less nesting
This commit is contained in:
565
dap/worker.py
565
dap/worker.py
@ -114,365 +114,366 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
|
||||
print(f"({pulseid}) problem ({e}) to read peakfinder parameters file, worker : {worker}", flush=True)
|
||||
|
||||
events = dict(poller.poll(2000)) # check every 2 seconds in each worker
|
||||
if backend_socket in events:
|
||||
if backend_socket not in events:
|
||||
continue
|
||||
|
||||
metadata = backend_socket.recv_json(FLAGS)
|
||||
image = backend_socket.recv(FLAGS, copy=False, track=False)
|
||||
image = np.frombuffer(image, dtype=metadata["type"]).reshape(metadata["shape"])
|
||||
metadata = backend_socket.recv_json(FLAGS)
|
||||
image = backend_socket.recv(FLAGS, copy=False, track=False)
|
||||
image = np.frombuffer(image, dtype=metadata["type"]).reshape(metadata["shape"])
|
||||
|
||||
results = copy(metadata)
|
||||
if results["shape"][0] == 2 and results["shape"][1] == 2:
|
||||
continue
|
||||
results = copy(metadata)
|
||||
if results["shape"][0] == 2 and results["shape"][1] == 2:
|
||||
continue
|
||||
|
||||
pulseid = results.get("pulse_id", 0)
|
||||
results.update(peakfinder_parameters)
|
||||
pulseid = results.get("pulse_id", 0)
|
||||
results.update(peakfinder_parameters)
|
||||
|
||||
detector = results.get("detector_name", "")
|
||||
detector = results.get("detector_name", "")
|
||||
|
||||
results["laser_on"] = False
|
||||
results["number_of_spots"] = 0
|
||||
results["is_hit_frame"] = False
|
||||
results["laser_on"] = False
|
||||
results["number_of_spots"] = 0
|
||||
results["is_hit_frame"] = False
|
||||
|
||||
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)
|
||||
event_ppicker = bool((daq_rec >> 19) & 1)
|
||||
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)
|
||||
event_ppicker = bool((daq_rec >> 19) & 1)
|
||||
|
||||
if not event_darkshot:
|
||||
results["laser_on"] = event_laser
|
||||
if not event_darkshot:
|
||||
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", False)
|
||||
if select_only_ppicker_events and not event_ppicker:
|
||||
continue
|
||||
select_only_ppicker_events = results.get("select_only_ppicker_events", False)
|
||||
if select_only_ppicker_events and not event_ppicker:
|
||||
continue
|
||||
|
||||
# special settings for p20270, only few shots were opened by pulse-picker
|
||||
# if detector in ["JF06T32V02"]:
|
||||
# if event_ppicker:
|
||||
# results["number_of_spots"] = 50
|
||||
# results["is_hit_frame"] = True
|
||||
# if detector in ["JF06T32V02"]:
|
||||
# if event_ppicker:
|
||||
# results["number_of_spots"] = 50
|
||||
# results["is_hit_frame"] = True
|
||||
|
||||
double_pixels = results.get("double_pixels", "mask")
|
||||
double_pixels = results.get("double_pixels", "mask")
|
||||
|
||||
pedestal_file_name = metadata.get("pedestal_name", None)
|
||||
if pedestal_file_name is not None and pedestal_file_name != pedestal_file_name_saved:
|
||||
pixel_mask_current = ju_stream_adapter.handler.pixel_mask
|
||||
ju_stream_adapter.handler.pixel_mask = pixel_mask_current
|
||||
pedestal_file_name_saved = pedestal_file_name
|
||||
pedestal_file_name = metadata.get("pedestal_name", None)
|
||||
if pedestal_file_name is not None and pedestal_file_name != pedestal_file_name_saved:
|
||||
pixel_mask_current = ju_stream_adapter.handler.pixel_mask
|
||||
ju_stream_adapter.handler.pixel_mask = pixel_mask_current
|
||||
pedestal_file_name_saved = pedestal_file_name
|
||||
|
||||
data = ju_stream_adapter.process(image, metadata, double_pixels=double_pixels)
|
||||
data = ju_stream_adapter.process(image, metadata, double_pixels=double_pixels)
|
||||
|
||||
# pedestal file is not in stream, skip this frame
|
||||
if ju_stream_adapter.handler.pedestal_file is None or ju_stream_adapter.handler.pedestal_file == "":
|
||||
continue
|
||||
# pedestal file is not in stream, skip this frame
|
||||
if ju_stream_adapter.handler.pedestal_file is None or ju_stream_adapter.handler.pedestal_file == "":
|
||||
continue
|
||||
|
||||
data = np.ascontiguousarray(data)
|
||||
data = np.ascontiguousarray(data)
|
||||
|
||||
# starting from ju 3.3.1 pedestal file is cached in library, re-calculated only if parameters (and/or pedestal file) are changed
|
||||
id_pixel_mask_1 = id(pixel_mask_corrected)
|
||||
pixel_mask_corrected = ju_stream_adapter.handler.get_pixel_mask(geometry=True, gap_pixels=True, double_pixels=double_pixels)
|
||||
id_pixel_mask_2 = id(pixel_mask_corrected)
|
||||
# starting from ju 3.3.1 pedestal file is cached in library, re-calculated only if parameters (and/or pedestal file) are changed
|
||||
id_pixel_mask_1 = id(pixel_mask_corrected)
|
||||
pixel_mask_corrected = ju_stream_adapter.handler.get_pixel_mask(geometry=True, gap_pixels=True, double_pixels=double_pixels)
|
||||
id_pixel_mask_2 = id(pixel_mask_corrected)
|
||||
|
||||
if id_pixel_mask_1 != id_pixel_mask_2:
|
||||
keep_pixels = None
|
||||
r_radial_integration = None
|
||||
if pixel_mask_corrected is not None:
|
||||
#pixel_mask_corrected = np.ascontiguousarray(pixel_mask_corrected)
|
||||
pixel_mask_pf = np.ascontiguousarray(pixel_mask_corrected).astype(np.int8, copy=False)
|
||||
if id_pixel_mask_1 != id_pixel_mask_2:
|
||||
keep_pixels = None
|
||||
r_radial_integration = None
|
||||
if pixel_mask_corrected is not None:
|
||||
#pixel_mask_corrected = np.ascontiguousarray(pixel_mask_corrected)
|
||||
pixel_mask_pf = np.ascontiguousarray(pixel_mask_corrected).astype(np.int8, copy=False)
|
||||
|
||||
else:
|
||||
pixel_mask_pf = None
|
||||
else:
|
||||
pixel_mask_pf = None
|
||||
|
||||
# add additional mask at the edge of modules for JF06T08
|
||||
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
|
||||
pixel_mask_pf[0:1030, 1100] = 0
|
||||
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
|
||||
pixel_mask_pf[0:1030, 1100] = 0
|
||||
|
||||
pixel_mask_pf[1106:2136, 1131] = 0
|
||||
pixel_mask_pf[1039:2069, 1168] = 0
|
||||
pixel_mask_pf[1106:2136, 1131] = 0
|
||||
pixel_mask_pf[1039:2069, 1168] = 0
|
||||
|
||||
pixel_mask_pf[1039:2069, 1718] = 0
|
||||
pixel_mask_pf[1039:2069, 1681] = 0
|
||||
pixel_mask_pf[1039:2069, 1718] = 0
|
||||
pixel_mask_pf[1039:2069, 1681] = 0
|
||||
|
||||
pixel_mask_pf[1106:2136, 618] = 0
|
||||
pixel_mask_pf[1106:2136, 618] = 0
|
||||
|
||||
pixel_mask_pf[1106:2136, 581] = 0
|
||||
pixel_mask_pf[1106:2136, 581] = 0
|
||||
|
||||
pixel_mask_pf[67:1097,513] = 0
|
||||
pixel_mask_pf[67:1097,513] = 0
|
||||
|
||||
pixel_mask_pf[67:1097, 550] = 0
|
||||
pixel_mask_pf[67:1097, 550] = 0
|
||||
|
||||
pixel_mask_pf[0:1030, 1650] = 0
|
||||
pixel_mask_pf[0:1030, 1650] = 0
|
||||
|
||||
pixel_mask_pf[0:1030, 1613] = 0
|
||||
pixel_mask_pf[0:1030, 1613] = 0
|
||||
|
||||
pixel_mask_pf[1106, 68:582] = 0
|
||||
pixel_mask_pf[1106, 68:582] = 0
|
||||
|
||||
pixel_mask_pf[1096, 550:1064] = 0
|
||||
pixel_mask_pf[1106, 618:1132] = 0
|
||||
pixel_mask_pf[1096, 550:1064] = 0
|
||||
pixel_mask_pf[1106, 618:1132] = 0
|
||||
|
||||
pixel_mask_pf[1029, 1100:1614] = 0
|
||||
pixel_mask_pf[1039, 1168:1682] = 0
|
||||
pixel_mask_pf[1029, 1100:1614] = 0
|
||||
pixel_mask_pf[1039, 1168:1682] = 0
|
||||
|
||||
pixel_mask_pf[1039, 1718:2230] = 0
|
||||
pixel_mask_pf[1039, 1718:2230] = 0
|
||||
|
||||
pixel_mask_pf[1096, 0:513] = 0
|
||||
pixel_mask_pf[1096, 0:513] = 0
|
||||
|
||||
pixel_mask_pf[1029, 1650:2163] = 0
|
||||
pixel_mask_pf[1029, 1650:2163] = 0
|
||||
|
||||
pixel_mask_pf[2068, 1168:2232] = 0
|
||||
pixel_mask_pf[2068, 1168:2232] = 0
|
||||
|
||||
pixel_mask_pf[67,0:1063] = 0
|
||||
pixel_mask_pf[67,0:1063] = 0
|
||||
|
||||
#bad region in left bottom inner module
|
||||
pixel_mask_pf[842:1097, 669:671] = 0
|
||||
#bad region in left bottom inner module
|
||||
pixel_mask_pf[842:1097, 669:671] = 0
|
||||
|
||||
#second bad region in left bottom inner module
|
||||
pixel_mask_pf[1094, 620:807] = 0
|
||||
#second bad region in left bottom inner module
|
||||
pixel_mask_pf[1094, 620:807] = 0
|
||||
|
||||
# vertical line in upper left bottom module
|
||||
pixel_mask_pf[842:1072, 87:90] = 0
|
||||
# vertical line in upper left bottom module
|
||||
pixel_mask_pf[842:1072, 87:90] = 0
|
||||
|
||||
pixel_mask_pf[1794, 1503:1550] = 0
|
||||
pixel_mask_pf[1794, 1503:1550] = 0
|
||||
|
||||
if detector == "JF17T16V01" and apply_additional_mask:
|
||||
# mask module 11
|
||||
pixel_mask_pf[2619:3333,1577:2607] = 0
|
||||
if detector == "JF17T16V01" and apply_additional_mask:
|
||||
# mask module 11
|
||||
pixel_mask_pf[2619:3333,1577:2607] = 0
|
||||
|
||||
if pixel_mask_corrected is not None:
|
||||
data_s = copy(image)
|
||||
saturated_pixels_coordinates = ju_stream_adapter.handler.get_saturated_pixels(data_s, mask=True, geometry=True, gap_pixels=True, double_pixels=double_pixels)
|
||||
results["saturated_pixels"] = len(saturated_pixels_coordinates[0])
|
||||
results["saturated_pixels_x"] = saturated_pixels_coordinates[1].tolist()
|
||||
results["saturated_pixels_y"] = saturated_pixels_coordinates[0].tolist()
|
||||
if pixel_mask_corrected is not None:
|
||||
data_s = copy(image)
|
||||
saturated_pixels_coordinates = ju_stream_adapter.handler.get_saturated_pixels(data_s, mask=True, geometry=True, gap_pixels=True, double_pixels=double_pixels)
|
||||
results["saturated_pixels"] = len(saturated_pixels_coordinates[0])
|
||||
results["saturated_pixels_x"] = saturated_pixels_coordinates[1].tolist()
|
||||
results["saturated_pixels_y"] = saturated_pixels_coordinates[0].tolist()
|
||||
|
||||
# pump probe analysis
|
||||
do_radial_integration = results.get("do_radial_integration", False)
|
||||
do_radial_integration = results.get("do_radial_integration", False)
|
||||
|
||||
if do_radial_integration:
|
||||
if do_radial_integration:
|
||||
|
||||
data_copy_1 = np.copy(data)
|
||||
data_copy_1 = np.copy(data)
|
||||
|
||||
if keep_pixels is None and pixel_mask_pf is not None:
|
||||
keep_pixels = pixel_mask_pf!=0
|
||||
if center_radial_integration is None:
|
||||
center_radial_integration = [results["beam_center_x"], results["beam_center_y"]]
|
||||
r_radial_integration = None
|
||||
if r_radial_integration is None:
|
||||
r_radial_integration, nr_radial_integration = prepare_radial_profile(data_copy_1, center_radial_integration, keep_pixels)
|
||||
r_min_max = [int(np.min(r_radial_integration)), int(np.max(r_radial_integration)) + 1]
|
||||
if keep_pixels is None and pixel_mask_pf is not None:
|
||||
keep_pixels = pixel_mask_pf!=0
|
||||
if center_radial_integration is None:
|
||||
center_radial_integration = [results["beam_center_x"], results["beam_center_y"]]
|
||||
r_radial_integration = None
|
||||
if r_radial_integration is None:
|
||||
r_radial_integration, nr_radial_integration = prepare_radial_profile(data_copy_1, center_radial_integration, keep_pixels)
|
||||
r_min_max = [int(np.min(r_radial_integration)), int(np.max(r_radial_integration)) + 1]
|
||||
|
||||
|
||||
apply_threshold = results.get("apply_threshold", False)
|
||||
|
||||
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
|
||||
if threshold_max > threshold_min:
|
||||
data_copy_1[data_copy_1 > threshold_max] = np.nan
|
||||
|
||||
rp = radial_profile(data_copy_1, r_radial_integration, nr_radial_integration, keep_pixels)
|
||||
|
||||
silent_region_min = results.get("radial_integration_silent_min", None)
|
||||
silent_region_max = results.get("radial_integration_silent_max", None)
|
||||
|
||||
if (
|
||||
silent_region_min is not None and
|
||||
silent_region_max is not None and
|
||||
silent_region_max > silent_region_min and
|
||||
silent_region_min > r_min_max[0] and
|
||||
silent_region_max < r_min_max[1]
|
||||
):
|
||||
|
||||
integral_silent_region = np.sum(rp[silent_region_min:silent_region_max])
|
||||
rp = rp / integral_silent_region
|
||||
results["radint_normalised"] = [silent_region_min, silent_region_max]
|
||||
|
||||
results["radint_I"] = list(rp[r_min_max[0]:])
|
||||
results["radint_q"] = r_min_max
|
||||
|
||||
#copy image to work with peakfinder, just in case
|
||||
d = np.copy(data)
|
||||
|
||||
# make all masked pixels values nans
|
||||
if pixel_mask_pf is not None:
|
||||
d[pixel_mask_pf != 1] = np.nan
|
||||
|
||||
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 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
|
||||
data_copy_1[data_copy_1 < threshold_min] = np.nan
|
||||
if threshold_max > threshold_min:
|
||||
d[d > threshold_max] = threshold_value
|
||||
data_copy_1[data_copy_1 > threshold_max] = np.nan
|
||||
|
||||
# if roi calculation request is present, make it
|
||||
roi_x1 = results.get("roi_x1", [])
|
||||
roi_x2 = results.get("roi_x2", [])
|
||||
roi_y1 = results.get("roi_y1", [])
|
||||
roi_y2 = results.get("roi_y2", [])
|
||||
rp = radial_profile(data_copy_1, r_radial_integration, nr_radial_integration, keep_pixels)
|
||||
|
||||
if len(roi_x1) > 0 and len(roi_x1) == len(roi_x2) and len(roi_x1) == len(roi_y1) and len(roi_x1) == len(roi_y2):
|
||||
roi_results = [0] * len(roi_x1)
|
||||
roi_results_normalised = [0] * len(roi_x1)
|
||||
silent_region_min = results.get("radial_integration_silent_min", None)
|
||||
silent_region_max = results.get("radial_integration_silent_max", None)
|
||||
|
||||
if pixel_mask_pf is not None:
|
||||
if (
|
||||
silent_region_min is not None and
|
||||
silent_region_max is not None and
|
||||
silent_region_max > silent_region_min and
|
||||
silent_region_min > r_min_max[0] and
|
||||
silent_region_max < r_min_max[1]
|
||||
):
|
||||
|
||||
results["roi_intensities_x"] = []
|
||||
results["roi_intensities_proj_x"] = []
|
||||
integral_silent_region = np.sum(rp[silent_region_min:silent_region_max])
|
||||
rp = rp / integral_silent_region
|
||||
results["radint_normalised"] = [silent_region_min, silent_region_max]
|
||||
|
||||
for iRoi in range(len(roi_x1)):
|
||||
data_roi = np.copy(d[roi_y1[iRoi]:roi_y2[iRoi], roi_x1[iRoi]:roi_x2[iRoi]])
|
||||
results["radint_I"] = list(rp[r_min_max[0]:])
|
||||
results["radint_q"] = r_min_max
|
||||
|
||||
roi_results[iRoi] = np.nansum(data_roi)
|
||||
if threshold_value_choice == "NaN":
|
||||
roi_results_normalised[iRoi] = roi_results[iRoi] / ((roi_y2[iRoi] - roi_y1[iRoi]) * (roi_x2[iRoi] - roi_x1[iRoi]))
|
||||
else:
|
||||
roi_results_normalised[iRoi] = np.nanmean(data_roi)
|
||||
#copy image to work with peakfinder, just in case
|
||||
d = np.copy(data)
|
||||
|
||||
results["roi_intensities_x"].append([roi_x1[iRoi], roi_x2[iRoi]])
|
||||
results["roi_intensities_proj_x"].append(np.nansum(data_roi,axis=0).tolist())
|
||||
# make all masked pixels values nans
|
||||
if pixel_mask_pf is not None:
|
||||
d[pixel_mask_pf != 1] = np.nan
|
||||
|
||||
results["roi_intensities"] = [float(r) for r in roi_results]
|
||||
results["roi_intensities_normalised"] = [float(r) for r in roi_results_normalised ]
|
||||
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 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
|
||||
if threshold_max > threshold_min:
|
||||
d[d > threshold_max] = threshold_value
|
||||
|
||||
# if roi calculation request is present, make it
|
||||
roi_x1 = results.get("roi_x1", [])
|
||||
roi_x2 = results.get("roi_x2", [])
|
||||
roi_y1 = results.get("roi_y1", [])
|
||||
roi_y2 = results.get("roi_y2", [])
|
||||
|
||||
if len(roi_x1) > 0 and len(roi_x1) == len(roi_x2) and len(roi_x1) == len(roi_y1) and len(roi_x1) == len(roi_y2):
|
||||
roi_results = [0] * len(roi_x1)
|
||||
roi_results_normalised = [0] * len(roi_x1)
|
||||
|
||||
if pixel_mask_pf is not None:
|
||||
|
||||
results["roi_intensities_x"] = []
|
||||
results["roi_intensities_proj_x"] = []
|
||||
|
||||
for iRoi in range(len(roi_x1)):
|
||||
data_roi = np.copy(d[roi_y1[iRoi]:roi_y2[iRoi], roi_x1[iRoi]:roi_x2[iRoi]])
|
||||
|
||||
roi_results[iRoi] = np.nansum(data_roi)
|
||||
if threshold_value_choice == "NaN":
|
||||
roi_results_normalised[iRoi] = roi_results[iRoi] / ((roi_y2[iRoi] - roi_y1[iRoi]) * (roi_x2[iRoi] - roi_x1[iRoi]))
|
||||
else:
|
||||
roi_results_normalised[iRoi] = np.nanmean(data_roi)
|
||||
|
||||
results["roi_intensities_x"].append([roi_x1[iRoi], roi_x2[iRoi]])
|
||||
results["roi_intensities_proj_x"].append(np.nansum(data_roi,axis=0).tolist())
|
||||
|
||||
results["roi_intensities"] = [float(r) for r in roi_results]
|
||||
results["roi_intensities_normalised"] = [float(r) for r in roi_results_normalised ]
|
||||
|
||||
# SPI analysis
|
||||
do_spi_analysis = results.get("do_spi_analysis", False)
|
||||
do_spi_analysis = results.get("do_spi_analysis", False)
|
||||
|
||||
if do_spi_analysis 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:
|
||||
if "spi_limit" in results and len(results["spi_limit"]) == 2:
|
||||
|
||||
number_of_spots = 0
|
||||
if results["roi_intensities_normalised"][0] >= results["spi_limit"][0]:
|
||||
number_of_spots += 25
|
||||
if results["roi_intensities_normalised"][1] >= results["spi_limit"][1]:
|
||||
number_of_spots += 50
|
||||
number_of_spots = 0
|
||||
if results["roi_intensities_normalised"][0] >= results["spi_limit"][0]:
|
||||
number_of_spots += 25
|
||||
if results["roi_intensities_normalised"][1] >= results["spi_limit"][1]:
|
||||
number_of_spots += 50
|
||||
|
||||
results["number_of_spots"] = number_of_spots
|
||||
if number_of_spots > 0:
|
||||
results["is_hit_frame"] = True
|
||||
|
||||
# in case all needed parameters are present, make peakfinding
|
||||
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"]
|
||||
hitfinder_min_pix_count = int(results["hitfinder_min_pix_count"])
|
||||
hitfinder_adc_thresh = results["hitfinder_adc_thresh"]
|
||||
|
||||
asic_ny, asic_nx = d.shape
|
||||
nasics_y, nasics_x = 1, 1
|
||||
hitfinder_max_pix_count = 100
|
||||
max_num_peaks = 10000
|
||||
|
||||
# usually don't need to change this value, rather robust
|
||||
hitfinder_local_bg_radius= 20.
|
||||
|
||||
# in case of further modification with the mask, make a new one, independent from real mask
|
||||
maskPr = np.copy(pixel_mask_pf)
|
||||
|
||||
y, x = np.indices(d.shape)
|
||||
pix_r = np.sqrt((x-x_beam)**2 + (y-y_beam)**2)
|
||||
|
||||
peak_list_x, peak_list_y, peak_list_value = peakfinder_8(
|
||||
max_num_peaks,
|
||||
d.astype(np.float32),
|
||||
maskPr.astype(np.int8),
|
||||
pix_r.astype(np.float32),
|
||||
asic_nx, asic_ny,
|
||||
nasics_x, nasics_y,
|
||||
hitfinder_adc_thresh,
|
||||
hitfinder_min_snr,
|
||||
hitfinder_min_pix_count,
|
||||
hitfinder_max_pix_count,
|
||||
hitfinder_local_bg_radius
|
||||
)
|
||||
|
||||
|
||||
number_of_spots = len(peak_list_x)
|
||||
results["number_of_spots"] = number_of_spots
|
||||
if number_of_spots != 0:
|
||||
results["spot_x"] = [-1.0] * number_of_spots
|
||||
results["spot_y"] = [-1.0] * number_of_spots
|
||||
results["spot_intensity"] = copy(peak_list_value)
|
||||
for i in range(number_of_spots):
|
||||
results["spot_x"][i] = peak_list_x[i] + 0.5
|
||||
results["spot_y"][i] = peak_list_y[i] + 0.5
|
||||
else:
|
||||
results["spot_x"] = []
|
||||
results["spot_y"] = []
|
||||
results["spot_intensity"] = []
|
||||
|
||||
npeaks_threshold_hit = results.get("npeaks_threshold_hit", 15)
|
||||
|
||||
if number_of_spots >= npeaks_threshold_hit:
|
||||
if number_of_spots > 0:
|
||||
results["is_hit_frame"] = True
|
||||
|
||||
forceSendVisualisation = False
|
||||
if data.dtype != np.uint16:
|
||||
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 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 and "aggregation_max" in results:
|
||||
if data_summed is not None:
|
||||
data += data_summed
|
||||
n_aggregated_images += 1
|
||||
data_summed = data.copy()
|
||||
data_summed[data == -np.nan] = -np.nan
|
||||
results["aggregated_images"] = n_aggregated_images
|
||||
results["worker"] = worker
|
||||
if n_aggregated_images >= results["aggregation_max"]:
|
||||
forceSendVisualisation = True
|
||||
data_summed = None
|
||||
n_aggregated_images = 1
|
||||
data[pixel_mask_pf == 0] = np.NaN
|
||||
# in case all needed parameters are present, make peakfinding
|
||||
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"]
|
||||
hitfinder_min_pix_count = int(results["hitfinder_min_pix_count"])
|
||||
hitfinder_adc_thresh = results["hitfinder_adc_thresh"]
|
||||
|
||||
else:
|
||||
data = image
|
||||
asic_ny, asic_nx = d.shape
|
||||
nasics_y, nasics_x = 1, 1
|
||||
hitfinder_max_pix_count = 100
|
||||
max_num_peaks = 10000
|
||||
|
||||
results["type"] = str(data.dtype)
|
||||
results["shape"] = data.shape
|
||||
# usually don't need to change this value, rather robust
|
||||
hitfinder_local_bg_radius= 20.
|
||||
|
||||
# in case of further modification with the mask, make a new one, independent from real mask
|
||||
maskPr = np.copy(pixel_mask_pf)
|
||||
|
||||
y, x = np.indices(d.shape)
|
||||
pix_r = np.sqrt((x-x_beam)**2 + (y-y_beam)**2)
|
||||
|
||||
peak_list_x, peak_list_y, peak_list_value = peakfinder_8(
|
||||
max_num_peaks,
|
||||
d.astype(np.float32),
|
||||
maskPr.astype(np.int8),
|
||||
pix_r.astype(np.float32),
|
||||
asic_nx, asic_ny,
|
||||
nasics_x, nasics_y,
|
||||
hitfinder_adc_thresh,
|
||||
hitfinder_min_snr,
|
||||
hitfinder_min_pix_count,
|
||||
hitfinder_max_pix_count,
|
||||
hitfinder_local_bg_radius
|
||||
)
|
||||
|
||||
|
||||
accumulator_socket.send_json(results, FLAGS)
|
||||
|
||||
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)
|
||||
else:
|
||||
data = np.empty((2, 2), dtype=np.uint16)
|
||||
results["type"] = str(data.dtype)
|
||||
results["shape"] = data.shape
|
||||
visualisation_socket.send_json(results, FLAGS | zmq.SNDMORE)
|
||||
visualisation_socket.send(data, FLAGS, copy=True, track=True)
|
||||
number_of_spots = len(peak_list_x)
|
||||
results["number_of_spots"] = number_of_spots
|
||||
if number_of_spots != 0:
|
||||
results["spot_x"] = [-1.0] * number_of_spots
|
||||
results["spot_y"] = [-1.0] * number_of_spots
|
||||
results["spot_intensity"] = copy(peak_list_value)
|
||||
for i in range(number_of_spots):
|
||||
results["spot_x"][i] = peak_list_x[i] + 0.5
|
||||
results["spot_y"][i] = peak_list_y[i] + 0.5
|
||||
else:
|
||||
if results["is_good_frame"] and (results["is_hit_frame"] or randint(1, skip_frames_rate) == 1):
|
||||
visualisation_socket.send_json(results, FLAGS | zmq.SNDMORE)
|
||||
visualisation_socket.send(data, FLAGS, copy=True, track=True)
|
||||
else:
|
||||
data = np.empty((2, 2), dtype=np.uint16)
|
||||
results["type"] = str(data.dtype)
|
||||
results["shape"] = data.shape
|
||||
visualisation_socket.send_json(results, FLAGS | zmq.SNDMORE)
|
||||
visualisation_socket.send(data, FLAGS, copy=True, track=True)
|
||||
results["spot_x"] = []
|
||||
results["spot_y"] = []
|
||||
results["spot_intensity"] = []
|
||||
|
||||
npeaks_threshold_hit = results.get("npeaks_threshold_hit", 15)
|
||||
|
||||
if number_of_spots >= npeaks_threshold_hit:
|
||||
results["is_hit_frame"] = True
|
||||
|
||||
forceSendVisualisation = False
|
||||
if data.dtype != np.uint16:
|
||||
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 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 and "aggregation_max" in results:
|
||||
if data_summed is not None:
|
||||
data += data_summed
|
||||
n_aggregated_images += 1
|
||||
data_summed = data.copy()
|
||||
data_summed[data == -np.nan] = -np.nan
|
||||
results["aggregated_images"] = n_aggregated_images
|
||||
results["worker"] = worker
|
||||
if n_aggregated_images >= results["aggregation_max"]:
|
||||
forceSendVisualisation = True
|
||||
data_summed = None
|
||||
n_aggregated_images = 1
|
||||
data[pixel_mask_pf == 0] = np.NaN
|
||||
|
||||
else:
|
||||
data = image
|
||||
|
||||
results["type"] = str(data.dtype)
|
||||
results["shape"] = data.shape
|
||||
|
||||
|
||||
accumulator_socket.send_json(results, FLAGS)
|
||||
|
||||
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)
|
||||
else:
|
||||
data = np.empty((2, 2), dtype=np.uint16)
|
||||
results["type"] = str(data.dtype)
|
||||
results["shape"] = data.shape
|
||||
visualisation_socket.send_json(results, FLAGS | zmq.SNDMORE)
|
||||
visualisation_socket.send(data, FLAGS, copy=True, track=True)
|
||||
else:
|
||||
if results["is_good_frame"] and (results["is_hit_frame"] or randint(1, skip_frames_rate) == 1):
|
||||
visualisation_socket.send_json(results, FLAGS | zmq.SNDMORE)
|
||||
visualisation_socket.send(data, FLAGS, copy=True, track=True)
|
||||
else:
|
||||
data = np.empty((2, 2), dtype=np.uint16)
|
||||
results["type"] = str(data.dtype)
|
||||
results["shape"] = data.shape
|
||||
visualisation_socket.send_json(results, FLAGS | zmq.SNDMORE)
|
||||
visualisation_socket.send(data, FLAGS, copy=True, track=True)
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user