In flr/mydas: Tools for conducting Management Strategy Evaluation for data limited stocks

An example of running an MSE in parallel, downloading data from dropbox and google drive, and uploading results to Amazon Web Server.

library(knitr)
## Global options
opts_chunk$set(cache     =TRUE,
               cache.path='cache/parallel/',
               echo      =TRUE,
               eval      =!TRUE,
               prompt    =FALSE,
               comment   =NA,
               message   =FALSE,
               warning   =FALSE,
               tidy      =FALSE,
               fig.height=6,
               fig.width =8,
               fig.path  ='tex/parallel-')


iFig=0

Load Libraries

library(plyr)
library(reshape)

library(FLCore)
library(FLBRP)
library(FLasher)
library(ggplotFL)
library(FLife)
library(mydas)

theme_set(theme_bw())
options(digits=3)

library(doParallel)
library(foreach)

registerDoParallel(4)

#library(googledrive)
#fls=drive_find()
#drive_download("turbot.RData",path="turbot.RData")
#load("turbot.RData")

load("/home/laurence/Desktop/sea++/mydas/project/data/OM/turbot.RData")

### Stochasticity
nits=dim(om)[6]
set.seed(1234)
srDev=FLife:::rlnoise(nits,FLQuant(0,dimnames=list(year=1:100)),0.2,b=0.0)

### OEM
uDev =FLife:::rlnoise(nits,FLQuant(0,dimnames=list(year=1:100)),0.3,b=0.0)

## MSE for Derivate empirical MP
scen=expand.grid(k1=seq(0.2,1.0,0.8),k2=seq(0.2,1.0,0.8),gamma=seq(1,2),
                 stringsAsFactors=FALSE)

registerDoParallel(4)
mseSBTD=mydas:::mseSBTD
empD<-foreach(i=(seq(dim(scen)[1])), 
              .combine=rbind,
              .multicombine=TRUE,
              .packages=c("FLCore","FLasher","FLBRP","FLife","plyr","reshape")) %dopar%{

               omRes=mseSBTD(om,eq,control=with(scen[i,],c(k1=k1,k2=k2,gamma=gamma)),start=60,end=100,
                             sr_deviances=srDev,u_deviances=uDev)

               res  =cbind(scen=i,k1=scen[i,"k1"],k2=scen[i,"k2"],gamma=scen[i,"gamma"],
                            omSmry(omRes,eq,lh))

               res}

#library(googledrive)
#fls=drive_find()
#drive_download("turbot.RData",path="turbot.RData")
#load("turbot.RData")

#library(googledrive)
#fls=drive_find()
#drive_download("turbot.RData",path="turbot.RData")
#load("turbot.RData")

### Stochasticity
nits=dim(om)[6]
set.seed(1234)
srDev=FLife:::rlnoise(nits,FLQuant(0,dimnames=list(year=1:100)),0.2,b=0.0)

### OEM
uDev =FLife:::rlnoise(nits,FLQuant(0,dimnames=list(year=1:100)),0.3,b=0.0)

## MSE for Derivate empirical MP
scen=expand.grid(k1=seq(0.2,1.0,0.8),k2=seq(0.2,1.0,0.8),gamma=seq(1,2),
                 stringsAsFactors=FALSE)

### Stochasticity
nits=dim(om)[6]
set.seed(1234)
srDev=FLife:::rlnoise(nits,FLQuant(0,dimnames=list(year=1:100)),0.2,b=0.0)

### OEM
uDev =FLife:::rlnoise(nits,FLQuant(0,dimnames=list(year=1:100)),0.3,b=0.0)

## MSE for Derivate empirical MP
scen=expand.grid(k1=seq(0.2,1.0,0.8),k2=seq(0.2,1.0,0.8),gamma=seq(1,2),
                 stringsAsFactors=FALSE)

## MSE for Derivate empirical MP
scen=expand.grid(k1=seq(0.2,1.0,0.8),k2=seq(0.2,1.0,0.8),gamma=seq(1,2),
                 stringsAsFactors=FALSE)

empD<-foreach(i=(seq(dim(scen)[1])), 
              .combine=rbind,
              .multicombine=TRUE,
              .packages=c("FLCore","FLasher","FLBRP","FLife","plyr","reshape")) %dopar%{

               omRes=mseSBTD(om,eq,control=with(scen[i,],c(k1=k1,k2=k2,gamma=gamma)),start=60,end=100,
                             sr_deviances=srDev,u_deviances=uDev)

               res  =cbind(scen=i,k1=scen[i,"k1"],k2=scen[i,"k2"],gamma=scen[i,"gamma"],
                            omSmry(omRes,eq,lh))

               res}

empD<-foreach(i=(seq(dim(scen)[1])), 
              .combine=rbind,
              .multicombine=TRUE,
              .packages=c("FLCore","FLasher","FLBRP","FLife","plyr","reshape")) %dopar%{

               omRes=mseSBTD(om,eq,control=with(scen[i,],c(k1=k1,k2=k2,gamma=gamma)),start=60,end=100,
                             sr_deviances=srDev,u_deviances=uDev)

               res  =cbind(scen=i,k1=scen[i,"k1"],k2=scen[i,"k2"],gamma=scen[i,"gamma"],
                            omSmry(omRes,eq,lh))

               res}

library(RPostgreSQL)
library(dplyr)
library(plyr)
library(reshape)

drv  = dbDriver("PostgreSQL")
con1 = dbConnect(drv, host = 'postgresql-seascope.csffkpr9jjjn.eu-west-2.rds.amazonaws.com',
                 dbname='FLRout',
                 port = 5432,
                 user = 'MydasAdmin',
                 password = 'datapoor1!')
##write
res$om="Turbot"
dbWriteTable(con1, "data_om", value = res, append=FALSE,overwrite=FALSE,row.names=FALSE)

##read
omtest = dbGetQuery(con1, paste0("SELECT *from data_om"))

scenarios=expand.grid(stock=c("brill","turbot","ray","pollack","sprat","lobster","razor"),
                      mp   =c("xsa","mpb","sra","lbspr","2/3","pid","sbt1","sbt2","irate"))
xsaOptions=expand.grid(mp="xsa",ftar=c(0.7,1,1,2),interval=1:3)
scenarios=merge(scenarios,xsaOptions,all=TRUE)

res$om="Turbot"
dbWriteTable(con1, "data_om", value = res, append=FALSE,overwrite=FALSE,row.names=FALSE)

omtest = dbGetQuery(con1, paste0("SELECT *from data_om"))

scenarios=expand.grid(stock=c("brill","turbot","ray","pollack","sprat","lobster","razor"),
                      mp   =c("xsa","mpb","sra","lbspr","2/3","pid","sbt1","sbt2","irate"))
xsaOptions=expand.grid(mp="xsa",ftar=c(0.7,1,1,2),interval=1:3)
scenarios=merge(scenarios,xsaOptions,all=TRUE)

Software Versions

• r version$version.string
• FLCore: r packageVersion('FLCore')
• FLPKG: r # packageVersion('FLPKG')
• Compiled: r date()
• Git Hash: r system("git log --pretty=format:'%h' -n 1", intern=TRUE)

Author information

Laurence KELL. [e-mail]

Acknowledgements

References {#References}

flr/mydas documentation built on Jan. 19, 2024, 10:33 a.m.