def plot_seasonality(self, name, ax=None, uncertainty=True):
"""Plot a custom seasonal component.
Parameters
----------
ax: Optional matplotlib Axes to plot on. One will be created if
this is not provided.
uncertainty: Optional boolean to plot uncertainty intervals.
Returns
-------
a list of matplotlib artists
"""
artists = []
if not ax:
fig = plt.figure(facecolor='w', figsize=(10, 6))
ax = fig.add_subplot(111)
# Compute seasonality from Jan 1 through a single period.
start = pd.to_datetime('2017-01-01 0000')
period = self.seasonalities[name]['period']
end = start + pd.Timedelta(days=period)
plot_points = 200
days = pd.to_datetime(np.linspace(start.value, end.value, plot_points))
df_y = self.seasonality_plot_df(days)
seas = self.predict_seasonal_components(df_y)
artists += ax.plot(df_y['ds'].dt.to_pydatetime(), seas[name], ls='-',
c='#0072B2')
if uncertainty:
artists += [ax.fill_between(
df_y['ds'].dt.to_pydatetime(), seas[name + '_lower'],
seas[name + '_upper'], color='#0072B2', alpha=0.2)]
ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
xticks = pd.to_datetime(np.linspace(start.value, end.value, 7)
).to_pydatetime()
ax.set_xticks(xticks)
if period <= 2:
fmt_str = '{dt:%T}'
elif period < 14:
fmt_str = '{dt:%m}/{dt:%d} {dt:%R}'
else:
fmt_str = '{dt:%m}/{dt:%d}'
ax.xaxis.set_major_formatter(FuncFormatter(
lambda x, pos=None: fmt_str.format(dt=num2date(x))))
ax.set_xlabel('ds')
ax.set_ylabel(name)
return artists
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