def test_LSCv2(verbose=False):
xyz1 = np.array([[1, 0, 0], [2, 0, 0], [3, 0, 0]], dtype='float32')
xyz2 = np.array([[1, 0, 0], [1, 2, 0], [1, 3, 0]], dtype='float32')
xyz3 = np.array([[1.1, 0, 0], [1, 2, 0], [1, 3, 0]], dtype='float32')
xyz4 = np.array([[1, 0, 0], [2.1, 0, 0], [3, 0, 0]], dtype='float32')
xyz5 = np.array([[100, 0, 0], [200, 0, 0], [300, 0, 0]], dtype='float32')
xyz6 = np.array([[0, 20, 0], [0, 40, 0], [300, 50, 0]], dtype='float32')
T = [xyz1, xyz2, xyz3, xyz4, xyz5, xyz6]
pf.local_skeleton_clustering(T, 0.2)
pf.local_skeleton_clustering_3pts(T, 0.2)
for i in range(40):
xyz = np.random.rand(3, 3).astype('f4')
T.append(xyz)
from time import time
t1 = time()
C3 = pf.local_skeleton_clustering(T, .5)
t2 = time()
if verbose:
print(t2 - t1)
print(len(C3))
t1 = time()
C4 = pf.local_skeleton_clustering_3pts(T, .5)
t2 = time()
if verbose:
print(t2 - t1)
print(len(C4))
for c in C3:
assert_equal(np.sum(C3[c]['hidden'] - C4[c]['hidden']), 0)
T2 = []
for i in range(10**4):
xyz = np.random.rand(10, 3).astype('f4')
T2.append(xyz)
t1 = time()
C5 = pf.local_skeleton_clustering(T2, .5)
t2 = time()
if verbose:
print(t2 - t1)
print(len(C5))
fname = get_fnames('fornix')
fornix = load_tractogram(fname, 'same', bbox_valid_check=False).streamlines
T3 = set_number_of_points(fornix, 6)
if verbose:
print('lenT3', len(T3))
C = pf.local_skeleton_clustering(T3, 10.)
if verbose:
print('lenC', len(C))
"""
def test_LSCv2():
xyz1=np.array([[1,0,0],[2,0,0],[3,0,0]],dtype='float32')
xyz2=np.array([[1,0,0],[1,2,0],[1,3,0]],dtype='float32')
xyz3=np.array([[1.1,0,0],[1,2,0],[1,3,0]],dtype='float32')
xyz4=np.array([[1,0,0],[2.1,0,0],[3,0,0]],dtype='float32')
xyz5=np.array([[100,0,0],[200,0,0],[300,0,0]],dtype='float32')
xyz6=np.array([[0,20,0],[0,40,0],[300,50,0]],dtype='float32')
T=[xyz1,xyz2,xyz3,xyz4,xyz5,xyz6]
C=pf.local_skeleton_clustering(T,0.2)
#print C
#print len(C)
C2=pf.local_skeleton_clustering_3pts(T,0.2)
#print C2
#print len(C2)
#"""
for i in range(40):
xyz=np.random.rand(3,3).astype('f4')
T.append(xyz)
from time import time
t1=time()
C3=pf.local_skeleton_clustering(T,.5)
t2=time()
print t2-t1
print len(C3)
t1=time()
C4=pf.local_skeleton_clustering_3pts(T,.5)
t2=time()
print t2-t1
print len(C4)
for c in C3:
assert_equal(np.sum(C3[c]['hidden']-C4[c]['hidden']),0)
T2=[]
for i in range(10**4):
xyz=np.random.rand(10,3).astype('f4')
T2.append(xyz)
t1=time()
C5=pf.local_skeleton_clustering(T2,.5)
t2=time()
print t2-t1
print len(C5)
from dipy.data import get_data
from nibabel import trackvis as tv
try:
from dipy.viz import fvtk
except ImportError, e:
raise nose.plugins.skip.SkipTest(
'Fails to import dipy.viz due to %s' % str(e))
def test_LSCv2():
xyz1 = np.array([[1, 0, 0], [2, 0, 0], [3, 0, 0]], dtype='float32')
xyz2 = np.array([[1, 0, 0], [1, 2, 0], [1, 3, 0]], dtype='float32')
xyz3 = np.array([[1.1, 0, 0], [1, 2, 0], [1, 3, 0]], dtype='float32')
xyz4 = np.array([[1, 0, 0], [2.1, 0, 0], [3, 0, 0]], dtype='float32')
xyz5 = np.array([[100, 0, 0], [200, 0, 0], [300, 0, 0]], dtype='float32')
xyz6 = np.array([[0, 20, 0], [0, 40, 0], [300, 50, 0]], dtype='float32')
T = [xyz1, xyz2, xyz3, xyz4, xyz5, xyz6]
C = pf.local_skeleton_clustering(T, 0.2)
#print C
#print len(C)
C2 = pf.local_skeleton_clustering_3pts(T, 0.2)
#print C2
#print len(C2)
#"""
for i in range(40):
xyz = np.random.rand(3, 3).astype('f4')
T.append(xyz)
from time import time
t1 = time()
C3 = pf.local_skeleton_clustering(T, .5)
t2 = time()
print t2 - t1
print len(C3)
t1 = time()
C4 = pf.local_skeleton_clustering_3pts(T, .5)
t2 = time()
print t2 - t1
print len(C4)
for c in C3:
assert_equal(np.sum(C3[c]['hidden'] - C4[c]['hidden']), 0)
T2 = []
for i in range(10**4):
xyz = np.random.rand(10, 3).astype('f4')
T2.append(xyz)
t1 = time()
C5 = pf.local_skeleton_clustering(T2, .5)
t2 = time()
print t2 - t1
print len(C5)
from dipy.data import get_data
from nibabel import trackvis as tv
try:
from dipy.viz import fvtk
except ImportError, e:
raise nose.plugins.skip.SkipTest('Fails to import dipy.viz due to %s' %
str(e))
def test_LSCv2():
xyz1 = np.array([[1, 0, 0], [2, 0, 0], [3, 0, 0]], dtype='float32')
xyz2 = np.array([[1, 0, 0], [1, 2, 0], [1, 3, 0]], dtype='float32')
xyz3 = np.array([[1.1, 0, 0], [1, 2, 0], [1, 3, 0]], dtype='float32')
xyz4 = np.array([[1, 0, 0], [2.1, 0, 0], [3, 0, 0]], dtype='float32')
xyz5 = np.array([[100, 0, 0], [200, 0, 0], [300, 0, 0]], dtype='float32')
xyz6 = np.array([[0, 20, 0], [0, 40, 0], [300, 50, 0]], dtype='float32')
T = [xyz1, xyz2, xyz3, xyz4, xyz5, xyz6]
C = pf.local_skeleton_clustering(T, 0.2)
# print C
# print len(C)
C2 = pf.local_skeleton_clustering_3pts(T, 0.2)
# print C2
# print len(C2)
# """
for i in range(40):
xyz = np.random.rand(3, 3).astype('f4')
T.append(xyz)
from time import time
t1 = time()
C3 = pf.local_skeleton_clustering(T, .5)
t2 = time()
print(t2 - t1)
print(len(C3))
t1 = time()
C4 = pf.local_skeleton_clustering_3pts(T, .5)
t2 = time()
print(t2 - t1)
print(len(C4))
for c in C3:
assert_equal(np.sum(C3[c]['hidden'] - C4[c]['hidden']), 0)
T2 = []
for i in range(10**4):
xyz = np.random.rand(10, 3).astype('f4')
T2.append(xyz)
t1 = time()
C5 = pf.local_skeleton_clustering(T2, .5)
t2 = time()
print(t2 - t1)
print(len(C5))
from dipy.data import get_data
from nibabel import trackvis as tv
try:
from dipy.viz import window, actor
except ImportError as e:
raise nose.plugins.skip.SkipTest('Fails to import dipy.viz due to %s' %
str(e))
streams, hdr = tv.read(get_data('fornix'))
T3 = [tm.downsample(s[0], 6) for s in streams]
print('lenT3', len(T3))
C = pf.local_skeleton_clustering(T3, 10.)
print('lenC', len(C))
"""
def test_LSCv2():
xyz1 = np.array([[1, 0, 0], [2, 0, 0], [3, 0, 0]], dtype='float32')
xyz2 = np.array([[1, 0, 0], [1, 2, 0], [1, 3, 0]], dtype='float32')
xyz3 = np.array([[1.1, 0, 0], [1, 2, 0], [1, 3, 0]], dtype='float32')
xyz4 = np.array([[1, 0, 0], [2.1, 0, 0], [3, 0, 0]], dtype='float32')
xyz5 = np.array([[100, 0, 0], [200, 0, 0], [300, 0, 0]], dtype='float32')
xyz6 = np.array([[0, 20, 0], [0, 40, 0], [300, 50, 0]], dtype='float32')
T = [xyz1, xyz2, xyz3, xyz4, xyz5, xyz6]
pf.local_skeleton_clustering(T, 0.2)
# print C
# print len(C)
pf.local_skeleton_clustering_3pts(T, 0.2)
# print C2
# print len(C2)
# """
for i in range(40):
xyz = np.random.rand(3, 3).astype('f4')
T.append(xyz)
from time import time
t1 = time()
C3 = pf.local_skeleton_clustering(T, .5)
t2 = time()
print(t2-t1)
print(len(C3))
t1 = time()
C4 = pf.local_skeleton_clustering_3pts(T, .5)
t2 = time()
print(t2-t1)
print(len(C4))
for c in C3:
assert_equal(np.sum(C3[c]['hidden']-C4[c]['hidden']), 0)
T2 = []
for i in range(10**4):
xyz = np.random.rand(10, 3).astype('f4')
T2.append(xyz)
t1 = time()
C5 = pf.local_skeleton_clustering(T2, .5)
t2 = time()
print(t2-t1)
print(len(C5))
from dipy.data import get_fnames
from nibabel import trackvis as tv
streams, hdr = tv.read(get_fnames('fornix'))
T3 = [tm.downsample(s[0], 6) for s in streams]
print('lenT3', len(T3))
C = pf.local_skeleton_clustering(T3, 10.)
print('lenC', len(C))
"""