X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FgetData.R;h=9ed021714c45175f40f12fae62300e06e3519abe;hb=7f90df63d1ac659d450a180b50b994a76d5fc7b9;hp=8d1a6faa9c29bcae9cb27fc9fbc20158a240abb6;hpb=44a9990b6d608ffcd2e99d5193fa8b9e6cbdb436;p=talweg.git

diff --git a/pkg/R/getData.R b/pkg/R/getData.R
index 8d1a6fa..9ed0217 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
@@ -14,13 +13,14 @@
 #'   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
 #'
 #' @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"))
-#' getData(ts_data, exo_data, input_tz="Europe/Paris", working_tz="Europe/Paris", limit=150)
+#' 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)
@@ -49,19 +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() #new("Data")
+	data = Data$new()
 	i = 1 #index of a cell in data
 	while (line <= nb_lines)
 	{
@@ -78,18 +80,21 @@ 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$append(time, centered_serie, level, exo_hat, exo_Jm1) #too slow; TODO: use R6 class
-		data[[length(data)+1]] = list("time"=time, "serie"=centered_serie, "level"=level,
-			"exo_hat"=exo_hat, "exo"=exo)
+		data$append(time, centered_serie, level, exo, exo_hat)
 		if (i >= limit)
 			break
 		i = i + 1
 	}
-	new("Data",data=data)
+	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
 }