Deep learning for NeuroImaging in Python.

Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the gallery for the big picture.

surfify.utils.sampling.interpolate_data(data, by=1, up_indices=None)[source]¶

Interpolate data/texture on the icosahedron to an upper order.

Parameters:

data : array (n_samples, n_vertices, n_features)

data to be upsampled.

by : int, default 1

number of orders to increase the icosahedron by.

up_indices : list of array, default None

optionally specify the list of consecutive upsampling vertices indices.

Returns:

upsampled_data : array (n_samples, new_n_vertices, n_features)

upsampled data.

Examples

>>> from surfify.utils import icosahedron, interpolate_data
>>> from surfify.datasets import make_classification
>>> import matplotlib.pyplot as plt
>>> from surfify.plotting import plot_trisurf
>>> ico2_verts, ico2_tris = icosahedron(order=2)
>>> ico4_verts, ico4_tris = icosahedron(order=4)
>>> X, y = make_classification(ico2_verts, n_samples=1, n_classes=3,
                               scale=1, seed=42)
>>> y = y.reshape(1, -1, 1)
>>> y_up = interpolate_data(y, by=2).squeeze()
>>> plot_trisurf(ico4_verts, triangles=ico4_tris, texture=y_up,
                 is_label=False)
>>> plt.show()