Metadata-Version: 2.4
Name: astro-parsnip
Version: 1.4.3
Summary: Deep generative modeling of astronomical transient light curves
Home-page: https://github.com/LSSTDESC/parsnip
Author: Kyle Boone
Author-email: kyboone@uw.edu
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: astropy
Requires-Dist: extinction
Requires-Dist: lcdata>=1.1.1
Requires-Dist: lightgbm>=2.3.1
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: sncosmo>=2.6
Requires-Dist: torch
Requires-Dist: tqdm
Provides-Extra: docs
Requires-Dist: numpy; extra == "docs"
Requires-Dist: sphinx; extra == "docs"
Requires-Dist: sphinx_rtd_theme; extra == "docs"
Requires-Dist: pillow; extra == "docs"
Requires-Dist: numpydoc; extra == "docs"
Dynamic: license-file

# ParSNIP

Deep generative modeling of astronomical transient light curves

[![Documentation Status](https://readthedocs.org/projects/parsnip/badge/?version=latest)](https://parsnip.readthedocs.io/en/latest/?badge=latest)

## About

ParSNIP learns a generative model of transients from a large dataset
of transient light curves. This code has many applications including
classification of transients, cosmological distance estimation, and
identifying novel transients. A full description of the algorithms
in this code can be found in Boone 2021 (submitted to ApJ).

## Installation and Usage

Instructions on how to install and use ParSNIP can be found on the [ParSNIP
readthedocs page](https://parsnip.readthedocs.io/en/latest/).
