Feature/jax merge

autogalaxy/__init__.py

@@ -3,7 +3,6 @@
 from autoarray.dataset.imaging.dataset import Imaging  # noqa
 from autoarray.dataset.interferometer.dataset import Interferometer  # noqa
 from autoarray.dataset.dataset_model import DatasetModel
-from autoarray.dataset.over_sampling import OverSamplingDataset
 from autoarray.inversion.inversion.mapper_valued import MapperValued
 from autoarray.inversion.pixelization import mesh  # noqa
 from autoarray.inversion import regularization as reg  # noqa
@@ -35,11 +34,7 @@
 from autoarray.structures.grids.uniform_1d import Grid1D  # noqa
 from autoarray.structures.grids.uniform_2d import Grid2D  # noqa
 from autoarray.structures.grids.irregular_2d import Grid2DIrregular  # noqa
-from autoarray.structures.grids.irregular_2d import Grid2DIrregularUniform  # noqa
-from autoarray.operators.over_sampling.uniform import OverSamplingUniform  # noqa
-from autoarray.operators.over_sampling.uniform import OverSamplerUniform  # noqa
-from autoarray.operators.over_sampling.iterate import OverSamplingIterate
-from autoarray.operators.over_sampling.iterate import OverSamplerIterate
+from autoarray.operators.over_sampling.over_sampler import OverSampler  # noqa
 from autoarray.structures.mesh.rectangular_2d import Mesh2DRectangular  # noqa
 from autoarray.structures.mesh.voronoi_2d import Mesh2DVoronoi  # noqa
 from autoarray.structures.mesh.delaunay_2d import Mesh2DDelaunay  # noqa
@@ -113,4 +108,4 @@
 
 conf.instance.register(__file__)
 
-__version__ = "2024.9.21.2"
+__version__ = "2025.1.18.7"

autogalaxy/aggregator/dataset_model.py

@@ -47,16 +47,17 @@ def _dataset_model_from(
         except AttributeError:
             dataset_model = None
 
-    if len(fit.children) > 0:
-        logger.info(
-            """
-            Using database for a fit with multiple summed Analysis objects.
-
-            DatasetModel objects do not fully support this yet (e.g. variables across Analysis objects may not be correct)
-            so proceed with caution!
-            """
-        )
-
-        return [dataset_model] * len(fit.children)
+    if fit.children is not None:
+        if len(fit.children) > 0:
+            logger.info(
+                """
+                Using database for a fit with multiple summed Analysis objects.
+
+                DatasetModel objects do not fully support this yet (e.g. variables across Analysis objects may not be correct)
+                so proceed with caution!
+                """
+            )
+
+            return [dataset_model] * len(fit.children)
 
     return [dataset_model]

autogalaxy/aggregator/galaxies.py

@@ -42,13 +42,15 @@ def _galaxies_from(fit: af.Fit, instance: af.ModelInstance) -> List[Galaxy]:
         galaxies = instance.galaxies
 
         if hasattr(instance, "extra_galaxies"):
-            galaxies = galaxies + fit.instance.extra_galaxies
+            if fit.instance.extra_galaxies is not None:
+                galaxies = galaxies + fit.instance.extra_galaxies
 
     else:
         galaxies = fit.instance.galaxies
 
         if hasattr(fit.instance, "extra_galaxies"):
-            galaxies = galaxies + fit.instance.extra_galaxies
+            if fit.instance.extra_galaxies is not None:
+                galaxies = galaxies + fit.instance.extra_galaxies
 
     if fit.children is not None:
         if len(fit.children) > 0:

autogalaxy/aggregator/imaging/imaging.py

@@ -50,20 +50,40 @@ def _imaging_from(
         except AttributeError:
             psf = None
 
-        over_sampling = fit.value(name="dataset.over_sampling")
-
         dataset = aa.Imaging(
             data=data,
             noise_map=noise_map,
             psf=psf,
-            over_sampling=over_sampling,
             check_noise_map=False,
         )
 
         mask = aa.Mask2D.from_primary_hdu(primary_hdu=fit.value(name="dataset.mask"))
 
         dataset = dataset.apply_mask(mask=mask)
 
+        try:
+            over_sample_size_lp = aa.Array2D.from_primary_hdu(
+                primary_hdu=fit.value(name="dataset.over_sample_size_lp")
+            ).native
+            over_sample_size_lp = over_sample_size_lp.apply_mask(mask=mask)
+        except AttributeError:
+            over_sample_size_lp = 1
+
+        try:
+            over_sample_size_pixelization = aa.Array2D.from_primary_hdu(
+                primary_hdu=fit.value(name="dataset.over_sample_size_pixelization")
+            ).native
+            over_sample_size_pixelization = over_sample_size_pixelization.apply_mask(
+                mask=mask
+            )
+        except AttributeError:
+            over_sample_size_pixelization = 1
+
+        dataset = dataset.apply_over_sampling(
+            over_sample_size_lp=over_sample_size_lp,
+            over_sample_size_pixelization=over_sample_size_pixelization,
+        )
+
         dataset_list.append(dataset)
 
     return dataset_list

autogalaxy/aggregator/interferometer/interferometer.py

@@ -56,15 +56,13 @@ def _interferometer_from(
             )
         )
 
-        over_sampling = fit.value(name="dataset.over_sampling")
         transformer_class = fit.value(name="dataset.transformer_class")
 
         dataset = aa.Interferometer(
             data=data,
             noise_map=noise_map,
             uv_wavelengths=uv_wavelengths,
             real_space_mask=real_space_mask,
-            over_sampling=over_sampling,
             transformer_class=transformer_class,
         )
 

autogalaxy/analysis/analysis/analysis.py

@@ -60,7 +60,7 @@ def galaxies_via_instance_from(
             if getattr(instance, "extra_galaxies", None) is not None:
                 return Galaxies(
                     galaxies=instance.galaxies + instance.extra_galaxies,
-                    run_time_dict=run_time_dict
+                    run_time_dict=run_time_dict,
                 )
 
         return Galaxies(galaxies=instance.galaxies, run_time_dict=run_time_dict)
@@ -139,7 +139,7 @@ def profile_log_likelihood_function(
         An `info_dict` is also created which stores information on aspects of the model and dataset that dictate
         run times, so the profiled times can be interpreted with this context.
 
-        The results of this profiling are then output to hard-disk in the `prefiling` folder of the model-fit results,
+        The results of this profiling are then output to hard-disk in the `profiling` folder of the model-fit results,
         which they can be inspected to ensure run-times are as expected.
 
         Parameters
@@ -191,19 +191,19 @@ def profile_log_likelihood_function(
         fit.figure_of_merit
 
         try:
-            info_dict["image_pixels"] = self.dataset.grids.uniform.shape_slim
-            info_dict["sub_total_light_profiles"] = (
-                self.dataset.grids.uniform.over_sampler.sub_total
-            )
+            info_dict["image_pixels"] = self.dataset.grids.lp.shape_slim
+            info_dict[
+                "sub_total_light_profiles"
+            ] = self.dataset.grids.lp.over_sampler.sub_total
         except AttributeError:
             pass
 
         if fit.model_obj.has(cls=aa.Pixelization):
             info_dict["use_w_tilde"] = fit.inversion.settings.use_w_tilde
             try:
-                info_dict["sub_total_pixelization"] = (
-                    self.dataset.grids.pixelization.over_sampler.sub_total
-                )
+                info_dict[
+                    "sub_total_pixelization"
+                ] = self.dataset.grids.pixelization.over_sampler.sub_total
             except AttributeError:
                 pass
             info_dict["use_positive_only_solver"] = (

autogalaxy/analysis/analysis/dataset.py

@@ -103,9 +103,14 @@ def save_attributes(self, paths: af.DirectoryPaths):
             hdu=self.dataset.noise_map.hdu_for_output,
             prefix="dataset",
         )
-        paths.save_json(
-            name="over_sampling",
-            object_dict=to_dict(self.dataset.over_sampling),
+        paths.save_fits(
+            name="over_sample_size_lp",
+            hdu=self.dataset.grids.lp.over_sample_size.native.hdu_for_output,
+            prefix="dataset",
+        )
+        paths.save_fits(
+            name="over_sample_size_pixelization",
+            hdu=self.dataset.grids.pixelization.over_sample_size.native.hdu_for_output,
             prefix="dataset",
         )
         paths.save_json(

autogalaxy/analysis/chaining_util.py

@@ -204,6 +204,9 @@ def extra_galaxies_from(
 
     # extra_galaxies = result.instance.extra_galaxies.as_model((LightProfile, mp.MassProfile,), fixed="centre", prior_pass=True)
 
+    if not hasattr(result.instance, "extra_galaxies"):
+        return None
+
     if result.instance.extra_galaxies is None:
         return None
 

autogalaxy/analysis/plotter_interface.py

@@ -318,7 +318,7 @@ def should_plot(name):
             reconstructed_image=should_plot("reconstructed_image"),
             reconstruction=should_plot("reconstruction"),
             mesh_pixels_per_image_pixels=should_plot("mesh_pixels_per_image_pixels"),
-            errors=should_plot("errors"),
+            reconstruction_noise_map=should_plot("reconstruction_noise_map"),
             regularization_weights=should_plot("regularization_weights"),
         )
 
@@ -347,7 +347,7 @@ def should_plot(name):
                 pixelization_index=0,
                 reconstructed_image=True,
                 reconstruction=True,
-                errors=True,
+                reconstruction_noise_map=True,
                 regularization_weights=True,
             )
 
@@ -366,7 +366,7 @@ def should_plot(name):
                 pixelization_index=0,
                 reconstructed_image=True,
                 reconstruction=True,
-                errors=True,
+                reconstruction_noise_map=True,
                 regularization_weights=True,
                 interpolate_to_uniform=True,
             )

autogalaxy/config/general.yaml

@@ -1,6 +1,7 @@
 fits:
   flip_for_ds9: true
 grid:
+  remove_projected_centre: false   # Whether 1D plots of a light profile should remove the central point to avoid the large numerical central value skewing the y axis.
   max_evaluation_grid_size: 1000   # An evaluation grid whose shape is adaptive chosen is used to compute quantities like critical curves, this integer is the max size of the grid ensuring faster run times.
 adapt:
   adapt_minimum_percent: 0.01