Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.connectome.vec_to_sym_matrix¶

nilearn.connectome.vec_to_sym_matrix(vec, diagonal=None)[source]¶

Return the symmetric matrix given its flattened lower triangular part.

Acts on the last dimension of the array if not 1-dimensional. Diagonal can be encompassed in vec or given separately. In both cases, note that diagonal elements are multiplied by sqrt(2).

Added in Nilearn 0.3.

Parameters:

vecnumpy.ndarray or list of numpy arrays, shape (…, n_columns * (n_columns + 1) /2) or: (…, (n_columns - 1) * n_columns / 2) if diagonal is given separately. The input array.
diagonalnumpy.ndarray, shape (…, n_columns), default=None: The diagonal array to be stacked to vec. If None, the diagonal is assumed to be included in vec.

Returns:

symnumpy.ndarray, shape (…, n_columns, n_columns).: The output symmetric matrix.

See also

nilearn.connectome.sym_matrix_to_vec

Notes

This function is meant to be the inverse of sym_matrix_to_vec. If you have discarded the diagonal in sym_matrix_to_vec, you need to provide it separately to reconstruct the symmetric matrix. For instance this can be useful for correlation matrices for which we know the diagonal is 1.

Examples

Create a vector representing the flattened lower triangular part (including the diagonal) of a symmetric matrix >>> import numpy as np >>> vec = np.arange(1, 7)

>>> from nilearn.connectome import vec_to_sym_matrix
>>> sym = vec_to_sym_matrix(vec)
>>> sym
array([[1.41421356, 2.        , 4.        ],
       [2.        , 4.24264069, 5.        ],
       [4.        , 5.        , 8.48528137]])