ENH: Add stacklevel to all (or almost all) our function calls

1 files changed, 9 insertions, 9 deletions

diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py
index c2fc92ebf..7f7aea9bc 100644
--- a/numpy/lib/nanfunctions.py
+++ b/numpy/lib/nanfunctions.py
@@ -236,7 +236,7 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue):
         # Fast, but not safe for subclasses of ndarray
         res = np.fmin.reduce(a, axis=axis, out=out, **kwargs)
         if np.isnan(res).any():
-            warnings.warn("All-NaN axis encountered", RuntimeWarning)
+            warnings.warn("All-NaN axis encountered", RuntimeWarning, stacklevel=2)
     else:
         # Slow, but safe for subclasses of ndarray
         a, mask = _replace_nan(a, +np.inf)
@@ -248,7 +248,7 @@ 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)
+            warnings.warn("All-NaN axis encountered", RuntimeWarning, stacklevel=2)
     return res
 
 
@@ -343,7 +343,7 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue):
         # Fast, but not safe for subclasses of ndarray
         res = np.fmax.reduce(a, axis=axis, out=out, **kwargs)
         if np.isnan(res).any():
-            warnings.warn("All-NaN slice encountered", RuntimeWarning)
+            warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=2)
     else:
         # Slow, but safe for subclasses of ndarray
         a, mask = _replace_nan(a, -np.inf)
@@ -355,7 +355,7 @@ 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)
+            warnings.warn("All-NaN axis encountered", RuntimeWarning, stacklevel=2)
     return res
 
 
@@ -821,7 +821,7 @@ 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)
+        warnings.warn("Mean of empty slice", RuntimeWarning, stacklevel=2)
         # NaN is the only possible bad value, so no further
         # action is needed to handle bad results.
     return avg
@@ -835,7 +835,7 @@ def _nanmedian1d(arr1d, overwrite_input=False):
     c = np.isnan(arr1d)
     s = np.where(c)[0]
     if s.size == arr1d.size:
-        warnings.warn("All-NaN slice encountered", RuntimeWarning)
+        warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=3)
         return np.nan
     elif s.size == 0:
         return np.median(arr1d, overwrite_input=overwrite_input)
@@ -887,7 +887,7 @@ 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)
+        warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=3)
     if out is not None:
         out[...] = m.filled(np.nan)
         return out
@@ -1161,7 +1161,7 @@ def _nanpercentile1d(arr1d, q, overwrite_input=False, interpolation='linear'):
     c = np.isnan(arr1d)
     s = np.where(c)[0]
     if s.size == arr1d.size:
-        warnings.warn("All-NaN slice encountered", RuntimeWarning)
+        warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=3)
         if q.ndim == 0:
             return np.nan
         else:
@@ -1317,7 +1317,7 @@ 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)
+        warnings.warn("Degrees of freedom <= 0 for slice.", RuntimeWarning, stacklevel=2)
         # NaN, inf, or negative numbers are all possible bad
         # values, so explicitly replace them with NaN.
         var = _copyto(var, np.nan, isbad)