mars.tensor.mgrid

mars.tensor.mgrid = <mars.tensor.lib.index_tricks.nd_grid object>

Construct a multi-dimensional “meshgrid”.

grid = nd_grid() creates an instance which will return a mesh-grid when indexed. The dimension and number of the output arrays are equal to the number of indexing dimensions. If the step length is not a complex number, then the stop is not inclusive.

However, if the step length is a complex number (e.g. 5j), then the integer part of its magnitude is interpreted as specifying the number of points to create between the start and stop values, where the stop value is inclusive.

If instantiated with an argument of sparse=True, the mesh-grid is open (or not fleshed out) so that only one-dimension of each returned argument is greater than 1.

Parameters

sparse (bool, optional) – Whether the grid is sparse or not. Default is False.

Notes

Two instances of nd_grid are made available in the Mars.tensor namespace, mgrid and ogrid:

mgrid = nd_grid(sparse=False)
ogrid = nd_grid(sparse=True)

Users should use these pre-defined instances instead of using nd_grid directly.

Examples

>>> import mars.tensor as mt

>>> mgrid = mt.lib.index_tricks.nd_grid()
>>> mgrid[0:5,0:5]
array([[[0, 0, 0, 0, 0],
        [1, 1, 1, 1, 1],
        [2, 2, 2, 2, 2],
        [3, 3, 3, 3, 3],
        [4, 4, 4, 4, 4]],
       [[0, 1, 2, 3, 4],
        [0, 1, 2, 3, 4],
        [0, 1, 2, 3, 4],
        [0, 1, 2, 3, 4],
        [0, 1, 2, 3, 4]]])
>>> mgrid[-1:1:5j]
array([-1. , -0.5,  0. ,  0.5,  1. ])

>>> ogrid = mt.lib.index_tricks.nd_grid(sparse=True)
>>> ogrid[0:5,0:5]
[array([[0],
        [1],
        [2],
        [3],
        [4]]), array([[0, 1, 2, 3, 4]])]