ActSNClass

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.

Dependencies

For code:

• 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

For documentation:

• sphinx>=2.1.2

Install

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