Rectangular mesh renames

Author: Jammy2211

autoarray/dataset/interferometer/dataset.py

@@ -162,6 +162,7 @@ def apply_w_tilde(
         self,
         curvature_preload=None,
         batch_size: int = 128,
+        chunk_k: int = 2048,
         show_progress: bool = False,
         show_memory: bool = False,
         use_jax: bool = False,
@@ -193,13 +194,16 @@ def apply_w_tilde(
 
         if curvature_preload is None:
 
-            logger.info("INTERFEROMETER - Computing W-Tilde; runtime scales with visibility count and mask resolution, extreme inputs may exceed hours.")
+            logger.info(
+                "INTERFEROMETER - Computing W-Tilde; runtime scales with visibility count and mask resolution, extreme inputs may exceed hours."
+            )
 
             curvature_preload = inversion_interferometer_util.w_tilde_curvature_preload_interferometer_from(
                 noise_map_real=self.noise_map.array.real,
                 uv_wavelengths=self.uv_wavelengths,
                 shape_masked_pixels_2d=self.transformer.grid.mask.shape_native_masked_pixels,
                 grid_radians_2d=self.transformer.grid.mask.derive_grid.all_false.in_radians.native.array,
+                chunk_k=chunk_k,
                 show_memory=show_memory,
                 show_progress=show_progress,
                 use_jax=use_jax,

autoarray/exc.py

@@ -72,7 +72,7 @@ class MeshException(Exception):
     """
     Raises exceptions associated with the `inversion/mesh` modules and `Mesh` classes.
 
-    For example if a `RectangularMagnification` mesh has dimensions below 3x3.
+    For example if a `RectangularAdaptDensity` mesh has dimensions below 3x3.
     """
 
     pass

autoarray/fit/fit_imaging.py

@@ -55,9 +55,7 @@ def __init__(
 
     @property
     def data(self) -> ty.DataLike:
-        if self.dataset_model.background_sky_level != 0.0:
-            return self.dataset.data - self.dataset_model.background_sky_level
-        return self.dataset.data
+        return self.dataset.data - self.dataset_model.background_sky_level
 
     @property
     def blurred_image(self) -> Array2D:

autoarray/inversion/inversion/abstract.py

@@ -160,8 +160,8 @@ def cls_list_from(self, cls: Type, cls_filtered: Optional[Type] = None) -> List:
 
         - If the input is `cls=aa.mesh.Mesh`, a list containing all pixelizations in the class are returned.
 
-        - If `cls=aa.mesh.Mesh` and `cls_filtered=aa.mesh.RectangularMagnification`, a list of all pixelizations
-        excluding those which are `RectangularMagnification` pixelizations will be returned.
+        - If `cls=aa.mesh.Mesh` and `cls_filtered=aa.mesh.RectangularAdaptDensity`, a list of all pixelizations
+        excluding those which are `RectangularAdaptDensity` pixelizations will be returned.
 
         Parameters
         ----------

autoarray/inversion/inversion/interferometer/inversion_interferometer_util.py

@@ -276,7 +276,7 @@ def w_tilde_curvature_preload_interferometer_via_np_from(
     K = uv_wavelengths.shape[0]
     n_chunks = (K + chunk_k - 1) // chunk_k
 
-    w = 1.0 / (noise_map_real ** 2)
+    w = 1.0 / (noise_map_real**2)
     ku = 2.0 * np.pi * uv_wavelengths[:, 0]
     kv = 2.0 * np.pi * uv_wavelengths[:, 1]
 

autoarray/inversion/inversion/mapper_valued.py

@@ -245,7 +245,7 @@ def magnification_via_mesh_from(
 
                 To compute the magnification of a `Delaunay` mesh, use the method `magnification_via_interpolation_from`.
 
-                This method only supports a `RectangularMagnification`.
+                This method only supports a `RectangularAdaptDensity`.
                 """
             )
 

autoarray/inversion/linear_obj/neighbors.py

@@ -4,14 +4,14 @@
 class Neighbors(np.ndarray):
     def __new__(cls, arr: np.ndarray, sizes: np.ndarray):
         """
-        Class packaging ndarrays describing the neighbors of every pixel in a mesh (e.g. `RectangularMagnification`).
+        Class packaging ndarrays describing the neighbors of every pixel in a mesh (e.g. `RectangularAdaptDensity`).
 
         The array `arr` contains the pixel indexes of the neighbors of every pixel. Its has shape [total_pixels,
         max_neighbors_in_single_pixel].
 
         The array `sizes` contains the number of neighbors of every pixel in the pixelixzation.
 
-        For example, for a 3x3 `RectangularMagnification` grid:
+        For example, for a 3x3 `RectangularAdaptDensity` grid:
 
         - `total_pixels=9` and `max_neighbors_in_single_pixel=4` (because the central pixel has 4 neighbors whereas
           edge / corner pixels have 3 and 2).

autoarray/inversion/pixelization/mappers/abstract.py

@@ -369,7 +369,7 @@ def mapped_to_source_from(self, array: Array2D) -> np.ndarray:
         """
         return mapper_util.mapped_to_source_via_mapping_matrix_from(
             mapping_matrix=np.array(self.mapping_matrix),
-            array_slim=array.slim,
+            array_slim=array.slim.array,
         )
 
     def extent_from(

autoarray/inversion/pixelization/mappers/delaunay.py

@@ -158,10 +158,10 @@ def pix_sub_weights(self) -> PixSubWeights:
         The `sub_slim_index` refers to the masked data sub-pixels and `pix_indexes` the pixelization pixel indexes,
         for example:
 
-        - `pix_indexes_for_sub_slim_index[0, 0] = 2`: The data's first (index 0) sub-pixel maps to the RectangularMagnification
+        - `pix_indexes_for_sub_slim_index[0, 0] = 2`: The data's first (index 0) sub-pixel maps to the RectangularAdaptDensity
         pixelization's third (index 2) pixel.
 
-        - `pix_indexes_for_sub_slim_index[2, 0] = 4`: The data's third (index 2) sub-pixel maps to the RectangularMagnification
+        - `pix_indexes_for_sub_slim_index[2, 0] = 4`: The data's third (index 2) sub-pixel maps to the RectangularAdaptDensity
         pixelization's fifth (index 4) pixel.
 
         The second dimension of the array `pix_indexes_for_sub_slim_index`, which is 0 in both examples above, is used

autoarray/inversion/pixelization/mappers/rectangular.py

@@ -39,7 +39,7 @@ class MapperRectangular(AbstractMapper):
     `Delaunay` triangulation, where every `grid_slim` pixel maps to three Delaunay pixels (the corners of the
     triangles) with varying interpolation weights .
 
-    For a `RectangularMagnification` mesh every pixel in the masked data maps to only one pixel, thus the second
+    For a `RectangularAdaptDensity` mesh every pixel in the masked data maps to only one pixel, thus the second
     dimension of `pix_indexes_for_sub_slim_index` is always of size 1.
 
     The mapper allows us to create a mapping matrix, which is a matrix representing the mapping between every
@@ -64,7 +64,7 @@ def shape_native(self) -> Tuple[int, ...]:
     def pix_sub_weights(self) -> PixSubWeights:
         """
         Computes the following three quantities describing the mappings between of every sub-pixel in the masked data
-        and pixel in the `RectangularMagnification` mesh.
+        and pixel in the `RectangularAdaptDensity` mesh.
 
         - `pix_indexes_for_sub_slim_index`: the mapping of every data pixel (given its `sub_slim_index`)
         to mesh pixels (given their `pix_indexes`).
@@ -79,18 +79,18 @@ def pix_sub_weights(self) -> PixSubWeights:
         The `sub_slim_index` refers to the masked data sub-pixels and `pix_indexes` the mesh pixel indexes,
         for example:
 
-        - `pix_indexes_for_sub_slim_index[0, 0] = 2`: The data's first (index 0) sub-pixel maps to the RectangularMagnification
+        - `pix_indexes_for_sub_slim_index[0, 0] = 2`: The data's first (index 0) sub-pixel maps to the RectangularAdaptDensity
         mesh's third (index 2) pixel.
 
-        - `pix_indexes_for_sub_slim_index[2, 0] = 4`: The data's third (index 2) sub-pixel maps to the RectangularMagnification
+        - `pix_indexes_for_sub_slim_index[2, 0] = 4`: The data's third (index 2) sub-pixel maps to the RectangularAdaptDensity
         mesh's fifth (index 4) pixel.
 
         The second dimension of the array `pix_indexes_for_sub_slim_index`, which is 0 in both examples above, is used
         for cases where a data pixel maps to more than one mesh pixel (for example a `Delaunay` triangulation
         where each data pixel maps to 3 Delaunay triangles with interpolation weights). The weights of multiple mappings
         are stored in the array `pix_weights_for_sub_slim_index`.
 
-        For a RectangularMagnification pixelization each data sub-pixel maps to a single mesh pixel, thus the second
+        For a RectangularAdaptDensity pixelization each data sub-pixel maps to a single mesh pixel, thus the second
         dimension of the array `pix_indexes_for_sub_slim_index` 1 and all entries in `pix_weights_for_sub_slim_index`
         are equal to 1.0.
         """