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Merge pull request #20 from hollorol/feature/convex-dependency-refinement

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solving the old dependency group problem
This commit is contained in:
Roland Hollós 2023-06-20 13:14:49 +00:00 committed by GitHub
commit 12b6d580de
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6 changed files with 162 additions and 478 deletions
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1
RBBGCMuso/R/assistantFunctions.R
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@ -150,6 +150,7 @@ readValuesFromFile <- function(filename, linums){
return(values) return(values)
} }
#' readMeasuredMuso #' readMeasuredMuso
#' #'
#' MuSo data reader #' MuSo data reader

23
RBBGCMuso/R/calibrateMuso.R
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@ -12,6 +12,7 @@ calibrateMuso <- function(measuredData, parameters =read.csv("parameters.csv", s
skipSpinup = TRUE, plotName = "calib.jpg", skipSpinup = TRUE, plotName = "calib.jpg",
modifyOriginal=TRUE, likelihood, uncertainity = NULL, modifyOriginal=TRUE, likelihood, uncertainity = NULL,
naVal = NULL, postProcString = NULL, naVal = NULL, postProcString = NULL,
sourceFile=NULL, # bases for musoRand if dependecy group is not fully defined by parameters.csv
thread_prefix="thread", numCores = max(c(parallel::detectCores()-1,1)), pb = txtProgressBar(min=0, max=iterations, style=3), thread_prefix="thread", numCores = max(c(parallel::detectCores()-1,1)), pb = txtProgressBar(min=0, max=iterations, style=3),
maxLikelihoodEpc=TRUE, maxLikelihoodEpc=TRUE,
pbUpdate = setTxtProgressBar, outputLoc="./", method="GLUE",lg = FALSE, w=NULL, ...){ pbUpdate = setTxtProgressBar, outputLoc="./", method="GLUE",lg = FALSE, w=NULL, ...){
@ -44,6 +45,7 @@ calibrateMuso <- function(measuredData, parameters =read.csv("parameters.csv", s
future({ future({
tryCatch( tryCatch(
musoSingleThread(measuredData, parameters, startDate, musoSingleThread(measuredData, parameters, startDate,
sourceFile=settings$epc[2], # EPC SPECIFIC
endDate, formatString, endDate, formatString,
dataVar, outLoc, dataVar, outLoc,
preTag, settings, preTag, settings,
@ -52,6 +54,7 @@ calibrateMuso <- function(measuredData, parameters =read.csv("parameters.csv", s
modifyOriginal, likelihood, uncertainity, modifyOriginal, likelihood, uncertainity,
naVal, postProcString, i) naVal, postProcString, i)
, error = function(e){ , error = function(e){
# browser()
writeLines(as.character(iterations),"progress.txt") writeLines(as.character(iterations),"progress.txt")
}) })
@ -127,6 +130,19 @@ calibrateMuso <- function(measuredData, parameters =read.csv("parameters.csv", s
switch(method, switch(method,
"GLUE"={ "GLUE"={
musoGlue(results, parameters=parameters,settings=settings, w=w, lg=lg) musoGlue(results, parameters=parameters,settings=settings, w=w, lg=lg)
liks <- results[,sprintf("%s_likelihood",names(likelihood))]
epcIndexes <- future::value(fut[[1]], stdout = FALSE, signal=FALSE)
if(ncol(liks) == 1){
ml_place <- which.max(liks)
} else {
ml_place <- which.max(as.matrix(liks) %*% as.matrix(w))
}
epcVals <- results[ml_place,1:length(epcIndexes)]
epcPlace <- file.path(dirname(settings$inputFiles),settings$epc)[2]
changemulline(filePaths= epcPlace, epcIndexes,
epcVals, src =epcPlace,# settings$epcInput[2],
outFiles = file.path(outputLoc, "maxLikelihood_epc.epc"))
names(epcVals) <- epcIndexes
}, },
"agromo"={ "agromo"={
liks <- results[,sprintf("%s_likelihood",names(likelihood))] liks <- results[,sprintf("%s_likelihood",names(likelihood))]
@ -174,6 +190,7 @@ copyToThreadDirs <- function(prefix="thread", numcores=parallel::detectCores()-1
} }
musoSingleThread <- function(measuredData, parameters = NULL, startDate = NULL, musoSingleThread <- function(measuredData, parameters = NULL, startDate = NULL,
sourceFile=NULL,
endDate = NULL, formatString = "%Y-%m-%d", endDate = NULL, formatString = "%Y-%m-%d",
dataVar, outLoc = "./calib", dataVar, outLoc = "./calib",
preTag = "cal-", settings = setupMuso(), preTag = "cal-", settings = setupMuso(),
@ -234,10 +251,10 @@ musoSingleThread <- function(measuredData, parameters = NULL, startDate = NULL,
## row numbers ## row numbers
print("optiMuso is randomizing the epc parameters now...",quote = FALSE) print("optiMuso is randomizing the epc parameters now...",quote = FALSE)
if(iterations < 3000){ if(iterations < 3000){
randVals <- musoRand(parameters = parameters,constrains = NULL, iterations = 3000) randVals <- musoRand(parameters = parameters,constrains = NULL, iterations = 3000,sourceFile=sourceFile)
randVals[[2]]<- randVals[[2]][sample(1:3000,iterations),] randVals[[2]]<- randVals[[2]][sample(1:3000,iterations),] # TODO: last not random
} else { } else {
randVals <- musoRand(parameters = parameters,constrains = NULL, iterations = iterations) randVals <- musoRand(parameters = parameters,constrains = NULL, iterations = iterations,sourceFile=sourceFile)
} }
origEpc <- readValuesFromFile(settings$epc[2],randVals[[1]]) origEpc <- readValuesFromFile(settings$epc[2],randVals[[1]])

3
RBBGCMuso/R/musoMonte.R
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@ -18,6 +18,7 @@
musoMonte <- function(settings=NULL, musoMonte <- function(settings=NULL,
parameters=NULL, parameters=NULL,
sourceFile=NULL,
inputDir = "./", inputDir = "./",
outLoc = "./calib", outLoc = "./calib",
iterations = 10, iterations = 10,
@ -100,7 +101,7 @@ musoMonte <- function(settings=NULL,
##reading the original epc file at the specified ##reading the original epc file at the specified
## row numbers ## row numbers
if(iterations < 3000){ if(iterations < 3000){
randVals <- musoRand(parameters = parameters,fileType="epc", iterations = 3000) randVals <- musoRand(parameters = parameters,fileType="epc", iterations = 3000,sourceFile=sourceFile)
randVals[[2]]<- randVals[[2]][sample(1:3000,iterations),] randVals[[2]]<- randVals[[2]][sample(1:3000,iterations),]
} else { } else {
randVals <- musoRand(parameters = parameters,fileType="epc", iterations = iterations) randVals <- musoRand(parameters = parameters,fileType="epc", iterations = iterations)

229
RBBGCMuso/R/musoRand.R
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@ -8,147 +8,182 @@
#' @importFrom limSolve xsample #' @importFrom limSolve xsample
#' @export #' @export
musoRand <- function(parameters, iterations=3000, fileType="epc", constrains = NULL, burnin = NULL){ musoRand <- function(parameters, iterations=3000, fileType="epc", sourceFile=NULL, constrains = NULL, burnin = NULL){
if(is.null(constrains)){ if(is.null(constrains)){
constMatrix <- constrains constMatrix <- constrains
constMatrix <- getOption("RMuso_constMatrix")[[fileType]][[as.character(getOption("RMuso_version"))]] constMatrix <- getOption("RMuso_constMatrix")[[fileType]][[as.character(getOption("RMuso_version"))]]
} else { } else {
constMatrix <- constrains constMatrix <- constrains
} }
parameters <- parameters[,-1] parameters <- parameters[,-1]
constMatrix <- constMatrix[,-1] constMatrix <- constMatrix[,-1]
depTableMaker <- function(constMatrix,parameters){ depTableMaker <- function(constMatrix,parameters){
# browser()
parameters <- parameters[order(parameters[,1]),] ## BUG!!! parameters <- parameters[order(parameters[,1]),] ## BUG!!!
selectedRows <- constMatrix[,"INDEX"] %in% parameters[,1] selectedRows <- constMatrix[,"INDEX"] %in% parameters[,1]
# constMatrix[constMatrix[,"INDEX"] %in% parameters[,1],]
rankList <- rank(constMatrix[selectedRows,2]) rankList <- rank(constMatrix[selectedRows,2])
constMatrix[selectedRows,c(5,6)] <- parameters[rankList,c(2,3)] constMatrix[selectedRows,c(5,6)] <- parameters[rankList,c(2,3)]
logiConstrain <- (constMatrix[,"GROUP"] %in% constMatrix[constMatrix[,"INDEX"] %in% parameters[,1],"GROUP"] &
(constMatrix[,"GROUP"]!=0)) | ((constMatrix[,"INDEX"] %in% parameters[,1]) & (constMatrix[,"GROUP"] == 0))
constMatrix <- constMatrix[logiConstrain,]
constMatrix <- constMatrix[order(apply(constMatrix[,7:8],1,function(x){x[1]/10+abs(x[2])})),]
constMatrix
logiConstrain <- (constMatrix[,"GROUP"] %in% constMatrix[constMatrix[,"INDEX"] %in% parameters[,1],"GROUP"] &
(constMatrix[,"GROUP"]!=0)) | ((constMatrix[,"INDEX"] %in% parameters[,1]) & (constMatrix[,"GROUP"] == 0))
constMatrix <- constMatrix[logiConstrain,]
constMatrix <- constMatrix[order(apply(constMatrix[,7:8],1,function(x){x[1]/10+abs(x[2])})),]
paragroups <- unique(constMatrix[constMatrix[,"GROUP"] != 0,"GROUP"])
missingMembers <- list()
for(group in paragroups){
groupMembers <- constMatrix[constMatrix[,"GROUP"] == group,"INDEX"]
missingMemberElems <- groupMembers[is.na(match(groupMembers,parameters[,1]))]
if(length(missingMemberElems) > 0){
missingMembers$indices <- c(missingMembers$indices,
match(missingMemberElems,constMatrix[,"INDEX"]))
if(is.null(sourceFile)){
stop(sprintf("All group members of the group (%s) have to be in parameters if sourceFile not(epc file)",group))
}
missingMembers$values <- c(missingMembers$values,
suppressWarnings(readValuesFromFile(sourceFile,missingMemberElems)))
}
}
list(depTable=constMatrix,missingMembers=missingMembers)
} }
# browser() # browser()
genMat0 <- function(dep){ genMat0 <- function(dep, missingIndices){
numberOfVariable <- nrow(dep) numberOfVariable <- nrow(dep)
G <- rbind(diag(numberOfVariable), -1*diag(numberOfVariable)) if(length(missingMembers) != 0){
h <- c(dependences[,5], -1*dependences[,6]) dep <- dep[-missingIndices,]
return(list(G=G,h=h)) }
G <- rbind(diag(numberOfVariable), -1*diag(numberOfVariable))
h <- c(dependences[,5], -1*dependences[,6])
return(list(G=G,h=h))
} }
genMat1 <- function(dep, N){ genMat1 <- function(dep, N){
## Range <- sapply(list(min,max),function(x){ ## Range <- sapply(list(min,max),function(x){
## x(as.numeric(rownames(dep))) ## x(as.numeric(rownames(dep)))
## }) It is more elegant, more general, but slower ## }) It is more elegant, more general, but slower
Range <- (function(x){ Range <- (function(x){
c(min(x), max(x)) c(min(x), max(x))
})(as.numeric(dep[,"rowIndex"])) })(as.numeric(dep[,"rowIndex"]))
numberOfVariables <- nrow(dep) numberOfVariables <- nrow(dep)
G<- -1*diag(numberOfVariables) G<- -1*diag(numberOfVariables)
for(i in 1:numberOfVariables){ for(i in 1:numberOfVariables){
if(dep[i,4]!=0){ if(dep[i,4]!=0){
G[i,dep[i,4]] <- 1 G[i,dep[i,4]] <- 1
} }
}
# browser()
G<-G[dep[,4]!=0,]
}
# browser()
G<-G[dep[,4]!=0,]
if(is.null(nrow(G))){ if(is.null(nrow(G))){
G<-t(as.matrix(G)) G<-t(as.matrix(G))
} }
numRowsInG <- nrow(G) numRowsInG <- nrow(G)
if(Range[1]==1){ if(Range[1]==1){
G<-cbind(G,matrix(ncol=(N-Range[2]),nrow=numRowsInG,data=0)) G<-cbind(G,matrix(ncol=(N-Range[2]),nrow=numRowsInG,data=0))
} else{ } else{
if(Range[2]==N){ if(Range[2]==N){
G<-cbind(matrix(ncol=(Range[1]-1),nrow=numRowsInG,data=0),G) G<-cbind(matrix(ncol=(Range[1]-1),nrow=numRowsInG,data=0),G)
} else { } else {
G <- cbind(matrix(ncol=(Range[1]-1),nrow=numRowsInG,data=0),G,matrix(ncol=(N-Range[2]),nrow=numRowsInG,data=0)) G <- cbind(matrix(ncol=(Range[1]-1),
} nrow=numRowsInG,data=0),
} G,
return(list(G=-1*G,h=-1*rep(0,nrow(G)))) matrix(ncol=(N-Range[2]),
nrow=numRowsInG,data=0))
}
}
return(list(G=-1*G,h=-1*rep(0,nrow(G))))
} }
genMat2 <- function(dep, N){ genMat2 <- function(dep, N){
G <- rep(1,nrow(dep)) G <- rep(1,nrow(dep))
Range <- (function(x){ Range <- (function(x){
c(min(x), max(x)) c(min(x), max(x))
})(as.numeric(dep[,"rowIndex"])) })(as.numeric(dep[,"rowIndex"]))
if(Range[1]==1){ if(Range[1]==1){
G<-c(G, numeric(N-Range[2])) G<-c(G, numeric(N-Range[2]))
} else{ } else{
if(Range[2]==N){ if(Range[2]==N){
G<-c(numeric(Range[1]-1), G) G<-c(numeric(Range[1]-1), G)
} else { } else {
G <- c(numeric(Range[1]-1), G, numeric(N-Range[2])) G <- c(numeric(Range[1]-1), G, numeric(N-Range[2]))
} }
} }
G <- t(matrix(sign(dep[2,4])*G)) G <- t(matrix(sign(dep[2,4])*G))
h <- abs(dep[1,4]) h <- abs(dep[1,4])
if(dep[1,"TYPE"]==2){ # This is not needed, I'll have to remove the if part, and keep the content if(dep[1,"TYPE"]==2){ # This is not needed, I'll have to remove the if part, and keep the content
G <- G*(-1) G <- G*(-1)
h <- h*(-1) h <- h*(-1)
} }
return(list(G=G,h=h)) return(list(G=G,h=h))
} }
genMat3 <- function(dep, N){ genMat3 <- function(dep, N){
Range <- (function(x){ Range <- (function(x){
c(min(x), max(x)) c(min(x), max(x))
})(as.numeric(dep[,"rowIndex"])) })(as.numeric(dep[,"rowIndex"]))
E <- rep(1,nrow(dep)) E <- rep(1,nrow(dep))
if(Range[1]==1){ if(Range[1]==1){
E<-c(E, numeric(N-Range[2])) E<-c(E, numeric(N-Range[2]))
} else{ } else{
if(Range[2]==N){ if(Range[2]==N){
E<-c(numeric(Range[1]-1), E) E<-c(numeric(Range[1]-1), E)
} else { } else {
E <- c(numeric(Range[1]-1), E, numeric(N-Range[2])) E <- c(numeric(Range[1]-1), E, numeric(N-Range[2]))
} }
} }
E <- t(matrix(E)) E <- t(matrix(E))
f <- dep[1,4] f <- dep[1,4]
return(list(E=E,f=f)) return(list(E=E,f=f))
} }
applyRandTypeG <- function(dep,N){ applyRandTypeG <- function(dep,N){
type <- unique(dep[,"TYPE"]) type <- unique(dep[,"TYPE"])
minR <- min(dep[,"rowIndex"]) minR <- min(dep[,"rowIndex"])
maxR <- max(dep[,"rowIndex"]) maxR <- max(dep[,"rowIndex"])
switch(type, switch(type,
invisible(Gh <- genMat1(dep, N)), invisible(Gh <- genMat1(dep, N)),
invisible(Gh <- genMat2(dep, N))) invisible(Gh <- genMat2(dep, N)))
return(Gh) return(Gh)
} }
applyRandTypeE <- function(dep,N){ applyRandTypeE <- function(dep,N){
type <- unique(dep[,"TYPE"]) type <- unique(dep[,"TYPE"])
minR <- min(dep[,"rowIndex"]) minR <- min(dep[,"rowIndex"])
maxR <- max(dep[,"rowIndex"]) maxR <- max(dep[,"rowIndex"])
switch(-type, switch(-type,
stop("Not implemented yet"), stop("Not implemented yet"),
stop("Not implemented yet"), stop("Not implemented yet"),
invisible(Ef <- genMat3(dep, N))) invisible(Ef <- genMat3(dep, N)))
return(Ef) return(Ef)
} }
dependences <- depTableMaker(constMatrix, parameters) dependences <- depTableMaker(constMatrix, parameters)
missingMembers <- dependences$missingMembers
dependences <- dependences$depTable
dependences <- cbind(dependences,1:nrow(dependences)) dependences <- cbind(dependences,1:nrow(dependences))
colnames(dependences)[ncol(dependences)] <- "rowIndex" colnames(dependences)[ncol(dependences)] <- "rowIndex"
# browser() # browser()
@ -167,21 +202,31 @@ musoRand <- function(parameters, iterations=3000, fileType="epc", constrains = N
Ef[[i]] <- applyRandTypeE(splitedDeps[[i]],nrow(dependences)) Ef[[i]] <- applyRandTypeE(splitedDeps[[i]],nrow(dependences))
} }
} }
Gh0<- genMat0(dependences) Gh0<- genMat0(dependences, missingMembers$indices)
G <- do.call(rbind,lapply(Gh,function(x){x$G})) G <- do.call(rbind,lapply(Gh,function(x){x$G}))
G<- rbind(Gh0$G,G) G<- rbind(Gh0$G,G)
h <- do.call(c,lapply(Gh,function(x){x$h})) h <- do.call(c,lapply(Gh,function(x){x$h}))
h <- c(Gh0$h,h) h <- c(Gh0$h,h)
E <- do.call(rbind,lapply(Ef,function(x){x$E})) E <- do.call(rbind,lapply(Ef,function(x){x$E}))
f <- do.call(c,lapply(Ef,function(x){x$f})) f <- do.call(c,lapply(Ef,function(x){x$f}))
# browser() # browser()
if(length(missingMembers$indices)!=0){
Ep <- matrix(data=0,ncol=numberOfVariable,nrow=length(missingMembers$indices))
Ep[1:length(missingMembers$indices),missingMembers$indices] <- 1
E <- rbind(E,Ep)
f <- c(f,missingMembers$values)
}
randVal <- suppressWarnings(limSolve::xsample(G=G,H=h,E=E,F=f,burninlength=burnin, iter = iterations))$X randVal <- suppressWarnings(limSolve::xsample(G=G,H=h,E=E,F=f,burninlength=burnin, iter = iterations))$X
} else{ } else{
Gh0<-genMat0(dependences) Gh0<-genMat0(dependences,NULL)
randVal <- suppressWarnings(xsample(G=Gh0$G,H=Gh0$h, iter = iterations))$X randVal <- suppressWarnings(xsample(G=Gh0$G,H=Gh0$h, iter = iterations))$X
} }
if(length(missingMembers$indices)!=0){
return(list(INDEX = dependences$INDEX[-missingMembers$indices],
randVal=randVal[,-missingMembers$indices]))
}
results <- list(INDEX =dependences$INDEX, randVal=randVal) results <- list(INDEX =dependences$INDEX, randVal=randVal)
return(results) return(results)
} }

2
RBBGCMuso/R/musoSensi.R
View File

@ -31,6 +31,7 @@ musoSensi <- function(monteCarloFile = NULL,
plotTitle = "Sensitivity", plotTitle = "Sensitivity",
skipSpinup = TRUE, skipSpinup = TRUE,
skipZero = TRUE, skipZero = TRUE,
sourceFile=NULL,
postProcString=NULL, postProcString=NULL,
modifyOut=TRUE, modifyOut=TRUE,
dpi=300){ dpi=300){
@ -87,6 +88,7 @@ musoSensi <- function(monteCarloFile = NULL,
if(is.null(monteCarloFile)){ if(is.null(monteCarloFile)){
M <- musoMonte(parameters = parameters, M <- musoMonte(parameters = parameters,
settings = settings, settings = settings,
sourceFile=NULL,
inputDir = inputDir, inputDir = inputDir,
outLoc = outLoc, outLoc = outLoc,
iterations = iterations, iterations = iterations,

382
test.R
View File

@ -1,382 +0,0 @@
parameters <-
getOption("RMuso_constMatrix")[["epc"]][["6"]]
NAME
yearday to start new growth
yearday to end new growth
transfer growth period as fraction of growing season
litterfall as fraction of growing season
base temperature
minimum temperature for growth displayed on current day
optimal1 temperature for growth displayed on current day
optimal2 temperature for growth displayed on current day
maxmimum temperature for growth displayed on current day
minimum temperature for carbon assimilation displayed on current day
optimal1 temperature for carbon assimilation displayed on current day
optimal2 temperature for carbon assimilation displayed on current day
maxmimum temperature for carbon assimilation displayed on current day
annual leaf and fine root turnover fraction
annual live wood turnover fraction
annual fire mortality fraction
whole-plant mortality paramter for vegetation period
C:N of leaves
C:N of leaf litter
C:N of fine roots
C:N of fruit
C:N of softstem
C:N of live wood
C:N of dead wood
dry matter content of leaves
dry matter content of leaf litter
dry matter content of fine roots
dry matter content of fruit
dry matter content of softstem
dry matter content of live wood
dry matter content of dead wood
leaf litter labile proportion
leaf litter cellulose proportion
fine root labile proportion
fine root cellulose proportion
fruit labile proportion
fruit cellulose proportion
softstem labile proportion
softstem cellulose proportion
dead wood cellulose proportion
canopy water interception coefficient
canopy light extinction coefficient
potential radiation use efficiency
radiation parameter1 (Jiang et al.2015)
radiation parameter1 (Jiang et al.2015)
all-sided to projected leaf area ratio
ratio of shaded SLA:sunlit SLA
fraction of leaf N in Rubisco
fraction of leaf N in PeP
maximum stomatal conductance
cuticular conductance
boundary layer conductance
maximum height of plant
stem weight corresponding to maximum height
plant height function shape parameter (slope)
maximum depth of rooting zone
root distribution parameter
root weight corresponding to max root depth
root depth function shape parameter (slope)
root weight to rooth length conversion factor
growth resp per unit of C grown
maintenance respiration in kgC/day per kg of tissue N
theoretical maximum prop. of non-structural and structural carbohydrates
prop. of non-structural carbohydrates available for maintanance resp
symbiotic+asymbiotic fixation of N
time delay for temperature in photosynthesis acclimation
critical VWCratio (prop. to FC-WP) in germination
critical photoslow daylength
slope of relative photoslow development rate
critical vernalization temperature 1
critical vernalization temperature 2
critical vernalization temperature 3
critical vernalization temperature 4
slope of relative vernalization development rate
required vernalization days (in vernalization development rate)
critical flowering heat stress temperature 1
critical flowering heat stress temperature 2
theoretical maximum of flowering thermal stress mortality
VWC ratio to calc. soil moisture limit 1 (prop. to FC-WP)
VWC ratio to calc. soil moisture limit 2 (prop. to SAT-FC)
minimum of soil moisture limit2 multiplicator (full anoxic stress value)
vapor pressure deficit: start of conductance reduction
vapor pressure deficit: complete conductance reduction
maximum senescence mortality coefficient of aboveground plant material
maximum senescence mortality coefficient of belowground plant material
maximum senescence mortality coefficient of non-structured plant material
lower limit extreme high temperature effect on senescence mortality
upper limit extreme high temperature effect on senescence mortality
turnover rate of wilted standing biomass to litter
turnover rate of cut-down non-woody biomass to litter
turnover rate of cut-down woody biomass to litter
drought tolerance parameter (critical value of day since water stress)
effect of soilstress factor on photosynthesis
crit. amount of snow limiting photosyn.
limit1 (under:full constrained) of HEATSUM index
limit2 (above:unconstrained) of HEATSUM index
limit1 (under:full constrained) of TMIN index
limit2 (above:unconstrained) of TMIN index
limit1 (above:full constrained) of VPD index
limit2 (under:unconstrained) of VPD index
limit1 (under:full constrained) of DAYLENGTH index
limit2 (above:unconstrained) of DAYLENGTH index
moving average (to avoid the effects of extreme events)
GSI limit1 (greater that limit -> start of vegper)
GSI limit2 (less that limit -> end of vegper)
length of phenophase (GDD)-0
leaf ALLOCATION -0
fine root ALLOCATION-0
fruit ALLOCATION -0
soft stem ALLOCATION-0
live woody stem ALLOCATION -0
dead woody stem ALLOCATION -0
live coarse root ALLOCATION-0
dead coarse root ALLOCATION -0
canopy average specific leaf area-0
current growth proportion-0
maximal lifetime of plant tissue-0
length of phenophase (GDD)-1
leaf ALLOCATION -1
fine root ALLOCATION-1
fruit ALLOCATION -1
soft stem ALLOCATION-1
live woody stem ALLOCATION -1
dead woody stem ALLOCATION -1
live coarse root ALLOCATION-1
dead coarse root ALLOCATION -1
canopy average specific leaf area-1
current growth proportion-1
maximal lifetime of plant tissue-1
length of phenophase (GDD)-2
leaf ALLOCATION -2
fine root ALLOCATION-2
fruit ALLOCATION -2
soft stem ALLOCATION-2
live woody stem ALLOCATION -2
dead woody stem ALLOCATION -2
live coarse root ALLOCATION-2
dead coarse root ALLOCATION -2
canopy average specific leaf area-2
current growth proportion-2
maximal lifetime of plant tissue-2
length of phenophase (GDD)-3
leaf ALLOCATION -3
fine root ALLOCATION-3
fruit ALLOCATION -3
soft stem ALLOCATION-3
live woody stem ALLOCATION -3
dead woody stem ALLOCATION -3
live coarse root ALLOCATION-3
dead coarse root ALLOCATION -3
canopy average specific leaf area-3
current growth proportion-3
maximal lifetime of plant tissue-3
length of phenophase (GDD)-4
leaf ALLOCATION -4
fine root ALLOCATION-4
fruit ALLOCATION -4
soft stem ALLOCATION-4
live woody stem ALLOCATION -4
dead woody stem ALLOCATION -4
live coarse root ALLOCATION-4
dead coarse root ALLOCATION -4
canopy average specific leaf area-4
current growth proportion-4
maximal lifetime of plant tissue-4
length of phenophase (GDD)-5
leaf ALLOCATION -5
fine root ALLOCATION-5
fruit ALLOCATION -5
soft stem ALLOCATION-5
live woody stem ALLOCATION -5
dead woody stem ALLOCATION -5
live coarse root ALLOCATION-5
dead coarse root ALLOCATION -5
canopy average specific leaf area-5
current growth proportion-5
maximal lifetime of plant tissue-5
length of phenophase (GDD)-6
leaf ALLOCATION -6
fine root ALLOCATION-6
fruit ALLOCATION -6
soft stem ALLOCATION-6
live woody stem ALLOCATION -6
dead woody stem ALLOCATION -6
live coarse root ALLOCATION-6
dead coarse root ALLOCATION -6
canopy average specific leaf area-6
current growth proportion-6
maximal lifetime of plant tissue-6
INDEX UNIT DEPENDENCE MIN MAX GROUP TYPE
9.00 yday NA 0.00000 364.0000 0 0
10.00 yday NA 0.00000 364.0000 0 0
11.00 prop NA 0.00000 1.0000 0 0
12.00 prop NA 0.00000 1.0000 0 0
13.00 Celsius NA 0.00000 12.0000 0 0
14.00 Celsius 0 0.00000 10.0000 1 1
15.00 Celsius 1 10.00000 20.0000 1 1
16.00 Celsius 2 20.00000 40.0000 1 1
17.00 Celsius 3 30.00000 50.0000 1 1
18.00 Celsius 0 0.00000 10.0000 2 1
19.00 Celsius 1 10.00000 20.0000 2 1
20.00 Celsius 2 20.00000 40.0000 2 1
21.00 Celsius 3 30.00000 50.0000 2 1
22.00 1/yr NA 0.10000 0.4000 0 0
23.00 1/yr NA 0.50000 1.0000 0 0
24.00 1/yr NA 0.00000 1.0000 0 0
25.00 1/vegper NA 0.00000 0.5000 0 0
26.00 kgC/kgN 0 10.00000 100.0000 3 1
27.00 kgC/kgN 1 10.00000 60.0000 3 1
28.00 kgC/kgN 1 10.00000 60.0000 3 1
29.00 kgC/kgN 1 10.00000 60.0000 3 1
30.00 kgC/kgN 1 10.00000 60.0000 3 1
31.00 kgC/kgN 0 50.00000 100.0000 4 1
32.00 kgC/kgN 1 300.00000 800.0000 4 1
33.00 kgC/kgDM NA 0.20000 0.6000 0 0
34.00 kgC/kgDM NA 0.20000 0.6000 0 0
35.00 kgC/kgDM NA 0.20000 0.6000 0 0
36.00 kgC/kgDM NA 0.20000 0.6000 0 0
37.00 kgC/kgDM NA 0.20000 0.6000 0 0
38.00 kgC/kgDM NA 0.20000 0.6000 0 0
39.00 kgC/kgDM NA 0.20000 0.6000 0 0
40.00 prop 1 0.10000 0.6000 5 2
41.00 prop 1 0.10000 0.6000 5 2
42.00 prop 1 0.10000 0.6000 6 2
43.00 prop 1 0.10000 0.6000 6 2
44.00 prop 1 0.10000 0.6000 7 2
45.00 prop 1 0.10000 0.6000 7 2
46.00 prop 1 0.10000 0.6000 8 2
47.00 prop 1 0.10000 0.6000 8 2
48.00 prop NA 0.50000 0.9000 0 0
49.00 1/LAI/d NA 0.01000 0.1000 0 0
50.00 dimless NA 0.20000 0.8000 0 0
51.00 g/MJ NA 2.00000 2.0000 0 0
52.00 dimless NA 0.78100 0.7810 0 0
53.00 dimless NA -13.59600 -13.5960 0 0
54.00 dimless NA 2.00000 2.0000 0 0
55.00 dimless NA 2.00000 2.0000 0 0
56.00 dimless NA 0.01000 0.2000 0 0
57.00 dimless NA 0.04240 0.0424 0 0
58.00 m/s NA 0.00100 0.1000 0 0
59.00 m/s NA 0.00001 0.0001 0 0
60.00 m/s NA 0.01000 0.0900 0 0
61.00 m NA 0.10000 10.0000 0 0
62.00 kgC NA 0.10000 100.0000 0 0
63.00 dimless NA 0.50000 0.5000 0 0
64.00 m NA 0.10000 10.0000 0 0
65.00 prop NA 3.67000 3.6700 0 0
66.00 kgC/m2 NA 0.40000 0.4000 0 0
67.00 prop NA 0.50000 0.5000 0 0
68.00 m/kg NA 1000.00000 1000.0000 0 0
69.00 prop NA 0.10000 0.5000 0 0
70.00 kgC/kgN/d NA 0.10000 0.5000 0 0
71.00 dimless NA 0.00000 1.0000 0 0
72.00 dimless NA 0.00000 1.0000 0 0
73.00 kgN/m2/yr NA 0.00000 0.0010 0 0
74.00 day NA 0.00000 50.0000 0 0
79.00 prop NA 0.00000 1.0000 0 0
81.00 hour NA 14.00000 18.0000 0 0
82.00 dimless NA 0.00500 0.0050 0 0
84.00 Celsius 0 -5.00000 5.0000 9 1
85.00 Celsius 1 0.00000 10.0000 9 1
86.00 Celsius 2 5.00000 15.0000 9 1
87.00 Celsius 3 10.00000 20.0000 9 1
88.00 dimless NA 0.04000 0.0400 0 0
89.00 dimless NA 30.00000 70.0000 0 0
91.00 Celsius 0 30.00000 40.0000 10 1
92.00 Celsius 1 30.00000 50.0000 10 1
93.00 prop NA 0.00000 0.4000 0 0
96.00 prop NA 0.50000 1.0000 0 0
97.00 prop NA 0.50000 1.0000 0 0
98.00 prop NA 0.00000 1.0000 0 0
99.00 Pa NA 500.00000 1500.0000 0 0
100.00 Pa NA 1500.00000 3500.0000 0 0
101.00 prop 0 0.00000 0.1000 0 0
102.00 prop 1 0.00000 0.1000 0 0
103.00 prop NA 0.00000 0.1000 0 0
104.00 Celsius NA 30.00000 40.0000 0 0
105.00 Celsius NA 30.00000 50.0000 0 0
106.00 prop NA 0.00000 0.1000 0 0
107.00 prop NA 0.00000 0.1000 0 0
108.00 prop NA 0.00000 0.1000 0 0
109.00 n_day NA 0.00000 100.0000 0 0
110.00 dimless NA 0.00000 1.0000 0 0
113.00 kg/m2 NA 0.00000 20.0000 0 0
114.00 Celsius 0 0.00000 50.0000 11 1
115.00 Celsius 1 0.00000 100.0000 11 1
116.00 Celsius 0 -5.00000 5.0000 12 1
117.00 Celsius 1 0.00000 10.0000 12 1
118.00 Pa 0 2000.00000 600.0000 13 1
119.00 Pa 1 500.00000 1500.0000 13 1
120.00 s 0 0.00000 0.0000 14 1
121.00 s 1 0.00000 0.0000 14 1
122.00 n_day NA 2.00000 20.0000 0 0
123.00 dimless NA 0.00000 0.2000 0 0
124.00 dimless NA 0.00000 0.1000 0 0
128.60 Celsius NA 0.00000 10000.0000 0 0
129.60 prop 1 0.00000 1.0000 15 -3
130.60 prop 1 0.00000 1.0000 15 -3
131.60 prop 1 0.00000 1.0000 15 -3
132.60 prop 1 0.00000 1.0000 15 -3
133.60 prop 1 0.00000 1.0000 15 -3
134.60 prop 1 0.00000 1.0000 15 -3
135.60 prop 1 0.00000 1.0000 15 -3
136.60 prop 1 0.00000 1.0000 15 -3
137.60 m2/kg NA 0.00000 2.0000 0 0
138.60 prop NA 0.00000 0.0000 0 0
139.60 Celsius NA 1.00000 20000.0000 0 0
128.61 Celsius NA 0.00000 10000.0000 0 0
129.61 prop 1 0.00000 1.0000 16 -3
130.61 prop 1 0.00000 1.0000 16 -3
131.61 prop 1 0.00000 1.0000 16 -3
132.61 prop 1 0.00000 1.0000 16 -3
133.61 prop 1 0.00000 1.0000 16 -3
134.61 prop 1 0.00000 1.0000 16 -3
135.61 prop 1 0.00000 1.0000 16 -3
136.61 prop 1 0.00000 1.0000 16 -3
137.61 m2/kg NA 0.00000 2.0000 0 0
138.61 prop NA 0.00000 0.0000 0 0
139.61 Celsius NA 1.00000 20000.0000 0 0
128.62 Celsius NA 0.00000 10000.0000 0 0
129.62 prop 1 0.00000 1.0000 17 -3
130.62 prop 1 0.00000 1.0000 17 -3
131.62 prop 1 0.00000 1.0000 17 -3
132.62 prop 1 0.00000 1.0000 17 -3
133.62 prop 1 0.00000 1.0000 17 -3
134.62 prop 1 0.00000 1.0000 17 -3
135.62 prop 1 0.00000 1.0000 17 -3
136.62 prop 1 0.00000 1.0000 17 -3
137.62 m2/kg NA 0.00000 2.0000 0 0
138.62 prop NA 0.00000 0.0000 0 0
139.62 Celsius NA 1.00000 20000.0000 0 0
128.63 Celsius NA 0.00000 10000.0000 0 0
129.63 prop 1 0.00000 1.0000 18 -3
130.63 prop 1 0.00000 1.0000 18 -3
131.63 prop 1 0.00000 1.0000 18 -3
132.63 prop 1 0.00000 1.0000 18 -3
133.63 prop 1 0.00000 1.0000 18 -3
134.63 prop 1 0.00000 1.0000 18 -3
135.63 prop 1 0.00000 1.0000 18 -3
136.63 prop 1 0.00000 1.0000 18 -3
137.63 m2/kg NA 0.00000 2.0000 0 0
138.63 prop NA 0.00000 0.0000 0 0
139.63 Celsius NA 1.00000 20000.0000 0 0
128.64 Celsius NA 0.00000 10000.0000 0 0
129.64 prop 1 0.00000 1.0000 19 -3
130.64 prop 1 0.00000 1.0000 19 -3
131.64 prop 1 0.00000 1.0000 19 -3
132.64 prop 1 0.00000 1.0000 19 -3
133.64 prop 1 0.00000 1.0000 19 -3
134.64 prop 1 0.00000 1.0000 19 -3
135.64 prop 1 0.00000 1.0000 19 -3
136.64 prop 1 0.00000 1.0000 19 -3
137.64 m2/kg NA 0.00000 2.0000 0 0
138.64 prop NA 0.00000 0.0000 0 0
139.64 Celsius NA 1.00000 20000.0000 0 0
128.65 Celsius NA 0.00000 10000.0000 0 0
129.65 prop 1 0.00000 1.0000 20 -3
130.65 prop 1 0.00000 1.0000 20 -3
131.65 prop 1 0.00000 1.0000 20 -3
132.65 prop 1 0.00000 1.0000 20 -3
133.65 prop 1 0.00000 1.0000 20 -3
134.65 prop 1 0.00000 1.0000 20 -3
135.65 prop 1 0.00000 1.0000 20 -3
136.65 prop 1 0.00000 1.0000 20 -3
137.65 m2/kg NA 0.00000 2.0000 0 0
138.65 prop NA 0.00000 0.0000 0 0
139.65 Celsius NA 1.00000 20000.0000 0 0
128.66 Celsius NA 0.00000 10000.0000 0 0
129.66 prop 1 0.00000 1.0000 21 -3
130.66 prop 1 0.00000 1.0000 21 -3
131.66 prop 1 0.00000 1.0000 21 -3
132.66 prop 1 0.00000 1.0000 21 -3
133.66 prop 1 0.00000 1.0000 21 -3
134.66 prop 1 0.00000 1.0000 21 -3
135.66 prop 1 0.00000 1.0000 21 -3
136.66 prop 1 0.00000 1.0000 21 -3
137.66 m2/kg NA 0.00000 2.0000 0 0
138.66 prop NA 0.00000 0.0000 0 0
139.66 Celsius NA 1.00000 20000.0000 0 0