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#' Function that loads forcings data for Bream spatialized model and performs the interpolation
#'
#' @param userpath the path where folder containing model inputs and outputs is located
#' @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 stats utils
#'
Bream_spatial_dataloader<-function(userpath) {
# Reads point forcing files
DaF=read.csv(paste0(userpath,"/Bream_spatial/Inputs/Point forcings//Feeding.csv"),sep=",",header=FALSE)
# Reads starting and ending dates for spatialized forcings
Spatial_dates=read.csv(paste0(userpath,"/Bream_spatial/Inputs/Spatial forcings//Spatial_dates.csv"),sep=",",header=FALSE)
# Reads integration extremes
Param_matrix=read.csv(paste0(userpath,"/Bream_spatial/Inputs/Parameters//Parameters.csv"),sep=",") # Reading the matrix containing parameters and their description
#Extracts vectors from the forcing files
Dates=Param_matrix[22:23,3] # Vector containing the starting and ending date of the simulation
timeT=as.matrix(Spatial_dates[2,2:3]) # Vector of the times of Temperature measurements
timeG=as.matrix(DaF[,1]) # Vector of the times of feeding dose
G=as.double(as.matrix(DaF[,2])) # Vector of the individual feeding dose time series (daily series)
# Times needed to perform interpolation
t0=min(as.numeric(as.Date(timeT[1], "%d/%m/%Y")), as.numeric(as.Date(timeG[1], "%d/%m/%Y")))
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]
# Prepare t data for Temperature and Feeding interpolation
timeGseries=as.numeric(as.Date(timeG, "%d/%m/%Y"))-t0 # Days at which food measurements are available
# Interpolation of Feeding forcing
Gtem=as.vector(matrix(0,nrow=ti-1)) # Initialize vector Gint
i=ti:tf+1 # Interpolation base points
Gtem2=approx(timeGseries,G,xout=i) # G interpolation according to base points
Gint=c(Gtem, Gtem2$y) # Interpolated G values starting at t0
daysG <- seq(as.Date(timeG[1], format = "%d/%m/%Y"), by = "days", length = length(Gint))
# Check if forcings are Ok with integration extremes
if ((ti<(as.numeric(as.Date(timeT[1], "%d/%m/%Y"))-t0))|(ti<(as.numeric(as.Date(timeG[1], "%d/%m/%Y"))-t0))) {
cat("ERROR: forcings are beginning after the specified integration start\n")
cat("Impossible to proceed with interpolation\n")
}
if ((ti>(as.numeric(as.Date(timeT[length(timeT)], "%d/%m/%Y"))-t0))|(ti>(as.numeric(as.Date(timeG[length(timeG)], "%d/%m/%Y"))-t0))) {
cat("ERROR: forcing are ending before the specified integration end\n")
cat("Impossible to proceed with interpolation\n")
}
# Spatialized forcings upload
# read .nc files
sst <- raster::stack(paste0(userpath,"/Bream_spatial/Inputs/Spatial forcings//sst.nc"))
# transform rasters to points
pixel_sst <- t(raster::rasterToPoints(sst))
sst_export <- pixel_sst[-c(1,2),]
#colnames(pixel_sst) <- gsub("V", "sst", colnames(pixel_sst))
write.csv(sst_export,paste0(userpath,"/Bream_spatial/Inputs/Spatial forcings//sst.csv"))
sst <- read.csv(paste0(userpath,"/Bream_spatial/Inputs/Spatial forcings//sst.csv"), header = TRUE)
sst <- sst[,-(1)]
colnames(sst) <- gsub("V", "sst", colnames(sst))
# save coordinates points
write.csv(pixel_sst[1:2,],paste0(userpath,"/Bream_spatial/Inputs/Spatial forcings//coordinates.csv"))
coord <- read.csv(paste0(userpath,"/Bream_spatial/Inputs/Spatial forcings//coordinates.csv"))
forcings=list(timeT,sst,daysG,Gint,coord)
return(forcings)
}
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