Feature/pixelization log10

autoarray/config/visualize/plots.yaml

@@ -12,6 +12,7 @@ imaging:                                   # Settings for plots of imaging datas
    psf: false
 fit:                                       # Settings for plots of all fits (e.g. FitImagingPlotter, FitInterferometerPlotter).
   subplot_fit: true                        # Plot subplot of all fit quantities for any dataset (e.g. the model data, residual-map, etc.)?
+  subplot_fit_log10: true                  # Plot subplot of all fit quantities for any dataset using log10 color maps (e.g. the model data, residual-map, etc.)?
   all_at_end_png: true                     # Plot all individual plots listed below as .png (even if False)?
   all_at_end_fits: true                    # Plot all individual plots listed below as .fits (even if False)?
   all_at_end_pdf: false                    # Plot all individual plots listed below as publication-quality .pdf (even if False)?
@@ -29,6 +30,7 @@ inversion:                                 # Settings for plots of inversions (e
   all_at_end_png: true                     # Plot all individual plots listed below as .png (even if False)?
   all_at_end_fits: true                    # Plot all individual plots listed below as .fits (even if False)?
   all_at_end_pdf: false                    # Plot all individual plots listed below as publication-quality .pdf (even if False)?
+  data_subtracted: false                   # Plot individual plots of the data with the other inversion linear objects subtracted?
   errors: false                            # Plot image of the errors of every mesh-pixel reconstructed value?
   mesh_pixels_per_image_pixels : false     # Plot the number of image-plane mesh pixels per masked data pixels?
   reconstructed_image: false               # Plot image of the reconstructed data (e.g. in the image-plane)?

autoarray/inversion/inversion/abstract.py

@@ -637,6 +637,34 @@ def mapped_reconstructed_image(self) -> Array2D:
         """
         return sum(self.mapped_reconstructed_image_dict.values())
 
+    @cached_property
+    def data_subtracted_dict(self) -> Dict[LinearObj, Array2D]:
+        """
+        Returns a dictionary of the data subtracted by the reconstructed images of combinations of all but one of the
+        linear objects the inversion.
+
+        This produces images of the data showing what each linear object is actually fitted to, after accounting for
+        the signal in the other linear objects.
+
+        Returns
+        -------
+        A dictionary of the data subtracted by the reconstructed images of combinations of all but one of the
+        linear objects the inversion.
+        """
+
+        data_subtracted_dict = {}
+
+        for linear_obj in self.linear_obj_list:
+            data_subtracted_dict[linear_obj] = copy.copy(self.data)
+
+            for linear_obj_other in self.linear_obj_list:
+                if linear_obj != linear_obj_other:
+                    data_subtracted_dict[
+                        linear_obj
+                    ] -= self.mapped_reconstructed_image_dict[linear_obj_other]
+
+        return data_subtracted_dict
+
     @cached_property
     @profile_func
     def regularization_term(self) -> float:

autoarray/inversion/plot/inversion_plotters.py

@@ -103,6 +103,7 @@ def figures_2d(self, reconstructed_image: bool = False):
     def figures_2d_of_pixelization(
         self,
         pixelization_index: int = 0,
+        data_subtracted: bool = False,
         reconstructed_image: bool = False,
         reconstruction: bool = False,
         errors: bool = False,
@@ -143,6 +144,17 @@ def figures_2d_of_pixelization(
 
         mapper_plotter = self.mapper_plotter_from(mapper_index=pixelization_index)
 
+        if data_subtracted:
+            array = self.inversion.data_subtracted_dict[mapper_plotter.mapper]
+
+            self.mat_plot_2d.plot_array(
+                array=array,
+                visuals_2d=self.get_visuals_2d_for_data(),
+                auto_labels=AutoLabels(
+                    title="Data Subtracted", filename="data_subtracted"
+                ),
+            )
+
         if reconstructed_image:
             array = self.inversion.mapped_reconstructed_image_dict[
                 mapper_plotter.mapper
@@ -248,10 +260,8 @@ def subplot_of_mapper(
 
         self.include_2d._mapper_image_plane_mesh_grid = False
 
-        self.mat_plot_2d.plot_array(
-            array=self.inversion.data,
-            visuals_2d=self.get_visuals_2d_for_data(),
-            auto_labels=AutoLabels(title=f" Data"),
+        self.figures_2d_of_pixelization(
+            pixelization_index=mapper_index, data_subtracted=True
         )
 
         self.figures_2d_of_pixelization(
@@ -274,45 +284,55 @@ def subplot_of_mapper(
         )
 
         self.mat_plot_2d.use_log10 = False
-        self.mat_plot_2d.contour = contour_original
 
-        self.include_2d._mapper_image_plane_mesh_grid = True
         self.figures_2d_of_pixelization(
-            pixelization_index=mapper_index, reconstructed_image=True
+            pixelization_index=mapper_index, reconstruction=True
         )
 
-        self.include_2d._mapper_image_plane_mesh_grid = False
+        self.set_title(label="Source Reconstruction (Unzoomed)")
         self.figures_2d_of_pixelization(
-            pixelization_index=mapper_index, mesh_pixels_per_image_pixels=True
+            pixelization_index=mapper_index,
+            reconstruction=True,
+            zoom_to_brightest=False,
         )
+        self.set_title(label=None)
 
-        self.include_2d._mapper_image_plane_mesh_grid = mapper_image_plane_mesh_grid
+        self.mat_plot_2d.use_log10 = True
+
+        self.set_title(label="Source Reconstruction (log10)")
 
         self.figures_2d_of_pixelization(
             pixelization_index=mapper_index, reconstruction=True
         )
 
-        self.set_title(label="Source Reconstruction (Unzoomed)")
+        self.set_title(label="Source Reconstruction (Unzoomed log10)")
         self.figures_2d_of_pixelization(
             pixelization_index=mapper_index,
             reconstruction=True,
             zoom_to_brightest=False,
         )
         self.set_title(label=None)
 
-        self.figures_2d_of_pixelization(pixelization_index=mapper_index, errors=True)
+        self.mat_plot_2d.use_log10 = False
+        self.mat_plot_2d.contour = contour_original
+
+        self.include_2d._mapper_image_plane_mesh_grid = True
+        self.figures_2d_of_pixelization(
+            pixelization_index=mapper_index, reconstructed_image=True
+        )
+
+        self.include_2d._mapper_image_plane_mesh_grid = False
+        self.figures_2d_of_pixelization(
+            pixelization_index=mapper_index, mesh_pixels_per_image_pixels=True
+        )
+
+        self.include_2d._mapper_image_plane_mesh_grid = mapper_image_plane_mesh_grid
+
         self.set_title(label="Errors (Unzoomed)")
         self.figures_2d_of_pixelization(
             pixelization_index=mapper_index, errors=True, zoom_to_brightest=False
         )
 
-        try:
-            self.figures_2d_of_pixelization(
-                pixelization_index=mapper_index, regularization_weights=True
-            )
-        except IndexError:
-            pass
-
         self.set_title(label="Regularization Weights (Unzoomed)")
         try:
             self.figures_2d_of_pixelization(

autoarray/plot/mat_plot/two_d.py

@@ -239,6 +239,9 @@ def plot_array(
         if array is None or np.all(array == 0):
             return
 
+        if self.use_log10 and (np.all(array == array[0]) or np.all(array < 0)):
+            return
+
         if array.pixel_scales is None and self.units.use_scaled:
             raise exc.ArrayException(
                 "You cannot plot an array using its scaled unit_label if the input array does not have "
@@ -624,6 +627,7 @@ def _plot_delaunay_mapper(
             colorbar_tickparams=self.colorbar_tickparams,
             aspect=aspect_inv,
             ax=ax,
+            use_log10=self.use_log10,
         )
 
         self.title.set(auto_title=auto_labels.title)
@@ -690,6 +694,7 @@ def _plot_voronoi_mapper(
                 colorbar=self.colorbar,
                 colorbar_tickparams=self.colorbar_tickparams,
                 ax=ax,
+                use_log10=self.use_log10,
             )
 
         else:
@@ -702,6 +707,7 @@ def _plot_voronoi_mapper(
                 colorbar_tickparams=self.colorbar_tickparams,
                 aspect=aspect_inv,
                 ax=ax,
+                use_log10=self.use_log10,
             )
 
         self.title.set(auto_title=auto_labels.title)

autoarray/plot/wrap/base/colorbar.py

@@ -161,7 +161,13 @@ def set(
         return cb
 
     def set_with_color_values(
-        self, units: Units, cmap: str, color_values: np.ndarray, ax=None, norm=None
+        self,
+        units: Units,
+        cmap: str,
+        color_values: np.ndarray,
+        ax=None,
+        norm=None,
+        use_log10: bool = False,
     ):
         """
         Set the figure's colorbar using an array of already known color values.
@@ -178,15 +184,16 @@ def set_with_color_values(
             The values of the pixels on the Voronoi mesh which are used to create the colorbar.
         """
 
-        mappable = cm.ScalarMappable(cmap=cmap)
+        mappable = cm.ScalarMappable(norm=norm, cmap=cmap)
         mappable.set_array(color_values)
 
-        manual_tick_values = self.tick_values_from(norm=norm)
-        manual_tick_labels = self.tick_labels_from(
-            manual_tick_values=manual_tick_values, units=units
+        tick_values = self.tick_values_from(norm=norm, use_log10=use_log10)
+        tick_labels = self.tick_labels_from(
+            manual_tick_values=tick_values,
+            units=units,
         )
 
-        if manual_tick_values is None and manual_tick_labels is None:
+        if tick_values is None and tick_labels is None:
             cb = plt.colorbar(
                 mappable=mappable,
                 ax=ax,
@@ -196,11 +203,11 @@ def set_with_color_values(
             cb = plt.colorbar(
                 mappable=mappable,
                 ax=ax,
-                ticks=manual_tick_values,
+                ticks=tick_values,
                 **self.config_dict,
             )
             cb.ax.set_yticklabels(
-                labels=manual_tick_labels, va=self.manual_alignment or "center"
+                labels=tick_labels, va=self.manual_alignment or "center"
             )
 
         return cb

autoarray/plot/wrap/two_d/interpolated_reconstruction.py

@@ -39,6 +39,7 @@ def imshow_reconstruction(
         colorbar_tickparams: wb.ColorbarTickParams = None,
         aspect=None,
         ax=None,
+        use_log10: bool = False,
     ):
         """
         Given a `Mapper` and a corresponding array of `pixel_values` (e.g. the reconstruction values of a Delaunay
@@ -76,6 +77,10 @@ def imshow_reconstruction(
         if pixel_values is None:
             return
 
+        interpolation_array = mapper.interpolated_array_from(values=pixel_values)
+
+        norm = cmap.norm_from(array=interpolation_array, use_log10=use_log10)
+
         vmin = cmap.vmin_from(array=pixel_values)
         vmax = cmap.vmax_from(array=pixel_values)
 
@@ -86,16 +91,20 @@ def imshow_reconstruction(
 
         if colorbar is not None:
             colorbar = colorbar.set_with_color_values(
-                units=units, cmap=cmap, color_values=color_values, ax=ax
+                units=units,
+                cmap=cmap,
+                norm=norm,
+                color_values=color_values,
+                ax=ax,
+                use_log10=use_log10,
             )
             if colorbar is not None and colorbar_tickparams is not None:
                 colorbar_tickparams.set(cb=colorbar)
 
-        interpolation_array = mapper.interpolated_array_from(values=pixel_values)
-
         plt.imshow(
             X=interpolation_array.native,
             cmap=cmap,
+            norm=norm,
             extent=mapper.source_plane_mesh_grid.geometry.extent_square,
             aspect=aspect,
         )

autoarray/plot/wrap/two_d/voronoi_drawer.py

@@ -31,6 +31,7 @@ def draw_voronoi_pixels(
         colorbar: Optional[wb.Colorbar],
         colorbar_tickparams: Optional[wb.ColorbarTickParams] = None,
         ax=None,
+        use_log10: bool = False,
     ):
         """
         Draws the Voronoi pixels of the input `mapper` using its `mesh_grid` which contains the (y,x)
@@ -46,6 +47,12 @@ def draw_voronoi_pixels(
             The colormap used to plot each Voronoi cell.
         colorbar
             The `Colorbar` object in `mat_base` used to set the colorbar of the figure the Voronoi mesh is plotted on.
+        colorbar_tickparams
+            The `ColorbarTickParams` object in `mat_base` used to set the tick labels of the colorbar.
+        ax
+            The matplotlib axis the Voronoi mesh is plotted on.
+        use_log10
+            If `True`, the colorbar is plotted using a log10 scale.
         """
 
         if ax is None:
@@ -54,6 +61,12 @@ def draw_voronoi_pixels(
         regions, vertices = mesh_util.voronoi_revised_from(voronoi=mapper.voronoi)
 
         if pixel_values is not None:
+            norm = cmap.norm_from(array=pixel_values, use_log10=use_log10)
+
+            if use_log10:
+                pixel_values[pixel_values < 1e-4] = 1e-4
+                pixel_values = np.log10(pixel_values)
+
             vmin = cmap.vmin_from(array=pixel_values)
             vmax = cmap.vmax_from(array=pixel_values)
 
@@ -69,7 +82,12 @@ def draw_voronoi_pixels(
 
             if colorbar is not None:
                 cb = colorbar.set_with_color_values(
-                    units=units, cmap=cmap, color_values=color_values, ax=ax
+                    units=units,
+                    norm=norm,
+                    cmap=cmap,
+                    color_values=color_values,
+                    ax=ax,
+                    use_log10=use_log10,
                 )
 
                 if cb is not None and colorbar_tickparams is not None:

test_autoarray/inversion/inversion/test_abstract.py

@@ -469,6 +469,38 @@ def test__mapped_reconstructed_image():
     assert (inversion.mapped_reconstructed_image == 3.0 * np.ones(2)).all()
 
 
+def test__data_subtracted_dict():
+    linear_obj_0 = aa.m.MockLinearObj()
+
+    mapped_reconstructed_data_dict = {linear_obj_0: np.ones(3)}
+
+    # noinspection PyTypeChecker
+    inversion = aa.m.MockInversion(
+        data=3.0 * np.ones(3),
+        linear_obj_list=[linear_obj_0],
+        mapped_reconstructed_data_dict=mapped_reconstructed_data_dict,
+    )
+
+    assert (inversion.data_subtracted_dict[linear_obj_0] == 3.0 * np.ones(3)).all()
+
+    linear_obj_1 = aa.m.MockLinearObj()
+
+    mapped_reconstructed_data_dict = {
+        linear_obj_0: np.ones(3),
+        linear_obj_1: 2.0 * np.ones(3),
+    }
+
+    # noinspection PyTypeChecker
+    inversion = aa.m.MockInversion(
+        data=3.0 * np.ones(3),
+        linear_obj_list=[linear_obj_0, linear_obj_1],
+        mapped_reconstructed_data_dict=mapped_reconstructed_data_dict,
+    )
+
+    assert (inversion.data_subtracted_dict[linear_obj_0] == np.ones(3)).all()
+    assert (inversion.data_subtracted_dict[linear_obj_1] == 2.0 * np.ones(3)).all()
+
+
 def test__reconstruction_raises_exception_for_linalg_error():
     # noinspection PyTypeChecker
     inversion = aa.m.MockInversion(

test_autoarray/plot/wrap/two_d/test_voronoi_drawer.py

@@ -9,7 +9,7 @@ def test__draws_voronoi_pixels_for_sensible_input(voronoi_mapper_9_3x3):
     voronoi_drawer.draw_voronoi_pixels(
         mapper=voronoi_mapper_9_3x3,
         pixel_values=None,
-        units=None,
+        units=aplt.Units(),
         cmap=aplt.Cmap(),
         colorbar=None,
     )
@@ -20,7 +20,7 @@ def test__draws_voronoi_pixels_for_sensible_input(voronoi_mapper_9_3x3):
     voronoi_drawer.draw_voronoi_pixels(
         mapper=voronoi_mapper_9_3x3,
         pixel_values=values,
-        units=None,
+        units=aplt.Units(),
         cmap=aplt.Cmap(),
         colorbar=aplt.Colorbar(fraction=0.1, pad=0.05),
     )