meshgrid(*xi, sparse=False, indexing="xy")¶
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:
>>> 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) [[0.] [1.]]