`numpy:ix_`

source: /numpy/lib/_index_tricks_impl.py :31

Signature

def ix_ ( * args )

Summary

Construct an open mesh from multiple sequences.

Extended Summary

This function takes N 1-D sequences and returns N outputs with N dimensions each, such that the shape is 1 in all but one dimension and the dimension with the non-unit shape value cycles through all N dimensions.

Using ix_ one can quickly construct index arrays that will index the cross product. a[np.ix_([1,3],[2,5])] returns the array [[a[1,2] a[1,5]], [a[3,2] a[3,5]]].

Parameters

args : 1-D sequences: Each sequence should be of integer or boolean type. Boolean sequences will be interpreted as boolean masks for the corresponding dimension (equivalent to passing in np.nonzero(boolean_sequence)).

Returns

out : tuple of ndarrays: N arrays with N dimensions each, with N the number of input sequences. Together these arrays form an open mesh.

Examples

import numpy as np
a = np.arange(10).reshape(2, 5)
a
ixgrid = np.ix_([0, 1], [2, 4])
ixgrid
ixgrid[0].shape, ixgrid[1].shape
a[ixgrid]

✓

ixgrid = np.ix_([True, True], [2, 4])
a[ixgrid]
ixgrid = np.ix_([True, True], [False, False, True, False, True])
a[ixgrid]

✓

Aliases

numpy.ix_