pyEMLearn 0.2.0

A computational E&M package for learning the fundimentals

pyEMLearn

A computational E&M library written in python, intended for learning the fundamentals

Installation

pyEMLearn is available through pip:

 $ pip3 install pyEMLearn

(or just pip if you are on windows, or have aliased your python3 installation)

Development

To install toward developing this package: fork and download this repo from github, open a terminal in the head directory and run:

 $ python3 -m venv venv
 $ source venv/bin/activate
 (venv) $ pip install -r requirements.txt
 (venv) $ python setup.py install

This will create a virtual environment, activate it, install the required packages, then install the current version of pyEMLearn into your virtual environment.

After you have made changes to the source code, re-run the last line to install your modified version.

Creating source packages

 $ python3 setup.py sdist bdist_wheel

Uploading to PyPI

 $ python3 -m twine upload dist/*

Development Philosophy

This package is really intended as a learning tool, rather than a full computational workhorse. As a result, much of the core code is deliberately inefficient so as to be more closely connected with the theory of the scattering (transfer) matrix methods. There are plenty of other computational E&M libraries which are both more efficient and have more sophisticated features (like EMpy, EMUstack and Rigorous-Couple-Wave-Analysis ] to name a few). So, development in pyEMLearn will always prioritize user-clarity over code-efficiency.

Acknowledgements

The authors would like to thank Dr. Raymond Rumpf (of UTEP) for his extensive educational resources, on which this project is principally based.

To Do

• Comment Everything . . .
• Add some descriptive examples
• Add some better examples
• Restructure the catalog
• Add more materials to the catalog
• Add transmittance matrix approach
• Create source object, which can produce k-vecs and polarization vecs
• Create field object? which can track E?
• Add Poynting vector functionality to field object
• Begin thinking about RCWA
• Fix RCWA, its broke AF

V1 To Do, Obsolete

• Flesh out interface object with "t" matrix
• Add interface object to layers objects (and halfplane objects)
• Modify utils objects to be more general
• Change materials repr functions to NOT return parameters, put that in a "summary" function
• Finish Docstring-ing
• Restructure the materials catalog
• Add more asserts to System.init to make sure that transmission layers arn't actually injection layers, or gap layers arn't actually just thickness zero layers, etc.
• Improve computational efficiency for LHI materials? (using 2c.pdf)
• Spend some time making all the class variable names consistent in their case/-/_ usage
• Add config and license files
• Get Setup on PyPi
• Add anisotropy functionality
• Make some better examples (bragg reflectors)
• Add parallelization options for parameter sweep calculations
• Add ellipsometric variable support
• Add internal field support