refactor complete

Author: Jammy2211

autolens/__init__.py

@@ -20,11 +20,9 @@
 from autoarray.inversion.pixelization.mesh.abstract import AbstractMesh
 from autoarray.inversion.regularization.abstract import AbstractRegularization
 from autoarray.inversion.pixelization.pixelization import Pixelization
-from autoarray.inversion.inversion.settings import SettingsInversion
+from autoarray.settings import Settings
 from autoarray.inversion.inversion.factory import inversion_from as Inversion
 from autoarray.inversion.pixelization.mappers.abstract import AbstractMapper
-from autoarray.inversion.pixelization.mappers.mapper_grids import MapperGrids
-from autoarray.inversion.pixelization.mappers.factory import mapper_from as Mapper
 from autoarray.inversion.pixelization.border_relocator import BorderRelocator
 from autoarray.operators.transformer import TransformerDFT
 from autoarray.operators.transformer import TransformerNUFFT
@@ -37,8 +35,8 @@
 from autoarray.structures.grids.uniform_1d import Grid1D
 from autoarray.structures.grids.uniform_2d import Grid2D
 from autoarray.structures.grids.irregular_2d import Grid2DIrregular
-from autoarray.inversion.pixelization.mesh_grid.rectangular_2d import Mesh2DRectangular
-from autoarray.inversion.pixelization.mesh_grid.delaunay_2d import Mesh2DDelaunay
+from autoarray.inversion.pixelization.mesh_grid.rectangular import Mesh2DRectangular
+from autoarray.inversion.pixelization.mesh_grid.delaunay import Mesh2DDelaunay
 from autoarray.structures.triangles.shape import Circle
 from autoarray.structures.triangles.shape import Triangle
 from autoarray.structures.triangles.shape import Square

autolens/aggregator/fit_imaging.py

@@ -15,7 +15,7 @@
 def _fit_imaging_from(
     fit: af.Fit,
     instance: Optional[af.ModelInstance] = None,
-    settings_inversion: aa.SettingsInversion = None,
+    settings: aa.Settings = None,
 ) -> List[FitImaging]:
     """
     Returns a list of `FitImaging` object from a `PyAutoFit` loaded directory `Fit` or sqlite database `Fit` object.
@@ -25,7 +25,7 @@ def _fit_imaging_from(
 
     - The imaging data, noise-map, PSF and settings as .fits files (e.g. `dataset/data.fits`).
     - The mask used to mask the `Imaging` data structure in the fit (`dataset.fits[hdu=0]`).
-    - The settings of inversions used by the fit (`dataset/settings_inversion.json`).
+    - The settings of inversions used by the fit (`dataset/settings.json`).
 
     Each individual attribute can be loaded from the database via the `fit.value()` method.
 
@@ -36,7 +36,7 @@ def _fit_imaging_from(
     is instead used to load lists of the data, noise-map, PSF and mask and combine them into a list of
     `FitImaging` objects.
 
-    The settings of an inversion can be overwritten by inputting a `settings_inversion` object, for example
+    The settings of an inversion can be overwritten by inputting a `settings` object, for example
     if you want to use a grid with a different inversion solver.
 
     Parameters
@@ -47,8 +47,8 @@ def _fit_imaging_from(
     instance
         A manual instance that overwrites the max log likelihood instance in fit (e.g. for drawing the instance
         randomly from the PDF).
-    settings_inversion
-        Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
+    settings
+        Optionally overwrite the `Settings` of the `Inversion` object that is created from the fit.
     """
 
     dataset_list = _imaging_from(fit=fit)
@@ -59,7 +59,7 @@ def _fit_imaging_from(
 
     adapt_images_list = agg_util.adapt_images_from(fit=fit)
 
-    settings_inversion = settings_inversion or fit.value(name="settings_inversion")
+    settings = settings or fit.value(name="settings")
 
     fit_dataset_list = []
 
@@ -75,7 +75,7 @@ def _fit_imaging_from(
                 tracer=tracer,
                 dataset_model=dataset_model,
                 adapt_images=adapt_images,
-                settings_inversion=settings_inversion,
+                settings=settings,
             )
         )
 
@@ -86,7 +86,7 @@ class FitImagingAgg(af.AggBase):
     def __init__(
         self,
         aggregator: af.Aggregator,
-        settings_inversion: Optional[aa.SettingsInversion] = None,
+        settings: Optional[aa.Settings] = None,
     ):
         """
             Interfaces with an `PyAutoFit` aggregator object to create instances of `FitImaging` objects from the results
@@ -97,7 +97,7 @@ def __init__(
 
             - The imaging data, noise-map, PSF and settings as .fits files (e.g. `dataset/data.fits`).
             - The mask used to mask the `Imaging` data structure in the fit (`dataset.fits[hdu=0]`).
-            - The settings of inversions used by the fit (`dataset/settings_inversion.json`).
+            - The settings of inversions used by the fit (`dataset/settings.json`).
 
             The `aggregator` contains the path to each of these files, and they can be loaded individually. This class
             can load them all at once and create an `FitImaging` object via the `_fit_imaging_from` method.
@@ -119,16 +119,16 @@ def __init__(
             ----------
             aggregator
                 A `PyAutoFit` aggregator object which can load the results of model-fits.
-            settings_inversion
-                Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
+            settings
+                Optionally overwrite the `Settings` of the `Inversion` object that is created from the fit.
             use_preloaded_grid
                 Certain pixelization's construct their mesh in the source-plane from a stochastic KMeans algorithm. This
                 grid may be output to hard-disk after the model-fit and loaded via the database to ensure the same grid is
                 used as the fit.
         """
         super().__init__(aggregator=aggregator)
 
-        self.settings_inversion = settings_inversion
+        self.settings = settings
 
     def object_via_gen_from(
         self, fit, instance: Optional[af.ModelInstance] = None
@@ -150,5 +150,5 @@ def object_via_gen_from(
         return _fit_imaging_from(
             fit=fit,
             instance=instance,
-            settings_inversion=self.settings_inversion,
+            settings=self.settings,
         )

autolens/aggregator/fit_interferometer.py

@@ -15,7 +15,7 @@
 def _fit_interferometer_from(
     fit: af.Fit,
     instance: Optional[af.ModelInstance] = None,
-    settings_inversion: aa.SettingsInversion = None,
+    settings: aa.Settings = None,
 ) -> List[FitInterferometer]:
     """
     Returns a list of `FitInterferometer` objects from a `PyAutoFit` loaded directory `Fit` or sqlite database `Fit` object.
@@ -25,7 +25,7 @@ def _fit_interferometer_from(
 
     - The interferometer data, noise-map, uv-wavelengths and settings as .fits files (e.g. `dataset/data.fits`).
     - The real space mask defining the grid of the interferometer for the FFT (`dataset/real_space_mask.fits`).
-    - The settings of inversions used by the fit (`dataset/settings_inversion.json`).
+    - The settings of inversions used by the fit (`dataset/settings.json`).
 
     Each individual attribute can be loaded from the database via the `fit.value()` method.
 
@@ -37,7 +37,7 @@ def _fit_interferometer_from(
     method is instead used to load lists of the data, noise-map, PSF and mask and combine them into a list of
     `FitInterferometer` objects.
 
-    The settings of an inversion can be overwritten by inputting a `settings_inversion` object, for
+    The settings of an inversion can be overwritten by inputting a `settings` object, for
     example if you want to use a grid with a different inversion solver.
 
     Parameters
@@ -48,8 +48,8 @@ def _fit_interferometer_from(
     instance
         A manual instance that overwrites the max log likelihood instance in fit (e.g. for drawing the instance
         randomly from the PDF).
-    settings_inversion
-        Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
+    settings
+        Optionally overwrite the `Settings` of the `Inversion` object that is created from the fit.
     """
     dataset_list = _interferometer_from(
         fit=fit,
@@ -59,7 +59,7 @@ def _fit_interferometer_from(
 
     adapt_images_list = agg_util.adapt_images_from(fit=fit)
 
-    settings_inversion = settings_inversion or fit.value(name="settings_inversion")
+    settings = settings or fit.value(name="settings")
 
     fit_dataset_list = []
 
@@ -75,7 +75,7 @@ def _fit_interferometer_from(
                 tracer=tracer,
                 dataset_model=dataset_model,
                 adapt_images=adapt_images,
-                settings_inversion=settings_inversion,
+                settings=settings,
             )
         )
 
@@ -86,7 +86,7 @@ class FitInterferometerAgg(af.AggBase):
     def __init__(
         self,
         aggregator: af.Aggregator,
-        settings_inversion: Optional[aa.SettingsInversion] = None,
+        settings: Optional[aa.Settings] = None,
     ):
         """
             Interfaces with an `PyAutoFit` aggregator object to create instances of `FitInterferometer` objects from the
@@ -97,7 +97,7 @@ def __init__(
 
             - The interferometer data, noise-map, uv-wavelengths and settings as .fits files (e.g. `dataset/data.fits`).
             - The real space mask defining the grid of the interferometer for the FFT (`dataset/real_space_mask.fits`).
-            - The settings of inversions used by the fit (`dataset/settings_inversion.json`).
+            - The settings of inversions used by the fit (`dataset/settings.json`).
 
             The `aggregator` contains the path to each of these files, and they can be loaded individually. This class
             can load them all at once and create an `FitInterferometer` object via the `_fit_interferometer_from` method.
@@ -115,16 +115,16 @@ def __init__(
             ----------
             aggregator
                 A `PyAutoFit` aggregator object which can load the results of model-fits.
-            settings_inversion
-                Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
+            settings
+                Optionally overwrite the `Settings` of the `Inversion` object that is created from the fit.
             use_preloaded_grid
                 Certain pixelization's construct their mesh in the source-plane from a stochastic KMeans algorithm. This
                 grid may be output to hard-disk after the model-fit and loaded via the database to ensure the same grid is
                 used as the fit.
         """
         super().__init__(aggregator=aggregator)
 
-        self.settings_inversion = settings_inversion
+        self.settings = settings
 
     def object_via_gen_from(
         self, fit, instance: Optional[af.ModelInstance] = None
@@ -146,5 +146,5 @@ def object_via_gen_from(
         return _fit_interferometer_from(
             fit=fit,
             instance=instance,
-            settings_inversion=self.settings_inversion,
+            settings=self.settings,
         )

autolens/aggregator/subhalo.py

@@ -10,15 +10,15 @@ class SubhaloAgg:
     def __init__(
         self,
         aggregator_grid_search: af.GridSearchAggregator,
-        settings_inversion: Optional[aa.SettingsInversion] = None,
+        settings: Optional[aa.Settings] = None,
     ):
         """
         Wraps a PyAutoFit aggregator in order to create generators of fits to imaging data, corresponding to the
         results of a non-linear search model-fit.
         """
 
         self.aggregator_grid_search = aggregator_grid_search
-        self.settings_inversion = settings_inversion
+        self.settings = settings
 
         if len(aggregator_grid_search) == 0:
             raise exc.AggregatorException(

autolens/analysis/analysis/dataset.py

@@ -30,7 +30,7 @@ def __init__(
         positions_likelihood_list: Optional[List[PositionsLH]] = None,
         adapt_images: Optional[ag.AdaptImages] = None,
         cosmology: ag.cosmo.LensingCosmology = None,
-        settings_inversion: aa.SettingsInversion = None,
+        settings: aa.Settings = None,
         preloads: aa.Preloads = None,
         raise_inversion_positions_likelihood_exception: bool = True,
         title_prefix: str = None,
@@ -60,7 +60,7 @@ def __init__(
             reconstructed galaxy's morphology.
         cosmology
             The Cosmology assumed for this analysis.
-        settings_inversion
+        settings
             Settings controlling how an inversion is fitted during the model-fit, for example which linear algebra
             formalism is used.
         raise_inversion_positions_likelihood_exception
@@ -74,7 +74,7 @@ def __init__(
             dataset=dataset,
             adapt_images=adapt_images,
             cosmology=cosmology,
-            settings_inversion=settings_inversion,
+            settings=settings,
             preloads=preloads,
             title_prefix=title_prefix,
             use_jax=use_jax,

autolens/imaging/fit_imaging.py

@@ -22,7 +22,7 @@ def __init__(
         tracer: Tracer,
         dataset_model : Optional[aa.DatasetModel] = None,
         adapt_images: Optional[ag.AdaptImages] = None,
-        settings_inversion: aa.SettingsInversion = aa.SettingsInversion(),
+        settings: aa.Settings = None,
         preloads: aa.Preloads = None,
         xp=np
     ):
@@ -61,19 +61,19 @@ def __init__(
         adapt_images
             Contains the adapt-images which are used to make a pixelization's mesh and regularization adapt to the
             reconstructed galaxy's morphology.
-        settings_inversion
+        settings
             Settings controlling how an inversion is fitted for example which linear algebra formalism is used.
         """
 
         super().__init__(dataset=dataset, dataset_model=dataset_model, xp=xp)
         AbstractFitInversion.__init__(
-            self=self, model_obj=tracer, settings_inversion=settings_inversion, xp=xp
+            self=self, model_obj=tracer, settings=settings, xp=xp
         )
 
         self.tracer = tracer
 
         self.adapt_images = adapt_images
-        self.settings_inversion = settings_inversion
+        self.settings = settings or aa.Settings()
         self.preloads = preloads
 
     @property
@@ -119,7 +119,7 @@ def tracer_to_inversion(self) -> TracerToInversion:
             dataset=dataset,
             tracer=self.tracer,
             adapt_images=self.adapt_images,
-            settings_inversion=self.settings_inversion,
+            settings=self.settings,
             preloads=self.preloads,
             xp=self._xp,
         )

autolens/imaging/model/analysis.py

@@ -107,7 +107,7 @@ def fit_from(
             tracer=tracer,
             dataset_model=dataset_model,
             adapt_images=adapt_images,
-            settings_inversion=self.settings_inversion,
+            settings=self.settings,
             preloads=self.preloads,
             xp=self._xp
         )

autolens/interferometer/fit_interferometer.py

@@ -19,7 +19,7 @@ def __init__(
         tracer: Tracer,
         dataset_model: Optional[aa.DatasetModel] = None,
         adapt_images: Optional[ag.AdaptImages] = None,
-        settings_inversion: aa.SettingsInversion = aa.SettingsInversion(),
+        settings: aa.Settings = None,
         preloads: aa.Preloads = None,
         xp=np,
     ):
@@ -59,23 +59,23 @@ def __init__(
         adapt_images
             Contains the adapt-images which are used to make a pixelization's mesh and regularization adapt to the
             reconstructed galaxy's morphology.
-        settings_inversion
+        settings
             Settings controlling how an inversion is fitted for example which linear algebra formalism is used.
         """
 
         self.tracer = tracer
 
         self.adapt_images = adapt_images
 
-        self.settings_inversion = settings_inversion
+        self.settings = settings
 
         super().__init__(
             dataset=dataset,
             dataset_model=dataset_model,
             xp=xp,
         )
         AbstractFitInversion.__init__(
-            self=self, model_obj=tracer, settings_inversion=settings_inversion, xp=xp
+            self=self, model_obj=tracer, settings=settings, xp=xp
         )
 
         self.preloads = preloads
@@ -122,7 +122,7 @@ def tracer_to_inversion(self) -> TracerToInversion:
             dataset=dataset,
             tracer=self.tracer,
             adapt_images=self.adapt_images,
-            settings_inversion=self.settings_inversion,
+            settings=self.settings,
             preloads=self.preloads,
             xp=self._xp,
         )