`numpy:mask_indices`

source: /numpy/lib/_twodim_base_impl.py:838

Signature

def mask_indices(n, mask_func, k = 0)

Summary

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

Extended Summary

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.

Parameters

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.

Returns

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

Examples

import numpy as np

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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)

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For example, if `a` is a 3x3 array:

a = np.arange(9).reshape(3, 3)
a
a[iu]

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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)

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with which we now extract only three elements:

a[iu1]

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array([1, 2, 5])

Aliases

numpy.mask_indices