R/Fill365Date.R

In rSQM: Statistical Downscaling Toolkit for Climate Change Scenario using Non Parametric Quantile Mapping

Fill365Date <- function(df, sdate) {

  sdate <- as.Date(sdate)
  #edate <- as.Date(edate)
  syear <- as.numeric (format(sdate,"%Y"))
  nrow <- length(df[,1])
  eyear <- syear+ nrow/365 - 1
  #  as.numeric (format(edate,"%Y"))
  nyear <- eyear - syear + 1

  year <- c(syear:eyear)
  years <- sapply(year, function (x) rep(x,365))
  years <- as.vector(years)
  years <- as.data.frame(years)
  colnames(years) <- "year"

  mons <- rep(c(rep(1,31), rep(2,28), rep(3,31), rep(4,30), rep(5,31), rep(6,30), rep(7,31), rep(8,31), rep(9,30), rep(10,31), rep(11,30), rep(12,31)),nyear)
  #mons <- as.vector(mon)
  mons <- as.data.frame(mons)
  colnames(mons) <- "month"

  days <- rep(c(c(1:31),c(1:28),c(1:31),c(1:30),c(1:31),c(1:30),c(1:31),c(1:31),c(1:30),c(1:31),c(1:30),c(1:31)),nyear)
  days <- as.data.frame(days)
  colnames(days) <- "day"

  datestr <- paste(years[[1]],mons[[1]],days[[1]],sep="-")

  date <- as.Date(datestr,"%Y-%m-%d")

  srow <- which(date[]==sdate)
  nrow <- length(df[,1])
  erow <- srow + nrow -1

  date <- date[srow:erow]

  df <- cbind.data.frame(date, df)

  df[,1] <- as.Date(df[,1],"%Y-%m-%d")

  df <- df[!is.na(df[,1]),]

  return(df)

}