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BenthisWorkflow2020.r
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#-------------------------------------------------------------------------------
#
# Initially named "the Benthis WP2 workflow"
# Designed by: Francois Bastardie, Niels Hintzen
#
# Updated by F. Bastardie to feed the EMFF Danish sandbank project
# Runs with: R version 3.6.3
# and VMStools version: 0.71
#
#-------------------------------------------------------------------------------
rm(list=ls())
library(vmstools)
library(maps)
library(mapdata)
if(.Platform$OS.type == "unix") {
codePath <- file.path("/zhome","fe","8","43283","BENTHIS")
dataPath <- file.path("/zhome","fe","8","43283","BENTHIS","EflaloAndTacsat")
#outPath <- file.path("~","BENTHIS", "outputs")
outPath <- file.path("/zhome","fe","8","43283","BENTHIS", "outputs2020")
polPath <- file.path("/zhome","fe","8","43283","BENTHIS", "BalanceMaps")
##First read in the arguments listed at the command line
args=(commandArgs(TRUE))
##args is now a list of character vectors
## First check to see if arguments are passed.
## Then cycle through each element of the list and evaluate the expressions.
if(length(args)==0){
print("No arguments supplied.")
##supply default values
a_year <- 2019
}else{
for(i in 1:length(args)){
eval(parse(text=args[[i]]))
}
}
}
if(.Platform$OS.type == "windows") {
codePath <- "D:/FBA/BENTHIS_2020/"
dataPath <- "D:/FBA/BENTHIS_2020/EflaloAndTacsat/"
outPath <- file.path("D:","FBA","BENTHIS_2020", "outputs2020")
polPath <- "D:/FBA/BENTHIS/BalanceMaps"
#a_year <- 2005
#a_year <- 2006
#a_year <- 2007
#a_year <- 2008
#a_year <- 2009
#a_year <- 2010
#a_year <- 2011
#a_year <- 2012
#a_year <- 2013
#a_year <- 2014
#a_year <- 2015
#a_year <- 2016
#a_year <- 2017
#a_year <- 2018
a_year <- 2019
}
overwrite <- TRUE
dir.create(file.path(outPath))
dir.create(file.path(outPath, a_year))
library(vmstools)
#if(TRUE){
# DO IT ONCE FOR ALL IN ORDER TO SAVE TIME LOADING BIG DATA:
#Caution: Because unfortunately the RData format has been impacted on
# the Unix side after 3.5.0 we need to save the RData with version=2 to ensure back compatibility
#assign (paste0("tacsat_", a_year),
# read.table(file.path(dataPath,paste("tacsat2_", a_year,".csv", sep='')), sep=",", header=TRUE)) # get the tacsat object
#save(list=paste0("tacsat_", a_year), file=file.path(dataPath,paste("tacsat_", a_year,".RData", sep='')), version=2)
#assign (paste0("eflalo_", a_year),
# read.table(file.path(dataPath,paste("eflalo4_", a_year,".csv", sep='')), sep=",", header=TRUE)) # get the eflalo object
#save(list=paste0("eflalo_", a_year), file=file.path(dataPath,paste("eflalo_", a_year,".RData", sep='')), version=2)
print(file.path(dataPath,paste("eflalo_", a_year,".RData", sep='')))
load(file.path(dataPath,paste("eflalo_", a_year,".RData", sep=''))); # get the eflalo object
load(file.path(dataPath,paste("tacsat_", a_year,".RData", sep=''))); # get the tacsat object
if(a_year>=2016){
tacsat <- formatTacsat(get(paste0("tacsat_", a_year))) # format each of the columns to the specified class
eflalo <- formatEflalo(get(paste0("eflalo_", a_year))) # format each of the columns to the specified class
} else{
tacsat <- formatTacsat(get(paste0("tacsat"))) # format each of the columns to the specified class
eflalo <- formatEflalo(get(paste0("eflalo"))) # format each of the columns to the specified class
}
# drop the species catch and euro composition
idx <- kgeur(colnames(eflalo))
eflalo$LE_KG_SPECS <- rowSums(eflalo[,grep("LE_KG_",colnames(eflalo))],na.rm=T)
eflalo$LE_EURO_SPECS <- rowSums(eflalo[,grep("LE_EURO_",colnames(eflalo))],na.rm=T)
eflalo <- eflalo[,-idx]
# country-specific
ctry <- "DNK"
eflalo <- eflalo[ grep(ctry, as.character(eflalo$VE_REF)),] # keep the national vessels only.
#VMS_ping_rate_in_hour <- 115/60 # Dutch data (rev(sort(table(intervalTacsat(sortTacsat(tacsat),level="vessel")$INTV))[1])
VMS_ping_rate_in_hour <- 1 # e.g. 1 hour ping for Denmark (rev(sort(table(intervalTacsat(sortTacsat(tacsat),level="vessel")$INTV))[1])
# Gear codes to keep (with assumed severe bottom impact)
gears2keep <- c("TBB","OTT","OTB","SSC","SDN","PTB","DRB")
towedGears <- c("TBB","OTT","OTB","PTB","DRB")
seineGears <- c("SSC","SDN")
if(.Platform$OS.type == "windows")
data(euharbours)
if(.Platform$OS.type == "unix")
data(harbours)
data(ICESareas)
data(europa)
#-----------------------------------------------------------------------------
# A UTILS FUNCTION
#-----------------------------------------------------------------------------
compute_swept_area <- function(
tacsatIntGearVEREF=tacsatIntGearVEREF,
gear_param_per_metier=gear_param_per_metier,
towedGears=towedGears,
seineGears=seineGears,
VMS_ping_rate_in_hour=VMS_ping_rate_in_hour,
already_informed_width_for=NULL
){
if(is.null(already_informed_width_for)){
tacsatIntGearVEREF <- tacsatIntGearVEREF[,!colnames(tacsatIntGearVEREF) %in%
c('GEAR_WIDTH', 'GEAR_WIDTH_LOWER', 'GEAR_WIDTH_UPPER', 'SWEPT_AREA_KM2', 'SWEPT_AREA_KM2_LOWER', 'SWEPT_AREA_KM2_UPPER')] # remove columns if exists
} else{
tacsatIntGearVEREF <- tacsatIntGearVEREF[,!colnames(tacsatIntGearVEREF) %in%
c('SWEPT_AREA_KM2', 'SWEPT_AREA_KM2_LOWER', 'SWEPT_AREA_KM2_UPPER')] # remove columns if exists
}
if(is.null(already_informed_width_for)){
# MERGE WITH GEAR WIDTH
GearWidth <- tacsatIntGearVEREF[!duplicated(data.frame(tacsatIntGearVEREF$VE_REF,tacsatIntGearVEREF$LE_MET,tacsatIntGearVEREF$VE_KW,tacsatIntGearVEREF$VE_LEN)), ]
GearWidth <- GearWidth[,c('VE_REF','LE_MET','VE_KW', 'VE_LEN') ]
GearWidth$GEAR_WIDTH <- NA
GearWidth$GEAR_WIDTH_LOWER <- NA
GearWidth$GEAR_WIDTH_UPPER <- NA
for (i in 1:nrow(GearWidth)) { # brute force...
kW <- GearWidth$VE_KW[i]
LOA <- GearWidth$VE_LEN[i]
this <- gear_param_per_metier[gear_param_per_metier$a_metier==as.character(GearWidth$LE_MET[i]),]
a <- NULL ; b <- NULL
a <- this[this$param=='a', 'Estimate']
b <- this[this$param=='b', 'Estimate']
GearWidth[i,"GEAR_WIDTH"] <- eval(parse(text= as.character(this[1, 'equ']))) / 1000 # converted in km
a <- this[this$param=='a', 'Estimate']
b <- this[this$param=='b', 'Estimate'] +2*this[this$param=='b', 'Std..Error']
GearWidth[i,"GEAR_WIDTH_UPPER"] <- eval(parse(text= as.character(this[1, 'equ']))) / 1000 # converted in km
a <- this[this$param=='a', 'Estimate']
b <- this[this$param=='b', 'Estimate'] -2*this[this$param=='b', 'Std..Error']
GearWidth[i,"GEAR_WIDTH_LOWER"] <- eval(parse(text= as.character(this[1, 'equ']))) / 1000 # converted in km
}
tacsatIntGearVEREF <- merge(tacsatIntGearVEREF, GearWidth,by=c("VE_REF","LE_MET","VE_KW","VE_LEN"),
all.x=T,all.y=F)
}
# the swept area (note that could work oustide the loop area as well....)
# for the trawlers...
if(tacsatIntGearVEREF$LE_GEAR[1] %in% towedGears){
tacsatIntGearVEREF$SWEPT_AREA_KM2 <- NA
tacsatIntGearVEREF <- orderBy(~SI_DATIM,data=tacsatIntGearVEREF)
a_dist <- distance(c(tacsatIntGearVEREF$SI_LONG[-1],0), c(tacsatIntGearVEREF$SI_LATI[-1],0),
tacsatIntGearVEREF$SI_LONG, tacsatIntGearVEREF$SI_LATI)
a_dist[length(a_dist)] <- rev(a_dist)[2]
tacsatIntGearVEREF$SWEPT_AREA_KM2 <- a_dist * tacsatIntGearVEREF$GEAR_WIDTH
tacsatIntGearVEREF$SWEPT_AREA_KM2_LOWER <- a_dist * tacsatIntGearVEREF$GEAR_WIDTH_LOWER
tacsatIntGearVEREF$SWEPT_AREA_KM2_UPPER <- a_dist * tacsatIntGearVEREF$GEAR_WIDTH_UPPER
# correct the transition between sequential fishing events
#idx <- which(diff(tacsatIntGearVEREF$SI_DATIM)/60 > 15) # if interval > 15 min then points belong to a different fishing event
# CORRECTION Sep18:
idx <- which( as.numeric(diff(tacsatIntGearVEREF$SI_DATIM), units='mins') > 15) # if interval > 15 min
idx <- c(idx, nrow(tacsatIntGearVEREF)) # to exclude the last observation
tacsatIntGearVEREF[ idx, c('SWEPT_AREA_KM2', 'SWEPT_AREA_KM2_LOWER', 'SWEPT_AREA_KM2_UPPER')] <- NA
}
# for the seiners...
if(tacsatIntGearVEREF$LE_GEAR[1] %in% seineGears){
tacsatIntGearVEREF$SWEPT_AREA_KM2 <- pi*(tacsatIntGearVEREF$GEAR_WIDTH/(2*pi))^2
tacsatIntGearVEREF$SWEPT_AREA_KM2_LOWER <- pi*(tacsatIntGearVEREF$GEAR_WIDTH_LOWER/(2*pi))^2
tacsatIntGearVEREF$SWEPT_AREA_KM2_UPPER <- pi*(tacsatIntGearVEREF$GEAR_WIDTH_UPPER/(2*pi))^2
haul_duration <- 3 # assumption of a mean duration based from questionnaires to seiners
tacsatIntGearVEREF$SWEPT_AREA_KM2 <- tacsatIntGearVEREF$SWEPT_AREA_KM2 * VMS_ping_rate_in_hour / haul_duration # correction to avoid counting the same circle are several time.
tacsatIntGearVEREF$SWEPT_AREA_KM2_LOWER <- tacsatIntGearVEREF$SWEPT_AREA_KM2_LOWER * VMS_ping_rate_in_hour / haul_duration # correction to avoid counting the same circle are several time.
tacsatIntGearVEREF$SWEPT_AREA_KM2_UPPER <- tacsatIntGearVEREF$SWEPT_AREA_KM2_UPPER * VMS_ping_rate_in_hour / haul_duration # correction to avoid counting the same circle are several time.
idx <- grep('SSC', as.character(tacsatIntGearVEREF$LE_GEAR))
tacsatIntGearVEREF[idx, 'SWEPT_AREA_KM2'] <- tacsatIntGearVEREF[idx, 'SWEPT_AREA_KM2'] *1.5 # ad hoc correction to account for the SSC specificities
tacsatIntGearVEREF[idx, 'SWEPT_AREA_KM2_LOWER'] <- tacsatIntGearVEREF[idx, 'SWEPT_AREA_KM2_LOWER'] *1.5 # ad hoc correction to account for the SSC specificities
tacsatIntGearVEREF[idx, 'SWEPT_AREA_KM2_UPPER'] <- tacsatIntGearVEREF[idx, 'SWEPT_AREA_KM2_UPPER'] *1.5 # ad hoc correction to account for the SSC specificities
}
return(tacsatIntGearVEREF)
}
# check if the files are not already existing. If yes, avoid rebuilding them because too time consuming
do_it <- TRUE
fls <- dir(file.path(outPath, a_year))
fls <- fls[grep("tacsatMerged.RData", fls)]
if(length(fls)!=0 && overwrite==FALSE) do_it <- FALSE
if(do_it){
#-----------------------------------------------------------------------------
# Cleaning tacsat (keep track of removed records)
#-----------------------------------------------------------------------------
remrecsTacsat <- matrix(NA,nrow=6,ncol=2,dimnames= list(c("total","duplicates","notPossible",
"pseudoDuplicates","harbour","land"),
c("rows","percentage")))
remrecsTacsat["total",] <- c(nrow(tacsat),"100%")
# Remove duplicate records
tacsat$SI_DATIM <- as.POSIXct(paste(tacsat$SI_DATE, tacsat$SI_TIME, sep=" "),
tz="GMT", format="%d/%m/%Y %H:%M")
uniqueTacsat <- paste(tacsat$VE_REF,tacsat$SI_LATI,tacsat$SI_LONG,tacsat$SI_DATIM)
tacsat <- tacsat[!duplicated(uniqueTacsat),]
remrecsTacsat["duplicates",] <- c(nrow(tacsat),100+round((nrow(tacsat) -
an(remrecsTacsat["total",1]))/an(remrecsTacsat["total",1])*100,2))
# Remove points that cannot be possible
spThres <- 20 #Maximum speed threshold in analyses in nm
idx <- which(abs(tacsat$SI_LATI) > 90 | abs(tacsat$SI_LONG) > 180)
idx <- unique(c(idx,which(tacsat$SI_HE < 0 | tacsat$SI_HE > 360)))
idx <- unique(c(idx,which(tacsat$SI_SP > spThres)))
if(length(idx)>0)
tacsat <- tacsat[-idx,]
remrecsTacsat["notPossible",] <- c(nrow(tacsat),100+round((nrow(tacsat) -
an(remrecsTacsat["total",1]))/an(remrecsTacsat["total",1])*100,2))
# Remove points which are pseudo duplicates as they have an interval rate < x minutes
intThres <- 5 # Minimum difference in time interval in minutes to prevent pseudo duplicates
tacsat <- sortTacsat(tacsat)
tacsatp <- intervalTacsat(tacsat,level="vessel",fill.na=T)
tacsat <- tacsatp[which(tacsatp$INTV > intThres | is.na(tacsatp$INTV)==T),-grep("INTV",colnames(tacsatp))]
remrecsTacsat["pseudoDuplicates",] <- c(nrow(tacsat),100+round((nrow(tacsat) -
an(remrecsTacsat["total",1]))/an(remrecsTacsat["total",1])*100,2))
# Remove points in harbour
idx <- pointInHarbour(tacsat$SI_LONG,tacsat$SI_LATI,harbours)
pih <- tacsat[which(idx == 1),]
save(pih,file=paste(outPath, a_year, "pointInHarbour.RData",sep=""))
tacsat <- tacsat[which(idx == 0),]
remrecsTacsat["harbour",] <- c(nrow(tacsat),100+round((nrow(tacsat) -
an(remrecsTacsat["total",1]))/an(remrecsTacsat["total",1])*100,2))
# Remove points on land
pols <- lonLat2SpatialPolygons(lst=lapply(as.list(sort(unique(europa$SID))),
function(x){data.frame(SI_LONG=subset(europa,SID==x)$X,
SI_LATI=subset(europa,SID==x)$Y)}))
idx <- pointOnLand(tacsat,pols)
pol <- tacsat[which(idx == 1),]
save(pol,file=file.path(outPath,a_year,"pointOnLand.RData"))
tacsat <- tacsat[which(idx == 0),]
remrecsTacsat["land",] <- c(nrow(tacsat),100+round((nrow(tacsat) -
an(remrecsTacsat["total",1]))/an(remrecsTacsat["total",1])*100,2))
# Save the remrecsTacsat file
save(remrecsTacsat,file=file.path(outPath,a_year,"remrecsTacsat.RData"))
# remove (otherwise we will see interpolation from 0s!)
tacsat <- tacsat[tacsat$SI_LATI!=0,]
save(tacsat,file=file.path(outPath,a_year,"cleanTacsat.RData"))
#-----------------------------------------------------------------------------
# Cleaning Eflalo
#-----------------------------------------------------------------------------
# Keep track of removed points
remrecsEflalo <- matrix(NA,nrow=5,ncol=2,dimnames=list(c("total","duplicated","impossible time",
"before 1st Jan","departArrival"),
c("rows","percentage")))
remrecsEflalo["total",] <- c(nrow(eflalo),"100%")
# Remove non-unique trip numbers
eflalo <- eflalo[!duplicated(paste(eflalo$LE_ID,eflalo$LE_CDAT,sep="-")),]
remrecsEflalo["duplicated",] <- c(nrow(eflalo),100+round((nrow(eflalo) -
an(remrecsEflalo["total",1]))/an(remrecsEflalo["total",1])*100,2))
# Remove impossible time stamp records
eflalo$FT_DDATIM <- as.POSIXct(paste(eflalo$FT_DDAT,eflalo$FT_DTIME, sep = " "),
tz = "GMT", format = "%d/%m/%Y %H:%M")
eflalo$FT_LDATIM <- as.POSIXct(paste(eflalo$FT_LDAT,eflalo$FT_LTIME, sep = " "),
tz = "GMT", format = "%d/%m/%Y %H:%M")
eflalo <- eflalo[!(is.na(eflalo$FT_DDATIM) |is.na(eflalo$FT_LDATIM)),]
remrecsEflalo["impossible time",] <- c(nrow(eflalo),100+round((nrow(eflalo) -
an(remrecsEflalo["total",1]))/an(remrecsEflalo["total",1])*100,2))
# Remove trip starting before 1st Jan
# year <- min(year(eflalo$FT_DDATIM)) # deprecated?
eflalo <- eflalo[eflalo$FT_DDATIM>=strptime(paste(a_year,"-01-01 00:00:00",sep=''),
"%Y-%m-%d %H:%M"),]
remrecsEflalo["before 1st Jan",] <- c(nrow(eflalo),100+round((nrow(eflalo) -
an(remrecsEflalo["total",1]))/an(remrecsEflalo["total",1])*100,2))
# Remove records with arrival date before departure date
eflalop <- eflalo
eflalop$FT_DDATIM <- as.POSIXct(paste(eflalo$FT_DDAT, eflalo$FT_DTIME, sep=" "),
tz="GMT", format="%d/%m/%Y %H:%M")
eflalop$FT_LDATIM <- as.POSIXct(paste(eflalo$FT_LDAT, eflalo$FT_LTIME, sep=" "),
tz="GMT", format="%d/%m/%Y %H:%M")
idx <- which(eflalop$FT_LDATIM >= eflalop$FT_DDATIM)
eflalo <- eflalo[idx,]
remrecsEflalo["departArrival",] <- c(nrow(eflalo),100+round((nrow(eflalo) -
an(remrecsEflalo["total",1]))/an(remrecsEflalo["total",1])*100,2))
# Save the remrecsEflalo file
save(remrecsEflalo,file=file.path(outPath,a_year,"remrecsEflalo.RData"))
# Save the cleaned eflalo file
save(eflalo,file=file.path(outPath,a_year,"cleanEflalo.RData"))
#-----------------------------------------------------------------------------
# Make gear code selection and calculate effort for each gear
#-----------------------------------------------------------------------------
# effort < 15m vs >15m
eflalo$length_class <- cut(as.numeric(as.character(eflalo$VE_LEN)), breaks=c(0,15,100)) # DCF but VMS!
# compute effort
eflalo <- subset(eflalo,FT_REF != 0)
eflalo <- orderBy(~VE_REF+FT_DDATIM+FT_REF, data=eflalo)
eflalo$ID <- paste(eflalo$VE_REF,eflalo$FT_REF,sep="")
eflalo$LE_EFF <- an(difftime(eflalo$FT_LDATIM, eflalo$FT_DDATIM, units="hours"))
eflalo$dummy <- 1
eflalo$LE_EFF <- eflalo$LE_EFF / merge(eflalo,aggregate(eflalo$dummy,by=list(eflalo$ID),FUN=sum),by.x="ID",by.y="Group.1",all.x=T)$x
eflalo <- eflalo[which(eflalo$LE_GEAR %in% gears2keep),]
aggregate(eflalo$LE_EFF, list(eflalo$length_class), sum,na.rm=T)
gc(reset=TRUE)
#-----------------------------------------------------------------------------
# Merge eflalo and tacsat
#-----------------------------------------------------------------------------
tacsatp <- mergeEflalo2Tacsat(eflalo,tacsat)
tacsatp$LE_GEAR <- eflalo$LE_GEAR[match(tacsatp$FT_REF,eflalo$FT_REF)]
tacsatp$VE_LEN <- eflalo$VE_LEN[ match(tacsatp$FT_REF,eflalo$FT_REF)]
tacsatp$LE_MET <- eflalo$LE_MET[ match(tacsatp$FT_REF,eflalo$FT_REF)]
tacsatp$VE_KW <- eflalo$VE_KW[ match(tacsatp$FT_REF,eflalo$FT_REF)]
if("LE_WIDTH" %in% colnames(eflalo))
tacsatp$LE_WIDTH <- eflalo$LE_WIDTH[ match(tacsatp$FT_REF,eflalo$FT_REF)]
save(tacsatp,file=file.path(outPath,a_year,"tacsatMerged.RData"))
# Save not merged tacsat data
tacsatpmin <- subset(tacsatp,FT_REF == 0)
save(tacsatpmin, file=file.path(outPath,a_year,"tacsatNotMerged.RData"))
} # end do_it
load(file=file.path(outPath,a_year,"tacsatMerged.RData"))
#-----------------------------------------------------------------------------
# transform into WP2 BENTHIS metier - HANDMADE WORK NEEDED: ADAPT TO YOUR OWN METIER LIST!!!
#-----------------------------------------------------------------------------
tacsatp <- subset(tacsatp,FT_REF != 0)
ctry <- "DNK"
if(ctry=="DNK"){
tacsatp$LE_MET_init <- tacsatp$LE_MET
tacsatp$LE_MET <- factor(tacsatp$LE_MET)
print(levels(tacsatp$LE_MET))
if(a_year=="2005"){
levels(tacsatp$LE_MET) <- c( ## REPLACE LEVELS WITH CAUTION ## adapt to your own list!!
"DRB_MOL", "OT_DMF", "NA", "OT_CRU", "OT_CRU", "OT_CRU", "OT_CRU", "OT_CRU", "OT_CRU", "OT_CRU",
"OT_MIX_DMF_PEL", "OT_DMF", "OT_DMF", "OT_DMF", "OT_MIX_DMF_PEL", "OT_MIX_DMF_PEL", "OT_DMF", "OT_DMF", "OT_DMF", "OT_DMF",
"OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_CRU", "OT_MIX_DMF_PEL", "OT_DMF", "OT_DMF", "OT_DMF",
"OT_MIX_DMF_PEL", "OT_DMF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "SDN_DEM", "SDN_DEM", "SDN_DEM",
"SDN_DEM", "SSC_DEM", "SSC_DEM","SSC_DEM", "TBB_CRU", "TBB_DMF", "TBB_DMF")
} else{
if(a_year=="2006"){
levels(tacsatp$LE_MET) <- c( ## REPLACE LEVELS WITH CAUTION ## adapt to your own list!!
"DRB_MOL", "NA", "OT_CRU", "OT_CRU", "OT_CRU", "OT_CRU", "OT_CRU","OT_CRU","OT_CRU", "OT_CRU",
"OT_MIX_DMF_PEL", "OT_DMF", "OT_DMF", "OT_DMF", "OT_MIX_DMF_PEL", "OT_MIX_DMF_PEL", "OT_DMF","OT_DMF", "OT_DMF", "OT_DMF",
"OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_CRU", "OT_CRU", "OT_MIX_DMF_PEL", "OT_DMF",
"OT_DMF", "OT_DMF", "OT_MIX_DMF_PEL", "OT_MIX_DMF_PEL", "OT_DMF", "OT_SPF","OT_SPF","OT_SPF", "OT_SPF", "OT_SPF",
"OT_SPF", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SSC_DEM", "SSC_DEM", "SSC_DEM", "SSC_DEM", "SSC_DEM",
"TBB_CRU", "TBB_DMF", "TBB_DMF", "TBB_DMF")
} else{
if(a_year=="2007"){
levels(tacsatp$LE_MET) <- c( ## REPLACE LEVELS WITH CAUTION ## adapt to your own list!!
"DRB_MOL", "NA", "OT_CRU", "OT_CRU", "OT_CRU", "OT_CRU","OT_CRU","OT_CRU", "OT_MIX_DMF_PEL", "OT_DMF",
"OT_DMF", "OT_DMF", "OT_MIX_DMF_PEL", "OT_MIX_DMF_PEL", "OT_DMF", "OT_DMF", "OT_DMF", "OT_SPF", "OT_SPF", "OT_SPF",
"OT_SPF", "OT_SPF", "OT_CRU", "OT_MIX_DMF_PEL", "OT_DMF", "OT_DMF", "OT_MIX_DMF_PEL", "OT_DMF", "OT_DMF", "OT_DMF",
"OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SSC_DEM", "SSC_DEM",
"SSC_DEM", "SSC_DEM", "SSC_DEM", "TBB_CRU", "TBB_DMF", "TBB_DMF")
} else{
if(a_year=="2008"){
levels(tacsatp$LE_MET) <- c( ## REPLACE LEVELS WITH CAUTION ## adapt to your own list!!
"DRB_MOL", "OT_DMF", "NA", "OT_CRU", "OT_CRU", "OT_CRU", "OT_CRU", "OT_CRU", "OT_CRU", "OT_MIX_DMF_PEL",
"OT_DMF", "OT_DMF", "OT_DMF", "OT_MIX_DMF_PEL", "OT_MIX_DMF_PEL", "OT_DMF", "OT_DMF", "OT_DMF", "OT_DMF", "OT_SPF",
"OT_SPF", "OT_SPF", "OT_SPF", "OT_CRU", "OT_MIX_DMF_PEL", "OT_DMF", "OT_DMF", "OT_DMF", "OT_DMF", "OT_SPF",
"OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "SDN_DEM", "SDN_DEM", "SDN_DEM","SDN_DEM", "SSC_DEM", "SSC_DEM",
"SSC_DEM", "SSC_DEM", "TBB_CRU", "TBB_DMF")
tacsatp$LE_MET <- as.character(tacsatp$LE_MET)
} else{
if(a_year=="2009"){
levels(tacsatp$LE_MET) <- c( ## REPLACE LEVELS WITH CAUTION ## adapt to your own list!!
"DRB_MOL", "NA", "OT_CRU", "OT_CRU", "OT_CRU", "OT_CRU", "OT_CRU", "OT_CRU", "OT_MIX_DMF_PEL", "OT_DMF", "OT_DMF",
"OT_DMF", "OT_MIX_DMF_PEL", "OT_MIX_DMF_PEL", "OT_DMF", "OT_DMF", "OTB_DMF", "OT_SPF", "OT_SPF", "OT_SPF",
"OT_SPF", "OT_SPF", "OT_CRU", "OT_MIX_DMF_PEL", "OT_DMF", "OT_DMF", "OT_MIX_DMF_PEL", "OT_DMF", "OT_SPF", "OT_SPF",
"OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SSC_DEM", "SSC_DEM",
"SSC_DEM", "SSC_DEM", "TBB_CRU", "TBB_DMF")
tacsatp$LE_MET <- as.character(tacsatp$LE_MET)
} else{
if(a_year=="2010"){
levels(tacsatp$LE_MET) <- c( ## REPLACE LEVELS WITH CAUTION ## adapt to your own list!!
"DRB_MOL", "NA", "OT_CRU","OT_CRU", "OT_CRU", "OT_CRU", "OT_CRU",
"OT_CRU", "OT_MIX_DMF_PEL", "OT_DMF", "OT_DMF", "OT_DMF", "OT_DMF", "OT_MIX_DMF_PEL",
"OT_MIX_DMF_PEL", "OT_DMF", "OT_DMF", "OT_DMF", "OT_DMF", "OT_SPF", "OT_SPF",
"OT_SPF", "OT_SPF", "OT_SPF", "OT_MIX_DMF_PEL", "OT_CRU", "OT_MIX_DMF_PEL", "OT_DMF",
"OT_DMF", "OT_DMF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "SDN_DEM",
"SDN_DEM", "SDN_DEM", "SDN_DEM", "SSC_DEM", "SSC_DEM", "TBB_CRU", "TBB_DMF")
tacsatp$LE_MET <- as.character(tacsatp$LE_MET)
} else{
if(a_year=="2011"){
levels(tacsatp$LE_MET) <- c( ## REPLACE LEVELS WITH CAUTION ## adapt to your own list!!
"DRB_MOL", "NA", "OT_CRU", "OT_CRU", "OT_CRU", "OT_MIX_DMF_PEL", "OT_DMF", "OT_MIX_DMF_PEL",
"OT_MIX_DMF_PEL", "OT_DMF", "OT_MIX_NEP", "OT_MIX_NEP", "OT_MIX_NEP", "OT_MIX_NEP", "OT_SPF", "OT_SPF",
"OT_SPF", "OT_MIX_DMF_PEL", "OT_SPF", "OT_CRU", "OT_MIX_DMF_PEL", "OT_DMF", "OT_MIX_DMF_PEL", "OT_MIX_NEP",
"OT_MIX_NEP", "OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "SDN_DEM", "SDN_DEM", "SDN_DEM",
"SDN_DEM", "SSC_DEM", "SSC_DEM", "TBB_CRU", "TBB_DMF", "TBB_DMF")
}else{
if(a_year=="2012") {
levels(tacsatp$LE_MET) <- c("DRB_MOL", "NA", "OT_CRU", "OT_CRU", "OT_CRU", "OT_DMF", "OT_DMF", "OT_MIX_DMF_PEL",
"OT_MIX_DMF_PEL", "OT_DMF", "OT_MIX_NEP", "OT_MIX_NEP", "OT_MIX_NEP", "OT_MIX_NEP",
"OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_MIX_DMF_PEL", "OT_DMF", "OT_MIX_NEP",
"OT_MIX_NEP", "OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF",
"SDN_DEM", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SSC_DEM", "SSC_DEM", "TBB_CRU", "TBB_DMF")
} else{
if(a_year=="2013") {
levels(tacsatp$LE_MET) <- c(
"DRB_MOL","NA", "OT_CRU", "OT_CRU",
"OT_CRU", "OT_DMF" ,"OT_DMF","OT_MIX_DMF_PEL",
"OT_MIX_DMF_PEL", "OT_DMF", "OT_MIX_NEP","OT_MIX_NEP","OT_MIX_NEP", "OT_MIX_NEP",
"OT_SPF","OT_SPF" , "OT_SPF", "OT_MIX_DMF_PEL", "OT_SPF", "OT_MIX_DMF_PEL",
"OT_DMF", "OT_MIX_DMF_PEL", "OT_MIX_NEP", "OT_SPF",
"OT_SPF", "OT_SPF", "OT_MIX_DMF_PEL", "OT_SPF", "OT_SPF",
"SDN_DEM", "SDN_DEM", "SDN_DEM",
"SDN_DEM", "SSC_DEM", "SSC_DEM", "TBB_CRU", "TBB_DMF")
} else{
if(a_year=="2014"){
levels (tacsatp$LE_MET) <- c(
"DRB_MOL", "NA", "OT_CRU", "OT_CRU", "OT_DMF_PEL", "OT_DMF", "OT_DMF_PEL", "OT_DMF_PEL", "OT_DMF", "OT_DMF",
"OT_MIX_NEP", "OT_MIX_NEP", "OT_MIX_NEP", "OT_MIX_NEP", "OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_DMF_PEL",
"OT_DMF", "OT_DMF_PEL", "OT_MIX_NEP", "OT_SPF", "OT_SPF", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SSC_DEM", "TBB_CRU" )
} else{
if(a_year=="2015"){
levels (tacsatp$LE_MET) <- c(
"DRB_MOL", "NA", "OT_CRU", "OT_CRU", "OT_DMF_PEL", "OT_DMF", "OT_DMF_PEL", "OT_DMF_PEL", "OT_DMF", "OT_DMF",
"OT_MIX_NEP", "OT_MIX_NEP", "OT_MIX_NEP", "OT_MIX_NEP", "OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_DMF_PEL",
"OT_DMF", "OT_SPF", "OT_SPF", "OT_DMF_PEL", "OT_DMF_PEL", "OT_SPF", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SSC_DEM", "SSC_DEM", "TBB_CRU", "TBB_DMF" )
} else{
if(a_year=="2016"){
levels (tacsatp$LE_MET) <- c(
"DRB_MOL", "NA", "NA", "OT_CRU", "OT_CRU", "OT_DMF_PEL", "OT_DMF", "OT_DMF_PEL", "OT_DMF_PEL", "OT_DMF",
"OT_MIX_NEP", "OT_MIX_NEP", "OT_MIX_NEP", "OT_MIX_NEP", "OT_SPF", "OT_SPF", "OT_SPF", "OT_SPF", "OT_DMF_PEL", "OT_DMF",
"OT_MIX_NEP", "OT_MIX_NEP", "OT_SPF", "OT_SPF", "OT_SPF", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SSC_DEM", "SSC_DEM", "TBB_CRU", "TBB_DMF" )
} else{
if(a_year=="2017"){
levels (tacsatp$LE_MET) <- c(
"DRB_MOL", "OT_MIX_NEP","OT_CRU", "OT_MIX_NEP", "OT_MIX_NEP", "OT_MIX_NEP","OT_DMF_PEL","OT_DMF","OT_DMF","OT_DMF",
"OT_DMF_PEL", "OT_DMF_PEL", "OT_DMF", "OT_DMF", "OT_DMF", "OT_DMF", "OT_SPF", "OT_DMF_PEL", "OT_DMF", "OT_MIX_NEP",
"OT_DMF_PEL", "OT_DMF", "OT_SPF", "OT_SPF", "OT_SPF", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SDN_DEM",
"SSC_DEM", "SSC_DEM", "TBB_CRU", "TBB_DMF", "TBB_DMF")
} else{
if(a_year=="2018"){
levels (tacsatp$LE_MET) <- c(
"DRB_MOL", "OT_MIX_NEP", "OT_CRU", "OT_CRU", "OT_CRU", "OT_MIX_NEP", "OT_DMF_PEL", "OT_DMF", "OT_DMF", "OT_DMF",
"OT_DMF_PEL", "OT_DMF_PEL", "OT_DMF", "OT_DMF", "OT_DMF", "OT_SPF", "OT_SPF", "OT_MIX_NEP", "OT_DEM_PEL", "OT_DMF",
"OT_DMF", "OT_DMF", "OT_SPF", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SDN_DEM", "SSC_DEM", "TBB_CRU", "TBB_DMF"
)
} else{
if(a_year=="2019"){
levels (tacsatp$LE_MET) <- c(
"DRB_MOL", "OT_MIX_NEP", "OT_CRU", "OT_MIX_NEP", "OT_MIX_NEP", "OT_MIX_NEP", "OT_DEM_PEL", "OT_DMF", "OT_DMF", "OT_DMF",
"OT_DMF_PEL", "OT_DMF_PEL", "OT_DMF", "OT_DMF", "OT_DMF", "OT_SPF", "OT_MIX_NEP", "OT_MIX_NEP", "OT_DMF", "SDN_DEM",
"SDN_DEM", "SDN_DEM", "SDN_DEM", "SSC_DEM", "SSC_DEM", "SSC_DEM", "TBB_CRU", "TBB_CRU", "TBB_DMF", "TBB_DMF"
)
}else{
stop('adapt the BENTHIS metiers for this year')
}
}
}}}}}}}}}}}}
}
initVersusBenthisMetiers <- tacsatp [!duplicated(data.frame(tacsatp$LE_MET_init, tacsatp$LE_MET)),
c('LE_MET_init', 'LE_MET')]
save(initVersusBenthisMetiers, file=file.path(outPath,a_year,"initVersusBenthisMetiers.RData"))
}
#-----------------------------------------------------------------------------
# Define activity
#-----------------------------------------------------------------------------
save(tacsatp,file=file.path(outPath,a_year,"tacsatMergedAndMet.RData"))
load(file=file.path(outPath,a_year,"tacsatMergedAndMet.RData"))
idx <- which(is.na(tacsatp$VE_REF) == T | is.na(tacsatp$SI_LONG) == T | is.na(tacsatp$SI_LATI) == T |
is.na(tacsatp$SI_DATIM) == T | is.na(tacsatp$SI_SP) == T)
if(length(idx)>0) tacsatp <- tacsatp[-idx,]
if(.Platform$OS.type == "windows" && FALSE) {
storeScheme <- activityTacsatAnalyse(tacsatp, units = "year", analyse.by = "LE_GEAR",identify="means")
storeScheme <- storeScheme[which(is.na(storeScheme$analyse.by)==F),]
storeScheme$years <- as.numeric(as.character(storeScheme$years))
storeScheme <- storeScheme[storeScheme$years==a_year,]
save(storeScheme, file=file.path(outPath,a_year,"storeScheme.RData"))
} else{ # actually, storeScheme copied/pasted from a year to the next because assumed constant. So do the copy/paste before running those lines
load(file.path(outPath,a_year,"storeScheme.RData"))
storeScheme$years <- a_year
}
tacsatp$year <- format(tacsatp$SI_DATIM, "%Y")
require(mixtools)
activity <- activityTacsat(tacsatp,units="year",analyse.by="LE_GEAR", storeScheme,
plot=FALSE, level="all")
tacsatp$SI_STATE <- NA
tacsatp$SI_STATE <- activity
#- Plot the result
if(FALSE){
result <- table(tacsatp$SI_STATE,tacsatp$SI_SP,tacsatp$LE_GEAR)
par(mfrow=rep(ceiling(sqrt(length(unique(tacsatp$LE_GEAR)))),2))
for(i in 1:dim(result)[3])
barplot(result[,,i],col=1:3)
}
# General speed rules for remaining records
idx <- which(is.na(tacsatp$SI_STATE))
if(length(idx)>0){
tacsatp$SI_STATE[idx[which(tacsatp$SI_SP[idx] >= 1.5 &
tacsatp$SI_SP[idx] <= 7.5)]] <- 'f'
tacsatp$SI_STATE[idx[which(tacsatp$SI_SP[idx] < 1.5)]] <- 'h'
tacsatp$SI_STATE[idx[which(tacsatp$SI_SP[idx] > 7.5)]] <- 's'
}
save(tacsatp, file=file.path(outPath,a_year,"tacsatActivity.RData"))
# Labelling each haul (caution: to do before discarding the steaming points...)
tacsatp <- labellingHauls(tacsatp)
# fuel use
table.fuelcons.per.engine <- read.table(file= file.path(dataPath, "IBM_datainput_engine_consumption.txt"), header=TRUE,sep="")
linear.model <- lm(calc_cons_L_per_hr_max_rpm~ kW2, data=table.fuelcons.per.engine) # conso = a*Kw +b # to guess its fuel consumption at maximal speed
max_vessel_speed <- tapply(as.numeric(as.character(tacsatp$SI_SP)), tacsatp$VE_REF, quantile, 0.95)
tacsatp$max_vessel_speed <- max_vessel_speed[tacsatp$VE_REF]
tacsatp$max_consumed <- predict(linear.model, newdata=data.frame(kW2=as.numeric(as.character(tacsatp$VE_KW))))
fuel_per_h <- function (a,x) a*(x^3) # cubic law
tacsatp$a <- tacsatp$max_consumed/ (tacsatp$max_vessel_speed^3) # scaling factor
tacsatp$LITRE_FUEL <- fuel_per_h(tacsatp$a, tacsatp$SI_SP)* round(VMS_ping_rate_in_hour*1.0)
# get info to later dispatch steaming cons on fishing points since we will be removing steaming when interpolating
tacsatps <- tacsatp[tacsatp$SI_STATE=="s",]
steaming_cons_per_VE_REF_FT_REF <- tapply(tacsatps$LITRE_FUEL, paste0(tacsatps$VE_REF,"_",tacsatps$FT_REF), sum)
save(steaming_cons_per_VE_REF_FT_REF, file=file.path(outPath,a_year,"steaming_cons_per_VE_REF_FT_REF.RData")) # save steaming_cons_per_VE_REF_FT_REF
#-----------------------------------------------------------------------------
# Interpolation (of fishing sequences only)
#-----------------------------------------------------------------------------
dir.create(file.path(outPath,a_year,"interpolated"))
tacsatp <- orderBy(~VE_REF+SI_DATIM,data=tacsatp)
# KEEP ONLY fish. seq. bounded by steaming points
tacsatp$SI_STATE_num <- NA
tacsatp$SI_STATE_num[which(tacsatp$SI_STATE=="h")] <- 1
tacsatp$SI_STATE_num[tacsatp$SI_STATE=="f"] <- 2
tacsatp$SI_STATE_num[tacsatp$SI_STATE=="s"] <- 3
is_transition <- c(0,diff(tacsatp$SI_STATE_num))
is_transition2 <- c(diff(tacsatp$SI_STATE_num), 0)
tacsatp <- tacsatp[ !is.na(tacsatp$SI_STATE_num) & (tacsatp$SI_STATE_num ==2 |
is_transition!=0 | is_transition2!=0),]
tacsatp <- tacsatp[,-grep("SI_STATE_num",colnames(tacsatp))]
tacsatp$SI_STATE <- "f"
#- Gear specific fm parameters (parameters tuned with st=c(4,8))
#- (should be informed for all below towedGears)
fm <- list(TBB=0.13,
#OTB=0.4217848,
#OTT=0.4217848,
#PTB=0.4217848,
OTB=0.13, # DNK close to straight line (fm at 0)
OTT=0.13,
PTB=0.13,
HMD=0,
DRB=0)
fls <- dir(file.path(outPath, a_year,"interpolated"))
fls <- fls[grep("tacsatSweptArea_", fls)]
# per gear per vessel
for(iGr in towedGears){
tacsatpGear <- tacsatp[!is.na(tacsatp$LE_GEAR) & tacsatp$LE_GEAR==iGr,]
for(iVE_REF in sort(unique(tacsatpGear$VE_REF))){
tacsatpGearVEREF <- tacsatpGear[tacsatpGear$VE_REF %in% iVE_REF,]
if(nrow(tacsatpGearVEREF)>3){
cat(paste(iGr, " ", iVE_REF, "...\n"))
# avoid redoing if the outcome file already there for this vessel-gear combination
do_it <-TRUE
if(length(fls)!=0 && length(grep(paste("tacsatSweptArea_",iVE_REF, "_", iGr, ".RData", sep=""),fls)!=0) && overwrite==FALSE) do_it <- FALSE
#Interpolate according to the cubic-hermite spline interpolation
er <- FALSE
if(do_it) er <- try({
interpolationcHs <- interpolateTacsat(tacsatpGearVEREF,
interval= VMS_ping_rate_in_hour*60, # THE PING RATE IS COUNTRY-SPECIFIC ##
margin = round(VMS_ping_rate_in_hour*60*0.1), # i.e. will make disconnected interpolations if interval out of the 50 70min range
res = 100,
method = "cHs",
params = list(fm=fm[[iGr]],distscale=0,sigline=0,st=c(2,6)), # rmenber that st not in use....
headingAdjustment=0,
fast = FALSE)
# Get the ranges of the total picture
if(FALSE){
ranges <- do.call(rbind,lapply(interpolationcHs,function(x){return(apply(x[-1,],2,range))}))
xrange <- range(ranges[,"x"])
yrange <- range(ranges[,"y"])
plot(tacsatpGearVEREF$SI_LONG, tacsatpGearVEREF$SI_LATI,
xlim=xrange,ylim=yrange,pch=19,col="blue",xlab="Longitude",ylab="Latitude")
for(iInt in 1:length(interpolationcHs))
lines(interpolationcHs[[iInt]][-1,1],interpolationcHs[[iInt]][-1,2])
}
# Convert the interpolation to tacsat style data
medx <- median(tacsatpGearVEREF$SI_LONG,na.rm=T); medy <- median(tacsatpGearVEREF$SI_LATI,na.rm=T)
npoints <- ceiling(median(tacsatpGearVEREF$SI_SP,na.rm=T) * 1.852 * VMS_ping_rate_in_hour /
mean( vmstools::distance(medx,medy,medx+1/60,medy), vmstools::distance(medx,medy,medx,medy+1/60))) + 1
npoints <- npoints*2 # double the accuracy (here for Danish fleet it is 12 points)
tacsatIntGearVEREF <- interpolation2Tacsat(interpolationcHs, tacsatpGearVEREF,npoints=ifelse(npoints<2,2,npoints))
tacsatIntGearVEREF <- tacsatIntGearVEREF[!duplicated(apply(tacsatIntGearVEREF[,c("SI_LONG","SI_LATI","SI_DATIM")],1,paste,collapse="_")),]
save(tacsatIntGearVEREF, file=file.path(outPath,a_year,"interpolated",
paste("tacsatSweptArea_",iVE_REF, "_", iGr, ".RData", sep="")),compress=T)
cat(paste(iGr, " ", iVE_REF, "...done\n"))
}, silent=TRUE)
if(class(er)=="try-error") print('error for this interpolation')
}
}
}
# per gear per vessel
for(iGr in seineGears){ # no interpolation
tacsatpGear <- tacsatp[!is.na(tacsatp$LE_GEAR) & tacsatp$LE_GEAR==iGr,]
for(iVE_REF in sort(unique(tacsatpGear$VE_REF))){
cat(paste(iGr, " ", iVE_REF, "\n"))
tacsatpGearVEREF <- tacsatpGear[tacsatpGear$VE_REF %in% iVE_REF,]
tacsatpGearVEREF <- tacsatpGearVEREF[tacsatpGearVEREF$SI_STATE=='f',] # keep fishing pings only
tacsatIntGearVEREF <- tacsatpGearVEREF
save(tacsatIntGearVEREF, file=file.path(outPath,a_year,"interpolated",
paste("tacsatSweptArea_",iVE_REF, "_", iGr, ".RData", sep="")))
}
}
#} # end TRUE/FALSE
cat(paste("All interpolations done", "\n"))
#-----------------------------------------------------------------------------
# compute (discrete point) effort_days and effort_KWdays
#-----------------------------------------------------------------------------
if(FALSE){
library(doBy)
tacsatp <- orderBy(~VE_REF+SI_DATIM+FT_REF,data=tacsatp)
tacsatp$effort_days <- as.numeric(as.character(difftime(c(tacsatp$SI_DATIM[-1],0),tacsatp$SI_DATIM,units="days")))
tacsatp$effort_KWdays <- tacsatp$effort_days * as.numeric(as.character(tacsatp$VE_KW))
tacsatp$effort_days[tacsatp$effort_days>0.014] <- 0 # correct (i.e. set at 0 if >3hours as a sign for a change of trip)
}
#-----------------------------------------------------------------------------
# Add "gear width-vessel size" relationships table of parameters.
#-----------------------------------------------------------------------------
#gear_param_per_metier <- read.table(file=file.path(dataPath, "estimates_for_gear_param_per_metier.txt"))
# an equivalent is:
gear_param_per_metier <- data.frame(
a_metier=c('OT_CRU','OT_CRU','OT_DMF','OT_DMF','OT_MIX','OT_MIX','OT_MIX_ARA','OT_MIX_ARA','OT_MIX_DMF_BEN','OT_MIX_DMF_BEN','OT_MIX_DMF_PEL','OT_MIX_DMF_PEL','OT_MIX_DPS','OT_MIX_DPS','OT_MIX_NEP','OT_MIX_NEP','OT_MIX_TGS_CTC','OT_MIX_TGS_CTC','OT_MIX_TGS_OCC','OT_MIX_TGS_OCC','OT_SPF','OT_SPF','TBB_CRU','TBB_CRU','TBB_DMF','TBB_DMF','TBB_MOL','TBB_MOL','DRB_MOL','DRB_MOL','SDN_DEM','SDN_DEM','SSC_DEM','SSC_DEM'),
param=c('a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b'),
Estimate=c(5.10393560454806,0.468985756915913,9.6053549509854,0.433672763959314,10.6607888271164,0.292055014993337,37.5271604597435,0.149004797319136,3.21410379943408,77.981158829069,6.63707197355847,0.770594580782091,26.6738247840508,0.210221545999405,3.92727763464472,35.8253721834011,6.23686411376723,0.767375050454527,0.0192465419797634,119.140335982507,0.965238378524667,68.3889717127507,1.48117115311386,0.457788539321641,0.660086393453441,0.507845311175148,0.953001905566232,0.709356826689359,0.314245137194503,1.24544036138755,1948.83466676682,0.236271746198865,4461.27004311913,0.117589220782479),
Std..Error=c(1.81527145191998,0.0597519960969362,3.98228885098937,0.067572002767068,6.69386377505425,0.104413257104915,10.6717875588847,0.044963446750424,1.67854244656697,40.9297885227685,2.69086696344053,0.126123213329976,5.37466576335144,0.030829495804396,0.928442484509969,21.0228522096513,1.46159830273852,0.0732116002636393,0.000552819642352548,0.510207569180525,0.205245990518183,7.45180177818494,0.278399892100703,0.0346555048025894,0.172902115850281,0.0388684340513048,0.315715856194751,0.138412196798781,0.110027479611801,0.10614681568516,637.25152416296,0.0636712369543136,1665.50234108383,0.118756519107319),
t.value=c(2.81166521907769,7.84887179593252,2.41201864314765,6.41793562718951,1.59262112068153,2.79710664230959,3.51648308708138,3.31390958851994,1.91481830322951,1.90524216331315,2.46651806415295,6.10985527910701,4.96288066244663,6.81884476260001,4.22996329893018,1.70411568450042,4.26715336360309,10.4816046595234,34.8152281598731,233.513462322532,4.70283670871103,9.17750817164227,5.32030074414718,13.2096918492278,3.81768835047121,13.0657517744299,3.01854305657162,5.12495894939517,2.8560604887363,11.733186279291,3.05818753329251,3.71080816866175,2.67863330664306,0.990170658978435),
Pr...t..=c(0.00613312535554725,1.21619365805854e-11,0.021410083292817,2.48114253493853e-07,0.114790848188445,0.00631861326022122,0.000513087659147687,0.0010462790834138,0.0692370736030276,0.0705334706657513,0.0147045751318625,7.39218704723967e-09,1.2637878625965e-05,2.97113026239585e-08,0.000166717383514359,0.097483711710908,0.000314181622785133,5.0948672020349e-10,9.05842416252619e-12,5.10054218622276e-20,0.000204968683311441,5.36482029322678e-08,0.00313939649832079,4.44157761915604e-05,0.000458495488420268,5.11509704563588e-16,0.00678642704689924,5.16047183433098e-05,0.0075895814688592,6.18091407283774e-13,0.00391206507124518,0.000614325243514857,0.0438919330122769,0.367557330382699),
equ=c('DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=(a*LOA)+b','DoS=(a*LOA)+b','DoS=a*(LOA^b)','DoS=a*(LOA^b)','DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=(a*LOA)+b','DoS=(a*LOA)+b','DoS=a*(LOA^b)','DoS=a*(LOA^b)','DoS=(a*kW)+b','DoS=(a*kW)+b','DoS=(a*LOA)+b','DoS=(a*LOA)+b','beamw=a*(kW^b)','beamw=a*(kW^b)','beamw=a*(kW^b)','beamw=a*(kW^b)','beamw=a*(LOA^b)','beamw=a*(LOA^b)','dredgew=a*(LOA^b)','dredgew=a*(LOA^b)','seineropel=a*(kW^b)','seineropel=a*(kW^b)','seineropel=a*(LOA^b)','seineropel=a*(LOA^b)'),
nb_records=c(124,124,39,39,94,94,271,271,48,48,190,190,45,45,53,53,24,24,12,12,19,19,7,7,42,42,22,22,33,33,47,47,8,8)
)
#-----------------------------------------------------------------------------
# Create one swept area dataset
#-----------------------------------------------------------------------------
#for(a_year in c(2005:2019)) {
#print(a_year)
#outPath <- file.path("D:","FBA","BENTHIS_2020", "outputs2020")
#dataPath <- "D:/FBA/BENTHIS_2020/EflaloAndTacsat/"
#load(file=file.path(outPath,a_year,"tacsatActivity.RData"))
#gears2keep <- c("TBB","OTT","OTB","SSC","SDN","PTB","DRB")
#towedGears <- c("TBB","OTT","OTB","PTB","DRB")
#seineGears <- c("SSC","SDN")
#VMS_ping_rate_in_hour <- 1
## for computing fuel use
#table.fuelcons.per.engine <- read.table(file= file.path(dataPath, "IBM_datainput_engine_consumption.txt"), header=TRUE,sep="")
#linear.model <- lm(calc_cons_L_per_hr_max_rpm~ kW2, data=table.fuelcons.per.engine) # conso = a*Kw +b # to guess its fuel consumption at maximal speed
#fuel_per_h <- function (a,x) a*(x^3) # cubic law
load(file=file.path(outPath,a_year,"steaming_cons_per_VE_REF_FT_REF.RData")) # get steaming_cons_per_VE_REF_FT_REF
fls <- dir(file.path(outPath, a_year,"interpolated"))
fls <- fls[grep("tacsatSweptArea_", fls)]
lst <- list(); count <- 0
vid_with_errors <- NA
cols2keep <- c("SI_LATI","SI_LONG","SI_DATE","LE_GEAR","LE_MET","SWEPT_AREA_KM2","SWEPT_AREA_KM2_LOWER","SWEPT_AREA_KM2_UPPER", "GEAR_WIDTH", "SI_DATIM", "SI_FT", "LE_KG_LITRE_FUEL" )
for(iFile in fls){
cat(paste(iFile, "\n"))
count <- count+1
load(file.path(outPath,a_year,"interpolated",iFile))
nbpoints <- 12 # caution: chek this for your case.
a_vessel <- sapply(strsplit(gsub(".RData","",iFile), split="_"), function(x)x[2])
a_gear <- sapply(strsplit(gsub(".RData","",iFile), split="_"), function(x)x[3])
a_max_vessel_speed <- quantile(as.numeric(as.character(tacsatp[tacsatp$VE_REF==a_vessel, 'SI_SP'])), 0.95) # we assume the towing is done at maximal load
if(a_gear%in% c(towedGears,"SSC")){
#for towed gears and SSC: assume full load when dragging the trawl
# fuel use
max_consumed <- predict(linear.model, newdata=data.frame(kW2=as.numeric(as.character(tacsatp[tacsatp$VE_REF==a_vessel, 'VE_KW'][1]))))
a <- max_consumed/ (a_max_vessel_speed^3) # scaling factor
full_load_factor <- 0.9 # they fish at 90% full load
tacsatIntGearVEREF$LITRE_FUEL_FISHING <- (fuel_per_h(as.numeric(as.character(a)), as.numeric(as.character(a_max_vessel_speed))) * full_load_factor) /nbpoints
tacsatIntGearVEREF$VE_REF_FT_REF <- paste0(tacsatIntGearVEREF$VE_REF,"_",tacsatIntGearVEREF$FT_REF)
nb_fishing_pts_per_VE_REF_FT_REF <- table(tacsatIntGearVEREF$VE_REF_FT_REF) # for dispatching evenly on fishing pts
tacsatIntGearVEREF$FUEL_LITRE_STEAMING <- steaming_cons_per_VE_REF_FT_REF[tacsatIntGearVEREF$VE_REF_FT_REF] / table(tacsatIntGearVEREF$VE_REF_FT_REF)[tacsatIntGearVEREF$VE_REF_FT_REF]
tacsatIntGearVEREF$LE_KG_LITRE_FUEL <- tacsatIntGearVEREF$LITRE_FUEL_FISHING + tacsatIntGearVEREF$FUEL_LITRE_STEAMING
tacsatIntGearVEREF <- tacsatIntGearVEREF[, !colnames(tacsatIntGearVEREF) %in% c("VE_REF_FT_REF", "max_vessel_speed", "max_consumed", "a", "FUEL_LITRE_STEAMING")] # cleaning
}
if(a_gear%in% "SDN"){
#for seiners SDN gears: actual speed is enough and a good proxy
# fuel use
max_consumed <- predict(linear.model, newdata=data.frame(kW2=as.numeric(as.character(tacsatp[tacsatp$VE_REF==a_vessel, 'VE_KW'][1]))))
a <- max_consumed/ (a_max_vessel_speed^3) # scaling factor
tacsatIntGearVEREF$LITRE_FUEL_FISHING <- fuel_per_h(as.numeric(as.character(a)), (as.numeric(as.character(tacsatIntGearVEREF$SI_SP))))* VMS_ping_rate_in_hour*1.0
tacsatIntGearVEREF$VE_REF_FT_REF <- paste0(tacsatIntGearVEREF$VE_REF,"_",tacsatIntGearVEREF$FT_REF)
nb_fishing_pts_per_VE_REF_FT_REF <- table(tacsatIntGearVEREF$VE_REF_FT_REF) # for dispatching evenly on fishing pts
tacsatIntGearVEREF$FUEL_LITRE_STEAMING <- steaming_cons_per_VE_REF_FT_REF[tacsatIntGearVEREF$VE_REF_FT_REF] / table(tacsatIntGearVEREF$VE_REF_FT_REF)[tacsatIntGearVEREF$VE_REF_FT_REF]
tacsatIntGearVEREF$LE_KG_LITRE_FUEL <- tacsatIntGearVEREF$LITRE_FUEL_FISHING + tacsatIntGearVEREF$FUEL_LITRE_STEAMING
tacsatIntGearVEREF <- tacsatIntGearVEREF[, !colnames(tacsatIntGearVEREF) %in% c("VE_REF_FT_REF", "max_vessel_speed", "max_consumed", "a", "FUEL_LITRE_STEAMING")] # cleaning
}
#- Make selection for gears where you already have gear width and which not
ctry <- "XXX"
if(ctry=="NLD"){
# compute the swept area
tacsatIntGearVEREF <- compute_swept_area (tacsatIntGearVEREF, gear_param_per_metier, towedGears, seineGears, VMS_ping_rate_in_hour, already_informed_width_for=c('DRB', 'TBB'))
} else{
# compute the swept area
tacsatIntGearVEREF <- compute_swept_area (tacsatIntGearVEREF, gear_param_per_metier, towedGears, seineGears, VMS_ping_rate_in_hour, already_informed_width_for=NULL)
}
if(any(tacsatIntGearVEREF$SWEPT_AREA_KM2>100, na.rm = TRUE) ) {
print(paste('check for lat long at 0!! for ', iFile))
vid_with_errors <- c(vid_with_errors, iFile)
tacsatIntGearVEREF[!is.na(tacsatIntGearVEREF$SWEPT_AREA_KM2) & tacsatIntGearVEREF$SWEPT_AREA_KM2>100, c("SWEPT_AREA_KM2", "SWEPT_AREA_KM2_LOWER", "SWEPT_AREA_KM2_UPPER")] <- NA
}
if(nrow(tacsatIntGearVEREF[is.na(tacsatIntGearVEREF$SWEPT_AREA_KM2),])!=0 ) print('check for NAs')
# NAs are acceptable if the metier was not informed (e.g. No_Matrix6)...
print(unique(tacsatIntGearVEREF[is.na(tacsatIntGearVEREF$SWEPT_AREA_KM2),"LE_MET_init"]))
lst[[count]] <- tacsatIntGearVEREF[,cols2keep]
}
tacsatSweptArea <- do.call(rbind,lst)
# check NAs (approx. 2% of the records)
nrow(tacsatSweptArea[is.na(tacsatSweptArea$SWEPT_AREA_KM2),])
# save
save(tacsatSweptArea, file=file.path(outPath,a_year, paste("tacsatSweptArea.RData", sep="")),compress=T)
#} # end FALSE
#-----------------------------------------------------------------------------
# Create the aggregated swept area dataset
# (TO BE DELIVERED BY EACH PARTNER TO THE WP2 COORDINATOR)
#-----------------------------------------------------------------------------
if(FALSE){
library(vmstools)
years <- 2017:2019
#- Combine tacsatSweptArea files over years to obtain aggregated swept areas per grid cell (caution, not SAR yet)
for(iYr in years){
#- read data for this year
load(file.path(outPath, iYr, paste("tacsatSweptArea.RData", sep="")))
#- collate
if(iYr == years[1]) tacsatSweptAreaTot <- cbind(tacsatSweptArea,SI_YEAR=iYr)
if(iYr != years[1]) tacsatSweptAreaTot <- rbind(tacsatSweptAreaTot,cbind(tacsatSweptArea,SI_YEAR=iYr))
}
tacsatSweptArea <- tacsatSweptAreaTot; rm(tacsatSweptAreaTot)
#- add months and days
tacsatSweptArea$SI_DATE <- as.POSIXct(paste(tacsatSweptArea$SI_DATE, sep=" "), tz="GMT", format="%d/%m/%Y")
tacsatSweptArea$MONTH <- format(tacsatSweptArea$SI_DATE, "%m")
tacsatSweptArea$DAY <- format(tacsatSweptArea$SI_DATE, "%j")
#- Get outer ranges of latitude and longitude in dataset
xrange <- range(tacsatSweptArea$SI_LONG,na.rm=T)
yrange <- range(tacsatSweptArea$SI_LATI,na.rm=T)
xrange <- c(floor(xrange[1]),ceiling(xrange[2]))
yrange <- c(floor(yrange[1]),ceiling(yrange[2]))
print(xrange); print(yrange)
#- If xrange and yrange are inappropriate, set your own (rounded) xrange and yrange
if(TRUE){
#xrange[1] <- floor(xrange[1]); xrange[2] <- ceiling(xrange[2])
#yrange[1] <- floor(yrange[1]); yrange[2] <- ceiling(yrange[2])
xrange <- c(-7,25) # do not change to be able to make the overlay over countries
yrange <- c(50,65) # do not change to be able to make the overlay over countries
}
## Set grid
#resx <- 1/60 #1 minute
#resy <- 1/60 #1 minute
#grd <- createGrid(xrange,yrange,resx=1/60,resy=1/60,type="SpatialGrid",exactBorder=T)
## Grid all tacsatSweptArea data
# Convert all tacsat poins first to SpatialPoints
#coords <- SpatialPoints(cbind(SI_LONG=tacsatSweptArea$SI_LONG,SI_LATI=tacsatSweptArea$SI_LATI))
#idx <- over(coords,grd)
#tacsatSweptArea$grID <- idx
## Remove records that are not in the study area
#tacsatSweptArea <- subset(tacsatSweptArea,is.na(grID)==F)
#replaced by:
grid_degrees <- 0.01 # less than 1 minute
tacsatSweptArea$c_square <- vmstools::CSquare (lon=tacsatSweptArea[,"SI_LONG"], lat=tacsatSweptArea[,"SI_LATI"], degrees=grid_degrees)
# 1. Aggregate the results by metier and grid ID (here: c_square) (aggregate() can be slow: be patient)
aggTacsatSweptArea <- aggregate(tacsatSweptArea[,c("SWEPT_AREA_KM2",
"SWEPT_AREA_KM2_LOWER",
"SWEPT_AREA_KM2_UPPER")],
by=list(tacsatSweptArea$LE_MET,tacsatSweptArea$c_square,tacsatSweptArea$SI_YEAR),sum,na.rm=T)
colnames(aggTacsatSweptArea)[1:3] <- c("LE_MET","c_square", "Year")
# Add midpoint of gridcell to dataset
#aggResult <- cbind(aggTacsatSweptArea,CELL_LONG=coordinates(grd)[aggTacsatSweptArea$grID,1],
#CELL_LATI=coordinates(grd)[aggTacsatSweptArea$c_square,2])
aggResult <- cbind.data.frame(aggTacsatSweptArea, CSquare2LonLat(aggTacsatSweptArea$c_square, grid_degrees)) # get the mid point coordinates
save(aggResult,file=file.path(outPath,paste0("AggregatedSweptArea_",range(years)[1],"-",range(years)[2],".RData")))
# 2. Aggregate the results by metier and grid ID and BY MONTH(aggregate() can be slow: be patient)
aggTacsatSweptArea2 <- aggregate(tacsatSweptArea[,c("SWEPT_AREA_KM2",
"SWEPT_AREA_KM2_LOWER", "SWEPT_AREA_KM2_UPPER")],
by=list(tacsatSweptArea$LE_MET,tacsatSweptArea$MONTH,tacsatSweptArea$c_square,tacsatSweptArea$SI_YEAR),sum,na.rm=T)
colnames(aggTacsatSweptArea2)[1:4] <- c("LE_MET","MONTH", "c_square", "Year")
# Add midpoint of gridcell to dataset
#aggResult <- cbind(aggTacsatSweptArea2,CELL_LONG=coordinates(grd)[aggTacsatSweptArea2$c_square,1],
#CELL_LATI=coordinates(grd)[aggTacsatSweptArea2$c_square,2])
aggResult <- cbind.data.frame(aggTacsatSweptArea2, CSquare2LonLat(aggTacsatSweptArea2$c_square, grid_degrees)) # get the mid point coordinates
save(aggResult,file=file.path(outPath,paste0("AggregatedSweptAreaMonth_",range(years)[1],"-",range(years)[2],".RData")))
# 3. Aggregate the results by metier and grid ID and BY DAY(aggregate() can be slow: be patient)
aggTacsatSweptArea3 <- aggregate(tacsatSweptArea[,c("SWEPT_AREA_KM2",
"SWEPT_AREA_KM2_LOWER", "SWEPT_AREA_KM2_UPPER")],
by=list(tacsatSweptArea$LE_MET, tacsatSweptArea$DAY, tacsatSweptArea$c_square,tacsatSweptArea$SI_YEAR),sum,na.rm=T)
colnames(aggTacsatSweptArea3)[1:4] <- c("LE_MET","DAY", "c_square", "Year")
#- Add midpoint of gridcell to dataset
#aggResult <- cbind(aggTacsatSweptArea3, CELL_LONG=coordinates(grd)[aggTacsatSweptArea3$c_square,1],
#CELL_LATI=coordinates(grd)[aggTacsatSweptArea3$c_square,2])
aggResult <- cbind.data.frame(aggTacsatSweptArea3, CSquare2LonLat(aggTacsatSweptArea3$c_square, grid_degrees)) # get the mid point coordinates
save(aggResult,file=file.path(outPath,paste0("AggregatedSweptAreaDay_",range(years)[1],"-",range(years)[2],".RData")))
#-----------------------------------------------------------------------------
# Create the "missing effort" dataset
# (TO BE DELIVERED BY EACH PARTNER TO THE WP2 COORDINATOR)
#-----------------------------------------------------------------------------
#- Code to get the missing effort in percentages per ICES rectangle
# i.e. the total effort in eflalo compared to the total effort in eflalo from
# VMS-equipped vessels
library(vmstools)
# once you have your three years ready....
aggResult <- NULL
#- load eflalo
for(iYr in years){
#- read data for this year
load(file.path(outPath,iYr,"cleanEflalo.RData"))
load(file.path(outPath,iYr,"tacsatSweptArea.RData"))
#- Load the datasets
#data(europa)
#- Convert time stamps to posixct formats
eflalo$LE_CDATIM <- as.POSIXct(eflalo$LE_CDAT,format="%d/%m/%Y",tz="GMT")
#- Calculate the effort (INTVDAY) in eflalo
eflalo$INTV <- c(difftime(eflalo$FT_LDATIM,eflalo$FT_DDATIM,units="mins"))
eflalo$dummy <- 1
eflalo <- merge(eflalo,aggregate(eflalo$dummy,by=list(eflalo$FT_REF,eflalo$LE_CDATIM),FUN=sum,na.rm=T),by.x=c("FT_REF","LE_CDATIM"),by.y=c("Group.1","Group.2"),all.x=T)
colnames(eflalo)[length(colnames(eflalo))] <- "NR_FT_REF"
eflalo$INTVDAY <- eflalo$INTV / eflalo$NR_FT_REF
# - we need to retrieve the BENTHIS metiers if not already informed in eflalo.
# this following object is in the workflow line 347, so partners should have it.
# if not, then they should apply the same procedure you did to assign BENTHIS metier
ctry <- "DEN"
if(ctry=="DEN"){
load(file=file.path(outPath,iYr,"initVersusBenthisMetiers.RData"))
eflalo$LE_MET_BENTHIS <- initVersusBenthisMetiers[match(eflalo$LE_MET, initVersusBenthisMetiers$LE_MET_init),'LE_MET']
}
# note that the metiers at NAs correspond to all the metiers which are not BENTHIS e.g., gillnets, pots, etc.
# - partner might adapt here if needed (the goal is to subset eflalo for the VMS-equipped vessels only)
fls <- dir(file.path(outPath,iYr,"interpolated"))
vms_equipped <- unique(sapply(fls, function (x) unlist(strsplit(x, "_"))[2]))
eflalo_vms <- eflalo[eflalo$VE_REF %in% vms_equipped,]
# - do the aggregation
aggResult_vms<- aggregate(eflalo_vms$INTVDAY, list(eflalo_vms$LE_RECT, eflalo_vms$LE_MET_BENTHIS), sum, na.rm=TRUE)
colnames(aggResult_vms) <- c('LE_RECT', 'LE_MET', 'INTVDAY')
aggResult_tot<- aggregate(eflalo$INTVDAY, list(eflalo$LE_RECT, eflalo$LE_MET_BENTHIS), sum, na.rm=TRUE)