inst/Updates/LFA 27-32/Contextual.Indicators.Updates.r
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
require(bio.lobster)
require(bio.utilities)
require(PBSmapping)
###for cohort analysis and numbers landed
landings.numbers = T
if(landings.numbers){
atSea.clean = lobster.db('atSea.clean')
atSea.clean$PORT = ifelse(atSea.clean$PORT==-99,0,atSea.clean$PORT)
g = lobster.db('annual.landings')
p = lobster.db('seasonal.landings')
mls = read.csv(file=file.path(project.datadirectory('bio.lobster'),'data','inputs','MLS.Changes.all.LFA.csv'))
mls$X = NULL
load(file.path(project.datadirectory('bio.lobster'),'outputs','deltaTsSimBH.rdata')) #DTs
ad = as.data.frame(unique(cbind(atSea.clean$LFA,atSea.clean$SYEAR)))
ad = ad[order(ad[,1],ad[,2]),]
names(ad) = c('LFA','YEAR')
names(mls)[1] <- 'YEAR'
ad = merge(ad,mls)
ad = subset(ad,YEAR<2030 & LFA<33)
atsea = atSea.clean
cG = lobster.db('community.to.grid.historic')
cH = lobster.db('community.to.grid.contemporary')
out = list()
outN = list()
outS = list()
for(i in 1:nrow(ad)) {
print(ad[i,])
po = ad[i,'LFA']
yo = ad[i,'YEAR']
mm = ad[i,'MLS_MM']
da = atSeaWeightings(atSea = atsea, comGridHist =subset(cG,LFA==ad[i,'LFA']),comGridCont = subset(cH,LFA==ad[i,'LFA'] & SYEAR==ad[i,'YEAR']), year=ad[i,'YEAR'],lfa=ad[i,'LFA'],females.only=F,at.sea.samples=T,mls=mm)
op = weightedCLF(x=da,returnLF=T,at.sea.samples=T)
os = op
os$vec<-NULL
outS[[i]] <- os
# brad ran to here to get data for a plot
#outS[[i]] <- op$vec
# }
#save(outS,file=file.path(project.datadirectory("bio.lobster"),"outputs","atSeaCLF.rdata"))
#Tc is fractional year of catch
if(po == 27) {ll = "LFA27-30"; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('27N',names(DTs))]]; Tc = 0.67}
if(po == 28) {ll = 'LFA28,30'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('28',names(DTs))]]; Tc = 0.67}
if(po == 29) {ll = 'LFA29'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('29',names(DTs))]]; Tc = 0.67}
if(po == 30) {ll = 'LFA28,30'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('30',names(DTs))]]; Tc = 0.67}
if(po == '31A') {ll = 'LFA29'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('31A',names(DTs))]]; Tc = 0.67}
if(po == '31B') {ll = 'LFA32'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('31B',names(DTs))]]; Tc = 0.67}
if(po == 32) {ll = 'LFA32'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('32',names(DTs))]]; Tc = 0.67}
if(po == 33) {ll = 'LFA33'; lle = 'all areas'; lp = p[,c('SYEAR',names(p)[grep(po,names(p))])]; lp = rename.df(lp,'SYEAR','YR'); dt = DTs[[grep('33W',names(DTs))]]; Tc = 0.3}
if(!is.null(op)){
vec = mm:250
oo = op$vec[op$vec>mm & op$vec<250]
v0 = hist(oo, breaks=vec,plot=F)
v0$wts = lobLW(v0$mids)
v0$bwts = v0$counts * v0$wts
le = subset(lp,YR == yo)[,2]
if(po ==33) le = subset(lp,substr(YR,6,9) == yo)[,2]
v0$acWt = v0$bwts / sum(v0$bwts) * le
v0$N = v0$acWt / v0$wts # tons / g = #'s in '000000
outN[[i]] = data.frame(N = v0$N, Len = v0$mids,LFA = po, Year = yo,MLS=mm)
#newly recruited fraction
outS[[i]] = c(outS[[i]], new.rec = sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]) / sum(v0$N[v0$mids %in% seq(mm,mm+100,by=0.5)]))
outS[[i]] = c(outS[[i]], recWt = sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]*v0$wts[v0$mids %in% seq(mm,mm+11,by=0.5)]) / sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]))
#Production
iw = v0$mids>mm
pMature = sum(pMat(lfa=ll,cl=v0$mids[iw]) * v0$N[iw]) /sum(v0$N[iw])
eggProd = sum(pMat(lfa=ll,cl=v0$mids) * Fecundity(lle,v0$mids) * v0$N * as.numeric(outS[[i]]['prop.female']))
outS[[i]] = c(outS[[i]], PropMating = pMature,EggProduction = eggProd)
brks = seq(mm,max(as.numeric(names(dt))),by=5) #throws error
dt = dt[which.min(abs(brks[1]-as.numeric(names(dt)))):length(dt)]
dt =data.frame(dt=dt,brks = as.numeric(names(dt)))
dt$dt = dt$dt / 365 #* (5 / (as.numeric(names(dt))*0.15))
dt = dt[1:length(brks),]
dt$brks = brks
v0$LCA = brks[findInterval(v0$mids,vec=brks)]
LCAN = aggregate(v0$N~v0$LCA,FUN=sum)
LCA = merge(LCAN,dt,by.x='v0$LCA',by.y = 'brks')
k = which(LCA[,2]==0)[1]
LCA = LCA[1:(k-1),]
###need to get dts
ca = cohortAnalysis(lens = LCA[,1], N = LCA[,2], dt = LCA[,3]) #annual
LCA$LFA = po
LCA$Year = yo
LCA$MLS = mm
out[[i]] = c(LFA = po, YEAR=yo, MLS=mm, N = sum(v0$N),Land = le,expl =ca$expl, F = ca$wF,M = ca$M,tF = ca$termF)
}
}
}
out.temp=out
out = as.data.frame(do.call(rbind,out.temp))
out = toNums(out,2:ncol(out))
save(out,file = file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNumbersLandedLFA27-33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNumbersLandedLFA27-33.rdata'))
outN = as.data.frame(do.call(rbind,outN))
save(outN,file = file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNatSizeLFA27-33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNatSizeLFA27-33.rdata'))
outS = as.data.frame(do.call(rbind,outS))
outS = toNums(outS,2:ncol(outS))
save(outS,file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryatSeaIndicatorsDataLFA27-33.rdata'))
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryatSeaIndicatorsDataLFA27-33.rdata'))
#####three year windowed expls using the data loaded above
runLCA = list()
m=0
lfa = c(27,29,30,'31A','31B',32,33)
for(i in 1:length(lfa)){
o = subset(outN,LFA==lfa[i])
#specific years where data was available for ~3 in a row
if(lfa[i]==27) yrs = list(1990:1992, 1991:1993, 1992:1994, 1993:1995, 1994:1997,1995:1999,1997:2000,1999:2001, 2000:2002,2001:2003, 2002:2004,2003:2005,2004:2007,2005:2009,2007:2010,2009:2011,2010:2012,2011:2013,2012:2014,2013:2015)
if(lfa[i]==29) yrs = list(1990:1993, 2008:2015)
if(lfa[i]==30) yrs = list(1999:2001,2000:2002,2001:2003,2002:2004,2003:2005,2004:2007,2005:2008,2007:2009)
if(lfa[i]=='31A') yrs = list(2001:2003, 2007:2009,2008:2010,2009:2011,2010:2012,2011:2013,2012:2014,2013:2015)
if(lfa[i]=='31B') yrs = list(2002:2004,2008:2010,2009:2011,2010:2012,2011:2013,2012:2014,2013:2015)
if(lfa[i]=='32') yrs = list(2001:2003,2002:2004,2009:2011,2010:2012,2011:2013,2012:2014,2013:2015)
if(lfa[i]=='33') yrs = list(1985:1987,2001:2003,2002:2004,2009:2012,2010:2014)
if(lfa[i] == 27) {dt = DTs[[grep('27N',names(DTs))]]; Tc = 0.67}
if(lfa[i] == 29) {dt = DTs[[grep('29',names(DTs))]]; Tc = 0.67}
if(lfa[i] == 30) {dt = DTs[[grep('30',names(DTs))]]; Tc = 0.67}
if(lfa[i] == '31A') {dt = DTs[[grep('31A',names(DTs))]]; Tc = 0.67}
if(lfa[i] == '31B') {dt = DTs[[grep('31B',names(DTs))]]; Tc = 0.67}
if(lfa[i] == 32) {dt = DTs[[grep('32',names(DTs))]]; Tc = 0.67}
if(lfa[i] == 33) {dt = DTs[[grep('33W',names(DTs))]]; Tc = 0.3}
for(j in 1:length(yrs)){
m =m+1
print(m)
p = subset(o,Year %in% yrs[[j]])
mm = max(unique(p$MLS))
p = subset(p,Len>=mm)
brks = seq(mm,max(as.numeric(names(dt))),by=5)
dt1 = dt[which.min(abs(brks[1]-as.numeric(names(dt)))):length(dt)]
dt1 =data.frame(dt=dt1,brks = as.numeric(names(dt1)))
dt1$dt = dt1$dt / 365 #* (5 / (as.numeric(names(dt))*0.15))
dt1 = dt1[1:length(brks),]
dt1$brks = brks
p$LCA = brks[findInterval(p$Len,vec=brks)]
p = merge(p,dt1,by.x='LCA',by.y = 'brks')
p$I = 1
LCAN = aggregate(cbind(N,I,dt)~LCA,data = p,FUN=sum)
w = which.max(LCAN$LCA[LCAN$I==15])
LCAN = LCAN[1:w,]
LCAN$dt = LCAN$dt/LCAN$I
k = which(LCAN$N==0)[1]
if(!is.na(k))LCAN = LCAN[1:(k-1),]
ca = cohortAnalysis(lens = LCAN$LCA, N = LCAN$N, dt = LCAN$dt)
runLCA[[m]] = c(LFA = lfa[i],Year.min = min(yrs[[j]]),Year.max = max(yrs[[j]]),F = ca$wF, expl = ca$expl)
}
}
runLCA = as.data.frame(do.call(rbind,runLCA))
runLCA = toNums(runLCA,c('Year.min','Year.max','F','expl'))
# save(runLCA,file = file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsExploitationAggregatedLFA27-33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsExploitationAggregatedLFA27-33.rdata') )
}
#sensitivity analysis
oo = c()
tf = seq(0.1,2,by=0.1)
for(i in tf){
oo = c(oo,cohortAnalysis(lens = LCAN$LCA, N = LCAN$N, dt = LCAN$dt,termF=i)$wF)
}
plot(tf,oo,xlab='Terminal F',ylab='Estimated Exploitation',type='b',ylim=c(0.8,0.9))
savePlot(file='/backup/bio_data/bio.lobster/figures/CAsensitivityToTermF.png',type='png')
tf = seq(0.1,0.2,by=0.01)
oo = c()
for(i in tf){
oo = c(oo,cohortAnalysis(lens = LCAN$LCA, N = LCAN$N, dt = LCAN$dt,M=i)$expl)
}
plot(tf,oo,xlab='Natural Mortality',ylab='Estimated Exploitation',type='b',ylim=c(0.5,0.65))
savePlot(file='/backup/bio_data/bio.lobster/figures/CAsensitivityToM.png',type='png')
#Cohort Analysis Plots
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNumbersLandedLFA27-33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsExploitationAggregatedLFA27-33.rdata'))
CAplots(ann = out, yr3 = runLCA)
#sample sizes
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsLFA27-33.rdata'))
ouS = out[,c('LFA','Year','TotalLobsters')]
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNumbersLandedLFA27-33.rdata'))
out$Year = out$YEAR
####FSRS commercial samples
fsrs.commercial.samples = T
if(fsrs.commercial.samples){
lobster.db('fsrs.commercial.samples')
fs = subset(fsrs.comm,SYEAR>2002)
p = lobster.db('seasonal.landings')
load(file.path(project.datadirectory('bio.lobster'),'outputs','deltaTsSimBH.rdata')) #DTs
dt = DTs[grep('33',names(DTs))]
ad = 2004:2017
cH = lobster.db('community.to.grid.contemporary')
cH = subset(cH, LFA==33)
cG = lobster.db('community.to.grid.historic')
cG = subset(cG, LFA==33)
outN = list()
out = list()
outS = list()
for(i in 1:length(ad)) {
print(ad[i])
da = atSeaWeightings(atSea = fs, comGridCont = subset(cH, SYEAR==ad[i]),comGridHist = cG, year=ad[i],females.only=F,fsrs.commercial.samples=T,mls=10)
op = weightedCLF(x=da,returnLF=T,fsrs.commercial.samples=T)
os = op
os$vec<-NULL
outS[[i]] <- unlist(os)
#Tc is fractional year of catch
ll = 'LFA33'
lle = 'LFA33'
yo = ad[i]
mm = 10.5
lp = p[,c('SYEAR',names(p)[grep(ll,names(p))])]
lp = rename.df(lp,'SYEAR','YR')
dt = DTs[[grep('33W',names(DTs))]]
dt = dt[which(names(dt)==85):which(names(dt)==130)]
dt = c(dt[1],mean(dt[2:3]),mean(dt[4:5]),mean(dt[6:7]),mean(dt[8:9]))
Tc = 0.3
if(!is.null(op)){
vec = c(10.5,11,12,13,14)
oo = op$vec[op$vec>=mm & op$vec<15]
v0 = table(oo)
wts = lobLW(c(85,95,105,115,125))
bwts = v0 * wts
le = subset(lp,substr(YR,6,9) == yo)[,2]
acWt = bwts / sum(bwts) * le
N = acWt / wts # tons / g = #'s in '000000
outN[[i]] = data.frame(N = N, Len = names(v0),Year = yo,MLS=mm)
if(i==13)browser()
outS[[i]] = c(outS[[i]], new.rec = as.numeric(v0[1] / sum(v0)))
ca = cohortAnalysis(lens = as.numeric(names(N)), N = as.numeric(N), dt = c(dt[1],dt[2:length(dt)]*2)/365) #annual
out[[i]] = c(YEAR=yo, MLS=mm, N = sum(N),Land = le,expl =ca$expl, F = ca$wF,M = ca$M,tF = ca$termF)
}
}
out = as.data.frame(do.call(rbind,out))
out = toNums(out,2:ncol(out))
out$LFA=33
save(out,file = file.path(project.datadirectory('bio.lobster'),'outputs','fsrsNumbersLanded33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','fsrsNumbersLanded33.rdata'))
outN = as.data.frame(do.call(rbind,outN))
save(outN,file = file.path(project.datadirectory('bio.lobster'),'outputs','fsrsNatSizeLFA33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','fsrsNatSizeLFA33.rdata'))
outS = as.data.frame(do.call(rbind,outS))
outS = toNums(outS,2:ncol(outS))
save(outS,file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryfsrsCommercialSamplesLanded33.rdata'))
#3-year running
o = outN
runLCA = list()
#specific years where data was available for ~3 in a row
yrs = list(2004:2006,2005:2007,2006:2008,2007:2009,2008:2010,2009:2011,2010:2012,2011:2013,2012:2014,2013:2015,2014:2016)
dt = DTs[[grep('33W',names(DTs))]]
dt = dt[which(names(dt)==85):which(names(dt)==130)]
dt = c(dt[1],mean(dt[2:3])*2,mean(dt[4:5])*2,mean(dt[6:7])*2,mean(dt[8:9])*2) / 365
for(j in 1:length(yrs)){
p = subset(o,Year %in% yrs[[j]])
LCAN=aggregate(N.Freq~Len,data=p,FUN=sum)
ca = cohortAnalysis(lens = as.numeric(LCAN$Len), N = LCAN$N.Freq, dt = as.numeric(dt))
runLCA[[j]] = c(Year.min = min(yrs[[j]]),Year.max = max(yrs[[j]]),F = ca$wF, expl = ca$expl)
}
runLCA = as.data.frame(do.call(rbind,runLCA))
runLCA = toNums(runLCA,c('Year.min','Year.max','F','expl'))
runLCA$LFA=33
save(runLCA,file = file.path(project.datadirectory('bio.lobster'),'outputs','fsrsExploitationAggregated33.rdata'))
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','fsrsExploitationAggregated33.rdata'))
##fsrs ca plots
load(file.path(project.datadirectory('bio.lobster'),'outputs','fsrsNatSizeLFA33.rdata'))
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','fsrsExploitationAggregated33.rdata'))
CAplots(ann=out,yr3=runLCA,fsrs=T)
}
###port samples
redo.port.samples=T
if(redo.port.samples){
lobster.db('port.sampling')
port = subset(port,!is.na(PORT_CODE))
port[,c('N_MALES','N_FEM','NBF')] = na.zero(port[,c('N_MALES','N_FEM','NBF')])
g = lobster.db('annual.landings')
p = lobster.db('seasonal.landings')
mls = read.csv(file=file.path(project.datadirectory('bio.lobster'),'data','inputs','MLS.Changes.all.LFA.csv'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','deltaTsSimBH.rdata')) #DTs
ad = as.data.frame(unique(cbind(port$LFA,port$SYEAR)))
ad = ad[order(ad[,1],ad[,2]),]
names(ad) = c('LFA','YEAR')
names(mls)[1] <- 'YEAR'
ad = merge(ad,mls)
ad = subset(ad, LFA<34)
cG = lobster.db('community.to.grid.historic')
cH = lobster.db('community.to.grid.contemporary')
out = list()
outN = list()
outS = list()
for(i in 1:nrow(ad)) {
print(ad[i,])
po = ad[i,'LFA']
yo = ad[i,'YEAR']
mm = ad[i,'MLS_MM']
da = atSeaWeightings(atSea = port, comGridHist =subset(cG,LFA==ad[i,'LFA']),comGridCont = subset(cH,LFA==ad[i,'LFA'] & SYEAR==ad[i,'YEAR']), year=ad[i,'YEAR'],lfa=ad[i,'LFA'],females.only=F,fsrs.commercial.samples=F,port.samples=T)
op = NULL
if(!is.null(da)) {
op = weightedCLF(x=da,returnLF=T,port.samples=T)
os = op
os$vec<-NULL
outS[[i]] <- unlist(os)
#Tc is fractional year of catch
if(po == 27) {ll = "LFA27-30"; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('27N',names(DTs))]]; Tc = 0.67}
if(po == 28) {ll = 'LFA28,30'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('28',names(DTs))]]; Tc = 0.67}
if(po == 29) {ll = 'LFA29'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('29',names(DTs))]]; Tc = 0.67}
if(po == 30) {ll = 'LFA28,30'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('30',names(DTs))]]; Tc = 0.67}
if(po == '31A') {ll = 'LFA29'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('31A',names(DTs))]]; Tc = 0.67}
if(po == '31B') {ll = 'LFA32'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('31B',names(DTs))]]; Tc = 0.67}
if(po == 32) {ll = 'LFA32'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('32',names(DTs))]]; Tc = 0.67}
if(po == 33) {ll = 'LFA33'; lle = 'all areas'; lp = p[,c('SYEAR',names(p)[grep(po,names(p))])]; lp = rename.df(lp,'SYEAR','YR'); dt = DTs[[grep('33W',names(DTs))]]; Tc = 0.3}
if(!is.null(op)){
vec = mm:250
oo = op$vec[op$vec>mm & op$vec<250]
v0 = hist(oo, breaks=vec,plot=F)
v0$wts = lobLW(v0$mids)
v0$bwts = v0$counts * v0$wts
le = subset(lp,YR == yo)[,2]
if(po ==33) le = subset(lp,substr(YR,6,9) == yo)[,2]
v0$acWt = v0$bwts / sum(v0$bwts) * le
v0$N = v0$acWt / v0$wts # tons / g = #'s in '000000
outN[[i]] = data.frame(N = v0$N, Len = v0$mids,LFA = po, Year = yo,MLS=mm)
outS[[i]] = c(outS[[i]], new.rec = sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]) / sum(v0$N[v0$mids %in% seq(mm,mm+110,by=0.5)]))
outS[[i]] = c(outS[[i]], recWt = sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]*v0$wts[v0$mids %in% seq(mm,mm+11,by=0.5)]) / sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]))
outS[[i]] = c(outS[[i]], landWt = sum(v0$N*v0$wts) / sum(v0$N))
iw = v0$mids>mm
pMature = sum(pMat(lfa=ll,cl=v0$mids[iw]) * v0$N[iw]) /sum(v0$N[iw])
eggProd = sum(pMat(lfa=ll,cl=v0$mids) * Fecundity(lle,v0$mids) * v0$N * as.numeric(outS[[i]]['prop.female']))
outS[[i]] = c(outS[[i]], PropMating = pMature,EggProduction = eggProd)
brks = seq(mm,max(as.numeric(names(dt))),by=5)
dt = dt[which.min(abs(brks[1]-as.numeric(names(dt)))):length(dt)]
dt =data.frame(dt=dt,brks = as.numeric(names(dt)))
dt$dt = dt$dt / 365 #* (5 / (as.numeric(names(dt))*0.15))
dt = dt[1:length(brks),]
dt$brks = brks
v0$LCA = brks[findInterval(v0$mids,vec=brks)]
LCAN = aggregate(v0$N~v0$LCA,FUN=sum)
LCA = merge(LCAN,dt,by.x='v0$LCA',by.y = 'brks')
k = which(LCA[,2]==0)[1]
LCA = LCA[1:(k-1),]
###need to get dts
ca = cohortAnalysis(lens = LCA[,1], N = LCA[,2], dt = LCA[,3]) #annual
LCA$LFA = po
LCA$Year = yo
LCA$MLS = mm
out[[i]] = c(LFA = po, YEAR=yo, MLS=mm, N = sum(v0$N),Land = le,expl =ca$expl, F = ca$wF,M = ca$M,tF = ca$termF)
}
}
}
out = as.data.frame(do.call(rbind,out))
out = toNums(out,2:ncol(out))
save(out,file = file.path(project.datadirectory('bio.lobster'),'outputs','portNumbersLanded27-33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','portNumbersLanded27-33.rdata'))
outN = as.data.frame(do.call(rbind,outN))
save(outN,file = file.path(project.datadirectory('bio.lobster'),'outputs','portNatSizeLFA27-33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','portNatSizeLFA27-33.rdata'))
outS = as.data.frame(do.call(rbind,outS))
outS = toNums(outS,2:ncol(outS))
save(outS,file = file.path(project.datadirectory('bio.lobster'),'outputs','portSummaryLFA27-33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','portSummaryLFA27-33.rdata'))
#####three year windowed expls using the data loaded above
runLCA = list()
m=0
lfa = c(27,29,'31A','31B',32,33)
for(i in 1:length(lfa)){
o = subset(outN,LFA==lfa[i])
#specific years where data was available for ~3 in a row
if(lfa[i]==27) yrs = list(1989:1991,1990:1994, 1994:1996, 1995:1997, 1996:1998, 1997:1999,1998:2001,1999:2002,2001:2003)
if(lfa[i]==29) yrs = list(1989:1993, 1990:1994, 1993:1995, 1994:1997, 1995:1998, 1997:1999, 1998:2000, 1999:2001, 2000:2002, 2001:2003)
if(lfa[i]=='31A') yrs = list(1985:1987,1986:1988,1987:1989, 1988:1990, 1989:1991, 1990:1992, 1991:1993, 1992:1994, 1998:2000,1999:2001,2000:2002,2001:2003,2002:2004, 2003:2005, 2004:2006)
if(lfa[i]=='31B') yrs = list(1986:1988,1987:1989, 1988:1990, 1989:1992, 1990:1993, 1992:1994, 1993:1996, 1994:1998,1996:1999,1998:2000,1999:2001,2000:2002,2001:2003, 2002:2004, 2003:2005)
if(lfa[i]=='32') yrs = list(1985:1987,1986:1988,1987:1989, 1988:1991, 1989:1992, 1991:1993, 1992:1994, 1993:1996, 1994:1998,1996:1999,1998:2000,1999:2001,2000:2002,2001:2003)
if(lfa[i]=='33') yrs = list(1992:1994, 1993:1995, 1994:1996, 1995:1997, 1996:1998, 1997:1999, 2007:2010, 2009:2011, 2010:2013)
if(lfa[i] == 27) {dt = DTs[[grep('27N',names(DTs))]]; Tc = 0.67}
if(lfa[i] == 29) {dt = DTs[[grep('29',names(DTs))]]; Tc = 0.67}
if(lfa[i] == '31A') {dt = DTs[[grep('31A',names(DTs))]]; Tc = 0.67}
if(lfa[i] == '31B') {dt = DTs[[grep('31B',names(DTs))]]; Tc = 0.67}
if(lfa[i] == 32) {dt = DTs[[grep('32',names(DTs))]]; Tc = 0.67}
if(lfa[i] == 33) {dt = DTs[[grep('33W',names(DTs))]]; Tc = 0.3}
for(j in 1:length(yrs)){
m =m+1
print(m)
p = subset(o,Year %in% yrs[[j]])
mm = max(unique(p$MLS))
p = subset(p,Len>=mm)
brks = seq(mm,max(as.numeric(names(dt))),by=5)
dt1 = dt[which.min(abs(brks[1]-as.numeric(names(dt)))):length(dt)]
dt1 =data.frame(dt=dt1,brks = as.numeric(names(dt1)))
dt1$dt = dt1$dt / 365 #* (5 / (as.numeric(names(dt))*0.15))
dt1 = dt1[1:length(brks),]
dt1$brks = brks
p$LCA = brks[findInterval(p$Len,vec=brks)]
p = merge(p,dt1,by.x='LCA',by.y = 'brks')
p$I = 1
LCAN = aggregate(cbind(N,I,dt)~LCA,data = p,FUN=sum)
lop = min(LCAN$I,na.rm=T)
w = which.max(LCAN$LCA[LCAN$I==lop])
LCAN = LCAN[1:w,]
LCAN$dt = LCAN$dt/LCAN$I
k = which(LCAN$N==0)[1]
if(!is.na(k))LCAN = LCAN[1:(k-1),]
ca = cohortAnalysis(lens = LCAN$LCA, N = LCAN$N, dt = LCAN$dt)
runLCA[[m]] = c(LFA = lfa[i],Year.min = min(yrs[[j]]),Year.max = max(yrs[[j]]),F = ca$wF, expl = ca$expl)
}
}
runLCA = as.data.frame(do.call(rbind,runLCA))
runLCA = toNums(runLCA,c('Year.min','Year.max','F','expl'))
save(runLCA,file = file.path(project.datadirectory('bio.lobster'),'outputs','PORTatSeaIndicatorsExploitationAggregatedLFA27-33.rdata'))
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','PORTatSeaIndicatorsExploitationAggregatedLFA27-33.rdata'))
CAplots(ann=out,yr3=runLCA,port=T)
}
#############
fsrs.recruit.samples = T
if(fsrs.recruit.samples){
lobster.db('fsrs')
fsrs$LFA = ifelse(fsrs$LFA=='31.1','31A',fsrs$LFA)
fsrs$LFA = ifelse(fsrs$LFA=='31.2','31B',fsrs$LFA)
#these shouldbe in lobster.db
fsrs = addSYEAR(fsrs,date.field='HAUL_DATE')
season.dates = lobster.db('season.dates')
fsrs$WOS = NA
lfa = unique(fsrs$LFA)
for(i in 1:length(lfa)) {
h = season.dates[season.dates$LFA==lfa[i],]
for(j in unique(fsrs$SYEAR[fsrs$LFA==lfa[i]])){
fsrs$WOS[fsrs$LFA==lfa[i] & fsrs$SYEAR==j] = floor(as.numeric(fsrs$HAUL_DATE[fsrs$LFA==lfa[i] & fsrs$SYEAR==j]-min(h$START_DATE[h$SYEAR==j]))/7)+1
}
}
fsrs = subset(fsrs,SYEAR>2003)
p = lobster.db('seasonal.landings')
g = lobster.db('annual.landings')
p = lobster.db('seasonal.landings')
mls = mls2fsrs()
mls = rename.df(mls,'Year','YEAR')
load(file.path(project.datadirectory('bio.lobster'),'outputs','deltaTsSimBH.rdata')) #DTs
ad = as.data.frame(unique(cbind(fsrs$LFA,fsrs$SYEAR)))
ad = ad[order(ad[,1],ad[,2]),]
names(ad) = c('LFA','YEAR')
ad = merge(ad,mls)
ad = subset(ad,YEAR<2019 & LFA<34)
cG = lobster.db('community.to.grid.historic')
cH = lobster.db('community.to.grid.contemporary')
outS = list()
for(i in 1:nrow(ad)) {
print(ad[i,])
da = atSeaWeightings(atSea = fsrs, comGridHist =subset(cG,LFA==ad[i,'LFA']),comGridCont = subset(cH,LFA==ad[i,'LFA'] & SYEAR==ad[i,'YEAR']),mls=ad[i,'fsrs'], year=ad[i,'YEAR'],lfa=ad[i,'LFA'],females.only=F,fsrs.recruit.samples=T)
op = weightedCLF(x=da,returnLF=T,fsrs.recruit.samples=T,lab=paste("LFA",ad[i,"LFA"],ad[i,"YEAR"]))
os = op
os$vec<-NULL
outS[[i]] <- unlist(os)
####No cohort analysis
}
outS = as.data.frame(do.call(rbind,outS))
outS = toNums(outS,2:ncol(outS))
save(outS,file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryfsrsrecruitmentSamplesLanded27-33.rdata'))
#3-year running
}
#Overall plots
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','PORTatSeaIndicatorsExploitationAggregatedLFA27-33.rdata'))
po = runLCA
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','fsrsExploitationAggregated33.rdata'))
fs = runLCA
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsExploitationAggregatedLFA27-33.rdata'))
aS = runLCA
CAplotsMultDataSets(atSea = aS, fsrs = fs, port = po)
LobsterScience/bio.lobster documentation built on Feb. 14, 2025, 3:28 p.m.
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require(bio.lobster)
require(bio.utilities)
require(PBSmapping)
###for cohort analysis and numbers landed
landings.numbers = T
if(landings.numbers){
atSea.clean = lobster.db('atSea.clean')
atSea.clean$PORT = ifelse(atSea.clean$PORT==-99,0,atSea.clean$PORT)
g = lobster.db('annual.landings')
p = lobster.db('seasonal.landings')
mls = read.csv(file=file.path(project.datadirectory('bio.lobster'),'data','inputs','MLS.Changes.all.LFA.csv'))
mls$X = NULL
load(file.path(project.datadirectory('bio.lobster'),'outputs','deltaTsSimBH.rdata')) #DTs
ad = as.data.frame(unique(cbind(atSea.clean$LFA,atSea.clean$SYEAR)))
ad = ad[order(ad[,1],ad[,2]),]
names(ad) = c('LFA','YEAR')
names(mls)[1] <- 'YEAR'
ad = merge(ad,mls)
ad = subset(ad,YEAR<2030 & LFA<33)
atsea = atSea.clean
cG = lobster.db('community.to.grid.historic')
cH = lobster.db('community.to.grid.contemporary')
out = list()
outN = list()
outS = list()
for(i in 1:nrow(ad)) {
print(ad[i,])
po = ad[i,'LFA']
yo = ad[i,'YEAR']
mm = ad[i,'MLS_MM']
da = atSeaWeightings(atSea = atsea, comGridHist =subset(cG,LFA==ad[i,'LFA']),comGridCont = subset(cH,LFA==ad[i,'LFA'] & SYEAR==ad[i,'YEAR']), year=ad[i,'YEAR'],lfa=ad[i,'LFA'],females.only=F,at.sea.samples=T,mls=mm)
op = weightedCLF(x=da,returnLF=T,at.sea.samples=T)
os = op
os$vec<-NULL
outS[[i]] <- os
# brad ran to here to get data for a plot
#outS[[i]] <- op$vec
# }
#save(outS,file=file.path(project.datadirectory("bio.lobster"),"outputs","atSeaCLF.rdata"))
#Tc is fractional year of catch
if(po == 27) {ll = "LFA27-30"; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('27N',names(DTs))]]; Tc = 0.67}
if(po == 28) {ll = 'LFA28,30'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('28',names(DTs))]]; Tc = 0.67}
if(po == 29) {ll = 'LFA29'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('29',names(DTs))]]; Tc = 0.67}
if(po == 30) {ll = 'LFA28,30'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('30',names(DTs))]]; Tc = 0.67}
if(po == '31A') {ll = 'LFA29'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('31A',names(DTs))]]; Tc = 0.67}
if(po == '31B') {ll = 'LFA32'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('31B',names(DTs))]]; Tc = 0.67}
if(po == 32) {ll = 'LFA32'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('32',names(DTs))]]; Tc = 0.67}
if(po == 33) {ll = 'LFA33'; lle = 'all areas'; lp = p[,c('SYEAR',names(p)[grep(po,names(p))])]; lp = rename.df(lp,'SYEAR','YR'); dt = DTs[[grep('33W',names(DTs))]]; Tc = 0.3}
if(!is.null(op)){
vec = mm:250
oo = op$vec[op$vec>mm & op$vec<250]
v0 = hist(oo, breaks=vec,plot=F)
v0$wts = lobLW(v0$mids)
v0$bwts = v0$counts * v0$wts
le = subset(lp,YR == yo)[,2]
if(po ==33) le = subset(lp,substr(YR,6,9) == yo)[,2]
v0$acWt = v0$bwts / sum(v0$bwts) * le
v0$N = v0$acWt / v0$wts # tons / g = #'s in '000000
outN[[i]] = data.frame(N = v0$N, Len = v0$mids,LFA = po, Year = yo,MLS=mm)
#newly recruited fraction
outS[[i]] = c(outS[[i]], new.rec = sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]) / sum(v0$N[v0$mids %in% seq(mm,mm+100,by=0.5)]))
outS[[i]] = c(outS[[i]], recWt = sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]*v0$wts[v0$mids %in% seq(mm,mm+11,by=0.5)]) / sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]))
#Production
iw = v0$mids>mm
pMature = sum(pMat(lfa=ll,cl=v0$mids[iw]) * v0$N[iw]) /sum(v0$N[iw])
eggProd = sum(pMat(lfa=ll,cl=v0$mids) * Fecundity(lle,v0$mids) * v0$N * as.numeric(outS[[i]]['prop.female']))
outS[[i]] = c(outS[[i]], PropMating = pMature,EggProduction = eggProd)
brks = seq(mm,max(as.numeric(names(dt))),by=5) #throws error
dt = dt[which.min(abs(brks[1]-as.numeric(names(dt)))):length(dt)]
dt =data.frame(dt=dt,brks = as.numeric(names(dt)))
dt$dt = dt$dt / 365 #* (5 / (as.numeric(names(dt))*0.15))
dt = dt[1:length(brks),]
dt$brks = brks
v0$LCA = brks[findInterval(v0$mids,vec=brks)]
LCAN = aggregate(v0$N~v0$LCA,FUN=sum)
LCA = merge(LCAN,dt,by.x='v0$LCA',by.y = 'brks')
k = which(LCA[,2]==0)[1]
LCA = LCA[1:(k-1),]
###need to get dts
ca = cohortAnalysis(lens = LCA[,1], N = LCA[,2], dt = LCA[,3]) #annual
LCA$LFA = po
LCA$Year = yo
LCA$MLS = mm
out[[i]] = c(LFA = po, YEAR=yo, MLS=mm, N = sum(v0$N),Land = le,expl =ca$expl, F = ca$wF,M = ca$M,tF = ca$termF)
}
}
}
out.temp=out
out = as.data.frame(do.call(rbind,out.temp))
out = toNums(out,2:ncol(out))
save(out,file = file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNumbersLandedLFA27-33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNumbersLandedLFA27-33.rdata'))
outN = as.data.frame(do.call(rbind,outN))
save(outN,file = file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNatSizeLFA27-33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNatSizeLFA27-33.rdata'))
outS = as.data.frame(do.call(rbind,outS))
outS = toNums(outS,2:ncol(outS))
save(outS,file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryatSeaIndicatorsDataLFA27-33.rdata'))
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryatSeaIndicatorsDataLFA27-33.rdata'))
#####three year windowed expls using the data loaded above
runLCA = list()
m=0
lfa = c(27,29,30,'31A','31B',32,33)
for(i in 1:length(lfa)){
o = subset(outN,LFA==lfa[i])
#specific years where data was available for ~3 in a row
if(lfa[i]==27) yrs = list(1990:1992, 1991:1993, 1992:1994, 1993:1995, 1994:1997,1995:1999,1997:2000,1999:2001, 2000:2002,2001:2003, 2002:2004,2003:2005,2004:2007,2005:2009,2007:2010,2009:2011,2010:2012,2011:2013,2012:2014,2013:2015)
if(lfa[i]==29) yrs = list(1990:1993, 2008:2015)
if(lfa[i]==30) yrs = list(1999:2001,2000:2002,2001:2003,2002:2004,2003:2005,2004:2007,2005:2008,2007:2009)
if(lfa[i]=='31A') yrs = list(2001:2003, 2007:2009,2008:2010,2009:2011,2010:2012,2011:2013,2012:2014,2013:2015)
if(lfa[i]=='31B') yrs = list(2002:2004,2008:2010,2009:2011,2010:2012,2011:2013,2012:2014,2013:2015)
if(lfa[i]=='32') yrs = list(2001:2003,2002:2004,2009:2011,2010:2012,2011:2013,2012:2014,2013:2015)
if(lfa[i]=='33') yrs = list(1985:1987,2001:2003,2002:2004,2009:2012,2010:2014)
if(lfa[i] == 27) {dt = DTs[[grep('27N',names(DTs))]]; Tc = 0.67}
if(lfa[i] == 29) {dt = DTs[[grep('29',names(DTs))]]; Tc = 0.67}
if(lfa[i] == 30) {dt = DTs[[grep('30',names(DTs))]]; Tc = 0.67}
if(lfa[i] == '31A') {dt = DTs[[grep('31A',names(DTs))]]; Tc = 0.67}
if(lfa[i] == '31B') {dt = DTs[[grep('31B',names(DTs))]]; Tc = 0.67}
if(lfa[i] == 32) {dt = DTs[[grep('32',names(DTs))]]; Tc = 0.67}
if(lfa[i] == 33) {dt = DTs[[grep('33W',names(DTs))]]; Tc = 0.3}
for(j in 1:length(yrs)){
m =m+1
print(m)
p = subset(o,Year %in% yrs[[j]])
mm = max(unique(p$MLS))
p = subset(p,Len>=mm)
brks = seq(mm,max(as.numeric(names(dt))),by=5)
dt1 = dt[which.min(abs(brks[1]-as.numeric(names(dt)))):length(dt)]
dt1 =data.frame(dt=dt1,brks = as.numeric(names(dt1)))
dt1$dt = dt1$dt / 365 #* (5 / (as.numeric(names(dt))*0.15))
dt1 = dt1[1:length(brks),]
dt1$brks = brks
p$LCA = brks[findInterval(p$Len,vec=brks)]
p = merge(p,dt1,by.x='LCA',by.y = 'brks')
p$I = 1
LCAN = aggregate(cbind(N,I,dt)~LCA,data = p,FUN=sum)
w = which.max(LCAN$LCA[LCAN$I==15])
LCAN = LCAN[1:w,]
LCAN$dt = LCAN$dt/LCAN$I
k = which(LCAN$N==0)[1]
if(!is.na(k))LCAN = LCAN[1:(k-1),]
ca = cohortAnalysis(lens = LCAN$LCA, N = LCAN$N, dt = LCAN$dt)
runLCA[[m]] = c(LFA = lfa[i],Year.min = min(yrs[[j]]),Year.max = max(yrs[[j]]),F = ca$wF, expl = ca$expl)
}
}
runLCA = as.data.frame(do.call(rbind,runLCA))
runLCA = toNums(runLCA,c('Year.min','Year.max','F','expl'))
# save(runLCA,file = file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsExploitationAggregatedLFA27-33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsExploitationAggregatedLFA27-33.rdata') )
}
#sensitivity analysis
oo = c()
tf = seq(0.1,2,by=0.1)
for(i in tf){
oo = c(oo,cohortAnalysis(lens = LCAN$LCA, N = LCAN$N, dt = LCAN$dt,termF=i)$wF)
}
plot(tf,oo,xlab='Terminal F',ylab='Estimated Exploitation',type='b',ylim=c(0.8,0.9))
savePlot(file='/backup/bio_data/bio.lobster/figures/CAsensitivityToTermF.png',type='png')
tf = seq(0.1,0.2,by=0.01)
oo = c()
for(i in tf){
oo = c(oo,cohortAnalysis(lens = LCAN$LCA, N = LCAN$N, dt = LCAN$dt,M=i)$expl)
}
plot(tf,oo,xlab='Natural Mortality',ylab='Estimated Exploitation',type='b',ylim=c(0.5,0.65))
savePlot(file='/backup/bio_data/bio.lobster/figures/CAsensitivityToM.png',type='png')
#Cohort Analysis Plots
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNumbersLandedLFA27-33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsExploitationAggregatedLFA27-33.rdata'))
CAplots(ann = out, yr3 = runLCA)
#sample sizes
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsLFA27-33.rdata'))
ouS = out[,c('LFA','Year','TotalLobsters')]
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNumbersLandedLFA27-33.rdata'))
out$Year = out$YEAR
####FSRS commercial samples
fsrs.commercial.samples = T
if(fsrs.commercial.samples){
lobster.db('fsrs.commercial.samples')
fs = subset(fsrs.comm,SYEAR>2002)
p = lobster.db('seasonal.landings')
load(file.path(project.datadirectory('bio.lobster'),'outputs','deltaTsSimBH.rdata')) #DTs
dt = DTs[grep('33',names(DTs))]
ad = 2004:2017
cH = lobster.db('community.to.grid.contemporary')
cH = subset(cH, LFA==33)
cG = lobster.db('community.to.grid.historic')
cG = subset(cG, LFA==33)
outN = list()
out = list()
outS = list()
for(i in 1:length(ad)) {
print(ad[i])
da = atSeaWeightings(atSea = fs, comGridCont = subset(cH, SYEAR==ad[i]),comGridHist = cG, year=ad[i],females.only=F,fsrs.commercial.samples=T,mls=10)
op = weightedCLF(x=da,returnLF=T,fsrs.commercial.samples=T)
os = op
os$vec<-NULL
outS[[i]] <- unlist(os)
#Tc is fractional year of catch
ll = 'LFA33'
lle = 'LFA33'
yo = ad[i]
mm = 10.5
lp = p[,c('SYEAR',names(p)[grep(ll,names(p))])]
lp = rename.df(lp,'SYEAR','YR')
dt = DTs[[grep('33W',names(DTs))]]
dt = dt[which(names(dt)==85):which(names(dt)==130)]
dt = c(dt[1],mean(dt[2:3]),mean(dt[4:5]),mean(dt[6:7]),mean(dt[8:9]))
Tc = 0.3
if(!is.null(op)){
vec = c(10.5,11,12,13,14)
oo = op$vec[op$vec>=mm & op$vec<15]
v0 = table(oo)
wts = lobLW(c(85,95,105,115,125))
bwts = v0 * wts
le = subset(lp,substr(YR,6,9) == yo)[,2]
acWt = bwts / sum(bwts) * le
N = acWt / wts # tons / g = #'s in '000000
outN[[i]] = data.frame(N = N, Len = names(v0),Year = yo,MLS=mm)
if(i==13)browser()
outS[[i]] = c(outS[[i]], new.rec = as.numeric(v0[1] / sum(v0)))
ca = cohortAnalysis(lens = as.numeric(names(N)), N = as.numeric(N), dt = c(dt[1],dt[2:length(dt)]*2)/365) #annual
out[[i]] = c(YEAR=yo, MLS=mm, N = sum(N),Land = le,expl =ca$expl, F = ca$wF,M = ca$M,tF = ca$termF)
}
}
out = as.data.frame(do.call(rbind,out))
out = toNums(out,2:ncol(out))
out$LFA=33
save(out,file = file.path(project.datadirectory('bio.lobster'),'outputs','fsrsNumbersLanded33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','fsrsNumbersLanded33.rdata'))
outN = as.data.frame(do.call(rbind,outN))
save(outN,file = file.path(project.datadirectory('bio.lobster'),'outputs','fsrsNatSizeLFA33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','fsrsNatSizeLFA33.rdata'))
outS = as.data.frame(do.call(rbind,outS))
outS = toNums(outS,2:ncol(outS))
save(outS,file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryfsrsCommercialSamplesLanded33.rdata'))
#3-year running
o = outN
runLCA = list()
#specific years where data was available for ~3 in a row
yrs = list(2004:2006,2005:2007,2006:2008,2007:2009,2008:2010,2009:2011,2010:2012,2011:2013,2012:2014,2013:2015,2014:2016)
dt = DTs[[grep('33W',names(DTs))]]
dt = dt[which(names(dt)==85):which(names(dt)==130)]
dt = c(dt[1],mean(dt[2:3])*2,mean(dt[4:5])*2,mean(dt[6:7])*2,mean(dt[8:9])*2) / 365
for(j in 1:length(yrs)){
p = subset(o,Year %in% yrs[[j]])
LCAN=aggregate(N.Freq~Len,data=p,FUN=sum)
ca = cohortAnalysis(lens = as.numeric(LCAN$Len), N = LCAN$N.Freq, dt = as.numeric(dt))
runLCA[[j]] = c(Year.min = min(yrs[[j]]),Year.max = max(yrs[[j]]),F = ca$wF, expl = ca$expl)
}
runLCA = as.data.frame(do.call(rbind,runLCA))
runLCA = toNums(runLCA,c('Year.min','Year.max','F','expl'))
runLCA$LFA=33
save(runLCA,file = file.path(project.datadirectory('bio.lobster'),'outputs','fsrsExploitationAggregated33.rdata'))
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','fsrsExploitationAggregated33.rdata'))
##fsrs ca plots
load(file.path(project.datadirectory('bio.lobster'),'outputs','fsrsNatSizeLFA33.rdata'))
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','fsrsExploitationAggregated33.rdata'))
CAplots(ann=out,yr3=runLCA,fsrs=T)
}
###port samples
redo.port.samples=T
if(redo.port.samples){
lobster.db('port.sampling')
port = subset(port,!is.na(PORT_CODE))
port[,c('N_MALES','N_FEM','NBF')] = na.zero(port[,c('N_MALES','N_FEM','NBF')])
g = lobster.db('annual.landings')
p = lobster.db('seasonal.landings')
mls = read.csv(file=file.path(project.datadirectory('bio.lobster'),'data','inputs','MLS.Changes.all.LFA.csv'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','deltaTsSimBH.rdata')) #DTs
ad = as.data.frame(unique(cbind(port$LFA,port$SYEAR)))
ad = ad[order(ad[,1],ad[,2]),]
names(ad) = c('LFA','YEAR')
names(mls)[1] <- 'YEAR'
ad = merge(ad,mls)
ad = subset(ad, LFA<34)
cG = lobster.db('community.to.grid.historic')
cH = lobster.db('community.to.grid.contemporary')
out = list()
outN = list()
outS = list()
for(i in 1:nrow(ad)) {
print(ad[i,])
po = ad[i,'LFA']
yo = ad[i,'YEAR']
mm = ad[i,'MLS_MM']
da = atSeaWeightings(atSea = port, comGridHist =subset(cG,LFA==ad[i,'LFA']),comGridCont = subset(cH,LFA==ad[i,'LFA'] & SYEAR==ad[i,'YEAR']), year=ad[i,'YEAR'],lfa=ad[i,'LFA'],females.only=F,fsrs.commercial.samples=F,port.samples=T)
op = NULL
if(!is.null(da)) {
op = weightedCLF(x=da,returnLF=T,port.samples=T)
os = op
os$vec<-NULL
outS[[i]] <- unlist(os)
#Tc is fractional year of catch
if(po == 27) {ll = "LFA27-30"; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('27N',names(DTs))]]; Tc = 0.67}
if(po == 28) {ll = 'LFA28,30'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('28',names(DTs))]]; Tc = 0.67}
if(po == 29) {ll = 'LFA29'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('29',names(DTs))]]; Tc = 0.67}
if(po == 30) {ll = 'LFA28,30'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('30',names(DTs))]]; Tc = 0.67}
if(po == '31A') {ll = 'LFA29'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('31A',names(DTs))]]; Tc = 0.67}
if(po == '31B') {ll = 'LFA32'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('31B',names(DTs))]]; Tc = 0.67}
if(po == 32) {ll = 'LFA32'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('32',names(DTs))]]; Tc = 0.67}
if(po == 33) {ll = 'LFA33'; lle = 'all areas'; lp = p[,c('SYEAR',names(p)[grep(po,names(p))])]; lp = rename.df(lp,'SYEAR','YR'); dt = DTs[[grep('33W',names(DTs))]]; Tc = 0.3}
if(!is.null(op)){
vec = mm:250
oo = op$vec[op$vec>mm & op$vec<250]
v0 = hist(oo, breaks=vec,plot=F)
v0$wts = lobLW(v0$mids)
v0$bwts = v0$counts * v0$wts
le = subset(lp,YR == yo)[,2]
if(po ==33) le = subset(lp,substr(YR,6,9) == yo)[,2]
v0$acWt = v0$bwts / sum(v0$bwts) * le
v0$N = v0$acWt / v0$wts # tons / g = #'s in '000000
outN[[i]] = data.frame(N = v0$N, Len = v0$mids,LFA = po, Year = yo,MLS=mm)
outS[[i]] = c(outS[[i]], new.rec = sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]) / sum(v0$N[v0$mids %in% seq(mm,mm+110,by=0.5)]))
outS[[i]] = c(outS[[i]], recWt = sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]*v0$wts[v0$mids %in% seq(mm,mm+11,by=0.5)]) / sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]))
outS[[i]] = c(outS[[i]], landWt = sum(v0$N*v0$wts) / sum(v0$N))
iw = v0$mids>mm
pMature = sum(pMat(lfa=ll,cl=v0$mids[iw]) * v0$N[iw]) /sum(v0$N[iw])
eggProd = sum(pMat(lfa=ll,cl=v0$mids) * Fecundity(lle,v0$mids) * v0$N * as.numeric(outS[[i]]['prop.female']))
outS[[i]] = c(outS[[i]], PropMating = pMature,EggProduction = eggProd)
brks = seq(mm,max(as.numeric(names(dt))),by=5)
dt = dt[which.min(abs(brks[1]-as.numeric(names(dt)))):length(dt)]
dt =data.frame(dt=dt,brks = as.numeric(names(dt)))
dt$dt = dt$dt / 365 #* (5 / (as.numeric(names(dt))*0.15))
dt = dt[1:length(brks),]
dt$brks = brks
v0$LCA = brks[findInterval(v0$mids,vec=brks)]
LCAN = aggregate(v0$N~v0$LCA,FUN=sum)
LCA = merge(LCAN,dt,by.x='v0$LCA',by.y = 'brks')
k = which(LCA[,2]==0)[1]
LCA = LCA[1:(k-1),]
###need to get dts
ca = cohortAnalysis(lens = LCA[,1], N = LCA[,2], dt = LCA[,3]) #annual
LCA$LFA = po
LCA$Year = yo
LCA$MLS = mm
out[[i]] = c(LFA = po, YEAR=yo, MLS=mm, N = sum(v0$N),Land = le,expl =ca$expl, F = ca$wF,M = ca$M,tF = ca$termF)
}
}
}
out = as.data.frame(do.call(rbind,out))
out = toNums(out,2:ncol(out))
save(out,file = file.path(project.datadirectory('bio.lobster'),'outputs','portNumbersLanded27-33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','portNumbersLanded27-33.rdata'))
outN = as.data.frame(do.call(rbind,outN))
save(outN,file = file.path(project.datadirectory('bio.lobster'),'outputs','portNatSizeLFA27-33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','portNatSizeLFA27-33.rdata'))
outS = as.data.frame(do.call(rbind,outS))
outS = toNums(outS,2:ncol(outS))
save(outS,file = file.path(project.datadirectory('bio.lobster'),'outputs','portSummaryLFA27-33.rdata'))
load(file.path(project.datadirectory('bio.lobster'),'outputs','portSummaryLFA27-33.rdata'))
#####three year windowed expls using the data loaded above
runLCA = list()
m=0
lfa = c(27,29,'31A','31B',32,33)
for(i in 1:length(lfa)){
o = subset(outN,LFA==lfa[i])
#specific years where data was available for ~3 in a row
if(lfa[i]==27) yrs = list(1989:1991,1990:1994, 1994:1996, 1995:1997, 1996:1998, 1997:1999,1998:2001,1999:2002,2001:2003)
if(lfa[i]==29) yrs = list(1989:1993, 1990:1994, 1993:1995, 1994:1997, 1995:1998, 1997:1999, 1998:2000, 1999:2001, 2000:2002, 2001:2003)
if(lfa[i]=='31A') yrs = list(1985:1987,1986:1988,1987:1989, 1988:1990, 1989:1991, 1990:1992, 1991:1993, 1992:1994, 1998:2000,1999:2001,2000:2002,2001:2003,2002:2004, 2003:2005, 2004:2006)
if(lfa[i]=='31B') yrs = list(1986:1988,1987:1989, 1988:1990, 1989:1992, 1990:1993, 1992:1994, 1993:1996, 1994:1998,1996:1999,1998:2000,1999:2001,2000:2002,2001:2003, 2002:2004, 2003:2005)
if(lfa[i]=='32') yrs = list(1985:1987,1986:1988,1987:1989, 1988:1991, 1989:1992, 1991:1993, 1992:1994, 1993:1996, 1994:1998,1996:1999,1998:2000,1999:2001,2000:2002,2001:2003)
if(lfa[i]=='33') yrs = list(1992:1994, 1993:1995, 1994:1996, 1995:1997, 1996:1998, 1997:1999, 2007:2010, 2009:2011, 2010:2013)
if(lfa[i] == 27) {dt = DTs[[grep('27N',names(DTs))]]; Tc = 0.67}
if(lfa[i] == 29) {dt = DTs[[grep('29',names(DTs))]]; Tc = 0.67}
if(lfa[i] == '31A') {dt = DTs[[grep('31A',names(DTs))]]; Tc = 0.67}
if(lfa[i] == '31B') {dt = DTs[[grep('31B',names(DTs))]]; Tc = 0.67}
if(lfa[i] == 32) {dt = DTs[[grep('32',names(DTs))]]; Tc = 0.67}
if(lfa[i] == 33) {dt = DTs[[grep('33W',names(DTs))]]; Tc = 0.3}
for(j in 1:length(yrs)){
m =m+1
print(m)
p = subset(o,Year %in% yrs[[j]])
mm = max(unique(p$MLS))
p = subset(p,Len>=mm)
brks = seq(mm,max(as.numeric(names(dt))),by=5)
dt1 = dt[which.min(abs(brks[1]-as.numeric(names(dt)))):length(dt)]
dt1 =data.frame(dt=dt1,brks = as.numeric(names(dt1)))
dt1$dt = dt1$dt / 365 #* (5 / (as.numeric(names(dt))*0.15))
dt1 = dt1[1:length(brks),]
dt1$brks = brks
p$LCA = brks[findInterval(p$Len,vec=brks)]
p = merge(p,dt1,by.x='LCA',by.y = 'brks')
p$I = 1
LCAN = aggregate(cbind(N,I,dt)~LCA,data = p,FUN=sum)
lop = min(LCAN$I,na.rm=T)
w = which.max(LCAN$LCA[LCAN$I==lop])
LCAN = LCAN[1:w,]
LCAN$dt = LCAN$dt/LCAN$I
k = which(LCAN$N==0)[1]
if(!is.na(k))LCAN = LCAN[1:(k-1),]
ca = cohortAnalysis(lens = LCAN$LCA, N = LCAN$N, dt = LCAN$dt)
runLCA[[m]] = c(LFA = lfa[i],Year.min = min(yrs[[j]]),Year.max = max(yrs[[j]]),F = ca$wF, expl = ca$expl)
}
}
runLCA = as.data.frame(do.call(rbind,runLCA))
runLCA = toNums(runLCA,c('Year.min','Year.max','F','expl'))
save(runLCA,file = file.path(project.datadirectory('bio.lobster'),'outputs','PORTatSeaIndicatorsExploitationAggregatedLFA27-33.rdata'))
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','PORTatSeaIndicatorsExploitationAggregatedLFA27-33.rdata'))
CAplots(ann=out,yr3=runLCA,port=T)
}
#############
fsrs.recruit.samples = T
if(fsrs.recruit.samples){
lobster.db('fsrs')
fsrs$LFA = ifelse(fsrs$LFA=='31.1','31A',fsrs$LFA)
fsrs$LFA = ifelse(fsrs$LFA=='31.2','31B',fsrs$LFA)
#these shouldbe in lobster.db
fsrs = addSYEAR(fsrs,date.field='HAUL_DATE')
season.dates = lobster.db('season.dates')
fsrs$WOS = NA
lfa = unique(fsrs$LFA)
for(i in 1:length(lfa)) {
h = season.dates[season.dates$LFA==lfa[i],]
for(j in unique(fsrs$SYEAR[fsrs$LFA==lfa[i]])){
fsrs$WOS[fsrs$LFA==lfa[i] & fsrs$SYEAR==j] = floor(as.numeric(fsrs$HAUL_DATE[fsrs$LFA==lfa[i] & fsrs$SYEAR==j]-min(h$START_DATE[h$SYEAR==j]))/7)+1
}
}
fsrs = subset(fsrs,SYEAR>2003)
p = lobster.db('seasonal.landings')
g = lobster.db('annual.landings')
p = lobster.db('seasonal.landings')
mls = mls2fsrs()
mls = rename.df(mls,'Year','YEAR')
load(file.path(project.datadirectory('bio.lobster'),'outputs','deltaTsSimBH.rdata')) #DTs
ad = as.data.frame(unique(cbind(fsrs$LFA,fsrs$SYEAR)))
ad = ad[order(ad[,1],ad[,2]),]
names(ad) = c('LFA','YEAR')
ad = merge(ad,mls)
ad = subset(ad,YEAR<2019 & LFA<34)
cG = lobster.db('community.to.grid.historic')
cH = lobster.db('community.to.grid.contemporary')
outS = list()
for(i in 1:nrow(ad)) {
print(ad[i,])
da = atSeaWeightings(atSea = fsrs, comGridHist =subset(cG,LFA==ad[i,'LFA']),comGridCont = subset(cH,LFA==ad[i,'LFA'] & SYEAR==ad[i,'YEAR']),mls=ad[i,'fsrs'], year=ad[i,'YEAR'],lfa=ad[i,'LFA'],females.only=F,fsrs.recruit.samples=T)
op = weightedCLF(x=da,returnLF=T,fsrs.recruit.samples=T,lab=paste("LFA",ad[i,"LFA"],ad[i,"YEAR"]))
os = op
os$vec<-NULL
outS[[i]] <- unlist(os)
####No cohort analysis
}
outS = as.data.frame(do.call(rbind,outS))
outS = toNums(outS,2:ncol(outS))
save(outS,file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryfsrsrecruitmentSamplesLanded27-33.rdata'))
#3-year running
}
#Overall plots
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','PORTatSeaIndicatorsExploitationAggregatedLFA27-33.rdata'))
po = runLCA
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','fsrsExploitationAggregated33.rdata'))
fs = runLCA
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsExploitationAggregatedLFA27-33.rdata'))
aS = runLCA
CAplotsMultDataSets(atSea = aS, fsrs = fs, port = po)
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