def summarize_simulations(self,mean_values,sim_vector,date_index,h,past_values):				
		error_bars = []
		for pre in range(5,100,5):
			error_bars.append(np.insert([np.percentile(i,pre) for i in sim_vector] - mean_values[(len(mean_values)-h):(len(mean_values))],0,0))
		forecasted_values = mean_values[(len(mean_values)-h-1):(len(mean_values))]
		plot_values = mean_values[len(mean_values)-h-past_values:len(mean_values)]
		plot_index = date_index[len(date_index)-h-past_values:len(date_index)]
		return error_bars, forecasted_values, plot_values, plot_index
		
	# Produces T-step ahead forecast for the series
	# This code is very inefficient; needs amending
only displays those above 1% relative abundance
dataset/ETHPy150Open RJT1990/pyflux/pyflux/arma/arma.py/ARIMA.summarize_simulations--crossover with--dataset/ETHPy150Open CGATOxford/cgat/obsolete/Lca.py/LcaRelativeAbundance.__call__