**Note:** Inputs must be sequences of same length.
"""
from dipy.segment.clustering import QuickBundles
from dipy.segment.metric import SumPointwiseEuclideanMetric
# Get some streamlines.
streamlines = get_streamlines() # Previously defined.
# Make sure our streamlines have the same number of points.
from dipy.tracking.streamline import set_number_of_points
nb_points = 12
streamlines = set_number_of_points(streamlines, nb_points=nb_points)
# Create the instance of `SumPointwiseEuclideanMetric` to use.
metric = SumPointwiseEuclideanMetric()
qb = QuickBundles(threshold=10.*nb_points, metric=metric)
clusters = qb.cluster(streamlines)
print("Nb. clusters:", len(clusters))
print("Cluster sizes:", map(len, clusters))
"""
::
Nb. clusters: 4
Cluster sizes: [64, 191, 44, 1]
from nibabel import trackvis as tv
from dipy.data import get_data
from dipy.viz import fvtk
fname = get_data('fornix')
streams, hdr = tv.read(fname)
streamlines = [i[0] for i in streams]
"""
Perform QuickBundles clustering using the metric `SumPointwiseEuclideanMetric`
and our `ArcLengthFeature`.
"""
from dipy.segment.clustering import QuickBundles
from dipy.segment.metric import SumPointwiseEuclideanMetric
metric = SumPointwiseEuclideanMetric(feature=ArcLengthFeature())
qb = QuickBundles(threshold=2., metric=metric)
clusters = qb.cluster(streamlines)
"""
We will now visualize the clustering result.
"""
# Color each streamline according to the cluster they belong to.
colormap = fvtk.create_colormap(np.ravel(clusters.centroids))
colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
colormap_full[cluster.indices] = color
ren = fvtk.ren()
ren.SetBackground(1, 1, 1)
fvtk.add(ren, fvtk.streamtube(streamlines, colormap_full))