def plot_yearly(self, ax=None, uncertainty=True, yearly_start=0):
"""Plot the yearly component of the forecast.
Parameters
----------
ax: Optional matplotlib Axes to plot on. One will be created if
this is not provided.
uncertainty: Optional boolean to plot uncertainty intervals.
yearly_start: Optional int specifying the start day of the yearly
seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts
by 1 day to Jan 2, and so on.
Returns
-------
a list of matplotlib artists
"""
artists = []
if not ax:
fig = plt.figure(facecolor='w', figsize=(10, 6))
ax = fig.add_subplot(111)
# Compute yearly seasonality for a Jan 1 - Dec 31 sequence of dates.
days = (pd.date_range(start='2017-01-01', periods=365) +
pd.Timedelta(days=yearly_start))
df_y = self.seasonality_plot_df(days)
seas = self.predict_seasonal_components(df_y)
artists += ax.plot(
df_y['ds'].dt.to_pydatetime(), seas['yearly'], ls='-', c='#0072B2')
if uncertainty:
artists += [ax.fill_between(
df_y['ds'].dt.to_pydatetime(), seas['yearly_lower'],
seas['yearly_upper'], color='#0072B2', alpha=0.2)]
ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
months = MonthLocator(range(1, 13), bymonthday=1, interval=2)
ax.xaxis.set_major_formatter(FuncFormatter(
lambda x, pos=None: '{dt:%B} {dt.day}'.format(dt=num2date(x))))
ax.xaxis.set_major_locator(months)
ax.set_xlabel('Day of year')
ax.set_ylabel('yearly')
return artists
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