nmma

A pythonic library for probing nuclear physics and cosmology with multimessenger analysis

https://github.com/nuclear-multimessenger-astronomy/nmma

Science Score: 59.0%

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 7 DOI reference(s) in README
  • Academic publication links
    Links to: arxiv.org, nature.com
  • Committers with academic emails
    8 of 18 committers (44.4%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (13.0%) to scientific vocabulary
Last synced: 6 months ago · JSON representation

Repository

A pythonic library for probing nuclear physics and cosmology with multimessenger analysis

Basic Info
Statistics
  • Stars: 38
  • Watchers: 5
  • Forks: 61
  • Open Issues: 43
  • Releases: 7
Created about 4 years ago · Last pushed 7 months ago
Metadata Files
Readme License Citation

README.md

NMMA

NMMA

a pythonic library for probing nuclear physics and cosmology with multimessenger analysis



GitHub Repo stars GitHub forks Conda Downloads PyPI - Downloads Coverage Status CI PyPI version Python version

Citations to the NMMA code: Citation record

Read our official documentation: NMMA Documentation

Check out our contribution guide: For contributors

A tutorial on how to produce simulations of lightcurves is given here tutorial-lightcurve_simulation.ipynb

Citing NMMA

When utilizing this code for a publication, kindly make a reference to the package by its name, NMMA, and a citation to the companion paper An updated nuclear-physics and multi-messenger astrophysics framework for binary neutron star mergers. The BibTeX entry for the paper is: bibtex @article{Pang:2022rzc, title={An updated nuclear-physics and multi-messenger astrophysics framework for binary neutron star mergers}, author={Peter T. H. Pang and Tim Dietrich and Michael W. Coughlin and Mattia Bulla and Ingo Tews and Mouza Almualla and Tyler Barna and Weizmann Kiendrebeogo and Nina Kunert and Gargi Mansingh and Brandon Reed and Niharika Sravan and Andrew Toivonen and Sarah Antier and Robert O. VandenBerg and Jack Heinzel and Vsevolod Nedora and Pouyan Salehi and Ritwik Sharma and Rahul Somasundaram and Chris Van Den Broeck}, journal={Nature Communications}, year={2023}, month={Dec}, day={20}, volume={14}, number={1}, pages={8352}, issn={2041-1723}, doi={10.1038/s41467-023-43932-6}, url={https://doi.org/10.1038/s41467-023-43932-6} } If you are using the systematics error, please also cite the paper Data-driven approach for modeling the temporal and spectral evolution of kilonova systematic uncertainties. The BibTeX entry for the paper is: bibtex @article{Jhawar:2024ezm, author = "Jhawar, Sahil and Wouters, Thibeau and Pang, Peter T. H. and Bulla, Mattia and Coughlin, Michael W. and Dietrich, Tim", title = "{Data-driven approach for modeling the temporal and spectral evolution of kilonova systematic uncertainties}", eprint = "2410.21978", archivePrefix = "arXiv", primaryClass = "astro-ph.HE", doi = "10.1103/PhysRevD.111.043046", journal = "Phys. Rev. D", volume = "111", number = "4", pages = "043046", year = "2025" }

If you are using likelihood free inference, please also cite the paper Rapid parameter estimation for kilonovae using likelihood-free inference. The BibTeX entry for the paper is: bibtex @article{Desai:2024hlp, author = "Desai, Malina and Chatterjee, Deep and Jhawar, Sahil and Harris, Philip and Katsavounidis, Erik and Coughlin, Michael", title = "{Kilonova Light Curve Parameter Estimation Using Likelihood-Free Inference}", eprint = "2408.06947", archivePrefix = "arXiv", primaryClass = "astro-ph.IM", doi = "10.1093/mnras/staf1045", month = "8", year = "2024" }

Acknowledgments

If you benefited from participating in our community, we ask that you please acknowledge the Nuclear Multi-Messenger Astronomy collaboration, and particular individuals who helped you, in any publications. Please use the following text for this acknowledgment:

We acknowledge the Nuclear Multi-Messenger Astronomy collective as an open community of multi-domain experts and collaborators. This community and <names of individuals>, in particular, were important for the development of this project.

Funding

We gratefully acknowledge previous and current support from the U.S. National Science Foundation (NSF) Harnessing the Data Revolution (HDR) Institute for Accelerating AI Algorithms for Data Driven Discovery (A3D3) under Cooperative Agreement No. PHY-2117997 and the European Research Council (ERC) under the European Union's Starting Grant (Grant No. 101076369).

A3D3 NSF ERC

Owner

  • Name: Nuclear Multimessenger Astronomy
  • Login: nuclear-multimessenger-astronomy
  • Kind: organization
  • Email: nuclear_multimessenger_astronomy@googlegroups.com

GitHub Events

Total
  • Create event: 5
  • Release event: 1
  • Issues event: 12
  • Watch event: 6
  • Delete event: 3
  • Member event: 1
  • Issue comment event: 32
  • Push event: 51
  • Pull request event: 20
  • Pull request review comment event: 3
  • Pull request review event: 13
  • Fork event: 4
Last Year
  • Create event: 5
  • Release event: 1
  • Issues event: 12
  • Watch event: 6
  • Delete event: 3
  • Member event: 1
  • Issue comment event: 32
  • Push event: 51
  • Pull request event: 20
  • Pull request review comment event: 3
  • Pull request review event: 13
  • Fork event: 4

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 223
  • Total Committers: 18
  • Avg Commits per committer: 12.389
  • Development Distribution Score (DDS): 0.538
Past Year
  • Commits: 129
  • Committers: 17
  • Avg Commits per committer: 7.588
  • Development Distribution Score (DDS): 0.628
Top Committers
Name Email Commits
Michael Coughlin m****n@g****m 103
Ramodgwendé Weizmann Kiendrébéogo k****7@g****m 39
Peter T. H. Pang t****g@g****m 19
ninakuni 4****i@u****m 11
ritwiksharma049 8****9@u****m 9
Ritwik Sharma s****4@g****m 7
FabioRagosta 5****a@u****m 5
Nina Kunert n****t@e****e 5
Peter T. H. Pang t****g@g****m 4
Michael Coughlin m****n@l****g 4
Tyler Barna b****4@u****u 3
Niharika Sravan n****a@l****u 3
Shreya Anand s****d@l****g 3
Daniel Finstad d****d@b****u 3
Niharika Sravan n****a@l****u 2
Andrew Michael Toivonen a****n@l****u 1
Andrew Toivonen 4****3@u****m 1
tahumada t****4@g****m 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 106
  • Total pull requests: 264
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 8 days
  • Total issue authors: 17
  • Total pull request authors: 26
  • Average comments per issue: 2.42
  • Average comments per pull request: 1.63
  • Merged pull requests: 211
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 8
  • Pull requests: 17
  • Average time to close issues: about 15 hours
  • Average time to close pull requests: about 10 hours
  • Issue authors: 4
  • Pull request authors: 4
  • Average comments per issue: 1.75
  • Average comments per pull request: 1.18
  • Merged pull requests: 11
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • tylerbarna (32)
  • bfhealy (20)
  • sahiljhawar (14)
  • mcoughlin (12)
  • ritwiksharma049 (5)
  • bking-astro (3)
  • tsunhopang (3)
  • haukekoehn (2)
  • Keerthiiz (2)
  • weizmannk (2)
  • ThibeauWouters (1)
  • s-paramvir (1)
  • phelipedarc (1)
  • prashantstar123 (1)
  • atoivonen13 (1)
Pull Request Authors
  • sahiljhawar (82)
  • bfhealy (81)
  • weizmannk (28)
  • tylerbarna (20)
  • haukekoehn (15)
  • ritwiksharma049 (11)
  • bking-astro (10)
  • tsunhopang (10)
  • atoivonen13 (9)
  • ninakuni (7)
  • mcoughlin (7)
  • malinadesai (4)
  • Theodlz (4)
  • shreyasahasram08 (4)
  • FabioRagosta (4)
Top Labels
Issue Labels
enhancement (32) bug (27) documentation (12) question (8) good first issue (3) wontfix (2) repo (2) minor (1)
Pull Request Labels
enhancement (14) documentation (6) help wanted (4) bug (2) minor (2) repo (1)

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 43 last-month
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 1
    (may contain duplicates)
  • Total versions: 26
  • Total maintainers: 4
pypi.org: nmma

A nuclear physics multi-messenger Bayesian inference library

  • Versions: 25
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 43 Last month
Rankings
Forks count: 6.3%
Dependent packages count: 7.4%
Stargazers count: 13.1%
Average: 17.2%
Dependent repos count: 22.2%
Downloads: 36.8%
Last synced: 7 months ago
conda-forge.org: nmma
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Forks count: 33.9%
Dependent repos count: 34.0%
Average: 42.3%
Stargazers count: 50.2%
Dependent packages count: 51.2%
Last synced: 7 months ago

Dependencies

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.github/workflows/publish-to-pypi.yml actions
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  • pypa/gh-action-pypi-publish master composite
.github/workflows/continous_integration.yml actions
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.github/workflows/delete-branch-cache.yml actions
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api/Dockerfile docker
  • condaforge/mambaforge 4.9.2-5 build
  • gcr.io/distroless/base-debian10 latest build
docker-compose.yml docker
doc_requirements.txt pypi
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  • sphinx ==4.4.0
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api/environment.yml pypi