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Welcome to `pgmuvi`'s documentation!
====================================
`pgmuvi` is a package for interpreting astronomical timeseries data (although there's no reason you can't use it for other kinds of data!) using Gaussian processes.
It is built on top of the `gpytorch `_ package, and is designed to be easy to use and flexible.
It is currently under active development, and we welcome contributions!
User Guide
----------
.. toctree::
:maxdepth: 2
notebooks/pgmuvi_tutorial
notebooks/pgmuvi_tutorial_mcmc
Installation and Quickstart
---------------------------
`pgmuvi` can be installed easily with pip::
$ pip install pgmuvi
You can also clone the latest version of `pgmuvi` from Github, for all the latest bugs but increased risk of features::
$ git clone git://github.com/ICSM/pgmuvi.git
and then you can install it::
$ cd pgmuvi
$ pip install .
If you want to contribute to `pgmuvi` and develop new features, you might want an *editable* install::
$ pip install -e .
this way you can test how things change as you go along.
Citing `pgmuvi`
----------------
`pgmuvi` is currently under review in the Journal of Open Source Software.
If you use `pgmuvi` in your research, please cite the paper (details will be given here when the paper is accepted!)
API reference
-------------
.. toctree::
:maxdepth: 2
api
Contributing
------------
We very much welcome contributions to `pgmuvi`! Please take a look at our `github repository `_ for more information on how to contribute!
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`