X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FgetData.R;h=b944dfb58dea49eb0a9b6830f56100e86c4a170a;hb=72b9c50162bcdcf6c99fbb8b2ec6ea9ba98379cb;hp=4f9e179a56378a858c148a76d95a0631484658dd;hpb=dea7ff860b42b3e246c8fa7ce2fb514561b8bc43;p=talweg.git diff --git a/pkg/R/getData.R b/pkg/R/getData.R index 4f9e179..b944dfb 100644 --- a/pkg/R/getData.R +++ b/pkg/R/getData.R @@ -1,35 +1,43 @@ #' @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}). Current limitation: series (in working_tz) must start at -#' right after midnight (to keep in sync with exogenous vars) +#' @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 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) +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)") @@ -38,61 +46,61 @@ 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 - } + # 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)) { + 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) + # 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$new() i = 1 #index of a cell in data while (line <= nb_lines) { time = c() serie = c() + hat_serie = c() repeat { { time = c(time, ts_df[line,1]) + hat_serie = c(serie, ts_df[line,3]) serie = c(serie, ts_df[line,2]) line = line + 1 }; - if (line >= nb_lines + 1 || as.POSIXlt(ts_df[line-1,1])$hour == predict_at) + if (line >= nb_lines + 1 + || as.POSIXlt(ts_df[line-1,1],tz=working_tz)$hour == predict_at) + { break + } } -##TODO: fix note comment ! --> triche: exo contient les mesures du jour, pas forcément toutes available - # NOTE: if predict_at does not cut days at midnight, - # for the exogenous to be synchronized they need to be shifted - if (predict_at > 0) - { - exo_hat = as.data.frame(exo_df[max(1,i-1),(1+nb_exos+1):(1+2*nb_exos)]) - exo = as.data.frame(exo_df[max(1,i-1),2:(1+nb_exos)]) - } - else - { - exo_hat = as.data.frame(exo_df[i,(1+nb_exos+1):(1+2*nb_exos)]) - exo = as.data.frame(exo_df[i,2:(1+nb_exos)]) - } + hat_exo = as.data.frame( exo_df[i,(1+nb_exos+1):(1+2*nb_exos)] ) + exo = as.data.frame( exo_df[i,2:(1+nb_exos)] ) + data$appendHat(time, hat_serie, hat_exo) + data$append(serie, exo) #in realtime, this call comes hours later + if (i >= limit) + break i = i + 1 - #center data - level = mean(serie, na.rm=TRUE) - centered_serie = serie - level -# data$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) } - 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 }