Nothing
bin_TempTS <- function(ts, res=8, verbose=FALSE){
sm0 <- plyr::ddply(ts,c("date"),function(x)c(nrecTempTS=nrow(x[which(!is.na(x$Temperature) & !is.na(x$Depth)),]))) ## check if dates missing!
if(any(sm0$nrecTempTS == 0)) warning('no Temperature/DepthTS available for dates:\n',paste(sm0$date[which(sm0$nrecTempTS == 0)],collapse=", "))
if(!is.null(ts$Corrected.Depth)){
warning('Corrected.Depth column found. This data will be used instead!')
ts$Depth <- ts$Corrected.Depth
}
ts <- ts[which(!is.na(ts$Temperature) & !is.na(ts$Depth)),]
sm <- plyr::ddply(ts,c('date'),function(x)c(nrecTempTS=nrow(x)))
dates <- as.character(sm$date)
fdates <- c()
pdt.rec <- c()
d <- dates[1]
for(d in dates){
if(verbose) cat(paste(d,"\n"))
i <- which(ts$date == d)
x <- ts[i,]
###################################################" round bin time series data in res m intervals
depth.range <- range(x$Depth)
depth <- x$Depth/res
ii <- which((x$Depth%%res)/res >= 0.5)
depth[ii] <- ceiling(depth[ii])*res
ii <- which((x$Depth%%res)/res < 0.5)
depth[ii] <- trunc(depth[ii])*res
x$Depth <- depth
unique_depths <- unique(depth)
if(length(unique_depths) > 2){
h <- hist(depth,breaks = unique_depths,plot=F)
identifiers <- c('DeployID','Serial','Ptt')
identifiers <- identifiers[which(identifiers %in% names(ts))]
add <- plyr::ddply(x[,which(names(x) %in% c(identifiers,'date','Depth','Temperature'))],
c(identifiers,'date','Depth'), function(x)
c(nrecs=nrow(x),
MeanTemp=mean(round(x$Temp,2)),
MinTemp=min(round(x$Temp,2)),
MaxTemp=max(round(x$Temp,2))))
add$bin <- 1:nrow(add)
pdt.rec <- rbind(pdt.rec,add)
}
}
pdt.rec$MeanPDT <- rowMeans(cbind(pdt.rec$MaxTemp, pdt.rec$MinTemp))
pdt.rec$date <- as.Date(pdt.rec$date)
return(pdt.rec)
}
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