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OM.R
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library(tidyr)
library(dplyr)
library(ggplot2)
library(cowplot)
library(FLash)
### use mse fork from shfischer/mse, branch mseDL2.0
### remotes::install_github("shfischer/mse", ref = "mseDL2.0)
library(mse)
source("funs.R")
source("funs_GA.R")
### parallel environment
library(doParallel)
cl <- makeCluster(10)
registerDoParallel(cl)
clusterEvalQ(cl, {source("funs.R");source("funs_GA.R")})
### ------------------------------------------------------------------------ ###
### fishing history dimensions ####
### ------------------------------------------------------------------------ ###
n_iter <- 500
yrs_hist <- 100
yrs_proj <- 50
set.seed(2)
### ------------------------------------------------------------------------ ###
### with uniform distribution and random F trajectories ####
### ------------------------------------------------------------------------ ###
fhist <- "one-way"#"random"#
if (identical(fhist, "random")) {
start <- rep(0, n_iter)
middle <- runif(n = n_iter, min = 0, max = 1)
end <- runif(n = n_iter, min = 0, max = 1)
df <- t(sapply(seq(n_iter),
function(x) {
c(approx(x = c(1, yrs_hist/2),
y = c(start[x], middle[x]),
n = yrs_hist/2)$y,
approx(x = c(yrs_hist/2, yrs_hist + 1),
y = c(middle[x], end[x]),
n = (yrs_hist/2) + 1)$y[-1])
}))
df2 <- as.data.frame(df)
rownames(df2) <- seq(n_iter)
colnames(df2) <- seq(yrs_hist)
df2$iter <- seq(n_iter)
df2 %>%
gather(key = "year", value = "value", 1:100) %>%
mutate(year = as.numeric(as.character(year))) %>%
ggplot(aes(x = year, y = value, group = as.factor(iter))) +
geom_line(alpha = 0.5) +
theme_bw()
f_array <- array(dim = c(yrs_hist, 3, n_iter),
dimnames = list(seq(yrs_hist), c("min","val","max"),
iter = seq(n_iter)))
f_array[, "val", ] <- c(t(df))
}
### ------------------------------------------------------------------------ ###
### create OMs ####
### ------------------------------------------------------------------------ ###
### get lhist for stocks
stocks <- read.csv("input/stocks.csv", stringsAsFactors = FALSE)
### BRPs from Fischer et al. (2020)
# brps <- readRDS("input/OMs/brps.rds")$new_baseline
# brps <- brps[match(x = stocks$stock_old, table = names(brps))]
# names(brps) <- stocks$stock
# ### calculate Blim
# brps <- lapply(brps, function(brp) {
# bv <- function(SSB, a, b) a*SSB/(b + SSB)
# solve <- function(SSB) {
# rec = bv(a = c(params(brp)["a"]),
# b = c(params(brp)["b"]), SSB = SSB)
# abs((c(refpts(brp)["virgin", "rec"]) * 0.7) - rec)
# }
# attr(brp, "Blim") <- optimize(f = solve, lower = 1, upper = 1000)$minimum
# return(brp)
# })
# saveRDS(brps, file = "input/brps.rds")
brps <- readRDS("input/brps.rds")
# sapply(brps, function(x) {
# c(refpts(x)["crash", "harvest"]/refpts(x)["msy", "harvest"])
# })
### create FLStocks
stocks_subset <- stocks$stock#"pol"
stks_hist <- foreach(stock = stocks_subset, .errorhandling = "pass",
.packages = c("FLCore", "FLash", "FLBRP")) %dopar% {
stk <- as(brps[[stock]], "FLStock")
refpts <- refpts(brps[[stock]])
stk <- qapply(stk, function(x) {#browser()
dimnames(x)$year <- as.numeric(dimnames(x)$year) - 1; return(x)
})
stk <- stf(stk, yrs_hist + yrs_proj - dims(stk)$year + 1)
stk <- propagate(stk, n_iter)
### create stock recruitment model
stk_sr <- FLSR(params = params(brps[[stock]]), model = model(brps[[stock]]))
### create residuals for (historical) projection
set.seed(0)
residuals(stk_sr) <- rlnoise(dim(stk)[6], rec(stk) %=% 0,
sd = 0.6, b = 0)
### replicate residuals from catch rule paper for historical period
set.seed(0)
residuals <- rlnoise(dim(stk)[6], (rec(stk) %=% 0)[, ac(1:100)],
sd = 0.6, b = 0)
residuals(stk_sr)[, ac(1:100)] <- residuals[, ac(1:100)]
### fishing history from previous paper
if (isTRUE(fhist == "one-way")) {
### 0.5Fmsy until year 75, then increase to 0.8Fcrash
fs <- rep(c(refpts["msy", "harvest"]) * 0.5, 74)
f0 <- c(refpts["msy", "harvest"]) * 0.5
fmax <- c(refpts["crash", "harvest"]) * 0.8
rate <- exp((log(fmax) - log(f0)) / (25))
fs <- c(fs, rate ^ (1:25) * f0)
### control object
ctrl <- fwdControl(data.frame(year = 2:100, quantity = "f", val = fs))
### random F trajectories
} else if (isTRUE(fhist == "random")) {
### control object template
ctrl <- fwdControl(data.frame(year = seq(yrs_hist),
quantity = c("f"), val = NA))
### add iterations
ctrl@trgtArray <- f_array
### target * Fcrash
ctrl@trgtArray[,"val",] <- ctrl@trgtArray[,"val",] *
c(refpts["crash", "harvest"]) * 1
}
### project fishing history
stk_stf <- fwd(stk, ctrl, sr = stk_sr, sr.residuals = residuals(stk_sr),
sr.residuals.mult = TRUE, maxF = 5)
#plot(stk_stf, iter = 1:50)
#plot(ssb(stk_stf), iter = 1:50)
### run a few times to get closer to target
# for (i in 1:5) {
# stk_stf <- fwd(stk_stf, ctrl, sr = stk_sr,
# sr.residuals.mult = TRUE, maxF = 4)
# }
name(stk_stf) <- stock
path <- paste0("input/", n_iter, "_", yrs_proj, "/OM_1_hist/", fhist, "/")
dir.create(path, recursive = TRUE)
saveRDS(list(stk = stk_stf, sr = stk_sr), file = paste0(path, stock, ".rds"))
return(NULL)
#return(list(stk = stk_stf, sr = stk_sr))
}
# names(stks_hist) <- stocks_subset
### stock status
res <- lapply(stocks_subset, function(stock) {
stk <- readRDS(paste0("input/", n_iter, "_", yrs_proj, "/OM_1_hist/", fhist,
"/", stock, ".rds"))$stk
ssb(stk)[, ac(100)] / refpts(brps[[stock]])["msy", "ssb"]
})
# plot(stk_stf)
# plot(stk_stf, iter = 1:100)
# hist(ssb(stk_stf)[, ac(201)])
# ssb(stk_stf)[, ac(199:201),,,, 1]
# ctrl@trgtArray[ac(199:200),,]
# summary(c(ssb(stk_stf)[, ac(201)]) / ctrl@trgtArray[ac(200),"val",])
# plot(fbar(stk_stf)[, ac(101:200)])
### plot history for all stocks
# for (stock in stocks_subset) {
# stk1 <- readRDS(paste0("input/", n_iter, "_", yrs_proj, "/OM_1_hist/", stock,
# ".rds"))$stk
# stk1 <- window(stk1, start = -100)
# stk1[, ac(-100:50)] <- stk1[, ac(0:150)]
# plot(window(stk1, end = 0),
# probs = c(0.05, 0.25, 0.5, 0.75, 0.95),
# iter = 1:10) +
# ylim(0, NA) +
# labs(x = "year") +
# geom_hline(data = data.frame(qname = "SSB",
# data = c(refpts(brps[[stock]])["msy", "ssb"])),
# aes(yintercept = data), linetype = "dashed", alpha = 0.5) +
# geom_hline(data = data.frame(qname = "F",
# data = c(refpts(brps[[stock]])["msy", "harvest"])),
# aes(yintercept = data), linetype = "dashed", alpha = 0.5)
# ggsave(filename = paste0("input/", n_iter, "_", yrs_proj, "/SSB_hist_",
# stock, ".png"),
# width = 30, height = 20, units = "cm", dpi = 300, type = "cairo")
# }
# rm(stk1); gc()
### ------------------------------------------------------------------------ ###
### prepare OMs for flr/mse MP ####
### ------------------------------------------------------------------------ ###
stks_mp <- foreach(stock = stocks_subset, .errorhandling = "pass",
.packages = c("FLCore", "mse")) %do% {
### load stock
tmp <- readRDS(paste0("input/", n_iter, "_", yrs_proj, "/OM_1_hist/", fhist,
"/", stock, ".rds"))
stk_fwd <- tmp$stk
stk_sr <- tmp$sr
### life-history data
lhist <- stocks[stocks$stock == stock, ]
#range(stk_stf)
### cut of history
stk_fwd <- window(stk_fwd, start = 50)
stk_sr@residuals <- window(stk_sr@residuals, start = 50)
### length data
pars_l <- FLPar(a = lhist$a,
b = lhist$b,
Lc = calc_lc(stk = stk_fwd[, ac(75:100)],
a = lhist$a, b = lhist$b))
### indices
q <- 1/(1 + exp(-1*(an(dimnames(stk_fwd)$age) - dims(stk_fwd)$max/10)))
idx <- FLQuants(
sel = stk_fwd@mat %=% q,
idxB = quantSums(stk_fwd@stock.n * stk_fwd@stock.wt * (stk_fwd@mat %=% q)),
idxL = lmean(stk = stk_fwd, params = pars_l),
PA_status = ssb(stk_fwd) %=% NA_integer_)
### index deviation
PA_status_dev <- FLQuant(NA, dimnames = list(age = c("positive", "negative"),
year = dimnames(stk_fwd)$year,
iter = dimnames(stk_fwd)$iter))
set.seed(1)
PA_status_dev["positive"] <- rbinom(n = PA_status_dev["positive"],
size = 1, prob = 0.9886215)
set.seed(2)
PA_status_dev["negative"] <- rbinom(n = PA_status_dev["negative"],
size = 1, prob = 1 - 0.4216946)
set.seed(696)
idx_dev <- FLQuants(sel = stk_fwd@mat %=% 1,
idxB = rlnoise(n = dims(idx$idxB)$iter, idx$idxB %=% 0,
sd = 0.2, b = 0),
idxL = rlnoise(n = dims(idx$idxL)$iter, idx$idxL %=% 0,
sd = 0.2, b = 0),
PA_status = PA_status_dev)
### iem deviation
set.seed(205)
iem_dev <- FLQuant(rlnoise(n = dims(stk_fwd)$iter, catch(stk_fwd) %=% 0,
sd = 0.1, b = 0))
### lowest observed index in last 50 years
I_loss <- list()
I_loss$SSB_idx <- apply(ssb(stk_fwd)[, ac(50:100)], 6, min)
I_loss$SSB_idx_dev <- apply((ssb(stk_fwd) * idx_dev$idxB)[, ac(50:100)],
6, min)
I_loss$idx <- apply(idx$idxB[, ac(50:100)], 6, min)
I_loss$idx_dev <- apply((idx$idxB * idx_dev$idxB)[, ac(50:100)], 6, min)
### parameters for components
pars_est <- list(
comp_r = TRUE, comp_f = TRUE, comp_b = TRUE,
comp_c = TRUE, comp_m = 1,
idxB_lag = 1, idxB_range_1 = 2, idxB_range_2 = 3, idxB_range_3 = 1,
catch_lag = 1, catch_range = 1,
interval = 2,
idxL_lag = 1, idxL_range = 1,
exp_r = 1, exp_f = 1, exp_b = 1,
Lref = rep((lhist$linf + 2*1.5*c(pars_l["Lc"])) / (1 + 2*1.5), n_iter),
B_lim = rep(brps[[stock]]@Blim, n_iter),
I_trigger = c(I_loss$idx_dev * 1.4), ### default, can be overwritten later
pa_buffer = FALSE, pa_size = 0.8, pa_duration = 3,
upper_constraint = Inf,
lower_constraint = 0
)
### operating model
om <- FLom(stock = stk_fwd, ### stock
sr = stk_sr, ### stock recruitment and precompiled residuals
fleetBehaviour = mseCtrl(),
projection = mseCtrl(method = fwd_attr,
args = list(dupl_trgt = TRUE)))
tracking = c("comp_c", "comp_i", "comp_r", "comp_f", "comp_b",
"multiplier", "exp_r", "exp_f", "exp_b")
oem <- FLoem(method = obs_generic,
observations = list(stk = stk_fwd, idx = idx),
deviances = list(stk = FLQuant(), idx = idx_dev),
args = list(idx_dev = TRUE, ssb = FALSE,
lngth = TRUE, lngth_dev = TRUE,
lngth_par = pars_l,
PA_status = FALSE, PA_status_dev = FALSE,
PA_Bmsy = c(refpts(brps[[stock]])["msy", "ssb"]),
PA_Fmsy = c(refpts(brps[[stock]])["msy", "harvest"])))
ctrl <- mpCtrl(list(
est = mseCtrl(method = est_comps,
args = pars_est),
phcr = mseCtrl(method = phcr_comps,
args = pars_est),
hcr = mseCtrl(method = hcr_comps,
args = pars_est),
isys = mseCtrl(method = is_comps,
args = pars_est)
))
iem <- FLiem(method = iem_comps,
args = list(use_dev = FALSE, iem_dev = iem_dev))
### args
args <- list(fy = dims(stk_fwd)$maxyear, ### final simulation year
y0 = dims(stk_fwd)$minyear, ### first data year
iy = 100, ### first simulation (intermediate) year
nsqy = 3, ### not used, but has to provided
nblocks = 1, ### block for parallel processing
seed = 1, ### random number seed before starting MSE
seed_part = FALSE
)
### get reference points
refpts <- refpts(brps[[stock]])
Blim <- attr(brps[[stock]], "Blim")
### list with input to mp()
input <- list(om = om, oem = oem, iem = iem, ctrl = ctrl,
args = args,
scenario = "GA", tracking = tracking,
verbose = TRUE,
refpts = refpts, Blim = Blim, I_loss = I_loss)
### save OM
path <- paste0("input/", n_iter, "_", yrs_proj, "/OM_2_mp_input/", fhist, "/")
dir.create(path, recursive = TRUE)
saveRDS(object = input, file = paste0(path, stock, ".rds"))
return(NULL)
}
# debugonce(wklife_3.2.1_est)
# debugonce(wklife_3.2.1_obs)
# debugonce(input$ctrl$hcr@method)
# debugonce(mp)
# debugonce(goFishDL)
# input$args$nblocks = 250
# res <- do.call(mp, input)
#
# ### timing
# system.time({res1 <- do.call(mp, input)})