name instead of year; ipynb generator debugged, with logging

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

diff --git a/pkg/R/getData.R b/pkg/R/getData.R
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+#' @title Acquire data in a clean format
+#'
+#' @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}).
+#'
+#' @param ts_data Time-series, as a data frame (DB style: 2 columns, first is date/time,
+#'   second is value) or a CSV file
+#' @param exo_data Exogenous variables, as a data frame or a CSV file; first comlumn is dates,
+#'   next block are measurements for the day, and final block are exogenous forecasts
+#' @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 \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"))
+#' 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)
+{
+       # 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)")
+       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)")
+       if (!is.character(date_format))
+               stop("Bad date_format (character)")
+       date_format = date_format[1]
+
+       ts_df =
+               if (is.character(ts_data))
+                       read.csv(ts_data)
+               else
+                       ts_data
+       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)
+
+       line = 1 #index in PM10 file (24 lines for 1 cell)
+       nb_lines = nrow(ts_df)
+       nb_exos = ( ncol(exo_df) - 1 ) / 2
+       data = Data$new()
+       i = 1 #index of a cell in data
+       while (line <= nb_lines)
+       {
+               time = c()
+               serie = c()
+               repeat
+               {
+                       {
+                               time = c(time, ts_df[line,1])
+                               serie = c(serie, ts_df[line,2])
+                               line = line + 1
+                       };
+                       if (line >= nb_lines + 1 || as.POSIXlt(ts_df[line-1,1])$hour == predict_at)
+                               break
+               }
+
+               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$append(time, centered_serie, level, exo, exo_hat)
+               if (i >= limit)
+                       break
+               i = i + 1
+       }
+       if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
+               data$removeFirst()
+       if (length(data$getCenteredSerie(data$getSize()))
+               < length(data$getCenteredSerie(data$getSize()-1)))
+       {
+               data$removeLast()
+       }
+       data
+}