def _generate_back_track_tree(n, dev):
tree = Section(
np.array([[0., 0., 0., 0.2, 1., 0.,
0.], [0., 1., 0., 0.15, 1., 0., 0.],
[0., 2., 0., 0.14, 1., 0., 0.]]))
ch0 = Section(
np.array([[0., 2., 0., 0.14, 1., 0.,
0.], [1., 3., 0., 0.15, 1., 0., 0.],
[2., 4., 0., 0.11, 1., 0., 0.]]))
ch1 = Section(
np.array([[0., 2., 0., 0.14, 1., 0., 0.],
[1., -3., 0., 0.15, 1., 0., 0.],
[2., -4., 0., 0.12, 1., 0., 0.],
[dev[0], dev[1], dev[2], 0.11, 1., 0., 0.],
[3., -5., 0., 0.1, 1., 0., 0.],
[4., -6., 0., 0.1, 1., 0., 0.]]))
tree.add_child(ch0)
tree.add_child(ch1)
tree.children[1].points[3] += tree.children[n].points[1]
tree.type = NeuriteType.undefined
return Neurite(tree)
def _genetate_tree_non_monotonic_section_boundary():
tree = Section(
np.array([[0, 0, 0, 1.0, 0, 0], [0, 0, 0, 0.75, 0, 0],
[0, 0, 0, 0.5, 0, 0], [0, 0, 0, 0.25, 0, 0]]))
ch0 = Section(
np.array([[0, 0, 0, 0.375, 0, 0], [0, 0, 0, 0.125, 0, 0],
[0, 0, 0, 0.0625, 0, 0]]))
tree.add_child(ch0)
tree.type = NeuriteType.undefined
return Neurite(tree)
def _genetate_tree_non_monotonic_section_boundary():
tree = Section(np.array([[0, 0, 0, 1.0, 0, 0],
[0, 0, 0, 0.75, 0, 0],
[0, 0, 0, 0.5, 0, 0],
[0, 0, 0, 0.25, 0, 0]]))
ch0 = Section(np.array([[0, 0, 0, 0.375, 0, 0],
[0, 0, 0, 0.125, 0, 0],
[0, 0, 0, 0.0625, 0, 0]]))
tree.add_child(ch0)
tree.type = NeuriteType.undefined
return Neurite(tree)
def _generate_back_track_tree(n, dev):
tree = Section(np.array([[0., 0., 0., 0.2, 1., 0., 0.],
[0., 1., 0., 0.15, 1., 0., 0.],
[0., 2., 0., 0.14, 1., 0., 0.]]))
ch0 = Section(np.array([[0., 2., 0., 0.14, 1., 0., 0.],
[1., 3., 0., 0.15, 1., 0., 0.],
[2., 4., 0., 0.11, 1., 0., 0.]]))
ch1 = Section(np.array([[0., 2., 0., 0.14, 1., 0., 0.],
[1., -3., 0., 0.15, 1., 0., 0.],
[2., -4., 0., 0.12, 1., 0., 0.],
[dev[0], dev[1], dev[2], 0.11, 1., 0., 0.],
[3., -5., 0., 0.1, 1., 0., 0.],
[4., -6., 0., 0.1, 1., 0., 0.]]))
tree.add_child(ch0)
tree.add_child(ch1)
tree.children[1].points[3] += tree.children[n].points[1]
tree.type = NeuriteType.undefined
return Neurite(tree)
def test_principal_direction_extents():
# test with a realistic neuron
nrn = nm.load_neuron(os.path.join(H5_PATH, 'bio_neuron-000.h5'))
p_ref = [
1672.9694359427331, 142.43704397865031, 226.45895382204986,
415.50612748523838, 429.83008974193206, 165.95410536922873,
346.83281498399697
]
p = _nf.principal_direction_extents(nrn)
_close(np.array(p), np.array(p_ref))
s0 = Section(42)
s1 = s0.add_child(Section(42))
s2 = s0.add_child(Section(42))
s3 = s0.add_child(Section(42))
s4 = s1.add_child(Section(42))
s5 = s1.add_child(Section(42))
s6 = s4.add_child(Section(42))
s7 = s4.add_child(Section(42))
def test_n_bifurcation_points():
nt.assert_equal(_nf.n_bifurcation_points(Neurite(s0)), 2)
nt.assert_equal(_nf.n_bifurcation_points(Neurite(s1)), 2)
nt.assert_equal(_nf.n_bifurcation_points(Neurite(s2)), 0)
nt.assert_equal(_nf.n_bifurcation_points(Neurite(s3)), 0)
nt.assert_equal(_nf.n_bifurcation_points(Neurite(s4)), 1)
nt.assert_equal(_nf.n_bifurcation_points(Neurite(s5)), 0)
RADIUS = 4.
POINTS0 = np.array([[0., 0., 0., RADIUS], [0., 0., 1., RADIUS],
[0., 0., 2., RADIUS], [0., 0., 3., RADIUS],
[1., 0., 3., RADIUS], [2., 0., 3., RADIUS],
[3., 0., 3., RADIUS]])
POINTS1 = np.array([[3., 0., 3., RADIUS], [3., 0., 4., RADIUS],
[3., 0., 5., RADIUS], [3., 0., 6., RADIUS],
[4., 0., 6., RADIUS], [5., 0., 6., RADIUS],
[6., 0., 6., RADIUS]])
REF_LEN = 12
ROOT_NODE = Section(POINTS0)
ROOT_NODE.add_child(Section(POINTS1))
def test_init():
nrt = Neurite(ROOT_NODE)
nt.eq_(nrt.type, nm.NeuriteType.undefined)
nt.eq_(len(nrt.points), 13)
def test_neurite_type():
root_node = Section(POINTS0, section_type=nm.AXON)
nrt = Neurite(root_node)
nt.eq_(nrt.type, nm.AXON)
root_node = Section(POINTS0, section_type=nm.BASAL_DENDRITE)
nrt = Neurite(root_node)
[2., 0., 3., RADIUS],
[3., 0., 3., RADIUS]])
POINTS1 = np.array([[3., 0., 3., RADIUS],
[3., 0., 4., RADIUS],
[3., 0., 5., RADIUS],
[3., 0., 6., RADIUS],
[4., 0., 6., RADIUS],
[5., 0., 6., RADIUS],
[6., 0., 6., RADIUS]])
REF_LEN = 12
ROOT_NODE = Section(POINTS0)
ROOT_NODE.add_child(Section(POINTS1))
def test_init():
nrt = Neurite(ROOT_NODE)
nt.eq_(nrt.type, nm.NeuriteType.undefined)
nt.eq_(len(nrt.points), 13)
def test_neurite_type():
root_node = Section(POINTS0, section_type=nm.AXON)
nrt = Neurite(root_node)
nt.eq_(nrt.type, nm.AXON)
root_node = Section(POINTS0, section_type=nm.BASAL_DENDRITE)
nrt = Neurite(root_node)
def test_principal_direction_extents():
# test with a realistic neuron
nrn = nm.load_neuron(os.path.join(H5_PATH, 'bio_neuron-000.h5'))
p_ref = [1672.9694359427331, 142.43704397865031, 226.45895382204986,
415.50612748523838, 429.83008974193206, 165.95410536922873,
346.83281498399697]
p = _nf.principal_direction_extents(nrn)
_close(np.array(p), np.array(p_ref))
s0 = Section(42)
s1 = s0.add_child(Section(42))
s2 = s0.add_child(Section(42))
s3 = s0.add_child(Section(42))
s4 = s1.add_child(Section(42))
s5 = s1.add_child(Section(42))
s6 = s4.add_child(Section(42))
s7 = s4.add_child(Section(42))
def test_n_bifurcation_points():
nt.assert_equal(_nf.n_bifurcation_points(Neurite(s0)), 2)
nt.assert_equal(_nf.n_bifurcation_points(Neurite(s1)), 2)
nt.assert_equal(_nf.n_bifurcation_points(Neurite(s2)), 0)
nt.assert_equal(_nf.n_bifurcation_points(Neurite(s3)), 0)
nt.assert_equal(_nf.n_bifurcation_points(Neurite(s4)), 1)
nt.assert_equal(_nf.n_bifurcation_points(Neurite(s5)), 0)
