Fixed a dtype bug in GFFT

nifty/field.py

@@ -151,11 +151,11 @@ class Field(Loggable, Versionable, object):
 
     def power_analyze(self, spaces=None, log=False, nbin=None, binbounds=None,
                       real_signal=True):
-        # assert that all spaces in `self.domain` are either harmonic or
+        # check if all spaces in `self.domain` are either harmonic or
         # power_space instances
         for sp in self.domain:
             if not sp.harmonic and not isinstance(sp, PowerSpace):
-                raise AttributeError(
+                self.logger.info(
                     "Field has a space in `domain` which is neither "
                     "harmonic nor a PowerSpace.")
 
@@ -288,11 +288,11 @@ class Field(Loggable, Versionable, object):
     def power_synthesize(self, spaces=None, real_signal=True,
                          mean=None, std=None):
 
-        # assert that all spaces in `self.domain` are either of signal-type or
+        # check if all spaces in `self.domain` are either of signal-type or
         # power_space instances
         for sp in self.domain:
             if not sp.harmonic and not isinstance(sp, PowerSpace):
-                raise AttributeError(
+                self.logger.info(
                     "Field has a space in `domain` which is neither "
                     "harmonic nor a PowerSpace.")
 
@@ -352,11 +352,7 @@ class Field(Loggable, Versionable, object):
                                     preserve_gaussian_variance=True)[0]
                                for x in result_list]
         else:
-#            # if the synthesized field is complex in signal space,
-#            # one must correct the variance here, since one draws
-#            # sqrt(twice) the power via real- and imaginary-part
-#            result_val_list = [x.val*np.sqrt(0.5) for x in result_list]
-             result_val_list = [x.val for x in result_list]
+            result_val_list = [x.val for x in result_list]
 
         # weight the random fields with the power spectrum
         # therefore get the pindex from the power space

nifty/operators/fft_operator/transformations/rg_transforms.py

@@ -592,6 +592,7 @@ class GFFT(Transform):
 
         # Array for storing the result
         return_val = None
+        result_dtype = np.result_type(np.complex, self.codomain.dtype)
 
         for slice_list in utilities.get_slice_list(temp_inp.shape, axes):
 
@@ -601,7 +602,7 @@ class GFFT(Transform):
             else:
                 # initialize the return_val object if needed
                 if return_val is None:
-                    return_val = np.empty_like(temp_inp)
+                    return_val = np.empty_like(temp_inp, dtype=result_dtype)
                 inp = temp_inp[slice_list]
 
             inp = self.fft_machine.gfft(
@@ -622,12 +623,11 @@ class GFFT(Transform):
             else:
                 return_val[slice_list] = inp
 
-        result_dtype = np.result_type(np.complex, self.codomain.dtype)
         if isinstance(val, distributed_data_object):
             new_val = val.copy_empty(dtype=result_dtype)
             new_val.set_full_data(return_val, copy=False)
             return_val = new_val
         else:
-            return_val = return_val.astype(result_type, copy=False)
+            return_val = return_val.astype(result_dtype, copy=False)
 
         return return_val