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BUG: distance arg of np.gradient must be scalar, fix docstring #7618

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Merged
merged 1 commit into from
May 11, 2016
Merged

BUG: distance arg of np.gradient must be scalar, fix docstring #7618

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12 changes: 7 additions & 5 deletions numpy/lib/function_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1410,9 +1410,10 @@ def gradient(f, *varargs, **kwargs):

Returns
-------
gradient : list of ndarray
Each element of `list` has the same shape as `f` giving the derivative
of `f` with respect to each dimension.
gradient : ndarray or list of ndarray
A set of ndarrays (or a single ndarray if there is only one dimension)
correposnding to the derivatives of f with respect to each dimension.
Each derivative has the same shape as f.

Examples
--------
Expand All @@ -1432,9 +1433,8 @@ def gradient(f, *varargs, **kwargs):
[ 1. , 1. , 1. ]])]

>>> x = np.array([0, 1, 2, 3, 4])
>>> dx = np.gradient(x)
>>> y = x**2
>>> np.gradient(y, dx, edge_order=2)
>>> np.gradient(y, edge_order=2)
array([-0., 2., 4., 6., 8.])

The axis keyword can be used to specify a subset of axes of which the gradient is calculated
Expand Down Expand Up @@ -1472,6 +1472,8 @@ def gradient(f, *varargs, **kwargs):
else:
raise SyntaxError(
"invalid number of arguments")
if any([not np.isscalar(dxi) for dxi in dx]):
raise ValueError("distances must be scalars")

edge_order = kwargs.pop('edge_order', 1)
if kwargs:
Expand Down
3 changes: 3 additions & 0 deletions numpy/lib/tests/test_function_base.py
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Expand Up @@ -666,6 +666,9 @@ def test_badargs(self):
assert_raises(SyntaxError, gradient, x, np.array([1., 1.]),
np.array([1., 1.]), np.array([1., 1.]))

# disallow arrays as distances, see gh-6847
assert_raises(ValueError, gradient, np.arange(5), np.ones(5))

def test_masked(self):
# Make sure that gradient supports subclasses like masked arrays
x = np.ma.array([[1, 1], [3, 4]],
Expand Down