def test_eq(self):
other = ezy.load_neuron(self.filename)
nt.assert_true(self.neuron == other)
def test_construct_neuron_non_consecutive_ids_loads():
filename = os.path.join(SWC_PATH, 'non_sequential_trunk_off_1_16pt.swc')
ezy.load_neuron(filename)
def setUp(self):
self.filename = os.path.join(SWC_PATH, 'Neuron.swc')
#self.filename2 = os.path.join(SWC_PATH, '')
self.neuron = ezy.load_neuron(self.filename)
def test_construct_neuron_no_soma_raises_SomaError():
filename = os.path.join(SWC_PATH, 'Single_apical_no_soma.swc')
ezy.load_neuron(filename)
def test_construct_neuron_invalid_id_sequence_raises():
filename = os.path.join(SWC_PATH, 'non_increasing_trunk_off_1_16pt.swc')
ezy.load_neuron(filename)
def test_construct_neuron():
filename = os.path.join(SWC_PATH, 'Neuron.swc')
ezy.load_neuron(filename)
def test_construct_neuron():
filename = os.path.join(SWC_PATH, 'Neuron.swc')
ezy.load_neuron(filename)
_path = os.path.dirname(os.path.abspath(__file__))
DATA_PATH = os.path.join(_path, '../test_data')
SWC_PATH = os.path.join(DATA_PATH, 'swc')
def random_color():
'''Random color generation'''
return np.random.rand(3, 1)
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()
if __name__ == '__main__':
# define set of files containing relevant neurons
file_nms = [os.path.join(SWC_PATH, file_nm) for file_nm in ['Soma_origin.swc',
'Soma_translated_1.swc',
'Soma_translated_2.swc']]
# load from file and plot
sms = [ezy.load_neuron(file_nm).soma for file_nm in file_nms]
plot_somas(sms)
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
from os.path import join as joinp
from nose import tools as nt
from neurom.core.population import Population
from neurom.ezy import Neuron, load_neuron
_path = os.path.dirname(os.path.abspath(__file__))
DATA_PATH = joinp(_path, "../../../test_data")
NRN1 = load_neuron(joinp(DATA_PATH, "swc/Neuron.swc"))
NRN2 = load_neuron(joinp(DATA_PATH, "swc/Single_basal.swc"))
NRN3 = load_neuron(joinp(DATA_PATH, "swc/Neuron_small_radius.swc"))
NEURONS = [NRN1, NRN2, NRN3]
TOT_NEURITES = sum(N.get_n_neurites() for N in NEURONS)
def test_population():
pop = Population(NEURONS, name="foo")
nt.assert_equal(len(pop.neurons), 3)
nt.ok_(pop.neurons[0].name, "Neuron")
nt.ok_(pop.neurons[1].name, "Single_basal")
nt.ok_(pop.neurons[2].name, "Neuron_small_radius")
def test_eq(self):
other = ezy.load_neuron(self.filename)
nt.assert_true(self.neuron == other)
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()
if __name__ == '__main__':
# define set of files containing relevant neurons
file_nms = [
os.path.join(SWC_PATH, file_nm) for file_nm in
['Soma_origin.swc', 'Soma_translated_1.swc', 'Soma_translated_2.swc']
]
# load from file and plot
sms = [ezy.load_neuron(file_nm).soma for file_nm in file_nms]
plot_somas(sms)
def setUp(self):
self.filename = os.path.join(SWC_PATH, 'Neuron.swc')
#self.filename2 = os.path.join(SWC_PATH, '')
self.neuron = ezy.load_neuron(self.filename)
def test_construct_neuron_non_consecutive_ids_raises_NonConsecutiveIDsError():
filename = os.path.join(SWC_PATH, 'non_sequential_trunk_off_1_16pt.swc')
ezy.load_neuron(filename)
def test_construct_neuron_no_soma_raises_SomaError():
filename = os.path.join(SWC_PATH, 'Single_apical_no_soma.swc')
ezy.load_neuron(filename)
def test_is_flat(): neu_tree = load_neuron(os.path.join(SWC_PATH, 'Neuron.swc')).neurites[0] nt.assert_false(is_flat(neu_tree, 1e-6, method='tolerance')) nt.assert_false(is_flat(neu_tree, 0.1, method='ratio'))
from __future__ import print_function
from itertools import imap
from neurom import ezy
from neurom.core.tree import isection
from neurom.core.tree import ibifurcation_point
from neurom.core.dataformat import COLS
from neurom.analysis import morphmath as mm
from neurom.analysis import morphtree as mt
import numpy as np
if __name__ == '__main__':
filename = 'test_data/swc/Neuron.swc'
# load a neuron from an SWC file
nrn = ezy.load_neuron(filename)
# Some examples of what can be done using iteration
# instead of pre-packaged functions that return lists.
# The iterations give us a lot of flexibility: we can map
# any function that takes a segment or section.
# Get length of all neurites in cell by iterating over sections,
# and summing the section lengths
print('Total neurite length:', sum(nrn.iter_sections(mm.path_distance)))
# Get length of all neurites in cell by iterating over segments,
# and summing the segment lengths.
# This should yield the same result as iterating over sections.
print('Total neurite length:', sum(nrn.iter_segments(mm.segment_length)))
centre_mass = neurite_centre_of_mass(neurite)
sum_sqr_distance = 0
N = 0
for segment in tree.val_iter(tree.isegment(neurite)):
sum_sqr_distance = sum_sqr_distance + distance_sqr(centre_mass, segment)
N += 1
return np.sqrt(sum_sqr_distance / N)
def mean_rad_of_gyration(neurites):
'''Calculate mean radius of gyration for set of neurites.'''
return np.mean([radius_of_gyration(n) for n in neurites])
if __name__ == '__main__':
# load a neuron from an SWC file
filename = 'test_data/swc/Neuron.swc'
nrn = ezy.load_neuron(filename)
# for every neurite, print (number of segments, radius of gyration, neurite type)
print([(sum(1 for _ in tree.isegment(nrte)),
radius_of_gyration(nrte), nrte.type) for nrte in nrn.neurites])
# print mean radius of gyration per neurite type
print('Mean radius of gyration for axons: ',
mean_rad_of_gyration(n for n in nrn.neurites if n.type == ezy.TreeType.axon))
print('Mean radius of gyration for basal dendrites: ',
mean_rad_of_gyration(n for n in nrn.neurites if n.type == ezy.TreeType.basal_dendrite))
print('Mean radius of gyration for apical dendrites: ',
mean_rad_of_gyration(n for n in nrn.neurites if n.type == ezy.TreeType.apical_dendrite))
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from nose import tools as nt
from neurom.core.population import Population
from neurom.ezy import Neuron, load_neuron
from neurom.analysis.morphtree import i_section_length
from neurom.analysis.morphtree import i_segment_length
NRN1 = load_neuron('test_data/swc/Neuron.swc')
NRN2 = load_neuron('test_data/swc/Single_basal.swc')
NRN3 = load_neuron('test_data/swc/Neuron_small_radius.swc')
NEURONS = [NRN1, NRN2, NRN3]
TOT_NEURITES = sum(N.get_n_neurites() for N in NEURONS)
def test_population():
pop = Population(NEURONS, name='foo')
nt.assert_equal(len(pop.neurons), 3)
nt.ok_(pop.neurons[0].name, 'Neuron')
nt.ok_(pop.neurons[1].name, 'Single_basal')
nt.ok_(pop.neurons[2].name, 'Neuron_small_radius')
nt.assert_equal(len(pop.somata), 3)
