Feature/zoom array

autoarray/__init__.py

@@ -53,6 +53,7 @@
 from .mask.derive.mask_2d import DeriveMask2D
 from .mask.derive.grid_1d import DeriveGrid1D
 from .mask.derive.grid_2d import DeriveGrid2D
+from .mask.derive.zoom_2d import Zoom2D
 from .mask.mask_1d import Mask1D
 from .mask.mask_2d import Mask2D
 from .operators.convolver import Convolver

autoarray/config/visualize/general.yaml

@@ -4,7 +4,6 @@ general:
   log10_min_value: 1.0e-4               # If negative values are being plotted on a log10 scale, values below this value are rounded up to it (e.g. to remove negative values).
   log10_max_value: 1.0e99               # If positive values are being plotted on a log10 scale, values above this value are rounded down to it (e.g. to prevent white blobs).
   zoom_around_mask: true                # If True, plots of data structures with a mask automatically zoom in the masked region.
-  disable_zoom_for_fits: true           # If True, the zoom-in around the masked region is disabled when outputting .fits files, which is useful to retain the same dimensions as the input data.
 inversion:
   reconstruction_vmax_factor: 0.5
   total_mappings_pixels : 8                 # The number of source pixels used when plotting the subplot_mappings of a pixelization.

autoarray/config/visualize/plots.yaml

@@ -3,6 +3,9 @@
 # For example, if `plots: fit: subplot_fit=True``, the ``fit_dataset.png`` subplot file will 
 # be plotted every time visualization is performed.
 
+subplot_format: [png]                      # Output format of all subplots, can be png, pdf or both (e.g. [png, pdf])
+fits_are_zoomed: true                      # If true, output .fits files are zoomed in on the center of the unmasked region image, saving hard-disk space.
+
 dataset:                                   # Settings for plots of all datasets (e.g. ImagingPlotter, InterferometerPlotter).
   subplot_dataset: true                    # Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?
 imaging:                                   # Settings for plots of imaging datasets (e.g. ImagingPlotter)

autoarray/geometry/geometry_util.py

@@ -182,7 +182,7 @@ def convert_pixel_scales_2d(pixel_scales: ty.PixelScales) -> Tuple[float, float]
 
 @numba_util.jit()
 def central_pixel_coordinates_2d_from(
-    shape_native: Tuple[int, int]
+    shape_native: Tuple[int, int],
 ) -> Tuple[float, float]:
     """
     Returns the central pixel coordinates of a 2D geometry (and therefore a 2D data structure like an ``Array2D``)
@@ -737,7 +737,7 @@ def grid_pixel_centres_2d_from(
 
 
 def extent_symmetric_from(
-    extent: Tuple[float, float, float, float]
+    extent: Tuple[float, float, float, float],
 ) -> Tuple[float, float, float, float]:
     """
     Given an input extent of the form (x_min, x_max, y_min, y_max), this function returns an extent which is

autoarray/inversion/inversion/abstract.py

@@ -509,12 +509,12 @@ def reconstruction(self) -> np.ndarray:
 
                 solutions = np.zeros(np.shape(self.curvature_reg_matrix)[0])
 
-                solutions[
-                    values_to_solve
-                ] = inversion_util.reconstruction_positive_only_from(
-                    data_vector=data_vector_input,
-                    curvature_reg_matrix=curvature_reg_matrix_input,
-                    settings=self.settings,
+                solutions[values_to_solve] = (
+                    inversion_util.reconstruction_positive_only_from(
+                        data_vector=data_vector_input,
+                        curvature_reg_matrix=curvature_reg_matrix_input,
+                        settings=self.settings,
+                    )
                 )
                 return solutions
             else:

autoarray/inversion/inversion/imaging/abstract.py

@@ -104,11 +104,13 @@ def operated_mapping_matrix_list(self) -> List[np.ndarray]:
         """
 
         return [
-            self.convolver.convolve_mapping_matrix(
-                mapping_matrix=linear_obj.mapping_matrix
+            (
+                self.convolver.convolve_mapping_matrix(
+                    mapping_matrix=linear_obj.mapping_matrix
+                )
+                if linear_obj.operated_mapping_matrix_override is None
+                else self.linear_func_operated_mapping_matrix_dict[linear_obj]
             )
-            if linear_obj.operated_mapping_matrix_override is None
-            else self.linear_func_operated_mapping_matrix_dict[linear_obj]
             for linear_obj in self.linear_obj_list
         ]
 
@@ -156,9 +158,9 @@ def linear_func_operated_mapping_matrix_dict(self) -> Dict:
                     mapping_matrix=linear_func.mapping_matrix
                 )
 
-            linear_func_operated_mapping_matrix_dict[
-                linear_func
-            ] = operated_mapping_matrix
+            linear_func_operated_mapping_matrix_dict[linear_func] = (
+                operated_mapping_matrix
+            )
 
         return linear_func_operated_mapping_matrix_dict
 

autoarray/inversion/pixelization/border_relocator.py

@@ -117,12 +117,12 @@ def sub_border_pixel_slim_indexes_from(
             int(border_pixel)
         ]
 
-        sub_border_pixels[
-            border_1d_index
-        ] = grid_2d_util.furthest_grid_2d_slim_index_from(
-            grid_2d_slim=sub_grid_2d_slim,
-            slim_indexes=sub_border_pixels_of_border_pixel,
-            coordinate=mask_centre,
+        sub_border_pixels[border_1d_index] = (
+            grid_2d_util.furthest_grid_2d_slim_index_from(
+                grid_2d_slim=sub_grid_2d_slim,
+                slim_indexes=sub_border_pixels_of_border_pixel,
+                coordinate=mask_centre,
+            )
         )
 
     return sub_border_pixels

autoarray/inversion/pixelization/mesh/mesh_util.py

@@ -7,7 +7,7 @@
 
 @numba_util.jit()
 def rectangular_neighbors_from(
-    shape_native: Tuple[int, int]
+    shape_native: Tuple[int, int],
 ) -> Tuple[np.ndarray, np.ndarray]:
     """
     Returns the 4 (or less) adjacent neighbors of every pixel on a rectangular pixelization as an ndarray of shape

autoarray/inversion/plot/inversion_plotters.py

@@ -215,10 +215,9 @@ def figures_2d_of_pixelization(
                         "inversion"
                     ]["reconstruction_vmax_factor"]
 
-                    self.mat_plot_2d.cmap.kwargs[
-                        "vmax"
-                    ] = reconstruction_vmax_factor * np.max(
-                        self.inversion.reconstruction
+                    self.mat_plot_2d.cmap.kwargs["vmax"] = (
+                        reconstruction_vmax_factor
+                        * np.max(self.inversion.reconstruction)
                     )
                     vmax_custom = True