Added missing tests to git

Author: Jammy2211

autoarray/operators/inversion/inversions.py

@@ -17,7 +17,7 @@ def inversion(masked_dataset, mapper, regularization):
             mapper=mapper,
             regularization=regularization,
         )
-    
+
     elif isinstance(masked_dataset, md.MaskedInterferometer):
 
         return InversionInterferometer.from_data_mapper_and_regularization(
@@ -30,7 +30,6 @@ def inversion(masked_dataset, mapper, regularization):
 
 
 class Inversion(object):
-
     def __init__(
         self,
         noise_map,
@@ -152,9 +151,14 @@ def __init__(
             The vector containing the reconstructed fit to the hyper_galaxies.
         """
 
-        super(InversionImaging, self).__init__(noise_map=noise_map, mapper=mapper, regularization=regularization,
-                                               regularization_matrix=regularization_matrix, curvature_reg_matrix=curvature_reg_matrix,
-                                               reconstruction=reconstruction)
+        super(InversionImaging, self).__init__(
+            noise_map=noise_map,
+            mapper=mapper,
+            regularization=regularization,
+            regularization_matrix=regularization_matrix,
+            curvature_reg_matrix=curvature_reg_matrix,
+            reconstruction=reconstruction,
+        )
 
         self.image = image
         self.blurred_mapping_matrix = blurred_mapping_matrix
@@ -288,9 +292,14 @@ def __init__(
             The vector containing the reconstructed fit to the hyper_galaxies.
         """
 
-        super(InversionInterferometer, self).__init__(noise_map=noise_map, mapper=mapper, regularization=regularization,
-                                               regularization_matrix=regularization_matrix, curvature_reg_matrix=curvature_reg_matrix,
-                                               reconstruction=reconstruction)
+        super(InversionInterferometer, self).__init__(
+            noise_map=noise_map,
+            mapper=mapper,
+            regularization=regularization,
+            regularization_matrix=regularization_matrix,
+            curvature_reg_matrix=curvature_reg_matrix,
+            reconstruction=reconstruction,
+        )
 
         self.visibilities = visibilities
         self.transformed_mapping_matrices = transformed_mapping_matrices
@@ -306,24 +315,24 @@ def from_data_mapper_and_regularization(
 
         real_data_vector = inversion_util.data_vector_from_transformed_mapping_matrix_and_data(
             transformed_mapping_matrix=transformed_mapping_matrices[0],
-            visibilities=visibilities[:,0],
-            noise_map=noise_map[:,0],
+            visibilities=visibilities[:, 0],
+            noise_map=noise_map[:, 0],
         )
 
         imag_data_vector = inversion_util.data_vector_from_transformed_mapping_matrix_and_data(
             transformed_mapping_matrix=transformed_mapping_matrices[1],
-            visibilities=visibilities[:,1],
-            noise_map=noise_map[:,1],
+            visibilities=visibilities[:, 1],
+            noise_map=noise_map[:, 1],
         )
 
         real_curvature_matrix = inversion_util.curvature_matrix_from_transformed_mapping_matrix(
             transformed_mapping_matrix=transformed_mapping_matrices[0],
-            noise_map=noise_map[:,0],
+            noise_map=noise_map[:, 0],
         )
 
         imag_curvature_matrix = inversion_util.curvature_matrix_from_transformed_mapping_matrix(
             transformed_mapping_matrix=transformed_mapping_matrices[1],
-            noise_map=noise_map[:,1],
+            noise_map=noise_map[:, 1],
         )
 
         regularization_matrix = regularization.regularization_matrix_from_mapper(
@@ -334,7 +343,9 @@ def from_data_mapper_and_regularization(
         imag_curvature_reg_matrix = np.add(imag_curvature_matrix, regularization_matrix)
 
         data_vector = np.add(real_data_vector, imag_data_vector)
-        curvature_reg_matrix = np.add(real_curvature_reg_matrix, imag_curvature_reg_matrix)
+        curvature_reg_matrix = np.add(
+            real_curvature_reg_matrix, imag_curvature_reg_matrix
+        )
 
         try:
             values = np.linalg.solve(curvature_reg_matrix, data_vector)
@@ -352,6 +363,13 @@ def from_data_mapper_and_regularization(
             reconstruction=values,
         )
 
+    @property
+    def mapped_reconstructed_image(self):
+        return inversion_util.mapped_reconstructed_data_from_mapping_matrix_and_reconstruction(
+            mapping_matrix=self.mapper.mapping_matrix,
+            reconstruction=self.reconstruction,
+        )
+
     @property
     def mapped_reconstructed_visibilities(self):
         real_visibilities = inversion_util.mapped_reconstructed_data_from_mapping_matrix_and_reconstruction(
@@ -364,4 +382,6 @@ def mapped_reconstructed_visibilities(self):
             reconstruction=self.reconstruction,
         )
 
-        return vis.Visibilities(visibilities_1d=np.stack((real_visibilities, imag_visibilities), axis=-1))
+        return vis.Visibilities(
+            visibilities_1d=np.stack((real_visibilities, imag_visibilities), axis=-1)
+        )

autoarray/operators/inversion/mappers.py

@@ -127,9 +127,9 @@ def all_sub_mask_1d_indexes_for_pixelization_1d_index(self):
         for mask_1d_index, pix_1_index in enumerate(
             self.pixelization_1d_index_for_sub_mask_1d_index
         ):
-            all_sub_mask_1d_indexes_for_pixelization_1d_index[
-                pix_1_index
-            ].append(mask_1d_index)
+            all_sub_mask_1d_indexes_for_pixelization_1d_index[pix_1_index].append(
+                mask_1d_index
+            )
 
         return all_sub_mask_1d_indexes_for_pixelization_1d_index
 

autoarray/util/inversion_util.py

@@ -26,9 +26,9 @@ def data_vector_from_blurred_mapping_matrix_and_data(
     for mask_1d_index in range(mapping_shape[0]):
         for pix_1_index in range(mapping_shape[1]):
             data_vector[pix_1_index] += (
-                    image[mask_1d_index]
-                    * blurred_mapping_matrix[mask_1d_index, pix_1_index]
-                    / (noise_map[mask_1d_index] ** 2.0)
+                image[mask_1d_index]
+                * blurred_mapping_matrix[mask_1d_index, pix_1_index]
+                / (noise_map[mask_1d_index] ** 2.0)
             )
 
     return data_vector
@@ -80,8 +80,8 @@ def curvature_matrix_from_blurred_mapping_matrix_jit(
         for pix_1_index in range(blurred_mapping_matrix.shape[1]):
             if blurred_mapping_matrix[mask_1d_index, pix_1_index] > 0.0:
                 flist[index] = (
-                        blurred_mapping_matrix[mask_1d_index, pix_1_index]
-                        / noise_map[mask_1d_index]
+                    blurred_mapping_matrix[mask_1d_index, pix_1_index]
+                    / noise_map[mask_1d_index]
                 )
                 iflist[index] = pix_1_index
                 index += 1
@@ -115,16 +115,14 @@ def mapped_reconstructed_data_from_mapping_matrix_and_reconstruction(
     mapped_reconstructred_data = np.zeros(mapping_matrix.shape[0])
     for i in range(mapping_matrix.shape[0]):
         for j in range(reconstruction.shape[0]):
-            mapped_reconstructred_data[i] += (
-                    reconstruction[j] * mapping_matrix[i, j]
-            )
+            mapped_reconstructred_data[i] += reconstruction[j] * mapping_matrix[i, j]
 
     return mapped_reconstructred_data
 
 
 @decorator_util.jit()
 def data_vector_from_transformed_mapping_matrix_and_data(
-        transformed_mapping_matrix, visibilities, noise_map
+    transformed_mapping_matrix, visibilities, noise_map
 ):
     """Compute the hyper_galaxies vector *D* from a transformed util matrix *f* and the 1D image *d* and 1D noise-map *\sigma* \
     (see Warren & Dye 2003).
@@ -143,15 +141,18 @@ def data_vector_from_transformed_mapping_matrix_and_data(
 
     for vis_1d_index in range(transformed_mapping_matrix.shape[0]):
         for pix_1d_index in range(transformed_mapping_matrix.shape[1]):
-            data_vector[pix_1d_index] += visibilities[vis_1d_index] \
-                    * transformed_mapping_matrix[vis_1d_index, pix_1d_index] \
-                    / (noise_map[vis_1d_index] ** 2.0)
+            data_vector[pix_1d_index] += (
+                visibilities[vis_1d_index]
+                * transformed_mapping_matrix[vis_1d_index, pix_1d_index]
+                / (noise_map[vis_1d_index] ** 2.0)
+            )
 
     return data_vector
 
+
 @decorator_util.jit()
 def curvature_matrix_from_transformed_mapping_matrix(
-        transformed_mapping_matrix, noise_map,
+    transformed_mapping_matrix, noise_map
 ):
     """Compute the curvature matrix *F* from a transformed util matrix *f* and the 1D noise-map *\sigma* \
     (see Warren & Dye 2003).
@@ -172,12 +173,13 @@ def curvature_matrix_from_transformed_mapping_matrix(
     )
 
     for pix_1d_index_0 in range(transformed_mapping_matrix.shape[1]):
-        for pix_1d_index_1 in range(pix_1d_index_0+1):
+        for pix_1d_index_1 in range(pix_1d_index_0 + 1):
             for vis_1d_index in range(transformed_mapping_matrix.shape[0]):
-                curvature_matrix[pix_1d_index_0, pix_1d_index_1] += \
-                    transformed_mapping_matrix[vis_1d_index, pix_1d_index_0] * \
-                    transformed_mapping_matrix[vis_1d_index, pix_1d_index_1] / \
-                        noise_map[vis_1d_index]**2
+                curvature_matrix[pix_1d_index_0, pix_1d_index_1] += (
+                    transformed_mapping_matrix[vis_1d_index, pix_1d_index_0]
+                    * transformed_mapping_matrix[vis_1d_index, pix_1d_index_1]
+                    / noise_map[vis_1d_index] ** 2
+                )
 
     for i in range(transformed_mapping_matrix.shape[1]):
         for j in range(transformed_mapping_matrix.shape[1]):
@@ -205,8 +207,8 @@ def inversion_residual_map_from_pixelization_values_and_reconstructed_data_1d(
             sub_mask_total += 1
             mask_1d_index = mask_1d_index_for_sub_mask_1d_index[sub_mask_1d_index]
             residual = (
-                    mapped_reconstructed_data[mask_1d_index]
-                    - pixelization_values[pix_1_index]
+                mapped_reconstructed_data[mask_1d_index]
+                - pixelization_values[pix_1_index]
             )
             residual_map[pix_1_index] += np.abs(residual)
 
@@ -237,8 +239,8 @@ def inversion_normalized_residual_map_from_pixelization_values_and_reconstructed
             sub_mask_total += 1
             mask_1d_index = mask_1d_index_for_sub_mask_1d_index[sub_mask_1d_index]
             residual = (
-                    mapped_reconstructed_data[mask_1d_index]
-                    - pixelization_values[pix_1_index]
+                mapped_reconstructed_data[mask_1d_index]
+                - pixelization_values[pix_1_index]
             )
             normalized_residual_map[pix_1_index] += np.abs(
                 (residual / noise_map_1d[mask_1d_index])
@@ -268,8 +270,8 @@ def inversion_chi_squared_map_from_pixelization_values_and_reconstructed_data_1d
             sub_mask_total += 1
             mask_1d_index = mask_1d_index_for_sub_mask_1d_index[sub_mask_1d_index]
             residual = (
-                    mapped_reconstructed_data[mask_1d_index]
-                    - pixelization_values[pix_1_index]
+                mapped_reconstructed_data[mask_1d_index]
+                - pixelization_values[pix_1_index]
             )
             chi_squared_map[pix_1_index] += (
                 residual / noise_map_1d[mask_1d_index]