A modern machine learning library for high-energy physics data analysis
Project description
ColliderML
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
-
Activate your environment:
conda activate collider-dev
-
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
-
Build documentation:
mkdocs build mkdocs serve # View at http://127.0.0.1:8000
License
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
colliderml-0.1.1.tar.gz
(419.2 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file colliderml-0.1.1.tar.gz.
File metadata
- Download URL: colliderml-0.1.1.tar.gz
- Upload date:
- Size: 419.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.26.0 requests-toolbelt/0.9.1 urllib3/1.26.7 tqdm/4.62.3 importlib-metadata/8.5.0 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.8.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
2f6d43f34f9987fb99a44a85b9402abdc3b503badd41a2fb4b0b934818314a9c
|
|
| MD5 |
01da5c451fe6fc756f5e8b3371884b90
|
|
| BLAKE2b-256 |
08fbcd225fe154ecfa531de07b0313f68c16a994514d84f179feee5cb6b0d985
|
File details
Details for the file colliderml-0.1.1-py3-none-any.whl.
File metadata
- Download URL: colliderml-0.1.1-py3-none-any.whl
- Upload date:
- Size: 13.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.26.0 requests-toolbelt/0.9.1 urllib3/1.26.7 tqdm/4.62.3 importlib-metadata/8.5.0 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.8.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
6a57dc4ec8b316abff0e03672ddcd5a57d326de330f1b5fc62a1204ce03e4671
|
|
| MD5 |
ae8674a9f4c0479859eb1050db26db9d
|
|
| BLAKE2b-256 |
3847c29c1592ca86056fda4a7925e991887e39c5ea9326237f79ca266aa901ad
|