We have a Dropbox Teams account, which gives us about north of 1 TB of storage. The idea is to install Dropbox on each netbook that monitors a test site, and to have it save its monitored data to its Dropbox folder. Then when/if that netbook has internet access, it will automatically upload the data to a shared folder on our Dropbox account, from which it will be re-distributed to all interested parties:

    X
   / \
  /   \
 /     \
/+-----+\
 |     |   +---------+
 |     |...|netbook-1|....
 |     |   +---------+   .
 +-----+                 .
  site 1                 .
                         .
                         .
                         .
    X                    .
   / \                   .
  /   \                 +-----------------------------------------+
 /     \                |  ____                  _                |
/+-----+\               | |  _ \ _ __ ___  _ __ | |__   _____  __ |
 |     |   +---------+  | | | | | '__/ _ \| '_ \| '_ \ / _ \ \/ / |
 |     |...|netbook-2|..| | |_| | | | (_) | |_) | |_) | (_) >  <  |.
 |     |   +---------+  | |____/|_|  \___/| .__/|_.__/ \___/_/\_\ |
 +-----+                |                 |_|                     |
  site 2                +-----------------------------------------+
                         .
                         .
                         .
    X                    .
   / \                   .
  /   \                  .
 /     \                 .
/+-----+\                .
 |     |   +---------+   .
 |     |...|netbook-3|....
 |     |   +---------+
 +-----+
  site 3

For simplicity (and for licencing costs) the same user account is linked to each netbook. For historic reasons, that account is nagios@neurobat.net (yes, a Nagios server is going to monitor that data too).

Since we don’t want data from, say, site 3 to be copied over to the local drive of netbook-1, we enable the “Selective Sync” feature on the netbooks so that each netbook sees only a folder dedicated to that site. The folder layout thus looks like this:

Monitoring/
├── site 1
│   └── sensors
├── site 2
│   └── sensors
└── site 3

The Monitoring folder is the globally shared folder, shared by nagios@neurobat.net with all Neurobat users interested in analyzing the data.

The only downside we have identified with this approach is that whenever a new site is added, that site’s data will by default also be synchronized with all existing sites. Selective sync will only let you exclude folders that already exist; as far as I know, you cannot ask Dropbox to not sync new folders in that shared folder. And I am apparently not the only one to find this annoying.

An alternative might have been to have a separate shared folder for each site. However, for each new site one would then have to share that folder with all Neurobat users. Furthermore, since all sites use the same Dropbox user, you would still end up with that site’s data being sync’ed all over the place.

That being said, we have found the above scheme to work out very well in practice. The automatic synchronization of Dropbox makes it a superior alternative to, say, hand-written FTP-calling scripts we might have used in the past century. And if you have notifications turned on for your Dropbox account (I personally don’t) it will be very reassuring to regularly see your test site data being updated on your local machine without any human intervention.

No related posts.

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

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

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.

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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.

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I used to use Ubuntu’s Timer applet to alert me to take a break, but a month ago I found what I now believe to be the perfect complement to the Pomodoro Technique: a nice little application called Workrave.

Workrave will let you define work intervals after which it will, shall we say, strongly encourage you to take a break and propose a couple of physical exercises. I’ve installed Workrave under Ubuntu (I believe it runs also on Windows) and configured it for:

• no micro-breaks
• a 5-min rest break after 25 min of work
• a daily limit of 8 hours (never reached)

Try it out! It’s completely free and quite nice. I can also recommend the Pomodoro Technique Illustrated book from the Pragmatic Programmers, but you might also want to begin with the free “official” Pomodoro book.

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When you use Sweave and produce high-quality plots in both EPS and PDF formats, you sometimes want to include them in Word or OpenOffice documents.

You can’t directly include PDF files, and you can only include EPS files when they have a preview, which Sweave will not do by default. And anyway, the EPS version of the graphs are made for black and white printers and will not be in color.

I’m using convert from the ImageMagick package to convert the PDF plots to PNG files. However, by default convert will give low-quality pictures. To have better images, I do:

convert -density 300 XXX.pdf XXX.png

If you feel geeky enough you can even include this in a Makefile:

figures: $(patsubst %.pdf, %.png, $(wildcard report-*.pdf))

report-%.png : report-%.pdf
    convert -density 300 $< $@

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I’ve been a user of free VCSs for a while now, beginning with my first exposure to CVS at CERN in 2002, through my discovery of Subversion during my doctoral years at EPFL, culminating in my current infatuation with Git as a front-end to Subversion. I’m now a complete convert to that system and could not imaging working without it. Every week I discover new use cases for this tool that I had not thought about before (and that I suspect the Git developers didn’t, either).

This week I found such a use case for Git: enforcing scientific reproducibility. Let me explain. I’m currently working on prototype software written in MATLAB that implements some advanced algorithms for the smart, predictive control of heating in buildings. As part of that work we need to evaluate several competing algorithm designs, and try out different parameters for the algorithms.

The traditional way of doing this is, of course, to set all your parameters right in your code for the first simulation, to run it, then to set the parameters right for the second one, to run it again, and so on. There are several problems with this approach.

First, you need a really good naming convention for the data you are going to generate to make sure that you know exactly which parameters you set for each run. And coming up with a good naming scheme for data files is not trivial.

Second, even if your data file naming convention is good enough that you can easily reproduce the experiment, how can you be sure that the settings are exactly right? That you didn’t, perhaps, tweak just that little extra configuration file just to work around that little bug in the software?

Third, how will you reproduce those results? Even assuming that you ran all your simulations based on a given, well-known revision number in your VCS (you do use a VCS, don’t you?), you will still need to dive in the code and set those configuration parameters yourself. A tedious, error-prone process, even if you manage to keep them all to one source file.

I think a system like Git solves all these problems. Here is how I did it.

I needed to run 7 simulations with different parameters, based on a stable version of our software, say r1409 in our Subversion repository.

I’m using Git as a front-end to Subversion. I began by creating a local branch (something Git, not Subversion, will let you do):

$ git checkout -b simulations_based_on_r1409

This will create a new branch from the current HEAD. Now the idea is to make a local commit on that local branch for each different set of parameters. Here is how:

1. Edit your source code so that all parameters are set right.
2. Commit the changes on your local branch:

   $ git ci -am "With parameter X set to Y"
   [simulations_based_on_r1409 66cea68] With parameter X set to   

3. Note the 7 characters (66cea68 above) next to the branch name. These are the first 7 characters of the SHA-1 hash of your entire project, as computed by Git.
4. Run your simulation. Log the results, along with the short hash.
5. Repeat the steps above for each different configuration you want to run the experiment with.

By the end of this process, you should have in your logbook a list of experimental results along with the short hash of the entire project as it looked during that experiment. It might, for instance, look something like this:

As you can see there are at least two reasons why it’s important to record the short hash:

1. It will let you go back in time and reproduce an experiment exactly as it was when you ran it first.
2. It will force you to commit all changes before running the experiment, which is a good thing.

I’ve been running a series of simulations using a variation on this process, whereby I actually run several simulations in parallel on my 8-core machine. For this to work you need to clone your entire project, once per simulation. Then for each simulation you checkout the right version of your project, and run the experiment.

Quite seriously, I would never have been able to do anything remotely like this with a centralized version control system. The possibility to create local branches and to commit to them is a truly awesome feature of distributed version control systems such as Git. I don’t suppose the Git developers had scientists and engineers in mind when they developed this system, but hey, here we are.

Are you a scientist or an engineer wishing to dramatically improve your way of working? Then run, do not walk, to read the best book on Git there is.

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Now before you dismiss this post, this author and this blog as just another bible-thumping fanatic, remember that in certain countries, certain political parties profess strict adherence to Scripture while being overtly skeptical about the whole climate warming problem. I think they are wrong and here’s why.

Let’s first review why, from a scriptural point of view, one could in principle argue that whether we take action or let things run as they are makes no difference. I’ve heard some of these arguments from very good (christian) friends, and I hope I’m not going to offend anyone by refuting them later in this post:

• Revelation 21:1 tells us that all of creation will eventually be destroyed and replaced by a new one:

Then I saw a new heaven and a new earth; for the first heaven and the first earth passed away, and there is no longer any sea.

• God is sovereign, so no matter what we do, things will run according to His will.

• It is highly arrogant for Man to believe that they can do anything about the climate.

The last two arguments are probably the easiest to answer. God is certainly sovereign, but that doesn’t remove our responsibility for doing the right things and making the right choices in life. In fact, God intends us to be co-creators with Him and to participate, so to speak, in the creative act. This point has been persuasively argued for by several authors such as C.S. Lewis and J.I. Packer.

The big problem with the first argument is that, even though the current creation is indeed doomed in the long run, God asked us from the beginning to take care of it, cf. Genesis 1:28:

God blessed them; and God said to them, “Be fruitful and multiply, and fill the earth, and subdue it; and rule over the fish of the sea and over the birds of the sky and over every living thing that moves on the earth.”

See that? Commandment nr 3 in the whole Bible: subdue the Earth and rule over it. No mention here of letting things run its course simply because the creation is about (in the biblical perspective) to be replaced.

Ah but you might argue that this command was given before the Fall, and that everything went downhill since then. You’re right about the downhill part, but look at this, Gen 3:23:

therefore the Lord God sent him out from the garden of Eden, to cultivate the ground from which he was taken.

Man is kicked out of the Garden of Eden, and what is he to do? Essentially the same thing, e.g. rule over the Earth and cultivate it and take care of it. The only difference being, of course, that now it’s going to be painful to do so (Gen 3:19).

The mandate to take care of creation is repeated several times, for instance right after Noah comes out of the Ark after the Flood, Gen 9:1-2:

And God blessed Noah and his sons and said to them, “Be fruitful and multiply, and fill the earth. The fear of you and the terror of you will be on every beast of the earth and on every bird of the sky; with everything that creeps on the ground, and all the fish of the sea, into your hand they are given.”

Or of course Psalm 8:5-6:

Yet You have made him a little lower than God, And You crown him with glory and majesty! You make him to rule over the works of Your hands; You have put all things under his feet.

God intends us clearly to rule and manage His creation, no matter what ultimate fate awaits it.

But the real suprise came to me while re-reading the following (Deut. 22:6-7):

“If you happen to come upon a bird’s nest along the way, in any tree or on the ground, with young ones or eggs, and the mother sitting on the young or on the eggs, you shall not take the mother with the young; you shall certainly let the mother go, but the young you may take for yourself, in order that it may be well with you and that you may prolong your days.

This was one of the many commands given Israel before entering the promised land. The spirit of this passage, and of others like it, is unambiguous: God is asking us simply to be utmostly careful in managing our natural resources. We are forbidden to view Earth as just a vast source of riches to be exploited as quickly and efficiently as possible. We are explicitly commanded to make sure that Earth can go on being such a source of riches, indefinitely if needed.

(I’ve even read somewhere that the number of years Israel spent in babylonian captivity, 70, corresponds to the number of years the land should have been allowed to rest since Israel took possession of it, but didn’t. Here again, the importance of allowing natural resources to replenish themselves is evident.)

The concept of “rest” is a potent one in Scripture. We are to rest once a week. The land was to rest once every 7 years. We were supposed to leave alone the corners of our fields that weren’t harvested. In other words, Scripture is full of passages mandating a careful management of our natural resources. Arguing that we can do whatever we want with Earth simply because it is doomed to the eternal fire anyway is not only lazy and criminal, it is also doctrinally false.

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This also marks the end of almost three years spent building enterprise integration systems in Java for a certain coffeeshop. I’m now moving back to my original topics of interest, namely the intelligent control and simulation of buildings. Indeed, without disclosing too much, the very first project I will be working on is the implementation of certain ideas formulated during the Neurobat research project carried out aeons ago at LESO-PB. Except this time the systems won’t be running in the quiet and safe environment of an experimental building whose occupants have a history of forgiveness towards enthusiastic graduate students and their ideas—including myself. No, this time we mean business, that is, embedded systems that must be build rock-solid and run unattended for years, or possibly decades.

One issue that’s come up more than once was whether we should keep MATLAB as our lingua franca for prototyping and trying out new ideas and concepts before porting them to languages more, shall we say, closer to the machine. Or should we just dump it (including its non-negligible licencing costs, especially for an non-academic organization) and work directly as close to the metal as we dare?

Personally, without wanting to sound overly smug or anything, I think that someone asking this question has obviously never tried multiplying two matrices in C. The implementation contributed by James Trevelyan to the Numerical Recipes in C website runs to about 33 lines:

void dmmult( double **a, int a_rows, int a_cols, 
             double **b, int b_rows, int b_cols, double **y)
/* multiply two matrices a, b, result in y. y must not be same as a or b */
{
   int i, j, k;
   double sum;

   if ( a_cols != b_rows ) {
      fprintf(stderr,"a_cols  b_rows (%d,%d): dmmult\n", a_cols, b_rows);
      exit(1);
   }

#ifdef V_CHECK
   if ( !valid_dmatrix_b( a ) ) 
      nrerror("Invalid 1st matrix: dmmult\n");    
   if ( !valid_dmatrix_b( b ) ) 
      nrerror("Invalid 2nd matrix: dmmult\n");    
   if ( !valid_dmatrix_b( y ) ) 
      nrerror("Invalid result matrix: dmmult\n"); 
#endif

   /*  getchar();
       dmdump( stdout, "Matrix a", a, a_rows, a_cols, "%8.2lf");
       dmdump( stdout, "Matrix b", b, b_rows, b_cols, "%8.2lf");
       getchar();
   */
   for ( i=1; i<=a_rows; i++ ) 
      for ( j=1; j<=b_cols; j++ ) {
         sum = 0.0;
         for ( k=1; k<=a_cols; k++ ) sum += a[i][k]*b[k][j];
         y[i][j] = sum;
      }
}

Give me instead MATLAB's

y = a * b

anytime. Now of course I realize the comparison is completely unfair. The C version includes error checking, comments, etc. But still, C is, after all, originally a systems programming language, while MATLAB-the-language is a DSL for doing precisely this sort of stuff. I never wanted to prove that C sucked at doing linear algebra—I just wanted to show that most trivial operations in MATLAB would have to be—by us—re-implemented in C before we can even begin using them. And I don't think we have that sort of time. Not outside of academia.

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Where can I find a list of publications relevant to the field of building simulation? I’m particularly interested in refereed journals and books.

The ensuing thread has been extremely helpful, in particular Shanta Tucker who pointed me to the IBPSA website. There, on the References link, you will find a fairly complete listing of articles, resources and books, including the full contents of every IBPSA conference paper. What’s not to like?

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I’d like to tell you about a software company co-founded by a friend and fellow Toastmaster of mine, David Portabella. The company’s name is DB4ALL, and they specialize in software for retrieving structured data from the web.

(Disclaimer: I am not affiliated with this company. I have had the opportunity to play with their tool, which I sincerely think is a high-quality one, but I derive no remuneration from writing this piece.)

They’ve developed `Webminer’, a Java library for extracting data in a structured manner from any website. Suppose, for instance, that you need a relational database with the data from the CIA World Factbook. That data, though in the public domain, cannot be obtained in the form of a relational database, but only by clicking around on the CIA website. But with ‘Webminer’, the smart guys at DB4ALL can write a custom application that will know how to navigate such websites, ‘scrape’ and ‘normalize’ its data, and save it to a relational database for you.

On DB4ALL’s website you will find references to the two most popular datasets that they’ve mined: the above-mentioned CIA World Factbook, and the SourceForge database of open-source projects. Having such data in a relational form is invaluable for any researcher or marketing analyst. Suppose for instance that you want scientific data on the popularity of different programming languages over time in open-source projects. Well with these datasets you have all you need to get started.

This, for instance, is a screenshot of the SourceForge dataset opened in Excel:

All in all, if you need publicly available data from a website stored in a relational database form, you should definitely consider using DB4ALL‘s services.

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