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chaospy

Chaospy - Toolbox for performing uncertainty quantification.

https://github.com/jonathf/chaospy

Science Score: 54.0%

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
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    4 of 26 committers (15.4%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (15.1%) to scientific vocabulary

Keywords

gaussian-quadrature probability-distribution sensitivity-analysis sparse-grids statistics uncertainty-quantification variance-reduction

Keywords from Contributors

mesh stellarator interpretability evolutionary-algorithms metaheuristic sequences projection interactive optim hacking
Last synced: 6 months ago · JSON representation ·

Repository

Chaospy - Toolbox for performing uncertainty quantification.

Basic Info
Statistics
  • Stars: 466
  • Watchers: 22
  • Forks: 88
  • Open Issues: 61
  • Releases: 9
Topics
gaussian-quadrature probability-distribution sensitivity-analysis sparse-grids statistics uncertainty-quantification variance-reduction
Created over 11 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.rst 

.. image:: https://github.com/jonathf/chaospy/raw/master/docs/_static/chaospy_logo.svg
   :height: 200 px
   :width: 200 px
   :align: center

|circleci| |codecov| |readthedocs| |downloads| |pypi|

.. |circleci| image:: https://img.shields.io/circleci/build/github/jonathf/chaospy/master
    :target: https://circleci.com/gh/jonathf/chaospy/tree/master
.. |codecov| image:: https://img.shields.io/codecov/c/github/jonathf/chaospy
    :target: https://codecov.io/gh/jonathf/chaospy
.. |readthedocs| image:: https://img.shields.io/readthedocs/chaospy
    :target: https://chaospy.readthedocs.io/en/master/?badge=master
.. |downloads| image:: https://img.shields.io/pypi/dm/chaospy
    :target: https://pypistats.org/packages/chaospy
.. |pypi| image:: https://img.shields.io/pypi/v/chaospy
    :target: https://pypi.org/project/chaospy

* `Documentation `_
* `Interactive tutorials with Binder `_
* `Code of conduct `_
* `Contribution guideline `_
* `Changelog `_
* `License `_

Chaospy is a numerical toolbox designed for performing uncertainty
quantification through polynomial chaos expansions and advanced Monte
Carlo methods implemented in Python. It includes a comprehensive suite
of tools for low-discrepancy sampling, quadrature creation, polynomial
manipulations, and much more.

The philosophy behind ``chaospy`` is not to serve as a single solution
for all uncertainty quantification challenges, but rather to provide
specific tools that empower users to solve problems themselves. This
approach accommodates well-established problems but also serves as a
foundry for experimenting with new, emerging problems. Emphasis is
placed on the following:

* Focus on an easy-to-use interface that embraces the `pythonic code
  style `.
* Ensure the code is "composable," meaning it's designed so that users
  can easily and effectively modify parts of the code with their own
  solutions.
* Strive to support a broad range of methods for uncertainty
  quantification where it makes sense to use ``chaospy``.
* Ensure that ``chaospy`` integrates well with a wide array of other
  projects, including `numpy `, `scipy
  `, `scikit-learn `,
  `statsmodels `, `openturns
  `, and `gstools
  `, among others.
* Contribute all code as open source to the community.

Installation
============

Installation is straightforward via `pip `_:

.. code-block:: bash

    pip install chaospy

Alternatively, if you prefer `Conda `_:

.. code-block:: bash

    conda install -c conda-forge chaospy

After installation, visit the `documentation
`_ to learn how to use the
toolbox.

Development
===========

To install ``chaospy`` and its dependencies in developer mode:

.. code-block:: bash

    pip install -e .[dev]

Testing
-------

To run tests on your local system:

.. code-block:: bash

    pytest --doctest-modules chaospy/ tests/ README.rst

Documentation
-------------

Ensure that ``pandoc`` is installed and available in your path to
build the documentation.

From the ``docs/`` directory, build the documentation locally using:

.. code-block:: bash

    cd docs/
    make html

Run ``make`` without arguments to view other build targets.
The HTML documentation will be output to ``doc/.build/html``.

Owner

  • Name: Jonathan Feinberg
  • Login: jonathf
  • Kind: user
  • Location: Oslo
  • Company: @expertanalytics

Citation (CITATIONS.bib) 

@article{chaospy_2015,
  title = {Chaospy: An open source tool for designing methods of uncertainty quantification},
  journal = {Journal of Computational Science},
  volume = {11},
  pages = {46-57},
  year = {2015},
  issn = {1877-7503},
  doi = {https://doi.org/10.1016/j.jocs.2015.08.008},
  url = {https://www.sciencedirect.com/science/article/pii/S1877750315300119},
  author = {Feinberg, Jonathan and Langtangen, Hans Petter},
  keywords = {Uncertainty quantification, polynomial chaos expansions, Monte Carlo simulation, Rosenblatt transformations, Python package},
  abstract = {The paper describes the philosophy, design, functionality, and usage of the Python software toolbox Chaospy for performing uncertainty quantification via polynomial chaos expansions and Monte Carlo simulation. The paper compares Chaospy to similar packages and demonstrates a stronger focus on defining reusable software building blocks that can easily be assembled to construct new, tailored algorithms for uncertainty quantification. For example, a Chaospy user can in a few lines of high-level computer code define custom distributions, polynomials, integration rules, sampling schemes, and statistical metrics for uncertainty analysis. In addition, the software introduces some novel methodological advances, like a framework for computing Rosenblatt transformations and a new approach for creating polynomial chaos expansions with dependent stochastic variables.}
}

@article{chaospy_2018,
  author = {Feinberg, Jonathan and Eck, Vinzenz Gregor and Langtangen, Hans Petter},
  title = {Multivariate Polynomial Chaos Expansions with Dependent Variables},
  journal = {SIAM Journal on Scientific Computing},
  volume = {40},
  number = {1},
  pages = {A199-A223},
  year = {2018},
  doi = {10.1137/15M1020447},
  url = {https://doi.org/10.1137/15M1020447},
  keywords = {uncertainty quantification, polynomial chaos expansions, dependent stochastic variables, variable transformations, blood flow simulation, wave propagation}
  abstract = {This paper describes a new approach for handling dependent stochastic variables in polynomial chaos expansions for uncertainty quantification. The methodology is based on a decorrelation algorithm that only requires raw statistical moments of multivariate random variables. When the mapping from input in probability space to the response is not smooth, polynomial chaos expansions may converge slowly. The remedy proposed in this paper is to introduce a transformation of the input parameters to create a smoother mapping in an alternative probability space. However, such a transformation quickly leads to dependent stochastic variables and hence a need for handling dependency. We consider three cases to demonstrate how variable transformations and the new framework can significantly increase the convergence rate of polynomial chaos expansions. The first case involves an analytical, nonsmooth mapping to exemplify serious convergence problems and the power of transforming the variables. The second case concerns diffusion in multimaterial/multidomain models with uncertain internal boundaries and uncertain material properties. We investigate in detail a simplified version of this physical problem where the performance of the method can be understood. The third case is a blood flow simulation model for arterial systems, which involves a network of one-dimensional nonlinear partial differential equations. Here we investigate the pressure discontinuity over an arterial bifurcation. In all of the examples, the standard polynomial chaos expansions converge very slowly, but with variable transformations, leading to dependent variables, we are able to achieve significantly faster convergence compared with state-of-the-art methods.}
}

GitHub Events

Total
  • Issues event: 9
  • Watch event: 26
  • Issue comment event: 33
  • Push event: 9
  • Pull request event: 3
  • Fork event: 2
  • Create event: 5
Last Year
  • Issues event: 9
  • Watch event: 26
  • Issue comment event: 33
  • Push event: 9
  • Pull request event: 3
  • Fork event: 2
  • Create event: 5

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 584
  • Total Committers: 26
  • Avg Commits per committer: 22.462
  • Development Distribution Score (DDS): 0.168
Past Year
  • Commits: 14
  • Committers: 2
  • Avg Commits per committer: 7.0
  • Development Distribution Score (DDS): 0.071
Top Committers
Name Email Commits
Jonathan Feinberg j****f@g****m 486
Hans Petter Langtangen h****l@s****o 26
Simen Tennøe s****n@g****m 15
Florian Künzner f****r@g****t 13
dependabot[bot] 4****] 7
davidovitch d****t@g****m 6
ma6yu s****a@g****m 5
lindert blonk l****k@d****m 4
Jacob Sturdy j****y@g****m 3
Régis LEBRUN r****n 2
Jonathan Feinberg j****n@f****o 2
jumu j****u@d****k 1
joergbuchwald 4****d 1
beroda 1****a 1
Zachary Burnett z****t@n****v 1
Yoel y****s@g****m 1
Vincent Vanlaer 1****r 1
Christopher Albert a****t@a****t 1
Christopher Teubert c****t@n****v 1
David W Wright d****t@g****m 1
Davide Fioriti 6****f 1
Iztok Fister Jr i****k@i****u 1
Jacob Hwang y****4@g****m 1
John Vouvakis Manousakis 7****m 1
Novermars n****s@o****m 1
Shun Zhang s****z@u****u 1
Committer Domains (Top 20 + Academic)
umich.edu: 1 iztok-jr-fister.eu: 1 nasa.gov: 1 alumni.tugraz.at: 1 noaa.gov: 1 dtu.dk: 1 feinberg.no: 1 dnvgl.com: 1 gmx.net: 1 simula.no: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 89
  • Total pull requests: 42
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 4 days
  • Total issue authors: 60
  • Total pull request authors: 12
  • Average comments per issue: 3.93
  • Average comments per pull request: 1.38
  • Merged pull requests: 36
  • Bot issues: 0
  • Bot pull requests: 7
Past Year
  • Issues: 12
  • Pull requests: 2
  • Average time to close issues: 4 days
  • Average time to close pull requests: 4 days
  • Issue authors: 10
  • Pull request authors: 1
  • Average comments per issue: 2.92
  • Average comments per pull request: 8.5
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • KIDDProgress (6)
  • hamzabuw (6)
  • oarcelus (5)
  • AmosJoseph (4)
  • yurivict (2)
  • John3221 (2)
  • marcrocasalonso (2)
  • rhaghi (2)
  • damar-wicaksono (2)
  • ahenkes1 (2)
  • takafusui (2)
  • dirge1 (2)
  • ehsansaleh (2)
  • goghino (2)
  • Sumit9013 (2)
Pull Request Authors
  • jonathf (25)
  • dependabot[bot] (7)
  • ioannis-vm (2)
  • davide-f (2)
  • gboehl (1)
  • Novermars (1)
  • yoelcortes (1)
  • teubert (1)
  • firefly-cpp (1)
  • beroda (1)
  • regislebrun (1)
  • VincentVanlaer (1)
  • ghost (1)
Top Labels
Issue Labels
question (43) bug (25) enhancement (7)
Pull Request Labels
dependencies (7)

Packages

  • Total packages: 15
  • Total downloads:
    • pypi 8,717 last-month
  • Total docker downloads: 10
  • Total dependent packages: 19
    (may contain duplicates)
  • Total dependent repositories: 35
    (may contain duplicates)
  • Total versions: 159
  • Total maintainers: 3
pypi.org: chaospy

Numerical tool for performing uncertainty quantification

  • Versions: 90
  • Dependent Packages: 17
  • Dependent Repositories: 34
  • Downloads: 8,717 Last month
  • Docker Downloads: 10
Rankings
Dependent packages count: 0.7%
Dependent repos count: 2.5%
Average: 2.5%
Downloads: 3.3%
Docker downloads count: 3.6%
Maintainers (1)
jonathf
Last synced: 6 months ago
alpine-v3.18: py3-chaospy-pyc

Precompiled Python bytecode for py3-chaospy

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Average: 6.3%
Forks count: 12.2%
Stargazers count: 13.0%
Maintainers (1)
iztok@iztok-jr-fister.eu
Last synced: 6 months ago
alpine-v3.18: py3-chaospy

Numerical tool for performing uncertainty quantification

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Average: 6.3%
Forks count: 12.3%
Stargazers count: 13.0%
Maintainers (1)
iztok@iztok-jr-fister.eu
Last synced: 6 months ago
alpine-edge: py3-chaospy

Numerical tool for performing uncertainty quantification

  • Versions: 18
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Average: 10.5%
Forks count: 13.3%
Stargazers count: 14.1%
Dependent packages count: 14.6%
Maintainers (1)
iztok@iztok-jr-fister.eu
Last synced: 6 months ago
alpine-edge: py3-chaospy-pyc

Precompiled Python bytecode for py3-chaospy

  • Versions: 14
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Average: 10.6%
Forks count: 13.6%
Stargazers count: 14.3%
Dependent packages count: 14.4%
Maintainers (1)
wpsinatra@gmail.com
Last synced: 6 months ago
alpine-v3.17: py3-chaospy

Numerical tool for performing uncertainty quantification

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Forks count: 11.6%
Stargazers count: 11.9%
Average: 12.7%
Dependent packages count: 27.3%
Maintainers (1)
iztok@iztok-jr-fister.eu
Last synced: 6 months ago
conda-forge.org: chaospy
  • Versions: 26
  • Dependent Packages: 2
  • Dependent Repositories: 1
Rankings
Dependent packages count: 19.6%
Stargazers count: 19.9%
Forks count: 20.4%
Average: 21.1%
Dependent repos count: 24.3%
Last synced: 6 months ago
alpine-v3.19: py3-chaospy-pyc

Precompiled Python bytecode for py3-chaospy

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Average: 100%
Last synced: 6 months ago
alpine-v3.22: py3-chaospy-pyc

Precompiled Python bytecode for py3-chaospy

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Average: 100%
Maintainers (1)
wpsinatra@gmail.com
Last synced: 6 months ago
alpine-v3.20: py3-chaospy

Numerical tool for performing uncertainty quantification

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Average: 100%
Maintainers (1)
iztok@iztok-jr-fister.eu
Last synced: 6 months ago
alpine-v3.21: py3-chaospy

Numerical tool for performing uncertainty quantification

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Average: 100%
Maintainers (1)
wpsinatra@gmail.com
Last synced: 6 months ago
alpine-v3.21: py3-chaospy-pyc

Precompiled Python bytecode for py3-chaospy

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Average: 100%
Maintainers (1)
wpsinatra@gmail.com
Last synced: 6 months ago
alpine-v3.19: py3-chaospy

Numerical tool for performing uncertainty quantification

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Average: 100%
Maintainers (1)
iztok@iztok-jr-fister.eu
Last synced: 6 months ago
alpine-v3.22: py3-chaospy

Numerical tool for performing uncertainty quantification

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Average: 100%
Maintainers (1)
wpsinatra@gmail.com
Last synced: 6 months ago
alpine-v3.20: py3-chaospy-pyc

Precompiled Python bytecode for py3-chaospy

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Average: 100%
Maintainers (1)
iztok@iztok-jr-fister.eu
Last synced: 6 months ago

Dependencies

docs/requirements.txt pypi
  • Sphinx <4.3
  • markupsafe ==2.0.1
  • nbsphinx ==0.8.5
  • pydata_sphinx_theme ==0.6.3
  • sphinxcontrib-bibtex ==2.2.0
.github/workflows/pipe.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • codecov/codecov-action v2 composite
  • pypa/gh-action-pypi-publish v1.4.2 composite
requirements.txt pypi
  • chaospy *
  • matplotlib *
  • numpoly *
  • numpy *
  • scipy *