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.
'''
view.tree(population.neurites[0], new_fig=new_fig)
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_one_point_branch_without_diameter():
test_tree = PointTree(np.array([1., 1., 1., 0.5, 2, 1, 0]))
try:
view.tree(test_tree, diameter=False)
nt.ok_(True)
except:
nt.ok_(False)
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.
'''
view.tree(population.neurites[0], new_fig=new_fig)
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_tree():
axes = []
for tree in pt_neuron.neurites:
fig, ax = view.tree(tree)
axes.append(ax)
nt.ok_(axes[0].get_data_ratio() > 1.00 )
nt.ok_(axes[1].get_data_ratio() > 0.80 )
nt.ok_(axes[2].get_data_ratio() > 1.00 )
nt.ok_(axes[3].get_data_ratio() > 0.85 )
tree0 = pt_neuron.neurites[0]
fig, ax = view.tree(tree0, treecolor='black', diameter=False, alpha=1., linewidth=1.2)
c = ax.collections[0]
nt.ok_(c.get_linewidth()[0] == 1.2 )
nt.ok_(np.allclose(c.get_color(), np.array([[ 0., 0., 0., 1.]])) )
fig, ax = view.tree(tree0, plane='wrong')
nt.ok_(ax == 'No such plane found! Please select one of: xy, xz, yx, yz, zx, zy.')
plt.close('all')
def test_tree():
axes = []
for tree in fst_neuron.neurites:
fig, ax = view.tree(tree)
axes.append(ax)
nt.ok_(axes[0].get_data_ratio() > 1.00 )
nt.ok_(axes[1].get_data_ratio() > 0.80 )
nt.ok_(axes[2].get_data_ratio() > 1.00 )
nt.ok_(axes[3].get_data_ratio() > 0.85 )
tree0 = fst_neuron.neurites[0]
fig, ax = view.tree(tree0, treecolor='black', diameter=False, alpha=1., linewidth=1.2)
c = ax.collections[0]
nt.eq_(c.get_linewidth()[0], 1.2)
nt.ok_(np.allclose(c.get_color(), np.array([[ 0., 0., 0., 1.]])))
fig, ax = view.tree(tree0, plane='wrong')
nt.ok_(ax == 'No such plane found! Please select one of: xy, xz, yx, yz, zx, zy.')
plt.close('all')
def test_one_point_branch():
test_section = Section(points=np.array([[1., 1., 1., 0.5, 2, 1, 0]]))
for diameter, linewidth in it.product((True, False),
(0.0, 1.2)):
view.tree(test_section, diameter=diameter, linewidth=linewidth)
view.tree3d(test_section, diameter=diameter, linewidth=linewidth)
