| Name | ptyrad JSON |
| Version |
0.1.0b9
JSON |
| download |
| home_page | None |
| Summary | PtyRAD: Ptychographic Reconstruction with Automatic Differentiation |
| upload_time | 2025-07-10 06:35:06 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
ptychography
|
| VCS |
 |
| bugtrack_url |
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| requirements |
No requirements were recorded.
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|
# PtyRAD: Ptychographic Reconstruction with Automatic Differentiation
[](https://anaconda.org/conda-forge/ptyrad)
[](https://anaconda.org/conda-forge/ptyrad)
[](https://anaconda.org/conda-forge/ptyrad)
[](https://arxiv.org/abs/2505.07814)
[](https://doi.org/10.5281/zenodo.15392805)
[**Docs**](https://ptyrad.readthedocs.io/en/latest/index.html)
| [**Install Guide**](https://ptyrad.readthedocs.io/en/latest/installation.html)
| [**Quickstart**](https://ptyrad.readthedocs.io/en/latest/quickstart.html)
| [**Paper**](https://arxiv.org/abs/2505.07814)
| [**Youtube**](https://www.youtube.com/@ptyrad_official)
*PtyRAD* performs ptychographic reconstruction using an [automatic differention](https://en.wikipedia.org/wiki/Automatic_differentiation) (AD) framework powered by [*PyTorch*](https://pytorch.org/), which enables flexible and efficient implementation of gradient descent optimization. See our [arXiv paper](https://arxiv.org/abs/2505.07814) and the [Zenodo record](https://doi.org/10.5281/zenodo.15273176) for more information and demo datasets.
## Features
- Gradient descent algorithms (Adam, SGD, LBFGS, etc.)
- Mixed-state probe and object
- Position correction
- Position-dependent object tilt correction
- Interoperability with *PtychoShelves (fold_slice)* and *py4DSTEM*
- Streamlined preprocessing of cropping, padding, resampling, adding noises, and many more
- Hyperparameter tuning
- Multi-GPU reconstructions
## Recommended Tools
We recommend using [*Miniforge*](https://github.com/conda-forge/miniforge) for Python environment management, and
[*Visual Studio Code*](https://code.visualstudio.com/Download) for code editing and execution.
## Major dependencies
* Python 3.10 or above
* PyTorch 2.0 or above
* While *PtyRAD* can run on CPU, GPU is strongly suggested for high-speed ptychographic reconstructions.
- *PtyRAD* supports both NVIDIA GPUs with CUDA and Apple Silicon (MPS)
* *PtyRAD* was tested on Windows, MacOS, and Linux
## Installation
We recommend installing *PtyRAD* using `pip` inside a fresh conda environment.
First, create and activate a new conda environment **(ptyrad)** with Python > 3.10:
```sh
conda create -n ptyrad python=3.12
conda activate ptyrad
```
> 💡 **Note:** After activating the environment, your terminal prompt should show **(ptyrad)** at the beginning, indicating that the environment is active.
Then install *PtyRAD* using:
```sh
pip install ptyrad
```
*PtyRAD* can also be installed via `conda`. For detailed instructions on installing *PtyRAD* on different machines or pinning specific CUDA versions, see [the installation guide](https://ptyrad.readthedocs.io/en/latest/installation.html).
### How do I check if my installed PtyRAD has the GPU support?
CUDA version, GPU support, and PyTorch build across platforms can be extremely confusing, so *PtyRAD* provides handy CLI tools to help check these information for you!
Once you activated `(ptyrad)` environment and installed *PtyRAD* via `pip insall ptyrad`, you'll have access to the following command:
```bash
# You can run this command anywhere from your terminal, as long as `ptyrad` is installed in the environment
ptyrad check-gpu
```
This command will print out relevant information of your CUDA information if available.
## Get Started with the Demo
> 💡 **Note:** Download the demo datasets and scripts from our [Box folder](https://cornell.box.com/s/n5balzf88jixescp9l15ojx7di4xn1uo), which contains `demo/` and other tutorial slides and videos.
Before running the demo, please check the following:
1. Demo datasets are downloaded and placed to the correct location under `demo/data/`
2. `(ptyrad)` environment is created and activated (in VS Code it's the "Select Kernel")
3. *PtyRAD* is installed in the `(ptyrad)` environment
Now you're ready to run a quick demo using one of two interfaces:
- **Interactive Jupyter interface (Recommended)**
Use `demo/scripts/run_ptyrad_quick_example.ipynb` to quickly reconstruct the demo dataset in a Jupyter notebook
- **Command-line interface** (like your *Miniforge Prompt* terminal)
```bash
# Here we assume working directory is set at `demo/`
ptyrad run --params_path "params/tBL_WSe2_reconstruct.yml"
```
## Documentation
*PtyRAD* documentation is available at https://ptyrad.readthedocs.io/en/latest/index.html.
## Author
Chia-Hao Lee (cl2696@cornell.edu)
Developed at the Muller Group, Cornell University.
## Acknowledgments
Besides great support from the entire Muller group, this package gets inspiration from lots of community efforts, and specifically from the following packages. Some of the functions in *PtyRAD* are directly translated or modified from these packages as noted in their docstrings/comments to give explicit acknowledgment.
* [PtychoShelves](https://journals.iucr.org/j/issues/2020/02/00/zy5001/index.html)
* [fold_slice](https://github.com/yijiang1/fold_slice)
* [py4dstem](https://github.com/py4dstem/py4DSTEM)
* [adorym](https://github.com/mdw771/adorym)
* [SciComPty](https://www.mdpi.com/2410-3896/6/4/36)
## Other resources
* [ptycho-packages](https://github.com/chiahao3/ptycho-packages) lists many available ptychography packages
* [Cornell Box folder](https://cornell.box.com/s/n5balzf88jixescp9l15ojx7di4xn1uo) compiled by myself that keeps demo data, tutorial recordings, and slides for PtyRAD
* [Argonne Box folder](https://anl.box.com/s/f7lk410lf62rnia70fztd5l7n567btyv) compiled by Dr. Yi Jiang that holds tutorial slides of `fold_slice`
* [Blog post](https://chiahao-blog.super.site/posts/theory-algorithm-and-code-structure-of-ptychoshelves) written by myself that details the algorithms and code structure of `PtychoShelves` / `fold_slice`
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"description": "# PtyRAD: Ptychographic Reconstruction with Automatic Differentiation\n\n[](https://anaconda.org/conda-forge/ptyrad)\n[](https://anaconda.org/conda-forge/ptyrad)\n[](https://anaconda.org/conda-forge/ptyrad)\n[](https://arxiv.org/abs/2505.07814)\n[](https://doi.org/10.5281/zenodo.15392805)\n\n[**Docs**](https://ptyrad.readthedocs.io/en/latest/index.html)\n| [**Install Guide**](https://ptyrad.readthedocs.io/en/latest/installation.html)\n| [**Quickstart**](https://ptyrad.readthedocs.io/en/latest/quickstart.html)\n| [**Paper**](https://arxiv.org/abs/2505.07814)\n| [**Youtube**](https://www.youtube.com/@ptyrad_official)\n\n*PtyRAD* performs ptychographic reconstruction using an [automatic differention](https://en.wikipedia.org/wiki/Automatic_differentiation) (AD) framework powered by [*PyTorch*](https://pytorch.org/), which enables flexible and efficient implementation of gradient descent optimization. See our [arXiv paper](https://arxiv.org/abs/2505.07814) and the [Zenodo record](https://doi.org/10.5281/zenodo.15273176) for more information and demo datasets.\n\n## Features\n- Gradient descent algorithms (Adam, SGD, LBFGS, etc.)\n- Mixed-state probe and object\n- Position correction\n- Position-dependent object tilt correction\n- Interoperability with *PtychoShelves (fold_slice)* and *py4DSTEM*\n- Streamlined preprocessing of cropping, padding, resampling, adding noises, and many more\n- Hyperparameter tuning\n- Multi-GPU reconstructions\n\n## Recommended Tools\nWe recommend using [*Miniforge*](https://github.com/conda-forge/miniforge) for Python environment management, and \n[*Visual Studio Code*](https://code.visualstudio.com/Download) for code editing and execution.\n\n## Major dependencies\n\n* Python 3.10 or above\n* PyTorch 2.0 or above\n* While *PtyRAD* can run on CPU, GPU is strongly suggested for high-speed ptychographic reconstructions. \n - *PtyRAD* supports both NVIDIA GPUs with CUDA and Apple Silicon (MPS)\n* *PtyRAD* was tested on Windows, MacOS, and Linux\n\n## Installation\n\n\n\nWe recommend installing *PtyRAD* using `pip` inside a fresh conda environment.\n\nFirst, create and activate a new conda environment **(ptyrad)** with Python > 3.10:\n```sh\nconda create -n ptyrad python=3.12\nconda activate ptyrad\n```\n> \ud83d\udca1 **Note:** After activating the environment, your terminal prompt should show **(ptyrad)** at the beginning, indicating that the environment is active.\n\nThen install *PtyRAD* using:\n```sh\npip install ptyrad\n```\n\n*PtyRAD* can also be installed via `conda`. For detailed instructions on installing *PtyRAD* on different machines or pinning specific CUDA versions, see [the installation guide](https://ptyrad.readthedocs.io/en/latest/installation.html).\n\n\n\n### How do I check if my installed PtyRAD has the GPU support?\nCUDA version, GPU support, and PyTorch build across platforms can be extremely confusing, so *PtyRAD* provides handy CLI tools to help check these information for you!\n\nOnce you activated `(ptyrad)` environment and installed *PtyRAD* via `pip insall ptyrad`, you'll have access to the following command:\n\n```bash\n# You can run this command anywhere from your terminal, as long as `ptyrad` is installed in the environment\nptyrad check-gpu\n```\n\nThis command will print out relevant information of your CUDA information if available.\n\n## Get Started with the Demo\n\n> \ud83d\udca1 **Note:** Download the demo datasets and scripts from our [Box folder](https://cornell.box.com/s/n5balzf88jixescp9l15ojx7di4xn1uo), which contains `demo/` and other tutorial slides and videos.\n\nBefore running the demo, please check the following:\n1. 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Some of the functions in *PtyRAD* are directly translated or modified from these packages as noted in their docstrings/comments to give explicit acknowledgment.\n* [PtychoShelves](https://journals.iucr.org/j/issues/2020/02/00/zy5001/index.html)\n* [fold_slice](https://github.com/yijiang1/fold_slice)\n* [py4dstem](https://github.com/py4dstem/py4DSTEM)\n* [adorym](https://github.com/mdw771/adorym)\n* [SciComPty](https://www.mdpi.com/2410-3896/6/4/36)\n\n## Other resources\n\n* [ptycho-packages](https://github.com/chiahao3/ptycho-packages) lists many available ptychography packages\n* [Cornell Box folder](https://cornell.box.com/s/n5balzf88jixescp9l15ojx7di4xn1uo) compiled by myself that keeps demo data, tutorial recordings, and slides for PtyRAD\n* [Argonne Box folder](https://anl.box.com/s/f7lk410lf62rnia70fztd5l7n567btyv) compiled by Dr. Yi Jiang that holds tutorial slides of `fold_slice`\n* [Blog post](https://chiahao-blog.super.site/posts/theory-algorithm-and-code-structure-of-ptychoshelves) written by myself that details the algorithms and code structure of `PtychoShelves` / `fold_slice`\n",
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