def load_neuron(input_file,
line_delimiter='\n',
soma_type=None,
tree_types=None,
remove_duplicates=True):
'''
Io method to load an swc or h5 file into a Neuron object.
TODO: Check if tree is connected to soma, otherwise do
not include it in neuron structure and warn the user
that there are disconnected components
'''
if tree_types is not None:
td.update(tree_types)
# Definition of swc types from type_dict function
if soma_type is None:
soma_index = TYPE_DCT['soma']
else:
soma_index = soma_type
# Make neuron with correct filename and load data
if os.path.splitext(input_file)[-1] == '.swc':
data = swc_to_data(
read_swc(input_file=input_file, line_delimiter=line_delimiter))
neuron = Neuron.Neuron(name=input_file.replace('.swc', ''))
elif os.path.splitext(input_file)[-1] == '.h5':
data = read_h5(input_file=input_file,
remove_duplicates=remove_duplicates)
neuron = Neuron.Neuron(name=input_file.replace('.h5', ''))
try:
soma_ids = _np.where(_np.transpose(data)[1] == soma_index)[0]
except IndexError:
raise LoadNeuronError('Soma points not in the expected format')
# Extract soma information from swc
soma = Soma.Soma(x=_np.transpose(data)[SWC_DCT['x']][soma_ids],
y=_np.transpose(data)[SWC_DCT['y']][soma_ids],
z=_np.transpose(data)[SWC_DCT['z']][soma_ids],
d=_np.transpose(data)[SWC_DCT['radius']][soma_ids])
# Save soma in Neuron
neuron.set_soma(soma)
p = _np.array(_np.transpose(data)[6],
dtype=int) - _np.transpose(data)[0][0]
# return p, soma_ids
try:
dA = sp.csr_matrix((_np.ones(len(p) - len(soma_ids)),
(range(len(soma_ids), len(p)), p[len(soma_ids):])),
shape=(len(p), len(p)))
except Exception:
raise LoadNeuronError(
'Cannot create connectivity, nodes not connected correctly.')
# assuming soma points are in the beginning of the file.
comp = cs.connected_components(dA[len(soma_ids):, len(soma_ids):])
# Extract trees
for i in range(comp[0]):
tree_ids = _np.where(comp[1] == i)[0] + len(soma_ids)
tree = make_tree(data[tree_ids])
neuron.append_tree(tree, td=td)
return neuron
def test_neuron_set_soma():
neu1 = Neuron.Neuron()
neu1.set_soma(soma_test)
nt.ok_(neu1.soma.is_equal(soma_test))
def test_neuron_rename():
neu1 = Neuron.Neuron()
neu1.rename('test')
nt.ok_(neu1.name == 'test')
def test_copy_neuron():
neu1 = Neuron.Neuron()
neu2 = neu1.copy_neuron()
nt.ok_(neu1.is_equal(neu2))
nt.ok_(neu1 != neu2)
import numpy as np
from tmd.Neuron import Neuron
from tmd.Soma import Soma
from tmd.Tree import Tree
from tmd.utils import TREE_TYPE_DICT as td
soma_test = Soma.Soma([0.],[0.],[0.],[12.])
soma_test1 = Soma.Soma([0.],[0.],[0.],[6.])
apical_test = Tree.Tree(x=np.array([5., 5.]), y=np.array([6., 6.]), z=np.array([ 7., 7.]),
d=np.array([16., 16.]), t=np.array([4, 4]), p=np.array([-1, 0]))
basal_test = Tree.Tree(x=np.array([4.]), y=np.array([5.]), z=np.array([6.]),
d=np.array([14.]), t=np.array([3]), p=np.array([-1]))
axon_test = Tree.Tree(x=np.array([ 3.]), y=np.array([4.]), z=np.array([5.]),
d=np.array([12.]), t=np.array([2 ]), p=np.array([-1]))
neu_test = Neuron.Neuron()
neu_test.set_soma(soma_test)
neu_test.append_tree(apical_test, td)
def test_neuron_init_():
neu1 = Neuron.Neuron()
nt.ok_(neu1.name == 'Neuron')
nt.ok_(isinstance(neu1.soma, Soma.Soma))
nt.ok_(neu1.axon == [])
nt.ok_(neu1.basal == [])
nt.ok_(neu1.apical == [])
nt.ok_(neu1.neurites == [])
neu1 = Neuron.Neuron(name='test')
nt.ok_(neu1.name == 'test')
def test_neuron_rename():