prepare script selects shape with fewest slices for meta.csv

mu_map/data/prepare.py

@@ -406,7 +406,7 @@ if __name__ == "__main__":
                     ), f"Not all pixel spacings of the reconstructions are equal: {pixel_spacings}"
                     pixel_spacing = pixel_spacings[0]
 
-                    # extract shapes and assert that they are equal for all reconstruction images
+                    # use the shape with the fewest slices, all other images will be aligned to that
                     _map_lists = map(
                         lambda image: [image.Rows, image.Columns, image.NumberOfSlices],
                         recon_images,
@@ -414,9 +414,7 @@ if __name__ == "__main__":
                     _map_lists = map(lambda shape: list(map(int, shape)), _map_lists)
                     _map_ndarrays = map(lambda shape: np.array(shape), _map_lists)
                     shapes = list(_map_ndarrays)
-                    _equal = all(map(lambda shape: (shape == shapes[0]).all(), shapes))
-                    # assert _equal, f"Not all shapes of the reconstructions are equal: {shapes}"
-                    # print(shapes)
+                    shapes.sort(key=lambda shape: shape[2])
                     shape = shapes[0]
 
                     # exctract and sort energy windows