version used in July Alvra experiment (conversion and compression of files using Ivan's export_file.py code based on jungfrau_utils); make list of frames in crystfel notation to separate laser light/dark

2026-05-09 07:32:04 +02:00 · 2020-07-27 14:46:13 +02:00
parent 44a60a8d27
commit df51bb6ca5
3 changed files with 282 additions and 3 deletions
@@ -0,0 +1,98 @@
+import argparse
+import json
+import warnings
+
+import h5py
+import numpy as np
+
+import jungfrau_utils as ju
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("file_in", type=str)
+parser.add_argument("file_out", type=str)
+parser.add_argument("json_run", type=str)
+parser.add_argument("json_detector", type=str)
+
+args = parser.parse_args()
+
+with open(args.json_detector, "r") as detector_file:
+    data = json.load(detector_file)
+
+    detector_name = data["detector_name"]
+    gain_file = data["gain_file"]
+    pedestal_file = data["pedestal_file"]
+
+with open(args.json_run, "r") as run_file:
+    data = json.load(run_file)
+    detector_params = data["detectors"][detector_name]
+
+    compression = detector_params.get("compression", True)
+    conversion = detector_params.get("adc_to_energy", True)
+    if conversion:
+        mask = detector_params.get("mask", True)
+        mask_double_pixels = detector_params.get("mask_double_pixels", True)
+        geometry = detector_params.get("geometry", False)
+        gap_pixels = detector_params.get("gap_pixels", True)
+        factor = detector_params.get("factor", None)
+    else:
+        mask = False
+        mask_double_pixels = False
+        geometry = False
+        gap_pixels = False
+        factor = None
+
+if not mask and mask_double_pixels:
+    warnings.warn("mask_double_pixels set to False")
+    mask_double_pixels = False
+
+if factor:
+    dtype = np.int32
+else:
+    dtype = None
+
+with ju.File(
+    args.file_in,
+    gain_file=gain_file,
+    pedestal_file=pedestal_file,
+    conversion=conversion,
+    mask=mask,
+    gap_pixels=gap_pixels,
+    geometry=geometry,
+    parallel=False,
+) as juf:
+    n_input_frames = len(juf["data"])
+    good_frames = np.nonzero(juf["is_good_frame"])[0]
+    n_output_frames = len(good_frames)
+
+    juf.handler.mask_double_pixels = mask_double_pixels
+    juf.export(args.file_out, index=good_frames, roi=None, compression=compression, factor=factor, dtype=dtype, batch_size=500)
+    pixel_mask = juf.handler.get_pixel_mask(gap_pixels=gap_pixels, geometry=geometry)
+
+# Postprocessing
+with h5py.File(args.file_out, "r+") as h5f:
+    h5f[f"/data/{detector_name}/pixel_mask"] = np.invert(pixel_mask)
+    if conversion:
+        print("daq_rec:", h5f[f"/data/{detector_name}/daq_rec"][0, 0])
+        del h5f[f"/data/{detector_name}/daq_rec"]
+
+        frame_index = h5f[f"/data/{detector_name}/frame_index"][:]
+        print("frame_index range:", (np.min(frame_index), np.max(frame_index)))
+        del h5f[f"/data/{detector_name}/frame_index"]
+
+        del h5f[f"/data/{detector_name}/is_good_frame"]
+
+print("input frames:", n_input_frames)
+print("bad frames:", n_input_frames - n_output_frames)
+print("output frames:", n_output_frames)
+
+print("gain_file:", gain_file)
+print("pedestal_file:", pedestal_file)
+print("conversion:", conversion)
+print("mask:", mask)
+print("mask_double_pixels:", mask_double_pixels)
+print("geometry:", geometry)
+print("gap_pixels:", gap_pixels)
+print("compression:", compression)
+print("factor:", factor)
+