ix_(*args)
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]]]
.
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)
).
N arrays with N dimensions each, with N the number of input sequences. Together these arrays form an open mesh.
Construct an open mesh from multiple sequences.
>>> a = np.arange(10).reshape(2, 5)
... a array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])
>>> ixgrid = np.ix_([0, 1], [2, 4])
... ixgrid (array([[0], [1]]), array([[2, 4]]))
>>> ixgrid[0].shape, ixgrid[1].shape ((2, 1), (1, 2))
>>> a[ixgrid] array([[2, 4], [7, 9]])
>>> ixgrid = np.ix_([True, True], [2, 4])
... a[ixgrid] array([[2, 4], [7, 9]])
>>> ixgrid = np.ix_([True, True], [False, False, True, False, True])See :
... a[ixgrid] array([[2, 4], [7, 9]])
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.ix_
numpy.cross
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