PyTransit
Fast and easytouse tools for exoplanet transit light curve modelling with Python. PyTransit offers optimised CPU and GPU implementations of popular exoplanet transit models with a unified interface, and thrives to be the fastest and the most versatile tool for transit modelling in Python.
PyTransit makes transit model evaluation trivial whether modelling straightforward singlepassband transit light curves or more complex sciencecases, such as transmission spectroscopy. Further, the model can be evaluated for a large set of parameter sets simultaneously in parallel to optimize the evaluation speed with populationbased MCMC samplers and optimization methods, such as emcee and DEMCMC.
from pytransit import RoadRunnerModel
tm = RoadRunnerModel('quadratic')
tm.set_data(times)
tm.evaluate(k=0.1, ldc=[0.2, 0.1], t0=0.0, p=1.0, a=3.0, i=0.5*pi)
tm.evaluate(k=[0.10, 0.12], ldc=[[0.2, 0.1, 0.5, 0.1]], t0=0.0, p=1.0, a=3.0, i=0.5*pi)
tm.evaluate(k=[[0.10, 0.12], [0.11, 0.13]], ldc=[[0.2, 0.1, 0.5, 0.1],[0.4, 0.2, 0.75, 0.1]],
t0=[0.0, 0.01], p=[1, 1], a=[3.0, 2.9], i=[.5*pi, .5*pi])
The package has been used in research since 2010, and is described in Parviainen (2015), Parviainen (2020a), and Parviainen (2020b).
Examples and tutorials
RoadRunner transit model
RoadRunner (Parviainen, 2020a) is a fast exoplanet transit model that can use any radially symmetric function to model stellar limb darkening while still being faster to evaluate than the analytical transit model for quadratic limb darkening.
 RRModel example 1 shows how to use RoadRunner with the included limb darkening models.
 RRModel example 2 shows how to use RoadRunner with your own limb darkening model.
 RRModel example 3 shows how to use an LDTkbased limb darkening model LDTkM with RoadRunner.
Transmission spectroscopy transit model
Transmission spectroscopy transit model (TSModel) is a special version of the RoadRunner model dedicated to modelling transmission spectrum light curves.
Documentation
Read the docs at pytransit.readthedocs.io.
News

21.10.2020, version 2.5
 Version 2.5 makes modelling of TTVs trivial with
pytransit.RoadRunnerModel
andpytransit.QuadraticModel
.  See the TTV modelling example notebook for an example of how to evaluate the models for a TTV analysis.
 Version 2.5 makes modelling of TTVs trivial with

14.10.2020, version 2.4
 Version 2.4 adds
pytransit.EclipseModel
to model secondary eclipses with as little hassle as possible.  See the secondary eclipse model example notebook for an example of how to use it.
 Version 2.4 adds

16.9.2020, Version 2.3
 Version 2.3 adds
OblateStarModel
to model transits over gravitydarkened rapidly rotating star as presented by Barnes (2009).  See the oblate star model example notebook for an example of usage.
 Version 2.3 adds

13.9.2020, Version 2.2
 Version 2.2 brings a significant speedup to the evaluation speed of all models. The normalised planetstar distances are now calculated using a Taylorseries expansion of the planet's (x,y) skyplane position. The method gives a 26 x speedup to the transit model evaluation and is detailed in Parviainen & Korth (2020, submitted to MNRAS)

7.7.2020, Version 2.1
 Version 2.1 introduces the RoadRunner transit model (Parviainen, submitted to MNRAS, 2020), a fast exoplanet transit model that can use any radially symmetric function to model stellar limb darkening while still being faster to evaluate than the analytical transit model for quadratic limb darkening.
 See the basic example notebook, the custom limb darkening notebook, and the LDTk limb darkening model example notebook.
Installation
PyPI
The easiest way to install PyTransit is by using pip
pip install pytransit
GitHub
Clone the repository from github and do the normal python package installation
git clone https://github.com/hpparvi/PyTransit.git
cd PyTransit
python setup.py install
Citing
If you use PyTransit in your reserach, please cite
Parviainen, H. MNRAS 450, 3233–3238 (2015) (DOI:10.1093/mnras/stv894).
or use this readymade BibTeX entry
@article{Parviainen2015,
author = {Parviainen, Hannu},
doi = {10.1093/mnras/stv894},
journal = {MNRAS},
number = {April},
pages = {32333238},
title = {{PYTRANSIT: fast and easy exoplanet transit modelling in PYTHON}},
url = {http://mnras.oxfordjournals.org/cgi/doi/10.1093/mnras/stv894},
volume = {450},
year = {2015}
}
Author
 Hannu Parviainen, Instituto de Astrofísica de Canarias