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Author: Jammy2211

autoarray/fit/fit_util.py

@@ -174,12 +174,8 @@ def noise_normalization_complex_from(*, noise_map: jnp.ndarray) -> float:
     noise_map
         The masked noise-map of the dataset.
     """
-    noise_normalization_real = jnp.sum(
-        jnp.log(2 * jnp.pi * noise_map.real ** 2.0)
-    )
-    noise_normalization_imag = jnp.sum(
-        jnp.log(2 * jnp.pi * noise_map.imag ** 2.0)
-    )
+    noise_normalization_real = jnp.sum(jnp.log(2 * jnp.pi * noise_map.real**2.0))
+    noise_normalization_imag = jnp.sum(jnp.log(2 * jnp.pi * noise_map.imag**2.0))
     return noise_normalization_real + noise_normalization_imag
 
 

autoarray/mask/derive/mask_2d.py

@@ -324,9 +324,9 @@ def edge_buffed(self) -> Mask2D:
         """
         from autoarray.mask.mask_2d import Mask2D
 
-        edge_buffed_mask = mask_2d_util.buffed_mask_2d_from(
-            mask_2d=self.mask
-        ).astype("bool")
+        edge_buffed_mask = mask_2d_util.buffed_mask_2d_from(mask_2d=self.mask).astype(
+            "bool"
+        )
 
         return Mask2D(
             mask=edge_buffed_mask,

autoarray/mask/mask_1d.py

@@ -153,7 +153,9 @@ def from_fits(
         """
 
         return cls(
-            mask=array_1d_util.numpy_array_1d_via_fits_from(file_path=file_path, hdu=hdu),
+            mask=array_1d_util.numpy_array_1d_via_fits_from(
+                file_path=file_path, hdu=hdu
+            ),
             pixel_scales=pixel_scales,
             origin=origin,
         )

autoarray/mask/mask_1d_util.py

@@ -36,4 +36,4 @@ def native_index_for_slim_index_1d_from(
 
     if isinstance(mask_1d, np.ndarray):
         return np.flatnonzero(~mask_1d)
-    return jnp.flatnonzero(~mask_1d)
+    return jnp.flatnonzero(~mask_1d)

autoarray/plot/visuals/two_d.py

@@ -55,9 +55,7 @@ def plot_via_plotter(self, plotter, grid_indexes=None, mapper=None, geometry=Non
             )
 
         if self.mask is not None:
-            plotter.mask_scatter.scatter_grid(
-                grid=self.mask.derive_grid.edge.array
-            )
+            plotter.mask_scatter.scatter_grid(grid=self.mask.derive_grid.edge.array)
 
         if self.border is not None:
             plotter.border_scatter.scatter_grid(grid=self.border.array)

autoarray/plot/wrap/base/text.py

@@ -9,9 +9,11 @@ class Text(AbstractMatWrap):
 
     - plt.text: https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.pyplot.text.html
     """
-    import matplotlib.pyplot as plt
 
     def set(self):
+
+        import matplotlib.pyplot as plt
+
         if "x" not in self.kwargs and "y" not in self.kwargs and "s" not in self.kwargs:
             return
 

autoarray/structures/grids/grid_2d_util.py

@@ -258,9 +258,9 @@ def grid_2d_slim_via_mask_from(
         pixel_scales = np.array(pixel_scales)
         sign = np.array([-1.0, 1.0])
         return (
-                (np.stack(np.nonzero(~mask_2d.astype(bool))).T - centres_scaled)
-                * sign
-                * pixel_scales
+            (np.stack(np.nonzero(~mask_2d.astype(bool))).T - centres_scaled)
+            * sign
+            * pixel_scales
         )
 
     centres_scaled = jnp.array(centres_scaled)

autoarray/structures/mock/mock_decorators.py

@@ -157,5 +157,3 @@ def ndarray_yx_2d_list_from(self, grid, *args, **kwargs):
         Such functions are common in **PyAutoGalaxy** for light and mass profile objects.
         """
         return [np.multiply(1.0, grid.array), np.multiply(2.0, grid.array)]
-
-

test_autoarray/conftest.py

@@ -1,8 +1,10 @@
 import jax.numpy as jnp
 
+
 def pytest_configure():
     _ = jnp.sum(jnp.array([0.0]))  # Force backend init
 
+
 import os
 from os import path
 import pytest

test_autoarray/fit/test_fit_util.py

@@ -407,7 +407,7 @@ def test__log_likelihood_from__with_mask():
 
 
 def test__log_likelihood_from__complex_data():
-    
+
     data = np.array([10.0 + 10.0j, 10.0 + 10.0j])
     noise_map = np.array([2.0 + 1.0j, 2.0 + 1.0j])
     model_data = np.array([9.0 + 12.0j, 9.0 + 12.0j])