def test_trunk_origin_azimuths():
n0 = MockNeuron()
n1 = MockNeuron()
n2 = MockNeuron()
n3 = MockNeuron()
n4 = MockNeuron()
n5 = MockNeuron()
t = PointTree((0, 0, 0, 2))
t.type = NeuriteType.basal_dendrite
n0.neurites = [t]
n1.neurites = [t]
n2.neurites = [t]
n3.neurites = [t]
n4.neurites = [t]
n5.neurites = [t]
pop = [n0, n1, n2, n3, n4, n5]
s0 = make_soma([[0, 0, 1, 4]])
s1 = make_soma([[0, 0, -1, 4]])
s2 = make_soma([[0, 0, 0, 4]])
s3 = make_soma([[-1, 0, -1, 4]])
s4 = make_soma([[-1, 0, 0, 4]])
s5 = make_soma([[1, 0, 0, 4]])
pop[0].soma = s0
pop[1].soma = s1
pop[2].soma = s2
pop[3].soma = s3
pop[4].soma = s4
pop[5].soma = s5
nt.assert_true(np.all(get_feat('trunk_origin_azimuths', pop) ==
[-np.pi/2., np.pi/2., 0.0, np.pi/4., 0.0, np.pi]))
nt.eq_(len(get_feat('trunk_origin_azimuths', pop, neurite_type=NeuriteType.axon)), 0)
def _make_tree():
'''This tree has 3 branching points'''
p = [0.0, 0.0, 0.0, 1.0, 1, 1, 2]
T = PointTree(p)
T1 = T.add_child(PointTree([0.0, 1.0, 0.0, 2.0, 1, 1, 2]))
T2 = T1.add_child(PointTree([0.0, 2.0, 0.0, 3.0, 1, 1, 2]))
T3 = T2.add_child(PointTree([0.0, 4.0, 0.0, 4.0, 1, 1, 2]))
T4 = T3.add_child(PointTree([0.0, 5.0, 0.0, 5.0, 1, 1, 2]))
T5 = T4.add_child(PointTree([2.0, 5.0, 0.0, 6.0, 1, 1, 2]))
T6 = T4.add_child(PointTree([0.0, 5.0, 2.0, 7.0, 1, 1, 2]))
return T
def _make_neuron_tree():
p = [0.0, 0.0, 0.0, 1.0, 1, 1, 2]
T = PointTree(p)
T1 = T.add_child(PointTree([0.0, 1.0, 0.0, 1.0, 1, 1, 2]))
T2 = T1.add_child(PointTree([0.0, 2.0, 0.0, 1.0, 1, 1, 2]))
T3 = T2.add_child(PointTree([0.0, 4.0, 0.0, 2.0, 1, 1, 2]))
T4 = T3.add_child(PointTree([0.0, 5.0, 0.0, 2.0, 1, 1, 2]))
T5 = T4.add_child(PointTree([2.0, 5.0, 0.0, 1.0, 1, 1, 2]))
T6 = T4.add_child(PointTree([0.0, 5.0, 2.0, 1.0, 1, 1, 2]))
T7 = T5.add_child(PointTree([3.0, 5.0, 0.0, 0.75, 1, 1, 2]))
T8 = T7.add_child(PointTree([4.0, 5.0, 0.0, 0.75, 1, 1, 2]))
T9 = T6.add_child(PointTree([0.0, 5.0, 3.0, 0.75, 1, 1, 2]))
T10 = T9.add_child(PointTree([0.0, 6.0, 3.0, 0.75, 1, 1, 2]))
return T
def _make_simple_tree():
p = [0.0, 0.0, 0.0, 1.0, 1, 1, 1]
T = PointTree(p)
T1 = T.add_child(PointTree([0.0, 2.0, 0.0, 1.0, 1, 1, 1]))
T2 = T1.add_child(PointTree([0.0, 4.0, 0.0, 1.0, 1, 1, 1]))
T3 = T2.add_child(PointTree([0.0, 6.0, 0.0, 1.0, 1, 1, 1]))
T4 = T3.add_child(PointTree([0.0, 8.0, 0.0, 1.0, 1, 1, 1]))
T5 = T.add_child(PointTree([0.0, 0.0, 2.0, 1.0, 1, 1, 1]))
T6 = T5.add_child(PointTree([0.0, 0.0, 4.0, 1.0, 1, 1, 1]))
T7 = T6.add_child(PointTree([0.0, 0.0, 6.0, 1.0, 1, 1, 1]))
T8 = T7.add_child(PointTree([0.0, 0.0, 8.0, 1.0, 1, 1, 1]))
return T
def _make_odd_tree():
''' The infamous invisible tree. This should fail check
in case of integer division in partition
'''
p = [0.0, 0.0, 0.0, 0.0, 0, 0, 0]
T = PointTree(p)
T.add_child(PointTree([0.0, 0.0, 0.0, 0.0, 0, 0, 0]))
T.add_child(PointTree([0.0, 0.0, 0.0, 0.0, 0, 0, 0]))
T.children[0].add_child(PointTree([0.0, 0.0, 0.0, 0.0, 0, 0, 0]))
T.children[0].add_child(PointTree([0.0, 0.0, 0.0, 0.0, 0, 0, 0]))
T.children[1].add_child(PointTree([0.0, 0.0, 0.0, 0.0, 0, 0, 0]))
T.children[1].add_child(PointTree([0.0, 0.0, 0.0, 0.0, 0, 0, 0]))
T.children[1].children[0].add_child(PointTree([0.0, 0.0, 0.0, 0.0, 0, 0, 0]))
T.children[1].children[0].add_child(PointTree([0.0, 0.0, 0.0, 0.0, 0, 0, 0]))
return T
def test_principal_direction_extent():
points = np.array([[-10., 0., 0.],
[-9., 0., 0.],
[9., 0., 0.],
[10., 0., 0.]])
tree = PointTree(np.array([points[0][0], points[0][1], points[0][2], 1., 0., 0.]))
tree.add_child(PointTree(np.array([points[1][0], points[1][1], points[1][2], 1., 0., 0.])))
tree.children[0].add_child(PointTree(np.array([points[2][0], points[2][1], points[2][2], 1., 0., 0.])))
tree.children[0].add_child(PointTree(np.array([points[3][0], points[3][1], points[3][2], 1., 0., 0.])))
extent = mtr.principal_direction_extent(tree)
nt.assert_true(np.allclose(extent, [20., 0., 0.]))
def _form_branching_tree():
p = [0.0, 0.0, 0.0, 1.0, 1, 1, 2]
T = PointTree(p)
T1 = T.add_child(PointTree([0.0, 1.0, 0.0, 1.0, 1, 1, 2]))
T2 = T1.add_child(PointTree([0.0, 2.0, 0.0, 1.0, 1, 1, 2]))
T3 = T2.add_child(PointTree([0.0, 4.0, 0.0, 2.0, 1, 1, 2]))
T4 = T3.add_child(PointTree([0.0, 5.0, 0.0, 2.0, 1, 1, 2]))
T5 = T4.add_child(PointTree([2.0, 5.0, 0.0, 1.0, 1, 1, 2]))
T6 = T4.add_child(PointTree([0.0, 5.0, 2.0, 1.0, 1, 1, 2]))
T7 = T5.add_child(PointTree([3.0, 5.0, 0.0, 0.75, 1, 1, 2]))
T8 = T7.add_child(PointTree([4.0, 5.0, 0.0, 0.75, 1, 1, 2]))
T9 = T6.add_child(PointTree([0.0, 5.0, 3.0, 0.75, 1, 1, 2]))
T10 = T9.add_child(PointTree([0.0, 6.0, 3.0, 0.75, 1, 1, 2]))
T11 = T9.add_child(PointTree([0.0, 6.0, 4.0, 0.75, 1, 1, 2]))
T33 = T3.add_child(PointTree([1.0, 5.0, 0.0, 2.0, 1, 1, 2]))
T331 = T33.add_child(PointTree([15.0, 15.0, 0.0, 2.0, 1, 1, 2]))
return T
def test_principal_directions_extents():
points = np.array([[-10., 0., 0.],
[-9., 0., 0.],
[9., 0., 0.],
[10., 0., 0.]])
tree = PointTree(np.array([points[0][0], points[0][1], points[0][2], 1., 0., 0.]))
tree.add_child(PointTree(np.array([points[1][0], points[1][1], points[1][2], 1., 0., 0.])))
tree.children[0].add_child(PointTree(np.array([points[2][0], points[2][1], points[2][2], 1., 0., 0.])))
tree.children[0].add_child(PointTree(np.array([points[3][0], points[3][1], points[3][2], 1., 0., 0.])))
neurites = [tree, tree, tree]
extents0 = get_feat('principal_direction_extents', neurites, direction='first')
nt.assert_true(np.allclose(extents0, [20., 20., 20.]))
extents1 = get_feat('principal_direction_extents', neurites, direction='second')
nt.assert_true(np.allclose(extents1, [0., 0., 0.]))
extents2 = get_feat('principal_direction_extents', neurites, direction='third')
nt.assert_true(np.allclose(extents2, [0., 0., 0.]))
def trunk_section_length(tree):
'''Length of the initial tree section
Returns:
Length of first section of tree or 0 if single point tree
'''
try:
_it = imap_val(mm.section_length, ptr.isection(tree))
return _it.next()
except StopIteration:
return 0.0
def _make_branching_tree_zero_length_bif_segments():
'''This tree has 3 branching points'''
p = [0.0, 0.0, 0.0, 1.0, 1, 1, 2]
T = PointTree(p)
T1 = T.add_child(PointTree([0.0, 1.0, 0.0, 1.0, 1, 1, 2]))
T2 = T1.add_child(PointTree([0.0, 2.0, 0.0, 1.0, 1, 1, 2]))
T3 = T2.add_child(PointTree([0.0, 4.0, 0.0, 2.0, 1, 1, 2]))
T4 = T3.add_child(PointTree([0.0, 5.0, 0.0, 2.0, 1, 1, 2]))
T5_0 = T4.add_child(PointTree([0.0, 5.0, 0.0, 2.0, 1, 1, 2]))
T5 = T5_0.add_child(PointTree([2.0, 5.0, 0.0, 1.0, 1, 1, 2]))
T6_0 = T4.add_child(PointTree([0.0, 5.0, 0.0, 2.0, 1, 1, 2]))
T6 = T6_0.add_child(PointTree([0.0, 5.0, 2.0, 1.0, 1, 1, 2]))
T7 = T5.add_child(PointTree([3.0, 5.0, 0.0, 0.75, 1, 1, 2]))
T8 = T7.add_child(PointTree([4.0, 5.0, 0.0, 0.75, 1, 1, 2]))
T9 = T6.add_child(PointTree([0.0, 5.0, 3.0, 0.75, 1, 1, 2]))
T10_0 = T9.add_child(PointTree([0.0, 5.0, 3.0, 0.75, 1, 1, 2]))
T10 = T10_0.add_child(PointTree([0.0, 6.0, 3.0, 0.75, 1, 1, 2]))
T11 = T9.add_child(PointTree([0.0, 6.0, 4.0, 0.75, 1, 1, 2]))
T33 = T3.add_child(PointTree([1.0, 5.0, 0.0, 2.0, 1, 1, 2]))
T331 = T33.add_child(PointTree([15.0, 15.0, 0.0, 2.0, 1, 1, 2]))
return T
def i_section_radial_dist(tree, pos=None, use_start_point=False):
'''Return an iterator of radial distances of tree sections to a given point
The radial distance is the euclidian distance between the either the
end-point or rhe start point of the section and the point in question.
Parameters:
tree: tree object
pos: origin to which distances are measured. It must have at least 3\
components. The first 3 components are (x, y, z).\
(default tree origin)
use_start_point: If true, calculate distance from section start point,\
else from end-point (default, False)
'''
pos = tree.value if pos is None else pos
sec_idx = 0 if use_start_point else -1
return imap_val(lambda s: mm.point_dist(s[sec_idx], pos), ptr.isection(tree))
def test_trunk_origin_elevations():
n0 = MockNeuron()
n1 = MockNeuron()
s = make_soma([[0, 0, 0, 4]])
t0 = PointTree((1, 0, 0, 2))
t0.type = NeuriteType.basal_dendrite
t1 = PointTree((0, 1, 0, 2))
t1.type = NeuriteType.basal_dendrite
n0.neurites = [t0, t1]
n0.soma = s
t2 = PointTree((0, -1, 0, 2))
t2.type = NeuriteType.basal_dendrite
n1.neurites = [t2]
n1.soma = s
pop = [n0, n1]
nt.assert_true(np.all(get_feat('trunk_origin_elevations', pop) ==
[0.0, np.pi/2., -np.pi/2.]))
nt.eq_(len(get_feat('trunk_origin_elevations', pop, neurite_type=NeuriteType.axon)), 0)
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from nose import tools as nt
import os
from neurom.point_neurite.core import iter_neurites
from neurom.point_neurite import io
from neurom.point_neurite.io.utils import make_neuron
from neurom.point_neurite.point_tree import PointTree, isection
from neurom.point_neurite import segments as seg
from neurom.point_neurite import sections as sec
# Mock tree holding integers, not points
MOCK_TREE = PointTree(0)
MOCK_TREE.add_child(PointTree(11))
MOCK_TREE.add_child(PointTree(12))
MOCK_TREE.children[0].add_child(PointTree(111))
MOCK_TREE.children[0].add_child(PointTree(112))
MOCK_TREE.children[1].add_child(PointTree(121))
MOCK_TREE.children[1].add_child(PointTree(122))
MOCK_TREE.children[1].children[0].add_child(PointTree(1211))
MOCK_TREE.children[1].children[0].children[0].add_child(PointTree(12111))
MOCK_TREE.children[1].children[0].children[0].add_child(PointTree(12112))
class MockNeuron(object):
pass
def n_segments(tree):
"""
Return number of segments in tree
"""
return sum(1 for _ in ptr.isegment(tree))
def n_sections(tree):
"""
Return number of sections in tree
"""
return sum(1 for _ in ptr.isection(tree))
def test_trunk_origin_radius():
t = PointTree((0, 0, 0, 42))
t.add_child(PointTree((1, 0, 0, 4)))
nt.assert_equal(mtr.trunk_origin_radius(t), 42.0)
def test_trunk_section_length():
t = PointTree((0, 0, 0, 42))
tt = t.add_child(PointTree((10, 0, 0, 4)))
tt.add_child(PointTree((10, 15, 0, 4)))
nt.assert_almost_equal(mtr.trunk_section_length(t), 25.0)
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from nose import tools as nt
from copy import deepcopy
from neurom.core._neuron import Neuron
from neurom.core import make_soma
from neurom.point_neurite.point_tree import PointTree
from neurom.point_neurite.point_tree import val_iter
from itertools import izip
import numpy as np
SOMA_SINGLE_PTS = [[11, 22, 33, 44, 1, 1, -1]]
TREE = PointTree([0.0, 0.0, 0.0, 1.0, 1, 1, 2])
T1 = TREE.add_child(PointTree([0.0, 1.0, 0.0, 1.0, 1, 1, 2]))
T2 = T1.add_child(PointTree([0.0, 2.0, 0.0, 1.0, 1, 1, 2]))
T3 = T2.add_child(PointTree([0.0, 4.0, 0.0, 2.0, 1, 1, 2]))
T4 = T3.add_child(PointTree([0.0, 5.0, 0.0, 2.0, 1, 1, 2]))
T5 = T4.add_child(PointTree([2.0, 5.0, 0.0, 1.0, 1, 1, 2]))
T6 = T4.add_child(PointTree([0.0, 5.0, 2.0, 1.0, 1, 1, 2]))
T7 = T5.add_child(PointTree([3.0, 5.0, 0.0, 0.75, 1, 1, 2]))
T8 = T7.add_child(PointTree([4.0, 5.0, 0.0, 0.75, 1, 1, 2]))
T9 = T6.add_child(PointTree([0.0, 5.0, 3.0, 0.75, 1, 1, 2]))
T10 = T9.add_child(PointTree([0.0, 6.0, 3.0, 0.75, 1, 1, 2]))
def test_deep_copy():
soma = make_soma([[0, 0, 0, 1, 1, 1, -1]])
import numpy as np
from nose import tools as nt
from neurom.point_neurite.point_tree import PointTree
from neurom.point_neurite.core import Neuron
from neurom.core import make_soma
from neurom.core import NeuriteType
from neurom.point_neurite.treefunc import set_tree_type
import neurom.point_neurite.dendrogram as dm
TREE = PointTree(np.array([0., 0., 0., 10., 4., 0., 0.]))
TREE.add_child(PointTree(np.array([3., 3., 3., 9., 4., 0., 0.])))
TREE.children[0].add_child(PointTree(np.array([10., 10., 10., 5., 4., 0., 0.])))
TREE.children[0].add_child(PointTree(np.array([-10., -10., -10., 7., 4., 0., 0.])))
set_tree_type(TREE)
SOMA = make_soma(np.array([[0., 0., 0., 1., 1., 1., -1.]]))
NEURON = Neuron(SOMA, [TREE, TREE, TREE])
OLD_OFFS = [1.2, -1.2]
NEW_OFFS = [2.3, -2.3]
SPACING = (40., 0.)
def test_n_rectangles_tree():
nt.assert_equal(dm._n_rectangles(TREE), 5)
import numpy as np
DATA_PATH = './test_data'
SWC_PATH = os.path.join(DATA_PATH, 'swc/')
data = io.load_data(SWC_PATH + 'Neuron.swc')
neuron0 = make_neuron(data)
tree0 = neuron0.neurites[0]
tree_types = [NeuriteType.axon,
NeuriteType.basal_dendrite,
NeuriteType.basal_dendrite,
NeuriteType.apical_dendrite]
# Mock tree holding integers, not points
MOCK_TREE = PointTree(0)
MOCK_TREE.add_child(PointTree(11))
MOCK_TREE.add_child(PointTree(12))
MOCK_TREE.children[0].add_child(PointTree(111))
MOCK_TREE.children[0].add_child(PointTree(112))
MOCK_TREE.children[1].add_child(PointTree(121))
MOCK_TREE.children[1].add_child(PointTree(122))
MOCK_TREE.children[1].children[0].add_child(PointTree(1211))
MOCK_TREE.children[1].children[0].children[0].add_child(PointTree(12111))
MOCK_TREE.children[1].children[0].children[0].add_child(PointTree(12112))
T1111 = MOCK_TREE.children[0].children[0].add_child(PointTree(1111))
T11111 = T1111.add_child(PointTree(11111))
T11112 = T1111.add_child(PointTree(11112))
T11113 = T1111.add_child(PointTree(11113))
REF_TREE3 = PointTree(np.array([0.,0.,0.,1.,0.,0.,0.]))
