def _onselect(eclick, erelease, tfr, pos, ch_type, itmin, itmax, ifmin, ifmax,
              cmap, fig, layout=None):
    import matplotlib.pyplot as plt
    pos, _ = _check_outlines(pos, outlines='head', head_pos=None)
    ax = eclick.inaxes
    xmin = min(eclick.xdata, erelease.xdata)
    xmax = max(eclick.xdata, erelease.xdata)
    ymin = min(eclick.ydata, erelease.ydata)
    ymax = max(eclick.ydata, erelease.ydata)
    indices = [i for i in range(len(pos)) if pos[i][0] < xmax and
               pos[i][0] > xmin and pos[i][1] < ymax and pos[i][1] > ymin]
    for idx, circle in enumerate(ax.artists):
        if idx in indices:
            circle.set_color('r')
        else:
            circle.set_color('black')
    plt.gcf().canvas.draw()
    if not indices:
        return
    data = tfr.data
    if ch_type == 'mag':
        picks = pick_types(tfr.info, meg=ch_type, ref_meg=False)
        data = np.mean(data[indices, ifmin:ifmax, itmin:itmax], axis=0)
        chs = [tfr.ch_names[picks[x]] for x in indices]
    elif ch_type == 'grad':
        picks = pick_types(tfr.info, meg=ch_type, ref_meg=False)
        from ..channels.layout import _pair_grad_sensors
        grads = _pair_grad_sensors(tfr.info, layout=layout,
                                   topomap_coords=False)
        idxs = list()
        for idx in indices:
            idxs.append(grads[idx * 2])
            idxs.append(grads[idx * 2 + 1])  # pair of grads
        data = np.mean(data[idxs, ifmin:ifmax, itmin:itmax], axis=0)
        chs = [tfr.ch_names[x] for x in idxs]
    elif ch_type == 'eeg':
        picks = pick_types(tfr.info, meg=False, eeg=True, ref_meg=False)
        data = np.mean(data[indices, ifmin:ifmax, itmin:itmax], axis=0)
        chs = [tfr.ch_names[picks[x]] for x in indices]
    logger.info('Averaging TFR over channels ' + str(chs))
    if len(fig) == 0:
        fig.append(figure_nobar())
    if not plt.fignum_exists(fig[0].number):
        fig[0] = figure_nobar()
    ax = fig[0].add_subplot(111)
    itmax = min(itmax, len(tfr.times) - 1)
    ifmax = min(ifmax, len(tfr.freqs) - 1)
    extent = (tfr.times[itmin] * 1e3, tfr.times[itmax] * 1e3, tfr.freqs[ifmin],
              tfr.freqs[ifmax])

    title = 'Average over %d %s channels.' % (len(chs), ch_type)
    ax.set_title(title)
    ax.set_xlabel('Time (ms)')
    ax.set_ylabel('Frequency (Hz)')
    img = ax.imshow(data, extent=extent, aspect="auto", origin="lower",
                    cmap=cmap)
    if len(fig[0].get_axes()) < 2:
        fig[0].get_axes()[1].cbar = fig[0].colorbar(mappable=img)
    else:
        fig[0].get_axes()[1].cbar.on_mappable_changed(mappable=img)
    fig[0].canvas.draw()
    plt.figure(fig[0].number)
    plt_show(True)
Create floating IP.
dataset/ETHPy150Open mne-tools/mne-python/mne/viz/topomap.py/_onselect--crossover with--dataset/ETHPy150Open openstack/neutron/neutron/services/l3_router/l3_router_plugin.py/L3RouterPlugin.create_floatingip