Feature/zoom array

autolens/aggregator/subplot.py

@@ -17,7 +17,7 @@ class FITSFit(Enum):
     The HDUs that can be extracted from the fit.fits file.
     """
 
-    model_image = "MODEL_IMAGE"
+    model_data = "MODEL_DATA"
     residual_map = "RESIDUAL_MAP"
     normalized_residual_map = "NORMALIZED_RESIDUAL_MAP"
     chi_squared_map = "CHI_SQUARED_MAP"
@@ -68,7 +68,7 @@ class SubplotFit(Enum):
     data = (0, 0)
     data_source_scale = (1, 0)
     signal_to_noise_map = (2, 0)
-    model_image = (3, 0)
+    model_data = (3, 0)
     lens_light_model = (1, 0)
     lens_light_subtracted_image = (1, 1)
     source_model_image = (1, 2)
@@ -89,7 +89,7 @@ class SubplotFitLog10(Enum):
     data = (0, 0)
     data_source_scale = (1, 0)
     signal_to_noise_map = (2, 0)
-    model_image = (3, 0)
+    model_data = (3, 0)
     lens_light_model = (1, 0)
     lens_light_subtracted_image = (1, 1)
     source_model_image = (1, 2)

autolens/analysis/plotter_interface.py

@@ -65,14 +65,20 @@ def should_plot(name):
             tracer_plotter.subplot_galaxies_images()
 
         if should_plot("fits_tracer"):
+
+            zoom = aa.Zoom2D(mask=grid.mask)
+            mask = zoom.mask_2d_from(buffer=1)
+            grid_zoom = aa.Grid2D.from_mask(mask=mask)
+
+            image_list = [
+                tracer.convergence_2d_from(grid=grid_zoom).native,
+                tracer.potential_2d_from(grid=grid_zoom).native,
+                tracer.deflections_yx_2d_from(grid=grid_zoom).native[:, :, 0],
+                tracer.deflections_yx_2d_from(grid=grid_zoom).native[:, :, 1],
+            ]
+
             hdu_list = hdu_list_for_output_from(
-                values_list=[
-                    grid.mask.astype("float"),
-                    tracer.convergence_2d_from(grid=grid).native,
-                    tracer.potential_2d_from(grid=grid).native,
-                    tracer.deflections_yx_2d_from(grid=grid).native[:, :, 0],
-                    tracer.deflections_yx_2d_from(grid=grid).native[:, :, 1],
-                ],
+                values_list=[image_list[0].mask.astype("float")] + image_list,
                 ext_name_list=[
                     "mask",
                     "convergence",

autolens/config/visualize/plots.yaml

@@ -11,6 +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.
 
 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.)?
@@ -37,7 +38,7 @@ tracer:                                    # Settings for plots of tracers (e.g.
 inversion:                                 # Settings for plots of inversions (e.g. InversionPlotter).
   subplot_inversion: true                  # Plot subplot of all quantities in each inversion (e.g. reconstrucuted image, reconstruction)?
   subplot_mappings: true                   # Plot subplot of the image-to-source pixels mappings of each pixelization?
-  fits_reconstruction: false               # output reconstruction.fits containing the reconstructed pixelization and noise map on the adaptive mesh?
+  csv_reconstruction: false                # output reconstruction.csv containing the source-plane mesh y, x, reconstruction and noise map values.
 
 adapt:                                     # Settings for plots of adapt images used by adaptive pixelizations.
   subplot_adapt_images: true               # Plot subplot showing each adapt image used for adaptive pixelization?

autolens/imaging/model/visualizer.py

@@ -102,8 +102,10 @@ def visualize(
 
         tracer = fit.tracer_linear_light_profiles_to_light_profiles
 
-        extent = fit.data.extent_of_zoomed_array(buffer=0)
-        shape_native = fit.data.zoomed_around_mask(buffer=0).shape_native
+        zoom = ag.Zoom2D(mask=fit.mask)
+
+        extent = zoom.extent_from(buffer=0)
+        shape_native = zoom.shape_native
 
         grid = ag.Grid2D.from_extent(extent=extent, shape_native=shape_native)
 

autolens/imaging/plot/fit_imaging_plotters.py

@@ -80,12 +80,11 @@ def tracer_plotter(self) -> TracerPlotter:
         Returns an `TracerPlotter` corresponding to the `Tracer` in the `FitImaging`.
         """
 
-        extent = self.fit.data.extent_of_zoomed_array(buffer=0)
-        shape_native = self.fit.data.zoomed_around_mask(buffer=0).shape_native
+        zoom = aa.Zoom2D(mask=self.fit.mask)
 
         grid = aa.Grid2D.from_extent(
-            extent=extent,
-            shape_native=shape_native
+            extent=zoom.extent_from(buffer=0),
+            shape_native=zoom.shape_native
         )
 
         return TracerPlotter(

test_autolens/analysis/test_plotter_interface.py

@@ -33,7 +33,7 @@ def test__tracer(
         file_path=path.join(plot_path, "tracer.fits"), hdu=0
     )
 
-    assert image.shape == (7, 7)
+    assert image.shape == (5, 5)
 
 
 def test__image_with_positions(

test_autolens/imaging/model/test_plotter_interface_imaging.py

@@ -34,13 +34,13 @@ def test__fit_imaging(
         file_path=path.join(plot_path, "fit.fits"), hdu=0
     )
 
-    assert image.shape == (7, 7)
+    assert image.shape == (5, 5)
 
     image = al.ndarray_via_fits_from(
         file_path=path.join(plot_path, "model_galaxy_images.fits"), hdu=0
     )
 
-    assert image.shape == (7, 7)
+    assert image.shape == (5, 5)
 
 def test__fit_imaging_combined(
     fit_imaging_x2_plane_inversion_7x7, plot_path, plot_patch

test_autolens/interferometer/model/test_plotter_interface_interferometer.py

@@ -36,7 +36,7 @@ def test__fit_interferometer(
         file_path=path.join(plot_path, "model_galaxy_images.fits"), hdu=0
     )
 
-    assert image.shape == (7, 7)
+    assert image.shape == (5, 5)
 
     image = al.ndarray_via_fits_from(
         file_path=path.join(plot_path, "dirty_images.fits"), hdu=0