Month: May 2011

homeR: an R package for building physics

For the past few weeks we’ve been very busy here at Neurobat with the analysis of field tests results. In the process of doing that, we had to implement several functions in R that relate to building physics.

We thought it might be useful for the community to have access to those functions, so we decided to begin submitting them to CRAN. That’s why we’re pleased to announce the first release of the homeR package, which we will fill over time with such functions.

This first release, version 0.1, contains just `pmv`, a function to calculate the so-called Predicted Mean Vote, i.e. a measure from -3 (cold) to +3 (hot) of thermal comfort as a function of several variables, including air temperature, clothing and metabolism.

Here I show how with this function one can derive a contour plot showing, for given clothing and metabolism, the optimal indoor temperature (assuming 50% relative humidity). We’re basically going to solve `pmv(clo, met, temp, sat) = 0` equation for `temp` across a grid of `clo` and `met` values with the `uniroot` function.

> clo <- seq(0,2,length=21) > met <- seq(0.6,3.2,length=21) > zero.pmv <- function(clo, met) uniroot(function(temp) pmv(clo,met,temp,50), c(-10,40))$root > contourplot((outer(clo,met,Vectorize(zero.pmv))),
cuts=20,
aspect=”fill”,
panel=function(…) {
panel.grid(h=-1,v=-1,…)
panel.contourplot(…)
},
row.values=clo, column.values=met,
xlim=extendrange(clo), ylim=extendrange(met),
xlab=”[Clo]”, ylab=”[Met]”)

And here is the resulting plot:

Predicted Mean Vote contour plot
Predicted Mean Vote contour plot

As you can see, this is pretty similar to that sort of plots one finds in standard textbooks on the subject, such as Claude-Alain Roulet’s Santé et qualité de l’environnement intérieur dans les bâtiments:

PMV contour plot from textbook
PMV contour plot from textbook

Please give the `homeR` package a try, and give us your feedback. There’s only the `pmv` function in there at the time of writing but we plan to extend the package in the weeks to come.

Posted on by David Lindelöf 2

Saikoro game engine in Lisp

Here at Neurobat we consecrate one day per sprint as a *Lab Day*, i.e. a day when, not unlike what Google does, we are free to work on whatever we want.

Today was Lab Day and I took the opportunity to brush up my Lisp skills by writing a game, inspiring myself heavily from Conrad Burski’s wonderful book Land of Lisp, which will teach you the necessary Lisp skills for the most important programming gig ever, that is, writing games.

I wrote a game engine for the Saikoro boardgame, a game simple enough to be amenable to the kind of AI described in the book. Without doing any major kind of optimization, the computer will play a perfect game on a 4 x 4 board by computing exhaustively a tree of all possible games from a starting position.

Here is what a typical game session looks like:

$ sbcl --load saikoro.lisp
; snip some noise
Current player: A
| A[ 0] 3[ 1] 3[ 2] 2[ 3] |
| 1[ 4] 4[ 5] 2[ 6] 2[ 7] |
| 3[ 8] 4[ 9] 4[10] 2[11] |
| 1[12] 3[13] 4[14] B[15] |
Choose your move: 
1. 0 -> 4 
2. 0 -> 10 
3. 0 -> 1 
4. 0 -> 5 
2
Current player: B
| 0[ 0] 3[ 1] 3[ 2] 2[ 3] |
| 1[ 4] 4[ 5] 2[ 6] 2[ 7] |
| 3[ 8] 4[ 9] A[10] 2[11] |
| 1[12] 3[13] 4[14] B[15] |
Current player: A
| 0[ 0] 3[ 1] 3[ 2] 2[ 3] |
| 1[ 4] B[ 5] 2[ 6] 2[ 7] |
| 3[ 8] 4[ 9] A[10] 2[11] |
| 1[12] 3[13] 4[14] 0[15] |
Choose your move: 
1. 10 -> 1 
2. 10 -> 7 
2
Current player: B
| 0[ 0] 3[ 1] 3[ 2] 2[ 3] |
| 1[ 4] B[ 5] 2[ 6] A[ 7] |
| 3[ 8] 4[ 9] 0[10] 2[11] |
| 1[12] 3[13] 4[14] 0[15] |
Current player: A
| 0[ 0] 3[ 1] 3[ 2] 2[ 3] |
| B[ 4] 0[ 5] 2[ 6] A[ 7] |
| 3[ 8] 4[ 9] 0[10] 2[11] |
| 1[12] 3[13] 4[14] 0[15] |
Choose your move: 
1. 7 -> 1 
1
Current player: B
| 0[ 0] A[ 1] 3[ 2] 2[ 3] |
| B[ 4] 0[ 5] 2[ 6] 0[ 7] |
| 3[ 8] 4[ 9] 0[10] 2[11] |
| 1[12] 3[13] 4[14] 0[15] |
Current player: A
| 0[ 0] A[ 1] 3[ 2] 2[ 3] |
| 0[ 4] 0[ 5] 2[ 6] 0[ 7] |
| 3[ 8] 4[ 9] 0[10] 2[11] |
| 1[12] B[13] 4[14] 0[15] |
Choose your move: 
1. 1 -> 6 
2. 1 -> 3 
3. 1 -> 2 
3
Current player: B
| 0[ 0] 0[ 1] A[ 2] 2[ 3] |
| 0[ 4] 0[ 5] 2[ 6] 0[ 7] |
| 3[ 8] 4[ 9] 0[10] 2[11] |
| 1[12] B[13] 4[14] 0[15] |
Current player: A
| 0[ 0] 0[ 1] A[ 2] 2[ 3] |
| 0[ 4] 0[ 5] 2[ 6] 0[ 7] |
| 3[ 8] 4[ 9] 0[10] 2[11] |
| B[12] 0[13] 4[14] 0[15] |
B wins!
* (quit)

As you can see the UI is very rudimentary for the moment but I wanted to concentrate on getting the AI right first. Next I plan to optimize the AI, and make it work with a non-exhaustive game tree so it would work on larger boards.

If you’re interested in trying it out, feel free to clone my repository.

Posted on by David Lindelöf 2