binarycpython 1.0.0

This is a python API for binary_c (versions 2.2.4) by David Hendriks, Rob Izzard and collaborators. Based on the initial set up by Jeff andrews.

# Python module for binary_c  

[](https://doi.org/10.21105/joss.04642)

We present our package [binary_c-python](https://binary_c.gitlab.io/binary_c-python/), a population synthesis code which is aimed to provide a convenient and easy-to-use interface to the [binary_c](https://binary_c.gitlab.io/binary_c/) framework, allowing the user to rapidly evolve single stellar systems and populations of star systems. Based on earlier Perl versions by [Robert Izzard](https://robizzard.gitlab.io/), updated and extended for Python 3 by [David Hendriks](https://davidhendriks.com/), Robert Izzard. Credits to Jeff Andrews for early efforts on the python-c interface.

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](https://binary_c.gitlab.io/binary_c-python/example_notebooks.html) in the [online documentation](https://binary_c.gitlab.io/binary_c-python)).

This is the development branch for binary_c-python version [1.0.1](https://gitlab.com/binary_c/binary_c-python/-/tree/releases/1.0.1/2.2.4) and binary_c version [2.2.4](https://gitlab.com/binary_c/binary_c/-/tree/releases/2.2.4).

The latest release branch is binary_c-python version [1.0.0](https://gitlab.com/binary_c/binary_c-python/-/tree/releases/1.0.0/2.2.4) and binary_c version [2.2.4](https://gitlab.com/binary_c/binary_c/-/tree/releases/2.2.4).

## Installation Below we provide the installation instructions for binary_c-python.

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

• Python version 3.9 up to 3.12 (it’s recommended to use [Pyenv](https://github.com/pyenv/pyenv) to manage your Python versions)

• binary_c version 2.2.4 (See [installation section](https://binary_c.gitlab.io/binary_c#magicparlabel-156))

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

To build the documentation the following additional software is required:

• pandoc available on https://pandoc.org/ or via sudo apt-get install pandoc

### Environment variables Before building binary_c-python please make sure to have defined the following 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

### Installing binary_c-python There are several ways to install binary_c-python:

### 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 of binary_c-python is the same version as the latest stable version on Pip.

### Installation from source: To install the binary_c-python from source, which is useful for development versions and customisation, run

` ./install.sh `

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

### Use of code without installation It is possible to use parts of the code without installing it, by adding the root directory of this repo to your $PYTHONPATH.

• Download binary_c-python, via e.g. git clone https://gitlab.com/binary_c/binary_c-python.git

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

It will not be possible to actually run systems through binary_c though.

## Usage Instructions on how to use binary_c-python are available in the form of [tutorial and example use-case notebooks](https://binary_c.gitlab.io/binary_c-python/example_notebooks.html) in the [online documentation](https://binary_c.gitlab.io/binary_c-python).

The documentation for the latest stable release of binary_c-python is available on https://binary_c.gitlab.io/binary_c-python/.

The documentation for binary_c is available on https://binary_c.gitlab.io/binary_c.

### Usage notes Make sure that with every change/recompilation you make in binary_c, you also rebuild binary_c-python. Whenever you change the source code of this package, you need to re-install it into your virtualenvironment as well.

### Unit tests After installing binary_c-python from source you can run the unit tests to make sure the code works as it should.

There are two suites of unit tests for binary_c-python. The first includes only the actual code of the project, and is located at binarycpython/test/main.py. The second includes only the tutorial and example notebooks, and is located at python binarycpython/tests/test_notebooks.py.

## Development: 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 contribution. Please see HOW_TO_CONTRIBUTE.

Some useful commands to generate documentation and coverage reports are stored in the commands/ directory.

We use the following naming convention for development and release branches:

` development/<binary_c-python version>/<binary_c version> releases/<binary_c-python version>/<binary_c version> `

### Generating documentation To build the documentation manually, run

` ./generate_docs.sh `

from within the commands/ directory. Note: this requires pandoc to be installed on your machine.

### Generating docstring and test coverage report To generate the unit test and docstring coverage report, run

` ./generate_reports.sh `

from within the commands/ directory.

## JOSS paper We’ve written a JOSS paper for binary_c-python, which is stored in [papers/joss/paper.pdf](https://gitlab.com/binary_c/binary_c-python/-/blob/master/papers/joss/paper.pdf).

Paper review status: [](https://joss.theoj.org/papers/7c43806e6d1f82c2945e12ae500f03b2)

## FAQ/Issues: If you encounter an issue with the code, or if you want to suggest extra features or changes in the code, please submit an issue at https://gitlab.com/binary_c/binary_c-python/-/issues/new.

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/mesasdk_init.sh 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.

Other: - 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.