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https://github.com/magis-slac/gradoptics

differentiable machine-learning optics-simulation pytorch ray-tracing simulator tomography

Last synced: about 2 months ago
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Host: GitHub
URL: https://github.com/magis-slac/gradoptics
Owner: magis-slac
License: mit
Created: 2021-08-06T13:53:16.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2024-09-23T23:32:51.000Z (4 months ago)
Last Synced: 2024-11-16T21:06:42.954Z (2 months ago)
Topics: differentiable, machine-learning, optics-simulation, pytorch, ray-tracing, simulator, tomography
Language: Python
Homepage: https://magis-slac.github.io/gradoptics
Size: 21.4 MB
Stars: 10
Watchers: 6
Forks: 2
Open Issues: 2
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE

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README

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# Differentiable Optics via Ray Tracing

[*gradoptics*](https://github.com/magis-slac/gradoptics) is a ray tracing based optical simulator built using PyTorch [[1]](#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]](#2). 

## Getting Started

[Getting Started](https://github.com/magis-slac/gradoptics/blob/main/docs/tutorials/Quick-Start.ipynb)

## Installation

```commandline

pip install gradoptics

```

Then, you should be ready to go!

```python

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*](https://github.com/magis-slac/gradoptics).  

#### Testing

If you modify [*gradoptics*](https://github.com/magis-slac/gradoptics), please use pytest for checking your code.

```commandline

pytest tests/tests.py 

```

## Support

[*gradoptics*](https://github.com/magis-slac/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.