Commit 0f53962d authored by matbuoro's avatar matbuoro
Browse files

Update 2016

parent 265babba
TO DO LIST:
- Modélisation hierarchique de la probabilité de capture au barrage d'Ohla
- Modélisation hierarchique de la probabilité de capture au barrage d'Ohla (adult)
STRUCTURE:
- analyse_coda_tacon.R # Permet d'obtenir des fichiers textes avec les estimations finales d'abondance à partir des CODA (S. Servanty)
\ No newline at end of file
- analyse_coda_tacon.R # Permet d'obtenir des fichiers textes avec les estimations finales d'abondance à partir des CODA (S. Servanty)
......@@ -18,9 +18,8 @@ stade <- "adult"
## WORKING DIRECTORY:
# work.dir<-paste("~/Documents/RESEARCH/PROJECTS/ORE/Abundance",site,stade,sep="/")
# work.dir<-paste("/media/ORE/Abundance",site,stade,sep="/")
# setwd(work.dir)
work.dir<-paste("/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance",site,stade,sep="/")
setwd(work.dir)
##-----------------------------DATA ----------------------------------##
......@@ -51,11 +50,12 @@ model
filename <- file.path(work.dir, model)
#system(paste("cp",model,paste(stade,"-",site,".txt",sep=""),sep=""))
#---------------------------ANALYSIS-----------------------------##
nChains = length(inits) # Number of chains to run.
adaptSteps = 1000 # Number of steps to "tune" the samplers.
nburnin=2000 # Number of steps to "burn-in" the samplers.
nstore=5000 # Total number of steps in chains to save.
nburnin=500 # Number of steps to "burn-in" the samplers.
nstore=1000 # Total number of steps in chains to save.
nthin=1 # Number of steps to "thin" (1=keep every step).
#nPerChain = ceiling( ( numSavedSteps * thinSteps ) / nChains ) # Steps per chain.
......@@ -78,10 +78,12 @@ fit <- bugs(
## cleaning
system("rm bugs/CODA*")
### Save inits ###
# save last values for inits
#inits <- fit$last.values
#if(site == "Nivelle") {save(inits,file=paste('inits/inits_',stade,year,'.Rdata',sep=""))}
#bugs.inits(inits, n.chains=1,digits=3, inits.files = paste('inits/init-',site,'-',stade,year,'.txt',sep=""))
# inits <- fit$last.values
# if(site == "Nivelle") {
# save(inits,file=paste('inits/inits_',stade,year,'.Rdata',sep=""))
# }
######### JAGS ##########
......
This diff is collapsed.
......@@ -4,7 +4,7 @@ modelCompile(1)
modelSetRN(1)
modelInits('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Nivelle/adult/bugs/inits1.txt',1)
modelGenInits()
modelUpdate(2000,1,2000)
modelUpdate(500,1,500)
samplesSet(mup_11_1)
samplesSet(sigmap_11_1)
samplesSet(mup_11_2)
......@@ -139,7 +139,7 @@ summarySet(c_3SW)
summarySet(c_tot)
summarySet(P_1SW)
summarySet(P_MSW)
modelUpdate(5000,1,5000)
modelUpdate(1000,1,1000)
samplesCoda('*', '/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Nivelle/adult/bugs//')
summaryStats('*')
modelQuit('y')
=============================
DIAGNOSTICS
=============================
---------------------------
Heidelberger and Welch's convergence diagnostic
heidel.diag is a run length control diagnostic based on a criterion of relative accuracy for the estimate of the mean. The default setting corresponds to a relative accuracy of two significant digits.
heidel.diag also implements a convergence diagnostic, and removes up to half the chain in order to ensure that the means are estimated from a chain that has converged.
Stationarity start p-value
test iteration
mup_11_1 passed 1 0.9355
sigmap_11_1 passed 1 0.4399
mup_11_2 passed 1 0.9306
sigmap_11_2 passed 1 0.7552
mupi_EF passed 1 0.4076
sigmapi_EF passed 1 0.1762
mup_21 passed 15001 0.1293
sigmap_21 passed 1 0.8320
k_1 passed 1 0.1568
k_2 passed 1 0.0764
eta_1 passed 1 0.8680
eta_2 passed 1 0.7807
rho passed 1 0.1695
shape_lambda passed 1 0.8315
rate_lambda passed 1 0.6545
lambda_tot0 passed 1 0.4739
a_1.1SW passed 1 0.9653
a_2.1SW passed 1 0.9477
Halfwidth Mean Halfwidth
test
mup_11_1 passed 0.2408 0.002251
sigmap_11_1 passed 0.4827 0.023425
mup_11_2 passed 0.1251 0.001064
sigmap_11_2 passed 0.2834 0.019319
mupi_EF passed 0.2487 0.000784
sigmapi_EF passed 0.7620 0.007365
mup_21 passed 0.6475 0.000906
sigmap_21 passed 0.5694 0.005585
k_1 passed 1.2151 0.017923
k_2 passed 2.4371 0.036742
eta_1 passed 3.6573 0.026820
eta_2 passed 5.0776 0.027071
rho passed 0.9424 0.002062
shape_lambda passed 2.8614 0.021615
rate_lambda passed 0.0149 0.000121
lambda_tot0 passed 140.8386 1.528503
a_1.1SW passed 10.1088 0.078443
a_2.1SW passed 2.9689 0.021459
---------------------------
Geweke's convergence diagnostic
Geweke (1992) proposed a convergence diagnostic for Markov chains based on a test for equality of the means of the first and last part of a Markov chain (by default the first 10% and the last 50%).
If the samples are drawn from the stationary distribution of the chain, the two means are equal and Geweke's statistic has an asymptotically standard normal distribution.
The test statistic is a standard Z-score: the difference between the two sample means divided by its estimated standard error. The standard error is estimated from the spectral density at zero and so takes into account any autocorrelation.
The Z-score is calculated under the assumption that the two parts of the chain are asymptotically independent, which requires that the sum of frac1 and frac2 be strictly less than 1.
Fraction in 1st window = 0.1
Fraction in 2nd window = 0.5
mup_11_1 sigmap_11_1 mup_11_2 sigmap_11_2 mupi_EF sigmapi_EF
0.15132 0.27278 0.66386 -0.60176 1.41349 0.68013
mup_21 sigmap_21 k_1 k_2 eta_1 eta_2
-1.17622 0.01639 -1.24499 -1.66276 -0.13923 -0.48727
rho shape_lambda rate_lambda lambda_tot0 a_1.1SW a_2.1SW
1.22970 0.81724 0.60608 -1.51675 -1.06795 -0.96842
---------------------------
Raftery and Lewis's diagnostic
Quantile (q) = 0.025
Accuracy (r) = +/- 0.005
Probability (s) = 0.95
Burn-in Total Lower bound Dependence
(M) (N) (Nmin) factor (I)
mup_11_1 20 25775 3746 6.88
sigmap_11_1 360 373560 3746 99.70
mup_11_2 52 62842 3746 16.80
sigmap_11_2 391 410975 3746 110.00
mupi_EF 6 8462 3746 2.26
sigmapi_EF 16 17728 3746 4.73
mup_21 9 13881 3746 3.71
sigmap_21 36 43812 3746 11.70
k_1 112 128492 3746 34.30
k_2 90 93340 3746 24.90
eta_1 4 4701 3746 1.25
eta_2 4 4622 3746 1.23
rho 128 138464 3746 37.00
shape_lambda 15 16899 3746 4.51
rate_lambda 15 16089 3746 4.29
lambda_tot0 12 18152 3746 4.85
a_1.1SW 12 14553 3746 3.88
a_2.1SW 12 13128 3746 3.50
......@@ -9,48 +9,48 @@ heidel.diag is a run length control diagnostic based on a criterion of relative
heidel.diag also implements a convergence diagnostic, and removes up to half the chain in order to ensure that the means are estimated from a chain that has converged.
Stationarity start p-value
test iteration
mup_11_1 passed 1 0.2294
sigmap_11_1 passed 1 0.1678
mup_11_2 passed 1 0.4416
sigmap_11_2 failed NA 0.0198
mupi_EF passed 1 0.1983
sigmapi_EF passed 1 0.1726
mup_21 passed 1 0.6430
sigmap_21 passed 1 0.1533
k_1 passed 1 0.2935
k_2 passed 1 0.7129
eta_1 passed 1 0.1334
eta_2 passed 1 0.2324
rho passed 1 0.6888
shape_lambda passed 1 0.2802
rate_lambda passed 1 0.3441
lambda_tot0 passed 1 0.8005
a_1.1SW passed 1 0.8186
a_2.1SW passed 1 0.6540
Stationarity start p-value
test iteration
mup_11_1 passed 1 4.04e-01
sigmap_11_1 failed NA 2.25e-06
mup_11_2 passed 401 1.18e-01
sigmap_11_2 passed 1 9.81e-01
mupi_EF passed 101 1.96e-01
sigmapi_EF passed 1 5.78e-01
mup_21 passed 101 3.24e-01
sigmap_21 passed 1 8.06e-01
k_1 passed 1 5.27e-01
k_2 passed 1 4.58e-01
eta_1 passed 1 4.09e-01
eta_2 passed 1 1.45e-01
rho passed 1 5.06e-01
shape_lambda passed 1 6.97e-01
rate_lambda passed 1 5.58e-01
lambda_tot0 passed 1 6.10e-01
a_1.1SW passed 1 3.33e-01
a_2.1SW passed 1 3.16e-01
Halfwidth Mean Halfwidth
test
mup_11_1 passed 0.2447 0.005560
sigmap_11_1 failed 0.4376 0.068686
mup_11_2 passed 0.1204 0.002275
sigmap_11_2 <NA> NA NA
mupi_EF passed 0.2476 0.002445
sigmapi_EF passed 0.7760 0.023879
mup_21 passed 0.6521 0.002417
sigmap_21 passed 0.5789 0.015989
k_1 passed 1.1704 0.064811
k_2 passed 2.4299 0.160761
eta_1 passed 3.3534 0.062855
eta_2 passed 5.1254 0.117029
rho passed 0.9452 0.009248
shape_lambda passed 2.8566 0.068897
rate_lambda passed 0.0151 0.000405
lambda_tot0 passed 142.0949 4.915053
a_1.1SW passed 10.0370 0.246490
a_2.1SW passed 2.9461 0.073931
mup_11_1 passed 0.2437 0.013806
sigmap_11_1 <NA> NA NA
mup_11_2 passed 0.1283 0.004165
sigmap_11_2 failed 0.3165 0.083794
mupi_EF passed 0.2514 0.005447
sigmapi_EF passed 0.7237 0.043936
mup_21 passed 0.6517 0.005495
sigmap_21 passed 0.5410 0.041786
k_1 passed 1.1453 0.097843
k_2 passed 2.3712 0.151751
eta_1 passed 3.3987 0.118404
eta_2 passed 5.3360 0.181535
rho passed 0.9475 0.011612
shape_lambda passed 2.7168 0.174404
rate_lambda passed 0.0143 0.000985
lambda_tot0 passed 138.7444 8.752523
a_1.1SW passed 10.1821 0.526254
a_2.1SW passed 2.9996 0.150857
---------------------------
Geweke's convergence diagnostic
......@@ -65,10 +65,12 @@ The Z-score is calculated under the assumption that the two parts of the chain a
Fraction in 1st window = 0.1
Fraction in 2nd window = 0.5
mup_11_1 sigmap_11_1 mup_11_2 sigmap_11_2 mupi_EF sigmapi_EF mup_21 sigmap_21 k_1
-0.6974 -2.6615 -0.3153 0.6576 -2.2736 -0.2349 0.3858 1.2018 0.5753
k_2 eta_1 eta_2 rho shape_lambda rate_lambda lambda_tot0 a_1.1SW a_2.1SW
-0.0455 1.1900 1.4736 -0.5396 -0.5237 -0.5010 -0.5615 0.8885 0.8970
mup_11_1 sigmap_11_1 mup_11_2 sigmap_11_2 mupi_EF sigmapi_EF
-0.3224 -0.4088 -0.8603 -1.3132 1.5172 0.5646
mup_21 sigmap_21 k_1 k_2 eta_1 eta_2
-2.2853 -1.0174 -1.6749 -0.6190 1.0087 -2.4604
rho shape_lambda rate_lambda lambda_tot0 a_1.1SW a_2.1SW
1.2729 -0.4802 -0.1552 0.0936 0.8232 0.6989
---------------------------
......@@ -77,25 +79,5 @@ Raftery and Lewis's diagnostic
Quantile (q) = 0.025
Accuracy (r) = +/- 0.005
Probability (s) = 0.95
Burn-in Total Lower bound Dependence
(M) (N) (Nmin) factor (I)
mup_11_1 4 5124 3746 1.37
sigmap_11_1 56 55960 3746 14.90
mup_11_2 10 11628 3746 3.10
sigmap_11_2 60 50640 3746 13.50
mupi_EF 3 4338 3746 1.16
sigmapi_EF 12 13334 3746 3.56
mup_21 4 5062 3746 1.35
sigmap_21 22 19268 3746 5.14
k_1 36 40242 3746 10.70
k_2 58 61786 3746 16.50
eta_1 4 4816 3746 1.29
eta_2 4 5038 3746 1.34
rho 44 48640 3746 13.00
shape_lambda 9 9892 3746 2.64
rate_lambda 12 13658 3746 3.65
lambda_tot0 5 5973 3746 1.59
a_1.1SW 10 12194 3746 3.26
a_2.1SW 9 9483 3746 2.53
You need a sample size of at least 3746 with these values of q, r and s
This diff is collapsed.
......@@ -8,7 +8,7 @@
library(coda)
##-----------------------------DATA ----------------------------------##
year <- 2015
#year <- 2016
site <- "Nivelle"
stade <- "tacon"
......@@ -824,7 +824,7 @@ for (y in 2:Y_last) {
}
# Write the results in tables
cnames <- c("mean", "sd","0.025", "0.05", "0.25","0.5","0.75","0.95","0.975")
cnames <- c("mean", "sd","q0.025", "q0.05", "q0.25","q0.5","q0.75","q0.95","q0.975")
write.table(YOY_tot_q,file="results/YOY_tot.txt", row.names=F, col.names=cnames, sep = "\t")
write.table(YOYnat_q,file="results/YOYnat.txt", row.names=F, col.names=cnames, sep = "\t")
write.table(YOYcomp_q,file="results/YOYcomp.txt", row.names=F, col.names=cnames, sep = "\t")
......
......@@ -18,8 +18,8 @@ stade <- "tacon"
## WORKING DIRECTORY:
# work.dir<-paste("/media/ORE/Abundance",site,stade,sep="/")
# setwd(work.dir)
work.dir<-paste("/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance",site,stade,sep="/")
setwd(work.dir)
##-----------------------------DATA ----------------------------------##
......@@ -39,7 +39,8 @@ if(!file.exists(paste("inits/init-",site,"-",stade,year,".txt",sep=""))){
}
#load(paste('inits/inits_',stade,'.Rdata',sep="")) # chargement des inits
#if(site == "Bresle" && stade == "adult") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
if(site == "Nivelle") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
#if(site == "Nivelle") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))
#------------------------MODEL----------------------------------##
model <- paste("model/model_",stade,"-",site,".R",sep="") # path of the model
......@@ -49,11 +50,12 @@ model
filename <- file.path(work.dir, model)
#system(paste("cp",model,paste(stade,"-",site,".txt",sep=""),sep=""))
#---------------------------ANALYSIS-----------------------------##
nChains = length(inits) # Number of chains to run.
adaptSteps = 1000 # Number of steps to "tune" the samplers.
nburnin=500 # Number of steps to "burn-in" the samplers.
nstore=1000 # Total number of steps in chains to save.
nburnin=1000 # Number of steps to "burn-in" the samplers.
nstore=2000 # Total number of steps in chains to save.
nthin=1 # Number of steps to "thin" (1=keep every step).
#nPerChain = ceiling( ( numSavedSteps * thinSteps ) / nChains ) # Steps per chain.
......@@ -76,10 +78,13 @@ fit <- bugs(
## cleaning
system("rm bugs/CODA*")
### Save inits ###
# save last values for inits
#inits <- fit$last.values
#if(site == "Nivelle") {save(inits,file=paste('inits/inits_',stade,year,'.Rdata',sep=""))}
#bugs.inits(inits, n.chains=1,digits=3, inits.files = paste('inits/init-',site,'-',stade,year,'.txt',sep=""))
# inits <- fit$last.values
# if(site == "Nivelle") {
# save(inits,file=paste('inits/inits_',stade,year,'.Rdata',sep=""))
# }
######### JAGS ##########
## Compile & adapt
......
****** Sorry something went wrong in procedure Mark in module Kernel ******
This diff is collapsed.
......@@ -4,7 +4,7 @@ modelCompile(1)
modelSetRN(1)
modelInits('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Nivelle/tacon/bugs/inits1.txt',1)
modelGenInits()
modelUpdate(500,1,500)
modelUpdate(1000,1,1000)
samplesSet(mu_p_srem)
samplesSet(sd_logit_p_srem)
samplesSet(epsilon_p)
......@@ -67,7 +67,7 @@ summarySet(jres_ns_runs)
summarySet(dj)
summarySet(dj_nat)
summarySet(dj_comp)
modelUpdate(1000,1,1000)
modelUpdate(2000,1,2000)
samplesCoda('*', '/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Nivelle/tacon/bugs//')
summaryStats('*')
modelQuit('y')
> cat("=============================\n")
=============================
> cat("DIAGNOSTICS\n")
DIAGNOSTICS
> cat("=============================\n")
=============================
> if (nChains > 1) {
+ cat("Convergence: gelman-Rubin R test\n")
+ gelman.diag(fit.mcmc[,which(varnames(fit.mcmc)%in%parameterstotest)])
+ }
> cat("\n---------------------------\n")
---------------------------
> cat("Heidelberger and Welch's convergence diagnostic\n")
Heidelberger and Welch's convergence diagnostic
> cat("
+ heidel.diag is a run length control diagnostic based on a criterion of relative accuracy for the estimate of the mean. The default setting c ..." ... [TRUNCATED]
heidel.diag is a run length control diagnostic based on a criterion of relative accuracy for the estimate of the mean. The default setting corresponds to a relative accuracy of two significant digits.
heidel.diag also implements a convergence diagnostic, and removes up to half the chain in order to ensure that the means are estimated from a chain that has converged.
Stationarity start p-value
test iteration
mu_p_srem passed 1 0.185689
sd_logit_p_srem failed NA 0.000693
epsilon_p passed 1 0.177055
pi_dj passed 1 0.913764
zeta_alpha_dj failed NA 0.014602
mu_dj_nat passed 1 0.506568
k_cpue passed 1 0.295180
eta_cpue passed 1 0.997085
rho_s passed 1 0.684125
sd_s_rec passed 1 0.096969
p_cpue passed 1 0.770388
> heidel.diag(fit.mcmc[,which(varnames(fit.mcmc)%in%parameterstotest)], eps=0.1, pvalue=0.05)
Stationarity start p-value
test iteration
mu_p_srem passed 601 0.0669
sd_logit_p_srem passed 601 0.2366
epsilon_p passed 801 0.4381
pi_dj passed 601 0.1008
zeta_alpha_dj passed 1 0.1208
mu_dj_nat passed 1 0.0641
k_cpue passed 801 0.1195
eta_cpue passed 1 0.5970
rho_s passed 1 0.3307
sd_s_rec passed 1 0.3200
p_cpue passed 1 0.5303
Halfwidth Mean Halfwidth
test
mu_p_srem passed 0.517 0.00536
sd_logit_p_srem <NA> NA NA
epsilon_p passed 0.559 0.00854
pi_dj passed 0.729 0.00472
zeta_alpha_dj <NA> NA NA
mu_dj_nat passed 0.127 0.00193
k_cpue passed 123.399 0.80628
eta_cpue passed 2.243 0.18749
rho_s passed 0.152 0.00506
sd_s_rec passed 0.452 0.00366
p_cpue passed 0.627 0.01003
mu_p_srem passed 0.544 0.01652
sd_logit_p_srem passed 1.024 0.03291
epsilon_p passed 0.504 0.01790
pi_dj passed 0.731 0.01765
zeta_alpha_dj passed 3.906 0.10157
mu_dj_nat passed 0.129 0.00664
k_cpue passed 130.697 3.53667
eta_cpue failed 2.127 0.79120
rho_s failed 0.177 0.01827
sd_s_rec passed 0.442 0.01248
p_cpue passed 0.628 0.04318
> cat("\n---------------------------\n")
---------------------------
> cat("Geweke's convergence diagnostic\n")
Geweke's convergence diagnostic
> cat("
+ Geweke (1992) proposed a convergence diagnostic for Markov chains based on a test for equality of the means of the first and last part of a ..." ... [TRUNCATED]
Geweke (1992) proposed a convergence diagnostic for Markov chains based on a test for equality of the means of the first and last part of a Markov chain (by default the first 10% and the last 50%).
If the samples are drawn from the stationary distribution of the chain, the two means are equal and Geweke's statistic has an asymptotically standard normal distribution.
The test statistic is a standard Z-score: the difference between the two sample means divided by its estimated standard error. The standard error is estimated from the spectral density at zero and so takes into account any autocorrelation.
......@@ -48,35 +75,31 @@ The test statistic is a standard Z-score: the difference between the two sample
The Z-score is calculated under the assumption that the two parts of the chain are asymptotically independent, which requires that the sum of frac1 and frac2 be strictly less than 1.
> geweke.diag(fit.mcmc[,which(varnames(fit.mcmc)%in%parameterstotest)], frac1 = 0.1, frac2 = 0.5)
Fraction in 1st window = 0.1
Fraction in 2nd window = 0.5
mu_p_srem sd_logit_p_srem epsilon_p pi_dj zeta_alpha_dj
1.4412 -1.5036 -1.7661 -0.2559 3.1065
mu_dj_nat k_cpue eta_cpue rho_s sd_s_rec
-1.2165 1.6109 -1.7121 -0.5301 2.2931
p_cpue
-0.5272
mu_p_srem sd_logit_p_srem epsilon_p pi_dj zeta_alpha_dj mu_dj_nat k_cpue eta_cpue
-6.641 7.805 6.139 1.559 -5.074 0.192 -5.779 -1.319
rho_s sd_s_rec p_cpue
-0.594 0.359 -1.285
> cat("\n---------------------------\n")
---------------------------
> cat("Raftery and Lewis's diagnostic\n")
Raftery and Lewis's diagnostic
> raftery.diag(fit.mcmc[,which(varnames(fit.mcmc)%in%parameterstotest)], q=0.025, r=0.005, s=0.95, converge.eps=0.001)
Quantile (q) = 0.025
Accuracy (r) = +/- 0.005
Probability (s) = 0.95
Burn-in Total Lower bound Dependence
(M) (N) (Nmin) factor (I)
mu_p_srem 126 122994 3746 32.80
sd_logit_p_srem 110 127270 3746 34.00
epsilon_p 220 216420 3746 57.80
pi_dj 24 29700 3746 7.93
zeta_alpha_dj 6 8164 3746 2.18
mu_dj_nat 143 153153 3746 40.90
k_cpue 88 99902 3746 26.70
eta_cpue 36 38880 3746 10.40
rho_s 32 45056 3746 12.00
sd_s_rec 50 55560 3746 14.80
p_cpue 36 38880 3746 10.40
You need a sample size of at least 3746 with these values of q, r and s
> # Stop writing to the file
> sink()
This diff is collapsed.
This diff is collapsed.
"mean" "sd" "0.025" "0.05" "0.25" "0.5" "0.75" "0.95" "0.975"
"mean" "sd" "q0.025" "q0.05" "q0.25" "q0.5" "q0.75" "q0.95" "q0.975"
NA NA NA NA NA NA NA NA NA
1339.9318 248.895807219039 917 973 1164 1317 1491 1786 1891
8081.12902 607.845530589492 6980 7138 7660 8049 8469 9131 9364
11828.93904 787.007407066194 10386 10594 11283 11792 12339 13176 13466
11148.29376 1157.63879343109 9102.975 9378.95 10343.75 11070 11869 13179 13643.025
10705.38568 888.134877235514 9050.975 9286 10100 10676 11274 12212.05 12536.025
5853.49724 558.206428304722 4849 4988 5467 5824 6205 6817 7040
7045.34904 838.374203537526 5573 5776 6459 6991 7562 8493.05 8847
5005.97636 915.612676786767 3421 3641 4366 4929 5560 6647.05 7047
4780.85668 710.376964581492 3566 3725 4285 4718 5202 6066 6388
13926.00988 1784.19119634273 10844 11267.95 12686 13786 15005 17039 17859.025
5944.80506 719.2643215687 4653 4844 5443 5898 6401 7190 7475
7290.6159 1146.90766524208 5306 5549 6484 7206 8003 9321 9762
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2563.75232 570.725961225765 1603 1724 2155 2514 2911 3581 3837
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4773.03726 974.401131907701 3082 3312 4077.75 4704 5375 6494.05 6908
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10722.9815 1115.12921706102 8823.875 9013.95 9913 10662.5 11374.75 12727.45 13193.025
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4393.676 539.188833641018 3422 3572.85 4020.75 4378 4720.25 5328.2 5494.25
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2453.3355 547.308525651552 1527.925 1644.95 2065.25 2390 2777.25 3507.05 3730.025
3095.064 587.276810311769 2018.925 2161.9 2689.5 3092 3476.25 4090.05 4287.3
5198.5305 985.680612675755 3499.95 3682.9 4529 5112 5804 6987.45 7375.1
2788.618 629.792794145645 1719.9 1868.9 2332.75 2730.5 3171 3914.5 4175.6