diff --git a/DESCRIPTION b/DESCRIPTION
index 00b5589d42b3f82eb4c7574702cc8e6348248322..83c5ba3280a2b8f20537c2d7069af81eaaef6880 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
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"),
diff --git a/NAMESPACE b/NAMESPACE
index 067f5e391acee4c748aa33335eb24559fa35cf8e..7c6fd5c88e1a7d9271ffbc4c7854533b8184f809 100644
--- a/NAMESPACE
+++ b/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)
diff --git a/NEWS.md b/NEWS.md
index 9e82f16aedc0c9333b551596322288de7d95dd1c..6fdd93f079db1a45f4e07800481dc3f8f578f8e4 100644
--- a/NEWS.md
+++ b/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>.
____________________________________________________________________________________
diff --git a/R/SeriesAggreg2.InputsModel.R b/R/SeriesAggreg.InputsModel.R
similarity index 60%
rename from R/SeriesAggreg2.InputsModel.R
rename to R/SeriesAggreg.InputsModel.R
index c969f36a9f77b345185e43acc8a6dbeb1afa6bfe..26ebb067a9d643d58b7aa0325ab5144b4e82e7ef 100644
--- a/R/SeriesAggreg2.InputsModel.R
+++ b/R/SeriesAggreg.InputsModel.R
@@ -1,10 +1,10 @@
-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
diff --git a/R/SeriesAggreg2.OutputsModel.R b/R/SeriesAggreg.OutputsModel.R
similarity index 56%
rename from R/SeriesAggreg2.OutputsModel.R
rename to R/SeriesAggreg.OutputsModel.R
index 978ffc1315ae8a8834be3b6096dfb75dd8440c4f..a1179e3e5ffcb2da5e295bf3c3fa388bf1b81af3 100644
--- a/R/SeriesAggreg2.OutputsModel.R
+++ b/R/SeriesAggreg.OutputsModel.R
@@ -1,10 +1,10 @@
-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
diff --git a/R/SeriesAggreg.R b/R/SeriesAggreg.R
index 209afb36de7f5ce2d05b6d93a08a74aa0d25550d..b4fd59fd619e8b17da1f4c9de8d8e6a9d86dbc92 100644
--- a/R/SeriesAggreg.R
+++ b/R/SeriesAggreg.R
@@ -1,174 +1,3 @@
-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)
-
-
-}
\ No newline at end of file
+SeriesAggreg <- function(TabSeries, Format, ...) {
+ UseMethod("SeriesAggreg")
+}
diff --git a/R/SeriesAggreg2.data.frame.R b/R/SeriesAggreg.data.frame.R
similarity index 98%
rename from R/SeriesAggreg2.data.frame.R
rename to R/SeriesAggreg.data.frame.R
index f50fa3adf753b716818990f83949bb2cfe6cd684..cb6320fe2ead581cd7bab381d6f5d17272bc1c77 100644
--- a/R/SeriesAggreg2.data.frame.R
+++ b/R/SeriesAggreg.data.frame.R
@@ -1,4 +1,4 @@
-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
diff --git a/R/SeriesAggreg2.list.R b/R/SeriesAggreg.list.R
similarity index 88%
rename from R/SeriesAggreg2.list.R
rename to R/SeriesAggreg.list.R
index fbf7ec495fff2e5c3e452206d50dac5ac99de613..c8dacf1fd6296dabe1b23e668b142c60edf99635 100644
--- a/R/SeriesAggreg2.list.R
+++ b/R/SeriesAggreg.list.R
@@ -1,4 +1,4 @@
-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
diff --git a/R/SeriesAggreg2.R b/R/SeriesAggreg2.R
deleted file mode 100644
index 3dabbcabc641dbbf2487a35f02a12b7374da7f8a..0000000000000000000000000000000000000000
--- a/R/SeriesAggreg2.R
+++ /dev/null
@@ -1,3 +0,0 @@
-SeriesAggreg2 <- function(TabSeries, Format, ...) {
- UseMethod("SeriesAggreg2")
-}
diff --git a/man/SeriesAggreg.Rd b/man/SeriesAggreg.Rd
index 4498eb6e09ef89614ef7561ff74e7e061e581363..3c15f379a355109c10c9ded24382ee913b4d8394 100644
--- a/man/SeriesAggreg.Rd
+++ b/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
}
diff --git a/man/SeriesAggreg2.Rd b/man/SeriesAggreg2.Rd
deleted file mode 100644
index a67601e021b7983c9bf58b83b4d8c4eb0c2bdd3c..0000000000000000000000000000000000000000
--- a/man/SeriesAggreg2.Rd
+++ /dev/null
@@ -1,101 +0,0 @@
-\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
-}
-
diff --git a/tests/testthat/test-SeriesAggreg2.R b/tests/testthat/test-SeriesAggreg.R
similarity index 88%
rename from tests/testthat/test-SeriesAggreg2.R
rename to tests/testthat/test-SeriesAggreg.R
index 677e2e524cfbe08fb2aab7c72bb000a54607d8ba..6a29bcffcf9c9540d6df9b0ecc9341a023b1322b 100644
--- a/tests/testthat/test-SeriesAggreg2.R
+++ b/tests/testthat/test-SeriesAggreg.R
@@ -1,4 +1,4 @@
-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)
})