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progress_bar: FloatProgress, optional
Returns
-------
"""
data = np.asarray(data)
if ax is None:
fig, ax = plt.subplots(figsize=(12, 6))
if group_inds is not None:
ugroup_inds = np.unique(group_inds)
handles = []
if progress_bar is not None:
this_iter = ProgressBar(enumerate(ugroup_inds), desc='plotting spikes', leave=False, total=len(ugroup_inds))
progress_bar = this_iter.container
else:
this_iter = enumerate(ugroup_inds)
for i, ui in this_iter:
color = colors[ugroup_inds[i] % len(colors)]
lineoffsets = np.where(group_inds == ui)[0] + offset
event_collection = ax.eventplot(data[group_inds == ui],
orientation='horizontal',
lineoffsets=lineoffsets,
color=color)
handles.append(event_collection[0])
if show_legend:
ax.legend(handles=handles[::-1], labels=list(labels[ugroup_inds][::-1]), loc='upper left',
bbox_to_anchor=(1.01, 1))
else:
-------
matplotlib.pyplot.Figure
"""
if time_window is None:
time_window = [get_min_spike_time(units), get_max_spike_time(units)]
if units_window is None:
units_window = [0, len(units)]
if order is None:
order = np.arange(len(units), dtype='int')
if progress_bar:
this_iter = ProgressBar(order, desc='reading spike data', leave=False)
progress_bar = this_iter.container
else:
this_iter = order
data = []
for unit in this_iter:
data.append(get_spike_times(units, unit, time_window))
if show_obs_intervals:
unobserved_intervals_list = get_unobserved_intervals(units, time_window, order)
else:
unobserved_intervals_list = None
ax = plot_grouped_events(data, time_window, group_inds=group_inds, labels=labels, show_legend=show_legend,
offset=units_window[0], unobserved_intervals_list=unobserved_intervals_list,
progress_bar=progress_bar)
ax.set_ylabel('unit #')