cleanups

nifty/data_objects/distributed_do.py

@@ -4,7 +4,7 @@ from mpi4py import MPI
 
 __all__ = ["ntask", "rank", "master", "local_shape", "data_object", "full",
            "empty", "zeros", "ones", "empty_like", "vdot", "abs", "exp",
-           "log", "sqrt", "bincount", "from_object", "from_random",
+           "log", "sqrt", "from_object", "from_random",
            "local_data", "ibegin", "np_allreduce_sum", "distaxis",
            "from_local_data", "from_global_data", "to_global_data",
            "redistribute", "default_distaxis"]
@@ -290,13 +290,6 @@ def sqrt(a, out=None):
     return _math_helper(a, np.sqrt, out)
 
 
-def bincount(x, weights=None, minlength=None):
-    if weights is not None:
-        weights = weights._data
-    res = np.bincount(x._data, weights, minlength)
-    return data_object(res)
-
-
 def from_object(object, dtype=None, copy=True):
     return data_object(object._shape, np.array(object._data, dtype=dtype,
                                                copy=copy),

nifty/data_objects/numpy_do.py

@@ -3,12 +3,12 @@
 import numpy as np
 from numpy import ndarray as data_object
 from numpy import full, empty, empty_like, sqrt, ones, zeros, vdot, abs, \
-                  bincount, exp, log
+                  exp, log
 from .random import Random
 
 __all__ = ["ntask", "rank", "master", "local_shape", "data_object", "full",
            "empty", "zeros", "ones", "empty_like", "vdot", "abs", "exp",
-           "log", "sqrt", "bincount", "from_object", "from_random",
+           "log", "sqrt", "from_object", "from_random",
            "local_data", "ibegin", "np_allreduce_sum", "distaxis",
            "from_local_data", "from_global_data", "to_global_data",
            "redistribute", "default_distaxis"]

nifty/operators/fft_operator_support.py

@@ -66,12 +66,12 @@ class RGRGTransformation(Transformation):
             tmpax = (dobj.distaxis(x.val),)
             tmp = dobj.redistribute(x.val, nodist=tmpax)
             ldat = dobj.local_data(tmp)
-            ldat = fftn(ldat, axes=tmpax)
-            if len(axes) ==1:  # we are done
-                ldat = ldat.real+ldat.imag
+            if len(axes) ==1:  # only one transform needed
+                ldat = hartley(ldat, axes=tmpax)
                 tmp = dobj.from_local_data(tmp.shape,ldat,distaxis=dobj.distaxis(tmp))
                 tmp = dobj.redistribute(tmp, dist=tmpax[0])
-            else:
+            else:  # two separate transforms
+                ldat = fftn(ldat, axes=tmpax)
                 tmp = dobj.from_local_data(tmp.shape,ldat,distaxis=dobj.distaxis(tmp))
                 tmp = dobj.redistribute(tmp, dist=tmpax[0])
                 tmpax = tuple (i for i in axes if i not in tmpax)

nifty/spaces/power_space.py

@@ -150,13 +150,12 @@ class PowerSpace(Space):
                                    minlength=nbin)
             temp_rho = dobj.np_allreduce_sum(temp_rho)
             assert not (temp_rho == 0).any(), "empty bins detected"
+            # The explicit conversion to float64 is necessary because bincount
+            # sometimes returns its result as an integer array, even when
+            # floating-point weights are present ...
             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)
+                minlength=nbin).astype(np.float64)
             temp_k_lengths = dobj.np_allreduce_sum(temp_k_lengths) / temp_rho
             temp_dvol = temp_rho*pdvol
             self._powerIndexCache[key] = (binbounds,