intermediate: R6, too slow

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

diff --git a/pkg/R/getData.R b/pkg/R/getData.R
index 4f9e179..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
@@ -14,22 +13,37 @@
 #'   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)
+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))
-               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,
        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,
        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)
        {
@@ -73,26 +85,26 @@ getData = function(ts_data, exo_data,
                                break
                }
 
-##TODO: fix note comment ! --> triche: exo contient les mesures du jour, pas forcément toutes available
-               # NOTE: if predict_at does not cut days at midnight,
-               # for the exogenous to be synchronized they need to be shifted
-               if (predict_at > 0)
-               {
-                       exo_hat = as.data.frame(exo_df[max(1,i-1),(1+nb_exos+1):(1+2*nb_exos)])
-                       exo = as.data.frame(exo_df[max(1,i-1),2:(1+nb_exos)])
-               }
-               else
-               {
-                       exo_hat = as.data.frame(exo_df[i,(1+nb_exos+1):(1+2*nb_exos)])
-                       exo = as.data.frame(exo_df[i,2:(1+nb_exos)])
-               }
-               i = i + 1
-               #center data
+               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$append(time, centered_serie, level, exo_hat, exo_Jm1) #TODO: slow: why ?
-               data[[length(data)+1]] = list("time"=time, "serie"=centered_serie, "level"=level,
-                       "exo_hat"=exo_hat, "exo"=exo)
+               data[[i]] = list("time"=time, "centered_serie"=centered_serie, "level"=level,
+                       "exo"=exo, "exo_hat"=exo_hat)
+               if (i >= limit)
+                       break
+               i = i + 1
+       }
+       start = 1
+       end = length(data)
+       if (length(data[[1]]$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
        }
-       new("Data",data=data)
+       if (start>1 || end<length(data))
+               data = data[start:end]
+       data
 }