def rotate(obj, axis, angle, origin=None):
'''
Rotation around unit vector following the right hand rule
Input:
obj : obj to be rotated (e.g. tree, neuron)
axis : unit vector for the axis of rotation
angle : rotation angle in rads
Returns:
A copy of the object with the applied translation.
'''
R = _rodriguesToRotationMatrix(axis, angle)
if isinstance(obj, Tree):
res_tree = make_copy(obj)
_affineTransformTree(res_tree, R, np.zeros(3), origin)
return res_tree
elif isinstance(obj, Neuron):
res_nrn = obj.copy()
_affineTransformNeuron(res_nrn, R, np.zeros(3), origin)
return res_nrn
def test_make_copy():
tree_copy = make_copy(REF_TREE3)
# assert that the two trees have the same values
# first by total nodes
nt.assert_true(len(list(ipreorder(tree_copy))) == len(list(ipreorder(REF_TREE3))))
# then node by node
for val1, val2 in izip(val_iter(ipreorder(tree_copy)), val_iter(ipreorder(REF_TREE3))):
nt.assert_true(all(val1 == val2))
# assert that the tree values do not have the same identity
for val1, val2 in izip(val_iter(ipreorder(tree_copy)), val_iter(ipreorder(REF_TREE3))):
nt.assert_false(val1 is val2)
# create a deepcopy of the original tree for validation
validation_tree = deepcopy(REF_TREE3)
# modify copied tree
tree_copy.value[0:3] = np.array([1000.0, 1000.0, -1000.0])
tree_copy.children[0].add_child(Tree(np.array([0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0])))
# check if anything changed in REF_TREE3 with respect to the validation deepcopy
nt.assert_true(len(list(ipreorder(validation_tree))) == len(list(ipreorder(REF_TREE3))))
for val1, val2 in izip(val_iter(ipreorder(REF_TREE3)), val_iter(ipreorder(validation_tree))):
nt.assert_true(all(val1 == val2))
def translate(obj, t):
'''
Translate object of supported type.
Input :
obj : object with one of the following types:
'TreeType', 'Neuron', 'ezyNeuron'
Returns: copy of the object with the applied translation
'''
if isinstance(obj, Tree):
res_tree = make_copy(obj)
_affineTransformTree(res_tree, np.identity(3), t)
return res_tree
elif isinstance(obj, Neuron):
res_nrn = obj.copy()
_affineTransformNeuron(res_nrn, np.identity(3), t)
return res_nrn
def test_affineTransformTree():
# rotate 180 and translate, translate back and rotate 180
# change origin as well
new_orig = np.array([10. , 45., 50.])
t = np.array([0.1, - 0.1, 40.3])
R = gtr._rodriguesToRotationMatrix(TEST_UVEC, np.pi)
# change origin, rotate 180 and translate
m = make_copy(TREE)
gtr._affineTransformTree(m, R, t, origin=new_orig)
# translate back
gtr._affineTransformTree(m, np.identity(3), -t, origin=np.zeros(3))
# rotate back
gtr._affineTransformTree(m, R, np.zeros(3), origin=new_orig)
_evaluate(TREE, m, lambda x, y: np.allclose(x, y))
def copy(self):
'''Return a copy of the Neuron object.
'''
return Neuron(deepcopy(self.soma),
[make_copy(neu) for neu in self.neurites],
self.name)
