fix mergeconflict
This commit is contained in:
Hollos Roland
commit
539eacefc0
@ -168,14 +168,26 @@ multiSiteCalib <- function(measurements,
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tryCatch(
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{
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multiSiteThread(measuredData = measurements, parameters = parameters, calTable=calTable,
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result <- multiSiteThread(measuredData = measurements, parameters = parameters, calTable=calTable,
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dataVar = dataVar, iterations = threadCount[i],
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likelihood = likelihood, threadNumber= i, constraints=constraints, th=th)
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<<<<<<< HEAD
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##setwd("../")
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}
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, error = function(e){
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=======
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setwd("../../")
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return(result)
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}
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, error = function(e){
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# browser()
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sink("error.txt")
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print(e)
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sink()
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>>>>>>> origin/CIRM
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saveRDS(e,"error.RDS")
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writeLines(as.character(iterations),"progress.txt")
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})
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@ -245,25 +257,22 @@ multiSiteCalib <- function(measurements,
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write.csv(results,"result.csv")
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calibrationPar <- future::value(fut[[1]], stdout = FALSE, signal=FALSE)[["calibrationPar"]]
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if(!is.null(constraints)){
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notForTree <- c(seq(from = (length(calibrationPar)+1), length.out=3))
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notForTree <- c(notForTree,which(sapply(seq_along(calibrationPar),function(i){sd(results[,i])==0})))
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treeData <- results[,-notForTree]
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treeData["failType"] <- as.factor(results$failType)
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if(ncol(treeData) > 4){
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tryCatch({
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rp <- rpart(failType ~ .,data=treeData,control=treeControl)
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svg("treeplot.svg")
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rpart.plot(rp)
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dev.off()
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tryCatch({
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notForTree <- c(seq(from = (length(calibrationPar)+1), length.out=3))
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notForTree <- c(notForTree,which(sapply(seq_along(calibrationPar),function(i){sd(results[,i])==0})))
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treeData <- results[,-notForTree]
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treeData["failType"] <- as.factor(results$failType)
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if(ncol(treeData) > 4){
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rp <- rpart(failType ~ .,data=treeData,control=treeControl)
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svg("treeplot.svg")
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rpart.plot(rp)
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dev.off()
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}
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, error = function(e){
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print(e)
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})
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}
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}, error = function(e){
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print(e)
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})
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}
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origModOut <- future::value(fut[[1]], stdout = FALSE, signal=FALSE)[["origModOut"]]
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origModOut <- future::value(fut[[1]], stdout = FALSE, signal=FALSE)[["origModOut"]]
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# Just single objective version TODO:Multiobjective
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results <- results[results[,"Const"] == 1,]
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if(nrow(results)==0){
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@ -295,8 +304,12 @@ multiSiteCalib <- function(measurements,
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res[["calibrationPar"]] <- calibrationPar
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res[["parameters"]] <- parameters
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<<<<<<< HEAD
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# browser()
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res[["comparison"]] <- compareCalibratedWithOriginal(key = names(dataVar)[1], modOld=origModOut, modNew=aposteriori, mes=measurements,
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=======
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res[["comparison"]] <- compareCalibratedWithOriginal(key = names(dataVar), modOld=origModOut, modNew=aposteriori, mes=measurements,
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>>>>>>> origin/CIRM
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likelihoods = likelihood,
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alignIndexes = alignIndexes,
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musoCodeToIndex = musoCodeToIndex,
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@ -318,8 +331,8 @@ multiSiteCalib <- function(measurements,
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max(c(measured,original,calibrated))),
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xlim=c(min(c(measured,original,calibrated)),
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max(c(measured,original,calibrated))),
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xlab=expression("measured "~(kg[C]~m^-2)),
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ylab=expression("simulated "~(kg[C]~m^-2)),
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xlab=expression("measured "~(kg[DM]~m^-2)),
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ylab=expression("simulated "~(kg[DM]~m^-2)),
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cex.lab=1.3,
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col="red",
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pch=19,
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@ -488,9 +501,12 @@ multiSiteThread <- function(measuredData, parameters = NULL, startDate = NULL,
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writeLines(as.character(i-1),"progress.txt") #UNCOMMENT IMPORTANT
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}
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}
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if(threadNumber == 1){
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return(originalRun)
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}
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return(0)
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}
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distributeCores <- function(iterations, numCores){
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perProcess<- iterations %/% numCores
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@ -540,10 +556,11 @@ calcLikelihoodsForGroups <- function(dataVar, mod, mes,
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measuredGroups[[domain_id]][alignIndexes[[domain_id]]$meas,]
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}))
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measured <- measured[measured$var_id == key,]
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res <- c(likelihoods[[key]](modelled, measured),
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sqrt(mean((modelled-measured$mean)^2))
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)
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print(abs(mean(modelled)-mean(measured$mean)))
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res
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})
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@ -12595,7 +12595,7 @@
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},
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{
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"codes": 2585,
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"names": "hydr_conductEND[6]",
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"names": "rootdepth5",
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"units": "ms-1",
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"descriptions": "Hydraulic conductivity at the end of the day of soil layer 7 (120-150 cm)"
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},
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25554
docs/CIRM/Martonvasar.wth
Normal file
25554
docs/CIRM/Martonvasar.wth
Normal file
File diff suppressed because it is too large
Load Diff
29
docs/CIRM/Martonvasar_maize.obs
Normal file
29
docs/CIRM/Martonvasar_maize.obs
Normal file
@ -0,0 +1,29 @@
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var_id;domain_id;date;mean;sd;min;max
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fruit_DM;211;1991-10-04;6537;1012;4981;7910
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fruit_DM;211;1992-10-04;5799;2906;1071;11141
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fruit_DM;211;1993-10-04;4161;1226;1581;6409
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fruit_DM;211;1994-10-04;5335;966;3026;6860
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fruit_DM;211;1995-10-04;6359;2981;1700;10132
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fruit_DM;211;1996-10-04;8675;1085;6596;10668
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fruit_DM;211;1997-10-04;8865;995;6622;10234
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fruit_DM;211;1998-10-04;7931;1908;4046;9554
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fruit_DM;211;1999-10-04;9447;1348;7404;11535
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fruit_DM;211;2000-10-04;6614;2695;2936;11237
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fruit_DM;211;2001-10-04;8667;881;6372;10275
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fruit_DM;211;2002-10-04;6321;1034;5100;8139
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||||
fruit_DM;211;2003-10-04;5328;1688;2508;7183
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||||
fruit_DM;211;2004-10-04;7965;1193;5869;9690
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||||
fruit_DM;211;2005-10-04;8543;679;7242;9439
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||||
fruit_DM;211;2006-10-04;10124;1146;8308;12402
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||||
fruit_DM;211;2007-10-04;4901;2088;957;7574
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fruit_DM;211;2008-10-04;10024;2012;7364;12852
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fruit_DM;211;2009-10-04;6713;1672;4601;10515
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fruit_DM;211;2010-10-04;9081;2009;6585;12011
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fruit_DM;211;2011-10-04;10536;1708;8958;13566
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fruit_DM;211;2012-10-04;5023;2072;1776;9325
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fruit_DM;211;2013-10-04;6958;2732;3729;11645
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fruit_DM;211;2014-10-04;10162;629;9444;10974
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fruit_DM;211;2015-10-04;8636;1780;5681;11586
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||||
fruit_DM;211;2016-10-04;9904;1098;7276;11397
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||||
fruit_DM;211;2017-10-04;6731;1351;4694;10166
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fruit_DM;211;2018-10-04;9092;1227;6746;10827
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29
docs/CIRM/Martonvasar_maize_KSH_Fejer.obs
Normal file
29
docs/CIRM/Martonvasar_maize_KSH_Fejer.obs
Normal file
@ -0,0 +1,29 @@
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var_id;domain_id;date;mean;sd;min;max
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fruit_DM;211;1991-10-04;6840;274;6019;7661
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fruit_DM;211;1992-10-04;3990;160;3511;4469
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fruit_DM;211;1993-10-04;3130;125;2754;3506
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fruit_DM;211;1994-10-04;3740;150;3291;4189
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fruit_DM;211;1995-10-04;4680;187;4118;5242
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fruit_DM;211;1996-10-04;6210;248;5465;6955
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fruit_DM;211;1997-10-04;6710;268;5905;7515
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fruit_DM;211;1998-10-04;6500;260;5720;7280
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fruit_DM;211;1999-10-04;7150;286;6292;8008
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fruit_DM;211;2000-10-04;4160;166;3661;4659
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fruit_DM;211;2001-10-04;6890;276;6063;7717
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fruit_DM;211;2002-10-04;5650;226;4972;6328
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fruit_DM;211;2003-10-04;4100;164;3608;4592
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fruit_DM;211;2004-10-04;7440;298;6547;8333
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fruit_DM;211;2005-10-04;9010;360;7929;10091
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fruit_DM;211;2006-10-04;7740;310;6811;8669
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fruit_DM;211;2007-10-04;2890;116;2543;3237
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fruit_DM;211;2008-10-04;8450;338;7436;9464
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fruit_DM;211;2009-10-04;7110;284;6257;7963
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fruit_DM;211;2010-10-04;7420;297;6530;8310
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fruit_DM;211;2011-10-04;7580;303;6670;8490
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fruit_DM;211;2012-10-04;4080;163;3590;4570
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fruit_DM;211;2013-10-04;5910;236;5201;6619
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fruit_DM;211;2014-10-04;8380;335;7374;9386
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fruit_DM;211;2015-10-04;6210;248;5465;6955
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fruit_DM;211;2016-10-04;9370;375;8246;10494
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fruit_DM;211;2017-10-04;6700;268;5896;7504
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fruit_DM;211;2018-10-04;10060;402;8853;11267
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86
docs/CIRM/README.md
Normal file
86
docs/CIRM/README.md
Normal file
@ -0,0 +1,86 @@
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This is a si
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## Preparations
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Before using this script, make sure, your current working directory looks like
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```{verbatim}
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.
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├── glue.R
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├── kichen_sink.R
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├── make_individual_trees.R
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├── Martonvasar_maize_KSH_Fejer.obs
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├── Martonvasar_maize.obs
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├── README.md
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├── statistics.R
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└── tree_accuracy.R
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```
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### Loading the RBBGCMuso package and the necessary functions
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```{r}
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library(RBBGCMuso)
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source("make_individual_trees.R") # The DT creation and update algorithms
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source("glue.R") # GLUE optimizer algorithms
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```
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The file containing the path to the observation files (Martonvasar_maize.obs), and the parameter intervals (Martonj)
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### Reading the observations
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The mean yield had to be adjust. see in art.
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```{r}
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measureFile <- "Martonvasar_maize.obs"
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measurements <- read.csv2(measureFile, stringsAsFactors=FALSE)
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measurements$mean <- measurements$mean / 10000
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measurements$sd <- measurements$sd / 10000
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```
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### Define conditioning functions
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constraints.json
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```{json}
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{
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"constraints": [
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{
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"Expression": "SELECT(harvest_index, max)|median",
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"Min": 0.45,
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"Max": 0.55
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},
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{
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"Expression": "SELECT(proj_lai, max)|quantile(.,0.5)",
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"Min": 2.7,
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"Max": 5
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},
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{
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"Expression": "SELECT(rootdepth5, max)|quantile(.,0.5)",
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"Min": 1.40,
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"Max": 1.80
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},
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{
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"Expression": "SELECT(flower_date, max)|quantile(.,0.5)",
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"Min": 180,
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"Max": 190
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}
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],
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"treshold": 80
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}
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```
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```{r}
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constraints <- jsonlite::read_json("constraints.json",simplifyVector=TRUE)
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```
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### Cal file:
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```{verbatim}
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Martonvasar_maize.obs
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Martonvasar_maize.set
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site
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Martonvasar_maize;211
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```
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55
docs/CIRM/glue.R
Normal file
55
docs/CIRM/glue.R
Normal file
@ -0,0 +1,55 @@
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zero_var <- function(m){
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apply(m,2, function(v){
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var(v) != 0
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})
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}
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glue <- function(results="result.csv",res_r="results.RDS",output ="gplot.pdf",epcname="maize_glue.epc"){
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res <- read.csv(results)[-1]
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res <- res[-1,]
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colnames(res)
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non_zero <- res[,1:(ncol(res)-4)]
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colnames(non_zero) <- colnames(res)[1:(ncol(res)-4)]
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impvars <- zero_var(non_zero)
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nonzero <- non_zero[,impvars]
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likelihoods <- res[,(ncol(res)-3)]
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rmse <- res[,(ncol(res)-2)]
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const <- res$Const
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namess <- gsub("__.*","",colnames(nonzero))
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likelihoods <- likelihoods[res$Const==1]
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goods <- res[res$Const==1,]
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medlik <- median(likelihoods[likelihoods >= quantile(likelihoods,0.95)])
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medlik_place <- which.min(abs(likelihoods - medlik))
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parameters <- readRDS(res_r)
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glue_opt <- goods[medlik_place, 1:(ncol(res)-4)][impvars]
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nonka <- goods[likelihoods >= quantile(likelihoods,0.95),1:(ncol(res)-4)]
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med_opt <- apply(nonka,2,median)[impvars]
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# med_opt <- apply(nonka,2,mean)[impvars]
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ml_opt <- goods[which.max(likelihoods),1:(ncol(res)-4)][impvars]
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calibrationPar <- parameters$calibrationPar[impvars]
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changemulline(src="maize.epc", calibrationPar = calibrationPar, contents=glue_opt, outFiles = epcname)
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changemulline(src="maize.epc", calibrationPar = calibrationPar, contents=med_opt, outFiles = "maize_median.epc")
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changemulline(src="maize.epc", calibrationPar = calibrationPar, contents=ml_opt, outFiles = "maize_ml.epc")
|
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|
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|
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|
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print(output)
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pdf(output)
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for(i in 1:ncol(nonzero)){
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plot(nonzero[,i],res[,(ncol(res)-3)],main="",col="lightgray", pch=20, cex=0.4, xlab=namess[i],ylab="logLikelihood")
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points(nonzero[const==1,i],res[const==1,(ncol(res)-3)],pch=20, cex=0.6, col="red",type="p",xlab=namess[i],ylab="logLikelihood")
|
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abline(v=glue_opt[i],col="green")
|
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abline(v=med_opt[i],col="blue")
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abline(v=ml_opt[i],col="black")
|
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}
|
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|
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dev.off()
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}
|
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|
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45
docs/CIRM/kichen_sink.R
Normal file
45
docs/CIRM/kichen_sink.R
Normal file
@ -0,0 +1,45 @@
|
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start_intervals <- read.csv("1/Martonvasar_maize.set",skip=1,stringsAsFactors=FALSE)
|
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|
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indices <- which(start_intervals[,3] != start_intervals[,4])
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|
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png("kichen_sink.png",width=30,height=30,res=600,units = "cm")
|
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|
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par(mfrow=c(5,4))
|
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|
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for(i in indices){
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ranges <- start_intervals[i,3:4]
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optimes <- numeric(10)
|
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for(j in 1:10){
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base_table <- read.csv(paste(j,"Martonvasar_maize_after_tree.set",sep="/"),
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skip=1, stringsAsFactors=FALSE)
|
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ranges <- rbind(ranges,base_table[i,3:4])
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optimes[j] <- unlist(readRDS(paste0(j,"/results.RDS"))$parameters[start_intervals[indices,1]][indices==i])
|
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}
|
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plot(ranges[,1],11:1,type="l",xlim=range(ranges),main=base_table[i,1],xlab="",ylab="iterations",yaxt="n")
|
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axis(2,at=11:1,labels = 0:10)
|
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points(optimes,10:1)
|
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lines(ranges[,2],11:1,type="l")
|
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}
|
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dev.off()
|
||||
|
||||
postscript("kichen_sink.eps",paper="a4")
|
||||
|
||||
par(mfrow=c(5,4))
|
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|
||||
for(i in indices){
|
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ranges <- start_intervals[i,3:4]
|
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optimes <- numeric(10)
|
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for(j in 1:10){
|
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base_table <- read.csv(paste(j,"Martonvasar_maize_after_tree.set",sep="/"),
|
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skip=1, stringsAsFactors=FALSE)
|
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ranges <- rbind(ranges,base_table[i,3:4])
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optimes[j] <- unlist(readRDS(paste0(j,"/results.RDS"))$parameters[start_intervals[indices,1]][indices==i])
|
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}
|
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plot(ranges[,1],11:1,type="l",xlim=range(ranges),main=base_table[i,1],xlab="",ylab="iterations",yaxt="n")
|
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axis(2,at=11:1,labels = 0:10)
|
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points(optimes,10:1)
|
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lines(ranges[,2],11:1,type="l")
|
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}
|
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dev.off()
|
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|
||||
|
||||
131
docs/CIRM/make_individual_trees.R
Normal file
131
docs/CIRM/make_individual_trees.R
Normal file
@ -0,0 +1,131 @@
|
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library(rpart)
|
||||
library(rpart.plot)
|
||||
zero_var <- function(m){
|
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apply(m,2, function(v){
|
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var(v) != 0
|
||||
})
|
||||
|
||||
}
|
||||
|
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decbin <- function(decnum){
|
||||
if(decnum < 2){
|
||||
return(decnum)
|
||||
}
|
||||
c(decbin((decnum %/% 2)),decnum %% 2)
|
||||
}
|
||||
|
||||
decpad <- function(decnum,len){
|
||||
binrep <- decbin(decnum)
|
||||
c(rep(0,len-length(binrep)),binrep)
|
||||
}
|
||||
|
||||
|
||||
tree_per_const <- function(results="result.csv",output ="tree_per_const.pdf",
|
||||
parameters_file="Martonvasar_maize.set"){
|
||||
varname <-readLines(parameters_file)[1]
|
||||
parameters <- read.csv(parameters_file,skip=1,stringsAsFactors=FALSE)
|
||||
|
||||
|
||||
results <- read.csv(results, stringsAsFactors=FALSE)
|
||||
# likelihoods <- results[,ncol(results)-3]
|
||||
# results <- results[likelihoods>=quantile(likelihoods,0.95),]
|
||||
len <- round(log(max(results$failType),2))
|
||||
failTypes <- do.call(rbind,lapply(results$failType,function(x){decpad(x,len)}))
|
||||
pdf(output)
|
||||
sapply(1:len, function(const){
|
||||
nonzero <- results[,1:(ncol(results)-4)]
|
||||
nonzero <- nonzero[,-1]
|
||||
nonzero <- nonzero[,zero_var(nonzero)]
|
||||
colnames(nonzero) <- gsub("__.*","",colnames(nonzero))
|
||||
constraint <- failTypes[,const]
|
||||
baseTable <- cbind.data.frame(nonzero,constraint = as.factor(constraint))
|
||||
try({
|
||||
rp = rpart(constraint ~ .,data = baseTable)
|
||||
})
|
||||
try({
|
||||
parameters <<- update_parameters_based_on_tree(rp, parameters)
|
||||
})
|
||||
try({
|
||||
rpart.plot(rp)
|
||||
})
|
||||
|
||||
|
||||
})
|
||||
dev.off()
|
||||
outname <- paste0(tools::file_path_sans_ext(parameters_file),"_after_tree.",
|
||||
tools::file_ext(parameters_file))
|
||||
writeLines(varname,outname)
|
||||
write.table(parameters,outname,row.names=FALSE,append=TRUE,sep=",",quote=FALSE)
|
||||
}
|
||||
update_parameters_based_on_tree <- function(rp, parameters){
|
||||
frm <- rp$frame
|
||||
nodes <- labels(rp)
|
||||
names(nodes) <- row.names(frm)
|
||||
node <- get_start_node(frm)
|
||||
parameters <- change_parameters_on_node(nodes,node,parameters)
|
||||
while(node !=1){
|
||||
node <- get_parent_node(node)
|
||||
if(node == 1){
|
||||
break()
|
||||
}
|
||||
parameters <- change_parameters_on_node(nodes,node,parameters)
|
||||
}
|
||||
parameters
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
parse_rule_row <- function(string){
|
||||
|
||||
rule_row <- regmatches(string,regexec("([a-zA-Z_0-9]+)([>=< ]+)(.*)",string,perl=TRUE))[[1]][-1]
|
||||
if(rule_row[2] == ">="){
|
||||
rule_num <- 1
|
||||
} else {
|
||||
rule_num <- 2
|
||||
}
|
||||
rule <- list(c(rule_num,as.numeric(rule_row[3])))
|
||||
names(rule) <- rule_row[1]
|
||||
return(rule)
|
||||
}
|
||||
|
||||
get_start_node <- function(frm){
|
||||
nfrm <- frm[frm$yval == 2,]
|
||||
nfrm <- nfrm[nfrm[,"var"] == "<leaf>",]
|
||||
pot_start <- as.numeric(row.names(nfrm))[which.max(nfrm$n)]
|
||||
pot_start
|
||||
}
|
||||
|
||||
get_parent_node <- function(node_id){
|
||||
as.integer(node_id/2)
|
||||
}
|
||||
|
||||
change_parameters_on_node <- function(nodes,node,parameters2){
|
||||
crule <- parse_rule_row(nodes[as.character(node)])
|
||||
minmax <- unlist(parameters2[parameters2[,1] == names(crule),c(3,4)])
|
||||
|
||||
if(crule[[1]][1] == 1){
|
||||
if(minmax[1]<=crule[[1]][2]){
|
||||
minmax[1] <- crule[[1]][2]
|
||||
if(minmax[1] <= minmax[2]){
|
||||
parameters2[parameters2[,1] == names(crule),c(3,4)] <- minmax
|
||||
} else {
|
||||
write(sprintf("WARNING: %s's minimum(%s) > maximum(%s)", parameters2[,1],
|
||||
minmax[1], minmax[2]), "errorlog.txt", append=TRUE)
|
||||
}
|
||||
}
|
||||
} else {
|
||||
if(minmax[2]>=crule[[1]][2]){
|
||||
minmax[2] <- crule[[1]][2]
|
||||
if(minmax[1] <= minmax[2]){
|
||||
parameters2[parameters2[,1] == names(crule),c(3,4)] <- minmax
|
||||
} else {
|
||||
write(sprintf("WARNING: %s's minimum(%s) > maximum(%s)", parameters2[,1],
|
||||
minmax[1], minmax[2]), "errorlog.txt", append=TRUE)
|
||||
}
|
||||
}
|
||||
}
|
||||
parameters2
|
||||
}
|
||||
|
||||
|
||||
BIN
docs/CIRM/muso
Normal file
BIN
docs/CIRM/muso
Normal file
Binary file not shown.
76
docs/CIRM/statistics.R
Normal file
76
docs/CIRM/statistics.R
Normal file
@ -0,0 +1,76 @@
|
||||
library(RBBGCMuso)
|
||||
file.copy("../../start_set/maize.epc","./start_set/maize.epc",overwrite=TRUE)
|
||||
setwd("start_set/")
|
||||
|
||||
rmse <- function(modelled, measured){
|
||||
sqrt(mean((modelled-measured)**2))
|
||||
}
|
||||
|
||||
r2 <- function(modelled, measured){
|
||||
summary(lm("mod ~ meas", data= data.frame(mod=modelled,meas=measured)))$r.squared
|
||||
}
|
||||
|
||||
modeff <- function(modelled, measured){
|
||||
1 - (sum((modelled-measured)**2) / sum((measured - mean(measured))**2))
|
||||
}
|
||||
|
||||
bias <- function(modelled, measured){
|
||||
mean(modelled) - mean(measured)
|
||||
}
|
||||
|
||||
get_stats <- function(modelled,measured){
|
||||
c(r2=r2(modelled,measured),
|
||||
rmse=rmse(modelled,measured),
|
||||
bias=bias(modelled,measured),
|
||||
modeff=modeff(modelled,measured))
|
||||
}
|
||||
|
||||
get_modelled <- function(obsTable,settings, ...){
|
||||
simulation <- runMuso(settings, ...)
|
||||
yield <- simulation[,"fruit_DM"]
|
||||
modelled <- yield[match(as.Date(obsTable$date),as.Date(names(yield),"%d.%m.%Y"))] * 10
|
||||
modelled
|
||||
}
|
||||
|
||||
obsTable <- read.csv2("Martonvasar_maize.obs",stringsAsFactors=FALSE)
|
||||
obsTable$mean <- obsTable$mean / 1000 * 0.85
|
||||
obsTable$sd <- obsTable$sd / 1000
|
||||
measured <- obsTable$mean
|
||||
|
||||
# apriori
|
||||
settings <- setupMuso(iniInput=c("n.ini","n.ini"))
|
||||
modelled <- get_modelled(obsTable, settings)
|
||||
results <- matrix(ncol=4,nrow=11)
|
||||
colnames(results) <- c("r2","rmse","bias","modeff")
|
||||
row.names(results) <- 0:10
|
||||
results[1,] <- get_stats(modelled,measured)
|
||||
# max_likelihood_stats
|
||||
for(i in 1:10){
|
||||
file.copy(sprintf("../%s/maize_ml.epc",i),"maize.epc",overwrite=TRUE)
|
||||
settings <- setupMuso(iniInput=c("n.ini","n.ini"))
|
||||
modelled <- get_modelled(obsTable, settings)
|
||||
results[i+1,] <- get_stats(modelled, measured)
|
||||
}
|
||||
# median_stats
|
||||
results_med <- results
|
||||
|
||||
for(i in 1:10){
|
||||
file.copy(sprintf("../maize_median_step%02d_corrected.epc",i),"maize.epc",overwrite=TRUE)
|
||||
settings <- setupMuso(iniInput=c("n.ini","n.ini"))
|
||||
modelled <- get_modelled(obsTable, settings)
|
||||
results_med[i+1,] <- get_stats(modelled, measured)
|
||||
}
|
||||
results_med
|
||||
|
||||
succes_ratio <- numeric(10)
|
||||
for(i in 1:10){
|
||||
succes_ratio[i] <- sum(read.csv(sprintf("../%s/result.csv",i),stringsAsFactors=FALSE)$Const[-1])/10000
|
||||
}
|
||||
names(succes_ratio) <- 1:10
|
||||
png("../success_rate.png",height=30,width=30,res=300, units="cm")
|
||||
barplot(succes_ratio,ylim=c(0,1),xlab="iteration number",ylab="Succes rate")
|
||||
dev.off()
|
||||
|
||||
postscript("../success_rate.eps")
|
||||
barplot(succes_ratio,ylim=c(0,1),xlab="iteration number",ylab="Succes rate")
|
||||
dev.off()
|
||||
70
docs/CIRM/tree_accuracy.R
Normal file
70
docs/CIRM/tree_accuracy.R
Normal file
@ -0,0 +1,70 @@
|
||||
library(rpart)
|
||||
library(rpart.plot)
|
||||
|
||||
accuracy <- function(x,rp){
|
||||
# Accuracy = (TP + TN)/(TP + TN + FP + FP)
|
||||
# TP: True Positive
|
||||
# TN: True Negative
|
||||
# FP: False Positive
|
||||
# FN: False Negative
|
||||
|
||||
predicted <- rpart.predict(rp,type = "vector")
|
||||
predicted[predicted==1] <- 0
|
||||
predicted[predicted==2] <- 1
|
||||
(sum(x*predicted) + sum((x + predicted) == 0)) / length(x)
|
||||
}
|
||||
|
||||
zero_var <- function(m){
|
||||
apply(m,2, function(v){
|
||||
var(v) != 0
|
||||
})
|
||||
|
||||
}
|
||||
|
||||
decbin <- function(decnum){
|
||||
if(decnum < 2){
|
||||
return(decnum)
|
||||
}
|
||||
c(decbin((decnum %/% 2)),decnum %% 2)
|
||||
}
|
||||
|
||||
decpad <- function(decnum,len){
|
||||
binrep <- decbin(decnum)
|
||||
c(rep(0,len-length(binrep)),binrep)
|
||||
}
|
||||
|
||||
|
||||
tree_per_const <- function(results="result.csv",output ="tree_per_const.pdf",
|
||||
parameters_file="Martonvasar_maize.set"){
|
||||
varname <-readLines(parameters_file)[1]
|
||||
parameters <- read.csv(parameters_file,skip=1,stringsAsFactors=FALSE)
|
||||
|
||||
|
||||
results <- read.csv(results, stringsAsFactors=FALSE)
|
||||
# likelihoods <- results[,ncol(results)-3]
|
||||
# results <- results[likelihoods>=quantile(likelihoods,0.95),]
|
||||
len <- round(log(max(results$failType),2))
|
||||
failTypes <- do.call(rbind,lapply(results$failType,function(x){decpad(x,len)}))
|
||||
sapply(1:len, function(const){
|
||||
nonzero <- results[,1:(ncol(results)-4)]
|
||||
nonzero <- nonzero[,-1]
|
||||
nonzero <- nonzero[,zero_var(nonzero)]
|
||||
colnames(nonzero) <- gsub("__.*","",colnames(nonzero))
|
||||
constraint <- failTypes[,const]
|
||||
baseTable <- cbind.data.frame(nonzero,constraint = as.factor(constraint))
|
||||
tryCatch({
|
||||
rp <- rpart(constraint ~ .,data = baseTable)
|
||||
accuracy(constraint, rp)
|
||||
}, error = function(e){NA})
|
||||
})
|
||||
}
|
||||
|
||||
results <- matrix(nrow=10,ncol=4)
|
||||
row.names(results) <- 1:10
|
||||
colnames(results) <- c("Harvest Index", "LAI", "Root depth in phen. 5", "Flowering date")
|
||||
for(i in 1:10){
|
||||
setwd(as.character(i))
|
||||
results[i,] <- tree_per_const(parameters_file="Martonvasar_maize_after_tree.set")
|
||||
setwd("../")
|
||||
}
|
||||
results
|
||||
382
test.R
Normal file
382
test.R
Normal file
@ -0,0 +1,382 @@
|
||||
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
|
||||
Loading…
Reference in New Issue
Block a user