Skip to main content

ROOT I/O in pure Python and Numpy.

Project description

uproot

uproot (originally μproot, for “micro-Python ROOT”) is a reader and a writer of the ROOT file format using only Python and Numpy. Unlike the standard C++ ROOT implementation, uproot is only an I/O library, primarily intended to stream data into machine learning libraries in Python. Unlike PyROOT and root_numpy, uproot does not depend on C++ ROOT. Instead, it uses Numpy to cast blocks of data from the ROOT file as Numpy arrays.

Python does not necessarily mean slow. As long as the data blocks (“baskets”) are large, this “array at a time” approach can even be faster than “event at a time” C++. Below, the rate of reading data into arrays with uproot is shown to be faster than C++ ROOT (left) and root_numpy (right), as long as the baskets are tens of kilobytes or larger (for a variable number of muons per event in an ensemble of different physics samples; higher is better).

https://raw.githubusercontent.com/scikit-hep/uproot3/master/docs/root-none-muon.png https://raw.githubusercontent.com/scikit-hep/uproot3/master/docs/rootnumpy-none-muon.png

uproot is not maintained by the ROOT project team, so post bug reports here as GitHub issues, not on a ROOT forum. Thanks!

Installation

Install uproot like any other Python package:

pip install uproot3                      # maybe with sudo or --user, or in virtualenv

The pip installer automatically installs strict dependencies; the conda installer also installs optional dependencies (except for Pandas).

Strict dependencies:

Optional dependencies:

  • lz4 to read/write lz4-compressed ROOT files

  • xxhash to read/write lz4-compressed ROOT files

  • lzma to read/write lzma-compressed ROOT files in Python 2

  • xrootd to access remote files through XRootD

  • requests to access remote files through HTTP

  • pandas to fill Pandas DataFrames instead of Numpy arrays

Reminder: you do not need C++ ROOT to run uproot.

Tutorial

See the project homepage for a tutorial.

Run that tutorial on Binder.

Tutorial contents:

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

uproot3-3.14.1.tar.gz (142.3 kB view details)

Uploaded Source

Built Distribution

uproot3-3.14.1-py3-none-any.whl (117.6 kB view details)

Uploaded Python 3

File details

Details for the file uproot3-3.14.1.tar.gz.

File metadata

  • Download URL: uproot3-3.14.1.tar.gz
  • Upload date:
  • Size: 142.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.24.0 setuptools/49.6.0.post20201009 requests-toolbelt/0.9.1 tqdm/4.52.0 CPython/3.8.5

File hashes

Hashes for uproot3-3.14.1.tar.gz
Algorithm Hash digest
SHA256 aac706eee309447cd2c2d7a2b23f11e06e72b2899da55bead607adef8e015fcd
MD5 a3f4b86b1cf4bc51f8f5cd0ab62c8f98
BLAKE2b-256 f0c91eaf2b351fc0bb8ad6506ff6c2fedc9bc3b489f3fc14c86065f19e4046c0

See more details on using hashes here.

File details

Details for the file uproot3-3.14.1-py3-none-any.whl.

File metadata

  • Download URL: uproot3-3.14.1-py3-none-any.whl
  • Upload date:
  • Size: 117.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.24.0 setuptools/49.6.0.post20201009 requests-toolbelt/0.9.1 tqdm/4.52.0 CPython/3.8.5

File hashes

Hashes for uproot3-3.14.1-py3-none-any.whl
Algorithm Hash digest
SHA256 3e6393e4b987f89316c02abebcc8ec78fa62d02f0af6e6be759a9ad9466e1da2
MD5 bfdd946d3a8de9ba83bb4f9703c3267d
BLAKE2b-256 79623f6fe3becb6a4ab3a07051ccca9fd7b5e3b0b115d9d4198bf104fe062bdb

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page