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#' Seabass bioenergetic population model preprocessor
#'
#' @param userpath the path where folder containing model inputs and outputs is located
#' @param forcings a list containing model forcings
#' @return a list containing the time series in the odd positions and realted forcings in the even positions. Forcings returned are: Water temperature [Celsius degrees] and feeding rate [g/individual x d]
#' @export
#'
#' @import matrixStats plotrix rstudioapi
#'
#' @import grDevices graphics utils stats
#'
Bass_pop_pre<-function(userpath,forcings){
rm(list=ls()) # Clean workspace
cat("Data preprocessing")
# Extracts forcings values from the list
timeT=forcings[[1]]
Tint=forcings[[2]]
timeG=forcings[[3]]
Gint=forcings[[4]]
# Read forcings and parameters from .csv files
Param_matrix=read.csv(paste0(userpath,"/Bass_population/Inputs/Parameters//Parameters.csv"),sep=",") # Reading the matrix containing parameters and their description
Food=read.csv(paste0(userpath,"/Bass_population/Inputs/Forcings//Food_characterization.csv"),sep=",",header=FALSE) # Reading the food composition (Proteins, Lipids, Carbohydrates) data
# Extract parameters and forcing values from parameters matrix and convert to type 'double' the vector contents
Param=as.matrix(Param_matrix[1:21,3]) # Vector containing all parameters
Param=suppressWarnings(as.numeric(Param))
Dates=Param_matrix[22:23,3] # Vector containing the starting and ending date of the simulation
#IC=as.double(as.matrix(Param_matrix[24,3])) # Initial weight condition
CS=as.double(as.matrix(Param_matrix[25,3])) # Commercial size
Food=as.double(as.matrix(Food[,1])) # Food composition (Proteins, Lipids, Carbohydrates) data
# Prepare data for ODE solution
t0=min(as.numeric(as.Date(timeT[1], "%d/%m/%Y")), as.numeric(as.Date(timeG[1], "%d/%m/%Y")), as.numeric(as.Date(Dates[1], "%d/%m/%Y"))) # Minimum starting date for forcings and observations
timestep=1 # Time step for integration [day]
ti=as.numeric(as.Date(Dates[1], "%d/%m/%Y"))-t0 # Start of integration [day]
tf=as.numeric(as.Date(Dates[2], "%d/%m/%Y"))-t0 # End of integration [day]
#weight=as.vector(matrix(0,nrow=ti)) # Initialize vector weight
#weight[ti]=IC # Weight initial value [g]
times<-cbind(ti, tf, timestep,t0) # Vector with integration data
# Food composition vector
Pcont=Food[1] # [-] Percentage of proteins in the food
Lcont=Food[2] # [-] Percentage of lipids in the food
Ccont=Food[3] # [-] Percentage of carbohydrates in the food
# Read files with population parameters and management strategies
Pop_matrix=read.csv(paste0(userpath,"/Bass_population/Inputs/Parameters//Population.csv"),sep=",") # Reading the matrix containing population parameters and their description
Management=read.csv(paste0(userpath,"/Bass_population/Inputs/Population_management//Management.csv"),sep=",") # Reading the matrix containing seeding and harvesting management
# Extract population parameters
meanW=as.double(as.matrix(Pop_matrix[1,3])) # [g] Dry weight average
IC=meanW
deltaW=as.double(as.matrix(Pop_matrix[2,3])) # [g] Dry weight standard deviation
Wlb=as.double(as.matrix(Pop_matrix[3,3])) # [g] Dry weight lower bound
meanImax=as.double(as.matrix(Pop_matrix[4,3])) # [l/d gDW] Clearence rate average
deltaImax=as.double(as.matrix(Pop_matrix[5,3])) # [l/d gDW] Clearance rate standard deviation
Nseed=as.double(as.matrix(Pop_matrix[6,3])) # [-] number of seeded individuals
mortmyt=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])
}
}
# Population differential equation solution
N<-Pop_fun(Nseed, mortmyt, manag, times)
# Print to screen inserted parameters
cat(" \n")
cat('The model will be executed with the following parameters:\n');
cat(" \n")
for (i in 1:21){
cat(paste0(toString(Param_matrix[i,2]), ": ", toString(Param_matrix[i,3]), " " ,toString(Param_matrix[i,4])),"\n")
}
cat(" \n")
cat("Integration is performed between ", toString(Dates[1]), " and ", toString(Dates[2]),"\n")
cat(" \n")
cat("The food has the following composition: \n")
cat(toString(Pcont*100),"% proteins\n")
cat(toString(Lcont*100),"% lipids\n")
cat(toString(Ccont*100),"% carbohydrates\n")
cat(" \n")
cat('Commercial size is ', toString(CS)," g")
cat(" \n")
# Print to screen population characteristics
cat(" \n")
cat('The population is simulated by assuming that initial weight and maximum ingestion rate are normally distributed:\n');
cat(" \n")
for (i in 1:5){
cat(paste0(toString(Pop_matrix[i,2]), ": ", toString(Pop_matrix[i,3]), " " ,toString(Pop_matrix[i,4])),"\n")
}
cat(" \n")
cat("The population is initially composed by ", toString(Pop_matrix[7,3]), " Individuals\n")
cat(" \n")
cat("The mortality rate is:", toString(Pop_matrix[8,3]),'1/d\n' )
# Print to screen management actions
cat(" \n")
cat('The population is managed according with following list (h:harvesting s:seeding):\n');
cat(" \n")
for (i in 1:length(Management[,1])){
cat(paste0(toString(Management[i,1])," ", toString(Management[i,2]), " " ,toString(Management[i,3])),"individuals\n")
}
cat(" \n")
cat("The individual model will be executed ", toString(nruns), " times in order to simulate a population\n")
cat(" \n")
# Plot to file inserted forcing functions
cat(" \n")
cat("Forcings are represented in graphs available at the following folder:\n")
cat(paste0(userpath,"/Bass_population/Inputs/Forcings_plots\n"))
# Plot Temperature forcing
Tintsave=Tint[(ti+1):tf]
currentpath=getwd()
filepath=paste0(userpath,"/Bass_population/Inputs/Forcings_plots//Water_temperature.jpeg")
jpeg(filepath,800,600)
days <- seq(as.Date(Dates[1], format = "%d/%m/%Y"), by = "days", length = tf-ti)
plot(days, Tintsave, ylab="Water temperature (Celsius degrees)", xlab="",xaxt = "n",type="l",cex.lab=1.4)
labDates <- seq(as.Date(Dates[1], format = "%d/%m/%Y"), tail(days, 1), by = "months")
axis.Date(side = 1, days, at = labDates, format = "%d %b %y", las = 2)
dev.off()
# Plot feeding rate forcing
Gintsave=Gint[(ti+1):tf]
currentpath=getwd()
filepath=paste0(userpath,"/Bass_population/Inputs/Forcings_plots//Feeding.jpeg")
jpeg(filepath,800,600)
days <- seq(as.Date(Dates[1], format = "%d/%m/%Y"), by = "days", length = tf-ti)
plot(days, Gintsave, ylab="Feed (g/d)", xlab="", xaxt = "n",type="l",cex.lab=1.4)
labDates <- seq(as.Date(Dates[1], format = "%d/%m/%Y"), tail(days, 1), by = "months")
axis.Date(side = 1, days, at = labDates, format = "%d %b %y", las = 2)
dev.off()
# plot population dynamics
Nsave=N[(ti+1):tf]
currentpath=getwd()
filepath=paste0(userpath,"/Bass_population/Inputs/Forcings_plots//Population.jpeg")
jpeg(filepath,800,600)
days <- seq(as.Date(Dates[1], format = "%d/%m/%Y"), by = "days", length = tf-ti)
plot(days, Nsave, ylab="Individuals", xlab="", xaxt = "n",type="l",cex.lab=1.4)
labDates <- seq(as.Date(Dates[1], format = "%d/%m/%Y"), tail(days, 1), by = "months")
axis.Date(side = 1, days, at = labDates, format = "%d %b %y", las = 2)
dev.off()
output=list(Param, Tint, Gint, Food, IC, times, Dates, N, CS)
}
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