def _integration_tree():
xyzdtp = np.array([
[ 5., -2., 5., 0.2, 3., -1.],
[ 11., -5., 12., 0.2, 3., 0.],
[ 18., -8., 19., 0.4, 3., 1.],
[ 24., -11., 26., 0.2, 3., 2.],
[ 31., -14., 33., 0.4, 3., 3.],
[ 38., -16., 40., 0.6, 3., 4.],
[ 45., -18., 47., 0.8, 3., 5.],
[ 52., -19., 53., 0.6, 3., 6.],
[ 37., -17., 39., 0.4, 3., 4.],
[ 44., -20., 46., 0.2, 3., 8.],
[ 50., -23., 53., 0.2, 3., 9.],
[ 57., -26., 60., 0.4, 3., 10.],
[ 63., -29., 67., 0.6, 3., 11.],
[ 70., -32., 74., 0.8, 3., 12.],
[ 24., -10., 26., 1.0, 3., 2.],
[ 31., -12., 33., 0.8, 3., 14.],
[ 39., -13., 40., 0.6, 3., 15.]])
return Tree.Tree(
x=xyzdtp[:, 0], y=xyzdtp[:, 1], z=xyzdtp[:, 2],
d=xyzdtp[:, 3], t=xyzdtp[:, 4].astype(int), p=xyzdtp[:, 5].astype(int)
)
def test_tree_init_():
tree1 = Tree.Tree(x=x1, y=y1, z=z1, d=d1, t=t1, p=p1)
nt.ok_(np.allclose(tree1.x, x1))
nt.ok_(np.allclose(tree1.y, y1))
nt.ok_(np.allclose(tree1.z, z1))
nt.ok_(np.allclose(tree1.d, d1))
nt.ok_(np.allclose(tree1.t, t1))
nt.ok_(np.allclose(tree1.p, p1))
def make_tree(data):
'''Make tree structure from loaded data.
Returns a tree of tmd.Tree
type.
'''
tr_data = _np.transpose(data)
parents = [_np.where(tr_data[0] == i)[0][0]
if len(_np.where(tr_data[0] == i)[0]) > 0
else - 1 for i in tr_data[6]]
return Tree.Tree(x=tr_data[SWC_DCT['x']], y=tr_data[SWC_DCT['y']], z=tr_data[SWC_DCT['z']],
d=tr_data[SWC_DCT['radius']], t=tr_data[SWC_DCT['type']], p=parents)
def test_parents_children():
tree = Tree.Tree(x=np.zeros(5),
y=np.zeros(5),
z=np.zeros(5),
d=np.zeros(5),
t=np.zeros(5),
p=np.array([-1, 0, 1, 2, 2]))
parents, children = tree.parents_children
assert parents == {0: -1, 2: 0, 3: 2, 4: 2}
expected_children = {2: [3, 4]}
for key, values in expected_children.items():
npt.assert_array_equal(children[key], values)
# 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_load_swc_neuron():
neuron = io.load_neuron(basic_file)
def test_copy_tree():
tree1 = Tree.Tree(x=x1, y=y1, z=z1, d=d1, t=t1, p=p1)
tree2 = tree1.copy_tree()
nt.ok_(tree1.is_equal(tree2))
nt.ok_(tree1 != tree2)
def test_tree_is_equal():
tree1 = Tree.Tree(x=x1, y=y1, z=z1, d=d1, t=t1, p=p1)
tree2 = Tree.Tree(x=x1, y=y1, z=z1, d=d1, t=t1, p=p1)
nt.ok_(tree1.is_equal(tree2))
tree3 = Tree.Tree(x=x2, y=y1, z=z1, d=d1, t=t1, p=p1)
nt.ok_(not tree1.is_equal(tree3))
])
secs_h5_end_points = np.array([
16, 17, 21, 30, 52, 78, 86, 91, 190, 196, 219, 222, 230, 249, 256, 301,
330, 334, 385, 406, 409, 454, 482, 494, 508, 519, 522, 612, 640, 645, 678,
682, 684, 710, 730, 738, 772, 795, 804, 828, 829, 832, 838
])
x1 = np.array([0., 1., 2., 3., 4.])
y1 = np.array([0., 2., 3., 4., 5.])
z1 = np.array([0., 3., 4., 5., 6.])
d1 = np.array([2., 4., 6., 8., 10.])
t1 = np.array([2, 2, 2, 2, 2])
p1 = np.array([-1, 0, 1, 2, 1])
tree = Tree.Tree(x=x1, y=y1, z=z1, d=d1, t=t1, p=p1)
def test_rd():
nt.ok_(methods._rd([0, 0], [0, 1]) == 1.)
nt.ok_(methods._rd([0, 0, 0], [0, 0, 1]) == 1.)
nt.ok_(methods._rd([1, 2, 0], [0, 2, 1]) == np.sqrt(2.))
# def test_rd_w():
# nt.ok_(methods._rd_w([0,0], [0,1], w=[0., 1.]) == 1.)
# nt.ok_(methods._rd_w([0,0], [1,1], w=[0., 2.], normed=False) == 2.)
# nt.ok_(methods._rd_w([0,0], [1,1], w=[0., 0.], normed=False) == 0.)
# nt.ok_(methods._rd_w([1,2,0], [0,2,1], normed=False) == methods._rd([1,2,0], [0,2,1]))
DATA_PATH = os.path.join(_path, '../../../test_data')
sample_ph_0 = os.path.join(DATA_PATH, 'sample_ph_0.txt')
sample_ph_1 = os.path.join(DATA_PATH, 'sample_ph_1.txt')
x1 = np.array([0., 0., 0., 1., 1.])
y1 = np.array([0., 1., -1., 1., 0.])
z1 = np.array([0., 0., 0, 0., -1.])
d1 = np.array([2., 2., 2., 2., 2.])
t1 = np.array([ 1, 1, 1, 1, 1])
p1 = np.array([-1, 0, 1, 2, 3])
x2 = np.array([0., 3., 4., 5., 4.])
p2 = np.array([-1, 0, 1, 1, 1])
tree = Tree.Tree(x=x1, y=y1, z=z1, d=d1, t=t1, p=p1)
tree_trifork = Tree.Tree(x=x1, y=y1, z=z1, d=d1, t=t1, p=p2)
# ===================================== tree0 =======================================
x2 = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., -1., -2.,
-3., -4., -5., 1., 2., 3., 4., 5., 5., 5., 5., 5., 5.,
5., 5., 5., 5., 5.])
y2 = np.array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 10., 10.,
10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.,
10., 10., 10., 10., 10.])
z2 = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 1., 2., 3., 4., 5.,
-1., -2., -3., -4., -5.])
d2 = np.array([0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
def convert_morphio_trees(morphio_neuron):
"""Convert morphio morphology's trees to tmd ones
Args:
morphio_neuron (Union[morphio.Morphology, morphio.mut.Morphology]):
morphio neuron object
Yields:
tuple:
- tree (Tree): The constructed tmd Tree
- tree_types (dict): The neuron tree types
"""
total_points = len(morphio_neuron.diameters)
x = np.empty(total_points, dtype=np.float32)
y = np.empty(total_points, dtype=np.float32)
z = np.empty(total_points, dtype=np.float32)
d = np.empty(total_points, dtype=np.float32)
t = np.empty(total_points, dtype=np.int32)
p = np.empty(total_points, dtype=np.int64)
section_final_nodes = np.empty(total_points, dtype=np.int)
tree_end = 0
for root in morphio_neuron.root_sections:
tree_length = 0
tree_beg = tree_end
for section in root.iter():
# root sections have parent -1
if section.is_root:
start = 0
parent = -1
else:
# tmd does not use a duplicate point representation
# thus the first point of the section is dropped
start = 1
parent = section_final_nodes[section.parent.id]
n, data = _section_to_data(section, tree_length, start, parent)
x[tree_end:n + tree_end] = data.points[:, 0]
y[tree_end:n + tree_end] = data.points[:, 1]
z[tree_end:n + tree_end] = data.points[:, 2]
d[tree_end:n + tree_end] = data.diameters
t[tree_end:n + tree_end] = data.section_type
p[tree_end:n + tree_end] = data.parents
tree_end += n
tree_length += n
# keep track of the last node in the section because we need
# to establish the correct connectivity when we ommit the first
# point from the children sections
section_final_nodes[section.id] = tree_length - 1
yield Tree.Tree(x=x[tree_beg:tree_end],
y=y[tree_beg:tree_end],
z=z[tree_beg:tree_end],
d=d[tree_beg:tree_end],
t=t[tree_beg:tree_end],
p=p[tree_beg:tree_end])