Metadata-Version: 2.1
Name: capcalc
Version: 1.2.3
Summary: Tools for performing coactivation pattern analysis on fMRI data.
Home-page: https://github.com/bbfrederick/capcalc
Author: Blaise Frederick
Author-email: bbfrederick@mclean.harvard.edu
License: Apache Software License
Description: 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.
        
        
        
Keywords: fMRI,correlation,clustering,states
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Provides-Extra: doc
