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#' OHC Likelihood in Parallel
#'
#' Calculate Ocean Heat Content (OHC) likelihood surface in parallel
#'
#' @param pdt input PDT data see \code{\link{extract.pdt}}
#' @param filename is the first part of the filename specified to the download
#' function \code{\link{get.env}}. For example, if downloaded files were
#' specific to a particular dataset, you may want to identify that with a name
#' like 'tuna' or 'shark1'. This results in a downloaded filename of, for
#' example, 'tuna_date.nc'. This filename is required here so the calc
#' function knows where to get the env data.
#' @param isotherm default '' in which isotherm is calculated on the fly based
#' on daily tag data. Otherwise, numeric isotherm constraint can be specified
#' (e.g. 20 deg C).
#' @param ohc.dir directory of downloaded hycom (or other) data
#' @param dateVec vector of complete dates for data range. This should be in
#' 'Date' format
#' @param bathy is logical indicating whether or not a bathymetric mask should
#' be applied
#' @param use.se is logical indicating whether or not to use SE when using
#' regression to predict temperature at specific depth levels.
#' @param ncores specify number of cores, or leave blank and use whatever you
#' have!
#'
#' @return a raster brick of OHC likelihood
#' @seealso \code{\link{calc.ohc}}
#' @references Luo J, Ault JS, Shay LK, Hoolihan JP, Prince ED, Brown C a.,
#' Rooker JR (2015) Ocean Heat Content Reveals Secrets of Fish Migrations.
#' PLoS One 10:e0141101
#' @export
#' @importFrom foreach "%dopar%"
#'
calc.ohc.par <- function(pdt, filename, isotherm = '', ohc.dir, dateVec, bathy = TRUE, use.se = TRUE, ncores = NULL){
options(warn=1)
if (is.null(ncores)) ncores <- ceiling(parallel::detectCores() * .9)
if (is.na(ncores) | ncores < 0) ncores <- ceiling(as.numeric(system('nproc', intern=T)) * .9)
t0 <- Sys.time()
print(paste('Starting OHC likelihood calculation...'))
# constants for OHC calc
cp <- 3.993 # kJ/kg*C <- heat capacity of seawater
rho <- 1025 # kg/m3 <- assumed density of seawater
# calculate midpoint of tag-based min/max temps
pdt$MidTemp <- (pdt$MaxTemp + pdt$MinTemp) / 2
# get unique time points
dateVec = lubridate::parse_date_time(dateVec, '%Y-%m-%d')
udates <- unique(lubridate::parse_date_time(pdt$Date, orders = '%Y-%m-%d %H%:%M:%S'))
T <- length(udates)
if(isotherm != '') iso.def <- TRUE else iso.def <- FALSE
print(paste0('Generating OHC likelihood for ', udates[1], ' through ', udates[length(udates)]))
# open nc and get the indices for the vars
nc1 = RNetCDF::open.nc(dir(ohc.dir, full.names = T)[1])
ncnames = NULL
nmax <- RNetCDF::file.inq.nc(nc1)$nvars - 1
for(ii in 0:nmax) ncnames[ii + 1] <- RNetCDF::var.inq.nc(nc1, ii)$name
temp.idx <- grep('temp', ncnames, ignore.case=TRUE) - 1
lat.idx <- grep('lat', ncnames, ignore.case=TRUE) - 1
lon.idx <- grep('lon', ncnames, ignore.case=TRUE) - 1
dep.idx <- grep('dep', ncnames, ignore.case=TRUE) - 1
# get attributes, if they exist
ncatts <- NULL
nmax <- RNetCDF::var.inq.nc(nc1, temp.idx)$natts - 1
for(ii in 0:nmax) ncatts[ii + 1] <- RNetCDF::att.inq.nc(nc1, temp.idx, ii)$name
scale.idx <- grep('scale', ncatts, ignore.case=TRUE) - 1
if(length(scale.idx) != 0){
scale <- RNetCDF::att.get.nc(nc1, temp.idx, attribute=scale.idx)
} else{
scale <- 1
}
off.idx <- grep('off', ncatts, ignore.case=TRUE) - 1
if(length(off.idx) != 0){
offset <- RNetCDF::att.get.nc(nc1, temp.idx, attribute=off.idx)
} else{
offset <- 1
}
# get and check the vars
depth <- RNetCDF::var.get.nc(nc1, dep.idx)
lon <- RNetCDF::var.get.nc(nc1, lon.idx)
if(length(dim(lon)) == 2) lon <- lon[,1]
if(!any(lon < 180)) lon <- lon - 360
lat <- RNetCDF::var.get.nc(nc1, lat.idx)
if(length(dim(lat)) == 2) lat <- lat[1,]
# results will be array of likelihood surfaces
L.ohc <- array(0, dim = c(length(lon), length(lat), length(dateVec)))
start.t <- Sys.time()
# BEGIN PARALLEL STUFF
print('Processing in parallel... ')
cl = parallel::makeCluster(ncores)
doParallel::registerDoParallel(cl, cores = ncores)
ans = foreach::foreach(i = 1:T) %dopar%{
time <- as.Date(udates[i])
pdt.i <- pdt[which(pdt$Date == time),]
# open day's hycom data
nc <- RNetCDF::open.nc(paste(ohc.dir, filename, '_', as.Date(time), '.nc', sep=''))
dat <- RNetCDF::var.get.nc(nc, temp.idx) * scale + offset
#extracts depth from tag data for day i
y <- pdt.i$Depth[!is.na(pdt.i$Depth)]
y[y<0] <- 0
#extract temperature from tag data for day i
x <- pdt.i$MidTemp[!is.na(pdt.i$Depth)]
# use the which.min
depIdx = unique(apply(as.data.frame(pdt.i$Depth), 1, FUN=function(x) which.min((x - depth) ^ 2)))
hycomDep <- depth[depIdx]
if(bathy){
mask <- dat[,,max(depIdx)]
mask[is.na(mask)] <- NA
mask[!is.na(mask)] <- 1
for(bb in 1:length(depth)){
dat[,,bb] <- dat[,,bb] * mask
}
}
# make predictions based on the regression model earlier for the temperature at standard WOA depth levels for low and high temperature at that depth
suppressWarnings(
fit.low <- locfit::locfit(pdt.i$MinTemp ~ pdt.i$Depth)
)
suppressWarnings(
fit.high <- locfit::locfit(pdt.i$MaxTemp ~ pdt.i$Depth)
)
n = length(hycomDep)
#suppressWarnings(
pred.low = stats::predict(fit.low, newdata = hycomDep, se = T, get.data = T)
#suppressWarnings(
pred.high = stats::predict(fit.high, newdata = hycomDep, se = T, get.data = T)
if (use.se){
# data frame for next step
df = data.frame(low = pred.low$fit - pred.low$se.fit * sqrt(n),
high = pred.high$fit + pred.high$se.fit * sqrt(n),
depth = hycomDep)
} else{
# data frame for next step
df = data.frame(low = pred.low$fit,# - pred.low$se.fit * sqrt(n),
high = pred.high$fit,# + pred.high$se.fit * sqrt(n),
depth = hycomDep)
}
# isotherm is minimum temperature recorded for that time point
if(iso.def == FALSE) isotherm <- min(df$low, na.rm = T)
# perform tag data integration at limits of model fits
minT.ohc <- cp * rho * sum(df$low - isotherm, na.rm = T) / 10000
maxT.ohc <- cp * rho * sum(df$high - isotherm, na.rm = T) / 10000
# Perform hycom integration
#dat[dat < isotherm] <- NA
dat <- dat - isotherm
ohc <- cp * rho * apply(dat[,,depIdx], 1:2, sum, na.rm = T) / 10000
ohc[ohc == 0] <- NA
# calc sd of OHC
# focal calc on mean temp and write to sd var
r = raster::flip(raster::raster(t(ohc)), 2)
sdx = raster::focal(r, w = matrix(1, nrow = 9, ncol = 9),
fun = function(x) stats::sd(x, na.rm = T))
sdx = t(raster::as.matrix(raster::flip(sdx, 2)))
# compare hycom to that day's tag-based ohc
#lik.ohc <- likint3(ohc, sdx, minT.ohc, maxT.ohc)
lik.try <- try(likint3(ohc, sdx, minT.ohc, maxT.ohc), TRUE)
if(class(lik.try) == 'try-error' & use.se == FALSE){
# try ohc again with use.se = T
df = data.frame(low = pred.low$fit - pred.low$se.fit * sqrt(n),
high = pred.high$fit + pred.high$se.fit * sqrt(n),
depth = hycomDep)
minT.ohc <- cp * rho * sum(df$low - isotherm, na.rm = T) / 10000
maxT.ohc <- cp * rho * sum(df$high - isotherm, na.rm = T) / 10000
lik.try <- try(likint3(ohc, sdx, minT.ohc, maxT.ohc), TRUE)
if (class(lik.try) == 'try-error'){
lik.try <- ohc * 0
warning(paste('Warning: likint3 failed after trying with and without SE prediction of depth-temp profiles. This is most likely a divergent integral for ', time, '...', sep=''))
}
} else if (class(lik.try) == 'try-error' & use.se == TRUE){
lik.try <- ohc * 0
warning(paste('Warning: likint3 failed after trying with and without SE prediction of depth-temp profiles. This is most likely a divergent integral for ', time, '...', sep=''))
}
lik.ohc <- lik.try
}
parallel::stopCluster(cl)
# make index of dates for filling in L.ohc
didx = base::match(udates, dateVec)
# lapply to put parallel answers back together
lik.ohc = lapply(ans, function(x) x / max(x, na.rm = T))
ii = 1
for(i in didx){
L.ohc[,,i] = lik.ohc[[ii]]
ii = ii+1
}
print(paste('Making final likelihood raster...'))
crs <- "+proj=longlat +datum=WGS84 +ellps=WGS84"
if(!any(lon < 180)) lon <- lon - 360
list.ohc <- list(x = lon, y = lat, z = L.ohc)
ex <- raster::extent(list.ohc)
L.ohc <- raster::brick(list.ohc$z, xmn=ex[1], xmx=ex[2], ymn=ex[3], ymx=ex[4], transpose=T, crs)
L.ohc <- raster::flip(L.ohc, direction = 'y')
L.ohc[L.ohc < 0] <- 0
names(L.ohc) = as.character(dateVec)
t1 <- Sys.time()
print(paste('OHC calculations took ', round(as.numeric(difftime(t1, t0, units='mins')), 2), 'minutes...'))
# return ohc likelihood surfaces
return(L.ohc)
}
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