after merg

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

diff --git a/pkg/R/getData.R b/pkg/R/getData.R
index a6401f9..f1f8861 100644 (file)
--- a/pkg/R/getData.R
+++ b/pkg/R/getData.R
@@ -10,11 +10,8 @@
 #' @param exo_data Exogenous variables, as a data frame or a CSV file; first column is
 #'   dates, next block are measurements for the day, and final block are exogenous
 #'   forecasts (for the same day).
-#' @param input_tz Timezone in the input files ("GMT" or e.g. "Europe/Paris")
 #' @param date_format How date/time are stored (e.g. year/month/day hour:minutes;
 #'   see ?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
@@ -24,14 +21,9 @@
 #' 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)
+getData = function(ts_data, exo_data, date_format="%d/%m/%Y %H:%M", 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)) ||
                        (!is.data.frame(exo_data) && !is.character(exo_data)) )
                stop("Bad time-series / exogenous input (data frame or CSV file)")
@@ -39,22 +31,20 @@ getData = function(ts_data, exo_data, input_tz="GMT", date_format="%d/%m/%Y %H:%
                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)")
        if (!is.character(date_format))
                stop("Bad date_format (character)")
        date_format = date_format[1]
+       if (!is.numeric(limit) || limit < 0)
+               stop("limit: positive integer")
 
        ts_df =
                if (is.character(ts_data))
                        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)
+       # Convert to GMT (pretend it's GMT; no impact)
+       dates_POSIXlt = strptime(as.character(ts_df[,1]), date_format, tz="GMT")
+       ts_df[,1] = format(as.POSIXct(dates_POSIXlt, tz="GMT"), tz="GMT", usetz=TRUE)
 
        exo_df =
                if (is.character(exo_data))
@@ -80,29 +70,18 @@ getData = function(ts_data, exo_data, input_tz="GMT", date_format="%d/%m/%Y %H:%
                                line = line + 1
                        };
                        if (line >= nb_lines + 1
-                               || as.POSIXlt(ts_df[line-1,1],tz=working_tz)$hour == predict_at)
+                               || as.POSIXlt(ts_df[line-1,1],tz="GMT")$hour == 0)
                        {
                                break
                        }
                }
 
-               exo = as.data.frame( exo_df[i,2:(1+nb_exos)] )
-               exo_hat =
-                       if (i < nrow(exo_df))
-                               as.data.frame( exo_df[i+1,(1+nb_exos+1):(1+2*nb_exos)] )
-                       else
-                               NA #exogenous prediction for next day are useless on last day
-               data$append(time, serie, exo, exo_hat)
+               # TODO: 2 modes, "operational" and "testing"; would need PM10 predictions
+               data$append(time=time, value=serie, level_hat=mean(serie,na.rm=TRUE),
+                       exo=exo_df[i,2:(1+nb_exos)], exo_hat=exo_df[i,(1+nb_exos+1):(1+2*nb_exos)])
                if (i >= limit)
                        break
                i = i + 1
        }
-       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
 }