Skip to main content

The Multi-Mission Maximum Likelihood framework

Project description

CI Conda Build and Publish Test Against XSPEC codecov Documentation Status License DOI

GitHub pull requests GitHub issues

PyPi

PyPI version fury.io PyPI - Downloads PyPI - Python Version Install using pip

Conda

Conda Conda

drawing

The Multi-Mission Maximum Likelihood framework (3ML)

A framework for multi-wavelength/multi-messenger analysis for astronomy/astrophysics.


Astrophysical sources are observed by different instruments at different wavelengths with an unprecedented quality. Putting all these data together to form a coherent view, however, is a very difficult task. Indeed, each instrument and data type has its own ad-hoc software and handling procedure, which present steep learning curves and do not talk to each other.

The Multi-Mission Maximum Likelihood framework (3ML) provides a common high-level interface and model definition, which allows for an easy, coherent and intuitive modeling of sources using all the available data, no matter their origin. At the same time, thanks to its architecture based on plug-ins, 3ML uses under the hood the official software of each instrument, the only one certified and maintained by the collaboration which built the instrument itself. This guarantees that 3ML is always using the best possible methodology to deal with the data of each instrument.

drawing

Though Maximum Likelihood is in the name for historical reasons, 3ML is an interface to several Bayesian inference algorithms such as MCMC and nested sampling as well as likelihood optimization algorithms. Each approach to analysis can be seamlessly switched between allowing users to try different approaches quickly and without having to rewrite their model or data interfaces.

Like your XPSEC models? You can use them in 3ML as well as our growing selection of 1-,2- and 3-D models from our fast and customizable modeling language astromodels.

Installation

Installing with pip or conda is easy. However, you want to include models from XSPEC, the process can get tougher and we recommend the more detailed instructions:

pip install astromodels threeml
conda  install astromodels threeml -c threeml conda-forge 

Please refer to the Installation instructions for more details and trouble-shooting.

Press

Who is using 3ML?

Here is a highlight list of teams and their publications using 3ML.

A full list of publications using 3ML is here.

Citing

If you find this package useful in you analysis, or the code in your own custom data tools, please cite:

Vianello et al. (2015)

Acknowledgements

3ML makes use of the Spanish Virtual Observatory's Filter Profile service (http://svo2.cab.inta-csic.es/svo/theory/fps3/index.php?mode=browse&gname=NIRT).

If you use these profiles in your research, please consider citing them by using the following suggested sentence in your paper:

"This research has made use of the SVO Filter Profile Service (http://svo2.cab.inta-csic.es/theory/fps/) supported from the Spanish MINECO through grant AyA2014-55216"

and citing the following publications:

The SVO Filter Profile Service. Rodrigo, C., Solano, E., Bayo, A. http://ivoa.net/documents/Notes/SVOFPS/index.html The Filter Profile Service Access Protocol. Rodrigo, C., Solano, E. http://ivoa.net/documents/Notes/SVOFPSDAL/index.html

ThreeML is supported by National Science Foundation (NSF)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

threeML-2.4.2.dev1.tar.gz (51.4 MB view details)

Uploaded Source

Built Distribution

threeML-2.4.2.dev1-py3-none-any.whl (51.5 MB view details)

Uploaded Python 3

File details

Details for the file threeML-2.4.2.dev1.tar.gz.

File metadata

  • Download URL: threeML-2.4.2.dev1.tar.gz
  • Upload date:
  • Size: 51.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.17

File hashes

Hashes for threeML-2.4.2.dev1.tar.gz
Algorithm Hash digest
SHA256 bb921365ec287caaebcd948fed82af5ed23d73f868c6359c4546f8fb517e99a2
MD5 a69eafd6623f8f4f90a841ca82905244
BLAKE2b-256 fc1b227dc4b3a45f95741be37c20b21a7a704d5076c9233a1a3816bc4a83815e

See more details on using hashes here.

File details

Details for the file threeML-2.4.2.dev1-py3-none-any.whl.

File metadata

File hashes

Hashes for threeML-2.4.2.dev1-py3-none-any.whl
Algorithm Hash digest
SHA256 63d5e34ca406a32d896a1f9400c5e8594cd09b1f75869a731ca706f2498a1d9e
MD5 2a7fcec1e51f13ac3e708f159f72d1fa
BLAKE2b-256 ce41731b8c9d9476262bcd0f677c267029f5ac21f2bfb565ebc6c2cbfb1e5e97

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page