

@verbose
def plot_epochs_psd_topomap(epochs, bands=None, vmin=None, vmax=None, tmin=None, tmax=None, proj=False, bandwidth=None, adaptive=False, low_bias=True, normalization='length', ch_type=None, layout=None, cmap='RdBu_r', agg_fun=None, dB=False, n_jobs=1, normalize=False, cbar_fmt='%0.3f', outlines='head', show=True, verbose=None):
    'Plot the topomap of the power spectral density across epochs\n\n    Parameters\n    ----------\n    epochs : instance of Epochs\n        The epochs object\n    bands : list of tuple | None\n        The lower and upper frequency and the name for that band. If None,\n        (default) expands to:\n\n        bands = [(0, 4, \'Delta\'), (4, 8, \'Theta\'), (8, 12, \'Alpha\'),\n                 (12, 30, \'Beta\'), (30, 45, \'Gamma\')]\n\n    vmin : float | callable | None\n        The value specifying the lower bound of the color range.\n        If None np.min(data) is used. If callable, the output equals\n        vmin(data).\n    vmax : float | callable | None\n        The value specifying the upper bound of the color range.\n        If None, the maximum absolute value is used. If callable, the output\n        equals vmax(data). Defaults to None.\n    tmin : float | None\n        Start time to consider.\n    tmax : float | None\n        End time to consider.\n    proj : bool\n        Apply projection.\n    bandwidth : float\n        The bandwidth of the multi taper windowing function in Hz. The default\n        value is a window half-bandwidth of 4 Hz.\n    adaptive : bool\n        Use adaptive weights to combine the tapered spectra into PSD\n        (slow, use n_jobs >> 1 to speed up computation).\n    low_bias : bool\n        Only use tapers with more than 90% spectral concentration within\n        bandwidth.\n    normalization : str\n        Either "full" or "length" (default). If "full", the PSD will\n        be normalized by the sampling rate as well as the length of\n        the signal (as in nitime).\n    ch_type : \'mag\' | \'grad\' | \'planar1\' | \'planar2\' | \'eeg\' | None\n        The channel type to plot. For \'grad\', the gradiometers are collected in\n        pairs and the RMS for each pair is plotted. If None, then first\n        available channel type from order given above is used. Defaults to\n        None.\n    layout : None | Layout\n        Layout instance specifying sensor positions (does not need to\n        be specified for Neuromag data). If possible, the correct layout\n        file is inferred from the data; if no appropriate layout file was\n        found, the layout is automatically generated from the sensor\n        locations.\n    cmap : matplotlib colormap\n        Colormap. For magnetometers and eeg defaults to \'RdBu_r\', else\n        \'Reds\'.\n    agg_fun : callable\n        The function used to aggregate over frequencies.\n        Defaults to np.sum. if normalize is True, else np.mean.\n    dB : bool\n        If True, transform data to decibels (with ``10 * np.log10(data)``)\n        following the application of `agg_fun`. Only valid if normalize is\n        False.\n    n_jobs : int\n        Number of jobs to run in parallel.\n    normalize : bool\n        If True, each band will be divided by the total power. Defaults to\n        False.\n    cbar_fmt : str\n        The colorbar format. Defaults to \'%0.3f\'.\n    outlines : \'head\' | \'skirt\' | dict | None\n        The outlines to be drawn. If \'head\', the default head scheme will be\n        drawn. If \'skirt\' the head scheme will be drawn, but sensors are\n        allowed to be plotted outside of the head circle. If dict, each key\n        refers to a tuple of x and y positions, the values in \'mask_pos\' will\n        serve as image mask, and the \'autoshrink\' (bool) field will trigger\n        automated shrinking of the positions due to points outside the outline.\n        Alternatively, a matplotlib patch object can be passed for advanced\n        masking options, either directly or as a function that returns patches\n        (required for multi-axis plots). If None, nothing will be drawn.\n        Defaults to \'head\'.\n    show : bool\n        Show figure if True.\n    verbose : bool, str, int, or None\n        If not None, override default verbose level (see mne.verbose).\n\n    Returns\n    -------\n    fig : instance of matplotlib figure\n        Figure distributing one image per channel across sensor topography.\n    '
    from ..channels import _get_ch_type
    ch_type = _get_ch_type(epochs, ch_type)
    (picks, pos, merge_grads, names, ch_type) = _prepare_topo_plot(epochs, ch_type, layout)
    (psds, freqs) = psd_multitaper(epochs, tmin=tmin, tmax=tmax, bandwidth=bandwidth, adaptive=adaptive, low_bias=low_bias, normalization=normalization, picks=picks, proj=proj, n_jobs=n_jobs)
    psds = np.mean(psds, axis=0)
    if merge_grads:
        from ..channels.layout import _merge_grad_data
        psds = _merge_grad_data(psds)
    return plot_psds_topomap(psds=psds, freqs=freqs, pos=pos, agg_fun=agg_fun, vmin=vmin, vmax=vmax, bands=bands, cmap=cmap, dB=dB, normalize=normalize, cbar_fmt=ch_type, outlines=outlines, show=show)

dataset/ETHPy150Open mne-tools/mne-python/mne/viz/topomap.py plot_epochs_psd_topomap/VarMisuse@263/1933 `cbar_fmt`->`ch_type`