Skip to main content

An adaptive optics alignment tool for ALS beamlines utilizing gpCAM.

Project description

Tsuchinoko

PyPI License Build Status Documentation Status Test Coverage Slack Status

Tsuchinoko is a Qt application for adaptive experiment execution and tuning. Live visualizations show details of measurements, and provide feedback on the adaptive engine's decision-making process. The parameters of the adaptive engine can also be tuned live to explore and optimize the search procedure.

While Tsuchinoko is designed to allow custom adaptive engines to drive experiments, the gpCAM engine is a featured inclusion. This tool is based on a flexible and powerful Gaussian process regression at the core.

A Tsuchinoko system includes 4 distinct components: the GUI client, an adaptive engine, and execution engine, and a core service. These components are separable to allow flexibility with a variety of distributed designs.

Tsuchinoko running simulated measurements

Installation

The latest stable Tsuchinoko version is available on PyPI, and is installable with pip.

pip install tsuchinoko

For more information, see the installation documentation.

Resources

About the name

Japanese folklore describes the Tsuchinoko as a wide and short snake-like creature living in the mountains of western Japan. This creature has a cultural following similar to the Bigfoot of North America. Much like the global optimum of a non-convex function, its elusive nature is infamous.

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

tsuchinoko-1.0.4.tar.gz (67.3 kB view details)

Uploaded Source

Built Distribution

tsuchinoko-1.0.4-py3-none-any.whl (56.2 kB view details)

Uploaded Python 3

File details

Details for the file tsuchinoko-1.0.4.tar.gz.

File metadata

  • Download URL: tsuchinoko-1.0.4.tar.gz
  • Upload date:
  • Size: 67.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.1

File hashes

Hashes for tsuchinoko-1.0.4.tar.gz
Algorithm Hash digest
SHA256 16dd833eabe7a8c2025394c37435cf84a8c11bad2179769d872d3a18f69ccef3
MD5 5b3851e2a63ad7b61dccc4257d389759
BLAKE2b-256 9114c98d4eeea1009960131addb0fe2080c7af0723d1978747f1697441b5699c

See more details on using hashes here.

File details

Details for the file tsuchinoko-1.0.4-py3-none-any.whl.

File metadata

  • Download URL: tsuchinoko-1.0.4-py3-none-any.whl
  • Upload date:
  • Size: 56.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.1

File hashes

Hashes for tsuchinoko-1.0.4-py3-none-any.whl
Algorithm Hash digest
SHA256 84a5b205c94a2908c59b0a44025a670af7f86069b90fd3a607a21bdb159cc22a
MD5 53c77d783833064a869c2d0dd49f987c
BLAKE2b-256 d56e4daf20609a0bd29c5f881aac10f0f7efcb57fb3ee4c9d861993cb614afe1

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