def area(self):
'''Return the surface area of this section.
The area is calculated from the segments, as defined by this
section's points
'''
return sum(morphmath.segment_area(s) for s in iter_segments(self))
def save_morph_data(self, morph_stats_filename):
if not os.path.exists(morph_stats_filename):
# load a neuron from an SWC file
nrn = nm.load_neuron(self.morph_file)
morph_stats = {}
morph_stats['cell_id'] = self.cell_id
morph_stats['soma_suface'] = nm.get('soma_surface_areas', nrn)[0]
morph_stats['soma_radius'] = np.mean(nm.get('soma_radii', nrn))
# Morph stats
for nrn_type_ in NEURITES:
morph_stats['length' + '.' +
str(nrn_type_).split('.')[1]] = np.sum(
nm.get('segment_lengths',
nrn,
neurite_type=nrn_type_))
morph_stats['area' + '.' + str(nrn_type_).split('.')[1]] = sum(
mm.segment_area(s) for s in nm.iter_segments(
nrn, neurite_filter=tree_type_checker(nrn_type_)))
morph_stats[
'volume' + '.' + str(nrn_type_).split('.')[1]] = sum(
mm.segment_volume(s) for s in nm.iter_segments(
nrn, neurite_filter=tree_type_checker(nrn_type_)))
morph_stats['taper_rate' + '.' + str(nrn_type_).split('.')[1]] = \
np.mean([mm.segment_taper_rate(s) for s in nm.iter_segments(
nrn, neurite_filter=tree_type_checker(nrn_type_))])
utility.save_json(morph_stats_filename, morph_stats)
def __init__(self, points):
"""Initialize a SomaCyliners object."""
super().__init__(points)
self.area = sum(
morphmath.segment_area((p0, p1))
for p0, p1 in zip(points, points[1:]))
self.radius = math.sqrt(self.area / (4. * math.pi))
def area(self):
"""Return the surface area of this section.
The area is calculated from the segments, as defined by this
section's points
"""
return sum(morphmath.segment_area(s) for s in iter_segments(self))
def soma_to_single_point(soma):
'''surface preserving cylindrical soma to a single point sphere'''
logger.info('Converting soma to a single point sphere, while preserving the surface')
neurom_points = np.hstack((soma.points, 0.5 * soma.diameters[:, None]))
surface_area = sum(morphmath.segment_area(seg)
for seg in zip(neurom_points[1:], neurom_points[:-1]))
soma.points = np.mean(soma.points, axis=0)[None, :]
soma.diameters = [float((surface_area / np.pi) ** 0.5)]
def test_segment_area():
p0 = Point(0.0, 0.0, 0.0, 3.0)
p1 = Point(2.0, 0.0, 0.0, 3.0)
p2 = Point(4.0, 0.0, 0.0, 3.0)
p3 = Point(4.0, 0.0, 0.0, 6.0)
p4 = Point(1.0, 0.0, 0.0, 3.0)
p5 = Point(4.0, 0.0, 0.0, 3.0)
a01 = mm.segment_area((p0, p1))
a02 = mm.segment_area((p0, p2))
a03 = mm.segment_area((p0, p3))
a04 = mm.segment_area((p0, p4))
a45 = mm.segment_area((p4, p5))
a05 = mm.segment_area((p0, p5))
assert_almost_equal(a01, 37.6991118, decimal=6)
assert_almost_equal(2 * a01, a02)
assert_almost_equal(a03, 141.3716694, decimal=6)
assert_almost_equal(a45, a05 - a04)
assert_almost_equal(mm.segment_area((p0, p3)), mm.segment_area((p3, p0)))
def test_segment_area():
p0 = Point(0.0, 0.0, 0.0, 3.0, 1)
p1 = Point(2.0, 0.0, 0.0, 3.0, 1)
p2 = Point(4.0, 0.0, 0.0, 3.0, 1)
p3 = Point(4.0, 0.0, 0.0, 6.0, 1)
p4 = Point(1.0, 0.0, 0.0, 3.0, 1)
p5 = Point(4.0, 0.0, 0.0, 3.0, 1)
a01 = mm.segment_area((p0, p1))
a02 = mm.segment_area((p0, p2))
a03 = mm.segment_area((p0, p3))
a04 = mm.segment_area((p0, p4))
a45 = mm.segment_area((p4, p5))
a05 = mm.segment_area((p0, p5))
nt.assert_almost_equal(a01, 37.6991118, places=6)
nt.assert_almost_equal(2*a01, a02)
nt.assert_almost_equal(a03, 141.3716694, places=6)
nt.assert_almost_equal(a45, a05 - a04)
nt.assert_almost_equal(mm.segment_area((p0, p3)), mm.segment_area((p3, p0)))
def __init__(self, points):
super(SomaCylinders, self).__init__(points)
self.area = sum(
morphmath.segment_area((p0, p1))
for p0, p1 in zip(points, points[1:]))
self.radius = math.sqrt(self.area / (4. * math.pi))
def __init__(self, points):
super(SomaCylinders, self).__init__(points)
self.area = sum(morphmath.segment_area((p0, p1))
for p0, p1 in zip(points, points[1:]))
self.radius = math.sqrt(self.area / (4. * math.pi))
def segment_areas(neurites, neurite_type=NeuriteType.all):
"""Areas of the segments in a collection of neurites."""
return [morphmath.segment_area(seg) for seg
in iter_segments(neurites, is_type(neurite_type))]
# Get length of all neurites in cell by iterating over segments,
# and summing the segment lengths.
# This should yield the same result as iterating over sections.
print('Total neurite length (segments):',
sum(mm.segment_length(s) for s in nm.iter_segments(nrn)))
# get volume of all neurites in cell by summing over segment
# volumes
print('Total neurite volume:',
sum(mm.segment_volume(s) for s in nm.iter_segments(nrn)))
# get area of all neurites in cell by summing over segment
# areas
print('Total neurite surface area:',
sum(mm.segment_area(s) for s in nm.iter_segments(nrn)))
# get total number of neurite points in cell.
def n_points(sec):
"""number of points in a section."""
n = len(sec.points)
# Non-root sections have duplicate first point
return n if sec.parent is None else n - 1
print('Total number of points:',
sum(n_points(s) for s in nm.iter_sections(nrn)))
# get mean radius of neurite points in cell.
# p[COLS.R] yields the radius for point p.
# Note: this includes duplicated points at beginning of
# non-trunk sections
# Get length of all neurites in cell by iterating over segments,
# and summing the segment lengths.
# This should yield the same result as iterating over sections.
print('Total neurite length (segments):',
sum(mm.segment_length(s) for s in nm.iter_segments(nrn)))
# get volume of all neurites in cell by summing over segment
# volumes
print('Total neurite volume:',
sum(mm.segment_volume(s) for s in nm.iter_segments(nrn)))
# get area of all neurites in cell by summing over segment
# areas
print('Total neurite surface area:',
sum(mm.segment_area(s) for s in nm.iter_segments(nrn)))
# get total number of neurite points in cell.
def n_points(sec):
'''number of points in a section'''
n = len(sec.points)
# Non-root sections have duplicate first point
return n if sec.parent is None else n - 1
print('Total number of points:',
sum(n_points(s) for s in nm.iter_sections(nrn)))
# get mean radius of neurite points in cell.
# p[COLS.R] yields the radius for point p.
# Note: this includes duplicated points at beginning of
# non-trunk sections
