# This file should be named Sensitivity_function.R
# A function that returns the sensitivity of the annual surveys in 2000-2018
# for all the regions and Finland in one array, in which
# dimension 1 = years, dimension 2 = regions (regions columns 1-15, Finland column 16), dimension 3 = iterations

Sensitivity <- function(scenario, DP_wood, DP_Monochamus, DPr, site_wood, site_Monochamus, n_i){
  
  ##########
  # CALLING FOR THE DATA NEEDED
  
  # The number of wood objects suitable for sampling per inspection site
  p_wood = p_wood_data(n_y,n_i)
  
  # The number of Monochamus adults per inspection site
  p_Monochamus = p_Monochamus_data(n_y,n_i)
  
  # The number of wood samples collected per inspection site
  n_wood <- round(rpert(n_i,1,2,5,1))
  
  # The number of inspected sites in the wood sampling component of the survey
  N_wood = N_wood_data(n_y,n_i)
  
  # The number of Monochamus samples collected per inspection site
  n_Monochamus = n_Monochamus_data(n_y,n_i)
  
  # The number of inspected sites in the Monochamus trapping component of the survey
  N_Monochamus = N_Monochamus_data(n_y,n_i)
  
  # The relative probability of invasion in the regions
  RP = RP_data(n_y,n_i)
  
  # The effective probability of invasion for the import-export survey and 
  # the regional-level design prevalence for the early detection survey 
  DPr_adj = DPr_adj_data(scenario,n_y,n_i)
  
  ##########
  # CALCULATING INSPECTION SENSITIVITY
  
  ISe_wood = 1 - (1 - ((n_wood)/(p_wood - 0.5*(p_wood*DP_wood-1))))^(p_wood*DP_wood)
  ISe_Monochamus = 1 - (1 - ((n_Monochamus)/(p_Monochamus - 0.5*(p_Monochamus*DP_Monochamus-1))))^(p_Monochamus*DP_Monochamus)
  
  #####
  # CALCULATING THE SENSITIVITY OF ANNUAL SURVEYS IN THE REGIONS
  # (separately for wood sampling and Monochamus trapping)
  
  GSe_wood  = 1 - (1 - (DPr_adj*ISe_wood))^N_wood
  GSe_Monochamus = 1 - (1 - (DPr_adj*ISe_Monochamus))^N_Monochamus
  
  #####
  # CALCULATING THE SENSITIVITY OF ANNUAL SURVEYS IN THE REGIONS AND IN FINLAND
  # (wood sampling and Monochamus trapping combined) 
  
  # Arrays for storing the results 
  # w = wood, m = Monochamus, wm = wood and Monochamus
  GSe_wm <- array(0,dim=c(n_y,15,n_i))
  SSe_w_FI <- array(0,dim=c(n_y,n_i))
  SSe_m_FI <- array(0,dim=c(n_y,n_i))
  SSe_wm_FI <- array(0,dim=c(n_y,n_i))

  for (k in 1:n_i){
    for(t in 1:n_y){
      
      # Regions
      GSe_wm[t,,k] = 1 - (1-GSe_wood[t,,k])*(1-GSe_Monochamus[t,,k])
      
      # Finland
      # Import-export
      if(scenario == 1){
        SSe_w_FI[t,k] = 1 - prod(1-GSe_wood[t,,k])
        SSe_m_FI[t,k] = 1 - prod(1-GSe_Monochamus[t,,k])
        SSe_wm_FI[t,k] = 1 - (1-SSe_w_FI[t,k])*(1-SSe_m_FI[t,k])
      # Early detection
      }else{
        SSe_wm_FI[t,k] = sum(GSe_wm[t,,k]*RP[t,,k])
      }
    }
  }
  
  #####
  # RETURN ALL THE RESULTS IN ONE ARRAY
  
  # Regions in columns 1-15, Finland in column 16
  Sensitivity_all <- array(0,dim=c(n_y,16,n_i))
  Sensitivity_all[,1:15,] = GSe_wm 
  Sensitivity_all[,16,] = SSe_wm_FI
  
  return(Sensitivity_all)}