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experiment2.R
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experiment2.R
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#!/usr/local/bin/Rscript
source("R/helpers.R")
source("R/flock_metrics.R")
source("R/directions_angles.R")
# source("R/directions_angles.R")
library(parallel)
library(patchwork)
library(tictoc)
library(RcppTOML)
library(rextendr)
library(ggplot2)
library(dplyr)
library(readr)
library(purrr)
library(stringr)
library(tidyr)
library(changepoint)
library(ggridges)
options(dplyr.summarise.inform = FALSE)
# make sure boidr is loaded
{
wd <- getwd()
setwd("~/Source/Repos/boids_rust/boidr")
rextendr::document()
devtools::load_all(".")
setwd(wd)
}
# if(FALSE) {
run_experiment <- function(config, experiment_no_simulations, experiment_name = "experiment1",
no_cores = 1, regen_graphs = TRUE, plot_splits = FALSE, gen_graphs = TRUE, stats_regen = TRUE, filter_noise = FALSE) {
print(paste0("starting ", experiment_name))
# set up the experiment
experiment_data_folder <- paste0("Data/", experiment_name, "/")
config$save_locations_path = experiment_data_folder
# ensure data folder exists
if (!dir.exists(experiment_data_folder)) {
dir.create(experiment_data_folder)
}
existing_simulations <- length(list.files(experiment_data_folder, pattern="(prepro_)?boids-data.*"))
sample_rate <- config$sample_rate
# collect data
run_simulation <- function(i){
rlang::exec(flock_detailed_no_return, !!!config)
return(i)
}
runs <- experiment_no_simulations - existing_simulations
stats_file_path <- paste0(experiment_data_folder, "results_stats.csv")
tic("data generation")
# if stats file does not exist, regenerate
# otherwise look if there is more work to be don or if we have specifically said
# not to regenerate stats
if(!file.exists(stats_file_path) || (runs > 0 && stats_regen)) {
# preserve config
write_config <- config
write_config$experiment_name = experiment_name
jsonlite::write_json(write_config, paste0(experiment_data_folder, "config.json"))
mclapply(1:runs,
run_simulation,
mc.cores = no_cores)
}
toc()
simulation_files <- list.files(experiment_data_folder, pattern="(prepro_)?boids-data.*")[1:experiment_no_simulations]
fetch_file <- function(file, init_width, init_height, no_boids, version = 3){
if (version != 3) {
return(read_csv(file, col_types = cols()))
}
# this inflates the files by about 1.5x, but is 7.5x times quicker and most importantly is able to chew through >300MB files
prep_file <- file
if(!startsWith(basename(file), "prepro_")) {
prep_file <- file.path(dirname(file), paste0("prepro_", basename(file)))
}
if (!file.exists(prep_file)) {
# prepro file does not exist
if (!file.exists(file)) {
# nor does the original
stop(paste0("Did not find the raw or preprocessed data file, file: ", file))
} else {
# preprocess the file
preprocess_file(file, init_width = init_width, init_height = init_height, no_boids = no_boids)
# remove the original
if (file.exists(prep_file)) {
file.remove(file)
} else {
stop(paste0("Something went wrong with file preprocessing, file: ", file))
}
}
}
return(read_csv(prep_file, col_types = cols()))
}
# file_no is the ith file in a data folder
# metric is a function that expects boid_data and returns a scalar for each time point
tic("evaluation")
collect_results <- function(file_no, metric, process = function(x)x, fetch = fetch_file){
fetch_file(
paste0(experiment_data_folder, simulation_files[[file_no]]),
init_width = config$init_width,
init_height = config$init_height,
no_boids = config$init_boids
) |>
process() |>
metric() |>
into_experiment_record()
}
# process <- function(x){ get_directional_boid_data2(x, if_else(config$boundary_config == "{\"type\": \"Toroidal\"}", F, T))}
process = function(x, version = 3){
remove_boundary = if_else(config$boundary_config == "{\"type\": \"Toroidal\"}", F, T);
if (version != 3) {
get_directional_boid_data2(x, remove_boundary)
} else {
get_directional_boid_data3(x, remove_boundary)
}
}
if (stats_regen || !file.exists(stats_file_path))
{
tic("results average norm vel")
results_average_norm_vel2 <- mclapply(
1:experiment_no_simulations,
function(file_no){
collect_results(file_no, metric = function(x){get_average_norm_vel2(x, FALSE)}, process = process)
},
mc.cores = no_cores
) |>
map_dfr(bind_rows)
toc()
tic("results average norm vel")
results_average_norm_vel <- mclapply(
1:experiment_no_simulations,
function(file_no){
collect_results(file_no, metric = function(x){get_average_norm_vel(x, FALSE)}, process = process)
},
mc.cores = no_cores
) |>
map_dfr(bind_rows)
toc()
tic("results voronoi counts")
results_voronoi_counts <- mclapply(
1:experiment_no_simulations,
function(file_no){
collect_results(file_no, metric = function(x) get_voronoi_area(x, config), process = process)
},
mc.cores = no_cores
) |>
map_dfr(bind_rows)
toc()
if (config$dbscan_clustering) {
tic("results convex hull")
results_convex_hull <- mclapply(
1:experiment_no_simulations,
function(file_no){
collect_results(file_no, get_convex_hull_data, process = process)
},
mc.cores = no_cores
) |>
map_dfr(bind_rows)
toc()
tic("results no flocks")
results_no_flocks <- mclapply(
1:experiment_no_simulations,
function(file_no){
collect_results(file_no, get_no_flocks, process = process)
},
mc.cores = no_cores
) |>
map_dfr(bind_rows)
toc()
}
tic("visualisation")
results_stats <- tibble(
t = 1:ncol(results_average_norm_vel) * sample_rate,
mean_average_norm_vel = apply(results_average_norm_vel, 2, mean),
mean_average_norm_vel2 = apply(results_average_norm_vel2, 2, mean),
var_average_norm_vel = apply(results_average_norm_vel, 2, var),
var_average_norm_vel2 = apply(results_average_norm_vel2, 2, var),
var_voronoi_counts = apply(results_voronoi_counts, 2, var),
mean_voronoi_counts = apply(results_voronoi_counts, 2, mean),
)
if (config$dbscan_clustering) {
results_stats <- results_stats |> bind_cols(
mean_no_flocks = apply(results_no_flocks, 2, mean),
var_no_flocks = apply(results_no_flocks, 2, var),
mean_convex_hull = apply(results_convex_hull, 2, mean),
var_convex_hull = apply(results_convex_hull, 2, var)
)
}
write_csv(results_stats, stats_file_path)
}
if (!gen_graphs) {
toc()
tic.clear()
return()
}
experiment_plot_folder <- paste0(experiment_data_folder, "plots/")
if(!dir.exists(experiment_plot_folder)) {
dir.create(experiment_plot_folder)
}
plots_splits_folder <- paste0(experiment_data_folder, "plots_splits/")
if(!dir.exists(plots_splits_folder) && plot_splits) {
dir.create(plots_splits_folder)
}
tic("setting change points")
init_chang_pts <- c()
if(plot_splits) {
res <- 200
if (config$dbscan_clustering) {
png(filename = paste0(plots_splits_folder, "mean_no_flocks.png"), width = 6 * res, height = 4 * res, res = res)
change_points <- cpt.mean(results_stats$mean_no_flocks, method = "AMOC")
plot(change_points)
dev.off()
init_chang_pts <- c(init_chang_pts, cpts(change_points)[1])
png(filename = paste0(plots_splits_folder, "var_no_flocks.png"), width = 6 * res, height = 4 * res, res = res)
change_points <- cpt.mean(results_stats$var_no_flocks, method = "AMOC")
plot(change_points)
dev.off()
init_chang_pts <- c(init_chang_pts, cpts(change_points)[1])
png(filename = paste0(plots_splits_folder, "mean_convex_hull.png"), width = 6 * res, height = 4 * res, res = res)
change_points <- cpt.mean(results_stats$mean_convex_hull, method = "AMOC")
plot(change_points)
dev.off()
init_chang_pts <- c(init_chang_pts, cpts(change_points)[1])
}
png(filename = paste0(plots_splits_folder, "mean_average_norm_vel.png"), width = 6 * res, height = 4 * res, res = res)
change_points <- cpt.var(results_stats$mean_average_norm_vel, method = "AMOC")
plot(change_points)
dev.off()
init_chang_pts <- c(init_chang_pts, cpts(change_points)[1])
png(filename = paste0(plots_splits_folder, "var_average_norm_vel.png"), width = 6 * res, height = 4 * res, res = res)
change_points <- cpt.var(results_stats$var_average_norm_vel, method = "AMOC")
plot(change_points)
dev.off()
init_chang_pts <- c(init_chang_pts, cpts(change_points)[1])
png(filename = paste0(plots_splits_folder, "var_voronoi_counts.png"), width = 6 * res, height = 4 * res, res = res)
change_points <- cpt.var(results_stats$var_voronoi_counts, method = "AMOC")
plot(change_points)
dev.off()
init_chang_pts <- c(init_chang_pts, cpts(change_points)[1])
png(filename = paste0(plots_splits_folder, "mean_voronoi_counts.png"), width = 6 * res, height = 4 * res, res = res)
change_points <- cpt.var(results_stats$mean_voronoi_counts, method = "AMOC")
plot(change_points)
dev.off()
init_chang_pts <- c(init_chang_pts, cpts(change_points)[1])
}
toc()
# change_points <- cpt.var(results_stats$mean_no_flocks)
# plot(change_points)
# change_points <- cpt.meanvar(results_stats$mean_no_flocks)
# plot(change_points)
max_change_point <- {
if(plot_splits && sum(!is.na(init_chang_pts)) != 0) {
m <- max(init_chang_pts[!is.na(init_chang_pts)])
if (is.na(m) || m > 10000 / sample_rate) { 10000 / sample_rate } else { m }
}
else {
0
}}
max_change_point <- as.integer(max_change_point)
print(paste0("max change points:", max_change_point))
figure_postfix <- paste0("_", experiment_name, ".jpg")
figure_height <- 6
figure_width <- 10
figure_units <- "in"
figure_dpi <- "retina" # screen/print/retina or a number
figure_subtitle_config <- paste(
"agents:", config$init_boids, ";",
"iter:", config$no_iter, ";",
"smpl:", config$sample_rate, ";",
"env:", paste0(config$init_width, "×", config$init_width), ";"
)
plot_with_splits <- function(my_data, split, plt, byrow = TRUE){
if (plot_splits) {
if (byrow) {
split_a <- my_data[1:split,]
split_b <- my_data[(split + 1):nrow(my_data),]
plt(split_a, "_split_a", 0)
plt(split_b, "_split_b", split * config$sample_rate)
} else {
split_a <- my_data[,1:split]
split_b <- my_data[,(split + 1):ncol(my_data)]
plt(split_a, "_split_a", 0)
plt(split_b, "_split_b", split * config$sample_rate)
}
}
plt(my_data, "_none", 0)
}
if (config$dbscan_clustering) {
plot_with_splits(results_stats, max_change_point, function(res_data, split_label, time_start) {
ggplot(
data = res_data |> mutate(t = t + time_start),
aes(x = t, y = mean_no_flocks)
) +
geom_point() +
# geom_smooth(method = "gam", level = .9) +
theme_bw() +
labs(x = "time t", y = "mean_t average # flocks",
title = "mean number of flocks;",
subtitle = figure_subtitle_config,
caption = paste0("split: ", str_replace(split_label, "_", ""))) +
scale_x_continuous(limits = c(time_start, NA))
ggsave(paste0("mean_no_flocks", split_label, figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
})
plot_with_splits(results_stats, max_change_point, function(res_data, split_label, time_start) {
ggplot(
data = res_data,
aes(x = t,
y = var_no_flocks)
) +
geom_point() +
# geom_smooth(method = "loess", level = .9) +
theme_bw() +
labs(x = "time t", y = "var_t mean # flocks",
title = "var number of flocks",
subtitle = figure_subtitle_config,
caption = paste0("split: ", str_replace(split_label, "_", ""))) +
scale_x_continuous(limits = c(time_start, NA))
ggsave(paste0("var_no_flocks", split_label, figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
})
plot_with_splits(results_stats, max_change_point, function(res_data, split_label, time_start) {
ggplot(
data = results_stats,
aes(x = t,
y = mean_convex_hull)
) +
geom_point() +
# geom_smooth(method = "gam", level = .9) +
theme_bw() +
labs(x = "time t", y = "mean_t mean flock area",
title = "mean flock area",
subtitle = figure_subtitle_config,
caption = paste0("split: ", str_replace(split_label, "_", ""))) +
scale_x_continuous(limits = c(time_start, NA))
ggsave(paste0("mean_area_flock", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
})
plot_with_splits(results_stats, max_change_point, function(res_data, split_label, time_start) {
ggplot(
data = results_stats,
aes(x = t,
y = var_convex_hull)
) +
geom_point() +
# geom_smooth(method = "gam", level = .9) +
theme_bw() +
labs(x = "time t", y = "var_t mean flock area",
title = "var flock area",
subtitle = figure_subtitle_config,
caption = paste0("split: ", str_replace(split_label, "_", ""))) +
scale_x_continuous(limits = c(time_start, NA))
ggsave(paste0("var_area_flock", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
})
}
plot_with_splits(results_stats, max_change_point, function(res_data, split_label, time_start) {
ggplot(
data = results_stats,
aes(x = t,
y = mean_average_norm_vel)
) +
geom_line() +
theme_bw() +
labs(x = "time t", y = "mean average norm vel",
title = "average norm vel",
subtitle = figure_subtitle_config,
caption = paste0("split: ", str_replace(split_label, "_", ""))) +
scale_x_continuous(limits = c(time_start, NA))
ggsave(paste0("mean_avg_norm_vel", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
})
plot_with_splits(results_stats, max_change_point, function(res_data, split_label, time_start) {
ggplot(
data = results_stats,
aes(x = t,
y = mean_average_norm_vel2)
) +
geom_line() +
theme_bw() +
labs(x = "time t", y = "mean average norm vel",
title = "average norm vel",
subtitle = figure_subtitle_config,
caption = paste0("split: ", str_replace(split_label, "_", ""))) +
scale_x_continuous(limits = c(time_start, NA))
ggsave(paste0("mean_avg_norm_vel2", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
})
plot_with_splits(results_stats, max_change_point, function(res_data, split_label, time_start) {
ggplot(
data = results_stats,
aes(x = t,
y = var_average_norm_vel)
) +
geom_line() +
theme_bw() +
labs(x = "time t", y = "var average norm vel",
title = "var average norm vel",
subtitle = figure_subtitle_config,
caption = paste0("split: ", str_replace(split_label, "_", ""))) +
scale_x_continuous(limits = c(time_start, NA))
ggsave(paste0("var_avg_norm_vel", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
})
plot_with_splits(results_stats, max_change_point, function(res_data, split_label, time_start) {
ggplot(
data = results_stats,
aes(x = t,
y = var_average_norm_vel2)
) +
geom_line() +
theme_bw() +
labs(x = "time t", y = "var average norm vel",
title = "var average norm vel",
subtitle = figure_subtitle_config,
caption = paste0("split: ", str_replace(split_label, "_", ""))) +
scale_x_continuous(limits = c(time_start, NA))
ggsave(paste0("var_avg_norm_vel2", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
})
plot_with_splits(results_stats, max_change_point, function(res_data, split_label, time_start) {
ggplot(
data = results_stats,
aes(x = t,
y = mean_voronoi_counts)
) +
geom_line() +
theme_bw() +
labs(x = "time t", y = "mean Count(voronoi cells area < pi*R^2)",
title = "average voronoi cell area",
subtitle = figure_subtitle_config,
caption = paste0("split: ", str_replace(split_label, "_", ""))) +
scale_x_continuous(limits = c(time_start, NA))
ggsave(paste0("mean_count_voronoi_cell_area", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
})
plot_with_splits(results_stats, max_change_point, function(res_data, split_label, time_start) {
ggplot(
data = results_stats,
aes(x = t, y = var_voronoi_counts)
) +
geom_line() +
theme_bw() +
labs(x = "time t", y = "var Count(voronoi cells area < pi*R^2)",
title = "var voronoi cell area",
subtitle = figure_subtitle_config,
caption = paste0("split: ", str_replace(split_label, "_", ""))) +
scale_x_continuous(limits = c(time_start, NA))
ggsave(paste0("var_count_voronoi_cell_area", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
})
first_n_average_norm_vel <- results_average_norm_vel |>
slice_head(n = 4) |>
mutate(sim_no = row_number()) |>
pivot_longer(cols = starts_with("t_"), values_to = "vals", names_pattern = "t_(.+)", names_transform = as.integer) |>
select(sim_no, time = name, vals) |>
mutate(label = sprintf("s_%d", sim_no), time = time * sample_rate) |>
select(time, vals, label) |>
pivot_wider(values_from = vals, names_from = label)
first_n_average_norm_vel2 <- results_average_norm_vel2 |>
slice_head(n = 4) |>
mutate(sim_no = row_number()) |>
pivot_longer(cols = starts_with("t_"), values_to = "vals", names_pattern = "t_(.+)", names_transform = as.integer) |>
select(sim_no, time = name, vals) |>
mutate(label = sprintf("s_%d", sim_no), time = time * sample_rate) |>
select(time, vals, label) |>
pivot_wider(values_from = vals, names_from = label)
# http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/81-ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/
plot_with_splits(results_average_norm_vel, max_change_point, function(res_data, split_label, time_start){
print(time_start)
res_data |>
slice_head(n = 5) |>
mutate(sim_no = row_number()) |>
pivot_longer(cols = starts_with("t_"), values_to = "vals", names_pattern = "t_(.+)", names_transform = as.integer) |>
select(sim_no, time = name, vals) |>
mutate(label = sprintf("s_%d", sim_no), time = time * sample_rate) |>
ggplot(aes(x = time, y = vals)) +
geom_line() +
scale_x_continuous(limits = c(time_start, NA)) +
facet_grid(rows = vars(label)) +
theme_bw() +
labs(x = "time t", y = "mean average norm vel",
title = "average norm vel of first n simulations",
subtitle = figure_subtitle_config,
caption = paste0("split: ", str_replace(split_label, "_", ""))
)
ggsave(paste0("first_n_avg_norm_vel", split_label, figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
}, byrow = F)
plot_with_splits(results_average_norm_vel2, max_change_point, function(res_data, split_label, time_start){
print(time_start)
res_data |>
slice_head(n = 5) |>
mutate(sim_no = row_number()) |>
pivot_longer(cols = starts_with("t_"), values_to = "vals", names_pattern = "t_(.+)", names_transform = as.integer) |>
select(sim_no, time = name, vals) |>
mutate(label = sprintf("s_%d", sim_no), time = time * sample_rate) |>
ggplot(aes(x = time, y = vals)) +
geom_line() +
scale_x_continuous(limits = c(time_start, NA)) +
facet_grid(rows = vars(label)) +
theme_bw() +
labs(x = "time t", y = "mean average norm vel",
title = "average norm vel of first n simulations",
subtitle = figure_subtitle_config,
caption = paste0("split: ", str_replace(split_label, "_", ""))
)
ggsave(paste0("first_n_avg_norm_vel2", split_label, figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
}, byrow = F)
toc()
# 4 by 4 faceted plots
tic("4 by 4 faceted plots")
no_facets <- 4
batch_factor <-6
direction_df <- mclapply(
1:no_facets,
function(file_no){
# somehow there is a few (units) records missing here
df <- fetch_file(paste0(experiment_data_folder, simulation_files[[file_no]])) |>
process() |>
mutate(result_no = file_no) |>
arrange(desc(time), desc(id))
n <- nrow(df)
section_size <- n %/% batch_factor
breaks = c(seq(1, n, section_size + if_else(n %% batch_factor != 0, 1, 0)), n + 1)
labels = 1:batch_factor
return(
df |>
mutate(timeline_facet = cut(1:n+1, breaks = breaks, labels = labels))
)
},
mc.cores = no_cores
) |>
purrr::map_dfr(dplyr::bind_rows)
create_radial_density_plot <- function(data, angle_col, time_col, title = "Radial Density Plot", sub_sampling, bin_size, no_bins) {
data |>
filter(time < no_bins * bin_size) |>
mutate(time_bin = ((time - 1) %/% bin_size) * bin_size + 1) |>
ggplot(aes(x = !!sym(angle_col), y = !!sym(time_col), group = !!sym(time_col))) +
geom_density_ridges(alpha = 0.5, bandwidth = 0.1) +
scale_x_continuous(breaks = seq(0, 2 * pi, pi / 2), labels = c("[0]", "π*1/2", "π", "3π/2", "[2π]"), limits = c(0, 2 * pi)) +
coord_polar(theta = "x", start = 0, clip = "off") +
labs(title = title,
x = "Heading angle (radians)",
y = "Time Bin",
caption = paste0("total timepoints captured: ", sub_sampling * no_bins * bin_size, " (", sub_sampling * bin_size, " per layer)")) +
theme_bw() +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 6))
}
timeline_labs <- sapply(1:batch_factor, function(x)paste0("k = ",x))
names(timeline_labs) <- 1:batch_factor
result_labs <- sapply(1:no_facets, function(x)paste0("run ",x,"."))
names(result_labs) <- 1:no_facets
direction_df |>
create_radial_density_plot("headings", "time_bin", title = "Radial Density Plot of agent headings",
sub_sampling = sample_rate, bin_size = 1, no_bins = 13) +
facet_wrap(~result_no, labeller = labeller(result_no = result_labs))
ggsave(paste0("radial_density_first_n_t_headings.jpg", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
no_bins <- 360
direction_counts_df <- direction_df |>
group_by(timeline_facet, result_no) |>
get_directional_counts(no_bins = no_bins)
# test$dd
#
# direction_counts_df <- direction_df |>
# group_by(timeline_facet, result_no) |>
# reframe(dd = get_directional_counts2(tibble(id, headings, bearings))) %>% unnest_wider(dd)
# mutate(heading_bin = cut(headings, breaks = seq(0, 2*pi, length.out = no_bins + 1), labels = F)) |>
# mutate(bearing_bin = cut(bearings, breaks = seq(-pi, pi, length.out = no_bins + 1), labels = F)) |>
# reframe(bin = 1:no_bins, heading_count = c(count_em_up(heading_bin, c(1, no_bins))), bearing_count = c(count_em_up(bearing_bin, c(1, no_bins))))
write_csv(direction_counts_df, paste0(experiment_data_folder, "direction_counts"))
ggplot(direction_counts_df, aes(x = bin, y = heading_count)) +
coord_polar(theta = "x", start = 0) +
geom_bar(stat = "identity", fill = "orange", width = .9) +
labs(title = "Heading Angles",
x = paste0("k-th iterration section"),
subtitle = figure_subtitle_config,
y = "headings count",
caption = paste0("n = ", no_facets, " runs divided into k = ", batch_factor, " time sections; oriented w.r.t. North")) +
theme_bw() +
theme(axis.text.x = element_blank()) +
facet_grid(
result_no~timeline_facet,
labeller = labeller(
timeline_facet = timeline_labs,
result_no = result_labs)
)
ggsave(paste0("heading_top_k_overview", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
ggplot(direction_counts_df, aes(x = bin, y = log(heading_count))) +
coord_polar(theta = "x", start = 0) +
geom_bar(stat = "identity", fill = "orange", width = .9) +
labs(title = "Heading Angles",
x = paste0("k-th iterration section"),
subtitle = figure_subtitle_config,
y = "log(headings count)",
caption = paste0("n = ", no_facets, " runs divided into k = ", batch_factor, " time sections; oriented w.r.t. North")) +
theme_bw() +
theme(axis.text.x = element_blank()) +
facet_grid(
result_no~timeline_facet,
labeller = labeller(
timeline_facet = timeline_labs,
result_no = result_labs)
)
ggsave(paste0("heading_log_top_k_overview", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
ggplot(direction_counts_df, aes(x = bin, y = bearing_count)) +
coord_polar(theta = "x", start = pi) +
geom_bar(stat = "identity", fill = "deeppink", width = .9) +
labs(title = "Turning Angles",
x = paste0("k-th iterration section "),
subtitle = figure_subtitle_config,
y = "turns count",
caption = paste0("n = ", no_facets, " runs divided into k = ", batch_factor, " time sections; oriented w.r.t. North")
) +
theme_bw() +
theme(axis.text.x = element_blank()) +
facet_grid(
result_no~timeline_facet,
labeller = labeller(
timeline_facet = timeline_labs,
result_no = result_labs)
)
ggsave(paste0("bearing_top_k_overview", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
ggplot(direction_counts_df, aes(x = bin, y = log(bearing_count))) +
coord_polar(theta = "x", start = pi) +
geom_bar(stat = "identity", fill = "deeppink", width = .9) +
labs(title = "Turning Angles",
subtitle = figure_subtitle_config,
y = "log(turns count)",
caption = paste0("n = ", no_facets, " runs divided into k = ", batch_factor, " time sections; oriented w.r.t. North")
) +
theme_bw() +
facet_grid(
result_no~timeline_facet,
labeller = labeller(
timeline_facet = timeline_labs,
result_no = result_labs)
)
ggsave(paste0("bearing_log_top_k_overview", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
tau <- 5
labels = direction_df$result_no
tic("results voronoi counts")
curvature_result <- mclapply(
(direction_df |> select(result_no) |> distinct())$result_no,
function(rn){
direction_df |> filter(result_no == rn) |>
select(-timeline_facet) |>
get_curvature_order_data(config, tau) |>
mutate(result_no = rep(rn, n()), label_time = 1:n() * config$sample_rate, label = sprintf("s_%d", result_no))
# bind_cols(tibble(result_no = rep(rn, length(time)), label_time = 1:length(time) * config$sample_rate))
},
mc.cores = no_cores
) |>
map_dfr(bind_rows)
toc()
# quit early for large no_iter numbers as the code bellow is really memory inefficient
if (config$no_iter > 2^16) {
tic.clear()
return()
}
ggplot(curvature_result, aes(x = label_time, y = rop)) +
theme_bw() +
labs(
title = paste0("mean flock path curvature with tau = ", tau),
subtitle = figure_subtitle_config,
x = "rate"
) +
geom_line() +
facet_grid(rows = vars(label))
ggsave(paste0("curvature_top_k_overview", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
toc()
tic("direction data graphs")
# testing these
direction_df |>
arrange(time) |>
group_by(result_no, id) |>
mutate(bearings_ma =rollapply(bearings, tau ,mean,align='right',fill=NA)) |>
ungroup() |>
mutate(noise = cluster_id == 0) |>
group_by(result_no, time, noise) |>
summarise(bearings_ma = mean(bearings_ma)) |>
ungroup() |>
filter(!is.na(bearings_ma)) |>
group_by(result_no) |>
mutate(time_label = 1:n() * config$sample_rate, label = sprintf("s_%d", result_no)) |>
ggplot(aes(x = time_label, y = bearings_ma)) +
geom_point(size = 1.2, alpha = .3, aes(color = noise)) +
stat_summary(aes(y = bearings_ma, color = noise), alpha = .8, fun=mean, geom="line") +
theme_bw() +
facet_grid(rows = vars(label)) +
labs(
title = paste0("mean average flock curvature tau = ", tau),
subtitle = figure_subtitle_config,
x = "time"
)
# mutate(label = sprintf("s_%d", sim_no), time = time * sample_rate) |>
# ggplot(aes(x = time, y = vals)) +
# geom_line() +
# facet_grid(rows = vars(label))
ggsave(paste0("dir_data_graph_1", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
direction_df |>
arrange(time) |>
group_by(result_no, id) |>
mutate(breaings_ma=rollapply(bearings, tau,mean,align='right',fill=NA)) |>
ungroup() |>
group_by(time, cluster_id) |>
summarise(breaings_ma = mean(breaings_ma)) |>
mutate(noise = cluster_id == 0) |>
ungroup() |>
filter(!is.na(breaings_ma)) |>
mutate(time_label = 1:n() * config$sample_rate) |>
ggplot(aes(x = time_label, y = breaings_ma)) +
geom_point(size = .5, alpha = .1, aes(color = noise)) +
stat_summary(aes(y = breaings_ma, color = noise), fun=mean, geom="line") +
theme_bw() +
labs(
title = paste0("mean average flock curvature tau = ", tau, "; coerced runs = ", length(table(direction_df$result_no))),
subtitle = figure_subtitle_config,
x = "time"
)
ggsave(paste0("dir_data_graph_2", figure_postfix), path = experiment_plot_folder, width = figure_width, height = figure_height, units = figure_units, dpi = figure_dpi)
toc()
toc()
toc()
}
# testing different configs -
# a - toroidal b/d vs reflective/euclidean b/d
# b - "stringy ball hybrid, mars = modified diamonds"
# c - "large stringy"
# e - ?
# f - one run of g with a longer sequence
# g - testing out different noise levels
# h - do longer runs trully stabilise?
# the previous runs were all done with old rules
# j is running the Vicsek config for different densities, given by the density table (attempt at replicating figure b 1995 paper)
if (FALSE) {
tic("experiment2_a1 start")
config <- get_config(
"basic.toml",
overwrite = list(
init_boids = 2^11,
no_iter = 2^15,
init_width = 4000,
init_height = 4000,
sample_rate = 64,
boundary_config = "{\"type\": \"Toroidal\"}",
distance_config = "{\"type\": \"EucEnclosed\"}"
)
)
no_cores <- 6
experiment_no_simulations <- 120
tryCatch(
expr = run_experiment(config, experiment_no_simulations, no_cores = no_cores, experiment_name = "0301_experiment2_a1")
)
toc()
tic("experiment2_a12 start")
config <- get_config(
"basic2.toml",
overwrite = list(
init_boids = 2^11,
no_iter = 2^15,
init_width = 4000,
init_height = 4000,
sample_rate = 64,
boundary_config = "{\"type\": \"Toroidal\"}",
distance_config = "{\"type\": \"EucEnclosed\"}"
)
)
no_cores <- 8
experiment_no_simulations <- 120
tryCatch(
expr = run_experiment(config, experiment_no_simulations, no_cores = no_cores, experiment_name = "0319_experiment2_a12")
)
toc()
tic("experiment2_a2 start")
config <- get_config(
"basic2.toml",
overwrite = list(
init_boids = 2^11,
no_iter = 2^15,
init_width = 4000,
init_height = 4000,
sample_rate = 64,
boundary_config = "{\"type\": \"Toroidal\"}",
distance_config = "{\"type\": \"EucToroidal\"}"
)
)
no_cores <- 8
experiment_no_simulations <- 120
run_experiment(config, experiment_no_simulations, no_cores = no_cores, experiment_name = "0319_experiment2_a2")
toc()
tic("experiment2_a3 start")
config <- get_config(
"basic2.toml",
overwrite = list(
init_boids = 2^11,
no_iter = 2^15,
init_width = 4000,
init_height = 4000,
sample_rate = 64,
boundary_config = "{\"type\": \"Reflective\"}",
distance_config = "{\"type\": \"EucEnclosed\"}"
)
)
no_cores <- 8
experiment_no_simulations <- 120
run_experiment(config, experiment_no_simulations, no_cores = no_cores, experiment_name = "0319_experiment2_a3")
toc()
tic("experiment3_a12 start")
config <- get_config(
"basic3-blows.toml",
overwrite = list(
init_boids = 2^10,
no_iter = 2^15,
init_width = 1000,
init_height = 1000,
sample_rate = 32,
boundary_config = "{\"type\": \"Toroidal\"}",
distance_config = "{\"type\": \"EucEnclosed\"}"
# dbscan_clustering = FALSE
)
)
# print(Sys.setenv(RUST_BACKTRACE = "1")) # `A+C` could also be used
# Sys.getenv("RUST_BACKTRACE")
no_cores <- 6
experiment_no_simulations <- 36
tryCatch(
expr = run_experiment(config, experiment_no_simulations, no_cores = no_cores, experiment_name = "00_test_0323_experiment3_a12")
)
# Sys.unsetenv("RUST_BACKTRACE") #
toc()
tic("experiment2_a2 start")
config <- get_config(
"basic3.toml",
overwrite = list(
init_boids = 2^10,
no_iter = 2^15,
init_width = 1000,
init_height = 1000,
sample_rate = 32,
boundary_config = "{\"type\": \"Toroidal\"}",
distance_config = "{\"type\": \"EucToroidal\"}"
)
)
no_cores <- 8
experiment_no_simulations <- 120
run_experiment(config, experiment_no_simulations, no_cores = no_cores, experiment_name = "0323_experiment3_a2")
toc()
tic("experiment3_a3 start")
config <- get_config(
"basic3.toml",
overwrite = list(
init_boids = 2^10,
no_iter = 2^15,
init_width = 1000,
init_height = 1000,
sample_rate = 32,
boundary_config = "{\"type\": \"Reflective\"}",
distance_config = "{\"type\": \"EucEnclosed\"}"
)
)
no_cores <- 8
experiment_no_simulations <- 120
run_experiment(config, experiment_no_simulations, no_cores = no_cores, experiment_name = "0323_experiment3_a3")
toc()
tic("0324_experiment3_a4 start")
config <- get_config(
"basic3.toml",
overwrite = list(
init_boids = 2^10,
no_iter = 2^15,
init_width = 1000,
init_height = 1000,
sample_rate = 32,
boundary_config = "{\"type\": \"Toroidal\"}",
distance_config = "{\"type\": \"EucToroidal\"}",
alignment_coef = 0.6,
cohesion_coef = 0.2,
separation_coef = 1.0,
wander_on = FALSE
)
)
no_cores <- 8
experiment_no_simulations <- 60
run_experiment(config, experiment_no_simulations, no_cores = no_cores, experiment_name = "0324_experiment3_a4")
toc()
tic("0324_experiment3_a5 start")
config <- get_config(
"basic3.toml",
overwrite = list(
init_boids = 2^10,
no_iter = 2^15,