def test_make_neuron_post_tree_action():
def post_action(t):
t.bar = 'foo'
rd = RAW_DATA[0]
nrn = utils.make_neuron(rd, post_action)
for t in nrn.neurites:
nt.ok_(hasattr(t, 'bar') and t.bar == 'foo')
def test_make_neuron_post_tree_action():
def post_action(t):
t.bar = 'foo'
rd = RAW_DATA[0]
nrn = utils.make_neuron(rd, post_action)
for t in nrn.neurites:
nt.ok_(hasattr(t, 'bar') and t.bar == 'foo')
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
'''Calculate inter-segment angles'''
import logging
import numpy as np
from neurom.core.dataformat import COLS
from neurom.io.readers import load_data
from neurom.io.utils import make_neuron
from neurom.analysis.morphtree import i_segment_meander_angle
# root level logger. This would be a top level application logger.
logging.basicConfig()
LOG = logging.getLogger()
LOG.setLevel(logging.DEBUG)
fmt = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
LOG.handlers[0].setFormatter(fmt)
if __name__ == '__main__':
filename = 'test_data/swc/Neuron.swc'
rd = load_data(filename)
nrn = make_neuron(rd)
for tt in nrn.neurites:
print 'Tree ID: {0}, type: {1}'.format(tt.value[COLS.ID],
tt.value[COLS.TYPE])
for a in i_segment_meander_angle(tt):
LOG.debug('Angle %f', a)
if np.isnan(a):
LOG.warn('Found NaN angle. Check for zero length segments!')
from neurom import segments as seg
from neurom import iter_neurites
import math
from itertools import izip
class MockNeuron(object):
pass
DATA_PATH = './test_data'
SWC_PATH = os.path.join(DATA_PATH, 'swc/')
data = io.load_data(SWC_PATH + 'Neuron.swc')
neuron0 = make_neuron(data)
tree0 = neuron0.neurites[0]
def _make_neuron_tree():
p = [0.0, 0.0, 0.0, 1.0, 1, 1, 2]
T = Tree(p)
T1 = T.add_child(Tree([0.0, 1.0, 0.0, 1.0, 1, 1, 2]))
T2 = T1.add_child(Tree([0.0, 2.0, 0.0, 1.0, 1, 1, 2]))
T3 = T2.add_child(Tree([0.0, 4.0, 0.0, 2.0, 1, 1, 2]))
T4 = T3.add_child(Tree([0.0, 5.0, 0.0, 2.0, 1, 1, 2]))
T5 = T4.add_child(Tree([2.0, 5.0, 0.0, 1.0, 1, 1, 2]))
T6 = T4.add_child(Tree([0.0, 5.0, 2.0, 1.0, 1, 1, 2]))
T7 = T5.add_child(Tree([3.0, 5.0, 0.0, 0.75, 1, 1, 2]))
T8 = T7.add_child(Tree([4.0, 5.0, 0.0, 0.75, 1, 1, 2]))
T9 = T6.add_child(Tree([0.0, 5.0, 3.0, 0.75, 1, 1, 2]))
T10 = T9.add_child(Tree([0.0, 6.0, 3.0, 0.75, 1, 1, 2]))
from neurom.analysis.morphtree import n_terminations
from neurom.analysis.morphtree import trunk_radius
from neurom.analysis.morphtree import trunk_length
from neurom.analysis.morphtree import trunk_direction
from neurom.analysis.morphtree import trunk_elevation
from neurom.analysis.morphtree import trunk_azimuth
from neurom.analysis.morphtree import get_bounding_box
import math
import numpy as np
from itertools import izip
DATA_PATH = './test_data'
SWC_PATH = os.path.join(DATA_PATH, 'swc/')
data = load_data(SWC_PATH + 'Neuron.swc')
neuron0 = make_neuron(data)
tree0 = neuron0.neurites[0]
tree_types = [TreeType.axon,
TreeType.basal_dendrite,
TreeType.basal_dendrite,
TreeType.apical_dendrite]
# Mock tree holding integers, not points
MOCK_TREE = Tree(0)
MOCK_TREE.add_child(Tree(11))
MOCK_TREE.add_child(Tree(12))
MOCK_TREE.children[0].add_child(Tree(111))
MOCK_TREE.children[0].add_child(Tree(112))
MOCK_TREE.children[1].add_child(Tree(121))
MOCK_TREE.children[1].add_child(Tree(122))
'''Calculate inter-segment angles'''
import logging
import numpy as np
from neurom.core.dataformat import COLS
from neurom.io.readers import load_data
from neurom.io.utils import make_neuron
from neurom.analysis.morphtree import i_segment_meander_angle
# root level logger. This would be a top level application logger.
logging.basicConfig()
LOG = logging.getLogger()
LOG.setLevel(logging.DEBUG)
fmt = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
LOG.handlers[0].setFormatter(fmt)
if __name__ == '__main__':
filename = 'test_data/swc/Neuron.swc'
rd = load_data(filename)
nrn = make_neuron(rd)
for tt in nrn.neurites:
print 'Tree ID: {0}, type: {1}'.format(tt.value[COLS.ID], tt.value[COLS.TYPE])
for a in i_segment_meander_angle(tt):
LOG.debug('Angle %f', a)
if np.isnan(a):
LOG.warn('Found NaN angle. Check for zero length segments!')
soma = neuron.make_soma([rd.get_row(si) for si in get_soma_ids(rd)])
for tr in trees:
for p in point_iter(tree.ipreorder(tr)):
print p
print 'Initial segment IDs:', init_seg_ids
nrn = neuron.Neuron(soma, trees)
print 'Neuron soma raw data', [r for r in nrn.soma.iter()]
print 'Neuron soma points', [as_point(p) for p in nrn.soma.iter()]
print 'Neuron tree init points, types'
for tt in nrn.neurites:
print tt.value[COLS.ID], tt.value[COLS.TYPE]
print 'Making neuron 2'
nrn2 = make_neuron(rd)
print 'Neuron 2 soma points', [r for r in nrn2.soma.iter()]
print 'Neuron 2 soma points', [as_point(p) for p in nrn2.soma.iter()]
print 'Neuron 2 tree init points, types'
for tt in nrn2.neurites:
print tt.value[COLS.ID], tt.value[COLS.TYPE]
print 'Print neuron leaves as points'
for tt in nrn2.neurites:
for p in point_iter(tree.ileaf(tt)):
print p
def test_make_neuron_no_soma_raises_SomaError():
utils.make_neuron(NO_SOMA_RAW_DATA)
def test_make_neuron():
rd = RAW_DATA[0]
nrn = utils.make_neuron(rd)
nt.ok_(np.all([s[COLS.ID] for s in nrn.soma.iter()] == SOMA_IDS[0]))
_check_trees(nrn.neurites)
for tr in trees:
for p in point_iter(tree.ipreorder(tr)):
LOG.debug(p)
LOG.info('Initial segment IDs: %s', init_seg_ids)
nrn = neuron.Neuron(soma, trees)
LOG.info('Neuron soma raw data % s', [r for r in nrn.soma.iter()])
LOG.info('Neuron soma points %s', [as_point(p)
for p in nrn.soma.iter()])
LOG.info('Neuron tree init points, types')
for tt in nrn.neurites:
LOG.info('%s, %s', tt.value[COLS.ID], tt.value[COLS.TYPE])
LOG.info('Making neuron 2')
nrn2 = make_neuron(rd)
LOG.debug('Neuron 2 soma points %s', [r for r in nrn2.soma.iter()])
LOG.debug('Neuron 2 soma points %s', [as_point(p)
for p in nrn2.soma.iter()])
LOG.info('Neuron 2 tree init points, types')
for tt in nrn2.neurites:
LOG.info('%s %s', tt.value[COLS.ID], tt.value[COLS.TYPE])
LOG.debug('Print neuron leaves as points')
for tt in nrn2.neurites:
for p in point_iter(tree.ileaf(tt)):
LOG.debug(p)
def test_make_neuron_no_soma_raises_SomaError():
utils.make_neuron(NO_SOMA_RAW_DATA)
def test_make_neuron():
rd = RAW_DATA[0]
nrn = utils.make_neuron(rd)
nt.ok_(np.all([s[COLS.ID] for s in nrn.soma.iter()] == SOMA_IDS[0]))
_check_trees(nrn.neurites)
from nose import tools as nt
from neurom.io.utils import make_neuron
from neurom.io.utils import load_neuron
from neurom.io.readers import load_data
from neurom.view import view
from neurom.analysis.morphtree import find_tree_type
import os
import numpy as np
import pylab as plt
DATA_PATH = './test_data'
SWC_PATH = os.path.join(DATA_PATH, 'swc/')
data = load_data(SWC_PATH + 'Neuron.swc')
neuron0 = make_neuron(data, find_tree_type)
soma0 = neuron0.soma
def test_tree():
axes = []
for tree in neuron0.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 = neuron0.neurites[0]
fig, ax = view.tree(tree0, treecolor='black', diameter=False, alpha=1., linewidth=1.2)
c = ax.collections[0]