def test_merge_nearby_nodes():
"""
--2 2
/ /
-- /
/ /
1 /
\ /
0-----------5 => 0------------5
/
3
|
4
"""
coords = [[ 0, 0],
[-1, 1],
[ 2, 6],
[-1, -1],
[-1, -2],
[ 5, 0]]
edges = [(0, 1), (1, 2), (0, 3), (3, 4), (0, 5)]
geo = GeoGraph(coords=dict(enumerate(coords)), data=edges)
geo.merge_nearby_nodes(radius=2.0)
assert geo.edges() == [(0, 2), (0, 5)],\
"nodes were not merged correctly"
def build_network(demand_nodes,
existing=None,
min_node_count=2,
single_network=True,
network_algorithm='mod_boruvka',
one_based=False
):
"""
project demand nodes onto optional existing supply network and
return the 'optimized' network
Args:
demand_nodes: GeoGraph of demand nodes
existing: GeoGraph of existing grid (assumes node ids
don't conflict with demand_nodes
min_node_count: minimum number of nodes allowed in a subgraph
of the result
network_algorithm: Algorithm from ALGOS to run
one_based: Whether result GeoGraph's nodes should be one_based
(if not, they are 0 based)
Returns:
msf: GeoGraph of minimum spanning forest proposed by the chosen
network algorithm
existing: The existing grid GeoGraph (None if it doesn't exist)
"""
geo_graph = subgraphs = rtree = None
if existing:
log.info("merging network and nodes")
geo_graph, subgraphs, rtree = \
merge_network_and_nodes(existing, demand_nodes,
single_network=single_network)
else:
geo_graph = demand_nodes
log.info("running {} on {} demand nodes and {} total nodes".format(
network_algorithm, len(demand_nodes), len(geo_graph)))
# now run the selected algorithm
network_algo = NetworkerRunner.ALGOS[network_algorithm]
result_geo_graph = network_algo(geo_graph, subgraphs=subgraphs,
rtree=rtree)
# TODO: Remove unreferenced fake nodes?
# now filter out subnetworks via minimum node count
# TODO: update union_all to support GeoGraph?
filtered_graph = nx.union_all(filter(
lambda sub: len(sub.node) >= min_node_count,
nx.connected_component_subgraphs(result_geo_graph)))
# map coords back to geograph
# NOTE: explicit relabel to int as somewhere in filtering above, some
# node ids are set to numpy types which screws up comparisons to tuples
# in write op
# NOTE: relabeling nodes in-place here drops node attributes for some
# reason so create a copy for now
def id_label(i):
id = int(i+1) if one_based else int(i)
return id
msf = None
if filtered_graph:
coords = {id_label(i): result_geo_graph.coords[i]
for i in filtered_graph}
relabeled = nx.relabel_nodes(filtered_graph, {i: id_label(i)
for i in filtered_graph}, copy=True)
msf = GeoGraph(result_geo_graph.srs, coords=coords, data=relabeled)
log.info("filtered result has {} nodes and {} edges".format(
len(msf.nodes()), len(msf.edges())))
return msf
def build_network(demand_nodes,
existing=None,
min_node_count=2,
single_network=True,
network_algorithm='mod_boruvka',
spherical_accuracy=False,
one_based=False):
"""
project demand nodes onto optional existing supply network and
return the 'optimized' network
Args:
demand_nodes: GeoGraph of demand nodes
existing: GeoGraph of existing grid (assumes node ids
don't conflict with demand_nodes
min_node_count: minimum number of nodes allowed in a subgraph
of the result
network_algorithm: Algorithm from ALGOS to run
spherical_accuracy: Whether to connect nodes to network on a sphere
one_based: Whether result GeoGraph's nodes should be one_based
(if not, they are 0 based)
Returns:
msf: GeoGraph of minimum spanning forest proposed by the chosen
network algorithm
existing: The existing grid GeoGraph (None if it doesn't exist)
"""
geo_graph = subgraphs = rtree = None
if existing:
log.info("merging network and nodes")
geo_graph, subgraphs, rtree = \
merge_network_and_nodes(existing, demand_nodes,
single_network=single_network,
spherical_accuracy=spherical_accuracy)
else:
geo_graph = demand_nodes
log.info("running {} on {} demand nodes and {} total nodes".format(
network_algorithm, len(demand_nodes), len(geo_graph)))
# now run the selected algorithm
network_algo = NetworkerRunner.ALGOS[network_algorithm]
result_geo_graph = network_algo(geo_graph, subgraphs=subgraphs,
rtree=rtree)
filtered_graph = filter_min_node_subnetworks(result_geo_graph,
min_node_count)
# map coords back to geograph
# NOTE: explicit relabel to int as somewhere in filtering above, some
# node ids are set to numpy types which screws up comparisons to tuples
# in write op
# NOTE: relabeling nodes in-place here drops node attributes for some
# reason so create a copy for now
def id_label(i):
id = int(i+1) if one_based else int(i)
return id
msf = GeoGraph(result_geo_graph.srs)
if filtered_graph:
coords = {id_label(i): result_geo_graph.coords[i]
for i in filtered_graph}
relabeled = nx.relabel_nodes(filtered_graph, {i: id_label(i)
for i in filtered_graph},
copy=True)
msf = GeoGraph(result_geo_graph.srs, coords=coords, data=relabeled)
log.info("filtered result has {} nodes and {} edges".format(
len(msf.nodes()), len(msf.edges())))
return msf