v1.6.8.11 refactor: Rename functions SeriesAggreg2 to SeriesAggreg

Refs #

DESCRIPTION

 Package: airGR
 Type: Package
 Title: Suite of GR Hydrological Models for Precipitation-Runoff Modelling
-Version: 1.6.8.10
-Date: 2020-11-20
+Version: 1.6.8.11
+Date: 2020-11-24
 Authors@R: c(
   person("Laurent", "Coron", role = c("aut", "trl"), comment = c(ORCID = "0000-0002-1503-6204")),
   person("Olivier", "Delaigue", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7668-8468"), email = "airGR@inrae.fr"),

NAMESPACE

@@ -9,10 +9,10 @@ useDynLib(airGR, .registration = TRUE)
 ##            S3 methods           ##
 #####################################
 S3method("plot", "OutputsModel")
-S3method("SeriesAggreg2", "data.frame")
-S3method("SeriesAggreg2", "list")
-S3method("SeriesAggreg2", "InputsModel")
-S3method("SeriesAggreg2", "OutputsModel")
+S3method("SeriesAggreg", "data.frame")
+S3method("SeriesAggreg", "list")
+S3method("SeriesAggreg", "InputsModel")
+S3method("SeriesAggreg", "OutputsModel")
 
 
 
@@ -51,11 +51,10 @@ export(RunModel_GR5J)
 export(RunModel_GR6J)
 export(RunModel_Lag)
 export(SeriesAggreg)
-export(SeriesAggreg2)
-export(SeriesAggreg2.list)
-export(SeriesAggreg2.data.frame)
-export(SeriesAggreg2.InputsModel)
-export(SeriesAggreg2.OutputsModel)
+export(SeriesAggreg.list)
+export(SeriesAggreg.data.frame)
+export(SeriesAggreg.InputsModel)
+export(SeriesAggreg.OutputsModel)
 export(TransfoParam)
 export(TransfoParam_CemaNeige)
 export(TransfoParam_CemaNeigeHyst)

NEWS.md

@@ -6,12 +6,12 @@
 
 #### New features
 
-- Added <code>SeriesAggreg2</code> S3 method with functions for `InputsModel`, `OutputsModel`, `list`, `data.frame` class objects. This new version of the <code>SeriesAggreg()</code> function allows to compute regimes.
+- Added <code>SeriesAggreg</code> S3 method with functions for `InputsModel`, `OutputsModel`, `list`, `data.frame` class objects. This new version of the <code>SeriesAggreg()</code> function allows to compute regimes.
 - Added<code>.AggregConvertFun()</code> private function in order to choose automatically the <code>ConvertFun</code> to apply on each element of <code>InputsModel</code> and <code>OutputsModel</code> objects.
 
 #### Bug fixes
 
-- <code>TimeLag</code> of the <code>SeriesAggreg2()</code> function now runs when <code>TimeLag >= 3600</code>.
+- <code>TimeLag</code> of the <code>SeriesAggreg()</code> function now runs when <code>TimeLag >= 3600</code>.
 
 ____________________________________________________________________________________
 

R/SeriesAggreg2.InputsModel.R → R/SeriesAggreg.InputsModel.R

-SeriesAggreg2.InputsModel <- function(TabSeries, Format, ...) {
+SeriesAggreg.InputsModel <- function(TabSeries, Format, ...) {
 
   if (!inherits(TabSeries, "InputsModel")) {
     stop("to be used with 'InputsModel' object")
   }
 
-  res <- SeriesAggreg2.list(TabSeries = TabSeries, Format, ...)
+  res <- SeriesAggreg.list(TabSeries = TabSeries, Format, ...)
 
   if (inherits(TabSeries, "CemaNeige")) {
     res$ZLayers <- TabSeries$ZLayers

R/SeriesAggreg2.OutputsModel.R → R/SeriesAggreg.OutputsModel.R

-SeriesAggreg2.OutputsModel <- function(TabSeries, Format, ...) {
+SeriesAggreg.OutputsModel <- function(TabSeries, Format, ...) {
 
   if (!inherits(TabSeries, "OutputsModel")) {
     stop("to be used with 'OutputsModel' object")
   }
 
-  res <- SeriesAggreg2.list(TabSeries, Format, ...)
+  res <- SeriesAggreg.list(TabSeries, Format, ...)
 
   res$StateEnd <- TabSeries$StateEnd
 

R/SeriesAggreg.R

-SeriesAggreg <- function(TabSeries,
-                         TimeFormat, NewTimeFormat,
-                         ConvertFun,
-                         YearFirstMonth = 1, TimeLag = 0,
-                         verbose = TRUE) {
-  
-  
-  ##_Arguments_check
-  
-  ##check_TabSeries
-  if (!is.data.frame(TabSeries)) {
-    stop("'TabSeries' must be a data.frame containing the dates and data to be aggregated")
-  }
-  if (ncol(TabSeries) < 2) {
-    stop("'TabSeries' must contain at least two columns (including the column of dates)")
-  }
-  ##check_TimeFormat
-  if (!any(class(TabSeries[, 1]) %in% "POSIXt")) {
-    stop("'TabSeries' first column must be a vector of class 'POSIXlt' or 'POSIXct'")
-  }
-  if (any(class(TabSeries[, 1]) %in% "POSIXlt")) {
-    TabSeries[, 1] <- as.POSIXct(TabSeries[, 1])
-  }
-  for (iCol in 2:ncol(TabSeries)) {
-    if (!is.numeric(TabSeries[,iCol])) {
-      stop("'TabSeries' columns (other than the first one) be of numeric class")
-    }
-  }
-  ##check TimeFormat and NewTimeFormat
-  TimeStep <- c("hourly", "daily", "monthly", "yearly")
-  TimeFormat    <- match.arg(TimeFormat   , choices = TimeStep)
-  NewTimeFormat <- match.arg(NewTimeFormat, choices = TimeStep)
-  ##check_ConvertFun
-  if (!is.vector(ConvertFun)) {
-    stop("'ConvertFun' must be a vector of character")
-  }
-  if (!is.character(ConvertFun)) {
-    stop("'ConvertFun' must be a vector of character")
-  }
-  if (length(ConvertFun) != (ncol(TabSeries) - 1)) {
-    stop(
-      paste("'ConvertFun' must be of length", ncol(TabSeries) - 1, "(length=ncol(TabSeries)-1)")
-    )
-  }
-  if (sum(ConvertFun %in% c("sum", "mean") == FALSE) != 0) {
-    stop("'ConvertFun' elements must be one of 'sum' or 'mean'")
-  }
-  ##check_YearFirstMonth
-  if (!is.vector(YearFirstMonth)) {
-    stop("'YearFirstMonth' must be an integer between 1 and 12")
-  }
-  if (!is.numeric(YearFirstMonth)) {
-    stop("'YearFirstMonth' must be an integer between 1 and 12")
-  }
-  YearFirstMonth <- as.integer(YearFirstMonth)
-  if (length(YearFirstMonth) != 1) {
-    stop("'YearFirstMonth' must be only one integer between 1 and 12")
-  }
-  if (YearFirstMonth %in% (1:12) == FALSE) {
-    stop("'YearFirstMonth' must be only one integer between 1 and 12")
-  } 
-  ##check_DatesR_integrity
-  by <- switch(TimeFormat,
-               'hourly'  = "hours",
-               'daily'   = "days",
-               'monthly' = "months",
-               'yearly'  = "years")
-  TmpDatesR <- seq(from = TabSeries[1, 1], to = tail(TabSeries[, 1], 1), by = by)
-  if (!identical(TabSeries[, 1], TmpDatesR)) {
-    stop("some dates might not be ordered or are missing in 'TabSeries'")
-  }
-  ##check_conversion_direction
-  if ((TimeFormat == "daily"   & NewTimeFormat %in% c("hourly")                  ) |
-      (TimeFormat == "monthly" & NewTimeFormat %in% c("hourly","daily")          ) |
-      (TimeFormat == "yearly"  & NewTimeFormat %in% c("hourly","daily","monthly"))) { 
-    stop("only time aggregation can be performed")
-  } 
-  ##check_if_conversion_not_needed
-  if ((TimeFormat == "hourly"  & NewTimeFormat == "hourly" ) |
-      (TimeFormat == "daily"   & NewTimeFormat == "daily"  ) |
-      (TimeFormat == "monthly" & NewTimeFormat == "monthly") |
-      (TimeFormat == "yearly"  & NewTimeFormat == "yearly" )) { 
-    if (verbose) {
-      warning("the old and new format are identical \n\t -> no time-step conversion was performed")
-      return(TabSeries)
-    }
-  }
-  
-  
-  ##_Time_step_conversion
-  
-  ##_Handle_conventional_difference_between_hourly_series_and_others
-  TmpDatesR <- TabSeries[, 1]
-  #if (TimeFormat=="hourly") { TmpDatesR <- TmpDatesR - 60*60; }
-  TmpDatesR <- TmpDatesR + TimeLag
-  Hmax <- "00"
-  if (TimeFormat == "hourly") {
-    Hmax <- "23"
-  }
-  
-  ##_Identify_the_part_of_the_series_to_be_aggregated
-  NDaysInMonth <- list("31", c("28", "29"), "31", "30", "31", "30", "31", "31", "30", "31", "30", "31")
-  NDaysAndMonth <- sprintf("%02i%s", c(1:2, 2:12), unlist(NDaysInMonth))
-  YearLastMonth <- YearFirstMonth + 11
-  if (YearLastMonth > 12) {
-    YearLastMonth <- YearLastMonth - 12
-  }
-  YearFirstMonthTxt <- formatC(YearFirstMonth, format = "d", width = 2, flag = "0")
-  YearLastMonthTxt  <- formatC(YearLastMonth , format = "d", width = 2, flag = "0")
-  if (NewTimeFormat == "daily") {
-    Ind1 <- which(format(TmpDatesR,    "%H") == "00")
-    Ind2 <- which(format(TmpDatesR,    "%H") == Hmax)
-    if (Ind2[1] < Ind1[1]) {
-      Ind2 <- Ind2[2:length(Ind2)]
-    }
-    if (tail(Ind1, 1) > tail(Ind2, 1)) {
-      Ind1 <- Ind1[1:(length(Ind1) - 1)]
-    }
-    ### Aggr <- NULL; iii <- 0; for(kkk in 1:length(Ind1)) {
-    ### iii <- iii+1; Aggr <- c(Aggr,rep(iii,length(Ind1[kkk]:Ind2[kkk]))); }
-    Aggr <- as.numeric(format(TmpDatesR[min(Ind1):max(Ind2)], "%Y%m%d"))
-    ### more efficient
-    NewDatesR <- data.frame(seq(from = TmpDatesR[min(Ind1)], to = TmpDatesR[max(Ind2)], by = "days"))
-  }
-  if (NewTimeFormat=="monthly") {
-    Ind1 <- which(format(TmpDatesR,  "%d%H") == "0100")
-    Ind2 <- which(format(TmpDatesR,"%m%d%H") %in% paste0(NDaysAndMonth, Hmax))
-    Ind2[1:(length(Ind2) - 1)][diff(Ind2) == 1] <- NA
-    Ind2 <- Ind2[!is.na(Ind2)] ### to keep only feb 29 if both feb 28 and feb 29 exists
-    if (Ind2[1] < Ind1[1]) {
-      Ind2 <- Ind2[2:length(Ind2)]
-    }
-    if (tail(Ind1, 1) > tail(Ind2, 1)) {
-      Ind1 <- Ind1[1:(length(Ind1) - 1)]
-    }
-    ### Aggr <- NULL; iii <- 0; for(kkk in 1:length(Ind1)) { 
-    ### iii <- iii+1; Aggr <- c(Aggr,rep(iii,length(Ind1[kkk]:Ind2[kkk]))); }
-    Aggr <- as.numeric(format(TmpDatesR[min(Ind1):max(Ind2)],"%Y%m"));  ### more efficient
-    NewDatesR <- data.frame(seq(from=TmpDatesR[min(Ind1)],to=TmpDatesR[max(Ind2)],by="months"))
-  }
-  if (NewTimeFormat == "yearly") {
-    Ind1 <- which(format(TmpDatesR, "%m%d%H") %in% paste0(YearFirstMonthTxt, "0100"))
-    Ind2 <- which(format(TmpDatesR, "%m%d%H") %in% paste0(YearLastMonthTxt, NDaysInMonth[[YearLastMonth]], Hmax))
-    Ind2[1:(length(Ind2) - 1)][diff(Ind2) == 1] <- NA
-    Ind2 <- Ind2[!is.na(Ind2)]
-    ### to keep only feb 29 if both feb 28 and feb 29 exists
-    if (Ind2[1] < Ind1[1]) {
-      Ind2 <- Ind2[2:length(Ind2)]
-    }
-    if (tail(Ind1, 1) > tail(Ind2, 1)) {
-      Ind1 <- Ind1[1:(length(Ind1) - 1)]
-    }
-    Aggr <- NULL
-    iii <- 0
-    for (kkk in 1:length(Ind1)) {
-      iii <- iii + 1
-      Aggr <- c(Aggr, rep(iii, length(Ind1[kkk]:Ind2[kkk])))
-    }
-    ### Aggr <- as.numeric(format(TmpDatesR[min(Ind1):max(Ind2)],"%Y")); ### not working if YearFirstMonth != 01
-    NewDatesR <- data.frame(seq(from = TmpDatesR[min(Ind1)], to = TmpDatesR[max(Ind2)], by = "years"))
-  }
-  ##_Aggreation_and_export
-  NewTabSeries <- data.frame(NewDatesR)
-  for (iCol in 2:ncol(TabSeries)) {
-    AggregData <- aggregate(TabSeries[min(Ind1):max(Ind2), iCol],
-                            by = list(Aggr),
-                            FUN = ConvertFun[iCol - 1], na.rm = FALSE)[, 2]
-    NewTabSeries <- data.frame(NewTabSeries, AggregData)
-  }
-  names(NewTabSeries) <- names(TabSeries)
-  return(NewTabSeries)
-  
-  
-}
+SeriesAggreg <- function(TabSeries, Format, ...) {
+  UseMethod("SeriesAggreg")
+}
\ No newline at end of file

R/SeriesAggreg2.data.frame.R → R/SeriesAggreg.data.frame.R

-SeriesAggreg2.data.frame <- function(TabSeries, Format, ConvertFun, TimeFormat = NULL, NewTimeFormat = NULL,
+SeriesAggreg.data.frame <- function(TabSeries, Format, ConvertFun, TimeFormat = NULL, NewTimeFormat = NULL,
                                   YearFirstMonth = 1, TimeLag = 0, verbose = TRUE, ...) {
 
   ## Arguments checks

R/SeriesAggreg2.list.R → R/SeriesAggreg.list.R

-SeriesAggreg2.list <- function(TabSeries, Format, simplify = FALSE, ...) {
+SeriesAggreg.list <- function(TabSeries, Format, simplify = FALSE, ...) {
 
   if (!inherits(TabSeries, c("InputsModel", "OutputsModel"))) {
     stop("to be used with InputsModel', or 'OutputsModel' object")
@@ -35,7 +35,7 @@ SeriesAggreg2.list <- function(TabSeries, Format, simplify = FALSE, ...) {
     }
     CemaNeigeLayersAggreg <- lapply(CemaNeigeLayers, function(iLayer) {
       tmp <- cbind(TabSeries$DatesR, as.data.frame(iLayer))
-      res <- SeriesAggreg2(tmp, Format, ..., ConvertFun = .AggregConvertFun(gsub("[.].*", "", colnames(tmp)[-1L])))
+      res <- SeriesAggreg(tmp, Format, ..., ConvertFun = .AggregConvertFun(gsub("[.].*", "", colnames(tmp)[-1L])))
       res <- res[, -1L]
       colnames(res) <- gsub(".*[.]", "", colnames(res))
       res <- as.list(res)
@@ -45,7 +45,7 @@ SeriesAggreg2.list <- function(TabSeries, Format, simplify = FALSE, ...) {
 
   TabSeries2 <- TabSeries[1:which(names(TabSeries) %in% lastCol)]
   TabSeries2 <- as.data.frame.list(TabSeries2)
-  NewTabSeries <- SeriesAggreg2(TabSeries = TabSeries2, Format, ..., ConvertFun = .AggregConvertFun(colnames(TabSeries2)[-1L]))
+  NewTabSeries <- SeriesAggreg(TabSeries = TabSeries2, Format, ..., ConvertFun = .AggregConvertFun(colnames(TabSeries2)[-1L]))
   NewTabSeries$zzz <- NULL
 
 

R/SeriesAggreg2.R

-SeriesAggreg2 <- function(TabSeries, Format, ...) {
-  UseMethod("SeriesAggreg2")
-}

man/SeriesAggreg.Rd

@@ -3,6 +3,10 @@
 
 \name{SeriesAggreg}
 \alias{SeriesAggreg}
+\alias{SeriesAggreg.list}
+\alias{SeriesAggreg.data.frame}
+\alias{SeriesAggreg.InputsModel}
+\alias{SeriesAggreg.OutputsModel}
 
 
 \title{Conversion of time series to another time step (aggregation only)}
@@ -10,34 +14,59 @@
 
 \description{
 Conversion of time series to another time step (aggregation only). \cr
-Warning : on the aggregated outputs, the dates correspond to the beginning of the time step \cr
-(e.g. for daily time-series 2005-03-01 00:00 = value for period 2005-03-01 00:00 - 2005-03-01 23:59) \cr
-(e.g. for monthly time-series 2005-03-01 00:00 = value for period 2005-03-01 00:00 - 2005-03-31 23:59) \cr
-(e.g. for yearly time-series 2005-03-01 00:00 = value for period 2005-03-01 00:00 - 2006-02-28 23:59)
+Warning: on the aggregated outputs, the dates correspond to the beginning of the time step \cr
+(e.g. for daily time series 2005-03-01 00:00 = value for period 2005-03-01 00:00 - 2005-03-01 23:59) \cr
+(e.g. for monthly time series 2005-03-01 00:00 = value for period 2005-03-01 00:00 - 2005-03-31 23:59) \cr
+(e.g. for yearly time series 2005-03-01 00:00 = value for period 2005-03-01 00:00 - 2006-02-28 23:59)
+}
+
+\details{
+  \code{\link{SeriesAggreg.InputsModel}} and \code{\link{SeriesAggreg.OutputsModel}}
+  call \code{\link{SeriesAggreg.list}} which itself calls \code{\link{SeriesAggreg.data.frame}}.
+  So, all arguments passed to any \code{\link{SeriesAggreg}} method will be passed to \code{\link{SeriesAggreg.data.frame}}.
 }
 
 
 \usage{
-SeriesAggreg(TabSeries, TimeFormat, NewTimeFormat, ConvertFun,
-             YearFirstMonth = 1, TimeLag = 0, verbose = TRUE)
+\method{SeriesAggreg}{data.frame}(TabSeries,
+Format, ConvertFun, TimeFormat = NULL, NewTimeFormat = NULL,
+YearFirstMonth = 1, TimeLag = 0,
+verbose = TRUE, \dots)
+
+\method{SeriesAggreg}{list}(TabSeries,
+Format, simplify = FALSE, \dots)
+
+\method{SeriesAggreg}{InputsModel}(TabSeries,
+Format, \dots)
+
+\method{SeriesAggreg}{OutputsModel}(TabSeries,
+Format, \dots)
 }
 
 
 \arguments{
 \item{TabSeries}{[POSIXt+numeric] data.frame containing the vector of dates (POSIXt) and the time series values numeric)}
 
-\item{TimeFormat}{[character] input time-step format (i.e. \code{"hourly"}, \code{"daily"}, \code{"monthly"} or \code{"yearly"})}
+\item{Format}{[character] output time step format (i.e. yearly times series: \code{"\%Y"}, monthly time series: \code{"\%Y\%m"}, daily time series: \code{"\%Y\%m\%d"}, monthly regimes \code{"\%m"}, daily regimes \code{"\%d"})}
+
+\item{TimeFormat}{(deprecated) [character] input time step format (i.e. \code{"hourly"}, \code{"daily"}, \code{"monthly"} or \code{"yearly"})}
 
-\item{NewTimeFormat}{[character] output time-step format (i.e. \code{"hourly"}, \code{"daily"}, \code{"monthly"} or \code{"yearly"})}
+\item{NewTimeFormat}{(deprecated) [character] output time step format (i.e. \code{"hourly"}, \code{"daily"}, \code{"monthly"} or \code{"yearly"}). Use the \code{TabSeries} argument instead}
 
-\item{ConvertFun}{[character] names of aggregation functions (e.g. for P[mm], T[degC], Q[mm] : \code{ConvertFun = c("sum", "mean", "sum"}))}
+\item{ConvertFun}{[character] names of aggregation functions (e.g. for P[mm], T[degC], Q[mm]: \code{ConvertFun = c("sum", "mean", "sum"}))}
 
-\item{YearFirstMonth}{(optional) [numeric] integer used when \code{NewTimeFormat = "yearly"} to set when the starting month of the year (e.g. 01 for calendar year or 09 for hydrological year starting in September)}
+\item{YearFirstMonth}{(optional) [numeric] integer used when \code{Format = "\%Y"} to set when the starting month of the year (e.g. 01 for calendar year or 09 for hydrological year starting in September)}
 
-\item{TimeLag}{(optional) [numeric] numeric indicating a time lag (in seconds) for the time series aggregation (especially useful to aggregate hourly time series in daily time series)}
+\item{TimeLag}{(optional) [numeric] numeric indicating a time lag (in seconds) for the time series aggregation (especially useful to aggregate hourly time series into daily time series)}
 
-\item{verbose}{(optional) [boolean] boolean indicating if the function is run in verbose mode or not, default = \code{FALSE}}
+\item{verbose}{(optional) [boolean] boolean indicating if the function is run in verbose mode or not, default = \code{TRUE}}
+
+\item{simplify}{(optional) [boolean] XXXXXX, default = \code{FALSE}}
+
+\item{\dots}{Arguments passed to \code{\link{SeriesAggreg.list}} and then to \code{\link{SeriesAggreg.data.frame}}}
 }
+
+
 \value{
 [POSIXct+numeric] data.frame containing a vector of aggregated dates (POSIXct) and time series values numeric)
 }
@@ -52,20 +81,21 @@ data(L0123002)
 ## preparation of the initial time series data frame at the daily time step
 TabSeries <- BasinObs[, c("DatesR", "P", "E", "T", "Qmm")]
 
-## conversion at the monthly time step
+## monthly time series
 NewTabSeries <- SeriesAggreg(TabSeries = TabSeries,
-                             TimeFormat = "daily", NewTimeFormat = "monthly",
-                             ConvertFun = c("sum", "sum", "mean", "sum"))
+                              Format = "\%Y\%m",
+                              ConvertFun = c("sum", "sum", "mean", "sum"))
+str(NewTabSeries)
 
-## conversion at the yearly time step
+## monthly regimes
 NewTabSeries <- SeriesAggreg(TabSeries = TabSeries,
-                             TimeFormat = "daily", NewTimeFormat = "yearly",
-                             ConvertFun = c("sum", "sum", "mean", "sum"))
-
+                              Format = "\%m",
+                              ConvertFun = c("sum", "sum", "mean", "sum"))
+str(NewTabSeries)
 }
 
 
 \author{
-Laurent Coron
+Olivier Delaigue
 }
 

man/SeriesAggreg2.Rd

-\encoding{UTF-8}
-
-
-\name{SeriesAggreg2}
-\alias{SeriesAggreg2}
-\alias{SeriesAggreg2.list}
-\alias{SeriesAggreg2.data.frame}
-\alias{SeriesAggreg2.InputsModel}
-\alias{SeriesAggreg2.OutputsModel}
-
-
-\title{Conversion of time series to another time step (aggregation only)}
-
-
-\description{
-Conversion of time series to another time step (aggregation only). \cr
-Warning: on the aggregated outputs, the dates correspond to the beginning of the time step \cr
-(e.g. for daily time series 2005-03-01 00:00 = value for period 2005-03-01 00:00 - 2005-03-01 23:59) \cr
-(e.g. for monthly time series 2005-03-01 00:00 = value for period 2005-03-01 00:00 - 2005-03-31 23:59) \cr
-(e.g. for yearly time series 2005-03-01 00:00 = value for period 2005-03-01 00:00 - 2006-02-28 23:59)
-}
-
-\details{
-  \code{\link{SeriesAggreg2.InputsModel}} and \code{\link{SeriesAggreg2.OutputsModel}}
-  call \code{\link{SeriesAggreg2.list}} which itself calls \code{\link{SeriesAggreg2.data.frame}}.
-  So, all arguments passed to any \code{\link{SeriesAggreg2}} method will be passed to \code{\link{SeriesAggreg2.data.frame}}.
-}
-
-
-\usage{
-\method{SeriesAggreg2}{data.frame}(TabSeries,
-Format, ConvertFun, TimeFormat = NULL, NewTimeFormat = NULL,
-YearFirstMonth = 1, TimeLag = 0,
-verbose = TRUE, \dots)
-
-\method{SeriesAggreg2}{list}(TabSeries,
-Format, simplify = FALSE, \dots)
-
-\method{SeriesAggreg2}{InputsModel}(TabSeries,
-Format, \dots)
-
-\method{SeriesAggreg2}{OutputsModel}(TabSeries,
-Format, \dots)
-}
-
-
-\arguments{
-\item{TabSeries}{[POSIXt+numeric] data.frame containing the vector of dates (POSIXt) and the time series values numeric)}
-
-\item{Format}{[character] output time step format (i.e. yearly times series: \code{"\%Y"}, monthly time series: \code{"\%Y\%m"}, daily time series: \code{"\%Y\%m\%d"}, monthly regimes \code{"\%m"}, daily regimes \code{"\%d"})}
-
-\item{TimeFormat}{(deprecated) [character] input time step format (i.e. \code{"hourly"}, \code{"daily"}, \code{"monthly"} or \code{"yearly"})}
-
-\item{NewTimeFormat}{(deprecated) [character] output time step format (i.e. \code{"hourly"}, \code{"daily"}, \code{"monthly"} or \code{"yearly"}). Use the \code{TabSeries} argument instead}
-
-\item{ConvertFun}{[character] names of aggregation functions (e.g. for P[mm], T[degC], Q[mm]: \code{ConvertFun = c("sum", "mean", "sum"}))}
-
-\item{YearFirstMonth}{(optional) [numeric] integer used when \code{Format = "\%Y"} to set when the starting month of the year (e.g. 01 for calendar year or 09 for hydrological year starting in September)}
-
-\item{TimeLag}{(optional) [numeric] numeric indicating a time lag (in seconds) for the time series aggregation (especially useful to aggregate hourly time series into daily time series)}
-
-\item{verbose}{(optional) [boolean] boolean indicating if the function is run in verbose mode or not, default = \code{TRUE}}
-
-\item{simplify}{(optional) [boolean] XXXXXX, default = \code{FALSE}}
-
-\item{\dots}{Arguments passed to \code{\link{SeriesAggreg2.list}} and then to \code{\link{SeriesAggreg2.data.frame}}}
-}
-
-
-\value{
-[POSIXct+numeric] data.frame containing a vector of aggregated dates (POSIXct) and time series values numeric)
-}
-
-
-\examples{
-library(airGR)
-
-## loading catchment data
-data(L0123002)
-
-## preparation of the initial time series data frame at the daily time step
-TabSeries <- BasinObs[, c("DatesR", "P", "E", "T", "Qmm")]
-
-## monthly time series
-NewTabSeries <- SeriesAggreg2(TabSeries = TabSeries,
-                              Format = "\%Y\%m",
-                              ConvertFun = c("sum", "sum", "mean", "sum"))
-str(NewTabSeries)
-
-## monthly regimes
-NewTabSeries <- SeriesAggreg2(TabSeries = TabSeries,
-                              Format = "\%m",
-                              ConvertFun = c("sum", "sum", "mean", "sum"))
-str(NewTabSeries)
-}
-
-
-\author{
-Olivier Delaigue
-}
-

tests/testthat/test-SeriesAggreg2.R → tests/testthat/test-SeriesAggreg.R

-context("SeriesAggreg2")
+context("SeriesAggreg")
 
 test_that("No warning with InputsModel Cemaneige'", {
   ## load of catchment data
@@ -9,6 +9,6 @@ test_that("No warning with InputsModel Cemaneige'", {
                                    ZInputs = BasinInfo$HypsoData[51], HypsoData=BasinInfo$HypsoData,
                                    NLayers = 5)
   expect_warning(
-    SeriesAggreg2(InputsModel, "%m"),
+    SeriesAggreg(InputsModel, "%m"),
     regexp = NA)
 })