Feature/unconvolved images

autolens/aggregator/subplot.py

@@ -1,6 +1,15 @@
 from enum import Enum
 
 
+class FITSGalaxyImages(Enum):
+    """
+    The HDUs that can be extracted from the fit.fits file.
+    """
+
+    lens_light_image = "GALAXY_0"
+    lensed_source_image = "GALAXY_1"
+
+
 class FITSModelGalaxyImages(Enum):
     """
     The HDUs that can be extracted from the fit.fits file.

autolens/config/visualize/plots.yaml

@@ -11,7 +11,7 @@
 # These can be disabled to save on hard-disk space but will lead to certain database functionality being disabled.
 
 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.
+fits_are_zoomed: false                     # 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.)?
@@ -26,7 +26,8 @@ fit:                                       # Settings for plots of all fits (e.g
   subplot_of_planes: false                 # Plot subplot of the model-image, subtracted image and other quantities of each plane?
   subplot_galaxies_images: false              # Plot subplot of the image of each plane in the model?
   fits_fit: false                          # Output a .fits file containing the fit model data, residual map, normalized residual map and chi-squared?
-  fits_model_galaxy_images : false         # Output a .fits file containing the model images of every galaxy?
+  fits_galaxy_images : false               # Output a .fits file containing the images (e.g. without PSF convolution) of every galaxy?
+  fits_model_galaxy_images : false         # Output a .fits file containing the model images (e.g. with PSF convolution) of every galaxy?
 
 fit_imaging: {}                            # Settings for plots of fits to imaging datasets (e.g. FitImagingPlotter).
 

autolens/imaging/fit_imaging.py

@@ -67,7 +67,7 @@ def __init__(
 
         super().__init__(dataset=dataset, dataset_model=dataset_model, xp=xp)
         AbstractFitInversion.__init__(
-            self=self, model_obj=tracer, settings_inversion=settings_inversion
+            self=self, model_obj=tracer, settings_inversion=settings_inversion,xp=xp
         )
 
         self.tracer = tracer
@@ -151,10 +151,35 @@ def model_data(self) -> aa.Array2D:
 
         if self.perform_inversion:
 
-            return self.blurred_image + self.inversion.mapped_reconstructed_data
+            return self.blurred_image + self.inversion.mapped_reconstructed_operated_data
 
         return self.blurred_image
 
+    @property
+    def galaxy_image_dict(self) -> Dict[ag.Galaxy, np.ndarray]:
+        """
+        A dictionary which associates every galaxy in the tracer before operation (e.g. no PSF convolution
+        or NUFFT performed).
+
+        This image is the image of the sum of:
+
+        - The images of all ordinary light profiles in that plane summed before any operation is performed on them.
+        - The images of all linear objects (e.g. linear light profiles / pixelizations), where the images are solved
+          for first via the inversion.
+
+        This dictionary is used to output to .fits file the galaxy images.
+        """
+
+        galaxy_image_2d_dict = self.tracer.galaxy_image_2d_dict_from(
+            grid=self.grids.lp,
+        )
+
+        galaxy_linear_obj_image_dict = self.galaxy_linear_obj_data_dict_from(
+            use_operated=False,
+        )
+
+        return {**galaxy_image_2d_dict, **galaxy_linear_obj_image_dict}
+
     @property
     def galaxy_model_image_dict(self) -> Dict[ag.Galaxy, np.ndarray]:
         """
@@ -177,7 +202,7 @@ def galaxy_model_image_dict(self) -> Dict[ag.Galaxy, np.ndarray]:
         )
 
         galaxy_linear_obj_image_dict = self.galaxy_linear_obj_data_dict_from(
-            use_image=True
+            use_operated=True,
         )
 
         return {**galaxy_blurred_image_2d_dict, **galaxy_linear_obj_image_dict}

autolens/imaging/plot/fit_imaging_plotters.py

@@ -789,7 +789,7 @@ def subplot_mappings_of_plane(
                 inversion_plotter.visuals_2d.indexes = indexes
 
                 inversion_plotter.figures_2d_of_pixelization(
-                    pixelization_index=pixelization_index, reconstructed_image=True
+                    pixelization_index=pixelization_index, reconstructed_operated_data=True
                 )
 
                 self.visuals_2d.source_plane_mesh_indexes = [

autolens/interferometer/fit_interferometer.py

@@ -75,7 +75,7 @@ def __init__(
             xp=xp,
         )
         AbstractFitInversion.__init__(
-            self=self, model_obj=tracer, settings_inversion=settings_inversion
+            self=self, model_obj=tracer, settings_inversion=settings_inversion, xp=xp
         )
 
         self.preloads = preloads
@@ -152,12 +152,15 @@ def model_data(self) -> aa.Visibilities:
         """
 
         if self.perform_inversion:
-            return self.profile_visibilities + self.inversion.mapped_reconstructed_data
+            return (
+                self.profile_visibilities
+                + self.inversion.mapped_reconstructed_operated_data
+            )
 
         return self.profile_visibilities
 
     @property
-    def galaxy_model_image_dict(self) -> Dict[ag.Galaxy, np.ndarray]:
+    def galaxy_image_dict(self) -> Dict[ag.Galaxy, np.ndarray]:
         """
         A dictionary which associates every galaxy in the tracer with its `image`.
 
@@ -170,15 +173,13 @@ def galaxy_model_image_dict(self) -> Dict[ag.Galaxy, np.ndarray]:
         For modeling, this dictionary is used to set up the `adapt_images` that adapt certain pixelizations to the
         data being fitted.
         """
-        galaxy_model_image_dict = self.tracer.galaxy_image_2d_dict_from(
-            grid=self.grids.lp
-        )
+        galaxy_image_dict = self.tracer.galaxy_image_2d_dict_from(grid=self.grids.lp)
 
         galaxy_linear_obj_image_dict = self.galaxy_linear_obj_data_dict_from(
-            use_image=True
+            use_operated=False
         )
 
-        return {**galaxy_model_image_dict, **galaxy_linear_obj_image_dict}
+        return {**galaxy_image_dict, **galaxy_linear_obj_image_dict}
 
     @property
     def galaxy_model_visibilities_dict(self) -> Dict[ag.Galaxy, np.ndarray]:
@@ -197,7 +198,7 @@ def galaxy_model_visibilities_dict(self) -> Dict[ag.Galaxy, np.ndarray]:
         )
 
         galaxy_linear_obj_visibilities_dict = self.galaxy_linear_obj_data_dict_from(
-            use_image=False
+            use_operated=True
         )
 
         return {**galaxy_model_visibilities_dict, **galaxy_linear_obj_visibilities_dict}

autolens/interferometer/plot/fit_interferometer_plotters.py

@@ -284,7 +284,7 @@ def figures_2d(
                 inversion_plotter = self.inversion_plotter_of_plane(
                     plane_index=plane_index
                 )
-                inversion_plotter.figures_2d(reconstructed_image=True)
+                inversion_plotter.figures_2d(reconstructed_operated_data=True)
 
         if dirty_model_image:
             self.mat_plot_2d.plot_array(
@@ -467,7 +467,7 @@ def subplot_mappings_of_plane(
             inversion_plotter.visuals_2d.radial_critical_curves = None
 
             inversion_plotter.figures_2d_of_pixelization(
-                pixelization_index=pixelization_index, reconstructed_image=True
+                pixelization_index=pixelization_index, reconstructed_operated_data=True
             )
 
             self.visuals_2d.source_plane_mesh_indexes = [
@@ -516,7 +516,7 @@ def subplot_fit_real_space(self):
             inversion_plotter = self.inversion_plotter_of_plane(plane_index=1)
 
             inversion_plotter.figures_2d_of_pixelization(
-                pixelization_index=0, reconstructed_image=True
+                pixelization_index=0, reconstructed_operated_data=True
             )
             inversion_plotter.figures_2d_of_pixelization(
                 pixelization_index=0, reconstruction=True

test_autolens/analysis/test_analysis.py

@@ -121,7 +121,7 @@ def test__use_border_relocator__determines_if_border_pixel_relocation_is_used(
 
     grid = fit.inversion.linear_obj_list[0].source_plane_data_grid.over_sampled
 
-    assert grid[2] == pytest.approx([-82.89544515, 52.7491249], 1.0e-4)
+    assert grid[2] == pytest.approx([-82.991148773, 52.81254921], 1.0e-4)
 
 
 def test__modify_before_fit__inversion_no_positions_likelihood__raises_exception(

test_autolens/analysis/test_result.py

@@ -164,7 +164,7 @@ def test__source_plane_centre(analysis_imaging_7x7):
     )
 
     assert result.source_plane_centre_from().in_list[0] == pytest.approx(
-        (0.916666, -0.916666), 1.0e-4
+        (-0.916666673333, -0.916666), 1.0e-4
     )
 
 

test_autolens/config/general.yaml

@@ -15,6 +15,7 @@ inversion:
   use_positive_only_solver: false    # If True, inversion's use a positive-only linear algebra solver by default, which is slower but prevents unphysical negative values in the reconstructed solutuion.
   no_regularization_add_to_curvature_diag_value: 1.0e-8 # The default value added to the curvature matrix's diagonal when regularization is not applied to a linear object, which prevents inversion's failing due to the matrix being singular.
   positive_only_uses_p_initial: false  # If True, the positive-only solver of an inversion's uses an initial guess of the reconstructed data's values as which values should be positive, speeding up the solver.
+  use_border_relocator: false          # If True, by default a pixelization's border is used to relocate all pixels outside its border to the border.
 numba:
   cache: true
   nopython: true

test_autolens/config/visualize.yaml

@@ -50,13 +50,15 @@ mat_wrap_2d:
       marker: .
       s: 17
 plots:
+  fits_are_zoomed: true
   dataset:
     subplot_dataset: true
   fit:
     subplot_fit: true
     subplots_of_planes: false
     fits_fit: true                          # Output a .fits file containing the fit model data, residual map, normalized residual map and chi-squared?
-    fits_model_galaxy_images : true         # Output a .fits file containing the model images of every galaxy?
+    fits_galaxy_images : true                # Output a .fits file containing the images (e.g. without PSF convolution) of every galaxy?
+    fits_model_galaxy_images : true          # Output a .fits file containing the model images (e.g. with PSF convolution) of every galaxy?
   fit_imaging: {}
   fit_interferometer:
     subplot_fit_real_space: true

test_autolens/imaging/test_fit_imaging.py

@@ -155,7 +155,7 @@ def test__fit_figure_of_merit(masked_imaging_7x7, masked_imaging_covariance_7x7)
     fit = al.FitImaging(dataset=masked_imaging_7x7, tracer=tracer)
 
     assert fit.perform_inversion is True
-    assert fit.figure_of_merit == pytest.approx(-22.79906, 1.0e-4)
+    assert fit.figure_of_merit == pytest.approx(-22.801816971, 1.0e-4)
 
     g0_operated = al.Galaxy(
         redshift=0.5,
@@ -341,7 +341,7 @@ def test__fit_figure_of_merit__sub_2(image_7x7, psf_3x3, noise_map_7x7, mask_2d_
     fit = al.FitImaging(dataset=masked_imaging_7x7, tracer=tracer)
 
     assert fit.perform_inversion is True
-    assert fit.figure_of_merit == pytest.approx( -22.8021113243233, 1.0e-4)
+    assert fit.figure_of_merit == pytest.approx( -22.8051006673, 1.0e-4)
 
     g0_operated = al.Galaxy(
         redshift=0.5,
@@ -459,7 +459,77 @@ def test__fit__model_dataset__grid_offset__handles_special_behaviour(masked_imag
     fit = al.FitImaging(dataset=masked_imaging_7x7, tracer=tracer,
                         dataset_model=al.DatasetModel(grid_offset=(1.0, 2.0))
                         )
-    assert fit.figure_of_merit == pytest.approx(-22.8021112977481, 1.0e-4)
+    assert fit.figure_of_merit == pytest.approx(-22.8051006, 1.0e-4)
+
+
+def test__galaxy_image_dict(masked_imaging_7x7):
+
+    # Normal Light Profiles Only
+
+    g0 = al.Galaxy(
+        redshift=0.5,
+        bulge=al.lp.Sersic(centre=(0.05, 0.05), intensity=1.0),
+        mass_profile=al.mp.IsothermalSph(centre=(0.05, 0.05), einstein_radius=1.0),
+    )
+    g1 = al.Galaxy(redshift=1.0, bulge=al.lp.Sersic(centre=(0.05, 0.05), intensity=1.0))
+    g2 = al.Galaxy(redshift=1.0)
+
+    tracer = al.Tracer(galaxies=[g0, g1, g2])
+
+    fit = al.FitImaging(dataset=masked_imaging_7x7, tracer=tracer)
+
+    image_2d_list = tracer.image_2d_list_from(
+        grid=masked_imaging_7x7.grids.lp,
+    )
+
+    assert fit.galaxy_image_dict[g0] == pytest.approx(
+        image_2d_list[0].array, 1.0e-4
+    )
+    assert fit.galaxy_image_dict[g1] == pytest.approx(
+        image_2d_list[1].array, 1.0e-4
+    )
+    assert (fit.galaxy_image_dict[g2] == np.zeros(9)).all()
+
+    # Normal light + Linear Light PRofiles + Pixelization + Regularizaiton
+
+    g0_linear = al.Galaxy(
+        redshift=0.5,
+        bulge=al.lp_linear.Sersic(),
+        mass_profile=al.mp.IsothermalSph(einstein_radius=1.0),
+    )
+
+    pixelization = al.Pixelization(
+        mesh=al.mesh.RectangularUniform(shape=(3, 3)),
+        regularization=al.reg.Constant(coefficient=1.0),
+    )
+
+    galaxy_pix_0 = al.Galaxy(redshift=1.0, pixelization=pixelization)
+
+    galaxy_pix_1 = al.Galaxy(redshift=1.0, pixelization=pixelization)
+
+    tracer = al.Tracer(
+        galaxies=[g0, g0_linear, g2, galaxy_pix_0, galaxy_pix_1]
+    )
+
+    masked_imaging_7x7.data[0] = 3.0
+
+    fit = al.FitImaging(dataset=masked_imaging_7x7, tracer=tracer)
+
+    assert fit.galaxy_image_dict[g0] == pytest.approx(
+        image_2d_list[0].array, 1.0e-4
+    )
+
+    assert fit.galaxy_image_dict[g0_linear][4] == pytest.approx(
+        -27.926926, 1.0e-4
+    )
+
+    assert fit.galaxy_image_dict[galaxy_pix_0][4] == pytest.approx(
+        0.94954435552, 1.0e-4
+    )
+    assert fit.galaxy_image_dict[galaxy_pix_1][4] == pytest.approx(
+        0.94954436798, 1.0e-4
+    )
+    assert (fit.galaxy_image_dict[g2] == np.zeros(9)).all()
 
 
 def test__galaxy_model_image_dict(masked_imaging_7x7):
@@ -688,7 +758,7 @@ def test__model_images_of_planes_list(masked_imaging_7x7_sub_2):
         1.0e-4,
     )
 
-    assert fit.model_images_of_planes_list[2][0] == pytest.approx(1.56110288, 1.0e-4)
+    assert fit.model_images_of_planes_list[2][0] == pytest.approx(1.5614163282, 1.0e-4)
 
 
 def test__subtracted_images_of_planes_list(masked_imaging_7x7_no_blur, masked_imaging_7x7_no_blur_sub_2):