Python tree.val_iter函数代码示例

def _evaluate(tr1, tr2, comp_func):

    for v1, v2 in izip(val_iter(ipreorder(tr1)), val_iter(ipreorder(tr2))):
        #print "v1 : ", v1[:COLS.R]
        #print "v2 : ", v2[:COLS.R]
        #print "-" * 10
        nt.assert_true(comp_func(v1[:COLS.R], v2[:COLS.R]))

    def test_iter_points(self):
        ref_point_radii = []
        for t in self.neuron.neurites:
            ref_point_radii.extend(p[COLS.R] for p in val_iter(ipreorder(t)))

        rads = [r for r in self.neuron.iter_points(lambda p: p[COLS.R])]
        nt.assert_true(np.all(ref_point_radii == rads))

def point_iter(iterator):
    '''Transform tree iterator into a point iterator

    Args:
        iterator: tree iterator for a tree holding raw data rows.
    '''
    return imap(as_point, tree.val_iter(iterator))

def test_copy():

    soma = neuron.make_soma([[0, 0, 0, 1, 1, 1, -1]])   
    nrn1 = neuron.Neuron(soma, [TREE], name="Rabbit of Caerbannog")
    nrn2 = nrn1.copy()

    # check if two neurons are identical

    # somata
    nt.assert_true(isinstance(nrn2.soma, type(nrn1.soma)))
    nt.assert_true(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(ipreorder(neu1)), val_iter(ipreorder(neu2))):

            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.

    nt.assert_false(nrn1.soma.radius == nrn2.soma.radius)
    # neurites
    for neu1, neu2 in izip(nrn1.neurites, nrn2.neurites):

        for v1, v2 in izip(val_iter(ipreorder(neu1)), val_iter(ipreorder(neu2))):

            v2 = np.array([-1000., -1000., -1000., 1000., -100., -100., -100.])
            nt.assert_false(any(v1 == v2))

def test_segment_iteration():

    nt.assert_equal(list(val_iter(isegment(REF_TREE))),
           [(0, 11),(11, 111),(11, 112),
            (0, 12),(12, 121),(121,1211),
            (1211,12111),(1211,12112),(12, 122)])

    nt.assert_equal(list(val_iter(isegment(REF_TREE.children[0]))),
           [(0, 11), (11, 111),(11, 112)])

    nt.assert_equal(list(val_iter(isegment(REF_TREE.children[0].children[0]))),
                    [(11, 111)])

    nt.assert_equal(list(val_iter(isegment(REF_TREE.children[0].children[1]))),
                    [(11, 112)])

    nt.assert_equal(list(val_iter(isegment(REF_TREE.children[1]))),
           [(0, 12), (12, 121), (121, 1211),
            (1211, 12111), (1211, 12112), (12, 122)])

    nt.assert_equal(list(val_iter(isegment(REF_TREE.children[1].children[0]))),
                    [(12, 121), (121, 1211), (1211, 12111), (1211, 12112)])

    nt.assert_equal(list(val_iter(isegment(REF_TREE.children[1].children[1]))),
                    [(12, 122)])

def calculate_and_plot_end_to_end_distance(path):
    '''Calculate and plot the end-to-end distance vs the number of segments for
    an increasingly larger part of a given path.

    Note that the plots are not very meaningful for bifurcating trees.'''
    end_to_end_distance = [morphmath.point_dist(segment[1], path.value)
                           for segment in tree.val_iter(tree.isegment(path))]
    make_end_to_end_distance_plot(np.arange(len(end_to_end_distance)) + 1, end_to_end_distance,
                                  path.type)

def test_ibifurcation_point_upstream():
    leaves = [l for l in ileaf(REF_TREE2)]
    ref_paths = [
        [11, 0], [11, 0], [11, 0], [11, 0],
        [1211, 12, 0], [1211, 12, 0], [12, 0]
    ]

    for l, ref in zip(leaves, ref_paths):
        nt.assert_equal([s for s in val_iter(ibifurcation_point(l, iupstream))], ref)

def find_tree_type(tree):

    """
    Calculates the 'mean' type of the tree.
    Accepted tree types are:
    'undefined', 'axon', 'basal', 'apical'
    The 'mean' tree type is defined as the type
    that is shared between at least 51% of the tree's points.
    Returns:
        The type of the tree
    """

    tree_types = tuple(TreeType)

    types = [node[COLS.TYPE] for node in tr.val_iter(tr.ipreorder(tree))]
    types = [node[COLS.TYPE] for node in tr.val_iter(tr.ipreorder(tree))]

    return tree_types[int(np.median(types))]

def neurite_centre_of_mass(neurite):
    '''Calculate and return centre of mass of a neurite.'''
    centre_of_mass = np.zeros(3)
    total_volume = 0
    for segment in tree.val_iter(tree.isegment(neurite)):
        seg_volume = morphmath.segment_volume(segment)
        centre_of_mass = centre_of_mass + seg_volume * segment_centre_of_mass(segment)
        total_volume += seg_volume
    return centre_of_mass / total_volume

def radius_of_gyration(neurite):
    '''Calculate and return radius of gyration of a given neurite.'''
    centre_mass = neurite_centre_of_mass(neurite)
    sum_sqr_distance = 0
    N = 0
    for segment in tree.val_iter(tree.isegment(neurite)):
        sum_sqr_distance = sum_sqr_distance + distance_sqr(centre_mass, segment)
        N += 1
    return np.sqrt(sum_sqr_distance / N)

def test_leaf_iteration():
    nt.ok_(list(val_iter(ileaf(REF_TREE))) == [111, 112, 12111, 12112, 122])
    nt.ok_(list(val_iter(ileaf(REF_TREE.children[0]))) == [111, 112])
    nt.ok_(list(val_iter(ileaf(REF_TREE.children[1]))) == [12111, 12112, 122])
    nt.ok_(list(val_iter(ileaf(REF_TREE.children[0].children[0]))) == [111])
    nt.ok_(list(val_iter(ileaf(REF_TREE.children[0].children[1]))) == [112])
    nt.ok_(list(val_iter(ileaf(REF_TREE.children[1].children[0]))) == [12111, 12112])
    nt.ok_(list(val_iter(ileaf(REF_TREE.children[1].children[1]))) == [122])

def _generate_dendro(current_node, lines, colors, n, max_dims,
                     spacing, offsets, show_diameters=True):
    '''Recursive function for dendrogram line computations
    '''

    start_x = _spacingx(current_node, max_dims, offsets, spacing)

    radii = [0., 0.]
    # store the parent radius in order to construct polygonal segments
    # isntead of simple line segments
    radii[0] = current_node.value[3] if show_diameters else 0.

    for child in current_node.children:

        # segment length
        length = segment_length(list(val_iter((current_node, child))))

        # extract the radius of the child node. Need both radius for
        # realistic segment representation
        radii[1] = child.value[3] if show_diameters else 0.

        # number of leaves in child
        terminations = n_terminations(child)

        # horizontal spacing with respect to the number of
        # terminations
        new_offsets = (start_x + spacing[0] * terminations / 2.,
                       offsets[1] + spacing[1] * 2. + length)

        # vertical segment
        lines[n[0]] = _vertical_segment(offsets, new_offsets, spacing, radii)

        # assign segment id to color array
        colors[n[0]] = child.value[4]
        n[0] += 1

        if offsets[1] + spacing[1] * 2 + length > max_dims[1]:
            max_dims[1] = offsets[1] + spacing[1] * 2. + length

        # recursive call to self.
        _generate_dendro(child, lines, colors, n, max_dims,
                         spacing, new_offsets, show_diameters=show_diameters)

        # update the starting position for the next child
        start_x += terminations * spacing[0]

        # write the horizontal lines only for bifurcations, where the are actual horizontal lines
        # and not zero ones
        if offsets[0] != new_offsets[0]:

            # horizontal segment
            lines[n[0]] = _horizontal_segment(offsets, new_offsets, spacing, 0.)
            colors[n[0]] = current_node.value[4]
            n[0] += 1

def nonzero_neurite_radii(neuron, threshold=0.0):
    '''Check presence of neurite points with radius not above threshold

    Arguments:
        neuron: Neuron object whose segments will be tested
        threshold: value above which a radius is considered to be non-zero
    Return: list of IDs of zero-radius points
    '''

    ids = [[i[COLS.ID] for i in val_iter(ipreorder(t))
            if i[COLS.R] <= threshold] for t in neuron.neurites]
    return [i for i in chain(*ids)]

def nonzero_segment_lengths(neuron, threshold=0.0):
    '''Check presence of neuron segments with length not above threshold

    Arguments:
        neuron: Neuron object whose segments will be tested
        threshold: value above which a segment length is considered to be non-zero
    Return: list of (first_id, second_id) of zero length segments
    '''
    l = [[s for s in val_iter(isegment(t))
          if segment_length(s) <= threshold]
         for t in neuron.neurites]
    return [(i[0][COLS.ID], i[1][COLS.ID]) for i in chain(*l)]

def nonzero_section_lengths(neuron, threshold=0.0):
    '''Check presence of neuron sections with length not above threshold

    Arguments:
        neuron: Neuron object whose segments will be tested
        threshold: value above which a section length is considered to be non-zero
    Return: list of ids of first point in bad sections
    '''
    l = [[s for s in val_iter(isection(t))
          if section_length(s) <= threshold]
         for t in neuron.neurites]
    return [i[0][COLS.ID] for i in chain(*l)]