def draw(obj, mode='2d', **kwargs):
"""Draw a morphology object.
Arguments:
obj: morphology object to be drawn (neuron, tree, soma).
mode (Optional[str]): drawing mode ('2d', '3d', 'dendrogram'). Defaults to '2d'.
**kwargs: keyword arguments for underlying neurom.view.view functions.
Raises:
InvalidDrawModeError if mode is not valid
NotDrawableError if obj is not drawable
NotDrawableError if obj type and mode combination is not drawable
Examples:
>>> nrn = ... # load a neuron
>>> fig, _ = viewer.draw(nrn) # 2d plot
>>> fig.show()
>>> fig3d, _ = viewer.draw(nrn, mode='3d') # 3d plot
>>> fig3d.show()
>>> fig, _ = viewer.draw(nrn.neurites[0]) # 2d plot of neurite tree
>>> dend, _ = viewer.draw(nrn, mode='dendrogram')
"""
if mode not in MODES:
raise InvalidDrawModeError('Invalid drawing mode %s' % mode)
if 'realistic_diameters' in kwargs and mode == '3d':
if kwargs['realistic_diameters']:
raise NotImplementedError(
'Option realistic_diameter not implemented for 3D plots')
del kwargs['realistic_diameters']
fig, ax = (common.get_figure() if mode in ('2d', 'dendrogram') else
common.get_figure(params={'projection': '3d'}))
if isinstance(obj, Neuron):
tag = 'neuron'
elif isinstance(obj, (Section, Neurite)):
tag = 'tree'
elif isinstance(obj, Soma):
tag = 'soma'
else:
raise NotDrawableError('draw not implemented for %s' % obj.__class__)
viewer = '%s_%s' % (tag, mode)
try:
plotter = _VIEWERS[viewer]
except KeyError as e:
raise NotDrawableError('No drawer for class %s, mode=%s' %
(obj.__class__, mode)) from e
output_path = kwargs.pop('output_path', None)
plotter(ax, obj, **kwargs)
if mode != 'dendrogram':
common.plot_style(fig=fig, ax=ax, **kwargs)
if output_path:
common.save_plot(fig=fig, output_path=output_path, **kwargs)
return fig, ax
def plot_density(population, # pylint: disable=too-many-arguments, too-many-locals
bins=100, new_fig=True, subplot=111, levels=None, plane='xy',
colorlabel='Nodes per unit area', labelfontsize=16,
color_map='Reds', no_colorbar=False, threshold=0.01,
neurite_type=NeuriteType.basal_dendrite, **kwargs):
'''Plots the 2d histogram of the center
coordinates of segments in the selected plane.
'''
fig, ax = fig, ax = common.get_figure(new_fig=new_fig, subplot=subplot)
H1, xedges1, yedges1 = extract_density(population, plane=plane, bins=bins,
neurite_type=neurite_type)
mask = H1 < threshold # mask = H1==0
H2 = np.ma.masked_array(H1, mask)
getattr(plt.cm, color_map).set_bad(color='white', alpha=None)
plots = ax.contourf((xedges1[:-1] + xedges1[1:]) / 2,
(yedges1[:-1] + yedges1[1:]) / 2,
np.transpose(H2), # / np.max(H2),
cmap=getattr(plt.cm, color_map), levels=levels)
if not no_colorbar:
cbar = plt.colorbar(plots)
cbar.ax.set_ylabel(colorlabel, fontsize=labelfontsize)
kwargs['title'] = kwargs.get('title', '')
kwargs['xlabel'] = kwargs.get('xlabel', plane[0])
kwargs['ylabel'] = kwargs.get('ylabel', plane[1])
return common.plot_style(fig=fig, ax=ax, **kwargs)
def plot_somas(somas):
"""Plot set of somas on same figure as spheres, each with different color."""
_, ax = common.get_figure(new_fig=True, subplot=111,
params={'projection': '3d', 'aspect': 'equal'})
for s in somas:
common.plot_sphere(ax, s.center, s.radius, color=random_color(), alpha=1)
plt.show()
def boxplots(data_all, new_fig=True, subplot=False,
feature_titles=features, **kwargs):
'''Plots a list of boxplots for each feature in feature_list for object 1.
Then presents the value of object 2 for each feature as an colored objected
in the same boxplot.
Parameters:
data_all:\
A list of pairs of flattened data for each feature.
new_fig (Optional[bool]):\
Default is False, which returns the default matplotlib axes 111\
If a subplot needs to be specified, it should be provided in xxx format.
subplot (Optional[bool]):\
Default is False, which returns a matplotlib figure object. If True,\
returns a matplotlib axis object, for use as a subplot.
Returns:
fig:\
A figure which contains the list of boxplots.
'''
from neurom.view import common
fig, ax = common.get_figure(new_fig=new_fig, subplot=subplot)
ax.boxplot(list(np.transpose(np.array(data_all))[0]), vert=False)
for idata, data in enumerate(data_all):
ax.scatter(np.median(data[1]), len(data_all) - idata, s=100, color='r', marker='s')
ax.set_yticklabels(feature_titles)
fig, ax = common.plot_style(fig, ax, xlabel='Normalized units (dimensionless)', ylabel='',
title='Summarizing features', tight=True, **kwargs)
return fig, ax
def plot_somas(somas):
'''Plot set of somas on same figure as spheres, each with different color'''
_, ax = common.get_figure(new_fig=True, subplot=111,
params={'projection': '3d', 'aspect': 'equal'})
for s in somas:
common.plot_sphere(ax, s.center, s.radius, color=random_color(), alpha=1)
plt.show()
def boxplot(neurons, feature, new_fig=True, subplot=False):
'''
Plot a histogram of the selected feature for the population of neurons.
Plots x-axis versus y-axis on a scatter|histogram|binned values plot.
More information about the plot and how it works.
Parameters
----------
neurons : list
List of Neurons. Single neurons must be encapsulated in a list.
feature : str
The feature of interest.
Options
-------
subplot : bool
Default is False, which returns a matplotlib figure object. If True,
returns a matplotlib axis object, for use as a subplot.
'''
feature_values = [getattr(neu, 'get_' + feature)() for neu in neurons]
_, ax = common.get_figure(new_fig=new_fig, subplot=subplot)
ax.boxplot(feature_values)
x_labels = ['neuron_id' for _ in neurons]
ax.set_xticklabels(x_labels)
def plot_neuron_on_density(
population, # pylint: disable=too-many-arguments
bins=100,
new_fig=True,
subplot=111,
levels=None,
plane='xy',
colorlabel='Nodes per unit area',
labelfontsize=16,
color_map='Reds',
no_colorbar=False,
threshold=0.01,
neurite_type=NeuriteType.basal_dendrite,
**kwargs):
"""Plots the 2d histogram of the center
coordinates of segments in the selected plane
and superimposes the view of the first neurite of the collection.
"""
_, ax = common.get_figure(new_fig=new_fig)
view.plot_tree(ax, population.neurites[0])
return plot_density(population,
plane=plane,
bins=bins,
new_fig=False,
subplot=subplot,
colorlabel=colorlabel,
labelfontsize=labelfontsize,
levels=levels,
color_map=color_map,
no_colorbar=no_colorbar,
threshold=threshold,
neurite_type=neurite_type,
**kwargs)
def test_plot_cylinder():
fig0, ax0 = get_figure(params={'projection': '3d'})
start, end = np.array([0, 0, 0]), np.array([1, 0, 0])
plot_cylinder(ax0, start=start, end=end,
start_radius=0, end_radius=10.,
color='black', alpha=1.)
nt.ok_(ax0.has_data())
def plot_somas(somas):
"""Plot set of somas on same figure as spheres, each with different color"""
fig, ax = common.get_figure(new_fig=True, subplot=111, params={"projection": "3d", "aspect": "equal"})
for s in somas:
center = s.center
radius = s.radius
common.plot_sphere(fig, ax, center, radius, color=random_color(), alpha=1)
plt.show()
def histogram(neurons, feature, new_fig=True, subplot=False, normed=False, **kwargs):
'''
Plot a histogram of the selected feature for the population of neurons.
Plots x-axis versus y-axis on a scatter|histogram|binned values plot.
More information about the plot and how it works.
Parameters :
neurons : list
List of Neurons. Single neurons must be encapsulated in a list.
feature : str
The feature of interest.
bins : int
Number of bins for the histogram.
cumulative : bool
Sets cumulative histogram on.
subplot : bool
Default is False, which returns a matplotlib figure object. If True,
returns a matplotlib axis object, for use as a subplot.
Returns :
figure_output : list
[fig|ax, figdata, figtext]
The first item is either a figure object (if subplot is False) or an
axis object. The second item is an object containing the data used to
generate the figure. The final item is text used in report generation
as a figure legend. This text needs to be manually entered in each
figure file.
'''
bins = kwargs.get('bins', 25)
cumulative = kwargs.get('cumulative', False)
fig, ax = common.get_figure(new_fig=new_fig, subplot=subplot)
kwargs['xlabel'] = kwargs.get('xlabel', feature)
kwargs['ylabel'] = kwargs.get('ylabel', feature + ' fraction')
kwargs['title'] = kwargs.get('title', feature + ' histogram')
feature_values = [getattr(neu, 'get_' + feature)() for neu in neurons]
neu_labels = [neu.name for neu in neurons]
ax.hist(feature_values, bins=bins, cumulative=cumulative, label=neu_labels, normed=normed)
kwargs['no_legend'] = len(neu_labels) == 1
return common.plot_style(fig=fig, ax=ax, **kwargs)
def histogram(neurons, feature, new_fig=True, subplot=False, normed=False, **kwargs):
'''
Plot a histogram of the selected feature for the population of neurons.
Plots x-axis versus y-axis on a scatter|histogram|binned values plot.
More information about the plot and how it works.
Parameters :
neurons : list
List of Neurons. Single neurons must be encapsulated in a list.
feature : str
The feature of interest.
bins : int
Number of bins for the histogram.
cumulative : bool
Sets cumulative histogram on.
subplot : bool
Default is False, which returns a matplotlib figure object. If True,
returns a matplotlib axis object, for use as a subplot.
Returns :
figure_output : list
[fig|ax, figdata, figtext]
The first item is either a figure object (if subplot is False) or an
axis object. The second item is an object containing the data used to
generate the figure. The final item is text used in report generation
as a figure legend. This text needs to be manually entered in each
figure file.
'''
bins = kwargs.get('bins', 25)
cumulative = kwargs.get('cumulative', False)
fig, ax = common.get_figure(new_fig=new_fig, subplot=subplot)
kwargs['xlabel'] = kwargs.get('xlabel', feature)
kwargs['ylabel'] = kwargs.get('ylabel', feature + ' fraction')
kwargs['title'] = kwargs.get('title', feature + ' histogram')
feature_values = [getattr(neu, 'get_' + feature)() for neu in neurons]
neu_labels = [neu.name for neu in neurons]
ax.hist(feature_values, bins=bins, cumulative=cumulative, label=neu_labels, normed=normed)
kwargs['no_legend'] = len(neu_labels) == 1
return common.plot_style(fig=fig, ax=ax, **kwargs)
def test_plot_cylinder():
fig0, ax0 = get_figure(params={'projection': '3d'})
start, end = np.array([0, 0, 0]), np.array([1, 0, 0])
plot_cylinder(ax0,
start=start,
end=end,
start_radius=0,
end_radius=10.,
color='black',
alpha=1.)
nt.ok_(ax0.has_data())
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
Options:
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 soma. \
Soma : "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.
Returns:
A 3D matplotlib figure with a soma view.
'''
treecolor = kwargs.get('treecolor', None)
# Initialization of matplotlib figure and axes.
fig, ax = common.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 = common.get_color(treecolor, tree_type=TreeType.soma)
xs = sm.center[0]
ys = sm.center[1]
zs = sm.center[2]
# Plot the soma as a circle.
fig, ax = common.plot_sphere(fig, ax, center=[xs, ys, zs], radius=sm.radius, 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 common.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
Options:
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 soma. \
Soma : "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.
Returns:
A 3D matplotlib figure with a soma view.
"""
treecolor = kwargs.get("treecolor", None)
# Initialization of matplotlib figure and axes.
fig, ax = common.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 = common.get_color(treecolor, tree_type=TreeType.soma)
xs = sm.center[0]
ys = sm.center[1]
zs = sm.center[2]
# Plot the soma as a circle.
fig, ax = common.plot_sphere(
fig, ax, center=[xs, ys, zs], radius=sm.radius, 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 common.plot_style(fig=fig, ax=ax, **kwargs)
def test_get_figure():
fig_old = plt.figure()
fig, ax = get_figure(new_fig=False, subplot=False)
nt.ok_(fig == fig_old)
nt.ok_(ax.colNum == 0)
nt.ok_(ax.rowNum == 0)
fig1, ax1 = get_figure(new_fig=True, subplot=224)
nt.ok_(fig1 != fig_old)
nt.ok_(ax1.colNum == 1)
nt.ok_(ax1.rowNum == 1)
fig = get_figure(new_fig=True, no_axes=True)
nt.ok_(type(fig) == plt.Figure)
fig2, ax2 = get_figure(new_fig=True, subplot=[1, 1, 1])
nt.ok_(ax2.colNum == 0)
nt.ok_(ax2.rowNum == 0)
plt.close('all')
fig = plt.figure()
ax = fig.add_subplot(111)
fig2, ax2 = get_figure(new_fig=False, new_axes=False)
nt.ok_(fig2 == plt.gcf())
nt.ok_(ax2 == plt.gca())
plt.close('all')
def test_get_figure():
fig_old = plt.figure()
fig, ax = get_figure(new_fig=False, subplot=False)
nt.ok_(fig == fig_old)
nt.ok_(ax.colNum == 0)
nt.ok_(ax.rowNum == 0)
fig1, ax1 = get_figure(new_fig=True, subplot=224)
nt.ok_(fig1 != fig_old)
nt.ok_(ax1.colNum == 1)
nt.ok_(ax1.rowNum == 1)
fig = get_figure(new_fig=True, no_axes=True)
nt.ok_(type(fig) == plt.Figure)
fig2, ax2 = get_figure(new_fig=True, subplot=[1,1,1])
nt.ok_(ax2.colNum == 0)
nt.ok_(ax2.rowNum == 0)
plt.close('all')
fig = plt.figure()
ax = fig.add_subplot(111)
fig2, ax2 = get_figure(new_fig=False, new_axes=False)
nt.ok_(fig2 == plt.gcf())
nt.ok_(ax2 == plt.gca())
plt.close('all')
def test_get_figure():
fig_old = plt.figure()
fig, ax = get_figure(new_fig=False)
nt.eq_(fig, fig_old)
nt.eq_(ax.get_subplotspec().colspan.start, 0)
nt.eq_(ax.get_subplotspec().rowspan.start, 0)
fig1, ax1 = get_figure(new_fig=True, subplot=224)
nt.ok_(fig1 != fig_old)
nt.eq_(ax1.get_subplotspec().colspan.start, 1)
nt.eq_(ax1.get_subplotspec().rowspan.start, 1)
fig2, ax2 = get_figure(new_fig=True, subplot=[1, 1, 1])
nt.eq_(ax2.get_subplotspec().colspan.start, 0)
nt.eq_(ax2.get_subplotspec().rowspan.start, 0)
plt.close('all')
fig = plt.figure()
ax = fig.add_subplot(111)
fig2, ax2 = get_figure(new_fig=False)
nt.eq_(fig2, plt.gcf())
nt.eq_(ax2, plt.gca())
plt.close('all')
def test_get_figure():
fig_old = plt.figure()
fig, ax = get_figure(new_fig=False)
nt.eq_(fig, fig_old)
nt.eq_(ax.colNum, 0)
nt.eq_(ax.rowNum, 0)
fig1, ax1 = get_figure(new_fig=True, subplot=224)
nt.ok_(fig1 != fig_old)
nt.eq_(ax1.colNum, 1)
nt.eq_(ax1.rowNum, 1)
fig2, ax2 = get_figure(new_fig=True, subplot=[1, 1, 1])
nt.eq_(ax2.colNum, 0)
nt.eq_(ax2.rowNum, 0)
plt.close('all')
fig = plt.figure()
ax = fig.add_subplot(111)
fig2, ax2 = get_figure(new_fig=False)
nt.eq_(fig2, plt.gcf())
nt.eq_(ax2, plt.gca())
plt.close('all')
def test_get_figure():
fig_old = plt.figure()
fig, ax = get_figure(new_fig=False)
nt.eq_(fig, fig_old)
nt.eq_(ax.colNum, 0)
nt.eq_(ax.rowNum, 0)
fig1, ax1 = get_figure(new_fig=True, subplot=224)
nt.ok_(fig1 != fig_old)
nt.eq_(ax1.colNum, 1)
nt.eq_(ax1.rowNum, 1)
fig2, ax2 = get_figure(new_fig=True, subplot=[1, 1, 1])
nt.eq_(ax2.colNum, 0)
nt.eq_(ax2.rowNum, 0)
plt.close('all')
fig = plt.figure()
ax = fig.add_subplot(111)
fig2, ax2 = get_figure(new_fig=False)
nt.eq_(fig2, plt.gcf())
nt.eq_(ax2, plt.gca())
plt.close('all')
def test_get_figure():
fig_old = plt.figure()
fig, ax = get_figure(new_fig=False)
assert fig == fig_old
assert ax.get_subplotspec().colspan.start == 0
assert ax.get_subplotspec().rowspan.start == 0
fig1, ax1 = get_figure(new_fig=True, subplot=224)
assert fig1 != fig_old
assert ax1.get_subplotspec().colspan.start == 1
assert ax1.get_subplotspec().rowspan.start == 1
fig2, ax2 = get_figure(new_fig=True, subplot=[1, 1, 1])
assert ax2.get_subplotspec().colspan.start == 0
assert ax2.get_subplotspec().rowspan.start == 0
plt.close('all')
fig = plt.figure()
ax = fig.add_subplot(111)
fig2, ax2 = get_figure(new_fig=False)
assert fig2 == plt.gcf()
assert ax2 == plt.gca()
plt.close('all')
def dendrogram(tree_object,
show_diameters=False,
new_fig=True,
subplot=False,
**kwargs):
'''Generates the deondrogram of the input neurite
Arguments:
tree_object : input tree object
Options:
show_diameters : bool for showing segment diameters
subplot : Default is False, which returns a matplotlib figure object. If True,
returns a matplotlib axis object, for use as a subplot.
Returns:
figure_output : list
[fig|ax, figdata, figtext]
The first item is either a figure object (if subplot is False) or an
axis object. The second item is an object containing the data used to
generate the figure. The final item is text used in report generation
as a figure legend. This text needs to be manually entered in each
figure file.
'''
collection, linked_colors = _generate_segment_collection(
tree_object, show_diameters)
fig, ax = common.get_figure(new_fig=new_fig, subplot=subplot)
ax.add_collection(collection)
ax.autoscale(enable=True, tight=None)
# dummy plots for color bar labels
for color, label in set(linked_colors):
ax.plot((0., 0.), (0., 0.), c=color, label=label)
# customization settings
kwargs['xticks'] = []
kwargs['title'] = kwargs.get('title', 'Morphology Dendrogram')
kwargs['xlabel'] = kwargs.get('xlabel', '')
kwargs['ylabel'] = kwargs.get('ylabel', '')
kwargs['no_legend'] = False
return common.plot_style(fig=fig, ax=ax, **kwargs)
def main(data_dir, mtype_file): # pylint: disable=too-many-locals
'''Run the stuff'''
# data structure to store results
stuff = load_neurite_features(data_dir)
sim_params = json.load(open(mtype_file))
# load histograms, distribution parameter sets and figures into arrays.
# To plot figures, do
# plots[i].fig.show()
# To modify an axis, do
# plots[i].ax.something()
_plots = []
for feat, d in stuff.items():
for typ, data in d.items():
dist = sim_params['components'][typ].get(feat, None)
print('Type = %s, Feature = %s, Distribution = %s' %
(typ, feat, dist))
# if no data available, skip this feature
if not data:
print("No data found for feature %s (%s)" % (feat, typ))
continue
# print 'DATA', data
num_bins = 100
limits = calc_limits(data, dist)
bin_edges = np.linspace(limits[0], limits[1], num_bins + 1)
histo = np.histogram(data, bin_edges, normed=True)
print('PLOT LIMITS:', limits)
# print 'DATA:', data
# print 'BIN HEIGHT', histo[0]
plot = Plot(*view_utils.get_figure(new_fig=True, subplot=111))
view_utils.plot_limits(plot.fig,
plot.ax,
xlim=limits,
no_ylim=True)
plot.ax.bar(histo[1][:-1], histo[0], width=bin_widths(histo[1]))
dp, bc = dist_points(histo[1], dist)
# print 'BIN CENTERS:', bc, len(bc)
if dp is not None:
# print 'DIST POINTS:', dp, len(dp)
plot.ax.plot(bc, dp, 'r*')
plot.ax.set_title('%s (%s)' % (feat, typ))
_plots.append(plot)
return _plots
def plot_neuron_on_density(population, # pylint: disable=too-many-arguments
bins=100, new_fig=True, subplot=111, levels=None, plane='xy',
colorlabel='Nodes per unit area', labelfontsize=16,
color_map='Reds', no_colorbar=False, threshold=0.01,
neurite_type=NeuriteType.basal_dendrite, **kwargs):
'''Plots the 2d histogram of the center
coordinates of segments in the selected plane
and superimposes the view of the first neurite of the collection.
'''
_, ax = common.get_figure(new_fig=new_fig)
view.plot_tree(ax, population.neurites[0])
return plot_density(population, plane=plane, bins=bins, new_fig=False, subplot=subplot,
colorlabel=colorlabel, labelfontsize=labelfontsize, levels=levels,
color_map=color_map, no_colorbar=no_colorbar, threshold=threshold,
neurite_type=neurite_type, **kwargs)
def plot_density(
population, # pylint: disable=too-many-arguments, too-many-locals
bins=100,
new_fig=True,
subplot=111,
levels=None,
plane='xy',
colorlabel='Nodes per unit area',
labelfontsize=16,
color_map='Reds',
no_colorbar=False,
threshold=0.01,
neurite_type=NeuriteType.basal_dendrite,
**kwargs):
"""Plots the 2d histogram of the center
coordinates of segments in the selected plane.
"""
fig, ax = common.get_figure(new_fig=new_fig, subplot=subplot)
H1, xedges1, yedges1 = extract_density(population,
plane=plane,
bins=bins,
neurite_type=neurite_type)
mask = H1 < threshold # mask = H1==0
H2 = np.ma.masked_array(H1, mask)
getattr(plt.cm, color_map).set_bad(color='white', alpha=None)
plots = ax.contourf(
(xedges1[:-1] + xedges1[1:]) / 2,
(yedges1[:-1] + yedges1[1:]) / 2,
np.transpose(H2), # / np.max(H2),
cmap=getattr(plt.cm, color_map),
levels=levels)
if not no_colorbar:
cbar = plt.colorbar(plots)
cbar.ax.set_ylabel(colorlabel, fontsize=labelfontsize)
kwargs['title'] = kwargs.get('title', '')
kwargs['xlabel'] = kwargs.get('xlabel', plane[0])
kwargs['ylabel'] = kwargs.get('ylabel', plane[1])
return common.plot_style(fig=fig, ax=ax, **kwargs)
def dendrogram(tree_object, show_diameters=False, new_fig=True,
subplot=False, **kwargs):
'''Generates the deondrogram of the input neurite
Arguments:
tree_object : input tree object
Options:
show_diameters : bool for showing segment diameters
subplot : Default is False, which returns a matplotlib figure object. If True,
returns a matplotlib axis object, for use as a subplot.
Returns:
figure_output : list
[fig|ax, figdata, figtext]
The first item is either a figure object (if subplot is False) or an
axis object. The second item is an object containing the data used to
generate the figure. The final item is text used in report generation
as a figure legend. This text needs to be manually entered in each
figure file.
'''
collection, linked_colors = _generate_segment_collection(tree_object, show_diameters)
fig, ax = common.get_figure(new_fig=new_fig, subplot=subplot)
ax.add_collection(collection)
ax.autoscale(enable=True, tight=None)
# dummy plots for color bar labels
for color, label in set(linked_colors):
ax.plot((0., 0.), (0., 0.), c=color, label=label)
# customization settings
kwargs['xticks'] = []
kwargs['title'] = kwargs.get('title', 'Morphology Dendrogram')
kwargs['xlabel'] = kwargs.get('xlabel', '')
kwargs['ylabel'] = kwargs.get('ylabel', '')
kwargs['no_legend'] = False
return common.plot_style(fig=fig, ax=ax, **kwargs)
def main(data_dir, mtype_file): # pylint: disable=too-many-locals
'''Run the stuff'''
# data structure to store results
stuff = load_neurite_features(data_dir)
sim_params = json.load(open(mtype_file))
# load histograms, distribution parameter sets and figures into arrays.
# To plot figures, do
# plots[i].fig.show()
# To modify an axis, do
# plots[i].ax.something()
_plots = []
for feat, d in stuff.items():
for typ, data in d.items():
dist = sim_params['components'][typ].get(feat, None)
print('Type = %s, Feature = %s, Distribution = %s' % (typ, feat, dist))
# if no data available, skip this feature
if not data:
print("No data found for feature %s (%s)" % (feat, typ))
continue
# print 'DATA', data
num_bins = 100
limits = calc_limits(data, dist)
bin_edges = np.linspace(limits[0], limits[1], num_bins + 1)
histo = np.histogram(data, bin_edges, normed=True)
print('PLOT LIMITS:', limits)
# print 'DATA:', data
# print 'BIN HEIGHT', histo[0]
plot = Plot(*view_utils.get_figure(new_fig=True, subplot=111))
plot.ax.set_xlim(*limits)
plot.ax.bar(histo[1][:-1], histo[0], width=bin_widths(histo[1]))
dp, bc = dist_points(histo[1], dist)
# print 'BIN CENTERS:', bc, len(bc)
if dp is not None:
# print 'DIST POINTS:', dp, len(dp)
plot.ax.plot(bc, dp, 'r*')
plot.ax.set_title('%s (%s)' % (feat, typ))
_plots.append(plot)
return _plots
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 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_plot_sphere():
fig0, ax0 = get_figure(params={'projection': '3d'})
plot_sphere(ax0, [0, 0, 0], 10., color='black', alpha=1.)
assert ax0.has_data()
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 test_plot_sphere():
fig0, ax0 = get_figure(params={'projection':'3d'})
fig1, ax1 = plot_sphere(fig0, ax0, [0,0,0], 10., color='black', alpha=1.)
nt.ok_(ax1.has_data() == True)
def test_plot_sphere():
fig0, ax0 = get_figure(params={'projection': '3d'})
fig1, ax1 = plot_sphere(fig0, ax0, [0, 0, 0], 10., color='black', alpha=1.)
nt.ok_(ax1.has_data() == True)
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 soma(sm, plane='xy', new_fig=True, subplot=False, **kwargs):
'''
Generates a 2d figure of the soma.
Parameters:
soma: Soma \
neurom.Soma 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 soma. \
Soma: 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.
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 2D matplotlib figure with a soma view, at the selected plane.
'''
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 = common.get_figure(new_fig=new_fig, subplot=subplot)
# Definition of the tree color depending on the tree type.
treecolor = common.get_color(treecolor, tree_type=TreeType.soma)
# Plot the outline of the soma as a circle, is outline is selected.
if not outline:
soma_circle = common.plt.Circle(sm.center, sm.radius, color=treecolor,
alpha=get_default('alpha', **kwargs))
ax.add_artist(soma_circle)
else:
horz = []
vert = []
for s_point in sm.iter():
horz.append(s_point[getattr(COLS, plane[0].capitalize())])
vert.append(s_point[getattr(COLS, plane[1].capitalize())])
horz.append(horz[0]) # To close the loop for a soma viewer. This might be modified!
vert.append(vert[0]) # To close the loop for a soma viewer. This might be modified!
common.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 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 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 soma(sm, plane='xy', new_fig=True, subplot=False, **kwargs):
'''Generates a 2d figure of the soma.
Parameters:
soma: Soma
neurom.Soma 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 soma. \
Soma: 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.
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 2D matplotlib figure with a soma view, at the selected plane.
'''
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 = common.get_figure(new_fig=new_fig, subplot=subplot)
# Definition of the tree color depending on the tree type.
treecolor = common.get_color(treecolor, tree_type=TreeType.soma)
# Plot the outline of the soma as a circle, is outline is selected.
if not outline:
soma_circle = common.plt.Circle(sm.center, sm.radius, color=treecolor,
alpha=get_default('alpha', **kwargs))
ax.add_artist(soma_circle)
else:
horz = []
vert = []
for s_point in sm.iter():
horz.append(s_point[getattr(COLS, plane[0].capitalize())])
vert.append(s_point[getattr(COLS, plane[1].capitalize())])
horz.append(horz[0]) # To close the loop for a soma viewer. This might be modified!
vert.append(vert[0]) # To close the loop for a soma viewer. This might be modified!
common.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 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
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 dendrogram(obj, show_diameters=True, 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:
obj: Neuron or tree \
neurom.Neuron, neurom.Tree
show_diameters : boolean \
Determines if node diameters will \
be show or not.
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.
Returns:
A 2D matplotlib figure with a dendrogram view.
'''
from neurom.analysis.dendrogram import Dendrogram
# create dendrogram and generate rectangle collection
dnd = Dendrogram(obj, show_diameters=show_diameters)
dnd.generate()
fig, ax = common.get_figure(new_fig=new_fig, new_axes=new_axes, subplot=subplot)
# render dendrogram and take into account neurite displacement which
# starts as zero. It is important to avoid overlapping of neurites
# and to determine tha limits of the figure.
displacement = _render_dendrogram(dnd, ax, 0.)
# customization settings
kwargs['xlim'] = [- dnd.dims[0][0] * 0.5, dnd.dims[-1][0] * 0.5 + displacement]
kwargs['title'] = kwargs.get('title', 'Morphology Dendrogram')
kwargs['xlabel'] = kwargs.get('xlabel', 'micrometers (um)')
kwargs['ylabel'] = kwargs.get('ylabel', 'micrometers (um)')
kwargs['no_legend'] = False
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)
