def plot_ica_components(ica, picks=None, ch_type=None, res=64,
                        layout=None, vmin=None, vmax=None, cmap='RdBu_r',
                        sensors=True, colorbar=False, title=None,
                        show=True, outlines='head', contours=6,
                        image_interp='bilinear', head_pos=None):
    """Project unmixing matrix on interpolated sensor topogrpahy.

    Parameters
    ----------
    ica : instance of mne.preprocessing.ICA
        The ICA solution.
    picks : int | array-like | None
        The indices of the sources to be plotted.
        If None all are plotted in batches of 20.
    ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None
        The channel type to plot. For 'grad', the gradiometers are
        collected in pairs and the RMS for each pair is plotted.
        If None, then channels are chosen in the order given above.
    res : int
        The resolution of the topomap image (n pixels along each side).
    layout : None | Layout
        Layout instance specifying sensor positions (does not need to
        be specified for Neuromag data). If possible, the correct layout is
        inferred from the data.
    vmin : float | callable | None
        The value specifying the lower bound of the color range.
        If None, and vmax is None, -vmax is used. Else np.min(data).
        If callable, the output equals vmin(data). Defaults to None.
    vmax : float | callable | None
        The value specifying the upper bound of the color range.
        If None, the maximum absolute value is used. If callable, the output
        equals vmax(data). Defaults to None.
    cmap : matplotlib colormap
        Colormap.
    sensors : bool | str
        Add markers for sensor locations to the plot. Accepts matplotlib
        plot format string (e.g., 'r+' for red plusses). If True, a circle
        will be used (via .add_artist). Defaults to True.
    colorbar : bool
        Plot a colorbar.
    title : str | None
        Title to use.
    show : bool
        Show figure if True.
    outlines : 'head' | 'skirt' | dict | None
        The outlines to be drawn. If 'head', the default head scheme will be
        drawn. If 'skirt' the head scheme will be drawn, but sensors are
        allowed to be plotted outside of the head circle. If dict, each key
        refers to a tuple of x and y positions, the values in 'mask_pos' will
        serve as image mask, and the 'autoshrink' (bool) field will trigger
        automated shrinking of the positions due to points outside the outline.
        Alternatively, a matplotlib patch object can be passed for advanced
        masking options, either directly or as a function that returns patches
        (required for multi-axis plots). If None, nothing will be drawn.
        Defaults to 'head'.
    contours : int | False | None
        The number of contour lines to draw. If 0, no contours will be drawn.
    image_interp : str
        The image interpolation to be used. All matplotlib options are
        accepted.
    head_pos : dict | None
        If None (default), the sensors are positioned such that they span
        the head circle. If dict, can have entries 'center' (tuple) and
        'scale' (tuple) for what the center and scale of the head should be
        relative to the electrode locations.

    Returns
    -------
    fig : instance of matplotlib.pyplot.Figure or list
        The figure object(s).
    """
    import matplotlib.pyplot as plt
    from mpl_toolkits.axes_grid import make_axes_locatable
    from ..channels import _get_ch_type

    if picks is None:  # plot components by sets of 20
        ch_type = _get_ch_type(ica, ch_type)
        n_components = ica.mixing_matrix_.shape[1]
        p = 20
        figs = []
        for k in range(0, n_components, p):
            picks = range(k, min(k + p, n_components))
            fig = plot_ica_components(ica, picks=picks,
                                      ch_type=ch_type, res=res, layout=layout,
                                      vmax=vmax, cmap=cmap, sensors=sensors,
                                      colorbar=colorbar, title=title,
                                      show=show, outlines=outlines,
                                      contours=contours,
                                      image_interp=image_interp)
            figs.append(fig)
        return figs
    elif np.isscalar(picks):
        picks = [picks]
    ch_type = _get_ch_type(ica, ch_type)

    data = np.dot(ica.mixing_matrix_[:, picks].T,
                  ica.pca_components_[:ica.n_components_])

    if ica.info is None:
        raise RuntimeError('The ICA\'s measurement info is missing. Please '
                           'fit the ICA or add the corresponding info object.')

    data_picks, pos, merge_grads, names, _ = _prepare_topo_plot(ica, ch_type,
                                                                layout)
    pos, outlines = _check_outlines(pos, outlines, head_pos)
    if outlines not in (None, 'head'):
        image_mask, pos = _make_image_mask(outlines, pos, res)
    else:
        image_mask = None

    data = np.atleast_2d(data)
    data = data[:, data_picks]

    # prepare data for iteration
    fig, axes = _prepare_trellis(len(data), max_col=5)
    if title is None:
        title = 'ICA components'
    fig.suptitle(title)

    if merge_grads:
        from ..channels.layout import _merge_grad_data
    for ii, data_, ax in zip(picks, data, axes):
        ax.set_title('IC #%03d' % ii, fontsize=12)
        data_ = _merge_grad_data(data_) if merge_grads else data_
        vmin_, vmax_ = _setup_vmin_vmax(data_, vmin, vmax)
        im = plot_topomap(data_.flatten(), pos, vmin=vmin_, vmax=vmax_,
                          res=res, axes=ax, cmap=cmap, outlines=outlines,
                          image_mask=image_mask, contours=contours,
                          image_interp=image_interp, show=False)[0]
        if colorbar:
            divider = make_axes_locatable(ax)
            cax = divider.append_axes("right", size="5%", pad=0.05)
            cbar = plt.colorbar(im, cax=cax, format='%3.2f', cmap=cmap)
            cbar.ax.tick_params(labelsize=12)
            cbar.set_ticks((vmin_, vmax_))
            cbar.ax.set_title('AU', fontsize=10)
        _hide_frame(ax)
    tight_layout(fig=fig)
    fig.subplots_adjust(top=0.95)
    fig.canvas.draw()
    plt_show(show)
    return fig
dataset/ETHPy150Open mne-tools/mne-python/mne/viz/topomap.py/plot_ica_components/original