Parser module

Defines the command line options and their help messages in create_parser() and read the input command line in parse(), dealing with different possible configurations.

The fancy short/long help formatting, as well as the automatic help creation from docstrings is entirely due to Francesco Montesano.

class parser_mp.MpArgumentParser(prog=None, usage=None, description=None, epilog=None, version=None, parents=[], formatter_class=<class 'argparse.HelpFormatter'>, prefix_chars='-', fromfile_prefix_chars=None, argument_default=None, conflict_handler='error', add_help=True)[source]

Bases: argparse.ArgumentParser

Extension of the default ArgumentParser


Override method to raise error :Parameters: message (string) –

error message

Allows to set a default subparser

This trick is there to maintain the previous way of calling

set_default_subparser(default, args=None)[source]

If no subparser option is found, add the default one


This function relies on the fact that all calls to MontePython will start with a -. If this came to change, this function should be revisited

parser_mp.add_subparser(sp, name, **kwargs)[source]

Add a parser to the subparser sp with name.

All the logic common to all subparsers should go here

  • sp (subparser instance)
  • name (str) – name of the subparser
  • kwargs (dict) – keywords to pass to the subparser
  • output
  • ——
  • sparser (Argparse instance) – new subparser

Definition of the parser command line options

The main parser has so far two subparsers, corresponding to the two main modes of operating the code, namely run and info. If you simply call python montepython/ -h, you will find only this piece of information. To go further, and find the command line options specific to these two submodes, one should then do: python montepython/ run -h, or info -h.

All command line arguments are defined below, for each of the two subparsers. This function create the automatic help command.

Each flag outputs the following argument to a destination variable, specified by the dest keyword argument in the source code. Please check there to understand the variable names associated with each option.


-N : int
number of steps in the chain (OBL). Note that when running on a cluster, your run might be stopped before reaching this number.
-o : str
output folder (OBL). For instance -o chains/myexperiments/mymodel. Note that in this example, the folder chains/myexperiments must already exist.
-p : str
input parameter file (OBL). For example -p input/exoticmodel.param.
-c : str

input covariance matrix (OPT). A covariance matrix is created when analyzing previous runs.

Note that the list of parameters in the input covariance matrix and in the run do not necessarily coincide.

-j : str

jumping method (global (default), sequential or fast) (OPT).

With the global method the code generates a new random direction at each step, with the sequential one it cycles over the eigenvectors of the proposal density (= input covariance matrix).

The global method the acceptance rate is usually lower but the points in the chains are less correlated. We recommend using the sequential method to get started in difficult cases, when the proposal density is very bad, in order to accumulate points and generate a covariance matrix to be used later with the default jumping method.

The fast method implements the Cholesky decomposition presented in by Antony Lewis.

-m : str

sampling method, by default ‘MH’ for Metropolis-Hastings, can be set to ‘NS’ for Nested Sampling (using Multinest wrapper PyMultiNest), ‘CH’ for Cosmo Hammer (using the Cosmo Hammer wrapper to emcee algorithm), and finally ‘IS’ for importance sampling.

Note that when running with Importance sampling, you need to specify a folder to start from.

–update : int
update frequency for Metropolis Hastings. If greater than zero, number of steps after which the proposal covariance matrix is updated automatically (OPT).
-f : float

jumping factor (>= 0, default to 2.4) (OPT).

The proposal density is given by the input covariance matrix (or a diagonal matrix with elements given by the square of the input sigma’s) multiplied by the square of this factor. In other words, a typical jump will have an amplitude given by sigma times this factor.

The default is the famous factor 2.4, advertised by Dunkley et al. to be an optimal trade-off between high acceptance rate and high correlation of chain elements, at least for multivariate gaussian posterior probabilities. It can be a good idea to reduce this factor for very non-gaussian posteriors.

Using -f 0 -N 1 is a convenient way to get the likelihood exactly at the starting point passed in input.

–conf : str
configuration file (default to default.conf) (OPT). This file contains the path to your cosmological module directory.
–chain-number : str

arbitrary number of the output chain, to overcome the automatic one (OPT).

By default, the chains are named yyyy-mm-dd_N__i.txt with year, month and day being extracted, N being the number of steps, and i an automatically updated index.

This means that running several times the code with the same command will create different chains automatically.

This option is a way to enforce a particular number i. This can be useful when running on a cluster: for instance you may ask your script to use the job number as i.

-r : str

restart from last point in chain, to avoid the burn-in stage (OPT).

At the beginning of the run, the previous chain will be deleted, and its content transfered to the beginning of the new chain.

-b : str
start a new chain from the bestfit file computed with analyze. (OPT)
–fisher : None
Calculates the inverse of the fisher matrix to use as proposal distribution
–silent : None
silence the standard output (useful when running on clusters)
–Der-target-folder : str
Add additional derived params to this folder. It has to be used in conjunction with Der-param-list, and the method set to Der: -m Der. (OPT)
–Der-param-list : str
Specify a number of derived parameters to be added. A complete example would be to add Omega_Lambda as a derived parameter: python montepython/ run -o existing_folder -m Der --Der-target-folder non_existing_folder --Der-param-list Omega_Lambda
–IS-starting-folder : str
Perform Importance Sampling from this folder or set of chains (OPT)

For Nested Sampling and Cosmo Hammer arguments, see nested_sampling and cosmo_hammer.


Replaces the old -info command, which is deprecated but still available.
files : string/list of strings

you can specify either single files, or a complete folder, for example info chains/my-run/2012-10-26*, or info chains/my-run.

If you specify several folders (or set of files), a comparison will be performed.

–minimal : None
use this flag to avoid computing the posterior distribution. This will decrease the time needed for the analysis, especially when analyzing big folders.
–bins : int
number of bins in the histograms used to derive posterior probabilities and credible intervals (default to 20). Decrease this number for smoother plots at the expense of masking details.
–no-mean : None
remove the mean likelihood from the plot. By default, when plotting marginalised 1D posteriors, the code also shows the mean likelihood per bin with dashed lines; this flag switches off the dashed lines.
–extra : str

extra file to customize the output plots. You can actually set all the possible options in this file, including line-width, ticknumber, ticksize, etc... You can specify four fields, info.redefine (dict with keys set to the previous variable, and the value set to a numerical computation that should replace this variable), info.to_change (dict with keys set to the old variable name, and value set to the new variable name), info.to_plot (list of variables with new names to plot), and info.new_scales (dict with keys set to the new variable names, and values set to the number by which it should be multiplied in the graph). For instance,

–noplot : bool
do not produce any plot, simply compute the posterior (OPT) (flag)
–noplot-2d : bool
produce only the 1d posterior plot (OPT) (flag)
–contours-only : bool
do not fill the contours on the 2d plots (OPT) (flag)
–all : None
output every subplot and data in separate files (OPT) (flag)
–ext : str
change the extension for the output file. Any extension handled by matplotlib can be used. (pdf (default), png (faster))
–fontsize : int
desired fontsize (default to 16)
–ticksize : int
desired ticksize (default to 14)
–line-width : int
set line width (default to 4)
–decimal : int
number of decimal places on ticks (default to 3)
–ticknumber : int
number of ticks on each axis (default to 3)
–legend-style : str
specify the style of the legend, to choose from sides or top.
–keep-non-markovian : bool
Use this flag to keep the non-markovian part of the chains produced at the beginning of runs with –update mode This option is only relevant when the chains were produced with –update (OPT) (flag)
–keep-fraction : float
after burn-in removal, analyze only last fraction of each chain. (between 0 and 1). Normally one would not use this for runs with –update mode, unless –keep-non-markovian is switched on (OPT)
–want-covmat : bool
calculate the covariant matrix when analyzing the chains. Warning: this will interfere with ongoing runs utilizing update mode (OPT) (flag)
Returns:args (NameSpace) – parsed input arguments

Create a custom help action.

It expects split_string to appear in groups of three. If the option string is ‘-h’, then uses the short description between the first two split_string. If the option string is ‘-h’, then uses all that is between the first and the third split_string, stripping the first one.

  • split_string (str) – string to use to select the help string and how to select them. They must appear in groups of 3
  • output
  • ——
  • CustomHelp (class definition)

Check if the file exists. If not raise an error

Parameters:fname (string) – file name to parse
Returns:fname (string)
parser_mp.get_dict_from_docstring(key_symbol='<**>', description_symbol='<++>')[source]

Create the decorator

  • key_symbol (str) – identifies the key of a argument/option
  • description_symbol (str) – identify the description of a argument/option
  • Returns
  • ——
  • wrapper (function)

Create the argument parser and returns it :Parameters: * kwargs (dictionary) –

keyword to pass to the parser
  • output
  • ——
  • p (MpArgumentParser instance) – parser with some keyword added

Check some basic organization of the folder, and exit the program in case something goes wrong.

Keyword Arguments:
 custom_command (str) – For testing purposes, instead of reading the command line argument, read instead the given string. It should ommit the start of the command, so e.g.: ‘-N 10 -o toto/’
parser_mp.parse_docstring(docstring, key_symbol='<**>', description_symbol='<++>')[source]

Extract from the docstring the keys and description, return it as a dict

  • docstring (str)
  • key_symbol (str) – identifies the key of an argument/option
  • description_symbol (str) – identify the description of an argument/option
  • output
  • ——
  • helpdic (dict) – help strings for the parser

Check if the input is integer positive :Parameters: * string (string) –

string to parse
  • output (int) – return the integer