renamed variable d to pfdata
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@ -180,11 +180,11 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
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#copy image to work with peakfinder, just in case
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#copy image to work with peakfinder, just in case
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d = np.copy(data)
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pfdata = np.copy(data)
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# make all masked pixels values nans
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# make all masked pixels values nans
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if pixel_mask_pf is not None:
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if pixel_mask_pf is not None:
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d[pixel_mask_pf != 1] = np.nan
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pfdata[pixel_mask_pf != 1] = np.nan
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apply_threshold = results.get("apply_threshold", False)
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apply_threshold = results.get("apply_threshold", False)
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threshold_value_choice = results.get("threshold_value", "NaN")
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threshold_value_choice = results.get("threshold_value", "NaN")
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@ -192,9 +192,9 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
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if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
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if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
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threshold_min = float(results["threshold_min"])
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threshold_min = float(results["threshold_min"])
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threshold_max = float(results["threshold_max"])
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threshold_max = float(results["threshold_max"])
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d[d < threshold_min] = threshold_value
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pfdata[pfdata < threshold_min] = threshold_value
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if threshold_max > threshold_min:
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if threshold_max > threshold_min:
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d[d > threshold_max] = threshold_value
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pfdata[pfdata > threshold_max] = threshold_value
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# if roi calculation request is present, make it
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# if roi calculation request is present, make it
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roi_x1 = results.get("roi_x1", [])
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roi_x1 = results.get("roi_x1", [])
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@ -212,7 +212,7 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
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results["roi_intensities_proj_x"] = []
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results["roi_intensities_proj_x"] = []
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for iRoi in range(len(roi_x1)):
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for iRoi in range(len(roi_x1)):
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data_roi = np.copy(d[roi_y1[iRoi]:roi_y2[iRoi], roi_x1[iRoi]:roi_x2[iRoi]])
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data_roi = np.copy(pfdata[roi_y1[iRoi]:roi_y2[iRoi], roi_x1[iRoi]:roi_x2[iRoi]])
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roi_results[iRoi] = np.nansum(data_roi)
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roi_results[iRoi] = np.nansum(data_roi)
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if threshold_value_choice == "NaN":
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if threshold_value_choice == "NaN":
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@ -252,7 +252,7 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
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hitfinder_min_pix_count = int(results["hitfinder_min_pix_count"])
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hitfinder_min_pix_count = int(results["hitfinder_min_pix_count"])
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hitfinder_adc_thresh = results["hitfinder_adc_thresh"]
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hitfinder_adc_thresh = results["hitfinder_adc_thresh"]
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asic_ny, asic_nx = d.shape
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asic_ny, asic_nx = pfdata.shape
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nasics_y, nasics_x = 1, 1
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nasics_y, nasics_x = 1, 1
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hitfinder_max_pix_count = 100
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hitfinder_max_pix_count = 100
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max_num_peaks = 10000
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max_num_peaks = 10000
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@ -263,12 +263,12 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
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# in case of further modification with the mask, make a new one, independent from real mask
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# in case of further modification with the mask, make a new one, independent from real mask
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maskPr = np.copy(pixel_mask_pf)
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maskPr = np.copy(pixel_mask_pf)
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y, x = np.indices(d.shape)
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y, x = np.indices(pfdata.shape)
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pix_r = np.sqrt((x-x_beam)**2 + (y-y_beam)**2)
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pix_r = np.sqrt((x-x_beam)**2 + (y-y_beam)**2)
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peak_list_x, peak_list_y, peak_list_value = peakfinder_8(
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peak_list_x, peak_list_y, peak_list_value = peakfinder_8(
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max_num_peaks,
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max_num_peaks,
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d.astype(np.float32),
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pfdata.astype(np.float32),
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maskPr.astype(np.int8),
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maskPr.astype(np.int8),
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pix_r.astype(np.float32),
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pix_r.astype(np.float32),
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asic_nx, asic_ny,
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asic_nx, asic_ny,
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