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-2.4.3.tar.gz (51.4 MB view details)

Uploaded Source

Built Distribution

threeML-2.4.3-py3-none-any.whl (51.5 MB view details)

Uploaded Python 3

File details

Details for the file threeml-2.4.3.tar.gz.

File metadata

  • Download URL: threeml-2.4.3.tar.gz
  • Upload date:
  • Size: 51.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for threeml-2.4.3.tar.gz
Algorithm Hash digest
SHA256 8020289db4fa4543c8287abc1f11967c2c8cabd6e3eaf4b767233e3b92869693
MD5 f3fb4f131c8542b7bf3062a3f4db5d17
BLAKE2b-256 ac99405fa0b5b8c4e889ba424baf423af0e1d8593c0bbadd1d196ee7443603be

See more details on using hashes here.

Provenance

The following attestation bundles were made for threeml-2.4.3.tar.gz:

Publisher: build_and_test.yml on threeML/threeML

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file threeML-2.4.3-py3-none-any.whl.

File metadata

  • Download URL: threeML-2.4.3-py3-none-any.whl
  • Upload date:
  • Size: 51.5 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for threeML-2.4.3-py3-none-any.whl
Algorithm Hash digest
SHA256 002aebb0afdca01f26f8df82175ad55dafd2138d0f1b83f541bdbc02090f5cc2
MD5 8a9c66e461e13ea7a4f1fcc778231fb4
BLAKE2b-256 b7fc4cb730e658c2eb0ad461e1bdde86b18afe9815c92407378aa021dc7001ea

See more details on using hashes here.

Provenance

The following attestation bundles were made for threeML-2.4.3-py3-none-any.whl:

Publisher: build_and_test.yml on threeML/threeML

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

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