def trunk_vectors(nrn, neurite_type=NeuriteType.all):
"""Calculates the vectors between all the trunks of the neuron and the soma center."""
neurite_filter = is_type(neurite_type)
nrns = neuron_population(nrn)
return np.array([morphmath.vector(s.root_node.points[0], n.soma.center)
for n in nrns
for s in n.neurites if neurite_filter(s)])
def _elevation(section, soma):
"""Elevation of a section."""
vector = morphmath.vector(section[0], soma.center)
norm_vector = np.linalg.norm(vector)
if norm_vector >= np.finfo(type(norm_vector)).eps:
return np.arcsin(vector[COLS.Y] / norm_vector)
raise ValueError("Norm of vector between soma center and section is almost zero.")
def _elevation(section, soma):
'''Elevation of a section'''
vector = mm.vector(section[0], soma.center)
norm_vector = np.linalg.norm(vector)
if norm_vector >= np.finfo(type(norm_vector)).eps:
return np.arcsin(vector[COLS.Y] / norm_vector)
else:
raise ValueError("Norm of vector between soma center and section is almost zero.")
def test_vector():
vec1 = (12.0, 3, 6.0)
vec2 = (14.0, 1.0, 6.0)
vec = mm.vector(vec1, vec2)
assert np.all(vec == (-2.0, 2.0, 0.0))
def _azimuth(section, soma):
"""Azimuth of a section."""
vector = morphmath.vector(section[0], soma.center)
return np.arctan2(vector[COLS.Z], vector[COLS.X])
def test_vector():
vec1 = (12.0, 3, 6.0)
vec2 = (14.0, 1.0, 6.0)
vec = mm.vector(vec1,vec2)
nt.ok_(np.all(vec ==(-2.0, 2.0, 0.0)))
def trunk_origin_direction(tree, soma):
'''Vector of trunk origin direction defined as
(initial tree point - soma center) of the tree.
'''
return mm.vector(tree.value, soma.center)
def _azimuth(section, soma):
'''Azimuth of a section'''
vector = mm.vector(section[0], soma.center)
return np.arctan2(vector[COLS.Z], vector[COLS.X])