ENH: __array_function__ support for np.lib, part 2/2 (#12119)

* ENH: __array_function__ support for np.lib, part 2

xref GH12028

np.lib.npyio through np.lib.ufunclike

* Fix failures in numpy/core/tests/test_overrides.py

* CLN: handle depreaction in dispatchers for np.lib.ufunclike

* CLN: fewer dispatchers in lib.twodim_base

* CLN: fewer dispatchers in lib.shape_base

* CLN: more dispatcher consolidation

* BUG: fix test failure

* Use all method instead of function in assert_equal

* DOC: indicate n is array_like in scimath.logn

* MAINT: updates per review

* MAINT: more conservative changes in assert_array_equal

* MAINT: add back in comment

* MAINT: casting tweaks in assert_array_equal

* MAINT: fixes and tests for assert_array_equal on subclasses

1 files changed, 16 insertions, 11 deletions

diff --git a/numpy/testing/_private/utils.py b/numpy/testing/_private/utils.py
index a3832fcde..b37dac69d 100644
--- a/numpy/testing/_private/utils.py
+++ b/numpy/testing/_private/utils.py
@@ -19,7 +19,7 @@ from warnings import WarningMessage
 import pprint
 
 from numpy.core import(
-     float32, empty, arange, array_repr, ndarray, isnat, array)
+     bool_, float32, empty, arange, array_repr, ndarray, isnat, array)
 from numpy.lib.utils import deprecate
 
 if sys.version_info[0] >= 3:
@@ -352,7 +352,7 @@ def assert_equal(actual, desired, err_msg='', verbose=True):
     # XXX: catch ValueError for subclasses of ndarray where iscomplex fail
     try:
         usecomplex = iscomplexobj(actual) or iscomplexobj(desired)
-    except ValueError:
+    except (ValueError, TypeError):
         usecomplex = False
 
     if usecomplex:
@@ -705,15 +705,20 @@ def assert_array_compare(comparison, x, y, err_msg='', verbose=True,
         at the same locations.
 
         """
-        # Both the != True comparison here and the cast to bool_ at the end are
-        # done to deal with `masked`, which cannot be compared usefully, and
-        # for which np.all yields masked.  The use of the function np.all is
-        # for back compatibility with ndarray subclasses that changed the
-        # return values of the all method.  We are not committed to supporting
-        # such subclasses, but some used to work.
         x_id = func(x)
         y_id = func(y)
-        if npall(x_id == y_id) != True:
+        # We include work-arounds here to handle three types of slightly
+        # pathological ndarray subclasses:
+        # (1) all() on `masked` array scalars can return masked arrays, so we
+        #     use != True
+        # (2) __eq__ on some ndarray subclasses returns Python booleans
+        #     instead of element-wise comparisons, so we cast to bool_() and
+        #     use isinstance(..., bool) checks
+        # (3) subclasses with bare-bones __array_function__ implemenations may
+        #     not implement np.all(), so favor using the .all() method
+        # We are not committed to supporting such subclasses, but it's nice to
+        # support them if possible.
+        if bool_(x_id == y_id).all() != True:
             msg = build_err_msg([x, y],
                                 err_msg + '\nx and y %s location mismatch:'
                                 % (hasval), verbose=verbose, header=header,
@@ -721,9 +726,9 @@ def assert_array_compare(comparison, x, y, err_msg='', verbose=True,
             raise AssertionError(msg)
         # If there is a scalar, then here we know the array has the same
         # flag as it everywhere, so we should return the scalar flag.
-        if x_id.ndim == 0:
+        if isinstance(x_id, bool) or x_id.ndim == 0:
             return bool_(x_id)
-        elif y_id.ndim == 0:
+        elif isinstance(x_id, bool) or y_id.ndim == 0:
             return bool_(y_id)
         else:
             return y_id