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https://github.com/slsdetectorgroup/aare.git
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mazzol_a
288152716b
stuff needed for JungfrauCalibration: - added fit boundaries to fit function (needed to only fit a certain range, faster than iterating over it in python) - added Jungfrau Geometry definitions - added mean_over_dataset (takes the mean over some aqcuisitions in a file - added read_current_source (reads current_source data from Jungfrau and averages) - added configurable option to fit elastic scattering in fit modules - added bad channel mask to PedestalTrackingHistogram
62 lines
2.7 KiB
Python
62 lines
2.7 KiB
Python
from pathlib import Path
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from aare import JungfrauDataFile
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import timeit
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import numpy as np
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def read_current_source_data(current_source_data : Path, num_samples, num_acq_per_exposure : int = 10) -> np.ndarray:
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"""
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Read the current source data from the raw data file and average over the number of acquisitions per exposure time.
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Parameters
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----------
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current_source_data : Path
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The path to the raw data file containing the current source data.
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exposure_times : np.ndarray
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The exposure times to read.
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num_acq_per_exposure : int, optional
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The number of acquisitions per exposure time. Default is 10.
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output_file : Path, optional
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The path to the file where the averaged current source data will be saved. If None, the data is not saved. Default is None.
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Returns
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-------
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np.ndarray
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A 3D array containing the averaged current source data for each exposure time. The shape of the array is (rows, cols, num_exposure_times)
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"""
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num_exposure_times = num_samples
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columns_per_subcolumn : int = 64 # number of columns per subcolumn - 64 for Jungfrau 1M - Jungfrau has 4 subcolumns per module a 64 columns
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cs_file = JungfrauDataFile(current_source_data)
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if ((num_exposure_times + 3) * num_acq_per_exposure * columns_per_subcolumn != cs_file.total_frames):
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raise ValueError(f"The number of frames in the current source data file does not match the expected number of frames. Expected: {(num_exposure_times + 3) * num_acq_per_exposure * columns_per_subcolumn}, got: {cs_file.total_frames}")
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mean_cs = np.zeros((cs_file.rows, cs_file.cols, num_exposure_times), dtype=np.float64) # TODO: use mean and np.uint16
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cs_file.seek(3*num_acq_per_exposure*columns_per_subcolumn) # skip first 3 frames used to check gain switching
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for i in range(num_exposure_times):
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for _ in range(num_acq_per_exposure):
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for k in range(columns_per_subcolumn):
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_, frame = cs_file.read_frame()
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mean_cs[:256, k:-1:columns_per_subcolumn, i] += frame[:256, k:-1:columns_per_subcolumn]
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# flipped for top modules
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flipped_k = columns_per_subcolumn - 1 - k
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mean_cs[256:, flipped_k:-1:columns_per_subcolumn, i] += frame[256:, flipped_k:-1:columns_per_subcolumn]
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mean_cs[:, :, i] /= num_acq_per_exposure
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return mean_cs.astype(np.uint16).copy()
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num_calls = 10
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my_file = Path("/home/mazzol_a/Documents/temp_jungfrau/"
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"CS_M749_2026-05-19_HGOG1G2_000000.dat")
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time = timeit.timeit(
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lambda: read_current_source_data(my_file, 97, num_acq_per_exposure=10),
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number=num_calls
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)
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print(f"Per call: {time / num_calls:.6f} s") |