Merge branch 'develop'

Author: Jammy2211

.cache/v/cache/lastfailed

@@ -0,0 +1,15 @@
+{
+  "test/util/test_array_util.py": true,
+  "test/util/test_grid_util.py": true,
+  "test/util/test_mask_util.py": true,
+  "test/util/test_sparse_util.py": true,
+  "test/test_convolution.py": true, 
+  "test/test_fourier_transform.py": true, 
+  "test/test_grids.py": true, 
+  "test/test_mask.py": true, 
+  "test/test_scaled_array.py": true, 
+  "test/util/test_array_util.py": true, 
+  "test/util/test_binning_util.py": true, 
+  "test/util/test_grid_util.py": true, 
+  "test/util/test_mask_util.py": true
+}

.gitignore

@@ -3,7 +3,8 @@ __pycache__
 *.swp
 .eggs/
 autoarray.egg-info/
-
+*cache
+*pytest_cache
 build//
 build/
 dist/

autoarray/__init__.py

@@ -1,17 +1,40 @@
 from autoarray.mask.mask import Mask as mask
 from autoarray.structures.arrays import Array as array, MaskedArray as masked_array
-from autoarray.structures.grids import Grid as grid, MaskedGrid as masked_grid
+from autoarray.structures.grids import (
+    Grid as grid,
+    MaskedGrid as masked_grid,
+    IrregularGrid as irregular_grid,
+)
 from autoarray.structures.kernel import Kernel as kernel
+from autoarray.structures.visibilities import Visibilities as visibilities
+from autoarray import conf
+from autoarray import plotters as plot
+from autoarray import util
 from autoarray.data import data_converter
 from autoarray.data.imaging import Imaging as imaging
+from autoarray.data.imaging import SimulatedImaging
 from autoarray.data.interferometer import Interferometer as interferometer
+from autoarray.fit.fit import (
+    DataFit as fit,
+    ImagingFit as fit_imaging,
+    InterferometerFit as fit_interferometer,
+)
+from autoarray.fit.masked_data import (
+    MaskedImaging as masked_imaging,
+    MaskedInterferometer as masked_interferometer,
+)
+from autoarray.mask.mask import Mask as mask
 from autoarray.operators.convolution import Convolver as convolver
 from autoarray.operators.fourier_transform import Transformer as transformer
-from autoarray.operators.inversion import pixelizations as pix, regularization as reg, mappers, inversions
-from autoarray.fit.fit import DataFit as fit, ImagingFit as fit_imaging
-from autoarray.fit.masked_data import MaskedImaging as masked_imaging, MaskedInterferometer as masked_interferometer
-from autoarray import util
-from autoarray import plotters as plot
-from autoarray import conf
+from autoarray.operators.inversion import (
+    pixelization_grids as pix_grid,
+    pixelizations as pix,
+    regularization as reg,
+    mappers,
+    inversions,
+)
+from autoarray.structures.arrays import Array as array, MaskedArray as masked_array
+from autoarray.structures.grids import Grid as grid, MaskedGrid as masked_grid
+from autoarray.structures.kernel import Kernel as kernel
 
-__version__ = '0.2.0'
+__version__ = '0.2.1'

autoarray/data/abstract_data.py

@@ -58,7 +58,9 @@ def signal_to_noise_max(self):
     @property
     def absolute_signal_to_noise_map(self):
         """The estimated absolute_signal-to-noise_maps mappers of the image."""
-        return self.mapping.array_from_array_1d(array_1d=np.divide(np.abs(self.data), self.noise_map))
+        return self.mapping.array_from_array_1d(
+            array_1d=np.divide(np.abs(self.data), self.noise_map)
+        )
 
     @property
     def absolute_signal_to_noise_max(self):
@@ -69,7 +71,9 @@ def absolute_signal_to_noise_max(self):
     def potential_chi_squared_map(self):
         """The potential chi-squared map of the imaging data_type. This represents how much each pixel can contribute to \
         the chi-squared map, assuming the model fails to fit it at all (e.g. model value = 0.0)."""
-        return self.mapping.array_from_array_1d(array_1d=np.square(self.absolute_signal_to_noise_map))
+        return self.mapping.array_from_array_1d(
+            array_1d=np.square(self.absolute_signal_to_noise_map)
+        )
 
     @property
     def potential_chi_squared_max(self):
@@ -123,12 +127,8 @@ def image_counts(self):
 
 class ExposureTimeMap(arrays.Array):
     @classmethod
-    def from_exposure_time_and_inverse_noise_map(
-        cls, exposure_time, inverse_noise_map
-    ):
-        relative_background_noise_map = inverse_noise_map / np.max(
-            inverse_noise_map
-        )
+    def from_exposure_time_and_inverse_noise_map(cls, exposure_time, inverse_noise_map):
+        relative_background_noise_map = inverse_noise_map / np.max(inverse_noise_map)
         return np.abs(exposure_time * (relative_background_noise_map))
 
 
@@ -207,8 +207,7 @@ def load_exposure_time_map(
 
         if exposure_time_map_from_inverse_noise_map:
             return ExposureTimeMap.from_exposure_time_and_inverse_noise_map(
-                exposure_time=exposure_time,
-                inverse_noise_map=inverse_noise_map,
+                exposure_time=exposure_time, inverse_noise_map=inverse_noise_map
             )
 
 

autoarray/data/data_converter.py

@@ -5,18 +5,31 @@
 # TODO : The way these work dpeending on if the input array (e.g. the weight map) are an auto array or not is a bit
 # TODO : clunky. For example, we can currently overwrite pixel_scales. We need to decade how we approach this.
 
-def array_using_correct_dimensions(array, pixel_scales=None, sub_size=1, origin=(0.0, 0.0)):
+
+def array_using_correct_dimensions(
+    array, pixel_scales=None, sub_size=1, origin=(0.0, 0.0)
+):
 
     if isinstance(array, arrays.AbstractArray):
         return array
 
     if len(array.shape) == 1:
-        return arrays.Array.manual_1d(array=array, shape_2d=array.shape_2d, pixel_scales=pixel_scales, sub_size=sub_size, origin=origin)
+        return arrays.Array.manual_1d(
+            array=array,
+            shape_2d=array.shape_2d,
+            pixel_scales=pixel_scales,
+            sub_size=sub_size,
+            origin=origin,
+        )
     elif len(array.shape) == 2:
-        return arrays.Array.manual_2d(array=array, pixel_scales=pixel_scales, sub_size=sub_size, origin=origin)
+        return arrays.Array.manual_2d(
+            array=array, pixel_scales=pixel_scales, sub_size=sub_size, origin=origin
+        )
 
 
-def noise_map_from_weight_map(weight_map, pixel_scales=None, sub_size=1, origin=(0.0, 0.0)):
+def noise_map_from_weight_map(
+    weight_map, pixel_scales=None, sub_size=1, origin=(0.0, 0.0)
+):
     """Setup the noise-map from a weight map, which is a form of noise-map that comes via HST image-reduction and \
     the software package MultiDrizzle.
 
@@ -37,9 +50,14 @@ def noise_map_from_weight_map(weight_map, pixel_scales=None, sub_size=1, origin=
     np.seterr(divide="ignore")
     noise_map = 1.0 / np.sqrt(weight_map)
     noise_map[noise_map > 1.0e8] = 1.0e8
-    return array_using_correct_dimensions(array=noise_map, pixel_scales=pixel_scales, sub_size=sub_size, origin=origin)
+    return array_using_correct_dimensions(
+        array=noise_map, pixel_scales=pixel_scales, sub_size=sub_size, origin=origin
+    )
 
-def noise_map_from_inverse_noise_map(inverse_noise_map, pixel_scales=None, sub_size=1, origin=(0.0, 0.0)):
+
+def noise_map_from_inverse_noise_map(
+    inverse_noise_map, pixel_scales=None, sub_size=1, origin=(0.0, 0.0)
+):
     """Setup the noise-map from an root-mean square standard deviation map, which is a form of noise-map that \
     comes via HST image-reduction and the software package MultiDrizzle.
 
@@ -57,43 +75,58 @@ def noise_map_from_inverse_noise_map(inverse_noise_map, pixel_scales=None, sub_s
     inverse_noise_map : ndarray
         The inverse noise_map value of each pixel which is converted to a variance.
     """
-    return array_using_correct_dimensions(array=1.0 / inverse_noise_map, pixel_scales=pixel_scales, sub_size=sub_size, origin=origin)
+    return array_using_correct_dimensions(
+        array=1.0 / inverse_noise_map,
+        pixel_scales=pixel_scales,
+        sub_size=sub_size,
+        origin=origin,
+    )
+
 
 def noise_map_from_image_and_background_noise_map(
     image,
     background_noise_map,
     exposure_time_map,
     gain=None,
     convert_from_electrons=False,
-    convert_from_adus=False, pixel_scales=None, sub_size=1, origin=(0.0, 0.0)
+    convert_from_adus=False,
+    pixel_scales=None,
+    sub_size=1,
+    origin=(0.0, 0.0),
 ):
 
     if not convert_from_electrons and not convert_from_adus:
-        noise_map = np.sqrt(
+        noise_map = (
+            np.sqrt(
                 np.abs(
                     ((background_noise_map) * exposure_time_map) ** 2.0
                     + (image) * exposure_time_map
-                )) / exposure_time_map
+                )
+            )
+            / exposure_time_map
+        )
 
     elif convert_from_electrons:
-        noise_map = np.sqrt(
-                np.abs(background_noise_map ** 2.0 + image)
-            )
+        noise_map = np.sqrt(np.abs(background_noise_map ** 2.0 + image))
     elif convert_from_adus:
-        noise_map = np.sqrt(
-                np.abs(
-                    (gain * background_noise_map) ** 2.0 + gain * image
-                )
-            ) / gain
+        noise_map = (
+            np.sqrt(np.abs((gain * background_noise_map) ** 2.0 + gain * image)) / gain
+        )
+
+    return array_using_correct_dimensions(
+        array=noise_map, pixel_scales=pixel_scales, sub_size=sub_size, origin=origin
+    )
 
-    return array_using_correct_dimensions(array=noise_map, pixel_scales=pixel_scales, sub_size=sub_size, origin=origin)
 
 def poisson_noise_map_from_image_and_exposure_time_map(
     image,
     exposure_time_map,
     gain=None,
     convert_from_electrons=False,
-    convert_from_adus=False, pixel_scales=None, sub_size=1, origin=(0.0, 0.0)
+    convert_from_adus=False,
+    pixel_scales=None,
+    sub_size=1,
+    origin=(0.0, 0.0),
 ):
     if not convert_from_electrons and not convert_from_adus:
         noise_map = np.sqrt(np.abs(image) * exposure_time_map) / exposure_time_map
@@ -103,4 +136,6 @@ def poisson_noise_map_from_image_and_exposure_time_map(
     elif convert_from_adus:
         noise_map = np.sqrt(gain * np.abs(image)) / gain
 
-    return array_using_correct_dimensions(array=noise_map, pixel_scales=pixel_scales, sub_size=sub_size, origin=origin)
+    return array_using_correct_dimensions(
+        array=noise_map, pixel_scales=pixel_scales, sub_size=sub_size, origin=origin
+    )