Merge branch 'new_iterator' - new iterator, ufunc update, restore 1.5 ABI

New Iterator - Read doc/neps/new-iterator-ufunc.rst.
UFunc Update - Change all ufunc functions to use the new iterator.  This
               replaces the inline buffering with iterator buffering, except
               for the reductions and generalized ufunc which use updateifcopy
               at the moment.  Also adds out= and order= parameters to
               all ufuncs.
Restore 1.5 ABI - This was done by moving the new type numbers to the end
               of the type enumeration, and replacing all type promotion
               code with a table-based approach.  The ArrFuncs was
               restored by putting the new type cast functions into the
               cast dictionary, originally designed just for custom
               types.

Conflicts:
	numpy/core/src/multiarray/ctors.c
	numpy/core/tests/test_regression.py

1 files changed, 13 insertions, 6 deletions

diff --git a/numpy/testing/print_coercion_tables.py b/numpy/testing/print_coercion_tables.py
index 0c8a87d9a..7b5320d7e 100755
--- a/numpy/testing/print_coercion_tables.py
+++ b/numpy/testing/print_coercion_tables.py
@@ -14,6 +14,8 @@ class GenericObject:
     def __radd__(self, other):
         return self
 
+    dtype = np.dtype('O')
+
 def print_cancast_table(ntypes):
     print 'X',
     for char in ntypes: print char,
@@ -24,7 +26,7 @@ def print_cancast_table(ntypes):
             print int(np.can_cast(row, col)),
         print
 
-def print_coercion_table(ntypes, inputfirstvalue, inputsecondvalue, firstarray):
+def print_coercion_table(ntypes, inputfirstvalue, inputsecondvalue, firstarray, use_promote_types=False):
     print '+',
     for char in ntypes: print char,
     print
@@ -46,11 +48,14 @@ def print_coercion_table(ntypes, inputfirstvalue, inputsecondvalue, firstarray):
                 else:
                     rowvalue = rowtype(inputfirstvalue)
                 colvalue = coltype(inputsecondvalue)
-                value = np.add(rowvalue,colvalue)
-                if isinstance(value, np.ndarray):
-                    char = value.dtype.char
+                if use_promote_types:
+                    char = np.promote_types(rowvalue.dtype, colvalue.dtype).char
                 else:
-                    char = np.dtype(type(value)).char
+                    value = np.add(rowvalue,colvalue)
+                    if isinstance(value, np.ndarray):
+                        char = value.dtype.char
+                    else:
+                        char = np.dtype(type(value)).char
             except ValueError:
                 char = '!'
             except OverflowError:
@@ -76,4 +81,6 @@ print_coercion_table(np.typecodes['All'], 0, 0, True)
 print
 print "array + neg scalar"
 print_coercion_table(np.typecodes['All'], 0, -1, True)
-
+print
+print "promote_types"
+print_coercion_table(np.typecodes['All'], 0, 0, False, True)