Commit d02946b8 authored by Iago Mosqueira's avatar Iago Mosqueira
Browse files

Corrected folder

parent f57db2be
......@@ -46,3 +46,26 @@ license: Creative Commons Attribution-ShareAlike 4.0 International Public Licens
6. Potential modifications after initial fitting
7. Stock status summary
8. Projections etc.
# ICES Suggested details to deal with in benchmark assessments.
- What have been the key areas of focus for the benchmark?
- Have comments in previous technical minutes been addressed?
- Have environmental issues and species interactions been taken into account?
- Are the catch statistics representative of the removals from the stocks (discards, underreporting, misreporting)?
- Has the quality of the survey data been evaluated and presented in the report?
- Has the quality of fishery dependent data (catch, effort, cpue) been evaluated and presented in the report?
- Has the sampling level (commercial catch, discards, surveys) been presented and evaluated?
## Stock assessment modelling:
- Has the sensitivity to model assumptions (different model formulations) been explored?
- Has the sensitivity of parameter estimates to data been evaluated and presented (e.g. bootstrap analysis)?
- Has retrospective analysis been carried out and presented?
- Is the final assessment acceptable to: 1) show the historical stock development 2) show the present status, and 3) be used for a short term forecast?
- Are the recruitment estimates presented and credible?
- If a short term forecast is presented:
- Is the short term forecast consistent with the stock assessment output?
- Has the sensitivity of the short term forecast been discussed (including the current year assumption)?
- Are the (previous) reference points (still) appropriate?
> btsisis <- aap(stock, indices[c("BTS-ISIS", "SNS")], control=control,
+ wkdir="model/aap/isis")
Model and results are stored in working directory [model/aap/isis-15]
Initial statistics: 262 variables; iteration 0; function evaluation 0; phase 1
Function value 8.8122680e+04; maximum gradient component mag -5.7518e+06
- final statistics:
267 variables; iteration 297; function evaluation 326
Function value -1.2230e+03; maximum gradient component mag 6.1724e-05
> btsgam <- aap(stock, indices[c("GAM", "SNS")], control=control,
+ wkdir="model/aap/base", stdfile=btsisis@stdfile)
Model and results are stored in working directory [model/aap/base-5]
Initial statistics: 262 variables; iteration 0; function evaluation 0; phase 1
Function value -5.1683661e+02; maximum gradient component mag -3.3264e+04
- final statistics:
267 variables; iteration 65; function evaluation 91
Function value -1.3242e+03; maximum gradient component mag 5.0039e-05
> gamisis <- aap(stock, indices[c("GAM-ISIS", "SNS")], control=control,
+ wkdir="model/aap/gisis", stdfile=btsisis@stdfile)
Model and results are stored in working directory [model/aap/gisis-1]
Initial statistics: 262 variables; iteration 0; function evaluation 0; phase 1
Function value -8.0530928e+02; maximum gradient component mag -1.4266e+04
Intermediate statistics: 262 variables; iteration 10; function evaluation 21; phase 1
# TMP.R - DESC
# /TMP.R
# Copyright Iago MOSQUEIRA (WMR), 2020
# Author: Iago MOSQUEIRA (WMR) <iago.mosqueira@wur.nl>
#
# Distributed under the terms of the EUPL-1.2
library(FLa4a)
load('data/data.RData')
# CHECK residuals()
control <- AAP.control(pGrp=TRUE, qplat.surveys=7, qplat.Fmatrix=8,
Fage.knots=6, Ftime.knots=22, Wtime.knots=5, mcmc=FALSE)
run <- aap(stock, indices[c("BTS-ISIS", "SNS")], control=control,
wkdir=tempfile())
x <- run@index.res[[1]][-10,ac(1985:2018)] -
(log(index(indices[[1]])) - log(run@index.hat[[1]][-10,ac(1985:2018)]))
# TEST effect of maturity ogive on fit
control <- AAP.control(pGrp=TRUE, qplat.surveys=7, qplat.Fmatrix=8,
Fage.knots=6, Ftime.knots=22, Wtime.knots=5, mcmc=FALSE)
stk <- stock
mat(stk) <- c(0,0.5,0.9,rep(1,7))
run <- aap(stock, indices[c("BTS-ISIS", "SNS")], control=control,
wkdir=tempfile())
residuals <- FLQuants(c(run@index.res, list(
catch.n=residuals(catch.n(run) + 0.5, catch.n(stock)),
catch=residuals(catch(run) + 0.5, catch(stock)))))
ggplot(residuals, aes(x=year, y=as.factor(age))) +
geom_point(aes(size=abs(data), fill=as.factor(sign(data))),
shape=21, alpha=0.4, na.rm=TRUE) + facet_wrap(~qname) +
scale_fill_manual(values=c("black", NA, "white")) +
scale_size(range = c(0.1, 5)) +
theme(legend.position="none") + xlab("") + ylab("")
# TEST effect of control changes on residual patterns
control <- AAP.control(pGrp=TRUE, qplat.surveys=7, qplat.Fmatrix=8,
Fage.knots=6, Ftime.knots=22, Wtime.knots=5, mcmc=FALSE)
run1 <- aap(stock, indices[c("BTS-ISIS", "SNS")], control=control,
wkdir=tempfile())
control <- AAP.control(pGrp=TRUE, qplat.surveys=7, qplat.Fmatrix=8,
Fage.knots=6, Ftime.knots=25, Wtime.knots=5, mcmc=FALSE)
run2 <- aap(stock, indices[c("BTS-ISIS", "SNS")], control=control,
wkdir=tempfile())
# NPIN
# NPIN
pin <- btsisis@stdfile
idx <- which(pin$name == 'log_sel_coff1')
npin <- rbind(pin[1:idx[length(idx)],], pin[seq(idx[1], length=12),], pin[165:441,])
npin$index <- seq(1, nrow(npin))
npin[seq(165, length=12),]
npin[seq(160, length=22),]
control <- AAP.control(pGrp=TRUE, qplat.surveys=7, qplat.Fmatrix=8,
Fage.knots=6, Ftime.knots=26, Wtime.knots=5, mcmc=FALSE)
pbtsisis <- aap(stock, indices[c("BTS-ISIS", "SNS")], control=control,
wkdir="model/aap/pin", stdfile=npin)
# functions.R - DESC
# /functions.R
# Copyright Iago MOSQUEIRA (WMR), 2019
# Author: Iago MOSQUEIRA (WMR) <iago.mosqueira@wur.nl>
#
# Distributed under the terms of the GPL 3.0
# readVPAInterCatch {{{
readVPAInterCatch <- function(file) {
contents <- scan(file=file, what="", sep="\n", quiet=TRUE)
# 1. Catch category
# 2. stock
# 3. 1 2
# 4. year
year <- as.numeric(strsplit(contents[4], " ")[[1]])[1]
# 5. ages
age <- do.call(seq, as.list(as.numeric(strsplit(contents[5], " ")[[1]])))
# 6. 1
# 7. units
units <- switch(contents[7],
"in numbers" = "1",
"in grams" = "gr",
"NA")
# 8. data: 300,999.876
data <- as.numeric(strsplit(gsub(",", "",
gsub("\\s+", "-", contents[8])), "-")[[1]])
if(units == "1") {
data <- data / 1000
units <- "thousands"
} else if (units == "gr") {
data <- data / 1000
units <- "kg"
}
return(FLQuant(data, dimnames=list(age=age, year=year), units=units))
} # }}}
# model.R - DESC
# /model.R
# Copyright Iago MOSQUEIRA (WMR), 2019
# Author: Iago MOSQUEIRA (WMR) <iago.mosqueira@wur.nl>
# Distributed under the terms of the GPL 3.0
library(AAP)
load('data/data.RData')
# RUN models
# AAP 2019 settings
control <- AAP.control(pGrp=TRUE, qplat.surveys=7, qplat.Fmatrix=7,
Fage.knots=6, Ftime.knots=22, Wtime.knots=5, mcmc=FALSE)
run <- aap(stock, indices, control=control, wkdir="model/aap")
result <- run + stock
plot(result)
# EXPLORE diagnostics
library(FLa4a)
# stdlogres(obs, fit)
catchres <- stdlogres(catch.n(stock), catch.n(run))
catchres[is.na(catchres)] <- 0
bubbles(age~year, data=catchres)
# TODO SURVEY residuals
sres <- mapply(function(x,y) Reduce('-', intersect(x,y)), index(run),
index.hat(run), SIMPLIFY=FALSE)
# RUN short term forecast
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sorted: 0.02629976292 0.6908716718 0.8731479198 0.9641392449 1.099703725 1.225518152 1.338607239 1.418811874 1.429699769 1.482063500 1.509492448 1.535070885 1.548211999 1.588603149 1.600275948 1.611525023 1.624806387 1.627625795 2.701261530 2.954331425 4.286354717 4.882183643 7.149445796 7.673783473 8.080024076 8.946406696 10.45354897 11.52140567 11.77226008 12.02949079 12.27936028 12.81615694 13.01193821 13.54730353 14.00938002 14.51346101 15.00283947 15.12641598 15.70032855 15.92060190 16.17326323 16.44971990 16.65273289 16.97420981 17.05762764 17.40977894 17.55552732 17.78592287 18.17054656 19.07350841 19.36686784 20.00704748 20.58748873 20.73657217 21.42902558 21.62028302 22.20459678 22.44086789 24.17291576 25.32892189 27.26737381 27.81752666 28.69447744 29.20871309 30.39483950 31.33936617 31.64529000 32.59962720 32.75053901 33.20113587 33.34017560 34.83409054 35.05108557 36.68732144 36.94566610 38.34357055 38.81012706 40.09833424 40.45294176 41.28036840 41.64753713 42.22093980 43.71131842 44.22635192 45.26322395 45.50322326 46.81977133 47.05520130 47.26003914 48.62469869 49.02431706 50.29207064 50.77747546 51.17507364 51.68339446 52.37712423 52.78717109 53.38546778 53.98419166 54.52739364 54.92173091 55.29146766 56.14447903 56.37140607 57.74064576 58.32103336 59.39914582 60.38325946 61.55116049 63.47750314 65.07329531 66.32768061 68.23890074 68.82233428 71.44531098 71.66696531 71.72817336 73.61890753 75.22537486 81.67526307 83.05913727 83.11717644 86.52321711 88.98367688 89.77507064 94.59484372 96.95692558 98.78817334 101.2088526 106.6684553 109.3378597 113.4994468 115.9851143 116.4809248 118.1950373 121.4577796 122.5238630 124.9951118 125.2688514 128.7250395 130.1904944 133.1750372 135.3043051 135.6091581 137.3756113 138.6277575 139.7994747 141.0821689 141.6870513 142.9560949 144.9152442 145.3366287 146.2587417 149.4233091 152.0363731 152.4137221 155.4619069 158.5025965 159.7725763 163.6867795 166.6886099 169.0971475 174.9836407 176.7260106 177.7345076 179.2367346 181.1954704 186.0460626 188.4126798 191.7633850 194.6905247 199.0152234 201.7590378 204.2962489 211.4267301 212.3434260 214.1775365 215.6396909 220.4652267 226.5208050 227.1201718 228.3461308 228.8549255 230.3063246 232.5629129 232.8289954 233.5476351 237.1793703 238.6756243 239.5933460 240.7808653 241.4388662 242.6413715 244.9942004 246.6705029 252.2276222 258.3638197 261.7702834 268.6820157 274.0283234 278.0132654 281.6108564 285.1773318 285.5833020 286.0050943 287.0128286 287.7313684 288.7188939 291.2468780 293.6199367 297.8166461 301.3955947 302.9346905 303.3561814 306.9575159 313.3807691 326.2573395 338.2014674 357.7668817 385.5022772 411.8608468 475.8910071 491.5649443 528.8634184 545.9628596 596.9542879 613.9830291 669.4023692 729.5189504 800.6460834 858.0609579 901.4761024 924.6483967 960.8682983 973.2135230 1063.036319 1096.325866 1149.363939 1169.611842 1314.069146 1326.994579 1503.924857 1590.276259 2276.753701 2578.738196 2775.807834 3384.177002 9729.650650 16333.71492 18231.00229 35527.70210 47032.13948 82231.40758 138319.6484 213472.6361 806684.8565 1062631.070 1518097.674 2983284.860 3080377.881 3094482.034
# Number of parameters = 256 Objective function value = -1305.12291777643 Maximum gradient component = 9.08435076909520e-05
# logsigmaL:
1.31819877527287 -1.02018069595641 0.0787155227034619
# logsigmaD:
-1.92462809531395 0.741341373271693 -0.0567210549769002
# logsigmaU:
-0.563525691234822 -0.268188624571199 0.0387646931972705
-0.607304954016883 -0.130048221995859 0.0535319753248117
# logsigmaLWTS:
-2.53541673222485 -0.404033320635065 0.0406214586285955
# logsigmaSWTS:
-3.00877107217849 -0.284646180969251 0.0363493632324616
# loga0:
0.899999787328
# logSWfact:
-0.0607534840251
# log_sel_coff1:
-1.40644298501 -8.28041052859 -5.94616932807 -7.26713158007 -5.40913354191 -0.925212304205 -2.62643305762 -2.04802636358 -2.43261816764 -3.04655592461 -1.78829206017 -2.40428658600 -4.28948727888 -2.80019056802 -3.32967188927 -0.495939632658 -3.16253733134 0.0800599003094 -1.96800240961 -1.47168528648 -1.69512552618 -1.25733915120 -1.32625710708 -0.151337067103 -1.74497046600 -0.728088584664 0.0529214270161 0.108696893202 0.354374729623 1.34708598552 1.63062317415 1.72563802814 1.36918262018 1.42747111023 1.33558299401 1.73890928522 1.58903503984 1.30234038423 0.908364574967 0.973331948827 1.48836706295 1.47340120770 1.44536779260 1.39051004987 1.47443141496 0.923212161478 1.38739651873 0.607778501426 0.871124549054 0.180196769261 0.659770112782 0.869616414719 1.32453942518 0.737037647863 1.38745871168 1.71656974293 1.67268983870 1.72580688929 1.51239899175 1.80646499615 1.68566327154 1.88948914552 1.63580931644 1.68622428843 1.47302298945 2.07271735337 1.78673886132 1.97935481263 1.55008957651 1.71360479770 1.43916404512 1.59960502238 1.52139261675 1.06237334720 1.56489583640 0.490954908253 1.05090782471 1.38077004769 0.960006266080 1.47782255198 1.23288241205 1.45360425090 1.45681501808 1.34325877387 1.46409561397 1.51284663194 1.65920646492 1.50359365399 1.45286254169 1.72103233633 1.69589314942 1.96489912460 1.67506743052 1.80746771085 1.54652924154 1.45475308229 1.46834045994 1.31158623728 1.33894144991 -0.368486676591 0.588510873600 0.762798465890 1.27909408762 1.01781252038 0.410413970232 1.02443341609 0.833188811044 1.39029840592 0.917034539742 1.33636816727 1.42562094601 1.61960464998 1.37902405691 1.23107019422 1.71075090480 1.73387191154 1.70380942254 1.59505092038 1.48585544634 1.40252344930 1.38339066785 1.46764676168 1.45802044394 1.60089190796 0.774563086938 0.132031434092 0.694176370069 0.965698031312 0.487496059982 0.379702342454 0.542088436391 0.777207418682 0.950181995196 1.11917866151 0.945393878117 1.00322995530 1.12823914510 1.02862602496 0.997829943038 1.12116008586 1.39309475705 1.49624810939 1.36118769235 1.01523144020 1.18034631512 1.00045884799 1.24974813467 1.16414034166 1.16978866689 0.674699837860
# log_sel_cofU:
-7.84770296446 -7.81383392607 -8.29340630271 -8.24058865732 -8.47601718094 -8.28530788450
-4.04593824094 -4.70840811570 -5.88101491861 -5.89997527671 -9.54287033907 2.66046409721
# disc_curve:
0.874780021236 -1.81407868294
# logK:
1.48840408725
# log_temp_wts_Linf:
0.242700000000 0.405400000000 0.409300000000 0.269700000000 0.281100000000
# log_initpop:
11.2303595544 11.4101002810 11.0759902574 9.79058249766 9.77582756957 10.3541911079 9.42780091006 8.04402012980 10.6619868088 11.8089307353 11.7142637371 13.0148388759 10.6436201287 11.1494032797 9.31712919144 9.45625569982 13.3392016585 11.9326705940 10.9055784318 11.3392206671 11.7039046977 11.4127563611 12.2623141376 10.9358643945 11.6019832916 11.9004107472 11.7231349085 10.9861200101 11.8367729538 12.0546192655 11.0413891037 9.76938483102 12.1305037696 12.3269414634 12.2387910629 12.1799256789 11.4427899036 11.5928952688 11.9800625925 11.2627960247 13.3056937438 11.7068463694 12.3208635020 11.4054206525 13.1522318661 11.6729555178 11.3239576780 11.7557805143 11.1868698189 12.5940551307 11.8380019693 11.6363102087 11.8906134030 11.2247152906 12.2449849592 11.5496276030 10.8541423664 10.8665880656 12.0405613378 11.1176550661 11.2547475638 11.5276121538 12.1442680971 12.0643460521 10.9836325542 11.4639645230 12.0735376759 11.7849116897 11.2303816079 11.8379114053 11.5205736434
# Number of parameters = 261 Objective function value = -1305.12299596970 Maximum gradient component = 9.78734974808049e-05
# logsigmaL:
1.31819941624316 -1.02018097258144 0.0787155469336595
# logsigmaD:
-1.92462820541090 0.741341416602445 -0.0567210584401751
# logsigmaU:
-0.563525182230913 -0.268188885634862 0.0387647187392415
-0.607304507545599 -0.130048483833581 0.0535320074014307
# logsigmaLWTS:
-2.53556590509348 -0.404073457522432 0.0406295879061330
# logsigmaSWTS:
-3.00869112095662 -0.284679641521732 0.0363535424900057
# loga0:
0.899999785788
# logSWfact:
-0.0607589908578
# log_sel_coff1:
-1.40644317655 -8.28041058074 -5.94616939389 -7.26713164507 -5.40913363817 -0.925212321501 -2.62643305584 -2.04802640471 -2.43261820374 -3.04655595770 -1.78829202524 -2.40428662028 -4.28948739339 -2.80019069419 -3.32967200990 -0.495939717837 -3.16253733995 0.0800598985982 -1.96800139882 -1.47168508072 -1.69512535777 -1.25733906754 -1.32625647798 -0.151336674534 -1.74496969031 -0.728088214396 0.0529222848998 0.108697714091 0.354374833088 1.34708565336 1.63062282645 1.72563765368 1.36918243278 1.42747141275 1.33558309101 1.73890908417 1.58903483953 1.30234032653 0.908364416592 0.973332364601 1.48836720714 1.47340160702 1.44536763593 1.39051017339 1.47443199545 0.923211894810 1.38739651703 0.607778417280 0.871124675980 0.180197488088 0.659770440105 0.869616619939 1.32453968325 0.737037706899 1.38745862504 1.71656984015 1.67269009020 1.72580681529 1.51239956845 1.80646453702 1.68566388454 1.88948925168 1.63580945066 1.68622444865 1.47302315015 2.07271761526 1.78673889575 1.97935495657 1.55008979376 1.71360465878 1.43916403948 1.59960531867 1.52139196768 1.06237246140 1.56489650744 0.490955057892 1.05090833270 1.38077018508 0.960006974356 1.47782273448 1.23288240692 1.45360426819 1.45681545357 1.34325856572 1.46409563441 1.51284699724 1.65920604581 1.50359414664 1.45286281255 1.72103241201 1.69589323815 1.96489908728 1.67506801854 1.80746775356 1.54652953285 1.45475313744 1.46834012512 1.31158545227 1.33894012095 -0.368487553717 0.588511057503 0.762798496040 1.27909431644 1.01781277441 0.410414712190 1.02443279779 0.833189581950 1.39029814485 0.917035108030 1.33636843112 1.42562098500 1.61960476915 1.37902404820 1.23107066126 1.71075107141 1.73387177324 1.70380958771 1.59505084063 1.48585517643 1.40252349601 1.38339095776 1.46764667865 1.45801963725 1.60088907203 0.774561506397 0.132031072629 0.694176745311 0.965698222281 0.487496318660 0.379703244353 0.542088700026 0.777207040980 0.950182201059 1.11917863139 0.945394656223 1.00322969959 1.12823932822 1.02862579744 0.997830110073 1.12116008643 1.39309519695 1.49624848008 1.36118785488 1.01523182642 1.18034638058 1.00045887975 1.24974781501 1.16413966384 1.16978554854 0.674692373020
# log_sel_cofU:
-7.84770265345 -7.81383344005 -8.29340661558 -8.24058886300 -8.47601747064 -8.28530876244
-4.04593851769 -4.70840780078 -5.88101457098 -5.89997588337 -9.54287043833 2.66046413742
# disc_curve:
0.874779958754 -1.81407808185
# logK:
1.48834072832
# log_temp_wts_Linf:
0.242713264211 0.405372041044 0.409275599034 0.269694780632 0.281118737963
# log_initpop:
11.2303595333 11.4101004603 11.0759901753 9.79058249524 9.77582746391 10.3541912400 9.42780125011 8.04402071281 10.6619867137 11.8089308223 11.7142638087 13.0148389035 10.6436200011 11.1494030244 9.31712907549 9.45625573851 13.3392014822 11.9326701682 10.9055778218 11.3392202207 11.7039042164 11.4127559192 12.2623137601 10.9358638249 11.6019825244 11.9004103287 11.7231348066 10.9861203041 11.8367728631 12.0546187507 11.0413886786 9.76938479458 12.1305037893 12.3269412472 12.2387903738 12.1799255641 11.4427895973 11.5928952290 11.9800624936 11.2627957281 13.3056934342 11.7068462457 12.3208634067 11.4054205993 13.1522319444 11.6729553830 11.3239575591 11.7557804117 11.1868699109 12.5940553219 11.8380019920 11.6363098030 11.8906129366 11.2247153577 12.2449850649 11.5496277521 10.8541422678 10.8665878302 12.0405614015 11.1176551252 11.2547475526 11.5276121224 12.1442684455 12.0643464468 10.9836328661 11.4639646524 12.0735378349 11.7849111389 11.2303814940 11.8379110691 11.5205734589
#logsigmaL
1.321 -1.025 0.07919
#logsigmaD
-1.883 0.7224 -0.05479
#logsigmaU
-0.6847 -0.1066 0.02817 -0.6407 -0.1326 0.05477
#logsigmaLWTS
-2.536 -0.4041 0.04063
#logsigmaSWTS
-3.009 -0.2847 0.03635
#loga0
0.9
#logSWfact
-0.06076
#log_sel_coff1
-1.434 -8.24 -5.91 -7.226 -5.374 -0.8984 -2.608 -2.011 -2.417 -3.011 -1.787 -2.386 -4.339 -2.972 -3.123 -0.4201 -3.11 -0.06443 -1.935 -1.412 -1.687 -1.321 -1.499 -0.203 -1.625 -0.7012 0.07772 0.1343 0.3804 1.37 1.66 1.758 1.404 1.447 1.376 1.748 1.677 1.321 1.142 1.059 1.325 1.421 1.506 1.436 1.48 0.9119 1.377 0.5187 0.5991 0.2874 0.6868 0.8948 1.35 0.7625 1.413 1.741 1.701 1.752 1.539 1.837 1.705 1.935 1.64 1.784 1.516 2.031 1.765 2.065 1.595 1.754 1.43 1.573 1.442 0.7262 1.269 0.5165 1.075 1.405 0.9845 1.502 1.257 1.477 1.483 1.364 1.498 1.522 1.697 1.524 1.486 1.74 1.723 1.965 1.713 1.835 1.602 1.473 1.479 1.162 1.067 -0.9007 0.6139 0.7865 1.303 1.042 0.4341 1.049 0.8552 1.416 0.9376 1.363 1.455 1.621 1.427 1.242 1.769 1.758 1.732 1.596 1.492 1.448 1.427 1.489 1.343 1.157 0.3067 0.1592 0.7192 0.9909 0.5126 0.4054 0.5669 0.8022 0.9725 1.147 0.9613 1.045 1.144 1.061 1.046 1.17 1.468 1.562 1.433 1.01 1.203 1.055 1.308 1.136 0.7507 -0.2114
#log_sel_cofU
-9.029 -8.711 -9.626 -9.767 -10.12 -10.61 -4.064 -4.729 -5.901 -5.979 -9.53 2.383
#disc_curve
0.8751 -1.826
#logK
1.488
#log_temp_wts_Linf
0.2427 0.4054 0.4093 0.2697 0.2811
#log_initpop
11.23 11.41 11.08 9.791 9.776 10.35 9.426 8.043 10.66 11.81 11.71 13.02 10.64 11.15 9.318 9.457 13.34 11.93 10.91 11.34 11.71 11.42 12.28 10.95 11.61 11.91 11.73 10.99 11.84 12.06 11.06 9.787 12.12 12.33 12.27 12.23 11.45 11.61 12.01 11.37 13.46 11.8 12.38 11.37 13.04 11.42 11.1 11.66 11.18 12.68 11.95 11.71 11.94 11.24 12.29 11.52 10.82 10.81 12.06 11.09 11.27 11.57 12.26 12.18 11.07 11.62 12.33 11.86 11.25 11.75 11.46
#SSBe
53203 54289 61603 66700 110770 93349 74822 56075 44105 129640 118110 91674 71058 64476 54039 61546 44103 42905 47442 46724 37352 44170 53400 40863 25988 42538 53405 54731 53379 38562 36003 44590 38677 125770 93133 88701 65207 99017 68886 40588 32391 24633 50387 42460 31997 29894 23727 35170 29592 21208 17223 28314 26388 24786 24827 34684 42447 37575 39564 53223 58088 55302
#SSBo
62961 65191 68483 70312 104550 86517 69135 51835 42002 111320 105040 88477 68546 64007 55336 63484 45570 45731 48042 46481 36716 42572 52643 40317 26366 38573 49870 51931 48089 38000 34151 42257 38941 126000 90709 87999 58727 88713 66581 37839 31348 23706 52043 43689 33707 33004 26407 41095 33128 24975 17591 33815 30917 30498 28909 33952 39268 36870 38294 53051 53483 50809
#Fbar
0.2206 0.2121 0.2187 0.2553 0.3214 0.364 0.3233 0.2659 0.258 0.3186 0.4199 0.4871 0.5089 0.5179 0.5277 0.5389 0.5448 0.5409 0.5189 0.4797 0.4544 0.4649 0.5054 0.5442 0.5551 0.5644 0.5976 0.6414 0.6522 0.6006 0.5327 0.4935 0.4891 0.4948 0.4924 0.5023 0.5326 0.5815 0.6348 0.6494 0.6442 0.644 0.6544 0.6638 0.6326 0.5871 0.5671 0.5717 0.5665 0.5132 0.4483 0.4226 0.448 0.4857 0.4748 0.4172 0.3524 0.302 0.2666 0.2329 0.2144 0.2369
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