dpnp.ix_

dpnp.ix_(*args)[source]

Construct an open mesh from multiple sequences.

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

Parameters:

  • x1 ({dpnp.ndarray, usm_ndarray}) -- 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 dpnp.nonzero(boolean_sequence)).

  • x2 ({dpnp.ndarray, usm_ndarray}) -- 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 dpnp.nonzero(boolean_sequence)).

  • ... ({dpnp.ndarray, usm_ndarray}) -- 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 dpnp.nonzero(boolean_sequence)).

  • xn ({dpnp.ndarray, usm_ndarray}) -- 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 dpnp.nonzero(boolean_sequence)).

Returns:

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

Return type:

tuple of dpnp.ndarray

See also

dpnp.mgrid

Return a dense multi-dimensional “meshgrid”.

dpnp.ogrid

Return an open multi-dimensional “meshgrid”.

dpnp.meshgrid

Return a tuple of coordinate matrices from coordinate vectors.

Examples

>>> import dpnp as np
>>> a = np.arange(10).reshape(2, 5)
>>> a
array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])
>>> x1 = np.array([0, 1])
>>> x2 = np.array([2, 4])
>>> ixgrid = np.ix_(x1, x2)
>>> ixgrid
(array([[0],
       [1]]), array([[2, 4]]))
>>> ixgrid[0].shape, ixgrid[1].shape
((2, 1), (1, 2))
>>> a[ixgrid]
array([[2, 4],
       [7, 9]])

>>> x1 = np.array([True, True])
>>> x2 = np.array([2, 4])
>>> ixgrid = np.ix_(x1, x2)
>>> a[ixgrid]
array([[2, 4],
       [7, 9]])
>>> x1 = np.array([True, True])
>>> x2 = np.array([False, False, True, False, True])
>>> ixgrid = np.ix_(x1, x2)
>>> a[ixgrid]
array([[2, 4],
       [7, 9]])