Skip to main content

Automated analysis of MEA datasets

Project description

automea

automea (Automated analysis of MEA datasets) is a open-source Python package for the analysis of Micro-Electrode Array (MEA) datasets.

Bursts are detected using the Max Interval Method. Users can manually set Max Interval Parameters, or can use a machine learning model that dynamically predicts optimal parameters for specific recording times. Several models are distributed with automea, and users are free to fine-tune the existing models for their specific needs, or upload new models completely.

The machine-learning-based burst detection routine is explained in the paper accompanying the package.

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

automea-0.0.6.tar.gz (40.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

automea-0.0.6-py3-none-any.whl (43.0 kB view details)

Uploaded Python 3

File details

Details for the file automea-0.0.6.tar.gz.

File metadata

  • Download URL: automea-0.0.6.tar.gz
  • Upload date:
  • Size: 40.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.13

File hashes

Hashes for automea-0.0.6.tar.gz
Algorithm Hash digest
SHA256 176ae1cdc22555ddb6ff7bda7ff3f58e80589382105546bae3cc75106c46736d
MD5 efbee81f3c34c97af007807f1a7c5a60
BLAKE2b-256 12d0d7528bb8fdf858b0f2d308bc2b67e82e77ba23f2ed7ce8c7fba4376218e4

See more details on using hashes here.

File details

Details for the file automea-0.0.6-py3-none-any.whl.

File metadata

  • Download URL: automea-0.0.6-py3-none-any.whl
  • Upload date:
  • Size: 43.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.13

File hashes

Hashes for automea-0.0.6-py3-none-any.whl
Algorithm Hash digest
SHA256 b740beccdbfeea438ef7908a9d536f237036c1c1ba4ee1dda29ee9c2cce511fa
MD5 620eafeadd8f61552ffe768c46148a1e
BLAKE2b-256 5bd710439da7a760012385b481d386107350bff46cdd225efa33b516ce8c7e07

See more details on using hashes here.

Supported by

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