ModelRun.r

library(parallel)

progress <- FALSE

# Custom version of pbapply (library pbapply) for writing the calibration status on "progess.txt"
pbapply_custom <- function (X, FUN, ..., cl = NULL) 
{
  FUN <- match.fun(FUN)
  if (!is.vector(X) || is.object(X)) 
    X <- as.list(X)
  if (!length(X)) 
    return(lapply(X, FUN, ...))
  if (!is.null(cl)) {
    if (identical(cl, "future")) {
      if (!requireNamespace("future") || !requireNamespace("future.apply")) {
        warning("You need some packages for cl='future' to work: install.packages('future.apply')")
        cl <- NULL
      }
    }
    else {
      if (.Platform$OS.type == "windows") {
        if (!inherits(cl, "cluster")) 
          cl <- NULL
      }
      
      else {
        if (inherits(cl, "cluster")) {
          if (length(cl) < 2L) 
            cl <- NULL
        }
        else {
          if (cl < 2) 
            cl <- NULL
        }
      }
    }
  }
  nout <- as.integer(getOption("pboptions")$nout)
  if (is.null(cl)) {
    if (!dopb()) 
      return(lapply(X, FUN, ...))
    Split <- splitpb(length(X), 1L, nout = nout)
    B <- length(Split)
    pb <- startpb(0, B)
    on.exit(closepb(pb), add = TRUE)
    rval <- vector("list", B)
    for (i in seq_len(B)) {
      rval[i] <- list(lapply(X[Split[[i]]], FUN, ...))
      setpb(pb, i)
    }
  }
  else {
    if (inherits(cl, "cluster")) {
      PAR_FUN <- if (isTRUE(getOption("pboptions")$use_lb)) 
        parallel::parLapplyLB
      else parallel::parLapply
      if (!dopb()) 
        return(PAR_FUN(cl, X, FUN, ...))
      Split <- splitpb(length(X), length(cl), nout = nout)
      B <- length(Split)
      pb <- startpb(0, B)
      on.exit(closepb(pb), add = TRUE)
      rval <- vector("list", B)
      for (i in seq_len(B)) {
        rval[i] <- list(PAR_FUN(cl, X[Split[[i]]], FUN, 
                                ...))
        setpb(pb, i)
        write.table(i/B*100,file = paste0(OD,'process.txt'), row.names = FALSE, col.names = FALSE)
      }
    }
    else if (identical(cl, "future")) {
      requireNamespace("future")
      requireNamespace("future.apply")
      if (!dopb()) 
        return(future.apply::future_lapply(X, FUN, ..., 
                                           future.stdout = FALSE))
      Split <- splitpb(length(X), future::nbrOfWorkers(), 
                       nout = nout)
      B <- length(Split)
      pb <- startpb(0, B)
      on.exit(closepb(pb), add = TRUE)
      rval <- vector("list", B)
      for (i in seq_len(B)) {
        rval[i] <- list(future.apply::future_lapply(X[Split[[i]]], 
                                                    FUN, ..., future.stdout = FALSE))
        setpb(pb, i)
      }
    }
    else {
      if (!dopb()) 
        return(parallel::mclapply(X, FUN, ..., mc.cores = as.integer(cl), 
                                  mc.silent = TRUE))
      Split <- splitpb(length(X), as.integer(cl), nout = nout)
      B <- length(Split)
      pb <- startpb(0, B)
      on.exit(closepb(pb), add = TRUE)
      rval <- vector("list", B)
      for (i in seq_len(B)) {
        rval[i] <- list(parallel::mclapply(X[Split[[i]]], 
                                           FUN, ..., mc.cores = as.integer(cl), mc.silent = TRUE))
        setpb(pb, i)
      }
    }
  }
  rval <- do.call(c, rval, quote = TRUE)
  names(rval) <- names(X)
  rval
}

##This is the function to run the DSSAT model.
ModelRun<-function(WD, OD, DSSATD, GD, CropName, GenotypeFileName, CultivarID, RoundOfGLUE, TotalParameterNumber, 
                   NumberOfModelRun, RandomMatrix, CoresAvailable, EcotypeID, EcotypeParameters, ModelSelect, CTR)
{
  CTR
  ListModelRun<- 1:NumberOfModelRun
  
  ParameterSetIndex<-c();

  run_simulations <<- function(i) {
    core_dir_name <<- paste0(OD,'/core_',Sys.getpid())
    
    if(!dir.exists(core_dir_name)){
      dir.create(core_dir_name)
      write('',paste0(core_dir_name,'/Evaluate_output.txt'))
      file.copy(paste0(OD,'/DSSBatch.v48'), core_dir_name)
      file.copy(paste0(GD,'/',GenotypeFileName,'.CUL'), core_dir_name)
      file.copy(paste0(GD,'/',GenotypeFileName,'.ECO'), core_dir_name) #added to run on HiPerGator
      file.copy(paste0(GD,'/',GenotypeFileName,'.SPE'), core_dir_name) #added to run on HiPerGator
      file.copy(paste0(GD,'/',GenotypeFileName,'.CUL'), OD) #adding a copy since GLUE needs to read the header after ModelRun.r
      writeLines(CTR,paste0(core_dir_name,"/DSCSM048.CTR")) #writing control file in each core directory
    }
    setwd(core_dir_name);
    #Set the path for program to call the bath file running.

    ################### 1. Model Run ##################
    ModelRunNumber<- i
    #print(ModelRunNumber)
    eval(parse(text = paste("source('",WD,"/GenotypeChange.r')",sep = '')));#Tell the location of the function.
    GenotypeChange(GD, DSSATD, core_dir_name, CropName, GenotypeFileName, CultivarID, TotalParameterNumber, ModelRunNumber, RandomMatrix, EcotypeID, EcotypeParameters); #Change the genotype file.
    
    
    #cat(RandomMatrix[ModelRunNumber,])
    #check which OS GLUE is running in order to run the simulations
    if(.Platform$OS.type == "windows"){
      eval(parse(text = paste("try(system('",DSSATD,"/DSCSM048.EXE ",ModelSelect," B ",OD,"DSSBatch.v48 DSCSM048.CTR'))",sep = '')));
    }else{
      eval(parse(text = paste("try(system('",DSSATD,"/dscsm048 ",ModelSelect," B ","DSSBatch.v48 DSCSM048.CTR'))",sep = '')));
    }
    
    #Call the batch file to run the model.
    if (file.exists("Evaluate.OUT")== F)
    {
      write('',paste0(core_dir_name,'/Error_list_',Sys.getpid(),'.txt'))
      cat(paste0("An error occurred in ", core_dir_name, " when using the following cultivar coefficients:\n"), file = paste0(core_dir_name,"/Error_list_",Sys.getpid(),".txt"),append = T)
      eval(parse(text = paste("cat(",list(RandomMatrix[ModelRunNumber,(1:(TotalParameterNumber -EcotypeParameters))]),", file = '",core_dir_name,"/Error_list_",Sys.getpid(),".txt',append = T)",sep = '')));
      #next;
      if(EcotypeParameters>0){
        cat(paste0("and ecotype coefficients:\n"), file = paste0(core_dir_name,"/Error_list_",Sys.getpid(),".txt"),append = T)
          eval(parse(text = paste("cat(",list(RandomMatrix[ModelRunNumber,(TotalParameterNumber -EcotypeParameters:TotalParameterNumber)]),"\n, file = '",core_dir_name,"/Error_list_",Sys.getpid(),".txt',append = T)",sep = '')));
      }
    }else
    {
      eval(parse(text = paste("EvaluateFile<-readLines('",core_dir_name,"/Evaluate_output.txt',n=-1)",sep = '')));
      
      ##If there is any errors in the Evaluate.OUT file.
      Error1Address<-match('NaN',EvaluateFile);
      Error2Address<-match("********",EvaluateFile);
      
      if (is.na(Error1Address) & is.na(Error2Address)){
        eval(parse(text = paste("EvaluateOut<<-readLines('",core_dir_name,"/Evaluate.OUT',n=-1)",sep = '')));
        #Read the output in evaluate file.
        
        eval(parse(text = paste("write(EvaluateOut, file = '",core_dir_name,"/Evaluate_output.txt',append = F)",sep = '')));
        #Save the evaluate output, but replace the previous output.
  
        ################### 2. Data Processing ##################
        
        
        FileLength<-length(EvaluateFile);
        TreatmentNumber<-(FileLength-3);

        #eval(parse(text = paste("write('.', file = '",core_dir_name,"/Error_list_",Sys.getpid(),".txt',append = T)",sep = '')));
        
        eval(parse(text = paste("source('",WD,"/OutputProcessing.r')",sep = '')));
        OutputProcessing(WD, core_dir_name, CropName, RoundOfGLUE, ModelRunNumber);
        #Call the function to process the output data of evaluate and plant growth in each model run.
        
        ParameterSetIndex<-c(ParameterSetIndex,i);
        #Select the parameter set that match the requirement, i.e. do not have bad outputs.            
      }else{
      write('',paste0(core_dir_name,'/Error_list_',Sys.getpid(),'.txt'))
      cat(paste0("Missing values found in ", core_dir_name, " when using the following combination of cultivar coefficients:\n"), file = paste0(core_dir_name,"/Error_list_",Sys.getpid(),".txt"),append = T)
      eval(parse(text = paste("cat(",list(RandomMatrix[ModelRunNumber,(1:(TotalParameterNumber -EcotypeParameters))]),'\n',", file = '",core_dir_name,"/Error_list_",Sys.getpid(),".txt',append = T)",sep = '')));
        if(EcotypeParameters>0){
          cat(paste0("\nand ecotype coefficients:\n"), file = paste0(core_dir_name,"/Error_list_",Sys.getpid(),".txt"),append = T)
            eval(parse(text = paste("cat(",list(RandomMatrix[ModelRunNumber,(TotalParameterNumber -EcotypeParameters:TotalParameterNumber)]),'\n',", file = '",core_dir_name,"/Error_list_",Sys.getpid(),".txt',append = T)",sep = '')));
        }
      }
    }
    
    if(RoundOfGLUE==1)
    {
      RealRandomSets<-RandomMatrix[ParameterSetIndex,];
      eval(parse(text = paste("write(t(RealRandomSets), file = '",core_dir_name,"/RealRandomSets_1.txt',,append = T, ncolumns =TotalParameterNumber)",sep = '')));
      ##Get and save really used random parameter sets as a table for future use.
    } else
    {
      RealRandomSets<-RandomMatrix[ParameterSetIndex,];
      eval(parse(text = paste("write(t(RealRandomSets), file = '",core_dir_name,"/RealRandomSets_2.txt',,append = T, ncolumns =TotalParameterNumber)",sep = '')));
    }
    

  }
    
    #
    #mclapply(ListModelRun, run_simulations, mc.cores = CoresAvailable)
    
    cl <- makePSOCKcluster(CoresAvailable)
    setDefaultCluster(cl)
    clusterExport(NULL, c('ModelSelect', 'run_simulations','WD', 'OD', 'DSSATD', 'GD', 'CropName', 
                          'GenotypeFileName', 'CultivarID', 'RoundOfGLUE', 'TotalParameterNumber', 
                          'NumberOfModelRun', 'RandomMatrix', 'EcotypeID', 'EcotypeParameters'))

    if(progress==TRUE){
      
      list.of.packages <- c("pbapply")
      new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
      if(length(new.packages)) install.packages(new.packages)
      library(pbapply)
      
      pbo = pboptions(type="txt")
      #pblapply(ListModelRun, function(z) run_simulations(z), cl = cl)
      pbapply_custom(ListModelRun, function(z) run_simulations(z), cl = cl)
    }else{
      parLapply(NULL, ListModelRun, function(z) run_simulations(z))
    }
    stopCluster(cl)

}