Fixed #728 scalar coercion problem with mixed types and r_

1 files changed, 13 insertions, 9 deletions

diff --git a/numpy/lib/index_tricks.py b/numpy/lib/index_tricks.py
index c45148057..22b8ef388 100644
--- a/numpy/lib/index_tricks.py
+++ b/numpy/lib/index_tricks.py
@@ -7,7 +7,8 @@ __all__ = ['unravel_index',
 
 import sys
 import numpy.core.numeric as _nx
-from numpy.core.numeric import asarray, ScalarType, array
+from numpy.core.numeric import asarray, ScalarType, array, dtype
+from numpy.core.numerictypes import find_common_type
 import math
 
 import function_base
@@ -225,7 +226,8 @@ class AxisConcatenator(object):
             key = (key,)
         objs = []
         scalars = []
-        final_dtypedescr = None
+        arraytypes = []
+        scalartypes = []
         for k in range(len(key)):
             scalar = False
             if type(key[k]) is slice:
@@ -272,6 +274,7 @@ class AxisConcatenator(object):
                 newobj = array(key[k],ndmin=ndmin)
                 scalars.append(k)
                 scalar = True
+                scalartypes.append(newobj.dtype)
             else:
                 newobj = key[k]
                 if ndmin > 1:
@@ -289,14 +292,15 @@ class AxisConcatenator(object):
                         newobj = newobj.transpose(axes)
                     del tempobj
             objs.append(newobj)
-            if isinstance(newobj, _nx.ndarray) and not scalar:
-                if final_dtypedescr is None:
-                    final_dtypedescr = newobj.dtype
-                elif newobj.dtype > final_dtypedescr:
-                    final_dtypedescr = newobj.dtype
-        if final_dtypedescr is not None:
+            if not scalar and isinstance(newobj, _nx.ndarray):
+                arraytypes.append(newobj.dtype)
+                
+        #  Esure that scalars won't up-cast unless warranted
+        final_dtype = find_common_type(arraytypes, scalartypes)
+        if final_dtype is not None:
             for k in scalars:
-                objs[k] = objs[k].astype(final_dtypedescr)
+                objs[k] = objs[k].astype(final_dtype)
+
         res = _nx.concatenate(tuple(objs),axis=self.axis)
         return self._retval(res)