removed moved functions

Author: hschryver

src/xenium_analysis_tools/alignment/align_sections.py

@@ -39,34 +39,6 @@
     add_micron_coord_sys,
 )
 
-# def _is_multiscale(element):
-#     return (
-#         hasattr(element, 'keys')
-#         and callable(element.keys)
-#         and not isinstance(element, GeoDataFrame)
-#     )
-
-# def get_microns_scales(sdata, element_name):
-#     el = sdata[element_name]
-#     if _is_multiscale(el):
-#         img = sd.get_pyramid_levels(el, n=0)
-#         img = img.image if hasattr(img, 'image') else img
-#     else:
-#         img = el.image if hasattr(el, 'image') else el
-
-#     # Get transforms from the actual image DataArray, not the DataTree
-#     el_transforms = get_transformation(img, get_all=True)
-#     microns_tf = el_transforms.get('microns', None)
-#     if microns_tf is None:
-#         ps = sdata['table'].uns['section_metadata']['pixel_size']
-#         microns_tf = Scale([ps, ps], axes=('x', 'y'))
-#         set_transformation(img, microns_tf, to_coordinate_system='microns')
-#     if len(microns_tf.scale) >= 2:
-#         x_y_axes = ('x', 'y')
-#         x_y_tf = [microns_tf.axes.index(axis) for axis in x_y_axes if axis in microns_tf.axes]
-#         microns_tf = Scale([microns_tf.scale[i] for i in x_y_tf], x_y_axes)
-#     return microns_tf
-
 def get_zstack_zarr(alignment_folder, paths, zstack_fov_size=None):
     zstack_folder = alignment_folder / 'zstacks'
     zstack_folder.mkdir(parents=True, exist_ok=True)
@@ -322,265 +294,6 @@ def make_element_3d(
 
     return sdata
 
-
-
-# ####### Functions to handle landmarks #########
-# def get_biwarp_params(bigwarp_json_path):
-#     import json
-#     with open(bigwarp_json_path, 'r') as f:
-#         bw_json = json.load(f)
-#     for source, source_data in bw_json['Sources'].items():
-#         if source_data['isMoving']:
-#             if 'confocal' in source_data['uri'].lower():
-#                 continue
-#             moving_image_path = Path(source_data['uri'])
-#             if moving_image_path.name=='?':
-#                 moving_image_path = moving_image_path.parent
-#         else:
-#             reference_image_path = Path(source_data['uri'])
-#             if reference_image_path.name=='?':
-#                 reference_image_path = reference_image_path.parent
-#     if 'zstack' in moving_image_path.name.lower() or 'z_stack' in moving_image_path.name.lower():
-#         moving_image = 'czstack'
-#         reference_image = 'xenium_section'
-#     transform_type = bw_json['Transform']['type']
-#     bigwarp_params = {'moving_image': moving_image,
-#                     'reference_image': reference_image,
-#                     'moving_image_path': moving_image_path,
-#                     'reference_image_path': reference_image_path,
-#                     'transform_type': transform_type}
-#     return bigwarp_params
-
-# def invert_xenium_y_landmarks(landmarks, landmarked_image_path):
-#     with tifffile.TiffFile(landmarked_image_path) as tif:
-#         landmarked_image_shape = tif.pages[0].shape
-#     full_y_size = landmarked_image_shape[0]
-#     landmarks['xenium_y'] = full_y_size - landmarks['xenium_y']
-#     return landmarks
-
-# def remove_landmark_buffer(landmarks, czstack_buffer=None, xenium_buffer=None):
-#     if czstack_buffer is not None:
-#         landmarks['czstack_y'] = landmarks['czstack_y'] - czstack_buffer.get('y', 0)
-#         landmarks['czstack_x'] = landmarks['czstack_x'] - czstack_buffer.get('x', 0)
-#         landmarks['czstack_z'] = landmarks['czstack_z'] - czstack_buffer.get('z', 0)
-#     if xenium_buffer is not None:
-#         landmarks['xenium_y'] = landmarks['xenium_y'] - xenium_buffer.get('y', 0)
-#         landmarks['xenium_x'] = landmarks['xenium_x'] - xenium_buffer.get('x', 0)
-#         landmarks['xenium_z'] = landmarks['xenium_z'] - xenium_buffer.get('z', 0)
-#     return landmarks
-
-# def get_section_landmarks(landmarks_path, dims_order=['x','y','z'], bigwarp_project_path=None, moving_img=None):
-#     if bigwarp_project_path is not None:
-#         bigwarp_params = get_biwarp_params(bigwarp_project_path)
-#     elif moving_img is not None:
-#         bigwarp_params = {'moving_image': moving_img}
-#     else:
-#         print("No BigWarp project path or moving image specified - assuming czstack was moving image")
-#         bigwarp_params = {'moving_image': 'czstack'}
-#     print(f"Loading landmarks from: {landmarks_path}")
-#     landmarks = pd.read_csv(landmarks_path, header=None)
-#     if bigwarp_params['moving_image']=='czstack':
-#         # Flatten the lists using + operator to concatenate them
-#         landmarks.columns = ['landmark_name', 'active'] + [f'czstack_{dim}' for dim in dims_order] + [f'xenium_{dim}' for dim in dims_order]
-#     else:
-#         landmarks.columns = ['landmark_name', 'active'] + [f'xenium_{dim}' for dim in dims_order] + [f'czstack_{dim}' for dim in dims_order]
-
-#     return landmarks, bigwarp_params
-
-# def get_landmarked_image_props(landmarked_image_path, sdata, landmarks, 
-#                                 section_n, invert_y=False):
-#     with tifffile.TiffFile(landmarked_image_path) as tif:
-#         landmarked_image_shape = tif.pages[0].shape
-#     print(f"Landmarked image shape: {landmarked_image_shape}")
-
-#     bbox_xmin = 0
-#     bbox_ymin = 0
-
-#     cell_labels = sd.get_pyramid_levels(sdata['cell_labels'], n=0)
-#     section_shape_y, section_shape_x = cell_labels.data.shape
-
-#     bbox_dict = sdata['table'].uns.get('sections_bboxes', None)
-
-#     if bbox_dict is not None and str(section_n) in bbox_dict:
-#         # Paired: invert within image space FIRST, then scale to full slide + offset
-#         if invert_y:
-#             landmarks['xenium_y'] = landmarked_image_shape[0] - landmarks['xenium_y']
-        
-#         section_bbox = bbox_dict[str(section_n)]
-#         bbox_xmin = section_bbox['x_min']
-#         bbox_ymin = section_bbox['y_min']
-#         full_slide_shape_y = np.max([bbox['y_max'] for bbox in bbox_dict.values()])
-#         full_slide_shape_x = np.max([bbox['x_max'] for bbox in bbox_dict.values()])
-#         scale_factor_y = full_slide_shape_y / landmarked_image_shape[0]
-#         scale_factor_x = full_slide_shape_x / landmarked_image_shape[1]
-#         print(f"Paired section — full slide shape: {[full_slide_shape_y, full_slide_shape_x]}")
-#         print(f"Image downsampled by: (yx) [{scale_factor_y:.4f}, {scale_factor_x:.4f}]")
-#         print(f"Section bbox offset: {section_bbox}")
-#     else:
-#         # Standalone: scale to full section first, then invert within section space
-#         scale_factor_y = section_shape_y / landmarked_image_shape[0]
-#         scale_factor_x = section_shape_x / landmarked_image_shape[1]
-#         print(f"Standalone section — section array shape: {[section_shape_y, section_shape_x]}")
-#         print(f"Image downsampled by: (yx) [{scale_factor_y:.4f}, {scale_factor_x:.4f}]")
-
-#     landmarks['xenium_x'] = landmarks['xenium_x'] * scale_factor_x
-#     landmarks['xenium_y'] = landmarks['xenium_y'] * scale_factor_y
-#     landmarks['xenium_x'] = landmarks['xenium_x'] - bbox_xmin
-#     landmarks['xenium_y'] = landmarks['xenium_y'] - bbox_ymin
-
-#     if bbox_dict is None or str(section_n) not in bbox_dict:
-#         # Standalone: invert after scaling, using full section height
-#         if invert_y:
-#             landmarks['xenium_y'] = section_shape_y - landmarks['xenium_y']
-
-#     return landmarks
-
-# def format_landmarks(sdata, 
-#                     landmarks_path, 
-#                     section_n, 
-#                     bigwarp_project_paths=None,
-#                     moving_img=None,
-#                     czstack_buffer=None,
-#                     invert_lm_y=False, 
-#                     fix_cropped_landmarks=False,
-#                     landmarked_image_path=None,
-#                     dims_order=['x','y','z']):
-
-#     landmarks, bigwarp_params = get_section_landmarks(
-#                                     landmarks_path=landmarks_path, 
-#                                     bigwarp_project_path=bigwarp_project_paths,
-#                                     moving_img=moving_img,
-#                                     dims_order=dims_order)
-
-#     if czstack_buffer is not None:
-#         landmarks = remove_landmark_buffer(landmarks, 
-#                                             czstack_buffer=czstack_buffer)
-
-#     if fix_cropped_landmarks and landmarked_image_path is not None:
-#         # invert_y is handled inside get_landmarked_image_props at the right point
-#         landmarks = get_landmarked_image_props(
-#                             landmarked_image_path, 
-#                             sdata, 
-#                             landmarks, 
-#                             section_n,
-#                             invert_y=invert_lm_y)
-#     elif invert_lm_y and landmarked_image_path is not None:
-#         # sections where fix_cropped_landmarks=False
-#         landmarks = invert_xenium_y_landmarks(landmarks, landmarked_image_path)
-
-#     # Adjust landmarks resolutions
-#     full_scale_pixel_size = sdata['table'].uns['section_metadata']['pixel_size']
-#     landmarks = landmarks.rename(columns={'xenium_x': 'x', 'xenium_y': 'y', 'xenium_z': 'z'})
-#     landmarks = PointsModel.parse(landmarks)
-#     set_transformation(landmarks, Identity(), to_coordinate_system='global')
-#     set_transformation(
-#         landmarks,
-#         Scale([full_scale_pixel_size, full_scale_pixel_size], axes=('x', 'y')),
-#         to_coordinate_system='microns'
-#     )
-#     return landmarks
-
-# ####### Functions to find transforms ##########
-# def get_affine_from_landmarks_flat(moving_coords, ref_coords):
-#     moving_2d = moving_coords[:, :2]
-#     ref_2d = ref_coords[:, :2]
-#     n = moving_2d.shape[0]
-#     A_xy = np.hstack([moving_2d, np.ones((n, 1))])
-#     result, _, _, _ = np.linalg.lstsq(A_xy, ref_2d, rcond=None)
-
-#     # Z offset: mean difference between ref and moving Z coords
-#     # mat[2,2] = 1.0 keeps matrix invertible (required by Napari)
-#     z_offset = float(np.mean(ref_coords[:, 2] - moving_coords[:, 2]))
-
-#     mat = np.eye(4)
-#     mat[0, 0] = result[0, 0]; mat[0, 1] = result[1, 0]; mat[0, 3] = result[2, 0]
-#     mat[1, 0] = result[0, 1]; mat[1, 1] = result[1, 1]; mat[1, 3] = result[2, 1]
-#     mat[2, 2] = 1.0   # MUST be 1.0 - keeps matrix invertible for Napari rendering
-#     mat[2, 3] = z_offset
-
-#     return Affine(mat, input_axes=('x', 'y', 'z'), output_axes=('x', 'y', 'z'))
-
-# def tilt_affines(moving_pts, fixed_pts, flat_affine):
-#     # moving_pts = Xenium (2D: x, y, 0)
-#     # fixed_pts = CZStack (3D: x, y, z)
-    
-#     # Fit Z_stack = a*X_xe + b*Y_xe + c
-#     A = np.column_stack([moving_pts[:, 0], moving_pts[:, 1], np.ones(len(moving_pts))])
-#     coeffs, _, _, _ = lstsq(A, fixed_pts[:, 2])
-#     a, b, c = coeffs
-    
-#     # Build the 3D matrix using the 2D XY results
-#     tilt_mat = np.array(flat_affine.matrix).copy()
-#     tilt_mat[2, 0] = a   # Effect of Xenium X on Stack Z
-#     tilt_mat[2, 1] = b   # Effect of Xenium Y on Stack Z
-#     tilt_mat[2, 2] = 1.0 # Keeps matrix invertible
-#     tilt_mat[2, 3] = c   # The base Z-slice offset
-    
-#     return Affine(tilt_mat, input_axes=('x', 'y', 'z'), output_axes=('x', 'y', 'z'))
-
-# def _is_multiscale(element):
-#     return (
-#         hasattr(element, 'keys')
-#         and callable(element.keys)
-#         and not isinstance(element, GeoDataFrame)
-#     )
-
-# def get_microns_scales(sdata, element_name):
-#     el = sdata[element_name]
-#     if _is_multiscale(el):
-#         img = sd.get_pyramid_levels(el, n=0)
-#         img = img.image if hasattr(img, 'image') else img
-#     else:
-#         img = el.image if hasattr(el, 'image') else el
-
-#     # Get transforms from the actual image DataArray, not the DataTree
-#     el_transforms = get_transformation(img, get_all=True)
-#     microns_tf = el_transforms.get('microns', None)
-#     if microns_tf is None:
-#         ps = sdata['table'].uns['section_metadata']['pixel_size']
-#         microns_tf = Scale([ps, ps], axes=('x', 'y'))
-#         set_transformation(img, microns_tf, to_coordinate_system='microns')
-#     if len(microns_tf.scale) >= 2:
-#         x_y_axes = ('x', 'y')
-#         x_y_tf = [microns_tf.axes.index(axis) for axis in x_y_axes if axis in microns_tf.axes]
-#         microns_tf = Scale([microns_tf.scale[i] for i in x_y_tf], x_y_axes)
-#     return microns_tf
-
-# def add_affine_to_element(element, affine_tf, coord_sys_name, 
-#                           microns_tf=None, microns_tf_position='before',
-#                           overwrite_existing=False):
-#     """
-#     microns_tf_position: 'before' puts microns_tf before affine_tf (section px → µm → czstack px)
-#                          'after'  puts microns_tf after  affine_tf (section px → czstack px → czstack µm)
-#     """
-#     def _build_tf(tf_to_fullres):
-#         if microns_tf is not None:
-#             if microns_tf_position == 'after':
-#                 return Sequence([tf_to_fullres, affine_tf, microns_tf])
-#             else:
-#                 return Sequence([tf_to_fullres, microns_tf, affine_tf])
-#         return Sequence([tf_to_fullres, affine_tf])
-
-#     if _is_multiscale(element):
-#         for n_l, level in enumerate(element.keys()):
-#             element_obj = sd.get_pyramid_levels(element, n=n_l)
-#             tf_to_fullres = get_transformation(element_obj, to_coordinate_system='global')
-#             if isinstance(tf_to_fullres, Identity):
-#                 tf_to_fullres = Scale([1.0, 1.0, 1.0], axes=('x', 'y', 'z'))
-#             if coord_sys_name in element_obj.attrs['transform'] and not overwrite_existing:
-#                 continue
-#             set_transformation(element_obj, _build_tf(tf_to_fullres), 
-#                                to_coordinate_system=coord_sys_name)
-#     else:
-#         element_obj = element
-#         tf_to_fullres = get_transformation(element_obj, to_coordinate_system='global')
-#         if isinstance(tf_to_fullres, Identity):
-#             tf_to_fullres = Scale([1.0, 1.0, 1.0], axes=('x', 'y', 'z'))
-#         if coord_sys_name in element_obj.attrs['transform'] and not overwrite_existing:
-#             return
-#         set_transformation(element_obj, _build_tf(tf_to_fullres),
-#                            to_coordinate_system=coord_sys_name)
-
 ####### QC alignment #########
 def get_section_z_stats(affines_dict, landmarks_dict, czstack_shape_yx=(512, 512)):
     y_max, x_max = czstack_shape_yx