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#' Hycom Profile LIkelihood
#'
#' Calculate Hycom profile likelihood surface
#'
#' @param pdt input PDT data output from \code{\link{read.wc}} and
#' \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 hycom.dir directory of downloaded hycom (or other) data
#' @param focalDim is integer for dimensions of raster::focal used to calculate
#' sd() of temperature grid cell. Recommend focalDim = 9 for Hycom data at
#' 0.08deg resolution.
#' @param dateVec vector of complete dates (from tag to pop by day) for data
#' range. This should be in 'Date' format
#' @param use.se is logical indicating whether or not to use SE when using
#' regression to predict temperature at specific depth levels.
#'
#' @return a raster brick of Hycom profile likelihood
#' @export
#' @seealso \code{\link{calc.hycom.par}}
#' @importFrom foreach %dopar%
#'
calc.hycom <- function(pdt, filename, hycom.dir, focalDim = 9, dateVec, use.se = TRUE){
options(warn=-1)
t0 <- Sys.time()
print(paste('Starting Hycom profile likelihood calculation...'))
# 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)
print(paste0('Generating profile likelihood for ', udates[1], ' through ', udates[length(udates)]))
# open nc and get the indices for the vars
nc1 = RNetCDF::open.nc(dir(hycom.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,]
# result will be array of likelihood surfaces
L.hycom <- array(0, dim = c(length(lon), length(lat), length(dateVec)))
print(paste('Starting iterations through deployment period ', '...'))
for(i in 1:T){
time <- as.Date(udates[i])
pdt.i <- pdt[which(pdt$Date == time),]
print(paste('Starting ', time,'...',sep=''))
# open day's hycom data
nc <- RNetCDF::open.nc(paste(hycom.dir, filename, '_', as.Date(time), '.nc', sep=''))
dat <- RNetCDF::var.get.nc(nc, temp.idx) * scale + offset
#extracts depth from tag data for day i
y <- pdt.i$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]
# 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)
}
# calculate sd using Le Bris neighbor method and focal()
sd.i = array(NA, dim = c(dim(dat)[1:2], length(depIdx)))
for(ii in 1:length(depIdx)){
r = raster::flip(raster::raster(t(dat[,,depIdx[ii]])), 2)
f1 = raster::focal(r, w = matrix(1, nrow = focalDim, ncol = focalDim), fun = function(x) stats::sd(x, na.rm = T))
f1 = t(raster::as.matrix(raster::flip(f1, 2)))
sd.i[,,ii] = f1
}
# make index of dates for filling in lik.prof
didx = base::match(udates, dateVec)
# setup the likelihood array for each day. Will have length (dim[3]) = n depths
lik.pdt = array(NA, dim = c(dim(dat)[1], dim(dat)[2], length(depIdx)))
for (b in 1:length(depIdx)) {
#calculate the likelihood for each depth level, b
lik.try <- try(likint3(dat[,,depIdx[b]], sd.i[,,b], df[b, 1], df[b, 2]), TRUE)
class.try <- class(lik.try)
if(!any(which(lik.try > 0))) class.try <- 'try-error'
if(class.try == 'try-error' & use.se == FALSE){
df[b,1] <- pred.low$fit[b] - pred.low$se.fit[b] * sqrt(n)
df[b,2] <- pred.high$fit[b] - pred.high$se.fit[b] * sqrt(n)
lik.try <- try(likint3(dat[,,depIdx[b]], sd.i[,,b], df[b, 1], df[b, 2]), TRUE)
class.try <- class(lik.try)
if(!any(which(lik.try > 0))) class.try <- 'try-error'
if (class.try == 'try-error'){
lik.try <- dat[,,depIdx[b]] * 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.try == 'try-error' & use.se == TRUE){
lik.try <- dat[,,depIdx[b]] * 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.pdt[,,b] <- lik.try
}
lik.pdt0 <- lik.pdt
lik.pdt0[is.na(lik.pdt0)] <- 0
use.idx <- unique(which(lik.pdt0 != 0, arr.ind=T)[,3])
# multiply likelihood across depth levels for each day
lik.pdt <- apply(lik.pdt[,,use.idx], 1:2, FUN=function(x) prod(x, na.rm=F))
if(i == 1){
# result will be array of likelihood surfaces
L.hycom <- array(0, dim = c(dim(lik.pdt), length(dateVec)))
}
idx <- which(dateVec == as.Date(time))
L.hycom[,,idx] = lik.pdt / max(lik.pdt, na.rm=T)
}
#parallel::stopCluster(cl)
# make index of dates for filling in L.hycom
#didx <- base::match(udates, dateVec)
# lapply to normalize
#lik.pdt <- lapply(ans, function(x) x / max(x, na.rm = T))
# fill in L.hycom from the list output
#ii = 1
#for(i in didx){
# L.hycom[,,i] = lik.pdt[[ii]]
# ii = ii+1
#}
print(paste('Making final likelihood raster...'))
crs <- "+proj=longlat +datum=WGS84 +ellps=WGS84"
L.hycom <- raster::brick(L.hycom, xmn=min(lon), xmx=max(lon), ymn=min(lat), ymx=max(lat), transpose=T, crs)
L.hycom <- raster::flip(L.hycom, direction = 'y')
#L.hycom[L.hycom < 0] <- 0
names(L.hycom) = as.character(dateVec)
t1 <- Sys.time()
print(paste('Hycom profile calculations took ', round(as.numeric(difftime(t1, t0, units='mins')), 2), 'minutes...'))
options(warn=2)
# return hycom likelihood surfaces
return(L.hycom)
}
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