def test_trunk_origin_elevations():
class Mock(object):
pass
n0 = Mock()
n1 = Mock()
s = make_soma([[0, 0, 0, 4]])
t0 = Section(((1, 0, 0, 2), (2, 1, 1, 2)))
t0.type = NeuriteType.basal_dendrite
t1 = Section(((0, 1, 0, 2), (1, 2, 1, 2)))
t1.type = NeuriteType.basal_dendrite
n0.neurites = [Neurite(t0), Neurite(t1)]
n0.soma = s
t2 = Section(((0, -1, 0, 2), (-1, -2, -1, 2)))
t2.type = NeuriteType.basal_dendrite
n1.neurites = [Neurite(t2)]
n1.soma = s
pop = Population([n0, n1])
nt.eq_(list(_nf.trunk_origin_elevations(pop)),
[0.0, np.pi/2., -np.pi/2.])
nt.eq_(
list(_nf.trunk_origin_elevations(pop, neurite_type=NeuriteType.basal_dendrite)),
[0.0, np.pi/2., -np.pi/2.])
nt.eq_(len(_nf.trunk_origin_elevations(pop, neurite_type=NeuriteType.axon)),
0)
nt.eq_(len(_nf.trunk_origin_elevations(pop, neurite_type=NeuriteType.apical_dendrite)),
0)
def test_trunk_origin_elevations():
class Mock(object):
pass
n0 = Mock()
n1 = Mock()
s = make_soma([[0, 0, 0, 4]])
t0 = Section(((1, 0, 0, 2), (2, 1, 1, 2)))
t0.type = NeuriteType.basal_dendrite
t1 = Section(((0, 1, 0, 2), (1, 2, 1, 2)))
t1.type = NeuriteType.basal_dendrite
n0.neurites = [Neurite(t0), Neurite(t1)]
n0.soma = s
t2 = Section(((0, -1, 0, 2), (-1, -2, -1, 2)))
t2.type = NeuriteType.basal_dendrite
n1.neurites = [Neurite(t2)]
n1.soma = s
pop = Population([n0, n1])
nt.eq_(list(_nf.trunk_origin_elevations(pop)), [0.0, np.pi / 2.0, -np.pi / 2.0])
nt.eq_(
list(_nf.trunk_origin_elevations(pop, neurite_type=NeuriteType.basal_dendrite)),
[0.0, np.pi / 2.0, -np.pi / 2.0],
)
nt.eq_(len(_nf.trunk_origin_elevations(pop, neurite_type=NeuriteType.axon)), 0)
nt.eq_(len(_nf.trunk_origin_elevations(pop, neurite_type=NeuriteType.apical_dendrite)), 0)
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)
