Merge branch 'develop' of https://github.com/jammy2211/PyAutoArray into develop

Author: Jammy2211

autoarray/operators/inversion/regularization.py

@@ -123,18 +123,18 @@ def __init__(self):
         """
 
 
-class Constant(Regularization):
+class instance(Regularization):
     def __init__(self, coefficient=1.0):
-        """A constant-regularization scheme (regularization is described in the *Regularization* class above).
+        """A instance-regularization scheme (regularization is described in the *Regularization* class above).
 
-        For the constant regularization_matrix scheme, there is only 1 regularization coefficient that is applied to \
+        For the instance regularization_matrix scheme, there is only 1 regularization coefficient that is applied to \
         all neighboring pixels. This means that we when write B, we only need to regularize pixels in one direction \
         (e.g. pixel 0 regularizes pixel 1, but NOT visa versa). For example:
 
         B = [-1, 1]  [0->1]
             [0, -1]  1 does not regularization with 0
 
-        A small numerical value of 1.0e-8 is added to all elements in a constant regularization matrix, to ensure that \
+        A small numerical value of 1.0e-8 is added to all elements in a instance regularization matrix, to ensure that \
         it is positive definite.
 
         Parameters
@@ -143,13 +143,13 @@ def __init__(self, coefficient=1.0):
             The regularization coefficient which controls the degree of smooth of the inversion reconstruction.
         """
         self.coefficient = coefficient
-        super(Constant, self).__init__()
+        super(instance, self).__init__()
 
     def regularization_weights_from_mapper(self, mapper):
         return self.coefficient * np.ones(mapper.pixelization_grid.pixels)
 
     def regularization_matrix_from_mapper(self, mapper):
-        return regularization_util.constant_regularization_matrix_from_pixel_neighbors(
+        return regularization_util.instance_regularization_matrix_from_pixel_neighbors(
             coefficient=self.coefficient,
             pixel_neighbors=mapper.pixelization_grid.pixel_neighbors,
             pixel_neighbors_size=mapper.pixelization_grid.pixel_neighbors_size,
@@ -158,20 +158,20 @@ def regularization_matrix_from_mapper(self, mapper):
 
 class AdaptiveBrightness(Regularization):
     def __init__(self, inner_coefficient=1.0, outer_coefficient=1.0, signal_scale=1.0):
-        """ A constant-regularization scheme (regularization is described in the *Regularization* class above).
+        """ A instance-regularization scheme (regularization is described in the *Regularization* class above).
 
         For the weighted regularization scheme, each pixel is given an 'effective regularization weight', which is \
         applied when each set of pixel neighbors are regularized with one another. The motivation of this is that \
         different regions of a pixelization require different levels of regularization (e.g., high smoothing where the \
         no signal is present and less smoothing where it is, see (Nightingale, Dye and Massey 2018)).
 
-        Unlike the constant regularization_matrix scheme, neighboring pixels must now be regularized with one another \
+        Unlike the instance regularization_matrix scheme, neighboring pixels must now be regularized with one another \
         in both directions (e.g. if pixel 0 regularizes pixel 1, pixel 1 must also regularize pixel 0). For example:
 
         B = [-1, 1]  [0->1]
             [-1, -1]  1 now also regularizes 0
 
-        For a constant regularization coefficient this would NOT produce a positive-definite matrix. However, for
+        For a instance regularization coefficient this would NOT produce a positive-definite matrix. However, for
         the weighted scheme, it does!
 
         The regularize weights change the B matrix as shown below - we simply multiply each pixel's effective \

autoarray/util/regularization_util.py

@@ -3,10 +3,10 @@
 
 
 @decorator_util.jit()
-def constant_regularization_matrix_from_pixel_neighbors(
+def instance_regularization_matrix_from_pixel_neighbors(
     coefficient, pixel_neighbors, pixel_neighbors_size
 ):
-    """From the pixel-neighbors, setup the regularization matrix using the constant regularization scheme.
+    """From the pixel-neighbors, setup the regularization matrix using the instance regularization scheme.
 
     Parameters
     ----------

test_autoarray/unit/dataset/test_imaging.py

@@ -321,7 +321,7 @@ def test__image_and_exposure_times_range_of_values__background_has_value_9___noi
     def test__image_and_exposure_times_and_background_are_all_ranges_of_values__noise_estimates_correct(
         self
     ):
-        # Use same pattern as above, noting that we are now also using a variable background signal_to_noise_ratio map.
+        # Use same pattern as above, noting that we are now also using a model background signal_to_noise_ratio map.
 
         image = aa.array.manual_2d(array=[[5.0, 3.0], [10.0, 20.0]])
 
@@ -2277,7 +2277,7 @@ def test__simulate_function__turns_exposure_time_and_sky_level_to_arrays(self):
 
         background_sky_map = aa.array.full(fill_value=16.0, shape_2d=image.mask.shape)
 
-        imaging_variable = aa.imaging.simulate(
+        imaging_model = aa.imaging.simulate(
             image=image,
             exposure_time=1.0,
             exposure_time_map=exposure_time_map,
@@ -2309,14 +2309,14 @@ def test__simulate_function__turns_exposure_time_and_sky_level_to_arrays(self):
         )
 
         assert (
-            imaging_variable.exposure_time_map.in_2d
+            imaging_model.exposure_time_map.in_2d
             == imaging_simulated.exposure_time_map.in_2d
         ).all()
-        assert imaging_variable.image.in_2d == pytest.approx(
+        assert imaging_model.image.in_2d == pytest.approx(
             imaging_simulated.image.in_2d, 1e-4
         )
         assert (
-            imaging_variable.background_noise_map.in_2d
+            imaging_model.background_noise_map.in_2d
             == imaging_simulated.background_noise_map.in_2d
         ).all()
 

test_autoarray/unit/operators/inversion/test_integration.py

@@ -62,7 +62,7 @@ def test__5_simple_grid__no_sub_grid(self):
         assert mapper.shape_2d == (3, 3)
         assert (mapper.hyper_image == np.ones((2, 2))).all()
 
-        reg = aa.reg.Constant(coefficient=1.0)
+        reg = aa.reg.instance(coefficient=1.0)
         regularization_matrix = reg.regularization_matrix_from_mapper(mapper=mapper)
 
         assert (
@@ -175,7 +175,7 @@ def test__15_grid__no_sub_grid(self):
         ).all()
         assert mapper.shape_2d == (3, 3)
 
-        reg = aa.reg.Constant(coefficient=1.0)
+        reg = aa.reg.instance(coefficient=1.0)
         regularization_matrix = reg.regularization_matrix_from_mapper(mapper=mapper)
 
         assert (
@@ -287,7 +287,7 @@ def test__5_simple_grid__include_sub_grid(self):
         ).all()
         assert mapper.shape_2d == (3, 3)
 
-        reg = aa.reg.Constant(coefficient=1.0)
+        reg = aa.reg.instance(coefficient=1.0)
         regularization_matrix = reg.regularization_matrix_from_mapper(mapper=mapper)
 
         assert (
@@ -372,7 +372,7 @@ def test__grid__requires_border_relocation(self):
         ).all()
         assert mapper.shape_2d == (3, 3)
 
-        reg = aa.reg.Constant(coefficient=1.0)
+        reg = aa.reg.instance(coefficient=1.0)
         regularization_matrix = reg.regularization_matrix_from_mapper(mapper=mapper)
 
         assert (
@@ -432,7 +432,7 @@ def test__interferometer(self):
             grid=grid, sparse_grid=None, inversion_uses_border=False
         )
 
-        reg = aa.reg.Constant(coefficient=0.0)
+        reg = aa.reg.instance(coefficient=0.0)
 
         visibilities = aa.visibilities.manual_1d(
             visibilities=[
@@ -547,7 +547,7 @@ def test__3x3_simple_grid(self):
             )
         ).all()
 
-        reg = aa.reg.Constant(coefficient=1.0)
+        reg = aa.reg.instance(coefficient=1.0)
         regularization_matrix = reg.regularization_matrix_from_mapper(mapper=mapper)
 
         assert (
@@ -639,7 +639,7 @@ def test__3x3_simple_grid__include_mask(self):
             )
         ).all()
 
-        reg = aa.reg.Constant(coefficient=1.0)
+        reg = aa.reg.instance(coefficient=1.0)
         regularization_matrix = reg.regularization_matrix_from_mapper(mapper=mapper)
 
         assert (
@@ -748,7 +748,7 @@ def test__3x3_simple_grid__include_mask_and_sub_grid(self):
             )
         ).all()
 
-        reg = aa.reg.Constant(coefficient=1.0)
+        reg = aa.reg.instance(coefficient=1.0)
         regularization_matrix = reg.regularization_matrix_from_mapper(mapper=mapper)
 
         assert (
@@ -838,7 +838,7 @@ def test__3x3_simple_grid__include_mask_with_offset_centre(self):
             )
         ).all()
 
-        reg = aa.reg.Constant(coefficient=1.0)
+        reg = aa.reg.instance(coefficient=1.0)
         regularization_matrix = reg.regularization_matrix_from_mapper(mapper=mapper)
 
         assert (
@@ -896,7 +896,7 @@ def test__interferometer(self):
             grid=grid, sparse_grid=sparse_grid, inversion_uses_border=False
         )
 
-        reg = aa.reg.Constant(coefficient=0.0)
+        reg = aa.reg.instance(coefficient=0.0)
 
         visibilities = aa.visibilities.manual_1d(
             visibilities=[

test_autoarray/unit/operators/inversion/test_regularization.py

@@ -4,7 +4,7 @@
 from test_autoarray.mock.mock_inversion import MockPixelizationGrid, MockRegMapper
 
 
-class TestRegularizationConstant:
+class TestRegularizationinstance:
     def test__regularization_matrix__compare_to_regularization_util(self):
 
         pixel_neighbors = np.array(
@@ -29,10 +29,10 @@ def test__regularization_matrix__compare_to_regularization_util(self):
 
         mapper = MockRegMapper(pixelization_grid=pixelization_grid)
 
-        reg = aa.reg.Constant(coefficient=1.0)
+        reg = aa.reg.instance(coefficient=1.0)
         regularization_matrix = reg.regularization_matrix_from_mapper(mapper=mapper)
 
-        regularization_matrix_util = aa.util.regularization.constant_regularization_matrix_from_pixel_neighbors(
+        regularization_matrix_util = aa.util.regularization.instance_regularization_matrix_from_pixel_neighbors(
             coefficient=1.0,
             pixel_neighbors=pixel_neighbors,
             pixel_neighbors_size=pixel_neighbors_size,

test_autoarray/unit/test_files/config/label.ini

@@ -31,5 +31,5 @@ HyperGalaxy = hg
 Rectangular = rect
 VoronoiMagnification = voro_mag
 VoronoiBrightnessImage = voro_image
-Constant = const
+instance = const
 AdaptiveBrightness = adapt_bright

test_autoarray/unit/test_files/config/priors/default/regularization.ini

@@ -1,4 +1,4 @@
-[Constant]
+[instance]
 coefficient = u,0,1
 
 [AdaptiveBrightness]

test_autoarray/unit/util/test_regularization_util.py

@@ -4,7 +4,7 @@
 import pytest
 
 
-class TestRegularizationConstantMatrix:
+class TestRegularizationinstanceMatrix:
     def test__1_b_matrix_size_3x3__weights_all_1s__makes_correct_regularization_matrix(
         self
     ):
@@ -26,7 +26,7 @@ def test__1_b_matrix_size_3x3__weights_all_1s__makes_correct_regularization_matr
             test_b_matrix.T, test_b_matrix
         ) + 1e-8 * np.identity(3)
 
-        regularization_matrix = aa.util.regularization.constant_regularization_matrix_from_pixel_neighbors(
+        regularization_matrix = aa.util.regularization.instance_regularization_matrix_from_pixel_neighbors(
             coefficient=1.0,
             pixel_neighbors=pixel_neighbors,
             pixel_neighbors_size=pixel_neighbors_size,
@@ -53,7 +53,7 @@ def test__1_b_matrix_size_4x4__weights_all_1s__makes_correct_regularization_matr
 
         pixel_neighbors_size = np.array([2, 2, 2, 2])
 
-        regularization_matrix = aa.util.regularization.constant_regularization_matrix_from_pixel_neighbors(
+        regularization_matrix = aa.util.regularization.instance_regularization_matrix_from_pixel_neighbors(
             coefficient=1.0,
             pixel_neighbors=pixel_neighbors,
             pixel_neighbors_size=pixel_neighbors_size,
@@ -80,7 +80,7 @@ def test__1_b_matrix_size_4x4__coefficient_2__makes_correct_regularization_matri
             test_b_matrix.T, test_b_matrix
         ) + 1e-8 * np.identity(4)
 
-        regularization_matrix = aa.util.regularization.constant_regularization_matrix_from_pixel_neighbors(
+        regularization_matrix = aa.util.regularization.instance_regularization_matrix_from_pixel_neighbors(
             coefficient=2.0,
             pixel_neighbors=pixel_neighbors,
             pixel_neighbors_size=pixel_neighbors_size,
@@ -146,7 +146,7 @@ def test__1_b_matrix_size_9x9__coefficient_2__makes_correct_regularization_matri
             + 1e-8 * np.identity(9)
         )
 
-        regularization_matrix = aa.util.regularization.constant_regularization_matrix_from_pixel_neighbors(
+        regularization_matrix = aa.util.regularization.instance_regularization_matrix_from_pixel_neighbors(
             coefficient=1.0,
             pixel_neighbors=pixel_neighbors,
             pixel_neighbors_size=pixel_neighbors_size,
@@ -394,7 +394,7 @@ def test__4_b_matrices_size_6x6__weights_all_1s__makes_correct_regularization_ma
             test_regularization_matrix, 1.0e-4
         )
 
-    def test__2_b_matrices_size_4x4_variables_regularization_weights__makes_correct_regularization_matrix(
+    def test__2_b_matrices_size_4x4_models_regularization_weights__makes_correct_regularization_matrix(
         self
     ):
         # Simple case, where we have just one regularization direction, regularizing pixel 0 -> 1 and 1 -> 2.