Python gaussian processes for multiwavelength variability inference
pgmuvi is based on GPyTorch and intended for us in infering the properties of astronomical sources with multiwavelength variability. It uses spectral-mixture kernels to learn an approximation of the PSD of the variability, which have been shown to be very effective for pattern discovery (see https://arxiv.org/pdf/1302.4245.pdf).
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.
We very much welcome contributions to pgmuvi! Please take a look at our contributing guide for more information on how to contribute! But don't forget to read our code of conduct before you get started.
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!)
You can find full documentation for pgmuvi at https://pgmuvi.readthedocs.io/. This includes a quickstart guide and a set of tutorials intended to get you up and running.