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peterhollenderpeterhollender
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add loading functions to xdc.util
To aid in using the functionality in #439
1 parent 83c1310 commit 1f24481

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‎src/openlifu/xdc/util.py‎

Lines changed: 274 additions & 0 deletions
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from __future__ import annotations
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import json
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import re
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import pandas as pd
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import xarray as xa
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from openlifu.util.types import PathLike
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from openlifu.xdc.transducer import Transducer
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from openlifu.xdc.transducerarray import TransducerArray
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FOCAL_GAIN_LUT = xa.DataArray.from_dict(
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{'dims': ('f0', 'crosstalk'),
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'attrs': {},
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'data': [[2.807589054107666,
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'coords': {'f0': {'dims': ('f0',),
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'attrs': {},
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'data': [130000.0,
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'crosstalk': {'dims': ('crosstalk',),
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'attrs': {},
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'data': [0.0,
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0.05,
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0.30000000000000004,
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0.35000000000000003,
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0.4,
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0.5]}},
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'name': 'focal_gain'})
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def load_transducer_from_file(transducer_filepath : PathLike, convert_array:bool = True) -> Transducer|TransducerArray:
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"""Load a Transducer or TransducerArray from file, depending on the "type" field in the file.
@@ -28,3 +260,45 @@ def load_transducer_from_file(transducer_filepath : PathLike, convert_array:bool
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else:
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transducer = Transducer.from_file(transducer_filepath)
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return transducer
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def read_test_report(filename: PathLike) -> pd.DataFrame:
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sections = [{"name": "info", "start_row": 3, "nrows": 3},
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{"name": "txm", "start_row": 9, "nrows": 5},
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{"name": "console", "start_row": 17, "nrows": 3},
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{"name": "scans", "start_row": 23, "nrows": 7},
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{"name": "freq", "start_row": 33, "nrows": 9},
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{"name": "voltage", "start_row": 45, "nrows": 7}]
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all_data = []
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for section in sections:
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report_df = pd.read_excel(filename, sheet_name="Report", skiprows=section["start_row"], nrows=section["nrows"], index_col=0, usecols="A:C")
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report_df["Section"] = section["name"]
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all_data.append(report_df)
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report_df = pd.concat(all_data)
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return report_df
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def report_to_matrix_dict(report_df: pd.DataFrame, focal_gain_lut=FOCAL_GAIN_LUT) -> dict:
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ROW_SN = 'B.1'
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ROW_FREQ = 'B.2'
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ROW_VOLTAGE = 'E.1'
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LIFU_400 = {'id': r'txm_400_{sn}', 'name': r'TXM 400kHz (S/N {sn})', 'nx': 8, 'ny': 8, 'pitch': 5, 'frequency': 400e3, 'kerf': 0.3, 'crosstalk_frac': 0.12, 'crosstalk_dist': 5.05e-3}
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LIFU_155 = {'id': r'txm_155_{sn}', 'name': r'TXM 155kHz (S/N {sn})', 'nx': 8, 'ny': 8, 'pitch': 5, 'frequency': 155e3, 'kerf': 0.3, 'crosstalk_frac': 0.12, 'crosstalk_dist': 5.05e-3}
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LIFU_MODULES = {400: LIFU_400, 155: LIFU_155}
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freq_kHz = report_df.loc[ROW_FREQ]["Value"]
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voltage = report_df.loc[ROW_VOLTAGE]["Value"]
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sn = report_df.loc[ROW_SN]["Value"]
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pattern = r'[^a-zA-Z0-9\-\_]'
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replacement = ''
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sn = re.sub(pattern, replacement, sn)
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matrix_dict = LIFU_MODULES[freq_kHz]
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freq_df = report_df[report_df["Section"] == "freq"].copy().drop(columns=["Section"])
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freq_df = freq_df.rename(columns={"Value": "PNP"})
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freq_df = freq_df[freq_df['Item'].str.startswith("PNP")]
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freq_df["Frequency"] = freq_df['Item'].apply(lambda x: float(re.search(r"(?<=^PNP \()\d+(?= kHz\)$)", x).group(0)))
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freq_df['focal_gain'] = freq_df['Frequency'].apply(lambda f: focal_gain_lut.interp(f0=f*1e3, crosstalk=matrix_dict['crosstalk_frac']).item())
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freq_df['Sensitivity'] = freq_df['PNP'].astype(float)*1e6/freq_df['focal_gain']/voltage
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matrix_dict['sensitivity'] = {f*1e3:sens for f, sens, in zip(freq_df["Frequency"], freq_df['Sensitivity'])}
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matrix_dict['id'] = matrix_dict['id'].format(sn=sn.lower())
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matrix_dict['name'] = matrix_dict['name'].format(sn=sn)
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return matrix_dict

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