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prognling_latest2.R
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prognling_latest2.R
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library(Rgadget)
library(mfdb)
## input parameters
lastyearTAC <- 8598
path <- '06-ling/13-new_ass_preprogn/'
mainfile <- 'main'
num.years <- 10
num.iter <- 1
params.file <- 'WGTS/params.final'
pre <- 'PROGN'
imm.stock <- 'lingimm'
mat.stock <- 'lingmat'
options(stringsAsFactors = FALSE)
block.size <- 5
rec.step <- 1
ref.fleet <- "lln"
harvest.rate <- seq(0,1,by=0.01)
asserr.cv <- 0.26
asserr.rho <- 0.8
ref.cm <- 75
bloss <- 9929629
bpa <- bloss
blim <- bpa/exp(1.645*0.2) ## always set as B_pa/1.4
btrigger <- bloss
#load('08-ling/99-docs/bootfit.Rdata')
## read functions
readoutput <- function(x){
tmp <- readLines(x)
file.remove(x)
preamble <- tmp[grepl(';',tmp)]
body <- tmp[!grepl(';',tmp)]
header <- preamble[grepl('year-step-area',preamble)] %>%
gsub('; (*)','\\1',.) %>%
str_split('-') %>%
unlist() %>%
gsub(' ','_',.)
body %>%
paste(collapse='\n') %>%
read.table(text=.,
col.names = header,fill=TRUE) %>%
dplyr::mutate(trial=cut(1:length(year),c(0,which(diff(year)<0),1e9),labels = FALSE)-1) %>%
tibble::as_tibble()
}
readcatch <- function(x){
tmp <- readLines(x)
preamble <- tmp[grepl(';',tmp)]
body <- tmp[!grepl(';',tmp)]
header <- preamble[grepl('year',preamble)] %>%
gsub('; ','',.) %>%
str_split('\t') %>%
unlist()
body %>%
paste(collapse='\n') %>%
read.table(text=.,
col.names = header,fill=TRUE) %>%
tibble::as_tibble()
}
#repeats the first (most important) fleet to create a 'fill' fleet
#that fills out the last two quarters of this fishing year with enough
#catch to fill the TAC exactly
pre.fleets <- data_frame(base_fleet = c(ref.fleet,'lln','bmt','gil'))
ref.points <-
data_frame(switch = c('lingmat.btrigger','lingmat.blim','lln.hr.low','lln.hr.high',
'fleet.prop.predict.lln','fleet.prop.predict.bmt','fleet.prop.predict.gil'),
value = c(0,bloss,0,0.2,
0.66,0.27,.07),
lower = 0.5*value,
upper = pmax(1.5*value,1),
optimise = 0)
## Prediction fleets are specified as a data.frame
## these borrow selection functions from the model fleets
## if base_fleet is not defined it is assumed that the fleet_name column
## is the base_fleet. Also if not specified the fleet will be a totalfleet
pre.fleets$fleet_name <- pre.fleets$base_fleet%||%pre.fleets$fleet_name
pre.fleets$fleet_name[1] <- paste0(ref.fleet,'.fill')
pre.fleets$fleet_type <- pre.fleets$fleet_type%||%'totalfleet'
params <- read.gadget.parameters(file=sprintf('%s/%s',path,params.file))
## Read in the model:
run.dir <- gadget.variant.dir(path = path,variant_dir = pre)
main <- read.gadget.file(path,mainfile,file_type = 'main')
stocks <-
main$stock$stockfiles %>%
purrr::map(~gadgetstock(.,path)) %>%
set_names(.,map(.,~.[[1]]$stockname) %>% unlist())
## collect the model fleets
fleets <-
main$fleet$fleetfiles %>%
purrr::map(~gadgetfleet(.,path))
## check if the projection fleets are defined
tmp <-
fleets %>%
purrr::flatten() %>%
purrr::map(~.[[1]]) %>%
unlist() %>%
setdiff(pre.fleets$base_fleet,.) %>%
map(~stop(sprintf('Projection fleet %s not found',.)))
## set the projection period
class(main[[1]]$timefile) <- c('gadgettime',class(main[[1]]$timefile))
attr(main[[1]]$timefile,'file_config')$file_type <- 'time'
attr(main[[1]]$timefile,'file_config')$mainfile_section <- 'timefile'
main[[1]]$timefile %>%
gadget_update(lastyear = .[[1]]$lastyear + num.years) %>%
write.gadget.file(run.dir)
## create a timing schedule for the fleet operations
schedule <-
expand.grid(year = seq(main[[1]]$timefile[[1]]$lastyear,
by=1,
length.out = num.years+1),
step = 1:main[[1]]$timefile[[1]]$notimesteps[1],
area = main[[1]]$areafile[[1]]$areas) %>%
dplyr::filter(!(year==main[[1]]$timefile[[1]]$lastyear &
step <= main[[1]]$timefile[[1]]$laststep),
!(year==max(year) & step > main[[1]]$timefile[[1]]$laststep)) %>%
dplyr::arrange(year,step,area)
## update the area file to include temperature data in the projection
attr(main[[1]]$areafile,'file_config')$file_type <- 'area'
attr(main[[1]]$areafile,'file_config')$mainfile_section <- 'areafile'
main[[1]]$areafile[[2]] <-
main[[1]]$areafile[[2]] %>%
bind_rows(schedule %>%
mutate(mean=3)) ## this bit could be user defined
main[[1]]$areafile %>%
write.gadget.file(run.dir)
## setup hockey stick spawning function
attr(stocks[[mat.stock]],'file_config')$mainfile_overwrite = TRUE
stocks[[mat.stock]] %>%
gadget_update('doesspawn',
spawnfile = gadgetfile('hockeyspawnfile',
component=list(list(spawnsteps = stocks[[imm.stock]]$doesrenew$normalparamfile[[1]]$step %>% unique(),
spawnareas = .[[1]]$livesonareas,
firstspawnyear = min(schedule$year)-stocks[imm.stock][[1]][[1]]$minage,
lastspawnyear = max(schedule$year),
spawnstocksandratios = paste('hockeystock',1),
proportionfunction = 'constant 1',
mortalityfunction = 'constant 0',
weightlossfunction = 'constant 0',
recruitment = 'hockeystick',
gadgetfile('hockeyrec',
file_type = 'timevariable',
components=list(list('hockeyrec',
data =
tibble::data_frame(year = main[[1]]$timefile[[1]]$firstyear,
step = main[[1]]$timefile[[1]]$firststep,
value = "0") %>%
dplyr::bind_rows(expand.grid(year = unique(schedule$year),
step = stocks[[imm.stock]]$doesrenew$normalparamfile[[1]]$step %>% unique()) %>%
dplyr::mutate(value = paste0('#',imm.stock,'.rec.',year),
year = year - stocks[[imm.stock]][[1]]$minage)) %>%
dplyr::select(year,step,value) %>%
as.data.frame()))),
sprintf('#%s.blim',mat.stock),
stockparameters = stocks[[imm.stock]]$doesrenew$normalparamfile[[1]] %>% ## what about other types of recruitment files?
tail(1) %>%
select(mean:beta)%>%
unlist() %>%
map(to.gadget.formulae) %>%
unlist()
)))) %>%
write.gadget.file(run.dir)
stocks[[imm.stock]] %>%
write.gadget.file(run.dir)
## setup hockeystick recruitment
gadgetstock('hockeystock',run.dir,missingOkay = TRUE) %>%
gadget_update('refweight',data=stocks[[imm.stock]][[1]]$refweightfile[[1]]) %>%
gadget_update('stock',
minage=0,
maxage=stocks[imm.stock][[1]][[1]]$minage,
minlength = stocks[imm.stock][[1]][[1]]$minlength,
maxlength = stocks[imm.stock][[1]][[1]]$maxlength,
dl = stocks[imm.stock][[1]][[1]]$dl,
livesonareas = stocks[imm.stock][[1]][[1]]$livesonareas) %>%
gadget_update('naturalmortality',rep(0,.[[1]]$maxage+1)) %>%
gadget_update('doesgrow',0) %>%
## to get multiannual recruitment you will need to define multiple hockeystocks or use straying (some other day)
gadget_update('doesmove',
transitionstocksandratios = sprintf('%s 1',imm.stock),
transitionstep = stocks[[imm.stock]]$doesrenew$normalparamfile[[1]]$step %>% unique() %>% head(1)) %>%
gadget_update('initialconditions',
normalparam = data_frame(age = .[[1]]$minage:.[[1]]$maxage,
area = .[[1]]$livesonareas,
age.factor = 0,
area.factor = 0,
mean = .[[1]]$minlength,
stddev = 1,
alpha = 0.00001, ## never used
beta = 3)) %>%
write.gadget.file(run.dir)
suits <-
## can define multiple fleets with the same selection
setNames(pre.fleets$base_fleet%||%pre.fleets$fleet_name,
pre.fleets$fleet_name) %>%
map(function(x){
fleets %>%
purrr::map(~purrr::keep(.,~.[[1]]==x)) %>%
purrr::flatten() %>%
purrr::map(c('suitability')) %>%
purrr::flatten()
}) %>%
purrr::map(~bind_rows(.)) %>%
dplyr::bind_rows(.id='fleet') %>%
tidyr::gather(stock,val,-c(1)) %>%
dplyr::group_by(fleet,stock) %>%
dplyr::mutate(n=paste0('n',1:n())) %>%
tidyr::spread(n,val,fill= '') %>%
dplyr::ungroup()
## define fleet amounts that are parametrised by year, step, area
fleet.amount <-
# pre.fleets$fleet_name %>%
# setNames(.,.) %>%
setNames(pre.fleets$base_fleet%||%pre.fleets$fleet_name,
pre.fleets$fleet_name) %>%
purrr::map(function(x)
schedule %>%
dplyr::mutate(year = year-1,number= 1) %>% #paste('#fleet',x,year,step,area,sep='.')) %>%
structure(area_group = lift(mfdb_group)(main[[1]]$areafile[[1]]$areas %>%
setNames(.,.))))
#the first fleet is fixed as the one with filled data
dat <-
readcatch(paste(path,'Data/fleet.lln.data',sep = '/')) %>%
full_join(readcatch(paste(path,'Data/fleet.bmt.data',sep = '/'))) %>%
full_join(readcatch(paste(path,'Data/fleet.gil.data',sep = '/'))) %>%
full_join(readcatch(paste(path,'Data/fleet.foreign.data',sep = '/')))
fleet.amount[[1]] <- data_frame(year = main[[1]]$timefile[[1]]$lastyear,
step = c(2,3),
area = 1,
total_weight = dat %>%
filter(year==2018 | (year==2017 & step==4) ) %>%
mutate(all = 'all') %>%
group_by(all) %>%
summarise(restperstep = (lastyearTAC*1000-sum(total_weight))/2) %>%
select(restperstep) %>%
unlist()
) %>%
structure(area_group = lift(mfdb_group)(main[[1]]$areafile[[1]]$areas %>%
setNames(.,.)))
## define the projection fleets
fleet.tmp <-
gadgetfleet('fleet.predict',path,missingOkay = TRUE)
for(i in seq_along(pre.fleets$fleet_name)){
pfx <- ifelse(i==1, '','predict.')
fleet.tmp <-
fleet.tmp %>%
gadget_update(pre.fleets$fleet_type[i],
name = paste0(pfx,pre.fleets$fleet_name[i]),
suitability =
suits %>%
dplyr::filter(fleet == pre.fleets$fleet_name[i]) %>%
tidyr::unite(col,-matches('fleet'),sep='\t') %>%
dplyr::select(-fleet) %>%
unlist() %>%
paste(collapse="\n") %>%
sprintf('\n%s',.),
data = fleet.amount[[pre.fleets$fleet_name[i]]])
}
fleet.tmp %>%
write.gadget.file(run.dir)
## Define the prognosis likelihood
pre.fleet.names <-
fleet.tmp %>%
map(~.[[1]]) %>%
unlist() %>%
as.character() %>%
grep('predict',.,value=T)
## assumes expL50 as that is the only thing allowed
biocoeffs <-
suits %>%
dplyr::filter(fleet == ref.fleet,tolower(n2) == 'exponentiall50') %>%
dplyr::select(-c(1:4)) %>%
tidyr::unite(col,everything(),sep='\t') %>%
dplyr::mutate(col=paste(col,1,sep='\t')) %>%
unlist() %>%
paste(collapse="\n")
progn <-
progn <-
gadgetfile('fleet.likelihood',
file_type = 'likelihood',
components = list(list('[component]',name = 'prognosis',
weight = 1,
type = "proglikelihood",
fleetnames = pre.fleet.names,
stocknames = c(imm.stock,mat.stock),
biocoeffs = c(-100,ref.cm,1,-100,ref.cm,1),## biocoeffs, this doesn't work atm
## only want the mature biomass as the trigger biomass
triggercoeffs=c(0,0,0,0,0,1),
triggervalues=c(0, sprintf('#%s.btrigger',mat.stock)),
harvestrates = sprintf(c('#%s.hr.low','#%s.hr.high'),ref.fleet),
quotasteps = c(3, 4, 5, 6),
## assume equal fishing intensity for now
quotaproportions = c(0.25, 0.25, 0.25, 0.25),
fleetproportions= sprintf('#fleet.prop.%s',pre.fleet.names),
weightoflastyearstac=0,
maxchange=4,
functionnumber=1,
firsttacyear=min(schedule$year)-1,
assessmentstep=1,
asserr=gadgetfile('asserr',
file_type = 'timevariable',
components=list(list('asserr',
data = data_frame(year = main[[1]]$timefile[[1]]$firstyear,
step = main[[1]]$timefile[[1]]$firststep,
value = "0") %>%
bind_rows(expand.grid(year = unique(schedule$year)-1,
step = 1) %>%
dplyr::mutate(value = paste0('#',mat.stock,'.asserr.',year))) %>%
as.data.frame()))))))
attr(progn,'file_config')$mainfile_overwrite = TRUE
progn %>%
write.gadget.file(run.dir)
#
## projections (stochastic)
rep.params <- function(params){
expand.grid(iter=1:num.iter,harvest.rate=harvest.rate) %>%
dplyr::mutate(run.id=1:n()) %>%
split(.$run.id) %>%
purrr::map(function(x){
block.rec <-
imm.stock %>%
purrr::set_names(.,.) %>%
purrr::map(~stocks[[.]]$doesrenew$normalparamfile[[1]] %>%
dplyr::filter(year < min(schedule$year)) %>%
dplyr::mutate(number = number %>%
unlist() %>%
map(~eval(.,setNames( nm=params$switch,params$value) %>%
as.list())) %>%
unlist())) %>%
dplyr::bind_rows(.id='stock') %>%
split(.$stock) %>%
purrr::map(function(x) {
schedule %>%
filter(step==1) %>%
mutate(number = mean(tail(head(x$number,-2),3)))
}) %>%
bind_rows(.id='stock') %>%
dplyr::transmute(switch=sprintf('%s.rec.%s',stock,year),
value = number,
upper=1.5*number,
lower=0.5*number,
optimise = 0)
asserr <-
mat.stock %>%
purrr::set_names(.,.) %>%
purrr::map(~schedule %>%
dplyr::select(year) %>%
dplyr::distinct() %>%
dplyr::mutate(number = 1)) %>% #arima.sim(list(ar = asserr.rho), n = n(),
# rand.gen = function(x) rnorm(x,sd=asserr.cv*sqrt(1-asserr.rho^2))) %>%
#exp())) %>%
dplyr::bind_rows(.id='stock') %>%
dplyr::ungroup() %>%
dplyr::transmute(switch=sprintf('%s.asserr.%s',stock,year-1),
value = number,
upper=1.5*number,
lower=0.5*number,
optimise = 0)
params %>%
bind_rows(asserr,block.rec,ref.points) %>%
dplyr::mutate(value = ifelse(grepl('hr.high',switch),x$harvest.rate,value))
})
}
#params.pre <- rep.params(params)
tmp <- main[[1]]$timefile[[1]]$lastyear
params.pre <-
params %>%
rep.params() %>%
bind_rows(.id='repl') %>%
## hack
mutate(value = ifelse(switch %in% sprintf('ling.rec.%s',c(tmp-1,tmp)),
mean(value[switch %in% sprintf('ling.rec.%s',c((tmp-4):(tmp-2)))])
,value)) %>%
select(repl,switch,value) %>%
tidyr::spread(switch,value)
## define the output, needs to be very compact
print <-
gadgetfile('pre.print',
components = list(list('[component]',
type = 'stockprinter',
stocknames = mat.stock,
areaaggfile = gadgetdata(sprintf('Aggfiles/%s.area.agg',mat.stock),
data = data.frame(name = sprintf('area%s',stocks[[mat.stock]][[1]]$livesonareas),
value = stocks[[mat.stock]][[1]]$livesonareas)),
ageaggfile = gadgetdata(sprintf('Aggfiles/%s.ssb.age.agg',mat.stock),
data = data.frame(name = 'allages',
value = paste(stocks[[mat.stock]][[1]]$minage:stocks[[mat.stock]][[1]]$maxage,
collapse = ' '))),
lenaggfile = gadgetdata(paste0('Aggfiles/', stocks[[mat.stock]][[1]]$stockname, '.stock.len.agg'),
data = data.frame(name = 'alllen',
value = paste(stocks[[mat.stock]][[1]]$minlength,stocks[[mat.stock]][[1]]$maxlength,
sep = ' '))),
printfile = gadgetfile(sprintf('out/%s.ssb',mat.stock)),
printatstart = 1,
yearsandsteps = 'all 1'),
list('[component]',
type = 'stockprinter',
stocknames = c(imm.stock,mat.stock),
areaaggfile = gadgetdata(sprintf('Aggfiles/%s.%s.area.agg',imm.stock,mat.stock),
data = data.frame(name = sprintf('area%s',stocks[[mat.stock]][[1]]$livesonareas),
value = stocks[[mat.stock]][[1]]$livesonareas)),
ageaggfile = gadgetdata(sprintf('Aggfiles/%s.%s.ssb.age.agg',imm.stock,mat.stock),
data = data.frame(name = 'allages',
value = paste(stocks[[imm.stock]][[1]]$minage:stocks[[mat.stock]][[1]]$maxage,
collapse = ' '))),
lenaggfile = gadgetdata('Aggfiles/refbio.len.agg',
data = data.frame(name = 'alllen',
value = paste(ref.cm,stocks[[mat.stock]][[1]]$maxlength,
sep = ' '))),
printfile = gadgetfile('out/refbio'),
printatstart = 1,
yearsandsteps = 'all all'),
list('[component]',
type = 'stockprinter',
stocknames = c(imm.stock,mat.stock),
areaaggfile = gadgetdata(sprintf('Aggfiles/%s.%s.area.agg',imm.stock,mat.stock),
data = data.frame(name = sprintf('area%s',stocks[[mat.stock]][[1]]$livesonareas),
value = stocks[[mat.stock]][[1]]$livesonareas)),
ageaggfile = gadgetdata(sprintf('Aggfiles/%s.%s.totalbio.age.agg',imm.stock,mat.stock),
data = data.frame(name = 'allages',
value = paste(stocks[[imm.stock]][[1]]$minage:stocks[[mat.stock]][[1]]$maxage,
collapse = ' '))),
lenaggfile = gadgetdata('Aggfiles/totalbio.len.agg',
data = data.frame(name = 'alllen',
value = paste(stocks[[imm.stock]][[1]]$minlength,stocks[[mat.stock]][[1]]$maxlength,
sep = ' '))),
printfile = gadgetfile('out/totalbio'),
printatstart = 1,
yearsandsteps = 'all all'),
list('[component]',
type = 'stockprinter',
stocknames = imm.stock,
areaaggfile = gadgetdata(sprintf('Aggfiles/%s.area.agg',imm.stock),
data = data.frame(name = sprintf('area%s',stocks[[imm.stock]][[1]]$livesonareas),
value = stocks[[imm.stock]][[1]]$livesonareas)),
ageaggfile = gadgetdata(sprintf('Aggfiles/%s.age.agg',imm.stock),
data = data.frame(name = stocks[[imm.stock]][[1]]$minage,
value = 3)),
lenaggfile = gadgetdata(paste0('Aggfiles/', stocks[[imm.stock]][[1]]$stockname, '.stock.len.agg'),
data = data.frame(name = 'alllen',
value = paste(stocks[[imm.stock]][[1]]$minlength,stocks[[imm.stock]][[1]]$maxlength,
sep = ' '))),
printfile = gadgetfile(sprintf('out/%s.rec',imm.stock)),
printatstart = 1,
yearsandsteps = 'all 2'),
list('[component]',
type = 'predatorpreyprinter',
predatornames = c(pre.fleets$fleet_name,pre.fleet.names,'foreign'),
preynames = c(imm.stock,mat.stock),
areaaggfile = gadgetdata(sprintf('Aggfiles/%s.area.agg',mat.stock),
data = data.frame(name = sprintf('area%s',stocks[[mat.stock]][[1]]$livesonareas),
value = stocks[[mat.stock]][[1]]$livesonareas)),
ageaggfile = gadgetdata(sprintf('Aggfiles/%s.stock.allage.agg',mat.stock),
data = tibble::data_frame(value = paste(stocks[[imm.stock]][[1]]$minage:stocks[[mat.stock]][[1]]$maxage,
collapse = ' '),
name = 'allages') %>%
dplyr::select(name,value) %>%
as.data.frame()),
lenaggfile = gadgetdata(paste0('Aggfiles/', stocks[[mat.stock]][[1]]$stockname, '.stock.len.agg'),
data = data.frame(name = 'alllen',
value = paste(stocks[[imm.stock]][[1]]$minlength,stocks[[mat.stock]][[1]]$maxlength,
sep = ' '))),
printfile = gadgetfile('out/catch.lw'),
yearsandsteps = 'all all'),
list('[component]',
type = 'predatorpreyprinter',
predatornames = c(pre.fleets$fleet_name,pre.fleet.names,'foreign'),
preynames = mat.stock,
areaaggfile = gadgetdata(sprintf('Aggfiles/%s.area.agg',mat.stock),
data = data.frame(name = sprintf('area%s',stocks[[mat.stock]][[1]]$livesonareas),
value = stocks[[mat.stock]][[1]]$livesonareas)),
ageaggfile = gadgetdata(sprintf('Aggfiles/%s.stock.Fage.agg',mat.stock),
data = tibble::data_frame(value = stocks[[mat.stock]][[1]]$maxage,
name = 'allages') %>%
dplyr::select(name,value) %>%
as.data.frame()),
lenaggfile = gadgetdata(paste0('Aggfiles/', stocks[[mat.stock]][[1]]$stockname, '.stock.len.agg'),
data = data.frame(name = 'alllen',
value = paste(stocks[[imm.stock]][[1]]$minlength,stocks[[mat.stock]][[1]]$maxlength,
sep = ' '))),
printfile = gadgetfile('out/catch.F'),
yearsandsteps = 'all all')))
attr(print,'file_config')$mainfile_section <- 'printfiles'
#write.gadget.file(print,run.dir)
Sys.setenv(GADGET_WORKING_DIR=normalizePath(path))
run.func <- function(x,asserr=TRUE,btrigger=FALSE){
curr.dir <- sprintf('%s/run%s',tempdir(),attr(x,'run_id'))
dir.create(curr.dir)
dir.create(paste(curr.dir,'out',sep='/'))
vd <- gadget.variant.dir(path,variant_dir = curr.dir,mainfile = 'main')
vd[[1]] <- ''
read.gadget.file(run.dir,'main',recursive = FALSE) %>%
write.gadget.file(vd,recursive=FALSE)
print %>% write.gadget.file(vd)
x$lingmat.blim <- bloss
if(!asserr){
x<-
x %>%
mutate_at(vars(matches("asserr")),function(x) 1)
}
if(btrigger){
x <-
x %>%
mutate_at(vars(matches("btrigger")),function(x) bpa)
}
write.gadget.parameters(x,file=paste(curr.dir,'params.pre',sep='/'),columns = FALSE)
callGadget(s=1,
main=attr(vd,'mainfile'),
i=paste(curr.dir,'params.pre',sep='/'),
ignore.stderr = FALSE,
# gadget.exe = '~/bin/gadget')
gadget.exe = '~/Documents/gadget/gadget')
list.files(paste(curr.dir,'out',sep='/'),full.names = TRUE) %>%
purrr::set_names(.,.) %>%
purrr::map(readoutput)
}
attr(params.pre,'run_id') <- 'tmp'
tmp <- run.func(params.pre %>% select(-repl) %>% filter(lln.hr.high %in% c(0,0.18)))
names(tmp) <- names(tmp) %>% str_split('/') %>% map(~tail(.,1)) %>% unlist()
lastadviceyear <- main[[1]]$timefile[[1]]$lastyear + 4
progn <-
tmp$catch.F %>%
filter(year < lastadviceyear) %>%
group_by(year,trial) %>%
summarise(F=mean(mortality)) %>%
left_join(tmp$refbio %>%
filter(step==1,year < lastadviceyear) %>%
group_by(year,trial) %>%
summarise(refbio=sum(number*mean_weight))) %>%
left_join(tmp$lingmat.ssb %>%
filter(step==1,year < lastadviceyear) %>%
group_by(year,trial) %>%
summarise(ssb=sum(number*mean_weight))) %>%
left_join(tmp$totalbio %>%
filter(step==1,year < lastadviceyear) %>%
group_by(year,trial) %>%
summarise(totalbio=sum(number*mean_weight))) %>%
left_join(tmp$catch.lw %>%
filter(year < lastadviceyear) %>%
group_by(year,trial) %>%
summarise(catch=sum(biomass_consumed))) %>%
left_join(tmp$lingimm.rec %>%
filter(year < lastadviceyear) %>%
select(year,rec=number,trial)) %>%
left_join(tmp$refbio %>%
mutate(bio = number*mean_weight) %>%
left_join(tmp$catch.lw %>%
select(year,step,trial,catch=biomass_consumed)) %>%
mutate(hr=4*catch/bio) %>%
filter(year < lastadviceyear) %>%
group_by(year,trial) %>%
summarise(hr=mean(hr)))
progn_by_adyear <-
tmp$catch.F %>%
mutate(ad.year = ifelse(step==4,paste(year,year+1,sep='/'),paste(year-1,year,sep='/'))) %>%
filter(ad.year < lastadviceyear) %>%
group_by(ad.year,trial) %>%
summarise(F=mean(mortality)) %>%
left_join(tmp$refbio %>%
mutate(ad.year = ifelse(step==4,paste(year,year+1,sep='/'),paste(year-1,year,sep='/'))) %>%
filter(step==1,year < lastadviceyear) %>%
group_by(ad.year,trial) %>%
summarise(refbio=sum(number*mean_weight))) %>%
left_join(tmp$lingmat.ssb %>%
mutate(ad.year = ifelse(step==4,paste(year,year+1,sep='/'),paste(year-1,year,sep='/'))) %>%
filter(step==1,year < lastadviceyear) %>%
group_by(ad.year,trial) %>%
summarise(ssb=sum(number*mean_weight))) %>%
left_join(tmp$totalbio %>%
mutate(ad.year = ifelse(step==4,paste(year,year+1,sep='/'),paste(year-1,year,sep='/'))) %>%
filter(step==1,year < lastadviceyear) %>%
group_by(ad.year,trial) %>%
summarise(totalbio=sum(number*mean_weight))) %>%
left_join(tmp$catch.lw %>%
mutate(ad.year = ifelse(step==4,paste(year,year+1,sep='/'),paste(year-1,year,sep='/'))) %>%
filter(year < lastadviceyear) %>%
group_by(ad.year,trial) %>%
summarise(catch=sum(biomass_consumed))) %>%
left_join(tmp$lingimm.rec %>%
mutate(ad.year = ifelse(step==4,paste(year,year+1,sep='/'),paste(year-1,year,sep='/'))) %>%
filter(year < lastadviceyear) %>%
select(ad.year,rec=number,trial)) %>%
left_join(tmp$refbio %>%
mutate(ad.year = ifelse(step==4,paste(year,year+1,sep='/'),paste(year-1,year,sep='/')),
bio = number*mean_weight) %>%
left_join(tmp$catch.lw %>%
mutate(ad.year = ifelse(step==4,paste(year,year+1,sep='/'),paste(year-1,year,sep='/'))) %>%
select(ad.year,step,trial,catch=biomass_consumed)) %>%
mutate(hr=4*catch/bio) %>%
filter(year < lastadviceyear) %>%
group_by(ad.year,trial) %>%
summarise(hr=mean(hr)))
write.csv2(progn,file='ling_progn.csv')
write.csv2(progn_by_adyear,file='ling_progn_by_adyear.csv')