mages' blog

Since Christmas I have been playing around with a Raspberry Pi. It is certainly not the fastest computer, but what a great little toy! Here are a few experiences and online resources that I found helpful.

Setup

Initially I connected the Raspberry Pi via HDMI to a TV; together with keyboard, mouse and an old USB Wifi adapter. Everything worked out of the box and I could install Raspbian and set up the network.

HDMI to VGA

Using an old VGA computer monitor via an adapter required changes to the file /boot/config.txt. You can find the parameters that match your monitor on Raspberry Pi StackExchange. In my case I had to set:

hdmi_group=2
hdmi_mode=35 # 1280x1024 @ 60Hz

Remote Access

But who needs a monitor when you can access the Pi remotely anyway? The command ifconfig tells me the local IP address of the Raspberry Pi.

With XQuartz on my Mac running I can connect to the Raspberry Pi via ssh with the X session forwarded:

ssh -X pi@your.ip.address.here

However, the performance is a bit sluggish and online comments suggest to use VNC instead. Nothing easier than that, install the VNC server on the Pi and use Screen Sharing on your Mac to access the Pi. Mitch Malone has a great post on this subject. Following the VNC setup on the Raspberry Pi I can type:

vnc://pi@your.ip.address.here:5901

into Safari and that will bring up the Screen Sharing App; see screen shot below.

AirPrint and AirPlay

Ok, let's give the Pi something to do: Rohan Kapoor explains how to set up the Raspberry Pi as a print server with AirPrint.

How about AirPlay as well? Follow Thorin Klosowski's steps on Lifehacker and you can stream music from your iOS devices to the Raspberry Pi's audio out.

Mathematica and R

Just before Christmas Stephen Wolfram announced that Mathematica would be made freely available for the Raspberry Pi. I had used Mathematica a little at university and was curious to see it on the Pi. Alex Newman posted the installation instructions on the Wolfram community site. It is as simple as:

sudo apt-get update && sudo apt-get install wolfram-engine

As a little toy example I run Paul Nylander's Mathematica code for a Feigenbaum diagram:

Surprisingly, it took about 3.5 minutes to run. Curious to find out if my old R routine would run faster I installed R on my Pi:

sudo apt-get install r-base

and adapted the code to match the parameters of the Mathematica routine (well, so I think):

The R code finished after about 10 seconds. Surely, there must be ways to speed up the Mathematica code that I have to investigate.

The other day I found some old basic code I had written about 15 years ago on a Mac Classic II to plot the Feigenbaum diagram for the logistic map. I remember, it took the little computer the whole night to produce the bifurcation chart.

With today's computers even a for-loop in a scripting language like R takes only a few seconds.

logistic.map <- function(r, x, N, M){
  ## r: bifurcation parameter
  ## x: initial value
  ## N: number of iteration
  ## M: number of iteration points to be returned
  z <- 1:N
  z[1] <- x
  for(i in c(1:(N-1))){
    z[i+1] <- r *z[i]  * (1 - z[i])
  }
  ## Return the last M iterations 
  z[c((N-M):N)]
}

## Set scanning range for bifurcation parameter r
my.r <- seq(2.5, 4, by=0.003)
system.time(Orbit <- sapply(my.r, logistic.map,  x=0.1, N=1000, M=300))
##   user  system elapsed (on a 2.4GHz Core2Duo)
##   2.910   0.018   2.919 

Orbit <- as.vector(Orbit)
r <- sort(rep(my.r, 301))

plot(Orbit ~ r, pch=".")

Let's not forget when Mitchell Feigenbaum started this work in 1975 he did this on his little calculator!

Update, 18 March 2012

The comment from Berend has helped to speedup the code by a factor of about four, thanks to byte compiling (using the same parameters as above), and Owe got me thinking about the alpha value of the plotting colour. Here is the updated result, with the R code below:

library(compiler) ## requires R >= 2.13.0
logistic.map <- cmpfun(logistic.map) # same function as above
my.r <- seq(2.5, 4, by=0.001)
N <- 2000; M <- 500; start.x <- 0.1
orbit <- sapply(my.r, logistic.map,  x=start.x, N=N, M=M)
Orbit <- as.vector(orbit)
r <- sort(rep(my.r, (M+1)))
plot(Orbit ~ r, pch=".", col=rgb(0,0,0,0.05))