revert operator copy

nifty/library/critical_filter/critical_power_curvature.py

@@ -33,23 +33,6 @@ class CriticalPowerCurvature(InvertibleOperatorMixin, EndomorphicOperator):
                                                  preconditioner=preconditioner,
                                                  **kwargs)
 
-    def _add_attributes_to_copy(self, copy, **kwargs):
-        copy._domain = self._domain
-        if 'theta' in kwargs:
-            theta = kwargs['theta']
-            copy.theta = DiagonalOperator(theta.domain, diagonal=theta)
-        else:
-            copy.theta = self.theta.copy()
-
-        if 'T' in kwargs:
-            copy.T = kwargs['T']
-        else:
-            copy.T = self.T
-
-        copy = super(CriticalPowerCurvature,
-                     self)._add_attributes_to_copy(copy, **kwargs)
-        return copy
-
     def _times(self, x, spaces):
         return self.T(x) + self.theta(x)
 

nifty/library/critical_filter/critical_power_energy.py

@@ -54,8 +54,7 @@ class CriticalPowerEnergy(Energy):
     # ---Overwritten properties and methods---
 
     def __init__(self, position, m, D=None, alpha=1.0, q=0.,
-                 smoothness_prior=0., logarithmic=True, samples=3, w=None,
-                 old_curvature=None):
+                 smoothness_prior=0., logarithmic=True, samples=3, w=None):
         super(CriticalPowerEnergy, self).__init__(position=position)
         self.m = m
         self.D = D
@@ -67,8 +66,6 @@ class CriticalPowerEnergy(Energy):
                                     logarithmic=logarithmic)
         self.rho = self.position.domain[0].rho
         self._w = w if w is not None else None
-        self._old_curvature = old_curvature
-        self._curvature = None
 
     # ---Mandatory properties and methods---
 
@@ -76,11 +73,9 @@ class CriticalPowerEnergy(Energy):
         return self.__class__(position, self.m, D=self.D, alpha=self.alpha,
                               q=self.q, smoothness_prior=self.smoothness_prior,
                               logarithmic=self.logarithmic,
-                              w=self.w, samples=self.samples,
-                              old_curvature=self._curvature)
+                              w=self.w, samples=self.samples)
 
     @property
-    @memo
     def value(self):
         energy = self._theta.sum()
         energy += self.position.vdot(self._rho_prime, bare=True)
@@ -88,7 +83,6 @@ class CriticalPowerEnergy(Energy):
         return energy.real
 
     @property
-    @memo
     def gradient(self):
         gradient = -self._theta.weight(-1)
         gradient += (self._rho_prime).weight(-1)
@@ -98,14 +92,9 @@ class CriticalPowerEnergy(Energy):
 
     @property
     def curvature(self):
-        if self._curvature is None:
-            if self._old_curvature is None:
-                self._curvature = CriticalPowerCurvature(
-                                        theta=self._theta.weight(-1), T=self.T)
-            else:
-                self._curvature = self._old_curvature.copy(
-                                        theta=self._theta.weight(-1), T=self.T)
-        return self._curvature
+        curvature = CriticalPowerCurvature(theta=self._theta.weight(-1),
+                                           T=self.T)
+        return curvature
 
     # ---Added properties and methods---
 

nifty/library/log_normal_wiener_filter/log_normal_wiener_filter_curvature.py

@@ -48,23 +48,6 @@ class LogNormalWienerFilterCurvature(InvertibleOperatorMixin,
                                                  preconditioner=preconditioner,
                                                  **kwargs)
 
-    def _add_attributes_to_copy(self, copy, **kwargs):
-        copy._cache = {}
-        copy._domain = self._domain
-        copy.R = self.R.copy()
-        copy.N = self.N.copy()
-        copy.S = self.S.copy()
-        copy.d = self.d.copy()
-        if 'position' in kwargs:
-            copy.position = kwargs['position']
-        else:
-            copy.position = self.position.copy()
-        copy._fft = self._fft
-
-        copy = super(LogNormalWienerFilterCurvature,
-                     self)._add_attributes_to_copy(copy, **kwargs)
-        return copy
-
     @property
     def domain(self):
         return self._domain

nifty/library/log_normal_wiener_filter/log_normal_wiener_filter_energy.py

@@ -24,7 +24,7 @@ class LogNormalWienerFilterEnergy(Energy):
         The prior signal covariance in harmonic space.
     """
 
-    def __init__(self, position, d, R, N, S, fft4exp=None, old_curvature=None):
+    def __init__(self, position, d, R, N, S, fft4exp=None):
         super(LogNormalWienerFilterEnergy, self).__init__(position=position)
         self.d = d
         self.R = R
@@ -37,13 +37,9 @@ class LogNormalWienerFilterEnergy(Energy):
         else:
             self._fft = fft4exp
 
-        self._old_curvature = old_curvature
-        self._curvature = None
-
     def at(self, position):
         return self.__class__(position=position, d=self.d, R=self.R, N=self.N,
-                              S=self.S, fft4exp=self._fft,
-                              old_curvature=self._curvature)
+                              S=self.S, fft4exp=self._fft)
 
     @property
     @memo
@@ -57,20 +53,11 @@ class LogNormalWienerFilterEnergy(Energy):
         return self._Sp + self._exppRNRexppd
 
     @property
+    @memo
     def curvature(self):
-        if self._curvature is None:
-            if self._old_curvature is None:
-                self._curvature = LogNormalWienerFilterCurvature(
-                                                      R=self.R,
-                                                      N=self.N,
-                                                      S=self.S,
-                                                      d=self.d,
-                                                      position=self.position,
-                                                      fft4exp=self._fft)
-            else:
-                self._curvature = \
-                    self._old_curvature.copy(position=self.position)
-        return self._curvature
+        return LogNormalWienerFilterCurvature(R=self.R, N=self.N, S=self.S,
+                                              d=self.d, position=self.position,
+                                              fft4exp=self._fft)
 
     @property
     def _expp(self):

nifty/library/wiener_filter/wiener_filter_curvature.py

@@ -35,15 +35,6 @@ class WienerFilterCurvature(InvertibleOperatorMixin, EndomorphicOperator):
                                                  preconditioner=preconditioner,
                                                  **kwargs)
 
-    def _add_attributes_to_copy(self, copy, **kwargs):
-        copy._domain = self._domain
-        copy.R = self.R.copy()
-        copy.N = self.N.copy()
-        copy.S = self.S.copy()
-        copy = super(WienerFilterCurvature, self)._add_attributes_to_copy(
-                                                                copy, **kwargs)
-        return copy
-
     @property
     def domain(self):
         return self._domain

nifty/library/wiener_filter/wiener_filter_energy.py

@@ -23,17 +23,16 @@ class WienerFilterEnergy(Energy):
         The prior signal covariance in harmonic space.
     """
 
-    def __init__(self, position, d, R, N, S, old_curvature=None):
+    def __init__(self, position, d, R, N, S):
         super(WienerFilterEnergy, self).__init__(position=position)
         self.d = d
         self.R = R
         self.N = N
         self.S = S
-        self._curvature = old_curvature
 
     def at(self, position):
         return self.__class__(position=position, d=self.d, R=self.R, N=self.N,
-                              S=self.S, old_curvature=self.curvature)
+                              S=self.S)
 
     @property
     @memo
@@ -46,12 +45,9 @@ class WienerFilterEnergy(Energy):
         return self._Dx - self._j
 
     @property
+    @memo
     def curvature(self):
-        if self._curvature is None:
-            self._curvature = WienerFilterCurvature(R=self.R,
-                                                    N=self.N,
-                                                    S=self.S)
-        return self._curvature
+        return WienerFilterCurvature(R=self.R, N=self.N, S=self.S)
 
     @property
     @memo

nifty/operators/composed_operator/composed_operator.py

@@ -85,14 +85,6 @@ class ComposedOperator(LinearOperator):
                                 "instances of the LinearOperator-baseclass")
             self._operator_store += (op,)
 
-    def _add_attributes_to_copy(self, copy, **kwargs):
-        copy._operator_store = ()
-        for op in self._operator_store:
-            copy._operator_store += (op.copy(),)
-        copy = super(ComposedOperator, self)._add_attributes_to_copy(
-                                                                copy, **kwargs)
-        return copy
-
     def _check_input_compatibility(self, x, spaces, inverse=False):
         """
         The input check must be disabled for the ComposedOperator, since it

nifty/operators/diagonal_operator/diagonal_operator.py

@@ -90,15 +90,6 @@ class DiagonalOperator(EndomorphicOperator):
         self._unitary = None
         self.set_diagonal(diagonal=diagonal, bare=bare, copy=copy)
 
-    def _add_attributes_to_copy(self, copy, **kwargs):
-        copy._domain = self._domain
-        copy.set_diagonal(diagonal=self.diagonal(bare=True), bare=True)
-        copy._self_adjoint = self._self_adjoint
-        copy._unitary = self._unitary
-        copy = super(DiagonalOperator, self)._add_attributes_to_copy(copy,
-                                                                     **kwargs)
-        return copy
-
     def _times(self, x, spaces):
         return self._times_helper(x, spaces, operation=lambda z: z.__mul__)
 
@@ -107,8 +98,7 @@ class DiagonalOperator(EndomorphicOperator):
                                   operation=lambda z: z.adjoint().__mul__)
 
     def _inverse_times(self, x, spaces):
-        return self._times_helper(
-                            x, spaces, operation=lambda z: z.__rtruediv__)
+        return self._times_helper(x, spaces, operation=lambda z: z.__rtruediv__)
 
     def _adjoint_inverse_times(self, x, spaces):
         return self._times_helper(x, spaces,

nifty/operators/fft_operator/fft_operator.py

@@ -125,15 +125,6 @@ class FFTOperator(LinearOperator):
         self._backward_transformation = backward_class(
             self.target[0], self.domain[0])
 
-    def _add_attributes_to_copy(self, copy, **kwargs):
-        copy._domain = self._domain
-        copy._target = self._target
-        copy._forward_transformation = self._forward_transformation
-        copy._backward_transformation = self._backward_transformation
-        copy = super(FFTOperator, self)._add_attributes_to_copy(copy,
-                                                                **kwargs)
-        return copy
-
     def _times(self, x, spaces):
         spaces = utilities.cast_axis_to_tuple(spaces, len(x.domain))
         if spaces is None:

nifty/operators/invertible_operator_mixin/invertible_operator_mixin.py

@@ -57,23 +57,6 @@ class InvertibleOperatorMixin(object):
         self.__backward_x0 = backward_x0
         super(InvertibleOperatorMixin, self).__init__(*args, **kwargs)
 
-    def _add_attributes_to_copy(self, copy, **kwargs):
-        copy.__preconditioner = self.__preconditioner
-        copy.__inverter = self.__inverter
-        try:
-            copy.__forward_x0 = self.__forward_x0.copy()
-        except AttributeError:
-            copy.__forward_x0 = self.__forward_x0
-
-        try:
-            copy.__backward_x0 = self.__backward_x0.copy()
-        except AttributeError:
-            copy.__backward_x0 = self.__backward_x0
-
-        copy = super(InvertibleOperatorMixin, self)._add_attributes_to_copy(
-                                                                copy, **kwargs)
-        return copy
-
     def _times(self, x, spaces):
         if self.__forward_x0 is not None:
             x0 = self.__forward_x0

nifty/operators/laplace_operator/laplace_operator.py

@@ -64,15 +64,6 @@ class LaplaceOperator(EndomorphicOperator):
         self._dposc[1:] += self._dpos
         self._dposc *= 0.5
 
-    def _add_attributes_to_copy(self, copy, **kwargs):
-        copy._domain = self._domain
-        copy._logarithmic = self._logarithmic
-        copy._dpos = self._dpos
-        copy._dposc = self._dposc
-        copy = super(LaplaceOperator, self)._add_attributes_to_copy(copy,
-                                                                    **kwargs)
-        return copy
-
     @property
     def target(self):
         return self._domain

nifty/operators/linear_operator/linear_operator.py

@@ -70,20 +70,6 @@ class LinearOperator(with_metaclass(
     def __init__(self, default_spaces=None):
         self._default_spaces = default_spaces
 
-    def copy(self, **kwargs):
-        class EmptyCopy(self.__class__):
-            def __init__(self):
-                pass
-
-        result = EmptyCopy()
-        result.__class__ = self.__class__
-        result = self._add_attributes_to_copy(result, **kwargs)
-        return result
-
-    def _add_attributes_to_copy(self, copy, **kwargs):
-        copy._default_spaces = self.default_spaces
-        return copy
-
     @staticmethod
     def _parse_domain(domain):
         return utilities.parse_domain(domain)

nifty/operators/projection_operator/projection_operator.py

@@ -87,13 +87,6 @@ class ProjectionOperator(EndomorphicOperator):
         self._projection_field = projection_field
         self._unitary = None
 
-    def _add_attributes_to_copy(self, copy, **kwargs):
-        copy._projection_field = self._projection_field
-        copy._unitary = self._unitary
-        copy = super(ProjectionOperator, self)._add_attributes_to_copy(
-                                                                copy, **kwargs)
-        return copy
-
     def _times(self, x, spaces):
         # if the domain matches directly
         # -> multiply the fields directly

nifty/operators/response_operator/response_operator.py

@@ -61,7 +61,7 @@ class ResponseOperator(LinearOperator):
 
         for ii in range(len(kernel_smoothing)):
             kernel_smoothing[ii] = FFTSmoothingOperator(self._domain[ii],
-                                                     sigma=sigma[ii])
+                                                    sigma=sigma[ii])
             kernel_exposure[ii] = DiagonalOperator(self._domain[ii],
                                                    diagonal=exposure[ii])
 
@@ -74,15 +74,6 @@ class ResponseOperator(LinearOperator):
 
         self._target = self._parse_domain(target_list)
 
-    def _add_attributes_to_copy(self, copy, **kwargs):
-        copy._domain = self._domain
-        copy._target = self._target
-        copy._composed_kernel = self._composed_kernel.copy()
-        copy._composed_exposure = self._composed_exposure.copy()
-        copy = super(DiagonalOperator, self)._add_attributes_to_copy(copy,
-                                                                     **kwargs)
-        return copy
-
     @property
     def domain(self):
         return self._domain

nifty/operators/smoothing_operator/direct_smoothing_operator.py

@@ -57,13 +57,6 @@ class DirectSmoothingOperator(EndomorphicOperator):
     def log_distances(self):
         return self._log_distances
 
-    def _add_attributes_to_copy(self, copy, **kwargs):
-        copy.effective_smoothing_width = self.effective_smoothing_width
-
-        copy = super(DirectSmoothingOperator, self)._add_attributes_to_copy(
-                                                                copy, **kwargs)
-        return copy
-
     def _precompute(self, x, sigma, dxmax=None):
         """ Does precomputations for Gaussian smoothing on a 1D irregular grid.
 

nifty/operators/smoothing_operator/fft_smoothing_operator.py

@@ -53,13 +53,6 @@ class FFTSmoothingOperator(EndomorphicOperator):
     def sigma(self):
         return self._sigma
 
-    def _add_attributes_to_copy(self, copy, **kwargs):
-        copy._transformator_cache = self._transformator_cache
-
-        copy = super(FFTSmoothingOperator, self)._add_attributes_to_copy(
-                                                                copy, **kwargs)
-        return copy
-
     def _smooth(self, x, spaces):
         # transform to the (global-)default codomain and perform all remaining
         # steps therein

nifty/operators/smoothness_operator/smoothness_operator.py

@@ -48,15 +48,6 @@ class SmoothnessOperator(EndomorphicOperator):
         self._laplace = LaplaceOperator(domain=self.domain,
                                         logarithmic=logarithmic)
 
-    def _add_attributes_to_copy(self, copy, **kwargs):
-        copy._domain = self._domain
-        copy._strength = self._strength
-        copy._laplace = self._laplace.copy()
-
-        copy = super(SmoothnessOperator, self)._add_attributes_to_copy(
-                                                                copy, **kwargs)
-        return copy
-
     # ---Mandatory properties and methods---
 
     @property