Upsample data from a lower resolution fsaverage template to fsaverage for visualization

This example shows how data in a lower resolution fsaverage template (e.g., fsaverage5 or fsaverage6) can be upsampled to the high resolution fsaverage template for visualization.

import matplotlib.pyplot as plt
import numpy as np

import cortex

subject = "fsaverage"

# First we check if the fsaverage template is already in the pycortex filestore. If not,
# we download the template from the web and add it to the filestore.
if subject not in cortex.db.subjects:
    cortex.download_subject(subject)

# Next we create some data on fsaverage5. Each hemisphere has 10242 vertices.
n_vertices_fsaverage5 = 10242
data_fs5 = np.arange(1, n_vertices_fsaverage5 + 1)
# We concatenate the data to itself to create a vector of length 20484, corresponding to
# the two hemispheres together.
data_fs5 = np.concatenate((data_fs5, data_fs5))
# Finally, we upsample the data to fsaverage.
data_fs7 = cortex.freesurfer.upsample_to_fsaverage(data_fs5, "fsaverage5")

# Now that the data is in the fsaverage template, we can visualize it in PyCortex as any
# other vertex dataset.
vtx = cortex.Vertex(data_fs7, subject, vmin=0, vmax=n_vertices_fsaverage5, cmap="turbo")
cortex.quickshow(vtx, with_curvature=False, with_colorbar=False)
plt.show()