def has_no_fat_ends(neuron, multiple_of_mean=2.0, final_point_count=5):
'''Check if leaf points are too large
Arguments:
neuron(Neuron): The neuron object to test
multiple_of_mean(float): how many times larger the final radius
has to be compared to the mean of the final points
final_point_count(int): how many points to include in the mean
Returns:
CheckResult with result list of ids of bad sections
Note:
A fat end is defined as a leaf segment whose last point is larger
by a factor of `multiple_of_mean` than the mean of the points in
`final_point_count`
'''
bad_ids = []
for leaf in _nf.iter_sections(neuron.neurites, iterator_type=Tree.ileaf):
mean_radius = np.mean(leaf.points[1:][-final_point_count:, COLS.R])
if mean_radius * multiple_of_mean <= leaf.points[-1, COLS.R]:
bad_ids.append((leaf.id, leaf.points[-1:]))
return CheckResult(len(bad_ids) == 0, bad_ids)
def has_no_fat_ends(neuron, multiple_of_mean=2.0, final_point_count=5):
'''Check if leaf points are too large
Arguments:
neuron(Neuron): The neuron object to test
multiple_of_mean(float): how many times larger the final radius
has to be compared to the mean of the final points
final_point_count(int): how many points to include in the mean
Returns:
CheckResult with result list of ids of bad sections
Note:
A fat end is defined as a leaf segment whose last point is larger
by a factor of `multiple_of_mean` than the mean of the points in
`final_point_count`
'''
bad_ids = []
for leaf in _nf.iter_sections(neuron.neurites, iterator_type=Tree.ileaf):
mean_radius = np.mean(leaf.points[1:][-final_point_count:, COLS.R])
if mean_radius * multiple_of_mean <= leaf.points[-1, COLS.R]:
bad_ids.append((leaf.id, leaf.points[-1:]))
return CheckResult(len(bad_ids) == 0, bad_ids)
def test_section_tortuosity():
sec_a = load_neuron(StringIO(u"""
((CellBody) (0 0 0 2))
((Dendrite)
(0 0 0 2)
(1 0 0 2)
(2 0 0 2)
(3 0 0 2))"""),
reader='asc').sections[1]
sec_b = load_neuron(StringIO(u"""
((CellBody) (0 0 0 2))
((Dendrite)
(0 0 0 2)
(1 0 0 2)
(1 2 0 2)
(0 2 0 2))"""),
reader='asc').sections[1]
nt.eq_(_sf.section_tortuosity(sec_a), 1.0)
nt.eq_(_sf.section_tortuosity(sec_b), 4.0 / 2.0)
for s in _nf.iter_sections(NRN):
nt.eq_(
_sf.section_tortuosity(s),
mmth.section_length(s.points) /
mmth.point_dist(s.points[0], s.points[-1]))
def test_neuron_sections():
all_nodes = set(NRN.sections)
neurite_nodes = set(_nf.iter_sections(NRN.neurites))
# check no duplicates
nt.assert_true(len(all_nodes) == len(NRN.sections))
# check all neurite tree nodes are
# in sections attribute
nt.assert_true(len(set(NRN.sections) - neurite_nodes) > 0)
def test_neuron_sections():
all_nodes = set(NRN.sections)
neurite_nodes = set(_nf.iter_sections(NRN.neurites))
# check no duplicates
nt.assert_true(len(all_nodes) == len(NRN.sections))
# check all neurite tree nodes are
# in sections attribute
nt.assert_true(len(set(NRN.sections) - neurite_nodes) > 0)
def test_section_tortuosity():
sec_a = Section([(0, 0, 0), (1, 0, 0), (2, 0, 0), (3, 0, 0)])
sec_b = Section([(0, 0, 0), (1, 0, 0), (1, 2, 0), (0, 2, 0)])
nt.eq_(_sf.section_tortuosity(sec_a), 1.0)
nt.eq_(_sf.section_tortuosity(sec_b), 4.0 / 2.0)
for s in _nf.iter_sections(NRN):
nt.eq_(_sf.section_tortuosity(s), mmth.section_length(s.points) / mmth.point_dist(s.points[0], s.points[-1]))
def test_section_tortuosity():
sec_a = Section([(0, 0, 0), (1, 0, 0), (2, 0, 0), (3, 0, 0)])
sec_b = Section([(0, 0, 0), (1, 0, 0), (1, 2, 0), (0, 2, 0)])
nt.eq_(_sf.section_tortuosity(sec_a), 1.0)
nt.eq_(_sf.section_tortuosity(sec_b), 4.0 / 2.0)
for s in _nf.iter_sections(NRN):
nt.eq_(
_sf.section_tortuosity(s),
mmth.section_length(s.points) /
mmth.point_dist(s.points[0], s.points[-1]))
def has_all_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:
status and list of ids bad sections
'''
bad_ids = [s.id for s in _nf.iter_sections(neuron.neurites)
if section_length(s.points) <= threshold]
return CheckResult(len(bad_ids) == 0, bad_ids)
def has_all_nonzero_section_lengths(neuron, threshold=0.0):
'''Check presence of neuron sections with length not above threshold
Arguments:
neuron(Neuron): The neuron object to test
threshold(float): value above which a section length is considered
to be non-zero
Returns:
CheckResult with result including list of ids of bad sections
'''
bad_ids = [s.id for s in _nf.iter_sections(neuron.neurites)
if section_length(s.points) <= threshold]
return CheckResult(len(bad_ids) == 0, bad_ids)
def has_all_nonzero_section_lengths(neuron, threshold=0.0):
'''Check presence of neuron sections with length not above threshold
Arguments:
neuron(Neuron): The neuron object to test
threshold(float): value above which a section length is considered
to be non-zero
Returns:
CheckResult with result including list of ids bad sections
'''
bad_ids = [s.id for s in _nf.iter_sections(neuron.neurites)
if section_length(s.points) <= threshold]
return CheckResult(len(bad_ids) == 0, bad_ids)
def has_no_fat_ends(neuron, multiple_of_mean=2.0, final_point_count=5):
'''Check if leaf points are too large
Arguments:
neuron: Neuron object whose soma will be tested
multiple_of_mean(float): how many times larger the final radius
has to be compared to the mean of the final points
final_point_count(int): how many points to include in the mean
'''
bad_ids = []
for leaf in _nf.iter_sections(neuron.neurites, iterator_type=Tree.ileaf):
mean_radius = np.mean(leaf.points[-final_point_count:, COLS.R])
if mean_radius * multiple_of_mean < leaf.points[-1, COLS.R]:
bad_ids.append((leaf.id, len(leaf.points)))
return CheckResult(len(bad_ids) == 0, bad_ids)
def has_all_nonzero_segment_lengths(neuron, threshold=0.0):
'''Check presence of neuron segments with length not above threshold
Arguments:
neuron: Neuron object whose segments will be tested
threshold: value above which a segment length is considered to be non-zero
Return:
status and list of (section_id, segment_id) of zero length segments
'''
bad_ids = []
for sec in _nf.iter_sections(neuron):
p = sec.points
for i, s in enumerate(izip(p[:-1], p[1:])):
if segment_length(s) <= threshold:
bad_ids.append((sec.id, i))
return CheckResult(len(bad_ids) == 0, bad_ids)
def has_all_nonzero_neurite_radii(neuron, threshold=0.0):
'''Check presence of neurite points with radius not above threshold
Arguments:
neuron: Neuron object whose segments will be tested
threshold: value above which a radius is considered to be non-zero
Return:
status and list of (section ID, point ID) pairs of zero-radius points
'''
bad_ids = []
seen_ids = set()
for s in _nf.iter_sections(neuron):
for i, p in enumerate(s.points):
info = (s.id, i)
if p[COLS.R] <= threshold and info not in seen_ids:
seen_ids.add(info)
bad_ids.append(info)
return CheckResult(len(bad_ids) == 0, bad_ids)
def has_no_jumps(neuron, max_distance=30.0, axis='z'):
'''Check if there are jumps (large movements in the `axis`)
Arguments:
neuron(Neuron): The neuron object to test
max_z_distance(float): value above which consecutive z-values are
considered a jump
axis(str): one of x/y/z, which axis to check for jumps
Returns:
CheckResult with result list of ids bad sections
'''
bad_ids = []
axis = {'x': COLS.X, 'y': COLS.Y, 'z': COLS.Z, }[axis.lower()]
for sec in _nf.iter_sections(neuron):
for i, (p0, p1) in enumerate(iter_segments(sec)):
info = (sec.id, i)
if max_distance < abs(p0[axis] - p1[axis]):
bad_ids.append(info)
return CheckResult(len(bad_ids) == 0, bad_ids)
def has_all_nonzero_segment_lengths(neuron, threshold=0.0):
'''Check presence of neuron segments with length not above threshold
Arguments:
neuron(Neuron): The neuron object to test
threshold(float): value above which a segment length is considered to
be non-zero
Returns:
CheckResult with result including list of (section_id, segment_id)
of zero length segments
'''
bad_ids = []
for sec in _nf.iter_sections(neuron):
p = sec.points
for i, s in enumerate(zip(p[:-1], p[1:])):
if segment_length(s) <= threshold:
bad_ids.append((sec.id, i))
return CheckResult(len(bad_ids) == 0, bad_ids)
def has_all_nonzero_neurite_radii(neuron, threshold=0.0):
'''Check presence of neurite points with radius not above threshold
Arguments:
neuron(Neuron): The neuron object to test
threshold: value above which a radius is considered to be non-zero
Returns:
CheckResult with result including list of (section ID, point ID) pairs
of zero-radius points
'''
bad_ids = []
seen_ids = set()
for s in _nf.iter_sections(neuron):
for i, p in enumerate(s.points):
info = (s.id, i)
if p[COLS.R] <= threshold and info not in seen_ids:
seen_ids.add(info)
bad_ids.append(info)
return CheckResult(len(bad_ids) == 0, bad_ids)
def has_no_jumps(neuron, max_distance=30.0, axis='z'):
'''Check if there are jumps (large movements in the `axis`)
Arguments:
neuron: Neuron object whose neurites will be tested
max_z_distance: value above which consecutive z-values are
considered a jump
axis(str): one of x/y/z, which axis to check for jumps
Return:
status and list of ids bad sections
'''
bad_ids = []
axis = {'x': COLS.X, 'y': COLS.Y, 'z': COLS.Z, }[axis.lower()]
for s in _nf.iter_sections(neuron):
it = izip(s.points, s.points[1:])
for i, (p0, p1) in enumerate(it):
info = (s.id, i)
if max_distance < abs(p0[axis] - p1[axis]):
bad_ids.append(info)
return CheckResult(len(bad_ids) == 0, bad_ids)
def has_no_jumps(neuron, max_distance=30.0, axis='z'):
'''Check if there are jumps (large movements in the `axis`)
Arguments:
neuron(Neuron): The neuron object to test
max_z_distance(float): value above which consecutive z-values are
considered a jump
axis(str): one of x/y/z, which axis to check for jumps
Returns:
CheckResult with result list of ids bad sections
'''
bad_ids = []
axis = {
'x': COLS.X,
'y': COLS.Y,
'z': COLS.Z,
}[axis.lower()]
for sec in _nf.iter_sections(neuron):
for i, (p0, p1) in enumerate(iter_segments(sec)):
info = (sec.id, i)
if max_distance < abs(p0[axis] - p1[axis]):
bad_ids.append(info)
return CheckResult(len(bad_ids) == 0, bad_ids)
def _check_fst_neurite_rotate(nrt_a, nrt_b, rot_mat):
for sa, sb in izip(_nf.iter_sections(nrt_a),
_nf.iter_sections(nrt_b)):
nt.assert_true(np.allclose(sb.points[:, 0:3],
_apply_rot(sa.points[:, 0:3], rot_mat)))
def _check_fst_neurite_translate(nrts_a, nrts_b, t):
# neurite sections
for sa, sb in zip(_nf.iter_sections(nrts_a),
_nf.iter_sections(nrts_b)):
nt.assert_true(np.allclose((sb.points[:, 0:3] - sa.points[:, 0:3]), t))
def _check_fst_neurite_translate(nrts_a, nrts_b, t):
# neurite sections
for sa, sb in izip(_nf.iter_sections(nrts_a),
_nf.iter_sections(nrts_b)):
nt.assert_true(np.allclose((sb.points[:, 0:3] - sa.points[:, 0:3]), t))
def _check_fst_neurite_rotate(nrt_a, nrt_b, rot_mat):
for sa, sb in zip(_nf.iter_sections(nrt_a),
_nf.iter_sections(nrt_b)):
nt.assert_true(np.allclose(sb.points[:, 0:3],
_apply_rot(sa.points[:, 0:3], rot_mat)))
