def test_tree():
with get_fig_2d() as (fig, ax):
tree = fst_neuron.neurites[0]
view.plot_tree(ax,
tree,
color='black',
diameter_scale=None,
alpha=1.,
linewidth=1.2)
collection = ax.collections[0]
nt.eq_(collection.get_linewidth()[0], 1.2)
np.testing.assert_allclose(collection.get_color(),
np.array([[0., 0., 0., 1.]]))
with get_fig_2d() as (fig, ax):
nt.assert_raises(AssertionError,
view.plot_tree,
ax,
tree,
plane='wrong')
with get_fig_2d() as (fig, ax):
tree = simple_neuron.neurites[0]
view.plot_tree(ax, tree)
np.testing.assert_allclose(ax.dataLim.bounds, (-5., 0., 11., 5.),
atol=1e-10)
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_tree():
neuron = load_neuron(
os.path.join(SWC_PATH, 'simple-different-section-types.swc'))
expected_colors = {
'black': np.array([[0., 0., 0., 1.] for _ in range(3)]),
None: [[1., 0., 0., 1.], [1., 0., 0., 1.],
[0.501961, 0., 0.501961, 1.]]
}
for input_color, expected_colors in expected_colors.items():
with get_fig_2d() as (fig, ax):
tree = neuron.neurites[0]
view.plot_tree(ax,
tree,
color=input_color,
diameter_scale=None,
alpha=1.,
linewidth=1.2)
collection = ax.collections[0]
eq_(collection.get_linewidth()[0], 1.2)
assert_array_almost_equal(collection.get_colors(), expected_colors)
with get_fig_2d() as (fig, ax):
assert_raises(AssertionError, view.plot_tree, ax, tree, plane='wrong')
with get_fig_2d() as (fig, ax):
tree = simple_neuron.neurites[0]
view.plot_tree(ax, tree)
assert_allclose(ax.dataLim.bounds, (-5., 0., 11., 5.), atol=1e-10)
def test_one_point_branch():
test_section = Section(points=np.array([[1., 1., 1., 0.5, 2, 1, 0]]))
for diameter_scale, linewidth in it.product((1.0, None),
(0.0, 1.2)):
with get_fig_2d() as (fig, ax):
view.plot_tree(ax, test_section, diameter_scale=diameter_scale, linewidth=linewidth)
with get_fig_3d() as (fig, ax):
view.plot_tree3d(ax, test_section, diameter_scale=diameter_scale, linewidth=linewidth)
def test_one_point_branch():
test_section = Neurite(Section(points=np.array([[1., 1., 1., 0.5, 2, 1, 0]])))
for diameter_scale, linewidth in it.product((1.0, None),
(0.0, 1.2)):
with get_fig_2d() as (fig, ax):
view.plot_tree(ax, test_section, diameter_scale=diameter_scale, linewidth=linewidth)
with get_fig_3d() as (fig, ax):
view.plot_tree3d(ax, test_section, diameter_scale=diameter_scale, linewidth=linewidth)
def test_neuron(get_fig_2d):
fig, ax = get_fig_2d
view.plot_neuron(ax, fst_neuron)
assert ax.get_title() == fst_neuron.name
assert_allclose(ax.dataLim.get_points(), [
[-40.32853516, -57.600172],
[64.74726272, 48.51626225],
])
with pytest.raises(AssertionError):
view.plot_tree(ax, fst_neuron, plane='wrong')
def test_tree_diameter_real(get_fig_2d):
fig, ax = get_fig_2d
tree = neuron_different.neurites[0]
for input_color, expected_colors in tree_colors.items():
view.plot_tree(ax,
tree,
color=input_color,
alpha=1.,
linewidth=1.2,
realistic_diameters=True)
collection = ax.collections[0]
assert collection.get_linewidth()[0] == 1.0
assert_array_almost_equal(collection.get_facecolors(), expected_colors)
fig.clear()
def test_tree():
with get_fig_2d() as (fig, ax):
tree = fst_neuron.neurites[0]
view.plot_tree(ax, tree, color='black', diameter_scale=None, alpha=1., linewidth=1.2)
collection = ax.collections[0]
nt.eq_(collection.get_linewidth()[0], 1.2)
np.testing.assert_allclose(collection.get_color(), np.array([[0., 0., 0., 1.]]))
with get_fig_2d() as (fig, ax):
nt.assert_raises(AssertionError, view.plot_tree, ax, tree, plane='wrong')
with get_fig_2d() as (fig, ax):
tree = simple_neuron.neurites[0]
view.plot_tree(ax, tree)
np.testing.assert_allclose(ax.dataLim.bounds, (-5., 0., 11., 5.), atol=1e-10)
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_tree_bounds(get_fig_2d):
fig, ax = get_fig_2d
view.plot_tree(ax, neuron_different.neurites[0])
np.testing.assert_allclose(ax.dataLim.bounds, (-5., 0., 11., 5.))
def test_tree_invalid(get_fig_2d):
fig, ax = get_fig_2d
with pytest.raises(AssertionError):
view.plot_tree(ax, neuron_different.neurites[0], plane='wrong')
