R/removefromcap_catch.R
In amandafaig/catchfunction: BSAI catch function
Defines functions
removefromcap_catch
removefromcap_catch <- function(ABC.DATA,scenario,spptomult,improvedcatchscale) {
FISH.DATA <- ABC.DATA
FISH.DATA$ABCboth <- FISH.DATA$ABC.BSAI.202 + FISH.DATA$ABC.BS.201
FISH.DATA$ABCboth.UB.150 <- as.numeric(FISH.DATA$ABC.BS.201 + FISH.DATA$ABC.BSAI.202 >= 1.5e6)
FISH.DATA$pollock.bs.UB <- as.numeric(FISH.DATA$ABC.BS.201 > 1.2e6)
FISH.DATA$flex <- 1 # introduction of flatfish flex
FISH.DATA$A80 <- 1 # introduction of A80
FISH.DATA$po10 <- 1
FISH.DATA$pre97 <- 0
FISH.DATA$is93 <- 0
FISH.DATA$WAISSLadj <- 1
FISH.DATA$solegone <- 1
FISH.DATA$plaicegone <- 1
FISH.DATA$kamsplit <- 1
FISH.DATA$AFA <- 1
FISH.DATA$pollockAIchange <- 1
FISH.DATA$A28 <- 1
FISH.DATA$atkadisp <- 0
FISH.DATA$SSL <- 1 # stellar sea lion closure
FISH.DATA$WAISSL <- 0
FISH.DATA$A80.ask.POP <- 1
FISH.DATA$is93 <- 0
FISH.DATA$A82 <- 1
# I start by giving myself a list of all the species numbers
allspp <- c("141",
"204",
"103",
"102",
"147",
"303",
"60",
"100",
"310",
"202",
"106",
"301",
"201",
"104",
"307",
"203",
"400",
"65",
"326",
"90",
"50",
"140")
# And their corresponding names, alphabetically.
# Honestly this could be saved in sysdata and loaded but it's not bad to have
# handy in case if you forget what number = what species
sppnames <- c("Arrowtooth",
"Atka",
"Flathead",
"Greenland",
"Kamchatka",
"Northern",
"Octopus",
"OtherFlat",
"OtherRock",
"PCod",
"Plaice",
"POP",
"Pollock",
"Rock",
"Rougheye",
"Sablefish",
"Sculpin",
"Shark",
"Shortraker",
"Skate",
"Squid",
"Yellowfin")
# calculate TAC.
# rule 1: if pollock has been removed from 2mmt cap, assume everything else is TAC = ABC
if (is.element("pollock",spptomult)) {
FISH.DATA$TAC.BSAI.201 <- 0
TAC.BOTHBIND <- FISH.DATA
TAC.BOTHBIND.FLATSUR <- FISH.DATA
# check sum < 2mmt
check2mmt <- function(DT_orig) {
DT <- DT_orig #%>% select(starts_with("TAC"))
NETTAC <- rowSums(DT, na.rm = TRUE)
if (NETTAC > 2e6) {
# first bring down the least valuable/zero value bycatch species
# These are first because these species really are almost nuisance
# catches.
TAC.BOTHBIND <- predict.tac.function(predictmethod = 1, model="SUR",fit=tac_BOTHBIND_loglin_sur,FISH.DATA)
DT_orig$TAC.BSAI.60 <- TAC.BOTHBIND$TAC.BSAI.60 # octopus
DT_orig$TAC.BSAI.65 <- TAC.BOTHBIND$TAC.BSAI.65 # sharks
DT_orig$TAC.BSAI.90 <- TAC.BOTHBIND$TAC.BSAI.90 #skates
DT_orig$TAC.BSAI.400 <- TAC.BOTHBIND$TAC.BSAI.400 #skates
}
#check again
DT <- DT_orig #%>% select(starts_with("TAC"))
NETTAC <- rowSums(DT, na.rm = TRUE)
if (NETTAC > 2e6) {
# if still over 2mmt, next bring down species for various reasons (see below)
DT_orig$TAC.BSAI.50 <- TAC.BOTHBIND$TAC.BSAI.50 # squid -- pollock bycatch. Valuable (because pollock) but still pure bycatch
DT_orig$TAC.BSAI.141 <- TAC.BOTHBIND$TAC.BSAI.141 # arrowtooth -- halibut will continue to be limiting
DT_orig$TAC.BSAI.104 <- TAC.BOTHBIND$TAC.BSAI.104 # rock sole -- worry about market limits
}
#check again
DT <- DT_orig #%>% select(starts_with("TAC"))
NETTAC <- rowSums(DT, na.rm = TRUE)
if (NETTAC > 2e6) {
# if still over 2mmt, next bring down basically everything that doesnt
# tend to go up with pollock or isn't directly listed as a targeted species
#
#
# non-AFA trawl cp targets: yfin, rock sole, flathead sole,atka, pop
# while cod isn't technically a target, its effectively one in terms of its value (as a bycatch)
# species that go up w. pollock going down: arrowtooth, flathead sole, rock sole, yellowfin
DT_orig$TAC.BSAI.326 <- TAC.BOTHBIND$TAC.BSAI.326 # shortraker
DT_orig$TAC.BSAI.307 <- TAC.BOTHBIND$TAC.BSAI.307 # rougheye
DT_orig$TAC.BS.310 <- TAC.BOTHBIND$TAC.BS.310 # orock BS
DT_orig$TAC.AI.310 <- TAC.BOTHBIND$TAC.AI.310 # orock AI
DT_orig$TAC.BSAI.303 <- TAC.BOTHBIND$TAC.BSAI.303 # northern
DT_orig$TAC.BSAI.147 <- TAC.BOTHBIND$TAC.BSAI.147 # kamchatka
DT_orig$TAC.BSAI.100 <- TAC.BOTHBIND$TAC.BSAI.307 # oflat
DT_orig$TAC.BS.102 <- TAC.BOTHBIND$TAC.BS.102 # greenland BS
DT_orig$TAC.AI.102 <- TAC.BOTHBIND$TAC.AI.102 # greenland AI
DT_orig$TAC.BSAI.106 <- TAC.BOTHBIND$TAC.AI.106 # plaice
}
#honestly at this point if we still have nettac > 2mmt something very weird is going on...
DT <- DT_orig # %>% select(starts_with("TAC"))
NETTAC <- rowSums(DT, na.rm = TRUE)
if (NETTAC > 2e6) {stop('NETTAC still over 2e6 even though pollock removed. Line 82 removefromcap_catch.R')}
}
} else {
TAC.BOTHBIND <- predict.tac.function(predictmethod = 1, model="SUR",fit=tac_BOTHBIND_loglin_sur,FISH.DATA)
TAC.BOTHBIND.FLATSUR <- predict.tac.function(predictmethod = 1, model="FLATSUR",fit=tac_BOTHBIND_FLATSUR_loglin_sur,FISH.DATA)
for (i in 1:length(spptomult)) {
eval(parse(text = paste("TAC.BOTHBIND$TAC.BSAI.",allspp[match(spptomult[i],sppnames)],"<- 0",sep="")))
eval(parse(text = paste("TAC.BOTHBIND$TAC.BS.",allspp[match(spptomult[i],sppnames)],"<- 0",sep="")))
eval(parse(text = paste("TAC.BOTHBIND$TAC.AI.",allspp[match(spptomult[i],sppnames)],"<- 0",sep="")))
}
for (i in 1:length(spptomult)) {
eval(parse(text = paste("TAC.BOTHBIND.FLATSUR$TAC.BSAI.",allspp[match(spptomult[i],sppnames)],"<- 0",sep="")))
eval(parse(text = paste("TAC.BOTHBIND.FLATSUR$TAC.BS.",allspp[match(spptomult[i],sppnames)],"<- 0",sep="")))
eval(parse(text = paste("TAC.BOTHBIND.FLATSUR$TAC.AI.",allspp[match(spptomult[i],sppnames)],"<- 0",sep="")))
}
# Reallocate the remaining TAC.
#
distributefun <- function(goal,abcdt,dt) {
while (goal > 0) {
# calculate the distance (from tac to abc) to tac ratio for each flatfish.
disttoabc_to_tac_ratio <- (abcdt - dt)/dt
disttoabc_to_tac_ratio[disttoabc_to_tac_ratio < 1e-9] <- 0 # close enough to zero
# the minimum, nonzero, ratio is the one that will determine how the goal amt is spread across the species, this round
if (min(disttoabc_to_tac_ratio, na.rm = T) < -1e-6) stop('x is negative!! something went horribly wrong') # close enough to zero--a little coarser, was running in to trouble here
xvec <- disttoabc_to_tac_ratio
xvec[xvec > 0] <- min(disttoabc_to_tac_ratio[disttoabc_to_tac_ratio > 0], na.rm = T)
xvec[is.na(xvec)] <- 0
xvec[xvec < 0] <- 0
if (sum(xvec*dt)==0 & goal > 1e-9) stop('infinite loop')
# check that another loop is needed at all.
if (sum(xvec*dt) < goal) {
# decrease remaining goalamt to distribute
goal <- goal - sum(xvec*dt)
# increase all TAC proportionally, by the allowable distance given tac<abc constraint
dt <- dt + xvec*dt
# and go back to the beginning and do it again until goalamt = 0
if (goal < -1e-6) stop('goalamt is negative, but it shouldnt be. something went horribly wrong') else if (goal < 1e-8) {goal <- 0} # if it's between 1e-9 and -1e-9 just set it to 0; it's close enough.
} else {
dtvec <- dt
dtvec[xvec == 0] <- 0 # so you don't go above abc!
# increase all TAC proportionally, by the remaining goalamt
dt <- dt + (dtvec/sum(dtvec))*goal
# calculate remaining goalamt
goal <- goal - sum((dtvec/sum(dtvec))*goal)
if (goal > 1e-9) stop('goalamt should be 0, but its not. something went horribly wrong') else goal <- 0 #numerically it'll get super close but due to rounding you have to manually set to 0
}
}
return(dt)
}
TAC.reallocate <- function(DT_orig,split_param) {
DT <- DT_orig #%>% select(starts_with("TAC"))
# If prediction exceeds cap, trim down from the LARGEST stocks
NETTAC <- rowSums(DT, na.rm = TRUE)
EXTRATAC <- as.numeric(NETTAC < 2e6)*(2e6 - NETTAC)
DT <- DT[order(DT, decreasing = T)]
# If there is excess, give split_param*excess to pollock and cod, up to ABC
# Give the rest to the rest
FORWHITEFISH <- EXTRATAC*split_param
# goal, to increase pollock + cod TAC by the minimum of: forwhitefish or ABC-TAC
goal <- pmin(FORWHITEFISH,FISH.DATA$ABC.BS.201 - DT$TAC.BS.201 + FISH.DATA$ABC.BSAI.202 - DT$TAC.BSAI.202)
goalamt <- goal
dt <- DT[c("TAC.BS.201","TAC.BSAI.202") ]# just to keep things clean..
abcdt <- FISH.DATA[c("ABC.BS.201","ABC.BSAI.202")]
dt <- distributefun(goalamt,abcdt,dt)
DT_orig$TAC.BS.201 <- dt$TAC.BS.201
DT_orig$TAC.BSAI.202 <- dt$TAC.BSAI.202
# increase first the species that empirically see increases when pollock is low
# this is yellowfin, arrowtooth, rock sole and flathead. (see manuscript table)
# expert opinion is others (e.g. northern rockfish) should also go up but
# couldn't get a statistically significant result when this was tested, so leaving
# that out.
#
FORPOLRESFISH <- EXTRATAC - goalamt
if (FORPOLRESFISH > 0) {
dt <- DT[c("TAC.BSAI.140",
"TAC.BSAI.104",
"TAC.BSAI.103",
"TAC.BSAI.141")] # just to keep things clean..
abcdt <- FISH.DATA[c("ABC.BSAI.140",
"ABC.BSAI.104",
"ABC.BSAI.103",
"ABC.BSAI.141")] # just to keep things clean..
goalamt <- pmin(FORPOLRESFISH, sum(abcdt) - sum(dt))
dt <- distributefun(goalamt,abcdt,dt)
# put the results into the prediction dataframe and move on to the next row
DT_orig[c('TAC.BSAI.140',
'TAC.BSAI.104',
'TAC.BSAI.103',
'TAC.BSAI.141')] <- dt[c('TAC.BSAI.140',
'TAC.BSAI.104',
'TAC.BSAI.103',
'TAC.BSAI.141')]
#
# Finally if there is still any remaining 2mmt use it to increase everything else.
FORRESTFISH <- FORPOLRESFISH - goalamt
if (FORRESTFISH > 0) {
dt <- DT[-c('TAC.BSAI.60',
'TAC.BSAI.65',
'TAC.BSAI.90',
'TAC.BSAI.400',
'TAC.BSAI.326',
'TAC.BSAI.307',
'TAC.BSAI.310',
'TAC.BSAI.303',
'TAC.BSAI.301',
'TAC.BS.203',
'TAC.AI.203',
'TAC.BSAI.204',
'TAC.BSAI.147',
'TAC.BSAI.100',
'TAC.BS.102',
'TAC.AI.102',
'TAC.BSAI.106')] # just to keep things clean..
abcdt <- FISH.DATA[c('ABC.BSAI.60',
'ABC.BSAI.65',
'ABC.BSAI.90',
'ABC.BSAI.400',
'ABC.BSAI.326',
'ABC.BSAI.307',
'ABC.BSAI.310',
'ABC.BSAI.303',
'ABC.BSAI.301',
'ABC.BS.203',
'ABC.AI.203',
'ABC.BSAI.204',
'ABC.BSAI.147',
'ABC.BSAI.100',
'ABC.BS.102',
'ABC.AI.102',
'ABC.BSAI.106')] # just to keep things clean..
goalamt <- pmin(FORRESTFISH, sum(abcdt) - sum(dt))
dt <- distributefun(goalamt,abcdt,dt)
# put the results into the prediction dataframe and move on to the next row
DT_orig[c('TAC.BSAI.60',
'TAC.BSAI.65',
'TAC.BSAI.90',
'TAC.BSAI.400',
'TAC.BSAI.326',
'TAC.BSAI.307',
'TAC.BSAI.310',
'TAC.BSAI.303',
'TAC.BSAI.301',
'TAC.BS.203',
'TAC.AI.203',
'TAC.BSAI.204',
'TAC.BSAI.147',
'TAC.BSAI.100',
'TAC.BS.102',
'TAC.AI.102',
'TAC.BSAI.106')] <- dt[c('TAC.BSAI.60',
'TAC.BSAI.65',
'TAC.BSAI.90',
'TAC.BSAI.400',
'TAC.BSAI.326',
'TAC.BSAI.307',
'TAC.BSAI.310',
'TAC.BSAI.303',
'TAC.BSAI.301',
'TAC.BS.203',
'TAC.AI.203',
'TAC.BSAI.204',
'TAC.BSAI.147',
'TAC.BSAI.100',
'TAC.BS.102',
'TAC.AI.102',
'TAC.BSAI.106')]
}
}
return(DT_orig)
}
TAC.BOTHBIND <- TAC.reallocate(TAC.BOTHBIND,0.7)
TAC.BOTHBIND.FLATSUR <- TAC.reallocate(TAC.BOTHBIND.FLATSUR,0.7)
}
# catch depending on
CATCH.BOTHBIND.SURSUR <- predict.catch.function(model="SUR",fit=catch_BOTHBIND_loglin_sur,TAC.BOTHBIND )
CATCH.BOTHBIND.SUROLS <- predict.catch.function(model="NOSUR",fit = catch_BOTHBIND_loglin_nosur,TAC.BOTHBIND )
CATCH.BOTHBIND.FLATSUR.SURSUR <- predict.catch.function(model="FLATSUR",fit=catch_BOTHBIND_FLATSUR_loglin_sur,TAC.BOTHBIND.FLATSUR )
# create ensemble
#
CATCH.PRED <- (CATCH.BOTHBIND.SURSUR + CATCH.BOTHBIND.SUROLS + CATCH.BOTHBIND.FLATSUR.SURSUR)/3
if (scenario == 5.4) {
# Improved catch scenario
# Note from Amanda to Alan: There is an easier way to automate this list, I'm sure of it, but
# I'm really tired right now and my contractions are picking up. so while this is a bit more work for
# you to edit later, since it requires you edit multiple lists, its less likely I code it wrong right now
# because its more straightforward... and thats valuable.
#
# Take any 'leftover' from catch to 2MMT and spread it out across pollock, cod, A80 targets
#
NETTAC <- rowSums(CATCH.PRED, na.rm = TRUE)
EXTRACATCH <- as.numeric(NETTAC < 2e6)*(2e6 - NETTAC)
CATCH.PRED <- CATCH.PRED[order(CATCH.PRED, decreasing = T)]
# If there's any remaining TAC give it to pollock (201), cod (202), yfin sole (140), sablefish (203), atka (204), POP (301), Flathead (103), Rock sole (104), Greenland turbot (102)
# goal: to increase above listed species catch up to ABC/2MMT
# Note: we're only increasing BS catch. That's a fun little assumption? If you want to do AI too, just.. add the CATCH.AI.X list.
#
# Here is the list of species that will be getting extra catch.
DT <- CATCH.PRED[c("CATCH.BS.201", #pollock
"CATCH.BS.202", #cod
"CATCH.BS.140", # yfin
"CATCH.BS.203", # sablefish
"CATCH.BS.204", #atka
"CATCH.BS.301", #POP
"CATCH.BS.103", #flathead
"CATCH.BS.104", #rock sole
"CATCH.BS.102")] #greenland
# We need to do the ABCs too. Easiest to just copy and paste the above list and ctrl+F "in selection" CATCH.BS <- ABC.BSAI
# Remember there are 5 species that get BS allocated in the bering sea and ai
# seperately: pollock, sablefish, other rock fish, POP and Greenaldn turbot. In these it should be ABC.BS.X
ABCDT <- FISH.DATA[c("ABC.BS.201", #pollock
"ABC.BSAI.202", #cod
"ABC.BSAI.140", # yfin
"ABC.BS.203", # sablefish
"ABC.BSAI.204", #atka
"ABC.BS.301", #POP
"ABC.BSAI.103", #flathead
"ABC.BSAI.104", #rock sole
"ABC.BS.102")] #greenland
# goal: to increase above listed species catch up to ABC/2MMT
goalamt <- pmin(EXTRACATCH,rowSums(ABCDT) - rowSums(DT))
DT <- distributefun(goalamt,ABCDT,DT) # already coded up on line 149
# Replace old catch with these new ones.
# One more place you need to add/remove species. Sorry. I just copy lines 357-end and paste as appropriate, and that works well..
CATCH.PRED[c("CATCH.BS.201", #pollock
"CATCH.BS.202", #cod
"CATCH.BS.140", # yfin
"CATCH.BS.203", # sablefish
"CATCH.BS.204", #atka
"CATCH.BS.301", #POP
"CATCH.BS.103", #flathead
"CATCH.BS.104", #rock sole
"CATCH.BS.102")] <- DT[c("CATCH.BS.201", #pollock
"CATCH.BS.202", #cod
"CATCH.BS.140", # yfin
"CATCH.BS.203", # sablefish
"CATCH.BS.204", #atka
"CATCH.BS.301", #POP
"CATCH.BS.103", #flathead
"CATCH.BS.104", #rock sole
"CATCH.BS.102")]#greenland
}
# # This is the old version of 5.4, where we scaled from catch to TAC. This didn't work because, well.. TAC < catch in some cases. (Due to in-season management)
# I'll eventaully just delete this all, since its a relic and messy, its just here while I'm on Mat leave so other ppl can follow what happened, if need be.
#
# if (scenario == 5.4) {
# # improved catch--linear combination of predicted catch and improved catch on a scale of 0 to 1
# TAC <- TAC.BOTHBIND[c("TAC.BSAI.141",
# "TAC.BSAI.204",
# "TAC.BSAI.103",
# "TAC.BS.102",
# "TAC.BSAI.147",
# "TAC.BSAI.303",
# "TAC.BSAI.60",
# "TAC.BSAI.100",
# "TAC.BS.310",
# "TAC.BSAI.202",
# "TAC.BSAI.106",
# "TAC.BS.301",
# "TAC.BS.201",
# "TAC.AI.201",
# "TAC.BSAI.104",
# "TAC.BSAI.307",
# "TAC.BS.203",
# "TAC.BSAI.400",
# "TAC.BSAI.65",
# "TAC.BSAI.326",
# "TAC.BSAI.90",
# "TAC.BSAI.50",
# "TAC.BSAI.140")]/2 + TAC.BOTHBIND.FLATSUR[c("TAC.BSAI.141",
# "TAC.BSAI.204",
# "TAC.BSAI.103",
# "TAC.BS.102",
# "TAC.BSAI.147",
# "TAC.BSAI.303",
# "TAC.BSAI.60",
# "TAC.BSAI.100",
# "TAC.BS.310",
# "TAC.BSAI.202",
# "TAC.BSAI.106",
# "TAC.BS.301",
# "TAC.BS.201",
# "TAC.AI.201",
# "TAC.BSAI.104",
# "TAC.BSAI.307",
# "TAC.BS.203",
# "TAC.BSAI.400",
# "TAC.BSAI.65",
# "TAC.BSAI.326",
# "TAC.BSAI.90",
# "TAC.BSAI.50",
# "TAC.BSAI.140")]/2
#
# catchisTAC <- TAC[c("TAC.BSAI.141")]
# names(catchisTAC) <- c("CATCH.BSAI.141")
# catchisTAC$CATCH.BSAI.141 <- TAC$TAC.BSAI.141*CATCH.AVG.BS$CODE.141
# catchisTAC$CATCH.BSAI.204 <- TAC$TAC.BSAI.204*CATCH.AVG.BS$CODE.204
# catchisTAC$CATCH.BSAI.103 <- TAC$TAC.BSAI.103*CATCH.AVG.BS$CODE.103
# catchisTAC$CATCH.BS.102 <- TAC$TAC.BS.102
# catchisTAC$CATCH.BSAI.147 <- TAC$TAC.BSAI.147*CATCH.AVG.BS$CODE.147
# catchisTAC$CATCH.BSAI.303 <- TAC$TAC.BSAI.303*CATCH.AVG.BS$CODE.303
# catchisTAC$CATCH.BSAI.60 <- TAC$TAC.BSAI.60*CATCH.AVG.BS$CODE.60
# catchisTAC$CATCH.BSAI.100 <- TAC$TAC.BSAI.100*CATCH.AVG.BS$CODE.100
# catchisTAC$CATCH.BS.310 <- TAC$TAC.BS.310
# catchisTAC$CATCH.BSAI.202 <- TAC$TAC.BSAI.202*CATCH.AVG.BS$CODE.202
# catchisTAC$CATCH.BSAI.106 <- TAC$TAC.BSAI.106*CATCH.AVG.BS$CODE.106
# catchisTAC$CATCH.BS.301 <- TAC$TAC.BS.301
# catchisTAC$CATCH.BS.201 <- (TAC$TAC.BS.201 + TAC$TAC.AI.201)*CATCH.AVG.BS$CODE.201
# catchisTAC$CATCH.BSAI.104 <- TAC$TAC.BSAI.104*CATCH.AVG.BS$CODE.104
# catchisTAC$CATCH.BSAI.307 <- TAC$TAC.BSAI.307*CATCH.AVG.BS$CODE.307
# catchisTAC$CATCH.BS.203 <- TAC$TAC.BS.203
# catchisTAC$CATCH.BSAI.400 <- TAC$TAC.BSAI.400*CATCH.AVG.BS$CODE.400
# catchisTAC$CATCH.BSAI.65 <- TAC$TAC.BSAI.65*CATCH.AVG.BS$CODE.65
# catchisTAC$CATCH.BSAI.326 <- TAC$TAC.BSAI.326*CATCH.AVG.BS$CODE.326
# catchisTAC$CATCH.BSAI.90 <- TAC$TAC.BSAI.90*CATCH.AVG.BS$CODE.90
# catchisTAC$CATCH.BSAI.50 <- TAC$TAC.BSAI.50*CATCH.AVG.BS$CODE.50
# catchisTAC$CATCH.BSAI.140 <- TAC$TAC.BSAI.140*CATCH.AVG.BS$CODE.140
#
# # TAC.BS DNE for many species!!!
#
# output <- improvedcatchscale*catchisTAC + (1-improvedcatchscale)*output
# }
# This just ensures the output is in a predictable order, and that we are only returning BS catch since this is a BS function.
output <- CATCH.PRED[c("CATCH.BS.141",
"CATCH.BS.204",
"CATCH.BS.103",
"CATCH.BS.102",
"CATCH.BS.147",
"CATCH.BS.303",
"CATCH.BS.60",
"CATCH.BS.100",
"CATCH.BS.310",
"CATCH.BS.202",
"CATCH.BS.106",
"CATCH.BS.301",
"CATCH.BS.201",
"CATCH.BS.104",
"CATCH.BS.307",
"CATCH.BS.203",
"CATCH.BS.400",
"CATCH.BS.65",
"CATCH.BS.326",
"CATCH.BS.90",
"CATCH.BS.50",
"CATCH.BS.140")]
return (output)
}
amandafaig/catchfunction documentation built on March 17, 2023, 11:52 p.m.
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Defines functions removefromcap_catch
removefromcap_catch <- function(ABC.DATA,scenario,spptomult,improvedcatchscale) {
FISH.DATA <- ABC.DATA
FISH.DATA$ABCboth <- FISH.DATA$ABC.BSAI.202 + FISH.DATA$ABC.BS.201
FISH.DATA$ABCboth.UB.150 <- as.numeric(FISH.DATA$ABC.BS.201 + FISH.DATA$ABC.BSAI.202 >= 1.5e6)
FISH.DATA$pollock.bs.UB <- as.numeric(FISH.DATA$ABC.BS.201 > 1.2e6)
FISH.DATA$flex <- 1 # introduction of flatfish flex
FISH.DATA$A80 <- 1 # introduction of A80
FISH.DATA$po10 <- 1
FISH.DATA$pre97 <- 0
FISH.DATA$is93 <- 0
FISH.DATA$WAISSLadj <- 1
FISH.DATA$solegone <- 1
FISH.DATA$plaicegone <- 1
FISH.DATA$kamsplit <- 1
FISH.DATA$AFA <- 1
FISH.DATA$pollockAIchange <- 1
FISH.DATA$A28 <- 1
FISH.DATA$atkadisp <- 0
FISH.DATA$SSL <- 1 # stellar sea lion closure
FISH.DATA$WAISSL <- 0
FISH.DATA$A80.ask.POP <- 1
FISH.DATA$is93 <- 0
FISH.DATA$A82 <- 1
# I start by giving myself a list of all the species numbers
allspp <- c("141",
"204",
"103",
"102",
"147",
"303",
"60",
"100",
"310",
"202",
"106",
"301",
"201",
"104",
"307",
"203",
"400",
"65",
"326",
"90",
"50",
"140")
# And their corresponding names, alphabetically.
# Honestly this could be saved in sysdata and loaded but it's not bad to have
# handy in case if you forget what number = what species
sppnames <- c("Arrowtooth",
"Atka",
"Flathead",
"Greenland",
"Kamchatka",
"Northern",
"Octopus",
"OtherFlat",
"OtherRock",
"PCod",
"Plaice",
"POP",
"Pollock",
"Rock",
"Rougheye",
"Sablefish",
"Sculpin",
"Shark",
"Shortraker",
"Skate",
"Squid",
"Yellowfin")
# calculate TAC.
# rule 1: if pollock has been removed from 2mmt cap, assume everything else is TAC = ABC
if (is.element("pollock",spptomult)) {
FISH.DATA$TAC.BSAI.201 <- 0
TAC.BOTHBIND <- FISH.DATA
TAC.BOTHBIND.FLATSUR <- FISH.DATA
# check sum < 2mmt
check2mmt <- function(DT_orig) {
DT <- DT_orig #%>% select(starts_with("TAC"))
NETTAC <- rowSums(DT, na.rm = TRUE)
if (NETTAC > 2e6) {
# first bring down the least valuable/zero value bycatch species
# These are first because these species really are almost nuisance
# catches.
TAC.BOTHBIND <- predict.tac.function(predictmethod = 1, model="SUR",fit=tac_BOTHBIND_loglin_sur,FISH.DATA)
DT_orig$TAC.BSAI.60 <- TAC.BOTHBIND$TAC.BSAI.60 # octopus
DT_orig$TAC.BSAI.65 <- TAC.BOTHBIND$TAC.BSAI.65 # sharks
DT_orig$TAC.BSAI.90 <- TAC.BOTHBIND$TAC.BSAI.90 #skates
DT_orig$TAC.BSAI.400 <- TAC.BOTHBIND$TAC.BSAI.400 #skates
}
#check again
DT <- DT_orig #%>% select(starts_with("TAC"))
NETTAC <- rowSums(DT, na.rm = TRUE)
if (NETTAC > 2e6) {
# if still over 2mmt, next bring down species for various reasons (see below)
DT_orig$TAC.BSAI.50 <- TAC.BOTHBIND$TAC.BSAI.50 # squid -- pollock bycatch. Valuable (because pollock) but still pure bycatch
DT_orig$TAC.BSAI.141 <- TAC.BOTHBIND$TAC.BSAI.141 # arrowtooth -- halibut will continue to be limiting
DT_orig$TAC.BSAI.104 <- TAC.BOTHBIND$TAC.BSAI.104 # rock sole -- worry about market limits
}
#check again
DT <- DT_orig #%>% select(starts_with("TAC"))
NETTAC <- rowSums(DT, na.rm = TRUE)
if (NETTAC > 2e6) {
# if still over 2mmt, next bring down basically everything that doesnt
# tend to go up with pollock or isn't directly listed as a targeted species
#
#
# non-AFA trawl cp targets: yfin, rock sole, flathead sole,atka, pop
# while cod isn't technically a target, its effectively one in terms of its value (as a bycatch)
# species that go up w. pollock going down: arrowtooth, flathead sole, rock sole, yellowfin
DT_orig$TAC.BSAI.326 <- TAC.BOTHBIND$TAC.BSAI.326 # shortraker
DT_orig$TAC.BSAI.307 <- TAC.BOTHBIND$TAC.BSAI.307 # rougheye
DT_orig$TAC.BS.310 <- TAC.BOTHBIND$TAC.BS.310 # orock BS
DT_orig$TAC.AI.310 <- TAC.BOTHBIND$TAC.AI.310 # orock AI
DT_orig$TAC.BSAI.303 <- TAC.BOTHBIND$TAC.BSAI.303 # northern
DT_orig$TAC.BSAI.147 <- TAC.BOTHBIND$TAC.BSAI.147 # kamchatka
DT_orig$TAC.BSAI.100 <- TAC.BOTHBIND$TAC.BSAI.307 # oflat
DT_orig$TAC.BS.102 <- TAC.BOTHBIND$TAC.BS.102 # greenland BS
DT_orig$TAC.AI.102 <- TAC.BOTHBIND$TAC.AI.102 # greenland AI
DT_orig$TAC.BSAI.106 <- TAC.BOTHBIND$TAC.AI.106 # plaice
}
#honestly at this point if we still have nettac > 2mmt something very weird is going on...
DT <- DT_orig # %>% select(starts_with("TAC"))
NETTAC <- rowSums(DT, na.rm = TRUE)
if (NETTAC > 2e6) {stop('NETTAC still over 2e6 even though pollock removed. Line 82 removefromcap_catch.R')}
}
} else {
TAC.BOTHBIND <- predict.tac.function(predictmethod = 1, model="SUR",fit=tac_BOTHBIND_loglin_sur,FISH.DATA)
TAC.BOTHBIND.FLATSUR <- predict.tac.function(predictmethod = 1, model="FLATSUR",fit=tac_BOTHBIND_FLATSUR_loglin_sur,FISH.DATA)
for (i in 1:length(spptomult)) {
eval(parse(text = paste("TAC.BOTHBIND$TAC.BSAI.",allspp[match(spptomult[i],sppnames)],"<- 0",sep="")))
eval(parse(text = paste("TAC.BOTHBIND$TAC.BS.",allspp[match(spptomult[i],sppnames)],"<- 0",sep="")))
eval(parse(text = paste("TAC.BOTHBIND$TAC.AI.",allspp[match(spptomult[i],sppnames)],"<- 0",sep="")))
}
for (i in 1:length(spptomult)) {
eval(parse(text = paste("TAC.BOTHBIND.FLATSUR$TAC.BSAI.",allspp[match(spptomult[i],sppnames)],"<- 0",sep="")))
eval(parse(text = paste("TAC.BOTHBIND.FLATSUR$TAC.BS.",allspp[match(spptomult[i],sppnames)],"<- 0",sep="")))
eval(parse(text = paste("TAC.BOTHBIND.FLATSUR$TAC.AI.",allspp[match(spptomult[i],sppnames)],"<- 0",sep="")))
}
# Reallocate the remaining TAC.
#
distributefun <- function(goal,abcdt,dt) {
while (goal > 0) {
# calculate the distance (from tac to abc) to tac ratio for each flatfish.
disttoabc_to_tac_ratio <- (abcdt - dt)/dt
disttoabc_to_tac_ratio[disttoabc_to_tac_ratio < 1e-9] <- 0 # close enough to zero
# the minimum, nonzero, ratio is the one that will determine how the goal amt is spread across the species, this round
if (min(disttoabc_to_tac_ratio, na.rm = T) < -1e-6) stop('x is negative!! something went horribly wrong') # close enough to zero--a little coarser, was running in to trouble here
xvec <- disttoabc_to_tac_ratio
xvec[xvec > 0] <- min(disttoabc_to_tac_ratio[disttoabc_to_tac_ratio > 0], na.rm = T)
xvec[is.na(xvec)] <- 0
xvec[xvec < 0] <- 0
if (sum(xvec*dt)==0 & goal > 1e-9) stop('infinite loop')
# check that another loop is needed at all.
if (sum(xvec*dt) < goal) {
# decrease remaining goalamt to distribute
goal <- goal - sum(xvec*dt)
# increase all TAC proportionally, by the allowable distance given tac<abc constraint
dt <- dt + xvec*dt
# and go back to the beginning and do it again until goalamt = 0
if (goal < -1e-6) stop('goalamt is negative, but it shouldnt be. something went horribly wrong') else if (goal < 1e-8) {goal <- 0} # if it's between 1e-9 and -1e-9 just set it to 0; it's close enough.
} else {
dtvec <- dt
dtvec[xvec == 0] <- 0 # so you don't go above abc!
# increase all TAC proportionally, by the remaining goalamt
dt <- dt + (dtvec/sum(dtvec))*goal
# calculate remaining goalamt
goal <- goal - sum((dtvec/sum(dtvec))*goal)
if (goal > 1e-9) stop('goalamt should be 0, but its not. something went horribly wrong') else goal <- 0 #numerically it'll get super close but due to rounding you have to manually set to 0
}
}
return(dt)
}
TAC.reallocate <- function(DT_orig,split_param) {
DT <- DT_orig #%>% select(starts_with("TAC"))
# If prediction exceeds cap, trim down from the LARGEST stocks
NETTAC <- rowSums(DT, na.rm = TRUE)
EXTRATAC <- as.numeric(NETTAC < 2e6)*(2e6 - NETTAC)
DT <- DT[order(DT, decreasing = T)]
# If there is excess, give split_param*excess to pollock and cod, up to ABC
# Give the rest to the rest
FORWHITEFISH <- EXTRATAC*split_param
# goal, to increase pollock + cod TAC by the minimum of: forwhitefish or ABC-TAC
goal <- pmin(FORWHITEFISH,FISH.DATA$ABC.BS.201 - DT$TAC.BS.201 + FISH.DATA$ABC.BSAI.202 - DT$TAC.BSAI.202)
goalamt <- goal
dt <- DT[c("TAC.BS.201","TAC.BSAI.202") ]# just to keep things clean..
abcdt <- FISH.DATA[c("ABC.BS.201","ABC.BSAI.202")]
dt <- distributefun(goalamt,abcdt,dt)
DT_orig$TAC.BS.201 <- dt$TAC.BS.201
DT_orig$TAC.BSAI.202 <- dt$TAC.BSAI.202
# increase first the species that empirically see increases when pollock is low
# this is yellowfin, arrowtooth, rock sole and flathead. (see manuscript table)
# expert opinion is others (e.g. northern rockfish) should also go up but
# couldn't get a statistically significant result when this was tested, so leaving
# that out.
#
FORPOLRESFISH <- EXTRATAC - goalamt
if (FORPOLRESFISH > 0) {
dt <- DT[c("TAC.BSAI.140",
"TAC.BSAI.104",
"TAC.BSAI.103",
"TAC.BSAI.141")] # just to keep things clean..
abcdt <- FISH.DATA[c("ABC.BSAI.140",
"ABC.BSAI.104",
"ABC.BSAI.103",
"ABC.BSAI.141")] # just to keep things clean..
goalamt <- pmin(FORPOLRESFISH, sum(abcdt) - sum(dt))
dt <- distributefun(goalamt,abcdt,dt)
# put the results into the prediction dataframe and move on to the next row
DT_orig[c('TAC.BSAI.140',
'TAC.BSAI.104',
'TAC.BSAI.103',
'TAC.BSAI.141')] <- dt[c('TAC.BSAI.140',
'TAC.BSAI.104',
'TAC.BSAI.103',
'TAC.BSAI.141')]
#
# Finally if there is still any remaining 2mmt use it to increase everything else.
FORRESTFISH <- FORPOLRESFISH - goalamt
if (FORRESTFISH > 0) {
dt <- DT[-c('TAC.BSAI.60',
'TAC.BSAI.65',
'TAC.BSAI.90',
'TAC.BSAI.400',
'TAC.BSAI.326',
'TAC.BSAI.307',
'TAC.BSAI.310',
'TAC.BSAI.303',
'TAC.BSAI.301',
'TAC.BS.203',
'TAC.AI.203',
'TAC.BSAI.204',
'TAC.BSAI.147',
'TAC.BSAI.100',
'TAC.BS.102',
'TAC.AI.102',
'TAC.BSAI.106')] # just to keep things clean..
abcdt <- FISH.DATA[c('ABC.BSAI.60',
'ABC.BSAI.65',
'ABC.BSAI.90',
'ABC.BSAI.400',
'ABC.BSAI.326',
'ABC.BSAI.307',
'ABC.BSAI.310',
'ABC.BSAI.303',
'ABC.BSAI.301',
'ABC.BS.203',
'ABC.AI.203',
'ABC.BSAI.204',
'ABC.BSAI.147',
'ABC.BSAI.100',
'ABC.BS.102',
'ABC.AI.102',
'ABC.BSAI.106')] # just to keep things clean..
goalamt <- pmin(FORRESTFISH, sum(abcdt) - sum(dt))
dt <- distributefun(goalamt,abcdt,dt)
# put the results into the prediction dataframe and move on to the next row
DT_orig[c('TAC.BSAI.60',
'TAC.BSAI.65',
'TAC.BSAI.90',
'TAC.BSAI.400',
'TAC.BSAI.326',
'TAC.BSAI.307',
'TAC.BSAI.310',
'TAC.BSAI.303',
'TAC.BSAI.301',
'TAC.BS.203',
'TAC.AI.203',
'TAC.BSAI.204',
'TAC.BSAI.147',
'TAC.BSAI.100',
'TAC.BS.102',
'TAC.AI.102',
'TAC.BSAI.106')] <- dt[c('TAC.BSAI.60',
'TAC.BSAI.65',
'TAC.BSAI.90',
'TAC.BSAI.400',
'TAC.BSAI.326',
'TAC.BSAI.307',
'TAC.BSAI.310',
'TAC.BSAI.303',
'TAC.BSAI.301',
'TAC.BS.203',
'TAC.AI.203',
'TAC.BSAI.204',
'TAC.BSAI.147',
'TAC.BSAI.100',
'TAC.BS.102',
'TAC.AI.102',
'TAC.BSAI.106')]
}
}
return(DT_orig)
}
TAC.BOTHBIND <- TAC.reallocate(TAC.BOTHBIND,0.7)
TAC.BOTHBIND.FLATSUR <- TAC.reallocate(TAC.BOTHBIND.FLATSUR,0.7)
}
# catch depending on
CATCH.BOTHBIND.SURSUR <- predict.catch.function(model="SUR",fit=catch_BOTHBIND_loglin_sur,TAC.BOTHBIND )
CATCH.BOTHBIND.SUROLS <- predict.catch.function(model="NOSUR",fit = catch_BOTHBIND_loglin_nosur,TAC.BOTHBIND )
CATCH.BOTHBIND.FLATSUR.SURSUR <- predict.catch.function(model="FLATSUR",fit=catch_BOTHBIND_FLATSUR_loglin_sur,TAC.BOTHBIND.FLATSUR )
# create ensemble
#
CATCH.PRED <- (CATCH.BOTHBIND.SURSUR + CATCH.BOTHBIND.SUROLS + CATCH.BOTHBIND.FLATSUR.SURSUR)/3
if (scenario == 5.4) {
# Improved catch scenario
# Note from Amanda to Alan: There is an easier way to automate this list, I'm sure of it, but
# I'm really tired right now and my contractions are picking up. so while this is a bit more work for
# you to edit later, since it requires you edit multiple lists, its less likely I code it wrong right now
# because its more straightforward... and thats valuable.
#
# Take any 'leftover' from catch to 2MMT and spread it out across pollock, cod, A80 targets
#
NETTAC <- rowSums(CATCH.PRED, na.rm = TRUE)
EXTRACATCH <- as.numeric(NETTAC < 2e6)*(2e6 - NETTAC)
CATCH.PRED <- CATCH.PRED[order(CATCH.PRED, decreasing = T)]
# If there's any remaining TAC give it to pollock (201), cod (202), yfin sole (140), sablefish (203), atka (204), POP (301), Flathead (103), Rock sole (104), Greenland turbot (102)
# goal: to increase above listed species catch up to ABC/2MMT
# Note: we're only increasing BS catch. That's a fun little assumption? If you want to do AI too, just.. add the CATCH.AI.X list.
#
# Here is the list of species that will be getting extra catch.
DT <- CATCH.PRED[c("CATCH.BS.201", #pollock
"CATCH.BS.202", #cod
"CATCH.BS.140", # yfin
"CATCH.BS.203", # sablefish
"CATCH.BS.204", #atka
"CATCH.BS.301", #POP
"CATCH.BS.103", #flathead
"CATCH.BS.104", #rock sole
"CATCH.BS.102")] #greenland
# We need to do the ABCs too. Easiest to just copy and paste the above list and ctrl+F "in selection" CATCH.BS <- ABC.BSAI
# Remember there are 5 species that get BS allocated in the bering sea and ai
# seperately: pollock, sablefish, other rock fish, POP and Greenaldn turbot. In these it should be ABC.BS.X
ABCDT <- FISH.DATA[c("ABC.BS.201", #pollock
"ABC.BSAI.202", #cod
"ABC.BSAI.140", # yfin
"ABC.BS.203", # sablefish
"ABC.BSAI.204", #atka
"ABC.BS.301", #POP
"ABC.BSAI.103", #flathead
"ABC.BSAI.104", #rock sole
"ABC.BS.102")] #greenland
# goal: to increase above listed species catch up to ABC/2MMT
goalamt <- pmin(EXTRACATCH,rowSums(ABCDT) - rowSums(DT))
DT <- distributefun(goalamt,ABCDT,DT) # already coded up on line 149
# Replace old catch with these new ones.
# One more place you need to add/remove species. Sorry. I just copy lines 357-end and paste as appropriate, and that works well..
CATCH.PRED[c("CATCH.BS.201", #pollock
"CATCH.BS.202", #cod
"CATCH.BS.140", # yfin
"CATCH.BS.203", # sablefish
"CATCH.BS.204", #atka
"CATCH.BS.301", #POP
"CATCH.BS.103", #flathead
"CATCH.BS.104", #rock sole
"CATCH.BS.102")] <- DT[c("CATCH.BS.201", #pollock
"CATCH.BS.202", #cod
"CATCH.BS.140", # yfin
"CATCH.BS.203", # sablefish
"CATCH.BS.204", #atka
"CATCH.BS.301", #POP
"CATCH.BS.103", #flathead
"CATCH.BS.104", #rock sole
"CATCH.BS.102")]#greenland
}
# # This is the old version of 5.4, where we scaled from catch to TAC. This didn't work because, well.. TAC < catch in some cases. (Due to in-season management)
# I'll eventaully just delete this all, since its a relic and messy, its just here while I'm on Mat leave so other ppl can follow what happened, if need be.
#
# if (scenario == 5.4) {
# # improved catch--linear combination of predicted catch and improved catch on a scale of 0 to 1
# TAC <- TAC.BOTHBIND[c("TAC.BSAI.141",
# "TAC.BSAI.204",
# "TAC.BSAI.103",
# "TAC.BS.102",
# "TAC.BSAI.147",
# "TAC.BSAI.303",
# "TAC.BSAI.60",
# "TAC.BSAI.100",
# "TAC.BS.310",
# "TAC.BSAI.202",
# "TAC.BSAI.106",
# "TAC.BS.301",
# "TAC.BS.201",
# "TAC.AI.201",
# "TAC.BSAI.104",
# "TAC.BSAI.307",
# "TAC.BS.203",
# "TAC.BSAI.400",
# "TAC.BSAI.65",
# "TAC.BSAI.326",
# "TAC.BSAI.90",
# "TAC.BSAI.50",
# "TAC.BSAI.140")]/2 + TAC.BOTHBIND.FLATSUR[c("TAC.BSAI.141",
# "TAC.BSAI.204",
# "TAC.BSAI.103",
# "TAC.BS.102",
# "TAC.BSAI.147",
# "TAC.BSAI.303",
# "TAC.BSAI.60",
# "TAC.BSAI.100",
# "TAC.BS.310",
# "TAC.BSAI.202",
# "TAC.BSAI.106",
# "TAC.BS.301",
# "TAC.BS.201",
# "TAC.AI.201",
# "TAC.BSAI.104",
# "TAC.BSAI.307",
# "TAC.BS.203",
# "TAC.BSAI.400",
# "TAC.BSAI.65",
# "TAC.BSAI.326",
# "TAC.BSAI.90",
# "TAC.BSAI.50",
# "TAC.BSAI.140")]/2
#
# catchisTAC <- TAC[c("TAC.BSAI.141")]
# names(catchisTAC) <- c("CATCH.BSAI.141")
# catchisTAC$CATCH.BSAI.141 <- TAC$TAC.BSAI.141*CATCH.AVG.BS$CODE.141
# catchisTAC$CATCH.BSAI.204 <- TAC$TAC.BSAI.204*CATCH.AVG.BS$CODE.204
# catchisTAC$CATCH.BSAI.103 <- TAC$TAC.BSAI.103*CATCH.AVG.BS$CODE.103
# catchisTAC$CATCH.BS.102 <- TAC$TAC.BS.102
# catchisTAC$CATCH.BSAI.147 <- TAC$TAC.BSAI.147*CATCH.AVG.BS$CODE.147
# catchisTAC$CATCH.BSAI.303 <- TAC$TAC.BSAI.303*CATCH.AVG.BS$CODE.303
# catchisTAC$CATCH.BSAI.60 <- TAC$TAC.BSAI.60*CATCH.AVG.BS$CODE.60
# catchisTAC$CATCH.BSAI.100 <- TAC$TAC.BSAI.100*CATCH.AVG.BS$CODE.100
# catchisTAC$CATCH.BS.310 <- TAC$TAC.BS.310
# catchisTAC$CATCH.BSAI.202 <- TAC$TAC.BSAI.202*CATCH.AVG.BS$CODE.202
# catchisTAC$CATCH.BSAI.106 <- TAC$TAC.BSAI.106*CATCH.AVG.BS$CODE.106
# catchisTAC$CATCH.BS.301 <- TAC$TAC.BS.301
# catchisTAC$CATCH.BS.201 <- (TAC$TAC.BS.201 + TAC$TAC.AI.201)*CATCH.AVG.BS$CODE.201
# catchisTAC$CATCH.BSAI.104 <- TAC$TAC.BSAI.104*CATCH.AVG.BS$CODE.104
# catchisTAC$CATCH.BSAI.307 <- TAC$TAC.BSAI.307*CATCH.AVG.BS$CODE.307
# catchisTAC$CATCH.BS.203 <- TAC$TAC.BS.203
# catchisTAC$CATCH.BSAI.400 <- TAC$TAC.BSAI.400*CATCH.AVG.BS$CODE.400
# catchisTAC$CATCH.BSAI.65 <- TAC$TAC.BSAI.65*CATCH.AVG.BS$CODE.65
# catchisTAC$CATCH.BSAI.326 <- TAC$TAC.BSAI.326*CATCH.AVG.BS$CODE.326
# catchisTAC$CATCH.BSAI.90 <- TAC$TAC.BSAI.90*CATCH.AVG.BS$CODE.90
# catchisTAC$CATCH.BSAI.50 <- TAC$TAC.BSAI.50*CATCH.AVG.BS$CODE.50
# catchisTAC$CATCH.BSAI.140 <- TAC$TAC.BSAI.140*CATCH.AVG.BS$CODE.140
#
# # TAC.BS DNE for many species!!!
#
# output <- improvedcatchscale*catchisTAC + (1-improvedcatchscale)*output
# }
# This just ensures the output is in a predictable order, and that we are only returning BS catch since this is a BS function.
output <- CATCH.PRED[c("CATCH.BS.141",
"CATCH.BS.204",
"CATCH.BS.103",
"CATCH.BS.102",
"CATCH.BS.147",
"CATCH.BS.303",
"CATCH.BS.60",
"CATCH.BS.100",
"CATCH.BS.310",
"CATCH.BS.202",
"CATCH.BS.106",
"CATCH.BS.301",
"CATCH.BS.201",
"CATCH.BS.104",
"CATCH.BS.307",
"CATCH.BS.203",
"CATCH.BS.400",
"CATCH.BS.65",
"CATCH.BS.326",
"CATCH.BS.90",
"CATCH.BS.50",
"CATCH.BS.140")]
return (output)
}
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