Here is a work in progress. It’s a transcription of Alimento di Sangue Illustre, a late 17th century Neapolitan treatise on fencing and horsemanship by Francesco Giovanni Angelo di Nuzzo.
The documentation on the IN2P3 Computing Centre portal does not contain any information specific to MontePython. Boris Bolliet provides some very useful instructions on his website. Aside from this, there is nothing at all. So I thought I would provide a few tips, especially for Euclid consortium members.
If you are a member, you can request an account on the cluster.
The first thing to do is to create a folder on /sps/euclid/Users/. You may choose any name (e.g. your username: /sps/euclid/Users/username).
In order to use MontePython, you will need to install software packages. Just follow the instructions on Boris Bolliet’s page.
The tricky bit is the job submission script. I am grateful for Quentin Le Boulc’h for what follows. We spent a lot of time trying to pin down the right parameters for the resource options.
You have two options.
#$ -N yourscript
#$ -P P_euclid
#$ -q pa_long
#$ -l sps=1
#$ -j n
#$ -l os=cl7 -pe openmpi 4
#$ -o $JOB_ID.out
#$ -e $JOB_ID.err
mpirun -np $NSLOTS montepython/montepython/MontePython.py run etc.
#$ -N yourscript
#$ -P P_euclid
#$ -q pa_long
#$ -l sps=1
#$ -j n
#$ -l os=cl7 -pe openmpi_8 32
#$ -o $JOB_ID.out
#$ -e $JOB_ID.err
mpirun -np 4 -pernode montepython/montepython/MontePython.py run etc.
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:
The key's randomart image is:
| . |
| . . 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.
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'
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.
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 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 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 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
$ 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
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
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 -N JOBNAME
#PBS -o $PBS_JOBID.out
#PBS -e $PBS_JOBID.err
#PBS -q furious
#PBS -m bea
#PBS -M firstname.lastname@example.org
#PBS -l nodes=1:ppn=32,mem=64GB,walltime=200:00:00
/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:
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.
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.
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’.
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.
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
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.
Matt Davey (2013), Neutrino
Introduction to neutrino physics, and references therein.