/usr/lib/python2.7/dist-packages/dipy/segment/tests/test_feature.py is in python-dipy 0.10.1-1.
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import dipy.segment.metric as dipymetric
from dipy.segment.featurespeed import extract
from nose.tools import assert_true, assert_false, assert_equal
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_raises, run_module_suite)
dtype = "float32"
s1 = np.array([np.arange(10, dtype=dtype)]*3).T # 10x3
s2 = np.arange(3*10, dtype=dtype).reshape((-1, 3))[::-1] # 10x3
s3 = np.random.rand(5, 4).astype(dtype) # 5x4
s4 = np.random.rand(5, 3).astype(dtype) # 5x3
def test_identity_feature():
# Test subclassing Feature
class IdentityFeature(dipymetric.Feature):
def __init__(self):
super(IdentityFeature, self).__init__(is_order_invariant=False)
def infer_shape(self, streamline):
return streamline.shape
def extract(self, streamline):
return streamline
for feature in [dipymetric.IdentityFeature(), IdentityFeature()]:
for s in [s1, s2, s3, s4]:
# Test method infer_shape
assert_equal(feature.infer_shape(s), s.shape)
# Test method extract
features = feature.extract(s)
assert_equal(features.shape, s.shape)
assert_array_equal(features, s)
# 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, s[::-1])
assert_true(np.any(np.not_equal(features, features_flip)))
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_feature_center_of_mass():
# Test subclassing Feature
class CenterOfMassFeature(dipymetric.Feature):
def __init__(self):
super(CenterOfMassFeature, self).__init__(is_order_invariant=True)
def infer_shape(self, streamline):
return (1, streamline.shape[1])
def extract(self, streamline):
return np.mean(streamline, axis=0)[None, :]
for feature in [dipymetric.CenterOfMassFeature(), CenterOfMassFeature()]:
for s in [s1, s2, s3, s4]:
# Test method infer_shape
assert_equal(feature.infer_shape(s), (1, s.shape[1]))
# Test method extract
features = feature.extract(s)
assert_equal(features.shape, (1, s.shape[1]))
assert_array_almost_equal(features, np.mean(s, axis=0)[None, :])
# This feature type is order invariant
assert_true(feature.is_order_invariant)
for s in [s1, s2, s3, s4]:
features = feature.extract(s)
features_flip = feature.extract(s[::-1])
assert_array_almost_equal(features, features_flip)
def test_feature_midpoint():
# Test subclassing Feature
class MidpointFeature(dipymetric.Feature):
def __init__(self):
super(MidpointFeature, self).__init__(is_order_invariant=False)
def infer_shape(self, streamline):
return (1, streamline.shape[1])
def extract(self, streamline):
return streamline[[len(streamline)//2]]
for feature in [dipymetric.MidpointFeature(), MidpointFeature()]:
for s in [s1, s2, s3, s4]:
# Test method infer_shape
assert_equal(feature.infer_shape(s), (1, s.shape[1]))
# Test method extract
features = feature.extract(s)
assert_equal(features.shape, (1, s.shape[1]))
assert_array_almost_equal(features, s[len(s)//2][None, :])
# 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])
if len(s) % 2 == 0:
assert_true(np.any(np.not_equal(features, features_flip)))
else:
assert_array_equal(features, features_flip)
def test_feature_arclength():
from dipy.tracking.streamline import length
# Test subclassing Feature
class ArcLengthFeature(dipymetric.Feature):
def __init__(self):
super(ArcLengthFeature, self).__init__(is_order_invariant=True)
def infer_shape(self, streamline):
return (1, 1)
def extract(self, streamline):
return length(streamline)[None, None]
for feature in [dipymetric.ArcLengthFeature(), ArcLengthFeature()]:
for s in [s1, s2, s3, s4]:
# Test method infer_shape
assert_equal(feature.infer_shape(s), (1, 1))
# Test method extract
features = feature.extract(s)
assert_equal(features.shape, (1, 1))
assert_array_almost_equal(features, length(s)[None, None])
# This feature type is order invariant
assert_true(feature.is_order_invariant)
for s in [s1, s2, s3, s4]:
features = feature.extract(s)
features_flip = feature.extract(s[::-1])
assert_array_almost_equal(features, features_flip)
def test_feature_vector_of_endpoints():
# Test subclassing Feature
class VectorOfEndpointsFeature(dipymetric.Feature):
def __init__(self):
super(VectorOfEndpointsFeature, self).__init__(False)
def infer_shape(self, streamline):
return (1, streamline.shape[1])
def extract(self, streamline):
return streamline[[-1]] - streamline[[0]]
feature_types = [dipymetric.VectorOfEndpointsFeature(),
VectorOfEndpointsFeature()]
for feature in feature_types:
for s in [s1, s2, s3, s4]:
# Test method infer_shape
assert_equal(feature.infer_shape(s), (1, s.shape[1]))
# Test method extract
features = feature.extract(s)
assert_equal(features.shape, (1, s.shape[1]))
assert_array_almost_equal(features, s[[-1]] - s[[0]])
# 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])
# The flip features are simply the negative of the features.
assert_array_almost_equal(features, -features_flip)
def test_feature_extract():
# Test that features are automatically cast into float32 when coming from Python space
class CenterOfMass64bit(dipymetric.Feature):
def infer_shape(self, streamline):
return streamline.shape[1]
def extract(self, streamline):
return np.mean(streamline.astype(np.float64), axis=0)
nb_streamlines = 100
feature_shape = (1, 3) # One N-dimensional point
feature = CenterOfMass64bit()
streamlines = [np.arange(np.random.randint(20, 30) * 3).reshape((-1, 3)).astype(np.float32) for i in range(nb_streamlines)]
features = extract(feature, streamlines)
assert_equal(len(features), len(streamlines))
assert_equal(features[0].shape, feature_shape)
# Test that scalar features
class ArcLengthFeature(dipymetric.Feature):
def infer_shape(self, streamline):
return 1
def extract(self, streamline):
return np.sum(np.sqrt(np.sum((streamline[1:] - streamline[:-1]) ** 2)))
nb_streamlines = 100
feature_shape = (1, 1) # One scalar represented as a 2D array
feature = ArcLengthFeature()
streamlines = [np.arange(np.random.randint(20, 30) * 3).reshape((-1, 3)).astype(np.float32) for i in range(nb_streamlines)]
features = extract(feature, streamlines)
assert_equal(len(features), len(streamlines))
assert_equal(features[0].shape, feature_shape)
# Try if streamlines are readonly
for s in streamlines:
s.setflags(write=False)
features = extract(feature, streamlines)
def test_subclassing_feature():
class EmptyFeature(dipymetric.Feature):
pass
feature = EmptyFeature()
assert_raises(NotImplementedError, feature.infer_shape, None)
assert_raises(NotImplementedError, feature.extract, None)
def test_using_python_feature_with_cython_metric():
class Identity(dipymetric.Feature):
def infer_shape(self, streamline):
return streamline.shape
def extract(self, streamline):
return streamline
# Test using Python Feature with Cython Metric
feature = Identity()
metric = dipymetric.AveragePointwiseEuclideanMetric(feature)
d1 = dipymetric.dist(metric, s1, s2)
features1 = metric.feature.extract(s1)
features2 = metric.feature.extract(s2)
d2 = metric.dist(features1, features2)
assert_equal(d1, d2)
if __name__ == '__main__':
run_module_suite()
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