black

Author: Jammy2211

autoarray/__init__.py

@@ -1,3 +1,8 @@
+from autoconf.dictable import register_parser
+from autofit import conf
+
+conf.instance.register(__file__)
+
 from . import exc
 from . import type
 from . import util

autoarray/dataset/grids.py

@@ -11,6 +11,7 @@
 
 from autoarray import exc
 
+
 class GridsDataset:
     def __init__(
         self,
@@ -88,6 +89,7 @@ def __init__(
             mask=self.mask, sub_size=self.over_sample_size_pixelization
         )
 
+
 class GridsInterface:
     def __init__(
         self,

autoarray/exc.py

@@ -104,4 +104,3 @@ class PlottingException(Exception):
     """
 
     pass
-

autoarray/inversion/inversion/abstract.py

@@ -81,7 +81,6 @@ def __init__(
                 "https://pyautolens.readthedocs.io/en/latest/installation/overview.html"
             )
 
-
         self.dataset = dataset
 
         self.linear_obj_list = linear_obj_list

autoarray/inversion/linear_obj/func_list.py

@@ -9,6 +9,7 @@
 from autoarray.inversion.regularization.abstract import AbstractRegularization
 from autoarray.type import Grid1D2DLike
 
+
 class AbstractLinearObjFuncList(LinearObj):
     def __init__(
         self,

autoarray/operators/over_sampling/over_sample_util.py

@@ -166,13 +166,15 @@ def sub_size_radial_bins_from(
 
     return sub_size_list[bin_indices]
 
+
 from autoarray.geometry import geometry_util
 
+
 def grid_2d_slim_over_sampled_via_mask_from(
-        mask_2d: np.ndarray,
-        pixel_scales: ty.PixelScales,
-        sub_size: np.ndarray,
-        origin: Tuple[float, float] = (0.0, 0.0),
+    mask_2d: np.ndarray,
+    pixel_scales: ty.PixelScales,
+    sub_size: np.ndarray,
+    origin: Tuple[float, float] = (0.0, 0.0),
 ) -> np.ndarray:
     """
     For a sub-grid, every unmasked pixel of its 2D mask with shape (total_y_pixels, total_x_pixels) is divided into
@@ -216,11 +218,9 @@ def grid_2d_slim_over_sampled_via_mask_from(
     pixels_in_mask = (np.size(mask_2d) - np.sum(mask_2d)).astype(int)
 
     if isinstance(sub_size, int):
-        sub_size = np.full(
-            fill_value=sub_size, shape=pixels_in_mask
-        )
+        sub_size = np.full(fill_value=sub_size, shape=pixels_in_mask)
 
-    total_sub_pixels = np.sum(sub_size ** 2)
+    total_sub_pixels = np.sum(sub_size**2)
 
     grid_slim = np.zeros(shape=(total_sub_pixels, 2))
 
@@ -248,10 +248,10 @@ def grid_2d_slim_over_sampled_via_mask_from(
                 for y1 in range(sub):
                     for x1 in range(sub):
                         grid_slim[sub_index, 0] = -(
-                                y_scaled - y_sub_half + y1 * y_sub_step + (y_sub_step / 2.0)
+                            y_scaled - y_sub_half + y1 * y_sub_step + (y_sub_step / 2.0)
                         )
                         grid_slim[sub_index, 1] = (
-                                x_scaled - x_sub_half + x1 * x_sub_step + (x_sub_step / 2.0)
+                            x_scaled - x_sub_half + x1 * x_sub_step + (x_sub_step / 2.0)
                         )
                         sub_index += 1
 

autoarray/structures/grids/uniform_2d.py

@@ -189,17 +189,16 @@ def over_sampled(self):
             return self._over_sampled
 
         over_sampled = over_sample_util.grid_2d_slim_over_sampled_via_mask_from(
-                mask_2d=np.array(self.mask),
-                pixel_scales=self.mask.pixel_scales,
-                sub_size=self.over_sampler.sub_size.array.astype("int"),
-                origin=self.mask.origin,
-            )
+            mask_2d=np.array(self.mask),
+            pixel_scales=self.mask.pixel_scales,
+            sub_size=self.over_sampler.sub_size.array.astype("int"),
+            origin=self.mask.origin,
+        )
 
         self._over_sampled = Grid2DIrregular(values=over_sampled)
 
         return self._over_sampled
 
-
     @classmethod
     def no_mask(
         cls,
@@ -703,7 +702,7 @@ def subtracted_from(self, offset: Tuple[(float, float), np.ndarray]) -> "Grid2D"
             values=self - np.array(offset),
             mask=mask,
             over_sample_size=self.over_sample_size,
-            over_sampled=self.over_sampled - np.array(offset)
+            over_sampled=self.over_sampled - np.array(offset),
         )
 
     @property

test_autoarray/fit/test_fit_imaging.py

@@ -102,4 +102,3 @@ def test__data_and_model_are_identical__inversion_included__changes_certain_prop
         fit.chi_squared + 2.0 + 3.0 - 4.0 + fit.noise_normalization
     )
     assert fit.figure_of_merit == fit.log_evidence
-

test_autoarray/test_decorators.py

@@ -8,6 +8,3 @@ def __init__(self, value):
     @property
     def value(self):
         return self._value
-
-
-