Regularization edits

SimPEG/directives/directives.py

@@ -1401,9 +1401,8 @@ def angleScale(self):
 
         for reg, var in zip(self.reg.objfcts[1:], max_s):
             for obj in reg.objfcts:
-                obj.add_set_weights(
-                    {"angle_scale": np.ones(obj.shape[0]) * max_p / var}
-                )
+                # TODO Need to make weights_shapes a public method
+                obj.set_weights(angle_scale=np.ones(obj._weights_shapes[0]) * max_p / var)
 
     def validate(self, directiveList):
         # check if a linear preconditioner is in the list, if not warn else
@@ -1587,7 +1586,7 @@ def update(self):
         for reg in self.reg.objfcts:
             if not isinstance(reg, BaseSimilarityMeasure):
                 for sub_reg in reg.objfcts:
-                    sub_reg.add_set_weights({"sensitivity": sub_reg.mapping * wr})
+                    sub_reg.set_weights(sensitivity=sub_reg.mapping * wr)
 
     def validate(self, directiveList):
         # check if a beta estimator is in the list after setting the weights

SimPEG/objective_function.py

@@ -293,10 +293,6 @@ def __len__(self):
     def __getitem__(self, key):
         return self.multipliers[key], self.objfcts[key]
 
-    @property
-    def __len__(self):
-        return self.objfcts.__len__
-
     @property
     def multipliers(self):
         return self._multipliers
@@ -312,7 +308,7 @@ def multipliers(self, value):
 
         assert len(value) == len(self.objfcts), (
             "the length of multipliers should be the same as the number of"
-            " objective functions ({}), not {}".format(len(self.objfcts, len(value)))
+            " objective functions ({}), not {}".format(len(self.objfcts), len(value))
         )
 
         self._multipliers = value

SimPEG/regularization/__init__.py

@@ -29,18 +29,36 @@ def __init__(self, mesh=None, **kwargs):
 
 @deprecate_class(removal_version="0.x.0", future_warn=True)
 class Simple(LeastSquaresRegularization):
-    def __init__(self, mesh=None, **kwargs):
-        super().__init__(mesh=mesh, **kwargs)
+    def __init__(self, mesh=None, alpha_x=1.0, alpha_y=1.0, alpha_z=1.0, **kwargs):
+        # These alphas are now refered to as length_scalse in the
+        # new LeastSquaresRegularization
+        super().__init__(
+            mesh=mesh,
+            length_scale_x=alpha_x,
+            length_scale_y=alpha_y,
+            length_scale_z=alpha_z,
+            **kwargs
+        )
 
 
 @deprecate_class(removal_version="0.x.0", future_warn=True)
 class Tikhonov(LeastSquaresRegularization):
-    pass
+    def __init__(
+        self, mesh=None, alpha_s=1e-6, alpha_x=1.0, alpha_y=1.0, alpha_z=1.0, **kwargs
+    ):
+        super().__init__(
+            mesh=mesh,
+            alpha_s=alpha_s,
+            alpha_x=alpha_x,
+            alpha_y=alpha_y,
+            alpha_z=alpha_z,
+            **kwargs
+        )
 
 
 @deprecate_class(removal_version="0.x.0", future_warn=True)
 class PGIwithNonlinearRelationshipsSmallness(PGIsmallness):
-    def __init__(self, gmm):
+    def __init__(self, gmm, **kwargs):
         super().__init__(gmm, non_linear_relationships=True, **kwargs)