PyTransit: fast and versatile exoplanet transit light curve modelling in Python. PyTransit provides a set of optimised transit models with a unified API that makes modelling complex sets of heterogeneous light curve (nearly) as easy as modelling individual transit light curves. The models are optimised with Numba which allows for model evaluation speeds paralleling Fortran and C-implementations but with hassle-free platform-independent multithreading.
The package has been under continuous development since 2009, and is described in Parviainen (2015), Parviainen (2020a), and Parviainen & Korth (2020b).
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])
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 LDTk-based limb darkening model LDTkM with RoadRunner.
Transmission spectroscopy transit model (TSModel) is a special version of the RoadRunner model dedicated to modelling transmission spectrum light curves.
Read the docs at pytransit.readthedocs.io.
The easiest way to install PyTransit is by using pip
pip install pytransit
Clone the repository from github and do the normal python package installation
git clone https://github.com/hpparvi/PyTransit.git
cd PyTransit
pip install .
If you use PyTransit in your reserach, please cite
Parviainen, H. MNRAS 450, 3233–3238 (2015) (DOI:10.1093/mnras/stv894).
or use this ready-made BibTeX entry
@article{Parviainen2015,
author = {Parviainen, Hannu},
doi = {10.1093/mnras/stv894},
journal = {MNRAS},
number = {April},
pages = {3233--3238},
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}
}
- Hannu Parviainen, Instituto de Astrofísica de Canarias