def persistence_image_add(Z2,
Z1,
new_fig=True,
subplot=111,
xlims=None,
ylims=None,
norm=True,
vmin=0,
vmax=2.,
cmap=jet_map,
**kwargs):
"""Takes as input two images as exported from the gaussian kernel
plotting function, and plots their addition.
"""
if xlims is None or xlims is None:
xlims, ylims = ((0, 100), (0, 100))
addition = analysis.get_image_add_data(Z1, Z2, norm=norm)
fig, ax = _cm.get_figure(new_fig=new_fig, subplot=subplot)
ax.imshow(_np.rot90(addition),
vmin=vmin,
vmax=vmax,
cmap=cmap,
interpolation='bilinear',
extent=xlims + ylims)
kwargs['xlim'] = xlims
kwargs['ylim'] = ylims
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def diagram(ph,
new_fig=True,
subplot=False,
color='b',
alpha=1.0,
edgecolors='black',
s=30,
**kwargs):
"""
Generates a 2d figure (ph diagram) of the persistent homology of a tree.
"""
# Initialization of matplotlib figure and axes.
fig, ax = _cm.get_figure(new_fig=new_fig, subplot=subplot)
bounds_max = _np.max(_np.max(ph))
bounds_min = _np.min(_np.min(ph))
_cm.plt.plot([bounds_min, bounds_max], [bounds_min, bounds_max], c='black')
ax.scatter(_np.array(ph)[:, 0],
_np.array(ph)[:, 1],
c=color,
alpha=alpha,
edgecolors=edgecolors,
s=s)
kwargs['title'] = kwargs.get('title', 'Persistence diagram')
kwargs['xlabel'] = kwargs.get('xlabel', 'End radial distance from soma')
kwargs['ylabel'] = kwargs.get('ylabel', 'Start radial distance from soma')
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def all_trunks3d(nrn,
new_fig=True,
new_axes=True,
subplot=False,
neurite_type='all',
N=10,
**kwargs):
'''Generates a figure of the neuron,
that contains a soma and a list of trees.
Parameters
----------
neuron: Neuron
neurom.Neuron object
'''
# Initialization of matplotlib figure and axes.
fig, ax = _cm.get_figure(new_fig=new_fig,
new_axes=new_axes,
subplot=subplot,
params={'projection': '3d'})
kwargs['new_fig'] = False
kwargs['new_axes'] = False
kwargs['subplot'] = subplot
soma3d(nrn.soma, **kwargs)
to_plot = []
if neurite_type == 'all':
to_plot = nrn.neurites
else:
for neu_type in neurite_type:
to_plot = to_plot + getattr(nrn, neu_type)
for temp_tree in to_plot:
trunk3d(temp_tree, N=N, **kwargs)
kwargs['title'] = kwargs.get('title', nrn.name)
kwargs['xlim'] = kwargs.get(
'xlim',
[nrn.soma.get_center()[0] - 2. * N,
nrn.soma.get_center()[0] + 2. * N])
kwargs['ylim'] = kwargs.get(
'ylim',
[nrn.soma.get_center()[1] - 2. * N,
nrn.soma.get_center()[1] + 2. * N])
kwargs['zlim'] = kwargs.get(
'zlim',
[nrn.soma.get_center()[2] - 2. * N,
nrn.soma.get_center()[2] + 2. * N])
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def all_trunks(nrn,
plane='xy',
new_fig=True,
subplot=False,
hadd=0.0,
vadd=0.0,
neurite_type='all',
N=10,
**kwargs):
'''Generates a 2d figure of the neuron,
that contains a soma and a list of trees.
Parameters
----------
neuron: Neuron
neurom.Neuron object
'''
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 = _cm.get_figure(new_fig=new_fig, subplot=subplot)
kwargs['new_fig'] = False
kwargs['subplot'] = subplot
soma(nrn.soma, plane=plane, hadd=hadd, vadd=vadd, **kwargs)
to_plot = []
if neurite_type == 'all':
to_plot = nrn.neurites
else:
for neu_type in neurite_type:
to_plot = to_plot + getattr(nrn, neu_type)
for temp_tree in to_plot:
trunk(temp_tree, N=N, **kwargs)
kwargs['title'] = kwargs.get('title', nrn.name)
kwargs['xlabel'] = kwargs.get('xlabel', plane[0])
kwargs['ylabel'] = kwargs.get('ylabel', plane[1])
kwargs['xlim'] = kwargs.get(
'xlim',
[nrn.soma.get_center()[0] - 2. * N,
nrn.soma.get_center()[0] + 2. * N])
kwargs['ylim'] = kwargs.get(
'ylim',
[nrn.soma.get_center()[1] - 2. * N,
nrn.soma.get_center()[1] + 2. * N])
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def population3d(pop, new_fig=True, new_axes=True, subplot=False, **kwargs):
'''Generates a figure of the population,
that contains the pop somata and a set of list of trees.
Parameters
----------
pop: Population
neurom.Population object
'''
# Initialization of matplotlib figure and axes.
fig, ax = _cm.get_figure(new_fig=new_fig,
new_axes=new_axes,
subplot=subplot,
params={'projection': '3d'})
kwargs['new_fig'] = False
kwargs['new_axes'] = False
kwargs['subplot'] = subplot
h = []
v = []
d = []
for nrn in pop.neurons:
# soma3d(nrn.soma, **kwargs)
for temp_tree in nrn.neurites:
bounding_box = temp_tree.get_bounding_box()
h.append([
bounding_box[0][_utils.term_dict['x']],
bounding_box[1][_utils.term_dict['x']]
])
v.append([
bounding_box[0][_utils.term_dict['y']],
bounding_box[1][_utils.term_dict['y']]
])
d.append([
bounding_box[0][_utils.term_dict['z']],
bounding_box[1][_utils.term_dict['z']]
])
tree3d(temp_tree, **kwargs)
kwargs['title'] = kwargs.get('title', '')
white_space = _get_default('white_space', **kwargs)
kwargs['xlim'] = kwargs.get(
'xlim', [_np.min(h) - white_space,
_np.max(h) + white_space])
kwargs['ylim'] = kwargs.get(
'ylim', [_np.min(v) - white_space,
_np.max(v) + white_space])
kwargs['zlim'] = kwargs.get(
'zlim', [_np.min(d) - white_space,
_np.max(d) + white_space])
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def soma(sm,
plane='xy',
new_fig=True,
subplot=False,
hadd=0.0,
vadd=0.0,
**kwargs):
'''Generates a 2d figure of the soma.
Parameters
----------
soma: Soma
neurom.Soma object
'''
treecolor = kwargs.get('treecolor', None)
outline = kwargs.get('outline', True)
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 = _cm.get_figure(new_fig=new_fig, subplot=subplot)
# Definition of the tree color depending on the tree type.
treecolor = _cm.get_color(treecolor, tree_type='soma')
# Plot the outline of the soma as a circle, is outline is selected.
if not outline:
soma_circle = _cm.plt.Circle(sm.get_center() + [hadd, vadd, 0.0],
sm.get_diameter() / 2.,
color=treecolor,
alpha=_get_default('alpha', **kwargs))
ax.add_artist(soma_circle)
else:
horz = getattr(sm, plane[0]) + hadd
vert = getattr(sm, plane[1]) + vadd
horz = _np.append(
horz, horz[0]) + hadd # To close the loop for a soma viewer.
vert = _np.append(
vert, vert[0]) + vadd # To close the loop for a soma viewer.
_cm.plt.plot(horz,
vert,
color=treecolor,
alpha=_get_default('alpha', **kwargs),
linewidth=_get_default('linewidth', **kwargs))
kwargs['title'] = kwargs.get('title', 'Soma view')
kwargs['xlabel'] = kwargs.get('xlabel', plane[0])
kwargs['ylabel'] = kwargs.get('ylabel', plane[1])
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def density_cloud(obj,
new_fig=True,
subplot=111,
new_axes=True,
neurite_type='all',
bins=100,
plane='xy',
color_map=blues_map,
alpha=0.8,
centered=True,
colorbar=True,
**kwargs):
"""
View the neuron morphologies of a population as a density cloud.
"""
x1 = []
y1 = []
if neurite_type == 'all':
ntypes = 'neurites'
else:
ntypes = neurite_type
fig, ax = _cm.get_figure(new_fig=new_fig,
new_axes=new_axes,
subplot=subplot)
for neu in obj.neurons:
for tr in getattr(neu, ntypes):
if centered:
dx = neu.soma.get_center()[0]
dy = neu.soma.get_center()[1]
else:
dx = 0
dy = 0
x1 = x1 + list(getattr(tr, plane[0]) - dx)
y1 = y1 + list(getattr(tr, plane[1]) - dy)
H1, xedges1, yedges1 = _np.histogram2d(x1, y1, bins=(bins, bins))
mask = H1 < 0.05
H2 = _np.ma.masked_array(H1, mask)
color_map.set_bad(color='white', alpha=None)
plots = ax.contourf((xedges1[:-1] + xedges1[1:]) / 2,
(yedges1[:-1] + yedges1[1:]) / 2,
_np.transpose(H2),
cmap=color_map,
alhpa=alpha)
if colorbar:
_cm.plt.colorbar(plots)
# soma(neu.soma, new_fig=False)
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def diagram_enhanced(ph,
new_fig=True,
subplot=False,
alpha=1.0,
valID=2,
cmap=jet_map,
edgecolors='black',
s=30,
**kwargs):
"""
Generates a 2d figure (diagram) of the persistent homology
of a tree enhanced by a parameter encodes in ph[valID]
"""
# Initialization of matplotlib figure and axes.
fig, ax = cm.get_figure(new_fig=new_fig, subplot=subplot)
val_max = np.max(ph, axis=0)[valID]
# Hack for colorbar creation
Z = [[0, 0], [0, 0]]
levels = np.linspace(0.0, val_max, 200)
CS3 = plt.contourf(Z, levels, cmap=cmap)
def sort_ph_enhanced(ph, valID):
"""
Sorts barcode according to length.
"""
ph_sort = [p[:valID + 1] + [np.abs(p[0] - p[1])] for p in ph]
ph_sort.sort(key=lambda x: x[valID + 1])
return ph_sort
ph_sort = sort_ph_enhanced(ph, valID)
bounds_max = np.max(np.max(ph_sort))
bounds_min = np.min(np.min(ph_sort))
plt.plot([bounds_min, bounds_max], [bounds_min, bounds_max], c='black')
colors = [cmap(p[valID] / val_max) for p in ph_sort]
ax.scatter(np.array(ph_sort)[:, 0],
np.array(ph_sort)[:, 1],
c=colors,
alpha=alpha,
edgecolors=edgecolors,
s=s)
kwargs['title'] = kwargs.get('title', 'Persistence diagram')
kwargs['xlabel'] = kwargs.get('xlabel', 'End radial distance from soma')
kwargs['ylabel'] = kwargs.get('ylabel', 'Start radial distance from soma')
plt.ylim([-1, len(ph_sort)])
plt.colorbar(CS3)
return cm.plot_style(fig=fig, ax=ax, **kwargs)
def persistence_image(ph,
new_fig=True,
subplot=111,
xlims=None,
ylims=None,
masked=False,
colorbar=False,
norm_factor=None,
threshold=0.01,
vmin=None,
vmax=None,
cmap=jet_map,
bw_method=None,
**kwargs):
'''Plots the gaussian kernel
of the ph diagram that is given.
'''
if xlims is None or xlims is None:
xlims, ylims = analysis.get_limits(ph, coll=False)
# pylint: disable=unexpected-keyword-arg
Zn = analysis.get_persistence_image_data(ph,
norm_factor=norm_factor,
bw_method=bw_method,
xlims=xlims,
ylims=ylims)
fig, ax = cm.get_figure(new_fig=new_fig, subplot=subplot)
if masked:
Zn = np.ma.masked_where((threshold > Zn), Zn)
cax = ax.imshow(np.rot90(Zn),
vmin=vmin,
vmax=vmax,
cmap=cmap,
interpolation='bilinear',
extent=xlims + ylims)
if colorbar:
plt.colorbar(cax)
kwargs['xlim'] = xlims
kwargs['ylim'] = ylims
kwargs['title'] = kwargs.get('title', 'Persistence image')
kwargs['xlabel'] = kwargs.get('xlabel', 'End radial distance from soma')
kwargs['ylabel'] = kwargs.get('ylabel', 'Start radial distance from soma')
return Zn, cm.plot_style(fig=fig, ax=ax, **kwargs)
def histogram_stepped_population(ph_list,
new_fig=True,
subplot=False,
color='b',
alpha=0.7,
**kwargs):
'''Extracts and plots the stepped histogram of a list of persistence diagrams.
The histogram is normalized acording to the number of persistence diagrams.
'''
fig, ax = _cm.get_figure(new_fig=new_fig, subplot=subplot)
hist_data = analysis.histogram_stepped(analysis.collapse(ph_list))
ax.fill_between(hist_data[0][:-1],
0,
hist_data[1] / len(ph_list),
color=color,
alpha=alpha)
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def histogram_stepped(ph,
new_fig=True,
subplot=False,
color='b',
alpha=0.7,
**kwargs):
'''Extracts and plots the stepped histogram of a persistent
homology array.
'''
fig, ax = _cm.get_figure(new_fig=new_fig, subplot=subplot)
hist_data = analysis.histogram_stepped(ph)
ax.fill_between(hist_data[0][:-1],
0,
hist_data[1],
color=color,
alpha=alpha)
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def histogram_horizontal(ph,
new_fig=True,
subplot=False,
bins=100,
color='b',
alpha=0.7,
**kwargs):
'''Extracts and plots the binned histogram of a persistent
homology array.
'''
fig, ax = cm.get_figure(new_fig=new_fig, subplot=subplot)
hist_data = analysis.histogram_horizontal(ph, num_bins=bins)
ax.fill_between(hist_data[0][:-1],
0,
hist_data[1],
color=color,
alpha=alpha)
return cm.plot_style(fig=fig, ax=ax, **kwargs)
def start_length_diagram(ph,
new_fig=True,
subplot=False,
color='b',
alpha=1.0,
**kwargs):
'''Plots the transformed ph diagram that represents lengths and starting points
of a component.
'''
ph_transformed = transform_ph_to_length(ph)
fig, ax = cm.get_figure(new_fig=new_fig, subplot=subplot)
for p in ph_transformed:
ax.scatter(p[0], p[1], c=color, edgecolors='black', alpha=alpha)
kwargs['title'] = kwargs.get('title', 'Transformed Persistence diagram')
kwargs['xlabel'] = kwargs.get('xlabel', 'Start of the component')
kwargs['ylabel'] = kwargs.get('ylabel', 'Length of the component')
return cm.plot_style(fig=fig, ax=ax, **kwargs)
def persistence_image_average(ph_list,
new_fig=True,
subplot=111,
xlims=None,
ylims=None,
norm_factor=1.0,
vmin=None,
vmax=None,
cmap=jet_map,
weighted=False,
**kwargs):
"""Merges a list of ph diagrams and plots their respective average image.
"""
# pylint: disable=unexpected-keyword-arg
av_imgs = analysis.get_average_persistence_image(ph_list,
xlims=xlims,
ylims=ylims,
norm_factor=norm_factor,
weighted=weighted)
if xlims is None or xlims is None:
xlims, ylims = analysis.get_limits(ph_list)
if vmin is None:
vmin = np.min(av_imgs)
if vmax is None:
vmax = np.max(av_imgs)
fig, ax = cm.get_figure(new_fig=new_fig, subplot=subplot)
ax.imshow(np.rot90(av_imgs),
vmin=vmin,
vmax=vmax,
cmap=cmap,
interpolation='bilinear',
extent=xlims + ylims)
kwargs['xlim'] = xlims
kwargs['ylim'] = ylims
kwargs['title'] = kwargs.get('title', 'Average persistence image')
kwargs['xlabel'] = kwargs.get('xlabel', 'End radial distance from soma')
kwargs['ylabel'] = kwargs.get('ylabel', 'Start radial distance from soma')
return av_imgs, cm.plot_style(fig=fig, ax=ax, **kwargs)
def barcode_enhanced(ph,
new_fig=True,
subplot=False,
linewidth=1.2,
valID=2,
cmap=jet_map,
**kwargs):
"""
Generates a 2d figure (barcode) of the persistent homology
of a tree enhanced by a parameter encodes in ph[valID]
"""
# Initialization of matplotlib figure and axes.
fig, ax = _cm.get_figure(new_fig=new_fig, subplot=subplot)
val_max = _np.max(ph, axis=0)[valID]
# Hack for colorbar creation
Z = [[0, 0], [0, 0]]
levels = _np.linspace(0.0, val_max, 200)
CS3 = _cm.plt.contourf(Z, levels, cmap=cmap)
def sort_ph_enhanced(ph, valID):
"""
Sorts barcode according to length.
"""
ph_sort = [p[:valID + 1] + [_np.abs(p[0] - p[1])] for p in ph]
ph_sort.sort(key=lambda x: x[valID + 1])
return ph_sort
ph_sort = sort_ph_enhanced(ph, valID)
for ip, p in enumerate(ph_sort):
ax.plot(p[:2], [ip, ip],
c=cmap(p[valID] / val_max),
linewidth=linewidth)
kwargs['title'] = kwargs.get('title', 'Barcode of p.h.')
kwargs['xlabel'] = kwargs.get('xlabel', 'Lifetime')
_cm.plt.ylim([-1, len(ph_sort)])
_cm.plt.colorbar(CS3)
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def soma3d(sm, new_fig=True, new_axes=True, subplot=False, **kwargs):
'''Generates a 3d figure of the soma.
Parameters
----------
soma: Soma
neurom.Soma object
'''
treecolor = kwargs.get('treecolor', None)
# Initialization of matplotlib figure and axes.
fig, ax = _cm.get_figure(new_fig=new_fig,
new_axes=new_axes,
subplot=subplot,
params={'projection': '3d'})
# Definition of the tree color depending on the tree type.
treecolor = _cm.get_color(treecolor, tree_type='soma')
center = sm.get_center()
xs = center[0]
ys = center[1]
zs = center[2]
# Plot the soma as a circle.
fig, ax = _cm.plot_sphere(fig,
ax,
center=[xs, ys, zs],
radius=sm.get_diameter(),
color=treecolor,
alpha=_get_default('alpha', **kwargs))
kwargs['title'] = kwargs.get('title', 'Soma view')
kwargs['xlabel'] = kwargs.get('xlabel', 'X')
kwargs['ylabel'] = kwargs.get('ylabel', 'Y')
kwargs['zlabel'] = kwargs.get('zlabel', 'Z')
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def barcode(ph,
new_fig=True,
subplot=False,
color='b',
linewidth=1.2,
**kwargs):
"""
Generates a 2d figure (barcode) of the persistent homology
of a tree as it has been computed by
Topology.get_persistent_homology method.
"""
# Initialization of matplotlib figure and axes.
fig, ax = _cm.get_figure(new_fig=new_fig, subplot=subplot)
ph_sort = analysis.sort_ph(ph)
for ip, p in enumerate(ph_sort):
ax.plot(p[:2], [ip, ip], c=color, linewidth=linewidth)
kwargs['title'] = kwargs.get('title', 'Persistence barcode')
kwargs['xlabel'] = kwargs.get('xlabel',
'Lifetime: radial distance from soma')
_cm.plt.ylim([-1, len(ph_sort)])
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def tree(tr,
plane='xy',
new_fig=True,
subplot=False,
hadd=0.0,
vadd=0.0,
**kwargs):
'''Generates a 2d figure of the tree.
Parameters
----------
tr: Tree
neurom.Tree object
'''
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 = _cm.get_figure(new_fig=new_fig, subplot=subplot)
# Data needed for the viewer: x,y,z,r
bounding_box = tr.get_bounding_box()
def _seg_2d(seg, x_add=0.0, y_add=0.0):
"""2d coordinates required for the plotting of a segment"""
horz = _utils.term_dict[plane[0]]
vert = _utils.term_dict[plane[1]]
horz1 = seg[0][horz] + x_add
horz2 = seg[1][horz] + x_add
vert1 = seg[0][vert] + y_add
vert2 = seg[1][vert] + y_add
return ((horz1, vert1), (horz2, vert2))
segs = [_seg_2d(seg, hadd, vadd) for seg in tr.get_segments()]
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)
linewidth = [d * scale for d in tr.d]
if tr.get_type() not in _utils.tree_type:
treecolor = 'black'
else:
treecolor = _cm.get_color(_get_default('treecolor', **kwargs),
_utils.tree_type[tr.get_type()])
# Plot the collection of lines.
collection = _LC(segs,
color=treecolor,
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])
white_space = _get_default('white_space', **kwargs)
kwargs['xlim'] = kwargs.get('xlim', [
bounding_box[0][_utils.term_dict[plane[0]]] - white_space,
bounding_box[1][_utils.term_dict[plane[0]]] + white_space
])
kwargs['ylim'] = kwargs.get('ylim', [
bounding_box[0][_utils.term_dict[plane[1]]] - white_space,
bounding_box[1][_utils.term_dict[plane[1]]] + white_space
])
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def neuron(nrn,
plane='xy',
new_fig=True,
subplot=False,
hadd=0.0,
vadd=0.0,
neurite_type='all',
rotation=None,
nosoma=False,
new_axes=True,
**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'
linewidth: float
Defines the linewidth of the tree and soma
of the neuron, if diameter is set to False.
Default value is 1.2.
alpha: float
Defines the transparency of the neuron.
0.0 transparent through 1.0 opaque.
Default value is 0.8.
treecolor: str or None
Defines the color of the trees.
If None the default values will be used,
depending on the type of tree:
Soma: "black"
Basal dendrite: "red"
Axon : "blue"
Apical dendrite: "purple"
Undefined tree: "black"
Default value is None.
new_fig: boolean
Defines if the neuron 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 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 = _cm.get_figure(new_fig=new_fig,
subplot=subplot,
new_axes=new_axes)
kwargs['new_fig'] = False
kwargs['subplot'] = subplot
if not nosoma:
soma(nrn.soma, plane=plane, hadd=hadd, vadd=vadd, **kwargs)
h = []
v = []
to_plot = []
if rotation == 'apical':
angle = _np.arctan2(nrn.apical[0].get_pca()[0],
nrn.apical[0].get_pca()[1])
angle = _np.arctan2(rotation[1], rotation[0])
if neurite_type == 'all':
to_plot = nrn.neurites
else:
for neu_type in neurite_type:
to_plot = to_plot + getattr(nrn, neu_type)
for temp_tree in to_plot:
if rotation is not None:
temp_tree.rotate_xy(angle)
bounding_box = temp_tree.get_bounding_box()
h.append([
bounding_box[0][_utils.term_dict[plane[0]]],
bounding_box[1][_utils.term_dict[plane[0]]]
])
v.append([
bounding_box[0][_utils.term_dict[plane[1]]],
bounding_box[1][_utils.term_dict[plane[1]]]
])
tree(temp_tree, hadd=hadd, vadd=vadd, **kwargs)
kwargs['title'] = kwargs.get('title', nrn.name)
kwargs['xlabel'] = kwargs.get('xlabel', plane[0])
kwargs['ylabel'] = kwargs.get('ylabel', plane[1])
white_space = _get_default('white_space', **kwargs)
kwargs['xlim'] = kwargs.get(
'xlim',
[_np.min(h) - white_space + hadd,
_np.max(h) + white_space + hadd])
kwargs['ylim'] = kwargs.get(
'ylim',
[_np.min(v) - white_space + vadd,
_np.max(v) + white_space + vadd])
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def neuron3d(nrn,
new_fig=True,
new_axes=True,
subplot=False,
neurite_type='all',
**kwargs):
'''Generates a figure of the neuron,
that contains a soma and a list of trees.
Parameters
----------
neuron: Neuron
neurom.Neuron object
'''
# Initialization of matplotlib figure and axes.
fig, ax = _cm.get_figure(new_fig=new_fig,
new_axes=new_axes,
subplot=subplot,
params={'projection': '3d'})
kwargs['new_fig'] = False
kwargs['new_axes'] = False
kwargs['subplot'] = subplot
soma3d(nrn.soma, **kwargs)
h = []
v = []
d = []
to_plot = []
if neurite_type == 'all':
to_plot = nrn.neurites
else:
for neu_type in neurite_type:
to_plot = to_plot + getattr(nrn, neu_type)
for temp_tree in to_plot:
bounding_box = temp_tree.get_bounding_box()
h.append([
bounding_box[0][_utils.term_dict['x']],
bounding_box[1][_utils.term_dict['x']]
])
v.append([
bounding_box[0][_utils.term_dict['y']],
bounding_box[1][_utils.term_dict['y']]
])
d.append([
bounding_box[0][_utils.term_dict['z']],
bounding_box[1][_utils.term_dict['z']]
])
tree3d(temp_tree, **kwargs)
kwargs['title'] = kwargs.get('title', nrn.name)
white_space = _get_default('white_space', **kwargs)
kwargs['xlim'] = kwargs.get(
'xlim', [_np.min(h) - white_space,
_np.max(h) + white_space])
kwargs['ylim'] = kwargs.get(
'ylim', [_np.min(v) - white_space,
_np.max(v) + white_space])
kwargs['zlim'] = kwargs.get(
'zlim', [_np.min(d) - white_space,
_np.max(d) + white_space])
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def population(pop,
plane='xy',
new_fig=True,
subplot=False,
hadd=0.0,
vadd=0.0,
neurite_type='all',
**kwargs):
'''Generates a 2d figure of the population,
that contains a soma and a list of trees.
Parameters
----------
pop: Population
neurom.Population object
Options
-------
plane: str
Accepted values: Any pair of of xyz
Default value is 'xy'
linewidth: float
Defines the linewidth of the tree and soma
of the neuron, if diameter is set to False.
Default value is 1.2.
alpha: float
Defines the transparency of the neuron.
0.0 transparent through 1.0 opaque.
Default value is 0.8.
treecolor: str or None
Defines the color of the trees.
If None the default values will be used,
depending on the type of tree:
Soma: "black"
Basal dendrite: "red"
Axon : "blue"
Apical dendrite: "purple"
Undefined tree: "black"
Default value is None.
new_fig: boolean
Defines if the neuron 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 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 = _cm.get_figure(new_fig=new_fig, subplot=subplot)
kwargs['new_fig'] = False
kwargs['subplot'] = subplot
h = []
v = []
for nrn in pop.neurons:
soma(nrn.soma, plane=plane, hadd=hadd, vadd=vadd, **kwargs)
if neurite_type == 'all':
neurite_list = ['basal', 'apical', 'axon']
else:
neurite_list = [neurite_type]
for nt in neurite_list:
for temp_tree in getattr(nrn, nt):
bounding_box = temp_tree.get_bounding_box()
h.append([
bounding_box[0][_utils.term_dict[plane[0]]] + hadd,
bounding_box[1][_utils.term_dict[plane[1]]] + vadd
])
v.append([
bounding_box[0][_utils.term_dict[plane[0]]] + hadd,
bounding_box[1][_utils.term_dict[plane[1]]] + vadd
])
tree(temp_tree, plane=plane, hadd=hadd, vadd=vadd, **kwargs)
kwargs['title'] = kwargs.get('title', 'Neuron view')
kwargs['xlabel'] = kwargs.get('xlabel', plane[0])
kwargs['ylabel'] = kwargs.get('ylabel', plane[1])
kwargs['xlim'] = kwargs.get('xlim', [_np.min(h), _np.max(h)])
kwargs['ylim'] = kwargs.get('ylim', [_np.min(v), _np.max(v)])
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
def trunk3d(tr, new_fig=True, new_axes=True, subplot=False, N=10, **kwargs):
'''Generates a figure of the trunk in 3d.
Parameters
----------
tr: Tree
neurom.Tree object
'''
# pylint: disable=import-outside-toplevel
from mpl_toolkits.mplot3d.art3d import Line3DCollection
# Initialization of matplotlib figure and axes.
fig, ax = _cm.get_figure(new_fig=new_fig,
new_axes=new_axes,
subplot=subplot,
params={'projection': '3d'})
def _seg_3d(seg):
"""3d coordinates needed for the plotting of a segment"""
horz = _utils.term_dict['x']
vert = _utils.term_dict['y']
depth = _utils.term_dict['z']
horz1 = seg[0][horz]
horz2 = seg[1][horz]
vert1 = seg[0][vert]
vert2 = seg[1][vert]
depth1 = seg[0][depth]
depth2 = seg[1][depth]
return ((horz1, vert1, depth1), (horz2, vert2, depth2))
if len(tr.get_segments()) < N:
N = len(tr.get_segments())
segs = [_seg_3d(seg) for seg in tr.get_segments()[:N]]
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)
linewidth = [d * scale for d in tr.d]
treecolor = _cm.get_color(_get_default('treecolor', **kwargs),
_utils.tree_type[tr.get_type()])
# Plot the collection of lines.
collection = Line3DCollection(segs,
color=treecolor,
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')
return _cm.plot_style(fig=fig, ax=ax, **kwargs)
