mirror of
https://github.com/paulscherrerinstitute/sf_daq_buffer.git
synced 2026-04-20 14:24:36 +02:00
Dmitry Ozerov
119 lines
3.5 KiB
Python
119 lines
3.5 KiB
Python
import argparse
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import json
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import warnings
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import h5py
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import numpy as np
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import jungfrau_utils as ju
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import sys
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sys.path.append('/home/dbe/git/sf_daq_buffer/scripts')
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import postprocess_raw
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import os
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parser = argparse.ArgumentParser()
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parser.add_argument("file_in", type=str)
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parser.add_argument("file_out", type=str)
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parser.add_argument("json_run", type=str)
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parser.add_argument("json_detector", type=str)
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args = parser.parse_args()
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with open(args.json_detector, "r") as detector_file:
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data = json.load(detector_file)
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detector_name = data["detector_name"]
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gain_file = data["gain_file"]
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pedestal_file = data["pedestal_file"]
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with open(args.json_run, "r") as run_file:
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data = json.load(run_file)
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detector_params = data["detectors"][detector_name]
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compression = detector_params.get("compression", False)
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conversion = detector_params.get("adc_to_energy", False)
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disabled_modules = detector_params.get("disabled_modules", [])
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if conversion:
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mask = detector_params.get("mask", True)
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mask_double_pixels = detector_params.get("mask_double_pixels", True)
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geometry = detector_params.get("geometry", False)
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gap_pixels = detector_params.get("gap_pixels", True)
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factor = detector_params.get("factor", None)
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else:
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mask = False
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mask_double_pixels = False
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geometry = False
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gap_pixels = False
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factor = None
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if not mask and mask_double_pixels:
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warnings.warn("mask_double_pixels set to False")
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mask_double_pixels = False
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file_tmp = args.file_in
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if len(disabled_modules)>0:
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print(f"Will reduce data file, disabled_modules: {disabled_modules}")
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if conversion:
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file_tmp = args.file_out+".tmp"
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else:
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file_tmp = args.file_out
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postprocess_raw.postprocess_raw(args.file_in, file_tmp, compression=compression, disabled_modules=disabled_modules)
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if conversion:
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with ju.File(
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file_tmp,
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gain_file=gain_file,
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pedestal_file=pedestal_file,
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conversion=conversion,
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mask=mask,
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gap_pixels=gap_pixels,
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geometry=geometry,
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parallel=False,
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) as juf:
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n_input_frames = len(juf["data"])
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good_frames = np.nonzero(juf["is_good_frame"])[0]
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n_output_frames = len(good_frames)
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juf.handler.mask_double_pixels = mask_double_pixels
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juf.export(
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args.file_out,
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index=good_frames,
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roi=None,
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compression=compression,
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factor=factor,
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dtype=None,
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batch_size=35,
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)
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os.remove(file_tmp)
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else:
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with h5py.File(file_tmp, "r") as juf:
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n_input_frames = len(juf[f"data/{detector_name}/data"])
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good_frames = np.nonzero(juf[f"data/{detector_name}/is_good_frame"])[0]
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n_output_frames = len(good_frames)
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# Utility info
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with h5py.File(args.file_out, "r") as h5f:
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print("daq_rec:", h5f[f"/data/{detector_name}/daq_rec"][0, 0])
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frame_index = h5f[f"/data/{detector_name}/frame_index"][:]
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print("frame_index range:", (np.min(frame_index), np.max(frame_index)))
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print("input frames:", n_input_frames)
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print("bad frames:", n_input_frames - n_output_frames)
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print("output frames:", n_output_frames)
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print("gain_file:", gain_file)
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print("pedestal_file:", pedestal_file)
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print("conversion:", conversion)
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print("mask:", mask)
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print("mask_double_pixels:", mask_double_pixels)
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print("geometry:", geometry)
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print("gap_pixels:", gap_pixels)
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print("compression:", compression)
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print("factor:", factor)
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