Monte Python, a Monte Carlo Markov Chain code (with Class!)

Main developer

Thejs Brinckmann <brinckmann@physik.rwth-aachen.de>

Author

Benjamin Audren <benjamin.audren@epfl.ch>

License

MIT

The code is under the MIT license. As an additional clause, when using the code in a scientific publication you are also required to cite the v3.0 release paper MontePython 3: boosted MCMC sampler and other features and the original release paper Conservative Constraints on Early Cosmology (see the tail of this document for the bibtex entries).

Recent changelog

v3.4.0, Feb 25, 2021 * Added BBN likelihoods from 1907.11594 (N. Schoeneberg, D. C. Hooper)

• Added H0LICOW likelihoods from 1907.04869 (S. Taubenberger, S. Suyu)
• New feature: experiment specifications passed in .param files now overwrite ones in .data files, which allows for testing different likelihood specifications simultaneously (N. Schoeneberg)
• New plotting features: to_derive allows to add new parameters in the plot file without overwriting the old parameter, and to_reorder allows to choose the order of parameters for the plots (N. Schoeneberg)
• Significant speed-up of Pantheon likelihood (J. Renk)
• Improved treatment of tick limits, sigma bounds, and Omega_m / omega_m (N. Schoeneberg)
• Various bugfixes related to python2 / python3 compatibility (D. C. Hooper, N. Schoeneberg, F. Agocs, J. Renk)
• Various additional bugfixes

v3.3.0, Jan 23, 2020

• Added python 3 compatibility, python 2.7 still supported (N. Schöneberg, F. Köhlinger)
• Added Lyman-alpha alpha-beta-gamma likelihood from 1907.01496 (M. Archidiacono, D.C. Hooper, R. Murgia)
• Added new type of parameters, derived_lkl, that behave like derived parameters of the likelihood and are not passed to class (D.C. Hooper)
• Added joint prior on sz nuisance parameters for all Planck 18 likelihoods. Bestfits and covmats updated to reflect this (D.C. Hooper)
• Various bugfixes and minor improvements, e.g. updated example plot file with more options and clarifications

v3.2.0, Aug 21, 2019

• Added Planck 2018 likelihoods and example param files (Deanna Hooper)
• Added BOSS DR12 and eBOSS DR14 Lya and QSO BAO likelihoods from 1906.11628 (James Farr)
• Added KiDS+VIKING-450 likelihood from 1812.06076 (KiDS collaboration)
• Added KiDS-450 correlation function likelihood from 1809.01406 (Fabian Köhlinger)
• Various bugfixes

Details and Examples

If you are searching for further details or specific examples of a work session, please refer to the online documentation. See also the Monte Python 3 paper for details on the code, including a summary of features as of v3.0.

Note the Monte Python 3 paper contains an overview of all likelihoods currently implemented in the code, with some details on those likelihoods, such as datasets, last updated, type and relevant papers to cite when using the likelihood. In the future, the overview of likelihoods will be maintained on the official Monte Python website.

You can find installation details below and on the archived Monte Python 2 wiki. The Monte Python 3 forum contains a collection of already answered questions, and can be used to discuss the code. Also refer to the archived Monte Python 2 forum for additional previously answered questions, but please post all new issues on the Monte Python 3 forum.

The official Monte Python website, the course page of Julien Lesgourgues, and the hi_class website contain Monte Python (and Class) lectures, examples and exercises.

Want to contribute?

Monte Python is developed and maintained by volunteer workers and we are always happy for new people to contribute. Do not hesitate to contact us if you believe you have something to add, this can be e.g. new likelihoods, new samplers, improvements to the plotting, bug fixes, or ideas for how to improve the code. Additionally, everyone is encouraged to assist in resolving issues on the forum, so do not hesitate to reply if you think you can help.

In particular, if you would like to have your likelihood added to the public github repository, please make sure it is well documented and add all relevant information to the .data file, e.g. authors and references.

Prerequisites

• You need the python program version 2.7.x or version 3.x. Note that lower versions of python may work, down to 2.6, if you add manually two extra packages (ordereddict and argparse).
• Your python of choice must have numpy (version >= 1.4.1) and cython. The later is used to wrap CLASS in python.
• [optional] If you want to use fully the plotting capabilities of Monte Python, you also need the scipy, with interpolate, and matplotlib modules.
• [optional] You can now use Multi Nest and the CosmoHammer with Monte Python, though you need to install them. Please refer to the documentation.

The MontePython part

Move the .tar.bz2 file to the place of your convenience, untar its content

$ bunzip2 montepython-vx.y.tar.bz2
$ tar -xvf montepython-vx.y.tar

This will create a directory named montepython into your current directory. You can add the following line to your .bashrc file:

export PATH=/path/to/MontePython/montepython/:$PATH

to be able to call the program from anywhere.

You will need to adapt only two files to your local configuration. The first is the main file of the code montepython/MontePython.py, and it will be the only time you will have to edit it, and it is simply to accommodate different possible configurations of your computer.

Its first line reads

#!/usr/bin/python

This should be changed to wherever is your preferred python distribution installed. For standard distribution, this should already be working. Now, you should be able to execute directly the file, i.e. instead of calling:

The second file to modify is located in the root directory of Monte Python : default.conf. This file will be read (and stored) whenever you execute the program, and will search for your cosmological code path, your data path, and your wmap wrapper path. You can alternatively create a second one, my.conf, containing your setup, and then run the code providing this file (with the flag --conf)

The Class part

Go to your class directory, and do make clean, then make. This builds the libclass.a, needed for the next step. From there,

$ cd python/
$ python setup.py build
$ python setup.py install --user

This will compile the file classy.pyx, which is the python wrapper for CLASS, into a library, classy.so, located in the build/ subdirectory. This is the library called in Monte Python afterwards.

If this step fails, check that you have cython installed, numpy (a numerical package for python), python (well... did I say this code was in python ?) with a version > 2.6. If this step fails again, kindly ask your system admin, (s)he is there for this, after all. Note that the installation (last command) is not strictly speaking mandatory.

Take care to use the same Python version when compiling CLASS as will be used when running Monte Python.

Remember that if you modify CLASS to implement some new physics, you will need to perform this part again for the new CLASS.

The Planck likelihood part

Written by Deanna C. Hooper <hooper@physik.rwth-aachen.de>

The Planck 2018 data can be found on the Planck Legacy Archive. The Planck Likelihood Code (plc) is based on a library called clik. It will be extracted, alongside several .clik folders that contain the likelihoods. The code uses an auto installer device, called waf. Here we detail the full installation.

Move to the directory where you want Planck 2018

$ cd path/to/planck

Download the code and baseline data (will need 300 Mb of space)

$ wget -O COM_Likelihood_Code-v3.0_R3.01.tar.gz "http://pla.esac.esa.int/pla/aio/product-action?COSMOLOGY.FILE_ID=COM_Likelihood_Code-v3.0_R3.01.tar.gz"
$ wget -O COM_Likelihood_Data-baseline_R3.00.tar.gz "http://pla.esac.esa.int/pla/aio/product-action?COSMOLOGY.FILE_ID=COM_Likelihood_Data-baseline_R3.00.tar.gz"

Uncompress the code and the likelihood, and do some clean-up

$ tar -xvzf COM_Likelihood_Code-v3.0_R3.01.tar.gz
$ tar -xvzf COM_Likelihood_Data-baseline_R3.00.tar.gz
$ rm COM_Likelihood_*tar.gz

Move into the code directory

$ cd code/plc_3.0/plc-3.01

Configure the code. Note that you are strongly advised to configure clik with the Intel mkl library, and not with lapack. There is a massive gain in execution time: without it, the code is dominated by the execution of the low-l polarisation data. Before the next step make sure you do NOT have any old Planck likelihoods sourced!

$ ./waf configure --lapack_mkl=${MKLROOT} --install_all_deps

If everything went well, you are ready to install the code

$ ./waf install

You now need to source the likelihood. If you are running on a bash shell, simply type

$ source bin/clik_profile.sh

If you are running in a z-shell, you will first need to create a .zsh version of the above file. This can be done in many ways, for example

$ cp bin/clik_profile.sh bin/clik_profile.zsh
$ sed -i 's/addvar PATH /PATH=$PATH:/g' bin/clik_profile.zsh
$ sed -i 's/addvar PYTHONPATH /PYTHONPATH=$PYTHONPATH:/g' bin/clik_profile.zsh
$ sed -i 's/addvar LD_LIBRARY_PATH /LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/g' bin/clik_profile.zsh
$ source bin/clik_profile.zsh

You need to add 'source /path/to/planck/code/plc_3.0/plc-3.01/bin/clik_profile.sh' to your .bashrc (or the .zsh to your .zshrc on a z-shell), and you should put it in your scripts for cluster computing.

In your Monte Python configuration file, you will need to add

path['clik'] = '/path/to/planck/code/plc_3.0/plc-3.01'

There are nine Planck 2018 likelihoods defined in Monte Python: Planck_highl_TT, Planck_highl_TT_lite, Planck_highl_TTTEEE, Planck_highl_TTTEEE_lite, Planck_lensing, Planck_lowl_TT, Planck_lowl_EE, Planck_lowl_EEBB, Planck_lowl_BB, as well as five sets of parameter files, bestfit files, and covmats.

Enjoying the difference

Now the code is installed. Go anywhere, and just call

$ python montepython/MontePython.py --help
$ python montepython/MontePython.py run --help
$ python montepython/MontePython.py info --help

To see a list of all commands. For the run subcommand, there are two essential ones, without which the program will not start. At minimum, you should precise an output folder (-o) and a parameter file (-p). An example of parameter file is found in the main directory of MontePython (test.param, for instance).

A typical call would then be:

$ python montepython/MontePython.py run -o test -p example.param

If non existent, the test/ folder will be created, and a run with the number of steps described in example.param will be started. To run a chain with more steps, one can type:

$ python montepython/MontePython.py run -o test -p example.param -N 100

If you want to analyse the run, then just type

$ python montepython/MontePython.py info test/

Note that you probably want more than a hundred points before analyzing a folder.

Bibtex entry

When using Monte Python in a publication, please acknowledge the code by citing the following papers. If you used Class, MultiNest, PolyChord or Cosmo Hammer, you should also cite the original works.

Please also cite the relevant papers for each likelihood used: as of v3.0 we have a list of references for all likelihoods in the first of the papers below. In the future the list will be maintained on the official Monte Python website. Otherwise, this information can often be found in the .data file of the likelihood folder.

In order to encourage people to both develop and share likelihoods with the community, to the benefit of all users, we optionally encourage users to cite the paper in which the Monte Python likelihood was first used, in addition to the papers in which data and/or likelihoods were published.

@article{Brinckmann:2018cvx,
      author         = "Brinckmann, Thejs and Lesgourgues, Julien",
      title          = "{MontePython 3: boosted MCMC sampler and other features}",
      year           = "2018",
      eprint         = "1804.07261",
      archivePrefix  = "arXiv",
      primaryClass   = "astro-ph.CO",
      SLACcitation   = "%%CITATION = ARXIV:1804.07261;%%"
}
@article{Audren:2012wb,
      author         = "Audren, Benjamin and Lesgourgues, Julien and Benabed,
                        Karim and Prunet, Simon",
      title          = "{Conservative Constraints on Early Cosmology: an
                        illustration of the Monte Python cosmological parameter
                        inference code}",
      journal        = "JCAP",
      volume         = "1302",
      pages          = "001",
      doi            = "10.1088/1475-7516/2013/02/001",
      year           = "2013",
      eprint         = "1210.7183",
      archivePrefix  = "arXiv",
      primaryClass   = "astro-ph.CO",
      reportNumber   = "CERN-PH-TH-2012-290, LAPTH-048-12",
      SLACcitation   = "%%CITATION = ARXIV:1210.7183;%%",
}