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cleaned/restructured

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augustin_s
2024-07-31 11:09:46 +02:00
parent 1db20f8b82
commit a25bd23b67
1 changed files with 22 additions and 11 deletions
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33
dap/algos/roi.py
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@ -20,26 +20,37 @@ def calc_roi(results, data, pixel_mask_pf, threshold_value_choice):
if not (len(roi_x1) == len(roi_x2) == len(roi_y1) == len(roi_y2)):
return
roi_results = [0] * len(roi_x1)
roi_results_normalised = [0] * len(roi_x1)
roi_results = []
roi_results_normalised = []
results["roi_intensities_x"] = []
results["roi_intensities_proj_x"] = []
roi_intensities_x = []
roi_intensities_proj_x = []
for iRoi in range(len(roi_x1)):
data_roi = data[roi_y1[iRoi]:roi_y2[iRoi], roi_x1[iRoi]:roi_x2[iRoi]]
for ix1, ix2, iy1, iy2 in zip(roi_x1, roi_x2, roi_y1, roi_y2):
data_roi = data[iy1:iy2, ix1:ix2]
roi_sum = np.nansum(data_roi)
roi_results.append(roi_sum)
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]))
roi_area = (y2 - y1) * (x2 - x1)
roi_sum_norm = roi_sum / roi_area
else:
roi_results_normalised[iRoi] = np.nanmean(data_roi)
roi_sum_norm = 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())
roi_results_normalised.append(roi_sum_norm)
roi_intensity_x = [x1, x2]
roi_intensity_proj_x = np.nansum(data_roi, axis=0).tolist()
roi_intensities_x.append(roi_intensity_x)
roi_intensities_proj_x.append(roi_intensity_proj_x)
results["roi_intensities"] = [float(r) for r in roi_results]
results["roi_intensities_normalised"] = [float(r) for r in roi_results_normalised]
results["roi_intensities_x"] = roi_intensities_x
results["roi_intensities_proj_x"] = roi_intensities_proj_x