PyElli is an open source numerical solver for spectral ellipsometry employing well-known 2x2 and 4x4 algorithms. It is intended for a broad case of problems including simple fitting of layered structures, anisotropic layers and any other light interaction with layered 1D structures. It serves as a system for the day to day ellipsometry task at hand and is easily extendable with your own dispersion models, EMAs or solvers. Our goal is to provide a reproducible and flexible tool for the needs of scientists working with spectral ellipsometry.
If you have questions using pyElli please feel free to open a discussion in the Q&A or join our discord channel.
The installers for all releases are available at the Python Package Index (PyPI).
To install run:
pip install pyElli[fitting]
This installs pyElli with the additional fitting capabilities and interactive widgets.
If you don't want to have this functionality just drop the [fitting]
in the end.
To increase performance of the 4x4 Solver, it is recommended to install PyTorch manually, as it is too big to include in the standard installation. Installation information can be found at the PyTorch Website. The CPU variant is sufficient, if you want to save some space.
A complete environment for pyElli is also available as a Docker Container. To pull and run it directly just execute
docker run -p 8888:8888 domna/pyelli
from your local docker install. After startup a link should appear in your console. Click it and you will be directed to a jupyter server with the latest release of pyElli available.
To install the latest development version use:
pip install "pyElli[fitting] @ git+https://github.com/PyEllips/pyElli.git"
The source code is hosted on GitHub, to manually install from source, clone the repository and run pip install -e .
in
the folder to install it in development mode:
git clone https://github.com/PyEllips/pyElli
cd pyElli
pip install -e ".[fitting]"
Until we have published a Paper on pyElli, we have prepared a Zenodo entry with DOIs for every pyElli Version. The can be found here.
@MarJMue receives financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant No. 398143140 (FOR 2824).