def plot_weekly()

in python/prophet/plot.py [0:0]


def plot_weekly(m, ax=None, uncertainty=True, weekly_start=0, figsize=(10, 6), name='weekly'):
    """Plot the weekly component of the forecast.

    Parameters
    ----------
    m: Prophet model.
    ax: Optional matplotlib Axes to plot on. One will be created if this
        is not provided.
    uncertainty: Optional boolean to plot uncertainty intervals, which will
        only be done if m.uncertainty_samples > 0.
    weekly_start: Optional int specifying the start day of the weekly
        seasonality plot. 0 (default) starts the week on Sunday. 1 shifts
        by 1 day to Monday, and so on.
    figsize: Optional tuple width, height in inches.
    name: Name of seasonality component if changed from default 'weekly'.

    Returns
    -------
    a list of matplotlib artists
    """
    artists = []
    if not ax:
        fig = plt.figure(facecolor='w', figsize=figsize)
        ax = fig.add_subplot(111)
    # Compute weekly seasonality for a Sun-Sat sequence of dates.
    days = (pd.date_range(start='2017-01-01', periods=7) +
            pd.Timedelta(days=weekly_start))
    df_w = seasonality_plot_df(m, days)
    seas = m.predict_seasonal_components(df_w)
    days = days.day_name()
    artists += ax.plot(range(len(days)), seas[name], ls='-',
                    c='#0072B2')
    if uncertainty and m.uncertainty_samples:
        artists += [ax.fill_between(range(len(days)),
                                    seas[name + '_lower'], seas[name + '_upper'],
                                    color='#0072B2', alpha=0.2)]
    ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
    ax.set_xticks(range(len(days)))
    ax.set_xticklabels(days)
    ax.set_xlabel('Day of week')
    ax.set_ylabel(name)
    if m.seasonalities[name]['mode'] == 'multiplicative':
        ax = set_y_as_percent(ax)
    return artists