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.
Raw data
{
"_id": null,
"home_page": "https://github.com/bbfrederick/capcalc",
"name": "capcalc",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "fMRI, correlation, clustering, states",
"author": "Blaise Frederick",
"author_email": "bbfrederick@mclean.harvard.edu",
"download_url": "https://files.pythonhosted.org/packages/42/d0/64eaf3cbe199bb738d157c5be7d5de607960643be294add4378b3a5ac45f/capcalc-1.3.9.tar.gz",
"platform": null,
"description": "Capcalc\n=======\n\ncapcalc is a suite of python programs used to perform coactivation\npattern analysis on time series data. It uses K-Means clustering to find\na set of \u201cactivation states\u201d that represent the covarying patterns in\nthe data.\n\nHTML documentation is here: http://capcalc.readthedocs.io/en/latest/\n\nNOTE\n====\n\nThis is an evolving code base. I\u2019m constantly tinkering with it. That\nsaid, now that I\u2019m releasing this to the world, I\u2019m being somewhat more\nresponsible about locking down stable release points. In between\nreleases, however, I\u2019ll be messing with things. **It\u2019s very possible I\ncould break something while doing this, so check back for status updates\nif you download the code in between releases**. I\u2019ve finally become a\nlittle more modern and started adding automated testing, so as time goes\nby hopefully the \u201cin between\u201d releases will be somewhat more reliable.\nCheck back often for exciting new features and bug fixes!\n\nOk, I\u2019m sold. What\u2019s in here?\n=============================\n\n- **roidecompose** - This program uses an atlas to extract timecourses\n from a 4D nifti file, producing a text file with the averaged\n timecourse from each region in the atlas (each integral value in\n file) in each column. This can be input to capfromtcs. There are\n various options for normalizing the timecourses.\n\n- **capfromtcs** - This does the actual CAP calculation, performing a\n k-means cluster analysis on the set of timecourses to find the best\n representitive set of \u201cstates\u201d in the file. Outputs the states found\n and the dominant state in each timepoint of the timecourse.\n\n- **maptoroi** - The inverse of roidecompose. Give it a set of cluster\n timecourses and a template file, and it maps the values back onto the\n rois\n\n- **statematch** - Use this for aligning two state output files. Takes\n two state timecourse files, and determines which states in the second\n correspond to which states in the first. Generates a new \u2018remapped\u2019\n file with the states in the second file expressed as states in the\n first.\n\n\n\n\n",
"bugtrack_url": null,
"license": "Apache Software License",
"summary": "Tools for performing coactivation pattern analysis on fMRI data.",
"version": "1.3.9",
"project_urls": {
"Homepage": "https://github.com/bbfrederick/capcalc"
},
"split_keywords": [
"fmri",
" correlation",
" clustering",
" states"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "4833cbf38f3ca3abce81ca74ddf0c7fb1e885b10c4fbeccd0fb6c2c16da79143",
"md5": "8985e8f9857d8a0e49008d15d955c308",
"sha256": "374fc42d935c2dedf7f45f3e0d24e0da794d2f9b57c97809a4c47a7bb486ed58"
},
"downloads": -1,
"filename": "capcalc-1.3.9-py2.py3-none-any.whl",
"has_sig": false,
"md5_digest": "8985e8f9857d8a0e49008d15d955c308",
"packagetype": "bdist_wheel",
"python_version": "py2.py3",
"requires_python": null,
"size": 126159,
"upload_time": "2024-04-25T21:37:36",
"upload_time_iso_8601": "2024-04-25T21:37:36.725790Z",
"url": "https://files.pythonhosted.org/packages/48/33/cbf38f3ca3abce81ca74ddf0c7fb1e885b10c4fbeccd0fb6c2c16da79143/capcalc-1.3.9-py2.py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "42d064eaf3cbe199bb738d157c5be7d5de607960643be294add4378b3a5ac45f",
"md5": "a898b40f863d44e62c86df5a0b4d7d5c",
"sha256": "03b4dcccfb63d472d7b80a66acb700622e4ae9bc00445481aa01f2dba657731e"
},
"downloads": -1,
"filename": "capcalc-1.3.9.tar.gz",
"has_sig": false,
"md5_digest": "a898b40f863d44e62c86df5a0b4d7d5c",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 121831,
"upload_time": "2024-04-25T21:37:38",
"upload_time_iso_8601": "2024-04-25T21:37:38.470558Z",
"url": "https://files.pythonhosted.org/packages/42/d0/64eaf3cbe199bb738d157c5be7d5de607960643be294add4378b3a5ac45f/capcalc-1.3.9.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-04-25 21:37:38",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "bbfrederick",
"github_project": "capcalc",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "capcalc"
}
JSON
