MAINT: Refactor AxisConcatenator to not use find_common_type

Rather, use `result_type` instead.  There are some exceedingly small
theoretical changes, since `result_type` currently uses value-inspection
logic.
`find_common_type` did not, because it pre-dates the value inspection
logic.
(I.e. in theory, this switches it to value-based promotion, just to
partially undo that in NEP 50; although more changes there.)

The only place where it is fathomable to matter is if someone is using
`np.c_[uint8_arr, -1]` to append 255 to an unsigned integer array.

1 files changed, 16 insertions, 15 deletions

diff --git a/numpy/lib/index_tricks.py b/numpy/lib/index_tricks.py
index 4f414925d..4c407d643 100644
--- a/numpy/lib/index_tricks.py
+++ b/numpy/lib/index_tricks.py
@@ -7,7 +7,7 @@ import numpy.core.numeric as _nx
 from numpy.core.numeric import (
     asarray, ScalarType, array, alltrue, cumprod, arange, ndim
 )
-from numpy.core.numerictypes import find_common_type, issubdtype
+from numpy.core.numerictypes import issubdtype
 
 import numpy.matrixlib as matrixlib
 from .function_base import diff
@@ -339,9 +339,8 @@ class AxisConcatenator:
         axis = self.axis
 
         objs = []
-        scalars = []
-        arraytypes = []
-        scalartypes = []
+        # dtypes or scalars for weak scalar handling in result_type
+        result_type_objs = []
 
         for k, item in enumerate(key):
             scalar = False
@@ -387,10 +386,8 @@ class AxisConcatenator:
                 except (ValueError, TypeError) as e:
                     raise ValueError("unknown special directive") from e
             elif type(item) in ScalarType:
-                newobj = array(item, ndmin=ndmin)
-                scalars.append(len(objs))
                 scalar = True
-                scalartypes.append(newobj.dtype)
+                newobj = array(item, ndmin=ndmin)
             else:
                 item_ndim = ndim(item)
                 newobj = array(item, copy=False, subok=True, ndmin=ndmin)
@@ -402,15 +399,19 @@ class AxisConcatenator:
                     defaxes = list(range(ndmin))
                     axes = defaxes[:k1] + defaxes[k2:] + defaxes[k1:k2]
                     newobj = newobj.transpose(axes)
+
             objs.append(newobj)
-            if not scalar and isinstance(newobj, _nx.ndarray):
-                arraytypes.append(newobj.dtype)
-
-        # Ensure 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_dtype)
+            if scalar:
+                result_type_objs.append(item)
+            else:
+                result_type_objs.append(newobj.dtype)
+
+        # Ensure that scalars won't up-cast unless warranted, for 0, drops
+        # through to error in concatenate.
+        if len(result_type_objs) != 0:
+            final_dtype = _nx.result_type(*result_type_objs)
+            # concatenate could do cast, but that can be overriden:
+            objs = [obj.astype(final_dtype, copy=False) for obj in objs]
 
         res = self.concatenate(tuple(objs), axis=axis)