def test_feature_resample():
from dipy.tracking.streamline import set_number_of_points
# Test subclassing Feature
class ResampleFeature(dipymetric.Feature):
def __init__(self, nb_points):
super(ResampleFeature, self).__init__(is_order_invariant=False)
self.nb_points = nb_points
if nb_points <= 0:
raise ValueError(
"ResampleFeature: `nb_points` must be strictly positive: {0}"
.format(nb_points))
def infer_shape(self, streamline):
return (self.nb_points, streamline.shape[1])
def extract(self, streamline):
return set_number_of_points(streamline, self.nb_points)
assert_raises(ValueError, dipymetric.ResampleFeature, nb_points=0)
assert_raises(ValueError, ResampleFeature, nb_points=0)
max_points = max(map(len, [s1, s2, s3, s4]))
for nb_points in [1, 5, 2 * max_points]:
for feature in [
dipymetric.ResampleFeature(nb_points),
ResampleFeature(nb_points)
]:
for s in [s1, s2, s3, s4]:
# Test method infer_shape
assert_equal(feature.infer_shape(s), (nb_points, s.shape[1]))
# Test method extract
features = feature.extract(s)
assert_equal(features.shape, (nb_points, s.shape[1]))
assert_array_almost_equal(features,
set_number_of_points(s, nb_points))
# This feature type is not order invariant
assert_false(feature.is_order_invariant)
for s in [s1, s2, s3, s4]:
features = feature.extract(s)
features_flip = feature.extract(s[::-1])
assert_array_equal(features_flip,
set_number_of_points(s[::-1], nb_points))
assert_true(np.any(np.not_equal(features, features_flip)))
def test_quickbundles_shape_uncompatibility():
# QuickBundles' old default metric (AveragePointwiseEuclideanMetric,
# aka MDF) requires that all streamlines have the same number of points.
metric = dipymetric.AveragePointwiseEuclideanMetric()
qb = QuickBundles(threshold=20., metric=metric)
assert_raises(ValueError, qb.cluster, data)
# QuickBundles' new default metric (AveragePointwiseEuclideanMetric,
# aka MDF combined with ResampleFeature) will automatically resample
# streamlines so they all have 18 points.
qb = QuickBundles(threshold=20.)
clusters1 = qb.cluster(data)
feature = dipymetric.ResampleFeature(nb_points=18)
metric = dipymetric.AveragePointwiseEuclideanMetric(feature)
qb = QuickBundles(threshold=20., metric=metric)
clusters2 = qb.cluster(data)
assert_array_equal(list(itertools.chain(*clusters1)),
list(itertools.chain(*clusters2)))