inst/scripts/02.figures.r
In jae0/bio.snowcrab: Snow crab assessment suite using aegis* and associated projects and data streams.
# Figures obtained after completion of data assimilation and processing up to the end of "01.snowcrab.r"
year.assessment = 2023
p = bio.snowcrab::load.environment( year.assessment=year.assessment )
# ------------------------------------------
# Time-series: Fisheries landings
figure.landings.timeseries( yearmax=p$year.assessment, outdir=file.path( p$annual.results, "timeseries","fishery"), outfile="landings.ts", outfile2="landings.ts.sm" )
# ------------------------------------------
# Time-series: Fisheries effort
figure.effort.timeseries( yearmax=p$year.assessment, outdir=file.path( p$annual.results,"timeseries", "fishery"), outfile="effort.ts", outfile2="effort.ts.sm" )
# ------------------------------------------
# Time-series: Fisheries CPUE
figure.cpue.timeseries( yearmax=p$year.assessment, outdir=file.path( p$annual.results,"timeseries", "fishery"), outfile="cpue.ts", outfile2="cpue.sm.ts" )
# ------------------------------------------
# Size frequency distributions, broken down by moult category from at-sea observed data
figure.observed.size.freq( regions = c("cfanorth", "cfasouth", "cfa4x"), years="all", outdir=file.path( p$annual.results, "figures", "size.freq", "observer") )
figure.sizefreq.carapacecondition( X=snowcrab.db( p=p, DS="det.georeferenced" ), cwbr=4, regions=c("cfanorth", "cfasouth", "cfa4x"),
outdir=file.path( p$annual.results, "figures", "size.freq", "carapacecondition" ) )
# ------------------------------------------
# Size-frequency distributions of snow crab cw from trawl data, broken down by maturity classes
# take subset in years
years = as.character( c(-9:0) + year.assessment )
# years = as.character(2004:2013)
# years = as.character(1996:2003)
regions=c("cfanorth", "cfasouth", "cfa4x")
# outdir=file.path( p$annual.results, "figures", "size.freq", "survey_1996_2003" )
# outdir=file.path( p$annual.results, "figures", "size.freq", "survey_2004_2013" )
outdir=file.path( p$annual.results, "figures", "size.freq", "survey" )
M = size_distributions(p=p, toget="simple_direct", xrange=xrange, dx=dx, Y=years )
# NOTE :: these produce png files (instead of pdfs) change as required.
# den=arithmetic mean density, denl = geometric mean density
plot_histogram_carapace_width( M=M, years=years, regions=regions, plot_sex="female", yvar="denl",
outdir=outdir, cols = c("slategray", "gray95" ) )
plot_histogram_carapace_width( M=M, years=years, regions=regions, plot_sex="male", yvar="denl",
outdir=outdir, cols = c("slategray", "gray95" ) )
if (0) {
# deprecated methods:
histograms.size.maturity.update( outdir=file.path( p$annual.results, "figures", "size.freq", "survey"), redo.data=T )
histograms.size.maturity.single.area( outdir=file.path( p$annual.results, "figures", "size.freq", "survey"), area='cfa4x',redo.data=T ) #area = cfanorth, cfasouth of cfa4x
if (oneoff_2022){
histograms.size.maturity_oneoff(p=p)
figure.timeseries.survey_oneoff(p=p,
outdir=file.path(p$annual.results, "timeseries", "survey", "oneoff"),
vlab="R0.mass", variables="totmass", plotyears=2004:p$year.assessment) # just R0 to see
}
}
# ------------------------------------------
# Timeseries of all survey variables
figure.timeseries.survey(p=p, outdir=file.path(p$annual.results, "timeseries", "survey"), variables="R0.mass", plotyears=2004:p$year.assessment) # just R0 to see
figure.timeseries.survey(p=p, outdir=file.path(p$annual.results, "timeseries", "survey"), variables=c("sexratio.all","sexratio.mat","sexratio.imm"))
figure.timeseries.survey(p=p, outdir=file.path(p$annual.results, "timeseries", "survey"), variables="cw.male.mat.mean", plotyears=2004:p$year.assessment, backtransform=TRUE)
figure.timeseries.survey(p=p, outdir=file.path(p$annual.results, "timeseries", "survey"), plotyears=2004:p$year.assessment) # all variables
figure.timeseries.survey(p=p, outdir=file.path(p$annual.results, "timeseries", "observer"),plotyears=2004:p$year.assessment,type='observer')
# no longer relevant (incomplete) as temp is now created through temp db. and not in gshyd
# figure.timeseries.survey(p=p, outdir=file.path(p$annual.results, "timeseries", "survey"), plotyears=2004:p$year.assessment,type='groundfish.t') # groundfish survey temperature
# area-specific figures
figure_area_based_extraction_from_carstm(DS="temperature", year.assessment ) # can only do done once we have an sppoly for snow crab
#-----------------------------------------------
#Timeseries: geometric mean biomass of by-catch from snow crab survey
# predators and competitors
#cod, haddock, halibut, plaice, wolfish, thornyskate, smoothskate, winterskate, northernshrimp, jonahcrab, lessertoadcrab
species = c(10, 11, 30, 40, 201, 50, 2521, 2511, 202, 204, 2211)
figure.timeseries.bycatch(p=p, species=species, plotyears=2004:p$year.assessment, outdir=file.path(p$annual.results,"timeseries", "survey"), type="no" )
figure.timeseries.bycatch(p=p, species=species, plotyears=2004:p$year.assessment, outdir=file.path(p$annual.results,"timeseries", "survey"), type="mass" )
# Naive estimation of by catch: directly from observations
# Using at-sea-observed catch rate estimates and re-scaling these
# to total snow crab fishery effort:
## -----------------------------------------
## --> this has its own file. Copy it to a work directory e.g., bio.data/bio.snowcrab/assessments/ and run it from there as a Quarto document (https://quarto.org/):
## --> bycatch.qmd
## --> NOTE:: 02.tables.qmd is also a quarto document so copy that to the above location and run from there as well
## -----------------------------------------
# ------------------------------------------
# Map: Interpolated mean/geometric mean of various variables in the set data table
# p$do.parallel=F
p$corners = data.frame(plon=c(220, 990), plat=c(4750, 5270) )
p$mapyears = year.assessment + c(-5:0 )
outdir = file.path( p$project.outputdir, "maps", "survey", "snowcrab","annual" )
# just for the roadshow
map.set.information( p=p, outdir=outdir, variables=c('totmass.male.com', 'totmass.female.mat'),mapyears=p$mapyears)
map.set.information( p=p, outdir=outdir, variables=c('R0.mass'),mapyears=p$mapyears)
map.set.information( p=p, variables='t',mapyears=p$mapyears,outdir=outdir,log.variable=F,add.zeros=F,theta=100)
# bycatch (geometric means)
# predators and competitors
# cod, haddock, halibut, plaice, wolfish, thornyskate, smoothskate, winterskate, northernshrimp, jonahcrab, lessertoadcrab
species = c(10, 11, 30, 40, 201, 50, 2521, 2511, 202, 204, 2211)
bc.vars = c(paste("ms.mass",species,sep='.'),paste("ms.no",species,sep='.'))
outdir.bc= file.path( p$project.outputdir, "maps", "survey", "snowcrab","annual", "bycatch" )
map.set.information( p, variables=bc.vars, mapyears=p$mapyears, outdir=outdir.bc,probs=c(0,0.975)) #
# all variables (geometric means)
#map.set.information( p, outdir=outdir) # takes a long time
# Means
# variables that shouldn't be logged
set = snowcrab.db( p=p, DS="set.biologicals")
variables = bio.snowcrab::snowcrab.variablelist("all.data")
variables = intersect( variables, names(set) )
nolog.variables = c("t","z", "julian", variables[grep("cw",variables)])
map.set.information( p=p, variables=nolog.variables,outdir=outdir,log.variable=F,add.zeros=F,theta=35)
# logit transform for ratios
map.set.information( p=p, variables=c("sexratio.all","sexratio.mat","sexratio.imm"),outdir=outdir,log.variable=F,add.zeros=F,theta=40)
# Geometric Means
# all except variables that shouldn't be logged
mass.vars = variables[!variables%in%nolog.variables][grep('mass',variables[!variables%in%nolog.variables])]
no.vars = variables[!variables%in%nolog.variables][grep('no',variables[!variables%in%nolog.variables])]
map.set.information( p=p, variables= mass.vars,outdir=outdir)
map.set.information( p=p, variables= no.vars,outdir=outdir,probs=c(0,0.975))
# ------------------------------------------
# Map: Survey locations
map.survey.locations( p=p, basedir=file.path(p$project.outputdir, "maps", "survey.locations"), newyear=F, map.method="lattice" )
# map.survey.locations( p, basedir=file.path(p$project.outputdir, "maps", "survey.locations"), newyear=F, map.method="googleearth" )
# ------------------------------------------
# Map: Observer locations
map.observer.locations( p=p, basedir=file.path(p$project.outputdir, "maps","observer.locations" ), newyear=F , map.method="lattice" )
# ------------------------------------------
# Map: Logbook recorded locations
map.logbook.locations( p=p, basedir=file.path(p$project.outputdir, "maps","logbook.locations" ), newyear=F , map.method="lattice" )
# ------------------------------------------
# Map: Logbook data
map.fisheries.data(
outdir=file.path( p$project.outputdir, "maps", "logbook","snowcrab","annual" ),
probs=c(0,0.975),
plot_crs=st_crs( p$aegis_proj4string_planar_km ),
outformat="png"
)
# ------------------
# counts of stations in each area
# check towquality .. this should always == 1
set = snowcrab.db(p=p, DS="set.clean")
if (length( unique( set$towquality) ) != 1 ) print("error -- not good tows")
out = data.frame(yr=sort( unique(set$yr )) )
for (reg in c("cfaall", "cfanorth", "cfasouth","cfa4x" ) ) {
d = polygon_inside(set[,c("lon","lat")], reg)
e = as.data.frame( xtabs(~yr, data=set[d,]) )
names(e) = c("yr", reg)
e$yr = as.numeric(as.character(e$yr) )
out = merge(out, e, by="yr", all=T)
}
print(out)
plot.new()
year = p$year.assessment
setdata = set[ which(set$yr==year),]
# row numbers:
N = polygon_inside(setdata[,c("lon","lat")], "cfanorth")
S = polygon_inside(setdata[,c("lon","lat")], "cfasouth")
X = polygon_inside(setdata[,c("lon","lat")], "cfa4x")
plot(setdata$lon, setdata$lat)
points(setdata$lon[N], setdata$lat[N],col="red",pch=20)
points(setdata$lon[S], setdata$lat[S],col="blue",pch=20)
points(setdata$lon[X], setdata$lat[X],col="black",pch=20)
# -------------------------------------------------------------------------------------
# deprecated ?
# % mat calculations:
#as above but
loc = file.path(sc.R, "size.data")
dir.create(path=loc, recursive=T, showWarnings=F)
outfilename = paste( c("mi", "mm", "fi", "fm"), "rdata", sep=".")
outfile = file.path(loc, paste(outfilename))
for (f in outfile) load(f)
f.i = f.imm[which( rownames(f.imm)%in% sids ) ,]
f.i.means = apply(X=f.i, MARGIN=2, FUN=mean)
f.m = f.mat[which( rownames(f.mat)%in% sids ) ,]
f.m.means = apply(X=f.m, MARGIN=2, FUN=mean)
toplot = rbind(f.m.means, f.i.means)
ii = as.data.frame(t(toplot))
ii$cw = as.numeric(rownames(ii))
ii$pmat = ii[,1]/ (ii[,1]+ii[,2]) * 100
plot(ii$cw, ii$pmat)
abline(h=50)
str(ii)
#`data.frame': 70 obs. of 4 variables:
# $ f.m.means: num 0 0 0 0 0 ...
# $ f.i.means: num 2.80 6.19 20.05 24.29 74.11 ...
# $ cw : num 12 14 16 18 20 22 24 26 28 30 ...
# $ pmat : num 0 0 0 0 0 ...
# ---------------------------------------- NOT USED ____________
# Carapace condition from trawl data < 95mm CW ... not kriged .. simple proportions
det0 = snowcrab.db( p=p, DS="det.georeferenced" )
det0$fishyr = det0$yr ## the counting routine expectes this variable
det = det0[ which( det0$cw < 95 ) ,] # commerical sized crab only
years = sort( unique( det$yr ) )
res = NULL
for (r in p$regions) {
for (y in years) {
out = proportion.cc (det, region=r, year=y)
res = rbind( res, cbind( r, y, t(out)) )
}}
cnames = c("region", "fishyr", c(1:5), "ntot")
colnames(res) = cnames
print(res)
res = as.data.frame(res)
for (i in cnames[-1]) res[,i] = as.numeric(as.character((res[,i])))
(res)
##########################################################################
############################### Retired figures #########################
# ------------------------------------------
# Map: Scotian Shelf with CFA lines and labels .. using gmt
# this is the basemap from map.r which is then post-labelled in sodipodi
# p$outdir = file.path(p$annual.results,"figures")
# p$outfile.basename = file.path(p$outdir, "map.CFAs")
# map.basemap.with.cfa.lines( p, conversions=c("ps2png") )
# ------------------------------------------
# Habitat usage comparisons (bivariate) ... requires the full "set.rdata" database and "logbook.dZ.rdata" database
# habitat.usage( usevar="totno.all", covariate="depth", outdir = file.path(p$annual.results, "habitat.templates") )
# ------------------------------------------
# Habitat usage comparisons (bivariate) ... requires the full "set.rdata" database and "logbook.dZ.rdata" database
#habitat.usage( usevar="totno.all", covariate="temperature", outdir = file.path(p$annual.results, "habitat.templates") )
# ------------------------------------------
# Habitat usage comparisons (bivariate) ... requires the full "set.rdata" database and "logbook.dZ.rdata" database
# habitat.usage( usevar="totno.all", covariate="bottom.slope", outdir = file.path(p$annual.results, "habitat.templates") )
# ------------------------------------------
# Habitat usage comparisons (bivariate) ... requires the full "set.rdata" database and "logbook.dZ.rdata" database
#habitat.usage( usevar="totno.all", covariate="bottom.curvature", outdir = file.path(p$annual.results, "habitat.templates") )
# ------------------------------------------
# Habitat usage comparisons (bivariate) ... requires the full "set.rdata" database and "logbook.dZ.rdata" database
#habitat.usage( usevar="totno.all", covariate="substrate", outdir = file.path(p$annual.results, "habitat.templates") )
# ------------------------------------------
# Timeseries: Larval brachyura from the SSIP data
##figure.timeseries.larvae( outdir=file.path(p$project.outputdir, "timeseries", "larvae") )
# ------------------------------------------
# Growth as a a function of instar for Scotian Shelf snow crab
figure.growth.instar( outdir=file.path(p$project.outputdir, "growth") )
# ------------------------------------------
# Map: Larval distributions from the Scotian Shelf Ichtyoplankton Program data
map.larvae( p=p, outdir=file.path(p$project.outputdir, "maps", "larvae"), conversions=conversions )
# ------------------------------------------
# Map: Spatial representation of maturity patterns of snow crab
#MG Not sure we use these maps either, check with Adam and Jae
# map.maturity( p, outdir=file.path(p$project.outputdir, "maps", "maturity"), newyear=T )
res = maturity_region_year(p) # timeseries of maturity
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# Figures obtained after completion of data assimilation and processing up to the end of "01.snowcrab.r"
year.assessment = 2023
p = bio.snowcrab::load.environment( year.assessment=year.assessment )
# ------------------------------------------
# Time-series: Fisheries landings
figure.landings.timeseries( yearmax=p$year.assessment, outdir=file.path( p$annual.results, "timeseries","fishery"), outfile="landings.ts", outfile2="landings.ts.sm" )
# ------------------------------------------
# Time-series: Fisheries effort
figure.effort.timeseries( yearmax=p$year.assessment, outdir=file.path( p$annual.results,"timeseries", "fishery"), outfile="effort.ts", outfile2="effort.ts.sm" )
# ------------------------------------------
# Time-series: Fisheries CPUE
figure.cpue.timeseries( yearmax=p$year.assessment, outdir=file.path( p$annual.results,"timeseries", "fishery"), outfile="cpue.ts", outfile2="cpue.sm.ts" )
# ------------------------------------------
# Size frequency distributions, broken down by moult category from at-sea observed data
figure.observed.size.freq( regions = c("cfanorth", "cfasouth", "cfa4x"), years="all", outdir=file.path( p$annual.results, "figures", "size.freq", "observer") )
figure.sizefreq.carapacecondition( X=snowcrab.db( p=p, DS="det.georeferenced" ), cwbr=4, regions=c("cfanorth", "cfasouth", "cfa4x"),
outdir=file.path( p$annual.results, "figures", "size.freq", "carapacecondition" ) )
# ------------------------------------------
# Size-frequency distributions of snow crab cw from trawl data, broken down by maturity classes
# take subset in years
years = as.character( c(-9:0) + year.assessment )
# years = as.character(2004:2013)
# years = as.character(1996:2003)
regions=c("cfanorth", "cfasouth", "cfa4x")
# outdir=file.path( p$annual.results, "figures", "size.freq", "survey_1996_2003" )
# outdir=file.path( p$annual.results, "figures", "size.freq", "survey_2004_2013" )
outdir=file.path( p$annual.results, "figures", "size.freq", "survey" )
M = size_distributions(p=p, toget="simple_direct", xrange=xrange, dx=dx, Y=years )
# NOTE :: these produce png files (instead of pdfs) change as required.
# den=arithmetic mean density, denl = geometric mean density
plot_histogram_carapace_width( M=M, years=years, regions=regions, plot_sex="female", yvar="denl",
outdir=outdir, cols = c("slategray", "gray95" ) )
plot_histogram_carapace_width( M=M, years=years, regions=regions, plot_sex="male", yvar="denl",
outdir=outdir, cols = c("slategray", "gray95" ) )
if (0) {
# deprecated methods:
histograms.size.maturity.update( outdir=file.path( p$annual.results, "figures", "size.freq", "survey"), redo.data=T )
histograms.size.maturity.single.area( outdir=file.path( p$annual.results, "figures", "size.freq", "survey"), area='cfa4x',redo.data=T ) #area = cfanorth, cfasouth of cfa4x
if (oneoff_2022){
histograms.size.maturity_oneoff(p=p)
figure.timeseries.survey_oneoff(p=p,
outdir=file.path(p$annual.results, "timeseries", "survey", "oneoff"),
vlab="R0.mass", variables="totmass", plotyears=2004:p$year.assessment) # just R0 to see
}
}
# ------------------------------------------
# Timeseries of all survey variables
figure.timeseries.survey(p=p, outdir=file.path(p$annual.results, "timeseries", "survey"), variables="R0.mass", plotyears=2004:p$year.assessment) # just R0 to see
figure.timeseries.survey(p=p, outdir=file.path(p$annual.results, "timeseries", "survey"), variables=c("sexratio.all","sexratio.mat","sexratio.imm"))
figure.timeseries.survey(p=p, outdir=file.path(p$annual.results, "timeseries", "survey"), variables="cw.male.mat.mean", plotyears=2004:p$year.assessment, backtransform=TRUE)
figure.timeseries.survey(p=p, outdir=file.path(p$annual.results, "timeseries", "survey"), plotyears=2004:p$year.assessment) # all variables
figure.timeseries.survey(p=p, outdir=file.path(p$annual.results, "timeseries", "observer"),plotyears=2004:p$year.assessment,type='observer')
# no longer relevant (incomplete) as temp is now created through temp db. and not in gshyd
# figure.timeseries.survey(p=p, outdir=file.path(p$annual.results, "timeseries", "survey"), plotyears=2004:p$year.assessment,type='groundfish.t') # groundfish survey temperature
# area-specific figures
figure_area_based_extraction_from_carstm(DS="temperature", year.assessment ) # can only do done once we have an sppoly for snow crab
#-----------------------------------------------
#Timeseries: geometric mean biomass of by-catch from snow crab survey
# predators and competitors
#cod, haddock, halibut, plaice, wolfish, thornyskate, smoothskate, winterskate, northernshrimp, jonahcrab, lessertoadcrab
species = c(10, 11, 30, 40, 201, 50, 2521, 2511, 202, 204, 2211)
figure.timeseries.bycatch(p=p, species=species, plotyears=2004:p$year.assessment, outdir=file.path(p$annual.results,"timeseries", "survey"), type="no" )
figure.timeseries.bycatch(p=p, species=species, plotyears=2004:p$year.assessment, outdir=file.path(p$annual.results,"timeseries", "survey"), type="mass" )
# Naive estimation of by catch: directly from observations
# Using at-sea-observed catch rate estimates and re-scaling these
# to total snow crab fishery effort:
## -----------------------------------------
## --> this has its own file. Copy it to a work directory e.g., bio.data/bio.snowcrab/assessments/ and run it from there as a Quarto document (https://quarto.org/):
## --> bycatch.qmd
## --> NOTE:: 02.tables.qmd is also a quarto document so copy that to the above location and run from there as well
## -----------------------------------------
# ------------------------------------------
# Map: Interpolated mean/geometric mean of various variables in the set data table
# p$do.parallel=F
p$corners = data.frame(plon=c(220, 990), plat=c(4750, 5270) )
p$mapyears = year.assessment + c(-5:0 )
outdir = file.path( p$project.outputdir, "maps", "survey", "snowcrab","annual" )
# just for the roadshow
map.set.information( p=p, outdir=outdir, variables=c('totmass.male.com', 'totmass.female.mat'),mapyears=p$mapyears)
map.set.information( p=p, outdir=outdir, variables=c('R0.mass'),mapyears=p$mapyears)
map.set.information( p=p, variables='t',mapyears=p$mapyears,outdir=outdir,log.variable=F,add.zeros=F,theta=100)
# bycatch (geometric means)
# predators and competitors
# cod, haddock, halibut, plaice, wolfish, thornyskate, smoothskate, winterskate, northernshrimp, jonahcrab, lessertoadcrab
species = c(10, 11, 30, 40, 201, 50, 2521, 2511, 202, 204, 2211)
bc.vars = c(paste("ms.mass",species,sep='.'),paste("ms.no",species,sep='.'))
outdir.bc= file.path( p$project.outputdir, "maps", "survey", "snowcrab","annual", "bycatch" )
map.set.information( p, variables=bc.vars, mapyears=p$mapyears, outdir=outdir.bc,probs=c(0,0.975)) #
# all variables (geometric means)
#map.set.information( p, outdir=outdir) # takes a long time
# Means
# variables that shouldn't be logged
set = snowcrab.db( p=p, DS="set.biologicals")
variables = bio.snowcrab::snowcrab.variablelist("all.data")
variables = intersect( variables, names(set) )
nolog.variables = c("t","z", "julian", variables[grep("cw",variables)])
map.set.information( p=p, variables=nolog.variables,outdir=outdir,log.variable=F,add.zeros=F,theta=35)
# logit transform for ratios
map.set.information( p=p, variables=c("sexratio.all","sexratio.mat","sexratio.imm"),outdir=outdir,log.variable=F,add.zeros=F,theta=40)
# Geometric Means
# all except variables that shouldn't be logged
mass.vars = variables[!variables%in%nolog.variables][grep('mass',variables[!variables%in%nolog.variables])]
no.vars = variables[!variables%in%nolog.variables][grep('no',variables[!variables%in%nolog.variables])]
map.set.information( p=p, variables= mass.vars,outdir=outdir)
map.set.information( p=p, variables= no.vars,outdir=outdir,probs=c(0,0.975))
# ------------------------------------------
# Map: Survey locations
map.survey.locations( p=p, basedir=file.path(p$project.outputdir, "maps", "survey.locations"), newyear=F, map.method="lattice" )
# map.survey.locations( p, basedir=file.path(p$project.outputdir, "maps", "survey.locations"), newyear=F, map.method="googleearth" )
# ------------------------------------------
# Map: Observer locations
map.observer.locations( p=p, basedir=file.path(p$project.outputdir, "maps","observer.locations" ), newyear=F , map.method="lattice" )
# ------------------------------------------
# Map: Logbook recorded locations
map.logbook.locations( p=p, basedir=file.path(p$project.outputdir, "maps","logbook.locations" ), newyear=F , map.method="lattice" )
# ------------------------------------------
# Map: Logbook data
map.fisheries.data(
outdir=file.path( p$project.outputdir, "maps", "logbook","snowcrab","annual" ),
probs=c(0,0.975),
plot_crs=st_crs( p$aegis_proj4string_planar_km ),
outformat="png"
)
# ------------------
# counts of stations in each area
# check towquality .. this should always == 1
set = snowcrab.db(p=p, DS="set.clean")
if (length( unique( set$towquality) ) != 1 ) print("error -- not good tows")
out = data.frame(yr=sort( unique(set$yr )) )
for (reg in c("cfaall", "cfanorth", "cfasouth","cfa4x" ) ) {
d = polygon_inside(set[,c("lon","lat")], reg)
e = as.data.frame( xtabs(~yr, data=set[d,]) )
names(e) = c("yr", reg)
e$yr = as.numeric(as.character(e$yr) )
out = merge(out, e, by="yr", all=T)
}
print(out)
plot.new()
year = p$year.assessment
setdata = set[ which(set$yr==year),]
# row numbers:
N = polygon_inside(setdata[,c("lon","lat")], "cfanorth")
S = polygon_inside(setdata[,c("lon","lat")], "cfasouth")
X = polygon_inside(setdata[,c("lon","lat")], "cfa4x")
plot(setdata$lon, setdata$lat)
points(setdata$lon[N], setdata$lat[N],col="red",pch=20)
points(setdata$lon[S], setdata$lat[S],col="blue",pch=20)
points(setdata$lon[X], setdata$lat[X],col="black",pch=20)
# -------------------------------------------------------------------------------------
# deprecated ?
# % mat calculations:
#as above but
loc = file.path(sc.R, "size.data")
dir.create(path=loc, recursive=T, showWarnings=F)
outfilename = paste( c("mi", "mm", "fi", "fm"), "rdata", sep=".")
outfile = file.path(loc, paste(outfilename))
for (f in outfile) load(f)
f.i = f.imm[which( rownames(f.imm)%in% sids ) ,]
f.i.means = apply(X=f.i, MARGIN=2, FUN=mean)
f.m = f.mat[which( rownames(f.mat)%in% sids ) ,]
f.m.means = apply(X=f.m, MARGIN=2, FUN=mean)
toplot = rbind(f.m.means, f.i.means)
ii = as.data.frame(t(toplot))
ii$cw = as.numeric(rownames(ii))
ii$pmat = ii[,1]/ (ii[,1]+ii[,2]) * 100
plot(ii$cw, ii$pmat)
abline(h=50)
str(ii)
#`data.frame': 70 obs. of 4 variables:
# $ f.m.means: num 0 0 0 0 0 ...
# $ f.i.means: num 2.80 6.19 20.05 24.29 74.11 ...
# $ cw : num 12 14 16 18 20 22 24 26 28 30 ...
# $ pmat : num 0 0 0 0 0 ...
# ---------------------------------------- NOT USED ____________
# Carapace condition from trawl data < 95mm CW ... not kriged .. simple proportions
det0 = snowcrab.db( p=p, DS="det.georeferenced" )
det0$fishyr = det0$yr ## the counting routine expectes this variable
det = det0[ which( det0$cw < 95 ) ,] # commerical sized crab only
years = sort( unique( det$yr ) )
res = NULL
for (r in p$regions) {
for (y in years) {
out = proportion.cc (det, region=r, year=y)
res = rbind( res, cbind( r, y, t(out)) )
}}
cnames = c("region", "fishyr", c(1:5), "ntot")
colnames(res) = cnames
print(res)
res = as.data.frame(res)
for (i in cnames[-1]) res[,i] = as.numeric(as.character((res[,i])))
(res)
##########################################################################
############################### Retired figures #########################
# ------------------------------------------
# Map: Scotian Shelf with CFA lines and labels .. using gmt
# this is the basemap from map.r which is then post-labelled in sodipodi
# p$outdir = file.path(p$annual.results,"figures")
# p$outfile.basename = file.path(p$outdir, "map.CFAs")
# map.basemap.with.cfa.lines( p, conversions=c("ps2png") )
# ------------------------------------------
# Habitat usage comparisons (bivariate) ... requires the full "set.rdata" database and "logbook.dZ.rdata" database
# habitat.usage( usevar="totno.all", covariate="depth", outdir = file.path(p$annual.results, "habitat.templates") )
# ------------------------------------------
# Habitat usage comparisons (bivariate) ... requires the full "set.rdata" database and "logbook.dZ.rdata" database
#habitat.usage( usevar="totno.all", covariate="temperature", outdir = file.path(p$annual.results, "habitat.templates") )
# ------------------------------------------
# Habitat usage comparisons (bivariate) ... requires the full "set.rdata" database and "logbook.dZ.rdata" database
# habitat.usage( usevar="totno.all", covariate="bottom.slope", outdir = file.path(p$annual.results, "habitat.templates") )
# ------------------------------------------
# Habitat usage comparisons (bivariate) ... requires the full "set.rdata" database and "logbook.dZ.rdata" database
#habitat.usage( usevar="totno.all", covariate="bottom.curvature", outdir = file.path(p$annual.results, "habitat.templates") )
# ------------------------------------------
# Habitat usage comparisons (bivariate) ... requires the full "set.rdata" database and "logbook.dZ.rdata" database
#habitat.usage( usevar="totno.all", covariate="substrate", outdir = file.path(p$annual.results, "habitat.templates") )
# ------------------------------------------
# Timeseries: Larval brachyura from the SSIP data
##figure.timeseries.larvae( outdir=file.path(p$project.outputdir, "timeseries", "larvae") )
# ------------------------------------------
# Growth as a a function of instar for Scotian Shelf snow crab
figure.growth.instar( outdir=file.path(p$project.outputdir, "growth") )
# ------------------------------------------
# Map: Larval distributions from the Scotian Shelf Ichtyoplankton Program data
map.larvae( p=p, outdir=file.path(p$project.outputdir, "maps", "larvae"), conversions=conversions )
# ------------------------------------------
# Map: Spatial representation of maturity patterns of snow crab
#MG Not sure we use these maps either, check with Adam and Jae
# map.maturity( p, outdir=file.path(p$project.outputdir, "maps", "maturity"), newyear=T )
res = maturity_region_year(p) # timeseries of maturity
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