mpdaf

.. image:: https://github.com/musevlt/mpdaf/workflows/Run%20unit%20tests/badge.svg :target: https://github.com/musevlt/mpdaf

.. image:: https://codecov.io/gh/musevlt/mpdaf/branch/master/graph/badge.svg :target: https://codecov.io/gh/musevlt/mpdaf

MPDAF, the MUSE Python Data Analysis Framework, is an open-source (BSD licensed) Python package, developed and maintained by CRAL <https://cral.univ-lyon1.fr/>_ and partially funded by the ERC advanced grant 339659-MUSICOS (see Authors and Credits <http://mpdaf.readthedocs.io/en/stable/credits.html>_ for more details). It has been developed and used in the MUSE Consortium <http://muse-vlt.eu/science/>_ for several years, and is available freely for the community.

It provides tools to work with MUSE-specific data (raw data, pixel tables, etc.), and with more general data like spectra, images and data cubes. Although its main use is to work with MUSE data, it is also possible to use it with other data, for example HST images.

MPDAF also provides MUSELET, a SExtractor-based tool to detect emission lines in a datacube, and a format to gather all the information on a source in one FITS file.

Bug reports, comments, and help with development are very welcome.

MPDAF 3.0 requires Python 3.5 or later. It is the first version that supports only Python 3. Older versions <https://pypi.org/project/mpdaf/#history>_ can be installed for users that still need Python 2.

Links

• Documentation <http://mpdaf.readthedocs.io/en/stable/>_.
• Source, issues and pull requests on Github <https://github.com/musevlt/mpdaf/>_ and on a Gitlab <https://git-cral.univ-lyon1.fr/MUSE/mpdaf>_ instance.
• Releases on PyPI <https://pypi.org/project/mpdaf/>_.
• Mailing list <[e-mail]>_ to get help or discuss issues.

Reporting Issues

If you have found a bug in MPDAF please create an issue on Github <https://github.com/musevlt/mpdaf/issues/new>_.

Citing

MPDAF can be cited with the ASCL <http://ascl.net/1611.003>_ reference (ADS <http://adsabs.harvard.edu/abs/2016ascl.soft11003B>, BibTeX <http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2016ascl.soft11003B&data_type=BIBTEX&db_key=AST&nocookieset=1>), and was also presented at ADASS XXVI, for which the proceedings is on the arXiv <https://arxiv.org/abs/1710.03554>_ (ADS <http://adsabs.harvard.edu/abs/2017arXiv171003554P>, BibTeX <http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2017arXiv171003554P&data_type=BIBTEX&db_key=PRE&nocookieset=1>).