mxnet.numpy.function_base

Numpy basic functions.

Functions

meshgrid(*xi, **kwargs)

Return coordinate matrices from coordinate vectors.
mxnet.numpy.function_base.meshgrid(*xi, **kwargs)[source]

Return 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.

Parameters:

  • x1 (ndarrays) – 1-D arrays representing the coordinates of a grid.

  • x2 (ndarrays) – 1-D arrays representing the coordinates of a grid.

  • ... (ndarrays) – 1-D arrays representing the coordinates of a grid.

  • xn (ndarrays) – 1-D arrays representing the coordinates of a grid.

  • indexing ({'xy', 'ij'}, optional) – Cartesian (‘xy’, default) or matrix (‘ij’) indexing of output. See Notes for more details.

  • sparse (bool, optional) – If True a sparse grid is returned in order to conserve memory. Default is False. Please note that sparse=True is currently not supported.

  • copy (bool, optional) – If False, a view into the original arrays are returned in order to conserve memory. Default is True. Please note that copy=False is currently not supported.

Returns:

X1, X2,…, XN – 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.

Return type:

ndarray

Notes

This function supports both indexing conventions through the indexing keyword argument. Giving the string ‘ij’ returns a meshgrid with matrix indexing, while ‘xy’ returns a meshgrid with Cartesian indexing. 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. The difference is illustrated by the following code snippet:

xv, yv = np.meshgrid(x, y, sparse=False, indexing='ij')
for i in range(nx):
    for j in range(ny):
        # treat xv[i,j], yv[i,j]

xv, yv = np.meshgrid(x, y, sparse=False, indexing='xy')
for i in range(nx):
    for j in range(ny):
        # treat xv[j,i], yv[j,i]

In the 1-D and 0-D case, the indexing and sparse keywords have no effect.