dir.create('data')
dir.create('output')

source('libraries.R')

source('R/LCE.R')

download_emi_files(emi_sas = Sys.getenv("EMI_SAS"),emi_endpoint = "https://emidatasets.blob.core.windows.net/publicdata")

df_branch_rentals = read.csv('data/pricing_year_20_21_transmission_rentals.csv')
df_branch_regions = read.csv('data/final_branch_mapping.csv')

#Get branch mapping of S1 investments
#See https://www.emi.ea.govt.nz/Wholesale/Datasets/_AdditionalInformation/SupportingInformationAndAnalysis/2019/20190723_TPM_2019_IssuesPaper/2019_Proposal_Impacts_modelling
df_s1_branch_mapping = read_excel('data/LCE rental refund module.xlsx',
                                  sheet = 'Branch_to_Projects')

#Load scaling of rentals from LCE data
df_tp_revenue = read_excel('data/Historic LCE Breakdown Jun 2021.xlsx',
                           sheet = 'Monthly Data',
                           range = 'b2:l269') %>% 
  mutate(month_date = floor_date(Month, 'month') %>% as.Date) %>% 
  select(month_date, scale_factor = `Scale Factor`)

#Load allocators for simple method BBCs
df_ipm_allocators = read.csv('data/indicative_pricing_allocators.csv')
df_ipm_allocators<-df_ipm_allocators[,-c(2:8)]

#Map rentals to simple method regions or historic investments
df_branches_final = branch_rental_mapping(df_branch_rentals, df_s1_branch_mapping)

#Create version that doesn't use S1 allocators
df_non_s1_rentals_investment = calculate_non_s1_rentals(df_branches_final, df_tp_revenue)
df_non_s1_rentals = rental_customer_allocation(df_non_s1_rentals_investment, df_tp_revenue, df_ipm_allocators)

write_csv(df_non_s1_rentals,
          'output/rental_attribution_to_customer_simpleBB_py2021.csv')