Skip to main content

Tree traversals using JAX

Project description

Hyperiax: Tree traversals using JAX

Introduction

Hyperiax is a framework for tree traversal and computations on large-scale tree. Its primary purpose is to facilitate efficient message passing and operation execution on large trees. Hyperiax uses JAX for fast execution and automatic differentiation. Hyperiax is currently developed and maintained by CCEM, UCPH actively.

Initially, Hyperiax was designed specifically for phylogenetic analysis of biological shape data, particularly enabling statistical inference with continuous time stochastic processes along the edges of the trees. For this purpose, is integrated with JAXGeometry, a computational differential geometry toolbox implemented in JAX. However, Hyperiax's messaging system and operations are general, which means that they can be easily adapted for use in other contexts. With minor modifications, Hyperiax can be used for any application where fast tree-level computations are necessary. Included examples cover such cases with inference in Gaussian graphical models, phylogenetic mean computation, and recursive shape matching in binary trees.

Installation

# Install Hyperiax directly using pip
pip install hyperiax

# Install Hyperiax from the repository, for the newest version
pip install git+https://github.com/ComputationalEvolutionaryMorphometry/hyperiax.git

# Install Hyperiax for development
git clone git@github.com:ComputationalEvolutionaryMorphometry/hyperiax.git
# or (if you haven't set up ssh)
git clone https://github.com/ComputationalEvolutionaryMorphometry/hyperiax.git
# and then install by
pip install -e hyperiax[dev]
# and optionally
pip install -e hyperiax[examples]
# to install the dependencies for all the example notebooks

Documentation

  • Usage: See Usage for different examples of Hyperiax.
  • Full API Documentation: See Hyperiax API

Todo

Contribution

Contributions, issues and feature requests are all welcome! Please refer to the contributing guidelines before you want to contribute to the project.

Contact

If you experience problems or have technical questions, please open an issue. For questions related to the Hyperiax project or CCEM, please contact Stefan Sommer.

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

hyperiax-2.0.1.tar.gz (4.0 MB view details)

Uploaded Source

File details

Details for the file hyperiax-2.0.1.tar.gz.

File metadata

  • Download URL: hyperiax-2.0.1.tar.gz
  • Upload date:
  • Size: 4.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.6

File hashes

Hashes for hyperiax-2.0.1.tar.gz
Algorithm Hash digest
SHA256 0f57c1bf5b03fff30d8c2e08d39a04236b739a744fd9ddc577cd03b21beca215
MD5 6361255558674aa7cacb892c0ea9951e
BLAKE2b-256 bfee1c44bb8fd09b9681577d5bef967bec400680650450ea9f0cc705432a3d3d

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