hep-viz 0.1.1

A local visualizer for HEP data

hep-viz

A local visualizer for High Energy Physics (HEP) data.

hep-viz is a lightweight, high-performance tool designed to visualize high-dimensional particle tracking and calorimeter data directly from your local machine or Python environment. It bridges the gap between massive local datasets and fluid web-based visualization.

Features

• 🚀 Local & Fast: Runs a local web server to stream data on demand—no cloud upload required.
• 🐍 Python API: Seamlessly integrates with Jupyter Notebooks and Python scripts.
• 💻 CLI Support: Quickly visualize datasets from your terminal.
• ✨ 3D Visualization: Interactive 3D view of particles, tracks, and calorimeter hits.
• 📂 Parquet Support: Optimized for reading large datasets stored in Parquet format.

Installation

Requires Python 3.8+.

Option 1: Install from PyPI

pip install hep-viz

Option 2: Install from Conda

conda install finnbarwilson::hep-viz

Option 3: Install from Source

git clone https://github.com/FinnbarWilson/hep-viz.git
cd hep-viz
pip install .

Usage

1. Command Line Interface (CLI)

Visualize a directory containing your Parquet data files:

hep-viz view /path/to/data/directory

Options:

• --port <int>: Specify the port to run the server on (default: 8000).
• --no-browser: Prevent the browser from opening automatically.

2. Python API

Use hep-viz directly within your Python scripts or Jupyter Notebooks. This is perfect for visualizing in-memory data (e.g., from Hugging Face Datasets).

Example with Hugging Face Datasets:

import hep_viz
from datasets import load_dataset

# Load a compatible dataset (e.g., OpenDataDetector)
dataset = load_dataset("OpenDataDetector/ColliderML_higgs_pu0")

# Launches the server and opens the visualization
hep_viz.view(dataset)

Example with Custom Dictionary:

import hep_viz

# Data should be a dictionary of lists
data = {
    "particles": [...],
    "tracks": [...],
    "tracker_hits": [...],
    "calo_hits": [...]
}

hep_viz.view(data)

Data Format

hep-viz expects data to be organized into four main categories. If using the CLI, these should be Parquet files in your target directory. If using the Python API, these should be keys in your dictionary.

Required Categories

1. particles: Truth-level particle information.
   • Columns: event_id, particle_id, pdg_id, px, py, vx, vy
2. tracks: Reconstructed track information (optional, or derived from hits).
   • Columns: event_id, particle_id, pT, pdg_id
3. tracker_hits: Individual hits in the tracker layers.
   • Columns: event_id, particle_id, x, y, z, volume_id
4. calo_hits: Energy deposits in the calorimeter.
   • Columns: event_id, cell_id, x, y, z, total_energy, contrib_particle_ids (list), contrib_energies (list)

File Naming (CLI)

For the CLI to automatically detect files, they should contain the category name (e.g., my_particles.parquet). You can split data across multiple files using the pattern events<start>-<end> (e.g., particles.events0-999.parquet).

Development

To contribute or modify hep-viz:

1. Clone the repository:

   git clone https://github.com/yourusername/hep-viz.git
   cd hep-viz
   

2. Install in editable mode:

   pip install -e .
   

3. Run Tests:

   # Install test dependencies
   pip install pytest httpx
   
   # Run the test suite
   pytest
   

License

This project is licensed under the MIT License.

Copyright (c) 2025 Finnbar Wilson