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GenerateMetiersVariousFiles.R
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GenerateMetiersVariousFiles.R
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##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!VARIOUS METIERS FILES!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!met_target_names.dat!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!metier_fspeed.dat!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!metierspe_mls_cat_semesterXX.dat!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!percent_revenue_completenesses.dat!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!metier_gear_widths_param_a.dat!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!metier_gear_widths_param_b.dat!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!metier_gear_widths_model_type.dat!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!20loss_after_one_passage_per_landscape_per_func_group.dat!!!##
##!!!!!!!!!!!!!!!metiersspe_discardratio_limits_semester.dat
##!!!!!!!!!!!!!!!metiersspe_avoided_stocks_semester.dat
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
# some args for the bunch of vessels to be created....
# Usage:
# GenerateMetiersVariousFiles.R application gis_path input_application_path
# GENERAL SETTINGS
args <- commandArgs(trailingOnly = TRUE)
general <- list()
if (length(args) < 2) {
if(.Platform$OS.type == "windows") {
general$application <- "testexample" # ...or myfish
general$main_path_gis <- file.path("C:","Users","fbas","Documents","GitHub","DISPLACE_input_gis", general$application)
general$main.path.ibm <- file.path("C:","Users","fbas","Documents","GitHub",paste("DISPLACE_input_", general$application, sep=''))
general$igraph <- 56 # caution: should be consistent with existing vessels already built upon a given graph
do_plot <- TRUE
}
} else {
general$application <- args[1]
general$main_path_gis <- args[2]
general$main.path.ibm <- args[3]
general$igraph <- args[4] # caution: should be consistent with existing vessels already built upon a given graph
do_plot <- FALSE
}
cat(paste("START \n"))
dir.create(file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep='')))
a_size_group_bin_in_cm <- 5 # caution: hardcoding....
mid <- a_size_group_bin_in_cm/2
cat(paste("caution: harcoded bin size \n"))
spp_table <- read.table(file=file.path(general$main_path_gis, "POPULATIONS",
paste("pop_names_",general$application,".txt",sep='')), header=TRUE)
spp <- as.character(spp_table$spp)
options(scipen=999)
# reuse the exported metier names in GenerateVesselConfigFiles.R
metier_names <- read.table( file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''), "metier_names.dat"), header=TRUE)
#########################################
## metier_target_name ###################
#########################################
write(c("LE_MET_level6", "mapped_stk_code"),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"met_target_names.dat"), ncolumns=2,
sep= ' ', append=FALSE)
cat(paste("Write met_target_names.dat \n"))
# by default, create a fake selectivity ogive i.e. all at 1 (and not species-specific...)
for (met in unique(metier_names$idx) ) {
met_target_names <- NULL
the_met <- as.character(metier_names[metier_names[, 'idx']==met, "name"])
demersal_gear <- FALSE ; pelagic_gear <- FALSE ; demersal_gear_cru <- FALSE ; passive_gear <- FALSE ; dredge_gear <- FALSE
if (length (grep("DEF", the_met))!=0 || length (grep("MCD", the_met))!=0 || length (grep("CRU", the_met))!=0) {demersal_gear <- TRUE; pelagic_gear <- FALSE ;demersal_gear_cru <- FALSE ; passive_gear <- FALSE; dredge_gear <- FALSE}
if (length (grep("MCD", the_met))!=0 || length (grep("CRU", the_met))!=0) {demersal_gear <- FALSE; pelagic_gear <- FALSE; demersal_gear_cru <- TRUE ; passive_gear <- FALSE; dredge_gear <- FALSE}
if (length (grep("SPF", the_met))!=0 ) {demersal_gear <- FALSE; pelagic_gear <- TRUE; demersal_gear_cru <- FALSE ; passive_gear <- FALSE; dredge_gear <- FALSE}
if (length (grep("LLD", the_met))!=0 || length (grep("GNS", the_met))!=0 || length (grep("other", the_met))!=0) {demersal_gear <- FALSE; pelagic_gear <- FALSE; demersal_gear_cru <- TRUE ; passive_gear <- FALSE; dredge_gear <- FALSE}
if (length (grep("MOL", the_met))!=0) {demersal_gear <- FALSE; pelagic_gear <- FALSE; demersal_gear_cru <- FALSE ; passive_gear <- FALSE; dredge_gear <- TRUE}
all_species <- sapply(spp, function (spp) substr(spp,1,3))
count <- -1
for (sp in all_species ) {
count <- count+1 # idx sp
demersal_sp <- FALSE ; pelagic_sp <- FALSE ; crustacean_sp <-FALSE
if(sp %in% c('COD', 'FLE', 'PLE', 'SOL', 'WHG', 'DAB', 'TUR') ) {pelagic_sp<- FALSE; demersal_sp <- TRUE; crustacean_sp <-FALSE; molluscs_sp <- FALSE}
if(sp %in% c("PRA", "NEP") ) {pelagic_sp<- FALSE; demersal_sp <- FALSE; crustacean_sp <-TRUE; molluscs_sp<-TRUE}
if(sp %in% c('HER', 'SPR', 'NOP', 'MAC')) {pelagic_sp<- TRUE; demersal_sp <- FALSE; crustacean_sp <-FALSE; molluscs_sp <- FALSE}
if(sp %in% c('MUS', 'OYE')) {pelagic_sp<- FALSE; demersal_sp <- FALSE; crustacean_sp <-FALSE; molluscs_sp <- TRUE}
if(
demersal_gear && demersal_sp ||
demersal_gear_cru && crustacean_sp ||
dredge_gear && molluscs_sp ||
passive_gear && demersal_sp ||
pelagic_gear && pelagic_sp
){
write(c(met, count),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"met_target_names.dat"), ncolumns=2,
sep= ' ', append=TRUE)
}
}
}
cat(paste("Write met_target_names.dat....done \n"))
#########################################
## metier_suitable_seabottomtypes ###################
#########################################
write(c("LE_MET_level6", "mapped_stk_code"),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"metier_suitable_seabottomtypes.dat"), ncolumns=2,
sep= ' ', append=FALSE)
cat(paste("Write met_target_names.dat \n"))
landscape_types <- unique(read.table(file=file.path(general$main_path_gis, "GRAPH",
paste("coord",general$igraph,"_with_landscape.dat",sep='')), header=FALSE)[,1])
bottomtype <- substr(landscape_types, 1,1) # here we use the BALANCE project coding
bottom_levels <- factor(bottomtype)
levels(bottom_levels) <- c('Mud', 'Bedrock', 'Hard Bottom', 'Sand', 'Hard Clay', 'Mud')
# by default, create a fake selectivity ogive i.e. all at 1 (and not species-specific...)
for (met in unique(metier_names$idx) ) {
met_target_names <- NULL
the_met <- as.character(metier_names[metier_names[, 'idx']==met, "name"])
demersal_gear <- FALSE ; pelagic_gear <- FALSE ; demersal_gear_cru <- FALSE ; passive_gear <- FALSE ; dredge_gear <- FALSE
if (length (grep("DEF", the_met))!=0 || length (grep("MCD", the_met))!=0 || length (grep("CRU", the_met))!=0) {demersal_gear <- TRUE; pelagic_gear <- FALSE ;demersal_gear_cru <- FALSE ; passive_gear <- FALSE; dredge_gear <- FALSE}
if (length (grep("MCD", the_met))!=0 || length (grep("CRU", the_met))!=0) {demersal_gear <- FALSE; pelagic_gear <- FALSE; demersal_gear_cru <- TRUE ; passive_gear <- FALSE; dredge_gear <- FALSE}
if (length (grep("SPF", the_met))!=0 ) {demersal_gear <- FALSE; pelagic_gear <- TRUE; demersal_gear_cru <- FALSE ; passive_gear <- FALSE; dredge_gear <- FALSE}
if (length (grep("LLD", the_met))!=0 || length (grep("GNS", the_met))!=0 || length (grep("other", the_met))!=0) {demersal_gear <- FALSE; pelagic_gear <- FALSE; demersal_gear_cru <- TRUE ; passive_gear <- FALSE; dredge_gear <- FALSE}
if (length (grep("MOL", the_met))!=0) {demersal_gear <- FALSE; pelagic_gear <- FALSE; demersal_gear_cru <- FALSE ; passive_gear <- FALSE; dredge_gear <- TRUE}
for (a_landscape in landscape_types ) {
hard <- FALSE ; soft <- FALSE ; mud <-FALSE
if(a_landscape %in% landscape_types[bottom_levels %in% c("Hard Bottom", "Hard")] ) {hard <- TRUE ; soft <- FALSE ; mud <-FALSE}
if(a_landscape %in% landscape_types[bottom_levels %in% c("Sand", "Clay")] ) {hard <- FALSE ; soft <- TRUE ; mud <-FALSE}
if(a_landscape %in% landscape_types[bottom_levels %in% c('0', 'Sand', 'Clay', 'Mud')]) {hard <- FALSE ; soft <- FALSE ; mud <-TRUE}
if(
demersal_gear && soft ||
demersal_gear_cru && mud ||
dredge_gear && mud ||
passive_gear && hard ||
pelagic_gear && hard ||
pelagic_gear && soft ||
pelagic_gear && mud
){
write(c(met, a_landscape),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"metier_suitable_seabottomtypes.dat"), ncolumns=2,
sep= ' ', append=TRUE)
}
}
}
cat(paste("Write metier_suitable_seabottomtypes.dat....done \n"))
#########################################
## metier_fspeed ########################
#########################################
write(c("LE_MET_level6", "fspeed"),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"metier_fspeed.dat"), ncolumns=2,
sep= ' ', append=FALSE)
# by default, create a fake selectivity ogive i.e. all at 1 (and not species-specific...)
for (met in unique(metier_names$idx) ) {
fspeed <- 4 # knots
write(c(met, fspeed),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"metier_fspeed.dat"), ncolumns=2,
sep= ' ', append=TRUE)
}
cat(paste("Write metier_fspeed.dat....done \n"))
#########################################
## metierspe_mls_cat_semesterXX #########
#########################################
# pop parameters
pa <- read.csv(file=file.path(general$main_path_gis, "POPULATIONS",
paste("Stock_biological_traits.csv", sep='')),
sep=';', header=TRUE)
rownames(pa) <- pa$stock
write(c("LE_MET_level6", "mls"),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"metierspe_mls_cat_semester1.dat"), ncolumns=2,
sep= ' ', append=FALSE)
write(c("LE_MET_level6", "mls"),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"metierspe_mls_cat_semester2.dat"), ncolumns=2,
sep= ' ', append=FALSE)
for (met in unique(metier_names$idx) ) {
for (sp in spp) {
sz <- pa[sp, "mls_cat"]
write(c(met, sz),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"metierspe_mls_cat_semester1.dat"), ncolumns=2,
sep= ' ', append=TRUE)
write(c(met, sz),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"metierspe_mls_cat_semester2.dat"), ncolumns=2,
sep= ' ', append=TRUE)
}
}
cat(paste("Write metierspe_mls_cat_semesterXX.dat....done \n"))
#########################################
## percent_revenue_completenesses########
#########################################
write(c("LE_MET_level6", "completeness"),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"percent_revenue_completenesses.dat"), ncolumns=2,
sep= ' ', append=FALSE)
for (met in unique(metier_names$idx) ) {
percent_revenue_completenesses <- 100 # % revenue of this metier from described spp
write(c(met, percent_revenue_completenesses),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"percent_revenue_completenesses.dat"), ncolumns=2,
sep= ' ', append=TRUE)
}
cat(paste("Write percent_revenue_completenesses.dat....done \n"))
#########################################
## metier_gear_widths_model_type ########
## metier_gear_widths_param_a #########
## metier_gear_widths_param_b ##########
#########################################
write(c("LE_MET_level6", "model_type"),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"metier_gear_widths_model_type.dat"), ncolumns=2,
sep= ' ', append=FALSE)
write(c("LE_MET_level6", "param_a"),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"metier_gear_widths_param_a.dat"), ncolumns=2,
sep= ' ', append=FALSE)
write(c("LE_MET_level6", "param_b"),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"metier_gear_widths_param_b.dat"), ncolumns=2,
sep= ' ', append=FALSE)
for (met in unique(metier_names$idx) ) {
met_target_names <- NULL
the_met <- as.character(metier_names[metier_names[, 'idx']==met, "name"])
trawl_gear <- TRUE ; net_gear <- FALSE ; seine_gear <- TRUE # default
if (length (grep("OTB", the_met))!=0 || length (grep("OTT", the_met))!=0 || length (grep("PTM", the_met))!=0) {trawl_gear <- TRUE; net_gear <- FALSE; seine_gear <- FALSE}
if (length (grep("GNS", the_met))!=0) {trawl_gear <- FALSE; net_gear <- TRUE; seine_gear <- FALSE}
if (length (grep("SDN", the_met))!=0) {trawl_gear <- FALSE; net_gear <- FALSE; seine_gear <- TRUE}
model_type <- "DoS=a*(kW^b)" ; param_a <- 9.6053549509854; param_b <- 0.433672763959314 # default
if(trawl_gear ){ model_type <- "a*(kW^b)" ; param_a <- 9.6053549509854; param_b <- 0.433672763959314}
if(net_gear ){ model_type <- "if_LOA<20_a_else_b" ; param_a <- 0.001; param_b <- 0.5}
if(seine_gear ){ model_type <- "a*(LOA^b)" ; param_a <- 4461.27004311913; param_b <- 0.117589220782479}
write(c(met, model_type),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"metier_gear_widths_model_type.dat"), ncolumns=2,
sep= ' ', append=TRUE)
write(c(met, param_a),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"metier_gear_widths_param_a.dat"), ncolumns=2,
sep= ' ', append=TRUE)
write(c(met, param_b),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"metier_gear_widths_param_b.dat"), ncolumns=2,
sep= ' ', append=TRUE)
}
cat(paste("Write metier_gear_widths_model_type.dat....done \n"))
cat(paste("Write metier_gear_widths_param_a.dat....done \n"))
cat(paste("Write metier_gear_widths_param_b.dat....done \n"))
#########################################
## combined_met_types.dat ###############
#########################################
write(c("LE_MET_level6", "model_type"),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"combined_met_types.dat"), ncolumns=2,
sep= ' ', append=FALSE)
for (met in unique(metier_names$idx) ) {
met_target_names <- NULL
the_met <- as.character(metier_names[metier_names[, 'idx']==met, "name"])
trawl_gear <- TRUE ; net_gear <- FALSE ; seine_gear <- TRUE # default
if (length (grep("OTB", the_met))!=0 || length (grep("OTT", the_met))!=0 || length (grep("PTM", the_met))!=0) {trawl_gear <- TRUE; net_gear <- FALSE; seine_gear <- FALSE}
if (length (grep("GNS", the_met))!=0) {trawl_gear <- FALSE; net_gear <- TRUE; seine_gear <- FALSE}
if (length (grep("SDN", the_met))!=0) {trawl_gear <- FALSE; net_gear <- FALSE; seine_gear <- TRUE}
combined_met_types <- 1 # default
if(trawl_gear ){ combined_met_types <- 1}
if(net_gear ){ combined_met_types <- 0}
if(seine_gear ){ combined_met_types <- 0}
write(c(met, combined_met_types),
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
"combined_met_types.dat"), ncolumns=2,
sep= ' ', append=TRUE)
cat(paste("Write combined_met_types.dat....done \n"))
}
#########################################
## metiersspe_discardratio_limits_semester.dat ###############
#########################################
for (a.semester in 1:2){
#-----------
#-----------
## METIER SPE----------
# export betas specific to the metier given this pop
# mean estimates
nb_met <- (nrow(metier_names))
nb_stk <- length(spp)
# Note that we assume exp(vesseleffect+metiereffect+stockdensityeffect*sel) in the catch equation
# so you will have to put -20 if this actually metier not catching this stock at all....for now we put 0
metiersspe_discardratio_limits_semester <- data.frame(rep(metier_names[,1], each=nb_stk), rep(2, each=nb_met*nb_stk))
# disc/land = 2 by default, which is high but then not that limiting
colnames(metiersspe_discardratio_limits_semester) <- c('LE_MET_level6', 'discardratio_limits')
if(length(unique(metiersspe_discardratio_limits_semester[,'LE_MET_level6'])) != nb_met) stop("missing metier(s) for info on metier effects")
# reorder:
library(doBy)
metiersspe_discardratio_limits_semester <- orderBy(~LE_MET_level6, data=metiersspe_discardratio_limits_semester)
# save .dat files
write.table(metiersspe_discardratio_limits_semester,
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
paste("metierspe_discardratio_limits_semester", a.semester,".dat",sep='')),
col.names=TRUE, row.names=FALSE, quote=FALSE, append=FALSE, sep = " ")
} # end a.semester
#########################################
## metiersspe_avoided_stocks_semester.dat ###############
#########################################
for (a.semester in 1:2){
#-----------
#-----------
## METIER SPE----------
# export betas specific to the metier given this pop
# mean estimates
nb_met <- (nrow(metier_names))
nb_stk <- length(spp)
# CODED 0/1
# Note that this info will be used in predicting where to fish and if should move away (possibly in the dtrees)
# espacially relevant in a EU LO context
metiersspe_avoided_stocks_semester <- data.frame(rep(metier_names[,1], each=nb_stk), rep(1, each=nb_met*nb_stk)) # 1s by default
# disc/land = 2 by default, which is high but then not that limiting
colnames(metiersspe_avoided_stocks_semester) <- c('LE_MET_level6', 'is_avoided_stocks')
if(length(unique(metiersspe_avoided_stocks_semester[,'LE_MET_level6'])) != nb_met) stop("missing metier(s) for info on metier effects")
# reorder:
library(doBy)
metiersspe_avoided_stocks_semester <- orderBy(~LE_MET_level6, data=metiersspe_avoided_stocks_semester)
# save .dat files
write.table(metiersspe_avoided_stocks_semester,
file=file.path(general$main.path.ibm, paste("metiersspe_", general$application, sep=''),
paste("metiersspe_is_avoided_stocks_semester", a.semester,".dat",sep='')),
col.names=TRUE, row.names=FALSE, quote=FALSE, append=FALSE, sep = " ")
} # end a.semester
cat(paste(".......done \n"))