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DiffinyTrace

DiffinyTrace is a Python library for differentiable ray tracing and optical system optimization using PyTorch. It enables automatic differentiation through optical systems, making it possible to optimize lens designs, mirror configurations, and other optical components using gradient-based methods.

The source code is available at the GitHub repository.

Key Features

Transformation example

Flexible Transformations — apply general transformations such as rotations and translations to optical components, with full control over the parameters and their role in the transformation.

CAD export example

Seamless CAD Export — generate lenses and mirrors that can be exported to standard CAD file formats.

B-spline surface example

Freeform Surfaces — design complex optical elements with advanced B-spline representations for maximum flexibility.

  • Differentiable Ray Tracing: Full automatic differentiation support through optical systems
  • Constraint Optimization: Advanced optimization with PyTorch and SciPy integration
  • Illumination Design: Algorithms for computing lens surfaces to achieve desired illumination profiles
  • GPU Acceleration: CUDA support for high-performance computations

Installation

  1. Create a new Enviroment via conda:

    conda create -n dit python==3.12
    

    activate enviroment via

    conda activate dit
    

    install pip

    conda install pip
    
  2. Install PyTorch

    Check your cuda version with

    nvcc --version
    

    Diffinytrace only has been tested with 2.10.0+cu130. Make sure to install the appropriate version of PyTorch for your system. You can find the installation instructions on the PyTorch website. DiffinyTrace should work for both cpu and cuda versions.

  3. Install DiffinyTrace Install all other dependencies and the library itself via:

    pip install diffinytrace
    

    or directly in the folder via

    pip install -r requirements.txt
    

Basic Usage Example

import diffinytrace as dit
import torch
NBK7 = dit.materials["NBK7"]

wave_len = 1.024
light_transform = dit.transforms.Offset(torch.tensor([0.0,0.0,0.0]))
source = dit.source.CollimatedMonochromatic(light_transform,8.0,wave_len)

plane_surface = dit.Plane()
surface2 = dit.Aspheric(-1/50.)
transf1 = dit.transforms.Distance(10.0,parent_transform=source)
lens1 = dit.Lens(transf1,5.,plane_surface,surface2,NBK7,13.0)
transf2 = dit.transforms.Distance(15.0,parent_transform=lens1)
detector = dit.Detector(transf2,plane_surface,8.0)
system = dit.SequentialOpticalSystem({"source":source, "lens":lens1, "detector":detector})

x,weights = source.sample(10)
O,D,wave_len,_,meta_data = system(x,["source","lens","detector"])
dit.plotting.system2D.plot(system,meta_data)

Documentation

For comprehensive documentation, tutorials, and API reference, visit the full documentation.

License

DiffinyTrace is licensed under the MIT License. See the repository for full license details.

Release files for diffinytrace 2.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for diffinytrace 2.4
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Table of built distributions (wheels) for diffinytrace 2.4
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diffinytrace-2.4-py3-none-any.whl Python 3 none any Details

Total release size: 169.8 kB

Release files / diffinytrace-2.4.tar.gz

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