Merge pull request #248 from LSSTDESC/issue/226/read-me

Issue/226/read me

README.md

@@ -4,32 +4,41 @@
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 | [![Build Status](https://dev.azure.com/zcicg57/snmachine/_apis/build/status/LSSTDESC.snmachine?branchName=master)](https://dev.azure.com/zcicg57/snmachine/_build/latest?definitionId=3&branchName=master) | [![Build Status](https://dev.azure.com/zcicg57/snmachine/_apis/build/status/LSSTDESC.snmachine?branchName=dev)](https://dev.azure.com/zcicg57/snmachine/_build/latest?definitionId=3&branchName=dev) |
 
-Welcome to version 1.0 of `snmachine`! As described in ([Lochner et al. (2016)](https://arxiv.org/abs/1603.00882)), this is a flexible python library for reading in photometric supernova light curves, extracting useful features from them and subsequently performing supervised machine learning to classify supernovae based on their light curves. The library is also flexible enough to easily extend to general transient classification.
+Welcome to version 2.0.0 of `snmachine`! As described in ([Lochner et al. (2016)](https://arxiv.org/abs/1603.00882)), this is a flexible python library for reading in photometric supernova light curves, extracting useful features from them and subsequently performing supervised machine learning to classify supernovae based on their light curves. The library is also flexible enough to easily extend to general transient classification.
 
 Up-to-date documentation of `snmachine` can be found via the following Github Pages link.
 
 [Online Documentation](https://lsstdesc.github.io/snmachine/)
 
 ## Usage Policy
 
-This code is made available within the LSST DESC Collaboration. `snmachine` was developed within the DESC, using DESC resources, and so meets the criteria given in the DESC Publication Policy for being a “DESC product” ([DESC Publication Policy](http://lsstdesc.org/sites/default/files/LSST_DESC_Publication_Policy.pdf)). We are aware that the codebase might be useful within other collaborations and welcome requests for access to the code for non-DESC use. If you wish to use the code outside DESC please submit your request [here](https://docs.google.com/forms/d/e/1FAIpQLSfHKNf-GeIGeRWODtwpVz_byXsUDBYISjlQk5lv1W9M0hgB3g/viewform?usp=sf_link).
+`snmachine` was developed within the DESC, using DESC resources, and so meets the criteria given in the DESC Publication Policy for being a “DESC product” ([DESC Publication Policy](http://lsstdesc.org/sites/default/files/LSST_DESC_Publication_Policy.pdf)). This software is released with a BSD 3-Clause License.
+
+## Release
+
+The list of released versions of this package can be found [here](https://github.com/LSSTDESC/snmachine/releases), with the `dev` branch including the most recent (non-released) development.
 
 ## Contributors
 
 The following people have contributed to snmachine v1.0:
 Michelle Lochner, Robert Schuhmann, Jason McEwen, Hiranya Peiris, Rahul Biswas, Ofer Lahav, Johnny Holland, Max Winter
 
+The following people have contributed to snmachine v2.0.0:
+Michelle Lochner, Jason McEwen, Hiranya Peiris, Catarina Alves, Tarek Allam Jr, Rahul Biswas, Christian Setzer, Robert Schuhmann
+
 ## Contributing to snmachine
 
 We welcome developers! Simply fork it into your own private repository and submit a pull request when ready. You can contribute by adding new dataset-reading methods, new feature extraction methods or new classification algorithms. Please create an issue if you have any questions or problems with the code.
 
+See [this page](https://github.com/LSSTDESC/snmachine/blob/dev/CONTRIBUTING.md) for a useful guide to contributing to `snmachine`.
+
 ## Citation
 
 If you use snmachine in your work please cite ([BibTex](http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2016ApJS..225...31L&data_type=BIBTEX&db_key=AST&nocookieset=1)):
 Lochner, M., McEwen, J., Peiris, H., Lahav, O., Winter, M. (2016) “Photometric Supernova Classification with Machine Learning”, The Astrophysical Journal Supplement Series, 225, 31
 
 ## Installation
 
-snmachine is now compatible with Python2 and Python3.
+`snmachine` is compatible with Python3.
 
 The installation is detailed in the online documentation. ([Install Guide](https://lsstdesc.github.io/snmachine/install.html)):