How to use the fbprophet.plot function in fbprophet

# Calculate seasonal components 
                self.forecast = self.model.predict_seasonal_components(df_y)

            else:
                # Prepare the seasonality data frame
                start = pd.to_datetime('2017-01-01 0000')
                period = self.model.seasonalities[self.seasonality]['period']
                
                end = start + pd.Timedelta(days=period)
                # plot_points = 200
                # plot_points is used instead of period below in fbprophet/forecaster.py. 
                # However, it seems to make more sense to use period given the expected usage in Qlik
                intervals = pd.to_datetime(np.linspace(start.value, end.value, period)) 
                
                df_x = plot.seasonality_plot_df(self.model, intervals)

                # Calculate seasonal components 
                self.forecast = self.model.predict_seasonal_components(df_x)
            
            # Set the correct sort order for the response
            try:
                self.forecast = self.forecast.reindex(self.sort_order.index)
            except AttributeError:
                pass
        
        # For standard forecast the output rows equal the input rows
        else:
            # Prepare the forecast
            self.forecast = self.model.predict(self.future_df)
            
            # For return=y_then_yhat[_upper / _lower] we return y values followed by relevant results for the forecast periods

def _forecast(self):
        """
        Execute the forecast algorithm according to the request type
        """
        
        # If this is a seasonality request, we need to return the relevant seasonlity component
        if self.is_seasonality_request:

            if self.seasonality == 'weekly':
                # Prepare the seasonality data frame
                # Parameter start needs to be any arbitrary week starting on a Sunday
                days = (pd.date_range(start='2017-01-01', periods=7) + pd.Timedelta(days=self.weekly_start))
                df_w = plot.seasonality_plot_df(self.model, days)

                # Calculate seasonal components 
                self.forecast = self.model.predict_seasonal_components(df_w)

            elif self.seasonality == 'yearly':
                # Prepare the seasonality data frame
                # Parameter start needs to be 1st January for any arbitrary year
                days = (pd.date_range(start='2017-01-01', periods=365) + pd.Timedelta(days=self.yearly_start))
                df_y = plot.seasonality_plot_df(self.model, days)

                # Calculate seasonal components 
                self.forecast = self.model.predict_seasonal_components(df_y)

            else:
                # Prepare the seasonality data frame
                start = pd.to_datetime('2017-01-01 0000')