wbplot 1.0.4

A package for automated plotting of neuroimaging maps using Connectome Workbench.

Automated plotting of neuroimaging maps from Python using 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

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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

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Assuming x is a NumPy array containing scalar values mapped onto each of the 360 parcels in the Human Connectome Project's MMP1.0 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

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• 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

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• 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.