def update(
self,
index: int,
start_label: str = "start_time",
before: float = 0.0,
after: float = 1.0,
order=None,
group_inds=None,
labels=None,
align_to_zero=False,
fig: go.FigureWidget = None,
):
data, time_ts_aligned = self.align_data(start_label, before, after,
index)
if group_inds is None:
group_inds = np.zeros(len(self.trials), dtype=np.int)
if align_to_zero:
for trial_no in order:
data_zero_id = bisect(time_ts_aligned[trial_no], 0)
data[trial_no] -= data[trial_no][data_zero_id]
fig = fig if fig is not None else go.FigureWidget()
fig.data = []
fig.layout = {}
return self.plot_group(group_inds, data, time_ts_aligned, fig, order)
def update(
self,
index: int,
start_label: str = "start_time",
before: float = 0.0,
after: float = 1.0,
order=None,
group_inds=None,
labels=None,
align_to_zero=False,
sem=False,
fig: go.FigureWidget = None,
):
data, time_ts_aligned = self.align_data(start_label, before, after,
index)
if group_inds is None:
group_inds = np.zeros(len(self.trials), dtype=np.int)
if align_to_zero:
for trial_no in order:
data_zero_id = bisect(time_ts_aligned[trial_no], 0)
data[trial_no] -= data[trial_no][data_zero_id]
fig = go.FigureWidget() if fig is None else fig
fig.data = []
fig.layout = {}
if sem:
group_stats = []
for group in np.unique(group_inds):
this_mean = np.nanmean(data[group_inds == group, :], axis=0)
err = scipy.stats.sem(data[group_inds == group, :],
axis=0,
nan_policy="omit")
group_stats.append(
dict(
mean=this_mean,
lower=this_mean - 2 * err,
upper=this_mean + 2 * err,
group=group,
))
for stats in group_stats:
plot_kwargs = dict()
color = color_wheel[stats["group"]]
if labels is not None:
plot_kwargs.update(text=labels[stats["group"]])
fig.add_scattergl(x=time_ts_aligned[0],
y=stats["lower"],
line_color=color)
fig.add_scattergl(x=time_ts_aligned[0],
y=stats["upper"],
line_color=color,
fill='tonexty',
opacity=0.2)
fig.add_scattergl(x=time_ts_aligned[0],
y=stats["mean"],
line_color=color,
**plot_kwargs)
else:
fig = self.plot_group(group_inds, data, time_ts_aligned, fig,
order)
return fig