DIAD Diffraction & Radiography Data Toolkit

Jupyter‑friendly helpers for visualising, analysing and cleaning the dual‑stream HDF5 data generated by the DIAD beamline at Diamond Light Source.

Features

• Inspect radiography and diffraction frames side‑by‑side in an interactive widget.
• Click‑to‑plot 1‑D diffraction profiles mapped to any pixel in the radiograph.
• Batch peak‑find / integrate hundreds of patterns and export CSV or pickle.
• Flat‑field & dark‑correct radiography stacks in seconds.
• Pure‑Python stack built on familiar libraries (numpy, h5py, matplotlib, ipywidgets).

---

Installation

1. Clone this repository

git clone https://github.com/DIAD-beamline/DataPostprocessing.git
cd DataPostprocessing

2. Create & activate a virtual environment (optional but recommended)

python -m venv diad_vis
source diad_vis/bin/activate   # Linux/macOS
diad_vis\Scripts\activate      # Windows

3. Install dependencies

pip install -r requirements.txt
# or, manually:
pip install numpy h5py matplotlib ipywidgets ipympl scipy pillow tifffile

Note: On Diamond’s analysis cluster simply module load python then pip install --user -r requirements.txt.

---

Quick Start

jupyter lab      # or jupyter notebook

Open one of the ExampleNotebook_* notebooks and run the first cell. A small GUI will pop up asking you to select your .h5 dataset.

---

Typical Workflows

Task	Notebook	What you get
Visual exploration & annotation	ExampleNotebook_DiffractionDataExplorer.ipynb	Scroll through projections, click the sample image to see its diffraction, overlay multiple profiles, save PNGs.
Batch peak analysis	ExampleNotebook_DiffractionDataAnalysis.ipynb	Load ROI‑averaged 1‑D profiles, detect peaks, integrate intensities, export results table and plots.
Radiography normalisation	ExampleNotebook_RadiographyDataNormalize.ipynb	Apply dark subtraction, flat‑field division, optional median filter; outputs TIFF stack ready for reconstruction.

---

Repository Structure

DataPostprocessing/
├── diffractionDataExplorer_class.py
├── diffractionDataAnalysis_class.py
├── radiographyDataNormalize_class.py
├── notebooks/
│   ├── ExampleNotebook_DiffractionDataExplorer.ipynb
│   ├── ExampleNotebook_DiffractionDataAnalysis.ipynb
│   └── ExampleNotebook_RadiographyDataNormalize.ipynb
├── tests/
└── README.md

---

Contributing

Bug reports, feature requests and pull requests are very welcome! Please open an issue first if you plan major changes.

1. Fork the repo
2. Create a feature branch: git checkout -b awesome‑feature
3. Commit and push: git push origin awesome‑feature
4. Open a pull request

---

License

Distributed under the MIT License. See LICENSE for details.

---

Citation

If this toolkit helps your research, please cite:

Watch this space

---

Contact

Questions? Open an issue or reach us at diad@diamond.ac.uk.

---

Acknowledgments

This software was developed by the DIAD beamline team and the wider Diamond Light Source community.