

def plot_evoked_white(evoked, noise_cov, show=True):
    'Plot whitened evoked response\n\n    Plots the whitened evoked response and the whitened GFP as described in\n    [1]_. If one single covariance object is passed, the GFP panel (bottom)\n    will depict different sensor types. If multiple covariance objects are\n    passed as a list, the left column will display the whitened evoked\n    responses for each channel based on the whitener from the noise covariance\n    that has the highest log-likelihood. The left column will depict the\n    whitened GFPs based on each estimator separately for each sensor type.\n    Instead of numbers of channels the GFP display shows the estimated rank.\n    Note. The rank estimation will be printed by the logger for each noise\n    covariance estimator that is passed.\n\n    Parameters\n    ----------\n    evoked : instance of mne.Evoked\n        The evoked response.\n    noise_cov : list | instance of Covariance | str\n        The noise covariance as computed by ``mne.cov.compute_covariance``.\n    show : bool\n        Show figure if True.\n\n    Returns\n    -------\n    fig : instance of matplotlib.figure.Figure\n        The figure object containing the plot.\n\n    References\n    ----------\n    .. [1] Engemann D. and Gramfort A. (2015) Automated model selection in\n           covariance estimation and spatial whitening of MEG and EEG\n           signals, vol. 108, 328-342, NeuroImage.\n    '
    return _plot_evoked_white(evoked=evoked, noise_cov=noise_cov, scalings=None, rank=None, show=show)

dataset/ETHPy150Open mne-tools/mne-python/mne/viz/evoked.py plot_evoked_white/original