Metadata-Version: 2.1
Name: wbplot
Version: 1.0.8
Summary: A package for automated plotting of neuroimaging maps using Connectome Workbench.
Home-page: https://github.com/jbburt/wbplot
Author: Joshua Burt
Author-email: joshua.burt@yale.edu
License: UNKNOWN
Description: Automated plotting of neuroimaging maps from Python using [Connectome Workbench](https://www.humanconnectome.org/software/connectome-workbench).
        
        This package is intended for users who want to generate images
        which illustrate scalar data on a brain surface, from within their Python scripts. 
        
        Installation
        ============
        ---
        
        ### Step 1. Make sure you have Connectome Workbench v1.3+ installed.
        * If running `wb_view` from a terminal yields `command not found`, see  <https://www.humanconnectome.org/software/connectome-workbench/>
        * If you have Workbench 1.2 or below installed, you will get an error message
        
        ### Step 2. Install `wbplot` and dependencies.
        * Clone the repository and install manually: `git clone https://github.com/jbburt/wbplot.git`
        * Or just use pip: `pip install wbplot`
        
        Usage
        =====
        ---
        Assuming `x` is a NumPy array containing scalar values mapped onto each of the
        360 parcels in the [Human Connectome Project](http://www.humanconnectomeproject.org/)'s [MMP1.0](https://www.nature.com/articles/nature18933) parcellation:
        ```
        from wbplot import pscalar
        pscalar("/path/to/image.png", x)
        ```
        
        Assuming `y` is a NumPy array containing dense scalar values mapped onto the 59412
        surface vertices in a standard bilateral 32k surface mesh:
        ```
        from wbplot import dscalar
        dscalar("/path/to/image.png", y)
        ```
        
        Notes
        =====
        ---
        - `wbplot` currently only supports cortical data. Parcellated data must also be in the
        HCP MMP1.0 parcellation. Dense data must be
        registered to a standard 32k surface mesh. 
        - Down the line I'd be open to adding subcortical
        support and other functionality if anyone ever actually uses this package.
        - More detailed explanations of the functionality can be found in the scripts in the `examples` directory. 
        
        
        Change Log
        ==========
        ---
        
        * 1.0 Initial release.
        * 1.0.1 Small error in README.
        * 1.0.2 Error in MANIFEST.in -- not all necessary data files included in build.
        * 1.0.3 Entirely changed the way data are read from and written to, to circumnavigate permissions issues. 
        * 1.0.4 ImageParcellated loaded into dense scenes resulted in error messages printed to console.
        * 1.0.5 Last patch didn't fix the problem.
        * 1.0.6 Added errors raised to docstrings and cleaned up a few files.
        * 1.0.7 Fixed type checking bug in `images` module
        * 1.0.8 Fixed it for real now.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Description-Content-Type: text/markdown
