changes to joss paper

In the current scientific climate `Python` is ubiquitous, and while lower-level codes written in, e.g., `Fortran` or `C` are still widely used, much of the newer software is written in `Python`, either entirely or as a wrapper around other codes and libraries. Education in programming also often includes `Python` courses because of its ease of use and its flexibility. Moreover, `Python` has a large community with many resources and tutorials. We have created `binary_c-python` to allow students and scientists alike to explore current scientific issues while enjoying the familiar syntax, and at the same time make use of the plentiful scientific and astrophysical packages like `Numpy`, `Scipy`, `Pandas`, `Astropy` and platforms like `Jupyter`.

Earlier versions of `binary_c-python` were written in Perl, where much of the logic and structure were developed and debugged. This made porting to `Python` relatively easy.

Over time, many population synthesis codes have been published. Recent ones are BPASS/HOKI, SeBa, MOBSE, COSMIC and COMPAS. While these codes sometimes have a slightly different focus, like gravitational wave populations or spectral synthesis, most of them have, or are, python interfaces. This further highlights the need for the development and release of a modern `Python` interface to `binary_c`, i.e. `binary_c-python`.

The previous interface to `binary_c`, `binary_grid` was written in `Perl`, where much of the logic and structure were developed and debugged. The initial porting to `Python` and the development of `binary_c-python` greatly benefitted from the existence of this earlier interface. While much of the code-base of `binary_c-python` has changed significantly from its `Perl` predecessor, many core parts are still heavily inspired by it.

# Projects that use `binary_c-python`

`binary_c-python` has already been used in a variety of situations, ranging from pure research to educational purposes, as well as in outreach events. In the summer of 2021 we used `binary_c-python` as the basis for the interactive classes on stellar ecosystems during the [International Max-Planck Research School summer school 2021 in Heidelberg](https://www2.mpia-hd.mpg.de/imprs-hd/SummerSchools/2021/), where students were introduced to the topic of population synthesis and were able to use our notebooks to perform their own calculations. `binary_c-python` has been used in @mirouh_etal22, where improvements to tidal interactions between stars were implemented, and initial birth parameter distributions were varied to match to observed binary systems in star clusters. A Master's thesis project, aimed at finding the birth system parameters of the V106 stellar system, comparing observations to results of `binary_c` and calculating the maximum likelihood with Bayesian inference through Markov chain Monte Carlo sampling. The project made use of `binary_c-python` and the `Emcee` package.

`binary_c-python` has already been used in a variety of situations, ranging from pure research to educational purposes, as well as in outreach events. In the summer of 2021 we used `binary_c-python` as the basis for the interactive classes on stellar ecosystems during the [International Max-Planck Research School summer school 2021 in Heidelberg](https://www2.mpia-hd.mpg.de/imprs-hd/SummerSchools/2021/), where students were introduced to the topic of population synthesis and were able to use our notebooks to perform their own calculations. `binary_c-python` has been used in @mirouh_etal22, where improvements to tidal interactions between stars were implemented, and initial birth parameter distributions were varied to match to observed binary systems in star clusters. A Master's thesis project, aimed at finding the birth system parameters of the V106 stellar system, comparing observations to results of `binary_c` and performing uncertainty inference with Bayesian inference through Markov chain Monte Carlo sampling. The project made use of `binary_c-python` and the `Emcee` package.

Currently `binary_c-python` is used in several ongoing projects that study the effect of birth distributions on the occurrence of carbon-enhanced metal-poor (CEMP) stars, the occurrence and properties of accretion disks in main-sequence stars and the predicted observable black hole distribution by combining star formation and metallicity distributions with the output of `binary_c`. Moreover, we use the *ensemble* output structure to generate datasets for galactic chemical evolution on cosmological timescales, where we rely heavily on the utilities of `binary_c-python`.