MAINT: Fix stacklevels for the new C dispatcher not adding one

3 files changed, 13 insertions, 13 deletions

diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 7a69c3c81..11a5a3ad0 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -2695,7 +2695,7 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
 
     if fact <= 0:
         warnings.warn("Degrees of freedom <= 0 for slice",
-                      RuntimeWarning, stacklevel=3)
+                      RuntimeWarning, stacklevel=2)
         fact = 0.0
 
     X -= avg[:, None]
@@ -2844,7 +2844,7 @@ def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue, *,
     if bias is not np._NoValue or ddof is not np._NoValue:
         # 2015-03-15, 1.10
         warnings.warn('bias and ddof have no effect and are deprecated',
-                      DeprecationWarning, stacklevel=3)
+                      DeprecationWarning, stacklevel=2)
     c = cov(x, y, rowvar, dtype=dtype)
     try:
         d = diag(c)
@@ -3684,7 +3684,7 @@ def msort(a):
     warnings.warn(
         "msort is deprecated, use np.sort(a, axis=0) instead",
         DeprecationWarning,
-        stacklevel=3,
+        stacklevel=2,
     )
     b = array(a, subok=True, copy=True)
     b.sort(0)
@@ -5398,7 +5398,7 @@ def insert(arr, obj, values, axis=None):
             warnings.warn(
                 "in the future insert will treat boolean arrays and "
                 "array-likes as a boolean index instead of casting it to "
-                "integer", FutureWarning, stacklevel=3)
+                "integer", FutureWarning, stacklevel=2)
             indices = indices.astype(intp)
             # Code after warning period:
             #if obj.ndim != 1:
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py
index 786d2021e..7e5528646 100644
--- a/numpy/lib/nanfunctions.py
+++ b/numpy/lib/nanfunctions.py
@@ -169,7 +169,7 @@ def _remove_nan_1d(arr1d, overwrite_input=False):
     s = np.nonzero(c)[0]
     if s.size == arr1d.size:
         warnings.warn("All-NaN slice encountered", RuntimeWarning,
-                      stacklevel=5)
+                      stacklevel=6)
         return arr1d[:0], True
     elif s.size == 0:
         return arr1d, overwrite_input
@@ -343,7 +343,7 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
         res = np.fmin.reduce(a, axis=axis, out=out, **kwargs)
         if np.isnan(res).any():
             warnings.warn("All-NaN slice encountered", RuntimeWarning,
-                          stacklevel=3)
+                          stacklevel=2)
     else:
         # Slow, but safe for subclasses of ndarray
         a, mask = _replace_nan(a, +np.inf)
@@ -357,7 +357,7 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
         if np.any(mask):
             res = _copyto(res, np.nan, mask)
             warnings.warn("All-NaN axis encountered", RuntimeWarning,
-                          stacklevel=3)
+                          stacklevel=2)
     return res
 
 
@@ -476,7 +476,7 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
         res = np.fmax.reduce(a, axis=axis, out=out, **kwargs)
         if np.isnan(res).any():
             warnings.warn("All-NaN slice encountered", RuntimeWarning,
-                          stacklevel=3)
+                          stacklevel=2)
     else:
         # Slow, but safe for subclasses of ndarray
         a, mask = _replace_nan(a, -np.inf)
@@ -490,7 +490,7 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,
         if np.any(mask):
             res = _copyto(res, np.nan, mask)
             warnings.warn("All-NaN axis encountered", RuntimeWarning,
-                          stacklevel=3)
+                          stacklevel=2)
     return res
 
 
@@ -1049,7 +1049,7 @@ 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=3)
+        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
@@ -1109,7 +1109,7 @@ def _nanmedian_small(a, axis=None, out=None, overwrite_input=False):
     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=4)
+                      stacklevel=5)
 
     fill_value = np.timedelta64("NaT") if m.dtype.kind == "m" else np.nan
     if out is not None:
@@ -1763,7 +1763,7 @@ 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,
-                      stacklevel=3)
+                      stacklevel=2)
         # NaN, inf, or negative numbers are all possible bad
         # values, so explicitly replace them with NaN.
         var = _copyto(var, np.nan, isbad)
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py
index 0f7ab0334..fb036108a 100644
--- a/numpy/lib/polynomial.py
+++ b/numpy/lib/polynomial.py
@@ -672,7 +672,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
     # warn on rank reduction, which indicates an ill conditioned matrix
     if rank != order and not full:
         msg = "Polyfit may be poorly conditioned"
-        warnings.warn(msg, RankWarning, stacklevel=4)
+        warnings.warn(msg, RankWarning, stacklevel=2)
 
     if full:
         return c, resids, rank, s, rcond