XRayPlot Tutorial: From Raw Images to Publication-Ready Plots

XRayPlot: A Complete Guide to Visualizing X-Ray Data

What XRayPlot is

XRayPlot is a visualization tool designed to process and display X‑ray imaging and diffraction data. It converts raw detector outputs into publication‑quality plots, supports common X‑ray file formats, and provides interactive controls for contrast, color mapping, and annotations.

Key features

• File support: Reads common formats (e.g., DICOM, TIFF, EDF, CBF).
• Image processing: Dark/flat correction, background subtraction, normalization.
• Visualization: Linear/log intensity scaling, colormaps, gamma correction, zoom/pan.
• Analysis tools: Radial integration, peak finding, line profiles, 2D to 1D conversions.
• Annotations & export: Add scale bars, labels, ROI overlays; export PNG, SVG, PDF, CSV.
• Batch processing: Apply pipelines to multiple images with scripting or CLI.
• Interactivity: GUI widgets for real‑time parameter tuning; optional notebook integration.

Typical workflow

1. Load raw files (single or batch).
2. Apply preprocessing (dark/flat, normalization).
3. Select visualization settings (scaling, colormap).
4. Run analysis (integrate, fit peaks, extract profiles).
5. Annotate and export figures and data.

Common use cases

• Medical imaging visualization and QC.
• Materials science: powder diffraction, crystallography.
• Synchrotron beamline data processing.
• Educational demonstrations of X‑ray image processing.

Example commands (CLI-style)

Code
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xrayplot load dataset/*.edf  –dark dark.edf –flat flat.edf   –normalize   –integrate radial –bins 1000   –export plot.svg results.csv 

Tips for better plots

• Use linear scaling for structural detail; log scaling for high dynamic range.
• Apply appropriate colormaps (sequential for intensity, diverging for differences).
• Always include scale bars and intensity colorbars on published images.
• Document preprocessing steps when sharing results.

Limitations & considerations

• Quality depends on correct detector calibration (geometry, pixel size).
• Large datasets may require GPU acceleration or chunked processing.
• Ensure compliance with medical data standards when handling patient images.