consistent naming
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@ -20,8 +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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roi_results = []
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roi_results_normalised = []
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roi_intensities = []
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roi_intensities_normalised = []
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roi_intensities_x = []
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roi_intensities_proj_x = []
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@ -30,7 +30,7 @@ def calc_roi(results, data, pixel_mask_pf, threshold_value_choice):
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data_roi = data[iy1:iy2, ix1:ix2]
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roi_sum = np.nansum(data_roi)
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roi_results.append(roi_sum)
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roi_intensities.append(roi_sum)
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if threshold_value_choice == "NaN":
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roi_area = (y2 - y1) * (x2 - x1)
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@ -38,7 +38,7 @@ def calc_roi(results, data, pixel_mask_pf, threshold_value_choice):
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else:
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roi_sum_norm = np.nanmean(data_roi)
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roi_results_normalised.append(roi_sum_norm)
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roi_intensities_normalised.append(roi_sum_norm)
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roi_intensity_x = [x1, x2]
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roi_intensity_proj_x = np.nansum(data_roi, axis=0).tolist()
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@ -46,8 +46,8 @@ def calc_roi(results, data, pixel_mask_pf, threshold_value_choice):
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roi_intensities_x.append(roi_intensity_x)
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roi_intensities_proj_x.append(roi_intensity_proj_x)
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results["roi_intensities"] = [float(r) for r in roi_results]
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results["roi_intensities_normalised"] = [float(r) for r in roi_results_normalised]
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results["roi_intensities"] = [float(r) for r in roi_intensities]
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results["roi_intensities_normalised"] = [float(r) for r in roi_intensities_normalised]
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results["roi_intensities_x"] = roi_intensities_x
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results["roi_intensities_proj_x"] = roi_intensities_proj_x
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