Prepare and perform signal extraction.

Return the symmetric matrix with the given eigenvectors and eigenvalues transformed by function. Parameters ---------- function : fun

Apply transform to covariances matrices to get the connectivity matrices for the chosen kind. Parameters ---------- X

Matrix function, for real symmetric matrices. The function is applied to the eigenvalues of symmetric. Parameters ---------- function

Fit the covariance estimator to the given time series for each subject. Parameters ---------- X : list of numpy.ndarr

Raise a ValueError if the input matrix is not symmetric positive definite. Parameters ---------- matrix : numpy.ndarray Input

Raise a ValueError if the input matrix is square. Parameters ---------- matrix : numpy.ndarray Input array.

Return correlation matrix for a given covariance matrix. Parameters ---------- covariance : 2D numpy.ndarray The input covariance

Fetch ABIDE timeseries data from Open Science Framework (OSF) Parameters ---------- data_dir : string Path where data should be d

Fetch ACPI timeseries data from Open Science Framework (OSF) Parameters ---------- data_dir : string Path where data should be do

Fetch ADNI timeseries data from Open Science Framework (OSF) Parameters ---------- data_dir : string Path where data should be do

Fetch ADNIDOD timeseries data from Open Science Framework (OSF) Parameters ---------- data_dir : string Path where data should be

Fetch COBRE timeseries data from Open Science Framework (OSF) Parameters ---------- data_dir : string Path where data should be d

Fetch HCP1 timeseries data from Open Science Framework (OSF) Parameters ---------- data_dir : string Path where data should be do

Fetch HCP2 timeseries data from Open Science Framework (OSF) Parameters ---------- data_dir : string Path where data should be do

Load path to pandas data frame Parameters ---------- path : str Path to csv file Returns ------- data : pd.DataFrame

Return partial correlation matrix for a given precision matrix. Parameters ---------- precision : 2D numpy.ndarray The input prec

New names assigned to each atlas

New names assigned to each classifier

New names assigned to each measure

Return the flattened lower triangular part of an array. If diagonal is kept, diagonal elements are divided by sqrt(2) to conserve the norm.

Compute the geometric mean of symmetric positive definite matrices. The geometric mean of n positive definite matrices M_1, ..., M_n is the m