This repository contains code allowing reproducibility of results presented in Leoni et al., 2022, Astronomy & Astrophysics, Volume 663, id.A13, 10 pp.
We list below a general description of each script/notebook.
The data necessary to reproduce these results are available through zenodo.
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actsnfink/sigmoid.py: functions related to the sigmoid feature extraction
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actsnfink/classifier_sigmoid.py: functions related to filtering points on the rise and concatenation with extra features (SNR, npoints, chi2) with sigmoid fit parameters
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actsnfink/early_sn_classifier.py: global functions for feature extraction and learning loop.
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actsnfink/notebooks/mean_model.ipynb: Extract best performing model from a given query strategy, save pkl file and generate list of alerts used of training.
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actsnfink/notebooks/0X with X \in [1,2,3,4,5,7]: Jupyter notebooks for reproducing the plots in Leoni et al., 2022
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actsnfink/scripts/run_loop.py Example script on how to use this package.
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LICENSE: MIT License
Create a virtual environment following these instructions. Source it and install the actsnclass package.
Then you can install the other dependencies using pip:
python3 -m pip install -r requirements.txt
Then you can install the functionalities of this package.
python setup.py install
If you wish to use the Rainbow features from Russeil et al., 2023 it is also necessary to install the light_curve package by Malanchev et al., 2021 as modified by E. Russeil here.