1 files changed, 21 insertions, 27 deletions

diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index c2680b016..ef8a26fe3 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
@@ -772,14 +770,14 @@ def copy(a, order='K'):
 # Basic operations
 
 
-def _gradient_dispatcher(f, *varargs, **kwargs):
+def _gradient_dispatcher(f, *varargs, axis=None, edge_order=None):
     yield f
     for v in varargs:
         yield v
 
 
 @array_function_dispatch(_gradient_dispatcher)
-def gradient(f, *varargs, **kwargs):
+def gradient(f, *varargs, axis=None, edge_order=1):
     """
     Return the gradient of an N-dimensional array.
 
@@ -956,11 +954,10 @@ def gradient(f, *varargs, **kwargs):
     f = np.asanyarray(f)
     N = f.ndim  # number of dimensions
 
-    axes = kwargs.pop('axis', None)
-    if axes is None:
+    if axis is None:
         axes = tuple(range(N))
     else:
-        axes = _nx.normalize_axis_tuple(axes, N)
+        axes = _nx.normalize_axis_tuple(axis, N)
 
     len_axes = len(axes)
     n = len(varargs)
@@ -974,13 +971,18 @@ 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
@@ -990,10 +992,6 @@ def gradient(f, *varargs, **kwargs):
     else:
         raise TypeError("invalid number of arguments")
 
-    edge_order = kwargs.pop('edge_order', 1)
-    if kwargs:
-        raise TypeError('"{}" are not valid keyword arguments.'.format(
-                                                  '", "'.join(kwargs.keys())))
     if edge_order > 2:
         raise ValueError("'edge_order' greater than 2 not supported")
 
@@ -1019,8 +1017,12 @@ 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 +1865,7 @@ 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)
@@ -4056,13 +4058,13 @@ def trapz(y, x=None, dx=1.0, axis=-1):
     return ret
 
 
-def _meshgrid_dispatcher(*xi, **kwargs):
+def _meshgrid_dispatcher(*xi, copy=None, sparse=None, indexing=None):
     return xi
 
 
 # Based on scitools meshgrid
 @array_function_dispatch(_meshgrid_dispatcher)
-def meshgrid(*xi, **kwargs):
+def meshgrid(*xi, copy=True, sparse=False, indexing='xy'):
     """
     Return coordinate matrices from coordinate vectors.
 
@@ -4168,14 +4170,6 @@ def meshgrid(*xi, **kwargs):
     """
     ndim = len(xi)
 
-    copy_ = kwargs.pop('copy', True)
-    sparse = kwargs.pop('sparse', False)
-    indexing = kwargs.pop('indexing', 'xy')
-
-    if kwargs:
-        raise TypeError("meshgrid() got an unexpected keyword argument '%s'"
-                        % (list(kwargs)[0],))
-
     if indexing not in ['xy', 'ij']:
         raise ValueError(
             "Valid values for `indexing` are 'xy' and 'ij'.")
@@ -4193,7 +4187,7 @@ def meshgrid(*xi, **kwargs):
         # Return the full N-D matrix (not only the 1-D vector)
         output = np.broadcast_arrays(*output, subok=True)
 
-    if copy_:
+    if copy:
         output = [x.copy() for x in output]
 
     return output