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https://github.com/matthewfilipovich/torchoptics
Python library for differentiable Fourier optics simulations with PyTorch.
https://github.com/matthewfilipovich/torchoptics
computational-optics deep-learning diffraction fourier-optics holography imaging inverse-design machine-learning microscopy optics physics pytorch
Last synced: about 1 month ago
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Python library for differentiable Fourier optics simulations with PyTorch.
- Host: GitHub
- URL: https://github.com/matthewfilipovich/torchoptics
- Owner: MatthewFilipovich
- License: mit
- Created: 2024-11-27T22:32:02.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-12-09T03:14:19.000Z (about 1 month ago)
- Last Synced: 2024-12-09T03:24:52.537Z (about 1 month ago)
- Topics: computational-optics, deep-learning, diffraction, fourier-optics, holography, imaging, inverse-design, machine-learning, microscopy, optics, physics, pytorch
- Language: Python
- Homepage: https://torchoptics.readthedocs.io
- Size: 15.6 MB
- Stars: 10
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
> TorchOptics is an open-source Python library for differentiable Fourier optics simulations with PyTorch.
[](https://github.com/MatthewFilipovich/torchoptics/actions/workflows/build.yml)
[](https://codecov.io/gh/MatthewFilipovich/torchoptics)
[](https://torchoptics.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.org/project/torchoptics/)
[](https://www.python.org/downloads/)
[](https://github.com/MatthewFilipovich/torchoptics/blob/main/LICENSE)
# Key Features
- **Differentiable Fourier Optics Simulations**: A comprehensive framework for modeling, analyzing, and designing optical systems using differentiable Fourier optics.
- **Built on PyTorch**: Leverages PyTorch for GPU acceleration, batch processing, automatic differentiation, and efficient gradient-based optimization.
- **End-to-End Optimization**: Enables optimization of optical hardware and deep learning models within a unified, differentiable pipeline.
- **Wide Range of Optical Elements and Spatial Profiles**: Includes standard elements like lenses and modulators, along with commonly used spatial profiles such as Hermite-Gaussian and Laguerre-Gaussian beams.
- **Polarized Light Simulation**: Simulates polarized light interactions using matrix Fourier optics with Jones calculus.
- **Spatial Coherence Support**: Models optical fields with arbitrary spatial coherence through the mutual coherence function.
Our research paper, available on [arXiv](https://arxiv.org/abs/2411.18591), introduces the TorchOptics library and provides a comprehensive review of its features and applications.
## Documentation
Access the latest documentation at [torchoptics.readthedocs.io](https://torchoptics.readthedocs.io/).
## Installation
TorchOptics and its dependencies can be installed using [pip](https://pypi.org/project/torchoptics/):
```sh
pip install torchoptics
```
To install the library in development mode, first clone the GitHub repository and then use pip to install it in editable mode:
```sh
git clone https://github.com/MatthewFilipovich/torchoptics
pip install -e ./torchoptics
```
## Usage
[](https://colab.research.google.com/github/MatthewFilipovich/torchoptics/blob/main/docs/source/tutorials/4f_system.ipynb)
This example demonstrates simulating a 4f imaging system using TorchOptics.
The field at each focal plane along the z-axis is computed and visualized:
```python
import torch
import torchoptics
from torchoptics import Field, System
from torchoptics.elements import Lens
from torchoptics.profiles import checkerboard
# Set simulation properties
shape = 1000 # Number of grid points in each dimension
spacing = 10e-6 # Spacing between grid points (m)
wavelength = 700e-9 # Field wavelength (m)
focal_length = 200e-3 # Lens focal length (m)
tile_length = 400e-6 # Checkerboard tile length (m)
num_tiles = 15 # Number of tiles in each dimension
# Determine device
device = "cuda" if torch.cuda.is_available() else "cpu"
# Configure torchoptics default properties
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)
# Initialize input field with checkerboard pattern
field_data = checkerboard(shape, tile_length, num_tiles)
input_field = Field(field_data).to(device)
# Define 4f optical system with two lenses
system = System(
Lens(shape, focal_length, z=1 * focal_length),
Lens(shape, focal_length, z=3 * focal_length),
).to(device)
# Measure field at focal planes along the z-axis
measurements = [
system.measure_at_z(input_field, z=i * focal_length)
for i in range(5)
]
# Visualize the measured intensity distributions
for i, measurement in enumerate(measurements):
measurement.visualize(title=f"z={i}f", vmax=1)
```
Intensity distributions at different focal planes in the 4f system.
Propagation of the intensity distribution.
_For more examples and detailed usage, please refer to the [documentation](https://torchoptics.readthedocs.io/)._
## Contributing
We welcome all bug reports and suggestions for future features and enhancements, which can be filed as GitHub issues. To contribute a feature:
1. Fork it ()
2. Create your feature branch (`git checkout -b feature/fooBar`)
3. Commit your changes (`git commit -am 'Add some fooBar'`)
4. Push to the branch (`git push origin feature/fooBar`)
5. Submit a Pull Request
## Citing TorchOptics
If you are using TorchOptics for research purposes, we kindly request that you cite the following paper:
> M.J. Filipovich and A.I. Lvovsky, _TorchOptics: An open-source Python library for differentiable Fourier optics simulations_, arXiv preprint [arXiv:2411.18591](https://arxiv.org/abs/2411.18591) (2024).
## License
TorchOptics is distributed under the MIT License. See the [LICENSE](https://github.com/MatthewFilipovich/torchoptics/blob/main/LICENSE) file for more details.