# dist_thr (distance threshold) which affects number of clusters and their size
# pts (number of points in each streamline) which will be used for downsampling before clustering
# Default values : dist_thr = 4 & pts = 12
qb = QuickBundles(streamlines, dist_thr=20, pts=20)
clusters = qb.clusters()
print('Number of clusters %i' % qb.total_clusters)
print('Cluster size', qb.clusters_sizes())
# Display streamlines
ren = window.Renderer()
ren.add(actor.streamtube(streamlines, window.colors.white))
window.show(ren)
window.record(ren, out_path=filename + '_stream_lines_eu.png', size=(600, 600))
# Display centroids
window.clear(ren)
colormap = actor.create_colormap(np.arange(qb.total_clusters))
ren.add(actor.streamtube(streamlines, window.colors.white, opacity=0.1))
ren.add(actor.streamtube(qb.centroids, colormap, linewidth=0.5))
window.show(ren)
window.record(ren, out_path=filename + '_centroids_eu.png', size=(600, 600))
# Display tracks
window.clear(ren)
colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters.items(), colormap):
colormap_full[cluster[1]['indices']] = color
ren.add(actor.streamtube(streamlines, colormap_full))
window.show(ren)
window.record(ren,
out_path=filename + '_stream_line_cluster_eu.png',
if interactive:
window.show(ren)
"""
We can extract the peaks from the ODF, and plot these as well
"""
sf_peaks = dpp.peaks_from_model(sf_model,
data_small,
sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
return_sh=False)
window.clear(ren)
fodf_peaks = actor.peak_slicer(sf_peaks.peak_dirs, sf_peaks.peak_values, scale=1.3)
ren.add(fodf_peaks)
print('Saving illustration as sf_peaks.png')
window.record(ren, out_path='sf_peaks.png', size=(1000, 1000))
if interactive:
window.show(ren)
"""
Finally, we plot both the peaks and the ODFs, overlayed:
"""
fodf_spheres.GetProperty().SetOpacity(0.4)
ren.add(fodf_spheres)
