piscola 1.0.2

Type Ia Supernova Light-curve fitting code

PISCOLA: Python for Intelligent Supernova-COsmology Light-curve Analysis

Supernova light-curve fitting code in python

Although the main purpose of PISCOLA is to fit type Ia supernovae, it can be used to fit other types of supernovae or even other transients.

Read the full documentation at: piscola.readthedocs.io. See below for a summary.

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Installation

PISCOLA can be installed in the usual ways, via pip:

pip install piscola

or from source:

git clone https://github.com/temuller/piscola.git
cd piscola
pip install .

Requirements

PISCOLA has the following requirements:

numpy
pandas
matplotlib
peakutils
requests
sfdmap
extinction
astropy
scipy
george
pickle5
pytest  (optional: for testing the code)

Tests

To run the tests, go to the parent directory and run the following command:

pytest -v

Using PISCOLA

PISCOLA can fit the supernova light curves and correct them in a few lines of code:

sn = piscola.call_sn(<sn_file>)
sn.fit()

The light-curve parameters are saved in a dictionary and can be accessed directly:

sn.lc_parameters  # dictionary
sn.dm15

You can find an example of input file in the data directory.

Citing PISCOLA

If you make use of PISCOLA in your projects, please cite Müller-Bravo et al. (2022). See below for the bibtex format:

@ARTICLE{2022MNRAS.512.3266M,
       author = {{M{\"u}ller-Bravo}, Tom{\'a}s E. and {Sullivan}, Mark and {Smith}, Mathew and {Frohmaier}, Chris and {Guti{\'e}rrez}, Claudia P. and {Wiseman}, Philip and {Zontou}, Zoe},
        title = "{PISCOLA: a data-driven transient light-curve fitter}",
      journal = {\mnras},
     keywords = {supernovae: general, cosmology: observations, distance scale, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics},
         year = 2022,
        month = may,
       volume = {512},
       number = {3},
        pages = {3266-3283},
          doi = {10.1093/mnras/stab3065},
archivePrefix = {arXiv},
       eprint = {2110.11340},
 primaryClass = {astro-ph.HE},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2022MNRAS.512.3266M},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Contributing and raising an issue

The recommended way is to use the issues page or send a pull request. Otherwise, you can contact me directly.