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## Do this in a separate tmp.R file to check the documentation
#library(devtools)
#document()
#load_all(as.package("../../onlineforecast"))
#?make_tday
#' Make an hour-of-day forecast matrix
#'
#' This function creates a data.frame with k-steps-ahead values of hour of day,
#' such that it can be added to a data.list and used inputs to a forecast model.
#'
#' @param time vector of times of class "POSIXct" "POSIXt".
#' @param kseq vector of integers, representing the desired "k-steps ahead".
#' @param tstep step time of k in seconds.
#' @return Returns a forecast matrix (data.frame) with rownames = times, colnames = k1, k2, k3, ...
#' The content of the data frame is the hour of day.
#' @keywords hourofday lags data.frame
#' @seealso make_periodic
#' @examples
#' # Create a time sequence of 30 min sample period
#' tseq <- seq(ct("2019-01-01"), ct("2019-02-01 12:00"), by=1800)
#'
#' # Make the time of day sequence (assuming time between k steps is same as for tseq)
#' make_tday(tseq, 1:10)
#'
#' # With 0.5 hour steps, kstep in hours
#' make_tday(tseq, 1:10, tstep=3600)
#'
#'
#' @export
make_tday <- function(time, kseq, tstep=NA){
# If tstep not given, then take it from time assuming same k-step is the sampling period
if(is.na(tstep)){
tstep <- as.numeric(time[2] - time[1], units="secs")
}
# The time of day (in the specified units)
tday <- sapply(kseq, function(k){
tk <- time + k * tstep
as.numeric( tk - trunc(tk, units="days"), units="hours")
})
# set row and column names
nams(tday) <- paste0('k', kseq)
return( as.data.frame(tday) )
}
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