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#' Calculate WOA profile in parallel
#'
#' Calculate Depth-temperature profile based likelihood
#'
#' \code{calc.woa.par} calculates likelihood of animal position based on
#' summarized depth-temperature profiles
#'
#' Tag-based depth-temperature profile summaries are compared to climatological
#' profiles from the World Ocean Atlas (WOA) "matched" to generate position
#' likelihoods. This essentially attempts to estimate animal position based on
#' the water mass it is in, particularly if extensive diving performs thorough
#' sampling of the environment. However, remember the in situ data is being
#' compared to climatological means or the results of an oceanographic model.
#'
#' @param pdt input PDT data output from \code{\link{read.wc}} and
#' \code{\link{extract.pdt}}
#' @param ptt is unique tag identifier
#' @param woa.data is (typically) a list of monthly global 1/4deg climatology
#' data from WOA13. See \code{\link{get.env}}.
#' @param focalDim is integer for dimensions of raster::focal used to calculate
#' sd() of temperature grid cell. Recommend focalDim = 3 if woa.data = woa.one
#' and 9 if using woa.quarter.
#' @param dateVec is vector of dates from tag to pop-up in 1 day increments.
#' @param sp.lim is list of limits as \code{list(xmin, xmax, ymin, ymax)}
#' @param use.se is logical indicating whether or not to use SE when using
#' regression to predict temperature at specific depth levels.
#'
#' @export
#' @return raster brick of likelihood
#' @importFrom foreach "%dopar%"
#' @seealso \code{\link{calc.ohc}}
calc.woa <- function(pdt, ptt, woa.data = NULL, dateVec, sp.lim = NULL, focalDim = NULL, use.se = TRUE){
options(warn=-1)
t0 <- Sys.time()
print(paste('Starting WOA likelihood calculation...'))
if(is.null(woa.data)){
stop('Error: data must be specified')
}
if (is.null(focalDim)){
stop('Error: focalDim must be specified.')
}
if (!is.null(sp.lim)){
lon.idx <- c(which.min(abs(woa.data$lon - sp.lim[[1]])):
which.min(abs(woa.data$lon - sp.lim[[2]])))
lat.idx <- c(which.min(abs(woa.data$lat - sp.lim[[3]])):
which.min(abs(woa.data$lat - sp.lim[[4]])))
woa.data[[1]] <- woa.data[[1]][lon.idx, lat.idx,,]
woa.data$lon <- woa.data$lon[lon.idx]
woa.data$lat <- woa.data$lat[lat.idx]
}
depth <- c(0, seq(2.5, 97.5, by=5), seq(112.5, 487.5, by=25), seq(525, 1475, by=50))
# get unique time points
dateVec = lubridate::parse_date_time(dateVec, '%Y-%m-%d')
udates <- unique(lubridate::parse_date_time(pdt$Date, orders = '%Y-%m-%d %H%:%M:%S'))
T <- length(udates)
pdt$MidTemp <- (pdt$MaxTemp + pdt$MinTemp) / 2
print(paste0('Generating WOA profile likelihood for ', udates[1], ' through ', udates[length(udates)]))
L.prof <- array(0, dim = c(length(woa.data$lon), length(woa.data$lat), length(dateVec)))
for (i in 1:T){
# define time based on tag data
time <- as.Date(udates[i])
pdt.i <- pdt[which(pdt$Date == time), ]
print(time)
#extracts depth from tag data for day i
y <- pdt.i$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$Depth)]
# use the which.min
depIdx = apply(as.data.frame(pdt.i$Depth), 1, FUN = function(x) which.min((x - depth) ^ 2))
# 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(depth[depIdx])
pred.low <- stats::predict(fit.low, newdata = depth[depIdx], se = T, get.data = T)
pred.high <- stats::predict(fit.high, newdata = depth[depIdx], 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 = depth[depIdx])
} 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 = depth[depIdx])
}
pdtMonth <-
as.numeric(format(as.Date(pdt.i$Date), format = '%m'))[1]
wdat = woa.data[[1]]
dat.i = wdat[, , , pdtMonth] #extract months climatology
# calculate sd using Le Bris neighbor method and focal()
sd.i = array(NA, dim = c(dim(dat.i)[1:2], length(depIdx)))
t1 <- Sys.time()
for(ii in 1:length(depIdx)){
r = raster::flip(raster::raster(t(dat.i[,,depIdx[ii]])), 2)
f1 = raster::focal(r, w = matrix(1, nrow = focalDim, ncol = focalDim), fun = function(x) stats::sd(x, na.rm = T))
f1 = t(raster::as.matrix(raster::flip(f1, 2)))
sd.i[,,ii] = f1
}
# make index of dates for filling in lik.prof
didx <- base::match(udates, dateVec)
# setup the likelihood array for each day. Will have length (dim[3]) = n depths
lik.pdt = array(NA, dim = c(dim(dat.i)[1], dim(dat.i)[2], length(depIdx)))
for (b in 1:length(depIdx)) {
#calculate the likelihood for each depth level, b
lik.try <- try(likint3(dat.i[,,depIdx[b]], sd.i[,,b], df[b, 1], df[b, 2]), TRUE)
if(class(lik.try) == 'try-error' & use.se == FALSE){
df[b,1] <- pred.low$fit[b] - pred.low$se.fit[b] * sqrt(n)
df[b,2] <- pred.high$fit[b] - pred.high$se.fit[b] * sqrt(n)
lik.try <- try(likint3(dat.i[,,depIdx[b]], sd.i[,,b], df[b, 1], df[b, 2]), TRUE)
if (class(lik.try) == 'try-error'){
lik.try <- dat.i[,,depIdx[b]] * 0
warning(paste('Warning: likint3 failed after trying with and without SE prediction of depth-temp profiles. This is most likely a divergent integral for ', time, '...', sep=''))
}
} else if (class(lik.try) == 'try-error' & use.se == TRUE){
lik.try <- dat.i[,,depIdx[b]] * 0
warning(paste('Warning: likint3 failed after trying with and without SE prediction of depth-temp profiles. This is most likely a divergent integral for ', time, '...', sep=''))
}
lik.pdt[,,b] <- lik.try
}
lik.pdt0 <- lik.pdt
lik.pdt0[is.na(lik.pdt0)] <- 0
use.idx <- unique(which(lik.pdt0 != 0, arr.ind=T)[,3])
lik.pdt[lik.pdt == 1] <- NA
# multiply likelihood across depth levels for each day
lik.pdt <- apply(lik.pdt[,,use.idx], 1:2, FUN=function(x) prod(x, na.rm=F))
# identify date index and add completed likelihood to L.pdt array
idx <- which(dateVec == as.Date(time))
L.prof[,,idx] = (lik.pdt / max(lik.pdt, na.rm=T)) - 0.2
}
print(paste('Making final likelihood raster...'))
crs <- "+proj=longlat +datum=WGS84 +ellps=WGS84"
L.ras <- raster::brick(L.prof, xmn = min(woa.data$lon), xmx = max(woa.data$lon), ymn = min(woa.data$lat), ymx = max(woa.data$lat), transpose = T, crs)
L.ras <- raster::flip(L.ras, direction = 'y')
t1 <- Sys.time()
print(paste('WOA calculations took ', round(as.numeric(difftime(t1, t0, units='mins')), 2), 'minutes...'))
options(warn = 2)
return(L.ras)
}
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