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diff --git a/pkg/R/getData.R b/pkg/R/getData.R
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-#' @title Acquire data in a clean format
-#'
-#' @description Take in input data frames and/or files containing raw data, and timezones, and
-#'   output a Data object, roughly corresponding to a list where each cell contains all value
-#'   for one day (see \code{?Data}).
-#'
-#' @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 comlumn is dates,
-#'   next block are measurements for the day, and final block are exogenous forecasts
-#' @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 \code{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 A list where data[[i]] contains
-#'   \itemize{
-#'     \item time: vector of times
-#'     \item centered_serie: centered serie
-#'     \item level: corresponding level
-#'     \item exo: exogenous variables
-#'     \item exo_hat: predicted exogenous variables
-#'   }
-#'
-#' @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
-	exo_df =
-		if (is.character(exo_data))
-			read.csv(exo_data)
-		else
-			exo_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=working_tz, usetz=TRUE)
-
-	line = 1 #index in PM10 file (24 lines for 1 cell)
-	nb_lines = nrow(ts_df)
-	nb_exos = ( ncol(exo_df) - 1 ) / 2
-	data = list()
-	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])$hour == predict_at)
-				break
-		}
-
-		exo = as.data.frame( exo_df[i,2:(1+nb_exos)] )
-		exo_hat = as.data.frame( exo_df[i,(1+nb_exos+1):(1+2*nb_exos)] )
-		level = mean(serie, na.rm=TRUE)
-		centered_serie = serie - level
-		data[[i]] = list("time"=time, "centered_serie"=centered_serie, "level"=level,
-			"exo"=exo, "exo_hat"=exo_hat)
-		if (i >= limit)
-			break
-		i = i + 1
-	}
-	start = 1
-	end = length(data)
-	if (length(data[[1]]$centered_serie) < length(data[[2]]$centered_serie))
-		start = 2
-	if (length(data[[length(data)]]$centered_serie) <
-		length(data[[length(data)-1]]$centered_serie))
-	{
-		end = end-1
-	}
-	if (start>1 || end<length(data))
-		data = data[start:end]
-	data
-}