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title: TexTOM Overview
---

# 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.