X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FgetData.R;h=e13cf86cb8ae578e4bad37b65a9e5ca2ccdc40a0;hb=6deb700590d0000c4f8cce050990394f381ddecf;hp=df94895af633b137f29c6fc1462a6724d178f97e;hpb=25b75559e2d9bf84e2de35b851d93fefdae36e17;p=talweg.git

diff --git a/pkg/R/getData.R b/pkg/R/getData.R
index df94895..e13cf86 100644
--- a/pkg/R/getData.R
+++ b/pkg/R/getData.R
@@ -15,14 +15,7 @@
 #' @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
-#'   }
+#' @return An object of class Data
 #'
 #' @examples
 #' ts_data = read.csv(system.file("extdata","pm10_mesures_H_loc.csv",package="talweg"))
@@ -56,19 +49,21 @@ getData = function(ts_data, exo_data, input_tz="GMT", date_format="%d/%m/%Y %H:%
 			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
-	# 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)
+	# 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 = list()
+	data = Data$new()
 	i = 1 #index of a cell in data
 	while (line <= nb_lines)
 	{
@@ -81,7 +76,7 @@ getData = function(ts_data, exo_data, input_tz="GMT", date_format="%d/%m/%Y %H:%
 				serie = c(serie, ts_df[line,2])
 				line = line + 1
 			};
-			if (line >= nb_lines + 1 || as.POSIXlt(ts_df[line-1,1])$hour == predict_at)
+			if (line >= nb_lines + 1 || as.POSIXlt(ts_df[line-1,1],tz=working_tz)$hour == predict_at)
 				break
 		}
 
@@ -89,22 +84,17 @@ getData = function(ts_data, exo_data, input_tz="GMT", date_format="%d/%m/%Y %H:%
 		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)
+		data$append(time, centered_serie, level, exo, exo_hat)
 		if (i >= limit)
 			break
 		i = i + 1
 	}
-	start = 1
-	end = length(data)
-	if (length(data[[1]]$centered_serie) < length(data[[2]]$centered_serie))
-		start = 2
-	if (length(data[[length(data)]]$centered_serie) <
-		length(data[[length(data)-1]]$centered_serie))
+	if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
+		data$removeFirst()
+	if (length(data$getCenteredSerie(data$getSize()))
+		< length(data$getCenteredSerie(data$getSize()-1)))
 	{
-		end = end-1
+		data$removeLast()
 	}
-	if (start>1 || end<length(data))
-		data = data[start:end]
 	data
 }