def test_segment_iteration():
nt.assert_equal(list(val_iter(isegment(REF_TREE))),
[(0, 11),(11, 111),(11, 112),
(0, 12),(12, 121),(121,1211),
(1211,12111),(1211,12112),(12, 122)])
nt.assert_equal(list(val_iter(isegment(REF_TREE.children[0]))),
[(0, 11), (11, 111),(11, 112)])
nt.assert_equal(list(val_iter(isegment(REF_TREE.children[0].children[0]))),
[(11, 111)])
nt.assert_equal(list(val_iter(isegment(REF_TREE.children[0].children[1]))),
[(11, 112)])
nt.assert_equal(list(val_iter(isegment(REF_TREE.children[1]))),
[(0, 12), (12, 121), (121, 1211),
(1211, 12111), (1211, 12112), (12, 122)])
nt.assert_equal(list(val_iter(isegment(REF_TREE.children[1].children[0]))),
[(12, 121), (121, 1211), (1211, 12111), (1211, 12112)])
nt.assert_equal(list(val_iter(isegment(REF_TREE.children[1].children[1]))),
[(12, 122)])
def test_segment_iteration():
nt.assert_equal(list(val_iter(isegment(REF_TREE))), [(0, 11), (11, 111),
(11, 112), (0, 12),
(12, 121),
(121, 1211),
(1211, 12111),
(1211, 12112),
(12, 122)])
nt.assert_equal(list(val_iter(isegment(REF_TREE.children[0]))),
[(0, 11), (11, 111), (11, 112)])
nt.assert_equal(list(val_iter(isegment(REF_TREE.children[0].children[0]))),
[(11, 111)])
nt.assert_equal(list(val_iter(isegment(REF_TREE.children[0].children[1]))),
[(11, 112)])
nt.assert_equal(list(val_iter(isegment(REF_TREE.children[1]))),
[(0, 12), (12, 121), (121, 1211), (1211, 12111),
(1211, 12112), (12, 122)])
nt.assert_equal(list(val_iter(isegment(REF_TREE.children[1].children[0]))),
[(12, 121), (121, 1211), (1211, 12111), (1211, 12112)])
nt.assert_equal(list(val_iter(isegment(REF_TREE.children[1].children[1]))),
[(12, 122)])
def calculate_and_plot_end_to_end_distance(path):
'''Calculate and plot the end-to-end distance vs the number of segments for
an increasingly larger part of a given path.
Note that the plots are not very meaningful for bifurcating trees.'''
end_to_end_distance = [morphmath.point_dist(segment[1], path.value)
for segment in tree.val_iter(tree.isegment(path))]
make_end_to_end_distance_plot(np.arange(len(end_to_end_distance)) + 1, end_to_end_distance,
path.type)
def neurite_centre_of_mass(neurite):
'''Calculate and return centre of mass of a neurite.'''
centre_of_mass = np.zeros(3)
total_volume = 0
for segment in tree.val_iter(tree.isegment(neurite)):
seg_volume = morphmath.segment_volume(segment)
centre_of_mass = centre_of_mass + seg_volume * segment_centre_of_mass(segment)
total_volume += seg_volume
return centre_of_mass / total_volume
def radius_of_gyration(neurite):
'''Calculate and return radius of gyration of a given neurite.'''
centre_mass = neurite_centre_of_mass(neurite)
sum_sqr_distance = 0
N = 0
for segment in tree.val_iter(tree.isegment(neurite)):
sum_sqr_distance = sum_sqr_distance + distance_sqr(centre_mass, segment)
N += 1
return np.sqrt(sum_sqr_distance / N)
def neurite_centre_of_mass(neurite):
'''Calculate and return centre of mass of a neurite.'''
centre_of_mass = np.zeros(3)
total_volume = 0
for segment in tree.val_iter(tree.isegment(neurite)):
seg_volume = morphmath.segment_volume(segment)
centre_of_mass = centre_of_mass + seg_volume * segment_centre_of_mass(
segment)
total_volume += seg_volume
return centre_of_mass / total_volume
def radius_of_gyration(neurite):
'''Calculate and return radius of gyration of a given neurite.'''
centre_mass = neurite_centre_of_mass(neurite)
sum_sqr_distance = 0
N = 0
for segment in tree.val_iter(tree.isegment(neurite)):
sum_sqr_distance = sum_sqr_distance + distance_sqr(
centre_mass, segment)
N += 1
return np.sqrt(sum_sqr_distance / N)
def 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: list of (first_id, second_id) of zero length segments
'''
l = [[s for s in val_iter(isegment(t)) if segment_length(s) <= threshold]
for t in neuron.neurites]
return [(i[0][COLS.ID], i[1][COLS.ID]) for i in chain(*l)]
def test_segment_upstream_iteration():
leaves = [l for l in ileaf(REF_TREE2)]
ref_paths = [[(1111, 11111), (111, 1111), (11, 111), (0, 11)],
[(1111, 11112), (111, 1111), (11, 111), (0, 11)],
[(1111, 11113), (111, 1111), (11, 111), (0, 11)],
[(11, 112), (0, 11)],
[(1211, 12111), (121, 1211), (12, 121), (0, 12)],
[(1211, 12112), (121, 1211), (12, 121), (0, 12)],
[(12, 122), (0, 12)]]
for l, ref in zip(leaves, ref_paths):
nt.assert_equal([s for s in val_iter(isegment(l, iupstream))], ref)
def _check_trees(trees):
for t in trees:
nt.ok_(len(list(tree.ileaf(t))) == 11)
nt.ok_(len(list(tree.iforking_point(t))) == 10)
nt.ok_(len(list(tree.ipreorder(t))) == 211)
nt.ok_(len(list(tree.ipostorder(t))) == 211)
nt.ok_(len(list(tree.isegment(t))) == 210)
leaves = [l for l in tree.ileaf(t)]
# path length from each leaf to root node.
branch_order = [21, 31, 41, 51, 61, 71, 81, 91, 101, 111, 111]
for i, l in enumerate(leaves):
nt.ok_(len(list(tree.iupstream(l))) == branch_order[i])
def 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: list of (first_id, second_id) of zero length segments
'''
l = [[s for s in val_iter(isegment(t))
if segment_length(s) <= threshold]
for t in neuron.neurites]
return [(i[0][COLS.ID], i[1][COLS.ID]) for i in chain(*l)]
def _check_trees(trees):
for t in trees:
nt.ok_(len(list(tree.ileaf(t))) == 11)
nt.ok_(len(list(tree.iforking_point(t))) == 10)
nt.ok_(len(list(tree.ipreorder(t))) == 211)
nt.ok_(len(list(tree.ipostorder(t))) == 211)
nt.ok_(len(list(tree.isegment(t))) == 210)
leaves = [l for l in tree.ileaf(t)]
# path length from each leaf to root node.
branch_order = [21, 31, 41, 51, 61, 71, 81, 91, 101, 111, 111]
for i, l in enumerate(leaves):
nt.ok_(len(list(tree.iupstream(l))) == branch_order[i])
def i_segment_radial_dist(pos, tree):
'''Return an iterator of radial distances of tree segments to a given point
The radial distance is the euclidian distance between the mid-point of
the segment and the point in question.
Parameters:
pos: origin to which disrances are measured. It must have at least 3
components. The first 3 components are (x, y, z).
tree: tree of raw data rows.
'''
return tr.imap_val(lambda s: mm.segment_radial_dist(s, pos), tr.isegment(tree))
def test_segment_upstream_iteration():
leaves = [l for l in ileaf(REF_TREE2)]
ref_paths = [
[(1111, 11111), (111, 1111), (11, 111), (0, 11)],
[(1111, 11112), (111, 1111), (11, 111), (0, 11)],
[(1111, 11113), (111, 1111), (11, 111), (0, 11)],
[(11, 112), (0, 11)],
[(1211, 12111), (121, 1211), (12, 121), (0, 12)],
[(1211, 12112), (121, 1211), (12, 121), (0, 12)],
[(12, 122), (0, 12)]
]
for l, ref in zip(leaves, ref_paths):
nt.assert_equal([s for s in val_iter(isegment(l, iupstream))], ref)
def i_segment_radial_dist(pos, tree):
'''Return an iterator of radial distances of tree segments to a given point
The radial distance is the euclidian distance between the mid-point of
the segment and the point in question.
Parameters:
pos: origin to which disrances are measured. It must have at least 3
components. The first 3 components are (x, y, z).
tree: tree of raw data rows.
'''
return tr.imap_val(lambda s: mm.segment_radial_dist(s, pos),
tr.isegment(tree))
def tree3d(tr, new_fig=True, new_axes=True, subplot=False, **kwargs):
'''Generates a figure of the tree in 3d.
Parameters:
tr: Tree
neurom.Tree object
Options:
linewidth: float \
Defines the linewidth of the tree, \
if diameter is set to False. \
Default value is 1.2.
alpha: float \
Defines throughe transparency of the tree. \
0.0 transparent through 1.0 opaque. \
Default value is 0.8.
treecolor: str or None \
Defines the color of the tree. \
If None the default values will be used, \
depending on the type of tree: \
Basal dendrite: "red" \
Axon : "blue" \
Apical dendrite: "purple" \
Undefined tree: "black" \
Default value is None.
new_fig: boolean \
Defines if the tree will be plotted \
in the current figure (False) \
or in a new figure (True) \
Default value is True.
subplot: matplotlib subplot value or False \
If False the default subplot 111 will be used. \
For any other value a matplotlib subplot \
will be generated. \
Default value is False.
diameter: boolean
If True the diameter, scaled with diameter_scale factor, \
will define the width of the tree lines. \
If False use linewidth to select the width of the tree lines. \
Default value is True.
diameter_scale: float \
Defines the scale factor that will be multiplied \
with the diameter to define the width of the tree line. \
Default value is 1.
white_space: float \
Defines the white space around \
the boundary box of the morphology. \
Default value is 1.
Returns:
A 3D matplotlib figure with a tree view.
'''
from mpl_toolkits.mplot3d.art3d import Line3DCollection
# Initialization of matplotlib figure and axes.
fig, ax = common.get_figure(new_fig=new_fig, new_axes=new_axes,
subplot=subplot, params={'projection': '3d'})
# Data needed for the viewer: x,y,z,r
bounding_box = get_bounding_box(tr)
def _seg_3d(seg):
'''2d coordinates needed for the plotting of a segment'''
horz = getattr(COLS, 'X')
vert = getattr(COLS, 'Y')
depth = getattr(COLS, 'Z')
parent_point = seg[0]
child_point = seg[1]
horz1 = parent_point[horz]
horz2 = child_point[horz]
vert1 = parent_point[vert]
vert2 = child_point[vert]
depth1 = parent_point[depth]
depth2 = child_point[depth]
return ((horz1, vert1, depth1), (horz2, vert2, depth2))
segs = [_seg_3d(seg) for seg in val_iter(isegment(tr))]
linewidth = get_default('linewidth', **kwargs)
# Definition of the linewidth according to diameter, if diameter is True.
if get_default('diameter', **kwargs):
# TODO: This was originally a numpy array. Did it have to be one?
linewidth = [2 * d * get_default('diameter_scale', **kwargs) for d in i_segment_radius(tr)]
# Plot the collection of lines.
collection = Line3DCollection(segs,
color=common.get_color(get_default('treecolor', **kwargs),
get_tree_type(tr)),
linewidth=linewidth, alpha=get_default('alpha', **kwargs))
ax.add_collection3d(collection)
kwargs['title'] = kwargs.get('title', 'Tree 3d-view')
kwargs['xlabel'] = kwargs.get('xlabel', 'X')
kwargs['ylabel'] = kwargs.get('ylabel', 'Y')
kwargs['zlabel'] = kwargs.get('zlabel', 'Z')
kwargs['xlim'] = kwargs.get('xlim', [bounding_box[0][0] - get_default('white_space', **kwargs),
bounding_box[1][0] + get_default('white_space', **kwargs)])
kwargs['ylim'] = kwargs.get('ylim', [bounding_box[0][1] - get_default('white_space', **kwargs),
bounding_box[1][1] + get_default('white_space', **kwargs)])
kwargs['zlim'] = kwargs.get('zlim', [bounding_box[0][2] - get_default('white_space', **kwargs),
bounding_box[1][2] + get_default('white_space', **kwargs)])
return common.plot_style(fig=fig, ax=ax, **kwargs)
def i_segment_area(tree):
''' return an iterator of tree segment areas
'''
return tr.imap_val(mm.segment_area, tr.isegment(tree))
def i_segment_volume(tree):
''' return an iterator of tree segment volumes
'''
return tr.imap_val(mm.segment_volume, tr.isegment(tree))
def i_segment_radius(tree):
''' return an iterator of tree segment radii
'''
return tr.imap_val(mm.segment_radius, tr.isegment(tree))
def i_segment_volume(tree):
''' return an iterator of tree segment volumes
'''
return tr.imap_val(mm.segment_volume, tr.isegment(tree))
def tree3d(tr, new_fig=True, new_axes=True, subplot=False, **kwargs):
"""Generates a figure of the tree in 3d.
Parameters:
tr: Tree
neurom.Tree object
Options:
linewidth: float \
Defines the linewidth of the tree, \
if diameter is set to False. \
Default value is 1.2.
alpha: float \
Defines throughe transparency of the tree. \
0.0 transparent through 1.0 opaque. \
Default value is 0.8.
treecolor: str or None \
Defines the color of the tree. \
If None the default values will be used, \
depending on the type of tree: \
Basal dendrite: "red" \
Axon : "blue" \
Apical dendrite: "purple" \
Undefined tree: "black" \
Default value is None.
new_fig: boolean \
Defines if the tree will be plotted \
in the current figure (False) \
or in a new figure (True) \
Default value is True.
subplot: matplotlib subplot value or False \
If False the default subplot 111 will be used. \
For any other value a matplotlib subplot \
will be generated. \
Default value is False.
diameter: boolean
If True the diameter, scaled with diameter_scale factor, \
will define the width of the tree lines. \
If False use linewidth to select the width of the tree lines. \
Default value is True.
diameter_scale: float \
Defines the scale factor that will be multiplied \
with the diameter to define the width of the tree line. \
Default value is 1.
white_space: float \
Defines the white space around \
the boundary box of the morphology. \
Default value is 1.
Returns:
A 3D matplotlib figure with a tree view.
"""
from mpl_toolkits.mplot3d.art3d import Line3DCollection
# Initialization of matplotlib figure and axes.
fig, ax = common.get_figure(new_fig=new_fig, new_axes=new_axes, subplot=subplot, params={"projection": "3d"})
# Data needed for the viewer: x,y,z,r
bounding_box = get_bounding_box(tr)
def _seg_3d(seg):
"""2d coordinates needed for the plotting of a segment"""
horz = getattr(COLS, "X")
vert = getattr(COLS, "Y")
depth = getattr(COLS, "Z")
parent_point = seg[0]
child_point = seg[1]
horz1 = parent_point[horz]
horz2 = child_point[horz]
vert1 = parent_point[vert]
vert2 = child_point[vert]
depth1 = parent_point[depth]
depth2 = child_point[depth]
return ((horz1, vert1, depth1), (horz2, vert2, depth2))
segs = [_seg_3d(seg) for seg in val_iter(isegment(tr))]
linewidth = get_default("linewidth", **kwargs)
# Definition of the linewidth according to diameter, if diameter is True.
if get_default("diameter", **kwargs):
# TODO: This was originally a numpy array. Did it have to be one?
linewidth = [2 * d * get_default("diameter_scale", **kwargs) for d in i_segment_radius(tr)]
# Plot the collection of lines.
collection = Line3DCollection(
segs,
color=common.get_color(get_default("treecolor", **kwargs), get_tree_type(tr)),
linewidth=linewidth,
alpha=get_default("alpha", **kwargs),
)
ax.add_collection3d(collection)
kwargs["title"] = kwargs.get("title", "Tree 3d-view")
kwargs["xlabel"] = kwargs.get("xlabel", "X")
kwargs["ylabel"] = kwargs.get("ylabel", "Y")
kwargs["zlabel"] = kwargs.get("zlabel", "Z")
kwargs["xlim"] = kwargs.get(
"xlim",
[
bounding_box[0][0] - get_default("white_space", **kwargs),
bounding_box[1][0] + get_default("white_space", **kwargs),
],
)
kwargs["ylim"] = kwargs.get(
"ylim",
[
bounding_box[0][1] - get_default("white_space", **kwargs),
bounding_box[1][1] + get_default("white_space", **kwargs),
],
)
kwargs["zlim"] = kwargs.get(
"zlim",
[
bounding_box[0][2] - get_default("white_space", **kwargs),
bounding_box[1][2] + get_default("white_space", **kwargs),
],
)
return common.plot_style(fig=fig, ax=ax, **kwargs)
def i_segment_area(tree):
''' return an iterator of tree segment areas
'''
return tr.imap_val(mm.segment_area, tr.isegment(tree))
def n_segments(tree):
"""
Return number of segments in tree
"""
return sum(1 for _ in tr.isegment(tree))
def i_segment_length(tree):
''' return an iterator of tree segment lengths
'''
return tr.imap_val(mm.segment_length, tr.isegment(tree))
def tree3d(tr, new_fig=True, new_axes=True, subplot=False, **kwargs):
'''
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.
Parameters:
tr: Tree \
neurom.Tree object
linewidth: float \
Defines the linewidth of the tree, \
if diameter is set to False. \
Default value is 1.2.
alpha: float \
Defines throughe transparency of the tree. \
0.0 transparent through 1.0 opaque. \
Default value is 0.8.
treecolor: str or None \
Defines the color of the tree. \
If None the default values will be used, \
depending on the type of tree: \
Basal dendrite: "red" \
Axon : "blue" \
Apical dendrite: "purple" \
Undefined tree: "black" \
Default value is None.
new_fig: boolean \
Defines if the tree will be plotted \
in the current figure (False) \
or in a new figure (True) \
Default value is True.
subplot: matplotlib subplot value or False \
If False the default subplot 111 will be used. \
For any other value a matplotlib subplot \
will be generated. \
Default value is False.
diameter: boolean \
If True the diameter, scaled with diameter_scale factor, \
will define the width of the tree lines. \
If False use linewidth to select the width of the tree lines. \
Default value is True.
diameter_scale: float \
Defines the scale factor that will be multiplied \
with the diameter to define the width of the tree line. \
Default value is 1.
white_space: float \
Defines the white space around \
the boundary box of the morphology. \
Default value is 1.
Returns:
A 3D matplotlib figure with a tree view.
'''
from mpl_toolkits.mplot3d.art3d import Line3DCollection
# Initialization of matplotlib figure and axes.
fig, ax = common.get_figure(new_fig=new_fig, new_axes=new_axes,
subplot=subplot, params={'projection': '3d'})
# Data needed for the viewer: x,y,z,r
bounding_box = get_bounding_box(tr)
def _seg_3d(seg):
'''2d coordinates needed for the plotting of a segment'''
horz = getattr(COLS, 'X')
vert = getattr(COLS, 'Y')
depth = getattr(COLS, 'Z')
parent_point = seg[0]
child_point = seg[1]
horz1 = parent_point[horz]
horz2 = child_point[horz]
vert1 = parent_point[vert]
vert2 = child_point[vert]
depth1 = parent_point[depth]
depth2 = child_point[depth]
return ((horz1, vert1, depth1), (horz2, vert2, depth2))
segs = [_seg_3d(seg) for seg in val_iter(isegment(tr))]
linewidth = get_default('linewidth', **kwargs)
# Definition of the linewidth according to diameter, if diameter is True.
if get_default('diameter', **kwargs):
# TODO: This was originally a numpy array. Did it have to be one?
linewidth = [2 * d * get_default('diameter_scale', **kwargs)
for d in iter_neurites(tr, segment_radius)]
if len(linewidth) == 0:
linewidth = get_default('linewidth', **kwargs)
# Plot the collection of lines.
collection = Line3DCollection(segs,
color=common.get_color(get_default('treecolor', **kwargs),
get_tree_type(tr)),
linewidth=linewidth, alpha=get_default('alpha', **kwargs))
ax.add_collection3d(collection)
kwargs['title'] = kwargs.get('title', 'Tree 3d-view')
kwargs['xlabel'] = kwargs.get('xlabel', 'X')
kwargs['ylabel'] = kwargs.get('ylabel', 'Y')
kwargs['zlabel'] = kwargs.get('zlabel', 'Z')
kwargs['xlim'] = kwargs.get('xlim', [bounding_box[0][0] - get_default('white_space', **kwargs),
bounding_box[1][0] + get_default('white_space', **kwargs)])
kwargs['ylim'] = kwargs.get('ylim', [bounding_box[0][1] - get_default('white_space', **kwargs),
bounding_box[1][1] + get_default('white_space', **kwargs)])
kwargs['zlim'] = kwargs.get('zlim', [bounding_box[0][2] - get_default('white_space', **kwargs),
bounding_box[1][2] + get_default('white_space', **kwargs)])
return common.plot_style(fig=fig, ax=ax, **kwargs)
def tree(tr, plane='xy', new_fig=True, subplot=False, **kwargs):
'''
Generates a 2d figure of the tree's segments. \
If the tree contains one single point the plot will be empty \
since no segments can be constructed.
Parameters:
tr: Tree \
neurom.Tree object
plane: str \
Accepted values: Any pair of of xyz \
Default value is 'xy'.treecolor
linewidth: float \
Defines the linewidth of the tree, \
if diameter is set to False. \
Default value is 1.2.
alpha: float \
Defines throughe transparency of the tree. \
0.0 transparent through 1.0 opaque. \
Default value is 0.8.
treecolor: str or None \
Defines the color of the tree. \
If None the default values will be used, \
depending on the type of tree: \
Basal dendrite: "red" \
Axon : "blue" \
Apical dendrite: "purple" \
Undefined tree: "black" \
Default value is None.
new_fig: boolean \
Defines if the tree will be plotted \
in the current figure (False) \
or in a new figure (True) \
Default value is True.
subplot: matplotlib subplot value or False \
If False the default subplot 111 will be used. \
For any other value a matplotlib subplot \
will be generated. \
Default value is False.
diameter: boolean
If True the diameter, scaled with diameter_scale factor, \
will define the width of the tree lines. \
If False use linewidth to select the width of the tree lines. \
Default value is True.
diameter_scale: float \
Defines the scale factor that will be multiplied \
with the diameter to define the width of the tree line. \
Default value is 1.
limits: list or boolean \
List of type: [[xmin, ymin, zmin], [xmax, ymax, zmax]] \
If False the figure will not be scaled. \
If True the figure will be scaled according to tree limits. \
Default value is False.
white_space: float \
Defines the white space around \
the boundary box of the morphology. \
Default value is 1.
Returns:
A 2D matplotlib figure with a tree view, at the selected plane.
'''
if plane not in ('xy', 'yx', 'xz', 'zx', 'yz', 'zy'):
return None, 'No such plane found! Please select one of: xy, xz, yx, yz, zx, zy.'
# Initialization of matplotlib figure and axes.
fig, ax = common.get_figure(new_fig=new_fig, subplot=subplot)
# Data needed for the viewer: x,y,z,r
bounding_box = get_bounding_box(tr)
def _seg_2d(seg):
'''2d coordinates needed for the plotting of a segment'''
horz = getattr(COLS, plane[0].capitalize())
vert = getattr(COLS, plane[1].capitalize())
parent_point = seg[0]
child_point = seg[1]
horz1 = parent_point[horz]
horz2 = child_point[horz]
vert1 = parent_point[vert]
vert2 = child_point[vert]
return ((horz1, vert1), (horz2, vert2))
segs = [_seg_2d(seg) for seg in val_iter(isegment(tr))]
linewidth = get_default('linewidth', **kwargs)
# Definition of the linewidth according to diameter, if diameter is True.
if get_default('diameter', **kwargs):
scale = get_default('diameter_scale', **kwargs)
# TODO: This was originally a numpy array. Did it have to be one?
linewidth = [2 * d * scale for d in iter_neurites(tr, segment_radius)]
if len(linewidth) == 0:
linewidth = get_default('linewidth', **kwargs)
# Plot the collection of lines.
collection = LineCollection(segs,
color=common.get_color(get_default('treecolor', **kwargs),
get_tree_type(tr)),
linewidth=linewidth, alpha=get_default('alpha', **kwargs))
ax.add_collection(collection)
kwargs['title'] = kwargs.get('title', 'Tree view')
kwargs['xlabel'] = kwargs.get('xlabel', plane[0])
kwargs['ylabel'] = kwargs.get('ylabel', plane[1])
kwargs['xlim'] = kwargs.get('xlim', [bounding_box[0][getattr(COLS, plane[0].capitalize())] -
get_default('white_space', **kwargs),
bounding_box[1][getattr(COLS, plane[0].capitalize())] +
get_default('white_space', **kwargs)])
kwargs['ylim'] = kwargs.get('ylim', [bounding_box[0][getattr(COLS, plane[1].capitalize())] -
get_default('white_space', **kwargs),
bounding_box[1][getattr(COLS, plane[1].capitalize())] +
get_default('white_space', **kwargs)])
return common.plot_style(fig=fig, ax=ax, **kwargs)
def path_length(tree):
'''Get the path length from a sub-tree to the root node'''
return np.sum(s for s in
tr.imap_val(mm.segment_length, tr.isegment(tree, tr.iupstream)))
def path_length(tree):
'''Get the path length from a sub-tree to the root node'''
return np.sum(s for s in tr.imap_val(mm.segment_length,
tr.isegment(tree, tr.iupstream)))
def n_segments(tree):
"""
Return number of segments in tree
"""
return sum(1 for _ in tr.isegment(tree))
def segments(self):
'''Returns segment iterator
'''
return isegment(self._tree)
centre_mass = neurite_centre_of_mass(neurite)
sum_sqr_distance = 0
N = 0
for segment in tree.val_iter(tree.isegment(neurite)):
sum_sqr_distance = sum_sqr_distance + distance_sqr(centre_mass, segment)
N += 1
return np.sqrt(sum_sqr_distance / N)
def mean_rad_of_gyration(neurites):
'''Calculate mean radius of gyration for set of neurites.'''
return np.mean([radius_of_gyration(n) for n in neurites])
if __name__ == '__main__':
# load a neuron from an SWC file
filename = 'test_data/swc/Neuron.swc'
nrn = ezy.load_neuron(filename)
# for every neurite, print (number of segments, radius of gyration, neurite type)
print([(sum(1 for _ in tree.isegment(nrte)),
radius_of_gyration(nrte), nrte.type) for nrte in nrn.neurites])
# print mean radius of gyration per neurite type
print('Mean radius of gyration for axons: ',
mean_rad_of_gyration(n for n in nrn.neurites if n.type == ezy.TreeType.axon))
print('Mean radius of gyration for basal dendrites: ',
mean_rad_of_gyration(n for n in nrn.neurites if n.type == ezy.TreeType.basal_dendrite))
print('Mean radius of gyration for apical dendrites: ',
mean_rad_of_gyration(n for n in nrn.neurites if n.type == ezy.TreeType.apical_dendrite))
def i_segment_length(tree):
''' return an iterator of tree segment lengths
'''
return tr.imap_val(mm.segment_length, tr.isegment(tree))
N += 1
return np.sqrt(sum_sqr_distance / N)
def mean_rad_of_gyration(neurites):
'''Calculate mean radius of gyration for set of neurites.'''
return np.mean([radius_of_gyration(n) for n in neurites])
if __name__ == '__main__':
# load a neuron from an SWC file
filename = 'test_data/swc/Neuron.swc'
nrn = ezy.load_neuron(filename)
# for every neurite, print (number of segments, radius of gyration, neurite type)
print([(sum(1 for _ in tree.isegment(nrte)), radius_of_gyration(nrte),
nrte.type) for nrte in nrn.neurites])
# print mean radius of gyration per neurite type
print(
'Mean radius of gyration for axons: ',
mean_rad_of_gyration(n for n in nrn.neurites
if n.type == ezy.TreeType.axon))
print(
'Mean radius of gyration for basal dendrites: ',
mean_rad_of_gyration(n for n in nrn.neurites
if n.type == ezy.TreeType.basal_dendrite))
print(
'Mean radius of gyration for apical dendrites: ',
mean_rad_of_gyration(n for n in nrn.neurites
if n.type == ezy.TreeType.apical_dendrite))
def i_segment_radius(tree):
''' return an iterator of tree segment radii
'''
return tr.imap_val(mm.segment_radius, tr.isegment(tree))
def tree(tr, plane='xy', new_fig=True, subplot=False, **kwargs):
'''Generates a 2d figure of the tree.
Parameters:
tr: Tree \
neurom.Tree object
Options:
plane: str \
Accepted values: Any pair of of xyz \
Default value is 'xy'.treecolor
linewidth: float \
Defines the linewidth of the tree, \
if diameter is set to False. \
Default value is 1.2.
alpha: float \
Defines throughe transparency of the tree. \
0.0 transparent through 1.0 opaque. \
Default value is 0.8.
treecolor: str or None \
Defines the color of the tree. \
If None the default values will be used, \
depending on the type of tree: \
Basal dendrite: "red" \
Axon : "blue" \
Apical dendrite: "purple" \
Undefined tree: "black" \
Default value is None.
new_fig: boolean \
Defines if the tree will be plotted \
in the current figure (False) \
or in a new figure (True) \
Default value is True.
subplot: matplotlib subplot value or False \
If False the default subplot 111 will be used. \
For any other value a matplotlib subplot \
will be generated. \
Default value is False.
diameter: boolean
If True the diameter, scaled with diameter_scale factor, \
will define the width of the tree lines. \
If False use linewidth to select the width of the tree lines. \
Default value is True.
diameter_scale: float \
Defines the scale factor that will be multiplied \
with the diameter to define the width of the tree line. \
Default value is 1.
limits: list or boolean \
List of type: [[xmin, ymin, zmin], [xmax, ymax, zmax]] \
If False the figure will not be scaled. \
If True the figure will be scaled according to tree limits. \
Default value is False.
white_space: float \
Defines the white space around \
the boundary box of the morphology. \
Default value is 1.
Returns:
A 2D matplotlib figure with a tree view, at the selected plane.
'''
if plane not in ('xy', 'yx', 'xz', 'zx', 'yz', 'zy'):
return None, 'No such plane found! Please select one of: xy, xz, yx, yz, zx, zy.'
# Initialization of matplotlib figure and axes.
fig, ax = common.get_figure(new_fig=new_fig, subplot=subplot)
# Data needed for the viewer: x,y,z,r
bounding_box = get_bounding_box(tr)
def _seg_2d(seg):
'''2d coordinates needed for the plotting of a segment'''
horz = getattr(COLS, plane[0].capitalize())
vert = getattr(COLS, plane[1].capitalize())
parent_point = seg[0]
child_point = seg[1]
horz1 = parent_point[horz]
horz2 = child_point[horz]
vert1 = parent_point[vert]
vert2 = child_point[vert]
return ((horz1, vert1), (horz2, vert2))
segs = [_seg_2d(seg) for seg in val_iter(isegment(tr))]
linewidth = get_default('linewidth', **kwargs)
# Definition of the linewidth according to diameter, if diameter is True.
if get_default('diameter', **kwargs):
scale = get_default('diameter_scale', **kwargs)
# TODO: This was originally a numpy array. Did it have to be one?
linewidth = [2 * d * scale for d in i_segment_radius(tr)]
# Plot the collection of lines.
collection = LineCollection(segs,
color=common.get_color(get_default('treecolor', **kwargs),
get_tree_type(tr)),
linewidth=linewidth, alpha=get_default('alpha', **kwargs))
ax.add_collection(collection)
kwargs['title'] = kwargs.get('title', 'Tree view')
kwargs['xlabel'] = kwargs.get('xlabel', plane[0])
kwargs['ylabel'] = kwargs.get('ylabel', plane[1])
kwargs['xlim'] = kwargs.get('xlim', [bounding_box[0][getattr(COLS, plane[0].capitalize())] -
get_default('white_space', **kwargs),
bounding_box[1][getattr(COLS, plane[0].capitalize())] +
get_default('white_space', **kwargs)])
kwargs['ylim'] = kwargs.get('ylim', [bounding_box[0][getattr(COLS, plane[1].capitalize())] -
get_default('white_space', **kwargs),
bounding_box[1][getattr(COLS, plane[1].capitalize())] +
get_default('white_space', **kwargs)])
return common.plot_style(fig=fig, ax=ax, **kwargs)
'''Calculate and plot the end-to-end distance vs the number of segments for
an increasingly larger part of a given path.
Note that the plots are not very meaningful for bifurcating trees.'''
end_to_end_distance = [morphmath.point_dist(segment[1], path.value)
for segment in tree.val_iter(tree.isegment(path))]
make_end_to_end_distance_plot(np.arange(len(end_to_end_distance)) + 1, end_to_end_distance,
path.type)
if __name__ == '__main__':
# load a neuron from an SWC file
filename = 'test_data/swc/Neuron_3_random_walker_branches.swc'
nrn = ezy.load_neuron(filename)
# print mean end-to-end distance per neurite type
print('Mean end-to-end distance for axons: ',
mean_end_to_end_dist(n for n in nrn.neurites if n.type == ezy.TreeType.axon))
print('Mean end-to-end distance for basal dendrites: ',
mean_end_to_end_dist(n for n in nrn.neurites if n.type == ezy.TreeType.basal_dendrite))
print('Mean end-to-end distance for apical dendrites: ',
mean_end_to_end_dist(n for n in nrn.neurites if n.type == ezy.TreeType.apical_dendrite))
print 'End-to-end distance per neurite (nb segments, end-to-end distance, neurite type):'
for nrte in nrn.neurites:
# plot end-to-end distance for increasingly larger parts of neurite
calculate_and_plot_end_to_end_distance(nrte)
# print (number of segments, end-to-end distance, neurite type)
print(sum(1 for _ in tree.isegment(nrte)),
path_end_to_end_distance(nrte), nrte.type)