inst/Updates/LFA35-38/Previous Updates/Update35_2018.R
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
####### Update 35: Sept 2018
require(bio.lobster)
require(ggplot2)
require(bio.utilities)
## Landings
AnnualSlip<-lobster.db( DS="annual.landings")
SeasonalSlip<-lobster.db( DS="seasonal.landings")
SeasonalSlip$YEAR<-as.numeric(substr(SeasonalSlip$SYEAR,6,9))
dat = subset(SeasonalSlip,YEAR<2018)
dat$SYEAR <- NULL
#Historic<-lobster.db( DS="historical.landings")
###stacked bar plot
library(reshape2)
dat2 <- melt(dat, id.vars = "YEAR")
dat2 = subset(dat2, variable %in% c('LFA35','LFA36','LFA38'))
Da = aggregate(value~YEAR,data=dat2,FUN=sum)
Da = as.data.frame(mavg(Da))
Da$V1 = Da$V1+1
Da = na.omit(Da)
names(Da) = c('YEAR','value')
Da$variable = 'Running Mean'
library(ggplot2)
ggplot(dat2, aes(x=YEAR, y=value, fill=variable)) +
geom_bar(stat="identity") +
theme_bw()+
scale_fill_grey()+
scale_x_continuous(breaks=seq(1980,2015,5),labels=paste(seq(1979,2014,5),seq(1980,2015,5),sep="-"))+
scale_y_continuous(breaks=seq(0,15000,2500))+
geom_line(data=Da,color='red',size=1.5) +
geom_hline(yintercept=c(1575,788),linetype=c('dashed','dotted'),color=c('black','black'),size=1.5)+
theme(legend.position='none',axis.text.x=element_text(angle=90, hjust=1, vjust=0.5,size=10, colour='black'),panel.background=element_rect(fill="white", colour='white'),panel.border=element_rect(,fill=NA,colour='black'),axis.text.y=element_text(size=10, colour='black'),
axis.title.y= element_text(size=15),panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + xlab('') + ylab('Landings (t)')
savePlot(file.path( project.figuredirectory("bio.lobster"), "LFA35-38Landings2018.png"),type='png')
dev.off()
####each LFA alone
#Seasonal35<-subset(SeasonalSlip,select=c("LFA35","SYEAR","YEAR"))
Seasonal35<-subset(SeasonalSlip,select=c("LFA38","SYEAR","YEAR"))
require(ggplot2)
cols = c(rep('navyblue',times=42),'green')
# Plot Landings (1892-present) Figure 2
pdf(file.path( project.figuredirectory("bio.lobster"), "LFA38Landings2018.pdf"),8,6)
ggplot(subset(Seasonal35),aes(YEAR,LFA38)) + geom_bar(data=Seasonal35,fill=cols,stat='identity') +
scale_fill_manual(values=cols) +
geom_line(data=Seasonal35,colour='red',size=1) +
scale_y_continuous(breaks=seq(0, 25000, 2000)) + scale_x_continuous(breaks=seq(1890, 2020, 5)) +
geom_hline(yintercept=c(518,259),linetype=c('dashed','dotted'),color=c('black','black'),size=1.5) +
theme(axis.text.x=element_text(angle=90, hjust=1, vjust=0.5,size=10, colour='black'),panel.background=element_rect(fill="white", colour='black'),panel.border=element_rect(,fill=NA,colour='black'),axis.text.y=element_text(size=10, colour='black'),
axis.title.y= element_text(size=15)) + xlab('') + ylab('Landings (t)')
dev.off()
#35-38 combined
SeasonalSlip$YEAR<-as.numeric(substr(SeasonalSlip$SYEAR,6,9))
SeasonalSlip$LFA35 = SeasonalSlip$LFA35 + SeasonalSlip$LFA36 + SeasonalSlip$LFA38
Seasonal35<-subset(SeasonalSlip,select=c("LFA35","SYEAR","YEAR"))
require(ggplot2)
cols = c(rep('navyblue',times=42),'green')
# Plot Landings (1892-present) Figure 2
pdf(file.path( project.figuredirectory("bio.lobster"), "LFA35-38Landings2018.pdf"),8,6)
ggplot(subset(Seasonal35),aes(YEAR,LFA35)) + geom_bar(data=Seasonal35,fill=cols,stat='identity') +
scale_fill_manual(values=cols) +
geom_line(data=Seasonal35,colour='red',size=1) +
scale_y_continuous(breaks=seq(0, 25000, 2000)) + scale_x_continuous(breaks=seq(1890, 2020, 5)) +
geom_hline(yintercept=c(1636,746),linetype=c('dashed','dotted'),color=c('yellow','yellow'),size=1.5) +
theme(axis.text.x=element_text(angle=90, hjust=1, vjust=0.5,size=10, colour='black'),panel.background=element_rect(fill="white", colour='black'),panel.border=element_rect(,fill=NA,colour='black'),axis.text.y=element_text(size=10, colour='black'),
axis.title.y= element_text(size=15)) + xlab('') + ylab('Landings (t)')
dev.off()
## Commercial catch rate index
logDat <- lobster.db('process.logs')
logDat <- subset(logDat,SYEAR %in% 2003:2018 & LFA %in% c(38))
catch.new <- with(logDat,tapply(WEIGHT_KG,SYEAR,sum))
effort.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,sum))
n.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,length))
cpueLFA35.dat <- data.frame(year=2005:2018,n=c(n.new),catch=c(catch.new),effort=c(effort.new),cpue=c(catch.new/effort.new))
write.csv(cpueLFA35.dat,file.path(project.datadirectory('bio.lobster'),'data',"LFA38CPUE2018.csv"),row.names=F)
KgPTH<- cpueLFA35.dat$cpue
yrs<-sort(as.numeric(cpueLFA35.dat$year))
rmKgPTH<-mavg(KgPTH)
# Plot Commercial CPUE Figure 3
pdf(file.path( project.figuredirectory("bio.lobster"), "LFA38CommercialCPUE2018.pdf"),8,6)
plot(yrs,KgPTH,pch=16,ylim=c(0,max(KgPTH)),xlab='',ylab='CPUE (Kg/TH)',las=1, main="LFA 38 - Commercial Log CPUE",xaxt='n',col=c(rep('black',times=(length(yrs)-1)),'green'))
axis(1,yrs,lab=paste(yrs-1,substr(yrs,3,4),sep='-'),las=2)
lines(yrs[-(1:2)],rmKgPTH[!is.na(rmKgPTH)],lty=2,col='orange',lwd=2)
abline(h=median(KgPTH[1:4])*.5,col=rgb(0,0,1,0.5))
text(max(yrs)+.5,median(KgPTH[2:5])*.85,"Upper Stock Reference",col=rgb(0,0,1,0.5),pos=2,cex=0.8)
dev.off()
#lfa 35-38
#with current imcomplete year
logDat <- lobster.db('process.logs')
logDat <- subset(logDat,SYEAR %in% 2005:2018 & LFA %in% c(35,36,38))
catch.new <- with(logDat,tapply(WEIGHT_KG,SYEAR,sum))
effort.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,sum))
n.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,length))
cpueLFA35.dat <- data.frame(year=2005:2018,n=c(n.new),catch=c(catch.new),effort=c(effort.new),cpue=c(catch.new/effort.new))
write.csv(cpueLFA35.dat,file.path(project.datadirectory('bio.lobster'),'data',"LFA35-38CPUE2018.csv"),row.names=F)
KgPTH<- cpueLFA35.dat$cpue
yrs<-sort(as.numeric(cpueLFA35.dat$year))
rmKgPTH<-mavg(KgPTH)
# Plot Commercial CPUE Figure 3
pdf(file.path( project.figuredirectory("bio.lobster"), "LFA35-38CommercialCPUE2018.pdf"),8,6)
plot(yrs,KgPTH,pch=16,ylim=c(0,max(KgPTH)),xlab='',ylab='CPUE (Kg/TH)',las=1, main="LFA 35 - 38 - Commercial Log CPUE",xaxt='n',col=c(rep('black',times=(length(yrs)-1)),'green'))
axis(1,yrs,lab=paste(yrs-1,substr(yrs,3,4),sep='-'),las=2)
lines(yrs[-(1:2)],rmKgPTH[!is.na(rmKgPTH)],lty=2,col='orange',lwd=2)
abline(h=median(KgPTH[2:5])*.5,col=rgb(0,0,1,0.5))
text(max(yrs)+.5,median(KgPTH[2:5])*.85,"Upper Stock Reference",col=rgb(0,0,1,0.5),pos=2,cex=0.8)
dev.off()
####with only complete years
#lfa 35-38
#with current imcomplete year
logDat <- lobster.db('process.logs')
logDat <- subset(logDat,SYEAR %in% 2005:2017 & LFA %in% c(35,36,38))
catch.new <- with(logDat,tapply(WEIGHT_KG,SYEAR,sum))
effort.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,sum))
n.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,length))
cpueLFA35.dat <- data.frame(year=2005:2017,n=c(n.new),catch=c(catch.new),effort=c(effort.new),cpue=c(catch.new/effort.new))
write.csv(cpueLFA35.dat,file.path(project.datadirectory('bio.lobster'),'data',"LFA35-38CPUEcomplete2018.csv"),row.names=F)
KgPTH<- cpueLFA35.dat$cpue
yrs<-sort(as.numeric(cpueLFA35.dat$year))
rmKgPTH<-mavg(KgPTH)
# Plot Commercial CPUE Figure 3
plot(yrs,KgPTH,pch=16,ylim=c(0,max(KgPTH)),xlab='',ylab='CPUE (Kg/TH)',las=1, main="LFA 35 - 38 - Commercial Log CPUE",xaxt='n',col='black')
axis(1,yrs,lab=paste(yrs-1,substr(yrs,3,4),sep='-'),las=2)
lines(yrs[-(1:2)],rmKgPTH[!is.na(rmKgPTH)],lty=2,col='orange',lwd=2)
abline(h=median(KgPTH[2:5])*.5,col=rgb(0,0,1,0.5))
text(max(yrs)+.5,median(KgPTH[2:5])*.85,"Upper Stock Reference",col=rgb(0,0,1,0.5),pos=2,cex=0.8)
savePlot(file.path( project.figuredirectory("bio.lobster"), "LFA35-38CPUE2018.png"),type='png')
###############RV survey
require(bio.survey)
require(bio.lobster)
require(bio.groundfish)
la()
p = bio.lobster::load.environment()
p$libs = NULL
fp = file.path(project.datadirectory('bio.lobster'),"analysis")
load_all('~/git/bio.survey/')
if(get.odbc.data){
p = bio.groundfish::load.groundfish.environment(assessment.year = 2018)
# these should be run on a windows machine: NULL values get mangled for some reason
p$odbc.data.yrs=1970:2018 # <<<<< ---- DATA YEAR can be a single year update too
groundfish.db( DS="odbc.redo", datayrs=p$odbc.data.yrs )
groundfish.db( DS="gscat.redo" )
groundfish.db( DS="gsdet.redo" )
groundfish.db( DS="gsinf.redo" )
}
##############################################################
#DFO RV Setup
p$series =c('summer')# p$series =c('georges');p$series =c('fall')
p$define.by.polygons = F
p$lobster.subunits=F
p$area = 'custom'
p$years.to.estimate = c(1970:2018)
p$length.based = F
p$by.sex = F
p$bootstrapped.ci=T
p$strata.files.return=F
p$vessel.correction.fixed=1.2
p$strat = 490:495
p$clusters = c( rep( "localhost", 7) )
p$strata.efficiencies = F
p = make.list(list(yrs=p$years.to.estimate),Y=p)
p$reweight.strata = F #this subsets
aout= dfo.rv.analysis(DS='stratified.estimates.redo',p=p)
#Figure
p$add.reference.lines = T
p$time.series.start.year = p$years.to.estimate[1]
p$time.series.end.year = p$years.to.estimate[length(p$years.to.estimate)]
p$metric = 'numbers' #weights
p$measure = 'stratified.mean' #'stratified.total'
p$figure.title = ""
p$reference.measure = 'mean' # mean, geomean
p$file.name = 'lfa35-382018.png'
p$error.polygon=F
p$y.maximum = NULL # NULL # if ymax is too high for one year
p$show.truncated.numbers = F #if using ymax and want to show the numbers that are cut off as values on figure
p$legend = FALSE
p$running.mean = T
p$running.length = 3
p$running.median = F #can only have rmedian or rmean
p$error.bars=T
p$reference.start.year = 1985
p$reference.end.year = 2016
p$ref.level = 1.9
p$add.primary.line = T
p$ylim=c(0,150)
p$custom.legend = T
p$legend.details = list(legend.placement='topleft',legend=c('Annual Mean','Running mean','USR'), line.types=c(1,1,1), col.types=c('black','salmon','blue'))
ref.out= figure.stratified.analysis(x=aout,out.dir = 'bio.lobster', p=p)
a = groundfish.db('gsinf')
a = subset(a,grepl('TEL2018023',id) & strat %in% c(490:495))
a = makePBS(a,polygon=F)
LobsterMap('BoF')
addPoints(a,pch=16,col='red')
title(2018)
savePlot(file.path(project.figuredirectory('bio.lobster'),'GSSetLocations2018.png'),type='png')
a = groundfish.db('gsinf')
a = subset(a,grepl('2017',id) & strat %in% c(490:495))
a = makePBS(a,polygon=F)
LobsterMap('')
title(2017)
addPoints(a,pch=16,col='red')
savePlot(file.path(project.figuredirectory('bio.lobster'),'GSSetLocations2017.png'),type='png')
a = groundfish.db('gsinf')
a = subset(a,yr>2009)
a = makePBS(a,polygon=F)
LobsterMap('west')
addPoints(a,pch=16,col='red')
savePlot(file.path(project.figuredirectory('bio.lobster'),'GSSetLocations2010-2018.png'),type='png')
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####### Update 35: Sept 2018
require(bio.lobster)
require(ggplot2)
require(bio.utilities)
## Landings
AnnualSlip<-lobster.db( DS="annual.landings")
SeasonalSlip<-lobster.db( DS="seasonal.landings")
SeasonalSlip$YEAR<-as.numeric(substr(SeasonalSlip$SYEAR,6,9))
dat = subset(SeasonalSlip,YEAR<2018)
dat$SYEAR <- NULL
#Historic<-lobster.db( DS="historical.landings")
###stacked bar plot
library(reshape2)
dat2 <- melt(dat, id.vars = "YEAR")
dat2 = subset(dat2, variable %in% c('LFA35','LFA36','LFA38'))
Da = aggregate(value~YEAR,data=dat2,FUN=sum)
Da = as.data.frame(mavg(Da))
Da$V1 = Da$V1+1
Da = na.omit(Da)
names(Da) = c('YEAR','value')
Da$variable = 'Running Mean'
library(ggplot2)
ggplot(dat2, aes(x=YEAR, y=value, fill=variable)) +
geom_bar(stat="identity") +
theme_bw()+
scale_fill_grey()+
scale_x_continuous(breaks=seq(1980,2015,5),labels=paste(seq(1979,2014,5),seq(1980,2015,5),sep="-"))+
scale_y_continuous(breaks=seq(0,15000,2500))+
geom_line(data=Da,color='red',size=1.5) +
geom_hline(yintercept=c(1575,788),linetype=c('dashed','dotted'),color=c('black','black'),size=1.5)+
theme(legend.position='none',axis.text.x=element_text(angle=90, hjust=1, vjust=0.5,size=10, colour='black'),panel.background=element_rect(fill="white", colour='white'),panel.border=element_rect(,fill=NA,colour='black'),axis.text.y=element_text(size=10, colour='black'),
axis.title.y= element_text(size=15),panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + xlab('') + ylab('Landings (t)')
savePlot(file.path( project.figuredirectory("bio.lobster"), "LFA35-38Landings2018.png"),type='png')
dev.off()
####each LFA alone
#Seasonal35<-subset(SeasonalSlip,select=c("LFA35","SYEAR","YEAR"))
Seasonal35<-subset(SeasonalSlip,select=c("LFA38","SYEAR","YEAR"))
require(ggplot2)
cols = c(rep('navyblue',times=42),'green')
# Plot Landings (1892-present) Figure 2
pdf(file.path( project.figuredirectory("bio.lobster"), "LFA38Landings2018.pdf"),8,6)
ggplot(subset(Seasonal35),aes(YEAR,LFA38)) + geom_bar(data=Seasonal35,fill=cols,stat='identity') +
scale_fill_manual(values=cols) +
geom_line(data=Seasonal35,colour='red',size=1) +
scale_y_continuous(breaks=seq(0, 25000, 2000)) + scale_x_continuous(breaks=seq(1890, 2020, 5)) +
geom_hline(yintercept=c(518,259),linetype=c('dashed','dotted'),color=c('black','black'),size=1.5) +
theme(axis.text.x=element_text(angle=90, hjust=1, vjust=0.5,size=10, colour='black'),panel.background=element_rect(fill="white", colour='black'),panel.border=element_rect(,fill=NA,colour='black'),axis.text.y=element_text(size=10, colour='black'),
axis.title.y= element_text(size=15)) + xlab('') + ylab('Landings (t)')
dev.off()
#35-38 combined
SeasonalSlip$YEAR<-as.numeric(substr(SeasonalSlip$SYEAR,6,9))
SeasonalSlip$LFA35 = SeasonalSlip$LFA35 + SeasonalSlip$LFA36 + SeasonalSlip$LFA38
Seasonal35<-subset(SeasonalSlip,select=c("LFA35","SYEAR","YEAR"))
require(ggplot2)
cols = c(rep('navyblue',times=42),'green')
# Plot Landings (1892-present) Figure 2
pdf(file.path( project.figuredirectory("bio.lobster"), "LFA35-38Landings2018.pdf"),8,6)
ggplot(subset(Seasonal35),aes(YEAR,LFA35)) + geom_bar(data=Seasonal35,fill=cols,stat='identity') +
scale_fill_manual(values=cols) +
geom_line(data=Seasonal35,colour='red',size=1) +
scale_y_continuous(breaks=seq(0, 25000, 2000)) + scale_x_continuous(breaks=seq(1890, 2020, 5)) +
geom_hline(yintercept=c(1636,746),linetype=c('dashed','dotted'),color=c('yellow','yellow'),size=1.5) +
theme(axis.text.x=element_text(angle=90, hjust=1, vjust=0.5,size=10, colour='black'),panel.background=element_rect(fill="white", colour='black'),panel.border=element_rect(,fill=NA,colour='black'),axis.text.y=element_text(size=10, colour='black'),
axis.title.y= element_text(size=15)) + xlab('') + ylab('Landings (t)')
dev.off()
## Commercial catch rate index
logDat <- lobster.db('process.logs')
logDat <- subset(logDat,SYEAR %in% 2003:2018 & LFA %in% c(38))
catch.new <- with(logDat,tapply(WEIGHT_KG,SYEAR,sum))
effort.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,sum))
n.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,length))
cpueLFA35.dat <- data.frame(year=2005:2018,n=c(n.new),catch=c(catch.new),effort=c(effort.new),cpue=c(catch.new/effort.new))
write.csv(cpueLFA35.dat,file.path(project.datadirectory('bio.lobster'),'data',"LFA38CPUE2018.csv"),row.names=F)
KgPTH<- cpueLFA35.dat$cpue
yrs<-sort(as.numeric(cpueLFA35.dat$year))
rmKgPTH<-mavg(KgPTH)
# Plot Commercial CPUE Figure 3
pdf(file.path( project.figuredirectory("bio.lobster"), "LFA38CommercialCPUE2018.pdf"),8,6)
plot(yrs,KgPTH,pch=16,ylim=c(0,max(KgPTH)),xlab='',ylab='CPUE (Kg/TH)',las=1, main="LFA 38 - Commercial Log CPUE",xaxt='n',col=c(rep('black',times=(length(yrs)-1)),'green'))
axis(1,yrs,lab=paste(yrs-1,substr(yrs,3,4),sep='-'),las=2)
lines(yrs[-(1:2)],rmKgPTH[!is.na(rmKgPTH)],lty=2,col='orange',lwd=2)
abline(h=median(KgPTH[1:4])*.5,col=rgb(0,0,1,0.5))
text(max(yrs)+.5,median(KgPTH[2:5])*.85,"Upper Stock Reference",col=rgb(0,0,1,0.5),pos=2,cex=0.8)
dev.off()
#lfa 35-38
#with current imcomplete year
logDat <- lobster.db('process.logs')
logDat <- subset(logDat,SYEAR %in% 2005:2018 & LFA %in% c(35,36,38))
catch.new <- with(logDat,tapply(WEIGHT_KG,SYEAR,sum))
effort.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,sum))
n.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,length))
cpueLFA35.dat <- data.frame(year=2005:2018,n=c(n.new),catch=c(catch.new),effort=c(effort.new),cpue=c(catch.new/effort.new))
write.csv(cpueLFA35.dat,file.path(project.datadirectory('bio.lobster'),'data',"LFA35-38CPUE2018.csv"),row.names=F)
KgPTH<- cpueLFA35.dat$cpue
yrs<-sort(as.numeric(cpueLFA35.dat$year))
rmKgPTH<-mavg(KgPTH)
# Plot Commercial CPUE Figure 3
pdf(file.path( project.figuredirectory("bio.lobster"), "LFA35-38CommercialCPUE2018.pdf"),8,6)
plot(yrs,KgPTH,pch=16,ylim=c(0,max(KgPTH)),xlab='',ylab='CPUE (Kg/TH)',las=1, main="LFA 35 - 38 - Commercial Log CPUE",xaxt='n',col=c(rep('black',times=(length(yrs)-1)),'green'))
axis(1,yrs,lab=paste(yrs-1,substr(yrs,3,4),sep='-'),las=2)
lines(yrs[-(1:2)],rmKgPTH[!is.na(rmKgPTH)],lty=2,col='orange',lwd=2)
abline(h=median(KgPTH[2:5])*.5,col=rgb(0,0,1,0.5))
text(max(yrs)+.5,median(KgPTH[2:5])*.85,"Upper Stock Reference",col=rgb(0,0,1,0.5),pos=2,cex=0.8)
dev.off()
####with only complete years
#lfa 35-38
#with current imcomplete year
logDat <- lobster.db('process.logs')
logDat <- subset(logDat,SYEAR %in% 2005:2017 & LFA %in% c(35,36,38))
catch.new <- with(logDat,tapply(WEIGHT_KG,SYEAR,sum))
effort.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,sum))
n.new <- with(logDat,tapply(NUM_OF_TRAPS,SYEAR,length))
cpueLFA35.dat <- data.frame(year=2005:2017,n=c(n.new),catch=c(catch.new),effort=c(effort.new),cpue=c(catch.new/effort.new))
write.csv(cpueLFA35.dat,file.path(project.datadirectory('bio.lobster'),'data',"LFA35-38CPUEcomplete2018.csv"),row.names=F)
KgPTH<- cpueLFA35.dat$cpue
yrs<-sort(as.numeric(cpueLFA35.dat$year))
rmKgPTH<-mavg(KgPTH)
# Plot Commercial CPUE Figure 3
plot(yrs,KgPTH,pch=16,ylim=c(0,max(KgPTH)),xlab='',ylab='CPUE (Kg/TH)',las=1, main="LFA 35 - 38 - Commercial Log CPUE",xaxt='n',col='black')
axis(1,yrs,lab=paste(yrs-1,substr(yrs,3,4),sep='-'),las=2)
lines(yrs[-(1:2)],rmKgPTH[!is.na(rmKgPTH)],lty=2,col='orange',lwd=2)
abline(h=median(KgPTH[2:5])*.5,col=rgb(0,0,1,0.5))
text(max(yrs)+.5,median(KgPTH[2:5])*.85,"Upper Stock Reference",col=rgb(0,0,1,0.5),pos=2,cex=0.8)
savePlot(file.path( project.figuredirectory("bio.lobster"), "LFA35-38CPUE2018.png"),type='png')
###############RV survey
require(bio.survey)
require(bio.lobster)
require(bio.groundfish)
la()
p = bio.lobster::load.environment()
p$libs = NULL
fp = file.path(project.datadirectory('bio.lobster'),"analysis")
load_all('~/git/bio.survey/')
if(get.odbc.data){
p = bio.groundfish::load.groundfish.environment(assessment.year = 2018)
# these should be run on a windows machine: NULL values get mangled for some reason
p$odbc.data.yrs=1970:2018 # <<<<< ---- DATA YEAR can be a single year update too
groundfish.db( DS="odbc.redo", datayrs=p$odbc.data.yrs )
groundfish.db( DS="gscat.redo" )
groundfish.db( DS="gsdet.redo" )
groundfish.db( DS="gsinf.redo" )
}
##############################################################
#DFO RV Setup
p$series =c('summer')# p$series =c('georges');p$series =c('fall')
p$define.by.polygons = F
p$lobster.subunits=F
p$area = 'custom'
p$years.to.estimate = c(1970:2018)
p$length.based = F
p$by.sex = F
p$bootstrapped.ci=T
p$strata.files.return=F
p$vessel.correction.fixed=1.2
p$strat = 490:495
p$clusters = c( rep( "localhost", 7) )
p$strata.efficiencies = F
p = make.list(list(yrs=p$years.to.estimate),Y=p)
p$reweight.strata = F #this subsets
aout= dfo.rv.analysis(DS='stratified.estimates.redo',p=p)
#Figure
p$add.reference.lines = T
p$time.series.start.year = p$years.to.estimate[1]
p$time.series.end.year = p$years.to.estimate[length(p$years.to.estimate)]
p$metric = 'numbers' #weights
p$measure = 'stratified.mean' #'stratified.total'
p$figure.title = ""
p$reference.measure = 'mean' # mean, geomean
p$file.name = 'lfa35-382018.png'
p$error.polygon=F
p$y.maximum = NULL # NULL # if ymax is too high for one year
p$show.truncated.numbers = F #if using ymax and want to show the numbers that are cut off as values on figure
p$legend = FALSE
p$running.mean = T
p$running.length = 3
p$running.median = F #can only have rmedian or rmean
p$error.bars=T
p$reference.start.year = 1985
p$reference.end.year = 2016
p$ref.level = 1.9
p$add.primary.line = T
p$ylim=c(0,150)
p$custom.legend = T
p$legend.details = list(legend.placement='topleft',legend=c('Annual Mean','Running mean','USR'), line.types=c(1,1,1), col.types=c('black','salmon','blue'))
ref.out= figure.stratified.analysis(x=aout,out.dir = 'bio.lobster', p=p)
a = groundfish.db('gsinf')
a = subset(a,grepl('TEL2018023',id) & strat %in% c(490:495))
a = makePBS(a,polygon=F)
LobsterMap('BoF')
addPoints(a,pch=16,col='red')
title(2018)
savePlot(file.path(project.figuredirectory('bio.lobster'),'GSSetLocations2018.png'),type='png')
a = groundfish.db('gsinf')
a = subset(a,grepl('2017',id) & strat %in% c(490:495))
a = makePBS(a,polygon=F)
LobsterMap('')
title(2017)
addPoints(a,pch=16,col='red')
savePlot(file.path(project.figuredirectory('bio.lobster'),'GSSetLocations2017.png'),type='png')
a = groundfish.db('gsinf')
a = subset(a,yr>2009)
a = makePBS(a,polygon=F)
LobsterMap('west')
addPoints(a,pch=16,col='red')
savePlot(file.path(project.figuredirectory('bio.lobster'),'GSSetLocations2010-2018.png'),type='png')
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