Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- eval=FALSE, message=FALSE-----------------------------------------------
# library(landsepi)
## -----------------------------------------------------------------------------
myDesign <- data.frame(nTSpY = c(120, 110, 100, 90, 80)
, pI0 = c(0, 1E-4, 5E-4, 1E-3, 1E-2)
, infection_rate = c(0.5, 0.4, 0.3, 0.2, 0.1)
, id_landscape = 1:5
, aggreg = c(0.07, 0.07, 0.25, 10, 10)
, R_efficiency = c(1.00, 0.90, 0.80, 0.70, 0.60)
, growth_rate = c(0.1, 0.2, 0.3, 0.1, 0.1)
## create columns to store outputs
, durab_MG1 = NA
, durab_MG2 = NA
, mean_audpc = NA)
## -----------------------------------------------------------------------------
n <- nrow(myDesign)
myDesign <- cbind(simul = 1:n, seed = sample(n*100, n), myDesign)
myDesign
## ---- eval=FALSE--------------------------------------------------------------
# simul_params <- createSimulParams(outputDir = getwd())
## ---- eval=FALSE--------------------------------------------------------------
# for (i in 1:n){
# print(paste("Running simulation", i, "/", n))
#
# ## Set Nyears and nTSpY
# simul_params <- setTime(simul_params
# , Nyears = 6
# , nTSpY = myDesign$nTSpY[i]) ## update nTSpY
#
# ## set seed (to run stochastic replicates)
# simul_params <- setSeed(simul_params, myDesign$seed[i]) ## update seed
#
# ## Pathogen parameters
# simul_params@ReproSexProb <- logical(0) ## initialize vector of sexual probability (one for each time step)
# basic_patho_param <- loadPathogen("rust")
# basic_patho_param$infection_rate <- myDesign$infection_rate[i] ## update inf. rate
# simul_params <- setPathogen(simul_params, basic_patho_param)
#
# ## Initial conditions
# simul_params <- setInoculum(simul_params, myDesign$pI0[i]) ## update pI0
#
# ## Landscape parameters
# landscape <- loadLandscape(myDesign$id_landscape[i]) ## update landscape
# simul_params <- setLandscape(simul_params, landscape)
#
# ## Dispersal parameters
# disp_patho_clonal <- loadDispersalPathogen(myDesign$id_landscape[i])[[1]] ## update dispersal
# simul_params <- setDispersalPathogen(simul_params, disp_patho_clonal)
#
# ## Genes
# gene1 <- loadGene(name = "MG 1", type = "majorGene")
# gene1$mutation_prob<-1e-4
# gene2 <- loadGene(name = "MG 2", type = "majorGene")
# gene2$mutation_prob<-1e-4
# genes <- data.frame(rbind(gene1, gene2), stringsAsFactors = FALSE)
# genes$efficiency <- myDesign$R_efficiency[i] ## update resistance efficiency
# simul_params <- setGenes(simul_params, genes)
#
# ## Cultivars
# cultivar1 <- loadCultivar(name = "Susceptible", type = "growingHost")
# cultivar2 <- loadCultivar(name = "Resistant1", type = "growingHost")
# cultivar3 <- loadCultivar(name = "Resistant2", type = "growingHost")
# cultivars <- data.frame(rbind(cultivar1, cultivar2, cultivar3)
# , stringsAsFactors = FALSE)
# cultivars$growth_rate <- myDesign$growth_rate[i] ## update growth rate
# simul_params <- setCultivars(simul_params, cultivars)
#
# ## Allocate genes to cultivars
# simul_params <- allocateCultivarGenes(simul_params, "Resistant1", c("MG 1"))
# simul_params <- allocateCultivarGenes(simul_params, "Resistant2", c("MG 2"))
#
# ## Allocate cultivars to croptypes
# croptypes <- loadCroptypes(simul_params, names = c("Susceptible crop"
# , "Resistant crop 1"
# , "Resistant crop 2"))
# croptypes <- allocateCroptypeCultivars(croptypes, "Susceptible crop", "Susceptible")
# croptypes <- allocateCroptypeCultivars(croptypes, "Resistant crop 1", "Resistant1")
# croptypes <- allocateCroptypeCultivars(croptypes, "Resistant crop 2", "Resistant2")
# simul_params <- setCroptypes(simul_params, croptypes)
#
# ## Allocate croptypes to landscape
# rotation_sequence <- croptypes$croptypeID ## No rotation: 1 rotation_sequence element
# rotation_period <- 0 ## same croptypes every years
# prop <- c(1/3, 1/3, 1/3) ## croptypes proportions
# aggreg <- myDesign$aggreg[i]
# simul_params <- allocateLandscapeCroptypes(simul_params,
# rotation_period = rotation_period,
# rotation_sequence = rotation_sequence,
# rotation_realloc = FALSE,
# prop = prop,
# aggreg = aggreg,
# graphic = FALSE)
#
# ## configure outputs
# outputlist <- loadOutputs(epid_outputs = "audpc", evol_outputs = "durability")
# simul_params <- setOutputs(simul_params, outputlist)
#
# ## Check, (save) and run simulation
# checkSimulParams(simul_params)
# # simul_params <- saveDeploymentStrategy(simul_params, overwrite = TRUE)
# res <- runSimul(simul_params, writeTXT=FALSE, graphic = FALSE)
#
# ## Extract outputs
# myDesign$durab_MG1[i] <- res$evol_outputs$durability[,"MG 1"]
# myDesign$durab_MG2[i] <- res$evol_outputs$durability[,"MG 2"]
# myDesign$mean_audpc[i] <- mean(res$epid_outputs$audpc$total)
#
# ## Create myDesign.txt at first simulation and then append
# if (i ==1){
# write.table(myDesign[i,], paste(simul_params@OutputDir, "/myDesign.txt", sep="")
# , append=FALSE, row.names = FALSE, col.names = TRUE)
# } else {
# write.table(myDesign[i,], paste(simul_params@OutputDir, "/myDesign.txt", sep="")
# , append=TRUE, row.names = FALSE, col.names = FALSE)
# }
# }
#
# myDesign
## ---- eval=FALSE--------------------------------------------------------------
# ## Disable computation of outputs:
# outputlist <- loadOutputs(epid_outputs = "", evol_outputs = "")
# simul_params <- setOutputs(simul_params, outputlist)
#
# ## Run simulation and keep raw binary files:
# runSimul(simul_params, writeTXT=FALSE, graphic = FALSE, keepRawResults = TRUE)
## ---- eval=FALSE--------------------------------------------------------------
# ## retrieve parameters from the object simul_params
# path <- simul_params@OutputDir
# Nyears <- simul_params@TimeParam$Nyears
# nTSpY <- simul_params@TimeParam$nTSpY
# nTS <- Nyears * nTSpY ## Total number of time-steps
#
# Npoly <- nrow(simul_params@Landscape)
# Nhost <- nrow(simul_params@Cultivars)
# Npatho <- prod(simul_params@Genes$Nlevels_aggressiveness)
#
# ## Initialise lists
# H <- as.list(1:nTS)
# Hjuv <- as.list(1:nTS)
# P <- as.list(1:nTS)
# L <- as.list(1:nTS)
# I <- as.list(1:nTS)
# R <- as.list(1:nTS)
# index <- 0
#
# ## Read binary files and store values in the lists as matrices or arrays
# for (year in 1:Nyears) {
#
# binfileH <- file(paste(path, sprintf("/H-%02d", year), ".bin", sep = ""), "rb")
# H.tmp <- readBin(con = binfileH, what = "int", n = Npoly * Nhost * nTSpY, size = 4
# , signed = T, endian = "little")
# close(binfileH)
#
# binfileHjuv = file(paste(path, sprintf("/Hjuv-%02d", year), ".bin",sep=""), "rb")
# Hjuv.tmp <- readBin(con=binfileHjuv, what="int", n=Npoly*Nhost*nTSpY, size = 4
# , signed=T,endian="little")
# close(binfileHjuv)
#
# binfileP <- file(paste(path, sprintf("/P-%02d", year), ".bin", sep = ""), "rb")
# P.tmp <- readBin(con = binfileP, what = "int", n = Npoly * Npatho * nTSpY, size = 4
# , signed = T, endian = "little")
# close(binfileP)
#
# binfileL <- file(paste(path, sprintf("/L-%02d", year), ".bin", sep = ""), "rb")
# L.tmp <- readBin(con = binfileL, what = "int", n = Npoly * Npatho * Nhost * nTSpY
# , size = 4 , signed = T, endian = "little")
# close(binfileL)
#
# binfileI <- file(paste(path, sprintf("/I-%02d", year), ".bin", sep = ""), "rb")
# I.tmp <- readBin(con = binfileI, what = "int", n = Npoly * Npatho * Nhost * nTSpY
# , size = 4 , signed = T, endian = "little")
# close(binfileI)
#
# binfileR <- file(paste(path, sprintf("/R-%02d", year), ".bin", sep = ""), "rb")
# R.tmp <- readBin(con = binfileR, what = "int", n = Npoly * Npatho * Nhost * nTSpY
# , size = 4 , signed = T, endian = "little")
# close(binfileR)
#
# ## Convert vectors in matrices or arrays
# for (t in 1:nTSpY) {
# H[[t + index]] <- matrix(H.tmp[((Nhost * Npoly) * (t-1)+1):(t * (Nhost * Npoly))]
# , ncol = Nhost, byrow = T)
# Hjuv[[t + index]] <- matrix(Hjuv.tmp[((Nhost*Npoly)*(t-1)+1):(t*(Nhost*Npoly))]
# , ncol=Nhost,byrow=T)
# P[[t + index]] <- matrix(P.tmp[((Npatho * Npoly) * (t-1)+1):(t * (Npatho * Npoly))]
# , ncol = Npatho, byrow = T)
# L[[t + index]] <- array(data = L.tmp[((Npatho * Npoly * Nhost) *
# (t-1)+1):(t * (Npatho * Npoly * Nhost))]
# , dim = c(Nhost, Npatho, Npoly))
# I[[t + index]] <- array(data = I.tmp[((Npatho * Npoly * Nhost) *
# (t-1)+1):(t * (Npatho * Npoly * Nhost))]
# , dim = c(Nhost, Npatho, Npoly))
# R[[t + index]] <- array(data = R.tmp[((Npatho * Npoly * Nhost) *
# (t-1)+1):(t * (Npatho * Npoly * Nhost))]
# , dim = c(Nhost, Npatho, Npoly))
# }
#
# index <- index + nTSpY
# }
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