ptychi 1.3.0

Pty-chi is a package of ptychography reconstruction engines

Welcome to the repository of Pty-chi, a PyTorch-based ptychography reconstruction library!

Installation

Standard installation

The easiest way to install the latest release is through PyPI.

First, create a new conda environment with Python 3.11:

conda create -n ptychi python=3.11

Then install Pty-Chi using:

pip install ptychi

Developer installation

Use developer installation when you want to modify the code and test the changes, or when you run into build issues that drive you to install the package from source. We recommend using Conda/pip or uv for environment and package management.

Option 1: Installation with uv

Uv is a modern lightweight package manager for Python featuring fast speed and deterministic builds. When creating a uv virtual environment, the environment directory and all the packages inatalled in it are kept in the current working directory – unlike Conda, where the environments are centrally managed. Therefore, first cd into the root level of your local clone of Pty-Chi, and then create a new uv virtual environment with Python 3.11:

uv venv --python 3.11

Then install Pty-Chi and its dependencies using:

uv sync

You can now run scripts inside the project directory with:

uv run <script.py>

without activating the environment. Alternatively, you can activate the environment with:

source .venv/bin/activate

and then run scripts with:

python <script.py>

This allows you to run scripts located anywhere in your system.

Option 2: Installation with Conda and pip

To install the latest code in the main branch, clone the repository to your workspace, and create a new conda environment using:

conda create -n ptychi python=3.11

Then install the package and its dependencies using:

pip install -r requirements.txt
pip install -e .

How to run test scripts

1. Contact the developers to be given access to the APS GitLab repository that holds test data. You need to have an account on APS GitLab.

2. After gaining access, clone the GitLab data repository to your hard drive.

3. Set PTYCHO_CI_DATA_DIR to the ci_data directory of the data repository: export PTYCHO_CI_DATA_DIR="path_to_data_repo/ci_data".

4. Run any test scripts in tests with Python.

To use non-Nvidia GPUs

Pty-Chi works on GPUs from different vendors than NVidia. For example, Intel. To run Pty-Chi with Intel GPUs, add these lines right after importing torch and ptychi:

torch.set_default_device("xpu")
ptychi.device.set_torch_accelerator_module(torch.xpu)

Reading documentations

Pty-Chi’s documentation is hosted on Read the Docs.

You can also build the docs and view them in your browser locally. To build the docs, install the dependencies as the first step:

pip install -r docs/requirements.txt

Then:

cd docs
make html

You can then view the docs by opening docs/build/html/index.html in your browser.

Developer’s Guide

Please refer to the developer’s guide in the Wiki page for more information on how to contribute to the project.

🛑 For major changes such as adding a new reconstructor or those involving extensive structural changes to the codebase, please first discuss with the maintainers by creating an issue. Consider creating a new package inheriting Pty-Chi’s reconstructors and data structures for experimental features and algorithms. See Developer’s guide: Extending Pty-Chi with external algorithm packages.

Citation

If Pty-Chi is useful for your research, please consider citing the following paper:

@misc{ptychi_2025_arxiv,
    title = {Pty-Chi: A PyTorch-based modern ptychographic data analysis package},
    author = {Ming Du and Hanna Ruth and Steven Henke and Yi Jiang and Viktor Nikitin and Ashish Tripathi and Junjing Deng and Jeffrey Klug and Peco Myint and Tao Zhou and Nicholas Schwarz and Mathew Cherukara and Alec Sandy and Stefan Vogt},
    year = {2025},
    eprint = {2510.20929},
    archivePrefix = {arXiv},
    primaryClass = {physics.optics},
    url = {https://arxiv.org/abs/2510.20929},
}