mindspore.numpy.gradient(f, *varargs, axis=None, edge_order=1)[source]

Returns the gradient of a N-dimensional array. The gradient is computed using second order accurate central differences in the interior points and either first or second order accurate one-sides (forward or backwards) differences at the boundaries. The returned gradient hence has the same shape as the input array.

Note

Currently we only support edge_order=1 and uniform spacing of varargs.

Parameters
• f (Union[tuple, list, Tensor]) – An N-dimensional array containing samples of a scalar function.

• varargs (Union[tuple[number], tuple[tensor scalar]], optional) – Spacing between f values. Default unitary spacing for all dimensions. Spacing can be specified using: 1. single scalar to specify a sample distance for all dimensions. 2. N scalars to specify a constant sample distance for each dimension.

• edge_order (int) – Gradient is calculated using N-th order accurate differences at the boundaries. Default: 1.

• axis (Union[None, int, tuple(int), list(int)], optional) – Gradient is calculated only along the given axis or axes. The default (axis = None) is to calculate the gradient for all the axes of the input tensor. axis may be negative, in which case it counts from the last to the first axis.

Returns

gradient, a list of tensors (or a single tensor if there is only one dimension to be calculated). Each derivative has the same shape as f.

Raises
• TypeError – if the inputs have types not specified above.

• ValueError – if axis values out of bounds, or shape of f has entries < 1.

• NotImplementedError – if edge_order != 1, or varargs contains non-scalar entries.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> output = np.gradient([[1, 2, 6], [3, 4, 5]], axis=-1)
>>> print(output)
[[1.  2.5 4. ]
[1.  1.  1. ]]