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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
50 lines
2.3 KiB
Python
50 lines
2.3 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_backplane_pulsing_signals(backplane_pulsing_data : Path, num_signals : int = 220, num_acquisitions_per_signal : int = 10) -> np.ndarray:
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"""
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Read the backplane pulsing signals from the raw data file and average over the number of acquisitions.
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Params:
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backplane_pulsing_data : Path
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The path to the raw data file containing the backplane pulsing signals.
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num_signals : int, optional
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The number of backplane pulsing signals to read. Default is 220.
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num_acquisitions_per_signal : int, optional
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The number of acquisitions per signal. Default is 10.
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"""
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backplane_pulsing_file = JungfrauDataFile(backplane_pulsing_data) #MultiThreadedFileReader(backplane_pulsing_data, ) #
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nrows, ncols = backplane_pulsing_file.rows, backplane_pulsing_file.cols
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backplane_pulsing_signals = np.empty(shape=(nrows, ncols, num_signals), dtype=np.uint16) # TODO should we use float? - copy in fit - maybe better for mean - not aligned with detector
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# parallelizing over signals is harder - not sure if file_reading gets faster - cant use parallel file reading class either
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for signal_idx in range(num_signals):
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_ , ADU_values = backplane_pulsing_file.read_n(num_acquisitions_per_signal) # TODO: use parallel file reader
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# TODO doesnt fit into L3 cache - and update mean on the go in c++
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# maybe a bit unorthodox but could use seperate PedestalTrackingHistogram and process_pedestal_file for each signal and parallel file reading - also copy not neccessary
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backplane_pulsing_signals[:, :, signal_idx] = np.mean(ADU_values, axis=0) # mean over acquisitions -> bad striding
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backplane_pulsing_signals = backplane_pulsing_signals.transpose(1, 2, 0).copy() # transpose to (rows, cols, signals) and copy to avoid striding issues
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return backplane_pulsing_signals
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num_calls = 10
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my_file = Path("/home/mazzol_a/Documents/temp_jungfrau/"
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"BP_M749_2026-05-19_HGOG1G2_000000.dat")
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time = timeit.timeit(
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lambda: read_backplane_pulsing_signals(my_file, num_signals=220, num_acquisitions_per_signal=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") |