added adaptive matern kernel

Author: Jammy2211

autoarray/inversion/regularization/matern_adaptive_brightness_kernel.py

@@ -0,0 +1,52 @@
+from autoarray.inversion.regularization.abstract import AbstractRegularization
+
+from autoarray.inversion.regularization.matern_kernel import matern_cov_matrix_from
+
+
+class AdaptiveBrightnessMatern(AbstractRegularization):
+    def __init__(
+            self,
+            coefficient: float = 1.0,
+            scale: float = 1.0,
+            nu: float = 0.5,
+            rho: float = 1.0,
+    ):
+        super().__init__(coefficient=coefficient, scale=scale, rho=rho)
+        self.nu = nu
+
+    def covariance_kernel_weights_from(self, linear_obj: LinearObj) -> np.ndarray:
+        pixel_signals = linear_obj.pixel_signals_from(signal_scale=1.0)
+        return np.exp(-self.rho * (1 - pixel_signals / pixel_signals.max()))
+
+    def regularization_matrix_from(self, linear_obj: LinearObj) -> np.ndarray:
+        kernel_weights = self.covariance_kernel_weights_from(linear_obj=linear_obj)
+
+        covariance_matrix = matern_cov_matrix_from(
+            scale=self.scale,
+            pixel_points=linear_obj.source_plane_mesh_grid,
+            nu=self.nu,
+            weights=kernel_weights,
+        )
+
+        return self.coefficient * np.linalg.inv(covariance_matrix)
+
+    def regularization_weights_from(self, linear_obj: LinearObj) -> np.ndarray:
+        """
+        Returns the regularization weights of this regularization scheme.
+
+        The regularization weights define the level of regularization applied to each parameter in the linear object
+        (e.g. the ``pixels`` in a ``Mapper``).
+
+        For standard regularization (e.g. ``Constant``) are weights are equal, however for adaptive schemes
+        (e.g. ``AdaptiveBrightness``) they vary to adapt to the data being reconstructed.
+
+        Parameters
+        ----------
+        linear_obj
+            The linear object (e.g. a ``Mapper``) which uses these weights when performing regularization.
+
+        Returns
+        -------
+        The regularization weights.
+        """
+        return 1.0/self.covariance_kernel_weights_from(linear_obj=linear_obj) #meaningless, but consistent with other regularization schemes