PtyLab.py

PtyLab is an inverse modeling toolbox for Conventional (CP) and Fourier (FP) ptychography in a unified framework. For more information please check the paper.

Getting started

The simplest way to get started is to check the below demo in Google Colab.

To explore more use cases of PtyLab, check the example_scripts and jupyter_tutorials directories. However, please install the package first as described in the below sections.

Installation

To install the package from source,

pip install git+https://github.com/PtyLab/PtyLab.py.git

This package uses cupy to utilize GPU for faster reconstruction. Please check their instructions for installing this dependency.

Development

Please clone this repository and navigate to the root folder

git clone git@github.com:PtyLab/PtyLab.py.git
cd PtyLab.py

Inside a virtual environment (preferably with conda), please install ptylab and its dependencies from the pinned versions specified under requirements.txt:

conda create --name ptylab_venv python=3.11.5 # or python version satisfying ">=3.9, <3.12"
conda activate ptylab_venv
pip install -e . -r requirements.txt

To use the GPU, cupy can be additionally installed in this environment.

conda install -c conda-forge cupy

If you would like to contribute to this package, especially if it involves modifying dependencies, please checkout the CONTRIBUTING.md file.

Citation

If you use this package in your work, cite us as below.

@article{Loetgering:23,
        author = {Lars Loetgering and Mengqi Du and Dirk Boonzajer Flaes and Tomas Aidukas and Felix Wechsler and Daniel S. Penagos Molina and Max Rose and Antonios Pelekanidis and Wilhelm Eschen and J\"{u}rgen Hess and Thomas Wilhein and Rainer Heintzmann and Jan Rothhardt and Stefan Witte},
        journal = {Opt. Express},
        number = {9},
        pages = {13763--13797},
        publisher = {Optica Publishing Group},
        title = {PtyLab.m/py/jl: a cross-platform, open-source inverse modeling toolbox for conventional and Fourier ptychography},
        volume = {31},
        month = {Apr},
        year = {2023},
        doi = {10.1364/OE.485370},
}