simpler logic
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@ -1,7 +1,7 @@
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import numpy as np
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def calc_roi(results, data, pixel_mask_pf, threshold_value_choice):
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def calc_roi(results, data, pixel_mask_pf):
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if pixel_mask_pf is None:
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return
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@ -20,6 +20,8 @@ def calc_roi(results, data, pixel_mask_pf, threshold_value_choice):
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if not (len(roi_x1) == len(roi_x2) == len(roi_y1) == len(roi_y2)):
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return
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threshold_value = results.get("threshold_value", "NaN")
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roi_intensities = []
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roi_intensities_normalised = []
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roi_intensities_x = []
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@ -30,7 +32,7 @@ def calc_roi(results, data, pixel_mask_pf, threshold_value_choice):
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roi_sum = np.nansum(data_roi)
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if threshold_value_choice == "NaN":
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if threshold_value == "NaN":
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roi_area = (ix2 - ix1) * (iy2 - iy1)
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roi_sum_norm = roi_sum / roi_area
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else:
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@ -15,7 +15,5 @@ def calc_apply_threshold(results, data):
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if threshold_max > threshold_min:
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data[data > threshold_max] = threshold_value
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return threshold_value_choice
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@ -184,9 +184,8 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
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if pixel_mask_pf is not None:
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pfdata[pixel_mask_pf != 1] = np.nan
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threshold_value_choice = calc_apply_threshold(results, pfdata)
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calc_roi(results, pfdata, pixel_mask_pf, threshold_value_choice)
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calc_apply_threshold(results, pfdata)
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calc_roi(results, pfdata, pixel_mask_pf)
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calc_spi_analysis(results)
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calc_peakfinder_analysis(results, pfdata, pixel_mask_pf)
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