rustpix 1.1.0

High-performance TPX3 pixel detector data processing for neutron imaging

rustpix

High-performance pixel detector data processing for neutron imaging. Supports Timepix3 (TPX3) with 96M+ hits/sec throughput. Features multiple clustering algorithms, centroid extraction, and Python bindings.

Features

• Fast TPX3 Processing: Parallel packet parsing with memory-mapped I/O
• Multiple Clustering Algorithms:
  • ABS (Adjacency-Based Search) - 8-connectivity clustering
  • DBSCAN - Density-based with spatial indexing
  • Graph - Union-find connected components
  • Grid - Parallel grid-based clustering
• Streaming Architecture: Process files larger than RAM
• Python Bindings: Thin wrappers with NumPy integration
• CLI Tool: Command-line interface for batch processing
• GUI Application: Interactive analysis with real-time visualization
• Multiple Output Formats: HDF5, Arrow, CSV

Installation

Python (pip)

pip install rustpix

macOS (Homebrew)

brew tap ornlneutronimaging/rustpix
brew install --cask rustpix  # GUI app

Rust (cargo)

# CLI tool
cargo install rustpix-cli

# Library
cargo add rustpix-core rustpix-algorithms

From Source

# Using pixi (recommended)
git clone https://github.com/ornlneutronimaging/rustpix
cd rustpix
pixi install
pixi run build

# Or with cargo
cargo build --release --workspace

Quick Start

Python

import rustpix

# Process TPX3 file to neutron events
config = rustpix.ClusteringConfig(radius=5.0, temporal_window_ns=75.0)
neutrons = rustpix.process_tpx3_neutrons(
    "data.tpx3",
    clustering_config=config,
    algorithm="abs"
)

# Convert to NumPy
data = neutrons.to_numpy()
print(f"Found {len(data['x'])} neutron events")

Command Line

# Process file
rustpix process input.tpx3 -o output.h5

# Show file info
rustpix info input.tpx3

# Benchmark algorithms
rustpix benchmark input.tpx3

GUI Application

Launch the GUI for interactive analysis:

# macOS (Homebrew)
open -a Rustpix

# From source
pixi run gui
cargo run -p rustpix-gui --release

Workspace Structure

Crate	Description
rustpix-core	Core traits and types
rustpix-tpx	TPX3 packet parser and hit types
rustpix-algorithms	Clustering algorithms (ABS, DBSCAN, Graph, Grid)
rustpix-io	File I/O with memory-mapped reading
rustpix-python	Python bindings (PyO3)
rustpix-cli	Command-line interface
rustpix-gui	GUI application (egui)

Python API

Read Hits

import rustpix

# Read all hits
hits = rustpix.read_tpx3_hits("input.tpx3")
hits_np = hits.to_numpy()

# Stream hits in batches
for batch in rustpix.stream_tpx3_hits("input.tpx3"):
    process(batch.to_numpy())

Process Neutrons

# Configure clustering
clustering = rustpix.ClusteringConfig(
    radius=5.0,
    temporal_window_ns=75.0,
    min_cluster_size=1
)

# Configure centroid extraction
extraction = rustpix.ExtractionConfig(
    super_resolution_factor=8.0,
    weighted_by_tot=True,
    min_tot_threshold=10
)

# Stream processing (low memory)
for batch in rustpix.process_tpx3_neutrons(
    "input.tpx3",
    clustering_config=clustering,
    extraction_config=extraction,
    algorithm="abs"
):
    save_batch(batch.to_numpy())

# Batch processing (collect all)
neutrons = rustpix.process_tpx3_neutrons(
    "input.tpx3",
    clustering_config=clustering,
    extraction_config=extraction,
    algorithm="abs",
    collect=True
)

CLI Usage

# Process with custom parameters
rustpix process input.tpx3 -o output.csv \
    --algorithm dbscan \
    --spatial-epsilon 2.0 \
    --temporal-epsilon 500

# Show file information
rustpix info input.tpx3

# Benchmark different algorithms
rustpix benchmark input.tpx3 --iterations 5

# Convert formats
rustpix convert input.tpx3 -f hdf5 -o output.h5

Performance

• Throughput: 96M+ hits/sec on modern hardware
• Memory: Streaming architecture processes files larger than RAM
• Parallel: Multi-threaded clustering with rayon
• Optimized: SIMD-friendly data layouts (SoA)

Development

# Install dependencies with pixi
pixi install

# Run tests
pixi run test

# Format and lint
pixi run lint

# Build documentation
pixi run docs

# Run GUI in debug mode
pixi run gui-debug

Documentation

• User Guide: ornlneutronimaging.github.io/rustpix
• Rust API: docs.rs/rustpix-core
• Design Docs: docs/ directory

Citation

If you use rustpix in your research, please cite:

@software{rustpix2026,
  title = {rustpix: High-performance pixel detector data processing},
  author = {{ORNL Neutron Imaging Team}},
  year = {2026},
  url = {https://github.com/ornlneutronimaging/rustpix},
  doi = {10.5281/zenodo.18496371}
}

License

MIT License - see LICENSE for details.

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

Acknowledgments

Developed by the Neutron Imaging Team at Oak Ridge National Laboratory.