How to use the nwbwidgets.utils.timeseries.get_timeseries_tt function in nwbwidgets

Returns
    -------

    """

    if time_window is None:
        t_ind_start = 0
        t_ind_stop = None
    else:
        t_ind_start = timeseries_time_to_ind(time_series, time_window[0])
        t_ind_stop = timeseries_time_to_ind(time_series, time_window[1])

    if trace_window is None:
        trace_window = [0, time_series.data.shape[1]]
    tt = get_timeseries_tt(time_series, t_ind_start, t_ind_stop)
    if time_series.data.shape[1] == len(tt):  # fix of orientation is incorrect
        mini_data = time_series.data[trace_window[0]:trace_window[1], t_ind_start:t_ind_stop].T
    else:
        mini_data = time_series.data[t_ind_start:t_ind_stop, trace_window[0]:trace_window[1]]

    gap = np.median(np.nanstd(mini_data, axis=0)) * 20
    offsets = np.arange(trace_window[1] - trace_window[0]) * gap

    fig, ax = plt.subplots()
    ax.figure.set_size_inches(12, 6)
    ax.plot(tt, mini_data + offsets)
    ax.set_xlabel('time (s)')
    if np.isfinite(gap):
        ax.set_ylim(-gap, offsets[-1] + gap)
        ax.set_xlim(tt[0], tt[-1])
        ax.set_yticks(offsets)

def show_timeseries_mpl(node: TimeSeries, neurodata_vis_spec=None, istart=0, istop=None, ax=None, zero_start=False,
                        xlabel=None, ylabel=None, title=None, **kwargs):
    if xlabel is None:
        xlabel = 'time (s)'

    if ylabel is None and node.unit:
        ylabel = node.unit

    if ax is None:
        fig, ax = plt.subplots()

    tt = get_timeseries_tt(node, istart=istart, istop=istop)
    if zero_start:
        tt = tt - tt[0]
    data, unit = get_timeseries_in_units(node, istart=istart, istop=istop)

    ax.plot(tt, data, **kwargs)
    ax.set_xlabel(xlabel)
    if node.unit:
        ax.set_ylabel(ylabel)
    if title is not None:
        ax.set_title(title)

    ax.autoscale(enable=True, axis='x', tight=True)

    return ax

def show_spatial_series_over_time(node: SpatialSeries, **kwargs):

    text_widget = base.show_text_fields(
        node, exclude=('timestamps_unit', 'comments', 'data', 'timestamps', 'interval'))

    data, unit = get_timeseries_in_units(node)

    if len(data.shape) == 1:
        ndims = 1
    else:
        ndims = data.shape[1]

    tt = get_timeseries_tt(node)

    if ndims == 1:
        fig, ax = plt.subplots()
        ax.plot(tt, data, **kwargs)
        ax.set_xlabel('t (sec)')
        if unit:
            ax.set_ylabel('x ({})'.format(unit))
        else:
            ax.set_ylabel('x')

    else:
        fig, axs = plt.subplots(ndims, 1, sharex=True)

        for i, (ax, dim_label) in enumerate(zip(axs, ('x', 'y', 'z'))):
            ax.plot(tt, data[:, i], **kwargs)
            if unit:

def show_spatial_series(node: SpatialSeries, **kwargs):

    data, unit = get_timeseries_in_units(node)
    tt = get_timeseries_tt(node)

    if len(data.shape) == 1:
        fig, ax = plt.subplots()
        ax.plot(tt, data, **kwargs)
        ax.set_xlabel('t (sec)')
        if unit:
            ax.set_xlabel('x ({})'.format(unit))
        else:
            ax.set_xlabel('x')
        ax.set_ylabel('x')

    elif data.shape[1] == 2:
        fig, ax = plt.subplots()
        ax.plot(data[:, 0], data[:, 1], **kwargs)
        if unit:
            ax.set_xlabel('x ({})'.format(unit))