implement disable modules possibility in detector retrieve process

scripts/export_file.py

@@ -7,6 +7,12 @@ import numpy as np
 
 import jungfrau_utils as ju
 
+import sys
+sys.path.append('/home/dbe/git/sf_daq_buffer/scripts')
+import postprocess_raw
+
+import os
+
 parser = argparse.ArgumentParser()
 
 parser.add_argument("file_in", type=str)
@@ -27,8 +33,9 @@ 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)
+    compression = detector_params.get("compression", False)
+    conversion = detector_params.get("adc_to_energy", False)
+    disabled_modules = detector_params.get("disabled_modules", [])
     if conversion:
         mask = detector_params.get("mask", True)
         mask_double_pixels = detector_params.get("mask_double_pixels", True)
@@ -46,30 +53,48 @@ if not mask and mask_double_pixels:
     warnings.warn("mask_double_pixels set to False")
     mask_double_pixels = False
 
-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)
+file_tmp = args.file_in
+if len(disabled_modules)>0:
+    print(f"Will reduce data file, disabled_modules: {disabled_modules}")
+    if conversion:
+        file_tmp = args.file_out+".tmp"
+    else:
+        file_tmp = args.file_out
+    postprocess_raw.postprocess_raw(args.file_in, file_tmp, compression=compression, disabled_modules=disabled_modules)
 
-    juf.handler.mask_double_pixels = mask_double_pixels
-    juf.export(
-        args.file_out,
-        index=good_frames,
-        roi=None,
-        compression=compression,
-        factor=factor,
-        dtype=None,
-        batch_size=35,
-    )
+if conversion:
+
+    with ju.File(
+        file_tmp,
+        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=None,
+            batch_size=35,
+        )
+    os.remove(file_tmp)
+
+else:
+    with h5py.File(file_tmp, "r") as juf:
+        n_input_frames = len(juf[f"data/{detector_name}/data"])
+        good_frames = np.nonzero(juf[f"data/{detector_name}/is_good_frame"])[0]
+        n_output_frames = len(good_frames)
 
 # Utility info
 with h5py.File(args.file_out, "r") as h5f: