Python library for designing and producing masks for Micro Optical Elements.
Mask design features:
- Designing multi-layer (grayscale) masks from analytical and/or numerical (e.g. Gerchberg–Saxton) methods
- Designing single layer (binary) dithered masks from grayscale masks
- Designing rudimentary metasurfaces masks
- Numerical scalar propagation methods (Rayleigh-Sommerfeld, Fresnel, Fraunhofer) of electric fields from (grayscale) masks
Mask production features:
- Image file (binary/grayscale) <-> CAD files input-output conversion
- Operations with/within mask files (e.g. change layer numbers, make intances, CAD file import, merge shapes, ...)
For background, theory, package structure, practical results and discussion of package's performance, please refer to the publication: https://doi.org/10.1016/j.cpc.2024.109331
For a practical walk through on how to use the package, besides the aforementioned publication, please follow the example notebooks inside notebooks\ directory. For running we recommend having Jupyter Notebook or Lab with a Python distribution >3.7, as well as required dependencies.
For documentation please visit https://pymoe-doc.readthedocs.io
A list of dependencies (with versions) is at https://github.com/INLnano/pyMOE/blob/main/requirements.txt . To use the package straighforwardly please make sure you have these dependencies (and versions) installed.
From root folder run
pytest
If pyMOE was useful for you, please cite the companion paper published in Computer Physics Communications (https://doi.org/10.1016/j.cpc.2024.109331).
Bibtex entry:
@article{CUNHA2024109331,
author = {Joao Cunha and José Queiroz and Carlos Silva and Fabio Gentile and Diogo E. Aguiam},
title = {pyMOE: Mask Design and Modelling for Micro Optical Elements and Flat Optics},
journal = {Computer Physics Communications},
pages = {109331},
year = {2024},
issn = {0010-4655},
doi = {https://doi.org/10.1016/j.cpc.2024.109331}
}
pyMOE releases are also mirrored in Zenodo at https://zenodo.org/records/12737201.
To interact/contribute please use the following:
- Email contact: Please feel free to contact via email - for email addresses, please use the email(s) provided at https://doi.org/10.1016/j.cpc.2024.109331.
- Issues page: Please feel free to post suggestions, bug reports, code malfunctions, or requests in the Issues page (https://github.com/INLnano/pyMOE/issues). Those will be cathegorized and addressed in due time.
- Code contributions: To contribute, please (1) fork the repository, (2) commit your code changes in your forked repository, and (3) make a pull request to a dev-(...) branch of this pyMOE repository (pull requests directly to pyMOE's main branch will not be accepted).