Skip to main content

heparchy

DOI

Hierarchical database storage and access for high energy physics event data.

Docstrings are available, markdown documentation coming soon.

Installation

pip install heparchy

Features

  • Fast and efficient storage
  • Writes and reads from HDF5 files
  • Data stored hierarchically
    • Files contain processes
    • Processes contain events
    • Events contain datasets for the final state particles
  • Process level metadata can be attached
  • Context managers provide access to these containers

Data writing interface

Process

  • Metadata writing methods:
    • string(): MadGraph formatted string, ie. p p > t t~
    • decay(): pdgids of incoming and outgoing particles for the hard event
    • com_energy()
    • signal_id(): pdgid of the particle of interest in the hard event
    • custom(): extend with your own key / value metadata pair for the process

Events

  • Data writing methods for final state particles:
    • pmu(): 2d numpy array of 4-momenta, each row [px, py, pz, e]
    • pdg(): pdgids
    • is_signal(): boolean tags identifying if particle constituent of signal
    • custom(): extend with your own key / value dataset pair for the event

Coming soon

  • Direct interface from HepMC files to HDF5 format
  • Jupyter notebook examples
  • Pip installation script

Warning: before the first release, the read interface may change to improve consistency with the write interface.

Breaking changes will be avoided following the iminent release of 1.0.0.

Metadata

Release files for heparchy 0.2.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for heparchy 0.2.3
File Size Uploaded
heparchy-0.2.3.tar.gz 21.1 kB Details

Release files / heparchy-0.2.3.tar.gz

Download URL heparchy-0.2.3.tar.gz
Size 21.1 kB
Tags Source
SHA-256 checksum
How to use checksums
c669d5f6b55ba179a6839fbe300b87beec3105d815e541581605a0a5cff19d64
BLAKE2b-256 checksum
How to use checksums
808502a07d94b23673f2cd510075c534f23c1ae17d02515f94d82029cb869a7e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.12.9

Release history Release notifications | RSS feed

This release

0.2.3 This release

1 release file

0.2.1

1 release file

0.2.0

1 release file

0.2b4

2 release files

0.1.2

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page