A Python implementation of the moving average principal components analysis methods from GIFT.

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for eurunuela from gravatar.com eurunuela Avatar for handwerkerd from gravatar.com handwerkerd Avatar for notzaki from gravatar.com notzaki Avatar for tsalo from gravatar.com tsalo

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Project description

mapca

A Python implementation of the moving average principal components analysis methods from GIFT

Latest Version PyPI - Python Version License CircleCI Codecov Average time to resolve an issue Percentage of issues still open Join the chat at https://gitter.im/ME-ICA/mapca

About

mapca is a Python package that performs dimensionality reduction with principal component analysis (PCA) on functional magnetic resonance imaging (fMRI) data. It is a translation to Python of the dimensionality reduction technique used in the MATLAB-based GIFT package and introduced by Li et al. 2007[^1].

[^1]: Li, Y. O., Adali, T., & Calhoun, V. D. (2007). Estimating the number of independent components for functional magnetic resonance imaging data. Human Brain Mapping, 28(11), 1251–1266. https://doi.org/10.1002/hbm.20359

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for eurunuela from gravatar.com eurunuela Avatar for handwerkerd from gravatar.com handwerkerd Avatar for notzaki from gravatar.com notzaki Avatar for tsalo from gravatar.com tsalo

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Release history Release notifications | RSS feed

0.0.5

0.0.4

0.0.3

This version

0.0.2

0.0.1

0.0.1rc0 pre-release

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mapca-0.0.2.tar.gz (32.2 kB view details)

Uploaded Source

Built Distribution

mapca-0.0.2-py3-none-any.whl (25.3 kB view details)

Uploaded Python 3

File details

Details for the file mapca-0.0.2.tar.gz.

File metadata

  • Download URL: mapca-0.0.2.tar.gz
  • Upload date:
  • Size: 32.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.10.1 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.12

File hashes

Hashes for mapca-0.0.2.tar.gz
Algorithm Hash digest
SHA256 a7c6a6a9812da6bbdbfe7835665cbbafa70e3269a06283529de36aec537c7c33
MD5 5d3665031ec6be6b53a379d3393eeb25
BLAKE2b-256 923d57378e19dae88b07599ffbb87464a268c05656a79ad09607bca3f3fcc761

See more details on using hashes here.

File details

Details for the file mapca-0.0.2-py3-none-any.whl.

File metadata

  • Download URL: mapca-0.0.2-py3-none-any.whl
  • Upload date:
  • Size: 25.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.10.1 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.12

File hashes

Hashes for mapca-0.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 3607daf3883e55454629bb0299039c024a97343c7e7055c46540cd684e358675
MD5 6d1539b4616fadc6040c704a9f36119f
BLAKE2b-256 a565989e0e81fd8b8cdf6ffab155892d78106e051c9dbf3ab8ad4eb27fac0d84

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