test_things.R
In peterkuriyama/hlsimulator_old: Simulate Hook and Line Survey
#habitat specific movement, when patchy certain attraction to
#certain cells
setwd("/Users/peterkuriyama/School/Research/hlsimulator")
library(devtools)
library(plyr)
library(dplyr)
library(reshape2)
library(ggplot2)
# library(purrr)
library(devtools)
install_github('peterkuriyama/hlsimulator@master')
install('../hlsimulator')
# load_all('../hlsimulator')
library(hlsimulator)
#update documentation
move_fish_hs(fish_area = initialize_population(distribute = 'patchy', numrow = 10, numcol = 10, seed = 5,
nfish = 1000, percent = .50),
hotspots = data.frame('row' = c(3, 2), 'column' = c(3, 5)),
max_prob = 0.1)
#--------------------------------------------------------------------------------------------
##TO DO
#Number of vessels fishing...?
#Number of anglers fishing...?
#Add in new probability distribution
##Put in no movement function
## Depends on how much of population we sample
##Recruitment functions?
#Things that might affect cpue relationship
#number of locations sampled
#distribution of fish
#number of fish
#percentage of distributed fish (if patchy distribution)
#-------------------------------------------------------------------------------------------
#Get back into this
#start with lowest level function call,
tt <- initialize_population(distribute = 'patchy', numrow = 10, numcol = 10, seed = 5,
nfish = 1000, percent = .50)
#Input locations for each vessel as a data.frame
#Location input with columns for vessel, row, column
location <- data.frame(vessel = c(1, 1, 2), x = c(3, 3, 8), y = c(3, 5, 8))
set.seed(1)
fish_population(fish_area = tt, location = data.frame(vessel = c(1, 1, 2), x = c(3, 3, 8),
y = c(3, 5, 8)),
scope = 2, nhooks = 5, ndrops = 3, process = "equal_prob")
#-------------------------------------------------------------------------------------------
#Ideal CPUE Figure
Linf <- 1
k <- .2
t0 <- 6
x <- 1:40
y <- Linf * (1 - exp(-k * (x - t0)))
pdf(width = 7, height = 7, file = 'figs/ideal_cpue.pdf')
par(mar = c(5.1, 5.1, 1, 1.5))
plot(x, y, type = 'l', axes = F, ann = F, lwd = 4, ylim = c(-1.5, 1),
xaxs = 'i', yaxs = 'i')
axis(side = 1, at = c(0, 10, 20, 30, 40), labels = c(0, '1000', '2000', '3000', '4000'),
cex.axis = 1.5)
axis(side = 2, at = c(-1.5, -1, -.5, 0, .5, 1),
labels = c(0, .2, .4, .6, .8, 1), las = 2, cex.axis = 1.5)
mtext(side = 1, "Number of Fish", line = 3.5, cex = 2)
mtext(side = 2, 'CPUE', line = 3.2, cex = 2)
dev.off()
#-------------------------------------------------------------------------------------------
#Plot actual CPUE stuff from data
load(paste0('data/',list.files('data'))[1])
load(paste0('data/',list.files('data'))[2])
# #Massage data so that columns are aligned
dat <- Grand.2014.JF
boc <- subset(dat, ComName == 'Bocaccio')
ver <- subset(dat, ComName == 'Vermilion Rockfish')
boc <- boc[, c('Year', 'SiteName' ,'VesName', 'Length.cm', 'Weight.kg')]
png(width = 7, height = 7, res = 200, units = 'in',
file = 'figs/boc_lengths.png')
ggplot(boc, aes(x = Length.cm)) + geom_histogram() + theme_bw() +
facet_wrap(~ Year)
dev.off()
hist(boc$Length.cm)
dat %>% group_by(Year, SiteName) %>% summarise(nfish = sum(SurvFish),
nhooks = length(SurvFish), cpue = nfish / nhooks) %>%
as.data.frame -> site_cpue
# ggplot(subset(site_cpue, SiteName %in% high_sites), aes(x = Year, y = cpue, group = SiteName)) +
# geom_line()
site_cpue_plot <- ggplot(site_cpue, aes(x = Year, y = cpue, group = SiteName)) +
geom_line() + facet_wrap(~ SiteName) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(),
axis.text.y = element_blank())
site_cpue <- site_cpue[-33, ]
ggplot(site_cpue, )
# hist(site_cpue$nfish, breaks = 30)
pdf(width = 10, height = 9,
file = 'figs/site_cpue.pdf')
print(site_cpue_plot)
dev.off()
#-------------------------------------------------------------------------------------------
#One Cell
#Evaluate survey in one cell multiple times
# run_and_plot(numrow = 1,
# numcol = 1, nfish = 1000,
# seed = 301, distribute = 'uniform',
# nyears = 1,
# location_list = list(c(1, 1)),
# move_func = 'move_fish_none',
# nhooks = 15, ndrops = 5, scope = 0,
# process = 'equal_prob',
# p0 = .4)
#Fish in one cell
to_loop_over <- expand.grid(1:400, seq(.1, 1, by = .1))
names(to_loop_over) <- c('nfish', 'p0')
one_cell_results <- vector('list', length = nrow(to_loop_over))
for(ii in 1:nrow(to_loop_over)){
if(ii %% 100 == 0) print(ii)
temp <- run_and_plot(numrow = 1, numcol = 1,
nfish = to_loop_over[ii, 1],
seed = 301, distribute = 'uniform',
nyears = 1,
location_list = list(c(1, 1)),
move_func = 'move_fish_none',
nhooks = 15, ndrops = 5, scope = 0,
process = 'equal_prob',
p0 = to_loop_over[ii, 2])
temp$cpue$nfish <- to_loop_over[ii, 1]
temp$cpue$p0 <- to_loop_over[ii, 2]
one_cell_results[[ii]] <- temp$cpue
}
one_cell_results <- ldply(one_cell_results)
mean_cpue <- one_cell_results %>% group_by(nfish, p0) %>%
summarise(cpue = mean(value)) %>% as.data.frame
unique_p0 <- unique(mean_cpue$p0)
grays <- gray.colors(length(unique_p0), start = .4, end = 1)
#----------------------------------------------------------------
#Plot only one p0 value
four <- subset(mean_cpue, p0 == .4)
pdf(width = 7, height = 7, file = 'figs/one_cell_p0_.4.pdf')
plot(four$nfish, four$cpue, type = 'o', ylim = c(0, 1.05), pch = 19,
xaxs = 'i', yaxs = 'i', xpd = FALSE,
xlab = 'Population Size (# fish)', ylab = 'CPUE', xlim = c(0, 200),
cex.axis = 1.5)
dev.off()
#----------------------------------------------------------------
#Plot all p0 values
pdf(width = 7, height = 7, file = 'figs/one_cell_all_p0.pdf')
plot(mean_cpue$nfish, mean_cpue$p0, type = 'n', ylim = c(0, 1),
xlab = 'Population Size (# fish)', ylab = 'CPUE',
xlim = c(0, 200), cex.axis = 1.5)
for(ii in 1:length(unique_p0)){
temp <- subset(mean_cpue, p0 == unique_p0[ii])
lines(temp$nfish, temp$cpue, col = grays[ii],
lwd = 2)
}
legend('bottomright', legend = rev(unique_p0), lwd = 2,
col = rev(grays), bty = 'n', cex = 1.4)
dev.off()
plot(subset(mean_cpue, p0 == 0.1)$nfish,
subset(mean_cpue, p0 == 0.1)$cpue, type = 'o', pch = 19)
ggplot(mean_cpue, aes(x = nfish, y = cpue, group = p0)) +
geom_point(aes(colour = p0))
fish_one_cell <- seq(0, 200, by = 1)
one_cell_results <- vector('list', length = length(fish_one_cell))
for(ii in 1:length(fish_one_cell)){
temp <- run_and_plot(numrow = 1,
numcol = 1, nfish = fish_one_cell[ii],
seed = 301, distribute = 'uniform',
nyears = 1,
location_list = list(c(1, 1)),
move_func = 'move_fish_none',
nhooks = 15, ndrops = 5, scope = 0,
process = 'equal_prob',
p0 = .4)
one_cell_results[[ii]] <- temp$cpue
}
names(one_cell_results) <- fish_one_cell
ocr <- ldply(one_cell_results)
names(ocr)[1] <- 'nfish'
ocr$nfish <- as.integer(ocr$nfish)
ocr_cpue <- ocr %>% group_by(nfish) %>% dplyr::summarise(cpue = mean(value))
plot(ocr_cpue$nfish, ocr_cpue$cpue, type = 'o', pch = 19)
plot(ocr$nfish, ocr$value)
ocr %>% group_by(nfish) %>% summarise(cpue = mean(value))
ggplot(ocr, aes(x = nfish, y = value)) + geom_point()
#-------------------------------------------------------------------------------------------
location_list1 <- list(c(2, 2),
c(3, 1),
c(3, 4),
c(3, 7),
c(4, 9),
c(2, 6),
c(7, 1),
c(9, 3),
c(8, 5),
c(8, 7),
c(9, 10),
c(10, 6))
run1 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 10000,
distribute = 'patchy',
seed = 301,
nyears = 15,
random_locations = FALSE,
location_list = location_list1,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 5, scope = 0, print_text = TRUE,
process = 'equal_prob', p0 = .4, percent = .5)
inits <- subset(run1$init_nfish, year == 1)
inits <- inits[order(inits$value, decreasing = TRUE), ]
best_sites <- sample(which(inits$value > 0), size = 12)
inits[best_sites, ]
best_locations <- list(c(4, 5),
c(5, 8),
c(3, 2),
c(2, 5),
c(5, 2),
c(8, 7),
c(9, 6),
c(3, 3),
c(5, 10),
c(8, 1),
c(5, 9),
c(10, 4))
# half_good_locations <- list()
half_good_locations <- list(c(4, 5),
c(7, 2),
c(3, 2),
c(10, 8),
c(5, 2),
c(8, 8),
c(9, 6),
c(3, 3),
c(6, 4),
c(1, 6),
c(9, 10),
c(10, 4))
#Modify number of fish
pdf(width = 7, height = 7, file = 'figs/nomovement_best_locs_15000.pdf')
run15_best <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 15000,
distribute = 'patchy',
seed = 301,
nyears = 15,
random_locations = FALSE,
location_list = best_locations,
move_func = 'move_fish_none', move_prob = .7,
nhooks = 15, ndrops = 5, scope = 0, print_text = TRUE,
process = 'equal_prob', p0 = .4, percent = .5)
dev.off()
pdf(width = 7, height = 7, file = 'figs/cwmovement_best_locs_15000.pdf')
run15_best <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 15000,
distribute = 'patchy',
seed = 301,
nyears = 15,
random_locations = FALSE,
location_list = best_locations,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 5, scope = 0, print_text = TRUE,
process = 'equal_prob', p0 = .4, percent = .5)
dev.off()
pdf(width = 7, height = 7, file = 'figs/cwmovement_half_best_locs_15000.pdf')
run15_half <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 15000,
distribute = 'patchy',
seed = 301,
nyears = 15,
random_locations = FALSE,
location_list = half_good_locations,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 5, scope = 0, print_text = TRUE,
process = 'equal_prob', p0 = .4, percent = .5)
dev.off()
pdf(width = 7, height = 7, file = 'figs/cwmovement_best_locs_10.pdf')
run15_half <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 10000,
distribute = 'patchy',
seed = 301,
nyears = 15,
random_locations = FALSE,
location_list = best_locations,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 5, scope = 0, print_text = TRUE,
process = 'equal_prob', p0 = .4, percent = .5)
dev.off()
pdf(width = 7, height = 7, file = 'figs/cwmovement_half_best_locs_10.pdf')
run15_half <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 10000,
distribute = 'patchy',
seed = 301,
nyears = 15,
random_locations = FALSE,
location_list = half_good_locations,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 5, scope = 0, print_text = TRUE,
process = 'equal_prob', p0 = .4, percent = .5)
dev.off()
inits <- subset(run5$init_nfish, year == 1)
subset(run5$init_nfish, year == 1)[order(subset(run5$init_nfish, year == 1)$value), ]
matrix(pp2$init_nfish$value, nrow = 10, ncol = 10, byrow = T)
pes <- 3 #potential exploitable stock size
p0 <- .5
f0 <- .5
f <- p0 / 5
pes <- 5
nn <- pes / p0
function(pes, p0 = .5, f0 = .5){
nn <- pes / p0
}
#Define probabilities, with same hook
fish0 <- f0 ^ nn
fish1 <- 5 * (f + f0) ^ nn - f0 ^ nn
fish2 <- 10 * ((2 * f + f0) ^ nn - 2 * (f + f0) ^ nn + f0 ^ nn)
fish31 <- (3 * f + f0) ^ nn
fish32 <- -3 * (2 * f + f0) ^ nn
fish33 <- 3 * (f + f0) ^ nn
fish34 <- -f0 ^ nn
fish3 <- 10 * (fish31 + fish32 + fish33 + fish34)
fish41 <- (4 * f + f0) ^ nn
fish42 <- -4 * (3 * f + f0) ^ nn
fish43 <- 6 * (2 * f + f0) ^ nn
fish44 <- -4 * (f + f0) ^ nn
fish45 <- f0 ^ nn
fish4 <- 5 * (fish41 + fish42 + fish43 + fish44 + fish45)
fish5 <- 1 - (fish0 + fish1 + fish2 + fish3 + fish4)
data.frame(fish0, fish1, fish2, fish3, fish4, fish5)
#Same hook probabilities
ff <- .2
f0 <- 1
#-------------------------------------------------------------------------------------------
#Define Probabilities of
#Specify hook probabilities for nhooks
prob_capture <- .2 #probability that fish detect gear
#probability of catching no fish
zero_prob <- 1
#define hook probabilities, prob of not catching anything,
hook_probs <- c(.2, .2, .2, .2, .2) #prob of no fish, hook 1, hook2 etc
zero_prob * hook_probs
nfish <- 1
#check hook probabilities
# if(zero_prob + sum(hook_probs) != 1) stop('zero_prob and hook_probs must sum to 1')
#calculate probabilities of hooking fish given these parameters
hook_combos <- expand.grid(rep(list(0:1), 5)) #combination of hooks
hook_combos$row_sum <- rowSums(hook_combos)
#Specify probabilities of each hook-catch configuration
hook_combos$prob <- 1
nhooks <- length(hook_probs)
#-------------------------------------------------------------------------------------------
#only one hook
ones <- hook_combos[which(hook_combos$row_sum == 1), ]
for(ll in 1:nrow(ones)){
temp <- ones[ll, ]
f_i <- hook_probs[which(temp[1, 1:nhooks] == 1)]
ones[ll, 'prob'] <- (f_i + zero_prob) ^ nfish - zero_prob ^ nfish
}
#-------------------------------------------------------------------------------------------
#Two hooks
twos <- hook_combos[which(hook_combos$row_sum == 2), ]
for(ll in 1:nrow(twos)){
temp <- twos[ll, ]
yes <- which(temp[1, 1:nhooks] == 1)
f_1 <- hook_probs[yes[1]]
f_2 <- hook_probs[yes[2]]
twos[ll, 'prob'] <- (f_1 + f_2 + zero_prob) ^ nfish - (f_1 + zero_prob) ^ nfish -
(f_2 + zero_prob) ^ nfish - zero_prob ^ nfish
}
#-------------------------------------------------------------------------------------------
#Three Hooks
threes <- hook_combos[which(hook_combos$row_sum == 3), ]
for(ll in 1:nrow(threes)){
temp <- which(threes[ll, 1:nhooks] == 1)
}
sapply(ones, function(x) which(x[1:nhooks] == 1))
(hook_probs[which(ones[1, 1:5] == 1)] + zero_prob) ^ nfish - zero_prob ^ nfish
dd <- apply(ones, MAR = 1,
FUN = function(x) (hook_probs[which(ones[x, 1:5] == 1)] + zero_prob) ^ nfish - zero_prob ^ nfish)
apply(hook_combos, MAR = 1, FUN = function(x) paste(which(x[1:5] == 1)))
for(ii in 1:nhooks){
print(ii)
}
ones$prob <-
#Calculate probabilities based on
hook_combos %>% group_by(row_sum) %>% mutate(prob = function(x))
zero_index <- which(hook_combos$row_sum == 0)
hook_combos
hook_combos[which(hook_combos$row_sum == 0), 'prob'] <-
which(hook_combos$row_sum == 1)
hook_combos %>% filter(row_sum == 0) %>% mutate(prob = zero_prob ^ nfish)
sum
#-------------------------------------------------------------------------------------------
#NO recruitment and strong local depletion effects with year after year sampling
#-------------------------------------------------------------------------------------------
##Compare the effect of fish movement on fixed location sampling
#Specify 10 locations
location_list1 <- list(c(2, 2),
c(3, 1),
c(3, 4),
c(3, 7),
c(4, 9),
c(2, 6),
c(7, 1),
c(9, 3),
c(8, 5),
c(8, 7),
c(9, 10),
c(10, 6))
pdf(width = 13, height = 5.65,
file = 'figs/fixed_location_different_movement.pdf')
par(mfcol = c(1, 3), oma = c(2, 2, 0, 0))
#no fish movement and uniform initial distribution
pp1 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = FALSE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_none',
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#clockwise fish movement
pp2 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = FALSE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#left fish movement
pp3 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = FALSE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_left', max_prob = .7, min_prob = 0,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
dev.off()
#-------------------------------------------------------------------------------------------
#random locations and various movement patterns
pdf(width = 13, height = 5.65,
file = 'figs/random_location_different_movement.pdf')
par(mfcol = c(1, 3), oma = c(2, 2, 0, 0))
#no fish movement and uniform initial distribution
pp1 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = TRUE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_none',
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#clockwise fish movement
pp2 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = TRUE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#left fish movement
pp3 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = TRUE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_left', max_prob = .7, min_prob = 0,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
dev.off()
#-------------------------------------------------------------------------------------------
#Fixed locations and moving fish
pdf(width = 13, height = 5.65,
file = 'figs/fixed_location_clockwise_movement_different_distributions.pdf')
par(mfcol = c(1, 3), oma = c(2, 2, 0, 0))
#no fish movement and uniform initial distribution
pp1 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = FALSE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#clockwise fish movement
pp2 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'patchy',
seed = 302,
nyears = 15,
random_locations = FALSE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#left fish movement
pp3 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'area',
area = 'upperright',
seed = 302,
nyears = 15,
random_locations = FALSE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
dev.off()
#-------------------------------------------------------------------------------------------
#Random locations and moving fish
pdf(width = 13, height = 5.65,
file = 'figs/random_location_clockwise_movement_different_distributions.pdf')
par(mfcol = c(1, 3), oma = c(2, 2, 0, 0))
#no fish movement and uniform initial distribution
pp1 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = TRUE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#clockwise fish movement
pp2 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'patchy',
seed = 302,
nyears = 15,
random_locations = TRUE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#left fish movement
pp3 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'area',
area = 'upperright',
seed = 302,
nyears = 15,
random_locations = TRUE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
dev.off()
#-------------------------------------------------------------------------------------------
par(mfcol = c(1, 3))
pp1 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 1000,
distribute = 'uniform',
area = 'upperleft',
seed = 302,
nyears = 30,
random_locations = TRUE,
location_list = list(c(2, 2), c(8, 8)),
nlocs = 10,
move_func = 'move_fish_none',
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE,
xlim_s = c(0, 10000))
pp2 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
area = 'upperleft',
seed = 302,
nyears = 30,
random_locations = TRUE,
location_list = list(c(2, 2), c(8, 8)),
nlocs = 10,
move_func = 'move_fish_none',
nhooks = 15, ndrops = 3, scope = 0,
xlim_s = c(0, 10000))
pp3 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 10000,
distribute = 'uniform',
area = 'upperleft',
seed = 302,
nyears = 30,
random_locations = TRUE,
location_list = list(c(2, 2), c(8, 8)),
nlocs = 10,
move_func = 'move_fish_none',
nhooks = 15, ndrops = 3, scope = 0)
#high level wrapper to run and plot
pp <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'area',
area = 'upperleft',
seed = 302,
nyears = 15,
random_locations = FALSE,
location_list = list(c(2, 2), c(8, 8)),
nlocs = 50,
move_func = 'move_fish_none',
nhooks = 15, ndrops = 3, scope = 0)
pp <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 1000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = TRUE,
# location_list = list(c(2, 2), c(8, 8)),
nlocs = 10,
move_func = 'move_fish_cw',
move_prob = .8,
nhooks = 15, ndrops = 3, scope = 0)
xx301[[4]] == xx300.2[[4]]
xx300.1$end_nfish == xx300.2$end_nfish
xx301$end_nfish == xx300.2$end_nfish
run_and_plot(numrow = 10, numcol = 10, nfish = 10000,
distribute = 'uniform', seed = 300, nyears = 15, random_locations = TRUE,
nlocs = 10, move_func = 'move_fish_left', max_prob = .2, min_prob = .01)
#-------------------------------------------------------------------------------------------
#develop function to move fish around ontogenetically or
xx <- survey_over_years(numrow = 10, numcol = 10, nfish = 100000,
distribute = 'uniform',
seed = 300, nyears = 15, location_list,
random_locations = TRUE, nlocs = 100, move_func = move_fish_cw, move_prob = .8,
scope = 1, process = 'equal_prob', ndrops = 3, nhooks = 5)
thing <- parse_master_list(xx)
nfish_cpue <- merge(thing$end_nfish %>% group_by(year) %>% summarise(nfish = sum(value)),
cpue %>% group_by(year) %>% summarise(cpue = mean(value)) %>% as.data.frame,
by = 'year')
plot(nfish_cpue$nfish, nfish_cpue$cpue, ylim = c(0, 1), pch = 19,
xaxs = 'i', yaxs = 'i', xlim = c(0, max(pretty(nfish_cpue$nfish))))
ggplot(cpue, aes(x = variable, y = value, colour = year, group = year)) + geom_line() +
facet_wrap(~ location) + scale_color_gradientn(colors = gray.colors(length(cpue$year)))
#separate elements from each iteration
#everything a list for now
fish_list <- lapply(xx, FUN = function(x) x$sampled_area)
fish_melt <- melt(fish_list)
names(fish_melt) <- c('row', 'column', 'nfish', 'year')
fish_melt$density <- fish_melt$nfish / 100
ggplot(fish_melt, aes(x = row, y = column)) + geom_raster(aes(fill = density)) +
facet_wrap(~ year)
ggplot(fish_melt)
melt(fish_list)
melt(fish_list[[1]])
ggplot(fish_list[[1]] aes(x))
cpue_list <- lapply(xx, FUN = function(x) x$cpue)
#extract number of fish in each year
nfish <- sapply(xx, FUN = function(x) {
sum(unlist(x$sampled_area))
})
cpue <- sapply(xx, FUN = function(x) {
mean(unlist(x$cpue[, 2:4]))
})
plot(nfish, cpue, pch = 19, type = 'o')
to.plot <- lapply(xx, FUN = function(x) x$cpue)
sapply(to.plot, FUN = function(x) mean(unlist(x[, 2:4])))
survey_over_years(nfish = 10000, distribute = 'uniform', seed = 300, nyears = 15,
random_locations = TRUE, nlocs = 10)
#-------------------------------------------------------------------------------------------
#Look at number of locations
cpues <- explore_nlocs_cpue(numrow = 10, numcol = 10, nfish = 2000, seed = 200,
numlocs = 10, distribute = 'patchy',
percent = .5, scope = 0)
to.plot <- melt(cpues)
names(to.plot)[1] <- 'nlocs'
xx <- sapply(to.plot$location, FUN = function(x) eval(parse(text = x)))
to.plot$x <- as.vector(xx[1, ])
to.plot$y <- as.vector(xx[2, ])
#-------------------------------------------------------------------------------------------
#Look at different numbers of fish
nfish.vec <- seq(100, 10000, by = 100)
avg.cpue <- nfish.vec
for(ii in 1:length(nfish.vec)){
init <- initialize_population(numrow = 10, numcol = 10, nfish = nfish.vec[ii], distribute = 'uniform',
percent = .3, seed = 301)
temp <- conduct_survey(fish_area = init, location_list = list(c(4, 10),
c(8, 2),
c(3, 3)), scope = 1, nhooks = 15, ndrops = 5)
avg.cpue[ii] <- mean(unlist(temp$cpue))
}
plot(nfish.vec, avg.cpue, type = 'o', pch = 19, ylim = c(0, 1), xaxs = 'i',
yaxs = 'i', xlim = c(0, max(nfish.vec)))
png(file = '/Users/peterkuriyama/Desktop/hl_cpue_check.png')
plot(nfish, avg.cpue, type = 'o', pch = 19, yli = c(0, 1), xaxs = 'i',
yaxs = 'i', xlim = c(0, max(nfish)))
dev.off()
conduct_survey(fish_area = init, location_list = list(c(4, 10),
c(8, 2),
c(3, 3)),
scope = 1, nhooks = 15, ndrops = 5)
#------------------------------------------------------
#------------------------------------------------------
#Scraps
# fish.range * move.prob
# #Define movement probabilities
# 1 - (fish.range / nfish.range)
# #Each fish has a probability of catching a hook
# #Probability depends on nuber
# #conusmption by one fish
# check_cons <- function(max.prob = .9, nhooks){
# zz <- (max.prob * nhooks) / (.6 + nhooks)
# return(zz)
# }
# tt <- data.frame(hook = 1:75, prob = sapply(1:75,
# FUN = function(x) check_cons(nhooks = x)))
# tt$prob.many <- 1 - exp(-(tt$prob * 50))
# plot(tt$hook, tt$prob.many, pch = 19, ylim = c(0, 1),
# main = '70 fish and 1:75 hooks')
# #Highest probability of hook saturation:
# #Five fish in location and more than five fish in nfish.range
# if(fish.loc >= nhooks & (nfish.range - fish.loc) >= nhooks){
# hook.prob <- seq(from = max.prob, to = 0, by = -delta.prob)
# }
# #High probability of hook saturation:
# #if more than five fish in location and fewer than nhooks fish surrounding
# if(fish.loc >= nhooks & (nfish.range - fish.loc) < nhooks){
# hook.prob <- seq(from = max.prob - (2 * delta.prob), to = 0, by = -delta.prob)
# }
# #Medium prob of hook saturation
# #fewer than five fish in specific location, more than five surrounding
# if(fish.loc < nhooks & (nfish.range - fish.loc) >= nhooks){
# hook.prob <- seq(from = max.prob - (4 * delta.prob), to = 0, by = -delta.prob)
# }
# #Low prob of hook saturation
# #fewer than five in speicifc location, fewer than five surrounding
# if(fish.loc < nhooks & (nfish.range - fish.loc) < nhooks & nfish.range >= nhooks){
# hook.prob <- seq(from = max.prob - (6 * delta.prob), to = 0, by = -delta.prob)
# }
# #Lowest prob of hook saturation
# #fewer than nhooks fish in entire range
# if(fish.loc < nhooks & (nfish.range - fish.loc) < nhooks & nfish.range < nhooks){
# hook.prob <- seq(from = max.prob - (8 * delta.prob), to = 0, by = -delta.prob)
# }
# #Subset hook probabilites based on number of hooks
# hook.prob <- hook.prob[1:nhooks]
# #Sample fish
# fish <- rbinom(n = nhooks, size = 1, prob = hook.prob)
# #Subtract sampled fish from number of fish in fish.range matrix
# xx <- melt(fish.range)
# xx[which(xx$value != 0), 'prob'] <- 1 / sum(xx$value != 0) #equal probabilities for now
# # xx$prob <- xx$value / sum(xx$value)
# # use a multinomial sample to find which cells to subtract sampled fish from
# fish.caught <- rmultinom(prob = xx$prob, n = 1, size = sum(fish))
# xx$value <- xx$value - fish.caught
# #update whole matrix
# return(xx)
# #Keep
# # while(sum(fish) > nfish.range){
# # fish <- rbinom(n = nhooks, size = 1, prob = hook.prob)
# # }
#-------------------------------------------------------------------------------------------
#Move fish cw
# fish_area1 <- initialize_population(distribute = 'area', area = 'upperright', numrow = 10, numcol = 10,
# nfish = 1000)
# tt <- fish_area1
# ttp <- vector('list', length = 8)
# for(ll in 1:8){
# temp <- move_fish_cw(fish_area = tt, move_prob = .8)
# tt <- temp$final
# ttp[[ll]] <- tt
# }
# ttp <- melt(ttp)
# ggplot(ttp, aes(x = Var1, y = Var2)) + geom_raster(aes(fill = value)) +
# theme_bw() + facet_wrap(~ L1)
# ggplot(ttp[1], )
# move_fish_cw(fish_area = fish_area1, move_prob = .8)
# #Update numbers of fish based on catches
# #Record catches
# prob.catch <- fish.loc
# #check this is working
# #------------------------------------------------------------------------------------------
# # check.this <- vector('list', length = 1000000)
# # for(ii in 1:length(check.this)){
# # check.this[[ii]] <- rbinom(n = nhooks, size = 1, prob = hook.prob)
# # }
# # ct <- ldply(check.this)
# # colSums(ct) / length(check.this)
# #------------------------------------------------------------------------------------------
# fishArea, location, prob.max, prob.delta
peterkuriyama/hlsimulator_old documentation built on May 25, 2019, 1:51 a.m.
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#habitat specific movement, when patchy certain attraction to
#certain cells
setwd("/Users/peterkuriyama/School/Research/hlsimulator")
library(devtools)
library(plyr)
library(dplyr)
library(reshape2)
library(ggplot2)
# library(purrr)
library(devtools)
install_github('peterkuriyama/hlsimulator@master')
install('../hlsimulator')
# load_all('../hlsimulator')
library(hlsimulator)
#update documentation
move_fish_hs(fish_area = initialize_population(distribute = 'patchy', numrow = 10, numcol = 10, seed = 5,
nfish = 1000, percent = .50),
hotspots = data.frame('row' = c(3, 2), 'column' = c(3, 5)),
max_prob = 0.1)
#--------------------------------------------------------------------------------------------
##TO DO
#Number of vessels fishing...?
#Number of anglers fishing...?
#Add in new probability distribution
##Put in no movement function
## Depends on how much of population we sample
##Recruitment functions?
#Things that might affect cpue relationship
#number of locations sampled
#distribution of fish
#number of fish
#percentage of distributed fish (if patchy distribution)
#-------------------------------------------------------------------------------------------
#Get back into this
#start with lowest level function call,
tt <- initialize_population(distribute = 'patchy', numrow = 10, numcol = 10, seed = 5,
nfish = 1000, percent = .50)
#Input locations for each vessel as a data.frame
#Location input with columns for vessel, row, column
location <- data.frame(vessel = c(1, 1, 2), x = c(3, 3, 8), y = c(3, 5, 8))
set.seed(1)
fish_population(fish_area = tt, location = data.frame(vessel = c(1, 1, 2), x = c(3, 3, 8),
y = c(3, 5, 8)),
scope = 2, nhooks = 5, ndrops = 3, process = "equal_prob")
#-------------------------------------------------------------------------------------------
#Ideal CPUE Figure
Linf <- 1
k <- .2
t0 <- 6
x <- 1:40
y <- Linf * (1 - exp(-k * (x - t0)))
pdf(width = 7, height = 7, file = 'figs/ideal_cpue.pdf')
par(mar = c(5.1, 5.1, 1, 1.5))
plot(x, y, type = 'l', axes = F, ann = F, lwd = 4, ylim = c(-1.5, 1),
xaxs = 'i', yaxs = 'i')
axis(side = 1, at = c(0, 10, 20, 30, 40), labels = c(0, '1000', '2000', '3000', '4000'),
cex.axis = 1.5)
axis(side = 2, at = c(-1.5, -1, -.5, 0, .5, 1),
labels = c(0, .2, .4, .6, .8, 1), las = 2, cex.axis = 1.5)
mtext(side = 1, "Number of Fish", line = 3.5, cex = 2)
mtext(side = 2, 'CPUE', line = 3.2, cex = 2)
dev.off()
#-------------------------------------------------------------------------------------------
#Plot actual CPUE stuff from data
load(paste0('data/',list.files('data'))[1])
load(paste0('data/',list.files('data'))[2])
# #Massage data so that columns are aligned
dat <- Grand.2014.JF
boc <- subset(dat, ComName == 'Bocaccio')
ver <- subset(dat, ComName == 'Vermilion Rockfish')
boc <- boc[, c('Year', 'SiteName' ,'VesName', 'Length.cm', 'Weight.kg')]
png(width = 7, height = 7, res = 200, units = 'in',
file = 'figs/boc_lengths.png')
ggplot(boc, aes(x = Length.cm)) + geom_histogram() + theme_bw() +
facet_wrap(~ Year)
dev.off()
hist(boc$Length.cm)
dat %>% group_by(Year, SiteName) %>% summarise(nfish = sum(SurvFish),
nhooks = length(SurvFish), cpue = nfish / nhooks) %>%
as.data.frame -> site_cpue
# ggplot(subset(site_cpue, SiteName %in% high_sites), aes(x = Year, y = cpue, group = SiteName)) +
# geom_line()
site_cpue_plot <- ggplot(site_cpue, aes(x = Year, y = cpue, group = SiteName)) +
geom_line() + facet_wrap(~ SiteName) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(),
axis.text.y = element_blank())
site_cpue <- site_cpue[-33, ]
ggplot(site_cpue, )
# hist(site_cpue$nfish, breaks = 30)
pdf(width = 10, height = 9,
file = 'figs/site_cpue.pdf')
print(site_cpue_plot)
dev.off()
#-------------------------------------------------------------------------------------------
#One Cell
#Evaluate survey in one cell multiple times
# run_and_plot(numrow = 1,
# numcol = 1, nfish = 1000,
# seed = 301, distribute = 'uniform',
# nyears = 1,
# location_list = list(c(1, 1)),
# move_func = 'move_fish_none',
# nhooks = 15, ndrops = 5, scope = 0,
# process = 'equal_prob',
# p0 = .4)
#Fish in one cell
to_loop_over <- expand.grid(1:400, seq(.1, 1, by = .1))
names(to_loop_over) <- c('nfish', 'p0')
one_cell_results <- vector('list', length = nrow(to_loop_over))
for(ii in 1:nrow(to_loop_over)){
if(ii %% 100 == 0) print(ii)
temp <- run_and_plot(numrow = 1, numcol = 1,
nfish = to_loop_over[ii, 1],
seed = 301, distribute = 'uniform',
nyears = 1,
location_list = list(c(1, 1)),
move_func = 'move_fish_none',
nhooks = 15, ndrops = 5, scope = 0,
process = 'equal_prob',
p0 = to_loop_over[ii, 2])
temp$cpue$nfish <- to_loop_over[ii, 1]
temp$cpue$p0 <- to_loop_over[ii, 2]
one_cell_results[[ii]] <- temp$cpue
}
one_cell_results <- ldply(one_cell_results)
mean_cpue <- one_cell_results %>% group_by(nfish, p0) %>%
summarise(cpue = mean(value)) %>% as.data.frame
unique_p0 <- unique(mean_cpue$p0)
grays <- gray.colors(length(unique_p0), start = .4, end = 1)
#----------------------------------------------------------------
#Plot only one p0 value
four <- subset(mean_cpue, p0 == .4)
pdf(width = 7, height = 7, file = 'figs/one_cell_p0_.4.pdf')
plot(four$nfish, four$cpue, type = 'o', ylim = c(0, 1.05), pch = 19,
xaxs = 'i', yaxs = 'i', xpd = FALSE,
xlab = 'Population Size (# fish)', ylab = 'CPUE', xlim = c(0, 200),
cex.axis = 1.5)
dev.off()
#----------------------------------------------------------------
#Plot all p0 values
pdf(width = 7, height = 7, file = 'figs/one_cell_all_p0.pdf')
plot(mean_cpue$nfish, mean_cpue$p0, type = 'n', ylim = c(0, 1),
xlab = 'Population Size (# fish)', ylab = 'CPUE',
xlim = c(0, 200), cex.axis = 1.5)
for(ii in 1:length(unique_p0)){
temp <- subset(mean_cpue, p0 == unique_p0[ii])
lines(temp$nfish, temp$cpue, col = grays[ii],
lwd = 2)
}
legend('bottomright', legend = rev(unique_p0), lwd = 2,
col = rev(grays), bty = 'n', cex = 1.4)
dev.off()
plot(subset(mean_cpue, p0 == 0.1)$nfish,
subset(mean_cpue, p0 == 0.1)$cpue, type = 'o', pch = 19)
ggplot(mean_cpue, aes(x = nfish, y = cpue, group = p0)) +
geom_point(aes(colour = p0))
fish_one_cell <- seq(0, 200, by = 1)
one_cell_results <- vector('list', length = length(fish_one_cell))
for(ii in 1:length(fish_one_cell)){
temp <- run_and_plot(numrow = 1,
numcol = 1, nfish = fish_one_cell[ii],
seed = 301, distribute = 'uniform',
nyears = 1,
location_list = list(c(1, 1)),
move_func = 'move_fish_none',
nhooks = 15, ndrops = 5, scope = 0,
process = 'equal_prob',
p0 = .4)
one_cell_results[[ii]] <- temp$cpue
}
names(one_cell_results) <- fish_one_cell
ocr <- ldply(one_cell_results)
names(ocr)[1] <- 'nfish'
ocr$nfish <- as.integer(ocr$nfish)
ocr_cpue <- ocr %>% group_by(nfish) %>% dplyr::summarise(cpue = mean(value))
plot(ocr_cpue$nfish, ocr_cpue$cpue, type = 'o', pch = 19)
plot(ocr$nfish, ocr$value)
ocr %>% group_by(nfish) %>% summarise(cpue = mean(value))
ggplot(ocr, aes(x = nfish, y = value)) + geom_point()
#-------------------------------------------------------------------------------------------
location_list1 <- list(c(2, 2),
c(3, 1),
c(3, 4),
c(3, 7),
c(4, 9),
c(2, 6),
c(7, 1),
c(9, 3),
c(8, 5),
c(8, 7),
c(9, 10),
c(10, 6))
run1 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 10000,
distribute = 'patchy',
seed = 301,
nyears = 15,
random_locations = FALSE,
location_list = location_list1,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 5, scope = 0, print_text = TRUE,
process = 'equal_prob', p0 = .4, percent = .5)
inits <- subset(run1$init_nfish, year == 1)
inits <- inits[order(inits$value, decreasing = TRUE), ]
best_sites <- sample(which(inits$value > 0), size = 12)
inits[best_sites, ]
best_locations <- list(c(4, 5),
c(5, 8),
c(3, 2),
c(2, 5),
c(5, 2),
c(8, 7),
c(9, 6),
c(3, 3),
c(5, 10),
c(8, 1),
c(5, 9),
c(10, 4))
# half_good_locations <- list()
half_good_locations <- list(c(4, 5),
c(7, 2),
c(3, 2),
c(10, 8),
c(5, 2),
c(8, 8),
c(9, 6),
c(3, 3),
c(6, 4),
c(1, 6),
c(9, 10),
c(10, 4))
#Modify number of fish
pdf(width = 7, height = 7, file = 'figs/nomovement_best_locs_15000.pdf')
run15_best <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 15000,
distribute = 'patchy',
seed = 301,
nyears = 15,
random_locations = FALSE,
location_list = best_locations,
move_func = 'move_fish_none', move_prob = .7,
nhooks = 15, ndrops = 5, scope = 0, print_text = TRUE,
process = 'equal_prob', p0 = .4, percent = .5)
dev.off()
pdf(width = 7, height = 7, file = 'figs/cwmovement_best_locs_15000.pdf')
run15_best <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 15000,
distribute = 'patchy',
seed = 301,
nyears = 15,
random_locations = FALSE,
location_list = best_locations,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 5, scope = 0, print_text = TRUE,
process = 'equal_prob', p0 = .4, percent = .5)
dev.off()
pdf(width = 7, height = 7, file = 'figs/cwmovement_half_best_locs_15000.pdf')
run15_half <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 15000,
distribute = 'patchy',
seed = 301,
nyears = 15,
random_locations = FALSE,
location_list = half_good_locations,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 5, scope = 0, print_text = TRUE,
process = 'equal_prob', p0 = .4, percent = .5)
dev.off()
pdf(width = 7, height = 7, file = 'figs/cwmovement_best_locs_10.pdf')
run15_half <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 10000,
distribute = 'patchy',
seed = 301,
nyears = 15,
random_locations = FALSE,
location_list = best_locations,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 5, scope = 0, print_text = TRUE,
process = 'equal_prob', p0 = .4, percent = .5)
dev.off()
pdf(width = 7, height = 7, file = 'figs/cwmovement_half_best_locs_10.pdf')
run15_half <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 10000,
distribute = 'patchy',
seed = 301,
nyears = 15,
random_locations = FALSE,
location_list = half_good_locations,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 5, scope = 0, print_text = TRUE,
process = 'equal_prob', p0 = .4, percent = .5)
dev.off()
inits <- subset(run5$init_nfish, year == 1)
subset(run5$init_nfish, year == 1)[order(subset(run5$init_nfish, year == 1)$value), ]
matrix(pp2$init_nfish$value, nrow = 10, ncol = 10, byrow = T)
pes <- 3 #potential exploitable stock size
p0 <- .5
f0 <- .5
f <- p0 / 5
pes <- 5
nn <- pes / p0
function(pes, p0 = .5, f0 = .5){
nn <- pes / p0
}
#Define probabilities, with same hook
fish0 <- f0 ^ nn
fish1 <- 5 * (f + f0) ^ nn - f0 ^ nn
fish2 <- 10 * ((2 * f + f0) ^ nn - 2 * (f + f0) ^ nn + f0 ^ nn)
fish31 <- (3 * f + f0) ^ nn
fish32 <- -3 * (2 * f + f0) ^ nn
fish33 <- 3 * (f + f0) ^ nn
fish34 <- -f0 ^ nn
fish3 <- 10 * (fish31 + fish32 + fish33 + fish34)
fish41 <- (4 * f + f0) ^ nn
fish42 <- -4 * (3 * f + f0) ^ nn
fish43 <- 6 * (2 * f + f0) ^ nn
fish44 <- -4 * (f + f0) ^ nn
fish45 <- f0 ^ nn
fish4 <- 5 * (fish41 + fish42 + fish43 + fish44 + fish45)
fish5 <- 1 - (fish0 + fish1 + fish2 + fish3 + fish4)
data.frame(fish0, fish1, fish2, fish3, fish4, fish5)
#Same hook probabilities
ff <- .2
f0 <- 1
#-------------------------------------------------------------------------------------------
#Define Probabilities of
#Specify hook probabilities for nhooks
prob_capture <- .2 #probability that fish detect gear
#probability of catching no fish
zero_prob <- 1
#define hook probabilities, prob of not catching anything,
hook_probs <- c(.2, .2, .2, .2, .2) #prob of no fish, hook 1, hook2 etc
zero_prob * hook_probs
nfish <- 1
#check hook probabilities
# if(zero_prob + sum(hook_probs) != 1) stop('zero_prob and hook_probs must sum to 1')
#calculate probabilities of hooking fish given these parameters
hook_combos <- expand.grid(rep(list(0:1), 5)) #combination of hooks
hook_combos$row_sum <- rowSums(hook_combos)
#Specify probabilities of each hook-catch configuration
hook_combos$prob <- 1
nhooks <- length(hook_probs)
#-------------------------------------------------------------------------------------------
#only one hook
ones <- hook_combos[which(hook_combos$row_sum == 1), ]
for(ll in 1:nrow(ones)){
temp <- ones[ll, ]
f_i <- hook_probs[which(temp[1, 1:nhooks] == 1)]
ones[ll, 'prob'] <- (f_i + zero_prob) ^ nfish - zero_prob ^ nfish
}
#-------------------------------------------------------------------------------------------
#Two hooks
twos <- hook_combos[which(hook_combos$row_sum == 2), ]
for(ll in 1:nrow(twos)){
temp <- twos[ll, ]
yes <- which(temp[1, 1:nhooks] == 1)
f_1 <- hook_probs[yes[1]]
f_2 <- hook_probs[yes[2]]
twos[ll, 'prob'] <- (f_1 + f_2 + zero_prob) ^ nfish - (f_1 + zero_prob) ^ nfish -
(f_2 + zero_prob) ^ nfish - zero_prob ^ nfish
}
#-------------------------------------------------------------------------------------------
#Three Hooks
threes <- hook_combos[which(hook_combos$row_sum == 3), ]
for(ll in 1:nrow(threes)){
temp <- which(threes[ll, 1:nhooks] == 1)
}
sapply(ones, function(x) which(x[1:nhooks] == 1))
(hook_probs[which(ones[1, 1:5] == 1)] + zero_prob) ^ nfish - zero_prob ^ nfish
dd <- apply(ones, MAR = 1,
FUN = function(x) (hook_probs[which(ones[x, 1:5] == 1)] + zero_prob) ^ nfish - zero_prob ^ nfish)
apply(hook_combos, MAR = 1, FUN = function(x) paste(which(x[1:5] == 1)))
for(ii in 1:nhooks){
print(ii)
}
ones$prob <-
#Calculate probabilities based on
hook_combos %>% group_by(row_sum) %>% mutate(prob = function(x))
zero_index <- which(hook_combos$row_sum == 0)
hook_combos
hook_combos[which(hook_combos$row_sum == 0), 'prob'] <-
which(hook_combos$row_sum == 1)
hook_combos %>% filter(row_sum == 0) %>% mutate(prob = zero_prob ^ nfish)
sum
#-------------------------------------------------------------------------------------------
#NO recruitment and strong local depletion effects with year after year sampling
#-------------------------------------------------------------------------------------------
##Compare the effect of fish movement on fixed location sampling
#Specify 10 locations
location_list1 <- list(c(2, 2),
c(3, 1),
c(3, 4),
c(3, 7),
c(4, 9),
c(2, 6),
c(7, 1),
c(9, 3),
c(8, 5),
c(8, 7),
c(9, 10),
c(10, 6))
pdf(width = 13, height = 5.65,
file = 'figs/fixed_location_different_movement.pdf')
par(mfcol = c(1, 3), oma = c(2, 2, 0, 0))
#no fish movement and uniform initial distribution
pp1 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = FALSE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_none',
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#clockwise fish movement
pp2 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = FALSE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#left fish movement
pp3 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = FALSE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_left', max_prob = .7, min_prob = 0,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
dev.off()
#-------------------------------------------------------------------------------------------
#random locations and various movement patterns
pdf(width = 13, height = 5.65,
file = 'figs/random_location_different_movement.pdf')
par(mfcol = c(1, 3), oma = c(2, 2, 0, 0))
#no fish movement and uniform initial distribution
pp1 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = TRUE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_none',
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#clockwise fish movement
pp2 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = TRUE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#left fish movement
pp3 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = TRUE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_left', max_prob = .7, min_prob = 0,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
dev.off()
#-------------------------------------------------------------------------------------------
#Fixed locations and moving fish
pdf(width = 13, height = 5.65,
file = 'figs/fixed_location_clockwise_movement_different_distributions.pdf')
par(mfcol = c(1, 3), oma = c(2, 2, 0, 0))
#no fish movement and uniform initial distribution
pp1 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = FALSE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#clockwise fish movement
pp2 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'patchy',
seed = 302,
nyears = 15,
random_locations = FALSE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#left fish movement
pp3 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'area',
area = 'upperright',
seed = 302,
nyears = 15,
random_locations = FALSE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
dev.off()
#-------------------------------------------------------------------------------------------
#Random locations and moving fish
pdf(width = 13, height = 5.65,
file = 'figs/random_location_clockwise_movement_different_distributions.pdf')
par(mfcol = c(1, 3), oma = c(2, 2, 0, 0))
#no fish movement and uniform initial distribution
pp1 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = TRUE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#clockwise fish movement
pp2 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'patchy',
seed = 302,
nyears = 15,
random_locations = TRUE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
#left fish movement
pp3 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'area',
area = 'upperright',
seed = 302,
nyears = 15,
random_locations = TRUE,
location_list = location_list1,
nlocs = 12,
move_func = 'move_fish_cw', move_prob = .7,
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE)
dev.off()
#-------------------------------------------------------------------------------------------
par(mfcol = c(1, 3))
pp1 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 1000,
distribute = 'uniform',
area = 'upperleft',
seed = 302,
nyears = 30,
random_locations = TRUE,
location_list = list(c(2, 2), c(8, 8)),
nlocs = 10,
move_func = 'move_fish_none',
nhooks = 15, ndrops = 3, scope = 0, print_text = TRUE,
xlim_s = c(0, 10000))
pp2 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'uniform',
area = 'upperleft',
seed = 302,
nyears = 30,
random_locations = TRUE,
location_list = list(c(2, 2), c(8, 8)),
nlocs = 10,
move_func = 'move_fish_none',
nhooks = 15, ndrops = 3, scope = 0,
xlim_s = c(0, 10000))
pp3 <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 10000,
distribute = 'uniform',
area = 'upperleft',
seed = 302,
nyears = 30,
random_locations = TRUE,
location_list = list(c(2, 2), c(8, 8)),
nlocs = 10,
move_func = 'move_fish_none',
nhooks = 15, ndrops = 3, scope = 0)
#high level wrapper to run and plot
pp <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 5000,
distribute = 'area',
area = 'upperleft',
seed = 302,
nyears = 15,
random_locations = FALSE,
location_list = list(c(2, 2), c(8, 8)),
nlocs = 50,
move_func = 'move_fish_none',
nhooks = 15, ndrops = 3, scope = 0)
pp <- run_and_plot(numrow = 10,
numcol = 10,
nfish = 1000,
distribute = 'uniform',
seed = 302,
nyears = 15,
random_locations = TRUE,
# location_list = list(c(2, 2), c(8, 8)),
nlocs = 10,
move_func = 'move_fish_cw',
move_prob = .8,
nhooks = 15, ndrops = 3, scope = 0)
xx301[[4]] == xx300.2[[4]]
xx300.1$end_nfish == xx300.2$end_nfish
xx301$end_nfish == xx300.2$end_nfish
run_and_plot(numrow = 10, numcol = 10, nfish = 10000,
distribute = 'uniform', seed = 300, nyears = 15, random_locations = TRUE,
nlocs = 10, move_func = 'move_fish_left', max_prob = .2, min_prob = .01)
#-------------------------------------------------------------------------------------------
#develop function to move fish around ontogenetically or
xx <- survey_over_years(numrow = 10, numcol = 10, nfish = 100000,
distribute = 'uniform',
seed = 300, nyears = 15, location_list,
random_locations = TRUE, nlocs = 100, move_func = move_fish_cw, move_prob = .8,
scope = 1, process = 'equal_prob', ndrops = 3, nhooks = 5)
thing <- parse_master_list(xx)
nfish_cpue <- merge(thing$end_nfish %>% group_by(year) %>% summarise(nfish = sum(value)),
cpue %>% group_by(year) %>% summarise(cpue = mean(value)) %>% as.data.frame,
by = 'year')
plot(nfish_cpue$nfish, nfish_cpue$cpue, ylim = c(0, 1), pch = 19,
xaxs = 'i', yaxs = 'i', xlim = c(0, max(pretty(nfish_cpue$nfish))))
ggplot(cpue, aes(x = variable, y = value, colour = year, group = year)) + geom_line() +
facet_wrap(~ location) + scale_color_gradientn(colors = gray.colors(length(cpue$year)))
#separate elements from each iteration
#everything a list for now
fish_list <- lapply(xx, FUN = function(x) x$sampled_area)
fish_melt <- melt(fish_list)
names(fish_melt) <- c('row', 'column', 'nfish', 'year')
fish_melt$density <- fish_melt$nfish / 100
ggplot(fish_melt, aes(x = row, y = column)) + geom_raster(aes(fill = density)) +
facet_wrap(~ year)
ggplot(fish_melt)
melt(fish_list)
melt(fish_list[[1]])
ggplot(fish_list[[1]] aes(x))
cpue_list <- lapply(xx, FUN = function(x) x$cpue)
#extract number of fish in each year
nfish <- sapply(xx, FUN = function(x) {
sum(unlist(x$sampled_area))
})
cpue <- sapply(xx, FUN = function(x) {
mean(unlist(x$cpue[, 2:4]))
})
plot(nfish, cpue, pch = 19, type = 'o')
to.plot <- lapply(xx, FUN = function(x) x$cpue)
sapply(to.plot, FUN = function(x) mean(unlist(x[, 2:4])))
survey_over_years(nfish = 10000, distribute = 'uniform', seed = 300, nyears = 15,
random_locations = TRUE, nlocs = 10)
#-------------------------------------------------------------------------------------------
#Look at number of locations
cpues <- explore_nlocs_cpue(numrow = 10, numcol = 10, nfish = 2000, seed = 200,
numlocs = 10, distribute = 'patchy',
percent = .5, scope = 0)
to.plot <- melt(cpues)
names(to.plot)[1] <- 'nlocs'
xx <- sapply(to.plot$location, FUN = function(x) eval(parse(text = x)))
to.plot$x <- as.vector(xx[1, ])
to.plot$y <- as.vector(xx[2, ])
#-------------------------------------------------------------------------------------------
#Look at different numbers of fish
nfish.vec <- seq(100, 10000, by = 100)
avg.cpue <- nfish.vec
for(ii in 1:length(nfish.vec)){
init <- initialize_population(numrow = 10, numcol = 10, nfish = nfish.vec[ii], distribute = 'uniform',
percent = .3, seed = 301)
temp <- conduct_survey(fish_area = init, location_list = list(c(4, 10),
c(8, 2),
c(3, 3)), scope = 1, nhooks = 15, ndrops = 5)
avg.cpue[ii] <- mean(unlist(temp$cpue))
}
plot(nfish.vec, avg.cpue, type = 'o', pch = 19, ylim = c(0, 1), xaxs = 'i',
yaxs = 'i', xlim = c(0, max(nfish.vec)))
png(file = '/Users/peterkuriyama/Desktop/hl_cpue_check.png')
plot(nfish, avg.cpue, type = 'o', pch = 19, yli = c(0, 1), xaxs = 'i',
yaxs = 'i', xlim = c(0, max(nfish)))
dev.off()
conduct_survey(fish_area = init, location_list = list(c(4, 10),
c(8, 2),
c(3, 3)),
scope = 1, nhooks = 15, ndrops = 5)
#------------------------------------------------------
#------------------------------------------------------
#Scraps
# fish.range * move.prob
# #Define movement probabilities
# 1 - (fish.range / nfish.range)
# #Each fish has a probability of catching a hook
# #Probability depends on nuber
# #conusmption by one fish
# check_cons <- function(max.prob = .9, nhooks){
# zz <- (max.prob * nhooks) / (.6 + nhooks)
# return(zz)
# }
# tt <- data.frame(hook = 1:75, prob = sapply(1:75,
# FUN = function(x) check_cons(nhooks = x)))
# tt$prob.many <- 1 - exp(-(tt$prob * 50))
# plot(tt$hook, tt$prob.many, pch = 19, ylim = c(0, 1),
# main = '70 fish and 1:75 hooks')
# #Highest probability of hook saturation:
# #Five fish in location and more than five fish in nfish.range
# if(fish.loc >= nhooks & (nfish.range - fish.loc) >= nhooks){
# hook.prob <- seq(from = max.prob, to = 0, by = -delta.prob)
# }
# #High probability of hook saturation:
# #if more than five fish in location and fewer than nhooks fish surrounding
# if(fish.loc >= nhooks & (nfish.range - fish.loc) < nhooks){
# hook.prob <- seq(from = max.prob - (2 * delta.prob), to = 0, by = -delta.prob)
# }
# #Medium prob of hook saturation
# #fewer than five fish in specific location, more than five surrounding
# if(fish.loc < nhooks & (nfish.range - fish.loc) >= nhooks){
# hook.prob <- seq(from = max.prob - (4 * delta.prob), to = 0, by = -delta.prob)
# }
# #Low prob of hook saturation
# #fewer than five in speicifc location, fewer than five surrounding
# if(fish.loc < nhooks & (nfish.range - fish.loc) < nhooks & nfish.range >= nhooks){
# hook.prob <- seq(from = max.prob - (6 * delta.prob), to = 0, by = -delta.prob)
# }
# #Lowest prob of hook saturation
# #fewer than nhooks fish in entire range
# if(fish.loc < nhooks & (nfish.range - fish.loc) < nhooks & nfish.range < nhooks){
# hook.prob <- seq(from = max.prob - (8 * delta.prob), to = 0, by = -delta.prob)
# }
# #Subset hook probabilites based on number of hooks
# hook.prob <- hook.prob[1:nhooks]
# #Sample fish
# fish <- rbinom(n = nhooks, size = 1, prob = hook.prob)
# #Subtract sampled fish from number of fish in fish.range matrix
# xx <- melt(fish.range)
# xx[which(xx$value != 0), 'prob'] <- 1 / sum(xx$value != 0) #equal probabilities for now
# # xx$prob <- xx$value / sum(xx$value)
# # use a multinomial sample to find which cells to subtract sampled fish from
# fish.caught <- rmultinom(prob = xx$prob, n = 1, size = sum(fish))
# xx$value <- xx$value - fish.caught
# #update whole matrix
# return(xx)
# #Keep
# # while(sum(fish) > nfish.range){
# # fish <- rbinom(n = nhooks, size = 1, prob = hook.prob)
# # }
#-------------------------------------------------------------------------------------------
#Move fish cw
# fish_area1 <- initialize_population(distribute = 'area', area = 'upperright', numrow = 10, numcol = 10,
# nfish = 1000)
# tt <- fish_area1
# ttp <- vector('list', length = 8)
# for(ll in 1:8){
# temp <- move_fish_cw(fish_area = tt, move_prob = .8)
# tt <- temp$final
# ttp[[ll]] <- tt
# }
# ttp <- melt(ttp)
# ggplot(ttp, aes(x = Var1, y = Var2)) + geom_raster(aes(fill = value)) +
# theme_bw() + facet_wrap(~ L1)
# ggplot(ttp[1], )
# move_fish_cw(fish_area = fish_area1, move_prob = .8)
# #Update numbers of fish based on catches
# #Record catches
# prob.catch <- fish.loc
# #check this is working
# #------------------------------------------------------------------------------------------
# # check.this <- vector('list', length = 1000000)
# # for(ii in 1:length(check.this)){
# # check.this[[ii]] <- rbinom(n = nhooks, size = 1, prob = hook.prob)
# # }
# # ct <- ldply(check.this)
# # colSums(ct) / length(check.this)
# #------------------------------------------------------------------------------------------
# fishArea, location, prob.max, prob.delta
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