diff options
Diffstat (limited to 'numpy/lib')
| -rw-r--r-- | numpy/lib/function_base.py | 4 | ||||
| -rw-r--r-- | numpy/lib/tests/test_function_base.py | 4 |
2 files changed, 7 insertions, 1 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 989030e03..91e7dc616 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -1697,8 +1697,10 @@ def gradient(f, *varargs, **kwargs): for i, distances in enumerate(dx): if distances.ndim == 0: continue + elif distances.ndim != 1: + raise ValueError("distances must be either scalars or 1d") if len(distances) != f.shape[axes[i]]: - raise ValueError("distances must be either scalars or match " + raise ValueError("when 1d, distances must match " "the length of the corresponding dimension") diffx = np.diff(dx[i]) # if distances are constant reduce to the scalar case diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index ee2cc166a..d7d00758e 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -756,6 +756,10 @@ class TestGradient(TestCase): # 2D but axis specified gradient(f_2d, dx, axis=1) + # 2d coordinate arguments are not yet allowed + assert_raises_regex(ValueError, '.*scalars or 1d', + gradient, f_2d, np.stack([dx]*2, axis=-1), 1) + def test_badargs(self): f_2d = np.arange(25).reshape(5, 5) x = np.cumsum(np.ones(5)) |