Sampling in COMPAS

Here are some basic instructions for efficient sampling of the COMPAS input parameters, using the python sampling package Stroopwafel. Below that are instructions for how to sample from the correlated parameter distributions outlined in Moe & DiStefano 2017.

Note that the intended Stroopwafel functionality for "Adaptive Importance Sampling" is not yet implemented, but is currently in development.


If you have not already, you will need to install Stroopwafel. If you have admin rights, Stroopwafel can be installed on your system with pip install stroopwafel.


To use Stroopwafel sampling, copy preProcessing/ into your working directory.


NOTE: This sampling method is currently being updated as part of an upgrade in our method to parse user-defined options. We plan to address this shortly. Please bear with us and contact the COMPAS team if an urgent solution is needed.

  1. runSubmit

If you are running COMPAS on default settings, skip this section.

If you have many non-default COMPAS arguments, you may want to set them in the compasConfigDefault.yaml, that is read and executed by the file in the same directory. For now, the file must be named this way and placed in the same directory as the file.

A configurable runSubmit file can be found in the preProcessing/ directory.

Set your desired options, then set the userunSubmit parameter to True in the

  1. Stroopwafel inputs

The lines below userunSubmit represent stroopwafel inputs. These are treated as defaults, but can be overridden by command-line arguments to stroopwafel. See python3 --help.

num_systems is the total number of binaries you would like to evolve. This value overrides the value set in the compasConfigDefault.yaml file.

num_cores is the number of cores you would like to use to parallelize your run. More cores means your run will finish sooner, but may reduce your ability to run other tasks while you wait. On linux systems, the command echo $(nproc) will tell you how many (virtual) CPUs you have available.

num_per_core is the number of systems to run on a core at a given time. This translates to the number of systems in a single batch file. This is more relevant for adaptive importance sampling.

mc_only specifies if you would like to do naive MC sampling only. Currently, this option must be set to True

run_on_hpc specifies if you are running on a High-Performance Computer (HPC). If so, see docs/ for assistance.

output_folder a string specifying the output folder. Relative paths will be appended onto the current directory path.

output_filename a string specifying the name of the output samples file.

debug whether to print the COMPAS output/error.

  1. Sampling parameters

Sampled parameters will be combined into grid files which COMPAS then reads in. Users should choose which parameters they would like to be sampled over, as well as the relevant distributions.

See the COMPAS Documentation for details on which sets of parameters are allowed/required.

See Stroopwafel Documentation for details on which distributions are available.

  1. Run Stroopwafel

When your satisfied with your settings, simply run with python3 The output will be collected into batch containers in your output folder. To postprocess the output, see

Moe & DiStefano

To sample from the Moe & DiStefano 2017 distributions, the sampler script can be found in preProcessing/ As described in the header, only the number of systems and upper and lower mass bounds can be set (the parameter correlations break if you try to set the other bounds). These values are set at the bottom of the script.