BUG: Increment stacklevel for warnings to account for NEP-18 overrides (#13589)

* Increment stacklevel for warnings to account for NEP-18 overrides

For NumPy functions that make use of `__array_function__`, the appropriate the
stack level for warnings should generally be increased by 1 to account for
the override function defined in numpy.core.overrides.

Fixes GH-13329

* Update numpy/lib/type_check.py

Co-Authored-By: Sebastian Berg <sebastian@sipsolutions.net>

1 files changed, 16 insertions, 9 deletions

diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py
index 77c851fcf..f497aca63 100644
--- a/numpy/lib/nanfunctions.py
+++ b/numpy/lib/nanfunctions.py
@@ -165,7 +165,8 @@ def _remove_nan_1d(arr1d, overwrite_input=False):
     c = np.isnan(arr1d)
     s = np.nonzero(c)[0]
     if s.size == arr1d.size:
-        warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=4)
+        warnings.warn("All-NaN slice encountered", RuntimeWarning,
+                      stacklevel=5)
         return arr1d[:0], True
     elif s.size == 0:
         return arr1d, overwrite_input
@@ -318,7 +319,8 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue):
         # which do not implement isnan (gh-9009), or fmin correctly (gh-8975)
         res = np.fmin.reduce(a, axis=axis, out=out, **kwargs)
         if np.isnan(res).any():
-            warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=2)
+            warnings.warn("All-NaN slice encountered", RuntimeWarning,
+                          stacklevel=3)
     else:
         # Slow, but safe for subclasses of ndarray
         a, mask = _replace_nan(a, +np.inf)
@@ -330,7 +332,8 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue):
         mask = np.all(mask, axis=axis, **kwargs)
         if np.any(mask):
             res = _copyto(res, np.nan, mask)
-            warnings.warn("All-NaN axis encountered", RuntimeWarning, stacklevel=2)
+            warnings.warn("All-NaN axis encountered", RuntimeWarning,
+                          stacklevel=3)
     return res
 
 
@@ -431,7 +434,8 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue):
         # which do not implement isnan (gh-9009), or fmax correctly (gh-8975)
         res = np.fmax.reduce(a, axis=axis, out=out, **kwargs)
         if np.isnan(res).any():
-            warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=2)
+            warnings.warn("All-NaN slice encountered", RuntimeWarning,
+                          stacklevel=3)
     else:
         # Slow, but safe for subclasses of ndarray
         a, mask = _replace_nan(a, -np.inf)
@@ -443,7 +447,8 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue):
         mask = np.all(mask, axis=axis, **kwargs)
         if np.any(mask):
             res = _copyto(res, np.nan, mask)
-            warnings.warn("All-NaN axis encountered", RuntimeWarning, stacklevel=2)
+            warnings.warn("All-NaN axis encountered", RuntimeWarning,
+                          stacklevel=3)
     return res
 
 
@@ -947,7 +952,7 @@ def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
 
     isbad = (cnt == 0)
     if isbad.any():
-        warnings.warn("Mean of empty slice", RuntimeWarning, stacklevel=2)
+        warnings.warn("Mean of empty slice", RuntimeWarning, stacklevel=3)
         # NaN is the only possible bad value, so no further
         # action is needed to handle bad results.
     return avg
@@ -959,7 +964,7 @@ def _nanmedian1d(arr1d, overwrite_input=False):
     See nanmedian for parameter usage
     """
     arr1d, overwrite_input = _remove_nan_1d(arr1d,
-        overwrite_input=overwrite_input)
+                                            overwrite_input=overwrite_input)
     if arr1d.size == 0:
         return np.nan
 
@@ -1002,7 +1007,8 @@ def _nanmedian_small(a, axis=None, out=None, overwrite_input=False):
     a = np.ma.masked_array(a, np.isnan(a))
     m = np.ma.median(a, axis=axis, overwrite_input=overwrite_input)
     for i in range(np.count_nonzero(m.mask.ravel())):
-        warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=3)
+        warnings.warn("All-NaN slice encountered", RuntimeWarning,
+                      stacklevel=4)
     if out is not None:
         out[...] = m.filled(np.nan)
         return out
@@ -1547,7 +1553,8 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):
 
     isbad = (dof <= 0)
     if np.any(isbad):
-        warnings.warn("Degrees of freedom <= 0 for slice.", RuntimeWarning, stacklevel=2)
+        warnings.warn("Degrees of freedom <= 0 for slice.", RuntimeWarning,
+                      stacklevel=3)
         # NaN, inf, or negative numbers are all possible bad
         # values, so explicitly replace them with NaN.
         var = _copyto(var, np.nan, isbad)