.im_bf[i] + im0 = self.im_bf[i].float() if edge_filter: pad_symmetric = wx.shape[-1] // 2 @@ -1945,7 +1961,7 @@ def shift_images_torch( if not blend: # simple shift per-image without padding/blending — keep original behavior - imgs = images.unsqueeze(1) + imgs = images.float().unsqueeze(1) grid_y, grid_x = torch.meshgrid( torch.linspace(-1, 1, H, device=images.device), torch.linspace(-1, 1, W, device=images.device), @@ -2326,12 +2342,13 @@ def dscan_correct( dataset, iterations, upsample_factor: int = 100, - plot_aligned: bool = True, + plot: bool = True, edge_blend: float = 2.0, device="cpu", method="autocorrelation", fit_shifts=True, mode="linear", + batch_size: int | None = None, ): """ Align diffraction patterns using autocorrelation. @@ -2344,7 +2361,7 @@ def dscan_correct( Number of refinement iterations upsample_factor : int Upsampling factor for sub-pixel accuracy - plot_aligned : bool + plot : bool Whether to plot results after each iteration edge_blend : float Edge blending parameter for Tukey window @@ -2365,6 +2382,9 @@ def dscan_correct( reference (torch.Tensor). """ H_rs, W_rs, H_dp, W_dp = dataset.shape + n_pos = H_rs * W_rs + if batch_size is None: + batch_size = max(1, min(n_pos, 256)) w = ( tukey_torch( @@ -2408,22 +2428,88 @@ def dscan_correct( G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) if method == "autocorrelation": - # Vectorize over the scan grid by flattening (H_rs, W_rs) into a batch dimension. - dp_batch = (w * shifted_dps).reshape(H_rs * W_rs, H_dp, W_dp) - G = torch.fft.fft2(dp_batch, dim=(-2, -1)) - G_flipped = torch.conj(G) - - shifts = ( - -cross_correlation_shift_torch( - G, - G_flipped, - upsample_factor=upsample_factor, - fft_input=True, + shifts_flat = torch.zeros((n_pos, 2), device=device, dtype=torch.float32) + shifted_dps_flat = shifted_dps.reshape(n_pos, H_dp, W_dp) + + for batch_start in tqdm( + range(0, n_pos, batch_size), + desc=f"Iteration {iteration + 1}/{iterations} (autocorrelation)", + ): + batch_end = min(batch_start + batch_size, n_pos) + dp_b = w * shifted_dps_flat[batch_start:batch_end] + G_b = torch.fft.fft2(dp_b, dim=(-2, -1)) + G_flipped = torch.conj(G_b) + shifts_flat[batch_start:batch_end] = ( + -cross_correlation_shift_torch( + G_b, + G_flipped, + upsample_factor=upsample_factor, + fft_input=True, + ) + / 2.0 ) - / 2.0 - ) + del dp_b, G_b, G_flipped + torch.cuda.empty_cache() if torch.cuda.is_available() else None + + diffraction_shifts[:, :, :] = shifts_flat.reshape(H_rs, W_rs, 2) + + if method == "direct_fitting": + centers = torch.zeros((n_pos, 2), device=device, dtype=torch.float32) + shifted_dps_flat = shifted_dps.reshape(n_pos, H_dp, W_dp) + + for batch_start in tqdm( + range(0, n_pos, batch_size), + desc=f"Iteration {iteration + 1}/{iterations} (direct fitting)", + ): + batch_end = min(batch_start + batch_size, n_pos) + dp_b = shifted_dps_flat[batch_start:batch_end].float() + B = dp_b.shape[0] + batch_idx = torch.arange(B, device=device) + + # argmax: integer center estimate + flat_idx = torch.argmax(dp_b.reshape(B, -1), dim=1) + row_peak = flat_idx // W_dp + col_peak = flat_idx % W_dp + + # log-parabolic sub-pixel refinement — row direction + row_safe = row_peak.clamp(1, H_dp - 2) + vr_m = dp_b[batch_idx, row_safe - 1, col_peak].clamp(min=1e-6).log() + vr_0 = dp_b[batch_idx, row_safe, col_peak].clamp(min=1e-6).log() + vr_p = dp_b[batch_idx, row_safe + 1, col_peak].clamp(min=1e-6).log() + denom_r = vr_m + vr_p - 2.0 * vr_0 + dr = torch.where( + (denom_r < -1e-6) & (row_peak > 0) & (row_peak < H_dp - 1), + ((vr_m - vr_p) / (2.0 * denom_r)).clamp(-1.0, 1.0), + torch.zeros(B, device=device), + ) + + # log-parabolic sub-pixel refinement — col direction + col_safe = col_peak.clamp(1, W_dp - 2) + vc_m = dp_b[batch_idx, row_peak, col_safe - 1].clamp(min=1e-6).log() + vc_0 = dp_b[batch_idx, row_peak, col_safe].clamp(min=1e-6).log() + vc_p = dp_b[batch_idx, row_peak, col_safe + 1].clamp(min=1e-6).log() + denom_c = vc_m + vc_p - 2.0 * vc_0 + dc = torch.where( + (denom_c < -1e-6) & (col_peak > 0) & (col_peak < W_dp - 1), + ((vc_m - vc_p) / (2.0 * denom_c)).clamp(-1.0, 1.0), + torch.zeros(B, device=device), + ) + + centers[batch_start:batch_end, 0] = row_peak.float() + dr + centers[batch_start:batch_end, 1] = col_peak.float() + dc + + del dp_b, flat_idx, row_peak, col_peak, batch_idx + del vr_m, vr_0, vr_p, vc_m, vc_0, vc_p, dr, dc + torch.cuda.empty_cache() if torch.cuda.is_available() else None + + # fit a plane to centers across the real-space scan grid + centers_2d = centers.reshape(H_rs, W_rs, 2) + centers_fit_r, _ = fit_surface_lstsq(centers_2d[:, :, 0], mode="linear") + centers_fit_c, _ = fit_surface_lstsq(centers_2d[:, :, 1], mode="linear") - diffraction_shifts[:, :, :] = shifts.reshape(H_rs, W_rs, 2) + # shifts = mean_center - fitted_center: moves each DP toward the global mean + diffraction_shifts[:, :, 0] = H_dp / 2 - centers_fit_r + diffraction_shifts[:, :, 1] = W_dp / 2 - centers_fit_c if fit_shifts: diffraction_shifts_1, _ = fit_surface_lstsq(diffraction_shifts[:, :, 0], mode=mode) @@ -2431,25 +2517,29 @@ def dscan_correct( diffraction_shifts_old = diffraction_shifts.clone() diffraction_shifts = torch.stack((diffraction_shifts_1, diffraction_shifts_2), dim=2) - # Recompute fitted shifts in one batched pass over all scan positions. - dp_batch = (w * shifted_dps).reshape(H_rs * W_rs, H_dp, W_dp) - G_batch = torch.fft.fft2(dp_batch, dim=(-2, -1)) - - shifts_batch = diffraction_shifts.reshape(H_rs * W_rs, 2) - phase_ramp = torch.exp( - -1j - * torch.pi - * ( - kr.unsqueeze(0) * shifts_batch[:, 0][:, None, None] - + kc.unsqueeze(0) * shifts_batch[:, 1][:, None, None] + # Apply fitted shifts in batches over all scan positions. + shifted_dps_flat = shifted_dps.reshape(n_pos, H_dp, W_dp) + shifts_flat = diffraction_shifts.reshape(n_pos, 2) + + for batch_start in range(0, n_pos, batch_size): + batch_end = min(batch_start + batch_size, n_pos) + G_b = torch.fft.fft2(w * shifted_dps_flat[batch_start:batch_end], dim=(-2, -1)) + s_b = shifts_flat[batch_start:batch_end] + phase_ramp = torch.exp( + -1j + * torch.pi + * ( + kr.unsqueeze(0) * s_b[:, 0][:, None, None] + + kc.unsqueeze(0) * s_b[:, 1][:, None, None] + ) ) - ) - G_shift = G_batch * phase_ramp - shifted_dps[:, :, :, :] = torch.fft.ifft2(G_shift, dim=(-2, -1)).real.reshape( - H_rs, W_rs, H_dp, W_dp - ) + shifted_dps_flat[batch_start:batch_end] = torch.fft.ifft2( + G_b * phase_ramp, dim=(-2, -1) + ).real + del G_b, s_b, phase_ramp + torch.cuda.empty_cache() if torch.cuda.is_available() else None - if plot_aligned: + if plot: if fit_shifts: show_2d( [ From 05f6b011f62d6747c55ff22287bbb5f7b1ec7373 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Tue, 2 Jun 2026 11:49:10 -0700 Subject: [PATCH 137/140] Revert "MAPED direct fitting descan correction implemented." This reverts commit 140c56300dbdaf26aa81b14b921e620b107c28e6. --- src/quantem/core/io/__init__.py | 15 +-- src/quantem/core/io/file_readers.py | 94 +-------------- src/quantem/diffraction/maped.py | 174 +++++++--------------------- 3 files changed, 51 insertions(+), 232 deletions(-) diff --git a/src/quantem/core/io/__init__.py b/src/quantem/core/io/__init__.py index 6b1085d9..2780eae4 100644 --- a/src/quantem/core/io/__init__.py +++ b/src/quantem/core/io/__init__.py @@ -1,13 +1,8 @@ +from quantem.core.io.file_readers import read_2d as read_2d +from quantem.core.io.file_readers import read_4dstem as read_4dstem +from quantem.core.io.file_readers import ( + read_emdfile_to_4dstem as read_emdfile_to_4dstem, +) from quantem.core.io.serialize import AutoSerialize as AutoSerialize from quantem.core.io.serialize import load as load from quantem.core.io.serialize import print_file as print_file - -_LAZY = {"read_2d", "read_4dstem", "read_emdfile_to_4dstem"} - - -def __getattr__(name: str): - if name in _LAZY: - from quantem.core.io import file_readers - - return getattr(file_readers, name) - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index bcf6585a..5267c0fc 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -5,10 +5,10 @@ import h5py -from quantem.core.datastructures.dataset import Dataset as Dataset -from quantem.core.datastructures.dataset2d import Dataset2d as Dataset2d -from quantem.core.datastructures.dataset3d import Dataset3d as Dataset3d -from quantem.core.datastructures.dataset4dstem import Dataset4dstem as Dataset4dstem +from quantem.core.datastructures import Dataset as Dataset +from quantem.core.datastructures import Dataset2d as Dataset2d +from quantem.core.datastructures import Dataset3d as Dataset3d +from quantem.core.datastructures import Dataset4dstem as Dataset4dstem def read_4dstem( @@ -37,92 +37,6 @@ def read_4dstem( -------- Dataset4dstem """ - - def _reshape_3d_to_4d( - imported_data: dict, - *, - dataset_index_local: int | None, - scan_length_local: int, - scan_axis_local: int, - transpose_scan_axes_local: bool, - ) -> dict: - data = imported_data["data"] - if data.ndim != 3: - raise ValueError( - f"Expected 3D data to reshape, got ndim={data.ndim} with shape {data.shape}" - ) - - if scan_axis_local not in (0, 1): - raise ValueError(f"scan_axis must be 0 or 1, got {scan_axis_local}") - - # Move scan axis to front so it becomes the frame axis - if scan_axis_local != 0: - data = np.moveaxis(data, scan_axis_local, 0) - - n_frames, ny, nx = data.shape - - if scan_length_local <= 0: - raise ValueError(f"scan_length must be positive, got {scan_length_local}") - if n_frames % scan_length_local != 0: - raise ValueError( - f"scan_length={scan_length_local} is not compatible with n_frames={n_frames}; " - f"n_frames % scan_length = {n_frames % scan_length_local}" - ) - - scan_y = n_frames // scan_length_local - scan_x = scan_length_local - - data_4d = data.reshape(scan_y, scan_x, ny, nx) - - if transpose_scan_axes_local: - data_4d = np.transpose(data_4d, (1, 0, 2, 3)) - scan_y, scan_x = scan_x, scan_y - - old_axes = imported_data.get("axes", None) - if old_axes is None or len(old_axes) != 3: - raise ValueError( - f"Expected 3 axes for 3D data when reshaping to 4D; got axes={old_axes}" - ) - - ax_scan_y = { - "scale": 1.0, - "offset": 0.0, - "units": "pixels", - "name": "scan_y", - } - ax_scan_x = { - "scale": 1.0, - "offset": 0.0, - "units": "pixels", - "name": "scan_x", - } - - ax_qy = dict(old_axes[1]) - ax_qx = dict(old_axes[2]) - - imported_data_4d = imported_data.copy() - imported_data_4d["data"] = data_4d - imported_data_4d["axes"] = [ax_scan_y, ax_scan_x, ax_qy, ax_qx] - - original_shape = imported_data["data"].shape - new_shape = data_4d.shape - if dataset_index_local is not None: - print( - f"Using 3D dataset {dataset_index_local} with shape {original_shape} " - f"interpreted as 4D with shape={new_shape} " - f"(scan_axis={scan_axis_local}, scan_length={scan_length_local}, " - f"transpose_scan_axes={transpose_scan_axes_local})." - ) - else: - print( - f"Using 3D dataset with shape {original_shape} " - f"interpreted as 4D with shape={new_shape} " - f"(scan_axis={scan_axis_local}, scan_length={scan_length_local}, " - f"transpose_scan_axes={transpose_scan_axes_local})." - ) - - return imported_data_4d - if file_type is None: file_type = Path(file_path).suffix.lower().lstrip(".") diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 93409ba2..aa2dfe83 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -14,7 +14,6 @@ from scipy.signal.windows import tukey from tqdm import tqdm -from quantem.core import config from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize from quantem.core.utils.imaging_utils import weighted_cross_correlation_shift @@ -828,8 +827,8 @@ class MAPEDTorch(AutoSerialize): def __init__( self, datasets: list[torch.Tensor], + device: str | Any, dtype: str | Any, - device: str | int | None = None, _token: object | None = None, ): if _token is not self._token: @@ -840,19 +839,6 @@ def __init__( self.device = device self.dtype = dtype - @property - def device(self) -> str: - if hasattr(self, "_device"): - return self._device - return config.get_device() - - @device.setter - def device(self, device: str | int | None) -> None: - if device is not None: - dev, _id = config.validate_device(device) - self._device = dev - # if None, leave unset so the property falls back to config.get_device() - @classmethod def from_datasets(cls, datasets: Sequence[torch.Tensor]) -> MAPED: """ @@ -878,13 +864,13 @@ def from_datasets(cls, datasets: Sequence[torch.Tensor]) -> MAPED: ) ds_list.append(d) - dtypes = [dataset.dtype for dataset in datasets] - devices = [str(dataset.device) for dataset in datasets] + dtypes = np.array([dataset.dtype for dataset in datasets]) + devices = np.array([dataset.device for dataset in datasets]) # check that all datasets have the same dtype and device - if len(set(str(d) for d in dtypes)) > 1: + if not np.all(dtypes == dtypes[0]): raise TypeError("All datasets need to have the same type") - if len(set(devices)) > 1: + if not np.all(devices == devices[0]): raise TypeError("All datasets need to have the same device") if not ds_list: @@ -1056,11 +1042,10 @@ def dscan_align( iterations: int, upsample_factor: int = 100, method: str = "autocorrelation", - plot: bool = True, + plot_aligned: bool = True, edge_blend: float = 2.0, fit_shifts: bool = True, mode: str = "linear", - batch_size: int | None = None, ): for i, dataset in enumerate(self.datasets): _, aligned_dataset = dscan_correct( @@ -1068,12 +1053,11 @@ def dscan_align( iterations, method=method, upsample_factor=upsample_factor, - plot=plot, + plot_aligned=plot_aligned, edge_blend=edge_blend, device=self.device, fit_shifts=fit_shifts, mode=mode, - batch_size=batch_size, ) self.datasets[i] = aligned_dataset @@ -1310,7 +1294,7 @@ def real_space_align( base_pad = torch.zeros((n, Hp, Wp), dtype=torch.float32, device=self.device) for i in range(n): - im0 = self.im_bf[i].float() + im0 = self.im_bf[i] if edge_filter: pad_symmetric = wx.shape[-1] // 2 @@ -1961,7 +1945,7 @@ def shift_images_torch( if not blend: # simple shift per-image without padding/blending — keep original behavior - imgs = images.float().unsqueeze(1) + imgs = images.unsqueeze(1) grid_y, grid_x = torch.meshgrid( torch.linspace(-1, 1, H, device=images.device), torch.linspace(-1, 1, W, device=images.device), @@ -2342,13 +2326,12 @@ def dscan_correct( dataset, iterations, upsample_factor: int = 100, - plot: bool = True, + plot_aligned: bool = True, edge_blend: float = 2.0, device="cpu", method="autocorrelation", fit_shifts=True, mode="linear", - batch_size: int | None = None, ): """ Align diffraction patterns using autocorrelation. @@ -2361,7 +2344,7 @@ def dscan_correct( Number of refinement iterations upsample_factor : int Upsampling factor for sub-pixel accuracy - plot : bool + plot_aligned : bool Whether to plot results after each iteration edge_blend : float Edge blending parameter for Tukey window @@ -2382,9 +2365,6 @@ def dscan_correct( reference (torch.Tensor). """ H_rs, W_rs, H_dp, W_dp = dataset.shape - n_pos = H_rs * W_rs - if batch_size is None: - batch_size = max(1, min(n_pos, 256)) w = ( tukey_torch( @@ -2428,88 +2408,22 @@ def dscan_correct( G_ref = G_ref * (ind / (ind + 1)) + G_shift / (ind + 1) if method == "autocorrelation": - shifts_flat = torch.zeros((n_pos, 2), device=device, dtype=torch.float32) - shifted_dps_flat = shifted_dps.reshape(n_pos, H_dp, W_dp) - - for batch_start in tqdm( - range(0, n_pos, batch_size), - desc=f"Iteration {iteration + 1}/{iterations} (autocorrelation)", - ): - batch_end = min(batch_start + batch_size, n_pos) - dp_b = w * shifted_dps_flat[batch_start:batch_end] - G_b = torch.fft.fft2(dp_b, dim=(-2, -1)) - G_flipped = torch.conj(G_b) - shifts_flat[batch_start:batch_end] = ( - -cross_correlation_shift_torch( - G_b, - G_flipped, - upsample_factor=upsample_factor, - fft_input=True, - ) - / 2.0 - ) - del dp_b, G_b, G_flipped - torch.cuda.empty_cache() if torch.cuda.is_available() else None - - diffraction_shifts[:, :, :] = shifts_flat.reshape(H_rs, W_rs, 2) - - if method == "direct_fitting": - centers = torch.zeros((n_pos, 2), device=device, dtype=torch.float32) - shifted_dps_flat = shifted_dps.reshape(n_pos, H_dp, W_dp) - - for batch_start in tqdm( - range(0, n_pos, batch_size), - desc=f"Iteration {iteration + 1}/{iterations} (direct fitting)", - ): - batch_end = min(batch_start + batch_size, n_pos) - dp_b = shifted_dps_flat[batch_start:batch_end].float() - B = dp_b.shape[0] - batch_idx = torch.arange(B, device=device) - - # argmax: integer center estimate - flat_idx = torch.argmax(dp_b.reshape(B, -1), dim=1) - row_peak = flat_idx // W_dp - col_peak = flat_idx % W_dp - - # log-parabolic sub-pixel refinement — row direction - row_safe = row_peak.clamp(1, H_dp - 2) - vr_m = dp_b[batch_idx, row_safe - 1, col_peak].clamp(min=1e-6).log() - vr_0 = dp_b[batch_idx, row_safe, col_peak].clamp(min=1e-6).log() - vr_p = dp_b[batch_idx, row_safe + 1, col_peak].clamp(min=1e-6).log() - denom_r = vr_m + vr_p - 2.0 * vr_0 - dr = torch.where( - (denom_r < -1e-6) & (row_peak > 0) & (row_peak < H_dp - 1), - ((vr_m - vr_p) / (2.0 * denom_r)).clamp(-1.0, 1.0), - torch.zeros(B, device=device), - ) - - # log-parabolic sub-pixel refinement — col direction - col_safe = col_peak.clamp(1, W_dp - 2) - vc_m = dp_b[batch_idx, row_peak, col_safe - 1].clamp(min=1e-6).log() - vc_0 = dp_b[batch_idx, row_peak, col_safe].clamp(min=1e-6).log() - vc_p = dp_b[batch_idx, row_peak, col_safe + 1].clamp(min=1e-6).log() - denom_c = vc_m + vc_p - 2.0 * vc_0 - dc = torch.where( - (denom_c < -1e-6) & (col_peak > 0) & (col_peak < W_dp - 1), - ((vc_m - vc_p) / (2.0 * denom_c)).clamp(-1.0, 1.0), - torch.zeros(B, device=device), + # Vectorize over the scan grid by flattening (H_rs, W_rs) into a batch dimension. + dp_batch = (w * shifted_dps).reshape(H_rs * W_rs, H_dp, W_dp) + G = torch.fft.fft2(dp_batch, dim=(-2, -1)) + G_flipped = torch.conj(G) + + shifts = ( + -cross_correlation_shift_torch( + G, + G_flipped, + upsample_factor=upsample_factor, + fft_input=True, ) + / 2.0 + ) - centers[batch_start:batch_end, 0] = row_peak.float() + dr - centers[batch_start:batch_end, 1] = col_peak.float() + dc - - del dp_b, flat_idx, row_peak, col_peak, batch_idx - del vr_m, vr_0, vr_p, vc_m, vc_0, vc_p, dr, dc - torch.cuda.empty_cache() if torch.cuda.is_available() else None - - # fit a plane to centers across the real-space scan grid - centers_2d = centers.reshape(H_rs, W_rs, 2) - centers_fit_r, _ = fit_surface_lstsq(centers_2d[:, :, 0], mode="linear") - centers_fit_c, _ = fit_surface_lstsq(centers_2d[:, :, 1], mode="linear") - - # shifts = mean_center - fitted_center: moves each DP toward the global mean - diffraction_shifts[:, :, 0] = H_dp / 2 - centers_fit_r - diffraction_shifts[:, :, 1] = W_dp / 2 - centers_fit_c + diffraction_shifts[:, :, :] = shifts.reshape(H_rs, W_rs, 2) if fit_shifts: diffraction_shifts_1, _ = fit_surface_lstsq(diffraction_shifts[:, :, 0], mode=mode) @@ -2517,29 +2431,25 @@ def dscan_correct( diffraction_shifts_old = diffraction_shifts.clone() diffraction_shifts = torch.stack((diffraction_shifts_1, diffraction_shifts_2), dim=2) - # Apply fitted shifts in batches over all scan positions. - shifted_dps_flat = shifted_dps.reshape(n_pos, H_dp, W_dp) - shifts_flat = diffraction_shifts.reshape(n_pos, 2) - - for batch_start in range(0, n_pos, batch_size): - batch_end = min(batch_start + batch_size, n_pos) - G_b = torch.fft.fft2(w * shifted_dps_flat[batch_start:batch_end], dim=(-2, -1)) - s_b = shifts_flat[batch_start:batch_end] - phase_ramp = torch.exp( - -1j - * torch.pi - * ( - kr.unsqueeze(0) * s_b[:, 0][:, None, None] - + kc.unsqueeze(0) * s_b[:, 1][:, None, None] - ) + # Recompute fitted shifts in one batched pass over all scan positions. + dp_batch = (w * shifted_dps).reshape(H_rs * W_rs, H_dp, W_dp) + G_batch = torch.fft.fft2(dp_batch, dim=(-2, -1)) + + shifts_batch = diffraction_shifts.reshape(H_rs * W_rs, 2) + phase_ramp = torch.exp( + -1j + * torch.pi + * ( + kr.unsqueeze(0) * shifts_batch[:, 0][:, None, None] + + kc.unsqueeze(0) * shifts_batch[:, 1][:, None, None] ) - shifted_dps_flat[batch_start:batch_end] = torch.fft.ifft2( - G_b * phase_ramp, dim=(-2, -1) - ).real - del G_b, s_b, phase_ramp - torch.cuda.empty_cache() if torch.cuda.is_available() else None + ) + G_shift = G_batch * phase_ramp + shifted_dps[:, :, :, :] = torch.fft.ifft2(G_shift, dim=(-2, -1)).real.reshape( + H_rs, W_rs, H_dp, W_dp + ) - if plot: + if plot_aligned: if fit_shifts: show_2d( [ From ff8ea7070357a7d4624d1c6c2f94a0474ad98b94 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Tue, 2 Jun 2026 14:53:15 -0700 Subject: [PATCH 138/140] Updates the output merged dataset to use the from_tensor initialization to stop forced switch to CPU memory --- src/quantem/diffraction/maped.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/diffraction/maped.py b/src/quantem/diffraction/maped.py index 93409ba2..6c623c0f 100644 --- a/src/quantem/diffraction/maped.py +++ b/src/quantem/diffraction/maped.py @@ -1734,7 +1734,7 @@ def merge_datasets( else: merged_out = merged.to(dtype=dtype_out) - dataset_merged = Dataset4dstem.from_array(array=merged_out.cpu().numpy()) + dataset_merged = Dataset4dstem.from_tensor(tensor=merged_out) dataset_merged.im_bf_merged = self.im_bf_merged dataset_merged.dp_mean_merged = self.dp_mean_merged From d725281575e6d82f99fe561c20f69f7de9f4e239 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Fri, 26 Jun 2026 09:25:04 -0700 Subject: [PATCH 139/140] Making uv.lock a valid lockfile. --- uv.lock | 3719 +++++++++++++++++-------------------------------------- 1 file changed, 1109 insertions(+), 2610 deletions(-) diff --git a/uv.lock b/uv.lock index a414c36d..ba90d574 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 1 +revision = 3 requires-python = ">=3.11" resolution-markers = [ "python_full_version >= '3.14'", @@ -17,49 +17,36 @@ members = [ name = "absl-py" version = "2.4.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "alembic" -version = "1.18.4" +version = "1.18.5" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "mako" }, { name = "sqlalchemy" }, { name = "typing-extensions" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/94/13/8b084e0f2efb0275a1d534838844926f798bd766566b1375174e2448cd31/alembic-1.18.4.tar.gz", hash = "sha256:cb6e1fd84b6174ab8dbb2329f86d631ba9559dd78df550b57804d607672cedbc", size = 2056725, upload-time = "2026-02-10T16:00:47.195Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d2/29/6533c317b74f707ea28f8d633734dbda2119bbadfc61b2f3640ba835d0f7/alembic-1.18.4-py3-none-any.whl", hash = "sha256:a5ed4adcf6d8a4cb575f3d759f071b03cd6e5c7618eb796cb52497be25bfe19a", size = 263893, upload-time = "2026-02-10T16:00:49.997Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/02/a6/74c8cadc2882977d80ad756a13857857dbcf9bd405bc80b662eb10651282/alembic-1.17.2.tar.gz", hash = "sha256:bbe9751705c5e0f14877f02d46c53d10885e377e3d90eda810a016f9baa19e8e", size = 1988064 } +sdist = { url = "https://files.pythonhosted.org/packages/1a/cc/ac0bed8e562e7407fe55c3ba85a4dce86e6dbd8730887bd1e406a6c5c18a/alembic-1.18.5.tar.gz", hash = "sha256:1554982221dd17e9a749b53902407578eb305e453f71999e8c7f0a48389fff8e", size = 2060480, upload-time = "2026-06-25T15:20:54.888Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/ba/88/6237e97e3385b57b5f1528647addea5cc03d4d65d5979ab24327d41fb00d/alembic-1.17.2-py3-none-any.whl", hash = "sha256:f483dd1fe93f6c5d49217055e4d15b905b425b6af906746abb35b69c1996c4e6", size = 248554 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/96/78/5fe6dc3a3a5b2f5a2a4faef8bfe336d5fa049a38884ab3172e0098160c01/alembic-1.18.5-py3-none-any.whl", hash = "sha256:06d8ba9d04558022f5395e9317de03d270f3dced49cee01f89fe7a13c26f14bc", size = 264664, upload-time = "2026-06-25T15:20:56.673Z" }, ] [[package]] name = "anyio" -version = "4.13.0" +version = "4.14.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "idna" }, { name = "typing-extensions", marker = "python_full_version < '3.13'" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/19/14/2c5dd9f512b66549ae92767a9c7b330ae88e1932ca57876909410251fe13/anyio-4.13.0.tar.gz", hash = "sha256:334b70e641fd2221c1505b3890c69882fe4a2df910cba14d97019b90b24439dc", size = 231622, upload-time = "2026-03-24T12:59:09.671Z" } +sdist = { url = "https://files.pythonhosted.org/packages/3b/72/5562aabb8dd7181e8e860622a38bea08d17842b99ecd4c91f84ac95251b0/anyio-4.14.1.tar.gz", hash = "sha256:8d648a3544c1a700e3ff78615cd679e4c5c3f149904287e73687b2596963629e", size = 254831, upload-time = "2026-06-24T20:56:06.017Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/da/42/e921fccf5015463e32a3cf6ee7f980a6ed0f395ceeaa45060b61d86486c2/anyio-4.13.0-py3-none-any.whl", hash = "sha256:08b310f9e24a9594186fd75b4f73f4a4152069e3853f1ed8bfbf58369f4ad708", size = 114353, upload-time = "2026-03-24T12:59:08.246Z" }, + { url = "https://files.pythonhosted.org/packages/b0/7b/90df4a0a816d98d6ea26f559d87836d494a2cf1fcf063be67df50a7bcc30/anyio-4.14.1-py3-none-any.whl", hash = "sha256:4e5533c5b8ff0a24f5d7a176cbe6877129cd183893f66b537f8f227d10527d72", size = 124875, upload-time = "2026-06-24T20:56:04.413Z" }, ] [[package]] @@ -74,20 +61,15 @@ dependencies = [ sdist = { url = "https://files.pythonhosted.org/packages/79/31/0491d707c674b34267f55d96d6a7148e55e7b6718a271686232cf295fbe2/anywidget-0.11.0.tar.gz", hash = "sha256:6695fbef9449cf8c27f421b96c5837aa37f909ec1f60cfa33add333e1b70b169", size = 426999, upload-time = "2026-04-27T23:42:09.576Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/8e/c2/8fec8e8e2eb920cc2280f569144080cd58622a2eda83bfa4c0c354a63264/anywidget-0.11.0-py3-none-any.whl", hash = "sha256:c574d9acc6503ad27b37a9acea48f957a8ba7c9c9876cfcb37898931c098ce9d", size = 317341, upload-time = "2026-04-27T23:42:08.356Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc", size = 109097 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "appnope" version = "0.1.4" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170 } +sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170, upload-time = "2024-02-06T09:43:11.258Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321 }, + { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" }, ] [[package]] @@ -97,9 +79,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "argon2-cffi-bindings" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/0e/89/ce5af8a7d472a67cc819d5d998aa8c82c5d860608c4db9f46f1162d7dab9/argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1", size = 45706 } +sdist = { url = "https://files.pythonhosted.org/packages/0e/89/ce5af8a7d472a67cc819d5d998aa8c82c5d860608c4db9f46f1162d7dab9/argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1", size = 45706, upload-time = "2025-06-03T06:55:32.073Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/4f/d3/a8b22fa575b297cd6e3e3b0155c7e25db170edf1c74783d6a31a2490b8d9/argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741", size = 14657 }, + { url = "https://files.pythonhosted.org/packages/4f/d3/a8b22fa575b297cd6e3e3b0155c7e25db170edf1c74783d6a31a2490b8d9/argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741", size = 14657, upload-time = "2025-06-03T06:55:30.804Z" }, ] [[package]] @@ -109,28 +91,28 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "cffi" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/5c/2d/db8af0df73c1cf454f71b2bbe5e356b8c1f8041c979f505b3d3186e520a9/argon2_cffi_bindings-25.1.0.tar.gz", hash = "sha256:b957f3e6ea4d55d820e40ff76f450952807013d361a65d7f28acc0acbf29229d", size = 1783441 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/60/97/3c0a35f46e52108d4707c44b95cfe2afcafc50800b5450c197454569b776/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:3d3f05610594151994ca9ccb3c771115bdb4daef161976a266f0dd8aa9996b8f", size = 54393 }, - { url = "https://files.pythonhosted.org/packages/9d/f4/98bbd6ee89febd4f212696f13c03ca302b8552e7dbf9c8efa11ea4a388c3/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:8b8efee945193e667a396cbc7b4fb7d357297d6234d30a489905d96caabde56b", size = 29328 }, - { url = "https://files.pythonhosted.org/packages/43/24/90a01c0ef12ac91a6be05969f29944643bc1e5e461155ae6559befa8f00b/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:3c6702abc36bf3ccba3f802b799505def420a1b7039862014a65db3205967f5a", size = 31269 }, - { url = "https://files.pythonhosted.org/packages/d4/d3/942aa10782b2697eee7af5e12eeff5ebb325ccfb86dd8abda54174e377e4/argon2_cffi_bindings-25.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a1c70058c6ab1e352304ac7e3b52554daadacd8d453c1752e547c76e9c99ac44", size = 86558 }, - { url = "https://files.pythonhosted.org/packages/0d/82/b484f702fec5536e71836fc2dbc8c5267b3f6e78d2d539b4eaa6f0db8bf8/argon2_cffi_bindings-25.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e2fd3bfbff3c5d74fef31a722f729bf93500910db650c925c2d6ef879a7e51cb", size = 92364 }, - { url = "https://files.pythonhosted.org/packages/c9/c1/a606ff83b3f1735f3759ad0f2cd9e038a0ad11a3de3b6c673aa41c24bb7b/argon2_cffi_bindings-25.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:c4f9665de60b1b0e99bcd6be4f17d90339698ce954cfd8d9cf4f91c995165a92", size = 85637 }, - { url = "https://files.pythonhosted.org/packages/44/b4/678503f12aceb0262f84fa201f6027ed77d71c5019ae03b399b97caa2f19/argon2_cffi_bindings-25.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ba92837e4a9aa6a508c8d2d7883ed5a8f6c308c89a4790e1e447a220deb79a85", size = 91934 }, - { url = "https://files.pythonhosted.org/packages/f0/c7/f36bd08ef9bd9f0a9cff9428406651f5937ce27b6c5b07b92d41f91ae541/argon2_cffi_bindings-25.1.0-cp314-cp314t-win32.whl", hash = "sha256:84a461d4d84ae1295871329b346a97f68eade8c53b6ed9a7ca2d7467f3c8ff6f", size = 28158 }, - { url = "https://files.pythonhosted.org/packages/b3/80/0106a7448abb24a2c467bf7d527fe5413b7fdfa4ad6d6a96a43a62ef3988/argon2_cffi_bindings-25.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:b55aec3565b65f56455eebc9b9f34130440404f27fe21c3b375bf1ea4d8fbae6", size = 32597 }, - { url = "https://files.pythonhosted.org/packages/05/b8/d663c9caea07e9180b2cb662772865230715cbd573ba3b5e81793d580316/argon2_cffi_bindings-25.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:87c33a52407e4c41f3b70a9c2d3f6056d88b10dad7695be708c5021673f55623", size = 28231 }, - { url = "https://files.pythonhosted.org/packages/1d/57/96b8b9f93166147826da5f90376e784a10582dd39a393c99bb62cfcf52f0/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:aecba1723ae35330a008418a91ea6cfcedf6d31e5fbaa056a166462ff066d500", size = 54121 }, - { url = "https://files.pythonhosted.org/packages/0a/08/a9bebdb2e0e602dde230bdde8021b29f71f7841bd54801bcfd514acb5dcf/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:2630b6240b495dfab90aebe159ff784d08ea999aa4b0d17efa734055a07d2f44", size = 29177 }, - { url = "https://files.pythonhosted.org/packages/b6/02/d297943bcacf05e4f2a94ab6f462831dc20158614e5d067c35d4e63b9acb/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:7aef0c91e2c0fbca6fc68e7555aa60ef7008a739cbe045541e438373bc54d2b0", size = 31090 }, - { url = "https://files.pythonhosted.org/packages/c1/93/44365f3d75053e53893ec6d733e4a5e3147502663554b4d864587c7828a7/argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e021e87faa76ae0d413b619fe2b65ab9a037f24c60a1e6cc43457ae20de6dc6", size = 81246 }, - { url = "https://files.pythonhosted.org/packages/09/52/94108adfdd6e2ddf58be64f959a0b9c7d4ef2fa71086c38356d22dc501ea/argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d3e924cfc503018a714f94a49a149fdc0b644eaead5d1f089330399134fa028a", size = 87126 }, - { url = "https://files.pythonhosted.org/packages/72/70/7a2993a12b0ffa2a9271259b79cc616e2389ed1a4d93842fac5a1f923ffd/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:c87b72589133f0346a1cb8d5ecca4b933e3c9b64656c9d175270a000e73b288d", size = 80343 }, - { url = "https://files.pythonhosted.org/packages/78/9a/4e5157d893ffc712b74dbd868c7f62365618266982b64accab26bab01edc/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:1db89609c06afa1a214a69a462ea741cf735b29a57530478c06eb81dd403de99", size = 86777 }, - { url = "https://files.pythonhosted.org/packages/74/cd/15777dfde1c29d96de7f18edf4cc94c385646852e7c7b0320aa91ccca583/argon2_cffi_bindings-25.1.0-cp39-abi3-win32.whl", hash = "sha256:473bcb5f82924b1becbb637b63303ec8d10e84c8d241119419897a26116515d2", size = 27180 }, - { url = "https://files.pythonhosted.org/packages/e2/c6/a759ece8f1829d1f162261226fbfd2c6832b3ff7657384045286d2afa384/argon2_cffi_bindings-25.1.0-cp39-abi3-win_amd64.whl", hash = "sha256:a98cd7d17e9f7ce244c0803cad3c23a7d379c301ba618a5fa76a67d116618b98", size = 31715 }, - { url = "https://files.pythonhosted.org/packages/42/b9/f8d6fa329ab25128b7e98fd83a3cb34d9db5b059a9847eddb840a0af45dd/argon2_cffi_bindings-25.1.0-cp39-abi3-win_arm64.whl", hash = "sha256:b0fdbcf513833809c882823f98dc2f931cf659d9a1429616ac3adebb49f5db94", size = 27149 }, +sdist = { url = "https://files.pythonhosted.org/packages/5c/2d/db8af0df73c1cf454f71b2bbe5e356b8c1f8041c979f505b3d3186e520a9/argon2_cffi_bindings-25.1.0.tar.gz", hash = "sha256:b957f3e6ea4d55d820e40ff76f450952807013d361a65d7f28acc0acbf29229d", size = 1783441, upload-time = "2025-07-30T10:02:05.147Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/60/97/3c0a35f46e52108d4707c44b95cfe2afcafc50800b5450c197454569b776/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:3d3f05610594151994ca9ccb3c771115bdb4daef161976a266f0dd8aa9996b8f", size = 54393, upload-time = "2025-07-30T10:01:40.97Z" }, + { url = "https://files.pythonhosted.org/packages/9d/f4/98bbd6ee89febd4f212696f13c03ca302b8552e7dbf9c8efa11ea4a388c3/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:8b8efee945193e667a396cbc7b4fb7d357297d6234d30a489905d96caabde56b", size = 29328, upload-time = "2025-07-30T10:01:41.916Z" }, + { url = "https://files.pythonhosted.org/packages/43/24/90a01c0ef12ac91a6be05969f29944643bc1e5e461155ae6559befa8f00b/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:3c6702abc36bf3ccba3f802b799505def420a1b7039862014a65db3205967f5a", size = 31269, upload-time = "2025-07-30T10:01:42.716Z" }, + { url = "https://files.pythonhosted.org/packages/d4/d3/942aa10782b2697eee7af5e12eeff5ebb325ccfb86dd8abda54174e377e4/argon2_cffi_bindings-25.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a1c70058c6ab1e352304ac7e3b52554daadacd8d453c1752e547c76e9c99ac44", size = 86558, upload-time = "2025-07-30T10:01:43.943Z" }, + { url = "https://files.pythonhosted.org/packages/0d/82/b484f702fec5536e71836fc2dbc8c5267b3f6e78d2d539b4eaa6f0db8bf8/argon2_cffi_bindings-25.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e2fd3bfbff3c5d74fef31a722f729bf93500910db650c925c2d6ef879a7e51cb", size = 92364, upload-time = "2025-07-30T10:01:44.887Z" }, + { url = "https://files.pythonhosted.org/packages/c9/c1/a606ff83b3f1735f3759ad0f2cd9e038a0ad11a3de3b6c673aa41c24bb7b/argon2_cffi_bindings-25.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:c4f9665de60b1b0e99bcd6be4f17d90339698ce954cfd8d9cf4f91c995165a92", size = 85637, upload-time = "2025-07-30T10:01:46.225Z" }, + { url = "https://files.pythonhosted.org/packages/44/b4/678503f12aceb0262f84fa201f6027ed77d71c5019ae03b399b97caa2f19/argon2_cffi_bindings-25.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ba92837e4a9aa6a508c8d2d7883ed5a8f6c308c89a4790e1e447a220deb79a85", size = 91934, upload-time = "2025-07-30T10:01:47.203Z" }, + { url = "https://files.pythonhosted.org/packages/f0/c7/f36bd08ef9bd9f0a9cff9428406651f5937ce27b6c5b07b92d41f91ae541/argon2_cffi_bindings-25.1.0-cp314-cp314t-win32.whl", hash = "sha256:84a461d4d84ae1295871329b346a97f68eade8c53b6ed9a7ca2d7467f3c8ff6f", size = 28158, upload-time = "2025-07-30T10:01:48.341Z" }, + { url = "https://files.pythonhosted.org/packages/b3/80/0106a7448abb24a2c467bf7d527fe5413b7fdfa4ad6d6a96a43a62ef3988/argon2_cffi_bindings-25.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:b55aec3565b65f56455eebc9b9f34130440404f27fe21c3b375bf1ea4d8fbae6", size = 32597, upload-time = "2025-07-30T10:01:49.112Z" }, + { url = "https://files.pythonhosted.org/packages/05/b8/d663c9caea07e9180b2cb662772865230715cbd573ba3b5e81793d580316/argon2_cffi_bindings-25.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:87c33a52407e4c41f3b70a9c2d3f6056d88b10dad7695be708c5021673f55623", size = 28231, upload-time = "2025-07-30T10:01:49.92Z" }, + { url = "https://files.pythonhosted.org/packages/1d/57/96b8b9f93166147826da5f90376e784a10582dd39a393c99bb62cfcf52f0/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:aecba1723ae35330a008418a91ea6cfcedf6d31e5fbaa056a166462ff066d500", size = 54121, upload-time = "2025-07-30T10:01:50.815Z" }, + { url = "https://files.pythonhosted.org/packages/0a/08/a9bebdb2e0e602dde230bdde8021b29f71f7841bd54801bcfd514acb5dcf/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:2630b6240b495dfab90aebe159ff784d08ea999aa4b0d17efa734055a07d2f44", size = 29177, upload-time = "2025-07-30T10:01:51.681Z" }, + { url = "https://files.pythonhosted.org/packages/b6/02/d297943bcacf05e4f2a94ab6f462831dc20158614e5d067c35d4e63b9acb/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:7aef0c91e2c0fbca6fc68e7555aa60ef7008a739cbe045541e438373bc54d2b0", size = 31090, upload-time = "2025-07-30T10:01:53.184Z" }, + { url = "https://files.pythonhosted.org/packages/c1/93/44365f3d75053e53893ec6d733e4a5e3147502663554b4d864587c7828a7/argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e021e87faa76ae0d413b619fe2b65ab9a037f24c60a1e6cc43457ae20de6dc6", size = 81246, upload-time = "2025-07-30T10:01:54.145Z" }, + { url = "https://files.pythonhosted.org/packages/09/52/94108adfdd6e2ddf58be64f959a0b9c7d4ef2fa71086c38356d22dc501ea/argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d3e924cfc503018a714f94a49a149fdc0b644eaead5d1f089330399134fa028a", size = 87126, upload-time = "2025-07-30T10:01:55.074Z" }, + { url = "https://files.pythonhosted.org/packages/72/70/7a2993a12b0ffa2a9271259b79cc616e2389ed1a4d93842fac5a1f923ffd/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:c87b72589133f0346a1cb8d5ecca4b933e3c9b64656c9d175270a000e73b288d", size = 80343, upload-time = "2025-07-30T10:01:56.007Z" }, + { url = "https://files.pythonhosted.org/packages/78/9a/4e5157d893ffc712b74dbd868c7f62365618266982b64accab26bab01edc/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:1db89609c06afa1a214a69a462ea741cf735b29a57530478c06eb81dd403de99", size = 86777, upload-time = "2025-07-30T10:01:56.943Z" }, + { url = "https://files.pythonhosted.org/packages/74/cd/15777dfde1c29d96de7f18edf4cc94c385646852e7c7b0320aa91ccca583/argon2_cffi_bindings-25.1.0-cp39-abi3-win32.whl", hash = "sha256:473bcb5f82924b1becbb637b63303ec8d10e84c8d241119419897a26116515d2", size = 27180, upload-time = "2025-07-30T10:01:57.759Z" }, + { url = "https://files.pythonhosted.org/packages/e2/c6/a759ece8f1829d1f162261226fbfd2c6832b3ff7657384045286d2afa384/argon2_cffi_bindings-25.1.0-cp39-abi3-win_amd64.whl", hash = "sha256:a98cd7d17e9f7ce244c0803cad3c23a7d379c301ba618a5fa76a67d116618b98", size = 31715, upload-time = "2025-07-30T10:01:58.56Z" }, + { url = "https://files.pythonhosted.org/packages/42/b9/f8d6fa329ab25128b7e98fd83a3cb34d9db5b059a9847eddb840a0af45dd/argon2_cffi_bindings-25.1.0-cp39-abi3-win_arm64.whl", hash = "sha256:b0fdbcf513833809c882823f98dc2f931cf659d9a1429616ac3adebb49f5db94", size = 27149, upload-time = "2025-07-30T10:01:59.329Z" }, ] [[package]] @@ -141,94 +123,70 @@ dependencies = [ { name = "python-dateutil" }, { name = "tzdata" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/b9/33/032cdc44182491aa708d06a68b62434140d8c50820a087fac7af37703357/arrow-1.4.0.tar.gz", hash = "sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7", size = 152931 } +sdist = { url = "https://files.pythonhosted.org/packages/b9/33/032cdc44182491aa708d06a68b62434140d8c50820a087fac7af37703357/arrow-1.4.0.tar.gz", hash = "sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7", size = 152931, upload-time = "2025-10-18T17:46:46.761Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/ed/c9/d7977eaacb9df673210491da99e6a247e93df98c715fc43fd136ce1d3d33/arrow-1.4.0-py3-none-any.whl", hash = "sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205", size = 68797 }, + { url = "https://files.pythonhosted.org/packages/ed/c9/d7977eaacb9df673210491da99e6a247e93df98c715fc43fd136ce1d3d33/arrow-1.4.0-py3-none-any.whl", hash = "sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205", size = 68797, upload-time = "2025-10-18T17:46:45.663Z" }, ] [[package]] name = "asttokens" version = "3.0.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/be/a5/8e3f9b6771b0b408517c82d97aed8f2036509bc247d46114925e32fe33f0/asttokens-3.0.1.tar.gz", hash = "sha256:71a4ee5de0bde6a31d64f6b13f2293ac190344478f081c3d1bccfcf5eacb0cb7", size = 62308 } +sdist = { url = "https://files.pythonhosted.org/packages/be/a5/8e3f9b6771b0b408517c82d97aed8f2036509bc247d46114925e32fe33f0/asttokens-3.0.1.tar.gz", hash = "sha256:71a4ee5de0bde6a31d64f6b13f2293ac190344478f081c3d1bccfcf5eacb0cb7", size = 62308, upload-time = "2025-11-15T16:43:48.578Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/d2/39/e7eaf1799466a4aef85b6a4fe7bd175ad2b1c6345066aa33f1f58d4b18d0/asttokens-3.0.1-py3-none-any.whl", hash = "sha256:15a3ebc0f43c2d0a50eeafea25e19046c68398e487b9f1f5b517f7c0f40f976a", size = 27047 }, + { url = "https://files.pythonhosted.org/packages/d2/39/e7eaf1799466a4aef85b6a4fe7bd175ad2b1c6345066aa33f1f58d4b18d0/asttokens-3.0.1-py3-none-any.whl", hash = "sha256:15a3ebc0f43c2d0a50eeafea25e19046c68398e487b9f1f5b517f7c0f40f976a", size = 27047, upload-time = "2025-11-15T16:43:16.109Z" }, ] [[package]] name = "async-lru" version = "2.3.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/e8/1f/989ecfef8e64109a489fff357450cb73fa73a865a92bd8c272170a6922c2/async_lru-2.3.0.tar.gz", hash = "sha256:89bdb258a0140d7313cf8f4031d816a042202faa61d0ab310a0a538baa1c24b6", size = 16332, upload-time = "2026-03-19T01:04:32.413Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/e5/e2/c2e3abf398f80732e58b03be77bde9022550d221dd8781bf586bd4d97cc1/async_lru-2.3.0-py3-none-any.whl", hash = "sha256:eea27b01841909316f2cc739807acea1c623df2be8c5cfad7583286397bb8315", size = 8403, upload-time = "2026-03-19T01:04:30.883Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/b2/4d/71ec4d3939dc755264f680f6c2b4906423a304c3d18e96853f0a595dfe97/async_lru-2.0.5.tar.gz", hash = "sha256:481d52ccdd27275f42c43a928b4a50c3bfb2d67af4e78b170e3e0bb39c66e5bb", size = 10380 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/03/49/d10027df9fce941cb8184e78a02857af36360d33e1721df81c5ed2179a1a/async_lru-2.0.5-py3-none-any.whl", hash = "sha256:ab95404d8d2605310d345932697371a5f40def0487c03d6d0ad9138de52c9943", size = 6069 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "attrs" version = "26.1.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/9a/8e/82a0fe20a541c03148528be8cac2408564a6c9a0cc7e9171802bc1d26985/attrs-26.1.0.tar.gz", hash = "sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32", size = 952055, upload-time = "2026-03-19T14:22:25.026Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/64/b4/17d4b0b2a2dc85a6df63d1157e028ed19f90d4cd97c36717afef2bc2f395/attrs-26.1.0-py3-none-any.whl", hash = "sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309", size = 67548, upload-time = "2026-03-19T14:22:23.645Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/6b/5c/685e6633917e101e5dcb62b9dd76946cbb57c26e133bae9e0cd36033c0a9/attrs-25.4.0.tar.gz", hash = "sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11", size = 934251 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "babel" version = "2.18.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/7d/b2/51899539b6ceeeb420d40ed3cd4b7a40519404f9baf3d4ac99dc413a834b/babel-2.18.0.tar.gz", hash = "sha256:b80b99a14bd085fcacfa15c9165f651fbb3406e66cc603abf11c5750937c992d", size = 9959554, upload-time = "2026-02-01T12:30:56.078Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/77/f5/21d2de20e8b8b0408f0681956ca2c69f1320a3848ac50e6e7f39c6159675/babel-2.18.0-py3-none-any.whl", hash = "sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35", size = 10196845, upload-time = "2026-02-01T12:30:53.445Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/7d/6b/d52e42361e1aa00709585ecc30b3f9684b3ab62530771402248b1b1d6240/babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d", size = 9951852 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "beautifulsoup4" -version = "4.14.3" +version = "4.15.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "soupsieve" }, { name = "typing-extensions" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/c3/b0/1c6a16426d389813b48d95e26898aff79abbde42ad353958ad95cc8c9b21/beautifulsoup4-4.14.3.tar.gz", hash = "sha256:6292b1c5186d356bba669ef9f7f051757099565ad9ada5dd630bd9de5fa7fb86", size = 627737, upload-time = "2025-11-30T15:08:26.084Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/1a/39/47f9197bdd44df24d67ac8893641e16f386c984a0619ef2ee4c51fbbc019/beautifulsoup4-4.14.3-py3-none-any.whl", hash = "sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb", size = 107721, upload-time = "2025-11-30T15:08:24.087Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/77/e9/df2358efd7659577435e2177bfa69cba6c33216681af51a707193dec162a/beautifulsoup4-4.14.2.tar.gz", hash = "sha256:2a98ab9f944a11acee9cc848508ec28d9228abfd522ef0fad6a02a72e0ded69e", size = 625822 } +sdist = { url = "https://files.pythonhosted.org/packages/43/65/318323f98dbee45d42dff61d8f047181bc6f2268a9068cfad035a46be5af/beautifulsoup4-4.15.0.tar.gz", hash = "sha256:288e3ca7d54b06f2ac191970bc275c1939cb46d450b255bf6718b04aa37ab4f7", size = 632571, upload-time = "2026-06-07T16:44:20.453Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/94/fe/3aed5d0be4d404d12d36ab97e2f1791424d9ca39c2f754a6285d59a3b01d/beautifulsoup4-4.14.2-py3-none-any.whl", hash = "sha256:5ef6fa3a8cbece8488d66985560f97ed091e22bbc4e9c2338508a9d5de6d4515", size = 106392 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/88/c6/92fcd42f1ba33e1184263f25bfabf3d27c383410470f169e4b8163bf9c17/beautifulsoup4-4.15.0-py3-none-any.whl", hash = "sha256:d6f88de62e1d4e38ecb1077eb9724cd0eff29d2a08ca16a401e9b9e93f117cf9", size = 109924, upload-time = "2026-06-07T16:44:21.566Z" }, ] [[package]] name = "bleach" -version = "6.3.0" +version = "6.4.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "webencodings" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/07/18/3c8523962314be6bf4c8989c79ad9531c825210dd13a8669f6b84336e8bd/bleach-6.3.0.tar.gz", hash = "sha256:6f3b91b1c0a02bb9a78b5a454c92506aa0fdf197e1d5e114d2e00c6f64306d22", size = 203533 } +sdist = { url = "https://files.pythonhosted.org/packages/48/3c/e12ac860709702bd5ebeb9b56a4fe334f1001246ee1b8f2b7ee28912df7d/bleach-6.4.0.tar.gz", hash = "sha256:4202482733d85cedd04e59fcb2f89f4e4c7c385a78d3c3c23c30446843a37452", size = 204857, upload-time = "2026-06-05T13:01:13.734Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/cd/3a/577b549de0cc09d95f11087ee63c739bba856cd3952697eec4c4bb91350a/bleach-6.3.0-py3-none-any.whl", hash = "sha256:fe10ec77c93ddf3d13a73b035abaac7a9f5e436513864ccdad516693213c65d6", size = 164437 }, + { url = "https://files.pythonhosted.org/packages/58/9d/40b6267367182187139a4000b82a3b287d84d745bccd808e75d916920e9d/bleach-6.4.0-py3-none-any.whl", hash = "sha256:4b6b6a54fff2e69a3dde9d21cc6301220bee3c3cb792187d11403fd795031081", size = 165109, upload-time = "2026-06-05T13:01:12.504Z" }, ] [package.optional-dependencies] @@ -238,17 +196,11 @@ css = [ [[package]] name = "certifi" -version = "2026.5.20" +version = "2026.6.17" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/f3/ce/ee2ecad540810a79593028e88299baeae54d346cc7a0d94b6199988b89b1/certifi-2026.5.20.tar.gz", hash = "sha256:69dea482ab64caa7b9f6aba1c6bf48bb6a5448d1c0f1b17ab42ad8c763a5344d", size = 135422, upload-time = "2026-05-20T11:46:50.073Z" } +sdist = { url = "https://files.pythonhosted.org/packages/c9/c7/424b75da314c1045981bd9777432fad05a9e0c69daa4ed7e308bbaffe405/certifi-2026.6.17.tar.gz", hash = "sha256:024c88eeec92ca068db80f02b8b07c9cef7b9fe261d1d535abfd5abd6f6af432", size = 134594, upload-time = "2026-06-17T10:31:07.894Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/59/8c/57e832b7af6d7c5abe66eb3fbe3a3a32f4d11ea23a1aa7131371035be991/certifi-2026.5.20-py3-none-any.whl", hash = "sha256:3c52e209ba0a4ad7aebe60436a4ab349c39e1e602e8c134221e546902ad25897", size = 134134, upload-time = "2026-05-20T11:46:48.578Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/a2/8c/58f469717fa48465e4a50c014a0400602d3c437d7c0c468e17ada824da3a/certifi-2025.11.12.tar.gz", hash = "sha256:d8ab5478f2ecd78af242878415affce761ca6bc54a22a27e026d7c25357c3316", size = 160538 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/70/7d/9bc192684cea499815ff478dfcdc13835ddf401365057044fb721ec6bddb/certifi-2025.11.12-py3-none-any.whl", hash = "sha256:97de8790030bbd5c2d96b7ec782fc2f7820ef8dba6db909ccf95449f2d062d4b", size = 159438 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/ef/2f/c5464532e965badff2f4c4c1a3a83f5697f0d7c407ed0cda44aaa99bb451/certifi-2026.6.17-py3-none-any.whl", hash = "sha256:2227dcbaafe0d2f59279d1762ddddc37783ed4354594f194ffc31d20f41fc3db", size = 133289, upload-time = "2026-06-17T10:31:06.348Z" }, ] [[package]] @@ -258,89 +210,82 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "pycparser", marker = "implementation_name != 'PyPy'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/12/4a/3dfd5f7850cbf0d06dc84ba9aa00db766b52ca38d8b86e3a38314d52498c/cffi-2.0.0-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:b4c854ef3adc177950a8dfc81a86f5115d2abd545751a304c5bcf2c2c7283cfe", size = 184344 }, - { url = "https://files.pythonhosted.org/packages/4f/8b/f0e4c441227ba756aafbe78f117485b25bb26b1c059d01f137fa6d14896b/cffi-2.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2de9a304e27f7596cd03d16f1b7c72219bd944e99cc52b84d0145aefb07cbd3c", size = 180560 }, - { url = "https://files.pythonhosted.org/packages/b1/b7/1200d354378ef52ec227395d95c2576330fd22a869f7a70e88e1447eb234/cffi-2.0.0-cp311-cp311-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:baf5215e0ab74c16e2dd324e8ec067ef59e41125d3eade2b863d294fd5035c92", size = 209613 }, - { url = "https://files.pythonhosted.org/packages/b8/56/6033f5e86e8cc9bb629f0077ba71679508bdf54a9a5e112a3c0b91870332/cffi-2.0.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:730cacb21e1bdff3ce90babf007d0a0917cc3e6492f336c2f0134101e0944f93", size = 216476 }, - { url = "https://files.pythonhosted.org/packages/dc/7f/55fecd70f7ece178db2f26128ec41430d8720f2d12ca97bf8f0a628207d5/cffi-2.0.0-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:6824f87845e3396029f3820c206e459ccc91760e8fa24422f8b0c3d1731cbec5", size = 203374 }, - { url = "https://files.pythonhosted.org/packages/84/ef/a7b77c8bdc0f77adc3b46888f1ad54be8f3b7821697a7b89126e829e676a/cffi-2.0.0-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:9de40a7b0323d889cf8d23d1ef214f565ab154443c42737dfe52ff82cf857664", size = 202597 }, - { url = "https://files.pythonhosted.org/packages/d7/91/500d892b2bf36529a75b77958edfcd5ad8e2ce4064ce2ecfeab2125d72d1/cffi-2.0.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8941aaadaf67246224cee8c3803777eed332a19d909b47e29c9842ef1e79ac26", size = 215574 }, - { url = "https://files.pythonhosted.org/packages/44/64/58f6255b62b101093d5df22dcb752596066c7e89dd725e0afaed242a61be/cffi-2.0.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:a05d0c237b3349096d3981b727493e22147f934b20f6f125a3eba8f994bec4a9", size = 218971 }, - { url = "https://files.pythonhosted.org/packages/ab/49/fa72cebe2fd8a55fbe14956f9970fe8eb1ac59e5df042f603ef7c8ba0adc/cffi-2.0.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:94698a9c5f91f9d138526b48fe26a199609544591f859c870d477351dc7b2414", size = 211972 }, - { url = "https://files.pythonhosted.org/packages/0b/28/dd0967a76aab36731b6ebfe64dec4e981aff7e0608f60c2d46b46982607d/cffi-2.0.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:5fed36fccc0612a53f1d4d9a816b50a36702c28a2aa880cb8a122b3466638743", size = 217078 }, - { url = "https://files.pythonhosted.org/packages/2b/c0/015b25184413d7ab0a410775fdb4a50fca20f5589b5dab1dbbfa3baad8ce/cffi-2.0.0-cp311-cp311-win32.whl", hash = "sha256:c649e3a33450ec82378822b3dad03cc228b8f5963c0c12fc3b1e0ab940f768a5", size = 172076 }, - { url = "https://files.pythonhosted.org/packages/ae/8f/dc5531155e7070361eb1b7e4c1a9d896d0cb21c49f807a6c03fd63fc877e/cffi-2.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:66f011380d0e49ed280c789fbd08ff0d40968ee7b665575489afa95c98196ab5", size = 182820 }, - { url = "https://files.pythonhosted.org/packages/95/5c/1b493356429f9aecfd56bc171285a4c4ac8697f76e9bbbbb105e537853a1/cffi-2.0.0-cp311-cp311-win_arm64.whl", hash = "sha256:c6638687455baf640e37344fe26d37c404db8b80d037c3d29f58fe8d1c3b194d", size = 177635 }, - { url = "https://files.pythonhosted.org/packages/ea/47/4f61023ea636104d4f16ab488e268b93008c3d0bb76893b1b31db1f96802/cffi-2.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6d02d6655b0e54f54c4ef0b94eb6be0607b70853c45ce98bd278dc7de718be5d", size = 185271 }, - { url = "https://files.pythonhosted.org/packages/df/a2/781b623f57358e360d62cdd7a8c681f074a71d445418a776eef0aadb4ab4/cffi-2.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8eca2a813c1cb7ad4fb74d368c2ffbbb4789d377ee5bb8df98373c2cc0dee76c", size = 181048 }, - { url = "https://files.pythonhosted.org/packages/ff/df/a4f0fbd47331ceeba3d37c2e51e9dfc9722498becbeec2bd8bc856c9538a/cffi-2.0.0-cp312-cp312-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe", size = 212529 }, - { url = "https://files.pythonhosted.org/packages/d5/72/12b5f8d3865bf0f87cf1404d8c374e7487dcf097a1c91c436e72e6badd83/cffi-2.0.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b21e08af67b8a103c71a250401c78d5e0893beff75e28c53c98f4de42f774062", size = 220097 }, - { url = "https://files.pythonhosted.org/packages/c2/95/7a135d52a50dfa7c882ab0ac17e8dc11cec9d55d2c18dda414c051c5e69e/cffi-2.0.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e", size = 207983 }, - { url = "https://files.pythonhosted.org/packages/3a/c8/15cb9ada8895957ea171c62dc78ff3e99159ee7adb13c0123c001a2546c1/cffi-2.0.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:81afed14892743bbe14dacb9e36d9e0e504cd204e0b165062c488942b9718037", size = 206519 }, - { url = "https://files.pythonhosted.org/packages/78/2d/7fa73dfa841b5ac06c7b8855cfc18622132e365f5b81d02230333ff26e9e/cffi-2.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3e17ed538242334bf70832644a32a7aae3d83b57567f9fd60a26257e992b79ba", size = 219572 }, - { url = "https://files.pythonhosted.org/packages/07/e0/267e57e387b4ca276b90f0434ff88b2c2241ad72b16d31836adddfd6031b/cffi-2.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3925dd22fa2b7699ed2617149842d2e6adde22b262fcbfada50e3d195e4b3a94", size = 222963 }, - { url = "https://files.pythonhosted.org/packages/b6/75/1f2747525e06f53efbd878f4d03bac5b859cbc11c633d0fb81432d98a795/cffi-2.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2c8f814d84194c9ea681642fd164267891702542f028a15fc97d4674b6206187", size = 221361 }, - { url = "https://files.pythonhosted.org/packages/7b/2b/2b6435f76bfeb6bbf055596976da087377ede68df465419d192acf00c437/cffi-2.0.0-cp312-cp312-win32.whl", hash = "sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18", size = 172932 }, - { url = "https://files.pythonhosted.org/packages/f8/ed/13bd4418627013bec4ed6e54283b1959cf6db888048c7cf4b4c3b5b36002/cffi-2.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5", size = 183557 }, - { url = "https://files.pythonhosted.org/packages/95/31/9f7f93ad2f8eff1dbc1c3656d7ca5bfd8fb52c9d786b4dcf19b2d02217fa/cffi-2.0.0-cp312-cp312-win_arm64.whl", hash = "sha256:4671d9dd5ec934cb9a73e7ee9676f9362aba54f7f34910956b84d727b0d73fb6", size = 177762 }, - { url = "https://files.pythonhosted.org/packages/4b/8d/a0a47a0c9e413a658623d014e91e74a50cdd2c423f7ccfd44086ef767f90/cffi-2.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:00bdf7acc5f795150faa6957054fbbca2439db2f775ce831222b66f192f03beb", size = 185230 }, - { url = "https://files.pythonhosted.org/packages/4a/d2/a6c0296814556c68ee32009d9c2ad4f85f2707cdecfd7727951ec228005d/cffi-2.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:45d5e886156860dc35862657e1494b9bae8dfa63bf56796f2fb56e1679fc0bca", size = 181043 }, - { url = "https://files.pythonhosted.org/packages/b0/1e/d22cc63332bd59b06481ceaac49d6c507598642e2230f201649058a7e704/cffi-2.0.0-cp313-cp313-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:07b271772c100085dd28b74fa0cd81c8fb1a3ba18b21e03d7c27f3436a10606b", size = 212446 }, - { url = "https://files.pythonhosted.org/packages/a9/f5/a2c23eb03b61a0b8747f211eb716446c826ad66818ddc7810cc2cc19b3f2/cffi-2.0.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d48a880098c96020b02d5a1f7d9251308510ce8858940e6fa99ece33f610838b", size = 220101 }, - { url = "https://files.pythonhosted.org/packages/f2/7f/e6647792fc5850d634695bc0e6ab4111ae88e89981d35ac269956605feba/cffi-2.0.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:f93fd8e5c8c0a4aa1f424d6173f14a892044054871c771f8566e4008eaa359d2", size = 207948 }, - { url = "https://files.pythonhosted.org/packages/cb/1e/a5a1bd6f1fb30f22573f76533de12a00bf274abcdc55c8edab639078abb6/cffi-2.0.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:dd4f05f54a52fb558f1ba9f528228066954fee3ebe629fc1660d874d040ae5a3", size = 206422 }, - { url = "https://files.pythonhosted.org/packages/98/df/0a1755e750013a2081e863e7cd37e0cdd02664372c754e5560099eb7aa44/cffi-2.0.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c8d3b5532fc71b7a77c09192b4a5a200ea992702734a2e9279a37f2478236f26", size = 219499 }, - { url = "https://files.pythonhosted.org/packages/50/e1/a969e687fcf9ea58e6e2a928ad5e2dd88cc12f6f0ab477e9971f2309b57c/cffi-2.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:d9b29c1f0ae438d5ee9acb31cadee00a58c46cc9c0b2f9038c6b0b3470877a8c", size = 222928 }, - { url = "https://files.pythonhosted.org/packages/36/54/0362578dd2c9e557a28ac77698ed67323ed5b9775ca9d3fe73fe191bb5d8/cffi-2.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6d50360be4546678fc1b79ffe7a66265e28667840010348dd69a314145807a1b", size = 221302 }, - { url = "https://files.pythonhosted.org/packages/eb/6d/bf9bda840d5f1dfdbf0feca87fbdb64a918a69bca42cfa0ba7b137c48cb8/cffi-2.0.0-cp313-cp313-win32.whl", hash = "sha256:74a03b9698e198d47562765773b4a8309919089150a0bb17d829ad7b44b60d27", size = 172909 }, - { url = "https://files.pythonhosted.org/packages/37/18/6519e1ee6f5a1e579e04b9ddb6f1676c17368a7aba48299c3759bbc3c8b3/cffi-2.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:19f705ada2530c1167abacb171925dd886168931e0a7b78f5bffcae5c6b5be75", size = 183402 }, - { url = "https://files.pythonhosted.org/packages/cb/0e/02ceeec9a7d6ee63bb596121c2c8e9b3a9e150936f4fbef6ca1943e6137c/cffi-2.0.0-cp313-cp313-win_arm64.whl", hash = "sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91", size = 177780 }, - { url = "https://files.pythonhosted.org/packages/92/c4/3ce07396253a83250ee98564f8d7e9789fab8e58858f35d07a9a2c78de9f/cffi-2.0.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fc33c5141b55ed366cfaad382df24fe7dcbc686de5be719b207bb248e3053dc5", size = 185320 }, - { url = "https://files.pythonhosted.org/packages/59/dd/27e9fa567a23931c838c6b02d0764611c62290062a6d4e8ff7863daf9730/cffi-2.0.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c654de545946e0db659b3400168c9ad31b5d29593291482c43e3564effbcee13", size = 181487 }, - { url = "https://files.pythonhosted.org/packages/d6/43/0e822876f87ea8a4ef95442c3d766a06a51fc5298823f884ef87aaad168c/cffi-2.0.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:24b6f81f1983e6df8db3adc38562c83f7d4a0c36162885ec7f7b77c7dcbec97b", size = 220049 }, - { url = "https://files.pythonhosted.org/packages/b4/89/76799151d9c2d2d1ead63c2429da9ea9d7aac304603de0c6e8764e6e8e70/cffi-2.0.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:12873ca6cb9b0f0d3a0da705d6086fe911591737a59f28b7936bdfed27c0d47c", size = 207793 }, - { url = "https://files.pythonhosted.org/packages/bb/dd/3465b14bb9e24ee24cb88c9e3730f6de63111fffe513492bf8c808a3547e/cffi-2.0.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:d9b97165e8aed9272a6bb17c01e3cc5871a594a446ebedc996e2397a1c1ea8ef", size = 206300 }, - { url = "https://files.pythonhosted.org/packages/47/d9/d83e293854571c877a92da46fdec39158f8d7e68da75bf73581225d28e90/cffi-2.0.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:afb8db5439b81cf9c9d0c80404b60c3cc9c3add93e114dcae767f1477cb53775", size = 219244 }, - { url = "https://files.pythonhosted.org/packages/2b/0f/1f177e3683aead2bb00f7679a16451d302c436b5cbf2505f0ea8146ef59e/cffi-2.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:737fe7d37e1a1bffe70bd5754ea763a62a066dc5913ca57e957824b72a85e205", size = 222828 }, - { url = "https://files.pythonhosted.org/packages/c6/0f/cafacebd4b040e3119dcb32fed8bdef8dfe94da653155f9d0b9dc660166e/cffi-2.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:38100abb9d1b1435bc4cc340bb4489635dc2f0da7456590877030c9b3d40b0c1", size = 220926 }, - { url = "https://files.pythonhosted.org/packages/3e/aa/df335faa45b395396fcbc03de2dfcab242cd61a9900e914fe682a59170b1/cffi-2.0.0-cp314-cp314-win32.whl", hash = "sha256:087067fa8953339c723661eda6b54bc98c5625757ea62e95eb4898ad5e776e9f", size = 175328 }, - { url = "https://files.pythonhosted.org/packages/bb/92/882c2d30831744296ce713f0feb4c1cd30f346ef747b530b5318715cc367/cffi-2.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:203a48d1fb583fc7d78a4c6655692963b860a417c0528492a6bc21f1aaefab25", size = 185650 }, - { url = "https://files.pythonhosted.org/packages/9f/2c/98ece204b9d35a7366b5b2c6539c350313ca13932143e79dc133ba757104/cffi-2.0.0-cp314-cp314-win_arm64.whl", hash = "sha256:dbd5c7a25a7cb98f5ca55d258b103a2054f859a46ae11aaf23134f9cc0d356ad", size = 180687 }, - { url = "https://files.pythonhosted.org/packages/3e/61/c768e4d548bfa607abcda77423448df8c471f25dbe64fb2ef6d555eae006/cffi-2.0.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:9a67fc9e8eb39039280526379fb3a70023d77caec1852002b4da7e8b270c4dd9", size = 188773 }, - { url = "https://files.pythonhosted.org/packages/2c/ea/5f76bce7cf6fcd0ab1a1058b5af899bfbef198bea4d5686da88471ea0336/cffi-2.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:7a66c7204d8869299919db4d5069a82f1561581af12b11b3c9f48c584eb8743d", size = 185013 }, - { url = "https://files.pythonhosted.org/packages/be/b4/c56878d0d1755cf9caa54ba71e5d049479c52f9e4afc230f06822162ab2f/cffi-2.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7cc09976e8b56f8cebd752f7113ad07752461f48a58cbba644139015ac24954c", size = 221593 }, - { url = "https://files.pythonhosted.org/packages/e0/0d/eb704606dfe8033e7128df5e90fee946bbcb64a04fcdaa97321309004000/cffi-2.0.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:92b68146a71df78564e4ef48af17551a5ddd142e5190cdf2c5624d0c3ff5b2e8", size = 209354 }, - { url = "https://files.pythonhosted.org/packages/d8/19/3c435d727b368ca475fb8742ab97c9cb13a0de600ce86f62eab7fa3eea60/cffi-2.0.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:b1e74d11748e7e98e2f426ab176d4ed720a64412b6a15054378afdb71e0f37dc", size = 208480 }, - { url = "https://files.pythonhosted.org/packages/d0/44/681604464ed9541673e486521497406fadcc15b5217c3e326b061696899a/cffi-2.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:28a3a209b96630bca57cce802da70c266eb08c6e97e5afd61a75611ee6c64592", size = 221584 }, - { url = "https://files.pythonhosted.org/packages/25/8e/342a504ff018a2825d395d44d63a767dd8ebc927ebda557fecdaca3ac33a/cffi-2.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:7553fb2090d71822f02c629afe6042c299edf91ba1bf94951165613553984512", size = 224443 }, - { url = "https://files.pythonhosted.org/packages/e1/5e/b666bacbbc60fbf415ba9988324a132c9a7a0448a9a8f125074671c0f2c3/cffi-2.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:6c6c373cfc5c83a975506110d17457138c8c63016b563cc9ed6e056a82f13ce4", size = 223437 }, - { url = "https://files.pythonhosted.org/packages/a0/1d/ec1a60bd1a10daa292d3cd6bb0b359a81607154fb8165f3ec95fe003b85c/cffi-2.0.0-cp314-cp314t-win32.whl", hash = "sha256:1fc9ea04857caf665289b7a75923f2c6ed559b8298a1b8c49e59f7dd95c8481e", size = 180487 }, - { url = "https://files.pythonhosted.org/packages/bf/41/4c1168c74fac325c0c8156f04b6749c8b6a8f405bbf91413ba088359f60d/cffi-2.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:d68b6cef7827e8641e8ef16f4494edda8b36104d79773a334beaa1e3521430f6", size = 191726 }, - { url = "https://files.pythonhosted.org/packages/ae/3a/dbeec9d1ee0844c679f6bb5d6ad4e9f198b1224f4e7a32825f47f6192b0c/cffi-2.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9", size = 184195 }, +sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/12/4a/3dfd5f7850cbf0d06dc84ba9aa00db766b52ca38d8b86e3a38314d52498c/cffi-2.0.0-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:b4c854ef3adc177950a8dfc81a86f5115d2abd545751a304c5bcf2c2c7283cfe", size = 184344, upload-time = "2025-09-08T23:22:26.456Z" }, + { url = "https://files.pythonhosted.org/packages/4f/8b/f0e4c441227ba756aafbe78f117485b25bb26b1c059d01f137fa6d14896b/cffi-2.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2de9a304e27f7596cd03d16f1b7c72219bd944e99cc52b84d0145aefb07cbd3c", size = 180560, upload-time = "2025-09-08T23:22:28.197Z" }, + { url = "https://files.pythonhosted.org/packages/b1/b7/1200d354378ef52ec227395d95c2576330fd22a869f7a70e88e1447eb234/cffi-2.0.0-cp311-cp311-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:baf5215e0ab74c16e2dd324e8ec067ef59e41125d3eade2b863d294fd5035c92", size = 209613, upload-time = "2025-09-08T23:22:29.475Z" }, + { url = "https://files.pythonhosted.org/packages/b8/56/6033f5e86e8cc9bb629f0077ba71679508bdf54a9a5e112a3c0b91870332/cffi-2.0.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:730cacb21e1bdff3ce90babf007d0a0917cc3e6492f336c2f0134101e0944f93", size = 216476, upload-time = "2025-09-08T23:22:31.063Z" }, + { url = "https://files.pythonhosted.org/packages/dc/7f/55fecd70f7ece178db2f26128ec41430d8720f2d12ca97bf8f0a628207d5/cffi-2.0.0-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:6824f87845e3396029f3820c206e459ccc91760e8fa24422f8b0c3d1731cbec5", size = 203374, upload-time = "2025-09-08T23:22:32.507Z" }, + { url = "https://files.pythonhosted.org/packages/84/ef/a7b77c8bdc0f77adc3b46888f1ad54be8f3b7821697a7b89126e829e676a/cffi-2.0.0-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:9de40a7b0323d889cf8d23d1ef214f565ab154443c42737dfe52ff82cf857664", size = 202597, upload-time = "2025-09-08T23:22:34.132Z" }, + { url = "https://files.pythonhosted.org/packages/d7/91/500d892b2bf36529a75b77958edfcd5ad8e2ce4064ce2ecfeab2125d72d1/cffi-2.0.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8941aaadaf67246224cee8c3803777eed332a19d909b47e29c9842ef1e79ac26", size = 215574, upload-time = "2025-09-08T23:22:35.443Z" }, + { url = "https://files.pythonhosted.org/packages/44/64/58f6255b62b101093d5df22dcb752596066c7e89dd725e0afaed242a61be/cffi-2.0.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:a05d0c237b3349096d3981b727493e22147f934b20f6f125a3eba8f994bec4a9", size = 218971, upload-time = "2025-09-08T23:22:36.805Z" }, + { url = "https://files.pythonhosted.org/packages/ab/49/fa72cebe2fd8a55fbe14956f9970fe8eb1ac59e5df042f603ef7c8ba0adc/cffi-2.0.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:94698a9c5f91f9d138526b48fe26a199609544591f859c870d477351dc7b2414", size = 211972, upload-time = "2025-09-08T23:22:38.436Z" }, + { url = "https://files.pythonhosted.org/packages/0b/28/dd0967a76aab36731b6ebfe64dec4e981aff7e0608f60c2d46b46982607d/cffi-2.0.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:5fed36fccc0612a53f1d4d9a816b50a36702c28a2aa880cb8a122b3466638743", size = 217078, upload-time = "2025-09-08T23:22:39.776Z" }, + { url = "https://files.pythonhosted.org/packages/2b/c0/015b25184413d7ab0a410775fdb4a50fca20f5589b5dab1dbbfa3baad8ce/cffi-2.0.0-cp311-cp311-win32.whl", hash = "sha256:c649e3a33450ec82378822b3dad03cc228b8f5963c0c12fc3b1e0ab940f768a5", size = 172076, upload-time = "2025-09-08T23:22:40.95Z" }, + { url = "https://files.pythonhosted.org/packages/ae/8f/dc5531155e7070361eb1b7e4c1a9d896d0cb21c49f807a6c03fd63fc877e/cffi-2.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:66f011380d0e49ed280c789fbd08ff0d40968ee7b665575489afa95c98196ab5", size = 182820, upload-time = "2025-09-08T23:22:42.463Z" }, + { url = "https://files.pythonhosted.org/packages/95/5c/1b493356429f9aecfd56bc171285a4c4ac8697f76e9bbbbb105e537853a1/cffi-2.0.0-cp311-cp311-win_arm64.whl", hash = "sha256:c6638687455baf640e37344fe26d37c404db8b80d037c3d29f58fe8d1c3b194d", size = 177635, upload-time = "2025-09-08T23:22:43.623Z" }, + { url = "https://files.pythonhosted.org/packages/ea/47/4f61023ea636104d4f16ab488e268b93008c3d0bb76893b1b31db1f96802/cffi-2.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:6d02d6655b0e54f54c4ef0b94eb6be0607b70853c45ce98bd278dc7de718be5d", size = 185271, upload-time = "2025-09-08T23:22:44.795Z" }, + { url = "https://files.pythonhosted.org/packages/df/a2/781b623f57358e360d62cdd7a8c681f074a71d445418a776eef0aadb4ab4/cffi-2.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8eca2a813c1cb7ad4fb74d368c2ffbbb4789d377ee5bb8df98373c2cc0dee76c", size = 181048, upload-time = "2025-09-08T23:22:45.938Z" }, + { url = "https://files.pythonhosted.org/packages/ff/df/a4f0fbd47331ceeba3d37c2e51e9dfc9722498becbeec2bd8bc856c9538a/cffi-2.0.0-cp312-cp312-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe", size = 212529, upload-time = "2025-09-08T23:22:47.349Z" }, + { url = "https://files.pythonhosted.org/packages/d5/72/12b5f8d3865bf0f87cf1404d8c374e7487dcf097a1c91c436e72e6badd83/cffi-2.0.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b21e08af67b8a103c71a250401c78d5e0893beff75e28c53c98f4de42f774062", size = 220097, upload-time = "2025-09-08T23:22:48.677Z" }, + { url = "https://files.pythonhosted.org/packages/c2/95/7a135d52a50dfa7c882ab0ac17e8dc11cec9d55d2c18dda414c051c5e69e/cffi-2.0.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e", size = 207983, upload-time = "2025-09-08T23:22:50.06Z" }, + { url = "https://files.pythonhosted.org/packages/3a/c8/15cb9ada8895957ea171c62dc78ff3e99159ee7adb13c0123c001a2546c1/cffi-2.0.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:81afed14892743bbe14dacb9e36d9e0e504cd204e0b165062c488942b9718037", size = 206519, upload-time = "2025-09-08T23:22:51.364Z" }, + { url = "https://files.pythonhosted.org/packages/78/2d/7fa73dfa841b5ac06c7b8855cfc18622132e365f5b81d02230333ff26e9e/cffi-2.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3e17ed538242334bf70832644a32a7aae3d83b57567f9fd60a26257e992b79ba", size = 219572, upload-time = "2025-09-08T23:22:52.902Z" }, + { url = "https://files.pythonhosted.org/packages/07/e0/267e57e387b4ca276b90f0434ff88b2c2241ad72b16d31836adddfd6031b/cffi-2.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3925dd22fa2b7699ed2617149842d2e6adde22b262fcbfada50e3d195e4b3a94", size = 222963, upload-time = "2025-09-08T23:22:54.518Z" }, + { url = "https://files.pythonhosted.org/packages/b6/75/1f2747525e06f53efbd878f4d03bac5b859cbc11c633d0fb81432d98a795/cffi-2.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2c8f814d84194c9ea681642fd164267891702542f028a15fc97d4674b6206187", size = 221361, upload-time = "2025-09-08T23:22:55.867Z" }, + { url = "https://files.pythonhosted.org/packages/7b/2b/2b6435f76bfeb6bbf055596976da087377ede68df465419d192acf00c437/cffi-2.0.0-cp312-cp312-win32.whl", hash = "sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18", size = 172932, upload-time = "2025-09-08T23:22:57.188Z" }, + { url = "https://files.pythonhosted.org/packages/f8/ed/13bd4418627013bec4ed6e54283b1959cf6db888048c7cf4b4c3b5b36002/cffi-2.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5", size = 183557, upload-time = "2025-09-08T23:22:58.351Z" }, + { url = "https://files.pythonhosted.org/packages/95/31/9f7f93ad2f8eff1dbc1c3656d7ca5bfd8fb52c9d786b4dcf19b2d02217fa/cffi-2.0.0-cp312-cp312-win_arm64.whl", hash = "sha256:4671d9dd5ec934cb9a73e7ee9676f9362aba54f7f34910956b84d727b0d73fb6", size = 177762, upload-time = "2025-09-08T23:22:59.668Z" }, + { url = "https://files.pythonhosted.org/packages/4b/8d/a0a47a0c9e413a658623d014e91e74a50cdd2c423f7ccfd44086ef767f90/cffi-2.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:00bdf7acc5f795150faa6957054fbbca2439db2f775ce831222b66f192f03beb", size = 185230, upload-time = "2025-09-08T23:23:00.879Z" }, + { url = "https://files.pythonhosted.org/packages/4a/d2/a6c0296814556c68ee32009d9c2ad4f85f2707cdecfd7727951ec228005d/cffi-2.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:45d5e886156860dc35862657e1494b9bae8dfa63bf56796f2fb56e1679fc0bca", size = 181043, upload-time = "2025-09-08T23:23:02.231Z" }, + { url = "https://files.pythonhosted.org/packages/b0/1e/d22cc63332bd59b06481ceaac49d6c507598642e2230f201649058a7e704/cffi-2.0.0-cp313-cp313-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:07b271772c100085dd28b74fa0cd81c8fb1a3ba18b21e03d7c27f3436a10606b", size = 212446, upload-time = "2025-09-08T23:23:03.472Z" }, + { url = "https://files.pythonhosted.org/packages/a9/f5/a2c23eb03b61a0b8747f211eb716446c826ad66818ddc7810cc2cc19b3f2/cffi-2.0.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d48a880098c96020b02d5a1f7d9251308510ce8858940e6fa99ece33f610838b", size = 220101, upload-time = "2025-09-08T23:23:04.792Z" }, + { url = "https://files.pythonhosted.org/packages/f2/7f/e6647792fc5850d634695bc0e6ab4111ae88e89981d35ac269956605feba/cffi-2.0.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:f93fd8e5c8c0a4aa1f424d6173f14a892044054871c771f8566e4008eaa359d2", size = 207948, upload-time = "2025-09-08T23:23:06.127Z" }, + { url = "https://files.pythonhosted.org/packages/cb/1e/a5a1bd6f1fb30f22573f76533de12a00bf274abcdc55c8edab639078abb6/cffi-2.0.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:dd4f05f54a52fb558f1ba9f528228066954fee3ebe629fc1660d874d040ae5a3", size = 206422, upload-time = "2025-09-08T23:23:07.753Z" }, + { url = "https://files.pythonhosted.org/packages/98/df/0a1755e750013a2081e863e7cd37e0cdd02664372c754e5560099eb7aa44/cffi-2.0.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c8d3b5532fc71b7a77c09192b4a5a200ea992702734a2e9279a37f2478236f26", size = 219499, upload-time = "2025-09-08T23:23:09.648Z" }, + { url = "https://files.pythonhosted.org/packages/50/e1/a969e687fcf9ea58e6e2a928ad5e2dd88cc12f6f0ab477e9971f2309b57c/cffi-2.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:d9b29c1f0ae438d5ee9acb31cadee00a58c46cc9c0b2f9038c6b0b3470877a8c", size = 222928, upload-time = "2025-09-08T23:23:10.928Z" }, + { url = "https://files.pythonhosted.org/packages/36/54/0362578dd2c9e557a28ac77698ed67323ed5b9775ca9d3fe73fe191bb5d8/cffi-2.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6d50360be4546678fc1b79ffe7a66265e28667840010348dd69a314145807a1b", size = 221302, upload-time = "2025-09-08T23:23:12.42Z" }, + { url = "https://files.pythonhosted.org/packages/eb/6d/bf9bda840d5f1dfdbf0feca87fbdb64a918a69bca42cfa0ba7b137c48cb8/cffi-2.0.0-cp313-cp313-win32.whl", hash = "sha256:74a03b9698e198d47562765773b4a8309919089150a0bb17d829ad7b44b60d27", size = 172909, upload-time = "2025-09-08T23:23:14.32Z" }, + { url = "https://files.pythonhosted.org/packages/37/18/6519e1ee6f5a1e579e04b9ddb6f1676c17368a7aba48299c3759bbc3c8b3/cffi-2.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:19f705ada2530c1167abacb171925dd886168931e0a7b78f5bffcae5c6b5be75", size = 183402, upload-time = "2025-09-08T23:23:15.535Z" }, + { url = "https://files.pythonhosted.org/packages/cb/0e/02ceeec9a7d6ee63bb596121c2c8e9b3a9e150936f4fbef6ca1943e6137c/cffi-2.0.0-cp313-cp313-win_arm64.whl", hash = "sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91", size = 177780, upload-time = "2025-09-08T23:23:16.761Z" }, + { url = "https://files.pythonhosted.org/packages/92/c4/3ce07396253a83250ee98564f8d7e9789fab8e58858f35d07a9a2c78de9f/cffi-2.0.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fc33c5141b55ed366cfaad382df24fe7dcbc686de5be719b207bb248e3053dc5", size = 185320, upload-time = "2025-09-08T23:23:18.087Z" }, + { url = "https://files.pythonhosted.org/packages/59/dd/27e9fa567a23931c838c6b02d0764611c62290062a6d4e8ff7863daf9730/cffi-2.0.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c654de545946e0db659b3400168c9ad31b5d29593291482c43e3564effbcee13", size = 181487, upload-time = "2025-09-08T23:23:19.622Z" }, + { url = "https://files.pythonhosted.org/packages/d6/43/0e822876f87ea8a4ef95442c3d766a06a51fc5298823f884ef87aaad168c/cffi-2.0.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:24b6f81f1983e6df8db3adc38562c83f7d4a0c36162885ec7f7b77c7dcbec97b", size = 220049, upload-time = "2025-09-08T23:23:20.853Z" }, + { url = "https://files.pythonhosted.org/packages/b4/89/76799151d9c2d2d1ead63c2429da9ea9d7aac304603de0c6e8764e6e8e70/cffi-2.0.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:12873ca6cb9b0f0d3a0da705d6086fe911591737a59f28b7936bdfed27c0d47c", size = 207793, upload-time = "2025-09-08T23:23:22.08Z" }, + { url = "https://files.pythonhosted.org/packages/bb/dd/3465b14bb9e24ee24cb88c9e3730f6de63111fffe513492bf8c808a3547e/cffi-2.0.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:d9b97165e8aed9272a6bb17c01e3cc5871a594a446ebedc996e2397a1c1ea8ef", size = 206300, upload-time = "2025-09-08T23:23:23.314Z" }, + { url = "https://files.pythonhosted.org/packages/47/d9/d83e293854571c877a92da46fdec39158f8d7e68da75bf73581225d28e90/cffi-2.0.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:afb8db5439b81cf9c9d0c80404b60c3cc9c3add93e114dcae767f1477cb53775", size = 219244, upload-time = "2025-09-08T23:23:24.541Z" }, + { url = "https://files.pythonhosted.org/packages/2b/0f/1f177e3683aead2bb00f7679a16451d302c436b5cbf2505f0ea8146ef59e/cffi-2.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:737fe7d37e1a1bffe70bd5754ea763a62a066dc5913ca57e957824b72a85e205", size = 222828, upload-time = "2025-09-08T23:23:26.143Z" }, + { url = "https://files.pythonhosted.org/packages/c6/0f/cafacebd4b040e3119dcb32fed8bdef8dfe94da653155f9d0b9dc660166e/cffi-2.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:38100abb9d1b1435bc4cc340bb4489635dc2f0da7456590877030c9b3d40b0c1", size = 220926, upload-time = "2025-09-08T23:23:27.873Z" }, + { url = "https://files.pythonhosted.org/packages/3e/aa/df335faa45b395396fcbc03de2dfcab242cd61a9900e914fe682a59170b1/cffi-2.0.0-cp314-cp314-win32.whl", hash = "sha256:087067fa8953339c723661eda6b54bc98c5625757ea62e95eb4898ad5e776e9f", size = 175328, upload-time = "2025-09-08T23:23:44.61Z" }, + { url = "https://files.pythonhosted.org/packages/bb/92/882c2d30831744296ce713f0feb4c1cd30f346ef747b530b5318715cc367/cffi-2.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:203a48d1fb583fc7d78a4c6655692963b860a417c0528492a6bc21f1aaefab25", size = 185650, upload-time = "2025-09-08T23:23:45.848Z" }, + { url = "https://files.pythonhosted.org/packages/9f/2c/98ece204b9d35a7366b5b2c6539c350313ca13932143e79dc133ba757104/cffi-2.0.0-cp314-cp314-win_arm64.whl", hash = "sha256:dbd5c7a25a7cb98f5ca55d258b103a2054f859a46ae11aaf23134f9cc0d356ad", size = 180687, upload-time = "2025-09-08T23:23:47.105Z" }, + { url = "https://files.pythonhosted.org/packages/3e/61/c768e4d548bfa607abcda77423448df8c471f25dbe64fb2ef6d555eae006/cffi-2.0.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:9a67fc9e8eb39039280526379fb3a70023d77caec1852002b4da7e8b270c4dd9", size = 188773, upload-time = "2025-09-08T23:23:29.347Z" }, + { url = "https://files.pythonhosted.org/packages/2c/ea/5f76bce7cf6fcd0ab1a1058b5af899bfbef198bea4d5686da88471ea0336/cffi-2.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:7a66c7204d8869299919db4d5069a82f1561581af12b11b3c9f48c584eb8743d", size = 185013, upload-time = "2025-09-08T23:23:30.63Z" }, + { url = "https://files.pythonhosted.org/packages/be/b4/c56878d0d1755cf9caa54ba71e5d049479c52f9e4afc230f06822162ab2f/cffi-2.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7cc09976e8b56f8cebd752f7113ad07752461f48a58cbba644139015ac24954c", size = 221593, upload-time = "2025-09-08T23:23:31.91Z" }, + { url = "https://files.pythonhosted.org/packages/e0/0d/eb704606dfe8033e7128df5e90fee946bbcb64a04fcdaa97321309004000/cffi-2.0.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:92b68146a71df78564e4ef48af17551a5ddd142e5190cdf2c5624d0c3ff5b2e8", size = 209354, upload-time = "2025-09-08T23:23:33.214Z" }, + { url = "https://files.pythonhosted.org/packages/d8/19/3c435d727b368ca475fb8742ab97c9cb13a0de600ce86f62eab7fa3eea60/cffi-2.0.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:b1e74d11748e7e98e2f426ab176d4ed720a64412b6a15054378afdb71e0f37dc", size = 208480, upload-time = "2025-09-08T23:23:34.495Z" }, + { url = "https://files.pythonhosted.org/packages/d0/44/681604464ed9541673e486521497406fadcc15b5217c3e326b061696899a/cffi-2.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:28a3a209b96630bca57cce802da70c266eb08c6e97e5afd61a75611ee6c64592", size = 221584, upload-time = "2025-09-08T23:23:36.096Z" }, + { url = "https://files.pythonhosted.org/packages/25/8e/342a504ff018a2825d395d44d63a767dd8ebc927ebda557fecdaca3ac33a/cffi-2.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:7553fb2090d71822f02c629afe6042c299edf91ba1bf94951165613553984512", size = 224443, upload-time = "2025-09-08T23:23:37.328Z" }, + { url = "https://files.pythonhosted.org/packages/e1/5e/b666bacbbc60fbf415ba9988324a132c9a7a0448a9a8f125074671c0f2c3/cffi-2.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:6c6c373cfc5c83a975506110d17457138c8c63016b563cc9ed6e056a82f13ce4", size = 223437, upload-time = "2025-09-08T23:23:38.945Z" }, + { url = "https://files.pythonhosted.org/packages/a0/1d/ec1a60bd1a10daa292d3cd6bb0b359a81607154fb8165f3ec95fe003b85c/cffi-2.0.0-cp314-cp314t-win32.whl", hash = "sha256:1fc9ea04857caf665289b7a75923f2c6ed559b8298a1b8c49e59f7dd95c8481e", size = 180487, upload-time = "2025-09-08T23:23:40.423Z" }, + { url = "https://files.pythonhosted.org/packages/bf/41/4c1168c74fac325c0c8156f04b6749c8b6a8f405bbf91413ba088359f60d/cffi-2.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:d68b6cef7827e8641e8ef16f4494edda8b36104d79773a334beaa1e3521430f6", size = 191726, upload-time = "2025-09-08T23:23:41.742Z" }, + { url = "https://files.pythonhosted.org/packages/ae/3a/dbeec9d1ee0844c679f6bb5d6ad4e9f198b1224f4e7a32825f47f6192b0c/cffi-2.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9", size = 184195, upload-time = "2025-09-08T23:23:43.004Z" }, ] [[package]] name = "cfgv" version = "3.5.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/4e/b5/721b8799b04bf9afe054a3899c6cf4e880fcf8563cc71c15610242490a0c/cfgv-3.5.0.tar.gz", hash = "sha256:d5b1034354820651caa73ede66a6294d6e95c1b00acc5e9b098e917404669132", size = 7334, upload-time = "2025-11-19T20:55:51.612Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/db/3c/33bac158f8ab7f89b2e59426d5fe2e4f63f7ed25df84c036890172b412b5/cfgv-3.5.0-py2.py3-none-any.whl", hash = "sha256:a8dc6b26ad22ff227d2634a65cb388215ce6cc96bbcc5cfde7641ae87e8dacc0", size = 7445, upload-time = "2025-11-19T20:55:50.744Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/11/74/539e56497d9bd1d484fd863dd69cbbfa653cd2aa27abfe35653494d85e94/cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560", size = 7114 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c5/55/51844dd50c4fc7a33b653bfaba4c2456f06955289ca770a5dbd5fd267374/cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9", size = 7249 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "charset-normalizer" version = "3.4.7" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/e7/a1/67fe25fac3c7642725500a3f6cfe5821ad557c3abb11c9d20d12c7008d3e/charset_normalizer-3.4.7.tar.gz", hash = "sha256:ae89db9e5f98a11a4bf50407d4363e7b09b31e55bc117b4f7d80aab97ba009e5", size = 144271, upload-time = "2026-04-02T09:28:39.342Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/c2/d7/b5b7020a0565c2e9fa8c09f4b5fa6232feb326b8c20081ccded47ea368fd/charset_normalizer-3.4.7-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:7641bb8895e77f921102f72833904dcd9901df5d6d72a2ab8f31d04b7e51e4e7", size = 309705, upload-time = "2026-04-02T09:26:02.191Z" }, @@ -424,102 +369,27 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/7a/1d/29d32e0fb40864b1f878c7f5a0b343ae676c6e2b271a2d55cc3a152391da/charset_normalizer-3.4.7-cp314-cp314t-win_amd64.whl", hash = "sha256:03853ed82eeebbce3c2abfdbc98c96dc205f32a79627688ac9a27370ea61a49c", size = 174168, upload-time = "2026-04-02T09:27:49.795Z" }, { url = "https://files.pythonhosted.org/packages/de/32/d92444ad05c7a6e41fb2036749777c163baf7a0301a040cb672d6b2b1ae9/charset_normalizer-3.4.7-cp314-cp314t-win_arm64.whl", hash = "sha256:c35abb8bfff0185efac5878da64c45dafd2b37fb0383add1be155a763c1f083d", size = 153018, upload-time = "2026-04-02T09:27:51.116Z" }, { url = "https://files.pythonhosted.org/packages/db/8f/61959034484a4a7c527811f4721e75d02d653a35afb0b6054474d8185d4c/charset_normalizer-3.4.7-py3-none-any.whl", hash = "sha256:3dce51d0f5e7951f8bb4900c257dad282f49190fdbebecd4ba99bcc41fef404d", size = 61958, upload-time = "2026-04-02T09:28:37.794Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/13/69/33ddede1939fdd074bce5434295f38fae7136463422fe4fd3e0e89b98062/charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a", size = 129418 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ed/27/c6491ff4954e58a10f69ad90aca8a1b6fe9c5d3c6f380907af3c37435b59/charset_normalizer-3.4.4-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:6e1fcf0720908f200cd21aa4e6750a48ff6ce4afe7ff5a79a90d5ed8a08296f8", size = 206988 }, - { url = "https://files.pythonhosted.org/packages/94/59/2e87300fe67ab820b5428580a53cad894272dbb97f38a7a814a2a1ac1011/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5f819d5fe9234f9f82d75bdfa9aef3a3d72c4d24a6e57aeaebba32a704553aa0", size = 147324 }, - { url = "https://files.pythonhosted.org/packages/07/fb/0cf61dc84b2b088391830f6274cb57c82e4da8bbc2efeac8c025edb88772/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:a59cb51917aa591b1c4e6a43c132f0cdc3c76dbad6155df4e28ee626cc77a0a3", size = 142742 }, - { url = "https://files.pythonhosted.org/packages/62/8b/171935adf2312cd745d290ed93cf16cf0dfe320863ab7cbeeae1dcd6535f/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:8ef3c867360f88ac904fd3f5e1f902f13307af9052646963ee08ff4f131adafc", size = 160863 }, - { url = "https://files.pythonhosted.org/packages/09/73/ad875b192bda14f2173bfc1bc9a55e009808484a4b256748d931b6948442/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d9e45d7faa48ee908174d8fe84854479ef838fc6a705c9315372eacbc2f02897", size = 157837 }, - { url = "https://files.pythonhosted.org/packages/6d/fc/de9cce525b2c5b94b47c70a4b4fb19f871b24995c728e957ee68ab1671ea/charset_normalizer-3.4.4-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:840c25fb618a231545cbab0564a799f101b63b9901f2569faecd6b222ac72381", size = 151550 }, - { url = "https://files.pythonhosted.org/packages/55/c2/43edd615fdfba8c6f2dfbd459b25a6b3b551f24ea21981e23fb768503ce1/charset_normalizer-3.4.4-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:ca5862d5b3928c4940729dacc329aa9102900382fea192fc5e52eb69d6093815", size = 149162 }, - { url = "https://files.pythonhosted.org/packages/03/86/bde4ad8b4d0e9429a4e82c1e8f5c659993a9a863ad62c7df05cf7b678d75/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d9c7f57c3d666a53421049053eaacdd14bbd0a528e2186fcb2e672effd053bb0", size = 150019 }, - { url = "https://files.pythonhosted.org/packages/1f/86/a151eb2af293a7e7bac3a739b81072585ce36ccfb4493039f49f1d3cae8c/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:277e970e750505ed74c832b4bf75dac7476262ee2a013f5574dd49075879e161", size = 143310 }, - { url = "https://files.pythonhosted.org/packages/b5/fe/43dae6144a7e07b87478fdfc4dbe9efd5defb0e7ec29f5f58a55aeef7bf7/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:31fd66405eaf47bb62e8cd575dc621c56c668f27d46a61d975a249930dd5e2a4", size = 162022 }, - { url = "https://files.pythonhosted.org/packages/80/e6/7aab83774f5d2bca81f42ac58d04caf44f0cc2b65fc6db2b3b2e8a05f3b3/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:0d3d8f15c07f86e9ff82319b3d9ef6f4bf907608f53fe9d92b28ea9ae3d1fd89", size = 149383 }, - { url = "https://files.pythonhosted.org/packages/4f/e8/b289173b4edae05c0dde07f69f8db476a0b511eac556dfe0d6bda3c43384/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:9f7fcd74d410a36883701fafa2482a6af2ff5ba96b9a620e9e0721e28ead5569", size = 159098 }, - { url = "https://files.pythonhosted.org/packages/d8/df/fe699727754cae3f8478493c7f45f777b17c3ef0600e28abfec8619eb49c/charset_normalizer-3.4.4-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:ebf3e58c7ec8a8bed6d66a75d7fb37b55e5015b03ceae72a8e7c74495551e224", size = 152991 }, - { url = "https://files.pythonhosted.org/packages/1a/86/584869fe4ddb6ffa3bd9f491b87a01568797fb9bd8933f557dba9771beaf/charset_normalizer-3.4.4-cp311-cp311-win32.whl", hash = "sha256:eecbc200c7fd5ddb9a7f16c7decb07b566c29fa2161a16cf67b8d068bd21690a", size = 99456 }, - { url = "https://files.pythonhosted.org/packages/65/f6/62fdd5feb60530f50f7e38b4f6a1d5203f4d16ff4f9f0952962c044e919a/charset_normalizer-3.4.4-cp311-cp311-win_amd64.whl", hash = "sha256:5ae497466c7901d54b639cf42d5b8c1b6a4fead55215500d2f486d34db48d016", size = 106978 }, - { url = "https://files.pythonhosted.org/packages/7a/9d/0710916e6c82948b3be62d9d398cb4fcf4e97b56d6a6aeccd66c4b2f2bd5/charset_normalizer-3.4.4-cp311-cp311-win_arm64.whl", hash = "sha256:65e2befcd84bc6f37095f5961e68a6f077bf44946771354a28ad434c2cce0ae1", size = 99969 }, - { url = "https://files.pythonhosted.org/packages/f3/85/1637cd4af66fa687396e757dec650f28025f2a2f5a5531a3208dc0ec43f2/charset_normalizer-3.4.4-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:0a98e6759f854bd25a58a73fa88833fba3b7c491169f86ce1180c948ab3fd394", size = 208425 }, - { url = "https://files.pythonhosted.org/packages/9d/6a/04130023fef2a0d9c62d0bae2649b69f7b7d8d24ea5536feef50551029df/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b5b290ccc2a263e8d185130284f8501e3e36c5e02750fc6b6bdeb2e9e96f1e25", size = 148162 }, - { url = "https://files.pythonhosted.org/packages/78/29/62328d79aa60da22c9e0b9a66539feae06ca0f5a4171ac4f7dc285b83688/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74bb723680f9f7a6234dcf67aea57e708ec1fbdf5699fb91dfd6f511b0a320ef", size = 144558 }, - { url = "https://files.pythonhosted.org/packages/86/bb/b32194a4bf15b88403537c2e120b817c61cd4ecffa9b6876e941c3ee38fe/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f1e34719c6ed0b92f418c7c780480b26b5d9c50349e9a9af7d76bf757530350d", size = 161497 }, - { url = "https://files.pythonhosted.org/packages/19/89/a54c82b253d5b9b111dc74aca196ba5ccfcca8242d0fb64146d4d3183ff1/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:2437418e20515acec67d86e12bf70056a33abdacb5cb1655042f6538d6b085a8", size = 159240 }, - { url = "https://files.pythonhosted.org/packages/c0/10/d20b513afe03acc89ec33948320a5544d31f21b05368436d580dec4e234d/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:11d694519d7f29d6cd09f6ac70028dba10f92f6cdd059096db198c283794ac86", size = 153471 }, - { url = "https://files.pythonhosted.org/packages/61/fa/fbf177b55bdd727010f9c0a3c49eefa1d10f960e5f09d1d887bf93c2e698/charset_normalizer-3.4.4-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:ac1c4a689edcc530fc9d9aa11f5774b9e2f33f9a0c6a57864e90908f5208d30a", size = 150864 }, - { url = "https://files.pythonhosted.org/packages/05/12/9fbc6a4d39c0198adeebbde20b619790e9236557ca59fc40e0e3cebe6f40/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:21d142cc6c0ec30d2efee5068ca36c128a30b0f2c53c1c07bd78cb6bc1d3be5f", size = 150647 }, - { url = "https://files.pythonhosted.org/packages/ad/1f/6a9a593d52e3e8c5d2b167daf8c6b968808efb57ef4c210acb907c365bc4/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:5dbe56a36425d26d6cfb40ce79c314a2e4dd6211d51d6d2191c00bed34f354cc", size = 145110 }, - { url = "https://files.pythonhosted.org/packages/30/42/9a52c609e72471b0fc54386dc63c3781a387bb4fe61c20231a4ebcd58bdd/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:5bfbb1b9acf3334612667b61bd3002196fe2a1eb4dd74d247e0f2a4d50ec9bbf", size = 162839 }, - { url = "https://files.pythonhosted.org/packages/c4/5b/c0682bbf9f11597073052628ddd38344a3d673fda35a36773f7d19344b23/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:d055ec1e26e441f6187acf818b73564e6e6282709e9bcb5b63f5b23068356a15", size = 150667 }, - { url = "https://files.pythonhosted.org/packages/e4/24/a41afeab6f990cf2daf6cb8c67419b63b48cf518e4f56022230840c9bfb2/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:af2d8c67d8e573d6de5bc30cdb27e9b95e49115cd9baad5ddbd1a6207aaa82a9", size = 160535 }, - { url = "https://files.pythonhosted.org/packages/2a/e5/6a4ce77ed243c4a50a1fecca6aaaab419628c818a49434be428fe24c9957/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:780236ac706e66881f3b7f2f32dfe90507a09e67d1d454c762cf642e6e1586e0", size = 154816 }, - { url = "https://files.pythonhosted.org/packages/a8/ef/89297262b8092b312d29cdb2517cb1237e51db8ecef2e9af5edbe7b683b1/charset_normalizer-3.4.4-cp312-cp312-win32.whl", hash = "sha256:5833d2c39d8896e4e19b689ffc198f08ea58116bee26dea51e362ecc7cd3ed26", size = 99694 }, - { url = "https://files.pythonhosted.org/packages/3d/2d/1e5ed9dd3b3803994c155cd9aacb60c82c331bad84daf75bcb9c91b3295e/charset_normalizer-3.4.4-cp312-cp312-win_amd64.whl", hash = "sha256:a79cfe37875f822425b89a82333404539ae63dbdddf97f84dcbc3d339aae9525", size = 107131 }, - { url = "https://files.pythonhosted.org/packages/d0/d9/0ed4c7098a861482a7b6a95603edce4c0d9db2311af23da1fb2b75ec26fc/charset_normalizer-3.4.4-cp312-cp312-win_arm64.whl", hash = "sha256:376bec83a63b8021bb5c8ea75e21c4ccb86e7e45ca4eb81146091b56599b80c3", size = 100390 }, - { url = "https://files.pythonhosted.org/packages/97/45/4b3a1239bbacd321068ea6e7ac28875b03ab8bc0aa0966452db17cd36714/charset_normalizer-3.4.4-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:e1f185f86a6f3403aa2420e815904c67b2f9ebc443f045edd0de921108345794", size = 208091 }, - { url = "https://files.pythonhosted.org/packages/7d/62/73a6d7450829655a35bb88a88fca7d736f9882a27eacdca2c6d505b57e2e/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b39f987ae8ccdf0d2642338faf2abb1862340facc796048b604ef14919e55ed", size = 147936 }, - { url = "https://files.pythonhosted.org/packages/89/c5/adb8c8b3d6625bef6d88b251bbb0d95f8205831b987631ab0c8bb5d937c2/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3162d5d8ce1bb98dd51af660f2121c55d0fa541b46dff7bb9b9f86ea1d87de72", size = 144180 }, - { url = "https://files.pythonhosted.org/packages/91/ed/9706e4070682d1cc219050b6048bfd293ccf67b3d4f5a4f39207453d4b99/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:81d5eb2a312700f4ecaa977a8235b634ce853200e828fbadf3a9c50bab278328", size = 161346 }, - { url = "https://files.pythonhosted.org/packages/d5/0d/031f0d95e4972901a2f6f09ef055751805ff541511dc1252ba3ca1f80cf5/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5bd2293095d766545ec1a8f612559f6b40abc0eb18bb2f5d1171872d34036ede", size = 158874 }, - { url = "https://files.pythonhosted.org/packages/f5/83/6ab5883f57c9c801ce5e5677242328aa45592be8a00644310a008d04f922/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a8a8b89589086a25749f471e6a900d3f662d1d3b6e2e59dcecf787b1cc3a1894", size = 153076 }, - { url = "https://files.pythonhosted.org/packages/75/1e/5ff781ddf5260e387d6419959ee89ef13878229732732ee73cdae01800f2/charset_normalizer-3.4.4-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:bc7637e2f80d8530ee4a78e878bce464f70087ce73cf7c1caf142416923b98f1", size = 150601 }, - { url = "https://files.pythonhosted.org/packages/d7/57/71be810965493d3510a6ca79b90c19e48696fb1ff964da319334b12677f0/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f8bf04158c6b607d747e93949aa60618b61312fe647a6369f88ce2ff16043490", size = 150376 }, - { url = "https://files.pythonhosted.org/packages/e5/d5/c3d057a78c181d007014feb7e9f2e65905a6c4ef182c0ddf0de2924edd65/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:554af85e960429cf30784dd47447d5125aaa3b99a6f0683589dbd27e2f45da44", size = 144825 }, - { url = "https://files.pythonhosted.org/packages/e6/8c/d0406294828d4976f275ffbe66f00266c4b3136b7506941d87c00cab5272/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:74018750915ee7ad843a774364e13a3db91682f26142baddf775342c3f5b1133", size = 162583 }, - { url = "https://files.pythonhosted.org/packages/d7/24/e2aa1f18c8f15c4c0e932d9287b8609dd30ad56dbe41d926bd846e22fb8d/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:c0463276121fdee9c49b98908b3a89c39be45d86d1dbaa22957e38f6321d4ce3", size = 150366 }, - { url = "https://files.pythonhosted.org/packages/e4/5b/1e6160c7739aad1e2df054300cc618b06bf784a7a164b0f238360721ab86/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:362d61fd13843997c1c446760ef36f240cf81d3ebf74ac62652aebaf7838561e", size = 160300 }, - { url = "https://files.pythonhosted.org/packages/7a/10/f882167cd207fbdd743e55534d5d9620e095089d176d55cb22d5322f2afd/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9a26f18905b8dd5d685d6d07b0cdf98a79f3c7a918906af7cc143ea2e164c8bc", size = 154465 }, - { url = "https://files.pythonhosted.org/packages/89/66/c7a9e1b7429be72123441bfdbaf2bc13faab3f90b933f664db506dea5915/charset_normalizer-3.4.4-cp313-cp313-win32.whl", hash = "sha256:9b35f4c90079ff2e2edc5b26c0c77925e5d2d255c42c74fdb70fb49b172726ac", size = 99404 }, - { url = "https://files.pythonhosted.org/packages/c4/26/b9924fa27db384bdcd97ab83b4f0a8058d96ad9626ead570674d5e737d90/charset_normalizer-3.4.4-cp313-cp313-win_amd64.whl", hash = "sha256:b435cba5f4f750aa6c0a0d92c541fb79f69a387c91e61f1795227e4ed9cece14", size = 107092 }, - { url = "https://files.pythonhosted.org/packages/af/8f/3ed4bfa0c0c72a7ca17f0380cd9e4dd842b09f664e780c13cff1dcf2ef1b/charset_normalizer-3.4.4-cp313-cp313-win_arm64.whl", hash = "sha256:542d2cee80be6f80247095cc36c418f7bddd14f4a6de45af91dfad36d817bba2", size = 100408 }, - { url = "https://files.pythonhosted.org/packages/2a/35/7051599bd493e62411d6ede36fd5af83a38f37c4767b92884df7301db25d/charset_normalizer-3.4.4-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:da3326d9e65ef63a817ecbcc0df6e94463713b754fe293eaa03da99befb9a5bd", size = 207746 }, - { url = "https://files.pythonhosted.org/packages/10/9a/97c8d48ef10d6cd4fcead2415523221624bf58bcf68a802721a6bc807c8f/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8af65f14dc14a79b924524b1e7fffe304517b2bff5a58bf64f30b98bbc5079eb", size = 147889 }, - { url = "https://files.pythonhosted.org/packages/10/bf/979224a919a1b606c82bd2c5fa49b5c6d5727aa47b4312bb27b1734f53cd/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74664978bb272435107de04e36db5a9735e78232b85b77d45cfb38f758efd33e", size = 143641 }, - { url = "https://files.pythonhosted.org/packages/ba/33/0ad65587441fc730dc7bd90e9716b30b4702dc7b617e6ba4997dc8651495/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:752944c7ffbfdd10c074dc58ec2d5a8a4cd9493b314d367c14d24c17684ddd14", size = 160779 }, - { url = "https://files.pythonhosted.org/packages/67/ed/331d6b249259ee71ddea93f6f2f0a56cfebd46938bde6fcc6f7b9a3d0e09/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d1f13550535ad8cff21b8d757a3257963e951d96e20ec82ab44bc64aeb62a191", size = 159035 }, - { url = "https://files.pythonhosted.org/packages/67/ff/f6b948ca32e4f2a4576aa129d8bed61f2e0543bf9f5f2b7fc3758ed005c9/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ecaae4149d99b1c9e7b88bb03e3221956f68fd6d50be2ef061b2381b61d20838", size = 152542 }, - { url = "https://files.pythonhosted.org/packages/16/85/276033dcbcc369eb176594de22728541a925b2632f9716428c851b149e83/charset_normalizer-3.4.4-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:cb6254dc36b47a990e59e1068afacdcd02958bdcce30bb50cc1700a8b9d624a6", size = 149524 }, - { url = "https://files.pythonhosted.org/packages/9e/f2/6a2a1f722b6aba37050e626530a46a68f74e63683947a8acff92569f979a/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c8ae8a0f02f57a6e61203a31428fa1d677cbe50c93622b4149d5c0f319c1d19e", size = 150395 }, - { url = "https://files.pythonhosted.org/packages/60/bb/2186cb2f2bbaea6338cad15ce23a67f9b0672929744381e28b0592676824/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:47cc91b2f4dd2833fddaedd2893006b0106129d4b94fdb6af1f4ce5a9965577c", size = 143680 }, - { url = "https://files.pythonhosted.org/packages/7d/a5/bf6f13b772fbb2a90360eb620d52ed8f796f3c5caee8398c3b2eb7b1c60d/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:82004af6c302b5d3ab2cfc4cc5f29db16123b1a8417f2e25f9066f91d4411090", size = 162045 }, - { url = "https://files.pythonhosted.org/packages/df/c5/d1be898bf0dc3ef9030c3825e5d3b83f2c528d207d246cbabe245966808d/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:2b7d8f6c26245217bd2ad053761201e9f9680f8ce52f0fcd8d0755aeae5b2152", size = 149687 }, - { url = "https://files.pythonhosted.org/packages/a5/42/90c1f7b9341eef50c8a1cb3f098ac43b0508413f33affd762855f67a410e/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:799a7a5e4fb2d5898c60b640fd4981d6a25f1c11790935a44ce38c54e985f828", size = 160014 }, - { url = "https://files.pythonhosted.org/packages/76/be/4d3ee471e8145d12795ab655ece37baed0929462a86e72372fd25859047c/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:99ae2cffebb06e6c22bdc25801d7b30f503cc87dbd283479e7b606f70aff57ec", size = 154044 }, - { url = "https://files.pythonhosted.org/packages/b0/6f/8f7af07237c34a1defe7defc565a9bc1807762f672c0fde711a4b22bf9c0/charset_normalizer-3.4.4-cp314-cp314-win32.whl", hash = "sha256:f9d332f8c2a2fcbffe1378594431458ddbef721c1769d78e2cbc06280d8155f9", size = 99940 }, - { url = "https://files.pythonhosted.org/packages/4b/51/8ade005e5ca5b0d80fb4aff72a3775b325bdc3d27408c8113811a7cbe640/charset_normalizer-3.4.4-cp314-cp314-win_amd64.whl", hash = "sha256:8a6562c3700cce886c5be75ade4a5db4214fda19fede41d9792d100288d8f94c", size = 107104 }, - { url = "https://files.pythonhosted.org/packages/da/5f/6b8f83a55bb8278772c5ae54a577f3099025f9ade59d0136ac24a0df4bde/charset_normalizer-3.4.4-cp314-cp314-win_arm64.whl", hash = "sha256:de00632ca48df9daf77a2c65a484531649261ec9f25489917f09e455cb09ddb2", size = 100743 }, - { url = "https://files.pythonhosted.org/packages/0a/4c/925909008ed5a988ccbb72dcc897407e5d6d3bd72410d69e051fc0c14647/charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f", size = 53402 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "click" -version = "8.4.1" +version = "8.4.2" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "colorama", marker = "sys_platform == 'win32'" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/9b/98/518d8e5081007684232226f475082b30087d0f585e8457db087298259f49/click-8.4.1.tar.gz", hash = "sha256:918b5633eddf6b41c32d4f454bf0de810065c74e3f7dbf8ee5452f8be88d3e96", size = 353007, upload-time = "2026-05-22T04:08:37.769Z" } +sdist = { url = "https://files.pythonhosted.org/packages/76/d4/81420972a676e8ffea40450d8c8c92943e7218a78fe9b64359836cc9876b/click-8.4.2.tar.gz", hash = "sha256:9a6cea6e60b17ebe0a44c5cc636d94f09bd66142c1cd7d8b4cd731c4917a15f6", size = 338000, upload-time = "2026-06-24T17:45:15.148Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/c7/0d/67e5b4109ea4a837e80daa87c2c696711955e40449a97e8926672534def2/click-8.4.1-py3-none-any.whl", hash = "sha256:482be17c6991b8c19c5429a1e995d9b0efdbb63172824c41f99965dc0ade8ec2", size = 116639, upload-time = "2026-05-22T04:08:35.26Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/fb/e2/79c688af8b210d232694e31e59da9f6ec747bae31c3f5946e4e9b98860d5/click-8.4.2-py3-none-any.whl", hash = "sha256:e6f9f66136c816745b9d65817da91d61d957fb16e02e4dcd0552553c5a197b76", size = 119243, upload-time = "2026-06-24T17:45:13.73Z" }, ] [[package]] name = "cloudpickle" version = "3.1.2" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/27/fb/576f067976d320f5f0114a8d9fa1215425441bb35627b1993e5afd8111e5/cloudpickle-3.1.2.tar.gz", hash = "sha256:7fda9eb655c9c230dab534f1983763de5835249750e85fbcef43aaa30a9a2414", size = 22330 } +sdist = { url = "https://files.pythonhosted.org/packages/27/fb/576f067976d320f5f0114a8d9fa1215425441bb35627b1993e5afd8111e5/cloudpickle-3.1.2.tar.gz", hash = "sha256:7fda9eb655c9c230dab534f1983763de5835249750e85fbcef43aaa30a9a2414", size = 22330, upload-time = "2025-11-03T09:25:26.604Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/88/39/799be3f2f0f38cc727ee3b4f1445fe6d5e4133064ec2e4115069418a5bb6/cloudpickle-3.1.2-py3-none-any.whl", hash = "sha256:9acb47f6afd73f60dc1df93bb801b472f05ff42fa6c84167d25cb206be1fbf4a", size = 22228 }, + { url = "https://files.pythonhosted.org/packages/88/39/799be3f2f0f38cc727ee3b4f1445fe6d5e4133064ec2e4115069418a5bb6/cloudpickle-3.1.2-py3-none-any.whl", hash = "sha256:9acb47f6afd73f60dc1df93bb801b472f05ff42fa6c84167d25cb206be1fbf4a", size = 22228, upload-time = "2025-11-03T09:25:25.534Z" }, ] [[package]] @@ -529,20 +399,21 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "colorspacious" }, { name = "matplotlib" }, - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/2f/ca/39152b506133881be74bd46cced27ec10d9bbc85db5bdfc22c8f8e4011f9/cmasher-1.9.2.tar.gz", hash = "sha256:6c90c2cec87146e3210d3e7f2321fceee38ac7d87c136cd0de25160a14f57ff5", size = 520860 } +sdist = { url = "https://files.pythonhosted.org/packages/2f/ca/39152b506133881be74bd46cced27ec10d9bbc85db5bdfc22c8f8e4011f9/cmasher-1.9.2.tar.gz", hash = "sha256:6c90c2cec87146e3210d3e7f2321fceee38ac7d87c136cd0de25160a14f57ff5", size = 520860, upload-time = "2024-11-19T11:17:02.084Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/91/d2/6fcb93a60777fef7662d84ba60846d2dee9b6b70b4a62472515110f79cee/cmasher-1.9.2-py3-none-any.whl", hash = "sha256:2fe45fde06051050dda5c023a527ba9066ca21f161c793f22839a6ebe6e4bbbb", size = 506496 }, + { url = "https://files.pythonhosted.org/packages/91/d2/6fcb93a60777fef7662d84ba60846d2dee9b6b70b4a62472515110f79cee/cmasher-1.9.2-py3-none-any.whl", hash = "sha256:2fe45fde06051050dda5c023a527ba9066ca21f161c793f22839a6ebe6e4bbbb", size = 506496, upload-time = "2024-11-19T11:16:58.549Z" }, ] [[package]] name = "colorama" version = "0.4.6" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697 } +sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 }, + { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" }, ] [[package]] @@ -552,9 +423,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "colorama", marker = "sys_platform == 'win32'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/a2/61/f083b5ac52e505dfc1c624eafbf8c7589a0d7f32daa398d2e7590efa5fda/colorlog-6.10.1.tar.gz", hash = "sha256:eb4ae5cb65fe7fec7773c2306061a8e63e02efc2c72eba9d27b0fa23c94f1321", size = 17162 } +sdist = { url = "https://files.pythonhosted.org/packages/a2/61/f083b5ac52e505dfc1c624eafbf8c7589a0d7f32daa398d2e7590efa5fda/colorlog-6.10.1.tar.gz", hash = "sha256:eb4ae5cb65fe7fec7773c2306061a8e63e02efc2c72eba9d27b0fa23c94f1321", size = 17162, upload-time = "2025-10-16T16:14:11.978Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/6d/c1/e419ef3723a074172b68aaa89c9f3de486ed4c2399e2dbd8113a4fdcaf9e/colorlog-6.10.1-py3-none-any.whl", hash = "sha256:2d7e8348291948af66122cff006c9f8da6255d224e7cf8e37d8de2df3bad8c9c", size = 11743 }, + { url = "https://files.pythonhosted.org/packages/6d/c1/e419ef3723a074172b68aaa89c9f3de486ed4c2399e2dbd8113a4fdcaf9e/colorlog-6.10.1-py3-none-any.whl", hash = "sha256:2d7e8348291948af66122cff006c9f8da6255d224e7cf8e37d8de2df3bad8c9c", size = 11743, upload-time = "2025-10-16T16:14:10.512Z" }, ] [[package]] @@ -562,20 +433,21 @@ name = "colorspacious" version = "1.1.2" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/75/e4/aa41ae14c5c061205715006c8834496d86ec7500f1edda5981f0f0190cc6/colorspacious-1.1.2.tar.gz", hash = "sha256:5e9072e8cdca889dac445c35c9362a22ccf758e97b00b79ff0d5a7ba3e11b618", size = 688573 } +sdist = { url = "https://files.pythonhosted.org/packages/75/e4/aa41ae14c5c061205715006c8834496d86ec7500f1edda5981f0f0190cc6/colorspacious-1.1.2.tar.gz", hash = "sha256:5e9072e8cdca889dac445c35c9362a22ccf758e97b00b79ff0d5a7ba3e11b618", size = 688573, upload-time = "2018-04-08T04:27:30.83Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/ab/a1/318b9aeca7b9856410ededa4f52d6f82174d1a41e64bdd70d951e532675a/colorspacious-1.1.2-py2.py3-none-any.whl", hash = "sha256:c78befa603cea5dccb332464e7dd29e96469eebf6cd5133029153d1e69e3fd6f", size = 37735 }, + { url = "https://files.pythonhosted.org/packages/ab/a1/318b9aeca7b9856410ededa4f52d6f82174d1a41e64bdd70d951e532675a/colorspacious-1.1.2-py2.py3-none-any.whl", hash = "sha256:c78befa603cea5dccb332464e7dd29e96469eebf6cd5133029153d1e69e3fd6f", size = 37735, upload-time = "2018-04-08T04:27:22.143Z" }, ] [[package]] name = "comm" version = "0.2.3" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/4c/13/7d740c5849255756bc17888787313b61fd38a0a8304fc4f073dfc46122aa/comm-0.2.3.tar.gz", hash = "sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971", size = 6319 } +sdist = { url = "https://files.pythonhosted.org/packages/4c/13/7d740c5849255756bc17888787313b61fd38a0a8304fc4f073dfc46122aa/comm-0.2.3.tar.gz", hash = "sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971", size = 6319, upload-time = "2025-07-25T14:02:04.452Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294 }, + { url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294, upload-time = "2025-07-25T14:02:02.896Z" }, ] [[package]] @@ -583,181 +455,166 @@ name = "contourpy" version = "1.3.3" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "numpy" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/91/2e/c4390a31919d8a78b90e8ecf87cd4b4c4f05a5b48d05ec17db8e5404c6f4/contourpy-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1", size = 288773 }, - { url = "https://files.pythonhosted.org/packages/0d/44/c4b0b6095fef4dc9c420e041799591e3b63e9619e3044f7f4f6c21c0ab24/contourpy-1.3.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:23416f38bfd74d5d28ab8429cc4d63fa67d5068bd711a85edb1c3fb0c3e2f381", size = 270149 }, - { url = "https://files.pythonhosted.org/packages/30/2e/dd4ced42fefac8470661d7cb7e264808425e6c5d56d175291e93890cce09/contourpy-1.3.3-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:929ddf8c4c7f348e4c0a5a3a714b5c8542ffaa8c22954862a46ca1813b667ee7", size = 329222 }, - { url = "https://files.pythonhosted.org/packages/f2/74/cc6ec2548e3d276c71389ea4802a774b7aa3558223b7bade3f25787fafc2/contourpy-1.3.3-cp311-cp311-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9e999574eddae35f1312c2b4b717b7885d4edd6cb46700e04f7f02db454e67c1", size = 377234 }, - { url = "https://files.pythonhosted.org/packages/03/b3/64ef723029f917410f75c09da54254c5f9ea90ef89b143ccadb09df14c15/contourpy-1.3.3-cp311-cp311-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0bf67e0e3f482cb69779dd3061b534eb35ac9b17f163d851e2a547d56dba0a3a", size = 380555 }, - { url = "https://files.pythonhosted.org/packages/5f/4b/6157f24ca425b89fe2eb7e7be642375711ab671135be21e6faa100f7448c/contourpy-1.3.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:51e79c1f7470158e838808d4a996fa9bac72c498e93d8ebe5119bc1e6becb0db", size = 355238 }, - { url = "https://files.pythonhosted.org/packages/98/56/f914f0dd678480708a04cfd2206e7c382533249bc5001eb9f58aa693e200/contourpy-1.3.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:598c3aaece21c503615fd59c92a3598b428b2f01bfb4b8ca9c4edeecc2438620", size = 1326218 }, - { url = "https://files.pythonhosted.org/packages/fb/d7/4a972334a0c971acd5172389671113ae82aa7527073980c38d5868ff1161/contourpy-1.3.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:322ab1c99b008dad206d406bb61d014cf0174df491ae9d9d0fac6a6fda4f977f", size = 1392867 }, - { url = "https://files.pythonhosted.org/packages/75/3e/f2cc6cd56dc8cff46b1a56232eabc6feea52720083ea71ab15523daab796/contourpy-1.3.3-cp311-cp311-win32.whl", hash = "sha256:fd907ae12cd483cd83e414b12941c632a969171bf90fc937d0c9f268a31cafff", size = 183677 }, - { url = "https://files.pythonhosted.org/packages/98/4b/9bd370b004b5c9d8045c6c33cf65bae018b27aca550a3f657cdc99acdbd8/contourpy-1.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:3519428f6be58431c56581f1694ba8e50626f2dd550af225f82fb5f5814d2a42", size = 225234 }, - { url = "https://files.pythonhosted.org/packages/d9/b6/71771e02c2e004450c12b1120a5f488cad2e4d5b590b1af8bad060360fe4/contourpy-1.3.3-cp311-cp311-win_arm64.whl", hash = "sha256:15ff10bfada4bf92ec8b31c62bf7c1834c244019b4a33095a68000d7075df470", size = 193123 }, - { url = "https://files.pythonhosted.org/packages/be/45/adfee365d9ea3d853550b2e735f9d66366701c65db7855cd07621732ccfc/contourpy-1.3.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:b08a32ea2f8e42cf1d4be3169a98dd4be32bafe4f22b6c4cb4ba810fa9e5d2cb", size = 293419 }, - { url = "https://files.pythonhosted.org/packages/53/3e/405b59cfa13021a56bba395a6b3aca8cec012b45bf177b0eaf7a202cde2c/contourpy-1.3.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6", size = 273979 }, - { url = "https://files.pythonhosted.org/packages/d4/1c/a12359b9b2ca3a845e8f7f9ac08bdf776114eb931392fcad91743e2ea17b/contourpy-1.3.3-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7", size = 332653 }, - { url = "https://files.pythonhosted.org/packages/63/12/897aeebfb475b7748ea67b61e045accdfcf0d971f8a588b67108ed7f5512/contourpy-1.3.3-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b2e8faa0ed68cb29af51edd8e24798bb661eac3bd9f65420c1887b6ca89987c8", size = 379536 }, - { url = "https://files.pythonhosted.org/packages/43/8a/a8c584b82deb248930ce069e71576fc09bd7174bbd35183b7943fb1064fd/contourpy-1.3.3-cp312-cp312-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea", size = 384397 }, - { url = "https://files.pythonhosted.org/packages/cc/8f/ec6289987824b29529d0dfda0d74a07cec60e54b9c92f3c9da4c0ac732de/contourpy-1.3.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1", size = 362601 }, - { url = "https://files.pythonhosted.org/packages/05/0a/a3fe3be3ee2dceb3e615ebb4df97ae6f3828aa915d3e10549ce016302bd1/contourpy-1.3.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7", size = 1331288 }, - { url = "https://files.pythonhosted.org/packages/33/1d/acad9bd4e97f13f3e2b18a3977fe1b4a37ecf3d38d815333980c6c72e963/contourpy-1.3.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411", size = 1403386 }, - { url = "https://files.pythonhosted.org/packages/cf/8f/5847f44a7fddf859704217a99a23a4f6417b10e5ab1256a179264561540e/contourpy-1.3.3-cp312-cp312-win32.whl", hash = "sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69", size = 185018 }, - { url = "https://files.pythonhosted.org/packages/19/e8/6026ed58a64563186a9ee3f29f41261fd1828f527dd93d33b60feca63352/contourpy-1.3.3-cp312-cp312-win_amd64.whl", hash = "sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b", size = 226567 }, - { url = "https://files.pythonhosted.org/packages/d1/e2/f05240d2c39a1ed228d8328a78b6f44cd695f7ef47beb3e684cf93604f86/contourpy-1.3.3-cp312-cp312-win_arm64.whl", hash = "sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc", size = 193655 }, - { url = "https://files.pythonhosted.org/packages/68/35/0167aad910bbdb9599272bd96d01a9ec6852f36b9455cf2ca67bd4cc2d23/contourpy-1.3.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5", size = 293257 }, - { url = "https://files.pythonhosted.org/packages/96/e4/7adcd9c8362745b2210728f209bfbcf7d91ba868a2c5f40d8b58f54c509b/contourpy-1.3.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1", size = 274034 }, - { url = "https://files.pythonhosted.org/packages/73/23/90e31ceeed1de63058a02cb04b12f2de4b40e3bef5e082a7c18d9c8ae281/contourpy-1.3.3-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286", size = 334672 }, - { url = "https://files.pythonhosted.org/packages/ed/93/b43d8acbe67392e659e1d984700e79eb67e2acb2bd7f62012b583a7f1b55/contourpy-1.3.3-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5", size = 381234 }, - { url = "https://files.pythonhosted.org/packages/46/3b/bec82a3ea06f66711520f75a40c8fc0b113b2a75edb36aa633eb11c4f50f/contourpy-1.3.3-cp313-cp313-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67", size = 385169 }, - { url = "https://files.pythonhosted.org/packages/4b/32/e0f13a1c5b0f8572d0ec6ae2f6c677b7991fafd95da523159c19eff0696a/contourpy-1.3.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9", size = 362859 }, - { url = "https://files.pythonhosted.org/packages/33/71/e2a7945b7de4e58af42d708a219f3b2f4cff7386e6b6ab0a0fa0033c49a9/contourpy-1.3.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659", size = 1332062 }, - { url = "https://files.pythonhosted.org/packages/12/fc/4e87ac754220ccc0e807284f88e943d6d43b43843614f0a8afa469801db0/contourpy-1.3.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7", size = 1403932 }, - { url = "https://files.pythonhosted.org/packages/a6/2e/adc197a37443f934594112222ac1aa7dc9a98faf9c3842884df9a9d8751d/contourpy-1.3.3-cp313-cp313-win32.whl", hash = "sha256:b20c7c9a3bf701366556e1b1984ed2d0cedf999903c51311417cf5f591d8c78d", size = 185024 }, - { url = "https://files.pythonhosted.org/packages/18/0b/0098c214843213759692cc638fce7de5c289200a830e5035d1791d7a2338/contourpy-1.3.3-cp313-cp313-win_amd64.whl", hash = "sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263", size = 226578 }, - { url = "https://files.pythonhosted.org/packages/8a/9a/2f6024a0c5995243cd63afdeb3651c984f0d2bc727fd98066d40e141ad73/contourpy-1.3.3-cp313-cp313-win_arm64.whl", hash = "sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9", size = 193524 }, - { url = "https://files.pythonhosted.org/packages/c0/b3/f8a1a86bd3298513f500e5b1f5fd92b69896449f6cab6a146a5d52715479/contourpy-1.3.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d", size = 306730 }, - { url = "https://files.pythonhosted.org/packages/3f/11/4780db94ae62fc0c2053909b65dc3246bd7cecfc4f8a20d957ad43aa4ad8/contourpy-1.3.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216", size = 287897 }, - { url = "https://files.pythonhosted.org/packages/ae/15/e59f5f3ffdd6f3d4daa3e47114c53daabcb18574a26c21f03dc9e4e42ff0/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae", size = 326751 }, - { url = "https://files.pythonhosted.org/packages/0f/81/03b45cfad088e4770b1dcf72ea78d3802d04200009fb364d18a493857210/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20", size = 375486 }, - { url = "https://files.pythonhosted.org/packages/0c/ba/49923366492ffbdd4486e970d421b289a670ae8cf539c1ea9a09822b371a/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99", size = 388106 }, - { url = "https://files.pythonhosted.org/packages/9f/52/5b00ea89525f8f143651f9f03a0df371d3cbd2fccd21ca9b768c7a6500c2/contourpy-1.3.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b", size = 352548 }, - { url = "https://files.pythonhosted.org/packages/32/1d/a209ec1a3a3452d490f6b14dd92e72280c99ae3d1e73da74f8277d4ee08f/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a", size = 1322297 }, - { url = "https://files.pythonhosted.org/packages/bc/9e/46f0e8ebdd884ca0e8877e46a3f4e633f6c9c8c4f3f6e72be3fe075994aa/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e", size = 1391023 }, - { url = "https://files.pythonhosted.org/packages/b9/70/f308384a3ae9cd2209e0849f33c913f658d3326900d0ff5d378d6a1422d2/contourpy-1.3.3-cp313-cp313t-win32.whl", hash = "sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3", size = 196157 }, - { url = "https://files.pythonhosted.org/packages/b2/dd/880f890a6663b84d9e34a6f88cded89d78f0091e0045a284427cb6b18521/contourpy-1.3.3-cp313-cp313t-win_amd64.whl", hash = "sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8", size = 240570 }, - { url = "https://files.pythonhosted.org/packages/80/99/2adc7d8ffead633234817ef8e9a87115c8a11927a94478f6bb3d3f4d4f7d/contourpy-1.3.3-cp313-cp313t-win_arm64.whl", hash = "sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301", size = 199713 }, - { url = "https://files.pythonhosted.org/packages/72/8b/4546f3ab60f78c514ffb7d01a0bd743f90de36f0019d1be84d0a708a580a/contourpy-1.3.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a", size = 292189 }, - { url = "https://files.pythonhosted.org/packages/fd/e1/3542a9cb596cadd76fcef413f19c79216e002623158befe6daa03dbfa88c/contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77", size = 273251 }, - { url = "https://files.pythonhosted.org/packages/b1/71/f93e1e9471d189f79d0ce2497007731c1e6bf9ef6d1d61b911430c3db4e5/contourpy-1.3.3-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5", size = 335810 }, - { url = "https://files.pythonhosted.org/packages/91/f9/e35f4c1c93f9275d4e38681a80506b5510e9327350c51f8d4a5a724d178c/contourpy-1.3.3-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4", size = 382871 }, - { url = "https://files.pythonhosted.org/packages/b5/71/47b512f936f66a0a900d81c396a7e60d73419868fba959c61efed7a8ab46/contourpy-1.3.3-cp314-cp314-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36", size = 386264 }, - { url = "https://files.pythonhosted.org/packages/04/5f/9ff93450ba96b09c7c2b3f81c94de31c89f92292f1380261bd7195bea4ea/contourpy-1.3.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3", size = 363819 }, - { url = "https://files.pythonhosted.org/packages/3e/a6/0b185d4cc480ee494945cde102cb0149ae830b5fa17bf855b95f2e70ad13/contourpy-1.3.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b", size = 1333650 }, - { url = "https://files.pythonhosted.org/packages/43/d7/afdc95580ca56f30fbcd3060250f66cedbde69b4547028863abd8aa3b47e/contourpy-1.3.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36", size = 1404833 }, - { url = "https://files.pythonhosted.org/packages/e2/e2/366af18a6d386f41132a48f033cbd2102e9b0cf6345d35ff0826cd984566/contourpy-1.3.3-cp314-cp314-win32.whl", hash = "sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d", size = 189692 }, - { url = "https://files.pythonhosted.org/packages/7d/c2/57f54b03d0f22d4044b8afb9ca0e184f8b1afd57b4f735c2fa70883dc601/contourpy-1.3.3-cp314-cp314-win_amd64.whl", hash = "sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd", size = 232424 }, - { url = "https://files.pythonhosted.org/packages/18/79/a9416650df9b525737ab521aa181ccc42d56016d2123ddcb7b58e926a42c/contourpy-1.3.3-cp314-cp314-win_arm64.whl", hash = "sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339", size = 198300 }, - { url = "https://files.pythonhosted.org/packages/1f/42/38c159a7d0f2b7b9c04c64ab317042bb6952b713ba875c1681529a2932fe/contourpy-1.3.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772", size = 306769 }, - { url = "https://files.pythonhosted.org/packages/c3/6c/26a8205f24bca10974e77460de68d3d7c63e282e23782f1239f226fcae6f/contourpy-1.3.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77", size = 287892 }, - { url = "https://files.pythonhosted.org/packages/66/06/8a475c8ab718ebfd7925661747dbb3c3ee9c82ac834ccb3570be49d129f4/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13", size = 326748 }, - { url = "https://files.pythonhosted.org/packages/b4/a3/c5ca9f010a44c223f098fccd8b158bb1cb287378a31ac141f04730dc49be/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe", size = 375554 }, - { url = "https://files.pythonhosted.org/packages/80/5b/68bd33ae63fac658a4145088c1e894405e07584a316738710b636c6d0333/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f", size = 388118 }, - { url = "https://files.pythonhosted.org/packages/40/52/4c285a6435940ae25d7410a6c36bda5145839bc3f0beb20c707cda18b9d2/contourpy-1.3.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0", size = 352555 }, - { url = "https://files.pythonhosted.org/packages/24/ee/3e81e1dd174f5c7fefe50e85d0892de05ca4e26ef1c9a59c2a57e43b865a/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4", size = 1322295 }, - { url = "https://files.pythonhosted.org/packages/3c/b2/6d913d4d04e14379de429057cd169e5e00f6c2af3bb13e1710bcbdb5da12/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f", size = 1391027 }, - { url = "https://files.pythonhosted.org/packages/93/8a/68a4ec5c55a2971213d29a9374913f7e9f18581945a7a31d1a39b5d2dfe5/contourpy-1.3.3-cp314-cp314t-win32.whl", hash = "sha256:e74a9a0f5e3fff48fb5a7f2fd2b9b70a3fe014a67522f79b7cca4c0c7e43c9ae", size = 202428 }, - { url = "https://files.pythonhosted.org/packages/fa/96/fd9f641ffedc4fa3ace923af73b9d07e869496c9cc7a459103e6e978992f/contourpy-1.3.3-cp314-cp314t-win_amd64.whl", hash = "sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc", size = 250331 }, - { url = "https://files.pythonhosted.org/packages/ae/8c/469afb6465b853afff216f9528ffda78a915ff880ed58813ba4faf4ba0b6/contourpy-1.3.3-cp314-cp314t-win_arm64.whl", hash = "sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b", size = 203831 }, - { url = "https://files.pythonhosted.org/packages/a5/29/8dcfe16f0107943fa92388c23f6e05cff0ba58058c4c95b00280d4c75a14/contourpy-1.3.3-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:cd5dfcaeb10f7b7f9dc8941717c6c2ade08f587be2226222c12b25f0483ed497", size = 278809 }, - { url = "https://files.pythonhosted.org/packages/85/a9/8b37ef4f7dafeb335daee3c8254645ef5725be4d9c6aa70b50ec46ef2f7e/contourpy-1.3.3-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:0c1fc238306b35f246d61a1d416a627348b5cf0648648a031e14bb8705fcdfe8", size = 261593 }, - { url = "https://files.pythonhosted.org/packages/0a/59/ebfb8c677c75605cc27f7122c90313fd2f375ff3c8d19a1694bda74aaa63/contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:70f9aad7de812d6541d29d2bbf8feb22ff7e1c299523db288004e3157ff4674e", size = 302202 }, - { url = "https://files.pythonhosted.org/packages/3c/37/21972a15834d90bfbfb009b9d004779bd5a07a0ec0234e5ba8f64d5736f4/contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ed3657edf08512fc3fe81b510e35c2012fbd3081d2e26160f27ca28affec989", size = 329207 }, - { url = "https://files.pythonhosted.org/packages/0c/58/bd257695f39d05594ca4ad60df5bcb7e32247f9951fd09a9b8edb82d1daa/contourpy-1.3.3-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:3d1a3799d62d45c18bafd41c5fa05120b96a28079f2393af559b843d1a966a77", size = 225315 }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/91/2e/c4390a31919d8a78b90e8ecf87cd4b4c4f05a5b48d05ec17db8e5404c6f4/contourpy-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1", size = 288773, upload-time = "2025-07-26T12:01:02.277Z" }, + { url = "https://files.pythonhosted.org/packages/0d/44/c4b0b6095fef4dc9c420e041799591e3b63e9619e3044f7f4f6c21c0ab24/contourpy-1.3.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:23416f38bfd74d5d28ab8429cc4d63fa67d5068bd711a85edb1c3fb0c3e2f381", size = 270149, upload-time = "2025-07-26T12:01:04.072Z" }, + { url = "https://files.pythonhosted.org/packages/30/2e/dd4ced42fefac8470661d7cb7e264808425e6c5d56d175291e93890cce09/contourpy-1.3.3-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:929ddf8c4c7f348e4c0a5a3a714b5c8542ffaa8c22954862a46ca1813b667ee7", size = 329222, upload-time = "2025-07-26T12:01:05.688Z" }, + { url = "https://files.pythonhosted.org/packages/f2/74/cc6ec2548e3d276c71389ea4802a774b7aa3558223b7bade3f25787fafc2/contourpy-1.3.3-cp311-cp311-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9e999574eddae35f1312c2b4b717b7885d4edd6cb46700e04f7f02db454e67c1", size = 377234, upload-time = "2025-07-26T12:01:07.054Z" }, + { url = "https://files.pythonhosted.org/packages/03/b3/64ef723029f917410f75c09da54254c5f9ea90ef89b143ccadb09df14c15/contourpy-1.3.3-cp311-cp311-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0bf67e0e3f482cb69779dd3061b534eb35ac9b17f163d851e2a547d56dba0a3a", size = 380555, upload-time = "2025-07-26T12:01:08.801Z" }, + { url = "https://files.pythonhosted.org/packages/5f/4b/6157f24ca425b89fe2eb7e7be642375711ab671135be21e6faa100f7448c/contourpy-1.3.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:51e79c1f7470158e838808d4a996fa9bac72c498e93d8ebe5119bc1e6becb0db", size = 355238, upload-time = "2025-07-26T12:01:10.319Z" }, + { url = "https://files.pythonhosted.org/packages/98/56/f914f0dd678480708a04cfd2206e7c382533249bc5001eb9f58aa693e200/contourpy-1.3.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:598c3aaece21c503615fd59c92a3598b428b2f01bfb4b8ca9c4edeecc2438620", size = 1326218, upload-time = "2025-07-26T12:01:12.659Z" }, + { url = "https://files.pythonhosted.org/packages/fb/d7/4a972334a0c971acd5172389671113ae82aa7527073980c38d5868ff1161/contourpy-1.3.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:322ab1c99b008dad206d406bb61d014cf0174df491ae9d9d0fac6a6fda4f977f", size = 1392867, upload-time = "2025-07-26T12:01:15.533Z" }, + { url = "https://files.pythonhosted.org/packages/75/3e/f2cc6cd56dc8cff46b1a56232eabc6feea52720083ea71ab15523daab796/contourpy-1.3.3-cp311-cp311-win32.whl", hash = "sha256:fd907ae12cd483cd83e414b12941c632a969171bf90fc937d0c9f268a31cafff", size = 183677, upload-time = "2025-07-26T12:01:17.088Z" }, + { url = "https://files.pythonhosted.org/packages/98/4b/9bd370b004b5c9d8045c6c33cf65bae018b27aca550a3f657cdc99acdbd8/contourpy-1.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:3519428f6be58431c56581f1694ba8e50626f2dd550af225f82fb5f5814d2a42", size = 225234, upload-time = "2025-07-26T12:01:18.256Z" }, + { url = "https://files.pythonhosted.org/packages/d9/b6/71771e02c2e004450c12b1120a5f488cad2e4d5b590b1af8bad060360fe4/contourpy-1.3.3-cp311-cp311-win_arm64.whl", hash = "sha256:15ff10bfada4bf92ec8b31c62bf7c1834c244019b4a33095a68000d7075df470", size = 193123, upload-time = "2025-07-26T12:01:19.848Z" }, + { url = "https://files.pythonhosted.org/packages/be/45/adfee365d9ea3d853550b2e735f9d66366701c65db7855cd07621732ccfc/contourpy-1.3.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:b08a32ea2f8e42cf1d4be3169a98dd4be32bafe4f22b6c4cb4ba810fa9e5d2cb", size = 293419, upload-time = "2025-07-26T12:01:21.16Z" }, + { url = "https://files.pythonhosted.org/packages/53/3e/405b59cfa13021a56bba395a6b3aca8cec012b45bf177b0eaf7a202cde2c/contourpy-1.3.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6", size = 273979, upload-time = "2025-07-26T12:01:22.448Z" }, + { url = "https://files.pythonhosted.org/packages/d4/1c/a12359b9b2ca3a845e8f7f9ac08bdf776114eb931392fcad91743e2ea17b/contourpy-1.3.3-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7", size = 332653, upload-time = "2025-07-26T12:01:24.155Z" }, + { url = "https://files.pythonhosted.org/packages/63/12/897aeebfb475b7748ea67b61e045accdfcf0d971f8a588b67108ed7f5512/contourpy-1.3.3-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b2e8faa0ed68cb29af51edd8e24798bb661eac3bd9f65420c1887b6ca89987c8", size = 379536, upload-time = "2025-07-26T12:01:25.91Z" }, + { url = "https://files.pythonhosted.org/packages/43/8a/a8c584b82deb248930ce069e71576fc09bd7174bbd35183b7943fb1064fd/contourpy-1.3.3-cp312-cp312-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea", size = 384397, upload-time = "2025-07-26T12:01:27.152Z" }, + { url = "https://files.pythonhosted.org/packages/cc/8f/ec6289987824b29529d0dfda0d74a07cec60e54b9c92f3c9da4c0ac732de/contourpy-1.3.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1", size = 362601, upload-time = "2025-07-26T12:01:28.808Z" }, + { url = "https://files.pythonhosted.org/packages/05/0a/a3fe3be3ee2dceb3e615ebb4df97ae6f3828aa915d3e10549ce016302bd1/contourpy-1.3.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7", size = 1331288, upload-time = "2025-07-26T12:01:31.198Z" }, + { url = "https://files.pythonhosted.org/packages/33/1d/acad9bd4e97f13f3e2b18a3977fe1b4a37ecf3d38d815333980c6c72e963/contourpy-1.3.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411", size = 1403386, upload-time = "2025-07-26T12:01:33.947Z" }, + { url = "https://files.pythonhosted.org/packages/cf/8f/5847f44a7fddf859704217a99a23a4f6417b10e5ab1256a179264561540e/contourpy-1.3.3-cp312-cp312-win32.whl", hash = "sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69", size = 185018, upload-time = "2025-07-26T12:01:35.64Z" }, + { url = "https://files.pythonhosted.org/packages/19/e8/6026ed58a64563186a9ee3f29f41261fd1828f527dd93d33b60feca63352/contourpy-1.3.3-cp312-cp312-win_amd64.whl", hash = "sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b", size = 226567, upload-time = "2025-07-26T12:01:36.804Z" }, + { url = "https://files.pythonhosted.org/packages/d1/e2/f05240d2c39a1ed228d8328a78b6f44cd695f7ef47beb3e684cf93604f86/contourpy-1.3.3-cp312-cp312-win_arm64.whl", hash = "sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc", size = 193655, upload-time = "2025-07-26T12:01:37.999Z" }, + { url = "https://files.pythonhosted.org/packages/68/35/0167aad910bbdb9599272bd96d01a9ec6852f36b9455cf2ca67bd4cc2d23/contourpy-1.3.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5", size = 293257, upload-time = "2025-07-26T12:01:39.367Z" }, + { url = "https://files.pythonhosted.org/packages/96/e4/7adcd9c8362745b2210728f209bfbcf7d91ba868a2c5f40d8b58f54c509b/contourpy-1.3.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1", size = 274034, upload-time = "2025-07-26T12:01:40.645Z" }, + { url = "https://files.pythonhosted.org/packages/73/23/90e31ceeed1de63058a02cb04b12f2de4b40e3bef5e082a7c18d9c8ae281/contourpy-1.3.3-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286", size = 334672, upload-time = "2025-07-26T12:01:41.942Z" }, + { url = "https://files.pythonhosted.org/packages/ed/93/b43d8acbe67392e659e1d984700e79eb67e2acb2bd7f62012b583a7f1b55/contourpy-1.3.3-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5", size = 381234, upload-time = "2025-07-26T12:01:43.499Z" }, + { url = "https://files.pythonhosted.org/packages/46/3b/bec82a3ea06f66711520f75a40c8fc0b113b2a75edb36aa633eb11c4f50f/contourpy-1.3.3-cp313-cp313-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67", size = 385169, upload-time = "2025-07-26T12:01:45.219Z" }, + { url = "https://files.pythonhosted.org/packages/4b/32/e0f13a1c5b0f8572d0ec6ae2f6c677b7991fafd95da523159c19eff0696a/contourpy-1.3.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9", size = 362859, upload-time = "2025-07-26T12:01:46.519Z" }, + { url = "https://files.pythonhosted.org/packages/33/71/e2a7945b7de4e58af42d708a219f3b2f4cff7386e6b6ab0a0fa0033c49a9/contourpy-1.3.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659", size = 1332062, upload-time = "2025-07-26T12:01:48.964Z" }, + { url = "https://files.pythonhosted.org/packages/12/fc/4e87ac754220ccc0e807284f88e943d6d43b43843614f0a8afa469801db0/contourpy-1.3.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7", size = 1403932, upload-time = "2025-07-26T12:01:51.979Z" }, + { url = "https://files.pythonhosted.org/packages/a6/2e/adc197a37443f934594112222ac1aa7dc9a98faf9c3842884df9a9d8751d/contourpy-1.3.3-cp313-cp313-win32.whl", hash = "sha256:b20c7c9a3bf701366556e1b1984ed2d0cedf999903c51311417cf5f591d8c78d", size = 185024, upload-time = "2025-07-26T12:01:53.245Z" }, + { url = "https://files.pythonhosted.org/packages/18/0b/0098c214843213759692cc638fce7de5c289200a830e5035d1791d7a2338/contourpy-1.3.3-cp313-cp313-win_amd64.whl", hash = "sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263", size = 226578, upload-time = "2025-07-26T12:01:54.422Z" }, + { url = "https://files.pythonhosted.org/packages/8a/9a/2f6024a0c5995243cd63afdeb3651c984f0d2bc727fd98066d40e141ad73/contourpy-1.3.3-cp313-cp313-win_arm64.whl", hash = "sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9", size = 193524, upload-time = "2025-07-26T12:01:55.73Z" }, + { url = "https://files.pythonhosted.org/packages/c0/b3/f8a1a86bd3298513f500e5b1f5fd92b69896449f6cab6a146a5d52715479/contourpy-1.3.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d", size = 306730, upload-time = "2025-07-26T12:01:57.051Z" }, + { url = "https://files.pythonhosted.org/packages/3f/11/4780db94ae62fc0c2053909b65dc3246bd7cecfc4f8a20d957ad43aa4ad8/contourpy-1.3.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216", size = 287897, upload-time = "2025-07-26T12:01:58.663Z" }, + { url = "https://files.pythonhosted.org/packages/ae/15/e59f5f3ffdd6f3d4daa3e47114c53daabcb18574a26c21f03dc9e4e42ff0/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae", size = 326751, upload-time = "2025-07-26T12:02:00.343Z" }, + { url = "https://files.pythonhosted.org/packages/0f/81/03b45cfad088e4770b1dcf72ea78d3802d04200009fb364d18a493857210/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20", size = 375486, upload-time = "2025-07-26T12:02:02.128Z" }, + { url = "https://files.pythonhosted.org/packages/0c/ba/49923366492ffbdd4486e970d421b289a670ae8cf539c1ea9a09822b371a/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99", size = 388106, upload-time = "2025-07-26T12:02:03.615Z" }, + { url = "https://files.pythonhosted.org/packages/9f/52/5b00ea89525f8f143651f9f03a0df371d3cbd2fccd21ca9b768c7a6500c2/contourpy-1.3.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b", size = 352548, upload-time = "2025-07-26T12:02:05.165Z" }, + { url = "https://files.pythonhosted.org/packages/32/1d/a209ec1a3a3452d490f6b14dd92e72280c99ae3d1e73da74f8277d4ee08f/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a", size = 1322297, upload-time = "2025-07-26T12:02:07.379Z" }, + { url = "https://files.pythonhosted.org/packages/bc/9e/46f0e8ebdd884ca0e8877e46a3f4e633f6c9c8c4f3f6e72be3fe075994aa/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e", size = 1391023, upload-time = "2025-07-26T12:02:10.171Z" }, + { url = "https://files.pythonhosted.org/packages/b9/70/f308384a3ae9cd2209e0849f33c913f658d3326900d0ff5d378d6a1422d2/contourpy-1.3.3-cp313-cp313t-win32.whl", hash = "sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3", size = 196157, upload-time = "2025-07-26T12:02:11.488Z" }, + { url = "https://files.pythonhosted.org/packages/b2/dd/880f890a6663b84d9e34a6f88cded89d78f0091e0045a284427cb6b18521/contourpy-1.3.3-cp313-cp313t-win_amd64.whl", hash = "sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8", size = 240570, upload-time = "2025-07-26T12:02:12.754Z" }, + { url = "https://files.pythonhosted.org/packages/80/99/2adc7d8ffead633234817ef8e9a87115c8a11927a94478f6bb3d3f4d4f7d/contourpy-1.3.3-cp313-cp313t-win_arm64.whl", hash = "sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301", size = 199713, upload-time = "2025-07-26T12:02:14.4Z" }, + { url = "https://files.pythonhosted.org/packages/72/8b/4546f3ab60f78c514ffb7d01a0bd743f90de36f0019d1be84d0a708a580a/contourpy-1.3.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a", size = 292189, upload-time = "2025-07-26T12:02:16.095Z" }, + { url = "https://files.pythonhosted.org/packages/fd/e1/3542a9cb596cadd76fcef413f19c79216e002623158befe6daa03dbfa88c/contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77", size = 273251, upload-time = "2025-07-26T12:02:17.524Z" }, + { url = "https://files.pythonhosted.org/packages/b1/71/f93e1e9471d189f79d0ce2497007731c1e6bf9ef6d1d61b911430c3db4e5/contourpy-1.3.3-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5", size = 335810, upload-time = "2025-07-26T12:02:18.9Z" }, + { url = "https://files.pythonhosted.org/packages/91/f9/e35f4c1c93f9275d4e38681a80506b5510e9327350c51f8d4a5a724d178c/contourpy-1.3.3-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4", size = 382871, upload-time = "2025-07-26T12:02:20.418Z" }, + { url = "https://files.pythonhosted.org/packages/b5/71/47b512f936f66a0a900d81c396a7e60d73419868fba959c61efed7a8ab46/contourpy-1.3.3-cp314-cp314-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36", size = 386264, upload-time = "2025-07-26T12:02:21.916Z" }, + { url = "https://files.pythonhosted.org/packages/04/5f/9ff93450ba96b09c7c2b3f81c94de31c89f92292f1380261bd7195bea4ea/contourpy-1.3.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3", size = 363819, upload-time = "2025-07-26T12:02:23.759Z" }, + { url = "https://files.pythonhosted.org/packages/3e/a6/0b185d4cc480ee494945cde102cb0149ae830b5fa17bf855b95f2e70ad13/contourpy-1.3.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b", size = 1333650, upload-time = "2025-07-26T12:02:26.181Z" }, + { url = "https://files.pythonhosted.org/packages/43/d7/afdc95580ca56f30fbcd3060250f66cedbde69b4547028863abd8aa3b47e/contourpy-1.3.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36", size = 1404833, upload-time = "2025-07-26T12:02:28.782Z" }, + { url = "https://files.pythonhosted.org/packages/e2/e2/366af18a6d386f41132a48f033cbd2102e9b0cf6345d35ff0826cd984566/contourpy-1.3.3-cp314-cp314-win32.whl", hash = "sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d", size = 189692, upload-time = "2025-07-26T12:02:30.128Z" }, + { url = "https://files.pythonhosted.org/packages/7d/c2/57f54b03d0f22d4044b8afb9ca0e184f8b1afd57b4f735c2fa70883dc601/contourpy-1.3.3-cp314-cp314-win_amd64.whl", hash = "sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd", size = 232424, upload-time = "2025-07-26T12:02:31.395Z" }, + { url = "https://files.pythonhosted.org/packages/18/79/a9416650df9b525737ab521aa181ccc42d56016d2123ddcb7b58e926a42c/contourpy-1.3.3-cp314-cp314-win_arm64.whl", hash = "sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339", size = 198300, upload-time = "2025-07-26T12:02:32.956Z" }, + { url = "https://files.pythonhosted.org/packages/1f/42/38c159a7d0f2b7b9c04c64ab317042bb6952b713ba875c1681529a2932fe/contourpy-1.3.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772", size = 306769, upload-time = "2025-07-26T12:02:34.2Z" }, + { url = "https://files.pythonhosted.org/packages/c3/6c/26a8205f24bca10974e77460de68d3d7c63e282e23782f1239f226fcae6f/contourpy-1.3.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77", size = 287892, upload-time = "2025-07-26T12:02:35.807Z" }, + { url = "https://files.pythonhosted.org/packages/66/06/8a475c8ab718ebfd7925661747dbb3c3ee9c82ac834ccb3570be49d129f4/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13", size = 326748, upload-time = "2025-07-26T12:02:37.193Z" }, + { url = "https://files.pythonhosted.org/packages/b4/a3/c5ca9f010a44c223f098fccd8b158bb1cb287378a31ac141f04730dc49be/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe", size = 375554, upload-time = "2025-07-26T12:02:38.894Z" }, + { url = "https://files.pythonhosted.org/packages/80/5b/68bd33ae63fac658a4145088c1e894405e07584a316738710b636c6d0333/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f", size = 388118, upload-time = "2025-07-26T12:02:40.642Z" }, + { url = "https://files.pythonhosted.org/packages/40/52/4c285a6435940ae25d7410a6c36bda5145839bc3f0beb20c707cda18b9d2/contourpy-1.3.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0", size = 352555, upload-time = "2025-07-26T12:02:42.25Z" }, + { url = "https://files.pythonhosted.org/packages/24/ee/3e81e1dd174f5c7fefe50e85d0892de05ca4e26ef1c9a59c2a57e43b865a/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4", size = 1322295, upload-time = "2025-07-26T12:02:44.668Z" }, + { url = "https://files.pythonhosted.org/packages/3c/b2/6d913d4d04e14379de429057cd169e5e00f6c2af3bb13e1710bcbdb5da12/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f", size = 1391027, upload-time = "2025-07-26T12:02:47.09Z" }, + { url = "https://files.pythonhosted.org/packages/93/8a/68a4ec5c55a2971213d29a9374913f7e9f18581945a7a31d1a39b5d2dfe5/contourpy-1.3.3-cp314-cp314t-win32.whl", hash = "sha256:e74a9a0f5e3fff48fb5a7f2fd2b9b70a3fe014a67522f79b7cca4c0c7e43c9ae", size = 202428, upload-time = "2025-07-26T12:02:48.691Z" }, + { url = "https://files.pythonhosted.org/packages/fa/96/fd9f641ffedc4fa3ace923af73b9d07e869496c9cc7a459103e6e978992f/contourpy-1.3.3-cp314-cp314t-win_amd64.whl", hash = "sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc", size = 250331, upload-time = "2025-07-26T12:02:50.137Z" }, + { url = "https://files.pythonhosted.org/packages/ae/8c/469afb6465b853afff216f9528ffda78a915ff880ed58813ba4faf4ba0b6/contourpy-1.3.3-cp314-cp314t-win_arm64.whl", hash = "sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b", size = 203831, upload-time = "2025-07-26T12:02:51.449Z" }, + { url = "https://files.pythonhosted.org/packages/a5/29/8dcfe16f0107943fa92388c23f6e05cff0ba58058c4c95b00280d4c75a14/contourpy-1.3.3-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:cd5dfcaeb10f7b7f9dc8941717c6c2ade08f587be2226222c12b25f0483ed497", size = 278809, upload-time = "2025-07-26T12:02:52.74Z" }, + { url = "https://files.pythonhosted.org/packages/85/a9/8b37ef4f7dafeb335daee3c8254645ef5725be4d9c6aa70b50ec46ef2f7e/contourpy-1.3.3-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:0c1fc238306b35f246d61a1d416a627348b5cf0648648a031e14bb8705fcdfe8", size = 261593, upload-time = "2025-07-26T12:02:54.037Z" }, + { url = "https://files.pythonhosted.org/packages/0a/59/ebfb8c677c75605cc27f7122c90313fd2f375ff3c8d19a1694bda74aaa63/contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:70f9aad7de812d6541d29d2bbf8feb22ff7e1c299523db288004e3157ff4674e", size = 302202, upload-time = "2025-07-26T12:02:55.947Z" }, + { url = "https://files.pythonhosted.org/packages/3c/37/21972a15834d90bfbfb009b9d004779bd5a07a0ec0234e5ba8f64d5736f4/contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ed3657edf08512fc3fe81b510e35c2012fbd3081d2e26160f27ca28affec989", size = 329207, upload-time = "2025-07-26T12:02:57.468Z" }, + { url = "https://files.pythonhosted.org/packages/0c/58/bd257695f39d05594ca4ad60df5bcb7e32247f9951fd09a9b8edb82d1daa/contourpy-1.3.3-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:3d1a3799d62d45c18bafd41c5fa05120b96a28079f2393af559b843d1a966a77", size = 225315, upload-time = "2025-07-26T12:02:58.801Z" }, ] [[package]] name = "coverage" -version = "7.14.1" -source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/54/fd/0ab2772530e946e1be1abd0bc09e647ec9b02e88f0867857601fefca8953/coverage-7.14.1.tar.gz", hash = "sha256:30c08f7d90415aa98b3c990385dea2939b0da55f38515e5b369b83655f8523be", size = 920132, upload-time = "2026-05-26T20:41:36.783Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/7d/d7/477ad149490e6cb849f28abea1dabb9c823cea72e7500c81b4240ce619c0/coverage-7.14.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:478b5bcd63c2e1357c5c7e16c070690df7b07f676b1c114d7b93e533c664309f", size = 219848, upload-time = "2026-05-26T20:38:38.715Z" }, - { url = "https://files.pythonhosted.org/packages/91/82/a5eb47257c50601bb7b9a9d2857c67b7a3a85ad74180eb2c98bb1fbe0ce5/coverage-7.14.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a24a81f9715ee42ef59a316cc11611c98fe23920f7c81861315c9f3ff4a230f4", size = 220354, upload-time = "2026-05-26T20:38:40.232Z" }, - { url = "https://files.pythonhosted.org/packages/43/8b/78419b5391a5cb706b6544390507e469d83ffc9a8248b02c4011aceb9365/coverage-7.14.1-cp311-cp311-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:196a13319ad88d6d8ef5ab489ec4f44ddde2143c0c7d5b27786f6c3ffd56a7e1", size = 250771, upload-time = "2026-05-26T20:38:41.782Z" }, - { url = "https://files.pythonhosted.org/packages/77/63/e77aaacd491182210d639636b7a8bba23ffffa9b82aa3762da9431855fa9/coverage-7.14.1-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:3d452fd08b5c72c5167c93e6867b5c08500bd40f2a21e1e854a500550b6cc36f", size = 252683, upload-time = "2026-05-26T20:38:43.305Z" }, - { url = "https://files.pythonhosted.org/packages/65/1c/a022e3cfbec2ac241640003cb3a817e161d9c7f5aa9b49173756cdc03204/coverage-7.14.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:23bf7fa51ac02e07fc7c96849b82946da47ae862dc8f86d183b2a4864fc38129", size = 254791, upload-time = "2026-05-26T20:38:45.361Z" }, - { url = "https://files.pythonhosted.org/packages/61/d6/967e408aca4c1ceb88cb0cc677169110ae7f5995fb5eaf5fb1f5a1bb8f5d/coverage-7.14.1-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:bcaa50684dcaadfa599ac48f81103c756d791cfd85c97203d2217c593d48b860", size = 256748, upload-time = "2026-05-26T20:38:46.91Z" }, - { url = "https://files.pythonhosted.org/packages/b8/be/869188f7fe28638078ec479331ace6dc5f7b40b7153eb616f47ab79404d8/coverage-7.14.1-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:4ea1c034f95c9b056e856b794630b17f9fa3d57e4800ff1e503d3be0f9c9078c", size = 250907, upload-time = "2026-05-26T20:38:48.493Z" }, - { url = "https://files.pythonhosted.org/packages/07/aa/adb7d3b4278d690e68703abcd76ab1b948242e3668d921711551b78f9ddb/coverage-7.14.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c7e057326434e441306226fbeb5d1aaf14a2637efe97ba668306635835f32ad7", size = 252483, upload-time = "2026-05-26T20:38:50.074Z" }, - { url = "https://files.pythonhosted.org/packages/43/61/331c74103c62dcb0c4b9b3a0de9a61aca016208b0a90f109592a9f9ecc28/coverage-7.14.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:59baf88468dbc8d63b1887afd92bda52e40bb1561696e5819670601403810cec", size = 250545, upload-time = "2026-05-26T20:38:51.613Z" }, - { url = "https://files.pythonhosted.org/packages/f6/b6/c5dae3c104d89be04828f61810e6b3473825482e4c288cc4ed04553e08ae/coverage-7.14.1-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:d34d75f892b3ab73ba11cab5442cce7b3e168fd64162b16f0e1e0d09c508edef", size = 254310, upload-time = "2026-05-26T20:38:53.503Z" }, - { url = "https://files.pythonhosted.org/packages/ad/a1/2b9d5863e3b83c01ad8199e3c597802fbb3a9dc90b058885804c20296d31/coverage-7.14.1-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:3a56abc20a472baf0304c455721bc601477440d28ecfde8a03dde79ede07e0df", size = 250266, upload-time = "2026-05-26T20:38:55.414Z" }, - { url = "https://files.pythonhosted.org/packages/7f/5e/0e511fbdb269359be26fe678a1c3fa1f2aa2a01573cc3f54268c8d6d4797/coverage-7.14.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:6a3cb83d1552c0cd1b4906655b6a33fd4a8473229633a901c6b73bf86914dee9", size = 251174, upload-time = "2026-05-26T20:38:57.141Z" }, - { url = "https://files.pythonhosted.org/packages/85/10/e55307b622b3dd9671cb321824502dc10f93e72f2802b9946159a8edadeb/coverage-7.14.1-cp311-cp311-win32.whl", hash = "sha256:10274a1fbeb8ec5d72966e17bb198a3104257aca4ac09d98667c5f8aca8c8548", size = 222354, upload-time = "2026-05-26T20:38:58.727Z" }, - { url = "https://files.pythonhosted.org/packages/71/cf/107421693cfb71e4f1ca5bf70443f64d4161878068d07a3e51c7ad21d17b/coverage-7.14.1-cp311-cp311-win_amd64.whl", hash = "sha256:87ebdf787d4888e3f3f2d523eadc6e18c6d18c6d0eb173801a189641627fb37e", size = 223290, upload-time = "2026-05-26T20:39:00.413Z" }, - { url = "https://files.pythonhosted.org/packages/b8/1d/3e3644585eb29e9dafefb19555078529a4d7cce12bd21929664eea989277/coverage-7.14.1-cp311-cp311-win_arm64.whl", hash = "sha256:dd34767fa19848d35659ffc0a75314f58c7af3f1cd87ec521e8292a1238398a3", size = 221953, upload-time = "2026-05-26T20:39:02.159Z" }, - { url = "https://files.pythonhosted.org/packages/3d/b7/bdbb725ba02c5b42825b200c940f38b7a54fcad24627b7192f78f8110d76/coverage-7.14.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:a06c76364a9360e33d6d23769aefdf7f66f38e2ffb60ceb1baaa4989d83b695c", size = 220022, upload-time = "2026-05-26T20:39:03.702Z" }, - { url = "https://files.pythonhosted.org/packages/72/81/fdc0898a55c6219223291ec1a1fe89966ef212ce82276aa0899df84b5de0/coverage-7.14.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fad54e871165f6ec2f536063ac74c3104508a12963e64072ba44bd822de52b0c", size = 220379, upload-time = "2026-05-26T20:39:05.381Z" }, - { url = "https://files.pythonhosted.org/packages/de/72/de048c4a25e13bce59ac6a339351c10bdf2515e07459afcdaf04dc3143a2/coverage-7.14.1-cp312-cp312-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:84b535f00655ecafe1d929d1fb00ed5d6fa3051ea643ab2c161a3887b86f294b", size = 251888, upload-time = "2026-05-26T20:39:07.367Z" }, - { url = "https://files.pythonhosted.org/packages/28/30/300c343f68beb9d4cbb64ec81e58c5b6b80b56927f72d2b38654ac26e013/coverage-7.14.1-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:6b6b0853b895fe0e98cbfc580d1ec3393d9302b4b1e96a77b3f5c91fdab899e6", size = 254624, upload-time = "2026-05-26T20:39:09.037Z" }, - { url = "https://files.pythonhosted.org/packages/b1/ed/7b25642496e8170b6bac14adce00537c6e5fa2d586159401a4de3e8b49e6/coverage-7.14.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:442cc9c952b2df400cda54bb04ab87330cf2cd08a8692cbbea36773531eb6f37", size = 255739, upload-time = "2026-05-26T20:39:10.889Z" }, - { url = "https://files.pythonhosted.org/packages/7f/a2/abd210b8c4e29c24e4624916db97bb519097a91034aaeb767f937e7da794/coverage-7.14.1-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:8270544c361ed405a27a060dbc9ed2c124b084d96dfdc2d9a2510482aef981ad", size = 257998, upload-time = "2026-05-26T20:39:12.722Z" }, - { url = "https://files.pythonhosted.org/packages/7f/24/7c50beed3792fe62f6ce0545c6686ce83379719e2c0276179333d97eae92/coverage-7.14.1-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:48b283b1dd6372e8de2a7a9a4c4d5dc06f4d4fd209b876f3c88a7a205a0c8f84", size = 252296, upload-time = "2026-05-26T20:39:14.259Z" }, - { url = "https://files.pythonhosted.org/packages/15/05/0f874628ebcbfc77ead559ff210281ef06a97db08481832e7dd39274a135/coverage-7.14.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:5b0c99ba93a07d56f6df340bb79be53202a082b2fdb81bfe6190b741a3470d54", size = 253658, upload-time = "2026-05-26T20:39:15.923Z" }, - { url = "https://files.pythonhosted.org/packages/99/6f/ca6ad067364b337ef997802115e7ecad2abd2248b05471464b0dea02b4d4/coverage-7.14.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:e471bc5769ff073b058cfadb0d736b56ce067c8560eabeb0da88462df98c23e7", size = 251803, upload-time = "2026-05-26T20:39:17.537Z" }, - { url = "https://files.pythonhosted.org/packages/c0/30/b9b4d377cd9f40baf228068f5a81faf8450c6228503011bd499708483a50/coverage-7.14.1-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:f497a1ea81d4cd7c10ddcaa685135b9aabd291af3d55775a9ddf3cb7a364cdd9", size = 255873, upload-time = "2026-05-26T20:39:19.414Z" }, - { url = "https://files.pythonhosted.org/packages/3c/21/7c721a9e5e6bb88547d30a787aefb97512d3f54c1324c7488d9b3743f7f9/coverage-7.14.1-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:2222be86d0b54f5dd5a38f45f17f315f737245e857bf0bdedc70734f84a13c02", size = 251372, upload-time = "2026-05-26T20:39:21.169Z" }, - { url = "https://files.pythonhosted.org/packages/9d/8c/f8ae5a2200130e1503cd7661a6cd3b2b7bacef98277fbf3571fb13f8b766/coverage-7.14.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:85e85586565842f6932abebd4c18bcb1074223dc0b3576e7d173ca710622813a", size = 253245, upload-time = "2026-05-26T20:39:23.097Z" }, - { url = "https://files.pythonhosted.org/packages/34/62/70a9024672a5f6910517d9628c52c9afbdd3cf8f46426af52bb148a56fff/coverage-7.14.1-cp312-cp312-win32.whl", hash = "sha256:4a28fd227808366b196a75476dced2eb35b351d6766ba9c858dc93319e87f4f1", size = 222567, upload-time = "2026-05-26T20:39:24.868Z" }, - { url = "https://files.pythonhosted.org/packages/f6/81/8b7cd386839b039ebe1855733b9f9449a8dec5d79564018234f185a7fa70/coverage-7.14.1-cp312-cp312-win_amd64.whl", hash = "sha256:54acdb6674a4661768d7bf7db32dfb9f46ab1d764f8aba6df75ce1a6a088724e", size = 223372, upload-time = "2026-05-26T20:39:26.603Z" }, - { url = "https://files.pythonhosted.org/packages/ae/ba/b44d472022f620d289d95fa830143235c0c36461c6f2437ea8d51e5481ed/coverage-7.14.1-cp312-cp312-win_arm64.whl", hash = "sha256:99cd41ff91afd94896fea3bc002706b6ae4ce95727d06e4a0f39c0a8d8bd8b1a", size = 221989, upload-time = "2026-05-26T20:39:28.242Z" }, - { url = "https://files.pythonhosted.org/packages/8a/9e/5f6d56327c62b185225d145191c607e07515294a0aa6338e58805cd4a5ac/coverage-7.14.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:be9f2c802dcfce3f71298303aa5dad0dce440a76c52f2f60dacd8656dab78793", size = 220044, upload-time = "2026-05-26T20:39:29.902Z" }, - { url = "https://files.pythonhosted.org/packages/75/92/e82aca356744cbbc0f77a0b623e38918c1872361963413a3bab5d0340393/coverage-7.14.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:6223a72fd0e4c7156353ec0f08a5f93623e1d3034d0e2683b9bb8ea674131b1d", size = 220412, upload-time = "2026-05-26T20:39:31.561Z" }, - { url = "https://files.pythonhosted.org/packages/27/c9/385bde0bf7ed0f4bf3a7ee5367060a86b5d218718cfd6fb943c0f836b34f/coverage-7.14.1-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:7279d2110a28cebc738b6459ecda2771735a4c18465fbbd36b3288fe5ed92247", size = 251412, upload-time = "2026-05-26T20:39:33.337Z" }, - { url = "https://files.pythonhosted.org/packages/51/8c/23faf6a2343a0d17f960a4bd56c43bc7eb4cf312f774dd6ceebd82c7d8fc/coverage-7.14.1-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:9eeb3fcbc13ba40dfbdb22d01d196a28e9cef9ed4c29b60061a1e0e823a9929d", size = 254008, upload-time = "2026-05-26T20:39:35.009Z" }, - { url = "https://files.pythonhosted.org/packages/42/06/36f4aa9ca8a815e6036156e80706a67828bb97bd826948244f6996dda957/coverage-7.14.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5f0cfc27c539f07cf5c0a4cfe211d0b6cae039f8f40526dbaa71944e64b50a7b", size = 255241, upload-time = "2026-05-26T20:39:36.71Z" }, - { url = "https://files.pythonhosted.org/packages/ca/79/95266316352f90f6b1c6736bb413302edfde2453fb32422d3911642691b3/coverage-7.14.1-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:221c70f316241a78e77e607c227cefc8808d4e08f28d99c04f35694690e940be", size = 257373, upload-time = "2026-05-26T20:39:38.412Z" }, - { url = "https://files.pythonhosted.org/packages/e3/9c/58316d1f66c488b5fca8a0eb3e98348807813efa8a0d0833b9021be27488/coverage-7.14.1-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:da028256b04ec30e5e0114b6f76172938c313991f0a2d3d894271315cf5d5e43", size = 251635, upload-time = "2026-05-26T20:39:40.268Z" }, - { url = "https://files.pythonhosted.org/packages/ef/5a/ca2398a568e16fed7bb713e84ba3603a7164fb65779abe645c565ec890d5/coverage-7.14.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:76a085d7005236a767e3426148b2c407e53ad61695c562f8a81da2d373324901", size = 253373, upload-time = "2026-05-26T20:39:42.145Z" }, - { url = "https://files.pythonhosted.org/packages/6e/2c/0396562c32deaebe7be51d865b3a41e9a87d7561acafe1a28f53b07e019a/coverage-7.14.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:b553d04b5e778a8e56d57eb134aff42a92718ecba45e79c4764ecfa40efd92ff", size = 251341, upload-time = "2026-05-26T20:39:43.907Z" }, - { url = "https://files.pythonhosted.org/packages/fd/8f/a94f9221184c9cae1ee115820e3798e48b6b17777a9f19e46fb9a0c8dc74/coverage-7.14.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:46f714d2fb8ae2f4f29f23ada7f1e79b759fff5a70f94a1dac23af204c3ec9e4", size = 255497, upload-time = "2026-05-26T20:39:46.166Z" }, - { url = "https://files.pythonhosted.org/packages/71/69/505d70e47db1eaebcd002c39759707621ef184cd6b1ae084d9f41293f323/coverage-7.14.1-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:1896f5e19ff3f0431c7ce2172adc54890fd97f86b59ced8ca1649145d9ffe35d", size = 251159, upload-time = "2026-05-26T20:39:48.03Z" }, - { url = "https://files.pythonhosted.org/packages/e0/aa/58681c383aa33a9d2ed40a02d7a22fbf780d1fa4d575396365777828198c/coverage-7.14.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:62fd185ef9df3c33d1c8178c5af105f762afbad96038de9a4ae100aa6297ca33", size = 252934, upload-time = "2026-05-26T20:39:49.872Z" }, - { url = "https://files.pythonhosted.org/packages/eb/fd/11c928cd6bdffc7074bb5965c173d9ebf517fb00205e1da524b98d29ef92/coverage-7.14.1-cp313-cp313-win32.whl", hash = "sha256:ab4af6352741a604c431c6072fce5bee33bf0f20dc7a56618d6bf6bb89e9810c", size = 222584, upload-time = "2026-05-26T20:39:51.68Z" }, - { url = "https://files.pythonhosted.org/packages/6f/92/fb416fc26d340dcba19518c418d6048e913186e17243982c5e435e41fa7a/coverage-7.14.1-cp313-cp313-win_amd64.whl", hash = "sha256:7af486dabe8954d03b087f0021540897afe084f04e16ff5579e08cc46f871416", size = 223394, upload-time = "2026-05-26T20:39:53.472Z" }, - { url = "https://files.pythonhosted.org/packages/73/c6/02d56e3867972f77d5036de924643f26c056e848f00452cafb4dbc3c29b4/coverage-7.14.1-cp313-cp313-win_arm64.whl", hash = "sha256:2224f89ffd0c5605ccce1ed7a584da162bc7c55f601ab1c946bc9de31a486b42", size = 222015, upload-time = "2026-05-26T20:39:55.374Z" }, - { url = "https://files.pythonhosted.org/packages/4d/9e/fcc77914050df73f7662fa1f00902774c79c075a8388ab334074574bf77e/coverage-7.14.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:de286598cc65d2b489411174b1faec2f5a7775fb3201fd925db2a76b4030f37d", size = 220733, upload-time = "2026-05-26T20:39:57.189Z" }, - { url = "https://files.pythonhosted.org/packages/f7/67/2963cbdaf5cbadec44efa3a1e39eaa1f02df4079585f05387607a221e126/coverage-7.14.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:042c46ded7c288aeb07cf14a28b6c1e10b78fcba40171c3fa1e939377eeef0b5", size = 221086, upload-time = "2026-05-26T20:39:59.019Z" }, - { url = "https://files.pythonhosted.org/packages/c8/c5/8701645574e11881f2f47d8930f98bc48b5d43b25eb5b4430dfc4a2f9f48/coverage-7.14.1-cp313-cp313t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:f4ddbe407477f04c45115d1a4e5bc480f753553b534d338d4c3358b1cdd0ea52", size = 262381, upload-time = "2026-05-26T20:40:00.822Z" }, - { url = "https://files.pythonhosted.org/packages/7c/28/7a64d73598263e0c5abd5084211a8474488d31b3c552ff531c719dfcff62/coverage-7.14.1-cp313-cp313t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:d13e6725992e2d2fd7d81d4f5241952d13740121dfd501da09201be39b2c003a", size = 264458, upload-time = "2026-05-26T20:40:02.506Z" }, - { url = "https://files.pythonhosted.org/packages/fa/d8/4969179db9f7eb4df218e69540adf829d1c835f59452513d065d15446802/coverage-7.14.1-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f747dc8edcfe740130f28f32f3995e955494285717e86ee25af51db2219df08a", size = 266884, upload-time = "2026-05-26T20:40:04.421Z" }, - { url = "https://files.pythonhosted.org/packages/a6/78/a45d5794dbc9bafd97afc96a4377c86c7820d78b6cf51b89bc1d4e919275/coverage-7.14.1-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ced2f09ef276fd58611a1ef502164ad266d2b75174e5a40cabbdb4033f9f6cf2", size = 268022, upload-time = "2026-05-26T20:40:06.298Z" }, - { url = "https://files.pythonhosted.org/packages/21/cb/4f5e354e9e3e67af96bd4e57113e6db6b22298c7168b13eec408a549903d/coverage-7.14.1-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b84800013769a78ccb9ef4659402e26d06867e337b61ec365f77ad008adea80e", size = 261631, upload-time = "2026-05-26T20:40:08.226Z" }, - { url = "https://files.pythonhosted.org/packages/ec/49/eced49af4cb996d5d8b7e94e736175c513e4facd3398507b89892b4326d8/coverage-7.14.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:ea8cd6ca0ee9f616aaef3afc6882e32c2cbf18b00d96313ffd76af650574034d", size = 264443, upload-time = "2026-05-26T20:40:10.137Z" }, - { url = "https://files.pythonhosted.org/packages/f1/d8/5603a88a7c5913a6b54f6cb1a8c46f7b39cbb30f27cd3f492908da09b2d7/coverage-7.14.1-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:aa5e304a873fabddc11e484e9b6b738bd38bd7bed17b09aa84eecf5332e8b8bb", size = 262069, upload-time = "2026-05-26T20:40:11.999Z" }, - { url = "https://files.pythonhosted.org/packages/f0/59/2ae3cb79da554a06c8619d6c88ea19dd1e4aed4b834b6a83bb1fa243bdc5/coverage-7.14.1-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:5a1c5215be81035e629d5bc756650634d0bf31991038db7a0eccb90f025ce16d", size = 265780, upload-time = "2026-05-26T20:40:13.858Z" }, - { url = "https://files.pythonhosted.org/packages/af/5f/b130c1dc999031f2648bd25317fbce505ad8d5562079b4ed81e736a84967/coverage-7.14.1-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:79058c47dae6788504b5effb319961bcd72d7240551464b91d474bc0ed186d69", size = 260970, upload-time = "2026-05-26T20:40:16.142Z" }, - { url = "https://files.pythonhosted.org/packages/87/d1/ec13ccddeb48ec963bdfa72a11224bac2584bd045ba13beca82f8113e9c7/coverage-7.14.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:370c5afae3fa0658e11694a32b24c2778f6bc2d17718121f94ee185e69f26b54", size = 263157, upload-time = "2026-05-26T20:40:18.382Z" }, - { url = "https://files.pythonhosted.org/packages/cf/c2/cd91ead503045161092d3845f7bb95ea2f25131ce96d3e314dd835d91b9c/coverage-7.14.1-cp313-cp313t-win32.whl", hash = "sha256:3758dd0a7f1fa57365ef2e781df0f0731d38b6e3772259d13dae4bd8a958d4b1", size = 223259, upload-time = "2026-05-26T20:40:20.381Z" }, - { url = "https://files.pythonhosted.org/packages/71/9f/1e28d97e6bd2c76b07f38b7c02870f1371255ff6717f54eca578fcbbdd0e/coverage-7.14.1-cp313-cp313t-win_amd64.whl", hash = "sha256:6ff665fb023a77386fe11685190cee1f60a7d635994a30d9b0a061533d470fce", size = 224320, upload-time = "2026-05-26T20:40:22.316Z" }, - { url = "https://files.pythonhosted.org/packages/a9/e0/d936e908f0e1efa55e52b91e01b52f1055cef5e1ab2718493390ed8e2fb8/coverage-7.14.1-cp313-cp313t-win_arm64.whl", hash = "sha256:17a5a241e5997621a956a7f402a7433ef4221e5152809b785bec79e2323799f1", size = 222577, upload-time = "2026-05-26T20:40:24.894Z" }, - { url = "https://files.pythonhosted.org/packages/d6/34/fc2f101b151af3799a101f0550b0454aa008afdc0add677394ec4aa8ea10/coverage-7.14.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:d5ed429d0b8edaac649e889b4ffcedb6c80b06629a3f93050e3dddfb99235bee", size = 220091, upload-time = "2026-05-26T20:40:27.249Z" }, - { url = "https://files.pythonhosted.org/packages/3d/a7/1ebae2ab5b961b5c79bb09fe7b3ac99edb190d8be4a8c510b2cf66f46468/coverage-7.14.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:8011224a62280e50dab346960c03cf47aca1a1e09e608c0fb33fd6e0cc8e9500", size = 220421, upload-time = "2026-05-26T20:40:30.084Z" }, - { url = "https://files.pythonhosted.org/packages/5e/90/92aca9cf0acc95123c96cd1eb1f08917897a7f5dee01e15738922971ec31/coverage-7.14.1-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:12c42ec1e14f553c4f817e989365982e646e27211f10a0f717855b94a79c8906", size = 251466, upload-time = "2026-05-26T20:40:32.542Z" }, - { url = "https://files.pythonhosted.org/packages/26/2b/78048cbe3b999f6cbf9cc0d90abba6a88a3e0863a8c1c6cbc762f3f8802f/coverage-7.14.1-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:06144cd511cf2624873a035c5069cf297144f6e77a73ee3d7a55b605ec5efb42", size = 253973, upload-time = "2026-05-26T20:40:34.473Z" }, - { url = "https://files.pythonhosted.org/packages/8e/21/c2e33b29d1cfde484a19d437afc343c6cd30b08d78cbbf9f5aff14e57b2b/coverage-7.14.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a311d8e1da24be5c1ccf85cbfb06315dbaa1703d5a1eab3f6432c72b837917c8", size = 255318, upload-time = "2026-05-26T20:40:38.154Z" }, - { url = "https://files.pythonhosted.org/packages/8e/ee/aad2f108d63b769121005302f16bf66db8625c88ceaba466942e09a2607e/coverage-7.14.1-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c79cead5b5bc584d9c71451cb984d0e3a84e0c0937379c8efcbf27c8d661b851", size = 257633, upload-time = "2026-05-26T20:40:40.164Z" }, - { url = "https://files.pythonhosted.org/packages/c2/f8/11a2c29b4fd76d9849f81d0bb812ec0017a9396df3217214e38934a8c837/coverage-7.14.1-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:dcbf65f1f66a26cdd88c35cf68fb4729c5d1cd2e88added72420541dfb212034", size = 251488, upload-time = "2026-05-26T20:40:42.631Z" }, - { url = "https://files.pythonhosted.org/packages/c9/b8/9a5820de4b8ac2b71d85e3b5fb49108d7469c665f0e2ad0dd7569023e305/coverage-7.14.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fd86572566fb40189a8260446158235159bc7a82dfbc87a3b39cf4fb57fcec1c", size = 253329, upload-time = "2026-05-26T20:40:45.208Z" }, - { url = "https://files.pythonhosted.org/packages/6b/ff/f33e4823667e27548e8fd8df44217515303f9808d0ff29817db56f87d990/coverage-7.14.1-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:7771b601718fdde84832c3a434ca9bbf4ae9adbc49d84198b4110700c3c77c36", size = 251291, upload-time = "2026-05-26T20:40:47.502Z" }, - { url = "https://files.pythonhosted.org/packages/68/9b/489db0ebb209054766b90a9014a45f6d26eb724c02ec21311c3733b5a644/coverage-7.14.1-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:39b21e212c55af06fa375e3dbf90a8a8e38792f3a910c580066d23563830ddd5", size = 255564, upload-time = "2026-05-26T20:40:49.372Z" }, - { url = "https://files.pythonhosted.org/packages/27/b5/16bc2d4c2409b23c7737edb68c83bc89e345f378050549fe1d75ac7d34d5/coverage-7.14.1-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:f2302660e32562a532b442480121aef8aa61a5bdb20b30bf0adab29f10a5a4b4", size = 251107, upload-time = "2026-05-26T20:40:51.677Z" }, - { url = "https://files.pythonhosted.org/packages/7d/0c/2629997469a00cd069d588a41c9dc887610f2775ae89d250c4791e65272a/coverage-7.14.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:03a6f93c1ec3b7f2e77b5dbcc5573a2c21f12529a5c6bbe0f16f72303cc2fa4d", size = 252764, upload-time = "2026-05-26T20:40:54.267Z" }, - { url = "https://files.pythonhosted.org/packages/d2/ee/f78d63c8f079e0d7211c7e2401fa17e311514534ba61bae03e4b287ce4ab/coverage-7.14.1-cp314-cp314-win32.whl", hash = "sha256:8a3ce026d73290f42f08dafecbd82c193a74df280461fbf97300fec51fd133ee", size = 222837, upload-time = "2026-05-26T20:40:56.496Z" }, - { url = "https://files.pythonhosted.org/packages/dc/b9/be539854f93a70dfbeec69117f33ec70dc42ff0b65b5b07ab8d40d04228e/coverage-7.14.1-cp314-cp314-win_amd64.whl", hash = "sha256:114c95ef29302423b87d159075805f4ab973254a2638a5d7d046c94887cc87d7", size = 223650, upload-time = "2026-05-26T20:40:58.351Z" }, - { url = "https://files.pythonhosted.org/packages/fe/9e/24e2842fef40f35ac82ba3a7719c8023d011bf3bf652d0675316a9d088a1/coverage-7.14.1-cp314-cp314-win_arm64.whl", hash = "sha256:a07891c3f4805442b31b71e84ba3cf29ed1aa9a428284e06deeb4b23e5b46343", size = 222218, upload-time = "2026-05-26T20:41:00.321Z" }, - { url = "https://files.pythonhosted.org/packages/0a/1d/ac0a9df5fe31c1e8bdd658074905fc12844a05c1a7e3fdb8417e97c31e23/coverage-7.14.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:1101a5ebb083aecb625ebb6209d4105b58f647b093cb2dc8122d7b33f743cfe1", size = 220822, upload-time = "2026-05-26T20:41:02.281Z" }, - { url = "https://files.pythonhosted.org/packages/32/cf/f964fd9aff20323f9f1a726c97135f8a76bcd87b92dad141a456a43f3c64/coverage-7.14.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:851b9e1e4e8a4608e77c79714b2e77c0970d2ed7202a05e92ae407817481887b", size = 221084, upload-time = "2026-05-26T20:41:04.593Z" }, - { url = "https://files.pythonhosted.org/packages/d8/5e/7e5ef2aba844de2b80d678619fcf0841b42e3f37f16411226f3fe4c1016f/coverage-7.14.1-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:d5b89cdfb2ee051b71e8c3c70bd81a9eff81100f736a269136fe1a68efe00474", size = 262454, upload-time = "2026-05-26T20:41:06.641Z" }, - { url = "https://files.pythonhosted.org/packages/64/62/75809bded87015cc4935524218a2a8ed8dd1a8498bfed30a2f4f7a4b4d34/coverage-7.14.1-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:0177614a0370f227888b4e436a7c55686d6a9f90eb1ade2b624ba685a1686e86", size = 264578, upload-time = "2026-05-26T20:41:08.556Z" }, - { url = "https://files.pythonhosted.org/packages/f3/42/d33392dc14633525012d2d504fa1a33b05538bf535f5c1d64675e5754b78/coverage-7.14.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2d69af5dea2de76fc485a83032a630523f985198b7e25be901ec60181587b01e", size = 266981, upload-time = "2026-05-26T20:41:10.824Z" }, - { url = "https://files.pythonhosted.org/packages/2a/49/0157c4428c2aca7f1e09d5565930586fd5ae36f1655f08b0daa7cf1fcae1/coverage-7.14.1-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:35ab22d91de736e8966b980dc355cbcdd2c6dbbcfe275f9a2991bc8a91b3df65", size = 268112, upload-time = "2026-05-26T20:41:12.966Z" }, - { url = "https://files.pythonhosted.org/packages/96/26/86b9ce71f4092b1ed325ce1421698081df1286b833400b6836912834d6e0/coverage-7.14.1-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:357d4e32935c36588aaba057d734fa32428c360c9fc2e4442afbf1b646beee6e", size = 261558, upload-time = "2026-05-26T20:41:15Z" }, - { url = "https://files.pythonhosted.org/packages/20/4c/c311210c5472cf5401d8422b0d7812cdd520f24417673afabda6c323faca/coverage-7.14.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:51bd64741cc6fa065abd300ede1afe5a5291ece9c31da8b24884deda48bcc3f8", size = 264447, upload-time = "2026-05-26T20:41:17.369Z" }, - { url = "https://files.pythonhosted.org/packages/fb/71/59513f8710ed3e6b0ac0a050a5b7e977bb9c9e880354863b5d00d8809256/coverage-7.14.1-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:9132cd363a68a4c3daa7c8704a654b1e39d3360f6f5b8ddd470608a945236c07", size = 262048, upload-time = "2026-05-26T20:41:19.309Z" }, - { url = "https://files.pythonhosted.org/packages/84/8d/bceed32dc494f5bbf50f775cd2e78ca814953942b5ea28d3c1c3ac316f14/coverage-7.14.1-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:07c6290b1697b862c0478eab545eec949a0d0e4d6d03497f446d706da3b4f2de", size = 265781, upload-time = "2026-05-26T20:41:21.559Z" }, - { url = "https://files.pythonhosted.org/packages/e7/c5/9348fe40dbfd4991aaf78df2c6c3098bfb2cc834d1fd362a64b4efef855a/coverage-7.14.1-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:5ea0c297e27133853b4d8a3eb799bff5a2dbd9f2f41537a240d337ac9b4df890", size = 260896, upload-time = "2026-05-26T20:41:23.428Z" }, - { url = "https://files.pythonhosted.org/packages/ca/92/1ea0f03929da7cf87206b1fa24f4c8e9c158be0455481af29ec0a1f3503f/coverage-7.14.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:01b7733daad0237daa01ef80fe2dfceffc911e6a17fa7b55d14aa8214eaaaecd", size = 263214, upload-time = "2026-05-26T20:41:25.419Z" }, - { url = "https://files.pythonhosted.org/packages/f6/a9/b2493c054c0e01a643266742ab45e15744e60743f9260cd930c7142b1124/coverage-7.14.1-cp314-cp314t-win32.whl", hash = "sha256:6adc5a36984624a70bf11d7184e20fa0a49aa7c47ffab43804106a1a695ea22e", size = 223624, upload-time = "2026-05-26T20:41:27.795Z" }, - { url = "https://files.pythonhosted.org/packages/fc/bd/3e1e6a57fccd2d7c83fcdf338e93ba98eb85c6e877dd34731ac585375490/coverage-7.14.1-cp314-cp314t-win_amd64.whl", hash = "sha256:ddf799247318f34dbcd2efa8c95a8d0642674e926bb1774cf9b63dfd2a389d1c", size = 224728, upload-time = "2026-05-26T20:41:30.098Z" }, - { url = "https://files.pythonhosted.org/packages/bb/d7/31066cf1d2f0c6c797fce911bcfa01dd35642dc6da992a950256097c5860/coverage-7.14.1-cp314-cp314t-win_arm64.whl", hash = "sha256:145986fe66647eb489f18d9a997567a3fd358584c4b5a808769113abc07466af", size = 222752, upload-time = "2026-05-26T20:41:32.123Z" }, - { url = "https://files.pythonhosted.org/packages/8a/3c/1a983b9a745d7f83d53f057bcc5bf79ba6a2bbc08266b3f0c7d6fe630c9b/coverage-7.14.1-py3-none-any.whl", hash = "sha256:a252f21c27e38347e60111a3266b03827422a7d5525951aceee313aa68bab1d2", size = 211815, upload-time = "2026-05-26T20:41:34.078Z" }, +version = "7.14.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/b4/91/0a7c28934e50d8ac9a7b117712d176f2953c3170bccced5eaacfa3e96175/coverage-7.14.3.tar.gz", hash = "sha256:1a7563a443f3d53fdeb040ec8c9f7466aed7ca3dc5891aa09d3ca3625fa4387f", size = 924398, upload-time = "2026-06-22T23:10:25.584Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f1/24/efb17eb94018dd3415d0e8a76a4786a866e8964aa9c50f033399d23939c2/coverage-7.14.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:e574801e1d643561594aa021206c46d80b257e9853087090ba97bed8b0a509d3", size = 220501, upload-time = "2026-06-22T23:08:02.182Z" }, + { url = "https://files.pythonhosted.org/packages/76/93/32f1bfca6cdd34259c8af42820a034b7a28dfb44969a13ed38c17e0ba5b0/coverage-7.14.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f82b6bb7d75a2613e85d07cefa3a8c973d0544a8993337f6e2728e4a1e94c305", size = 221008, upload-time = "2026-06-22T23:08:03.701Z" }, + { url = "https://files.pythonhosted.org/packages/eb/88/0d0f974855ff905d15a64f7873d00bdc4182e2736267486c6634f4af293c/coverage-7.14.3-cp311-cp311-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:a2335ea5fed26af2e831094964fa3f8fae60b45f7e37fcc2d3b615b2add3ad87", size = 251420, upload-time = "2026-06-22T23:08:05.211Z" }, + { url = "https://files.pythonhosted.org/packages/39/7f/117dd2ec65e4140576f8ef991d88220f9b806769f7a8c20e0550c0f924e2/coverage-7.14.3-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:fbb8c3a98e779013786ae01d229662aeacbc77100efbd3f2f245219ace5af700", size = 253331, upload-time = "2026-06-22T23:08:06.672Z" }, + { url = "https://files.pythonhosted.org/packages/87/55/f0bd6d6538e3f16829fb8a44b6c0d2fe9da638bbfdd6a20f8b5da8f4fa81/coverage-7.14.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ac082660de8f429ba0ea363595abb838998570b9a7546777c60f413ab902bbde", size = 255441, upload-time = "2026-06-22T23:08:08.208Z" }, + { url = "https://files.pythonhosted.org/packages/1e/98/aa71f7879019c846a8a9662579ea4484b0202cf1e252ffeed647075e7eca/coverage-7.14.3-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:8ac012839ff7e396030f1e94e10553a431d14e4de2ab65cb3acb72bbd5628ca2", size = 257398, upload-time = "2026-06-22T23:08:09.749Z" }, + { url = "https://files.pythonhosted.org/packages/f3/4f/5fd367e59844190f5965015d7bee899e67a89d13eb2760118479bf836f2f/coverage-7.14.3-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:5952f8c1bda2a5347154450379316e6dfa4d934d62ca35f6784451e6f55074fb", size = 251558, upload-time = "2026-06-22T23:08:11.37Z" }, + { url = "https://files.pythonhosted.org/packages/8f/de/5383a6ee5a6376701fe07d980fa8e4a66c0c377fead16712720340d701a3/coverage-7.14.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8cf0f2509acb4619e2471a1951089054dd58ebea7a912066d2ea56dd4c24ca4a", size = 253134, upload-time = "2026-06-22T23:08:13.04Z" }, + { url = "https://files.pythonhosted.org/packages/01/99/09542b1a99f788e3daec7f0fadc288821e71aca9ea298d51bfa1ba79fed5/coverage-7.14.3-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:2e41fd3aab806770008279a93879b0924b16247e09ab537c043d08bbca53b4ab", size = 251195, upload-time = "2026-06-22T23:08:14.606Z" }, + { url = "https://files.pythonhosted.org/packages/02/9d/722fe8c13f0fbb064491b9e8656e56a606286792e5068c47ca1042e773e8/coverage-7.14.3-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:f0a47095963cfe054e0df178daca95aec21e680d6076da807c3add28dfe920f7", size = 254959, upload-time = "2026-06-22T23:08:16.431Z" }, + { url = "https://files.pythonhosted.org/packages/fb/58/943627179ff1d82da9e54d0a5b0bb907bb19cf19515599ccd921de50b469/coverage-7.14.3-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:a090cbf9521e78ffdb2fcf448b72902afe9f5923ff6a12d5c0d0120200348af9", size = 250914, upload-time = "2026-06-22T23:08:18.03Z" }, + { url = "https://files.pythonhosted.org/packages/a5/d4/803efcbf9ae5567454a0c71e983589529448e2704ee0da2dc0163d482f18/coverage-7.14.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:4d310baf69a4fbe8a098ce727e4808a34866ac718a6f759ae659cbd3221358bc", size = 251824, upload-time = "2026-06-22T23:08:19.704Z" }, + { url = "https://files.pythonhosted.org/packages/32/79/3f78ea9563132746eed5cecb75d2e576f9d8fec45a47242b5ae0950b82a3/coverage-7.14.3-cp311-cp311-win32.whl", hash = "sha256:74fdd718d88fe144f4579b8747873a07ec3f04cb837d5faec5a25d9e22fa31a8", size = 222594, upload-time = "2026-06-22T23:08:21.311Z" }, + { url = "https://files.pythonhosted.org/packages/85/22/9ebbc5a2ab42ac5d0eea1f48648629e1de9bbe41ec243ed6b93d55a5a53f/coverage-7.14.3-cp311-cp311-win_amd64.whl", hash = "sha256:cc96aa922e21d4bc5d5ed3c915cef27dfcbc13686f47d5e378d647fbfba655a2", size = 223073, upload-time = "2026-06-22T23:08:23.318Z" }, + { url = "https://files.pythonhosted.org/packages/71/af/69d5fcc16cb555153f99cec5467922f226be0369f7335a9506856d2a7bd0/coverage-7.14.3-cp311-cp311-win_arm64.whl", hash = "sha256:c66f9f9d4f1e9712eb9b1de5310f881d4e2188cfcba5065e1a8490f38687f2c4", size = 222617, upload-time = "2026-06-22T23:08:25.054Z" }, + { url = "https://files.pythonhosted.org/packages/bd/b0/8a911f6ffe6974dac4df95b468ab9a2899d0e59f0f99a489afeec39f00bc/coverage-7.14.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:3d74ff26299c4879ce3a4d826f9d3d4d556fd285fde7bbce3c0ef5a8ab1cec24", size = 220672, upload-time = "2026-06-22T23:08:26.621Z" }, + { url = "https://files.pythonhosted.org/packages/36/16/0fc0cb52538783dbbae0934b834f5a58fd5354380ee6cad4a07b15dc845d/coverage-7.14.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:96150a9cf3468ea20f0bc5d0e21b3df8972c31480ef90fa7614b773cc6429665", size = 221035, upload-time = "2026-06-22T23:08:28.372Z" }, + { url = "https://files.pythonhosted.org/packages/77/e2/421ccfbb48335ac49e93301478cf5d623b0c2bf1c0cadd8e2b2fc6c0c710/coverage-7.14.3-cp312-cp312-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:27d07a46500ba23515b838dbcf52512026af04090755cf6cc64166d88c9b9a1a", size = 252540, upload-time = "2026-06-22T23:08:30.226Z" }, + { url = "https://files.pythonhosted.org/packages/06/c2/05b8c890097c61a7f4406b35396b997a635200ded0339eda83dfbe526c5f/coverage-7.14.3-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:621e13c6108234d7960aaf5762ab5c3c00f33c30c15af06dcbff0c73bf112727", size = 255274, upload-time = "2026-06-22T23:08:31.876Z" }, + { url = "https://files.pythonhosted.org/packages/dc/be/b6d9efe447f8ba3c3c854195f326bd64c54b907d936cd2fdebf8767ec72e/coverage-7.14.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4b60ca6d8af70473491a15a343cbabab2e8f9ea66a4376e81c7aa24876a6f977", size = 256389, upload-time = "2026-06-22T23:08:33.843Z" }, + { url = "https://files.pythonhosted.org/packages/d4/3c/f26e50acc429e608bc534ac06f0a3c169019c798178ec5e9de3dbc0df9c9/coverage-7.14.3-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c90a7cdd5e380e1ce02f19792e2ac2fbfbf177e35a27e69fd3e873b30d895c0c", size = 258648, upload-time = "2026-06-22T23:08:35.481Z" }, + { url = "https://files.pythonhosted.org/packages/9e/a2/01c1fabf816c8e1dae197e258edf878a3d3ddc86fbda34b76e5794277d8f/coverage-7.14.3-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:5d788e5fd55347eef06ca0732c77d04a264de67e8ff24631270cdff3767a60cf", size = 252949, upload-time = "2026-06-22T23:08:37.562Z" }, + { url = "https://files.pythonhosted.org/packages/89/c6/941166dd79c31fd44a13063780ae8d552eee0089a0a0930b9bdb7df554ed/coverage-7.14.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:62c7f79db2851c95ef020e5d28b97afde3daf9f7febcd35b53e05638f729063f", size = 254310, upload-time = "2026-06-22T23:08:39.174Z" }, + { url = "https://files.pythonhosted.org/packages/10/31/80b1fd028201a961033ce95be3cd1e39e521b3762e6b4a1ac1616cb291e7/coverage-7.14.3-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:90f7608aeb5d9b60b523b9fb2a4ee1973867cc4865a3f26fe6c7577073b70205", size = 252453, upload-time = "2026-06-22T23:08:40.84Z" }, + { url = "https://files.pythonhosted.org/packages/5f/85/c3d9addd94c4b524f3f4af0232075f5fe7170ce99a1386edff803e5934db/coverage-7.14.3-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:1e3b91f9c4740aeb571ecf82e5e8d8e4ab62d34fcb5a5d4e5baa38c6f7d2857c", size = 256522, upload-time = "2026-06-22T23:08:42.494Z" }, + { url = "https://files.pythonhosted.org/packages/91/14/e5a0575f73795af3a7a9ae13dadf812e17d32422896839987dc3f86947e1/coverage-7.14.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:c946099774a7699de03cbd0ff0a64e21aed4525eed9d959adde4afe6d15758ef", size = 252023, upload-time = "2026-06-22T23:08:44.243Z" }, + { url = "https://files.pythonhosted.org/packages/38/9b/9652ee531937ce3b8a63a8896885b2b4a2d56adc30e53c9540c666286d88/coverage-7.14.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:16b206e521feb8b7133a45754643dead0538489cf8b783b90cf5f4e3299625fd", size = 253893, upload-time = "2026-06-22T23:08:46.113Z" }, + { url = "https://files.pythonhosted.org/packages/b1/05/42678841c8c38e4b08bdfc48269f5a16dfbf5806000fe6a89b4cece3c691/coverage-7.14.3-cp312-cp312-win32.whl", hash = "sha256:ea3169c7116eb6cdf7608c6c7da9ecfcb3da40688e3a510fac2d1d2bafd6dc35", size = 222734, upload-time = "2026-06-22T23:08:47.858Z" }, + { url = "https://files.pythonhosted.org/packages/df/87/07a4fcee55177a25f1b52331a8e92cf4f2c53b1a9c75ce2981fd59c684ad/coverage-7.14.3-cp312-cp312-win_amd64.whl", hash = "sha256:7ea52fc08f007bcc494d4bb3df3851e95843d881860ba38fe2c64dc100db5e7d", size = 223266, upload-time = "2026-06-22T23:08:49.494Z" }, + { url = "https://files.pythonhosted.org/packages/aa/34/2b8b66a989282ea7b370beb49f50bab29470dc30bb0b03935b6b802782f7/coverage-7.14.3-cp312-cp312-win_arm64.whl", hash = "sha256:8cec0ad652ec57790970d817490105bd917d783c2f7b38d6b58a0ca312e1a336", size = 222655, upload-time = "2026-06-22T23:08:51.766Z" }, + { url = "https://files.pythonhosted.org/packages/a9/83/7fefbf5df23ed2b7f489907564a7b34b9b07098128e12e0fdfa92626e456/coverage-7.14.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:47968988b367990ae4ab17523790c38cd125e02c6bfd379b6022be2d40bdc38c", size = 220699, upload-time = "2026-06-22T23:08:53.522Z" }, + { url = "https://files.pythonhosted.org/packages/31/e6/38c3653ff6d56d704b29241362387ca824e38e15b76fdcb7096538195790/coverage-7.14.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:0ee68f5c34812780f3a7063382c0a9fcbb99985b7ddcdcaa626e4f3fb2e0783a", size = 221068, upload-time = "2026-06-22T23:08:55.571Z" }, + { url = "https://files.pythonhosted.org/packages/20/86/4f5c45d51c5cd10a128933f0fd235393c9146abbfd2ce2dfa68b3267ead3/coverage-7.14.3-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:fa9e5c6857a7e80fa22ace5cf3550ae392bbfc322f1d8dd2d2d5a8be38cec027", size = 252060, upload-time = "2026-06-22T23:08:57.464Z" }, + { url = "https://files.pythonhosted.org/packages/82/50/dfce42eff2cecabcd5a9bbad5489449c87db3415f408d23ffee417ce01f6/coverage-7.14.3-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:98a0859b0e98e43e1178a9402e19c8127766b14f7109a374d976e5a62c0e5c73", size = 254657, upload-time = "2026-06-22T23:08:59.453Z" }, + { url = "https://files.pythonhosted.org/packages/ba/d2/639ceb1bc8038fd0d66768278d5dc22df3391918b8278c2a21aa2602a531/coverage-7.14.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:69918344541ed9c8368566c2adc03c0e33d4550d7faa87d1b35e49b6a3286ea9", size = 255892, upload-time = "2026-06-22T23:09:01.291Z" }, + { url = "https://files.pythonhosted.org/packages/8b/96/002094a10e113512500dc1e10430a449417e17b0f90f7d496bcb820208b7/coverage-7.14.3-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b7f300ac92cd4b570724c8ffbbd0c130fee298d2447f41d5a3abf58976fae1de", size = 258026, upload-time = "2026-06-22T23:09:03.017Z" }, + { url = "https://files.pythonhosted.org/packages/0b/ec/286a5d2fad9c4bee59bd724feeb7d5bf8303c6c9200b51d1dd945a9c72b0/coverage-7.14.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:11a7ec9f97ab950f4c5af62229befc7faf208fdbc0116d3902d7e306cf2c5abd", size = 252285, upload-time = "2026-06-22T23:09:04.773Z" }, + { url = "https://files.pythonhosted.org/packages/d9/7d/a17753a0b12dd48d0d50f5fab079ad99d3be1eac790494d89f3a417ca0b9/coverage-7.14.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a571bd889cd36c5922ce8e42e059f9d37d02301531d11374afa4c87a578625d5", size = 254023, upload-time = "2026-06-22T23:09:06.513Z" }, + { url = "https://files.pythonhosted.org/packages/86/ef/a76c6ceba6a2c313f905310abf2701d534cada22d372db11731831e9e209/coverage-7.14.3-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:de76caefc8deabb0dd1678b6a980be97d14c8d87e213ac194dbf8b09e96d63fb", size = 251989, upload-time = "2026-06-22T23:09:08.382Z" }, + { url = "https://files.pythonhosted.org/packages/d9/39/353013a75fec0fb49f7553519f9d52b4441e902e5178c93f38eb6c07cedb/coverage-7.14.3-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:d20a15c622194234161535459affa8f7905830391c9ccfa060d495dbfe3a1c7f", size = 256144, upload-time = "2026-06-22T23:09:10.369Z" }, + { url = "https://files.pythonhosted.org/packages/29/0e/613878555d734def11c5b20a2701a15cb3781b9e9ea749da27c5f436e928/coverage-7.14.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:b488bd4b23397db62e7a9459129d01ff06a846582a732efd24834b24a6ada498", size = 251808, upload-time = "2026-06-22T23:09:12.057Z" }, + { url = "https://files.pythonhosted.org/packages/af/76/359c058c9cfdcf1e8b107663881225b03b364a320017eda24a2a66e55102/coverage-7.14.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6a3693b4153394d265f44fb855fdc80e72403024d4d6f91c4871b334d028e4e0", size = 253579, upload-time = "2026-06-22T23:09:13.858Z" }, + { url = "https://files.pythonhosted.org/packages/1d/d9/4ba2f060933a30ebe363cef9f67a365b0a317e580c0d5d9169d56a73ef1c/coverage-7.14.3-cp313-cp313-win32.whl", hash = "sha256:338b19131ab1a6b767b462bfcbaa692e7ae22f24463e39d49b02a83410ff6b37", size = 222741, upload-time = "2026-06-22T23:09:15.636Z" }, + { url = "https://files.pythonhosted.org/packages/76/e8/196ebc25d8f34c06d43a6e9c8513c9266ef8dbf3b5672beb1a00cf5e29fa/coverage-7.14.3-cp313-cp313-win_amd64.whl", hash = "sha256:b3d77f7f196abdef7e01415de1bce09f216189e83e58159cfeef2b92d0464994", size = 223283, upload-time = "2026-06-22T23:09:17.478Z" }, + { url = "https://files.pythonhosted.org/packages/7c/af/51d2aac6417523a286f10fb25f09eb9518a84df9f1151e93ff6871f34849/coverage-7.14.3-cp313-cp313-win_arm64.whl", hash = "sha256:e6230e688c7c3e65cedd41a774eb4ec221adc6bfee13768231015b702d5e4150", size = 222678, upload-time = "2026-06-22T23:09:19.7Z" }, + { url = "https://files.pythonhosted.org/packages/61/56/14e3b97facbfa1304dd19e676e26599ad359f04714bed32f7f1c5a88efdc/coverage-7.14.3-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:605ab2b566a22bd94834529d66d295c364aba84afd3e5498285c7a524017b1fc", size = 220741, upload-time = "2026-06-22T23:09:21.616Z" }, + { url = "https://files.pythonhosted.org/packages/12/1d/db378b5cca433b90b893f26dab728b280ddd89f272a1fdfed4aeaa05c686/coverage-7.14.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:a3c2134809e80fac091bfed18a6991b5a5eb5df5ae32b17ac4f4f99864b73dd7", size = 221068, upload-time = "2026-06-22T23:09:23.452Z" }, + { url = "https://files.pythonhosted.org/packages/47/f0/3f8421b20d9c4fcd39be9a8ca3c3fda8bc204b44efbd09fede153afd3e2f/coverage-7.14.3-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:c02efd507227bde9969cab0db8f48890eb3b5dcad6afac57a4792df4133543ce", size = 252117, upload-time = "2026-06-22T23:09:25.458Z" }, + { url = "https://files.pythonhosted.org/packages/27/ca/59ea35fb99743549ec8b37eff141ece4431fea590c89e536ed8032ef45cf/coverage-7.14.3-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:1bb93c2aa61d2a5b38f1526546d95cf4132cb681e541a337bf8dfd092be816e5", size = 254622, upload-time = "2026-06-22T23:09:27.523Z" }, + { url = "https://files.pythonhosted.org/packages/c8/25/ec6de51ae7493b92a1cf74d1b763121c29636759167e2a593ba4db5881e4/coverage-7.14.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f502e948e03e866538048bba081c075caaa62e5bda6ea5b7432e45f587eb462a", size = 255968, upload-time = "2026-06-22T23:09:29.43Z" }, + { url = "https://files.pythonhosted.org/packages/5d/05/c8bfc77823f42b4664fb25842f13b567022f6f84a4c83c8ecbb16734b7cb/coverage-7.14.3-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9973ef2463f8e6cfb61a6324126bb3e17d67a85f22f58d856e583ea2e3ca6501", size = 258284, upload-time = "2026-06-22T23:09:31.397Z" }, + { url = "https://files.pythonhosted.org/packages/f6/15/1d1b242027124a32b26ef01f82018b8c4ef34ef174aa6aeba7b1eeef48e8/coverage-7.14.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:9be4e7d4c5ca0427889f8f9d614bd630c2be741b1de7699bca3b2b6c0e41003e", size = 252143, upload-time = "2026-06-22T23:09:33.256Z" }, + { url = "https://files.pythonhosted.org/packages/74/b6/d2a9842fd2a5d7d27f1ac851c043a734a494ad75402c5331db3da79ed691/coverage-7.14.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:a574912f3bde4b0619f6e97d01aa590b70998859244793769eb3a6df78ee56d3", size = 253976, upload-time = "2026-06-22T23:09:35.351Z" }, + { url = "https://files.pythonhosted.org/packages/fd/30/e1600ddf7e226db5558bb5323d2186fff00f505c4b764643ec89ce5d8175/coverage-7.14.3-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:e343fb086c9cd780b38622fea7c369acd64c1a0724312149b5d769c387a2b1f5", size = 251942, upload-time = "2026-06-22T23:09:37.313Z" }, + { url = "https://files.pythonhosted.org/packages/d9/2c/9159de64f9dd648e324328d588a44cfab1e331eb5259ce1141afe2a92dfb/coverage-7.14.3-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:3c68df8e61f1e09633fefc7538297145623957a048534368c9d212782aa5e845", size = 256220, upload-time = "2026-06-22T23:09:39.165Z" }, + { url = "https://files.pythonhosted.org/packages/91/67/b7f536cc2c124f48e91b22fbb741d2261f4e3d310faf6f76007f47566e5d/coverage-7.14.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:3e5b550a128419373c2f6cec28a244207013ef15f5cbcff6a5ca09d1dfaaf027", size = 251756, upload-time = "2026-06-22T23:09:41.056Z" }, + { url = "https://files.pythonhosted.org/packages/dd/ec/f3718038e2d4860c715a55428377ca7f6c75872caf98cabd982e1d76967d/coverage-7.14.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2bfc4dd0a912329eccc7484a7d0b2a38032b38c40663b1e1ac595f10c457954b", size = 253413, upload-time = "2026-06-22T23:09:43.306Z" }, + { url = "https://files.pythonhosted.org/packages/b8/a5/91f11efeef89b3cc9b30461128db15b0511ef813ab889a7b7ab636b3a497/coverage-7.14.3-cp314-cp314-win32.whl", hash = "sha256:0423d64c013057a06e70f070f073cec4b0cbc7d2b27f3c7007292f2ff1d52965", size = 222946, upload-time = "2026-06-22T23:09:45.261Z" }, + { url = "https://files.pythonhosted.org/packages/58/fd/98ac9f524d9ec378de831c034dbdeb544ca7ef7d2d9c9996daf232a037fd/coverage-7.14.3-cp314-cp314-win_amd64.whl", hash = "sha256:92c22e19ce64ca3f2ad751f16f14df1468b4c231bd6af97185063a9c292a0cb3", size = 223436, upload-time = "2026-06-22T23:09:47.177Z" }, + { url = "https://files.pythonhosted.org/packages/b4/a0/7cd612d650a772a0ae80144443406bf61981c896c3d57c9e6e79fb2cdbd1/coverage-7.14.3-cp314-cp314-win_arm64.whl", hash = "sha256:41de778bd41780586e2b04912079c73089ab5d839624e28db3bdb26de638da92", size = 222861, upload-time = "2026-06-22T23:09:49.384Z" }, + { url = "https://files.pythonhosted.org/packages/55/57/017353fab573779c0d00448e47d102edd36c792f7b6f233a4d89a7a08384/coverage-7.14.3-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:8427f370ca67db4c975d2a26acfc0e5783ca0b52444dbc50278ace0f35445949", size = 221474, upload-time = "2026-06-22T23:09:51.417Z" }, + { url = "https://files.pythonhosted.org/packages/69/92/90cf1f1a5c468a9c1b7ba2716e0e205293ad9b02f5f573a6de4318b15ba1/coverage-7.14.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:d8e88f335544a47e22ae2e45b344772925ec65166555c958720d5ed971880891", size = 221738, upload-time = "2026-06-22T23:09:53.487Z" }, + { url = "https://files.pythonhosted.org/packages/a4/c0/4df964fa539f8399fd7679c09c472d73744de334686fd3f01e3a2465ce4e/coverage-7.14.3-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:beaab199b9e5ceaf5a225e16a9d4df136f2a1eae0a5c20de1e277c8a5225f388", size = 263101, upload-time = "2026-06-22T23:09:55.895Z" }, + { url = "https://files.pythonhosted.org/packages/06/76/e5d33b2576ae3bf2be2058cd1cae57774b61e400f2c3c58f3783dc2ffb4a/coverage-7.14.3-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b3ff255799f5a1676c71c1c32ec01fd043aa09d57b3d95764b24992757184784", size = 265225, upload-time = "2026-06-22T23:09:57.904Z" }, + { url = "https://files.pythonhosted.org/packages/61/d2/e52419afe391a39ba27fdefaf0737d8e34bf03faef6ab3b3006545bbd0d0/coverage-7.14.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:878832eaac515b62decfa76965aed558775f86bf1fc8cca76993c0c84ae31aed", size = 267643, upload-time = "2026-06-22T23:09:59.938Z" }, + { url = "https://files.pythonhosted.org/packages/58/7a/f2625d8d5006b6b20fba5afaef00b24a763fe96476ea798a3076cbc1f84e/coverage-7.14.3-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:611e62cb9386096d81b63e0a05330750268617231e7bd598e1fe77482a2c58a5", size = 268762, upload-time = "2026-06-22T23:10:01.943Z" }, + { url = "https://files.pythonhosted.org/packages/7d/bf/908024006bba57127354d74e938954b9c3cd765cc2e0412dc9c37b415cda/coverage-7.14.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:02c41de2a88011b893050fc9830267d927a50a215f7ad5ec17349db7090ccf26", size = 262208, upload-time = "2026-06-22T23:10:03.954Z" }, + { url = "https://files.pythonhosted.org/packages/34/a0/d4f9296441b909817442fdb26bd77a698f08272ec683a7394b00eb2e47a0/coverage-7.14.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:526ce9721116af23b1065089f0b75046fe521e7772ab94b641cd66b7a0421889", size = 265096, upload-time = "2026-06-22T23:10:05.936Z" }, + { url = "https://files.pythonhosted.org/packages/e8/da/4ae4f3f4e477b56a4ce1e5c48a35eff38a94b50130ce5bdc897024741cfc/coverage-7.14.3-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:e4ed44705ca4bead6fc977a8b741f2145608289b33c8a9b42a95d0f15aedbf4d", size = 262699, upload-time = "2026-06-22T23:10:07.973Z" }, + { url = "https://files.pythonhosted.org/packages/d8/7a/6927148073ff32856d78baa77b4ddc07a9be7e90020f9db0661c4ca523a1/coverage-7.14.3-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:2415902f385a23dcc4ccd26e0ba803249a169af6a930c003a4c715eeb9a5444e", size = 266433, upload-time = "2026-06-22T23:10:10.145Z" }, + { url = "https://files.pythonhosted.org/packages/f7/a7/774f658dbe9c4c3f5daa86a87e0459ac3832e4e3cc67affe078547f727b9/coverage-7.14.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:b75ee850fc2d7c831e883220c445b035f2224de2ba6103f1e56dbd237ab913f7", size = 261547, upload-time = "2026-06-22T23:10:12.191Z" }, + { url = "https://files.pythonhosted.org/packages/3d/14/a0c18c0376c43cbf973f43ef6ca20019c950597180e6396232f7b6a27102/coverage-7.14.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:dc9b4e35e7c3920e925ba7f14886fd5fbe481232754624e832ddba66c7535635", size = 263859, upload-time = "2026-06-22T23:10:14.492Z" }, + { url = "https://files.pythonhosted.org/packages/10/ac/43a3d0f460af524b131a6191805bc5d18b806ab4e828fbf82e8c8c3af446/coverage-7.14.3-cp314-cp314t-win32.whl", hash = "sha256:7b27c822a8161afbe48e99f1adfb098d270ae7e0f7d7b0555ce110529bdb69cc", size = 223250, upload-time = "2026-06-22T23:10:16.758Z" }, + { url = "https://files.pythonhosted.org/packages/3f/5f/d5e5c56b0712e96ce8f69fe7dbf229ff938b437bc50862743c8a0d2cea84/coverage-7.14.3-cp314-cp314t-win_amd64.whl", hash = "sha256:39e1dbbb6ff2c338e0196a482558a792a1de3aa64261196f5cdb3da016ad9cda", size = 224082, upload-time = "2026-06-22T23:10:19.23Z" }, + { url = "https://files.pythonhosted.org/packages/62/35/947cbd5be1d3bcbbdc43d6791de8a56c6501903311d42915ae06a82815f0/coverage-7.14.3-cp314-cp314t-win_arm64.whl", hash = "sha256:68520c90babfa2d560eca6d497921ed3a4f469623bd709733124491b2aa8ef3f", size = 223400, upload-time = "2026-06-22T23:10:21.24Z" }, + { url = "https://files.pythonhosted.org/packages/eb/e3/a0aa32bfa3a081951f60a23bc0e7b512891ef0eecda1153cf1d8ba36c6b1/coverage-7.14.3-py3-none-any.whl", hash = "sha256:fb7e18afb6e903c1a92401a2f0501ac277dca527bb9ca6fe1f691a8a0026a0e8", size = 212469, upload-time = "2026-06-22T23:10:23.405Z" }, ] [package.optional-dependencies] @@ -831,83 +688,20 @@ nvrtc = [ ] nvtx = [ { name = "nvidia-nvtx", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/e3/66/7e97aa77af7cf6afbff26e3651b564fe41932599bc2d3dce0b2f73d4829a/crc32c-2.8.tar.gz", hash = "sha256:578728964e59c47c356aeeedee6220e021e124b9d3e8631d95d9a5e5f06e261c", size = 48179 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/dc/0b/5e03b22d913698e9cc563f39b9f6bbd508606bf6b8e9122cd6bf196b87ea/crc32c-2.8-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:e560a97fbb96c9897cb1d9b5076ef12fc12e2e25622530a1afd0de4240f17e1f", size = 66329 }, - { url = "https://files.pythonhosted.org/packages/6b/38/2fe0051ffe8c6a650c8b1ac0da31b8802d1dbe5fa40a84e4b6b6f5583db5/crc32c-2.8-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6762d276d90331a490ef7e71ffee53b9c0eb053bd75a272d786f3b08d3fe3671", size = 62988 }, - { url = "https://files.pythonhosted.org/packages/3e/30/5837a71c014be83aba1469c58820d287fc836512a0cad6b8fdd43868accd/crc32c-2.8-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:60670569f5ede91e39f48fb0cb4060e05b8d8704dd9e17ede930bf441b2f73ef", size = 61522 }, - { url = "https://files.pythonhosted.org/packages/ca/29/63972fc1452778e2092ae998c50cbfc2fc93e3fa9798a0278650cd6169c5/crc32c-2.8-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:711743da6ccc70b3c6718c328947b0b6f34a1fe6a6c27cc6c1d69cc226bf70e9", size = 80200 }, - { url = "https://files.pythonhosted.org/packages/cb/3a/60eb49d7bdada4122b3ffd45b0df54bdc1b8dd092cda4b069a287bdfcff4/crc32c-2.8-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5eb4094a2054774f13b26f21bf56792bb44fa1fcee6c6ad099387a43ffbfb4fa", size = 81757 }, - { url = "https://files.pythonhosted.org/packages/f5/63/6efc1b64429ef7d23bd58b75b7ac24d15df327e3ebbe9c247a0f7b1c2ed1/crc32c-2.8-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:fff15bf2bd3e95780516baae935ed12be88deaa5ebe6143c53eb0d26a7bdc7b7", size = 80830 }, - { url = "https://files.pythonhosted.org/packages/e1/eb/0ae9f436f8004f1c88f7429e659a7218a3879bd11a6b18ed1257aad7e98b/crc32c-2.8-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:4c0e11e3826668121fa53e0745635baf5e4f0ded437e8ff63ea56f38fc4f970a", size = 80095 }, - { url = "https://files.pythonhosted.org/packages/9e/81/4afc9d468977a4cd94a2eb62908553345009a7c0d30e74463a15d4b48ec3/crc32c-2.8-cp311-cp311-win32.whl", hash = "sha256:38f915336715d1f1353ab07d7d786f8a789b119e273aea106ba55355dfc9101d", size = 64886 }, - { url = "https://files.pythonhosted.org/packages/d6/e8/94e839c9f7e767bf8479046a207afd440a08f5c59b52586e1af5e64fa4a0/crc32c-2.8-cp311-cp311-win_amd64.whl", hash = "sha256:60e0a765b1caab8d31b2ea80840639253906a9351d4b861551c8c8625ea20f86", size = 66639 }, - { url = "https://files.pythonhosted.org/packages/b6/36/fd18ef23c42926b79c7003e16cb0f79043b5b179c633521343d3b499e996/crc32c-2.8-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:572ffb1b78cce3d88e8d4143e154d31044a44be42cb3f6fbbf77f1e7a941c5ab", size = 66379 }, - { url = "https://files.pythonhosted.org/packages/7f/b8/c584958e53f7798dd358f5bdb1bbfc97483134f053ee399d3eeb26cca075/crc32c-2.8-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:cf827b3758ee0c4aacd21ceca0e2da83681f10295c38a10bfeb105f7d98f7a68", size = 63042 }, - { url = "https://files.pythonhosted.org/packages/62/e6/6f2af0ec64a668a46c861e5bc778ea3ee42171fedfc5440f791f470fd783/crc32c-2.8-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:106fbd79013e06fa92bc3b51031694fcc1249811ed4364ef1554ee3dd2c7f5a2", size = 61528 }, - { url = "https://files.pythonhosted.org/packages/17/8b/4a04bd80a024f1a23978f19ae99407783e06549e361ab56e9c08bba3c1d3/crc32c-2.8-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:6dde035f91ffbfe23163e68605ee5a4bb8ceebd71ed54bb1fb1d0526cdd125a2", size = 80028 }, - { url = "https://files.pythonhosted.org/packages/21/8f/01c7afdc76ac2007d0e6a98e7300b4470b170480f8188475b597d1f4b4c6/crc32c-2.8-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e41ebe7c2f0fdcd9f3a3fd206989a36b460b4d3f24816d53e5be6c7dba72c5e1", size = 81531 }, - { url = "https://files.pythonhosted.org/packages/32/2b/8f78c5a8cc66486be5f51b6f038fc347c3ba748d3ea68be17a014283c331/crc32c-2.8-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ecf66cf90266d9c15cea597d5cc86c01917cd1a238dc3c51420c7886fa750d7e", size = 80608 }, - { url = "https://files.pythonhosted.org/packages/db/86/fad1a94cdeeeb6b6e2323c87f970186e74bfd6fbfbc247bf5c88ad0873d5/crc32c-2.8-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:59eee5f3a69ad0793d5fa9cdc9b9d743b0cd50edf7fccc0a3988a821fef0208c", size = 79886 }, - { url = "https://files.pythonhosted.org/packages/d5/db/1a7cb6757a1e32376fa2dfce00c815ea4ee614a94f9bff8228e37420c183/crc32c-2.8-cp312-cp312-win32.whl", hash = "sha256:a73d03ce3604aa5d7a2698e9057a0eef69f529c46497b27ee1c38158e90ceb76", size = 64896 }, - { url = "https://files.pythonhosted.org/packages/bf/8e/2024de34399b2e401a37dcb54b224b56c747b0dc46de4966886827b4d370/crc32c-2.8-cp312-cp312-win_amd64.whl", hash = "sha256:56b3b7d015247962cf58186e06d18c3d75a1a63d709d3233509e1c50a2d36aa2", size = 66645 }, - { url = "https://files.pythonhosted.org/packages/e8/d8/3ae227890b3be40955a7144106ef4dd97d6123a82c2a5310cdab58ca49d8/crc32c-2.8-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:36f1e03ee9e9c6938e67d3bcb60e36f260170aa5f37da1185e04ef37b56af395", size = 66380 }, - { url = "https://files.pythonhosted.org/packages/bd/8b/178d3f987cd0e049b484615512d3f91f3d2caeeb8ff336bb5896ae317438/crc32c-2.8-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:b2f3226b94b85a8dd9b3533601d7a63e9e3e8edf03a8a169830ee8303a199aeb", size = 63048 }, - { url = "https://files.pythonhosted.org/packages/f2/a1/48145ae2545ebc0169d3283ebe882da580ea4606bfb67cf4ca922ac3cfc3/crc32c-2.8-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:6e08628bc72d5b6bc8e0730e8f142194b610e780a98c58cb6698e665cb885a5b", size = 61530 }, - { url = "https://files.pythonhosted.org/packages/06/4b/cf05ed9d934cc30e5ae22f97c8272face420a476090e736615d9a6b53de0/crc32c-2.8-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:086f64793c5ec856d1ab31a026d52ad2b895ac83d7a38fce557d74eb857f0a82", size = 80001 }, - { url = "https://files.pythonhosted.org/packages/15/ab/4b04801739faf36345f6ba1920be5b1c70282fec52f8280afd3613fb13e2/crc32c-2.8-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bcf72ee7e0135b3d941c34bb2c26c3fc6bc207106b49fd89aaafaeae223ae209", size = 81543 }, - { url = "https://files.pythonhosted.org/packages/a9/1b/6e38dde5bfd2ea69b7f2ab6ec229fcd972a53d39e2db4efe75c0ac0382ce/crc32c-2.8-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:8a717dd9c3fd777d9bc6603717eae172887d402c4ab589d124ebd0184a83f89e", size = 80644 }, - { url = "https://files.pythonhosted.org/packages/ce/45/012176ffee90059ae8ec7131019c71724ea472aa63e72c0c8edbd1fad1d7/crc32c-2.8-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0450bb845b3c3c7b9bdc0b4e95620ec9a40824abdc8c86d6285c919a90743c1a", size = 79919 }, - { url = "https://files.pythonhosted.org/packages/f0/2b/f557629842f9dec2b3461cb3a0d854bb586ec45b814cea58b082c32f0dde/crc32c-2.8-cp313-cp313-win32.whl", hash = "sha256:765d220bfcbcffa6598ac11eb1e10af0ee4802b49fe126aa6bf79f8ddb9931d1", size = 64896 }, - { url = "https://files.pythonhosted.org/packages/d0/db/fd0f698c15d1e21d47c64181a98290665a08fcbb3940cd559e9c15bda57e/crc32c-2.8-cp313-cp313-win_amd64.whl", hash = "sha256:171ff0260d112c62abcce29332986950a57bddee514e0a2418bfde493ea06bb3", size = 66646 }, - { url = "https://files.pythonhosted.org/packages/db/b9/8e5d7054fe8e7eecab10fd0c8e7ffb01439417bdb6de1d66a81c38fc4a20/crc32c-2.8-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:b977a32a3708d6f51703c8557008f190aaa434d7347431efb0e86fcbe78c2a50", size = 66203 }, - { url = "https://files.pythonhosted.org/packages/55/5f/cc926c70057a63cc0c98a3c8a896eb15fc7e74d3034eadd53c94917c6cc3/crc32c-2.8-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:7399b01db4adaf41da2fb36fe2408e75a8d82a179a9564ed7619412e427b26d6", size = 62956 }, - { url = "https://files.pythonhosted.org/packages/a1/8a/0660c44a2dd2cb6ccbb529eb363b9280f5c766f1017bc8355ed8d695bd94/crc32c-2.8-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:4379f73f9cdad31958a673d11a332ec725ca71572401ca865867229f5f15e853", size = 61442 }, - { url = "https://files.pythonhosted.org/packages/f5/5a/6108d2dfc0fe33522ce83ba07aed4b22014911b387afa228808a278e27cd/crc32c-2.8-cp313-cp313t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:2e68264555fab19bab08331550dab58573e351a63ed79c869d455edd3b0aa417", size = 79109 }, - { url = "https://files.pythonhosted.org/packages/84/1e/c054f9e390090c197abf3d2936f4f9effaf0c6ee14569ae03d6ddf86958a/crc32c-2.8-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b48f2486727b8d0e7ccbae4a34cb0300498433d2a9d6b49cb13cb57c2e3f19cb", size = 80987 }, - { url = "https://files.pythonhosted.org/packages/c8/ad/1650e5c3341e4a485f800ea83116d72965030c5d48ccc168fcc685756e4d/crc32c-2.8-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:ecf123348934a086df8c8fde7f9f2d716d523ca0707c5a1367b8bb00d8134823", size = 79994 }, - { url = "https://files.pythonhosted.org/packages/d7/3b/f2ed924b177729cbb2ab30ca2902abff653c31d48c95e7b66717a9ca9fcc/crc32c-2.8-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e636ac60f76de538f7a2c0d0f3abf43104ee83a8f5e516f6345dc283ed1a4df7", size = 79046 }, - { url = "https://files.pythonhosted.org/packages/4b/80/413b05ee6ace613208b31b3670c3135ee1cf451f0e72a9c839b4946acc04/crc32c-2.8-cp313-cp313t-win32.whl", hash = "sha256:8dd4a19505e0253892e1b2f1425cc3bd47f79ae5a04cb8800315d00aad7197f2", size = 64837 }, - { url = "https://files.pythonhosted.org/packages/3b/1b/85eddb6ac5b38496c4e35c20298aae627970c88c3c624a22ab33e84f16c7/crc32c-2.8-cp313-cp313t-win_amd64.whl", hash = "sha256:4bb18e4bd98fb266596523ffc6be9c5b2387b2fa4e505ec56ca36336f49cb639", size = 66574 }, - { url = "https://files.pythonhosted.org/packages/aa/df/50e9079b532ff53dbfc0e66eed781374bd455af02ed5df8b56ad538de4ff/crc32c-2.8-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:3a3b2e4bcf7b3ee333050e7d3ff38e2ba46ea205f1d73d8949b248aaffe937ac", size = 66399 }, - { url = "https://files.pythonhosted.org/packages/5a/2e/67e3b0bc3d30e46ea5d16365cc81203286387671e22f2307eb41f19abb9c/crc32c-2.8-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:445e559e66dff16be54f8a4ef95aa6b01db799a639956d995c5498ba513fccc2", size = 63044 }, - { url = "https://files.pythonhosted.org/packages/36/ea/1723b17437e4344ed8d067456382ecb1f5b535d83fdc5aaebab676c6d273/crc32c-2.8-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:bf3040919e17afa5782e01b1875d6a05f44b8f19c05f211d8b9f8a1deb8bbd9c", size = 61541 }, - { url = "https://files.pythonhosted.org/packages/4c/6a/cbec8a235c5b46a01f319939b538958662159aec0ed3a74944e3a6de21f1/crc32c-2.8-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:5607ab8221e1ffd411f64aa40dbb6850cf06dd2908c9debd05d371e1acf62ff3", size = 80139 }, - { url = "https://files.pythonhosted.org/packages/21/31/d096722fe74b692d6e8206c27da1ea5f6b2a12ff92c54a62a6ba2f376254/crc32c-2.8-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c7f5db4f16816926986d3c94253314920689706ae13a9bf4888b47336c6735ce", size = 81736 }, - { url = "https://files.pythonhosted.org/packages/f6/a2/f75ef716ff7e3c22f385ba6ef30c5de80c19a21ebe699dc90824a1903275/crc32c-2.8-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:70b0153c4d418b673309d3529334d117e1074c4a3b2d7f676e430d72c14de67b", size = 80795 }, - { url = "https://files.pythonhosted.org/packages/d8/94/6d647a12d96ab087d9b8eacee3da073f981987827d57c7072f89ffc7b6cd/crc32c-2.8-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5c8933531442042438753755a5c8a9034e4d88b01da9eb796f7e151b31a7256c", size = 80042 }, - { url = "https://files.pythonhosted.org/packages/cd/dc/32b8896b40a0afee7a3c040536d0da5a73e68df2be9fadd21770fd158e16/crc32c-2.8-cp314-cp314-win32.whl", hash = "sha256:cdc83a3fe6c4e5df9457294cfd643de7d95bd4e9382c1dd6ed1e0f0f9169172c", size = 64914 }, - { url = "https://files.pythonhosted.org/packages/f2/b4/4308b27d307e8ecaf8dd1dcc63bbb0e47ae1826d93faa3e62d1ee00ee2d5/crc32c-2.8-cp314-cp314-win_amd64.whl", hash = "sha256:509e10035106df66770fe24b9eb8d9e32b6fb967df17744402fb67772d8b2bc7", size = 66723 }, - { url = "https://files.pythonhosted.org/packages/90/d5/a19d2489fa997a143bfbbf971a5c9a43f8b1ba9e775b1fb362d8fb15260c/crc32c-2.8-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:864359a39777a07b09b28eb31337c0cc603d5c1bf0fc328c3af736a8da624ec0", size = 66201 }, - { url = "https://files.pythonhosted.org/packages/98/c2/5f82f22d2c1242cb6f6fe92aa9a42991ebea86de994b8f9974d9c1d128e2/crc32c-2.8-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:14511d7cfc5d9f5e1a6c6b64caa6225c2bdc1ed00d725e9a374a3e84073ce180", size = 62956 }, - { url = "https://files.pythonhosted.org/packages/9b/61/3d43d33489cf974fb78bfb3500845770e139ae6d1d83473b660bd8f79a6c/crc32c-2.8-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:918b7999b52b5dcbcea34081e9a02d46917d571921a3f209956a9a429b2e06e5", size = 61443 }, - { url = "https://files.pythonhosted.org/packages/52/6d/f306ce64a352a3002f76b0fc88a1373f4541f9d34fad3668688610bab14b/crc32c-2.8-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:cc445da03fc012a5a03b71da1df1b40139729e6a5571fd4215ab40bfb39689c7", size = 79106 }, - { url = "https://files.pythonhosted.org/packages/a5/b7/1f74965dd7ea762954a69d172dfb3a706049c84ffa45d31401d010a4a126/crc32c-2.8-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e3dde2ec59a8a830511d72a086ead95c0b0b7f0d418f93ea106244c5e77e350", size = 80983 }, - { url = "https://files.pythonhosted.org/packages/1b/50/af93f0d91ccd61833ce77374ebfbd16f5805f5c17d18c6470976d9866d76/crc32c-2.8-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:61d51681a08b6a2a2e771b7f0cd1947fb87cb28f38ed55a01cb7c40b2ac4cdd8", size = 80009 }, - { url = "https://files.pythonhosted.org/packages/ee/fa/94f394beb68a88258af694dab2f1284f55a406b615d7900bdd6235283bc4/crc32c-2.8-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:67c0716c3b1a02d5235be649487b637eed21f2d070f2b3f63f709dcd2fefb4c7", size = 79066 }, - { url = "https://files.pythonhosted.org/packages/91/c6/a6050e0c64fd73c67a97da96cb59f08b05111e00b958fb87ecdce99f17ac/crc32c-2.8-cp314-cp314t-win32.whl", hash = "sha256:2e8fe863fbbd8bdb6b414a2090f1b0f52106e76e9a9c96a413495dbe5ebe492a", size = 64869 }, - { url = "https://files.pythonhosted.org/packages/08/1f/c7735034e401cb1ea14f996a224518e3a3fa9987cb13680e707328a7d779/crc32c-2.8-cp314-cp314t-win_amd64.whl", hash = "sha256:20a9cfb897693eb6da19e52e2a7be2026fd4d9fc8ae318f086c0d71d5dd2d8e0", size = 66633 }, - { url = "https://files.pythonhosted.org/packages/a7/1d/dd926c68eb8aac8b142a1a10b8eb62d95212c1cf81775644373fe7cceac2/crc32c-2.8-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:5833f4071da7ea182c514ba17d1eee8aec3c5be927d798222fbfbbd0f5eea02c", size = 62345 }, - { url = "https://files.pythonhosted.org/packages/51/be/803404e5abea2ef2c15042edca04bbb7f625044cca879e47f186b43887c2/crc32c-2.8-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:1dc4da036126ac07b39dd9d03e93e585ec615a2ad28ff12757aef7de175295a8", size = 61229 }, - { url = "https://files.pythonhosted.org/packages/fc/3a/00cc578cd27ed0b22c9be25cef2c24539d92df9fa80ebd67a3fc5419724c/crc32c-2.8-pp311-pypy311_pp73-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:15905fa78344654e241371c47e6ed2411f9eeb2b8095311c68c88eccf541e8b4", size = 64108 }, - { url = "https://files.pythonhosted.org/packages/6b/bc/0587ef99a1c7629f95dd0c9d4f3d894de383a0df85831eb16c48a6afdae4/crc32c-2.8-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c596f918688821f796434e89b431b1698396c38bf0b56de873621528fe3ecb1e", size = 64815 }, - { url = "https://files.pythonhosted.org/packages/73/42/94f2b8b92eae9064fcfb8deef2b971514065bd606231f8857ff8ae02bebd/crc32c-2.8-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:8d23c4fe01b3844cb6e091044bc1cebdef7d16472e058ce12d9fadf10d2614af", size = 66659 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "cycler" version = "0.12.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/a9/95/a3dbbb5028f35eafb79008e7522a75244477d2838f38cbb722248dabc2a8/cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c", size = 7615 } +sdist = { url = "https://files.pythonhosted.org/packages/a9/95/a3dbbb5028f35eafb79008e7522a75244477d2838f38cbb722248dabc2a8/cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c", size = 7615, upload-time = "2023-10-07T05:32:18.335Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321 }, + { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321, upload-time = "2023-10-07T05:32:16.783Z" }, ] [[package]] name = "dask" -version = "2026.3.0" +version = "2026.6.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "click" }, @@ -919,115 +713,76 @@ dependencies = [ { name = "pyyaml" }, { name = "toolz" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/d7/2a/5d8cc1579590af86576dde890254440e478c7174b93a02095ecfc2e6ba38/dask-2026.3.0.tar.gz", hash = "sha256:f7d96c8274e8a900d217c1ff6ea8d1bbf0b4c2c21e74a409644498d925eb8f85", size = 11000710, upload-time = "2026-03-18T07:10:14.945Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/4a/f3/00bb1e867fba351e2d784170955713bee200c43ea306c59f30bd7e748192/dask-2026.3.0-py3-none-any.whl", hash = "sha256:be614b9242b0b38288060fb2d7696125946469c98a1c30e174883fd199e0428d", size = 1485630, upload-time = "2026-03-18T07:10:12.832Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/db/33/eacaa72731f7fc64868caaf2d35060d50049eff889bd217263e68f76472f/dask-2025.11.0.tar.gz", hash = "sha256:23d59e624b80ee05b7cc8df858682cca58262c4c3b197ccf61da0f6543c8f7c3", size = 10984781 } +sdist = { url = "https://files.pythonhosted.org/packages/60/ca/58434f10ebb45d2ddc6edd6e2988abcd38da1ab1897a5f6f402711a594e9/dask-2026.6.0.tar.gz", hash = "sha256:ae3436bd31ebce2be75edf952bd1fc687a1f11ec03fe8b1bec2903d222344a45", size = 11544529, upload-time = "2026-06-11T17:48:43.316Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/1d/54/a46920229d12c3a6e9f0081d1bdaeffad23c1826353ace95714faee926e5/dask-2025.11.0-py3-none-any.whl", hash = "sha256:08c35a8146c05c93b34f83cf651009129c42ee71762da7ca452fb7308641c2b8", size = 1477108 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/02/17/b82b537a30be67ba178fc0b0fbda8fcbaeda188fb039d6ad8a84e89353a2/dask-2026.6.0-py3-none-any.whl", hash = "sha256:1539859071065dca379ca592ff76e911cd7965dc466da0040354ab466179189b", size = 1488995, upload-time = "2026-06-11T17:48:41.008Z" }, ] [package.optional-dependencies] array = [ - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, ] [[package]] name = "debugpy" -version = "1.8.20" -source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/e0/b7/cd8080344452e4874aae67c40d8940e2b4d47b01601a8fd9f44786c757c7/debugpy-1.8.20.tar.gz", hash = "sha256:55bc8701714969f1ab89a6d5f2f3d40c36f91b2cbe2f65d98bf8196f6a6a2c33", size = 1645207, upload-time = "2026-01-29T23:03:28.199Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/51/56/c3baf5cbe4dd77427fd9aef99fcdade259ad128feeb8a786c246adb838e5/debugpy-1.8.20-cp311-cp311-macosx_15_0_universal2.whl", hash = "sha256:eada6042ad88fa1571b74bd5402ee8b86eded7a8f7b827849761700aff171f1b", size = 2208318, upload-time = "2026-01-29T23:03:36.481Z" }, - { url = "https://files.pythonhosted.org/packages/9a/7d/4fa79a57a8e69fe0d9763e98d1110320f9ecd7f1f362572e3aafd7417c9d/debugpy-1.8.20-cp311-cp311-manylinux_2_34_x86_64.whl", hash = "sha256:7de0b7dfeedc504421032afba845ae2a7bcc32ddfb07dae2c3ca5442f821c344", size = 3171493, upload-time = "2026-01-29T23:03:37.775Z" }, - { url = "https://files.pythonhosted.org/packages/7d/f2/1e8f8affe51e12a26f3a8a8a4277d6e60aa89d0a66512f63b1e799d424a4/debugpy-1.8.20-cp311-cp311-win32.whl", hash = "sha256:773e839380cf459caf73cc533ea45ec2737a5cc184cf1b3b796cd4fd98504fec", size = 5209240, upload-time = "2026-01-29T23:03:39.109Z" }, - { url = "https://files.pythonhosted.org/packages/d5/92/1cb532e88560cbee973396254b21bece8c5d7c2ece958a67afa08c9f10dc/debugpy-1.8.20-cp311-cp311-win_amd64.whl", hash = "sha256:1f7650546e0eded1902d0f6af28f787fa1f1dbdbc97ddabaf1cd963a405930cb", size = 5233481, upload-time = "2026-01-29T23:03:40.659Z" }, - { url = "https://files.pythonhosted.org/packages/14/57/7f34f4736bfb6e00f2e4c96351b07805d83c9a7b33d28580ae01374430f7/debugpy-1.8.20-cp312-cp312-macosx_15_0_universal2.whl", hash = "sha256:4ae3135e2089905a916909ef31922b2d733d756f66d87345b3e5e52b7a55f13d", size = 2550686, upload-time = "2026-01-29T23:03:42.023Z" }, - { url = "https://files.pythonhosted.org/packages/ab/78/b193a3975ca34458f6f0e24aaf5c3e3da72f5401f6054c0dfd004b41726f/debugpy-1.8.20-cp312-cp312-manylinux_2_34_x86_64.whl", hash = "sha256:88f47850a4284b88bd2bfee1f26132147d5d504e4e86c22485dfa44b97e19b4b", size = 4310588, upload-time = "2026-01-29T23:03:43.314Z" }, - { url = "https://files.pythonhosted.org/packages/c1/55/f14deb95eaf4f30f07ef4b90a8590fc05d9e04df85ee379712f6fb6736d7/debugpy-1.8.20-cp312-cp312-win32.whl", hash = "sha256:4057ac68f892064e5f98209ab582abfee3b543fb55d2e87610ddc133a954d390", size = 5331372, upload-time = "2026-01-29T23:03:45.526Z" }, - { url = "https://files.pythonhosted.org/packages/a1/39/2bef246368bd42f9bd7cba99844542b74b84dacbdbea0833e610f384fee8/debugpy-1.8.20-cp312-cp312-win_amd64.whl", hash = "sha256:a1a8f851e7cf171330679ef6997e9c579ef6dd33c9098458bd9986a0f4ca52e3", size = 5372835, upload-time = "2026-01-29T23:03:47.245Z" }, - { url = "https://files.pythonhosted.org/packages/15/e2/fc500524cc6f104a9d049abc85a0a8b3f0d14c0a39b9c140511c61e5b40b/debugpy-1.8.20-cp313-cp313-macosx_15_0_universal2.whl", hash = "sha256:5dff4bb27027821fdfcc9e8f87309a28988231165147c31730128b1c983e282a", size = 2539560, upload-time = "2026-01-29T23:03:48.738Z" }, - { url = "https://files.pythonhosted.org/packages/90/83/fb33dcea789ed6018f8da20c5a9bc9d82adc65c0c990faed43f7c955da46/debugpy-1.8.20-cp313-cp313-manylinux_2_34_x86_64.whl", hash = "sha256:84562982dd7cf5ebebfdea667ca20a064e096099997b175fe204e86817f64eaf", size = 4293272, upload-time = "2026-01-29T23:03:50.169Z" }, - { url = "https://files.pythonhosted.org/packages/a6/25/b1e4a01bfb824d79a6af24b99ef291e24189080c93576dfd9b1a2815cd0f/debugpy-1.8.20-cp313-cp313-win32.whl", hash = "sha256:da11dea6447b2cadbf8ce2bec59ecea87cc18d2c574980f643f2d2dfe4862393", size = 5331208, upload-time = "2026-01-29T23:03:51.547Z" }, - { url = "https://files.pythonhosted.org/packages/13/f7/a0b368ce54ffff9e9028c098bd2d28cfc5b54f9f6c186929083d4c60ba58/debugpy-1.8.20-cp313-cp313-win_amd64.whl", hash = "sha256:eb506e45943cab2efb7c6eafdd65b842f3ae779f020c82221f55aca9de135ed7", size = 5372930, upload-time = "2026-01-29T23:03:53.585Z" }, - { url = "https://files.pythonhosted.org/packages/33/2e/f6cb9a8a13f5058f0a20fe09711a7b726232cd5a78c6a7c05b2ec726cff9/debugpy-1.8.20-cp314-cp314-macosx_15_0_universal2.whl", hash = "sha256:9c74df62fc064cd5e5eaca1353a3ef5a5d50da5eb8058fcef63106f7bebe6173", size = 2538066, upload-time = "2026-01-29T23:03:54.999Z" }, - { url = "https://files.pythonhosted.org/packages/c5/56/6ddca50b53624e1ca3ce1d1e49ff22db46c47ea5fb4c0cc5c9b90a616364/debugpy-1.8.20-cp314-cp314-manylinux_2_34_x86_64.whl", hash = "sha256:077a7447589ee9bc1ff0cdf443566d0ecf540ac8aa7333b775ebcb8ce9f4ecad", size = 4269425, upload-time = "2026-01-29T23:03:56.518Z" }, - { url = "https://files.pythonhosted.org/packages/c5/d9/d64199c14a0d4c476df46c82470a3ce45c8d183a6796cfb5e66533b3663c/debugpy-1.8.20-cp314-cp314-win32.whl", hash = "sha256:352036a99dd35053b37b7803f748efc456076f929c6a895556932eaf2d23b07f", size = 5331407, upload-time = "2026-01-29T23:03:58.481Z" }, - { url = "https://files.pythonhosted.org/packages/e0/d9/1f07395b54413432624d61524dfd98c1a7c7827d2abfdb8829ac92638205/debugpy-1.8.20-cp314-cp314-win_amd64.whl", hash = "sha256:a98eec61135465b062846112e5ecf2eebb855305acc1dfbae43b72903b8ab5be", size = 5372521, upload-time = "2026-01-29T23:03:59.864Z" }, - { url = "https://files.pythonhosted.org/packages/e0/c3/7f67dea8ccf8fdcb9c99033bbe3e90b9e7395415843accb81428c441be2d/debugpy-1.8.20-py2.py3-none-any.whl", hash = "sha256:5be9bed9ae3be00665a06acaa48f8329d2b9632f15fd09f6a9a8c8d9907e54d7", size = 5337658, upload-time = "2026-01-29T23:04:17.404Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/15/ad/71e708ff4ca377c4230530d6a7aa7992592648c122a2cd2b321cf8b35a76/debugpy-1.8.17.tar.gz", hash = "sha256:fd723b47a8c08892b1a16b2c6239a8b96637c62a59b94bb5dab4bac592a58a8e", size = 1644129 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d8/53/3af72b5c159278c4a0cf4cffa518675a0e73bdb7d1cac0239b815502d2ce/debugpy-1.8.17-cp311-cp311-macosx_15_0_universal2.whl", hash = "sha256:d3fce3f0e3de262a3b67e69916d001f3e767661c6e1ee42553009d445d1cd840", size = 2207154 }, - { url = "https://files.pythonhosted.org/packages/8f/6d/204f407df45600e2245b4a39860ed4ba32552330a0b3f5f160ae4cc30072/debugpy-1.8.17-cp311-cp311-manylinux_2_34_x86_64.whl", hash = "sha256:c6bdf134457ae0cac6fb68205776be635d31174eeac9541e1d0c062165c6461f", size = 3170322 }, - { url = "https://files.pythonhosted.org/packages/f2/13/1b8f87d39cf83c6b713de2620c31205299e6065622e7dd37aff4808dd410/debugpy-1.8.17-cp311-cp311-win32.whl", hash = "sha256:e79a195f9e059edfe5d8bf6f3749b2599452d3e9380484cd261f6b7cd2c7c4da", size = 5155078 }, - { url = "https://files.pythonhosted.org/packages/c2/c5/c012c60a2922cc91caa9675d0ddfbb14ba59e1e36228355f41cab6483469/debugpy-1.8.17-cp311-cp311-win_amd64.whl", hash = "sha256:b532282ad4eca958b1b2d7dbcb2b7218e02cb934165859b918e3b6ba7772d3f4", size = 5179011 }, - { url = "https://files.pythonhosted.org/packages/08/2b/9d8e65beb2751876c82e1aceb32f328c43ec872711fa80257c7674f45650/debugpy-1.8.17-cp312-cp312-macosx_15_0_universal2.whl", hash = "sha256:f14467edef672195c6f6b8e27ce5005313cb5d03c9239059bc7182b60c176e2d", size = 2549522 }, - { url = "https://files.pythonhosted.org/packages/b4/78/eb0d77f02971c05fca0eb7465b18058ba84bd957062f5eec82f941ac792a/debugpy-1.8.17-cp312-cp312-manylinux_2_34_x86_64.whl", hash = "sha256:24693179ef9dfa20dca8605905a42b392be56d410c333af82f1c5dff807a64cc", size = 4309417 }, - { url = "https://files.pythonhosted.org/packages/37/42/c40f1d8cc1fed1e75ea54298a382395b8b937d923fcf41ab0797a554f555/debugpy-1.8.17-cp312-cp312-win32.whl", hash = "sha256:6a4e9dacf2cbb60d2514ff7b04b4534b0139facbf2abdffe0639ddb6088e59cf", size = 5277130 }, - { url = "https://files.pythonhosted.org/packages/72/22/84263b205baad32b81b36eac076de0cdbe09fe2d0637f5b32243dc7c925b/debugpy-1.8.17-cp312-cp312-win_amd64.whl", hash = "sha256:e8f8f61c518952fb15f74a302e068b48d9c4691768ade433e4adeea961993464", size = 5319053 }, - { url = "https://files.pythonhosted.org/packages/50/76/597e5cb97d026274ba297af8d89138dfd9e695767ba0e0895edb20963f40/debugpy-1.8.17-cp313-cp313-macosx_15_0_universal2.whl", hash = "sha256:857c1dd5d70042502aef1c6d1c2801211f3ea7e56f75e9c335f434afb403e464", size = 2538386 }, - { url = "https://files.pythonhosted.org/packages/5f/60/ce5c34fcdfec493701f9d1532dba95b21b2f6394147234dce21160bd923f/debugpy-1.8.17-cp313-cp313-manylinux_2_34_x86_64.whl", hash = "sha256:3bea3b0b12f3946e098cce9b43c3c46e317b567f79570c3f43f0b96d00788088", size = 4292100 }, - { url = "https://files.pythonhosted.org/packages/e8/95/7873cf2146577ef71d2a20bf553f12df865922a6f87b9e8ee1df04f01785/debugpy-1.8.17-cp313-cp313-win32.whl", hash = "sha256:e34ee844c2f17b18556b5bbe59e1e2ff4e86a00282d2a46edab73fd7f18f4a83", size = 5277002 }, - { url = "https://files.pythonhosted.org/packages/46/11/18c79a1cee5ff539a94ec4aa290c1c069a5580fd5cfd2fb2e282f8e905da/debugpy-1.8.17-cp313-cp313-win_amd64.whl", hash = "sha256:6c5cd6f009ad4fca8e33e5238210dc1e5f42db07d4b6ab21ac7ffa904a196420", size = 5319047 }, - { url = "https://files.pythonhosted.org/packages/de/45/115d55b2a9da6de812696064ceb505c31e952c5d89c4ed1d9bb983deec34/debugpy-1.8.17-cp314-cp314-macosx_15_0_universal2.whl", hash = "sha256:045290c010bcd2d82bc97aa2daf6837443cd52f6328592698809b4549babcee1", size = 2536899 }, - { url = "https://files.pythonhosted.org/packages/5a/73/2aa00c7f1f06e997ef57dc9b23d61a92120bec1437a012afb6d176585197/debugpy-1.8.17-cp314-cp314-manylinux_2_34_x86_64.whl", hash = "sha256:b69b6bd9dba6a03632534cdf67c760625760a215ae289f7489a452af1031fe1f", size = 4268254 }, - { url = "https://files.pythonhosted.org/packages/86/b5/ed3e65c63c68a6634e3ba04bd10255c8e46ec16ebed7d1c79e4816d8a760/debugpy-1.8.17-cp314-cp314-win32.whl", hash = "sha256:5c59b74aa5630f3a5194467100c3b3d1c77898f9ab27e3f7dc5d40fc2f122670", size = 5277203 }, - { url = "https://files.pythonhosted.org/packages/b0/26/394276b71c7538445f29e792f589ab7379ae70fd26ff5577dfde71158e96/debugpy-1.8.17-cp314-cp314-win_amd64.whl", hash = "sha256:893cba7bb0f55161de4365584b025f7064e1f88913551bcd23be3260b231429c", size = 5318493 }, - { url = "https://files.pythonhosted.org/packages/b0/d0/89247ec250369fc76db477720a26b2fce7ba079ff1380e4ab4529d2fe233/debugpy-1.8.17-py2.py3-none-any.whl", hash = "sha256:60c7dca6571efe660ccb7a9508d73ca14b8796c4ed484c2002abba714226cfef", size = 5283210 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) +version = "1.8.21" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/f2/aa/12037145b7a56eaa5b29b41872f7a21b538e807e13f32c4d3c46e59be084/debugpy-1.8.21.tar.gz", hash = "sha256:a3c53278e84c94e11bd87c53970ec391d1a67396c8b22609fcac576520e611a6", size = 1697577, upload-time = "2026-06-01T19:30:35.156Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/89/fb/cbf306d6e07a313a91e7171a98669054502840931432c227cfd505ee367f/debugpy-1.8.21-cp311-cp311-macosx_15_0_universal2.whl", hash = "sha256:da456226c7b4c69e35dbe35dcee6623d912000a77816db7856a41af1c72a0264", size = 2203120, upload-time = "2026-06-01T19:30:43.964Z" }, + { url = "https://files.pythonhosted.org/packages/aa/57/aa739bd4ad2cbf96aeb1b20b56918ddd5ae4c28b68709bfcd327f02123ee/debugpy-1.8.21-cp311-cp311-manylinux_2_34_x86_64.whl", hash = "sha256:f68b891688e61bdc08b8d364d919ff0051e0b94657b39dcd027bc3173edb7cdc", size = 3059958, upload-time = "2026-06-01T19:30:45.622Z" }, + { url = "https://files.pythonhosted.org/packages/a8/31/453d2c9a23d133fe2c8ec7ca1d816ded52a913487fe3ffef7c01b4b706af/debugpy-1.8.21-cp311-cp311-win32.whl", hash = "sha256:f843a8b08c2edeaf9b1582eed4f25441af21a297c22ff16bf76a662557aa9c9e", size = 5236515, upload-time = "2026-06-01T19:30:47.461Z" }, + { url = "https://files.pythonhosted.org/packages/60/94/6660de2f2d7bf388f229335ba4637646eebabdbf38564cb439a95a9193c9/debugpy-1.8.21-cp311-cp311-win_amd64.whl", hash = "sha256:84c564d8cc701d41843b29a92814c1f1bef6798724ca9d675c284ad9f6a547d7", size = 5256138, upload-time = "2026-06-01T19:30:49.113Z" }, + { url = "https://files.pythonhosted.org/packages/a2/df/bf625547431a9cadc9f4cbfeda38866e2b17f6aed147b625377e87834449/debugpy-1.8.21-cp312-cp312-macosx_15_0_universal2.whl", hash = "sha256:9f96713896f39c3dff0ee841f47320c3f2983d33c341e009361bb0ebc79adc4e", size = 2483609, upload-time = "2026-06-01T19:30:50.794Z" }, + { url = "https://files.pythonhosted.org/packages/bf/09/59324b903599031ff9faaec1758292409f6561a0ec2492fe4b703327705a/debugpy-1.8.21-cp312-cp312-manylinux_2_34_x86_64.whl", hash = "sha256:c193d474f0a211191f2b4449d2d06157c689013035bd952f3b617e0ef422b176", size = 3968900, upload-time = "2026-06-01T19:30:52.341Z" }, + { url = "https://files.pythonhosted.org/packages/14/cd/27f65b805d7fe005c44e1a36b9183ecdfbcdbf9d3e721a5115d461ecc7ee/debugpy-1.8.21-cp312-cp312-win32.whl", hash = "sha256:4743373c1cac7f9e74a1b9915bf1dbe0e900eca657ffb170ae07ac8363205ae9", size = 5336340, upload-time = "2026-06-01T19:30:54.047Z" }, + { url = "https://files.pythonhosted.org/packages/77/1d/c84e30c0c674184948b66f076ab271c01d940618a2824c23cd035a27bc20/debugpy-1.8.21-cp312-cp312-win_amd64.whl", hash = "sha256:bd7ba9dd3daa7c2f942c6ca8d4695a16bf9ac16b63615261c7982bc74f7ed20c", size = 5374751, upload-time = "2026-06-01T19:30:55.891Z" }, + { url = "https://files.pythonhosted.org/packages/77/6b/d817e1f8cc77aa055d37fba092e0febfdff40fe652d8d53d4cd7a86ad98d/debugpy-1.8.21-cp313-cp313-macosx_15_0_universal2.whl", hash = "sha256:13678151fc401e2d68c9880b91e28714f797d40422994572b24560ef80910a88", size = 2477398, upload-time = "2026-06-01T19:30:57.644Z" }, + { url = "https://files.pythonhosted.org/packages/48/57/412421516afc3055fa577516f00beec3d663f9b0ab330639547ae6c57720/debugpy-1.8.21-cp313-cp313-manylinux_2_34_x86_64.whl", hash = "sha256:ecbd158386c31ffe71d46f72d44d56e66331ab9b16cad649156d514368f23ab2", size = 3962096, upload-time = "2026-06-01T19:30:59.235Z" }, + { url = "https://files.pythonhosted.org/packages/c1/62/2c616337cf6ba7b07ebbc97f02c6c945a8e2f76b365e33ee809c32ee36d1/debugpy-1.8.21-cp313-cp313-win32.whl", hash = "sha256:2c2ae706dec41d99a9ca1f7ebc987a83e65578363be6f6b3ac9067504917fae1", size = 5336288, upload-time = "2026-06-01T19:31:00.79Z" }, + { url = "https://files.pythonhosted.org/packages/f8/99/9175103392f84c4b1bf7622888cdc68da07f0ff7d9e581266428f6776033/debugpy-1.8.21-cp313-cp313-win_amd64.whl", hash = "sha256:aa648733047443eb1d07682c4ef287d36a54507b643ffdf38b09a3ef002c72a0", size = 5376567, upload-time = "2026-06-01T19:31:02.56Z" }, + { url = "https://files.pythonhosted.org/packages/ce/3d/f4bbb323a548bfab2af3d6b4ffd9bf22636e55956a1285d317a1de643aad/debugpy-1.8.21-cp314-cp314-macosx_15_0_universal2.whl", hash = "sha256:9bb2a685287a2ac9b181cde89edcec64845cb51de7faaa75badb9a698bc24782", size = 2477209, upload-time = "2026-06-01T19:31:04.157Z" }, + { url = "https://files.pythonhosted.org/packages/8c/2d/6e7ec524984a1702777868de49a4c53202bddac2a432a76a093469587750/debugpy-1.8.21-cp314-cp314-manylinux_2_34_x86_64.whl", hash = "sha256:3d6922439bf33fd38a3e2c447869ebc7b97da5cd3d329ff1ef9bc06c4903437e", size = 3927115, upload-time = "2026-06-01T19:31:05.863Z" }, + { url = "https://files.pythonhosted.org/packages/97/47/d1aa6d64005a98a9144647d99306b419396f9ad7bf1d73c119e17a81fb4d/debugpy-1.8.21-cp314-cp314-win32.whl", hash = "sha256:15d4963bd5ffa48f0da0947fd06757fa7621945048a14ad7705431566d3c0e7c", size = 5336724, upload-time = "2026-06-01T19:31:07.711Z" }, + { url = "https://files.pythonhosted.org/packages/5f/67/b905b90d163af11878c1af8abafa4a25206335e112e284e413454543a6da/debugpy-1.8.21-cp314-cp314-win_amd64.whl", hash = "sha256:fe0744a12353406de0ae8ccff0d0a4a666f00801a3db8fd04e7a5f761cd520e8", size = 5373803, upload-time = "2026-06-01T19:31:09.469Z" }, + { url = "https://files.pythonhosted.org/packages/95/51/67e7cf11a53e40694f720457d5b3a1cdaaa3d5a9a633e482f225456b93ff/debugpy-1.8.21-py2.py3-none-any.whl", hash = "sha256:b1e37d333663c8851516a47364ef473da127f9caebe4417e6df6f5825a7e9a92", size = 5352888, upload-time = "2026-06-01T19:31:25.186Z" }, ] [[package]] name = "decorator" version = "5.3.1" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/60/8b/32f9823da46cde7df2087faa08cd98d01b908f8dcab982cdba9c84e85355/decorator-5.3.1.tar.gz", hash = "sha256:4cbcdd55a6efadb9dbea26b858f4fb3264567b52d69ca0d25b721b553f60ea82", size = 58084, upload-time = "2026-05-18T06:03:28.057Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/05/7f/798705f5296a58ca505d600456748d1be48078eac8a7050d8a98bc9edb89/decorator-5.3.1-py3-none-any.whl", hash = "sha256:f47fe6fdbd2edd623ecfe36875d37aba411624e2670dd395dddae1358689bb3c", size = 10365, upload-time = "2026-05-18T06:03:26.517Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/43/fa/6d96a0978d19e17b68d634497769987b16c8f4cd0a7a05048bec693caa6b/decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360", size = 56711 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/4e/8c/f3147f5c4b73e7550fe5f9352eaa956ae838d5c51eb58e7a25b9f3e2643b/decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a", size = 9190 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "defusedxml" version = "0.7.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/0f/d5/c66da9b79e5bdb124974bfe172b4daf3c984ebd9c2a06e2b8a4dc7331c72/defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69", size = 75520 } +sdist = { url = "https://files.pythonhosted.org/packages/0f/d5/c66da9b79e5bdb124974bfe172b4daf3c984ebd9c2a06e2b8a4dc7331c72/defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69", size = 75520, upload-time = "2021-03-08T10:59:26.269Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604 }, + { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" }, ] [[package]] name = "dill" version = "0.4.1" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/81/e1/56027a71e31b02ddc53c7d65b01e68edf64dea2932122fe7746a516f75d5/dill-0.4.1.tar.gz", hash = "sha256:423092df4182177d4d8ba8290c8a5b640c66ab35ec7da59ccfa00f6fa3eea5fa", size = 187315, upload-time = "2026-01-19T02:36:56.85Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/1e/77/dc8c558f7593132cf8fefec57c4f60c83b16941c574ac5f619abb3ae7933/dill-0.4.1-py3-none-any.whl", hash = "sha256:1e1ce33e978ae97fcfcff5638477032b801c46c7c65cf717f95fbc2248f79a9d", size = 120019, upload-time = "2026-01-19T02:36:55.663Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/12/80/630b4b88364e9a8c8c5797f4602d0f76ef820909ee32f0bacb9f90654042/dill-0.4.0.tar.gz", hash = "sha256:0633f1d2df477324f53a895b02c901fb961bdbf65a17122586ea7019292cbcf0", size = 186976 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/50/3d/9373ad9c56321fdab5b41197068e1d8c25883b3fea29dd361f9b55116869/dill-0.4.0-py3-none-any.whl", hash = "sha256:44f54bf6412c2c8464c14e8243eb163690a9800dbe2c367330883b19c7561049", size = 119668 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "distlib" -version = "0.4.0" +version = "0.4.3" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/96/8e/709914eb2b5749865801041647dc7f4e6d00b549cfe88b65ca192995f07c/distlib-0.4.0.tar.gz", hash = "sha256:feec40075be03a04501a973d81f633735b4b69f98b05450592310c0f401a4e0d", size = 614605 } +sdist = { url = "https://files.pythonhosted.org/packages/c9/02/bd72be9134d25ed783ecbbc38a539ffaefbf90c78418c7fb7229600dbac7/distlib-0.4.3.tar.gz", hash = "sha256:f152097224a0ae24be5a0f6bae1b9359af82133bce63f98a95f86cae1aede9ed", size = 615141, upload-time = "2026-06-12T08:04:52.847Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/33/6b/e0547afaf41bf2c42e52430072fa5658766e3d65bd4b03a563d1b6336f57/distlib-0.4.0-py2.py3-none-any.whl", hash = "sha256:9659f7d87e46584a30b5780e43ac7a2143098441670ff0a49d5f9034c54a6c16", size = 469047 }, + { url = "https://files.pythonhosted.org/packages/02/08/9c41fb51ab5b43eb21674aff13df270e8ba6c4b29c8624e328dc7a9482af/distlib-0.4.3-py2.py3-none-any.whl", hash = "sha256:4b0ce306c966eb73bc3a7b6abad017c556dadd92c44701562cd528ac7fde4d5b", size = 470628, upload-time = "2026-06-12T08:04:50.506Z" }, ] [[package]] @@ -1037,42 +792,36 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "pyyaml" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/25/71/80cc718ff6d7abfbabacb1f57aaa42e9c1552bfdd01e64ddd704e4a03638/donfig-0.8.1.post1.tar.gz", hash = "sha256:3bef3413a4c1c601b585e8d297256d0c1470ea012afa6e8461dc28bfb7c23f52", size = 19506 } +sdist = { url = "https://files.pythonhosted.org/packages/25/71/80cc718ff6d7abfbabacb1f57aaa42e9c1552bfdd01e64ddd704e4a03638/donfig-0.8.1.post1.tar.gz", hash = "sha256:3bef3413a4c1c601b585e8d297256d0c1470ea012afa6e8461dc28bfb7c23f52", size = 19506, upload-time = "2024-05-23T14:14:31.513Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/0c/d5/c5db1ea3394c6e1732fb3286b3bd878b59507a8f77d32a2cebda7d7b7cd4/donfig-0.8.1.post1-py3-none-any.whl", hash = "sha256:2a3175ce74a06109ff9307d90a230f81215cbac9a751f4d1c6194644b8204f9d", size = 21592 }, + { url = "https://files.pythonhosted.org/packages/0c/d5/c5db1ea3394c6e1732fb3286b3bd878b59507a8f77d32a2cebda7d7b7cd4/donfig-0.8.1.post1-py3-none-any.whl", hash = "sha256:2a3175ce74a06109ff9307d90a230f81215cbac9a751f4d1c6194644b8204f9d", size = 21592, upload-time = "2024-05-23T14:13:55.283Z" }, ] [[package]] name = "executing" version = "2.2.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/cc/28/c14e053b6762b1044f34a13aab6859bbf40456d37d23aa286ac24cfd9a5d/executing-2.2.1.tar.gz", hash = "sha256:3632cc370565f6648cc328b32435bd120a1e4ebb20c77e3fdde9a13cd1e533c4", size = 1129488 } +sdist = { url = "https://files.pythonhosted.org/packages/cc/28/c14e053b6762b1044f34a13aab6859bbf40456d37d23aa286ac24cfd9a5d/executing-2.2.1.tar.gz", hash = "sha256:3632cc370565f6648cc328b32435bd120a1e4ebb20c77e3fdde9a13cd1e533c4", size = 1129488, upload-time = "2025-09-01T09:48:10.866Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/c1/ea/53f2148663b321f21b5a606bd5f191517cf40b7072c0497d3c92c4a13b1e/executing-2.2.1-py2.py3-none-any.whl", hash = "sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017", size = 28317 }, + { url = "https://files.pythonhosted.org/packages/c1/ea/53f2148663b321f21b5a606bd5f191517cf40b7072c0497d3c92c4a13b1e/executing-2.2.1-py2.py3-none-any.whl", hash = "sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017", size = 28317, upload-time = "2025-09-01T09:48:08.5Z" }, ] [[package]] name = "fastjsonschema" version = "2.21.2" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/20/b5/23b216d9d985a956623b6bd12d4086b60f0059b27799f23016af04a74ea1/fastjsonschema-2.21.2.tar.gz", hash = "sha256:b1eb43748041c880796cd077f1a07c3d94e93ae84bba5ed36800a33554ae05de", size = 374130 } +sdist = { url = "https://files.pythonhosted.org/packages/20/b5/23b216d9d985a956623b6bd12d4086b60f0059b27799f23016af04a74ea1/fastjsonschema-2.21.2.tar.gz", hash = "sha256:b1eb43748041c880796cd077f1a07c3d94e93ae84bba5ed36800a33554ae05de", size = 374130, upload-time = "2025-08-14T18:49:36.666Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/cb/a8/20d0723294217e47de6d9e2e40fd4a9d2f7c4b6ef974babd482a59743694/fastjsonschema-2.21.2-py3-none-any.whl", hash = "sha256:1c797122d0a86c5cace2e54bf4e819c36223b552017172f32c5c024a6b77e463", size = 24024 }, + { url = "https://files.pythonhosted.org/packages/cb/a8/20d0723294217e47de6d9e2e40fd4a9d2f7c4b6ef974babd482a59743694/fastjsonschema-2.21.2-py3-none-any.whl", hash = "sha256:1c797122d0a86c5cace2e54bf4e819c36223b552017172f32c5c024a6b77e463", size = 24024, upload-time = "2025-08-14T18:49:34.776Z" }, ] [[package]] name = "filelock" -version = "3.29.0" +version = "3.29.4" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/b5/fe/997687a931ab51049acce6fa1f23e8f01216374ea81374ddee763c493db5/filelock-3.29.0.tar.gz", hash = "sha256:69974355e960702e789734cb4871f884ea6fe50bd8404051a3530bc07809cf90", size = 57571, upload-time = "2026-04-19T15:39:10.068Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/81/47/dd9a212ef6e343a6857485ffe25bba537304f1913bdbed446a23f7f592e1/filelock-3.29.0-py3-none-any.whl", hash = "sha256:96f5f6344709aa1572bbf631c640e4ebeeb519e08da902c39a001882f30ac258", size = 39812, upload-time = "2026-04-19T15:39:08.752Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/58/46/0028a82567109b5ef6e4d2a1f04a583fb513e6cf9527fcdd09afd817deeb/filelock-3.20.0.tar.gz", hash = "sha256:711e943b4ec6be42e1d4e6690b48dc175c822967466bb31c0c293f34334c13f4", size = 18922 } +sdist = { url = "https://files.pythonhosted.org/packages/e6/dc/be6cbe99670cd6e4ad387123647cb08e0c32975e223f82551e914c5568a6/filelock-3.29.4.tar.gz", hash = "sha256:10cdb3656fc44541cdf30652a93fb10ec6b05325620eb316bd26893e4201538a", size = 63028, upload-time = "2026-06-13T16:12:00.744Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/76/91/7216b27286936c16f5b4d0c530087e4a54eead683e6b0b73dd0c64844af6/filelock-3.20.0-py3-none-any.whl", hash = "sha256:339b4732ffda5cd79b13f4e2711a31b0365ce445d95d243bb996273d072546a2", size = 16054 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/13/37/a065dc3bd6e49423a6532c642ca7378d3f467b1ef44c2800c937af7f9739/filelock-3.29.4-py3-none-any.whl", hash = "sha256:dac1648087d5115554850d113e7dd8c83ab2d38e3435dde2d4f163847e57b767", size = 42757, upload-time = "2026-06-13T16:11:59.582Z" }, ] [[package]] @@ -1082,9 +831,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/55/b0/8a21e330561c65653d010ef112bf38f60890051d244ede197ddaa08e50c1/flexcache-0.3.tar.gz", hash = "sha256:18743bd5a0621bfe2cf8d519e4c3bfdf57a269c15d1ced3fb4b64e0ff4600656", size = 15816 } +sdist = { url = "https://files.pythonhosted.org/packages/55/b0/8a21e330561c65653d010ef112bf38f60890051d244ede197ddaa08e50c1/flexcache-0.3.tar.gz", hash = "sha256:18743bd5a0621bfe2cf8d519e4c3bfdf57a269c15d1ced3fb4b64e0ff4600656", size = 15816, upload-time = "2024-03-09T03:21:07.555Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/27/cd/c883e1a7c447479d6e13985565080e3fea88ab5a107c21684c813dba1875/flexcache-0.3-py3-none-any.whl", hash = "sha256:d43c9fea82336af6e0115e308d9d33a185390b8346a017564611f1466dcd2e32", size = 13263 }, + { url = "https://files.pythonhosted.org/packages/27/cd/c883e1a7c447479d6e13985565080e3fea88ab5a107c21684c813dba1875/flexcache-0.3-py3-none-any.whl", hash = "sha256:d43c9fea82336af6e0115e308d9d33a185390b8346a017564611f1466dcd2e32", size = 13263, upload-time = "2024-03-09T03:21:05.635Z" }, ] [[package]] @@ -1094,16 +843,15 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/82/99/b4de7e39e8eaf8207ba1a8fa2241dd98b2ba72ae6e16960d8351736d8702/flexparser-0.4.tar.gz", hash = "sha256:266d98905595be2ccc5da964fe0a2c3526fbbffdc45b65b3146d75db992ef6b2", size = 31799 } +sdist = { url = "https://files.pythonhosted.org/packages/82/99/b4de7e39e8eaf8207ba1a8fa2241dd98b2ba72ae6e16960d8351736d8702/flexparser-0.4.tar.gz", hash = "sha256:266d98905595be2ccc5da964fe0a2c3526fbbffdc45b65b3146d75db992ef6b2", size = 31799, upload-time = "2024-11-07T02:00:56.249Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/fe/5e/3be305568fe5f34448807976dc82fc151d76c3e0e03958f34770286278c1/flexparser-0.4-py3-none-any.whl", hash = "sha256:3738b456192dcb3e15620f324c447721023c0293f6af9955b481e91d00179846", size = 27625 }, + { url = "https://files.pythonhosted.org/packages/fe/5e/3be305568fe5f34448807976dc82fc151d76c3e0e03958f34770286278c1/flexparser-0.4-py3-none-any.whl", hash = "sha256:3738b456192dcb3e15620f324c447721023c0293f6af9955b481e91d00179846", size = 27625, upload-time = "2024-11-07T02:00:54.523Z" }, ] [[package]] name = "fonttools" version = "4.63.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/84/69/c97f2c18e0db87d2c7b15da1974dace76ae938f1cfa22e2727a648b7ed43/fonttools-4.63.0.tar.gz", hash = "sha256:caeb583deeb5168e694b65cda8b4ee62abedfa66cf88488734466f2366b9c4e0", size = 3597189, upload-time = "2026-05-14T12:04:30.958Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/75/2b/a7f1545bdf5da69c4bda0cea2a5781f0ad2a6623e0277267672db43c5fe6/fonttools-4.63.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:2b8ae05d9eacf6081414d759c0a352769ac28ce31280d6bb8e77b03f9e3c449f", size = 2881793, upload-time = "2026-05-14T12:02:56.645Z" }, @@ -1147,70 +895,24 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/34/3b/214dcc19ee31d3d38fb5ad2755c11ef0514e5dc300bbaf41c0b69f393799/fonttools-4.63.0-cp314-cp314t-win32.whl", hash = "sha256:948428a275741f0b64b113c955425a953314f4b9ab9997f73a72c83e68e569c8", size = 2359326, upload-time = "2026-05-14T12:04:24.22Z" }, { url = "https://files.pythonhosted.org/packages/dd/1e/3ff1a9b523058c2eeb6a9d50f5574e2a738200d0d94107d5bc4105e8da3f/fonttools-4.63.0-cp314-cp314t-win_amd64.whl", hash = "sha256:6d4741eb179121cab9eea4cb2393d24492373a260d7945006358c08cfbf45419", size = 2425829, upload-time = "2026-05-14T12:04:26.829Z" }, { url = "https://files.pythonhosted.org/packages/2c/47/c99d5268f354002ce80f8d029cd9d7d872969da1de8b93d32de4dc56d6f4/fonttools-4.63.0-py3-none-any.whl", hash = "sha256:445af2eab030a16b9171ea8bdda7ebf7d96bda2df88ee182a464252f6e05e20d", size = 1164562, upload-time = "2026-05-14T12:04:29.092Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/4b/42/97a13e47a1e51a5a7142475bbcf5107fe3a68fc34aef331c897d5fb98ad0/fonttools-4.60.1.tar.gz", hash = "sha256:ef00af0439ebfee806b25f24c8f92109157ff3fac5731dc7867957812e87b8d9", size = 3559823 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ea/85/639aa9bface1537e0fb0f643690672dde0695a5bbbc90736bc571b0b1941/fonttools-4.60.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:7b4c32e232a71f63a5d00259ca3d88345ce2a43295bb049d21061f338124246f", size = 2831872 }, - { url = "https://files.pythonhosted.org/packages/6b/47/3c63158459c95093be9618794acb1067b3f4d30dcc5c3e8114b70e67a092/fonttools-4.60.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3630e86c484263eaac71d117085d509cbcf7b18f677906824e4bace598fb70d2", size = 2356990 }, - { url = "https://files.pythonhosted.org/packages/94/dd/1934b537c86fcf99f9761823f1fc37a98fbd54568e8e613f29a90fed95a9/fonttools-4.60.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5c1015318e4fec75dd4943ad5f6a206d9727adf97410d58b7e32ab644a807914", size = 5042189 }, - { url = "https://files.pythonhosted.org/packages/d2/d2/9f4e4c4374dd1daa8367784e1bd910f18ba886db1d6b825b12edf6db3edc/fonttools-4.60.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e6c58beb17380f7c2ea181ea11e7db8c0ceb474c9dd45f48e71e2cb577d146a1", size = 4978683 }, - { url = "https://files.pythonhosted.org/packages/cc/c4/0fb2dfd1ecbe9a07954cc13414713ed1eab17b1c0214ef07fc93df234a47/fonttools-4.60.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:ec3681a0cb34c255d76dd9d865a55f260164adb9fa02628415cdc2d43ee2c05d", size = 5021372 }, - { url = "https://files.pythonhosted.org/packages/0c/d5/495fc7ae2fab20223cc87179a8f50f40f9a6f821f271ba8301ae12bb580f/fonttools-4.60.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f4b5c37a5f40e4d733d3bbaaef082149bee5a5ea3156a785ff64d949bd1353fa", size = 5132562 }, - { url = "https://files.pythonhosted.org/packages/bc/fa/021dab618526323c744e0206b3f5c8596a2e7ae9aa38db5948a131123e83/fonttools-4.60.1-cp311-cp311-win32.whl", hash = "sha256:398447f3d8c0c786cbf1209711e79080a40761eb44b27cdafffb48f52bcec258", size = 2230288 }, - { url = "https://files.pythonhosted.org/packages/bb/78/0e1a6d22b427579ea5c8273e1c07def2f325b977faaf60bb7ddc01456cb1/fonttools-4.60.1-cp311-cp311-win_amd64.whl", hash = "sha256:d066ea419f719ed87bc2c99a4a4bfd77c2e5949cb724588b9dd58f3fd90b92bf", size = 2278184 }, - { url = "https://files.pythonhosted.org/packages/e3/f7/a10b101b7a6f8836a5adb47f2791f2075d044a6ca123f35985c42edc82d8/fonttools-4.60.1-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:7b0c6d57ab00dae9529f3faf187f2254ea0aa1e04215cf2f1a8ec277c96661bc", size = 2832953 }, - { url = "https://files.pythonhosted.org/packages/ed/fe/7bd094b59c926acf2304d2151354ddbeb74b94812f3dc943c231db09cb41/fonttools-4.60.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:839565cbf14645952d933853e8ade66a463684ed6ed6c9345d0faf1f0e868877", size = 2352706 }, - { url = "https://files.pythonhosted.org/packages/c0/ca/4bb48a26ed95a1e7eba175535fe5805887682140ee0a0d10a88e1de84208/fonttools-4.60.1-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:8177ec9676ea6e1793c8a084a90b65a9f778771998eb919d05db6d4b1c0b114c", size = 4923716 }, - { url = "https://files.pythonhosted.org/packages/b8/9f/2cb82999f686c1d1ddf06f6ae1a9117a880adbec113611cc9d22b2fdd465/fonttools-4.60.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:996a4d1834524adbb423385d5a629b868ef9d774670856c63c9a0408a3063401", size = 4968175 }, - { url = "https://files.pythonhosted.org/packages/18/79/be569699e37d166b78e6218f2cde8c550204f2505038cdd83b42edc469b9/fonttools-4.60.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a46b2f450bc79e06ef3b6394f0c68660529ed51692606ad7f953fc2e448bc903", size = 4911031 }, - { url = "https://files.pythonhosted.org/packages/cc/9f/89411cc116effaec5260ad519162f64f9c150e5522a27cbb05eb62d0c05b/fonttools-4.60.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:6ec722ee589e89a89f5b7574f5c45604030aa6ae24cb2c751e2707193b466fed", size = 5062966 }, - { url = "https://files.pythonhosted.org/packages/62/a1/f888221934b5731d46cb9991c7a71f30cb1f97c0ef5fcf37f8da8fce6c8e/fonttools-4.60.1-cp312-cp312-win32.whl", hash = "sha256:b2cf105cee600d2de04ca3cfa1f74f1127f8455b71dbad02b9da6ec266e116d6", size = 2218750 }, - { url = "https://files.pythonhosted.org/packages/88/8f/a55b5550cd33cd1028601df41acd057d4be20efa5c958f417b0c0613924d/fonttools-4.60.1-cp312-cp312-win_amd64.whl", hash = "sha256:992775c9fbe2cf794786fa0ffca7f09f564ba3499b8fe9f2f80bd7197db60383", size = 2267026 }, - { url = "https://files.pythonhosted.org/packages/7c/5b/cdd2c612277b7ac7ec8c0c9bc41812c43dc7b2d5f2b0897e15fdf5a1f915/fonttools-4.60.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:6f68576bb4bbf6060c7ab047b1574a1ebe5c50a17de62830079967b211059ebb", size = 2825777 }, - { url = "https://files.pythonhosted.org/packages/d6/8a/de9cc0540f542963ba5e8f3a1f6ad48fa211badc3177783b9d5cadf79b5d/fonttools-4.60.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:eedacb5c5d22b7097482fa834bda0dafa3d914a4e829ec83cdea2a01f8c813c4", size = 2348080 }, - { url = "https://files.pythonhosted.org/packages/2d/8b/371ab3cec97ee3fe1126b3406b7abd60c8fec8975fd79a3c75cdea0c3d83/fonttools-4.60.1-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b33a7884fabd72bdf5f910d0cf46be50dce86a0362a65cfc746a4168c67eb96c", size = 4903082 }, - { url = "https://files.pythonhosted.org/packages/04/05/06b1455e4bc653fcb2117ac3ef5fa3a8a14919b93c60742d04440605d058/fonttools-4.60.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2409d5fb7b55fd70f715e6d34e7a6e4f7511b8ad29a49d6df225ee76da76dd77", size = 4960125 }, - { url = "https://files.pythonhosted.org/packages/8e/37/f3b840fcb2666f6cb97038793606bdd83488dca2d0b0fc542ccc20afa668/fonttools-4.60.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c8651e0d4b3bdeda6602b85fdc2abbefc1b41e573ecb37b6779c4ca50753a199", size = 4901454 }, - { url = "https://files.pythonhosted.org/packages/fd/9e/eb76f77e82f8d4a46420aadff12cec6237751b0fb9ef1de373186dcffb5f/fonttools-4.60.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:145daa14bf24824b677b9357c5e44fd8895c2a8f53596e1b9ea3496081dc692c", size = 5044495 }, - { url = "https://files.pythonhosted.org/packages/f8/b3/cede8f8235d42ff7ae891bae8d619d02c8ac9fd0cfc450c5927a6200c70d/fonttools-4.60.1-cp313-cp313-win32.whl", hash = "sha256:2299df884c11162617a66b7c316957d74a18e3758c0274762d2cc87df7bc0272", size = 2217028 }, - { url = "https://files.pythonhosted.org/packages/75/4d/b022c1577807ce8b31ffe055306ec13a866f2337ecee96e75b24b9b753ea/fonttools-4.60.1-cp313-cp313-win_amd64.whl", hash = "sha256:a3db56f153bd4c5c2b619ab02c5db5192e222150ce5a1bc10f16164714bc39ac", size = 2266200 }, - { url = "https://files.pythonhosted.org/packages/9a/83/752ca11c1aa9a899b793a130f2e466b79ea0cf7279c8d79c178fc954a07b/fonttools-4.60.1-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:a884aef09d45ba1206712c7dbda5829562d3fea7726935d3289d343232ecb0d3", size = 2822830 }, - { url = "https://files.pythonhosted.org/packages/57/17/bbeab391100331950a96ce55cfbbff27d781c1b85ebafb4167eae50d9fe3/fonttools-4.60.1-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8a44788d9d91df72d1a5eac49b31aeb887a5f4aab761b4cffc4196c74907ea85", size = 2345524 }, - { url = "https://files.pythonhosted.org/packages/3d/2e/d4831caa96d85a84dd0da1d9f90d81cec081f551e0ea216df684092c6c97/fonttools-4.60.1-cp314-cp314-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:e852d9dda9f93ad3651ae1e3bb770eac544ec93c3807888798eccddf84596537", size = 4843490 }, - { url = "https://files.pythonhosted.org/packages/49/13/5e2ea7c7a101b6fc3941be65307ef8df92cbbfa6ec4804032baf1893b434/fonttools-4.60.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:154cb6ee417e417bf5f7c42fe25858c9140c26f647c7347c06f0cc2d47eff003", size = 4944184 }, - { url = "https://files.pythonhosted.org/packages/0c/2b/cf9603551c525b73fc47c52ee0b82a891579a93d9651ed694e4e2cd08bb8/fonttools-4.60.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:5664fd1a9ea7f244487ac8f10340c4e37664675e8667d6fee420766e0fb3cf08", size = 4890218 }, - { url = "https://files.pythonhosted.org/packages/fd/2f/933d2352422e25f2376aae74f79eaa882a50fb3bfef3c0d4f50501267101/fonttools-4.60.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:583b7f8e3c49486e4d489ad1deacfb8d5be54a8ef34d6df824f6a171f8511d99", size = 4999324 }, - { url = "https://files.pythonhosted.org/packages/38/99/234594c0391221f66216bc2c886923513b3399a148defaccf81dc3be6560/fonttools-4.60.1-cp314-cp314-win32.whl", hash = "sha256:66929e2ea2810c6533a5184f938502cfdaea4bc3efb7130d8cc02e1c1b4108d6", size = 2220861 }, - { url = "https://files.pythonhosted.org/packages/3e/1d/edb5b23726dde50fc4068e1493e4fc7658eeefcaf75d4c5ffce067d07ae5/fonttools-4.60.1-cp314-cp314-win_amd64.whl", hash = "sha256:f3d5be054c461d6a2268831f04091dc82753176f6ea06dc6047a5e168265a987", size = 2270934 }, - { url = "https://files.pythonhosted.org/packages/fb/da/1392aaa2170adc7071fe7f9cfd181a5684a7afcde605aebddf1fb4d76df5/fonttools-4.60.1-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:b6379e7546ba4ae4b18f8ae2b9bc5960936007a1c0e30b342f662577e8bc3299", size = 2894340 }, - { url = "https://files.pythonhosted.org/packages/bf/a7/3b9f16e010d536ce567058b931a20b590d8f3177b2eda09edd92e392375d/fonttools-4.60.1-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:9d0ced62b59e0430b3690dbc5373df1c2aa7585e9a8ce38eff87f0fd993c5b01", size = 2375073 }, - { url = "https://files.pythonhosted.org/packages/9b/b5/e9bcf51980f98e59bb5bb7c382a63c6f6cac0eec5f67de6d8f2322382065/fonttools-4.60.1-cp314-cp314t-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:875cb7764708b3132637f6c5fb385b16eeba0f7ac9fa45a69d35e09b47045801", size = 4849758 }, - { url = "https://files.pythonhosted.org/packages/e3/dc/1d2cf7d1cba82264b2f8385db3f5960e3d8ce756b4dc65b700d2c496f7e9/fonttools-4.60.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a184b2ea57b13680ab6d5fbde99ccef152c95c06746cb7718c583abd8f945ccc", size = 5085598 }, - { url = "https://files.pythonhosted.org/packages/5d/4d/279e28ba87fb20e0c69baf72b60bbf1c4d873af1476806a7b5f2b7fac1ff/fonttools-4.60.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:026290e4ec76583881763fac284aca67365e0be9f13a7fb137257096114cb3bc", size = 4957603 }, - { url = "https://files.pythonhosted.org/packages/78/d4/ff19976305e0c05aa3340c805475abb00224c954d3c65e82c0a69633d55d/fonttools-4.60.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:f0e8817c7d1a0c2eedebf57ef9a9896f3ea23324769a9a2061a80fe8852705ed", size = 4974184 }, - { url = "https://files.pythonhosted.org/packages/63/22/8553ff6166f5cd21cfaa115aaacaa0dc73b91c079a8cfd54a482cbc0f4f5/fonttools-4.60.1-cp314-cp314t-win32.whl", hash = "sha256:1410155d0e764a4615774e5c2c6fc516259fe3eca5882f034eb9bfdbee056259", size = 2282241 }, - { url = "https://files.pythonhosted.org/packages/8a/cb/fa7b4d148e11d5a72761a22e595344133e83a9507a4c231df972e657579b/fonttools-4.60.1-cp314-cp314t-win_amd64.whl", hash = "sha256:022beaea4b73a70295b688f817ddc24ed3e3418b5036ffcd5658141184ef0d0c", size = 2345760 }, - { url = "https://files.pythonhosted.org/packages/c7/93/0dd45cd283c32dea1545151d8c3637b4b8c53cdb3a625aeb2885b184d74d/fonttools-4.60.1-py3-none-any.whl", hash = "sha256:906306ac7afe2156fcf0042173d6ebbb05416af70f6b370967b47f8f00103bbb", size = 1143175 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "fqdn" version = "1.5.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/30/3e/a80a8c077fd798951169626cde3e239adeba7dab75deb3555716415bd9b0/fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f", size = 6015 } +sdist = { url = "https://files.pythonhosted.org/packages/30/3e/a80a8c077fd798951169626cde3e239adeba7dab75deb3555716415bd9b0/fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f", size = 6015, upload-time = "2021-03-11T07:16:29.08Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/cf/58/8acf1b3e91c58313ce5cb67df61001fc9dcd21be4fadb76c1a2d540e09ed/fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014", size = 9121 }, + { url = "https://files.pythonhosted.org/packages/cf/58/8acf1b3e91c58313ce5cb67df61001fc9dcd21be4fadb76c1a2d540e09ed/fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014", size = 9121, upload-time = "2021-03-11T07:16:28.351Z" }, ] [[package]] name = "fsspec" -version = "2026.4.0" +version = "2026.6.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/d5/8d/1c51c094345df128ca4a990d633fe1a0ff28726c9e6b3c41ba65087bba1d/fsspec-2026.4.0.tar.gz", hash = "sha256:301d8ac70ae90ef3ad05dcf94d6c3754a097f9b5fe4667d2787aa359ec7df7e4", size = 312760, upload-time = "2026-04-29T20:42:38.635Z" } +sdist = { url = "https://files.pythonhosted.org/packages/10/a1/ae4e3e5003468d6391d2c77b6fa1cd73bd5d13511d81c642d7b28ac90ed4/fsspec-2026.6.0.tar.gz", hash = "sha256:f5bac145310fe30e16e1471bd6840b2d990d609e872251d7e674241822abf01a", size = 313646, upload-time = "2026-06-16T01:57:28.105Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/d5/0c/043d5e551459da400957a1395e0febbf771446ff34291afcbe3d8be2a279/fsspec-2026.4.0-py3-none-any.whl", hash = "sha256:11ef7bb35dab8a394fde6e608221d5cf3e8499401c249bebaeaad760a1a8dec2", size = 203402, upload-time = "2026-04-29T20:42:36.842Z" }, + { url = "https://files.pythonhosted.org/packages/e5/22/4222d7ddf3da30f363edaa98e329c2bce6c65497c9cb2810931c8b2c0fbc/fsspec-2026.6.0-py3-none-any.whl", hash = "sha256:02e0b71817df9b2169dc30a16832045764def1191b43dcff5bb85bdee212d2a1", size = 203949, upload-time = "2026-06-16T01:57:26.358Z" }, ] [[package]] @@ -1241,226 +943,129 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/08/a5/7b059810934a09fb3ccb657e0843813c1fee1183d3bc2c8041800374aa2c/google_crc32c-1.8.0-cp314-cp314-win_amd64.whl", hash = "sha256:d511b3153e7011a27ab6ee6bb3a5404a55b994dc1a7322c0b87b29606d9790e2", size = 34878, upload-time = "2025-12-16T00:35:23.142Z" }, { url = "https://files.pythonhosted.org/packages/52/c5/c171e4d8c44fec1422d801a6d2e5d7ddabd733eeda505c79730ee9607f07/google_crc32c-1.8.0-pp311-pypy311_pp73-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:87fa445064e7db928226b2e6f0d5304ab4cd0339e664a4e9a25029f384d9bb93", size = 28615, upload-time = "2025-12-16T00:40:29.298Z" }, { url = "https://files.pythonhosted.org/packages/9c/97/7d75fe37a7a6ed171a2cf17117177e7aab7e6e0d115858741b41e9dd4254/google_crc32c-1.8.0-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f639065ea2042d5c034bf258a9f085eaa7af0cd250667c0635a3118e8f92c69c", size = 28800, upload-time = "2025-12-16T00:40:30.322Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/24/7f/2747c0d332b9acfa75dc84447a066fdf812b5a6b8d30472b74d309bfe8cb/fsspec-2025.10.0.tar.gz", hash = "sha256:b6789427626f068f9a83ca4e8a3cc050850b6c0f71f99ddb4f542b8266a26a59", size = 309285 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/eb/02/a6b21098b1d5d6249b7c5ab69dde30108a71e4e819d4a9778f1de1d5b70d/fsspec-2025.10.0-py3-none-any.whl", hash = "sha256:7c7712353ae7d875407f97715f0e1ffcc21e33d5b24556cb1e090ae9409ec61d", size = 200966 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "greenlet" -version = "3.5.1" -source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/6d/6e/802acd792aebb2256fbbee8cacf2727faaeb6f240ac11008f09eae4414bc/greenlet-3.5.1.tar.gz", hash = "sha256:5a56aeb7d5d9cc4b3a735efb5095bd4b4f6f0e4f93e5ca876d0e2315137b7829", size = 197356, upload-time = "2026-05-20T15:05:03.917Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/42/3c/ff890b466eaba2b0f5e6bdfff025f8c75f41b8ffdc3dbc3d24ad261e764a/greenlet-3.5.1-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:73f78f9b9f0a5c06e5c946ba1e8e36f5114923b6be109ee618c54f079c3ea14f", size = 284764, upload-time = "2026-05-20T13:09:10.204Z" }, - { url = "https://files.pythonhosted.org/packages/81/0e/5e5457be3d256918f6a4756f073548a3f0190836e2cc94aa6d0d617a940b/greenlet-3.5.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a0cbed8bb44e23c5b199f888f4e4ce096b45ad9f25ff74a7ad0213875e936bb2", size = 603479, upload-time = "2026-05-20T14:00:04.757Z" }, - { url = "https://files.pythonhosted.org/packages/6d/e1/f89a21d58d308298e6f275f13a1b472ed96c680b601a371b08be6a725989/greenlet-3.5.1-cp311-cp311-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a203a8bd0acb0701653d3bbb26e404854a68674139ed5cbb778830f42b09bb33", size = 615495, upload-time = "2026-05-20T14:05:40.87Z" }, - { url = "https://files.pythonhosted.org/packages/75/de/af6cef182862d2ccd6975440d21c9058a77c3f9b469abf94e322dfd2e0e3/greenlet-3.5.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8a271fcd66c74615cda6a964fda3f304267a12e50a084472218a39bb0376f563", size = 614754, upload-time = "2026-05-20T13:14:24.947Z" }, - { url = "https://files.pythonhosted.org/packages/1a/c6/50e520283a9f19388a7326b05f9e8637e566003475eacaadad04f558c68d/greenlet-3.5.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:ded7b068c7c31c1a8657d4fd42d886b3e051ae29f88b80c5ff9d502257b0f071", size = 1574097, upload-time = "2026-05-20T14:02:24.003Z" }, - { url = "https://files.pythonhosted.org/packages/21/1c/13abd1f4860d987fa5e1170a01930d6e6cd40d328de487a3c9fdaff0ffd0/greenlet-3.5.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:d0932b81d72f552ded9d810d00021b64d89f2195a91ce115b893f943b7a4ab3c", size = 1641058, upload-time = "2026-05-20T13:14:31.83Z" }, - { url = "https://files.pythonhosted.org/packages/f5/56/5f332b7705545eac2dc01b4e9254d24a793f2656d55d5cc6b94ee59d22ae/greenlet-3.5.1-cp311-cp311-win_amd64.whl", hash = "sha256:88e300d136eac057b2397aa1cfd7328b4c87c7eb66a09c7bc6a1292234db474e", size = 238089, upload-time = "2026-05-20T13:14:03.229Z" }, - { url = "https://files.pythonhosted.org/packages/d9/a9/a3c2fa886c5b94863fb0e61b3bc14610b7aa94cf4f17f8741b11708305fc/greenlet-3.5.1-cp311-cp311-win_arm64.whl", hash = "sha256:cc6ab7e555c8a112ad3a76e368e86e12a2754bcae1652a5602e133ec7b635523", size = 234989, upload-time = "2026-05-20T13:08:27.715Z" }, - { url = "https://files.pythonhosted.org/packages/c4/37/4549f149c9797c21b32c2683c33522af22522099de128b2406672526d005/greenlet-3.5.1-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:fa4f98af3a528f0c3fd592a26df7f376f93329c8f4d987f6bb979057af8bf5e2", size = 286220, upload-time = "2026-05-20T13:07:28.463Z" }, - { url = "https://files.pythonhosted.org/packages/38/ff/a4f436709716965eaab9f36ea7b906c8a927fbe32fb1372a2071d964f6b1/greenlet-3.5.1-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ffea73584b216150eab159b6d12348fb253e68757974de1e2c40d8a318ac89ed", size = 601585, upload-time = "2026-05-20T14:00:06.141Z" }, - { url = "https://files.pythonhosted.org/packages/65/ad/54bc3fcee3ad368a61b19b67d88117f7a8c29727bf71fffdeda81fbd946e/greenlet-3.5.1-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1072b4f9edcc1e192d9283a66a3e68d6b84c561de33a83d7858beb9ba1effe10", size = 614215, upload-time = "2026-05-20T14:05:42.675Z" }, - { url = "https://files.pythonhosted.org/packages/40/69/b91cda0647df839483201545913514c2827ebea5e5ccdf931842763bc127/greenlet-3.5.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:add5217d68b31130f0beca584d7fef4878327d2e31642b66618a14eef312b63b", size = 611358, upload-time = "2026-05-20T13:14:26.37Z" }, - { url = "https://files.pythonhosted.org/packages/59/90/3cf77e080350cd02fa307bb2abf05df48f4482c240275bbd2c203ba8bb1c/greenlet-3.5.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a5ea42a752d47a145eae922b605cd1634665ac3d5ec1e72402d5048e8d60d207", size = 1570475, upload-time = "2026-05-20T14:02:25.29Z" }, - { url = "https://files.pythonhosted.org/packages/65/2c/18cece62045e74598c3c393f70dce4a63f56222015ba29a5d4eeb04f764c/greenlet-3.5.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c5551170cf4f5ff5623e9af81323751979fee2c731e2287b61f73cd27257b823", size = 1635625, upload-time = "2026-05-20T13:14:34.027Z" }, - { url = "https://files.pythonhosted.org/packages/30/f5/310d104ddf41eb5a70f4c268d22508dfb0c3c8e86fec152be34d0d2ed819/greenlet-3.5.1-cp312-cp312-win_amd64.whl", hash = "sha256:3c8bb982ad117d29478ef8f5533e97df21f1e2befd17a299257b0c96d1371c0b", size = 238791, upload-time = "2026-05-20T13:10:39.018Z" }, - { url = "https://files.pythonhosted.org/packages/62/90/ceca11f504cd23a8047a3dea31919adc48df9b626dd0c13f0d858734fdfd/greenlet-3.5.1-cp312-cp312-win_arm64.whl", hash = "sha256:80eb4b04dadc4e67df3fae179a32c4706a3f495bc7f22fc8a81115d5f5512188", size = 235580, upload-time = "2026-05-20T13:08:45.056Z" }, - { url = "https://files.pythonhosted.org/packages/27/69/7f7e5372d998b81001899b1c0823c957aa413ba0f2662e65821611cc31e4/greenlet-3.5.1-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:51518ff74664078fc51bffcc6fc529b0df5ae58da192691cee765d45ce944a2b", size = 285060, upload-time = "2026-05-20T13:08:51.899Z" }, - { url = "https://files.pythonhosted.org/packages/b1/bf/387f9b6b865fd2ae0d0be09e0004827295a01b71be76ed350dd1e28a91a4/greenlet-3.5.1-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1ffdb3c0bb002c99cd8f298957e046c3dbf6006b5b7cdf11a4e19194624a0a0a", size = 604370, upload-time = "2026-05-20T14:00:07.492Z" }, - { url = "https://files.pythonhosted.org/packages/32/f5/169ce3d4e4c67291bd18f8cbe0299c9f3e45102c7f1fb3c14780c93e4532/greenlet-3.5.1-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7715a5a2c3378ba602c3a440558261e13a820bb53a82693aacd7b7f6d964e283", size = 616987, upload-time = "2026-05-20T14:05:44.237Z" }, - { url = "https://files.pythonhosted.org/packages/ee/e5/7f2e41d5273be07e77560d61ea4e56485b4d6c316d2a84518c62d1364061/greenlet-3.5.1-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:dc71ff466927a201b08305acac451ebe1aedfcea002f62f1f2f2ac2ac1e6a135", size = 613911, upload-time = "2026-05-20T13:14:27.539Z" }, - { url = "https://files.pythonhosted.org/packages/c5/a4/fbdc67579b73615a1f91615e814303cc71e06128f7baaba87be79b8fb90c/greenlet-3.5.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:cd443683db272ebaaca03af98c0b063ab30db70ea8a31a1559f35e3f7b744ccd", size = 1570689, upload-time = "2026-05-20T14:02:27.225Z" }, - { url = "https://files.pythonhosted.org/packages/e6/b4/77abbe35078be39718a46cd49caf16bceb35662f97a34101dca28aa98e47/greenlet-3.5.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:089fff7a6ce8d9316d1f65ebc00273a56be258c1725b32b94de90a3a979557e1", size = 1635602, upload-time = "2026-05-20T13:14:36.344Z" }, - { url = "https://files.pythonhosted.org/packages/37/f7/129f27ca700845b8ee8ca88ce7f43435a1239c2eddb7677fc938822762cf/greenlet-3.5.1-cp313-cp313-win_amd64.whl", hash = "sha256:110a1ca7b49b014b097f6078272c3f4ed31af45b254de5228b79adba879f6af9", size = 238683, upload-time = "2026-05-20T13:11:50.57Z" }, - { url = "https://files.pythonhosted.org/packages/6d/5c/a485a36e87df8d8fd0632ee01511244f5156a20ed3746cc6599340326395/greenlet-3.5.1-cp313-cp313-win_arm64.whl", hash = "sha256:f16ba1efc0715b680a18b8123d90dad887c6112ae3555b4b5c32c149540c6b4e", size = 235499, upload-time = "2026-05-20T13:12:42.028Z" }, - { url = "https://files.pythonhosted.org/packages/8a/cb/c62454606daf5640369c94d8a9dd540599b1bfc090e2d2180cb77f4038d2/greenlet-3.5.1-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:d8ab31c9de8651a2facdd5c5bb0011f2380dd1a7af78ce2adf4b56095294fc07", size = 285579, upload-time = "2026-05-20T13:08:56.396Z" }, - { url = "https://files.pythonhosted.org/packages/ec/71/c4270398c2eba968a6071af1dfbdcaeee6ec1c24bc8b435b8cc452700da6/greenlet-3.5.1-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5e300185139abc337ade480c327183adf42a875ac7181bfe66d7d4efea31fbea", size = 651106, upload-time = "2026-05-20T14:00:09.448Z" }, - { url = "https://files.pythonhosted.org/packages/1a/ab/71e34b78a44ec271fb5f550c17bc46d301ddc5953890d935f270b0dcdb5a/greenlet-3.5.1-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7ffdb990dcaa0234cf9845aead5df2e3c3a8b6507d409274dd87e0d5ab05ffc2", size = 663478, upload-time = "2026-05-20T14:05:45.88Z" }, - { url = "https://files.pythonhosted.org/packages/77/96/4efd6fa5c62c85426a0c19077a586258ebc3a2a146ff2493e4312a697a22/greenlet-3.5.1-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2f82b3597e9d83b63408affed0b48fd0f54935edac4302237b9a837be0dae33c", size = 660800, upload-time = "2026-05-20T13:14:29.129Z" }, - { url = "https://files.pythonhosted.org/packages/7a/e0/6c71401a25cac7000261304e866a2f2cc04dc74810d40e2f118aa4799495/greenlet-3.5.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c0141e37414c10164e702b8fb1473304221ad98f71600850c6ef7ff4880feba0", size = 1617518, upload-time = "2026-05-20T14:02:28.662Z" }, - { url = "https://files.pythonhosted.org/packages/41/26/c5c06643e8c0af9e7bf18e16cb51d0ab7625155f0392e1c9015d66d556cd/greenlet-3.5.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:50ae25a67bea74ea41fb14b960bc532df73eb713417b2d61892dced82fe8d3bc", size = 1681593, upload-time = "2026-05-20T13:14:39.417Z" }, - { url = "https://files.pythonhosted.org/packages/8a/bd/e11a108317485075e68af9d23039619b86b28130c3b50d227d42edece64b/greenlet-3.5.1-cp314-cp314-win_amd64.whl", hash = "sha256:8a17c42330e261299766b75ac1ea32caa437a9453c8f65d16a13140db378ecd3", size = 239800, upload-time = "2026-05-20T13:09:30.128Z" }, - { url = "https://files.pythonhosted.org/packages/47/f8/8e8e8417b7bf28639a5a56356ef934d0375e1d0c70a57e04d7701e870ffe/greenlet-3.5.1-cp314-cp314-win_arm64.whl", hash = "sha256:7b5f5fae05b8ac6d176a61b60c394a8cbdc2b5b91b81793066e68745cf165e54", size = 236862, upload-time = "2026-05-20T13:09:10.498Z" }, - { url = "https://files.pythonhosted.org/packages/90/12/41bf27fde4d3605d3773ae57751eda182b8be2f5398011c041173b1d9534/greenlet-3.5.1-cp314-cp314t-macosx_11_0_universal2.whl", hash = "sha256:ea8da1e900d758d078810d4255d8c6aa572181896a31ec79d779eb79c3adc9ad", size = 293637, upload-time = "2026-05-20T13:12:35.529Z" }, - { url = "https://files.pythonhosted.org/packages/44/44/ba14b23e9757707050c2f397d305bbcae62e5d7cad122f8b6baec5ae4a1f/greenlet-3.5.1-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a19570c52a21420dcbc94e661994bc325c0b5b11304540fed514586da5dc8f2e", size = 650840, upload-time = "2026-05-20T14:00:11.079Z" }, - { url = "https://files.pythonhosted.org/packages/a8/37/5ddc2b686a6844f91abecef43411842426da2e1573f60b49ecf2547f4ae1/greenlet-3.5.1-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3d955c89b75eeca4723d7cc14135f393cd47c32e2a6cb4a8e4c6e760a26b0986", size = 656416, upload-time = "2026-05-20T14:05:47.118Z" }, - { url = "https://files.pythonhosted.org/packages/e1/f0/d17510297c35a2992712f0bf84de3779749999f7d3d63aa1f09db7c62dbe/greenlet-3.5.1-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:de2daaaebd1a5aa88c49045b6baf9310b3263796bd88db713edf37cf53e7bb4e", size = 654397, upload-time = "2026-05-20T13:14:30.696Z" }, - { url = "https://files.pythonhosted.org/packages/37/eb/147387705bb89092645b012586e7273cb5ed3c90ef7eaf3a69173eaf0209/greenlet-3.5.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:3bfbd69cc349e43bf3a8ae1c85548ff0718efc887615c2db16c3833d7b0b072d", size = 1614469, upload-time = "2026-05-20T14:02:30.192Z" }, - { url = "https://files.pythonhosted.org/packages/a6/4e/37ee0da7732b7aa9896f17e15579a9df34b9fcb9dd494f0adfa749af6623/greenlet-3.5.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:4378720dd888136c27215a0214d32a4d37c3852765d45bc37aad0623423cfd78", size = 1675115, upload-time = "2026-05-20T13:14:40.972Z" }, - { url = "https://files.pythonhosted.org/packages/57/f3/97dfcf4a6eb5077f8a672234216fb5923eb89f2cab7081cb10b2cf75b605/greenlet-3.5.1-cp314-cp314t-win_amd64.whl", hash = "sha256:45718441607f9325d948db98cbc691276059316d0358c188c246da4e1d4d23d2", size = 245246, upload-time = "2026-05-20T13:12:22.646Z" }, - { url = "https://files.pythonhosted.org/packages/5d/73/d7f72e34b582f694f4a9b248162db7b09cc458a259ba8f0c0bfa1a34ea7d/greenlet-3.5.1-cp315-cp315-macosx_11_0_universal2.whl", hash = "sha256:2baee5ca02031757ffe8cc3d69f0cc0aec7065ce362622da74f32d3bcab1c541", size = 285575, upload-time = "2026-05-20T13:12:07.043Z" }, - { url = "https://files.pythonhosted.org/packages/df/59/fa9c6e87dc8ad27a95dabe2f29f372b733d05a8a67470f6c901ed9975655/greenlet-3.5.1-cp315-cp315-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9b1ec3274918a81d3ea778b9e75b56b72b33f300edb6cf7f3a7fe1dae56683de", size = 656428, upload-time = "2026-05-20T14:00:12.556Z" }, - { url = "https://files.pythonhosted.org/packages/f6/f9/e753408871eaa61dfe35e619cfc67512b036fde99893685d50eea9e07146/greenlet-3.5.1-cp315-cp315-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:111e2390ffffc47d5840b01711dd7fac07d4c09283d0283e7f3264b14e284c64", size = 667064, upload-time = "2026-05-20T14:05:48.662Z" }, - { url = "https://files.pythonhosted.org/packages/96/27/5565b5b40389f1c7753003a07e21892fda8660926787036d5bc0308b8113/greenlet-3.5.1-cp315-cp315-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e630136e905fe5ff43e86945ae41220b6d1470956a39220e708110ac48d01ea5", size = 665697, upload-time = "2026-05-20T13:14:32.943Z" }, - { url = "https://files.pythonhosted.org/packages/cf/82/e7de4178c0c2d1c9a5a3be3cc0b33e46a85b3ee4a77c071bf7ad8600e079/greenlet-3.5.1-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:975eac34b44a7077ca4d421348455b94f0f518246a7f14bc6d2fdcfe5b584368", size = 1621256, upload-time = "2026-05-20T14:02:31.91Z" }, - { url = "https://files.pythonhosted.org/packages/00/10/f2dddcf7dacac17dfc68691809589adad06135eb28930429cf58a6467a2f/greenlet-3.5.1-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:9ab3c3a0b2ae6198e67c898dad5215a49f9ae0d0081b3c3ec59f333e39eeca26", size = 1685956, upload-time = "2026-05-20T13:14:42.55Z" }, - { url = "https://files.pythonhosted.org/packages/22/17/4a232b32133230ada52f70e9d7f5b65b0caef8772f01849bd8d149e7e4ca/greenlet-3.5.1-cp315-cp315-win_amd64.whl", hash = "sha256:cbfc69be86e10dcfef5b1e6269d1d6926552aa89ee39e1de3353360c1b6989ab", size = 239802, upload-time = "2026-05-20T13:13:15.481Z" }, - { url = "https://files.pythonhosted.org/packages/c2/ae/4e623a7e6d4d2a5f4cb8e4c82de4169fc637942caae68d6e676b8a128ac5/greenlet-3.5.1-cp315-cp315-win_arm64.whl", hash = "sha256:92fd6d44ac5e5a887c8a5dc4a8ba0ba908527c31c12f78c6bc7dcfe8aab279f6", size = 236853, upload-time = "2026-05-20T13:15:37.301Z" }, - { url = "https://files.pythonhosted.org/packages/7a/57/816d9cff29119da3505b3d6a5e14a8af89006ac36f47f891ff293ee05af1/greenlet-3.5.1-cp315-cp315t-macosx_11_0_universal2.whl", hash = "sha256:a6fdf2433a5441ef9a95464f7c3e674775da1c8c1177fff311cee1acad4626ed", size = 293877, upload-time = "2026-05-20T13:10:19.078Z" }, - { url = "https://files.pythonhosted.org/packages/23/a1/59b0a7c7d140ff1a75626680b9a9899b79a9176cab298b394968fb023295/greenlet-3.5.1-cp315-cp315t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7546556f0d649f99f6a361098a55f761181bb2ea12ff150bb16d26092ad88244", size = 655333, upload-time = "2026-05-20T14:00:14.758Z" }, - { url = "https://files.pythonhosted.org/packages/72/1b/5efe127597625042218939d01855109f352779050768b670b52edcc16a6c/greenlet-3.5.1-cp315-cp315t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:d5ee3ea898009fa898f85f9982255d35278c477bebe185beca249cab42d4526c", size = 659443, upload-time = "2026-05-20T14:05:50.159Z" }, - { url = "https://files.pythonhosted.org/packages/6c/6d/c404246ea4d22d097a7426d0efb5b781bd7eb67715f09e79001bd552ab18/greenlet-3.5.1-cp315-cp315t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a5c81f74d204d3edd136ebfd50dce53acbb776995d721a0fe801626cfc93b8cd", size = 658356, upload-time = "2026-05-20T13:14:35.091Z" }, - { url = "https://files.pythonhosted.org/packages/51/02/f8ee37fb6d2219329f350af241c27fcf12df57e723d11f6fc6d3bacdadaa/greenlet-3.5.1-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:2c18ef16bf6d4dd410e4dd52996888ea1497be26892fe5bbc73580aba4287b8e", size = 1619216, upload-time = "2026-05-20T14:02:33.403Z" }, - { url = "https://files.pythonhosted.org/packages/93/c5/3dc9475ace2c7a3680da12372cddd7f1ac874eb410a1ac48d3e9dab83782/greenlet-3.5.1-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:17d86354f0ae6b61bf9be5148d0dd34e06c3cb7c602c671f79f29ac3b150e659", size = 1678427, upload-time = "2026-05-20T13:14:43.71Z" }, - { url = "https://files.pythonhosted.org/packages/df/4e/750c15c317a41ffb36f0bf40b933e3d744a7dede61889f74443ea69690cf/greenlet-3.5.1-cp315-cp315t-win_amd64.whl", hash = "sha256:e7516cf6ae6b8a582c2770a0caed47b8a48373ed732c33d69a72913ae6ac923e", size = 245225, upload-time = "2026-05-20T13:13:59.366Z" }, - { url = "https://files.pythonhosted.org/packages/4f/fd/d3baea2eeb7b617efd47e87ca06e2ec2c6118d303aa9e918e0ce16eadc10/greenlet-3.5.1-cp315-cp315t-win_arm64.whl", hash = "sha256:5028648bf2253ec4745add746129d3904121fa7fe871a76bed23c5720573ce0a", size = 239590, upload-time = "2026-05-20T13:13:37.382Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/03/b8/704d753a5a45507a7aab61f18db9509302ed3d0a27ac7e0359ec2905b1a6/greenlet-3.2.4.tar.gz", hash = "sha256:0dca0d95ff849f9a364385f36ab49f50065d76964944638be9691e1832e9f86d", size = 188260 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/a4/de/f28ced0a67749cac23fecb02b694f6473f47686dff6afaa211d186e2ef9c/greenlet-3.2.4-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:96378df1de302bc38e99c3a9aa311967b7dc80ced1dcc6f171e99842987882a2", size = 272305 }, - { url = "https://files.pythonhosted.org/packages/09/16/2c3792cba130000bf2a31c5272999113f4764fd9d874fb257ff588ac779a/greenlet-3.2.4-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:1ee8fae0519a337f2329cb78bd7a8e128ec0f881073d43f023c7b8d4831d5246", size = 632472 }, - { url = "https://files.pythonhosted.org/packages/ae/8f/95d48d7e3d433e6dae5b1682e4292242a53f22df82e6d3dda81b1701a960/greenlet-3.2.4-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:94abf90142c2a18151632371140b3dba4dee031633fe614cb592dbb6c9e17bc3", size = 644646 }, - { url = "https://files.pythonhosted.org/packages/d5/5e/405965351aef8c76b8ef7ad370e5da58d57ef6068df197548b015464001a/greenlet-3.2.4-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:4d1378601b85e2e5171b99be8d2dc85f594c79967599328f95c1dc1a40f1c633", size = 640519 }, - { url = "https://files.pythonhosted.org/packages/25/5d/382753b52006ce0218297ec1b628e048c4e64b155379331f25a7316eb749/greenlet-3.2.4-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0db5594dce18db94f7d1650d7489909b57afde4c580806b8d9203b6e79cdc079", size = 639707 }, - { url = "https://files.pythonhosted.org/packages/1f/8e/abdd3f14d735b2929290a018ecf133c901be4874b858dd1c604b9319f064/greenlet-3.2.4-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2523e5246274f54fdadbce8494458a2ebdcdbc7b802318466ac5606d3cded1f8", size = 587684 }, - { url = "https://files.pythonhosted.org/packages/5d/65/deb2a69c3e5996439b0176f6651e0052542bb6c8f8ec2e3fba97c9768805/greenlet-3.2.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:1987de92fec508535687fb807a5cea1560f6196285a4cde35c100b8cd632cc52", size = 1116647 }, - { url = "https://files.pythonhosted.org/packages/3f/cc/b07000438a29ac5cfb2194bfc128151d52f333cee74dd7dfe3fb733fc16c/greenlet-3.2.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:55e9c5affaa6775e2c6b67659f3a71684de4c549b3dd9afca3bc773533d284fa", size = 1142073 }, - { url = "https://files.pythonhosted.org/packages/67/24/28a5b2fa42d12b3d7e5614145f0bd89714c34c08be6aabe39c14dd52db34/greenlet-3.2.4-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c9c6de1940a7d828635fbd254d69db79e54619f165ee7ce32fda763a9cb6a58c", size = 1548385 }, - { url = "https://files.pythonhosted.org/packages/6a/05/03f2f0bdd0b0ff9a4f7b99333d57b53a7709c27723ec8123056b084e69cd/greenlet-3.2.4-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:03c5136e7be905045160b1b9fdca93dd6727b180feeafda6818e6496434ed8c5", size = 1613329 }, - { url = "https://files.pythonhosted.org/packages/d8/0f/30aef242fcab550b0b3520b8e3561156857c94288f0332a79928c31a52cf/greenlet-3.2.4-cp311-cp311-win_amd64.whl", hash = "sha256:9c40adce87eaa9ddb593ccb0fa6a07caf34015a29bf8d344811665b573138db9", size = 299100 }, - { url = "https://files.pythonhosted.org/packages/44/69/9b804adb5fd0671f367781560eb5eb586c4d495277c93bde4307b9e28068/greenlet-3.2.4-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:3b67ca49f54cede0186854a008109d6ee71f66bd57bb36abd6d0a0267b540cdd", size = 274079 }, - { url = "https://files.pythonhosted.org/packages/46/e9/d2a80c99f19a153eff70bc451ab78615583b8dac0754cfb942223d2c1a0d/greenlet-3.2.4-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ddf9164e7a5b08e9d22511526865780a576f19ddd00d62f8a665949327fde8bb", size = 640997 }, - { url = "https://files.pythonhosted.org/packages/3b/16/035dcfcc48715ccd345f3a93183267167cdd162ad123cd93067d86f27ce4/greenlet-3.2.4-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:f28588772bb5fb869a8eb331374ec06f24a83a9c25bfa1f38b6993afe9c1e968", size = 655185 }, - { url = "https://files.pythonhosted.org/packages/31/da/0386695eef69ffae1ad726881571dfe28b41970173947e7c558d9998de0f/greenlet-3.2.4-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:5c9320971821a7cb77cfab8d956fa8e39cd07ca44b6070db358ceb7f8797c8c9", size = 649926 }, - { url = "https://files.pythonhosted.org/packages/68/88/69bf19fd4dc19981928ceacbc5fd4bb6bc2215d53199e367832e98d1d8fe/greenlet-3.2.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c60a6d84229b271d44b70fb6e5fa23781abb5d742af7b808ae3f6efd7c9c60f6", size = 651839 }, - { url = "https://files.pythonhosted.org/packages/19/0d/6660d55f7373b2ff8152401a83e02084956da23ae58cddbfb0b330978fe9/greenlet-3.2.4-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3b3812d8d0c9579967815af437d96623f45c0f2ae5f04e366de62a12d83a8fb0", size = 607586 }, - { url = "https://files.pythonhosted.org/packages/8e/1a/c953fdedd22d81ee4629afbb38d2f9d71e37d23caace44775a3a969147d4/greenlet-3.2.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:abbf57b5a870d30c4675928c37278493044d7c14378350b3aa5d484fa65575f0", size = 1123281 }, - { url = "https://files.pythonhosted.org/packages/3f/c7/12381b18e21aef2c6bd3a636da1088b888b97b7a0362fac2e4de92405f97/greenlet-3.2.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:20fb936b4652b6e307b8f347665e2c615540d4b42b3b4c8a321d8286da7e520f", size = 1151142 }, - { url = "https://files.pythonhosted.org/packages/27/45/80935968b53cfd3f33cf99ea5f08227f2646e044568c9b1555b58ffd61c2/greenlet-3.2.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ee7a6ec486883397d70eec05059353b8e83eca9168b9f3f9a361971e77e0bcd0", size = 1564846 }, - { url = "https://files.pythonhosted.org/packages/69/02/b7c30e5e04752cb4db6202a3858b149c0710e5453b71a3b2aec5d78a1aab/greenlet-3.2.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:326d234cbf337c9c3def0676412eb7040a35a768efc92504b947b3e9cfc7543d", size = 1633814 }, - { url = "https://files.pythonhosted.org/packages/e9/08/b0814846b79399e585f974bbeebf5580fbe59e258ea7be64d9dfb253c84f/greenlet-3.2.4-cp312-cp312-win_amd64.whl", hash = "sha256:a7d4e128405eea3814a12cc2605e0e6aedb4035bf32697f72deca74de4105e02", size = 299899 }, - { url = "https://files.pythonhosted.org/packages/49/e8/58c7f85958bda41dafea50497cbd59738c5c43dbbea5ee83d651234398f4/greenlet-3.2.4-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:1a921e542453fe531144e91e1feedf12e07351b1cf6c9e8a3325ea600a715a31", size = 272814 }, - { url = "https://files.pythonhosted.org/packages/62/dd/b9f59862e9e257a16e4e610480cfffd29e3fae018a68c2332090b53aac3d/greenlet-3.2.4-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cd3c8e693bff0fff6ba55f140bf390fa92c994083f838fece0f63be121334945", size = 641073 }, - { url = "https://files.pythonhosted.org/packages/f7/0b/bc13f787394920b23073ca3b6c4a7a21396301ed75a655bcb47196b50e6e/greenlet-3.2.4-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:710638eb93b1fa52823aa91bf75326f9ecdfd5e0466f00789246a5280f4ba0fc", size = 655191 }, - { url = "https://files.pythonhosted.org/packages/f2/d6/6adde57d1345a8d0f14d31e4ab9c23cfe8e2cd39c3baf7674b4b0338d266/greenlet-3.2.4-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:c5111ccdc9c88f423426df3fd1811bfc40ed66264d35aa373420a34377efc98a", size = 649516 }, - { url = "https://files.pythonhosted.org/packages/7f/3b/3a3328a788d4a473889a2d403199932be55b1b0060f4ddd96ee7cdfcad10/greenlet-3.2.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d76383238584e9711e20ebe14db6c88ddcedc1829a9ad31a584389463b5aa504", size = 652169 }, - { url = "https://files.pythonhosted.org/packages/ee/43/3cecdc0349359e1a527cbf2e3e28e5f8f06d3343aaf82ca13437a9aa290f/greenlet-3.2.4-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:23768528f2911bcd7e475210822ffb5254ed10d71f4028387e5a99b4c6699671", size = 610497 }, - { url = "https://files.pythonhosted.org/packages/b8/19/06b6cf5d604e2c382a6f31cafafd6f33d5dea706f4db7bdab184bad2b21d/greenlet-3.2.4-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:00fadb3fedccc447f517ee0d3fd8fe49eae949e1cd0f6a611818f4f6fb7dc83b", size = 1121662 }, - { url = "https://files.pythonhosted.org/packages/a2/15/0d5e4e1a66fab130d98168fe984c509249c833c1a3c16806b90f253ce7b9/greenlet-3.2.4-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:d25c5091190f2dc0eaa3f950252122edbbadbb682aa7b1ef2f8af0f8c0afefae", size = 1149210 }, - { url = "https://files.pythonhosted.org/packages/1c/53/f9c440463b3057485b8594d7a638bed53ba531165ef0ca0e6c364b5cc807/greenlet-3.2.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6e343822feb58ac4d0a1211bd9399de2b3a04963ddeec21530fc426cc121f19b", size = 1564759 }, - { url = "https://files.pythonhosted.org/packages/47/e4/3bb4240abdd0a8d23f4f88adec746a3099f0d86bfedb623f063b2e3b4df0/greenlet-3.2.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ca7f6f1f2649b89ce02f6f229d7c19f680a6238af656f61e0115b24857917929", size = 1634288 }, - { url = "https://files.pythonhosted.org/packages/0b/55/2321e43595e6801e105fcfdee02b34c0f996eb71e6ddffca6b10b7e1d771/greenlet-3.2.4-cp313-cp313-win_amd64.whl", hash = "sha256:554b03b6e73aaabec3745364d6239e9e012d64c68ccd0b8430c64ccc14939a8b", size = 299685 }, - { url = "https://files.pythonhosted.org/packages/22/5c/85273fd7cc388285632b0498dbbab97596e04b154933dfe0f3e68156c68c/greenlet-3.2.4-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:49a30d5fda2507ae77be16479bdb62a660fa51b1eb4928b524975b3bde77b3c0", size = 273586 }, - { url = "https://files.pythonhosted.org/packages/d1/75/10aeeaa3da9332c2e761e4c50d4c3556c21113ee3f0afa2cf5769946f7a3/greenlet-3.2.4-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:299fd615cd8fc86267b47597123e3f43ad79c9d8a22bebdce535e53550763e2f", size = 686346 }, - { url = "https://files.pythonhosted.org/packages/c0/aa/687d6b12ffb505a4447567d1f3abea23bd20e73a5bed63871178e0831b7a/greenlet-3.2.4-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:c17b6b34111ea72fc5a4e4beec9711d2226285f0386ea83477cbb97c30a3f3a5", size = 699218 }, - { url = "https://files.pythonhosted.org/packages/dc/8b/29aae55436521f1d6f8ff4e12fb676f3400de7fcf27fccd1d4d17fd8fecd/greenlet-3.2.4-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:b4a1870c51720687af7fa3e7cda6d08d801dae660f75a76f3845b642b4da6ee1", size = 694659 }, - { url = "https://files.pythonhosted.org/packages/92/2e/ea25914b1ebfde93b6fc4ff46d6864564fba59024e928bdc7de475affc25/greenlet-3.2.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:061dc4cf2c34852b052a8620d40f36324554bc192be474b9e9770e8c042fd735", size = 695355 }, - { url = "https://files.pythonhosted.org/packages/72/60/fc56c62046ec17f6b0d3060564562c64c862948c9d4bc8aa807cf5bd74f4/greenlet-3.2.4-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:44358b9bf66c8576a9f57a590d5f5d6e72fa4228b763d0e43fee6d3b06d3a337", size = 657512 }, - { url = "https://files.pythonhosted.org/packages/23/6e/74407aed965a4ab6ddd93a7ded3180b730d281c77b765788419484cdfeef/greenlet-3.2.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:2917bdf657f5859fbf3386b12d68ede4cf1f04c90c3a6bc1f013dd68a22e2269", size = 1612508 }, - { url = "https://files.pythonhosted.org/packages/0d/da/343cd760ab2f92bac1845ca07ee3faea9fe52bee65f7bcb19f16ad7de08b/greenlet-3.2.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:015d48959d4add5d6c9f6c5210ee3803a830dce46356e3bc326d6776bde54681", size = 1680760 }, - { url = "https://files.pythonhosted.org/packages/e3/a5/6ddab2b4c112be95601c13428db1d8b6608a8b6039816f2ba09c346c08fc/greenlet-3.2.4-cp314-cp314-win_amd64.whl", hash = "sha256:e37ab26028f12dbb0ff65f29a8d3d44a765c61e729647bf2ddfbbed621726f01", size = 303425 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) +version = "3.5.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/dd/8b/befc3cb36965f397d87e86fb3b00e3ec0dc67c1ecb0986d7f54ee528f018/greenlet-3.5.2.tar.gz", hash = "sha256:c1b906220d83c140361cdd12eef970fb5881a168b98ee58a43786426173da14c", size = 199243, upload-time = "2026-06-17T20:19:01.317Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/21/68/371ee6dad168be3386c46030bedaa8e3e7e3cf3d203621d4529e78ff36ef/greenlet-3.5.2-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:d7792398872f89466c6671d5d193537eff163ecf7fac78d82e6ddc25017fb4f5", size = 286925, upload-time = "2026-06-17T17:33:17.928Z" }, + { url = "https://files.pythonhosted.org/packages/26/16/ed5706c26b4d26f3fabceb79abca992654eac8b0fa435def2ac6dbd92122/greenlet-3.5.2-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:711028c953cd6ce5dc01bbb5a1747e3ad6bd8b2f7ded73778bb936e8dab9e3b6", size = 606036, upload-time = "2026-06-17T18:07:18.538Z" }, + { url = "https://files.pythonhosted.org/packages/8e/32/f9c77093af9f5f96615922b7e3fe3690a9faff02adb89f1d74e21578b147/greenlet-3.5.2-cp311-cp311-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:5eba55076d79e8a5176e6925295cfb901ebc95dae493342ede22230f75d8bee2", size = 617821, upload-time = "2026-06-17T18:29:41.317Z" }, + { url = "https://files.pythonhosted.org/packages/bd/d4/642833e778c17d32b5cabb793e14ce7364c55952462fc506fecdee55d485/greenlet-3.5.2-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c1c1e5ad80f1f38ea479b83b39dccb20874cfe9ad5e52f87225fa294ba4d39a1", size = 616877, upload-time = "2026-06-17T17:39:26.564Z" }, + { url = "https://files.pythonhosted.org/packages/d3/cc/7120f83e78b8be3cf7acbe2306b3b7bd2cbf99f5ad12e85e2f05d7b31961/greenlet-3.5.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:9e194b996aa1b89d933cfe136e5eb39b22a8b72ba59d376ef39a55bca4dbf47f", size = 1577274, upload-time = "2026-06-17T18:22:10.692Z" }, + { url = "https://files.pythonhosted.org/packages/fa/d8/05a0074ee485dd51c320fd706fd7ed48006b9cad3443092d7df1a655f0d2/greenlet-3.5.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:4e554809538bd4867f24421b43abde170f9c9b8192149b30df5e164bcac6124f", size = 1643566, upload-time = "2026-06-17T17:40:05.452Z" }, + { url = "https://files.pythonhosted.org/packages/35/fe/9fe2060bdeece682e38d381184ae66045b48ed183c107ab3f88b9886a630/greenlet-3.5.2-cp311-cp311-win_amd64.whl", hash = "sha256:e063263ce9047878480d7e536012fc8b7c8e1922989eb5f03b9ab998a2ee7b7e", size = 238643, upload-time = "2026-06-17T17:37:03.039Z" }, + { url = "https://files.pythonhosted.org/packages/41/13/a9db72f5b6b700977ebd371d6a1f2984a08838357de924fcd5571607b1bf/greenlet-3.5.2-cp311-cp311-win_arm64.whl", hash = "sha256:a3f76a94e2d6e1fee8f302265679d8cc47d71a203936dd03c6e2ace0f9cfd46d", size = 237135, upload-time = "2026-06-17T17:34:34.14Z" }, + { url = "https://files.pythonhosted.org/packages/3f/7a/6bc2a7835731387ed303b9390ce68a116ab053df05450a59181239200454/greenlet-3.5.2-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:76dae33e97b52743a19210931ee3e78a88fe1438bc2fc4ee5e7512d289bfad4f", size = 288351, upload-time = "2026-06-17T17:36:17.019Z" }, + { url = "https://files.pythonhosted.org/packages/57/1b/bd98062fcef6d0e9d0873ab6f2d029772e6ea342972ae43275bd6177900f/greenlet-3.5.2-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:30252d191d6959df1d040b559a38fc017139606c5ecc2ad00416557c0355d742", size = 604273, upload-time = "2026-06-17T18:07:20.296Z" }, + { url = "https://files.pythonhosted.org/packages/25/e6/fe392c522bf45d976abe7db2793f6ef4e87b053ebb869deeaae46aeb54da/greenlet-3.5.2-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1adc23c50f22b0f5979521909a8360ab4a3d3bef8b641ce633a04cf1b1c967ea", size = 616536, upload-time = "2026-06-17T18:29:43.205Z" }, + { url = "https://files.pythonhosted.org/packages/68/4a/399ff81fa93a19d6a9df394cef0355f082dbc19ad41aba9593cd0ad444e2/greenlet-3.5.2-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1f052fff492c52fdfa99bd3b3c1389a53de37dae76a0562741417f0d018f02b3", size = 613749, upload-time = "2026-06-17T17:39:28.148Z" }, + { url = "https://files.pythonhosted.org/packages/a5/75/f519593f12ad43d08e28c03a95cfe2eeae011707dbc9dab0c4a263ce90f9/greenlet-3.5.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:120b77c2a18ebf629c3a7886f68c6d01e065654844ad468f15bb93ace66f2094", size = 1573725, upload-time = "2026-06-17T18:22:12.023Z" }, + { url = "https://files.pythonhosted.org/packages/f1/bc/bc1ea4b0754c6c51bbf9d94677b0b1f7fbda8cbb404e44a896854fc0a940/greenlet-3.5.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:a850f6224088ef7dcc70f1a545cb6b3d119c35d6dca63b925b9f35da0635cdad", size = 1638132, upload-time = "2026-06-17T17:40:06.971Z" }, + { url = "https://files.pythonhosted.org/packages/36/c0/f0f5a34247df60de285f75f22e57f14027f4b3c43820981854b5b643ca6d/greenlet-3.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:89da99ee8345b458ea2f16831dad31c88ddcdec454b48704d569a0b8fb28f146", size = 239393, upload-time = "2026-06-17T17:33:47.09Z" }, + { url = "https://files.pythonhosted.org/packages/09/17/a8544e165445f30aea67a8d9cf2786d2bb0eb1b0e0d224b4d9bd80e2d587/greenlet-3.5.2-cp312-cp312-win_arm64.whl", hash = "sha256:ca92411942154023c65851e6077d8ca0d00f19de5fa80bb2c6f196ff6c920ba9", size = 237723, upload-time = "2026-06-17T17:36:47.776Z" }, + { url = "https://files.pythonhosted.org/packages/d0/3c/bb37b9d40d65b0741a8b040ca5c307034d0a9822994dff5f825c88dd7a6b/greenlet-3.5.2-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:0629377725977252159de1ebd3c6e49c170a63856e585446797bb3d66d4d9c34", size = 287178, upload-time = "2026-06-17T17:35:25.132Z" }, + { url = "https://files.pythonhosted.org/packages/f0/a6/0c5902393f492f8ceb19d0b5cf139284e3a11b333a049739643b1036b6f8/greenlet-3.5.2-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a2ddf9eddc617681108dd071b3feabf3f4a4cd64846254aec4d4ceda098b639a", size = 606900, upload-time = "2026-06-17T18:07:21.692Z" }, + { url = "https://files.pythonhosted.org/packages/d8/7c/42899c31d4b87148ae4e3f87f63e13398824be6241f4dde42ded95768a34/greenlet-3.5.2-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f41feb9f2b59e2e61ac9bea4e344ddd9396bf3cacb2583f73a3595ed7df6f8e7", size = 619265, upload-time = "2026-06-17T18:29:44.837Z" }, + { url = "https://files.pythonhosted.org/packages/d3/52/4ff8c98d3cfe62b4515f8584ae14510a58f35c549cc5292b78d9b7a40b70/greenlet-3.5.2-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:09201fa698768db245920b00fdc86ee3e73540f01ca6db162be9632642e1a473", size = 616187, upload-time = "2026-06-17T17:39:29.473Z" }, + { url = "https://files.pythonhosted.org/packages/c9/a6/269c8bf9aefc13361ce1088f0e392b154cb21005de7862e42b5d782b81fd/greenlet-3.5.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a1759fa4f14c398508cf20dc8037de55cc23ae8bd14c185c2718257837195ca5", size = 1573778, upload-time = "2026-06-17T18:22:13.497Z" }, + { url = "https://files.pythonhosted.org/packages/1f/9b/391d015cbc6323e81b14c02cf825fdca7e0049c9bb489bf4ac72883118ba/greenlet-3.5.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b9318cdeb9abdbfdd8bc8464ee4a06dffde2c7846e1def138365a6240ab2c9a5", size = 1638092, upload-time = "2026-06-17T17:40:08.163Z" }, + { url = "https://files.pythonhosted.org/packages/49/53/5b4df711f4356c62e85d9f819d87966d526d1cfb32bae49a8f7d6fc36ea4/greenlet-3.5.2-cp313-cp313-win_amd64.whl", hash = "sha256:2c3b3311af72b3d3b03cc0f1ffd11f072e834be5d0444105cf715fc44434e39c", size = 239352, upload-time = "2026-06-17T17:38:51.593Z" }, + { url = "https://files.pythonhosted.org/packages/bb/b6/18efc3a329ec035c3f344b8f2b60356451950ddf9b7b64ff00023778a1dd/greenlet-3.5.2-cp313-cp313-win_arm64.whl", hash = "sha256:f9bbd6216c45a563c2a61e478e038b439d9f248bde44f775ea37d339da643af4", size = 237635, upload-time = "2026-06-17T17:35:36.632Z" }, + { url = "https://files.pythonhosted.org/packages/c7/89/aaafc8e14de4ac882e02ccb963225329b0e8578aba4365e71eb678e45722/greenlet-3.5.2-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:1c31219badba285858ba8ed117f403dea7fafee6bade9a1991875aae530c3ceb", size = 287676, upload-time = "2026-06-17T17:33:31.514Z" }, + { url = "https://files.pythonhosted.org/packages/b8/fc/2308249206c12ac70de7b9a00970f84f07d10b3cd60e05d2fbcaa84124e8/greenlet-3.5.2-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6f96ed6f4adc1066954ae95f45717657cb67468ef3b89e9a3632e14a625a8f39", size = 653552, upload-time = "2026-06-17T18:07:23.493Z" }, + { url = "https://files.pythonhosted.org/packages/7c/24/47730d1f8f1336b9b089237521ed7a26eee997065dcb4cab81cdca333abc/greenlet-3.5.2-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:5795e883e915333c0d5648faaa691857fbc7180136883edc377f50f0d509c2a8", size = 665756, upload-time = "2026-06-17T18:29:46.616Z" }, + { url = "https://files.pythonhosted.org/packages/99/69/d6c99db15dc0b5e892ac3cc7b942c8b21f4a9cc3bd9ea0bc3b0f339ffbd4/greenlet-3.5.2-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:26aed8d9503ca78889141a9739d71b383efea5f472a7c522b5410f7eb2a1b163", size = 663228, upload-time = "2026-06-17T17:39:31.073Z" }, + { url = "https://files.pythonhosted.org/packages/4f/88/9e603f448e2bc107c883e95817b980fb9b45ba6aea0299b2e9978124bea2/greenlet-3.5.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:dbebc038fcdda8f8f21cce985fd04e34e0f42007e7fc7ab7ad285caf77974b95", size = 1620723, upload-time = "2026-06-17T18:22:14.817Z" }, + { url = "https://files.pythonhosted.org/packages/11/91/26da17e3777858c16fdb8d020a4c68f3a03cb92f238de8f5351d5d5186e9/greenlet-3.5.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:a207023f1cf8695fd82580b8099c09c5809be18bc2282362cdfb965dd884a317", size = 1684227, upload-time = "2026-06-17T17:40:09.536Z" }, + { url = "https://files.pythonhosted.org/packages/2d/44/b3a11f7aa34cb38f1b7f3df8bcd9fcd09bac9d342c2a2c9b8686c804bcd2/greenlet-3.5.2-cp314-cp314-win_amd64.whl", hash = "sha256:c674a1dd4fe41f6a93febe7ab366ceabf15080ea31a9307811c56dac5f435f73", size = 240257, upload-time = "2026-06-17T17:35:23.359Z" }, + { url = "https://files.pythonhosted.org/packages/de/e3/3b62145fe917311732041a258adb218248add00542e3131c48bd047fbed5/greenlet-3.5.2-cp314-cp314-win_arm64.whl", hash = "sha256:3c417cd6c593bbbef6f7aa31a79f37d3db7d18832fc56b694a2150130bde784e", size = 239038, upload-time = "2026-06-17T17:37:56.792Z" }, + { url = "https://files.pythonhosted.org/packages/47/ac/d3bad483e9f6cd1848604fdffa32cac25846dd6dfcec0e6f81c790185518/greenlet-3.5.2-cp314-cp314t-macosx_11_0_universal2.whl", hash = "sha256:a96457a30384de52d9c5d2fd33abf6c1daae3db392cd556738f408b1a79a1cf0", size = 295668, upload-time = "2026-06-17T17:36:02.293Z" }, + { url = "https://files.pythonhosted.org/packages/00/e9/3a7e557b895fd0469b00cd0b2bd498ba950e8bfdf6d7adeecf2c5e4130a6/greenlet-3.5.2-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e4af5d4961818ab651d09c1448a03b1ba2a1726a076266ebb62330bab9f3238c", size = 652820, upload-time = "2026-06-17T18:07:24.95Z" }, + { url = "https://files.pythonhosted.org/packages/78/67/6225d5c5e4afc04be0fd161eec82e4b72017e8a100d222f25d7b42b0140d/greenlet-3.5.2-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a1789a6244ea1ba61fd4386c9a6a31873e9b0234762103364be98ef87dcb19f3", size = 658697, upload-time = "2026-06-17T18:29:48.365Z" }, + { url = "https://files.pythonhosted.org/packages/fa/99/6324b8ef916dcaddccb340b304c992ca3f947614ce0f2685d438187300b8/greenlet-3.5.2-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3be00501fb4a8c37f6b4b3c4773808ceb26ea65c7ea64fd5735d0f330b3786de", size = 656436, upload-time = "2026-06-17T17:39:32.509Z" }, + { url = "https://files.pythonhosted.org/packages/a9/ee/f5bf9daac27c5e1b011965f64b5630a32b415daf7381b312943629e12c2a/greenlet-3.5.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:1d554cd96841a68d464d75a3736f8e87408a7b02b1930a75fa32feb408ad62f8", size = 1617193, upload-time = "2026-06-17T18:22:16.252Z" }, + { url = "https://files.pythonhosted.org/packages/8a/21/b05d5b12715bda92ce27c118d64971d21e9b8f3563ed959a7d271e2d4223/greenlet-3.5.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:3dff6cd3aac35f6cd3fc23460105acf576f5faf6c378de0bc088bf37c913864a", size = 1677512, upload-time = "2026-06-17T17:40:10.771Z" }, + { url = "https://files.pythonhosted.org/packages/b8/97/1b8f1314b868041b327dc1051603e8142b826480cb0ecb8a7b7632aee9c4/greenlet-3.5.2-cp314-cp314t-win_amd64.whl", hash = "sha256:36cfea2aa075d544617176b2e84450480f0797070ad8799a8c41ada2fe449d32", size = 243145, upload-time = "2026-06-17T17:34:37.502Z" }, + { url = "https://files.pythonhosted.org/packages/36/07/1b5311775e04c718a118c504d7a3a312430e2a1bd1347226aff4774e4549/greenlet-3.5.2-cp315-cp315-macosx_11_0_universal2.whl", hash = "sha256:a0314aa832c94633355dc6f3ee54f195159533355a323f26926fc63b98b2ccbb", size = 288315, upload-time = "2026-06-17T17:34:34.04Z" }, + { url = "https://files.pythonhosted.org/packages/ed/cc/6abcd2a486b58b9f77b7a93b690d59cb2c11a5906ed2ad4c63c7b9c1113d/greenlet-3.5.2-cp315-cp315-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:24c59cb7db9d5c694cb8fd0c76eef8e456b2123afdfa7e4b8f2a67a0860d7682", size = 659130, upload-time = "2026-06-17T18:07:26.354Z" }, + { url = "https://files.pythonhosted.org/packages/f2/12/f4aaad6d3d383233f700ab322568a4f29f2c701a4861d85f4811d99689b2/greenlet-3.5.2-cp315-cp315-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7bb811753703739ad318112f16eccfaabdac050037b6d092debaa8b23566b4ce", size = 669724, upload-time = "2026-06-17T18:29:50.13Z" }, + { url = "https://files.pythonhosted.org/packages/91/2a/a089811fc31c6bf8742f40a4e73470d6d401cef18e4314eb20dc399b377c/greenlet-3.5.2-cp315-cp315-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6d78b5c1c178dad90447f1b8452262709d3eef4c98f825569e74c9d0b2260ac9", size = 668089, upload-time = "2026-06-17T17:39:33.808Z" }, + { url = "https://files.pythonhosted.org/packages/0f/1c/2f47c7d5fcfa98a62b705bf9a0505d86f4563c0d81cab1f7159ff1e743b7/greenlet-3.5.2-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:0977af2df83136f81c1f76e76d4e2fe7d0dc56ea9c101a86af26a95190b9ca32", size = 1625684, upload-time = "2026-06-17T18:22:17.664Z" }, + { url = "https://files.pythonhosted.org/packages/b9/bf/661dd24624f70b7b32972d7693d0344ecde10278f647d7b828baf739899c/greenlet-3.5.2-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:f9ed777c6891d8253e54468576f55e27f8fc1a662a664f946a191003574c0a74", size = 1688043, upload-time = "2026-06-17T17:40:12.403Z" }, + { url = "https://files.pythonhosted.org/packages/60/49/d9bde1d15a21296b3b521fe083eb8aabd54ac05d15de9832918f3d639543/greenlet-3.5.2-cp315-cp315-win_amd64.whl", hash = "sha256:c0ea4eb3de23f0bac1d75205e10ccfa9b418b17b01a2d7bf19e3b69dda08900a", size = 240531, upload-time = "2026-06-17T17:35:47.448Z" }, + { url = "https://files.pythonhosted.org/packages/7f/4d/86d7768bd53e9907de0333df215c2018cd01a593b3715cbd79aa82dd94b7/greenlet-3.5.2-cp315-cp315-win_arm64.whl", hash = "sha256:7a7bfc200be40d04961d7e80e8337d726c0c1a50777e588123c3ed8ba731dcb9", size = 239579, upload-time = "2026-06-17T17:39:39.954Z" }, + { url = "https://files.pythonhosted.org/packages/92/15/907be5e8900901039bae752fa9a31c03a3c1e064833f35a4e49449184581/greenlet-3.5.2-cp315-cp315t-macosx_11_0_universal2.whl", hash = "sha256:98a52d6a50d4deaba304331d83ee3e10ebbdc1517fcca40b2715d1de4534065c", size = 296697, upload-time = "2026-06-17T17:37:15.887Z" }, + { url = "https://files.pythonhosted.org/packages/95/5c/08c57be575c3d6a3c023bbf22144a1c7dc6ed4d134527bb36ded4dbf04a8/greenlet-3.5.2-cp315-cp315t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1587ff8b58fdf806993ed1490a06ac19c22d47b219c68b30954380029045d8d4", size = 656710, upload-time = "2026-06-17T18:07:28.046Z" }, + { url = "https://files.pythonhosted.org/packages/8c/d0/749f917bdc9fc90fceea4aa65fbf6556e617a50714d1496bdc8ad190bb36/greenlet-3.5.2-cp315-cp315t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:feb721811d2754bfd16b48de151dd6b1f222c048e625151f2ca44cfdfd69f59c", size = 662629, upload-time = "2026-06-17T18:29:51.728Z" }, + { url = "https://files.pythonhosted.org/packages/5a/a5/68cefae3a07f6d0093a490cf28ab604f14578f3e60205a2a2b2d5cd70af2/greenlet-3.5.2-cp315-cp315t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7fe6062b1f35534e1e8fb28dfed406cf4eeff3e0bca3a0d9f8ff69f20a4abb00", size = 660147, upload-time = "2026-06-17T17:39:35.068Z" }, + { url = "https://files.pythonhosted.org/packages/d2/6b/b9156d8397e4750220f54c7c5c34650f1e740a8d2f66eab9cfd1b7b53b69/greenlet-3.5.2-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:b4ac902af825cbac8e9b2fccab8122236fd2ba6c8b71a080116d2c2ec72671b1", size = 1621675, upload-time = "2026-06-17T18:22:18.873Z" }, + { url = "https://files.pythonhosted.org/packages/b0/e3/d3250f4fa01c211a93d04e34fded63187e648dbec17b9b1a14d388040593/greenlet-3.5.2-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:6f1e473c06ae8be00c9034c2bb10fa277b08a93287e3111c395b839f01d27e1f", size = 1680577, upload-time = "2026-06-17T17:40:14.055Z" }, + { url = "https://files.pythonhosted.org/packages/55/ba/eaee8bda4419770d7096b5a009ebff0ab20a2a28cdd83c4b591bfdf36fa9/greenlet-3.5.2-cp315-cp315t-win_amd64.whl", hash = "sha256:3c2315045f9983e2e50d7e89d95405c21bddb8745f2da4487bc080ab3525f904", size = 243482, upload-time = "2026-06-17T17:37:34.741Z" }, + { url = "https://files.pythonhosted.org/packages/37/45/f794a81c91e9942c61f9110bd1f9a38a0ea565eab57f8b08cd53d3131e48/greenlet-3.5.2-cp315-cp315t-win_arm64.whl", hash = "sha256:db548d5ab6c2a8ead82c013f875090d79b5d7d2b67fc513934ce6cf66492ad7f", size = 242062, upload-time = "2026-06-17T17:35:39.814Z" }, ] [[package]] name = "grpcio" -version = "1.81.0" +version = "1.81.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "typing-extensions" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/15/f3/23f47b24f8d8c2028eba501db3acfbb2f592cbb5995eaa6e363a627b74d7/grpcio-1.81.0.tar.gz", hash = "sha256:a5acd7efd3b1fe9b4eb0bcaaa1507eed68a0ad0678b654c3f7b464df9ba9dca5", size = 13032272, upload-time = "2026-06-01T05:56:22.827Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/45/a8/9916ab10a0201f4c7afb6918125aa2f38a7626ee18ffbc066dd9cb04a74d/grpcio-1.81.0-cp311-cp311-linux_armv7l.whl", hash = "sha256:794e6aa648e8df47d8f908dc8c3b42347d04ec58438f1dcd4e445f09b4f6b0ce", size = 6093557, upload-time = "2026-06-01T05:54:32.64Z" }, - { url = "https://files.pythonhosted.org/packages/a7/43/99e969a048904a65df3129ee53c5f523b7c4e43127786460cac4bee82470/grpcio-1.81.0-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:cd78145b7f7784661c524624f3526c9c6f891b30a4b54cb93a40806d0d0d61e9", size = 12075345, upload-time = "2026-06-01T05:54:35.77Z" }, - { url = "https://files.pythonhosted.org/packages/83/70/4c3a204e190333768d4f63f4ff56bd0bf405f05b9188f3a59a8bcf161f8b/grpcio-1.81.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:638ccc1b86f7540170a169cb900799b9296a1381e47879ce60b0de9d3db73d33", size = 6640664, upload-time = "2026-06-01T05:54:38.854Z" }, - { url = "https://files.pythonhosted.org/packages/2e/a9/0fa17ac8b4e29cf59b26915be6cab8c0d4583ce24a6208a287b6e5f6d072/grpcio-1.81.0-cp311-cp311-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:21ec30b9ea320c8207ea7cd05873ad64aa69fdd0e81b6758b3347983ba20b50a", size = 7332542, upload-time = "2026-06-01T05:54:41.39Z" }, - { url = "https://files.pythonhosted.org/packages/f4/18/7c8e3d0dda2fb7a17076fcd6c9085209eabad3354696c64230f87b3a14eb/grpcio-1.81.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:dbdb99986548a7e87f8343805ef315fd4eb50ffaabf4fb1206e42f2542bb805d", size = 6842564, upload-time = "2026-06-01T05:54:43.57Z" }, - { url = "https://files.pythonhosted.org/packages/f6/19/2f1726c2e03ad3f3fe241e6b41534532ad580d595de14a4054ad84999c80/grpcio-1.81.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c36f5d5e97944cbda2d4096b4ae262e6e68506246b61582acf1b8591607f3ccc", size = 7446236, upload-time = "2026-06-01T05:54:46.042Z" }, - { url = "https://files.pythonhosted.org/packages/a7/dc/0321f892212e2c0bfe248cea24c00d7d7111639688ec5ffd8e36b5c02fe6/grpcio-1.81.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:9f355384e5543ab77a755a7085225ecc19f32b76032e851cbd8145715d79dec8", size = 8445633, upload-time = "2026-06-01T05:54:48.809Z" }, - { url = "https://files.pythonhosted.org/packages/e5/20/0e7ea7494955cf1beea3077b2fd2c04c84d4480c2ae85a1e1cfa150c62d7/grpcio-1.81.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:77eb4e9fe61486bd1198cc7236ebb0f70e66234e63c0348f40bc2553ed16a88b", size = 7873958, upload-time = "2026-06-01T05:54:52.135Z" }, - { url = "https://files.pythonhosted.org/packages/d3/9e/6438e226046c2a0778060e2b1d791a4827277bbd9d223013c2c63ee7435e/grpcio-1.81.0-cp311-cp311-win32.whl", hash = "sha256:7915a2e63acdc05264a206e1bddfd8e1fb8a29e406c18d72d30f8c124e021374", size = 4202110, upload-time = "2026-06-01T05:54:54.134Z" }, - { url = "https://files.pythonhosted.org/packages/42/6b/d0895e93d65b186f5f1737fcc186d7faa487e2d9d934eda111a37a309869/grpcio-1.81.0-cp311-cp311-win_amd64.whl", hash = "sha256:5e925a70fe99fe5794f7beca0ea034c75f068afcc356d79047e73f99cdcca34c", size = 4940942, upload-time = "2026-06-01T05:54:56.749Z" }, - { url = "https://files.pythonhosted.org/packages/82/d5/896a3aaf07068d707d88b282a04914b872db4d32d3c7e6d88e43a3b911fa/grpcio-1.81.0-cp312-cp312-linux_armv7l.whl", hash = "sha256:57b3b0e73a518fa286959b40c3eddd02703504ca186e8b7b2945954519bd8b2c", size = 6053538, upload-time = "2026-06-01T05:54:58.965Z" }, - { url = "https://files.pythonhosted.org/packages/68/6a/7e3eafa4727cd405ff917605ed2949e2af162f233f5cbdd773723a5fea7d/grpcio-1.81.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:8bb1789c94322a13336a2b6c58d9c14d68f8628b6e24205a799c69f5bf8516ce", size = 12053447, upload-time = "2026-06-01T05:55:01.862Z" }, - { url = "https://files.pythonhosted.org/packages/16/79/a4302aa82428de48a922421f522b027a1a727ab4d0926368454aa953d36d/grpcio-1.81.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e4d053900a0d24b75d7521139a3872150301b3d6bde3bed5e12318fb25791e4d", size = 6595872, upload-time = "2026-06-01T05:55:04.946Z" }, - { url = "https://files.pythonhosted.org/packages/b4/1f/7ff2850eaefbecf99af3f624dbb28dd1ad6c5fd4c1d8c26909ed6482673b/grpcio-1.81.0-cp312-cp312-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:db217c2e52931719f9937bd12082cd4d7b495b35803d5760686975c285924bf8", size = 7303857, upload-time = "2026-06-01T05:55:07.205Z" }, - { url = "https://files.pythonhosted.org/packages/e2/98/1f3896a9baae1f2aedf4e99c55291d6fa1f30ad9603d63bc18bda967b53e/grpcio-1.81.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:19f201da7b4e5c0559198abe5a97157e726f3abe6e8f5e832d4a50740f6dcc22", size = 6809676, upload-time = "2026-06-01T05:55:09.513Z" }, - { url = "https://files.pythonhosted.org/packages/34/8b/3441983718095208c5d797fd3239882e97ea89a629f41c8df94b4eef4df9/grpcio-1.81.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:275144b0115353339dbb8a6f28a9cf8997b5bf40e37f8f66ac0b0ea57e95b43f", size = 7412654, upload-time = "2026-06-01T05:55:12.777Z" }, - { url = "https://files.pythonhosted.org/packages/3c/98/1eddf07df6e4fe85cf67502a793f7b05468b2dca3d1ef35b972cf5d54468/grpcio-1.81.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:5192857589f223e5a98ff0e31f6e551b19040e647d17bfe10116c8a2ce3b8696", size = 8408026, upload-time = "2026-06-01T05:55:15.514Z" }, - { url = "https://files.pythonhosted.org/packages/5c/73/3860341e6a1f5347be6ab35c6c0e1e3a8eb59d010388207fd561dcf01a88/grpcio-1.81.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c6ff087cb1f563f47b504b4e29e684129fc5ae4863faf3ebca08a327764ee6cb", size = 7849498, upload-time = "2026-06-01T05:55:18.078Z" }, - { url = "https://files.pythonhosted.org/packages/ae/3f/0ea06bd85c701966aa3f8f37314f2ed83520d2b7590f42d643d445d8bc8b/grpcio-1.81.0-cp312-cp312-win32.whl", hash = "sha256:98c6240f563178fc5877bd50e6ff274463e53e1472128f4110742450739659fa", size = 4184161, upload-time = "2026-06-01T05:55:20.127Z" }, - { url = "https://files.pythonhosted.org/packages/39/e3/a7c387406827a86f99ad7838b995bf9b4a182ffe2d2c439ed2873efec952/grpcio-1.81.0-cp312-cp312-win_amd64.whl", hash = "sha256:87e33b7afcfb3585121b5f007d2c52b8c534104d18f556e840d35193ca2a9141", size = 4929958, upload-time = "2026-06-01T05:55:22.736Z" }, - { url = "https://files.pythonhosted.org/packages/f3/29/779ee53c931d0fd55c1d459fde43e485172caa3ac87cbd43d003a13a0185/grpcio-1.81.0-cp313-cp313-linux_armv7l.whl", hash = "sha256:62bbe463c9f0f2ff24e31bd25f8dd8b4bae78900e315915a3195a0ef1471a855", size = 6054973, upload-time = "2026-06-01T05:55:25.043Z" }, - { url = "https://files.pythonhosted.org/packages/9e/b6/7211807926b5a17f8d9a5d47c739a163d6812fefe3e4714e81cf92945ed7/grpcio-1.81.0-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:43c121e135ae44d1559b430db2b2dfad7421cbbe40e1deba506c7dc62b439719", size = 12048662, upload-time = "2026-06-01T05:55:28.453Z" }, - { url = "https://files.pythonhosted.org/packages/64/89/b1b93ef6b34bd20bbaf707fa99133bc9cc302139d5ec6f77a165c7169796/grpcio-1.81.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f345de40ef2e65f63645d53d251824e6070e07804827c5b00ec2e44555f9f901", size = 6599116, upload-time = "2026-06-01T05:55:31.185Z" }, - { url = "https://files.pythonhosted.org/packages/eb/bc/c89f9b9d1c22895715356a1e009554dae66319e97826bb4d30bcda7d29e8/grpcio-1.81.0-cp313-cp313-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:8c0855a350886f713b9e458e2a10d208009dcaa849f574e39cd6067db1fe1279", size = 7307591, upload-time = "2026-06-01T05:55:33.463Z" }, - { url = "https://files.pythonhosted.org/packages/65/4a/1df2a4cb4a1386e066ab7e4175e34bb884b35ccb60d3621c09c84af6aabb/grpcio-1.81.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a524cd530900bd24511fcb7f2ed144da4ea37711c4b094475d0bceca7a93a170", size = 6811797, upload-time = "2026-06-01T05:55:36.731Z" }, - { url = "https://files.pythonhosted.org/packages/8d/dc/fa189d20601a1be25b08850cfb733879bbb1047b62a8feec3a60e3e1a87b/grpcio-1.81.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:e7746ba3e6efc9e2b748eff59470a2b8684d5a9ec607c6580bcaa5be175820bc", size = 7415131, upload-time = "2026-06-01T05:55:39.451Z" }, - { url = "https://files.pythonhosted.org/packages/ad/a3/5625c48cb48d23c6631b3e5294f88e4c751f22a52591ae78859fab96dca1/grpcio-1.81.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:aaaa4f7f2057d795952e4eacf3f342be8b5b156992f6ac85023c8b98794ebd47", size = 8408398, upload-time = "2026-06-01T05:55:42.219Z" }, - { url = "https://files.pythonhosted.org/packages/75/34/0f8202c6809a46c2b4d69125ef3667c40b1c211f8e19930e5fa1f1197039/grpcio-1.81.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0fba53cb96004b2b7fb758b46b2288cb49d0b658316a4e73f3ef67230616ee65", size = 7844481, upload-time = "2026-06-01T05:55:44.849Z" }, - { url = "https://files.pythonhosted.org/packages/c0/95/c3366b5b5edf4c4adc90f2e29ca16e57965a8e56dc8d2ee89565ba1905bb/grpcio-1.81.0-cp313-cp313-win32.whl", hash = "sha256:c197e2ef75a442528072b29e9755da299110e8610e8bcbb59a6b4cf55384f005", size = 4182777, upload-time = "2026-06-01T05:55:47.459Z" }, - { url = "https://files.pythonhosted.org/packages/a9/a7/932f2f748511a32e641a2aba0d30dded3ed6e8bc330e0924e4d5d86853e6/grpcio-1.81.0-cp313-cp313-win_amd64.whl", hash = "sha256:194eddfacc84d80f50512e9fd4ee851d5f2499f18f299c95aa8fb4748f0537e0", size = 4928085, upload-time = "2026-06-01T05:55:50.158Z" }, - { url = "https://files.pythonhosted.org/packages/c5/1d/28b231333857deb840bc3d182ae087510170ea6d68f21393aeb0fe499530/grpcio-1.81.0-cp314-cp314-linux_armv7l.whl", hash = "sha256:a9351055f52660b58f3d4890ea66188b5134399f82b11aa0c55bd4b99eff5390", size = 6055712, upload-time = "2026-06-01T05:55:52.889Z" }, - { url = "https://files.pythonhosted.org/packages/e8/b8/999c14f9dff0fc47549d2e827cba1343ddc18e1d1bf0d06d2cf628eecbd9/grpcio-1.81.0-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:300f3337b6425fd16ead9a4f9b2ac25801acb64aa5bc0b99eb69901645b2b1d2", size = 12057189, upload-time = "2026-06-01T05:55:55.952Z" }, - { url = "https://files.pythonhosted.org/packages/1e/3d/1fbde079572562af65351151d840525a13879eb7b481d35b55cd64c6127a/grpcio-1.81.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:97bbd623f7ded558fd4f7cb5a4f600c4d4de65c5dd364c83a5b14b2a10a2d3b5", size = 6608136, upload-time = "2026-06-01T05:55:59.069Z" }, - { url = "https://files.pythonhosted.org/packages/32/89/1f17cb6882abfd8e5a303a25d5d1665abef5a8c499a96198c65a651d1b85/grpcio-1.81.0-cp314-cp314-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:ff83d889e3ebf6341c8c7864ad8031591ad5ca61599072fc511644d1eb962d2b", size = 7307045, upload-time = "2026-06-01T05:56:02.376Z" }, - { url = "https://files.pythonhosted.org/packages/48/5a/f98e91b2e755652e637ea2144318b0229b290062199f761b445fe1fa6015/grpcio-1.81.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c4fe218c5a35e1d87a5a26544237f1fa41dfd9cbd3c856b0810a30061f8b0aaf", size = 6812794, upload-time = "2026-06-01T05:56:05.777Z" }, - { url = "https://files.pythonhosted.org/packages/0a/0c/77892d715ac41e7ec0ace2a50080ffb64e189188056f607a66fe0014d1ee/grpcio-1.81.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:b8b025b6af43ee0ad4a70307025d77bcab5adde7c4597786010d802c203e9fc5", size = 7422767, upload-time = "2026-06-01T05:56:08.524Z" }, - { url = "https://files.pythonhosted.org/packages/3f/b8/aa04590c6564714d94954515f15a236e59d4b9b3ad01e615f1b706d7792d/grpcio-1.81.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:3d4e0ce5a40a998cf608c8ba60ecfe18fdf364a9aa193ae4ac3faeecd0e86757", size = 8408551, upload-time = "2026-06-01T05:56:11.283Z" }, - { url = "https://files.pythonhosted.org/packages/43/3d/4f4a3450a1973568910c6909cb74abbf2126f68aefae5976962f9f7ad50d/grpcio-1.81.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:aa948712c8e5fa40ec250870bda14bc7578e1bb832a8912d9d2a0f720518edbe", size = 7846468, upload-time = "2026-06-01T05:56:14.536Z" }, - { url = "https://files.pythonhosted.org/packages/88/f4/5827fd248221ad3b44161c23ce9b5f4ee405b04fc6da5fd402a9aa87a84a/grpcio-1.81.0-cp314-cp314-win32.whl", hash = "sha256:fbbe81314a9d92156abce8b62c09364eb8bafc0ca2a19919a45ec64b5c6cb664", size = 4264427, upload-time = "2026-06-01T05:56:17.192Z" }, - { url = "https://files.pythonhosted.org/packages/0c/e8/127dc2b246096ad50ef7c8d9b7b31d757787aeb796368bcdd4454e4204c4/grpcio-1.81.0-cp314-cp314-win_amd64.whl", hash = "sha256:b93cee313cae4e113fbb3a0ce1ea5633db6f63cfde2b2dc1d817429026b2a50b", size = 5070848, upload-time = "2026-06-01T05:56:19.735Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/b6/e0/318c1ce3ae5a17894d5791e87aea147587c9e702f24122cc7a5c8bbaeeb1/grpcio-1.76.0.tar.gz", hash = "sha256:7be78388d6da1a25c0d5ec506523db58b18be22d9c37d8d3a32c08be4987bd73", size = 12785182 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/a0/00/8163a1beeb6971f66b4bbe6ac9457b97948beba8dd2fc8e1281dce7f79ec/grpcio-1.76.0-cp311-cp311-linux_armv7l.whl", hash = "sha256:2e1743fbd7f5fa713a1b0a8ac8ebabf0ec980b5d8809ec358d488e273b9cf02a", size = 5843567 }, - { url = "https://files.pythonhosted.org/packages/10/c1/934202f5cf335e6d852530ce14ddb0fef21be612ba9ecbbcbd4d748ca32d/grpcio-1.76.0-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:a8c2cf1209497cf659a667d7dea88985e834c24b7c3b605e6254cbb5076d985c", size = 11848017 }, - { url = "https://files.pythonhosted.org/packages/11/0b/8dec16b1863d74af6eb3543928600ec2195af49ca58b16334972f6775663/grpcio-1.76.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:08caea849a9d3c71a542827d6df9d5a69067b0a1efbea8a855633ff5d9571465", size = 6412027 }, - { url = "https://files.pythonhosted.org/packages/d7/64/7b9e6e7ab910bea9d46f2c090380bab274a0b91fb0a2fe9b0cd399fffa12/grpcio-1.76.0-cp311-cp311-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:f0e34c2079d47ae9f6188211db9e777c619a21d4faba6977774e8fa43b085e48", size = 7075913 }, - { url = "https://files.pythonhosted.org/packages/68/86/093c46e9546073cefa789bd76d44c5cb2abc824ca62af0c18be590ff13ba/grpcio-1.76.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8843114c0cfce61b40ad48df65abcfc00d4dba82eae8718fab5352390848c5da", size = 6615417 }, - { url = "https://files.pythonhosted.org/packages/f7/b6/5709a3a68500a9c03da6fb71740dcdd5ef245e39266461a03f31a57036d8/grpcio-1.76.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8eddfb4d203a237da6f3cc8a540dad0517d274b5a1e9e636fd8d2c79b5c1d397", size = 7199683 }, - { url = "https://files.pythonhosted.org/packages/91/d3/4b1f2bf16ed52ce0b508161df3a2d186e4935379a159a834cb4a7d687429/grpcio-1.76.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:32483fe2aab2c3794101c2a159070584e5db11d0aa091b2c0ea9c4fc43d0d749", size = 8163109 }, - { url = "https://files.pythonhosted.org/packages/5c/61/d9043f95f5f4cf085ac5dd6137b469d41befb04bd80280952ffa2a4c3f12/grpcio-1.76.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:dcfe41187da8992c5f40aa8c5ec086fa3672834d2be57a32384c08d5a05b4c00", size = 7626676 }, - { url = "https://files.pythonhosted.org/packages/36/95/fd9a5152ca02d8881e4dd419cdd790e11805979f499a2e5b96488b85cf27/grpcio-1.76.0-cp311-cp311-win32.whl", hash = "sha256:2107b0c024d1b35f4083f11245c0e23846ae64d02f40b2b226684840260ed054", size = 3997688 }, - { url = "https://files.pythonhosted.org/packages/60/9c/5c359c8d4c9176cfa3c61ecd4efe5affe1f38d9bae81e81ac7186b4c9cc8/grpcio-1.76.0-cp311-cp311-win_amd64.whl", hash = "sha256:522175aba7af9113c48ec10cc471b9b9bd4f6ceb36aeb4544a8e2c80ed9d252d", size = 4709315 }, - { url = "https://files.pythonhosted.org/packages/bf/05/8e29121994b8d959ffa0afd28996d452f291b48cfc0875619de0bde2c50c/grpcio-1.76.0-cp312-cp312-linux_armv7l.whl", hash = "sha256:81fd9652b37b36f16138611c7e884eb82e0cec137c40d3ef7c3f9b3ed00f6ed8", size = 5799718 }, - { url = "https://files.pythonhosted.org/packages/d9/75/11d0e66b3cdf998c996489581bdad8900db79ebd83513e45c19548f1cba4/grpcio-1.76.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:04bbe1bfe3a68bbfd4e52402ab7d4eb59d72d02647ae2042204326cf4bbad280", size = 11825627 }, - { url = "https://files.pythonhosted.org/packages/28/50/2f0aa0498bc188048f5d9504dcc5c2c24f2eb1a9337cd0fa09a61a2e75f0/grpcio-1.76.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d388087771c837cdb6515539f43b9d4bf0b0f23593a24054ac16f7a960be16f4", size = 6359167 }, - { url = "https://files.pythonhosted.org/packages/66/e5/bbf0bb97d29ede1d59d6588af40018cfc345b17ce979b7b45424628dc8bb/grpcio-1.76.0-cp312-cp312-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:9f8f757bebaaea112c00dba718fc0d3260052ce714e25804a03f93f5d1c6cc11", size = 7044267 }, - { url = "https://files.pythonhosted.org/packages/f5/86/f6ec2164f743d9609691115ae8ece098c76b894ebe4f7c94a655c6b03e98/grpcio-1.76.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:980a846182ce88c4f2f7e2c22c56aefd515daeb36149d1c897f83cf57999e0b6", size = 6573963 }, - { url = "https://files.pythonhosted.org/packages/60/bc/8d9d0d8505feccfdf38a766d262c71e73639c165b311c9457208b56d92ae/grpcio-1.76.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:f92f88e6c033db65a5ae3d97905c8fea9c725b63e28d5a75cb73b49bda5024d8", size = 7164484 }, - { url = "https://files.pythonhosted.org/packages/67/e6/5d6c2fc10b95edf6df9b8f19cf10a34263b7fd48493936fffd5085521292/grpcio-1.76.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:4baf3cbe2f0be3289eb68ac8ae771156971848bb8aaff60bad42005539431980", size = 8127777 }, - { url = "https://files.pythonhosted.org/packages/3f/c8/dce8ff21c86abe025efe304d9e31fdb0deaaa3b502b6a78141080f206da0/grpcio-1.76.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:615ba64c208aaceb5ec83bfdce7728b80bfeb8be97562944836a7a0a9647d882", size = 7594014 }, - { url = "https://files.pythonhosted.org/packages/e0/42/ad28191ebf983a5d0ecef90bab66baa5a6b18f2bfdef9d0a63b1973d9f75/grpcio-1.76.0-cp312-cp312-win32.whl", hash = "sha256:45d59a649a82df5718fd9527ce775fd66d1af35e6d31abdcdc906a49c6822958", size = 3984750 }, - { url = "https://files.pythonhosted.org/packages/9e/00/7bd478cbb851c04a48baccaa49b75abaa8e4122f7d86da797500cccdd771/grpcio-1.76.0-cp312-cp312-win_amd64.whl", hash = "sha256:c088e7a90b6017307f423efbb9d1ba97a22aa2170876223f9709e9d1de0b5347", size = 4704003 }, - { url = "https://files.pythonhosted.org/packages/fc/ed/71467ab770effc9e8cef5f2e7388beb2be26ed642d567697bb103a790c72/grpcio-1.76.0-cp313-cp313-linux_armv7l.whl", hash = "sha256:26ef06c73eb53267c2b319f43e6634c7556ea37672029241a056629af27c10e2", size = 5807716 }, - { url = "https://files.pythonhosted.org/packages/2c/85/c6ed56f9817fab03fa8a111ca91469941fb514e3e3ce6d793cb8f1e1347b/grpcio-1.76.0-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:45e0111e73f43f735d70786557dc38141185072d7ff8dc1829d6a77ac1471468", size = 11821522 }, - { url = "https://files.pythonhosted.org/packages/ac/31/2b8a235ab40c39cbc141ef647f8a6eb7b0028f023015a4842933bc0d6831/grpcio-1.76.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:83d57312a58dcfe2a3a0f9d1389b299438909a02db60e2f2ea2ae2d8034909d3", size = 6362558 }, - { url = "https://files.pythonhosted.org/packages/bd/64/9784eab483358e08847498ee56faf8ff6ea8e0a4592568d9f68edc97e9e9/grpcio-1.76.0-cp313-cp313-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:3e2a27c89eb9ac3d81ec8835e12414d73536c6e620355d65102503064a4ed6eb", size = 7049990 }, - { url = "https://files.pythonhosted.org/packages/2b/94/8c12319a6369434e7a184b987e8e9f3b49a114c489b8315f029e24de4837/grpcio-1.76.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:61f69297cba3950a524f61c7c8ee12e55c486cb5f7db47ff9dcee33da6f0d3ae", size = 6575387 }, - { url = "https://files.pythonhosted.org/packages/15/0f/f12c32b03f731f4a6242f771f63039df182c8b8e2cf8075b245b409259d4/grpcio-1.76.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6a15c17af8839b6801d554263c546c69c4d7718ad4321e3166175b37eaacca77", size = 7166668 }, - { url = "https://files.pythonhosted.org/packages/ff/2d/3ec9ce0c2b1d92dd59d1c3264aaec9f0f7c817d6e8ac683b97198a36ed5a/grpcio-1.76.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:25a18e9810fbc7e7f03ec2516addc116a957f8cbb8cbc95ccc80faa072743d03", size = 8124928 }, - { url = "https://files.pythonhosted.org/packages/1a/74/fd3317be5672f4856bcdd1a9e7b5e17554692d3db9a3b273879dc02d657d/grpcio-1.76.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:931091142fd8cc14edccc0845a79248bc155425eee9a98b2db2ea4f00a235a42", size = 7589983 }, - { url = "https://files.pythonhosted.org/packages/45/bb/ca038cf420f405971f19821c8c15bcbc875505f6ffadafe9ffd77871dc4c/grpcio-1.76.0-cp313-cp313-win32.whl", hash = "sha256:5e8571632780e08526f118f74170ad8d50fb0a48c23a746bef2a6ebade3abd6f", size = 3984727 }, - { url = "https://files.pythonhosted.org/packages/41/80/84087dc56437ced7cdd4b13d7875e7439a52a261e3ab4e06488ba6173b0a/grpcio-1.76.0-cp313-cp313-win_amd64.whl", hash = "sha256:f9f7bd5faab55f47231ad8dba7787866b69f5e93bc306e3915606779bbfb4ba8", size = 4702799 }, - { url = "https://files.pythonhosted.org/packages/b4/46/39adac80de49d678e6e073b70204091e76631e03e94928b9ea4ecf0f6e0e/grpcio-1.76.0-cp314-cp314-linux_armv7l.whl", hash = "sha256:ff8a59ea85a1f2191a0ffcc61298c571bc566332f82e5f5be1b83c9d8e668a62", size = 5808417 }, - { url = "https://files.pythonhosted.org/packages/9c/f5/a4531f7fb8b4e2a60b94e39d5d924469b7a6988176b3422487be61fe2998/grpcio-1.76.0-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:06c3d6b076e7b593905d04fdba6a0525711b3466f43b3400266f04ff735de0cd", size = 11828219 }, - { url = "https://files.pythonhosted.org/packages/4b/1c/de55d868ed7a8bd6acc6b1d6ddc4aa36d07a9f31d33c912c804adb1b971b/grpcio-1.76.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fd5ef5932f6475c436c4a55e4336ebbe47bd3272be04964a03d316bbf4afbcbc", size = 6367826 }, - { url = "https://files.pythonhosted.org/packages/59/64/99e44c02b5adb0ad13ab3adc89cb33cb54bfa90c74770f2607eea629b86f/grpcio-1.76.0-cp314-cp314-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:b331680e46239e090f5b3cead313cc772f6caa7d0fc8de349337563125361a4a", size = 7049550 }, - { url = "https://files.pythonhosted.org/packages/43/28/40a5be3f9a86949b83e7d6a2ad6011d993cbe9b6bd27bea881f61c7788b6/grpcio-1.76.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2229ae655ec4e8999599469559e97630185fdd53ae1e8997d147b7c9b2b72cba", size = 6575564 }, - { url = "https://files.pythonhosted.org/packages/4b/a9/1be18e6055b64467440208a8559afac243c66a8b904213af6f392dc2212f/grpcio-1.76.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:490fa6d203992c47c7b9e4a9d39003a0c2bcc1c9aa3c058730884bbbb0ee9f09", size = 7176236 }, - { url = "https://files.pythonhosted.org/packages/0f/55/dba05d3fcc151ce6e81327541d2cc8394f442f6b350fead67401661bf041/grpcio-1.76.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:479496325ce554792dba6548fae3df31a72cef7bad71ca2e12b0e58f9b336bfc", size = 8125795 }, - { url = "https://files.pythonhosted.org/packages/4a/45/122df922d05655f63930cf42c9e3f72ba20aadb26c100ee105cad4ce4257/grpcio-1.76.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:1c9b93f79f48b03ada57ea24725d83a30284a012ec27eab2cf7e50a550cbbbcc", size = 7592214 }, - { url = "https://files.pythonhosted.org/packages/4a/6e/0b899b7f6b66e5af39e377055fb4a6675c9ee28431df5708139df2e93233/grpcio-1.76.0-cp314-cp314-win32.whl", hash = "sha256:747fa73efa9b8b1488a95d0ba1039c8e2dca0f741612d80415b1e1c560febf4e", size = 4062961 }, - { url = "https://files.pythonhosted.org/packages/19/41/0b430b01a2eb38ee887f88c1f07644a1df8e289353b78e82b37ef988fb64/grpcio-1.76.0-cp314-cp314-win_amd64.whl", hash = "sha256:922fa70ba549fce362d2e2871ab542082d66e2aaf0c19480ea453905b01f384e", size = 4834462 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) +sdist = { url = "https://files.pythonhosted.org/packages/b0/b5/1ff353970a87eda4c98251e34d2dfd214abd4982dc89119c9252a2a482d2/grpcio-1.81.1.tar.gz", hash = "sha256:6fa10a767143a5e82e8eaab53918af0cd8909a57a27f8cb2288b80a613ac671b", size = 13026582, upload-time = "2026-06-11T12:46:51.673Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/52/ea/1c2fa386b718ff493225e61cfc052ef400b4d6ffc54cbe261026432624b5/grpcio-1.81.1-cp311-cp311-linux_armv7l.whl", hash = "sha256:d71d30f2d92f67d944631c523713934fee37292469e182ebcd2c1dd8a64ce53f", size = 6093112, upload-time = "2026-06-11T12:44:52.131Z" }, + { url = "https://files.pythonhosted.org/packages/2b/18/acf45fa8bd1bc5d7b0c2fd3dc4c209379fbd5bb396b440b68a83342226b7/grpcio-1.81.1-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:b137f4bf3ada9dc44d411478decc6ff09a79ed30b306cd2abaa98408c3588137", size = 12074277, upload-time = "2026-06-11T12:44:55.354Z" }, + { url = "https://files.pythonhosted.org/packages/48/d7/ee86a60699b7db039f772a2c4a7e4facc7138984ff42c0130933a0063884/grpcio-1.81.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:a3acb384427816dd5d470f47e62137b87f74da694faa8a50147012cf40df276a", size = 6640348, upload-time = "2026-06-11T12:44:59.223Z" }, + { url = "https://files.pythonhosted.org/packages/26/ee/d2de5e47378ffc207d476c230fea3be4d2601edbce9995f4fe45535d4896/grpcio-1.81.1-cp311-cp311-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:f9a0ebbe45c29b5e5866593c12b78bd9035f0f0f0d4bc8361680cd580d99db49", size = 7331842, upload-time = "2026-06-11T12:45:02.001Z" }, + { url = "https://files.pythonhosted.org/packages/23/d6/abeda5c2b896a0b341584fe5ac411bbf72e197a9a374c355fb90965e08d2/grpcio-1.81.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0a37165cc80b1a368384b383e63a4c38116a10467ae44c904d2d7468c4470ec2", size = 6842229, upload-time = "2026-06-11T12:45:04.76Z" }, + { url = "https://files.pythonhosted.org/packages/10/1c/1f0da7d590b4aeee006826ba568d0e419ca14b23e18f901a3da3e9fba613/grpcio-1.81.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:6282caffb41ec326d4cb67ca9cf53b739d1b2f975a2acb498c7418e9f7d9a416", size = 7446096, upload-time = "2026-06-11T12:45:07.499Z" }, + { url = "https://files.pythonhosted.org/packages/6a/81/5c505d508f7c887aa7982d21443a4126597c80d34b0bcf40f9cec576d7f3/grpcio-1.81.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:a35009284d0d3d5c2c9601c164a911b8b4331608d98a9a66d47d97bb2f522b70", size = 8445238, upload-time = "2026-06-11T12:45:10.243Z" }, + { url = "https://files.pythonhosted.org/packages/f7/b2/524847365122ee509ca17bcc4e092198b700e94af7bfd5bb5e6dd9f3ee66/grpcio-1.81.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:1b22c80559854b789a01fd89e8929b3798a156c0829b5282a8939f33ad4115ad", size = 7873989, upload-time = "2026-06-11T12:45:13.102Z" }, + { url = "https://files.pythonhosted.org/packages/18/fa/07c037c50b006909d1d13a5848774f8aa7b242f70dc03a035c64eea0e6db/grpcio-1.81.1-cp311-cp311-win32.whl", hash = "sha256:428bec0161b48d8cf583c068591bc0016d0d9cfff52462b72b3884861ea768c5", size = 4202223, upload-time = "2026-06-11T12:45:16.166Z" }, + { url = "https://files.pythonhosted.org/packages/41/ed/6bff15376920942fac6b95b9802752b837437172c9e8fc2d3170546b89cc/grpcio-1.81.1-cp311-cp311-win_amd64.whl", hash = "sha256:30e825f6848d9f18bba350ed6c75c1b02a0b5184474a31db9a32b1fa66fd8c79", size = 4941303, upload-time = "2026-06-11T12:45:18.724Z" }, + { url = "https://files.pythonhosted.org/packages/85/07/9a979c81738863a738dc23d65177056e71fbb2db817740ed870b33434e7a/grpcio-1.81.1-cp312-cp312-linux_armv7l.whl", hash = "sha256:8b39472beafc0bdcafc4c8c73ad082ebfdb449d566897a61e7acb4fa88089115", size = 6053264, upload-time = "2026-06-11T12:45:21.017Z" }, + { url = "https://files.pythonhosted.org/packages/75/95/539706ca0d3bd40dbad583dc56fd883da941f37556b629132da5762781b9/grpcio-1.81.1-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:12b7524c88d4026d3dcb7b0ebe16b6714f3b4af402ddd0f0639ab064a00c87c3", size = 12052560, upload-time = "2026-06-11T12:45:23.652Z" }, + { url = "https://files.pythonhosted.org/packages/e0/44/f257b7e0bd69c93b06c6cb8ac8d1b901ccb42bedabd83c1a4c77a71f8810/grpcio-1.81.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:1e123f9b37edb8375fd74130d1f69c944bbf0a7b06761ae7211154b8759e94d2", size = 6595983, upload-time = "2026-06-11T12:45:26.963Z" }, + { url = "https://files.pythonhosted.org/packages/b9/f3/19782aa04c960968bef8c5539329d8e3bbc3364e2e46d19eb5e5cc5e43b7/grpcio-1.81.1-cp312-cp312-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:2c2e2ae6867c2966b8daccc836d54a13218e0007e9a490aeb81dd05be64d22d7", size = 7303455, upload-time = "2026-06-11T12:45:29.707Z" }, + { url = "https://files.pythonhosted.org/packages/eb/8c/dea020b6d91508cd84463917a63149ec196ee7db505d032ae43fcb3303b9/grpcio-1.81.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:766bc7c9a9c340342f4c864ccbda8e78111e4751f13b895812b9c148fb79e9d0", size = 6809167, upload-time = "2026-06-11T12:45:32.52Z" }, + { url = "https://files.pythonhosted.org/packages/1c/c7/3030dd940408083bd32cd95d634777a71605ade4887154d93e8a89244946/grpcio-1.81.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:b259a04a737cb3496be0901328eb8b7552ed8df4865d8c8f1cf1bffcfc0776a3", size = 7412536, upload-time = "2026-06-11T12:45:35.403Z" }, + { url = "https://files.pythonhosted.org/packages/e0/dd/1172a9e42b168edcafefad6115346ef619a3fc02158bb170e66ced24bcdd/grpcio-1.81.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:85b10a45b8993d195c4f3ff57025b8d1e11834909ee475c403bfa60cb4caefaf", size = 8408276, upload-time = "2026-06-11T12:45:37.78Z" }, + { url = "https://files.pythonhosted.org/packages/25/7a/71437c7f3596e5246155c515852795a85a1a8d228190212432b13b97a95d/grpcio-1.81.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:8ea1936c26b99999b27479853039a7f34713f56c49375ad52b38535ec93a796c", size = 7849660, upload-time = "2026-06-11T12:45:40.627Z" }, + { url = "https://files.pythonhosted.org/packages/65/40/7debc0da45d2efebafb82da75644be347497fe4ee250514b8cd3b86ae8bf/grpcio-1.81.1-cp312-cp312-win32.whl", hash = "sha256:a185a04039df6cae8648bc8ab6d6fde7bf94f7188ecf7828e76ac52eef1e41d6", size = 4185819, upload-time = "2026-06-11T12:45:43.027Z" }, + { url = "https://files.pythonhosted.org/packages/2e/b9/8fe3ba5ed462067774ebc1f9c7f26aa7ebcc280ddd476be107153de1339e/grpcio-1.81.1-cp312-cp312-win_amd64.whl", hash = "sha256:3ad74f8bb1a18963914c5452d289422830b39459e8776ebbcd207be1fbfb1d94", size = 4930461, upload-time = "2026-06-11T12:45:45.775Z" }, + { url = "https://files.pythonhosted.org/packages/7a/42/dcc2e4b600538ef18327c0839d56b7d3c3812337c5d710df5877dbb39b1e/grpcio-1.81.1-cp313-cp313-linux_armv7l.whl", hash = "sha256:b10e1ff4756ed27d5a29d7fc79cfce7ef1ff56ad20025b89bac7cf79e09abbbe", size = 6054466, upload-time = "2026-06-11T12:45:48.43Z" }, + { url = "https://files.pythonhosted.org/packages/7b/4a/a36e03210183a8a7d4c80c3936acee679f4bd77d5861f369db47b2cc5f05/grpcio-1.81.1-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:819edbdcb42ab8598b494bcf0222684bbb7a3c772bd1b1f0be7e029a6063c28e", size = 12048795, upload-time = "2026-06-11T12:45:54.011Z" }, + { url = "https://files.pythonhosted.org/packages/b0/d5/d68e30b29098f63beab6fe501100fe82674ff142b32c672532da86a99b3a/grpcio-1.81.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:c5bf2dc311127d91230cc79b92188c082634a06cf66c5234db49a43b910183b0", size = 6599094, upload-time = "2026-06-11T12:45:57.799Z" }, + { url = "https://files.pythonhosted.org/packages/3d/b3/e837954d279754f638a11cca5dcf6b24a005efb398984cefaf7735945a54/grpcio-1.81.1-cp313-cp313-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:e8ca6a1fcdb2943c9cbc1804a1baf3acb6071d72a471591678ded84218006e14", size = 7307182, upload-time = "2026-06-11T12:46:00.568Z" }, + { url = "https://files.pythonhosted.org/packages/0d/1e/b47957057e729adc6cdf519a47f8be2562b7140e280f1418443eb4022192/grpcio-1.81.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e64dd101d380a115cc5a0c7856788adb535f1a4e21fc543775602f8be95180ae", size = 6810962, upload-time = "2026-06-11T12:46:03.312Z" }, + { url = "https://files.pythonhosted.org/packages/40/26/569868e364e05b19ec8f969da53d230bcd89c962cd198f7c29943155c4d3/grpcio-1.81.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:98a07f9bf591e3a8919797bee1c53f026ba4acd587e5a4404c8e57c9ec36b2a5", size = 7415698, upload-time = "2026-06-11T12:46:06.005Z" }, + { url = "https://files.pythonhosted.org/packages/36/0c/5440a0582cb5653fc42a6e262eeb22700943313f8076f9dc927491b20a59/grpcio-1.81.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:c261d74b1a945cf895a9d6eccd1685a8e837531beaab782da4d630a8d12deffb", size = 8407779, upload-time = "2026-06-11T12:46:08.84Z" }, + { url = "https://files.pythonhosted.org/packages/ff/aa/66fe9f39871d766987d869a03ee0842a026f499c7b1e62decb9e78a8088e/grpcio-1.81.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:58ad1131c300d3c9b933802b3cc4dc69d380822935ba50b28703156ea826fbf7", size = 7844521, upload-time = "2026-06-11T12:46:12.171Z" }, + { url = "https://files.pythonhosted.org/packages/f0/9e/69bb7194861bcd28fb3193261d4f9c3831b4446993f002cf59068943e7ab/grpcio-1.81.1-cp313-cp313-win32.whl", hash = "sha256:78e29211f26da2fdd0e9c6d2b79f489476140cf7029b6a64808ade7ca4156a42", size = 4182786, upload-time = "2026-06-11T12:46:15.192Z" }, + { url = "https://files.pythonhosted.org/packages/0d/20/3da8bb0d637feccdc3e1e419bb511ce93651ce7d54164f95de22cc0b8b34/grpcio-1.81.1-cp313-cp313-win_amd64.whl", hash = "sha256:edb59506291b647a30884b1d51a599d605f40b20af4a7dc3d33786a47a31de60", size = 4928648, upload-time = "2026-06-11T12:46:17.823Z" }, + { url = "https://files.pythonhosted.org/packages/b6/58/19414622b1bf6981bc9c05a365bd548e71876c89000083b3af489251e9c0/grpcio-1.81.1-cp314-cp314-linux_armv7l.whl", hash = "sha256:506f48f2f9c29b143fca3dad7b0d518c188b6c9648c75a2ae6e2d9f2c13a060b", size = 6055336, upload-time = "2026-06-11T12:46:20.557Z" }, + { url = "https://files.pythonhosted.org/packages/32/f1/2ec88adb92b0eba970dd0e0e7dd086341daa3c75eba4f735f9e44bf684b0/grpcio-1.81.1-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:d865db4a6318e1c1bea83292e0ed231090538fc4ca45425b0f0480eb338bbc6e", size = 12056279, upload-time = "2026-06-11T12:46:24.255Z" }, + { url = "https://files.pythonhosted.org/packages/41/36/e8c5f8c6ec71de73733695ebc809e98b178b534ec6d8eaa31a7ebab4ad4c/grpcio-1.81.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e2aa72e3ce1770317ef534f63d397b55e130725f5149bd36077c3b539019db27", size = 6608225, upload-time = "2026-06-11T12:46:27.601Z" }, + { url = "https://files.pythonhosted.org/packages/30/22/96fc577a845ab093326d9ab1adb874bd4936c8cf98ac8ed2f3db13a0a2fb/grpcio-1.81.1-cp314-cp314-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:0490c30c261eded63f3f354979f9dc4502a9fb944cccb60cd9dc85f5a7349854", size = 7306576, upload-time = "2026-06-11T12:46:30.514Z" }, + { url = "https://files.pythonhosted.org/packages/76/7b/61dab5d5969f28d97fb1009cead1df0a5cd987d3315e1b37f18a4449f8bc/grpcio-1.81.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:410482da976329fe5f4067270401b12cf2bd552ff8020f054ecfaddb5475f9d6", size = 6812165, upload-time = "2026-06-11T12:46:33.699Z" }, + { url = "https://files.pythonhosted.org/packages/82/78/6e501929d4f5f96462fd82fd9f0f06e5f9612207582b862868d68757b27d/grpcio-1.81.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e3657301562ac3cb8018d30d0d3ebfa39932239f7b5703422057ef14b69949f5", size = 7422962, upload-time = "2026-06-11T12:46:36.511Z" }, + { url = "https://files.pythonhosted.org/packages/2a/7e/f2157589e66daa78ebb3165942d05a08bdea93b9d11c2bc1e172aef89685/grpcio-1.81.1-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:24c8e57504c8f45b237e40b99262d181071e5099a07053695b75d97bb53053a0", size = 8408176, upload-time = "2026-06-11T12:46:39.803Z" }, + { url = "https://files.pythonhosted.org/packages/da/df/c6717fef716e00d235ffb96123baf6dce76d6004f6233fa767c502861460/grpcio-1.81.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:b427c19380991a4eaab2f6144b64b99b412043314c6bf4ab544f97bb31ee4190", size = 7846681, upload-time = "2026-06-11T12:46:43.013Z" }, + { url = "https://files.pythonhosted.org/packages/36/84/3502e9f210a6a5c4438c8aca3f88edd2e04f6a27f3d41b26cf0a0024b096/grpcio-1.81.1-cp314-cp314-win32.whl", hash = "sha256:61233fe8951e5c85dff81c2458b6528624760166946b5b47ea150a589168411f", size = 4264615, upload-time = "2026-06-11T12:46:45.741Z" }, + { url = "https://files.pythonhosted.org/packages/ff/b0/4af731ff7492c68a96e4c71bfd0f4590acde92b31c6fe4894e6465c10ff6/grpcio-1.81.1-cp314-cp314-win_amd64.whl", hash = "sha256:3768a5ff1b2125e6f552e561b6b2dca0e64982d8949689b4df145cf8b98d7821", size = 5070275, upload-time = "2026-06-11T12:46:48.486Z" }, ] [[package]] name = "h11" version = "0.16.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1", size = 101250 } +sdist = { url = "https://files.pythonhosted.org/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1", size = 101250, upload-time = "2025-04-24T03:35:25.427Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515 }, + { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" }, ] [[package]] @@ -1468,9 +1073,9 @@ name = "h5py" version = "3.16.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/db/33/acd0ce6863b6c0d7735007df01815403f5589a21ff8c2e1ee2587a38f548/h5py-3.16.0.tar.gz", hash = "sha256:a0dbaad796840ccaa67a4c144a0d0c8080073c34c76d5a6941d6818678ef2738", size = 446526, upload-time = "2026-03-06T13:49:08.07Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/ba/95/a825894f3e45cbac7554c4e97314ce886b233a20033787eda755ca8fecc7/h5py-3.16.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:719439d14b83f74eeb080e9650a6c7aa6d0d9ea0ca7f804347b05fac6fbf18af", size = 3721663, upload-time = "2026-03-06T13:47:49.599Z" }, @@ -1513,58 +1118,23 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/41/8c/bbe98f813722b4873818a8db3e15aa3e625b59278566905ac439725e8070/h5py-3.16.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:346df559a0f7dcb31cf8e44805319e2ab24b8957c45e7708ce503b2ec79ba725", size = 5300253, upload-time = "2026-03-06T13:49:02.033Z" }, { url = "https://files.pythonhosted.org/packages/32/9e/87e6705b4d6890e7cecdf876e2a7d3e40654a2ae37482d79a6f1b87f7b92/h5py-3.16.0-cp314-cp314t-win_amd64.whl", hash = "sha256:4c6ab014ab704b4feaa719ae783b86522ed0bf1f82184704ed3c9e4e3228796e", size = 3381671, upload-time = "2026-03-06T13:49:04.351Z" }, { url = "https://files.pythonhosted.org/packages/96/91/9fad90cfc5f9b2489c7c26ad897157bce82f0e9534a986a221b99760b23b/h5py-3.16.0-cp314-cp314t-win_arm64.whl", hash = "sha256:faca8fb4e4319c09d83337adc80b2ca7d5c5a343c2d6f1b6388f32cfecca13c1", size = 2740706, upload-time = "2026-03-06T13:49:06.347Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/4d/6a/0d79de0b025aa85dc8864de8e97659c94cf3d23148394a954dc5ca52f8c8/h5py-3.15.1.tar.gz", hash = "sha256:c86e3ed45c4473564de55aa83b6fc9e5ead86578773dfbd93047380042e26b69", size = 426236 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/41/fd/8349b48b15b47768042cff06ad6e1c229f0a4bd89225bf6b6894fea27e6d/h5py-3.15.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:5aaa330bcbf2830150c50897ea5dcbed30b5b6d56897289846ac5b9e529ec243", size = 3434135 }, - { url = "https://files.pythonhosted.org/packages/c1/b0/1c628e26a0b95858f54aba17e1599e7f6cd241727596cc2580b72cb0a9bf/h5py-3.15.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c970fb80001fffabb0109eaf95116c8e7c0d3ca2de854e0901e8a04c1f098509", size = 2870958 }, - { url = "https://files.pythonhosted.org/packages/f9/e3/c255cafc9b85e6ea04e2ad1bba1416baa1d7f57fc98a214be1144087690c/h5py-3.15.1-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:80e5bb5b9508d5d9da09f81fd00abbb3f85da8143e56b1585d59bc8ceb1dba8b", size = 4504770 }, - { url = "https://files.pythonhosted.org/packages/8b/23/4ab1108e87851ccc69694b03b817d92e142966a6c4abd99e17db77f2c066/h5py-3.15.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5b849ba619a066196169763c33f9f0f02e381156d61c03e000bb0100f9950faf", size = 4700329 }, - { url = "https://files.pythonhosted.org/packages/a4/e4/932a3a8516e4e475b90969bf250b1924dbe3612a02b897e426613aed68f4/h5py-3.15.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:e7f6c841efd4e6e5b7e82222eaf90819927b6d256ab0f3aca29675601f654f3c", size = 4152456 }, - { url = "https://files.pythonhosted.org/packages/2a/0a/f74d589883b13737021b2049ac796328f188dbb60c2ed35b101f5b95a3fc/h5py-3.15.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:ca8a3a22458956ee7b40d8e39c9a9dc01f82933e4c030c964f8b875592f4d831", size = 4617295 }, - { url = "https://files.pythonhosted.org/packages/23/95/499b4e56452ef8b6c95a271af0dde08dac4ddb70515a75f346d4f400579b/h5py-3.15.1-cp311-cp311-win_amd64.whl", hash = "sha256:550e51131376889656feec4aff2170efc054a7fe79eb1da3bb92e1625d1ac878", size = 2882129 }, - { url = "https://files.pythonhosted.org/packages/ce/bb/cfcc70b8a42222ba3ad4478bcef1791181ea908e2adbd7d53c66395edad5/h5py-3.15.1-cp311-cp311-win_arm64.whl", hash = "sha256:b39239947cb36a819147fc19e86b618dcb0953d1cd969f5ed71fc0de60392427", size = 2477121 }, - { url = "https://files.pythonhosted.org/packages/62/b8/c0d9aa013ecfa8b7057946c080c0c07f6fa41e231d2e9bd306a2f8110bdc/h5py-3.15.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:316dd0f119734f324ca7ed10b5627a2de4ea42cc4dfbcedbee026aaa361c238c", size = 3399089 }, - { url = "https://files.pythonhosted.org/packages/a4/5e/3c6f6e0430813c7aefe784d00c6711166f46225f5d229546eb53032c3707/h5py-3.15.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b51469890e58e85d5242e43aab29f5e9c7e526b951caab354f3ded4ac88e7b76", size = 2847803 }, - { url = "https://files.pythonhosted.org/packages/00/69/ba36273b888a4a48d78f9268d2aee05787e4438557450a8442946ab8f3ec/h5py-3.15.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8a33bfd5dfcea037196f7778534b1ff7e36a7f40a89e648c8f2967292eb6898e", size = 4914884 }, - { url = "https://files.pythonhosted.org/packages/3a/30/d1c94066343a98bb2cea40120873193a4fed68c4ad7f8935c11caf74c681/h5py-3.15.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:25c8843fec43b2cc368aa15afa1cdf83fc5e17b1c4e10cd3771ef6c39b72e5ce", size = 5109965 }, - { url = "https://files.pythonhosted.org/packages/81/3d/d28172116eafc3bc9f5991b3cb3fd2c8a95f5984f50880adfdf991de9087/h5py-3.15.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a308fd8681a864c04423c0324527237a0484e2611e3441f8089fd00ed56a8171", size = 4561870 }, - { url = "https://files.pythonhosted.org/packages/a5/83/393a7226024238b0f51965a7156004eaae1fcf84aa4bfecf7e582676271b/h5py-3.15.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f4a016df3f4a8a14d573b496e4d1964deb380e26031fc85fb40e417e9131888a", size = 5037161 }, - { url = "https://files.pythonhosted.org/packages/cf/51/329e7436bf87ca6b0fe06dd0a3795c34bebe4ed8d6c44450a20565d57832/h5py-3.15.1-cp312-cp312-win_amd64.whl", hash = "sha256:59b25cf02411bf12e14f803fef0b80886444c7fe21a5ad17c6a28d3f08098a1e", size = 2874165 }, - { url = "https://files.pythonhosted.org/packages/09/a8/2d02b10a66747c54446e932171dd89b8b4126c0111b440e6bc05a7c852ec/h5py-3.15.1-cp312-cp312-win_arm64.whl", hash = "sha256:61d5a58a9851e01ee61c932bbbb1c98fe20aba0a5674776600fb9a361c0aa652", size = 2458214 }, - { url = "https://files.pythonhosted.org/packages/88/b3/40207e0192415cbff7ea1d37b9f24b33f6d38a5a2f5d18a678de78f967ae/h5py-3.15.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c8440fd8bee9500c235ecb7aa1917a0389a2adb80c209fa1cc485bd70e0d94a5", size = 3376511 }, - { url = "https://files.pythonhosted.org/packages/31/96/ba99a003c763998035b0de4c299598125df5fc6c9ccf834f152ddd60e0fb/h5py-3.15.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:ab2219dbc6fcdb6932f76b548e2b16f34a1f52b7666e998157a4dfc02e2c4123", size = 2826143 }, - { url = "https://files.pythonhosted.org/packages/6a/c2/fc6375d07ea3962df7afad7d863fe4bde18bb88530678c20d4c90c18de1d/h5py-3.15.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d8cb02c3a96255149ed3ac811eeea25b655d959c6dd5ce702c9a95ff11859eb5", size = 4908316 }, - { url = "https://files.pythonhosted.org/packages/d9/69/4402ea66272dacc10b298cca18ed73e1c0791ff2ae9ed218d3859f9698ac/h5py-3.15.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:121b2b7a4c1915d63737483b7bff14ef253020f617c2fb2811f67a4bed9ac5e8", size = 5103710 }, - { url = "https://files.pythonhosted.org/packages/e0/f6/11f1e2432d57d71322c02a97a5567829a75f223a8c821764a0e71a65cde8/h5py-3.15.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:59b0d63b318bf3cc06687def2b45afd75926bbc006f7b8cd2b1a231299fc8599", size = 4556042 }, - { url = "https://files.pythonhosted.org/packages/18/88/3eda3ef16bfe7a7dbc3d8d6836bbaa7986feb5ff091395e140dc13927bcc/h5py-3.15.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e02fe77a03f652500d8bff288cbf3675f742fc0411f5a628fa37116507dc7cc0", size = 5030639 }, - { url = "https://files.pythonhosted.org/packages/e5/ea/fbb258a98863f99befb10ed727152b4ae659f322e1d9c0576f8a62754e81/h5py-3.15.1-cp313-cp313-win_amd64.whl", hash = "sha256:dea78b092fd80a083563ed79a3171258d4a4d307492e7cf8b2313d464c82ba52", size = 2864363 }, - { url = "https://files.pythonhosted.org/packages/5d/c9/35021cc9cd2b2915a7da3026e3d77a05bed1144a414ff840953b33937fb9/h5py-3.15.1-cp313-cp313-win_arm64.whl", hash = "sha256:c256254a8a81e2bddc0d376e23e2a6d2dc8a1e8a2261835ed8c1281a0744cd97", size = 2449570 }, - { url = "https://files.pythonhosted.org/packages/a0/2c/926eba1514e4d2e47d0e9eb16c784e717d8b066398ccfca9b283917b1bfb/h5py-3.15.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:5f4fb0567eb8517c3ecd6b3c02c4f4e9da220c8932604960fd04e24ee1254763", size = 3380368 }, - { url = "https://files.pythonhosted.org/packages/65/4b/d715ed454d3baa5f6ae1d30b7eca4c7a1c1084f6a2edead9e801a1541d62/h5py-3.15.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:954e480433e82d3872503104f9b285d369048c3a788b2b1a00e53d1c47c98dd2", size = 2833793 }, - { url = "https://files.pythonhosted.org/packages/ef/d4/ef386c28e4579314610a8bffebbee3b69295b0237bc967340b7c653c6c10/h5py-3.15.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fd125c131889ebbef0849f4a0e29cf363b48aba42f228d08b4079913b576bb3a", size = 4903199 }, - { url = "https://files.pythonhosted.org/packages/33/5d/65c619e195e0b5e54ea5a95c1bb600c8ff8715e0d09676e4cce56d89f492/h5py-3.15.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:28a20e1a4082a479b3d7db2169f3a5034af010b90842e75ebbf2e9e49eb4183e", size = 5097224 }, - { url = "https://files.pythonhosted.org/packages/30/30/5273218400bf2da01609e1292f562c94b461fcb73c7a9e27fdadd43abc0a/h5py-3.15.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fa8df5267f545b4946df8ca0d93d23382191018e4cda2deda4c2cedf9a010e13", size = 4551207 }, - { url = "https://files.pythonhosted.org/packages/d3/39/a7ef948ddf4d1c556b0b2b9559534777bccc318543b3f5a1efdf6b556c9c/h5py-3.15.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:99d374a21f7321a4c6ab327c4ab23bd925ad69821aeb53a1e75dd809d19f67fa", size = 5025426 }, - { url = "https://files.pythonhosted.org/packages/b6/d8/7368679b8df6925b8415f9dcc9ab1dab01ddc384d2b2c24aac9191bd9ceb/h5py-3.15.1-cp314-cp314-win_amd64.whl", hash = "sha256:9c73d1d7cdb97d5b17ae385153472ce118bed607e43be11e9a9deefaa54e0734", size = 2865704 }, - { url = "https://files.pythonhosted.org/packages/d3/b7/4a806f85d62c20157e62e58e03b27513dc9c55499768530acc4f4c5ce4be/h5py-3.15.1-cp314-cp314-win_arm64.whl", hash = "sha256:a6d8c5a05a76aca9a494b4c53ce8a9c29023b7f64f625c6ce1841e92a362ccdf", size = 2465544 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "hdf5plugin" -version = "6.0.0" +version = "7.0.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "h5py" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/a1/4f/9130151e3aa475b3e4e9a611bf608107fe5c72d277d74c4cf36f164b7c81/hdf5plugin-6.0.0.tar.gz", hash = "sha256:847ed9e96b451367a110f0ba64a3b260d38d64bbf3f25751858d3b56e094cfe0", size = 66372085 } +sdist = { url = "https://files.pythonhosted.org/packages/3d/64/0fc6b68e5bc671e7b81d67b930fbed3a4e8a2a92dc4af0f7282b2a2ff988/hdf5plugin-7.0.0.tar.gz", hash = "sha256:e6e6b1f8b0c4d2ca87e616ddc31d08330b36207e466357040f95269e5e0401c8", size = 68284761, upload-time = "2026-06-25T20:59:40.102Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/9c/13/15017f6210bfea843316d62f0f121e364e17bb129444ed803a256a213036/hdf5plugin-6.0.0-py3-none-macosx_10_13_universal2.whl", hash = "sha256:a59fbd5d4290a8a5334d82ccb4c6b9bfc7aaf586de7fedb88762e8601bc05fd4", size = 13339413 }, - { url = "https://files.pythonhosted.org/packages/40/bf/d1f3765fb879820d7331e30e860b684f5b78d3ec17324e8f54130cbe560b/hdf5plugin-6.0.0-py3-none-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d301f4b9295872bacf277c70628d4c5e965ee47db762d8fde2d4849f201b9897", size = 42858563 }, - { url = "https://files.pythonhosted.org/packages/0a/67/37d0b84fbbf26bf0d6a99a8f98bcd82bb6d437dc8cabee259fb3d7506ec7/hdf5plugin-6.0.0-py3-none-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:78b082ea355fe46bf5b396024de1fb662a1aaf9a5e11861ad61a5a2a6316d59d", size = 45126124 }, - { url = "https://files.pythonhosted.org/packages/ed/2f/1046d464ad1db29a4f6c70ba4e19b39baa8a6542c719eaa4e765108f07f1/hdf5plugin-6.0.0-py3-none-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:79e0524d18ddc41c0cf2e1bb2e529d4e154c286f6a1bd85f3d44019d2a17574a", size = 44857273 }, - { url = "https://files.pythonhosted.org/packages/61/b3/75478bdfee85533777de4204373f563aa7a1074355300743c3aedc33cac5/hdf5plugin-6.0.0-py3-none-win_amd64.whl", hash = "sha256:99866f90be1ceac5519e6e038669564be326c233618d59ba1f38c9dd8c32099e", size = 3379316 }, + { url = "https://files.pythonhosted.org/packages/03/5a/00d0f491d420d491b5134ee044cd8ead5103382d88f4c8aee623258a6348/hdf5plugin-7.0.0-py3-none-macosx_10_13_x86_64.whl", hash = "sha256:e0ff0a81e6319575ffc8bf230b8df43f3f19f6fe96843e113e04c5ba9f7f3141", size = 6941923, upload-time = "2026-06-25T20:59:24.517Z" }, + { url = "https://files.pythonhosted.org/packages/96/ad/b28f4102e619c262b29bfcd13ccf6799e26f9ff113d2fdce19050ae29448/hdf5plugin-7.0.0-py3-none-macosx_11_0_arm64.whl", hash = "sha256:97ea4ff6114223c5e8ccce7e23cc6cd398b58c02f4e988e967aeea914fcb8030", size = 6259223, upload-time = "2026-06-25T20:59:26.246Z" }, + { url = "https://files.pythonhosted.org/packages/1f/f4/67263173ff61c49800eec4f16b4d84e6a39170be8d4871d4eb0d540b71ee/hdf5plugin-7.0.0-py3-none-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0f3ba9f4e2a370340b45e7042d1b1b1577ab259c1ddf00956264836fa33052ef", size = 42789778, upload-time = "2026-06-25T20:59:28.438Z" }, + { url = "https://files.pythonhosted.org/packages/75/d2/66673e2d0ef8499d08dd7061caa194e4ddec12df73ab512431c60538f675/hdf5plugin-7.0.0-py3-none-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1a4cb2207ec3ac538fc728e4596e9e49aed6c4487a641071fb370c04a361e7bf", size = 45295544, upload-time = "2026-06-25T20:59:31.765Z" }, + { url = "https://files.pythonhosted.org/packages/d7/0b/855e50e27eab8338c71c3157672c065cd2a2ab38887b3ea4bf128cbd89a1/hdf5plugin-7.0.0-py3-none-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4ad4ab0d3367699d132e61b1cc382a0c640a7dba56536e5105508205ebbe8762", size = 45012013, upload-time = "2026-06-25T20:59:35.029Z" }, + { url = "https://files.pythonhosted.org/packages/4e/14/32cf2aae083c74b95678875e11d25d0cb9e51a33d27f4218eb9aeacfcd70/hdf5plugin-7.0.0-py3-none-win_amd64.whl", hash = "sha256:2e052af8d7848e8bac92646584617503a08bb9b466cfa810a49ecd93e89b7ffa", size = 3523827, upload-time = "2026-06-25T20:59:37.758Z" }, ] [[package]] @@ -1575,9 +1145,9 @@ dependencies = [ { name = "certifi" }, { name = "h11" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/06/94/82699a10bca87a5556c9c59b5963f2d039dbd239f25bc2a63907a05a14cb/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8", size = 85484 } +sdist = { url = "https://files.pythonhosted.org/packages/06/94/82699a10bca87a5556c9c59b5963f2d039dbd239f25bc2a63907a05a14cb/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8", size = 85484, upload-time = "2025-04-24T22:06:22.219Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/7e/f5/f66802a942d491edb555dd61e3a9961140fd64c90bce1eafd741609d334d/httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55", size = 78784 }, + { url = "https://files.pythonhosted.org/packages/7e/f5/f66802a942d491edb555dd61e3a9961140fd64c90bce1eafd741609d334d/httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55", size = 78784, upload-time = "2025-04-24T22:06:20.566Z" }, ] [[package]] @@ -1590,39 +1160,27 @@ dependencies = [ { name = "httpcore" }, { name = "idna" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406 } +sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406, upload-time = "2024-12-06T15:37:23.222Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517 }, + { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517, upload-time = "2024-12-06T15:37:21.509Z" }, ] [[package]] name = "identify" version = "2.6.19" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/52/63/51723b5f116cc04b061cb6f5a561790abf249d25931d515cd375e063e0f4/identify-2.6.19.tar.gz", hash = "sha256:6be5020c38fcb07da56c53733538a3081ea5aa70d36a156f83044bfbf9173842", size = 99567, upload-time = "2026-04-17T18:39:50.265Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/94/84/d9273cd09688070a6523c4aee4663a8538721b2b755c4962aafae0011e72/identify-2.6.19-py2.py3-none-any.whl", hash = "sha256:20e6a87f786f768c092a721ad107fc9df0eb89347be9396cadf3f4abbd1fb78a", size = 99397, upload-time = "2026-04-17T18:39:49.221Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/ff/e7/685de97986c916a6d93b3876139e00eef26ad5bbbd61925d670ae8013449/identify-2.6.15.tar.gz", hash = "sha256:e4f4864b96c6557ef2a1e1c951771838f4edc9df3a72ec7118b338801b11c7bf", size = 99311 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/0f/1c/e5fd8f973d4f375adb21565739498e2e9a1e54c858a97b9a8ccfdc81da9b/identify-2.6.15-py2.py3-none-any.whl", hash = "sha256:1181ef7608e00704db228516541eb83a88a9f94433a8c80bb9b5bd54b1d81757", size = 99183 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "idna" -version = "3.17" +version = "3.18" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/b9/28/99c51f664567218d824af024c0251650fb27e4ca066df188dab0769c5b91/idna-3.17.tar.gz", hash = "sha256:5eb0cb53bc467c12eadcf6de83163ad8527cec9416f44b9b61b19caedad2b87f", size = 196048, upload-time = "2026-05-28T14:32:38.55Z" } +sdist = { url = "https://files.pythonhosted.org/packages/cd/63/9496c57188a2ee585e0f1db071d75089a11e98aa86eb99d9d7618fc1edce/idna-3.18.tar.gz", hash = "sha256:ffb385a7e039654cef1ab9ef32c6fafe283c0c0467bba1d9029738ce4a14a848", size = 196711, upload-time = "2026-06-02T14:34:07.794Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/de/a7/f76514cc40ad6234098ecdebda08732d75964776c51a42845b7da10649e2/idna-3.17-py3-none-any.whl", hash = "sha256:466e48829084efe2548012b855df21540b96f2e20e51bd124c851536556a592c", size = 65316, upload-time = "2026-05-28T14:32:37.035Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/6f/6d/0703ccc57f3a7233505399edb88de3cbd678da106337b9fcde432b65ed60/idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902", size = 194582 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea", size = 71008 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/1e/5e/d4e9f1a599fb8e573b7b87160658329fbf28d19eac2718f51fc3def3aa5a/idna-3.18-py3-none-any.whl", hash = "sha256:7f952cbe720b688055e3f87de14f5c3e5fdaa8bc3928985c4077ca689de849a2", size = 65455, upload-time = "2026-06-02T14:34:06.319Z" }, ] [[package]] @@ -1630,18 +1188,13 @@ name = "imageio" version = "2.37.3" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, { name = "pillow" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/b1/84/93bcd1300216ea50811cee96873b84a1bebf8d0489ffaf7f2a3756bab866/imageio-2.37.3.tar.gz", hash = "sha256:bbb37efbfc4c400fcd534b367b91fcd66d5da639aaa138034431a1c5e0a41451", size = 389673, upload-time = "2026-03-09T11:31:12.573Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/49/fa/391e437a34e55095173dca5f24070d89cbc233ff85bf1c29c93248c6588d/imageio-2.37.3-py3-none-any.whl", hash = "sha256:46f5bb8522cd421c0f5ae104d8268f569d856b29eb1a13b92829d1970f32c9f0", size = 317646, upload-time = "2026-03-09T11:31:10.771Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/a3/6f/606be632e37bf8d05b253e8626c2291d74c691ddc7bcdf7d6aaf33b32f6a/imageio-2.37.2.tar.gz", hash = "sha256:0212ef2727ac9caa5ca4b2c75ae89454312f440a756fcfc8ef1993e718f50f8a", size = 389600 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/fb/fe/301e0936b79bcab4cacc7548bf2853fc28dced0a578bab1f7ef53c9aa75b/imageio-2.37.2-py3-none-any.whl", hash = "sha256:ad9adfb20335d718c03de457358ed69f141021a333c40a53e57273d8a5bd0b9b", size = 317646 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1651,29 +1204,23 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "zipp", marker = "python_full_version < '3.12'" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/a9/01/15bb152d77b21318514a96f43af312635eb2500c96b55398d020c93d86ea/importlib_metadata-9.0.0.tar.gz", hash = "sha256:a4f57ab599e6a2e3016d7595cfd72eb4661a5106e787a95bcc90c7105b831efc", size = 56405, upload-time = "2026-03-20T06:42:56.999Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/38/3d/2d244233ac4f76e38533cfcb2991c9eb4c7bf688ae0a036d30725b8faafe/importlib_metadata-9.0.0-py3-none-any.whl", hash = "sha256:2d21d1cc5a017bd0559e36150c21c830ab1dc304dedd1b7ea85d20f45ef3edd7", size = 27789, upload-time = "2026-03-20T06:42:55.665Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/76/66/650a33bd90f786193e4de4b3ad86ea60b53c89b669a5c7be931fac31cdb0/importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000", size = 56641 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/20/b0/36bd937216ec521246249be3bf9855081de4c5e06a0c9b4219dbeda50373/importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd", size = 27656 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "iniconfig" version = "2.3.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503 } +sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484 }, + { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484, upload-time = "2025-10-18T21:55:41.639Z" }, ] [[package]] name = "ipykernel" -version = "7.2.0" +version = "7.3.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "appnope", marker = "sys_platform == 'darwin'" }, @@ -1683,27 +1230,21 @@ dependencies = [ { name = "jupyter-client" }, { name = "jupyter-core" }, { name = "matplotlib-inline" }, - { name = "nest-asyncio" }, + { name = "nest-asyncio2" }, { name = "packaging" }, { name = "psutil" }, { name = "pyzmq" }, { name = "tornado" }, { name = "traitlets" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/ca/8d/b68b728e2d06b9e0051019640a40a9eb7a88fcd82c2e1b5ce70bef5ff044/ipykernel-7.2.0.tar.gz", hash = "sha256:18ed160b6dee2cbb16e5f3575858bc19d8f1fe6046a9a680c708494ce31d909e", size = 176046, upload-time = "2026-02-06T16:43:27.403Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/82/b9/e73d5d9f405cba7706c539aa8b311b49d4c2f3d698d9c12f815231169c71/ipykernel-7.2.0-py3-none-any.whl", hash = "sha256:3bbd4420d2b3cc105cbdf3756bfc04500b1e52f090a90716851f3916c62e1661", size = 118788, upload-time = "2026-02-06T16:43:25.149Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/b9/a4/4948be6eb88628505b83a1f2f40d90254cab66abf2043b3c40fa07dfce0f/ipykernel-7.1.0.tar.gz", hash = "sha256:58a3fc88533d5930c3546dc7eac66c6d288acde4f801e2001e65edc5dc9cf0db", size = 174579 } +sdist = { url = "https://files.pythonhosted.org/packages/3d/c4/e4a38f579de4225a561305666f7541cdabb30075def2aa1ac17bd73c1fb5/ipykernel-7.3.0.tar.gz", hash = "sha256:9acaaaf97d16355166e4085afe9d225bfbdf2b7ef520f9df3be8f2b248275e09", size = 184899, upload-time = "2026-06-10T08:41:25.481Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/a3/17/20c2552266728ceba271967b87919664ecc0e33efca29c3efc6baf88c5f9/ipykernel-7.1.0-py3-none-any.whl", hash = "sha256:763b5ec6c5b7776f6a8d7ce09b267693b4e5ce75cb50ae696aaefb3c85e1ea4c", size = 117968 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/3d/02/77b271f5dc58bfbc0b577c877b2365d1ffea2afe66a80c13f2312820348c/ipykernel-7.3.0-py3-none-any.whl", hash = "sha256:897eb64da762549ef610698fca5e9675195ec6ac8ec7f19d81ce1ca20c876057", size = 120583, upload-time = "2026-06-10T08:41:23.648Z" }, ] [[package]] name = "ipython" -version = "9.14.0" +version = "9.15.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "colorama", marker = "sys_platform == 'win32'" }, @@ -1713,21 +1254,15 @@ dependencies = [ { name = "matplotlib-inline" }, { name = "pexpect", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, { name = "prompt-toolkit" }, - { name = "psutil", marker = "sys_platform != 'emscripten'" }, + { name = "psutil", marker = "sys_platform != 'cygwin' and sys_platform != 'emscripten'" }, { name = "pygments" }, { name = "stack-data" }, { name = "traitlets" }, { name = "typing-extensions", marker = "python_full_version < '3.12'" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/21/c2/c0064cf15d026501a1ef70e42efd9c3f818663089399aacc5e37a82901c1/ipython-9.14.0.tar.gz", hash = "sha256:6f27ff0f1d9ea050e0551f71568bc4b34d8aba579e8f111c5b4175f44ac6b4aa", size = 4432601, upload-time = "2026-05-29T15:13:24.611Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/14/a3/9e59340f02c1dc8f8c0a05b09244712b8609eb5439f9996e887e2b82f452/ipython-9.14.0-py3-none-any.whl", hash = "sha256:8fd984a3372c14b12790b084ba6b5cff5678c0cb063244a0034f06a51f20d6c2", size = 627457, upload-time = "2026-05-29T15:13:22.942Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/29/e6/48c74d54039241a456add616464ea28c6ebf782e4110d419411b83dae06f/ipython-9.7.0.tar.gz", hash = "sha256:5f6de88c905a566c6a9d6c400a8fed54a638e1f7543d17aae2551133216b1e4e", size = 4422115 } +sdist = { url = "https://files.pythonhosted.org/packages/53/59/165d3b4d75cc34add3122c4417ecb229085140ac573103c223cd01dde96f/ipython-9.15.0.tar.gz", hash = "sha256:da2819ce2aa83135257df830660b1176d986c3d2876db24df01974fa955b2756", size = 4442580, upload-time = "2026-06-26T11:03:35.913Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/05/aa/62893d6a591d337aa59dcc4c6f6c842f1fe20cd72c8c5c1f980255243252/ipython-9.7.0-py3-none-any.whl", hash = "sha256:bce8ac85eb9521adc94e1845b4c03d88365fd6ac2f4908ec4ed1eb1b0a065f9f", size = 618911 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/40/3a/948263ca3b9d65bb2b1b0c521b3a49fad5d59ada58724bd87d2bd5ff3f36/ipython-9.15.0-py3-none-any.whl", hash = "sha256:515ad9c3cdf0c932a5a9f6245419e8aba706b7bd03c3e1d3a1c83d9351d6aa6e", size = 630895, upload-time = "2026-06-26T11:03:33.809Z" }, ] [[package]] @@ -1737,9 +1272,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "pygments" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/ef/4c/5dd1d8af08107f88c7f741ead7a40854b8ac24ddf9ae850afbcf698aa552/ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81", size = 8393 } +sdist = { url = "https://files.pythonhosted.org/packages/ef/4c/5dd1d8af08107f88c7f741ead7a40854b8ac24ddf9ae850afbcf698aa552/ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81", size = 8393, upload-time = "2025-01-17T11:24:34.505Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/d9/33/1f075bf72b0b747cb3288d011319aaf64083cf2efef8354174e3ed4540e2/ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c", size = 8074 }, + { url = "https://files.pythonhosted.org/packages/d9/33/1f075bf72b0b747cb3288d011319aaf64083cf2efef8354174e3ed4540e2/ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c", size = 8074, upload-time = "2025-01-17T11:24:33.271Z" }, ] [[package]] @@ -1765,9 +1300,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "arrow" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/7c/1a/3c8edc664e06e6bd06cce40c6b22da5f1429aa4224d0c590f3be21c91ead/isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9", size = 11649 } +sdist = { url = "https://files.pythonhosted.org/packages/7c/1a/3c8edc664e06e6bd06cce40c6b22da5f1429aa4224d0c590f3be21c91ead/isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9", size = 11649, upload-time = "2020-11-01T11:00:00.312Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/7b/55/e5326141505c5d5e34c5e0935d2908a74e4561eca44108fbfb9c13d2911a/isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042", size = 11321 }, + { url = "https://files.pythonhosted.org/packages/7b/55/e5326141505c5d5e34c5e0935d2908a74e4561eca44108fbfb9c13d2911a/isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042", size = 11321, upload-time = "2020-11-01T10:59:58.02Z" }, ] [[package]] @@ -1777,15 +1312,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "parso" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/46/b7/a3635f6a2d7cf5b5dd98064fc1d5fbbafcb25477bcea204a3a92145d158b/jedi-0.20.0.tar.gz", hash = "sha256:c3f4ccbd276696f4b19c54618d4fb18f9fc24b0aef02acf704b23f487daa1011", size = 3119416, upload-time = "2026-05-01T23:38:47.814Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/9a/93/242e2eab5fe682ffcb8b0084bde703a41d51e17ee0f3a31ff0d9d813620a/jedi-0.20.0-py2.py3-none-any.whl", hash = "sha256:7bdd9c2634f56713299976f4cbd59cb3fa92165cc5e05ea811fb253480728b67", size = 4884812, upload-time = "2026-05-01T23:38:43.919Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/72/3a/79a912fbd4d8dd6fbb02bf69afd3bb72cf0c729bb3063c6f4498603db17a/jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0", size = 1231287 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9", size = 1572278 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1795,39 +1324,27 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "markupsafe" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115 } +sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115, upload-time = "2025-03-05T20:05:02.478Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899 }, + { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" }, ] [[package]] name = "json5" -version = "0.14.0" +version = "0.15.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/9c/4b/6f8906aaf67d501e259b0adab4d312945bb7211e8b8d4dcc77c92320edaa/json5-0.14.0.tar.gz", hash = "sha256:b3f492fad9f6cdbced8b7d40b28b9b1c9701c5f561bef0d33b81c2ff433fefcb", size = 52656, upload-time = "2026-03-27T22:50:48.108Z" } +sdist = { url = "https://files.pythonhosted.org/packages/e4/7d/05c46a96a78147ae3bf99c2f4169ce144a70220b8d6fcd56f6ec368b8ce9/json5-0.15.0.tar.gz", hash = "sha256:7424d1f1eb1d56da6e3d70643f53619862b4ce81440bdb8ecfd6f875e5ba4a71", size = 53278, upload-time = "2026-06-19T20:08:27.716Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/b8/42/cf027b4ac873b076189d935b135397675dac80cb29acb13e1ab86ad6c631/json5-0.14.0-py3-none-any.whl", hash = "sha256:56cf861bab076b1178eb8c92e1311d273a9b9acea2ccc82c276abf839ebaef3a", size = 36271, upload-time = "2026-03-27T22:50:47.073Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/12/ae/929aee9619e9eba9015207a9d2c1c54db18311da7eb4dcf6d41ad6f0eb67/json5-0.12.1.tar.gz", hash = "sha256:b2743e77b3242f8d03c143dd975a6ec7c52e2f2afe76ed934e53503dd4ad4990", size = 52191 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/85/e2/05328bd2621be49a6fed9e3030b1e51a2d04537d3f816d211b9cc53c5262/json5-0.12.1-py3-none-any.whl", hash = "sha256:d9c9b3bc34a5f54d43c35e11ef7cb87d8bdd098c6ace87117a7b7e83e705c1d5", size = 36119 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/eb/be/59527c99478aade6bb33a68d72e6e18dd4e6ff6eacfc7d01bdb15bc76912/json5-0.15.0-py3-none-any.whl", hash = "sha256:56636a30c0e8a4665fe2179c0212f32eae3796dea89ea6f649b9436ecdb39618", size = 36570, upload-time = "2026-06-19T20:08:26.748Z" }, ] [[package]] name = "jsonpointer" version = "3.1.1" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/18/c7/af399a2e7a67fd18d63c40c5e62d3af4e67b836a2107468b6a5ea24c4304/jsonpointer-3.1.1.tar.gz", hash = "sha256:0b801c7db33a904024f6004d526dcc53bbb8a4a0f4e32bfd10beadf60adf1900", size = 9068, upload-time = "2026-03-23T22:32:32.458Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/9e/6a/a83720e953b1682d2d109d3c2dbb0bc9bf28cc1cbc205be4ef4be5da709d/jsonpointer-3.1.1-py3-none-any.whl", hash = "sha256:8ff8b95779d071ba472cf5bc913028df06031797532f08a7d5b602d8b2a488ca", size = 7659, upload-time = "2026-03-23T22:32:31.568Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/6a/0a/eebeb1fa92507ea94016a2a790b93c2ae41a7e18778f85471dc54475ed25/jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef", size = 9114 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/71/92/5e77f98553e9e75130c78900d000368476aed74276eb8ae8796f65f00918/jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942", size = 7595 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1840,15 +1357,9 @@ dependencies = [ { name = "referencing" }, { name = "rpds-py" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/b3/fc/e067678238fa451312d4c62bf6e6cf5ec56375422aee02f9cb5f909b3047/jsonschema-4.26.0.tar.gz", hash = "sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326", size = 366583, upload-time = "2026-01-07T13:41:07.246Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/69/90/f63fb5873511e014207a475e2bb4e8b2e570d655b00ac19a9a0ca0a385ee/jsonschema-4.26.0-py3-none-any.whl", hash = "sha256:d489f15263b8d200f8387e64b4c3a75f06629559fb73deb8fdfb525f2dab50ce", size = 90630, upload-time = "2026-01-07T13:41:05.306Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/74/69/f7185de793a29082a9f3c7728268ffb31cb5095131a9c139a74078e27336/jsonschema-4.25.1.tar.gz", hash = "sha256:e4a9655ce0da0c0b67a085847e00a3a51449e1157f4f75e9fb5aa545e122eb85", size = 357342 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/bf/9c/8c95d856233c1f82500c2450b8c68576b4cf1c871db3afac5c34ff84e6fd/jsonschema-4.25.1-py3-none-any.whl", hash = "sha256:3fba0169e345c7175110351d456342c364814cfcf3b964ba4587f22915230a63", size = 90040 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [package.optional-dependencies] @@ -1871,14 +1382,27 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "referencing" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/19/74/a633ee74eb36c44aa6d1095e7cc5569bebf04342ee146178e2d36600708b/jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d", size = 32855 } +sdist = { url = "https://files.pythonhosted.org/packages/19/74/a633ee74eb36c44aa6d1095e7cc5569bebf04342ee146178e2d36600708b/jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d", size = 32855, upload-time = "2025-09-08T01:34:59.186Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/41/45/1a4ed80516f02155c51f51e8cedb3c1902296743db0bbc66608a0db2814f/jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe", size = 18437 }, + { url = "https://files.pythonhosted.org/packages/41/45/1a4ed80516f02155c51f51e8cedb3c1902296743db0bbc66608a0db2814f/jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe", size = 18437, upload-time = "2025-09-08T01:34:57.871Z" }, +] + +[[package]] +name = "jupyter-builder" +version = "1.0.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-core" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/fb/45/d0df8b43c10a61529c0f4a8af5e19ebe108f0c3af8f57e0fc358969907af/jupyter_builder-1.0.2.tar.gz", hash = "sha256:6155d78a5325010532a6419ffcba89eac643fd1aa56ea83115e661924d6f6aab", size = 968638, upload-time = "2026-06-12T02:33:25.767Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/28/b6/c418e0b3256f67c04933566b80bfce947350682db92c4b786a8653db32d6/jupyter_builder-1.0.2-py3-none-any.whl", hash = "sha256:b024f65d36e1d530542db597b00dd513261aa59842e0d0fbbb1015a9f1935e9c", size = 910789, upload-time = "2026-06-12T02:33:23.317Z" }, ] [[package]] name = "jupyter-client" -version = "8.8.0" +version = "8.9.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "jupyter-core" }, @@ -1886,16 +1410,11 @@ dependencies = [ { name = "pyzmq" }, { name = "tornado" }, { name = "traitlets" }, + { name = "typing-extensions" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/05/e4/ba649102a3bc3fbca54e7239fb924fd434c766f855693d86de0b1f2bec81/jupyter_client-8.8.0.tar.gz", hash = "sha256:d556811419a4f2d96c869af34e854e3f059b7cc2d6d01a9cd9c85c267691be3e", size = 348020, upload-time = "2026-01-08T13:55:47.938Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/2d/0b/ceb7694d864abc0a047649aec263878acb9f792e1fec3e676f22dc9015e3/jupyter_client-8.8.0-py3-none-any.whl", hash = "sha256:f93a5b99c5e23a507b773d3a1136bd6e16c67883ccdbd9a829b0bbdb98cd7d7a", size = 107371, upload-time = "2026-01-08T13:55:45.562Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/71/22/bf9f12fdaeae18019a468b68952a60fe6dbab5d67cd2a103cac7659b41ca/jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419", size = 342019 } +sdist = { url = "https://files.pythonhosted.org/packages/7d/dc/5512503b088997c2250b8bf18258fba9d9ce5ead641183700960d3c9d342/jupyter_client-8.9.1.tar.gz", hash = "sha256:a58f730dd9e728ba16ba1d62ebccf7ffe1ebbdbce4e95cfae941b7321ae1f4fa", size = 359256, upload-time = "2026-06-09T13:15:01.033Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/11/85/b0394e0b6fcccd2c1eeefc230978a6f8cb0c5df1e4cd3e7625735a0d7d1e/jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f", size = 106105 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/3f/6f/56d39bf385c5c27988aebaf0c18a2a17e960575740100973511018bd904e/jupyter_client-8.9.1-py3-none-any.whl", hash = "sha256:0b7a295bc46e8751e9adae84781f726c851c1d911bd793edc4a3bde942e3da81", size = 109828, upload-time = "2026-06-09T13:14:58.835Z" }, ] [[package]] @@ -1906,9 +1425,9 @@ dependencies = [ { name = "platformdirs" }, { name = "traitlets" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/02/49/9d1284d0dc65e2c757b74c6687b6d319b02f822ad039e5c512df9194d9dd/jupyter_core-5.9.1.tar.gz", hash = "sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508", size = 89814 } +sdist = { url = "https://files.pythonhosted.org/packages/02/49/9d1284d0dc65e2c757b74c6687b6d319b02f822ad039e5c512df9194d9dd/jupyter_core-5.9.1.tar.gz", hash = "sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508", size = 89814, upload-time = "2025-10-16T19:19:18.444Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/e7/e7/80988e32bf6f73919a113473a604f5a8f09094de312b9d52b79c2df7612b/jupyter_core-5.9.1-py3-none-any.whl", hash = "sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407", size = 29032 }, + { url = "https://files.pythonhosted.org/packages/e7/e7/80988e32bf6f73919a113473a604f5a8f09094de312b9d52b79c2df7612b/jupyter_core-5.9.1-py3-none-any.whl", hash = "sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407", size = 29032, upload-time = "2025-10-16T19:19:16.783Z" }, ] [[package]] @@ -1925,15 +1444,9 @@ dependencies = [ { name = "rfc3986-validator" }, { name = "traitlets" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/18/f8/475c4241b2b75af0deaae453ed003c6c851766dbc44d332d8baf245dc931/jupyter_events-0.12.1.tar.gz", hash = "sha256:faff25f77218335752f35f23c5fe6e4a392a7bd99a5939ccb9b8fbf594636cf3", size = 62854, upload-time = "2026-04-20T23:17:50.66Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/eb/6c/6fcde0c8f616ed360ffd3587f7db9e225a7e62b583a04494d2f069cf64ea/jupyter_events-0.12.1-py3-none-any.whl", hash = "sha256:c366585253f537a627da52fa7ca7410c5b5301fe893f511e7b077c2d93ec8bcf", size = 19512, upload-time = "2026-04-20T23:17:48.927Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/9d/c3/306d090461e4cf3cd91eceaff84bede12a8e52cd821c2d20c9a4fd728385/jupyter_events-0.12.0.tar.gz", hash = "sha256:fc3fce98865f6784c9cd0a56a20644fc6098f21c8c33834a8d9fe383c17e554b", size = 62196 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e2/48/577993f1f99c552f18a0428731a755e06171f9902fa118c379eb7c04ea22/jupyter_events-0.12.0-py3-none-any.whl", hash = "sha256:6464b2fa5ad10451c3d35fabc75eab39556ae1e2853ad0c0cc31b656731a97fb", size = 19430 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -1943,20 +1456,14 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "jupyter-server" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/36/ff/1e4a61f5170a9a1d978f3ac3872449de6c01fc71eaf89657824c878b1549/jupyter_lsp-2.3.1.tar.gz", hash = "sha256:fdf8a4aa7d85813976d6e29e95e6a2c8f752701f926f2715305249a3829805a6", size = 55677, upload-time = "2026-04-02T08:10:06.749Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/23/e8/9d61dcbd1dce8ef418f06befd4ac084b4720429c26b0b1222bc218685eff/jupyter_lsp-2.3.1-py3-none-any.whl", hash = "sha256:71b954d834e85ff3096400554f2eefaf7fe37053036f9a782b0f7c5e42dadb81", size = 77513, upload-time = "2026-04-02T08:10:01.753Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/eb/5a/9066c9f8e94ee517133cd98dba393459a16cd48bba71a82f16a65415206c/jupyter_lsp-2.3.0.tar.gz", hash = "sha256:458aa59339dc868fb784d73364f17dbce8836e906cd75fd471a325cba02e0245", size = 54823 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/1a/60/1f6cee0c46263de1173894f0fafcb3475ded276c472c14d25e0280c18d6d/jupyter_lsp-2.3.0-py3-none-any.whl", hash = "sha256:e914a3cb2addf48b1c7710914771aaf1819d46b2e5a79b0f917b5478ec93f34f", size = 76687 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "jupyter-server" -version = "2.19.0" +version = "2.20.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -1979,15 +1486,9 @@ dependencies = [ { name = "traitlets" }, { name = "websocket-client" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/76/a0/eb3c511f54df7b54ca5fc7bff3f4d2277d69052d6a7f521643dfed5279d6/jupyter_server-2.19.0.tar.gz", hash = "sha256:1731236bc32b680223e1ceb9d68209a845203475012ef68773a81434b46a31a7", size = 754561, upload-time = "2026-05-29T11:21:26.057Z" } +sdist = { url = "https://files.pythonhosted.org/packages/6b/dc/db3a582633170186f8c8b31298d7eb26ad0eb031a1f53476c258b64eed05/jupyter_server-2.20.0.tar.gz", hash = "sha256:b5778ba337d8015a3dc2b80803ecdd5ac18d3797fddf61a50ea5fb472b4ebe14", size = 756523, upload-time = "2026-06-17T12:09:09.435Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/c1/78/d2881e68894cecdcd05912a9c585cfb776ef1fb38b62c8dba98f12ab3adc/jupyter_server-2.19.0-py3-none-any.whl", hash = "sha256:cb76591b76d7093584c2ad2ae72ac3d58614a4b597507a1bb04e1f9f683cf9ea", size = 392244, upload-time = "2026-05-29T11:21:23.871Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/5b/ac/e040ec363d7b6b1f11304cc9f209dac4517ece5d5e01821366b924a64a50/jupyter_server-2.17.0.tar.gz", hash = "sha256:c38ea898566964c888b4772ae1ed58eca84592e88251d2cfc4d171f81f7e99d5", size = 731949 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/92/80/a24767e6ca280f5a49525d987bf3e4d7552bf67c8be07e8ccf20271f8568/jupyter_server-2.17.0-py3-none-any.whl", hash = "sha256:e8cb9c7db4251f51ed307e329b81b72ccf2056ff82d50524debde1ee1870e13f", size = 388221 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/f3/71/8c002223e873a870f5c41dc69b0a7c922301123e4a31d5d01ecb700aef77/jupyter_server-2.20.0-py3-none-any.whl", hash = "sha256:c3b67c93c471e947c18b5026f04f21614218adb706df8f48227d3ee8e0a7cdcc", size = 393143, upload-time = "2026-06-17T12:09:07.234Z" }, ] [[package]] @@ -1998,54 +1499,42 @@ dependencies = [ { name = "pywinpty", marker = "os_name == 'nt'" }, { name = "terminado" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/f4/a7/bcd0a9b0cbba88986fe944aaaf91bfda603e5a50bda8ed15123f381a3b2f/jupyter_server_terminals-0.5.4.tar.gz", hash = "sha256:bbda128ed41d0be9020349f9f1f2a4ab9952a73ed5f5ac9f1419794761fb87f5", size = 31770, upload-time = "2026-01-14T16:53:20.213Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/d1/2d/6674563f71c6320841fc300911a55143925112a72a883e2ca71fba4c618d/jupyter_server_terminals-0.5.4-py3-none-any.whl", hash = "sha256:55be353fc74a80bc7f3b20e6be50a55a61cd525626f578dcb66a5708e2007d14", size = 13704, upload-time = "2026-01-14T16:53:18.738Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/fc/d5/562469734f476159e99a55426d697cbf8e7eb5efe89fb0e0b4f83a3d3459/jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269", size = 31430 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/07/2d/2b32cdbe8d2a602f697a649798554e4f072115438e92249624e532e8aca6/jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa", size = 13656 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "jupyterlab" -version = "4.5.7" +version = "4.6.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "async-lru" }, { name = "httpx" }, { name = "ipykernel" }, { name = "jinja2" }, + { name = "jupyter-builder" }, { name = "jupyter-core" }, { name = "jupyter-lsp" }, { name = "jupyter-server" }, { name = "jupyterlab-server" }, { name = "notebook-shim" }, { name = "packaging" }, - { name = "setuptools" }, { name = "tornado" }, { name = "traitlets" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/2b/22/8440ec827762146e7cdecf04335bd348795899d29dc6ae82238707353a2c/jupyterlab-4.5.7.tar.gz", hash = "sha256:55a9822c4754da305f41e113452c68383e214dcf96de760146af89ce5d5117b0", size = 23992763, upload-time = "2026-04-29T16:43:51.328Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/3d/aa/537b8f7d80e799af19af35fb3ddfc970b951088a13c57dd9387dcfbb7f61/jupyterlab-4.5.7-py3-none-any.whl", hash = "sha256:fba4cb0e2c44a52859669d8c98b45de029d5e515f8407bf8534d2a8fc5f0964d", size = 12450123, upload-time = "2026-04-29T16:43:46.639Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/df/e5/4fa382a796a6d8e2cd867816b64f1ff27f906e43a7a83ad9eb389e448cd8/jupyterlab-4.5.0.tar.gz", hash = "sha256:aec33d6d8f1225b495ee2cf20f0514f45e6df8e360bdd7ac9bace0b7ac5177ea", size = 23989880 } +sdist = { url = "https://files.pythonhosted.org/packages/f8/3c/1ebd737b860cdbe61eed71536d06aab4e1781fbdcf07ecd5a1afe05b8adf/jupyterlab-4.6.0.tar.gz", hash = "sha256:6a8b88f2aae7ed4d012c634fc957c1a27f3aa217c32f0ced0175fac9ee17f9e5", size = 28181861, upload-time = "2026-06-18T13:52:56.039Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/6c/1e/5a4d5498eba382fee667ed797cf64ae5d1b13b04356df62f067f48bb0f61/jupyterlab-4.5.0-py3-none-any.whl", hash = "sha256:88e157c75c1afff64c7dc4b801ec471450b922a4eae4305211ddd40da8201c8a", size = 12380641 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/0a/eb/aa48075d0aa3d0188db34ba2704f53791757743c0bb02e18c4eef989b6de/jupyterlab-4.6.0-py3-none-any.whl", hash = "sha256:b6938cb8a1ef3d43860ff4745a680c62cc0a9385f9672295bb56cd2e7cfeebe2", size = 17143447, upload-time = "2026-06-18T13:52:51.42Z" }, ] [[package]] name = "jupyterlab-pygments" version = "0.3.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/90/51/9187be60d989df97f5f0aba133fa54e7300f17616e065d1ada7d7646b6d6/jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d", size = 512900 } +sdist = { url = "https://files.pythonhosted.org/packages/90/51/9187be60d989df97f5f0aba133fa54e7300f17616e065d1ada7d7646b6d6/jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d", size = 512900, upload-time = "2023-11-23T09:26:37.44Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/b1/dd/ead9d8ea85bf202d90cc513b533f9c363121c7792674f78e0d8a854b63b4/jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780", size = 15884 }, + { url = "https://files.pythonhosted.org/packages/b1/dd/ead9d8ea85bf202d90cc513b533f9c363121c7792674f78e0d8a854b63b4/jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780", size = 15884, upload-time = "2023-11-23T09:26:34.325Z" }, ] [[package]] @@ -2061,16 +1550,15 @@ dependencies = [ { name = "packaging" }, { name = "requests" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/d6/2c/90153f189e421e93c4bb4f9e3f59802a1f01abd2ac5cf40b152d7f735232/jupyterlab_server-2.28.0.tar.gz", hash = "sha256:35baa81898b15f93573e2deca50d11ac0ae407ebb688299d3a5213265033712c", size = 76996 } +sdist = { url = "https://files.pythonhosted.org/packages/d6/2c/90153f189e421e93c4bb4f9e3f59802a1f01abd2ac5cf40b152d7f735232/jupyterlab_server-2.28.0.tar.gz", hash = "sha256:35baa81898b15f93573e2deca50d11ac0ae407ebb688299d3a5213265033712c", size = 76996, upload-time = "2025-10-22T13:59:18.37Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/e0/07/a000fe835f76b7e1143242ab1122e6362ef1c03f23f83a045c38859c2ae0/jupyterlab_server-2.28.0-py3-none-any.whl", hash = "sha256:e4355b148fdcf34d312bbbc80f22467d6d20460e8b8736bf235577dd18506968", size = 59830 }, + { url = "https://files.pythonhosted.org/packages/e0/07/a000fe835f76b7e1143242ab1122e6362ef1c03f23f83a045c38859c2ae0/jupyterlab_server-2.28.0-py3-none-any.whl", hash = "sha256:e4355b148fdcf34d312bbbc80f22467d6d20460e8b8736bf235577dd18506968", size = 59830, upload-time = "2025-10-22T13:59:16.767Z" }, ] [[package]] name = "jupyterlab-widgets" version = "3.0.16" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/26/2d/ef58fed122b268c69c0aa099da20bc67657cdfb2e222688d5731bd5b971d/jupyterlab_widgets-3.0.16.tar.gz", hash = "sha256:423da05071d55cf27a9e602216d35a3a65a3e41cdf9c5d3b643b814ce38c19e0", size = 897423, upload-time = "2025-11-01T21:11:29.724Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/ab/b5/36c712098e6191d1b4e349304ef73a8d06aed77e56ceaac8c0a306c7bda1/jupyterlab_widgets-3.0.16-py3-none-any.whl", hash = "sha256:45fa36d9c6422cf2559198e4db481aa243c7a32d9926b500781c830c80f7ecf8", size = 914926, upload-time = "2025-11-01T21:11:28.008Z" }, @@ -2180,101 +1668,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/48/44/2b5b95b7aa39fb2d8d9d956e0f3d5d45aef2ae1d942d4c3ffac2f9cfed1a/kiwisolver-1.5.0-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:be4a51a55833dc29ab5d7503e7bcb3b3af3402d266018137127450005cdfe737", size = 79892, upload-time = "2026-03-09T13:15:49.694Z" }, { url = "https://files.pythonhosted.org/packages/52/7d/7157f9bba6b455cfb4632ed411e199fc8b8977642c2b12082e1bd9e6d173/kiwisolver-1.5.0-pp311-pypy311_pp73-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:daae526907e262de627d8f70058a0f64acc9e2641c164c99c8f594b34a799a16", size = 77603, upload-time = "2026-03-09T13:15:50.945Z" }, { url = "https://files.pythonhosted.org/packages/0a/dd/8050c947d435c8d4bc94e3252f4d8bb8a76cfb424f043a8680be637a57f1/kiwisolver-1.5.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:59cd8683f575d96df5bb48f6add94afc055012c29e28124fcae2b63661b9efb1", size = 73558, upload-time = "2026-03-09T13:15:52.112Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/5c/3c/85844f1b0feb11ee581ac23fe5fce65cd049a200c1446708cc1b7f922875/kiwisolver-1.4.9.tar.gz", hash = "sha256:c3b22c26c6fd6811b0ae8363b95ca8ce4ea3c202d3d0975b2914310ceb1bcc4d", size = 97564 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/6f/ab/c80b0d5a9d8a1a65f4f815f2afff9798b12c3b9f31f1d304dd233dd920e2/kiwisolver-1.4.9-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:eb14a5da6dc7642b0f3a18f13654847cd8b7a2550e2645a5bda677862b03ba16", size = 124167 }, - { url = "https://files.pythonhosted.org/packages/a0/c0/27fe1a68a39cf62472a300e2879ffc13c0538546c359b86f149cc19f6ac3/kiwisolver-1.4.9-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:39a219e1c81ae3b103643d2aedb90f1ef22650deb266ff12a19e7773f3e5f089", size = 66579 }, - { url = "https://files.pythonhosted.org/packages/31/a2/a12a503ac1fd4943c50f9822678e8015a790a13b5490354c68afb8489814/kiwisolver-1.4.9-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2405a7d98604b87f3fc28b1716783534b1b4b8510d8142adca34ee0bc3c87543", size = 65309 }, - { url = "https://files.pythonhosted.org/packages/66/e1/e533435c0be77c3f64040d68d7a657771194a63c279f55573188161e81ca/kiwisolver-1.4.9-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:dc1ae486f9abcef254b5618dfb4113dd49f94c68e3e027d03cf0143f3f772b61", size = 1435596 }, - { url = "https://files.pythonhosted.org/packages/67/1e/51b73c7347f9aabdc7215aa79e8b15299097dc2f8e67dee2b095faca9cb0/kiwisolver-1.4.9-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8a1f570ce4d62d718dce3f179ee78dac3b545ac16c0c04bb363b7607a949c0d1", size = 1246548 }, - { url = "https://files.pythonhosted.org/packages/21/aa/72a1c5d1e430294f2d32adb9542719cfb441b5da368d09d268c7757af46c/kiwisolver-1.4.9-cp311-cp311-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:cb27e7b78d716c591e88e0a09a2139c6577865d7f2e152488c2cc6257f460872", size = 1263618 }, - { url = "https://files.pythonhosted.org/packages/a3/af/db1509a9e79dbf4c260ce0cfa3903ea8945f6240e9e59d1e4deb731b1a40/kiwisolver-1.4.9-cp311-cp311-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:15163165efc2f627eb9687ea5f3a28137217d217ac4024893d753f46bce9de26", size = 1317437 }, - { url = "https://files.pythonhosted.org/packages/e0/f2/3ea5ee5d52abacdd12013a94130436e19969fa183faa1e7c7fbc89e9a42f/kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:bdee92c56a71d2b24c33a7d4c2856bd6419d017e08caa7802d2963870e315028", size = 2195742 }, - { url = "https://files.pythonhosted.org/packages/6f/9b/1efdd3013c2d9a2566aa6a337e9923a00590c516add9a1e89a768a3eb2fc/kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:412f287c55a6f54b0650bd9b6dce5aceddb95864a1a90c87af16979d37c89771", size = 2290810 }, - { url = "https://files.pythonhosted.org/packages/fb/e5/cfdc36109ae4e67361f9bc5b41323648cb24a01b9ade18784657e022e65f/kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:2c93f00dcba2eea70af2be5f11a830a742fe6b579a1d4e00f47760ef13be247a", size = 2461579 }, - { url = "https://files.pythonhosted.org/packages/62/86/b589e5e86c7610842213994cdea5add00960076bef4ae290c5fa68589cac/kiwisolver-1.4.9-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f117e1a089d9411663a3207ba874f31be9ac8eaa5b533787024dc07aeb74f464", size = 2268071 }, - { url = "https://files.pythonhosted.org/packages/3b/c6/f8df8509fd1eee6c622febe54384a96cfaf4d43bf2ccec7a0cc17e4715c9/kiwisolver-1.4.9-cp311-cp311-win_amd64.whl", hash = "sha256:be6a04e6c79819c9a8c2373317d19a96048e5a3f90bec587787e86a1153883c2", size = 73840 }, - { url = "https://files.pythonhosted.org/packages/e2/2d/16e0581daafd147bc11ac53f032a2b45eabac897f42a338d0a13c1e5c436/kiwisolver-1.4.9-cp311-cp311-win_arm64.whl", hash = "sha256:0ae37737256ba2de764ddc12aed4956460277f00c4996d51a197e72f62f5eec7", size = 65159 }, - { url = "https://files.pythonhosted.org/packages/86/c9/13573a747838aeb1c76e3267620daa054f4152444d1f3d1a2324b78255b5/kiwisolver-1.4.9-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:ac5a486ac389dddcc5bef4f365b6ae3ffff2c433324fb38dd35e3fab7c957999", size = 123686 }, - { url = "https://files.pythonhosted.org/packages/51/ea/2ecf727927f103ffd1739271ca19c424d0e65ea473fbaeea1c014aea93f6/kiwisolver-1.4.9-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:f2ba92255faa7309d06fe44c3a4a97efe1c8d640c2a79a5ef728b685762a6fd2", size = 66460 }, - { url = "https://files.pythonhosted.org/packages/5b/5a/51f5464373ce2aeb5194508298a508b6f21d3867f499556263c64c621914/kiwisolver-1.4.9-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4a2899935e724dd1074cb568ce7ac0dce28b2cd6ab539c8e001a8578eb106d14", size = 64952 }, - { url = "https://files.pythonhosted.org/packages/70/90/6d240beb0f24b74371762873e9b7f499f1e02166a2d9c5801f4dbf8fa12e/kiwisolver-1.4.9-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f6008a4919fdbc0b0097089f67a1eb55d950ed7e90ce2cc3e640abadd2757a04", size = 1474756 }, - { url = "https://files.pythonhosted.org/packages/12/42/f36816eaf465220f683fb711efdd1bbf7a7005a2473d0e4ed421389bd26c/kiwisolver-1.4.9-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:67bb8b474b4181770f926f7b7d2f8c0248cbcb78b660fdd41a47054b28d2a752", size = 1276404 }, - { url = "https://files.pythonhosted.org/packages/2e/64/bc2de94800adc830c476dce44e9b40fd0809cddeef1fde9fcf0f73da301f/kiwisolver-1.4.9-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2327a4a30d3ee07d2fbe2e7933e8a37c591663b96ce42a00bc67461a87d7df77", size = 1294410 }, - { url = "https://files.pythonhosted.org/packages/5f/42/2dc82330a70aa8e55b6d395b11018045e58d0bb00834502bf11509f79091/kiwisolver-1.4.9-cp312-cp312-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:7a08b491ec91b1d5053ac177afe5290adacf1f0f6307d771ccac5de30592d198", size = 1343631 }, - { url = "https://files.pythonhosted.org/packages/22/fd/f4c67a6ed1aab149ec5a8a401c323cee7a1cbe364381bb6c9c0d564e0e20/kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:d8fc5c867c22b828001b6a38d2eaeb88160bf5783c6cb4a5e440efc981ce286d", size = 2224963 }, - { url = "https://files.pythonhosted.org/packages/45/aa/76720bd4cb3713314677d9ec94dcc21ced3f1baf4830adde5bb9b2430a5f/kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:3b3115b2581ea35bb6d1f24a4c90af37e5d9b49dcff267eeed14c3893c5b86ab", size = 2321295 }, - { url = "https://files.pythonhosted.org/packages/80/19/d3ec0d9ab711242f56ae0dc2fc5d70e298bb4a1f9dfab44c027668c673a1/kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:858e4c22fb075920b96a291928cb7dea5644e94c0ee4fcd5af7e865655e4ccf2", size = 2487987 }, - { url = "https://files.pythonhosted.org/packages/39/e9/61e4813b2c97e86b6fdbd4dd824bf72d28bcd8d4849b8084a357bc0dd64d/kiwisolver-1.4.9-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:ed0fecd28cc62c54b262e3736f8bb2512d8dcfdc2bcf08be5f47f96bf405b145", size = 2291817 }, - { url = "https://files.pythonhosted.org/packages/a0/41/85d82b0291db7504da3c2defe35c9a8a5c9803a730f297bd823d11d5fb77/kiwisolver-1.4.9-cp312-cp312-win_amd64.whl", hash = "sha256:f68208a520c3d86ea51acf688a3e3002615a7f0238002cccc17affecc86a8a54", size = 73895 }, - { url = "https://files.pythonhosted.org/packages/e2/92/5f3068cf15ee5cb624a0c7596e67e2a0bb2adee33f71c379054a491d07da/kiwisolver-1.4.9-cp312-cp312-win_arm64.whl", hash = "sha256:2c1a4f57df73965f3f14df20b80ee29e6a7930a57d2d9e8491a25f676e197c60", size = 64992 }, - { url = "https://files.pythonhosted.org/packages/31/c1/c2686cda909742ab66c7388e9a1a8521a59eb89f8bcfbee28fc980d07e24/kiwisolver-1.4.9-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:a5d0432ccf1c7ab14f9949eec60c5d1f924f17c037e9f8b33352fa05799359b8", size = 123681 }, - { url = "https://files.pythonhosted.org/packages/ca/f0/f44f50c9f5b1a1860261092e3bc91ecdc9acda848a8b8c6abfda4a24dd5c/kiwisolver-1.4.9-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:efb3a45b35622bb6c16dbfab491a8f5a391fe0e9d45ef32f4df85658232ca0e2", size = 66464 }, - { url = "https://files.pythonhosted.org/packages/2d/7a/9d90a151f558e29c3936b8a47ac770235f436f2120aca41a6d5f3d62ae8d/kiwisolver-1.4.9-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1a12cf6398e8a0a001a059747a1cbf24705e18fe413bc22de7b3d15c67cffe3f", size = 64961 }, - { url = "https://files.pythonhosted.org/packages/e9/e9/f218a2cb3a9ffbe324ca29a9e399fa2d2866d7f348ec3a88df87fc248fc5/kiwisolver-1.4.9-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b67e6efbf68e077dd71d1a6b37e43e1a99d0bff1a3d51867d45ee8908b931098", size = 1474607 }, - { url = "https://files.pythonhosted.org/packages/d9/28/aac26d4c882f14de59041636292bc838db8961373825df23b8eeb807e198/kiwisolver-1.4.9-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5656aa670507437af0207645273ccdfee4f14bacd7f7c67a4306d0dcaeaf6eed", size = 1276546 }, - { url = "https://files.pythonhosted.org/packages/8b/ad/8bfc1c93d4cc565e5069162f610ba2f48ff39b7de4b5b8d93f69f30c4bed/kiwisolver-1.4.9-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:bfc08add558155345129c7803b3671cf195e6a56e7a12f3dde7c57d9b417f525", size = 1294482 }, - { url = "https://files.pythonhosted.org/packages/da/f1/6aca55ff798901d8ce403206d00e033191f63d82dd708a186e0ed2067e9c/kiwisolver-1.4.9-cp313-cp313-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:40092754720b174e6ccf9e845d0d8c7d8e12c3d71e7fc35f55f3813e96376f78", size = 1343720 }, - { url = "https://files.pythonhosted.org/packages/d1/91/eed031876c595c81d90d0f6fc681ece250e14bf6998c3d7c419466b523b7/kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:497d05f29a1300d14e02e6441cf0f5ee81c1ff5a304b0d9fb77423974684e08b", size = 2224907 }, - { url = "https://files.pythonhosted.org/packages/e9/ec/4d1925f2e49617b9cca9c34bfa11adefad49d00db038e692a559454dfb2e/kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:bdd1a81a1860476eb41ac4bc1e07b3f07259e6d55bbf739b79c8aaedcf512799", size = 2321334 }, - { url = "https://files.pythonhosted.org/packages/43/cb/450cd4499356f68802750c6ddc18647b8ea01ffa28f50d20598e0befe6e9/kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:e6b93f13371d341afee3be9f7c5964e3fe61d5fa30f6a30eb49856935dfe4fc3", size = 2488313 }, - { url = "https://files.pythonhosted.org/packages/71/67/fc76242bd99f885651128a5d4fa6083e5524694b7c88b489b1b55fdc491d/kiwisolver-1.4.9-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d75aa530ccfaa593da12834b86a0724f58bff12706659baa9227c2ccaa06264c", size = 2291970 }, - { url = "https://files.pythonhosted.org/packages/75/bd/f1a5d894000941739f2ae1b65a32892349423ad49c2e6d0771d0bad3fae4/kiwisolver-1.4.9-cp313-cp313-win_amd64.whl", hash = "sha256:dd0a578400839256df88c16abddf9ba14813ec5f21362e1fe65022e00c883d4d", size = 73894 }, - { url = "https://files.pythonhosted.org/packages/95/38/dce480814d25b99a391abbddadc78f7c117c6da34be68ca8b02d5848b424/kiwisolver-1.4.9-cp313-cp313-win_arm64.whl", hash = "sha256:d4188e73af84ca82468f09cadc5ac4db578109e52acb4518d8154698d3a87ca2", size = 64995 }, - { url = "https://files.pythonhosted.org/packages/e2/37/7d218ce5d92dadc5ebdd9070d903e0c7cf7edfe03f179433ac4d13ce659c/kiwisolver-1.4.9-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:5a0f2724dfd4e3b3ac5a82436a8e6fd16baa7d507117e4279b660fe8ca38a3a1", size = 126510 }, - { url = "https://files.pythonhosted.org/packages/23/b0/e85a2b48233daef4b648fb657ebbb6f8367696a2d9548a00b4ee0eb67803/kiwisolver-1.4.9-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:1b11d6a633e4ed84fc0ddafd4ebfd8ea49b3f25082c04ad12b8315c11d504dc1", size = 67903 }, - { url = "https://files.pythonhosted.org/packages/44/98/f2425bc0113ad7de24da6bb4dae1343476e95e1d738be7c04d31a5d037fd/kiwisolver-1.4.9-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:61874cdb0a36016354853593cffc38e56fc9ca5aa97d2c05d3dcf6922cd55a11", size = 66402 }, - { url = "https://files.pythonhosted.org/packages/98/d8/594657886df9f34c4177cc353cc28ca7e6e5eb562d37ccc233bff43bbe2a/kiwisolver-1.4.9-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:60c439763a969a6af93b4881db0eed8fadf93ee98e18cbc35bc8da868d0c4f0c", size = 1582135 }, - { url = "https://files.pythonhosted.org/packages/5c/c6/38a115b7170f8b306fc929e166340c24958347308ea3012c2b44e7e295db/kiwisolver-1.4.9-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92a2f997387a1b79a75e7803aa7ded2cfbe2823852ccf1ba3bcf613b62ae3197", size = 1389409 }, - { url = "https://files.pythonhosted.org/packages/bf/3b/e04883dace81f24a568bcee6eb3001da4ba05114afa622ec9b6fafdc1f5e/kiwisolver-1.4.9-cp313-cp313t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a31d512c812daea6d8b3be3b2bfcbeb091dbb09177706569bcfc6240dcf8b41c", size = 1401763 }, - { url = "https://files.pythonhosted.org/packages/9f/80/20ace48e33408947af49d7d15c341eaee69e4e0304aab4b7660e234d6288/kiwisolver-1.4.9-cp313-cp313t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:52a15b0f35dad39862d376df10c5230155243a2c1a436e39eb55623ccbd68185", size = 1453643 }, - { url = "https://files.pythonhosted.org/packages/64/31/6ce4380a4cd1f515bdda976a1e90e547ccd47b67a1546d63884463c92ca9/kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:a30fd6fdef1430fd9e1ba7b3398b5ee4e2887783917a687d86ba69985fb08748", size = 2330818 }, - { url = "https://files.pythonhosted.org/packages/fa/e9/3f3fcba3bcc7432c795b82646306e822f3fd74df0ee81f0fa067a1f95668/kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:cc9617b46837c6468197b5945e196ee9ca43057bb7d9d1ae688101e4e1dddf64", size = 2419963 }, - { url = "https://files.pythonhosted.org/packages/99/43/7320c50e4133575c66e9f7dadead35ab22d7c012a3b09bb35647792b2a6d/kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:0ab74e19f6a2b027ea4f845a78827969af45ce790e6cb3e1ebab71bdf9f215ff", size = 2594639 }, - { url = "https://files.pythonhosted.org/packages/65/d6/17ae4a270d4a987ef8a385b906d2bdfc9fce502d6dc0d3aea865b47f548c/kiwisolver-1.4.9-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dba5ee5d3981160c28d5490f0d1b7ed730c22470ff7f6cc26cfcfaacb9896a07", size = 2391741 }, - { url = "https://files.pythonhosted.org/packages/2a/8f/8f6f491d595a9e5912971f3f863d81baddccc8a4d0c3749d6a0dd9ffc9df/kiwisolver-1.4.9-cp313-cp313t-win_arm64.whl", hash = "sha256:0749fd8f4218ad2e851e11cc4dc05c7cbc0cbc4267bdfdb31782e65aace4ee9c", size = 68646 }, - { url = "https://files.pythonhosted.org/packages/6b/32/6cc0fbc9c54d06c2969faa9c1d29f5751a2e51809dd55c69055e62d9b426/kiwisolver-1.4.9-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:9928fe1eb816d11ae170885a74d074f57af3a0d65777ca47e9aeb854a1fba386", size = 123806 }, - { url = "https://files.pythonhosted.org/packages/b2/dd/2bfb1d4a4823d92e8cbb420fe024b8d2167f72079b3bb941207c42570bdf/kiwisolver-1.4.9-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:d0005b053977e7b43388ddec89fa567f43d4f6d5c2c0affe57de5ebf290dc552", size = 66605 }, - { url = "https://files.pythonhosted.org/packages/f7/69/00aafdb4e4509c2ca6064646cba9cd4b37933898f426756adb2cb92ebbed/kiwisolver-1.4.9-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:2635d352d67458b66fd0667c14cb1d4145e9560d503219034a18a87e971ce4f3", size = 64925 }, - { url = "https://files.pythonhosted.org/packages/43/dc/51acc6791aa14e5cb6d8a2e28cefb0dc2886d8862795449d021334c0df20/kiwisolver-1.4.9-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:767c23ad1c58c9e827b649a9ab7809fd5fd9db266a9cf02b0e926ddc2c680d58", size = 1472414 }, - { url = "https://files.pythonhosted.org/packages/3d/bb/93fa64a81db304ac8a246f834d5094fae4b13baf53c839d6bb6e81177129/kiwisolver-1.4.9-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:72d0eb9fba308b8311685c2268cf7d0a0639a6cd027d8128659f72bdd8a024b4", size = 1281272 }, - { url = "https://files.pythonhosted.org/packages/70/e6/6df102916960fb8d05069d4bd92d6d9a8202d5a3e2444494e7cd50f65b7a/kiwisolver-1.4.9-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f68e4f3eeca8fb22cc3d731f9715a13b652795ef657a13df1ad0c7dc0e9731df", size = 1298578 }, - { url = "https://files.pythonhosted.org/packages/7c/47/e142aaa612f5343736b087864dbaebc53ea8831453fb47e7521fa8658f30/kiwisolver-1.4.9-cp314-cp314-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d84cd4061ae292d8ac367b2c3fa3aad11cb8625a95d135fe93f286f914f3f5a6", size = 1345607 }, - { url = "https://files.pythonhosted.org/packages/54/89/d641a746194a0f4d1a3670fb900d0dbaa786fb98341056814bc3f058fa52/kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:a60ea74330b91bd22a29638940d115df9dc00af5035a9a2a6ad9399ffb4ceca5", size = 2230150 }, - { url = "https://files.pythonhosted.org/packages/aa/6b/5ee1207198febdf16ac11f78c5ae40861b809cbe0e6d2a8d5b0b3044b199/kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:ce6a3a4e106cf35c2d9c4fa17c05ce0b180db622736845d4315519397a77beaf", size = 2325979 }, - { url = "https://files.pythonhosted.org/packages/fc/ff/b269eefd90f4ae14dcc74973d5a0f6d28d3b9bb1afd8c0340513afe6b39a/kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:77937e5e2a38a7b48eef0585114fe7930346993a88060d0bf886086d2aa49ef5", size = 2491456 }, - { url = "https://files.pythonhosted.org/packages/fc/d4/10303190bd4d30de547534601e259a4fbf014eed94aae3e5521129215086/kiwisolver-1.4.9-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:24c175051354f4a28c5d6a31c93906dc653e2bf234e8a4bbfb964892078898ce", size = 2294621 }, - { url = "https://files.pythonhosted.org/packages/28/e0/a9a90416fce5c0be25742729c2ea52105d62eda6c4be4d803c2a7be1fa50/kiwisolver-1.4.9-cp314-cp314-win_amd64.whl", hash = "sha256:0763515d4df10edf6d06a3c19734e2566368980d21ebec439f33f9eb936c07b7", size = 75417 }, - { url = "https://files.pythonhosted.org/packages/1f/10/6949958215b7a9a264299a7db195564e87900f709db9245e4ebdd3c70779/kiwisolver-1.4.9-cp314-cp314-win_arm64.whl", hash = "sha256:0e4e2bf29574a6a7b7f6cb5fa69293b9f96c928949ac4a53ba3f525dffb87f9c", size = 66582 }, - { url = "https://files.pythonhosted.org/packages/ec/79/60e53067903d3bc5469b369fe0dfc6b3482e2133e85dae9daa9527535991/kiwisolver-1.4.9-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:d976bbb382b202f71c67f77b0ac11244021cfa3f7dfd9e562eefcea2df711548", size = 126514 }, - { url = "https://files.pythonhosted.org/packages/25/d1/4843d3e8d46b072c12a38c97c57fab4608d36e13fe47d47ee96b4d61ba6f/kiwisolver-1.4.9-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:2489e4e5d7ef9a1c300a5e0196e43d9c739f066ef23270607d45aba368b91f2d", size = 67905 }, - { url = "https://files.pythonhosted.org/packages/8c/ae/29ffcbd239aea8b93108de1278271ae764dfc0d803a5693914975f200596/kiwisolver-1.4.9-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:e2ea9f7ab7fbf18fffb1b5434ce7c69a07582f7acc7717720f1d69f3e806f90c", size = 66399 }, - { url = "https://files.pythonhosted.org/packages/a1/ae/d7ba902aa604152c2ceba5d352d7b62106bedbccc8e95c3934d94472bfa3/kiwisolver-1.4.9-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b34e51affded8faee0dfdb705416153819d8ea9250bbbf7ea1b249bdeb5f1122", size = 1582197 }, - { url = "https://files.pythonhosted.org/packages/f2/41/27c70d427eddb8bc7e4f16420a20fefc6f480312122a59a959fdfe0445ad/kiwisolver-1.4.9-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d8aacd3d4b33b772542b2e01beb50187536967b514b00003bdda7589722d2a64", size = 1390125 }, - { url = "https://files.pythonhosted.org/packages/41/42/b3799a12bafc76d962ad69083f8b43b12bf4fe78b097b12e105d75c9b8f1/kiwisolver-1.4.9-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7cf974dd4e35fa315563ac99d6287a1024e4dc2077b8a7d7cd3d2fb65d283134", size = 1402612 }, - { url = "https://files.pythonhosted.org/packages/d2/b5/a210ea073ea1cfaca1bb5c55a62307d8252f531beb364e18aa1e0888b5a0/kiwisolver-1.4.9-cp314-cp314t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:85bd218b5ecfbee8c8a82e121802dcb519a86044c9c3b2e4aef02fa05c6da370", size = 1453990 }, - { url = "https://files.pythonhosted.org/packages/5f/ce/a829eb8c033e977d7ea03ed32fb3c1781b4fa0433fbadfff29e39c676f32/kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:0856e241c2d3df4efef7c04a1e46b1936b6120c9bcf36dd216e3acd84bc4fb21", size = 2331601 }, - { url = "https://files.pythonhosted.org/packages/e0/4b/b5e97eb142eb9cd0072dacfcdcd31b1c66dc7352b0f7c7255d339c0edf00/kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:9af39d6551f97d31a4deebeac6f45b156f9755ddc59c07b402c148f5dbb6482a", size = 2422041 }, - { url = "https://files.pythonhosted.org/packages/40/be/8eb4cd53e1b85ba4edc3a9321666f12b83113a178845593307a3e7891f44/kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:bb4ae2b57fc1d8cbd1cf7b1d9913803681ffa903e7488012be5b76dedf49297f", size = 2594897 }, - { url = "https://files.pythonhosted.org/packages/99/dd/841e9a66c4715477ea0abc78da039832fbb09dac5c35c58dc4c41a407b8a/kiwisolver-1.4.9-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:aedff62918805fb62d43a4aa2ecd4482c380dc76cd31bd7c8878588a61bd0369", size = 2391835 }, - { url = "https://files.pythonhosted.org/packages/0c/28/4b2e5c47a0da96896fdfdb006340ade064afa1e63675d01ea5ac222b6d52/kiwisolver-1.4.9-cp314-cp314t-win_amd64.whl", hash = "sha256:1fa333e8b2ce4d9660f2cda9c0e1b6bafcfb2457a9d259faa82289e73ec24891", size = 79988 }, - { url = "https://files.pythonhosted.org/packages/80/be/3578e8afd18c88cdf9cb4cffde75a96d2be38c5a903f1ed0ceec061bd09e/kiwisolver-1.4.9-cp314-cp314t-win_arm64.whl", hash = "sha256:4a48a2ce79d65d363597ef7b567ce3d14d68783d2b2263d98db3d9477805ba32", size = 70260 }, - { url = "https://files.pythonhosted.org/packages/a3/0f/36d89194b5a32c054ce93e586d4049b6c2c22887b0eb229c61c68afd3078/kiwisolver-1.4.9-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:720e05574713db64c356e86732c0f3c5252818d05f9df320f0ad8380641acea5", size = 60104 }, - { url = "https://files.pythonhosted.org/packages/52/ba/4ed75f59e4658fd21fe7dde1fee0ac397c678ec3befba3fe6482d987af87/kiwisolver-1.4.9-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:17680d737d5335b552994a2008fab4c851bcd7de33094a82067ef3a576ff02fa", size = 58592 }, - { url = "https://files.pythonhosted.org/packages/33/01/a8ea7c5ea32a9b45ceeaee051a04c8ed4320f5add3c51bfa20879b765b70/kiwisolver-1.4.9-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:85b5352f94e490c028926ea567fc569c52ec79ce131dadb968d3853e809518c2", size = 80281 }, - { url = "https://files.pythonhosted.org/packages/da/e3/dbd2ecdce306f1d07a1aaf324817ee993aab7aee9db47ceac757deabafbe/kiwisolver-1.4.9-pp311-pypy311_pp73-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:464415881e4801295659462c49461a24fb107c140de781d55518c4b80cb6790f", size = 78009 }, - { url = "https://files.pythonhosted.org/packages/da/e9/0d4add7873a73e462aeb45c036a2dead2562b825aa46ba326727b3f31016/kiwisolver-1.4.9-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:fb940820c63a9590d31d88b815e7a3aa5915cad3ce735ab45f0c730b39547de1", size = 73929 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "lark" version = "1.3.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/da/34/28fff3ab31ccff1fd4f6c7c7b0ceb2b6968d8ea4950663eadcb5720591a0/lark-1.3.1.tar.gz", hash = "sha256:b426a7a6d6d53189d318f2b6236ab5d6429eaf09259f1ca33eb716eed10d2905", size = 382732 } +sdist = { url = "https://files.pythonhosted.org/packages/da/34/28fff3ab31ccff1fd4f6c7c7b0ceb2b6968d8ea4950663eadcb5720591a0/lark-1.3.1.tar.gz", hash = "sha256:b426a7a6d6d53189d318f2b6236ab5d6429eaf09259f1ca33eb716eed10d2905", size = 382732, upload-time = "2025-10-27T18:25:56.653Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/82/3d/14ce75ef66813643812f3093ab17e46d3a206942ce7376d31ec2d36229e7/lark-1.3.1-py3-none-any.whl", hash = "sha256:c629b661023a014c37da873b4ff58a817398d12635d3bbb2c5a03be7fe5d1e12", size = 113151 }, + { url = "https://files.pythonhosted.org/packages/82/3d/14ce75ef66813643812f3093ab17e46d3a206942ce7376d31ec2d36229e7/lark-1.3.1-py3-none-any.whl", hash = "sha256:c629b661023a014c37da873b4ff58a817398d12635d3bbb2c5a03be7fe5d1e12", size = 113151, upload-time = "2025-10-27T18:25:54.882Z" }, ] [[package]] @@ -2284,15 +1686,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "packaging" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/49/ac/21a1f8aa3777f5658576777ea76bfb124b702c520bbe90edf4ae9915eafa/lazy_loader-0.5.tar.gz", hash = "sha256:717f9179a0dbed357012ddad50a5ad3d5e4d9a0b8712680d4e687f5e6e6ed9b3", size = 15294, upload-time = "2026-03-06T15:45:09.054Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/8a/a1/8d812e53a5da1687abb10445275d41a8b13adb781bbf7196ddbcf8d88505/lazy_loader-0.5-py3-none-any.whl", hash = "sha256:ab0ea149e9c554d4ffeeb21105ac60bed7f3b4fd69b1d2360a4add51b170b005", size = 8044, upload-time = "2026-03-06T15:45:07.668Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/6f/6b/c875b30a1ba490860c93da4cabf479e03f584eba06fe5963f6f6644653d8/lazy_loader-0.4.tar.gz", hash = "sha256:47c75182589b91a4e1a85a136c074285a5ad4d9f39c63e0d7fb76391c4574cd1", size = 15431 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl", hash = "sha256:342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc", size = 12097 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2303,24 +1699,18 @@ dependencies = [ { name = "packaging" }, { name = "typing-extensions" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/f1/45/7fa8f56b17dc0f0a41ec70dd307ecd6787254483549843bef4c30ab5adce/lightning_utilities-0.15.3.tar.gz", hash = "sha256:792ae0204c79f6859721ac7f386c237a33b0ed06ba775009cb894e010a842033", size = 33553, upload-time = "2026-02-22T14:48:53.348Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/25/f4/ead6e0e37209b07c9baa3e984ccdb0348ca370b77cea3aaea8ddbb097e00/lightning_utilities-0.15.3-py3-none-any.whl", hash = "sha256:6c55f1bee70084a1cbeaa41ada96e4b3a0fea5909e844dd335bd80f5a73c5f91", size = 31906, upload-time = "2026-02-22T14:48:52.488Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/b8/39/6fc58ca81492db047149b4b8fd385aa1bfb8c28cd7cacb0c7eb0c44d842f/lightning_utilities-0.15.2.tar.gz", hash = "sha256:cdf12f530214a63dacefd713f180d1ecf5d165338101617b4742e8f22c032e24", size = 31090 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/de/73/3d757cb3fc16f0f9794dd289bcd0c4a031d9cf54d8137d6b984b2d02edf3/lightning_utilities-0.15.2-py3-none-any.whl", hash = "sha256:ad3ab1703775044bbf880dbf7ddaaac899396c96315f3aa1779cec9d618a9841", size = 29431 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "locket" version = "1.0.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/2f/83/97b29fe05cb6ae28d2dbd30b81e2e402a3eed5f460c26e9eaa5895ceacf5/locket-1.0.0.tar.gz", hash = "sha256:5c0d4c052a8bbbf750e056a8e65ccd309086f4f0f18a2eac306a8dfa4112a632", size = 4350 } +sdist = { url = "https://files.pythonhosted.org/packages/2f/83/97b29fe05cb6ae28d2dbd30b81e2e402a3eed5f460c26e9eaa5895ceacf5/locket-1.0.0.tar.gz", hash = "sha256:5c0d4c052a8bbbf750e056a8e65ccd309086f4f0f18a2eac306a8dfa4112a632", size = 4350, upload-time = "2022-04-20T22:04:44.312Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/db/bc/83e112abc66cd466c6b83f99118035867cecd41802f8d044638aa78a106e/locket-1.0.0-py2.py3-none-any.whl", hash = "sha256:b6c819a722f7b6bd955b80781788e4a66a55628b858d347536b7e81325a3a5e3", size = 4398 }, + { url = "https://files.pythonhosted.org/packages/db/bc/83e112abc66cd466c6b83f99118035867cecd41802f8d044638aa78a106e/locket-1.0.0-py2.py3-none-any.whl", hash = "sha256:b6c819a722f7b6bd955b80781788e4a66a55628b858d347536b7e81325a3a5e3", size = 4398, upload-time = "2022-04-20T22:04:42.23Z" }, ] [[package]] @@ -2330,218 +1720,157 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "markupsafe" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/00/62/791b31e69ae182791ec67f04850f2f062716bbd205483d63a215f3e062d3/mako-1.3.12.tar.gz", hash = "sha256:9f778e93289bd410bb35daadeb4fc66d95a746f0b75777b942088b7fd7af550a", size = 400219, upload-time = "2026-04-28T19:01:08.512Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/bc/b1/a0ec7a5a9db730a08daef1fdfb8090435b82465abbf758a596f0ea88727e/mako-1.3.12-py3-none-any.whl", hash = "sha256:8f61569480282dbf557145ce441e4ba888be453c30989f879f0d652e39f53ea9", size = 78521, upload-time = "2026-04-28T19:01:10.393Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/9e/38/bd5b78a920a64d708fe6bc8e0a2c075e1389d53bef8413725c63ba041535/mako-1.3.10.tar.gz", hash = "sha256:99579a6f39583fa7e5630a28c3c1f440e4e97a414b80372649c0ce338da2ea28", size = 392474 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/87/fb/99f81ac72ae23375f22b7afdb7642aba97c00a713c217124420147681a2f/mako-1.3.10-py3-none-any.whl", hash = "sha256:baef24a52fc4fc514a0887ac600f9f1cff3d82c61d4d700a1fa84d597b88db59", size = 78509 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "markdown" version = "3.10.2" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/2b/f4/69fa6ed85ae003c2378ffa8f6d2e3234662abd02c10d216c0ba96081a238/markdown-3.10.2.tar.gz", hash = "sha256:994d51325d25ad8aa7ce4ebaec003febcce822c3f8c911e3b17c52f7f589f950", size = 368805, upload-time = "2026-02-09T14:57:26.942Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/de/1f/77fa3081e4f66ca3576c896ae5d31c3002ac6607f9747d2e3aa49227e464/markdown-3.10.2-py3-none-any.whl", hash = "sha256:e91464b71ae3ee7afd3017d9f358ef0baf158fd9a298db92f1d4761133824c36", size = 108180, upload-time = "2026-02-09T14:57:25.787Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/7d/ab/7dd27d9d863b3376fcf23a5a13cb5d024aed1db46f963f1b5735ae43b3be/markdown-3.10.tar.gz", hash = "sha256:37062d4f2aa4b2b6b32aefb80faa300f82cc790cb949a35b8caede34f2b68c0e", size = 364931 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/70/81/54e3ce63502cd085a0c556652a4e1b919c45a446bd1e5300e10c44c8c521/markdown-3.10-py3-none-any.whl", hash = "sha256:b5b99d6951e2e4948d939255596523444c0e677c669700b1d17aa4a8a464cb7c", size = 107678 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "markupsafe" version = "3.0.3" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/7e/99/7690b6d4034fffd95959cbe0c02de8deb3098cc577c67bb6a24fe5d7caa7/markupsafe-3.0.3.tar.gz", hash = "sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698", size = 80313 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/08/db/fefacb2136439fc8dd20e797950e749aa1f4997ed584c62cfb8ef7c2be0e/markupsafe-3.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:1cc7ea17a6824959616c525620e387f6dd30fec8cb44f649e31712db02123dad", size = 11631 }, - { url = "https://files.pythonhosted.org/packages/e1/2e/5898933336b61975ce9dc04decbc0a7f2fee78c30353c5efba7f2d6ff27a/markupsafe-3.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4bd4cd07944443f5a265608cc6aab442e4f74dff8088b0dfc8238647b8f6ae9a", size = 12058 }, - { url = "https://files.pythonhosted.org/packages/1d/09/adf2df3699d87d1d8184038df46a9c80d78c0148492323f4693df54e17bb/markupsafe-3.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b5420a1d9450023228968e7e6a9ce57f65d148ab56d2313fcd589eee96a7a50", size = 24287 }, - { url = "https://files.pythonhosted.org/packages/30/ac/0273f6fcb5f42e314c6d8cd99effae6a5354604d461b8d392b5ec9530a54/markupsafe-3.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0bf2a864d67e76e5c9a34dc26ec616a66b9888e25e7b9460e1c76d3293bd9dbf", size = 22940 }, - { url = "https://files.pythonhosted.org/packages/19/ae/31c1be199ef767124c042c6c3e904da327a2f7f0cd63a0337e1eca2967a8/markupsafe-3.0.3-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:bc51efed119bc9cfdf792cdeaa4d67e8f6fcccab66ed4bfdd6bde3e59bfcbb2f", size = 21887 }, - { url = "https://files.pythonhosted.org/packages/b2/76/7edcab99d5349a4532a459e1fe64f0b0467a3365056ae550d3bcf3f79e1e/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:068f375c472b3e7acbe2d5318dea141359e6900156b5b2ba06a30b169086b91a", size = 23692 }, - { url = "https://files.pythonhosted.org/packages/a4/28/6e74cdd26d7514849143d69f0bf2399f929c37dc2b31e6829fd2045b2765/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:7be7b61bb172e1ed687f1754f8e7484f1c8019780f6f6b0786e76bb01c2ae115", size = 21471 }, - { url = "https://files.pythonhosted.org/packages/62/7e/a145f36a5c2945673e590850a6f8014318d5577ed7e5920a4b3448e0865d/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f9e130248f4462aaa8e2552d547f36ddadbeaa573879158d721bbd33dfe4743a", size = 22923 }, - { url = "https://files.pythonhosted.org/packages/0f/62/d9c46a7f5c9adbeeeda52f5b8d802e1094e9717705a645efc71b0913a0a8/markupsafe-3.0.3-cp311-cp311-win32.whl", hash = "sha256:0db14f5dafddbb6d9208827849fad01f1a2609380add406671a26386cdf15a19", size = 14572 }, - { url = "https://files.pythonhosted.org/packages/83/8a/4414c03d3f891739326e1783338e48fb49781cc915b2e0ee052aa490d586/markupsafe-3.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:de8a88e63464af587c950061a5e6a67d3632e36df62b986892331d4620a35c01", size = 15077 }, - { url = "https://files.pythonhosted.org/packages/35/73/893072b42e6862f319b5207adc9ae06070f095b358655f077f69a35601f0/markupsafe-3.0.3-cp311-cp311-win_arm64.whl", hash = "sha256:3b562dd9e9ea93f13d53989d23a7e775fdfd1066c33494ff43f5418bc8c58a5c", size = 13876 }, - { url = "https://files.pythonhosted.org/packages/5a/72/147da192e38635ada20e0a2e1a51cf8823d2119ce8883f7053879c2199b5/markupsafe-3.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e", size = 11615 }, - { url = "https://files.pythonhosted.org/packages/9a/81/7e4e08678a1f98521201c3079f77db69fb552acd56067661f8c2f534a718/markupsafe-3.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce", size = 12020 }, - { url = "https://files.pythonhosted.org/packages/1e/2c/799f4742efc39633a1b54a92eec4082e4f815314869865d876824c257c1e/markupsafe-3.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d", size = 24332 }, - { url = "https://files.pythonhosted.org/packages/3c/2e/8d0c2ab90a8c1d9a24f0399058ab8519a3279d1bd4289511d74e909f060e/markupsafe-3.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d", size = 22947 }, - { url = "https://files.pythonhosted.org/packages/2c/54/887f3092a85238093a0b2154bd629c89444f395618842e8b0c41783898ea/markupsafe-3.0.3-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a", size = 21962 }, - { url = "https://files.pythonhosted.org/packages/c9/2f/336b8c7b6f4a4d95e91119dc8521402461b74a485558d8f238a68312f11c/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b", size = 23760 }, - { url = "https://files.pythonhosted.org/packages/32/43/67935f2b7e4982ffb50a4d169b724d74b62a3964bc1a9a527f5ac4f1ee2b/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f", size = 21529 }, - { url = "https://files.pythonhosted.org/packages/89/e0/4486f11e51bbba8b0c041098859e869e304d1c261e59244baa3d295d47b7/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b", size = 23015 }, - { url = "https://files.pythonhosted.org/packages/2f/e1/78ee7a023dac597a5825441ebd17170785a9dab23de95d2c7508ade94e0e/markupsafe-3.0.3-cp312-cp312-win32.whl", hash = "sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d", size = 14540 }, - { url = "https://files.pythonhosted.org/packages/aa/5b/bec5aa9bbbb2c946ca2733ef9c4ca91c91b6a24580193e891b5f7dbe8e1e/markupsafe-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c", size = 15105 }, - { url = "https://files.pythonhosted.org/packages/e5/f1/216fc1bbfd74011693a4fd837e7026152e89c4bcf3e77b6692fba9923123/markupsafe-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f", size = 13906 }, - { url = "https://files.pythonhosted.org/packages/38/2f/907b9c7bbba283e68f20259574b13d005c121a0fa4c175f9bed27c4597ff/markupsafe-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795", size = 11622 }, - { url = "https://files.pythonhosted.org/packages/9c/d9/5f7756922cdd676869eca1c4e3c0cd0df60ed30199ffd775e319089cb3ed/markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219", size = 12029 }, - { url = "https://files.pythonhosted.org/packages/00/07/575a68c754943058c78f30db02ee03a64b3c638586fba6a6dd56830b30a3/markupsafe-3.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6", size = 24374 }, - { url = "https://files.pythonhosted.org/packages/a9/21/9b05698b46f218fc0e118e1f8168395c65c8a2c750ae2bab54fc4bd4e0e8/markupsafe-3.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676", size = 22980 }, - { url = "https://files.pythonhosted.org/packages/7f/71/544260864f893f18b6827315b988c146b559391e6e7e8f7252839b1b846a/markupsafe-3.0.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9", size = 21990 }, - { url = "https://files.pythonhosted.org/packages/c2/28/b50fc2f74d1ad761af2f5dcce7492648b983d00a65b8c0e0cb457c82ebbe/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1", size = 23784 }, - { url = "https://files.pythonhosted.org/packages/ed/76/104b2aa106a208da8b17a2fb72e033a5a9d7073c68f7e508b94916ed47a9/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc", size = 21588 }, - { url = "https://files.pythonhosted.org/packages/b5/99/16a5eb2d140087ebd97180d95249b00a03aa87e29cc224056274f2e45fd6/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12", size = 23041 }, - { url = "https://files.pythonhosted.org/packages/19/bc/e7140ed90c5d61d77cea142eed9f9c303f4c4806f60a1044c13e3f1471d0/markupsafe-3.0.3-cp313-cp313-win32.whl", hash = "sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed", size = 14543 }, - { url = "https://files.pythonhosted.org/packages/05/73/c4abe620b841b6b791f2edc248f556900667a5a1cf023a6646967ae98335/markupsafe-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5", size = 15113 }, - { url = "https://files.pythonhosted.org/packages/f0/3a/fa34a0f7cfef23cf9500d68cb7c32dd64ffd58a12b09225fb03dd37d5b80/markupsafe-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485", size = 13911 }, - { url = "https://files.pythonhosted.org/packages/e4/d7/e05cd7efe43a88a17a37b3ae96e79a19e846f3f456fe79c57ca61356ef01/markupsafe-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73", size = 11658 }, - { url = "https://files.pythonhosted.org/packages/99/9e/e412117548182ce2148bdeacdda3bb494260c0b0184360fe0d56389b523b/markupsafe-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37", size = 12066 }, - { url = "https://files.pythonhosted.org/packages/bc/e6/fa0ffcda717ef64a5108eaa7b4f5ed28d56122c9a6d70ab8b72f9f715c80/markupsafe-3.0.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19", size = 25639 }, - { url = "https://files.pythonhosted.org/packages/96/ec/2102e881fe9d25fc16cb4b25d5f5cde50970967ffa5dddafdb771237062d/markupsafe-3.0.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025", size = 23569 }, - { url = "https://files.pythonhosted.org/packages/4b/30/6f2fce1f1f205fc9323255b216ca8a235b15860c34b6798f810f05828e32/markupsafe-3.0.3-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6", size = 23284 }, - { url = "https://files.pythonhosted.org/packages/58/47/4a0ccea4ab9f5dcb6f79c0236d954acb382202721e704223a8aafa38b5c8/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f", size = 24801 }, - { url = "https://files.pythonhosted.org/packages/6a/70/3780e9b72180b6fecb83a4814d84c3bf4b4ae4bf0b19c27196104149734c/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb", size = 22769 }, - { url = "https://files.pythonhosted.org/packages/98/c5/c03c7f4125180fc215220c035beac6b9cb684bc7a067c84fc69414d315f5/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009", size = 23642 }, - { url = "https://files.pythonhosted.org/packages/80/d6/2d1b89f6ca4bff1036499b1e29a1d02d282259f3681540e16563f27ebc23/markupsafe-3.0.3-cp313-cp313t-win32.whl", hash = "sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354", size = 14612 }, - { url = "https://files.pythonhosted.org/packages/2b/98/e48a4bfba0a0ffcf9925fe2d69240bfaa19c6f7507b8cd09c70684a53c1e/markupsafe-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218", size = 15200 }, - { url = "https://files.pythonhosted.org/packages/0e/72/e3cc540f351f316e9ed0f092757459afbc595824ca724cbc5a5d4263713f/markupsafe-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287", size = 13973 }, - { url = "https://files.pythonhosted.org/packages/33/8a/8e42d4838cd89b7dde187011e97fe6c3af66d8c044997d2183fbd6d31352/markupsafe-3.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe", size = 11619 }, - { url = "https://files.pythonhosted.org/packages/b5/64/7660f8a4a8e53c924d0fa05dc3a55c9cee10bbd82b11c5afb27d44b096ce/markupsafe-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026", size = 12029 }, - { url = "https://files.pythonhosted.org/packages/da/ef/e648bfd021127bef5fa12e1720ffed0c6cbb8310c8d9bea7266337ff06de/markupsafe-3.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737", size = 24408 }, - { url = "https://files.pythonhosted.org/packages/41/3c/a36c2450754618e62008bf7435ccb0f88053e07592e6028a34776213d877/markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97", size = 23005 }, - { url = "https://files.pythonhosted.org/packages/bc/20/b7fdf89a8456b099837cd1dc21974632a02a999ec9bf7ca3e490aacd98e7/markupsafe-3.0.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d", size = 22048 }, - { url = "https://files.pythonhosted.org/packages/9a/a7/591f592afdc734f47db08a75793a55d7fbcc6902a723ae4cfbab61010cc5/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda", size = 23821 }, - { url = "https://files.pythonhosted.org/packages/7d/33/45b24e4f44195b26521bc6f1a82197118f74df348556594bd2262bda1038/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf", size = 21606 }, - { url = "https://files.pythonhosted.org/packages/ff/0e/53dfaca23a69fbfbbf17a4b64072090e70717344c52eaaaa9c5ddff1e5f0/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe", size = 23043 }, - { url = "https://files.pythonhosted.org/packages/46/11/f333a06fc16236d5238bfe74daccbca41459dcd8d1fa952e8fbd5dccfb70/markupsafe-3.0.3-cp314-cp314-win32.whl", hash = "sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9", size = 14747 }, - { url = "https://files.pythonhosted.org/packages/28/52/182836104b33b444e400b14f797212f720cbc9ed6ba34c800639d154e821/markupsafe-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581", size = 15341 }, - { url = "https://files.pythonhosted.org/packages/6f/18/acf23e91bd94fd7b3031558b1f013adfa21a8e407a3fdb32745538730382/markupsafe-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4", size = 14073 }, - { url = "https://files.pythonhosted.org/packages/3c/f0/57689aa4076e1b43b15fdfa646b04653969d50cf30c32a102762be2485da/markupsafe-3.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab", size = 11661 }, - { url = "https://files.pythonhosted.org/packages/89/c3/2e67a7ca217c6912985ec766c6393b636fb0c2344443ff9d91404dc4c79f/markupsafe-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175", size = 12069 }, - { url = "https://files.pythonhosted.org/packages/f0/00/be561dce4e6ca66b15276e184ce4b8aec61fe83662cce2f7d72bd3249d28/markupsafe-3.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634", size = 25670 }, - { url = "https://files.pythonhosted.org/packages/50/09/c419f6f5a92e5fadde27efd190eca90f05e1261b10dbd8cbcb39cd8ea1dc/markupsafe-3.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50", size = 23598 }, - { url = "https://files.pythonhosted.org/packages/22/44/a0681611106e0b2921b3033fc19bc53323e0b50bc70cffdd19f7d679bb66/markupsafe-3.0.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e", size = 23261 }, - { url = "https://files.pythonhosted.org/packages/5f/57/1b0b3f100259dc9fffe780cfb60d4be71375510e435efec3d116b6436d43/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5", size = 24835 }, - { url = "https://files.pythonhosted.org/packages/26/6a/4bf6d0c97c4920f1597cc14dd720705eca0bf7c787aebc6bb4d1bead5388/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523", size = 22733 }, - { url = "https://files.pythonhosted.org/packages/14/c7/ca723101509b518797fedc2fdf79ba57f886b4aca8a7d31857ba3ee8281f/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc", size = 23672 }, - { url = "https://files.pythonhosted.org/packages/fb/df/5bd7a48c256faecd1d36edc13133e51397e41b73bb77e1a69deab746ebac/markupsafe-3.0.3-cp314-cp314t-win32.whl", hash = "sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d", size = 14819 }, - { url = "https://files.pythonhosted.org/packages/1a/8a/0402ba61a2f16038b48b39bccca271134be00c5c9f0f623208399333c448/markupsafe-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9", size = 15426 }, - { url = "https://files.pythonhosted.org/packages/70/bc/6f1c2f612465f5fa89b95bead1f44dcb607670fd42891d8fdcd5d039f4f4/markupsafe-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa", size = 14146 }, +sdist = { url = "https://files.pythonhosted.org/packages/7e/99/7690b6d4034fffd95959cbe0c02de8deb3098cc577c67bb6a24fe5d7caa7/markupsafe-3.0.3.tar.gz", hash = "sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698", size = 80313, upload-time = "2025-09-27T18:37:40.426Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/08/db/fefacb2136439fc8dd20e797950e749aa1f4997ed584c62cfb8ef7c2be0e/markupsafe-3.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:1cc7ea17a6824959616c525620e387f6dd30fec8cb44f649e31712db02123dad", size = 11631, upload-time = "2025-09-27T18:36:18.185Z" }, + { url = "https://files.pythonhosted.org/packages/e1/2e/5898933336b61975ce9dc04decbc0a7f2fee78c30353c5efba7f2d6ff27a/markupsafe-3.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4bd4cd07944443f5a265608cc6aab442e4f74dff8088b0dfc8238647b8f6ae9a", size = 12058, upload-time = "2025-09-27T18:36:19.444Z" }, + { url = "https://files.pythonhosted.org/packages/1d/09/adf2df3699d87d1d8184038df46a9c80d78c0148492323f4693df54e17bb/markupsafe-3.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b5420a1d9450023228968e7e6a9ce57f65d148ab56d2313fcd589eee96a7a50", size = 24287, upload-time = "2025-09-27T18:36:20.768Z" }, + { url = "https://files.pythonhosted.org/packages/30/ac/0273f6fcb5f42e314c6d8cd99effae6a5354604d461b8d392b5ec9530a54/markupsafe-3.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0bf2a864d67e76e5c9a34dc26ec616a66b9888e25e7b9460e1c76d3293bd9dbf", size = 22940, upload-time = "2025-09-27T18:36:22.249Z" }, + { url = "https://files.pythonhosted.org/packages/19/ae/31c1be199ef767124c042c6c3e904da327a2f7f0cd63a0337e1eca2967a8/markupsafe-3.0.3-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:bc51efed119bc9cfdf792cdeaa4d67e8f6fcccab66ed4bfdd6bde3e59bfcbb2f", size = 21887, upload-time = "2025-09-27T18:36:23.535Z" }, + { url = "https://files.pythonhosted.org/packages/b2/76/7edcab99d5349a4532a459e1fe64f0b0467a3365056ae550d3bcf3f79e1e/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:068f375c472b3e7acbe2d5318dea141359e6900156b5b2ba06a30b169086b91a", size = 23692, upload-time = "2025-09-27T18:36:24.823Z" }, + { url = "https://files.pythonhosted.org/packages/a4/28/6e74cdd26d7514849143d69f0bf2399f929c37dc2b31e6829fd2045b2765/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:7be7b61bb172e1ed687f1754f8e7484f1c8019780f6f6b0786e76bb01c2ae115", size = 21471, upload-time = "2025-09-27T18:36:25.95Z" }, + { url = "https://files.pythonhosted.org/packages/62/7e/a145f36a5c2945673e590850a6f8014318d5577ed7e5920a4b3448e0865d/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f9e130248f4462aaa8e2552d547f36ddadbeaa573879158d721bbd33dfe4743a", size = 22923, upload-time = "2025-09-27T18:36:27.109Z" }, + { url = "https://files.pythonhosted.org/packages/0f/62/d9c46a7f5c9adbeeeda52f5b8d802e1094e9717705a645efc71b0913a0a8/markupsafe-3.0.3-cp311-cp311-win32.whl", hash = "sha256:0db14f5dafddbb6d9208827849fad01f1a2609380add406671a26386cdf15a19", size = 14572, upload-time = "2025-09-27T18:36:28.045Z" }, + { url = "https://files.pythonhosted.org/packages/83/8a/4414c03d3f891739326e1783338e48fb49781cc915b2e0ee052aa490d586/markupsafe-3.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:de8a88e63464af587c950061a5e6a67d3632e36df62b986892331d4620a35c01", size = 15077, upload-time = "2025-09-27T18:36:29.025Z" }, + { url = "https://files.pythonhosted.org/packages/35/73/893072b42e6862f319b5207adc9ae06070f095b358655f077f69a35601f0/markupsafe-3.0.3-cp311-cp311-win_arm64.whl", hash = "sha256:3b562dd9e9ea93f13d53989d23a7e775fdfd1066c33494ff43f5418bc8c58a5c", size = 13876, upload-time = "2025-09-27T18:36:29.954Z" }, + { url = "https://files.pythonhosted.org/packages/5a/72/147da192e38635ada20e0a2e1a51cf8823d2119ce8883f7053879c2199b5/markupsafe-3.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e", size = 11615, upload-time = "2025-09-27T18:36:30.854Z" }, + { url = "https://files.pythonhosted.org/packages/9a/81/7e4e08678a1f98521201c3079f77db69fb552acd56067661f8c2f534a718/markupsafe-3.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce", size = 12020, upload-time = "2025-09-27T18:36:31.971Z" }, + { url = "https://files.pythonhosted.org/packages/1e/2c/799f4742efc39633a1b54a92eec4082e4f815314869865d876824c257c1e/markupsafe-3.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d", size = 24332, upload-time = "2025-09-27T18:36:32.813Z" }, + { url = "https://files.pythonhosted.org/packages/3c/2e/8d0c2ab90a8c1d9a24f0399058ab8519a3279d1bd4289511d74e909f060e/markupsafe-3.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d", size = 22947, upload-time = "2025-09-27T18:36:33.86Z" }, + { url = "https://files.pythonhosted.org/packages/2c/54/887f3092a85238093a0b2154bd629c89444f395618842e8b0c41783898ea/markupsafe-3.0.3-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a", size = 21962, upload-time = "2025-09-27T18:36:35.099Z" }, + { url = "https://files.pythonhosted.org/packages/c9/2f/336b8c7b6f4a4d95e91119dc8521402461b74a485558d8f238a68312f11c/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b", size = 23760, upload-time = "2025-09-27T18:36:36.001Z" }, + { url = "https://files.pythonhosted.org/packages/32/43/67935f2b7e4982ffb50a4d169b724d74b62a3964bc1a9a527f5ac4f1ee2b/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f", size = 21529, upload-time = "2025-09-27T18:36:36.906Z" }, + { url = "https://files.pythonhosted.org/packages/89/e0/4486f11e51bbba8b0c041098859e869e304d1c261e59244baa3d295d47b7/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b", size = 23015, upload-time = "2025-09-27T18:36:37.868Z" }, + { url = "https://files.pythonhosted.org/packages/2f/e1/78ee7a023dac597a5825441ebd17170785a9dab23de95d2c7508ade94e0e/markupsafe-3.0.3-cp312-cp312-win32.whl", hash = "sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d", size = 14540, upload-time = "2025-09-27T18:36:38.761Z" }, + { url = "https://files.pythonhosted.org/packages/aa/5b/bec5aa9bbbb2c946ca2733ef9c4ca91c91b6a24580193e891b5f7dbe8e1e/markupsafe-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c", size = 15105, upload-time = "2025-09-27T18:36:39.701Z" }, + { url = "https://files.pythonhosted.org/packages/e5/f1/216fc1bbfd74011693a4fd837e7026152e89c4bcf3e77b6692fba9923123/markupsafe-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f", size = 13906, upload-time = "2025-09-27T18:36:40.689Z" }, + { url = "https://files.pythonhosted.org/packages/38/2f/907b9c7bbba283e68f20259574b13d005c121a0fa4c175f9bed27c4597ff/markupsafe-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795", size = 11622, upload-time = "2025-09-27T18:36:41.777Z" }, + { url = "https://files.pythonhosted.org/packages/9c/d9/5f7756922cdd676869eca1c4e3c0cd0df60ed30199ffd775e319089cb3ed/markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219", size = 12029, upload-time = "2025-09-27T18:36:43.257Z" }, + { url = "https://files.pythonhosted.org/packages/00/07/575a68c754943058c78f30db02ee03a64b3c638586fba6a6dd56830b30a3/markupsafe-3.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6", size = 24374, upload-time = "2025-09-27T18:36:44.508Z" }, + { url = "https://files.pythonhosted.org/packages/a9/21/9b05698b46f218fc0e118e1f8168395c65c8a2c750ae2bab54fc4bd4e0e8/markupsafe-3.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676", size = 22980, upload-time = "2025-09-27T18:36:45.385Z" }, + { url = "https://files.pythonhosted.org/packages/7f/71/544260864f893f18b6827315b988c146b559391e6e7e8f7252839b1b846a/markupsafe-3.0.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9", size = 21990, upload-time = "2025-09-27T18:36:46.916Z" }, + { url = "https://files.pythonhosted.org/packages/c2/28/b50fc2f74d1ad761af2f5dcce7492648b983d00a65b8c0e0cb457c82ebbe/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1", size = 23784, upload-time = "2025-09-27T18:36:47.884Z" }, + { url = "https://files.pythonhosted.org/packages/ed/76/104b2aa106a208da8b17a2fb72e033a5a9d7073c68f7e508b94916ed47a9/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc", size = 21588, upload-time = "2025-09-27T18:36:48.82Z" }, + { url = "https://files.pythonhosted.org/packages/b5/99/16a5eb2d140087ebd97180d95249b00a03aa87e29cc224056274f2e45fd6/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12", size = 23041, upload-time = "2025-09-27T18:36:49.797Z" }, + { url = "https://files.pythonhosted.org/packages/19/bc/e7140ed90c5d61d77cea142eed9f9c303f4c4806f60a1044c13e3f1471d0/markupsafe-3.0.3-cp313-cp313-win32.whl", hash = "sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed", size = 14543, upload-time = "2025-09-27T18:36:51.584Z" }, + { url = "https://files.pythonhosted.org/packages/05/73/c4abe620b841b6b791f2edc248f556900667a5a1cf023a6646967ae98335/markupsafe-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5", size = 15113, upload-time = "2025-09-27T18:36:52.537Z" }, + { url = "https://files.pythonhosted.org/packages/f0/3a/fa34a0f7cfef23cf9500d68cb7c32dd64ffd58a12b09225fb03dd37d5b80/markupsafe-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485", size = 13911, upload-time = "2025-09-27T18:36:53.513Z" }, + { url = "https://files.pythonhosted.org/packages/e4/d7/e05cd7efe43a88a17a37b3ae96e79a19e846f3f456fe79c57ca61356ef01/markupsafe-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73", size = 11658, upload-time = "2025-09-27T18:36:54.819Z" }, + { url = "https://files.pythonhosted.org/packages/99/9e/e412117548182ce2148bdeacdda3bb494260c0b0184360fe0d56389b523b/markupsafe-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37", size = 12066, upload-time = "2025-09-27T18:36:55.714Z" }, + { url = "https://files.pythonhosted.org/packages/bc/e6/fa0ffcda717ef64a5108eaa7b4f5ed28d56122c9a6d70ab8b72f9f715c80/markupsafe-3.0.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19", size = 25639, upload-time = "2025-09-27T18:36:56.908Z" }, + { url = "https://files.pythonhosted.org/packages/96/ec/2102e881fe9d25fc16cb4b25d5f5cde50970967ffa5dddafdb771237062d/markupsafe-3.0.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025", size = 23569, upload-time = "2025-09-27T18:36:57.913Z" }, + { url = "https://files.pythonhosted.org/packages/4b/30/6f2fce1f1f205fc9323255b216ca8a235b15860c34b6798f810f05828e32/markupsafe-3.0.3-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6", size = 23284, upload-time = "2025-09-27T18:36:58.833Z" }, + { url = "https://files.pythonhosted.org/packages/58/47/4a0ccea4ab9f5dcb6f79c0236d954acb382202721e704223a8aafa38b5c8/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f", size = 24801, upload-time = "2025-09-27T18:36:59.739Z" }, + { url = "https://files.pythonhosted.org/packages/6a/70/3780e9b72180b6fecb83a4814d84c3bf4b4ae4bf0b19c27196104149734c/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb", size = 22769, upload-time = "2025-09-27T18:37:00.719Z" }, + { url = "https://files.pythonhosted.org/packages/98/c5/c03c7f4125180fc215220c035beac6b9cb684bc7a067c84fc69414d315f5/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009", size = 23642, upload-time = "2025-09-27T18:37:01.673Z" }, + { url = "https://files.pythonhosted.org/packages/80/d6/2d1b89f6ca4bff1036499b1e29a1d02d282259f3681540e16563f27ebc23/markupsafe-3.0.3-cp313-cp313t-win32.whl", hash = "sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354", size = 14612, upload-time = "2025-09-27T18:37:02.639Z" }, + { url = "https://files.pythonhosted.org/packages/2b/98/e48a4bfba0a0ffcf9925fe2d69240bfaa19c6f7507b8cd09c70684a53c1e/markupsafe-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218", size = 15200, upload-time = "2025-09-27T18:37:03.582Z" }, + { url = "https://files.pythonhosted.org/packages/0e/72/e3cc540f351f316e9ed0f092757459afbc595824ca724cbc5a5d4263713f/markupsafe-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287", size = 13973, upload-time = "2025-09-27T18:37:04.929Z" }, + { url = "https://files.pythonhosted.org/packages/33/8a/8e42d4838cd89b7dde187011e97fe6c3af66d8c044997d2183fbd6d31352/markupsafe-3.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe", size = 11619, upload-time = "2025-09-27T18:37:06.342Z" }, + { url = "https://files.pythonhosted.org/packages/b5/64/7660f8a4a8e53c924d0fa05dc3a55c9cee10bbd82b11c5afb27d44b096ce/markupsafe-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026", size = 12029, upload-time = "2025-09-27T18:37:07.213Z" }, + { url = "https://files.pythonhosted.org/packages/da/ef/e648bfd021127bef5fa12e1720ffed0c6cbb8310c8d9bea7266337ff06de/markupsafe-3.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737", size = 24408, upload-time = "2025-09-27T18:37:09.572Z" }, + { url = "https://files.pythonhosted.org/packages/41/3c/a36c2450754618e62008bf7435ccb0f88053e07592e6028a34776213d877/markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97", size = 23005, upload-time = "2025-09-27T18:37:10.58Z" }, + { url = "https://files.pythonhosted.org/packages/bc/20/b7fdf89a8456b099837cd1dc21974632a02a999ec9bf7ca3e490aacd98e7/markupsafe-3.0.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d", size = 22048, upload-time = "2025-09-27T18:37:11.547Z" }, + { url = "https://files.pythonhosted.org/packages/9a/a7/591f592afdc734f47db08a75793a55d7fbcc6902a723ae4cfbab61010cc5/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda", size = 23821, upload-time = "2025-09-27T18:37:12.48Z" }, + { url = "https://files.pythonhosted.org/packages/7d/33/45b24e4f44195b26521bc6f1a82197118f74df348556594bd2262bda1038/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf", size = 21606, upload-time = "2025-09-27T18:37:13.485Z" }, + { url = "https://files.pythonhosted.org/packages/ff/0e/53dfaca23a69fbfbbf17a4b64072090e70717344c52eaaaa9c5ddff1e5f0/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe", size = 23043, upload-time = "2025-09-27T18:37:14.408Z" }, + { url = "https://files.pythonhosted.org/packages/46/11/f333a06fc16236d5238bfe74daccbca41459dcd8d1fa952e8fbd5dccfb70/markupsafe-3.0.3-cp314-cp314-win32.whl", hash = "sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9", size = 14747, upload-time = "2025-09-27T18:37:15.36Z" }, + { url = "https://files.pythonhosted.org/packages/28/52/182836104b33b444e400b14f797212f720cbc9ed6ba34c800639d154e821/markupsafe-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581", size = 15341, upload-time = "2025-09-27T18:37:16.496Z" }, + { url = "https://files.pythonhosted.org/packages/6f/18/acf23e91bd94fd7b3031558b1f013adfa21a8e407a3fdb32745538730382/markupsafe-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4", size = 14073, upload-time = "2025-09-27T18:37:17.476Z" }, + { url = "https://files.pythonhosted.org/packages/3c/f0/57689aa4076e1b43b15fdfa646b04653969d50cf30c32a102762be2485da/markupsafe-3.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab", size = 11661, upload-time = "2025-09-27T18:37:18.453Z" }, + { url = "https://files.pythonhosted.org/packages/89/c3/2e67a7ca217c6912985ec766c6393b636fb0c2344443ff9d91404dc4c79f/markupsafe-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175", size = 12069, upload-time = "2025-09-27T18:37:19.332Z" }, + { url = "https://files.pythonhosted.org/packages/f0/00/be561dce4e6ca66b15276e184ce4b8aec61fe83662cce2f7d72bd3249d28/markupsafe-3.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634", size = 25670, upload-time = "2025-09-27T18:37:20.245Z" }, + { url = "https://files.pythonhosted.org/packages/50/09/c419f6f5a92e5fadde27efd190eca90f05e1261b10dbd8cbcb39cd8ea1dc/markupsafe-3.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50", size = 23598, upload-time = "2025-09-27T18:37:21.177Z" }, + { url = "https://files.pythonhosted.org/packages/22/44/a0681611106e0b2921b3033fc19bc53323e0b50bc70cffdd19f7d679bb66/markupsafe-3.0.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e", size = 23261, upload-time = "2025-09-27T18:37:22.167Z" }, + { url = "https://files.pythonhosted.org/packages/5f/57/1b0b3f100259dc9fffe780cfb60d4be71375510e435efec3d116b6436d43/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5", size = 24835, upload-time = "2025-09-27T18:37:23.296Z" }, + { url = "https://files.pythonhosted.org/packages/26/6a/4bf6d0c97c4920f1597cc14dd720705eca0bf7c787aebc6bb4d1bead5388/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523", size = 22733, upload-time = "2025-09-27T18:37:24.237Z" }, + { url = "https://files.pythonhosted.org/packages/14/c7/ca723101509b518797fedc2fdf79ba57f886b4aca8a7d31857ba3ee8281f/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc", size = 23672, upload-time = "2025-09-27T18:37:25.271Z" }, + { url = "https://files.pythonhosted.org/packages/fb/df/5bd7a48c256faecd1d36edc13133e51397e41b73bb77e1a69deab746ebac/markupsafe-3.0.3-cp314-cp314t-win32.whl", hash = "sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d", size = 14819, upload-time = "2025-09-27T18:37:26.285Z" }, + { url = "https://files.pythonhosted.org/packages/1a/8a/0402ba61a2f16038b48b39bccca271134be00c5c9f0f623208399333c448/markupsafe-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9", size = 15426, upload-time = "2025-09-27T18:37:27.316Z" }, + { url = "https://files.pythonhosted.org/packages/70/bc/6f1c2f612465f5fa89b95bead1f44dcb607670fd42891d8fdcd5d039f4f4/markupsafe-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa", size = 14146, upload-time = "2025-09-27T18:37:28.327Z" }, ] [[package]] name = "matplotlib" -version = "3.10.9" +version = "3.11.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "contourpy" }, { name = "cycler" }, { name = "fonttools" }, { name = "kiwisolver" }, - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, { name = "packaging" }, { name = "pillow" }, { name = "pyparsing" }, { name = "python-dateutil" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/63/1b/4be5be87d43d327a0cf4de1a56e86f7f84c89312452406cf122efe2839e6/matplotlib-3.10.9.tar.gz", hash = "sha256:fd66508e8c6877d98e586654b608a0456db8d7e8a546eb1e2600efd957302358", size = 34811233, upload-time = "2026-04-24T00:14:13.539Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/4c/8c/290f021104741fea63769c31494f5324c0cd249bf536a65a4350767b1f22/matplotlib-3.10.9-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:68cfdcede415f7c8f5577b03303dd94526cdb6d11036cecdc205e08733b2d2bb", size = 8306860, upload-time = "2026-04-24T00:12:01.207Z" }, - { url = "https://files.pythonhosted.org/packages/51/18/325cd32ece1120d1da51cc4e4294c6580190699490183fc2fe8cb6d61ec5/matplotlib-3.10.9-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:dfca0129678bd56379db26c52b5d77ed7de314c047492fbdc763aa7501710cfb", size = 8199254, upload-time = "2026-04-24T00:12:04.239Z" }, - { url = "https://files.pythonhosted.org/packages/79/db/e28c1b83e3680740aa78925f5fb2ae4d16207207419ad75ea9fe604f8676/matplotlib-3.10.9-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8e436d155fa8a3399dc62683f8f5d0e2e50d25d0144a73edd73f82eec8f4abfb", size = 8777092, upload-time = "2026-04-24T00:12:06.793Z" }, - { url = "https://files.pythonhosted.org/packages/55/fa/3ce7adfe9ba101748f465211660d9c6374c876b671bdb8c2bb6d347e8b94/matplotlib-3.10.9-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:56fc0bd271b00025c6edfdc7c2dcd247372c8e1544971d62e1dc7c17367e8bf9", size = 9595691, upload-time = "2026-04-24T00:12:09.706Z" }, - { url = "https://files.pythonhosted.org/packages/36/c4/6960a76686ed668f2c60f84e9799ba4c0d56abdb36b1577b60c1d061d1ec/matplotlib-3.10.9-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:a5a6104ed666402ba5106d7f36e0e0cdca4e8d7fa4d39708ca88019e2835a2eb", size = 9659771, upload-time = "2026-04-24T00:12:12.766Z" }, - { url = "https://files.pythonhosted.org/packages/7e/0d/271aace3342157c64700c9ff4c59c7b392f3dbab393692e8db6fbe7ab96c/matplotlib-3.10.9-cp311-cp311-win_amd64.whl", hash = "sha256:d730e984eddf56974c3e72b6129c7ca462ac38dc624338f4b0b23eb23ecba00f", size = 8205112, upload-time = "2026-04-24T00:12:15.773Z" }, - { url = "https://files.pythonhosted.org/packages/e2/ee/cb57ad4754f3e7b9174ce6ce66d9205fb827067e48a9f58ac09d7e7d6b77/matplotlib-3.10.9-cp311-cp311-win_arm64.whl", hash = "sha256:51bf0ddbdc598e060d46c16b5590708f81a1624cefbaaf62f6a81bf9285b8c80", size = 8132310, upload-time = "2026-04-24T00:12:18.645Z" }, - { url = "https://files.pythonhosted.org/packages/35/c6/5581e26c72233ebb2a2a6fed2d24fb7c66b4700120b813f51b0555acf0b6/matplotlib-3.10.9-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:f0c3c28d9fbcc1fe7a03be236d73430cf6409c41fb2383a7ac52fe932b072cb1", size = 8319908, upload-time = "2026-04-24T00:12:21.323Z" }, - { url = "https://files.pythonhosted.org/packages/b7/18/4880dd762e40cd360c1bf06e890c5a97b997e91cb324602b1a19950ad5ce/matplotlib-3.10.9-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:41cb28c2bd769aa3e98322c6ab09854cbcc52ab69d2759d681bba3e327b2b320", size = 8216016, upload-time = "2026-04-24T00:12:23.4Z" }, - { url = "https://files.pythonhosted.org/packages/32/91/d024616abdba99e83120e07a20658976f6a343646710760c4a51df126029/matplotlib-3.10.9-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ae20801130378b82d647ff5047c07316295b68dc054ca6b3c13519d0ea624285", size = 8789336, upload-time = "2026-04-24T00:12:26.096Z" }, - { url = "https://files.pythonhosted.org/packages/5c/04/030a2f61ef2158f5e4c259487a92ac877732499fb33d871585d89e03c42d/matplotlib-3.10.9-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6c63ebcd8b4b169eb2f5c200552ae6b8be8999a005b6b507ed76fb8d7d674fe2", size = 9604602, upload-time = "2026-04-24T00:12:29.052Z" }, - { url = "https://files.pythonhosted.org/packages/fc/c2/541e4d09d87bb6b5830fc28b4c887a9a8cf4e1c6cee698a8c05552ae2003/matplotlib-3.10.9-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:d75d11c949914165976c621b2324f9ef162af7ebf4b057ddf95dd1dba7e5edcf", size = 9670966, upload-time = "2026-04-24T00:12:32.131Z" }, - { url = "https://files.pythonhosted.org/packages/04/a1/4571fc46e7702de8d0c2dc54ad1b2f8e29328dea3ee90831181f7353d93c/matplotlib-3.10.9-cp312-cp312-win_amd64.whl", hash = "sha256:d091f9d758b34aaaaa6331d13574bf01891d903b3dec59bfff458ef7551de5d6", size = 8217462, upload-time = "2026-04-24T00:12:35.226Z" }, - { url = "https://files.pythonhosted.org/packages/4b/d0/2269edb12aa30c13c8bcc9382892e39943ce1d28aab4ec296e0381798e81/matplotlib-3.10.9-cp312-cp312-win_arm64.whl", hash = "sha256:10cc5ce06d10231c36f40e875f3c7e8050362a4ee8f0ee5d29a6b3277d57bb42", size = 8136688, upload-time = "2026-04-24T00:12:37.442Z" }, - { url = "https://files.pythonhosted.org/packages/aa/d3/8d4f6afbecb49fc04e060a57c0fce39ea51cc163a6bd87303ccd698e4fa6/matplotlib-3.10.9-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:b580440f1ff81a0e34122051a3dfabb7e4b7f9e380629929bde0eff9af72165f", size = 8320331, upload-time = "2026-04-24T00:12:39.688Z" }, - { url = "https://files.pythonhosted.org/packages/63/d9/9e14bc7564bf92d5ffa801ae5fac819ce74b925dfb55e3ebde61a3bbad3e/matplotlib-3.10.9-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:b1b745c489cd1a77a0dc1120a05dc87af9798faebc913601feb8c73d89bf2d1e", size = 8216461, upload-time = "2026-04-24T00:12:42.494Z" }, - { url = "https://files.pythonhosted.org/packages/8a/17/4402d0d14ccf1dfc70932600b68097fbbf9c898a4871d2cbbe79c7801a32/matplotlib-3.10.9-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8f3bcac1ca5ed000a6f4337d47ba67dfddf37ed6a46c15fd7f014997f7bf865f", size = 8790091, upload-time = "2026-04-24T00:12:44.789Z" }, - { url = "https://files.pythonhosted.org/packages/3e/0b/322aeec06dd9b91411f92028b37d447342770a24392aa4813e317064dad5/matplotlib-3.10.9-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7a8d66a55def891c33147ba3ba9bfcabf0b526a43764c818acbb4525e5ed0838", size = 9605027, upload-time = "2026-04-24T00:12:47.583Z" }, - { url = "https://files.pythonhosted.org/packages/74/88/5f13482f55e7b00bcfc09838b093c2456e1379978d2a146844aae05350ad/matplotlib-3.10.9-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d843374407c4017a6403b59c6c81606773d136f3259d5b6da3131bc814542cc2", size = 9671269, upload-time = "2026-04-24T00:12:50.878Z" }, - { url = "https://files.pythonhosted.org/packages/c5/e0/0840fd2f93da988ec660b8ad1984abe9f25d2aed22a5e394ff1c68c88307/matplotlib-3.10.9-cp313-cp313-win_amd64.whl", hash = "sha256:f4399f64b3e94cd500195490972ae1ee81170df1636fa15364d157d5bdd7b921", size = 8217588, upload-time = "2026-04-24T00:12:53.784Z" }, - { url = "https://files.pythonhosted.org/packages/47/b9/d706d06dd605c49b9f83a2aed8c13e3e5db70697d7a80b7e3d7915de6b17/matplotlib-3.10.9-cp313-cp313-win_arm64.whl", hash = "sha256:ba7b3b8ef09eab7df0e86e9ae086faa433efbfbdb46afcb3aa16aabf779469a8", size = 8136913, upload-time = "2026-04-24T00:12:56.501Z" }, - { url = "https://files.pythonhosted.org/packages/9b/45/6e32d96978264c8ca8c4b1010adb955a1a49cfaf314e212bbc8908f04a61/matplotlib-3.10.9-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:09218df8a93712bd6ea133e83a153c755448cf7868316c531cffcc43f69d1cc9", size = 8368019, upload-time = "2026-04-24T00:12:58.896Z" }, - { url = "https://files.pythonhosted.org/packages/86/0a/c8e3d3bba245f0f7fc424937f8ff7ef77291a36af3edb97ccd78aa93d84f/matplotlib-3.10.9-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:82368699727bfb7b0182e1aa13082e3c08e092fa1a25d3e1fd92405bff96f6d4", size = 8264645, upload-time = "2026-04-24T00:13:01.406Z" }, - { url = "https://files.pythonhosted.org/packages/3d/aa/5bf5a14fe4fed73a4209a155606f8096ff797aad89c6c35179026571133e/matplotlib-3.10.9-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3225f4e1edcb8c86c884ddf79ebe20ecd0a67d30188f279897554ccd8fded4dc", size = 8802194, upload-time = "2026-04-24T00:13:03.702Z" }, - { url = "https://files.pythonhosted.org/packages/dd/5e/b4be852d6bba6fd15893fadf91ff26ae49cb91aac789e95dde9d342e664f/matplotlib-3.10.9-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:de2445a0c6690d21b7eb6ce071cebad6d40a2e9bdf10d039074a96ba19797b99", size = 9622684, upload-time = "2026-04-24T00:13:06.647Z" }, - { url = "https://files.pythonhosted.org/packages/4c/3d/ed428c971139112ef730f62770654d609467346d09d4b62617e1afd68a5a/matplotlib-3.10.9-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:b2b9516251cb89ff618d757daec0e2ed1bf21248013844a853d87ef85ab3081d", size = 9680790, upload-time = "2026-04-24T00:13:10.009Z" }, - { url = "https://files.pythonhosted.org/packages/e7/09/052e884aaf2b985c63cb79f715f1d5b6a3eaa7de78f6a52b9dbc077d5b53/matplotlib-3.10.9-cp313-cp313t-win_amd64.whl", hash = "sha256:e9fae004b941b23ff2edcf1567a857ed77bafc8086ffa258190462328434faf8", size = 8287571, upload-time = "2026-04-24T00:13:13.087Z" }, - { url = "https://files.pythonhosted.org/packages/f4/38/ae27288e788c35a4250491422f3db7750366fc8c97d6f36fbdecfc1f5518/matplotlib-3.10.9-cp313-cp313t-win_arm64.whl", hash = "sha256:6b63d9c7c769b88ab81e10dc86e4e0607cf56817b9f9e6cf24b2a5f1693b8e38", size = 8188292, upload-time = "2026-04-24T00:13:15.546Z" }, - { url = "https://files.pythonhosted.org/packages/d6/e6/3bd8afd04949f02eabc1c17115ea5255e19cacd4d06fc5abdde4eeb0052c/matplotlib-3.10.9-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:172db52c9e683f5d12eaf57f0f54834190e12581fe1cc2a19595a8f5acb4e77d", size = 8321276, upload-time = "2026-04-24T00:13:18.318Z" }, - { url = "https://files.pythonhosted.org/packages/41/86/86231232fff41c9f8e4a1a7d7a597d349a02527109c3af7d618366122139/matplotlib-3.10.9-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:97e35e8d39ccc85859095e01a53847432ba9a53ddf7986f7a54a11b73d0e143f", size = 8218218, upload-time = "2026-04-24T00:13:20.974Z" }, - { url = "https://files.pythonhosted.org/packages/85/8f/becc9722cafc64f5d2eb0b7c1bf5f585271c618a45dbd8fabeb021f898b6/matplotlib-3.10.9-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:aba1615dabe83188e19d4f75a253c6a08423e04c1425e64039f800050a69de6b", size = 9608145, upload-time = "2026-04-24T00:13:23.228Z" }, - { url = "https://files.pythonhosted.org/packages/32/5d/f7e914f7d9325abff4057cee62c0fa70263683189f774473cbfb534cd13b/matplotlib-3.10.9-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:34cf8167e023ad956c15f36302911d5406bd99a9862c1a8499ea6f7c0e015dc2", size = 9885085, upload-time = "2026-04-24T00:13:25.849Z" }, - { url = "https://files.pythonhosted.org/packages/a5/fd/fa69f2221534e80cc5772ac2b7d222011a2acafc2ec7216d5dd174c864ae/matplotlib-3.10.9-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:59476c6d29d612b8e9bb6ce8c5b631be6ba8f9e3a2421f22a02b192c7dd28716", size = 9672358, upload-time = "2026-04-24T00:13:28.906Z" }, - { url = "https://files.pythonhosted.org/packages/ab/1a/5a4f747a8b271cbb024946d2dd3c913ab5032ba430626f8c3528ada96b4b/matplotlib-3.10.9-cp314-cp314-win_amd64.whl", hash = "sha256:336b9acc64d309063126edcdaca00db9373af3c476bb94388fe9c5a53ad13e6f", size = 8349970, upload-time = "2026-04-24T00:13:31.904Z" }, - { url = "https://files.pythonhosted.org/packages/64/dc/95d60ecaefe30680a154b52ea96ab4b0dab547f1fd6aa12f5fb655e89cae/matplotlib-3.10.9-cp314-cp314-win_arm64.whl", hash = "sha256:2dc9477819ffd78ad12a20df1d9d6a6bd4fec6aaa9072681465fddca052f1456", size = 8272785, upload-time = "2026-04-24T00:13:34.511Z" }, - { url = "https://files.pythonhosted.org/packages/70/a0/005d68bc8b8418300ce6591f18586910a8526806e2ab663933d9f20a41e9/matplotlib-3.10.9-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:da4e09638420548f31c354032a6250e473c68e5a4e96899b4844cf39ddea23fe", size = 8367999, upload-time = "2026-04-24T00:13:36.962Z" }, - { url = "https://files.pythonhosted.org/packages/22/05/1236cc9290be70b2498af20ca348add76e3fffe7f67b477db5133a84f3ea/matplotlib-3.10.9-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:345f6f68ecc8da0ca56fad2ea08fde1a115eda530079eca185d50a7bc3e146c6", size = 8264543, upload-time = "2026-04-24T00:13:39.851Z" }, - { url = "https://files.pythonhosted.org/packages/cd/c2/071f5a5ff6c5bd63aaaf2f45c811d9bf2ced94bde188d9e1a519e21d0cba/matplotlib-3.10.9-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4edcfbd8565339aa62f1cd4012f7180926fdbe71850f7b0d3c379c175cd6b66c", size = 9622800, upload-time = "2026-04-24T00:13:42.296Z" }, - { url = "https://files.pythonhosted.org/packages/95/57/da7d1f10a85624b9e7db68e069dd94e58dc41dbf9463c5921632ecbe3661/matplotlib-3.10.9-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6be157fe17fc37cb95ac1d7374cf717ce9259616edec911a78d9d26dae8522d4", size = 9888561, upload-time = "2026-04-24T00:13:45.026Z" }, - { url = "https://files.pythonhosted.org/packages/67/b2/ef8d6bb59b0edb6c16c968b70f548aa13b54348972def5aa6ac85df67145/matplotlib-3.10.9-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:4e42042d54db34fda4e95a7bd3e5789c2a995d2dad3eb8850232ee534092fbbf", size = 9680884, upload-time = "2026-04-24T00:13:48.066Z" }, - { url = "https://files.pythonhosted.org/packages/61/1c/d21bfeb9931881ebe96bcfcff27c7ae4b160ae0ec291a714c42641a56d75/matplotlib-3.10.9-cp314-cp314t-win_amd64.whl", hash = "sha256:c27df8b3848f32a83d1767566595e43cfaa4460380974da06f4279a7ec143c39", size = 8432333, upload-time = "2026-04-24T00:13:51.008Z" }, - { url = "https://files.pythonhosted.org/packages/78/23/92493c3e6e1b635ccfff146f7b99e674808787915420373ac399283764c2/matplotlib-3.10.9-cp314-cp314t-win_arm64.whl", hash = "sha256:a49f1eadc84ca85fd72fa4e89e70e61bf86452df6f971af04b12c60761a0772c", size = 8324785, upload-time = "2026-04-24T00:13:53.633Z" }, - { url = "https://files.pythonhosted.org/packages/63/e2/9f66ca6a651a52abfe0d4964ce01439ed34f3f1e119de10ff3a07f403043/matplotlib-3.10.9-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:42fb814efabe95c06c1994d8ab5a8385f43a249e23badd3ba931d4308e5bca20", size = 8304420, upload-time = "2026-04-24T00:14:04.57Z" }, - { url = "https://files.pythonhosted.org/packages/e8/e8/467c03568218792906aa87b5e7bb379b605e056ed0c74fe00c051786d925/matplotlib-3.10.9-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:f76e640a5268850bfda54b5131b1b1941cc685e42c5fa98ed9f2d64038308cba", size = 8197981, upload-time = "2026-04-24T00:14:07.233Z" }, - { url = "https://files.pythonhosted.org/packages/6f/87/afead29192170917537934c6aff4b008c805fff7b1ccea0c79120d96beda/matplotlib-3.10.9-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3fc0364dfbe1d07f6d15c5ebd0c5bf89e126916e5a8667dd4a7a6e84c36653d4", size = 8774002, upload-time = "2026-04-24T00:14:09.816Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/ae/e2/d2d5295be2f44c678ebaf3544ba32d20c1f9ef08c49fe47f496180e1db15/matplotlib-3.10.7.tar.gz", hash = "sha256:a06ba7e2a2ef9131c79c49e63dad355d2d878413a0376c1727c8b9335ff731c7", size = 34804865 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/fc/bc/0fb489005669127ec13f51be0c6adc074d7cf191075dab1da9fe3b7a3cfc/matplotlib-3.10.7-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:53b492410a6cd66c7a471de6c924f6ede976e963c0f3097a3b7abfadddc67d0a", size = 8257507 }, - { url = "https://files.pythonhosted.org/packages/e2/6a/d42588ad895279ff6708924645b5d2ed54a7fb2dc045c8a804e955aeace1/matplotlib-3.10.7-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d9749313deb729f08207718d29c86246beb2ea3fdba753595b55901dee5d2fd6", size = 8119565 }, - { url = "https://files.pythonhosted.org/packages/10/b7/4aa196155b4d846bd749cf82aa5a4c300cf55a8b5e0dfa5b722a63c0f8a0/matplotlib-3.10.7-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2222c7ba2cbde7fe63032769f6eb7e83ab3227f47d997a8453377709b7fe3a5a", size = 8692668 }, - { url = "https://files.pythonhosted.org/packages/e6/e7/664d2b97016f46683a02d854d730cfcf54ff92c1dafa424beebef50f831d/matplotlib-3.10.7-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e91f61a064c92c307c5a9dc8c05dc9f8a68f0a3be199d9a002a0622e13f874a1", size = 9521051 }, - { url = "https://files.pythonhosted.org/packages/a8/a3/37aef1404efa615f49b5758a5e0261c16dd88f389bc1861e722620e4a754/matplotlib-3.10.7-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:6f1851eab59ca082c95df5a500106bad73672645625e04538b3ad0f69471ffcc", size = 9576878 }, - { url = "https://files.pythonhosted.org/packages/33/cd/b145f9797126f3f809d177ca378de57c45413c5099c5990de2658760594a/matplotlib-3.10.7-cp311-cp311-win_amd64.whl", hash = "sha256:6516ce375109c60ceec579e699524e9d504cd7578506f01150f7a6bc174a775e", size = 8115142 }, - { url = "https://files.pythonhosted.org/packages/2e/39/63bca9d2b78455ed497fcf51a9c71df200a11048f48249038f06447fa947/matplotlib-3.10.7-cp311-cp311-win_arm64.whl", hash = "sha256:b172db79759f5f9bc13ef1c3ef8b9ee7b37b0247f987fbbbdaa15e4f87fd46a9", size = 7992439 }, - { url = "https://files.pythonhosted.org/packages/be/b3/09eb0f7796932826ec20c25b517d568627754f6c6462fca19e12c02f2e12/matplotlib-3.10.7-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7a0edb7209e21840e8361e91ea84ea676658aa93edd5f8762793dec77a4a6748", size = 8272389 }, - { url = "https://files.pythonhosted.org/packages/11/0b/1ae80ddafb8652fd8046cb5c8460ecc8d4afccb89e2c6d6bec61e04e1eaf/matplotlib-3.10.7-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:c380371d3c23e0eadf8ebff114445b9f970aff2010198d498d4ab4c3b41eea4f", size = 8128247 }, - { url = "https://files.pythonhosted.org/packages/7d/18/95ae2e242d4a5c98bd6e90e36e128d71cf1c7e39b0874feaed3ef782e789/matplotlib-3.10.7-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d5f256d49fea31f40f166a5e3131235a5d2f4b7f44520b1cf0baf1ce568ccff0", size = 8696996 }, - { url = "https://files.pythonhosted.org/packages/7e/3d/5b559efc800bd05cb2033aa85f7e13af51958136a48327f7c261801ff90a/matplotlib-3.10.7-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:11ae579ac83cdf3fb72573bb89f70e0534de05266728740d478f0f818983c695", size = 9530153 }, - { url = "https://files.pythonhosted.org/packages/88/57/eab4a719fd110312d3c220595d63a3c85ec2a39723f0f4e7fa7e6e3f74ba/matplotlib-3.10.7-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:4c14b6acd16cddc3569a2d515cfdd81c7a68ac5639b76548cfc1a9e48b20eb65", size = 9593093 }, - { url = "https://files.pythonhosted.org/packages/31/3c/80816f027b3a4a28cd2a0a6ef7f89a2db22310e945cd886ec25bfb399221/matplotlib-3.10.7-cp312-cp312-win_amd64.whl", hash = "sha256:0d8c32b7ea6fb80b1aeff5a2ceb3fb9778e2759e899d9beff75584714afcc5ee", size = 8122771 }, - { url = "https://files.pythonhosted.org/packages/de/77/ef1fc78bfe99999b2675435cc52120887191c566b25017d78beaabef7f2d/matplotlib-3.10.7-cp312-cp312-win_arm64.whl", hash = "sha256:5f3f6d315dcc176ba7ca6e74c7768fb7e4cf566c49cb143f6bc257b62e634ed8", size = 7992812 }, - { url = "https://files.pythonhosted.org/packages/02/9c/207547916a02c78f6bdd83448d9b21afbc42f6379ed887ecf610984f3b4e/matplotlib-3.10.7-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:1d9d3713a237970569156cfb4de7533b7c4eacdd61789726f444f96a0d28f57f", size = 8273212 }, - { url = "https://files.pythonhosted.org/packages/bc/d0/b3d3338d467d3fc937f0bb7f256711395cae6f78e22cef0656159950adf0/matplotlib-3.10.7-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:37a1fea41153dd6ee061d21ab69c9cf2cf543160b1b85d89cd3d2e2a7902ca4c", size = 8128713 }, - { url = "https://files.pythonhosted.org/packages/22/ff/6425bf5c20d79aa5b959d1ce9e65f599632345391381c9a104133fe0b171/matplotlib-3.10.7-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b3c4ea4948d93c9c29dc01c0c23eef66f2101bf75158c291b88de6525c55c3d1", size = 8698527 }, - { url = "https://files.pythonhosted.org/packages/d0/7f/ccdca06f4c2e6c7989270ed7829b8679466682f4cfc0f8c9986241c023b6/matplotlib-3.10.7-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22df30ffaa89f6643206cf13877191c63a50e8f800b038bc39bee9d2d4957632", size = 9529690 }, - { url = "https://files.pythonhosted.org/packages/b8/95/b80fc2c1f269f21ff3d193ca697358e24408c33ce2b106a7438a45407b63/matplotlib-3.10.7-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b69676845a0a66f9da30e87f48be36734d6748024b525ec4710be40194282c84", size = 9593732 }, - { url = "https://files.pythonhosted.org/packages/e1/b6/23064a96308b9aeceeffa65e96bcde459a2ea4934d311dee20afde7407a0/matplotlib-3.10.7-cp313-cp313-win_amd64.whl", hash = "sha256:744991e0cc863dd669c8dc9136ca4e6e0082be2070b9d793cbd64bec872a6815", size = 8122727 }, - { url = "https://files.pythonhosted.org/packages/b3/a6/2faaf48133b82cf3607759027f82b5c702aa99cdfcefb7f93d6ccf26a424/matplotlib-3.10.7-cp313-cp313-win_arm64.whl", hash = "sha256:fba2974df0bf8ce3c995fa84b79cde38326e0f7b5409e7a3a481c1141340bcf7", size = 7992958 }, - { url = "https://files.pythonhosted.org/packages/4a/f0/b018fed0b599bd48d84c08794cb242227fe3341952da102ee9d9682db574/matplotlib-3.10.7-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:932c55d1fa7af4423422cb6a492a31cbcbdbe68fd1a9a3f545aa5e7a143b5355", size = 8316849 }, - { url = "https://files.pythonhosted.org/packages/b0/b7/bb4f23856197659f275e11a2a164e36e65e9b48ea3e93c4ec25b4f163198/matplotlib-3.10.7-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5e38c2d581d62ee729a6e144c47a71b3f42fb4187508dbbf4fe71d5612c3433b", size = 8178225 }, - { url = "https://files.pythonhosted.org/packages/62/56/0600609893ff277e6f3ab3c0cef4eafa6e61006c058e84286c467223d4d5/matplotlib-3.10.7-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:786656bb13c237bbcebcd402f65f44dd61ead60ee3deb045af429d889c8dbc67", size = 8711708 }, - { url = "https://files.pythonhosted.org/packages/d8/1a/6bfecb0cafe94d6658f2f1af22c43b76cf7a1c2f0dc34ef84cbb6809617e/matplotlib-3.10.7-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:09d7945a70ea43bf9248f4b6582734c2fe726723204a76eca233f24cffc7ef67", size = 9541409 }, - { url = "https://files.pythonhosted.org/packages/08/50/95122a407d7f2e446fd865e2388a232a23f2b81934960ea802f3171518e4/matplotlib-3.10.7-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:d0b181e9fa8daf1d9f2d4c547527b167cb8838fc587deabca7b5c01f97199e84", size = 9594054 }, - { url = "https://files.pythonhosted.org/packages/13/76/75b194a43b81583478a81e78a07da8d9ca6ddf50dd0a2ccabf258059481d/matplotlib-3.10.7-cp313-cp313t-win_amd64.whl", hash = "sha256:31963603041634ce1a96053047b40961f7a29eb8f9a62e80cc2c0427aa1d22a2", size = 8200100 }, - { url = "https://files.pythonhosted.org/packages/f5/9e/6aefebdc9f8235c12bdeeda44cc0383d89c1e41da2c400caf3ee2073a3ce/matplotlib-3.10.7-cp313-cp313t-win_arm64.whl", hash = "sha256:aebed7b50aa6ac698c90f60f854b47e48cd2252b30510e7a1feddaf5a3f72cbf", size = 8042131 }, - { url = "https://files.pythonhosted.org/packages/0d/4b/e5bc2c321b6a7e3a75638d937d19ea267c34bd5a90e12bee76c4d7c7a0d9/matplotlib-3.10.7-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:d883460c43e8c6b173fef244a2341f7f7c0e9725c7fe68306e8e44ed9c8fb100", size = 8273787 }, - { url = "https://files.pythonhosted.org/packages/86/ad/6efae459c56c2fbc404da154e13e3a6039129f3c942b0152624f1c621f05/matplotlib-3.10.7-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:07124afcf7a6504eafcb8ce94091c5898bbdd351519a1beb5c45f7a38c67e77f", size = 8131348 }, - { url = "https://files.pythonhosted.org/packages/a6/5a/a4284d2958dee4116359cc05d7e19c057e64ece1b4ac986ab0f2f4d52d5a/matplotlib-3.10.7-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c17398b709a6cce3d9fdb1595c33e356d91c098cd9486cb2cc21ea2ea418e715", size = 9533949 }, - { url = "https://files.pythonhosted.org/packages/de/ff/f3781b5057fa3786623ad8976fc9f7b0d02b2f28534751fd5a44240de4cf/matplotlib-3.10.7-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7146d64f561498764561e9cd0ed64fcf582e570fc519e6f521e2d0cfd43365e1", size = 9804247 }, - { url = "https://files.pythonhosted.org/packages/47/5a/993a59facb8444efb0e197bf55f545ee449902dcee86a4dfc580c3b61314/matplotlib-3.10.7-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:90ad854c0a435da3104c01e2c6f0028d7e719b690998a2333d7218db80950722", size = 9595497 }, - { url = "https://files.pythonhosted.org/packages/0d/a5/77c95aaa9bb32c345cbb49626ad8eb15550cba2e6d4c88081a6c2ac7b08d/matplotlib-3.10.7-cp314-cp314-win_amd64.whl", hash = "sha256:4645fc5d9d20ffa3a39361fcdbcec731382763b623b72627806bf251b6388866", size = 8252732 }, - { url = "https://files.pythonhosted.org/packages/74/04/45d269b4268d222390d7817dae77b159651909669a34ee9fdee336db5883/matplotlib-3.10.7-cp314-cp314-win_arm64.whl", hash = "sha256:9257be2f2a03415f9105c486d304a321168e61ad450f6153d77c69504ad764bb", size = 8124240 }, - { url = "https://files.pythonhosted.org/packages/4b/c7/ca01c607bb827158b439208c153d6f14ddb9fb640768f06f7ca3488ae67b/matplotlib-3.10.7-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1e4bbad66c177a8fdfa53972e5ef8be72a5f27e6a607cec0d8579abd0f3102b1", size = 8316938 }, - { url = "https://files.pythonhosted.org/packages/84/d2/5539e66e9f56d2fdec94bb8436f5e449683b4e199bcc897c44fbe3c99e28/matplotlib-3.10.7-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:d8eb7194b084b12feb19142262165832fc6ee879b945491d1c3d4660748020c4", size = 8178245 }, - { url = "https://files.pythonhosted.org/packages/77/b5/e6ca22901fd3e4fe433a82e583436dd872f6c966fca7e63cf806b40356f8/matplotlib-3.10.7-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b4d41379b05528091f00e1728004f9a8d7191260f3862178b88e8fd770206318", size = 9541411 }, - { url = "https://files.pythonhosted.org/packages/9e/99/a4524db57cad8fee54b7237239a8f8360bfcfa3170d37c9e71c090c0f409/matplotlib-3.10.7-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4a74f79fafb2e177f240579bc83f0b60f82cc47d2f1d260f422a0627207008ca", size = 9803664 }, - { url = "https://files.pythonhosted.org/packages/e6/a5/85e2edf76ea0ad4288d174926d9454ea85f3ce5390cc4e6fab196cbf250b/matplotlib-3.10.7-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:702590829c30aada1e8cef0568ddbffa77ca747b4d6e36c6d173f66e301f89cc", size = 9594066 }, - { url = "https://files.pythonhosted.org/packages/39/69/9684368a314f6d83fe5c5ad2a4121a3a8e03723d2e5c8ea17b66c1bad0e7/matplotlib-3.10.7-cp314-cp314t-win_amd64.whl", hash = "sha256:f79d5de970fc90cd5591f60053aecfce1fcd736e0303d9f0bf86be649fa68fb8", size = 8342832 }, - { url = "https://files.pythonhosted.org/packages/04/5f/e22e08da14bc1a0894184640d47819d2338b792732e20d292bf86e5ab785/matplotlib-3.10.7-cp314-cp314t-win_arm64.whl", hash = "sha256:cb783436e47fcf82064baca52ce748af71725d0352e1d31564cbe9c95df92b9c", size = 8172585 }, - { url = "https://files.pythonhosted.org/packages/58/8f/76d5dc21ac64a49e5498d7f0472c0781dae442dd266a67458baec38288ec/matplotlib-3.10.7-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:15112bcbaef211bd663fa935ec33313b948e214454d949b723998a43357b17b0", size = 8252283 }, - { url = "https://files.pythonhosted.org/packages/27/0d/9c5d4c2317feb31d819e38c9f947c942f42ebd4eb935fc6fd3518a11eaa7/matplotlib-3.10.7-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:d2a959c640cdeecdd2ec3136e8ea0441da59bcaf58d67e9c590740addba2cb68", size = 8116733 }, - { url = "https://files.pythonhosted.org/packages/9a/cc/3fe688ff1355010937713164caacf9ed443675ac48a997bab6ed23b3f7c0/matplotlib-3.10.7-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3886e47f64611046bc1db523a09dd0a0a6bed6081e6f90e13806dd1d1d1b5e91", size = 8693919 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) +sdist = { url = "https://files.pythonhosted.org/packages/1f/24/080c99d223d158d3a8902769269ab6da5b50f7a0e6e072513907e02b7a6c/matplotlib-3.11.0.tar.gz", hash = "sha256:68c0c7be01b30dcca3638934f7f591df73401235cbdbf0d1ab1c71e7db7f8b57", size = 33251176, upload-time = "2026-06-12T02:29:15.508Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ce/a2/78f662f1b18968531f67d3fcde1b7ea8496920bacd4f16ddb5b79d112e46/matplotlib-3.11.0-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:f857524b442f0f36e641868ce2171aafa88cb0bc0644f4e1d8a5df9b32649fef", size = 9436261, upload-time = "2026-06-12T02:27:34.161Z" }, + { url = "https://files.pythonhosted.org/packages/5e/92/044f1de43901310202f4c79acf4f141be53b2ca8d8380e2fcefb3d523a75/matplotlib-3.11.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:57baa92fdc82948ed716eae6d2579d4d6f40965cd8d2f416755b4a72580a3233", size = 9264669, upload-time = "2026-06-12T02:27:37.413Z" }, + { url = "https://files.pythonhosted.org/packages/53/f4/f0b4f9ba7ec14a7af8151f3ad71ecfe3561e6ba38cfab1db3681ba4ca112/matplotlib-3.11.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:630eee0e67d35cce2019a0e670719f4816e3b86aff0fa72729f6c69786fceb45", size = 10021076, upload-time = "2026-06-12T02:27:39.926Z" }, + { url = "https://files.pythonhosted.org/packages/d7/33/4d679c6dcd594a156542080ac907ddccf7b09ca11655c4b28eca8e9ee5da/matplotlib-3.11.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5106c444d0bf966eee2853548c03772af4ab7199118e086c62fbac8ccb07c055", size = 10828999, upload-time = "2026-06-12T02:27:42.433Z" }, + { url = "https://files.pythonhosted.org/packages/07/74/0a3683802037d8cd013144d77c247219b47f2aabace6fdde74faa12bacf7/matplotlib-3.11.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:4d7aea652b58e686444079be3376ef546bffa1eee9b9bb9c472b9fcf6cf410d3", size = 10913103, upload-time = "2026-06-12T02:27:44.827Z" }, + { url = "https://files.pythonhosted.org/packages/d0/9f/970fcbf381e82ec66fdf5da8ea76e2e9240f61a24011ce9fd1d42c37ac2d/matplotlib-3.11.0-cp311-cp311-win_amd64.whl", hash = "sha256:70a5b3e9a5dab708c0f039709ae7c68d5b4d254e291ef76492cdba230c8bb5e4", size = 9310945, upload-time = "2026-06-12T02:27:46.867Z" }, + { url = "https://files.pythonhosted.org/packages/14/4e/6e7cfed23611265ded53806852343b5c59339e506e84c474a9b5afc3b249/matplotlib-3.11.0-cp311-cp311-win_arm64.whl", hash = "sha256:3d68266213e73823ac3be90615bab0cf31f88851e114cdb1dd25dacf3b01e1a7", size = 8999304, upload-time = "2026-06-12T02:27:48.798Z" }, + { url = "https://files.pythonhosted.org/packages/da/17/f5276b496c61477a6c4fc5e7401f4bfe1c2e5ef7c6cd67896f2ade3809cb/matplotlib-3.11.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:06b5872e9cf11adc8f589ded3ce11bc3e1061ad498259664fabc1f6615beb918", size = 9449976, upload-time = "2026-06-12T02:27:50.989Z" }, + { url = "https://files.pythonhosted.org/packages/82/34/bdd77418adb2178a1d59f044bd67bfebb115896e91b840b8a197eb3f4f4e/matplotlib-3.11.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0515d495124be3124340e59f164d901ed4484e2246a5b74cfa483cac3b80bd97", size = 9279307, upload-time = "2026-06-12T02:27:53.247Z" }, + { url = "https://files.pythonhosted.org/packages/94/95/7f522393c88313336b20d70fc849555757b2e5febc22b83b3a3f0fd4bce9/matplotlib-3.11.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:be5f93a1d21981bfb802ded0d77a0caa92d4342a47d45754fac77e314a506344", size = 10031353, upload-time = "2026-06-12T02:27:55.215Z" }, + { url = "https://files.pythonhosted.org/packages/87/ce/8f25a0e3186aefd61913e7467d1b999465bcd0d0c03ac695c1b26ca559b7/matplotlib-3.11.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:41635d7909d19e52e924a521dde6d8f670b0f53ab1d0e8c331fa831554f681d1", size = 10839232, upload-time = "2026-06-12T02:27:57.746Z" }, + { url = "https://files.pythonhosted.org/packages/85/c2/db15da2bbdf9e3ca66df7db8e2c33a1dfed67be24a24d2c878efaaff01d6/matplotlib-3.11.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:94f5000f67ca9faa300863ea17f8bce9175cb67b88bec4bc7780502d53dd7c9e", size = 10923899, upload-time = "2026-06-12T02:28:00.223Z" }, + { url = "https://files.pythonhosted.org/packages/e5/2f/a58a4443a4d052a4ea77557478336aefc26c7981f6408d37adba763aa758/matplotlib-3.11.0-cp312-cp312-win_amd64.whl", hash = "sha256:ac6f1ef39f3d0f9e2463303013094992cdbe0f85f43bc54155bc472b2042768e", size = 9329528, upload-time = "2026-06-12T02:28:02.27Z" }, + { url = "https://files.pythonhosted.org/packages/61/0f/4b669589d47733b97ab9df4b58d6fc1e68acb5ea42a928dc7cbdd6bf5871/matplotlib-3.11.0-cp312-cp312-win_arm64.whl", hash = "sha256:9dd11fb612ce7bc60b1de5b4fc87ff959d22317b5de42aabf392f66f97af22eb", size = 9003413, upload-time = "2026-06-12T02:28:04.49Z" }, + { url = "https://files.pythonhosted.org/packages/55/41/aa47f156b061d14c98b906f76c428507397708ec63ff94f410ae1752b426/matplotlib-3.11.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:6ce3b839b34ae1f430b4616893a2945a2999debaa7e94e7e29a2a8bbf286f7b5", size = 9450532, upload-time = "2026-06-12T02:28:06.769Z" }, + { url = "https://files.pythonhosted.org/packages/8c/4f/5a9eb0375e81413953febf8af7b012a6b6357f53438a15c4f5ad86c6bbb5/matplotlib-3.11.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:373db8f91214e8ccaf35ac833cc1dd59dd961e148bbd55dd027141591dde1313", size = 9279760, upload-time = "2026-06-12T02:28:09.152Z" }, + { url = "https://files.pythonhosted.org/packages/a4/c0/1117d53077e3ac3152503a84e9cf7a5c239576805ee71276e80c2aaa7471/matplotlib-3.11.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:be152b7570324dc8d01574cc9474dd2d803237acf528bcbb5b211fa347461a09", size = 10031623, upload-time = "2026-06-12T02:28:11.26Z" }, + { url = "https://files.pythonhosted.org/packages/92/7e/e937138daffad65b71bf831a377809dcbc830fb4f31a31e067dc1faa2575/matplotlib-3.11.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:126f256df600652d7e4b394cf3164ff75210a00038f287c95a012a6f58d0e83f", size = 10839372, upload-time = "2026-06-12T02:28:14.102Z" }, + { url = "https://files.pythonhosted.org/packages/1d/c2/438ecc197ffb8023b6b9922915542f2172f5fd45b76703b0b4fc47322243/matplotlib-3.11.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:03acfeddf87b0dddb11b081ef7740ad445a3ca8bcb6b8e3011b08f2cf802b75c", size = 10924099, upload-time = "2026-06-12T02:28:16.383Z" }, + { url = "https://files.pythonhosted.org/packages/40/2e/395883da416f378b3ed2c9f3e843ac477eae1ce731b671b79adaa6f0bacd/matplotlib-3.11.0-cp313-cp313-win_amd64.whl", hash = "sha256:ab3722f04f3ff34c23b5012c5873d2894174e06c3822fcdac3610965a5ac7d06", size = 9329727, upload-time = "2026-06-12T02:28:18.581Z" }, + { url = "https://files.pythonhosted.org/packages/61/82/2c388956abf8bf392dfb5b8917c502f1082df6a941b781ab8c8e5ba2474b/matplotlib-3.11.0-cp313-cp313-win_arm64.whl", hash = "sha256:c945824670fb8915b4ac879e5e61f3c58e0913022f70a0de4c082b17372f8771", size = 9003506, upload-time = "2026-06-12T02:28:20.474Z" }, + { url = "https://files.pythonhosted.org/packages/c8/c1/34454baa44da7975ada82e9aea37105ec47059514dc967d3be14426ba8dc/matplotlib-3.11.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:3489c3dc487669b4a980bc3068f87856de7a1564248d3f6c629efb2a58b03f24", size = 9499838, upload-time = "2026-06-12T02:28:22.713Z" }, + { url = "https://files.pythonhosted.org/packages/b1/c3/98fe79a398cf232219f090163a7fa7e6766e9f2e0ad26df54d6f8934d8ee/matplotlib-3.11.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:6a98f5476ce784a50ce09998f4ae1e6a9f25043cef8a480c98949902eda74620", size = 9332298, upload-time = "2026-06-12T02:28:24.796Z" }, + { url = "https://files.pythonhosted.org/packages/95/e4/b4b7c33151e74e5c802f3cde1ba807ebfc38401e329b44e215a5888dd76d/matplotlib-3.11.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:565af866fd63e4bd3f987d580afe27c44c2552a3b3305f4ecbb85133601ea6f3", size = 10045491, upload-time = "2026-06-12T02:28:27.141Z" }, + { url = "https://files.pythonhosted.org/packages/71/28/394548efd68354110c1a1be11fe6b6e559e06d1a23da35908a0e316c55a9/matplotlib-3.11.0-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e6b3e64dea5062c570f04358e2711859f3531b459f29516274fbad889079e4f3", size = 10857059, upload-time = "2026-06-12T02:28:29.222Z" }, + { url = "https://files.pythonhosted.org/packages/c8/44/e7922e6e2a4d63bdfbc9dc4a53e3850ab438d46cf42e6779bb15ec92c948/matplotlib-3.11.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:942b37c5db1899610bd1543ce8e13e4ecff9a4633e7f63bb6aa9205d2644ebd1", size = 10939576, upload-time = "2026-06-12T02:28:31.66Z" }, + { url = "https://files.pythonhosted.org/packages/3d/be/b1ca96003a441d619b727fee21d671fdff7a5ce2f1bb797b2521aa2f679a/matplotlib-3.11.0-cp313-cp313t-win_amd64.whl", hash = "sha256:c08e649a6313e1291e713623b97a38e5bb4aa580b2a100a94a3309bc6b9c8eb3", size = 9379519, upload-time = "2026-06-12T02:28:33.888Z" }, + { url = "https://files.pythonhosted.org/packages/e3/72/4bf3b91821c34596dd6a7bdac5836d94f744144c8208939ef49d8ec43f7e/matplotlib-3.11.0-cp313-cp313t-win_arm64.whl", hash = "sha256:2746cd2c113742ff6ce37a864c5ac5fd7aa644568f445e66166e457ac78e40e0", size = 9055456, upload-time = "2026-06-12T02:28:35.878Z" }, + { url = "https://files.pythonhosted.org/packages/57/52/a94102ac99eb78e2fe9b826674f9ef9ee23327110ea6ab4776c1b4eb6209/matplotlib-3.11.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:3338e3e3de128cf50d0d2fb92a122815daf9c755bd882a474343c05f8fd7ec79", size = 9452137, upload-time = "2026-06-12T02:28:37.93Z" }, + { url = "https://files.pythonhosted.org/packages/7c/03/b8cdb625a21f710dfa11bbca1f48fb4057d2c0286975f8b415bf80942c99/matplotlib-3.11.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:25c2e5455efd8d99f41fb79871a31feb7d301569642e332ec58d72cfe9282bc3", size = 9281514, upload-time = "2026-06-12T02:28:40.028Z" }, + { url = "https://files.pythonhosted.org/packages/b7/2d/4e1240ea82ee197dfb3851e71f71c87eeeb975f1753b56a0588e4e80739a/matplotlib-3.11.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d9695457a467ff86d23f35037a43deb6f1134dd6d3e2ac8ce1e2087cff09ffb9", size = 10843005, upload-time = "2026-06-12T02:28:42.39Z" }, + { url = "https://files.pythonhosted.org/packages/29/dc/6377ecfaa5fef79430f74a1a16638b4e2aa30d4692bae2c19f9d76fe3b01/matplotlib-3.11.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:19c16c61dea63b3582918503e6b294193961261d9daa806d4ae2151f1ad05430", size = 11127459, upload-time = "2026-06-12T02:28:44.483Z" }, + { url = "https://files.pythonhosted.org/packages/6f/41/795c405aa7560443a3b01309424cde4a1113b85c90b8a63417444a749617/matplotlib-3.11.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2d72ea8b7924f3cb955e61518d21e43b3df1e6c8a793b480a0c1214f185d30ba", size = 10925160, upload-time = "2026-06-12T02:28:46.564Z" }, + { url = "https://files.pythonhosted.org/packages/1a/f7/3a9e6389a7cfaeff76c56e40c2dabcb13110e21e82f837228c834ebe748c/matplotlib-3.11.0-cp314-cp314-win_amd64.whl", hash = "sha256:1c02da0a629dfa9debf52725ea06866b74c1fb70a895bae05e4493d34074f9f2", size = 9485186, upload-time = "2026-06-12T02:28:49.344Z" }, + { url = "https://files.pythonhosted.org/packages/8b/c0/396478ee7cf2091d182db8b4a8695f6a37f1ddb978989cf9dbb84cd5c123/matplotlib-3.11.0-cp314-cp314-win_arm64.whl", hash = "sha256:aa55d73b3117d4b07f959cd9eb6f69b375d8df3414139c479388e551aa5d999d", size = 9160349, upload-time = "2026-06-12T02:28:51.382Z" }, + { url = "https://files.pythonhosted.org/packages/c5/6f/1c3bd51bb2b34eaacdcf3c3d859dbb357f952fc8020c617dc118ad7c9e38/matplotlib-3.11.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:a9d8c6e7cd2f0ddf11d8d92e520dd1d9d2abb0cf6ac8831e338666c81e905847", size = 9500921, upload-time = "2026-06-12T02:28:53.443Z" }, + { url = "https://files.pythonhosted.org/packages/e0/0d/4d861d0121840cb1a3fd4a10deb211efd6fccd481ed23e553f31f4f4da4a/matplotlib-3.11.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:be050fcf32f729eda99f7f75a80bf67612ce16ab9ac1c23a387dcaede95cb70e", size = 9332190, upload-time = "2026-06-12T02:28:55.623Z" }, + { url = "https://files.pythonhosted.org/packages/4b/cb/22f6bc35711a0b5639a784e74e653e77c86210bd4304449dd399a482f74e/matplotlib-3.11.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:dfabef0230d0697aa0d717385194dd41162e00207a68bf4abf94c2bf4c27dca0", size = 10854181, upload-time = "2026-06-12T02:28:57.856Z" }, + { url = "https://files.pythonhosted.org/packages/3f/7e/9a9eaca731a2939589da520f0ebe8fd8753d0f51fca98c7d20af6dbe261a/matplotlib-3.11.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1644db30e759199443493ac5e5caec24fdb775a8f6123021f85ba47c4133c3cb", size = 11137715, upload-time = "2026-06-12T02:29:00.555Z" }, + { url = "https://files.pythonhosted.org/packages/ef/f9/9b030b6088354acb0296871bb624b25befc1c42509d3c6cd17420c83a5b8/matplotlib-3.11.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:15b0d160079cb10699a0e98b5989c70677b2df7cacdc62af67c30f2facec46d9", size = 10939427, upload-time = "2026-06-12T02:29:02.527Z" }, + { url = "https://files.pythonhosted.org/packages/59/94/6b273eaee4ee250863567d100865da61a5c1527fa67f527b7ed22e0dd29c/matplotlib-3.11.0-cp314-cp314t-win_amd64.whl", hash = "sha256:446307e6b04b57b1f1239e228a1ec2af0d589a1008cebc3dfa3f5441d095cfb6", size = 9535809, upload-time = "2026-06-12T02:29:04.994Z" }, + { url = "https://files.pythonhosted.org/packages/60/95/1d36bddf2b7e2692c1540e78a6e5bc88bc1496b137e3e35a611f91b65ac3/matplotlib-3.11.0-cp314-cp314t-win_arm64.whl", hash = "sha256:652fb5696271d4c50f196d22a5ff4f8e4444c74f847423570d7dc0aa2bbd0159", size = 9209226, upload-time = "2026-06-12T02:29:07.033Z" }, + { url = "https://files.pythonhosted.org/packages/0f/c2/f5da6cd37ed6871f5c9b3c0507ddb69f14d6c36fac4541e4e0c60cb8cdfc/matplotlib-3.11.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:81ae77077a1e16d37a5b61096ccb07c8d90a99b518fa8256b8f21578932f2f62", size = 9434094, upload-time = "2026-06-12T02:29:09.135Z" }, + { url = "https://files.pythonhosted.org/packages/f8/07/56f66906e0f87a0c6d0d0acbd34dbc9432b1931d8f26ef618bd6f92932a9/matplotlib-3.11.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:ddef37840695f5eef65f9f070fe2d2f510f584c2156203f9f622a5b0584efffd", size = 9262183, upload-time = "2026-06-12T02:29:11.283Z" }, + { url = "https://files.pythonhosted.org/packages/0c/d8/c4ecab06b7ea36a570c4f3bd2d48d1799fd5d9174470e45c2194199431e7/matplotlib-3.11.0-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cf662e5ac5707658cb931e19972c4bd99f7b4f8b7bf79d3c821d239fa6b71e64", size = 10015653, upload-time = "2026-06-12T02:29:13.251Z" }, ] [[package]] @@ -2551,44 +1880,32 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "traitlets" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/bd/c0/9f7c9a46090390368a4d7bcb76bb87a4a36c421e4c0792cdb53486ffac7a/matplotlib_inline-0.2.2.tar.gz", hash = "sha256:72f3fe8fce36b70d4a5b612f899090cd0401deddc4ea90e1572b9f4bfb058c79", size = 8150, upload-time = "2026-05-08T17:33:33.49Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/41/09/5b161152e2d90f7b87f781c2e1267494aef9c32498df793f73ad0a0a494a/matplotlib_inline-0.2.2-py3-none-any.whl", hash = "sha256:3c821cf1c209f59fb2d2d64abbf5b23b67bcb2210d663f9918dd851c6da1fcf6", size = 9534, upload-time = "2026-05-08T17:33:32.055Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/c7/74/97e72a36efd4ae2bccb3463284300f8953f199b5ffbc04cbbb0ec78f74b1/matplotlib_inline-0.2.1.tar.gz", hash = "sha256:e1ee949c340d771fc39e241ea75683deb94762c8fa5f2927ec57c83c4dffa9fe", size = 8110 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/af/33/ee4519fa02ed11a94aef9559552f3b17bb863f2ecfe1a35dc7f548cde231/matplotlib_inline-0.2.1-py3-none-any.whl", hash = "sha256:d56ce5156ba6085e00a9d54fead6ed29a9c47e215cd1bba2e976ef39f5710a76", size = 9516 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "mistune" -version = "3.2.1" +version = "3.3.2" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/ca/84/620cc3f7e3adf6f5067e10f4dbae71295d8f9e16d5d3f9ef97c40f2f592c/mistune-3.2.1.tar.gz", hash = "sha256:7c8e5501d38bac1582e067e46c8343f17d57ea1aaa735823f3aba1fd59c88a28", size = 98003, upload-time = "2026-05-03T14:33:22.312Z" } +sdist = { url = "https://files.pythonhosted.org/packages/04/5f/007786743f962224423753b78f7d7acb0f2ade46d1604f2e0fa2bedf9020/mistune-3.3.2.tar.gz", hash = "sha256:e12ee4f1e74336e91aa1141e35f913b337c40bdf7c0cc49f21fb853a27e8b62f", size = 111284, upload-time = "2026-06-23T00:29:28.568Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/2a/7f/a946aa4f8752b37102b41e64dca18a1976ac705c3a0d1dfe74d820a02552/mistune-3.2.1-py3-none-any.whl", hash = "sha256:78cdb0ba5e938053ccf63651b352508d2efa9411dc8810bfb05f2dc5140c0048", size = 53749, upload-time = "2026-05-03T14:33:20.551Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/d7/02/a7fb8b21d4d55ac93cdcde9d3638da5dd0ebdd3a4fed76c7725e10b81cbe/mistune-3.1.4.tar.gz", hash = "sha256:b5a7f801d389f724ec702840c11d8fc48f2b33519102fc7ee739e8177b672164", size = 94588 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/7a/f0/8282d9641415e9e33df173516226b404d367a0fc55e1a60424a152913abc/mistune-3.1.4-py3-none-any.whl", hash = "sha256:93691da911e5d9d2e23bc54472892aff676df27a75274962ff9edc210364266d", size = 53481 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/44/43/894c2cbbcbdf53b57d1257a249811abe2ee9ab7ef76af301b40f1c054533/mistune-3.3.2-py3-none-any.whl", hash = "sha256:a678a56387d487db7368ede4647cb2ba1deff22ce61f92343e4ebe0ddfce4f2d", size = 61554, upload-time = "2026-06-23T00:29:27.088Z" }, ] [[package]] name = "mpmath" version = "1.3.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/e0/47/dd32fa426cc72114383ac549964eecb20ecfd886d1e5ccf5340b55b02f57/mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f", size = 508106 } +sdist = { url = "https://files.pythonhosted.org/packages/e0/47/dd32fa426cc72114383ac549964eecb20ecfd886d1e5ccf5340b55b02f57/mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f", size = 508106, upload-time = "2023-03-07T16:47:11.061Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198 }, + { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198, upload-time = "2023-03-07T16:47:09.197Z" }, ] [[package]] name = "nbclient" -version = "0.10.4" +version = "0.11.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "jupyter-client" }, @@ -2596,15 +1913,9 @@ dependencies = [ { name = "nbformat" }, { name = "traitlets" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/56/91/1c1d5a4b9a9ebba2b4e32b8c852c2975c872aec1fe42ab5e516b2cecd193/nbclient-0.10.4.tar.gz", hash = "sha256:1e54091b16e6da39e297b0ece3e10f6f29f4ac4e8ee515d29f8a7099bd6553c9", size = 62554, upload-time = "2025-12-23T07:45:46.369Z" } +sdist = { url = "https://files.pythonhosted.org/packages/28/a5/b3bae4b590c0cbcada2c63a34f7580024e834a8ba213e949a2f906705787/nbclient-0.11.0.tar.gz", hash = "sha256:04a134a5b087f2c5887f228aca155db50169b8cd9334dee6942c8e927e56081a", size = 62535, upload-time = "2026-06-05T07:52:41.746Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/83/a0/5b0c2f11142ed1dddec842457d3f65eaf71a0080894eb6f018755b319c3a/nbclient-0.10.4-py3-none-any.whl", hash = "sha256:9162df5a7373d70d606527300a95a975a47c137776cd942e52d9c7e29ff83440", size = 25465, upload-time = "2025-12-23T07:45:44.51Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/87/66/7ffd18d58eae90d5721f9f39212327695b749e23ad44b3881744eaf4d9e8/nbclient-0.10.2.tar.gz", hash = "sha256:90b7fc6b810630db87a6d0c2250b1f0ab4cf4d3c27a299b0cde78a4ed3fd9193", size = 62424 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/34/6d/e7fa07f03a4a7b221d94b4d586edb754a9b0dc3c9e2c93353e9fa4e0d117/nbclient-0.10.2-py3-none-any.whl", hash = "sha256:4ffee11e788b4a27fabeb7955547e4318a5298f34342a4bfd01f2e1faaeadc3d", size = 25434 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/36/c9/94d73e5a01c5b926c3fa2496e97d7a8dc28ed5a77c0b2ed712f1a62e6694/nbclient-0.11.0-py3-none-any.whl", hash = "sha256:ef7fa0d59d6e1d41103933d8a445a18d5de860ca6b613b87b8574accdb3c2895", size = 25288, upload-time = "2026-06-05T07:52:40.115Z" }, ] [[package]] @@ -2627,15 +1938,9 @@ dependencies = [ { name = "pygments" }, { name = "traitlets" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/01/b1/708e53fe2e429c103c6e6e159106bcf0357ac41aa4c28772bd8402339051/nbconvert-7.17.1.tar.gz", hash = "sha256:34d0d0a7e73ce3cbab6c5aae8f4f468797280b01fd8bd2ca746da8569eddd7d2", size = 865311, upload-time = "2026-04-08T00:44:14.914Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/67/f8/bb0a9d5f46819c821dc1f004aa2cc29b1d91453297dbf5ff20470f00f193/nbconvert-7.17.1-py3-none-any.whl", hash = "sha256:aa85c087b435e7bf1ffd03319f658e285f2b89eccab33bc1ba7025495ab3e7c8", size = 261927, upload-time = "2026-04-08T00:44:12.845Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/a3/59/f28e15fc47ffb73af68a8d9b47367a8630d76e97ae85ad18271b9db96fdf/nbconvert-7.16.6.tar.gz", hash = "sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582", size = 857715 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/cc/9a/cd673b2f773a12c992f41309ef81b99da1690426bd2f96957a7ade0d3ed7/nbconvert-7.16.6-py3-none-any.whl", hash = "sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b", size = 258525 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2648,48 +1953,36 @@ dependencies = [ { name = "jupyter-core" }, { name = "traitlets" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/6d/fd/91545e604bc3dad7dca9ed03284086039b294c6b3d75c0d2fa45f9e9caf3/nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a", size = 142749 } +sdist = { url = "https://files.pythonhosted.org/packages/6d/fd/91545e604bc3dad7dca9ed03284086039b294c6b3d75c0d2fa45f9e9caf3/nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a", size = 142749, upload-time = "2024-04-04T11:20:37.371Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b", size = 78454 }, + { url = "https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b", size = 78454, upload-time = "2024-04-04T11:20:34.895Z" }, ] [[package]] -name = "nest-asyncio" -version = "1.6.0" +name = "nest-asyncio2" +version = "1.7.2" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418 } +sdist = { url = "https://files.pythonhosted.org/packages/b4/73/731debf26e27e0a0323d7bda270dc2f634b398e38f040a09da1f4351d0aa/nest_asyncio2-1.7.2.tar.gz", hash = "sha256:1921d70b92cc4612c374928d081552efb59b83d91b2b789d935c665fa01729a8", size = 14743, upload-time = "2026-02-13T00:34:04.386Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195 }, + { url = "https://files.pythonhosted.org/packages/c5/3c/3179b85b0e1c3659f0369940200cd6d0fa900e6cefcc7ea0bc6dd0e29ffb/nest_asyncio2-1.7.2-py3-none-any.whl", hash = "sha256:f5dfa702f3f81f6a03857e9a19e2ba578c0946a4ad417b4c50a24d7ba641fe01", size = 7843, upload-time = "2026-02-13T00:34:02.691Z" }, ] [[package]] name = "networkx" version = "3.6.1" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/6a/51/63fe664f3908c97be9d2e4f1158eb633317598cfa6e1fc14af5383f17512/networkx-3.6.1.tar.gz", hash = "sha256:26b7c357accc0c8cde558ad486283728b65b6a95d85ee1cd66bafab4c8168509", size = 2517025, upload-time = "2025-12-08T17:02:39.908Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/9e/c9/b2622292ea83fbb4ec318f5b9ab867d0a28ab43c5717bb85b0a5f6b3b0a4/networkx-3.6.1-py3-none-any.whl", hash = "sha256:d47fbf302e7d9cbbb9e2555a0d267983d2aa476bac30e90dfbe5669bd57f3762", size = 2068504, upload-time = "2025-12-08T17:02:38.159Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/6c/4f/ccdb8ad3a38e583f214547fd2f7ff1fc160c43a75af88e6aec213404b96a/networkx-3.5.tar.gz", hash = "sha256:d4c6f9cf81f52d69230866796b82afbccdec3db7ae4fbd1b65ea750feed50037", size = 2471065 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl", hash = "sha256:0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec", size = 2034406 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "nodeenv" version = "1.10.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/24/bf/d1bda4f6168e0b2e9e5958945e01910052158313224ada5ce1fb2e1113b8/nodeenv-1.10.0.tar.gz", hash = "sha256:996c191ad80897d076bdfba80a41994c2b47c68e224c542b48feba42ba00f8bb", size = 55611, upload-time = "2025-12-20T14:08:54.006Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/88/b2/d0896bdcdc8d28a7fc5717c305f1a861c26e18c05047949fb371034d98bd/nodeenv-1.10.0-py2.py3-none-any.whl", hash = "sha256:5bb13e3eed2923615535339b3c620e76779af4cb4c6a90deccc9e36b274d3827", size = 23438, upload-time = "2025-12-20T14:08:52.782Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/43/16/fc88b08840de0e0a72a2f9d8c6bae36be573e475a6326ae854bcc549fc45/nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f", size = 47437 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d2/1d/1b658dbd2b9fa9c4c9f32accbfc0205d532c8c6194dc0f2a4c0428e7128a/nodeenv-1.9.1-py2.py3-none-any.whl", hash = "sha256:ba11c9782d29c27c70ffbdda2d7415098754709be8a7056d79a737cd901155c9", size = 22314 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -2699,9 +1992,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "jupyter-server" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/54/d2/92fa3243712b9a3e8bafaf60aac366da1cada3639ca767ff4b5b3654ec28/notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb", size = 13167 } +sdist = { url = "https://files.pythonhosted.org/packages/54/d2/92fa3243712b9a3e8bafaf60aac366da1cada3639ca767ff4b5b3654ec28/notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb", size = 13167, upload-time = "2024-02-14T23:35:18.353Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/f9/33/bd5b9137445ea4b680023eb0469b2bb969d61303dedb2aac6560ff3d14a1/notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef", size = 13307 }, + { url = "https://files.pythonhosted.org/packages/f9/33/bd5b9137445ea4b680023eb0469b2bb969d61303dedb2aac6560ff3d14a1/notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef", size = 13307, upload-time = "2024-02-14T23:35:16.286Z" }, ] [[package]] @@ -2709,10 +2002,10 @@ name = "numcodecs" version = "0.16.5" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, { name = "typing-extensions" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/44/bd/8a391e7c356366224734efd24da929cc4796fff468bfb179fe1af6548535/numcodecs-0.16.5.tar.gz", hash = "sha256:0d0fb60852f84c0bd9543cc4d2ab9eefd37fc8efcc410acd4777e62a1d300318", size = 6276387, upload-time = "2025-11-21T02:49:48.986Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/af/85/1ac101a40ead81eaa1c7dc49a8827a30e2e436211b43ebdc63c590eb1347/numcodecs-0.16.5-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:78382dcea50622f2ef1e6e7a71dbe7f861d8fe376b27b7c297c26907304fef1e", size = 1621795, upload-time = "2025-11-21T02:49:17.418Z" }, @@ -2735,37 +2028,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/4e/15/e2e1151b5a8b14a15dfd4bb4abccce7fff7580f39bc34092780088835f3a/numcodecs-0.16.5-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:49f7b7d24f103187f53135bed28bb9f0ed6b2e14c604664726487bb6d7c882e1", size = 8476987, upload-time = "2025-11-21T02:49:43.363Z" }, { url = "https://files.pythonhosted.org/packages/6d/30/16a57fc4d9fb0ba06c600408bd6634f2f1753c54a7a351c99c5e09b51ee2/numcodecs-0.16.5-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:aec9736d81b70f337d89c4070ee3ffeff113f386fd789492fa152d26a15043e4", size = 9102377, upload-time = "2025-11-21T02:49:45.508Z" }, { url = "https://files.pythonhosted.org/packages/31/a5/a0425af36c20d55a3ea884db4b4efca25a43bea9214ba69ca7932dd997b4/numcodecs-0.16.5-cp314-cp314-win_amd64.whl", hash = "sha256:b16a14303800e9fb88abc39463ab4706c037647ac17e49e297faa5f7d7dbbf1d", size = 819022, upload-time = "2025-11-21T02:49:47.39Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/f6/48/6188e359b90a9d8a1850f2bc888c023e66f4a8b2b496820babbea414f008/numcodecs-0.16.3.tar.gz", hash = "sha256:53d705865faaf0a7927c973af3777532001c8fbb653de119c1e844608614d799", size = 6275704 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d4/cc/917a85972537498f2bbd7914047efc98babc8667587ceb9dcb228378978a/numcodecs-0.16.3-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:95c9f2a49bef10cf91ad614a761cba9bfe96656b60c12540e1080de5d909b4ca", size = 1642356 }, - { url = "https://files.pythonhosted.org/packages/3b/6a/64c25a089e8537441fe67c09ecb7f3f7fb5d98cd04faf01f605d43aca41c/numcodecs-0.16.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e2afe73d5ebaf9ca0cd5c83aad945da80d29a33d860a80d43a7248491d8813ff", size = 1169186 }, - { url = "https://files.pythonhosted.org/packages/d8/a0/0de627baeb43e2045a3d4b3de99bf8b69af329a33df1ed4cda468d70c1fb/numcodecs-0.16.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:913f08194d82dcb37594e6705e6d4ae6ccd4b6571500b832fb3e4a155de1dfe8", size = 8341668 }, - { url = "https://files.pythonhosted.org/packages/b6/0f/49d1f74a216149240c4b9403218111f11670bd11af0919fda357bb056bf2/numcodecs-0.16.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:85a7f1cae9eb18b85709af46570bf9c60056e7155c4c8f610e8080c68124d0e5", size = 8866611 }, - { url = "https://files.pythonhosted.org/packages/aa/51/03aece765108fe247717105b5131856546e5428f22a56a14ffdebd017424/numcodecs-0.16.3-cp311-cp311-win_amd64.whl", hash = "sha256:f7bb7f2c46eb7ec8a1c5f8d8fe1a72c222256dd6d6df5af9eaac7a6b905f3575", size = 806787 }, - { url = "https://files.pythonhosted.org/packages/0d/78/e4b34803a3aa1d0769919695de4b133266c18c80c474d32ebc462fa1a9bd/numcodecs-0.16.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c77454d92941a335d148b0b822f5d4783103f392774d5d76283bbf7f21b49529", size = 1681108 }, - { url = "https://files.pythonhosted.org/packages/25/cf/ca36f463b03a4097767d2a1c1b72f31810e8c6384e9449dd9b925203783c/numcodecs-0.16.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:270e7a33ee96bdf5c957acf25a2487002a233811a125a155c400c2f036b69c73", size = 1165589 }, - { url = "https://files.pythonhosted.org/packages/ed/ae/670260c3c4b5ed34a0674561355f3d4ce7fcbdf09a667e5bc841526d271c/numcodecs-0.16.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:12f43fa4a347d1dba775c4506a1c9b15b90144c258433b81f79f1c1b1a990db5", size = 8316365 }, - { url = "https://files.pythonhosted.org/packages/bb/fa/94e022419c751a60ff0f53642ebae5ef81ed3cc3640f958588e3ad3dc18d/numcodecs-0.16.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:44869ef564a50aa545215c6a0d42ba5bbc34e9715523fb2336ada3d1fb2b331d", size = 8846228 }, - { url = "https://files.pythonhosted.org/packages/71/60/f23733589f3e059bf8589508acd23ffeec230bdf179f138a54f5ab16e0a6/numcodecs-0.16.3-cp312-cp312-win_amd64.whl", hash = "sha256:9aae6996172ba10c5f5111b2998709071b5aeba6b58b1ee0b26b61ed6aa7f2f4", size = 806260 }, - { url = "https://files.pythonhosted.org/packages/3c/d5/d3536d06ac1e5fb848a3186958204082b68b106364c9a3669652dd786731/numcodecs-0.16.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:947406b01c20f2ce7ce2e631e7f21b782e8a9d4b57b374a41c9e7b1341a8f3a2", size = 1677129 }, - { url = "https://files.pythonhosted.org/packages/e1/fd/b0513a3428dc2b38ec85eea771703ae69c49f09b9650d6c44c9105c80073/numcodecs-0.16.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:7cf50e351398a34b45817974c411527629e88937b7683695e276afd65da6ed6f", size = 1159058 }, - { url = "https://files.pythonhosted.org/packages/98/05/b7c127283cfb154a97abb284363825401b69302d71a28608af66f73257cc/numcodecs-0.16.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f7938502fcc060ed9543814f38ca67048b33d7bd2667756e36e6b1060455b17e", size = 8260987 }, - { url = "https://files.pythonhosted.org/packages/ff/46/320d960aff884bc63abaaf846ffa3de4803e83e8070b6f84c5688464839c/numcodecs-0.16.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:010d628c95be1214536fb22c0df4ced58da954b404b1fcb25ddebf64e4a3f7f3", size = 8805295 }, - { url = "https://files.pythonhosted.org/packages/31/ae/acc2e0f1f49ba32afa2174578f170673139248ef86f77e334f2619133867/numcodecs-0.16.3-cp313-cp313-win_amd64.whl", hash = "sha256:e83115e3c32de798c7b7164503e06aae9f9746c1cef564d029616eb44bd6cd90", size = 803204 }, -] - -[package.optional-dependencies] -crc32c = [ - { name = "crc32c" }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "numpy" version = "2.4.6" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD +resolution-markers = [ + "python_full_version < '3.12'", +] sdist = { url = "https://files.pythonhosted.org/packages/d0/ad/fed0499ce6a338d2a03ebae59cd15093910c8875328855781952abf6c2fe/numpy-2.4.6.tar.gz", hash = "sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda", size = 20735807, upload-time = "2026-05-18T23:37:14.07Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/b3/49/ec46835a70be8fa6446c495126ac84fdb28cb2558e1620ffb87a10c8b64c/numpy-2.4.6-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0280e0356c0829a18d9de1cb7eee50ec22ca639878d7240307ca0943d73cd2c4", size = 16969194, upload-time = "2026-05-18T23:33:13.503Z" }, @@ -2841,134 +2112,71 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/15/ce/e5ec180bc41812edcd8daeb8639d205622c0e8c02259d8ab25a0201b3c2a/numpy-2.4.6-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:2803abfebfc990042cd494d8ce2d5f82e9d847af6d35ec486923aa19dbad5e73", size = 12504263, upload-time = "2026-05-18T23:37:09.715Z" }, ] +[[package]] +name = "numpy" +version = "2.5.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.14'", + "python_full_version >= '3.12' and python_full_version < '3.14'", +] +sdist = { url = "https://files.pythonhosted.org/packages/e7/05/3d27272d30698dc0ecb7fdfaa41ad70303b444f81722bb99bce1d818638a/numpy-2.5.0.tar.gz", hash = "sha256:5a129578019311b6e56bdd714250f19b518f7dceeeb8d1af5490f4942d3f891c", size = 20652461, upload-time = "2026-06-21T20:57:51.95Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fa/0a/11486d02add7b1384dff7374d124b1cfbb0ee864dcc9f6a2c0380638cf84/numpy-2.5.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:489780423903667933b4ed6197b6ec3b75ea5dd17d1d8f0f38d798feb6921561", size = 16789987, upload-time = "2026-06-21T20:56:16.657Z" }, + { url = "https://files.pythonhosted.org/packages/55/b2/285f48640a181947b4587a3766d21ec1eaa7fea833d4b49957e09da467a2/numpy-2.5.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ece55976ced6bca95a03ae2839e2e5ccffe8eb6a3e7022415645eb154a81e4e6", size = 11760322, upload-time = "2026-06-21T20:56:19.813Z" }, + { url = "https://files.pythonhosted.org/packages/dd/67/b032db1eb03ca30d16eda3b0c22aaa615338b9263c2fd559d0f29451aca4/numpy-2.5.0-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:c83b664b0e6eee9594fa920cf0639d8af796606d3fad6cc70180c87e4b97c7be", size = 5319605, upload-time = "2026-06-21T20:56:22.173Z" }, + { url = "https://files.pythonhosted.org/packages/b9/83/03fc7300c7c6b6c84c487b1dc80d322817b95fbd1f4dd57a85e23b7198de/numpy-2.5.0-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:bf80333980bf37f523341ddd72c783f39d6829ec7736b9eb99086388a2d52cc2", size = 6653628, upload-time = "2026-06-21T20:56:23.914Z" }, + { url = "https://files.pythonhosted.org/packages/82/49/2ec21730bc63ccfda829323f7040a8ed4715b3852ce658689cf74ee96a8c/numpy-2.5.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a1a4874217b36d5ac8fc876f52e39df56f8182c88463e9e2dceabf7ca8b7efb8", size = 15153691, upload-time = "2026-06-21T20:56:25.631Z" }, + { url = "https://files.pythonhosted.org/packages/bb/6b/f4a3d0637692c49da8ef99d72d52526f92e0a8d6ac4f0ca9f31441b9d9ea/numpy-2.5.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:aaa760137137e8d3c920d27927748215b56014f92667dc9b6c27dfc61249255a", size = 16660066, upload-time = "2026-06-21T20:56:28.009Z" }, + { url = "https://files.pythonhosted.org/packages/3a/2f/c354ec86d1f3f5c19649463b0d39652e160736e5b0a4cd18dff0576715c4/numpy-2.5.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:7174ce8265fc7f7417d171c9ea8fe905220748893ea67a2a7abe726ec331c4b0", size = 16514638, upload-time = "2026-06-21T20:56:30.26Z" }, + { url = "https://files.pythonhosted.org/packages/06/34/43efdcb319988648580f93c11f1ae82cf7e2faa74925e98e454ae3aa95f8/numpy-2.5.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b8c3daaf99de52415d20b42f8e8155c78642cb04207d02f9d317a0dcf1b3fb54", size = 18419647, upload-time = "2026-06-21T20:56:32.41Z" }, + { url = "https://files.pythonhosted.org/packages/71/e2/f5d1676b1d7fb682eb5e9a1641e7ebd2414b3216c370661d1029778908b4/numpy-2.5.0-cp312-cp312-win32.whl", hash = "sha256:6206db0af545d73d068add6d992279145f158428d1da6cc49adc4b630c5d6ee5", size = 6056688, upload-time = "2026-06-21T20:56:34.657Z" }, + { url = "https://files.pythonhosted.org/packages/8f/7c/48f115d1c58a34032facebcd51fdf2d02df2c51d4a46a81dd1197bb2ea6b/numpy-2.5.0-cp312-cp312-win_amd64.whl", hash = "sha256:6f2d6873e2940c860a309d21e25b1e69af6aaffdd80aa056b04c16380db1c4f2", size = 12419237, upload-time = "2026-06-21T20:56:36.24Z" }, + { url = "https://files.pythonhosted.org/packages/86/26/2e0882f4044d1b1a1b63e875151fb2393389032022a8b7f5657a7996d3b2/numpy-2.5.0-cp312-cp312-win_arm64.whl", hash = "sha256:a55e1eb2bca2cfd17a16b213c99dfc8502d47b0d494224d2122277d0400935ca", size = 10339912, upload-time = "2026-06-21T20:56:38.733Z" }, + { url = "https://files.pythonhosted.org/packages/8a/33/07675aaad7f26ea013d5e884d9a0d784b79c6bd7566c333f5a52fa3c610b/numpy-2.5.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:520e6b8be0a4b65840ac8090d4f51cef4bed66e2b0894d5a520f099adc24a9b2", size = 16784890, upload-time = "2026-06-21T20:56:40.799Z" }, + { url = "https://files.pythonhosted.org/packages/85/4b/953118a730ee3b35e28645e0eb4cf9beec5bdbb954e1ac2f5fcefba6bbc3/numpy-2.5.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:146b81cdd3967fdb6beca8ba25f00c58741d8f3cbd797f55af0fbe0bfec3469c", size = 11754584, upload-time = "2026-06-21T20:56:43.094Z" }, + { url = "https://files.pythonhosted.org/packages/44/9b/56dd530c367c74ae17411027cea4135ca57e1e0583bf5594cee18bd83217/numpy-2.5.0-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:126b88d95e8ff9b00c9e717aa540469f21d6180162f84c0caec51b16215d49cd", size = 5313904, upload-time = "2026-06-21T20:56:45.503Z" }, + { url = "https://files.pythonhosted.org/packages/ce/b0/bcd672edad27ecca7da1f7bb0ce72cd1706a4f2d79ae94990afc97c13e1c/numpy-2.5.0-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:d4313cef1594c5ce46c31b6e54e918338f63f16ee9322304e8c9114d6d81c8bd", size = 6648504, upload-time = "2026-06-21T20:56:47.567Z" }, + { url = "https://files.pythonhosted.org/packages/80/9e/15cdfcbd30a1544a46c9e487a00df331c4672450216538705a9e51fa6710/numpy-2.5.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:750fb097caf26fa878746d9d119f6f9da12dedcbff1eea966c3e3447647c4a9e", size = 15150086, upload-time = "2026-06-21T20:56:49.352Z" }, + { url = "https://files.pythonhosted.org/packages/32/4e/8d7656ccaab3e81e97258b8a9bc5f0c8502513a92fb4ceb0a2cbfebc17bf/numpy-2.5.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3893adc2dc7c0412ba76777db55a049215d99c9aa3113003be8f49f4f1290ab9", size = 16647250, upload-time = "2026-06-21T20:56:51.542Z" }, + { url = "https://files.pythonhosted.org/packages/3c/81/97060281b602ed07f21b12f4ec409eac1f75a2f91fbc829ed8b2becf3ad4/numpy-2.5.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:835e454dd99b238cdc5a3f63bce2371296f5ebc53ca1e0f8e6ddbb6d92a29aab", size = 16512864, upload-time = "2026-06-21T20:56:55.401Z" }, + { url = "https://files.pythonhosted.org/packages/33/ab/4496208146911f8d8ddb54f68a972aafa6c8d44babcb2ea03b0e5cc87c9d/numpy-2.5.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6f9836778081a0a3c02a6a21493f3e9f5b311f8d2541934f31f05583dc999ea4", size = 18408407, upload-time = "2026-06-21T20:56:57.75Z" }, + { url = "https://files.pythonhosted.org/packages/d4/9f/a4df67c181e4ee8b467aa3332dc2db10fd5c515136831302f3ca48bc0a01/numpy-2.5.0-cp313-cp313-win32.whl", hash = "sha256:0b525be4744b60bb0557ac872d53ef07d085b5f39622bc579c98d3809d05b988", size = 6054431, upload-time = "2026-06-21T20:57:00.016Z" }, + { url = "https://files.pythonhosted.org/packages/30/53/491e1c47c55b62ccc6a63c1c5b8635c73fc2258dddeb9bda27cae4a0ae96/numpy-2.5.0-cp313-cp313-win_amd64.whl", hash = "sha256:44353e2878930039db472b99dc353d749826e4010bd4d2a7f835e94a97a5c748", size = 12414420, upload-time = "2026-06-21T20:57:01.815Z" }, + { url = "https://files.pythonhosted.org/packages/eb/4a/25c2906f541e9d9f4c5769764db732e6627be91a13f4724fa10634d77db4/numpy-2.5.0-cp313-cp313-win_arm64.whl", hash = "sha256:48f54b00711f83a5f796b70c518e8c2b3c5848dda03a54911f23eb68519b9b60", size = 10339533, upload-time = "2026-06-21T20:57:03.961Z" }, + { url = "https://files.pythonhosted.org/packages/86/ad/abc44aaceaf7b17ee1edde2bbb4458da591bc79574cffff50c4bb35f00d1/numpy-2.5.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:f27582c55ba4c750b7c58c8faf021d2cd9324a662b466229db8a417b41368af9", size = 16783807, upload-time = "2026-06-21T20:57:06.253Z" }, + { url = "https://files.pythonhosted.org/packages/5d/39/b72e168daf9c00fb20c9fc996d00437ccecdef3102387775d29d7a62576d/numpy-2.5.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:28e7137057d551e4a83c4ae414e3451f50568409db7569aacc7f9811ee06a446", size = 11765215, upload-time = "2026-06-21T20:57:08.547Z" }, + { url = "https://files.pythonhosted.org/packages/f7/a0/8400a9c0e3625182347593f5e1f57da9a617a534794805c8df5518154ddc/numpy-2.5.0-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:e1da54b53e75cd9fcfc23efcc7edab2c6aecf97b6037566d8a0fe804af8ec57c", size = 5324493, upload-time = "2026-06-21T20:57:11.012Z" }, + { url = "https://files.pythonhosted.org/packages/f6/8c/0d104deaa0401c93395a629ec902891618a2eff76d19229139cb5a887bfc/numpy-2.5.0-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:694d8f74e156f7fd01179f1aa8faa2f648ab6ae0f70b6c3fe57a03249aea2303", size = 6645211, upload-time = "2026-06-21T20:57:12.919Z" }, + { url = "https://files.pythonhosted.org/packages/6a/d9/4a4a628c812750363786afc3d33492709a5cd64b215469c16b0f6c7bb811/numpy-2.5.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1a7569a7b53c77716f036bb28cb1c91f166a26ec7d9502cd1e4bdfe502fdec22", size = 15166004, upload-time = "2026-06-21T20:57:14.717Z" }, + { url = "https://files.pythonhosted.org/packages/a0/5e/2a902317d7fc4aa93236e80c932662dadfc459b323d758329e01775125e1/numpy-2.5.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:39a0433bd4086ebd462960cf375e19195bb07b53dc1d87dd5fcf47ad78576f03", size = 16650797, upload-time = "2026-06-21T20:57:16.906Z" }, + { url = "https://files.pythonhosted.org/packages/e9/a0/a0090e6329f4ca5992c07847bb579c5259a19953dc57255bb08793142ffb/numpy-2.5.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:929f0c79ac38bcbd7154fe631dc907abfeddbcc5027a896bd1f7767323271e7a", size = 16524647, upload-time = "2026-06-21T20:57:19.165Z" }, + { url = "https://files.pythonhosted.org/packages/5e/7d/6caf27734c42b65837e7461ed0dbbd6b6fc835060c9714ec59d673bb383a/numpy-2.5.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:cc4f247a47bbf070bfd70be53ccdcf47b800af563535e7bbe172322197c30e21", size = 18411841, upload-time = "2026-06-21T20:57:21.638Z" }, + { url = "https://files.pythonhosted.org/packages/13/dc/26edadbd812536769a82c2e9e002234e33feb5da43061d47a044f6d309b7/numpy-2.5.0-cp314-cp314-win32.whl", hash = "sha256:5dc71423499fab3f46f7a7201155ade1669ea101f2f429d332df9e72f8161731", size = 6106361, upload-time = "2026-06-21T20:57:23.844Z" }, + { url = "https://files.pythonhosted.org/packages/f2/9e/4dd1459282229a72d92dece2ae9138e5cac94a72263a7ceb48f37434c925/numpy-2.5.0-cp314-cp314-win_amd64.whl", hash = "sha256:ebb81d9d5443e0309d6c54894c3fbed74ad7da0714352a67b6d773cd189eae73", size = 12551749, upload-time = "2026-06-21T20:57:25.945Z" }, + { url = "https://files.pythonhosted.org/packages/05/a7/6bc6384c080b86c7f6c85c5bc5b540b24f4f679cd144791d99574e90d462/numpy-2.5.0-cp314-cp314-win_arm64.whl", hash = "sha256:3b94d0d0deceebfad3e67ae5c0e5eb87371e8f7a0581cd04a779928c2450cf1e", size = 10617072, upload-time = "2026-06-21T20:57:28.175Z" }, + { url = "https://files.pythonhosted.org/packages/86/6b/4a2b71d66ada5608ae02b63f150dfad520f6940721cb7f029ad270befc0e/numpy-2.5.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:22f3d43e362d650bc39db1f17851302874a148ca95ba6981c1dfb5fa6862f35b", size = 11881067, upload-time = "2026-06-21T20:57:30.104Z" }, + { url = "https://files.pythonhosted.org/packages/dc/b2/d365eb40a20efb49d67e9feb90494ed8511282ee1f5fa16006675c65397d/numpy-2.5.0-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:243563efb4cd7528a264567e9fd206c87826457322521d06206a00bfa316c927", size = 5440290, upload-time = "2026-06-21T20:57:32.193Z" }, + { url = "https://files.pythonhosted.org/packages/fa/5e/e9c03188de5f9b767e46a8fe988bcfd3efad066a4a3fda8b9cb11a93f895/numpy-2.5.0-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:84881d825ca75249b189bbee875fcfe3238aa5c479e6100893cda566e8e86826", size = 6748371, upload-time = "2026-06-21T20:57:33.933Z" }, + { url = "https://files.pythonhosted.org/packages/fd/1d/68c186a38a5027bae2c4ddd5ea681fdaf8b4d30fb7301def6d8ad270390f/numpy-2.5.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:cda12aa4779d42b8771180aba759c96f527d43446d8f380ab59e2b35e8489efd", size = 15214643, upload-time = "2026-06-21T20:57:35.677Z" }, + { url = "https://files.pythonhosted.org/packages/8c/67/73f67b7c7e20635baae9c4c3ead4ae7326a005900297a6110971abd62eb5/numpy-2.5.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1c0121101093d2bd74981b10f8837d78e794a8ff57834eb27179f49e1ba11ac6", size = 16690128, upload-time = "2026-06-21T20:57:38.159Z" }, + { url = "https://files.pythonhosted.org/packages/eb/05/d4c1fb0c46d02a27d6b2b8b319a78c90937acec8631c1641874670b31e6f/numpy-2.5.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:d371c92cfa09da00022f501ab67fafaea813d752eb30ac44336d45b1e5b0268a", size = 16577902, upload-time = "2026-06-21T20:57:40.447Z" }, + { url = "https://files.pythonhosted.org/packages/9e/1d/771c797d50fa26e4888989cccf1d50ee51f530d4e455ad2692dcb64fa711/numpy-2.5.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:9990713e9c38154c6861e7547f1e3fc7a87e75ff09bab24ef1cc81d81c2835e9", size = 18452814, upload-time = "2026-06-21T20:57:42.875Z" }, + { url = "https://files.pythonhosted.org/packages/e8/46/52fc0d2a68d7643f0f149eeea5a5d8ea2a3507056ac8afa83c9212606e8b/numpy-2.5.0-cp314-cp314t-win32.whl", hash = "sha256:edadfbd4794b1086c0d822f81863e8a68fc129d132fd0bb9e31e955d7fbbbdb7", size = 6253168, upload-time = "2026-06-21T20:57:45.101Z" }, + { url = "https://files.pythonhosted.org/packages/2a/be/6c8d1118b5f13b2881dc095d5b345de19c6638b8959c17409b6eff84c8aa/numpy-2.5.0-cp314-cp314t-win_amd64.whl", hash = "sha256:f7e5fa4382967ae6548bd2f174219afb908e294b0d5f625af01166edd5f7d9aa", size = 12736286, upload-time = "2026-06-21T20:57:46.935Z" }, + { url = "https://files.pythonhosted.org/packages/fd/6a/d3a169aaf8536cf228d56a09e04bcb713a2fe4410d4e2105b9419b5a9c89/numpy-2.5.0-cp314-cp314t-win_arm64.whl", hash = "sha256:016623417bb330d719d579daf2d6b9a01ddc52e41a9ed61a47f39fde46dcd865", size = 10686451, upload-time = "2026-06-21T20:57:49.313Z" }, +] + [[package]] name = "nvidia-cublas" version = "13.1.1.3" -======= -sdist = { url = "https://files.pythonhosted.org/packages/76/65/21b3bc86aac7b8f2862db1e808f1ea22b028e30a225a34a5ede9bf8678f2/numpy-2.3.5.tar.gz", hash = "sha256:784db1dcdab56bf0517743e746dfb0f885fc68d948aba86eeec2cba234bdf1c0", size = 20584950 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/43/77/84dd1d2e34d7e2792a236ba180b5e8fcc1e3e414e761ce0253f63d7f572e/numpy-2.3.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:de5672f4a7b200c15a4127042170a694d4df43c992948f5e1af57f0174beed10", size = 17034641 }, - { url = "https://files.pythonhosted.org/packages/2a/ea/25e26fa5837106cde46ae7d0b667e20f69cbbc0efd64cba8221411ab26ae/numpy-2.3.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:acfd89508504a19ed06ef963ad544ec6664518c863436306153e13e94605c218", size = 12528324 }, - { url = "https://files.pythonhosted.org/packages/4d/1a/e85f0eea4cf03d6a0228f5c0256b53f2df4bc794706e7df019fc622e47f1/numpy-2.3.5-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:ffe22d2b05504f786c867c8395de703937f934272eb67586817b46188b4ded6d", size = 5356872 }, - { url = "https://files.pythonhosted.org/packages/5c/bb/35ef04afd567f4c989c2060cde39211e4ac5357155c1833bcd1166055c61/numpy-2.3.5-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:872a5cf366aec6bb1147336480fef14c9164b154aeb6542327de4970282cd2f5", size = 6893148 }, - { url = "https://files.pythonhosted.org/packages/f2/2b/05bbeb06e2dff5eab512dfc678b1cc5ee94d8ac5956a0885c64b6b26252b/numpy-2.3.5-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3095bdb8dd297e5920b010e96134ed91d852d81d490e787beca7e35ae1d89cf7", size = 14557282 }, - { url = "https://files.pythonhosted.org/packages/65/fb/2b23769462b34398d9326081fad5655198fcf18966fcb1f1e49db44fbf31/numpy-2.3.5-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8cba086a43d54ca804ce711b2a940b16e452807acebe7852ff327f1ecd49b0d4", size = 16897903 }, - { url = "https://files.pythonhosted.org/packages/ac/14/085f4cf05fc3f1e8aa95e85404e984ffca9b2275a5dc2b1aae18a67538b8/numpy-2.3.5-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:6cf9b429b21df6b99f4dee7a1218b8b7ffbbe7df8764dc0bd60ce8a0708fed1e", size = 16341672 }, - { url = "https://files.pythonhosted.org/packages/6f/3b/1f73994904142b2aa290449b3bb99772477b5fd94d787093e4f24f5af763/numpy-2.3.5-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:396084a36abdb603546b119d96528c2f6263921c50df3c8fd7cb28873a237748", size = 18838896 }, - { url = "https://files.pythonhosted.org/packages/cd/b9/cf6649b2124f288309ffc353070792caf42ad69047dcc60da85ee85fea58/numpy-2.3.5-cp311-cp311-win32.whl", hash = "sha256:b0c7088a73aef3d687c4deef8452a3ac7c1be4e29ed8bf3b366c8111128ac60c", size = 6563608 }, - { url = "https://files.pythonhosted.org/packages/aa/44/9fe81ae1dcc29c531843852e2874080dc441338574ccc4306b39e2ff6e59/numpy-2.3.5-cp311-cp311-win_amd64.whl", hash = "sha256:a414504bef8945eae5f2d7cb7be2d4af77c5d1cb5e20b296c2c25b61dff2900c", size = 13078442 }, - { url = "https://files.pythonhosted.org/packages/6d/a7/f99a41553d2da82a20a2f22e93c94f928e4490bb447c9ff3c4ff230581d3/numpy-2.3.5-cp311-cp311-win_arm64.whl", hash = "sha256:0cd00b7b36e35398fa2d16af7b907b65304ef8bb4817a550e06e5012929830fa", size = 10458555 }, - { url = "https://files.pythonhosted.org/packages/44/37/e669fe6cbb2b96c62f6bbedc6a81c0f3b7362f6a59230b23caa673a85721/numpy-2.3.5-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:74ae7b798248fe62021dbf3c914245ad45d1a6b0cb4a29ecb4b31d0bfbc4cc3e", size = 16733873 }, - { url = "https://files.pythonhosted.org/packages/c5/65/df0db6c097892c9380851ab9e44b52d4f7ba576b833996e0080181c0c439/numpy-2.3.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ee3888d9ff7c14604052b2ca5535a30216aa0a58e948cdd3eeb8d3415f638769", size = 12259838 }, - { url = "https://files.pythonhosted.org/packages/5b/e1/1ee06e70eb2136797abe847d386e7c0e830b67ad1d43f364dd04fa50d338/numpy-2.3.5-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:612a95a17655e213502f60cfb9bf9408efdc9eb1d5f50535cc6eb365d11b42b5", size = 5088378 }, - { url = "https://files.pythonhosted.org/packages/6d/9c/1ca85fb86708724275103b81ec4cf1ac1d08f465368acfc8da7ab545bdae/numpy-2.3.5-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:3101e5177d114a593d79dd79658650fe28b5a0d8abeb8ce6f437c0e6df5be1a4", size = 6628559 }, - { url = "https://files.pythonhosted.org/packages/74/78/fcd41e5a0ce4f3f7b003da85825acddae6d7ecb60cf25194741b036ca7d6/numpy-2.3.5-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8b973c57ff8e184109db042c842423ff4f60446239bd585a5131cc47f06f789d", size = 14250702 }, - { url = "https://files.pythonhosted.org/packages/b6/23/2a1b231b8ff672b4c450dac27164a8b2ca7d9b7144f9c02d2396518352eb/numpy-2.3.5-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0d8163f43acde9a73c2a33605353a4f1bc4798745a8b1d73183b28e5b435ae28", size = 16606086 }, - { url = "https://files.pythonhosted.org/packages/a0/c5/5ad26fbfbe2012e190cc7d5003e4d874b88bb18861d0829edc140a713021/numpy-2.3.5-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:51c1e14eb1e154ebd80e860722f9e6ed6ec89714ad2db2d3aa33c31d7c12179b", size = 16025985 }, - { url = "https://files.pythonhosted.org/packages/d2/fa/dd48e225c46c819288148d9d060b047fd2a6fb1eb37eae25112ee4cb4453/numpy-2.3.5-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b46b4ec24f7293f23adcd2d146960559aaf8020213de8ad1909dba6c013bf89c", size = 18542976 }, - { url = "https://files.pythonhosted.org/packages/05/79/ccbd23a75862d95af03d28b5c6901a1b7da4803181513d52f3b86ed9446e/numpy-2.3.5-cp312-cp312-win32.whl", hash = "sha256:3997b5b3c9a771e157f9aae01dd579ee35ad7109be18db0e85dbdbe1de06e952", size = 6285274 }, - { url = "https://files.pythonhosted.org/packages/2d/57/8aeaf160312f7f489dea47ab61e430b5cb051f59a98ae68b7133ce8fa06a/numpy-2.3.5-cp312-cp312-win_amd64.whl", hash = "sha256:86945f2ee6d10cdfd67bcb4069c1662dd711f7e2a4343db5cecec06b87cf31aa", size = 12782922 }, - { url = "https://files.pythonhosted.org/packages/78/a6/aae5cc2ca78c45e64b9ef22f089141d661516856cf7c8a54ba434576900d/numpy-2.3.5-cp312-cp312-win_arm64.whl", hash = "sha256:f28620fe26bee16243be2b7b874da327312240a7cdc38b769a697578d2100013", size = 10194667 }, - { url = "https://files.pythonhosted.org/packages/db/69/9cde09f36da4b5a505341180a3f2e6fadc352fd4d2b7096ce9778db83f1a/numpy-2.3.5-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:d0f23b44f57077c1ede8c5f26b30f706498b4862d3ff0a7298b8411dd2f043ff", size = 16728251 }, - { url = "https://files.pythonhosted.org/packages/79/fb/f505c95ceddd7027347b067689db71ca80bd5ecc926f913f1a23e65cf09b/numpy-2.3.5-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:aa5bc7c5d59d831d9773d1170acac7893ce3a5e130540605770ade83280e7188", size = 12254652 }, - { url = "https://files.pythonhosted.org/packages/78/da/8c7738060ca9c31b30e9301ee0cf6c5ffdbf889d9593285a1cead337f9a5/numpy-2.3.5-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:ccc933afd4d20aad3c00bcef049cb40049f7f196e0397f1109dba6fed63267b0", size = 5083172 }, - { url = "https://files.pythonhosted.org/packages/a4/b4/ee5bb2537fb9430fd2ef30a616c3672b991a4129bb1c7dcc42aa0abbe5d7/numpy-2.3.5-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:afaffc4393205524af9dfa400fa250143a6c3bc646c08c9f5e25a9f4b4d6a903", size = 6622990 }, - { url = "https://files.pythonhosted.org/packages/95/03/dc0723a013c7d7c19de5ef29e932c3081df1c14ba582b8b86b5de9db7f0f/numpy-2.3.5-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9c75442b2209b8470d6d5d8b1c25714270686f14c749028d2199c54e29f20b4d", size = 14248902 }, - { url = "https://files.pythonhosted.org/packages/f5/10/ca162f45a102738958dcec8023062dad0cbc17d1ab99d68c4e4a6c45fb2b/numpy-2.3.5-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:11e06aa0af8c0f05104d56450d6093ee639e15f24ecf62d417329d06e522e017", size = 16597430 }, - { url = "https://files.pythonhosted.org/packages/2a/51/c1e29be863588db58175175f057286900b4b3327a1351e706d5e0f8dd679/numpy-2.3.5-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ed89927b86296067b4f81f108a2271d8926467a8868e554eaf370fc27fa3ccaf", size = 16024551 }, - { url = "https://files.pythonhosted.org/packages/83/68/8236589d4dbb87253d28259d04d9b814ec0ecce7cb1c7fed29729f4c3a78/numpy-2.3.5-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:51c55fe3451421f3a6ef9a9c1439e82101c57a2c9eab9feb196a62b1a10b58ce", size = 18533275 }, - { url = "https://files.pythonhosted.org/packages/40/56/2932d75b6f13465239e3b7b7e511be27f1b8161ca2510854f0b6e521c395/numpy-2.3.5-cp313-cp313-win32.whl", hash = "sha256:1978155dd49972084bd6ef388d66ab70f0c323ddee6f693d539376498720fb7e", size = 6277637 }, - { url = "https://files.pythonhosted.org/packages/0c/88/e2eaa6cffb115b85ed7c7c87775cb8bcf0816816bc98ca8dbfa2ee33fe6e/numpy-2.3.5-cp313-cp313-win_amd64.whl", hash = "sha256:00dc4e846108a382c5869e77c6ed514394bdeb3403461d25a829711041217d5b", size = 12779090 }, - { url = "https://files.pythonhosted.org/packages/8f/88/3f41e13a44ebd4034ee17baa384acac29ba6a4fcc2aca95f6f08ca0447d1/numpy-2.3.5-cp313-cp313-win_arm64.whl", hash = "sha256:0472f11f6ec23a74a906a00b48a4dcf3849209696dff7c189714511268d103ae", size = 10194710 }, - { url = "https://files.pythonhosted.org/packages/13/cb/71744144e13389d577f867f745b7df2d8489463654a918eea2eeb166dfc9/numpy-2.3.5-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:414802f3b97f3c1eef41e530aaba3b3c1620649871d8cb38c6eaff034c2e16bd", size = 16827292 }, - { url = "https://files.pythonhosted.org/packages/71/80/ba9dc6f2a4398e7f42b708a7fdc841bb638d353be255655498edbf9a15a8/numpy-2.3.5-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5ee6609ac3604fa7780e30a03e5e241a7956f8e2fcfe547d51e3afa5247ac47f", size = 12378897 }, - { url = "https://files.pythonhosted.org/packages/2e/6d/db2151b9f64264bcceccd51741aa39b50150de9b602d98ecfe7e0c4bff39/numpy-2.3.5-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:86d835afea1eaa143012a2d7a3f45a3adce2d7adc8b4961f0b362214d800846a", size = 5207391 }, - { url = "https://files.pythonhosted.org/packages/80/ae/429bacace5ccad48a14c4ae5332f6aa8ab9f69524193511d60ccdfdc65fa/numpy-2.3.5-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:30bc11310e8153ca664b14c5f1b73e94bd0503681fcf136a163de856f3a50139", size = 6721275 }, - { url = "https://files.pythonhosted.org/packages/74/5b/1919abf32d8722646a38cd527bc3771eb229a32724ee6ba340ead9b92249/numpy-2.3.5-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1062fde1dcf469571705945b0f221b73928f34a20c904ffb45db101907c3454e", size = 14306855 }, - { url = "https://files.pythonhosted.org/packages/a5/87/6831980559434973bebc30cd9c1f21e541a0f2b0c280d43d3afd909b66d0/numpy-2.3.5-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ce581db493ea1a96c0556360ede6607496e8bf9b3a8efa66e06477267bc831e9", size = 16657359 }, - { url = "https://files.pythonhosted.org/packages/dd/91/c797f544491ee99fd00495f12ebb7802c440c1915811d72ac5b4479a3356/numpy-2.3.5-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:cc8920d2ec5fa99875b670bb86ddeb21e295cb07aa331810d9e486e0b969d946", size = 16093374 }, - { url = "https://files.pythonhosted.org/packages/74/a6/54da03253afcbe7a72785ec4da9c69fb7a17710141ff9ac5fcb2e32dbe64/numpy-2.3.5-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:9ee2197ef8c4f0dfe405d835f3b6a14f5fee7782b5de51ba06fb65fc9b36e9f1", size = 18594587 }, - { url = "https://files.pythonhosted.org/packages/80/e9/aff53abbdd41b0ecca94285f325aff42357c6b5abc482a3fcb4994290b18/numpy-2.3.5-cp313-cp313t-win32.whl", hash = "sha256:70b37199913c1bd300ff6e2693316c6f869c7ee16378faf10e4f5e3275b299c3", size = 6405940 }, - { url = "https://files.pythonhosted.org/packages/d5/81/50613fec9d4de5480de18d4f8ef59ad7e344d497edbef3cfd80f24f98461/numpy-2.3.5-cp313-cp313t-win_amd64.whl", hash = "sha256:b501b5fa195cc9e24fe102f21ec0a44dffc231d2af79950b451e0d99cea02234", size = 12920341 }, - { url = "https://files.pythonhosted.org/packages/bb/ab/08fd63b9a74303947f34f0bd7c5903b9c5532c2d287bead5bdf4c556c486/numpy-2.3.5-cp313-cp313t-win_arm64.whl", hash = "sha256:a80afd79f45f3c4a7d341f13acbe058d1ca8ac017c165d3fa0d3de6bc1a079d7", size = 10262507 }, - { url = "https://files.pythonhosted.org/packages/ba/97/1a914559c19e32d6b2e233cf9a6a114e67c856d35b1d6babca571a3e880f/numpy-2.3.5-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:bf06bc2af43fa8d32d30fae16ad965663e966b1a3202ed407b84c989c3221e82", size = 16735706 }, - { url = "https://files.pythonhosted.org/packages/57/d4/51233b1c1b13ecd796311216ae417796b88b0616cfd8a33ae4536330748a/numpy-2.3.5-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:052e8c42e0c49d2575621c158934920524f6c5da05a1d3b9bab5d8e259e045f0", size = 12264507 }, - { url = "https://files.pythonhosted.org/packages/45/98/2fe46c5c2675b8306d0b4a3ec3494273e93e1226a490f766e84298576956/numpy-2.3.5-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:1ed1ec893cff7040a02c8aa1c8611b94d395590d553f6b53629a4461dc7f7b63", size = 5093049 }, - { url = "https://files.pythonhosted.org/packages/ce/0e/0698378989bb0ac5f1660c81c78ab1fe5476c1a521ca9ee9d0710ce54099/numpy-2.3.5-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:2dcd0808a421a482a080f89859a18beb0b3d1e905b81e617a188bd80422d62e9", size = 6626603 }, - { url = "https://files.pythonhosted.org/packages/5e/a6/9ca0eecc489640615642a6cbc0ca9e10df70df38c4d43f5a928ff18d8827/numpy-2.3.5-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:727fd05b57df37dc0bcf1a27767a3d9a78cbbc92822445f32cc3436ba797337b", size = 14262696 }, - { url = "https://files.pythonhosted.org/packages/c8/f6/07ec185b90ec9d7217a00eeeed7383b73d7e709dae2a9a021b051542a708/numpy-2.3.5-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fffe29a1ef00883599d1dc2c51aa2e5d80afe49523c261a74933df395c15c520", size = 16597350 }, - { url = "https://files.pythonhosted.org/packages/75/37/164071d1dde6a1a84c9b8e5b414fa127981bad47adf3a6b7e23917e52190/numpy-2.3.5-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:8f7f0e05112916223d3f438f293abf0727e1181b5983f413dfa2fefc4098245c", size = 16040190 }, - { url = "https://files.pythonhosted.org/packages/08/3c/f18b82a406b04859eb026d204e4e1773eb41c5be58410f41ffa511d114ae/numpy-2.3.5-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2e2eb32ddb9ccb817d620ac1d8dae7c3f641c1e5f55f531a33e8ab97960a75b8", size = 18536749 }, - { url = "https://files.pythonhosted.org/packages/40/79/f82f572bf44cf0023a2fe8588768e23e1592585020d638999f15158609e1/numpy-2.3.5-cp314-cp314-win32.whl", hash = "sha256:66f85ce62c70b843bab1fb14a05d5737741e74e28c7b8b5a064de10142fad248", size = 6335432 }, - { url = "https://files.pythonhosted.org/packages/a3/2e/235b4d96619931192c91660805e5e49242389742a7a82c27665021db690c/numpy-2.3.5-cp314-cp314-win_amd64.whl", hash = "sha256:e6a0bc88393d65807d751a614207b7129a310ca4fe76a74e5c7da5fa5671417e", size = 12919388 }, - { url = "https://files.pythonhosted.org/packages/07/2b/29fd75ce45d22a39c61aad74f3d718e7ab67ccf839ca8b60866054eb15f8/numpy-2.3.5-cp314-cp314-win_arm64.whl", hash = "sha256:aeffcab3d4b43712bb7a60b65f6044d444e75e563ff6180af8f98dd4b905dfd2", size = 10476651 }, - { url = "https://files.pythonhosted.org/packages/17/e1/f6a721234ebd4d87084cfa68d081bcba2f5cfe1974f7de4e0e8b9b2a2ba1/numpy-2.3.5-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:17531366a2e3a9e30762c000f2c43a9aaa05728712e25c11ce1dbe700c53ad41", size = 16834503 }, - { url = "https://files.pythonhosted.org/packages/5c/1c/baf7ffdc3af9c356e1c135e57ab7cf8d247931b9554f55c467efe2c69eff/numpy-2.3.5-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:d21644de1b609825ede2f48be98dfde4656aefc713654eeee280e37cadc4e0ad", size = 12381612 }, - { url = "https://files.pythonhosted.org/packages/74/91/f7f0295151407ddc9ba34e699013c32c3c91944f9b35fcf9281163dc1468/numpy-2.3.5-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:c804e3a5aba5460c73955c955bdbd5c08c354954e9270a2c1565f62e866bdc39", size = 5210042 }, - { url = "https://files.pythonhosted.org/packages/2e/3b/78aebf345104ec50dd50a4d06ddeb46a9ff5261c33bcc58b1c4f12f85ec2/numpy-2.3.5-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:cc0a57f895b96ec78969c34f682c602bf8da1a0270b09bc65673df2e7638ec20", size = 6724502 }, - { url = "https://files.pythonhosted.org/packages/02/c6/7c34b528740512e57ef1b7c8337ab0b4f0bddf34c723b8996c675bc2bc91/numpy-2.3.5-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:900218e456384ea676e24ea6a0417f030a3b07306d29d7ad843957b40a9d8d52", size = 14308962 }, - { url = "https://files.pythonhosted.org/packages/80/35/09d433c5262bc32d725bafc619e095b6a6651caf94027a03da624146f655/numpy-2.3.5-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:09a1bea522b25109bf8e6f3027bd810f7c1085c64a0c7ce050c1676ad0ba010b", size = 16655054 }, - { url = "https://files.pythonhosted.org/packages/7a/ab/6a7b259703c09a88804fa2430b43d6457b692378f6b74b356155283566ac/numpy-2.3.5-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:04822c00b5fd0323c8166d66c701dc31b7fbd252c100acd708c48f763968d6a3", size = 16091613 }, - { url = "https://files.pythonhosted.org/packages/c2/88/330da2071e8771e60d1038166ff9d73f29da37b01ec3eb43cb1427464e10/numpy-2.3.5-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:d6889ec4ec662a1a37eb4b4fb26b6100841804dac55bd9df579e326cdc146227", size = 18591147 }, - { url = "https://files.pythonhosted.org/packages/51/41/851c4b4082402d9ea860c3626db5d5df47164a712cb23b54be028b184c1c/numpy-2.3.5-cp314-cp314t-win32.whl", hash = "sha256:93eebbcf1aafdf7e2ddd44c2923e2672e1010bddc014138b229e49725b4d6be5", size = 6479806 }, - { url = "https://files.pythonhosted.org/packages/90/30/d48bde1dfd93332fa557cff1972fbc039e055a52021fbef4c2c4b1eefd17/numpy-2.3.5-cp314-cp314t-win_amd64.whl", hash = "sha256:c8a9958e88b65c3b27e22ca2a076311636850b612d6bbfb76e8d156aacde2aaf", size = 13105760 }, - { url = "https://files.pythonhosted.org/packages/2d/fd/4b5eb0b3e888d86aee4d198c23acec7d214baaf17ea93c1adec94c9518b9/numpy-2.3.5-cp314-cp314t-win_arm64.whl", hash = "sha256:6203fdf9f3dc5bdaed7319ad8698e685c7a3be10819f41d32a0723e611733b42", size = 10545459 }, - { url = "https://files.pythonhosted.org/packages/c6/65/f9dea8e109371ade9c782b4e4756a82edf9d3366bca495d84d79859a0b79/numpy-2.3.5-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:f0963b55cdd70fad460fa4c1341f12f976bb26cb66021a5580329bd498988310", size = 16910689 }, - { url = "https://files.pythonhosted.org/packages/00/4f/edb00032a8fb92ec0a679d3830368355da91a69cab6f3e9c21b64d0bb986/numpy-2.3.5-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:f4255143f5160d0de972d28c8f9665d882b5f61309d8362fdd3e103cf7bf010c", size = 12457053 }, - { url = "https://files.pythonhosted.org/packages/16/a4/e8a53b5abd500a63836a29ebe145fc1ab1f2eefe1cfe59276020373ae0aa/numpy-2.3.5-pp311-pypy311_pp73-macosx_14_0_arm64.whl", hash = "sha256:a4b9159734b326535f4dd01d947f919c6eefd2d9827466a696c44ced82dfbc18", size = 5285635 }, - { url = "https://files.pythonhosted.org/packages/a3/2f/37eeb9014d9c8b3e9c55bc599c68263ca44fdbc12a93e45a21d1d56df737/numpy-2.3.5-pp311-pypy311_pp73-macosx_14_0_x86_64.whl", hash = "sha256:2feae0d2c91d46e59fcd62784a3a83b3fb677fead592ce51b5a6fbb4f95965ff", size = 6801770 }, - { url = "https://files.pythonhosted.org/packages/7d/e4/68d2f474df2cb671b2b6c2986a02e520671295647dad82484cde80ca427b/numpy-2.3.5-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ffac52f28a7849ad7576293c0cb7b9f08304e8f7d738a8cb8a90ec4c55a998eb", size = 14391768 }, - { url = "https://files.pythonhosted.org/packages/b8/50/94ccd8a2b141cb50651fddd4f6a48874acb3c91c8f0842b08a6afc4b0b21/numpy-2.3.5-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:63c0e9e7eea69588479ebf4a8a270d5ac22763cc5854e9a7eae952a3908103f7", size = 16729263 }, - { url = "https://files.pythonhosted.org/packages/2d/ee/346fa473e666fe14c52fcdd19ec2424157290a032d4c41f98127bfb31ac7/numpy-2.3.5-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:f16417ec91f12f814b10bafe79ef77e70113a2f5f7018640e7425ff979253425", size = 12967213 }, -] - -[[package]] -name = "nvidia-cublas-cu12" -version = "12.8.4.1" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/dc/61/e24b560ab2e2eaeb3c839129175fb330dfcfc29e5203196e5541a4c44682/nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:8ac4e771d5a348c551b2a426eda6193c19aa630236b418086020df5ba9667142", size = 594346921 }, -] - -[[package]] -name = "nvidia-cuda-cupti-cu12" -version = "12.8.90" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/f8/02/2adcaa145158bf1a8295d83591d22e4103dbfd821bcaf6f3f53151ca4ffa/nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ea0cb07ebda26bb9b29ba82cda34849e73c166c18162d3913575b0c9db9a6182", size = 10248621 }, -] - -[[package]] -name = "nvidia-cuda-nvrtc-cu12" -version = "12.8.93" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/05/6b/32f747947df2da6994e999492ab306a903659555dddc0fbdeb9d71f75e52/nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:a7756528852ef889772a84c6cd89d41dfa74667e24cca16bb31f8f061e3e9994", size = 88040029 }, -] - -[[package]] -name = "nvidia-cuda-runtime-cu12" -version = "12.8.90" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/0d/9b/a997b638fcd068ad6e4d53b8551a7d30fe8b404d6f1804abf1df69838932/nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:adade8dcbd0edf427b7204d480d6066d33902cab2a4707dcfc48a2d0fd44ab90", size = 954765 }, -] - -[[package]] -name = "nvidia-cudnn-cu12" -version = "9.10.2.21" ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "nvidia-cuda-nvrtc" }, ] wheels = [ -<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/a7/a1/0bd24ee8c8d03adac032fd2909426a00c88f8c57961b1277ded97f91119f/nvidia_cublas-13.1.1.3-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:b7a210458267ac818974c53038fbec2e969d5c99f305ab15c72522fa9f001dd5", size = 542848918, upload-time = "2026-04-08T18:46:22.985Z" }, { url = "https://files.pythonhosted.org/packages/3b/cd/154ca20c38269e05eff77c1464e6c1da89f50a6390b565e9d82e06bc11e1/nvidia_cublas-13.1.1.3-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:37936a16db8fe4ac1f065c2139360608a543a09275cb1a1af612e08cfa065436", size = 423138758, upload-time = "2026-04-08T18:46:58.655Z" }, -======= - { url = "https://files.pythonhosted.org/packages/ba/51/e123d997aa098c61d029f76663dedbfb9bc8dcf8c60cbd6adbe42f76d049/nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:949452be657fa16687d0930933f032835951ef0892b37d2d53824d1a84dc97a8", size = 706758467 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3006,7 +2214,6 @@ dependencies = [ { name = "nvidia-cublas" }, ] wheels = [ -<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/56/c5/83384d846b2fd17c44bd499b36c75a45ed4f095fbbb2252294e89cea5c5c/nvidia_cudnn_cu13-9.20.0.48-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:e31454ae00094b0c55319d9d15b6fa2fc50a9e1c0f5c8c80fb75258234e731e1", size = 444574296, upload-time = "2026-03-09T19:28:27.751Z" }, { url = "https://files.pythonhosted.org/packages/6e/5e/edb9c0ae051602c3ccaffe424256463636d639e27d7f302dde9975ef9e7a/nvidia_cudnn_cu13-9.20.0.48-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:0c45dd8eeb50b603f07995b1b300c62ffe6a1980482b82b3bcf94a4ca9d49304", size = 366173588, upload-time = "2026-03-09T19:29:34.474Z" }, ] @@ -3014,41 +2221,13 @@ wheels = [ [[package]] name = "nvidia-cufft" version = "12.0.0.61" -======= - { url = "https://files.pythonhosted.org/packages/1f/13/ee4e00f30e676b66ae65b4f08cb5bcbb8392c03f54f2d5413ea99a5d1c80/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74", size = 193118695 }, -] - -[[package]] -name = "nvidia-cufile-cu12" -version = "1.13.1.3" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/bb/fe/1bcba1dfbfb8d01be8d93f07bfc502c93fa23afa6fd5ab3fc7c1df71038a/nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1d069003be650e131b21c932ec3d8969c1715379251f8d23a1860554b1cb24fc", size = 1197834 }, -] - -[[package]] -name = "nvidia-curand-cu12" -version = "10.3.9.90" -source = { registry = "https://pypi.org/simple" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/fb/aa/6584b56dc84ebe9cf93226a5cde4d99080c8e90ab40f0c27bda7a0f29aa1/nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:b32331d4f4df5d6eefa0554c565b626c7216f87a06a4f56fab27c3b68a830ec9", size = 63619976 }, -] - -[[package]] -name = "nvidia-cusolver-cu12" -version = "11.7.3.90" ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "nvidia-nvjitlink" }, ] wheels = [ -<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/8b/ae/f417a75c0259e85c1d2f83ca4e960289a5f814ed0cea74d18c353d3e989d/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2708c852ef8cd89d1d2068bdbece0aa188813a0c934db3779b9b1faa8442e5f5", size = 214053554, upload-time = "2025-09-04T08:31:38.196Z" }, { url = "https://files.pythonhosted.org/packages/a8/2f/7b57e29836ea8714f81e9898409196f47d772d5ddedddf1592eadb8ab743/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6c44f692dce8fd5ffd3e3df134b6cdb9c2f72d99cf40b62c32dde45eea9ddad3", size = 214085489, upload-time = "2025-09-04T08:31:56.044Z" }, -======= - { url = "https://files.pythonhosted.org/packages/85/48/9a13d2975803e8cf2777d5ed57b87a0b6ca2cc795f9a4f59796a910bfb80/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450", size = 267506905 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3079,12 +2258,8 @@ dependencies = [ { name = "nvidia-nvjitlink" }, ] wheels = [ -<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/c8/c3/b30c9e935fc01e3da443ec0116ed1b2a009bb867f5324d3f2d7e533e776b/nvidia_cusolver-12.0.4.66-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:02c2457eaa9e39de20f880f4bd8820e6a1cfb9f9a34f820eb12a155aa5bc92d2", size = 223467760, upload-time = "2025-09-04T08:33:04.222Z" }, { url = "https://files.pythonhosted.org/packages/5f/67/cba3777620cdacb99102da4042883709c41c709f4b6323c10781a9c3aa34/nvidia_cusolver-12.0.4.66-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:0a759da5dea5c0ea10fd307de75cdeb59e7ea4fcb8add0924859b944babf1112", size = 200941980, upload-time = "2025-09-04T08:33:22.767Z" }, -======= - { url = "https://files.pythonhosted.org/packages/c2/f5/e1854cb2f2bcd4280c44736c93550cc300ff4b8c95ebe370d0aa7d2b473d/nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b", size = 288216466 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3095,12 +2270,8 @@ dependencies = [ { name = "nvidia-nvjitlink" }, ] wheels = [ -<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/f8/94/5c26f33738ae35276672f12615a64bd008ed5be6d1ebcb23579285d960a9/nvidia_cusparse-12.6.3.3-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:80bcc4662f23f1054ee334a15c72b8940402975e0eab63178fc7e670aa59472c", size = 162155568, upload-time = "2025-09-04T08:33:42.864Z" }, { url = "https://files.pythonhosted.org/packages/fa/18/623c77619c31d62efd55302939756966f3ecc8d724a14dab2b75f1508850/nvidia_cusparse-12.6.3.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2b3c89c88d01ee0e477cb7f82ef60a11a4bcd57b6b87c33f789350b59759360b", size = 145942937, upload-time = "2025-09-04T08:33:58.029Z" }, -======= - { url = "https://files.pythonhosted.org/packages/56/79/12978b96bd44274fe38b5dde5cfb660b1d114f70a65ef962bcbbed99b549/nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f1bb701d6b930d5a7cea44c19ceb973311500847f81b634d802b7b539dc55623", size = 287193691 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3108,12 +2279,8 @@ name = "nvidia-cusparselt-cu13" version = "0.8.1" source = { registry = "https://pypi.org/simple" } wheels = [ -<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/46/e1/cdc1797eadf82d3a9a575a19b33fdc871a97edbec42c00b5b5e914f4aff4/nvidia_cusparselt_cu13-0.8.1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:4dca476c50bf4780d46cd0bfbd82e2bc10a08e4fef7950917ce8d7578d22a23f", size = 221051344, upload-time = "2025-09-05T18:49:51.289Z" }, { url = "https://files.pythonhosted.org/packages/34/7d/2661f2fb3ac4302f3a246f5fc030213ac60c1fe0bce84f9783dbd831dbb7/nvidia_cusparselt_cu13-0.8.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:786ce87568c303fadb5afcc7102d454cd3040d75f6f8626f5db460d1871f4dd0", size = 170148586, upload-time = "2025-09-05T18:50:50.248Z" }, -======= - { url = "https://files.pythonhosted.org/packages/6e/89/f7a07dc961b60645dbbf42e80f2bc85ade7feb9a491b11a1e973aa00071f/nvidia_nccl_cu12-2.27.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ad730cf15cb5d25fe849c6e6ca9eb5b76db16a80f13f425ac68d8e2e55624457", size = 322348229 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3121,12 +2288,8 @@ name = "nvidia-nccl-cu13" version = "2.29.7" source = { registry = "https://pypi.org/simple" } wheels = [ -<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/72/0d/daf50d44177ee0cbc7ff0a0c91eb5ff676c82be42f9a970bc7597f440c3a/nvidia_nccl_cu13-2.29.7-py3-none-manylinux_2_18_aarch64.whl", hash = "sha256:674a12383e3c38a1bcccae7d4f3633b37852230b6047883cb2f4c2d1b36d9bf5", size = 206014712, upload-time = "2026-03-03T05:34:20.843Z" }, { url = "https://files.pythonhosted.org/packages/67/f4/58e4e91b6919367c7aafb8e36fce9aad1a3047e536bf7e2fd560927d3a4c/nvidia_nccl_cu13-2.29.7-py3-none-manylinux_2_18_x86_64.whl", hash = "sha256:edd81538446786ec3b73972543e53bb43bcaf0bfc8ef76cb679fcc390ffe136d", size = 205976000, upload-time = "2026-03-03T05:36:24.472Z" }, -======= - { url = "https://files.pythonhosted.org/packages/f6/74/86a07f1d0f42998ca31312f998bd3b9a7eff7f52378f4f270c8679c77fb9/nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:81ff63371a7ebd6e6451970684f916be2eab07321b73c9d244dc2b4da7f73b88", size = 39254836 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3134,12 +2297,8 @@ name = "nvidia-nvjitlink" version = "13.0.88" source = { registry = "https://pypi.org/simple" } wheels = [ -<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/56/7a/123e033aaff487c77107195fa5a2b8686795ca537935a24efae476c41f05/nvidia_nvjitlink-13.0.88-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:13a74f429e23b921c1109976abefacc69835f2f433ebd323d3946e11d804e47b", size = 40713933, upload-time = "2025-09-04T08:35:43.553Z" }, { url = "https://files.pythonhosted.org/packages/ab/2c/93c5250e64df4f894f1cbb397c6fd71f79813f9fd79d7cd61de3f97b3c2d/nvidia_nvjitlink-13.0.88-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e931536ccc7d467a98ba1d8b89ff7fa7f1fa3b13f2b0069118cd7f47bff07d0c", size = 38768748, upload-time = "2025-09-04T08:35:20.008Z" }, -======= - { url = "https://files.pythonhosted.org/packages/3b/6c/99acb2f9eb85c29fc6f3a7ac4dccfd992e22666dd08a642b303311326a97/nvidia_nvshmem_cu12-3.3.20-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d00f26d3f9b2e3c3065be895e3059d6479ea5c638a3f38c9fec49b1b9dd7c1e5", size = 124657145 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3147,7 +2306,6 @@ name = "nvidia-nvshmem-cu13" version = "3.4.5" source = { registry = "https://pypi.org/simple" } wheels = [ -<<<<<<< HEAD { url = "https://files.pythonhosted.org/packages/dc/0f/05cc9c720236dcd2db9c1ab97fff629e96821be2e63103569da0c9b72f19/nvidia_nvshmem_cu13-3.4.5-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:6dc2a197f38e5d0376ad52cd1a2a3617d3cdc150fd5966f4aee9bcebb1d68fe9", size = 60215947, upload-time = "2025-09-06T00:32:20.022Z" }, { url = "https://files.pythonhosted.org/packages/3c/35/a9bf80a609e74e3b000fef598933235c908fcefcef9026042b8e6dfde2a9/nvidia_nvshmem_cu13-3.4.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:290f0a2ee94c9f3687a02502f3b9299a9f9fe826e6d0287ee18482e78d495b80", size = 60412546, upload-time = "2025-09-06T00:32:41.564Z" }, ] @@ -3159,9 +2317,6 @@ source = { registry = "https://pypi.org/simple" } wheels = [ { url = "https://files.pythonhosted.org/packages/c2/f3/d86c845465a2723ad7e1e5c36dcd75ddb82898b3f53be47ebd429fb2fa5d/nvidia_nvtx-13.0.85-py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:4936d1d6780fbe68db454f5e72a42ff64d1fd6397df9f363ae786930fd5c1cd4", size = 148047, upload-time = "2025-09-04T08:29:01.761Z" }, { url = "https://files.pythonhosted.org/packages/a8/64/3708a90d1ebe202ffdeb7185f878a3c84d15c2b2c31858da2ce0583e2def/nvidia_nvtx-13.0.85-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cb7780edb6b14107373c835bf8b72e7a178bac7367e23da7acb108f973f157a6", size = 148878, upload-time = "2025-09-04T08:28:53.627Z" }, -======= - { url = "https://files.pythonhosted.org/packages/a2/eb/86626c1bbc2edb86323022371c39aa48df6fd8b0a1647bc274577f72e90b/nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5b17e2001cc0d751a5bc2c6ec6d26ad95913324a4adb86788c944f8ce9ba441f", size = 89954 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3171,69 +2326,52 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "alembic" }, { name = "colorlog" }, - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, { name = "packaging" }, { name = "pyyaml" }, { name = "sqlalchemy" }, { name = "tqdm" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/f4/aa/05f5e3f662cc96a4c478fc3446b8ed6359825a2b504ecb614a9ac84e4a4d/optuna-4.9.0.tar.gz", hash = "sha256:b322e5cbdf1655fb84c37646c4a7a1f391de1b47806bbe222e015825d0a82b87", size = 485834, upload-time = "2026-06-01T06:23:30.424Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/ab/f3/e5fcd5d9b15771ed6dc10e3a7eeddc672e418f4f4c4653d216cc1d857e2d/optuna-4.9.0-py3-none-any.whl", hash = "sha256:f52f3be6148654850c92a5860d398fd88ec6b2c84ab68d9c3d07dcff02e7afee", size = 425553, upload-time = "2026-06-01T06:23:28.804Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/6b/81/08f90f194eed78178064a9383432eca95611e2c5331e7b01e2418ce4b15a/optuna-4.6.0.tar.gz", hash = "sha256:89e38c2447c7f793a726617b8043f01e31f0bad54855040db17eb3b49404a369", size = 477444 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/58/de/3d8455b08cb6312f8cc46aacdf16c71d4d881a1db4a4140fc5ef31108422/optuna-4.6.0-py3-none-any.whl", hash = "sha256:4c3a9facdef2b2dd7e3e2a8ae3697effa70fae4056fcf3425cfc6f5a40feb069", size = 404708 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "overrides" version = "7.7.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/36/86/b585f53236dec60aba864e050778b25045f857e17f6e5ea0ae95fe80edd2/overrides-7.7.0.tar.gz", hash = "sha256:55158fa3d93b98cc75299b1e67078ad9003ca27945c76162c1c0766d6f91820a", size = 22812 } +sdist = { url = "https://files.pythonhosted.org/packages/36/86/b585f53236dec60aba864e050778b25045f857e17f6e5ea0ae95fe80edd2/overrides-7.7.0.tar.gz", hash = "sha256:55158fa3d93b98cc75299b1e67078ad9003ca27945c76162c1c0766d6f91820a", size = 22812, upload-time = "2024-01-27T21:01:33.423Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/2c/ab/fc8290c6a4c722e5514d80f62b2dc4c4df1a68a41d1364e625c35990fcf3/overrides-7.7.0-py3-none-any.whl", hash = "sha256:c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49", size = 17832 }, + { url = "https://files.pythonhosted.org/packages/2c/ab/fc8290c6a4c722e5514d80f62b2dc4c4df1a68a41d1364e625c35990fcf3/overrides-7.7.0-py3-none-any.whl", hash = "sha256:c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49", size = 17832, upload-time = "2024-01-27T21:01:31.393Z" }, ] [[package]] name = "packaging" version = "26.2" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/d7/f1/e7a6dd94a8d4a5626c03e4e99c87f241ba9e350cd9e6d75123f992427270/packaging-26.2.tar.gz", hash = "sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661", size = 228134, upload-time = "2026-04-24T20:15:23.917Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/df/b2/87e62e8c3e2f4b32e5fe99e0b86d576da1312593b39f47d8ceef365e95ed/packaging-26.2-py3-none-any.whl", hash = "sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e", size = 100195, upload-time = "2026-04-24T20:15:22.081Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/a1/d4/1fc4078c65507b51b96ca8f8c3ba19e6a61c8253c72794544580a7b6c24d/packaging-25.0.tar.gz", hash = "sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f", size = 165727 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl", hash = "sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484", size = 66469 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "pandocfilters" version = "1.5.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/70/6f/3dd4940bbe001c06a65f88e36bad298bc7a0de5036115639926b0c5c0458/pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e", size = 8454 } +sdist = { url = "https://files.pythonhosted.org/packages/70/6f/3dd4940bbe001c06a65f88e36bad298bc7a0de5036115639926b0c5c0458/pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e", size = 8454, upload-time = "2024-01-18T20:08:13.726Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/ef/af/4fbc8cab944db5d21b7e2a5b8e9211a03a79852b1157e2c102fcc61ac440/pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc", size = 8663 }, + { url = "https://files.pythonhosted.org/packages/ef/af/4fbc8cab944db5d21b7e2a5b8e9211a03a79852b1157e2c102fcc61ac440/pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc", size = 8663, upload-time = "2024-01-18T20:08:11.28Z" }, ] [[package]] name = "parso" version = "0.8.7" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/30/4b/90c937815137d43ce71ba043cd3566221e9df6b9c805f24b5d138c9d40a7/parso-0.8.7.tar.gz", hash = "sha256:eaaac4c9fdd5e9e8852dc778d2d7405897ec510f2a298071453e5e3a07914bb1", size = 401824, upload-time = "2026-05-01T23:13:02.138Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/99/5d/8268b644392ee874ee82a635cd0df1773de230bde356c38de28e298392cc/parso-0.8.7-py2.py3-none-any.whl", hash = "sha256:a8926eb2a1b915486941fdbd31e86a4baf88fe8c210f25f2f35ecec5b574ca1c", size = 107025, upload-time = "2026-05-01T23:12:58.867Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/d4/de/53e0bcf53d13e005bd8c92e7855142494f41171b34c2536b86187474184d/parso-0.8.5.tar.gz", hash = "sha256:034d7354a9a018bdce352f48b2a8a450f05e9d6ee85db84764e9b6bd96dafe5a", size = 401205 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/16/32/f8e3c85d1d5250232a5d3477a2a28cc291968ff175caeadaf3cc19ce0e4a/parso-0.8.5-py2.py3-none-any.whl", hash = "sha256:646204b5ee239c396d040b90f9e272e9a8017c630092bf59980beb62fd033887", size = 106668 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3244,9 +2382,9 @@ dependencies = [ { name = "locket" }, { name = "toolz" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/b2/3a/3f06f34820a31257ddcabdfafc2672c5816be79c7e353b02c1f318daa7d4/partd-1.4.2.tar.gz", hash = "sha256:d022c33afbdc8405c226621b015e8067888173d85f7f5ecebb3cafed9a20f02c", size = 21029 } +sdist = { url = "https://files.pythonhosted.org/packages/b2/3a/3f06f34820a31257ddcabdfafc2672c5816be79c7e353b02c1f318daa7d4/partd-1.4.2.tar.gz", hash = "sha256:d022c33afbdc8405c226621b015e8067888173d85f7f5ecebb3cafed9a20f02c", size = 21029, upload-time = "2024-05-06T19:51:41.945Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/71/e7/40fb618334dcdf7c5a316c0e7343c5cd82d3d866edc100d98e29bc945ecd/partd-1.4.2-py3-none-any.whl", hash = "sha256:978e4ac767ec4ba5b86c6eaa52e5a2a3bc748a2ca839e8cc798f1cc6ce6efb0f", size = 18905 }, + { url = "https://files.pythonhosted.org/packages/71/e7/40fb618334dcdf7c5a316c0e7343c5cd82d3d866edc100d98e29bc945ecd/partd-1.4.2-py3-none-any.whl", hash = "sha256:978e4ac767ec4ba5b86c6eaa52e5a2a3bc748a2ca839e8cc798f1cc6ce6efb0f", size = 18905, upload-time = "2024-05-06T19:51:39.271Z" }, ] [[package]] @@ -3256,16 +2394,15 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "ptyprocess" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/42/92/cc564bf6381ff43ce1f4d06852fc19a2f11d180f23dc32d9588bee2f149d/pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f", size = 166450 } +sdist = { url = "https://files.pythonhosted.org/packages/42/92/cc564bf6381ff43ce1f4d06852fc19a2f11d180f23dc32d9588bee2f149d/pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f", size = 166450, upload-time = "2023-11-25T09:07:26.339Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/9e/c3/059298687310d527a58bb01f3b1965787ee3b40dce76752eda8b44e9a2c5/pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523", size = 63772 }, + { url = "https://files.pythonhosted.org/packages/9e/c3/059298687310d527a58bb01f3b1965787ee3b40dce76752eda8b44e9a2c5/pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523", size = 63772, upload-time = "2023-11-25T06:56:14.81Z" }, ] [[package]] name = "pillow" version = "12.2.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/8c/21/c2bcdd5906101a30244eaffc1b6e6ce71a31bd0742a01eb89e660ebfac2d/pillow-12.2.0.tar.gz", hash = "sha256:a830b1a40919539d07806aa58e1b114df53ddd43213d9c8b75847eee6c0182b5", size = 46987819, upload-time = "2026-04-01T14:46:17.687Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/68/e1/748f5663efe6edcfc4e74b2b93edfb9b8b99b67f21a854c3ae416500a2d9/pillow-12.2.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:8be29e59487a79f173507c30ddf57e733a357f67881430449bb32614075a40ab", size = 5354347, upload-time = "2026-04-01T14:42:44.255Z" }, @@ -3347,89 +2484,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/c3/28/ec0fc38107fc32536908034e990c47914c57cd7c5a3ece4d8d8f7ffd7e27/pillow-12.2.0-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4a6c9fa44005fa37a91ebfc95d081e8079757d2e904b27103f4f5fa6f0bf78c0", size = 5355404, upload-time = "2026-04-01T14:46:06.33Z" }, { url = "https://files.pythonhosted.org/packages/5e/8b/51b0eddcfa2180d60e41f06bd6d0a62202b20b59c68f5a132e615b75aecf/pillow-12.2.0-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:25373b66e0dd5905ed63fa3cae13c82fbddf3079f2c8bf15c6fb6a35586324c1", size = 6002215, upload-time = "2026-04-01T14:46:08.83Z" }, { url = "https://files.pythonhosted.org/packages/bc/60/5382c03e1970de634027cee8e1b7d39776b778b81812aaf45b694dfe9e28/pillow-12.2.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:bfa9c230d2fe991bed5318a5f119bd6780cda2915cca595393649fc118ab895e", size = 7080946, upload-time = "2026-04-01T14:46:11.734Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/5a/b0/cace85a1b0c9775a9f8f5d5423c8261c858760e2466c79b2dd184638b056/pillow-12.0.0.tar.gz", hash = "sha256:87d4f8125c9988bfbed67af47dd7a953e2fc7b0cc1e7800ec6d2080d490bb353", size = 47008828 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/0e/5a/a2f6773b64edb921a756eb0729068acad9fc5208a53f4a349396e9436721/pillow-12.0.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:0fd00cac9c03256c8b2ff58f162ebcd2587ad3e1f2e397eab718c47e24d231cc", size = 5289798 }, - { url = "https://files.pythonhosted.org/packages/2e/05/069b1f8a2e4b5a37493da6c5868531c3f77b85e716ad7a590ef87d58730d/pillow-12.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a3475b96f5908b3b16c47533daaa87380c491357d197564e0ba34ae75c0f3257", size = 4650589 }, - { url = "https://files.pythonhosted.org/packages/61/e3/2c820d6e9a36432503ead175ae294f96861b07600a7156154a086ba7111a/pillow-12.0.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:110486b79f2d112cf6add83b28b627e369219388f64ef2f960fef9ebaf54c642", size = 6230472 }, - { url = "https://files.pythonhosted.org/packages/4f/89/63427f51c64209c5e23d4d52071c8d0f21024d3a8a487737caaf614a5795/pillow-12.0.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5269cc1caeedb67e6f7269a42014f381f45e2e7cd42d834ede3c703a1d915fe3", size = 8033887 }, - { url = "https://files.pythonhosted.org/packages/f6/1b/c9711318d4901093c15840f268ad649459cd81984c9ec9887756cca049a5/pillow-12.0.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:aa5129de4e174daccbc59d0a3b6d20eaf24417d59851c07ebb37aeb02947987c", size = 6343964 }, - { url = "https://files.pythonhosted.org/packages/41/1e/db9470f2d030b4995083044cd8738cdd1bf773106819f6d8ba12597d5352/pillow-12.0.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bee2a6db3a7242ea309aa7ee8e2780726fed67ff4e5b40169f2c940e7eb09227", size = 7034756 }, - { url = "https://files.pythonhosted.org/packages/cc/b0/6177a8bdd5ee4ed87cba2de5a3cc1db55ffbbec6176784ce5bb75aa96798/pillow-12.0.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:90387104ee8400a7b4598253b4c406f8958f59fcf983a6cea2b50d59f7d63d0b", size = 6458075 }, - { url = "https://files.pythonhosted.org/packages/bc/5e/61537aa6fa977922c6a03253a0e727e6e4a72381a80d63ad8eec350684f2/pillow-12.0.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:bc91a56697869546d1b8f0a3ff35224557ae7f881050e99f615e0119bf934b4e", size = 7125955 }, - { url = "https://files.pythonhosted.org/packages/1f/3d/d5033539344ee3cbd9a4d69e12e63ca3a44a739eb2d4c8da350a3d38edd7/pillow-12.0.0-cp311-cp311-win32.whl", hash = "sha256:27f95b12453d165099c84f8a8bfdfd46b9e4bda9e0e4b65f0635430027f55739", size = 6298440 }, - { url = "https://files.pythonhosted.org/packages/4d/42/aaca386de5cc8bd8a0254516957c1f265e3521c91515b16e286c662854c4/pillow-12.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:b583dc9070312190192631373c6c8ed277254aa6e6084b74bdd0a6d3b221608e", size = 6999256 }, - { url = "https://files.pythonhosted.org/packages/ba/f1/9197c9c2d5708b785f631a6dfbfa8eb3fb9672837cb92ae9af812c13b4ed/pillow-12.0.0-cp311-cp311-win_arm64.whl", hash = "sha256:759de84a33be3b178a64c8ba28ad5c135900359e85fb662bc6e403ad4407791d", size = 2436025 }, - { url = "https://files.pythonhosted.org/packages/2c/90/4fcce2c22caf044e660a198d740e7fbc14395619e3cb1abad12192c0826c/pillow-12.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:53561a4ddc36facb432fae7a9d8afbfaf94795414f5cdc5fc52f28c1dca90371", size = 5249377 }, - { url = "https://files.pythonhosted.org/packages/fd/e0/ed960067543d080691d47d6938ebccbf3976a931c9567ab2fbfab983a5dd/pillow-12.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:71db6b4c1653045dacc1585c1b0d184004f0d7e694c7b34ac165ca70c0838082", size = 4650343 }, - { url = "https://files.pythonhosted.org/packages/e7/a1/f81fdeddcb99c044bf7d6faa47e12850f13cee0849537a7d27eeab5534d4/pillow-12.0.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2fa5f0b6716fc88f11380b88b31fe591a06c6315e955c096c35715788b339e3f", size = 6232981 }, - { url = "https://files.pythonhosted.org/packages/88/e1/9098d3ce341a8750b55b0e00c03f1630d6178f38ac191c81c97a3b047b44/pillow-12.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:82240051c6ca513c616f7f9da06e871f61bfd7805f566275841af15015b8f98d", size = 8041399 }, - { url = "https://files.pythonhosted.org/packages/a7/62/a22e8d3b602ae8cc01446d0c57a54e982737f44b6f2e1e019a925143771d/pillow-12.0.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:55f818bd74fe2f11d4d7cbc65880a843c4075e0ac7226bc1a23261dbea531953", size = 6347740 }, - { url = "https://files.pythonhosted.org/packages/4f/87/424511bdcd02c8d7acf9f65caa09f291a519b16bd83c3fb3374b3d4ae951/pillow-12.0.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b87843e225e74576437fd5b6a4c2205d422754f84a06942cfaf1dc32243e45a8", size = 7040201 }, - { url = "https://files.pythonhosted.org/packages/dc/4d/435c8ac688c54d11755aedfdd9f29c9eeddf68d150fe42d1d3dbd2365149/pillow-12.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c607c90ba67533e1b2355b821fef6764d1dd2cbe26b8c1005ae84f7aea25ff79", size = 6462334 }, - { url = "https://files.pythonhosted.org/packages/2b/f2/ad34167a8059a59b8ad10bc5c72d4d9b35acc6b7c0877af8ac885b5f2044/pillow-12.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:21f241bdd5080a15bc86d3466a9f6074a9c2c2b314100dd896ac81ee6db2f1ba", size = 7134162 }, - { url = "https://files.pythonhosted.org/packages/0c/b1/a7391df6adacf0a5c2cf6ac1cf1fcc1369e7d439d28f637a847f8803beb3/pillow-12.0.0-cp312-cp312-win32.whl", hash = "sha256:dd333073e0cacdc3089525c7df7d39b211bcdf31fc2824e49d01c6b6187b07d0", size = 6298769 }, - { url = "https://files.pythonhosted.org/packages/a2/0b/d87733741526541c909bbf159e338dcace4f982daac6e5a8d6be225ca32d/pillow-12.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:9fe611163f6303d1619bbcb653540a4d60f9e55e622d60a3108be0d5b441017a", size = 7001107 }, - { url = "https://files.pythonhosted.org/packages/bc/96/aaa61ce33cc98421fb6088af2a03be4157b1e7e0e87087c888e2370a7f45/pillow-12.0.0-cp312-cp312-win_arm64.whl", hash = "sha256:7dfb439562f234f7d57b1ac6bc8fe7f838a4bd49c79230e0f6a1da93e82f1fad", size = 2436012 }, - { url = "https://files.pythonhosted.org/packages/62/f2/de993bb2d21b33a98d031ecf6a978e4b61da207bef02f7b43093774c480d/pillow-12.0.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:0869154a2d0546545cde61d1789a6524319fc1897d9ee31218eae7a60ccc5643", size = 4045493 }, - { url = "https://files.pythonhosted.org/packages/0e/b6/bc8d0c4c9f6f111a783d045310945deb769b806d7574764234ffd50bc5ea/pillow-12.0.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:a7921c5a6d31b3d756ec980f2f47c0cfdbce0fc48c22a39347a895f41f4a6ea4", size = 4120461 }, - { url = "https://files.pythonhosted.org/packages/5d/57/d60d343709366a353dc56adb4ee1e7d8a2cc34e3fbc22905f4167cfec119/pillow-12.0.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:1ee80a59f6ce048ae13cda1abf7fbd2a34ab9ee7d401c46be3ca685d1999a399", size = 3576912 }, - { url = "https://files.pythonhosted.org/packages/a4/a4/a0a31467e3f83b94d37568294b01d22b43ae3c5d85f2811769b9c66389dd/pillow-12.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c50f36a62a22d350c96e49ad02d0da41dbd17ddc2e29750dbdba4323f85eb4a5", size = 5249132 }, - { url = "https://files.pythonhosted.org/packages/83/06/48eab21dd561de2914242711434c0c0eb992ed08ff3f6107a5f44527f5e9/pillow-12.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:5193fde9a5f23c331ea26d0cf171fbf67e3f247585f50c08b3e205c7aeb4589b", size = 4650099 }, - { url = "https://files.pythonhosted.org/packages/fc/bd/69ed99fd46a8dba7c1887156d3572fe4484e3f031405fcc5a92e31c04035/pillow-12.0.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:bde737cff1a975b70652b62d626f7785e0480918dece11e8fef3c0cf057351c3", size = 6230808 }, - { url = "https://files.pythonhosted.org/packages/ea/94/8fad659bcdbf86ed70099cb60ae40be6acca434bbc8c4c0d4ef356d7e0de/pillow-12.0.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a6597ff2b61d121172f5844b53f21467f7082f5fb385a9a29c01414463f93b07", size = 8037804 }, - { url = "https://files.pythonhosted.org/packages/20/39/c685d05c06deecfd4e2d1950e9a908aa2ca8bc4e6c3b12d93b9cafbd7837/pillow-12.0.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0b817e7035ea7f6b942c13aa03bb554fc44fea70838ea21f8eb31c638326584e", size = 6345553 }, - { url = "https://files.pythonhosted.org/packages/38/57/755dbd06530a27a5ed74f8cb0a7a44a21722ebf318edbe67ddbd7fb28f88/pillow-12.0.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f4f1231b7dec408e8670264ce63e9c71409d9583dd21d32c163e25213ee2a344", size = 7037729 }, - { url = "https://files.pythonhosted.org/packages/ca/b6/7e94f4c41d238615674d06ed677c14883103dce1c52e4af16f000338cfd7/pillow-12.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6e51b71417049ad6ab14c49608b4a24d8fb3fe605e5dfabfe523b58064dc3d27", size = 6459789 }, - { url = "https://files.pythonhosted.org/packages/9c/14/4448bb0b5e0f22dd865290536d20ec8a23b64e2d04280b89139f09a36bb6/pillow-12.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d120c38a42c234dc9a8c5de7ceaaf899cf33561956acb4941653f8bdc657aa79", size = 7130917 }, - { url = "https://files.pythonhosted.org/packages/dd/ca/16c6926cc1c015845745d5c16c9358e24282f1e588237a4c36d2b30f182f/pillow-12.0.0-cp313-cp313-win32.whl", hash = "sha256:4cc6b3b2efff105c6a1656cfe59da4fdde2cda9af1c5e0b58529b24525d0a098", size = 6302391 }, - { url = "https://files.pythonhosted.org/packages/6d/2a/dd43dcfd6dae9b6a49ee28a8eedb98c7d5ff2de94a5d834565164667b97b/pillow-12.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:4cf7fed4b4580601c4345ceb5d4cbf5a980d030fd5ad07c4d2ec589f95f09905", size = 7007477 }, - { url = "https://files.pythonhosted.org/packages/77/f0/72ea067f4b5ae5ead653053212af05ce3705807906ba3f3e8f58ddf617e6/pillow-12.0.0-cp313-cp313-win_arm64.whl", hash = "sha256:9f0b04c6b8584c2c193babcccc908b38ed29524b29dd464bc8801bf10d746a3a", size = 2435918 }, - { url = "https://files.pythonhosted.org/packages/f5/5e/9046b423735c21f0487ea6cb5b10f89ea8f8dfbe32576fe052b5ba9d4e5b/pillow-12.0.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:7fa22993bac7b77b78cae22bad1e2a987ddf0d9015c63358032f84a53f23cdc3", size = 5251406 }, - { url = "https://files.pythonhosted.org/packages/12/66/982ceebcdb13c97270ef7a56c3969635b4ee7cd45227fa707c94719229c5/pillow-12.0.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:f135c702ac42262573fe9714dfe99c944b4ba307af5eb507abef1667e2cbbced", size = 4653218 }, - { url = "https://files.pythonhosted.org/packages/16/b3/81e625524688c31859450119bf12674619429cab3119eec0e30a7a1029cb/pillow-12.0.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:c85de1136429c524e55cfa4e033b4a7940ac5c8ee4d9401cc2d1bf48154bbc7b", size = 6266564 }, - { url = "https://files.pythonhosted.org/packages/98/59/dfb38f2a41240d2408096e1a76c671d0a105a4a8471b1871c6902719450c/pillow-12.0.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:38df9b4bfd3db902c9c2bd369bcacaf9d935b2fff73709429d95cc41554f7b3d", size = 8069260 }, - { url = "https://files.pythonhosted.org/packages/dc/3d/378dbea5cd1874b94c312425ca77b0f47776c78e0df2df751b820c8c1d6c/pillow-12.0.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7d87ef5795da03d742bf49439f9ca4d027cde49c82c5371ba52464aee266699a", size = 6379248 }, - { url = "https://files.pythonhosted.org/packages/84/b0/d525ef47d71590f1621510327acec75ae58c721dc071b17d8d652ca494d8/pillow-12.0.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:aff9e4d82d082ff9513bdd6acd4f5bd359f5b2c870907d2b0a9c5e10d40c88fe", size = 7066043 }, - { url = "https://files.pythonhosted.org/packages/61/2c/aced60e9cf9d0cde341d54bf7932c9ffc33ddb4a1595798b3a5150c7ec4e/pillow-12.0.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:8d8ca2b210ada074d57fcee40c30446c9562e542fc46aedc19baf758a93532ee", size = 6490915 }, - { url = "https://files.pythonhosted.org/packages/ef/26/69dcb9b91f4e59f8f34b2332a4a0a951b44f547c4ed39d3e4dcfcff48f89/pillow-12.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:99a7f72fb6249302aa62245680754862a44179b545ded638cf1fef59befb57ef", size = 7157998 }, - { url = "https://files.pythonhosted.org/packages/61/2b/726235842220ca95fa441ddf55dd2382b52ab5b8d9c0596fe6b3f23dafe8/pillow-12.0.0-cp313-cp313t-win32.whl", hash = "sha256:4078242472387600b2ce8d93ade8899c12bf33fa89e55ec89fe126e9d6d5d9e9", size = 6306201 }, - { url = "https://files.pythonhosted.org/packages/c0/3d/2afaf4e840b2df71344ababf2f8edd75a705ce500e5dc1e7227808312ae1/pillow-12.0.0-cp313-cp313t-win_amd64.whl", hash = "sha256:2c54c1a783d6d60595d3514f0efe9b37c8808746a66920315bfd34a938d7994b", size = 7013165 }, - { url = "https://files.pythonhosted.org/packages/6f/75/3fa09aa5cf6ed04bee3fa575798ddf1ce0bace8edb47249c798077a81f7f/pillow-12.0.0-cp313-cp313t-win_arm64.whl", hash = "sha256:26d9f7d2b604cd23aba3e9faf795787456ac25634d82cd060556998e39c6fa47", size = 2437834 }, - { url = "https://files.pythonhosted.org/packages/54/2a/9a8c6ba2c2c07b71bec92cf63e03370ca5e5f5c5b119b742bcc0cde3f9c5/pillow-12.0.0-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:beeae3f27f62308f1ddbcfb0690bf44b10732f2ef43758f169d5e9303165d3f9", size = 4045531 }, - { url = "https://files.pythonhosted.org/packages/84/54/836fdbf1bfb3d66a59f0189ff0b9f5f666cee09c6188309300df04ad71fa/pillow-12.0.0-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:d4827615da15cd59784ce39d3388275ec093ae3ee8d7f0c089b76fa87af756c2", size = 4120554 }, - { url = "https://files.pythonhosted.org/packages/0d/cd/16aec9f0da4793e98e6b54778a5fbce4f375c6646fe662e80600b8797379/pillow-12.0.0-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:3e42edad50b6909089750e65c91aa09aaf1e0a71310d383f11321b27c224ed8a", size = 3576812 }, - { url = "https://files.pythonhosted.org/packages/f6/b7/13957fda356dc46339298b351cae0d327704986337c3c69bb54628c88155/pillow-12.0.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:e5d8efac84c9afcb40914ab49ba063d94f5dbdf5066db4482c66a992f47a3a3b", size = 5252689 }, - { url = "https://files.pythonhosted.org/packages/fc/f5/eae31a306341d8f331f43edb2e9122c7661b975433de5e447939ae61c5da/pillow-12.0.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:266cd5f2b63ff316d5a1bba46268e603c9caf5606d44f38c2873c380950576ad", size = 4650186 }, - { url = "https://files.pythonhosted.org/packages/86/62/2a88339aa40c4c77e79108facbd307d6091e2c0eb5b8d3cf4977cfca2fe6/pillow-12.0.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:58eea5ebe51504057dd95c5b77d21700b77615ab0243d8152793dc00eb4faf01", size = 6230308 }, - { url = "https://files.pythonhosted.org/packages/c7/33/5425a8992bcb32d1cb9fa3dd39a89e613d09a22f2c8083b7bf43c455f760/pillow-12.0.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f13711b1a5ba512d647a0e4ba79280d3a9a045aaf7e0cc6fbe96b91d4cdf6b0c", size = 8039222 }, - { url = "https://files.pythonhosted.org/packages/d8/61/3f5d3b35c5728f37953d3eec5b5f3e77111949523bd2dd7f31a851e50690/pillow-12.0.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6846bd2d116ff42cba6b646edf5bf61d37e5cbd256425fa089fee4ff5c07a99e", size = 6346657 }, - { url = "https://files.pythonhosted.org/packages/3a/be/ee90a3d79271227e0f0a33c453531efd6ed14b2e708596ba5dd9be948da3/pillow-12.0.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c98fa880d695de164b4135a52fd2e9cd7b7c90a9d8ac5e9e443a24a95ef9248e", size = 7038482 }, - { url = "https://files.pythonhosted.org/packages/44/34/a16b6a4d1ad727de390e9bd9f19f5f669e079e5826ec0f329010ddea492f/pillow-12.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fa3ed2a29a9e9d2d488b4da81dcb54720ac3104a20bf0bd273f1e4648aff5af9", size = 6461416 }, - { url = "https://files.pythonhosted.org/packages/b6/39/1aa5850d2ade7d7ba9f54e4e4c17077244ff7a2d9e25998c38a29749eb3f/pillow-12.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d034140032870024e6b9892c692fe2968493790dd57208b2c37e3fb35f6df3ab", size = 7131584 }, - { url = "https://files.pythonhosted.org/packages/bf/db/4fae862f8fad0167073a7733973bfa955f47e2cac3dc3e3e6257d10fab4a/pillow-12.0.0-cp314-cp314-win32.whl", hash = "sha256:1b1b133e6e16105f524a8dec491e0586d072948ce15c9b914e41cdadd209052b", size = 6400621 }, - { url = "https://files.pythonhosted.org/packages/2b/24/b350c31543fb0107ab2599464d7e28e6f856027aadda995022e695313d94/pillow-12.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:8dc232e39d409036af549c86f24aed8273a40ffa459981146829a324e0848b4b", size = 7142916 }, - { url = "https://files.pythonhosted.org/packages/0f/9b/0ba5a6fd9351793996ef7487c4fdbde8d3f5f75dbedc093bb598648fddf0/pillow-12.0.0-cp314-cp314-win_arm64.whl", hash = "sha256:d52610d51e265a51518692045e372a4c363056130d922a7351429ac9f27e70b0", size = 2523836 }, - { url = "https://files.pythonhosted.org/packages/f5/7a/ceee0840aebc579af529b523d530840338ecf63992395842e54edc805987/pillow-12.0.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:1979f4566bb96c1e50a62d9831e2ea2d1211761e5662afc545fa766f996632f6", size = 5255092 }, - { url = "https://files.pythonhosted.org/packages/44/76/20776057b4bfd1aef4eeca992ebde0f53a4dce874f3ae693d0ec90a4f79b/pillow-12.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:b2e4b27a6e15b04832fe9bf292b94b5ca156016bbc1ea9c2c20098a0320d6cf6", size = 4653158 }, - { url = "https://files.pythonhosted.org/packages/82/3f/d9ff92ace07be8836b4e7e87e6a4c7a8318d47c2f1463ffcf121fc57d9cb/pillow-12.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fb3096c30df99fd01c7bf8e544f392103d0795b9f98ba71a8054bcbf56b255f1", size = 6267882 }, - { url = "https://files.pythonhosted.org/packages/9f/7a/4f7ff87f00d3ad33ba21af78bfcd2f032107710baf8280e3722ceec28cda/pillow-12.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7438839e9e053ef79f7112c881cef684013855016f928b168b81ed5835f3e75e", size = 8071001 }, - { url = "https://files.pythonhosted.org/packages/75/87/fcea108944a52dad8cca0715ae6247e271eb80459364a98518f1e4f480c1/pillow-12.0.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5d5c411a8eaa2299322b647cd932586b1427367fd3184ffbb8f7a219ea2041ca", size = 6380146 }, - { url = "https://files.pythonhosted.org/packages/91/52/0d31b5e571ef5fd111d2978b84603fce26aba1b6092f28e941cb46570745/pillow-12.0.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d7e091d464ac59d2c7ad8e7e08105eaf9dafbc3883fd7265ffccc2baad6ac925", size = 7067344 }, - { url = "https://files.pythonhosted.org/packages/7b/f4/2dd3d721f875f928d48e83bb30a434dee75a2531bca839bb996bb0aa5a91/pillow-12.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:792a2c0be4dcc18af9d4a2dfd8a11a17d5e25274a1062b0ec1c2d79c76f3e7f8", size = 6491864 }, - { url = "https://files.pythonhosted.org/packages/30/4b/667dfcf3d61fc309ba5a15b141845cece5915e39b99c1ceab0f34bf1d124/pillow-12.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:afbefa430092f71a9593a99ab6a4e7538bc9eabbf7bf94f91510d3503943edc4", size = 7158911 }, - { url = "https://files.pythonhosted.org/packages/a2/2f/16cabcc6426c32218ace36bf0d55955e813f2958afddbf1d391849fee9d1/pillow-12.0.0-cp314-cp314t-win32.whl", hash = "sha256:3830c769decf88f1289680a59d4f4c46c72573446352e2befec9a8512104fa52", size = 6408045 }, - { url = "https://files.pythonhosted.org/packages/35/73/e29aa0c9c666cf787628d3f0dcf379f4791fba79f4936d02f8b37165bdf8/pillow-12.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:905b0365b210c73afb0ebe9101a32572152dfd1c144c7e28968a331b9217b94a", size = 7148282 }, - { url = "https://files.pythonhosted.org/packages/c1/70/6b41bdcddf541b437bbb9f47f94d2db5d9ddef6c37ccab8c9107743748a4/pillow-12.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:99353a06902c2e43b43e8ff74ee65a7d90307d82370604746738a1e0661ccca7", size = 2525630 }, - { url = "https://files.pythonhosted.org/packages/1d/b3/582327e6c9f86d037b63beebe981425d6811104cb443e8193824ef1a2f27/pillow-12.0.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:b22bd8c974942477156be55a768f7aa37c46904c175be4e158b6a86e3a6b7ca8", size = 5215068 }, - { url = "https://files.pythonhosted.org/packages/fd/d6/67748211d119f3b6540baf90f92fae73ae51d5217b171b0e8b5f7e5d558f/pillow-12.0.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:805ebf596939e48dbb2e4922a1d3852cfc25c38160751ce02da93058b48d252a", size = 4614994 }, - { url = "https://files.pythonhosted.org/packages/2d/e1/f8281e5d844c41872b273b9f2c34a4bf64ca08905668c8ae730eedc7c9fa/pillow-12.0.0-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cae81479f77420d217def5f54b5b9d279804d17e982e0f2fa19b1d1e14ab5197", size = 5246639 }, - { url = "https://files.pythonhosted.org/packages/94/5a/0d8ab8ffe8a102ff5df60d0de5af309015163bf710c7bb3e8311dd3b3ad0/pillow-12.0.0-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:aeaefa96c768fc66818730b952a862235d68825c178f1b3ffd4efd7ad2edcb7c", size = 6986839 }, - { url = "https://files.pythonhosted.org/packages/20/2e/3434380e8110b76cd9eb00a363c484b050f949b4bbe84ba770bb8508a02c/pillow-12.0.0-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:09f2d0abef9e4e2f349305a4f8cc784a8a6c2f58a8c4892eea13b10a943bd26e", size = 5313505 }, - { url = "https://files.pythonhosted.org/packages/57/ca/5a9d38900d9d74785141d6580950fe705de68af735ff6e727cb911b64740/pillow-12.0.0-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bdee52571a343d721fb2eb3b090a82d959ff37fc631e3f70422e0c2e029f3e76", size = 5963654 }, - { url = "https://files.pythonhosted.org/packages/95/7e/f896623c3c635a90537ac093c6a618ebe1a90d87206e42309cb5d98a1b9e/pillow-12.0.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:b290fd8aa38422444d4b50d579de197557f182ef1068b75f5aa8558638b8d0a5", size = 6997850 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3442,39 +2496,27 @@ dependencies = [ { name = "platformdirs" }, { name = "typing-extensions" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/52/9d/b1379cdbd33a49d17d627bc24e2b63cca06a1c5343b38072d2889499e82e/pint-0.25.3.tar.gz", hash = "sha256:f8f5df6cf65314d74da1ade1bf96f8e3e4d0c41b51577ac53c49e7d44ca5acee", size = 255106, upload-time = "2026-03-19T21:57:08.72Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/1b/dd/a9fe6a0a09512da23951c68bf36466aeecd89def3183dc095edbc807ddc5/pint-0.25.3-py3-none-any.whl", hash = "sha256:27eb25143bd5de9fcc4d5a4b484f16faf6b4615aa93ece6b3373a8c1a3c1b97d", size = 307488, upload-time = "2026-03-19T21:57:07.022Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/5f/74/bc3f671997158aef171194c3c4041e549946f4784b8690baa0626a0a164b/pint-0.25.2.tar.gz", hash = "sha256:85a45d1da8fe9c9f7477fed8aef59ad2b939af3d6611507e1a9cbdacdcd3450a", size = 254467 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ab/88/550d41e81e6d43335603a960cd9c75c1d88f9cf01bc9d4ee8e86290aba7d/pint-0.25.2-py3-none-any.whl", hash = "sha256:ca35ab1d8eeeb6f7d9942b3cb5f34ca42b61cdd5fb3eae79531553dcca04dda7", size = 306762 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "platformdirs" version = "4.10.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/d7/47/e4501f49c178ae1d9f4a75073fda4204f52647993f075a9db4d14930e0c5/platformdirs-4.10.0.tar.gz", hash = "sha256:31e761a6a0ca04faf7353ea759bdba55652be214725111e5aac52dfa29d4bef7", size = 31224, upload-time = "2026-05-28T03:32:53.587Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/81/e6/cd9575ac904136b3cbf7aa7ee819ef86eedb7274e46f230e94ea4342e729/platformdirs-4.10.0-py3-none-any.whl", hash = "sha256:fb516cdb12eb0d857d0cd85a7c57cea4d060bee4578d6cf5a14dfdf8cbf8784a", size = 22743, upload-time = "2026-05-28T03:32:52.175Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/61/33/9611380c2bdb1225fdef633e2a9610622310fed35ab11dac9620972ee088/platformdirs-4.5.0.tar.gz", hash = "sha256:70ddccdd7c99fc5942e9fc25636a8b34d04c24b335100223152c2803e4063312", size = 21632 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/73/cb/ac7874b3e5d58441674fb70742e6c374b28b0c7cb988d37d991cde47166c/platformdirs-4.5.0-py3-none-any.whl", hash = "sha256:e578a81bb873cbb89a41fcc904c7ef523cc18284b7e3b3ccf06aca1403b7ebd3", size = 18651 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "pluggy" version = "1.6.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/f9/e2/3e91f31a7d2b083fe6ef3fa267035b518369d9511ffab804f839851d2779/pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3", size = 69412 } +sdist = { url = "https://files.pythonhosted.org/packages/f9/e2/3e91f31a7d2b083fe6ef3fa267035b518369d9511ffab804f839851d2779/pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3", size = 69412, upload-time = "2025-05-15T12:30:07.975Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538 }, + { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538, upload-time = "2025-05-15T12:30:06.134Z" }, ] [[package]] @@ -3488,30 +2530,18 @@ dependencies = [ { name = "pyyaml" }, { name = "virtualenv" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/8e/22/2de9408ac81acbb8a7d05d4cc064a152ccf33b3d480ebe0cd292153db239/pre_commit-4.6.0.tar.gz", hash = "sha256:718d2208cef53fdc38206e40524a6d4d9576d103eb16f0fec11c875e7716e9d9", size = 198525, upload-time = "2026-04-21T20:31:41.613Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/80/6e/4b28b62ecb6aae56769c34a8ff1d661473ec1e9519e2d5f8b2c150086b26/pre_commit-4.6.0-py2.py3-none-any.whl", hash = "sha256:e2cf246f7299edcabcf15f9b0571fdce06058527f0a06535068a86d38089f29b", size = 226472, upload-time = "2026-04-21T20:31:40.092Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/a6/49/7845c2d7bf6474efd8e27905b51b11e6ce411708c91e829b93f324de9929/pre_commit-4.4.0.tar.gz", hash = "sha256:f0233ebab440e9f17cabbb558706eb173d19ace965c68cdce2c081042b4fab15", size = 197501 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/27/11/574fe7d13acf30bfd0a8dd7fa1647040f2b8064f13f43e8c963b1e65093b/pre_commit-4.4.0-py2.py3-none-any.whl", hash = "sha256:b35ea52957cbf83dcc5d8ee636cbead8624e3a15fbfa61a370e42158ac8a5813", size = 226049 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "prometheus-client" version = "0.25.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/1b/fb/d9aa83ffe43ce1f19e557c0971d04b90561b0cfd50762aafb01968285553/prometheus_client-0.25.0.tar.gz", hash = "sha256:5e373b75c31afb3c86f1a52fa1ad470c9aace18082d39ec0d2f918d11cc9ba28", size = 86035, upload-time = "2026-04-09T19:53:42.359Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/8d/9b/d4b1e644385499c8346fa9b622a3f030dce14cd6ef8a1871c221a17a67e7/prometheus_client-0.25.0-py3-none-any.whl", hash = "sha256:d5aec89e349a6ec230805d0df882f3807f74fd6c1a2fa86864e3c2279059fed1", size = 64154, upload-time = "2026-04-09T19:53:41.324Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/23/53/3edb5d68ecf6b38fcbcc1ad28391117d2a322d9a1a3eff04bfdb184d8c3b/prometheus_client-0.23.1.tar.gz", hash = "sha256:6ae8f9081eaaaf153a2e959d2e6c4f4fb57b12ef76c8c7980202f1e57b48b2ce", size = 80481 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b8/db/14bafcb4af2139e046d03fd00dea7873e48eafe18b7d2797e73d6681f210/prometheus_client-0.23.1-py3-none-any.whl", hash = "sha256:dd1913e6e76b59cfe44e7a4b83e01afc9873c1bdfd2ed8739f1e76aeca115f99", size = 61145 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3521,43 +2551,30 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "wcwidth" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/a1/96/06e01a7b38dce6fe1db213e061a4602dd6032a8a97ef6c1a862537732421/prompt_toolkit-3.0.52.tar.gz", hash = "sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855", size = 434198 } +sdist = { url = "https://files.pythonhosted.org/packages/a1/96/06e01a7b38dce6fe1db213e061a4602dd6032a8a97ef6c1a862537732421/prompt_toolkit-3.0.52.tar.gz", hash = "sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855", size = 434198, upload-time = "2025-08-27T15:24:02.057Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/84/03/0d3ce49e2505ae70cf43bc5bb3033955d2fc9f932163e84dc0779cc47f48/prompt_toolkit-3.0.52-py3-none-any.whl", hash = "sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955", size = 391431 }, + { url = "https://files.pythonhosted.org/packages/84/03/0d3ce49e2505ae70cf43bc5bb3033955d2fc9f932163e84dc0779cc47f48/prompt_toolkit-3.0.52-py3-none-any.whl", hash = "sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955", size = 391431, upload-time = "2025-08-27T15:23:59.498Z" }, ] [[package]] name = "protobuf" -version = "7.35.0" +version = "7.35.1" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/60/fd/5b1491d9e4b586d621c54f4c36b888714164b6875f8d6afa3f9072906a51/protobuf-7.35.0.tar.gz", hash = "sha256:a2efd84605f41e559f1881b0912b44099d0a2ac9bf46b3474823f10fb393b0e6", size = 458677, upload-time = "2026-05-19T23:02:29.197Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/83/ee/93d06e358a4aa32280b00e722d3ea0a1f25fc3cc5778d80581c9cca2c10e/protobuf-7.35.0-cp310-abi3-macosx_10_9_universal2.whl", hash = "sha256:66be6c513931c794fa92c080ffee41671390da3d79da219cf9c0c0907f035dda", size = 433225, upload-time = "2026-05-19T23:02:19.884Z" }, - { url = "https://files.pythonhosted.org/packages/8b/39/1c76c2da93f3c507e958e0aecee2391cc44d4625de6c728bbc555195b5a8/protobuf-7.35.0-cp310-abi3-manylinux2014_aarch64.whl", hash = "sha256:fcbe42a4ac09d3ec9c987ddfcd956afd0b15f1ff613bd8371bde9405ffd5c8e5", size = 328847, upload-time = "2026-05-19T23:02:22.3Z" }, - { url = "https://files.pythonhosted.org/packages/91/1a/39f7ce90a238c1a987a4d81ec26379e02ca0aff367de68e4a1fa474215b9/protobuf-7.35.0-cp310-abi3-manylinux2014_s390x.whl", hash = "sha256:4cbf5cc286130e06a6c9bbefac442431173906dfcc979712183d4adcc01b37ee", size = 344030, upload-time = "2026-05-19T23:02:23.591Z" }, - { url = "https://files.pythonhosted.org/packages/70/5b/6baf9008817964454055ff3fe65f1de0b5f1e26c80c82f7fb108b7cd4ea3/protobuf-7.35.0-cp310-abi3-manylinux2014_x86_64.whl", hash = "sha256:6c0f98f10c8a05ea30f8993dfef2de093d27b490fdae78bb60c8343795d55011", size = 327130, upload-time = "2026-05-19T23:02:24.637Z" }, - { url = "https://files.pythonhosted.org/packages/8e/e5/e46adb0badc388bfb84877a5f9f026aff63f60e611016cf64dbe77e05446/protobuf-7.35.0-cp310-abi3-win32.whl", hash = "sha256:4c4617b83ade0e279d1d2bfe04025a1adb87f9ed657de038620dc0ff959357f6", size = 428946, upload-time = "2026-05-19T23:02:25.741Z" }, - { url = "https://files.pythonhosted.org/packages/a7/ab/547fbd9e16d879dd13c167478f8ae0a83a428008ca07a5e06acdc23ad473/protobuf-7.35.0-cp310-abi3-win_amd64.whl", hash = "sha256:f05bcadf9a2a6b8dda047007075135fb7d08c73d9177aabc067e1be46881a201", size = 439996, upload-time = "2026-05-19T23:02:26.808Z" }, - { url = "https://files.pythonhosted.org/packages/b8/ef/50433d346c56657a70d27f156c7b349ac59a068b01de4eb796e747eecc43/protobuf-7.35.0-py3-none-any.whl", hash = "sha256:c13f325cf242bad135c350629eeb5d54b24228eb472fb3e2e9ebbd4c5dc20ca0", size = 171659, upload-time = "2026-05-19T23:02:27.842Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/0a/03/a1440979a3f74f16cab3b75b0da1a1a7f922d56a8ddea96092391998edc0/protobuf-6.33.1.tar.gz", hash = "sha256:97f65757e8d09870de6fd973aeddb92f85435607235d20b2dfed93405d00c85b", size = 443432 } +sdist = { url = "https://files.pythonhosted.org/packages/da/01/9ef0afd7999eb9badb3a768b4aedd78c86d4c65cfaf1958ab276199e76b4/protobuf-7.35.1.tar.gz", hash = "sha256:ce115a26fe0c39a2c29973d914d327e516a6455464489fe3cd1e51a1b354f81a", size = 458717, upload-time = "2026-06-11T21:55:40.257Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/06/f1/446a9bbd2c60772ca36556bac8bfde40eceb28d9cc7838755bc41e001d8f/protobuf-6.33.1-cp310-abi3-win32.whl", hash = "sha256:f8d3fdbc966aaab1d05046d0240dd94d40f2a8c62856d41eaa141ff64a79de6b", size = 425593 }, - { url = "https://files.pythonhosted.org/packages/a6/79/8780a378c650e3df849b73de8b13cf5412f521ca2ff9b78a45c247029440/protobuf-6.33.1-cp310-abi3-win_amd64.whl", hash = "sha256:923aa6d27a92bf44394f6abf7ea0500f38769d4b07f4be41cb52bd8b1123b9ed", size = 436883 }, - { url = "https://files.pythonhosted.org/packages/cd/93/26213ff72b103ae55bb0d73e7fb91ea570ef407c3ab4fd2f1f27cac16044/protobuf-6.33.1-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:fe34575f2bdde76ac429ec7b570235bf0c788883e70aee90068e9981806f2490", size = 427522 }, - { url = "https://files.pythonhosted.org/packages/c2/32/df4a35247923393aa6b887c3b3244a8c941c32a25681775f96e2b418f90e/protobuf-6.33.1-cp39-abi3-manylinux2014_aarch64.whl", hash = "sha256:f8adba2e44cde2d7618996b3fc02341f03f5bc3f2748be72dc7b063319276178", size = 324445 }, - { url = "https://files.pythonhosted.org/packages/8e/d0/d796e419e2ec93d2f3fa44888861c3f88f722cde02b7c3488fcc6a166820/protobuf-6.33.1-cp39-abi3-manylinux2014_s390x.whl", hash = "sha256:0f4cf01222c0d959c2b399142deb526de420be8236f22c71356e2a544e153c53", size = 339161 }, - { url = "https://files.pythonhosted.org/packages/1d/2a/3c5f05a4af06649547027d288747f68525755de692a26a7720dced3652c0/protobuf-6.33.1-cp39-abi3-manylinux2014_x86_64.whl", hash = "sha256:8fd7d5e0eb08cd5b87fd3df49bc193f5cfd778701f47e11d127d0afc6c39f1d1", size = 323171 }, - { url = "https://files.pythonhosted.org/packages/08/b4/46310463b4f6ceef310f8348786f3cff181cea671578e3d9743ba61a459e/protobuf-6.33.1-py3-none-any.whl", hash = "sha256:d595a9fd694fdeb061a62fbe10eb039cc1e444df81ec9bb70c7fc59ebcb1eafa", size = 170477 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/10/03/8aeeb7458d22546bf64b5250ca1daeb5ff757d900e8e4a7476c6f0db843e/protobuf-7.35.1-cp310-abi3-macosx_10_9_universal2.whl", hash = "sha256:24f857477359a85c0c235261b8ba905fd51b2562f4a64ca1df5473f29850cbf6", size = 433226, upload-time = "2026-06-11T21:55:31.719Z" }, + { url = "https://files.pythonhosted.org/packages/37/4b/dfb89eb0e652a1ff073c39a59fb5e3a83cfe9b57a2c83fa6d78270101767/protobuf-7.35.1-cp310-abi3-manylinux2014_aarch64.whl", hash = "sha256:11d6b0ec246892d85215b0a13ca6e0233cf5284b68f0ac02646427f4ff88a799", size = 328847, upload-time = "2026-06-11T21:55:34.035Z" }, + { url = "https://files.pythonhosted.org/packages/0f/58/dc12f2cd484951524af6e3382c785869b9b3fb5e52ee95ae23add53ee8f9/protobuf-7.35.1-cp310-abi3-manylinux2014_s390x.whl", hash = "sha256:b73f9489a4b8b1c9cb1f8ed951c736392592edb24b9d6819f36d2e10b171d5b4", size = 344030, upload-time = "2026-06-11T21:55:34.941Z" }, + { url = "https://files.pythonhosted.org/packages/e4/be/5b3cfe508bfab6761414ff944e3366eb13be4fd71efcd69450f89ba39f43/protobuf-7.35.1-cp310-abi3-manylinux2014_x86_64.whl", hash = "sha256:74758715c53d7158fb76caf4f0cfdacc5329a4b1bb994f865d6cf302d413a1c4", size = 327130, upload-time = "2026-06-11T21:55:35.921Z" }, + { url = "https://files.pythonhosted.org/packages/d8/bc/6d6c7ba8709c85f8f2c390b2b118d6fb08a783676a572271851bf45a7d22/protobuf-7.35.1-cp310-abi3-win32.whl", hash = "sha256:353652e4efd0bca5b5fc2656abf8307ef351f0cf938c9eba09f0e09c20a25c30", size = 428945, upload-time = "2026-06-11T21:55:37.034Z" }, + { url = "https://files.pythonhosted.org/packages/0a/19/8d0cb6f20a1ef7b18f1c8986ad5783f22f84cce39c6ce9a6e645ea55192e/protobuf-7.35.1-cp310-abi3-win_amd64.whl", hash = "sha256:230a75ddfc2de4806e56696ce9640c1cdfdb6543b7cfce98d42a4c0a0e7bdb87", size = 439996, upload-time = "2026-06-11T21:55:38.123Z" }, + { url = "https://files.pythonhosted.org/packages/19/c7/5f7c636ec43e0c545e28d1f1db71990108306f7bdcb89f069ba97e428e7f/protobuf-7.35.1-py3-none-any.whl", hash = "sha256:4bc97768d8fe4ad6743c8a19403e314511ed9f6d13205b687e52421c023ac1b9", size = 171659, upload-time = "2026-06-11T21:55:39.155Z" }, ] [[package]] name = "psutil" version = "7.2.2" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/aa/c6/d1ddf4abb55e93cebc4f2ed8b5d6dbad109ecb8d63748dd2b20ab5e57ebe/psutil-7.2.2.tar.gz", hash = "sha256:0746f5f8d406af344fd547f1c8daa5f5c33dbc293bb8d6a16d80b4bb88f59372", size = 493740, upload-time = "2026-01-28T18:14:54.428Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/51/08/510cbdb69c25a96f4ae523f733cdc963ae654904e8db864c07585ef99875/psutil-7.2.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:2edccc433cbfa046b980b0df0171cd25bcaeb3a68fe9022db0979e7aa74a826b", size = 130595, upload-time = "2026-01-28T18:14:57.293Z" }, @@ -3609,96 +2626,56 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/fc/dc/40c6026c88d7f9220ecc913afe0501045a512c9b82f9b7e036bb089dc287/psygnal-0.15.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:1ac399566852fe4354ce26a1acbe12319232e8c2b615fe5ad1e114c547095cf6", size = 880863, upload-time = "2026-01-04T16:38:38.381Z" }, { url = "https://files.pythonhosted.org/packages/b7/85/b4f45ec3057c473b5622fc002b3a636a698c34d3a0917a064ff5247f1984/psygnal-0.15.1-cp314-cp314-win_amd64.whl", hash = "sha256:d3a03055f331ce91d44581c71edb79938ccc133a94af2ce7ad3a18fa57ac7be5", size = 423654, upload-time = "2026-01-04T16:38:39.7Z" }, { url = "https://files.pythonhosted.org/packages/46/49/7742544684bee728ec123515d2694cee859aa2a705951a461230b00f18cc/psygnal-0.15.1-py3-none-any.whl", hash = "sha256:4221140e633e45b076953c64bcb9b41a744833527f9a037c1ca98bc270798cbf", size = 90638, upload-time = "2026-01-04T16:38:40.841Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/e1/88/bdd0a41e5857d5d703287598cbf08dad90aed56774ea52ae071bae9071b6/psutil-7.1.3.tar.gz", hash = "sha256:6c86281738d77335af7aec228328e944b30930899ea760ecf33a4dba66be5e74", size = 489059 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/bd/93/0c49e776b8734fef56ec9c5c57f923922f2cf0497d62e0f419465f28f3d0/psutil-7.1.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0005da714eee687b4b8decd3d6cc7c6db36215c9e74e5ad2264b90c3df7d92dc", size = 239751 }, - { url = "https://files.pythonhosted.org/packages/6f/8d/b31e39c769e70780f007969815195a55c81a63efebdd4dbe9e7a113adb2f/psutil-7.1.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:19644c85dcb987e35eeeaefdc3915d059dac7bd1167cdcdbf27e0ce2df0c08c0", size = 240368 }, - { url = "https://files.pythonhosted.org/packages/62/61/23fd4acc3c9eebbf6b6c78bcd89e5d020cfde4acf0a9233e9d4e3fa698b4/psutil-7.1.3-cp313-cp313t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:95ef04cf2e5ba0ab9eaafc4a11eaae91b44f4ef5541acd2ee91d9108d00d59a7", size = 287134 }, - { url = "https://files.pythonhosted.org/packages/30/1c/f921a009ea9ceb51aa355cb0cc118f68d354db36eae18174bab63affb3e6/psutil-7.1.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1068c303be3a72f8e18e412c5b2a8f6d31750fb152f9cb106b54090296c9d251", size = 289904 }, - { url = "https://files.pythonhosted.org/packages/a6/82/62d68066e13e46a5116df187d319d1724b3f437ddd0f958756fc052677f4/psutil-7.1.3-cp313-cp313t-win_amd64.whl", hash = "sha256:18349c5c24b06ac5612c0428ec2a0331c26443d259e2a0144a9b24b4395b58fa", size = 249642 }, - { url = "https://files.pythonhosted.org/packages/df/ad/c1cd5fe965c14a0392112f68362cfceb5230819dbb5b1888950d18a11d9f/psutil-7.1.3-cp313-cp313t-win_arm64.whl", hash = "sha256:c525ffa774fe4496282fb0b1187725793de3e7c6b29e41562733cae9ada151ee", size = 245518 }, - { url = "https://files.pythonhosted.org/packages/2e/bb/6670bded3e3236eb4287c7bcdc167e9fae6e1e9286e437f7111caed2f909/psutil-7.1.3-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:b403da1df4d6d43973dc004d19cee3b848e998ae3154cc8097d139b77156c353", size = 239843 }, - { url = "https://files.pythonhosted.org/packages/b8/66/853d50e75a38c9a7370ddbeefabdd3d3116b9c31ef94dc92c6729bc36bec/psutil-7.1.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ad81425efc5e75da3f39b3e636293360ad8d0b49bed7df824c79764fb4ba9b8b", size = 240369 }, - { url = "https://files.pythonhosted.org/packages/41/bd/313aba97cb5bfb26916dc29cf0646cbe4dd6a89ca69e8c6edce654876d39/psutil-7.1.3-cp314-cp314t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8f33a3702e167783a9213db10ad29650ebf383946e91bc77f28a5eb083496bc9", size = 288210 }, - { url = "https://files.pythonhosted.org/packages/c2/fa/76e3c06e760927a0cfb5705eb38164254de34e9bd86db656d4dbaa228b04/psutil-7.1.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fac9cd332c67f4422504297889da5ab7e05fd11e3c4392140f7370f4208ded1f", size = 291182 }, - { url = "https://files.pythonhosted.org/packages/0f/1d/5774a91607035ee5078b8fd747686ebec28a962f178712de100d00b78a32/psutil-7.1.3-cp314-cp314t-win_amd64.whl", hash = "sha256:3792983e23b69843aea49c8f5b8f115572c5ab64c153bada5270086a2123c7e7", size = 250466 }, - { url = "https://files.pythonhosted.org/packages/00/ca/e426584bacb43a5cb1ac91fae1937f478cd8fbe5e4ff96574e698a2c77cd/psutil-7.1.3-cp314-cp314t-win_arm64.whl", hash = "sha256:31d77fcedb7529f27bb3a0472bea9334349f9a04160e8e6e5020f22c59893264", size = 245756 }, - { url = "https://files.pythonhosted.org/packages/ef/94/46b9154a800253e7ecff5aaacdf8ebf43db99de4a2dfa18575b02548654e/psutil-7.1.3-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:2bdbcd0e58ca14996a42adf3621a6244f1bb2e2e528886959c72cf1e326677ab", size = 238359 }, - { url = "https://files.pythonhosted.org/packages/68/3a/9f93cff5c025029a36d9a92fef47220ab4692ee7f2be0fba9f92813d0cb8/psutil-7.1.3-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:bc31fa00f1fbc3c3802141eede66f3a2d51d89716a194bf2cd6fc68310a19880", size = 239171 }, - { url = "https://files.pythonhosted.org/packages/ce/b1/5f49af514f76431ba4eea935b8ad3725cdeb397e9245ab919dbc1d1dc20f/psutil-7.1.3-cp36-abi3-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3bb428f9f05c1225a558f53e30ccbad9930b11c3fc206836242de1091d3e7dd3", size = 263261 }, - { url = "https://files.pythonhosted.org/packages/e0/95/992c8816a74016eb095e73585d747e0a8ea21a061ed3689474fabb29a395/psutil-7.1.3-cp36-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:56d974e02ca2c8eb4812c3f76c30e28836fffc311d55d979f1465c1feeb2b68b", size = 264635 }, - { url = "https://files.pythonhosted.org/packages/55/4c/c3ed1a622b6ae2fd3c945a366e64eb35247a31e4db16cf5095e269e8eb3c/psutil-7.1.3-cp37-abi3-win_amd64.whl", hash = "sha256:f39c2c19fe824b47484b96f9692932248a54c43799a84282cfe58d05a6449efd", size = 247633 }, - { url = "https://files.pythonhosted.org/packages/c9/ad/33b2ccec09bf96c2b2ef3f9a6f66baac8253d7565d8839e024a6b905d45d/psutil-7.1.3-cp37-abi3-win_arm64.whl", hash = "sha256:bd0d69cee829226a761e92f28140bec9a5ee9d5b4fb4b0cc589068dbfff559b1", size = 244608 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "ptyprocess" version = "0.7.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/20/e5/16ff212c1e452235a90aeb09066144d0c5a6a8c0834397e03f5224495c4e/ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220", size = 70762 } +sdist = { url = "https://files.pythonhosted.org/packages/20/e5/16ff212c1e452235a90aeb09066144d0c5a6a8c0834397e03f5224495c4e/ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220", size = 70762, upload-time = "2020-12-28T15:15:30.155Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35", size = 13993 }, + { url = "https://files.pythonhosted.org/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35", size = 13993, upload-time = "2020-12-28T15:15:28.35Z" }, ] [[package]] name = "pure-eval" version = "0.2.3" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/cd/05/0a34433a064256a578f1783a10da6df098ceaa4a57bbeaa96a6c0352786b/pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42", size = 19752 } +sdist = { url = "https://files.pythonhosted.org/packages/cd/05/0a34433a064256a578f1783a10da6df098ceaa4a57bbeaa96a6c0352786b/pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42", size = 19752, upload-time = "2024-07-21T12:58:21.801Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842 }, + { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842, upload-time = "2024-07-21T12:58:20.04Z" }, ] [[package]] name = "pycparser" version = "3.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/1b/7d/92392ff7815c21062bea51aa7b87d45576f649f16458d78b7cf94b9ab2e6/pycparser-3.0.tar.gz", hash = "sha256:600f49d217304a5902ac3c37e1281c9fe94e4d0489de643a9504c5cdfdfc6b29", size = 103492, upload-time = "2026-01-21T14:26:51.89Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/0c/c3/44f3fbbfa403ea2a7c779186dc20772604442dde72947e7d01069cbe98e3/pycparser-3.0-py3-none-any.whl", hash = "sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992", size = 48172, upload-time = "2026-01-21T14:26:50.693Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/fe/cf/d2d3b9f5699fb1e4615c8e32ff220203e43b248e1dfcc6736ad9057731ca/pycparser-2.23.tar.gz", hash = "sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2", size = 173734 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/a0/e3/59cd50310fc9b59512193629e1984c1f95e5c8ae6e5d8c69532ccc65a7fe/pycparser-2.23-py3-none-any.whl", hash = "sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934", size = 118140 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "pygments" version = "2.20.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/c3/b2/bc9c9196916376152d655522fdcebac55e66de6603a76a02bca1b6414f6c/pygments-2.20.0.tar.gz", hash = "sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f", size = 4955991, upload-time = "2026-03-29T13:29:33.898Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/f4/7e/a72dd26f3b0f4f2bf1dd8923c85f7ceb43172af56d63c7383eb62b332364/pygments-2.20.0-py3-none-any.whl", hash = "sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176", size = 1231151, upload-time = "2026-03-29T13:29:30.038Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/b0/77/a5b8c569bf593b0140bde72ea885a803b82086995367bf2037de0159d924/pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887", size = 4968631 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b", size = 1225217 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "pyparsing" version = "3.3.2" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/f3/91/9c6ee907786a473bf81c5f53cf703ba0957b23ab84c264080fb5a450416f/pyparsing-3.3.2.tar.gz", hash = "sha256:c777f4d763f140633dcb6d8a3eda953bf7a214dc4eff598413c070bcdc117cbc", size = 6851574, upload-time = "2026-01-21T03:57:59.36Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/10/bd/c038d7cc38edc1aa5bf91ab8068b63d4308c66c4c8bb3cbba7dfbc049f9c/pyparsing-3.3.2-py3-none-any.whl", hash = "sha256:850ba148bd908d7e2411587e247a1e4f0327839c40e2e5e6d05a007ecc69911d", size = 122781, upload-time = "2026-01-21T03:57:55.912Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/f2/a5/181488fc2b9d093e3972d2a472855aae8a03f000592dbfce716a512b3359/pyparsing-3.2.5.tar.gz", hash = "sha256:2df8d5b7b2802ef88e8d016a2eb9c7aeaa923529cd251ed0fe4608275d4105b6", size = 1099274 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/10/5e/1aa9a93198c6b64513c9d7752de7422c06402de6600a8767da1524f9570b/pyparsing-3.2.5-py3-none-any.whl", hash = "sha256:e38a4f02064cf41fe6593d328d0512495ad1f3d8a91c4f73fc401b3079a59a5e", size = 113890 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "pytest" -version = "9.0.3" +version = "9.1.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "colorama", marker = "sys_platform == 'win32'" }, @@ -3707,10 +2684,9 @@ dependencies = [ { name = "pluggy" }, { name = "pygments" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" } +sdist = { url = "https://files.pythonhosted.org/packages/e4/47/b9efed96c114afcfa3c9d3fe98a76a1d14c74a9e266d397cf6eb64be5e01/pytest-9.1.1.tar.gz", hash = "sha256:1088fbde8f2b49d95a549a195707afa7a76a3ce9bcadc26b6d71f0ffda5fe313", size = 1636369, upload-time = "2026-06-19T10:58:32.857Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249, upload-time = "2026-04-07T17:16:16.13Z" }, + { url = "https://files.pythonhosted.org/packages/24/25/1de2678b631f5a49215c6c96fff41ba892b0a34df68d6d80292b1b48aa7f/pytest-9.1.1-py3-none-any.whl", hash = "sha256:37a86b45efb9a47a61a36449063e8e18d0cab3161329fc099eb21783169c4f0c", size = 386536, upload-time = "2026-06-19T10:58:31.347Z" }, ] [[package]] @@ -3725,18 +2701,12 @@ dependencies = [ sdist = { url = "https://files.pythonhosted.org/packages/b1/51/a849f96e117386044471c8ec2bd6cfebacda285da9525c9106aeb28da671/pytest_cov-7.1.0.tar.gz", hash = "sha256:30674f2b5f6351aa09702a9c8c364f6a01c27aae0c1366ae8016160d1efc56b2", size = 55592, upload-time = "2026-03-21T20:11:16.284Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/9d/7a/d968e294073affff457b041c2be9868a40c1c71f4a35fcc1e45e5493067b/pytest_cov-7.1.0-py3-none-any.whl", hash = "sha256:a0461110b7865f9a271aa1b51e516c9a95de9d696734a2f71e3e78f46e1d4678", size = 22876, upload-time = "2026-03-21T20:11:14.438Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/07/56/f013048ac4bc4c1d9be45afd4ab209ea62822fb1598f40687e6bf45dcea4/pytest-9.0.1.tar.gz", hash = "sha256:3e9c069ea73583e255c3b21cf46b8d3c56f6e3a1a8f6da94ccb0fcf57b9d73c8", size = 1564125 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/0b/8b/6300fb80f858cda1c51ffa17075df5d846757081d11ab4aa35cef9e6258b/pytest-9.0.1-py3-none-any.whl", hash = "sha256:67be0030d194df2dfa7b556f2e56fb3c3315bd5c8822c6951162b92b32ce7dad", size = 373668 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "python-box" version = "7.4.1" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/0f/0f/34e7ee0a72f1464b4c7a2e8bafb389f230477256af586bc82bcfad85295a/python_box-7.4.1.tar.gz", hash = "sha256:e412e36c25fca8223560516d53ef6c7993591c3b0ec8bb4ec582bf7defdd79f0", size = 49859, upload-time = "2026-02-21T16:21:16.008Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/f8/a8/c8bcd3ff0905ec549273ea3485e6b9f2039f57baab419123fb18f964f829/python_box-7.4.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:3f76dad8be9d57d65a3edc792b952f7afe3991515aa6eba616cf5efb2fbb2e0c", size = 1870869, upload-time = "2026-02-21T16:21:34.16Z" }, @@ -3751,20 +2721,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/94/a2/771b5e526bba2214ac2d30e321209a66680c40788616a45cf01005e95204/python_box-7.4.1-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:33c6701faa51fd87f0dcc538873c0fad2b3a1cc3750eab85835cd071cadf1948", size = 1875508, upload-time = "2026-02-21T16:21:37.432Z" }, { url = "https://files.pythonhosted.org/packages/a6/5f/0e7ea7640ba60ff459ce37e340d816ac5e91b7a9a7c3c161f9dabe622be6/python_box-7.4.1-cp314-cp314-win_amd64.whl", hash = "sha256:ae8c540a0457f52350211d24690211251912018e1e0c1857f50792729d6f562c", size = 1314304, upload-time = "2026-02-21T16:22:22.173Z" }, { url = "https://files.pythonhosted.org/packages/06/a6/5d3f3abf46b37aa44b1f6788d287c8b4f2319b55013191dddf25b9e6d62c/python_box-7.4.1-py3-none-any.whl", hash = "sha256:a3b0d84d003882fb6abe505b1b883b3a5dcbf226b0fe168d24bc5ff75d9826e5", size = 30402, upload-time = "2026-02-21T16:21:14.78Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/29/f7/635eed8c500adf26208e86e985bbffb6ff039cd8950e3a4749ceca904218/python_box-7.3.2.tar.gz", hash = "sha256:028b9917129e67f311932d93347b8a4f1b500d7a5a2870ee3c035f4e7b19403b", size = 45771 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/1e/3f/133619c00d8a9d4f86efd8626c0e4ec356c8b8dabac66da18dac5cfaf70c/python_box-7.3.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:32163b1cb151883de0da62b0cd3572610dc72ccf0762f2447baf1d2562e25bea", size = 1834122 }, - { url = "https://files.pythonhosted.org/packages/b7/52/51b6081562daa864847692536260200b337ccb4176d1e5f626ae48a7d493/python_box-7.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:064cb59b41e25aaf7dbd39efe53151a5f6797cc1cb3c68610f0f21a9d406d67e", size = 4305556 }, - { url = "https://files.pythonhosted.org/packages/d4/e2/6cdc8649381ae14def88c3e2e93d5b8b17a622a95896e0d1c92861270b7d/python_box-7.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:488f0fba9a6416c3334b602366dcd92825adb0811e07e03753dfcf0ed79cd6f7", size = 1232328 }, - { url = "https://files.pythonhosted.org/packages/45/68/0c2f289d8055d3e1b156ff258847f0e8f1010063e284cf5a612f09435575/python_box-7.3.2-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:39009a2da5c20133718b24891a206592adbe09169856aedc450ad1600fc2e511", size = 1819681 }, - { url = "https://files.pythonhosted.org/packages/ce/5d/76b4d6d0e41edb676a229f032848a1ecea166890fa8d501513ea1a030f4d/python_box-7.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2e2a72e2f6fb97c7e472ff3272da207ecc615aa222e52e98352391428527c469", size = 4270424 }, - { url = "https://files.pythonhosted.org/packages/e4/6b/32484b2a3cd2fb5e5f56bfb53a4537d93a4d2014ccf7fc0c0017fa6f65e9/python_box-7.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:9eead914b9fb7d98a1473f5027dcfe27d26b3a10ffa33b9ba22cf948a23cd280", size = 1211252 }, - { url = "https://files.pythonhosted.org/packages/2f/39/8bec609e93dbc5e0d3ea26cfb5af3ca78915f7a55ef5414713462fedeb59/python_box-7.3.2-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:1dfc3b9b073f3d7cad1fa90de98eaaa684a494d0574bbc0666f74fa8307fd6b6", size = 1804675 }, - { url = "https://files.pythonhosted.org/packages/88/ae/baf3a8057d8129896a7e02619df43ea0d918fc5b2bb66eb6e2470595fbac/python_box-7.3.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3ca4685a7f764b5a71b6e08535ce2a96b7964bb63d8cb4df10f6bb7147b6c54b", size = 4265645 }, - { url = "https://files.pythonhosted.org/packages/43/90/72367e03033c11a5e82676ee389b572bf136647ff4e3081557392b37e1ad/python_box-7.3.2-cp313-cp313-win_amd64.whl", hash = "sha256:e143295f74d47a9ab24562ead2375c9be10629599b57f2e86717d3fff60f82a9", size = 1206740 }, - { url = "https://files.pythonhosted.org/packages/37/13/8a990c6e2b6cc12700dce16f3cb383324e6d9a30f604eca22a2fdf84c923/python_box-7.3.2-py3-none-any.whl", hash = "sha256:fd7d74d5a848623f93b5221fd9fb00b8c00ff0e130fa87f396277aa188659c92", size = 29479 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -3774,23 +2730,22 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "six" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432 } +sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432, upload-time = "2024-03-01T18:36:20.211Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892 }, + { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" }, ] [[package]] name = "python-discovery" -version = "1.4.0" +version = "1.4.2" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD dependencies = [ { name = "filelock" }, { name = "platformdirs" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/a6/12/38c1a0b1e64806780c9563e3fc9f6e472251839662587cfbe9bfaf2ae10a/python_discovery-1.4.0.tar.gz", hash = "sha256:eb8bc7daad3c226c147e45bb4e970a1feb1bf4048ee178e6db59e197b8010ce3", size = 68455, upload-time = "2026-05-28T01:15:37.639Z" } +sdist = { url = "https://files.pythonhosted.org/packages/0b/1a/cbbaf13b730abb0a16b964d984e19f2fe520c21a4dc664051359a3f5a9e7/python_discovery-1.4.2.tar.gz", hash = "sha256:8f3746c4b4968d22afbb97d36e1a0e5b66e6c0f297290f2e95f05b9b8bf18690", size = 70277, upload-time = "2026-06-11T16:10:42.383Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/c8/8d/3d316429f65029532bb1e28ff77b797d86b5ac3915bb44ca4e19aa283d43/python_discovery-1.4.0-py3-none-any.whl", hash = "sha256:26ed78d703e234879a66244c7d4114563fb13ec5cd30a2d1357e5fb4850782da", size = 33217, upload-time = "2026-05-28T01:15:36.573Z" }, + { url = "https://files.pythonhosted.org/packages/1a/82/a70006589557f267f15bd384c0642ad49f0d97b690c3a05b166b9dcbad3b/python_discovery-1.4.2-py3-none-any.whl", hash = "sha256:475803f53b7b2ed6e490e27373f9d8340f7d2eebf9acdaf645d7d714c97bb500", size = 33886, upload-time = "2026-06-11T16:10:41.192Z" }, ] [[package]] @@ -3800,97 +2755,81 @@ source = { registry = "https://pypi.org/simple" } sdist = { url = "https://files.pythonhosted.org/packages/f7/ff/3cc9165fd44106973cd7ac9facb674a65ed853494592541d339bdc9a30eb/python_json_logger-4.1.0.tar.gz", hash = "sha256:b396b9e3ed782b09ff9d6e4f1683d46c83ad0d35d2e407c09a9ebbf038f88195", size = 17573, upload-time = "2026-03-29T04:39:56.805Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/27/be/0631a861af4d1c875f096c07d34e9a63639560a717130e7a87cbc82b7e3f/python_json_logger-4.1.0-py3-none-any.whl", hash = "sha256:132994765cf75bf44554be9aa49b06ef2345d23661a96720262716438141b6b2", size = 15021, upload-time = "2026-03-29T04:39:55.266Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/29/bf/eca6a3d43db1dae7070f70e160ab20b807627ba953663ba07928cdd3dc58/python_json_logger-4.0.0.tar.gz", hash = "sha256:f58e68eb46e1faed27e0f574a55a0455eecd7b8a5b88b85a784519ba3cff047f", size = 17683 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/51/e5/fecf13f06e5e5f67e8837d777d1bc43fac0ed2b77a676804df5c34744727/python_json_logger-4.0.0-py3-none-any.whl", hash = "sha256:af09c9daf6a813aa4cc7180395f50f2a9e5fa056034c9953aec92e381c5ba1e2", size = 15548 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "pywinpty" -version = "3.0.3" +version = "3.0.5" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/f7/54/37c7370ba91f579235049dc26cd2c5e657d2a943e01820844ffc81f32176/pywinpty-3.0.3.tar.gz", hash = "sha256:523441dc34d231fb361b4b00f8c99d3f16de02f5005fd544a0183112bcc22412", size = 31309, upload-time = "2026-02-04T21:51:09.524Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/79/c3/3e75075c7f71735f22b66fab0481f2c98e3a4d58cba55cb50ba29114bcf6/pywinpty-3.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:dff25a9a6435f527d7c65608a7e62783fc12076e7d44487a4911ee91be5a8ac8", size = 2114430, upload-time = "2026-02-04T21:54:19.485Z" }, - { url = "https://files.pythonhosted.org/packages/8d/1e/8a54166a8c5e4f5cb516514bdf4090be4d51a71e8d9f6d98c0aa00fe45d4/pywinpty-3.0.3-cp311-cp311-win_arm64.whl", hash = "sha256:fbc1e230e5b193eef4431cba3f39996a288f9958f9c9f092c8a961d930ee8f68", size = 236191, upload-time = "2026-02-04T21:50:36.239Z" }, - { url = "https://files.pythonhosted.org/packages/7c/d4/aeb5e1784d2c5bff6e189138a9ca91a090117459cea0c30378e1f2db3d54/pywinpty-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:c9081df0e49ffa86d15db4a6ba61530630e48707f987df42c9d3313537e81fc0", size = 2113098, upload-time = "2026-02-04T21:54:37.711Z" }, - { url = "https://files.pythonhosted.org/packages/b9/53/7278223c493ccfe4883239cf06c823c56460a8010e0fc778eef67858dc14/pywinpty-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:15e79d870e18b678fb8a5a6105fd38496b55697c66e6fc0378236026bc4d59e9", size = 234901, upload-time = "2026-02-04T21:53:31.35Z" }, - { url = "https://files.pythonhosted.org/packages/e5/cb/58d6ed3fd429c96a90ef01ac9a617af10a6d41469219c25e7dc162abbb71/pywinpty-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9c91dbb026050c77bdcef964e63a4f10f01a639113c4d3658332614544c467ab", size = 2112686, upload-time = "2026-02-04T21:52:03.035Z" }, - { url = "https://files.pythonhosted.org/packages/fd/50/724ed5c38c504d4e58a88a072776a1e880d970789deaeb2b9f7bd9a5141a/pywinpty-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:fe1f7911805127c94cf51f89ab14096c6f91ffdcacf993d2da6082b2142a2523", size = 234591, upload-time = "2026-02-04T21:52:29.821Z" }, - { url = "https://files.pythonhosted.org/packages/f7/ad/90a110538696b12b39fd8758a06d70ded899308198ad2305ac68e361126e/pywinpty-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:3f07a6cf1c1d470d284e614733c3d0f726d2c85e78508ea10a403140c3c0c18a", size = 2112360, upload-time = "2026-02-04T21:55:33.397Z" }, - { url = "https://files.pythonhosted.org/packages/44/0f/7ffa221757a220402bc79fda44044c3f2cc57338d878ab7d622add6f4581/pywinpty-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:15c7c0b6f8e9d87aabbaff76468dabf6e6121332c40fc1d83548d02a9d6a3759", size = 233107, upload-time = "2026-02-04T21:51:45.455Z" }, - { url = "https://files.pythonhosted.org/packages/28/88/2ff917caff61e55f38bcdb27de06ee30597881b2cae44fbba7627be015c4/pywinpty-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:d4b6b7b0fe0cdcd02e956bd57cfe9f4e5a06514eecf3b5ae174da4f951b58be9", size = 2113282, upload-time = "2026-02-04T21:52:08.188Z" }, - { url = "https://files.pythonhosted.org/packages/63/32/40a775343ace542cc43ece3f1d1fce454021521ecac41c4c4573081c2336/pywinpty-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:34789d685fc0d547ce0c8a65e5a70e56f77d732fa6e03c8f74fefb8cbb252019", size = 234207, upload-time = "2026-02-04T21:51:58.687Z" }, - { url = "https://files.pythonhosted.org/packages/8d/54/5d5e52f4cb75028104ca6faf36c10f9692389b1986d34471663b4ebebd6d/pywinpty-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:0c37e224a47a971d1a6e08649a1714dac4f63c11920780977829ed5c8cadead1", size = 2112910, upload-time = "2026-02-04T21:52:30.976Z" }, - { url = "https://files.pythonhosted.org/packages/0a/44/dcd184824e21d4620b06c7db9fbb15c3ad0a0f1fa2e6de79969fb82647ec/pywinpty-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:c4e9c3dff7d86ba81937438d5819f19f385a39d8f592d4e8af67148ceb4f6ab5", size = 233425, upload-time = "2026-02-04T21:51:56.754Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/f3/bb/a7cc2967c5c4eceb6cc49cfe39447d4bfc56e6c865e7c2249b6eb978935f/pywinpty-3.0.2.tar.gz", hash = "sha256:1505cc4cb248af42cb6285a65c9c2086ee9e7e574078ee60933d5d7fa86fb004", size = 30669 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/a6/a1/409c1651c9f874d598c10f51ff586c416625601df4bca315d08baec4c3e3/pywinpty-3.0.2-cp311-cp311-win_amd64.whl", hash = "sha256:327790d70e4c841ebd9d0f295a780177149aeb405bca44c7115a3de5c2054b23", size = 2050304 }, - { url = "https://files.pythonhosted.org/packages/02/4e/1098484e042c9485f56f16eb2b69b43b874bd526044ee401512234cf9e04/pywinpty-3.0.2-cp312-cp312-win_amd64.whl", hash = "sha256:99fdd9b455f0ad6419aba6731a7a0d2f88ced83c3c94a80ff9533d95fa8d8a9e", size = 2050391 }, - { url = "https://files.pythonhosted.org/packages/fc/19/b757fe28008236a4a713e813283721b8a40aa60cd7d3f83549f2e25a3155/pywinpty-3.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:18f78b81e4cfee6aabe7ea8688441d30247b73e52cd9657138015c5f4ee13a51", size = 2050057 }, - { url = "https://files.pythonhosted.org/packages/cb/44/cbae12ecf6f4fa4129c36871fd09c6bef4f98d5f625ecefb5e2449765508/pywinpty-3.0.2-cp313-cp313t-win_amd64.whl", hash = "sha256:663383ecfab7fc382cc97ea5c4f7f0bb32c2f889259855df6ea34e5df42d305b", size = 2049874 }, - { url = "https://files.pythonhosted.org/packages/ca/15/f12c6055e2d7a617d4d5820e8ac4ceaff849da4cb124640ef5116a230771/pywinpty-3.0.2-cp314-cp314-win_amd64.whl", hash = "sha256:28297cecc37bee9f24d8889e47231972d6e9e84f7b668909de54f36ca785029a", size = 2050386 }, - { url = "https://files.pythonhosted.org/packages/de/24/c6907c5bb06043df98ad6a0a0ff5db2e0affcecbc3b15c42404393a3f72a/pywinpty-3.0.2-cp314-cp314t-win_amd64.whl", hash = "sha256:34b55ae9a1b671fe3eae071d86618110538e8eaad18fcb1531c0830b91a82767", size = 2049834 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) +sdist = { url = "https://files.pythonhosted.org/packages/a9/ef/2d27f30c59a67be7025b2d7858c8c2d282b74d66544b2384730b82de74fd/pywinpty-3.0.5.tar.gz", hash = "sha256:61db0db063de9865adbea66db294628f8577f608d9764a4c7d3384eeacc4e81b", size = 16223484, upload-time = "2026-06-11T00:11:58.93Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b0/5c/31feb3dd82d1b33ae0bd09ca601edb993d9da1b7f0226b3336d4b4c39e1e/pywinpty-3.0.5-cp311-cp311-win_amd64.whl", hash = "sha256:af7a8720c78776ddd6259b71dd567944f766a6cd67f8d2887fbc4973967bacda", size = 2092466, upload-time = "2026-06-10T23:44:24.453Z" }, + { url = "https://files.pythonhosted.org/packages/ee/fe/fe23e2229ffec0c10190cef5964f5c9b2dba179d23b69ae537b7ea90bcab/pywinpty-3.0.5-cp311-cp311-win_arm64.whl", hash = "sha256:c2406f54f699eab75953fb75ce805f2ae55a33a957cd070890abd454fb4b7680", size = 818395, upload-time = "2026-06-10T23:41:56.93Z" }, + { url = "https://files.pythonhosted.org/packages/45/34/942cc95ca4e26489875aa8a95192766247a687379ec29543eebe73ec945f/pywinpty-3.0.5-cp312-cp312-win_amd64.whl", hash = "sha256:d62946adf14b15b54c0b8d785f93fe18b04da23f4ad59e2e8c4612646e9abd23", size = 2090915, upload-time = "2026-06-10T23:43:14.98Z" }, + { url = "https://files.pythonhosted.org/packages/6a/70/5b9053004844139ea8bd86209c57ade12b134b2782f383a095784c8531ec/pywinpty-3.0.5-cp312-cp312-win_arm64.whl", hash = "sha256:e9391c05fbfa7a992a97e831fc6849887b4014a614192e3d984a7ca59592b376", size = 815934, upload-time = "2026-06-10T23:41:42.384Z" }, + { url = "https://files.pythonhosted.org/packages/b9/f4/2a464b9893cceb3b3f416356e94fdc3e1bca9476993927e4e6d99fe95382/pywinpty-3.0.5-cp313-cp313-win_amd64.whl", hash = "sha256:48db1b0ad9d0a1b81dcaaa7163a99a7808deaceb0c1b2344716dc1fc090c3c4c", size = 2090471, upload-time = "2026-06-10T23:42:11.071Z" }, + { url = "https://files.pythonhosted.org/packages/3c/2c/a138491a0afbdb50eb79395577bd326d4b0fbde7209417d1a8087ff2493a/pywinpty-3.0.5-cp313-cp313-win_arm64.whl", hash = "sha256:2c6008fb2d3774b48693b2fcb7f2cc317ade9dc581289a964ffeeaf81307c9b5", size = 815518, upload-time = "2026-06-10T23:42:02.363Z" }, + { url = "https://files.pythonhosted.org/packages/6f/15/54400049a380582acd1282665c70fcf11e0bd3713679aca78e24c3aae738/pywinpty-3.0.5-cp313-cp313t-win_amd64.whl", hash = "sha256:22ce1b780d89821cc52daf6eac0708af22d93d000ce9c7c07e37489db8594598", size = 2089920, upload-time = "2026-06-10T23:44:13.395Z" }, + { url = "https://files.pythonhosted.org/packages/94/0c/6f24f3c0799f502259b24bdf841a99ad2b0d59df5c2525b4e2a286d14be2/pywinpty-3.0.5-cp313-cp313t-win_arm64.whl", hash = "sha256:9c2919a81bc5cfb09b86fc5a002112b2de95ca4304a07413cbeeb746a1307a5c", size = 814520, upload-time = "2026-06-10T23:43:28.588Z" }, + { url = "https://files.pythonhosted.org/packages/e9/23/f3cd1b1e5fc56517f54452c49f92049e7dd9ffc8a63de22a495581f50d04/pywinpty-3.0.5-cp314-cp314-win_amd64.whl", hash = "sha256:03bb3c16d691d9242267201830bcd0e64a9b663170e9042bc84b210da9de15ac", size = 2090663, upload-time = "2026-06-10T23:43:59.845Z" }, + { url = "https://files.pythonhosted.org/packages/9d/dd/96d6cbfc6d9ddab5c1c2f92c26545ae8997446a2ba7ee2024cd43c81f49b/pywinpty-3.0.5-cp314-cp314-win_arm64.whl", hash = "sha256:89c5c6ef08997a3b4b277b214a35fe15cab4dd6d119f0140aa71df5b1168fdbc", size = 815700, upload-time = "2026-06-10T23:40:50.001Z" }, + { url = "https://files.pythonhosted.org/packages/30/36/d98087bce0acaa4cce7f196103cfa7be3f63ce65f52473bb3e38784ae5d9/pywinpty-3.0.5-cp314-cp314t-win_amd64.whl", hash = "sha256:7b566165e0c5fdd6abe167a5ac8b954be6a843eb55a85946576d6bc1dea03d6d", size = 2090093, upload-time = "2026-06-10T23:40:58.933Z" }, + { url = "https://files.pythonhosted.org/packages/57/fd/fe2b0db922ba052ce3976a08f3fc05d0c05047c8b4ebb6102e832b8ef563/pywinpty-3.0.5-cp314-cp314t-win_arm64.whl", hash = "sha256:24366280a8aa677323da87bec729cb3ea3b35367386cece0978bdc6e4695c690", size = 814517, upload-time = "2026-06-10T23:42:34.946Z" }, ] [[package]] name = "pyyaml" version = "6.0.3" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/05/8e/961c0007c59b8dd7729d542c61a4d537767a59645b82a0b521206e1e25c2/pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f", size = 130960 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/6d/16/a95b6757765b7b031c9374925bb718d55e0a9ba8a1b6a12d25962ea44347/pyyaml-6.0.3-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:44edc647873928551a01e7a563d7452ccdebee747728c1080d881d68af7b997e", size = 185826 }, - { url = "https://files.pythonhosted.org/packages/16/19/13de8e4377ed53079ee996e1ab0a9c33ec2faf808a4647b7b4c0d46dd239/pyyaml-6.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:652cb6edd41e718550aad172851962662ff2681490a8a711af6a4d288dd96824", size = 175577 }, - { url = "https://files.pythonhosted.org/packages/0c/62/d2eb46264d4b157dae1275b573017abec435397aa59cbcdab6fc978a8af4/pyyaml-6.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:10892704fc220243f5305762e276552a0395f7beb4dbf9b14ec8fd43b57f126c", size = 775556 }, - { url = "https://files.pythonhosted.org/packages/10/cb/16c3f2cf3266edd25aaa00d6c4350381c8b012ed6f5276675b9eba8d9ff4/pyyaml-6.0.3-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:850774a7879607d3a6f50d36d04f00ee69e7fc816450e5f7e58d7f17f1ae5c00", size = 882114 }, - { url = "https://files.pythonhosted.org/packages/71/60/917329f640924b18ff085ab889a11c763e0b573da888e8404ff486657602/pyyaml-6.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b8bb0864c5a28024fac8a632c443c87c5aa6f215c0b126c449ae1a150412f31d", size = 806638 }, - { url = "https://files.pythonhosted.org/packages/dd/6f/529b0f316a9fd167281a6c3826b5583e6192dba792dd55e3203d3f8e655a/pyyaml-6.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:1d37d57ad971609cf3c53ba6a7e365e40660e3be0e5175fa9f2365a379d6095a", size = 767463 }, - { url = "https://files.pythonhosted.org/packages/f2/6a/b627b4e0c1dd03718543519ffb2f1deea4a1e6d42fbab8021936a4d22589/pyyaml-6.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:37503bfbfc9d2c40b344d06b2199cf0e96e97957ab1c1b546fd4f87e53e5d3e4", size = 794986 }, - { url = "https://files.pythonhosted.org/packages/45/91/47a6e1c42d9ee337c4839208f30d9f09caa9f720ec7582917b264defc875/pyyaml-6.0.3-cp311-cp311-win32.whl", hash = "sha256:8098f252adfa6c80ab48096053f512f2321f0b998f98150cea9bd23d83e1467b", size = 142543 }, - { url = "https://files.pythonhosted.org/packages/da/e3/ea007450a105ae919a72393cb06f122f288ef60bba2dc64b26e2646fa315/pyyaml-6.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:9f3bfb4965eb874431221a3ff3fdcddc7e74e3b07799e0e84ca4a0f867d449bf", size = 158763 }, - { url = "https://files.pythonhosted.org/packages/d1/33/422b98d2195232ca1826284a76852ad5a86fe23e31b009c9886b2d0fb8b2/pyyaml-6.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196", size = 182063 }, - { url = "https://files.pythonhosted.org/packages/89/a0/6cf41a19a1f2f3feab0e9c0b74134aa2ce6849093d5517a0c550fe37a648/pyyaml-6.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0", size = 173973 }, - { url = "https://files.pythonhosted.org/packages/ed/23/7a778b6bd0b9a8039df8b1b1d80e2e2ad78aa04171592c8a5c43a56a6af4/pyyaml-6.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28", size = 775116 }, - { url = "https://files.pythonhosted.org/packages/65/30/d7353c338e12baef4ecc1b09e877c1970bd3382789c159b4f89d6a70dc09/pyyaml-6.0.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c", size = 844011 }, - { url = "https://files.pythonhosted.org/packages/8b/9d/b3589d3877982d4f2329302ef98a8026e7f4443c765c46cfecc8858c6b4b/pyyaml-6.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc", size = 807870 }, - { url = "https://files.pythonhosted.org/packages/05/c0/b3be26a015601b822b97d9149ff8cb5ead58c66f981e04fedf4e762f4bd4/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e", size = 761089 }, - { url = "https://files.pythonhosted.org/packages/be/8e/98435a21d1d4b46590d5459a22d88128103f8da4c2d4cb8f14f2a96504e1/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea", size = 790181 }, - { url = "https://files.pythonhosted.org/packages/74/93/7baea19427dcfbe1e5a372d81473250b379f04b1bd3c4c5ff825e2327202/pyyaml-6.0.3-cp312-cp312-win32.whl", hash = "sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5", size = 137658 }, - { url = "https://files.pythonhosted.org/packages/86/bf/899e81e4cce32febab4fb42bb97dcdf66bc135272882d1987881a4b519e9/pyyaml-6.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b", size = 154003 }, - { url = "https://files.pythonhosted.org/packages/1a/08/67bd04656199bbb51dbed1439b7f27601dfb576fb864099c7ef0c3e55531/pyyaml-6.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd", size = 140344 }, - { url = "https://files.pythonhosted.org/packages/d1/11/0fd08f8192109f7169db964b5707a2f1e8b745d4e239b784a5a1dd80d1db/pyyaml-6.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8", size = 181669 }, - { url = "https://files.pythonhosted.org/packages/b1/16/95309993f1d3748cd644e02e38b75d50cbc0d9561d21f390a76242ce073f/pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1", size = 173252 }, - { url = "https://files.pythonhosted.org/packages/50/31/b20f376d3f810b9b2371e72ef5adb33879b25edb7a6d072cb7ca0c486398/pyyaml-6.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c", size = 767081 }, - { url = "https://files.pythonhosted.org/packages/49/1e/a55ca81e949270d5d4432fbbd19dfea5321eda7c41a849d443dc92fd1ff7/pyyaml-6.0.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5", size = 841159 }, - { url = "https://files.pythonhosted.org/packages/74/27/e5b8f34d02d9995b80abcef563ea1f8b56d20134d8f4e5e81733b1feceb2/pyyaml-6.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6", size = 801626 }, - { url = "https://files.pythonhosted.org/packages/f9/11/ba845c23988798f40e52ba45f34849aa8a1f2d4af4b798588010792ebad6/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6", size = 753613 }, - { url = "https://files.pythonhosted.org/packages/3d/e0/7966e1a7bfc0a45bf0a7fb6b98ea03fc9b8d84fa7f2229e9659680b69ee3/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be", size = 794115 }, - { url = "https://files.pythonhosted.org/packages/de/94/980b50a6531b3019e45ddeada0626d45fa85cbe22300844a7983285bed3b/pyyaml-6.0.3-cp313-cp313-win32.whl", hash = "sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26", size = 137427 }, - { url = "https://files.pythonhosted.org/packages/97/c9/39d5b874e8b28845e4ec2202b5da735d0199dbe5b8fb85f91398814a9a46/pyyaml-6.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c", size = 154090 }, - { url = "https://files.pythonhosted.org/packages/73/e8/2bdf3ca2090f68bb3d75b44da7bbc71843b19c9f2b9cb9b0f4ab7a5a4329/pyyaml-6.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb", size = 140246 }, - { url = "https://files.pythonhosted.org/packages/9d/8c/f4bd7f6465179953d3ac9bc44ac1a8a3e6122cf8ada906b4f96c60172d43/pyyaml-6.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac", size = 181814 }, - { url = "https://files.pythonhosted.org/packages/bd/9c/4d95bb87eb2063d20db7b60faa3840c1b18025517ae857371c4dd55a6b3a/pyyaml-6.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310", size = 173809 }, - { url = "https://files.pythonhosted.org/packages/92/b5/47e807c2623074914e29dabd16cbbdd4bf5e9b2db9f8090fa64411fc5382/pyyaml-6.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7", size = 766454 }, - { url = "https://files.pythonhosted.org/packages/02/9e/e5e9b168be58564121efb3de6859c452fccde0ab093d8438905899a3a483/pyyaml-6.0.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788", size = 836355 }, - { url = "https://files.pythonhosted.org/packages/88/f9/16491d7ed2a919954993e48aa941b200f38040928474c9e85ea9e64222c3/pyyaml-6.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5", size = 794175 }, - { url = "https://files.pythonhosted.org/packages/dd/3f/5989debef34dc6397317802b527dbbafb2b4760878a53d4166579111411e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764", size = 755228 }, - { url = "https://files.pythonhosted.org/packages/d7/ce/af88a49043cd2e265be63d083fc75b27b6ed062f5f9fd6cdc223ad62f03e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35", size = 789194 }, - { url = "https://files.pythonhosted.org/packages/23/20/bb6982b26a40bb43951265ba29d4c246ef0ff59c9fdcdf0ed04e0687de4d/pyyaml-6.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac", size = 156429 }, - { url = "https://files.pythonhosted.org/packages/f4/f4/a4541072bb9422c8a883ab55255f918fa378ecf083f5b85e87fc2b4eda1b/pyyaml-6.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3", size = 143912 }, - { url = "https://files.pythonhosted.org/packages/7c/f9/07dd09ae774e4616edf6cda684ee78f97777bdd15847253637a6f052a62f/pyyaml-6.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3", size = 189108 }, - { url = "https://files.pythonhosted.org/packages/4e/78/8d08c9fb7ce09ad8c38ad533c1191cf27f7ae1effe5bb9400a46d9437fcf/pyyaml-6.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba", size = 183641 }, - { url = "https://files.pythonhosted.org/packages/7b/5b/3babb19104a46945cf816d047db2788bcaf8c94527a805610b0289a01c6b/pyyaml-6.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c", size = 831901 }, - { url = "https://files.pythonhosted.org/packages/8b/cc/dff0684d8dc44da4d22a13f35f073d558c268780ce3c6ba1b87055bb0b87/pyyaml-6.0.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702", size = 861132 }, - { url = "https://files.pythonhosted.org/packages/b1/5e/f77dc6b9036943e285ba76b49e118d9ea929885becb0a29ba8a7c75e29fe/pyyaml-6.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c", size = 839261 }, - { url = "https://files.pythonhosted.org/packages/ce/88/a9db1376aa2a228197c58b37302f284b5617f56a5d959fd1763fb1675ce6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065", size = 805272 }, - { url = "https://files.pythonhosted.org/packages/da/92/1446574745d74df0c92e6aa4a7b0b3130706a4142b2d1a5869f2eaa423c6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65", size = 829923 }, - { url = "https://files.pythonhosted.org/packages/f0/7a/1c7270340330e575b92f397352af856a8c06f230aa3e76f86b39d01b416a/pyyaml-6.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9", size = 174062 }, - { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341 }, +sdist = { url = "https://files.pythonhosted.org/packages/05/8e/961c0007c59b8dd7729d542c61a4d537767a59645b82a0b521206e1e25c2/pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f", size = 130960, upload-time = "2025-09-25T21:33:16.546Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6d/16/a95b6757765b7b031c9374925bb718d55e0a9ba8a1b6a12d25962ea44347/pyyaml-6.0.3-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:44edc647873928551a01e7a563d7452ccdebee747728c1080d881d68af7b997e", size = 185826, upload-time = "2025-09-25T21:31:58.655Z" }, + { url = "https://files.pythonhosted.org/packages/16/19/13de8e4377ed53079ee996e1ab0a9c33ec2faf808a4647b7b4c0d46dd239/pyyaml-6.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:652cb6edd41e718550aad172851962662ff2681490a8a711af6a4d288dd96824", size = 175577, upload-time = "2025-09-25T21:32:00.088Z" }, + { url = "https://files.pythonhosted.org/packages/0c/62/d2eb46264d4b157dae1275b573017abec435397aa59cbcdab6fc978a8af4/pyyaml-6.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:10892704fc220243f5305762e276552a0395f7beb4dbf9b14ec8fd43b57f126c", size = 775556, upload-time = "2025-09-25T21:32:01.31Z" }, + { url = "https://files.pythonhosted.org/packages/10/cb/16c3f2cf3266edd25aaa00d6c4350381c8b012ed6f5276675b9eba8d9ff4/pyyaml-6.0.3-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:850774a7879607d3a6f50d36d04f00ee69e7fc816450e5f7e58d7f17f1ae5c00", size = 882114, upload-time = "2025-09-25T21:32:03.376Z" }, + { url = "https://files.pythonhosted.org/packages/71/60/917329f640924b18ff085ab889a11c763e0b573da888e8404ff486657602/pyyaml-6.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b8bb0864c5a28024fac8a632c443c87c5aa6f215c0b126c449ae1a150412f31d", size = 806638, upload-time = "2025-09-25T21:32:04.553Z" }, + { url = "https://files.pythonhosted.org/packages/dd/6f/529b0f316a9fd167281a6c3826b5583e6192dba792dd55e3203d3f8e655a/pyyaml-6.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:1d37d57ad971609cf3c53ba6a7e365e40660e3be0e5175fa9f2365a379d6095a", size = 767463, upload-time = "2025-09-25T21:32:06.152Z" }, + { url = "https://files.pythonhosted.org/packages/f2/6a/b627b4e0c1dd03718543519ffb2f1deea4a1e6d42fbab8021936a4d22589/pyyaml-6.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:37503bfbfc9d2c40b344d06b2199cf0e96e97957ab1c1b546fd4f87e53e5d3e4", size = 794986, upload-time = "2025-09-25T21:32:07.367Z" }, + { url = "https://files.pythonhosted.org/packages/45/91/47a6e1c42d9ee337c4839208f30d9f09caa9f720ec7582917b264defc875/pyyaml-6.0.3-cp311-cp311-win32.whl", hash = "sha256:8098f252adfa6c80ab48096053f512f2321f0b998f98150cea9bd23d83e1467b", size = 142543, upload-time = "2025-09-25T21:32:08.95Z" }, + { url = "https://files.pythonhosted.org/packages/da/e3/ea007450a105ae919a72393cb06f122f288ef60bba2dc64b26e2646fa315/pyyaml-6.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:9f3bfb4965eb874431221a3ff3fdcddc7e74e3b07799e0e84ca4a0f867d449bf", size = 158763, upload-time = "2025-09-25T21:32:09.96Z" }, + { url = "https://files.pythonhosted.org/packages/d1/33/422b98d2195232ca1826284a76852ad5a86fe23e31b009c9886b2d0fb8b2/pyyaml-6.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196", size = 182063, upload-time = "2025-09-25T21:32:11.445Z" }, + { url = "https://files.pythonhosted.org/packages/89/a0/6cf41a19a1f2f3feab0e9c0b74134aa2ce6849093d5517a0c550fe37a648/pyyaml-6.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0", size = 173973, upload-time = "2025-09-25T21:32:12.492Z" }, + { url = "https://files.pythonhosted.org/packages/ed/23/7a778b6bd0b9a8039df8b1b1d80e2e2ad78aa04171592c8a5c43a56a6af4/pyyaml-6.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28", size = 775116, upload-time = "2025-09-25T21:32:13.652Z" }, + { url = "https://files.pythonhosted.org/packages/65/30/d7353c338e12baef4ecc1b09e877c1970bd3382789c159b4f89d6a70dc09/pyyaml-6.0.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c", size = 844011, upload-time = "2025-09-25T21:32:15.21Z" }, + { url = "https://files.pythonhosted.org/packages/8b/9d/b3589d3877982d4f2329302ef98a8026e7f4443c765c46cfecc8858c6b4b/pyyaml-6.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc", size = 807870, upload-time = "2025-09-25T21:32:16.431Z" }, + { url = "https://files.pythonhosted.org/packages/05/c0/b3be26a015601b822b97d9149ff8cb5ead58c66f981e04fedf4e762f4bd4/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e", size = 761089, upload-time = "2025-09-25T21:32:17.56Z" }, + { url = "https://files.pythonhosted.org/packages/be/8e/98435a21d1d4b46590d5459a22d88128103f8da4c2d4cb8f14f2a96504e1/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea", size = 790181, upload-time = "2025-09-25T21:32:18.834Z" }, + { url = "https://files.pythonhosted.org/packages/74/93/7baea19427dcfbe1e5a372d81473250b379f04b1bd3c4c5ff825e2327202/pyyaml-6.0.3-cp312-cp312-win32.whl", hash = "sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5", size = 137658, upload-time = "2025-09-25T21:32:20.209Z" }, + { url = "https://files.pythonhosted.org/packages/86/bf/899e81e4cce32febab4fb42bb97dcdf66bc135272882d1987881a4b519e9/pyyaml-6.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b", size = 154003, upload-time = "2025-09-25T21:32:21.167Z" }, + { url = "https://files.pythonhosted.org/packages/1a/08/67bd04656199bbb51dbed1439b7f27601dfb576fb864099c7ef0c3e55531/pyyaml-6.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd", size = 140344, upload-time = "2025-09-25T21:32:22.617Z" }, + { url = "https://files.pythonhosted.org/packages/d1/11/0fd08f8192109f7169db964b5707a2f1e8b745d4e239b784a5a1dd80d1db/pyyaml-6.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8", size = 181669, upload-time = "2025-09-25T21:32:23.673Z" }, + { url = "https://files.pythonhosted.org/packages/b1/16/95309993f1d3748cd644e02e38b75d50cbc0d9561d21f390a76242ce073f/pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1", size = 173252, upload-time = "2025-09-25T21:32:25.149Z" }, + { url = "https://files.pythonhosted.org/packages/50/31/b20f376d3f810b9b2371e72ef5adb33879b25edb7a6d072cb7ca0c486398/pyyaml-6.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c", size = 767081, upload-time = "2025-09-25T21:32:26.575Z" }, + { url = "https://files.pythonhosted.org/packages/49/1e/a55ca81e949270d5d4432fbbd19dfea5321eda7c41a849d443dc92fd1ff7/pyyaml-6.0.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5", size = 841159, upload-time = "2025-09-25T21:32:27.727Z" }, + { url = "https://files.pythonhosted.org/packages/74/27/e5b8f34d02d9995b80abcef563ea1f8b56d20134d8f4e5e81733b1feceb2/pyyaml-6.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6", size = 801626, upload-time = "2025-09-25T21:32:28.878Z" }, + { url = "https://files.pythonhosted.org/packages/f9/11/ba845c23988798f40e52ba45f34849aa8a1f2d4af4b798588010792ebad6/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6", size = 753613, upload-time = "2025-09-25T21:32:30.178Z" }, + { url = "https://files.pythonhosted.org/packages/3d/e0/7966e1a7bfc0a45bf0a7fb6b98ea03fc9b8d84fa7f2229e9659680b69ee3/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be", size = 794115, upload-time = "2025-09-25T21:32:31.353Z" }, + { url = "https://files.pythonhosted.org/packages/de/94/980b50a6531b3019e45ddeada0626d45fa85cbe22300844a7983285bed3b/pyyaml-6.0.3-cp313-cp313-win32.whl", hash = "sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26", size = 137427, upload-time = "2025-09-25T21:32:32.58Z" }, + { url = "https://files.pythonhosted.org/packages/97/c9/39d5b874e8b28845e4ec2202b5da735d0199dbe5b8fb85f91398814a9a46/pyyaml-6.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c", size = 154090, upload-time = "2025-09-25T21:32:33.659Z" }, + { url = "https://files.pythonhosted.org/packages/73/e8/2bdf3ca2090f68bb3d75b44da7bbc71843b19c9f2b9cb9b0f4ab7a5a4329/pyyaml-6.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb", size = 140246, upload-time = "2025-09-25T21:32:34.663Z" }, + { url = "https://files.pythonhosted.org/packages/9d/8c/f4bd7f6465179953d3ac9bc44ac1a8a3e6122cf8ada906b4f96c60172d43/pyyaml-6.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac", size = 181814, upload-time = "2025-09-25T21:32:35.712Z" }, + { url = "https://files.pythonhosted.org/packages/bd/9c/4d95bb87eb2063d20db7b60faa3840c1b18025517ae857371c4dd55a6b3a/pyyaml-6.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310", size = 173809, upload-time = "2025-09-25T21:32:36.789Z" }, + { url = "https://files.pythonhosted.org/packages/92/b5/47e807c2623074914e29dabd16cbbdd4bf5e9b2db9f8090fa64411fc5382/pyyaml-6.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7", size = 766454, upload-time = "2025-09-25T21:32:37.966Z" }, + { url = "https://files.pythonhosted.org/packages/02/9e/e5e9b168be58564121efb3de6859c452fccde0ab093d8438905899a3a483/pyyaml-6.0.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788", size = 836355, upload-time = "2025-09-25T21:32:39.178Z" }, + { url = "https://files.pythonhosted.org/packages/88/f9/16491d7ed2a919954993e48aa941b200f38040928474c9e85ea9e64222c3/pyyaml-6.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5", size = 794175, upload-time = "2025-09-25T21:32:40.865Z" }, + { url = "https://files.pythonhosted.org/packages/dd/3f/5989debef34dc6397317802b527dbbafb2b4760878a53d4166579111411e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764", size = 755228, upload-time = "2025-09-25T21:32:42.084Z" }, + { url = "https://files.pythonhosted.org/packages/d7/ce/af88a49043cd2e265be63d083fc75b27b6ed062f5f9fd6cdc223ad62f03e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35", size = 789194, upload-time = "2025-09-25T21:32:43.362Z" }, + { url = "https://files.pythonhosted.org/packages/23/20/bb6982b26a40bb43951265ba29d4c246ef0ff59c9fdcdf0ed04e0687de4d/pyyaml-6.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac", size = 156429, upload-time = "2025-09-25T21:32:57.844Z" }, + { url = "https://files.pythonhosted.org/packages/f4/f4/a4541072bb9422c8a883ab55255f918fa378ecf083f5b85e87fc2b4eda1b/pyyaml-6.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3", size = 143912, upload-time = "2025-09-25T21:32:59.247Z" }, + { url = "https://files.pythonhosted.org/packages/7c/f9/07dd09ae774e4616edf6cda684ee78f97777bdd15847253637a6f052a62f/pyyaml-6.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3", size = 189108, upload-time = "2025-09-25T21:32:44.377Z" }, + { url = "https://files.pythonhosted.org/packages/4e/78/8d08c9fb7ce09ad8c38ad533c1191cf27f7ae1effe5bb9400a46d9437fcf/pyyaml-6.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba", size = 183641, upload-time = "2025-09-25T21:32:45.407Z" }, + { url = "https://files.pythonhosted.org/packages/7b/5b/3babb19104a46945cf816d047db2788bcaf8c94527a805610b0289a01c6b/pyyaml-6.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c", size = 831901, upload-time = "2025-09-25T21:32:48.83Z" }, + { url = "https://files.pythonhosted.org/packages/8b/cc/dff0684d8dc44da4d22a13f35f073d558c268780ce3c6ba1b87055bb0b87/pyyaml-6.0.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702", size = 861132, upload-time = "2025-09-25T21:32:50.149Z" }, + { url = "https://files.pythonhosted.org/packages/b1/5e/f77dc6b9036943e285ba76b49e118d9ea929885becb0a29ba8a7c75e29fe/pyyaml-6.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c", size = 839261, upload-time = "2025-09-25T21:32:51.808Z" }, + { url = "https://files.pythonhosted.org/packages/ce/88/a9db1376aa2a228197c58b37302f284b5617f56a5d959fd1763fb1675ce6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065", size = 805272, upload-time = "2025-09-25T21:32:52.941Z" }, + { url = "https://files.pythonhosted.org/packages/da/92/1446574745d74df0c92e6aa4a7b0b3130706a4142b2d1a5869f2eaa423c6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65", size = 829923, upload-time = "2025-09-25T21:32:54.537Z" }, + { url = "https://files.pythonhosted.org/packages/f0/7a/1c7270340330e575b92f397352af856a8c06f230aa3e76f86b39d01b416a/pyyaml-6.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9", size = 174062, upload-time = "2025-09-25T21:32:55.767Z" }, + { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341, upload-time = "2025-09-25T21:32:56.828Z" }, ] [[package]] @@ -3900,55 +2839,55 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "cffi", marker = "implementation_name == 'pypy'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/04/0b/3c9baedbdf613ecaa7aa07027780b8867f57b6293b6ee50de316c9f3222b/pyzmq-27.1.0.tar.gz", hash = "sha256:ac0765e3d44455adb6ddbf4417dcce460fc40a05978c08efdf2948072f6db540", size = 281750 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/06/5d/305323ba86b284e6fcb0d842d6adaa2999035f70f8c38a9b6d21ad28c3d4/pyzmq-27.1.0-cp311-cp311-macosx_10_15_universal2.whl", hash = "sha256:226b091818d461a3bef763805e75685e478ac17e9008f49fce2d3e52b3d58b86", size = 1333328 }, - { url = "https://files.pythonhosted.org/packages/bd/a0/fc7e78a23748ad5443ac3275943457e8452da67fda347e05260261108cbc/pyzmq-27.1.0-cp311-cp311-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:0790a0161c281ca9723f804871b4027f2e8b5a528d357c8952d08cd1a9c15581", size = 908803 }, - { url = "https://files.pythonhosted.org/packages/7e/22/37d15eb05f3bdfa4abea6f6d96eb3bb58585fbd3e4e0ded4e743bc650c97/pyzmq-27.1.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c895a6f35476b0c3a54e3eb6ccf41bf3018de937016e6e18748317f25d4e925f", size = 668836 }, - { url = "https://files.pythonhosted.org/packages/b1/c4/2a6fe5111a01005fc7af3878259ce17684fabb8852815eda6225620f3c59/pyzmq-27.1.0-cp311-cp311-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5bbf8d3630bf96550b3be8e1fc0fea5cbdc8d5466c1192887bd94869da17a63e", size = 857038 }, - { url = "https://files.pythonhosted.org/packages/cb/eb/bfdcb41d0db9cd233d6fb22dc131583774135505ada800ebf14dfb0a7c40/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:15c8bd0fe0dabf808e2d7a681398c4e5ded70a551ab47482067a572c054c8e2e", size = 1657531 }, - { url = "https://files.pythonhosted.org/packages/ab/21/e3180ca269ed4a0de5c34417dfe71a8ae80421198be83ee619a8a485b0c7/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:bafcb3dd171b4ae9f19ee6380dfc71ce0390fefaf26b504c0e5f628d7c8c54f2", size = 2034786 }, - { url = "https://files.pythonhosted.org/packages/3b/b1/5e21d0b517434b7f33588ff76c177c5a167858cc38ef740608898cd329f2/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:e829529fcaa09937189178115c49c504e69289abd39967cd8a4c215761373394", size = 1894220 }, - { url = "https://files.pythonhosted.org/packages/03/f2/44913a6ff6941905efc24a1acf3d3cb6146b636c546c7406c38c49c403d4/pyzmq-27.1.0-cp311-cp311-win32.whl", hash = "sha256:6df079c47d5902af6db298ec92151db82ecb557af663098b92f2508c398bb54f", size = 567155 }, - { url = "https://files.pythonhosted.org/packages/23/6d/d8d92a0eb270a925c9b4dd039c0b4dc10abc2fcbc48331788824ef113935/pyzmq-27.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:190cbf120fbc0fc4957b56866830def56628934a9d112aec0e2507aa6a032b97", size = 633428 }, - { url = "https://files.pythonhosted.org/packages/ae/14/01afebc96c5abbbd713ecfc7469cfb1bc801c819a74ed5c9fad9a48801cb/pyzmq-27.1.0-cp311-cp311-win_arm64.whl", hash = "sha256:eca6b47df11a132d1745eb3b5b5e557a7dae2c303277aa0e69c6ba91b8736e07", size = 559497 }, - { url = "https://files.pythonhosted.org/packages/92/e7/038aab64a946d535901103da16b953c8c9cc9c961dadcbf3609ed6428d23/pyzmq-27.1.0-cp312-abi3-macosx_10_15_universal2.whl", hash = "sha256:452631b640340c928fa343801b0d07eb0c3789a5ffa843f6e1a9cee0ba4eb4fc", size = 1306279 }, - { url = "https://files.pythonhosted.org/packages/e8/5e/c3c49fdd0f535ef45eefcc16934648e9e59dace4a37ee88fc53f6cd8e641/pyzmq-27.1.0-cp312-abi3-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1c179799b118e554b66da67d88ed66cd37a169f1f23b5d9f0a231b4e8d44a113", size = 895645 }, - { url = "https://files.pythonhosted.org/packages/f8/e5/b0b2504cb4e903a74dcf1ebae157f9e20ebb6ea76095f6cfffea28c42ecd/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3837439b7f99e60312f0c926a6ad437b067356dc2bc2ec96eb395fd0fe804233", size = 652574 }, - { url = "https://files.pythonhosted.org/packages/f8/9b/c108cdb55560eaf253f0cbdb61b29971e9fb34d9c3499b0e96e4e60ed8a5/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:43ad9a73e3da1fab5b0e7e13402f0b2fb934ae1c876c51d0afff0e7c052eca31", size = 840995 }, - { url = "https://files.pythonhosted.org/packages/c2/bb/b79798ca177b9eb0825b4c9998c6af8cd2a7f15a6a1a4272c1d1a21d382f/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:0de3028d69d4cdc475bfe47a6128eb38d8bc0e8f4d69646adfbcd840facbac28", size = 1642070 }, - { url = "https://files.pythonhosted.org/packages/9c/80/2df2e7977c4ede24c79ae39dcef3899bfc5f34d1ca7a5b24f182c9b7a9ca/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_i686.whl", hash = "sha256:cf44a7763aea9298c0aa7dbf859f87ed7012de8bda0f3977b6fb1d96745df856", size = 2021121 }, - { url = "https://files.pythonhosted.org/packages/46/bd/2d45ad24f5f5ae7e8d01525eb76786fa7557136555cac7d929880519e33a/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:f30f395a9e6fbca195400ce833c731e7b64c3919aa481af4d88c3759e0cb7496", size = 1878550 }, - { url = "https://files.pythonhosted.org/packages/e6/2f/104c0a3c778d7c2ab8190e9db4f62f0b6957b53c9d87db77c284b69f33ea/pyzmq-27.1.0-cp312-abi3-win32.whl", hash = "sha256:250e5436a4ba13885494412b3da5d518cd0d3a278a1ae640e113c073a5f88edd", size = 559184 }, - { url = "https://files.pythonhosted.org/packages/fc/7f/a21b20d577e4100c6a41795842028235998a643b1ad406a6d4163ea8f53e/pyzmq-27.1.0-cp312-abi3-win_amd64.whl", hash = "sha256:9ce490cf1d2ca2ad84733aa1d69ce6855372cb5ce9223802450c9b2a7cba0ccf", size = 619480 }, - { url = "https://files.pythonhosted.org/packages/78/c2/c012beae5f76b72f007a9e91ee9401cb88c51d0f83c6257a03e785c81cc2/pyzmq-27.1.0-cp312-abi3-win_arm64.whl", hash = "sha256:75a2f36223f0d535a0c919e23615fc85a1e23b71f40c7eb43d7b1dedb4d8f15f", size = 552993 }, - { url = "https://files.pythonhosted.org/packages/60/cb/84a13459c51da6cec1b7b1dc1a47e6db6da50b77ad7fd9c145842750a011/pyzmq-27.1.0-cp313-cp313-android_24_arm64_v8a.whl", hash = "sha256:93ad4b0855a664229559e45c8d23797ceac03183c7b6f5b4428152a6b06684a5", size = 1122436 }, - { url = "https://files.pythonhosted.org/packages/dc/b6/94414759a69a26c3dd674570a81813c46a078767d931a6c70ad29fc585cb/pyzmq-27.1.0-cp313-cp313-android_24_x86_64.whl", hash = "sha256:fbb4f2400bfda24f12f009cba62ad5734148569ff4949b1b6ec3b519444342e6", size = 1156301 }, - { url = "https://files.pythonhosted.org/packages/a5/ad/15906493fd40c316377fd8a8f6b1f93104f97a752667763c9b9c1b71d42d/pyzmq-27.1.0-cp313-cp313t-macosx_10_15_universal2.whl", hash = "sha256:e343d067f7b151cfe4eb3bb796a7752c9d369eed007b91231e817071d2c2fec7", size = 1341197 }, - { url = "https://files.pythonhosted.org/packages/14/1d/d343f3ce13db53a54cb8946594e567410b2125394dafcc0268d8dda027e0/pyzmq-27.1.0-cp313-cp313t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:08363b2011dec81c354d694bdecaef4770e0ae96b9afea70b3f47b973655cc05", size = 897275 }, - { url = "https://files.pythonhosted.org/packages/69/2d/d83dd6d7ca929a2fc67d2c3005415cdf322af7751d773524809f9e585129/pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d54530c8c8b5b8ddb3318f481297441af102517602b569146185fa10b63f4fa9", size = 660469 }, - { url = "https://files.pythonhosted.org/packages/3e/cd/9822a7af117f4bc0f1952dbe9ef8358eb50a24928efd5edf54210b850259/pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6f3afa12c392f0a44a2414056d730eebc33ec0926aae92b5ad5cf26ebb6cc128", size = 847961 }, - { url = "https://files.pythonhosted.org/packages/9a/12/f003e824a19ed73be15542f172fd0ec4ad0b60cf37436652c93b9df7c585/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c65047adafe573ff023b3187bb93faa583151627bc9c51fc4fb2c561ed689d39", size = 1650282 }, - { url = "https://files.pythonhosted.org/packages/d5/4a/e82d788ed58e9a23995cee70dbc20c9aded3d13a92d30d57ec2291f1e8a3/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:90e6e9441c946a8b0a667356f7078d96411391a3b8f80980315455574177ec97", size = 2024468 }, - { url = "https://files.pythonhosted.org/packages/d9/94/2da0a60841f757481e402b34bf4c8bf57fa54a5466b965de791b1e6f747d/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:add071b2d25f84e8189aaf0882d39a285b42fa3853016ebab234a5e78c7a43db", size = 1885394 }, - { url = "https://files.pythonhosted.org/packages/4f/6f/55c10e2e49ad52d080dc24e37adb215e5b0d64990b57598abc2e3f01725b/pyzmq-27.1.0-cp313-cp313t-win32.whl", hash = "sha256:7ccc0700cfdf7bd487bea8d850ec38f204478681ea02a582a8da8171b7f90a1c", size = 574964 }, - { url = "https://files.pythonhosted.org/packages/87/4d/2534970ba63dd7c522d8ca80fb92777f362c0f321900667c615e2067cb29/pyzmq-27.1.0-cp313-cp313t-win_amd64.whl", hash = "sha256:8085a9fba668216b9b4323be338ee5437a235fe275b9d1610e422ccc279733e2", size = 641029 }, - { url = "https://files.pythonhosted.org/packages/f6/fa/f8aea7a28b0641f31d40dea42d7ef003fded31e184ef47db696bc74cd610/pyzmq-27.1.0-cp313-cp313t-win_arm64.whl", hash = "sha256:6bb54ca21bcfe361e445256c15eedf083f153811c37be87e0514934d6913061e", size = 561541 }, - { url = "https://files.pythonhosted.org/packages/87/45/19efbb3000956e82d0331bafca5d9ac19ea2857722fa2caacefb6042f39d/pyzmq-27.1.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:ce980af330231615756acd5154f29813d553ea555485ae712c491cd483df6b7a", size = 1341197 }, - { url = "https://files.pythonhosted.org/packages/48/43/d72ccdbf0d73d1343936296665826350cb1e825f92f2db9db3e61c2162a2/pyzmq-27.1.0-cp314-cp314t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1779be8c549e54a1c38f805e56d2a2e5c009d26de10921d7d51cfd1c8d4632ea", size = 897175 }, - { url = "https://files.pythonhosted.org/packages/2f/2e/a483f73a10b65a9ef0161e817321d39a770b2acf8bcf3004a28d90d14a94/pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7200bb0f03345515df50d99d3db206a0a6bee1955fbb8c453c76f5bf0e08fb96", size = 660427 }, - { url = "https://files.pythonhosted.org/packages/f5/d2/5f36552c2d3e5685abe60dfa56f91169f7a2d99bbaf67c5271022ab40863/pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:01c0e07d558b06a60773744ea6251f769cd79a41a97d11b8bf4ab8f034b0424d", size = 847929 }, - { url = "https://files.pythonhosted.org/packages/c4/2a/404b331f2b7bf3198e9945f75c4c521f0c6a3a23b51f7a4a401b94a13833/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:80d834abee71f65253c91540445d37c4c561e293ba6e741b992f20a105d69146", size = 1650193 }, - { url = "https://files.pythonhosted.org/packages/1c/0b/f4107e33f62a5acf60e3ded67ed33d79b4ce18de432625ce2fc5093d6388/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:544b4e3b7198dde4a62b8ff6685e9802a9a1ebf47e77478a5eb88eca2a82f2fd", size = 2024388 }, - { url = "https://files.pythonhosted.org/packages/0d/01/add31fe76512642fd6e40e3a3bd21f4b47e242c8ba33efb6809e37076d9b/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:cedc4c68178e59a4046f97eca31b148ddcf51e88677de1ef4e78cf06c5376c9a", size = 1885316 }, - { url = "https://files.pythonhosted.org/packages/c4/59/a5f38970f9bf07cee96128de79590bb354917914a9be11272cfc7ff26af0/pyzmq-27.1.0-cp314-cp314t-win32.whl", hash = "sha256:1f0b2a577fd770aa6f053211a55d1c47901f4d537389a034c690291485e5fe92", size = 587472 }, - { url = "https://files.pythonhosted.org/packages/70/d8/78b1bad170f93fcf5e3536e70e8fadac55030002275c9a29e8f5719185de/pyzmq-27.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:19c9468ae0437f8074af379e986c5d3d7d7bfe033506af442e8c879732bedbe0", size = 661401 }, - { url = "https://files.pythonhosted.org/packages/81/d6/4bfbb40c9a0b42fc53c7cf442f6385db70b40f74a783130c5d0a5aa62228/pyzmq-27.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:dc5dbf68a7857b59473f7df42650c621d7e8923fb03fa74a526890f4d33cc4d7", size = 575170 }, - { url = "https://files.pythonhosted.org/packages/4c/c6/c4dcdecdbaa70969ee1fdced6d7b8f60cfabe64d25361f27ac4665a70620/pyzmq-27.1.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:18770c8d3563715387139060d37859c02ce40718d1faf299abddcdcc6a649066", size = 836265 }, - { url = "https://files.pythonhosted.org/packages/3e/79/f38c92eeaeb03a2ccc2ba9866f0439593bb08c5e3b714ac1d553e5c96e25/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:ac25465d42f92e990f8d8b0546b01c391ad431c3bf447683fdc40565941d0604", size = 800208 }, - { url = "https://files.pythonhosted.org/packages/49/0e/3f0d0d335c6b3abb9b7b723776d0b21fa7f3a6c819a0db6097059aada160/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:53b40f8ae006f2734ee7608d59ed661419f087521edbfc2149c3932e9c14808c", size = 567747 }, - { url = "https://files.pythonhosted.org/packages/a1/cf/f2b3784d536250ffd4be70e049f3b60981235d70c6e8ce7e3ef21e1adb25/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f605d884e7c8be8fe1aa94e0a783bf3f591b84c24e4bc4f3e7564c82ac25e271", size = 747371 }, - { url = "https://files.pythonhosted.org/packages/01/1b/5dbe84eefc86f48473947e2f41711aded97eecef1231f4558f1f02713c12/pyzmq-27.1.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:c9f7f6e13dff2e44a6afeaf2cf54cee5929ad64afaf4d40b50f93c58fc687355", size = 544862 }, +sdist = { url = "https://files.pythonhosted.org/packages/04/0b/3c9baedbdf613ecaa7aa07027780b8867f57b6293b6ee50de316c9f3222b/pyzmq-27.1.0.tar.gz", hash = "sha256:ac0765e3d44455adb6ddbf4417dcce460fc40a05978c08efdf2948072f6db540", size = 281750, upload-time = "2025-09-08T23:10:18.157Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/06/5d/305323ba86b284e6fcb0d842d6adaa2999035f70f8c38a9b6d21ad28c3d4/pyzmq-27.1.0-cp311-cp311-macosx_10_15_universal2.whl", hash = "sha256:226b091818d461a3bef763805e75685e478ac17e9008f49fce2d3e52b3d58b86", size = 1333328, upload-time = "2025-09-08T23:07:45.946Z" }, + { url = "https://files.pythonhosted.org/packages/bd/a0/fc7e78a23748ad5443ac3275943457e8452da67fda347e05260261108cbc/pyzmq-27.1.0-cp311-cp311-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:0790a0161c281ca9723f804871b4027f2e8b5a528d357c8952d08cd1a9c15581", size = 908803, upload-time = "2025-09-08T23:07:47.551Z" }, + { url = "https://files.pythonhosted.org/packages/7e/22/37d15eb05f3bdfa4abea6f6d96eb3bb58585fbd3e4e0ded4e743bc650c97/pyzmq-27.1.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c895a6f35476b0c3a54e3eb6ccf41bf3018de937016e6e18748317f25d4e925f", size = 668836, upload-time = "2025-09-08T23:07:49.436Z" }, + { url = "https://files.pythonhosted.org/packages/b1/c4/2a6fe5111a01005fc7af3878259ce17684fabb8852815eda6225620f3c59/pyzmq-27.1.0-cp311-cp311-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5bbf8d3630bf96550b3be8e1fc0fea5cbdc8d5466c1192887bd94869da17a63e", size = 857038, upload-time = "2025-09-08T23:07:51.234Z" }, + { url = "https://files.pythonhosted.org/packages/cb/eb/bfdcb41d0db9cd233d6fb22dc131583774135505ada800ebf14dfb0a7c40/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:15c8bd0fe0dabf808e2d7a681398c4e5ded70a551ab47482067a572c054c8e2e", size = 1657531, upload-time = "2025-09-08T23:07:52.795Z" }, + { url = "https://files.pythonhosted.org/packages/ab/21/e3180ca269ed4a0de5c34417dfe71a8ae80421198be83ee619a8a485b0c7/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:bafcb3dd171b4ae9f19ee6380dfc71ce0390fefaf26b504c0e5f628d7c8c54f2", size = 2034786, upload-time = "2025-09-08T23:07:55.047Z" }, + { url = "https://files.pythonhosted.org/packages/3b/b1/5e21d0b517434b7f33588ff76c177c5a167858cc38ef740608898cd329f2/pyzmq-27.1.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:e829529fcaa09937189178115c49c504e69289abd39967cd8a4c215761373394", size = 1894220, upload-time = "2025-09-08T23:07:57.172Z" }, + { url = "https://files.pythonhosted.org/packages/03/f2/44913a6ff6941905efc24a1acf3d3cb6146b636c546c7406c38c49c403d4/pyzmq-27.1.0-cp311-cp311-win32.whl", hash = "sha256:6df079c47d5902af6db298ec92151db82ecb557af663098b92f2508c398bb54f", size = 567155, upload-time = "2025-09-08T23:07:59.05Z" }, + { url = "https://files.pythonhosted.org/packages/23/6d/d8d92a0eb270a925c9b4dd039c0b4dc10abc2fcbc48331788824ef113935/pyzmq-27.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:190cbf120fbc0fc4957b56866830def56628934a9d112aec0e2507aa6a032b97", size = 633428, upload-time = "2025-09-08T23:08:00.663Z" }, + { url = "https://files.pythonhosted.org/packages/ae/14/01afebc96c5abbbd713ecfc7469cfb1bc801c819a74ed5c9fad9a48801cb/pyzmq-27.1.0-cp311-cp311-win_arm64.whl", hash = "sha256:eca6b47df11a132d1745eb3b5b5e557a7dae2c303277aa0e69c6ba91b8736e07", size = 559497, upload-time = "2025-09-08T23:08:02.15Z" }, + { url = "https://files.pythonhosted.org/packages/92/e7/038aab64a946d535901103da16b953c8c9cc9c961dadcbf3609ed6428d23/pyzmq-27.1.0-cp312-abi3-macosx_10_15_universal2.whl", hash = "sha256:452631b640340c928fa343801b0d07eb0c3789a5ffa843f6e1a9cee0ba4eb4fc", size = 1306279, upload-time = "2025-09-08T23:08:03.807Z" }, + { url = "https://files.pythonhosted.org/packages/e8/5e/c3c49fdd0f535ef45eefcc16934648e9e59dace4a37ee88fc53f6cd8e641/pyzmq-27.1.0-cp312-abi3-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1c179799b118e554b66da67d88ed66cd37a169f1f23b5d9f0a231b4e8d44a113", size = 895645, upload-time = "2025-09-08T23:08:05.301Z" }, + { url = "https://files.pythonhosted.org/packages/f8/e5/b0b2504cb4e903a74dcf1ebae157f9e20ebb6ea76095f6cfffea28c42ecd/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3837439b7f99e60312f0c926a6ad437b067356dc2bc2ec96eb395fd0fe804233", size = 652574, upload-time = "2025-09-08T23:08:06.828Z" }, + { url = "https://files.pythonhosted.org/packages/f8/9b/c108cdb55560eaf253f0cbdb61b29971e9fb34d9c3499b0e96e4e60ed8a5/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:43ad9a73e3da1fab5b0e7e13402f0b2fb934ae1c876c51d0afff0e7c052eca31", size = 840995, upload-time = "2025-09-08T23:08:08.396Z" }, + { url = "https://files.pythonhosted.org/packages/c2/bb/b79798ca177b9eb0825b4c9998c6af8cd2a7f15a6a1a4272c1d1a21d382f/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:0de3028d69d4cdc475bfe47a6128eb38d8bc0e8f4d69646adfbcd840facbac28", size = 1642070, upload-time = "2025-09-08T23:08:09.989Z" }, + { url = "https://files.pythonhosted.org/packages/9c/80/2df2e7977c4ede24c79ae39dcef3899bfc5f34d1ca7a5b24f182c9b7a9ca/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_i686.whl", hash = "sha256:cf44a7763aea9298c0aa7dbf859f87ed7012de8bda0f3977b6fb1d96745df856", size = 2021121, upload-time = "2025-09-08T23:08:11.907Z" }, + { url = "https://files.pythonhosted.org/packages/46/bd/2d45ad24f5f5ae7e8d01525eb76786fa7557136555cac7d929880519e33a/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:f30f395a9e6fbca195400ce833c731e7b64c3919aa481af4d88c3759e0cb7496", size = 1878550, upload-time = "2025-09-08T23:08:13.513Z" }, + { url = "https://files.pythonhosted.org/packages/e6/2f/104c0a3c778d7c2ab8190e9db4f62f0b6957b53c9d87db77c284b69f33ea/pyzmq-27.1.0-cp312-abi3-win32.whl", hash = "sha256:250e5436a4ba13885494412b3da5d518cd0d3a278a1ae640e113c073a5f88edd", size = 559184, upload-time = "2025-09-08T23:08:15.163Z" }, + { url = "https://files.pythonhosted.org/packages/fc/7f/a21b20d577e4100c6a41795842028235998a643b1ad406a6d4163ea8f53e/pyzmq-27.1.0-cp312-abi3-win_amd64.whl", hash = "sha256:9ce490cf1d2ca2ad84733aa1d69ce6855372cb5ce9223802450c9b2a7cba0ccf", size = 619480, upload-time = "2025-09-08T23:08:17.192Z" }, + { url = "https://files.pythonhosted.org/packages/78/c2/c012beae5f76b72f007a9e91ee9401cb88c51d0f83c6257a03e785c81cc2/pyzmq-27.1.0-cp312-abi3-win_arm64.whl", hash = "sha256:75a2f36223f0d535a0c919e23615fc85a1e23b71f40c7eb43d7b1dedb4d8f15f", size = 552993, upload-time = "2025-09-08T23:08:18.926Z" }, + { url = "https://files.pythonhosted.org/packages/60/cb/84a13459c51da6cec1b7b1dc1a47e6db6da50b77ad7fd9c145842750a011/pyzmq-27.1.0-cp313-cp313-android_24_arm64_v8a.whl", hash = "sha256:93ad4b0855a664229559e45c8d23797ceac03183c7b6f5b4428152a6b06684a5", size = 1122436, upload-time = "2025-09-08T23:08:20.801Z" }, + { url = "https://files.pythonhosted.org/packages/dc/b6/94414759a69a26c3dd674570a81813c46a078767d931a6c70ad29fc585cb/pyzmq-27.1.0-cp313-cp313-android_24_x86_64.whl", hash = "sha256:fbb4f2400bfda24f12f009cba62ad5734148569ff4949b1b6ec3b519444342e6", size = 1156301, upload-time = "2025-09-08T23:08:22.47Z" }, + { url = "https://files.pythonhosted.org/packages/a5/ad/15906493fd40c316377fd8a8f6b1f93104f97a752667763c9b9c1b71d42d/pyzmq-27.1.0-cp313-cp313t-macosx_10_15_universal2.whl", hash = "sha256:e343d067f7b151cfe4eb3bb796a7752c9d369eed007b91231e817071d2c2fec7", size = 1341197, upload-time = "2025-09-08T23:08:24.286Z" }, + { url = "https://files.pythonhosted.org/packages/14/1d/d343f3ce13db53a54cb8946594e567410b2125394dafcc0268d8dda027e0/pyzmq-27.1.0-cp313-cp313t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:08363b2011dec81c354d694bdecaef4770e0ae96b9afea70b3f47b973655cc05", size = 897275, upload-time = "2025-09-08T23:08:26.063Z" }, + { url = "https://files.pythonhosted.org/packages/69/2d/d83dd6d7ca929a2fc67d2c3005415cdf322af7751d773524809f9e585129/pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d54530c8c8b5b8ddb3318f481297441af102517602b569146185fa10b63f4fa9", size = 660469, upload-time = "2025-09-08T23:08:27.623Z" }, + { url = "https://files.pythonhosted.org/packages/3e/cd/9822a7af117f4bc0f1952dbe9ef8358eb50a24928efd5edf54210b850259/pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6f3afa12c392f0a44a2414056d730eebc33ec0926aae92b5ad5cf26ebb6cc128", size = 847961, upload-time = "2025-09-08T23:08:29.672Z" }, + { url = "https://files.pythonhosted.org/packages/9a/12/f003e824a19ed73be15542f172fd0ec4ad0b60cf37436652c93b9df7c585/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c65047adafe573ff023b3187bb93faa583151627bc9c51fc4fb2c561ed689d39", size = 1650282, upload-time = "2025-09-08T23:08:31.349Z" }, + { url = "https://files.pythonhosted.org/packages/d5/4a/e82d788ed58e9a23995cee70dbc20c9aded3d13a92d30d57ec2291f1e8a3/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:90e6e9441c946a8b0a667356f7078d96411391a3b8f80980315455574177ec97", size = 2024468, upload-time = "2025-09-08T23:08:33.543Z" }, + { url = "https://files.pythonhosted.org/packages/d9/94/2da0a60841f757481e402b34bf4c8bf57fa54a5466b965de791b1e6f747d/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:add071b2d25f84e8189aaf0882d39a285b42fa3853016ebab234a5e78c7a43db", size = 1885394, upload-time = "2025-09-08T23:08:35.51Z" }, + { url = "https://files.pythonhosted.org/packages/4f/6f/55c10e2e49ad52d080dc24e37adb215e5b0d64990b57598abc2e3f01725b/pyzmq-27.1.0-cp313-cp313t-win32.whl", hash = "sha256:7ccc0700cfdf7bd487bea8d850ec38f204478681ea02a582a8da8171b7f90a1c", size = 574964, upload-time = "2025-09-08T23:08:37.178Z" }, + { url = "https://files.pythonhosted.org/packages/87/4d/2534970ba63dd7c522d8ca80fb92777f362c0f321900667c615e2067cb29/pyzmq-27.1.0-cp313-cp313t-win_amd64.whl", hash = "sha256:8085a9fba668216b9b4323be338ee5437a235fe275b9d1610e422ccc279733e2", size = 641029, upload-time = "2025-09-08T23:08:40.595Z" }, + { url = "https://files.pythonhosted.org/packages/f6/fa/f8aea7a28b0641f31d40dea42d7ef003fded31e184ef47db696bc74cd610/pyzmq-27.1.0-cp313-cp313t-win_arm64.whl", hash = "sha256:6bb54ca21bcfe361e445256c15eedf083f153811c37be87e0514934d6913061e", size = 561541, upload-time = "2025-09-08T23:08:42.668Z" }, + { url = "https://files.pythonhosted.org/packages/87/45/19efbb3000956e82d0331bafca5d9ac19ea2857722fa2caacefb6042f39d/pyzmq-27.1.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:ce980af330231615756acd5154f29813d553ea555485ae712c491cd483df6b7a", size = 1341197, upload-time = "2025-09-08T23:08:44.973Z" }, + { url = "https://files.pythonhosted.org/packages/48/43/d72ccdbf0d73d1343936296665826350cb1e825f92f2db9db3e61c2162a2/pyzmq-27.1.0-cp314-cp314t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1779be8c549e54a1c38f805e56d2a2e5c009d26de10921d7d51cfd1c8d4632ea", size = 897175, upload-time = "2025-09-08T23:08:46.601Z" }, + { url = "https://files.pythonhosted.org/packages/2f/2e/a483f73a10b65a9ef0161e817321d39a770b2acf8bcf3004a28d90d14a94/pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7200bb0f03345515df50d99d3db206a0a6bee1955fbb8c453c76f5bf0e08fb96", size = 660427, upload-time = "2025-09-08T23:08:48.187Z" }, + { url = "https://files.pythonhosted.org/packages/f5/d2/5f36552c2d3e5685abe60dfa56f91169f7a2d99bbaf67c5271022ab40863/pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:01c0e07d558b06a60773744ea6251f769cd79a41a97d11b8bf4ab8f034b0424d", size = 847929, upload-time = "2025-09-08T23:08:49.76Z" }, + { url = "https://files.pythonhosted.org/packages/c4/2a/404b331f2b7bf3198e9945f75c4c521f0c6a3a23b51f7a4a401b94a13833/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:80d834abee71f65253c91540445d37c4c561e293ba6e741b992f20a105d69146", size = 1650193, upload-time = "2025-09-08T23:08:51.7Z" }, + { url = "https://files.pythonhosted.org/packages/1c/0b/f4107e33f62a5acf60e3ded67ed33d79b4ce18de432625ce2fc5093d6388/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:544b4e3b7198dde4a62b8ff6685e9802a9a1ebf47e77478a5eb88eca2a82f2fd", size = 2024388, upload-time = "2025-09-08T23:08:53.393Z" }, + { url = "https://files.pythonhosted.org/packages/0d/01/add31fe76512642fd6e40e3a3bd21f4b47e242c8ba33efb6809e37076d9b/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:cedc4c68178e59a4046f97eca31b148ddcf51e88677de1ef4e78cf06c5376c9a", size = 1885316, upload-time = "2025-09-08T23:08:55.702Z" }, + { url = "https://files.pythonhosted.org/packages/c4/59/a5f38970f9bf07cee96128de79590bb354917914a9be11272cfc7ff26af0/pyzmq-27.1.0-cp314-cp314t-win32.whl", hash = "sha256:1f0b2a577fd770aa6f053211a55d1c47901f4d537389a034c690291485e5fe92", size = 587472, upload-time = "2025-09-08T23:08:58.18Z" }, + { url = "https://files.pythonhosted.org/packages/70/d8/78b1bad170f93fcf5e3536e70e8fadac55030002275c9a29e8f5719185de/pyzmq-27.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:19c9468ae0437f8074af379e986c5d3d7d7bfe033506af442e8c879732bedbe0", size = 661401, upload-time = "2025-09-08T23:08:59.802Z" }, + { url = "https://files.pythonhosted.org/packages/81/d6/4bfbb40c9a0b42fc53c7cf442f6385db70b40f74a783130c5d0a5aa62228/pyzmq-27.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:dc5dbf68a7857b59473f7df42650c621d7e8923fb03fa74a526890f4d33cc4d7", size = 575170, upload-time = "2025-09-08T23:09:01.418Z" }, + { url = "https://files.pythonhosted.org/packages/4c/c6/c4dcdecdbaa70969ee1fdced6d7b8f60cfabe64d25361f27ac4665a70620/pyzmq-27.1.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:18770c8d3563715387139060d37859c02ce40718d1faf299abddcdcc6a649066", size = 836265, upload-time = "2025-09-08T23:09:49.376Z" }, + { url = "https://files.pythonhosted.org/packages/3e/79/f38c92eeaeb03a2ccc2ba9866f0439593bb08c5e3b714ac1d553e5c96e25/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:ac25465d42f92e990f8d8b0546b01c391ad431c3bf447683fdc40565941d0604", size = 800208, upload-time = "2025-09-08T23:09:51.073Z" }, + { url = "https://files.pythonhosted.org/packages/49/0e/3f0d0d335c6b3abb9b7b723776d0b21fa7f3a6c819a0db6097059aada160/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:53b40f8ae006f2734ee7608d59ed661419f087521edbfc2149c3932e9c14808c", size = 567747, upload-time = "2025-09-08T23:09:52.698Z" }, + { url = "https://files.pythonhosted.org/packages/a1/cf/f2b3784d536250ffd4be70e049f3b60981235d70c6e8ce7e3ef21e1adb25/pyzmq-27.1.0-pp311-pypy311_pp73-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f605d884e7c8be8fe1aa94e0a783bf3f591b84c24e4bc4f3e7564c82ac25e271", size = 747371, upload-time = "2025-09-08T23:09:54.563Z" }, + { url = "https://files.pythonhosted.org/packages/01/1b/5dbe84eefc86f48473947e2f41711aded97eecef1231f4558f1f02713c12/pyzmq-27.1.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:c9f7f6e13dff2e44a6afeaf2cf54cee5929ad64afaf4d40b50f93c58fc687355", size = 544862, upload-time = "2025-09-08T23:09:56.509Z" }, ] [[package]] @@ -3962,11 +2901,13 @@ dependencies = [ { name = "h5py" }, { name = "hdf5plugin" }, { name = "matplotlib" }, - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, { name = "optuna" }, { name = "rosettasciio" }, { name = "scikit-image" }, - { name = "scipy" }, + { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "scipy", version = "1.18.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, { name = "tensorboard" }, { name = "torch" }, { name = "torchinfo" }, @@ -4045,7 +2986,8 @@ source = { editable = "widget" } dependencies = [ { name = "anywidget" }, { name = "matplotlib" }, - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, { name = "pillow" }, { name = "torch" }, { name = "traitlets" }, @@ -4070,9 +3012,9 @@ dependencies = [ { name = "rpds-py" }, { name = "typing-extensions", marker = "python_full_version < '3.13'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/22/f5/df4e9027acead3ecc63e50fe1e36aca1523e1719559c499951bb4b53188f/referencing-0.37.0.tar.gz", hash = "sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8", size = 78036 } +sdist = { url = "https://files.pythonhosted.org/packages/22/f5/df4e9027acead3ecc63e50fe1e36aca1523e1719559c499951bb4b53188f/referencing-0.37.0.tar.gz", hash = "sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8", size = 78036, upload-time = "2025-10-13T15:30:48.871Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/2c/58/ca301544e1fa93ed4f80d724bf5b194f6e4b945841c5bfd555878eea9fcb/referencing-0.37.0-py3-none-any.whl", hash = "sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231", size = 26766 }, + { url = "https://files.pythonhosted.org/packages/2c/58/ca301544e1fa93ed4f80d724bf5b194f6e4b945841c5bfd555878eea9fcb/referencing-0.37.0-py3-none-any.whl", hash = "sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231", size = 26766, upload-time = "2025-10-13T15:30:47.625Z" }, ] [[package]] @@ -4085,15 +3027,9 @@ dependencies = [ { name = "idna" }, { name = "urllib3" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/ac/c3/e2a2b89f2d3e2179abd6d00ebd70bff6273f37fb3e0cc209f48b39d00cbf/requests-2.34.2.tar.gz", hash = "sha256:f288924cae4e29463698d6d60bc6a4da69c89185ad1e0bcc4104f584e960b9ed", size = 142856, upload-time = "2026-05-14T19:25:27.735Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/a0/f4/c67b0b3f1b9245e8d266f0f112c500d50e5b4e83cb6f3b71b6528104182a/requests-2.34.2-py3-none-any.whl", hash = "sha256:2a0d60c172f83ac6ab31e4554906c0f3b3588d37b5cb939b1c061f4907e278e0", size = 73075, upload-time = "2026-05-14T19:25:26.443Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/c9/74/b3ff8e6c8446842c3f5c837e9c3dfcfe2018ea6ecef224c710c85ef728f4/requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf", size = 134517 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6", size = 64738 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -4103,18 +3039,18 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "six" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/28/ea/a9387748e2d111c3c2b275ba970b735e04e15cdb1eb30693b6b5708c4dbd/rfc3339_validator-0.1.4.tar.gz", hash = "sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b", size = 5513 } +sdist = { url = "https://files.pythonhosted.org/packages/28/ea/a9387748e2d111c3c2b275ba970b735e04e15cdb1eb30693b6b5708c4dbd/rfc3339_validator-0.1.4.tar.gz", hash = "sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b", size = 5513, upload-time = "2021-05-12T16:37:54.178Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/7b/44/4e421b96b67b2daff264473f7465db72fbdf36a07e05494f50300cc7b0c6/rfc3339_validator-0.1.4-py2.py3-none-any.whl", hash = "sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa", size = 3490 }, + { url = "https://files.pythonhosted.org/packages/7b/44/4e421b96b67b2daff264473f7465db72fbdf36a07e05494f50300cc7b0c6/rfc3339_validator-0.1.4-py2.py3-none-any.whl", hash = "sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa", size = 3490, upload-time = "2021-05-12T16:37:52.536Z" }, ] [[package]] name = "rfc3986-validator" version = "0.1.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/da/88/f270de456dd7d11dcc808abfa291ecdd3f45ff44e3b549ffa01b126464d0/rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055", size = 6760 } +sdist = { url = "https://files.pythonhosted.org/packages/da/88/f270de456dd7d11dcc808abfa291ecdd3f45ff44e3b549ffa01b126464d0/rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055", size = 6760, upload-time = "2019-10-28T16:00:19.144Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl", hash = "sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9", size = 4242 }, + { url = "https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl", hash = "sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9", size = 4242, upload-time = "2019-10-28T16:00:13.976Z" }, ] [[package]] @@ -4124,9 +3060,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "lark" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/2c/06/37c1a5557acf449e8e406a830a05bf885ac47d33270aec454ef78675008d/rfc3987_syntax-1.1.0.tar.gz", hash = "sha256:717a62cbf33cffdd16dfa3a497d81ce48a660ea691b1ddd7be710c22f00b4a0d", size = 14239 } +sdist = { url = "https://files.pythonhosted.org/packages/2c/06/37c1a5557acf449e8e406a830a05bf885ac47d33270aec454ef78675008d/rfc3987_syntax-1.1.0.tar.gz", hash = "sha256:717a62cbf33cffdd16dfa3a497d81ce48a660ea691b1ddd7be710c22f00b4a0d", size = 14239, upload-time = "2025-07-18T01:05:05.015Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/7e/71/44ce230e1b7fadd372515a97e32a83011f906ddded8d03e3c6aafbdedbb7/rfc3987_syntax-1.1.0-py3-none-any.whl", hash = "sha256:6c3d97604e4c5ce9f714898e05401a0445a641cfa276432b0a648c80856f6a3f", size = 8046 }, + { url = "https://files.pythonhosted.org/packages/7e/71/44ce230e1b7fadd372515a97e32a83011f906ddded8d03e3c6aafbdedbb7/rfc3987_syntax-1.1.0-py3-none-any.whl", hash = "sha256:6c3d97604e4c5ce9f714898e05401a0445a641cfa276432b0a648c80856f6a3f", size = 8046, upload-time = "2025-07-18T01:05:03.843Z" }, ] [[package]] @@ -4135,13 +3071,13 @@ version = "0.14.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "dask", extra = ["array"] }, - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, { name = "pint" }, { name = "python-box" }, { name = "python-dateutil" }, { name = "pyyaml" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/57/ca/dc7c3871b47e1bd89b7a11a34b7b68219504a03047788e52cff1ce823755/rosettasciio-0.14.0.tar.gz", hash = "sha256:4b21582f0d832f086e10236b1798b1c17e327dc6d610e845a8eddd4df817504f", size = 1245393, upload-time = "2026-05-27T15:29:54.331Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/9f/bf/0ecc513c23c2a61f1497c9801cc4144e40a996099d36b71407673ad1a975/rosettasciio-0.14.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:73c7bb64e6a456fd058807c9a698d50b6fa757985f0e505087b641b10dd50904", size = 876315, upload-time = "2026-05-27T15:28:58.173Z" }, @@ -4181,38 +3117,12 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/14/f3/3a5e2c3fa67fbf2219a914c6342a91f2f7ba19554ffe2b654fd44e13685c/rosettasciio-0.14.0-cp314-cp314t-win_amd64.whl", hash = "sha256:4a17300595ae1a83331ce4e015478341eac99cf8544f36c88f7c6de6e2662541", size = 869656, upload-time = "2026-05-27T15:29:49.836Z" }, { url = "https://files.pythonhosted.org/packages/55/3c/5f189a330ba363f04ee73e25b37f8e08ae1266ad72e6c93d65166a7c7c6e/rosettasciio-0.14.0-cp314-cp314t-win_arm64.whl", hash = "sha256:66ca5c8d08cd3caa5397b134f83803915807d8e799ad3ac2fda5b9607342371d", size = 857389, upload-time = "2026-05-27T15:29:51.353Z" }, { url = "https://files.pythonhosted.org/packages/d2/a3/5cf5424b16d68e8db9139d613570ef01825995d260343fdec704d3f646ba/rosettasciio-0.14.0-py3-none-any.whl", hash = "sha256:f64e045ced6827ba75e6b8b85c54812c0346eda6b76f08acded3d0f6e7fb5a27", size = 628709, upload-time = "2026-05-27T15:29:52.825Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/cc/4c/f1560842f8b9aba82973b670eb9a4e57cdfca0d998fd106140c6c74ae1e0/rosettasciio-0.10.0.tar.gz", hash = "sha256:0082ad325029fb815d1da8015f9069df90e979114fcba84bda94a63c2e63039b", size = 1186926 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d2/db/1d65725ed9fbd1ed9ce89d0bf38aed0a98d5683db13e898f25a2d8b239ca/rosettasciio-0.10.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:718ec836f2cf884c584be86cd0da188419692d7c32915665738d46ad6b6e3a37", size = 795531 }, - { url = "https://files.pythonhosted.org/packages/10/ef/5ffde08333c095978c231dd488e7f05d97ee5187cabe5670df83f96d8403/rosettasciio-0.10.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f75142a4bfa4b85384efdf1d69af21ec8d9a83e17dfd3e577166c3a87a796420", size = 789939 }, - { url = "https://files.pythonhosted.org/packages/84/ba/bfea3c8aa25b313d01e955821130bf1b4e5cc1eb48ee33fd0ef7db0cbf37/rosettasciio-0.10.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:21a5d7acf1501e979f8439995acc8edb2d24d42b9dc5ecfbe498c624cd84d4ee", size = 1255637 }, - { url = "https://files.pythonhosted.org/packages/6e/54/dbc75dcd3ff0fd6f0d631d636d6d2410c6c4feac0fd8507d9d133fc4715d/rosettasciio-0.10.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8b2ef07b84dfb8a9cb3dfa0cf4e071c6bee2a70cd173293c85522b791e7ceb07", size = 1258334 }, - { url = "https://files.pythonhosted.org/packages/ef/77/e8b57c6fde7a01af588448ee7be43b7f29fdf265d00c646eb7b8196653a8/rosettasciio-0.10.0-cp311-cp311-win_amd64.whl", hash = "sha256:a1fb511579431dd1c7b7aed6626f469ac2b77ab45f6728c834c3d054913ac71f", size = 787920 }, - { url = "https://files.pythonhosted.org/packages/e6/4f/94b8166640de28b7f2c8a98664288bfc674b22a5ba49d9953bb2af1dd932/rosettasciio-0.10.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:3441354fc7cb1d92c5e0bf8eccdcd26a5abc429299625a820087f95daed4cfa2", size = 796549 }, - { url = "https://files.pythonhosted.org/packages/f1/48/af8159fd1b9dfeb82a47c84587a81d1d98974f05f85e764952862a5206de/rosettasciio-0.10.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:96e08cd6a373dc794edc268da4c644b3fd93927e2324c5a3721c3dd71d3b592c", size = 789810 }, - { url = "https://files.pythonhosted.org/packages/99/9d/5cf650f4c39c649e3c09ff885b6a471aa3a0c57e52e1a538f88156083ca8/rosettasciio-0.10.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9e056f143c5c7e41ca55ee5dc59b5efaa2f14ace6f6f8039d5102f156385f2c9", size = 1257517 }, - { url = "https://files.pythonhosted.org/packages/2d/49/d0484376a7724aa8bbc3628d6e4331f0593dcbee3efd3ba193c81cc380f6/rosettasciio-0.10.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:465811041da11db7259f8fde1c72fb6838aaa14678c000feed8719c4f8d2dfe0", size = 1263282 }, - { url = "https://files.pythonhosted.org/packages/94/99/a70aeef2b316de92a315c8b6f5d3153fc1b36bbaedc96ad3caf70b45606c/rosettasciio-0.10.0-cp312-cp312-win_amd64.whl", hash = "sha256:fffc27d62c14e63818d8928d55458fe660f0153c406b4294e3cd4cbd63e1bea7", size = 788436 }, - { url = "https://files.pythonhosted.org/packages/bc/5f/ef4db517d63a657a9c1d626d65e9c843850fd8da8a364cfb674abe1e6fc4/rosettasciio-0.10.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8627f64c027361face099eaad76ea02eb7cfce4f7c66174826f36e236204e970", size = 795544 }, - { url = "https://files.pythonhosted.org/packages/90/45/d1590ce68aed0cf1c8e76cd52c1077a5a92f4e14c97d2a7456871296899c/rosettasciio-0.10.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:c5ca0b541c5744b9b9559f5e002b5f2362d4cb09a002b2650a9d358025477261", size = 788949 }, - { url = "https://files.pythonhosted.org/packages/b0/6f/680259cc71741d9fa0c418230653177cf9bea4f554014a26354ab8079380/rosettasciio-0.10.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ccf69c1d3e141bca2feca2275158a796f27299888a8ac7943eceec9779f1b811", size = 1249455 }, - { url = "https://files.pythonhosted.org/packages/24/4f/8b668c9312ab4d2c87a342e4d947a2c0c10cadda44391bff1154df5a623e/rosettasciio-0.10.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5a8929f285c2cfdebb5ae180dfe80e90ada916e37063e83d8ea7840be7851ddb", size = 1259454 }, - { url = "https://files.pythonhosted.org/packages/ba/5f/07ba8f617b121431b0d8f45f946bd9602256fb1f1d43c275f21fecfe9f27/rosettasciio-0.10.0-cp313-cp313-win_amd64.whl", hash = "sha256:c802e0292537cb0f4878eca631d227183562a7e13c2449fe1e6f23b4eae808ff", size = 788185 }, - { url = "https://files.pythonhosted.org/packages/40/2f/4515d05d896b13410c379eb21a6090c61fd654956fae0f54fce108a7379a/rosettasciio-0.10.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:a8ab4c344987364c479232001d2e072f00a855fe3d385cbcbac68641a0c2f802", size = 800276 }, - { url = "https://files.pythonhosted.org/packages/57/d7/2bd8a60a7758d5a894ae59192846bb72a68bd773b24daed9ea8c737c41bf/rosettasciio-0.10.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:e8bbf513d81e05242b6ee5d35d58ba06232431990adaff6473bd942518cf2b82", size = 795978 }, - { url = "https://files.pythonhosted.org/packages/35/54/dae525b83a98b27fbabe2152045d990b4baa858522024196c5e47d06bbc1/rosettasciio-0.10.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2b34b629ef130aec286062753b44d4e4d73caf1a4d9aba3530ec3801bd49755c", size = 1265997 }, - { url = "https://files.pythonhosted.org/packages/06/ae/a5969d089adc71df61451af8665df7e30a34957a0ebdc2614c449d2c37cf/rosettasciio-0.10.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:365750bd177c7cff56f88b9f8cf9b59d2c99b52e7d38db142282996e25e552c2", size = 1255782 }, - { url = "https://files.pythonhosted.org/packages/d2/38/7e9d8eb167419d9e8b34433b698532b88c4b06ed38cd6723f7e702260f9f/rosettasciio-0.10.0-cp313-cp313t-win_amd64.whl", hash = "sha256:a16dbadb662290be0d4ea0a2496109cff4484c155fd12eb6b57345e64d9015d9", size = 798624 }, - { url = "https://files.pythonhosted.org/packages/92/a6/a4cff62b7346daca9dfdd1d08c1aed5e0c27283845465b6b5c797fb41713/rosettasciio-0.10.0-py3-none-any.whl", hash = "sha256:a4e353365972865e915b7a689d2f9ba38a6f94aec8cd19c2e35513523b718b1f", size = 550949 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "rpds-py" version = "2026.5.1" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/2e/43/25a8dcd3feedd735039a8f0b5b7e3b118232b5eae288c4fd9ab200d41094/rpds_py-2026.5.1.tar.gz", hash = "sha256:07b24fea40541e28570e5b795a4a38fbdcd12550c06bd0748005ecc8116ca256", size = 64459, upload-time = "2026-05-28T12:02:13.232Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/4f/a0/acf8b6fc20bfdcd3a45bd3f57680fb198e157b7e997b9123b10763798bd2/rpds_py-2026.5.1-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:3397a5ed7174dc2786bb214030232fc36fe8e5584fec43a9952cc542b1a12036", size = 355609, upload-time = "2026-05-28T11:58:50.78Z" }, @@ -4344,158 +3254,31 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/1d/6f/93831a3bfe789542ed0c1d0d74b78b440f055d6dc3ea4640eba2d95e6e23/rpds_py-2026.5.1-pp311-pypy311_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:c74005a7bb87752acf351c93897ec63ad77a07a0da7ecad9c050e32e7286ba34", size = 557243, upload-time = "2026-05-28T12:02:08.013Z" }, { url = "https://files.pythonhosted.org/packages/1f/ff/0b3d604614ffc77522c6b288fdbce68957eb583da1002aa65ba38ac0ee40/rpds_py-2026.5.1-pp311-pypy311_pp73-musllinux_1_2_i686.whl", hash = "sha256:8213afbe8a3a906fb9acb2014423fe3359ee783d0bf90995f70623a3217bfa6c", size = 623541, upload-time = "2026-05-28T12:02:09.661Z" }, { url = "https://files.pythonhosted.org/packages/ea/ea/e7b0251441da9adfeaebcf29601d10f2a1455fcf0772fae9e7e19032bd96/rpds_py-2026.5.1-pp311-pypy311_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:8c43a8a973270fd173bf48cdf80bbe66312421cba68d40845034f174f2389049", size = 586326, upload-time = "2026-05-28T12:02:11.47Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/98/33/23b3b3419b6a3e0f559c7c0d2ca8fc1b9448382b25245033788785921332/rpds_py-0.29.0.tar.gz", hash = "sha256:fe55fe686908f50154d1dc599232016e50c243b438c3b7432f24e2895b0e5359", size = 69359 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/36/ab/7fb95163a53ab122c74a7c42d2d2f012819af2cf3deb43fb0d5acf45cc1a/rpds_py-0.29.0-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:9b9c764a11fd637e0322a488560533112837f5334ffeb48b1be20f6d98a7b437", size = 372344 }, - { url = "https://files.pythonhosted.org/packages/b3/45/f3c30084c03b0d0f918cb4c5ae2c20b0a148b51ba2b3f6456765b629bedd/rpds_py-0.29.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:3fd2164d73812026ce970d44c3ebd51e019d2a26a4425a5dcbdfa93a34abc383", size = 363041 }, - { url = "https://files.pythonhosted.org/packages/e3/e9/4d044a1662608c47a87cbb37b999d4d5af54c6d6ebdda93a4d8bbf8b2a10/rpds_py-0.29.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4a097b7f7f7274164566ae90a221fd725363c0e9d243e2e9ed43d195ccc5495c", size = 391775 }, - { url = "https://files.pythonhosted.org/packages/50/c9/7616d3ace4e6731aeb6e3cd85123e03aec58e439044e214b9c5c60fd8eb1/rpds_py-0.29.0-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:7cdc0490374e31cedefefaa1520d5fe38e82fde8748cbc926e7284574c714d6b", size = 405624 }, - { url = "https://files.pythonhosted.org/packages/c2/e2/6d7d6941ca0843609fd2d72c966a438d6f22617baf22d46c3d2156c31350/rpds_py-0.29.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:89ca2e673ddd5bde9b386da9a0aac0cab0e76f40c8f0aaf0d6311b6bbf2aa311", size = 527894 }, - { url = "https://files.pythonhosted.org/packages/8d/f7/aee14dc2db61bb2ae1e3068f134ca9da5f28c586120889a70ff504bb026f/rpds_py-0.29.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a5d9da3ff5af1ca1249b1adb8ef0573b94c76e6ae880ba1852f033bf429d4588", size = 412720 }, - { url = "https://files.pythonhosted.org/packages/2f/e2/2293f236e887c0360c2723d90c00d48dee296406994d6271faf1712e94ec/rpds_py-0.29.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8238d1d310283e87376c12f658b61e1ee23a14c0e54c7c0ce953efdbdc72deed", size = 392945 }, - { url = "https://files.pythonhosted.org/packages/14/cd/ceea6147acd3bd1fd028d1975228f08ff19d62098078d5ec3eed49703797/rpds_py-0.29.0-cp311-cp311-manylinux_2_31_riscv64.whl", hash = "sha256:2d6fb2ad1c36f91c4646989811e84b1ea5e0c3cf9690b826b6e32b7965853a63", size = 406385 }, - { url = "https://files.pythonhosted.org/packages/52/36/fe4dead19e45eb77a0524acfdbf51e6cda597b26fc5b6dddbff55fbbb1a5/rpds_py-0.29.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:534dc9df211387547267ccdb42253aa30527482acb38dd9b21c5c115d66a96d2", size = 423943 }, - { url = "https://files.pythonhosted.org/packages/a1/7b/4551510803b582fa4abbc8645441a2d15aa0c962c3b21ebb380b7e74f6a1/rpds_py-0.29.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d456e64724a075441e4ed648d7f154dc62e9aabff29bcdf723d0c00e9e1d352f", size = 574204 }, - { url = "https://files.pythonhosted.org/packages/64/ba/071ccdd7b171e727a6ae079f02c26f75790b41555f12ca8f1151336d2124/rpds_py-0.29.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:a738f2da2f565989401bd6fd0b15990a4d1523c6d7fe83f300b7e7d17212feca", size = 600587 }, - { url = "https://files.pythonhosted.org/packages/03/09/96983d48c8cf5a1e03c7d9cc1f4b48266adfb858ae48c7c2ce978dbba349/rpds_py-0.29.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:a110e14508fd26fd2e472bb541f37c209409876ba601cf57e739e87d8a53cf95", size = 562287 }, - { url = "https://files.pythonhosted.org/packages/40/f0/8c01aaedc0fa92156f0391f39ea93b5952bc0ec56b897763858f95da8168/rpds_py-0.29.0-cp311-cp311-win32.whl", hash = "sha256:923248a56dd8d158389a28934f6f69ebf89f218ef96a6b216a9be6861804d3f4", size = 221394 }, - { url = "https://files.pythonhosted.org/packages/7e/a5/a8b21c54c7d234efdc83dc034a4d7cd9668e3613b6316876a29b49dece71/rpds_py-0.29.0-cp311-cp311-win_amd64.whl", hash = "sha256:539eb77eb043afcc45314d1be09ea6d6cafb3addc73e0547c171c6d636957f60", size = 235713 }, - { url = "https://files.pythonhosted.org/packages/a7/1f/df3c56219523947b1be402fa12e6323fe6d61d883cf35d6cb5d5bb6db9d9/rpds_py-0.29.0-cp311-cp311-win_arm64.whl", hash = "sha256:bdb67151ea81fcf02d8f494703fb728d4d34d24556cbff5f417d74f6f5792e7c", size = 229157 }, - { url = "https://files.pythonhosted.org/packages/3c/50/bc0e6e736d94e420df79be4deb5c9476b63165c87bb8f19ef75d100d21b3/rpds_py-0.29.0-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:a0891cfd8db43e085c0ab93ab7e9b0c8fee84780d436d3b266b113e51e79f954", size = 376000 }, - { url = "https://files.pythonhosted.org/packages/3e/3a/46676277160f014ae95f24de53bed0e3b7ea66c235e7de0b9df7bd5d68ba/rpds_py-0.29.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:3897924d3f9a0361472d884051f9a2460358f9a45b1d85a39a158d2f8f1ad71c", size = 360575 }, - { url = "https://files.pythonhosted.org/packages/75/ba/411d414ed99ea1afdd185bbabeeaac00624bd1e4b22840b5e9967ade6337/rpds_py-0.29.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2a21deb8e0d1571508c6491ce5ea5e25669b1dd4adf1c9d64b6314842f708b5d", size = 392159 }, - { url = "https://files.pythonhosted.org/packages/8f/b1/e18aa3a331f705467a48d0296778dc1fea9d7f6cf675bd261f9a846c7e90/rpds_py-0.29.0-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9efe71687d6427737a0a2de9ca1c0a216510e6cd08925c44162be23ed7bed2d5", size = 410602 }, - { url = "https://files.pythonhosted.org/packages/2f/6c/04f27f0c9f2299274c76612ac9d2c36c5048bb2c6c2e52c38c60bf3868d9/rpds_py-0.29.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:40f65470919dc189c833e86b2c4bd21bd355f98436a2cef9e0a9a92aebc8e57e", size = 515808 }, - { url = "https://files.pythonhosted.org/packages/83/56/a8412aa464fb151f8bc0d91fb0bb888adc9039bd41c1c6ba8d94990d8cf8/rpds_py-0.29.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:def48ff59f181130f1a2cb7c517d16328efac3ec03951cca40c1dc2049747e83", size = 416015 }, - { url = "https://files.pythonhosted.org/packages/04/4c/f9b8a05faca3d9e0a6397c90d13acb9307c9792b2bff621430c58b1d6e76/rpds_py-0.29.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad7bd570be92695d89285a4b373006930715b78d96449f686af422debb4d3949", size = 395325 }, - { url = "https://files.pythonhosted.org/packages/34/60/869f3bfbf8ed7b54f1ad9a5543e0fdffdd40b5a8f587fe300ee7b4f19340/rpds_py-0.29.0-cp312-cp312-manylinux_2_31_riscv64.whl", hash = "sha256:5a572911cd053137bbff8e3a52d31c5d2dba51d3a67ad902629c70185f3f2181", size = 410160 }, - { url = "https://files.pythonhosted.org/packages/91/aa/e5b496334e3aba4fe4c8a80187b89f3c1294c5c36f2a926da74338fa5a73/rpds_py-0.29.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:d583d4403bcbf10cffc3ab5cee23d7643fcc960dff85973fd3c2d6c86e8dbb0c", size = 425309 }, - { url = "https://files.pythonhosted.org/packages/85/68/4e24a34189751ceb6d66b28f18159922828dd84155876551f7ca5b25f14f/rpds_py-0.29.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:070befbb868f257d24c3bb350dbd6e2f645e83731f31264b19d7231dd5c396c7", size = 574644 }, - { url = "https://files.pythonhosted.org/packages/8c/cf/474a005ea4ea9c3b4f17b6108b6b13cebfc98ebaff11d6e1b193204b3a93/rpds_py-0.29.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:fc935f6b20b0c9f919a8ff024739174522abd331978f750a74bb68abd117bd19", size = 601605 }, - { url = "https://files.pythonhosted.org/packages/f4/b1/c56f6a9ab8c5f6bb5c65c4b5f8229167a3a525245b0773f2c0896686b64e/rpds_py-0.29.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:8c5a8ecaa44ce2d8d9d20a68a2483a74c07f05d72e94a4dff88906c8807e77b0", size = 564593 }, - { url = "https://files.pythonhosted.org/packages/b3/13/0494cecce4848f68501e0a229432620b4b57022388b071eeff95f3e1e75b/rpds_py-0.29.0-cp312-cp312-win32.whl", hash = "sha256:ba5e1aeaf8dd6d8f6caba1f5539cddda87d511331714b7b5fc908b6cfc3636b7", size = 223853 }, - { url = "https://files.pythonhosted.org/packages/1f/6a/51e9aeb444a00cdc520b032a28b07e5f8dc7bc328b57760c53e7f96997b4/rpds_py-0.29.0-cp312-cp312-win_amd64.whl", hash = "sha256:b5f6134faf54b3cb83375db0f113506f8b7770785be1f95a631e7e2892101977", size = 239895 }, - { url = "https://files.pythonhosted.org/packages/d1/d4/8bce56cdad1ab873e3f27cb31c6a51d8f384d66b022b820525b879f8bed1/rpds_py-0.29.0-cp312-cp312-win_arm64.whl", hash = "sha256:b016eddf00dca7944721bf0cd85b6af7f6c4efaf83ee0b37c4133bd39757a8c7", size = 230321 }, - { url = "https://files.pythonhosted.org/packages/fd/d9/c5de60d9d371bbb186c3e9bf75f4fc5665e11117a25a06a6b2e0afb7380e/rpds_py-0.29.0-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:1585648d0760b88292eecab5181f5651111a69d90eff35d6b78aa32998886a61", size = 375710 }, - { url = "https://files.pythonhosted.org/packages/b3/b3/0860cdd012291dc21272895ce107f1e98e335509ba986dd83d72658b82b9/rpds_py-0.29.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:521807963971a23996ddaf764c682b3e46459b3c58ccd79fefbe16718db43154", size = 360582 }, - { url = "https://files.pythonhosted.org/packages/92/8a/a18c2f4a61b3407e56175f6aab6deacdf9d360191a3d6f38566e1eaf7266/rpds_py-0.29.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0a8896986efaa243ab713c69e6491a4138410f0fe36f2f4c71e18bd5501e8014", size = 391172 }, - { url = "https://files.pythonhosted.org/packages/fd/49/e93354258508c50abc15cdcd5fcf7ac4117f67bb6233ad7859f75e7372a0/rpds_py-0.29.0-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:1d24564a700ef41480a984c5ebed62b74e6ce5860429b98b1fede76049e953e6", size = 409586 }, - { url = "https://files.pythonhosted.org/packages/5a/8d/a27860dae1c19a6bdc901f90c81f0d581df1943355802961a57cdb5b6cd1/rpds_py-0.29.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e6596b93c010d386ae46c9fba9bfc9fc5965fa8228edeac51576299182c2e31c", size = 516339 }, - { url = "https://files.pythonhosted.org/packages/fc/ad/a75e603161e79b7110c647163d130872b271c6b28712c803c65d492100f7/rpds_py-0.29.0-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5cc58aac218826d054c7da7f95821eba94125d88be673ff44267bb89d12a5866", size = 416201 }, - { url = "https://files.pythonhosted.org/packages/b9/42/555b4ee17508beafac135c8b450816ace5a96194ce97fefc49d58e5652ea/rpds_py-0.29.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:de73e40ebc04dd5d9556f50180395322193a78ec247e637e741c1b954810f295", size = 395095 }, - { url = "https://files.pythonhosted.org/packages/cd/f0/c90b671b9031e800ec45112be42ea9f027f94f9ac25faaac8770596a16a1/rpds_py-0.29.0-cp313-cp313-manylinux_2_31_riscv64.whl", hash = "sha256:295ce5ac7f0cf69a651ea75c8f76d02a31f98e5698e82a50a5f4d4982fbbae3b", size = 410077 }, - { url = "https://files.pythonhosted.org/packages/3d/80/9af8b640b81fe21e6f718e9dec36c0b5f670332747243130a5490f292245/rpds_py-0.29.0-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1ea59b23ea931d494459c8338056fe7d93458c0bf3ecc061cd03916505369d55", size = 424548 }, - { url = "https://files.pythonhosted.org/packages/e4/0b/b5647446e991736e6a495ef510e6710df91e880575a586e763baeb0aa770/rpds_py-0.29.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f49d41559cebd608042fdcf54ba597a4a7555b49ad5c1c0c03e0af82692661cd", size = 573661 }, - { url = "https://files.pythonhosted.org/packages/f7/b3/1b1c9576839ff583d1428efbf59f9ee70498d8ce6c0b328ac02f1e470879/rpds_py-0.29.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:05a2bd42768ea988294ca328206efbcc66e220d2d9b7836ee5712c07ad6340ea", size = 600937 }, - { url = "https://files.pythonhosted.org/packages/6c/7b/b6cfca2f9fee4c4494ce54f7fb1b9f578867495a9aa9fc0d44f5f735c8e0/rpds_py-0.29.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:33ca7bdfedd83339ca55da3a5e1527ee5870d4b8369456b5777b197756f3ca22", size = 564496 }, - { url = "https://files.pythonhosted.org/packages/b9/fb/ba29ec7f0f06eb801bac5a23057a9ff7670623b5e8013bd59bec4aa09de8/rpds_py-0.29.0-cp313-cp313-win32.whl", hash = "sha256:20c51ae86a0bb9accc9ad4e6cdeec58d5ebb7f1b09dd4466331fc65e1766aae7", size = 223126 }, - { url = "https://files.pythonhosted.org/packages/3c/6b/0229d3bed4ddaa409e6d90b0ae967ed4380e4bdd0dad6e59b92c17d42457/rpds_py-0.29.0-cp313-cp313-win_amd64.whl", hash = "sha256:6410e66f02803600edb0b1889541f4b5cc298a5ccda0ad789cc50ef23b54813e", size = 239771 }, - { url = "https://files.pythonhosted.org/packages/e4/38/d2868f058b164f8efd89754d85d7b1c08b454f5c07ac2e6cc2e9bd4bd05b/rpds_py-0.29.0-cp313-cp313-win_arm64.whl", hash = "sha256:56838e1cd9174dc23c5691ee29f1d1be9eab357f27efef6bded1328b23e1ced2", size = 229994 }, - { url = "https://files.pythonhosted.org/packages/52/91/5de91c5ec7d41759beec9b251630824dbb8e32d20c3756da1a9a9d309709/rpds_py-0.29.0-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:37d94eadf764d16b9a04307f2ab1d7af6dc28774bbe0535c9323101e14877b4c", size = 365886 }, - { url = "https://files.pythonhosted.org/packages/85/7c/415d8c1b016d5f47ecec5145d9d6d21002d39dce8761b30f6c88810b455a/rpds_py-0.29.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:d472cf73efe5726a067dce63eebe8215b14beabea7c12606fd9994267b3cfe2b", size = 355262 }, - { url = "https://files.pythonhosted.org/packages/3d/14/bf83e2daa4f980e4dc848aed9299792a8b84af95e12541d9e7562f84a6ef/rpds_py-0.29.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:72fdfd5ff8992e4636621826371e3ac5f3e3b8323e9d0e48378e9c13c3dac9d0", size = 384826 }, - { url = "https://files.pythonhosted.org/packages/33/b8/53330c50a810ae22b4fbba5e6cf961b68b9d72d9bd6780a7c0a79b070857/rpds_py-0.29.0-cp313-cp313t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2549d833abdf8275c901313b9e8ff8fba57e50f6a495035a2a4e30621a2f7cc4", size = 394234 }, - { url = "https://files.pythonhosted.org/packages/cc/32/01e2e9645cef0e584f518cfde4567563e57db2257244632b603f61b40e50/rpds_py-0.29.0-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4448dad428f28a6a767c3e3b80cde3446a22a0efbddaa2360f4bb4dc836d0688", size = 520008 }, - { url = "https://files.pythonhosted.org/packages/98/c3/0d1b95a81affae2b10f950782e33a1fd2edd6ce2a479966cac98c9a66f57/rpds_py-0.29.0-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:115f48170fd4296a33938d8c11f697f5f26e0472e43d28f35624764173a60e4d", size = 409569 }, - { url = "https://files.pythonhosted.org/packages/fa/60/aa3b8678f3f009f675b99174fa2754302a7fbfe749162e8043d111de2d88/rpds_py-0.29.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8e5bb73ffc029820f4348e9b66b3027493ae00bca6629129cd433fd7a76308ee", size = 385188 }, - { url = "https://files.pythonhosted.org/packages/92/02/5546c1c8aa89c18d40c1fcffdcc957ba730dee53fb7c3ca3a46f114761d2/rpds_py-0.29.0-cp313-cp313t-manylinux_2_31_riscv64.whl", hash = "sha256:b1581fcde18fcdf42ea2403a16a6b646f8eb1e58d7f90a0ce693da441f76942e", size = 398587 }, - { url = "https://files.pythonhosted.org/packages/6c/e0/ad6eeaf47e236eba052fa34c4073078b9e092bd44da6bbb35aaae9580669/rpds_py-0.29.0-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:16e9da2bda9eb17ea318b4c335ec9ac1818e88922cbe03a5743ea0da9ecf74fb", size = 416641 }, - { url = "https://files.pythonhosted.org/packages/1a/93/0acedfd50ad9cdd3879c615a6dc8c5f1ce78d2fdf8b87727468bb5bb4077/rpds_py-0.29.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:28fd300326dd21198f311534bdb6d7e989dd09b3418b3a91d54a0f384c700967", size = 566683 }, - { url = "https://files.pythonhosted.org/packages/62/53/8c64e0f340a9e801459fc6456821abc15b3582cb5dc3932d48705a9d9ac7/rpds_py-0.29.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:2aba991e041d031c7939e1358f583ae405a7bf04804ca806b97a5c0e0af1ea5e", size = 592730 }, - { url = "https://files.pythonhosted.org/packages/85/ef/3109b6584f8c4b0d2490747c916df833c127ecfa82be04d9a40a376f2090/rpds_py-0.29.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:7f437026dbbc3f08c99cc41a5b2570c6e1a1ddbe48ab19a9b814254128d4ea7a", size = 557361 }, - { url = "https://files.pythonhosted.org/packages/ff/3b/61586475e82d57f01da2c16edb9115a618afe00ce86fe1b58936880b15af/rpds_py-0.29.0-cp313-cp313t-win32.whl", hash = "sha256:6e97846e9800a5d0fe7be4d008f0c93d0feeb2700da7b1f7528dabafb31dfadb", size = 211227 }, - { url = "https://files.pythonhosted.org/packages/3b/3a/12dc43f13594a54ea0c9d7e9d43002116557330e3ad45bc56097ddf266e2/rpds_py-0.29.0-cp313-cp313t-win_amd64.whl", hash = "sha256:f49196aec7c4b406495f60e6f947ad71f317a765f956d74bbd83996b9edc0352", size = 225248 }, - { url = "https://files.pythonhosted.org/packages/89/b1/0b1474e7899371d9540d3bbb2a499a3427ae1fc39c998563fe9035a1073b/rpds_py-0.29.0-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:394d27e4453d3b4d82bb85665dc1fcf4b0badc30fc84282defed71643b50e1a1", size = 363731 }, - { url = "https://files.pythonhosted.org/packages/28/12/3b7cf2068d0a334ed1d7b385a9c3c8509f4c2bcba3d4648ea71369de0881/rpds_py-0.29.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:55d827b2ae95425d3be9bc9a5838b6c29d664924f98146557f7715e331d06df8", size = 354343 }, - { url = "https://files.pythonhosted.org/packages/eb/73/5afcf8924bc02a749416eda64e17ac9c9b28f825f4737385295a0e99b0c1/rpds_py-0.29.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fc31a07ed352e5462d3ee1b22e89285f4ce97d5266f6d1169da1142e78045626", size = 385406 }, - { url = "https://files.pythonhosted.org/packages/c8/37/5db736730662508535221737a21563591b6f43c77f2e388951c42f143242/rpds_py-0.29.0-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c4695dd224212f6105db7ea62197144230b808d6b2bba52238906a2762f1d1e7", size = 396162 }, - { url = "https://files.pythonhosted.org/packages/70/0d/491c1017d14f62ce7bac07c32768d209a50ec567d76d9f383b4cfad19b80/rpds_py-0.29.0-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fcae1770b401167f8b9e1e3f566562e6966ffa9ce63639916248a9e25fa8a244", size = 517719 }, - { url = "https://files.pythonhosted.org/packages/d7/25/b11132afcb17cd5d82db173f0c8dab270ffdfaba43e5ce7a591837ae9649/rpds_py-0.29.0-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:90f30d15f45048448b8da21c41703b31c61119c06c216a1bf8c245812a0f0c17", size = 409498 }, - { url = "https://files.pythonhosted.org/packages/0f/7d/e6543cedfb2e6403a1845710a5ab0e0ccf8fc288e0b5af9a70bfe2c12053/rpds_py-0.29.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:44a91e0ab77bdc0004b43261a4b8cd6d6b451e8d443754cfda830002b5745b32", size = 382743 }, - { url = "https://files.pythonhosted.org/packages/75/11/a4ebc9f654293ae9fefb83b2b6be7f3253e85ea42a5db2f77d50ad19aaeb/rpds_py-0.29.0-cp314-cp314-manylinux_2_31_riscv64.whl", hash = "sha256:4aa195e5804d32c682e453b34474f411ca108e4291c6a0f824ebdc30a91c973c", size = 400317 }, - { url = "https://files.pythonhosted.org/packages/52/18/97677a60a81c7f0e5f64e51fb3f8271c5c8fcabf3a2df18e97af53d7c2bf/rpds_py-0.29.0-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:7971bdb7bf4ee0f7e6f67fa4c7fbc6019d9850cc977d126904392d363f6f8318", size = 416979 }, - { url = "https://files.pythonhosted.org/packages/f0/69/28ab391a9968f6c746b2a2db181eaa4d16afaa859fedc9c2f682d19f7e18/rpds_py-0.29.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:8ae33ad9ce580c7a47452c3b3f7d8a9095ef6208e0a0c7e4e2384f9fc5bf8212", size = 567288 }, - { url = "https://files.pythonhosted.org/packages/3b/d3/0c7afdcdb830eee94f5611b64e71354ffe6ac8df82d00c2faf2bfffd1d4e/rpds_py-0.29.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:c661132ab2fb4eeede2ef69670fd60da5235209874d001a98f1542f31f2a8a94", size = 593157 }, - { url = "https://files.pythonhosted.org/packages/e2/ac/a0fcbc2feed4241cf26d32268c195eb88ddd4bd862adfc9d4b25edfba535/rpds_py-0.29.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:bb78b3a0d31ac1bde132c67015a809948db751cb4e92cdb3f0b242e430b6ed0d", size = 554741 }, - { url = "https://files.pythonhosted.org/packages/0f/f1/fcc24137c470df8588674a677f33719d5800ec053aaacd1de8a5d5d84d9e/rpds_py-0.29.0-cp314-cp314-win32.whl", hash = "sha256:f475f103488312e9bd4000bc890a95955a07b2d0b6e8884aef4be56132adbbf1", size = 215508 }, - { url = "https://files.pythonhosted.org/packages/7b/c7/1d169b2045512eac019918fc1021ea07c30e84a4343f9f344e3e0aa8c788/rpds_py-0.29.0-cp314-cp314-win_amd64.whl", hash = "sha256:b9cf2359a4fca87cfb6801fae83a76aedf66ee1254a7a151f1341632acf67f1b", size = 228125 }, - { url = "https://files.pythonhosted.org/packages/be/36/0cec88aaba70ec4a6e381c444b0d916738497d27f0c30406e3d9fcbd3bc2/rpds_py-0.29.0-cp314-cp314-win_arm64.whl", hash = "sha256:9ba8028597e824854f0f1733d8b964e914ae3003b22a10c2c664cb6927e0feb9", size = 221992 }, - { url = "https://files.pythonhosted.org/packages/b1/fa/a2e524631717c9c0eb5d90d30f648cfba6b731047821c994acacb618406c/rpds_py-0.29.0-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:e71136fd0612556b35c575dc2726ae04a1669e6a6c378f2240312cf5d1a2ab10", size = 366425 }, - { url = "https://files.pythonhosted.org/packages/a2/a4/6d43ebe0746ff694a30233f63f454aed1677bd50ab7a59ff6b2bb5ac61f2/rpds_py-0.29.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:76fe96632d53f3bf0ea31ede2f53bbe3540cc2736d4aec3b3801b0458499ef3a", size = 355282 }, - { url = "https://files.pythonhosted.org/packages/fa/a7/52fd8270e0320b09eaf295766ae81dd175f65394687906709b3e75c71d06/rpds_py-0.29.0-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9459a33f077130dbb2c7c3cea72ee9932271fb3126404ba2a2661e4fe9eb7b79", size = 384968 }, - { url = "https://files.pythonhosted.org/packages/f4/7d/e6bc526b7a14e1ef80579a52c1d4ad39260a058a51d66c6039035d14db9d/rpds_py-0.29.0-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5c9546cfdd5d45e562cc0444b6dddc191e625c62e866bf567a2c69487c7ad28a", size = 394714 }, - { url = "https://files.pythonhosted.org/packages/c0/3f/f0ade3954e7db95c791e7eaf978aa7e08a756d2046e8bdd04d08146ed188/rpds_py-0.29.0-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:12597d11d97b8f7e376c88929a6e17acb980e234547c92992f9f7c058f1a7310", size = 520136 }, - { url = "https://files.pythonhosted.org/packages/87/b3/07122ead1b97009715ab9d4082be6d9bd9546099b2b03fae37c3116f72be/rpds_py-0.29.0-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28de03cf48b8a9e6ec10318f2197b83946ed91e2891f651a109611be4106ac4b", size = 409250 }, - { url = "https://files.pythonhosted.org/packages/c9/c6/dcbee61fd1dc892aedcb1b489ba661313101aa82ec84b1a015d4c63ebfda/rpds_py-0.29.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd7951c964069039acc9d67a8ff1f0a7f34845ae180ca542b17dc1456b1f1808", size = 384940 }, - { url = "https://files.pythonhosted.org/packages/47/11/914ecb6f3574cf9bf8b38aced4063e0f787d6e1eb30b181a7efbc6c1da9a/rpds_py-0.29.0-cp314-cp314t-manylinux_2_31_riscv64.whl", hash = "sha256:c07d107b7316088f1ac0177a7661ca0c6670d443f6fe72e836069025e6266761", size = 399392 }, - { url = "https://files.pythonhosted.org/packages/f5/fd/2f4bd9433f58f816434bb934313584caa47dbc6f03ce5484df8ac8980561/rpds_py-0.29.0-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1de2345af363d25696969befc0c1688a6cb5e8b1d32b515ef84fc245c6cddba3", size = 416796 }, - { url = "https://files.pythonhosted.org/packages/79/a5/449f0281af33efa29d5c71014399d74842342ae908d8cd38260320167692/rpds_py-0.29.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:00e56b12d2199ca96068057e1ae7f9998ab6e99cda82431afafd32f3ec98cca9", size = 566843 }, - { url = "https://files.pythonhosted.org/packages/ab/32/0a6a1ccee2e37fcb1b7ba9afde762b77182dbb57937352a729c6cd3cf2bb/rpds_py-0.29.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:3919a3bbecee589300ed25000b6944174e07cd20db70552159207b3f4bbb45b8", size = 593956 }, - { url = "https://files.pythonhosted.org/packages/4a/3d/eb820f95dce4306f07a495ede02fb61bef36ea201d9137d4fcd5ab94ec1e/rpds_py-0.29.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:e7fa2ccc312bbd91e43aa5e0869e46bc03278a3dddb8d58833150a18b0f0283a", size = 557288 }, - { url = "https://files.pythonhosted.org/packages/e9/f8/b8ff786f40470462a252918e0836e0db903c28e88e3eec66bc4a7856ee5d/rpds_py-0.29.0-cp314-cp314t-win32.whl", hash = "sha256:97c817863ffc397f1e6a6e9d2d89fe5408c0a9922dac0329672fb0f35c867ea5", size = 211382 }, - { url = "https://files.pythonhosted.org/packages/c9/7f/1a65ae870bc9d0576aebb0c501ea5dccf1ae2178fe2821042150ebd2e707/rpds_py-0.29.0-cp314-cp314t-win_amd64.whl", hash = "sha256:2023473f444752f0f82a58dfcbee040d0a1b3d1b3c2ec40e884bd25db6d117d2", size = 225919 }, - { url = "https://files.pythonhosted.org/packages/f2/ac/b97e80bf107159e5b9ba9c91df1ab95f69e5e41b435f27bdd737f0d583ac/rpds_py-0.29.0-pp311-pypy311_pp73-macosx_10_12_x86_64.whl", hash = "sha256:acd82a9e39082dc5f4492d15a6b6c8599aa21db5c35aaf7d6889aea16502c07d", size = 373963 }, - { url = "https://files.pythonhosted.org/packages/40/5a/55e72962d5d29bd912f40c594e68880d3c7a52774b0f75542775f9250712/rpds_py-0.29.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:715b67eac317bf1c7657508170a3e011a1ea6ccb1c9d5f296e20ba14196be6b3", size = 364644 }, - { url = "https://files.pythonhosted.org/packages/99/2a/6b6524d0191b7fc1351c3c0840baac42250515afb48ae40c7ed15499a6a2/rpds_py-0.29.0-pp311-pypy311_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f3b1b87a237cb2dba4db18bcfaaa44ba4cd5936b91121b62292ff21df577fc43", size = 393847 }, - { url = "https://files.pythonhosted.org/packages/1c/b8/c5692a7df577b3c0c7faed7ac01ee3c608b81750fc5d89f84529229b6873/rpds_py-0.29.0-pp311-pypy311_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:1c3c3e8101bb06e337c88eb0c0ede3187131f19d97d43ea0e1c5407ea74c0cbf", size = 407281 }, - { url = "https://files.pythonhosted.org/packages/f0/57/0546c6f84031b7ea08b76646a8e33e45607cc6bd879ff1917dc077bb881e/rpds_py-0.29.0-pp311-pypy311_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2b8e54d6e61f3ecd3abe032065ce83ea63417a24f437e4a3d73d2f85ce7b7cfe", size = 529213 }, - { url = "https://files.pythonhosted.org/packages/fa/c1/01dd5f444233605555bc11fe5fed6a5c18f379f02013870c176c8e630a23/rpds_py-0.29.0-pp311-pypy311_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3fbd4e9aebf110473a420dea85a238b254cf8a15acb04b22a5a6b5ce8925b760", size = 413808 }, - { url = "https://files.pythonhosted.org/packages/aa/0a/60f98b06156ea2a7af849fb148e00fbcfdb540909a5174a5ed10c93745c7/rpds_py-0.29.0-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:80fdf53d36e6c72819993e35d1ebeeb8e8fc688d0c6c2b391b55e335b3afba5a", size = 394600 }, - { url = "https://files.pythonhosted.org/packages/37/f1/dc9312fc9bec040ece08396429f2bd9e0977924ba7a11c5ad7056428465e/rpds_py-0.29.0-pp311-pypy311_pp73-manylinux_2_31_riscv64.whl", hash = "sha256:ea7173df5d86f625f8dde6d5929629ad811ed8decda3b60ae603903839ac9ac0", size = 408634 }, - { url = "https://files.pythonhosted.org/packages/ed/41/65024c9fd40c89bb7d604cf73beda4cbdbcebe92d8765345dd65855b6449/rpds_py-0.29.0-pp311-pypy311_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:76054d540061eda273274f3d13a21a4abdde90e13eaefdc205db37c05230efce", size = 426064 }, - { url = "https://files.pythonhosted.org/packages/a2/e0/cf95478881fc88ca2fdbf56381d7df36567cccc39a05394beac72182cd62/rpds_py-0.29.0-pp311-pypy311_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:9f84c549746a5be3bc7415830747a3a0312573afc9f95785eb35228bb17742ec", size = 575871 }, - { url = "https://files.pythonhosted.org/packages/ea/c0/df88097e64339a0218b57bd5f9ca49898e4c394db756c67fccc64add850a/rpds_py-0.29.0-pp311-pypy311_pp73-musllinux_1_2_i686.whl", hash = "sha256:0ea962671af5cb9a260489e311fa22b2e97103e3f9f0caaea6f81390af96a9ed", size = 601702 }, - { url = "https://files.pythonhosted.org/packages/87/f4/09ffb3ebd0cbb9e2c7c9b84d252557ecf434cd71584ee1e32f66013824df/rpds_py-0.29.0-pp311-pypy311_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:f7728653900035fb7b8d06e1e5900545d8088efc9d5d4545782da7df03ec803f", size = 564054 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "ruff" -version = "0.15.15" -source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/84/6f/a76f7d96e5c962f5b69cee865e49c15c1116897c01990faa8a57edb62e7f/ruff-0.15.15.tar.gz", hash = "sha256:b8dff018130b46d8e5bf0f926ef6b60cf871d6d5ae45fc9334e09632daa741d6", size = 4706985, upload-time = "2026-05-28T14:16:57.784Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/fa/9d/3a45c05b8ab04b4705989de70a79008e27c8003296a0feaee9edc18dd7e9/ruff-0.15.15-py3-none-linux_armv6l.whl", hash = "sha256:cf93e5388f412e1b108b1f8b34a6e036b70fe8aff89393befad96fe48670311b", size = 10710652, upload-time = "2026-05-28T14:16:06.701Z" }, - { url = "https://files.pythonhosted.org/packages/05/66/da974431624bf3b49f6ee1f9543c02d929ff1cba78b0d5a79c38cf21f744/ruff-0.15.15-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:ac5a646d1f6a7dadd5d50842dae2c1f9862ac887ef5d1b1375e02def791fde6e", size = 11096615, upload-time = "2026-05-28T14:16:23.313Z" }, - { url = "https://files.pythonhosted.org/packages/8c/09/7443452e5d290230a712103f2fdceeef7184f3ec99a2bd01c8be78aaceb5/ruff-0.15.15-py3-none-macosx_11_0_arm64.whl", hash = "sha256:77d955a431430c66f72dd94e379ad38a16daea3d25094872ac4edf9e797be530", size = 10436683, upload-time = "2026-05-28T14:16:40.974Z" }, - { url = "https://files.pythonhosted.org/packages/53/01/d330c26a57fa4f3943a14424904027428315b700fe4d14a84bb123a649e5/ruff-0.15.15-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7614ee79c69788cf6cedd568069ade9cecc22a1ad20494efe8d0c9ebb4b622d4", size = 10769064, upload-time = "2026-05-28T14:16:28.905Z" }, - { url = "https://files.pythonhosted.org/packages/1d/85/cc8770f8bdff541b1da8392d1634141fe4a0e3f4ee596605959b7906c27f/ruff-0.15.15-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3cdb1679e06a1f6b47bc384714ae96f6e2fb65ca441eb78c43d2ca554176ce1f", size = 10511987, upload-time = "2026-05-28T14:16:43.732Z" }, - { url = "https://files.pythonhosted.org/packages/7c/29/8c190c1472b63013583ba391f3342036e02010544c1270455ed8e519bdf3/ruff-0.15.15-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2728b93d7b23a603ea2c0ac6eb73d760bd38ec9de35f35fb41e18f7a3fee7622", size = 11275100, upload-time = "2026-05-28T14:16:55.244Z" }, - { url = "https://files.pythonhosted.org/packages/9f/6b/7e145ce2cc8e63d6834eca03d83a0e18d121def5c69f91b4cf4011ed4879/ruff-0.15.15-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:be582fcc0db438902c7792b08d6ddf6c9b9e21addaa10092c2c741cfb09e5a45", size = 12176903, upload-time = "2026-05-28T14:16:14.368Z" }, - { url = "https://files.pythonhosted.org/packages/80/a3/d5974637f68e451f7fadf015cf3101d1cd7d8ba5027cffe0b9e3826ebe6b/ruff-0.15.15-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7aa77465b8ecaf1a27bea098d696f7fed5e1eccbd10b321b682d6de586ae5627", size = 11404550, upload-time = "2026-05-28T14:16:20.138Z" }, - { url = "https://files.pythonhosted.org/packages/fe/1c/e6e5e568f22be4fb05d6244234aba384c06b451252453b821e1a529263cf/ruff-0.15.15-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:48decfa11d740de4889de623be1463308346312f2409a56e24aa280c86162dc4", size = 11382027, upload-time = "2026-05-28T14:16:46.615Z" }, - { url = "https://files.pythonhosted.org/packages/1d/01/170921b49fcd2e8858825593f91cf7146c3e40a5c3e6df763e4bb0484dde/ruff-0.15.15-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:a5015088452ca0081387063649ec67f06d3d1d6b8b936a1f836b5e9657ecd48c", size = 11366041, upload-time = "2026-05-28T14:16:26.247Z" }, - { url = "https://files.pythonhosted.org/packages/87/54/a7bad711d7de93254e15e06a4c375b89a03d18de45d3e5dcc86a4472fb1a/ruff-0.15.15-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:f5294aab6356c81600fcdea3a62bb1b924dfd5e91767c12318d3f68f86af57cd", size = 10741795, upload-time = "2026-05-28T14:16:17.11Z" }, - { url = "https://files.pythonhosted.org/packages/c9/31/38c075963668f8b41c6914ee0f6f318727fbe30ab9145cb29e6df464c5fa/ruff-0.15.15-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:db5bd4d802415cca656dc1616070b725952d6ae95eb5d4831e49fbd94a38f75f", size = 10511117, upload-time = "2026-05-28T14:16:31.767Z" }, - { url = "https://files.pythonhosted.org/packages/9d/96/6ff689e1f7e375d1d97075eca022f74c2bab59554a432fe4d2e6f091986a/ruff-0.15.15-py3-none-musllinux_1_2_i686.whl", hash = "sha256:587a6278ed42059191c1a466e490bd7930fb50bd2e255398bc29616c895a61cb", size = 10994867, upload-time = "2026-05-28T14:16:35.149Z" }, - { url = "https://files.pythonhosted.org/packages/c3/c2/5dce0ab9f92a8d534fa62b9bf9caca3eddb8c1a81b616f5e195ada4f0d6e/ruff-0.15.15-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:df0c1c084f5f4be9812f61518a45c440d3c30d69ce4bf6c5270e66d38338f02a", size = 11482101, upload-time = "2026-05-28T14:16:49.598Z" }, - { url = "https://files.pythonhosted.org/packages/b1/c0/1003b60edd697c649faf61f1a34094b1abb38fb3d1181e3f895781250a08/ruff-0.15.15-py3-none-win32.whl", hash = "sha256:29428ea79694afbe756d45fd59b36f22b6b020dc0443cf7de0173046236964b9", size = 10716774, upload-time = "2026-05-28T14:16:52.337Z" }, - { url = "https://files.pythonhosted.org/packages/02/a8/1269eddd6945a06c23f055ef7848886e37cf9d6a8bebb386a3115f01470c/ruff-0.15.15-py3-none-win_amd64.whl", hash = "sha256:8df0323902e15e24bc4bf246da830573d3cf3352bd0b9a164eab335d111ff4a4", size = 11868463, upload-time = "2026-05-28T14:16:11.333Z" }, - { url = "https://files.pythonhosted.org/packages/4e/b2/920464c907b191e37469d477a1aa8bc048b8f36c4c1610dfa4ab87b39e18/ruff-0.15.15-py3-none-win_arm64.whl", hash = "sha256:3c8ceca6792f38196b8f589bc92eccd03eef286602da92e5dc05cc42ef6441b7", size = 11138498, upload-time = "2026-05-28T14:16:38.425Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/82/fa/fbb67a5780ae0f704876cb8ac92d6d76da41da4dc72b7ed3565ab18f2f52/ruff-0.14.5.tar.gz", hash = "sha256:8d3b48d7d8aad423d3137af7ab6c8b1e38e4de104800f0d596990f6ada1a9fc1", size = 5615944 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/68/31/c07e9c535248d10836a94e4f4e8c5a31a1beed6f169b31405b227872d4f4/ruff-0.14.5-py3-none-linux_armv6l.whl", hash = "sha256:f3b8248123b586de44a8018bcc9fefe31d23dda57a34e6f0e1e53bd51fd63594", size = 13171630 }, - { url = "https://files.pythonhosted.org/packages/8e/5c/283c62516dca697cd604c2796d1487396b7a436b2f0ecc3fd412aca470e0/ruff-0.14.5-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:f7a75236570318c7a30edd7f5491945f0169de738d945ca8784500b517163a72", size = 13413925 }, - { url = "https://files.pythonhosted.org/packages/b6/f3/aa319f4afc22cb6fcba2b9cdfc0f03bbf747e59ab7a8c5e90173857a1361/ruff-0.14.5-py3-none-macosx_11_0_arm64.whl", hash = "sha256:6d146132d1ee115f8802356a2dc9a634dbf58184c51bff21f313e8cd1c74899a", size = 12574040 }, - { url = "https://files.pythonhosted.org/packages/f9/7f/cb5845fcc7c7e88ed57f58670189fc2ff517fe2134c3821e77e29fd3b0c8/ruff-0.14.5-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2380596653dcd20b057794d55681571a257a42327da8894b93bbd6111aa801f", size = 13009755 }, - { url = "https://files.pythonhosted.org/packages/21/d2/bcbedbb6bcb9253085981730687ddc0cc7b2e18e8dc13cf4453de905d7a0/ruff-0.14.5-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2d1fa985a42b1f075a098fa1ab9d472b712bdb17ad87a8ec86e45e7fa6273e68", size = 12937641 }, - { url = "https://files.pythonhosted.org/packages/a4/58/e25de28a572bdd60ffc6bb71fc7fd25a94ec6a076942e372437649cbb02a/ruff-0.14.5-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:88f0770d42b7fa02bbefddde15d235ca3aa24e2f0137388cc15b2dcbb1f7c7a7", size = 13610854 }, - { url = "https://files.pythonhosted.org/packages/7d/24/43bb3fd23ecee9861970978ea1a7a63e12a204d319248a7e8af539984280/ruff-0.14.5-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:3676cb02b9061fee7294661071c4709fa21419ea9176087cb77e64410926eb78", size = 15061088 }, - { url = "https://files.pythonhosted.org/packages/23/44/a022f288d61c2f8c8645b24c364b719aee293ffc7d633a2ca4d116b9c716/ruff-0.14.5-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b595bedf6bc9cab647c4a173a61acf4f1ac5f2b545203ba82f30fcb10b0318fb", size = 14734717 }, - { url = "https://files.pythonhosted.org/packages/58/81/5c6ba44de7e44c91f68073e0658109d8373b0590940efe5bd7753a2585a3/ruff-0.14.5-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f55382725ad0bdb2e8ee2babcbbfb16f124f5a59496a2f6a46f1d9d99d93e6e2", size = 14028812 }, - { url = "https://files.pythonhosted.org/packages/ad/ef/41a8b60f8462cb320f68615b00299ebb12660097c952c600c762078420f8/ruff-0.14.5-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7497d19dce23976bdaca24345ae131a1d38dcfe1b0850ad8e9e6e4fa321a6e19", size = 13825656 }, - { url = "https://files.pythonhosted.org/packages/7c/00/207e5de737fdb59b39eb1fac806904fe05681981b46d6a6db9468501062e/ruff-0.14.5-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:410e781f1122d6be4f446981dd479470af86537fb0b8857f27a6e872f65a38e4", size = 13959922 }, - { url = "https://files.pythonhosted.org/packages/bc/7e/fa1f5c2776db4be405040293618846a2dece5c70b050874c2d1f10f24776/ruff-0.14.5-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:c01be527ef4c91a6d55e53b337bfe2c0f82af024cc1a33c44792d6844e2331e1", size = 12932501 }, - { url = "https://files.pythonhosted.org/packages/67/d8/d86bf784d693a764b59479a6bbdc9515ae42c340a5dc5ab1dabef847bfaa/ruff-0.14.5-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:f66e9bb762e68d66e48550b59c74314168ebb46199886c5c5aa0b0fbcc81b151", size = 12927319 }, - { url = "https://files.pythonhosted.org/packages/ac/de/ee0b304d450ae007ce0cb3e455fe24fbcaaedae4ebaad6c23831c6663651/ruff-0.14.5-py3-none-musllinux_1_2_i686.whl", hash = "sha256:d93be8f1fa01022337f1f8f3bcaa7ffee2d0b03f00922c45c2207954f351f465", size = 13206209 }, - { url = "https://files.pythonhosted.org/packages/33/aa/193ca7e3a92d74f17d9d5771a765965d2cf42c86e6f0fd95b13969115723/ruff-0.14.5-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:c135d4b681f7401fe0e7312017e41aba9b3160861105726b76cfa14bc25aa367", size = 13953709 }, - { url = "https://files.pythonhosted.org/packages/cc/f1/7119e42aa1d3bf036ffc9478885c2e248812b7de9abea4eae89163d2929d/ruff-0.14.5-py3-none-win32.whl", hash = "sha256:c83642e6fccfb6dea8b785eb9f456800dcd6a63f362238af5fc0c83d027dd08b", size = 12925808 }, - { url = "https://files.pythonhosted.org/packages/3b/9d/7c0a255d21e0912114784e4a96bf62af0618e2190cae468cd82b13625ad2/ruff-0.14.5-py3-none-win_amd64.whl", hash = "sha256:9d55d7af7166f143c94eae1db3312f9ea8f95a4defef1979ed516dbb38c27621", size = 14331546 }, - { url = "https://files.pythonhosted.org/packages/e5/80/69756670caedcf3b9be597a6e12276a6cf6197076eb62aad0c608f8efce0/ruff-0.14.5-py3-none-win_arm64.whl", hash = "sha256:4b700459d4649e2594b31f20a9de33bc7c19976d4746d8d0798ad959621d64a4", size = 13433331 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) +version = "0.15.20" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/43/dc/35b341fc554ba02f217fc10da57d1a75168cfbcf75b0ef2202176d4c4f2d/ruff-0.15.20.tar.gz", hash = "sha256:1416eb04349192646b54de98f146c4f59afe37d0decfc02c3cbbf396f3a28566", size = 4755489, upload-time = "2026-06-25T17:20:37.578Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/94/d9/2d5014f0253ba541d2061d9fa7193f48e941c8b21bb88a7ff9bbe0bd0596/ruff-0.15.20-py3-none-linux_armv6l.whl", hash = "sha256:00e188c53e499c3c1637f73c91dcf2fb56d576cab76ce1be50a27c4e80e37078", size = 10839665, upload-time = "2026-06-25T17:19:44.702Z" }, + { url = "https://files.pythonhosted.org/packages/c6/d3/ac1798ba64f670698867fcfc591d50e7e421bef137db564858f619a30fcf/ruff-0.15.20-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:9ebd1fd9b9c95fc0bd7b2761aebec1f030013d2e193a2901b224af68fe47251b", size = 11208649, upload-time = "2026-06-25T17:19:48.787Z" }, + { url = "https://files.pythonhosted.org/packages/47/47/d3ac899991202095dfcf3d5176be4272642be3cf981a2f1a30f72a2afb95/ruff-0.15.20-py3-none-macosx_11_0_arm64.whl", hash = "sha256:c5b16cdd67ca108185cd36dce98c576350c03b1660a751de725fb049193a0632", size = 10622638, upload-time = "2026-06-25T17:19:51.354Z" }, + { url = "https://files.pythonhosted.org/packages/33/13/4e043fe30aa94d4ff5213a9881fc296d12960f5971b234a5263fdc225312/ruff-0.15.20-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3413bb3c3d2ca6a8208f1f4809cd2dca3c6de6d0b491c0e70847672bde6e6efd", size = 10984227, upload-time = "2026-06-25T17:19:54.044Z" }, + { url = "https://files.pythonhosted.org/packages/76/e6/92e7bf40388bc5800073b96564f56264f7e48bfd1a498f5ced6ae6d5a769/ruff-0.15.20-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:bd7ec42b3bb3da066488db093308a69c4ac5ee6d2af333a86ba6e2eb2e7dd44b", size = 10622882, upload-time = "2026-06-25T17:19:57.037Z" }, + { url = "https://files.pythonhosted.org/packages/13/7a/43460be3f24495a3aa46d4b16873e2c4941b3b5f0b00cf88c03b7b94b339/ruff-0.15.20-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e1a36ad0eb77fba9aabfb69ede54de6f376d04ac18ebea022847046d340a8267", size = 11474808, upload-time = "2026-06-25T17:20:00.357Z" }, + { url = "https://files.pythonhosted.org/packages/27/a0/f37077884873221c6b33b4ab49eb18f9f88e54a16a25a5bca59bef46dd66/ruff-0.15.20-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b6df3b1e4610432f0386dba04d853b5f08cbbc903410c6fcc02f620f05aff53c", size = 12293094, upload-time = "2026-06-25T17:20:03.446Z" }, + { url = "https://files.pythonhosted.org/packages/a6/74/165545b60256a9704c21ac0ec4a0d07933b320812f9584836c9f4aca4292/ruff-0.15.20-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e89f198a1ea6ef0d727c1cf16088bc91a6cb0ab947dedc966715691647186eae", size = 11526176, upload-time = "2026-06-25T17:20:06.301Z" }, + { url = "https://files.pythonhosted.org/packages/86/b1/a976a136d40ade83ce743578399865f57001003a409acadc0ecbb3051082/ruff-0.15.20-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:309809086c2acb67624950a3c8133e80f32d0d3e27106c0cd60ff26657c9f24b", size = 11520767, upload-time = "2026-06-25T17:20:09.191Z" }, + { url = "https://files.pythonhosted.org/packages/19/0f/f032696cb01c9b54c0263fa393474d7758f1cdc021a01b04e3cbc2500999/ruff-0.15.20-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:2d2374caa2f2c2f9e2b7da0a50802cfb8b79f55a9b5e49379f564544fbf56487", size = 11500132, upload-time = "2026-06-25T17:20:13.602Z" }, + { url = "https://files.pythonhosted.org/packages/4b/f4/51b1a14bc69e8c224b15dab9cce8e99b425e0455d462caa2b3c9be2b6a8e/ruff-0.15.20-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:a1ed17b65293e0c2f22fc387bc13198a5de94bf4429589b0ff6946b0feaf21a3", size = 10943828, upload-time = "2026-06-25T17:20:16.635Z" }, + { url = "https://files.pythonhosted.org/packages/71/4b/fe267640783cd02bf6c5cc290b1df1051be2ec294c678b5c15fe19e52343/ruff-0.15.20-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:f701305e66b38ea6c91882490eb73459796808e4c6362a1b765255e0cdcd4053", size = 10645418, upload-time = "2026-06-25T17:20:19.4Z" }, + { url = "https://files.pythonhosted.org/packages/b0/c0/a65aa4ec2f5e87a1df32dc3ec1fede434fe3dfd5cbcf3b503cafc676ab54/ruff-0.15.20-py3-none-musllinux_1_2_i686.whl", hash = "sha256:5b9c0c367ad8e5d0d5b5b8537864c469a0a0e55417aadfbeca41fa61333be9f4", size = 11211770, upload-time = "2026-06-25T17:20:22.033Z" }, + { url = "https://files.pythonhosted.org/packages/5a/a4/0caa331d954ae2723d729d351c989cb4ca8b6077d5c6c2cb6de75e98c041/ruff-0.15.20-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:01cc00dd58f0df339d0e902219dd53990ea99996a0344e5d9cc8d45d5307e460", size = 11618698, upload-time = "2026-06-25T17:20:25.259Z" }, + { url = "https://files.pythonhosted.org/packages/10/9b/5f14927848d2fd4aa891fd88d883788c5a7baba561c7874732364045708c/ruff-0.15.20-py3-none-win32.whl", hash = "sha256:ed65ef510e43a137207e0f01cfcf998aeddb1aeeda5c9d35023e910284d7cf21", size = 10857322, upload-time = "2026-06-25T17:20:28.612Z" }, + { url = "https://files.pythonhosted.org/packages/fa/f0/fe47c501f9dea92a26d788ff98bb5d92ed4cb4c88792c5c88af6b697dc8e/ruff-0.15.20-py3-none-win_amd64.whl", hash = "sha256:a525c81c70fb0380344dd1d8745d8cc1c890b7fc94a58d5a07bd8eb9557b8415", size = 11993274, upload-time = "2026-06-25T17:20:31.871Z" }, + { url = "https://files.pythonhosted.org/packages/d7/2b/9555445e1201d92b3195f45cdb153a0b68f24e0a4273f6e3d5ab46e212bb/ruff-0.15.20-py3-none-win_arm64.whl", hash = "sha256:2f5b2a6d614e8700388806a14996c40fab2c47b819ef57d790a34878858ed9ca", size = 11343498, upload-time = "2026-06-25T17:20:35.03Z" }, ] [[package]] @@ -4506,14 +3289,15 @@ dependencies = [ { name = "imageio" }, { name = "lazy-loader" }, { name = "networkx" }, - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, { name = "packaging" }, { name = "pillow" }, - { name = "scipy" }, + { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "scipy", version = "1.18.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, { name = "tifffile", version = "2026.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, { name = "tifffile", version = "2026.6.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/a1/b4/2528bb43c67d48053a7a649a9666432dc307d66ba02e3a6d5c40f46655df/scikit_image-0.26.0.tar.gz", hash = "sha256:f5f970ab04efad85c24714321fcc91613fcb64ef2a892a13167df2f3e59199fa", size = 22729739, upload-time = "2025-12-20T17:12:21.824Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/76/16/8a407688b607f86f81f8c649bf0d68a2a6d67375f18c2d660aba20f5b648/scikit_image-0.26.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b1ede33a0fb3731457eaf53af6361e73dd510f449dac437ab54573b26788baf0", size = 12355510, upload-time = "2025-12-20T17:10:31.628Z" }, @@ -4564,36 +3348,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/47/88/00a90402e1775634043c2a0af8a3c76ad450866d9fa444efcc43b553ba2d/scikit_image-0.26.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:2c1e7bd342f43e7a97e571b3f03ba4c1293ea1a35c3f13f41efdc8a81c1dc8f2", size = 14364151, upload-time = "2025-12-20T17:12:14.909Z" }, { url = "https://files.pythonhosted.org/packages/da/ca/918d8d306bd43beacff3b835c6d96fac0ae64c0857092f068b88db531a7c/scikit_image-0.26.0-cp314-cp314t-win_amd64.whl", hash = "sha256:b702c3bb115e1dcf4abf5297429b5c90f2189655888cbed14921f3d26f81d3a4", size = 12413484, upload-time = "2025-12-20T17:12:17.046Z" }, { url = "https://files.pythonhosted.org/packages/dc/cd/4da01329b5a8d47ff7ec3c99a2b02465a8017b186027590dc7425cee0b56/scikit_image-0.26.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0608aa4a9ec39e0843de10d60edb2785a30c1c47819b67866dd223ebd149acaf", size = 11769501, upload-time = "2025-12-20T17:12:19.339Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/c7/a8/3c0f256012b93dd2cb6fda9245e9f4bff7dc0486880b248005f15ea2255e/scikit_image-0.25.2.tar.gz", hash = "sha256:e5a37e6cd4d0c018a7a55b9d601357e3382826d3888c10d0213fc63bff977dde", size = 22693594 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c4/97/3051c68b782ee3f1fb7f8f5bb7d535cf8cb92e8aae18fa9c1cdf7e15150d/scikit_image-0.25.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f4bac9196fb80d37567316581c6060763b0f4893d3aca34a9ede3825bc035b17", size = 14003057 }, - { url = "https://files.pythonhosted.org/packages/19/23/257fc696c562639826065514d551b7b9b969520bd902c3a8e2fcff5b9e17/scikit_image-0.25.2-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:d989d64ff92e0c6c0f2018c7495a5b20e2451839299a018e0e5108b2680f71e0", size = 13180335 }, - { url = "https://files.pythonhosted.org/packages/ef/14/0c4a02cb27ca8b1e836886b9ec7c9149de03053650e9e2ed0625f248dd92/scikit_image-0.25.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b2cfc96b27afe9a05bc92f8c6235321d3a66499995675b27415e0d0c76625173", size = 14144783 }, - { url = "https://files.pythonhosted.org/packages/dd/9b/9fb556463a34d9842491d72a421942c8baff4281025859c84fcdb5e7e602/scikit_image-0.25.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:24cc986e1f4187a12aa319f777b36008764e856e5013666a4a83f8df083c2641", size = 14785376 }, - { url = "https://files.pythonhosted.org/packages/de/ec/b57c500ee85885df5f2188f8bb70398481393a69de44a00d6f1d055f103c/scikit_image-0.25.2-cp311-cp311-win_amd64.whl", hash = "sha256:b4f6b61fc2db6340696afe3db6b26e0356911529f5f6aee8c322aa5157490c9b", size = 12791698 }, - { url = "https://files.pythonhosted.org/packages/35/8c/5df82881284459f6eec796a5ac2a0a304bb3384eec2e73f35cfdfcfbf20c/scikit_image-0.25.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:8db8dd03663112783221bf01ccfc9512d1cc50ac9b5b0fe8f4023967564719fb", size = 13986000 }, - { url = "https://files.pythonhosted.org/packages/ce/e6/93bebe1abcdce9513ffec01d8af02528b4c41fb3c1e46336d70b9ed4ef0d/scikit_image-0.25.2-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:483bd8cc10c3d8a7a37fae36dfa5b21e239bd4ee121d91cad1f81bba10cfb0ed", size = 13235893 }, - { url = "https://files.pythonhosted.org/packages/53/4b/eda616e33f67129e5979a9eb33c710013caa3aa8a921991e6cc0b22cea33/scikit_image-0.25.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9d1e80107bcf2bf1291acfc0bf0425dceb8890abe9f38d8e94e23497cbf7ee0d", size = 14178389 }, - { url = "https://files.pythonhosted.org/packages/6b/b5/b75527c0f9532dd8a93e8e7cd8e62e547b9f207d4c11e24f0006e8646b36/scikit_image-0.25.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a17e17eb8562660cc0d31bb55643a4da996a81944b82c54805c91b3fe66f4824", size = 15003435 }, - { url = "https://files.pythonhosted.org/packages/34/e3/49beb08ebccda3c21e871b607c1cb2f258c3fa0d2f609fed0a5ba741b92d/scikit_image-0.25.2-cp312-cp312-win_amd64.whl", hash = "sha256:bdd2b8c1de0849964dbc54037f36b4e9420157e67e45a8709a80d727f52c7da2", size = 12899474 }, - { url = "https://files.pythonhosted.org/packages/e6/7c/9814dd1c637f7a0e44342985a76f95a55dd04be60154247679fd96c7169f/scikit_image-0.25.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:7efa888130f6c548ec0439b1a7ed7295bc10105458a421e9bf739b457730b6da", size = 13921841 }, - { url = "https://files.pythonhosted.org/packages/84/06/66a2e7661d6f526740c309e9717d3bd07b473661d5cdddef4dd978edab25/scikit_image-0.25.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:dd8011efe69c3641920614d550f5505f83658fe33581e49bed86feab43a180fc", size = 13196862 }, - { url = "https://files.pythonhosted.org/packages/4e/63/3368902ed79305f74c2ca8c297dfeb4307269cbe6402412668e322837143/scikit_image-0.25.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:28182a9d3e2ce3c2e251383bdda68f8d88d9fff1a3ebe1eb61206595c9773341", size = 14117785 }, - { url = "https://files.pythonhosted.org/packages/cd/9b/c3da56a145f52cd61a68b8465d6a29d9503bc45bc993bb45e84371c97d94/scikit_image-0.25.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8abd3c805ce6944b941cfed0406d88faeb19bab3ed3d4b50187af55cf24d147", size = 14977119 }, - { url = "https://files.pythonhosted.org/packages/8a/97/5fcf332e1753831abb99a2525180d3fb0d70918d461ebda9873f66dcc12f/scikit_image-0.25.2-cp313-cp313-win_amd64.whl", hash = "sha256:64785a8acefee460ec49a354706db0b09d1f325674107d7fa3eadb663fb56d6f", size = 12885116 }, - { url = "https://files.pythonhosted.org/packages/10/cc/75e9f17e3670b5ed93c32456fda823333c6279b144cd93e2c03aa06aa472/scikit_image-0.25.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:330d061bd107d12f8d68f1d611ae27b3b813b8cdb0300a71d07b1379178dd4cd", size = 13862801 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "scipy" version = "1.17.1" source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version < '3.12'", +] dependencies = [ - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/7a/97/5a3609c4f8d58b039179648e62dd220f89864f56f7357f5d4f45c29eb2cc/scipy-1.17.1.tar.gz", hash = "sha256:95d8e012d8cb8816c226aef832200b1d45109ed4464303e997c5b13122b297c0", size = 30573822, upload-time = "2026-02-23T00:26:24.851Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/df/75/b4ce781849931fef6fd529afa6b63711d5a733065722d0c3e2724af9e40a/scipy-1.17.1-cp311-cp311-macosx_10_14_x86_64.whl", hash = "sha256:1f95b894f13729334fb990162e911c9e5dc1ab390c58aa6cbecb389c5b5e28ec", size = 31613675, upload-time = "2026-02-23T00:16:00.13Z" }, @@ -4656,214 +3422,145 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/52/52/e57eceff0e342a1f50e274264ed47497b59e6a4e3118808ee58ddda7b74a/scipy-1.17.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:a77cbd07b940d326d39a1d1b37817e2ee4d79cb30e7338f3d0cddffae70fcaa2", size = 37682317, upload-time = "2026-02-23T00:22:18.513Z" }, { url = "https://files.pythonhosted.org/packages/11/2f/b29eafe4a3fbc3d6de9662b36e028d5f039e72d345e05c250e121a230dd4/scipy-1.17.1-cp314-cp314t-win_amd64.whl", hash = "sha256:eb092099205ef62cd1782b006658db09e2fed75bffcae7cc0d44052d8aa0f484", size = 37345327, upload-time = "2026-02-23T00:22:24.442Z" }, { url = "https://files.pythonhosted.org/packages/07/39/338d9219c4e87f3e708f18857ecd24d22a0c3094752393319553096b98af/scipy-1.17.1-cp314-cp314t-win_arm64.whl", hash = "sha256:200e1050faffacc162be6a486a984a0497866ec54149a01270adc8a59b7c7d21", size = 25489165, upload-time = "2026-02-23T00:22:29.563Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/0a/ca/d8ace4f98322d01abcd52d381134344bf7b431eba7ed8b42bdea5a3c2ac9/scipy-1.16.3.tar.gz", hash = "sha256:01e87659402762f43bd2fee13370553a17ada367d42e7487800bf2916535aecb", size = 30597883 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/9b/5f/6f37d7439de1455ce9c5a556b8d1db0979f03a796c030bafdf08d35b7bf9/scipy-1.16.3-cp311-cp311-macosx_10_14_x86_64.whl", hash = "sha256:40be6cf99e68b6c4321e9f8782e7d5ff8265af28ef2cd56e9c9b2638fa08ad97", size = 36630881 }, - { url = "https://files.pythonhosted.org/packages/7c/89/d70e9f628749b7e4db2aa4cd89735502ff3f08f7b9b27d2e799485987cd9/scipy-1.16.3-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:8be1ca9170fcb6223cc7c27f4305d680ded114a1567c0bd2bfcbf947d1b17511", size = 28941012 }, - { url = "https://files.pythonhosted.org/packages/a8/a8/0e7a9a6872a923505dbdf6bb93451edcac120363131c19013044a1e7cb0c/scipy-1.16.3-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:bea0a62734d20d67608660f69dcda23e7f90fb4ca20974ab80b6ed40df87a005", size = 20931935 }, - { url = "https://files.pythonhosted.org/packages/bd/c7/020fb72bd79ad798e4dbe53938543ecb96b3a9ac3fe274b7189e23e27353/scipy-1.16.3-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:2a207a6ce9c24f1951241f4693ede2d393f59c07abc159b2cb2be980820e01fb", size = 23534466 }, - { url = "https://files.pythonhosted.org/packages/be/a0/668c4609ce6dbf2f948e167836ccaf897f95fb63fa231c87da7558a374cd/scipy-1.16.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:532fb5ad6a87e9e9cd9c959b106b73145a03f04c7d57ea3e6f6bb60b86ab0876", size = 33593618 }, - { url = "https://files.pythonhosted.org/packages/ca/6e/8942461cf2636cdae083e3eb72622a7fbbfa5cf559c7d13ab250a5dbdc01/scipy-1.16.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0151a0749efeaaab78711c78422d413c583b8cdd2011a3c1d6c794938ee9fdb2", size = 35899798 }, - { url = "https://files.pythonhosted.org/packages/79/e8/d0f33590364cdbd67f28ce79368b373889faa4ee959588beddf6daef9abe/scipy-1.16.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:b7180967113560cca57418a7bc719e30366b47959dd845a93206fbed693c867e", size = 36226154 }, - { url = "https://files.pythonhosted.org/packages/39/c1/1903de608c0c924a1749c590064e65810f8046e437aba6be365abc4f7557/scipy-1.16.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:deb3841c925eeddb6afc1e4e4a45e418d19ec7b87c5df177695224078e8ec733", size = 38878540 }, - { url = "https://files.pythonhosted.org/packages/f1/d0/22ec7036ba0b0a35bccb7f25ab407382ed34af0b111475eb301c16f8a2e5/scipy-1.16.3-cp311-cp311-win_amd64.whl", hash = "sha256:53c3844d527213631e886621df5695d35e4f6a75f620dca412bcd292f6b87d78", size = 38722107 }, - { url = "https://files.pythonhosted.org/packages/7b/60/8a00e5a524bb3bf8898db1650d350f50e6cffb9d7a491c561dc9826c7515/scipy-1.16.3-cp311-cp311-win_arm64.whl", hash = "sha256:9452781bd879b14b6f055b26643703551320aa8d79ae064a71df55c00286a184", size = 25506272 }, - { url = "https://files.pythonhosted.org/packages/40/41/5bf55c3f386b1643812f3a5674edf74b26184378ef0f3e7c7a09a7e2ca7f/scipy-1.16.3-cp312-cp312-macosx_10_14_x86_64.whl", hash = "sha256:81fc5827606858cf71446a5e98715ba0e11f0dbc83d71c7409d05486592a45d6", size = 36659043 }, - { url = "https://files.pythonhosted.org/packages/1e/0f/65582071948cfc45d43e9870bf7ca5f0e0684e165d7c9ef4e50d783073eb/scipy-1.16.3-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:c97176013d404c7346bf57874eaac5187d969293bf40497140b0a2b2b7482e07", size = 28898986 }, - { url = "https://files.pythonhosted.org/packages/96/5e/36bf3f0ac298187d1ceadde9051177d6a4fe4d507e8f59067dc9dd39e650/scipy-1.16.3-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:2b71d93c8a9936046866acebc915e2af2e292b883ed6e2cbe5c34beb094b82d9", size = 20889814 }, - { url = "https://files.pythonhosted.org/packages/80/35/178d9d0c35394d5d5211bbff7ac4f2986c5488b59506fef9e1de13ea28d3/scipy-1.16.3-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:3d4a07a8e785d80289dfe66b7c27d8634a773020742ec7187b85ccc4b0e7b686", size = 23565795 }, - { url = "https://files.pythonhosted.org/packages/fa/46/d1146ff536d034d02f83c8afc3c4bab2eddb634624d6529a8512f3afc9da/scipy-1.16.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0553371015692a898e1aa858fed67a3576c34edefa6b7ebdb4e9dde49ce5c203", size = 33349476 }, - { url = "https://files.pythonhosted.org/packages/79/2e/415119c9ab3e62249e18c2b082c07aff907a273741b3f8160414b0e9193c/scipy-1.16.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:72d1717fd3b5e6ec747327ce9bda32d5463f472c9dce9f54499e81fbd50245a1", size = 35676692 }, - { url = "https://files.pythonhosted.org/packages/27/82/df26e44da78bf8d2aeaf7566082260cfa15955a5a6e96e6a29935b64132f/scipy-1.16.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1fb2472e72e24d1530debe6ae078db70fb1605350c88a3d14bc401d6306dbffe", size = 36019345 }, - { url = "https://files.pythonhosted.org/packages/82/31/006cbb4b648ba379a95c87262c2855cd0d09453e500937f78b30f02fa1cd/scipy-1.16.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c5192722cffe15f9329a3948c4b1db789fbb1f05c97899187dcf009b283aea70", size = 38678975 }, - { url = "https://files.pythonhosted.org/packages/c2/7f/acbd28c97e990b421af7d6d6cd416358c9c293fc958b8529e0bd5d2a2a19/scipy-1.16.3-cp312-cp312-win_amd64.whl", hash = "sha256:56edc65510d1331dae01ef9b658d428e33ed48b4f77b1d51caf479a0253f96dc", size = 38555926 }, - { url = "https://files.pythonhosted.org/packages/ce/69/c5c7807fd007dad4f48e0a5f2153038dc96e8725d3345b9ee31b2b7bed46/scipy-1.16.3-cp312-cp312-win_arm64.whl", hash = "sha256:a8a26c78ef223d3e30920ef759e25625a0ecdd0d60e5a8818b7513c3e5384cf2", size = 25463014 }, - { url = "https://files.pythonhosted.org/packages/72/f1/57e8327ab1508272029e27eeef34f2302ffc156b69e7e233e906c2a5c379/scipy-1.16.3-cp313-cp313-macosx_10_14_x86_64.whl", hash = "sha256:d2ec56337675e61b312179a1ad124f5f570c00f920cc75e1000025451b88241c", size = 36617856 }, - { url = "https://files.pythonhosted.org/packages/44/13/7e63cfba8a7452eb756306aa2fd9b37a29a323b672b964b4fdeded9a3f21/scipy-1.16.3-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:16b8bc35a4cc24db80a0ec836a9286d0e31b2503cb2fd7ff7fb0e0374a97081d", size = 28874306 }, - { url = "https://files.pythonhosted.org/packages/15/65/3a9400efd0228a176e6ec3454b1fa998fbbb5a8defa1672c3f65706987db/scipy-1.16.3-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:5803c5fadd29de0cf27fa08ccbfe7a9e5d741bf63e4ab1085437266f12460ff9", size = 20865371 }, - { url = "https://files.pythonhosted.org/packages/33/d7/eda09adf009a9fb81827194d4dd02d2e4bc752cef16737cc4ef065234031/scipy-1.16.3-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:b81c27fc41954319a943d43b20e07c40bdcd3ff7cf013f4fb86286faefe546c4", size = 23524877 }, - { url = "https://files.pythonhosted.org/packages/7d/6b/3f911e1ebc364cb81320223a3422aab7d26c9c7973109a9cd0f27c64c6c0/scipy-1.16.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0c3b4dd3d9b08dbce0f3440032c52e9e2ab9f96ade2d3943313dfe51a7056959", size = 33342103 }, - { url = "https://files.pythonhosted.org/packages/21/f6/4bfb5695d8941e5c570a04d9fcd0d36bce7511b7d78e6e75c8f9791f82d0/scipy-1.16.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7dc1360c06535ea6116a2220f760ae572db9f661aba2d88074fe30ec2aa1ff88", size = 35697297 }, - { url = "https://files.pythonhosted.org/packages/04/e1/6496dadbc80d8d896ff72511ecfe2316b50313bfc3ebf07a3f580f08bd8c/scipy-1.16.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:663b8d66a8748051c3ee9c96465fb417509315b99c71550fda2591d7dd634234", size = 36021756 }, - { url = "https://files.pythonhosted.org/packages/fe/bd/a8c7799e0136b987bda3e1b23d155bcb31aec68a4a472554df5f0937eef7/scipy-1.16.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eab43fae33a0c39006a88096cd7b4f4ef545ea0447d250d5ac18202d40b6611d", size = 38696566 }, - { url = "https://files.pythonhosted.org/packages/cd/01/1204382461fcbfeb05b6161b594f4007e78b6eba9b375382f79153172b4d/scipy-1.16.3-cp313-cp313-win_amd64.whl", hash = "sha256:062246acacbe9f8210de8e751b16fc37458213f124bef161a5a02c7a39284304", size = 38529877 }, - { url = "https://files.pythonhosted.org/packages/7f/14/9d9fbcaa1260a94f4bb5b64ba9213ceb5d03cd88841fe9fd1ffd47a45b73/scipy-1.16.3-cp313-cp313-win_arm64.whl", hash = "sha256:50a3dbf286dbc7d84f176f9a1574c705f277cb6565069f88f60db9eafdbe3ee2", size = 25455366 }, - { url = "https://files.pythonhosted.org/packages/e2/a3/9ec205bd49f42d45d77f1730dbad9ccf146244c1647605cf834b3a8c4f36/scipy-1.16.3-cp313-cp313t-macosx_10_14_x86_64.whl", hash = "sha256:fb4b29f4cf8cc5a8d628bc8d8e26d12d7278cd1f219f22698a378c3d67db5e4b", size = 37027931 }, - { url = "https://files.pythonhosted.org/packages/25/06/ca9fd1f3a4589cbd825b1447e5db3a8ebb969c1eaf22c8579bd286f51b6d/scipy-1.16.3-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:8d09d72dc92742988b0e7750bddb8060b0c7079606c0d24a8cc8e9c9c11f9079", size = 29400081 }, - { url = "https://files.pythonhosted.org/packages/6a/56/933e68210d92657d93fb0e381683bc0e53a965048d7358ff5fbf9e6a1b17/scipy-1.16.3-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:03192a35e661470197556de24e7cb1330d84b35b94ead65c46ad6f16f6b28f2a", size = 21391244 }, - { url = "https://files.pythonhosted.org/packages/a8/7e/779845db03dc1418e215726329674b40576879b91814568757ff0014ad65/scipy-1.16.3-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:57d01cb6f85e34f0946b33caa66e892aae072b64b034183f3d87c4025802a119", size = 23929753 }, - { url = "https://files.pythonhosted.org/packages/4c/4b/f756cf8161d5365dcdef9e5f460ab226c068211030a175d2fc7f3f41ca64/scipy-1.16.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:96491a6a54e995f00a28a3c3badfff58fd093bf26cd5fb34a2188c8c756a3a2c", size = 33496912 }, - { url = "https://files.pythonhosted.org/packages/09/b5/222b1e49a58668f23839ca1542a6322bb095ab8d6590d4f71723869a6c2c/scipy-1.16.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cd13e354df9938598af2be05822c323e97132d5e6306b83a3b4ee6724c6e522e", size = 35802371 }, - { url = "https://files.pythonhosted.org/packages/c1/8d/5964ef68bb31829bde27611f8c9deeac13764589fe74a75390242b64ca44/scipy-1.16.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:63d3cdacb8a824a295191a723ee5e4ea7768ca5ca5f2838532d9f2e2b3ce2135", size = 36190477 }, - { url = "https://files.pythonhosted.org/packages/ab/f2/b31d75cb9b5fa4dd39a0a931ee9b33e7f6f36f23be5ef560bf72e0f92f32/scipy-1.16.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e7efa2681ea410b10dde31a52b18b0154d66f2485328830e45fdf183af5aefc6", size = 38796678 }, - { url = "https://files.pythonhosted.org/packages/b4/1e/b3723d8ff64ab548c38d87055483714fefe6ee20e0189b62352b5e015bb1/scipy-1.16.3-cp313-cp313t-win_amd64.whl", hash = "sha256:2d1ae2cf0c350e7705168ff2429962a89ad90c2d49d1dd300686d8b2a5af22fc", size = 38640178 }, - { url = "https://files.pythonhosted.org/packages/8e/f3/d854ff38789aca9b0cc23008d607ced9de4f7ab14fa1ca4329f86b3758ca/scipy-1.16.3-cp313-cp313t-win_arm64.whl", hash = "sha256:0c623a54f7b79dd88ef56da19bc2873afec9673a48f3b85b18e4d402bdd29a5a", size = 25803246 }, - { url = "https://files.pythonhosted.org/packages/99/f6/99b10fd70f2d864c1e29a28bbcaa0c6340f9d8518396542d9ea3b4aaae15/scipy-1.16.3-cp314-cp314-macosx_10_14_x86_64.whl", hash = "sha256:875555ce62743e1d54f06cdf22c1e0bc47b91130ac40fe5d783b6dfa114beeb6", size = 36606469 }, - { url = "https://files.pythonhosted.org/packages/4d/74/043b54f2319f48ea940dd025779fa28ee360e6b95acb7cd188fad4391c6b/scipy-1.16.3-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:bb61878c18a470021fb515a843dc7a76961a8daceaaaa8bad1332f1bf4b54657", size = 28872043 }, - { url = "https://files.pythonhosted.org/packages/4d/e1/24b7e50cc1c4ee6ffbcb1f27fe9f4c8b40e7911675f6d2d20955f41c6348/scipy-1.16.3-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:f2622206f5559784fa5c4b53a950c3c7c1cf3e84ca1b9c4b6c03f062f289ca26", size = 20862952 }, - { url = "https://files.pythonhosted.org/packages/dd/3a/3e8c01a4d742b730df368e063787c6808597ccb38636ed821d10b39ca51b/scipy-1.16.3-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:7f68154688c515cdb541a31ef8eb66d8cd1050605be9dcd74199cbd22ac739bc", size = 23508512 }, - { url = "https://files.pythonhosted.org/packages/1f/60/c45a12b98ad591536bfe5330cb3cfe1850d7570259303563b1721564d458/scipy-1.16.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:8b3c820ddb80029fe9f43d61b81d8b488d3ef8ca010d15122b152db77dc94c22", size = 33413639 }, - { url = "https://files.pythonhosted.org/packages/71/bc/35957d88645476307e4839712642896689df442f3e53b0fa016ecf8a3357/scipy-1.16.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d3837938ae715fc0fe3c39c0202de3a8853aff22ca66781ddc2ade7554b7e2cc", size = 35704729 }, - { url = "https://files.pythonhosted.org/packages/3b/15/89105e659041b1ca11c386e9995aefacd513a78493656e57789f9d9eab61/scipy-1.16.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:aadd23f98f9cb069b3bd64ddc900c4d277778242e961751f77a8cb5c4b946fb0", size = 36086251 }, - { url = "https://files.pythonhosted.org/packages/1a/87/c0ea673ac9c6cc50b3da2196d860273bc7389aa69b64efa8493bdd25b093/scipy-1.16.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:b7c5f1bda1354d6a19bc6af73a649f8285ca63ac6b52e64e658a5a11d4d69800", size = 38716681 }, - { url = "https://files.pythonhosted.org/packages/91/06/837893227b043fb9b0d13e4bd7586982d8136cb249ffb3492930dab905b8/scipy-1.16.3-cp314-cp314-win_amd64.whl", hash = "sha256:e5d42a9472e7579e473879a1990327830493a7047506d58d73fc429b84c1d49d", size = 39358423 }, - { url = "https://files.pythonhosted.org/packages/95/03/28bce0355e4d34a7c034727505a02d19548549e190bedd13a721e35380b7/scipy-1.16.3-cp314-cp314-win_arm64.whl", hash = "sha256:6020470b9d00245926f2d5bb93b119ca0340f0d564eb6fbaad843eaebf9d690f", size = 26135027 }, - { url = "https://files.pythonhosted.org/packages/b2/6f/69f1e2b682efe9de8fe9f91040f0cd32f13cfccba690512ba4c582b0bc29/scipy-1.16.3-cp314-cp314t-macosx_10_14_x86_64.whl", hash = "sha256:e1d27cbcb4602680a49d787d90664fa4974063ac9d4134813332a8c53dbe667c", size = 37028379 }, - { url = "https://files.pythonhosted.org/packages/7c/2d/e826f31624a5ebbab1cd93d30fd74349914753076ed0593e1d56a98c4fb4/scipy-1.16.3-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:9b9c9c07b6d56a35777a1b4cc8966118fb16cfd8daf6743867d17d36cfad2d40", size = 29400052 }, - { url = "https://files.pythonhosted.org/packages/69/27/d24feb80155f41fd1f156bf144e7e049b4e2b9dd06261a242905e3bc7a03/scipy-1.16.3-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:3a4c460301fb2cffb7f88528f30b3127742cff583603aa7dc964a52c463b385d", size = 21391183 }, - { url = "https://files.pythonhosted.org/packages/f8/d3/1b229e433074c5738a24277eca520a2319aac7465eea7310ea6ae0e98ae2/scipy-1.16.3-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:f667a4542cc8917af1db06366d3f78a5c8e83badd56409f94d1eac8d8d9133fa", size = 23930174 }, - { url = "https://files.pythonhosted.org/packages/16/9d/d9e148b0ec680c0f042581a2be79a28a7ab66c0c4946697f9e7553ead337/scipy-1.16.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f379b54b77a597aa7ee5e697df0d66903e41b9c85a6dd7946159e356319158e8", size = 33497852 }, - { url = "https://files.pythonhosted.org/packages/2f/22/4e5f7561e4f98b7bea63cf3fd7934bff1e3182e9f1626b089a679914d5c8/scipy-1.16.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4aff59800a3b7f786b70bfd6ab551001cb553244988d7d6b8299cb1ea653b353", size = 35798595 }, - { url = "https://files.pythonhosted.org/packages/83/42/6644d714c179429fc7196857866f219fef25238319b650bb32dde7bf7a48/scipy-1.16.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:da7763f55885045036fabcebd80144b757d3db06ab0861415d1c3b7c69042146", size = 36186269 }, - { url = "https://files.pythonhosted.org/packages/ac/70/64b4d7ca92f9cf2e6fc6aaa2eecf80bb9b6b985043a9583f32f8177ea122/scipy-1.16.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ffa6eea95283b2b8079b821dc11f50a17d0571c92b43e2b5b12764dc5f9b285d", size = 38802779 }, - { url = "https://files.pythonhosted.org/packages/61/82/8d0e39f62764cce5ffd5284131e109f07cf8955aef9ab8ed4e3aa5e30539/scipy-1.16.3-cp314-cp314t-win_amd64.whl", hash = "sha256:d9f48cafc7ce94cf9b15c6bffdc443a81a27bf7075cf2dcd5c8b40f85d10c4e7", size = 39471128 }, - { url = "https://files.pythonhosted.org/packages/64/47/a494741db7280eae6dc033510c319e34d42dd41b7ac0c7ead39354d1a2b5/scipy-1.16.3-cp314-cp314t-win_arm64.whl", hash = "sha256:21d9d6b197227a12dcbf9633320a4e34c6b0e51c57268df255a0942983bac562", size = 26464127 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) +] + +[[package]] +name = "scipy" +version = "1.18.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.14'", + "python_full_version >= '3.12' and python_full_version < '3.14'", +] +dependencies = [ + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a7/25/c2700dfaf6442b4effaa91af24ebce5dc9d31bb4a69706313aae70d72cd0/scipy-1.18.0.tar.gz", hash = "sha256:67b2ad2ad54c72ca6d04975a9b2df8c3638c34ddd5b28738e94fc2b57929d378", size = 30774447, upload-time = "2026-06-19T15:01:43.456Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6a/19/ca10ead60b0acc80b2b833c2c4a4f2ff753d0f58b811f70d911c7e94a25c/scipy-1.18.0-cp312-cp312-macosx_10_15_x86_64.whl", hash = "sha256:7bd21faaf5a1a3b2eff922d02db5f191b99a6518db9078a8fb23169f6d22259a", size = 31056519, upload-time = "2026-06-19T14:59:45.203Z" }, + { url = "https://files.pythonhosted.org/packages/96/72/1e6442a00cd2924d361aa1b642ab6373ec35c6fabf311a760be9f76e0f13/scipy-1.18.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:265915e79107de9f946b855e50d7470d5893ec3f54b342e1aa6201cbdcd8bb6b", size = 28681889, upload-time = "2026-06-19T14:59:48.103Z" }, + { url = "https://files.pythonhosted.org/packages/9b/2d/11dd93d21e147a73ba22bd75c0b9208d3a2e0ec76d53170ce7d9029b1015/scipy-1.18.0-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:9ab7b758be6940954a713ee466e2043e9f6e2ed965c1fce5c91039f4be3d90a9", size = 20423580, upload-time = "2026-06-19T14:59:50.665Z" }, + { url = "https://files.pythonhosted.org/packages/9c/01/93552f75e0d2a7dd115a45e59209c51e8d514daff02fc887d2623be06fe1/scipy-1.18.0-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:97b6cddaaee0a779ef6b5ca83c9604b27cc16b2b8fc22c142652df8793319fb8", size = 23054441, upload-time = "2026-06-19T14:59:53.564Z" }, + { url = "https://files.pythonhosted.org/packages/3c/23/21f5e703643d66f21faa6b4c73195bfcad70c55efcb4f1ab327cd7c4101a/scipy-1.18.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:52a96e21517c7292375c0e27dd796a811f03fcea5fd4d108fdfea8145dcf17ab", size = 33968720, upload-time = "2026-06-19T14:59:56.415Z" }, + { url = "https://files.pythonhosted.org/packages/dd/aa/1b939f6c67ed68635bb538e6752d3dacc02f66535182e939a89581a44e9c/scipy-1.18.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1f55797419e16e7f30cf88ffb3113ce0467f00cfe3f70d5c281730b21769bfc2", size = 35287115, upload-time = "2026-06-19T14:59:59.411Z" }, + { url = "https://files.pythonhosted.org/packages/b6/ff/eec46be7e9234208f801062b53e1983085eddebd693f6c9bfb03b459830d/scipy-1.18.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ad033410e2e0672ffdc1042110cef20e1c46f8fd0616cee1d44d8d58fad8fc11", size = 35577989, upload-time = "2026-06-19T15:00:02.235Z" }, + { url = "https://files.pythonhosted.org/packages/84/ca/210d4759c7210bb7d269437421959b39a33434e2776b60c5cb8a763bb30a/scipy-1.18.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:4a55985d54c769c872e64b7f4c8a81cc30ef700cc04296abbbf3705439c126de", size = 37421717, upload-time = "2026-06-19T15:00:05.102Z" }, + { url = "https://files.pythonhosted.org/packages/2b/54/9a9edb45345bd6744da5ddfb6628e5d5185920494c6a67ec45b6381004cb/scipy-1.18.0-cp312-cp312-win_amd64.whl", hash = "sha256:71ccc8faa2dd16ac310233203474a8b5cb67f10dedd54a3116d34943f4b19132", size = 36597428, upload-time = "2026-06-19T15:00:08.112Z" }, + { url = "https://files.pythonhosted.org/packages/99/0e/33f32a2a58987e26aec0f7df252cbbad1e90ae77bdbc76f40dd4ed0cf0ea/scipy-1.18.0-cp312-cp312-win_arm64.whl", hash = "sha256:d88363fd9d8fbd3511bd273f1a49efb2a540773ddf92a91d57498ce7dd7f3e76", size = 24351481, upload-time = "2026-06-19T15:00:11.103Z" }, + { url = "https://files.pythonhosted.org/packages/05/52/9c0136c2de7ae0779b7b366447766cec6d9f0702c56bb8ffeb04c8fd3af4/scipy-1.18.0-cp313-cp313-macosx_10_15_x86_64.whl", hash = "sha256:09143f676d157d9f546d663504ef9c1becb819824f1afc018814176411942446", size = 31036107, upload-time = "2026-06-19T15:00:14.03Z" }, + { url = "https://files.pythonhosted.org/packages/02/73/0291a64843270f4efb86cdcf2ee0f2048631b65ec6b405398b2b4dbf11bf/scipy-1.18.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:5efe260f69417b97ddae455bfb5a95e8359f7f66ad7fa9522a60feb66f169520", size = 28663303, upload-time = "2026-06-19T15:00:16.819Z" }, + { url = "https://files.pythonhosted.org/packages/d3/0f/10ffa0b697a572f4e0d48b92a88895d366422f019f723e7e14a84c050dac/scipy-1.18.0-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:68363b7eaacd8b5dd426df56d782cc156468ac79a127a1b87ca597d6e2e82197", size = 20404960, upload-time = "2026-06-19T15:00:19.635Z" }, + { url = "https://files.pythonhosted.org/packages/7e/d2/e896cea21ba8edd6c81d4c55b1ffcc717e79698dcbebf9641b4cfb4c6622/scipy-1.18.0-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:c5557d8be5da8e41353fcd4d21491fdbab83b062fc579e94dc09a7c8ab4f669b", size = 23034074, upload-time = "2026-06-19T15:00:22.107Z" }, + { url = "https://files.pythonhosted.org/packages/ea/b2/e83ea34279a52c03374477c74006256ec78df65fc877baa4617d6de1d202/scipy-1.18.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0d13bca67c096d89fb95ced0d8921807300fce0275643aef9533cc63a0773468", size = 33942038, upload-time = "2026-06-19T15:00:24.964Z" }, + { url = "https://files.pythonhosted.org/packages/f6/af/e8fe5fb136f51e2b01678b92cb4106d10d8cd68ec147ead2e7cb0ac75398/scipy-1.18.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a46f9273dbd0eb1cefba61c9b8648b4dfe3cbc14a080176f9a73e44b8336dc7f", size = 35266390, upload-time = "2026-06-19T15:00:28.059Z" }, + { url = "https://files.pythonhosted.org/packages/3a/49/2c5cbb907b56695fc67517811d1db234dfd83381a84814ec220aded2794d/scipy-1.18.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5aba46108853ddfc77906b6557aac839d2b52e900c1d72a1180adaaab58d265f", size = 35551324, upload-time = "2026-06-19T15:00:31.014Z" }, + { url = "https://files.pythonhosted.org/packages/bb/73/eda39f7a2d306ff0ffc574afd13c0bbb6d10a603d9a413998ee269487a80/scipy-1.18.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b6f758e35f12757b5d95c00bc6de2438e229c2664b7a92e96f205959d9f2dfa4", size = 37404785, upload-time = "2026-06-19T15:00:34.072Z" }, + { url = "https://files.pythonhosted.org/packages/b7/d2/ae881ee28d014f38e0ccbfd974a06a919ba9af34f1f74bf42b5301891d63/scipy-1.18.0-cp313-cp313-win_amd64.whl", hash = "sha256:1afac4a847207c7ff8efd321734a50b06d0280b3b2a2c0fc2f413101747ad7c7", size = 36554943, upload-time = "2026-06-19T15:00:36.903Z" }, + { url = "https://files.pythonhosted.org/packages/70/3a/21154e2d54eb3639c6bf4dbae2e531c68356bfe95990daa30df33b30d556/scipy-1.18.0-cp313-cp313-win_arm64.whl", hash = "sha256:c5dbddf60e58c2312316d097271a8e73d40eaf2eabfa4d95ed7d3695bbf2ce7b", size = 24350911, upload-time = "2026-06-19T15:00:40.062Z" }, + { url = "https://files.pythonhosted.org/packages/78/b5/915a19b3de2f7430062b509653563db1633ddbb6f021b06731521115d4e2/scipy-1.18.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:4c256ee70c0d1a8a2ace807e199ccd4e3f57037433842abb3fb36bc17eaa9578", size = 31036253, upload-time = "2026-06-19T15:00:43.216Z" }, + { url = "https://files.pythonhosted.org/packages/d7/88/b72def7262e150d16be13fca37a96481138d624e700340bc3362a7588929/scipy-1.18.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:2ef3abc54a4ffc53765374b0d5728532dfdd2585ed23f6b11c206a1f0b1b9af8", size = 28673758, upload-time = "2026-06-19T15:00:46.663Z" }, + { url = "https://files.pythonhosted.org/packages/91/02/2e636a61a525632c373cf6a9c24442a3ffb79e364d38e98b32042964ac32/scipy-1.18.0-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:f2a6af57bd9e4a75d70e4117e78a1bbee84f79ae3fbb6d0111005d6ebcc4cb8d", size = 20415514, upload-time = "2026-06-19T15:00:49.399Z" }, + { url = "https://files.pythonhosted.org/packages/c9/b6/2135974442f6aba159d9d39d774a1c8cb19947016725d69fecc685df45bf/scipy-1.18.0-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:3f1ac564d3bf6c03d861d2cd87a1bea0da2887136f7fb1bf519c05a8971452d6", size = 23034398, upload-time = "2026-06-19T15:00:51.941Z" }, + { url = "https://files.pythonhosted.org/packages/f6/e6/ba89ec5abf6ee9257c0d1ec985573f3ae32742c24bc03e016388a40b1b15/scipy-1.18.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:40395a5fcd1abee49a5c7aaa98c29db393eedc835138560a588c47ec16156690", size = 33998032, upload-time = "2026-06-19T15:00:54.838Z" }, + { url = "https://files.pythonhosted.org/packages/7f/c4/bc41eb19b0fd0db868f4132920879019318d80cc522ad8f2bca4611af808/scipy-1.18.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8ca01e8ae69f1b18e9a58d91afead31be3cef0dd905a10249dac559ee15460a0", size = 35283333, upload-time = "2026-06-19T15:00:58.152Z" }, + { url = "https://files.pythonhosted.org/packages/53/a4/cbdeef6eb3830a8462a9d4ada814de5fc984345cc9ecf17cbec51a036f1e/scipy-1.18.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7a7f3b01647384dbc3a711e8c6778e0aabbe93959249fef5c7393396bcac0867", size = 35610216, upload-time = "2026-06-19T15:01:01.155Z" }, + { url = "https://files.pythonhosted.org/packages/80/4d/b2b82502b65f661d1b789c1665dcdf315d5f12194e06fc0b37946294ebae/scipy-1.18.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6aa94e78ec192a30063a5e72e561c28af769dc311190b24fe91774eff1969709", size = 37418960, upload-time = "2026-06-19T15:01:04.155Z" }, + { url = "https://files.pythonhosted.org/packages/93/3e/902d836831474b0ab5a37d16404f7bc5fafd9efba632890e271ba952635f/scipy-1.18.0-cp314-cp314-win_amd64.whl", hash = "sha256:2d8bbdc6c817f5b4006a54d799d4f5bab6f910193cbb9a1ff310833d4d270f61", size = 37288845, upload-time = "2026-06-19T15:01:07.822Z" }, + { url = "https://files.pythonhosted.org/packages/b6/43/8d73b337a3bdb14daa0314f0434210747c02d79d729ce1777574a817dcf6/scipy-1.18.0-cp314-cp314-win_arm64.whl", hash = "sha256:18e9575f1569b2c54174e6159d32942e03731177f63dce7975f0a0c88d102f5b", size = 24988971, upload-time = "2026-06-19T15:01:11.076Z" }, + { url = "https://files.pythonhosted.org/packages/b4/b4/f11918b0508a2787031a0499a03fbe3546f3bb5ca05d01038c45b278c09a/scipy-1.18.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:f351e0dd702687d12a402b867a1b4146a256923e1c38317cbc472f6372b94707", size = 31399325, upload-time = "2026-06-19T15:01:13.723Z" }, + { url = "https://files.pythonhosted.org/packages/7b/d1/1f287b57c0ff0ee5185dff3946d92c8017d39b0e431f0ae79a3ff1859512/scipy-1.18.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:7c7a51b33ce387193c97f228320cf8e87361daa1bba750638677729598b3e677", size = 29092110, upload-time = "2026-06-19T15:01:16.908Z" }, + { url = "https://files.pythonhosted.org/packages/ff/1a/7b74eb6c392fdcb27d414c0e7558a6d0231eb3b6d73571f479bb81ea8794/scipy-1.18.0-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:84031d7b052a54fae2f8632e0ec802073d385476eb9a63079bce6e23ef9283d4", size = 20833811, upload-time = "2026-06-19T15:01:20.488Z" }, + { url = "https://files.pythonhosted.org/packages/7c/ad/f3941716320a7b9cb4d68734a903b45fe16eff5fb7da7e16f2e619304979/scipy-1.18.0-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:56abf29a7c067dde59be8b9a22d606a4ea1b2f2a4b756d9d903c62818f5dacce", size = 23396644, upload-time = "2026-06-19T15:01:23.364Z" }, + { url = "https://files.pythonhosted.org/packages/22/22/1446b62ffe07f9719b7d9b1b6a4e05a772833ae8f441fe4c22c34c9b250f/scipy-1.18.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1ad44305cfa24b1ba5803cbbebf033590ccbac1aa5d612d727b785325ab408b0", size = 34079318, upload-time = "2026-06-19T15:01:26.002Z" }, + { url = "https://files.pythonhosted.org/packages/56/3b/b87da667098bb470fa30c7011b0ba351ee976dd395c78798c66e941665a3/scipy-1.18.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:945c1761b93f38d7f99ae81ae80c63e621471608c7eeead563f6df025585cd58", size = 35324320, upload-time = "2026-06-19T15:01:28.881Z" }, + { url = "https://files.pythonhosted.org/packages/f8/a1/c7932f91909759b0267f75fdea34e91309f96b895757534b76a90b6b4344/scipy-1.18.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:1a4441f15d620578772a49e5ab48c0ee1f7a0220e387110283062729136b2553", size = 35699541, upload-time = "2026-06-19T15:01:31.968Z" }, + { url = "https://files.pythonhosted.org/packages/f7/86/5185061a1fcc41d18c5dc2463969b3a3964b31d9ac67b2fb05d4c7ff7670/scipy-1.18.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:9aac6192fac56bf2ca534389d24623f07b39ff83317d58287285e7fbd622ff76", size = 37472480, upload-time = "2026-06-19T15:01:35.136Z" }, + { url = "https://files.pythonhosted.org/packages/31/8e/f04c68e39919a010d34f2ee1367fd705b0a25a02f609d755f0bfbc0a15fc/scipy-1.18.0-cp314-cp314t-win_amd64.whl", hash = "sha256:e40baea28ae7f5475c779741e2d90b1247c78531207b49c7030e698ff81cee3f", size = 37365390, upload-time = "2026-06-19T15:01:38.091Z" }, + { url = "https://files.pythonhosted.org/packages/d5/19/969dc072906c84dd0a3b05dcf57ea750936087d7873549e408b35cfc3f97/scipy-1.18.0-cp314-cp314t-win_arm64.whl", hash = "sha256:368e0a705903c466aa5f08eefb39e6b1b6b2d659e7352a31fd9e2438365be0f8", size = 25279661, upload-time = "2026-06-19T15:01:40.817Z" }, ] [[package]] name = "send2trash" version = "2.1.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/c5/f0/184b4b5f8d00f2a92cf96eec8967a3d550b52cf94362dad1100df9e48d57/send2trash-2.1.0.tar.gz", hash = "sha256:1c72b39f09457db3c05ce1d19158c2cbef4c32b8bedd02c155e49282b7ea7459", size = 17255, upload-time = "2026-01-14T06:27:36.056Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/1c/78/504fdd027da3b84ff1aecd9f6957e65f35134534ccc6da8628eb71e76d3f/send2trash-2.1.0-py3-none-any.whl", hash = "sha256:0da2f112e6d6bb22de6aa6daa7e144831a4febf2a87261451c4ad849fe9a873c", size = 17610, upload-time = "2026-01-14T06:27:35.218Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/fd/3a/aec9b02217bb79b87bbc1a21bc6abc51e3d5dcf65c30487ac96c0908c722/Send2Trash-1.8.3.tar.gz", hash = "sha256:b18e7a3966d99871aefeb00cfbcfdced55ce4871194810fc71f4aa484b953abf", size = 17394 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/40/b0/4562db6223154aa4e22f939003cb92514c79f3d4dccca3444253fd17f902/Send2Trash-1.8.3-py3-none-any.whl", hash = "sha256:0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9", size = 18072 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "setuptools" version = "81.0.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/0d/1c/73e719955c59b8e424d015ab450f51c0af856ae46ea2da83eba51cc88de1/setuptools-81.0.0.tar.gz", hash = "sha256:487b53915f52501f0a79ccfd0c02c165ffe06631443a886740b91af4b7a5845a", size = 1198299, upload-time = "2026-02-06T21:10:39.601Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/e1/e3/c164c88b2e5ce7b24d667b9bd83589cf4f3520d97cad01534cd3c4f55fdb/setuptools-81.0.0-py3-none-any.whl", hash = "sha256:fdd925d5c5d9f62e4b74b30d6dd7828ce236fd6ed998a08d81de62ce5a6310d6", size = 1062021, upload-time = "2026-02-06T21:10:37.175Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/18/5d/3bf57dcd21979b887f014ea83c24ae194cfcd12b9e0fda66b957c69d1fca/setuptools-80.9.0.tar.gz", hash = "sha256:f36b47402ecde768dbfafc46e8e4207b4360c654f1f3bb84475f0a28628fb19c", size = 1319958 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/a3/dc/17031897dae0efacfea57dfd3a82fdd2a2aeb58e0ff71b77b87e44edc772/setuptools-80.9.0-py3-none-any.whl", hash = "sha256:062d34222ad13e0cc312a4c02d73f059e86a4acbfbdea8f8f76b28c99f306922", size = 1201486 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "six" version = "1.17.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031 } +sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031, upload-time = "2024-12-04T17:35:28.174Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050 }, + { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" }, ] [[package]] -<<<<<<< HEAD -======= -name = "sniffio" -version = "1.3.1" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/a2/87/a6771e1546d97e7e041b6ae58d80074f81b7d5121207425c964ddf5cfdbd/sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc", size = 20372 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2", size = 10235 }, -] - -[[package]] ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) name = "soupsieve" version = "2.8.4" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/47/2c/0a5f6f8ee0d5589e48c7640213ed5175d52cf540a06725b628cc1a45d6ce/soupsieve-2.8.4.tar.gz", hash = "sha256:e121fd02e975c695e4e9e8774a5ee35d74714b59307868dcc5319ad2d9e3328e", size = 121110, upload-time = "2026-05-24T13:55:57.154Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/5e/f5/0c41cb68dcae6b7de4fac4188a3a9589e21fb31df21ea3a2e888db95e6c9/soupsieve-2.8.4-py3-none-any.whl", hash = "sha256:e7e6b0769c8f51ed59acab6e994b00621096cfb1c640a7509295987388fbaf65", size = 37304, upload-time = "2026-05-24T13:55:55.406Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/6d/e6/21ccce3262dd4889aa3332e5a119a3491a95e8f60939870a3a035aabac0d/soupsieve-2.8.tar.gz", hash = "sha256:e2dd4a40a628cb5f28f6d4b0db8800b8f581b65bb380b97de22ba5ca8d72572f", size = 103472 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/14/a0/bb38d3b76b8cae341dad93a2dd83ab7462e6dbcdd84d43f54ee60a8dc167/soupsieve-2.8-py3-none-any.whl", hash = "sha256:0cc76456a30e20f5d7f2e14a98a4ae2ee4e5abdc7c5ea0aafe795f344bc7984c", size = 36679 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "sqlalchemy" -version = "2.0.50" +version = "2.0.51" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "greenlet", marker = "platform_machine == 'AMD64' or platform_machine == 'WIN32' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'ppc64le' or platform_machine == 'win32' or platform_machine == 'x86_64'" }, { name = "typing-extensions" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/57/da/6fbf010c8ebb347679d0d100b22fe9ba5e13fd04046c5df7280d2f0bf706/sqlalchemy-2.0.50.tar.gz", hash = "sha256:af5607d11ef90fd6a5c0549fe0045dce1663d427426bcfb506dcb5346a85a3b9", size = 9907424, upload-time = "2026-05-24T19:20:04.018Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b6/5d/3172686af1770e4de2805f919a51441085f589ddadf3dd76ec582f84f497/sqlalchemy-2.0.50-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1aa6e403663a9c43c8fef7ce4bdb4cf48bcd8d352e91deda2a99f963270bd508", size = 2161366, upload-time = "2026-05-24T20:00:02.061Z" }, - { url = "https://files.pythonhosted.org/packages/0f/90/e98dedea3c3e663a17afcd003a34ba45efdac2cea3b6f2e4585e2b1e2537/sqlalchemy-2.0.50-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:51b637a84f9fa35ae1f9017e786cb142974a25305085e1b378b3647a67f65ad3", size = 3318926, upload-time = "2026-05-24T20:07:42.369Z" }, - { url = "https://files.pythonhosted.org/packages/3b/4f/501308c2babb62c11753ecb4ee88ba9eef019419a4d6cbf7cb13e2bad353/sqlalchemy-2.0.50-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2dab927761d9108550f0cf8e66ff21af56f907a0ce0a689793db615e2b55f62c", size = 3319199, upload-time = "2026-05-24T20:14:28.551Z" }, - { url = "https://files.pythonhosted.org/packages/ac/39/d88996c5e03ed6248c3a788d20f0b8d8b376b9f8a495e4bab9df7c72d2f8/sqlalchemy-2.0.50-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:545eae198d37bcf837a10ede3684e2af32458d6f35c597c35c2de7502dc38fc4", size = 3270301, upload-time = "2026-05-24T20:07:44.917Z" }, - { url = "https://files.pythonhosted.org/packages/42/1b/1ae0e65161b51cc43e5ca75430ef79d80e23b5042d645586c2c342c3b92e/sqlalchemy-2.0.50-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:0fec460e18cdbb4c7773531122ce9a27e96c6ca17af3933941d94da475ad2c86", size = 3293465, upload-time = "2026-05-24T20:14:30.501Z" }, - { url = "https://files.pythonhosted.org/packages/83/29/17c0003f2c0dfa6d1b97672475707e3ec5980db09defd7fa20beb6833bbd/sqlalchemy-2.0.50-cp311-cp311-win32.whl", hash = "sha256:e6e814658818fd165e749e3d8490ef16cc7f379a118c37ada8b0589ffbaaac22", size = 2120694, upload-time = "2026-05-24T20:08:09.237Z" }, - { url = "https://files.pythonhosted.org/packages/c9/18/280d00654cc19d1fccf236fa5070f6dd04b84dde6f1b2e637bde0ff340a7/sqlalchemy-2.0.50-cp311-cp311-win_amd64.whl", hash = "sha256:1c5f858fe79c9f5d8fda065c06186356acb7f8df3cd52dbd5ee3f200e4b144f5", size = 2145315, upload-time = "2026-05-24T20:08:10.952Z" }, - { url = "https://files.pythonhosted.org/packages/be/b0/a9d19b43f38f878b1278bca5b00b909f7540d41494396dd2561f9ad0956d/sqlalchemy-2.0.50-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:23ae23d8b9d344d30d0a92f06d45825024a5790f1c1dd4cf452636a50d3e58cb", size = 2159807, upload-time = "2026-05-24T19:27:53.086Z" }, - { url = "https://files.pythonhosted.org/packages/f5/2c/191dd58a248fd2cfd4780fa82c375c505e4ad98c8b522fa69ec492130d77/sqlalchemy-2.0.50-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:47b71b933e7b4ebad407c8fdfd70d2c4f08b78b3238bb30eebdd6eb32ca51b89", size = 3343358, upload-time = "2026-05-24T20:09:29.279Z" }, - { url = "https://files.pythonhosted.org/packages/8a/2b/514fce8a7df81cf5bad7ff7865de7ac0c5776a38cc043475c4703eb7fe8b/sqlalchemy-2.0.50-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:110fdac56ace278949f00de805edacbd6141e382d992f9ba28238b3a0827a600", size = 3357994, upload-time = "2026-05-24T20:17:13.495Z" }, - { url = "https://files.pythonhosted.org/packages/35/a6/a0e283f5494f92b0d77e319ff77e437b1ffe4a051ba67c81d53234825475/sqlalchemy-2.0.50-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0f5e4ac70e9e757f6b3e87c0491ff034442ecd8dfd36d041a50564c322dafc0e", size = 3289399, upload-time = "2026-05-24T20:09:32.239Z" }, - { url = "https://files.pythonhosted.org/packages/b7/96/1b07325ba71752d6a028b77d07bed1483ad545f794e8b1dc89b3ba3b3c68/sqlalchemy-2.0.50-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:724f3dcbe53dd0151e3cb5e7ec4ba4c620bede579caacd16275dc35ce06e8615", size = 3321216, upload-time = "2026-05-24T20:17:15.581Z" }, - { url = "https://files.pythonhosted.org/packages/ed/8e/bad6ed253e8a99edfc99af02f7173ec48a1d3ed1b9b35a1b8bc1700900cc/sqlalchemy-2.0.50-cp312-cp312-win32.whl", hash = "sha256:1208050441471d003b7c8cb4054fb084f185cf35ac3f0ea270803865bca9939a", size = 2119194, upload-time = "2026-05-24T19:50:04.943Z" }, - { url = "https://files.pythonhosted.org/packages/b6/2d/314a6690dda4b9cfc571eab1a63cf6fe6e1470aa3759ccda6aa016ee0f5a/sqlalchemy-2.0.50-cp312-cp312-win_amd64.whl", hash = "sha256:9d1af51558029a156a70986b7df88f042b3d158d7c8d8fb5072912d4b32d89c7", size = 2146186, upload-time = "2026-05-24T19:50:06.74Z" }, - { url = "https://files.pythonhosted.org/packages/0b/c4/c42356b527296e9862f67990efce31ef78b4cf69cd3f80873a528a060320/sqlalchemy-2.0.50-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:06a9210bdc5f4298cff0781087e2ff45683922252dacc452846373a58761f093", size = 2156697, upload-time = "2026-05-24T19:27:54.764Z" }, - { url = "https://files.pythonhosted.org/packages/60/a1/b1a70e3c4365ac7fe9e347f3710f19b562c866fb96d45e3c891588789a7b/sqlalchemy-2.0.50-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8b53784972ade4f8174b9aa661f31a06f8a936d2cfdd602913ff3c6dd40ae873", size = 3284260, upload-time = "2026-05-24T20:09:34.195Z" }, - { url = "https://files.pythonhosted.org/packages/3f/4a/f3ac3caa19f263d57b0a47f8c91bbf56583dc2d3fc63acfbf644abb24fe0/sqlalchemy-2.0.50-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:31648fa14460537e768a7303b078e4344d208e0d23e06867c1f376a227ed82db", size = 3302280, upload-time = "2026-05-24T20:17:17.825Z" }, - { url = "https://files.pythonhosted.org/packages/66/55/ccada3e3d62254587819749a0bc69f41173eb48a6e385d10e66d32a9c88e/sqlalchemy-2.0.50-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:03f4323c980ad0e918cc9e5369b015f759f4e534db5bbaf4dc36832c10d05064", size = 3231580, upload-time = "2026-05-24T20:09:36.406Z" }, - { url = "https://files.pythonhosted.org/packages/05/f6/6809349130a2de0e109e7f00fd7d431da9565b9b2868b32ee684754f672b/sqlalchemy-2.0.50-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:2b9dcc43afef8ac157cd92fce96985d6b8b0cfbd3df4d666f66b4d55a75d202f", size = 3269375, upload-time = "2026-05-24T20:17:20.34Z" }, - { url = "https://files.pythonhosted.org/packages/48/84/278a811ef4e07be9c89dc5cdd7be833268509a66a68c4897cf585e67428f/sqlalchemy-2.0.50-cp313-cp313-win32.whl", hash = "sha256:60922d6599065ddca2c6f376b9aa2f41a6b85a271725e0909490bbc50b1998a5", size = 2117229, upload-time = "2026-05-24T19:50:08.215Z" }, - { url = "https://files.pythonhosted.org/packages/f6/1c/067cc6187ed32d2ec222fe6d2643acc1659a6d0659f8a7cbc5ad3ae83280/sqlalchemy-2.0.50-cp313-cp313-win_amd64.whl", hash = "sha256:287086e67275a212c4582d166a6fb03a65ccc5551d80866270ce0dd9f34eccd3", size = 2143126, upload-time = "2026-05-24T19:50:09.691Z" }, - { url = "https://files.pythonhosted.org/packages/df/32/10ac51b4be7cdecd7e93d069251c86dfbf70b7adbd7c67b48ccea6c49e1c/sqlalchemy-2.0.50-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c966932507a4d7d0a37314927dbfcd89720e3f37d2a1e3352e7ae7939fa8e8a0", size = 2158519, upload-time = "2026-05-24T19:27:56.472Z" }, - { url = "https://files.pythonhosted.org/packages/5a/76/e703d2f7681d7d66c4c891af3f07c7ccf4c76ad7f18351de035b5eda007a/sqlalchemy-2.0.50-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:faffef4bcc20a1892e65e155293d99d60855bbbc79250ab712819cfd56a8e6bb", size = 3282063, upload-time = "2026-05-24T20:09:38.57Z" }, - { url = "https://files.pythonhosted.org/packages/31/26/ef168b184a25701f9995e8fb7e503fafd7a99c1c77cda1bc1a26ea2ed486/sqlalchemy-2.0.50-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6c206aec519a2e7bd08abbfb33436e325fd22c632d9c21a9047e376ce241646e", size = 3287069, upload-time = "2026-05-24T20:17:21.942Z" }, - { url = "https://files.pythonhosted.org/packages/c2/15/765acc2bc693bccc43ca4a95d5b69750da8aaf6db1b5c616536e087f8920/sqlalchemy-2.0.50-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:bef4ac756363227ef6402a75fee025a4bc690f92328e825868939b3b3a446a6d", size = 3230453, upload-time = "2026-05-24T20:09:40.398Z" }, - { url = "https://files.pythonhosted.org/packages/63/61/08e03c3adbf5db0087a0b6816746fec8f3032fb2f7fc899a9bb9b2a48ce4/sqlalchemy-2.0.50-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:96fbee6b19c19cd1556c8bf9419447cf2ec149ffcab7ab64348c23e54ef8547f", size = 3252413, upload-time = "2026-05-24T20:17:24.067Z" }, - { url = "https://files.pythonhosted.org/packages/03/0c/370a1f2db38436c615e10134c8a37de3688e74084792380695f3f5083860/sqlalchemy-2.0.50-cp314-cp314-win32.whl", hash = "sha256:8f00e3eb43ba30eb1b238ee03a8a62309486d1321eda3328bb611e0340033ad8", size = 2120063, upload-time = "2026-05-24T19:50:11.08Z" }, - { url = "https://files.pythonhosted.org/packages/7f/a0/fe92bb9817863bc13ba093bda931979a26cc2ca69f8e8f26d07add3d7c6f/sqlalchemy-2.0.50-cp314-cp314-win_amd64.whl", hash = "sha256:15708c613cd5005b7dffe1f66ee6a63ee8f5e46799f71c70ebad74178c676a39", size = 2145830, upload-time = "2026-05-24T19:50:12.452Z" }, - { url = "https://files.pythonhosted.org/packages/cc/ff/e5640a98a0b2f491eb8fde10fb6c773621a2e44340de231fafcc9370f4a9/sqlalchemy-2.0.50-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:3699dac4be410e97049a1658e9480da9cde956594aa0f3aebc60b88f21c5ba70", size = 2178435, upload-time = "2026-05-24T19:42:58.889Z" }, - { url = "https://files.pythonhosted.org/packages/b7/85/337116e186f1236375b5fb70c21cfac98e8e8ab0d3a47be838dc47a59e08/sqlalchemy-2.0.50-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f96233858e3df43932ac11589e22520da6e8aeb624b03fedfeebb0e8ea213086", size = 3566059, upload-time = "2026-05-24T20:01:20.848Z" }, - { url = "https://files.pythonhosted.org/packages/96/34/bb0e190e161c3c2c24314a65add57218be14a4a9486886b7f5047c1ff7c8/sqlalchemy-2.0.50-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c4e70c46fad30c3bcc6a4708bc0130a3173e11a5b25f0ea4a9d8911b450f1f52", size = 3535366, upload-time = "2026-05-24T20:03:56.768Z" }, - { url = "https://files.pythonhosted.org/packages/df/5a/a7f759f97e4fd499c5d4e4488c760d5a7fbecf3028b465a04274fcd52384/sqlalchemy-2.0.50-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:1918a3cf564d16d95bca7301005f41ab2ad50b07cd3b9da50d3ed986db148d6a", size = 3474879, upload-time = "2026-05-24T20:01:23.058Z" }, - { url = "https://files.pythonhosted.org/packages/9d/d9/2907ea38eb60687d297bf9c39e5ee58053c87b57fe8a9cae97090cecbf10/sqlalchemy-2.0.50-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:b00098cdbdbd38c7be3d568b0c9c3122b8c0ec62b911b57cd5e6e0254d60a76d", size = 3486117, upload-time = "2026-05-24T20:03:59.052Z" }, - { url = "https://files.pythonhosted.org/packages/f2/e3/5aa06f167559f8c0bdae487e297d23ba548150ab016a3418265d617a4985/sqlalchemy-2.0.50-cp314-cp314t-win32.whl", hash = "sha256:1fbd55a969d7ac44a98e3dec75016074f809fa08f871585ace58dde110d1bf3e", size = 2150823, upload-time = "2026-05-24T20:08:58.644Z" }, - { url = "https://files.pythonhosted.org/packages/65/9b/112fb8f977582d7489d036e409e3723948bcf5320b3ac465f3c481bbe8f9/sqlalchemy-2.0.50-cp314-cp314t-win_amd64.whl", hash = "sha256:c5c3cdb753a9004183e1ccb634b41611654c989e61bc68617ce878e46d6f1e51", size = 2185794, upload-time = "2026-05-24T20:09:00.319Z" }, - { url = "https://files.pythonhosted.org/packages/d0/10/f7220e9b784d295d241c86ed99aeb537f92afcd469a64861f2717e9bb077/sqlalchemy-2.0.50-py3-none-any.whl", hash = "sha256:92064363517a3ff8212b5a93b8c62876579d8dfd1ca5b561335f30152d884fa9", size = 1943861, upload-time = "2026-05-24T19:59:01.119Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/f0/f2/840d7b9496825333f532d2e3976b8eadbf52034178aac53630d09fe6e1ef/sqlalchemy-2.0.44.tar.gz", hash = "sha256:0ae7454e1ab1d780aee69fd2aae7d6b8670a581d8847f2d1e0f7ddfbf47e5a22", size = 9819830 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e3/81/15d7c161c9ddf0900b076b55345872ed04ff1ed6a0666e5e94ab44b0163c/sqlalchemy-2.0.44-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0fe3917059c7ab2ee3f35e77757062b1bea10a0b6ca633c58391e3f3c6c488dd", size = 2140517 }, - { url = "https://files.pythonhosted.org/packages/d4/d5/4abd13b245c7d91bdf131d4916fd9e96a584dac74215f8b5bc945206a974/sqlalchemy-2.0.44-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:de4387a354ff230bc979b46b2207af841dc8bf29847b6c7dbe60af186d97aefa", size = 2130738 }, - { url = "https://files.pythonhosted.org/packages/cb/3c/8418969879c26522019c1025171cefbb2a8586b6789ea13254ac602986c0/sqlalchemy-2.0.44-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c3678a0fb72c8a6a29422b2732fe423db3ce119c34421b5f9955873eb9b62c1e", size = 3304145 }, - { url = "https://files.pythonhosted.org/packages/94/2d/fdb9246d9d32518bda5d90f4b65030b9bf403a935cfe4c36a474846517cb/sqlalchemy-2.0.44-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3cf6872a23601672d61a68f390e44703442639a12ee9dd5a88bbce52a695e46e", size = 3304511 }, - { url = "https://files.pythonhosted.org/packages/7d/fb/40f2ad1da97d5c83f6c1269664678293d3fe28e90ad17a1093b735420549/sqlalchemy-2.0.44-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:329aa42d1be9929603f406186630135be1e7a42569540577ba2c69952b7cf399", size = 3235161 }, - { url = "https://files.pythonhosted.org/packages/95/cb/7cf4078b46752dca917d18cf31910d4eff6076e5b513c2d66100c4293d83/sqlalchemy-2.0.44-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:70e03833faca7166e6a9927fbee7c27e6ecde436774cd0b24bbcc96353bce06b", size = 3261426 }, - { url = "https://files.pythonhosted.org/packages/f8/3b/55c09b285cb2d55bdfa711e778bdffdd0dc3ffa052b0af41f1c5d6e582fa/sqlalchemy-2.0.44-cp311-cp311-win32.whl", hash = "sha256:253e2f29843fb303eca6b2fc645aca91fa7aa0aa70b38b6950da92d44ff267f3", size = 2105392 }, - { url = "https://files.pythonhosted.org/packages/c7/23/907193c2f4d680aedbfbdf7bf24c13925e3c7c292e813326c1b84a0b878e/sqlalchemy-2.0.44-cp311-cp311-win_amd64.whl", hash = "sha256:7a8694107eb4308a13b425ca8c0e67112f8134c846b6e1f722698708741215d5", size = 2130293 }, - { url = "https://files.pythonhosted.org/packages/62/c4/59c7c9b068e6813c898b771204aad36683c96318ed12d4233e1b18762164/sqlalchemy-2.0.44-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:72fea91746b5890f9e5e0997f16cbf3d53550580d76355ba2d998311b17b2250", size = 2139675 }, - { url = "https://files.pythonhosted.org/packages/d6/ae/eeb0920537a6f9c5a3708e4a5fc55af25900216bdb4847ec29cfddf3bf3a/sqlalchemy-2.0.44-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:585c0c852a891450edbb1eaca8648408a3cc125f18cf433941fa6babcc359e29", size = 2127726 }, - { url = "https://files.pythonhosted.org/packages/d8/d5/2ebbabe0379418eda8041c06b0b551f213576bfe4c2f09d77c06c07c8cc5/sqlalchemy-2.0.44-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9b94843a102efa9ac68a7a30cd46df3ff1ed9c658100d30a725d10d9c60a2f44", size = 3327603 }, - { url = "https://files.pythonhosted.org/packages/45/e5/5aa65852dadc24b7d8ae75b7efb8d19303ed6ac93482e60c44a585930ea5/sqlalchemy-2.0.44-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:119dc41e7a7defcefc57189cfa0e61b1bf9c228211aba432b53fb71ef367fda1", size = 3337842 }, - { url = "https://files.pythonhosted.org/packages/41/92/648f1afd3f20b71e880ca797a960f638d39d243e233a7082c93093c22378/sqlalchemy-2.0.44-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0765e318ee9179b3718c4fd7ba35c434f4dd20332fbc6857a5e8df17719c24d7", size = 3264558 }, - { url = "https://files.pythonhosted.org/packages/40/cf/e27d7ee61a10f74b17740918e23cbc5bc62011b48282170dc4c66da8ec0f/sqlalchemy-2.0.44-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2e7b5b079055e02d06a4308d0481658e4f06bc7ef211567edc8f7d5dce52018d", size = 3301570 }, - { url = "https://files.pythonhosted.org/packages/3b/3d/3116a9a7b63e780fb402799b6da227435be878b6846b192f076d2f838654/sqlalchemy-2.0.44-cp312-cp312-win32.whl", hash = "sha256:846541e58b9a81cce7dee8329f352c318de25aa2f2bbe1e31587eb1f057448b4", size = 2103447 }, - { url = "https://files.pythonhosted.org/packages/25/83/24690e9dfc241e6ab062df82cc0df7f4231c79ba98b273fa496fb3dd78ed/sqlalchemy-2.0.44-cp312-cp312-win_amd64.whl", hash = "sha256:7cbcb47fd66ab294703e1644f78971f6f2f1126424d2b300678f419aa73c7b6e", size = 2130912 }, - { url = "https://files.pythonhosted.org/packages/45/d3/c67077a2249fdb455246e6853166360054c331db4613cda3e31ab1cadbef/sqlalchemy-2.0.44-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:ff486e183d151e51b1d694c7aa1695747599bb00b9f5f604092b54b74c64a8e1", size = 2135479 }, - { url = "https://files.pythonhosted.org/packages/2b/91/eabd0688330d6fd114f5f12c4f89b0d02929f525e6bf7ff80aa17ca802af/sqlalchemy-2.0.44-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:0b1af8392eb27b372ddb783b317dea0f650241cea5bd29199b22235299ca2e45", size = 2123212 }, - { url = "https://files.pythonhosted.org/packages/b0/bb/43e246cfe0e81c018076a16036d9b548c4cc649de241fa27d8d9ca6f85ab/sqlalchemy-2.0.44-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2b61188657e3a2b9ac4e8f04d6cf8e51046e28175f79464c67f2fd35bceb0976", size = 3255353 }, - { url = "https://files.pythonhosted.org/packages/b9/96/c6105ed9a880abe346b64d3b6ddef269ddfcab04f7f3d90a0bf3c5a88e82/sqlalchemy-2.0.44-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b87e7b91a5d5973dda5f00cd61ef72ad75a1db73a386b62877d4875a8840959c", size = 3260222 }, - { url = "https://files.pythonhosted.org/packages/44/16/1857e35a47155b5ad927272fee81ae49d398959cb749edca6eaa399b582f/sqlalchemy-2.0.44-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:15f3326f7f0b2bfe406ee562e17f43f36e16167af99c4c0df61db668de20002d", size = 3189614 }, - { url = "https://files.pythonhosted.org/packages/88/ee/4afb39a8ee4fc786e2d716c20ab87b5b1fb33d4ac4129a1aaa574ae8a585/sqlalchemy-2.0.44-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:1e77faf6ff919aa8cd63f1c4e561cac1d9a454a191bb864d5dd5e545935e5a40", size = 3226248 }, - { url = "https://files.pythonhosted.org/packages/32/d5/0e66097fc64fa266f29a7963296b40a80d6a997b7ac13806183700676f86/sqlalchemy-2.0.44-cp313-cp313-win32.whl", hash = "sha256:ee51625c2d51f8baadf2829fae817ad0b66b140573939dd69284d2ba3553ae73", size = 2101275 }, - { url = "https://files.pythonhosted.org/packages/03/51/665617fe4f8c6450f42a6d8d69243f9420f5677395572c2fe9d21b493b7b/sqlalchemy-2.0.44-cp313-cp313-win_amd64.whl", hash = "sha256:c1c80faaee1a6c3428cecf40d16a2365bcf56c424c92c2b6f0f9ad204b899e9e", size = 2127901 }, - { url = "https://files.pythonhosted.org/packages/9c/5e/6a29fa884d9fb7ddadf6b69490a9d45fded3b38541713010dad16b77d015/sqlalchemy-2.0.44-py3-none-any.whl", hash = "sha256:19de7ca1246fbef9f9d1bff8f1ab25641569df226364a0e40457dc5457c54b05", size = 1928718 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) +sdist = { url = "https://files.pythonhosted.org/packages/02/f1/a7a892f18d4d224e6b26f706531eafccc41e37594d37d304786969ee13cb/sqlalchemy-2.0.51.tar.gz", hash = "sha256:804dccd8a4a6242c4e30ad961e540e18a588f6527202f2d6791b01845d59fdc9", size = 9912201, upload-time = "2026-06-15T15:41:20.012Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3a/69/a67c69e5f28fc9c99d6f7bd60bd50e91f2fed2423e3b30fb228fa00e51f3/sqlalchemy-2.0.51-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1aa10c0daee6705294d181daadaa793221e1a59ed55000a3fab1d42b088ce4ba", size = 2161838, upload-time = "2026-06-15T16:05:17.144Z" }, + { url = "https://files.pythonhosted.org/packages/9a/a4/c8c22b8438bddc0a030157c6ec0f6ef97b3c38effa444bdab2a27af04090/sqlalchemy-2.0.51-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a5b2ed6d828f1f09bd812861f4f59ca3bc3803f9df871f4555187f0faf018604", size = 3319402, upload-time = "2026-06-15T16:10:40.002Z" }, + { url = "https://files.pythonhosted.org/packages/90/54/44012d32fd77d991256d2ff793ba3807c51d40cb27a85b4796224f6744df/sqlalchemy-2.0.51-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:436728ce18a80f6951a1e11cc6112c2ede9faf20766f1a26195a7c441ca12dbd", size = 3319675, upload-time = "2026-06-15T16:12:25.658Z" }, + { url = "https://files.pythonhosted.org/packages/29/a5/de0592acaf5906cd7430874392d6f7e8b4a7c8437610953ee2d1501c0b44/sqlalchemy-2.0.51-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:dc261707bf5739aea8a541593f3cc1d463c2701fb05fbcbba0ce031b69a21260", size = 3270777, upload-time = "2026-06-15T16:10:42.125Z" }, + { url = "https://files.pythonhosted.org/packages/cb/14/a44c90739c780b362238e4ac3cb19dd0ca40d13e6ddc5daa112166ddab4f/sqlalchemy-2.0.51-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:a6d26094615306d116dd5e4a51b0304c99dd2356fc569eed6922a80a6bd3b265", size = 3293940, upload-time = "2026-06-15T16:12:27.156Z" }, + { url = "https://files.pythonhosted.org/packages/65/eb/fbd0f206a330e66f8c602a99c37c4e731f107faed62954b41b01f16dd9d9/sqlalchemy-2.0.51-cp311-cp311-win32.whl", hash = "sha256:ca8435d13829b92f4a97362d91975154a4015db3a2634154e1754e9a915e6b86", size = 2121183, upload-time = "2026-06-15T16:13:29.905Z" }, + { url = "https://files.pythonhosted.org/packages/ad/fd/005bf80f3cf6e5c62b5dd68616280f51cd012c60840fa74781b3ed7b1623/sqlalchemy-2.0.51-cp311-cp311-win_amd64.whl", hash = "sha256:4a011ea4510683319ce4ed274b56ee05194b39b6da9d09ca7a39388f0fa84dcc", size = 2145796, upload-time = "2026-06-15T16:13:31.283Z" }, + { url = "https://files.pythonhosted.org/packages/d5/70/e868bc5412acd101a8280f25c95f10eeae0771c4eb806b02491142810ee8/sqlalchemy-2.0.51-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:7d78702b26ba1c18b2d0fb2ea940ba7f17a9581b42e8361ff93920ebbee1235a", size = 2160291, upload-time = "2026-06-15T16:08:48.918Z" }, + { url = "https://files.pythonhosted.org/packages/e5/1c/71ee0f8a6b9d7316a1ccd30430b4c62b6c2e36adc96017a4e3a72dce49d6/sqlalchemy-2.0.51-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:581921d849d6e6f994d560389192955e80e2950e18fcdfe2ccea863e01158e6e", size = 3343835, upload-time = "2026-06-15T16:19:42.613Z" }, + { url = "https://files.pythonhosted.org/packages/2b/7c/7ab9f9aadc5944fdd06612484ed7918fe376ad871a5f50404dc1536e0194/sqlalchemy-2.0.51-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1d21ce524ab86c23046e992a5b81cb54c21079c6df6e78b8fc77d77cac70a6b9", size = 3358470, upload-time = "2026-06-15T16:26:38.011Z" }, + { url = "https://files.pythonhosted.org/packages/d0/7d/ff77169fee6186de145a7f2b87006c39638391130abbab2b1f63ac6ea583/sqlalchemy-2.0.51-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c5d98a2709840027f5a347c3af0a7c3d5f6c1ff93af2ca1c54494e23cba8f389", size = 3289874, upload-time = "2026-06-15T16:19:45.212Z" }, + { url = "https://files.pythonhosted.org/packages/6f/3b/6c505903710d781b55bc3141ee34a062bf9745a6b5bc7333305b9ed63b33/sqlalchemy-2.0.51-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:1181256e0f16479691b5616d36375dc2620ad8332b25978763c3d206ad3f3f1d", size = 3321692, upload-time = "2026-06-15T16:26:39.747Z" }, + { url = "https://files.pythonhosted.org/packages/3c/b7/c5ffe50aa2f4d947c9250e1519d939260329a07fe6272edfccd784b3d007/sqlalchemy-2.0.51-cp312-cp312-win32.whl", hash = "sha256:9f380393be5abeb6815f68fd39271b95127173511b6706b0a630a9995d53f8f5", size = 2119674, upload-time = "2026-06-15T16:23:09.543Z" }, + { url = "https://files.pythonhosted.org/packages/25/dc/46a65916af68a06ef6b972c6050ba4c8f97070fe3fb33097d34229d9bef6/sqlalchemy-2.0.51-cp312-cp312-win_amd64.whl", hash = "sha256:2cf39aabdf48e87c1c2c2ed6d20d33ffa0733b3071ce9c5f66357947dd009080", size = 2146670, upload-time = "2026-06-15T16:23:11.048Z" }, + { url = "https://files.pythonhosted.org/packages/54/fe/a210d52fd1a90ecfae8a78e9d8b27e18d733d60818a8bf250ff690b75120/sqlalchemy-2.0.51-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:7c2056838b6685b72fdb36c99996cf862753461a62f2e84f4196371d3b2d6a07", size = 2157184, upload-time = "2026-06-15T16:08:50.374Z" }, + { url = "https://files.pythonhosted.org/packages/17/6b/2dce8369b199cb855110e056032f94a9f66dacc2237d3d39c115a86eac56/sqlalchemy-2.0.51-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:483b11bd46bf35fc14c52faf338b04300c9e6ce554bce9b11be85bfec3bc3195", size = 3284735, upload-time = "2026-06-15T16:19:46.934Z" }, + { url = "https://files.pythonhosted.org/packages/53/ff/dbc495b8a14da840faffb353857a72d4190113cac33727906fb997047f0f/sqlalchemy-2.0.51-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1bed1ee8b01da6088210aa9412023326fb98a599ba502e6118308601dcbef77f", size = 3302756, upload-time = "2026-06-15T16:26:41.336Z" }, + { url = "https://files.pythonhosted.org/packages/cf/d5/fde8f4dddcf518ee15ab35a7c6a28acc32c8ba548d1d2aa451f96e6dbb0b/sqlalchemy-2.0.51-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:72ca54c952107ba5cd58854b67a5a6268631289d21651a1235396f3b98b47400", size = 3232055, upload-time = "2026-06-15T16:19:49.286Z" }, + { url = "https://files.pythonhosted.org/packages/67/d1/43d3a0ac955a58601c24fa23038b1c55ee3a1ec02c0f96ebb1eae2bcf614/sqlalchemy-2.0.51-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b3e693d15533a45cd5906f0589f9c35090bef6ef45bf1e8195c424aa0ae06a8d", size = 3269850, upload-time = "2026-06-15T16:26:43.017Z" }, + { url = "https://files.pythonhosted.org/packages/94/df/de669c7054cd47c4439ac34b1b2ee8b804a794791fbb10720e997a2c87c7/sqlalchemy-2.0.51-cp313-cp313-win32.whl", hash = "sha256:b93ab07b5292dbe7e6b8da89475275e7042744283921344b56105f3eeb0f828b", size = 2117721, upload-time = "2026-06-15T16:23:12.36Z" }, + { url = "https://files.pythonhosted.org/packages/d0/8a/403c51d064196bae20a0bc2476577f83a3f8dd299719a97417086b7f2ec5/sqlalchemy-2.0.51-cp313-cp313-win_amd64.whl", hash = "sha256:0f053118c30e53161857a953e4de667d90e274980dccbe5dd3829bbbeece72a5", size = 2143615, upload-time = "2026-06-15T16:23:13.906Z" }, + { url = "https://files.pythonhosted.org/packages/b1/49/a739be2e1d02a96a658eb71ab45d921c874249252358ad24a5bffdd02525/sqlalchemy-2.0.51-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:6ea306caaae6bd5afd0a46050003c88f6bf33227377a49298c498c3cb88ff491", size = 2158999, upload-time = "2026-06-15T16:08:51.759Z" }, + { url = "https://files.pythonhosted.org/packages/23/6b/2e0e38cf75c8780eca78d9b2e78164f8bcfd70125e5caa588ff5cbb9c9f4/sqlalchemy-2.0.51-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c45a496d6bc05dec41dcd4c3a2b183723f47473255c159cd80b503c8f246424d", size = 3282539, upload-time = "2026-06-15T16:19:51.065Z" }, + { url = "https://files.pythonhosted.org/packages/dd/a1/e77854cb5336fd37dc3c6ae3b71de242c98caac5725120be0b526b31cbd0/sqlalchemy-2.0.51-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4004ada0aafe8ae1991b2cd1d99c6d9146126e123bd6f883c260d974aa012e54", size = 3287545, upload-time = "2026-06-15T16:26:44.735Z" }, + { url = "https://files.pythonhosted.org/packages/f6/ab/9e17272fd4dac8df3b83c4fbe52b998a1c9d89a843c8c35ff29b74ff7364/sqlalchemy-2.0.51-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:0f6bcad487aee1c638d707235682fc96f741de00663619881ab235400d03289e", size = 3230929, upload-time = "2026-06-15T16:19:52.625Z" }, + { url = "https://files.pythonhosted.org/packages/02/3c/52f408ea701781caee975606beccc48845f2aee8711ac29843d612c0306c/sqlalchemy-2.0.51-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:39a76529db6305693d8d4affa58ad5b5e2e18edd62daea628b29b97930b3513d", size = 3252888, upload-time = "2026-06-15T16:26:46.454Z" }, + { url = "https://files.pythonhosted.org/packages/24/16/3efd2ee6bc4ca4693a30a1dd17a91b606cae15d517d2a4746611d9b73ce8/sqlalchemy-2.0.51-cp314-cp314-win32.whl", hash = "sha256:08a204d8b5638717c26a24df18fcf40af45a6b22e35b70b1d62f0113c2e278e8", size = 2120551, upload-time = "2026-06-15T16:23:15.629Z" }, + { url = "https://files.pythonhosted.org/packages/7b/78/55b12e70f45bccc40d9e483925c065027b3b98ea4cbbdf6f8c2546feaf6c/sqlalchemy-2.0.51-cp314-cp314-win_amd64.whl", hash = "sha256:96747bfbadb055466e5b46d572618170046b45ce5a4879167f50d70a5319a499", size = 2146318, upload-time = "2026-06-15T16:23:17.108Z" }, + { url = "https://files.pythonhosted.org/packages/21/db/a9574ed40fed418924b1b1a3e54f47ee3963053b3d3d325a0d36b41f2c08/sqlalchemy-2.0.51-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:e5ea1a213be1fcd5e49d9904c3b9939211ded90bc2a64e93f4c01963474285de", size = 2178920, upload-time = "2026-06-15T15:59:56.285Z" }, + { url = "https://files.pythonhosted.org/packages/bf/90/a1bb5c7cbba76b7bc1fbd586d0a5479a7bc9c27b4a8298f22ec9423b2bb3/sqlalchemy-2.0.51-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7c6b36ed71f41942bdcd2ad2522be46bfce09d5705be5640ecf19bbc7660e4b7", size = 3566534, upload-time = "2026-06-15T15:58:35.024Z" }, + { url = "https://files.pythonhosted.org/packages/15/4b/481f1fed30e0e9e8dd24aecbb49f29eb57fe7657ece5cf06ee9b84bb97d8/sqlalchemy-2.0.51-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0c2c62877097e1a0db401fba5cb4debee33265e5b2a55c4ccb489c02c53b4f72", size = 3535844, upload-time = "2026-06-15T16:02:43.973Z" }, + { url = "https://files.pythonhosted.org/packages/02/71/0aa64aeda645510af0a43f7d9ee70932f0d1dc4263aed34c50ee891d9df3/sqlalchemy-2.0.51-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:0378d055e9e8cd6ce4d8dff683bdd3d7d413533c4ee51d67a2b1e0f9eacc0f23", size = 3475355, upload-time = "2026-06-15T15:58:36.592Z" }, + { url = "https://files.pythonhosted.org/packages/05/db/6061db32316446135a3abae5f308d144ab988a34234726042da3e58b1c63/sqlalchemy-2.0.51-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:6e46fc36029eff666391e0531e5387b62ce6c4f1d8e50b3fb3099eaca1b42522", size = 3486591, upload-time = "2026-06-15T16:02:45.346Z" }, + { url = "https://files.pythonhosted.org/packages/0d/c9/f14fdf71bb8957e0c7e39db69bbdf12b5c80f4ef775fdfa127bf4e0d6760/sqlalchemy-2.0.51-cp314-cp314t-win32.whl", hash = "sha256:9161cfc9efce70d1715f47d6ff40f79c6778c00d53be4fbc09d70301e4b83ba7", size = 2151313, upload-time = "2026-06-15T16:03:39.127Z" }, + { url = "https://files.pythonhosted.org/packages/6a/c6/673e618e6f4f297e126d9b56ea2f6478708f6c1af4e3223835c22e2c3697/sqlalchemy-2.0.51-cp314-cp314t-win_amd64.whl", hash = "sha256:159bb6ba32059f57ad7375a8f50d844dd2f19d14954ecf820cd33e20debd46b2", size = 2186280, upload-time = "2026-06-15T16:03:40.569Z" }, + { url = "https://files.pythonhosted.org/packages/e2/22/dbf013a12ec759e54a34a119e9e217435b3f71b2dd5c61a7ade0a25dae87/sqlalchemy-2.0.51-py3-none-any.whl", hash = "sha256:bb024d8b621d0be75f4f44ecc7c950450026e76d66dc8f791bb5331d7fed59d5", size = 1944334, upload-time = "2026-06-15T16:09:22.418Z" }, ] [[package]] @@ -4875,9 +3572,9 @@ dependencies = [ { name = "executing" }, { name = "pure-eval" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/28/e3/55dcc2cfbc3ca9c29519eb6884dd1415ecb53b0e934862d3559ddcb7e20b/stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9", size = 44707 } +sdist = { url = "https://files.pythonhosted.org/packages/28/e3/55dcc2cfbc3ca9c29519eb6884dd1415ecb53b0e934862d3559ddcb7e20b/stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9", size = 44707, upload-time = "2023-09-30T13:58:05.479Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695", size = 24521 }, + { url = "https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695", size = 24521, upload-time = "2023-09-30T13:58:03.53Z" }, ] [[package]] @@ -4887,9 +3584,9 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "mpmath" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/83/d3/803453b36afefb7c2bb238361cd4ae6125a569b4db67cd9e79846ba2d68c/sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517", size = 7793921 } +sdist = { url = "https://files.pythonhosted.org/packages/83/d3/803453b36afefb7c2bb238361cd4ae6125a569b4db67cd9e79846ba2d68c/sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517", size = 7793921, upload-time = "2025-04-27T18:05:01.611Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353 }, + { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" }, ] [[package]] @@ -4900,7 +3597,8 @@ dependencies = [ { name = "absl-py" }, { name = "grpcio" }, { name = "markdown" }, - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, { name = "packaging" }, { name = "pillow" }, { name = "protobuf" }, @@ -4909,7 +3607,7 @@ dependencies = [ { name = "werkzeug" }, ] wheels = [ - { url = "https://files.pythonhosted.org/packages/9c/d9/a5db55f88f258ac669a92858b70a714bbbd5acd993820b41ec4a96a4d77f/tensorboard-2.20.0-py3-none-any.whl", hash = "sha256:9dc9f978cb84c0723acf9a345d96c184f0293d18f166bb8d59ee098e6cfaaba6", size = 5525680 }, + { url = "https://files.pythonhosted.org/packages/9c/d9/a5db55f88f258ac669a92858b70a714bbbd5acd993820b41ec4a96a4d77f/tensorboard-2.20.0-py3-none-any.whl", hash = "sha256:9dc9f978cb84c0723acf9a345d96c184f0293d18f166bb8d59ee098e6cfaaba6", size = 5525680, upload-time = "2025-07-17T19:20:49.638Z" }, ] [[package]] @@ -4917,9 +3615,9 @@ name = "tensorboard-data-server" version = "0.7.2" source = { registry = "https://pypi.org/simple" } wheels = [ - { url = "https://files.pythonhosted.org/packages/7a/13/e503968fefabd4c6b2650af21e110aa8466fe21432cd7c43a84577a89438/tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb", size = 2356 }, - { url = "https://files.pythonhosted.org/packages/b7/85/dabeaf902892922777492e1d253bb7e1264cadce3cea932f7ff599e53fea/tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60", size = 4823598 }, - { url = "https://files.pythonhosted.org/packages/73/c6/825dab04195756cf8ff2e12698f22513b3db2f64925bdd41671bfb33aaa5/tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530", size = 6590363 }, + { url = "https://files.pythonhosted.org/packages/7a/13/e503968fefabd4c6b2650af21e110aa8466fe21432cd7c43a84577a89438/tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb", size = 2356, upload-time = "2023-10-23T21:23:32.16Z" }, + { url = "https://files.pythonhosted.org/packages/b7/85/dabeaf902892922777492e1d253bb7e1264cadce3cea932f7ff599e53fea/tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60", size = 4823598, upload-time = "2023-10-23T21:23:33.714Z" }, + { url = "https://files.pythonhosted.org/packages/73/c6/825dab04195756cf8ff2e12698f22513b3db2f64925bdd41671bfb33aaa5/tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530", size = 6590363, upload-time = "2023-10-23T21:23:35.583Z" }, ] [[package]] @@ -4931,9 +3629,9 @@ dependencies = [ { name = "pywinpty", marker = "os_name == 'nt'" }, { name = "tornado" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/8a/11/965c6fd8e5cc254f1fe142d547387da17a8ebfd75a3455f637c663fb38a0/terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e", size = 32701 } +sdist = { url = "https://files.pythonhosted.org/packages/8a/11/965c6fd8e5cc254f1fe142d547387da17a8ebfd75a3455f637c663fb38a0/terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e", size = 32701, upload-time = "2024-03-12T14:34:39.026Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/6a/9e/2064975477fdc887e47ad42157e214526dcad8f317a948dee17e1659a62f/terminado-0.18.1-py3-none-any.whl", hash = "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0", size = 14154 }, + { url = "https://files.pythonhosted.org/packages/6a/9e/2064975477fdc887e47ad42157e214526dcad8f317a948dee17e1659a62f/terminado-0.18.1-py3-none-any.whl", hash = "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0", size = 14154, upload-time = "2024-03-12T14:34:36.569Z" }, ] [[package]] @@ -4943,9 +3641,8 @@ source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version < '3.12'", ] -<<<<<<< HEAD dependencies = [ - { name = "numpy", marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/c5/cb/2f6d79c7576e22c116352a801f4c3c8ace5957e9aced862012430b62e14f/tifffile-2026.3.3.tar.gz", hash = "sha256:d9a1266bed6f2ee1dd0abde2018a38b4f8b2935cb843df381d70ac4eac5458b7", size = 388745, upload-time = "2026-03-03T19:14:38.134Z" } wheels = [ @@ -4961,35 +3658,29 @@ resolution-markers = [ "python_full_version >= '3.12' and python_full_version < '3.14'", ] dependencies = [ - { name = "numpy", marker = "python_full_version >= '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/b7/38/5e2ecef5af2f4fd4a89bb8d6240de9458bab4d51a4cbd97aeb3a0cd618e2/tifffile-2026.6.1.tar.gz", hash = "sha256:626c892c0e899d959b9438e7c0e1491dc154a7fead1f1f37a991724a50eceba9", size = 429694, upload-time = "2026-05-31T23:57:12.165Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/81/59/208f71d70ddc6184f79b8c6d87d46eb7d7b12c19186a817dec9c9c3f3693/tifffile-2026.6.1-py3-none-any.whl", hash = "sha256:0d7382d2769b855b81ce358528e2b40c16d48aa39031746efa81215205332a8d", size = 267108, upload-time = "2026-05-31T23:57:10.597Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/2d/b5/0d8f3d395f07d25ec4cafcdfc8cab234b2cc6bf2465e9d7660633983fe8f/tifffile-2025.10.16.tar.gz", hash = "sha256:425179ec7837ac0e07bc95d2ea5bea9b179ce854967c12ba07fc3f093e58efc1", size = 371848 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/e6/5e/56c751afab61336cf0e7aa671b134255a30f15f59cd9e04f59c598a37ff5/tifffile-2025.10.16-py3-none-any.whl", hash = "sha256:41463d979c1c262b0a5cdef2a7f95f0388a072ad82d899458b154a48609d759c", size = 231162 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "tinycss2" -version = "1.4.0" +version = "1.5.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "webencodings" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/7a/fd/7a5ee21fd08ff70d3d33a5781c255cbe779659bd03278feb98b19ee550f4/tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7", size = 87085 } +sdist = { url = "https://files.pythonhosted.org/packages/a3/ae/2ca4913e5c0f09781d75482874c3a95db9105462a92ddd303c7d285d3df2/tinycss2-1.5.1.tar.gz", hash = "sha256:d339d2b616ba90ccce58da8495a78f46e55d4d25f9fd71dfd526f07e7d53f957", size = 88195, upload-time = "2025-11-23T10:29:10.082Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/e6/34/ebdc18bae6aa14fbee1a08b63c015c72b64868ff7dae68808ab500c492e2/tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289", size = 26610 }, + { url = "https://files.pythonhosted.org/packages/60/45/c7b5c3168458db837e8ceab06dc77824e18202679d0463f0e8f002143a97/tinycss2-1.5.1-py3-none-any.whl", hash = "sha256:3415ba0f5839c062696996998176c4a3751d18b7edaaeeb658c9ce21ec150661", size = 28404, upload-time = "2025-11-23T10:29:08.676Z" }, ] [[package]] name = "tomli" version = "2.4.1" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/22/de/48c59722572767841493b26183a0d1cc411d54fd759c5607c4590b6563a6/tomli-2.4.1.tar.gz", hash = "sha256:7c7e1a961a0b2f2472c1ac5b69affa0ae1132c39adcb67aba98568702b9cc23f", size = 17543, upload-time = "2026-03-25T20:22:03.828Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/f4/11/db3d5885d8528263d8adc260bb2d28ebf1270b96e98f0e0268d32b8d9900/tomli-2.4.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f8f0fc26ec2cc2b965b7a3b87cd19c5c6b8c5e5f436b984e85f486d652285c30", size = 154704, upload-time = "2026-03-25T20:21:10.473Z" }, @@ -5038,65 +3729,20 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/12/64/da524626d3b9cc40c168a13da8335fe1c51be12c0a63685cc6db7308daae/tomli-2.4.1-cp314-cp314t-win_amd64.whl", hash = "sha256:2c1c351919aca02858f740c6d33adea0c5deea37f9ecca1cc1ef9e884a619d26", size = 121196, upload-time = "2026-03-25T20:22:01.169Z" }, { url = "https://files.pythonhosted.org/packages/5a/cd/e80b62269fc78fc36c9af5a6b89c835baa8af28ff5ad28c7028d60860320/tomli-2.4.1-cp314-cp314t-win_arm64.whl", hash = "sha256:eab21f45c7f66c13f2a9e0e1535309cee140182a9cdae1e041d02e47291e8396", size = 100393, upload-time = "2026-03-25T20:22:02.137Z" }, { url = "https://files.pythonhosted.org/packages/7b/61/cceae43728b7de99d9b847560c262873a1f6c98202171fd5ed62640b494b/tomli-2.4.1-py3-none-any.whl", hash = "sha256:0d85819802132122da43cb86656f8d1f8c6587d54ae7dcaf30e90533028b49fe", size = 14583, upload-time = "2026-03-25T20:22:03.012Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/52/ed/3f73f72945444548f33eba9a87fc7a6e969915e7b1acc8260b30e1f76a2f/tomli-2.3.0.tar.gz", hash = "sha256:64be704a875d2a59753d80ee8a533c3fe183e3f06807ff7dc2232938ccb01549", size = 17392 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b3/2e/299f62b401438d5fe1624119c723f5d877acc86a4c2492da405626665f12/tomli-2.3.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:88bd15eb972f3664f5ed4b57c1634a97153b4bac4479dcb6a495f41921eb7f45", size = 153236 }, - { url = "https://files.pythonhosted.org/packages/86/7f/d8fffe6a7aefdb61bced88fcb5e280cfd71e08939da5894161bd71bea022/tomli-2.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:883b1c0d6398a6a9d29b508c331fa56adbcdff647f6ace4dfca0f50e90dfd0ba", size = 148084 }, - { url = "https://files.pythonhosted.org/packages/47/5c/24935fb6a2ee63e86d80e4d3b58b222dafaf438c416752c8b58537c8b89a/tomli-2.3.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d1381caf13ab9f300e30dd8feadb3de072aeb86f1d34a8569453ff32a7dea4bf", size = 234832 }, - { url = "https://files.pythonhosted.org/packages/89/da/75dfd804fc11e6612846758a23f13271b76d577e299592b4371a4ca4cd09/tomli-2.3.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a0e285d2649b78c0d9027570d4da3425bdb49830a6156121360b3f8511ea3441", size = 242052 }, - { url = "https://files.pythonhosted.org/packages/70/8c/f48ac899f7b3ca7eb13af73bacbc93aec37f9c954df3c08ad96991c8c373/tomli-2.3.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:0a154a9ae14bfcf5d8917a59b51ffd5a3ac1fd149b71b47a3a104ca4edcfa845", size = 239555 }, - { url = "https://files.pythonhosted.org/packages/ba/28/72f8afd73f1d0e7829bfc093f4cb98ce0a40ffc0cc997009ee1ed94ba705/tomli-2.3.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:74bf8464ff93e413514fefd2be591c3b0b23231a77f901db1eb30d6f712fc42c", size = 245128 }, - { url = "https://files.pythonhosted.org/packages/b6/eb/a7679c8ac85208706d27436e8d421dfa39d4c914dcf5fa8083a9305f58d9/tomli-2.3.0-cp311-cp311-win32.whl", hash = "sha256:00b5f5d95bbfc7d12f91ad8c593a1659b6387b43f054104cda404be6bda62456", size = 96445 }, - { url = "https://files.pythonhosted.org/packages/0a/fe/3d3420c4cb1ad9cb462fb52967080575f15898da97e21cb6f1361d505383/tomli-2.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:4dc4ce8483a5d429ab602f111a93a6ab1ed425eae3122032db7e9acf449451be", size = 107165 }, - { url = "https://files.pythonhosted.org/packages/ff/b7/40f36368fcabc518bb11c8f06379a0fd631985046c038aca08c6d6a43c6e/tomli-2.3.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d7d86942e56ded512a594786a5ba0a5e521d02529b3826e7761a05138341a2ac", size = 154891 }, - { url = "https://files.pythonhosted.org/packages/f9/3f/d9dd692199e3b3aab2e4e4dd948abd0f790d9ded8cd10cbaae276a898434/tomli-2.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:73ee0b47d4dad1c5e996e3cd33b8a76a50167ae5f96a2607cbe8cc773506ab22", size = 148796 }, - { url = "https://files.pythonhosted.org/packages/60/83/59bff4996c2cf9f9387a0f5a3394629c7efa5ef16142076a23a90f1955fa/tomli-2.3.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:792262b94d5d0a466afb5bc63c7daa9d75520110971ee269152083270998316f", size = 242121 }, - { url = "https://files.pythonhosted.org/packages/45/e5/7c5119ff39de8693d6baab6c0b6dcb556d192c165596e9fc231ea1052041/tomli-2.3.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4f195fe57ecceac95a66a75ac24d9d5fbc98ef0962e09b2eddec5d39375aae52", size = 250070 }, - { url = "https://files.pythonhosted.org/packages/45/12/ad5126d3a278f27e6701abde51d342aa78d06e27ce2bb596a01f7709a5a2/tomli-2.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:e31d432427dcbf4d86958c184b9bfd1e96b5b71f8eb17e6d02531f434fd335b8", size = 245859 }, - { url = "https://files.pythonhosted.org/packages/fb/a1/4d6865da6a71c603cfe6ad0e6556c73c76548557a8d658f9e3b142df245f/tomli-2.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:7b0882799624980785240ab732537fcfc372601015c00f7fc367c55308c186f6", size = 250296 }, - { url = "https://files.pythonhosted.org/packages/a0/b7/a7a7042715d55c9ba6e8b196d65d2cb662578b4d8cd17d882d45322b0d78/tomli-2.3.0-cp312-cp312-win32.whl", hash = "sha256:ff72b71b5d10d22ecb084d345fc26f42b5143c5533db5e2eaba7d2d335358876", size = 97124 }, - { url = "https://files.pythonhosted.org/packages/06/1e/f22f100db15a68b520664eb3328fb0ae4e90530887928558112c8d1f4515/tomli-2.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:1cb4ed918939151a03f33d4242ccd0aa5f11b3547d0cf30f7c74a408a5b99878", size = 107698 }, - { url = "https://files.pythonhosted.org/packages/89/48/06ee6eabe4fdd9ecd48bf488f4ac783844fd777f547b8d1b61c11939974e/tomli-2.3.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:5192f562738228945d7b13d4930baffda67b69425a7f0da96d360b0a3888136b", size = 154819 }, - { url = "https://files.pythonhosted.org/packages/f1/01/88793757d54d8937015c75dcdfb673c65471945f6be98e6a0410fba167ed/tomli-2.3.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:be71c93a63d738597996be9528f4abe628d1adf5e6eb11607bc8fe1a510b5dae", size = 148766 }, - { url = "https://files.pythonhosted.org/packages/42/17/5e2c956f0144b812e7e107f94f1cc54af734eb17b5191c0bbfb72de5e93e/tomli-2.3.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c4665508bcbac83a31ff8ab08f424b665200c0e1e645d2bd9ab3d3e557b6185b", size = 240771 }, - { url = "https://files.pythonhosted.org/packages/d5/f4/0fbd014909748706c01d16824eadb0307115f9562a15cbb012cd9b3512c5/tomli-2.3.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4021923f97266babc6ccab9f5068642a0095faa0a51a246a6a02fccbb3514eaf", size = 248586 }, - { url = "https://files.pythonhosted.org/packages/30/77/fed85e114bde5e81ecf9bc5da0cc69f2914b38f4708c80ae67d0c10180c5/tomli-2.3.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4ea38c40145a357d513bffad0ed869f13c1773716cf71ccaa83b0fa0cc4e42f", size = 244792 }, - { url = "https://files.pythonhosted.org/packages/55/92/afed3d497f7c186dc71e6ee6d4fcb0acfa5f7d0a1a2878f8beae379ae0cc/tomli-2.3.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ad805ea85eda330dbad64c7ea7a4556259665bdf9d2672f5dccc740eb9d3ca05", size = 248909 }, - { url = "https://files.pythonhosted.org/packages/f8/84/ef50c51b5a9472e7265ce1ffc7f24cd4023d289e109f669bdb1553f6a7c2/tomli-2.3.0-cp313-cp313-win32.whl", hash = "sha256:97d5eec30149fd3294270e889b4234023f2c69747e555a27bd708828353ab606", size = 96946 }, - { url = "https://files.pythonhosted.org/packages/b2/b7/718cd1da0884f281f95ccfa3a6cc572d30053cba64603f79d431d3c9b61b/tomli-2.3.0-cp313-cp313-win_amd64.whl", hash = "sha256:0c95ca56fbe89e065c6ead5b593ee64b84a26fca063b5d71a1122bf26e533999", size = 107705 }, - { url = "https://files.pythonhosted.org/packages/19/94/aeafa14a52e16163008060506fcb6aa1949d13548d13752171a755c65611/tomli-2.3.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:cebc6fe843e0733ee827a282aca4999b596241195f43b4cc371d64fc6639da9e", size = 154244 }, - { url = "https://files.pythonhosted.org/packages/db/e4/1e58409aa78eefa47ccd19779fc6f36787edbe7d4cd330eeeedb33a4515b/tomli-2.3.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:4c2ef0244c75aba9355561272009d934953817c49f47d768070c3c94355c2aa3", size = 148637 }, - { url = "https://files.pythonhosted.org/packages/26/b6/d1eccb62f665e44359226811064596dd6a366ea1f985839c566cd61525ae/tomli-2.3.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c22a8bf253bacc0cf11f35ad9808b6cb75ada2631c2d97c971122583b129afbc", size = 241925 }, - { url = "https://files.pythonhosted.org/packages/70/91/7cdab9a03e6d3d2bb11beae108da5bdc1c34bdeb06e21163482544ddcc90/tomli-2.3.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0eea8cc5c5e9f89c9b90c4896a8deefc74f518db5927d0e0e8d4a80953d774d0", size = 249045 }, - { url = "https://files.pythonhosted.org/packages/15/1b/8c26874ed1f6e4f1fcfeb868db8a794cbe9f227299402db58cfcc858766c/tomli-2.3.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:b74a0e59ec5d15127acdabd75ea17726ac4c5178ae51b85bfe39c4f8a278e879", size = 245835 }, - { url = "https://files.pythonhosted.org/packages/fd/42/8e3c6a9a4b1a1360c1a2a39f0b972cef2cc9ebd56025168c4137192a9321/tomli-2.3.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:b5870b50c9db823c595983571d1296a6ff3e1b88f734a4c8f6fc6188397de005", size = 253109 }, - { url = "https://files.pythonhosted.org/packages/22/0c/b4da635000a71b5f80130937eeac12e686eefb376b8dee113b4a582bba42/tomli-2.3.0-cp314-cp314-win32.whl", hash = "sha256:feb0dacc61170ed7ab602d3d972a58f14ee3ee60494292d384649a3dc38ef463", size = 97930 }, - { url = "https://files.pythonhosted.org/packages/b9/74/cb1abc870a418ae99cd5c9547d6bce30701a954e0e721821df483ef7223c/tomli-2.3.0-cp314-cp314-win_amd64.whl", hash = "sha256:b273fcbd7fc64dc3600c098e39136522650c49bca95df2d11cf3b626422392c8", size = 107964 }, - { url = "https://files.pythonhosted.org/packages/54/78/5c46fff6432a712af9f792944f4fcd7067d8823157949f4e40c56b8b3c83/tomli-2.3.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:940d56ee0410fa17ee1f12b817b37a4d4e4dc4d27340863cc67236c74f582e77", size = 163065 }, - { url = "https://files.pythonhosted.org/packages/39/67/f85d9bd23182f45eca8939cd2bc7050e1f90c41f4a2ecbbd5963a1d1c486/tomli-2.3.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:f85209946d1fe94416debbb88d00eb92ce9cd5266775424ff81bc959e001acaf", size = 159088 }, - { url = "https://files.pythonhosted.org/packages/26/5a/4b546a0405b9cc0659b399f12b6adb750757baf04250b148d3c5059fc4eb/tomli-2.3.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a56212bdcce682e56b0aaf79e869ba5d15a6163f88d5451cbde388d48b13f530", size = 268193 }, - { url = "https://files.pythonhosted.org/packages/42/4f/2c12a72ae22cf7b59a7fe75b3465b7aba40ea9145d026ba41cb382075b0e/tomli-2.3.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c5f3ffd1e098dfc032d4d3af5c0ac64f6d286d98bc148698356847b80fa4de1b", size = 275488 }, - { url = "https://files.pythonhosted.org/packages/92/04/a038d65dbe160c3aa5a624e93ad98111090f6804027d474ba9c37c8ae186/tomli-2.3.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:5e01decd096b1530d97d5d85cb4dff4af2d8347bd35686654a004f8dea20fc67", size = 272669 }, - { url = "https://files.pythonhosted.org/packages/be/2f/8b7c60a9d1612a7cbc39ffcca4f21a73bf368a80fc25bccf8253e2563267/tomli-2.3.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:8a35dd0e643bb2610f156cca8db95d213a90015c11fee76c946aa62b7ae7e02f", size = 279709 }, - { url = "https://files.pythonhosted.org/packages/7e/46/cc36c679f09f27ded940281c38607716c86cf8ba4a518d524e349c8b4874/tomli-2.3.0-cp314-cp314t-win32.whl", hash = "sha256:a1f7f282fe248311650081faafa5f4732bdbfef5d45fe3f2e702fbc6f2d496e0", size = 107563 }, - { url = "https://files.pythonhosted.org/packages/84/ff/426ca8683cf7b753614480484f6437f568fd2fda2edbdf57a2d3d8b27a0b/tomli-2.3.0-cp314-cp314t-win_amd64.whl", hash = "sha256:70a251f8d4ba2d9ac2542eecf008b3c8a9fc5c3f9f02c56a9d7952612be2fdba", size = 119756 }, - { url = "https://files.pythonhosted.org/packages/77/b8/0135fadc89e73be292b473cb820b4f5a08197779206b33191e801feeae40/tomli-2.3.0-py3-none-any.whl", hash = "sha256:e95b1af3c5b07d9e643909b5abbec77cd9f1217e6d0bca72b0234736b9fb1f1b", size = 14408 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "toolz" version = "1.1.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/11/d6/114b492226588d6ff54579d95847662fc69196bdeec318eb45393b24c192/toolz-1.1.0.tar.gz", hash = "sha256:27a5c770d068c110d9ed9323f24f1543e83b2f300a687b7891c1a6d56b697b5b", size = 52613 } +sdist = { url = "https://files.pythonhosted.org/packages/11/d6/114b492226588d6ff54579d95847662fc69196bdeec318eb45393b24c192/toolz-1.1.0.tar.gz", hash = "sha256:27a5c770d068c110d9ed9323f24f1543e83b2f300a687b7891c1a6d56b697b5b", size = 52613, upload-time = "2025-10-17T04:03:21.661Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/fb/12/5911ae3eeec47800503a238d971e51722ccea5feb8569b735184d5fcdbc0/toolz-1.1.0-py3-none-any.whl", hash = "sha256:15ccc861ac51c53696de0a5d6d4607f99c210739caf987b5d2054f3efed429d8", size = 58093 }, + { url = "https://files.pythonhosted.org/packages/fb/12/5911ae3eeec47800503a238d971e51722ccea5feb8569b735184d5fcdbc0/toolz-1.1.0-py3-none-any.whl", hash = "sha256:15ccc861ac51c53696de0a5d6d4607f99c210739caf987b5d2054f3efed429d8", size = 58093, upload-time = "2025-10-17T04:03:20.435Z" }, ] [[package]] name = "torch" -version = "2.12.0" +version = "2.12.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "cuda-bindings", marker = "sys_platform == 'linux'" }, @@ -5116,31 +3762,26 @@ dependencies = [ { name = "typing-extensions" }, ] wheels = [ -<<<<<<< HEAD - { url = "https://files.pythonhosted.org/packages/18/62/131124fb95df03811b8260d1d43dcc5ee85ea1a344b964613d7efe77fb08/torch-2.12.0-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:10802fd383bbfed646212e765a72c37d2185205d4f26eb197a254e8ac7ddcb25", size = 87990344, upload-time = "2026-05-13T14:55:42.154Z" }, - { url = "https://files.pythonhosted.org/packages/12/9c/dda0dbd547dc549839824135f223792fd0e725f28ed0715dda366b7acaa2/torch-2.12.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:c12592630aef72feaf18bd3f197ef587bbfa21131b31c38b23ab2e55fce92e36", size = 426362932, upload-time = "2026-05-13T14:54:15.295Z" }, - { url = "https://files.pythonhosted.org/packages/e2/d2/a7dd5a3f9bdaa7842124e8e2359202b317c48d47d2fc5816fafdf2049adb/torch-2.12.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:415c1b8d0412f67551c8e89a2daca0fb3e56694af0281ba155eaa9da481f58b4", size = 532170085, upload-time = "2026-05-13T14:55:20.788Z" }, - { url = "https://files.pythonhosted.org/packages/12/1b/a61ce2004f9ab0ea8964a6e6168133a127795667639e2ff4f8f2bdb16a65/torch-2.12.0-cp311-cp311-win_amd64.whl", hash = "sha256:dd37188ea325042cb1f6cafa56822b11ada2520c04791a52629b0af25bdfbfd9", size = 122953128, upload-time = "2026-05-13T14:54:52.744Z" }, - { url = "https://files.pythonhosted.org/packages/ef/bb/285d643f254731294c9b595a007eac39db4600a98682d7bca688f42ca164/torch-2.12.0-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:b41339df93d491435e790ff8bcbae1c0ce777175889bfd1281d119862793e6a2", size = 88010197, upload-time = "2026-05-13T14:55:35.414Z" }, - { url = "https://files.pythonhosted.org/packages/79/81/76debf1db1343bd929bbb5d74c89fb437c2ed88eb144712557e7bd3eea45/torch-2.12.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:8fbef9f108a863e7722a73740998967e3b074742a834fc5be3a535a2befa7057", size = 426376751, upload-time = "2026-05-13T14:55:03.353Z" }, - { url = "https://files.pythonhosted.org/packages/de/f0/80026028b603c4650ff270fc3785bdef4bd6738765a9cc5a0f5a637d65a2/torch-2.12.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:4b4f64c2c2b11f7510d93dd6412b87025ff6eddd6bb61c3b5a3d892ea20c4756", size = 532261691, upload-time = "2026-05-13T14:52:54.453Z" }, - { url = "https://files.pythonhosted.org/packages/b9/c2/64b06cbb7830fb3cd9be13e1158b31a3f36b68e6a209105ee3c9d9480be0/torch-2.12.0-cp312-cp312-win_amd64.whl", hash = "sha256:8b958caff4a14d3a3b0b2dfc6a378f64dda9728a9dad28c08a0db9ce4dafb549", size = 122988114, upload-time = "2026-05-13T14:54:42.153Z" }, - { url = "https://files.pythonhosted.org/packages/86/ca/01896c80ba921676aa45886b2c5b8d774912de2a1f719de48169c6f755cd/torch-2.12.0-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:90dd587a5f61bfe1307148b581e2084fc5bc4a06e2b90a20e9a36b81087ff16b", size = 88009511, upload-time = "2026-05-13T14:54:47.411Z" }, - { url = "https://files.pythonhosted.org/packages/a5/04/52bdaf4787eab6ac7d7f5851dff934e4def0bc8ead9c8fd2b69b3e529699/torch-2.12.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:864392c73b7654f4d2b3ae712f607937d0dbb1101c4555fbb41848106b297f39", size = 426383231, upload-time = "2026-05-13T14:53:32.129Z" }, - { url = "https://files.pythonhosted.org/packages/49/8a/94bdecd13f5aaa90d45920b89789d9fe7c6f4af8c3cdd7ce01fcb59908fc/torch-2.12.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:5d6b560dfa7d56291c07d615c3bb73e8d9943d9b6d87f76cd0d9d570c4797fa6", size = 532269288, upload-time = "2026-05-13T14:53:49.423Z" }, - { url = "https://files.pythonhosted.org/packages/3e/2f/bdbaaa267de519ef1b73054bf590d8c93c37a266c9a4e24a01bd38b6918f/torch-2.12.0-cp313-cp313-win_amd64.whl", hash = "sha256:3fee918902090ade827643e758e98363278815de583c75d111fdd665ebffde9f", size = 122987706, upload-time = "2026-05-13T14:54:00.335Z" }, - { url = "https://files.pythonhosted.org/packages/9b/ad/e95e822f3538171e22640a7fbe839a1fdb666600bf6487025de2ff03b11a/torch-2.12.0-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:10ee1448a9f304d3b987eb4656f664ba6e4d7b410ca7a5a7c642199777a2cf88", size = 88319556, upload-time = "2026-05-13T14:54:05.574Z" }, - { url = "https://files.pythonhosted.org/packages/b7/07/055d06d985b445d67422d25b033c11cf55bbb81785d4c4e68e28bca5820e/torch-2.12.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:af68dbf403439cae9ceaeaaf92f8352b460787dcd27b92aa05c40dd4a19c0f1e", size = 426397656, upload-time = "2026-05-13T14:52:38.84Z" }, - { url = "https://files.pythonhosted.org/packages/43/94/b0b4fdc3014122e0a7302fb90086d352aa48f2576f0b252561ebb38c01a8/torch-2.12.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:a6a2eebb237d3b1d9ad3b378e86d9b9e0782afdea8b1e0eba6a13646b9b49c07", size = 532183124, upload-time = "2026-05-13T14:53:16.178Z" }, - { url = "https://files.pythonhosted.org/packages/d8/c8/052405e6ad05d3237bfe5a4df78f917773956f8e17813a2d44c059068b74/torch-2.12.0-cp313-cp313t-win_amd64.whl", hash = "sha256:2140e373e9a51a3e22ef62e8d14366d0b470d18f0adf19fdc757368077133a34", size = 123232462, upload-time = "2026-05-13T14:52:27.26Z" }, - { url = "https://files.pythonhosted.org/packages/67/dc/ac069f8d6e8be701535921141055293b0d4819d3d7f224a4612cf157c7f9/torch-2.12.0-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:f7dfae4a519197dfa050e98d8e36378a0fb5899625a875c2b54445005a2e404e", size = 88027282, upload-time = "2026-05-13T14:53:05.258Z" }, - { url = "https://files.pythonhosted.org/packages/33/c3/1c1eb00e34555b536dddf792676026a988d710ed36981aa00499b36b0620/torch-2.12.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:891c769072637c74e9a5a77a3bc782894696d8ffec83b938df8536dee7f0ba78", size = 426386961, upload-time = "2026-05-13T14:51:28.406Z" }, - { url = "https://files.pythonhosted.org/packages/cd/d4/7e730dba0c7032a4154dc9056b76cf9625515e030e269cfbf8098fcfee7d/torch-2.12.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:e2ad3eb85d39c3cab62dfa93ed5a73516e6a53c6713cb97d004004fe089f0f1f", size = 532272265, upload-time = "2026-05-13T14:51:59.308Z" }, - { url = "https://files.pythonhosted.org/packages/f1/b4/92c80d1bbfee1c0036c06d1d2155a3065bd2423134c83bf8a47e65cd6b9b/torch-2.12.0-cp314-cp314-win_amd64.whl", hash = "sha256:c66696857e987efb8bc1777a37357ec4f60ab5e8af6250b83d6034437fa2d8f3", size = 122987138, upload-time = "2026-05-13T14:51:45.942Z" }, - { url = "https://files.pythonhosted.org/packages/7b/78/2e12b37ce50a19a037d7bc62d652a5a8f27385a7b05859d6bc9204f20cfe/torch-2.12.0-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:b4556715c8572758625d62b6e0ae3b1f76c440221913a6fb5e100f321fb4fb02", size = 88320100, upload-time = "2026-05-13T14:51:39.955Z" }, - { url = "https://files.pythonhosted.org/packages/56/5e/83c450ec7b0bb40a7b74611c1b5440f9260e33c54c90d556fd4a1f0fd955/torch-2.12.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:a43ac605a5e13116c72b64c359644cce0229f213dde48d2ae0ae5eb5becf7feb", size = 426391871, upload-time = "2026-05-13T14:52:14.989Z" }, - { url = "https://files.pythonhosted.org/packages/c9/e9/1a0b575d98d0afedd8f157d23fa3d2759421483660448e60d0a4b10b6daa/torch-2.12.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:6a7512adfdd7f6732e40de1c620831e3c75b39b98cef60b11d0c5f0a76473ec5", size = 532192241, upload-time = "2026-05-13T14:51:07.795Z" }, - { url = "https://files.pythonhosted.org/packages/88/21/afadd25ecd81b3cea1e11c73cf1ab41a983a50271548c3ec7ec3b9efc3e9/torch-2.12.0-cp314-cp314t-win_amd64.whl", hash = "sha256:5f96b63f8287f66a005dd1b5a6abba2920f11156c5e5c4d815f3e2050fd1aa16", size = 123231092, upload-time = "2026-05-13T14:51:18.854Z" }, + { url = "https://files.pythonhosted.org/packages/59/38/7028d3be540f1dcdf41660a2b01d0c51d2cb73915fe370d84e4d277a6d47/torch-2.12.1-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:ef81f503912effea2ce3d9b12a2e3a6ed488943e91271c90c7a829f60baf6aa2", size = 87975425, upload-time = "2026-06-17T21:08:34.094Z" }, + { url = "https://files.pythonhosted.org/packages/5a/e3/750b3e3548635ceac03ba255daa26dbc7ed66ca3484dc4b4d955ab7f4501/torch-2.12.1-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:107df6888624bdea41508f9aeb6149d9333c737a5530ceecb56c904e811369ae", size = 426379894, upload-time = "2026-06-17T21:06:55.077Z" }, + { url = "https://files.pythonhosted.org/packages/dc/ca/ed24783da629ff3e640ba3f70a7639e9045d3d88b93ee6bc47b8a28a1f2c/torch-2.12.1-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:6e29e7e74d05bda7d955c75e99459f878ebd970ef851b4057edbd3b34a5eb4a3", size = 532169264, upload-time = "2026-06-17T21:08:17.65Z" }, + { url = "https://files.pythonhosted.org/packages/46/61/c63f0158446f3a98ea672b004d761b848911eba567ea4a624c7db5aadc04/torch-2.12.1-cp311-cp311-win_amd64.whl", hash = "sha256:a513506cfda3c1c78dabeb6574c1597538c0254b3d39af174dde35d8177f4ce3", size = 122953086, upload-time = "2026-06-17T21:08:27.69Z" }, + { url = "https://files.pythonhosted.org/packages/f0/54/efb7ebca77970012b0cc21687a55d70eb2ba514b2c2b8e18d9fb1222f3be/torch-2.12.1-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:d2dd0f2c5f7ccbddaf34cade0deaf476808368f902b9cdb7f36a2ab42301bc0e", size = 87991951, upload-time = "2026-06-17T21:07:49.309Z" }, + { url = "https://files.pythonhosted.org/packages/1e/00/4210d76ca7424981f04033ebe7e48816ab83287a62538747a58825db770c/torch-2.12.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:2de4e19b88a481482c6c75291f2d6a52eda3ce51f311b29aa9b68499c830c07c", size = 426382721, upload-time = "2026-06-17T21:06:41.842Z" }, + { url = "https://files.pythonhosted.org/packages/76/1f/bc9f5a5aa569307076365f25afcebacb22e9c754b1bcfbaaa146627c7fda/torch-2.12.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:649e4ced014ba646f76f8cb9c9726735a6323eb321b7919f942790a923f90921", size = 532261322, upload-time = "2026-06-17T21:06:06.673Z" }, + { url = "https://files.pythonhosted.org/packages/9e/49/c549461daa008159d006a76a991fbc2f26fa8bac27a4030c858463dcb20f/torch-2.12.1-cp312-cp312-win_amd64.whl", hash = "sha256:e86550597877fb272ddc52db2f85b82cb601ea7bd932576a0340152cae2200b3", size = 122988095, upload-time = "2026-06-17T21:07:44.9Z" }, + { url = "https://files.pythonhosted.org/packages/ff/4a/0300261818e1560d72cc160ac826005507e8b7ca0a35788b591436d05b4a/torch-2.12.1-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:c75e93173c700bccd6bfcc4a9d19ce242ab6dacd1f1781483027a16239b9e650", size = 87992358, upload-time = "2026-06-17T21:07:40.299Z" }, + { url = "https://files.pythonhosted.org/packages/30/a7/874a5ca05e8f159211dca7921060f7057acc1adb26431e119fd150623efc/torch-2.12.1-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:fcb61ccd20784b62bdd78ec84238a5cfb383b4994902e03bac95505ab360884c", size = 426386134, upload-time = "2026-06-17T21:07:31.481Z" }, + { url = "https://files.pythonhosted.org/packages/e1/75/20bb8fe9c1ad6538cce8cd0391b51927ae5af0b17ed1eab44b8824465dc1/torch-2.12.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:f4afc8083dff08719edbea346644476e3cec0cf40ebe256be0ee5d5b7c7e8c0d", size = 532268019, upload-time = "2026-06-17T21:05:37.925Z" }, + { url = "https://files.pythonhosted.org/packages/d1/fa/824ddb662af55b2eabc0dbb7b57c7c0b1bcd93693754a2b8509ec4d16490/torch-2.12.1-cp313-cp313-win_amd64.whl", hash = "sha256:f92609e3b3ce72f25e2eb780d043ced2480c1a86c47c852604fc7a9108648386", size = 122987777, upload-time = "2026-06-17T21:07:09.49Z" }, + { url = "https://files.pythonhosted.org/packages/63/b7/1b49fe7086ea36839cc80abc43174c43d0ab6f676c0891c871c162f44fe3/torch-2.12.1-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:e9b6f7d2dd66ea87a3ae620069d31335d594c06effb1a383bdd21cfe61e44ece", size = 88010025, upload-time = "2026-06-17T21:07:03.934Z" }, + { url = "https://files.pythonhosted.org/packages/d7/06/5b44063a6545036dcc680d2d303b137d9176cfb2cc1e1863e3ef94abeb52/torch-2.12.1-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:7973ccd3d2cd35c74449213f7bded199bec6c6247e705cbeda7407af79703d91", size = 426392891, upload-time = "2026-06-17T21:05:52.261Z" }, + { url = "https://files.pythonhosted.org/packages/f8/dd/c9ce9a4b0eb3c5bb92d9ea56766e2c22559f0b45171149188494edcce80f/torch-2.12.1-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:c64ac4aac16be5e296dcd912305605804b203333c690bf98c55bc09494ee92ad", size = 532272494, upload-time = "2026-06-17T21:06:22.72Z" }, + { url = "https://files.pythonhosted.org/packages/21/7c/f3a601fc1b1f663ff269bfe553654e638651939aa6563e8daa7167c33098/torch-2.12.1-cp314-cp314-win_amd64.whl", hash = "sha256:f6dc4caf7eb4adb38a2d9f536b51db56310fdd1254e69a2d96767e1367c892b3", size = 122987254, upload-time = "2026-06-17T21:06:33.199Z" }, + { url = "https://files.pythonhosted.org/packages/e6/8c/b8087556cf81ddd808dbeb34afb8396d7ae7a1694ab489f08b1a0004e7d0/torch-2.12.1-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:2afbb2bdaa8a95040e733f05492ddf133c3967c9b7ce0abd218d704b6cab437d", size = 88303173, upload-time = "2026-06-17T21:05:06.603Z" }, + { url = "https://files.pythonhosted.org/packages/4a/07/fe09d1699fbed2afa10ebc692ff2b99d113f2605b6748cea633989e2789a/torch-2.12.1-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:97eba061fcb042fed191400b15568990073d67eaacaa6ee9b7ca01dd8b790fe9", size = 426404009, upload-time = "2026-06-17T21:04:57.557Z" }, + { url = "https://files.pythonhosted.org/packages/2e/f7/0ce4f6c1962c60ded7270e0a9eb560fb615c92b89d332cf9e3dff36d5ecc/torch-2.12.1-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:3867b861391701012adb2df93360efb88494dca245a185e3bb7624495cfe3f33", size = 532184292, upload-time = "2026-06-17T21:05:17.526Z" }, + { url = "https://files.pythonhosted.org/packages/70/db/e384c12aba30320ca92aaaf557456cbcb26f04b4df307728bb8f019f5000/torch-2.12.1-cp314-cp314t-win_amd64.whl", hash = "sha256:dd15595f8fc764cffde8c6361a3beb6ef69a028c851b1b3e70e077f615980d4e", size = 123231142, upload-time = "2026-06-17T21:05:27.061Z" }, ] [[package]] @@ -5150,32 +3791,6 @@ source = { registry = "https://pypi.org/simple" } sdist = { url = "https://files.pythonhosted.org/packages/53/d9/2b811d1c0812e9ef23e6cf2dbe022becbe6c5ab065e33fd80ee05c0cd996/torchinfo-1.8.0.tar.gz", hash = "sha256:72e94b0e9a3e64dc583a8e5b7940b8938a1ac0f033f795457f27e6f4e7afa2e9", size = 25880, upload-time = "2023-05-14T19:23:26.377Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/72/25/973bd6128381951b23cdcd8a9870c6dcfc5606cb864df8eabd82e529f9c1/torchinfo-1.8.0-py3-none-any.whl", hash = "sha256:2e911c2918603f945c26ff21a3a838d12709223dc4ccf243407bce8b6e897b46", size = 23377, upload-time = "2023-05-14T19:23:24.141Z" }, -======= - { url = "https://files.pythonhosted.org/packages/15/db/c064112ac0089af3d2f7a2b5bfbabf4aa407a78b74f87889e524b91c5402/torch-2.9.1-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:62b3fd888277946918cba4478cf849303da5359f0fb4e3bfb86b0533ba2eaf8d", size = 104220430 }, - { url = "https://files.pythonhosted.org/packages/56/be/76eaa36c9cd032d3b01b001e2c5a05943df75f26211f68fae79e62f87734/torch-2.9.1-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:d033ff0ac3f5400df862a51bdde9bad83561f3739ea0046e68f5401ebfa67c1b", size = 899821446 }, - { url = "https://files.pythonhosted.org/packages/47/cc/7a2949e38dfe3244c4df21f0e1c27bce8aedd6c604a587dd44fc21017cb4/torch-2.9.1-cp311-cp311-win_amd64.whl", hash = "sha256:0d06b30a9207b7c3516a9e0102114024755a07045f0c1d2f2a56b1819ac06bcb", size = 110973074 }, - { url = "https://files.pythonhosted.org/packages/1e/ce/7d251155a783fb2c1bb6837b2b7023c622a2070a0a72726ca1df47e7ea34/torch-2.9.1-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:52347912d868653e1528b47cafaf79b285b98be3f4f35d5955389b1b95224475", size = 74463887 }, - { url = "https://files.pythonhosted.org/packages/0f/27/07c645c7673e73e53ded71705045d6cb5bae94c4b021b03aa8d03eee90ab/torch-2.9.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:da5f6f4d7f4940a173e5572791af238cb0b9e21b1aab592bd8b26da4c99f1cd6", size = 104126592 }, - { url = "https://files.pythonhosted.org/packages/19/17/e377a460603132b00760511299fceba4102bd95db1a0ee788da21298ccff/torch-2.9.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:27331cd902fb4322252657f3902adf1c4f6acad9dcad81d8df3ae14c7c4f07c4", size = 899742281 }, - { url = "https://files.pythonhosted.org/packages/b1/1a/64f5769025db846a82567fa5b7d21dba4558a7234ee631712ee4771c436c/torch-2.9.1-cp312-cp312-win_amd64.whl", hash = "sha256:81a285002d7b8cfd3fdf1b98aa8df138d41f1a8334fd9ea37511517cedf43083", size = 110940568 }, - { url = "https://files.pythonhosted.org/packages/6e/ab/07739fd776618e5882661d04c43f5b5586323e2f6a2d7d84aac20d8f20bd/torch-2.9.1-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:c0d25d1d8e531b8343bea0ed811d5d528958f1dcbd37e7245bc686273177ad7e", size = 74479191 }, - { url = "https://files.pythonhosted.org/packages/20/60/8fc5e828d050bddfab469b3fe78e5ab9a7e53dda9c3bdc6a43d17ce99e63/torch-2.9.1-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:c29455d2b910b98738131990394da3e50eea8291dfeb4b12de71ecf1fdeb21cb", size = 104135743 }, - { url = "https://files.pythonhosted.org/packages/f2/b7/6d3f80e6918213babddb2a37b46dbb14c15b14c5f473e347869a51f40e1f/torch-2.9.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:524de44cd13931208ba2c4bde9ec7741fd4ae6bfd06409a604fc32f6520c2bc9", size = 899749493 }, - { url = "https://files.pythonhosted.org/packages/a6/47/c7843d69d6de8938c1cbb1eba426b1d48ddf375f101473d3e31a5fc52b74/torch-2.9.1-cp313-cp313-win_amd64.whl", hash = "sha256:545844cc16b3f91e08ce3b40e9c2d77012dd33a48d505aed34b7740ed627a1b2", size = 110944162 }, - { url = "https://files.pythonhosted.org/packages/28/0e/2a37247957e72c12151b33a01e4df651d9d155dd74d8cfcbfad15a79b44a/torch-2.9.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5be4bf7496f1e3ffb1dd44b672adb1ac3f081f204c5ca81eba6442f5f634df8e", size = 74830751 }, - { url = "https://files.pythonhosted.org/packages/4b/f7/7a18745edcd7b9ca2381aa03353647bca8aace91683c4975f19ac233809d/torch-2.9.1-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:30a3e170a84894f3652434b56d59a64a2c11366b0ed5776fab33c2439396bf9a", size = 104142929 }, - { url = "https://files.pythonhosted.org/packages/f4/dd/f1c0d879f2863ef209e18823a988dc7a1bf40470750e3ebe927efdb9407f/torch-2.9.1-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:8301a7b431e51764629208d0edaa4f9e4c33e6df0f2f90b90e261d623df6a4e2", size = 899748978 }, - { url = "https://files.pythonhosted.org/packages/1f/9f/6986b83a53b4d043e36f3f898b798ab51f7f20fdf1a9b01a2720f445043d/torch-2.9.1-cp313-cp313t-win_amd64.whl", hash = "sha256:2e1c42c0ae92bf803a4b2409fdfed85e30f9027a66887f5e7dcdbc014c7531db", size = 111176995 }, - { url = "https://files.pythonhosted.org/packages/40/60/71c698b466dd01e65d0e9514b5405faae200c52a76901baf6906856f17e4/torch-2.9.1-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:2c14b3da5df416cf9cb5efab83aa3056f5b8cd8620b8fde81b4987ecab730587", size = 74480347 }, - { url = "https://files.pythonhosted.org/packages/48/50/c4b5112546d0d13cc9eaa1c732b823d676a9f49ae8b6f97772f795874a03/torch-2.9.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:1edee27a7c9897f4e0b7c14cfc2f3008c571921134522d5b9b5ec4ebbc69041a", size = 74433245 }, - { url = "https://files.pythonhosted.org/packages/81/c9/2628f408f0518b3bae49c95f5af3728b6ab498c8624ab1e03a43dd53d650/torch-2.9.1-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:19d144d6b3e29921f1fc70503e9f2fc572cde6a5115c0c0de2f7ca8b1483e8b6", size = 104134804 }, - { url = "https://files.pythonhosted.org/packages/28/fc/5bc91d6d831ae41bf6e9e6da6468f25330522e92347c9156eb3f1cb95956/torch-2.9.1-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:c432d04376f6d9767a9852ea0def7b47a7bbc8e7af3b16ac9cf9ce02b12851c9", size = 899747132 }, - { url = "https://files.pythonhosted.org/packages/63/5d/e8d4e009e52b6b2cf1684bde2a6be157b96fb873732542fb2a9a99e85a83/torch-2.9.1-cp314-cp314-win_amd64.whl", hash = "sha256:d187566a2cdc726fc80138c3cdb260970fab1c27e99f85452721f7759bbd554d", size = 110934845 }, - { url = "https://files.pythonhosted.org/packages/bd/b2/2d15a52516b2ea3f414643b8de68fa4cb220d3877ac8b1028c83dc8ca1c4/torch-2.9.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:cb10896a1f7fedaddbccc2017ce6ca9ecaaf990f0973bdfcf405439750118d2c", size = 74823558 }, - { url = "https://files.pythonhosted.org/packages/86/5c/5b2e5d84f5b9850cd1e71af07524d8cbb74cba19379800f1f9f7c997fc70/torch-2.9.1-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:0a2bd769944991c74acf0c4ef23603b9c777fdf7637f115605a4b2d8023110c7", size = 104145788 }, - { url = "https://files.pythonhosted.org/packages/a9/8c/3da60787bcf70add986c4ad485993026ac0ca74f2fc21410bc4eb1bb7695/torch-2.9.1-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:07c8a9660bc9414c39cac530ac83b1fb1b679d7155824144a40a54f4a47bfa73", size = 899735500 }, - { url = "https://files.pythonhosted.org/packages/db/2b/f7818f6ec88758dfd21da46b6cd46af9d1b3433e53ddbb19ad1e0da17f9b/torch-2.9.1-cp314-cp314t-win_amd64.whl", hash = "sha256:c88d3299ddeb2b35dcc31753305612db485ab6f1823e37fb29451c8b2732b87e", size = 111163659 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -5184,229 +3799,143 @@ version = "1.9.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "lightning-utilities" }, - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, { name = "packaging" }, { name = "torch" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/81/34/39b8b749333db56c0585d7a11fa62a283c087bb1dfc897d69fb8cedbefb1/torchmetrics-1.9.0.tar.gz", hash = "sha256:a488609948600df52d3db4fcdab02e62aab2a85ef34da67037dc3e65b8512faa", size = 581765, upload-time = "2026-03-09T17:41:22.443Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/c3/a2/c7f6ebf546f8f644edf0f999aa98ece106986a77a7b922316bf6414ff825/torchmetrics-1.9.0-py3-none-any.whl", hash = "sha256:bfdcbff3dd1d96b3374bb2496eb39f23c4b28b8a845b6a18c313688e0d2d9ca1", size = 983384, upload-time = "2026-03-09T17:41:19.756Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/85/2e/48a887a59ecc4a10ce9e8b35b3e3c5cef29d902c4eac143378526e7485cb/torchmetrics-1.8.2.tar.gz", hash = "sha256:cf64a901036bf107f17a524009eea7781c9c5315d130713aeca5747a686fe7a5", size = 580679 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/02/21/aa0f434434c48490f91b65962b1ce863fdcce63febc166ca9fe9d706c2b6/torchmetrics-1.8.2-py3-none-any.whl", hash = "sha256:08382fd96b923e39e904c4d570f3d49e2cc71ccabd2a94e0f895d1f0dac86242", size = 983161 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "torchvision" -version = "0.27.0" +version = "0.27.1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "numpy" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, { name = "pillow" }, { name = "torch" }, ] wheels = [ -<<<<<<< HEAD - { url = "https://files.pythonhosted.org/packages/cf/d6/a7e71e981042d5c573e2e61891b9023b190c88adb75b18bed8594371250c/torchvision-0.27.0-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:df0c166b6bdf7c47f88e81e8b43bc085451d5c50d0c5d1691bc474c1227d6fed", size = 1758812, upload-time = "2026-05-13T14:57:16.662Z" }, - { url = "https://files.pythonhosted.org/packages/93/f9/f542fb7e4476603fb237ebdc64369a7d11f18eb5a129aa2559cbdb710aee/torchvision-0.27.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:9bb9251f64b854124efed95d02953a89f7e2726c3ca662d7ea0151129157297f", size = 7831148, upload-time = "2026-05-13T14:57:08.37Z" }, - { url = "https://files.pythonhosted.org/packages/f6/61/7aa7cc2c9e8750027f6fb9ae3a7393ef43860bcdfe3966e2f71fee800e31/torchvision-0.27.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:f44453f107c296d5446a79f7ac59733ad8bf5ddfa04c53805dfbae298a42a798", size = 7575519, upload-time = "2026-05-13T14:56:50.552Z" }, - { url = "https://files.pythonhosted.org/packages/19/aa/929b358b1a643849b81ec95569938044cc37dc65ab10c84eb6d82fe1bfbb/torchvision-0.27.0-cp311-cp311-win_amd64.whl", hash = "sha256:b4aacff70ea4b7377f996f9048989c850d221fef33658ddbcae42aa5bd4ca11a", size = 3749475, upload-time = "2026-05-13T14:57:11.007Z" }, - { url = "https://files.pythonhosted.org/packages/9e/c8/5cd91932f7f3671b0743dc4ae1a4c16b1d0b45bf4087976277d325bda718/torchvision-0.27.0-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:1a6dd742a150645126df9e0b2e449874c1d635897c773b322c2e067e98382dfe", size = 1758824, upload-time = "2026-05-13T14:57:15.227Z" }, - { url = "https://files.pythonhosted.org/packages/d9/36/7fb7d19477b3d93283b52fea11fa8ee30ab9064a08c97b4a6b91445e26cb/torchvision-0.27.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:65772ff3ec4f4f5d680e30019835555dd239e7fefee4b0a846375fe1cb1592ef", size = 7831034, upload-time = "2026-05-13T14:57:06.483Z" }, - { url = "https://files.pythonhosted.org/packages/62/43/dfd894c3f8b01b5b33fde990f0159c1926ebc7b6e2c4193e2efb7da3c4cb/torchvision-0.27.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:7a9966a088d06b4cf6c610e03be62de469efa6f2cd2e7c7eed8e925ed6af59ac", size = 7579774, upload-time = "2026-05-13T14:56:59.337Z" }, - { url = "https://files.pythonhosted.org/packages/ff/0c/722e989f9cf026e97ef7cb24a9bb1859e099f72d247ae35388fb89729f73/torchvision-0.27.0-cp312-cp312-win_amd64.whl", hash = "sha256:2c037709072ca9b19750c0cbe9e8bb6f91c9a1be1befa26df33e281deccbd8c7", size = 4021073, upload-time = "2026-05-13T14:57:00.848Z" }, - { url = "https://files.pythonhosted.org/packages/d8/ae/36547812e6e047c1d80bcacd1b17a340612b08a6e876e0aabf3d0b9228b0/torchvision-0.27.0-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:41d6dae73e1af09fa82ded597ae57f2a2314285acde54b25890a8f8e51b999d7", size = 1758826, upload-time = "2026-05-13T14:57:05.262Z" }, - { url = "https://files.pythonhosted.org/packages/ae/30/32c4ea842738728a14e3df8c576c62dedcf5ae5cb6a5c984c6429ebe7524/torchvision-0.27.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:70f071c6f74b60d5fe8851636d8d4cd5f4fa29d57fd9348a87a6f17b990b95ba", size = 7789501, upload-time = "2026-05-13T14:56:57.786Z" }, - { url = "https://files.pythonhosted.org/packages/f6/24/4d0d48684251bd0673f87d633d5d88ab00227983b00591156eed2f86c8d5/torchvision-0.27.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:aaafa6962c9d91f42503de1957d6fa349907d028c06f335bd95da7a5bc57147d", size = 7579868, upload-time = "2026-05-13T14:56:41.618Z" }, - { url = "https://files.pythonhosted.org/packages/ba/da/e6edd051d2ba25adf23b120fa97f458dff888d098c51e84724f17d2d1470/torchvision-0.27.0-cp313-cp313-win_amd64.whl", hash = "sha256:aee384a2782c89517c4ab9061d2720ba59fd2ffe5ef89d0a149cc2d43abdf521", size = 4092700, upload-time = "2026-05-13T14:57:09.729Z" }, - { url = "https://files.pythonhosted.org/packages/fa/23/95dfa40431360f42ca949bf861434bed51164adfa8fb9801e05bf3194f50/torchvision-0.27.0-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:c5121f1b9ab09a7f73e837871deb8321551f7eaeb19d87aa00de9191968eae44", size = 1845008, upload-time = "2026-05-13T14:57:03.768Z" }, - { url = "https://files.pythonhosted.org/packages/23/b9/9dbdf76b2b49a75ba8088df6f7c755bdb520afb6c6dbac0102b46cde5e99/torchvision-0.27.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:1c01f0d1091ae22b9dfc082b0a0fe5faaf053686a29b4fb082ba7691375c73cf", size = 7791430, upload-time = "2026-05-13T14:56:56.206Z" }, - { url = "https://files.pythonhosted.org/packages/5c/6a/e4a16cf2f3310c2ea7760dc5d9054496844391e0f4c1fae87fefac2f3d9e/torchvision-0.27.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:dadea3c5ecfd05bbb2a3312ab0374f213c58bf6459cb059122e2f4dfe13d10ed", size = 7668441, upload-time = "2026-05-13T14:57:02.127Z" }, - { url = "https://files.pythonhosted.org/packages/00/70/01b6461117a6a94b5af3f8ee166bb0f045056f3cf187750c110dabfdfffa/torchvision-0.27.0-cp313-cp313t-win_amd64.whl", hash = "sha256:a49e55055a39a8506fe7e59850522cab004efb2c3839f6057658889c1d69c815", size = 4141602, upload-time = "2026-05-13T14:56:53.449Z" }, - { url = "https://files.pythonhosted.org/packages/92/22/c0633677b3b3f3e69554a21ac087bf705f829c40cd5e3783507b8c006681/torchvision-0.27.0-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:c1fac0fc2a7adf29481fc1938a0e7845c57ba1147a986784109c4d98f434ea8c", size = 1758818, upload-time = "2026-05-13T14:56:54.988Z" }, - { url = "https://files.pythonhosted.org/packages/48/e8/55f9d9667b56dae470e69e31beac9b00d458ea393feec1aae95cc4f3f1c9/torchvision-0.27.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:cbf89764fc76f3f17fbf80c12d5a89c691e91cb9d82c38412aaf0568655ffb19", size = 7789667, upload-time = "2026-05-13T14:56:48.858Z" }, - { url = "https://files.pythonhosted.org/packages/00/bc/6f8681daf3bbc4c315bb0005110f99d28e3ecd675bf9c8f2c0d393fbac7a/torchvision-0.27.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:91f61b9865423037c327eb56afa207cc72de874e458c361840db9dcf5ce0c0eb", size = 7579848, upload-time = "2026-05-13T14:56:38.209Z" }, - { url = "https://files.pythonhosted.org/packages/19/6c/8d8020e6bd1e46c53e487c9c4e9457a07f2ee28931028fb5d71e2da40adc/torchvision-0.27.0-cp314-cp314-win_amd64.whl", hash = "sha256:5bb82fc3c55daf1788621e504310b0a286f1069627a8742f692aebb075ef25a7", size = 4119284, upload-time = "2026-05-13T14:56:46.625Z" }, - { url = "https://files.pythonhosted.org/packages/8d/7e/e78c48662a8d551606efdbe11c6b9c1d6d2391b92cd0e4591b9e6a2412b8/torchvision-0.27.0-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:2c4099a15150143b9b034730b404a56d572efe0b79489b4c765d929cb4eac7f3", size = 1758828, upload-time = "2026-05-13T14:56:52.293Z" }, - { url = "https://files.pythonhosted.org/packages/21/dd/d03ee9f9ee7bf11a8c7c776fb8e7fd6102f59c013791a2a4e5175bd6cba7/torchvision-0.27.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:b4c6bb0a670dcba017b3643e21902c9b8a1cc1c127d602f1488fa29ec3c6e865", size = 7790618, upload-time = "2026-05-13T14:56:44.721Z" }, - { url = "https://files.pythonhosted.org/packages/39/08/4002336a74742be70728603ec1769feb2b55e0d19c532c9ec9f92008de76/torchvision-0.27.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:1c2db4bde82bc48ebff73436a6adf34d4f809448268a70d9a1285f5c8f92313d", size = 7580217, upload-time = "2026-05-13T14:56:43.274Z" }, - { url = "https://files.pythonhosted.org/packages/ed/cb/4dd4783eb3565f526ba6e64b6f6ca26c00eacc924cdfe60455db9d91b84b/torchvision-0.27.0-cp314-cp314t-win_amd64.whl", hash = "sha256:72bf547e58ddb948689734eed6f4b6a2031f979dba4fb08e3690688b392e929f", size = 4226392, upload-time = "2026-05-13T14:56:40.235Z" }, -======= - { url = "https://files.pythonhosted.org/packages/e7/69/30f5f03752aa1a7c23931d2519b31e557f3f10af5089d787cddf3b903ecf/torchvision-0.24.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:056c525dc875f18fe8e9c27079ada166a7b2755cea5a2199b0bc7f1f8364e600", size = 1891436 }, - { url = "https://files.pythonhosted.org/packages/0c/69/49aae86edb75fe16460b59a191fcc0f568c2378f780bb063850db0fe007a/torchvision-0.24.1-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:1e39619de698e2821d71976c92c8a9e50cdfd1e993507dfb340f2688bfdd8283", size = 2387757 }, - { url = "https://files.pythonhosted.org/packages/11/c9/1dfc3db98797b326f1d0c3f3bb61c83b167a813fc7eab6fcd2edb8c7eb9d/torchvision-0.24.1-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:a0f106663e60332aa4fcb1ca2159ef8c3f2ed266b0e6df88de261048a840e0df", size = 8047682 }, - { url = "https://files.pythonhosted.org/packages/fa/bb/cfc6a6f6ccc84a534ed1fdf029ae5716dd6ff04e57ed9dc2dab38bf652d5/torchvision-0.24.1-cp311-cp311-win_amd64.whl", hash = "sha256:a9308cdd37d8a42e14a3e7fd9d271830c7fecb150dd929b642f3c1460514599a", size = 4037588 }, - { url = "https://files.pythonhosted.org/packages/f0/af/18e2c6b9538a045f60718a0c5a058908ccb24f88fde8e6f0fc12d5ff7bd3/torchvision-0.24.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:e48bf6a8ec95872eb45763f06499f87bd2fb246b9b96cb00aae260fda2f96193", size = 1891433 }, - { url = "https://files.pythonhosted.org/packages/9d/43/600e5cfb0643d10d633124f5982d7abc2170dfd7ce985584ff16edab3e76/torchvision-0.24.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:7fb7590c737ebe3e1c077ad60c0e5e2e56bb26e7bccc3b9d04dbfc34fd09f050", size = 2386737 }, - { url = "https://files.pythonhosted.org/packages/93/b1/db2941526ecddd84884132e2742a55c9311296a6a38627f9e2627f5ac889/torchvision-0.24.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:66a98471fc18cad9064123106d810a75f57f0838eee20edc56233fd8484b0cc7", size = 8049868 }, - { url = "https://files.pythonhosted.org/packages/69/98/16e583f59f86cd59949f59d52bfa8fc286f86341a229a9d15cbe7a694f0c/torchvision-0.24.1-cp312-cp312-win_amd64.whl", hash = "sha256:4aa6cb806eb8541e92c9b313e96192c6b826e9eb0042720e2fa250d021079952", size = 4302006 }, - { url = "https://files.pythonhosted.org/packages/e4/97/ab40550f482577f2788304c27220e8ba02c63313bd74cf2f8920526aac20/torchvision-0.24.1-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:8a6696db7fb71eadb2c6a48602106e136c785642e598eb1533e0b27744f2cce6", size = 1891435 }, - { url = "https://files.pythonhosted.org/packages/30/65/ac0a3f9be6abdbe4e1d82c915d7e20de97e7fd0e9a277970508b015309f3/torchvision-0.24.1-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:db2125c46f9cb25dc740be831ce3ce99303cfe60439249a41b04fd9f373be671", size = 2338718 }, - { url = "https://files.pythonhosted.org/packages/10/b5/5bba24ff9d325181508501ed7f0c3de8ed3dd2edca0784d48b144b6c5252/torchvision-0.24.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:f035f0cacd1f44a8ff6cb7ca3627d84c54d685055961d73a1a9fb9827a5414c8", size = 8049661 }, - { url = "https://files.pythonhosted.org/packages/5c/ec/54a96ae9ab6a0dd66d4bba27771f892e36478a9c3489fa56e51c70abcc4d/torchvision-0.24.1-cp313-cp313-win_amd64.whl", hash = "sha256:16274823b93048e0a29d83415166a2e9e0bf4e1b432668357b657612a4802864", size = 4319808 }, - { url = "https://files.pythonhosted.org/packages/d5/f3/a90a389a7e547f3eb8821b13f96ea7c0563cdefbbbb60a10e08dda9720ff/torchvision-0.24.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:e3f96208b4bef54cd60e415545f5200346a65024e04f29a26cd0006dbf9e8e66", size = 2005342 }, - { url = "https://files.pythonhosted.org/packages/a9/fe/ff27d2ed1b524078164bea1062f23d2618a5fc3208e247d6153c18c91a76/torchvision-0.24.1-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:f231f6a4f2aa6522713326d0d2563538fa72d613741ae364f9913027fa52ea35", size = 2341708 }, - { url = "https://files.pythonhosted.org/packages/b1/b9/d6c903495cbdfd2533b3ef6f7b5643ff589ea062f8feb5c206ee79b9d9e5/torchvision-0.24.1-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:1540a9e7f8cf55fe17554482f5a125a7e426347b71de07327d5de6bfd8d17caa", size = 8177239 }, - { url = "https://files.pythonhosted.org/packages/4f/2b/ba02e4261369c3798310483028495cf507e6cb3f394f42e4796981ecf3a7/torchvision-0.24.1-cp313-cp313t-win_amd64.whl", hash = "sha256:d83e16d70ea85d2f196d678bfb702c36be7a655b003abed84e465988b6128938", size = 4251604 }, - { url = "https://files.pythonhosted.org/packages/42/84/577b2cef8f32094add5f52887867da4c2a3e6b4261538447e9b48eb25812/torchvision-0.24.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:cccf4b4fec7fdfcd3431b9ea75d1588c0a8596d0333245dafebee0462abe3388", size = 2005319 }, - { url = "https://files.pythonhosted.org/packages/5f/34/ecb786bffe0159a3b49941a61caaae089853132f3cd1e8f555e3621f7e6f/torchvision-0.24.1-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:1b495edd3a8f9911292424117544f0b4ab780452e998649425d1f4b2bed6695f", size = 2338844 }, - { url = "https://files.pythonhosted.org/packages/51/99/a84623786a6969504c87f2dc3892200f586ee13503f519d282faab0bb4f0/torchvision-0.24.1-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:ab211e1807dc3e53acf8f6638df9a7444c80c0ad050466e8d652b3e83776987b", size = 8175144 }, - { url = "https://files.pythonhosted.org/packages/6d/ba/8fae3525b233e109317ce6a9c1de922ab2881737b029a7e88021f81e068f/torchvision-0.24.1-cp314-cp314-win_amd64.whl", hash = "sha256:18f9cb60e64b37b551cd605a3d62c15730c086362b40682d23e24b616a697d41", size = 4234459 }, - { url = "https://files.pythonhosted.org/packages/50/33/481602c1c72d0485d4b3a6b48c9534b71c2957c9d83bf860eb837bf5a620/torchvision-0.24.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ec9d7379c519428395e4ffda4dbb99ec56be64b0a75b95989e00f9ec7ae0b2d7", size = 2005336 }, - { url = "https://files.pythonhosted.org/packages/d0/7f/372de60bf3dd8f5593bd0d03f4aecf0d1fd58f5bc6943618d9d913f5e6d5/torchvision-0.24.1-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:af9201184c2712d808bd4eb656899011afdfce1e83721c7cb08000034df353fe", size = 2341704 }, - { url = "https://files.pythonhosted.org/packages/36/9b/0f3b9ff3d0225ee2324ec663de0e7fb3eb855615ca958ac1875f22f1f8e5/torchvision-0.24.1-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:9ef95d819fd6df81bc7cc97b8f21a15d2c0d3ac5dbfaab5cbc2d2ce57114b19e", size = 8177422 }, - { url = "https://files.pythonhosted.org/packages/d6/ab/e2bcc7c2f13d882a58f8b30ff86f794210b075736587ea50f8c545834f8a/torchvision-0.24.1-cp314-cp314t-win_amd64.whl", hash = "sha256:480b271d6edff83ac2e8d69bbb4cf2073f93366516a50d48f140ccfceedb002e", size = 4335190 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/64/46/bc0ebd93282aeedc1759f054a252c6fadf14b42a0535db3233c85cce4ae5/torchvision-0.27.1-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:ad8743a9c12c8c124ad0a1491e54c3ca0c749e91e374e3d92136060b22c9e0f4", size = 1852118, upload-time = "2026-06-17T21:09:32.448Z" }, + { url = "https://files.pythonhosted.org/packages/b2/00/752adc57b6aa8bb833f5b0672acb9538aa5535d64998b9d8dd48ee51fa80/torchvision-0.27.1-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:a726707e4cbe438fcc507d787af7acf6bca52de30bf4b03579f1dfc0675da829", size = 7831256, upload-time = "2026-06-17T21:09:26.767Z" }, + { url = "https://files.pythonhosted.org/packages/fa/8a/c474fb27faba02e84dc40e0ac9ea1aa828d6d3557a378f7d0a22468bb2a3/torchvision-0.27.1-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:a1d6a123009af59ad288459f579f67a65cbe8f59372dc7b97e41bc01a6a9b767", size = 7659995, upload-time = "2026-06-17T21:09:25.325Z" }, + { url = "https://files.pythonhosted.org/packages/eb/7c/e254f8e242a921adc2cc62c11674fa8a16d33e0a1b6c6f5436cb91628ee7/torchvision-0.27.1-cp311-cp311-win_amd64.whl", hash = "sha256:f3b57a984283896f15c9698562418282f828332886c77315bf269936e6ba0280", size = 3807497, upload-time = "2026-06-17T21:09:31.234Z" }, + { url = "https://files.pythonhosted.org/packages/88/82/2e8fdc19e4f0bbe31d403a55d78318bcea4afcd3083e1e4700ef61ebb893/torchvision-0.27.1-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:448abfc3baba984da4577f737209e445da6be93e3b5f4799d90162bf61e3f485", size = 1852105, upload-time = "2026-06-17T21:09:33.695Z" }, + { url = "https://files.pythonhosted.org/packages/43/42/103fa8f9366cfd1329fe449d6b1a25a640c0c17862ed48f21c4af94af322/torchvision-0.27.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:9edfb5a549fc2f30ccadb24eca907901e92e426c91a59316be6703a9360e5098", size = 7830902, upload-time = "2026-06-17T21:09:29.739Z" }, + { url = "https://files.pythonhosted.org/packages/97/70/fa6052a42110a3657fc94073648da6171220469f4bf9f27e6a0b9378075c/torchvision-0.27.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:ae3d49e57c4abc8eafc1a1971f80fc4948a6268fa69340737ca4466936def080", size = 7664211, upload-time = "2026-06-17T21:09:17.206Z" }, + { url = "https://files.pythonhosted.org/packages/d0/95/27aca854da7e536a339f46bab1ef67823ac2ac97c59ab2b3203b373d46cf/torchvision-0.27.1-cp312-cp312-win_amd64.whl", hash = "sha256:0b6e3aa98b7433506bbce1d0d05cb13ec787fc6eb8c5fbd998b26ce05f047543", size = 4079076, upload-time = "2026-06-17T21:09:15.907Z" }, + { url = "https://files.pythonhosted.org/packages/32/bb/b21e0f598ca191bb2a9e9fda2fee37c06ad113313b43c6769dbefa0e921d/torchvision-0.27.1-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:d60311a6d08df905f9656a3a312f0a8f55f0d46321bc737bad30a8dec9644309", size = 1852110, upload-time = "2026-06-17T21:09:22.577Z" }, + { url = "https://files.pythonhosted.org/packages/2f/90/d61171daa5d6cd5f9315f84f9ef947b047a9fdf283d53241327045a8dd6d/torchvision-0.27.1-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:08aa33bc8e062cca32aefa90ac714916c5a855cbe1ab4c6148fc0453eb40ca5a", size = 7789476, upload-time = "2026-06-17T21:09:13.105Z" }, + { url = "https://files.pythonhosted.org/packages/b8/dc/b21d7801562c23a770e7037989814582f22ca4db479204293561de4b62e8/torchvision-0.27.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:916448be4b19676677b0dbf47d08f68b7955ea0abec7fc79340c31e217a824ba", size = 7664256, upload-time = "2026-06-17T21:09:07.549Z" }, + { url = "https://files.pythonhosted.org/packages/b9/b3/4386976ff77eda55f0aed504a288564f3ff8d170b6db49ee22e172eddfac/torchvision-0.27.1-cp313-cp313-win_amd64.whl", hash = "sha256:18bc906235bfa901c135acd239f05b8c8ab90d502830cf1ef2cba3301e1f8a23", size = 4150710, upload-time = "2026-06-17T21:09:14.457Z" }, + { url = "https://files.pythonhosted.org/packages/ff/74/1d237c61f665bf46d02e15f67c9d40be42b1b634f87164b9cefd257450e7/torchvision-0.27.1-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:9f5ef59ad60e695796eca6b64e97cb9b21b9d5463cac5ac0ef86cfb72b6e5db9", size = 1852112, upload-time = "2026-06-17T21:09:21.445Z" }, + { url = "https://files.pythonhosted.org/packages/24/84/f0d772e7ed85891f084755bd5d7f6f7fd279992a02652c653c1c8429dd84/torchvision-0.27.1-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:ab2f8047c2da5bf6742fec6da86840e5feaeb0cea76930d0536f3520df31e166", size = 7789751, upload-time = "2026-06-17T21:09:11.51Z" }, + { url = "https://files.pythonhosted.org/packages/76/68/3febd41b6eef453a83fb7a0178446334fbb0405eb4b0c40b00efaf99a2dc/torchvision-0.27.1-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:b44ef28ad1963f8cba5bf82f3564c454c74be300df9f79efa43f773312d17d6c", size = 7664350, upload-time = "2026-06-17T21:09:04.486Z" }, + { url = "https://files.pythonhosted.org/packages/52/49/a23e199faf29e42a90f7d6b76437ade5d17e3185da3c64d368973ba8243e/torchvision-0.27.1-cp314-cp314-win_amd64.whl", hash = "sha256:b3e9bc71854fddbf94ddb69ed8d88983945f3f28f78ee104214b0088669af66a", size = 4177297, upload-time = "2026-06-17T21:09:10.273Z" }, + { url = "https://files.pythonhosted.org/packages/ba/48/b3240eaf0fe3676dcf677ce8930ef477fe77d7f69ebe58ca8d0941384952/torchvision-0.27.1-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:c2fd9902f23b56b6ac667213171672fb6c89287ff011918b04af053852a2c4eb", size = 1852118, upload-time = "2026-06-17T21:09:20.223Z" }, + { url = "https://files.pythonhosted.org/packages/73/01/6c8f3158994a9e5bb0c7b1bacc361d60e015ad79487af88fa4d7ce72c2b6/torchvision-0.27.1-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:8abb6d5cacd56486ca2240e5580750e53ac559412e472ea6a3cee83231a77ca7", size = 7791242, upload-time = "2026-06-17T21:09:02.062Z" }, + { url = "https://files.pythonhosted.org/packages/1b/e6/f66733fc411a9ce070c0d899c1ae562ff11654a0bc708511e23efe9d6872/torchvision-0.27.1-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:d11da1ce8a5cc7fc527f2d5e0fe25efba93687897fe9339382b593910b1d1c6e", size = 7664934, upload-time = "2026-06-17T21:09:06.221Z" }, + { url = "https://files.pythonhosted.org/packages/90/aa/d6179812ec52b70a7a8f5e99fe7937895d28c535106df1ca0d03f5f51425/torchvision-0.27.1-cp314-cp314t-win_amd64.whl", hash = "sha256:12deaee20d0d9dec6302025d3f93354266befeb692f5c50bca0137b395598b9e", size = 4284412, upload-time = "2026-06-17T21:09:08.989Z" }, ] [[package]] name = "tornado" -version = "6.5.6" -source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/50/57/6d7303a77ae439d9189108f76c0c4fd89ee5e2cc8387bffb55232565c4ed/tornado-6.5.6.tar.gz", hash = "sha256:9a365179fe8ff6b8766f602c0f67c185d778193e9bdd828b19f0b6ed7764177d", size = 518139, upload-time = "2026-05-27T15:35:54.646Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/1b/0d/b4f481e18c5a51864e6d12b9a05ecf72919696680b747c958c3fc1f4fbae/tornado-6.5.6-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:65fcfaafb079435c2c19dc9e07c0f1cf0fa9051759ed0a7d0a3ba7ea7f64919c", size = 447737, upload-time = "2026-05-27T15:35:38.122Z" }, - { url = "https://files.pythonhosted.org/packages/9e/9c/5430c39fcab1144d35860f457b15e9c08b4bc7ac86764354204e983d6183/tornado-6.5.6-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:38bc01b4acacded2de63ae78023548e41ebe6fbed3ec05a796d7ae3ad893887e", size = 445899, upload-time = "2026-05-27T15:35:40.519Z" }, - { url = "https://files.pythonhosted.org/packages/8b/79/fa7e14a2f939c807a8d30619b4eb604eab219601b78792516ebe22d40cf9/tornado-6.5.6-cp39-abi3-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b942e6a137fda31ff54bf8e6e2c8d1c37f1f50583f3ed53fb840b53b9601d104", size = 448964, upload-time = "2026-05-27T15:35:42.106Z" }, - { url = "https://files.pythonhosted.org/packages/a7/71/bd67d5f5199f937dafe03a49a37989f60f600ff6fef34c79412a829d97bd/tornado-6.5.6-cp39-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8666946e70171b8c3f1fc9b7876fac492e84822c4c7f3746f4e8f8bc9ac92a79", size = 449935, upload-time = "2026-05-27T15:35:43.906Z" }, - { url = "https://files.pythonhosted.org/packages/cc/a4/c24388c9cf5b3c3a513b56a158af9f23092c9a2810d789e294310797df21/tornado-6.5.6-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:1c34cfab7ad6d104f052f55de06d39bbafc5885cfeb4da688803308dbcfa90b7", size = 449767, upload-time = "2026-05-27T15:35:45.793Z" }, - { url = "https://files.pythonhosted.org/packages/a5/eb/6a07ad550c3f7b37244bd0becdf293ec3d3e961783d8b720a97df50de1b2/tornado-6.5.6-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:385f35e4e22fb52551dfcda4cdc8c30c61c2c001aef5ddad99cdfe116952efd3", size = 449174, upload-time = "2026-05-27T15:35:47.485Z" }, - { url = "https://files.pythonhosted.org/packages/bb/84/3469e098dccdb6763130e06aacd786bb4363fca7b590a55c101ddf34ed30/tornado-6.5.6-cp39-abi3-win32.whl", hash = "sha256:db475f1b67b2809b10bb16264829087724ca8d24fe4ed47f7b8675cae453ef86", size = 450230, upload-time = "2026-05-27T15:35:49.322Z" }, - { url = "https://files.pythonhosted.org/packages/d2/3c/273a04e0b9dd9016f1685cca0c1c8795a71ac88a34a8c889a0b443483226/tornado-6.5.6-cp39-abi3-win_amd64.whl", hash = "sha256:6739bf1e8eb09230f1280ddbd3236f0309db70f2c551a8dbc40f62babdf82f79", size = 450667, upload-time = "2026-05-27T15:35:51.194Z" }, - { url = "https://files.pythonhosted.org/packages/02/98/0cffe22a224f60c5fb1e3aa0b76f9da2e1ca78b0e9545e3d077c68ce60a7/tornado-6.5.6-cp39-abi3-win_arm64.whl", hash = "sha256:2543597b24a695d72338a9a77818362d72387c03ae173f1f169eadc5c91466ac", size = 449690, upload-time = "2026-05-27T15:35:52.902Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/09/ce/1eb500eae19f4648281bb2186927bb062d2438c2e5093d1360391afd2f90/tornado-6.5.2.tar.gz", hash = "sha256:ab53c8f9a0fa351e2c0741284e06c7a45da86afb544133201c5cc8578eb076a0", size = 510821 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/f6/48/6a7529df2c9cc12efd2e8f5dd219516184d703b34c06786809670df5b3bd/tornado-6.5.2-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:2436822940d37cde62771cff8774f4f00b3c8024fe482e16ca8387b8a2724db6", size = 442563 }, - { url = "https://files.pythonhosted.org/packages/f2/b5/9b575a0ed3e50b00c40b08cbce82eb618229091d09f6d14bce80fc01cb0b/tornado-6.5.2-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:583a52c7aa94ee046854ba81d9ebb6c81ec0fd30386d96f7640c96dad45a03ef", size = 440729 }, - { url = "https://files.pythonhosted.org/packages/1b/4e/619174f52b120efcf23633c817fd3fed867c30bff785e2cd5a53a70e483c/tornado-6.5.2-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b0fe179f28d597deab2842b86ed4060deec7388f1fd9c1b4a41adf8af058907e", size = 444295 }, - { url = "https://files.pythonhosted.org/packages/95/fa/87b41709552bbd393c85dd18e4e3499dcd8983f66e7972926db8d96aa065/tornado-6.5.2-cp39-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b186e85d1e3536d69583d2298423744740986018e393d0321df7340e71898882", size = 443644 }, - { url = "https://files.pythonhosted.org/packages/f9/41/fb15f06e33d7430ca89420283a8762a4e6b8025b800ea51796ab5e6d9559/tornado-6.5.2-cp39-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e792706668c87709709c18b353da1f7662317b563ff69f00bab83595940c7108", size = 443878 }, - { url = "https://files.pythonhosted.org/packages/11/92/fe6d57da897776ad2e01e279170ea8ae726755b045fe5ac73b75357a5a3f/tornado-6.5.2-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:06ceb1300fd70cb20e43b1ad8aaee0266e69e7ced38fa910ad2e03285009ce7c", size = 444549 }, - { url = "https://files.pythonhosted.org/packages/9b/02/c8f4f6c9204526daf3d760f4aa555a7a33ad0e60843eac025ccfd6ff4a93/tornado-6.5.2-cp39-abi3-musllinux_1_2_i686.whl", hash = "sha256:74db443e0f5251be86cbf37929f84d8c20c27a355dd452a5cfa2aada0d001ec4", size = 443973 }, - { url = "https://files.pythonhosted.org/packages/ae/2d/f5f5707b655ce2317190183868cd0f6822a1121b4baeae509ceb9590d0bd/tornado-6.5.2-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:b5e735ab2889d7ed33b32a459cac490eda71a1ba6857b0118de476ab6c366c04", size = 443954 }, - { url = "https://files.pythonhosted.org/packages/e8/59/593bd0f40f7355806bf6573b47b8c22f8e1374c9b6fd03114bd6b7a3dcfd/tornado-6.5.2-cp39-abi3-win32.whl", hash = "sha256:c6f29e94d9b37a95013bb669616352ddb82e3bfe8326fccee50583caebc8a5f0", size = 445023 }, - { url = "https://files.pythonhosted.org/packages/c7/2a/f609b420c2f564a748a2d80ebfb2ee02a73ca80223af712fca591386cafb/tornado-6.5.2-cp39-abi3-win_amd64.whl", hash = "sha256:e56a5af51cc30dd2cae649429af65ca2f6571da29504a07995175df14c18f35f", size = 445427 }, - { url = "https://files.pythonhosted.org/packages/5e/4f/e1f65e8f8c76d73658b33d33b81eed4322fb5085350e4328d5c956f0c8f9/tornado-6.5.2-cp39-abi3-win_arm64.whl", hash = "sha256:d6c33dc3672e3a1f3618eb63b7ef4683a7688e7b9e6e8f0d9aa5726360a004af", size = 444456 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) +version = "6.5.7" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/64/24/95ec527ad67b76d59299e5465b3935d05e4294b7e0290a3924b7487df30b/tornado-6.5.7.tar.gz", hash = "sha256:66c513a76cda70d53907bc27cf1447557699c2e95aa48ba27a442ff61c3ddfc2", size = 519252, upload-time = "2026-06-08T17:34:51.232Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/02/dc/c7043cab6fed8ae159fc1923ce829ada35c4dbd797d408a43858ffaf9639/tornado-6.5.7-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:148b2eb15c2c765a50796172c1e499649b35f30d2e3c3d3e15913cfa56bfb163", size = 448543, upload-time = "2026-06-08T17:34:38.052Z" }, + { url = "https://files.pythonhosted.org/packages/92/4f/090b1431e5a43df696feceffc268c5383cc079ecb5f08ce58f917109aafe/tornado-6.5.7-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:9da38de27f1da3b78a966f0dae12b5a1ea9afe72ca805d84ff06508272ddf100", size = 446707, upload-time = "2026-06-08T17:34:39.594Z" }, + { url = "https://files.pythonhosted.org/packages/37/d8/ef374952fd5da67d4463122c2b8e5a96536ec10b4b339254c6dcde81d01c/tornado-6.5.7-cp39-abi3-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:8d759e71906ee783f8867b93bf26a265743da4c1e2f4a018464c1ba019862972", size = 449774, upload-time = "2026-06-08T17:34:41.204Z" }, + { url = "https://files.pythonhosted.org/packages/35/37/d434c73f4c6e014b745b9b37085f34f40c022f007efff3d7fe65991899f3/tornado-6.5.7-cp39-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8a46347a18f23fb92b396beebe0fb78f61dda0cc302445202c16203d8a18848b", size = 450745, upload-time = "2026-06-08T17:34:42.531Z" }, + { url = "https://files.pythonhosted.org/packages/b6/2b/56b9aff361d7f1ab728a805ec7d7ea835f8807afa9f5cc690ea0e630efb9/tornado-6.5.7-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:7778b30bef919231265e91c69963ce0f49a1e9c07ac900bbe75b19ce2575ba92", size = 450578, upload-time = "2026-06-08T17:34:43.787Z" }, + { url = "https://files.pythonhosted.org/packages/02/30/a7444fb23aa76860a14198fab96ac79f1866b0a6e19e26c4381b0938e50f/tornado-6.5.7-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:e726f0c75da7726eec023aa62751ff8878bd2737e34fbdd33b1ae5897d2200f5", size = 449985, upload-time = "2026-06-08T17:34:45.326Z" }, + { url = "https://files.pythonhosted.org/packages/5c/42/5f0e56c01e8d9d36f4e23f367b85ae6cae0c1ecddd5e6977d8388ad27488/tornado-6.5.7-cp39-abi3-win32.whl", hash = "sha256:f8de3bf12d3efdd0cbe7c8887868198f8a91415e3f29fcf258d9b8eb7b1d9ae4", size = 451047, upload-time = "2026-06-08T17:34:46.784Z" }, + { url = "https://files.pythonhosted.org/packages/c9/a4/b393076ffb21b469eec5b328a0534cf03a3b90bfc6b1f09507cdd075d938/tornado-6.5.7-cp39-abi3-win_amd64.whl", hash = "sha256:de942f843533a039ef9fa3d9c88c7cd8a7c94553fb5ad0154270989b3d99a2c4", size = 451485, upload-time = "2026-06-08T17:34:48.248Z" }, + { url = "https://files.pythonhosted.org/packages/71/2e/7b1c769803121b809112cf9a00681c472eae1d80e32d7ec0e0bd61d0d0e1/tornado-6.5.7-cp39-abi3-win_arm64.whl", hash = "sha256:ff934fce95643af5f11efdae618eaa73d469dc588641e5c8d19295a0c65c4796", size = 450506, upload-time = "2026-06-08T17:34:49.702Z" }, ] [[package]] name = "tqdm" -version = "4.67.3" +version = "4.68.3" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "colorama", marker = "sys_platform == 'win32'" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/09/a9/6ba95a270c6f1fbcd8dac228323f2777d886cb206987444e4bce66338dd4/tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb", size = 169598, upload-time = "2026-02-03T17:35:53.048Z" } +sdist = { url = "https://files.pythonhosted.org/packages/87/d7/0535a28b1f5f24f6612fb3ff1e89fb1a8d160fee0f976e0aa6803862134b/tqdm-4.68.3.tar.gz", hash = "sha256:00dfa48452b6b6cfae3dd9885636c23d3422d1ec97c66d96818cbd5e0821d482", size = 170596, upload-time = "2026-06-17T07:36:52.105Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/16/e1/3079a9ff9b8e11b846c6ac5c8b5bfb7ff225eee721825310c91b3b50304f/tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf", size = 78374, upload-time = "2026-02-03T17:35:50.982Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/a8/4b/29b4ef32e036bb34e4ab51796dd745cdba7ed47ad142a9f4a1eb8e0c744d/tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2", size = 169737 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2", size = 78540 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/d8/8e/bb97bb0c71802080bfc8952937d174e49cfc50de5c951dd47b2496f0dcdb/tqdm-4.68.3-py3-none-any.whl", hash = "sha256:39832cc2def2789a6f29df83f172db7416cea70052c0907a57801c5f2fdccb03", size = 78337, upload-time = "2026-06-17T07:36:50.132Z" }, ] [[package]] name = "traitlets" -version = "5.15.0" +version = "5.15.1" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/1b/22/40f55b26baeab80c2d7b3f1db0682f8954e4617fee7d90ce634022ef05c6/traitlets-5.15.0.tar.gz", hash = "sha256:4fead733f81cf1c4c938e06f8ca4633896833c9d89eff878159457f4d4392971", size = 163197, upload-time = "2026-05-06T08:05:58.016Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/da/98/a9937a969d018a23badfea0b381f66783649d48e0ea6c41923265c3cbeb3/traitlets-5.15.0-py3-none-any.whl", hash = "sha256:fb36a18867a6803deab09f3c5e0fa81bb7b26a5c9e82501c9933f759166eff40", size = 85877, upload-time = "2026-05-06T08:05:55.853Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/eb/79/72064e6a701c2183016abbbfedaba506d81e30e232a68c9f0d6f6fcd1574/traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7", size = 161621 } +sdist = { url = "https://files.pythonhosted.org/packages/57/a9/a2584b8313b89f94869ddb3c4074617a691de1812a614d2d50e32ca5a7a6/traitlets-5.15.1.tar.gz", hash = "sha256:7b1c07854fe25acb39e009bae49f11b79ff6cbb2f27999104e9110e7a6b53722", size = 163344, upload-time = "2026-06-03T12:26:06.181Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f", size = 85359 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/96/8d/1080ee4c231f361b6ce4470d556c8c435b67c7e0753aaa641497ee92f88b/traitlets-5.15.1-py3-none-any.whl", hash = "sha256:770a53705f84b81ac107e83a1b3328ff2dae16094d8fc3cfc004e4b22dfd8e92", size = 85858, upload-time = "2026-06-03T12:26:04.395Z" }, ] [[package]] name = "triton" -version = "3.7.0" -source = { registry = "https://pypi.org/simple" } -wheels = [ -<<<<<<< HEAD - { url = "https://files.pythonhosted.org/packages/b8/c1/5d842314bb6c78442cc60437928781701c6050b8d479bc2a1aed691d37ca/triton-3.7.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a9e71fc392675fac364e0ecf4ef3f76f85b7f5433a16f4c3c5fe5f05a52c85fe", size = 188480277, upload-time = "2026-05-07T19:05:03.231Z" }, - { url = "https://files.pythonhosted.org/packages/13/31/8315ea5f8dd18e60970b3022e3a8b93fd37e0b784fbbef86e10c8e6e5ca1/triton-3.7.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:22bacffce443f54593dd20f05294d5a40622e0ea9ab632816f87154504356221", size = 201415942, upload-time = "2026-05-07T18:46:06.479Z" }, - { url = "https://files.pythonhosted.org/packages/f7/13/ec05adfcd87311d532ba61e3af143e8be59fcd26675884c4682841406a20/triton-3.7.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a4bf49b00a7a377a68a6da603a876e797614e6455a80e9021669c476a953ad9a", size = 188505104, upload-time = "2026-05-07T19:05:09.843Z" }, - { url = "https://files.pythonhosted.org/packages/62/7b/468a576e35beef1426e0828e28e9ba9e65f5474d496f16ee126c15646324/triton-3.7.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8f111161d49bf903c0eaedde3962353a3d841c08a836839b7cc1025b8426efcf", size = 201457567, upload-time = "2026-05-07T18:46:13.505Z" }, - { url = "https://files.pythonhosted.org/packages/01/e1/a59a583de59b8f62c495d67c80ee3ea97d09e91ac80c4c6e76456ed8d8ac/triton-3.7.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:abdf6beaa89b1bcfb9a43cd990536ce66091a997841a4814b260b7bee4c88c3c", size = 188503209, upload-time = "2026-05-07T19:05:17.935Z" }, - { url = "https://files.pythonhosted.org/packages/30/b1/b7507bb9815d403927c8dd51d4158ed2e11751a92dbc118a044f247b6848/triton-3.7.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a35d7afe3f3f058e7ec49fcce09794049e0ffc5c59019ac25ec3413741b8c4e7", size = 201453566, upload-time = "2026-05-07T18:46:20.427Z" }, - { url = "https://files.pythonhosted.org/packages/a6/8f/0bea7a6a0c989315c9135a1d7fb37e41905cfb3a17cbc1f10044ebd4cc3a/triton-3.7.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:cc1d61c172d257db80ddf42595131fb196ad2e9bdd751e90fe2ef13531734e8b", size = 188612899, upload-time = "2026-05-07T19:05:24.955Z" }, - { url = "https://files.pythonhosted.org/packages/e1/02/d96f57828d0912aec733b9bc7e0e7dbfd2c6f079a8fa433ac25cb93d1a30/triton-3.7.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:70fb9bbdc9f400afc54bbf6eb2670af28829a6ae3996863317964783141daf56", size = 201553816, upload-time = "2026-05-07T18:46:27.49Z" }, - { url = "https://files.pythonhosted.org/packages/40/fb/82a802dac4689f2a2fb2e69302e6a138eecc3e175bbe976ba3cfc717683a/triton-3.7.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c4a44a8476d0d3571eac4e4d1048e1ff75aad81a09ff4602ccfc56c6dea1672e", size = 188507879, upload-time = "2026-05-07T19:05:32.209Z" }, - { url = "https://files.pythonhosted.org/packages/8f/af/9904ec6d3c93d9b24e5ec360445bbdf758b7f00bfbeedb89cb0eb64eb8bb/triton-3.7.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b9b85e72968a9d8bba5ddb24e9b64aaabaf48affb042f2755cb7cfa92b7531ce", size = 201460637, upload-time = "2026-05-07T18:46:34.749Z" }, - { url = "https://files.pythonhosted.org/packages/a1/f9/4835a8ea746b88727d8899f4e3ccce4f9cacb38abfc3bb0a638266c53111/triton-3.7.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:18a160de426fd99f92b0baf509045360afbd3bfaa0b4a5171dde800ec9f09684", size = 188608706, upload-time = "2026-05-07T19:05:39.218Z" }, - { url = "https://files.pythonhosted.org/packages/c1/68/fa86e5a39608000f645535b2c124920126327ab731f8c4fafd5b07ff8d4b/triton-3.7.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ce061073102714b725f3660ec6939d94a1da7984b3aa99c921417cae273672f5", size = 201546766, upload-time = "2026-05-07T18:46:42.088Z" }, -======= - { url = "https://files.pythonhosted.org/packages/b0/72/ec90c3519eaf168f22cb1757ad412f3a2add4782ad3a92861c9ad135d886/triton-3.5.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:61413522a48add32302353fdbaaf92daaaab06f6b5e3229940d21b5207f47579", size = 170425802 }, - { url = "https://files.pythonhosted.org/packages/f2/50/9a8358d3ef58162c0a415d173cfb45b67de60176e1024f71fbc4d24c0b6d/triton-3.5.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d2c6b915a03888ab931a9fd3e55ba36785e1fe70cbea0b40c6ef93b20fc85232", size = 170470207 }, - { url = "https://files.pythonhosted.org/packages/27/46/8c3bbb5b0a19313f50edcaa363b599e5a1a5ac9683ead82b9b80fe497c8d/triton-3.5.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f3f4346b6ebbd4fad18773f5ba839114f4826037c9f2f34e0148894cd5dd3dba", size = 170470410 }, - { url = "https://files.pythonhosted.org/packages/37/92/e97fcc6b2c27cdb87ce5ee063d77f8f26f19f06916aa680464c8104ef0f6/triton-3.5.1-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0b4d2c70127fca6a23e247f9348b8adde979d2e7a20391bfbabaac6aebc7e6a8", size = 170579924 }, - { url = "https://files.pythonhosted.org/packages/a4/e6/c595c35e5c50c4bc56a7bac96493dad321e9e29b953b526bbbe20f9911d0/triton-3.5.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d0637b1efb1db599a8e9dc960d53ab6e4637db7d4ab6630a0974705d77b14b60", size = 170480488 }, - { url = "https://files.pythonhosted.org/packages/16/b5/b0d3d8b901b6a04ca38df5e24c27e53afb15b93624d7fd7d658c7cd9352a/triton-3.5.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bac7f7d959ad0f48c0e97d6643a1cc0fd5786fe61cb1f83b537c6b2d54776478", size = 170582192 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) +version = "3.7.1" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7b/f9/19d842d06a08559534fa1eaab6ca551b1bcf40f06620bddec1babaa2772d/triton-3.7.1-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d4a0e1cd4c4a76370ed74a8432a53cea28716827d19e40ffc732233e35ceb3f6", size = 184664887, upload-time = "2026-06-17T20:03:42.913Z" }, + { url = "https://files.pythonhosted.org/packages/cd/5e/fce69606f7f240297f163e25539906732b199530d486ce67ae319877e821/triton-3.7.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6744957e9fd610a29680ec2346057d0c86948ed3812468670719f391e94b44a5", size = 197701306, upload-time = "2026-06-17T19:53:13.673Z" }, + { url = "https://files.pythonhosted.org/packages/94/fa/f856e24deb462d5f18bd4b5a746957862ab9b6ee5834bda60605ec348366/triton-3.7.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9497f2e696ee368862a181a90b2dcc03ca978cc4f602abd67c7d81022a6988e1", size = 184692359, upload-time = "2026-06-17T20:03:48.288Z" }, + { url = "https://files.pythonhosted.org/packages/c4/6f/fb96d15db6f36d6eae4cafb998c2e0353bf59d7c4ea1662d7497f269134a/triton-3.7.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7e40869937a68206ec70d7f25bb7ec6433cb083f9135e1f36dbd318dc449a728", size = 197719725, upload-time = "2026-06-17T19:53:20.419Z" }, + { url = "https://files.pythonhosted.org/packages/00/42/c5089d4d9327fcd1e862c599cc2927f39418f84dd11a84cb2ccff9d4787a/triton-3.7.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:cdbfc09d9ec58bc5e68321525653220de7515c199e7a8097a97c85e62b52cd0a", size = 184694629, upload-time = "2026-06-17T20:03:53.444Z" }, + { url = "https://files.pythonhosted.org/packages/07/42/2c3ac59253ae8892b6f307875263dd23dc875cdf732d3aea40d6d41fb7cb/triton-3.7.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:58c0e131da05134a2a4788ccbcc0c1105cf0f54c8e98f19e34cd465396dc15eb", size = 197729241, upload-time = "2026-06-17T19:53:27.801Z" }, + { url = "https://files.pythonhosted.org/packages/40/71/e01aa7ad573883ed9456f130226babdec70b005e098c4d6226a6238e761b/triton-3.7.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fe4ea396a06171f1f1f58cbd39c70b09294398f7dd7c620939bab54ad6f934fa", size = 184705764, upload-time = "2026-06-17T20:03:59.064Z" }, + { url = "https://files.pythonhosted.org/packages/a4/09/5683146fda6a2b569deb78ccfd8fbfea8bfe55f726b081c0a6bb18dd6f28/triton-3.7.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2020153b08280415ec0da6607834e79166442147e78e144df06b508c75b186d2", size = 197729537, upload-time = "2026-06-17T19:53:35.516Z" }, + { url = "https://files.pythonhosted.org/packages/e9/f8/448220c3092019f9fdfab39ec47985968181d67da34b44f6a7f6280a5cbb/triton-3.7.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c58e4c61f0c73b5dba3b5d19b4a7093c32f90dc18b2a7f121a7c16ccd31107b7", size = 184814760, upload-time = "2026-06-17T20:04:04.984Z" }, + { url = "https://files.pythonhosted.org/packages/f0/ac/229b7d4589d2e5937310e72c6d46e89599d16a4a12b479ffa1499fee8eb8/triton-3.7.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:10ba85fa2cca4a2fbdeb36bf1cb082f2c252bda55bf9fccd74f65ec5bc647e68", size = 197824404, upload-time = "2026-06-17T19:53:42.772Z" }, ] [[package]] name = "typing-extensions" version = "4.15.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/72/94/1a15dd82efb362ac84269196e94cf00f187f7ed21c242792a923cdb1c61f/typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466", size = 109391 } +sdist = { url = "https://files.pythonhosted.org/packages/72/94/1a15dd82efb362ac84269196e94cf00f187f7ed21c242792a923cdb1c61f/typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466", size = 109391, upload-time = "2025-08-25T13:49:26.313Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614 }, + { url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614, upload-time = "2025-08-25T13:49:24.86Z" }, ] [[package]] name = "tzdata" version = "2026.2" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/ba/19/1b9b0e29f30c6d35cb345486df41110984ea67ae69dddbc0e8a100999493/tzdata-2026.2.tar.gz", hash = "sha256:9173fde7d80d9018e02a662e168e5a2d04f87c41ea174b139fbef642eda62d10", size = 198254, upload-time = "2026-04-24T15:22:08.651Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/ce/e4/dccd7f47c4b64213ac01ef921a1337ee6e30e8c6466046018326977efd95/tzdata-2026.2-py2.py3-none-any.whl", hash = "sha256:bbe9af844f658da81a5f95019480da3a89415801f6cc966806612cc7169bffe7", size = 349321, upload-time = "2026-04-24T15:22:05.876Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/95/32/1a225d6164441be760d75c2c42e2780dc0873fe382da3e98a2e1e48361e5/tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9", size = 196380 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "uri-template" version = "1.3.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/31/c7/0336f2bd0bcbada6ccef7aaa25e443c118a704f828a0620c6fa0207c1b64/uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7", size = 21678 } +sdist = { url = "https://files.pythonhosted.org/packages/31/c7/0336f2bd0bcbada6ccef7aaa25e443c118a704f828a0620c6fa0207c1b64/uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7", size = 21678, upload-time = "2023-06-21T01:49:05.374Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363", size = 11140 }, + { url = "https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363", size = 11140, upload-time = "2023-06-21T01:49:03.467Z" }, ] [[package]] name = "urllib3" version = "2.7.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/53/0c/06f8b233b8fd13b9e5ee11424ef85419ba0d8ba0b3138bf360be2ff56953/urllib3-2.7.0.tar.gz", hash = "sha256:231e0ec3b63ceb14667c67be60f2f2c40a518cb38b03af60abc813da26505f4c", size = 433602, upload-time = "2026-05-07T16:13:18.596Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/7f/3e/5db95bcf282c52709639744ca2a8b149baccf648e39c8cc87553df9eae0c/urllib3-2.7.0-py3-none-any.whl", hash = "sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897", size = 131087, upload-time = "2026-05-07T16:13:17.151Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/15/22/9ee70a2574a4f4599c47dd506532914ce044817c7752a79b6a51286319bc/urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760", size = 393185 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc", size = 129795 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "virtualenv" -version = "21.4.2" +version = "21.5.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "distlib" }, @@ -5414,57 +3943,45 @@ dependencies = [ { name = "platformdirs" }, { name = "python-discovery" }, ] -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/e1/0d/4e93c8e6d1001a75763f87d8f5ecda8ebc7f4aa2153dddfaf4ae8892821a/virtualenv-21.4.2.tar.gz", hash = "sha256:38e6ee0a555615c0ea9da2ac7e9998fe8dc3b911dd33ad8eaad2020957653b0c", size = 7613326, upload-time = "2026-05-31T17:01:22.827Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/bf/c4/557dc082be035381b85fdb2b74e21d3d21b57750b74f2b47a32f3a639ff9/virtualenv-21.4.2-py3-none-any.whl", hash = "sha256:854210ca524a1a4d0d744734f4acbc721c3ffe163b85bbf5d56d14d5ae2f0fae", size = 7594079, upload-time = "2026-05-31T17:01:20.735Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/20/28/e6f1a6f655d620846bd9df527390ecc26b3805a0c5989048c210e22c5ca9/virtualenv-20.35.4.tar.gz", hash = "sha256:643d3914d73d3eeb0c552cbb12d7e82adf0e504dbf86a3182f8771a153a1971c", size = 6028799 } +sdist = { url = "https://files.pythonhosted.org/packages/f1/a5/81f987504738e6defeed61ec1c47e2aefab3c35d8eeb87e1b3f38cf28254/virtualenv-21.5.1.tar.gz", hash = "sha256:dca3bf98275a59c652b69d68e73433e597d977c2da9198882479d1a7188009c8", size = 4578798, upload-time = "2026-06-16T16:23:58.603Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/79/0c/c05523fa3181fdf0c9c52a6ba91a23fbf3246cc095f26f6516f9c60e6771/virtualenv-20.35.4-py3-none-any.whl", hash = "sha256:c21c9cede36c9753eeade68ba7d523529f228a403463376cf821eaae2b650f1b", size = 6005095 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/2c/02/3623e6169bed617ed1e2d372f7c69f92ec28d54c4dfc997055c8578ec148/virtualenv-21.5.1-py3-none-any.whl", hash = "sha256:55aa670b67bbfb991b03fda39bd3276d92c419d702376e98c5df1c9989a26783", size = 4558820, upload-time = "2026-06-16T16:23:56.963Z" }, ] [[package]] name = "wcwidth" -version = "0.7.0" +version = "0.8.1" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD -sdist = { url = "https://files.pythonhosted.org/packages/2c/ee/afaf0f85a9a18fe47a67f1e4422ed6cf1fe642f0ae0a2f81166231303c52/wcwidth-0.7.0.tar.gz", hash = "sha256:90e3a7ea092341c44b99562e75d09e4d5160fe7a3974c6fb842a101a95e7eed0", size = 182132, upload-time = "2026-05-02T16:04:12.653Z" } +sdist = { url = "https://files.pythonhosted.org/packages/49/b4/51fe890511f0f242d07cb1ebe6a5b6db417262b9d2568b460347c57d95cc/wcwidth-0.8.1.tar.gz", hash = "sha256:faf5b4a5366a72dc49cad48cdf21f52bdf63bdda995178e483ba247ff79089b9", size = 1466072, upload-time = "2026-06-08T05:57:23.146Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/41/52/e465037f5375f43533d1a80b6923955201596a99142ed524d77b571a1418/wcwidth-0.7.0-py3-none-any.whl", hash = "sha256:5d69154c429a82910e241c738cd0e2976fac8a2dd47a1a805f4afed1c0f136f2", size = 110825, upload-time = "2026-05-02T16:04:11.033Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/24/30/6b0809f4510673dc723187aeaf24c7f5459922d01e2f794277a3dfb90345/wcwidth-0.2.14.tar.gz", hash = "sha256:4d478375d31bc5395a3c55c40ccdf3354688364cd61c4f6adacaa9215d0b3605", size = 102293 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/af/b5/123f13c975e9f27ab9c0770f514345bd406d0e8d3b7a0723af9d43f710af/wcwidth-0.2.14-py2.py3-none-any.whl", hash = "sha256:a7bb560c8aee30f9957e5f9895805edd20602f2d7f720186dfd906e82b4982e1", size = 37286 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) + { url = "https://files.pythonhosted.org/packages/bd/6e/95b0e537de1f4d4301f76f944642c6da50d1511cc7b3d64dc418a66c7509/wcwidth-0.8.1-py3-none-any.whl", hash = "sha256:f453740b1e4a4f3291faa37944c555d71056c4da08d59809b307ef4feba695c8", size = 323092, upload-time = "2026-06-08T05:57:21.413Z" }, ] [[package]] name = "webcolors" version = "25.10.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/1d/7a/eb316761ec35664ea5174709a68bbd3389de60d4a1ebab8808bfc264ed67/webcolors-25.10.0.tar.gz", hash = "sha256:62abae86504f66d0f6364c2a8520de4a0c47b80c03fc3a5f1815fedbef7c19bf", size = 53491 } +sdist = { url = "https://files.pythonhosted.org/packages/1d/7a/eb316761ec35664ea5174709a68bbd3389de60d4a1ebab8808bfc264ed67/webcolors-25.10.0.tar.gz", hash = "sha256:62abae86504f66d0f6364c2a8520de4a0c47b80c03fc3a5f1815fedbef7c19bf", size = 53491, upload-time = "2025-10-31T07:51:03.977Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/e2/cc/e097523dd85c9cf5d354f78310927f1656c422bd7b2613b2db3e3f9a0f2c/webcolors-25.10.0-py3-none-any.whl", hash = "sha256:032c727334856fc0b968f63daa252a1ac93d33db2f5267756623c210e57a4f1d", size = 14905 }, + { url = "https://files.pythonhosted.org/packages/e2/cc/e097523dd85c9cf5d354f78310927f1656c422bd7b2613b2db3e3f9a0f2c/webcolors-25.10.0-py3-none-any.whl", hash = "sha256:032c727334856fc0b968f63daa252a1ac93d33db2f5267756623c210e57a4f1d", size = 14905, upload-time = "2025-10-31T07:51:01.778Z" }, ] [[package]] name = "webencodings" version = "0.5.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/0b/02/ae6ceac1baeda530866a85075641cec12989bd8d31af6d5ab4a3e8c92f47/webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923", size = 9721 } +sdist = { url = "https://files.pythonhosted.org/packages/0b/02/ae6ceac1baeda530866a85075641cec12989bd8d31af6d5ab4a3e8c92f47/webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923", size = 9721, upload-time = "2017-04-05T20:21:34.189Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", size = 11774 }, + { url = "https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", size = 11774, upload-time = "2017-04-05T20:21:32.581Z" }, ] [[package]] name = "websocket-client" version = "1.9.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/2c/41/aa4bf9664e4cda14c3b39865b12251e8e7d239f4cd0e3cc1b6c2ccde25c1/websocket_client-1.9.0.tar.gz", hash = "sha256:9e813624b6eb619999a97dc7958469217c3176312b3a16a4bd1bc7e08a46ec98", size = 70576 } +sdist = { url = "https://files.pythonhosted.org/packages/2c/41/aa4bf9664e4cda14c3b39865b12251e8e7d239f4cd0e3cc1b6c2ccde25c1/websocket_client-1.9.0.tar.gz", hash = "sha256:9e813624b6eb619999a97dc7958469217c3176312b3a16a4bd1bc7e08a46ec98", size = 70576, upload-time = "2025-10-07T21:16:36.495Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/34/db/b10e48aa8fff7407e67470363eac595018441cf32d5e1001567a7aeba5d2/websocket_client-1.9.0-py3-none-any.whl", hash = "sha256:af248a825037ef591efbf6ed20cc5faa03d3b47b9e5a2230a529eeee1c1fc3ef", size = 82616 }, + { url = "https://files.pythonhosted.org/packages/34/db/b10e48aa8fff7407e67470363eac595018441cf32d5e1001567a7aeba5d2/websocket_client-1.9.0-py3-none-any.whl", hash = "sha256:af248a825037ef591efbf6ed20cc5faa03d3b47b9e5a2230a529eeee1c1fc3ef", size = 82616, upload-time = "2025-10-07T21:16:34.951Z" }, ] [[package]] @@ -5474,7 +3991,6 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "markupsafe" }, ] -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/dd/b2/381be8cfdee792dd117872481b6e378f85c957dd7c5bca38897b08f765fd/werkzeug-3.1.8.tar.gz", hash = "sha256:9bad61a4268dac112f1c5cd4630a56ede601b6ed420300677a869083d70a4c44", size = 875852, upload-time = "2026-04-02T18:49:14.268Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/93/8c/2e650f2afeb7ee576912636c23ddb621c91ac6a98e66dc8d29c3c69446e1/werkzeug-3.1.8-py3-none-any.whl", hash = "sha256:63a77fb8892bf28ebc3178683445222aa500e48ebad5ec77b0ad80f8726b1f50", size = 226459, upload-time = "2026-04-02T18:49:12.72Z" }, @@ -5487,11 +4003,6 @@ source = { registry = "https://pypi.org/simple" } sdist = { url = "https://files.pythonhosted.org/packages/bd/f4/c67440c7fb409a71b7404b7aefcd7569a9c0d6bd071299bf4198ae7a5d95/widgetsnbextension-4.0.15.tar.gz", hash = "sha256:de8610639996f1567952d763a5a41af8af37f2575a41f9852a38f947eb82a3b9", size = 1097402, upload-time = "2025-11-01T21:15:55.178Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/3f/0e/fa3b193432cfc60c93b42f3be03365f5f909d2b3ea410295cf36df739e31/widgetsnbextension-4.0.15-py3-none-any.whl", hash = "sha256:8156704e4346a571d9ce73b84bee86a29906c9abfd7223b7228a28899ccf3366", size = 2196503, upload-time = "2025-11-01T21:15:53.565Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/9f/69/83029f1f6300c5fb2471d621ab06f6ec6b3324685a2ce0f9777fd4a8b71e/werkzeug-3.1.3.tar.gz", hash = "sha256:60723ce945c19328679790e3282cc758aa4a6040e4bb330f53d30fa546d44746", size = 806925 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/52/24/ab44c871b0f07f491e5d2ad12c9bd7358e527510618cb1b803a88e986db1/werkzeug-3.1.3-py3-none-any.whl", hash = "sha256:54b78bf3716d19a65be4fceccc0d1d7b89e608834989dfae50ea87564639213e", size = 224498 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] @@ -5501,12 +4012,11 @@ source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version < '3.12'", ] -<<<<<<< HEAD dependencies = [ { name = "donfig", marker = "python_full_version < '3.12'" }, { name = "google-crc32c", marker = "python_full_version < '3.12'" }, { name = "numcodecs", marker = "python_full_version < '3.12'" }, - { name = "numpy", marker = "python_full_version < '3.12'" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, { name = "packaging", marker = "python_full_version < '3.12'" }, { name = "typing-extensions", marker = "python_full_version < '3.12'" }, ] @@ -5527,31 +4037,20 @@ dependencies = [ { name = "donfig", marker = "python_full_version >= '3.12'" }, { name = "google-crc32c", marker = "python_full_version >= '3.12'" }, { name = "numcodecs", marker = "python_full_version >= '3.12'" }, - { name = "numpy", marker = "python_full_version >= '3.12'" }, + { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, { name = "packaging", marker = "python_full_version >= '3.12'" }, { name = "typing-extensions", marker = "python_full_version >= '3.12'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/93/8d/aeb164004f87543b06ef54f885d02c342c31ceb274e2bbec470a98927621/zarr-3.2.1.tar.gz", hash = "sha256:71565b738a0e7e8ed226f0516eba8c6bb53440ad7669a8c48ebb3534a161d035", size = 675161, upload-time = "2026-05-05T12:37:22.383Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/88/0a/469e2bd01be1490336e6c8707386845655d59261543315778a3ccc7e8019/zarr-3.2.1-py3-none-any.whl", hash = "sha256:f78cdd3d9687ad0e9f9cba2c5683b64f0c52589c19f685eeabe872e93cc0d2c7", size = 319617, upload-time = "2026-05-05T12:37:20.66Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/d6/67/14be68a7bad15eecda09b1e81fca2420f7533645fe187bf4d6104c1aad52/zarr-3.1.3.tar.gz", hash = "sha256:01342f3e26a02ed5670db608a5576fbdb8d76acb5c280bd2d0082454b1ba6f79", size = 349125 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/1a/71/9de7229515a53d1cc5705ca9c411530f711a2242f962214d9dbfe2741aa4/zarr-3.1.3-py3-none-any.whl", hash = "sha256:45f67f87f65f14fa453f99dd8110a5936b7ac69f3a21981d33e90407c80c302a", size = 276427 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] [[package]] name = "zipp" version = "4.1.0" source = { registry = "https://pypi.org/simple" } -<<<<<<< HEAD sdist = { url = "https://files.pythonhosted.org/packages/b9/d8/eab98a517c14134c0b2eb4e2387bc5f457334293ec5d2dd3857ec2966802/zipp-4.1.0.tar.gz", hash = "sha256:4cb57381f544315db7688e976e922a2b18cdb513d21cc194eb42232ba2a3e602", size = 26214, upload-time = "2026-05-18T20:08:57.967Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/3a/13/547360d81e6d88d58492968ffda9f9542854f11310ee556fef14260cc886/zipp-4.1.0-py3-none-any.whl", hash = "sha256:25ad4e16390cd314347dd8f1de67a2ac538ae658ed4ab9db16029c07c188e97f", size = 10238, upload-time = "2026-05-18T20:08:57.045Z" }, -======= -sdist = { url = "https://files.pythonhosted.org/packages/e3/02/0f2892c661036d50ede074e376733dca2ae7c6eb617489437771209d4180/zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166", size = 25547 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276 }, ->>>>>>> dc4ee09 (faster strain, adding h5plugin to read arina data) ] From 90eb8e3983d0eb327432cfca4a3dd85c0589ba98 Mon Sep 17 00:00:00 2001 From: henrygbell Date: Fri, 26 Jun 2026 09:46:32 -0700 Subject: [PATCH 140/140] Fixed pre-existing docstring error with tomo/utils.py --- src/quantem/tomography/utils.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/quantem/tomography/utils.py b/src/quantem/tomography/utils.py index 2e8e7fd9..91cc762b 100644 --- a/src/quantem/tomography/utils.py +++ b/src/quantem/tomography/utils.py @@ -127,6 +127,9 @@ def get_TV_loss(tensor, factor=1e-3): return tv_loss * factor / (torch.prod(torch.tensor(tensor.shape))) + +def tv_loss_1d(x, reduction="mean"): + """ Encourages piecewise smoothness by penalizing differences between adjacent elements.