pycortex-guide

name	pycortex-guide
description	Domain-validated guidance for cortical surface visualization and brain surface rendering of fMRI data using pycortex: data types (Volume, Vertex, Dataset), 2D cortical flatmaps, 3D WebGL brain viewers, volume-to-surface mapping, FreeSurfer/fMRIPrep integration, ROI management, and surface analysis. Use this skill whenever the user mentions pycortex, `import cortex`, cortical surfaces, brain flatmaps, WebGL brain viewers, cortical surface mapping, or wants to visualize neuroimaging data on the cortex, even if they don't explicitly name pycortex.
version	1.0.0
authors	["Claude (AI-assisted)"]
review_status	ai-generated

Pycortex Guide

Purpose

Encodes domain knowledge for using pycortex — a Python library for visualizing fMRI and volumetric neuroimaging data on cortical surfaces. Covers data creation, 2D flatmap rendering, interactive 3D WebGL viewers, volume-to-surface mapping, subject database management, FreeSurfer/fMRIPrep integration, and surface geometry analysis.

When to Use This Skill

Activate when the user:

• Wants to visualize fMRI data on cortical surfaces
• Mentions pycortex, import cortex, flatmaps, or cortical surface visualization
• Needs to create Volume, Vertex, or Dataset objects for brain data
• Wants to generate 2D flatmaps or 3D interactive brain viewers
• Needs to import FreeSurfer or fMRIPrep subjects into pycortex
• Works with volume-to-surface mapping or cortical ROIs
• Needs to align functional data to anatomical surfaces
• Wants to compute surface properties (curvature, thickness, geodesic distance)

Reference Files

Topic	File	When to Read
Data types	references/dataset-types.md	User creates Volume, Vertex, RGB, 2D, or Dataset objects
Visualization	references/visualization.md	User wants flatmaps, WebGL viewers, exports, or screenshots
Database & subjects	references/database-subjects.md	User manages subjects, surfaces, transforms, or masks
Mapping & transforms	references/mapping-transforms.md	User maps volume↔surface, aligns data, or works with transforms
FreeSurfer & fMRIPrep	references/freesurfer-fmriprep.md	User imports subjects from FreeSurfer or fMRIPrep
Surface analysis	references/surface-analysis.md	User computes curvature, geodesic distance, ROIs, or distortion
MNI & utilities	references/mni-utils.md	User transforms to/from MNI space or uses volume utilities

Installation

pip install -U setuptools wheel numpy cython
pip install -U pycortex
# With headless rendering support:
pip install -U 'pycortex[headless]'

Requirements: Python 3.10+, Linux/macOS only. Key dependencies: numpy, scipy, matplotlib, nibabel, h5py, tornado, shapely, lxml.

Overview Pipeline

1. Import subject (FreeSurfer/fMRIPrep)  →  stored in cortex.db
2. Create data objects (Volume/Vertex)    →  wrap arrays with metadata
3. Visualize (quickflat / webgl / export) →  2D flatmaps or 3D viewers
4. Analyze surfaces (curvature, ROIs)     →  surface geometry tools

Quick Start

import cortex

# Create a random volume for demo subject "S1" with transform "fullhead"
vol = cortex.Volume.random("S1", "fullhead")

# 2D flatmap
fig = cortex.quickshow(vol, with_curvature=True, with_rois=True)
fig.savefig("flatmap.png")

# Interactive 3D viewer
cortex.webshow(vol)

# Create volume from your own data (3D numpy array)
import numpy as np
data = np.random.randn(31, 100, 100)  # must match transform shape
vol = cortex.Volume(data, "S1", "fullhead", cmap="RdBu_r", vmin=-2, vmax=2)
cortex.quickshow(vol)

# Save/load datasets
ds = cortex.Dataset(my_map=vol)
ds.save("results.hdf")
ds_loaded = cortex.load("results.hdf")

Key Data Types

Class	Description	Key Args
Volume	Volumetric data (3D/4D)	data, subject, xfmname, cmap, vmin, vmax
Vertex	Surface vertex data (1D/2D)	data, subject, cmap, vmin, vmax
VolumeRGB	RGB per voxel	red, green, blue, subject, xfmname, alpha
VertexRGB	RGB per vertex	red, green, blue, subject, alpha
Volume2D	Two volumes, 2D colormap	dim1, dim2, subject, xfmname, vmin, vmax, vmin2, vmax2
Vertex2D	Two vertex maps, 2D colormap	dim1, dim2, subject, vmin, vmax, vmin2, vmax2
Dataset	Container for multiple views	**named_dataviews

Key Visualization Functions

Function	Description
cortex.quickshow(data, ...)	2D flatmap → matplotlib Figure
cortex.quickflat.make_png(fname, data, ...)	Save flatmap as PNG
cortex.quickflat.make_svg(fname, data, ...)	Save flatmap as SVG
cortex.quickflat.make_gif(fname, volumes, ...)	Animated GIF from volumes
cortex.webshow(data, ...)	Interactive 3D WebGL viewer
cortex.webgl.make_static(path, data, ...)	Static HTML 3D viewer
cortex.export.save_3d_views(vol, ...)	Multi-angle PNG exports
cortex.export.plot_panels(vol, panels, ...)	Multi-panel figure

Common Pitfalls

1. Volume data shape must match the transform dimensions in the database. Use cortex.db.get_xfm(subject, xfmname) to check expected shape.
2. Subject and transform names must exist in cortex.db before creating Volume objects. Import from FreeSurfer first.
3. cortex.quickshow is an alias for cortex.quickflat.make_figure — they are the same function.
4. Vertex data length must match total vertex count (left + right hemisphere). Single-hemisphere data is auto-padded with zeros.
5. vmin/vmax default to 1st/99th percentile of data. Always set explicitly for consistent colorbars across subjects.
6. WebGL viewer (webshow) starts a Tornado server — it blocks in scripts. Use in IPython/Jupyter or set autoclose=True.
7. The pycortex filestore path is set in cortex.options.config. Check with cortex.database.default_filestore.
8. For headless rendering (no display), install with pip install 'pycortex[headless]' and use cortex.export.save_3d_views(..., headless=True).
9. cortex.db is a singleton — all operations share the same database instance.
10. When saving datasets with pack=True, subject geometry and transforms are embedded in the HDF5 file for portability.