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-rw-r--r--numpy/lib/function_base.py21
1 files changed, 14 insertions, 7 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index c2680b016..6c7dfbfd3 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -1,5 +1,3 @@
-from __future__ import division, absolute_import, print_function
-
try:
# Accessing collections abstract classes from collections
# has been deprecated since Python 3.3
@@ -974,13 +972,18 @@ def gradient(f, *varargs, **kwargs):
# scalar or 1d array for each axis
dx = list(varargs)
for i, distances in enumerate(dx):
- if np.ndim(distances) == 0:
+ distances = np.asanyarray(distances)
+ if distances.ndim == 0:
continue
- elif np.ndim(distances) != 1:
+ elif distances.ndim != 1:
raise ValueError("distances must be either scalars or 1d")
if len(distances) != f.shape[axes[i]]:
raise ValueError("when 1d, distances must match "
"the length of the corresponding dimension")
+ if np.issubdtype(distances.dtype, np.integer):
+ # Convert numpy integer types to float64 to avoid modular
+ # arithmetic in np.diff(distances).
+ distances = distances.astype(np.float64)
diffx = np.diff(distances)
# if distances are constant reduce to the scalar case
# since it brings a consistent speedup
@@ -1019,8 +1022,12 @@ def gradient(f, *varargs, **kwargs):
elif np.issubdtype(otype, np.inexact):
pass
else:
- # all other types convert to floating point
- otype = np.double
+ # All other types convert to floating point.
+ # First check if f is a numpy integer type; if so, convert f to float64
+ # to avoid modular arithmetic when computing the changes in f.
+ if np.issubdtype(otype, np.integer):
+ f = f.astype(np.float64)
+ otype = np.float64
for axis, ax_dx in zip(axes, dx):
if f.shape[axis] < edge_order + 1:
@@ -1863,7 +1870,7 @@ def _create_arrays(broadcast_shape, dim_sizes, list_of_core_dims, dtypes):
@set_module('numpy')
-class vectorize(object):
+class vectorize:
"""
vectorize(pyfunc, otypes=None, doc=None, excluded=None, cache=False,
signature=None)