def check_cloned_neuron(nrn1, nrn2):
# check if two neurons are identical
# somata
nt.assert_true(isinstance(nrn2.soma, type(nrn1.soma)))
nt.eq_(nrn1.soma.radius, nrn2.soma.radius)
for v1, v2 in izip(nrn1.soma.iter(), nrn2.soma.iter()):
nt.assert_true(np.allclose(v1, v2))
# neurites
for neu1, neu2 in izip(nrn1.neurites, nrn2.neurites):
nt.assert_true(isinstance(neu2, type(neu1)))
for v1, v2 in izip(val_iter(neu1.ipreorder()), val_iter(neu2.ipreorder())):
nt.assert_true(np.allclose(v1, v2))
# check if the ids are different
# somata
nt.assert_true(nrn1.soma is not nrn2.soma)
# neurites
for neu1, neu2 in izip(nrn1.neurites, nrn2.neurites):
nt.assert_true(neu1 is not neu2)
# check if changes are propagated between neurons
nrn2.soma.radius = 10.0
nt.ok_(nrn1.soma.radius != nrn2.soma.radius)
# neurites
for neu1, neu2 in izip(nrn1.neurites, nrn2.neurites):
for v1, v2 in izip(val_iter(neu1.ipreorder()), val_iter(neu2.ipreorder())):
v2 = np.array([-1000.0, -1000.0, -1000.0, 1000.0, -100.0, -100.0, -100.0])
nt.assert_false(any(v1 == v2))
def compare_trees(tree1, tree2):
'''
Comparison between all the nodes and their respective radii between two trees.
Ids are do not have to be identical between the trees, and swapping is allowed
Returns:
False if the trees are not identical. True otherwise.
'''
leaves1 = list(tr.ileaf(tree1))
leaves2 = list(tr.ileaf(tree2))
if len(leaves1) != len(leaves2):
return False
else:
nleaves = len(leaves1)
for leaf1, leaf2 in product(leaves1, leaves2):
is_equal = True
for node1, node2 in izip(val_iter(tr.iupstream(leaf1)),
val_iter(tr.iupstream(leaf2))):
if any(node1[0:5] != node2[0:5]):
is_equal = False
continue
if is_equal:
nleaves -= 1
return nleaves == 0
def get_bounding_box(tree):
"""
Returns:
The boundaries of the tree in three dimensions:
[[xmin, ymin, zmin],
[xmax, ymax, zmax]]
"""
min_xyz, max_xyz = (np.array([np.inf, np.inf, np.inf]),
np.array([np.NINF, np.NINF, np.NINF]))
for p in val_iter(tree.ipreorder()):
min_xyz = np.minimum(p[:COLS.R], min_xyz)
max_xyz = np.maximum(p[:COLS.R], max_xyz)
return np.array([min_xyz, max_xyz])
def test_branch_order():
branch_order_map = {
(0, 11): 1,
(11, 111, 1111): 2,
(1111, 11111): 3,
(1111, 11112): 3,
(1111, 11113): 3,
(11, 112): 2,
(0, 12): 1,
(12, 121, 1211): 2,
(1211, 12111): 3,
(1211, 12112): 3,
(12, 122): 2
}
for sec in ptr.isection(MOCK_TREE):
nt.assert_equal(mtr.branch_order(sec),
branch_order_map[tuple(p for p in ptr.val_iter(sec))])
