Esempio n. 1
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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
Esempio n. 2
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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))
Esempio n. 3
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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
Esempio n. 4
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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))
Esempio n. 5
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 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)