X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FgetData.R;h=df94895af633b137f29c6fc1462a6724d178f97e;hb=25b75559e2d9bf84e2de35b851d93fefdae36e17;hp=153a660f35a56667342232331fdad2c8992b8f97;hpb=1e20780ee1505fac6c7ed68d340892c497524561;p=talweg.git

diff --git a/pkg/R/getData.R b/pkg/R/getData.R
index 153a660..df94895 100644
--- a/pkg/R/getData.R
+++ b/pkg/R/getData.R
@@ -2,8 +2,7 @@
 #'
 #' @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}). Current limitation: series (in working_tz) must start at
-#'   right after midnight (to keep in sync with exogenous vars)
+#'   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
@@ -14,9 +13,21 @@
 #'   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 An object of class Data
+#' @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)
@@ -26,10 +37,13 @@ getData = function(ts_data, exo_data, input_tz="GMT", date_format="%d/%m/%Y %H:%
 		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))
-		stop("Bad time-series input (data frame or CSV file)")
+	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)")
@@ -38,17 +52,15 @@ getData = function(ts_data, exo_data, input_tz="GMT", date_format="%d/%m/%Y %H:%
 	date_format = date_format[1]
 
 	ts_df =
-		if (is.character(ts_data)) {
+		if (is.character(ts_data))
 			read.csv(ts_data)
-		} else {
+		else
 			ts_data
-		}
 	exo_df =
-		if (is.character(exo_data)) {
+		if (is.character(exo_data))
 			read.csv(exo_data)
-		} else {
+		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)
@@ -56,7 +68,7 @@ getData = function(ts_data, exo_data, input_tz="GMT", date_format="%d/%m/%Y %H:%
 	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() #new("Data")
+	data = list()
 	i = 1 #index of a cell in data
 	while (line <= nb_lines)
 	{
@@ -73,18 +85,26 @@ getData = function(ts_data, exo_data, input_tz="GMT", date_format="%d/%m/%Y %H:%
 				break
 		}
 
-		# NOTE: if predict_at does not cut days at midnight, exogenous vars need to be shifted
-		exo_hat = as.data.frame( exo_df[
-			ifelse(predict_at>0,max(1,i-1),i) , (1+nb_exos+1):(1+2*nb_exos) ] )
-		exo = as.data.frame( exo_df[ ifelse(predict_at>0,max(1,i-1),i) , 2:(1+nb_exos) ] )
+		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$append(time, centered_serie, level, exo_hat, exo_Jm1) #too slow; TODO: use R6 class
-		data[[length(data)+1]] = list("time"=time, "serie"=centered_serie, "level"=level,
-			"exo_hat"=exo_hat, "exo"=exo)
+		data[[i]] = list("time"=time, "centered_serie"=centered_serie, "level"=level,
+			"exo"=exo, "exo_hat"=exo_hat)
 		if (i >= limit)
 			break
 		i = i + 1
 	}
-	new("Data",data=data)
+	start = 1
+	end = length(data)
+	if (length(data[[1]]$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
 }