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This is a python API for binary_c (versions 2.1.7,2.2pre1,2.2.0,2.2.1) by David Hendriks, Rob Izzard and collaborators. Based on the initial set up by Jeff andrews.

Project description

Python module for binary_c

docstring coverage test coverage astropy

We present our package binary-c-python, a population synthesis code which is aimed to provide a convenient and easy-to-use interface to the binary_c framework, allowing the user to rapidly evolve single stellar systems and populations of star systems. Based on a early work by Jeff Andrews. Updated and extended for Python3 by David Hendriks, Robert Izzard.

binary_c-python is developed for students and scientists in the field of stellar astrophysics, who want to study the evolution of individual or populations of single and binary star systems (see the example use-case notebooks in the online documentation.

The current release is version version, and is designed and tested to work with binary_c version 2.2.1 (for older or newer versions we can't guarantee correct behaviour).

The current development branch is development_0.9.5/2.2.1.


To install binary_c-python we need to make sure we meet the requirements of installation, and

Python requirements

To run this code you need to at least have installations of:

  • Python 3.7 or higher (3.6 is EOL, and we are using 3.9 for development)
  • binary_c version 2.2.0 or higher

The packages that are required for this code to run are listed in the requirements.txt, which automatically gets read out by

Environment variables

Before compilation you need to have certain environment variables:


  • BINARY_C should point to the root directory of your binary_c installation
  • LD_LIBRARY_PATH should include $BINARY_C/src and whatever directories are required to run binary_c (e.g. locations of libgsl, libmemoize, librinterpolate, etc.)
  • LIBRARY_PATH should include whatever directories are required to build binary_c (e.g. locations of libgsl, libmemoize, librinterpolate, etc.)
  • GSL_DIR should point to the root location where you installed GSL to. This root dir should contain bin/, lib/ etc

Build instructions

First, make sure you have built binary_c (See $BINARY_C/doc/binary_c2.pdf section: installation for all the installation instructions for binary_c)) and that it functions correctly.

Installation via PIP:

To install this package via pip:

pip install binarycpython

This will install the latest stable installation that is available on Pip. The version on the master branch should be the same version as the latest stable version on Pip

Installation from source:

We can also install the package from source, which is useful for development versions and when you want to modify the code. It is recommended that you install this into a virtual environment. From within the commands/ directory, run


This will install the package, along with all the dependencies, into the current active (virtual) python environment.

After installation from source

After installing the code via source it is useful to run the test suite before doing any programming with it. The test suite is stored in binarycpython/tests and running python in there will run all the tests.

Use of code without installation

Because installing binary_c-python requires a working installation of binary_c, installing via pip or from source without this working installation of binary_c won't work. To still make use of some of the functions provided by binary_c-python, you can add the path to the code-base to your PYTHONPATH:

  • Download binary_c-python, via e.g. git clone
  • Add the path to the downloaded repo to your $PYTHONPATH, via e.g. export PYTHONPATH="~/binary_c-python:$PYTHONPATH"



See the examples/ directory for example scripts and notebooks. The documentation contains example pages as well.

Usage notes

Make sure that with every change/recompilation you make in binary_c, you also rebuild this package. Whenever you change the sourcecode of this package, you need to reinstall it into your virtualenvironment as well


Look in the docs/ directory. Within the build/html/ there is the html version of the documentation. The documentation is also hosted on but only for the most recent stable release.


If you want to contribute to the code, then it is recommended that you install the packages in development_requirements.txt:

pip install -r development_requirements.txt

Please do not hesitate to contact us to discuss any developments.

Generating documentation

To build the documentation manually, run


from within the commands/ directory

Generating docstring and test coverage report

To generate the unit test and docstring coverage report, run


from within the commands/ directory

Running unit tests

There are two versions of the general unit tests. The first includes only the actual code of the project, and is located at binarycpython/test/ The second includes the tutorial notebooks, and is located at binarycpython/test/ To run just the notebook tests run python binarycpython/tests/

Pulling the JOSS paper article repo

We've written a JOSS paper for binary_c-python, which is stored in, but is also added as a submodule to this repository. To initialise and pull the repo as a submodule, run

git submodule update --init --recursive


Here we provide a non-exhaustive list of some issues we encountered and solutions for these:

Building issues with binary_c itself:

  • see the documentation of binary_c (in doc/).
  • If you have MESA installed, make sure that the $MESASDK_ROOT/bin/ is not sourced. It comes with its own version of some programs, and those can interfere with installing.

When Pip install fails:

  • Run the installation with -v and/or --log <logfile> to get some more info
  • If gcc throws errors like gcc: error: unrecognized command line option ‘-ftz’; did you mean ‘-flto’?, this might be due to that the python on that system was built with a different compiler. It then passes the python3.6-config --cflags to the binarycpython installation, which, if done with gcc, will not work. Try a different python3.6. I suggest using pyenv to manage python versions. If installing a version of python with pyenv is not possible, then try to use a python version that is avaible to the machine that is built with the same compiler as binary_c was built with.
  • if pip installation results in No files/directories in /tmp/pip-1ckzg0p9-build/pip-egg-info (from PKG-INFO), try running it verbose (-v) to see what is actually going wrong.
  • If pip terminates with the error FileNotFoundError: [Errno 2] No such file or directory: '<...>/binary_c-config' Then make sure that the path to your main $BINARY_C directory is set correctly.


  • When running jupyter notebooks, make sure you are running the jupyter installation from the same virtual environment.
  • When the output of binary_c seems to be different than expected, you might need to rebuild this python package. Everytime binary_c is compiled, this package needs to be rebuilt too.

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