Differentiable Optics via Ray Tracing
Project description
Differentiable Optics via Ray Tracing
gradoptics is a ray tracing based optical simulator built using PyTorch [1] to enable automatic differentiation.
The API is designed similar to rendering softwares, and has been heavily inspired by Physically Based Rendering (Pharr, Jakob, Humphreys) [2].
Getting Started
Installation
pip install gradoptics
Then, you should be ready to go!
import gradoptics as optics
Work in progress
- Currently, some optical element normals are aligned with the optical axis -> more general orientations in progress
- Currently, monochromatic -> no chromatic aberrations
Project History
This project was started in 2020 by Michael Kagan and Maxime Vandegar at SLAC National Accelerator Laboratory.
Feedback and Contributions
Please use issues on GitHub for reporting bugs and suggesting features (including better documentation).
We appreciate all contributions. In general, we recommend using pull requests to make changes to gradoptics.
Testing
If you modify gradoptics, please use pytest for checking your code.
pytest tests/tests.py
Support
gradoptics was developed in the context of the MAGIS-100 experiment
References
[1] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. PyTorch: An imperative style, high-performance deep learning library. In NeurIPS, 2019.
[2] Matt Pharr, Wenzel Jakob, and Greg Humphreys. 2016. Physically Based Rendering: From Theory to Implementation (3rd ed.). Morgan Kaufmann Publishers Inc.
Project details
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