| Abstract: |
| X-ray chemical imaging techniques, such as XRD-CT, provide vital spatially-resolved information for materials science, yet data processing remains computationally demanding. We present nDTomo, an open-source Python toolkit designed for the simulation, reconstruction, and analysis of hyperspectral tomographic data. Unlike highly abstracted frameworks, nDTomo emphasises pedagogical clarity through a function-centric design, making it ideal for both research and education. The suite integrates standard reconstruction algorithms with deep learning capabilities, including the self-supervised PeakFitCNN for automated peak analysis and GPU-accelerated direct least squares reconstruction (DLSR). We demonstrate the utility of the toolkit through its application in battery research and catalysis, highlighting its ability to correct experimental artifacts such as motor jitter and beam decay. By combining an intuitive PyQt-based graphical user interface (nDTomoGUI) with a modular backend and interactive Jupyter notebooks, nDTomo provides a transparent and reproducible environment for chemical imaging workflows. |
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