def plot_multi_neuron(neurons, layout, to_save="", scalebar=(200,"$\mu m$"),fig_size=None):
"""Plot mutiple neurons in array manner.
neurons: neurom.population.Population.
layout: 1x2 tuple. including the row number and the colunm number.
to_save: the file to save as. If left empty, not to save.
fig_size: default: 2x2 inch multiple with layout."""
if fig_size is None:
fig_size=(2*layout[0], 2*layout[1])
fig, axs = plt.subplots(*layout, figsize=fig_size)
if layout[1]==1 and axs.ndim==1:
axs=axs[:,np.newaxis]
elif layout[0]==1 and axs.ndim==1:
axs=axs[np.newaxis,:]
for (ind, ax), neuron in zip(np.ndenumerate(np.array(axs)), neurons):
view.plot_neuron(ax, neuron)
ax.autoscale()
ax.set_title("")
ax.set_xlabel("")
ax.set_ylabel("")
ax.set_axis_off()
left = len(neurons) - layout[0] * layout[1]
if left<0:
for ax in axs.flat[left:]:
ax.set_visible(False)
plot_unified_scale_grid(fig, axs)
add_scalebar(axs.flat[-1], scalebar[0], scalebar[1], fig)
if bool(to_save):
to_save_figure(to_save)
def test_neuron():
with get_fig_2d() as (fig, ax):
view.plot_neuron(ax, fst_neuron)
nt.ok_(ax.get_title() == fst_neuron.name)
np.testing.assert_allclose(ax.dataLim.get_points(),
[[-40.32853516, -57.600172],
[64.74726272, 48.51626225], ])
with get_fig_2d() as (fig, ax):
nt.assert_raises(AssertionError, view.plot_tree, ax, fst_neuron, plane='wrong')
def test_neuron():
with get_fig_2d() as (fig, ax):
view.plot_neuron(ax, fst_neuron)
ok_(ax.get_title() == fst_neuron.name)
assert_allclose(ax.dataLim.get_points(),
[[-40.32853516, -57.600172],
[64.74726272, 48.51626225], ])
with get_fig_2d() as (fig, ax):
assert_raises(AssertionError, view.plot_tree, ax, fst_neuron, plane='wrong')
def plot(neuron, bbox, subplot, title, **kwargs):
'''2D neuron plot'''
ax = plt.subplot(subplot, facecolor='w', aspect='equal')
xlim = (bbox[0][0], bbox[1][0])
ylim = (bbox[0][2], bbox[1][2])
plot_neuron(ax, neuron, **kwargs)
ax.set_title(title)
ax.set_aspect('equal', adjustable='box')
ax.set_xlim(xlim)
ax.set_ylim(ylim)
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 plot_single_neuron(neuron, put_apical_upside=False, to_save=""):
"""Plot a neuron.
neuron: [neurom.fst._core.FstNeuron] The neuron to plot.
put_apical_upside: logical. Whether put apical dendrite upside."""
fig, ax = plt.subplots(subplot_kw={'aspect':'equal'})
if put_apical_upside:
neuron=apical_upside(neuron)
view.plot_neuron(ax, neuron)
ax.autoscale()
ax.set_title("")
ax.set_xlabel(None)
ax.set_ylabel(None)
ax.set_axis_off()
if bool(to_save):
def test_neuron_no_neurites():
filename = os.path.join(SWC_PATH, 'point_soma.swc')
with get_fig_2d() as (fig, ax):
view.plot_neuron(ax, load_neuron(filename))
def test_neuron_no_neurites():
filename = Path(SWC_PATH, 'point_soma.swc')
with get_fig_2d() as (fig, ax):
view.plot_neuron(ax, load_neuron(filename))
=======
>>>>>>> Stashed changes
"""Plot sholl analysis demo figure.
Display the apical and basal part of a neuron, and concentric circles of sholl analysis.
Args:
- neuron: [neurom.fst._core.FstNeuron], a neuron object.
- step_size: [int], the interval radius of the concentric circles.
- label_dict: [dict], a dictionary indicate the position of label.
the keys of dict are the label name, and the values are the (x, y) position.
Default: {'Apical': (x1, y1), 'Basal': (x2, y2)}, where (x1,y1) and (x2,y2) are computed automatically.
"""
neuron = apical_upside(neuron)
fig = plt.figure()
ax = fig.add_subplot(1,1,1, aspect='equal')
view.plot_neuron(ax, neuron)
# draw circles
center = neuron.soma.center[:2]
_dist = np.linalg.norm(neuron.points[:,:2]-center, axis=1).max()
radii = np.arange(step_size, _dist, step_size)
patches = []
for rad in radii:
circle = mpatches.Circle(center, rad, fill=False, edgecolor='dimgray')
patches.append(circle)
p = PatchCollection(patches, match_original=True)
ax.add_collection(p)
# add labels
if label_dict is None:
apical_points = np.concatenate([x.points for x in nm.iter_neurites(neuron, filt=lambda t: t.type==nm.APICAL_DENDRITE)])
def test_neuron_no_neurites(get_fig_2d):
filename = Path(SWC_PATH, 'point_soma.swc')
fig, ax = get_fig_2d
view.plot_neuron(ax, load_neuron(filename))
