intermediate: R6, too slow

[talweg.git] / pkg / R / getData.R

diff --git a/pkg/R/getData.R b/pkg/R/getData.R
index 8d1a6fa..df94895 100644 (file)
--- 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 +13,21 @@
 #'   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"))
-#' 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)
@@ -61,7 +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)
        {
@@ -78,18 +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
 }