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.
- Python>=3.7
- argparse>=1.1
- matplotlib>=3.1.1
- numpy>=1.17.0
- pandas>=0.25.0
- setuptools>=41.0.1
- scipy>=1.3.0
- scikit-learn>=0.20.3
- seaborn>=0.9.0
- xgboost>=1.6.2
- sphinx>=2.1.2
The current version runs in Python-3.7 or latter.
We recommend you use a virtual environment to ensure the correct package versions.
Once your environment is created, you can source it :
>> source <path_to_venv>/bin/activate
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) >> pip install --upgrade pip
(ActSNClass) >> pip install -r requirements.txt
(ActSNClass) >> python setup.py install