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A modern machine learning library for high-energy physics data analysis

Project description

ColliderML

Tests Coverage Python 3.10+ License: MIT

A modern machine learning library for high-energy physics data analysis.

Features

  • Efficient parallel data downloading with resume capability
  • Support for common HEP data formats
  • Machine learning utilities for particle physics
  • Visualization tools for physics data

Installation

For Users

# Create and activate environment
conda create -n collider-env python=3.11  # 3.10 or 3.11 recommended
conda activate collider-env

# Install package
pip install colliderml

For Developers

# Create and activate environment
conda create -n collider-dev python=3.11  # 3.10 or 3.11 recommended
conda activate collider-dev

# Clone repository
git clone https://github.com/murnanedaniel/colliderml.git
cd colliderml

# Install in development mode with extra dependencies
pip install -e ".[dev]"

Quick Start

from colliderml.core.data.manifest import ManifestClient
from colliderml.core.io import DataDownloader

manifest = ManifestClient()
files = manifest.select_files(campaign=None, datasets=["ttbar"], objects=["tracks"], max_events=1000)

downloader = DataDownloader()
results = downloader.download_files([f.path for f in files], local_dir="data", max_workers=4, resume=True)

for path, result in results.items():
    print(path, result.success, result.error)

Features

  • Manifest-driven: Always selects files from the latest portal manifest
  • Parallel Downloads: Download multiple files concurrently
  • Resume Capability: Optionally resume interrupted downloads
  • Progress Tracking: Real-time progress bars
  • Clear Errors: Helpful failure messages and HEAD checks

Development

  1. Activate your environment:

    conda activate collider-dev
    
  2. Run tests:

    # Run unit tests only
    pytest -v -m "not integration"
    
    # Run all tests including integration tests
    pytest -v
    
    # Run with coverage report
    pytest --cov=colliderml
    
  3. Build documentation:

    mkdocs build
    mkdocs serve  # View at http://127.0.0.1:8000
    

License

MIT License

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