diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 12 |
1 files changed, 7 insertions, 5 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index b119f667a..e858ad1c3 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -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 -------- @@ -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 @@ -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: |