Frites -> MNE-Connectivity

conn_mne.py

"""Converting Frites' outputs to MNE-connectivity.
"""
import numpy as np
import xarray as xr


def conn_frites_to_mne(conn):
    """Conversion of Frites' connectivity outputs to MNE-connectivity.

    For performing the conversion to MNE-Python this function requires the
    MNE-Connectivity package.

    Parameters
    ----------
    conn : xr.DataArray
        Connectivity array (output of of a frites.conn function)

    Returns
    -------
    conn_mne : mne_connectivity
        Connectivity object compatible with MNE-Python
    """
    assert isinstance(conn, xr.DataArray)

    # getting universal attributes
    indices = (conn.attrs['sources'], conn.attrs['targets'])
    method = conn.name
    attrs = conn.attrs

    # conversion according to connectivity type
    if conn.attrs['type'] in ['dfc']:
        from mne_connectivity import EpochTemporalConnectivity

        times = conn['times'].data
        names = conn['roi'].data

        conn_mne = EpochTemporalConnectivity(
            conn.data, times, len(names), names=names, indices=indices,
            method=method, attrs=attrs
        )

    return conn_mne


if __name__ == '__main__':
    import matplotlib.pyplot as plt

    from frites.conn import define_windows
    from frites.conn import conn_dfc
    from frites.simulations import StimSpecAR

    # data simulation using AR model
    ss = StimSpecAR()
    x = ss.fit(ar_type='hga', n_epochs=100, n_stim=2)

    # sliding window definition
    slwin_len = .3
    slwin_step = .02
    win_sample = define_windows(
        x['times'].data, slwin_len=slwin_len, slwin_step=slwin_step
    )[0]

    # compute amplitude "correlation"
    conn = conn_dfc(x, win_sample=win_sample, times='times', roi='roi')

    # plot the results
    # conn.groupby('trials').mean().plot(x='times', hue='trials')
    # plt.show()

    conn_mne = conn_frites_to_mne(conn)