more informative error

autoarray/config/general.yaml

@@ -10,7 +10,7 @@ numba:
   nopython: true
   parallel: false
 pixelization:
-  voronoi_nn_max_interpolation_neighbors: 100
+  voronoi_nn_max_interpolation_neighbors: 300
 structures:
   native_binned_only: false           # If True, data structures are only stored in their native and binned format. This is used to reduce memory usage in autocti.
 test:

autoarray/inversion/inversion/inversion_util.py

@@ -292,7 +292,7 @@ def reconstruction_positive_only_from(
                 P_initial=P_initial,
             )
 
-        except (RuntimeError, np.linalg.LinAlgError) as e:
+        except (RuntimeError, np.linalg.LinAlgError, ValueError) as e:
             raise exc.InversionException() from e
 
     else:

autoarray/mask/mask_2d_util.py

@@ -1129,7 +1129,7 @@ def rescaled_mask_2d_from(mask_2d: np.ndarray, rescale_factor: float) -> np.ndar
             scale=rescale_factor,
             mode="edge",
             anti_aliasing=False,
-          #  multichannel=False,
+            #  multichannel=False,
         )
 
     rescaled_mask_2d[0, :] = 1

autoarray/plot/mat_plot/two_d.py

@@ -241,7 +241,7 @@ def plot_array(
         # so that if a mask is irregular and zooming in creates white edges, that instead it doesnt have the eddge.
         # This could just be a matplotlib settings to change the edge color?
 
-        # array = array.resized_from(new_shape=(401, 401))
+        #        array = array.resized_from(new_shape=(401, 401))
 
         if array.mask.is_all_false:
             buffer = 0
@@ -338,14 +338,14 @@ def plot_array(
             )
             self.colorbar_tickparams.set(cb=cb)
 
-        # levels = np.logspace(np.log10(0.3), np.log10(20.0), 10)
-        # plt.contour(
-        #   #  array.mask.derive_grid.unmasked_sub_1,
-        #     array.native[::-1],
-        #     levels=levels,
-        #     colors="black",
-        #     extent=extent,
-        # )
+        levels = np.logspace(np.log10(0.3), np.log10(20.0), 10)
+        plt.contour(
+            #  array.mask.derive_grid.unmasked_sub_1,
+            array.native[::-1],
+            levels=levels,
+            colors="black",
+            extent=extent,
+        )
 
         grid_indexes = None
 

autoarray/plot/wrap/base/colorbar.py

@@ -18,7 +18,7 @@ def __init__(
         manual_tick_values: Optional[List[float]] = None,
         manual_alignment: Optional[str] = None,
         manual_unit: Optional[str] = None,
-        manual_log10 : bool = False,
+        manual_log10: bool = False,
         **kwargs,
     ):
         """
@@ -102,8 +102,12 @@ def manual_tick_labels_from(
             ]
 
             if self.manual_log10:
-                manual_tick_labels = ["{:.0e}".format(label) for label in manual_tick_labels]
-                manual_tick_labels = [label.replace("1e", "$10^{") + "}$" for label in manual_tick_labels]
+                manual_tick_labels = [
+                    "{:.0e}".format(label) for label in manual_tick_labels
+                ]
+                manual_tick_labels = [
+                    label.replace("1e", "$10^{") + "}$" for label in manual_tick_labels
+                ]
                 manual_tick_labels = [
                     label.replace("{-0", "{-").replace("{+0", "{+").replace("+", "")
                     for label in manual_tick_labels

autoarray/preloads.py

@@ -1,5 +1,6 @@
 import logging
 import numpy as np
+import os
 from typing import List
 
 from autoconf import conf
@@ -465,6 +466,9 @@ def check_via_fit(self, fit):
             settings_inversion=settings_inversion,
         )
 
+        if os.environ.get("PYAUTOFIT_TEST_MODE") == "1":
+            return
+
         try:
             if (
                 abs(

autoarray/structures/arrays/kernel_2d.py

@@ -465,7 +465,7 @@ def rescaled_with_odd_dimensions_from(
                 rescale_factor,
                 anti_aliasing=False,
                 mode="constant",
-             #   multichannel=False,
+                #   multichannel=False,
             )
 
         if kernel_rescaled.shape[0] % 2 == 0 and kernel_rescaled.shape[1] % 2 == 0:

autoarray/structures/structure_decorators.py

@@ -3,6 +3,7 @@
 from typing import List, Optional, Union
 
 from autoconf import conf
+from autoconf.exc import ConfigException
 from autoarray.structures.arrays.uniform_1d import Array1D
 from autoarray.structures.arrays.uniform_2d import Array2D
 from autoarray.structures.grids.uniform_1d import Grid1D
@@ -39,7 +40,7 @@ def wrapper(
         obj: object,
         grid: Union[Grid1D, Grid2D, Grid2DIterate, Grid2DIrregular],
         *args,
-        **kwargs
+        **kwargs,
     ) -> Union[Array1D, ArrayIrregular]:
         """
         This decorator homogenizes the input of a "grid_like" 2D structure (`Grid2D`, `Grid2DIterate`,
@@ -128,7 +129,7 @@ def wrapper(
         obj,
         grid: Union[Grid1D, Grid2D, Grid2DIterate, Grid2DIrregular],
         *args,
-        **kwargs
+        **kwargs,
     ) -> Union[Array1D, ArrayIrregular]:
         """
         This decorator homogenizes the output of functions which compute a 1D result, by inspecting the output
@@ -184,7 +185,7 @@ def wrapper(
         obj: object,
         grid: Union[np.ndarray, Grid2D, Grid2DIterate, Grid2DIrregular, Grid1D],
         *args,
-        **kwargs
+        **kwargs,
     ) -> Union[np.ndarray, Array2D, ArrayIrregular, Grid2D, Grid2DIrregular]:
         """
         This decorator homogenizes the input of a "grid_like" 2D structure (`Grid2D`, `Grid2DIterate`,
@@ -261,7 +262,7 @@ def wrapper(
         obj: object,
         grid: Union[np.ndarray, Grid2D, Grid2DIterate, Grid2DIrregular, Grid1D],
         *args,
-        **kwargs
+        **kwargs,
     ) -> List[Union[np.ndarray, Array2D, ArrayIrregular, Grid2D, Grid2DIrregular]]:
         """
         This decorator serves the same purpose as the `grid_2d_to_structure` decorator, but it deals with functions
@@ -329,7 +330,7 @@ def wrapper(
         obj: object,
         grid: Union[np.ndarray, Grid2D, Grid2DIterate, Grid2DIrregular, Grid1D],
         *args,
-        **kwargs
+        **kwargs,
     ) -> Union[np.ndarray, Array2D, ArrayIrregular, Grid2D, Grid2DIrregular]:
         """
         This decorator homogenizes the input of a "grid_like" 2D vector_yx (`Grid2D`, `Grid2DIterate`,
@@ -399,7 +400,7 @@ def wrapper(
         obj: object,
         grid: Union[np.ndarray, Grid2D, Grid2DIterate, Grid2DIrregular, Grid1D],
         *args,
-        **kwargs
+        **kwargs,
     ) -> List[Union[np.ndarray, Array2D, ArrayIrregular, Grid2D, Grid2DIrregular]]:
         """
         This decorator serves the same purpose as the `grid_2d_to_vector_yx` decorator, but it deals with functions
@@ -463,7 +464,7 @@ def wrapper(
             Grid2DIrregularTransformed,
         ],
         *args,
-        **kwargs
+        **kwargs,
     ) -> Union[
         np.ndarray,
         Grid2D,
@@ -526,7 +527,7 @@ def wrapper(
         cls,
         grid: Union[np.ndarray, Grid2D, Grid2DIrregular, Grid2DIterate],
         *args,
-        **kwargs
+        **kwargs,
     ) -> Union[np.ndarray, Grid2D, Grid2DIrregular, Grid2DIterate]:
         """
 
@@ -542,9 +543,32 @@ def wrapper(
             The grid_like object whose coordinates are radially moved from (0.0, 0.0).
         """
 
-        grid_radial_minimum = conf.instance["grids"]["radial_minimum"][
-            "radial_minimum"
-        ][cls.__class__.__name__]
+        try:
+            grid_radial_minimum = conf.instance["grids"]["radial_minimum"][
+                "radial_minimum"
+            ][cls.__class__.__name__]
+        except KeyError as e:
+            raise ConfigException(
+                rf"""
+                The {cls.__class__.__name__} profile you are using does not have a corresponding
+                entry in the `config/grid.yaml` config file.
+
+                When a profile is evaluated at (0.0, 0.0), they commonly break due to numericalinstabilities (e.g. 
+                division by zero). To prevent this, the code relocates the (y,x) coordinates of the grid to a 
+                minimum radial value, specified in the `config/grids.yaml` config file.
+
+                For example, if the value in `grid.yaml` is `radial_minimum: 1e-6`, then any (y,x) coordinates
+                with a radial distance less than 1e-6 to (0.0, 0.0) are relocated to 1e-6.
+
+                For a profile to be used it must have an entry in the `config/grids.yaml` config file. Go to this
+                file now and add your profile to the `radial_minimum` section. Adopting a value of 1e-6 is a good
+                default choice.
+
+                If you are going to make a pull request to add your profile to the source code, you should also
+                add an entry to the `config/grids.yaml` config file of the source code itself
+                (e.g. `PyAutoGalaxy/autogalaxy/config/grids.yaml`).
+                """
+            )
 
         with np.errstate(all="ignore"):  # Division by zero fixed via isnan
             grid_radii = cls.radial_grid_from(grid=grid)

test_autoarray/config/general.yaml

@@ -29,7 +29,7 @@ output:
   model_results_every_update: 100
   remove_files: false
 pixelization:
-  voronoi_nn_max_interpolation_neighbors: 100
+  voronoi_nn_max_interpolation_neighbors: 300
 profiling:
   perform: false
   repeats: 1