/usr/lib/python2.7/dist-packages/dipy/segment/benchmarks/bench_quickbundles.py is in python-dipy 0.10.1-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | """ Benchmarks for QuickBundles
Run all benchmarks with::
import dipy.segment as dipysegment
dipysegment.bench()
If you have doctests enabled by default in nose (with a noserc file or
environment variable), and you have a numpy version <= 1.6.1, this will also run
the doctests, let's hope they pass.
Run this benchmark with:
nosetests -s --match '(?:^|[\\b_\\.//-])[Bb]ench' /path/to/bench_quickbundles.py
"""
import numpy as np
import nibabel as nib
from dipy.data import get_data
import dipy.tracking.streamline as streamline_utils
from dipy.segment.metric import Metric
from dipy.segment.quickbundles import QuickBundles as QB_Old
from dipy.segment.clustering import QuickBundles as QB_New
from nose.tools import assert_equal
from dipy.testing import assert_arrays_equal
from numpy.testing import assert_array_equal, measure
class MDFpy(Metric):
def are_compatible(self, shape1, shape2):
return shape1 == shape2
def dist(self, features1, features2):
dist = np.sqrt(np.sum((features1-features2)**2, axis=1))
dist = np.sum(dist/len(features1))
return dist
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)
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