docuemnet preloasds and mapper_index_list -> mapper_indices

Author: Jammy2211

autoarray/inversion/inversion/abstract.py

@@ -254,20 +254,20 @@ def no_regularization_index_list(self) -> List[int]:
         return no_regularization_index_list
 
     @property
-    def mapper_index_list(self) -> List[int]:
+    def mapper_indices(self) -> np.ndarray[]:
 
-        if self.preloads.mapper_index_list is not None:
-            return self.preloads.mapper_index_list
+        if self.preloads.mapper_indices is not None:
+            return self.preloads.mapper_indices
 
-        mapper_index_list = []
+        mapper_indices = []
 
         param_range_list = self.param_range_list_from(cls=AbstractMapper)
 
         for param_range in param_range_list:
 
-            mapper_index_list += range(param_range[0], param_range[1])
+            mapper_indices += range(param_range[0], param_range[1])
 
-        return np.array(mapper_index_list)
+        return np.array(mapper_indices)
 
     @property
     def mask(self) -> Array2D:
@@ -360,7 +360,7 @@ def regularization_matrix_reduced(self) -> Optional[np.ndarray]:
             return self.regularization_matrix
 
         # ids of values which are on edge so zero-d and not solved for.
-        ids_to_keep = self.mapper_index_list
+        ids_to_keep = self.mapper_indices
 
         # Zero rows and columns in the matrix we want to ignore
         return self.regularization_matrix[ids_to_keep][:, ids_to_keep]
@@ -399,7 +399,7 @@ def curvature_reg_matrix_reduced(self) -> Optional[np.ndarray]:
             return self.curvature_reg_matrix
 
         # ids of values which are on edge so zero-d and not solved for.
-        ids_to_keep = self.mapper_index_list
+        ids_to_keep = self.mapper_indices
 
         # Zero rows and columns in the matrix we want to ignore
         return self.curvature_reg_matrix[ids_to_keep][:, ids_to_keep]
@@ -489,7 +489,7 @@ def reconstruction_reduced(self) -> np.ndarray:
             return self.reconstruction
 
         # ids of values which are on edge so zero-d and not solved for.
-        ids_to_keep = self.mapper_index_list
+        ids_to_keep = self.mapper_indices
 
         # Zero rows and columns in the matrix we want to ignore
         return self.reconstruction[ids_to_keep]

autoarray/preloads.py

@@ -1,12 +1,36 @@
 import logging
 
+import numpy as np
+
 logger = logging.getLogger(__name__)
 
 logger.setLevel(level="INFO")
 
 
 class Preloads:
 
-    def __init__(self, mapper_index_list = None):
+    def __init__(self, mapper_indices: np.ndarray = None):
+        """
+        Preload in memory arrays and matrices used to perform pixelized linear inversions, for both key functionality
+        and speeding up the run-time of the inversion.
+
+        Certain preloading arrays (e.g. `mapper_indices`) are stored here because JAX requires that they are
+        known and defined as static arrays before sampling. During each inversion, the preloads will be inspected
+        for these fixed arrays and used to change matrix shapes in an identical way for every likelihood evaluation.
+
+        Other preloading arrays are used purely to speed up the run-time of the inversion, such as 
+        the `curvature_matrix_preload` array. For certain models (e.g. if the source model is fixed and only the
+        lens light is being fitted for), certain quadrants of the `curvature_matrix` are fixed 
+        for every likelihood evaluation, meaning that they can be preloaded and used to speed up the inversion.
+
+
+        Parameters
+        ----------
+        mapper_indices
+            The integer indexes of the mapper pixels in a pixeized inversion, which separate their indexes from those
+            of linear light profiles in the inversion. This is used to extract `_reduced` 
+            matrices (e.g. `curvature_matrix_reduced`) to compute the `log_evidence` terms of the pixelized inversion
+            likelihood function.
+        """
 
-        self.mapper_index_list = mapper_index_list
+        self.mapper_indices = mapper_indices