WIP: Fix FFTW caching

- Refactor RGRGTransformation
- Add is_local to dictionary key hash

nifty/operators/fft_operator/transformations/rg_transforms.py

@@ -170,7 +170,9 @@ class FFTW(Transform):
         # generate a id-tuple which identifies the domain-codomain setting
         temp_id = (domain.__hash__() ^
                    (101 * codomain.__hash__()) ^
-                   (211 * transform_shape.__hash__()))
+                   (211 * transform_shape.__hash__()) ^
+                   (131 * is_local.__hash__())
+        )
 
         # generate the plan_and_info object if not already there
         if temp_id not in self.info_dict:

nifty/operators/fft_operator/transformations/rgrgtransformation.py

@@ -7,29 +7,29 @@ from nifty import RGSpace, nifty_configuration
 
 class RGRGTransformation(Transformation):
     def __init__(self, domain, codomain=None, module=None):
-        super(RGRGTransformation, self).__init__(domain, codomain, module)
+        super(RGRGTransformation, self).__init__(domain, codomain)
 
         if module is None:
             if nifty_configuration['fft_module'] == 'pyfftw':
-                self._transform = FFTW(domain, codomain)
+                self._transform = FFTW(self.domain, self.codomain)
             elif (nifty_configuration['fft_module'] == 'gfft' or
                   nifty_configuration['fft_module'] == 'gfft_dummy'):
                 self._transform = \
-                    GFFT(domain,
-                         codomain,
+                    GFFT(self.domain,
+                         self.codomain,
                          gdi.get(nifty_configuration['fft_module']))
             else:
                 raise ValueError('ERROR: unknow default FFT module:' +
                                  nifty_configuration['fft_module'])
         else:
             if module == 'pyfftw':
-                self._transform = FFTW(domain, codomain)
+                self._transform = FFTW(self.domain, self.codomain)
             elif module == 'gfft':
                 self._transform = \
-                    GFFT(domain, codomain, gdi.get('gfft'))
+                    GFFT(self.domain, self.codomain, gdi.get('gfft'))
             elif module == 'gfft_dummy':
                 self._transform = \
-                    GFFT(domain, codomain, gdi.get('gfft_dummy'))
+                    GFFT(self.domain, self.codomain, gdi.get('gfft_dummy'))
             else:
                 raise ValueError('ERROR: unknow FFT module:' + module)
 

nifty/operators/fft_operator/transformations/transformation.py

@@ -11,7 +11,7 @@ class Transformation(object, Loggable):
     """
     __metaclass__ = abc.ABCMeta
 
-    def __init__(self, domain, codomain=None, module=None):
+    def __init__(self, domain, codomain):
         if codomain is None:
             self.domain = domain
             self.codomain = self.get_codomain(domain)