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The MATLAB function ranksum is part of MATLAB’s Statistics Toolbox. Like many organizations who use network licensing for MATLAB and its toolboxes, my employer, The University of Manchester, sometimes runs out of licenses for this toolbox which leads to following error message when you attempt to evaluate ranksum.
??? License checkout failed. License Manager Error -4 Maximum number of users for Statistics_Toolbox reached. Try again later. To see a list of current users use the lmstat utility or contact your License Administrator.
An alternative to the Statistics Toolbox is the NAG Toolbox for MATLAB for which we have an unlimited number of licenses. Here’s how to replace ranksum with the NAG routine g08ah.
Original MATLAB / Statistics Toolbox code
x = [0.8147;0.9058;0.1270;0.9134;0.6324;0.0975;0.2785;0.5469;0.9575;0.9649]; y= [0.4076;1.220;1.207;0.735;1.0502;0.3918;0.671;1.165;1.0422;1.2094;0.9057;0.285;1.099;1.18;0.928]; p = ranksum(x,y)
The result is p = 0.0375
Code using the NAG Toolbox for MATLAB
x = [0.8147;0.9058;0.1270;0.9134;0.6324;0.0975;0.2785;0.5469;0.9575;0.9649]; y = [0.4076;1.220;1.207;0.735;1.0502;0.3918;0.671;1.165;1.0422;1.2094;0.9057;0.285;1.099;1.18;0.928]; tail = 'T'; [u, unor, p, ties, ranks, ifail] = g08ah(x, y, tail);
The value for p is the same as that calculated by ranksum: p = 0.0375
NAG’s g08ah routine returns a lot more than just the value p but, for this particular example, we can just ignore it all. In fact, if you have MATLAB 2009b or above then you could call g08ah like this
tail = 'T'; [~, ~, p, ~, ~, ~] = g08ah(x, y, tail);
Which explicitly indicates that you are not going to use any of the outputs other than p.
People at Manchester are using the NAG toolbox for MATLAB more and more; not only because we have a full site license for it but because it can sometimes be very fast. Here’s some more articles on the NAG toolbox you may find useful.
I was recently asked to install 32bit Gaussian 03 binaries on an Ubuntu 9.10 machine and when I tried to run a test job I got the following error message
Erroneous write during file extend. write -1 instead of 4096 Probably out of disk space. Erroneous write during file extend. write -1 instead of 4096 Probably out of disk space. Write error in NtrExt1 Write error in NtrExt1: Bad address Segmentation fault
A bit of googling suggested that the following might work
sudo echo 0 > /proc/sys/kernel/randomize_va_space
but this will result in permission denied (explanation here). The command you really want to use is
sudo bash -c "echo 0 > /proc/sys/kernel/randomize_va_space"
Once this was done, Gaussian worked as advertised. Maybe this post will help a googler sometime in the future.
MATLAB contains a function called pdist that calculates the ‘Pairwise distance between pairs of objects’. Typical usage is
X=rand(10,2); dists=pdist(X,'euclidean');
It’s a nice function but the problem with it is that it is part of the Statistics Toolbox and that costs extra. I was recently approached by a user who needed access to the pdist function but all of the statistics toolbox license tokens on our network were in use and this led to the error message
??? License checkout failed. License Manager Error -4 Maximum number of users for Statistics_Toolbox reached. Try again later. To see a list of current users use the lmstat utility or contact your License Administrator
One option, of course, is to buy more licenses for the statistics toolbox but there is another way. You may have heard of GNU Octave, a free,open-source MATLAB-like program that has been in development for many years. Well, Octave has a sister project called Octave-Forge which aims to provide a set of free toolboxes for Octave. It turns out that not only does Octave-forge contain a statistics toolbox but that toolbox contains an pdist function. I wondered how hard it would be to take Octave-forge’s pdist function and modify it so that it ran on MATLAB.
Not very! There is a script called oct2mat that is designed to automate some of the translation but I chose not to use it – I prefer to get my hands dirty you see. I named the resulting function octave_pdist to help clearly identify the fact that you are using an Octave function rather than a MATLAB function. This may matter if one or the other turns out to have bugs. It appears to work rather nicely:
dists_oct = octave_pdist(X,'euclidean');
% let's see if it agrees with the stats toolbox version
all( abs(dists_oct-dists)<1e-10)
ans =
1
Let’s look at timings on a slightly bigger problem.
>> X=rand(1000,2); >> tic;matdists=pdist(X,'euclidean');toc Elapsed time is 0.018972 seconds. >> tic;octdists=octave_pdist(X,'euclidean');toc Elapsed time is 6.644317 seconds.
Uh-oh! The Octave version is 350 times slower (for this problem) than the MATLAB version. Ouch! As far as I can tell, this isn’t down to my dodgy porting efforts, the original Octave pdist really does take that long on my machine (Ubuntu 9.10, Octave 3.0.5).
This was far too slow to be of practical use and we didn’t want to be modifying algorithms so we ditched this function and went with the NAG Toolbox for MATLAB instead (routine g03ea in case you are interested) since Manchester effectively has an infinite number of licenses for that product.
If,however, you’d like to play with my MATLAB port of Octave’s pdist then download it below.
- octave_pdist.m makes use of some functions in the excellent NaN Toolbox so you will need to download and install that package first.
I work for a large university in the UK and part of my job involves helping to look after our site licenses for software such as MATLAB, Origin, Labview, Mathematica,Abaqus and the NAG Library among many others. Now, anyone who works in this field will know that no two site licenses are alike. For example, one license might allow any member of the university to install it on any machine that they own whereas others are limited only to university owned machines. There are lots of little rules like this and I could go on for quite a while about some of the gotchas but I’d prefer to focus this post on something close to my heart right now – the matter of perpetual usage.
Imagine, if you will, that you are the coordinator for a degree course and you are completely overhauling the syllabus. Part of this overhaul will concern the software that you use and teach as part of this course. You want to standardize on just one package so that students won’t have to learn a new system every semester. After reviewing all of the possibilities, you finally decide that it is going to be either software A or software B. Both are powerful, easy to use, considered an industry standard etc etc. As far as functionality goes there really isn’t much between them. Which one to choose?
You invite your friendly,neighborhood software geek out for coffee and ask him to talk about the two software packages. He likes talking about this sort of stuff and usually has a lot to say. He talks about open-source alternatives, programming styles, speed, potentially useful books, other people in your field who use this type of software and so on. Eventually he starts talking about licensing and offers the following tidbit of information.
- Our license for Software A includes perpetual use. This means that if our funding dried up we could stop paying our maintenance agreement but we would never lose access to the software. Of course we’d not be able to upgrade to the latest version any more but we’d always have access to the version we have right now.
- Our license for Software B doesn’t include perpetual use. So, if we stopped paying our maintenance agreement then we can’t use the software anymore. We’d have to stop using it for teaching, research….everything. You’d have to go and buy your own license for it.
“How likely is it that we might stop funding either of them?” you ask.
“Oh, pretty unlikely,” comes the reply as he rests his coffee mug over that day’s newspaper headline “We are a big university and both of these packages are key to what we do. I’d say we are very unlikely to stop funding either of them.”
I have a question to you all. Would the lack of perpetual use for Software B be a factor in your decision concerning what you would use for your teaching and research?
The latest version of MATLAB – MATLAB 2010a – was released on Friday 5th March. As always, the full list of changes is extensive but some of the highlights from my point of view include the following.
Bug Fixes
MATLAB 2009b contained bugs in some core pieces of functionality. I am now very happy to report that these have been fixed.
- A serious bug in MATLAB 2009b fixed in 2010a
- A bug in the sort function that affected arrays of complex numbers on some platforms. Fixed in 2010a
Parallel and Multicore enhancements
Almost everyone has a multicore processor these days and they expect programs such as MATLAB to take full advantage of them. However, parallel programming can be hard work and so it is impossible to make every function become ‘multicore aware’ overnight. Almost every release of MATLAB gives us a few more functions that make use of our new multicore processors.
- Multicore support for several functions from the image processing toolbox. I’ve added these to my list of known ‘multicore aware’ functions in MATLAB.
- The Fast Fourier Transform function – fft – was originally made multicore-aware back in release 2009a and this has been improved on in 2010a. The multithreaded improvements for fft in 2009a were for multichannel Fast Fourier Transforms. In other words, when taking the FFT of a 2D array, MATLAB computes the FFT of each column on an independent thread. The improvements in 2010a actually multithread the calculation of a single FFT. Mathworks refer to this as “long vector” FFT since this speeds up the calculations of a very long fft.
Other articles you may be interested in on parallel programming in MATLAB.
- Parallel MATLAB with openmp mex files
- Parallelisation: Is MATLAB doing it wrong?
- Supercharge MATLAB with your graphics card
- A list of multicore functions in MATLAB
Changes to the symbolic toolbox
The symbolic toolbox for MATLAB is one of my favourite Mathworks products since it offers so much extra functionality. Most toolboxes are just a set of .m MATLAB files but the symbolic toolbox includes an entire program called MUPAD along with a set of interfaces to MATLAB itself. Lets look at some of the enhancements developed in 2010a in detail.
- MuPAD demonstrates better performance when handling some linear algebra operations on matrices containing symbolic elements. Let’s look at symbolic Matrix Inversion for example. Take the following piece of MUPAD code
x:= 1 + sin(a) + cos(b): A:= matrix( [ [11/x + a11, 12/x + a12, 13/x + a13, 14/x + a14], [21/x + a21, 22/x + a22, 23/x + a23, 24/x + a24], [31/x + a31, 32/x + a32, 33/x + a33, 34/x + a34], [41/x + a41, 42/x + a42, 43/x + a43, 44/x + a44] ]): time((B:= 1/A))*sec/1000.0;On my machine, 2009a did this calculation in 61.1 seconds whereas 2010a did it in 0.9 seconds. That’s one heck of a speedup!
- Enhanced simplification functions, simplify and Simplify, demonstrate better results for expressions involving trigonometric and hyperbolic functions, square roots, and sums over roots of unity. There’s a lot of improvements packed into this one bullet point but I’ll just focus on one particular example. Take the following MUPAD code.
f:= -( ((x + 1)^(1/2) - 1) *((1 - x)^(1/2) - 1)*(4*x*(x + 1)^(1/2) + 8*(1 - x)^(1/2)*(x + 1)^(1/2) + 4*(x + 1)^(1/2) + 12*(x + 1)^(3/2) - 4*x*(1 - x)^(1/2) + 4*(1 - x)^(1/2) + 12*(1 - x)^(3/2) - 8*x^2 - 40) ) /(6*((x + 1)^(1/2) + (1 - x)^(1/2) - 2)^3) - (1/3*(x + 1)^(3/2) + 1/3*(1 - x)^(3/2)): simplify(f)If you evaluate this in the MUPAD notebook of release 2009b then you get a big mess. Evaluating it in 2010a, on the other hand, results in 0. Much tidier!
- Polynomial division has improved functionality. In R2009b it was not possible to divide multivariate polynomials with remainder,only exact division was possible. R2010a returns the quotient and the remainder. Consider the following MUPAD code.
q := poly(x+y^2+1): p := poly(x^2+y)*q + poly(y^3+x)*q + poly(x^2+y^2+1): divide(p, q);
2009b MUPAD just gives an error message
Error: Illegal argument [divide]
2010a,on the other hand, gives us what we want returning the quotient and the remainder.
poly(x^2 + 2*x + y^3 - y^2 + y - 1, [x, y]), poly(y^4 + 3*y^2 + 2, [x, y])
- The symbolic ODE solver in MUPAD now handles many more equation types. Here’s a particular example of a 2nd order linear ODE with hypergeometric solutions that can now be solved.
eq:= x^2*(x^2+1)*diff(y(x),x,x)+x*(2*x^2+1)*diff(y(x),x)-(nu*(nu+1)*x^2+n^2)*y(x); solve(ode(eq,y(x)))
This is just a small selection of the enhancements that The Mathworks have made to the symbolic toolbox which is coming along in leaps and bounds in my humble opinion. Thanks to various members of staff at The Mathworks for supplying me with these examples (and a fair few more besides). I wouldn’t have been able to make this post without them.
Other articles on Walking Randomly about the symbolic toolbox for MATLAB:
- Review of the new symbolic toolbox in MATLAB 2008b (written just after the transfer from a Maple symbolic engine to the new Mupad one)
Changes to the Optimization toolbox
- The fmincon function now has a new algorithm available. The algorithm is called SQP which stands for Sequential Quadratic Programming. I don’t know much about it yet but may return to this in the future.
Licensing changes
Licensing and toolbox naming might be dull for most people – but it is a big deal to me since I work in a team that administers a large network license for MATLAB. Changes that might seem minor to most people can result in drastic improvements for the life of a network license administrator. On the other hand, some changes can result in massive amounts of work.
- Solaris as a network license platform was dropped. This is a shame because although my employer has very few Solaris client machines, it currently runs its MATLAB license server on Solaris and it has been (and continues to be) rock solid. Now we are going to have to move to Windows license servers and update every single client machine on campus before we can update to 2010a. Oh joys!
- The Genetic Algorithm and Direct Search toolbox is no more. It’s functionality is now part of the Global Optimization Toolbox. I’ve not used this one myself yet but it does have a small core set of users at my university.
Articles aimed at users of MATLAB network licenses include
- More MATLAB statistics toolbox licensing woes (and how to solve them)
- An alternative to binopdf using the NAG toolbox for MATLAB
Much of the fine detail in this article was provided to me by members of staff at The Mathworks. I thank them all for their time, patience and expertise in answering my incessant questions. Any mistakes, however, are my fault!
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According to a new website, if you plot the following equation
then you’ll get the following graph
Head over to the Inverse Graphing Calculator to generate your own. It would be interesting to solve these equations and see if the website is correct.
If you liked this post then you may like this one too: Secret messages hidden inside equations
The Problem
A MATLAB user came to me with a complaint recently. He had a piece of code that made use of the MATLAB Statistics toolbox but couldn’t get reliable access to a stats toolbox license from my employer’s server. You see, although we have hundreds of licenses for MATLAB itself, we only have a few dozen licenses for the statistics toolbox. This has always served us well in the past but use of this particular toolbox is on the rise and so we sometimes run out which means that users are more likely to get the following error message these days
??? License checkout failed. License Manager Error -4 Maximum number of users for Statistics_Toolbox reached. Try again later. To see a list of current users use the lmstat utility or contact your License Administrator.
The user had some options if he wanted to run his code via our shared network licenses for MATLAB (rather than rewrite in a free language or buy his own, personal license for MATLAB):
- Wait until a license became free.
- Wait until we found funds for more licenses.
- Buy an additional license token for himself and add it to the network (235 pounds +VAT currently)
- Allow me to gave a look at his code and come up with another way
He went for the final option. So, I sat with a colleague of mine and we looked through the code. It turned out that there was only one line in the entire program that used the Statistics toolbox and all that line did was call binopdf. That one function call almost cost him 235 quid!
Now, binopdf() simply evaluates the binomial probability density function and the definition doesn’t look too difficult so you may wonder why I didn’t just cook up my own version of binopdf for him? Quite simply, I don’t trust myself to do as good a job as The Mathworks. There’s bound to be gotchas and I am bound to not know them at first. What would you rather use, binpodf.m from Mathworks or binopdf.m from ‘Dodgy Sysadmin’s Functions Inc.’? Your research depends upon this function remember…
NAG to the rescue!
I wanted to use a version of this function that was written by someone I trust so my mind immediately turned to NAG (Numerical Algorithms Group) and their MATLAB toolbox which my employer has a full site license for. The NAG Toolbox for MATLAB has a function called g01bj which does what we need and a whole lot more. The basic syntax is
[plek, pgtk, peqk, ifail] = g01bj(n, p, x)
From the NAG documentation: Let X denote a random variable having a binomial distribution with parameters n and p. g01bj calculates the following probabilities
- plek = Prob{X<=x}
- pgtk = Prob{X > x}
- peqk = Prob{X = x}
MATLAB’s binopodf only calculates Prob(X=x) so the practical upshot is that for the following MATLAB code
n=200; x=0; p=0.02; y=binopdf(x,n,p)
the corresponding NAG Toolbox code (on a 32bit machine) is
n=int32(200); x=int32(0); p=0.02; [plek, pgtk, y, ifail] = g01bj(n, p, x);
both of these code snippets gives
y =
0.0176
Further tests show that NAG’s and MATLAB’s results agree either exactly or to within what is essentially numerical noise for a range of values. The only thing that remained was to make NAG’s function look more ‘normal’ for the user in question which meant creating a file called nag_binopdf.m which contained the following
function pbin = nag_binopdf(x,n,p) x=int32(x); n=int32(n); [~,~,pbin,~] = g01bj(n,p,x); end
Now the user had a function called nag_binopdf that behaved just like the Statistics toolbox’s binopdf, for his particular application, argument order and everything.
It’s not all plain sailing though!
As good as the NAG toolbox is, it’s not perfect. Note that I placed particular emphasis on the fact that we had come up with a suitable, drop-in replacement for binopdf for this user’s particular application. The salient points of his application are:
- His input argument, x, is just a single value – not a vector of values. The NAG library wasn’t written with MATLAB in mind, it’s written in Fortran, and so many of its functions are not vectorised (some of them are though). In Fortran this is no problem at all but in MATLAB it’s a pain. I could, of course, write a nag_binopodf that looks like it’s vectorised by putting g01bj in a loop hidden away from the user but I’d soon get found out because the performance would suck. To better support MATLAB users, NAG needs to write properly vectorised versions of its functions.
- He only wanted to run his code on 32bit machines. If he had wanted to run it on a 64bit machine then he would have to change all of those int32() calls to int64(). I agree that this is no big deal but it’s something that he wouldn’t have to worry about if he had coughed up the cash for a Mathworks statistics license.
Final thoughts
I use the NAG toolbox for MATLAB to do this sort of thing all the time and have saved researchers at my university several thousand pounds over the last year or so through unneeded Mathworks toolbox licenses. As well as statistics, the NAG toolbox is also good for optimisation (local and global), curve fitting, splines, wavelets, finance,partial differential equations and probably more. Not everyone likes this approach and it is not always suitable but when it works, it works well. Sometimes, you even get a significant speed boost into the bargain (check out my interp1 article for example).
Comments are, as always, welcomed.
This is not a serious post but then mathematics doesn’t have to be serious all the time does it? Ever wondered what the equation of a 3D heart looks like? An old post of mine will help you find the answer using Mathematica. Click on the image for more details (and a demonstration that you can run using the free Mathematica player).
I don’t save all of my love for Mathematica though. I’ve got some for MATLAB too:
%code to plot a heart shape in MATLAB %set up mesh n=100; x=linspace(-3,3,n); y=linspace(-3,3,n); z=linspace(-3,3,n); [X,Y,Z]=ndgrid(x,y,z); %Compute function at every point in mesh F=320 * ((-X.^2 .* Z.^3 -9.*Y.^2.*Z.^3/80) + (X.^2 + 9.* Y.^2/4 + Z.^2-1).^3); %generate plot isosurface(F,0) view([-67.5 2]);
Did you know that the equation for a heart (or a cardioid if you want to get technical) is very similar to the equation for a flower? The polar equation you need is 
and you get a rotated cardioid for n=1. Change n to 6 and you get a flower. Let’s use the free maths package, SAGE, this time.
First, define the function:
def eqn(x,n):
return(1-sin(n*x))
then plot it for n=1
polar_plot(eqn(x,1), (x,-pi, pi),aspect_ratio=1,axes=False)
and for n=7
polar_plot(eqn(x,7), (x,-pi, pi),aspect_ratio=1,axes=False)
Back to Mathematica and the Wolfram Demonstrations project. We have a Valentine’s version of the traditional Tangram puzzle.
Feel free to let me know of any other Valentine’s math that you discover, puzzles, fractals or equations, it’s all good 
A friend of mine got me interested in JavaFX recently and my interest grew when I discovered that it had some nice charting functionality. Dean Iverson has written some great tutorials on the subject over at his blog and includes a link to a demo showing some of the different plot types that are available.
The demo is called ChartDemo and can be found here
http://pleasingsoftware.blogspot.com/2009/06/this-is-test.html
In an ideal world you simply have to click on the demo’s screenshot for it to download and launch but that wasn’t what happened for me. What happened when I clicked on it was nothing. No error messages…just nothing. It’s difficult to google ‘nothing happened’ and get something useful so I downloaded the demo (which had the filename ChartDemo.jnlp) and tried to launch it from the command line using
javaws ChartDemo.jnlp
This gave me the error message
netx: Unexpected net.sourceforge.jnlp.ParseException: Invalid XML document syntax. at net.sourceforge.jnlp.Parser.getRootNode(Parser.java:1200)
What follows is the story of how I eventually got this demo to work in the hope that it will help someone out there.
So, first things first, what are some of the relevant system specs I am using? Well, I am running 32bit Ubuntu Linux 9.10 (Karmic Koala) and
java -version
gives
java version "1.6.0_0" OpenJDK Runtime Environment (IcedTea6 1.6.1) (6b16-1.6.1-3ubuntu1) OpenJDK Server VM (build 14.0-b16, mixed mode)
Now, when I googled the error message I discovered that Linux (more specifically, I guess, the OpenJDK) is much more sensitive to xml errors than Windows/Mac OS X (.jnlp files are written in xml). Take double quotes for example; according to the W3C XML recommendations you should not use \” inside an xml attribute but should use “"” instead. Some java implementations don’t seem to care but, at the time of writing at least, OpenJDK definitely does. Follow this link to see the original discussion thread where I learned this.
The practical upshot of this extra level of strictness is that .jnlp files that work just fine on Windows and Mac OS X won’t work on Linux and I guessed that was what as happening here. Sadly there were no examples of \” in ChartDemo.jnlp for me to change to “"” so there must be something else ‘wrong’ with it; but what?
I decided to try the ’stare at it until you figure it out’ approach to debugging and left the laptop on the side of the sofa while watching a movie on TV. About halfway through the movie, inspiration struck and I changed the line
<update check="background">
to
<update check="background"/>
which got things past the xml parsing stage. Sadly, I then hit another problem. Rather than a working ChartDemo, my efforts were rewarded with nothing more than just a blank window and a load of java errors in the terminal. When I say ‘a load’ I mean HUNDREDS and none of them looked particularly illuminating. I was starting to remember why I had avoided Java in the past but was not about to give up so easily.
Let’s take stock:
- The .nlbp file was fine (or at least didn’t return any parse errors)
- The ChartDemo code must be bug free because if it wasn’t then the author would have been told so rather quickly in the comments section of his blog
- My Java setup was presumably fine since I was able to run other JavaFX examples. For example I successfully worked through a JavaFX programming tutorial on Sun’s website without incident.
Of those three points I figured that the third one was the most likely to be wrong. It was OpenJDK’s handling of the .jnlp file that caused my first problem so maybe it was causing this second problem too. Could I switch from using OpenJDK to a different version of Java I wondered? Some googling ensued and I discovered some useful incantations.
I can list the versions of Java installed on my machine with the command
sudo update-java-alternatives -l
to get
java-6-openjdk 1061 /usr/lib/jvm/java-6-openjdk java-6-sun 63 /usr/lib/jvm/java-6-sun
I can change from the openjdk to sun-java with
sudo update-java-alternatives -s java-6-sun
Once I did this I tried to run the ChartDemo.nlbp file again:
javaws ChartDemo.jnlp
and it worked perfectly. I was rewarded with a very nice demo of JavaFX’s charting functionality and Dean’s tutorials proved to be very useful to me. So useful in fact that I bought his book
Incidentially, the java-6-sun version of java doesn’t care about the syntax of the .jnlp file quite so much as openjdk. However, if you want to change back to using openjdk you can do
sudo update-java-alternatives -s java-6-openjdk
I hope this little tale helps someone out there. Let me know if it does and also feel free to let me know if I have got anything wrong. My knowledge of all things Java is rather basic at the moment to say the least – something I am trying to change.
Did you make any New Year’s resolutions this year? If you did then who will they help? Just you? Your family? Your students? The whole world? If I am being honest then I have to say that most of the new-year’s resolutions I have made over the years tend to focus on myself because at my very core I am a bit selfish. So, my resolutions tend to be things like “I want to get fitter”, “I’ll not stay late at work so much” or “I want to learn more Python programming.”
If I keep these resolutions then I’m going to be fitter, more knowledgeable and have a better work-life balance. So far so selfish!
Over the last few days though I have made a rather different sort of new-years resolution. Yes, I admit that it’s a bit late but why limit change to an essentially arbitrary date? My new new year’s resolution is to give a little back to the community that has given me so much – the community of organisations and individuals who provide me with software – either for free or for such a trivial amount of money that it may as well be free.
Now I am not a rich man so I can’t give away great wads of cash and although I am a programmer I have neither the time nor the knowledge to provide significant amounts of code to any particualr project. So what can I do?
Donations
Well, although I am not minted, I can easily afford the occasional small donation or two so I will start making them. It’s already begun with my Sage bounty hunt and will continue with other projects over the year. Another small donation I made recently was to buy the ‘Premium’ version of Aldiko – a great ebook reader for Android smartphones. Aldiko is a free piece of software, has been downloaded by tens of thousands of users and is used by me on an almost daily basis. I noticed that they had a ‘Premium’ version available for $1.99 but it turns out that it is identical to the free version. The $1.99 simply represents a donation to the developers and it’s a donation I made without hesitation. Doing the right thing for less than two dollars – getting the warm fuzzies has never been easier!
Bug reports and feedback
Thanks to my job and to running Walking Randomly I get to see how researchers, teachers, students and average joes use mathematical software quite a lot. I get told about bugs, about feature wish lists, about gripes with licensing, performance issues…the list goes on and on. The best way to get bugs fixed is to report them – first tell the developers and, if the bug is interesting/severe enough, tell the world. I do this plenty with commercial software but I am going to make the effort more with free software from now on. Feedback is part of the lifeblood of free software and developers need both the good and the bad.
Did you try out Octave, Maxima, Sage or Smath Studio and it didn’t work out? Why didn’t it? What did these packages have missing that forced you to turn to alternatives? Try to be specific; saying ‘I tried it and it sucks’ is a rubbish piece of feedback because it’s just an opinion and gives the developers nothing to do. Saying something like ‘I tried to calculate XYZ and it gave the following incorrect result whereas it should have given ABC’ is MUCH more productive.
Tutorials, examples and documentation
One comment I have heard over and over again from people who have tried free mathematical software and then turned their back on it is that the ‘documentation isn’t good enough’. These people want more tutorials, more examples, more explanations and just more and better documentation. Do you like writing? Do you like fiddling with math software? I do and so I intend on giving as many examples and tutorials as I can. I also have a (moderately) successful blog so I can provide a publishing outlet for people who want to write such things but don’t want to start a blog of their own. This has already begun too with Greg Astley’s tutorial on how to plot direction fields for ODE’s in maxima. Contact me if you are interested in doing this yourself and we’ll discuss it.
Talk
I like to talk. Many people who know me personally would probably go so far as to say I talk too much but I can use this to help towards my new resolution too. I’m going to give short talks, demonstrations and seminars on free mathematical software to interested people over the coming year via various fora. Maybe you could too?
So, in summary, I plan to do the following to give back to free software over the next decade and I invite you to do the same.
- Give small, direct donations to some of my favourite open source and free software projects
- Set up bounty hunts for particular features I want in various packages
- Buy donation versions of Android software whenever possible
- Publish as many examples of using software such as Sage, octave and maxima as I can
- Help write tutorials and documentation
- Give talks to help spread the word
I’ll admit that none of this will change the world but it will possibly help a few more people than “I resolve to get myself fitter.”