Metadata-Version: 2.4
Name: torchoptics
Version: 1.0.0b3
Summary: Python library for differentiable Fourier optics simulations with PyTorch.
Project-URL: Homepage, https://github.com/MatthewFilipovich/torchoptics
Project-URL: Documentation, https://torchoptics.readthedocs.io/
Project-URL: Tracker, https://github.com/MatthewFilipovich/torchoptics/issues
Project-URL: Changelog, https://github.com/MatthewFilipovich/torchoptics/releases
Author-email: Matthew Filipovich <matthew.filipovich@physics.ox.ac.uk>
License-Expression: MIT
License-File: LICENSE
Keywords: Fourier optics,PyTorch,computational optics,differentiable optics,diffraction,holography,imaging,inverse design,machine learning,microscopy,optical neural networks,physics,wave optics
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.10
Requires-Dist: matplotlib
Requires-Dist: torch
Provides-Extra: dev
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Description-Content-Type: text/markdown

<p align="center">
  <img src="https://raw.githubusercontent.com/MatthewFilipovich/torchoptics/main/docs/source/_static/torchoptics_logo.png" width="700px">
</p>

<div align="center">

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</div>

> TorchOptics is a differentiable wave optics simulation library built on PyTorch.

# Key Features

- 🌊 **Differentiable Wave Optics** — Model, analyze, and optimize optical systems using Fourier optics.
- 🔥 **Built on PyTorch** — GPU acceleration, batch processing, and automatic differentiation.
- 🛠️ **End-to-End Optimization** — Joint optimization of optical hardware and machine learning models.
- 🔬 **Optical Elements** — Lenses, modulators, detectors, polarizers, and more.
- 🖼️ **Spatial Profiles** — Hermite-Gaussian, Laguerre-Gaussian, Zernike modes, and others.
- 🔆 **Polarization and Coherence** — Simulate polarized light and fields with arbitrary spatial coherence.

Learn more about TorchOptics in our research paper on [arXiv](https://arxiv.org/abs/2411.18591).

# Installation

TorchOptics is available on [PyPI](https://pypi.org/project/torchoptics/) and can be installed with:

```bash
pip install torchoptics
```

## Documentation

Read the full documentation at [torchoptics.readthedocs.io](https://torchoptics.readthedocs.io/).

## Usage

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MatthewFilipovich/torchoptics/blob/main/docs/source/_static/torchoptics_colab.ipynb)

This example shows how to simulate a 4f imaging system using TorchOptics, computing and visualizing the field at each focal plane along the optical axis:

```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 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)
```

<p align="center">
  <img src="https://raw.githubusercontent.com/MatthewFilipovich/torchoptics/main/docs/source/_static/4f_simulation.png" width="700px">
  <br>
  <em>Intensity distributions at different focal planes in the 4f system.</em>
</p>

<p align="center">
  <img width="300px" src="https://raw.githubusercontent.com/MatthewFilipovich/torchoptics/main/docs/source/_static/4f_propagation.gif">
  <br>
  <em>Propagation of the intensity distribution.</em>
</p>

_For more examples and detailed usage, please refer to the [documentation](https://torchoptics.readthedocs.io/)._

## Contributing

We welcome contributions! See our [Contributing Guide](https://github.com/MatthewFilipovich/torchoptics/blob/main/CONTRIBUTING.md) for details.

## Citing TorchOptics

If you use TorchOptics in your research, please cite our [paper](https://arxiv.org/abs/2411.18591):

```bibtex
@misc{filipovich2024torchoptics,
      title={TorchOptics: An open-source Python library for differentiable Fourier optics simulations},
      author={Matthew J. Filipovich and A. I. Lvovsky},
      year={2024},
      eprint={2411.18591},
      archivePrefix={arXiv},
      primaryClass={physics.optics},
      url={https://arxiv.org/abs/2411.18591},
}
```

## License

TorchOptics is distributed under the MIT License. See the [LICENSE](https://github.com/MatthewFilipovich/torchoptics/blob/main/LICENSE) file for more details.
