BCForestGroundSample: BC forest ground sample compilation

Documented in VRICompiler

#' VRI compiler - VRI specific
#'
#'
#' @description This function compiles VRI data by calling specific VRI functions. Unlike the original
#'              compiler (i.e., SAS compiler), the R version compiler hardcodes all the lookup tables in
#'              the compilation process. Please refer the descriptions for lookup table to see whether
#'              they are same as the original lookup table.
#'
#' @param oracleUserName character, User name to access to oracle database.
#' @param oraclePassword character, Password to access to oracle database.
#' @param asciiTxtPath character, Path to ascii txt files. By default, the arguement is specified to
#'                     \code{//albers/gis_tib/VRI/RDW/RDW_Data2/Work_Areas/VRI_ASCII_PROD/vri_raw}.
#'                     However, user can modify.
#' @param compilationPath character, Specifies the path that stores all the data/processes. By specifying this,
#'                         four folders will be created to record all the data/processes. Specifically,
#'                         raw_from_oracle stores the data just after oracle and ascii without editing;
#'                         compilation_sa stores key data (not all) that after editing and before volume and age compilation;
#'                         compilation_db stores compiled results for volume and age compilation at both tree level
#'                         and cluater level;
#'                         Archive_YYYYMMDD achives all the data mentioned above for the future use or reference.
#'                         By default, this path is set as \code{//albers/gis_tib/VRI/RDW/RDW_Data2/Work_Areas/VRI_ASCII_PROD/RCompilation},
#'                         which is consistent with our rdw system.
#'
#' @param fizmapPath character, Specifies the path to forest inventory zone map. By default,
#'                              it is set to \code{//spatialfiles2.bcgov/work/for/vic/hts/dam/workarea/data/infrastructure},
#'                              which is maintained by FAIB employee.
#' @param fizmapName character, Specifies the name of forest inventory zone map. By default,
#'                              it is set to \code{FIZ_REG_COMPARTMENT}, which is maintained by FAIB employee.
#' @param fizmapFormat character, Specifies the format of forest inventory zone map. Currently, it can be specified
#'                                as \code{gdb} for geodatabase format and \code{shp} for shapefile format.
#'                                By default, it is set to \code{gdb}, which is maintained by FAIB employee.
#'
#' @param equation character, Specifies the taper equation that is used for compiler. Currently supports
#'                            BEC-based (\code{KBEC}) and FIZ-based (\code{KFIZ}).
#' @param walkThru logical, Speciefies whether the data had been collected using work through method. Default is \code{TRUE},
#'                          if it is not specified.
#' @param logMinLength numeric, Specifies minimum length of log when doing log length adjustment,
#'                              see \code{\link{logMatrixAdjustment}} for details. If missing 0.1 is used.
#' @param stumpHeight numeric, Stump height. If missing 0.3 is used.
#' @param breastHeight numeric, Breast height. If missing 1.3 is used.
#' @param UTOPDIB numeric, Threshold inside-bark diameter for merchantable volume. If missing, UTOPDIB is 10.
#' @param utilLevel numeric, Specifies utilization level in summrizing tree volumes at cluster and species level. Default is 4.
#' @param weirdUtil character, Specifies weird utilization in summarizing tree volumes at cluster and species level.
#'                             Default is \code{no}, if missing. Otherwise, a number should be provided.
#' @param useExternalCoefRatio logical, Allow users to use prederived regression and ratio. Default is \code{FALSE}
#'                             for now.
#'
#'
#' @return This function compiles data and save outputs in \code{compilationPaths$compilation_db} and no file is returned.
#'
#' @importFrom data.table ':='
#' @importFrom fpCompare '%<=%' '%==%' '%>=%' '%!=%' '%>>%' '%<<%'
#' @references VRI compiler manual
#' @note
#'  Improvements include:
#'  \enumerate{
#'  \item specifies trees in B plots as height enhanced trees
#'  \item removes the sas-dependent sindex functions
#'  \item introduce the SIndexR package
#'  }
#'  Currently, the compiler supports compilation for the below sample types:
#'  \itemize{
#'  \item{\code{Q: }} {Regular VRI sample with five point clusters design}
#'  \item{\code{T: }} {??, sample protocol and sample design are same as Q samples}
#'  \item{\code{B: }} {??, same plot layout as Q sample, with height is measured for all trees and no call grading information}
#'  \item{\code{M: }} {CMI sample, with all trees have call grading information in the field}
#'  \item{\code{L: }} {LiDAR project, same plot layout and same design but without call grading information}
#'  \item{\code{Y: }} {YSM plots, population between 15 and 50 years}
#'  \item{\code{F: }} {NFI plots, trees measured all DBH, height and call grading}
#'  \item{\code{N: }} {NVAF plots}
#'  \item{\code{A: }} {VRI audit plots}
#'  }
#' @export
#' @docType methods
#' @rdname VRICompiler
#'
#' @author Yong Luo
#'

VRICompiler <- function(oracleUserName,
                        oraclePassword,
                        asciiTxtPath = "//albers/gis_tib/VRI/RDW/RDW_Data2/Work_Areas/VRI_ASCII_PROD/vri_raw",
                        compilationPath = "//albers/gis_tib/VRI/RDW/RDW_Data2/Work_Areas/VRI_ASCII_PROD/RCompilation",
                        fizmapPath = "//spatialfiles2.bcgov/work/for/vic/hts/dam/workarea/data/infrastructure",
                        fizmapName = "FIZ_REG_COMPARTMENT",
                        fizmapFormat = "gdb",
                        equation = "KBEC",
                        walkThru = TRUE,
                        logMinLength = 0.1,
                        stumpHeight = 0.3,
                        breastHeight = 1.3,
                        UTOPDIB = 10,
                        utilLevel = 4,
                        weirdUtil = "No",
                        useExternalCoefRatio = FALSE){
  cat(paste(Sys.time(), ": Prepare folders under compilation path.\n", sep = ""))
  compilationPaths <- compilerPathSetup(compilationPath)

  ## setup the folder to save coefficients and ratios
  ## and update coefficients and ratio annually
  coeffPath <- file.path(compilationPath, "Coeffs")
  if(!(dir.exists(coeffPath))){
    dir.create(coeffPath)
  }

  ### 2. load oracle and txt data for compilation
  cat(paste(Sys.time(), ": Load data from oracle.\n", sep = ""))
  loadVGIS(userName = oracleUserName,
           password = oraclePassword,
           saveThem = TRUE,
           savePath = compilationPaths$raw_from_oracle)

  alltxtfiletable <- data.table::data.table(alltxtfiles = dir(asciiTxtPath, full.names = TRUE))
  alltxtfiletable[, txtlength := nchar(alltxtfiles)]
  alltxtfiletable[, txtbeg := txtlength-3]
  alltxtfiletable <- alltxtfiletable[substr(alltxtfiles, txtbeg, txtlength) %in% c(".txt", ".TXT")]

  if(nrow(alltxtfiletable) > 0){
    cat(paste(Sys.time(), ": Load data from ASCII.\n", sep = ""))
    loadASCII(txtLocation = asciiTxtPath,
              saveThem = TRUE,
              savePath = compilationPaths$raw_from_oracle)
    ## 3 merge key tables for compilation
    cat(paste(Sys.time(), ": Merge data from two sources.\n", sep = ""))
    mergeOAData(oracleSourcePath = compilationPaths$raw_from_oracle,
                asciiSourcePath = compilationPaths$raw_from_oracle,
                fizmapPath = fizmapPath,
                fizmapName = fizmapName,
                fizmapFormat = fizmapFormat,
                outputPath = compilationPaths$compilation_sa)
  } else {
    mergeOAData(oracleSourcePath = compilationPaths$raw_from_oracle,
                asciiSourcePath = "NONE",
                fizmapPath = fizmapPath,
                fizmapName = fizmapName,
                fizmapFormat = fizmapFormat,
                outputPath = compilationPaths$compilation_sa)
  }



  ## 4 get tsa, bec and fiz information from maps


  ### 5.0 lookup table check
  # lookuptables <- c("vri_grp", "vri_bec", "spv_spc", "sp_cost", "spv_frd", "sp_type",
  #                   "dcy_v3", "dcy_v3x", "brk_99", "wst_v3")
  # for(inditable in lookuptables){
  #   indilog <- lookupCheck(inditable)
  # }
  # rm(lookuptables, indilog, inditable)
  ### 2.1 load cluster/plot header
  clusterplotheader_VRI <- VRIInit_clusterplot(dataSourcePath = compilationPaths$compilation_sa)
  samples <- data.table::copy(clusterplotheader_VRI)

  saveRDS(samples, file.path(compilationPaths$compilation_db, "samples.rds"))
  # write.csv(samples, file.path(compilationPaths$compilation_db, "samples.csv"), row.names = FALSE)

  rm(clusterplotheader_VRI)
  ### 2.2 load vi_c data
  ## vi_c contains the trees of: 1) fully measured trees in IPC (trees have dbh, height and call grading)
  ##                             2) enhanced trees in auxi plots (trees have dbh, height and call grading)
  ##                             3) H-enhanced trees in auxi plots (trees have dbh, height)
  ##                             4) B-sample trees in fixed area lidar projects (trees have dbh, height)
  tree_ms1 <- VRIInit_measuredTree(data.table::copy(samples),
                                   compilationPaths$compilation_sa,
                                   walkThru)

  ### 2.3 load vi_d data
  ## vi_d contains call grading data for fully measured trees and enhanced trees
  vi_d <- VRIInit_lossFactor(fullMeasuredTrees = tree_ms1[,.(CLSTR_ID, PLOT, TREE_NO)],
                             dataSourcePath = compilationPaths$compilation_sa)

  ### 2.4 load vi_i data
  ## vi_i has trees in auxi plots without height information (mostly), however, some of these trees are also in vi_c
  tree_ax1 <- VRIInit_auxTree(data.table::copy(samples),
                              compilationPaths$compilation_sa)
  tree_ax1 <- merge(tree_ax1, unique(lookup_species()[,.(SPECIES, SP0)], by = "SPECIES"),
                    by = "SPECIES")
  ### 2.5 load vi_h data
  ## vi_h data is the site age trees
  tree_ah1 <- VRIInit_siteTree(data.table::copy(samples),
                               compilationPaths$compilation_sa)



  ### 3. vi_c compilation
  cat(paste(Sys.time(), ": Compile full/enhanced and h-enhanced volume trees.\n", sep = ""))
  tree_ms1[LOG_G_1 == "*",
           MEAS_INTENSE := "H-ENHANCED"]
  tree_ms1[is.na(MEAS_INTENSE) & PLOT == "I",
           MEAS_INTENSE := "FULL"]
  tree_ms1[is.na(MEAS_INTENSE),
           MEAS_INTENSE := "ENHANCED"]
  ## for the full/enhanced trees, if the length of first log is missing, assign
  ## them with tree height
  tree_ms1[MEAS_INTENSE %in% c("FULL", "ENHANCED") & LOG_L_1 %in% c(NA, 0),
           LOG_L_1 := HEIGHT]
  ## for the zero tree height trees, force them as non-enhanced trees, which means
  ## they only have DBH information
  tree_ms1[HEIGHT %in% c(NA, 0), MEAS_INTENSE := "NON-ENHANCED"]
  nonenhancedtreedata <- tree_ms1[MEAS_INTENSE == "NON-ENHANCED",]
  voltrees <- data.table::copy(tree_ms1)[MEAS_INTENSE %in% c("FULL", "ENHANCED", "H-ENHANCED"),]
  voltrees <- merge(voltrees, unique(samples[,.(CLSTR_ID, FIZ, BGC_ZONE, BGC_SBZN, BGC_VAR)],
                                     by = "CLSTR_ID"),
                    by = "CLSTR_ID",
                    all.x = TRUE)
  tree_ms6 <- VRIVolTree(treeData = data.table::copy(voltrees),
                         equation = equation, logMinLength = logMinLength,
                         stumpHeight = stumpHeight, breastHeight = breastHeight, UTOPDIB = UTOPDIB)
  tree_ms6 <- rbindlist(list(tree_ms6, nonenhancedtreedata), fill = TRUE)
  rm(tree_ms1, voltrees, nonenhancedtreedata)

  ######################
  ###################### start the site age compilation
  ### 4. vi_h site age compilation
  cat(paste(Sys.time(), ": Compile age trees.\n", sep = ""))
  tree_ah1 <- merge_dupUpdate(tree_ah1, samples[,.(CLSTR_ID, PLOT, FIZ = as.character(FIZ))],
                              by = c("CLSTR_ID", "PLOT"), all.x = TRUE)
  tree_ah2 <- siteAgeCompiler(siteAgeData = data.table::copy(tree_ah1))
  saveRDS(tree_ah2, file.path(compilationPaths$compilation_db, "compiled_vi_h.rds"))
  # write.csv(tree_ah2, file.path(compilationPaths$compilation_db, "compiled_vi_h.csv"), row.names = FALSE)

  siteAgeSummaries <- siteAgeSummary(tree_ah2)
  cl_ah <- siteAgeSummaries$cl_ah
  saveRDS(cl_ah, file.path(compilationPaths$compilation_db,
                           "Smries_siteAge_byCL.rds"))
  # write.csv(cl_ah, file.path(compilationPaths$compilation_db,
  #                          "Smries_siteAge_byCL.csv"), row.names = FALSE)
  saveRDS(siteAgeSummaries$spc_ah,
          file.path(compilationPaths$compilation_db, "Smries_siteAge_byCLSP.rds"))
  # write.csv(siteAgeSummaries$spc_ah,
  #         file.path(compilationPaths$compilation_db, "Smries_siteAge_byCLSP.csv"), row.names = FALSE)
  rm(siteAgeSummaries, tree_ah1, tree_ah2)

  ######################
  ######################
  ### 5. start the decay, waste and breakage calculation for full/enhanced trees in vi_c
  cat(paste(Sys.time(), ": Compile DWB.\n", sep = ""))
  siteAgeTable <- merge_dupUpdate(cl_ah[,.(CLSTR_ID, AT_M_TLS, AT_M_TXO)],
                                  unique(samples[,.(CLSTR_ID, PROJ_ID, SAMP_NO, TYPE_CD)],
                                         by = "CLSTR_ID"),
                                  by = "CLSTR_ID",
                                  all.x = TRUE)
  tree_ms6 <- merge_dupUpdate(tree_ms6,
                              unique(samples[,.(CLSTR_ID, PROJ_ID, BGC_ZONE, BGC_SBZN, BGC_VAR,
                                                TSA, TYPE_CD)],
                                     by = "CLSTR_ID"),
                              by = "CLSTR_ID",
                              all.x = TRUE)
  tree_ms7 <- DWBCompiler(treeMS = tree_ms6[MEAS_INTENSE %in% c("FULL", "ENHANCED"),],
                          siteAge = unique(siteAgeTable, by = "CLSTR_ID"),
                          treeLossFactors = vi_d, equation = "KBEC")
  tree_ms7 <- rbindlist(list(tree_ms7,
                             tree_ms6[MEAS_INTENSE %in% c("H-ENHANCED", "NON-ENHANCED"),]),
                        fill = TRUE)
  saveRDS(tree_ms7, file.path(compilationPaths$compilation_db,
                              "compiled_vi_c.rds"))
  # write.csv(tree_ms7, file.path(compilationPaths$compilation_db,
  #                             "compiled_vi_c.csv"), row.names = FALSE)
  rm(vi_d, siteAgeTable, tree_ms6)



  #######
  ### 6. start to calculate tree volume components for H-enhanced and non-enhanced trees in auxi plots
  cat(paste(Sys.time(), ": Compile NON- and H-enhanced trees.\n", sep = ""))
  if(useExternalCoefRatio){
    # derive ratio and regression routine
    todayDate <- as.Date(Sys.time())
    todayYear <- substr(todayDate, 1, 4)
    if(todayDate >= as.Date(paste0(todayYear, "-01-01")) &
       todayDate < as.Date(paste0(todayYear, "-04-01"))){
      compilerYear <- as.character(as.numeric(todayYear) - 1)
    } else {
      compilerYear <- todayYear
    }
    if(!file.exists(file.path(coeffPath,
                              paste0("regRatioData", compilerYear, ".rds")))){
      alltreelist <- mergeAllVolTrees(treeMS = data.table::copy(tree_ms7),
                                      treeAX = data.table::copy(tree_ax1))
      samples_beccls <- unique(samples[,.(CLSTR_ID, BGC_ZONE)], by = "CLSTR_ID")
      alltreelist <- merge(alltreelist, samples_beccls, by = "CLSTR_ID", all.x = TRUE)
      allbecsplvd <- unique(alltreelist[,.(BGC_ZONE, SP0, LV_D)])

      cat("Start selecting regratio data and derive mixed effect model coefficients and ratios.")
      ## if the regratiodata can not be found in coeff folder
      ## generate regratiodata and derive coeff and ratio using mixed effect models
      regRatioData <- regRatioDataSelect(samples, tree_ms7)
      saveRDS(regRatioData, file.path(coeffPath, paste0("regRatioData", compilerYear, ".rds")))
      write.table(regRatioData,
                  file.path(coeffPath, paste0("regRatioData", compilerYear, ".txt")),
                  row.names = FALSE, sep = ",")

      coefs <- regBA_WSV(regRatioData, needCombs = allbecsplvd)
      saveRDS(coefs$fixedcoeff,
              file.path(coeffPath, paste0("fixedCoefs", compilerYear, ".rds")))
      saveRDS(coefs$randomcoeff,
              file.path(coeffPath, paste0("randomCoefs", compilerYear, ".rds")))
      write.table(coefs$fixedcoeff,
                  file.path(coeffPath, paste0("fixedCoefs", compilerYear, ".txt")),
                  row.names = FALSE, sep = ",")
      write.table(coefs$randomcoeff,
                  file.path(coeffPath, paste0("randomCoefs", compilerYear, ".txt")),
                  row.names = FALSE, sep = ",")

      ratios <- toWSVRatio_New(inputData = regRatioData, needCombs = allbecsplvd)
      saveRDS(ratios,
              file.path(coeffPath, paste0("ratios", compilerYear, ".rds")))

      write.table(ratios,
                  file.path(coeffPath, paste0("ratios", compilerYear, ".txt")),
                  row.names = FALSE, sep = ",")
      rm(coefs, ratios, regRatioData)
    }
    fixedcoeffs <- readRDS(file.path(coeffPath, paste0("fixedCoefs", compilerYear, ".rds")))
    randomcoeffs <- readRDS(file.path(coeffPath, paste0("randomCoefs", compilerYear, ".rds")))
    ratios <- readRDS(file.path(coeffPath, paste0("ratios", compilerYear, ".rds")))
    auxtreecompilation <- auxiTreeCompiler_useExternalCoeffRatio(fullMeasuredTrees = data.table::copy(tree_ms7),
                                                                 auxiTrees = data.table::copy(tree_ax1),
                                                                 clusterPlotHeader = samples,
                                                                 fixedCoeff = fixedcoeffs,
                                                                 randomCoeff = randomcoeffs,
                                                                 ratios = ratios)
  } else {
    # call vol_ha_2017 macro
    auxtreecompilation <- auxiTreeCompiler(fullMeasuredTrees = data.table::copy(tree_ms7),
                                           auxiTrees = data.table::copy(tree_ax1),
                                           clusterPlotHeader = samples)
    rm(tree_ms7)
    saveRDS(auxtreecompilation$regressionTable,
            file.path(compilationPaths$compilation_db, "regressiontable.rds"))
    # write.csv(auxtreecompilation$regressionTable,
    #         file.path(compilationPaths$compilation_db, "regressiontable.csv"), row.names = FALSE)
    saveRDS(auxtreecompilation$ratioTable,
            file.path(compilationPaths$compilation_db, "ratiotable.rds"))
    # write.csv(auxtreecompilation$ratioTable,
    #         file.path(compilationPaths$compilation_db, "ratiotable.csv"), row.names = FALSE)
  }
  prep_smy <- rbindlist(list(auxtreecompilation$fullenhancedtrees,
                             auxtreecompilation$HnonenhancedTrees),
                        fill = TRUE)
  prep_smy[MEAS_INTENSE %in% c("FULL", "ENHANCED", "H-ENHANCED"), VOL_SRCE := "Calc"]
  prep_smy[is.na(VOL_SRCE), VOL_SRCE := "Unk"]
  prep_smy <- merge(prep_smy, unique(lookup_species()[,.(SPECIES, SP_TYPE)], by = "SPECIES"),
                    by = "SPECIES", all.x = TRUE)
  saveRDS(prep_smy[order(CLSTR_ID, PLOT, TREE_NO),],
          file.path(compilationPaths$compilation_db, "treelist.rds"))
  # write.csv(prep_smy[order(CLSTR_ID, PLOT, TREE_NO),],
  #         file.path(compilationPaths$compilation_db, "treelist.csv"), row.names = FALSE)
  rm(auxtreecompilation)

  ## 7. sammarize and save compiled tree-level data at cluster and cluster/species level
  cat(paste(Sys.time(), ": Summarize volume and age.\n", sep = ""))
  vrisummaries <- VRISummaries(allVolumeTrees = data.table::copy(prep_smy),
                               clusterPlotHeader = samples,
                               utilLevel = utilLevel,
                               weirdUtil = weirdUtil,
                               equation = equation)
  saveRDS(vrisummaries$vol_bycs, file.path(compilationPaths$compilation_db, "Smries_volume_byCLSP.rds"))
  saveRDS(vrisummaries$vol_byc, file.path(compilationPaths$compilation_db, "Smries_volume_byCL.rds"))
  saveRDS(vrisummaries$vol_byc, file.path(compilationPaths$compilation_db, "Smries_height_byCL.rds"))
  saveRDS(vrisummaries$compositionsmry_byc, file.path(compilationPaths$compilation_db, "Smries_speciesComposition_byCL.rds"))

  # write.csv(vrisummaries$vol_bycs, file.path(compilationPaths$compilation_db, "Smries_volume_byCLSP.csv"), row.names = FALSE)
  # write.csv(vrisummaries$vol_byc, file.path(compilationPaths$compilation_db, "Smries_volume_byCL.csv"), row.names = FALSE)
  # write.csv(vrisummaries$vol_byc, file.path(compilationPaths$compilation_db, "Smries_height_byCL.csv"), row.names = FALSE)
  # write.csv(vrisummaries$compositionsmry_byc, file.path(compilationPaths$compilation_db, "Smries_speciesComposition_byCL.csv"), row.names = FALSE)


  ## 8. small tree and stump compilation
  ## stump data
  cat(paste(Sys.time(), ": Small tree and stump compilation.\n", sep = ""))
  vi_e <- readRDS(file.path(compilationPaths$compilation_sa, "vi_e.rds")) %>% data.table
  names(vi_e) <- toupper(names(vi_e))
  vi_e <- vi_e[CLSTR_ID %in% unique(samples$CLSTR_ID),]

  ## small tree data
  vi_f <- readRDS(file.path(compilationPaths$compilation_sa, "vi_f.rds")) %>% data.table
  names(vi_f) <- toupper(names(vi_f))
  vi_f[, obslength := length(TOTAL1), by = c("CLSTR_ID", "PLOT", "SPECIES")]
  vi_f <- unique(vi_f, by = c("CLSTR_ID", "PLOT", "SPECIES"))
  vi_f[, clusterplot := paste(CLSTR_ID, "_", PLOT, sep = "")]
  vi_e[, clusterplot := paste(CLSTR_ID, "_", PLOT, sep = "")]
  vi_f <- vi_f[clusterplot %in% unique(vi_e[PL_ORIG == "SML_TR",]$clusterplot),]
  smalltreecompile <- smallTreeVolSmry(smallTreeData = vi_f,
                                       smallTreePlotHeader = vi_e[PL_ORIG == "SML_TR",])
  saveRDS(smalltreecompile$clusterSummaries,
          file.path(compilationPaths$compilation_db, "Smries_smallTree_byCL.rds"))
  saveRDS(smalltreecompile$clusterSpeciesSummaries,
          file.path(compilationPaths$compilation_db, "Smries_smallTree_byCLSP.rds"))
  # write.csv(smalltreecompile$clusterSummaries,
  #         file.path(compilationPaths$compilation_db, "Smries_smallTree_byCL.csv"), row.names = FALSE)
  # write.csv(smalltreecompile$clusterSpeciesSummaries,
  # file.path(compilationPaths$compilation_db, "Smries_smallTree_byCLSP.csv"), row.names = FALSE)
  rm(smalltreecompile)
  vi_g <- readRDS(file.path(compilationPaths$compilation_sa, "vi_g.rds")) %>% data.table
  names(vi_g) <- toupper(names(vi_g))

  vi_g[, clusterplot := paste(CLSTR_ID, "_", PLOT, sep = "")]
  vi_g <- vi_g[clusterplot %in% unique(vi_e[PL_ORIG == "SML_TR",]$clusterplot),]

  ## plot header for stump and small trees
  stumpCompile <- stumpVolSmry(stumpData = vi_g,
                               stumpPlotHeader = vi_e[PL_ORIG == "STUMP",])
  saveRDS(stumpCompile$stmp_c,
          file.path(compilationPaths$compilation_db, "Smries_stump_byCL.rds"))
  saveRDS(stumpCompile$stmp_cs,
          file.path(compilationPaths$compilation_db, "Smries_stump_byCLSP.rds"))
  # write.csv(stumpCompile$stmp_c,
  #         file.path(compilationPaths$compilation_db, "Smries_stump_byCL.csv"), row.names = FALSE)
  # write.csv(stumpCompile$stmp_cs,
  #         file.path(compilationPaths$compilation_db, "Smries_stump_byCLSP.csv"), row.names = FALSE)
  #############################

  cat(paste(Sys.time(), ": Archive compilation to Archive_", gsub("-", "", Sys.Date()), ".\n", sep = ""))

  file.copy(from = compilationPaths$compilation_sa,
            to = compilationPaths$compilation_archive,
            recursive = TRUE)
  file.copy(from = compilationPaths$compilation_db,
            to = compilationPaths$compilation_archive,
            recursive = TRUE)
  file.copy(from = compilationPaths$raw_from_oracle,
            to = compilationPaths$compilation_archive,
            recursive = TRUE)

}