The code for Bengyat & Gal-Yam (2022).
Exploring the diversity of SNe, by their flux function through time and wavelength. Meant to be used on output from PyCoCo.
- Tested on Python 3.8, requires the basic packages
- Clone PyCoCo and run it on the SNe you wish to use. Or, to run
spectra_in_time on the SNe used in the paper, use the existing
info.dat
and receive the/Outputs
folder upon request. Alternatively, you can use the 67 templates from the original PyCoCo repo. - empca.py and yamada.py in an importable directory
- scikit-learn
- spectres
main.py
: Running everything.
analysis.py
: The Random Forest and data analysis routines.
snfuncs.py
: Some input + SN object class and functions for managing the data.
utils.py
: Some input + directories, visialization etc.
snlist.pickle
: Pickled list of SN objects, to spare recalculation of features, for when it takes long.
post-analysis shown in the paper:
data_vs_displacement.py
: testing use on degraded data
clusters.py
: examining average spectra of clusters