Updated P_M and P_Sparsity prox with common eval() signature

proxtoolbox/proxoperators/P_M.py

@@ -22,7 +22,7 @@ class P_M(ProxOperator):
         self.prop = experiment.propagator(experiment)
         self.invprop = experiment.inverse_propagator(experiment)
 
-    def eval(self, u, **kwargs):
+    def eval(self, u, prox_idx=None):
         """
         Applies the proxoperator P_M
         
@@ -35,8 +35,6 @@ class P_M(ProxOperator):
         array_like - [p_M,phat_M] ,where p_M = the projection IN THE PHYSICAL (time) DOMAIN
             and phat_M = projection IN THE FOURIER DOMAIN
         """
-        if kwargs:
-            raise NotImplementedError("Handling of keyword arguments is not yet supported")
         m = self.data
         a = self.prop.eval(u)
         b = magproj(a, m)
@@ -59,7 +57,7 @@ class P_M_masked(P_M):
         super(P_M_masked, self).__init__(experiment)
         self.mask = experiment.fmask == 0
 
-    def eval(self, u, **kwargs):
+    def eval(self, u, prox_idx=None):
         """
         Applies the proxoperator P_M_masked
 
@@ -71,8 +69,6 @@ class P_M_masked(P_M):
         -------
         array_like - p_M: the projection IN THE PHYSICAL (time) DOMAIN
         """
-        if kwargs:
-            raise NotImplementedError("Handling of keyword arguments is not yet supported")
         fourier_space_iterate = self.prop.eval(u)
         constrained = magproj(fourier_space_iterate.copy(), self.data)
         update = where(self.mask, fourier_space_iterate, constrained)
@@ -87,9 +83,7 @@ class Approx_P_M(P_M):
         self.data_ball = experiment.data_ball
         self.TOL2 = experiment.TOL2
 
-    def eval(self, u, **kwargs):
-        if kwargs:
-            raise NotImplementedError("Handling of keyword arguments is not yet supported")
+    def eval(self, u, prox_idx=None):
         u_hat = self.prop.eval(u)
         u_hat_sq = abs(u_hat)**2
 
@@ -117,9 +111,7 @@ class Approx_P_M_masked(P_M_masked):
         self.data_ball = experiment.data_ball
         self.TOL2 = experiment.TOL2
 
-    def eval(self, u, **kwargs):
-        if kwargs:
-            raise NotImplementedError("Handling of keyword arguments is not yet supported")
+    def eval(self, u, prox_idx=None):
         u_hat = self.prop.eval(u)
         u_hat_sq = abs(u_hat) ** 2
         tmp = u_hat_sq / self.data_sq

proxtoolbox/proxoperators/P_Sparsity.py

@@ -11,8 +11,6 @@ __all__ = ['P_Sparsity', 'P_Sparsity_real', 'P_Sparsity_Symmetric', 'P_Sparsity_
 class P_Sparsity(ProxOperator):
     """
     Projection subroutine for projecting onto a sparsity constraint
-    USAGE: p_Sparsity = P_Sparsity(config)
-           p_Sparsity.work(u)
     """
 
     def __init__(self, experiment):
@@ -48,10 +46,8 @@ class P_Sparsity(ProxOperator):
 
         self.value_selection = value_selection
 
-    def eval(self,  *args, **kwargs):
-        return self.work(*args, **kwargs)
 
-    def work(self, u, **kwargs):
+    def eval(self, u, prox_idx=None):
         """
         Parameters
         ----------
@@ -61,8 +57,6 @@ class P_Sparsity(ProxOperator):
         -------
         p_Sparsity : array_like, the projection
         """
-        if kwargs:
-            raise NotImplementedError("Handling of keyword arguments is not yet supported")
         u *= self.support  # apply support (simple 1 if no support)
         p_sparse = 0 * u
         sorting = np.argsort(abs(u), axis=None)  # gives indices of sorted array in ascending order
@@ -79,10 +73,8 @@ class P_Sparsity_real(P_Sparsity):
     order: realness, sparsity
     """
 
-    def work(self, u, **kwargs):
-        if kwargs:
-            raise NotImplementedError("Handling of keyword arguments is not yet supported")
-        return super(P_Sparsity_real, self).work(u.real)
+    def eval(self, u, prox_idx=None):
+        return super(P_Sparsity_real, self).eval(u.real, prox_idx)
 
 
 class P_Sparsity_Symmetric(P_Sparsity):
@@ -97,15 +89,13 @@ class P_Sparsity_Symmetric(P_Sparsity):
         self.symmetry = experiment.symmetry_type  # antisymmetric = -1, symmetric = 1
         self.symmetry_axis = experiment.symmetry_axis  # -1 for last, 0 for first, 'both', 'all' or None for full flip
 
-    def work(self, u, **kwargs):
-        if kwargs:
-            raise NotImplementedError("Handling of keyword arguments is not yet supported")
+    def eval(self, u, prox_idx=None):
         if self.symmetry_axis in ['both', 'all']:
             mirror = np.flip(u, axis=None)
         else:
             mirror = np.flip(u, axis=self.symmetry_axis)
         inp = (u + self.symmetry * mirror) / 2
-        out = super(P_Sparsity_Symmetric, self).work(inp)
+        out = super(P_Sparsity_Symmetric, self).eval(inp, prox_idx)
         return out
 
 
@@ -116,10 +106,8 @@ class P_Sparsity_Symmetric_real(P_Sparsity_Symmetric):
     order: realness, symmetry, sparsity
     """
 
-    def work(self, u, **kwargs):
-        if kwargs:
-            raise NotImplementedError("Handling of keyword arguments is not yet supported")
-        out = super(P_Sparsity_Symmetric_real, self).work(u.real)
+    def eval(self, u, prox_idx=None):
+        out = super(P_Sparsity_Symmetric_real, self).eval(u.real, prox_idx)
         return out
 
 
@@ -141,11 +129,9 @@ class P_Sparsity_Superpixel(P_Sparsity):
         assert test_shape == experiment.u0.shape, 'Given array size does not allow for binning'
         # TODO: allow for padding, then cut of the remainder after tile_array
 
-    def work(self, u, **kwargs):
-        if kwargs:
-            raise NotImplementedError("Handling of keyword arguments is not yet supported")
+    def eval(self, u, prox_idx=None):
         binned = bin_array(abs(u), self.superpixel_size)
-        sparse_array = super(P_Sparsity_Superpixel, self).work(binned)
+        sparse_array = super(P_Sparsity_Superpixel, self).eval(binned, prox_idx)
         mask = tile_array(sparse_array, self.superpixel_size, normalize=True) > 0
         return np.where(mask, u, 0)
 
@@ -154,8 +140,6 @@ class P_Sparsity_Superpixel_real(P_Sparsity_Superpixel):
     """
     Apply real-valued sparsity on superpixels, i.e. on the binned array
     """
-    def work(self, u, **kwargs):
-        if kwargs:
-            raise NotImplementedError("Handling of keyword arguments is not yet supported")
-        return super(P_Sparsity_Superpixel_real, self).work(u.real)
+    def eval(self, u, prox_idx=None):
+        return super(P_Sparsity_Superpixel_real, self).eval(u.real, prox_idx)