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

Standard Installation

The latest stable Tsuchinoko version is available on PyPI, and is installable with pip. It is recommended that you use Python 3.9 for this installation.

pip install tsuchinoko

For more information, see the installation documentation.

Easy Installation

For Mac OSX and Windows, pre-packaged installers are available. These do not require a base Python installation. See the installation documentation for more details.

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-1.1.0.tar.gz (94.3 kB view details)

Uploaded Source

Built Distribution

tsuchinoko-1.1.0-py3-none-any.whl (92.2 kB view details)

Uploaded Python 3

File details

Details for the file tsuchinoko-1.1.0.tar.gz.

File metadata

  • Download URL: tsuchinoko-1.1.0.tar.gz
  • Upload date:
  • Size: 94.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for tsuchinoko-1.1.0.tar.gz
Algorithm Hash digest
SHA256 0677f5b2b3a7c042a02e22686749547c73eb5467616876278325b1717493d014
MD5 2d69491f44a3a455aa92bade46f5afe1
BLAKE2b-256 a2d401f5d96935fcbe969be58aa98d9b2bfed9fbbfbf4777d1eecfc748ab8a73

See more details on using hashes here.

File details

Details for the file tsuchinoko-1.1.0-py3-none-any.whl.

File metadata

  • Download URL: tsuchinoko-1.1.0-py3-none-any.whl
  • Upload date:
  • Size: 92.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for tsuchinoko-1.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 500dde912732ef040c30365e79c7c08dbb8f2a32ed2801caee356cd79810606c
MD5 4ae8983fbfa7d7645d24580aee68f71d
BLAKE2b-256 4577338590cd46b212c20e5b174015e0f532c708882b99372ac0968302fa8341

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