README.md

# Exoplanet ML

Machine learning models and utilities for exoplanet science.

## Code Author

Chris Shallue: [@cshallue](https://github.com/cshallue)

## Quick Start

Jump to the [AstroNet walkthrough](astronet/README.md#walkthrough).

## Citation

If you find this code useful, please cite our paper:

Shallue, C. J., & Vanderburg, A. (2018). Identifying Exoplanets with Deep
Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet
around Kepler-90. *The Astronomical Journal*, 155(2), 94.

Full text available at [*The Astronomical Journal*](http://iopscience.iop.org/article/10.3847/1538-3881/aa9e09/meta).

## Directories

[astronet/](astronet/)

* A neural network for identifying exoplanets in light curves. Contains code for:
  * Downloading and preprocessing Kepler light curves.
  * Building different types of neural network classification models.
  * Training and evaluating a new model.
  * Using a trained model to generate new predictions.

[astrowavenet/](astrowavenet/)

* A generative model for light curves.

[light_curve/](light_curve)

* Utilities for operating on light curves. These include:
  * Reading Kepler data from `.fits` files.
  * Applying a median filter to smooth and normalize a light curve.
  * Phase folding, splitting, removing periodic events, etc.
* [light_curve/fast_ops/](light_curve/fast_ops) contains optimized C++ light
curve operations.

[tf_util/](tf_util)

* Shared TensorFlow utilities.

[third_party/](third_party/)

* Utilities derived from third party code.


# Setup

## Required Packages

* **TensorFlow** ([instructions](https://www.tensorflow.org/install/))
* **Pandas** ([instructions](http://pandas.pydata.org/pandas-docs/stable/install.html))
* **NumPy** ([instructions](https://docs.scipy.org/doc/numpy/user/install.html))
* **SciPy** ([instructions](https://scipy.org/install.html))
* **AstroPy** ([instructions](http://www.astropy.org/))
* **PyDl** ([instructions](https://pypi.python.org/pypi/pydl))
* **Bazel** ([instructions](https://docs.bazel.build/versions/master/install.html))
* **Abseil Python Common Libraries** ([instructions](https://github.com/abseil/abseil-py))

## Run Unit Tests

Verify that all dependencies are satisfied by running the unit tests:

```bash
bazel test astronet/... astrowavenet/... light_curve/... tf_util/... third_party/...
```