diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 13 |
1 files changed, 6 insertions, 7 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 91e7dc616..b616011d1 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -1674,7 +1674,6 @@ def gradient(f, *varargs, **kwargs): S0025-5718-1988-0935077-0/S0025-5718-1988-0935077-0.pdf>`_. """ f = np.asanyarray(f) - varargs = [asanyarray(d) for d in varargs] N = f.ndim # number of dimensions axes = kwargs.pop('axis', None) @@ -1687,17 +1686,17 @@ def gradient(f, *varargs, **kwargs): n = len(varargs) if n == 0: # no spacing argument - use 1 in all axes - dx = [np.array(1.0)] * len_axes - elif n == 1 and varargs[0].ndim == 0: + dx = [1.0] * len_axes + elif n == 1 and np.ndim(varargs[0]) == 0: # single scalar for all axes dx = varargs * len_axes elif n == len_axes: # scalar or 1d array for each axis - dx = varargs[:] + dx = list(varargs) for i, distances in enumerate(dx): - if distances.ndim == 0: + if np.ndim(distances) == 0: continue - elif distances.ndim != 1: + elif np.ndim(distances) != 1: raise ValueError("distances must be either scalars or 1d") if len(distances) != f.shape[axes[i]]: raise ValueError("when 1d, distances must match " @@ -1757,7 +1756,7 @@ def gradient(f, *varargs, **kwargs): # result allocation out = np.empty_like(y, dtype=otype) - uniform_spacing = dx[i].ndim == 0 + uniform_spacing = np.ndim(dx[i]) == 0 # Numerical differentiation: 2nd order interior slice1[axis] = slice(1, -1) |