A package to associate transients with host galaxies, and a database of 16k SNe-host galaxies in PS1.
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"At the last dim horizon, we search among ghostly errors of observations for landmarks that are scarcely more substantial. The search will continue. The urge is older than history. It is not satisfied and it will not be oppressed." --Edwin Hubble
Welcome to GHOST, the database for supernovae and their host galaxies. This database contains ~16k sources in PS1, which were used to predict supernova classes in Gagliano et al. (2020). Installation instructions for the analysis tools are below.
For details on installing and using GHOST, see the
Jdaviz documentation <https://astro-ghost.readthedocs.io/en/latest/>_.
This project is Copyright (c) Alexander Gagliano and licensed under
the terms of the BSD 3-Clause license. This package is based upon
Astropy package template <https://github.com/astropy/package-template>_
which is licensed under the BSD 3-clause license. See the licenses folder for
We love contributions! astro_ghost is open source, built on open source, and we'd love to have you hang out in our community.
Imposter syndrome disclaimer: We want your help. No, really.
There may be a little voice inside your head that is telling you that you're not ready to be an open source contributor; that your skills aren't nearly good enough to contribute. What could you possibly offer a project like this one?
We assure you - the little voice in your head is wrong. If you can write code at all, you can contribute code to open source. Contributing to open source projects is a fantastic way to advance one's coding skills. Writing perfect code isn't the measure of a good developer (that would disqualify all of us!); it's trying to create something, making mistakes, and learning from those mistakes. That's how we all improve, and we are happy to help others learn.
Being an open source contributor doesn't just mean writing code, either. You can help out by writing documentation, tests, or even giving feedback about the project (and yes - that includes giving feedback about the contribution process). Some of these contributions may be the most valuable to the project as a whole, because you're coming to the project with fresh eyes, so you can see the errors and assumptions that seasoned contributors have glossed over.
Note: This disclaimer was originally written by
Adrienne Lowe <https://github.com/adriennefriend>_ for a
PyCon talk <https://www.youtube.com/watch?v=6Uj746j9Heo>, and was adapted by
astro_ghost based on its use in the README file for the
MetPy project <https://github.com/Unidata/MetPy>.
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