Feature/zeroth mesh edge#58
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rhayes777
approved these changes
Mar 24, 2023
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| def test__brightness_zeroth_regularization_weights_from(): | ||
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| pixel_signals = np.array([1.0, 1.0, 1.0]) | ||
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| weight_list = aa.util.regularization.brightness_zeroth_regularization_weights_from( | ||
| coefficient=1.0, pixel_signals=pixel_signals | ||
| ) | ||
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| assert (weight_list == np.array([0.0, 0.0, 0.0])).all() | ||
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| pixel_signals = np.array([0.25, 0.5, 0.75]) | ||
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| weight_list = aa.util.regularization.brightness_zeroth_regularization_weights_from( | ||
| coefficient=1.0, pixel_signals=pixel_signals | ||
| ) | ||
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| assert (weight_list == np.array([0.75, 0.5, 0.25])).all() | ||
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| pixel_signals = np.array([0.25, 0.5, 0.75]) | ||
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| weight_list = aa.util.regularization.brightness_zeroth_regularization_weights_from( | ||
| coefficient=2.0, pixel_signals=pixel_signals | ||
| ) | ||
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| assert (weight_list == np.array([1.5, 1.0, 0.5])).all() |
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| def test__brightness_zeroth_regularization_matrix_from(): | ||
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| regularization_weights = np.ones(shape=(3,)) | ||
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| regularization_matrix = ( | ||
| aa.util.regularization.brightness_zeroth_regularization_matrix_from( | ||
| regularization_weights=regularization_weights, | ||
| ) | ||
| ) | ||
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| assert regularization_matrix == pytest.approx( | ||
| np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]), 1.0e-4 | ||
| ) | ||
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| regularization_weights = np.array([1.0, 2.0, 3.0]) | ||
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| regularization_matrix = ( | ||
| aa.util.regularization.brightness_zeroth_regularization_matrix_from( | ||
| regularization_weights=regularization_weights, | ||
| ) | ||
| ) | ||
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| assert regularization_matrix == pytest.approx( | ||
| np.array([[1.0, 0.0, 0.0], [0.0, 4.0, 0.0], [0.0, 0.0, 9.0]]), 1.0e-4 | ||
| ) |
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An experimental regularization scheme which applies adaptive zeroth-order regularization to the exterior regions of a inversion mesh (e.g. the regions of the source-plane that dont contain the source).
If this works, we will want to adjust PyAutoFit to allow a galaxy object to have multiple regularization schemes, which each lead to the summation of their regularization matrices.