Light propagation

Python app made to calculate field distribution transformation using the given propagation method based on article

Installation

Due to dependencies, it is suggested to run this app in a separate virtual environment like venv.

Initialization

python -m venv env

Activation (on Windows run activate.exe)

source env/bin/activate.sh

python -m pip install -r requirements.txt

To support linting and unify styling we suggest to use pre-commit

pip install pre-commit

Running pre-commit automatically

To run validation automatically before each commit, please use:

pre-commit install

This will add pre-commit to git hooks and perform all the checks defined in .pre-commit-config.yaml

Running pre-commit manually

To check stying in all files, please use

pre-commit run -a

Every pull request should pass pre-commit stage to be merged

For a quick start, please refer to our examples:

Exemplary usage of structure optimization through neural network optimization
```
    python doc/examples/nn_training.py
```
Exemplary usage of structure optimization using Gerchberg-Saxton algorithm.
```
    python doc/examples/gerchberg_saxton.py
```

lightprop/io - input/output functionalities, loading/saving images/files
lightprop/propagation - core set of classes defining propagation calculations (convolution, neural network)
lightprop/lightfield - field distribution representation using A exp(i phi) notation
lightprop/optimization - set of methods used for calculating phase distribution
lightprop/structures - optical structures, like lens

If you are interested in using/improving/developing this project, don't hesitate to contact us using email: pawel.komorowski@wat.edu.pl

This project is licensed under the terms of the MIT license.

UT are written using pytest framework. To start tests use:

python -m pytest