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Commit e006e776 authored by Manuela's avatar Manuela
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update output format

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R/fun_stoch_sim_wave.R
+ 26
− 26
View file @ e006e776
......@@ -3,7 +3,7 @@
prsim.wave <- function(data, station_id="Qobs", number_sim=1, win_h_length=15,
marginal=c("kappa","empirical"), n_par=4, n_wave=100, marginalpar=TRUE,
GoFtest=NULL, out_dir, verbose=TRUE, suppWarn=FALSE, ...){
GoFtest=NULL, verbose=TRUE, suppWarn=FALSE, ...){
### function for backtransformation of continuous wavelet transform
### inverse wavelet transform
......@@ -423,28 +423,28 @@ prsim.wave <- function(data, station_id="Qobs", number_sim=1, win_h_length=15,
if (GoFtest=="NULL") {
p_vals <- NULL
}
if(!is.na(out_dir)){
### set output directory
setwd(out_dir)
if(marginal != "empirical"){
if (marginalpar) { # also return intermediate results
# return(list(simulation=data_stoch, pars=par_day, p_val=p_vals))
out_list <- list(simulation=data_stoch, pars=par_day, p_val=p_vals)
save(file=paste(l,'_stoch_sim.rda',sep=''),out_list)
} else {
# return(list(simulation=data_stoch, pars=NULL, p_val=p_vals))
out_list <- list(simulation=data_stoch, pars=NULL, p_val=p_vals)
save(file=paste(l,'_stoch_sim.rda',sep=''),out_list)
}
}else{
# return(list(simulation=data_stoch))
out_list <- list(simulation=data_stoch, pars=NULL, p_val=NULL)
save(file=paste(l,'_stoch_sim.rda',sep=''),simulation=out_list)
}
}
if(is.na(out_dir)){
# if(!is.na(out_dir)){
# ### set output directory
# setwd(out_dir)
# if(marginal != "empirical"){
# if (marginalpar) { # also return intermediate results
# # return(list(simulation=data_stoch, pars=par_day, p_val=p_vals))
# out_list <- list(simulation=data_stoch, pars=par_day, p_val=p_vals)
# save(file=paste(l,'_stoch_sim.rda',sep=''),out_list)
# } else {
# # return(list(simulation=data_stoch, pars=NULL, p_val=p_vals))
# out_list <- list(simulation=data_stoch, pars=NULL, p_val=p_vals)
# save(file=paste(l,'_stoch_sim.rda',sep=''),out_list)
# }
# }else{
# # return(list(simulation=data_stoch))
# out_list <- list(simulation=data_stoch, pars=NULL, p_val=NULL)
# save(file=paste(l,'_stoch_sim.rda',sep=''),simulation=out_list)
# }
# }
#
# if(is.na(out_dir)){
### store values in list
if(marginal != "empirical"){
if (marginalpar) { # also return intermediate results
......@@ -458,10 +458,10 @@ prsim.wave <- function(data, station_id="Qobs", number_sim=1, win_h_length=15,
# return(list(simulation=data_stoch))
out_list[[l]] <- list(simulation=data_stoch, pars=NULL, p_val=NULL)
}
}
# }
### to to next station
}
if(is.na(out_dir)){
# if(is.na(out_dir)){
return(out_list)
}
# }
}
demo/PRSim_wave.R
+ 3
− 5
View file @ e006e776
dontrun{
# short demo
data(runoff_multi_sites)
### for empirical distribution
out <- prsim.wave(data=runoff_multi_sites, number_sim=1, marginal="empirical",out_dir=NA)
### for kappa distribution
prsim.wave(data=runoff_multi_sites, number_sim=1, marginal="kappa", GoFtest = "KS",out_dir=NA)
out <- prsim.wave(data=runoff_multi_sites, number_sim=1, marginal="kappa", GoFtest = "KS",out_dir=NA)
### for GEV distribution
require("evd")
......@@ -16,7 +14,7 @@ GEV_fit <- function( xdat, ...) gev.fit( xdat, show=FALSE, ...)$mle
### GEV
prsim.wave(data=runoff_multi_sites, number_sim=1, marginal="GEV", GoFtest = "KS", n_par=3, out_dir=NA)
out <- prsim.wave(data=runoff_multi_sites, number_sim=1, marginal="GEV", GoFtest = "KS", n_par=3, out_dir=NA)
### load stochastically simulated time series
......@@ -30,5 +28,5 @@ plot(sim$timestamp[1:1000], sim$Qobs[1:1000], type="l",
### add simulations
matlines(sim$timestamp[1:1000], sim[1:1000, grep("r", names(sim))],
lty=1, col="gray")
}
man/fun_stoch_sim_wave.Rd
+ 2
− 4
View file @ e006e776
......@@ -22,7 +22,6 @@ prsim.wave(data, station_id="Qobs", number_sim=1, win_h_length=15,
\item{verbose}{logical. Should progress be reported?}
\item{marginalpar}{logical. Should the estimated parameters of the distribution used be returned?}
\item{n_wave}{number of scales to be considered in the continuous wavelet transform.}
\item{out_dir}{path of output directory where the stochastic simulations for the stations considered are saved. If NA, list of data frames for the different sites is returned}
\item{suppWarn}{logical. See \sQuote{Details}.}
\item{...}{any other argument passed to the sub-function specifying the cdf for fitting. See \sQuote{Details} and \sQuote{Examples}. }
}
......@@ -58,8 +57,7 @@ Manuela Brunner
}
\examples{
data(runoff_multi_sites)
prsim.wave(runoff_multi_sites, "Qobs", 1, suppWarn=TRUE,
out_dir='~/PRSim-devel/data/simulations_multi_sites')
prsim.wave(runoff_multi_sites, "Qobs", 1, suppWarn=TRUE)
# warnings() # as a follow-up to `suppWarn=TRUE`
## Specifying particular CDFs:
......@@ -86,7 +84,7 @@ GB2_fit <- function( xdat, ...) ml.gb2( xdat, ...)$opt1$par
\dontrun{ # The following call requires half minute or so to execute.
Some warnings are issued
prsim.wave(runoff_multi_sites, "Qobs", 1, suppWarn=TRUE,
marginal="GB2", out_dir='dir_PRsim_wave_data/simulations_multi_sites')
marginal="GB2")
}
......
man/simulations_multi_sites.Rd
+ 13
− 5
View file @ e006e776
......@@ -43,15 +43,23 @@ par(mfrow=c(2,1),mar=c(3,3,2,1))
### determine ylim
ylim_max <- max(sim[[1]]$Qobs)*1.5
### observed
plot(sim[[1]]$Qobs[1:1000],ylab=expression(bold(paste("Specific discharge [mm/d]"))),xlab="Time [d]",type="l",col=col_vect_obs[1],ylim=c(0,ylim_max),
main='Observations')
plot(sim[[1]]$Qobs[1:1000],
ylab=expression(bold(
paste("Specific discharge [mm/d]"))),
xlab="Time [d]",type="l",col=col_vect_obs[1],
ylim=c(0,ylim_max),main='Observations')
for(l in 2:4){
lines(sim[[l]]$Qobs[1:1000],col=col_vect_obs[l])
}
legend('topleft',legend=c('Station 1','Station 2','Station 3','Station 4'),lty=1,col=col_vect_obs[1:4])
legend('topleft',legend=c('Station 1','Station 2',
'Station 3','Station 4'),
lty=1,col=col_vect_obs[1:4])
### simulated (one run)
plot(sim[[1]]$r1[1:1000],ylab=expression(bold(paste("Specific discharge [mm/d]"))),xlab="Time [d]",type="l",col=col_vect_sim[1],ylim=c(0,ylim_max),
main='Stochastic simulations')
plot(sim[[1]]$r1[1:1000],
ylab=expression(bold(paste("Specific discharge [mm/d]"))),
xlab="Time [d]",type="l",col=col_vect_sim[1],
ylim=c(0,ylim_max),
main='Stochastic simulations')
for(l in 2:4){
lines(sim[[l]]$r1[1:1000],col=col_vect_sim[l])
}
......
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