def morphology(neuron, neurite_type=None, reset_radii=True):
pdata = []
edges = []
if neurite_type is None:
it = iter_sections(neuron)
else:
it = iter_sections(neuron, neurite_filter=lambda n: n.type == neurite_type)
for section in it:
section_points = section.points[:, :4]
if reset_radii:
section_points[:, 3] = 0.01
section_points = section_points.tolist()
N = len(pdata)
neurite_edges = [[i, i + 1] for i in range(N, len(section_points) + N - 1)]
pdata.extend(section_points)
edges.extend(neurite_edges)
return numpy.asarray(pdata), numpy.asarray(edges)
def partition_asymmetries(neurites,
neurite_type=NeuriteType.all,
variant='branch-order'):
"""Partition asymmetry at bifurcation points of a collection of neurites.
Variant: length is a different definition, as the absolute difference in
downstream path lenghts, relative to the total neurite path length
"""
if variant not in {'branch-order', 'length'}:
raise ValueError(
'Please provide a valid variant for partition asymmetry,\
found %s' % variant)
if variant == 'branch-order':
return map(
partition_asymmetry,
iter_sections(neurites,
iterator_type=Tree.ibifurcation_point,
neurite_filter=is_type(neurite_type)))
asymmetries = list()
for neurite in iter_neurites(neurites, filt=is_type(neurite_type)):
neurite_length = total_length_per_neurite(neurite)[0]
for section in iter_sections(neurite,
iterator_type=Tree.ibifurcation_point,
neurite_filter=is_type(neurite_type)):
pathlength_diff = abs(
downstream_pathlength(section.children[0]) -
downstream_pathlength(section.children[1]))
asymmetries.append(pathlength_diff / neurite_length)
return asymmetries
def plot_tree3d(ax, tree,
diameter_scale=_DIAMETER_SCALE, linewidth=_LINEWIDTH,
color=None, alpha=_ALPHA):
"""Generates a figure of the tree in 3d.
If the tree contains one single point the plot will be empty \
since no segments can be constructed.
Args:
ax(matplotlib axes): on what to plot
tree(neurom.core.Tree or neurom.core.Neurite): plotted tree
diameter_scale(float): Scale factor multiplied with segment diameters before plotting
linewidth(float): all segments are plotted with this width, but only if diameter_scale=None
color(str or None): Color of plotted values, None corresponds to default choice
alpha(float): Transparency of plotted values
"""
section_segment_list = [(section, segment)
for section in iter_sections(tree)
for segment in iter_segments(section)]
segs = [(seg[0][COLS.XYZ], seg[1][COLS.XYZ]) for _, seg in section_segment_list]
colors = [_get_color(color, section.type) for section, _ in section_segment_list]
linewidth = _get_linewidth(tree, diameter_scale=diameter_scale, linewidth=linewidth)
collection = Line3DCollection(segs, colors=colors, linewidth=linewidth, alpha=alpha)
ax.add_collection3d(collection)
_update_3d_datalim(ax, tree)
def plot_tree(ax, tree, plane='xy',
diameter_scale=_DIAMETER_SCALE, linewidth=_LINEWIDTH,
color=None, alpha=_ALPHA):
"""Plots a 2d figure of the tree's segments.
Args:
ax(matplotlib axes): on what to plot
tree(neurom.core.Tree or neurom.core.Neurite): plotted tree
plane(str): Any pair of 'xyz'
diameter_scale(float): Scale factor multiplied with segment diameters before plotting
linewidth(float): all segments are plotted with this width, but only if diameter_scale=None
color(str or None): Color of plotted values, None corresponds to default choice
alpha(float): Transparency of plotted values
Note:
If the tree contains one single point the plot will be empty
since no segments can be constructed.
"""
plane0, plane1 = _plane2col(plane)
section_segment_list = [(section, segment)
for section in iter_sections(tree)
for segment in iter_segments(section)]
segs = [((seg[0][plane0], seg[0][plane1]),
(seg[1][plane0], seg[1][plane1]))
for _, seg in section_segment_list]
colors = [_get_color(color, section.type) for section, _ in section_segment_list]
linewidth = _get_linewidth(tree, diameter_scale=diameter_scale, linewidth=linewidth)
collection = LineCollection(segs, colors=colors, linewidth=linewidth, alpha=alpha)
ax.add_collection(collection)
def bifurcation_partitions(neurites, neurite_type=NeuriteType.all):
"""Partition at bifurcation points of a collection of neurites."""
return map(
bifurcationfunc.bifurcation_partition,
iter_sections(neurites,
iterator_type=Tree.ibifurcation_point,
neurite_filter=is_type(neurite_type)))
def terminal_path_lengths_per_neurite(neurites, neurite_type=NeuriteType.all):
"""Get the path lengths to each terminal point per neurite in a collection"""
return list(
sectionfunc.section_path_length(s)
for n in iter_neurites(neurites, filt=is_type(neurite_type))
for s in iter_sections(n, iterator_type=Tree.ileaf)
)
def test_iter_sections_filter():
for ntyp in nm.NEURITE_TYPES:
a = [s for n in filter(lambda nn: nn.type == ntyp, POP.neurites)
for s in n.iter_sections()]
b = [n for n in core.iter_sections(POP, neurite_filter=lambda n: n.type == ntyp)]
assert_sequence_equal(a, b)
def has_no_narrow_neurite_section(neuron,
neurite_filter,
radius_threshold=0.05,
considered_section_min_length=50):
'''Check if the neuron has dendrites with narrow sections
Arguments:
neuron(Neuron): The neuron object to test
neurite_filter(callable): filter the neurites by this callable
radius_threshold(float): radii below this are considered narro
considered_section_min_length(float): sections with length below
this are not taken into account
Returns:
CheckResult with result. result.info contains the narrow section ids and their
first point
'''
considered_sections = (sec for sec in iter_sections(neuron, neurite_filter=neurite_filter)
if sec.length > considered_section_min_length)
def narrow_section(section):
'''Select narrow sections'''
return section.points[:, COLS.R].mean() < radius_threshold
bad_ids = [(section.id, section.points[1])
for section in considered_sections if narrow_section(section)]
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_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 of bad sections
'''
bad_ids = []
axis = {
'x': COLS.X,
'y': COLS.Y,
'z': COLS.Z,
}[axis.lower()]
for neurite in iter_neurites(neuron):
section_segment = ((sec, seg) for sec in iter_sections(neurite)
for seg in iter_segments(sec))
for sec, (p0, p1) in islice(section_segment, 1,
None): # Skip neurite root segment
if max_distance < abs(p0[axis] - p1[axis]):
bad_ids.append((sec.id, [p0, p1]))
return CheckResult(len(bad_ids) == 0, bad_ids)
def test_iter_sections_filter():
for ntyp in nm.NEURITE_TYPES:
a = [s.id for n in filter(lambda nn: nn.type == ntyp, POP.neurites)
for s in n.iter_sections()]
b = [n.id for n in core.iter_sections(POP, neurite_filter=lambda n: n.type == ntyp)]
assert_sequence_equal(a, b)
def has_no_narrow_neurite_section(neuron,
neurite_filter,
radius_threshold=0.05,
considered_section_min_length=50):
'''Check if the neuron has dendrites with narrow sections
Arguments:
neuron(Neuron): The neuron object to test
neurite_filter(callable): filter the neurites by this callable
radius_threshold(float): radii below this are considered narro
considered_section_min_length(float): sections with length below
this are not taken into account
Returns:
CheckResult with result. result.info contains the narrow section ids and their
first point
'''
considered_sections = (
sec for sec in iter_sections(neuron, neurite_filter=neurite_filter)
if sec.length > considered_section_min_length)
def narrow_section(section):
'''Select narrow sections'''
return section.points[:, COLS.R].mean() < radius_threshold
bad_ids = [(section.id, section.points[1])
for section in considered_sections if narrow_section(section)]
return CheckResult(len(bad_ids) == 0, bad_ids)
def partition_asymmetries(neurites, neurite_type=NeuriteType.all):
'''Partition asymmetry at bifurcation points of a collection of neurites'''
return map(
_bifurcationfunc.partition_asymmetry,
iter_sections(neurites,
iterator_type=Tree.ibifurcation_point,
neurite_filter=is_type(neurite_type)))
def n_sections(neurites,
neurite_type=NeuriteType.all,
iterator_type=Tree.ipreorder):
'''Number of sections in a collection of neurites'''
return sum(1 for _ in iter_sections(neurites,
iterator_type=iterator_type,
neurite_filter=is_type(neurite_type)))
def test_iter_section_nrn():
ref = list(core.iter_sections(SIMPLE))
nt.eq_(len(ref), 6)
ref = list(
core.iter_sections(SIMPLE, neurite_filter=lambda n: n.type == nm.AXON))
nt.eq_(len(ref), 3)
ref = list(
core.iter_sections(
SIMPLE, neurite_filter=lambda n: n.type == nm.BASAL_DENDRITE))
nt.eq_(len(ref), 3)
ref = list(
core.iter_sections(
SIMPLE, neurite_filter=lambda n: n.type == nm.APICAL_DENDRITE))
nt.eq_(len(ref), 0)
def partition_pairs(neurites, neurite_type=NeuriteType.all):
'''Partition pairs at bifurcation points of a collection of neurites.
Partition pait is defined as the number of bifurcations at the two
daughters of the bifurcating section'''
return map(_bifurcationfunc.partition_pair,
iter_sections(neurites,
iterator_type=Tree.ibifurcation_point,
neurite_filter=is_type(neurite_type)))
def partition_pairs(neurites, neurite_type=NeuriteType.all):
'''Partition pairs at bifurcation points of a collection of neurites.
Partition pait is defined as the number of bifurcations at the two
daughters of the bifurcating section'''
return map(_bifurcationfunc.partition_pair,
iter_sections(neurites,
iterator_type=Tree.ibifurcation_point,
neurite_filter=is_type(neurite_type)))
def test_iter_sections_iforking():
ref = [
s for n in POP.neurites for s in n.iter_sections(Tree.iforking_point)
]
assert_sequence_equal(ref, [
n for n in core.iter_sections(POP, iterator_type=Tree.iforking_point)
])
def map_segments(func, neurites, neurite_type):
''' Map `func` to all the segments in a collection of neurites
`func` accepts a section and returns list of values corresponding to each segment.
'''
neurite_filter = is_type(neurite_type)
return [
s for ss in iter_sections(neurites, neurite_filter=neurite_filter) for s in func(ss)
]
def map_segments(func, neurites, neurite_type):
''' Map `func` to all the segments in a collection of neurites
`func` accepts a section and returns list of values corresponding to each segment.
'''
neurite_filter = is_type(neurite_type)
return [
s for ss in iter_sections(neurites, neurite_filter=neurite_filter) for s in func(ss)
]
def segment_lengths(neurites, neurite_type=NeuriteType.all):
"""Lengths of the segments in a collection of neurites"""
def _seg_len(sec):
"""list of segment lengths of a section"""
return np.linalg.norm(np.diff(sec.points[:, : COLS.R], axis=0), axis=1)
neurite_filter = is_type(neurite_type)
return [s for ss in iter_sections(neurites, neurite_filter=neurite_filter) for s in _seg_len(ss)]
def plot_tree(ax, tree, plane='xy',
diameter_scale=_DIAMETER_SCALE, linewidth=_LINEWIDTH,
color=None, alpha=_ALPHA, realistic_diameters=False):
"""Plots a 2d figure of the tree's segments.
Args:
ax(matplotlib axes): on what to plot
tree(neurom.core.Tree or neurom.core.Neurite): plotted tree
plane(str): Any pair of 'xyz'
diameter_scale(float): Scale factor multiplied with segment diameters before plotting
linewidth(float): all segments are plotted with this width, but only if diameter_scale=None
color(str or None): Color of plotted values, None corresponds to default choice
alpha(float): Transparency of plotted values
realistic_diameters(bool): scale linewidths with axis data coordinates
Note:
If the tree contains one single point the plot will be empty
since no segments can be constructed.
"""
plane0, plane1 = _plane2col(plane)
section_segment_list = [(section, segment)
for section in iter_sections(tree)
for segment in iter_segments(section)]
colors = [_get_color(color, section.type) for section, _ in section_segment_list]
if realistic_diameters:
def _get_rectangle(x, y, linewidth):
"""Draw a rectangle to represent a secgment."""
x, y = np.array(x), np.array(y)
diff = y - x
angle = np.arctan2(diff[1], diff[0]) % (2 * np.pi)
return Rectangle(x - linewidth / 2. * np.array([-np.sin(angle), np.cos(angle)]),
np.linalg.norm(diff),
linewidth,
np.rad2deg(angle))
segs = [_get_rectangle((seg[0][plane0], seg[0][plane1]),
(seg[1][plane0], seg[1][plane1]),
2 * segment_radius(seg) * diameter_scale)
for _, seg in section_segment_list]
collection = PatchCollection(segs, alpha=alpha, facecolors=colors)
else:
segs = [((seg[0][plane0], seg[0][plane1]),
(seg[1][plane0], seg[1][plane1]))
for _, seg in section_segment_list]
linewidth = _get_linewidth(
tree,
diameter_scale=diameter_scale,
linewidth=linewidth,
)
collection = LineCollection(segs, colors=colors, linewidth=linewidth, alpha=alpha)
ax.add_collection(collection)
def segment_radii(neurites, neurite_type=NeuriteType.all):
"""arithmetic mean of the radii of the points in segments in a collection of neurites"""
def _seg_radii(sec):
"""vectorized mean radii"""
pts = sec.points[:, COLS.R]
return np.divide(np.add(pts[:-1], pts[1:]), 2.0)
neurite_filter = is_type(neurite_type)
return [s for ss in iter_sections(neurites, neurite_filter=neurite_filter) for s in _seg_radii(ss)]
def segment_midpoints(neurites, neurite_type=NeuriteType.all):
"""Return a list of segment mid-points in a collection of neurites"""
def _seg_midpoint(sec):
"""Return the mid-points of segments in a section"""
pts = sec.points
return np.divide(np.add(pts[:-1], pts[1:])[:, :3], 2.0)
neurite_filter = is_type(neurite_type)
return [s for ss in iter_sections(neurites, neurite_filter=neurite_filter) for s in _seg_midpoint(ss)]
def map_sections(fun,
neurites,
neurite_type=NeuriteType.all,
iterator_type=Tree.ipreorder):
'''Map `fun` to all the sections in a collection of neurites'''
return map(
fun,
iter_sections(neurites,
iterator_type=iterator_type,
neurite_filter=is_type(neurite_type)))
def segment_lengths(neurites, neurite_type=NeuriteType.all):
'''Lengths of the segments in a collection of neurites'''
def _seg_len(sec):
'''list of segment lengths of a section'''
vecs = np.diff(sec.points, axis=0)[:, :3]
return np.sqrt([np.dot(p, p) for p in vecs])
neurite_filter = is_type(neurite_type)
return [s for ss in iter_sections(neurites, neurite_filter=neurite_filter)
for s in _seg_len(ss)]
def segment_lengths(neurites, neurite_type=NeuriteType.all):
'''Lengths of the segments in a collection of neurites'''
def _seg_len(sec):
'''list of segment lengths of a section'''
return np.linalg.norm(np.diff(sec.points[:, :COLS.R], axis=0), axis=1)
neurite_filter = is_type(neurite_type)
return [
s for ss in iter_sections(neurites, neurite_filter=neurite_filter)
for s in _seg_len(ss)
]
def sibling_ratios(neurites, neurite_type=NeuriteType.all, method='first'):
'''Sibling ratios at bifurcation points of a collection of neurites.
The sibling ratio is the ratio between the diameters of the
smallest and the largest child. It is a real number between
0 and 1. Method argument allows one to consider mean diameters
along the child section instead of diameter of the first point. '''
return map(
lambda bif_point: _bifurcationfunc.sibling_ratio(bif_point, method),
iter_sections(neurites,
iterator_type=Tree.ibifurcation_point,
neurite_filter=is_type(neurite_type)))
def section_radial_distances(neurites, neurite_type=NeuriteType.all, origin=None,
iterator_type=Tree.ipreorder):
'''Section radial distances in a collection of neurites.
The iterator_type can be used to select only terminal sections (ileaf)
or only bifurcations (ibifurcation_point).'''
dist = []
for n in iter_neurites(neurites, filt=is_type(neurite_type)):
pos = n.root_node.points[0] if origin is None else origin
dist.extend(sectionfunc.section_radial_distance(s, pos)
for s in iter_sections(n,
iterator_type=iterator_type))
return dist
def segment_midpoints(neurites, neurite_type=NeuriteType.all):
'''Return a list of segment mid-points in a collection of neurites'''
def _seg_midpoint(sec):
'''Return the mid-points of segments in a section'''
pts = sec.points
return np.divide(np.add(pts[:-1], pts[1:])[:, :3], 2.0)
neurite_filter = is_type(neurite_type)
return [
s for ss in iter_sections(neurites, neurite_filter=neurite_filter)
for s in _seg_midpoint(ss)
]
def segment_radii(neurites, neurite_type=NeuriteType.all):
'''arithmetic mean of the radii of the points in segments in a collection of neurites'''
def _seg_radii(sec):
'''vectorized mean radii'''
pts = sec.points[:, COLS.R]
return np.divide(np.add(pts[:-1], pts[1:]), 2.0)
neurite_filter = is_type(neurite_type)
return [
s for ss in iter_sections(neurites, neurite_filter=neurite_filter)
for s in _seg_radii(ss)
]
def section_radial_distances(neurites, neurite_type=NeuriteType.all, origin=None,
iterator_type=Tree.ipreorder):
'''Section radial distances in a collection of neurites.
The iterator_type can be used to select only terminal sections (ileaf)
or only bifurcations (ibifurcation_point).'''
dist = []
for n in iter_neurites(neurites, filt=is_type(neurite_type)):
pos = n.root_node.points[0] if origin is None else origin
dist.extend(sectionfunc.section_radial_distance(s, pos)
for s in iter_sections(n,
iterator_type=iterator_type))
return dist
def diameter_power_relations(neurites,
neurite_type=NeuriteType.all,
method='first'):
'''Calculate the diameter power relation at a bifurcation point
as defined in https://www.ncbi.nlm.nih.gov/pubmed/18568015
This quantity gives an indication of how far the branching is from
the Rall ratio (when =1).'''
return (
_bifurcationfunc.diameter_power_relation(bif_point, method)
for bif_point in iter_sections(neurites,
iterator_type=Tree.ibifurcation_point,
neurite_filter=is_type(neurite_type)))
def segment_taper_rates(neurites, neurite_type=NeuriteType.all):
"""taper rates of the segments in a collection of neurites
The taper rate is defined as the absolute radii differences divided by length of the section
"""
def _seg_taper_rates(sec):
"""vectorized taper rates"""
pts = sec.points[:, : COLS.TYPE]
diff = np.diff(pts, axis=0)
distance = np.linalg.norm(diff[:, : COLS.R], axis=1)
return np.divide(2 * np.abs(diff[:, COLS.R]), distance)
neurite_filter = is_type(neurite_type)
return [s for ss in iter_sections(neurites, neurite_filter=neurite_filter) for s in _seg_taper_rates(ss)]
def section_path_lengths(neurites, neurite_type=NeuriteType.all):
'''Path lengths of a collection of neurites '''
# Calculates and stores the section lengths in one pass,
# then queries the lengths in the path length iterations.
# This avoids repeatedly calculating the lengths of the
# same sections.
dist = {}
neurite_filter = is_type(neurite_type)
for s in iter_sections(neurites, neurite_filter=neurite_filter):
dist[s] = s.length
def pl2(node):
'''Calculate the path length using cached section lengths'''
return sum(dist[n] for n in node.iupstream())
return map_sections(pl2, neurites, neurite_type=neurite_type)
def section_path_lengths(neurites, neurite_type=NeuriteType.all):
"""Path lengths of a collection of neurites """
# Calculates and stores the section lengths in one pass,
# then queries the lengths in the path length iterations.
# This avoids repeatedly calculating the lengths of the
# same sections.
dist = {}
neurite_filter = is_type(neurite_type)
for s in iter_sections(neurites, neurite_filter=neurite_filter):
dist[s] = s.length
def pl2(node):
"""Calculate the path length using cached section lengths"""
return sum(dist[n] for n in node.iupstream())
return map_sections(pl2, neurites, neurite_type=neurite_type)
def segment_taper_rates(neurites, neurite_type=NeuriteType.all):
'''taper rates of the segments in a collection of neurites
The taper rate is defined as the absolute radii differences divided by length of the section
'''
def _seg_taper_rates(sec):
'''vectorized taper rates'''
pts = sec.points[:, :COLS.TYPE]
diff = np.diff(pts, axis=0)
distance = np.linalg.norm(diff[:, :COLS.R], axis=1)
return np.divide(2 * np.abs(diff[:, COLS.R]), distance)
neurite_filter = is_type(neurite_type)
return [
s for ss in iter_sections(neurites, neurite_filter=neurite_filter)
for s in _seg_taper_rates(ss)
]
def segment_path_lengths(neurites, neurite_type=NeuriteType.all):
'''Returns pathlengths between all non-root points and their root point'''
pathlength = {}
neurite_filter = is_type(neurite_type)
def _get_pathlength(section):
if section.id not in pathlength:
if section.parent:
pathlength[
section.id] = section.parent.length + _get_pathlength(
section.parent)
else:
pathlength[section.id] = 0
return pathlength[section.id]
return np.hstack([
_get_pathlength(section) + sectionfunc.segment_lengths(section)
for section in iter_sections(neurites, neurite_filter=neurite_filter)
])
def segment_path_lengths(neurites, neurite_type=NeuriteType.all):
"""Returns pathlengths between all non-root points and their root point."""
pathlength = {}
neurite_filter = is_type(neurite_type)
def _get_pathlength(section):
if section.id not in pathlength:
if section.parent:
pathlength[
section.id] = section.parent.length + _get_pathlength(
section.parent)
else:
pathlength[section.id] = 0
return pathlength[section.id]
result = [
_get_pathlength(section) +
np.cumsum(sectionfunc.segment_lengths(section))
for section in iter_sections(neurites, neurite_filter=neurite_filter)
]
return np.hstack(result) if result else np.array([])
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_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 of bad sections
'''
bad_ids = []
axis = {'x': COLS.X, 'y': COLS.Y, 'z': COLS.Z, }[axis.lower()]
for neurite in iter_neurites(neuron):
section_segment = ((sec, seg) for sec in iter_sections(neurite)
for seg in iter_segments(sec))
for sec, (p0, p1) in islice(section_segment, 1, None): # Skip neurite root segment
if max_distance < abs(p0[axis] - p1[axis]):
bad_ids.append((sec.id, [p0, p1]))
return CheckResult(len(bad_ids) == 0, bad_ids)
def has_no_narrow_neurite_section(neuron,
neurite_filter,
radius_threshold=0.05,
considered_section_min_length=50):
'''Check if the neuron has dendrites with narrow sections
sections below 'considered_section_min_length' are not taken into account
Returns:
CheckResult with result. result.info contains the narrow section ids and their
first point
'''
considered_sections = (
sec for sec in iter_sections(neuron, neurite_filter=neurite_filter)
if sec.length > considered_section_min_length)
def narrow_section(section):
'''Select narrow sections'''
return section.points[:, COLS.R].mean() < radius_threshold
bad_ids = [(section.id, section.points[1])
for section in considered_sections if narrow_section(section)]
return CheckResult(len(bad_ids) == 0, bad_ids)
def test_iter_sections_ipostorder():
ref = [s for n in POP.neurites for s in n.iter_sections(Tree.ipostorder)]
assert_sequence_equal(ref, [n for n in core.iter_sections(POP, iterator_type=Tree.ipostorder)])
def n_segments(neurites, neurite_type=NeuriteType.all):
'''Number of segments in a collection of neurites'''
return sum(len(s.points) - 1
for s in iter_sections(neurites, neurite_filter=is_type(neurite_type)))
def partition_asymmetries(neurites, neurite_type=NeuriteType.all):
'''Partition asymmetry at bifurcation points of a collection of neurites'''
return map(_bifurcationfunc.partition_asymmetry,
iter_sections(neurites,
iterator_type=Tree.ibifurcation_point,
neurite_filter=is_type(neurite_type)))
def test_iter_sections_default():
ref = [s for n in POP.neurites for s in n.iter_sections()]
assert_sequence_equal(ref,
[n for n in core.iter_sections(POP)])
def bifurcation_partitions(neurites, neurite_type=NeuriteType.all):
"""Partition at bifurcation points of a collection of neurites"""
return map(
_bifurcationfunc.bifurcation_partition,
iter_sections(neurites, iterator_type=Tree.ibifurcation_point, neurite_filter=is_type(neurite_type)),
)
def map_sections(fun, neurites, neurite_type=NeuriteType.all, iterator_type=Tree.ipreorder):
"""Map `fun` to all the sections in a collection of neurites"""
return map(fun, iter_sections(neurites, iterator_type=iterator_type, neurite_filter=is_type(neurite_type)))
def n_sections(neurites, neurite_type=NeuriteType.all, iterator_type=Tree.ipreorder):
"""Number of sections in a collection of neurites"""
return sum(1 for _ in iter_sections(neurites, iterator_type=iterator_type, neurite_filter=is_type(neurite_type)))
def n_segments(neurites, neurite_type=NeuriteType.all):
"""Number of segments in a collection of neurites"""
return sum(len(s.points) - 1 for s in iter_sections(neurites, neurite_filter=is_type(neurite_type)))
def test_iter_sections_ipostorder():
ref = [s for n in POP.neurites for s in n.iter_sections(Tree.ipostorder)]
assert_sequence_equal(
ref,
[n for n in core.iter_sections(POP, iterator_type=Tree.ipostorder)])
def bifurcation_partitions(neurites, neurite_type=NeuriteType.all):
'''Partition at bifurcation points of a collection of neurites'''
return map(bifurcation_partition,
iter_sections(neurites,
iterator_type=Tree.ibifurcation_point,
neurite_filter=is_type(neurite_type)))
def test_iter_sections_default():
ref = [s for n in POP.neurites for s in n.iter_sections()]
assert_sequence_equal(ref, [n for n in core.iter_sections(POP)])
def test_iter_sections_iforking():
ref = [s for n in POP.neurites for s in n.iter_sections(Tree.iforking_point)]
assert_sequence_equal(ref, [n for n in core.iter_sections(POP, iterator_type=Tree.iforking_point)])
