#' getData
#'
#' Acquire data as a Data object; see ?Data.
#'
#' Since series are given in columns (database format), this function builds series one
#' by one and incrementally grows a Data object which is finally returned.
#'
#' @param ts_data Time-series, as a data frame (DB style: 2 columns, first is date/time,
#'   second is value) or a CSV file.
#' @param exo_data Exogenous variables, as a data frame or a CSV file; first column is
#'   dates, next block are measurements for the day, and final block are exogenous
#'   forecasts (for the same day).
#' @param input_tz Timezone in the input files ("GMT" or e.g. "Europe/Paris")
#' @param date_format How date/time are stored (e.g. year/month/day hour:minutes;
#'   see ?strptime)
#' @param working_tz Timezone to work with ("GMT" or e.g. "Europe/Paris")
#' @param predict_at When does the prediction take place? Integer, in hours. Default: 0
#' @param limit Number of days to extract (default: Inf, for "all")
#'
#' @return An object of class Data
#'
#' @examples
#' ts_data = read.csv(system.file("extdata","pm10_mesures_H_loc.csv",package="talweg"))
#' exo_data = read.csv(system.file("extdata","meteo_extra_noNAs.csv",package="talweg"))
#' data = getData(ts_data, exo_data, limit=120)
#' @export
getData = function(ts_data, exo_data, input_tz="GMT", date_format="%d/%m/%Y %H:%M",
	working_tz="GMT", predict_at=0, limit=Inf)
{
	# Sanity checks (not full, but sufficient at this stage)
	if (!is.character(input_tz) || !is.character(working_tz))
		stop("Bad timezone (see ?timezone)")
	input_tz = input_tz[1]
	working_tz = working_tz[1]
	if ( (!is.data.frame(ts_data) && !is.character(ts_data)) ||
			(!is.data.frame(exo_data) && !is.character(exo_data)) )
		stop("Bad time-series / exogenous input (data frame or CSV file)")
	if (is.character(ts_data))
		ts_data = ts_data[1]
	if (is.character(exo_data))
		exo_data = exo_data[1]
	predict_at = as.integer(predict_at)[1]
	if (predict_at<0 || predict_at>23)
		stop("Bad predict_at (0-23)")
	if (!is.character(date_format))
		stop("Bad date_format (character)")
	date_format = date_format[1]

	ts_df =
		if (is.character(ts_data))
			read.csv(ts_data)
		else
			ts_data
	# Convert to the desired timezone (usually "GMT" or "Europe/Paris")
	formatted_dates_POSIXlt = strptime(as.character(ts_df[,1]), date_format, tz=input_tz)
	ts_df[,1] = format(
		as.POSIXct(formatted_dates_POSIXlt, tz=input_tz), tz=working_tz, usetz=TRUE)

	exo_df =
		if (is.character(exo_data))
			read.csv(exo_data)
		else
			exo_data
	# Times in exogenous variables file are ignored: no conversions required

	line = 1 #index in PM10 file (24 lines for 1 cell)
	nb_lines = nrow(ts_df)
	nb_exos = ( ncol(exo_df) - 1 ) / 2
	data = Data$new()
	i = 1 #index of a cell in data
	while (line <= nb_lines)
	{
		time = c()
		serie = c()
		repeat
		{
			{
				time = c(time, ts_df[line,1])
				serie = c(serie, ts_df[line,2])
				line = line + 1
			};
			if (line >= nb_lines + 1
				|| as.POSIXlt(ts_df[line-1,1],tz=working_tz)$hour == predict_at)
			{
				break
			}
		}

		exo = as.data.frame( exo_df[i,2:(1+nb_exos)] )
		exo_hat =
			if (i < nrow(exo_df))
				as.data.frame( exo_df[i+1,(1+nb_exos+1):(1+2*nb_exos)] )
			else
				NA #exogenous prediction for next day are useless on last day
		data$append(time, serie, exo, exo_hat)
		if (i >= limit)
			break
		i = i + 1
	}
	if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
		data$removeFirst()
	if (length(data$getCenteredSerie(data$getSize()))
		< length(data$getCenteredSerie(data$getSize()-1)))
	{
		data$removeLast()
	}
	data
}