fixes

nifty/data_objects/distributed_do.py

@@ -91,10 +91,10 @@ class data_object(object):
         if axis is None:
             res = np.array(getattr(self._data, op)())
             if (self._distaxis==-1):
-                return res[0]
-            res2 = np.empty(1,dtype=res.dtype)
+                return res[()]
+            res2 = np.empty((),dtype=res.dtype)
             _comm.Allreduce(res,res2,mpiop)
-            return res2[0]
+            return res2[()]
 
         if self._distaxis in axis:
             res = getattr(self._data, op)(axis=axis)
@@ -122,6 +122,10 @@ class data_object(object):
 
     def sum(self, axis=None):
         return self._contraction_helper("sum", MPI.SUM, axis)
+    def min(self, axis=None):
+        return self._contraction_helper("min", MPI.MIN, axis)
+    def max(self, axis=None):
+        return self._contraction_helper("max", MPI.MAX, axis)
 
     # FIXME: to be improved!
     def mean(self):
@@ -239,10 +243,10 @@ def empty_like(a, dtype=None):
 
 
 def vdot(a, b):
-    tmp = np.vdot(a._data, b._data)
-    res = np.empty(1,dtype=type(tmp))
+    tmp = np.array(np.vdot(a._data, b._data))
+    res = np.empty((),dtype=tmp.dtype)
     _comm.Allreduce(tmp,res,MPI.SUM)
-    return res[0]
+    return res[()]
 
 
 def _math_helper(x, function, out):
@@ -364,7 +368,7 @@ def redistribute (arr, dist=None, nodist=None):
         out = np.moveaxis(out, 0, arr._distaxis)
         return from_global_data (out, distaxis=-1)
     # real redistribution via Alltoallv
-    # temporary slow, but simple solution
+    # temporary slow, but simple solution for comparison purposes:
     #return redistribute(redistribute(arr,dist=-1),dist=dist)
 
     tmp = np.moveaxis(arr._data, (dist, arr._distaxis), (0, 1))

nifty/low_level_library.py

@@ -48,7 +48,7 @@ if not special_hartley:
 
     def _fill_array(tmp, res, axes):
         if axes is None:
-            axes = range(a.ndim)
+            axes = range(tmp.ndim)
         lastaxis = axes[-1]
         ntmplast = tmp.shape[lastaxis]
         slice1 = [slice(None)]*lastaxis + [slice(0, ntmplast)]
@@ -60,7 +60,7 @@ def hartley(a, axes=None):
     # Check if the axes provided are valid given the shape
     if axes is not None and \
             not all(axis < len(a.shape) for axis in axes):
-        raise ValueError("Provided axes does not match array shape")
+        raise ValueError("Provided axes do not match array shape")
     if issubclass(a.dtype.type, np.complexfloating):
         raise TypeError("Hartley tansform requires real-valued arrays.")