inst/IP/bycatchCSAS/Cod/4b.DesignbasedestimatesofCodCatch.R
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
##Design based estimates of Cod Catch
require(bio.lobster)
require(bio.utilities)
require(RODBC)
require(lubridate)
require(devtools)
options(stringAsFactors=F)
la()
wd = ('C:\\Users\\Cooka\\OneDrive - DFO-MPO\\BycatchLobster')
setwd(wd)
b=bycatch.db('logbook.merge')
aS = bycatch.db('SWLSS')
aO = bycatch.db('ISDB.reshape')
ao = aggregate(CodWt~TRIP+LFA+Cluster+GridGroup+Period,data=aO,FUN=mean)
aCs = aggregate(CodWt~TRIP+LFA+Cluster+GridGroup+Period,data=aO,FUN=var,na.rm=T)
aCs = rename.df(aCs,c('CodWt'),c('CodWtvar'))
aCC = aggregate(UID~TRIP+LFA+Cluster+GridGroup+Period,data=aO,FUN=function(x) length(unique(x)))
ao = merge(ao, aCC)
aCo = merge(ao, aCs)
aCo$mean =aCo$CodWt
aCo$var =aCo$CodWtvar
aCo$n = aCo$UID
ap = aggregate(CodWt~TRIP+LFA+Cluster+GridGroup+Period,data=aS,FUN=mean)
aCs = aggregate(CodWt~TRIP+LFA+Cluster+GridGroup+Period,data=aS,FUN=var,na.rm=T)
aCs = rename.df(aCs,c('CodWt'),c('CodWtvar'))
aCC = aggregate(UID~TRIP+LFA+Cluster+GridGroup+Period,data=aS,FUN=function(x) length(unique(x)))
ap = merge(ap, aCC)
aCp = merge(ap, aCs)
aCp$mean =aCp$CodWt
aCp$var =aCp$CodWtvar
aCp$n = aCp$UID
aT = as.data.frame(rbind(aCp,aCo))
aT$Strata = paste(aT$LFA,aT$GridGroup,aT$Period,sep='_')
op = list()
aTs = split(aT,f=list(aT$Strata))
aTs = rm.from.list(aTs)
for(i in 1:length(aTs)){
j = aTs[[i]]
op[[i]] =c(LFA =unique(j$LFA), GridGroup = unique(j$GridGroup), Period = unique(j$Period),Strata=unique(j$Strata),clusterStatistics(j))
}
aCo = as.data.frame(do.call(rbind,op))
aCo = toNums(aCo,c(5:8))
uu = readRDS('results/BumpedUpEffortByGridGroup.rds')
uu = subset(uu,LFA %in% 33:35 & SYEAR %in% c(2019,2020,2021))
uu$Strata = paste(uu$LFA,uu$GridGroup,uu$Period,sep='_')
uu = aggregate(cbind(BTTH,BuTH,BlTH)~Strata+LFA+GridGroup+Period,data=uu,FUN=mean)
au = merge(uu,aCo,all.x=T)
#4missing Strata with minimal effort
uy = read.csv('data/GridGroupsLocation.csv')
names(uy)[1] = 'LFA'
au = merge(au,uy)
au$var = (au$sd)^2
aug = split(au,f=list(au$Period, au$LFA))
ol = list()
aug = rm.from.list(aug)
for(i in 1:length(aug)){
j = aug[[i]]
if(all(!is.na(j$mean)) | all(is.na(j$mean))) {
ol[[i]] = j
} else {
ii = which(is.na(j$mean))
k = j[-ii,]
for(l in 1:length(ii)){
ll = j[ii[l],'Location']
lmo = subset(k,Location == ll)
if(nrow(lmo)==0){
j$mean[ii[l]] <- min(j$mean,na.rm=T)
j$sd[ii[l]] <- min(j$sd,na.rm=T)
j$n[ii[l]] <- 2
} else {
lk = clusterStatistics(lmo)
j$mean[ii[l]] <- lk[1]
j$sd[ii[l]] <- lk[2]
}
}
ol[[i]] <- j
}
}
ff = as.data.frame(do.call(rbind,ol))
fg = aggregate(mean~LFA+Period,data=ff,FUN=mean,na.rm=T)
fs = aggregate(mean~LFA+Period,data=ff,FUN=sd,na.rm=T)
names(fs)[3] = 'sd'
ffs = merge(fg,fs)
i = which(is.na(ff$mean))
ff$mean[i] = ffs[which(ffs$LFA==35 & ffs$Period==3),'mean']
ff$sd[i] = ffs[which(ffs$LFA==35 & ffs$Period==3),'sd']
i = which(is.na(ff$n))
ff$n[i] =2
#stratified totals
ff$Tot = ff$BTTH * ff$mean
ff$var = (ff$sd)^2
ff$sdTH = ((ff$BTTH-ff$BlTH)/5)
f1 = aggregate(BTTH~LFA,data=ff,FUN=sum)
names(f1)[2] = 'TotTr'
ff = merge(ff,f1)
fu = function(m,s,n) rnorm(n,mean=m,sd=s)
iter=1000
re = unique(ff$LFA)
out = list()
for(i in 1:length(re)){
k = subset(ff,LFA==re[i])
mu = sum(k$mean * k$BTTH)
v = sqrt(sum((k$BTTH)^2*(k$var/k$n)))
ql = qnorm(.025,mu,v)
qu = qnorm(.975,mu,v)
for(j in 2:(iter)){
kk = k[,c('BTTH','sdTH')]
kk$n=1
kw = unlist(Map(fu,kk$BTTH,kk$sdTH,kk$n))
mu = c(mu,sum(k$mean * kw))
v = c(v,sqrt(sum((kw)^2*(k$var/k$n))))
ql =c(ql,qnorm(.025,mu[j],v[j]))
qu = c(qu,qnorm(.975,mu[j],v[j]))
}
out[[i]] = c(re[i],mean(mu),mean(ql),mean(qu))
}
gs = as.data.frame(do.call(rbind,out))
names(gs) = c('LFA','TotCodWt','TotCodWtL','TotCodWtU')
##cbfha
b=bycatch.db('logbook.merge')
aS = bycatch.db('CBFHA')
ap = aggregate(CodWt~TRIP+LFA+Cluster+GridGroup+Period,data=aS,FUN=mean)
aCs = aggregate(CodWt~TRIP+LFA+Cluster+GridGroup+Period,data=aS,FUN=var,na.rm=T)
aCs = rename.df(aCs,c('CodWt'),c('CodWtvar'))
aCC = aggregate(UID~TRIP+LFA+Cluster+GridGroup+Period,data=aS,FUN=function(x) length(unique(x)))
ap = merge(ap, aCC)
aCp = merge(ap, aCs)
aCp$mean =aCp$CodWt
aCp$var =aCp$CodWtvar
aCp$n = aCp$UID
aT = aCp
aT$Strata = paste(aT$LFA,aT$GridGroup,aT$Period,sep='_')
op = list()
aTs = split(aT,f=list(aT$Strata))
aTs = rm.from.list(aTs)
for(i in 1:length(aTs)){
j = aTs[[i]]
op[[i]] =c(LFA =unique(j$LFA), GridGroup = unique(j$GridGroup), Period = unique(j$Period),Strata=unique(j$Strata),clusterStatistics(j))
}
aCo = as.data.frame(do.call(rbind,op))
aCo = toNums(aCo,c(5:8))
uu = readRDS('results/BumpedUpEffortByGridGroup.rds')
uu = subset(uu,LFA %in% 27)
uu$Strata = paste(uu$LFA,uu$GridGroup,uu$Period,sep='_')
uu = aggregate(cbind(BTTH,BuTH,BlTH)~Strata+LFA+GridGroup+Period,data=uu,FUN=mean)
au = merge(uu,aCo,all.x=T)
ff = au
#stratified totals
ff$Tot = ff$BTTH * ff$mean
ff$var = (ff$sd)^2
ff$sdTH = ((ff$BTTH-ff$BlTH)/5)
f1 = aggregate(BTTH~LFA,data=ff,FUN=sum)
names(f1)[2] = 'TotTr'
ff = merge(ff,f1)
fu = function(m,s,n) rnorm(n,mean=m,sd=s)
iter=1000
re = unique(ff$LFA)
out = list()
for(i in 1:length(re)){
k = subset(ff,LFA==re[i])
mu = sum(k$mean * k$BTTH)
v = sqrt(sum((k$BTTH)^2*(k$var/k$n)))
ql = qnorm(.025,mu,v)
qu = qnorm(.975,mu,v)
for(j in 2:(iter)){
kk = k[,c('BTTH','sdTH')]
kk$n=1
kw = unlist(Map(fu,kk$BTTH,kk$sdTH,kk$n))
mu = c(mu,sum(k$mean * kw))
v = c(v,sqrt(sum((kw)^2*(k$var/k$n))))
ql =c(ql,qnorm(.025,mu[j],v[j]))
qu = c(qu,qnorm(.975,mu[j],v[j]))
}
out[[i]] = c(re[i],mean(mu),mean(ql),mean(qu))
}
gs = as.data.frame(do.call(rbind,out))
gs = toNums(gs,1:4)
gs[,2:4] = gs[,2:4]/1000
#######################
##GCIFA
b=bycatch.db('logbook.merge')
aS = bycatch.db('GCIFA')
ap = aggregate(CodWt~TRIP+LFA+Cluster+Period+SYEAR,data=aS,FUN=mean)
aCs = aggregate(CodWt~TRIP+LFA+Cluster+Period+SYEAR,data=aS,FUN=var,na.rm=T)
aCs = rename.df(aCs,c('CodWt'),c('CodWtvar'))
aCC = aggregate(UID~TRIP+LFA+Cluster+Period+SYEAR,data=aS,FUN=function(x) length(unique(x)))
ap = merge(ap, aCC)
aCp = merge(ap, aCs)
aCp$mean =aCp$CodWt
aCp$var =aCp$CodWtvar
aCp$n = aCp$UID
aT = aCp
aT$Strata = paste(aT$LFA,aT$Period,sep='_')
op = list()
aTs = split(aT,f=list(aT$SYEAR,aT$Strata))
aTs = rm.from.list(aTs)
for(i in 1:length(aTs)){
j = aTs[[i]]
op[[i]] =c(SYEAR= unique(j$SYEAR),LFA =unique(j$LFA), Period = unique(j$Period),Strata=unique(j$Strata),clusterStatistics(j))
}
aCo = as.data.frame(do.call(rbind,op))
aCo = toNums(aCo,c(5:8))
uu = readRDS('results/BumpedUpEffortByGridGroup.rds')
uu = subset(uu,LFA %in% c('31A','31B') & SYEAR %in% c(2019,2018))
uu$Strata = paste(uu$LFA,uu$Period,sep='_')
uu = aggregate(cbind(BTTH,BlTH,BuTH)~SYEAR+LFA+Strata+Period, data=uu,FUN=sum)
au = merge(uu,aCo,all.x=T)
#for unsampled strata use the mean and of the time period from all others using the same clustering
i = which(is.na(au$mean))
ff = au
ff = toNums(ff,8:10)
#stratified totals
ff$Tot = ff$BTTH * ff$mean
ff$var = (ff$sd)^2
ff$sdTH = ((ff$BTTH-ff$BlTH)/5)
f1 = aggregate(BTTH~LFA+SYEAR,data=ff,FUN=sum)
names(f1)[3] = 'TotTr'
ff = merge(ff,f1)
fu = function(m,s,n) rnorm(n,mean=m,sd=s)
iter=1000
re = unique(cbind(ff$LFA,ff$SYEAR))
out = list()
for(i in 1:nrow(re)){
k = subset(ff,LFA==re[i,1] & SYEAR==re[i,2])
mu = sum(k$mean * k$BTTH)
v = sqrt(sum((k$BTTH)^2*(k$var/k$n)))
ql = qnorm(.025,mu,v)
qu = qnorm(.975,mu,v)
for(j in 2:(iter)){
kk = k[,c('BTTH','sdTH')]
kk$n=1
kw = unlist(Map(fu,kk$BTTH,kk$sdTH,kk$n))
mu = c(mu,sum(k$mean * kw))
v = c(v,sqrt(sum((kw)^2*(k$var/k$n))))
ql =c(ql,qnorm(.025,mu[j],v[j]))
qu = c(qu,qnorm(.975,mu[j],v[j]))
}
out[[i]] = c(re[i,],mean(mu),mean(ql),mean(qu))
}
gs = as.data.frame(do.call(rbind,out))
names(gs)= c('LFA','YR','TotCod','TotCodL','TotCodW')
gs = toNums(gs,2:5)
gs[,3:5] = gs[,3:5]/1000
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##Design based estimates of Cod Catch
require(bio.lobster)
require(bio.utilities)
require(RODBC)
require(lubridate)
require(devtools)
options(stringAsFactors=F)
la()
wd = ('C:\\Users\\Cooka\\OneDrive - DFO-MPO\\BycatchLobster')
setwd(wd)
b=bycatch.db('logbook.merge')
aS = bycatch.db('SWLSS')
aO = bycatch.db('ISDB.reshape')
ao = aggregate(CodWt~TRIP+LFA+Cluster+GridGroup+Period,data=aO,FUN=mean)
aCs = aggregate(CodWt~TRIP+LFA+Cluster+GridGroup+Period,data=aO,FUN=var,na.rm=T)
aCs = rename.df(aCs,c('CodWt'),c('CodWtvar'))
aCC = aggregate(UID~TRIP+LFA+Cluster+GridGroup+Period,data=aO,FUN=function(x) length(unique(x)))
ao = merge(ao, aCC)
aCo = merge(ao, aCs)
aCo$mean =aCo$CodWt
aCo$var =aCo$CodWtvar
aCo$n = aCo$UID
ap = aggregate(CodWt~TRIP+LFA+Cluster+GridGroup+Period,data=aS,FUN=mean)
aCs = aggregate(CodWt~TRIP+LFA+Cluster+GridGroup+Period,data=aS,FUN=var,na.rm=T)
aCs = rename.df(aCs,c('CodWt'),c('CodWtvar'))
aCC = aggregate(UID~TRIP+LFA+Cluster+GridGroup+Period,data=aS,FUN=function(x) length(unique(x)))
ap = merge(ap, aCC)
aCp = merge(ap, aCs)
aCp$mean =aCp$CodWt
aCp$var =aCp$CodWtvar
aCp$n = aCp$UID
aT = as.data.frame(rbind(aCp,aCo))
aT$Strata = paste(aT$LFA,aT$GridGroup,aT$Period,sep='_')
op = list()
aTs = split(aT,f=list(aT$Strata))
aTs = rm.from.list(aTs)
for(i in 1:length(aTs)){
j = aTs[[i]]
op[[i]] =c(LFA =unique(j$LFA), GridGroup = unique(j$GridGroup), Period = unique(j$Period),Strata=unique(j$Strata),clusterStatistics(j))
}
aCo = as.data.frame(do.call(rbind,op))
aCo = toNums(aCo,c(5:8))
uu = readRDS('results/BumpedUpEffortByGridGroup.rds')
uu = subset(uu,LFA %in% 33:35 & SYEAR %in% c(2019,2020,2021))
uu$Strata = paste(uu$LFA,uu$GridGroup,uu$Period,sep='_')
uu = aggregate(cbind(BTTH,BuTH,BlTH)~Strata+LFA+GridGroup+Period,data=uu,FUN=mean)
au = merge(uu,aCo,all.x=T)
#4missing Strata with minimal effort
uy = read.csv('data/GridGroupsLocation.csv')
names(uy)[1] = 'LFA'
au = merge(au,uy)
au$var = (au$sd)^2
aug = split(au,f=list(au$Period, au$LFA))
ol = list()
aug = rm.from.list(aug)
for(i in 1:length(aug)){
j = aug[[i]]
if(all(!is.na(j$mean)) | all(is.na(j$mean))) {
ol[[i]] = j
} else {
ii = which(is.na(j$mean))
k = j[-ii,]
for(l in 1:length(ii)){
ll = j[ii[l],'Location']
lmo = subset(k,Location == ll)
if(nrow(lmo)==0){
j$mean[ii[l]] <- min(j$mean,na.rm=T)
j$sd[ii[l]] <- min(j$sd,na.rm=T)
j$n[ii[l]] <- 2
} else {
lk = clusterStatistics(lmo)
j$mean[ii[l]] <- lk[1]
j$sd[ii[l]] <- lk[2]
}
}
ol[[i]] <- j
}
}
ff = as.data.frame(do.call(rbind,ol))
fg = aggregate(mean~LFA+Period,data=ff,FUN=mean,na.rm=T)
fs = aggregate(mean~LFA+Period,data=ff,FUN=sd,na.rm=T)
names(fs)[3] = 'sd'
ffs = merge(fg,fs)
i = which(is.na(ff$mean))
ff$mean[i] = ffs[which(ffs$LFA==35 & ffs$Period==3),'mean']
ff$sd[i] = ffs[which(ffs$LFA==35 & ffs$Period==3),'sd']
i = which(is.na(ff$n))
ff$n[i] =2
#stratified totals
ff$Tot = ff$BTTH * ff$mean
ff$var = (ff$sd)^2
ff$sdTH = ((ff$BTTH-ff$BlTH)/5)
f1 = aggregate(BTTH~LFA,data=ff,FUN=sum)
names(f1)[2] = 'TotTr'
ff = merge(ff,f1)
fu = function(m,s,n) rnorm(n,mean=m,sd=s)
iter=1000
re = unique(ff$LFA)
out = list()
for(i in 1:length(re)){
k = subset(ff,LFA==re[i])
mu = sum(k$mean * k$BTTH)
v = sqrt(sum((k$BTTH)^2*(k$var/k$n)))
ql = qnorm(.025,mu,v)
qu = qnorm(.975,mu,v)
for(j in 2:(iter)){
kk = k[,c('BTTH','sdTH')]
kk$n=1
kw = unlist(Map(fu,kk$BTTH,kk$sdTH,kk$n))
mu = c(mu,sum(k$mean * kw))
v = c(v,sqrt(sum((kw)^2*(k$var/k$n))))
ql =c(ql,qnorm(.025,mu[j],v[j]))
qu = c(qu,qnorm(.975,mu[j],v[j]))
}
out[[i]] = c(re[i],mean(mu),mean(ql),mean(qu))
}
gs = as.data.frame(do.call(rbind,out))
names(gs) = c('LFA','TotCodWt','TotCodWtL','TotCodWtU')
##cbfha
b=bycatch.db('logbook.merge')
aS = bycatch.db('CBFHA')
ap = aggregate(CodWt~TRIP+LFA+Cluster+GridGroup+Period,data=aS,FUN=mean)
aCs = aggregate(CodWt~TRIP+LFA+Cluster+GridGroup+Period,data=aS,FUN=var,na.rm=T)
aCs = rename.df(aCs,c('CodWt'),c('CodWtvar'))
aCC = aggregate(UID~TRIP+LFA+Cluster+GridGroup+Period,data=aS,FUN=function(x) length(unique(x)))
ap = merge(ap, aCC)
aCp = merge(ap, aCs)
aCp$mean =aCp$CodWt
aCp$var =aCp$CodWtvar
aCp$n = aCp$UID
aT = aCp
aT$Strata = paste(aT$LFA,aT$GridGroup,aT$Period,sep='_')
op = list()
aTs = split(aT,f=list(aT$Strata))
aTs = rm.from.list(aTs)
for(i in 1:length(aTs)){
j = aTs[[i]]
op[[i]] =c(LFA =unique(j$LFA), GridGroup = unique(j$GridGroup), Period = unique(j$Period),Strata=unique(j$Strata),clusterStatistics(j))
}
aCo = as.data.frame(do.call(rbind,op))
aCo = toNums(aCo,c(5:8))
uu = readRDS('results/BumpedUpEffortByGridGroup.rds')
uu = subset(uu,LFA %in% 27)
uu$Strata = paste(uu$LFA,uu$GridGroup,uu$Period,sep='_')
uu = aggregate(cbind(BTTH,BuTH,BlTH)~Strata+LFA+GridGroup+Period,data=uu,FUN=mean)
au = merge(uu,aCo,all.x=T)
ff = au
#stratified totals
ff$Tot = ff$BTTH * ff$mean
ff$var = (ff$sd)^2
ff$sdTH = ((ff$BTTH-ff$BlTH)/5)
f1 = aggregate(BTTH~LFA,data=ff,FUN=sum)
names(f1)[2] = 'TotTr'
ff = merge(ff,f1)
fu = function(m,s,n) rnorm(n,mean=m,sd=s)
iter=1000
re = unique(ff$LFA)
out = list()
for(i in 1:length(re)){
k = subset(ff,LFA==re[i])
mu = sum(k$mean * k$BTTH)
v = sqrt(sum((k$BTTH)^2*(k$var/k$n)))
ql = qnorm(.025,mu,v)
qu = qnorm(.975,mu,v)
for(j in 2:(iter)){
kk = k[,c('BTTH','sdTH')]
kk$n=1
kw = unlist(Map(fu,kk$BTTH,kk$sdTH,kk$n))
mu = c(mu,sum(k$mean * kw))
v = c(v,sqrt(sum((kw)^2*(k$var/k$n))))
ql =c(ql,qnorm(.025,mu[j],v[j]))
qu = c(qu,qnorm(.975,mu[j],v[j]))
}
out[[i]] = c(re[i],mean(mu),mean(ql),mean(qu))
}
gs = as.data.frame(do.call(rbind,out))
gs = toNums(gs,1:4)
gs[,2:4] = gs[,2:4]/1000
#######################
##GCIFA
b=bycatch.db('logbook.merge')
aS = bycatch.db('GCIFA')
ap = aggregate(CodWt~TRIP+LFA+Cluster+Period+SYEAR,data=aS,FUN=mean)
aCs = aggregate(CodWt~TRIP+LFA+Cluster+Period+SYEAR,data=aS,FUN=var,na.rm=T)
aCs = rename.df(aCs,c('CodWt'),c('CodWtvar'))
aCC = aggregate(UID~TRIP+LFA+Cluster+Period+SYEAR,data=aS,FUN=function(x) length(unique(x)))
ap = merge(ap, aCC)
aCp = merge(ap, aCs)
aCp$mean =aCp$CodWt
aCp$var =aCp$CodWtvar
aCp$n = aCp$UID
aT = aCp
aT$Strata = paste(aT$LFA,aT$Period,sep='_')
op = list()
aTs = split(aT,f=list(aT$SYEAR,aT$Strata))
aTs = rm.from.list(aTs)
for(i in 1:length(aTs)){
j = aTs[[i]]
op[[i]] =c(SYEAR= unique(j$SYEAR),LFA =unique(j$LFA), Period = unique(j$Period),Strata=unique(j$Strata),clusterStatistics(j))
}
aCo = as.data.frame(do.call(rbind,op))
aCo = toNums(aCo,c(5:8))
uu = readRDS('results/BumpedUpEffortByGridGroup.rds')
uu = subset(uu,LFA %in% c('31A','31B') & SYEAR %in% c(2019,2018))
uu$Strata = paste(uu$LFA,uu$Period,sep='_')
uu = aggregate(cbind(BTTH,BlTH,BuTH)~SYEAR+LFA+Strata+Period, data=uu,FUN=sum)
au = merge(uu,aCo,all.x=T)
#for unsampled strata use the mean and of the time period from all others using the same clustering
i = which(is.na(au$mean))
ff = au
ff = toNums(ff,8:10)
#stratified totals
ff$Tot = ff$BTTH * ff$mean
ff$var = (ff$sd)^2
ff$sdTH = ((ff$BTTH-ff$BlTH)/5)
f1 = aggregate(BTTH~LFA+SYEAR,data=ff,FUN=sum)
names(f1)[3] = 'TotTr'
ff = merge(ff,f1)
fu = function(m,s,n) rnorm(n,mean=m,sd=s)
iter=1000
re = unique(cbind(ff$LFA,ff$SYEAR))
out = list()
for(i in 1:nrow(re)){
k = subset(ff,LFA==re[i,1] & SYEAR==re[i,2])
mu = sum(k$mean * k$BTTH)
v = sqrt(sum((k$BTTH)^2*(k$var/k$n)))
ql = qnorm(.025,mu,v)
qu = qnorm(.975,mu,v)
for(j in 2:(iter)){
kk = k[,c('BTTH','sdTH')]
kk$n=1
kw = unlist(Map(fu,kk$BTTH,kk$sdTH,kk$n))
mu = c(mu,sum(k$mean * kw))
v = c(v,sqrt(sum((kw)^2*(k$var/k$n))))
ql =c(ql,qnorm(.025,mu[j],v[j]))
qu = c(qu,qnorm(.975,mu[j],v[j]))
}
out[[i]] = c(re[i,],mean(mu),mean(ql),mean(qu))
}
gs = as.data.frame(do.call(rbind,out))
names(gs)= c('LFA','YR','TotCod','TotCodL','TotCodW')
gs = toNums(gs,2:5)
gs[,3:5] = gs[,3:5]/1000
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