def bench_quickbundles():
dtype = "float32"
repeat = 10
nb_points = 18
streams, hdr = nib.trackvis.read(get_data('fornix'))
fornix = [s[0].astype(dtype) for s in streams]
fornix = streamline_utils.set_number_of_points(fornix, nb_points)
# Create eight copies of the fornix to be clustered (one in each octant).
streamlines = []
streamlines += [s + np.array([100, 100, 100], dtype) for s in fornix]
streamlines += [s + np.array([100, -100, 100], dtype) for s in fornix]
streamlines += [s + np.array([100, 100, -100], dtype) for s in fornix]
streamlines += [s + np.array([100, -100, -100], dtype) for s in fornix]
streamlines += [s + np.array([-100, 100, 100], dtype) for s in fornix]
streamlines += [s + np.array([-100, -100, 100], dtype) for s in fornix]
streamlines += [s + np.array([-100, 100, -100], dtype) for s in fornix]
streamlines += [s + np.array([-100, -100, -100], dtype) for s in fornix]
# The expected number of clusters of the fornix using threshold=10 is 4.
threshold = 10.
expected_nb_clusters = 4 * 8
print("Timing QuickBundles 1.0 vs. 2.0")
qb = QB_Old(streamlines, threshold, pts=None)
qb1_time = measure("QB_Old(streamlines, threshold, nb_points)", repeat)
print("QuickBundles time: {0:.4}sec".format(qb1_time))
assert_equal(qb.total_clusters, expected_nb_clusters)
sizes1 = [qb.partitions()[i]['N'] for i in range(qb.total_clusters)]
indices1 = [
qb.partitions()[i]['indices'] for i in range(qb.total_clusters)
]
qb2 = QB_New(threshold)
qb2_time = measure("clusters = qb2.cluster(streamlines)", repeat)
print("QuickBundles2 time: {0:.4}sec".format(qb2_time))
print("Speed up of {0}x".format(qb1_time / qb2_time))
clusters = qb2.cluster(streamlines)
sizes2 = map(len, clusters)
indices2 = map(lambda c: c.indices, clusters)
assert_equal(len(clusters), expected_nb_clusters)
assert_array_equal(sizes2, sizes1)
assert_arrays_equal(indices2, indices1)
qb = QB_New(threshold, metric=MDFpy())
qb3_time = measure("clusters = qb.cluster(streamlines)", repeat)
print("QuickBundles2_python time: {0:.4}sec".format(qb3_time))
print("Speed up of {0}x".format(qb1_time / qb3_time))
clusters = qb.cluster(streamlines)
sizes3 = map(len, clusters)
indices3 = map(lambda c: c.indices, clusters)
assert_equal(len(clusters), expected_nb_clusters)
assert_array_equal(sizes3, sizes1)
assert_arrays_equal(indices3, indices1)
def bench_quickbundles():
dtype = "float32"
repeat = 10
nb_points = 12
streams, hdr = nib.trackvis.read(get_fnames('fornix'))
fornix = [s[0].astype(dtype) for s in streams]
fornix = streamline_utils.set_number_of_points(fornix, nb_points)
# Create eight copies of the fornix to be clustered (one in each octant).
streamlines = []
streamlines += [s + np.array([100, 100, 100], dtype) for s in fornix]
streamlines += [s + np.array([100, -100, 100], dtype) for s in fornix]
streamlines += [s + np.array([100, 100, -100], dtype) for s in fornix]
streamlines += [s + np.array([100, -100, -100], dtype) for s in fornix]
streamlines += [s + np.array([-100, 100, 100], dtype) for s in fornix]
streamlines += [s + np.array([-100, -100, 100], dtype) for s in fornix]
streamlines += [s + np.array([-100, 100, -100], dtype) for s in fornix]
streamlines += [s + np.array([-100, -100, -100], dtype) for s in fornix]
# The expected number of clusters of the fornix using threshold=10 is 4.
threshold = 10.
expected_nb_clusters = 4 * 8
print("Timing QuickBundles 1.0 vs. 2.0")
qb = QB_Old(streamlines, threshold, pts=None)
qb1_time = measure("QB_Old(streamlines, threshold, nb_points)", repeat)
print("QuickBundles time: {0:.4}sec".format(qb1_time))
assert_equal(qb.total_clusters, expected_nb_clusters)
sizes1 = [qb.partitions()[i]['N'] for i in range(qb.total_clusters)]
indices1 = [qb.partitions()[i]['indices']
for i in range(qb.total_clusters)]
qb2 = QB_New(threshold)
qb2_time = measure("clusters = qb2.cluster(streamlines)", repeat)
print("QuickBundles2 time: {0:.4}sec".format(qb2_time))
print("Speed up of {0}x".format(qb1_time / qb2_time))
clusters = qb2.cluster(streamlines)
sizes2 = map(len, clusters)
indices2 = map(lambda c: c.indices, clusters)
assert_equal(len(clusters), expected_nb_clusters)
assert_array_equal(list(sizes2), sizes1)
assert_arrays_equal(indices2, indices1)
qb = QB_New(threshold, metric=MDFpy())
qb3_time = measure("clusters = qb.cluster(streamlines)", repeat)
print("QuickBundles2_python time: {0:.4}sec".format(qb3_time))
print("Speed up of {0}x".format(qb1_time / qb3_time))
clusters = qb.cluster(streamlines)
sizes3 = map(len, clusters)
indices3 = map(lambda c: c.indices, clusters)
assert_equal(len(clusters), expected_nb_clusters)
assert_array_equal(list(sizes3), sizes1)
assert_arrays_equal(indices3, indices1)
