def test_section_iteration():
REF_SECTIONS = ((0, 11), (11, 111, 1111), (1111, 11111), (1111, 11112),
(1111, 11113), (11, 112), (0, 12), (12, 121, 1211),
(1211, 12111), (1211, 12112), (12, 122))
for i, s in enumerate(isection(REF_TREE2)):
nt.assert_equal(REF_SECTIONS[i], tuple(tt.value for tt in s))
def test_section_iteration():
REF_SECTIONS = ((0, 11), (11, 111, 1111), (1111, 11111), (1111, 11112),
(1111, 11113), (11, 112), (0, 12), (12, 121, 1211),
(1211, 12111), (1211, 12112), (12, 122))
for i, s in enumerate(isection(REF_TREE2)):
nt.assert_equal(REF_SECTIONS[i], tuple(tt.value for tt in s))
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 = tr.imap_val(mm.section_length, tr.isection(tree))
return _it.next()
except StopIteration:
return 0.0
def nonzero_section_lengths(neuron, threshold=0.0):
'''Check presence of neuron sections with length not above threshold
Arguments:
neuron: Neuron object whose segments will be tested
threshold: value above which a section length is considered to be non-zero
Return: list of ids of first point in bad sections
'''
l = [[s for s in val_iter(isection(t)) if section_length(s) <= threshold]
for t in neuron.neurites]
return [i[0][COLS.ID] for i in chain(*l)]
def test_section_upstream_iteration():
leaves = [l for l in ileaf(REF_TREE2)]
ref_paths = [[(1111, 11111), (11, 111, 1111), (0, 11)],
[(1111, 11112), (11, 111, 1111), (0, 11)],
[(1111, 11113), (11, 111, 1111), (0, 11)], [(11, 112),
(0, 11)],
[(1211, 12111), (12, 121, 1211), (0, 12)],
[(1211, 12112), (12, 121, 1211), (0, 12)], [(12, 122),
(0, 12)]]
for l, ref in zip(leaves, ref_paths):
nt.assert_equal([s for s in val_iter(isection(l, iupstream))], ref)
def nonzero_section_lengths(neuron, threshold=0.0):
'''Check presence of neuron sections with length not above threshold
Arguments:
neuron: Neuron object whose segments will be tested
threshold: value above which a section length is considered to be non-zero
Return: list of ids of first point in bad sections
'''
l = [[s for s in val_iter(isection(t))
if section_length(s) <= threshold]
for t in neuron.neurites]
return [i[0][COLS.ID] for i in chain(*l)]
def i_section_path_length(tree, use_start_point=False):
'''Return an iterator of path lengths of tree sections
Path lengths are measured to the tree's root.
Parameters:
tree: tree object
use_start_point: If true, calculate path length from section start point,\
else from end-point (default, False)
'''
sec_idx = 0 if use_start_point else -1
return imap(lambda s: path_length(s[sec_idx]), tr.isection(tree))
def test_section_upstream_iteration():
leaves = [l for l in ileaf(REF_TREE2)]
ref_paths = [
[(1111, 11111), (11, 111, 1111), (0, 11)],
[(1111, 11112), (11, 111, 1111), (0, 11)],
[(1111, 11113), (11, 111, 1111), (0, 11)],
[(11, 112), (0, 11)],
[(1211, 12111), (12, 121, 1211), (0, 12)],
[(1211, 12112), (12, 121, 1211), (0, 12)],
[(12, 122), (0, 12)]]
for l, ref in zip(leaves, ref_paths):
nt.assert_equal([s for s in val_iter(isection(l, iupstream))], ref)
def i_section_path_length(tree, use_start_point=False):
'''Return an iterator of path lengths of tree sections
Path lengths are measured to the tree's root.
Parameters:
tree: tree object
use_start_point: If true, calculate path length from section start point,\
else from end-point (default, False)
'''
sec_idx = 0 if use_start_point else -1
return imap(lambda s: path_length(s[sec_idx]), tr.isection(tree))
def test_branch_order():
branch_order_map = {
(0, 11): 0,
(11, 111, 1111): 1,
(1111, 11111): 2,
(1111, 11112): 2,
(1111, 11113): 2,
(11, 112): 1,
(0, 12): 0,
(12, 121, 1211): 1,
(1211, 12111): 2,
(1211, 12112): 2,
(12, 122): 1
}
for sec in tr.isection(MOCK_TREE):
nt.assert_equal(branch_order(sec),
branch_order_map[tuple(p for p in tr.val_iter(sec))])
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 tr.imap_val(lambda s: mm.point_dist(s[sec_idx], pos), tr.isection(tree))
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 tr.imap_val(lambda s: mm.point_dist(s[sec_idx], pos),
tr.isection(tree))
# Number of bifurcation points.
# This uses the more generic iter_neurites method, in which
# we can decide the type of iteration. Here we iterate over
# bifurcation points.
print('Number of bifurcation points:',
sum(1 for _ in nrn.iter_neurites(ibifurcation_point)))
# Number of bifurcation points for apical dendrites
print(
'Number of bifurcation points (apical dendrites):',
sum(1 for _ in nrn.iter_neurites(
ibifurcation_point, neurite_type=ezy.TreeType.apical_dendrite)))
# Maximum branch order
# This is complicated and will be factored into a helper function.
# We iterate over sections, calcumating the branch order for each one.
# The reason we cannot simply call nen.iter_sections(mt.branch_order) is
# that mt.branch_order requires sections of tree nodes for navigation, but
# nrn.iter_sections iterates over the sections of points.
# TODO: This whole tree data business has to be refactored and simplified.
print(
'Maximum branch order:',
np.max([
bo for bo in nrn.iter_neurites(
lambda t: imap(mt.branch_order, isection(t)))
]))
# Neuron's bounding box
print('Bounding box ((min x, y, z), (max x, y, z))', nrn.bounding_box())
def sections(self):
'''Returns sections iterator
'''
return isection(self._tree)
def i_section_length(tree):
"""
Return an iterator of tree section lengths
"""
return tr.imap_val(mm.section_length, tr.isection(tree))
def n_sections(tree):
"""
Return number of sections in tree
"""
return sum(1 for _ in tr.isection(tree))
def i_section_length(tree):
"""
Return an iterator of tree section lengths
"""
return tr.imap_val(mm.section_length, tr.isection(tree))
'''Build sections from a tree
Sections are defined as points between forking points,
between the root node and forking points, or between
forking points and end-points
'''
from neurom.core import tree
REF_TREE = tree.Tree(0)
REF_TREE.add_child(tree.Tree(11))
REF_TREE.add_child(tree.Tree(12))
REF_TREE.children[0].add_child(tree.Tree(111))
REF_TREE.children[0].add_child(tree.Tree(112))
REF_TREE.children[1].add_child(tree.Tree(121))
REF_TREE.children[1].add_child(tree.Tree(122))
REF_TREE.children[1].children[0].add_child(tree.Tree(1211))
REF_TREE.children[1].children[0].children[0].add_child(tree.Tree(12111))
REF_TREE.children[1].children[0].children[0].add_child(tree.Tree(12112))
REF_TREE.children[0].children[0].add_child(tree.Tree(1111))
REF_TREE.children[0].children[0].children[0].add_child(tree.Tree(11111))
REF_TREE.children[0].children[0].children[0].add_child(tree.Tree(11112))
if __name__ == '__main__':
for s in tree.isection(REF_TREE):
print [tt.value for tt in s]
def n_sections(tree):
"""
Return number of sections in tree
"""
return sum(1 for _ in tr.isection(tree))
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
'''Build sections from a tree
Sections are defined as points between forking points,
between the root node and forking points, or between
forking points and end-points
'''
from neurom.core import tree
REF_TREE = tree.Tree(0)
REF_TREE.add_child(tree.Tree(11))
REF_TREE.add_child(tree.Tree(12))
REF_TREE.children[0].add_child(tree.Tree(111))
REF_TREE.children[0].add_child(tree.Tree(112))
REF_TREE.children[1].add_child(tree.Tree(121))
REF_TREE.children[1].add_child(tree.Tree(122))
REF_TREE.children[1].children[0].add_child(tree.Tree(1211))
REF_TREE.children[1].children[0].children[0].add_child(tree.Tree(12111))
REF_TREE.children[1].children[0].children[0].add_child(tree.Tree(12112))
REF_TREE.children[0].children[0].add_child(tree.Tree(1111))
REF_TREE.children[0].children[0].children[0].add_child(tree.Tree(11111))
REF_TREE.children[0].children[0].children[0].add_child(tree.Tree(11112))
if __name__ == '__main__':
for s in tree.isection(REF_TREE):
print[tt.value for tt in s]
", max=",
np.max(seg_taper_rate),
sep="",
)
# Number of bifurcation points.
# This uses the more generic iter_neurites method, in which
# we can decide the type of iteration. Here we iterate over
# bifurcation points.
print("Number of bifurcation points:", sum(1 for _ in nrn.iter_neurites(ibifurcation_point)))
# Number of bifurcation points for apical dendrites
print(
"Number of bifurcation points (apical dendrites):",
sum(1 for _ in nrn.iter_neurites(ibifurcation_point, neurite_type=ezy.TreeType.apical_dendrite)),
)
# Maximum branch order
# This is complicated and will be factored into a helper function.
# We iterate over sections, calcumating the branch order for each one.
# The reason we cannot simply call nen.iter_sections(mt.branch_order) is
# that mt.branch_order requires sections of tree nodes for navigation, but
# nrn.iter_sections iterates over the sections of points.
# TODO: This whole tree data business has to be refactored and simplified.
print(
"Maximum branch order:", np.max([bo for bo in nrn.iter_neurites(lambda t: imap(mt.branch_order, isection(t)))])
)
# Neuron's bounding box
print("Bounding box ((min x, y, z), (max x, y, z))", nrn.bounding_box())