R/SimulatedWorld_ROMS.R
In eeholmes/WRAP: WRAP Location Study
Defines functions
SimulateWorld_ROMS
Documented in
SimulateWorld_ROMS
#' Simulated World ROMS
#'
#' Function uses covariate data from ROMS to generate species distribution and abundance.
#'
#' The ROMS data are assumed to be in the working directory in a folder called 'Rasters_2d_monthly/gfdl/'. SST data are in a subfolder called 'sst_monthly'. If the ROMS data folder is different, you can pass that in via the 'dir' argument. However the ROMS data folder must have subfolders 'gfdl/sst_monthly' so that 'paste0(dir,"/gfdl/sst_monthly")' will find the SST ROMS data.
#' Download ROMS data from here: https://www.dropbox.com/sh/aaezimxwq3glwdy/AABHmZbmfjVJM7R4jcHCi4c9a?dl=0
#' Caution: this function uses the downscaled GCMs to simulate species distrubtion. It has slight differences to the other SimulateWorld_function that 'randomly' generates environmental data. Remember: 1980-2010 are not observed data (by design)
#'
#' @param PA_shape specifies how enviro suitability determines species presence-absence. takes values of "logistic" (SB original), "logistic_prev" (JS, reduces knife-edge), "linear" (JS, reduces knife edge, encourages more absences, also specifies prevalence and fits 'b')
#' @param abund_enviro specifies abundance if present, can be "lnorm_low" (SB original), "lnorm_high" (EW), or "poisson" (JS, increases abundance range)
#' @param covariates A vector with "sst" and/or "chla". One or both can be listed.
#' @param response.curve The response curve to use in \code{virtualspecies::generateSpFromFun()} in the \code{parameter} argument to generate the suitability surface.
#' @param convertToPA.options Values to pass to `virtualspecies::convertToPA()` call. Defaults are linear=list(a=NULL, b=NULL, species.prevalence=0.8), logistic_prev=list(beta = "random", alpha = -0.3, species.prevalence = 0.5), logistic=list(beta=0.5, alpha=-0.05,species.prevalence=NULL)). To change, pass in a list with all the values for your PA_shape, e.g. list(a=1, b=0, species.prevalence=NULL) could be passed in if PA_shape is linear.
#' @param dir (optional) The path to the directory where the folder 'gfdl' is.
#' @param roms.years The years for the ROMS data. The data files use the year and month. Default is 1980:2100.
#' @param n.samples The number of samples to take from the ROMS layers each year. Default is 400.
#' @param maxN max mean abundance at highest suitability
#' @param verbose FALSE means print minimal progress, TRUE means print verbose progress output
#'
#' @return Returns an object of class \code{\link[=OM_class]{OM}}, which is a list with "grid" and "meta". "meta" has all the information about the simulation including all the parameters passed into the function.
#'
#' @examples
#' \dontrun{
#' test <- SimulateWorld_ROMS(PA_shape="logistic", abund_enviro="lnorm_low")
#' head(test)
#' tail(test)
#' plot(aggregate(suitability~year, data=test, FUN=mean),type='b')
#' plot(aggregate(pres~year, data=test, FUN=sum),type='b')
#' plot(aggregate(abundance~year, data=test, FUN=sum),type='b')
#' plot(aggregate(sst~year, data=test, FUN=mean),type='b')
#' }
#'
#' @export
SimulateWorld_ROMS <- function(
PA_shape=c("logistic", "logistic_prev", "linear"),
abund_enviro=c("lnorm_low", "lnorm_high", "poisson"),
covariates=c("sst"),
response.curve = list(sst = c(fun="dnorm",mean=15,sd=4), chla=c(fun="dnorm",mean=1.6,sd=9)),
convertToPA.options=list(
linear=list(a=NULL, b=NULL,species.prevalence=0.8),
logistic_prev=list(beta="random", alpha=-0.3, species.prevalence=0.5),
logistic=list(beta=0.5, alpha=-0.05, species.prevalence=NULL)),
dir=file.path(here::here(),"Rasters_2d_monthly"),
roms.years=1980:2100,
n.samples=400,
maxN=50,
verbose=FALSE){
PA_shape <- match.arg(PA_shape)
abund_enviro <- match.arg(abund_enviro)
covariates <- match.arg(covariates, c("sst", "chla"), several.ok=TRUE)
if(!dir.exists(dir))
stop(paste("This function requires that dir points to the ROMSdata.\nCurrently dir is pointing to", dir, "\n"))
# save all the inputs for meta data
fun.args <- as.list(environment())
if(!all(covariates %in% c("sst", "chla"))) stop("Only sst and chla allowed in covariates.")
if(missing(convertToPA.options)) convertToPA.options <- convertToPA.options[[PA_shape]]
# Use file.path not paste0 to create platform specific file paths
sst_dr <- file.path(dir, "gfdl", "sst_monthly")
chl_dr <- file.path(dir, "gfdl", "chl_surface")
# Record the seed used for simulation
sim.seed <- .Random.seed
# number of ROMS years
n.year <- length(roms.years)
#----Create output file----
output <- as.data.frame(matrix(NA, nrow=n.samples*n.year, ncol=5+length(covariates)))
colnames(output) <- c("lon","lat","year","pres","suitability", covariates)
#----Load in rasters----
# sst_dr and chla_dr was created at top
if("sst" %in% covariates) files_sst <- list.files(sst_dr, full.names = TRUE, pattern=".grd") #should be 1452 files
if("chla" %in% covariates) files_chl <- list.files(chl_dr, full.names = TRUE, pattern=".grd")
months <- rep(1:12, n.year)
years <- rep(roms.years, each=12)
august_indexes <- which(months==8) #picking last month in summer
#loop through each year
for (y in august_indexes){
if(verbose) print(paste0("Creating environmental simulation for Year ",years[y]))
if("sst" %in% covariates) sst <- raster::raster(files_sst[y])
if("chla" %in% covariates){
chla <- raster::raster(files_chl[y])
chla <- log(chla)
}
#plot(sst)
#----Use Virtual Species to assign response curve----
if(identical(covariates, "sst")){
envir_stack <- raster::stack(sst) #must be in raster stack format
names(envir_stack) <- c('sst')
}
if(identical(covariates, "chla")){
envir_stack <- raster::stack(chla) #must be in raster stack format
names(envir_stack) <- c('chla')
}
if(length(covariates)==2 && "sst" %in% covariates && "chla" %in% covariates){
envir_stack <- raster::stack(sst, chla)
names(envir_stack) <- c('sst', 'chla')
}
#----assign response curve
if(identical(covariates, "sst")){
parameters <- virtualspecies::formatFunctions(
sst = response.curve$sst)
ref_max <- stats::dnorm(parameters$sst$args[1], mean=parameters$sst$args[1], sd=parameters$sst$args[2])
#JS/BM: potential maximum suitability based on optimum temperature
}
if(identical(covariates, "chla")){
parameters <- virtualspecies::formatFunctions(
chla = response.curve$chla)
ref_max <- stats::dnorm(parameters$chla$args[1], mean=parameters$chla$args[1], sd=parameters$chla$args[2])
}
if(length(covariates)==2 && "sst" %in% covariates && "chla" %in% covariates){
parameters <- virtualspecies::formatFunctions(
sst = response.curve$sst,
chla = response.curve$chla)
ref_max_sst <- stats::dnorm(parameters$sst$args[1], mean=parameters$sst$args[1], sd=parameters$sst$args[2])
ref_max_chl <- stats::dnorm(parameters$chla$args[1], mean=parameters$chla$args[1], sd=parameters$chla$args[2])
ref_max <- ref_max_sst * ref_max_chl #simple multiplication of layers.
}
#----convert temperature raster to species suitability----
envirosuitability <- virtualspecies::generateSpFromFun(
envir_stack,
parameters=parameters,
rescale = FALSE,
rescale.each.response = FALSE)
#rescale
envirosuitability$suitab.raster <- (1/ref_max)*envirosuitability$suitab.raster
#JS/BM: rescaling suitability, so the max suitbaility is only when optimum temp is encountered
#Plot suitability and response curve
# plot(envirosuitability$suitab.raster) #plot habitat suitability
# virtualspecies::plotResponse(envirosuitability) #plot response curves
#----Convert suitability to Presence-Absence----
if (PA_shape == "logistic") {
#SB: specifies alpha and beta of logistic - creates almost knife-edge absence -> presence
suitability_PA <- virtualspecies::convertToPA(
envirosuitability,
PA.method = "probability",
beta = convertToPA.options$beta,
alpha = convertToPA.options$alpha,
species.prevalence = convertToPA.options$species.prevalence,
plot = FALSE)
# plotSuitabilityToProba(suitability_PA) #Let's you plot the shape of conversion function
}
if (PA_shape == "logistic_prev") {
#JS: relaxes logistic a little bit, by specifing reduced prevalence and fitting beta (test diff prevalence values, but 0.5 seems realistic)
suitability_PA <- virtualspecies::convertToPA(
envirosuitability,
PA.method = "probability",
beta = convertToPA.options$beta,
alpha = convertToPA.options$alpha,
species.prevalence = convertToPA.options$species.prevalence,
plot = FALSE)
# plotSuitabilityToProba(suitability_PA) #Let's you plot the shape of conversion function
}
if (PA_shape == "linear") {
#JS: relaxes knife-edge absence -> presence further; also specifies prevalence and fits 'b'
suitability_PA <- virtualspecies::convertToPA(
envirosuitability,
PA.method = "probability",
prob.method = "linear",
a = convertToPA.options$a,
b = convertToPA.options$b,
species.prevalence = convertToPA.options$species.prevalence,
plot = FALSE)
# plotSuitabilityToProba(suitability_PA) #Let's you plot the shape of conversion function
}
# plot(suitability_PA$pa.raster)
#-----Sample Presences and Absences-----
presence.points <- virtualspecies::sampleOccurrences(
suitability_PA,
n = n.samples,
type = "presence-absence",
detection.probability = 1,
error.probability=0,
plot = FALSE)
df <- cbind(as.data.frame(round(presence.points$sample.points$x,1)),as.data.frame(round(presence.points$sample.points$y,1)))
colnames(df) <- c("x","y")
#----Extract data for each year----
if(verbose) print("Extracting suitability")
ei <- n.samples*which(august_indexes==y) #end location
se <- ei - (n.samples-1) #start location
output$lat[se:ei] <- df$y
output$lon[se:ei] <- df$x
output$year[se:ei] <- rep(years[y], n.samples)
output$pres[se:ei] <- presence.points$sample.points$Real
output$suitability[se:ei] <- raster::extract(envirosuitability$suitab.raster, y= df) #extract points from suitability file
if("sst" %in% covariates) output$sst[se:ei] <- raster::extract(sst, y= df)
if("chla" %in% covariates) output$chla[se:ei] <- raster::extract(chla, y= df)
}
#----Create abundance as a function of the environment----
if (abund_enviro == "lnorm_low") {
# SB: values in Ecography paper. I think initially they were based on flounder in EBS but not sure if I edited them
output$abundance <- ifelse(output$pres==1, stats::rlnorm(n.samples*n.year,2,0.1)*output$suitability,0)
}
if (abund_enviro == "lnorm_high") {
# EW: I'm cranking up the rlnorm parameters to make it more comparable to wc trawl survey estimates -- these new ones based on arrowtooth
# SB: rlnorm parameters (6,1) were too large for estimation model. GAMs had explained deviance <10%. Other suggestions?
output$abundance <- ifelse(output$pres==1, stats::rlnorm(n.samples*n.year,6,1)*output$suitability,0)
}
if (abund_enviro == "poisson") {
# JS: sample from a Poisson distbn, with suitability proportional to mean (slower, bc it re-samples distbn for each observation)
#maxN is mean abundance at highest suitability
output$abundance <- ifelse(output$pres==1, stats::rpois(n.samples*n.year, lambda=output$suitability*maxN),0)
}
# meta data is auto generated. You shouldn't need to edit
covgrid <- get(covariates[1])
meta=list(
version= utils::packageVersion("WRAP"),
func=deparse(as.list(match.call())[[1]]),
call=deparse( sys.call() ),
sim.seed=sim.seed,
grid.dimensions=c(attr(covgrid, "nrows"), attr(covgrid, "ncols"), attr(covgrid, "nrows")*attr(covgrid, "ncols")),
grid.resolution=c((attr(covgrid, "extent")@xmax-attr(covgrid, "extent")@xmin)/attr(covgrid, "ncols"), (attr(covgrid, "extent")@ymax-attr(covgrid, "extent")@ymin)/attr(covgrid, "nrows")),
grid.extent=c(attr(covgrid, "extent")@xmin, attr(covgrid, "extent")@xmax, attr(covgrid, "extent")@ymin, attr(covgrid, "extent")@ymax),
grid.crs=attr(covgrid, "crs"),
grid.unit="degree",
time=c(min(output$year), max(output$year)),
time.unit="year"
)
meta <- c(meta, fun.args) #add the function arguments
meta$dir <- c(sst_dr, chl_dr)
obj <- list(meta=meta, grid=output)
class(obj) <- "OM"
return(obj)
}
eeholmes/WRAP documentation built on Feb. 18, 2021, 10:51 a.m.
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Defines functions SimulateWorld_ROMS
Documented in SimulateWorld_ROMS
#' Simulated World ROMS
#'
#' Function uses covariate data from ROMS to generate species distribution and abundance.
#'
#' The ROMS data are assumed to be in the working directory in a folder called 'Rasters_2d_monthly/gfdl/'. SST data are in a subfolder called 'sst_monthly'. If the ROMS data folder is different, you can pass that in via the 'dir' argument. However the ROMS data folder must have subfolders 'gfdl/sst_monthly' so that 'paste0(dir,"/gfdl/sst_monthly")' will find the SST ROMS data.
#' Download ROMS data from here: https://www.dropbox.com/sh/aaezimxwq3glwdy/AABHmZbmfjVJM7R4jcHCi4c9a?dl=0
#' Caution: this function uses the downscaled GCMs to simulate species distrubtion. It has slight differences to the other SimulateWorld_function that 'randomly' generates environmental data. Remember: 1980-2010 are not observed data (by design)
#'
#' @param PA_shape specifies how enviro suitability determines species presence-absence. takes values of "logistic" (SB original), "logistic_prev" (JS, reduces knife-edge), "linear" (JS, reduces knife edge, encourages more absences, also specifies prevalence and fits 'b')
#' @param abund_enviro specifies abundance if present, can be "lnorm_low" (SB original), "lnorm_high" (EW), or "poisson" (JS, increases abundance range)
#' @param covariates A vector with "sst" and/or "chla". One or both can be listed.
#' @param response.curve The response curve to use in \code{virtualspecies::generateSpFromFun()} in the \code{parameter} argument to generate the suitability surface.
#' @param convertToPA.options Values to pass to `virtualspecies::convertToPA()` call. Defaults are linear=list(a=NULL, b=NULL, species.prevalence=0.8), logistic_prev=list(beta = "random", alpha = -0.3, species.prevalence = 0.5), logistic=list(beta=0.5, alpha=-0.05,species.prevalence=NULL)). To change, pass in a list with all the values for your PA_shape, e.g. list(a=1, b=0, species.prevalence=NULL) could be passed in if PA_shape is linear.
#' @param dir (optional) The path to the directory where the folder 'gfdl' is.
#' @param roms.years The years for the ROMS data. The data files use the year and month. Default is 1980:2100.
#' @param n.samples The number of samples to take from the ROMS layers each year. Default is 400.
#' @param maxN max mean abundance at highest suitability
#' @param verbose FALSE means print minimal progress, TRUE means print verbose progress output
#'
#' @return Returns an object of class \code{\link[=OM_class]{OM}}, which is a list with "grid" and "meta". "meta" has all the information about the simulation including all the parameters passed into the function.
#'
#' @examples
#' \dontrun{
#' test <- SimulateWorld_ROMS(PA_shape="logistic", abund_enviro="lnorm_low")
#' head(test)
#' tail(test)
#' plot(aggregate(suitability~year, data=test, FUN=mean),type='b')
#' plot(aggregate(pres~year, data=test, FUN=sum),type='b')
#' plot(aggregate(abundance~year, data=test, FUN=sum),type='b')
#' plot(aggregate(sst~year, data=test, FUN=mean),type='b')
#' }
#'
#' @export
SimulateWorld_ROMS <- function(
PA_shape=c("logistic", "logistic_prev", "linear"),
abund_enviro=c("lnorm_low", "lnorm_high", "poisson"),
covariates=c("sst"),
response.curve = list(sst = c(fun="dnorm",mean=15,sd=4), chla=c(fun="dnorm",mean=1.6,sd=9)),
convertToPA.options=list(
linear=list(a=NULL, b=NULL,species.prevalence=0.8),
logistic_prev=list(beta="random", alpha=-0.3, species.prevalence=0.5),
logistic=list(beta=0.5, alpha=-0.05, species.prevalence=NULL)),
dir=file.path(here::here(),"Rasters_2d_monthly"),
roms.years=1980:2100,
n.samples=400,
maxN=50,
verbose=FALSE){
PA_shape <- match.arg(PA_shape)
abund_enviro <- match.arg(abund_enviro)
covariates <- match.arg(covariates, c("sst", "chla"), several.ok=TRUE)
if(!dir.exists(dir))
stop(paste("This function requires that dir points to the ROMSdata.\nCurrently dir is pointing to", dir, "\n"))
# save all the inputs for meta data
fun.args <- as.list(environment())
if(!all(covariates %in% c("sst", "chla"))) stop("Only sst and chla allowed in covariates.")
if(missing(convertToPA.options)) convertToPA.options <- convertToPA.options[[PA_shape]]
# Use file.path not paste0 to create platform specific file paths
sst_dr <- file.path(dir, "gfdl", "sst_monthly")
chl_dr <- file.path(dir, "gfdl", "chl_surface")
# Record the seed used for simulation
sim.seed <- .Random.seed
# number of ROMS years
n.year <- length(roms.years)
#----Create output file----
output <- as.data.frame(matrix(NA, nrow=n.samples*n.year, ncol=5+length(covariates)))
colnames(output) <- c("lon","lat","year","pres","suitability", covariates)
#----Load in rasters----
# sst_dr and chla_dr was created at top
if("sst" %in% covariates) files_sst <- list.files(sst_dr, full.names = TRUE, pattern=".grd") #should be 1452 files
if("chla" %in% covariates) files_chl <- list.files(chl_dr, full.names = TRUE, pattern=".grd")
months <- rep(1:12, n.year)
years <- rep(roms.years, each=12)
august_indexes <- which(months==8) #picking last month in summer
#loop through each year
for (y in august_indexes){
if(verbose) print(paste0("Creating environmental simulation for Year ",years[y]))
if("sst" %in% covariates) sst <- raster::raster(files_sst[y])
if("chla" %in% covariates){
chla <- raster::raster(files_chl[y])
chla <- log(chla)
}
#plot(sst)
#----Use Virtual Species to assign response curve----
if(identical(covariates, "sst")){
envir_stack <- raster::stack(sst) #must be in raster stack format
names(envir_stack) <- c('sst')
}
if(identical(covariates, "chla")){
envir_stack <- raster::stack(chla) #must be in raster stack format
names(envir_stack) <- c('chla')
}
if(length(covariates)==2 && "sst" %in% covariates && "chla" %in% covariates){
envir_stack <- raster::stack(sst, chla)
names(envir_stack) <- c('sst', 'chla')
}
#----assign response curve
if(identical(covariates, "sst")){
parameters <- virtualspecies::formatFunctions(
sst = response.curve$sst)
ref_max <- stats::dnorm(parameters$sst$args[1], mean=parameters$sst$args[1], sd=parameters$sst$args[2])
#JS/BM: potential maximum suitability based on optimum temperature
}
if(identical(covariates, "chla")){
parameters <- virtualspecies::formatFunctions(
chla = response.curve$chla)
ref_max <- stats::dnorm(parameters$chla$args[1], mean=parameters$chla$args[1], sd=parameters$chla$args[2])
}
if(length(covariates)==2 && "sst" %in% covariates && "chla" %in% covariates){
parameters <- virtualspecies::formatFunctions(
sst = response.curve$sst,
chla = response.curve$chla)
ref_max_sst <- stats::dnorm(parameters$sst$args[1], mean=parameters$sst$args[1], sd=parameters$sst$args[2])
ref_max_chl <- stats::dnorm(parameters$chla$args[1], mean=parameters$chla$args[1], sd=parameters$chla$args[2])
ref_max <- ref_max_sst * ref_max_chl #simple multiplication of layers.
}
#----convert temperature raster to species suitability----
envirosuitability <- virtualspecies::generateSpFromFun(
envir_stack,
parameters=parameters,
rescale = FALSE,
rescale.each.response = FALSE)
#rescale
envirosuitability$suitab.raster <- (1/ref_max)*envirosuitability$suitab.raster
#JS/BM: rescaling suitability, so the max suitbaility is only when optimum temp is encountered
#Plot suitability and response curve
# plot(envirosuitability$suitab.raster) #plot habitat suitability
# virtualspecies::plotResponse(envirosuitability) #plot response curves
#----Convert suitability to Presence-Absence----
if (PA_shape == "logistic") {
#SB: specifies alpha and beta of logistic - creates almost knife-edge absence -> presence
suitability_PA <- virtualspecies::convertToPA(
envirosuitability,
PA.method = "probability",
beta = convertToPA.options$beta,
alpha = convertToPA.options$alpha,
species.prevalence = convertToPA.options$species.prevalence,
plot = FALSE)
# plotSuitabilityToProba(suitability_PA) #Let's you plot the shape of conversion function
}
if (PA_shape == "logistic_prev") {
#JS: relaxes logistic a little bit, by specifing reduced prevalence and fitting beta (test diff prevalence values, but 0.5 seems realistic)
suitability_PA <- virtualspecies::convertToPA(
envirosuitability,
PA.method = "probability",
beta = convertToPA.options$beta,
alpha = convertToPA.options$alpha,
species.prevalence = convertToPA.options$species.prevalence,
plot = FALSE)
# plotSuitabilityToProba(suitability_PA) #Let's you plot the shape of conversion function
}
if (PA_shape == "linear") {
#JS: relaxes knife-edge absence -> presence further; also specifies prevalence and fits 'b'
suitability_PA <- virtualspecies::convertToPA(
envirosuitability,
PA.method = "probability",
prob.method = "linear",
a = convertToPA.options$a,
b = convertToPA.options$b,
species.prevalence = convertToPA.options$species.prevalence,
plot = FALSE)
# plotSuitabilityToProba(suitability_PA) #Let's you plot the shape of conversion function
}
# plot(suitability_PA$pa.raster)
#-----Sample Presences and Absences-----
presence.points <- virtualspecies::sampleOccurrences(
suitability_PA,
n = n.samples,
type = "presence-absence",
detection.probability = 1,
error.probability=0,
plot = FALSE)
df <- cbind(as.data.frame(round(presence.points$sample.points$x,1)),as.data.frame(round(presence.points$sample.points$y,1)))
colnames(df) <- c("x","y")
#----Extract data for each year----
if(verbose) print("Extracting suitability")
ei <- n.samples*which(august_indexes==y) #end location
se <- ei - (n.samples-1) #start location
output$lat[se:ei] <- df$y
output$lon[se:ei] <- df$x
output$year[se:ei] <- rep(years[y], n.samples)
output$pres[se:ei] <- presence.points$sample.points$Real
output$suitability[se:ei] <- raster::extract(envirosuitability$suitab.raster, y= df) #extract points from suitability file
if("sst" %in% covariates) output$sst[se:ei] <- raster::extract(sst, y= df)
if("chla" %in% covariates) output$chla[se:ei] <- raster::extract(chla, y= df)
}
#----Create abundance as a function of the environment----
if (abund_enviro == "lnorm_low") {
# SB: values in Ecography paper. I think initially they were based on flounder in EBS but not sure if I edited them
output$abundance <- ifelse(output$pres==1, stats::rlnorm(n.samples*n.year,2,0.1)*output$suitability,0)
}
if (abund_enviro == "lnorm_high") {
# EW: I'm cranking up the rlnorm parameters to make it more comparable to wc trawl survey estimates -- these new ones based on arrowtooth
# SB: rlnorm parameters (6,1) were too large for estimation model. GAMs had explained deviance <10%. Other suggestions?
output$abundance <- ifelse(output$pres==1, stats::rlnorm(n.samples*n.year,6,1)*output$suitability,0)
}
if (abund_enviro == "poisson") {
# JS: sample from a Poisson distbn, with suitability proportional to mean (slower, bc it re-samples distbn for each observation)
#maxN is mean abundance at highest suitability
output$abundance <- ifelse(output$pres==1, stats::rpois(n.samples*n.year, lambda=output$suitability*maxN),0)
}
# meta data is auto generated. You shouldn't need to edit
covgrid <- get(covariates[1])
meta=list(
version= utils::packageVersion("WRAP"),
func=deparse(as.list(match.call())[[1]]),
call=deparse( sys.call() ),
sim.seed=sim.seed,
grid.dimensions=c(attr(covgrid, "nrows"), attr(covgrid, "ncols"), attr(covgrid, "nrows")*attr(covgrid, "ncols")),
grid.resolution=c((attr(covgrid, "extent")@xmax-attr(covgrid, "extent")@xmin)/attr(covgrid, "ncols"), (attr(covgrid, "extent")@ymax-attr(covgrid, "extent")@ymin)/attr(covgrid, "nrows")),
grid.extent=c(attr(covgrid, "extent")@xmin, attr(covgrid, "extent")@xmax, attr(covgrid, "extent")@ymin, attr(covgrid, "extent")@ymax),
grid.crs=attr(covgrid, "crs"),
grid.unit="degree",
time=c(min(output$year), max(output$year)),
time.unit="year"
)
meta <- c(meta, fun.args) #add the function arguments
meta$dir <- c(sst_dr, chl_dr)
obj <- list(meta=meta, grid=output)
class(obj) <- "OM"
return(obj)
}
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