solved merging conflict cnfw.py

Author: NiekWielders

autogalaxy/__init__.py

@@ -10,24 +10,17 @@
 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.inversion.pixelization import mesh  # noqa
+from autoarray.inversion.mesh import mesh  # noqa
 from autoarray.inversion import regularization as reg  # noqa
-from autoarray.inversion.pixelization import image_mesh
-from autoarray.inversion.pixelization.mappers.abstract import AbstractMapper  # noqa
-from autoarray.inversion.inversion.settings import SettingsInversion  # noqa
+from autoarray.inversion.mesh import image_mesh
+from autoarray.inversion.mappers.abstract import Mapper  # noqa
+from autoarray.settings import Settings  # noqa
 from autoarray.inversion.inversion.factory import inversion_from as Inversion  # noqa
-from autoarray.inversion.pixelization.image_mesh.abstract import AbstractImageMesh
-from autoarray.inversion.pixelization.mesh.abstract import AbstractMesh
+from autoarray.inversion.mesh.image_mesh.abstract import AbstractImageMesh
+from autoarray.inversion.mesh.mesh.abstract import AbstractMesh
 from autoarray.inversion.regularization.abstract import AbstractRegularization
-from autoarray.inversion.pixelization.pixelization import Pixelization  # noqa
-from autoarray.inversion.pixelization.mappers.abstract import AbstractMapper
-from autoarray.inversion.pixelization.mappers.mapper_grids import MapperGrids  # noqa
-from autoarray.inversion.pixelization.mappers.factory import (
-    mapper_from as Mapper,
-)  # noqa
-from autoarray.inversion.pixelization.border_relocator import BorderRelocator
-from autoarray.preloads import Preloads
-from autoarray.preloads import mapper_indices_from
+from autoarray.inversion.pixelization import Pixelization  # noqa
+from autoarray.inversion.mesh.border_relocator import BorderRelocator
 from autoarray.mask.mask_1d import Mask1D  # noqa
 from autoarray.mask.mask_2d import Mask2D  # noqa
 from autoarray.mask.derive.zoom_2d import Zoom2D
@@ -43,18 +36,22 @@
 from autoarray.structures.grids.uniform_2d import Grid2D  # noqa
 from autoarray.structures.grids.irregular_2d import Grid2DIrregular  # noqa
 from autoarray.operators.over_sampling.over_sampler import OverSampler  # noqa
-from autoarray.structures.mesh.rectangular_2d import Mesh2DRectangular  # noqa
-from autoarray.structures.mesh.rectangular_2d_uniform import (
-    Mesh2DRectangularUniform,
+from autoarray.operators.convolver import Convolver
+from autoarray.inversion.mesh.interpolator.rectangular import (
+    InterpolatorRectangular,
+)  # noqa
+from autoarray.inversion.mesh.interpolator.delaunay import (
+    InterpolatorDelaunay,
 )  # noqa
-from autoarray.structures.mesh.delaunay_2d import Mesh2DDelaunay  # noqa
 from autoarray.structures.vectors.uniform import VectorYX2D  # noqa
 from autoarray.structures.vectors.irregular import VectorYX2DIrregular  # noqa
 from autoarray.layout.region import Region1D  # noqa
 from autoarray.layout.region import Region2D  # noqa
-from autoarray.structures.arrays.kernel_2d import Kernel2D  # noqa
 from autoarray.structures.visibilities import Visibilities  # noqa
 from autoarray.structures.visibilities import VisibilitiesNoiseMap  # noqa
+from autoarray.inversion.mesh.mesh_geometry.rectangular import (
+    rectangular_edge_pixel_list_from,
+)
 
 from .analysis import model_util
 from .analysis.adapt_images.adapt_images import AdaptImages

autogalaxy/abstract_fit.py

@@ -15,7 +15,7 @@
 
 
 class AbstractFitInversion:
-    def __init__(self, model_obj, settings_inversion: aa.SettingsInversion, xp=np):
+    def __init__(self, model_obj, settings: aa.Settings, xp=np):
         """
         An abstract fit object which fits to datasets (e.g. imaging, interferometer) inherit from.
 
@@ -28,11 +28,11 @@ def __init__(self, model_obj, settings_inversion: aa.SettingsInversion, xp=np):
             The object which contains the model components (e.g. light profiles, galaxies, etc) which are used to
             create the model-data that fits the data. In PyAutoGalaxy this is a list of galaxies and PyAutoLens
             it is a `Tracer`.
-        settings_inversion
+        settings
             Settings controlling how an inversion is fitted for example which linear algebra formalism is used.
         """
         self.model_obj = model_obj
-        self.settings_inversion = settings_inversion
+        self.settings = settings or aa.Settings()
         self.use_jax = xp is not np
 
     @property
@@ -75,7 +75,7 @@ def perform_inversion(self) -> bool:
     def sparse_operator(self) -> Optional[aa.ImagingSparseOperator]:
         """
         Only call the `sparse_operator` property of a dataset used to perform efficient linear algebra calculations if
-        the SettingsInversion()` object has `use_sparse_operator=True`, to avoid unnecessary computation.
+        the Settings()` object has `use_sparse_operator=True`, to avoid unnecessary computation.
 
         Returns
         -------

autogalaxy/aggregator/agg_util.py

@@ -172,7 +172,7 @@ def adapt_images_from(
 
 
 def mesh_grids_of_planes_list_from(
-    fit: af.Fit, total_fits: int, use_preloaded_grid: bool
+    fit: af.Fit, total_fits: int
 ) -> List[Optional[aa.Grid2D]]:
     """
     Returns the image-plane pixelization grid(s) used by the fit.
@@ -196,69 +196,15 @@ def mesh_grids_of_planes_list_from(
         an output directory or from an sqlite database..
     total_fits
         The total number of `Analysis` objects summed to create 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.
 
     Returns
     -------
     The list of image-plane mesh centres used when creating the overall fit.
     """
-
-    if use_preloaded_grid:
-        if not fit.children:
-            return [fit.value(name="preload_mesh_grids_of_planes")]
-        else:
-            try:
-                return fit.child_values(name="preload_mesh_grids_of_planes")
-            except AttributeError:
-                return [None] * total_fits
+    if not fit.children:
+        return [fit.value(name="preload_mesh_grids_of_planes")]
     else:
-        return [None] * total_fits
-
-
-def preloads_from(
-    preloads_cls,
-    use_preloaded_grid: bool,
-    mesh_grids_of_planes: List,
-) -> aa.Preloads:
-    """
-    Returns a `Preloads` object associated with a fit loaded via the database.
-
-    When loading results via the database, the preloads class may have certain attributes associated with it
-    in order to perform the fit. The main purpose is to use the same image-plane mesh centres for pixelization where
-    the mesh is computed in the image-plane (see `agg_util.mesh_grids_of_planes_list_from`).
-
-    Parameters
-    ----------
-    preloads_cls
-        The `Preloads` object used to create the preloads (this varies across
-        projects like `autogalaxy` and `autolens`).
-    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.
-    mesh_grids_of_planes
-        The list of image-plane mesh centres used when creating the overall fit which are associated with the
-        preloads.
-
-    Returns
-    -------
-    The preloads object associated with the fit loaded via the database.
-    """
-    preloads = preloads_cls()
-
-    if use_preloaded_grid:
-        if mesh_grids_of_planes is not None:
-            preloads = preloads_cls(
-                image_plane_mesh_grid_pg_list=mesh_grids_of_planes,
-            )
-
-            if len(preloads.image_plane_mesh_grid_pg_list) == 2:
-                if type(preloads.image_plane_mesh_grid_pg_list[1]) != list:
-                    preloads.image_plane_mesh_grid_pg_list[1] = [
-                        preloads.image_plane_mesh_grid_pg_list[1]
-                    ]
-
-    return preloads
+        try:
+            return fit.child_values(name="preload_mesh_grids_of_planes")
+        except AttributeError:
+            return [None] * total_fits

autogalaxy/aggregator/ellipse/fit_ellipse.py

@@ -33,7 +33,7 @@ def _fit_ellipse_from(
     is instead used to load lists of the data, noise-map, PSF and mask and combine them into a list of
     `FitEllipse` 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

autogalaxy/aggregator/imaging/fit_imaging.py

@@ -16,7 +16,7 @@
 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` objects from a `PyAutoFit` loaded directory `Fit` or sqlite database `Fit` object.
@@ -26,7 +26,7 @@ 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.
 
@@ -37,7 +37,7 @@ 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
@@ -48,8 +48,8 @@ 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.
     """
 
     from autogalaxy.imaging.fit_imaging import FitImaging
@@ -62,7 +62,7 @@ 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 = []
 
@@ -79,7 +79,7 @@ def _fit_imaging_from(
                 galaxies=galaxies,
                 dataset_model=dataset_model,
                 adapt_images=adapt_images,
-                settings_inversion=settings_inversion,
+                settings=settings,
             )
         )
 
@@ -90,7 +90,7 @@ 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
@@ -101,7 +101,7 @@ 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.
@@ -123,19 +123,19 @@ 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.
 
         Parameters
         ----------
         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.
         """
         super().__init__(aggregator=aggregator)
 
-        self.settings_inversion = settings_inversion
+        self.settings = settings
 
     def object_via_gen_from(
         self, fit, instance: Optional[af.ModelInstance] = None
@@ -157,5 +157,5 @@ def object_via_gen_from(
         return _fit_imaging_from(
             fit=fit,
             instance=instance,
-            settings_inversion=self.settings_inversion,
+            settings=self.settings,
         )

autogalaxy/aggregator/imaging/imaging.py

@@ -59,8 +59,10 @@ def values_from(hdu: int, cls):
         noise_map = values_from(hdu=2, cls=aa.Array2D)
 
         try:
-            psf = values_from(hdu=3, cls=aa.Kernel2D)
+            kernel = values_from(hdu=3, cls=aa.Array2D)
+            psf = aa.Convolver(kernel=kernel)
         except (TypeError, IndexError):
+            kernel = None
             psf = None
 
         dataset = aa.Imaging(