code: def initial_seasonal_components(series, slen):
      seasonals = {}
      season_averages = []
      n_seasons = int(len(series) / slen)
      # compute season averages
      for j in range(n_seasons):
          season_averages.append(
              sum(series[slen * j : slen * j + slen]) / float(slen)
          )
      # compute initial values
      for i in range(slen):
          sum_of_vals_over_avg = 0.0
          for j in range(n_seasons):
              sum_of_vals_over_avg += (
                  series[slen * j + i] - season_averages[j]
              )
          seasonals[i] = sum_of_vals_over_avg / n_seasons
      return seasonals
  
imports: import pandas
  import numpy
  import statsmodels.api
  from scipy import signal, integrate
  from pykalman import UnscentedKalmanFilter
  from tsaug import *
  from fbprophet import Prophet
  import pylab
  from seasonal.periodogram import periodogram
label_desc: Use image node_id 9 for calling a global function with example usage: initial_seasonal_components(series, slen) and returns: seasonals