def watershed_hierarchy_by_dynamics(graph, edge_weights, canonize_tree=True):
"""
Watershed hierarchy by dynamics.
The definition of hierarchical watershed follows the one given in:
J. Cousty, L. Najman.
`Incremental algorithm for hierarchical minimum spanning forests and saliency of watershed cuts <https://hal-upec-upem.archives-ouvertes.fr/hal-00622505/document>`_.
ISMM 2011: 272-283.
The algorithm used is described in:
Laurent Najman, Jean Cousty, Benjamin Perret.
`Playing with Kruskal: Algorithms for Morphological Trees in Edge-Weighted Graphs <https://hal.archives-ouvertes.fr/file/index/docid/798621/filename/ismm2013-algo.pdf>`_.
ISMM 2013: 135-146.
:param graph: input graph
:param edge_weights: input graph edge weights
:param canonize_tree: if ``True`` (default), the resulting hierarchy is canonized (see function :func:`~higra.canonize_hierarchy`),
otherwise the returned hierarchy is a binary tree
:return: a tree (Concept :class:`~higra.CptHierarchy` is ``True`` and :class:`~higra.CptBinaryHierarchy` otherwise)
and its node altitudes
"""
return watershed_hierarchy_by_attribute(
graph, edge_weights, lambda tree, altitudes: hg.attribute_dynamics(
tree, altitudes, "increasing"), canonize_tree)
def test_attribute_dynamics2(self):
t = hg.Tree((11, 11, 9, 9, 8, 8, 13, 13, 10, 10, 12, 12, 14, 14, 14))
altitudes = np.asarray((0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 4, 8, 10.))
ref = np.asarray((3, 3, 0, 0, 10, 10, 2, 2, 10, 0, 10, 3, 10, 2, 10))
res = hg.attribute_dynamics(t, altitudes)
self.assertTrue(np.all(res == ref))
