def accumulate_on_contours(tree, node_weights, accumulator, leaf_graph):
"""
For each edge of the leaf graph, accumulates the weights of the nodes whose contour pass by this edge.
For any edge :math:`\{x,y\}`, let :math:`R_{\{x,y\}}` be the set of regions of the input tree :math:`T`
having :math:`\{x,y\}` in its contour:
.. math::
R_{\{x,y\}} = \{n \in T \, |\, |\{x,y\} \cap n| = 1 \}
The output value for the edge :math:`\{x,y\}` is then the accumulated weights of the nodes
in :math:`R_{\{x,y\}}`.
:Runtime complexity:
This algorithm runs in :math:`\mathcal{O}(n*k)` with :math:`n` the number of edges in the leaf graph and
:math:`k` the maximal depth of the tree (i.e. the number of edges on the longest downward path between
the root and a leaf).
:param tree: input tree (Concept :class:`~higra.CptHierarchy`)
:param node_weights: weights on the nodes of the tree
:param accumulator: see :class:`~higra.Accumulators`
:param leaf_graph: graph of the tree leaves (deduced from :class:`~higra.CptHierarchy`)
:return: returns leaf graph edge weights
"""
depth = hg.attribute_depth(tree)
res = hg.cpp._accumulate_on_contours(leaf_graph, tree, node_weights, depth,
accumulator)
return res
def test_random_binary_partition_tree_perfectly_unbalanced(self):
size = 32
tree, altitudes = hg.random_binary_partition_tree(size, 1)
depth = hg.attribute_depth(tree)
for i in range(32):
num_nodes = 1 if i == 0 else 2
self.assertTrue(np.sum(depth == i) == num_nodes)
def attribute_regular_altitudes(tree, depth=None):
"""
Regular altitudes is comprised between 0 and 1 and is inversely proportional to the depth of a node
:param tree: input tree
:param depth: depth of the tree node (provided by :func:`~higra.attribute_depth`)
:return: a nd array
"""
if depth is None:
depth = hg.attribute_depth(tree)
altitudes = 1 - depth / np.max(depth)
altitudes[:tree.num_leaves()] = 0
return altitudes
def test_depth(self):
t = hg.Tree((6, 6, 7, 8, 8, 8, 7, 9, 9, 9))
ref = np.asarray((3, 3, 2, 2, 2, 2, 2, 1, 1, 0))
res = hg.attribute_depth(t)
self.assertTrue(np.all(ref == res))
