R/observer.db.r
In jae0/bio.snowcrab: Snow crab assessment suite using aegis* and associated projects and data streams.
Defines functions
observer.db
observer.db = function( DS, p=NULL, yrs=NULL, fn_root=project.datadirectory("bio.snowcrab"),
region="cfaall", yrs_show=5 ) {
# sex codes
male = 0
female = 1
sex.unknown = 2
# maturity codes
immature = 0
mature = 1
mat.unknown = 2
if (DS %in% c("rawdata.redo", "rawdata") ) {
fn.loc = file.path( fn_root, "data", "observer", "datadump" )
dir.create( fn.loc, recursive = TRUE, showWarnings = FALSE )
if (DS=="rawdata") {
out = NULL
for ( YR in yrs ) {
fny = file.path( fn.loc, paste( YR, "rdata", sep="."))
if (file.exists(fny)) {
odb = NULL
load (fny)
setDT(odb)
if (!is.null(odb)) {
out = try( rbind( out, odb ) )
if (inherits(out, "try-error" )) {
stop( "problem: inconsistent number of variables", fny)
}
}
}
}
return (out)
}
# for the full list of tables:
# tbls = sqlTables(connect)
# gs.tables = tbls[ which(tbls[,2] == "GROUNDFISH"),]
# print(gs.tables)
con = ROracle::dbConnect(DBI::dbDriver("Oracle"),dbname=oracle.snowcrab.server , username=oracle.snowcrab.user, password=oracle.snowcrab.password, believeNRows=F)
for ( YR in yrs ) {
fny = file.path( fn.loc, paste( YR, "rdata", sep="."))
odbq = paste(
"SELECT s.LATITUDE, s.LONGITUDE, s.LANDING_DATE, s.SET_NO, s.PRODCD_ID, s.SETCD_ID, s.EST_CATCH, s.EST_KEPT_WT," ,
"s.NUM_HOOK_HAUL, d.BOARD_DATE, d.FISH_NO, d.SEXCD_ID, d.FISH_LENGTH, " ,
"d.FEMALE_ABDOMEN, d.CHELA_HEIGHT, d.SHELLCOND_CD, d.DUROMETRE, d.TRIP_ID, d.TRIP " ,
"FROM SNOWCRAB.SNCRABDETAILS_OBS d, SNOWCRAB.SNCRABSETS_OBS s " ,
"WHERE d.TRIP_ID = s.TRIP_ID " ,
"AND d.SET_NO = s.SET_NO " ,
"AND s.SETCD_ID = 1", ## to keep trips for actual fishery activity (and not CP activity etc)
"AND d.FISH_NO Is Not Null" ,
"AND EXTRACT(YEAR from d.BOARD_DATE) = ", YR )
odb = NULL
odb = ROracle::dbGetQuery(con, odbq )
save( odb, file=fny, compress=T)
gc() # garbage collection
print(YR)
}
ROracle::dbDisconnect(con)
return (yrs)
}
if (DS %in% c("bycatch.redo", "bycatch") ) {
fn.loc = file.path( fn_root, "data", "observer", "bycatch" )
dir.create( fn.loc, recursive = TRUE, showWarnings = FALSE )
if (DS=="bycatch") {
out = NULL
for ( YR in yrs ) {
fny = file.path( fn.loc, paste( YR, "rdata", sep="."))
if (file.exists(fny)) {
odb = NULL
load (fny)
setDT(odb)
if (!is.null(odb)) {
out = try( rbind( out, odb ) )
if (inherits(out, "try-error" )) {
stop( "problem: inconsistent number of variables", fny)
}
}
}
}
return (out)
}
con = ROracle::dbConnect(DBI::dbDriver("Oracle"),dbname=oracle.snowcrab.server , username=oracle.snowcrab.user, password=oracle.snowcrab.password, believeNRows=F)
for ( YR in yrs ) {
fny = file.path( fn.loc, paste( YR, "rdata", sep="."))
odbq = paste(
"SELECT trip.trip_id, trip.trip, trip.board_date, trip.landing_date, st.set_no, vess.vessel_name, vess.license_no, vess.cfv,",
" isp.LATITUDE, isp.LONGITUDE, isp.DEPTH, isp.WATER_TEMPERATURE, ",
" sc.common, st.comarea_id, st.nafarea_id, ",
" ca.speccd_id, ca.est_num_caught, ca.est_kept_wt, ca.est_discard_wt, st.NUM_HOOK_HAUL,",
" fish.fish_no , fish.fish_length, fish.fish_weight ",
"FROM istrips trip, isgears gr, isfishsets st, iscatches ca, isfish fish, issetprofile isp, isvessels vess, isobservercodes o, isspeciescodes sc ",
"WHERE trip.tripcd_id = 2509",
"AND vess.vess_id = trip.vess_id",
"AND vess.license_no = trip.license_no",
"AND o.obscd_id = trip.obscd_id",
"AND trip.trip_id = gr.trip_Id",
"AND st.fishset_id = isp.fishset_id ",
"AND isp.fishset_id = ca.fishset_id ",
"AND ca.speccd_id = sc.speccd_id ",
"AND trip.tripcd_id=2509 ",
"AND st.specscd_id=2526 ",
"AND isp.pntcd_id=4 ",
"AND (trip.trip_id = st.trip_id AND gr.gear_id = st.gear_id)",
"AND ca.catch_id = fish.catch_id(+)",
"AND EXTRACT(YEAR from trip.board_date) = ", YR )
odb = NULL
odb = ROracle::dbGetQuery(con, odbq )
save( odb, file=fny, compress=T)
gc() # garbage collection
print(YR)
}
ROracle::dbDisconnect(con)
return (yrs)
}
if (DS %in% c("bycatch_clean_data", "bycatch_clean_data.redo")) {
fn = file.path( project.datadirectory("bio.snowcrab"), "data", "observer", "odb_bycatch.rdata" )
if (DS=="bycatch_clean_data") {
load( fn )
return(obs)
}
obs = observer.db( DS="bycatch", p=p, yrs=yrs ) # 711,413 in 2023
names(obs) = tolower(names(obs))
setDT(obs)
# QA/QC
obs$lon = -obs$longitude
obs$lat = obs$latitude
obs$longitude = NULL
obs$latitude = NULL
obs[ !is.finite(est_kept_wt), "est_kept_wt" ] = 0
obs[ !is.finite(est_discard_wt), "est_discard_wt" ] = 0
# obs[ !is.finite(num_hook_haul), "num_hook_haul" ] = 5 # this is protocal (assume when not recorded it is 5)
obs[ , wgt:=est_kept_wt + est_discard_wt ]
obs[ num_hook_haul > 10, "num_hook_haul"] = NA # errors likely
obs[ est_discard_wt > 1000, "est_discard_wt"] = NA # errors likely .. these are estimated
obs[ , cpue:= est_discard_wt /num_hook_haul ]
obs[ cpue > 150, "cpue"] = NA # errors likely as capture higher than 150kg / trap unlikely .. note these are cpu of all positive valued catches .. zero-valued are not records
obs[ !is.finite(board_date), "board_date" ] = NA
obs[ , yr:=year(board_date) ]
# cfa 4X has a fishing season that spans two years recode "yr" to "fishyr" to accomodate this
cfa4x = polygon_inside(obs, aegis.polygons::polygon_internal_code("cfa4x"))
mnth = lubridate::month(obs$board_date)
to.offset = which(mnth >= 1 & mnth <= 7 )
to.offset = sort(intersect(cfa4x, to.offset))
obs$fishyr = obs$yr
obs$fishyr[to.offset] = obs$fishyr[to.offset] - 1
obs = obs[obs$fishyr >= 1996 ,] # years for which observer database are good
obs[ , uid:=paste(trip, set_no, sep="_") ] # length(unique(obs$id)) 32406
save(obs, file=fn, compress=TRUE)
return(fn)
}
if (DS %in% c("bycatch_summary")) {
# fishery rates broken down by year (kg, trap hauls)
lgbk = logbook.db( DS="logbook", p=p, yrs=yrs ) # 71,194
names(lgbk) = tolower(names(lgbk))
setDT(lgbk)
lgbk$fishyr = lgbk$yr # fishyr is coded as "yr" in logbook.db
lgbk = lgbk[lgbk$fishyr %in% c(yrs, min(yrs)-1, max(yrs)+1) ,]
j = polygon_inside( lgbk[, c("lon", "lat")], region=region )
lgbk = lgbk[j,]
lgyr = lgbk[, .(
totaleffort = sum(effort, na.rm=TRUE),
totallandings = sum(landings, na.rm=TRUE),
cpue = sum(landings, na.rm=TRUE)/ sum(effort, na.rm=TRUE)
),
by=.(fishyr)
]
lgbk = NULL
# observer data .. extra care needed as there are duplicated records, etc
obs = observer.db( DS="bycatch_clean_data", p=p, yrs=yrs ) # Prepare at sea observed data
i = polygon_inside( obs[, c("lon", "lat")], region=region )
oss = obs = obs[i,]
# drop unid fish, "STONES AND ROCKS", "SEAWEED ALGAE KELP", "SNOW CRAB QUEEN", "CORAL", "SPONGES", "LEATHERBACK SEA TURTLE", "BASKING SHARK", "SEALS", "WHALES"
obs = obs[ !(speccd_id %in% c(90, 233, 900, 920, 8332, 8600, 9200, 9300, 9435 )), ] # 711,412
obs = obs[!grep("NA", uid),] # remove data with NA's in uid
uid0 = unique( obs[, .(uid, fishyr, cfv, wk=week(board_date))] )
# sampling effort and catch .. unique as data entered seems sometimes not aggregated
# also the SQL pull should be roken down to get the correct parts without the outer join
observed_effort = obs[, .(no_traps=unique(num_hook_haul)), by=.(uid)][, .(no_traps=sum(no_traps, na.rm=TRUE)), by=.(uid)]
observed_effort[ no_traps==0, "no_traps"] = NA # 5 ?override with standard sampling procotol expectation
observed_catch = obs[, .(wgt=unique(wgt)), by=.(uid)][, .(wgt=sum(wgt, na.rm=TRUE)), by=.(uid)]
observed = observed_effort[observed_catch, on=.(uid) ]
# same but broken down by uid and species
observed_catch2 = obs[, .(wgt=unique(wgt)), by=.(uid, speccd_id)][, .(wgt=sum(wgt, na.rm=TRUE)), by=.(uid, speccd_id)]
observed_discard2 = obs[, .(est_discard_wt=unique(est_discard_wt)), by=.(uid, speccd_id)][, .(est_discard_wt=sum(est_discard_wt, na.rm=TRUE)), by=.(uid, speccd_id)]
observed_kept2 = obs[, .(est_kept_wt=unique(est_kept_wt)), by=.(uid, speccd_id)][, .(est_kept_wt=sum(est_kept_wt, na.rm=TRUE)), by=.(uid, speccd_id)]
# kept weight is mostly snow crab as this is for the snow crab fishery
# non-zero (buy very low) kept species include: 2520, 2523, 2525, 2527, 10, 51 (probably for personal/direct use or sampling activity)
# .. can otherwise be ignored as totals are mostly discarded
observed2 = observed_catch2[observed_discard2, on=.(uid, speccd_id) ]
observed2 = observed2[observed_kept2, on=.(uid, speccd_id) ]
# add set/trip stats
observed2 = observed[ observed2, on=.(uid) ]
setnames( observed2, "i.wgt", "est_catch_wt" )
setnames( observed2, "wgt", "est_total_catch_wt" )
observed2[ , discard_rate := est_discard_wt/est_catch_wt ]
observed2[ , fraction_of_catch := est_catch_wt/est_total_catch_wt ]
observed2[ , cpue_total := est_catch_wt/no_traps ]
observed2[ , cpue_kept := est_catch_wt/no_traps ]
# efficiency of snow crab capture
eff = observed2[ speccd_id==2526, ]
eff = uid0[eff, on="uid"]
eff_summ = eff[, .(discard_rate=mean(discard_rate, na.rm=TRUE), discard_rate_sd=sd(discard_rate, na.rm=TRUE)), by=.(fishyr) ]
# catch rates kg per trap
# number of sampling events
cpue = dcast( observed2, uid~speccd_id, value.var="cpue_total",
fun.aggregate=mean, fill=0, drop=FALSE, na.rm=TRUE)
# cpue = uid0[catch, on="uid"]
cpue_uid = cpue$uid
cpue = cpue[ , lapply(.SD, function(x) {ifelse(is.nan(x), NA, x) } ),
.SDcols=patterns("[[:digit:]]+") ] # reset NaNs
bct = colMeans(cpue, na.rm=TRUE)
specid = colnames(cpue)
tx = bio.taxonomy::taxonomy.recode( from="spec", to="taxa", tolookup=specid )
species = tx$vern
toshow = which(specid != "2526") #drop as snow crab rates are very high
spec = as.numeric(as.factor(specid[toshow]) )
# direct computation from effort by year/spec
bct_effort = cbind(data.table(uid=cpue_uid), cpue)
bct_effort = bct_effort[uid0, on=.(uid)]
bct_effort = bct_effort[ order(fishyr), lapply(.SD, function(x) {mean(x[is.finite(x)])}), .SDcols=patterns("[[:digit:]]+"), by=.(fishyr) ]
bct_effort = bct_effort[ , lapply(.SD, function(x) {ifelse(is.nan(x), NA, x) } ) ] # reset NaNs
bct_effort = lgyr[bct_effort, on=.(fishyr)]
bct_effort = bct_effort[ , lapply(.SD, function(x) {x*totaleffort}), .SDcols=patterns("[[:digit:]]+"), by=.(fishyr) ]
bct_effort_catchmean = colMeans(bct_effort[, .SD, .SDcols=patterns("[[:digit:]]+") ], na.rm=TRUE)
specs = names(bct_effort) [-1]
tx = bio.taxonomy::taxonomy.recode( from="spec", to="taxa", tolookup=specs )
bct_effort = bct_effort[ data.table(fishyr=yrs) , on=.(fishyr)]
bct_effort$fishyr = NULL
bct_effort = as.data.table( t(bct_effort) )
names( bct_effort) = as.character( yrs )
bct_effort = zapsmall( bct_effort, digits=9)
bct_effort$spec = specs
bct_effort$species = stringr::str_to_title(tx$vern)
bct_effort$taxa = tx$tx
# snow crab kept to this point as a sosuble check on computations
yrss = year.assessment - (yrs_show-1):0
yrsa = 1999:year.assessment
to_show = c( "species", as.character( yrss ) )
to_use = c( "species", as.character( yrsa ) )
to_keep = setdiff( which(bct_effort_catchmean > 1), which(names(bct_effort_catchmean) =="2526" ))
# check if years missing
missing_years = setdiff( to_use, names(bct_effort))
j = length(missing_years)
if ( j > 0) {
for (i in 1:j) {
vn = paste( "missing_years[", i, "]", sep="" )
bct_effort[, eval(parse(text=vn)) ] = NA
}
}
bctabe = bct_effort[to_keep, ..to_use]
i = 2:ncol(bctabe)
sums = data.table( species="Total", t(colSums( bct_effort[to_keep, ..to_use][,..i] ) ) )
bctabe = rbind(bctabe, sums )
lds = lgyr[ data.table(fishyr=yrsa) , on=.(fishyr)]
landings = round(t(lds[["totallandings"]]))
landings = data.table( "Landings/Débarquements (kg)", landings )
names(landings ) = c("species", yrsa )
bctabe = rbind(bctabe, landings )
effort2 = t(lds[["totaleffort"]])
effort2 = data.table( "Effort (trap hauls/casiers levés)", effort2 )
names(effort2 ) = c("species", yrsa )
bctabe = rbind(bctabe, effort2 )
obs_summ = observed[ uid0, on=.(uid) ]
coverage = obs_summ[order(fishyr), .(wgt=sum(wgt, na.rm=TRUE)), by=.(fishyr)]
coverage = coverage[ data.table(fishyr=yrsa) , on=.(fishyr)][["wgt"]]
coverage = data.table( "At sea observed catch \n/Débarquements observés en mer (kg)", t(coverage) )
names(coverage ) = c("species", yrsa )
bctabe = rbind(bctabe, coverage)
efforts = obs_summ[order(fishyr), .(no_traps=sum(no_traps, na.rm=TRUE)), by=.(fishyr)]
efforts = efforts[ data.table(fishyr=yrsa) , on=.(fishyr)][["no_traps"]]
efforts = data.table( "At sea observed effort (trap hauls) \n/Efforts observés en mer (casiers levés)", t(efforts) )
names(efforts ) = c("species", yrsa )
bctabe = rbind(bctabe, efforts)
bctabe$"Average/Moyen" = round(rowMeans(bctabe[,.SD,.SDcols=patterns("[[:digit:]]+")], na.rm=TRUE),2)
toprint = c("species", yrss, "Average/Moyen")
bctabe = bctabe[,..toprint]
# return classifying variables to cpue kg/trap , standaradized to snow crab total catch rates
obs2 = cbind( uid=cpue_uid, cpue/cpue[["2526"]] ) # cpu is total (kept+discard)
obs2 = uid0[ obs2, on="uid"]
# aggregate to year
bct_catch = obs2[order(fishyr), lapply(.SD, function(x) {mean(x[is.finite(x)])}), .SDcols=patterns("[[:digit:]]+"), by=.(fishyr) ]
bct_catch = bct_catch[ , lapply(.SD, function(x) {ifelse(is.nan(x), NA, x) } ) ] # reset NaNs
bct_catch = lgyr[ bct_catch, on=.(fishyr)]
bct_catch = eff_summ[ bct_catch, on=.(fishyr)]
bct_catch = bct_catch[ data.table(fishyr=yrs) , on=.(fishyr)]
yrs = bct_catch$fishyr
bct_catch$fishyr = NULL
cpue_fraction = colMeans(bct_catch[, .SD, .SDcols=patterns("[[:digit:]]+") ], na.rm=TRUE)
# compute by catch as a fraction of snow crab landings
bct_catch = bct_catch[, lapply(.SD, function(x) {x*totallandings* ( 1 / (1-discard_rate) ) }), .SDcols=patterns("[[:digit:]]+") ]
bct_catch = bct_catch[, lapply(.SD, function(x) ifelse(is.nan(x), NA, x))] #NaN's behave differently ..
specs = names(bct_catch)
tx = bio.taxonomy::taxonomy.recode( from="spec", to="taxa", tolookup=specs )
bct_catch = as.data.table( t(bct_catch) )
names( bct_catch) = as.character( yrs )
bct_catch = zapsmall( bct_catch, digits=9)
bct_catch$spec = specs
bct_catch$cpue_fraction = cpue_fraction # mean fraction of landing per year (weight/weight)
bct_catch$species = stringr::str_to_title(tx$vern)
bct_catch$taxa = tx$tx
# snow crab kept to this point as a sosuble check on computations
yrss = year.assessment - (yrs_show-1):0
yrsa = 1999:year.assessment
to_show = c( "species", as.character( yrss ) )
to_use = c( "species", as.character( yrsa ) )
to_keep = setdiff( which(cpue_fraction >1e-9), which(names(cpue_fraction) =="2526" ))
# check if years missing
missing_years = setdiff( to_use, names(bct_catch))
j = length(missing_years)
if ( j > 0) {
for (i in 1:j) {
vn = paste( "missing_years[", i, "]", sep="" )
bct_catch[, eval(parse(text=vn)) ] = NA
}
}
bctabc = bct_catch[to_keep, ..to_use]
i = 2:ncol(bctabc)
sums = data.table( species="Total", t(colSums( bct_catch[to_keep, ..to_use][,..i] ) ) )
bctabc = rbind(bctabc, sums )
lds = lgyr[ data.table(fishyr=yrsa) , on=.(fishyr)]
landings = round(t(lds[["totallandings"]]))
landings = data.table( "Landings/Débarquements (kg)", landings )
names(landings ) = c("species", yrsa )
bctabc = rbind(bctabc, landings )
effort2 = t(lds[["totaleffort"]])
effort2 = data.table( "Effort (trap hauls/casiers levés)", effort2 )
names(effort2 ) = c("species", yrsa )
bctabc = rbind(bctabc, effort2 )
obs_summ = observed[ uid0, on=.(uid) ]
coverage = obs_summ[order(fishyr), .(wgt=sum(wgt, na.rm=TRUE)), by=.(fishyr)]
coverage = coverage[ data.table(fishyr=yrsa) , on=.(fishyr)][["wgt"]]
coverage = data.table( "At sea observed catch \n/Débarquements observés en mer (kg)", t(coverage) )
names(coverage ) = c("species", yrsa )
bctabc = rbind(bctabc, coverage)
efforts = obs_summ[order(fishyr), .(no_traps=sum(no_traps, na.rm=TRUE)), by=.(fishyr)]
efforts = efforts[ data.table(fishyr=yrsa) , on=.(fishyr)][["no_traps"]]
efforts = data.table( "At sea observed effort (trap hauls) \n/Efforts observés en mer (casiers levés)", t(efforts) )
names(efforts ) = c("species", yrsa )
bctabc = rbind(bctabc, efforts)
bctabc$"Average/Moyen" = round(rowMeans(bctabc[,.SD,.SDcols=patterns("[[:digit:]]+")], na.rm=TRUE),2)
toprint = c("species", yrss, "Average/Moyen")
bctabc = bctabc[,..toprint]
out = list(
oss=oss,
eff_summ=eff_summ,
bct = bct[toshow],
bct_sc = round(bct[["2526"]], 1 ),
spec = spec, # as factor
specid = specid,
species = stringr::str_to_title(species[toshow]),
cpue_fraction= cpue_fraction,
bycatch_table = bctabe,
bycatch_table_catch = bctabc
)
return(out)
}
# ---------------------
if (DS %in% c("odb", "odb.redo")) {
fn = file.path( project.datadirectory("bio.snowcrab"), "data", "observer", "odb.rdata" )
if (DS=="odb") {
load( fn )
return(odb)
}
mod1 = allometry.snowcrab ( "cw.mass", "male")
mod2 = allometry.snowcrab ( "chela.mass", "male")
mod3 = allometry.snowcrab ( "cw.chela.mat", "male")
odb = observer.db( DS="rawdata", yrs=1996:(p$year.assessment + 1)) # add one to get new year fraction for 4x
names(odb) = rename.bio.snowcrab.variables( names(odb) )
i.m = which( odb$sex==1)
i.f = which( odb$sex==2)
i.o = setdiff( 1:nrow( odb ), c(i.m, i.f) )
odb$sex[ i.m ] = male
odb$sex[ i.f ] = female
odb$sex[ i.o ] = sex.unknown
odb$cw[ which(odb$cw < 40 | odb$cw > 185) ] = NA
odb$log.cw = log( odb$cw )
odb$mass =NA
odb$log.mass = predict( mod1, odb )
kk = intersect( which( !is.finite( odb$mass ) ), i.m )
odb$mass[kk] = exp( odb$log.mass[kk] )
kk = intersect( which( !is.finite( odb$mass ) ), i.m )
odb$log.chela = log( odb$chela )
log.mass2 = predict( mod2, odb )
odb$mass[kk] = exp( log.mass2[kk] )
rm (mod1, mod2)
odb$mat_tmp = predict( mod3, odb, type="response" )
odb$mat = NA
odb$mat [ which(odb$mat_tmp <0.5 & odb$sex==male) ] = immature
odb$mat [ which(odb$mat_tmp >=0.5 & odb$sex==male) ] = mature
odb$log.cw = NULL
odb$log.chela = NULL
odb$log.mass = NULL
odb$mat_tmp = NULL
odb$lon = -odb$lon
odb$timestamp = odb$sdate
odb$yr = lubridate::year(odb$timestamp)
# cfa 4X has a fishing season that spans two years recode "yr" to "fishyr" to accomodate this
cfa4x = polygon_inside(odb, aegis.polygons::polygon_internal_code("cfa4x"))
to.offset = which( lubridate::month(odb$timestamp) >= 1 & lubridate::month(odb$timestamp) <= 7 )
to.offset = sort(intersect(cfa4x, to.offset))
odb$fishyr = odb$yr
odb$fishyr[to.offset] = odb$fishyr[to.offset] - 1
odb = odb[odb$fishyr >= 1996 ,] # years for which observer database are good
# odb$cw[odb$cw>175] = NA
odb$tripset = paste( odb$trip, odb$set_no, sep="~")
odb$cpue.kg.trap = ( odb$totmass*1000)/odb$num_hook_haul
save(odb, file=fn, compress=T)
return( "complete" )
}
}
jae0/bio.snowcrab documentation built on Nov. 6, 2024, 10:10 p.m.
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Defines functions observer.db
observer.db = function( DS, p=NULL, yrs=NULL, fn_root=project.datadirectory("bio.snowcrab"),
region="cfaall", yrs_show=5 ) {
# sex codes
male = 0
female = 1
sex.unknown = 2
# maturity codes
immature = 0
mature = 1
mat.unknown = 2
if (DS %in% c("rawdata.redo", "rawdata") ) {
fn.loc = file.path( fn_root, "data", "observer", "datadump" )
dir.create( fn.loc, recursive = TRUE, showWarnings = FALSE )
if (DS=="rawdata") {
out = NULL
for ( YR in yrs ) {
fny = file.path( fn.loc, paste( YR, "rdata", sep="."))
if (file.exists(fny)) {
odb = NULL
load (fny)
setDT(odb)
if (!is.null(odb)) {
out = try( rbind( out, odb ) )
if (inherits(out, "try-error" )) {
stop( "problem: inconsistent number of variables", fny)
}
}
}
}
return (out)
}
# for the full list of tables:
# tbls = sqlTables(connect)
# gs.tables = tbls[ which(tbls[,2] == "GROUNDFISH"),]
# print(gs.tables)
con = ROracle::dbConnect(DBI::dbDriver("Oracle"),dbname=oracle.snowcrab.server , username=oracle.snowcrab.user, password=oracle.snowcrab.password, believeNRows=F)
for ( YR in yrs ) {
fny = file.path( fn.loc, paste( YR, "rdata", sep="."))
odbq = paste(
"SELECT s.LATITUDE, s.LONGITUDE, s.LANDING_DATE, s.SET_NO, s.PRODCD_ID, s.SETCD_ID, s.EST_CATCH, s.EST_KEPT_WT," ,
"s.NUM_HOOK_HAUL, d.BOARD_DATE, d.FISH_NO, d.SEXCD_ID, d.FISH_LENGTH, " ,
"d.FEMALE_ABDOMEN, d.CHELA_HEIGHT, d.SHELLCOND_CD, d.DUROMETRE, d.TRIP_ID, d.TRIP " ,
"FROM SNOWCRAB.SNCRABDETAILS_OBS d, SNOWCRAB.SNCRABSETS_OBS s " ,
"WHERE d.TRIP_ID = s.TRIP_ID " ,
"AND d.SET_NO = s.SET_NO " ,
"AND s.SETCD_ID = 1", ## to keep trips for actual fishery activity (and not CP activity etc)
"AND d.FISH_NO Is Not Null" ,
"AND EXTRACT(YEAR from d.BOARD_DATE) = ", YR )
odb = NULL
odb = ROracle::dbGetQuery(con, odbq )
save( odb, file=fny, compress=T)
gc() # garbage collection
print(YR)
}
ROracle::dbDisconnect(con)
return (yrs)
}
if (DS %in% c("bycatch.redo", "bycatch") ) {
fn.loc = file.path( fn_root, "data", "observer", "bycatch" )
dir.create( fn.loc, recursive = TRUE, showWarnings = FALSE )
if (DS=="bycatch") {
out = NULL
for ( YR in yrs ) {
fny = file.path( fn.loc, paste( YR, "rdata", sep="."))
if (file.exists(fny)) {
odb = NULL
load (fny)
setDT(odb)
if (!is.null(odb)) {
out = try( rbind( out, odb ) )
if (inherits(out, "try-error" )) {
stop( "problem: inconsistent number of variables", fny)
}
}
}
}
return (out)
}
con = ROracle::dbConnect(DBI::dbDriver("Oracle"),dbname=oracle.snowcrab.server , username=oracle.snowcrab.user, password=oracle.snowcrab.password, believeNRows=F)
for ( YR in yrs ) {
fny = file.path( fn.loc, paste( YR, "rdata", sep="."))
odbq = paste(
"SELECT trip.trip_id, trip.trip, trip.board_date, trip.landing_date, st.set_no, vess.vessel_name, vess.license_no, vess.cfv,",
" isp.LATITUDE, isp.LONGITUDE, isp.DEPTH, isp.WATER_TEMPERATURE, ",
" sc.common, st.comarea_id, st.nafarea_id, ",
" ca.speccd_id, ca.est_num_caught, ca.est_kept_wt, ca.est_discard_wt, st.NUM_HOOK_HAUL,",
" fish.fish_no , fish.fish_length, fish.fish_weight ",
"FROM istrips trip, isgears gr, isfishsets st, iscatches ca, isfish fish, issetprofile isp, isvessels vess, isobservercodes o, isspeciescodes sc ",
"WHERE trip.tripcd_id = 2509",
"AND vess.vess_id = trip.vess_id",
"AND vess.license_no = trip.license_no",
"AND o.obscd_id = trip.obscd_id",
"AND trip.trip_id = gr.trip_Id",
"AND st.fishset_id = isp.fishset_id ",
"AND isp.fishset_id = ca.fishset_id ",
"AND ca.speccd_id = sc.speccd_id ",
"AND trip.tripcd_id=2509 ",
"AND st.specscd_id=2526 ",
"AND isp.pntcd_id=4 ",
"AND (trip.trip_id = st.trip_id AND gr.gear_id = st.gear_id)",
"AND ca.catch_id = fish.catch_id(+)",
"AND EXTRACT(YEAR from trip.board_date) = ", YR )
odb = NULL
odb = ROracle::dbGetQuery(con, odbq )
save( odb, file=fny, compress=T)
gc() # garbage collection
print(YR)
}
ROracle::dbDisconnect(con)
return (yrs)
}
if (DS %in% c("bycatch_clean_data", "bycatch_clean_data.redo")) {
fn = file.path( project.datadirectory("bio.snowcrab"), "data", "observer", "odb_bycatch.rdata" )
if (DS=="bycatch_clean_data") {
load( fn )
return(obs)
}
obs = observer.db( DS="bycatch", p=p, yrs=yrs ) # 711,413 in 2023
names(obs) = tolower(names(obs))
setDT(obs)
# QA/QC
obs$lon = -obs$longitude
obs$lat = obs$latitude
obs$longitude = NULL
obs$latitude = NULL
obs[ !is.finite(est_kept_wt), "est_kept_wt" ] = 0
obs[ !is.finite(est_discard_wt), "est_discard_wt" ] = 0
# obs[ !is.finite(num_hook_haul), "num_hook_haul" ] = 5 # this is protocal (assume when not recorded it is 5)
obs[ , wgt:=est_kept_wt + est_discard_wt ]
obs[ num_hook_haul > 10, "num_hook_haul"] = NA # errors likely
obs[ est_discard_wt > 1000, "est_discard_wt"] = NA # errors likely .. these are estimated
obs[ , cpue:= est_discard_wt /num_hook_haul ]
obs[ cpue > 150, "cpue"] = NA # errors likely as capture higher than 150kg / trap unlikely .. note these are cpu of all positive valued catches .. zero-valued are not records
obs[ !is.finite(board_date), "board_date" ] = NA
obs[ , yr:=year(board_date) ]
# cfa 4X has a fishing season that spans two years recode "yr" to "fishyr" to accomodate this
cfa4x = polygon_inside(obs, aegis.polygons::polygon_internal_code("cfa4x"))
mnth = lubridate::month(obs$board_date)
to.offset = which(mnth >= 1 & mnth <= 7 )
to.offset = sort(intersect(cfa4x, to.offset))
obs$fishyr = obs$yr
obs$fishyr[to.offset] = obs$fishyr[to.offset] - 1
obs = obs[obs$fishyr >= 1996 ,] # years for which observer database are good
obs[ , uid:=paste(trip, set_no, sep="_") ] # length(unique(obs$id)) 32406
save(obs, file=fn, compress=TRUE)
return(fn)
}
if (DS %in% c("bycatch_summary")) {
# fishery rates broken down by year (kg, trap hauls)
lgbk = logbook.db( DS="logbook", p=p, yrs=yrs ) # 71,194
names(lgbk) = tolower(names(lgbk))
setDT(lgbk)
lgbk$fishyr = lgbk$yr # fishyr is coded as "yr" in logbook.db
lgbk = lgbk[lgbk$fishyr %in% c(yrs, min(yrs)-1, max(yrs)+1) ,]
j = polygon_inside( lgbk[, c("lon", "lat")], region=region )
lgbk = lgbk[j,]
lgyr = lgbk[, .(
totaleffort = sum(effort, na.rm=TRUE),
totallandings = sum(landings, na.rm=TRUE),
cpue = sum(landings, na.rm=TRUE)/ sum(effort, na.rm=TRUE)
),
by=.(fishyr)
]
lgbk = NULL
# observer data .. extra care needed as there are duplicated records, etc
obs = observer.db( DS="bycatch_clean_data", p=p, yrs=yrs ) # Prepare at sea observed data
i = polygon_inside( obs[, c("lon", "lat")], region=region )
oss = obs = obs[i,]
# drop unid fish, "STONES AND ROCKS", "SEAWEED ALGAE KELP", "SNOW CRAB QUEEN", "CORAL", "SPONGES", "LEATHERBACK SEA TURTLE", "BASKING SHARK", "SEALS", "WHALES"
obs = obs[ !(speccd_id %in% c(90, 233, 900, 920, 8332, 8600, 9200, 9300, 9435 )), ] # 711,412
obs = obs[!grep("NA", uid),] # remove data with NA's in uid
uid0 = unique( obs[, .(uid, fishyr, cfv, wk=week(board_date))] )
# sampling effort and catch .. unique as data entered seems sometimes not aggregated
# also the SQL pull should be roken down to get the correct parts without the outer join
observed_effort = obs[, .(no_traps=unique(num_hook_haul)), by=.(uid)][, .(no_traps=sum(no_traps, na.rm=TRUE)), by=.(uid)]
observed_effort[ no_traps==0, "no_traps"] = NA # 5 ?override with standard sampling procotol expectation
observed_catch = obs[, .(wgt=unique(wgt)), by=.(uid)][, .(wgt=sum(wgt, na.rm=TRUE)), by=.(uid)]
observed = observed_effort[observed_catch, on=.(uid) ]
# same but broken down by uid and species
observed_catch2 = obs[, .(wgt=unique(wgt)), by=.(uid, speccd_id)][, .(wgt=sum(wgt, na.rm=TRUE)), by=.(uid, speccd_id)]
observed_discard2 = obs[, .(est_discard_wt=unique(est_discard_wt)), by=.(uid, speccd_id)][, .(est_discard_wt=sum(est_discard_wt, na.rm=TRUE)), by=.(uid, speccd_id)]
observed_kept2 = obs[, .(est_kept_wt=unique(est_kept_wt)), by=.(uid, speccd_id)][, .(est_kept_wt=sum(est_kept_wt, na.rm=TRUE)), by=.(uid, speccd_id)]
# kept weight is mostly snow crab as this is for the snow crab fishery
# non-zero (buy very low) kept species include: 2520, 2523, 2525, 2527, 10, 51 (probably for personal/direct use or sampling activity)
# .. can otherwise be ignored as totals are mostly discarded
observed2 = observed_catch2[observed_discard2, on=.(uid, speccd_id) ]
observed2 = observed2[observed_kept2, on=.(uid, speccd_id) ]
# add set/trip stats
observed2 = observed[ observed2, on=.(uid) ]
setnames( observed2, "i.wgt", "est_catch_wt" )
setnames( observed2, "wgt", "est_total_catch_wt" )
observed2[ , discard_rate := est_discard_wt/est_catch_wt ]
observed2[ , fraction_of_catch := est_catch_wt/est_total_catch_wt ]
observed2[ , cpue_total := est_catch_wt/no_traps ]
observed2[ , cpue_kept := est_catch_wt/no_traps ]
# efficiency of snow crab capture
eff = observed2[ speccd_id==2526, ]
eff = uid0[eff, on="uid"]
eff_summ = eff[, .(discard_rate=mean(discard_rate, na.rm=TRUE), discard_rate_sd=sd(discard_rate, na.rm=TRUE)), by=.(fishyr) ]
# catch rates kg per trap
# number of sampling events
cpue = dcast( observed2, uid~speccd_id, value.var="cpue_total",
fun.aggregate=mean, fill=0, drop=FALSE, na.rm=TRUE)
# cpue = uid0[catch, on="uid"]
cpue_uid = cpue$uid
cpue = cpue[ , lapply(.SD, function(x) {ifelse(is.nan(x), NA, x) } ),
.SDcols=patterns("[[:digit:]]+") ] # reset NaNs
bct = colMeans(cpue, na.rm=TRUE)
specid = colnames(cpue)
tx = bio.taxonomy::taxonomy.recode( from="spec", to="taxa", tolookup=specid )
species = tx$vern
toshow = which(specid != "2526") #drop as snow crab rates are very high
spec = as.numeric(as.factor(specid[toshow]) )
# direct computation from effort by year/spec
bct_effort = cbind(data.table(uid=cpue_uid), cpue)
bct_effort = bct_effort[uid0, on=.(uid)]
bct_effort = bct_effort[ order(fishyr), lapply(.SD, function(x) {mean(x[is.finite(x)])}), .SDcols=patterns("[[:digit:]]+"), by=.(fishyr) ]
bct_effort = bct_effort[ , lapply(.SD, function(x) {ifelse(is.nan(x), NA, x) } ) ] # reset NaNs
bct_effort = lgyr[bct_effort, on=.(fishyr)]
bct_effort = bct_effort[ , lapply(.SD, function(x) {x*totaleffort}), .SDcols=patterns("[[:digit:]]+"), by=.(fishyr) ]
bct_effort_catchmean = colMeans(bct_effort[, .SD, .SDcols=patterns("[[:digit:]]+") ], na.rm=TRUE)
specs = names(bct_effort) [-1]
tx = bio.taxonomy::taxonomy.recode( from="spec", to="taxa", tolookup=specs )
bct_effort = bct_effort[ data.table(fishyr=yrs) , on=.(fishyr)]
bct_effort$fishyr = NULL
bct_effort = as.data.table( t(bct_effort) )
names( bct_effort) = as.character( yrs )
bct_effort = zapsmall( bct_effort, digits=9)
bct_effort$spec = specs
bct_effort$species = stringr::str_to_title(tx$vern)
bct_effort$taxa = tx$tx
# snow crab kept to this point as a sosuble check on computations
yrss = year.assessment - (yrs_show-1):0
yrsa = 1999:year.assessment
to_show = c( "species", as.character( yrss ) )
to_use = c( "species", as.character( yrsa ) )
to_keep = setdiff( which(bct_effort_catchmean > 1), which(names(bct_effort_catchmean) =="2526" ))
# check if years missing
missing_years = setdiff( to_use, names(bct_effort))
j = length(missing_years)
if ( j > 0) {
for (i in 1:j) {
vn = paste( "missing_years[", i, "]", sep="" )
bct_effort[, eval(parse(text=vn)) ] = NA
}
}
bctabe = bct_effort[to_keep, ..to_use]
i = 2:ncol(bctabe)
sums = data.table( species="Total", t(colSums( bct_effort[to_keep, ..to_use][,..i] ) ) )
bctabe = rbind(bctabe, sums )
lds = lgyr[ data.table(fishyr=yrsa) , on=.(fishyr)]
landings = round(t(lds[["totallandings"]]))
landings = data.table( "Landings/Débarquements (kg)", landings )
names(landings ) = c("species", yrsa )
bctabe = rbind(bctabe, landings )
effort2 = t(lds[["totaleffort"]])
effort2 = data.table( "Effort (trap hauls/casiers levés)", effort2 )
names(effort2 ) = c("species", yrsa )
bctabe = rbind(bctabe, effort2 )
obs_summ = observed[ uid0, on=.(uid) ]
coverage = obs_summ[order(fishyr), .(wgt=sum(wgt, na.rm=TRUE)), by=.(fishyr)]
coverage = coverage[ data.table(fishyr=yrsa) , on=.(fishyr)][["wgt"]]
coverage = data.table( "At sea observed catch \n/Débarquements observés en mer (kg)", t(coverage) )
names(coverage ) = c("species", yrsa )
bctabe = rbind(bctabe, coverage)
efforts = obs_summ[order(fishyr), .(no_traps=sum(no_traps, na.rm=TRUE)), by=.(fishyr)]
efforts = efforts[ data.table(fishyr=yrsa) , on=.(fishyr)][["no_traps"]]
efforts = data.table( "At sea observed effort (trap hauls) \n/Efforts observés en mer (casiers levés)", t(efforts) )
names(efforts ) = c("species", yrsa )
bctabe = rbind(bctabe, efforts)
bctabe$"Average/Moyen" = round(rowMeans(bctabe[,.SD,.SDcols=patterns("[[:digit:]]+")], na.rm=TRUE),2)
toprint = c("species", yrss, "Average/Moyen")
bctabe = bctabe[,..toprint]
# return classifying variables to cpue kg/trap , standaradized to snow crab total catch rates
obs2 = cbind( uid=cpue_uid, cpue/cpue[["2526"]] ) # cpu is total (kept+discard)
obs2 = uid0[ obs2, on="uid"]
# aggregate to year
bct_catch = obs2[order(fishyr), lapply(.SD, function(x) {mean(x[is.finite(x)])}), .SDcols=patterns("[[:digit:]]+"), by=.(fishyr) ]
bct_catch = bct_catch[ , lapply(.SD, function(x) {ifelse(is.nan(x), NA, x) } ) ] # reset NaNs
bct_catch = lgyr[ bct_catch, on=.(fishyr)]
bct_catch = eff_summ[ bct_catch, on=.(fishyr)]
bct_catch = bct_catch[ data.table(fishyr=yrs) , on=.(fishyr)]
yrs = bct_catch$fishyr
bct_catch$fishyr = NULL
cpue_fraction = colMeans(bct_catch[, .SD, .SDcols=patterns("[[:digit:]]+") ], na.rm=TRUE)
# compute by catch as a fraction of snow crab landings
bct_catch = bct_catch[, lapply(.SD, function(x) {x*totallandings* ( 1 / (1-discard_rate) ) }), .SDcols=patterns("[[:digit:]]+") ]
bct_catch = bct_catch[, lapply(.SD, function(x) ifelse(is.nan(x), NA, x))] #NaN's behave differently ..
specs = names(bct_catch)
tx = bio.taxonomy::taxonomy.recode( from="spec", to="taxa", tolookup=specs )
bct_catch = as.data.table( t(bct_catch) )
names( bct_catch) = as.character( yrs )
bct_catch = zapsmall( bct_catch, digits=9)
bct_catch$spec = specs
bct_catch$cpue_fraction = cpue_fraction # mean fraction of landing per year (weight/weight)
bct_catch$species = stringr::str_to_title(tx$vern)
bct_catch$taxa = tx$tx
# snow crab kept to this point as a sosuble check on computations
yrss = year.assessment - (yrs_show-1):0
yrsa = 1999:year.assessment
to_show = c( "species", as.character( yrss ) )
to_use = c( "species", as.character( yrsa ) )
to_keep = setdiff( which(cpue_fraction >1e-9), which(names(cpue_fraction) =="2526" ))
# check if years missing
missing_years = setdiff( to_use, names(bct_catch))
j = length(missing_years)
if ( j > 0) {
for (i in 1:j) {
vn = paste( "missing_years[", i, "]", sep="" )
bct_catch[, eval(parse(text=vn)) ] = NA
}
}
bctabc = bct_catch[to_keep, ..to_use]
i = 2:ncol(bctabc)
sums = data.table( species="Total", t(colSums( bct_catch[to_keep, ..to_use][,..i] ) ) )
bctabc = rbind(bctabc, sums )
lds = lgyr[ data.table(fishyr=yrsa) , on=.(fishyr)]
landings = round(t(lds[["totallandings"]]))
landings = data.table( "Landings/Débarquements (kg)", landings )
names(landings ) = c("species", yrsa )
bctabc = rbind(bctabc, landings )
effort2 = t(lds[["totaleffort"]])
effort2 = data.table( "Effort (trap hauls/casiers levés)", effort2 )
names(effort2 ) = c("species", yrsa )
bctabc = rbind(bctabc, effort2 )
obs_summ = observed[ uid0, on=.(uid) ]
coverage = obs_summ[order(fishyr), .(wgt=sum(wgt, na.rm=TRUE)), by=.(fishyr)]
coverage = coverage[ data.table(fishyr=yrsa) , on=.(fishyr)][["wgt"]]
coverage = data.table( "At sea observed catch \n/Débarquements observés en mer (kg)", t(coverage) )
names(coverage ) = c("species", yrsa )
bctabc = rbind(bctabc, coverage)
efforts = obs_summ[order(fishyr), .(no_traps=sum(no_traps, na.rm=TRUE)), by=.(fishyr)]
efforts = efforts[ data.table(fishyr=yrsa) , on=.(fishyr)][["no_traps"]]
efforts = data.table( "At sea observed effort (trap hauls) \n/Efforts observés en mer (casiers levés)", t(efforts) )
names(efforts ) = c("species", yrsa )
bctabc = rbind(bctabc, efforts)
bctabc$"Average/Moyen" = round(rowMeans(bctabc[,.SD,.SDcols=patterns("[[:digit:]]+")], na.rm=TRUE),2)
toprint = c("species", yrss, "Average/Moyen")
bctabc = bctabc[,..toprint]
out = list(
oss=oss,
eff_summ=eff_summ,
bct = bct[toshow],
bct_sc = round(bct[["2526"]], 1 ),
spec = spec, # as factor
specid = specid,
species = stringr::str_to_title(species[toshow]),
cpue_fraction= cpue_fraction,
bycatch_table = bctabe,
bycatch_table_catch = bctabc
)
return(out)
}
# ---------------------
if (DS %in% c("odb", "odb.redo")) {
fn = file.path( project.datadirectory("bio.snowcrab"), "data", "observer", "odb.rdata" )
if (DS=="odb") {
load( fn )
return(odb)
}
mod1 = allometry.snowcrab ( "cw.mass", "male")
mod2 = allometry.snowcrab ( "chela.mass", "male")
mod3 = allometry.snowcrab ( "cw.chela.mat", "male")
odb = observer.db( DS="rawdata", yrs=1996:(p$year.assessment + 1)) # add one to get new year fraction for 4x
names(odb) = rename.bio.snowcrab.variables( names(odb) )
i.m = which( odb$sex==1)
i.f = which( odb$sex==2)
i.o = setdiff( 1:nrow( odb ), c(i.m, i.f) )
odb$sex[ i.m ] = male
odb$sex[ i.f ] = female
odb$sex[ i.o ] = sex.unknown
odb$cw[ which(odb$cw < 40 | odb$cw > 185) ] = NA
odb$log.cw = log( odb$cw )
odb$mass =NA
odb$log.mass = predict( mod1, odb )
kk = intersect( which( !is.finite( odb$mass ) ), i.m )
odb$mass[kk] = exp( odb$log.mass[kk] )
kk = intersect( which( !is.finite( odb$mass ) ), i.m )
odb$log.chela = log( odb$chela )
log.mass2 = predict( mod2, odb )
odb$mass[kk] = exp( log.mass2[kk] )
rm (mod1, mod2)
odb$mat_tmp = predict( mod3, odb, type="response" )
odb$mat = NA
odb$mat [ which(odb$mat_tmp <0.5 & odb$sex==male) ] = immature
odb$mat [ which(odb$mat_tmp >=0.5 & odb$sex==male) ] = mature
odb$log.cw = NULL
odb$log.chela = NULL
odb$log.mass = NULL
odb$mat_tmp = NULL
odb$lon = -odb$lon
odb$timestamp = odb$sdate
odb$yr = lubridate::year(odb$timestamp)
# cfa 4X has a fishing season that spans two years recode "yr" to "fishyr" to accomodate this
cfa4x = polygon_inside(odb, aegis.polygons::polygon_internal_code("cfa4x"))
to.offset = which( lubridate::month(odb$timestamp) >= 1 & lubridate::month(odb$timestamp) <= 7 )
to.offset = sort(intersect(cfa4x, to.offset))
odb$fishyr = odb$yr
odb$fishyr[to.offset] = odb$fishyr[to.offset] - 1
odb = odb[odb$fishyr >= 1996 ,] # years for which observer database are good
# odb$cw[odb$cw>175] = NA
odb$tripset = paste( odb$trip, odb$set_no, sep="~")
odb$cpue.kg.trap = ( odb$totmass*1000)/odb$num_hook_haul
save(odb, file=fn, compress=T)
return( "complete" )
}
}
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