MNT: Improve comments on assert_array_compare nan/inf handling logic

- Use same language as elsewhere below to explain `!= True` used to handle np.ma.masked - Clarify committed to support standard MaskedArrays - Restore note lost in 7315145 comment changes about how the np.bool casts towards the end of the function handle np.ma.masked, and expand further.
numpy · ngoldbaum · Jul 13, 2025 · Jul 4, 2025 · Jul 4, 2025 · Jul 4, 2025
commit 32ba514866d26dd4d27f60ce09d376af643843a8
diff --git a/numpy/testing/_private/utils.py b/numpy/testing/_private/utils.py
@@ -771,15 +771,15 @@ def func_assert_same_pos(x, y, func=isnan, hasval='nan'):
         y_id = func(y)
         # 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
+        # (1) all() on fully masked arrays returns np.ma.masked, so we use != True
+        #     (np.ma.masked != True evaluates as np.ma.masked, which is falsy).
         # (2) __eq__ on some ndarray subclasses returns Python booleans
         #     instead of element-wise comparisons, so we cast to np.bool() in
         #     that case (or in case __eq__ returns some other value with no
         #     all() method).
         # (3) subclasses with bare-bones __array_function__ implementations may
         #     not implement np.all(), so favor using the .all() method
-        # We are not committed to supporting such subclasses, but it's nice to
+        # We are not committed to supporting cases (2) and (3), but it's nice to
         # support them if possible.
         result = x_id == y_id
         if not hasattr(result, "all") or not callable(result.all):
@@ -794,6 +794,9 @@ def func_assert_same_pos(x, y, func=isnan, hasval='nan'):
             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.
+        # np.ma.masked is also handled and converted to np.False_ (even if the other
+        # array has nans/infs etc.; that's OK given the handling later of fully-masked
+        # results).
         if isinstance(x_id, bool) or x_id.ndim == 0:
             return np.bool(x_id)
         elif isinstance(y_id, bool) or y_id.ndim == 0: