mindspore.numpy.indices

mindspore.numpy.indices(dimensions, dtype=mstype.int32, sparse=False)[source]

Returns an array representing the indices of a grid.

Computes an array where the subarrays contain index values 0, 1, … varying only along the corresponding axis.

Parameters

dimensions (tuple or list of ints) – The shape of the grid.
dtype (mindspore.dtype, optional) – Data type of the result.
sparse (boolean, optional) – Default: False . Return a sparse representation of the grid instead of a dense representation.

Returns

Tensor or tuple of Tensor, If sparse is False, returns one array of grid indices, grid.shape = (len(dimensions),) + tuple(dimensions). If sparse is True, returns a tuple of arrays, with grid[i].shape = (1, ..., 1, dimensions[i], 1, ..., 1) with dimensions[i] in the ith place

Raises

TypeError – If input dimensions is not a tuple or list.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> grid = np.indices((2, 3))
>>> print(grid)
[Tensor(shape=[2, 3], dtype=Int32, value=
[[0, 0, 0],
[1, 1, 1]]), Tensor(shape=[2, 3], dtype=Int32, value=
[[0, 1, 2],
[0, 1, 2]])]