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assessment_graphics.R
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assessment_graphics.R
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###############################################################################
# LIBRARIES
###############################################################################
library(extrafont)
library(ggplot2)
library(plyr)
library(latticeExtra)
library(gridExtra)
library(Hmisc)
library(RColorBrewer)
library(tidyr)
library(dplyr)
library(coda)
#------------------------------------------------------------------------------
# Graphics defaults
#------------------------------------------------------------------------------
#font_import()
#loadfonts(device="win")
windowsFonts(Times=windowsFont("TT Calibri"))
theme_set(theme_bw(base_size=18,base_family="Calibri")+
#theme(legend.position = c(0.25,0.9))+
theme(legend.direction = "vertical")+
theme(legend.margin = unit(1,"cm"))+
theme(axis.title.y=element_text(vjust=1))+
theme(axis.title.x=element_text(vjust=-0.5))+
theme(legend.title = element_blank())+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()))
#------------------------------------------------------------------------------
# Data calls
#------------------------------------------------------------------------------
.REP <- "model"
source("globals.R")
#------------------------------------------------------------------------------
# Model comparisons
#
# 'model' = global model with estimated M
# 'model_M' = global model with fixed M
#------------------------------------------------------------------------------
# -----------------------------------------------------------------------------
# ADMB report files - output defined in FINAL_CALCS section of .tpl file
#
# NOTE: you must have all four ADMB files written here: .par, .std, .cor, .rep
#
#------------------------------------------------------------------------------
model <- read.admb("model_output/model")
modelM <- read.admb("model_output/modelM")
# ADMB .std files (contain parameter values and standard deviation)
# - all defined parameters with whatever 'sdreport_vector / matrix'
# calls are defined in the .tpl file
par <- read.delim("model_output/model.std",header=TRUE,sep="")$value
var <- read.delim("model_output/model.std",header=TRUE,sep="")$std
parM <- read.delim("model_output/modelM.std",header=TRUE,sep="")$value
varM <- read.delim("model_output/modelM.std",header=TRUE,sep="")$std
# MCMC - primary parameters to check distributions and bounds
theta <- read.delim("model_output/theta.ps",header=FALSE,sep="")
thetaM <- read.delim("model_output/thetaM.ps",header=FALSE,sep="")
# MCMC - derived quantities
biomass <- read.delim("model_output/bm.ps",header=FALSE,sep="")
biomassM <- read.delim("model_output/bmM.ps",header=FALSE,sep="")
density <- read.delim("model_output/dens.ps",header=FALSE,sep="")
densityM <- read.delim("model_output/densM.ps",header=FALSE,sep="")
recruit <- read.delim("model_output/rec.ps",header=FALSE,sep="")
recruitM <- read.delim("model_output/recM.ps",header=FALSE,sep="")
spbiomass <- read.delim("model_output/sbm.ps",header=FALSE,sep="")
spbiomassM <- read.delim("model_output/sbmM.ps",header=FALSE,sep="")
#------------------------------------------------------------------------------
#
# RETROSPECTIVE ANALYSES
# You'll have to run these individually and manually
#
#-------------------------------------------------------------------------------
par_R1m <- read.delim("model_output/model_r1m.std",header=TRUE,sep="")$value
var_R1m <- read.delim("model_output/model_r1m.std",header=TRUE,sep="")$std
par_R2m <- read.delim("model_output/model_r2m.std",header=TRUE,sep="")$value
var_R2m <- read.delim("model_output/model_r2m.std",header=TRUE,sep="")$std
par_R4m <- read.delim("model_output/model_r4m.std",header=TRUE,sep="")$value
var_R4m <- read.delim("model_output/model_r4m.std",header=TRUE,sep="")$std
par_R5m <- read.delim("model_output/model_r5m.std",header=TRUE,sep="")$value
var_R5m <- read.delim("model_output/model_r5m.std",header=TRUE,sep="")$std
par_R9m <- read.delim("model_output/model_r9m.std",header=TRUE,sep="")$value
var_R9m <- read.delim("model_output/model_r9m.std",header=TRUE,sep="")$std
par_R10m <- read.delim("model_output/model_r10m.std",header=TRUE,sep="")$value
var_R10m <- read.delim("model_output/model_r10m.std",header=TRUE,sep="")$std
par_R1 <- read.delim("model_output/model_r1.std",header=TRUE,sep="")$value
var_R1 <- read.delim("model_output/model_r1.std",header=TRUE,sep="")$std
par_R2 <- read.delim("model_output/model_r2.std",header=TRUE,sep="")$value
var_R2 <- read.delim("model_output/model_r2.std",header=TRUE,sep="")$std
par_R6 <- read.delim("model_output/model_r6.std",header=TRUE,sep="")$value
var_R6 <- read.delim("model_output/model_r6.std",header=TRUE,sep="")$std
par_R9 <- read.delim("model_output/model_r9.std",header=TRUE,sep="")$value
var_R9 <- read.delim("model_output/model_r9.std",header=TRUE,sep="")$std
par_R10 <- read.delim("model_output/model_r10.std",header=TRUE,sep="")$value
var_R10 <- read.delim("model_output/model_r10.std",header=TRUE,sep="")$std
#------------------------------------------------------------------------------
# Parse and sort MCMC for variances
# These already have every 100th saved, and if I recall correctly, as ADMB
# begins the draws from the approximately multivariate normal of the Hessian
# matrix, a deleted burn-in isn't stricly necessary, but as I am a
# statistical dunce, I still do it.
#
# MCMC outputs of 1,000,000 draws w/100th saved = 10,000
# Trim first 20% = 8,000 remaining
#------------------------------------------------------------------------------
theta <- theta[c(2001:10000),]
thetaM <- thetaM[c(2001:10000),]
biomass <- biomass[c(2001:10000),]
biomassM <- biomassM[c(2001:10000),]
density <- density[c(2001:10000),]
densityM <- densityM[c(2001:10000),]
recruit <- recruit[c(2001:10000),]
recruitM <- recruitM[c(2001:10000),]
spbiomass <- spbiomass[c(2001:10000),]
spbiomassM <- spbiomassM[c(2001:10000),]
# Sort each MCMC matrix by column and select 5% and 95%
# ASSUMES ALL MCMC MATRICES ARE OF THE SAME DIMENSIONS
min<-0.05*length(theta[,1])
max<-0.95*length(theta[,1])
for(i in 1:10)
{
theta[,i]<-sort(theta[,i])
thetaM[,i]<-sort(thetaM[,i])
}
for(i in 1:31)
{
biomass[,i] <- sort(biomass[,1])
biomassM[,i] <- sort(biomassM[,1])
density[,i] <- sort(density[,1])
densityM[,i] <- sort(densityM[,1])
recruit[,i] <- sort(recruit[,1])
recruitM[,i] <- sort(recruitM[,1])
spbiomass[,i] <- sort(spbiomass[,1])
spbiomassM[,i] <- sort(spbiomassM[,1])
}
#------------------------------------------------------------------------------
# THETA PARAMETERS
#------------------------------------------------------------------------------
parMcmc <-data.frame(Par = c(unlist(theta[,1:10]),unlist(thetaM[,1:10])),
Name = c(rep(c(rep("M",8000),rep("Mean Recruitment",8000),
rep("Mean Year 1 N",8000), rep("Sigma Year 1",8000),
rep("Mean F",8000),rep("Mean F sport",8000),
rep("Slope (selectivity)",8000),
rep("Age 50% sel.",8000),
rep("Catchability (commercial)",8000),
rep("Catchability (IPHC survey)",8000)),2)),
Model = c(rep("Estimated M",80000),
rep("Fixed M",80000)))
plot.parmc <- function(df) {
ggplot(subset(df, Name %in% c("M","Mean Recruitment", "Mean Year 1 N",
"Sigma Year 1", "Mean F","Mean F sport",
"Slope (selectivity)", "Age 50% sel.",
"Catchability (commercial)",
"Catchability (IPHC survey)")),
aes(Par,group=interaction(Name,Model))) + geom_density(aes(Par, y=..scaled.., fill = Model),alpha=.2)+
facet_wrap(~Name,scales="free",ncol=2)+
labs(y="")+
labs(x="")+
theme(legend.margin=unit(-0.25, "cm"))+
guides(fill = guide_legend(ncol=3))+
theme(legend.position = 'bottom')+
theme(legend.key.size = unit(0.5, "cm"))+
scale_fill_manual(name='', values=c('Estimated M' = 'black',
'Fixed M' = 'blue'))
}
#------------------------------------------------------------------------------
# DENSITY GLOBAL
#------------------------------------------------------------------------------
dfD <- data.frame(Year = rep(seq(1985,2015,by=1),2),
Density = c(par[293:323],parM[292:322]),
Stdev = c(var[293:323],varM[292:322]),
Model = c(rep("Estimated M",31), rep("Fixed M",31)),
Rov = rep(c(rep(NA,10),2820,NA,2103.5,NA,1980,rep(NA,3),2839,NA,2357,NA,1050,NA,1930,NA,NA,752,986,NA,1641),2),
Var = rep(c(rep(NA,10),549.5,NA,474.5,NA,380,rep(NA,3),417.5,NA,424,NA,126.3,NA,320,NA,NA,97,217,NA,288),2))
plot.density <- function(df) {
df <- df %>%
mutate(p.var = 1.96 * Var) %>%
mutate(p.lower = Rov - p.var, p.upper = Rov + p.var)
df <- df %>%
mutate(err= 1.96 * Stdev) %>%
mutate(lower = Density - err, upper = Density + err)
ggplot(df,aes(Year,Density,group=Model)) + geom_line(aes(Year,Density,colour=Model),size=1)+
geom_ribbon(aes(ymin = lower, ymax = upper, fill=Model),alpha=0.2, show.legend=FALSE) +
geom_point(aes(x=Year,y= Rov), colour="black",size=4)+
geom_errorbar(aes(x = Year,ymin = p.lower, ymax = p.upper),colour="black",lwd=0.8, width=0.5)+
scale_colour_manual(name='', values=c('Estimated M' = 'black',
'Fixed M' = "blue"))+
scale_fill_manual(name='', values=c('Estimated M' = 'black',
'Fixed M' = "blue"))+
labs(x="Year")+
labs(y="Density (ind. per square kilometer)")+
theme(legend.position = c(0.8,0.88))+
theme(legend.direction = "vertical")+
#theme(legend.position = c(0.8,0.8))+
#ggtitle("Total density (individuals per square kilometer)")+
theme(legend.key = element_rect(fill = "white"))
}
#------------------------------------------------------------------------------
# SPAWNING BIOMASS
#------------------------------------------------------------------------------
dfS <- data.frame(Year = rep(seq(1985,2015,by=1),2),
Density = c(par[190:220],parM[189:219]),
Stdev = c(var[190:220],varM[189:219]),
Model = c(rep("Estimated M",31),rep("Fixed M",31)))
plot.spawners <- function(df) {
df <- df %>%
mutate(err= 1.96 * Stdev) %>%
mutate(lower = Density - err, upper = Density + err)
ggplot(df,aes(Year,Density,group=Model)) + geom_line(aes(Year,Density,colour=Model),size=1)+
geom_ribbon(aes(ymin = lower, ymax = upper, fill=Model),alpha=0.2, show.legend=FALSE) +
scale_colour_manual(name='', values=c('Estimated M' = 'black',
'UnEstimated M' = 'darkorange3',
'Fixed M' = "blue"))+
scale_fill_manual(name='', values=c('Estimated M' = 'black',
'UnEstimated M' = 'darkorange3',
'Fixed M' = "blue"))+
labs(x="Year")+
labs(y="Spawning biomass (tons)")+
theme(legend.position = c(0.8,0.88))+
theme(legend.direction = "vertical")+
#theme(legend.position = c(0.8,0.8))+
theme(legend.key.size = unit(1., "cm"))+
#ggtitle("Total density (individuals per square kilometer)")+
theme(legend.key = element_rect(fill = "white"))
}
#------------------------------------------------------------------------------
# RECRUITMENT
#------------------------------------------------------------------------------
dfR <- data.frame(Year = rep(seq(1985,2015,by=1),2),
Density = c(par[159:189],parM[158:188]),
Stdev = c(var[159:189],varM[158:188]),
Model = c(rep("Estimated M",31),rep("Fixed M",31)))
plot.recruit <- function(df) {
df <- df %>%
mutate(err= 1.96 * Stdev) %>%
mutate(lower = Density - err, upper = Density + err)
ggplot(df,aes(Year,Density,group=Model)) + geom_line(aes(Year,Density,colour=Model),size=1)+
geom_ribbon(aes(ymin = lower, ymax = upper, fill=Model),alpha=0.2, show.legend=FALSE) +
scale_colour_manual(name='', values=c('Estimated M' = 'black',
'Fixed M' = "blue"))+
scale_fill_manual(name='', values=c('Estimated M' = 'black',
'Fixed M' = "blue"))+
labs(x="Year")+
labs(y="Recruitment (thousands)")+
theme(legend.position = c(0.8,0.88))+
theme(legend.direction = "vertical")+
#theme(legend.position = c(0.8,0.8))+
theme(legend.key.size = unit(1., "cm"))+
#ggtitle("Total density (individuals per square kilometer)")+
theme(legend.key = element_rect(fill = "white"))
}
#------------------------------------------------------------------------------
# Abundance at age
#------------------------------------------------------------------------------
yearT <- seq(1985,2015,by=1)
yearX <- c('1988','1989',seq(1991,2005,by=1),seq(2008,2015,by=1))
age <- seq(8,75,by=1)
SD <- data.frame(Year = rep(as.numeric(yearT),length(age)),
Age = rep(as.numeric(age), each=length(yearT)),
p = as.vector(model$natage))
M <- data.frame(Year = rep(as.numeric(yearT),length(age)),
Age = rep(as.numeric(age), each=length(yearT)),
p = as.vector(modelM$natage))
Tr <- data.frame(Year = rep(as.numeric(yearX),length(age)),
Age = rep(as.numeric(age), each=length(yearX)),
p = as.vector(modelM$oac_fish))
png(file='figures/age1.png', res=300, width=7, height=9, units ="in", bg="transparent")
par(mfrow = c(2,1))
symbols(Tr$Year, Tr$Age, Tr$p, inches=0.2, xlim=c(1985,2015), ylim=c(8,75),
xlab="Year", ylab = "Observed age", cex.axis=1.5, cex.lab=1.5, cex.main=1.,main="Observed catch composition")
symbols(SD$Year, SD$Age, SD$p, inches=0.2, xlim=c(1985,2015), ylim=c(8,75),
xlab="Year", ylab = "Estimated age", cex.axis=1.5, cex.lab=1.5, cex.main=1.,main="Abundance at age - Estimated M")
dev.off()
png(file='figures/age2.png', res=300, width=7, height=9, units ="in", bg="transparent")
par(mfrow = c(2,1))
symbols(Tr$Year, Tr$Age, Tr$p, inches=0.2, xlim=c(1985,2015), ylim=c(8,75),
xlab="Year", ylab = "Observed age", cex.axis=1.5, cex.lab=1.5, cex.main=1.,main="Observed catch composition")
symbols(M$Year, M$Age, M$p, inches=0.2, xlim=c(1985,2015), ylim=c(8,75),
xlab="Year", ylab = "Estimated age", cex.axis=1.5, cex.lab=1.5, cex.main=1.,main="Abundance at age - fixed M")
dev.off()
#------------------------------------------------------------------------------
# Catch at age (looking for recruitment indicators)
#------------------------------------------------------------------------------
# yearC <- c('1988',seq(1991,2004,by=1),seq(2008,2015,by=1))
# yearS <- c('1985','1987','1988','1989', seq(1991,2004,by=1),'2008','2009','2010','2011','2012','2013')
# yearE <- c(seq(1991,2001,by=1),'2004','2005',seq(2008,2015,by=1))
# age <- seq(8,75,by=1)
#
#
# C <- data.frame(Year = rep(as.numeric(yearC),length(age)),
# Age = rep(as.numeric(age), each=length(yearC)),
# p = as.vector(model_s$oac_fishC))
#
# E <- data.frame(Year = rep(as.numeric(yearE),length(age)),
# Age = rep(as.numeric(age), each=length(yearE)),
# p = as.vector(model_s$oac_fishE))
#
# S <- data.frame(Year = rep(as.numeric(yearS),length(age)),
# Age = rep(as.numeric(age), each=length(yearS)),
# p = as.vector(model_s$oac_fishS))
#
# png(file='../../figures/catch_age_dataC.png', res=300, width=9, height=7, units ="in", bg="transparent")
# symbols(C$Year, C$Age, C$p, inches=0.2, xlim=c(1985,2015), ylim=c(8,75),
# xlab="Year", ylab = "Observed age", cex.axis=1.5, cex.lab=1.5, cex.main=1.,main="Observed catch composition CSEO")
# dev.off()
#
# png(file='../../figures/catch_age_dataE.png', res=300, width=9, height=7, units ="in", bg="transparent")
# symbols(E$Year, E$Age, E$p, inches=0.2, xlim=c(1985,2015), ylim=c(8,75),
# xlab="Year", ylab = "Observed age", cex.axis=1.5, cex.lab=1.5, cex.main=1.,main="Observed catch composition EYKT")
# dev.off()
#
# png(file='../../figures/catch_age_dataS.png', res=300, width=9, height=7, units ="in", bg="transparent")
# symbols(S$Year, S$Age, S$p, inches=0.2, xlim=c(1985,2015), ylim=c(8,75),
# xlab="Year", ylab = "Observed age", cex.axis=1.5, cex.lab=1.5, cex.main=1.,main="Observed catch composition SSEO")
#
# dev.off()
#
#------------------------------------------------------------------------------
# Selectivity
#------------------------------------------------------------------------------
dfSel <- data.frame(Year = rep(seq(8,75,by=1),2),
Sel = c(model$fish_sel,
modelM$fish_sel),
Model = c(rep("Estimated M",68),rep("Fixed M",68)))
plot.sel <- function(df) {
ggplot(df,aes(Year,Sel, group=Model, colour=Model)) + geom_line(aes(Year,Sel,group=Model, colour=Model),size=1)+
scale_colour_manual(name='', values=c('Estimated M' = 'black',
'UnEstimated M' = 'darkorange3',
'Fixed M' = 'blue'))+
labs(x="Age")+
labs(y="Selectivity")+
theme(legend.position = c(0.75,0.8))+
theme(legend.direction = "vertical")+
theme(legend.key = element_rect(fill = "white"))
}
#------------------------------------------------------------------------------
# Fishing mortality
#------------------------------------------------------------------------------
dfF <- data.frame(Year = rep(seq(1985,2015,by=1),2),
Mort = c(model$Fmort,
modelM$Fmort),
Model = c(rep("Estimated M",31),rep("Fixed M",31)))
plot.Fmt <- function(df) {
ggplot(df,aes(Year,Mort, group=Model, colour=Model)) +
geom_line(aes(Year,Mort,group=Model, colour=Model))+
geom_hline(yintercept=0.02)+
scale_colour_manual(name='', values=c('Estimated M' = 'black',
'UnEstimated M' = 'darkorange3',
'Fixed M' = 'blue'))+
labs(x="Year")+
labs(y="Mortality")+
theme(legend.position = c(0.75,0.85))+
theme(legend.direction = "vertical")+
theme(legend.key = element_rect(fill = "white"))
}
#------------------------------------------------------------------------------
# Catch age residuals
#------------------------------------------------------------------------------
yearT <- c('1988','1989',seq(1991,2005,by=1),seq(2008,2015,by=1))
age <- seq(8,75,by=1)
dfs <- data.frame(Year = rep(as.numeric(yearT),length(age)),
Age = rep(as.numeric(age), each=length(yearT)),
p = as.vector(model$res_age))
dfm <- data.frame(Year = rep(as.numeric(yearT),length(age)),
Age = rep(as.numeric(age), each=length(yearT)),
p = as.vector(modelM$res_age))
plot.age <- function(dfs,dfm) {
A <- ggplot(df,aes(Year,Age,size=p))+
geom_point(aes(colour=p))+
scale_size_area(max_size = 5, guide=FALSE)+
scale_colour_gradient2(limits=c(-0.04,0.08),low="red", high="blue", mid = "grey", midpoint = 0.0)+
xlab("Age")+
ylab("Residuals (o - p)")+
theme(legend.margin=unit(0.2, "cm"))+
theme(legend.position = "right")
B <- ggplot(dfm,aes(Year,Age,size=p))+
geom_point(aes(colour=p))+
scale_size_area(max_size = 5, guide=FALSE)+
scale_colour_gradient2(limits=c(-0.04,0.08),low="red", high="blue", mid = "grey", midpoint = 0.0)+
xlab("Age")+
ylab("Residuals (o - p)")+
theme(legend.margin=unit(0.2, "cm"))+
theme(legend.position = "right")
grid.arrange(A,B)
}
#------------------------------------------------------------------------------
# COMMERCIAL CPUE
#------------------------------------------------------------------------------
dfcp <- data.frame(Year = as.factor(c(rep(c(seq(1992,2005,by=1),seq(2008,2015,by=1)),3))),
CPUE = c(model$obs_cpue,model$pred_cpue,modelM$pred_cpue),
Var = c(model$var_cpue,var[324:345],varM[323:344]),
Model = c(rep("Observed",22),rep("Estimated M",22),rep("Fixed M",22)))
plot.cpue <- function(df) {
df <- df %>%
mutate(err= 1.96 * Var) %>%
mutate(lower = CPUE - err, upper = CPUE + err)
ggplot(df,aes(Year,CPUE, group=Model, colour=Model)) +
geom_line(data=df,aes(Year,CPUE,group=Model, colour=Model),lwd=1)+
geom_ribbon(aes(ymin = lower, ymax = upper, fill=Model, colour=NA),alpha=0.2, show.legend=FALSE) +
geom_point(data=df, aes(Year,CPUE,group=Model),size=2)+
labs(y="CPUE (pounds per hook)")+
scale_x_discrete(breaks=seq(1992,2015,by=4))+
theme(axis.text.x = element_text(angle=90, vjust=0.5))+
theme(legend.position = c(0.4,0.85))+
theme(legend.direction="vertical")+
scale_colour_manual(name='', values=c('Estimated M' = 'black', "Observed" = 'green', 'Fixed M' = 'blue'))+
scale_fill_manual(name='', values=c('Estimated M' = 'black', "Observed" = 'green', 'Fixed M' = 'blue'))
}
#------------------------------------------------------------------------------
# IPHC CPUE
#------------------------------------------------------------------------------
dfcpi<- data.frame(Year = as.factor(c(rep(seq(1998,2015,by=1),3))),
CPUE = c(model$obs_cpue_iphc,
par[346:363],parM[345:362]),
Var = c(model$var_cpue_iphc,var[346:363],
varM[345:362]),
Model = c(rep("Observed",18),rep("Estimated M",18),
rep("Fixed M",18)))
plot.cpuei <- function(df) {
df <- df %>%
mutate(err= 1.96 * Var) %>%
mutate(lower = CPUE - err, upper = CPUE + err)
ggplot(df,aes(Year,CPUE, group=Model, colour=Model)) +
geom_line(data=df,aes(Year,CPUE,group=Model, colour=Model), lwd=1)+
geom_ribbon(aes(ymin = lower, ymax = upper, fill=Model, colour=NA),alpha=0.2, show.legend=FALSE) +
geom_point(data=df,aes(Year,CPUE,group=Model),size=2)+
labs(y=("CPUE (individuals per hook)"))+
scale_x_discrete(breaks=seq(1992,2015,by=4))+
theme(legend.position = c(0.7,0.85))+
theme(legend.background = element_blank())+
theme(legend.margin = unit(0.5,"cm"))+
theme(legend.direction="vertical")+
theme(axis.text.x = element_text(angle=90, vjust=0.5))+
scale_colour_manual(name='', values=c('Estimated M' = 'black', "Observed" = 'green', "Fixed M" = "blue"))+
scale_fill_manual(name='', values=c('Estimated M' = 'black', "Observed" = 'green', "Fixed M" = 'blue'))
}
#------------------------------------------------------------------------------
# Spawning biomass projections
#------------------------------------------------------------------------------
dfproj <- data.frame(Year = rep(seq(2016,2030,by=1),2),
Density = c(par[367:381],parM[366:380]),
Error = c(var[367:381],varM[366:380]),
Model = c(rep("Recommended F = 0.022 (F_65) Estimated M",15),
rep("Recommended F = 0.022 (F_65) Fixed M",15)))
plot.proj<- function(df) {
df <- df %>%
mutate(err= 1.96 * Error) %>%
mutate(lower = Density - err, upper = Density + err)
ggplot(df,aes(Year,Density,group=Model)) + geom_line(aes(Year,Density,colour=Model),size=1)+
geom_ribbon(aes(ymin = lower, ymax = upper, fill=Model),alpha=0.2, show.legend=FALSE) +
scale_colour_manual(name='', values=c('Recommended F = 0.022 (F_65) Estimated M' = 'black',
'Recommended F = 0.022 (F_65) Fixed M' = 'blue'))+
scale_fill_manual(name='', values=c('Recommended F = 0.022 (F_65) Estiated M' = 'black',
'Recommended F = 0.022 (F_65) Fixed M' = 'blue'))+
labs(x="Year")+
ylim(0,6000)+
labs(y="Projected spawning biomass")+
theme(legend.position = c(0.5,0.2))+
theme(legend.direction = "vertical")+
theme(legend.key.size = unit(1., "cm"))+
theme(legend.background = element_blank())+
#ggtitle("Projected spawning biomass (mt)")+
theme(legend.key = element_rect(fill = "white"))
}
#------------------------------------------------------------------------------
# DENSITY GLOBAL RETROSPECTIVE
#------------------------------------------------------------------------------
dfDR <- data.frame(Year = rep(seq(1985,2015,by=1),6),
Density = c(par[283:313],
par_R1[217:246],NA,
par_R2[213:241],NA,NA,
par_R6[197:221],NA,NA,NA,NA,NA,NA,
par_R9[185:206],NA,NA,NA,NA,NA,NA,NA,NA,NA,
par_R10[181:201],NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
Error = c(var[283:313],
var_R1[217:246],NA,
var_R2[213:241],NA,NA,
var_R6[197:221],NA,NA,NA,NA,NA,NA,
var_R9[185:206],NA,NA,NA,NA,NA,NA,NA,NA,NA,
var_R10[181:201],NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
Model = c(rep("Full",31),rep("retro 1",31),rep("retro 2",31),rep("retro 6",31),
rep("retro 9",31), rep("retro 10",31)),
Rov = rep(c(rep(NA,10),2820,NA,2103.5,NA,1980,rep(NA,3),2839,NA,2357,NA,1050,NA,1930,NA,NA,752,986,NA,1641),6),
Var = rep(c(rep(NA,10),549.5,NA,474.5,NA,380,rep(NA,3),417.5,NA,424,NA,126.3,NA,320,NA,NA,97,217,NA,288),6))
dfDR$Model <- factor(dfDR$Model, as.character(dfDR$Model))
plot.retrodensity <- function(df) {
df <- df %>%
mutate(p.var = 1.96 * Var) %>%
mutate(p.lower = Rov - p.var, p.upper = Rov + p.var)
df <- df %>%
mutate(err= 1.96 * Error) %>%
mutate(lower = Density - err, upper = Density + err)
ggplot(df,aes(Year,Density,group=Model)) + geom_line(aes(Year,Density,colour=Model),size=1)+
geom_ribbon(aes(ymin = lower, ymax = upper, fill=Model),alpha=0.2, show.legend=FALSE) +
geom_point(aes(x=Year,y= Rov), colour="black",size=3)+
geom_errorbar(aes(x = Year,ymin = p.lower, ymax = p.upper),colour="black",lwd=0.8, width=0.5)+
scale_colour_manual(name='', values=c('Full' = '#000000', 'retro 1' = 'red', 'retro 2' = 'orange',
'retro 6' = 'green', 'retro 9' = 'darkblue', 'retro 10' = 'violet'))+
scale_fill_manual(name='', values=c('Full' = '#000000', 'retro 1' = 'red', 'retro 2' = 'orange',
'retro 6' = 'green', 'retro 9' = 'darkblue', 'retro 10' = 'violet'))+
labs(x="Year")+
labs(y="Density (ind. per square kilometer)")+
theme(legend.position = c(0.87,0.75))+
theme(legend.direction = "vertical")+
guides(fill=guide_legend(ncol=2))
}
#------------------------------------------------------------------------------
# DENSITY GLOBAL RETROSPECTIVE FIXED M
#------------------------------------------------------------------------------
dfDRm <- data.frame(Year = rep(seq(1985,2015,by=1),7),
Density = c(parM[282:312],
par_R1m[216:245],NA,
par_R2m[212:240],NA,NA,
par_R4m[204:230],NA,NA,NA,NA,
par_R5m[200:225],NA,NA,NA,NA,NA,
par_R9m[184:205],NA,NA,NA,NA,NA,NA,NA,NA,NA,
par_R10m[180:200],NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
Error = c(varM[282:312],
var_R1m[216:245],NA,
var_R2m[212:240],NA,NA,
var_R4m[204:230],NA,NA,NA,NA,
var_R5m[200:225],NA,NA,NA,NA,NA,
var_R9m[184:205],NA,NA,NA,NA,NA,NA,NA,NA,NA,
var_R10m[180:200],NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
Model = c(rep("Full",31),rep("retro 1",31),rep("retro 2",31),rep("retro 4",31),rep("retro 5",31),
rep("retro 9",31), rep("retro 10",31)),
Rov = rep(c(rep(NA,10),2820,NA,2103.5,NA,1980,rep(NA,3),2839,NA,2357,NA,1050,NA,1930,NA,NA,752,986,NA,1641),7),
Var = rep(c(rep(NA,10),549.5,NA,474.5,NA,380,rep(NA,3),417.5,NA,424,NA,126.3,NA,320,NA,NA,97,217,NA,288),7))
dfDRm$Model <- factor(dfDRm$Model, as.character(dfDRm$Model))
plot.retrodensitym <- function(df) {
df <- df %>%
mutate(p.var = 1.96 * Var) %>%
mutate(p.lower = Rov - p.var, p.upper = Rov + p.var)
df <- df %>%
mutate(err= 1.96 * Error) %>%
mutate(lower = Density - err, upper = Density + err)
ggplot(df,aes(Year,Density,group=Model)) + geom_line(aes(Year,Density,colour=Model),size=1)+
geom_ribbon(aes(ymin = lower, ymax = upper, fill=Model),alpha=0.2, show.legend=FALSE) +
geom_point(aes(x=Year,y= Rov), colour="black",size=3)+
geom_errorbar(aes(x = Year,ymin = p.lower, ymax = p.upper),colour="black",lwd=0.8, width=0.5)+
scale_colour_manual(name='', values=c('Full' = '#000000', 'retro 1' = 'red', 'retro 2' = 'orange', 'retro 4' = 'gold',
'retro 5' = 'green', 'retro 9' = 'darkblue', 'retro 10' = 'violet'))+
scale_fill_manual(name='', values=c('Full' = '#000000', 'retro 1' = 'red', 'retro 2' = 'orange','retro 4' = 'gold',
'retro 5' = 'green', 'retro 9' = 'darkblue', 'retro 10' = 'violet'))+
labs(x="Year")+
labs(y="Density (ind. per square kilometer)")+
theme(legend.position = c(0.87,0.75))+
theme(legend.direction = "vertical")+
guides(fill=guide_legend(ncol=2))
}
#------------------------------------------------------------------------------
# SPAWNING BIOMASS GLOBAL RETROSPECTIVE
#------------------------------------------------------------------------------
dfSR<-data.frame(Year = rep(seq(1985,2015,by=1),6),
Density = c(par[190:220],
par_R1[187:216],NA,
par_R2[184:212],NA,NA,
par_R6[172:196],NA,NA,NA,NA,NA,NA,
par_R9[163:184],NA,NA,NA,NA,NA,NA,NA,NA,NA,
par_R10[160:180],NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
Error = c(var[190:220],
var_R1[187:216],NA,
var_R2[184:212],NA,NA,
var_R6[172:196],NA,NA,NA,NA,NA,NA,
var_R9[163:184],NA,NA,NA,NA,NA,NA,NA,NA,NA,
var_R10[160:180],NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
Model = c(rep("Full",31),rep("retro 1",31),rep("retro 2",31),
rep("retro 6",31),rep("retro 9",31),
rep("retro 10",31)))
dfSR$Model <- factor(dfSR$Model, as.character(dfSR$Model))
plot.retrospawn <- function(df) {
df <- df %>%
mutate(err= 1.96 * Error) %>%
mutate(lower = Density - err, upper = Density + err)
ggplot(df,aes(Year,Density,group=Model)) + geom_line(aes(Year,Density,colour=Model),size=1)+
geom_ribbon(aes(ymin = lower, ymax = upper, fill=Model),alpha=0.2, show.legend=FALSE) +
scale_colour_manual(name='', values=c('Full' = '#000000', 'retro 1' = 'red', 'retro 2' = 'orange',
'retro 6' = 'green', 'retro 9' = 'darkblue', 'retro 10' = 'violet'))+
scale_fill_manual(name='', values=c('Full' = '#000000', 'retro 1' = 'red', 'retro 2' = 'orange',
'retro 6' = 'green', 'retro 9' = 'darkblue', 'retro 10' = 'violet'))+
#coord_cartesian(xlim=c(1994,2015))+
#scale_y_continuous(limits=c(500,6000))+
labs(x="Year")+
labs(y="Spawning biomass (tons)")+
theme(legend.position = c(0.85,0.70))+
theme(legend.direction = "vertical")+
guides(fill=guide_legend(ncol=2))
}
#------------------------------------------------------------------------------
# SPAWNING BIOMASS GLOBAL RETROSPECTIVE FIXED M
#------------------------------------------------------------------------------
dfSRm <- data.frame(Year = rep(seq(1985,2015,by=1),7),
Density = c(parM[189:219],
par_R1m[186:215],NA,
par_R2m[183:211],NA,NA,
par_R4m[177:203],NA,NA,NA,NA,
par_R5m[174:199],NA,NA,NA,NA,NA,
par_R9m[162:183],NA,NA,NA,NA,NA,NA,NA,NA,NA,
par_R10m[159:179],NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
Error = c(varM[189:219],
var_R1m[186:215],NA,
var_R2m[183:211],NA,NA,
var_R4m[177:203],NA,NA,NA,NA,
var_R5m[174:199],NA,NA,NA,NA,NA,
var_R9m[162:183],NA,NA,NA,NA,NA,NA,NA,NA,NA,
var_R10m[159:179],NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
Model = c(rep("Full",31),rep("retro 1",31),rep("retro 2",31),rep("retro 4",31),rep("retro 5",31),
rep("retro 9",31), rep("retro 10",31)))
dfSRm$Model <- factor(dfSRm$Model, as.character(dfSRm$Model))
plot.retrospawnm <- function(df) {
df <- df %>%
mutate(err= 1.96 * Error) %>%
mutate(lower = Density - err, upper = Density + err)
ggplot(df,aes(Year,Density,group=Model)) + geom_line(aes(Year,Density,colour=Model),size=1)+
geom_ribbon(aes(ymin = lower, ymax = upper, fill=Model),alpha=0.2, show.legend=FALSE) +
scale_colour_manual(name='', values=c('Full' = '#000000', 'retro 1' = 'red', 'retro 2' = 'orange', 'retro 4' = 'gold',
'retro 5' = 'green', 'retro 9' = 'darkblue', 'retro 10' = 'violet'))+
scale_fill_manual(name='', values=c('Full' = '#000000', 'retro 1' = 'red', 'retro 2' = 'orange','retro 4' = 'gold',
'retro 5' = 'green', 'retro 9' = 'darkblue', 'retro 10' = 'violet'))+
labs(x="Year")+
labs(y="Spawning biomass (tons)")+
theme(legend.position = c(0.87,0.75))+
theme(legend.direction = "vertical")+
guides(fill=guide_legend(ncol=2))
}
#------------------------------------------------------------------------------
# Recruitment GLOBAL RETROSPECTIVE
#------------------------------------------------------------------------------
dfRR<-data.frame(Year = rep(seq(1985,2015,by=1),6),
Density = c(par[159:189],
par_R1[157:186],NA,
par_R2[155:183],NA,NA,
par_R6[147:171],NA,NA,NA,NA,NA,NA,
par_R9[141:162],NA,NA,NA,NA,NA,NA,NA,NA,NA,
par_R10[139:159],NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
Error= c(var[159:189],
var_R1[157:186],NA,
var_R2[155:183],NA,NA,
var_R6[147:171],NA,NA,NA,NA,NA,NA,
var_R9[141:162],NA,NA,NA,NA,NA,NA,NA,NA,NA,
var_R10[139:159],NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
Model = c(rep("Full",31),rep("retro 1",31),rep("retro 2",31),
rep("retro 6",31),rep("retro 9",31),
rep("retro 10",31)))
dfRR$Model <- factor(dfRR$Model, as.character(dfRR$Model))
plot.retrorec <- function(df) {
df <- df %>%
mutate(err= 1.96 * Error) %>%
mutate(lower = Density - err, upper = Density + err)
ggplot(df,aes(Year,Density,group=Model)) + geom_line(aes(Year,Density,colour=Model),size=1)+
geom_ribbon(aes(ymin = lower, ymax = upper, fill=Model),alpha=0.2, show.legend=FALSE) +
scale_colour_manual(name='', values=c('Full' = '#000000', 'retro 1' = 'red', 'retro 2' = 'orange',
'retro 6' = 'green', 'retro 9' = 'darkblue', 'retro 10' = 'violet'))+
scale_fill_manual(name='', values=c('Full' = '#000000', 'retro 1' = 'red', 'retro 2' = 'orange',
'retro 6' = 'green', 'retro 9' = 'darkblue', 'retro 10' = 'violet'))+
labs(x="Year")+
labs(y="Age 8 recruits (thousands)")+
theme(legend.position = c(0.8,0.7))+
theme(legend.direction = "vertical")+
guides(fill=guide_legend(ncol=2))
}
#------------------------------------------------------------------------------
# RECRUITMENT GLOBAL RETROSPECTIVE FIXED M
#------------------------------------------------------------------------------
dfRRm <- data.frame(Year = rep(seq(1985,2015,by=1),7),
Density = c(parM[158:188],
par_R1m[156:185],NA,
par_R2m[154:182],NA,NA,
par_R4m[150:176],NA,NA,NA,NA,
par_R5m[148:173],NA,NA,NA,NA,NA,
par_R9m[140:161],NA,NA,NA,NA,NA,NA,NA,NA,NA,
par_R10m[138:158],NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
Error= c(varM[158:188],
var_R1m[156:185],NA,
var_R2m[154:182],NA,NA,
var_R4m[150:176],NA,NA,NA,NA,
var_R5m[148:173],NA,NA,NA,NA,NA,
var_R9m[140:161],NA,NA,NA,NA,NA,NA,NA,NA,NA,
var_R10m[138:158],NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
Model = c(rep("Full",31),rep("retro 1",31),rep("retro 2",31),rep("retro 4",31),rep("retro 5",31),
rep("retro 9",31), rep("retro 10",31)))
dfRRm$Model <- factor(dfRRm$Model, as.character(dfRRm$Model))
plot.retrorecm <- function(df) {
df <- df %>%
mutate(err= 1.96 * Error) %>%
mutate(lower = Density - err, upper = Density + err)
ggplot(df,aes(Year,Density,group=Model)) + geom_line(aes(Year,Density,colour=Model),size=1)+
geom_ribbon(aes(ymin = lower, ymax = upper, fill=Model),alpha=0.2, show.legend=FALSE) +
scale_colour_manual(name='', values=c('Full' = '#000000', 'retro 1' = 'red', 'retro 2' = 'orange', 'retro 4' = 'gold',
'retro 5' = 'green', 'retro 9' = 'darkblue', 'retro 10' = 'violet'))+
scale_fill_manual(name='', values=c('Full' = '#000000', 'retro 1' = 'red', 'retro 2' = 'orange','retro 4' = 'gold',
'retro 5' = 'green', 'retro 9' = 'darkblue', 'retro 10' = 'violet'))+
labs(x="Year")+
labs(y="Age 8 recruits (thousands)")+
theme(legend.position = c(0.87,0.75))+
theme(legend.direction = "vertical")+
guides(fill=guide_legend(ncol=2))
}
#------------------------------------------------------------------------------
# SDNR - Global
#------------------------------------------------------------------------------
# dfsdnr <- data.frame(Year = rep(yearT,4),
# Result= sdnr$Result,
# Iteration = sdnr$Iteration,
# Metric = sdnr$Metric)
#
# plot.sample <- function(df) {
# ggplot(df, aes(Year,Result,group=Iteration)) + geom_line(aes(Year,Result,colour=Iteration),size=1)+
# facet_wrap(~Metric, scales = "free", ncol = 1)+
# scale_colour_manual(name='', values=c('Data' = '#000000', 'First pass' = 'purple', 'Second pass' = 'blue',
# 'Third pass' = 'green'))+
# scale_fill_manual(name='', values=c('Data' = '#000000', 'First pass' = 'purple', 'Second pass' = 'blue',
# 'Third pass' = 'green'))+
# labs(x="Year")+
# labs(y="")+
# theme(legend.margin=unit(0.2, "cm"))+
# theme(axis.text.x = element_text(angle=90, vjust=0.5, size = 12))+
# theme(legend.direction = "vertical")+
# guides(fill=guide_legend(ncol=2))
# }
#
#------------------------------------------------------------------------------
# SDNR - Fixed M
# #------------------------------------------------------------------------------
# dfsdnrM <- data.frame(Year = rep(yearT,4),
# Result= sdnrm$Result,
# Iteration = sdnrm$Iteration,
# Metric = sdnrm$Metric)
#
# plot.sample <- function(df) {
# ggplot(df, aes(Year,Result,group=Iteration)) + geom_line(aes(Year,Result,colour=Iteration),size=1)+
# facet_wrap(~Metric, scales = "free", ncol = 1)+
# scale_colour_manual(name='', values=c('Data' = '#000000', 'First pass' = 'purple', 'Second pass' = 'blue',
# 'Third pass' = 'green'))+
# scale_fill_manual(name='', values=c('Data' = '#000000', 'First pass' = 'purple', 'Second pass' = 'blue',
# 'Third pass' = 'green'))+
# labs(x="Year")+
# labs(y="")+
# theme(legend.margin=unit(0.2, "cm"))+
# theme(axis.text.x = element_text(angle=90, vjust=0.5, size = 12))+
# theme(legend.direction = "vertical")+
# guides(fill=guide_legend(ncol=2))
# }
#
#------------------------------------------------------------------------------
# GELMAN
#------------------------------------------------------------------------------
# png(file='../figures/FUCKYOU.png', res=300, width=6, height=4, units ="in", bg="transparent")
# plot(gr_diag[,1],type="l",ylim=c(1,1.8), xlab="Parameter",ylab = "Gelman-Rubin score")
# lines(gr_diag[,2],col="red")
# dev.off()
#
#------------------------------------------------------------------------------
# FUNCTION CALLS
#------------------------------------------------------------------------------
d1 <- plot.parmc(df = parMcmc)
d2 <- plot.density(df = dfD)
d3 <- plot.spawners(df = dfS)
d4 <- plot.recruit(df = dfR)
d5 <- plot.sel(df = dfSel)
d6 <- plot.Fmt(df = dfF)
d7 <- plot.age(dfs = dfs, dfm = dfm)
d8 <- plot.cpue(df = dfcp)
d9 <- plot.cpuei(df = dfcpi)
d10 <- plot.proj(df = dfproj)
d11 <- plot.retrodensity(df = dfDR)
d12 <- plot.retrospawn(df = dfSR)
d13 <- plot.retrorec(df = dfRR)
#d14 <- plot.sample(df = dfsdnr)
#d15 <- plot.sample(df = dfsdnrM)
d16 <- plot.retrodensitym(df = dfDRm)
d17 <- plot.retrospawnm(df = dfSRm)
d18 <- plot.retrorecm(df = dfRRm)
#d19 <- plot.age(df = dfx)
#------------------------------------------------------------------------------
# EXPORT
#------------------------------------------------------------------------------
ggsave(d1,file='figures/theta.png', dpi=300, width=7, height=8, units ="in", bg="transparent")
ggsave(d2,file='figures/density.png', dpi=300, width=9, height=7, units ="in", bg="transparent")
ggsave(d3,file='figures/spawners.png', dpi=300, width=9, height=7, units ="in", bg="transparent")
ggsave(d4,file='figures/recruit.png', dpi=300, width=9, height=7, units ="in", bg="transparent")
ggsave(d5,file='figures/selectivity.png', dpi=300, width=7, height=4., units ="in", bg="transparent")
ggsave(d6,file='figures/Fmort.png', dpi=300, width=7, height=4., units ="in", bg="transparent")
ggsave(d7,file='figures/age_res.png', dpi=300, width=7, height=5, units ="in", bg="transparent")
ggsave(d8,file='figures/cpue.png', dpi=300, width=7, height=5, units ="in", bg="transparent")
ggsave(d9,file='figures/cpuei.png', dpi=300, width=7, height=5, units ="in", bg="transparent")
ggsave(d10,file='figures/spbm_proj.png', dpi=300, width=7, height=5, units ="in", bg="transparent")
ggsave(d11,file='figures/retro_den.png', dpi=300, width=7, height=5, units ="in", bg="transparent")
ggsave(d12,file='figures/retro_spawn.png', dpi=300, width=7, height=5, units ="in", bg="transparent")
ggsave(d13,file='figures/retro_rec.png', dpi=300, width=7, height=5, units ="in", bg="transparent")
#ggsave(d14,file='../../figures/sample.png', dpi=300, width=7, height=4, units ="in", bg="transparent")
#ggsave(d15,file='../../figures/sample2.png', dpi=300, width=7, height=4, units ="in", bg="transparent")
ggsave(d16,file='figures/retro_denm.png', dpi=300, width=7, height=5, units ="in", bg="transparent")
ggsave(d17,file='figures/retro_spawnm.png', dpi=300, width=7, height=5, units ="in", bg="transparent")
ggsave(d18,file='figures/retro_recm.png', dpi=300, width=7, height=5, units ="in", bg="transparent")
#ggsave(d19,file='../../figures/age_res_uncorrected.png', dpi=300, width=7, height=5, units ="in", bg="transparent")
# Ds <- read.delim("../model_output/dens_sdnr.ps",header=FALSE,sep="")
# Dg <- read.delim("../model_output/dens_g.ps",header=FALSE,sep="")
# Do <- read.delim("../model_output/dens_4.ps",header=FALSE,sep="")
#
#
# Ds<-Ds[c(10001:25000),]
# Dg<-Dg[c(430358:445357),]
# Do<-Do[c(5001:20000),]
#
# for(i in 1:31)
# {
# Ds[,i]<-sort(Ds[,i])
# Dg[,i]<-sort(Dg[,i])
# Do[,i]<-sort(Do[,i])
# }
#
# MenD<-as.matrix(c(colMeans(Ds),colMeans(Dg),colMeans(Do)))
# MinD<-as.matrix(c(Ds[min,],Dg[min,],Do[min,]))
# MaxD<-as.matrix(c(Ds[max,],Dg[max,],Do[max,]))
#
# dfD <- data.frame(Year = rep(seq(1985,2015,by=1),3),
# Density = MenD,
# MinD = unlist(MinD),
# MaxD = unlist(MaxD),
# Model = c(rep("S",31),rep("G",31),rep("O",31)),
# Rov = rep(c(rep(NA,10),2820,NA,2103.5,NA,1980,rep(NA,3),2839,NA,2357,NA,1050,NA,1930,NA,NA,752,986,NA,1641),3),
# Var = rep(c(rep(NA,10),549.5,NA,474.5,NA,380,rep(NA,3),417.5,NA,424,NA,126.3,NA,320,NA,NA,97,217,NA,288),3))
#
# plot.density <- function(dfD) {
# dfD <- dfD %>%
# mutate(p.var = 1.96 * Var) %>%
# mutate(p.lower = Rov - p.var, p.upper = Rov + p.var)
#
#
# ggplot(dfD,aes(Year,Density,group=Model)) + geom_line(aes(Year,Density,colour=Model),size=1)+
# geom_ribbon(aes(ymin = MinD, ymax = MaxD, fill=Model),alpha=0.2, show.legend=FALSE) +
# geom_point(aes(x=Year,y= Rov), colour="black",size=4)+
# geom_errorbar(aes(x = Year,ymin = p.lower, ymax = p.upper),colour="black",lwd=0.8, width=0.5)+
# scale_colour_manual(name='', values=c('S' = '#000000', 'G' = '#009E73', 'O' = '#e79f00'))+
# scale_fill_manual(name='', values=c('S' = '#000000', 'G' = '#009E73', 'O' = '#e79f00'))+