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Fast and easy exoplanet transit light curve modelling.

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PyTransit
=========

[![Licence](http://img.shields.io/badge/license-GPLv2-blue.svg?style=flat)](http://www.gnu.org/licenses/gpl-2.0.html)
[![MNRAS](https://img.shields.io/badge/MNRAS-10.1093%2Fmnras%2Fstv894-blue.svg)](http://mnras.oxfordjournals.org/content/450/3/3233)
[![arXiv](http://img.shields.io/badge/arXiv-1504.07433-blue.svg?style=flat)](http://arxiv.org/abs/1504.07433)
[![ASCL](https://img.shields.io/badge/ASCL-A1505.024-blue.svg?style=flat)](http://ascl.net/1505.024)
[![DOI](https://zenodo.org/badge/5871/hpparvi/PyTransit.svg)](https://zenodo.org/badge/latestdoi/5871/hpparvi/PyTransit)

*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)](http://arxiv.org/abs/1504.07433), 
[Parviainen (2020a)](https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.1633P/abstract), and [Parviainen & Korth (2020b)](https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.3356P/abstract).

```Python
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])
```

![](https://raw.githubusercontent.com/hpparvi/PyTransit/master/doc/source/basic_example_1.svg)
![](https://raw.githubusercontent.com/hpparvi/PyTransit/master/doc/source/basic_example_2.svg)
![](https://raw.githubusercontent.com/hpparvi/PyTransit/master/doc/source/basic_example_3.svg)

## Examples and tutorials

### EMAC Workshop introduction video

[![EMAC Workshop PyTransit introduction video](https://raw.githubusercontent.com/hpparvi/PyTransit/master/video1.png)](https://youtu.be/bLnxkFNrMDQ?si=OTjr4kUGK1kkhkLC)

### RoadRunner transit model

RoadRunner [(Parviainen, 2020a)](https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.1633P/abstract) 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](https://github.com/hpparvi/PyTransit/blob/dev/doc/source/notebooks/models/roadrunner/roadrunner_model_example_1.ipynb) 
  shows how to use RoadRunner with the included limb darkening models.
- [RRModel example 2](https://github.com/hpparvi/PyTransit/blob/dev/doc/source/notebooks/models/roadrunner/roadrunner_model_example_2.ipynb)
  shows how to use RoadRunner with your own limb darkening model.
- [RRModel example 3](https://github.com/hpparvi/PyTransit/blob/dev/doc/source/notebooks/models/roadrunner/roadrunner_model_example_3.ipynb) 
  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](https://github.com/hpparvi/PyTransit/blob/dev/notebooks/roadrunner/tsmodel_example_1.ipynb)

## Documentation

Read the docs at [pytransit.readthedocs.io](https://pytransit.readthedocs.io).

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
    pip 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](mailto:hpparvi@gmail.com), Instituto de Astrofísica de Canarias

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