fixes

nifty/data_objects/distributed_do.py

@@ -233,10 +233,10 @@ class data_object(object):
         return data_object(self._shape, np.abs(self._data), self._distaxis)
 
     def all(self):
-        return self._data.all()
+        return self.sum() == self.size
 
     def any(self):
-        return self._data.any()
+        return self.sum() != 0
 
 
 def full(shape, fill_value, dtype=None, distaxis=0):

nifty/operators/fft_operator_support.py

@@ -82,8 +82,12 @@ class RGRGTransformation(Transformation):
             Tval = Field(tdom, tmp)
         else:
             ldat = dobj.local_data(x.val)
-            ldat = fftn(ldat,axes=axes)
-            ldat = ldat.real+ldat.imag
+            # these two alternatives are equivalent, with the second being faster
+            if False:
+                ldat = fftn(ldat,axes=axes)
+                ldat = ldat.real+ldat.imag
+            else:
+                ldat = hartley(ldat,axes=axes)
             tmp = dobj.from_local_data(x.val.shape, ldat, distaxis=dobj.distaxis(x.val))
             Tval = Field(tdom,tmp)
         fct = self.fct_p2h if p2h else self.fct_h2p

nifty/spaces/power_space.py

@@ -153,6 +153,10 @@ class PowerSpace(Space):
             temp_k_lengths = np.bincount(dobj.local_data(temp_pindex).ravel(),
                 weights=dobj.local_data(k_length_array.val).ravel(),
                 minlength=nbin)
+            # This conversion is necessary because bincount sometimes returns
+            # its result as an integer array, even when floating-point weights
+            # are present ...
+            temp_k_lengths = temp_k_lengths.astype(np.float64)
             temp_k_lengths = dobj.np_allreduce_sum(temp_k_lengths) / temp_rho
             temp_dvol = temp_rho*pdvol
             self._powerIndexCache[key] = (binbounds,