This version of MontePython is modified to work with NeuralNest. The nested sampler can be invoked via

python montepython/MontePython.py run -o chains/test -p input/example.param -m NN --NN_sampler nested --NN_n_live_points 100

Note the parameter file requires bounded parameters for nested sampling.

The MCMC sampler (in development) can be invoked by

python montepython/MontePython.py run -o chains/test -p input/example.param -m NN --NN_sampler mcmc --NN_bootstrap_fileroot chains/file

where bootstrap_fileroot gives the root directory of some initial chains used to train the network. This is similar to estimating the covariance matrix and using the Cholesky decomposition for the proposal directions.

Please cite [Moss (2019)](https://arxiv.org/abs/1903.10860) if you find the package useful in your research, along with the relevant MontePython references.

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).

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.

• You need the python program version 2.7 or above, but less than 3.0. Note that lower versions of python will work, down to 2.6 (tested), 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.

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)

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.

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

The release of the Planck data comes with a likelihood program, called Clik, that one can recover from the ESA website, along with the data. Download all tgz files, extract them to the place of your convenience.

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 installation of the code is described in the archive, and it uses an auto installer device, called waf.

Go to your plc folder, and execute the following line, taking into account the mkl installation

$ ./waf configure --install_all_deps --mkl=...

In your configuration file, to use this code, you should add the following line

path['clik'] = 'path/to/your/plc/folder/'

The four likelihoods defined in for Planck are Planck_highl, Planck_lowl, Planck_lensing, lowlike (the polarization data from WMAP). In each of the respective data files for these likelihood, please make sure that the line, for instance,

Planck_highl.path_clik = data.path['clik']+'../something.clik'

points to the correct clik file. Do not forget to source your Planck likelihood every time you want to use it:

$ source Your/Plc/bin/clik_profile.sh

You can put this line in your .bashrc file, and you should put it in your scripts for cluster computing.

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.

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;%%",
}