Ivan Debono

A notebook of science, thoughts and reason

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The APC cluster (1): SSH without a password

Why would you need to avoid entering a password when you use SSH? Typing in your password each time you log in is tedious. You may also need to call ssh from within a shell script.

These instructions are not specific to the APC computing cluster (APCSSH and APCCLM) .  They will work on any Unix-like operating system.

You already have an APC account with a login name, which I shall call APClogin. I assume you have already succesfully logged into your APC account using your password. Write down your password somewhere, case you need it.

If you are using Mac OS X, you can do all of the steps below in Terminal. If you are using Windows, you need an ssh client such as PuTTY .

LocalLogin stands for the login name on your local machine.

Here’s how to do it.

1: Generate the authentication keys

Type the following in your terminal window.

$ ssh-keygen -t rsa

You will get a message saying:

Generating public/private rsa key pair.
Enter file in which to save the key (/Users/LocalLogin/.ssh/id_rsa):

If you wish to change the default location, go ahead and specify a file path. Better to keep it simple, and just press Enter.
You will get this message asking for a password (“passphrase”). Do not enter one. Just press Enter, twice.

Enter passphrase (empty for no passphrase):
Enter same passphrase again:

If you did everything properly you will get a message giving the file path to the keys, and the key fingerprint:

Your identification has been saved in /Users/LocalLogin/.ssh/id_rsa.
Your public key has been saved in /Users/LocalLogin/.ssh/id_rsa.pub.
The key fingerprint is:
SHA256:dWitjNhmrttyt7oCFmYdsu6wdA6Y5yao8UuoZ7Zzgnjsi22Q LocalLogin@apcdhcp24.in2p3.fr
The key's randomart image is:
+---[RSA 2048]----+
| |
| . |
| . . O .|
| . o o O + |
| S o B * =|
| ... . + o = = |
| Ho. . o = . .|
|o=+O.o .. o + o |
|o=O+*. ..+ .|

2: Create a .ssh directory on apcssh

Next, you need to create a .ssh directory on apcssh.in2p3.fr by typing:

$ ssh APClogin@apcssh.in2p3.fr mkdir -p .ssh

You will be asked for your password (that is why you need to have it written down somewhere). Type it in.

APClogin@apcssh.in2p3.fr's password:


3. Append your local public key to the authorised keys on apcssh

Enter the line below. You will then be asked for your password, which you need to enter.
$ cat .ssh/id_rsa.pub | ssh APClogin@apcssh.in2p3.fr 'cat >> .ssh/authorized_keys'
APClogin@apcssh.in2p3.fr's password:

4. Done

Now you should be able to log into apccsh.in2p3.fr using the usual ssh command without entering a password.

Doing the same for the APC cluster

If the above works, you can log into apcclm following the same steps, except that you need to log into apccssh first.

In summary:

1. Log into apccsh (which you can now do without a password)

2. Generate the authentication keys

3. Create a .ssh directory on apcclm by typing

$ ssh APClogin@apcclm mkdir -p .ssh

4. And you’re done

The APC cluster (2): Using Montepython

The official documentation is here http://monte-python.readthedocs.io/en/latest but it glosses over some important details. You may find more information here: http://www.iac.es/congreso/cosmo2017/media/montepython.pdf

Installing Montepython

Installing Montepython is quite straightforward if you follow the installation guide. Just make sure that that your version of Python is 2.7. There are some syntax changes in Python 3 which prevent the code from installing.

Running Montepython

Running Montepython on your local machine is easy if you follow the official documentation. For the code to be any use, however, you need to output chains with thousands of points. And that means running it on the APC cluster.

Here are some helpful tips.

The graphical backend

Montepython and the CLASS Python wrapper use Matplotlib. You need to log in with  the -Y option for both apcssh and apcclm:
$ ssh -Y APClogin@apcssh.in2p3.fr
followed by
$ ssh -Y apcclm

When you run Montepython on the cluster using a script, you will need to set this environment variable in the script itself (see below).

External programs within CLASS

If you modify CLASS by calling an external program (let’s call it PowerSpectrumExtension.py) to calculate some quantity, remember to make it executable by running
chmod +x PowerSpectrumExtension.py

Job submission

You need to write a script that gets the job done. This is described here https://www.apc.univ-paris7.fr/FACeWiki/pmwiki.php?n=Apc-cluster.Scheduler.

When you run jobs on a cluster, you are sharing resources with the other users. If you ask for resources (memory, number of nodes) that are unavailable, or ask for too much, your job will be sent to the back of the queue, or aborted.

Here’s an example of a message for an aborted run:
There are not enough slots available in the system to satisfy the 4 slots
that were requested by the application:

Either request fewer slots for your application, or make more slots available
for use.

You also need to set the right environment variables for the required libraries

This is an example of a script which ran succesfully on the APC cluster:

#PBS -o $PBS_JOBID.out
#PBS -e $PBS_JOBID.err
#PBS -q furious
#PBS -m bea
#PBS -M name.surname@apc.univ-paris7.fr
#PBS -l nodes=1:ppn=32,mem=64GB,walltime=200:00:00
export SCRATCH="/scratch/$USER.$PBS_JOBID"
export PATH=/usr/local/openmpi/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/openmpi/lib/:/usr/local/openmpi/lib/openmpi/:$LD_LIBRARY_PATH
set -e
cd ~/montepython
/usr/local/openmpi/bin/mpirun -np 4 env MPLBACKEND=Agg montepython/Montepython.py run -p input/lcdm.param -o chains/planck/lcdm -N 20000 --silent

The –silent command suppresses Montepython’s screen output (which you don’t need when you submit a cluster job).

Here are some good resources explaingin qsub settings:



Analysing the chains

Once the run has terminated, output the plots and information by running:

cd montepython
env MPLBACKEND=Agg montepython/Montepython.py info [path]/[to]/[chains]/*.txt --want-covmat

The option –want-covmat outputs the covariance matrix.

Make sure to include env MPLBACKEND=AGG or you will get the usual matplotlib display problems.

The first graph

Data visualisation is, we are told, the hottest keyword in data science. Like many items of jargon fashionable in these modern times (just like the phrase ‘data science’ itself), its meaning is at best vague. The tech industry like to think of data visualisation as somehow computer-related, or the final stage of a process of computation and data manipulation which involves coding or the use of software tools. But human beings have been manipulating data and doing data science for millennia, so it follows that the history of data visualisation goes back a long way, long before computers were invented.

Consider graphs, the most common form of data visualisation. A graph is a visual representation of the relation between two variables (I mean the kind that’s plotted on a flat surface).

Who drew the first graph?

In 1936, Howard Gray Funkhouser described an image in a manuscript discovered by Siegmund Günther in 1877. The manuscript, a copy of Macrobius’s commentary on Cicero’s Somnium Scipionis, is located in the Bayerische Staatsbibliothek in Munich (BSB Clm 14436). It appears to date from the first quarter of the 11th century.

The graph is in the appendix, which bears the title De cursu per zodiacum (‘On the movement through the zodiac’). It was possibly added by an unknown transcriber.  The graph seems to represent a plot of the inclinations of the planetary orbits as a function of the time. The zodiac is shown on a plane, with the horizontal axies showing time (divided into thirty parts), while the vertical axis shows the width of the zodiac.

The world’s first graph?
Folio 61 recto of a copy of Macrobius’s commentary on Cicero’s ‘Somnium Scipionis’. The lines show seven heavenly bodies: Venus, Mercury, Saturn, the Sun, Mars, Jupiter, and the Moon.

Is the the world’s first graph? The use of a grid is uncannily modern. But there are some difficulties.

Each line, belonging to a different planet, is plotted on a different scale, so the periods cannot be reconciled. It would be more accurate to call it a schematic diagram of the data. In other words, the values for the amplitudes which are described in the accompanying text, cannot be read off the graph.

A final note on data visualisation: Funkhouser’s research was motivated by the explosion in the use of graphical methods in the 1930s. That’s eighty years before DJ Patil popularised the term ‘data science’.

Forecasting an election result

In the run-up to the French presidential election in mid-2017, G. Elliott Morris at The Crosstab published some interesting forecasts using simulated Dirichlet draws. The post in question is no longer online. It was previously available on http://www.thecrosstab.com/2017/02/14/france-2017-methodology .

The method itself is fairly well-known (see e.g. Rigdon et al. 2009, A Bayesian Prediction Model for the U.S. Presidential Election ).

I thought it would be interesting to apply the technique when the poll samples and voter base are both tiny. So I did it for Malta, which held a snap election in June 2017. The main problem here is the inconsistency of the polls. For the last few data points, I used a series of voluntary online surveys carried out by a Facebook group calling themselves MaltaSurvey. In hindsight, the survey results appear to have been biased, but at the time there was no way of knowing how to model this bias.

So, without further ado, here is the link to the code on Github.




Musica universalis (14) – The inner space

Airwave (2000), Innerspace



Eugenio Calabi (1954), The space of Kähler metrics 

Shing-Tung Yau (1978), On the Ricci curvature of a compact kähler manifold and the complex Monge-Ampère equation

The scourge of graduate unemployment

Berlin's trendence institute surveyed over 300,000 European graduates on their perceived career prospects Illustration: Christine Oliver for the Guardian

Berlin’s trendence institute surveyed over 300,000 European graduates on their perceived career prospects Illustration: Christine Oliver for the Guardian



Or what to do with millions of extra graduates.

Europe isn’t alone in facing the problem of graduate unemployment. The BRIC countries are feeling it too.

The numbers are staggering. In India one in three graduates up to the age of 29 is unemployed, according to a Labour Ministry report released last November. Total unemployment in the country is officially closer to 12%.

In China this month a record 7.26 million will graduate from the country’s universities – more than seven times the number 15 years ago.

Unemployment among new Chines graduates six months after leaving university is officially around 15%.

The real unemployment rate could be closer to 30% – some 2.3 million unemployed from this year’s graduating cohort alone, according to Joseph Cheng, professor of political science at City University of Hong Kong.

Meanwhile, back home in Europe, graduates expect to submit an average of 60 applications before landing their first job.  The average wait between graduation and employment is approaching six months. That’s the average.


Musica universalis (2) – Neutrinos

Matt Davey (2013), Neutrino



Introduction to neutrino physics, and references therein.

Genius isn’t always rewarded

Ten cautionary tales from BBC Wired

10 inventors who didn’t get mega-rich from their inventions

First cosmological results from Planck

I don’t usually post stuff that’s already all over the news media, but the publication of the first results from Planck requires some clarification.

Big, expensive observation missions are put in place with one final aim: to test our model of the Universe. That’s what science is all about.

The results from Planck confirm Lambda CDM (the Condordance Model). Excellent. This increases our confidence in our model. Karl Popper’s “falsifiability” process is a trifle more difficult than good old confidence (used with huge success, and no hang-ups at all, by stock market modellers, but  I digress).

Upshot: we can trust our model.

There is another question though. Can we rule out other models? Now this is what keeps scientists in employment. If our current model were the final word, it would be the end of science (predicted countless times).

The short answer is no. Planck does not rule out models with an evolving dark energy equation of state. Huge sigh of relief all round. We’re not on the dole yet.

It also shows (sorry – “does not rule out the absence of”) stuff that shouldn’t be there with the current model of inflation. So some tweaking required there. The good news in that particular market is that inflation is so poorly understood anyway. So tweaking it won’t cause cataclysmic paradigm shifts.

Bottom line: a splendid job, with excellent results, and a good return on investment.

Planck will probably not give us any teflon-coated frying pans but at €700 million, it’s an absolute bargain, and well worth it.






The new privateers

It was bound to happen sooner or later. The rising number of pirate attacks on merchant and private vessels in the Indian Ocean has driven the already lucrative private security business seaward. A British company has set up a maritime security company to deter pirate attacks and, it is presumed, to engage in offensive action if extreme circumstances. In other words, a privateering venture.

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