def __init__(self, name='Neuron'):
'''Creates an empty Neuron object.
'''
self.soma = Soma.Soma()
self.axon = list()
self.apical = list()
self.basal = list()
self.undefined = list()
self.name = name
def load_neuron(input_file, line_delimiter='\n', soma_type=None,
user_tree_types=None, remove_duplicates=True):
"""
Io method to load an swc or h5 file into a Neuron object.
"""
tree_types = redefine_types(user_tree_types)
# Definition of swc types from type_dict function
if soma_type is None:
soma_index = SOMA_TYPE
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 as exc:
raise LoadNeuronError('Soma points not in the expected format') from exc
# 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 as exc:
raise LoadNeuronError('Cannot create connectivity, nodes not connected correctly.') from exc
# 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, tree_types)
return neuron
POP_PATH = os.path.join(_path, '../../../test_data/valid')
# Filenames for testing
basic_file = os.path.join(DATA_PATH, 'basic.swc')
nosecids_file = os.path.join(DATA_PATH, 'basic_no_sec_ids.swc')
sample_file = os.path.join(DATA_PATH, 'sample.swc')
sample_h5_v1_file = os.path.join(DATA_PATH, 'sample_v1.h5')
sample_h5_v2_file = os.path.join(DATA_PATH, 'sample_v2.h5')
sample_h5_v0_file = os.path.join(DATA_PATH, 'sample_v0.h5')
neuron_v1 = io.load_neuron(sample_h5_v1_file)
neuron_v2 = io.load_neuron(sample_h5_v2_file)
neuron1 = io.load_neuron(sample_file)
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]))
def test_type_dict():
nt.ok_(io.TYPE_DCT == {'soma': 1,
'basal': 3,
'apical': 4,
'axon': 2})
def test_copy_soma():
soma1 = Soma.Soma(x=x1, y=y1, z=z1, d=d1)
soma2 = soma1.copy_soma()
nt.ok_(soma1.is_equal(soma2))
nt.ok_(soma1 != soma2)
def test_soma_is_equal():
soma1 = Soma.Soma(x=x1, y=y1, z=z1, d=d1)
soma2 = Soma.Soma(x=x1, y=y1, z=z1, d=d1)
nt.ok_(soma1.is_equal(soma2))
soma2 = Soma.Soma(x=x2, y=y1, z=z1, d=d1)
nt.ok_(not soma1.is_equal(soma2))
def test_soma_init_():
soma1 = Soma.Soma(x=x1, y=y1, z=z1, d=d1)
nt.ok_(np.allclose(soma1.x, x1))
nt.ok_(np.allclose(soma1.y, y1))
nt.ok_(np.allclose(soma1.z, z1))
nt.ok_(np.allclose(soma1.d, d1))
'''Test tmd.soma'''
from nose import tools as nt
import numpy as np
from tmd.Soma import Soma
soma = Soma.Soma([0.], [0.], [0.], [12.])
soma1 = Soma.Soma([0., 0., 0.], [0., 1., 0.], [0., 0., 1.], [0., 0., 0.])
def test_get_center():
nt.ok_(np.allclose(soma.get_center(), [0., 0., 0.]))
nt.ok_(np.allclose(soma1.get_center(), [0., 1. / 3., 1. / 3.]))
def test_get_diameter():
nt.ok_(soma.get_diameter() == 12.)
nt.ok_(soma1.get_diameter() == 0.65403883526363049)
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
'''
import os
from scipy import sparse as sp
from scipy.sparse import csgraph as cs
from tmd.utils import tree_type as td
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', ''))
soma_ids = _np.where(_np.transpose(data)[1] == soma_index)[0]
# 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
# 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
