def _plot_timeseries_unified(bn, ch_idx, tmin, tmax, vmin, vmax, ylim, data,
                             color, times, vline=None, x_label=None,
                             y_label=None, colorbar=False, hline=None):
    """Aux function to show multiple time series on topo using a single axes"""
    import matplotlib.pyplot as plt
    if not (ylim and not any(v is None for v in ylim)):
        ylim = np.array([np.min(data), np.max(data)])
    # Translation and scale parameters to take data->under_ax normalized coords
    _compute_scalings(bn, (tmin, tmax), ylim)
    pos = bn.pos
    data_lines = bn.data_lines
    ax = bn.ax
    # XXX These calls could probably be made faster by using collections
    for data_, color_ in zip(data, color):
        data_lines.append(ax.plot(
            bn.x_t + bn.x_s * times, bn.y_t + bn.y_s * data_[ch_idx],
            color_, clip_on=True, clip_box=pos)[0])
    if vline:
        vline = np.array(vline) * bn.x_s + bn.x_t
        ax.vlines(vline, pos[1], pos[1] + pos[3], color='w', linewidth=0.5)
    if hline:
        hline = np.array(hline) * bn.y_s + bn.y_t
        ax.hlines(hline, pos[0], pos[0] + pos[2], color='w', linewidth=0.5)
    if x_label is not None:
        ax.text(pos[0] + pos[2] / 2., pos[1], x_label,
                horizontalalignment='center', verticalalignment='top')
    if y_label is not None:
        y_label = y_label[ch_idx] if isinstance(y_label, list) else y_label
        ax.text(pos[0], pos[1] + pos[3] / 2., y_label,
                horizontalignment='right', verticalalignment='middle',
                rotation=90)
    if colorbar:
        plt.colorbar()
dataset/ETHPy150Open mne-tools/mne-python/mne/viz/topo.py/_plot_timeseries_unified/original