Merge pull request #7148 from seberg/stacklevel+tests

ENH,TST: Bump stacklevel and add tests for warnings

6 files changed, 33 insertions, 33 deletions

diff --git a/numpy/lib/format.py b/numpy/lib/format.py
index e62677bd8..633aee675 100644
--- a/numpy/lib/format.py
+++ b/numpy/lib/format.py
@@ -556,7 +556,7 @@ def write_array(fp, array, version=None, allow_pickle=True, pickle_kwargs=None):
     # this warning can be removed when 1.9 has aged enough
     if version != (2, 0) and used_ver == (2, 0):
         warnings.warn("Stored array in format 2.0. It can only be"
-                      "read by NumPy >= 1.9", UserWarning)
+                      "read by NumPy >= 1.9", UserWarning, stacklevel=2)
 
     if array.itemsize == 0:
         buffersize = 0
@@ -759,7 +759,7 @@ def open_memmap(filename, mode='r+', dtype=None, shape=None,
             # this warning can be removed when 1.9 has aged enough
             if version != (2, 0) and used_ver == (2, 0):
                 warnings.warn("Stored array in format 2.0. It can only be"
-                              "read by NumPy >= 1.9", UserWarning)
+                              "read by NumPy >= 1.9", UserWarning, stacklevel=2)
             offset = fp.tell()
         finally:
             fp.close()
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index a866ed767..b74e04028 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -1383,7 +1383,7 @@ def select(condlist, choicelist, default=0):
         # 2014-02-24, 1.9
         warnings.warn("select with an empty condition list is not possible"
                       "and will be deprecated",
-                      DeprecationWarning)
+                      DeprecationWarning, stacklevel=2)
         return np.asarray(default)[()]
 
     choicelist = [np.asarray(choice) for choice in choicelist]
@@ -1418,7 +1418,7 @@ def select(condlist, choicelist, default=0):
         msg = "select condlists containing integer ndarrays is deprecated " \
             "and will be removed in the future. Use `.astype(bool)` to " \
             "convert to bools."
-        warnings.warn(msg, DeprecationWarning)
+        warnings.warn(msg, DeprecationWarning, stacklevel=2)
 
     if choicelist[0].ndim == 0:
         # This may be common, so avoid the call.
@@ -2692,7 +2692,7 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
         fact = w_sum - ddof*sum(w*aweights)/w_sum
 
     if fact <= 0:
-        warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning)
+        warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning, stacklevel=2)
         fact = 0.0
 
     X -= avg[:, None]
@@ -2766,7 +2766,7 @@ def corrcoef(x, y=None, rowvar=1, 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)
+                      DeprecationWarning, stacklevel=2)
     c = cov(x, y, rowvar)
     try:
         d = diag(c)
@@ -3774,7 +3774,7 @@ def _median(a, axis=None, out=None, overwrite_input=False):
         if rout.ndim == 0:
             if n == True:
                 warnings.warn("Invalid value encountered in median",
-                              RuntimeWarning)
+                              RuntimeWarning, stacklevel=3)
                 if out is not None:
                     out[...] = a.dtype.type(np.nan)
                     rout = out
@@ -3783,7 +3783,7 @@ def _median(a, axis=None, out=None, overwrite_input=False):
         elif np.count_nonzero(n.ravel()) > 0:
             warnings.warn("Invalid value encountered in median for" +
                           " %d results" % np.count_nonzero(n.ravel()),
-                          RuntimeWarning)
+                          RuntimeWarning, stacklevel=3)
             rout[n] = np.nan
         return rout
     else:
@@ -4039,7 +4039,7 @@ def _percentile(a, q, axis=None, out=None,
 
     if np.any(n):
         warnings.warn("Invalid value encountered in percentile",
-                              RuntimeWarning)
+                      RuntimeWarning, stacklevel=3)
         if zerod:
             if ap.ndim == 1:
                 if out is not None:
@@ -4403,7 +4403,7 @@ def delete(arr, obj, axis=None):
         # 2013-09-24, 1.9
         warnings.warn(
             "in the future the special handling of scalars will be removed "
-            "from delete and raise an error", DeprecationWarning)
+            "from delete and raise an error", DeprecationWarning, stacklevel=2)
         if wrap:
             return wrap(arr)
         else:
@@ -4470,7 +4470,7 @@ def delete(arr, obj, axis=None):
     if obj.dtype == bool:
         warnings.warn(
             "in the future insert will treat boolean arrays and array-likes "
-            "as boolean index instead of casting it to integer", FutureWarning)
+            "as boolean index instead of casting it to integer", FutureWarning, stacklevel=2)
         obj = obj.astype(intp)
     if isinstance(_obj, (int, long, integer)):
         # optimization for a single value
@@ -4498,7 +4498,7 @@ def delete(arr, obj, axis=None):
             # 2013-09-24, 1.9
             warnings.warn(
                 "using a non-integer array as obj in delete will result in an "
-                "error in the future", DeprecationWarning)
+                "error in the future", DeprecationWarning, stacklevel=2)
             obj = obj.astype(intp)
         keep = ones(N, dtype=bool)
 
@@ -4509,13 +4509,13 @@ def delete(arr, obj, axis=None):
             warnings.warn(
                 "in the future out of bounds indices will raise an error "
                 "instead of being ignored by `numpy.delete`.",
-                DeprecationWarning)
+                DeprecationWarning, stacklevel=2)
             obj = obj[inside_bounds]
         positive_indices = obj >= 0
         if not positive_indices.all():
             warnings.warn(
                 "in the future negative indices will not be ignored by "
-                "`numpy.delete`.", FutureWarning)
+                "`numpy.delete`.", FutureWarning, stacklevel=2)
             obj = obj[positive_indices]
 
         keep[obj, ] = False
@@ -4642,7 +4642,7 @@ def insert(arr, obj, values, axis=None):
         # 2013-09-24, 1.9
         warnings.warn(
             "in the future the special handling of scalars will be removed "
-            "from insert and raise an error", DeprecationWarning)
+            "from insert and raise an error", DeprecationWarning, stacklevel=2)
         arr = arr.copy(order=arrorder)
         arr[...] = values
         if wrap:
@@ -4664,7 +4664,7 @@ 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)
+                "integer", FutureWarning, stacklevel=2)
             indices = indices.astype(intp)
             # Code after warning period:
             #if obj.ndim != 1:
@@ -4714,7 +4714,7 @@ def insert(arr, obj, values, axis=None):
         # 2013-09-24, 1.9
         warnings.warn(
             "using a non-integer array as obj in insert will result in an "
-            "error in the future", DeprecationWarning)
+            "error in the future", DeprecationWarning, stacklevel=2)
         indices = indices.astype(intp)
 
     indices[indices < 0] += N
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)
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py
index 534aa695c..0b2fdfaba 100644
--- a/numpy/lib/npyio.py
+++ b/numpy/lib/npyio.py
@@ -944,7 +944,7 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None,
             # End of lines reached
             first_line = ''
             first_vals = []
-            warnings.warn('loadtxt: Empty input file: "%s"' % fname)
+            warnings.warn('loadtxt: Empty input file: "%s"' % fname, stacklevel=2)
         N = len(usecols or first_vals)
 
         dtype_types, packing = flatten_dtype(dtype)
@@ -1542,7 +1542,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
         # return an empty array if the datafile is empty
         first_line = asbytes('')
         first_values = []
-        warnings.warn('genfromtxt: Empty input file: "%s"' % fname)
+        warnings.warn('genfromtxt: Empty input file: "%s"' % fname, stacklevel=2)
 
     # Should we take the first values as names ?
     if names is True:
@@ -1827,7 +1827,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
                 raise ValueError(errmsg)
             # Issue a warning ?
             else:
-                warnings.warn(errmsg, ConversionWarning)
+                warnings.warn(errmsg, ConversionWarning, stacklevel=2)
 
     # Strip the last skip_footer data
     if skip_footer > 0:
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py
index d178ba4b3..81c72749a 100644
--- a/numpy/lib/polynomial.py
+++ b/numpy/lib/polynomial.py
@@ -588,7 +588,7 @@ 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)
+        warnings.warn(msg, RankWarning, stacklevel=2)
 
     if full:
         return c, resids, rank, s, rcond
diff --git a/numpy/lib/utils.py b/numpy/lib/utils.py
index a2191468f..133704d13 100644
--- a/numpy/lib/utils.py
+++ b/numpy/lib/utils.py
@@ -96,7 +96,7 @@ class _Deprecate(object):
 
         def newfunc(*args,**kwds):
             """`arrayrange` is deprecated, use `arange` instead!"""
-            warnings.warn(depdoc, DeprecationWarning)
+            warnings.warn(depdoc, DeprecationWarning, stacklevel=2)
             return func(*args, **kwds)
 
         newfunc = _set_function_name(newfunc, old_name)
@@ -152,7 +152,7 @@ def deprecate(*args, **kwargs):
     >>> olduint(6)
     /usr/lib/python2.5/site-packages/numpy/lib/utils.py:114:
     DeprecationWarning: uint32 is deprecated
-      warnings.warn(str1, DeprecationWarning)
+      warnings.warn(str1, DeprecationWarning, stacklevel=2)
     6
 
     """
@@ -1016,7 +1016,7 @@ class SafeEval(object):
     def __init__(self):
         # 2014-10-15, 1.10
         warnings.warn("SafeEval is deprecated in 1.10 and will be removed.",
-                      DeprecationWarning)
+                      DeprecationWarning, stacklevel=2)
 
     def visit(self, node):
         cls = node.__class__