Function plot_cov

mne/viz/misc.py:111–256

Plot Covariance data. Parameters ---------- cov : instance of Covariance The covariance matrix. %(info_not_none)s exclude : list of str | str List of channels to exclude. If empty do not exclude any channel. If 'bads', exclude info['bads']. colorbar :

(
    cov,
    info,
    exclude=(),
    colorbar=True,
    proj=False,
    show_svd=True,
    show=True,
    verbose=None,
)

@verbose
def plot_cov(
    cov,
    info,
    exclude=(),
    colorbar=True,
    proj=False,
    show_svd=True,
    show=True,
    verbose=None,
):
    """Plot Covariance data.

    Parameters
    ----------
    cov : instance of Covariance
        The covariance matrix.
    %(info_not_none)s
    exclude : list of str | str
        List of channels to exclude. If empty do not exclude any channel.
        If 'bads', exclude info['bads'].
    colorbar : bool
        Show colorbar or not.
    proj : bool
        Apply projections or not.
    show_svd : bool
        Plot also singular values of the noise covariance for each sensor
        type. We show square roots ie. standard deviations.
    show : bool
        Show figure if True.
    %(verbose)s

    Returns
    -------
    fig_cov : instance of matplotlib.figure.Figure
        The covariance plot.
    fig_svd : instance of matplotlib.figure.Figure | None
        The SVD plot of the covariance (i.e., the eigenvalues or "matrix spectrum").

    See Also
    --------
    mne.compute_rank

    Notes
    -----
    For each channel type, the rank is estimated using
    :func:`mne.compute_rank`.

    .. versionchanged:: 0.19
       Approximate ranks for each channel type are shown with red dashed lines.
    """
    import matplotlib.pyplot as plt
    from matplotlib.colors import Normalize

    from ..cov import Covariance

    info, C, ch_names, idx_names = _index_info_cov(info, cov, exclude)
    del cov, exclude