Merge branch 'master' into npy-2.1

1 files changed, 13 insertions, 4 deletions

diff --git a/numpy/testing/_private/utils.py b/numpy/testing/_private/utils.py
index c22348103..ee8eac9e8 100644
--- a/numpy/testing/_private/utils.py
+++ b/numpy/testing/_private/utils.py
@@ -20,7 +20,7 @@ from warnings import WarningMessage
 import pprint
 
 from numpy.core import(
-     float32, empty, arange, array_repr, ndarray, isnat, array)
+     intp, float32, empty, arange, array_repr, ndarray, isnat, array)
 from numpy.lib.utils import deprecate
 
 if sys.version_info[0] >= 3:
@@ -301,6 +301,11 @@ def assert_equal(actual, desired, err_msg='', verbose=True):
     check that all elements of these objects are equal. An exception is raised
     at the first conflicting values.
 
+    This function handles NaN comparisons as if NaN was a "normal" number.
+    That is, no assertion is raised if both objects have NaNs in the same
+    positions.  This is in contrast to the IEEE standard on NaNs, which says
+    that NaN compared to anything must return False.
+
     Parameters
     ----------
     actual : array_like
@@ -328,6 +333,11 @@ def assert_equal(actual, desired, err_msg='', verbose=True):
      ACTUAL: 5
      DESIRED: 6
 
+    The following comparison does not raise an exception.  There are NaNs
+    in the inputs, but they are in the same positions.
+
+    >>> np.testing.assert_equal(np.array([1.0, 2.0, np.nan]), [1, 2, np.nan])
+
     """
     __tracebackhide__ = True  # Hide traceback for py.test
     if isinstance(desired, dict):
@@ -784,18 +794,17 @@ def assert_array_compare(comparison, x, y, err_msg='', verbose=True,
 
         if isinstance(val, bool):
             cond = val
-            reduced = [0]
+            reduced = array([val])
         else:
             reduced = val.ravel()
             cond = reduced.all()
-            reduced = reduced.tolist()
 
         # The below comparison is a hack to ensure that fully masked
         # results, for which val.ravel().all() returns np.ma.masked,
         # do not trigger a failure (np.ma.masked != True evaluates as
         # np.ma.masked, which is falsy).
         if cond != True:
-            mismatch = 100.0 * reduced.count(0) / ox.size
+            mismatch = 100. * (reduced.size - reduced.sum(dtype=intp)) / ox.size
             remarks = ['Mismatch: {:.3g}%'.format(mismatch)]
 
             with errstate(invalid='ignore', divide='ignore'):