Fixed the return type of the FFTOperator.

Fixed violation of LSP for implemented keyword/property.

nifty/field.py

@@ -49,14 +49,17 @@ class Field(object):
 
         self.set_val(new_val=val, copy=copy)
 
-    def _parse_domain(self, domain, val):
+    def _parse_domain(self, domain, val=None):
         if domain is None:
             if isinstance(val, Field):
                 domain = val.domain
             else:
                 domain = ()
-        elif not isinstance(domain, tuple):
+        elif isinstance(domain, Space):
             domain = (domain,)
+        elif not isinstance(domain, tuple):
+            domain = tuple(domain)
+
         for d in domain:
             if not isinstance(d, Space):
                 raise TypeError(about._errors.cstring(
@@ -64,14 +67,16 @@ class Field(object):
                     "nifty.space."))
         return domain
 
-    def _parse_field_type(self, field_type, val):
+    def _parse_field_type(self, field_type, val=None):
         if field_type is None:
             if isinstance(val, Field):
                 field_type = val.field_type
             else:
                 field_type = ()
-        elif not isinstance(field_type, tuple):
+        elif isinstance(field_type, FieldType):
             field_type = (field_type,)
+        elif not isinstance(field_type, tuple):
+            field_type = tuple(field_type)
         for ft in field_type:
             if not isinstance(ft, FieldType):
                 raise TypeError(about._errors.cstring(

nifty/operators/diagonal_operator/diagonal_operator.py

@@ -16,11 +16,13 @@ class DiagonalOperator(EndomorphicOperator):
     # ---Overwritten properties and methods---
 
     def __init__(self, domain=(), field_type=(), implemented=False,
-                 diagonal=None, bare=False, datamodel=None, copy=True):
+                 diagonal=None, bare=False, copy=True, datamodel=None):
         super(DiagonalOperator, self).__init__(domain=domain,
                                                field_type=field_type,
                                                implemented=implemented)
 
+        self._implemented = bool(implemented)
+
         if datamodel is None:
             if isinstance(diagonal, distributed_data_object):
                 datamodel = diagonal.distribution_strategy
@@ -80,6 +82,10 @@ class DiagonalOperator(EndomorphicOperator):
 
     # ---Mandatory properties and methods---
 
+    @property
+    def implemented(self):
+        return self._implemented
+
     @property
     def symmetric(self):
         return self._symmetric
@@ -116,8 +122,16 @@ class DiagonalOperator(EndomorphicOperator):
                   datamodel=self.datamodel,
                   copy=copy)
 
-        # weight if the given values were `bare`
-        f.weight(inplace=True)
+        # weight if the given values were `bare` and `implemented` is True
+        # do inverse weightening if the other way around
+        if bare and self.implemented:
+            # If `copy` is True, we won't change external data by weightening
+            # Otherwise, inplace weightening would change the external field
+            f.weight(inplace=copy)
+        elif not bare and not self.implemented:
+            # If `copy` is True, we won't change external data by weightening
+            # Otherwise, inplace weightening would change the external field
+            f.weight(inplace=copy, power=-1)
 
         # check if the operator is symmetric:
         self._symmetric = (f.val.imag == 0).all()
@@ -127,4 +141,3 @@ class DiagonalOperator(EndomorphicOperator):
 
         # store the diagonal-field
         self._diagonal = f
-

nifty/operators/fft_operator/__init__.py

 from transformations import *
-from fft_operator import FFTOperator
+from fft_operator import FFTOperator
\ No newline at end of file

nifty/operators/fft_operator/fft_operator.py

@@ -8,41 +8,26 @@ class FFTOperator(LinearOperator):
 
     # ---Overwritten properties and methods---
 
-    def __init__(self, domain=(), field_type=(), target=(),
-                 field_type_target=(), implemented=True):
+    def __init__(self, domain=(), field_type=(), target=None):
         super(FFTOperator, self).__init__(domain=domain,
-                                          field_type=field_type,
-                                          implemented=implemented)
+                                          field_type=field_type)
 
-        if self.domain == ():
-            raise TypeError(about._errors.cstring(
-                'ERROR: TransformationOperator needs a single space as '
-                'input domain.'
-            ))
-        else:
-            if len(self.domain) > 1:
-                raise TypeError(about._errors.cstring(
-                    'ERROR: TransformationOperator accepts only a single '
-                    'space as input domain.'
-                ))
+        if len(self.domain) != 1:
+            raise ValueError(about._errors.cstring(
+                    'ERROR: TransformationOperator accepts only exactly one '
+                    'space as input domain.'))
 
         if self.field_type != ():
-            raise TypeError(about._errors.cstring(
-                'ERROR: TransformationOperator field-type has to be an '
+            raise ValueError(about._errors.cstring(
+                'ERROR: TransformationOperator field-type must be an '
                 'empty tuple.'
             ))
 
-        # currently not sanitizing the target
-        self._target = self._parse_domain(
-            utilities.get_default_codomain(self.domain[0])
-        )
-        self._field_type_target = self._parse_field_type(field_type_target)
+        if target is None:
+            target = utilities.get_default_codomain(self.domain[0])
 
-        if self.field_type_target != ():
-            raise TypeError(about._errors.cstring(
-                'ERROR: TransformationOperator target field-type has to be an '
-                'empty tuple.'
-            ))
+        self._target = self._parse_domain(
+                        utilities.get_default_codomain(self.domain[0]))
 
         self._forward_transformation = TransformationFactory.create(
             self.domain[0], self.target[0]
@@ -52,24 +37,37 @@ class FFTOperator(LinearOperator):
             self.target[0], self.domain[0]
         )
 
-    def adjoint_times(self, x, spaces=None, types=None):
-        return self.inverse_times(x, spaces, types)
+    def _times(self, x, spaces, types):
+        spaces = utilities.cast_axis_to_tuple(spaces, len(x.domain))
 
-    def adjoint_inverse_times(self, x, spaces=None, types=None):
-        return self.times(x, spaces, types)
+        new_val = self._forward_transformation.transform(x.val, axes=spaces)
 
-    def inverse_adjoint_times(self, x, spaces=None, types=None):
-        return self.times(x, spaces, types)
+        if spaces is None:
+            result_domain = self.target
+        else:
+            result_domain = list(x.domain)
+            result_domain[spaces[0]] = self.target[0]
 
-    def _times(self, x, spaces, types):
-        spaces = utilities.cast_axis_to_tuple(spaces, len(x.domain))
+        result_field = x.copy_empty(domain=result_domain)
+        result_field.set_val(new_val=new_val)
 
-        return self._forward_transformation.transform(x.val, axes=spaces)
+        return result_field
 
     def _inverse_times(self, x, spaces, types):
         spaces = utilities.cast_axis_to_tuple(spaces, len(x.domain))
 
-        return self._inverse_transformation.transform(x.val, axes=spaces)
+        new_val = self._inverse_transformation.transform(x.val, axes=spaces)
+
+        if spaces is None:
+            result_domain = self.domain
+        else:
+            result_domain = list(x.domain)
+            result_domain[spaces[0]] = self.domain[0]
+
+        result_field = x.copy_empty(domain=result_domain)
+        result_field.set_val(new_val=new_val)
+
+        return result_field
 
     # ---Mandatory properties and methods---
 
@@ -79,5 +77,12 @@ class FFTOperator(LinearOperator):
 
     @property
     def field_type_target(self):
-        return self._field_type_target
+        return self.field_type
+
+    @property
+    def implemented(self):
+        return True
 
+    @property
+    def unitary(self):
+        return True

nifty/operators/linear_operator/linear_operator.py

@@ -12,10 +12,9 @@ import nifty.nifty_utilities as utilities
 class LinearOperator(object):
     __metaclass__ = abc.ABCMeta
 
-    def __init__(self, domain=(), field_type=(), implemented=False):
+    def __init__(self, domain=(), field_type=()):
         self._domain = self._parse_domain(domain)
         self._field_type = self._parse_field_type(field_type)
-        self._implemented = bool(implemented)
 
     @property
     def domain(self):
@@ -36,8 +35,11 @@ class LinearOperator(object):
     def _parse_domain(self, domain):
         if domain is None:
             domain = ()
-        elif not isinstance(domain, tuple):
+        elif isinstance(domain, Space):
             domain = (domain,)
+        elif not isinstance(domain, tuple):
+            domain = tuple(domain)
+
         for d in domain:
             if not isinstance(d, Space):
                 raise TypeError(about._errors.cstring(
@@ -48,17 +50,20 @@ class LinearOperator(object):
     def _parse_field_type(self, field_type):
         if field_type is None:
             field_type = ()
-        elif not isinstance(field_type, tuple):
+        elif isinstance(field_type, FieldType):
             field_type = (field_type,)
+        elif not isinstance(field_type, tuple):
+            field_type = tuple(field_type)
+
         for ft in field_type:
             if not isinstance(ft, FieldType):
                 raise TypeError(about._errors.cstring(
                     "ERROR: Given object is not a nifty.FieldType."))
         return field_type
 
-    @property
+    @abc.abstractproperty
     def implemented(self):
-        return self._implemented
+        raise NotImplementedError
 
     @abc.abstractproperty
     def unitary(self):