https://github.com/bbfrederick/capcalc

Various programs for processing and displaying coactivation patterns in fMRI data

https://github.com/bbfrederick/capcalc

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Committers with academic emails
    2 of 4 committers (50.0%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (10.9%) to scientific vocabulary

Keywords from Contributors

interactive projection generic sequences archival neuroimaging embedded genomics observability autograding
Last synced: 10 months ago · JSON representation

Repository

Various programs for processing and displaying coactivation patterns in fMRI data

Basic Info
  • Host: GitHub
  • Owner: bbfrederick
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 19.3 MB
Statistics
  • Stars: 6
  • Watchers: 2
  • Forks: 3
  • Open Issues: 0
  • Releases: 20
Created over 9 years ago · Last pushed 11 months ago
Metadata Files
Readme Changelog License

README.rst

Capcalc
=======

capcalc is a suite of python programs used to perform coactivation
pattern analysis on time series data. It uses K-Means clustering to find
a set of “activation states” that represent the covarying patterns in
the data.

HTML documentation is here: http://capcalc.readthedocs.io/en/latest/

NOTE
====

This is an evolving code base. I’m constantly tinkering with it. That
said, now that I’m releasing this to the world, I’m being somewhat more
responsible about locking down stable release points. In between
releases, however, I’ll be messing with things. **It’s very possible I
could break something while doing this, so check back for status updates
if you download the code in between releases**. I’ve finally become a
little more modern and started adding automated testing, so as time goes
by hopefully the “in between” releases will be somewhat more reliable.
Check back often for exciting new features and bug fixes!

Ok, I’m sold. What’s in here?
=============================

-  **roidecompose** - This program uses an atlas to extract timecourses
   from a 4D nifti file, producing a text file with the averaged
   timecourse from each region in the atlas (each integral value in
   file) in each column. This can be input to capfromtcs. There are
   various options for normalizing the timecourses.

-  **capfromtcs** - This does the actual CAP calculation, performing a
   k-means cluster analysis on the set of timecourses to find the best
   representitive set of “states” in the file. Outputs the states found
   and the dominant state in each timepoint of the timecourse.

-  **maptoroi** - The inverse of roidecompose. Give it a set of cluster
   timecourses and a template file, and it maps the values back onto the
   rois

-  **statematch** - Use this for aligning two state output files. Takes
   two state timecourse files, and determines which states in the second
   correspond to which states in the first. Generates a new ‘remapped’
   file with the states in the second file expressed as states in the
   first.


Owner

  • Name: Blaise deB Frederick
  • Login: bbfrederick
  • Kind: user

MR Physicist who took a very wrong turn somewhere in the 80's, and ended up in psychiatry. Director of the Optomagnetic Group in the McLean Hospital BIC.

GitHub Events

Total
  • Release event: 5
  • Delete event: 9
  • Issue comment event: 2
  • Push event: 31
  • Pull request event: 27
  • Create event: 17
Last Year
  • Release event: 5
  • Delete event: 9
  • Issue comment event: 2
  • Push event: 31
  • Pull request event: 27
  • Create event: 17

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 476
  • Total Committers: 4
  • Avg Commits per committer: 119.0
  • Development Distribution Score (DDS): 0.384
Past Year
  • Commits: 116
  • Committers: 2
  • Avg Commits per committer: 58.0
  • Development Distribution Score (DDS): 0.155
Top Committers
Name Email Commits
Blaise deB Frederick b****k@g****m 293
Blaise Frederick b****k@m****u 154
dependabot[bot] 4****] 27
Daniel M. Drucker d****r@m****u 2
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 3
  • Total pull requests: 72
  • Average time to close issues: 4 days
  • Average time to close pull requests: 9 days
  • Total issue authors: 2
  • Total pull request authors: 2
  • Average comments per issue: 2.67
  • Average comments per pull request: 0.22
  • Merged pull requests: 54
  • Bot issues: 0
  • Bot pull requests: 70
Past Year
  • Issues: 0
  • Pull requests: 23
  • Average time to close issues: N/A
  • Average time to close pull requests: 4 days
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.13
  • Merged pull requests: 18
  • Bot issues: 0
  • Bot pull requests: 23
Top Authors
Issue Authors
  • dmd (2)
  • pstewa (1)
Pull Request Authors
  • dependabot[bot] (82)
  • dmd (2)
Top Labels
Issue Labels
bug (1)
Pull Request Labels
dependencies (82) github_actions (63) docker (19)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 55 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 15
  • Total maintainers: 1
pypi.org: capcalc

capcalc is a suite of python programs used to perform coactivation pattern analysis on time series data.

  • Versions: 15
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 55 Last month
Rankings
Dependent packages count: 6.6%
Downloads: 18.4%
Average: 19.1%
Forks count: 19.6%
Stargazers count: 20.5%
Dependent repos count: 30.6%
Maintainers (1)
Last synced: 11 months ago