particles

This package implements the d3-force algorithm developed by Mike Bostock in R, thus providing a way to run many types of particle simulations using its versatile interface.

While the first goal is to provide feature parity with its JavaScript origin, the intentions is to add more forces, constraints, etc. down the line. While d3-force is most well-known as a layout engine for visualising networks, it is capable of much more. Therefore, particles is provided as a very open framework to play with. Eventually ggraph will provide some shortcut layouts based on particles with the aim of facilitating network visualisation.

Usage

particles builds upon the framework provided by tidygraph and adds a set of verbs that defines the simulation:

simulate() : Creates a simulation based on the input graph, global parameters, and a genesis function that sets up the initial conditions of the simulation.
wield() : Adds a force to the simulation. All forces implemented in d3-force are available as well as some additionals.
impose() : Adds a constraint to the simulation. This function is a departure from d3-force, as d3-force only allowed for simple fixing of x and/or y coordinates through the use of the fx and fy accessors. particles formalises the use of simulation constraints and adds new functionalities.
evolve() : Progresses the simulation, either a predefined number of steps, or until the simulated annealing has cooled down.

Example

A recreation of the Les Miserable network in https://bl.ocks.org/mbostock/4062045

library(tidyverse)
library(ggraph)
library(tidygraph)
library(particles)

# Data preparation
d3_col <- c(
  '0' = "#98df8a",
  '1' = "#1f77b4",
  '2' = "#aec7e8",
  '3' = "#ff7f0e",
  '4' = "#ffbb78",
  '5' = "#2ca02c",
  '6' = "#d62728",
  '7' = "#ff9896",
  '8' = "#9467bd",
  '9' = "#c5b0d5",
  '10' =  "#8c564b"
)

raw_data <- 'https://gist.githubusercontent.com/mbostock/4062045/raw/5916d145c8c048a6e3086915a6be464467391c62/miserables.json'
miserable_data <- jsonlite::read_json(raw_data, simplifyVector = TRUE)
miserable_data$nodes$group <- as.factor(miserable_data$nodes$group)
miserable_data$links <- miserable_data$links |>  
  mutate(from = match(source, miserable_data$nodes$id),
         to = match(target, miserable_data$nodes$id))

# Actual particles part
mis_graph <- miserable_data |> 
  simulate() |> 
  wield(link_force) |> 
  wield(manybody_force) |> 
  wield(center_force) |> 
  evolve() |> 
  as_tbl_graph()

# Plotting with ggraph
ggraph(mis_graph, 'nicely') + 
  geom_edge_link(aes(width = sqrt(value)), colour = '#999999', alpha = 0.6) + 
  geom_node_point(aes(fill = group), shape = 21, colour = 'white', size = 4, 
                  stroke = 1.5) + 
  scale_fill_manual('Group', values = d3_col) + 
  scale_edge_width('Value', range = c(0.5, 3)) + 
  coord_fixed() +
  theme_graph()
#> Warning: Existing variables `x`, `y` overwritten by layout variables

If you intend to follow the steps of the simulation it is possible to attach an event handler that gets called ofter each generation of the simulation. If the handler produces a plot the result will be an animation of the simulation:

# Random overlapping circles
graph <- as_tbl_graph(igraph::erdos.renyi.game(100, 0)) |> 
  mutate(x = runif(100) - 0.5, 
         y = runif(100) - 0.5, 
         radius = runif(100, min = 0.1, 0.2))

# Plotting function
graph_plot <- function(sim) {
  gr <- as_tbl_graph(sim)
  p <- ggraph(gr, layout = as_tibble(gr)) +
    geom_node_circle(aes(r = radius), fill = 'forestgreen', alpha = 0.5) + 
    coord_fixed(xlim = c(-2.5, 2.5), ylim = c(-2.5, 2.5)) + 
    theme_graph()
  plot(p)
}

# Simulation
graph %>% simulate(velocity_decay = 0.7, setup = predefined_genesis(x, y)) |> 
  wield(collision_force, radius = radius, n_iter = 2) |> 
  wield(x_force, x = 0, strength = 0.002) |> 
  wield(y_force, y = 0, strength = 0.002) |> 
  evolve(on_generation = graph_plot)

Click here for resulting animation (GitHub don’t allow big gifs in readme)

Installation

You can install particles from CRAN using install.packages("particles") or alternatively install the development version from github with:

# install.packages("devtools")
devtools::install_github("thomasp85/particles")

Immense Thanks

A huge “Thank You” to Mike Bostock is in place. Without d3-force, particles wouldn’t exist and without d3 in general the world would be a sadder place.
The C++ quad tree implementation that powers manbody_force and collision_force is a modification of the implementation made by Andrei Kashcha and made available under MIT license. Big thanks to Andrei as well.

Possible issue with velocity decay

Hi Thomas, hope you're good.

I make generative art/animation (mostly in R) and I've been exploring your brilliant Particles library (inspired by your art) as I think there's massive potential for some really interesting pieces. Hoping I can get your help with an issue which could be a bug (but more likely I'm just misunderstanding something)

I'm working on some code where I want to create a simulation which reacts to a new (randomly generated) field force every 10 evolutions (10 is an arbitrary number - eventually this will be triggered by an audio input). Each evolution is plotted and exporting, capturing the simulation.

The idea is that the particles in the simulation will be 're-energised' every 10 frames, energetically reacting to the new field force.

What I'm finding however is that even though I have the velocity decay set to 0, the velocity decays to 0 after 20 frames or so and the following field forces don't have any effect.

I suspect I'm doing something wrong with the 'reheat' function but I can't find much in the way of documentation/examples.

Was wondering if you could possibly take a look at my code and let me know if you can see where I'm going wrong?

library(particles)
library(tidygraph)
library(ggplot2)
library(tidyr)
library(scales)
library(av)

root_dir <- "C:\\Local workspace\\Art\\simulations\\frames"

# function  to create flow field
generate_field_function <- function(size=10){
  
  # generate input
  grid <- long_grid(seq(1, size, length.out = size*size), seq(1, size, length.out = size*size))
  
  # generate perlin grid
  grid$noise <- gen_perlin(grid$x, grid$y)
  
  # rescale and reformat
  grid <- as.data.frame(grid)
  grid$noise <- scales::rescale(grid$noise, to=c(0,7))
  grid <- grid %>% pivot_wider(names_from = x, values_from = noise)
  colnames(grid) <- paste0("V",1:size*size)
  grid <- as.matrix(grid)
}


plot_function <- function(sim){
  
  # count number of existing files (used as counter)
  count_existing_files <- length(list.files(root_dir))
  new_file <- count_existing_files + 1
  
  # plot
  plt <-
    ggplot(as_tibble(sim)) + 
    geom_point(aes(x, y), size = 0.5, stroke=0.5, alpha=1) +
    theme_void()
  
  # save plot to directory
  frame_no <- str_pad(new_file, 4, pad = "0")
  dir.create(file.path(root_dir), showWarnings = FALSE)
  ggsave(paste0(frame_no,".png"),  plot = plt,  device = png, path = root_dir, height=600, width=600,  units="px")
  
  # if frame multiple of 10 then generate new flow field and reheat simulation
  if(new_file %% 10 == 0){
    
    print("new grid")
    
    new_grid <- generate_field_function(10)
    
    new_sim <- sim %>%
      unwield(1) %>%
      wield(field_force, angle=new_grid, vel=1) %>%
      reheat(1) 
    
    return(new_sim)
  }
}

# generate initial flow gird
grid <- generate_field_function(10)
  
# run simulation
create_empty(1000) %>% 
  simulate(setup = aquarium_genesis(), alpha_decay=0) %>% 
  wield(field_force, angle=grid, vel=1) %>% 
  evolve(100, on_generation= plot_function) 

# generate video from exported plots
png_files <- list.files(root_dir, pattern = ".*png$", full.names = TRUE)
av_encode_video(png_files, "C:\\Local workspace\\Art\\simulations\\simulation_export_1.mp4",
                framerate = 10)

Any ideas/tips you can give are appreciated

Thanks

Joe

thomasp85 / particles Goto Github PK

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