Rscript/TestCaseGenerator.R
In cstawitz/RMAS: Read and Write Input and Output for MAS Model
# # R script to generate inputs for an operating model in RMAS. The script
# # populates and runs the operating model and generates the .RData file
# # for a test case. Inputs are randomly chosen between min and max values
# # for each parameter in the model.
#
# library(RMAS)
# library(jsonlite)
# require(Rcpp)
# require(RMAS)
#
# r4mas <-
# Module("rmas", dyn.load(paste(
# d, "/libs/RMAS", .Platform$dynlib.ext, sep = ""
# )))
#
#
# #control values for random model construction
#
# number_of_years_min <- 10
# number_of_years_max <- 40
#
# number_of_seasons_min <- 1
# number_of_seasons_max <- 1
#
# number_of_ages_min <- 7
# number_of_ages_max <- 20
#
# number_of_areas_min <- 1
# number_of_areas_max <- 5
#
# number_of_populations_min <- 1
# number_of_populations_max <- 5
#
# sex_ratio_min <- 0.3
# sex_ratio_max <- 0.8
#
# number_of_fleets_min <- 1
# number_of_fleets_max <- 5
#
# number_of_surveys_min <- 1
# number_of_surveys_max <- 5
#
# # need to add Ricker here
# recruitment_type <- c(BevertonHolt = 1,
# BevertonHoltAlt = 2)
#
# R0_min <- 15000
# R0_max <- 200000
#
#
# bevertonholt_h_min <- 0.2
# bevertonholt_h_max <- 1.0
#
# bevertonholt_sigma_r_min <- 0.1
# bevertonholt_sigma_r_max <- 0.5
#
# bevertonholt_alt_h_min <- 0.2
# bevertonholt_alt_h_max <- 1.0
#
# bevertonholt_alt_sigma_r_min <- 0.1
# bevertonholt_alt_sigma_r_max <- 0.5
#
# growth_type <- c(VonBertalanffy = 1,
# VonBertalanffyModified = 2)
#
#
# vonbertalanffy_alpha_f_min<-0.000025
# vonbertalanffy_alpha_f_max<-0.000025
#
# vonbertalanffy_alpha_m_min<-0.000025
# vonbertalanffy_alpha_m_max<-0.000025
#
# vonbertalanffy_beta_f_min<-3.0
# vonbertalanffy_beta_f_max<-3.0
#
# vonbertalanffy_beta_m_min<-3.0
# vonbertalanffy_beta_m_max<-3.0
#
# vonbertalanffy_lmin_min<-10
# vonbertalanffy_lmin_max<-15
#
# vonbertalanffy_lmax_min<-60
# vonbertalanffy_lmax_max<-90
#
# vonbertalanffy_l_inf_min<-60
# vonbertalanffy_l_inf_max<-100
#
# vonbertalanffy_k_min<-0.2
# vonbertalanffy_k_max<-0.7
#
# vonbertalanffymodified_alpha_f_min<-0.000025
# vonbertalanffymodified_alpha_f_max<-0.000025
#
# vonbertalanffymodified_alpha_m_min<-0.000025
# vonbertalanffymodified_alpha_m_max<-0.000025
#
# vonbertalanffymodified_beta_f_min<-3.0
# vonbertalanffymodified_beta_f_max<-3.0
#
# vonbertalanffymodified_beta_m_min<-3.0
# vonbertalanffymodified_beta_m_max<-3.0
#
# vonbertalanffymodified_lmin_min<-10
# vonbertalanffymodified_lmin_max<-15
#
# vonbertalanffymodified_lmax_min<-60
# vonbertalanffymodified_lmax_max<-60
#
# vonbertalanffymodified_c_min<-0.2
# vonbertalanffymodified_c_max<-0.7
#
# maturity_a50_min <- 1.9
# maturity_a50_max <- 5.0
#
# maturity_slope_min <- 0.05
# maturity_slope_max <- 0.5
#
# natural_mortality_age_1_min <- 0.5
# natural_mortality_age_1_max <- 0.7
#
# natural_mortality_age_2_min <- 0.2
# natural_mortality_age_2_max <- 0.5
#
# natural_mortality_min <- 0.1
# natural_mortality_max <- 0.3
#
# movement_coefficient_min <- 0.2
# movement_coefficient_max <- 0.5
#
# selectivity_type <- c(Logistic = 1,
# DoubleLogistic = 2)
#
# fleet_logistic_selectivity_a50_min <- 1.75
# fleet_logistic_selectivity_a50_max <- 5.0
#
# fleet_logistic_selectivity_slope_min <- 0.05
# fleet_logistic_selectivity_slope_max <- 0.5
#
# fleet_double_logistic_alpha_asc_min <- 1
# fleet_double_logistic_alpha_asc_max <- 1
#
# fleet_double_logistic_alpha_desc_min <- 1
# fleet_double_logistic_alpha_desc_max <- 1
#
# fleet_double_logistic_beta_asc_min <- 1
# fleet_double_logistic_beta_asc_max <- 1
#
# fleet_double_logistic_beta_desc_min <- 1
# fleet_double_logistic_beta_desc_max <- 1
#
# fishing_mortality_min <- 0.1
# fishing_mortality_max <- 0.5
#
# survey_logistic_selectivity_a50_min <- 1.75
# survey_logistic_selectivity_a50_max <- 5.0
#
# survey_logistic_selectivity_slope_min <- 0.05
# survey_logistic_selectivity_slope_max <- 0.5
#
# survey_double_logistic_alpha_asc_min <- 1
# survey_double_logistic_alpha_asc_max <- 1
#
# survey_double_logistic_alpha_desc_min <- 1
# survey_double_logistic_alpha_desc_max <- 1
#
# survey_double_logistic_beta_asc_min <- 1
# survey_double_logistic_beta_asc_max <- 1
#
# survey_double_logistic_beta_desc_min <- 1
# survey_double_logistic_beta_desc_max <- 1
#
# survey_q_min <- 0.001
# survey_q_max <- 10.0
#
# #Begin model construction
# model <- new(r4mas$MASModel)
#
# nyears <-
# as.integer(runif(1, number_of_years_min, number_of_years_max))
# model$nyears <- nyears
#
# nseasons <-
# as.integer(runif(1, number_of_seasons_min, number_of_seasons_max))
# model$nseasons <- nseasons
#
# nareas <- as.integer(runif(1, number_of_areas_min, number_of_areas_max))
#
# nages <- as.integer(runif(1, number_of_ages_min, number_of_ages_max))
# ages <- c(0.1, seq(1, nages - 1))
# model$ages <- ages
# model$nages<-nages
# model$nyears<-nyears
# model$nseasons<-nseasons
# model$extended_plus_group<-nages
# npopulations <-
# as.integer(runif(1, number_of_populations_min, number_of_populations_max))
# nfleets <-
# as.integer(runif(1, number_of_fleets_min, number_of_fleets_max))
# nsurveys <-
# as.integer(runif(1, number_of_surveys_min, number_of_surveys_max))
#
# #create area models for the operating model
# for (i in 1:nareas) {
# a <- assign(paste0("area_", i), new(r4mas$Area))
# }
#
# #create populations for the operating model
# for (i in 1:npopulations) {
# p <- assign(paste0("population_", i), new(r4mas$Population))
#
# #sex ratio
# p$sex_ratio<-runif(1,sex_ratio_min, sex_ratio_max)
#
# #Recruitment
# recruitment_t <- sample(recruitment_type, 1)
# if (recruitment_t[1] == 1) {
# show(r4mas$Population)
# for (j in 1:nareas) {
# a <- get(paste0("area_", j))
# r <-
# assign(paste0(paste0(
# paste0("beverton-holt_recruitment_", i), "_"
# ), j), new(r4mas$BevertonHoltRecruitment))
# r$R0$value <- runif(1, R0_min, R0_max)
# r$h$value <- runif(1, bevertonholt_h_min, bevertonholt_h_max)
# r$sigma_r$value <-
# runif(1, bevertonholt_sigma_r_min, bevertonholt_sigma_r_max)
# p$AddRecruitment(r$id, 1, a$id)
# }
#
# } else if (recruitment_t[1] == 2) {
# for (j in 1:nareas) {
# a <- get(paste0("area_", j))
# r <-
# assign(paste0(paste0(
# paste0("beverton-holt_alt_recruitment_", i), "_"
# ), j),
# new(r4mas$BevertonHoltRecruitmentAlt))
# r$R0$value <- runif(1, R0_min, R0_max)
# r$h$value <-
# runif(1, bevertonholt_alt_h_min, bevertonholt_alt_h_max)
# r$sigma_r$value <-
# runif(1,
# bevertonholt_alt_sigma_r_min,
# bevertonholt_alt_sigma_r_max)
# p$AddRecruitment(r$id, 1, a$id)
# }
# }
#
# #Growth
# growth_t <- sample(growth_type, 1)
# if (growth_t[1] == 1) {
#
# g<-assign((paste0("VonBertalanffy_",i)), new(r4mas$VonBertalanffy))
#
# g$a_min$value<-ages[1]
# g$a_max$value<-ages[nages]
#
# g$alpha_f$value<-runif(1,vonbertalanffy_alpha_f_min,vonbertalanffy_alpha_f_max)
# g$alpha_m$value<-runif(1,vonbertalanffy_alpha_m_min,vonbertalanffy_alpha_m_max)
#
# g$beta_f$value<-runif(1,vonbertalanffy_beta_f_min,vonbertalanffy_beta_f_max)
# g$beta_m$value<-runif(1,vonbertalanffy_beta_m_min,vonbertalanffy_beta_m_max)
#
# g$k$value<-runif(1,vonbertalanffy_k_min,vonbertalanffy_k_max)
#
# g$l_inf$value<-runif(1,vonbertalanffy_l_inf_min,vonbertalanffy_l_inf_max)
#
# p$SetGrowth(g$id)
#
# } else if (growth_t[1] == 2) {
# g<-assign((paste0("VonBertalanffyModified_",i)), new(r4mas$VonBertalanffyModified))
#
# g$a_min$value<-ages[1]
# g$a_max$value<-ages[nages]
#
# g$alpha_f$value<-runif(1,vonbertalanffymodified_alpha_f_min,vonbertalanffymodified_alpha_f_max)
# g$alpha_m$value<-runif(1,vonbertalanffymodified_alpha_m_min,vonbertalanffymodified_alpha_m_max)
#
# g$beta_f$value<-runif(1,vonbertalanffymodified_beta_f_min,vonbertalanffymodified_beta_f_max)
# g$beta_m$value<-runif(1,vonbertalanffymodified_beta_m_min,vonbertalanffymodified_beta_m_max)
#
# g$c$value<-runif(1,vonbertalanffymodified_c_min,vonbertalanffymodified_c_max)
#
# p$SetGrowth(g$id)
#
# }
#
# #Maturity
# m<-assign(paste0("maturity_",i), new(r4mas$Maturity))
# t<-c(1:nages)
#
# m_a50<-runif(1,maturity_a50_min, maturity_a50_max)
# m_slope<-runif(1,maturity_slope_min, maturity_slope_max)
#
# for(j in 1:nages){
# t[j] <- 1.0/(1.0 + exp(-1.0*(ages[j]-m_a50)/m_slope))
# }
#
# m$values<-t*p$sex_ratio
#
# for( j in nareas){
# a <- get(paste0("area_", j))
# p$AddMaturity(m$id, a$id, "undifferentiated")
# }
#
#
# #Natural Mortality
# nm<-assign(paste0("natural_mortality_",i), new(r4mas$NaturalMortality))
# nmv<-c(1:nages)
# nmv[1]<-runif(1,natural_mortality_age_1_min,natural_mortality_age_1_max)
# nmv[2]<-runif(1,natural_mortality_age_2_min,natural_mortality_age_2_max)
#
# nnn<-runif(1,natural_mortality_min, natural_mortality_max)
# for(j in 3:nages){
# nmv[j]<- nnn
# }
# nm$values<-nmv
# for( j in nareas){
# a <- get(paste0("area_", j))
# p$AddNaturalMortality(nm$id, a$id, "undifferentiated")
# }
#
#
# #Movement
# coefficients<-c(nareas*nareas)
# for(j in 1:(nareas*nareas)){
# coefficients[j]<-runif(1,movement_coefficient_min,movement_coefficient_max)
# }
#
# mdat <- matrix( coefficients, nrow = nareas, ncol = nareas, byrow = TRUE)
# for(j in 1:nareas){
# mdat[j,]<-mdat[j,]/sum(mdat[j,])
# }
#
# seasonal_movement<-c(rep(as.vector(mdat), nseasons))
#
# mm<-assign(paste0("movement_",i), new(r4mas$Movement))
# mm$connectivity_females<-seasonal_movement
# mm$connectivity_males<-seasonal_movement
# mm$connectivity_recruits<-seasonal_movement
#
# for(y in 1:nyears){
# p$AddMovement(mm$id, y)
# }
#
# model$AddPopulation(p$id)
# }
#
# for(i in 1:nfleets){
# f<-assign(paste0("fleet_",i), new(r4mas$Fleet))
#
# #randomly choose fishing areas
# na<-as.integer(runif(1,1, nareas))
# fa <- as.integer(runif(na, 1, nareas))
#
# selectivity_t<-sample(selectivity_type,1)
# if(selectivity_t[1] == 1){
#
# for (j in 1:length(fa)) {
# a<-get(paste0("area_",fa[j]))
# selx <-
# assign(paste0(paste0(
# paste0("logistic_selectivity_", i), "_"
# ), fa[j]), new(r4mas$LogisticSelectivity))
#
# selx$a50$value<-runif(1,fleet_logistic_selectivity_a50_min, fleet_logistic_selectivity_a50_max)
# selx$slope$value<-runif(1,fleet_logistic_selectivity_slope_min, fleet_logistic_selectivity_slope_max)
# f$AddSelectivity(selx$id,1, a$id)
#
# }
#
# }else if(selectivity_t[1] == 2){
#
# for (j in 1:length(fa)) {
# a<-get(paste0("area_",fa[j]))
# selx <-
# assign(paste0(paste0(
# paste0("double_logistic_selectivity_", i), "_"
# ), fa[j]), new(r4mas$DoubleLogisticSelectivity))
#
# selx$alpha_asc<-runif(1,fleet_double_logistic_alpha_asc_min, fleet_double_logistic_alpha_asc_max)
# selx$alpha_desc<-runif(1,fleet_double_logistic_alpha_desc_min, fleet_double_logistic_alpha_desc_max)
# selx$beta_asc<-runif(1,fleet_double_logistic_beta_asc_min, fleet_double_logistic_beta_asc_max)
# selx$beta_desc<-runif(1,fleet_double_logistic_beta_desc_min, fleet_double_logistic_beta_desc_max)
# f$AddSelectivity(selx$id, 1,a$id)
# }
# }
#
# for(j in 1:nseasons){
# for (k in 1:length(fa)) {
# a<-get(paste0("area_",fa[k]))
#
# fm<-assign(paste0(paste0(paste0(paste0(paste0("fishing_mortality_",i)," "),j),""),k), new(r4mas$FishingMortality))
# fm$values<-c(rep(runif(fishing_mortality_min, fishing_mortality_max),1:nyears*nseasons))
# f$AddFishingMortality(fm$id,1,a$id)
# }
# }
# model$AddFleet(f$id)
# }
#
#
# for(i in 1:nsurveys){
# survey<-assign(paste0("survey_", i), new(r4mas$Survey))
#
# survey$q$value<-runif(1, survey_q_min, survey_q_max)
#
# na<-as.integer(runif(1,1, nareas))
# fa <- as.integer(runif(na, 1, nareas))
#
# selectivity_t<-sample(selectivity_type,1)
# if(selectivity_t[1] == 1){
#
# for (j in 1:length(fa)) {
# a<-get(paste0("area_",fa[j]))
# selx <-
# assign(paste0(paste0(
# paste0("logistic_selectivity_", i), "_"
# ), fa[j]), new(r4mas$LogisticSelectivity))
#
# selx$a50$value<-runif(1,survey_logistic_selectivity_a50_min, survey_logistic_selectivity_a50_max)
# selx$slope$value<-runif(1,survey_logistic_selectivity_slope_min, survey_logistic_selectivity_slope_max)
# survey$AddSelectivity(selx$id, 1,a$id)
#
# }
#
# }else if(selectivity_t[1] == 2){
#
# for (j in 1:length(fa)) {
# a<-get(paste0("area_",fa[j]))
# selx <-
# assign(paste0(paste0(
# paste0("double_logistic_selectivity_", i), "_"
# ), fa[j]), new(r4mas$DoubleLogisticSelectivity))
#
# selx$alpha_asc<-runif(1,survey_double_logistic_alpha_asc_min, survey_double_logistic_alpha_asc_max)
# selx$alpha_desc<-runif(1,survey_double_logistic_alpha_desc_min, survey_double_logistic_alpha_desc_max)
# selx$beta_asc<-runif(1,survey_double_logistic_beta_asc_min, survey_double_logistic_beta_asc_max)
# selx$beta_desc<-runif(1,survey_double_logistic_beta_desc_min, survey_double_logistic_beta_desc_max)
# survey$AddSelectivity(selx$id, 1,a$id)
# }
#
# model$AddSurvey(survey$id)
# }
#
# # model$Run()
#
# }
cstawitz/RMAS documentation built on Oct. 6, 2022, 3:31 a.m.
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# # R script to generate inputs for an operating model in RMAS. The script
# # populates and runs the operating model and generates the .RData file
# # for a test case. Inputs are randomly chosen between min and max values
# # for each parameter in the model.
#
# library(RMAS)
# library(jsonlite)
# require(Rcpp)
# require(RMAS)
#
# r4mas <-
# Module("rmas", dyn.load(paste(
# d, "/libs/RMAS", .Platform$dynlib.ext, sep = ""
# )))
#
#
# #control values for random model construction
#
# number_of_years_min <- 10
# number_of_years_max <- 40
#
# number_of_seasons_min <- 1
# number_of_seasons_max <- 1
#
# number_of_ages_min <- 7
# number_of_ages_max <- 20
#
# number_of_areas_min <- 1
# number_of_areas_max <- 5
#
# number_of_populations_min <- 1
# number_of_populations_max <- 5
#
# sex_ratio_min <- 0.3
# sex_ratio_max <- 0.8
#
# number_of_fleets_min <- 1
# number_of_fleets_max <- 5
#
# number_of_surveys_min <- 1
# number_of_surveys_max <- 5
#
# # need to add Ricker here
# recruitment_type <- c(BevertonHolt = 1,
# BevertonHoltAlt = 2)
#
# R0_min <- 15000
# R0_max <- 200000
#
#
# bevertonholt_h_min <- 0.2
# bevertonholt_h_max <- 1.0
#
# bevertonholt_sigma_r_min <- 0.1
# bevertonholt_sigma_r_max <- 0.5
#
# bevertonholt_alt_h_min <- 0.2
# bevertonholt_alt_h_max <- 1.0
#
# bevertonholt_alt_sigma_r_min <- 0.1
# bevertonholt_alt_sigma_r_max <- 0.5
#
# growth_type <- c(VonBertalanffy = 1,
# VonBertalanffyModified = 2)
#
#
# vonbertalanffy_alpha_f_min<-0.000025
# vonbertalanffy_alpha_f_max<-0.000025
#
# vonbertalanffy_alpha_m_min<-0.000025
# vonbertalanffy_alpha_m_max<-0.000025
#
# vonbertalanffy_beta_f_min<-3.0
# vonbertalanffy_beta_f_max<-3.0
#
# vonbertalanffy_beta_m_min<-3.0
# vonbertalanffy_beta_m_max<-3.0
#
# vonbertalanffy_lmin_min<-10
# vonbertalanffy_lmin_max<-15
#
# vonbertalanffy_lmax_min<-60
# vonbertalanffy_lmax_max<-90
#
# vonbertalanffy_l_inf_min<-60
# vonbertalanffy_l_inf_max<-100
#
# vonbertalanffy_k_min<-0.2
# vonbertalanffy_k_max<-0.7
#
# vonbertalanffymodified_alpha_f_min<-0.000025
# vonbertalanffymodified_alpha_f_max<-0.000025
#
# vonbertalanffymodified_alpha_m_min<-0.000025
# vonbertalanffymodified_alpha_m_max<-0.000025
#
# vonbertalanffymodified_beta_f_min<-3.0
# vonbertalanffymodified_beta_f_max<-3.0
#
# vonbertalanffymodified_beta_m_min<-3.0
# vonbertalanffymodified_beta_m_max<-3.0
#
# vonbertalanffymodified_lmin_min<-10
# vonbertalanffymodified_lmin_max<-15
#
# vonbertalanffymodified_lmax_min<-60
# vonbertalanffymodified_lmax_max<-60
#
# vonbertalanffymodified_c_min<-0.2
# vonbertalanffymodified_c_max<-0.7
#
# maturity_a50_min <- 1.9
# maturity_a50_max <- 5.0
#
# maturity_slope_min <- 0.05
# maturity_slope_max <- 0.5
#
# natural_mortality_age_1_min <- 0.5
# natural_mortality_age_1_max <- 0.7
#
# natural_mortality_age_2_min <- 0.2
# natural_mortality_age_2_max <- 0.5
#
# natural_mortality_min <- 0.1
# natural_mortality_max <- 0.3
#
# movement_coefficient_min <- 0.2
# movement_coefficient_max <- 0.5
#
# selectivity_type <- c(Logistic = 1,
# DoubleLogistic = 2)
#
# fleet_logistic_selectivity_a50_min <- 1.75
# fleet_logistic_selectivity_a50_max <- 5.0
#
# fleet_logistic_selectivity_slope_min <- 0.05
# fleet_logistic_selectivity_slope_max <- 0.5
#
# fleet_double_logistic_alpha_asc_min <- 1
# fleet_double_logistic_alpha_asc_max <- 1
#
# fleet_double_logistic_alpha_desc_min <- 1
# fleet_double_logistic_alpha_desc_max <- 1
#
# fleet_double_logistic_beta_asc_min <- 1
# fleet_double_logistic_beta_asc_max <- 1
#
# fleet_double_logistic_beta_desc_min <- 1
# fleet_double_logistic_beta_desc_max <- 1
#
# fishing_mortality_min <- 0.1
# fishing_mortality_max <- 0.5
#
# survey_logistic_selectivity_a50_min <- 1.75
# survey_logistic_selectivity_a50_max <- 5.0
#
# survey_logistic_selectivity_slope_min <- 0.05
# survey_logistic_selectivity_slope_max <- 0.5
#
# survey_double_logistic_alpha_asc_min <- 1
# survey_double_logistic_alpha_asc_max <- 1
#
# survey_double_logistic_alpha_desc_min <- 1
# survey_double_logistic_alpha_desc_max <- 1
#
# survey_double_logistic_beta_asc_min <- 1
# survey_double_logistic_beta_asc_max <- 1
#
# survey_double_logistic_beta_desc_min <- 1
# survey_double_logistic_beta_desc_max <- 1
#
# survey_q_min <- 0.001
# survey_q_max <- 10.0
#
# #Begin model construction
# model <- new(r4mas$MASModel)
#
# nyears <-
# as.integer(runif(1, number_of_years_min, number_of_years_max))
# model$nyears <- nyears
#
# nseasons <-
# as.integer(runif(1, number_of_seasons_min, number_of_seasons_max))
# model$nseasons <- nseasons
#
# nareas <- as.integer(runif(1, number_of_areas_min, number_of_areas_max))
#
# nages <- as.integer(runif(1, number_of_ages_min, number_of_ages_max))
# ages <- c(0.1, seq(1, nages - 1))
# model$ages <- ages
# model$nages<-nages
# model$nyears<-nyears
# model$nseasons<-nseasons
# model$extended_plus_group<-nages
# npopulations <-
# as.integer(runif(1, number_of_populations_min, number_of_populations_max))
# nfleets <-
# as.integer(runif(1, number_of_fleets_min, number_of_fleets_max))
# nsurveys <-
# as.integer(runif(1, number_of_surveys_min, number_of_surveys_max))
#
# #create area models for the operating model
# for (i in 1:nareas) {
# a <- assign(paste0("area_", i), new(r4mas$Area))
# }
#
# #create populations for the operating model
# for (i in 1:npopulations) {
# p <- assign(paste0("population_", i), new(r4mas$Population))
#
# #sex ratio
# p$sex_ratio<-runif(1,sex_ratio_min, sex_ratio_max)
#
# #Recruitment
# recruitment_t <- sample(recruitment_type, 1)
# if (recruitment_t[1] == 1) {
# show(r4mas$Population)
# for (j in 1:nareas) {
# a <- get(paste0("area_", j))
# r <-
# assign(paste0(paste0(
# paste0("beverton-holt_recruitment_", i), "_"
# ), j), new(r4mas$BevertonHoltRecruitment))
# r$R0$value <- runif(1, R0_min, R0_max)
# r$h$value <- runif(1, bevertonholt_h_min, bevertonholt_h_max)
# r$sigma_r$value <-
# runif(1, bevertonholt_sigma_r_min, bevertonholt_sigma_r_max)
# p$AddRecruitment(r$id, 1, a$id)
# }
#
# } else if (recruitment_t[1] == 2) {
# for (j in 1:nareas) {
# a <- get(paste0("area_", j))
# r <-
# assign(paste0(paste0(
# paste0("beverton-holt_alt_recruitment_", i), "_"
# ), j),
# new(r4mas$BevertonHoltRecruitmentAlt))
# r$R0$value <- runif(1, R0_min, R0_max)
# r$h$value <-
# runif(1, bevertonholt_alt_h_min, bevertonholt_alt_h_max)
# r$sigma_r$value <-
# runif(1,
# bevertonholt_alt_sigma_r_min,
# bevertonholt_alt_sigma_r_max)
# p$AddRecruitment(r$id, 1, a$id)
# }
# }
#
# #Growth
# growth_t <- sample(growth_type, 1)
# if (growth_t[1] == 1) {
#
# g<-assign((paste0("VonBertalanffy_",i)), new(r4mas$VonBertalanffy))
#
# g$a_min$value<-ages[1]
# g$a_max$value<-ages[nages]
#
# g$alpha_f$value<-runif(1,vonbertalanffy_alpha_f_min,vonbertalanffy_alpha_f_max)
# g$alpha_m$value<-runif(1,vonbertalanffy_alpha_m_min,vonbertalanffy_alpha_m_max)
#
# g$beta_f$value<-runif(1,vonbertalanffy_beta_f_min,vonbertalanffy_beta_f_max)
# g$beta_m$value<-runif(1,vonbertalanffy_beta_m_min,vonbertalanffy_beta_m_max)
#
# g$k$value<-runif(1,vonbertalanffy_k_min,vonbertalanffy_k_max)
#
# g$l_inf$value<-runif(1,vonbertalanffy_l_inf_min,vonbertalanffy_l_inf_max)
#
# p$SetGrowth(g$id)
#
# } else if (growth_t[1] == 2) {
# g<-assign((paste0("VonBertalanffyModified_",i)), new(r4mas$VonBertalanffyModified))
#
# g$a_min$value<-ages[1]
# g$a_max$value<-ages[nages]
#
# g$alpha_f$value<-runif(1,vonbertalanffymodified_alpha_f_min,vonbertalanffymodified_alpha_f_max)
# g$alpha_m$value<-runif(1,vonbertalanffymodified_alpha_m_min,vonbertalanffymodified_alpha_m_max)
#
# g$beta_f$value<-runif(1,vonbertalanffymodified_beta_f_min,vonbertalanffymodified_beta_f_max)
# g$beta_m$value<-runif(1,vonbertalanffymodified_beta_m_min,vonbertalanffymodified_beta_m_max)
#
# g$c$value<-runif(1,vonbertalanffymodified_c_min,vonbertalanffymodified_c_max)
#
# p$SetGrowth(g$id)
#
# }
#
# #Maturity
# m<-assign(paste0("maturity_",i), new(r4mas$Maturity))
# t<-c(1:nages)
#
# m_a50<-runif(1,maturity_a50_min, maturity_a50_max)
# m_slope<-runif(1,maturity_slope_min, maturity_slope_max)
#
# for(j in 1:nages){
# t[j] <- 1.0/(1.0 + exp(-1.0*(ages[j]-m_a50)/m_slope))
# }
#
# m$values<-t*p$sex_ratio
#
# for( j in nareas){
# a <- get(paste0("area_", j))
# p$AddMaturity(m$id, a$id, "undifferentiated")
# }
#
#
# #Natural Mortality
# nm<-assign(paste0("natural_mortality_",i), new(r4mas$NaturalMortality))
# nmv<-c(1:nages)
# nmv[1]<-runif(1,natural_mortality_age_1_min,natural_mortality_age_1_max)
# nmv[2]<-runif(1,natural_mortality_age_2_min,natural_mortality_age_2_max)
#
# nnn<-runif(1,natural_mortality_min, natural_mortality_max)
# for(j in 3:nages){
# nmv[j]<- nnn
# }
# nm$values<-nmv
# for( j in nareas){
# a <- get(paste0("area_", j))
# p$AddNaturalMortality(nm$id, a$id, "undifferentiated")
# }
#
#
# #Movement
# coefficients<-c(nareas*nareas)
# for(j in 1:(nareas*nareas)){
# coefficients[j]<-runif(1,movement_coefficient_min,movement_coefficient_max)
# }
#
# mdat <- matrix( coefficients, nrow = nareas, ncol = nareas, byrow = TRUE)
# for(j in 1:nareas){
# mdat[j,]<-mdat[j,]/sum(mdat[j,])
# }
#
# seasonal_movement<-c(rep(as.vector(mdat), nseasons))
#
# mm<-assign(paste0("movement_",i), new(r4mas$Movement))
# mm$connectivity_females<-seasonal_movement
# mm$connectivity_males<-seasonal_movement
# mm$connectivity_recruits<-seasonal_movement
#
# for(y in 1:nyears){
# p$AddMovement(mm$id, y)
# }
#
# model$AddPopulation(p$id)
# }
#
# for(i in 1:nfleets){
# f<-assign(paste0("fleet_",i), new(r4mas$Fleet))
#
# #randomly choose fishing areas
# na<-as.integer(runif(1,1, nareas))
# fa <- as.integer(runif(na, 1, nareas))
#
# selectivity_t<-sample(selectivity_type,1)
# if(selectivity_t[1] == 1){
#
# for (j in 1:length(fa)) {
# a<-get(paste0("area_",fa[j]))
# selx <-
# assign(paste0(paste0(
# paste0("logistic_selectivity_", i), "_"
# ), fa[j]), new(r4mas$LogisticSelectivity))
#
# selx$a50$value<-runif(1,fleet_logistic_selectivity_a50_min, fleet_logistic_selectivity_a50_max)
# selx$slope$value<-runif(1,fleet_logistic_selectivity_slope_min, fleet_logistic_selectivity_slope_max)
# f$AddSelectivity(selx$id,1, a$id)
#
# }
#
# }else if(selectivity_t[1] == 2){
#
# for (j in 1:length(fa)) {
# a<-get(paste0("area_",fa[j]))
# selx <-
# assign(paste0(paste0(
# paste0("double_logistic_selectivity_", i), "_"
# ), fa[j]), new(r4mas$DoubleLogisticSelectivity))
#
# selx$alpha_asc<-runif(1,fleet_double_logistic_alpha_asc_min, fleet_double_logistic_alpha_asc_max)
# selx$alpha_desc<-runif(1,fleet_double_logistic_alpha_desc_min, fleet_double_logistic_alpha_desc_max)
# selx$beta_asc<-runif(1,fleet_double_logistic_beta_asc_min, fleet_double_logistic_beta_asc_max)
# selx$beta_desc<-runif(1,fleet_double_logistic_beta_desc_min, fleet_double_logistic_beta_desc_max)
# f$AddSelectivity(selx$id, 1,a$id)
# }
# }
#
# for(j in 1:nseasons){
# for (k in 1:length(fa)) {
# a<-get(paste0("area_",fa[k]))
#
# fm<-assign(paste0(paste0(paste0(paste0(paste0("fishing_mortality_",i)," "),j),""),k), new(r4mas$FishingMortality))
# fm$values<-c(rep(runif(fishing_mortality_min, fishing_mortality_max),1:nyears*nseasons))
# f$AddFishingMortality(fm$id,1,a$id)
# }
# }
# model$AddFleet(f$id)
# }
#
#
# for(i in 1:nsurveys){
# survey<-assign(paste0("survey_", i), new(r4mas$Survey))
#
# survey$q$value<-runif(1, survey_q_min, survey_q_max)
#
# na<-as.integer(runif(1,1, nareas))
# fa <- as.integer(runif(na, 1, nareas))
#
# selectivity_t<-sample(selectivity_type,1)
# if(selectivity_t[1] == 1){
#
# for (j in 1:length(fa)) {
# a<-get(paste0("area_",fa[j]))
# selx <-
# assign(paste0(paste0(
# paste0("logistic_selectivity_", i), "_"
# ), fa[j]), new(r4mas$LogisticSelectivity))
#
# selx$a50$value<-runif(1,survey_logistic_selectivity_a50_min, survey_logistic_selectivity_a50_max)
# selx$slope$value<-runif(1,survey_logistic_selectivity_slope_min, survey_logistic_selectivity_slope_max)
# survey$AddSelectivity(selx$id, 1,a$id)
#
# }
#
# }else if(selectivity_t[1] == 2){
#
# for (j in 1:length(fa)) {
# a<-get(paste0("area_",fa[j]))
# selx <-
# assign(paste0(paste0(
# paste0("double_logistic_selectivity_", i), "_"
# ), fa[j]), new(r4mas$DoubleLogisticSelectivity))
#
# selx$alpha_asc<-runif(1,survey_double_logistic_alpha_asc_min, survey_double_logistic_alpha_asc_max)
# selx$alpha_desc<-runif(1,survey_double_logistic_alpha_desc_min, survey_double_logistic_alpha_desc_max)
# selx$beta_asc<-runif(1,survey_double_logistic_beta_asc_min, survey_double_logistic_beta_asc_max)
# selx$beta_desc<-runif(1,survey_double_logistic_beta_desc_min, survey_double_logistic_beta_desc_max)
# survey$AddSelectivity(selx$id, 1,a$id)
# }
#
# model$AddSurvey(survey$id)
# }
#
# # model$Run()
#
# }
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