Skip to content
Python-based (chemical kinetic) Model Automatic Reduction Software
Python TeX
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
conda.recipe
docs Changes integration behavior to reach steady state by default. Closes #… Jul 22, 2019
logo added logo Jun 21, 2019
paper (still) trying to fix pytables reference in paper [skip ci] Sep 6, 2019
pymars 💎 v1.1.0 release Sep 6, 2019
.coveragerc adds coverage config file May 12, 2019
.gitignore adding initial continuous integration files May 5, 2019
.travis.yml add sphinx bibtex package to non-tagged build Jun 21, 2019
AUTHORS.md improved setup and associated files May 10, 2018
CHANGELOG.md 💎 v1.1.0 release Sep 6, 2019
CITATION.md fixed software name May 22, 2018
CODE_OF_CONDUCT.md improved setup and associated files May 10, 2018
CONTRIBUTING.md improved setup and associated files May 10, 2018
LICENSE
MANIFEST.in moved all test files to assets directory for cleanup Jul 19, 2019
README.md Added Anaconda package badge to README Jul 5, 2019
azure-pipelines.yml Trim pipelines build Jul 24, 2019
build-environment.yaml bump v1.0.0a5. fixed tables/pytables in setup.py Jun 21, 2019
github_deploy_key_niemeyer_research_group_pymars.enc 💎 alpha release of v1.0.0, with doctr publishing docs Jun 21, 2019
setup.cfg Added automated test for DRGEPSA method Oct 10, 2018
setup.py moved all test files to assets directory for cleanup Jul 19, 2019
test-environment.yaml bump v1.0.0a5. fixed tables/pytables in setup.py Jun 21, 2019

README.md

pyMARS

DOI Build Status Build Status codecov License Code of Conduct Anaconda-Server Badge

Python-based (chemical kinetic) Model Automatic Reduction Software (pyMARS) implements multiple techniques for reducing the size and complexity of detailed chemical kinetic models.

An installation guide, usage examples, theory details, and API docs are provided in the online documentation: https://Niemeyer-Research-Group.github.io/pyMARS/

pyMARS currently consists of four methods for model reduction:

  1. Directed relation graph (DRG)
  2. Directed relation graph with error propagation (DRGEP)
  3. Path flux analysis (PFA)
  4. Sensitivity analysis (SA)

Sensitivity analysis may be run following one of the first three methods, or directly on the starting model; however, its computational expense is high, and applying this method alone is not recommended.

Installation

pyMARS supports Python 3.6 and 3.7, and can be installed easily using conda:

conda install -c cantera cantera
conda install -c niemeyer-research-group pymars

Usage

For detailed usage examples, see the online documentation. Once installed, the list of options can be found with:

pymars --help

pyMARS requires models in the Cantera format. However, running pyMARS with a CHEMKIN file will convert it into a Cantera file. pyMARS also provides the --convert option to convert a given model to/from the CHEMKIN format.

Citation

Please refer to the CITATION file for information about citing pyMARS when used in a scholarly work.

If you use this package as part of a scholarly publication, please consider citing the appropriate theory/method papers in addition to the software itself.

License

pyMARS is released under the MIT license; see LICENSE for details.

Code of Conduct

To ensure an open and welcoming community, pyMARS adheres to a code of conduct adapted from the Contributor Covenant code of conduct.

Please adhere to this code of conduct in any interactions you have in the pyMARS community. It is strictly enforced on all official PyKED repositories, websites, and resources. If you encounter someone violating these terms, please let the project lead (@kyleniemeyer) know via email at kyle.niemeyer@gmail.com and we will address it as soon as possible.

You can’t perform that action at this time.