CosmoSIS Reference

The main CosmoSIS code (as distinct from the science modules) is primarily designed to be used as a command line program, but you might need to delve in and modify or re-use various parts of it.

You might want to do this if:

• you are writing a new sampler
• you want to customize plotting, post-processing, or the output of chains
• you want to have a new way of setting up a likelihood pipeline
• you want to use a CosmoSIS pipeline for another purpose
• you just like reading code

Samplers

You create a CosmoSIS sampler by making a subclass cosmosis.samplers.Sampler or cosmosis.samplers.ParallelSampler, typically in a subdirectory of cosmosis/samplers.

New sampler subclasses must be called XYZSampler, where XYZ is the name of the sampler. This ensures that when a new Sampler subclass is defined and imported to samplers/init.py then it is automatically registered with CosmoSIS, so that if the user writes sampler=XYZ in their ini file runtime section then the right sampler is used.

Methods available to Sampler

The Sampler base class provides one method for subclasses to use:

Sampler.read_ini(self, option, option_type, default=None)

option is the name of an option to read from the confgiuration ini file for the sampler. option_type is a python type, one of float, int, bool, str which decides what type of option to look for. default is the default value to use if the option is not specified by the user (None means that no default is used and an error should be raised if the option is not set).

The ParallelSampler subclass provides two more methods for subclasses:

Sampler.is_master(self)
Sampler.worker(self)

The is_master method simply returns True if the sampler is at MPI rank zero in the pool of processes.

The worker method, which may be overridden by subclasses but often does not need to be, is called by all the non-master processes instead of the execute function below. It simply tells the process to wait for a function to be sent from master to run.

Methods a sampler subclass must implement

Sampler.config(self)

Sampler.execute(self)

Sampler.is_converged(self)

The interface a sampler must implement.

Postprocessing

The structure of postprocessors and their elements, including plotting and statistics. Often easier to use tweaks or extra plots. This may change soon.

Output

The interface a new output must implement. The existing output modes.

Parameter

The parameter class, initializing it, normalization, name scheme.

Module

The module class - describe functions for intitialzing and running.

Pipeline

The pipeline class, and in particular how it is created and loaded. The various methods for running the pipeline.

Inifile

The inifile class, reference the python ConfigParser docs, and describe extensions to it.

Prior

The prior class, and creating new priors.

MPI Pools

The MPI pool's behaviour and using it elsewhere. Limitations including the pickle issue.

Datablock

The datablock - probably do not go into this in too much detail since a) it's a very in-depth topic b) it is hard to modify c) most of the user-facing methods should be documented elsewhere.