Umami

Umami: A Python toolkit for jet flavour tagging - Published in JOSS (2024)

https://github.com/umami-hep/umami

Science Score: 95.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 1 DOI reference(s) in JOSS metadata
  • Academic publication links
  • Committers with academic emails
    26 of 30 committers (86.7%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
    Published in Journal of Open Source Software

Scientific Fields

Earth and Environmental Sciences Physical Sciences - 83% confidence
Artificial Intelligence and Machine Learning Computer Science - 62% confidence
Last synced: 4 months ago · JSON representation

Repository

Mirror of the gitlab umami project

Basic Info
  • Host: GitHub
  • Owner: umami-hep
  • License: apache-2.0
  • Language: Python
  • Default Branch: master
  • Size: 91.7 MB
Statistics
  • Stars: 1
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 2 years ago · Last pushed about 1 year ago
Metadata Files
Readme Changelog Contributing License

README.md

pipeline status coverage report Code style: black

Umami

The Umami documentation is avaliable here:

Umami docs

Below is included a brief summary on how to get started fast.

Installation

You can find the detailed described in the umami docs.

Testing & Linter

To better collaborate on this project, we require some code practices such as: - linting (flake8, yamllint) - unit tests - integration tests

More details can be found here.

Preprocessing

For the training of umami the ntuples are used as specified in the section MC Samples.

The ntuples need to be preprocessed following the Preprocessing Instructions.

Training

If you want to train or evaluate DL1r or DIPS please follow the Training Instructions.

Tutorial for Umami

At the FTAG Workshop in 2022 in Amsterdam, we gave a tutorial how to work with Umami. You can find the slides together with a recording of the talk here. The corresponding step-by-step tutorial can be found in the FTAG docs webpage here.

Owner

  • Name: umami-hep
  • Login: umami-hep
  • Kind: organization

JOSS Publication

Umami: A Python toolkit for jet flavour tagging
Published
October 08, 2024
Volume 9, Issue 102, Page 5833
Authors
Jackson Barr ORCID
University College London, United Kingdom
Joschka Birk ORCID
University of Hamburg, Germany
Maxence Draguet ORCID
University of Oxford, United Kingdom
Stefano Franchellucci ORCID
Université de Genève, Switzerland
Alexander Froch ORCID
Université de Genève, Switzerland
Philipp Gadow ORCID
European Laboratory for Particle Physics CERN, Switzerland
Daniel Hay Guest ORCID
Humboldt University Berlin, Germany
Manuel Guth ORCID
Université de Genève, Switzerland
Nicole Michelle Hartman ORCID
Technical University of Munich, Germany
Michael Kagan ORCID
SLAC National Accelerator Laboratory, United States of America
Osama Karkout ORCID
Nikhef National Institute for Subatomic Physics and University of Amsterdam, Netherlands
Dmitrii Kobylianskii ORCID
Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Israel
Ivan Oleksiyuk ORCID
Université de Genève, Switzerland
Nikita Ivvan Pond ORCID
University College London, United Kingdom
Frederic Renner ORCID
Deutsches Elektronen-Synchrotron DESY, Germany
Sebastien Rettie ORCID
European Laboratory for Particle Physics CERN, Switzerland
Victor Hugo Ruelas Rivera ORCID
Humboldt University Berlin, Germany
Tomke Schröer ORCID
Université de Genève, Switzerland
Martino Tanasini ORCID
Stony Brook University, United States of America
Samuel Van Stroud ORCID
University College London, United Kingdom
Janik Von Ahnen ORCID
Deutsches Elektronen-Synchrotron DESY, Germany
Editor
Matthew Feickert ORCID
Tags
Dockerfile high energy physics jet physics flavour tagging machine learning

GitHub Events

Total
Last Year

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 821
  • Total Committers: 30
  • Avg Commits per committer: 27.367
  • Development Distribution Score (DDS): 0.616
Past Year
  • Commits: 4
  • Committers: 2
  • Avg Commits per committer: 2.0
  • Development Distribution Score (DDS): 0.5
Top Committers
Name Email Commits
Alexander Froch a****h@c****h 315
Manuel Guth m****h@c****h 282
Joschka Birk j****k@c****h 66
Paul Philipp Gadow p****w@c****h 33
Samuel Van Stroud s****d@c****h 18
Maxence Draguet m****t@l****k 16
Tomke Schroer t****r@c****h 15
Frederic Renner f****r@c****h 9
Victor Hugo Ruelas Rivera v****a@c****h 9
Ivan Oleksiyuk i****k@g****m 8
Jackson Barr j****r@c****h 8
Janik Von Ahnen j****n@c****h 7
Maxence Draguet m****t@h****m 5
Stefano Franchellucci s****i@c****h 5
Sebastien Rettie s****e@c****h 4
Osama Karkout o****t@c****h 4
Nikita Ivvan Pond n****d@c****h 2
Martino Tanasini m****i@c****h 2
Dmitrii Kobylianskii d****i@c****h 2
Jan Iven j****n@c****h 1
Johnny Raine j****e@c****h 1
Maggie Chen m****n@c****h 1
Nicole Hartman n****2@s****u 1
Wei Sheng Lai w****i@c****h 1
te54@uni-freiburg.de f****4@l****t 1
Jacob Edwin Crosby j****y@c****h 1
Dan Guest d****t@c****h 1
Daniela Mascione d****e@c****h 1
Guth Manuel g****a@c****l 1
Bingxuan Liu b****u@c****h 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 6
  • Total pull requests: 0
  • Average time to close issues: 4 months
  • Average time to close pull requests: N/A
  • Total issue authors: 2
  • Total pull request authors: 0
  • Average comments per issue: 3.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • jpata (4)
  • matthewfeickert (2)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels

Dependencies

docker/umami/Dockerfile docker
  • ${BASE_IMAGE} latest build
docker/umami-slim/Dockerfile docker
  • ${BASE_IMAGE} latest build
docker/umamibase/Dockerfile docker
  • ${BASE_IMAGE} latest build
docker/umamibase-plus/Dockerfile docker
  • ${BASE_IMAGE} latest build
requirements.txt pypi
  • atlas-ftag-tools ==0.1.4
  • h5py ==3.7.0
  • matplotlib ==3.5.1
  • mlxtend ==0.19.0
  • numpy ==1.21.0
  • pandas ==1.3.5
  • puma-hep ==0.2.6
  • pydash ==5.1.0
  • pyyaml ==6.0
  • ruamel.yaml ==0.17.21
  • seaborn ==0.11.2
  • shap ==0.40.0
  • tables ==3.7.0
  • tqdm ==4.62.3
requirements_additional.txt pypi
  • hep_ml ==0.7.0
  • jupyter ==1.0.0
  • jupyterlab ==3.2.9
  • onnxruntime ==1.10.0
  • python-gitlab ==3.1.1
  • scikit-learn ==1.0.2
  • sphinx_rtd_theme *
  • tf2onnx ==1.9.3
  • uproot ==4.2.0
  • xarray ==0.21.1
  • xhistogram ==0.3.1
requirements_develop.txt pypi
  • black ==23.3.0 development
  • coverage ==6.3.1 development
  • darglint ==1.8.1 development
  • flake8 ==4.0.1 development
  • isort ==5.10.1 development
  • pre-commit ==2.17.0 development
  • pydot ==1.4.2 development
  • pylint ==2.12.2 development
  • pytest ==7.0.1 development
  • pytest-cov ==3.0.0 development
  • pytest-randomly ==3.11.0 development
  • yamllint ==1.26.3 development
pyproject.toml pypi
setup.py pypi
.github/workflows/draft-pdf.yml actions
  • actions/checkout v3 composite
  • actions/upload-artifact v1 composite
  • openjournals/openjournals-draft-action master composite