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UncertainSCI

UncertainSCI: A Python Package for Noninvasive Parametric Uncertainty Quantification of Simulation Pipelines - Published in JOSS (2023)

https://github.com/sciinstitute/uncertainsci

Science Score: 100.0%

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

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 4 DOI reference(s) in README and JOSS metadata
  • ✓
    Academic publication links
    Links to: joss.theoj.org, zenodo.org
  • ✓
    Committers with academic emails
    4 of 10 committers (40.0%) from academic institutions
  • ✓
    Institutional organization owner
    Organization sciinstitute has institutional domain (www.sci.utah.edu)
  • ✓
    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords from Contributors

mesh signal-processing

Scientific Fields

Mathematics Computer Science - 40% confidence
Last synced: 4 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: SCIInstitute
  • License: mit
  • Language: Python
  • Default Branch: master
  • Size: 8.6 MB
Statistics
  • Stars: 10
  • Watchers: 3
  • Forks: 11
  • Open Issues: 21
  • Releases: 14
Created almost 6 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

UncertainSCI

A Python-based toolkit that harnesses modern techniques to estimate model and parametric uncertainty, with a particular emphasis on needs for biomedical simulations and applications. This toolkit enables non-intrusive integration of these techniques with well-established biomedical simulation software.

UncertainSCI

All Builds Coverage Status status DOI

Overview

UncertainSCI is an open-source tool designed to make modern uncertainty quantification (UQ) techniques more accessible in biomedical simulation applications. UncertainSCI uses noninvasive UQ techniques, specifically polynomial Chaos estimation (PCE), with a similarly noninvasive interface to external modeling software that can be called in diverse ways. PCE and UncertainSCI allows users to propagate the effect of input uncertainty on model results, providing essential context for model stability and confidence needed in many modeling fields. Users can run UncertainSCI by setting input distributions for a model parameters, setting up PCE, sampling the parameter space, running the samples sets within the target model, and compiling output statistics based on PCE. This process is breifly describe in the getting started guide, and more fully explained in the API documentation, and supplied demos and tutorials.

Documentation

https://uncertainsci.readthedocs.io

Getting Started Guide

https://uncertainsci.readthedocs.io/en/latest/user_docs/getting_started.html

License

Distributed under the MIT license. See LICENSE for more information.

Publications

  • Akil Narayan, Zexin Liu, Jake Bergquist, Chantel Charlebois, Sumientra Rampersad, Lindsay Rupp, Dana Brooks, Dan White, Jess Tate, and Rob S MacLeod. UncertainSCI: Uncertainty quantification for com- putational models in biomedicine and bioengineering. Available at SSRN 4049696, 2022.
  • Kyle M. Burk, Akil Narayan, and Joseph A. Orr. Efficient sampling for polynomial chaos-based uncertainty quantification and sensitivity analysis using weighted approximate fekete points. International Journal for Numerical Methods in Biomedical Engineering, 36(11):e3395, 2020.
  • Jake Bergquist, Brian Zenger, Lindsay Rupp, Akil Narayan, Jess Tate, and Rob MacLeod. Uncertainty quantification in simulations of myocardial ischemia. In Computing in Cardiology, volume 48, September 2021.
  • Lindsay C Rupp, Jake A Bergquist, Brian Zenger, Karli Gillette, Akil Narayan, Jess Tate, Gernot Plank, and Rob S. MacLeod. The role of myocardial fiber direction in epicardial activation patterns via uncertainty quantification. In Computing in Cardiology, volume 48, September 2021.
  • Lindsay C Rupp, Zexin Liu, Jake A Bergquist, Sumientra Rampersad, Dan White, Jess D Tate, Dana H. Brooks, Akil Narayan, and Rob S. MacLeod. Using uncertainSCI to quantify uncertainty in cardiac simu- lations. In Computing in Cardiology, volume 47, September 2020.
  • Jess Tate, Sumientra Rampersad, Chantel Charlebois, Zexin Liu, Jake Bergquist, Dan White, Lindsay Rupp, Dana Brooks, Akil Narayan, and Rob MacLeod. Uncertainty quantification in brain stimulation using uncertainSCI. Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, 14(6):1659–1660, January 2021.
  • Jess D. Tate, Wilson W. Good, Nejib Zemzemi, Machteld Boonstra, Peter van Dam, Dana H. Brooks, Akil Narayan, and Rob S. MacLeod. Uncertainty quantification of the effects of segmentation variability in ECGI. In Functional Imaging and Modeling of the Heart, pages 515–522. Springer-Cham, Palo Alto, USA, 2021.
  • Jess D Tate, Nejib Zemzemi, Shireen Elhabian, Beáta Ondrusǔvá, Machteld Boonstra, Peter van Dam, Akil Narayan, Dana H Brooks, and Rob S MacLeod. Segmentation uncertainty quantification in cardiac propagation models. In 2022 Computing in Cardiology (CinC), volume 498, pages 1–4, 2022.

Acknowledgements

This project was supported by grants from the National Institute of Biomedical Imaging and Bioengineering (U24EB029012) from the National Institutes of Health.

Owner

  • Name: The Scientific Computing and Imaging Institute
  • Login: SCIInstitute
  • Kind: organization
  • Email: cibc-contact@sci.utah.edu
  • Location: Salt Lake City, Utah

JOSS Publication

UncertainSCI: A Python Package for Noninvasive Parametric Uncertainty Quantification of Simulation Pipelines
Published
October 27, 2023
DOI
Volume 8, Issue 90, Page 4249
Authors
Jess Tate ORCID
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
Zexin Liu ORCID
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA, Mathematics Department, University of Utah, Salt Lake City, UT, USA
Jake A. Bergquist ORCID
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA, Biomedical Engineering Department , University of Utah, Salt Lake City, UT, USA, Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
Sumientra Rampersad ORCID
Physics Department, University of Massachusetts, Boston, MA, USA, Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA
Dan White
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
Chantel Charlebois ORCID
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA, Biomedical Engineering Department , University of Utah, Salt Lake City, UT, USA
Lindsay Rupp ORCID
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA, Biomedical Engineering Department , University of Utah, Salt Lake City, UT, USA, Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
Dana H. Brooks ORCID
Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA
Rob S. MacLeod ORCID
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA, Biomedical Engineering Department , University of Utah, Salt Lake City, UT, USA, Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
Akil Narayan ORCID
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA, Mathematics Department, University of Utah, Salt Lake City, UT, USA
Editor
Kelly Rowland ORCID
Tags
uncertainty quantification computer modeling polynomial chaos bioelectricity
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Citation (CITATION.cff) 

cff-version: 1.1.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Tate
    given-names: Jess
    orcid: https://orcid.org/0000-0002-2934-1453 
  - family-names: Liu
    given-names: Zexin
    orcid: https://orcid.org/0000-0003-3409-5709
  - family-names: Bergquist
    given-names: Jake A
  - name: Jake A Bergquist
    orcid: https://orcid.org/0000-0002-4586-6911
  - family-names: Rampersad
    given-names: Sumientra
    orcid: https://orcid.org/0000-0001-9860-4459
  - family-names: White
    given-names: Dan
  - family-names: Charlebois
    given-names: Chantel
    orcid: https://orcid.org/0000-0002-4139-3539
  - family-names: Rupp
    given-names: Lindsay
    orcid: https://orcid.org/0000-0002-2688-7688
  - family-names: Brooks
    given-names: Dana H
    orcid: https://orcid.org/0000-0003-3231-6715
  - family-names: MacLeod
    given-names: Rob S
    orcid: https://orcid.org/0000-0002-0000-0356
  - family-names: Narayan
    given-names: Akil
    orcid: https://orcid.org/0000-0002-5914-4207
title: UncertainSCI: A Python Package for Noninvasive Parametric Uncertainty Quantification of Simulation Pipelines
version: v1.0.1
date-released: 2023-10-11

GitHub Events

Total
  • Watch event: 2
  • Fork event: 1
Last Year
  • Watch event: 2
  • Fork event: 1

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 357
  • Total Committers: 10
  • Avg Commits per committer: 35.7
  • Development Distribution Score (DDS): 0.683
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
jessdtate j****s@s****u 113
Akil Narayan a****n@g****m 113
Zexin Liu l****8@g****m 56
Daniel White d****e@s****u 48
Nidhi Patel n****1@g****m 13
Akil Narayan a****n@g****m 9
jab0707 j****7@w****u 2
dependabot[bot] 4****] 1
Kelly L. Rowland k****d@l****v 1
Jake Bergquist 3****7 1
Committer Domains (Top 20 + Academic)
sci.utah.edu: 2 lbl.gov: 1 westminstercollege.edu: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 47
  • Total pull requests: 53
  • Average time to close issues: 2 months
  • Average time to close pull requests: about 1 month
  • Total issue authors: 6
  • Total pull request authors: 7
  • Average comments per issue: 0.94
  • Average comments per pull request: 1.58
  • Merged pull requests: 48
  • Bot issues: 0
  • Bot pull requests: 2
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
View more stats
Top Authors
Issue Authors
  • akilnarayan (24)
  • dcwhite (15)
  • jab0707 (3)
  • jessdtate (3)
  • Mazze (1)
  • jcollfont (1)
Pull Request Authors
  • akilnarayan (23)
  • jessdtate (10)
  • ZEXINLIU (6)
  • dcwhite (6)
  • jab0707 (4)
  • dependabot[bot] (3)
  • nids2001 (2)
Top Labels
Issue Labels
enhancement (4) documentation (4) bug (2) good first issue (1)
Pull Request Labels
documentation (2) dependencies (2) enhancement (1) python (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 29 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 11
  • Total maintainers: 2
pypi.org: uncertainsci

A Non-invasive Uncertainty Quantification tool

  • Versions: 11
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 29 Last month
Rankings
Dependent packages count: 10.1%
Average: 16.5%
Downloads: 17.9%
Dependent repos count: 21.5%
Maintainers (2)
jessdtate akil
Last synced: 4 months ago

Dependencies

docs/requirements.txt pypi
  • docutils *
  • pygments *
  • recommonmark *
  • sphinx *
  • sphinx-issues *
  • sphinx-markdown-tables *
  • sphinx-notfound-page *
  • sphinx_rtd_theme *
  • sphinxcontrib-bibtex *
requirements.txt pypi
  • matplotlib ==3.1.3
  • matplotlib >=3.1.3
  • numpy ==1.15.2
  • numpy >=1.21.0
  • scipy ==1.4.1
  • scipy >=1.4.1
setup.py pypi
  • matplotlib ==3.1.3
  • matplotlib >=3.1.3
  • numpy ==1.15.2
  • numpy >=1.21.0
  • scipy ==1.4.1
  • scipy >=1.4.1
.github/workflows/draft-pdf.yml actions
  • actions/checkout v2 composite
  • actions/upload-artifact v1 composite
  • openjournals/openjournals-draft-action master composite
.github/workflows/pypublish.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • pypa/gh-action-pypi-publish master composite
.github/workflows/pythonapp.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite