Posts Tagged: animation


20
Oct 11

Queueing up in R, continued

Shown above is a queueing simulation. Each diamond represents a person. The vertical line up is the queue; at the bottom are 5 slots where the people are attended to. The size of each diamond is proportional to the log of the time it will take them to be attended. Color is used to tell one person from another and doesn’t have any other meaning. Code for this simulation, written in R, is here. This is my second post about queueing simulation, you can find the first one, including an earlier version of the code, here. Thanks as always to commenters for their suggestions.

A few notes about the simulation:

  • Creating an animation to go along with your simulation can take a while to program (unless, perhaps, you are coding in Flash), and it may seem like an extra, unnecessary step. But you can often learn a lot just by “watching”, and animations can help you spot bugs in the code. I noticed that sometimes smaller diamonds hung around for much longer then I expected, which led me to track down a tricky little error in the code.
  • As usual, I’ve put all of the configuration options at the beginning of the code. Try experimenting with different numbers of intervals and tellers/slots, or change the mean service time.
  • If you want to run the code, you’ll need to have ImageMagick installed. If you are on a PC, make sure to include the full path to “convert”, since Windows has a built-in convert tool might take precedence. Also, note how the files that represent the individual animation cells are named. That’s so that they are added in the animation in the right order, naming them sequentially without zeros at the beginning failed.
  • I used Photoshop to interlace the animated GIF and resave. This reduced the file size by over 90%
  • The code is still a work in progress, it needs cleanup and I still have some questions I want to “ask” of the simulation.

5
May 10

Game of Life in R

Before I decided to learn R in a serious way, I thought about learning Flash/Actionscript instead. Most of my work involves evolutionary models that take place over time. I need visual representations of change. It’s certainly possible to represent change and tell an evolving story with a single plot (see for example Tufte‘s favorite infographic), but there are a lot more options when you can use animations. Flash is also object oriented, well documented with hundreds of books and websites, and has a powerful (albeit challenging to learn) IDE which helps for large coding projects.

The drawbacks to Flash are that it is way behind R in terms of statistical tools, is a closed, expensive language to work with, and dispute widespread use it might be so weak that a single mobile computing company might kill it.

So I picked R, with the idea that when I needed animations, I would find a way to build them. The code below is my first test of using R to generate animations. It’s a variant of Conway’s Game of Life (not to be confused with the Milton Bradley version), where single celled lifeforms live or die based on how many living neighbors they have. In my version, the rules for each cell are determined randomly, in advance of the game. The board size is fixed (see the configuration options at the beginning), whereas Conway’s version was played on a theoretically infinite grid. Green cells are “alive”, black ones are “dead”. I tried for nearly an hour to match the Black=living, White=dead scheme of Conway but couldn’t get that to work, maybe you can figure out how to do it. I re-sized the resulting animated GIF with an external program, that’s another thing I still need to figure out in R.

par(mar=c(0,0,0,0))
library(abind)
library(gdata)
library(fields)
library(grDevices)
library(caTools)
#
times = 50
myWidth = 10
myHeight = 10
#   
# Set the 3D array of rules to determine transitions for each cell.
rulesArray = NULL
for(i in 1:9) {
	toBind <- matrix(sample(c(0,1),replace=T,(myWidth*myHeight)),ncol=myWidth)
	rulesArray <- abind(rulesArray, toBind, along=3)
}
#
first = T
frames = array(0, c(myWidth, myHeight, times))
for(i in 1:times) {
	if(first) {
		forFrame <- sample(c(0,1),replace=T,(myWidth*myHeight))
		first = F
	} else {
		# Convert last frame data to matrix to make comparing adjacent cells easier
		forFrame <- matrix(forFrame, ncol=myWidth)
		newFrame <- forFrame
		#
		for(m in 1:myHeight) {
			for(n in 1:myWidth) {
				adjLiving = 1 # cuz we start with array index 1
				#
				# Find out how many adjacent are living
				if(m > 1 &#038;&#038; n > 1) {
					# Look at top left
					adjLiving = adjLiving + forFrame[(m-1),(n-1)]
				}
				if(m > 1) {
					# Look at top center
					adjLiving = adjLiving + forFrame[(m-1),(n)]
				}
				if(m > 1 &#038;&#038; n < myWidth) {
					# Look at top right
					adjLiving = adjLiving + forFrame[(m-1),(n+1)]
				}
				if(n > 1) {
					# Look at left
					adjLiving = adjLiving + forFrame[(m),(n-1)]
				}
				if(n < myWidth) {
					# Look at right
					adjLiving = adjLiving + forFrame[(m),(n+1)]
				}
				if(m < myHeight &#038;&#038; n > 1) {
					# Look at bottom left
					adjLiving = adjLiving + forFrame[(m+1),(n-1)]
				}
				if(m < myHeight) {
					# Look at bottom center
					adjLiving = adjLiving + forFrame[(m+1),(n)]
				}
				if(m < myHeight &#038;&#038; n < myWidth) {
					# Look at bottom right
					adjLiving = adjLiving + forFrame[(m+1),(n+1)]
				}
				#
				# Find the corresponding rule for this cell
				newStatus = rulesArray[m,n,adjLiving]
				#		
				# Update the status of this cell depending on the rule and number of living adjacent
				newFrame[m,n] = newStatus			
			}
		}
#		
		# Pull it out of a matrix
		forFrame = unmatrix(newFrame)	
	}
	frames[,,i] <- forFrame
}
write.gif(frames, "gameOfLifeRevisited.gif", col=c("#FFFF00", "#000000") , delay=150)