numpy.mask_indices — NumPy v2.2 Manual (original) (raw)

numpy.mask_indices(n, mask_func, k=0)[source]#

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 asmask_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.

Parameters:

nint

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

mask_funccallable

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.

kscalar

An optional argument which is passed through to mask_func. Functions like triu, tril take a second argument that is interpreted as an offset.

Returns:

indicestuple of arrays.

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

Examples

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])