R/OM_functions.R

In MSEtool: Management Strategy Evaluation Toolkit

#' Subset a Stock, Fleet, Obs, or Imp object from an OM object
#'
#' A function that strips out a Stock, Fleet, Obs, or Imp object from a
#' complete OM object. Mainly used for internal functions.
#'
#' @param OM An operating model object (class OM)
#' @param Sub A character string specifying what object type to strip out
#' "Stock", "Fleet", "Obs", or "Imp"
#' @return An object of class Stock, Fleet, Obs, or Imp
#' @author A. Hordyk
#' @examples
#' Stock <- SubOM(testOM, "Stock")
#' class(Stock)
#' @seealso \link{Sub} for subsetting MSE output and \link{SubCpars} for subsetting by simulation and projection years.
#' @export
SubOM <- function(OM, Sub=c("Stock", "Fleet", "Obs", "Imp")) {
  if (!methods::is(OM,"OM")) stop("OM must be of class OM ", call.=FALSE)
  Sub <- match.arg(Sub)
  temp <- new(Sub)

  slots <- slotNames(temp)
  for (X in seq_along(slots))
    slot(temp, slots[X]) <- slot(OM, slots[X])

  colon <- gregexpr(":", temp@Name)
  space <- gregexpr("  ", temp@Name)
  ind <- switch(Sub, Stock=1, Fleet=2, Obs=3, Imp=4)

  if (length(colon) > 0) {
    if (all(colon[[1]] >0) & all(space[[1]]>0)) {
      if (ind < 4) temp@Name <- substr(temp@Name, colon[[1]][ind]+1, space[[1]][ind]-1)
      if (ind == 4) temp@Name <- substr(temp@Name, colon[[1]][ind]+1, nchar(temp@Name))

    }
  }
  temp
}


#' Replace an existing Stock, Fleet, Obs, or Imp object
#'
#' A function that replaces a Stock, Fleet, Obs, or Imp object from an
#' OM with one from another object.
#'
#' @param OM An operating model object (class OM) which will be updated with a sub-model from another OM
#' @param from An object of class `OM`, `Stock`, `Fleet`, `Obs`, or `Imp` to be replace the values in `OM`
#' @param Sub A character string specifying what object type to replace (only used if `from` is class `OM`)
#' "Stock", "Fleet", "Obs", or "Imp" (default is all four which is probably not what you want to do)
#' @param Name Character. Name for the new OM object (`OM@Name`)
#' @param silent Should messages be printed?
#'
# #' @templateVar url modifying-the-om
# #' @templateVar ref the-replace-function
# #' @template userguide_link
#'
#' @return An object of class OM
#' @author A. Hordyk
#' @examples
#' # Replace Stock
#' OM <- MSEtool::testOM
#' OM2 <- Replace(OM, Blue_shark)
#'
#' # Replace Fleet
#' OM <- MSEtool::testOM
#' OM2 <- Replace(OM, Generic_DecE)
#'
#' # Replace Fleet from another OM
#' # OM1 <- new("OM", Albacore, Generic_DecE, Perfect_Info, Overages)
#' # OM2 <- new("OM", Blue_shark, Generic_IncE, Generic_Obs, Perfect_Imp)
#' # OM1a <- Replace(OM1, OM2, "Fleet")
#'
#' @export
Replace <- function(OM, from,Sub=c("Stock", "Fleet", "Obs", "Imp"),  Name=NULL, silent=FALSE) {
  if (methods::is(OM, "character")) OM <- get(OM)
  if (!methods::is(OM, "OM")) stop("OM must be of class OM ", call.=FALSE)
  if (methods::is(from,"character")) from <- get(from)
  if (!class(from) %in% c("OM", "Stock", "Fleet", "Obs", "Imp"))
    stop("from must be class `OM`, `Stock`, `Fleet`, `Obs`, `Imp`", call.=FALSE)

  Stock <- SubOM(OM, "Stock")
  Fleet <- SubOM(OM, "Fleet")
  Obs <- SubOM(OM, "Obs")
  Imp <- SubOM(OM, "Imp")

  if (methods::is(from, "OM")) {
    Sub <- match.arg(Sub, several.ok=TRUE)
    if (length(Sub)==4) warning("Replacing all OM components. Probably not what you want to do ...")

    if(!silent) message("Replacing sub-models:", paste0(" ", Sub))
    for (x in 1:length(Sub)) {
      assign(Sub[x], SubOM(from, Sub[x]))
    }

  } else {
    if(!silent) message("Replacing sub-model: ", class(from))
    assign(class(from), from)
  }
  outOM <- new("OM", Stock, Fleet, Obs, Imp)


  OMsl <- slotNames('OM')
  allSl <- c(slotNames('Stock'), slotNames('Fleet'), slotNames('Obs'), slotNames('Imp'))
  repsl <- OMsl[!OMsl %in% allSl]
  for (sl in repsl) slot(outOM, sl) <- slot(OM, sl)

  if (is.null(Name)) {
    slot(outOM, 'Name') <- paste0('REPLACED -- ', slot(OM, 'Name'))
  } else {
    slot(outOM, 'Name') <- Name
  }

  outOM
}



# #' Forces correlation among operating model parameters for M, K, Linf and L50
# #'
# #' @description Uses typical correlations among estimated parameters to generate realistic samples for natural mortality # rate (M), growth rate (K), maximum length (Linf) and length at 50% maturity (L50), these are placed in the cpars slot
# #' @param OM An operating model object with M, growth, stock-recruitment and maturity parameters specified.
# #' @param nsim The number of simulated values to create (note that OM@nsim will be used preferentially).
# #' @param plot Should the sampled parameters and distributions be plotted?
# #' @return An object of class OM with a populated (or appended) cpars slot
# #' @author T. Carruthers (Canadian DFO grant)
# #' @export
# #' @examples
# #' testOM<-ForceCor(testOM)
# ForceCor<-function(OM,nsim=48,plot=T){
#   .Deprecated("LH2OM", msg="Life-history correlations are now calculated using data from FishBase.\nConsider using # `LH2OM` instead.")
#
#   if("nsim"%in%slotNames(OM))nsim<-OM@nsim
#   if("seed"%in%slotNames(OM))set.seed(OM@seed)
#
#   OM@nsim<-nsim
#
#   colline=makeTransparent('blue',60)
#   lwdline=4
#   histcol='black'
#   # Estimation cross correlation is typically -0.9 for Linf and K
#   sigma<-matrix(c(1,-0.9,-0.9,1),nrow=2)
#
#   # Other Parameters correlation  from meta analysis (technically incorrect in the sapce of other v
#   #                  M     K    F_linf    Linf
#   sigma=matrix(c(1.000,  0.721, -0.568, -0.721,
#                  0.721,  1.000, -0.107, -0.910,
#                  -0.568, -0.107, 1.000,  0.407,
#                  -0.721, -0.910, 0.407,  1.000),nrow=4)
#
#
#   means<-c(mean(OM@M),mean(OM@K),mean(OM@L50),mean(OM@Linf))
#
#   sim<-as.data.frame(mvtnorm::rmvnorm(nsim,rep(0,4),sigma))
#   sim<-sim-rep(apply(sim,2,mean),each=nsim)#mean 0
#   sim<-sim/rep(apply(sim,2,sd),each=nsim)# cv=1
#   cvs<-c(OM@M[2]-OM@M[1],OM@K[2]-OM@K[1],OM@L50[2]-OM@L50[1],OM@Linf[2]-OM@Linf[1])/(1.96*2)/means
#   sim<-exp(sim*rep(cvs,each=nsim))
#   sim<-sim/rep(apply(sim,2,mean),each=nsim)
#   sim<-sim*rep(means,each=nsim)
#
#   if(plot){
#     par(mfrow=c(4,4),mai=c(0.3,0.3,0.4,0.05),omi=c(0.02,0.02,0.3,0.02))
#
#     labs<-c("M","K","L50","Linf")
#     bounds<-matrix(c(OM@M,OM@K,OM@L50,OM@Linf),nrow=2)
#
#     for(i in 1:4){
#
#       for(j in 1:4){
#
#         if(i == j){
#
#           if(i==1){
#
#             hist(sim[,1],main="Natural mortality rate (M)",col=histcol,border='white',xlab="",axes=F)
#             axis(1)
#             abline(v=OM@M,col=colline,lwd=lwdline)
#
#           }else if(i==2){
#
#             hist(sim[,2],main="Growth rate (K)",col=histcol,border='white',xlab="",axes=F)
#             axis(1)
#             abline(v=OM@K,col=colline,lwd=lwdline)
#
#
#           }else if(i==4){
#             hist(sim[,4],main="Maximum length (Linf)",col=histcol,border='white',xlab="",axes=F)
#             axis(1)
#             abline(v=OM@Linf,col=colline,lwd=lwdline)
#
#           }else{
#
#             hist(sim[,3],main="Length at 50% maturity (L50)",col=histcol,border='white',xlab="",axes=F)
#             axis(1)
#             abline(v=OM@L50,col=colline,lwd=lwdline)
#
#           }
#
#         }else{ # not positive diagonal
#
#           plot(sim[,j],sim[,i],axes=F,col="white")
#           polygon(bounds[c(1,1,2,2),j],bounds[c(1,2,2,1),i],col=colline,border="white")
#           points(sim[,j],sim[,i],pch=19)
#
#           axis(1)
#           axis(2)
#
#         }
#
#       }
#     }
#
#     mtext("Sampled parameters and cross-correlations",3,font=2,outer=T)
#
#   } # if plot
#
#   names(sim)<-c("M","K","L50","Linf")
#
#   OM@cpars$M<-sim$M
#   OM@cpars$K<-sim$K
#   OM@cpars$L50<-sim$L50
#   OM@cpars$Linf<-sim$Linf
#
#   OM
#
# }
#

#' Predict missing life-history parameters
#'
#' Predict missing life-history based on taxonomic information and
#' hierarchical model fitted to FishBase life-history parameters
#'
#' @describeIn LH2OM Predict missing life-history and populate `OM@cpars`
#'
#' @param OM An object of class 'OM'
#' @param dist Character. Should parameters be sampled from a uniform (`unif`) or
#' normal (`norm`) distribution?
#' @param filterMK Logical or numeric specifying percentiles. See Details. 
#' e.g. `OM@M` and `OM@K`. Empty slots or slots with all values of 0 are considered unknown.
#' @param plot Logical. Should the plot be produced?
#' @param Class Optional higher order taxonomic information
#' @param Order Optional higher order taxonomic information
#' @param Family Optional higher order taxonomic information
#' @param msg Logical. Should messages be printed?
#'
# #' @templateVar url generating-correlated-life-history-parameters
# #' @templateVar ref NULL
# #' @template userguide_link
#'
#' @return LH2OM: An OM with `OM@cpars` populated with `OM@nsim` samples of M, K, Linf and L50
#' @author A. Hordyk
#' @references Thorson, J. T., S. B. Munch, J. M. Cope, and J. Gao. 2017.
#' Predicting life history parameters for all fishes worldwide. Ecological Applications. 27(8): 2262--2276
#' @source \url{https://github.com/James-Thorson-NOAA/FishLife}
#' 
#' @details 
#' ## filterMK
#' If filterMK is logical: Should the predicted M and K parameters be filtered within the range specified in `inpars`or `OM`?
#' 
#' Otherwise, filterMK must be numeric vector of length(2) specifying lower and upper percentiles that will be applied 
#' to the predicted M or K values
#' 
#' @export
LH2OM <- function(OM, dist=c("unif", "norm"), filterMK=FALSE, plot=TRUE,
                  Class = "predictive", Order = "predictive",
                  Family = "predictive", msg=TRUE, db=MSEtool::LHdatabase) {
  if (!methods::is(OM, 'OM')) stop("OM must be class 'OM'")
  dist <- match.arg(dist)
  set.seed(OM@seed)
  if (length(OM@nsim)<1) OM@nsim <- 48
  if (length(OM@cpars)>0) {
    cnames <- names(OM@cpars)
    if (any(c("Linf", "L50", "M", "K") %in% cnames)) {
      message("Life-history parameters already in OM@cpars.\nReturning original OM")
      return(OM)
    }
  }

  sls <- c("Linf", "L50", "K", "M")
  for (sl in sls) {
    slval <- slot(OM, sl)
    if(any(is.na(slval)) | all(slval==0) | length(slval)<1) {
      assign(sl, NA)
    } else {
      assign(sl, slval)
    }
  }

  if (length(OM@M)>2) {
    message("Age-dependant M has been set in OM. (length(OM@M) > 2)\nReturning original OM")
    return(OM)
  }

  Genus <- unlist(strsplit(OM@Species, " "))[1]
  Species <- unlist(strsplit(OM@Species, " "))[2]
  if (is.na(Genus) || nchar(Genus)<1) Genus <- "predictive"
  if (is.na(Species) || nchar(Species)<1) Species <- "predictive"

  Out <- predictLH(inpars=list(Linf=Linf, L50=L50, K=K, M=M),
                   Genus, Species, nsamp=OM@nsim, db=db, dist=dist,
                   filterMK=filterMK, plot=plot, Class=Class, Order=Order, Family=Family, msg=msg)
  if (is.null(Out)) {
    message('Could not complete prediction. Returning original OM')
    return(OM)
  }
  OM@Linf <- c(0,0)
  OM@L50 <- c(0,0)
  OM@M <- c(0,0)
  OM@K <- c(0,0)

  # check for negative parameters
  if (any(Out$Linf <=0)) warning("Some Linf values are <= 0")
  if (any(Out$M <=0)) warning("Some M values are <= 0")
  if (any(Out$K <=0)) warning("Some K values are <= 0")
  if (any(Out$L50 <=0)) warning("Some L50 values are <= 0")

  OM@cpars$Linf <- Out$Linf
  OM@cpars$M <- Out$M
  OM@cpars$K <- Out$K
  OM@cpars$L50 <- Out$L50
  OM
}

#' @describeIn LH2OM Predict missing life-history based on taxonomic information and
#' hierarchical model fitted to FishBase life-history parameters
#' @param inpars A named list with lower and upper bounds of provided parameters: *Linf*, *L50*,
#' *K* and *M* (must be length 2). Unknown or missing parameters should not be included. For example, an empty list assumes
#' that all four life history parameters are unknown and need to be estimated. See Details below for more information.
#' @param Genus Character string specifying the Genus name. Optional. Default is 'predictive'
#' @param Species Character string specifying the Species name. Optional. Default is 'predictive'. If full species name
#' (Genus + Species) is not found if FishLife database (based on FishBase) higher order taxonomy will be used (e.g., Family)
#' for the predictions.
#' @param nsamp The number of samples to return
#' @param db Database from FishLife model with fitted model results
#'
#' @details The model predicts missing life-history parameters based on provided parameters and taxonomic information.
#' If both *M* and *K* are provided in `inpars` or  `OM`, *K* values are predicted and predictions filtered
#' so that resulting *K* values are within bounds specified in `inpars$K` or `OM@K` (see `filterMK`).
#'
#' If both *Linf* and *L50* are provided in `inpars` or `OM`, *L50* values are predicted and values in `inpars$L50`
#' or `OM@L50` are ignored.
#'
#' @return predictLH: A data.frame with `nsamp` rows with `Linf`, `L50`, `K`, and `M` values.
#' @export
#'
predictLH <- function(inpars=list(), Genus="predictive", Species="predictive", nsamp=100,
                      db=MSEtool::LHdatabase, dist=c("unif", "norm"),
                      filterMK=TRUE, plot=TRUE, Class = "predictive", Order = "predictive",
                      Family = "predictive", msg=TRUE) {

  if (!requireNamespace("MASS", quietly = TRUE)) {
    stop("Package \"MASS\" needed for this function to work. Please install it.",
         call. = FALSE)
  }

  dist <- match.arg(dist)

  inpars_1 <- inpars
  # checks
  names <- names(inpars)
  lens <- lapply(inpars, length) == 2
  valnames <- c("Linf", "L50",  "K",  "M")
  if (!prod(names %in% valnames)) stop("invalid names in inpars. Valid names are: ", paste0(valnames, " "))

  notnames <- !valnames %in% names

  chkNA <- as.logical(unlist(lapply(lapply(inpars_1, is.na), prod)))
  notnames[chkNA] <- TRUE

  inpars_1[valnames[!valnames %in% names]] <- NA

  # names(inpars_1) <- valnames
  if (any(notnames)) {
    for(x in seq_along(valnames)) {
      if (notnames[x]) {
        nm <- valnames[x]
        inpars[[nm]] <- NA
        if (msg) message("Predicting ", nm)
      }
    }
  }

  if (prod(valnames[1:2] %in% names)) {
    if (lens['Linf']) {
      if (prod(valnames[1:2] %in% names)) {
        if (lens['Linf']) {
          if (msg) message("Predicting L50 from Linf")
          lens['L50'] <- FALSE
          inpars$L50 <- NA
        }
        if (lens['L50']) {
          if (msg) message("Predicting Linf from L50")
          lens['Linf'] <- FALSE
          inpars$Linf <- NA
        }
      }
      lens['L50'] <- FALSE
      inpars$L50 <- NA
    }
    if (lens['L50']) {
      if (msg) message("Predicting Linf from L50")
      lens['Linf'] <- FALSE
      inpars$Linf <- NA
    }
  }

  if (prod(valnames[3:4] %in% names)) {
    if (lens['M']) {
      if (msg) message("Predicting K from M")
      lens['K'] <- FALSE
      inpars$K <- NA
    }
    if (lens['K']) {
      if (msg) message("Predicting M from K")
      lens['M'] <- FALSE
      inpars$M <- NA
    }
  }
  multi <- 100
  if (is.logical(filterMK)) {
    filter <- 'OM'
    # filter M or K by bounds specified in OM
    filterM <- filterK <- FALSE
    if (prod(c("K", "M") %in% names) & filterMK & !(all(is.na(inpars_1$K)) || all(is.na(inpars_1$M)))) {
      if (all(is.na(inpars$M))) {
        filterM <- TRUE
        if (msg) message_info("Filtering predicted M within bounds:", paste0(inpars_1$M, collapse = '-'))
      }
      if (all(is.na(inpars$K))) {
        filterK <- TRUE
        if (msg) message_info("Filtering predicted K within bounds:", paste0(inpars_1$K, collapse = '-'))
      }
      multi <- 500
    }
  }
  if (is.numeric(filterMK)) {
    # filter by percentile range
    filter <- 'perc'
    if (length(filterMK) !=2) 
      stop('filterMK must be numeric values of length 2 (lower and upper percentiles) OR logical')
    if (all(is.na(inpars$M))) {
      filterM <- TRUE
      if (msg) message_info("Filtering predicted M within percentiles:", paste0(filterMK, collapse = '-'))
    }
    if (all(is.na(inpars$K))) {
      filterK <- TRUE
      if (msg) message_info("Filtering predicted K within percentiles:", paste0(filterMK, collapse = '-'))
    }
    multi <- 500
  }


  # get predictions from FishLife
  taxa <- gettaxa(Class, Order, Family, Genus, Species, msg=msg)
  if (is.null(taxa)) return(NULL)
  if (!methods::is(db, "list")) stop("db must be database list from FishLife", call.=FALSE)
  Which <- grep(taxa, db$ParentChild_gz[,'ChildName'])
  mu <- db$ParHat$beta_gj[Which,]
  covar <- db$Cov_gjj[Which,,]
  names(mu) <- gsub("Loo", "Linf", names(mu))
  names(mu) <- gsub("Lm", "L50", names(mu))

  sampvals <- exp(as.data.frame(MASS::mvrnorm(nsamp*multi, mu, covar))) %>% dplyr::select(Linf, K, M, L50)
  sampvals$relLm <- sampvals$L50/sampvals$Linf
  sampvals$MK <- sampvals$M/sampvals$K

  outpars <- inpars
  # sample input parameters

  if (dist=="unif") {
    for (x in names(outpars)) {
      if(!all(is.na(outpars[[x]]))) outpars[[x]] <- myrunif(nsamp*multi, min(outpars[[x]]), max(outpars[[x]]))
    }
  } else {
    for (x in names(outpars)) {
      if(!all(is.na(outpars[[x]]))) {
        varsd <- (max(outpars[[x]]) - mean(outpars[[x]]))/2
        varmean <- mean(outpars[[x]])
        outpars[[x]] <- rnorm(nsamp*multi, varmean, varsd)
      }
    }
  }

  # generate predicted parameters
  missing <- lapply(lapply(outpars, is.na), prod) ==1
  missnm <- names(outpars)[missing]
  for (x in missnm) {
    if (x == "L50") {
      outpars$L50 <- outpars$Linf * sampvals$relLm
    }
    if (x == "Linf") {
      outpars$Linf <- outpars$L50 / sampvals$relLm
    }
    if (x == "M") {
      outpars$M <- outpars$K * sampvals$MK
    }
    if (x == "K") {
      outpars$K <- outpars$M / sampvals$MK
    }
  }


  # Still missing
  missing <- lapply(lapply(outpars, is.na), prod) ==1
  missnm <- names(outpars)[missing]
  for (x in missnm) {
    outpars[[x]] <- sampvals[[x]]
  }

  Out <- as.data.frame(do.call("cbind", outpars))

  # drop any samples where L50 > 0.95 Linf
  ind <- Out$L50 > 0.95*Out$Linf
  Out <- Out[!ind,]

  if (filter=='OM') {
    if(filterK) {
      ind <- Out$K > min(inpars_1$K) & Out$K < max(inpars_1$K)
      if (sum(ind)<2) {
        warning('No samples of K within bounds: ', paste(as.character(inpars_1$K), collapse=" "),
                "\nIgnoring bounds on K")
      } else {
        Out <- Out[ind,]
      }
      
    }
    if (filterM) {
      ind <- Out$M > min(inpars_1$M) & Out$M < max(inpars_1$M)
      if (sum(ind)<2) {
        warning('No samples of M within bounds: ', paste(as.character(inpars_1$M), collapse=" "),
                "\nIgnoring bounds on M")
      } else {
        Out <- Out[ind,]
      }
    }
  }
  if (filter=='perc') {
    if(filterK) {
      ind <- Out$K > quantile(Out$K, filterMK[1]) & Out$K < quantile(Out$K, filterMK[2])
      Out <- Out[ind,]
    }
    if (filterM) {
      ind <- Out$M > quantile(Out$M, filterMK[1]) & Out$M < quantile(Out$M, filterMK[2])
      Out <- Out[ind,]
    }
  }


  if(nrow(Out) < nsamp) {
    warning("Could not generate ", nsamp, ' samples within specified bounds. Sampling with replacement')
    rows <- sample(1:nrow(Out), nsamp, replace=TRUE)
    Out <- Out[rows,]
  } else {
    Out <- Out[1:nsamp,]
  }

  Out <- Out %>% dplyr::select(valnames)

  if(plot){
    op <- par(no.readonly = TRUE)
    on.exit(par(op))
    par(mfrow=c(4,4),mai=c(0.3,0.3,0.4,0.05),omi=c(0.02,0.02,0.3,0.02))

    colline=MSEtool::makeTransparent('blue',60)
    lwdline=4
    histcol='black'
    if (length(inpars_1)>1) {
      dobounds <- TRUE
      bounds<-matrix(NA,nrow=2, ncol=4)
      count <- 0
      for (nm in valnames) {
        count <- count + 1
        tempval <- inpars_1[[nm]]
        if (length(tempval)>0) bounds[,count] <- range(tempval)
      }
    } else {
      dobounds <- FALSE
    }


    for(i in 1:4){
      if (length(inpars_1[[valnames[i]]])>1) {
        rng <- range(inpars_1[[valnames[i]]])
      } else {
        rng <- c(NA, NA)
      }

      rng2 <- range(Out[,i])
      # options(warn=-1)
      if (!all(is.na(rng))) {
        rng2[1] <- min(c(min(rng, na.rm=TRUE), min(rng2, na.rm=TRUE)), na.rm=TRUE)
        rng2[2] <- max(c(max(rng, na.rm=TRUE), max(rng2, na.rm=TRUE)), na.rm=TRUE)
      }

      # options(warn=0)

      for(j in 1:4){
        if(i == j){
          if(i==1){
            hist(Out[,1],main="Asymptotic length (Linf)",col=histcol,border='white',xlab="",axes=F, xlim=rng2, ylab="")
            axis(1)
            abline(v=rng,col=colline,lwd=lwdline)
          }else if(i==2){
            hist(Out[,2],main="Length at 50% maturity (L50)",col=histcol,border='white',xlab="",axes=F, xlim=rng2, ylab="")
            axis(1)
            abline(v=rng,col=colline,lwd=lwdline)
          }else if(i==3){
            hist(Out[,3],main="Growth rate (K)",col=histcol,border='white',xlab="",axes=F, xlim=rng2, ylab="")
            axis(1)
            abline(v=rng,col=colline,lwd=lwdline)
          }else{
            hist(Out[,4],main="Natural mortality rate (M)",col=histcol,border='white',xlab="",axes=F, xlim=rng2, ylab="")
            axis(1)
            abline(v=rng,col=colline,lwd=lwdline)
          }
        }else{ # not positive diagonal

          plot(Out[,j],Out[,i],axes=F,col="white", xlab="", ylab="")
          if (dobounds) polygon(bounds[c(1,1,2,2),j],bounds[c(1,2,2,1),i],col=colline,border="white")
          points(Out[,j],Out[,i],pch=19)

          axis(1)
          axis(2)
        }

      }
    }
  }
  Out
}

# modified from FishLife::Search_species
# https://github.com/James-Thorson/FishLife
gettaxa <- function(Class = "predictive", Order = "predictive",
                    Family = "predictive", Genus="predictive", Species="predictive",
                    ParentChild_gz=MSEtool::LHdatabase$ParentChild_gz,
                    msg=TRUE) {

  # if (!requireNamespace("rfishbase", quietly = TRUE)) {
  #   stop("Package \"rfishbase\" needed for this function to work. Please install it.",
  #        call. = FALSE)
  # }
  # 
  # if (msg)
  #   message_info('Loading FishBase database')
  # # Taxa_Table <- suppressMessages(rfishbase::load_taxa())
  Taxa_Table <-MSEtool::Taxa_Table
  Species2 <- strsplit(Taxa_Table$Species, " ")
  
  Match = 1:nrow(Taxa_Table)
  if (Class != "predictive")
    Match = Match[which(tolower(Taxa_Table$Class[Match]) == tolower(Class))]
  if (Order != "predictive")
    Match = Match[which(tolower(Taxa_Table$Order[Match]) == tolower(Order))]
  if (Family != "predictive")
    Match = Match[which(tolower(Taxa_Table$Family[Match]) == tolower(Family))]
  if (Genus != "predictive")
    Match = Match[which(tolower(Taxa_Table$Genus[Match]) == tolower(Genus))]
  if (Species != "predictive") {
    Species2 <- paste(Genus, Species)
    Match = Match[which(tolower(Taxa_Table$Species[Match]) == tolower(Species2))]
  }
  
  full_taxonomy <- c(Class, Order, Family, Genus, Species)
  spIn <- trimws(paste(gsub("predictive", "", full_taxonomy), collapse=" "))
  if (length(Match) == 0) {
    if(msg) message(spIn, ' not found in FishBase database')
    Class <- Order <- Family <- Genus <- Species <- "predictive"
  }
  full_taxonomy <- c(Class, Order, Family, Genus, Species)

  if (!all(Species == "predictive")) {
    if (length(unique(Taxa_Table$Species[Match])) != 1)
      stop("inputs are not unique")
    if (length(unique(Taxa_Table$Species[Match])) == 1) {
    tmp = unique(Taxa_Table$Species[Match])[1]
    full_taxonomy[5] <- strsplit(tmp, ' ')[[1]][2]
    }
  }
  if (!all(c(Species, Genus) == "predictive")) {
    if (length(unique(Taxa_Table$Genus[Match])) != 1)
      stop("inputs are not unique")
    if (length(unique(Taxa_Table$Genus[Match])) == 1)
      full_taxonomy[4] = unique(Taxa_Table$Genus[Match])[1]
  }
  if (!all(c(Species, Genus, Family) == "predictive")) {
    if (length(unique(Taxa_Table$Family[Match])) != 1)
      stop("inputs are not unique")
    if (length(unique(Taxa_Table$Family[Match])) == 1)
      full_taxonomy[3] = unique(Taxa_Table$Family[Match])[1]
  }
  if (!all(c(Species, Genus, Family, Order) == "predictive")) {
    if (length(unique(Taxa_Table$Order[Match])) != 1)
      stop("inputs are not unique")
    if (length(unique(Taxa_Table$Order[Match])) == 1)
      full_taxonomy[2] = unique(Taxa_Table$Order[Match])[1]
  }
  if (!all(c(Species, Genus, Family, Order, Class) == "predictive")) {
    if (length(unique(Taxa_Table$Class[Match])) != 1)
      stop("inputs are not unique")
    if (length(unique(Taxa_Table$Class[Match])) == 1)
      full_taxonomy[1] = unique(Taxa_Table$Class[Match])[1]
  }
  match_taxonomy = full_taxonomy
  
  fam_gen_sp <- tolower(paste(match_taxonomy[3:5], collapse = '_'))
  nm_ind <- which(grepl(fam_gen_sp, tolower(ParentChild_gz$ChildName)))
  if (length(nm_ind)==0) {
    # species not found in FishLife
    temp <- strsplit(fam_gen_sp,'_')[[1]]
    temp[3] <- 'predictive'
    fam_gen_sp <- paste0(temp, collapse="_")
    nm_ind <- which(grepl(fam_gen_sp, tolower(ParentChild_gz$ChildName)))
  }
  
  if (length(nm_ind)==0) {
    # family & species not found in FishLife
    temp <- strsplit(fam_gen_sp,'_')[[1]]
    temp[2] <- 'predictive'
    fam_gen_sp <- paste0(temp, collapse="_")
    nm_ind <- which(grepl(fam_gen_sp, tolower(ParentChild_gz$ChildName)))
  }
  
  fullname <- gsub("_", " ", ParentChild_gz$ChildName[nm_ind])
  if (length(fullname)>1)
    fullname <- fullname[length(fullname)]
  
  ind <- !grepl("predictive", strsplit(fullname, " ")[[1]])
  if (all(!ind)) {
    if (msg) message_info("Predicting from all species in FishBase")
  } else if (any(!ind)) {
    if (msg) message_info("Closest match: ", fullname)
  } else {
    if (msg) message_info("Species match: ", fullname)
  }
  match_taxonomy = unique(as.character(Add_predictive(ParentChild_gz$ChildName[nm_ind])))
  if (length(match_taxonomy)>1)
    match_taxonomy <- match_taxonomy[length(match_taxonomy)]
  match_taxonomy
}

# from FishLife::Search_species
# https://github.com/James-Thorson/FishLife
Add_predictive = function(char_vec) {
  return_vec = char_vec
  for (i in 1:length(return_vec)) {
    vec = strsplit(as.character(return_vec[i]), "_")[[1]]
    return_vec[i] = paste(c(vec, rep("predictive", 5 - length(vec))), collapse = "_")
  }
  return(return_vec)
}




#' Check OM object is complete
#'
#' @param OM An object of class `OM` 
#' @param msg Logical. Display messages?
#' @param stop_if_missing Logical. Stop with error is values are missing and there is no default?
#'
#' @return The OM object with default values (if needed)
#' @export
#'
#' @examples
#' testOM <- CheckOM(testOM)
CheckOM <- function(OM, msg=TRUE, stop_if_missing=TRUE) {
  if (msg)
    message_info('Checking OM for completeness')
  
  nms <- slotNames('OM')
  not_needed <-c("Name", "Agency", "Region", "Sponsor", "Latitude", 'Longitude', 'cpars',
                 'Source', 'Common_Name', 'Species', 'Misc')
  nms <- nms[!nms%in%not_needed]
  
  for (slot in nms) {
    OM <- checkSlot(slot, OM, msg=msg, stop_if_missing=stop_if_missing) 
  }
  OM
}


checkSlot <- function(slot, OM, msg=TRUE, stop_if_missing=TRUE) {
  
  # additional
  Var <- NULL # CRAN check hack
  df2 <- data.frame(Var=c('nsim', 'interval', 'proyears', 'reps', 'pstar',
                          'maxF', 'seed'),
                    Dim=NA,
                    Desc=NA,
                    Type=NA,
                    DimOM=c(1,1, 1,1, 1,1,1),
                    Default=c('48', '1', '50', '1', '0.5','0.8', '101'),
                    Comment=NA)
  df <- dplyr::bind_rows(cpars_info, df2)
  df <- dplyr::filter(df, Var==slot)
  if (nrow(df)>1) df <- df[1,]
  
  val <- methods::slot(OM, slot)
  if(length(val)==0) {
    # slot is missing value - check cpars
    cpars_val <- OM@cpars[[slot]]
    if (!is.null(cpars_val)) {
      # values in cpars
      methods::slot(OM, slot) <- range(cpars_val)
    } else {
      # missing - check for default
      OM <- checkDefault(OM, slot, df, msg, stop_if_missing)  
    }
    
  }
  # check length 
  val <- methods::slot(OM, slot)
  
  if (length(val)==0) return(OM) # will have stopped with error unless stop_if_missing=FALSE
  
  if (!is.na(df$DimOM)) {
    if (length(val) != df$DimOM) { # check for correct length
      if (length(val)==1 & df$DimOM==2) {
        if (msg) 
          message_info(
            'slot', slot, 'has only one value', paste0('(', val, '). Using this value for both lower and upper bounds.')
          )
        methods::slot(OM, slot) <- rep(val, 2)
      }
      if (length(val)==2 & df$DimOM==1) {
        if (msg) 
          message_info(
            'slot', slot, 'has two values, but only one required. Using first value', paste0('(', val[1], ').')
          )
        methods::slot(OM, slot) <- val
      }
      if (length(val)>2 & df$DimOM<=2) {
        n <- df$DimOM
        if (msg) 
          message_info(
            'slot', slot, 'has more than', n, 'values. Using first ', n, 'value(s)', paste0('(', val[1:n], ').')
          )
        methods::slot(OM, slot) <- val[1:n]
      }
    }
  }
  
  # check for NAs
  if (all(is.na(val))) {
    # slot is missing value - check cpars
    cpars_val <- OM@cpars[[slot]]
    if (!is.null(cpars_val)) {
      # values in cpars
      methods::slot(OM, slot) <- range(cpars_val)
    } else {
      # missing - check for default
      OM <- checkDefault(OM, slot, df, msg, stop_if_missing)  
    }
  }
  
  if (any(is.na(val))) {
    # is it a biascv slot?
    if (grepl('biascv',slot)) {
      # always length 1
      methods::slot(OM, slot) <- val[1]
    } else {
      # otherwise - repeat the same value 
      methods::slot(OM, slot) <- val[!is.na(val)]
    }
  }
    
  
  OM
}


checkDefault <- function(OM,slot, df, msg=TRUE, stop_if_missing=TRUE) {
  if (nrow(df)>1) df <- df[1,]
  default <- df$Default
  if (length(default)<1) default <- NA
  
  if (nrow(df)==0 | is.na(default)) {
    
    # parameter calculated internally
    growth_pars <- c('Linf', 'K', 't0')
    if (slot %in% growth_pars & !is.null(OM@cpars$Len_age)) {
      if (msg) {
        message_info(
        'slot', slot, 'is missing value(s). Will be calculated from `OM@cpars$Len_age`'
        )
      }
      
    } else {
      # no default
      if (stop_if_missing)
        stop('Slot ', slot, ' is missing required value(s)', call.=FALSE)
      
    }
    
  } else if (grepl('`', default)) {
    # it's code
    code <- gsub('`', '', default)
    methods::slot(OM, slot) <- as.numeric(eval(parse(text=code)))
    if (msg) 
      message_info(
        'slot', slot, 'is missing value(s). Using default value of:', methods::slot(OM, slot)
      )
  } else if (grepl('TRUE', default) | grepl('FALSE', default)) {
    # it's a logical
    methods::slot(OM, slot) <- as.logical(default)
  } else {
    # it's numeric
    default <- as.numeric(default)
    methods::slot(OM, slot) <- rep(default, df$DimOM)
    if (msg) 
      message_info(
        'slot', slot, 'is missing value(s). Using default value of:', default
      )
  }
  OM
}