############################################################################
# Copyright ESIEE Paris (2018) #
# #
# Contributor(s) : Benjamin Perret #
# #
# Distributed under the terms of the CECILL-B License. #
# #
# The full license is in the file LICENSE, distributed with this software. #
############################################################################
import higra as hg
import numpy as np
[docs]@hg.argument_helper(hg.CptHierarchy)
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