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)
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 neuron3d(nrn, new_fig=True, new_axes=True, subplot=False, **kwargs):
'''Generates a figure of the neuron,
that contains a soma and a list of trees.
Parameters:
neuron: Neuron
neurom.Neuron 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.
Returns:
A 3D matplotlib figure with a neuron view.
'''
# Initialization of matplotlib figure and axes.
fig, ax = common.get_figure(new_fig=new_fig, new_axes=new_axes,
subplot=subplot, params={'projection': '3d'})
kwargs['new_fig'] = False
kwargs['subplot'] = subplot
kwargs['new_axes'] = False
kwargs['title'] = kwargs.get('title', 'Neuron view')
soma3d(nrn.soma, **kwargs)
h = []
v = []
d = []
for temp_tree in nrn.neurites:
bounding_box = get_bounding_box(temp_tree)
h.append([bounding_box[0][getattr(COLS, 'X')],
bounding_box[1][getattr(COLS, 'X')]])
v.append([bounding_box[0][getattr(COLS, 'Y')],
bounding_box[1][getattr(COLS, 'Y')]])
d.append([bounding_box[0][getattr(COLS, 'Z')],
bounding_box[1][getattr(COLS, 'Z')]])
tree3d(temp_tree, **kwargs)
if h:
kwargs['xlim'] = kwargs.get('xlim', [np.min(h) - get_default('white_space', **kwargs),
np.max(h) + get_default('white_space', **kwargs)])
if v:
kwargs['ylim'] = kwargs.get('ylim', [np.min(v) - get_default('white_space', **kwargs),
np.max(v) + get_default('white_space', **kwargs)])
if d:
kwargs['zlim'] = kwargs.get('zlim', [np.min(d) - get_default('white_space', **kwargs),
np.max(d) + get_default('white_space', **kwargs)])
return common.plot_style(fig=fig, ax=ax, **kwargs)
def test_get_bounding_box():
box = np.array([[-33.25305769, -57.600172 , 0. ],
[ 0. , 0. , 49.70137991]])
bb = get_bounding_box(tree0)
nt.ok_(np.allclose(bb, box))
def neuron(nrn, plane='xy', new_fig=True, subplot=False, **kwargs):
'''Generates a 2d figure of the neuron,
that contains a soma and a list of trees.
Parameters:
neuron: Neuron
neurom.Neuron 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.
Returns:
A 3D 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)
kwargs['new_fig'] = False
kwargs['subplot'] = subplot
soma(nrn.soma, plane=plane, **kwargs)
kwargs['title'] = kwargs.get('title', 'Neuron view')
kwargs['xlabel'] = kwargs.get('xlabel', plane[0])
kwargs['ylabel'] = kwargs.get('ylabel', plane[1])
h = []
v = []
for temp_tree in nrn.neurites:
bounding_box = get_bounding_box(temp_tree)
h.append([bounding_box[0][getattr(COLS, plane[0].capitalize())],
bounding_box[1][getattr(COLS, plane[0].capitalize())]])
v.append([bounding_box[0][getattr(COLS, plane[1].capitalize())],
bounding_box[1][getattr(COLS, plane[1].capitalize())]])
tree(temp_tree, plane=plane, **kwargs)
if h:
kwargs['xlim'] = kwargs.get('xlim', [np.min(h) - get_default('white_space', **kwargs),
np.max(h) + get_default('white_space', **kwargs)])
if v:
kwargs['ylim'] = kwargs.get('ylim', [np.min(v) - get_default('white_space', **kwargs),
np.max(v) + 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 neuron3d(nrn, new_fig=True, new_axes=True, subplot=False, **kwargs):
'''
Generates a figure of the neuron,
that contains a soma and a list of trees.
Parameters:
neuron: Neuron \
neurom.Neuron 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.
Returns:
A 3D matplotlib figure with a neuron view.
'''
# Initialization of matplotlib figure and axes.
fig, ax = common.get_figure(new_fig=new_fig, new_axes=new_axes,
subplot=subplot, params={'projection': '3d'})
kwargs['new_fig'] = False
kwargs['subplot'] = subplot
kwargs['new_axes'] = False
kwargs['title'] = kwargs.get('title', 'Neuron view')
soma3d(nrn.soma, **kwargs)
h = []
v = []
d = []
for temp_tree in nrn.neurites:
bounding_box = get_bounding_box(temp_tree)
h.append([bounding_box[0][getattr(COLS, 'X')],
bounding_box[1][getattr(COLS, 'X')]])
v.append([bounding_box[0][getattr(COLS, 'Y')],
bounding_box[1][getattr(COLS, 'Y')]])
d.append([bounding_box[0][getattr(COLS, 'Z')],
bounding_box[1][getattr(COLS, 'Z')]])
tree3d(temp_tree, **kwargs)
if h:
kwargs['xlim'] = kwargs.get('xlim', [np.min(h) - get_default('white_space', **kwargs),
np.max(h) + get_default('white_space', **kwargs)])
if v:
kwargs['ylim'] = kwargs.get('ylim', [np.min(v) - get_default('white_space', **kwargs),
np.max(v) + get_default('white_space', **kwargs)])
if d:
kwargs['zlim'] = kwargs.get('zlim', [np.min(d) - get_default('white_space', **kwargs),
np.max(d) + get_default('white_space', **kwargs)])
return common.plot_style(fig=fig, ax=ax, **kwargs)
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 neuron(nrn, plane='xy', new_fig=True, subplot=False, **kwargs):
'''
Generates a 2d figure of the neuron, \
that contains a soma and a list of trees.
Parameters:
neuron: Neuron \
neurom.Neuron 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.
Returns:
A 3D 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)
kwargs['new_fig'] = False
kwargs['subplot'] = subplot
soma(nrn.soma, plane=plane, **kwargs)
kwargs['title'] = kwargs.get('title', 'Neuron view')
kwargs['xlabel'] = kwargs.get('xlabel', plane[0])
kwargs['ylabel'] = kwargs.get('ylabel', plane[1])
h = []
v = []
for temp_tree in nrn.neurites:
bounding_box = get_bounding_box(temp_tree)
h.append([bounding_box[0][getattr(COLS, plane[0].capitalize())],
bounding_box[1][getattr(COLS, plane[0].capitalize())]])
v.append([bounding_box[0][getattr(COLS, plane[1].capitalize())],
bounding_box[1][getattr(COLS, plane[1].capitalize())]])
tree(temp_tree, plane=plane, **kwargs)
if h:
kwargs['xlim'] = kwargs.get('xlim', [np.min(h) - get_default('white_space', **kwargs),
np.max(h) + get_default('white_space', **kwargs)])
if v:
kwargs['ylim'] = kwargs.get('ylim', [np.min(v) - get_default('white_space', **kwargs),
np.max(v) + get_default('white_space', **kwargs)])
return common.plot_style(fig=fig, ax=ax, **kwargs)
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)
