def test_segment_taper_rate():
p0 = (0.0, 0.0, 0.0, 1.0)
p1 = (1.0, 0.0, 0.0, 4.0)
p2 = (2.0, 0.0, 0.0, 4.0)
p3 = (3.0, 0.0, 0.0, 4.0)
assert_almost_equal(mm.segment_taper_rate((p0, p1)), 6.0)
assert_almost_equal(mm.segment_taper_rate((p0, p2)), 3.0)
assert_almost_equal(mm.segment_taper_rate((p0, p3)), 2.0)
def test_segment_taper_rate():
p0 = (0.0, 0.0, 0.0, 1.0)
p1 = (1.0, 0.0, 0.0, 4.0)
p2 = (2.0, 0.0, 0.0, 4.0)
p3 = (3.0, 0.0, 0.0, 4.0)
nt.assert_almost_equal(mm.segment_taper_rate((p0, p1)), 6.0)
nt.assert_almost_equal(mm.segment_taper_rate((p0, p2)), 3.0)
nt.assert_almost_equal(mm.segment_taper_rate((p0, p3)), 2.0)
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)
# 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
pts = [p[COLS.R] for s in nrn.sections[1:] for p in s.points]
print('Mean radius of points:', np.mean(pts))
# get mean radius of segments
print('Mean radius of segments:',
np.mean(list(mm.segment_radius(s) for s in nm.iter_segments(nrn))))
# get stats for the segment taper rate, for different types of neurite
for ttype in NEURITES:
ttt = ttype
seg_taper_rate = [
mm.segment_taper_rate(s)
for s in nm.iter_segments(nrn,
neurite_filter=tree_type_checker(ttt))
]
print('Segment taper rate (',
ttype,
'):\n mean=',
np.mean(seg_taper_rate),
', std=',
np.std(seg_taper_rate),
', min=',
np.min(seg_taper_rate),
', max=',
np.max(seg_taper_rate),
sep='')
# 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
pts = [p[COLS.R] for s in nrn.sections[1:] for p in s.points]
print('Mean radius of points:',
np.mean(pts))
# get mean radius of segments
print('Mean radius of segments:',
np.mean(list(mm.segment_radius(s) for s in nm.iter_segments(nrn))))
# get stats for the segment taper rate, for different types of neurite
for ttype in NEURITES:
ttt = ttype
seg_taper_rate = [mm.segment_taper_rate(s)
for s in nm.iter_segments(nrn, neurite_filter=tree_type_checker(ttt))]
print('Segment taper rate (', ttype,
'):\n mean=', np.mean(seg_taper_rate),
', std=', np.std(seg_taper_rate),
', min=', np.min(seg_taper_rate),
', max=', np.max(seg_taper_rate),
sep='')
# Number of bifurcation points.
print('Number of bifurcation points:',
sum(1 for _ in nm.iter_sections(nrn,
iterator_type=Tree.ibifurcation_point)))
# Number of bifurcation points for apical dendrites
