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.plotting.surface.plot_trisurf(vertices, triangles, texture=None, vmin=None, vmax=None, colorbar=True, fig=None, ax=None, is_label=False, alpha=1, edgecolors='black', linewidths=1, color_map=<matplotlib.colors.LinearSegmentedColormap object>)[source]¶

Display a triangular surface.

Parameters:

vertices : array (N, 3)

the surface vertices.

triangles : array (M, 3)

the surface triangles.

texture : array (N,), default None

a texture to display on the surface.

vmin : float, default None

minimum value to map.

vmax : float, default None

maximum value to map.

colorbar : bool, default True

display a colorbar.

fig : Figure, default None

the matplotlib figure.

ax : Axes3D, default None

axis to display the surface plot.

is_label : bool, default False

optionally specify that the texture contains labels in order to use most representative neighboor interpolation.

alpha : float, default 1

number between 0 and 1 defining the opacity of the faces. Not use when texture is None, where alpha is fixed at 0.1

edgecolors : string, default ‘black’

name of the matplotlib color to use for the edges

linewidths : float, default 1

edge line width.

color_map : Mapping, default cm.coolwarm

container for colormaps.