Skip to content

Robbybp/incidence_examples

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

51 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Incidence Examples

This is a small repository with some examples of the incidence_analysis contributed package in Pyomo. The incidence_analysis code can be found here.

incidence_analysis implements (using NetworkX wherever possible) three algorithms that are useful for debugging equation-oriented chemical process models:

  • Maximum matching
  • Block triangularization
  • Dulmage-Mendelsohn partition

Contents

This repo has three main parts:

  • "example1", an example using the Dulmage-Mendelsohn partition to debug degrees of freedom in a chemical looping model from IDAES
  • "example2", an example using a block triangularization (strongly connected components) to solve a subsystem of the same chemical looping model for initialization
  • "tutorial", a walkthrough of the three graph algorithms implemented applied to a small system representing the thermodynamics of a porous solid particle

Dependencies

These examples and tutorial depend on the following Python packages: idaes-pse, pyomo, scipy, networkx, matplotlib. I am not 100% sure this is an exhaustive list of dependencies. These packages (and any others I've forgotten) should be easily installable with pip.

The tutorial also relies on the PyNumero ASL library, for getting Jacobians, which can be obtained via pyomo build-extensions.

Instructions

To run these examples and tutorial, you need to download the code in this repository, probably by running:

git clone https://github.com/robbybp/incidence_examples.git

You then need to "install" this code by running:

python setup.py develop

You can check that you have the required dependencies and have "installed" this code properly by running pytest (this may require a pip install pytest if you don't have pytest already, although pytest is not the only test runner you could use here).

The examples and tutorial are provided as scripts as well as Jupyter notebooks (.ipynb files). To open a Jupyter notebook (say to follow along during a demonstration), navigate to the directory of the notebook you want to open and run:

jupyter notebook filename.ipynb

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published