XRD Analysis Collection

A collection of standalone Python tools for processing X-ray diffraction (XRD) data — 2D/1D data viewing, ROI imaging, azimuthal fitting, 1D simulation, and batch Rietveld/profile refinement with TOPAS. Built around scanning-XRD datasets stored as HDF5 (e.g. ESRF ID13-style files with an integrated 1D pattern and a raw 2D Eiger detector stack per scan point).

Repository structure

Path	Description
roi2d.py	ROI-from-2D Viewer — build a real-space image from a region of the raw 2D detector.
sxrd-viewer.py	Scanning XRD Viewer (v1) — explore a scan pixel-by-pixel or with a single polygon ROI.
sxrd_viewer2.py	Scanning XRD Viewer (v2) — adds multiple simultaneous polygon ROIs and background subtraction.
1Dsimulation/	Simulating / modeling 1D diffraction patterns.
batch_azimuthal/	Batch azimuthal integration and fitting across many frames.
batchrefinement/	Batch profile / Rietveld refinement automation driven by TOPAS.
preprocessing/	Preprocessing utilities for raw scan files ahead of analysis.

Each subfolder groups related scripts/notebooks for that stage of the workflow — open the folder for the specific tools it contains.

Tools

ROI-from-2D Viewer — roi2d.py

A focused PyQt5 GUI for one task: turning a region of the 2D diffraction pattern into a real-space (scan-position) image.

Workflow

1. Load the scan .h5 file — set the 1D and Eiger HDF5 paths and the map shape (rows × cols), then click Load.
2. The mean 1D pattern is shown on top. Drag a horizontal span (or use the min/max boxes) to pick a 2θ range — this builds the navigator map.
3. Click a few pixels on the navigator map. Their raw 2D detector frames are summed live and shown in the detector panel.
4. Draw a polygon on the summed detector frame to select a feature.
5. Click Build image from detector ROI — every scan position's 2D frame is integrated inside that polygon to produce a new real-space image.
6. Save the result as .npy + .png + .json.

python roi2d.py

Scanning XRD Viewer — sxrd-viewer.py (v1) and sxrd_viewer2.py (v2)

A desktop GUI for exploring scanning XRD datasets. Both viewers share the same core workflow; v2 adds multi-polygon ROI management and background subtraction on top of v1.

Workflow

1. Pick the scan .h5 file, choose the scan entry (e.g. 1.1), confirm the internal HDF5 paths, and click Load.
2. The mean 1D pattern is plotted on top — drag a span (or set 2θ min/max) to define the ROI used to build the spatial intensity map.
3. Click-pixel mode: click any pixel on the map to see its 1D pattern and raw 2D Eiger frame simultaneously.
4. Polygon mode: draw a polygon on the map. The 1D pattern (mean or sum over the polygon) appears immediately; click Average 2D over polygon to also build the averaged detector frame.
5. (v2 only) Add several polygons, step through them with ←/→, place a matching background region per polygon, and optionally subtract it from the 1D pattern.
6. Save the current 1D pattern (.xy), everything (.npz), or just the polygon vertices (.json/.npy).

python sxrd-viewer.py     # v1 — single polygon
python sxrd_viewer2.py    # v2 — multi-polygon + background subtraction

Requirements

All three viewers share the same dependencies:

• Python ≥ 3.8
• PyQt5
• matplotlib ≥ 3.5
• numpy
• h5py
• hdf5plugin (required to decode compressed/bitshuffled Eiger frames)

pip install PyQt5 "matplotlib>=3.5" numpy h5py hdf5plugin

Data format

The viewers expect an HDF5 scan file containing, per scan entry (e.g. 1.1):

• a 2θ (or q) axis, e.g. /1.1/eiger_integrate/integrated/2th
• an integrated intensity array, e.g. /1.1/eiger_integrate/integrated/intensity
• (optional) the raw 2D detector stack, e.g. /1.1/measurement/eiger

These paths and the scan map shape (rows × cols) are editable fields in each tool's UI — adjust them to match your beamline's file layout if it differs from the ESRF ID13-style defaults shown above.

License

No license file is currently included in this repository, which means all rights are reserved by default. Add a LICENSE file if you want to permit reuse.

Author

GVanTRAN