Merging complete

Author: Jammy2211

autoarray/mask/mapping.py

@@ -284,7 +284,7 @@ def grid_binned_from_sub_grid_1d(self, sub_grid_1d):
             grid_1d=np.stack((grid_1d_y, grid_1d_x), axis=-1), mask=self.mask_sub_1
         )
 
-    def trimmed_array_2d_from_padded_array_and_image_shape(
+    def trimmed_array_from_padded_array_and_image_shape(
         self, padded_array, image_shape
     ):
         """ Map a padded 1D array of values to its original 2D array, trimming all edge values.
@@ -297,10 +297,11 @@ def trimmed_array_2d_from_padded_array_and_image_shape(
 
         pad_size_0 = self.mask.shape[0] - image_shape[0]
         pad_size_1 = self.mask.shape[1] - image_shape[1]
-        return padded_array.in_2d_binned[
+        trimmed_array = padded_array.in_2d_binned[
             pad_size_0 // 2 : self.mask.shape[0] - pad_size_0 // 2,
             pad_size_1 // 2 : self.mask.shape[1] - pad_size_1 // 2,
         ]
+        return arrays.Array.manual_2d(array=trimmed_array, pixel_scales=self.mask.pixel_scales, sub_size=1, origin=self.mask.origin)
 
     def convolve_padded_array_1d_with_psf(self, padded_array_1d, psf):
         """Convolve a 1d padded array of values (e.g. image before PSF blurring) with a PSF, and then trim \
@@ -329,7 +330,7 @@ def convolve_padded_array_1d_with_psf(self, padded_array_1d, psf):
             sub_size=1,
         )
 
-    def unmasked_blurred_array_2d_from_padded_array_psf_and_image_shape(
+    def unmasked_blurred_array_from_padded_array_psf_and_image_shape(
         self, padded_array, psf, image_shape
     ):
         """For a padded grid and psf, compute an unmasked blurred image from an unmasked unblurred image.
@@ -347,7 +348,7 @@ def unmasked_blurred_array_2d_from_padded_array_psf_and_image_shape(
 
         blurred_image = psf.convolved_array_from_array(array=padded_array)
 
-        return self.trimmed_array_2d_from_padded_array_and_image_shape(
+        return self.trimmed_array_from_padded_array_and_image_shape(
             padded_array=blurred_image, image_shape=image_shape
         )
 

autoarray/mask/mask.py

@@ -373,7 +373,7 @@ def elliptical_annular(
         )
 
     @classmethod
-    def from_fits(cls, file_path, hdu, pixel_scales, sub_size, origin=(0.0, 0.0)):
+    def from_fits(cls, file_path, pixel_scales, hdu=0, sub_size=1, origin=(0.0, 0.0)):
         """
         Loads the image from a .fits file.
 
@@ -383,7 +383,7 @@ def from_fits(cls, file_path, hdu, pixel_scales, sub_size, origin=(0.0, 0.0)):
             The full path of the fits file.
         hdu : int
             The HDU number in the fits file containing the image image.
-        pixel_scales : (float, float)
+        pixel_scales : float or (float, float)
             The arc-second to pixel conversion factor of each pixel.
         """
 

autoarray/operators/inversion/inversions.py

@@ -119,7 +119,7 @@ def errors(self):
 
     @property
     def residual_map(self):
-        return inversion_util.pixelization_residual_map_from_pixelization_values_and_reconstructed_data_1d(
+        return inversion_util.inversion_residual_map_from_pixelization_values_and_reconstructed_data_1d(
             pixelization_values=self.reconstruction,
             reconstructed_data_1d=self.mapped_reconstructed_image,
             mask_1d_index_for_sub_mask_1d_index=self.mapper.grid.mask.regions._mask_1d_index_for_sub_mask_1d_index,
@@ -128,7 +128,7 @@ def residual_map(self):
 
     @property
     def normalized_residual_map(self):
-        return inversion_util.pixelization_normalized_residual_map_from_pixelization_values_and_reconstructed_data_1d(
+        return inversion_util.inversion_normalized_residual_map_from_pixelization_values_and_reconstructed_data_1d(
             pixelization_values=self.reconstruction,
             reconstructed_data_1d=self.mapped_reconstructed_image,
             noise_map_1d=self.noise_map,
@@ -138,7 +138,7 @@ def normalized_residual_map(self):
 
     @property
     def chi_squared_map(self):
-        return inversion_util.pixelization_chi_squared_map_from_pixelization_values_and_reconstructed_data_1d(
+        return inversion_util.inversion_chi_squared_map_from_pixelization_values_and_reconstructed_data_1d(
             pixelization_values=self.reconstruction,
             reconstructed_data_1d=self.mapped_reconstructed_image,
             noise_map_1d=self.noise_map,

autoarray/plotters/inversion_plotters.py

@@ -437,7 +437,7 @@ def residual_map(
     xyticksize=16,
     output_path=None,
     output_format="show",
-    output_filename="inversion_pixelization_residual_map",
+    output_filename="inversion_inversion_residual_map",
 ):
 
     if output_format is "fits":
@@ -516,7 +516,7 @@ def normalized_residual_map(
     xyticksize=16,
     output_path=None,
     output_format="show",
-    output_filename="inversion_pixelization_normalized_residual_map",
+    output_filename="inversion_inversion_normalized_residual_map",
 ):
 
     if output_format is "fits":
@@ -595,7 +595,7 @@ def chi_squared_map(
     xyticksize=16,
     output_path=None,
     output_format="show",
-    output_filename="inversion_pixelization_chi_squared_map",
+    output_filename="inversion_inversion_chi_squared_map",
 ):
 
     if output_format is "fits":
@@ -674,7 +674,7 @@ def regularization_weights(
     xyticksize=16,
     output_path=None,
     output_format="show",
-    output_filename="inversion_pixelization_regularization_weights",
+    output_filename="inversion_inversion_regularization_weights",
 ):
 
     if output_format is "fits":

autoarray/plotters/plotter_util.py

@@ -5,6 +5,8 @@
 matplotlib.use(backend)
 import matplotlib.pyplot as plt
 import numpy as np
+from autoarray import exc
+from autoarray.util import array_util
 
 
 def get_subplot_rows_columns_figsize(number_subplots):
@@ -120,7 +122,7 @@ def output_figure(array, as_subplot, output_path, output_filename, output_format
         elif output_format is "png":
             plt.savefig(output_path + output_filename + ".png", bbox_inches="tight")
         elif output_format is "fits":
-            aa.util.array.numpy_array_2d_to_fits(
+            array_util.numpy_array_2d_to_fits(
                 array_2d=array,
                 file_path=output_path + output_filename + ".fits",
                 overwrite=True,

autoarray/structures/arrays.py

@@ -311,7 +311,7 @@ def zeros(cls, shape_2d, pixel_scales=None, sub_size=1, origin=(0.0, 0.0)):
 
     @classmethod
     def from_fits(
-        cls, file_path, hdu, pixel_scales=None, sub_size=1, origin=(0.0, 0.0)
+        cls, file_path, hdu=0, pixel_scales=None, sub_size=1, origin=(0.0, 0.0)
     ):
         array_2d = array_util.numpy_array_2d_from_fits(file_path=file_path, hdu=hdu)
         return cls.manual_2d(

autoarray/util/inversion_util.py

@@ -122,7 +122,7 @@ def reconstructed_data_vector_from_blurred_mapping_matrix_and_solution_vector(
     return reconstructed_data_vector
 
 
-def pixelization_residual_map_from_pixelization_values_and_reconstructed_data_1d(
+def inversion_residual_map_from_pixelization_values_and_reconstructed_data_1d(
     pixelization_values,
     reconstructed_data_1d,
     mask_1d_index_for_sub_mask_1d_index,
@@ -149,7 +149,7 @@ def pixelization_residual_map_from_pixelization_values_and_reconstructed_data_1d
     return pixelization_residuals
 
 
-def pixelization_normalized_residual_map_from_pixelization_values_and_reconstructed_data_1d(
+def inversion_normalized_residual_map_from_pixelization_values_and_reconstructed_data_1d(
     pixelization_values,
     reconstructed_data_1d,
     noise_map_1d,
@@ -179,7 +179,7 @@ def pixelization_normalized_residual_map_from_pixelization_values_and_reconstruc
     return pixelization_normalized_residuals
 
 
-def pixelization_chi_squared_map_from_pixelization_values_and_reconstructed_data_1d(
+def inversion_chi_squared_map_from_pixelization_values_and_reconstructed_data_1d(
     pixelization_values,
     reconstructed_data_1d,
     noise_map_1d,

test_autoarray/unit/mask/test_mapping.py

@@ -559,11 +559,11 @@ def test__trimmed_array_2d_from_padded_array_1d_and_image_shape(self):
             mask=mask,
         )
 
-        array_2d = mask.mapping.trimmed_array_2d_from_padded_array_and_image_shape(
+        array = mask.mapping.trimmed_array_from_padded_array_and_image_shape(
             padded_array=array_1d, image_shape=(2, 2)
         )
 
-        assert (array_2d == np.array([[6.0, 7.0], [1.0, 2.0]])).all()
+        assert (array.in_2d == np.array([[6.0, 7.0], [1.0, 2.0]])).all()
 
         mask = aa.mask.unmasked(shape_2d=(5, 3))
 
@@ -572,11 +572,11 @@ def test__trimmed_array_2d_from_padded_array_1d_and_image_shape(self):
             mask=mask,
         )
 
-        array_2d = mask.mapping.trimmed_array_2d_from_padded_array_and_image_shape(
+        array = mask.mapping.trimmed_array_from_padded_array_and_image_shape(
             padded_array=array_1d, image_shape=(3, 1)
         )
 
-        assert (array_2d == np.array([[5.0], [8.0], [2.0]])).all()
+        assert (array.in_2d == np.array([[5.0], [8.0], [2.0]])).all()
 
         mask = aa.mask.unmasked(shape_2d=(3, 5))
 
@@ -585,11 +585,11 @@ def test__trimmed_array_2d_from_padded_array_1d_and_image_shape(self):
             mask=mask,
         )
 
-        array_2d = mask.mapping.trimmed_array_2d_from_padded_array_and_image_shape(
+        array = mask.mapping.trimmed_array_from_padded_array_and_image_shape(
             padded_array=array_1d, image_shape=(1, 3)
         )
 
-        assert (array_2d == np.array([[7.0, 8.0, 9.0]])).all()
+        assert (array.in_2d == np.array([[7.0, 8.0, 9.0]])).all()
 
 
 class TestMappingExceptions:

test_autoarray/unit/test_files/config/visualize.ini

@@ -1,7 +1,5 @@
 [figures]
 plot_mask_on_images = True
-extract_images_from_mask = True
-zoom_around_mask_of_images = True
 plot_positions_on_images = True
 plot_image_plane_adaptive_pixelization_grid = True
 plot_units = arcsec
@@ -27,27 +25,27 @@ plot_ray_tracing_potential = False
 plot_ray_tracing_deflections = False
 plot_ray_tracing_magnification = False
 
-plot_lens_fit_all_at_end_png = True
-plot_lens_fit_all_at_end_fits = True
-plot_lens_fit_as_subplot = True
-plot_lens_fit_of_planes_as_subplot=True
-plot_lens_fit_inversion_as_subplot = True
-plot_lens_fit_image = False
-plot_lens_fit_noise_map = False
-plot_lens_fit_signal_to_noise_map = False
-plot_lens_fit_lens_subtracted_image = False
-plot_lens_fit_model_image = False
-plot_lens_fit_residual_map = False
-plot_lens_fit_normalized_residual_map = False
-plot_lens_fit_chi_squared_map = False
-plot_lens_fit_contribution_maps = False
-plot_lens_fit_pixelization_residual_map = False
-plot_lens_fit_pixelization_normalized_residual_map = False
-plot_lens_fit_pixelization_chi_squared_map = False
-plot_lens_fit_pixelization_regularization_weight_map = False
-plot_lens_fit_subtracted_images_of_planes = False
-plot_lens_fit_model_images_of_planes = False
-plot_lens_fit_plane_images_of_planes = False
+plot_fit_all_at_end_png = True
+plot_fit_all_at_end_fits = True
+plot_fit_as_subplot = True
+plot_fit_of_planes_as_subplot=True
+plot_fit_inversion_as_subplot = True
+plot_fit_image = False
+plot_fit_noise_map = False
+plot_fit_signal_to_noise_map = False
+plot_fit_lens_subtracted_image = False
+plot_fit_model_image = False
+plot_fit_residual_map = False
+plot_fit_normalized_residual_map = False
+plot_fit_chi_squared_map = False
+plot_fit_contribution_maps = False
+plot_fit_inversion_residual_map = False
+plot_fit_inversion_normalized_residual_map = False
+plot_fit_inversion_chi_squared_map = False
+plot_fit_inversion_regularization_weight_map = False
+plot_fit_subtracted_images_of_planes = False
+plot_fit_model_images_of_planes = False
+plot_fit_plane_images_of_planes = False
 
 plot_hyper_galaxy_subplot = False
 plot_hyper_model_image = False

test_autoarray/unit/util/test_inversion_util.py

@@ -144,7 +144,7 @@ def test__pixelization_perfectly_reconstructed_data__quantities_like_residuals_a
             [2, 2, 2],
         ]
 
-        pixelization_residuals = aa.util.inversion.pixelization_residual_map_from_pixelization_values_and_reconstructed_data_1d(
+        pixelization_residuals = aa.util.inversion.inversion_residual_map_from_pixelization_values_and_reconstructed_data_1d(
             pixelization_values=pixelization_values,
             reconstructed_data_1d=reconstructed_data_1d,
             mask_1d_index_for_sub_mask_1d_index=mask_1d_index_for_sub_mask_1d_index,
@@ -164,7 +164,7 @@ def test__pixelization_not_perfect_fit__quantities_like_residuals_non_zero(self)
             [6, 7, 8],
         ]
 
-        pixelization_residuals = aa.util.inversion.pixelization_residual_map_from_pixelization_values_and_reconstructed_data_1d(
+        pixelization_residuals = aa.util.inversion.inversion_residual_map_from_pixelization_values_and_reconstructed_data_1d(
             pixelization_values=pixelization_values,
             reconstructed_data_1d=reconstructed_data_1d,
             mask_1d_index_for_sub_mask_1d_index=mask_1d_index_for_sub_mask_1d_index,
@@ -182,7 +182,7 @@ def test__pixelization_not_perfect_fit__quantities_like_residuals_non_zero(self)
             [6, 7, 8],
         ]
 
-        pixelization_residuals = aa.util.inversion.pixelization_residual_map_from_pixelization_values_and_reconstructed_data_1d(
+        pixelization_residuals = aa.util.inversion.inversion_residual_map_from_pixelization_values_and_reconstructed_data_1d(
             pixelization_values=pixelization_values,
             reconstructed_data_1d=reconstructed_data_1d,
             mask_1d_index_for_sub_mask_1d_index=mask_1d_index_for_sub_mask_1d_index,
@@ -207,7 +207,7 @@ def test__pixelization_perfectly_reconstructed_data__quantities_like_residuals_a
             [2, 2, 2],
         ]
 
-        pixelization_normalized_residuals = aa.util.inversion.pixelization_normalized_residual_map_from_pixelization_values_and_reconstructed_data_1d(
+        pixelization_normalized_residuals = aa.util.inversion.inversion_normalized_residual_map_from_pixelization_values_and_reconstructed_data_1d(
             pixelization_values=pixelization_values,
             reconstructed_data_1d=reconstructed_data_1d,
             noise_map_1d=noise_map_1d,
@@ -229,7 +229,7 @@ def test__pixelization_not_perfect_fit__quantities_like_residuals_non_zero(self)
             [6, 7, 8],
         ]
 
-        pixelization_normalized_residuals = aa.util.inversion.pixelization_normalized_residual_map_from_pixelization_values_and_reconstructed_data_1d(
+        pixelization_normalized_residuals = aa.util.inversion.inversion_normalized_residual_map_from_pixelization_values_and_reconstructed_data_1d(
             pixelization_values=pixelization_values,
             reconstructed_data_1d=reconstructed_data_1d,
             noise_map_1d=noise_map_1d,
@@ -249,7 +249,7 @@ def test__pixelization_not_perfect_fit__quantities_like_residuals_non_zero(self)
             [6, 7, 8],
         ]
 
-        pixelization_normalized_residuals = aa.util.inversion.pixelization_normalized_residual_map_from_pixelization_values_and_reconstructed_data_1d(
+        pixelization_normalized_residuals = aa.util.inversion.inversion_normalized_residual_map_from_pixelization_values_and_reconstructed_data_1d(
             pixelization_values=pixelization_values,
             reconstructed_data_1d=reconstructed_data_1d,
             noise_map_1d=noise_map_1d,
@@ -275,7 +275,7 @@ def test__pixelization_perfectly_reconstructed_data__quantities_like_residuals_a
             [2, 2, 2],
         ]
 
-        pixelization_chi_squareds = aa.util.inversion.pixelization_chi_squared_map_from_pixelization_values_and_reconstructed_data_1d(
+        pixelization_chi_squareds = aa.util.inversion.inversion_chi_squared_map_from_pixelization_values_and_reconstructed_data_1d(
             pixelization_values=pixelization_values,
             reconstructed_data_1d=reconstructed_data_1d,
             noise_map_1d=noise_map_1d,
@@ -297,7 +297,7 @@ def test__pixelization_not_perfect_fit__quantities_like_residuals_non_zero(self)
             [6, 7, 8],
         ]
 
-        pixelization_chi_squareds = aa.util.inversion.pixelization_chi_squared_map_from_pixelization_values_and_reconstructed_data_1d(
+        pixelization_chi_squareds = aa.util.inversion.inversion_chi_squared_map_from_pixelization_values_and_reconstructed_data_1d(
             pixelization_values=pixelization_values,
             reconstructed_data_1d=reconstructed_data_1d,
             noise_map_1d=noise_map_1d,
@@ -317,7 +317,7 @@ def test__pixelization_not_perfect_fit__quantities_like_residuals_non_zero(self)
             [6, 7, 8],
         ]
 
-        pixelization_chi_squareds = aa.util.inversion.pixelization_chi_squared_map_from_pixelization_values_and_reconstructed_data_1d(
+        pixelization_chi_squareds = aa.util.inversion.inversion_chi_squared_map_from_pixelization_values_and_reconstructed_data_1d(
             pixelization_values=pixelization_values,
             reconstructed_data_1d=reconstructed_data_1d,
             noise_map_1d=noise_map_1d,