Active Learning for Supernova Photometric Classification
This repository holds the code and data used in Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning, by Ishida, Beck, Gonzalez-Gaitan, de Souza, Krone-Martins, Barrett, Kennamer, Vilalta, Burgess, Quint, Vitorelli, Mahabal and Gangler, 2018.
This is one of the products of COIN Residence Program #4, which took place in August/2017 in Clermont-Ferrand (France).
We kindly ask you to include the full citation if you use this material in your research: Ishida et al, 2019, MNRAS, 483 (1), 2–18.
Full documentation can be found at readthedocs.
The current version runs in Python-3.7.
We recommend you use anaconda to create a suitable environment.
To set up the enviroment clone this repository, navigate to its location in the terminal and do::
>> conda env create -f environment.yml
Once the environment is created, activate it using::
>> conda activate ActSNClass
You will notice a
(ActSNCLass) to the left of your terminal line.
This means everything is ok!
In order to install this code you should clone this repository and do::
(ActSNClass) >> python setup.py install