mindspore.numpy.meshgrid(*xi, sparse=False, indexing="xy")[source]

Returns coordinate matrices from coordinate vectors.

Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,…, xn.


Numpy argument copy is not supported, and a copy is always returned.

  • *xi (Tensor) – 1-D arrays representing the coordinates of a grid.

  • indexing (‘xy’, ‘ij’, optional) – Cartesian (‘xy’, default) or matrix (‘ij’) indexing of output. In the 2-D case with inputs of length M and N, the outputs are of shape (N, M) for ‘xy’ indexing and (M, N) for ‘ij’ indexing. In the 3-D case with inputs of length M, N and P, outputs are of shape (N, M, P) for ‘xy’ indexing and (M, N, P) for ‘ij’ indexing.

  • sparse (bool, optional) – If True a sparse grid is returned in order to conserve memory. Default is False.


Tuple of tensors, for vectors x1, x2,…, xn with lengths Ni=len(xi), return (N1, N2, N3,…Nn) shaped arrays if indexing=’ij’ or (N2, N1, N3,…Nn) shaped arrays if indexing=’xy’ with the elements of xi repeated to fill the matrix along the first dimension for x1, the second for x2 and so on.


TypeError – if the input is not a tensor, or sparse is not boolean, or indexing is not ‘xy’ or ‘ij’.

Supported Platforms:

Ascend GPU CPU


>>> import mindspore.numpy as np
>>> x = np.linspace(0, 1, 3)
>>> y = np.linspace(0, 1, 2)
>>> xv, yv = np.meshgrid(x, y)
>>> print(xv)
[[0.  0.5 1. ]
[0.  0.5 1. ]]
>>> print(yv)
[[0.  0.  0.]
[1.  1.  1.]]
>>> xv, yv = np.meshgrid(x, y, sparse=True)
>>> print(xv)
[[0.  0.5  1. ]]
>>> print(yv)