xorbits.numpy.mask_indices(n, mask_func, k=0)#

Return the indices to access (n, n) arrays, given a masking function.

Assume mask_func is a function that, for a square array a of size (n, n) with a possible offset argument k, when called as mask_func(a, k) returns a new array with zeros in certain locations (functions like triu or tril do precisely this). Then this function returns the indices where the non-zero values would be located.

  • n (int) – The returned indices will be valid to access arrays of shape (n, n).

  • mask_func (callable) – A function whose call signature is similar to that of triu, tril. That is, mask_func(x, k) returns a boolean array, shaped like x. k is an optional argument to the function.

  • k (scalar) – An optional argument which is passed through to mask_func. Functions like triu, tril take a second argument that is interpreted as an offset.


indices – The n arrays of indices corresponding to the locations where mask_func(np.ones((n, n)), k) is True.

Return type

tuple of arrays.


New in version 1.4.0(numpy).


These are the indices that would allow you to access the upper triangular part of any 3x3 array:

>>> iu = np.mask_indices(3, np.triu)  

For example, if a is a 3x3 array:

>>> a = np.arange(9).reshape(3, 3)  
>>> a  
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])
>>> a[iu]  
array([0, 1, 2, 4, 5, 8])

An offset can be passed also to the masking function. This gets us the indices starting on the first diagonal right of the main one:

>>> iu1 = np.mask_indices(3, np.triu, 1)  

with which we now extract only three elements:

>>> a[iu1]  
array([1, 2, 5])


This method has not been implemented yet. Xorbits will try to execute it with numpy.

This docstring was copied from numpy.