def test_tree_propagate(self):
tree = TestTreeAccumulators.get_tree()
input_array = np.asarray(
((1, 8), (2, 7), (3, 6), (4, 5), (5, 4), (6, 3), (7, 2), (8, 1)),
dtype=np.float64)
condition = np.asarray(
(True, False, True, False, True, True, False, False))
output = hg.propagate_parallel(tree, input_array)
ref = np.asarray(
((6, 3), (6, 3), (7, 2), (7, 2), (7, 2), (8, 1), (8, 1), (8, 1)))
self.assertTrue(np.allclose(ref, output))
output = hg.propagate_parallel(tree, input_array, condition=condition)
ref = np.asarray(
((6, 3), (2, 7), (7, 2), (4, 5), (7, 2), (8, 1), (7, 2), (8, 1)))
self.assertTrue(np.allclose(ref, output))
output2 = hg.propagate_sequential(tree, input_array, condition)
ref2 = np.asarray(
((8, 1), (2, 7), (7, 2), (4, 5), (7, 2), (8, 1), (7, 2), (8, 1)))
self.assertTrue(np.allclose(ref2, output2))
output2 = hg.propagate_sequential_and_accumulate(
tree, input_array, hg.Accumulators.sum)
ref2 = np.asarray(((15, 12), (16, 11), (18, 9), (19, 8), (20, 7),
(14, 4), (15, 3), (8, 1)))
self.assertTrue(np.allclose(ref2, output2))
output2 = hg.propagate_sequential_and_accumulate(
tree, input_array, hg.Accumulators.prod, condition)
ref2 = np.asarray(((48, 24), (2, 7), (21, 12), (4, 5), (35, 8),
(48, 3), (7, 2), (8, 1)))
self.assertTrue(np.allclose(ref2, output2))
def reconstruct_leaf_data(tree, altitudes, deleted_nodes, leaf_graph=None):
"""
Each leaf of the tree takes the altitude of its closest non deleted ancestor.
:param tree: input tree (Concept :class:`~higra.CptHierarchy`)
:param altitudes: node altitudes of the input tree
:param deleted_nodes: binary node weights indicating which nodes are deleted
:param leaf_graph: graph of the tree leaves (optional, deduced from :class:`~higra.CptHierarchy`)
:return: Leaf weights
"""
reconstruction = hg.propagate_sequential(tree, altitudes, deleted_nodes)
leaf_weights = reconstruction[0:tree.num_leaves(), ...]
if leaf_graph is not None:
leaf_weights = hg.delinearize_vertex_weights(leaf_weights, leaf_graph)
return leaf_weights
def reconstruct_leaf_data(tree,
altitudes,
deleted_nodes=None,
leaf_graph=None):
"""
Each leaf of the tree takes the altitude of its closest non deleted ancestor.
The root node is never deleted.
In a component tree, leaves are always deleted.
If :attr:`deleted_nodes` is ``None`` then its default value is set to `np.zeros((tree.numvertices(),)`
(no nodes are deleted).
:param tree: input tree (Concept :class:`~higra.CptHierarchy`)
:param altitudes: node altitudes of the input tree
:param deleted_nodes: binary node weights indicating which nodes are deleted (optional)
:param leaf_graph: graph of the tree leaves (optional, deduced from :class:`~higra.CptHierarchy`)
:return: Leaf weights
"""
if deleted_nodes is None:
if tree.category() == hg.TreeCategory.PartitionTree:
leaf_weights = altitudes[0:tree.num_leaves(), ...]
elif tree.category() == hg.TreeCategory.ComponentTree:
parents = tree.parents()
leaf_weights = altitudes[parents[np.arange(tree.num_leaves())],
...]
else:
if tree.category() == hg.TreeCategory.ComponentTree:
deleted_nodes[:tree.num_leaves()] = True
reconstruction = hg.propagate_sequential(tree, altitudes,
deleted_nodes)
leaf_weights = reconstruction[0:tree.num_leaves(), ...]
if leaf_graph is not None:
leaf_weights = hg.delinearize_vertex_weights(leaf_weights, leaf_graph)
return leaf_weights