inst/scripts/93.biomass_index_carstm.r
In jae0/snowcrab: Snow crab assessment suite using aegis* and associated projects and data streams.
# -------------------------------------------------
# Snow crab --- Areal unit modelling Hurdle / Delta model
# combination of three models via posterior simulation
# 1. Poisson on positive valued numbers offset by swept are
# 2. Meansize in space and time
# 3 Presence-absence
# the convolution of all three after simulation is called a Hurdle or Delta model
# -------------------------------------------------
# TODO::: move plotting calls to self-contained functions:
# -------------------------------------------------
# Part 1 -- construct basic parameter list defining the main characteristics of the study
require(bio.snowcrab) # loadfunctions("bio.snowcrab")
year.assessment = 2023
yrs = 1999:year.assessment
spec_bio = bio.taxonomy::taxonomy.recode( from="spec", to="parsimonious", tolookup=2526 )
snowcrab_filter_class = "fb" # fishable biomass (including soft-shelled ) "m.mat" "f.mat" "imm"
carstm_model_label= paste( "default", snowcrab_filter_class, sep="_" )
# params for number
pN = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
carstm_model_label= carstm_model_label,
selection = list(
type = "number",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for mean size .. mostly the same as pN
pW = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
carstm_model_label= carstm_model_label,
selection = list(
type = "meansize",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for probability of observation
pH = snowcrab_parameters(
project_clastheta_inits="carstm",
yrs=yrs,
areal_units_type="tesselation",
carstm_model_label= carstm_model_label,
selection = list(
type = "presence_absence",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
sppoly=areal_units( p=pN )
pN$space_name = sppoly$AUID
pN$space_id = 1:nrow(sppoly) # must match M$space
pN$time_name = as.character(pN$yrs)
pN$time_id = 1:pN$ny
pN$cyclic_name = as.character(pN$cyclic_levels)
pN$cyclic_id = 1:pN$nw
pW$space_name = sppoly$AUID
pW$space_id = 1:nrow(sppoly) # must match M$space
pW$time_name = as.character(pW$yrs)
pW$time_id = 1:pW$ny
pW$cyclic_name = as.character(pW$cyclic_levels)
pW$cyclic_id = 1:pW$nw
pH$space_name = sppoly$AUID
pH$space_id = 1:nrow(sppoly) # must match M$space
pH$time_name = as.character(pH$yrs)
pH$time_id = 1:pH$ny
pH$cyclic_name = as.character(pH$cyclic_levels)
pH$cyclic_id = 1:pH$nw
M = snowcrab.db( p=pN, DS="carstm_inputs", sppoly=sppoly, redo=TRUE ) # will redo if not found
additional_features = snowcrab_mapping_features(pN) # for mapping below
# ------------------------------------------------
# Part 2 -- spatiotemporal statistical model
if ( spatiotemporal_model ) {
# total numbers
sppoly = areal_units( p=pN )
M = snowcrab.db( p=pN, DS="carstm_inputs", sppoly=sppoly ) # will redo if not found
io = which(M$tag=="observations")
ip = which(M$tag=="predictions")
iq = unique( c( which( M$totno > 0), ip ) )
iw = unique( c( which( M$totno > 5), ip ) ) # need a good sample to estimate mean size
# number
res = NULL; gc()
res = carstm_model( p=pN, data=M[ iq, ], sppoly=sppoly,
theta=c( 2.409, 1.874, 0.772, 2.092, -1.490, 5.145, 4.509, 2.178, 5.453, 0.182, 2.742, 0.525, 0.051, 0.779 ),
nposteriors=5000,
posterior_simulations_to_retain=c( "summary", "random_spatial", "predictions"),
family = "poisson",
verbose=TRUE,
# redo_fit=FALSE,
# debug = "summary",
# debug = "predictions",
num.threads="4:3"
)
# mean size
res = NULL; gc()
res = carstm_model( p=pW, data=M[ iw, ], sppoly = sppoly,
theta=c( 6.108, 8.632, 0.883, 2.946, 9.801, 7.265, 10.726, 12.214, 11.849, 9.826, 6.556, 3.456, 5.832, 2.939, 1.625 ),
nposteriors=5000,
posterior_simulations_to_retain=c( "summary", "random_spatial", "predictions"),
family = "gaussian",
verbose=TRUE,
# redo_fit=FALSE,
# debug = "summary",
# control.inla = list( strategy="laplace", int.strategy="eb" ),
num.threads="4:3"
)
# model pa using all data
res = NULL; gc()
res = carstm_model( p=pH, data=M, sppoly=sppoly,
theta = c( 0.926, 1.743, -0.401, 0.705, -2.574, 1.408, 2.390, 3.459, 3.321, -2.138, 3.083, -1.014, 3.558, 2.703 ),
nposteriors=5000,
posterior_simulations_to_retain=c( "summary", "random_spatial", "predictions"),
family = "binomial", # "binomial", # "nbinomial", "betabinomial", "zeroinflatedbinomial0" , "zeroinflatednbinomial0"
verbose=TRUE,
#redo_fit=FALSE,
# debug = "summary",
# control.family=list(control.link=list(model="logit")), # default for binomial .. no need to specify
# control.inla = list( strategy="laplace", int.strategy="eb" ),
num.threads="4:3"
)
} # end spatiotemporal model
# ----------------------
# Part 3: assimilation of models
assimilate_numbers_and_size = TRUE
if (assimilate_numbers_and_size ) {
# wgts_max = 1.1 # kg, hard upper limit
# N_max = NULL
# # quantile( M$totno[ipositive]/M$data_offset[ipositive], probs=0.95, na.rm=TRUE )
# posterior sims
sims = carstm_posterior_simulations( pN=pN, pW=pW, pH=pH, pa_threshold=0.05, qmax=0.95 )
sims = sims / 10^6 # units: kg ; div (10^6) -> kt ;;
# sims[ which(!is.finite(sppoly$npts)),, ] = 0
SM = aggregate_simulations(
sims=sims,
sppoly=sppoly,
fn=carstm_filenames( pN, returnvalue="filename", fn="aggregated_timeseries" ),
yrs=pN$yrs,
method="sum",
redo=TRUE
)
# units: kt/km^2
if (0) {
# to compute habitat prob
sims = carstm_posterior_simulations( pH=pH, pa_threshold=0.05, qmax=0.95 )
SM = aggregate_simulations(
sims=sims,
sppoly=sppoly,
fn=carstm_filenames( pN, returnvalue="filename", fn="aggregated_timeseries" ),
yrs=pN$yrs,
method="mean",
redo=TRUE
)
outputdir = file.path( carstm_filenames( pN, returnvalue="output_directory"), "aggregated_habitat_timeseries" )
RES= SM$RES
}
RES= SM$RES # units: kt
# RES = aggregate_simulations( fn=carstm_filenames( pN, returnvalue="filename", fn="aggregated_timeseries" ) )$RES
# note: using pN, even though this is biomass
outputdir = file.path( carstm_filenames( pN, returnvalue="output_directory"), "aggregated_biomass_timeseries" )
if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )
( fn = file.path( outputdir, "cfa_all.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfaall ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfaall_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfaall_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
( fn = file.path( outputdir, "cfa_south.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfasouth ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfasouth_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfasouth_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
( fn = file.path( outputdir, "cfa_north.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfanorth ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfanorth_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfanorth_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
( fn = file.path( outputdir, "cfa_4x.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfa4x ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfa4x_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfa4x_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
regions = c("cfanorth", "cfasouth", "cfa4x" )
region_label = c("N-ENS", "S-ENS", "4X")
a= cbind( "cfanorth", RES[,c("yrs", "cfanorth", "cfanorth_lb", "cfanorth_ub")] )
b= cbind( "cfasouth", RES[,c("yrs", "cfasouth", "cfasouth_lb", "cfasouth_ub")] )
c= cbind( "cfa4x", RES[,c("yrs", "cfa4x", "cfa4x_lb", "cfa4x_ub")] )
names(a) = names(b) = names(c) = c("region", "year", "mean", "lb", "ub")
tdb = rbind(a, b, c)
tdb$region = factor(tdb$region, levels=regions, labels =region_label)
tdb = tdb[(which(!is.na(tdb$region))), ]
fn = file.path( outputdir, "biomass_M0.png" )
require(ggplot2)
library(ggbreak)
color_map = c("#E69F00", "#56B4E9", "#CC79A7" )
out = ggplot(tdb, aes(x=year, y=mean, fill=region, colour=region)) +
geom_line( alpha=0.9, linewidth=1.2 ) +
geom_point(aes(shape=region), size=3, alpha=0.7 ) +
geom_errorbar(aes(ymin=lb,ymax=ub), linewidth=0.8, alpha=0.8, width=0.3) +
labs(x="Year/Année", y="Biomass index (kt) / Indice de biomasse (kt)", size = rel(1.5)) +
scale_colour_manual(values=color_map) +
scale_fill_manual(values=color_map) +
scale_shape_manual(values = c(15, 17, 19)) +
theme_light( base_size = 22) +
theme( legend.position="inside", legend.position.inside=c(0.75, 0.9), legend.title=element_blank()) +
scale_y_break(c(14, 28), scales = 1)
# scale_y_continuous( limits=c(0, 300) )
ggsave(filename=fn, plot=out, device="png", width=12, height = 8)
print(out)
# map it ..mean density
sppoly = areal_units( p=pN ) # to reload
vn = paste("biomass", "predicted", sep=".")
outputdir = file.path( carstm_filenames( pN, returnvalue="output_directory"), "predicted_biomass_densities" )
if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )
B = apply( sims, c(1,2), mean ) # sims units (kt);
B[ which(!is.finite(B)) ] = NA
# brks = pretty( log10( quantile( B[], probs=c(0.05, 0.95), na.rm=TRUE )* 10^6) )
sa = units::drop_units(sppoly$au_sa_km2)
brks = pretty( ( quantile( log(B * 10^6 / sa), probs=c(0.05, 0.95), na.rm=TRUE )) )
additional_features = snowcrab_mapping_features(pN) # for mapping below
for (i in 1:length(pN$yrs) ) {
y = as.character( pN$yrs[i] )
# u = log10( B[,y]* 10^6 ) ## Total kt->kg: log10( kg )
u = log( B[,y]* 10^6 / sa) # ;; density log10( kg /km^2 )
sppoly[,vn] = u
outfilename = file.path( outputdir , paste( "biomass", y, "png", sep=".") )
plt = carstm_map( sppoly=sppoly, vn=vn,
breaks=brks,
additional_features=additional_features,
title=y,
# title=paste( "log_10( Predicted biomass density; kg/km^2 )", y ),
colors=rev(RColorBrewer::brewer.pal(5, "RdYlBu")),
outfilename=outfilename
)
plt
}
} # end assimilate size and numbers
# prep data for discrete version
# Rdata files are ready load them through julia and model
# for production
sppoly_tweaks = list(
# vary params by variable as data densities vary for these size/age/sex groups
areal_units_constraint_ntarget= list( M0=8, M1=10, M2=14, M3=14, M4=14, f.mat=8 ),
n_iter_drop=list( M0=0, M1=1, M2=1, M3=1, M4=1, f.mat=0 )
)
fishery_model_data_inputs(
year.assessment=year.assessment,
type="biomass_dynamics",
snowcrab_filter_class="fb",
modeldir = pN$modeldir,
carstm_model_label= pN$carstm_model_label,
for_julia=TRUE )
## note the output directory .. this is used for the next script
# for development
# modeldir = file.path( homedir, "projects", "dynamical_model", "snowcrab", "data" )
# end
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# -------------------------------------------------
# Snow crab --- Areal unit modelling Hurdle / Delta model
# combination of three models via posterior simulation
# 1. Poisson on positive valued numbers offset by swept are
# 2. Meansize in space and time
# 3 Presence-absence
# the convolution of all three after simulation is called a Hurdle or Delta model
# -------------------------------------------------
# TODO::: move plotting calls to self-contained functions:
# -------------------------------------------------
# Part 1 -- construct basic parameter list defining the main characteristics of the study
require(bio.snowcrab) # loadfunctions("bio.snowcrab")
year.assessment = 2023
yrs = 1999:year.assessment
spec_bio = bio.taxonomy::taxonomy.recode( from="spec", to="parsimonious", tolookup=2526 )
snowcrab_filter_class = "fb" # fishable biomass (including soft-shelled ) "m.mat" "f.mat" "imm"
carstm_model_label= paste( "default", snowcrab_filter_class, sep="_" )
# params for number
pN = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
carstm_model_label= carstm_model_label,
selection = list(
type = "number",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for mean size .. mostly the same as pN
pW = snowcrab_parameters(
project_class="carstm",
yrs=yrs,
areal_units_type="tesselation",
carstm_model_label= carstm_model_label,
selection = list(
type = "meansize",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
# params for probability of observation
pH = snowcrab_parameters(
project_clastheta_inits="carstm",
yrs=yrs,
areal_units_type="tesselation",
carstm_model_label= carstm_model_label,
selection = list(
type = "presence_absence",
biologicals=list( spec_bio=spec_bio ),
biologicals_using_snowcrab_filter_class=snowcrab_filter_class
)
)
sppoly=areal_units( p=pN )
pN$space_name = sppoly$AUID
pN$space_id = 1:nrow(sppoly) # must match M$space
pN$time_name = as.character(pN$yrs)
pN$time_id = 1:pN$ny
pN$cyclic_name = as.character(pN$cyclic_levels)
pN$cyclic_id = 1:pN$nw
pW$space_name = sppoly$AUID
pW$space_id = 1:nrow(sppoly) # must match M$space
pW$time_name = as.character(pW$yrs)
pW$time_id = 1:pW$ny
pW$cyclic_name = as.character(pW$cyclic_levels)
pW$cyclic_id = 1:pW$nw
pH$space_name = sppoly$AUID
pH$space_id = 1:nrow(sppoly) # must match M$space
pH$time_name = as.character(pH$yrs)
pH$time_id = 1:pH$ny
pH$cyclic_name = as.character(pH$cyclic_levels)
pH$cyclic_id = 1:pH$nw
M = snowcrab.db( p=pN, DS="carstm_inputs", sppoly=sppoly, redo=TRUE ) # will redo if not found
additional_features = snowcrab_mapping_features(pN) # for mapping below
# ------------------------------------------------
# Part 2 -- spatiotemporal statistical model
if ( spatiotemporal_model ) {
# total numbers
sppoly = areal_units( p=pN )
M = snowcrab.db( p=pN, DS="carstm_inputs", sppoly=sppoly ) # will redo if not found
io = which(M$tag=="observations")
ip = which(M$tag=="predictions")
iq = unique( c( which( M$totno > 0), ip ) )
iw = unique( c( which( M$totno > 5), ip ) ) # need a good sample to estimate mean size
# number
res = NULL; gc()
res = carstm_model( p=pN, data=M[ iq, ], sppoly=sppoly,
theta=c( 2.409, 1.874, 0.772, 2.092, -1.490, 5.145, 4.509, 2.178, 5.453, 0.182, 2.742, 0.525, 0.051, 0.779 ),
nposteriors=5000,
posterior_simulations_to_retain=c( "summary", "random_spatial", "predictions"),
family = "poisson",
verbose=TRUE,
# redo_fit=FALSE,
# debug = "summary",
# debug = "predictions",
num.threads="4:3"
)
# mean size
res = NULL; gc()
res = carstm_model( p=pW, data=M[ iw, ], sppoly = sppoly,
theta=c( 6.108, 8.632, 0.883, 2.946, 9.801, 7.265, 10.726, 12.214, 11.849, 9.826, 6.556, 3.456, 5.832, 2.939, 1.625 ),
nposteriors=5000,
posterior_simulations_to_retain=c( "summary", "random_spatial", "predictions"),
family = "gaussian",
verbose=TRUE,
# redo_fit=FALSE,
# debug = "summary",
# control.inla = list( strategy="laplace", int.strategy="eb" ),
num.threads="4:3"
)
# model pa using all data
res = NULL; gc()
res = carstm_model( p=pH, data=M, sppoly=sppoly,
theta = c( 0.926, 1.743, -0.401, 0.705, -2.574, 1.408, 2.390, 3.459, 3.321, -2.138, 3.083, -1.014, 3.558, 2.703 ),
nposteriors=5000,
posterior_simulations_to_retain=c( "summary", "random_spatial", "predictions"),
family = "binomial", # "binomial", # "nbinomial", "betabinomial", "zeroinflatedbinomial0" , "zeroinflatednbinomial0"
verbose=TRUE,
#redo_fit=FALSE,
# debug = "summary",
# control.family=list(control.link=list(model="logit")), # default for binomial .. no need to specify
# control.inla = list( strategy="laplace", int.strategy="eb" ),
num.threads="4:3"
)
} # end spatiotemporal model
# ----------------------
# Part 3: assimilation of models
assimilate_numbers_and_size = TRUE
if (assimilate_numbers_and_size ) {
# wgts_max = 1.1 # kg, hard upper limit
# N_max = NULL
# # quantile( M$totno[ipositive]/M$data_offset[ipositive], probs=0.95, na.rm=TRUE )
# posterior sims
sims = carstm_posterior_simulations( pN=pN, pW=pW, pH=pH, pa_threshold=0.05, qmax=0.95 )
sims = sims / 10^6 # units: kg ; div (10^6) -> kt ;;
# sims[ which(!is.finite(sppoly$npts)),, ] = 0
SM = aggregate_simulations(
sims=sims,
sppoly=sppoly,
fn=carstm_filenames( pN, returnvalue="filename", fn="aggregated_timeseries" ),
yrs=pN$yrs,
method="sum",
redo=TRUE
)
# units: kt/km^2
if (0) {
# to compute habitat prob
sims = carstm_posterior_simulations( pH=pH, pa_threshold=0.05, qmax=0.95 )
SM = aggregate_simulations(
sims=sims,
sppoly=sppoly,
fn=carstm_filenames( pN, returnvalue="filename", fn="aggregated_timeseries" ),
yrs=pN$yrs,
method="mean",
redo=TRUE
)
outputdir = file.path( carstm_filenames( pN, returnvalue="output_directory"), "aggregated_habitat_timeseries" )
RES= SM$RES
}
RES= SM$RES # units: kt
# RES = aggregate_simulations( fn=carstm_filenames( pN, returnvalue="filename", fn="aggregated_timeseries" ) )$RES
# note: using pN, even though this is biomass
outputdir = file.path( carstm_filenames( pN, returnvalue="output_directory"), "aggregated_biomass_timeseries" )
if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )
( fn = file.path( outputdir, "cfa_all.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfaall ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfaall_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfaall_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
( fn = file.path( outputdir, "cfa_south.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfasouth ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfasouth_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfasouth_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
( fn = file.path( outputdir, "cfa_north.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfanorth ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfanorth_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfanorth_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
( fn = file.path( outputdir, "cfa_4x.png") )
png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )
plot( cfa4x ~ yrs, data=RES, lty="solid", lwd=4, pch=20, col="slateblue", type="b", ylab="Biomass index (kt)", xlab="")
lines( cfa4x_lb ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
lines( cfa4x_ub ~ yrs, data=RES, lty="dotted", lwd=2, col="slategray" )
dev.off()
regions = c("cfanorth", "cfasouth", "cfa4x" )
region_label = c("N-ENS", "S-ENS", "4X")
a= cbind( "cfanorth", RES[,c("yrs", "cfanorth", "cfanorth_lb", "cfanorth_ub")] )
b= cbind( "cfasouth", RES[,c("yrs", "cfasouth", "cfasouth_lb", "cfasouth_ub")] )
c= cbind( "cfa4x", RES[,c("yrs", "cfa4x", "cfa4x_lb", "cfa4x_ub")] )
names(a) = names(b) = names(c) = c("region", "year", "mean", "lb", "ub")
tdb = rbind(a, b, c)
tdb$region = factor(tdb$region, levels=regions, labels =region_label)
tdb = tdb[(which(!is.na(tdb$region))), ]
fn = file.path( outputdir, "biomass_M0.png" )
require(ggplot2)
library(ggbreak)
color_map = c("#E69F00", "#56B4E9", "#CC79A7" )
out = ggplot(tdb, aes(x=year, y=mean, fill=region, colour=region)) +
geom_line( alpha=0.9, linewidth=1.2 ) +
geom_point(aes(shape=region), size=3, alpha=0.7 ) +
geom_errorbar(aes(ymin=lb,ymax=ub), linewidth=0.8, alpha=0.8, width=0.3) +
labs(x="Year/Année", y="Biomass index (kt) / Indice de biomasse (kt)", size = rel(1.5)) +
scale_colour_manual(values=color_map) +
scale_fill_manual(values=color_map) +
scale_shape_manual(values = c(15, 17, 19)) +
theme_light( base_size = 22) +
theme( legend.position="inside", legend.position.inside=c(0.75, 0.9), legend.title=element_blank()) +
scale_y_break(c(14, 28), scales = 1)
# scale_y_continuous( limits=c(0, 300) )
ggsave(filename=fn, plot=out, device="png", width=12, height = 8)
print(out)
# map it ..mean density
sppoly = areal_units( p=pN ) # to reload
vn = paste("biomass", "predicted", sep=".")
outputdir = file.path( carstm_filenames( pN, returnvalue="output_directory"), "predicted_biomass_densities" )
if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )
B = apply( sims, c(1,2), mean ) # sims units (kt);
B[ which(!is.finite(B)) ] = NA
# brks = pretty( log10( quantile( B[], probs=c(0.05, 0.95), na.rm=TRUE )* 10^6) )
sa = units::drop_units(sppoly$au_sa_km2)
brks = pretty( ( quantile( log(B * 10^6 / sa), probs=c(0.05, 0.95), na.rm=TRUE )) )
additional_features = snowcrab_mapping_features(pN) # for mapping below
for (i in 1:length(pN$yrs) ) {
y = as.character( pN$yrs[i] )
# u = log10( B[,y]* 10^6 ) ## Total kt->kg: log10( kg )
u = log( B[,y]* 10^6 / sa) # ;; density log10( kg /km^2 )
sppoly[,vn] = u
outfilename = file.path( outputdir , paste( "biomass", y, "png", sep=".") )
plt = carstm_map( sppoly=sppoly, vn=vn,
breaks=brks,
additional_features=additional_features,
title=y,
# title=paste( "log_10( Predicted biomass density; kg/km^2 )", y ),
colors=rev(RColorBrewer::brewer.pal(5, "RdYlBu")),
outfilename=outfilename
)
plt
}
} # end assimilate size and numbers
# prep data for discrete version
# Rdata files are ready load them through julia and model
# for production
sppoly_tweaks = list(
# vary params by variable as data densities vary for these size/age/sex groups
areal_units_constraint_ntarget= list( M0=8, M1=10, M2=14, M3=14, M4=14, f.mat=8 ),
n_iter_drop=list( M0=0, M1=1, M2=1, M3=1, M4=1, f.mat=0 )
)
fishery_model_data_inputs(
year.assessment=year.assessment,
type="biomass_dynamics",
snowcrab_filter_class="fb",
modeldir = pN$modeldir,
carstm_model_label= pN$carstm_model_label,
for_julia=TRUE )
## note the output directory .. this is used for the next script
# for development
# modeldir = file.path( homedir, "projects", "dynamical_model", "snowcrab", "data" )
# end
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