PyTransit

Fast and easy-to-use 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 single-passband transit light curves or more complex science-cases, 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 population-based MCMC samplers and optimization methods, such as emcee and DE-MCMC.

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 LDTk-based 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.

• TSModel Example 1

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 and pytransit.QuadraticModel.
  • See the TTV modelling example notebook for an example of how to evaluate the models for a TTV analysis.

• 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.

• 16.9.2020, Version 2.3

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

• 13.9.2020, Version 2.2

  • Version 2.2 brings a significant speedup to the evaluation speed of all models. The normalised planet-star distances are now calculated using a Taylor-series expansion of the planet's (x,y) sky-plane position. The method gives a 2-6 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 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}
}

Author

• Hannu Parviainen, Instituto de Astrofísica de Canarias