diff --git a/R/Calibration.R b/R/Calibration.R
index b3f6ca4d695d069dfa113a008eb4c64ff8475203..9266177f3d8d695f73d444c813c61760b21f45ee 100644
--- a/R/Calibration.R
+++ b/R/Calibration.R
@@ -1,29 +1,4 @@
-#*************************************************************************************************
-#' Calibration algorithm which minimises the error criterion using the provided functions. \cr
-#*************************************************************************************************
-#' @title Calibration algorithm which minimises an error criterion on the model outputs using the provided functions
-#' @author Laurent Coron (June 2014)
-#' @seealso \code{\link{Calibration_Michel}}, \code{\link{Calibration_optim}},
-#' \code{\link{RunModel}}, \code{\link{ErrorCrit}}, \code{\link{TransfoParam}},
-#' \code{\link{CreateInputsModel}}, \code{\link{CreateRunOptions}},
-#' \code{\link{CreateInputsCrit}}, \code{\link{CreateCalibOptions}}.
-#' @example tests/example_Calibration.R
-#' @export
-#' @encoding UTF-8
-#_FunctionInputs__________________________________________________________________________________
-#' @param InputsModel [object of class \emph{InputsModel}] see \code{\link{CreateInputsModel}} for details
-#' @param RunOptions [object of class \emph{RunOptions}] see \code{\link{CreateRunOptions}} for details
-#' @param InputsCrit [object of class \emph{InputsCrit}] see \code{\link{CreateInputsCrit}} for details
-#' @param CalibOptions [object of class \emph{CalibOptions}] see \code{\link{CreateCalibOptions}} for details
-#' @param FUN_MOD [function] hydrological model function (e.g. RunModel_GR4J, RunModel_CemaNeigeGR4J)
-#' @param FUN_CRIT [function] error criterion function (e.g. ErrorCrit_RMSE, ErrorCrit_NSE)
-#' @param FUN_CALIB (optional) [function] calibration algorithm function (e.g. Calibration_Michel, Calibration_optim), default=Calibration_Michel
-#' @param FUN_TRANSFO (optional) [function] model parameters transformation function, if the FUN_MOD used is native in the package FUN_TRANSFO is automatically defined
-#' @param quiet (optional) [boolean] boolean indicating if the function is run in quiet mode or not, default=FALSE
-#_FunctionOutputs_________________________________________________________________________________
-#' @return [list] see \code{\link{Calibration_Michel}} or \code{\link{Calibration_optim}}
-#**************************************************************************************************
-Calibration <- function(InputsModel,RunOptions,InputsCrit,CalibOptions,FUN_MOD,FUN_CRIT,FUN_CALIB=Calibration_Michel,FUN_TRANSFO=NULL,quiet=FALSE){
- return( FUN_CALIB(InputsModel,RunOptions,InputsCrit,CalibOptions,FUN_MOD,FUN_CRIT,FUN_TRANSFO,quiet=quiet) )
+Calibration <- function(InputsModel,RunOptions,InputsCrit,CalibOptions,FUN_MOD,FUN_CRIT,FUN_CALIB=Calibration_Michel,FUN_TRANSFO=NULL, verbose = TRUE){
+ return( FUN_CALIB(InputsModel,RunOptions,InputsCrit,CalibOptions,FUN_MOD,FUN_CRIT,FUN_TRANSFO, verbose = verbose) )
}
diff --git a/R/Calibration_Michel.R b/R/Calibration_Michel.R
index 20600c56a269e6ac2fd7d1d8ef001cb96db27bff..873c0036bfb2e3f452261ba3bcb56f6327d3121c 100644
--- a/R/Calibration_Michel.R
+++ b/R/Calibration_Michel.R
@@ -1,63 +1,4 @@
-#*************************************************************************************************
-#' Calibration algorithm which minimises the error criterion. \cr
-#' \cr
-#' The algorithm is based on a local search procedure.
-#' First, a screening is performed using either a rough predefined grid or a list of parameter sets
-#' and then a simple steepest descent local search algorithm is performed.
-#'
-#' A screening is first performed either from a rough predefined grid (considering various initial
-#' values for each paramete) or from a list of initial parameter sets. \cr
-#' The best set identified in this screening is then used as a starting point for the steepest
-#' descent local search algorithm. \cr
-#' For this search, the parameters are used in a transformed version, to obtain uniform
-#' variation ranges (and thus a similar pace), while the true ranges might be quite different. \cr
-#' At each iteration, we start from a parameter set of NParam values (NParam being the number of
-#' free parameters of the chosen hydrological model) and we determine the 2*NParam-1 new candidates
-#' by changing one by one the different parameters (+/- pace). \cr
-#' All these candidates are tested and the best one kept to be the starting point for the next
-#' iteration. At the end of each iteration, the pace is either increased or decreased to adapt
-#' the progression speed. A diagonal progress can occasionally be done. \cr
-#' The calibration algorithm stops when the pace becomes too small. \cr
-#'
-#' To optimise the exploration of the parameter space, transformation functions are used to convert
-#' the model parameters. This is done using the TransfoParam functions.
-#*************************************************************************************************
-#' @title Calibration algorithm which minimises the error criterion using the Irstea-HBAN procedure
-#' @author Laurent Coron (August 2013)
-#' @references
-#' Michel, C. (1991),
-#' Hydrologie appliquée aux petits bassins ruraux, Hydrology handout (in French), Cemagref, Antony, France.
-#' @example tests/example_Calibration_Michel.R
-#' @seealso \code{\link{Calibration}}, \code{\link{Calibration_optim}},
-#' \code{\link{RunModel_GR4J}}, \code{\link{TransfoParam_GR4J}}, \code{\link{ErrorCrit_RMSE}},
-#' \code{\link{CreateInputsModel}}, \code{\link{CreateRunOptions}},
-#' \code{\link{CreateInputsCrit}}, \code{\link{CreateCalibOptions}}.
-#' @encoding UTF-8
-#' @export
-#_FunctionInputs__________________________________________________________________________________
-#' @param InputsModel [object of class \emph{InputsModel}] see \code{\link{CreateInputsModel}} for details
-#' @param RunOptions [object of class \emph{RunOptions}] see \code{\link{CreateRunOptions}} for details
-#' @param InputsCrit [object of class \emph{InputsCrit}] see \code{\link{CreateInputsCrit}} for details
-#' @param CalibOptions [object of class \emph{CalibOptions}] see \code{\link{CreateCalibOptions}} for details
-#' @param FUN_MOD [function] hydrological model function (e.g. RunModel_GR4J, RunModel_CemaNeigeGR4J)
-#' @param FUN_CRIT [function] error criterion function (e.g. ErrorCrit_RMSE, ErrorCrit_NSE)
-#' @param FUN_TRANSFO (optional) [function] model parameters transformation function, if the FUN_MOD used is native in the package FUN_TRANSFO is automatically defined
-#' @param quiet (optional) [boolean] boolean indicating if the function is run in quiet mode or not, default=FALSE
-#_FunctionOutputs_________________________________________________________________________________
-#' @return [list] list containing the function outputs organised as follows:
-#' \tabular{ll}{
-#' \emph{$ParamFinalR } \tab [numeric] parameter set obtained at the end of the calibration \cr
-#' \emph{$CritFinal } \tab [numeric] error criterion obtained at the end of the calibration \cr
-#' \emph{$NIter } \tab [numeric] number of iterations during the calibration \cr
-#' \emph{$NRuns } \tab [numeric] number of model runs done during the calibration \cr
-#' \emph{$HistParamR } \tab [numeric] table showing the progression steps in the search for optimal set: parameter values \cr
-#' \emph{$HistCrit } \tab [numeric] table showing the progression steps in the search for optimal set: criterion values \cr
-#' \emph{$MatBoolCrit } \tab [boolean] table giving the requested and actual time steps when the model is calibrated \cr
-#' \emph{$CritName } \tab [character] name of the calibration criterion \cr
-#' \emph{$CritBestValue} \tab [numeric] theoretical best criterion value \cr
-#' }
-#**************************************************************************************************
-Calibration_Michel <- function(InputsModel,RunOptions,InputsCrit,CalibOptions,FUN_MOD,FUN_CRIT,FUN_TRANSFO=NULL,quiet=FALSE){
+Calibration_Michel <- function(InputsModel,RunOptions,InputsCrit,CalibOptions,FUN_MOD,FUN_CRIT,FUN_TRANSFO=NULL, verbose = TRUE){
##_____Arguments_check_____________________________________________________________________
@@ -162,13 +103,13 @@ Calibration_Michel <- function(InputsModel,RunOptions,InputsCrit,CalibOptions,FU
##Loop_to_test_the_various_candidates______________________________________
iNewOptim <- 0;
Ncandidates <- nrow(CandidatesParamR);
- if(!quiet & Ncandidates>1){
+ if(verbose & Ncandidates>1){
if(PrefilteringType==1){ cat(paste("\t List-Screening in progress (",sep="")); }
if(PrefilteringType==2){ cat(paste("\t Grid-Screening in progress (",sep="")); }
cat("0%");
}
for(iNew in 1:nrow(CandidatesParamR)){
- if(!quiet & Ncandidates>1){
+ if(verbose & Ncandidates>1){
for(k in c(2,4,6,8)){ if(iNew==round(k/10*Ncandidates)){ cat(paste(" ",10*k,"%",sep="")); } }
}
##Model_run
@@ -187,7 +128,7 @@ Calibration_Michel <- function(InputsModel,RunOptions,InputsCrit,CalibOptions,FU
Multiplier <- OutputsCrit$Multiplier;
}
}
- if(!quiet & Ncandidates>1){ cat(" 100%) \n"); }
+ if(verbose & Ncandidates>1){ cat(" 100%) \n"); }
##End_of_first_step_Parameter_Screening____________________________________
@@ -195,7 +136,7 @@ Calibration_Michel <- function(InputsModel,RunOptions,InputsCrit,CalibOptions,FU
ParamStartT <- FUN_TRANSFO(ParamStartR,"RT");
CritStart <- CritOptim;
NRuns <- NRuns+nrow(CandidatesParamR);
- if(!quiet){
+ if(verbose){
if(Ncandidates> 1){ cat(paste("\t Screening completed (",NRuns," runs): \n",sep="")); }
if(Ncandidates==1){ cat(paste("\t Starting point for steepest-descent local search: \n",sep="")); }
cat(paste("\t Param = ",paste(formatC(ParamStartR,format="f",width=8,digits=3),collapse=" , "),"\n",sep=""));
@@ -249,7 +190,7 @@ Calibration_Michel <- function(InputsModel,RunOptions,InputsCrit,CalibOptions,FU
##Initialisation_of_variables
- if(!quiet){
+ if(verbose){
cat("\t Steepest-descent local search in progress \n");
}
Pace <- 0.64;
@@ -355,7 +296,7 @@ Calibration_Michel <- function(InputsModel,RunOptions,InputsCrit,CalibOptions,FU
HistParamR[ITER+1,] <- NewParamOptimR;
HistParamT[ITER+1,] <- NewParamOptimT;
HistCrit[ITER+1,] <- CritOptim;
- ### if(!quiet){ cat(paste("\t Iter ",formatC(ITER,format="d",width=3)," Crit ",formatC(CritOptim,format="f",digits=4)," Pace ",formatC(Pace,format="f",digits=4),"\n",sep="")); }
+ ### if(verbose){ cat(paste("\t Iter ",formatC(ITER,format="d",width=3)," Crit ",formatC(CritOptim,format="f",digits=4)," Pace ",formatC(Pace,format="f",digits=4),"\n",sep="")); }
@@ -364,7 +305,7 @@ Calibration_Michel <- function(InputsModel,RunOptions,InputsCrit,CalibOptions,FU
##Case_when_the_starting_parameter_set_remains_the_best_solution__________
- if(CritOptim==CritStart & !quiet){
+ if(CritOptim==CritStart & verbose){
cat("\t No progress achieved \n");
}
@@ -373,7 +314,7 @@ Calibration_Michel <- function(InputsModel,RunOptions,InputsCrit,CalibOptions,FU
ParamFinalT <- NewParamOptimT;
CritFinal <- CritOptim;
NIter <- 1+ITER;
- if(!quiet){
+ if(verbose){
cat(paste("\t Calibration completed (",NIter," iterations, ",NRuns," runs): \n",sep=""));
cat(paste("\t Param = ",paste(formatC(ParamFinalR,format="f",width=8,digits=3),collapse=" , "),"\n",sep=""));
cat(paste("\t Crit ",format(CritName,width=12,justify="left")," = ",formatC(CritFinal*Multiplier,format="f",digits=4),"\n",sep=""));
diff --git a/R/CreateInputsCrit.R b/R/CreateInputsCrit.R
index 09bc06d10c54cadc3aaf47dfc4fff11278793294..64a78ad159e4437657d897c55a18bf612e12ceab 100644
--- a/R/CreateInputsCrit.R
+++ b/R/CreateInputsCrit.R
@@ -1,34 +1,3 @@
-#*************************************************************************************************
-#' Creation of the InputsCrit object required to the ErrorCrit functions.
-#'
-#' Users wanting to use FUN_CRIT functions that are not included in
-#' the package must create their own InputsCrit object accordingly.
-#*************************************************************************************************
-#' @title Creation of the InputsCrit object required to the ErrorCrit functions
-#' @author Laurent Coron (June 2014)
-#' @seealso \code{\link{RunModel}}, \code{\link{CreateInputsModel}}, \code{\link{CreateRunOptions}}, \code{\link{CreateCalibOptions}}
-#' @example tests/example_ErrorCrit.R
-#' @encoding UTF-8
-#' @export
-#_FunctionInputs__________________________________________________________________________________
-#' @param FUN_CRIT [function] error criterion function (e.g. ErrorCrit_RMSE, ErrorCrit_NSE)
-#' @param InputsModel [object of class \emph{InputsModel}] see \code{\link{CreateInputsModel}} for details
-#' @param RunOptions [object of class \emph{RunOptions}] see \code{\link{CreateRunOptions}} for details
-#' @param Qobs [numeric] series of observed discharges [mm]
-#' @param BoolCrit (optional) [boolean] boolean giving the time steps to consider in the computation (all time steps are consider by default)
-#' @param transfo (optional) [character] name of the transformation (e.g. "", "sqrt", "log", "inv", "sort")
-#' @param Ind_zeroes (optional) [numeric] indices of the time-steps where zeroes are observed
-#' @param epsilon (optional) [numeric] epsilon to add to all Qobs and Qsim if \emph{$Ind_zeroes} is not empty
-#_FunctionOutputs_________________________________________________________________________________
-#' @return [list] object of class \emph{InputsCrit} containing the data required to evaluate the model outputs; it can include the following:
-#' \tabular{ll}{
-#' \emph{$BoolCrit } \tab [boolean] boolean giving the time steps to consider in the computation \cr
-#' \emph{$Qobs } \tab [numeric] series of observed discharges [mm] \cr
-#' \emph{$transfo } \tab [character] name of the transformation (e.g. "", "sqrt", "log", "inv", "sort") \cr
-#' \emph{$Ind_zeroes} \tab [numeric] indices of the time-steps where zeroes are observed \cr
-#' \emph{$epsilon } \tab [numeric] epsilon to add to all Qobs and Qsim if \emph{$Ind_zeroes} is not empty \cr
-#' }
-#**************************************************************************************************
CreateInputsCrit <- function(FUN_CRIT,InputsModel,RunOptions,Qobs,BoolCrit=NULL,transfo="",Ind_zeroes=NULL,epsilon=NULL){
ObjectClass <- NULL;
diff --git a/R/CreateInputsModel.R b/R/CreateInputsModel.R
index 53a42633712a799b4d373d6daaeee05b0f524bcb..fed45306a4296ae5ecf95747a32feacb1edeee70 100644
--- a/R/CreateInputsModel.R
+++ b/R/CreateInputsModel.R
@@ -1,39 +1,4 @@
-#*************************************************************************************************
-#' Creation of the InputsModel object required to the RunModel functions.
-#'
-#' Users wanting to use FUN_MOD functions that are not included in
-#' the package must create their own InputsModel object accordingly.
-#*************************************************************************************************
-#' @title Creation of the InputsModel object required to the RunModel functions
-#' @author Laurent Coron (June 2014)
-#' @seealso \code{\link{RunModel}}, \code{\link{CreateRunOptions}}, \code{\link{CreateInputsCrit}}, \code{\link{CreateCalibOptions}}, \code{\link{DataAltiExtrapolation_Valery}}
-#' @example tests/example_RunModel.R
-#' @encoding UTF-8
-#' @export
-#_FunctionInputs__________________________________________________________________________________
-#' @param FUN_MOD [function] hydrological model function (e.g. RunModel_GR4J, RunModel_CemaNeigeGR4J)
-#' @param DatesR [POSIXlt] vector of dates required to create the GR model and CemaNeige module inputs
-#' @param Precip [numeric] time series of total precipitation (catchment average) [mm], required to create the GR model and CemaNeige module inputs
-#' @param PotEvap [numeric] time series of potential evapotranspiration (catchment average) [mm], required to create the GR model inputs
-#' @param TempMean (optional) [numeric] time series of mean air temperature [degC], required to create the CemaNeige module inputs
-#' @param TempMin (optional) [numeric] time series of min air temperature [degC], possibly used to create the CemaNeige module inputs
-#' @param TempMax (optional) [numeric] time series of max air temperature [degC], possibly used to create the CemaNeige module inputs
-#' @param ZInputs (optional) [numeric] real giving the mean elevation of the Precip and Temp series (before extrapolation) [m]
-#' @param HypsoData (optional) [numeric] vector of 101 reals: min, q01 to q99 and max of catchment elevation distribution [m], required to create the GR model inputs, if not defined a single elevation is used for CemaNeige
-#' @param NLayers (optional) [numeric] integer giving the number of elevation layers requested [-], required to create the GR model inputs, default=5
-#' @param quiet (optional) [boolean] boolean indicating if the function is run in quiet mode or not, default=FALSE
-#_FunctionOutputs_________________________________________________________________________________
-#' @return [list] object of class \emph{InputsModel} containing the data required to evaluate the model outputs; it can include the following:
-#' \tabular{ll}{
-#' \emph{$DatesR } \tab [POSIXlt] vector of dates \cr
-#' \emph{$Precip } \tab [numeric] time series of total precipitation (catchment average) [mm] \cr
-#' \emph{$PotEvap } \tab [numeric] time series of potential evapotranspiration (catchment average) [mm], \cr\tab defined if FUN_MOD includes GR4H, GR4J, GR5J, GR6J, GR2M or GR1A \cr \cr
-#' \emph{$LayerPrecip } \tab [list] list of time series of precipitation (layer average) [mm], \cr\tab defined if FUN_MOD includes CemaNeige \cr \cr
-#' \emph{$LayerTempMean } \tab [list] list of time series of mean air temperature (layer average) [degC], \cr\tab defined if FUN_MOD includes CemaNeige \cr \cr
-#' \emph{$LayerFracSolidPrecip} \tab [list] list of time series of solid precip. fract. (layer average) [-], \cr\tab defined if FUN_MOD includes CemaNeige \cr \cr
-#' }
-#**************************************************************************************************
-CreateInputsModel <- function(FUN_MOD,DatesR,Precip,PotEvap=NULL,TempMean=NULL,TempMin=NULL,TempMax=NULL,ZInputs=NULL,HypsoData=NULL,NLayers=5,quiet=FALSE){
+CreateInputsModel <- function(FUN_MOD,DatesR,Precip,PotEvap=NULL,TempMean=NULL,TempMin=NULL,TempMax=NULL,ZInputs=NULL,HypsoData=NULL,NLayers=5, verbose = TRUE){
ObjectClass <- NULL;
@@ -109,11 +74,11 @@ CreateInputsModel <- function(FUN_MOD,DatesR,Precip,PotEvap=NULL,TempMean=NULL,T
if(is.na(ZInputs) | !is.numeric(ZInputs)){ stop("\t ZInputs must be a single numeric value if not null \n"); return(NULL); }
}
if(is.null(HypsoData)){
- if(!quiet){ warning("\t HypsoData is missing => a single layer is used and no extrapolation is made \n"); }
+ if(verbose){ warning("\t HypsoData is missing => a single layer is used and no extrapolation is made \n"); }
HypsoData <- as.numeric(rep(NA,101)); ZInputs <- as.numeric(NA); NLayers <- as.integer(1);
}
if(is.null(ZInputs)){
- if(!quiet & !identical(HypsoData,as.numeric(rep(NA,101)))){ warning("\t ZInputs is missing => HypsoData[51] is used \n"); }
+ if(verbose & !identical(HypsoData,as.numeric(rep(NA,101)))){ warning("\t ZInputs is missing => HypsoData[51] is used \n"); }
ZInputs <- HypsoData[51];
}
}
@@ -122,15 +87,15 @@ CreateInputsModel <- function(FUN_MOD,DatesR,Precip,PotEvap=NULL,TempMean=NULL,T
##check_NA_values
BOOL_NA <- rep(FALSE,length(DatesR));
if("GR" %in% ObjectClass){
- BOOL_NA_TMP <- (Precip < 0) | is.na(Precip ); if(sum(BOOL_NA_TMP)!=0){ BOOL_NA <- BOOL_NA | BOOL_NA_TMP; if(!quiet){ warning("\t Values < 0 or NA values detected in Precip series \n"); } }
- BOOL_NA_TMP <- (PotEvap < 0) | is.na(PotEvap); if(sum(BOOL_NA_TMP)!=0){ BOOL_NA <- BOOL_NA | BOOL_NA_TMP; if(!quiet){ warning("\t Values < 0 or NA values detected in PotEvap series \n"); } }
+ BOOL_NA_TMP <- (Precip < 0) | is.na(Precip ); if(sum(BOOL_NA_TMP)!=0){ BOOL_NA <- BOOL_NA | BOOL_NA_TMP; if(verbose){ warning("\t Values < 0 or NA values detected in Precip series \n"); } }
+ BOOL_NA_TMP <- (PotEvap < 0) | is.na(PotEvap); if(sum(BOOL_NA_TMP)!=0){ BOOL_NA <- BOOL_NA | BOOL_NA_TMP; if(verbose){ warning("\t Values < 0 or NA values detected in PotEvap series \n"); } }
}
if("CemaNeige" %in% ObjectClass){
- BOOL_NA_TMP <- (Precip < 0 ) | is.na(Precip ); if(sum(BOOL_NA_TMP)!=0){ BOOL_NA <- BOOL_NA | BOOL_NA_TMP; if(!quiet){ warning("\t Values < 0 or NA values detected in Precip series \n"); } }
- BOOL_NA_TMP <- (TempMean<(-150)) | is.na(TempMean); if(sum(BOOL_NA_TMP)!=0){ BOOL_NA <- BOOL_NA | BOOL_NA_TMP; if(!quiet){ warning("\t Values < -150) or NA values detected in TempMean series \n"); } }
+ BOOL_NA_TMP <- (Precip < 0 ) | is.na(Precip ); if(sum(BOOL_NA_TMP)!=0){ BOOL_NA <- BOOL_NA | BOOL_NA_TMP; if(verbose){ warning("\t Values < 0 or NA values detected in Precip series \n"); } }
+ BOOL_NA_TMP <- (TempMean<(-150)) | is.na(TempMean); if(sum(BOOL_NA_TMP)!=0){ BOOL_NA <- BOOL_NA | BOOL_NA_TMP; if(verbose){ warning("\t Values < -150) or NA values detected in TempMean series \n"); } }
if(!is.null(TempMin) & !is.null(TempMax)){
- BOOL_NA_TMP <- (TempMin<(-150)) | is.na(TempMin); if(sum(BOOL_NA_TMP)!=0){ BOOL_NA <- BOOL_NA | BOOL_NA_TMP; if(!quiet){ warning("\t Values < -150) or NA values detected in TempMin series \n"); } }
- BOOL_NA_TMP <- (TempMax<(-150)) | is.na(TempMax); if(sum(BOOL_NA_TMP)!=0){ BOOL_NA <- BOOL_NA | BOOL_NA_TMP; if(!quiet){ warning("\t Values < -150) or NA values detected in TempMax series \n"); } } }
+ BOOL_NA_TMP <- (TempMin<(-150)) | is.na(TempMin); if(sum(BOOL_NA_TMP)!=0){ BOOL_NA <- BOOL_NA | BOOL_NA_TMP; if(verbose){ warning("\t Values < -150) or NA values detected in TempMin series \n"); } }
+ BOOL_NA_TMP <- (TempMax<(-150)) | is.na(TempMax); if(sum(BOOL_NA_TMP)!=0){ BOOL_NA <- BOOL_NA | BOOL_NA_TMP; if(verbose){ warning("\t Values < -150) or NA values detected in TempMax series \n"); } } }
}
if(sum(BOOL_NA)!=0){
WTxt <- NULL;
@@ -144,14 +109,14 @@ CreateInputsModel <- function(FUN_MOD,DatesR,Precip,PotEvap=NULL,TempMean=NULL,T
DatesR <- DatesR[Select];
WTxt <- paste(WTxt,"\t -> data were trunced to keep the most recent available time-steps \n",sep="");
WTxt <- paste(WTxt,"\t -> ",length(Select)," time-steps were kept \n",sep="");
- if(!is.null(WTxt) & !quiet){ warning(WTxt); }
+ if(!is.null(WTxt) & verbose){ warning(WTxt); }
}
##DataAltiExtrapolation_Valery
if("CemaNeige" %in% ObjectClass){
- RESULT <- DataAltiExtrapolation_Valery(DatesR=DatesR,Precip=Precip,TempMean=TempMean,TempMin=TempMin,TempMax=TempMax,ZInputs=ZInputs,HypsoData=HypsoData,NLayers=NLayers,quiet=quiet);
- if(!quiet){ if(NLayers==1){ cat(paste("\t Input series were successfully created on 1 elevation layer for use by CemaNeige \n",sep=""));
+ RESULT <- DataAltiExtrapolation_Valery(DatesR=DatesR,Precip=Precip,TempMean=TempMean,TempMin=TempMin,TempMax=TempMax,ZInputs=ZInputs,HypsoData=HypsoData,NLayers=NLayers, verbose = verbose);
+ if(verbose){ if(NLayers==1){ cat(paste("\t Input series were successfully created on 1 elevation layer for use by CemaNeige \n",sep=""));
} else { cat(paste("\t Input series were successfully created on ",NLayers," elevation layers for use by CemaNeige \n",sep="")); } }
}
diff --git a/R/CreateRunOptions.R b/R/CreateRunOptions.R
index 7b943183b67a1518c2130452b9d6bb1f890000cb..ea7f6e772a9324f321674ccf79ded8148995db35 100644
--- a/R/CreateRunOptions.R
+++ b/R/CreateRunOptions.R
@@ -1,5 +1,5 @@
CreateRunOptions <- function(FUN_MOD,InputsModel,IndPeriod_WarmUp=NULL,IndPeriod_Run,IniStates=NULL,IniResLevels=NULL,
- Outputs_Cal=NULL,Outputs_Sim="all",RunSnowModule=TRUE,MeanAnSolidPrecip=NULL,quiet=FALSE){
+ Outputs_Cal=NULL,Outputs_Sim="all",RunSnowModule=TRUE,MeanAnSolidPrecip=NULL, verbose = TRUE){
ObjectClass <- NULL;
@@ -91,11 +91,11 @@ CreateRunOptions <- function(FUN_MOD,InputsModel,IndPeriod_WarmUp=NULL,IndPeriod
if((IndPeriod_Run[1]-1)!=tail(IndPeriod_WarmUp,1) & !identical(IndPeriod_WarmUp,as.integer(0))){
WTxt <- paste(WTxt,"\t Model warm-up period is not directly before the model run period \n",sep=""); }
}
- if(!is.null(WTxt) & !quiet){ warning(WTxt); }
+ if(!is.null(WTxt) & verbose){ warning(WTxt); }
##check_IniStates_and_IniResLevels
- if(is.null(IniStates) & is.null(IniResLevels) & !quiet){
+ if(is.null(IniStates) & is.null(IniResLevels) & verbose){
warning("\t Model states initialisation not defined -> default configuration used \n"); }
if("CemaNeige" %in% ObjectClass){ NLayers <- length(InputsModel$LayerPrecip); } else { NLayers <- 0; }
NState <- NULL;
@@ -190,7 +190,7 @@ CreateRunOptions <- function(FUN_MOD,InputsModel,IndPeriod_WarmUp=NULL,IndPeriod
if(inherits(InputsModel,"yearly" )){ Factor <- 1; }
if(is.null(Factor)){ stop("InputsModel must be of class 'hourly', 'daily', 'monthly' or 'yearly' \n"); return(NULL); }
MeanAnSolidPrecip <- rep(mean(SolidPrecip)*Factor,NLayers); ### default value: same Gseuil for all layers
- if(!quiet){ warning("\t MeanAnSolidPrecip not defined -> it was automatically set to c(",paste(round(MeanAnSolidPrecip),collapse=","),") \n"); }
+ if(verbose){ warning("\t MeanAnSolidPrecip not defined -> it was automatically set to c(",paste(round(MeanAnSolidPrecip),collapse=","),") \n"); }
}
if("CemaNeige" %in% ObjectClass & !is.null(MeanAnSolidPrecip)){
if(!is.vector( MeanAnSolidPrecip) ){ stop(paste("MeanAnSolidPrecip must be a vector of numeric values \n",sep="")); return(NULL); }
@@ -205,13 +205,13 @@ CreateRunOptions <- function(FUN_MOD,InputsModel,IndPeriod_WarmUp=NULL,IndPeriod
WTxt <- NULL;
WTxt <- paste(WTxt,"\t PliqAndMelt was not defined in Outputs_Cal but is needed to feed the hydrological model with the snow module outputs \n",sep="");
WTxt <- paste(WTxt,"\t -> it was automatically added \n",sep="");
- if(!is.null(WTxt) & !quiet){ warning(WTxt); }
+ if(!is.null(WTxt) & verbose){ warning(WTxt); }
Outputs_Cal <- c(Outputs_Cal,"PliqAndMelt"); }
if("PliqAndMelt" %in% Outputs_Sim == FALSE & "all" %in% Outputs_Sim == FALSE){
WTxt <- NULL;
WTxt <- paste(WTxt,"\t PliqAndMelt was not defined in Outputs_Sim but is needed to feed the hydrological model with the snow module outputs \n",sep="");
WTxt <- paste(WTxt,"\t -> it was automatically added \n",sep="");
- if(!is.null(WTxt) & !quiet){ warning(WTxt); }
+ if(!is.null(WTxt) & verbose){ warning(WTxt); }
Outputs_Sim <- c(Outputs_Sim,"PliqAndMelt"); }
}
diff --git a/R/DataAltiExtrapolation_Valery.R b/R/DataAltiExtrapolation_Valery.R
index 8b001bb1cd4001cdd501d9297d3efaf28f1f9664..2882efc713676f135d5b232941eacdf20c6edd08 100644
--- a/R/DataAltiExtrapolation_Valery.R
+++ b/R/DataAltiExtrapolation_Valery.R
@@ -1,46 +1,4 @@
-#*****************************************************************************************************************
-#' Function which extrapolates the precipitation and air temperature series for different elevation layers (method from Valery, 2010).
-#'
-#' Elevation layers of equal surface are created the 101 elevation quantiles (\emph{HypsoData})
-#' and the number requested elevation layers (\emph{NLayers}). \cr
-#' Forcing data (precipitation and air temperature) are extrapolated using gradients from Valery (2010).
-#' (e.g. gradP=0.0004 [m-1] for France and gradT=0.434 [degreC/100m] for January, 1st). \cr
-#' This function is used by the \emph{CreateInputsModel} function. \cr
-#*****************************************************************************************************************
-#' @title Altitudinal extrapolation of precipitation and temperature series
-#' @author Laurent Coron, Pierre Brigode (June 2014)
-#' @references
-#' Turcotte, R., L.-G. Fortin, V. Fortin, J.-P. Fortin and J.-P. Villeneuve (2007),
-#' Operational analysis of the spatial distribution and the temporal evolution of the snowpack water equivalent
-#' in southern Quebec, Canada, Nordic Hydrology, 38(3), 211, doi:10.2166/nh.2007.009. \cr
-#' Valéry, A. (2010), Modélisation précipitations-débit sous influence nivale ? : Elaboration d'un module neige
-#' et évaluation sur 380 bassins versants, PhD thesis (in french), AgroParisTech, Paris, France. \cr
-#' USACE (1956), Snow Hydrology, pp. 437, U.S. Army Coprs of Engineers (USACE) North Pacific Division, Portland, Oregon, USA.
-#' @seealso \code{\link{CreateInputsModel}}, \code{\link{RunModel_CemaNeigeGR4J}}
-#' @encoding UTF-8
-#' @export
-#_FunctionInputs__________________________________________________________________________________________________
-#' @param DatesR [POSIXlt] vector of dates
-#' @param Precip [numeric] time series of daily total precipitation (catchment average) [mm]
-#' @param TempMean [numeric] time series of daily mean air temperature [degC]
-#' @param TempMin (optional) [numeric] time series of daily min air temperature [degC]
-#' @param TempMax (optional) [numeric] time series of daily max air temperature [degC]
-#' @param ZInputs [numeric] real giving the mean elevation of the Precip and Temp series (before extrapolation) [m]
-#' @param HypsoData [numeric] vector of 101 reals: min, q01 to q99 and max of catchment elevation distribution [m]
-#' @param NLayers [numeric] integer giving the number of elevation layers requested [-]
-#' @param quiet (optional) [boolean] boolean indicating if the function is run in quiet mode or not, default=FALSE
-#_FunctionOutputs_________________________________________________________________________________________________
-#' @return list containing the extrapolated series of precip. and air temp. on each elevation layer
-#' \tabular{ll}{
-#' \emph{$LayerPrecip } \tab [list] list of time series of daily precipitation (layer average) [mm] \cr
-#' \emph{$LayerTempMean } \tab [list] list of time series of daily mean air temperature (layer average) [degC] \cr
-#' \emph{$LayerTempMin } \tab [list] list of time series of daily min air temperature (layer average) [degC] \cr
-#' \emph{$LayerTempMax } \tab [list] list of time series of daily max air temperature (layer average) [degC] \cr
-#' \emph{$LayerFracSolidPrecip} \tab [list] list of time series of daily solid precip. fract. (layer average) [-] \cr
-#' \emph{$ZLayers } \tab [numeric] vector of median elevation for each layer \cr
-#' }
-#*****************************************************************************************************************
-DataAltiExtrapolation_Valery <- function(DatesR,Precip,TempMean,TempMin=NULL,TempMax=NULL,ZInputs,HypsoData,NLayers,quiet=FALSE){
+DataAltiExtrapolation_Valery <- function(DatesR,Precip,TempMean,TempMin=NULL,TempMax=NULL,ZInputs,HypsoData,NLayers, verbose = TRUE){
##Altitudinal_gradient_functions_______________________________________________________________
diff --git a/R/ErrorCrit.R b/R/ErrorCrit.R
index 0f0a940fc273e85ce2726e921d6c07b88550498a..3354cf400f496a280c18717fdf4439029b65c712 100644
--- a/R/ErrorCrit.R
+++ b/R/ErrorCrit.R
@@ -1,22 +1,4 @@
-#*****************************************************************************************************************
-#' Function which computes an error criterion with the provided function.
-#*****************************************************************************************************************
-#' @title Error criterion using the provided function
-#' @author Laurent Coron (June 2014)
-#' @seealso \code{\link{ErrorCrit_RMSE}}, \code{\link{ErrorCrit_NSE}}, \code{\link{ErrorCrit_KGE}}
-#' @example tests/example_ErrorCrit.R
-#' @useDynLib airGR
-#' @encoding UTF-8
-#' @export
-#_FunctionInputs__________________________________________________________________________________________________
-#' @param InputsCrit [object of class \emph{InputsCrit}] see \code{\link{CreateInputsCrit}} for details
-#' @param OutputsModel [object of class \emph{OutputsModel}] see \code{\link{RunModel_GR4J}} or \code{\link{RunModel_CemaNeigeGR4J}} for details
-#' @param FUN_CRIT [function] error criterion function (e.g. ErrorCrit_RMSE, ErrorCrit_NSE)
-#' @param quiet (optional) [boolean] boolean indicating if the function is run in quiet mode or not, default=FALSE
-#_FunctionOutputs_________________________________________________________________________________________________
-#' @return [list] list containing the function outputs, see \code{\link{ErrorCrit_RMSE}} or \code{\link{ErrorCrit_NSE}} for details
-#*****************************************************************************************************************'
-ErrorCrit <- function(InputsCrit,OutputsModel,FUN_CRIT,quiet=FALSE){
- return( FUN_CRIT(InputsCrit,OutputsModel,quiet=quiet) )
+ErrorCrit <- function(InputsCrit,OutputsModel,FUN_CRIT, verbose = TRUE){
+ return( FUN_CRIT(InputsCrit,OutputsModel, verbose = verbose) )
}
diff --git a/R/ErrorCrit_KGE.R b/R/ErrorCrit_KGE.R
index b7d8972ba8b0d5f17c3e7e7fac6f1db86a0bb7ff..03dafffa4d4e2da555612c6b0563cb64058809d8 100644
--- a/R/ErrorCrit_KGE.R
+++ b/R/ErrorCrit_KGE.R
@@ -1,37 +1,4 @@
-#*****************************************************************************************************************
-#' Function which computes an error criterion based on the KGE formula proposed by Gupta et al. (2009).
-#'
-#' In addition to the criterion value, the function outputs include a multiplier (-1 or +1) which allows
-#' the use of the function for model calibration: the product CritValue*Multiplier is the criterion to be minimised
-#' (e.g. Multiplier=+1 for RMSE, Multiplier=-1 for NSE).
-#*****************************************************************************************************************
-#' @title Error criterion based on the KGE formula
-#' @author Laurent Coron (June 2014)
-#' @references
-#' Gupta, H. V., Kling, H., Yilmaz, K. K. and Martinez, G. F. (2009),
-#' Decomposition of the mean squared error and NSE performance criteria: Implications
-#' for improving hydrological modelling, Journal of Hydrology, 377(1-2), 80-91, doi:10.1016/j.jhydrol.2009.08.003. \cr
-#' @seealso \code{\link{ErrorCrit_RMSE}}, \code{\link{ErrorCrit_NSE}}, \code{\link{ErrorCrit_KGE2}}
-#' @examples ## see example of the ErrorCrit function
-#' @encoding UTF-8
-#' @export
-#_FunctionInputs__________________________________________________________________________________________________
-#' @param InputsCrit [object of class \emph{InputsCrit}] see \code{\link{CreateInputsCrit}} for details
-#' @param OutputsModel [object of class \emph{OutputsModel}] see \code{\link{RunModel_GR4J}} or \code{\link{RunModel_CemaNeigeGR4J}} for details
-#' @param quiet (optional) [boolean] boolean indicating if the function is run in quiet mode or not, default=FALSE
-#_FunctionOutputs_________________________________________________________________________________________________
-#' @return [list] list containing the function outputs organised as follows:
-#' \tabular{ll}{
-#' \emph{$CritValue } \tab [numeric] value of the criterion \cr
-#' \emph{$CritName } \tab [character] name of the criterion \cr
-#' \emph{$SubCritValues } \tab [numeric] values of the sub-criteria \cr
-#' \emph{$SubCritNames } \tab [character] names of the sub-criteria \cr
-#' \emph{$CritBestValue } \tab [numeric] theoretical best criterion value \cr
-#' \emph{$Multiplier } \tab [numeric] integer indicating whether the criterion is indeed an error (+1) or an efficiency (-1) \cr
-#' \emph{$Ind_notcomputed} \tab [numeric] indices of the time-steps where InputsCrit$BoolCrit=FALSE or no data is available \cr
-#' }
-#*****************************************************************************************************************
-ErrorCrit_KGE <- function(InputsCrit,OutputsModel,quiet=FALSE){
+ErrorCrit_KGE <- function(InputsCrit,OutputsModel, verbose = TRUE){
##Arguments_check________________________________
@@ -72,7 +39,7 @@ ErrorCrit_KGE <- function(InputsCrit,OutputsModel,quiet=FALSE){
if(inherits(OutputsModel,"daily" )){ WarningTS <- 365; }
if(inherits(OutputsModel,"monthly")){ WarningTS <- 12; }
if(inherits(OutputsModel,"yearly" )){ WarningTS <- 3; }
- if(sum(!TS_ignore)<WarningTS & !quiet){ warning(paste("\t criterion computed on less than ",WarningTS," time-steps \n",sep="")); }
+ if(sum(!TS_ignore)<WarningTS & verbose){ warning(paste("\t criterion computed on less than ",WarningTS," time-steps \n",sep="")); }
##Other_variables_preparation
meanVarObs <- mean(VarObs[!TS_ignore]);
meanVarSim <- mean(VarSim[!TS_ignore]);
diff --git a/R/ErrorCrit_KGE2.R b/R/ErrorCrit_KGE2.R
index 4874132895423cab05a140062c0f5c15c460973b..623c9ad025ac04895f536be36c7887b96d1815c2 100644
--- a/R/ErrorCrit_KGE2.R
+++ b/R/ErrorCrit_KGE2.R
@@ -1,40 +1,4 @@
-#*****************************************************************************************************************
-#' Function which computes an error criterion based on the KGE' formula proposed by Kling et al. (2012).
-#'
-#' In addition to the criterion value, the function outputs include a multiplier (-1 or +1) which allows
-#' the use of the function for model calibration: the product CritValue*Multiplier is the criterion to be minimised
-#' (e.g. Multiplier=+1 for RMSE, Multiplier=-1 for NSE).
-#*****************************************************************************************************************
-#' @title Error criterion based on the KGE' formula
-#' @author Laurent Coron (June 2014)
-#' @references
-#' Gupta, H. V., Kling, H., Yilmaz, K. K. and Martinez, G. F. (2009),
-#' Decomposition of the mean squared error and NSE performance criteria: Implications
-#' for improving hydrological modelling, Journal of Hydrology, 377(1-2), 80-91, doi:10.1016/j.jhydrol.2009.08.003. \cr
-#' Kling, H., Fuchs, M. and Paulin, M. (2012),
-#' Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios,
-#' Journal of Hydrology, 424-425, 264-277, doi:10.1016/j.jhydrol.2012.01.011.
-#' @seealso \code{\link{ErrorCrit_RMSE}}, \code{\link{ErrorCrit_NSE}}, \code{\link{ErrorCrit_KGE}}
-#' @examples ## see example of the ErrorCrit function
-#' @encoding UTF-8
-#' @export
-#_FunctionInputs__________________________________________________________________________________________________
-#' @param InputsCrit [object of class \emph{InputsCrit}] see \code{\link{CreateInputsCrit}} for details
-#' @param OutputsModel [object of class \emph{OutputsModel}] see \code{\link{RunModel_GR4J}} or \code{\link{RunModel_CemaNeigeGR4J}} for details
-#' @param quiet (optional) [boolean] boolean indicating if the function is run in quiet mode or not, default=FALSE
-#_FunctionOutputs_________________________________________________________________________________________________
-#' @return [list] list containing the function outputs organised as follows:
-#' \tabular{ll}{
-#' \emph{$CritValue } \tab [numeric] value of the criterion \cr
-#' \emph{$CritName } \tab [character] name of the criterion \cr
-#' \emph{$SubCritValues } \tab [numeric] values of the sub-criteria \cr
-#' \emph{$SubCritNames } \tab [character] names of the sub-criteria \cr
-#' \emph{$CritBestValue } \tab [numeric] theoretical best criterion value \cr
-#' \emph{$Multiplier } \tab [numeric] integer indicating whether the criterion is indeed an error (+1) or an efficiency (-1) \cr
-#' \emph{$Ind_notcomputed} \tab [numeric] indices of the time-steps where InputsCrit$BoolCrit=FALSE or no data is available \cr
-#' }
-#*****************************************************************************************************************'
-ErrorCrit_KGE2 <- function(InputsCrit,OutputsModel,quiet=FALSE){
+ErrorCrit_KGE2 <- function(InputsCrit,OutputsModel, verbose = TRUE){
##Arguments_check________________________________
@@ -75,7 +39,7 @@ ErrorCrit_KGE2 <- function(InputsCrit,OutputsModel,quiet=FALSE){
if(inherits(OutputsModel,"daily" )){ WarningTS <- 365; }
if(inherits(OutputsModel,"monthly")){ WarningTS <- 12; }
if(inherits(OutputsModel,"yearly" )){ WarningTS <- 3; }
- if(sum(!TS_ignore)<WarningTS & !quiet){ warning(paste("\t criterion computed on less than ",WarningTS," time-steps \n",sep="")); }
+ if(sum(!TS_ignore)<WarningTS & verbose){ warning(paste("\t criterion computed on less than ",WarningTS," time-steps \n",sep="")); }
##Other_variables_preparation
meanVarObs <- mean(VarObs[!TS_ignore]);
meanVarSim <- mean(VarSim[!TS_ignore]);
diff --git a/R/ErrorCrit_NSE.R b/R/ErrorCrit_NSE.R
index 3db43d1b16f53bfcaf3ccf34e01e970394d07384..3f50b480d5af0ba7cb6cca16e65d71c3b9ef4a2f 100644
--- a/R/ErrorCrit_NSE.R
+++ b/R/ErrorCrit_NSE.R
@@ -1,35 +1,4 @@
-#*****************************************************************************************************************
-#' Function which computes an error criterion based on the NSE formula proposed by Nash & Sutcliffe (1970).
-#'
-#' In addition to the criterion value, the function outputs include a multiplier (-1 or +1) which allows
-#' the use of the function for model calibration: the product CritValue*Multiplier is the criterion to be minimised
-#' (e.g. Multiplier=+1 for RMSE, Multiplier=-1 for NSE).
-#*****************************************************************************************************************
-#' @title Error criterion based on the NSE formula
-#' @author Laurent Coron (June 2014)
-#' @references
-#' Nash, J.E. and Sutcliffe, J.V. (1970),
-#' River flow forecasting through conceptual models part 1.
-#' A discussion of principles, Journal of Hydrology, 10(3), 282-290, doi:10.1016/0022-1694(70)90255-6. \cr
-#' @seealso \code{\link{ErrorCrit_RMSE}}, \code{\link{ErrorCrit_KGE}}, \code{\link{ErrorCrit_KGE2}}
-#' @examples ## see example of the ErrorCrit function
-#' @encoding UTF-8
-#' @export
-#_FunctionInputs__________________________________________________________________________________________________
-#' @param InputsCrit [object of class \emph{InputsCrit}] see \code{\link{CreateInputsCrit}} for details
-#' @param OutputsModel [object of class \emph{OutputsModel}] see \code{\link{RunModel_GR4J}} or \code{\link{RunModel_CemaNeigeGR4J}} for details
-#' @param quiet (optional) [boolean] boolean indicating if the function is run in quiet mode or not, default=FALSE
-#_FunctionOutputs_________________________________________________________________________________________________
-#' @return [list] list containing the function outputs organised as follows:
-#' \tabular{ll}{
-#' \emph{$CritValue } \tab [numeric] value of the criterion \cr
-#' \emph{$CritName } \tab [character] name of the criterion \cr
-#' \emph{$CritBestValue } \tab [numeric] theoretical best criterion value \cr
-#' \emph{$Multiplier } \tab [numeric] integer indicating whether the criterion is indeed an error (+1) or an efficiency (-1) \cr
-#' \emph{$Ind_notcomputed} \tab [numeric] indices of the time-steps where InputsCrit$BoolCrit=FALSE or no data is available \cr
-#' }
-#*****************************************************************************************************************
-ErrorCrit_NSE <- function(InputsCrit,OutputsModel,quiet=FALSE){
+ErrorCrit_NSE <- function(InputsCrit,OutputsModel, verbose = TRUE){
##Arguments_check________________________________
@@ -69,7 +38,7 @@ ErrorCrit_NSE <- function(InputsCrit,OutputsModel,quiet=FALSE){
if(inherits(OutputsModel,"daily" )){ WarningTS <- 365; }
if(inherits(OutputsModel,"monthly")){ WarningTS <- 12; }
if(inherits(OutputsModel,"yearly" )){ WarningTS <- 3; }
- if(sum(!TS_ignore)<WarningTS & !quiet){ warning(paste("\t criterion computed on less than ",WarningTS," time-steps \n",sep="")); }
+ if(sum(!TS_ignore)<WarningTS & verbose){ warning(paste("\t criterion computed on less than ",WarningTS," time-steps \n",sep="")); }
##Other_variables_preparation
meanVarObs <- mean(VarObs[!TS_ignore]);
meanVarSim <- mean(VarSim[!TS_ignore]);
diff --git a/R/ErrorCrit_RMSE.R b/R/ErrorCrit_RMSE.R
index b4d1d93a6c98bc696d82422544c903ded153f24f..25bc5f1637f54169c344b2634fb8b87fc12f72f3 100644
--- a/R/ErrorCrit_RMSE.R
+++ b/R/ErrorCrit_RMSE.R
@@ -1,31 +1,4 @@
-#*****************************************************************************************************************
-#' Function which computes an error criterion based on the root mean square error (RMSE).
-#'
-#' In addition to the criterion value, the function outputs include a multiplier (-1 or +1) which allows
-#' the use of the function for model calibration: the product CritValue*Multiplier is the criterion to be minimised
-#' (e.g. Multiplier=+1 for RMSE, Multiplier=-1 for NSE).
-#*****************************************************************************************************************
-#' @title Error criterion based on the RMSE
-#' @author Laurent Coron (June 2014)
-#' @seealso \code{\link{ErrorCrit_NSE}}, \code{\link{ErrorCrit_KGE}}, \code{\link{ErrorCrit_KGE2}}
-#' @examples ## see example of the ErrorCrit function
-#' @encoding UTF-8
-#' @export
-#_FunctionInputs__________________________________________________________________________________________________
-#' @param InputsCrit [object of class \emph{InputsCrit}] see \code{\link{CreateInputsCrit}} for details
-#' @param OutputsModel [object of class \emph{OutputsModel}] see \code{\link{RunModel_GR4J}} or \code{\link{RunModel_CemaNeigeGR4J}} for details
-#' @param quiet (optional) [boolean] boolean indicating if the function is run in quiet mode or not, default=FALSE
-#_FunctionOutputs_________________________________________________________________________________________________
-#' @return [list] list containing the function outputs organised as follows:
-#' \tabular{ll}{
-#' \emph{$CritValue } \tab [numeric] value of the criterion \cr
-#' \emph{$CritName } \tab [character] name of the criterion \cr
-#' \emph{$CritBestValue } \tab [numeric] theoretical best criterion value \cr
-#' \emph{$Multiplier } \tab [numeric] integer indicating whether the criterion is indeed an error (+1) or an efficiency (-1) \cr
-#' \emph{$Ind_notcomputed} \tab [numeric] indices of the time-steps where InputsCrit$BoolCrit=FALSE or no data is available \cr
-#' }
-#*****************************************************************************************************************
-ErrorCrit_RMSE <- function(InputsCrit,OutputsModel,quiet=FALSE){
+ErrorCrit_RMSE <- function(InputsCrit,OutputsModel, verbose = TRUE){
##Arguments_check________________________________
@@ -66,7 +39,7 @@ ErrorCrit_RMSE <- function(InputsCrit,OutputsModel,quiet=FALSE){
if(inherits(OutputsModel,"daily" )){ WarningTS <- 365; }
if(inherits(OutputsModel,"monthly")){ WarningTS <- 12; }
if(inherits(OutputsModel,"yearly" )){ WarningTS <- 3; }
- if(sum(!TS_ignore)<WarningTS & !quiet){ warning(paste("\t criterion computed on less than ",WarningTS," time-steps \n",sep="")); }
+ if(sum(!TS_ignore)<WarningTS & verbose){ warning(paste("\t criterion computed on less than ",WarningTS," time-steps \n",sep="")); }
##ErrorCrit______________________________________
diff --git a/R/PEdaily_Oudin.R b/R/PEdaily_Oudin.R
index 1f3b2c6d311a6b514459677e2772a0489961b80c..108616858fd75f9a13dd708069a623639a1d506f 100644
--- a/R/PEdaily_Oudin.R
+++ b/R/PEdaily_Oudin.R
@@ -1,26 +1,3 @@
-#*****************************************************************************************************************
-#' Function which computes daily PE using the formula from Oudin et al. (2005).
-#*****************************************************************************************************************
-#' @title Computation of daily series of potential evapotranspiration with Oudin's formula
-#' @author Laurent Coron (December 2013)
-#' @references
-#' Oudin, L., F. Hervieu, C. Michel, C. Perrin, V. Andréassian, F. Anctil and C. Loumagne (2005),
-#' Which potential evapotranspiration input for a lumped rainfall-runoff model?: Part 2-Towards a
-#' simple and efficient potential evapotranspiration model for rainfall-runoff modelling, Journal of Hydrology,
-#' 303(1-4), 290-306, doi:10.1016/j.jhydrol.2004.08.026.
-#' @examples
-#' require(airGR)
-#' data(L0123001)
-#' PotEvap <- PEdaily_Oudin(JD=as.POSIXlt(BasinObs$DatesR)$yday,Temp=BasinObs$T,LatRad=0.8)
-#' @encoding UTF-8
-#' @export
-#_FunctionInputs__________________________________________________________________________________________________
-#' @param JD [numeric] time series of julian day [-]
-#' @param Temp [numeric] time series of daily mean air temperature [degC]
-#' @param LatRad [numeric] latitude of measurement for the temperature series [rad]
-#_FunctionOutputs_________________________________________________________________________________________________
-#' @return [numeric] time series of daily potential evapotranspiration [mm/d]
-#*****************************************************************************************************************'
PEdaily_Oudin <- function(JD,Temp,LatRad){
PE_Oudin_D <- rep(NA,length(Temp));
diff --git a/R/RunModel.R b/R/RunModel.R
index d4b5086192566b5acdc1babb806c2cadf3be0f30..876daad09f683f992d4b8bea057b85f4cbce810f 100644
--- a/R/RunModel.R
+++ b/R/RunModel.R
@@ -1,21 +1,3 @@
-#*****************************************************************************************************************
-#' Function which performs a single model run with the provided function.
-#*****************************************************************************************************************
-#' @title Run with the provided hydrological model function
-#' @author Laurent Coron (June 2014)
-#' @seealso \code{\link{RunModel_GR4J}}, \code{\link{RunModel_CemaNeigeGR4J}}, \code{\link{CreateInputsModel}}, \code{\link{CreateRunOptions}}.
-#' @example tests/example_RunModel.R
-#' @useDynLib airGR
-#' @encoding UTF-8
-#' @export
-#_FunctionInputs__________________________________________________________________________________________________
-#' @param InputsModel [object of class \emph{InputsModel}] see \code{\link{CreateInputsModel}} for details
-#' @param RunOptions [object of class \emph{RunOptions}] see \code{\link{CreateRunOptions}} for details
-#' @param Param [numeric] vector of model parameters
-#' @param FUN_MOD [function] hydrological model function (e.g. RunModel_GR4J, RunModel_CemaNeigeGR4J)
-#_FunctionOutputs_________________________________________________________________________________________________
-#' @return [list] see \code{\link{RunModel_GR4J}} or \code{\link{RunModel_CemaNeigeGR4J}} for details
-#*****************************************************************************************************************'
RunModel <- function(InputsModel,RunOptions,Param,FUN_MOD){
return( FUN_MOD(InputsModel,RunOptions,Param) )
}
diff --git a/R/RunModel_GR1A.R b/R/RunModel_GR1A.R
index 1797df8ebfaa5047b9f135d0a7c6a915e0dba112..5c400d9213ab57fd041f535827690b3b1cb67949 100644
--- a/R/RunModel_GR1A.R
+++ b/R/RunModel_GR1A.R
@@ -1,36 +1,3 @@
-#*****************************************************************************************************************
-#' Function which performs a single run for the GR1A yearly lumped model.
-#'
-#' For further details on the model, see the references section.
-#' For further details on the argument structures and initialisation options, see \code{\link{CreateRunOptions}}.
-#*****************************************************************************************************************
-#' @title Run with the GR1A hydrological model
-#' @author Laurent Coron (March 2015)
-#' @example tests/example_RunModel_GR1A.R
-#' @references
-#' Mouelhi S. (2003),
-#' Vers une chaîne cohérente de modèles pluie-débit conceptuels globaux aux pas de temps pluriannuel, annuel, mensuel et journalier,
-#' PhD thesis (in French), ENGREF, Cemagref Antony, France. \cr
-#' @useDynLib airGR
-#' @encoding UTF-8
-#' @export
-#_FunctionInputs__________________________________________________________________________________________________
-#' @param InputsModel [object of class \emph{InputsModel}] see \code{\link{CreateInputsModel}} for details
-#' @param RunOptions [object of class \emph{RunOptions}] see \code{\link{CreateRunOptions}} for details
-#' @param Param [numeric] vector of 1 parameter
-#' \tabular{ll}{
-#' GR1A X1 \tab model parameter [mm] \cr
-#' }
-#_FunctionOutputs_________________________________________________________________________________________________
-#' @return [list] list containing the function outputs organised as follows:
-#' \tabular{ll}{
-#' \emph{$DatesR } \tab [POSIXlt] series of dates \cr
-#' \emph{$PotEvap } \tab [numeric] series of input potential evapotranspiration [mm/year] \cr
-#' \emph{$Precip } \tab [numeric] series of input total precipitation [mm/year] \cr
-#' \emph{$Qsim } \tab [numeric] series of Qsim [mm/year] \cr
-#' }
-#' (refer to the provided references or to the package source code for further details on these model outputs)
-#*****************************************************************************************************************
RunModel_GR1A <- function(InputsModel,RunOptions,Param){
NParam <- 1;
diff --git a/R/SeriesAggreg.R b/R/SeriesAggreg.R
index ec67f7ac0929dc8c339e369334e4f621015c3c42..4a819fb9b633a549827ec59dc1ecbcf3b9e24a2a 100644
--- a/R/SeriesAggreg.R
+++ b/R/SeriesAggreg.R
@@ -1,26 +1,4 @@
-#*************************************************************************************************
-#' Conversion of time series to another time-step (aggregation only).
-#' Warning : on the aggregated outputs, the dates correpond to the beginning ot the time-step
-#' (e.g. for daily time-series 01/03/2005 00:00 = value for period 01/03/2005 00:00 - 01/03/2005 23:59 )
-#' (e.g. for monthly time-series 01/03/2005 00:00 = value for period 01/03/2005 00:00 - 31/03/2005 23:59 )
-#' (e.g. for yearly time-series 01/03/2005 00:00 = value for period 01/03/2005 00:00 - 28/02/2006 23:59 )
-#*************************************************************************************************
-#' @title Conversion of time series to another time-step (aggregation only)
-#' @author Laurent Coron (March 2015)
-#' @example tests/example_SeriesAggreg.R
-#' @encoding UTF-8
-#' @export
-#_FunctionInputs__________________________________________________________________________________
-#' @param TabSeries [POSIXlt+numeric] dataframe containing the vector of dates and the time series values
-#' @param TimeFormat [character] desired format (i.e. "hourly", "daily", "monthly" or "yearly")
-#' @param NewTimeFormat [character] desired format (i.e. "hourly", "daily", "monthly" or "yearly")
-#' @param ConvertFun [character] names of aggregation functions (e.g. for P[mm],T[deg],Q[mm] : ConvertFun=c("sum","mean","sum"))
-#' @param YearFirstMonth (optional) [numeric] integer used when 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)
-#' @param quiet (optional) [boolean] boolean indicating if the function is run in quiet mode or not, default=FALSE
-#_FunctionOutputs_________________________________________________________________________________
-#' @return [POSIXlt+numeric] dataframe containing a vector of aggregated dates and time series values
-#**************************************************************************************************'
-SeriesAggreg <- function(TabSeries,TimeFormat,NewTimeFormat,ConvertFun,YearFirstMonth=01,quiet=FALSE){
+SeriesAggreg <- function(TabSeries,TimeFormat,NewTimeFormat,ConvertFun,YearFirstMonth=01, verbose = TRUE){
##_Arguments_check
@@ -76,7 +54,7 @@ SeriesAggreg <- function(TabSeries,TimeFormat,NewTimeFormat,ConvertFun,YearFirst
(TimeFormat == "daily" & NewTimeFormat=="daily" ) |
(TimeFormat == "monthly" & NewTimeFormat=="monthly") |
(TimeFormat == "yearly" & NewTimeFormat=="yearly" )){
- if(!quiet){ warning("\t The old and new format are identical \n\t -> no time-step conversion was performed \n"); return(TabSeries); } }
+ if(verbose){ warning("\t The old and new format are identical \n\t -> no time-step conversion was performed \n"); return(TabSeries); } }
##_Time_step_conversion
diff --git a/R/TransfoParam.R b/R/TransfoParam.R
index ae0da09cbbe560fc038fb2cde46cb8e18b31fe9d..70120d973942f23fcc37327af54118f304543812 100644
--- a/R/TransfoParam.R
+++ b/R/TransfoParam.R
@@ -1,18 +1,3 @@
-#**************************************************************************************************
-#' Function which transforms model parameters (from real to transformed parameters and vice versa) using the provided function.
-#**************************************************************************************************
-#' @title Transformation of the parameters using the provided function
-#' @author Laurent Coron (June 2014)
-#' @seealso \code{\link{TransfoParam_GR4J}}, \code{\link{TransfoParam_GR5J}}, \code{\link{TransfoParam_GR6J}}, \code{\link{TransfoParam_CemaNeige}}
-#' @example tests/example_TransfoParam.R
-#' @encoding UTF-8
-#' @export
-#_FunctionInputsOutputs____________________________________________________________________________
-#' @param ParamIn [numeric] matrix of parameter sets (sets in line, parameter values in column)
-#' @param Direction [character] direction of the transformation: use "RT" for Real->Transformed and "TR" for Transformed->Real
-#' @param FUN_TRANSFO [function] model parameters transformation function (e.g. TransfoParam_GR4J, TransfoParam_CemaNeigeGR4J)
-#' @return \emph{ParamOut} [numeric] matrix of parameter sets (sets in line, parameter values in column)
-#**************************************************************************************************'
TransfoParam <- function(ParamIn,Direction,FUN_TRANSFO){
return( FUN_TRANSFO(ParamIn,Direction) )
}
diff --git a/R/plot_OutputsModel.R b/R/plot_OutputsModel.R
index 0ef583f506607b21630698dad24df6bec8526e2e..6af27419689c871d3ec33564dee99a57af2e6954 100644
--- a/R/plot_OutputsModel.R
+++ b/R/plot_OutputsModel.R
@@ -1,28 +1,4 @@
-#*****************************************************************************************************************
-#' Function which creates a screen plot giving an overview of the model outputs
-#'
-#' Dashboard of results including various graphs (depending on the model):
-#' (1) time series of total precipitation and simulated flows (and observed flows if provided)
-#' (2) interannual median monthly simulated flow (and observed flows if provided)
-#' (3) correlation plot between simulated and observed flows (if observed flows provided)
-#' (4) cumulative frequency plot for simulated flows (and observed flows if provided)
-#*****************************************************************************************************************
-#' @title Default preview of model outputs
-#' @author Laurent Coron (June 2014)
-## @example tests/example_plot_OutputsModel.R
-#' @encoding UTF-8
-#' @export
-#_FunctionInputs__________________________________________________________________________________________________
-#' @param OutputsModel [object of class \emph{OutputsModel}] list of model outputs (which must at least include DatesR, Precip and Qsim) [POSIXlt, mm, mm]
-#' @param Qobs (optional) [numeric] time series of observed flow (for the same time-steps than simulated) [mm]
-#' @param IndPeriod_Plot (optional) [numeric] indices of the time-steps to be plotted (among the OutputsModel series)
-#' @param BasinArea (optional) [numeric] basin area [km2], used to plot flow axes in m3/s
-#' @param PlotChoice (optional) [character] choice of plots \cr (e.g. c("Precip","SnowPack","Flows","Regime","CumFreq","CorQQ")), default="all"
-#' @param quiet (optional) [boolean] boolean indicating if the function is run in quiet mode or not, default=FALSE
-#_FunctionOutputs_________________________________________________________________________________________________
-#' @return screen plot window
-#*****************************************************************************************************************
-plot_OutputsModel <- function(OutputsModel,Qobs=NULL,IndPeriod_Plot=NULL,BasinArea=NULL,PlotChoice="all",quiet=FALSE){
+plot_OutputsModel <- function(OutputsModel,Qobs=NULL,IndPeriod_Plot=NULL,BasinArea=NULL,PlotChoice="all", verbose = TRUE){
if(!inherits(OutputsModel,"GR") & !inherits(OutputsModel,"CemaNeige")){ stop(paste("OutputsModel not in the correct format for default plotting \n",sep="")); return(NULL); }
@@ -76,8 +52,8 @@ plot_OutputsModel <- function(OutputsModel,Qobs=NULL,IndPeriod_Plot=NULL,BasinAr
} else { PsolLayerMean <- PsolLayerMean + OutputsModel$CemaNeigeLayers[[iLayer]]$Psol/NLayers; } } }
BOOL_QobsZero <- FALSE; if(BOOL_Qobs){ SelectQobsNotZero <- (round(Qobs[IndPeriod_Plot] ,4)!=0); BOOL_QobsZero <- sum(!SelectQobsNotZero,na.rm=TRUE)>0; }
BOOL_QsimZero <- FALSE; if(BOOL_Qsim){ SelectQsimNotZero <- (round(OutputsModel$Qsim[IndPeriod_Plot],4)!=0); BOOL_QsimZero <- sum(!SelectQsimNotZero,na.rm=TRUE)>0; }
- if(BOOL_QobsZero & !quiet){ warning("\t zeroes detected in Qobs -> some plots in the log space will not be created using all time-steps \n"); }
- if(BOOL_QsimZero & !quiet){ warning("\t zeroes detected in Qsim -> some plots in the log space will not be created using all time-steps \n"); }
+ if(BOOL_QobsZero & verbose){ warning("\t zeroes detected in Qobs -> some plots in the log space will not be created using all time-steps \n"); }
+ if(BOOL_QsimZero & verbose){ warning("\t zeroes detected in Qsim -> some plots in the log space will not be created using all time-steps \n"); }
BOOL_FilterZero <- TRUE;
##Plots_choices