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#' Function that runs the Monte Carlo simulation for the Clam population model (alternative version)
#'
#' @param Param a vector containing model parameters
#' @param times integration extremes and integration timestep
#' @param IC initial condition
#' @param Tint the interpolated water temperature time series
#' @param Chlint the interpolated chlorophyll a time series
#' @param N time series with number of individuals
#' @param userpath the path where the working folder is located
#'
#' @return a list with RK solver outputs
#'
#' @import matrixStats plotrix rstudioapi
#'
ClamF_pop_loop<-function(Param, times, IC, Tint, Chlint, N,userpath) {
cat("Population processing\n")
ti=times[1]
tf=times[2]
t0=times[4]
# Read files with population parameters and management strategies
Pop_matrix=read.csv(paste0(userpath,"/ClamF_population/Inputs/Parameters//Population.csv"),sep=",") # Reading the matrix containing population parameters and their description
Management=read.csv(paste0(userpath,"/ClamF_population/Inputs/Population_management//Management.csv"),sep=",") # Reading the matrix containing seeding and harvesting management
# Extract population parameters
meanWw=as.double(as.matrix(Pop_matrix[1,3])) # [g] Wet weight average
deltaWw=as.double(as.matrix(Pop_matrix[2,3])) # [g] Wet weight standard deviation
Wwlb=as.double(as.matrix(Pop_matrix[3,3])) # [g] Wet weight lower bound
meanGdmax=as.double(as.matrix(Pop_matrix[4,3])) # [l/d gDW] Maximum growth rate on a dry weight average
deltaGdmax=as.double(as.matrix(Pop_matrix[5,3]))# [l/d gDW] Maximum growth rate on a dry weight standard deviation
Nseed=as.double(as.matrix(Pop_matrix[6,3])) # [-] number of seeded individuals
mort=as.double(as.matrix(Pop_matrix[7,3])) # [1/d] natural mortality rate
nruns=as.double(as.matrix(Pop_matrix[8,3])) # [-] number of runs for population simulation
# Prepare management values
manag=as.matrix(matrix(0,nrow=length(Management[,1]),ncol=2))
for (i in 1:length(Management[,1])) {
manag[i,1]=as.numeric(as.Date(Management[i,1], "%d/%m/%Y"))-t0
if ((Management[i,2])=="h") {
manag[i,2]=-as.numeric(Management[i,3])
} else {
manag[i,2]=as.numeric(Management[i,3])
}
}
# Vectors initialization
saveIC=as.vector(matrix(0,nrow=nruns))
saveGrmax=as.vector(matrix(0,nrow=nruns))
Wd=as.matrix(matrix(0,nrow=nruns,ncol=tf))
Ww=as.matrix(matrix(0,nrow=nruns,ncol=tf))
L=as.matrix(matrix(0,nrow=nruns,ncol=tf))
A=as.matrix(matrix(0,nrow=nruns,ncol=tf))
C=as.matrix(matrix(0,nrow=nruns,ncol=tf))
# sources Runge-Kutta solver
pb <- txtProgressBar(min = 0, max = nruns, style = 3)
for (ii in 1:nruns){
# Weight initialization
IC=rnorm(1,meanWw,deltaWw) # [g] initial weight extracted from a normal distribution
IC=max(IC, Wwlb) # Lower bound for weight distribution
saveIC[ii]=IC # Saves initial condition values on Wd for each run
# Maximum clearance rate initialization
Gdmax=rnorm(1,meanGdmax,deltaGdmax) # [l/d gDW] Maximum ingestion rate extracted from a normal distribution
Gdmax=max(Gdmax,0) # Forces maximum ingestion rate to be positive
saveGrmax[ii]=Gdmax # Saves initial condition values on Imax for each run
# Perturbe the parameters vector
Param[1]=Gdmax
# Solves ODE with perturbed parameters
output<-ClamF_pop_RKsolver(Param, times, IC, Tint, Chlint)
# Extract outputs
weight=t(output[[1]])
Tfun=output[[2]]
metab=output[[3]]
# Saves results of each run to compute statistics
Wd[ii,1:length(weight[,1])]=weight[,1] # Clam dry weight [g]
Ww[ii,1:length(weight[,2])]=weight[,2] # Clam wet weight [g]
L[ii,1:length(weight[,3])]=weight[,3] # Clam length [g]
A[ii,1:length(metab[,1])]=metab[,1] # Net anabolism [J/d]
C[ii,1:length(metab[,2])]=metab[,2] # Fasting catabolism [J/d]
setTxtProgressBar(pb, ii)
} # Close population loop
close(pb)
# Temperaure limitation functions
fgT=Tfun[,1] # Optimum dependance from temperature for ingestion
frT=Tfun[,2] # Exponential dependance from temperature for catabolism
# Statistics computation
Wd_stat=rbind(colMeans(Wd), colSds(Wd))
Ww_stat=rbind(colMeans(Ww), colSds(Ww))
L_stat=rbind(colMeans(L), colSds(L))
A_stat=rbind(colMeans(A), colSds(A))
C_stat=rbind(colMeans(C), colSds(C))
output=list(Wd_stat,Ww_stat,L_stat,A_stat,C_stat,fgT,frT)
return(output)
}
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