From 6ab34110019d91882e5441ae4a22e3cbc8876724 Mon Sep 17 00:00:00 2001 From: Jim Ianelli Date: Tue, 20 Feb 2024 19:11:13 -0800 Subject: [PATCH] Step one to start organizgin working examples --- examples/.run.sh.swp | Bin 12288 -> 0 bytes examples/Makefile | 80 - examples/README.md | 17 - examples/SS_example.R | 222 -- examples/admodel.cov | Bin 24 -> 0 bytes examples/admodel.dep | 2 - examples/admodel.hes | Bin 36 -> 0 bytes examples/ai_re.prj | 35 - examples/ai_re_age10_m_22_1_original.dat | 35 - .../ai_re_age10_m_22_1_original_spcat.dat | 37 - examples/ai_re_sppcat.dat | 55 - examples/ai_spm.dat | 55 - examples/akp.R | 127 - examples/ebswp_spcat.dat | 30 - examples/elasticity.csv | 0 examples/example.R | 124 - examples/goanrs_all_spcat.dat | 28 - examples/goars.R | 54 - .../more_examples/BSAI NRS/ForProjections.dat | 40 - examples/more_examples/BSAI NRS/spm.dat | 55 - .../Projection Model 2005 copy.pdf | Bin 1272482 -> 0 bytes .../more_examples/Projection Model 2005.pdf | Bin 1272482 -> 0 bytes examples/more_examples/atka/plot.R | 39 - 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-all: -ifneq "$(wildcard $(data) )" "" - cp $(data) spp_catch.dat - @echo $(outdir) - $(run) -ifneq "$(wildcard $(outdir) )" "" -else - mkdir $(outdir) -endif - mv *.out $(outdir)/ - mv spm.rep $(outdir)/report.out - $(RM) rm eigv.rpt variance admodel.* *.r0? *.p0? fmin.log *.b0? -else - # if it doesn't: - @echo "Oops...error, file " $(data) " appears to be missing... " -endif - - -clean: - $(RM) $(outdir)/* - - -EXEC = spm -DIST = ../src/ -ARGS = -nox -iprint 150 - -ifdef ComSpec - RM=del /F /Q -else - RM=rm -rf -endif - -all: mpd $(DIST)$(EXEC).tpl - -$(EXEC): $(DIST)$(EXEC).tpl - ln -sf $(DIST)$(EXEC) $@ - ln -sf $(DIST)$(EXEC).tpl $@.tpl - $(MAKE) --directory=../../src - -$(DIST)$(EXEC).tpl: - $(MAKE) --directory=../../src - - -mpd: $(EXEC) - ./$(EXEC) $(ARGS) - @$(RM) $(EXEC).*[0123456789] *.rpt *.log variance gradient.* *tmp admodel.* *.eva - -mcmc: $(EXEC) - ./$(EXEC) $(ARGS) -mcmc 3000000 -mcsave 600 - ./$(EXEC) -mceval - -proj: $(PROJ) - ./$(PROJ) - -debug: $(EXEC) - ./$(EXEC) $(ARGS) - -plot: - R CMD BATCH plot.R - -clean: - @$(RM) $(EXEC) - @$(RM) $(EXEC) $(EXEC).[brces]* $(EXEC).*[0123456789] *.rpt *.log variance gradient.* *tmp - @$(RM) admodel.* - @$(RM) checkfile.rep - @$(RM) mcout.rep - @$(RM) plot.Rout - @$(RM) Rplots.pdf - @$(RM) *.rep - @$(RM) Fprof.yld - @$(RM) *.prj - @$(RM) pm.par - @$(RM) SIS_out.rep - @$(RM) mceval.dat diff --git a/examples/README.md b/examples/README.md deleted file mode 100644 index 8c47d58..0000000 --- a/examples/README.md +++ /dev/null @@ -1,17 +0,0 @@ -# Example projection model run - -## To do list... - - - [ ] get datafiles updated in examples directory - - [ ] make sure script runs - - [ ] write R script for settings and inputs and running - - [ ] include post-processing R script - - -## Options - - make mydat - - run.bat mydat - - diff --git a/examples/SS_example.R b/examples/SS_example.R deleted file mode 100644 index be17f39..0000000 --- a/examples/SS_example.R +++ /dev/null @@ -1,222 +0,0 @@ -#' # Projection model work -#' Projection model work for demonstrating application of controls and input data - -library(tidyverse) -library(ggplot2) -library(ggthemes) -library(gtable) -dir = "C:/WORKING_FOLDER/Model19.14.48c_T" -Model_Name = "Model19.14.48c_T" - -source("../R/readData.R") - -#' ## Set initial "setup" parameters -thisyr=2019 -setup<-list( - Run_name = noquote("Std"), - Tier = 3 , - nalts = 7 , - alts = c(1,2,3,4,5,6,7), - tac_abc = 1, #' Flag to set TAC equal to ABC (1 means true, otherwise false) - srr = 1 , #' Stock-recruitment type (1=Ricker, 2=Bholt) - rec_proj = 1, #' projection rec form (default: 1 = use observed mean and std, option 2 = use estimated SRR and estimated sigma R) - srr_cond = 0 , #' SR-Conditioning (0 means no, 1 means use Fmsy == F35%?, 2 means Fmsy == F35% and Bmsy=B35% condition (affects SRR fits) - srr_prior = 0.0, #' Condition that there is a prior that mean historical recruitment is similar to expected recruitment at half mean SSB and double mean SSB 0 means don't use, otherwise specify CV - write_big = 1, #' Flag to write big file (of all simulations rather than a summary, 0 means don't do it, otherwise do it) Write_Big - nyrs_proj = 14, #' Number of projection years - nsims = 1000, #' Number of simulations - beg_yr_label = thisyr #' Begin Year -) - -#' ## Set up the species specific run file -config<-list( - nFixCatchYrs = 1, - nSpecies = 1, - OYMin = .1343248, - OYMax = 1943248, - dataFiles = noquote(paste0("data/",Model_Name,".dat")), - ABCMult = 1, - PoplnScalar = 1000, - AltFabcSPR = 0.75, - nTAC = 1, - TACIndices = 1, - Catch = c(2019,15000.) -) - - -## - -write_proj<-function(data_file="Model19.14.48c_T_Proj.dat", - data = mod1, - FY=1977, - LY=2019, - RY=2, - fleets=3, - sexes=1, - NAGES=10, - SSL=0, - Dorn = 0, - AUTHOR_F = 1, - SPR_ABC = 0.4, - SPR_MSY = 0.35, - SPAWN_M = 1, - FRATIO=noquote("0.3 0.2 0.5")) - { -## writing projection file -## mean 5 year F -Y5<-LY-5 -F_5<-mean(data$sprseries$Tot_Exploit[data$sprseries$Yr>Y5&data$sprseries$Yr<=LY]) -## population weight at age for females -WGT<-vector("list",length=sexes) -Nage_LY<-vector("list",length=sexes) -M1<-vector("list",length=sexes) -for(i in 1:sexes){ - WGT[[i]]<-(data.table(data$endgrowth)[Sex==i]$Wt_Beg*data.table(data$endgrowth)[Sex==i]$Mat_F_Natage)[2:(NAGES+1)] - Nage_LY[[i]]<-subset(data$natage,data$natage[,11]=="B"&data$natage$Yr==LY&data$natage$Sex==i) - M1[[i]]<-as.numeric(subset(data$M_at_age,data$M_at_age$Yr==(LY-10)&data$M_at_age$Sex==i)[,4:(NAGES+3)]) ## pulling array of Ms at age by sex for LY-10 -} - -## selectivity at age for fishery -sel_LY<-vector("list",length=fleets) -wt_LY<-vector("list",length=fleets) -for(i in 1:fleets){ - sel_LY[[i]]<-subset(data$ageselex,data$ageselex$Fleet==i&data$ageselex$Yr==LY-RY&data$ageselex$Factor=="Asel2") - wt_LY[[i]]<-subset(data$ageselex,data$ageselex$Fleet==i&data$ageselex$Yr==LY-RY&data$ageselex$Factor=="bodywt") -} - -## numbers at age -Rec_1<-as.numeric(data$natage[,14][data$natage$Yr<=LY&data$natage$Yr>=FY&data$natage$Sex==1&data$natage[,11]=="B"]) -N_rec<-length(Rec_1) - -SSB<-as.numeric(data$sprseries$SSB[data$sprseries$Yr<=LY&data$sprseries$Yr>=FY]) -#SSB<-SSB[1:(LY-FY)] -T1<-noquote(paste(data_file)) -write(T1,paste(data_file),ncolumns = 1 ) -T1<-noquote(paste0(SSL," # SSL Species???")) - write(T1,paste(data_file),ncolumns = 1,append=T) -T1<-noquote(paste0(Dorn," # Constant Buffer Dorn?")) - write(T1,paste(data_file),append = T) -T1<-noquote(paste0(fleets," # Number of fisheries")) - write(T1,paste(data_file),append = T) -T1<-noquote(paste0(sexes," # Number of Sexes")) - write(T1,paste(data_file),append = T) -T1<-noquote(paste(F_5,"# Average 5 year F")) - write(T1,paste(data_file),append = T) -T1<-noquote(paste0(AUTHOR_F," # Author f")) - write(T1,paste(data_file),append = T) -T1<-noquote(paste0(SPR_ABC," # SPR ABC")) - write(T1,paste(data_file),append = T) -T1<-noquote(paste0(SPR_MSY," # SPR MSY")) - write(T1,paste(data_file),append = T) -T1<-noquote(paste0(SPAWN_M," # Spawning month")) - write(T1,paste(data_file),append = T) -T1<-noquote(paste0(NAGES," # number of ages")) - write(T1,paste(data_file),append = T) -T1<-noquote(paste0(FRATIO," # Fratio")) - write(T1,paste(data_file),append = T) -T1<-noquote("# natural mortality") - write(T1,paste(data_file),append = T) - for (i in 1:sexes){ - write(M1[[i]],paste(data_file),append = T,ncolumns = 45) - } -T1<-noquote("# Maturity ") - write(T1,paste(data_file),append = T) - write(rep(1,NAGES),paste(data_file),append = T,ncolumns = 45) ## Female maturity?? -T1<-noquote("# wt spawn females") - write(T1,paste(data_file),append = T) - for(i in 1: sexes){ - write(round(as.numeric(WGT[[i]]),4),paste(data_file),append = T,ncolumns = 45) - } -T1<-noquote("# WtAge females by fishery") - write(T1,paste(data_file),append = T) - for(i in 1: fleets){ - write(round(as.numeric(wt_LY[[i]][1,9:(NAGES+8)]),4),paste(data_file),append = T,ncolumns = 45) - } -T1<-noquote("# Selectivity females by fishery") - write(T1,paste(data_file),append = T) - for(i in 1: fleets){ - write(round(as.numeric(sel_LY[[i]][1,9:(NAGES+8)]),4),paste(data_file),append = T,ncolumns = 45) - } - -T1<-noquote(paste0("# Numbers at age females males in ",LY)) - write(T1,paste(data_file),append = T) - for (i in 1:sexes){ - write(as.numeric(Nage_LY[[i]][1,14:ncol(Nage_LY[[i]])]),paste(data_file),append = T,ncolumns = 45) - } -T1<-noquote(paste0("# No Recruitments for ",FY, " to ",LY-RY)) - write(T1,paste(data_file),append = T) - write((N_rec-RY),paste(data_file),append = T,ncolumns = 45) -T1<-noquote("# Recruitment") - write(T1,paste(data_file),append = T) - write(round(Rec_1[1:(length(Rec_1)-RY)],1),paste(data_file),append = T,ncolumns = 45) -T1<-noquote(paste("# SSB ", FY,"-",LY,sep="")) - write(T1,paste(data_file),append = T) - write(SSB,paste(data_file),append = T,ncolumns = 45) -} - - -mod1<-SS_output(dir) - -write_proj(data_file=paste0("data/",Model_Name,".dat"),data=mod1, - FY=1977, - LY=2020, - RY=2, - fleets=3, - sexes=1, - NAGES=10, - SSL=0, - Dorn = 0, - AUTHOR_F = 1, - SPR_ABC = 0.4, - SPR_MSY = 0.35, - SPAWN_M = 1, - FRATIO=noquote("0.3 0.2 0.5")) - - -#' ## Save lists for running model to files expected by projection model -library(gbm) -# Setup.dat -list2dat(setup,"setup.dat") -# spp_catch.dat -list2dat(config,paste0("data/",Model_Name,"_spcat.dat")) - -file.copy(paste0("data/",Model_Name,"_spcat.dat"),"spp_catch.dat",overwrite=TRUE) - -#' ## Run projection model -system("../src/main") -#' ## Read in projection model mainfiles - .projdir= paste0(Model_Name,"/") - dir.create(.projdir) - file.copy(list.files(getwd(), pattern="out$"), .projdir,overwrite=TRUE) - file.remove(list.files(getwd(), pattern="out$")) - bf <- data.frame(read.table(paste0(.projdir,"bigfile.out"),header=TRUE,as.is=TRUE)) - bfs <- bf %>% filter(Sim<=30) - #write.csv(bfs,"data/proj.csv") - # head(bfs) - bfss <- bfs %>% filter(Alt==2) %>% select(Alt,Yr,Catch,SSB,Sim) - pf <- data.frame(read.table(paste0(.projdir,"percentdb.out"),header=F) ) - names(pf) <- c("stock","Alt","Yr","variable","value") -#' ## Make plot of projection model simulations - p1 <- pf %>% filter(substr(variable,1,1)=="C",variable!="CStdn",Alt==2) %>% select(Yr,variable,value) %>% spread(variable,value) %>% - ggplot(aes(x=Yr,y=CMean),width=1.2) + geom_ribbon(aes(ymax=CUCI,ymin=CLCI),fill="goldenrod",alpha=.5) + theme_few() + geom_line() + - scale_x_continuous(breaks=seq(thisyr,thisyr+14,2)) + xlab("Year") + ylab("Tier 3 ABC (kt)") + geom_point() + - expand_limits(y=0) + - geom_line(aes(y=Cabc)) + geom_line(aes(y=Cofl),linetype="dashed") + geom_line(data=bfss,aes(x=Yr,y=Catch,col=as.factor(Sim)))+ guides(size=FALSE,fill=FALSE,alpha=FALSE,col=FALSE) - p2 <- pf %>% filter(substr(variable,1,1)=="S",variable!="SSBStdn",Alt==2) %>% select(Yr,variable,value) %>% spread(variable,value) %>% - ggplot(aes(x=Yr,y=SSBMean),width=1.2) + geom_ribbon(aes(ymax=SSBUCI,ymin=SSBLCI),fill="coral",alpha=.5) + theme_few() + geom_line() + - scale_x_continuous(breaks=seq(thisyr,thisyr+14,2)) + xlab("Year") + ylab("Tier 3 Spawning biomass (kt)") + geom_point() + - expand_limits(y=0) + - geom_line(aes(y=SSBFabc)) + geom_line(aes(y=SSBFofl),linetype="dashed")+ geom_line(data=bfss,aes(x=Yr,y=SSB,col=as.factor(Sim)))+ guides(size=FALSE,fill=FALSE,alpha=FALSE,col=FALSE) - t3 <- grid.arrange(p1, p2, nrow=2) - ggsave(paste0(.projdir,"tier3_proj.pdf"),plot=t3,width=5.4,height=7,units="in") - - -#' ## Make tables - library(xtable) - # Stock Alt Sim Yr SSB Rec Tot_biom SPR_Implied F Ntot Catch ABC OFL AvgAge AvgAgeTot SexRatio FABC FOFL - bfsum <- bf %>% select(Alt,Yr,SSB,F,ABC ,Catch) %>% group_by(Alt,Yr) %>% summarise(Catch=mean(Catch),SSB=mean(SSB),F=mean(F),ABC=mean(ABC)) - t1 <- bfsum %>% select(Alt,Yr,Catch) %>% spread(Alt,Catch) - names(t1) <- c("Catch","Scenario 1","Scenario 2","Scenario 3","Scenario 4","Scenario 5","Scenario 6","Scenario 7") - - print_Tier3_tables(bf) - diff --git a/examples/admodel.cov b/examples/admodel.cov deleted file mode 100644 index 410c598b4f1146749a4eefdbf406dfb6e8ecc309..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 24 ScmZQ%fPn}0Fct&D2YUb!!U9kL diff --git a/examples/admodel.dep b/examples/admodel.dep deleted file mode 100644 index 971e366..0000000 --- a/examples/admodel.dep +++ /dev/null @@ -1,2 +0,0 @@ -1 0 -Bzero 0 diff --git a/examples/admodel.hes b/examples/admodel.hes deleted file mode 100644 index 154a7fc3f3dff06d950fd9a36800980393e3b551..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 36 WcmZQ%fCC3Oi{XR4>YxArVSE5KSOky& diff --git a/examples/ai_re.prj b/examples/ai_re.prj deleted file mode 100644 index 4894810..0000000 --- a/examples/ai_re.prj +++ /dev/null @@ -1,35 +0,0 @@ -Rougheye -0 # SSL Species??? -0 # Constant buffer of Dorn -1 # Number of fsheries -1 # Number of sexes?? -# 0.0261738 # average 5 yr f -# 0.0297296 # average F, 2022 and 2023 -# 0.0401244 # 2023 Fofl, start value -# 0.028858427 # 2023 Fofl, second iteration - 0.031 # 2024 Fofl, start value -# 0.0305124 # 2024 Fofl, second iteration -1 # author f -0.4 # ABC SPR -0.35 # MSY SPR -3 # Spawnmo -43 # Number of ages -1 # Fratio - # Natural mortality - 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 - # Maturity - 0.00340888 0.0044329 0.00576277 0.00748858 0.00972618 0.0126239 0.0163706 0.0212055 0.0274284 0.0354115 0.0456091 0.0585651 0.0749125 0.0953608 0.120663 0.151553 0.188655 0.232353 0.282646 0.339018 0.400358 0.464989 0.530817 0.59559 0.657196 0.713924 0.764628 0.808752 0.846267 0.877538 0.903175 0.923911 0.940499 0.953652 0.964008 0.972118 0.978442 0.983356 0.987164 0.99011 0.992385 0.99414 0.998196 - # Wt Spawn - 55 81 112 147 188 232 280 331 384 440 497 556 615 675 735 795 854 913 970 1027 1082 1137 1189 1240 1290 1338 1384 1429 1472 1513 1553 1591 1627 1662 1695 1727 1757 1786 1814 1840 1865 1888 2006.7 - # Wt Fish - 55 81 112 147 188 232 280 331 384 440 497 556 615 675 735 795 854 913 970 1027 1082 1137 1189 1240 1290 1338 1384 1429 1472 1513 1553 1591 1627 1662 1695 1727 1757 1786 1814 1840 1865 1888 2006.7 - # selectivity - 0.000742953 0.00147408 0.00292258 0.0057862 0.0114235 0.0224293 0.0435709 0.0829494 0.152252 0.262859 0.414529 0.584339 0.736236 0.847145 0.916695 0.956234 0.977468 0.988524 0.994187 0.997064 0.998519 0.999254 0.999624 0.999811 0.999905 0.999952 0.999976 0.999988 0.999994 0.999997 0.999998 0.999999 1 1 1 1 1 1 1 1 1 1 1 - # natage - 1.90469 1.8117 1.72321 1.48394 1.63 1.71119 1.65994 1.59983 1.32949 13.4142 1.05629 1.1426 1.22072 1.18872 0.931879 0.82203 0.933938 1.26836 0.821616 0.749373 0.705759 0.714874 0.449897 0.329574 0.251513 0.187913 0.14503 0.11613 0.0962753 0.0833661 0.0759574 0.0723329 0.0718631 0.0730712 0.0751903 0.0781308 0.0809843 0.0818001 0.0759746 0.0667667 0.0573137 0.0503982 0.792156 - # Nrec -35 - # rec - 1.15837 1.14404 1.16416 1.21007 1.22741 1.17776 1.0395 0.895728 0.773063 0.677399 0.603982 0.554082 0.533258 0.539249 0.576319 0.645214 0.748979 0.902494 1.1235 1.36938 1.73894 2.57075 2.36166 2.3346 2.38527 3.43462 2.35989 1.93733 2.04665 2.43209 2.32951 2.04021 1.77262 21.2472 1.99402 - # ssb - 4974.15 4968.42 4359.62 3715.78 3652.69 3626.45 3707.87 3827.03 3957.91 4098.91 4239.95 4368.17 4481.53 4444.2 4153.67 4180.48 3932.34 3747.46 3617.6 3551.1 3351.38 3128.31 3023.8 2959.06 2903.16 2781.58 2741.76 2727.58 2714.87 2725.5 2702.99 2691.78 2677.38 2661.55 2649.74 diff --git a/examples/ai_re_age10_m_22_1_original.dat b/examples/ai_re_age10_m_22_1_original.dat deleted file mode 100644 index 4894810..0000000 --- a/examples/ai_re_age10_m_22_1_original.dat +++ /dev/null @@ -1,35 +0,0 @@ -Rougheye -0 # SSL Species??? -0 # Constant buffer of Dorn -1 # Number of fsheries -1 # Number of sexes?? -# 0.0261738 # average 5 yr f -# 0.0297296 # average F, 2022 and 2023 -# 0.0401244 # 2023 Fofl, start value -# 0.028858427 # 2023 Fofl, second iteration - 0.031 # 2024 Fofl, start value -# 0.0305124 # 2024 Fofl, second iteration -1 # author f -0.4 # ABC SPR -0.35 # MSY SPR -3 # Spawnmo -43 # Number of ages -1 # Fratio - # Natural mortality - 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 0.0500307 - # Maturity - 0.00340888 0.0044329 0.00576277 0.00748858 0.00972618 0.0126239 0.0163706 0.0212055 0.0274284 0.0354115 0.0456091 0.0585651 0.0749125 0.0953608 0.120663 0.151553 0.188655 0.232353 0.282646 0.339018 0.400358 0.464989 0.530817 0.59559 0.657196 0.713924 0.764628 0.808752 0.846267 0.877538 0.903175 0.923911 0.940499 0.953652 0.964008 0.972118 0.978442 0.983356 0.987164 0.99011 0.992385 0.99414 0.998196 - # Wt Spawn - 55 81 112 147 188 232 280 331 384 440 497 556 615 675 735 795 854 913 970 1027 1082 1137 1189 1240 1290 1338 1384 1429 1472 1513 1553 1591 1627 1662 1695 1727 1757 1786 1814 1840 1865 1888 2006.7 - # Wt Fish - 55 81 112 147 188 232 280 331 384 440 497 556 615 675 735 795 854 913 970 1027 1082 1137 1189 1240 1290 1338 1384 1429 1472 1513 1553 1591 1627 1662 1695 1727 1757 1786 1814 1840 1865 1888 2006.7 - # selectivity - 0.000742953 0.00147408 0.00292258 0.0057862 0.0114235 0.0224293 0.0435709 0.0829494 0.152252 0.262859 0.414529 0.584339 0.736236 0.847145 0.916695 0.956234 0.977468 0.988524 0.994187 0.997064 0.998519 0.999254 0.999624 0.999811 0.999905 0.999952 0.999976 0.999988 0.999994 0.999997 0.999998 0.999999 1 1 1 1 1 1 1 1 1 1 1 - # natage - 1.90469 1.8117 1.72321 1.48394 1.63 1.71119 1.65994 1.59983 1.32949 13.4142 1.05629 1.1426 1.22072 1.18872 0.931879 0.82203 0.933938 1.26836 0.821616 0.749373 0.705759 0.714874 0.449897 0.329574 0.251513 0.187913 0.14503 0.11613 0.0962753 0.0833661 0.0759574 0.0723329 0.0718631 0.0730712 0.0751903 0.0781308 0.0809843 0.0818001 0.0759746 0.0667667 0.0573137 0.0503982 0.792156 - # Nrec -35 - # rec - 1.15837 1.14404 1.16416 1.21007 1.22741 1.17776 1.0395 0.895728 0.773063 0.677399 0.603982 0.554082 0.533258 0.539249 0.576319 0.645214 0.748979 0.902494 1.1235 1.36938 1.73894 2.57075 2.36166 2.3346 2.38527 3.43462 2.35989 1.93733 2.04665 2.43209 2.32951 2.04021 1.77262 21.2472 1.99402 - # ssb - 4974.15 4968.42 4359.62 3715.78 3652.69 3626.45 3707.87 3827.03 3957.91 4098.91 4239.95 4368.17 4481.53 4444.2 4153.67 4180.48 3932.34 3747.46 3617.6 3551.1 3351.38 3128.31 3023.8 2959.06 2903.16 2781.58 2741.76 2727.58 2714.87 2725.5 2702.99 2691.78 2677.38 2661.55 2649.74 diff --git a/examples/ai_re_age10_m_22_1_original_spcat.dat b/examples/ai_re_age10_m_22_1_original_spcat.dat deleted file mode 100644 index 25b398d..0000000 --- a/examples/ai_re_age10_m_22_1_original_spcat.dat +++ /dev/null @@ -1,37 +0,0 @@ - #_Number_of_years with specified catch (if begin-yr = 2004, and this number is "3", then subsequent values represent catches in 2004, 05, and 06 (to evaluate alts for 2007) - 2 - # Number of species - 1 - # OY Minimum -.1343248 # Note that this is for age-structured species 1330.148 - # OY Maximum -1943248 # Note that this is for age-structured species 1930.148 - # data files for each species - # 1 - data\ai_re_age10_m_22_1_original.dat - # ABC Multipliers - 1 - # scalars - 1 - # New Alt 4 Fabc SPRs (Rockfish = 0.75, other 0.6), Steller sea lion prey species between F40 and F60 (to meet OY Min) -0.75 - # Number of TAC model categories - 1 - # TAC model indices (for aggregating) - 1 - # Catch in each future year - 2022 305 - 2023 395 - - - - - - - - - - - - - diff --git a/examples/ai_re_sppcat.dat b/examples/ai_re_sppcat.dat deleted file mode 100644 index 08ab02a..0000000 --- a/examples/ai_re_sppcat.dat +++ /dev/null @@ -1,55 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -#nyrs_fixed_catch -2 -#nspp -1 -#OY_min -.1343248 # Note that this is for age-structured species 1330.148 -#OY_max -1943248 # Note that this is for age-structured species 1930.148 -# data files for each species -data\ai_re_age10_m_22_1_original.dat -#ABC_mults -1 -# scalars -1 -# New Alt 4 Fabc SPRs (Rockfish = 0.75, other 0.6), Steller sea lion prey species between F40 and F60 (to meet OY Min) -0.75 -# Number of TAC model categories -1 -# TAC model indices (for aggregating) -1 -# Catch in each future year - 2022 305 - 2023 395 diff --git a/examples/ai_spm.dat b/examples/ai_spm.dat deleted file mode 100644 index 9876642..0000000 --- a/examples/ai_spm.dat +++ /dev/null @@ -1,55 +0,0 @@ -#rn -AI_re -#Tier -3 -#nalts -7 -#alts -1 -2 -3 -4 -5 -6 -7 -#tac_flag -1 -#srr_type -1 -#srr_form -1 -#srr_conditioning -0 -#srr_reserved -0 -#spm_detail_flag -1 -#nprj_yrs -100 -#nsims -1000 -#beg_yr -2022 -#nyrs_fixed_catch -2 -#nspp -1 -#OY_min -10 -#OY_max -1943248 # Note that this is for age-structured species 1930.148 -#data_files -ai_re.prj -#ABC_mults -1 -#scalars -1 -#alt4_spr -0.6 -#nTAC_cat -1 -#TACind -1 -#fixed_catch - 2022 305 - 2023 395 diff --git a/examples/akp.R b/examples/akp.R deleted file mode 100644 index 58e8ca7..0000000 --- a/examples/akp.R +++ /dev/null @@ -1,127 +0,0 @@ -#' # Projection model work -#' Projection model work for demonstrating application of controls and input data - -library(tidyverse) -library(ggplot2) -library(ggthemes) -library(gtable) -source("../R/readData.R") - -#' ## Set initial "setup" parameters -thisyr=2019 -setup<-list( - Run_name = noquote("Std"), - Tier = 3 , - nalts = 7 , - alts = c(1,2,3,4,5,6,7), - tac_abc = 1, #' Flag to set TAC equal to ABC (1 means true, otherwise false) - srr = 1 , #' Stock-recruitment type (1=Ricker, 2=Bholt) - rec_proj = 1, #' projection rec form (default: 1 = use observed mean and std, option 2 = use estimated SRR and estimated sigma R) - srr_cond = 0 , #' SR-Conditioning (0 means no, 1 means use Fmsy == F35%?, 2 means Fmsy == F35% and Bmsy=B35% condition (affects SRR fits) - srr_prior = 0.0, #' Condition that there is a prior that mean historical recruitment is similar to expected recruitment at half mean SSB and double mean SSB 0 means don't use, otherwise specify CV - write_big = 1, #' Flag to write big file (of all simulations rather than a summary, 0 means don't do it, otherwise do it) Write_Big - nyrs_proj = 14, #' Number of projection years - nsims = 100, #' Number of simulations - beg_yr_label = thisyr #' Begin Year -) - -#' ## Set up the species specific run file -config<-list( - nFixCatchYrs = 2, - nSpecies = 1, - OYMin = .1343248, - OYMax = 1943248, - dataFiles = noquote("data/t1.dat"), - ABCMult = 1, - PoplnScalar = 1000, - AltFabcSPR = 0.75, - nTAC = 1, - TACIndices = 1, - Catch = c( 2016,55000., 2017,55000. ) -) - -#' ## Make list of main file w/ assessment model results -#' E.g., "data/bsai_atka.dat" -datfile <- list( - runname = noquote("M16.2"), - ssl_spp = 1, # SSL_spp - Dorn_buffer = 1, # Dorn_buffer - nfsh = 1, # N_fsh - nsex = 1, # N_sexes - avgF5yr = 0.0661399, # avg_5yr_F - F40_mult = 1, # F_40_multiplier - spr_abc = 0.4, # SPR_abc - spr_msy = 0.35, # SPR_msy - sp_mo = 8, # spawn_month - nages = 11, # N_ages - Frat = 1, # F_ratio - # M - M = c(0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3), - # Maturity - pmat = c(0.005,0.037,0.224,0.688,0.944,0.992,0.999,1,1,1,1), - # Wt_at_age_spawners - wtage_sp = c(44.8,161.377,398.272,557.695,652.113,719.573,863.744,948.744,921.397,885.912,1069.87), - # Wt_at_age_fsh - wtage_fsh = c(69.3778,253.522,408.211,614.731,668.483,718.137,803.017,798.707,788.117,842.468,960.006), - # select - sel = c(0.002576427,0.040030753,0.651104228,0.768404263,0.794886081,1,0.889293108,0.604815671,0.451169778,0.403195516,0.403195516), - # N - N = c(511.179,378.528,278.443,194.385,183.423,45.5404,61.1188,19.8073,39.6285,33.6501,51.7806), - # Nyrs - nyrs = 37, - # recruits - R = c(1578.51,479.509,357.919,443.588,318.981,413.125,514.351,600.987,536.301,692.34,452.279,1618.73,702.801,372.811,597.812,1136.19,402.862,424.179,1025.36,207.05,383.695,1054.64,2224.52,1379.34,1545.81,345.556,454.884,617.194,404.876,993.497,727.754,236.795,505.948,258.653,726.627,524.198,473.54), - # SSB - SSB = c(206.391,194.569,187.097,183.296,195.289,240.774,252.143,238.163,223.199,200.776,182.378,179.671,189.193,198.242,212.123,233.484,277.891,282.004,250.709,231.816,218.403,195.275,181.628,189.976,175.912,168.624,220.206,315.124,376.621,397.171,365.476,317.159,277.777,242.719,233.415,223.636,198.117,183.537,177.91) - ) - -#' ## Save lists for running model to files expected by projection model -# Setup.dat -list2dat(setup,"setup.dat") -# spp_catch.dat -list2dat(config,"data/t1_spcat.dat") -runfn<-"t1" -file.copy(paste0("data/",runfn,"_spcat.dat"),"spp_catch.dat",overwrite=TRUE) - -list2dat(datfile,"data/t1.dat") - -#' ## Run projection model -system("../src/main") -#' ## Read in projection model mainfiles - .projdir="akp_out/" - dir.create(.projdir) - file.copy(list.files(getwd(), pattern="out$"), .projdir,overwrite=TRUE) - file.remove(list.files(getwd(), pattern="out$")) - - bf <- data.frame(read.table(paste0(.projdir,"bigfile.out"),header=TRUE,as.is=TRUE)) - bfs <- bf %>% filter(Sim<=30) - #write.csv(bfs,"data/proj.csv") - # head(bfs) - bfss <- bfs %>% filter(Alt==2) %>% select(Alt,Yr,Catch,SSB,Sim) - pf <- data.frame(read.table(paste0(.projdir,"percentdb.out"),header=F) ) - names(pf) <- c("stock","Alt","Yr","variable","value") -#' ## Make plot of projection model simulations - p1 <- pf %>% filter(substr(variable,1,1)=="C",variable!="CStdn",Alt==2) %>% select(Yr,variable,value) %>% spread(variable,value) %>% - ggplot(aes(x=Yr,y=CMean),width=1.2) + geom_ribbon(aes(ymax=CUCI,ymin=CLCI),fill="goldenrod",alpha=.5) + theme_few() + geom_line() + - scale_x_continuous(breaks=seq(thisyr,thisyr+14,2)) + xlab("Year") + ylab("Tier 3 ABC (kt)") + geom_point() + - expand_limits(y=0) + - geom_line(aes(y=Cabc)) + geom_line(aes(y=Cofl),linetype="dashed") + geom_line(data=bfss,aes(x=Yr,y=Catch,col=as.factor(Sim)))+ guides(size=FALSE,fill=FALSE,alpha=FALSE,col=FALSE) - p2 <- pf %>% filter(substr(variable,1,1)=="S",variable!="SSBStdn",Alt==2) %>% select(Yr,variable,value) %>% spread(variable,value) %>% - ggplot(aes(x=Yr,y=SSBMean),width=1.2) + geom_ribbon(aes(ymax=SSBUCI,ymin=SSBLCI),fill="coral",alpha=.5) + theme_few() + geom_line() + - scale_x_continuous(breaks=seq(thisyr,thisyr+14,2)) + xlab("Year") + ylab("Tier 3 Spawning biomass (kt)") + geom_point() + - expand_limits(y=0) + - geom_line(aes(y=SSBFabc)) + geom_line(aes(y=SSBFofl),linetype="dashed")+ geom_line(data=bfss,aes(x=Yr,y=SSB,col=as.factor(Sim)))+ guides(size=FALSE,fill=FALSE,alpha=FALSE,col=FALSE) - t3 <- grid.arrange(p1, p2, nrow=2) - library(patchwork) - p1/p2 - ggsave(paste0(.projdir,"tier3_proj.pdf"),plot=t3,width=5.4,height=7,units="in") - - -#' ## Make tables - # Stock Alt Sim Yr SSB Rec Tot_biom SPR_Implied F Ntot Catch ABC OFL AvgAge AvgAgeTot SexRatio FABC FOFL - bfsum <- bf %>% select(Alt,Yr,SSB,F,ABC ,Catch) %>% group_by(Alt,Yr) %>% summarise(Catch=mean(Catch),SSB=mean(SSB),F=mean(F),ABC=mean(ABC)) - t1 <- bfsum %>% select(Alt,Yr,Catch) %>% spread(Alt,Catch) - names(t1) <- c("Catch","Scenario 1","Scenario 2","Scenario 3","Scenario 4","Scenario 5","Scenario 6","Scenario 7") - - print_Tier3_tables(bf) - diff --git a/examples/ebswp_spcat.dat b/examples/ebswp_spcat.dat deleted file mode 100755 index 4bfc68e..0000000 --- a/examples/ebswp_spcat.dat +++ /dev/null @@ -1,30 +0,0 @@ -#_SETUP_FILE_FOR_THE_BERING_SEA/AI_FISHERIES -#_Number_of_years with specified catch (if begin-yr = 2005, and this number is "3", then subsequent values represent catches in 2005, 06, and 07 (to evaluate alts for 2008) -3 -# Number of species -1 -# OY Minimum -0.000000 # Note that this is for age-structured species 1330.148 -# OY Maximum -3500.000 # Note that this is for age-structured species 1930.148 -../pm.prj -# ABC Multipliers -1 -# Population scalars -1 -# New Alt 4 Fabc SPRs (Rockfish = 0.75, other 0.6), Steller sea lion prey species between F40 and F60 (to meet OY Min) -0.6 -# Number of TAC model categories -1 -# TAC model indices (for aggregating) -1 -# Catch in each future year -2019 1390 -2020 1350 -2021 1324 -2022 1341 -2023 1354 -2024 1365 -2025 1375 -2026 1377 -2017 1350 diff --git a/examples/elasticity.csv b/examples/elasticity.csv deleted file mode 100644 index e69de29..0000000 diff --git a/examples/example.R b/examples/example.R deleted file mode 100644 index b57a9c2..0000000 --- a/examples/example.R +++ /dev/null @@ -1,124 +0,0 @@ -#' # Projection model work -#' Projection model work for demonstrating application of controls and input data - -library(tidyverse) -library(ggplot2) -library(ggthemes) -library(gtable) -source("../R/readData.R") - -#' ## Set initial "setup" parameters -thisyr=2019 -setup<-list( - Run_name = noquote("Std"), - Tier = 3 , - nalts = 7 , - alts = c(1,2,3,4,5,6,7), - tac_abc = 1, #' Flag to set TAC equal to ABC 1 means true, otherwise false - srr = 1 , #' Stock-recruitment type 1=Ricker, 2=Bholt - rec_proj = 1, #' projection rec form default: 1 = use observed mean and std, option 2 = use estimated SRR and estimated sigma R - srr_cond = 0 , #' SR-Conditioning 0 means no, 1 means use Fmsy == F35%?, 2 means Fmsy == F35% and Bmsy=B35% condition affects SRR fits - srr_prior = 0.0, #' Condition that there is a prior that mean historical recruitment is similar to expected recruitment at half mean SSB and double mean SSB 0 means don't use, otherwise specify CV - write_big = 1, #' Flag to write big file of all simulations rather than a summary, 0 means don't do it, otherwise do it Write_Big - nyrs_proj = 14, #' Number of projection years - nsims = 100, #' Number of simulations - beg_yr_label = thisyr #' Begin Year -) - -#' ## Set up the species specific run file -config<-list( - nFixCatchYrs = 2, - nSpecies = 1, - OYMin = .1343248, - OYMax = 1943248, - dataFiles = noquote("data/t1.dat"), - ABCMult = 1, - PoplnScalar = 1000, - AltFabcSPR = 0.75, - nTAC = 1, - TACIndices = 1, - Catch = c( 2016,55000., 2017,55000. ) -) - -#' ## Make list of main file w/ assessment model results -#' E.g., "data/bsai_atka.dat" -datfile <- list( - runname = noquote("M16.2"), - ssl_spp = 1, # SSL_spp - Dorn_buffer = 1, # Dorn_buffer - nfsh = 1, # N_fsh - nsex = 1, # N_sexes - avgF5yr = 0.0661399, # avg_5yr_F - F40_mult = 1, # F_40_multiplier - spr_abc = 0.4, # SPR_abc - spr_msy = 0.35, # SPR_msy - sp_mo = 8, # spawn_month - nages = 11, # N_ages - Frat = 1, # F_ratio - # M - M = c(0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3), - # Maturity - pmat = c(0.005,0.037,0.224,0.688,0.944,0.992,0.999,1,1,1,1), - # Wt_at_age_spawners - wtage_sp = c(44.8,161.377,398.272,557.695,652.113,719.573,863.744,948.744,921.397,885.912,1069.87), - # Wt_at_age_fsh - wtage_fsh = c(69.3778,253.522,408.211,614.731,668.483,718.137,803.017,798.707,788.117,842.468,960.006), - # select - sel = c(0.002576427,0.040030753,0.651104228,0.768404263,0.794886081,1,0.889293108,0.604815671,0.451169778,0.403195516,0.403195516), - # N - N = c(511.179,378.528,278.443,194.385,183.423,45.5404,61.1188,19.8073,39.6285,33.6501,51.7806), - # Nyrs - nyrs = 37, - # recruits - R = c(1578.51,479.509,357.919,443.588,318.981,413.125,514.351,600.987,536.301,692.34,452.279,1618.73,702.801,372.811,597.812,1136.19,402.862,424.179,1025.36,207.05,383.695,1054.64,2224.52,1379.34,1545.81,345.556,454.884,617.194,404.876,993.497,727.754,236.795,505.948,258.653,726.627,524.198,473.54), - # SSB - SSB = c(206.391,194.569,187.097,183.296,195.289,240.774,252.143,238.163,223.199,200.776,182.378,179.671,189.193,198.242,212.123,233.484,277.891,282.004,250.709,231.816,218.403,195.275,181.628,189.976,175.912,168.624,220.206,315.124,376.621,397.171,365.476,317.159,277.777,242.719,233.415,223.636,198.117,183.537,177.91) - ) - -#' ## Save lists for running model to files expected by projection model -# Setup.dat -list2dat(setup,"setup.dat") -# spp_catch.dat -list2dat(config,"data/t1_spcat.dat") -runfn<-"t1" -file.copy(paste0("data/",runfn,"_spcat.dat"),"spp_catch.dat",overwrite=TRUE) - -list2dat(datfile,"data/t1.dat") - -#' ## Run projection model -system("../src/main") -#' ## Read in projection model mainfiles - .projdir="t1/" - dir.create(.projdir) - file.copy(list.files(getwd(), pattern="out$"), .projdir,overwrite=TRUE) - file.remove(list.files(getwd(), pattern="out$")) - bf <- data.frame(read.table(paste0(.projdir,"bigfile.out"),header=TRUE,as.is=TRUE)) - bfs <- bf %>% filter(Sim<=30) - #write.csv(bfs,"data/proj.csv") - # head(bfs) - bfss <- bfs %>% filter(Alt==2) %>% select(Alt,Yr,Catch,SSB,Sim) - pf <- data.frame(read.table(paste0(.projdir,"percentdb.out"),header=F) ) - names(pf) <- c("stock","Alt","Yr","variable","value") -#' ## Make plot of projection model simulations - p1 <- pf %>% filter(substr(variable,1,1)=="C",variable!="CStdn",Alt==2) %>% select(Yr,variable,value) %>% spread(variable,value) %>% - ggplot(aes(x=Yr,y=CMean),width=1.2) + geom_ribbon(aes(ymax=CUCI,ymin=CLCI),fill="goldenrod",alpha=.5) + theme_few() + geom_line() + - scale_x_continuous(breaks=seq(thisyr,thisyr+14,2)) + xlab("Year") + ylab("Tier 3 ABC (kt)") + geom_point() + - expand_limits(y=0) + - geom_line(aes(y=Cabc)) + geom_line(aes(y=Cofl),linetype="dashed") + geom_line(data=bfss,aes(x=Yr,y=Catch,col=as.factor(Sim)))+ guides(size=FALSE,fill=FALSE,alpha=FALSE,col=FALSE) - p2 <- pf %>% filter(substr(variable,1,1)=="S",variable!="SSBStdn",Alt==2) %>% select(Yr,variable,value) %>% spread(variable,value) %>% - ggplot(aes(x=Yr,y=SSBMean),width=1.2) + geom_ribbon(aes(ymax=SSBUCI,ymin=SSBLCI),fill="coral",alpha=.5) + theme_few() + geom_line() + - scale_x_continuous(breaks=seq(thisyr,thisyr+14,2)) + xlab("Year") + ylab("Tier 3 Spawning biomass (kt)") + geom_point() + - expand_limits(y=0) + - geom_line(aes(y=SSBFabc)) + geom_line(aes(y=SSBFofl),linetype="dashed")+ geom_line(data=bfss,aes(x=Yr,y=SSB,col=as.factor(Sim)))+ guides(size=FALSE,fill=FALSE,alpha=FALSE,col=FALSE) - t3 <- grid.arrange(p1, p2, nrow=2) - ggsave(paste0(.projdir,"tier3_proj.pdf"),plot=t3,width=5.4,height=7,units="in") - - -#' ## Make tables - # Stock Alt Sim Yr SSB Rec Tot_biom SPR_Implied F Ntot Catch ABC OFL AvgAge AvgAgeTot SexRatio FABC FOFL - bfsum <- bf %>% select(Alt,Yr,SSB,F,ABC ,Catch) %>% group_by(Alt,Yr) %>% summarise(Catch=mean(Catch),SSB=mean(SSB),F=mean(F),ABC=mean(ABC)) - t1 <- bfsum %>% select(Alt,Yr,Catch) %>% spread(Alt,Catch) - names(t1) <- c("Catch","Scenario 1","Scenario 2","Scenario 3","Scenario 4","Scenario 5","Scenario 6","Scenario 7") - - print_Tier3_tables(bf) - diff --git a/examples/goanrs_all_spcat.dat b/examples/goanrs_all_spcat.dat deleted file mode 100644 index c3c9e3e..0000000 --- a/examples/goanrs_all_spcat.dat +++ /dev/null @@ -1,28 +0,0 @@ -#_SETUP_FILE_FOR_THE_BERING_SEA/AI_FISHERIES -#_Number_of_years with specified catch (if begin-yr = 2005, and this number is 3, then subsequent values represent catches in 2005, 06, and 7 (to evaluate alts for 2008 -3 -# Number of runs -3 -# OY Minimum -13.248 # Note that this is for age-structured species 1330.148 -# OY Maximum -1943.248 # Note that this is for age-structured species 1930.148 -# data files for each species -# Pollock Pacific cod sablefish Yellowfin Greenland turbot arrowtooth flounder Rock sole Flathead sole AK Plaice Pacific ocean perch Nrthrn RF Atka mackerel -# 1 2 3 4 5 6 7 8 9 10 11 12 -# data files for each species -data/goanrs.dat data/goanrs_western.dat data/goanrs_central.dat -# ABC Multipliers -1 1 1 -# Population scalars -1 1 1 -# New Alt 4 Fabc SPRs (Rockfish = 0.75, other 0.6), Steller sea lion prey species between F40 and F60 (to meet OY Min) -0.75 .75 .75 -# Number of TAC model categories -1 1 1 -# TAC model indices (for aggregating) -1 1 1 -# Catch in each future year -2021 683.728 666 333 -2022 683.728 666 333 -2023 683.728 666 333 diff --git a/examples/goars.R b/examples/goars.R deleted file mode 100644 index 8ca1232..0000000 --- a/examples/goars.R +++ /dev/null @@ -1,54 +0,0 @@ -library(tidyverse) -library(ggplot2) -library(ggthemes) -system("run.sh goanrs_central") -system("run.sh goanrs_western") -system("run.sh goanrs") - -# Or do all three above in one run -system("run.sh goanrs_all") - - -plot_ssb <- function(dirname,title=NULL){ - df <- read_table(paste0(dirname,"_out/bigfile.out")) - if(is.null(title)) title=dirname - df %>% mutate(Alt=as.factor(Alt)) %>% group_by(Yr,Alt) %>% - summarise(SSB=mean(SSB),Catch=mean(Catch),ABC=mean(ABC)) %>% - ggplot(aes(x=Yr,y=SSB,color=Alt)) + geom_line(size=2) + - expand_limits(y=0) + theme_few() + ggtitle(title) -} -plot_ssb(dirname="goanrs_central","N rock sole, Central") -plot_ssb(dirname="goanrs_western","N rock sole, western") - -plot_ssb <- function(dirname,alt=c(1,7),title=NULL){ - df <- read_table(paste0(dirname,"_out/bigfile.out")) - if(is.null(title)) title=dirname - df %>% filter(Alt %in% alt) %>% group_by(Yr,Stock,Alt) %>% - summarise(ub=quantile(SSB,.9),lb=quantile(SSB,.1), - SSB=mean(SSB), Catch=mean(Catch),ABC=mean(ABC)) %>% - mutate(Stock=as.factor(Stock),Alt=as.factor(Alt)) %>% - ggplot(aes(x=Yr,y=SSB,ymax=ub,ymin=lb,color=Stock:Alt,fill=Stock:Alt)) + geom_line(size=2) + - geom_ribbon(alpha=.2) + - expand_limits(y=0) + theme_few() + ggtitle(title) -} -names(df) -dirname <- "goanrs_all" -dirname="goanrs_all";title="N rock sole, GOA";alt=c(1,6) -plot_ssb(dirname="goanrs_all",title="N rock sole, GOA") - -plot_catch <- function(dirname,alt=c(1,7),title=NULL){ - df <- read_table(paste0(dirname,"_out/bigfile.out")) - if(is.null(title)) title=dirname - df %>% filter(Alt %in% alt) %>% group_by(Yr,Stock,Alt) %>% - summarise(ub=quantile(Catch,.9),lb=quantile(Catch,.1), - SSB=mean(SSB), Catch=mean(Catch),ABC=mean(ABC)) %>% - mutate(Stock=as.factor(Stock),Alt=as.factor(Alt)) %>% - ggplot(aes(x=Yr,y=Catch,ymax=ub,ymin=lb,color=Stock:Alt,fill=Stock:Alt)) + geom_line(size=2) + - geom_ribbon(alpha=.2) + - expand_limits(y=0) + theme_few() + ggtitle(title) -} -names(df) -plot_catch(dirname="goanrs_all",alt=1,title="N rock sole, GOA") -plot_ssb(dirname="goanrs_all",alt=c(7,6),title="N rock sole, GOA") -plot_catch(dirname="goanrs_all",alt=c(7,6),title="N rock sole, GOA") -plot_ssb(dirname="goanrs_western","lN rock sole, western") \ No newline at end of file diff --git a/examples/more_examples/BSAI NRS/ForProjections.dat b/examples/more_examples/BSAI NRS/ForProjections.dat deleted file mode 100644 index 5dce63e..0000000 --- a/examples/more_examples/BSAI NRS/ForProjections.dat +++ /dev/null @@ -1,40 +0,0 @@ -fm_projection_model_output -0 # SSL Species??? -0 # Constant buffer of Dorn? -1 # Number of fsheries -2 # Number of sexes?? -0.0698939 # averagei 5yr f -1.0 # author f -0.4 # SPR ABC -0.35 # SPR MSY -2 # Spawnmo -20 # Number of ages -1 # Fratio -#females first -0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 -#male -0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 0.172865 -# Maturity Females -0 0 0 0 0.001 0.008 0.061 0.2 0.505 0.75 0.888 0.929 0.955 0.979 0.983 0.991 0.996 0.997 0.997 0.997 -# Maturity Males same as females!! -0 0 0 0 0.001 0.008 0.061 0.2 0.505 0.75 0.888 0.929 0.955 0.979 0.983 0.991 0.996 0.997 0.997 0.997 -# Wt spawn females -0 0.0143 0.0426333 0.0880667 0.135333 0.249269 0.36119 0.4164 0.459727 0.488625 0.525333 0.61875 0.6755 0.7158 0.72725 0.629667 0.725308 0.7105 0.79275 0.927545 -# WtAge Females, by fishery -0 0.01 0.070674 0.119675 0.163832 0.243534 0.309472 0.388137 0.450988 0.527343 0.546709 0.566713 0.575558 0.548343 0.568974 0.600366 0.606937 0.644775 0.604785 0.65195 -# WtAge Males, by fishery -0 0.01 0.0706607 0.115078 0.181837 0.239772 0.280924 0.318739 0.399935 0.37515 0.421513 0.401573 0.314375 0.382681 0.415436 0.401458 0.411747 0.406325 0.449322 0.519749 -# Selectivity Females, by fishery -0.000311118 0.000842886 0.00228149 0.00616031 0.0165244 0.0435607 0.10989 0.250739 0.475649 0.71089 0.869541 0.947554 0.97999 0.992523 0.997229 0.998976 0.999622 0.999622 0.999622 0.999622 -# Selectivity males, by fishery -0.00020912 0.000738778 0.00260599 0.00914349 0.031494 0.10209 0.279024 0.547414 0.752056 0.840996 0.870105 0.878709 0.881174 0.881874 0.882073 0.882129 0.882144 0.882144 0.882144 0.882144 -# N at age in endyr Females, Males -440.236 361.536 1568.42 685.803 390.204 399.757 283.75 145.383 22.4396 36.8494 23.2145 15.9373 6.9824 8.43125 9.7935 20.1012 51.2928 33.37 31.563 87.4915 -440.236 353.362 1498.29 640.245 355.799 355.337 245.094 121.789 18.1604 28.7295 17.4556 11.5895 4.92134 5.79107 6.56302 13.109 32.6172 20.979 19.8522 53.4976 -# No Recruitments -40 -# Recruitment 1978-2004 -280.595 274.373 360.874 653.349 666.766 615.804 1010.65 874.832 835.076 1414.25 2165.87 751.508 643.514 1442.41 711.898 366.867 542.332 286.298 278.206 383.692 219.987 336.756 314.047 666.997 1037.49 1229.31 923.733 746.552 892.496 278.217 110.018 77.902 53.3355 100.487 120.679 158.777 80.5199 435.205 713.113 852.928 -# SSB -# used only for S/R analysis -54.1439 67.006 82.3962 94.4856 100.706 100.453 105.69 119.585 116.998 123.639 148.617 155.766 163.769 177.721 199.031 215.356 272.954 317.17 366.632 450.015 493.62 485.398 509.759 533.741 549.379 557.758 568.449 551.987 470.123 431.805 407.609 388.716 354.036 375.854 429.022 461.09 469.144 473.966 485.95 433.155 diff --git a/examples/more_examples/BSAI NRS/spm.dat b/examples/more_examples/BSAI NRS/spm.dat deleted file mode 100644 index 7e077f8..0000000 --- a/examples/more_examples/BSAI NRS/spm.dat +++ /dev/null @@ -1,55 +0,0 @@ -#rn -NRS_test -#Tier -3 -#nalts -7 -#alts -1 -2 -3 -4 -5 -6 -7 -#tac_flag -1 -#srr_type -1 -#srr_form -1 -#srr_conditioning -0 -#srr_reserved -0 -#spm_detail_flag -1 -#nprj_yrs -14 -#nsims -1000 -#beg_yr -2022 -#nyrs_fixed_catch -2 -#nspp -1 -#OY_min -10 -#OY_max -1943.248 -#data_files for each species -../fm.prj -#ABC_mults -1 -#scalars -1 -#alt4_spr -0.6 -#nTAC_cat -1 -#TACind -1 -#fixed_catch -2022 16.0143 #Beginning of October catch -2023 40.760 #10 yr average up to 2021. diff --git a/examples/more_examples/Projection Model 2005 copy.pdf b/examples/more_examples/Projection Model 2005 copy.pdf deleted file mode 100644 index f8bbb6729c01c962e33c3d6d69fcf174e12049e0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1272482 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zJU|{$Z@~Bfd4N1X9v}~>0>}g80rG%)1I7o)1LOhn0C_+aKpr3ukO$NoFg`#YAP2+#s|m)0C|8spx%J-0rCKOfIL7RPz8_&$OGg7^#+U&kO#;EH(-2# zJU|{G50D2`0ptPl0C_;Y0pkPY0rCKOfIOfIAP$OGg7@&I{26+j*!50D4c8!$dV9v}~p2gn1e z0P+BNfIOhyfbjwH0C|8sKps#9kO#;EJ1nlAP0>}g80rG%)1I7o)1LOhn0C_+aKpr3ukO$NoFg`#YAP-CmfH{&zDR8({zd diff --git a/examples/more_examples/atka/plot.R b/examples/more_examples/atka/plot.R deleted file mode 100644 index 8555737..0000000 --- a/examples/more_examples/atka/plot.R +++ /dev/null @@ -1,39 +0,0 @@ -radian -rm(list=ls()) -ls() -#source(paste0(here::here("R"),"/prelims.R")) -source("../../R/prelims.R") -#------------------------------------------------------------------------------- -# Visual compare runs -#------------------------------------------------------------------------------- -library(ggridges) - -source("../../R/compareRuns.r") -#--Projections--------- -pdf("proj.pdf") -pfn <- "amak" -pfn <- "5yr" -pdt <- read_csv(paste0("spm_detail.csv")) -pdt$Alternative <- as.factor(pdt$Alternative) -names(pdt) - -pt <- pdt%>%filter(Yr>2021,Alternative==2) %>% group_by(Yr,Alternative) %>% summarise(Catch=mean(Catch),ABC=mean(ABC),OFL=mean(OFL),SSB=median(SSB) ,lb=quantile(SSB,.2) ,ub=quantile(SSB,.8) ) -pt -ggplot(pt,aes(x=Yr,y=SSB,fill=Alternative)) + geom_line() + mytheme + ylim(c(0,340000)) + geom_ribbon(aes(ymin=lb,ymax=ub,fill=Alternative),alpha=0.25) + labs(y="Spawning biomass (kt)",x="Year") + scale_x_continuous(breaks=seq(2022,2035,2)) -c1 <- ggplot(pt,aes(x=Yr,y=Catch,color=Alternative,size=1.)) + geom_line(size=1.5) + mytheme + labs(y="Catch (kt)",x="Year") + scale_x_continuous(breaks=seq(2015,2032,2)) -c1 <- c1 + geom_line(aes(x=Yr,y=ABC),size=1) -#c1 <- c1 + geom_line(data=pt[as.numeric(Alternative)==2,.(Yr,ABC)],aes(x=Yr,y=ABC)) -c1 -pt[as.numeric(Alternative)==2,.(Yr,ABC)] -pt <- pdt[Yr>2018,.(Catch=mean(Catch),ABC=mean(ABC),OFL=mean(OFL)),.(Yr,Alternative)] -pt -ggplot(pt,aes(x=Yr,y=OFL,color=Alternative)) + geom_line() + mytheme -pdt -pdx <-rbind(pdt) -setkey(pdx,Yr,Alternative) - -pt <- pdx[.(Yr>2018,(Alternative)==1),.(Catch=mean(Catch),ABC=mean(ABC),OFL=mean(OFL),SSB=median(SSB) ),.(Yr,config)] -pt <- pdx[Yr>2018&Alternative==1,.(Catch=mean(Catch),ABC=mean(ABC),OFL=mean(OFL),SSB=median(SSB) ,lb=quantile(Catch,.1) ,ub=quantile(Catch,.9) ),.(Yr,config)] -ggplot(pt,aes(x=Yr,y=Catch,color=config,shape=config)) + geom_line() + geom_point() + mytheme + geom_ribbon(aes(ymin=lb,ymax=ub,fill=config),alpha=0.25) + labs(x="Year")+ scale_x_continuous(breaks=seq(2015,2032,2)) + ylim(c(0,130)) -dev.off() - diff --git a/examples/more_examples/atka/readme.txt b/examples/more_examples/atka/readme.txt deleted file mode 100644 index 0d84398..0000000 --- a/examples/more_examples/atka/readme.txt +++ /dev/null @@ -1 +0,0 @@ -added ABC output for Tier 1 under any scenario... diff --git a/examples/more_examples/atka/setup.dat b/examples/more_examples/atka/setup.dat deleted file mode 100644 index 2311eae..0000000 --- a/examples/more_examples/atka/setup.dat +++ /dev/null @@ -1,25 +0,0 @@ -#-------------------------------------------- -Std # Run name 5 scenarios -#-------------------------------------------- -7 # Number of Alternatives -#-------------------------------------------- -1 -2 # List of alternatives -3 -4 -5 -6 -7 -#-------------------------------------------- -1 # Flag to set TAC equal to ABC (1 means true, otherwise false) -#-------------------------------------------- -1 # Stock-recruitment type (1=Ricker, 2=Bholt) -1 # projection rec form (default: 1 = use observed mean and std, option 2 = use estimated SRR and estimated sigma R) -0 # SR-Conditioning (0 means no, 1 means use Fmsy == F35%?, 2 means Fmsy == F35% and Bmsy=B35% condition (affects SRR fits) -0.0 # Condition that there is a prior that mean historical recruitment is similar to expected recruitment at half mean SSB and double mean SSB 0 means don't use, otherwise specify CV -#-------------------------------------------- -1 # Flag to write big file (of all simulations rather than a summary, 0 means don't do it, otherwise do it) Write_Big -#-------------------------------------------- -14 #_Number of projection years -1000 #_Number of simulations -2021 #_Begin Year diff --git a/examples/more_examples/atka/spm.dat b/examples/more_examples/atka/spm.dat deleted file mode 100644 index d350203..0000000 --- a/examples/more_examples/atka/spm.dat +++ /dev/null @@ -1,53 +0,0 @@ -#_SETUP_FILE_FOR_skates -std # Run name 5 scenarios -# Tier -3 -#-------------------------------------------- -7 # Number of Alternatives -#-------------------------------------------- -1 -2 # List of alternatives -3 -4 -5 -6 -7 -#-------------------------------------------- -1 # Flag to set TAC equal to ABC (1 means true, otherwise false) -#-------------------------------------------- -2 # Stock-recruitment type (1=Ricker, 2=Bholt) -1 # projection rec form (default: 1 = use observed mean and std, option 2 = use estimated SRR and estimated sigma R) -0 # SR-Conditioning (0 means no, 1 means use Fmsy == F35%?, 2 means Fmsy == F35% and Bmsy=B35% condition (affects SRR fits) -0 # Condition that there is a prior that mean historical recruitment is similar to expected recruitment at half mean SSB and double mean SSB 0 means don't use, otherwise specify CV -#-------------------------------------------- -1 # Flag to write big file (of all simulations rather than a summary, 0 means don't do it, otherwise do it) Write_Big -#-------------------------------------------- -15 #_Number of projection years -1000 #_Number of simulations -2022 #_Begin Year -#_Number_of_years with specified catch -3 -# Number of species -1 -# OY Min -0 -# OY Max -2.00E+06 -# data files for each species -../amak.prj -# ABC Multipliers -1 -# scalars -1 -# New Alt 4 Fabc SPRs (Rockfish = 0.75, other 0.6), Steller sea lion prey species between F40 and F60 (to meet OY Min) -0.75 -# Number of TAC model categories -1 -# TAC model indices (for aggregating) -1 -# Catch in each future year -# Catch in each future year -2022 66481 -2023 83800 -2024 73495 - diff --git a/examples/more_examples/atka/spm.tpl b/examples/more_examples/atka/spm.tpl deleted file mode 100644 index b791fc1..0000000 --- a/examples/more_examples/atka/spm.tpl +++ /dev/null @@ -1,2509 +0,0 @@ - // DONT FORGET ABOU RHO!!! - //////////////////////////////////////////////////////////////////////////// - // spm.tpl - // Version 0.6 Released Oct 2022 - // - // NOTE: Draft subject to change - // Conventions: - // nspp, ispp = index for species/stock - // ngear, igear= index for gears used within age-structured species/stocks - // - // Oct 2005 add ability to compute objective function for maximizing yield while minimizing variability - // want to have the ability to compute the average first-diff squared and avg yield for each trajectory - // Alt3b_obj_fun = avg_yield - 0.5 * sqrd_1st_diff - // - //////////////////////////////////////////////////////////////////////////// -DATA_SECTION - !!CLASS ofstream means_out("means.out") - !!CLASS ofstream alts_proj("alt_proj.out") - !!CLASS ofstream percent_out("percentiles.out") - !!CLASS ofstream percent_db("percentdb.out") - // !!CLASS ofstream Alt3bstuff("alt3b.out") - !!CLASS ofstream detail_out("spm_detail.csv") - !!CLASS ofstream prof_F("F_profile.out"); - !!CLASS ofstream elasticity("elasticity.csv"); - int condition_SR - int ipro - int isim - number kink_adj - !! condition_SR = 0;// - int rnseed // Random number seed - !! rnseed = 123; - //////////////////////////////////////////////////////////////////////////// - // !! ad_comm::change_datafile_name("setup.dat"); Jim changed to spm.dat file - !! *(ad_comm::global_datafile) >> run_name; // Read in the name of this run - init_int Tier - int alt ; // Alternative specfications (relic of PSEIS) - vector rec_vector(1,300) // storage vector for historical and future recruitment... - vector wtd_rec(1,25) - vector wtd_div(1,25) // denominator of wtd recruitment - init_int nalts - init_ivector alt_list(1,nalts) - init_int TAC_ABC // Flag to set TAC equal to ABC (1 means true, otherwise false) - init_int SrType // Type of recruitment curve (1=Ricker, 2 Bholt) - // Specify form of recruitment generator (1 = use observed mean and std - // 2 = use estimated SRR and estimated sigma R - // 3 = use input parameters from srecpar.dat : - // 4 = use input parameters from srecpar.dat : - init_int Rec_Gen - init_int Fmsy_F35 // Specify if conditioned so that Fmsy = F35 (affects SRR fitting) may need to be species specific... - init_number Rec_Cond // Specify prior condition that recruitment at half and double average SSB is similar to average historical Rec - init_int Write_Big // Flag to write big file (of all simulations rather than a summary, 0 means don't do it, otherwise do it) - init_int npro // Number of projection years - init_int nsims // Number of simulaions - init_int styr // First year of projection - !! cout<< "First year:\t"<> spp_file_name(i); - - write_log(nyrs_catch_in); - write_log(nspp); - write_log(OY_min); - write_log(OY_max); - write_log(spp_file_name); - - END_CALCS - !! cout <<"OYMax "<> bzero_in; - *(ad_comm::global_datafile) >> phizero_in; - *(ad_comm::global_datafile) >> alpha_in; - *(ad_comm::global_datafile) >> sigmar_in; - *(ad_comm::global_datafile) >> rho_in; - // rho_in=0.82; - } - // Open up tac-model parameters - ad_comm::change_datafile_name("tacpar.dat"); - END_CALCS - init_int nntmp - init_int nnodes - init_vector maxabc(1,ntacspp) - init_matrix theta(0,nnodes,1,ntacspp) - !! cout<<"read tacpar"<> spname(i); // 1 - cout<> SSL_spp(i); // 2 - *(ad_comm::global_datafile) >> Const_Buffer(i); // 3 - *(ad_comm::global_datafile) >> ngear(i); // 4 - *(ad_comm::global_datafile) >> nsexes(i); // 6 - *(ad_comm::global_datafile) >> avg_5yrF(i); // 7 - *(ad_comm::global_datafile) >> FABC_Adj(i); // 8 - *(ad_comm::global_datafile) >> SPR_abc(i); // 9 - *(ad_comm::global_datafile) >> SPR_ofl(i); // 10 - *(ad_comm::global_datafile) >> spawnmo(i); // 11 - *(ad_comm::global_datafile) >> nages(i); // 12 - cout<<"nages: "<> Fratiotmp(i,j); // 13 - write_log( Fratiotmp(i)); - - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> M_Ftmp(i,k); // 14 - if (nsexes(i)==2) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> M_Mtmp(i,k); // 15 - write_log( M_Ftmp(i)); - - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> pmaturetmp_F(i,k); // 16 - write_log( pmaturetmp_F(i)); - - if (nsexes(i)==2) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> pmaturetmp_M(i,k); // 15 - write_log( pmaturetmp_M(i)); - cout << "Mature: "<< pmaturetmp_F(i)(1,nages(i)) <> wt_Ftmp(i,k); // 17 - write_log( wt_Ftmp(i)); - for (int j=1;j<=ngear(i);j++) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> wt_gear_Ftmp(i,j,k); // 18 - write_log( wt_gear_Ftmp(i)); - - if (nsexes(i)==2) - for (int j=1;j<=ngear(i);j++) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> wt_gear_Mtmp(i,j,k);// 19 - write_log( wt_gear_Mtmp(i)); - - for (int j=1;j<=ngear(i);j++) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> sel_Ftmp(i,j,k); // 20 - write_log( sel_Ftmp(i)); - - if (nsexes(i)==2) - for (int j=1;j<=ngear(i);j++) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> sel_Mtmp(i,j,k); // 21 - write_log( sel_Mtmp(i)); - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> n0_Ftmp(i,k); // 22 - write_log( n0_Ftmp(i)); - - if (nsexes(i)==2) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> n0_Mtmp(i,k); // 23 - write_log( n0_Mtmp(i)); - - cout<<"N: "<> nrec(i); // 24 - cout<<"nrec: "<> Rtmp(i,j); // 25 - cout<<"Rec: "<> SSBtmp(i,j); // 26 - cout<<"SSB: "<> Rsim(ispp,i,j); - Rsim(ispp,i,j) *= exp(rnorms(i,j)*.375); // about 15% CV to get historical mean - } - if (nsims<=5) Rsim(ispp,i,j) = AMeanRec(ispp); // XXX constant recruitment - } - } - envin.close(); - AMeanRec(ispp) *= .5; // Arithmetic mean - cout <<"recruits"< 0 ) - F_begin_yr(k,ispp) = SolveF2(n0_F(ispp), n0_M(ispp), Obs_Catch(k,ispp),ispp); // F_yr_one(ispp) = SolveF2(n0_F(ispp), n0_M(ispp), yr_one_catch(ispp),ispp); - else - F_begin_yr(ispp) = 0; // F_yr_one(ispp) = 0; // cout< 1) // Debugging if LP to be done or not - { - int on=0; - if ( (on=option_match(ad_comm::argc,ad_comm::argv,"-nolp"))>-1) - dolp=0; - else - dolp=1; - } - END_CALCS - - -PRELIMINARY_CALCS_SECTION - double tmp1; - write_alts_hdr(); - get_SB100(); - cout<<"SSB, Biomass at unfished: "<1) - nyrs_catch = nyrs_catch_in; - else - nyrs_catch = 1;// NOTA BUENO: this is changed under the new EIS Alternatives (May 06) - Do_Sims(); - // if (alt==2) write_alts(); - write_alts(); - } - -FUNCTION compute_obj_fun - dvariable tmp1; - dvariable tmp2; - tmp1.initialize(); - for (int ispp=1;ispp<=nspp;ispp++) - { - Get_Bzero(ispp); - // write_srec();exit(1); - if(Fmsy_F35>0) - { - get_msy(ispp); - switch (current_phase()) - { - case 1 : tmp1 = 1.e1*square(log(Fmsy(ispp))-log(F35(ispp))); break; - case 2 : tmp1 = 1.e2*square(log(Fmsy(ispp))-log(F35(ispp))); break; - case 3 : tmp1 = 1.e2*square(log(Fmsy(ispp))-log(F35(ispp))); break; - case 4 : tmp1 = 1.e3*square(log(Fmsy(ispp))-log(F35(ispp))); break; - default: tmp1 = 1.e3*square(log(Fmsy(ispp))-log(F35(ispp))); break; - } - if(Fmsy_F35==2) - switch (current_phase()) - { - case 1 : tmp1 += 1.e1*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - case 2 : tmp1 += 1.e2*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - case 3 : tmp1 += 1.e2*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - case 4 : tmp1 += 1.e3*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - default : tmp1+= 1.e3*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - } - obj_fun += tmp1; - } - if (Rec_Cond>0.) - { - // More conditioning here--make mean recruitment consistent with half and double - // recruitment at avg spawning biomass... - double vartmp = 2.*Rec_Cond*Rec_Cond; - /* - double ssb1 = AMeanSSB(ispp) * 0.5; - double ssb2 = AMeanSSB(ispp) ; - double ssb3 = AMeanSSB(ispp) * 2.0; - // double ssb4 = value(Bzero(ispp)) ; - tmp2.initialize(); - tmp2 += square(log(AMeanRec(ispp)) - log(SRecruit( ssb1, ispp)))/ vartmp; - tmp2 += square(log(AMeanRec(ispp)) - log(SRecruit( ssb2, ispp)))/ vartmp; - tmp2 += square(log(AMeanRec(ispp)) - log(SRecruit( ssb3, ispp)))/ vartmp; - // tmp2 += 24.* square(log(AMeanRec(ispp)) - log(SRecruit( ssb4, ispp))); - // tmp2 += 24.* square(log(AMeanSSB(ispp)) - log( ssb4 )); - */ - tmp2 = square(log(Bzero(ispp)) - log(SB100(ispp)))/ vartmp; - obj_fun += tmp2; - // cout << "SSB "<OY_max) - Actual_Catch = TAC/sum(TAC)*OY_max; - else - Actual_Catch = TAC; - } - /* ////////////////////////////////////////////////////////////////////////////// //int use_max = 1; // Need to move this into the setup file... double max_catch=1500.;// EBS POllock special case (CHANGE THIS) //if (use_max==1) for (int ispp=1;ispp<=nspp;ispp++) Actual_Catch(ispp) = min(TAC(ispp),max_catch); // cout<<"AC: "<2) - write_sim("Alternative ",ispp); - } - write_by_time(); - - -FUNCTION Alt4_TAC - // Return vector of Alt4 TACs (given Alt4_Fabc, and the SSL prey condition to be at least up to SSL MaxPerm) - // Get all ABCs under Alt4_Fabc - // Sum and test if below OY_min - double sumabc; - sumabc = sum(ABC); - if (sumabc3) - { - Alt4_Fcalc = 1; - double diff; - double ssl_sum=0.; - diff = OY_min-sumabc; - // if so, sum up SSL ABCs - ssl_sum = SSL_spp*ABC; - for (int ispp=1;ispp<=nspp;ispp++) - { - // then apportion them to difference between current ABC and target (OY_min) difference - if (SSL_spp(ispp)) - { - TAC(ispp) = ABC(ispp) + diff * ABC(ispp)/ssl_sum; - // Special for BSAI Pcod to subtract off 3%...and get the totals to match up - // if (ispp==2) TAC(ispp) /= 0.97; For EIS work - } - else - { - TAC(ispp) = ABC(ispp); - } - } - // cout << ipro<<" "<0.01) ftmp = SolveF2(N_F(ispp),N_M(ispp),TAC(ispp),ispp); - // cout <= 0.2 *SBzero(ispp) & SBtmp < SBKink(ispp) ) // NOTE Same as Am 56 here (until 20% bzero reached) - Ftmp = (Fabc(ispp)*(1/(1-alpha ))*(SBtmp/SBKink(ispp) - alpha )); - if (SBtmp > SBKink(ispp) ) - Ftmp = (Fabc(ispp)); - SBtmp = N_females * elem_prod( wt_mature_F(ispp), mfexp( -yrfrac(ispp)*(M_F(ispp) + Ftmp*F_age ))); - } - return(Ftmp); - -FUNCTION double Get_F_Am56(const dvector& F_age, const dvector& N_females, const int ispp ) - double Ftmp; // cout<< spname(ispp)<< endl; - { - for (ii=1;ii<=3;ii++) // Iterate to get month of spawning correct - { - if (SBtmp < alpha*SBKink(ispp)) - Ftmp = 0.; - if (SBtmp >= alpha*SBKink(ispp) & SBtmp < SBKink(ispp) ) - Ftmp = (Fabc(ispp)*(1/(1-alpha))*(SBtmp/SBKink(ispp) - alpha)); - if (SBtmp > SBKink(ispp) ) - Ftmp = (Fabc(ispp)); - SBtmp = N_females * elem_prod( wt_mature_F(ispp), mfexp( -yrfrac(ispp)*(M_F(ispp) + Ftmp*F_age ))); - } - } - return(Ftmp); - -FUNCTION double Get_Fofl_t2(const dvector& F_age, const dvector& N_females, const int ispp ) - double Ftmp; // cout<< spname(ispp)<< endl; - { - for (ii=1;ii<=3;ii++) // Iterate to get month of spawning correct - { - if (SBtmp < alpha*SBKink(ispp)) - Ftmp = 0.; - if (SBtmp >= alpha*SBKink(ispp) & SBtmp < SBKink(ispp) ) - Ftmp = (Fofl(ispp)*(1/(1-alpha))*(SBtmp/SBKink(ispp) - alpha)); - if (SBtmp > SBKink(ispp) ) - Ftmp = (Fofl(ispp)); - SBtmp = N_females * elem_prod( wt_mature_F(ispp), mfexp( -yrfrac(ispp)*(M_F(ispp) + Ftmp*F_age ))); - } - } - return(Ftmp); - -FUNCTION double Get_Fofl_t(const dvector& F_age, const dvector& N_females, const int ispp ) - double Ftmp; // cout<< spname(ispp)<< endl; - { - for (ii=1;ii<=3;ii++) // Iterate to get month of spawning correct - { - if (SBtmp < alpha*SBKink(ispp)) - Ftmp = 0.; - if (SBtmp >= alpha*SBKink(ispp) & SBtmp < SBKink(ispp) ) - Ftmp = (Fofl(ispp)*(1/(1-alpha))*(SBtmp/SBKink(ispp) - alpha)); - if (SBtmp > SBKink(ispp) ) - Ftmp = (Fofl(ispp)); - SBtmp = N_females * elem_prod( wt_mature_F(ispp), mfexp( -yrfrac(ispp)*(M_F(ispp) + Ftmp*F_age ))); - } - } - return(Ftmp); - - -FUNCTION double Get_F_t(const dvector& F_age, const dvector& N_females, const int ispp ) - double Ftmp; // cout<< spname(ispp)<< endl; - if (SSL_spp(ispp)) - { - Ftmp = Get_F_SSL_prey(F_age, N_females, ispp); - } - else - { - Ftmp = Get_F_Am56(F_age, N_females, ispp); - } - return(Ftmp); - -FUNCTION void Project_Pops(const int& isim, const int& i) - double ctmp; - if ( i == npro && isim%int(nsims/4)==0) cout << "Year "< 0) - { - if (i <= nyrs_catch || TAC_ABC==0) // Use TAC setting algorithm for alt 2 only, for all others, set TAC==ABC - { - Ftmp = SolveF2(N_F(ispp),N_M(ispp),Actual_Catch(ispp),ispp); - } - else - { - if (alt==7 && i<=3) - Ftmp = Get_F(1,ispp); // Set to the F rather than solving every time... - else - if (alt==77 && i<=2) - Ftmp = Get_F(1,ispp); // Set to the F rather than solving every time... - else - { - // if (alt==2 && sum(TAC)>OY_max) - if ((alt==2 ||alt==98||alt==97) && sum(TAC)>OY_max) - { - Ftmp = SolveF2(N_F(ispp),N_M(ispp),OY_max*TAC(ispp)/sum(TAC),ispp); - // cout<<"HERE "<< Ftmp<<" "< 0.) - { - for (m=1;m<=ngear(ispp);m++) - { - Ftottmp_F = Ftmp*Frat(ispp,m)*sel_F(ispp,m); - Ftottmp_spr += Ftottmp_F ; - Ftottmp_M = Ftmp*Frat(ispp,m)*sel_M(ispp,m); - ctmp += (wt_gear_F(ispp,m) * elem_prod(elem_div(Ftottmp_F, Z_F(ispp)),elem_prod(1.-S_F(ispp),N_F(ispp)))); // Catch equation (vectors) - ctmp += (wt_gear_M(ispp,m) * elem_prod(elem_div(Ftottmp_M, Z_M(ispp)),elem_prod(1.-S_M(ispp),N_M(ispp)))); // Catch equation (vectors) - } - SPRsim(ispp,isim,i) = Implied_SPR(Ftottmp_spr,ispp); - // cout <NUL "); - ifstream tac("tac.dat"); - tac>>TAC; - tac.close(); /* */ - for (int ispp=1;ispp<=nspp;ispp++) - Actual_Catch(ispp) = min(TAC(ispp),ABC(ispp)); - } - - -FUNCTION Avg_Age - Avg_Age_End.initialize(); - Avg_Age_Mat.initialize(); - for (int ispp=1;ispp<=nspp;ispp++) - { - // if(!isit_const(ispp) ) - { - dvector age_seq(1,nages(ispp)); - age_seq.initialize(); - age_seq.fill_seqadd(1,1); - dvector ntmp(1,nages(ispp)); - ntmp = N_F(ispp); - Avg_Age_End(ispp) += age_seq * ntmp /sum(ntmp); - Avg_Age_sum(ispp) += Avg_Age_End(ispp) ; - ntmp = elem_prod(ntmp,pmature_F(ispp)) ; - Avg_Age_Mat(ispp) += elem_prod(ntmp,pmature_F(ispp)) * age_seq/sum(ntmp); - // cout< 1e-6) - { - iter++; - ftmp += (TACin-cc) / btmp; - Ftottmp_F.initialize(); - Ftottmp_M.initialize(); - for (m=1;m<=ngear(ispp);m++) - { - Ftottmp_F += ftmp*Fratsel_F(m); - Ftottmp_M += ftmp*Fratsel_M(m); - } - Z_F = Ftottmp_F + M_F(ispp); - Z_M = Ftottmp_M + M_M(ispp); - S_F = mfexp( -Z_F ); - S_M = mfexp( -Z_M ); - cc = 0.0; - for (m=1;m<=ngear(ispp);m++) - { - cc += (wt_gear_F(ispp,m) * elem_prod(elem_div(ftmp*Fratsel_F(m), Z_F),elem_prod(1.-S_F,N_F))); // Catch equation (vectors) - cc += (wt_gear_M(ispp,m) * elem_prod(elem_div(ftmp*Fratsel_M(m), Z_M),elem_prod(1.-S_M,N_M))); // Catch equation (vectors) - } - dd = cc / TACin - 1.; - //cout << ispp<<" "<< ftmp << " "<< cc << " "<100) {cerr<<"Bombed on catch solver--check scales for "<nages(ispp)) - { - Tg += double(ii) * wt_mature_F(ispp,nages(ispp)) * ntmp; - tmp += wt_mature_F(ispp,nages(ispp)) * ntmp; - ntmp *= exp(-M_F(ispp,nages(ispp))); - } - else - { - Tg += double(ii) * wt_mature_F(ispp,ii) * ntmp; - tmp += wt_mature_F(ispp,ii) * ntmp; - ntmp *= exp(-M_F(ispp,ii)); - } - } - // Tg /= value(phizero(ispp)); - Tg /= tmp; - report << Tg<< " "; - } - report << endl; - report << "Options SR_Type Project_Recr_Assmption SR_Condition"< 1e-6) - means_out << mean_value <<" "; - else - means_out << " NA "; - } - means_out << endl; - } - means_out << endl; - -FUNCTION void write_TACs(const adstring& Title) - // This one prints out species over time (means only), but without headings - means_out <<"Alternative "< 1e-6) - means_out << mean_value <<" "; - else - means_out << " NA "; - } - means_out << endl; - } - means_out << endl; - - - -FUNCTION void write_sim(const adstring& Title, d3_array& Outtmp) - // This one prints out species over time (means only), but without headings - d3_array mtmp(1,nspp,1,npro,1,nsims); - for (int ispp=1;ispp<=nspp;ispp++) for (int i=1;i<=npro;i++) for (int k=1;k<=nsims;k++) - mtmp(ispp,i,k)=Outtmp(ispp,k,i); - means_out <<"Alternative "< 1e-6) - means_out << mean_value <<" "; - else - means_out << " NA "; - } - means_out << endl; - } - means_out << endl; - -FUNCTION void write_sim(const adstring& Title,const d3_array& Outtmp,const dvar_vector& bb) - // This one prints out species over time (means only) - d3_array mtmp(1,nspp,1,npro,1,nsims); - for (int ispp=1;ispp<=nspp;ispp++) for (int i=1;i<=npro;i++) for (int k=1;k<=nsims;k++) - mtmp(ispp,i,k)=Outtmp(ispp,k,i); - means_out <<"Alternative "<1e-6) - means_out << mean_value <<" "; - else - means_out << " NA "; - } - means_out << endl; - } - means_out << endl; - -FUNCTION void write_sim(const adstring& Title,const int& ispp) - // For each species separately - percent_out <<"Alternative "< - adstring xspname; - adstring_array targsppname(1,20); - adstring_array spp_file_name(1,20); - adstring_array gearname(1,8); - adstring_array spname(1,90); - adstring_array areaname(1,8); - adstring run_name; - - ofstream write_log("Input_Log.rep"); - #undef write_log - #define write_log(object) write_log << #object "\n" << object << endl; - - // A routine to get transpose, sort and return a matrix ---- - dmatrix TranSort (const dmatrix m1) - { - RETURN_ARRAYS_INCREMENT(); - dmatrix vtmp=trans(m1); - int npro=m1.colmax(); - int nsim=m1.rowmax(); - for (int i=1;i<=npro;i++) - vtmp(i) = sort(vtmp(i),nsim); - - RETURN_ARRAYS_DECREMENT(); - return(vtmp); - } - // #include - -FUNCTION dvariable SRecruit(const dvariable& Stmp,const int& ispp) - RETURN_ARRAYS_INCREMENT(); - dvariable RecTmp; - switch (SrType) - { - case 1: - RecTmp = (Stmp / phizero(ispp)) * mfexp( sr_alpha(ispp) * ( 1. - Stmp / Bzero(ispp) )) ; //Ricker form from Dorn - break; - case 2: - RecTmp = Stmp / ( sr_alpha(ispp) + beta(ispp) * Stmp); //Beverton-Holt form - break; - case 3: - // RecTmp = mfexp(log_avgrec); //Avg recruitment - break; - case 4: - RecTmp = Stmp * mfexp( sr_alpha(ispp) - Stmp * beta(ispp)) ; //old Ricker form - break; - } - - RETURN_ARRAYS_DECREMENT(); - return RecTmp; - - -FUNCTION dvar_vector SRecruit(const dvar_vector& Stmp,const int& ispp) - RETURN_ARRAYS_INCREMENT(); - dvar_vector RecTmp(Stmp.indexmin(),Stmp.indexmax()); - switch (SrType) - { - case 1: - RecTmp = elem_prod((Stmp / phizero(ispp)) , mfexp( sr_alpha(ispp) * ( 1. - Stmp / Bzero(ispp) ))) ; //Ricker form from Dorn - break; - case 2: - RecTmp = elem_prod(Stmp , 1. / ( sr_alpha(ispp) + beta(ispp) * Stmp)); //Beverton-Holt form - break; - case 3: - // RecTmp = mfexp(log_avgrec); //Avg recruitment - break; - case 4: - RecTmp = elem_prod(Stmp ,mfexp( sr_alpha(ispp) - Stmp * beta(ispp))); //old Ricker form - break; - } - RETURN_ARRAYS_DECREMENT(); - //cout <5||F1<0.01)) - // { - // ii=5; - // F1=M(ispp,nages(ispp)); // When things bomb (F <0 or F really big then just set it to F35...) - // } - // else - { - F2 = F1 + df*.5; - F3 = F2 - df; //F1 = double(ii)/100; - yld1 = yield(F1,Stmp,Rtmp,ispp); //cout <> run_name; // Read in the name of this run - init_int Tier - int alt ; // Alternative specfications (relic of PSEIS) - vector rec_vector(1,300) // storage vector for historical and future recruitment... - vector wtd_rec(1,25) - vector wtd_div(1,25) // denominator of wtd recruitment - init_int nalts - init_ivector alt_list(1,nalts) - init_int TAC_ABC // Flag to set TAC equal to ABC (1 means true, otherwise false) - init_int SrType // Type of recruitment curve (1=Ricker, 2 Bholt) - // Specify form of recruitment generator (1 = use observed mean and std - // 2 = use estimated SRR and estimated sigma R - // 3 = use input parameters from srecpar.dat : - // 4 = use input parameters from srecpar.dat : - init_int Rec_Gen - init_int Fmsy_F35 // Specify if conditioned so that Fmsy = F35 (affects SRR fitting) may need to be species specific... - init_number Rec_Cond // Specify prior condition that recruitment at half and double average SSB is similar to average historical Rec - init_int Write_Big // Flag to write big file (of all simulations rather than a summary, 0 means don't do it, otherwise do it) - init_int npro // Number of projection years - init_int nsims // Number of simulaions - init_int styr // First year of projection - !! cout<< "First year:\t"<> spp_file_name(i); - - write_log(nyrs_catch_in); - write_log(nspp); - write_log(OY_min); - write_log(OY_max); - write_log(spp_file_name); - - END_CALCS - !! cout <<"OYMax "<> bzero_in; - *(ad_comm::global_datafile) >> phizero_in; - *(ad_comm::global_datafile) >> alpha_in; - *(ad_comm::global_datafile) >> sigmar_in; - *(ad_comm::global_datafile) >> rho_in; - // rho_in=0.82; - } - // Open up tac-model parameters - ad_comm::change_datafile_name("tacpar.dat"); - END_CALCS - init_int nntmp - init_int nnodes - init_vector maxabc(1,ntacspp) - init_matrix theta(0,nnodes,1,ntacspp) - !! cout<<"read tacpar"<> spname(i); // 1 - cout<> SSL_spp(i); // 2 - *(ad_comm::global_datafile) >> Const_Buffer(i); // 3 - *(ad_comm::global_datafile) >> ngear(i); // 4 - *(ad_comm::global_datafile) >> nsexes(i); // 6 - *(ad_comm::global_datafile) >> avg_5yrF(i); // 7 - *(ad_comm::global_datafile) >> FABC_Adj(i); // 8 - *(ad_comm::global_datafile) >> SPR_abc(i); // 9 - *(ad_comm::global_datafile) >> SPR_ofl(i); // 10 - *(ad_comm::global_datafile) >> spawnmo(i); // 11 - *(ad_comm::global_datafile) >> nages(i); // 12 - cout<<"nages: "<> Fratiotmp(i,j); // 13 - write_log( Fratiotmp(i)); - - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> M_Ftmp(i,k); // 14 - if (nsexes(i)==2) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> M_Mtmp(i,k); // 15 - write_log( M_Ftmp(i)); - - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> pmaturetmp_F(i,k); // 16 - write_log( pmaturetmp_F(i)); - - if (nsexes(i)==2) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> pmaturetmp_M(i,k); // 15 - write_log( pmaturetmp_M(i)); - cout << "Mature: "<< pmaturetmp_F(i)(1,nages(i)) <> wt_Ftmp(i,k); // 17 - write_log( wt_Ftmp(i)); - for (int j=1;j<=ngear(i);j++) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> wt_gear_Ftmp(i,j,k); // 18 - write_log( wt_gear_Ftmp(i)); - - if (nsexes(i)==2) - for (int j=1;j<=ngear(i);j++) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> wt_gear_Mtmp(i,j,k);// 19 - write_log( wt_gear_Mtmp(i)); - - for (int j=1;j<=ngear(i);j++) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> sel_Ftmp(i,j,k); // 20 - write_log( sel_Ftmp(i)); - - if (nsexes(i)==2) - for (int j=1;j<=ngear(i);j++) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> sel_Mtmp(i,j,k); // 21 - write_log( sel_Mtmp(i)); - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> n0_Ftmp(i,k); // 22 - write_log( n0_Ftmp(i)); - - if (nsexes(i)==2) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> n0_Mtmp(i,k); // 23 - write_log( n0_Mtmp(i)); - - cout<<"N: "<> nrec(i); // 24 - cout<<"nrec: "<> Rtmp(i,j); // 25 - cout<<"Rec: "<> SSBtmp(i,j); // 26 - cout<<"SSB: "<> Rsim(ispp,i,j); - Rsim(ispp,i,j) *= exp(rnorms(i,j)*.375); // about 15% CV to get historical mean - } - if (nsims<=5) Rsim(ispp,i,j) = AMeanRec(ispp); // XXX constant recruitment - } - } - envin.close(); - AMeanRec(ispp) *= .5; // Goes to females only - cout <<"Mean recruits "<< AMeanRec(ispp) < 0 ) - F_begin_yr(k,ispp) = SolveF2(n0_F(ispp), n0_M(ispp), Obs_Catch(k,ispp),ispp); // F_yr_one(ispp) = SolveF2(n0_F(ispp), n0_M(ispp), yr_one_catch(ispp),ispp); - else - F_begin_yr(ispp) = 0; // F_yr_one(ispp) = 0; // cout< 1) // Debugging if LP to be done or not - { - int on=0; - if ( (on=option_match(ad_comm::argc,ad_comm::argv,"-nolp"))>-1) - dolp=0; - else - dolp=1; - } - END_CALCS - - -PRELIMINARY_CALCS_SECTION - double tmp1; - write_alts_hdr(); - get_SB100(); - cout<<"SSB, Biomass at unfished: "<1) - nyrs_catch = nyrs_catch_in; - //else - // nyrs_catch = 1;// NOTA BUENO: this is changed under the new EIS Alternatives (May 06) - Do_Sims(); - // if (alt==2) write_alts(); - write_alts(); - } - -FUNCTION compute_obj_fun - dvariable tmp1; - dvariable tmp2; - tmp1.initialize(); - for (int ispp=1;ispp<=nspp;ispp++) - { - Get_Bzero(ispp); - // write_srec();exit(1); - if(Fmsy_F35>0) - { - get_msy(ispp); - switch (current_phase()) - { - case 1 : tmp1 = 1.e1*square(log(Fmsy(ispp))-log(F35(ispp))); break; - case 2 : tmp1 = 1.e2*square(log(Fmsy(ispp))-log(F35(ispp))); break; - case 3 : tmp1 = 1.e2*square(log(Fmsy(ispp))-log(F35(ispp))); break; - case 4 : tmp1 = 1.e3*square(log(Fmsy(ispp))-log(F35(ispp))); break; - default: tmp1 = 1.e3*square(log(Fmsy(ispp))-log(F35(ispp))); break; - } - if(Fmsy_F35==2) - switch (current_phase()) - { - case 1 : tmp1 += 1.e1*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - case 2 : tmp1 += 1.e2*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - case 3 : tmp1 += 1.e2*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - case 4 : tmp1 += 1.e3*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - default : tmp1+= 1.e3*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - } - obj_fun += tmp1; - } - if (Rec_Cond>0.) - { - // More conditioning here--make mean recruitment consistent with half and double - // recruitment at avg spawning biomass... - double vartmp = 2.*Rec_Cond*Rec_Cond; - /* - double ssb1 = AMeanSSB(ispp) * 0.5; - double ssb2 = AMeanSSB(ispp) ; - double ssb3 = AMeanSSB(ispp) * 2.0; - // double ssb4 = value(Bzero(ispp)) ; - tmp2.initialize(); - tmp2 += square(log(AMeanRec(ispp)) - log(SRecruit( ssb1, ispp)))/ vartmp; - tmp2 += square(log(AMeanRec(ispp)) - log(SRecruit( ssb2, ispp)))/ vartmp; - tmp2 += square(log(AMeanRec(ispp)) - log(SRecruit( ssb3, ispp)))/ vartmp; - // tmp2 += 24.* square(log(AMeanRec(ispp)) - log(SRecruit( ssb4, ispp))); - // tmp2 += 24.* square(log(AMeanSSB(ispp)) - log( ssb4 )); - */ - tmp2 = square(log(Bzero(ispp)) - log(SB100(ispp)))/ vartmp; - obj_fun += tmp2; - // cout << "SSB "<OY_max) - Actual_Catch = TAC/sum(TAC)*OY_max; - else - Actual_Catch = TAC; - } - /* ////////////////////////////////////////////////////////////////////////////// //int use_max = 1; // Need to move this into the setup file... double max_catch=1500.;// EBS POllock special case (CHANGE THIS) //if (use_max==1) for (int ispp=1;ispp<=nspp;ispp++) Actual_Catch(ispp) = min(TAC(ispp),max_catch); // cout<<"AC: "<3) - { - Alt4_Fcalc = 1; - double diff; - double ssl_sum=0.; - diff = OY_min-sumabc; - // if so, sum up SSL ABCs - ssl_sum = SSL_spp*ABC; - for (int ispp=1;ispp<=nspp;ispp++) - { - // then apportion them to difference between current ABC and target (OY_min) difference - if (SSL_spp(ispp)) - { - TAC(ispp) = ABC(ispp) + diff * ABC(ispp)/ssl_sum; - // Special for BSAI Pcod to subtract off 3%...and get the totals to match up - // if (ispp==2) TAC(ispp) /= 0.97; For EIS work - } - else - { - TAC(ispp) = ABC(ispp); - } - } - // cout << ipro<<" "<0.01) ftmp = SolveF2(N_F(ispp),N_M(ispp),TAC(ispp),ispp); - // cout <= 0.2 *SBzero(ispp) & SBtmp < SBKink(ispp) ) // NOTE Same as Am 56 here (until 20% bzero reached) - Ftmp = (Fabc(ispp)*(1/(1-alpha ))*(SBtmp/SBKink(ispp) - alpha )); - if (SBtmp > SBKink(ispp) ) - Ftmp = (Fabc(ispp)); - SBtmp = N_females * elem_prod( wt_mature_F(ispp), mfexp( -yrfrac(ispp)*(M_F(ispp) + Ftmp*F_age ))); - } - return(Ftmp); - -FUNCTION double Get_F_Am56(const dvector& F_age, const dvector& N_females, const int ispp ) - double Ftmp; // cout<< spname(ispp)<< endl; - { - for (ii=1;ii<=3;ii++) // Iterate to get month of spawning correct - { - if (SBtmp < alpha*SBKink(ispp)) - Ftmp = 0.; - if (SBtmp >= alpha*SBKink(ispp) & SBtmp < SBKink(ispp) ) - Ftmp = (Fabc(ispp)*(1/(1-alpha))*(SBtmp/SBKink(ispp) - alpha)); - if (SBtmp > SBKink(ispp) ) - Ftmp = (Fabc(ispp)); - SBtmp = N_females * elem_prod( wt_mature_F(ispp), mfexp( -yrfrac(ispp)*(M_F(ispp) + Ftmp*F_age ))); - } - } - return(Ftmp); - -FUNCTION double Get_Fofl_t2(const dvector& F_age, const dvector& N_females, const int ispp ) - double Ftmp; // cout<< spname(ispp)<< endl; - { - for (ii=1;ii<=3;ii++) // Iterate to get month of spawning correct - { - if (SBtmp < alpha*SBKink(ispp)) - Ftmp = 0.; - if (SBtmp >= alpha*SBKink(ispp) & SBtmp < SBKink(ispp) ) - Ftmp = (Fofl(ispp)*(1/(1-alpha))*(SBtmp/SBKink(ispp) - alpha)); - if (SBtmp > SBKink(ispp) ) - Ftmp = (Fofl(ispp)); - SBtmp = N_females * elem_prod( wt_mature_F(ispp), mfexp( -yrfrac(ispp)*(M_F(ispp) + Ftmp*F_age ))); - } - } - return(Ftmp); - -FUNCTION double Get_Fofl_t(const dvector& F_age, const dvector& N_females, const int ispp ) - double Ftmp; // cout<< spname(ispp)<< endl; - { - for (ii=1;ii<=3;ii++) // Iterate to get month of spawning correct - { - if (SBtmp < alpha*SBKink(ispp)) - Ftmp = 0.; - if (SBtmp >= alpha*SBKink(ispp) & SBtmp < SBKink(ispp) ) - Ftmp = (Fofl(ispp)*(1/(1-alpha))*(SBtmp/SBKink(ispp) - alpha)); - if (SBtmp > SBKink(ispp) ) - Ftmp = (Fofl(ispp)); - SBtmp = N_females * elem_prod( wt_mature_F(ispp), mfexp( -yrfrac(ispp)*(M_F(ispp) + Ftmp*F_age ))); - } - } - return(Ftmp); - - -FUNCTION double Get_F_t(const dvector& F_age, const dvector& N_females, const int ispp ) - double Ftmp; // cout<< spname(ispp)<< endl; - if (SSL_spp(ispp)) - { - Ftmp = Get_F_SSL_prey(F_age, N_females, ispp); - } - else - { - Ftmp = Get_F_Am56(F_age, N_females, ispp); - } - return(Ftmp); - -FUNCTION void Project_Pops(const int& isim, const int& i) - double ctmp; - if ( i == npro && isim%int(nsims/4)==0) cout << "Year "< 0) - { - if (i <= nyrs_catch || TAC_ABC==0) // Use TAC setting algorithm for alt 2 only, for all others, set TAC==ABC - { - Ftmp = SolveF2(N_F(ispp),N_M(ispp),Actual_Catch(ispp),ispp); - } - else - { - if (alt==7 && i<=3) - Ftmp = Get_F(1,ispp); // Set to the F rather than solving every time... - else - if (alt==77 && i<=2) - Ftmp = Get_F(1,ispp); // Set to the F rather than solving every time... - else - { - // if (alt==2 && sum(TAC)>OY_max) - if ((alt==2 ||alt==98||alt==97) && sum(TAC)>OY_max) - { - Ftmp = SolveF2(N_F(ispp),N_M(ispp),OY_max*TAC(ispp)/sum(TAC),ispp); - // cout<<"HERE "<< Ftmp<<" "< 0.) - { - for (m=1;m<=ngear(ispp);m++) - { - Ftottmp_F = Ftmp*Frat(ispp,m)*sel_F(ispp,m); - Ftottmp_spr += Ftottmp_F ; - Ftottmp_M = Ftmp*Frat(ispp,m)*sel_M(ispp,m); - ctmp += (wt_gear_F(ispp,m) * elem_prod(elem_div(Ftottmp_F, Z_F(ispp)),elem_prod(1.-S_F(ispp),N_F(ispp)))); // Catch equation (vectors) - ctmp += (wt_gear_M(ispp,m) * elem_prod(elem_div(Ftottmp_M, Z_M(ispp)),elem_prod(1.-S_M(ispp),N_M(ispp)))); // Catch equation (vectors) - } - SPRsim(ispp,isim,i) = Implied_SPR(Ftottmp_spr,ispp); - // cout <NUL "); - ifstream tac("tac.dat"); - tac>>TAC; - tac.close(); /* */ - for (int ispp=1;ispp<=nspp;ispp++) - Actual_Catch(ispp) = min(TAC(ispp),ABC(ispp)); - } - - -FUNCTION Avg_Age - Avg_Age_End.initialize(); - Avg_Age_Mat.initialize(); - for (int ispp=1;ispp<=nspp;ispp++) - { - // if(!isit_const(ispp) ) - { - dvector age_seq(1,nages(ispp)); - age_seq.initialize(); - age_seq.fill_seqadd(1,1); - dvector ntmp(1,nages(ispp)); - ntmp = N_F(ispp); - Avg_Age_End(ispp) += age_seq * ntmp /sum(ntmp); - Avg_Age_sum(ispp) += Avg_Age_End(ispp) ; - ntmp = elem_prod(ntmp,pmature_F(ispp)) ; - Avg_Age_Mat(ispp) += elem_prod(ntmp,pmature_F(ispp)) * age_seq/sum(ntmp); - // cout< 1e-6) - { - iter++; - ftmp += (TACin-cc) / btmp; - Ftottmp_F.initialize(); - Ftottmp_M.initialize(); - for (m=1;m<=ngear(ispp);m++) - { - Ftottmp_F += ftmp*Fratsel_F(m); - Ftottmp_M += ftmp*Fratsel_M(m); - } - Z_F = Ftottmp_F + M_F(ispp); - Z_M = Ftottmp_M + M_M(ispp); - S_F = mfexp( -Z_F ); - S_M = mfexp( -Z_M ); - cc = 0.0; - for (m=1;m<=ngear(ispp);m++) - { - cc += (wt_gear_F(ispp,m) * elem_prod(elem_div(ftmp*Fratsel_F(m), Z_F),elem_prod(1.-S_F,N_F))); // Catch equation (vectors) - cc += (wt_gear_M(ispp,m) * elem_prod(elem_div(ftmp*Fratsel_M(m), Z_M),elem_prod(1.-S_M,N_M))); // Catch equation (vectors) - } - dd = cc / TACin - 1.; - //cout << ispp<<" "<< ftmp << " "<< cc << " "<100) {cerr<<"Bombed on catch solver--check scales for "<nages(ispp)) - { - Tg += double(ii) * wt_mature_F(ispp,nages(ispp)) * ntmp; - tmp += wt_mature_F(ispp,nages(ispp)) * ntmp; - ntmp *= exp(-M_F(ispp,nages(ispp))); - } - else - { - Tg += double(ii) * wt_mature_F(ispp,ii) * ntmp; - tmp += wt_mature_F(ispp,ii) * ntmp; - ntmp *= exp(-M_F(ispp,ii)); - } - } - // Tg /= value(phizero(ispp)); - Tg /= tmp; - report << Tg<< " "; - } - report << endl; - report << "Options SR_Type Project_Recr_Assmption SR_Condition"<2) - write_sim("Alternative ",ispp); - } - write_by_time(); - - // Writing routines here .... -FUNCTION write_by_time - write_sim("Catch", Csim,Cabc); // Total Catch - cout< 1e-6) - means_out << mean_value <<" "; - else - means_out << " NA "; - } - means_out << endl; - } - means_out << endl; - -FUNCTION void write_TACs(const adstring& Title) - // This one prints out species over time (means only), but without headings - means_out <<"Alternative "< 1e-6) - means_out << mean_value <<" "; - else - means_out << " NA "; - } - means_out << endl; - } - means_out << endl; - - - -FUNCTION void write_sim(const adstring& Title, d3_array& Outtmp) - // This one prints out species over time (means only), but without headings - d3_array mtmp(1,nspp,1,npro,1,nsims); - for (int ispp=1;ispp<=nspp;ispp++) for (int i=1;i<=npro;i++) for (int k=1;k<=nsims;k++) - mtmp(ispp,i,k)=Outtmp(ispp,k,i); - means_out <<"Alternative "< 1e-6) - means_out << mean_value <<" "; - else - means_out << " NA "; - } - means_out << endl; - } - means_out << endl; - -FUNCTION void write_sim(const adstring& Title,const d3_array& Outtmp,const dvar_vector& bb) - // This one prints out species over time (means only) - d3_array mtmp(1,nspp,1,npro,1,nsims); - for (int ispp=1;ispp<=nspp;ispp++) for (int i=1;i<=npro;i++) for (int k=1;k<=nsims;k++) - mtmp(ispp,i,k)=Outtmp(ispp,k,i); - means_out <<"Alternative "<1e-6) - means_out << mean_value <<" "; - else - means_out << " NA "; - } - means_out << endl; - } - means_out << endl; - -FUNCTION void write_sim(const adstring& Title,const int& ispp) - // For each species separately - percent_out <<"Alternative "< - adstring xspname; - adstring_array targsppname(1,20); - adstring_array spp_file_name(1,20); - adstring_array gearname(1,8); - adstring_array spname(1,90); - adstring_array areaname(1,8); - adstring run_name; - - ofstream write_log("Input_Log.rep"); - #undef write_log - #define write_log(object) write_log << #object "\n" << object << endl; - - // A routine to get transpose, sort and return a matrix ---- - dmatrix TranSort (const dmatrix m1) - { - RETURN_ARRAYS_INCREMENT(); - dmatrix vtmp=trans(m1); - int npro=m1.colmax(); - int nsim=m1.rowmax(); - for (int i=1;i<=npro;i++) - vtmp(i) = sort(vtmp(i),nsim); - - RETURN_ARRAYS_DECREMENT(); - return(vtmp); - } - // #include - -FUNCTION dvariable SRecruit(const dvariable& Stmp,const int& ispp) - RETURN_ARRAYS_INCREMENT(); - dvariable RecTmp; - switch (SrType) - { - case 1: - RecTmp = (Stmp / phizero(ispp)) * mfexp( sr_alpha(ispp) * ( 1. - Stmp / Bzero(ispp) )) ; //Ricker form from Dorn - break; - case 2: - RecTmp = Stmp / ( sr_alpha(ispp) + beta(ispp) * Stmp); //Beverton-Holt form - break; - case 3: - // RecTmp = mfexp(log_avgrec); //Avg recruitment - break; - case 4: - RecTmp = Stmp * mfexp( sr_alpha(ispp) - Stmp * beta(ispp)) ; //old Ricker form - break; - } - - RETURN_ARRAYS_DECREMENT(); - return RecTmp; - - -FUNCTION dvar_vector SRecruit(const dvar_vector& Stmp,const int& ispp) - RETURN_ARRAYS_INCREMENT(); - dvar_vector RecTmp(Stmp.indexmin(),Stmp.indexmax()); - switch (SrType) - { - case 1: - RecTmp = elem_prod((Stmp / phizero(ispp)) , mfexp( sr_alpha(ispp) * ( 1. - Stmp / Bzero(ispp) ))) ; //Ricker form from Dorn - break; - case 2: - RecTmp = elem_prod(Stmp , 1. / ( sr_alpha(ispp) + beta(ispp) * Stmp)); //Beverton-Holt form - break; - case 3: - // RecTmp = mfexp(log_avgrec); //Avg recruitment - break; - case 4: - RecTmp = elem_prod(Stmp ,mfexp( sr_alpha(ispp) - Stmp * beta(ispp))); //old Ricker form - break; - } - RETURN_ARRAYS_DECREMENT(); - //cout <5||F1<0.01)) - // { - // ii=5; - // F1=M(ispp,nages(ispp)); // When things bomb (F <0 or F really big then just set it to F35...) - // } - // else - { - F2 = F1 + df*.5; - F3 = F2 - df; //F1 = double(ii)/100; - yld1 = yield(F1,Stmp,Rtmp,ispp); //cout <Zgb2r#~kyz3Z{6m-mzG$Pb@3e(GiQ);Wz)wi|wO7zVC0ZSnToa*pCiayW2}Y zsp~c2rJqz)O)S0S>Y=l)y7Jw-yZ;qHf z^J>8(uc4VSajN5%ZtT4_xu0; z`+qI)|7Q!_cVYdw*v6LL4_u6YOX5e@KV{w=AA4-oJ`-Z2Ub!ayuvd0{`OxHFUK%qG zt?U<@TsjZ5grFpFP+wc65EsB!Hf|k1w8Sc&rw78Swn<pld;eALm=QOb9h=9&A!XF&ZN`{sGP@j8wrwu#-B5qrk5*xS5MfqYTk9=rNM zWpp>tpRvrZ+QZU4EgWa}^~?nP3SMWM*MV(umhp`geEP-7J%`7VeHJ`Zl-yCiX?0!Z z#&NO2;u4epbidf<{6OneYqR;%3OUKC$vm86EkJisR>J zdS;(%d&MRv+vN8M`Q5&rY({-KcOub-#!3qP{5H?*kC&kK{GyhGIedCL9y0xc7QBmt 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SSL Species??? - 0 # Constant Buffer Dorn -1 # Number of fisheries -2 # Number of Sexes -0.011785292 # 5 year average F -1 # Author f -0.4 # SPR ABC -0.35 # SPR MSY -1 # Spawning month -27 # number of ages -1 # Fratio -# natural mortality -0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 -0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 -# Maturity females -0.0117 0.025 0.0526 0.1074 0.2067 0.3608 0.5501 0.7259 0.8516 0.9255 0.9642 0.9831 0.9921 0.9964 0.9983 0.9992 0.9996 0.9998 0.9999 1 1 1 1 1 1 1 1 -# Maturity males -0.0117 0.025 0.0526 0.1074 0.2067 0.3608 0.5501 0.7259 0.8516 0.9255 0.9642 0.9831 0.9921 0.9964 0.9983 0.9992 0.9996 0.9998 0.9999 1 1 1 1 1 1 1 1 -# wt spawn females -0.0353 0.0823 0.1442 0.2148 0.2885 0.3612 0.4299 0.4929 0.5495 0.5994 0.6429 0.6803 0.7124 0.7396 0.7626 0.7819 0.7981 0.8117 0.823 0.8324 0.8402 0.8467 0.8521 0.8566 0.8603 0.8633 0.8676 -# WtAge by sex and fishery -0.0565 0.1117 0.1788 0.2516 0.3252 0.3962 0.4622 0.522 0.5753 0.6219 0.6623 0.697 0.7266 0.7516 0.7727 0.7904 0.8052 0.8176 0.8279 0.8365 0.8436 0.8495 0.8544 0.8585 0.8619 0.8647 0.8685 -0.0574 0.1081 0.1635 0.2175 0.2667 0.3095 0.3456 0.3753 0.3994 0.4187 0.434 0.446 0.4555 0.4629 0.4686 0.4731 0.4766 0.4793 0.4814 0.483 0.4842 0.4852 0.4859 0.4865 0.4869 0.4873 0.4877 -# Selectivity by sex and fishery -0.0034 0.0108 0.0302 0.0741 0.1593 0.2998 0.494 0.7126 0.9002 0.9956 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -0.0056 0.0184 0.0515 0.1236 0.2546 0.4503 0.6835 0.8905 0.996 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -# Numbers at age 3 - 29 females males 2022 -120770 50366.1 76654.2 88682.3 54823.4 33475.1 26167.7 23684 23439.7 26602.5 12136.6 8261.88 6154.39 5021.4 5327.77 3881.7 3510.42 2511.5 1806.43 1238.84 1589.31 1068.58 882.212 411.856 358.507 409.89 1708.1 -120769 50365.1 76648.1 88659.3 54783.9 33417.8 26076.4 23538.8 23222.8 26283.3 11972.6 8146.96 6067.35 4947.08 5244.6 3817.3 3447.73 2463.42 1769.13 1211.89 1555.35 1047.29 865.968 404.905 353.008 404.196 1688.55 -# No Recruitments -39 -# Recruitment age 3+ 1984-2022 -181867.1 190543.2 182161 172498.9 171655.4 124447.3 186303.4 140150 218847 142536.7 201373 178528 182753.2 177684.5 124868.4 115038.7 197229.6 190835.3 226307 140538.9 163402.8 181426.1 202833 179691.2 197863.5 149778.7 147575.4 159332.5 188346.6 332754 237017 194304.5 174764.2 182459.5 244273 323293 228730 123036.1 241539 -# SSB 1984-2022 -57829.2 63601.9 70420 76618.6 81093.9 83754.7 85217 85643.6 85653.5 84661.8 83105.5 81639.7 80569.8 79460 79086.6 79321 80033.2 80283 80030.8 79250.5 78089.5 77197.6 76855.5 76812.5 77003.8 76960.7 76769.1 76594.6 77115 77817.8 77929.6 77982.6 78510.7 79580.2 81880.5 84798.5 87356.9 89577.7 91832.1 diff --git a/examples/more_examples/goa fhs/setup.dat b/examples/more_examples/goa fhs/setup.dat deleted file mode 100644 index 790f9de..0000000 --- a/examples/more_examples/goa fhs/setup.dat +++ /dev/null @@ -1,18 +0,0 @@ -std #No idea what this is -7 #Number of alternatives??? -1 #Vector of alt numbers -2 -3 -4 -5 -6 -7 -1 #TAC_ABC -1 #SRType -1 #Rec_Gen -0 #Fmsy_F35 -0 #Rec_Cond -1 #Write_Big -17 #NProj -1000 #Nsims -2022 #Styr diff --git a/examples/more_examples/goa fhs/spp_catch.dat b/examples/more_examples/goa fhs/spp_catch.dat deleted file mode 100644 index a3a5ed5..0000000 --- a/examples/more_examples/goa fhs/spp_catch.dat +++ /dev/null @@ -1,28 +0,0 @@ -#_SETUP_FILE_FOR_THE_BERING_SEA/AI_FISHERIES -#_Number_of_years with specified catch (if begin-yr = 2005, and this number is 3, then subsequent values represent catches in 2005, 06, and 7 (to evaluate alts for 2008 -3 -# Number of runs -1 -# OY Minimum -116000 # Note that this is for age-structured species 1343.248,1330.148 -# OY Maximum -8e+05 # Note that this is for age-structured species 1943.248, 1930.148 -# data files for each species -# Pollock Pacific cod sablefish Yellowfin Greenland turbot arrowtooth flounder Rock sole Flathead sole AK Plaice Pacific ocean perch Nrthrn RF Atka mackerel -# 1 2 3 4 5 6 7 8 9 10 11 12 -# data files for each species -data/projection_data.dat -# ABC Multipliers -1 -# Population scalars -1000 -# New Alt 4 Fabc SPRs (Rockfish = 0.75, other 0.6), Steller sea lion prey species between F40 and F60 (to meet OY Min) -0.75 -# Number of TAC model categories -1 -# TAC model indices (for aggregating) -1 -# Catch in each future year -2022 687 -2023 1907.968 -2024 1907.968 diff --git a/examples/more_examples/goa fhs/tacpar.dat b/examples/more_examples/goa fhs/tacpar.dat deleted file mode 100644 index 6631707..0000000 --- a/examples/more_examples/goa fhs/tacpar.dat +++ /dev/null @@ -1,10 +0,0 @@ -7 -6 - 2.60197 0.417 0.372 0.361 0.296 0.2733 0.125 - -0.300856 -0.741664 -1.26797 -1.78614 -2.15198 -2.39595 -2.59939 - -0.00703968 -0.956592 -1.48909 -2.09122 -2.26005 -2.31812 -2.45321 - 0.372276 -1.35066 -1.76076 -2.22163 -2.31726 -2.28107 -2.34758 - 0.505535 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/dev/null @@ -1,26 +0,0 @@ -# a new file -#nFixCatchYrs -2 -#nSpecies -1 -#OYMin -0.1343248 -#OYMax -1943248 -#dataFiles -data/t1.dat -#ABCMult -1 -#PoplnScalar -1000 -#AltFabcSPR -0.75 -#nTAC -1 -#TACIndices -1 -#Catch -2016 -55000 -2017 -55000 diff --git a/examples/srecpar.dat b/examples/srecpar.dat deleted file mode 100644 index cf22bcb..0000000 --- a/examples/srecpar.dat +++ /dev/null @@ -1,4 +0,0 @@ -# Bzero, PhiZero, Alpha, sigmaR -675136 0.00306701 2.34603 0.6 -# Bzero, PhiZero, Alpha, sigmaR -675136 0.00306701 2.34603 0.6 diff --git a/examples/tacpar.dat b/examples/tacpar.dat deleted file mode 100644 index 6631707..0000000 --- a/examples/tacpar.dat +++ /dev/null @@ -1,10 +0,0 @@ -7 -6 - 2.60197 0.417 0.372 0.361 0.296 0.2733 0.125 - -0.300856 -0.741664 -1.26797 -1.78614 -2.15198 -2.39595 -2.59939 - -0.00703968 -0.956592 -1.48909 -2.09122 -2.26005 -2.31812 -2.45321 - 0.372276 -1.35066 -1.76076 -2.22163 -2.31726 -2.28107 -2.34758 - 0.505535 -1.19926 -1.68812 -2.39543 -2.40752 -2.5904 -2.58726 - 0.368722 -1.59025 -1.79548 -2.67422 -2.61358 -2.41092 -2.70204 - 0.308248 -1.78052 -2.23264 -2.85605 -3.39375 -3.05209 -2.94219 - 0.180676 -1.7586 -1.80222 -3.09402 -2.2618 -3.43215 -3.06417 diff --git a/examples/test.R b/examples/test.R deleted file mode 100644 index aec7b47..0000000 --- a/examples/test.R +++ /dev/null @@ -1,124 +0,0 @@ -#' # Projection model work -#' Projection model work for demonstrating application of controls and input data - -library(tidyverse) -library(ggplot2) -library(ggthemes) -library(gtable) -source("../R/readData.R") - -#' ## Set initial "setup" parameters -thisyr=2019 -setup<-list( - Run_name = noquote("Std"), - Tier = 3 , - nalts = 7 , - alts = c(1,2,3,4,5,6,7), - tac_abc = 1, #' Flag to set TAC equal to ABC (1 means true, otherwise false) - srr = 1 , #' Stock-recruitment type (1=Ricker, 2=Bholt) - rec_proj = 1, #' projection rec form (default: 1 = use observed mean and std, option 2 = use estimated SRR and estimated sigma R) - srr_cond = 0 , #' SR-Conditioning (0 means no, 1 means use Fmsy == F35%?, 2 means Fmsy == F35% and Bmsy=B35% condition (affects SRR fits) - srr_prior = 0.0, #' Condition that there is a prior that mean historical recruitment is similar to expected recruitment at half mean SSB and double mean SSB 0 means don't use, otherwise specify CV - write_big = 1, #' Flag to write big file (of all simulations rather than a summary, 0 means don't do it, otherwise do it) Write_Big - nyrs_proj = 14, #' Number of projection years - nsims = 100, #' Number of simulations - beg_yr_label = thisyr #' Begin Year -) - -#' ## Set up the species specific run file -config<-list( - nFixCatchYrs = 2, - nSpecies = 1, - OYMin = .1343248, - OYMax = 1943248, - dataFiles = noquote("data/t1.dat"), - ABCMult = 1, - PoplnScalar = 1000, - AltFabcSPR = 0.75, - nTAC = 1, - TACIndices = 1, - Catch = c( 2016,55000., 2017,55000. ) -) - -#' ## Make list of main file w/ assessment model results -#' E.g., "data/bsai_atka.dat" -datfile <- list( - runname = noquote("M16.2"), - ssl_spp = 1, # SSL_spp - Dorn_buffer = 1, # Dorn_buffer - nfsh = 1, # N_fsh - nsex = 1, # N_sexes - avgF5yr = 0.0661399, # avg_5yr_F - F40_mult = 1, # F_40_multiplier - spr_abc = 0.4, # SPR_abc - spr_msy = 0.35, # SPR_msy - sp_mo = 8, # spawn_month - nages = 11, # N_ages - Frat = 1, # F_ratio - # M - M = c(0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3), - # Maturity - pmat = c(0.005,0.037,0.224,0.688,0.944,0.992,0.999,1,1,1,1), - # Wt_at_age_spawners - wtage_sp = c(44.8,161.377,398.272,557.695,652.113,719.573,863.744,948.744,921.397,885.912,1069.87), - # Wt_at_age_fsh - wtage_fsh = c(69.3778,253.522,408.211,614.731,668.483,718.137,803.017,798.707,788.117,842.468,960.006), - # select - sel = c(0.002576427,0.040030753,0.651104228,0.768404263,0.794886081,1,0.889293108,0.604815671,0.451169778,0.403195516,0.403195516), - # N - N = c(511.179,378.528,278.443,194.385,183.423,45.5404,61.1188,19.8073,39.6285,33.6501,51.7806), - # Nyrs - nyrs = 37, - # recruits - R = c(1578.51,479.509,357.919,443.588,318.981,413.125,514.351,600.987,536.301,692.34,452.279,1618.73,702.801,372.811,597.812,1136.19,402.862,424.179,1025.36,207.05,383.695,1054.64,2224.52,1379.34,1545.81,345.556,454.884,617.194,404.876,993.497,727.754,236.795,505.948,258.653,726.627,524.198,473.54), - # SSB - SSB = c(206.391,194.569,187.097,183.296,195.289,240.774,252.143,238.163,223.199,200.776,182.378,179.671,189.193,198.242,212.123,233.484,277.891,282.004,250.709,231.816,218.403,195.275,181.628,189.976,175.912,168.624,220.206,315.124,376.621,397.171,365.476,317.159,277.777,242.719,233.415,223.636,198.117,183.537,177.91) - ) - -#' ## Save lists for running model to files expected by projection model -# Setup.dat -list2dat(setup,"setup.dat") -# spp_catch.dat -list2dat(config,"data/t1_spcat.dat") -runfn<-"t1" -file.copy(paste0("data/",runfn,"_spcat.dat"),"spp_catch.dat",overwrite=TRUE) - -list2dat(datfile,"data/t1.dat") - -#' ## Run projection model -system("../src/main") -#' ## Read in projection model mainfiles - .projdir="t1/" - dir.create(.projdir) - file.copy(list.files(getwd(), pattern="out$"), .projdir,overwrite=TRUE) - file.remove(list.files(getwd(), pattern="out$")) - bf <- data.frame(read.table(paste0(.projdir,"bigfile.out"),header=TRUE,as.is=TRUE)) - bfs <- bf %>% filter(Sim<=30) - #write.csv(bfs,"data/proj.csv") - # head(bfs) - bfss <- bfs %>% filter(Alt==2) %>% select(Alt,Yr,Catch,SSB,Sim) - pf <- data.frame(read.table(paste0(.projdir,"percentdb.out"),header=F) ) - names(pf) <- c("stock","Alt","Yr","variable","value") -#' ## Make plot of projection model simulations - p1 <- pf %>% filter(substr(variable,1,1)=="C",variable!="CStdn",Alt==2) %>% select(Yr,variable,value) %>% spread(variable,value) %>% - ggplot(aes(x=Yr,y=CMean),width=1.2) + geom_ribbon(aes(ymax=CUCI,ymin=CLCI),fill="goldenrod",alpha=.5) + theme_few() + geom_line() + - scale_x_continuous(breaks=seq(thisyr,thisyr+14,2)) + xlab("Year") + ylab("Tier 3 ABC (kt)") + geom_point() + - expand_limits(y=0) + - geom_line(aes(y=Cabc)) + geom_line(aes(y=Cofl),linetype="dashed") + geom_line(data=bfss,aes(x=Yr,y=Catch,col=as.factor(Sim)))+ guides(size=FALSE,fill=FALSE,alpha=FALSE,col=FALSE) - p2 <- pf %>% filter(substr(variable,1,1)=="S",variable!="SSBStdn",Alt==2) %>% select(Yr,variable,value) %>% spread(variable,value) %>% - ggplot(aes(x=Yr,y=SSBMean),width=1.2) + geom_ribbon(aes(ymax=SSBUCI,ymin=SSBLCI),fill="coral",alpha=.5) + theme_few() + geom_line() + - scale_x_continuous(breaks=seq(thisyr,thisyr+14,2)) + xlab("Year") + ylab("Tier 3 Spawning biomass (kt)") + geom_point() + - expand_limits(y=0) + - geom_line(aes(y=SSBFabc)) + geom_line(aes(y=SSBFofl),linetype="dashed")+ geom_line(data=bfss,aes(x=Yr,y=SSB,col=as.factor(Sim)))+ guides(size=FALSE,fill=FALSE,alpha=FALSE,col=FALSE) - t3 <- grid.arrange(p1, p2, nrow=2) - ggsave(paste0(.projdir,"tier3_proj.pdf"),plot=t3,width=5.4,height=7,units="in") - - -#' ## Make tables - # Stock Alt Sim Yr SSB Rec Tot_biom SPR_Implied F Ntot Catch ABC OFL AvgAge AvgAgeTot SexRatio FABC FOFL - bfsum <- bf %>% select(Alt,Yr,SSB,F,ABC ,Catch) %>% group_by(Alt,Yr) %>% summarise(Catch=mean(Catch),SSB=mean(SSB),F=mean(F),ABC=mean(ABC)) - t1 <- bfsum %>% select(Alt,Yr,Catch) %>% spread(Alt,Catch) - names(t1) <- c("Catch","Scenario 1","Scenario 2","Scenario 3","Scenario 4","Scenario 5","Scenario 6","Scenario 7") - - print_Tier3_tables(bf) - diff --git a/examples/test.Rmd b/examples/test.Rmd deleted file mode 100644 index 12e5e92..0000000 --- a/examples/test.Rmd +++ /dev/null @@ -1,151 +0,0 @@ -# Projection model work -Projection model work for demonstrating application of controls and input data - -```{r } -library(tidyverse) -library(ggplot2) -library(ggthemes) -library(gtable) -source("../R/readData.R") -``` - -## Set initial "setup" parameters - -```{r } -thisyr=2019 -setup<-list( - Run_name = noquote("Std"), - Tier = 3 , - nalts = 7 , - alts = c(1,2,3,4,5,6,7), - tac_abc = 1, #' Flag to set TAC equal to ABC (1 means true, otherwise false) - srr = 1 , #' Stock-recruitment type (1=Ricker, 2=Bholt) - rec_proj = 1, #' projection rec form (default: 1 = use observed mean and std, option 2 = use estimated SRR and estimated sigma R) - srr_cond = 0 , #' SR-Conditioning (0 means no, 1 means use Fmsy == F35%?, 2 means Fmsy == F35% and Bmsy=B35% condition (affects SRR fits) - srr_prior = 0.0, #' Condition that there is a prior that mean historical recruitment is similar to expected recruitment at half mean SSB and double mean SSB 0 means don't use, otherwise specify CV - write_big = 1, #' Flag to write big file (of all simulations rather than a summary, 0 means don't do it, otherwise do it) Write_Big - nyrs_proj = 14, #' Number of projection years - nsims = 100, #' Number of simulations - beg_yr_label = thisyr #' Begin Year -) -``` - -## Set up the species specific run file - -```{r } -config<-list( - nFixCatchYrs = 2, - nSpecies = 1, - OYMin = .1343248, - OYMax = 1943248, - dataFiles = noquote("data/t1.dat"), - ABCMult = 1, - PoplnScalar = 1000, - AltFabcSPR = 0.75, - nTAC = 1, - TACIndices = 1, - Catch = c( 2016,55000., 2017,55000. ) -) -``` - -## Make list of main file w/ assessment model results -E.g., "data/bsai_atka.dat" - -```{r } -datfile <- list( - runname = noquote("M16.2"), - ssl_spp = 1, # SSL_spp - Dorn_buffer = 1, # Dorn_buffer - nfsh = 1, # N_fsh - nsex = 1, # N_sexes - avgF5yr = 0.0661399, # avg_5yr_F - F40_mult = 1, # F_40_multiplier - spr_abc = 0.4, # SPR_abc - spr_msy = 0.35, # SPR_msy - sp_mo = 8, # spawn_month - nages = 11, # N_ages - Frat = 1, # F_ratio - # M - M = c(0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3), - # Maturity - pmat = c(0.005,0.037,0.224,0.688,0.944,0.992,0.999,1,1,1,1), - # Wt_at_age_spawners - wtage_sp = c(44.8,161.377,398.272,557.695,652.113,719.573,863.744,948.744,921.397,885.912,1069.87), - # Wt_at_age_fsh - wtage_fsh = c(69.3778,253.522,408.211,614.731,668.483,718.137,803.017,798.707,788.117,842.468,960.006), - # select - sel = c(0.002576427,0.040030753,0.651104228,0.768404263,0.794886081,1,0.889293108,0.604815671,0.451169778,0.403195516,0.403195516), - # N - N = c(511.179,378.528,278.443,194.385,183.423,45.5404,61.1188,19.8073,39.6285,33.6501,51.7806), - # Nyrs - nyrs = 37, - # recruits - R = c(1578.51,479.509,357.919,443.588,318.981,413.125,514.351,600.987,536.301,692.34,452.279,1618.73,702.801,372.811,597.812,1136.19,402.862,424.179,1025.36,207.05,383.695,1054.64,2224.52,1379.34,1545.81,345.556,454.884,617.194,404.876,993.497,727.754,236.795,505.948,258.653,726.627,524.198,473.54), - # SSB - SSB = c(206.391,194.569,187.097,183.296,195.289,240.774,252.143,238.163,223.199,200.776,182.378,179.671,189.193,198.242,212.123,233.484,277.891,282.004,250.709,231.816,218.403,195.275,181.628,189.976,175.912,168.624,220.206,315.124,376.621,397.171,365.476,317.159,277.777,242.719,233.415,223.636,198.117,183.537,177.91) - ) -``` - -## Save lists for running model to files expected by projection model - -```{r } -# Setup.dat -list2dat(setup,"setup.dat") -# spp_catch.dat -list2dat(config,"data/t1_spcat.dat") -runfn<-"t1" -file.copy(paste0("data/",runfn,"_spcat.dat"),"spp_catch.dat",overwrite=TRUE) - -list2dat(datfile,"data/t1.dat") -``` - -## Run projection model - -```{r } -system("../src/main") -``` - -## Read in projection model mainfiles - -```{r } - .projdir="t1/" - dir.create(.projdir) - file.copy(list.files(getwd(), pattern="out$"), .projdir,overwrite=TRUE) - file.remove(list.files(getwd(), pattern="out$")) - bf <- data.frame(read.table(paste0(.projdir,"bigfile.out"),header=TRUE,as.is=TRUE)) - bfs <- bf %>% filter(Sim<=30) - #write.csv(bfs,"data/proj.csv") - # head(bfs) - bfss <- bfs %>% filter(Alt==2) %>% select(Alt,Yr,Catch,SSB,Sim) - pf <- data.frame(read.table(paste0(.projdir,"percentdb.out"),header=F) ) - names(pf) <- c("stock","Alt","Yr","variable","value") -``` - -## Make plot of projection model simulations - -```{r } - p1 <- pf %>% filter(substr(variable,1,1)=="C",variable!="CStdn",Alt==2) %>% select(Yr,variable,value) %>% spread(variable,value) %>% - ggplot(aes(x=Yr,y=CMean),width=1.2) + geom_ribbon(aes(ymax=CUCI,ymin=CLCI),fill="goldenrod",alpha=.5) + theme_few() + geom_line() + - scale_x_continuous(breaks=seq(thisyr,thisyr+14,2)) + xlab("Year") + ylab("Tier 3 ABC (kt)") + geom_point() + - expand_limits(y=0) + - geom_line(aes(y=Cabc)) + geom_line(aes(y=Cofl),linetype="dashed") + geom_line(data=bfss,aes(x=Yr,y=Catch,col=as.factor(Sim)))+ guides(size=FALSE,fill=FALSE,alpha=FALSE,col=FALSE) - p2 <- pf %>% filter(substr(variable,1,1)=="S",variable!="SSBStdn",Alt==2) %>% select(Yr,variable,value) %>% spread(variable,value) %>% - ggplot(aes(x=Yr,y=SSBMean),width=1.2) + geom_ribbon(aes(ymax=SSBUCI,ymin=SSBLCI),fill="coral",alpha=.5) + theme_few() + geom_line() + - scale_x_continuous(breaks=seq(thisyr,thisyr+14,2)) + xlab("Year") + ylab("Tier 3 Spawning biomass (kt)") + geom_point() + - expand_limits(y=0) + - geom_line(aes(y=SSBFabc)) + geom_line(aes(y=SSBFofl),linetype="dashed")+ geom_line(data=bfss,aes(x=Yr,y=SSB,col=as.factor(Sim)))+ guides(size=FALSE,fill=FALSE,alpha=FALSE,col=FALSE) - t3 <- grid.arrange(p1, p2, nrow=2) - ggsave(paste0(.projdir,"tier3_proj.pdf"),plot=t3,width=5.4,height=7,units="in") -``` - -## Make tables - -```{r } - # Stock Alt Sim Yr SSB Rec Tot_biom SPR_Implied F Ntot Catch ABC OFL AvgAge AvgAgeTot SexRatio FABC FOFL - bfsum <- bf %>% select(Alt,Yr,SSB,F,ABC ,Catch) %>% group_by(Alt,Yr) %>% summarise(Catch=mean(Catch),SSB=mean(SSB),F=mean(F),ABC=mean(ABC)) - t1 <- bfsum %>% select(Alt,Yr,Catch) %>% spread(Alt,Catch) - names(t1) <- c("Catch","Scenario 1","Scenario 2","Scenario 3","Scenario 4","Scenario 5","Scenario 6","Scenario 7") - - print_Tier3_tables(bf) -``` - diff --git a/examples/test.html b/examples/test.html deleted file mode 100644 index 48a1625..0000000 --- a/examples/test.html +++ /dev/null @@ -1,455 +0,0 @@ - - - - - -Projection model work - - - - - - - - - - - - - - - - - - -

    Projection model work

    - -

    Projection model work for demonstrating application of controls and input data

    - -
    library(tidyverse)
    -library(ggplot2)
    -library(ggthemes)
    -library(gtable)
    -source("../R/readData.R")
    -
    - -

    Set initial “setup” parameters

    - -
    thisyr=2019
    -setup<-list(
    -  Run_name     = noquote("Std"),
    -  Tier         = 3    ,
    -  nalts        = 7    ,
    -  alts         = c(1,2,3,4,5,6,7),
    -  tac_abc      = 1,    #' Flag to set TAC equal to ABC (1 means true, otherwise false)
    -  srr          = 1 ,   #' Stock-recruitment type (1=Ricker, 2=Bholt)
    -  rec_proj     = 1,    #' projection rec form (default: 1 = use observed mean and std, option 2 = use estimated SRR and estimated sigma R)
    -  srr_cond     = 0 ,   #' SR-Conditioning (0 means no, 1 means use Fmsy == F35%?, 2 means Fmsy == F35% and Bmsy=B35%  condition (affects SRR fits)
    -  srr_prior    = 0.0,  #' Condition that there is a prior that mean historical recruitment is similar to expected recruitment at half mean SSB and double mean SSB 0 means don't use, otherwise specify CV
    -  write_big    = 1,    #' Flag to write big file (of all simulations rather than a summary, 0 means don't do it, otherwise do it) Write_Big
    -  nyrs_proj    = 14,   #' Number of projection years
    -  nsims        = 100, #' Number of simulations
    -  beg_yr_label = thisyr  #' Begin Year
    -)
    -
    - -

    Set up the species specific run file

    - -
    config<-list(
    -  nFixCatchYrs = 2,
    -  nSpecies     = 1,
    -  OYMin        = .1343248,
    -  OYMax        = 1943248,
    -  dataFiles    = noquote("data/t1.dat"),
    -  ABCMult      = 1,
    -  PoplnScalar  = 1000,
    -  AltFabcSPR   = 0.75,
    -  nTAC         = 1,
    -  TACIndices   = 1,
    -  Catch        = c( 2016,55000.,   2017,55000. )
    -)
    -
    - -

    Make list of main file w/ assessment model results

    - -

    E.g., “data/bsai_atka.dat”

    - -
    datfile <- list(
    -  runname     = noquote("M16.2"), 
    -  ssl_spp     = 1,         # SSL_spp
    -  Dorn_buffer = 1,         # Dorn_buffer
    -  nfsh        = 1,         # N_fsh
    -  nsex        = 1,         # N_sexes
    -  avgF5yr     = 0.0661399, # avg_5yr_F
    -  F40_mult    = 1,         # F_40_multiplier
    -  spr_abc     = 0.4,       # SPR_abc
    -  spr_msy     = 0.35,      # SPR_msy
    -  sp_mo       = 8,         # spawn_month
    -  nages       = 11,        # N_ages
    -  Frat        = 1,         # F_ratio
    -    # M
    -    M    = c(0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3), 
    -    #  Maturity
    -    pmat = c(0.005,0.037,0.224,0.688,0.944,0.992,0.999,1,1,1,1),
    -    #  Wt_at_age_spawners  
    -    wtage_sp  = c(44.8,161.377,398.272,557.695,652.113,719.573,863.744,948.744,921.397,885.912,1069.87),
    -    #  Wt_at_age_fsh
    -    wtage_fsh = c(69.3778,253.522,408.211,614.731,668.483,718.137,803.017,798.707,788.117,842.468,960.006), 
    -    # select
    -    sel = c(0.002576427,0.040030753,0.651104228,0.768404263,0.794886081,1,0.889293108,0.604815671,0.451169778,0.403195516,0.403195516),
    -    # N
    -    N   = c(511.179,378.528,278.443,194.385,183.423,45.5404,61.1188,19.8073,39.6285,33.6501,51.7806),
    -    # Nyrs
    -    nyrs = 37,
    -    # recruits
    -    R    = c(1578.51,479.509,357.919,443.588,318.981,413.125,514.351,600.987,536.301,692.34,452.279,1618.73,702.801,372.811,597.812,1136.19,402.862,424.179,1025.36,207.05,383.695,1054.64,2224.52,1379.34,1545.81,345.556,454.884,617.194,404.876,993.497,727.754,236.795,505.948,258.653,726.627,524.198,473.54),
    -    # SSB 
    -    SSB  = c(206.391,194.569,187.097,183.296,195.289,240.774,252.143,238.163,223.199,200.776,182.378,179.671,189.193,198.242,212.123,233.484,277.891,282.004,250.709,231.816,218.403,195.275,181.628,189.976,175.912,168.624,220.206,315.124,376.621,397.171,365.476,317.159,277.777,242.719,233.415,223.636,198.117,183.537,177.91)
    -    )
    -
    - -

    Save lists for running model to files expected by projection model

    - -
    # Setup.dat
    -list2dat(setup,"setup.dat")
    -# spp_catch.dat
    -list2dat(config,"data/t1_spcat.dat")
    -runfn<-"t1"
    -file.copy(paste0("data/",runfn,"_spcat.dat"),"spp_catch.dat",overwrite=TRUE)
    -
    - -
    ## [1] TRUE
    -
    - -
    list2dat(datfile,"data/t1.dat")
    -
    - -

    Run projection model

    - -
    system("../src/main")
    -
    - -

    Read in projection model mainfiles

    - -
      .projdir="t1/"
    -  dir.create(.projdir)
    -
    - -
    ## Warning in dir.create(.projdir): 't1' already exists
    -
    - -
      file.copy(list.files(getwd(), pattern="out$"), .projdir,overwrite=TRUE)      
    -
    - -
    ## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE
    -
    - -
      file.remove(list.files(getwd(), pattern="out$"))
    -
    - -
    ## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE
    -
    - -
      bf <- data.frame(read.table(paste0(.projdir,"bigfile.out"),header=TRUE,as.is=TRUE))
    -  bfs <- bf %>% filter(Sim<=30)
    -  #write.csv(bfs,"data/proj.csv")
    - # head(bfs)
    -  bfss <- bfs %>% filter(Alt==2) %>% select(Alt,Yr,Catch,SSB,Sim) 
    -  pf <- data.frame(read.table(paste0(.projdir,"percentdb.out"),header=F) )
    -  names(pf) <- c("stock","Alt","Yr","variable","value") 
    -
    - -

    Make plot of projection model simulations

    - -
      p1 <- pf %>% filter(substr(variable,1,1)=="C",variable!="CStdn",Alt==2) %>% select(Yr,variable,value) %>% spread(variable,value) %>%
    -    ggplot(aes(x=Yr,y=CMean),width=1.2) + geom_ribbon(aes(ymax=CUCI,ymin=CLCI),fill="goldenrod",alpha=.5) + theme_few() + geom_line() +
    -    scale_x_continuous(breaks=seq(thisyr,thisyr+14,2))  +  xlab("Year") + ylab("Tier 3 ABC (kt)") + geom_point() + 
    -    expand_limits(y=0) +
    -    geom_line(aes(y=Cabc)) + geom_line(aes(y=Cofl),linetype="dashed") + geom_line(data=bfss,aes(x=Yr,y=Catch,col=as.factor(Sim)))+ guides(size=FALSE,fill=FALSE,alpha=FALSE,col=FALSE) 
    -  p2 <- pf %>% filter(substr(variable,1,1)=="S",variable!="SSBStdn",Alt==2) %>% select(Yr,variable,value) %>% spread(variable,value) %>%
    -    ggplot(aes(x=Yr,y=SSBMean),width=1.2) + geom_ribbon(aes(ymax=SSBUCI,ymin=SSBLCI),fill="coral",alpha=.5) + theme_few() + geom_line() +
    -    scale_x_continuous(breaks=seq(thisyr,thisyr+14,2))  +  xlab("Year") + ylab("Tier 3 Spawning biomass (kt)") + geom_point() + 
    -    expand_limits(y=0) +
    -    geom_line(aes(y=SSBFabc)) + geom_line(aes(y=SSBFofl),linetype="dashed")+ geom_line(data=bfss,aes(x=Yr,y=SSB,col=as.factor(Sim)))+ guides(size=FALSE,fill=FALSE,alpha=FALSE,col=FALSE) 
    -  t3 <- grid.arrange(p1, p2, nrow=2)
    -
    - -

    plot of chunk unnamed-chunk-8

    - -
      ggsave(paste0(.projdir,"tier3_proj.pdf"),plot=t3,width=5.4,height=7,units="in")
    -
    - -

    Make tables

    - -
      # Stock Alt Sim Yr  SSB Rec Tot_biom SPR_Implied F Ntot Catch ABC OFL AvgAge AvgAgeTot SexRatio FABC FOFL
    -  bfsum <- bf %>% select(Alt,Yr,SSB,F,ABC ,Catch) %>% group_by(Alt,Yr) %>% summarise(Catch=mean(Catch),SSB=mean(SSB),F=mean(F),ABC=mean(ABC))
    -  t1 <- bfsum %>% select(Alt,Yr,Catch) %>% spread(Alt,Catch) 
    -  names(t1) <- c("Catch","Scenario 1","Scenario 2","Scenario 3","Scenario 4","Scenario 5","Scenario 6","Scenario 7")
    -
    -  print_Tier3_tables(bf)
    -
    - -
    ## <!-- html table generated in R 4.0.0 by xtable 1.8-4 package -->
    -## <!-- Tue Jun  9 08:53:57 2020 -->
    -## <table border=1>
    -## <caption align="top"> Tier 3 projections of BSAI Atka mackerel catch for the 7 scenarios. </caption>
    -## <tr> <th> Catch </th> <th> Scenario.1 </th> <th> Scenario.2 </th> <th> Scenario.3 </th> <th> Scenario.4 </th> <th> Scenario.5 </th> <th> Scenario.6 </th> <th> Scenario.7 </th>  </tr>
    -##   <tr> <td align="right"> 2019 </td> <td align="right"> 55 </td> <td align="right"> 55 </td> <td align="right"> 55 </td> <td align="right"> 55 </td> <td align="right"> 55 </td> <td align="right"> 55 </td> <td align="right"> 55 </td> </tr>
    -##   <tr> <td align="right"> 2020 </td> <td align="right"> 91 </td> <td align="right"> 55 </td> <td align="right"> 21 </td> <td align="right"> 27 </td> <td align="right"> 0 </td> <td align="right"> 106 </td> <td align="right"> 91 </td> </tr>
    -##   <tr> <td align="right"> 2021 </td> <td align="right"> 83 </td> <td align="right"> 85 </td> <td align="right"> 22 </td> <td align="right"> 28 </td> <td align="right"> 0 </td> <td align="right"> 91 </td> <td align="right"> 83 </td> </tr>
    -##   <tr> <td align="right"> 2022 </td> <td align="right"> 81 </td> <td align="right"> 80 </td> <td align="right"> 25 </td> <td align="right"> 30 </td> <td align="right"> 0 </td> <td align="right"> 85 </td> <td align="right"> 93 </td> </tr>
    -##   <tr> <td align="right"> 2023 </td> <td align="right"> 82 </td> <td align="right"> 81 </td> <td align="right"> 27 </td> <td align="right"> 33 </td> <td align="right"> 0 </td> <td align="right"> 88 </td> <td align="right"> 91 </td> </tr>
    -##   <tr> <td align="right"> 2024 </td> <td align="right"> 86 </td> <td align="right"> 84 </td> <td align="right"> 29 </td> <td align="right"> 35 </td> <td align="right"> 0 </td> <td align="right"> 92 </td> <td align="right"> 93 </td> </tr>
    -##   <tr> <td align="right"> 2025 </td> <td align="right"> 90 </td> <td align="right"> 88 </td> <td align="right"> 30 </td> <td align="right"> 37 </td> <td align="right"> 0 </td> <td align="right"> 96 </td> <td align="right"> 97 </td> </tr>
    -##   <tr> <td align="right"> 2026 </td> <td align="right"> 90 </td> <td align="right"> 88 </td> <td align="right"> 31 </td> <td align="right"> 38 </td> <td align="right"> 0 </td> <td align="right"> 96 </td> <td align="right"> 97 </td> </tr>
    -##   <tr> <td align="right"> 2027 </td> <td align="right"> 89 </td> <td align="right"> 86 </td> <td align="right"> 31 </td> <td align="right"> 38 </td> <td align="right"> 0 </td> <td align="right"> 94 </td> <td align="right"> 94 </td> </tr>
    -##   <tr> <td align="right"> 2028 </td> <td align="right"> 89 </td> <td align="right"> 86 </td> <td align="right"> 31 </td> <td align="right"> 38 </td> <td align="right"> 0 </td> <td align="right"> 95 </td> <td align="right"> 95 </td> </tr>
    -##   <tr> <td align="right"> 2029 </td> <td align="right"> 85 </td> <td align="right"> 82 </td> <td align="right"> 31 </td> <td align="right"> 37 </td> <td align="right"> 0 </td> <td align="right"> 90 </td> <td align="right"> 90 </td> </tr>
    -##   <tr> <td align="right"> 2030 </td> <td align="right"> 84 </td> <td align="right"> 80 </td> <td align="right"> 30 </td> <td align="right"> 37 </td> <td align="right"> 0 </td> <td align="right"> 88 </td> <td align="right"> 88 </td> </tr>
    -##   <tr> <td align="right"> 2031 </td> <td align="right"> 85 </td> <td align="right"> 83 </td> <td align="right"> 31 </td> <td align="right"> 37 </td> <td align="right"> 0 </td> <td align="right"> 91 </td> <td align="right"> 91 </td> </tr>
    -##   <tr> <td align="right"> 2032 </td> <td align="right"> 88 </td> <td align="right"> 85 </td> <td align="right"> 31 </td> <td align="right"> 38 </td> <td align="right"> 0 </td> <td align="right"> 93 </td> <td align="right"> 93 </td> </tr>
    -##    </table>
    -## <!-- html table generated in R 4.0.0 by xtable 1.8-4 package -->
    -## <!-- Tue Jun  9 08:53:57 2020 -->
    -## <table border=1>
    -## <caption align="top"> Tier 3 projections of BSAI Atka mackerel ABC for the 7 scenarios. </caption>
    -## <tr> <th> SSB </th> <th> Scenario.1 </th> <th> Scenario.2 </th> <th> Scenario.3 </th> <th> Scenario.4 </th> <th> Scenario.5 </th> <th> Scenario.6 </th> <th> Scenario.7 </th>  </tr>
    -##   <tr> <td align="right"> 2019 </td> <td align="right"> 172 </td> <td align="right"> 172 </td> <td align="right"> 172 </td> <td align="right"> 172 </td> <td align="right"> 172 </td> <td align="right"> 172 </td> <td align="right"> 172 </td> </tr>
    -##   <tr> <td align="right"> 2020 </td> <td align="right"> 156 </td> <td align="right"> 164 </td> <td align="right"> 172 </td> <td align="right"> 171 </td> <td align="right"> 177 </td> <td align="right"> 152 </td> <td align="right"> 156 </td> </tr>
    -##   <tr> <td align="right"> 2021 </td> <td align="right"> 138 </td> <td align="right"> 152 </td> <td align="right"> 178 </td> <td align="right"> 175 </td> <td align="right"> 192 </td> <td align="right"> 131 </td> <td align="right"> 138 </td> </tr>
    -##   <tr> <td align="right"> 2022 </td> <td align="right"> 130 </td> <td align="right"> 141 </td> <td align="right"> 187 </td> <td align="right"> 182 </td> <td align="right"> 209 </td> <td align="right"> 122 </td> <td align="right"> 128 </td> </tr>
    -##   <tr> <td align="right"> 2023 </td> <td align="right"> 132 </td> <td align="right"> 142 </td> <td align="right"> 204 </td> <td align="right"> 197 </td> <td align="right"> 233 </td> <td align="right"> 123 </td> <td align="right"> 126 </td> </tr>
    -##   <tr> <td align="right"> 2024 </td> <td align="right"> 135 </td> <td align="right"> 144 </td> <td align="right"> 218 </td> <td align="right"> 210 </td> <td align="right"> 254 </td> <td align="right"> 125 </td> <td align="right"> 127 </td> </tr>
    -##   <tr> <td align="right"> 2025 </td> <td align="right"> 139 </td> <td align="right"> 146 </td> <td align="right"> 232 </td> <td align="right"> 223 </td> <td align="right"> 275 </td> <td align="right"> 128 </td> <td align="right"> 129 </td> </tr>
    -##   <tr> <td align="right"> 2026 </td> <td align="right"> 140 </td> <td align="right"> 147 </td> <td align="right"> 243 </td> <td align="right"> 232 </td> <td align="right"> 292 </td> <td align="right"> 129 </td> <td align="right"> 129 </td> </tr>
    -##   <tr> <td align="right"> 2027 </td> <td align="right"> 139 </td> <td align="right"> 146 </td> <td align="right"> 249 </td> <td align="right"> 237 </td> <td align="right"> 302 </td> <td align="right"> 127 </td> <td align="right"> 127 </td> </tr>
    -##   <tr> <td align="right"> 2028 </td> <td align="right"> 138 </td> <td align="right"> 145 </td> <td align="right"> 252 </td> <td align="right"> 240 </td> <td align="right"> 309 </td> <td align="right"> 126 </td> <td align="right"> 126 </td> </tr>
    -##   <tr> <td align="right"> 2029 </td> <td align="right"> 137 </td> <td align="right"> 144 </td> <td align="right"> 254 </td> <td align="right"> 241 </td> <td align="right"> 314 </td> <td align="right"> 125 </td> <td align="right"> 125 </td> </tr>
    -##   <tr> <td align="right"> 2030 </td> <td align="right"> 134 </td> <td align="right"> 141 </td> <td align="right"> 251 </td> <td align="right"> 238 </td> <td align="right"> 313 </td> <td align="right"> 122 </td> <td align="right"> 122 </td> </tr>
    -##   <tr> <td align="right"> 2031 </td> <td align="right"> 134 </td> <td align="right"> 141 </td> <td align="right"> 252 </td> <td align="right"> 238 </td> <td align="right"> 315 </td> <td align="right"> 123 </td> <td align="right"> 123 </td> </tr>
    -##   <tr> <td align="right"> 2032 </td> <td align="right"> 137 </td> <td align="right"> 144 </td> <td align="right"> 255 </td> <td align="right"> 242 </td> <td align="right"> 319 </td> <td align="right"> 125 </td> <td align="right"> 125 </td> </tr>
    -##    </table>
    -## <!-- html table generated in R 4.0.0 by xtable 1.8-4 package -->
    -## <!-- Tue Jun  9 08:53:57 2020 -->
    -## <table border=1>
    -## <caption align="top"> Tier 3 projections of BSAI Atka mackerel fishing mortality for the 7 scenarios. </caption>
    -## <tr> <th> F </th> <th> Scenario.1 </th> <th> Scenario.2 </th> <th> Scenario.3 </th> <th> Scenario.4 </th> <th> Scenario.5 </th> <th> Scenario.6 </th> <th> Scenario.7 </th>  </tr>
    -##   <tr> <td align="right"> 2019 </td> <td align="right"> 0.169 </td> <td align="right"> 0.169 </td> <td align="right"> 0.169 </td> <td align="right"> 0.169 </td> <td align="right"> 0.169 </td> <td align="right"> 0.169 </td> <td align="right"> 0.169 </td> </tr>
    -##   <tr> <td align="right"> 2020 </td> <td align="right"> 0.299 </td> <td align="right"> 0.172 </td> <td align="right"> 0.066 </td> <td align="right"> 0.083 </td> <td align="right"> 0.000 </td> <td align="right"> 0.353 </td> <td align="right"> 0.299 </td> </tr>
    -##   <tr> <td align="right"> 2021 </td> <td align="right"> 0.299 </td> <td align="right"> 0.283 </td> <td align="right"> 0.066 </td> <td align="right"> 0.083 </td> <td align="right"> 0.000 </td> <td align="right"> 0.343 </td> <td align="right"> 0.299 </td> </tr>
    -##   <tr> <td align="right"> 2022 </td> <td align="right"> 0.284 </td> <td align="right"> 0.263 </td> <td align="right"> 0.066 </td> <td align="right"> 0.083 </td> <td align="right"> 0.000 </td> <td align="right"> 0.315 </td> <td align="right"> 0.330 </td> </tr>
    -##   <tr> <td align="right"> 2023 </td> <td align="right"> 0.277 </td> <td align="right"> 0.259 </td> <td align="right"> 0.066 </td> <td align="right"> 0.083 </td> <td align="right"> 0.000 </td> <td align="right"> 0.311 </td> <td align="right"> 0.317 </td> </tr>
    -##   <tr> <td align="right"> 2024 </td> <td align="right"> 0.275 </td> <td align="right"> 0.258 </td> <td align="right"> 0.066 </td> <td align="right"> 0.083 </td> <td align="right"> 0.000 </td> <td align="right"> 0.310 </td> <td align="right"> 0.312 </td> </tr>
    -##   <tr> <td align="right"> 2025 </td> <td align="right"> 0.276 </td> <td align="right"> 0.260 </td> <td align="right"> 0.066 </td> <td align="right"> 0.083 </td> <td align="right"> 0.000 </td> <td align="right"> 0.312 </td> <td align="right"> 0.314 </td> </tr>
    -##   <tr> <td align="right"> 2026 </td> <td align="right"> 0.277 </td> <td align="right"> 0.261 </td> <td align="right"> 0.066 </td> <td align="right"> 0.083 </td> <td align="right"> 0.000 </td> <td align="right"> 0.313 </td> <td align="right"> 0.314 </td> </tr>
    -##   <tr> <td align="right"> 2027 </td> <td align="right"> 0.277 </td> <td align="right"> 0.260 </td> <td align="right"> 0.066 </td> <td align="right"> 0.083 </td> <td align="right"> 0.000 </td> <td align="right"> 0.313 </td> <td align="right"> 0.313 </td> </tr>
    -##   <tr> <td align="right"> 2028 </td> <td align="right"> 0.278 </td> <td align="right"> 0.259 </td> <td align="right"> 0.066 </td> <td align="right"> 0.083 </td> <td align="right"> 0.000 </td> <td align="right"> 0.312 </td> <td align="right"> 0.312 </td> </tr>
    -##   <tr> <td align="right"> 2029 </td> <td align="right"> 0.277 </td> <td align="right"> 0.258 </td> <td align="right"> 0.066 </td> <td align="right"> 0.083 </td> <td align="right"> 0.000 </td> <td align="right"> 0.310 </td> <td align="right"> 0.310 </td> </tr>
    -##   <tr> <td align="right"> 2030 </td> <td align="right"> 0.275 </td> <td align="right"> 0.254 </td> <td align="right"> 0.066 </td> <td align="right"> 0.083 </td> <td align="right"> 0.000 </td> <td align="right"> 0.306 </td> <td align="right"> 0.306 </td> </tr>
    -##   <tr> <td align="right"> 2031 </td> <td align="right"> 0.274 </td> <td align="right"> 0.254 </td> <td align="right"> 0.066 </td> <td align="right"> 0.083 </td> <td align="right"> 0.000 </td> <td align="right"> 0.307 </td> <td align="right"> 0.307 </td> </tr>
    -##   <tr> <td align="right"> 2032 </td> <td align="right"> 0.275 </td> <td align="right"> 0.256 </td> <td align="right"> 0.066 </td> <td align="right"> 0.083 </td> <td align="right"> 0.000 </td> <td align="right"> 0.309 </td> <td align="right"> 0.309 </td> </tr>
    -##    </table>
    -## <!-- html table generated in R 4.0.0 by xtable 1.8-4 package -->
    -## <!-- Tue Jun  9 08:53:57 2020 -->
    -## <table border=1>
    -## <caption align="top"> Tier 3 projections of BSAI Atka mackerel spawning biomass for the 7 scenarios. </caption>
    -## <tr> <th> ABC </th> <th> Scenario.1 </th> <th> Scenario.2 </th> <th> Scenario.3 </th> <th> Scenario.4 </th> <th> Scenario.5 </th> <th> Scenario.6 </th> <th> Scenario.7 </th>  </tr>
    -##   <tr> <td align="right"> 2019 </td> <td align="right"> 93 </td> <td align="right"> 93 </td> <td align="right"> 22 </td> <td align="right"> 27 </td> <td align="right"> 0 </td> <td align="right"> 107 </td> <td align="right"> 107 </td> </tr>
    -##   <tr> <td align="right"> 2020 </td> <td align="right"> 91 </td> <td align="right"> 89 </td> <td align="right"> 21 </td> <td align="right"> 27 </td> <td align="right"> 0 </td> <td align="right"> 106 </td> <td align="right"> 106 </td> </tr>
    -##   <tr> <td align="right"> 2021 </td> <td align="right"> 83 </td> <td align="right"> 85 </td> <td align="right"> 22 </td> <td align="right"> 28 </td> <td align="right"> 0 </td> <td align="right"> 91 </td> <td align="right"> 96 </td> </tr>
    -##   <tr> <td align="right"> 2022 </td> <td align="right"> 81 </td> <td align="right"> 80 </td> <td align="right"> 25 </td> <td align="right"> 30 </td> <td align="right"> 0 </td> <td align="right"> 85 </td> <td align="right"> 93 </td> </tr>
    -##   <tr> <td align="right"> 2023 </td> <td align="right"> 82 </td> <td align="right"> 81 </td> <td align="right"> 27 </td> <td align="right"> 33 </td> <td align="right"> 0 </td> <td align="right"> 88 </td> <td align="right"> 91 </td> </tr>
    -##   <tr> <td align="right"> 2024 </td> <td align="right"> 86 </td> <td align="right"> 84 </td> <td align="right"> 29 </td> <td align="right"> 35 </td> <td align="right"> 0 </td> <td align="right"> 92 </td> <td align="right"> 93 </td> </tr>
    -##   <tr> <td align="right"> 2025 </td> <td align="right"> 90 </td> <td align="right"> 88 </td> <td align="right"> 30 </td> <td align="right"> 37 </td> <td align="right"> 0 </td> <td align="right"> 96 </td> <td align="right"> 97 </td> </tr>
    -##   <tr> <td align="right"> 2026 </td> <td align="right"> 90 </td> <td align="right"> 88 </td> <td align="right"> 31 </td> <td align="right"> 38 </td> <td align="right"> 0 </td> <td align="right"> 96 </td> <td align="right"> 97 </td> </tr>
    -##   <tr> <td align="right"> 2027 </td> <td align="right"> 89 </td> <td align="right"> 86 </td> <td align="right"> 31 </td> <td align="right"> 38 </td> <td align="right"> 0 </td> <td align="right"> 94 </td> <td align="right"> 94 </td> </tr>
    -##   <tr> <td align="right"> 2028 </td> <td align="right"> 89 </td> <td align="right"> 86 </td> <td align="right"> 31 </td> <td align="right"> 38 </td> <td align="right"> 0 </td> <td align="right"> 95 </td> <td align="right"> 95 </td> </tr>
    -##   <tr> <td align="right"> 2029 </td> <td align="right"> 85 </td> <td align="right"> 82 </td> <td align="right"> 31 </td> <td align="right"> 37 </td> <td align="right"> 0 </td> <td align="right"> 90 </td> <td align="right"> 90 </td> </tr>
    -##   <tr> <td align="right"> 2030 </td> <td align="right"> 84 </td> <td align="right"> 80 </td> <td align="right"> 30 </td> <td align="right"> 37 </td> <td align="right"> 0 </td> <td align="right"> 88 </td> <td align="right"> 88 </td> </tr>
    -##   <tr> <td align="right"> 2031 </td> <td align="right"> 85 </td> <td align="right"> 83 </td> <td align="right"> 31 </td> <td align="right"> 37 </td> <td align="right"> 0 </td> <td align="right"> 91 </td> <td align="right"> 91 </td> </tr>
    -##   <tr> <td align="right"> 2032 </td> <td align="right"> 88 </td> <td align="right"> 85 </td> <td align="right"> 31 </td> <td align="right"> 38 </td> <td align="right"> 0 </td> <td align="right"> 93 </td> <td align="right"> 93 </td> </tr>
    -##    </table>
    -
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-## Set initial "setup" parameters - - -```r -thisyr=2019 -setup<-list( - Run_name = noquote("Std"), - Tier = 3 , - nalts = 7 , - alts = c(1,2,3,4,5,6,7), - tac_abc = 1, #' Flag to set TAC equal to ABC (1 means true, otherwise false) - srr = 1 , #' Stock-recruitment type (1=Ricker, 2=Bholt) - rec_proj = 1, #' projection rec form (default: 1 = use observed mean and std, option 2 = use estimated SRR and estimated sigma R) - srr_cond = 0 , #' SR-Conditioning (0 means no, 1 means use Fmsy == F35%?, 2 means Fmsy == F35% and Bmsy=B35% condition (affects SRR fits) - srr_prior = 0.0, #' Condition that there is a prior that mean historical recruitment is similar to expected recruitment at half mean SSB and double mean SSB 0 means don't use, otherwise specify CV - write_big = 1, #' Flag to write big file (of all simulations rather than a summary, 0 means don't do it, otherwise do it) Write_Big - nyrs_proj = 14, #' Number of projection years - nsims = 100, #' Number of simulations - beg_yr_label = thisyr #' Begin Year -) -``` - -## Set up the species specific run file - - -```r -config<-list( - nFixCatchYrs = 2, - nSpecies = 1, - OYMin = .1343248, - OYMax = 1943248, - dataFiles = noquote("data/t1.dat"), - ABCMult = 1, - PoplnScalar = 1000, - AltFabcSPR = 0.75, - nTAC = 1, - TACIndices = 1, - Catch = c( 2016,55000., 2017,55000. ) -) -``` - -## Make list of main file w/ assessment model results -E.g., "data/bsai_atka.dat" - - -```r -datfile <- list( - runname = noquote("M16.2"), - ssl_spp = 1, # SSL_spp - Dorn_buffer = 1, # Dorn_buffer - nfsh = 1, # N_fsh - nsex = 1, # N_sexes - avgF5yr = 0.0661399, # avg_5yr_F - F40_mult = 1, # F_40_multiplier - spr_abc = 0.4, # SPR_abc - spr_msy = 0.35, # SPR_msy - sp_mo = 8, # spawn_month - nages = 11, # N_ages - Frat = 1, # F_ratio - # M - M = c(0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3), - # Maturity - pmat = c(0.005,0.037,0.224,0.688,0.944,0.992,0.999,1,1,1,1), - # Wt_at_age_spawners - wtage_sp = c(44.8,161.377,398.272,557.695,652.113,719.573,863.744,948.744,921.397,885.912,1069.87), - # Wt_at_age_fsh - wtage_fsh = c(69.3778,253.522,408.211,614.731,668.483,718.137,803.017,798.707,788.117,842.468,960.006), - # select - sel = c(0.002576427,0.040030753,0.651104228,0.768404263,0.794886081,1,0.889293108,0.604815671,0.451169778,0.403195516,0.403195516), - # N - N = c(511.179,378.528,278.443,194.385,183.423,45.5404,61.1188,19.8073,39.6285,33.6501,51.7806), - # Nyrs - nyrs = 37, - # recruits - R = c(1578.51,479.509,357.919,443.588,318.981,413.125,514.351,600.987,536.301,692.34,452.279,1618.73,702.801,372.811,597.812,1136.19,402.862,424.179,1025.36,207.05,383.695,1054.64,2224.52,1379.34,1545.81,345.556,454.884,617.194,404.876,993.497,727.754,236.795,505.948,258.653,726.627,524.198,473.54), - # SSB - SSB = c(206.391,194.569,187.097,183.296,195.289,240.774,252.143,238.163,223.199,200.776,182.378,179.671,189.193,198.242,212.123,233.484,277.891,282.004,250.709,231.816,218.403,195.275,181.628,189.976,175.912,168.624,220.206,315.124,376.621,397.171,365.476,317.159,277.777,242.719,233.415,223.636,198.117,183.537,177.91) - ) -``` - -## Save lists for running model to files expected by projection model - - -```r -# Setup.dat -list2dat(setup,"setup.dat") -# spp_catch.dat -list2dat(config,"data/t1_spcat.dat") -runfn<-"t1" -file.copy(paste0("data/",runfn,"_spcat.dat"),"spp_catch.dat",overwrite=TRUE) -``` - -``` -## [1] TRUE -``` - -```r -list2dat(datfile,"data/t1.dat") -``` - -## Run projection model - - -```r -system("../src/main") -``` - -## Read in projection model mainfiles - - -```r - .projdir="t1/" - dir.create(.projdir) -``` - -``` -## Warning in dir.create(.projdir): 't1' already exists -``` - -```r - file.copy(list.files(getwd(), pattern="out$"), .projdir,overwrite=TRUE) -``` - -``` -## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE -``` - -```r - file.remove(list.files(getwd(), pattern="out$")) -``` - -``` -## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE -``` - -```r - bf <- data.frame(read.table(paste0(.projdir,"bigfile.out"),header=TRUE,as.is=TRUE)) - bfs <- bf %>% filter(Sim<=30) - #write.csv(bfs,"data/proj.csv") - # head(bfs) - bfss <- bfs %>% filter(Alt==2) %>% select(Alt,Yr,Catch,SSB,Sim) - pf <- data.frame(read.table(paste0(.projdir,"percentdb.out"),header=F) ) - names(pf) <- c("stock","Alt","Yr","variable","value") -``` - -## Make plot of projection model simulations - - -```r - p1 <- pf %>% filter(substr(variable,1,1)=="C",variable!="CStdn",Alt==2) %>% select(Yr,variable,value) %>% spread(variable,value) %>% - ggplot(aes(x=Yr,y=CMean),width=1.2) + geom_ribbon(aes(ymax=CUCI,ymin=CLCI),fill="goldenrod",alpha=.5) + theme_few() + geom_line() + - scale_x_continuous(breaks=seq(thisyr,thisyr+14,2)) + xlab("Year") + ylab("Tier 3 ABC (kt)") + geom_point() + - expand_limits(y=0) + - geom_line(aes(y=Cabc)) + geom_line(aes(y=Cofl),linetype="dashed") + geom_line(data=bfss,aes(x=Yr,y=Catch,col=as.factor(Sim)))+ guides(size=FALSE,fill=FALSE,alpha=FALSE,col=FALSE) - p2 <- pf %>% filter(substr(variable,1,1)=="S",variable!="SSBStdn",Alt==2) %>% select(Yr,variable,value) %>% spread(variable,value) %>% - ggplot(aes(x=Yr,y=SSBMean),width=1.2) + geom_ribbon(aes(ymax=SSBUCI,ymin=SSBLCI),fill="coral",alpha=.5) + theme_few() + geom_line() + - scale_x_continuous(breaks=seq(thisyr,thisyr+14,2)) + xlab("Year") + ylab("Tier 3 Spawning biomass (kt)") + geom_point() + - expand_limits(y=0) + - geom_line(aes(y=SSBFabc)) + geom_line(aes(y=SSBFofl),linetype="dashed")+ geom_line(data=bfss,aes(x=Yr,y=SSB,col=as.factor(Sim)))+ guides(size=FALSE,fill=FALSE,alpha=FALSE,col=FALSE) - t3 <- grid.arrange(p1, p2, nrow=2) -``` - -![plot of chunk unnamed-chunk-8](figure/unnamed-chunk-8-1.png) - -```r - ggsave(paste0(.projdir,"tier3_proj.pdf"),plot=t3,width=5.4,height=7,units="in") -``` - -## Make tables - - -```r - # Stock Alt Sim Yr SSB Rec Tot_biom SPR_Implied F Ntot Catch ABC OFL AvgAge AvgAgeTot SexRatio FABC FOFL - bfsum <- bf %>% select(Alt,Yr,SSB,F,ABC ,Catch) %>% group_by(Alt,Yr) %>% summarise(Catch=mean(Catch),SSB=mean(SSB),F=mean(F),ABC=mean(ABC)) - t1 <- bfsum %>% select(Alt,Yr,Catch) %>% spread(Alt,Catch) - names(t1) <- c("Catch","Scenario 1","Scenario 2","Scenario 3","Scenario 4","Scenario 5","Scenario 6","Scenario 7") - - print_Tier3_tables(bf) -``` - -``` -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -##
    Tier 3 projections of BSAI Atka mackerel catch for the 7 scenarios.
    Catch Scenario.1 Scenario.2 Scenario.3 Scenario.4 Scenario.5 Scenario.6 Scenario.7
    2019 55 55 55 55 55 55 55
    2020 91 55 21 27 0 106 91
    2021 83 85 22 28 0 91 83
    2022 81 80 25 30 0 85 93
    2023 82 81 27 33 0 88 91
    2024 86 84 29 35 0 92 93
    2025 90 88 30 37 0 96 97
    2026 90 88 31 38 0 96 97
    2027 89 86 31 38 0 94 94
    2028 89 86 31 38 0 95 95
    2029 85 82 31 37 0 90 90
    2030 84 80 30 37 0 88 88
    2031 85 83 31 37 0 91 91
    2032 88 85 31 38 0 93 93
    -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -##
    Tier 3 projections of BSAI Atka mackerel ABC for the 7 scenarios.
    SSB Scenario.1 Scenario.2 Scenario.3 Scenario.4 Scenario.5 Scenario.6 Scenario.7
    2019 172 172 172 172 172 172 172
    2020 156 164 172 171 177 152 156
    2021 138 152 178 175 192 131 138
    2022 130 141 187 182 209 122 128
    2023 132 142 204 197 233 123 126
    2024 135 144 218 210 254 125 127
    2025 139 146 232 223 275 128 129
    2026 140 147 243 232 292 129 129
    2027 139 146 249 237 302 127 127
    2028 138 145 252 240 309 126 126
    2029 137 144 254 241 314 125 125
    2030 134 141 251 238 313 122 122
    2031 134 141 252 238 315 123 123
    2032 137 144 255 242 319 125 125
    -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -##
    Tier 3 projections of BSAI Atka mackerel fishing mortality for the 7 scenarios.
    F Scenario.1 Scenario.2 Scenario.3 Scenario.4 Scenario.5 Scenario.6 Scenario.7
    2019 0.169 0.169 0.169 0.169 0.169 0.169 0.169
    2020 0.299 0.172 0.066 0.083 0.000 0.353 0.299
    2021 0.299 0.283 0.066 0.083 0.000 0.343 0.299
    2022 0.284 0.263 0.066 0.083 0.000 0.315 0.330
    2023 0.277 0.259 0.066 0.083 0.000 0.311 0.317
    2024 0.275 0.258 0.066 0.083 0.000 0.310 0.312
    2025 0.276 0.260 0.066 0.083 0.000 0.312 0.314
    2026 0.277 0.261 0.066 0.083 0.000 0.313 0.314
    2027 0.277 0.260 0.066 0.083 0.000 0.313 0.313
    2028 0.278 0.259 0.066 0.083 0.000 0.312 0.312
    2029 0.277 0.258 0.066 0.083 0.000 0.310 0.310
    2030 0.275 0.254 0.066 0.083 0.000 0.306 0.306
    2031 0.274 0.254 0.066 0.083 0.000 0.307 0.307
    2032 0.275 0.256 0.066 0.083 0.000 0.309 0.309
    -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -## -##
    Tier 3 projections of BSAI Atka mackerel spawning biomass for the 7 scenarios.
    ABC Scenario.1 Scenario.2 Scenario.3 Scenario.4 Scenario.5 Scenario.6 Scenario.7
    2019 93 93 22 27 0 107 107
    2020 91 89 21 27 0 106 106
    2021 83 85 22 28 0 91 96
    2022 81 80 25 30 0 85 93
    2023 82 81 27 33 0 88 91
    2024 86 84 29 35 0 92 93
    2025 90 88 30 37 0 96 97
    2026 90 88 31 38 0 96 97
    2027 89 86 31 38 0 94 94
    2028 89 86 31 38 0 95 95
    2029 85 82 31 37 0 90 90
    2030 84 80 30 37 0 88 88
    2031 85 83 31 37 0 91 91
    2032 88 85 31 38 0 93 93
    -``` - diff --git a/examples/yfs.dat b/examples/yfs.dat deleted file mode 100644 index 49f8148..0000000 --- a/examples/yfs.dat +++ /dev/null @@ -1,41 +0,0 @@ -BSAI_YFINSS -0 # SSL Species??? -0 # Constant buffer of Dorn? -1 # Number of fisheries -2 # Number of sexes?? -0.0522 # average 5yr f -1.0 # author f -0.4 # SPR ABC -0.35 # SPR MSY -5 # Spawnmo -20 # Number of ages -1 # Fratio - # 0.5 0.5 # Fratio -#females first -0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 -#males -0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 -# Maturity Females first row used in projections (average of 2012 and 1992 studies) -0.0001 0.0003 0.0008 0.0018 0.0042 0.011 0.0382 0.097 0.2134 0.4134 0.6459 0.825 0.9195 0.9675 0.9855 0.996 0.9985 1 1 1 -# Maturity Males -0.00026 0.0006 0.00159 0.0035 0.0084 0.012 0.0463 0.1039 0.2167 0.3967 0.6118 0.7899 0.899 0.955 0.981 0.992 0.997 1 1 1 -# Wt spawn females -0.006 0.008 0.011 0.016 0.039 0.053 0.122 0.21 0.273 0.36 0.399 0.42 0.434 0.453 0.471 0.508 0.529 0.537 0.546 0.59 -# WtAge Females, by fishery -0.006 0.008 0.032 0.05 0.066 0.074 0.112 0.186 0.338 0.372 0.412 0.408 0.455 0.456 0.485 0.508 0.515 0.532 0.555 0.59 -# WtAge Males, by fishery -0.004 0.008 0.043 0.057 0.063 0.082 0.116 0.171 0.253 0.319 0.318 0.331 0.338 0.346 0.381 0.383 0.408 0.434 0.413 0.46 -# Selectivity Females, by fishery using last year in time-varying model -0.000701093 0.00157433 0.00353139 0.00790201 0.0175864 0.0386771 0.0829259 0.168903 0.313545 0.506555 0.697632 0.838332 0.920976 0.963226 0.983297 0.992499 0.996648 0.996648 0.996648 0.996648 -# Selectivity males, by fishery using last year in time-varying model -0.000663833 0.0014836 0.00331236 0.00737866 0.0163549 0.0358564 0.076796 0.156873 0.293871 0.482098 0.675547 0.823232 0.91241 0.958847 0.981173 0.991494 0.996179 0.996179 0.996179 0.996179 -# N at age in 2018 Females, Males -1179.8 1033.99 934.165 1186.63 1173.63 564.941 178.599 250.413 660.076 421.127 310.649 287.582 182.132 119.927 221.512 114.413 97.0637 58.0578 75.7241 362.037 -1179.8 1033.94 934.086 1186.45 1173.45 565.078 178.989 252.484 673.115 433.036 318.252 291.14 182.187 118.758 217.846 112.506 96.4746 58.1467 75.3316 360.317 -# Number of Recruits -33 -# Recruitment 1978-2010 -2467.15 1574.561 3038.13 2258.42 6528.73 1209.186 5401.76 1868.121 1435.065 1959.887 2685 2682.95 1339.353 1503.374 3331.54 1988.833 1679.79 1692.189 4175.32 1805.508 1499.919 1839.244 2606.42 1658.825 2284.2 2219.71 3523.92 1533.483 1844.777 2349.6 2112.732 2434.22 3272.3 -# SSB -# used only for S/R analysis -293.496 409.183 526.225 656.554 776.716 843.983 945.256 1024.84 1071.79 1058.17 1051.2 993.336 965.759 976.509 1057.25 1139.5 1174.45 1177.44 1176.38 1110.03 1073.52 1009.26 1000.98 987.701 983.504 981.707 990.525 1021.07 1036.84 1056.94 1061.63 1036.55 1000.41 975.522 952.413 934.539 924.131 881.156 872.721 874.66 856.504 854.096 diff --git a/src/spm.cpp b/src/spm.cpp deleted file mode 100644 index 43058d8..0000000 --- a/src/spm.cpp +++ /dev/null @@ -1,2909 +0,0 @@ -#ifdef DEBUG - #ifndef __SUNPRO_C - #include - #include - #endif -#endif - #include - adstring xspname; - adstring_array targsppname(1,20); - adstring_array spp_file_name(1,20); - adstring_array gearname(1,8); - adstring_array spname(1,90); - adstring_array areaname(1,8); - adstring run_name; - ofstream write_log("Input_Log.rep"); - #undef write_log - #define write_log(object) write_log << #object "\n" << object << endl; - // A routine to get transpose, sort and return a matrix ---- - dmatrix TranSort (const dmatrix m1) - { - RETURN_ARRAYS_INCREMENT(); - dmatrix vtmp=trans(m1); - int npro=m1.colmax(); - int nsim=m1.rowmax(); - for (int i=1;i<=npro;i++) - vtmp(i) = sort(vtmp(i),nsim); - RETURN_ARRAYS_DECREMENT(); - return(vtmp); - } - // #include -#ifdef DEBUG - #include -#endif -#include -#ifdef USE_ADMB_CONTRIBS -#include - -#endif - extern "C" { - void ad_boundf(int i); - } -#include - -model_data::model_data(int argc,char * argv[]) : ad_comm(argc,argv) -{ - adstring tmpstring; - tmpstring=adprogram_name + adstring(".dat"); - if (argc > 1) - { - int on=0; - if ( (on=option_match(argc,argv,"-ind"))>-1) - { - if (on>argc-2 || argv[on+1][0] == '-') - { - cerr << "Invalid input data command line option" - " -- ignored" << endl; - } - else - { - tmpstring = adstring(argv[on+1]); - } - } - } - global_datafile = new cifstream(tmpstring); - if (!global_datafile) - { - cerr << "Error: Unable to allocate global_datafile in model_data constructor."; - ad_exit(1); - } - if (!(*global_datafile)) - { - delete global_datafile; - global_datafile=NULL; - } - pad_means_out = new ofstream("means.out"); - pad_alts_proj = new ofstream("alt_proj.out"); - pad_percent_out = new ofstream("percentiles.out"); - pad_percent_db = new ofstream("percentdb.out"); - pad_detail_out = new ofstream("spm_detail.csv"); - pad_prof_F = new ofstream("F_profile.out");; - pad_elasticity = new ofstream("elasticity.csv");; - condition_SR = 0;// - rnseed = 123; - *(ad_comm::global_datafile) >> run_name; // Read in the name of this run - Tier.allocate("Tier"); - rec_vector.allocate(1,300); - wtd_rec.allocate(1,25); - wtd_div.allocate(1,25); - nalts.allocate("nalts"); - alt_list.allocate(1,nalts,"alt_list"); - TAC_ABC.allocate("TAC_ABC"); - SrType.allocate("SrType"); - Rec_Gen.allocate("Rec_Gen"); - Fmsy_F35.allocate("Fmsy_F35"); - Rec_Cond.allocate("Rec_Cond"); - Write_Big.allocate("Write_Big"); - npro.allocate("npro"); - nsims.allocate("nsims"); - styr.allocate("styr"); - cout<< "First year:\t"<> spp_file_name(i); - write_log(nyrs_catch_in); - write_log(nspp); - write_log(OY_min); - write_log(OY_max); - write_log(spp_file_name); - cout <<"OYMax "<> bzero_in; - *(ad_comm::global_datafile) >> phizero_in; - *(ad_comm::global_datafile) >> alpha_in; - *(ad_comm::global_datafile) >> sigmar_in; - *(ad_comm::global_datafile) >> rho_in; - // rho_in=0.82; - } - // Open up tac-model parameters - ad_comm::change_datafile_name("tacpar.dat"); - nntmp.allocate("nntmp"); - nnodes.allocate("nnodes"); - maxabc.allocate(1,ntacspp,"maxabc"); - theta.allocate(0,nnodes,1,ntacspp,"theta"); - cout<<"read tacpar"<> spname(i); // 1 - cout<> SSL_spp(i); // 2 - *(ad_comm::global_datafile) >> Const_Buffer(i); // 3 - *(ad_comm::global_datafile) >> ngear(i); // 4 - *(ad_comm::global_datafile) >> nsexes(i); // 6 - *(ad_comm::global_datafile) >> avg_5yrF(i); // 7 - *(ad_comm::global_datafile) >> FABC_Adj(i); // 8 - *(ad_comm::global_datafile) >> SPR_abc(i); // 9 - *(ad_comm::global_datafile) >> SPR_ofl(i); // 10 - *(ad_comm::global_datafile) >> spawnmo(i); // 11 - *(ad_comm::global_datafile) >> nages(i); // 12 - cout<<"nages: "<> Fratiotmp(i,j); // 13 - write_log( Fratiotmp(i)); - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> M_Ftmp(i,k); // 14 - if (nsexes(i)==2) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> M_Mtmp(i,k); // 15 - write_log( M_Ftmp(i)); - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> pmaturetmp_F(i,k); // 16 - write_log( pmaturetmp_F(i)); - if (nsexes(i)==2) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> pmaturetmp_M(i,k); // 15 - write_log( pmaturetmp_M(i)); - cout << "Mature: "<< pmaturetmp_F(i)(1,nages(i)) <> wt_Ftmp(i,k); // 17 - write_log( wt_Ftmp(i)); - for (int j=1;j<=ngear(i);j++) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> wt_gear_Ftmp(i,j,k); // 18 - write_log( wt_gear_Ftmp(i)); - if (nsexes(i)==2) - for (int j=1;j<=ngear(i);j++) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> wt_gear_Mtmp(i,j,k);// 19 - write_log( wt_gear_Mtmp(i)); - - for (int j=1;j<=ngear(i);j++) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> sel_Ftmp(i,j,k); // 20 - write_log( sel_Ftmp(i)); - if (nsexes(i)==2) - for (int j=1;j<=ngear(i);j++) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> sel_Mtmp(i,j,k); // 21 - write_log( sel_Mtmp(i)); - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> n0_Ftmp(i,k); // 22 - write_log( n0_Ftmp(i)); - if (nsexes(i)==2) - for (int k=1;k<=nages(i);k++) - *(ad_comm::global_datafile) >> n0_Mtmp(i,k); // 23 - write_log( n0_Mtmp(i)); - cout<<"N: "<> nrec(i); // 24 - cout<<"nrec: "<> Rtmp(i,j); // 25 - cout<<"Rec: "<> SSBtmp(i,j); // 26 - cout<<"SSB: "<> Rsim(ispp,i,j); - Rsim(ispp,i,j) *= exp(rnorms(i,j)*.375); // about 15% CV to get historical mean - } - if (nsims<=5) Rsim(ispp,i,j) = AMeanRec(ispp); // XXX constant recruitment - } - } - envin.close(); - AMeanRec(ispp) *= .5; // Goes to females only - cout <<"Mean recruits "<< AMeanRec(ispp) < 0 ) - F_begin_yr(k,ispp) = SolveF2(n0_F(ispp), n0_M(ispp), Obs_Catch(k,ispp),ispp); // F_yr_one(ispp) = SolveF2(n0_F(ispp), n0_M(ispp), yr_one_catch(ispp),ispp); - else - F_begin_yr(ispp) = 0; // F_yr_one(ispp) = 0; // cout< 1) // Debugging if LP to be done or not - { - int on=0; - if ( (on=option_match(ad_comm::argc,ad_comm::argv,"-nolp"))>-1) - dolp=0; - else - dolp=1; - } -} - -void model_parameters::preliminary_calculations(void) -{ - -#if defined(USE_ADPVM) - - admaster_slave_variable_interface(*this); - -#endif - double tmp1; - write_alts_hdr(); - get_SB100(); - cout<<"SSB, Biomass at unfished: "<GRAD_STACK1->total() << " out of " << gradient_structure::get_GRADSTACK_BUFFER_SIZE() << std::endl;; - std::cout << "DEBUG: Total dvariable address used is " << gradient_structure::get()->GRAD_LIST->total_addresses() << " out of " << gradient_structure::get_MAX_DLINKS() << std::endl;; - std::cout << "DEBUG: Total dvariable address used is " << gradient_structure::get()->ARR_LIST1->get_max_last_offset() << " out of " << gradient_structure::get_ARRAY_MEMBLOCK_SIZE() << std::endl;; -#endif -} - -void model_parameters::Run_Sim(void) -{ - ofstream& means_out= *pad_means_out; - ofstream& alts_proj= *pad_alts_proj; - ofstream& percent_out= *pad_percent_out; - ofstream& percent_db= *pad_percent_db; - ofstream& detail_out= *pad_detail_out; - ofstream& prof_F= *pad_prof_F; - ofstream& elasticity= *pad_elasticity; - detail_out<<"Stock,Alternative,Sim,Yr,SSB,Rec,Tot_biom,SPR_Implied,F,Ntot,Catch,ABC,OFL,AvgAge,AvgAgeTot,SexRatio,B100,B40,B35"<1) - nyrs_catch = nyrs_catch_in; - //else - // nyrs_catch = 1;// NOTA BUENO: this is changed under the new EIS Alternatives (May 06) - Do_Sims(); - // if (alt==2) write_alts(); - write_alts(); - } -} - -void model_parameters::compute_obj_fun(void) -{ - ofstream& means_out= *pad_means_out; - ofstream& alts_proj= *pad_alts_proj; - ofstream& percent_out= *pad_percent_out; - ofstream& percent_db= *pad_percent_db; - ofstream& detail_out= *pad_detail_out; - ofstream& prof_F= *pad_prof_F; - ofstream& elasticity= *pad_elasticity; - dvariable tmp1; - dvariable tmp2; - tmp1.initialize(); - for (int ispp=1;ispp<=nspp;ispp++) - { - Get_Bzero(ispp); - // write_srec();exit(1); - if(Fmsy_F35>0) - { - get_msy(ispp); - switch (current_phase()) - { - case 1 : tmp1 = 1.e1*square(log(Fmsy(ispp))-log(F35(ispp))); break; - case 2 : tmp1 = 1.e2*square(log(Fmsy(ispp))-log(F35(ispp))); break; - case 3 : tmp1 = 1.e2*square(log(Fmsy(ispp))-log(F35(ispp))); break; - case 4 : tmp1 = 1.e3*square(log(Fmsy(ispp))-log(F35(ispp))); break; - default: tmp1 = 1.e3*square(log(Fmsy(ispp))-log(F35(ispp))); break; - } - if(Fmsy_F35==2) - switch (current_phase()) - { - case 1 : tmp1 += 1.e1*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - case 2 : tmp1 += 1.e2*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - case 3 : tmp1 += 1.e2*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - case 4 : tmp1 += 1.e3*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - default : tmp1+= 1.e3*square(log(Bmsy(ispp))-log(0.35 * SB100(ispp))); break; - } - obj_fun += tmp1; - } - if (Rec_Cond>0.) - { - // More conditioning here--make mean recruitment consistent with half and double - // recruitment at avg spawning biomass... - double vartmp = 2.*Rec_Cond*Rec_Cond; - /* - double ssb1 = AMeanSSB(ispp) * 0.5; - double ssb2 = AMeanSSB(ispp) ; - double ssb3 = AMeanSSB(ispp) * 2.0; - // double ssb4 = value(Bzero(ispp)) ; - tmp2.initialize(); - tmp2 += square(log(AMeanRec(ispp)) - log(SRecruit( ssb1, ispp)))/ vartmp; - tmp2 += square(log(AMeanRec(ispp)) - log(SRecruit( ssb2, ispp)))/ vartmp; - tmp2 += square(log(AMeanRec(ispp)) - log(SRecruit( ssb3, ispp)))/ vartmp; - // tmp2 += 24.* square(log(AMeanRec(ispp)) - log(SRecruit( ssb4, ispp))); - // tmp2 += 24.* square(log(AMeanSSB(ispp)) - log( ssb4 )); - */ - tmp2 = square(log(Bzero(ispp)) - log(SB100(ispp)))/ vartmp; - obj_fun += tmp2; - // cout << "SSB "<OY_max) - Actual_Catch = TAC/sum(TAC)*OY_max; - else - Actual_Catch = TAC; - } - /* ////////////////////////////////////////////////////////////////////////////// //int use_max = 1; // Need to move this into the setup file... double max_catch=1500.;// EBS POllock special case (CHANGE THIS) //if (use_max==1) for (int ispp=1;ispp<=nspp;ispp++) Actual_Catch(ispp) = min(TAC(ispp),max_catch); // cout<<"AC: "<3) - { - Alt4_Fcalc = 1; - double diff; - double ssl_sum=0.; - diff = OY_min-sumabc; - // if so, sum up SSL ABCs - ssl_sum = SSL_spp*ABC; - for (int ispp=1;ispp<=nspp;ispp++) - { - // then apportion them to difference between current ABC and target (OY_min) difference - if (SSL_spp(ispp)) - { - TAC(ispp) = ABC(ispp) + diff * ABC(ispp)/ssl_sum; - // Special for BSAI Pcod to subtract off 3%...and get the totals to match up - // if (ispp==2) TAC(ispp) /= 0.97; For EIS work - } - else - { - TAC(ispp) = ABC(ispp); - } - } - // cout << ipro<<" "<0.01) ftmp = SolveF2(N_F(ispp),N_M(ispp),TAC(ispp),ispp); - // cout <= 0.2 *SBzero(ispp) & SBtmp < SBKink(ispp) ) // NOTE Same as Am 56 here (until 20% bzero reached) - Ftmp = (Fabc(ispp)*(1/(1-alpha ))*(SBtmp/SBKink(ispp) - alpha )); - if (SBtmp > SBKink(ispp) ) - Ftmp = (Fabc(ispp)); - SBtmp = N_females * elem_prod( wt_mature_F(ispp), mfexp( -yrfrac(ispp)*(M_F(ispp) + Ftmp*F_age ))); - } - return(Ftmp); -} - -double model_parameters::Get_F_Am56(const dvector& F_age, const dvector& N_females, const int ispp ) -{ - ofstream& means_out= *pad_means_out; - ofstream& alts_proj= *pad_alts_proj; - ofstream& percent_out= *pad_percent_out; - ofstream& percent_db= *pad_percent_db; - ofstream& detail_out= *pad_detail_out; - ofstream& prof_F= *pad_prof_F; - ofstream& elasticity= *pad_elasticity; - double Ftmp; // cout<< spname(ispp)<< endl; - { - for (ii=1;ii<=3;ii++) // Iterate to get month of spawning correct - { - if (SBtmp < alpha*SBKink(ispp)) - Ftmp = 0.; - if (SBtmp >= alpha*SBKink(ispp) & SBtmp < SBKink(ispp) ) - Ftmp = (Fabc(ispp)*(1/(1-alpha))*(SBtmp/SBKink(ispp) - alpha)); - if (SBtmp > SBKink(ispp) ) - Ftmp = (Fabc(ispp)); - SBtmp = N_females * elem_prod( wt_mature_F(ispp), mfexp( -yrfrac(ispp)*(M_F(ispp) + Ftmp*F_age ))); - } - } - return(Ftmp); -} - -double model_parameters::Get_Fofl_t2(const dvector& F_age, const dvector& N_females, const int ispp ) -{ - ofstream& means_out= *pad_means_out; - ofstream& alts_proj= *pad_alts_proj; - ofstream& percent_out= *pad_percent_out; - ofstream& percent_db= *pad_percent_db; - ofstream& detail_out= *pad_detail_out; - ofstream& prof_F= *pad_prof_F; - ofstream& elasticity= *pad_elasticity; - double Ftmp; // cout<< spname(ispp)<< endl; - { - for (ii=1;ii<=3;ii++) // Iterate to get month of spawning correct - { - if (SBtmp < alpha*SBKink(ispp)) - Ftmp = 0.; - if (SBtmp >= alpha*SBKink(ispp) & SBtmp < SBKink(ispp) ) - Ftmp = (Fofl(ispp)*(1/(1-alpha))*(SBtmp/SBKink(ispp) - alpha)); - if (SBtmp > SBKink(ispp) ) - Ftmp = (Fofl(ispp)); - SBtmp = N_females * elem_prod( wt_mature_F(ispp), mfexp( -yrfrac(ispp)*(M_F(ispp) + Ftmp*F_age ))); - } - } - return(Ftmp); -} - -double model_parameters::Get_Fofl_t(const dvector& F_age, const dvector& N_females, const int ispp ) -{ - ofstream& means_out= *pad_means_out; - ofstream& alts_proj= *pad_alts_proj; - ofstream& percent_out= *pad_percent_out; - ofstream& percent_db= *pad_percent_db; - ofstream& detail_out= *pad_detail_out; - ofstream& prof_F= *pad_prof_F; - ofstream& elasticity= *pad_elasticity; - double Ftmp; // cout<< spname(ispp)<< endl; - { - for (ii=1;ii<=3;ii++) // Iterate to get month of spawning correct - { - if (SBtmp < alpha*SBKink(ispp)) - Ftmp = 0.; - if (SBtmp >= alpha*SBKink(ispp) & SBtmp < SBKink(ispp) ) - Ftmp = (Fofl(ispp)*(1/(1-alpha))*(SBtmp/SBKink(ispp) - alpha)); - if (SBtmp > SBKink(ispp) ) - Ftmp = (Fofl(ispp)); - SBtmp = N_females * elem_prod( wt_mature_F(ispp), mfexp( -yrfrac(ispp)*(M_F(ispp) + Ftmp*F_age ))); - } - } - return(Ftmp); -} - -double model_parameters::Get_F_t(const dvector& F_age, const dvector& N_females, const int ispp ) -{ - ofstream& means_out= *pad_means_out; - ofstream& alts_proj= *pad_alts_proj; - ofstream& percent_out= *pad_percent_out; - ofstream& percent_db= *pad_percent_db; - ofstream& detail_out= *pad_detail_out; - ofstream& prof_F= *pad_prof_F; - ofstream& elasticity= *pad_elasticity; - double Ftmp; // cout<< spname(ispp)<< endl; - if (SSL_spp(ispp)) - { - Ftmp = Get_F_SSL_prey(F_age, N_females, ispp); - } - else - { - Ftmp = Get_F_Am56(F_age, N_females, ispp); - } - return(Ftmp); -} - -void model_parameters::Project_Pops(const int& isim, const int& i) -{ - ofstream& means_out= *pad_means_out; - ofstream& alts_proj= *pad_alts_proj; - ofstream& percent_out= *pad_percent_out; - ofstream& percent_db= *pad_percent_db; - ofstream& detail_out= *pad_detail_out; - ofstream& prof_F= *pad_prof_F; - ofstream& elasticity= *pad_elasticity; - double ctmp; - if ( i == npro && isim%int(nsims/4)==0) cout << "Year "< 0) - { - if (i <= nyrs_catch || TAC_ABC==0) // Use TAC setting algorithm for alt 2 only, for all others, set TAC==ABC - { - Ftmp = SolveF2(N_F(ispp),N_M(ispp),Actual_Catch(ispp),ispp); - } - else - { - if (alt==7 && i<=3) - Ftmp = Get_F(1,ispp); // Set to the F rather than solving every time... - else - if (alt==77 && i<=2) - Ftmp = Get_F(1,ispp); // Set to the F rather than solving every time... - else - { - // if (alt==2 && sum(TAC)>OY_max) - if ((alt==2 ||alt==98||alt==97) && sum(TAC)>OY_max) - { - Ftmp = SolveF2(N_F(ispp),N_M(ispp),OY_max*TAC(ispp)/sum(TAC),ispp); - // cout<<"HERE "<< Ftmp<<" "< 0.) - { - for (m=1;m<=ngear(ispp);m++) - { - Ftottmp_F = Ftmp*Frat(ispp,m)*sel_F(ispp,m); - Ftottmp_spr += Ftottmp_F ; - Ftottmp_M = Ftmp*Frat(ispp,m)*sel_M(ispp,m); - ctmp += (wt_gear_F(ispp,m) * elem_prod(elem_div(Ftottmp_F, Z_F(ispp)),elem_prod(1.-S_F(ispp),N_F(ispp)))); // Catch equation (vectors) - ctmp += (wt_gear_M(ispp,m) * elem_prod(elem_div(Ftottmp_M, Z_M(ispp)),elem_prod(1.-S_M(ispp),N_M(ispp)))); // Catch equation (vectors) - } - SPRsim(ispp,isim,i) = Implied_SPR(Ftottmp_spr,ispp); - // cout <NUL "); - ifstream tac("tac.dat"); - tac>>TAC; - tac.close(); /* */ - for (int ispp=1;ispp<=nspp;ispp++) - Actual_Catch(ispp) = min(TAC(ispp),ABC(ispp)); - } -} - -void model_parameters::Avg_Age(void) -{ - ofstream& means_out= *pad_means_out; - ofstream& alts_proj= *pad_alts_proj; - ofstream& percent_out= *pad_percent_out; - ofstream& percent_db= *pad_percent_db; - ofstream& detail_out= *pad_detail_out; - ofstream& prof_F= *pad_prof_F; - ofstream& elasticity= *pad_elasticity; - Avg_Age_End.initialize(); - Avg_Age_Mat.initialize(); - for (int ispp=1;ispp<=nspp;ispp++) - { - // if(!isit_const(ispp) ) - { - dvector age_seq(1,nages(ispp)); - age_seq.initialize(); - age_seq.fill_seqadd(1,1); - dvector ntmp(1,nages(ispp)); - ntmp = N_F(ispp); - Avg_Age_End(ispp) += age_seq * ntmp /sum(ntmp); - Avg_Age_sum(ispp) += Avg_Age_End(ispp) ; - ntmp = elem_prod(ntmp,pmature_F(ispp)) ; - Avg_Age_Mat(ispp) += elem_prod(ntmp,pmature_F(ispp)) * age_seq/sum(ntmp); - // cout< 1e-6) - { - iter++; - ftmp += (TACin-cc) / btmp; - Ftottmp_F.initialize(); - Ftottmp_M.initialize(); - for (m=1;m<=ngear(ispp);m++) - { - Ftottmp_F += ftmp*Fratsel_F(m); - Ftottmp_M += ftmp*Fratsel_M(m); - } - Z_F = Ftottmp_F + M_F(ispp); - Z_M = Ftottmp_M + M_M(ispp); - S_F = mfexp( -Z_F ); - S_M = mfexp( -Z_M ); - cc = 0.0; - for (m=1;m<=ngear(ispp);m++) - { - cc += (wt_gear_F(ispp,m) * elem_prod(elem_div(ftmp*Fratsel_F(m), Z_F),elem_prod(1.-S_F,N_F))); // Catch equation (vectors) - cc += (wt_gear_M(ispp,m) * elem_prod(elem_div(ftmp*Fratsel_M(m), Z_M),elem_prod(1.-S_M,N_M))); // Catch equation (vectors) - } - dd = cc / TACin - 1.; - //cout << ispp<<" "<< ftmp << " "<< cc << " "<100) {cerr<<"Bombed on catch solver--check scales for "<nages(ispp)) - { - Tg += double(ii) * wt_mature_F(ispp,nages(ispp)) * ntmp; - tmp += wt_mature_F(ispp,nages(ispp)) * ntmp; - ntmp *= exp(-M_F(ispp,nages(ispp))); - } - else - { - Tg += double(ii) * wt_mature_F(ispp,ii) * ntmp; - tmp += wt_mature_F(ispp,ii) * ntmp; - ntmp *= exp(-M_F(ispp,ii)); - } - } - // Tg /= value(phizero(ispp)); - Tg /= tmp; - report << Tg<< " "; - } - report << endl; - report << "Options SR_Type Project_Recr_Assmption SR_Condition"<2) - write_sim("Alternative ",ispp); - } - write_by_time(); - // Writing routines here .... -} - -void model_parameters::write_by_time(void) -{ - ofstream& means_out= *pad_means_out; - ofstream& alts_proj= *pad_alts_proj; - ofstream& percent_out= *pad_percent_out; - ofstream& percent_db= *pad_percent_db; - ofstream& detail_out= *pad_detail_out; - ofstream& prof_F= *pad_prof_F; - ofstream& elasticity= *pad_elasticity; - write_sim("Catch", Csim,Cabc); // Total Catch - cout< 1e-6) - means_out << mean_value <<" "; - else - means_out << " NA "; - } - means_out << endl; - } - means_out << endl; -} - -void model_parameters::write_TACs(const adstring& Title) -{ - ofstream& means_out= *pad_means_out; - ofstream& alts_proj= *pad_alts_proj; - ofstream& percent_out= *pad_percent_out; - ofstream& percent_db= *pad_percent_db; - ofstream& detail_out= *pad_detail_out; - ofstream& prof_F= *pad_prof_F; - ofstream& elasticity= *pad_elasticity; - // This one prints out species over time (means only), but without headings - means_out <<"Alternative "< 1e-6) - means_out << mean_value <<" "; - else - means_out << " NA "; - } - means_out << endl; - } - means_out << endl; -} - -void model_parameters::write_sim(const adstring& Title, d3_array& Outtmp) -{ - ofstream& means_out= *pad_means_out; - ofstream& alts_proj= *pad_alts_proj; - ofstream& percent_out= *pad_percent_out; - ofstream& percent_db= *pad_percent_db; - ofstream& detail_out= *pad_detail_out; - ofstream& prof_F= *pad_prof_F; - ofstream& elasticity= *pad_elasticity; - // This one prints out species over time (means only), but without headings - d3_array mtmp(1,nspp,1,npro,1,nsims); - for (int ispp=1;ispp<=nspp;ispp++) for (int i=1;i<=npro;i++) for (int k=1;k<=nsims;k++) - mtmp(ispp,i,k)=Outtmp(ispp,k,i); - means_out <<"Alternative "< 1e-6) - means_out << mean_value <<" "; - else - means_out << " NA "; - } - means_out << endl; - } - means_out << endl; -} - -void model_parameters::write_sim(const adstring& Title,const d3_array& Outtmp,const dvar_vector& bb) -{ - ofstream& means_out= *pad_means_out; - ofstream& alts_proj= *pad_alts_proj; - ofstream& percent_out= *pad_percent_out; - ofstream& percent_db= *pad_percent_db; - ofstream& detail_out= *pad_detail_out; - ofstream& prof_F= *pad_prof_F; - ofstream& elasticity= *pad_elasticity; - // This one prints out species over time (means only) - d3_array mtmp(1,nspp,1,npro,1,nsims); - for (int ispp=1;ispp<=nspp;ispp++) for (int i=1;i<=npro;i++) for (int k=1;k<=nsims;k++) - mtmp(ispp,i,k)=Outtmp(ispp,k,i); - means_out <<"Alternative "<1e-6) - means_out << mean_value <<" "; - else - means_out << " NA "; - } - means_out << endl; - } - means_out << endl; -} - -void model_parameters::write_sim(const adstring& Title,const int& ispp) -{ - ofstream& means_out= *pad_means_out; - ofstream& alts_proj= *pad_alts_proj; - ofstream& percent_out= *pad_percent_out; - ofstream& percent_db= *pad_percent_db; - ofstream& detail_out= *pad_detail_out; - ofstream& prof_F= *pad_prof_F; - ofstream& elasticity= *pad_elasticity; - // For each species separately - percent_out <<"Alternative "<5||F1<0.01)) - // { - // ii=5; - // F1=M(ispp,nages(ispp)); // When things bomb (F <0 or F really big then just set it to F35...) - // } - // else - { - F2 = F1 + df*.5; - F3 = F2 - df; //F1 = double(ii)/100; - yld1 = yield(F1,Stmp,Rtmp,ispp); //cout <(std::chrono::high_resolution_clock::now() - start).count() << " microseconds." << endl; - #ifndef __SUNPRO_C -bool failedtest = false; -if (std::fetestexcept(FE_DIVBYZERO)) - { cerr << "Error: Detected division by zero." << endl; failedtest = true; } -if (std::fetestexcept(FE_INVALID)) - { cerr << "Error: Detected invalid argument." << endl; failedtest = true; } -if (std::fetestexcept(FE_OVERFLOW)) - { cerr << "Error: Detected overflow." << endl; failedtest = true; } -if (std::fetestexcept(FE_UNDERFLOW)) - { cerr << "Error: Detected underflow." << endl; } -if (failedtest) { std::abort(); } - #endif -#endif - return 0; -} - -extern "C" { - void ad_boundf(int i) - { - /* so we can stop here */ - exit(i); - } -} diff --git a/src/spm.htp b/src/spm.htp deleted file mode 100644 index 39d4a84..0000000 --- a/src/spm.htp +++ /dev/null @@ -1,326 +0,0 @@ -#if !defined(_SPM_) -# define _SPM_ - -class model_data : public ad_comm{ - ofstream * pad_means_out; - ofstream * pad_alts_proj; - ofstream * pad_percent_out; - ofstream * pad_percent_db; - ofstream * pad_detail_out; - ofstream * pad_prof_F; - ofstream * pad_elasticity; - int condition_SR; - int ipro; - int isim; - double kink_adj; - int rnseed; - data_int Tier; - int alt; - dvector rec_vector; - dvector wtd_rec; - dvector wtd_div; - data_int nalts; - data_ivector alt_list; - data_int TAC_ABC; - data_int SrType; - data_int Rec_Gen; - data_int Fmsy_F35; - data_number Rec_Cond; - data_int Write_Big; - data_int npro; - data_int nsims; - data_int styr; - double bzero_in; - double phizero_in; - double alpha_in; - double sigmar_in; - double rho_in; - dmatrix rnorms; - dmatrix unifs; - dvector sst; - int nyrs_catch; - data_int nyrs_catch_in; - data_int nspp; - data_number OY_min; - data_number OY_max; - data_vector ABC_Multiplier; - data_vector N_scalar; - data_vector Alt4_SPR; - dvector Alt4_Fabc; - int Alt4_Fcalc; - dvector Alt3b_obj_fun; - data_int ntacspp; - data_ivector tac_ind; - dvector model_tacs; - data_matrix Obs_Catch; - data_int nntmp; - data_int nnodes; - data_vector maxabc; - data_matrix theta; - dvector agg_abc; - dvector agg_cat; - dvector agg_tac; - int i; - int k; - int dolp; - ivector SSL_spp; - ivector Const_Buffer; - double srprior_a; - double srprior_b; - ivector ngear; - ivector isit_const; - dvector FABC_Adj; - dvector spawnmo; - ivector nages; - dmatrix Fratiotmp; - dmatrix M_Ftmp; - dmatrix M_Mtmp; - dmatrix pmaturetmp_F; - dmatrix pmaturetmp_M; - dmatrix wt_Ftmp; - d3_array wt_gear_Ftmp; - d3_array wt_gear_Mtmp; - d3_array sel_Ftmp; - d3_array sel_Mtmp; - dmatrix n0_Ftmp; - dmatrix n0_Mtmp; - dmatrix Rtmp; - dmatrix SSBtmp; - ivector nrec; - dvector Expl_Biom; - int ntargspp; - dvector reserved; - ivector nsexes; - dvector avg_5yrF; - dvector SPR_abc; - dvector SPR_ofl; - dvector targ_SPR; - dmatrix M_F; - dmatrix M_M; - dmatrix pmature_F; - dmatrix pmature_M; - dmatrix wt_F; - dmatrix wt_M; - dmatrix Frat; - d3_array wt_gear_F; - d3_array wt_gear_M; - d3_array sel_F; - d3_array sel_M; - dmatrix n0_F; - dmatrix n0_M; - dmatrix R; - dmatrix SSB; - dmatrix wt_mature_F; - dmatrix wt_mature_M; - dvector yrfrac; - dvector Actual_Catch; - d3_array Rsim; - d3_array Fsim; - d3_array Bsim; - d3_array Nsim; - d3_array SRsim; - d3_array SBsim; - d3_array SPRsim; - d3_array Csim; - dmatrix TACs_by_yr; - dmatrix ABCs_by_yr; - dmatrix OFLs_by_yr; - dmatrix FABCs_by_yr; - dmatrix FOFLs_by_yr; - double gi_beta; - double gamma; - dvector cvrec; - double delta; - dvector TAC; - dvector ABC; - dvector OFL; - dvector AMeanSSB; - dvector AMeanRec; - dvector AMaxSSB; - dvector HMeanRec; - dvector Bcurrent; - double alpha; - int ii; - int m; - int j; - double alphaCI; - int UCI; - int LCI; - dmatrix N_F; - dmatrix N_M; - dmatrix Nnext_F; - dmatrix Nnext_M; - double chi_prev; - double chi; - dmatrix Z_F; - dmatrix Z_M; - dmatrix S_F; - dmatrix S_M; - dvector Cabc; - dvector Cofl; - dvector F35; - dvector Ftarg; - double Btmp; - double SBtmp; - d3_array C_F; - d3_array C_M; - dvector F_yr_one; - dmatrix F_begin_yr; - dvector Fabc; - dvector F40; - dvector Fofl; - dmatrix Ftotabc; - dmatrix Ftot40; - dmatrix Ftotofl; - dmatrix N; - dmatrix NsprF0; - dmatrix NsprM0; - dmatrix NsprFabc; - dmatrix NsprMabc; - dmatrix NsprF40; - dmatrix NsprM40; - dmatrix NsprFofl; - dmatrix NsprMofl; - dvector SB100; - dvector SBzero; - dvector SBFabc; - dvector SBF40; - dvector SBKink; - dvector SBFofl; - dvector Avg_Age_Mabc; - dvector Avg_Age_M0; - dvector Avg_Age_Fabc; - dvector Avg_Age_F0; - dvector Avg_Age_End; - dvector Avg_Age_Mat; - dvector Avg_Age_sum; - dvector BFabc; - dvector BF40; - dvector BFofl; - dvector B100; - double R_guess; - double phase_sr; - ~model_data(); - model_data(int argc,char * argv[]); - friend class model_parameters; -}; - -class model_parameters : public model_data , - public function_minimizer -{ -public: - ~model_parameters(); - void preliminary_calculations(void); - void set_runtime(void); - static int mc_phase(void) - { - return initial_params::mc_phase; - } - static int mceval_phase(void) - { - return initial_params::mceval_phase; - } - static int hessian_phase(void) - { - return initial_params::in_hessian_phase; - } - static int sd_phase(void) - { - return initial_params::sd_phase; - } - static int current_phase(void) - { - return initial_params::current_phase; - } - static int last_phase(void) - { - return (initial_params::current_phase - >=initial_params::max_number_phases); - } - static prevariable& current_feval(void) - { - return *objective_function_value::pobjfun; - } -private: - dvariable adromb(dvariable(model_parameters::*f)(const dvariable&), double a, double b, int ns) - { - using namespace std::placeholders; - _func func = std::bind(f, this, _1); - return function_minimizer::adromb(func, a, b, ns); - } - ivector integer_control_flags; - dvector double_control_flags; - param_init_bounded_vector log_Rzero; - param_init_bounded_vector steepness; - param_init_bounded_vector sigr; - param_init_number dummy; - param_vector sr_alpha; - param_vector beta; - param_vector Rzero; - param_vector rec_like; - param_stddev_vector Bzero; - param_vector phizero; - param_vector phi; - param_vector sigmaRsq; - param_vector MSY; - param_vector Fmsy; - param_vector Bmsy; - param_vector Rmsy; - param_vector sigmar; - param_number prior_function_value; - param_number likelihood_function_value; - objective_function_value obj_fun; -public: - virtual void userfunction(void); - virtual void report(const dvector& gradients); - virtual void final_calcs(void); - model_parameters(int sz,int argc, char * argv[]); - virtual void initializationfunction(void); - void Run_Sim(void); - void compute_obj_fun(void); - void opt_sim(void); - void Mainloop(int& isim); - void Alt4_TAC(void); - double Get_Catch(const int& thisalt, const int& ispp); - double Get_F(const int& thisalt,const int& ispp); - double Get_F_SSL_prey(const dvector& F_age, const dvector& N_females, const int ispp ); - double Get_F_Am56(const dvector& F_age, const dvector& N_females, const int ispp ); - double Get_Fofl_t2(const dvector& F_age, const dvector& N_females, const int ispp ); - double Get_Fofl_t(const dvector& F_age, const dvector& N_females, const int ispp ); - double Get_F_t(const dvector& F_age, const dvector& N_females, const int ispp ); - void Project_Pops(const int& isim, const int& i); - void Status_Determ(void); - void Fit_TAC(void); - void Avg_Age(void); - void get_SB100(void); - void compute_spr_rates(void); - double Implied_SPR( const dvector& F_age, const int& ispp); - double SolveF2(const dvector& N_F, const dvector& N_M, const double& TACin, const int& ispp); - void Get_SPR_Catches(const int& ispp); - void write_srec(void); - void Do_Sims(void); - void write_by_time(void); - void write_avg_age(const adstring& Title); - void write_alts(void); - void write_alts_hdr(void); - void write_ABCs(const adstring& Title); - void write_TACs(const adstring& Title); - void write_sim(const adstring& Title, d3_array& Outtmp); - void write_sim(const adstring& Title,const d3_array& Outtmp,const dvar_vector& bb); - void write_sim(const adstring& Title,const int& ispp); - void write_sim_hdr(const int& ispp); - void write_spp(const int& ispp); - dvariable SRecruit(const dvariable& Stmp,const int& ispp); - dvar_vector SRecruit(const dvar_vector& Stmp,const int& ispp); - dvariable Requil(const dvariable& phi,const int& ispp); - void Get_Bzero(const int& ispp); - void Recruitment_Likelihood(const int& ispp); - void Profile_F(const int& ispp); - void get_msy(const int& ispp); - dvariable yield(const dvariable& Ftmp, dvariable& Stmp,dvariable& Rtmp,const int& ispp); - double get_spr_rates(double spr_percent,int& ispp); - double spr_ratio(double& trial_F,dmatrix& sel_tmp_F, int& ispp); - void do_elasticity(); - -}; -#endif diff --git a/src/spm.obj b/src/spm.obj deleted file mode 100644 index c9e061fb38b40cfce59f9e58077cc7181ed027c2..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 880455 zcmbr{3A|2K`#=8MaE>HXMMyd_L?MLC^AHN93`OS1P^PGqD51$v#tNA#g_0pdgE2}f znJG~yDU@W0`hPxqU(dPQd#|$UG^Kj(hC*1E2>_HYm9)OF;n%RU~Pxm&NC zSrR!)CcIXrYyWOt1`g`x|Gz}y){Kcn*K-;E_kaEWy);*%OxJFmAMReX<^P`Oi=Fv@ z2VLz%uDWG94;b)2!~b0!B;HfQ5-nDj>EE@}z)netZW9GB1To^(4!az1g~BdZ?b>Aq zc6(%C(LN9UFJf-&lnS}DTrE0=-KB6f6fO%~BZbQh*G%C!zB?6e6$;Y@uF$`JZ8+X! z9*^Mv`Ry|N&r>jC#8KPa6qq>EqNaBUFp6kO8`e!iSXQ@>U5^8qjm#Bm%Z_a(w?hMAh#kC!jt zg7fxlO2))QY;3_g2i**qcNA_Z%o>H;46{q&4#NEAa9l5EVD3&e<&j(mJdQeuVZLmp z`qAC5aGa+;3ReNHAKYBA9HpsW&gTk6yb^F9!W|MDoVR4Yx4<2TDV8O^E_64++^=wb zVcvAO8xU_1%sz!X43qPc`0*7-y!>LY>orsT=xQn4RdC(l9(VN1`5mc68%gUgI%vB`%St_@XY&gD;@Ee?|MZ%UVk^i 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