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augustin_s
2024-03-22 23:06:35 +01:00
parent a22fc651f1
commit 6947b90614
1 changed files with 20 additions and 20 deletions
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40
dap/worker.py
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@ -145,7 +145,7 @@ def main():
results["number_of_spots"] = 0
results["is_hit_frame"] = False
daq_rec = results.get("daq_rec",0)
daq_rec = results.get("daq_rec", 0)
event_laser = bool((daq_rec >> 16) & 1)
event_darkshot = bool((daq_rec >> 17) & 1)
# event_fel = bool((daq_rec >> 18) & 1)
@ -155,7 +155,7 @@ def main():
results["laser_on"] = event_laser
# Filter only ppicker events, if requested; skipping all other events
select_only_ppicker_events = results.get("select_only_ppicker_events", 0)
select_only_ppicker_events = results.get("select_only_ppicker_events", False)
if select_only_ppicker_events and not event_ppicker:
continue
@ -197,7 +197,7 @@ def main():
pixel_mask_pf = None
# add additional mask at the edge of modules for JF06T08
apply_additional_mask = (results.get("apply_additional_mask", 0) == 1)
apply_additional_mask = results.get("apply_additional_mask", False)
if detector == "JF06T08V04" and apply_additional_mask:
# edge pixels
pixel_mask_pf[67:1097,1063] = 0
@ -262,9 +262,9 @@ def main():
results["saturated_pixels_y"] = saturated_pixels_coordinates[0].tolist()
# pump probe analysis
do_radial_integration = results.get("do_radial_integration", 0)
do_radial_integration = results.get("do_radial_integration", False)
if do_radial_integration != 0:
if do_radial_integration:
data_copy_1 = np.copy(data)
@ -278,9 +278,9 @@ def main():
r_min_max = [int(np.min(r_radial_integration)), int(np.max(r_radial_integration)) + 1]
apply_threshold = results.get("apply_threshold", 0)
apply_threshold = results.get("apply_threshold", False)
if apply_threshold != 0 and all(k in results for k in ("threshold_min", "threshold_max")):
if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
threshold_min = float(results["threshold_min"])
threshold_max = float(results["threshold_max"])
data_copy_1[data_copy_1 < threshold_min] = np.nan
@ -314,10 +314,10 @@ def main():
if pixel_mask_pf is not None:
d[pixel_mask_pf != 1] = np.nan
apply_threshold = results.get("apply_threshold", 0)
apply_threshold = results.get("apply_threshold", False)
threshold_value_choice = results.get("threshold_value", "NaN")
threshold_value = 0 if threshold_value_choice == "0" else np.nan
if apply_threshold != 0 and all(k in results for k in ("threshold_min", "threshold_max")):
if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
threshold_min = float(results["threshold_min"])
threshold_max = float(results["threshold_max"])
d[d < threshold_min] = threshold_value
@ -355,9 +355,9 @@ def main():
results["roi_intensities_normalised"] = [float(r) for r in roi_results_normalised ]
# SPI analysis
do_spi_analysis = results.get("do_spi_analysis", 0)
do_spi_analysis = results.get("do_spi_analysis", False)
if (do_spi_analysis != 0) and "roi_intensities_normalised" in results and len(results["roi_intensities_normalised"]) >= 2:
if do_spi_analysis and "roi_intensities_normalised" in results and len(results["roi_intensities_normalised"]) >= 2:
if "spi_limit" in results and len(results["spi_limit"]) == 2:
@ -372,8 +372,8 @@ def main():
results["is_hit_frame"] = True
# in case all needed parameters are present, make peakfinding
do_peakfinder_analysis = results.get("do_peakfinder_analysis", 0)
if do_peakfinder_analysis != 0 and pixel_mask_pf is not None and all(k in results for k in ("beam_center_x", "beam_center_y", "hitfinder_min_snr", "hitfinder_min_pix_count", "hitfinder_adc_thresh")):
do_peakfinder_analysis = results.get("do_peakfinder_analysis", False)
if do_peakfinder_analysis and pixel_mask_pf is not None and all(k in results for k in ("beam_center_x", "beam_center_y", "hitfinder_min_snr", "hitfinder_min_pix_count", "hitfinder_adc_thresh")):
x_beam = results["beam_center_x"] - 0.5 # to coordinates where position of first pixel/point is 0.5, 0.5
y_beam = results["beam_center_y"] - 0.5 # to coordinates where position of first pixel/point is 0.5, 0.5
hitfinder_min_snr = results["hitfinder_min_snr"]
@ -430,19 +430,19 @@ def main():
forceSendVisualisation = False
if data.dtype != np.uint16:
apply_threshold = results.get("apply_threshold", 0)
apply_aggregation = results.get("apply_aggregation", 0)
if apply_aggregation == 0:
apply_threshold = results.get("apply_threshold", False)
apply_aggregation = results.get("apply_aggregation", False)
if not apply_aggregation:
data_summed = None
n_aggregated_images = 1
if apply_threshold != 0 or apply_aggregation != 0:
if apply_threshold != 0 and all(k in results for k in ("threshold_min", "threshold_max")):
if apply_threshold or apply_aggregation:
if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
threshold_min = float(results["threshold_min"])
threshold_max = float(results["threshold_max"])
data[data < threshold_min] = 0.0
if threshold_max > threshold_min:
data[data > threshold_max] = 0.0
if (apply_aggregation != 0 ) and "aggregation_max" in results:
if apply_aggregation and "aggregation_max" in results:
if data_summed is not None:
data += data_summed
n_aggregated_images += 1
@ -465,7 +465,7 @@ def main():
accumulator_socket.send_json(results, FLAGS)
if (apply_aggregation != 0 ) and "aggregation_max" in results:
if apply_aggregation and "aggregation_max" in results:
if forceSendVisualisation:
visualisation_socket.send_json(results, FLAGS | zmq.SNDMORE)
visualisation_socket.send(data, FLAGS, copy=True, track=True)