TexTOM Overview#

TexTOM is an end-to-end toolbox for texture tomography, combining data integration, alignment, forward modelling, and orientation-distribution optimisation. This page introduces the moving parts so the rest of the documentation can dive directly into tasks and reference details.

Key Capabilities#

  • Data integration powered by pyFAI and GPU-friendly backends for azimuthal rebinning of detector frames.

  • Alignment routines adapted from the Mumott project, including optical-flow and phase-matching pipelines with coarse-to-fine regrouping.

  • Physical modelling through diffractlets (hyperspherical harmonics or grid-based) and projector generation that encode the sample footprint.

  • Optimisation engines that retrieve voxel-wise orientation distribution functions and related statistics.

  • Visualisation and export helpers for diagnostic plots, IPF/slice viewers, and Paraview-compatible outputs.

Command-Line Entry Points#

Command

Purpose

textom

Launch an IPython session with every public TexTOM function preloaded.

textom_config

Open the configuration template (textom/config.py) to set thread counts, GPU usage, and defaults.

textom_documentation

Open the legacy PDF manual for offline reading (optional).

Documentation Layout#

Section

Description

Tutorials

Hands-on walkthroughs of the recommended workflow—from data ingestion to optimisation and analysis.

How-to Guides

Task-oriented checklists (e.g., environment setup, remote execution, troubleshooting alignment).

Explanations

Background theory: diffractlets, projector construction, and parameter trade-offs.

References

API docs generated via Sphinx autodoc/autosummary for direct code lookup.

Treat this page as your map: jump to tutorials if you need an end-to-end flow, or open the guides/reference pages for targeted questions.