Maelstrom ========= .. image:: https://github.com/danhey/maelstrom/workflows/maelstrom-tests/badge.svg .. image:: https://github.com/danhey/maelstrom/workflows/Docs/badge.svg :target: https://danhey.github.io/maelstrom/ .. image:: https://img.shields.io/badge/powered_by-PyMC3-EB5368.svg?style=flat :target: https://docs.pymc.io .. image:: https://img.shields.io/badge/powered_by-exoplanet-EB5368.svg?style=flat :target: https://github.com/dfm/exoplanet .. image:: https://codecov.io/gh/danhey/maelstrom/branch/master/graph/badge.svg :target: https://codecov.io/gh/danhey/maelstrom *maelstrom* is a set of custom PyMC3 Models and solvers for modelling binary orbits through the `phase modulation technique `_. Unlike previous codes, *maelstrom* fits each individual datapoint in the time series by forward modelling the time delay onto the light curve. This approach fully captures variations in a light curve caused by an orbital companion. To install the current version:: git clone https://github.com/danhey/maelstrom.git cd maelstrom pip install -e . To get started:: from maelstrom import Maelstrom ms = Maelstrom(time, flux) ms.optimize() .. toctree:: :maxdepth: 2 :caption: User Guide user/install user/citation .. toctree:: :maxdepth: 2 :caption: Tutorials notebooks/Getting started.ipynb notebooks/Estimating frequencies.ipynb notebooks/Custom priors.ipynb notebooks/Recovering weak signals.ipynb notebooks/FAQ.ipynb .. toctree:: :maxdepth: 1 :caption: Case studies from paper case_studies/9651065.ipynb case_studies/6780873.ipynb case_studies/10080943.ipynb .. toctree:: :maxdepth: 2 :caption: API api/maelstrom api/orbit api/periodogram api/utils api/estimator api/eddy License & attribution --------------------- Copyright 2019 Daniel Hey, Daniel Foreman-Mackey, and Simon Murphy. The source code is made available under the terms of the MIT license. Changelog --------- .. include:: ../CHANGES.rst