# numpy.mask_indices

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.

  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.

  See also

  `triu`, `tril`, `triu_indices`, `tril_indices`

  Notes

  New in version 1.4.0.

  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])

previous

numpy.diag_indices_from

next

numpy.tril_indices