merge main

autoarray/fit/fit_util.py

@@ -302,17 +302,18 @@ def chi_squared_with_mask_fast_from(
     mask
         The mask applied to the chi-squared-map, where `False` entries are included in the calculation.
     """
-
-    return np.sum(
-        np.square(
-            np.divide(
-                np.subtract(
-                    data,
-                    model_data,
-                ),
-                noise_map,
+    return float(
+        np.sum(
+            np.square(
+                np.divide(
+                    np.subtract(
+                        data,
+                        model_data,
+                    )[np.asarray(mask) == 0],
+                    noise_map[np.asarray(mask) == 0],
+                )
             )
-        )[np.asarray(mask) == 0]
+        )
     )
 
 

autoarray/inversion/pixelization/mappers/mapper_util.py

@@ -1,4 +1,5 @@
 import numpy as np
+from scipy.spatial import cKDTree
 from typing import Tuple
 
 from autoconf import conf
@@ -172,6 +173,20 @@ def pix_indexes_for_sub_slim_index_delaunay_from(
     return pix_indexes_for_sub_slim_index, pix_indexes_for_sub_slim_index_sizes
 
 
+def nearest_pixelization_index_for_slim_index_from_kdtree(grid, mesh_grid):
+
+    kdtree = cKDTree(mesh_grid)
+
+    sparse_index_for_slim_index = []
+
+    for i in range(grid.shape[0]):
+
+        input_point = [grid[i, [0]], grid[i, 1]]
+        index = kdtree.query(input_point)[1]
+        sparse_index_for_slim_index.append(index)
+
+    return sparse_index_for_slim_index
+
 @numba_util.jit()
 def nearest_pixelization_index_for_slim_index_from(grid, mesh_grid):
     """

autoarray/inversion/pixelization/mesh/mesh_util.py

@@ -469,22 +469,6 @@ def voronoi_neighbors_from(
     -------
     The arrays containing the 1D index of every pixel's neighbors and the number of neighbors that each pixel has.
     """
-    """
-    Returns the neighbors and total number of neighbors of every cell on a Voronoi mesh. 
-    
-    Neighbors are returned as an ndarray of shape [total_pixels, max_neighbors_in_a_given_voronoi pixel], where 
-    entries have values of -1 if the pixel has no neighbor.
-    
-    The number of neighbors of every pixel is also returned as an ndarray of shape [total_pixels], where the values
-    are integers between 0 and the total neighbors in a given Voronoi pixel.
-    
-    Parameters
-    ----------
-    pixels
-        The number of pixels on the Voronoi mesh
-    ridge_points
-        Contains the information on every Voronoi pixel and its neighbors.
-    """
     neighbors_sizes = np.zeros(shape=(pixels))
 
     for ridge_index in range(ridge_points.shape[0]):

autoarray/plot/mat_plot/one_d.py

@@ -165,7 +165,7 @@ def plot_yx(
         text_manual_dict_y=None,
         bypass: bool = False,
     ):
-        if (y is None) or np.count_nonzero(y) == 0:
+        if (y is None) or np.count_nonzero(y) == 0 or np.isnan(y).all():
             return
 
         ax = None