def simple_nodes_disjoint_grid():
"""
return disjoint net plus nodes with fakes
fakes are associated with disjoint subnets
nodes by id (budget in parens)
(5) 0-------1 (5)
| |
+-+-+ +-+-+ <-- disjoint existing grid
Useful for testing treating existing grid as single grid
vs disjoint
"""
# setup grid
grid_coords = np.array([[-1.0, 0.0], [1.0, 0.0], [3.0, 0.0], [5.0, 0.0]])
grid = GeoGraph(gm.PROJ4_FLAT_EARTH,
{'grid-' + str(n): c
for n, c in enumerate(grid_coords)})
nx.set_node_attributes(grid, 'budget', {n: 0 for n in grid.nodes()})
grid.add_edges_from([('grid-0', 'grid-1'), ('grid-2', 'grid-3')])
# setup input nodes
node_coords = np.array([[0.0, 1.0], [4.0, 1.0]])
nodes = GeoGraph(gm.PROJ4_FLAT_EARTH, dict(enumerate(node_coords)))
budget_values = [5, 5]
nx.set_node_attributes(nodes, 'budget', dict(enumerate(budget_values)))
fakes = [2, 3]
return grid, nodes, fakes
def simple_nodes_disjoint_grid():
"""
return disjoint net plus nodes with fakes
fakes are associated with disjoint subnets
nodes by id (budget in parens)
(5) 0-------1 (5)
| |
+-+-+ +-+-+ <-- disjoint existing grid
Useful for testing treating existing grid as single grid
vs disjoint
"""
# setup grid
grid_coords = np.array([[-1.0, 0.0], [1.0, 0.0], [3.0, 0.0], [5.0, 0.0]])
grid = GeoGraph(gm.PROJ4_FLAT_EARTH, {'grid-' + str(n): c for n, c in
enumerate(grid_coords)})
nx.set_node_attributes(grid, 'budget', {n: 0 for n in grid.nodes()})
grid.add_edges_from([('grid-0', 'grid-1'), ('grid-2', 'grid-3')])
# setup input nodes
node_coords = np.array([[0.0, 1.0], [4.0, 1.0]])
nodes = GeoGraph(gm.PROJ4_FLAT_EARTH, dict(enumerate(node_coords)))
budget_values = [5, 5]
nx.set_node_attributes(nodes, 'budget', dict(enumerate(budget_values)))
fakes = [2, 3]
return grid, nodes, fakes
def nodes_plus_existing_grid():
"""
return net plus existing grid with certain properties for testing
nodes by id (budget in parens)
1 (3)
\
\
0 (2)
| 2 (5)
| |
+-+-+-+-+-+-+-+-+-+-+ <-- existing grid
"""
# setup grid
grid_coords = np.array([[-5.0, 0.0], [5.0, 0.0]])
grid = GeoGraph(gm.PROJ4_FLAT_EARTH,
{'grid-' + str(n): c
for n, c in enumerate(grid_coords)})
nx.set_node_attributes(grid, 'budget', {n: 0 for n in grid.nodes()})
grid.add_edges_from([('grid-0', 'grid-1')])
# setup input nodes
node_coords = np.array([[0.0, 2.0], [-1.0, 4.0], [4.0, 1.0]])
nodes = GeoGraph(gm.PROJ4_FLAT_EARTH, dict(enumerate(node_coords)))
budget_values = [2, 3, 5]
nx.set_node_attributes(nodes, 'budget', dict(enumerate(budget_values)))
# setup resulting edges when creating msf through the sequence of nodes
# Note: Fake nodes integer label begins at the total number of nodes + 1
# Hence why the fake node in the test is incremented by one on each
# iteration
edges_at_iteration = [
[(0, 1)], # 0 connects to fake_node
[(0, 2)], # 0, 1 can't connect
[(0, 3), (2, 5), (1, 0)]
] # 2 connects grid
return grid, nodes, edges_at_iteration
def nodes_plus_existing_grid():
"""
return net plus existing grid with certain properties for testing
nodes by id (budget in parens)
1 (3)
\
\
0 (2)
| 2 (5)
| |
+-+-+-+-+-+-+-+-+-+-+ <-- existing grid
"""
# setup grid
grid_coords = np.array([[-5.0, 0.0], [5.0, 0.0]])
grid = GeoGraph(gm.PROJ4_FLAT_EARTH, {'grid-' + str(n): c for n, c in
enumerate(grid_coords)})
nx.set_node_attributes(grid, 'budget', {n: 0 for n in grid.nodes()})
grid.add_edges_from([('grid-0', 'grid-1')])
# setup input nodes
node_coords = np.array([[0.0, 2.0], [-1.0, 4.0], [4.0, 1.0]])
nodes = GeoGraph(gm.PROJ4_FLAT_EARTH, dict(enumerate(node_coords)))
budget_values = [2, 3, 5]
nx.set_node_attributes(nodes, 'budget', dict(enumerate(budget_values)))
# setup resulting edges when creating msf through the sequence of nodes
# Note: Fake nodes integer label begins at the total number of nodes + 1
# Hence why the fake node in the test is incremented by one on each
# iteration
edges_at_iteration = [[(0, 1)], # 0 connects to fake_node
[(0, 2)], # 0, 1 can't connect
[(0, 3), (2, 5), (1, 0)]] # 2 connects grid
return grid, nodes, edges_at_iteration
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
def nodes_plus_grid():
"""
Return: nodes as graph and grid as UnionFind/Rtree combo
This example input demonstrates the "more" optimal nature
of mod_boruvka vs mod_kruskal.
2(10)
|
1(4) |
sqrt(5){ /| |
/ | |
/ | }3 | } 5
(2)0 | |
1{| | |
+-+-3-+-4-+-+-+-+-+-+-+5-+-+-+ <-- existing grid
In this case, all nodes will be connected via either algorithm,
but the graph produced by mod_kruskal will have edge (4,1) whereas
mod_boruvka will produce a graph with edge (0,1).
Therefore, the mod_boruvka graph is more optimal.
"""
mv_max_values = [2, 4, 10]
coords = np.array([[0.0, 1.0], [1.0, 3.0], [10.0, 5.0]])
coords_dict = dict(enumerate(coords))
nodes = GeoGraph(gm.PROJ4_FLAT_EARTH, coords=coords_dict)
nx.set_node_attributes(nodes, 'budget', dict(enumerate(mv_max_values)))
grid_coords = np.array([[-5.0, 0.0], [15.0, 0.0]])
grid = GeoGraph(gm.PROJ4_FLAT_EARTH,
{'grid-' + str(n): c
for n, c in enumerate(grid_coords)})
nx.set_node_attributes(grid, 'budget', {n: 0 for n in grid.nodes()})
grid.add_edges_from([('grid-0', 'grid-1')])
# now find projections onto grid
rtree = grid.get_rtree_index()
projected = grid.project_onto(nodes, rtree_index=rtree)
projected.remove_nodes_from(grid)
projected.remove_nodes_from(nodes)
# populate disjoint set of subgraphs
subgraphs = UnionFind()
# only one connected component, so just associate all nodes
# with first node of grid
parent = grid.nodes()[0]
subgraphs.add_component(parent, budget=grid.node[parent]['budget'])
for node in grid.nodes()[1:]:
subgraphs.add_component(node, budget=grid.node[node]['budget'])
subgraphs.union(parent, node, 0)
# and the projected "fake" nodes
for node in projected.nodes():
subgraphs.add_component(node, budget=np.inf)
subgraphs.union(parent, node, 0)
# add projected nodes to node set
nodes.add_nodes_from(projected, budget=np.inf)
# merge coords
nodes.coords = dict(nodes.coords, **projected.coords)
return nodes, subgraphs, rtree
def nodes_plus_grid():
"""
Return: nodes as graph and grid as UnionFind/Rtree combo
This example input demonstrates the "more" optimal nature
of mod_boruvka vs mod_kruskal.
2(10)
|
1(4) |
sqrt(5){ /| |
/ | |
/ | }3 | } 5
(2)0 | |
1{| | |
+-+-3-+-4-+-+-+-+-+-+-+5-+-+-+ <-- existing grid
In this case, all nodes will be connected via either algorithm,
but the graph produced by mod_kruskal will have edge (4,1) whereas
mod_boruvka will produce a graph with edge (0,1).
Therefore, the mod_boruvka graph is more optimal.
"""
mv_max_values = [2, 4, 10]
coords = np.array([[0.0, 1.0], [1.0, 3.0], [10.0, 5.0]])
coords_dict = dict(enumerate(coords))
nodes = GeoGraph(gm.PROJ4_FLAT_EARTH, coords=coords_dict)
nx.set_node_attributes(nodes, 'budget', dict(enumerate(mv_max_values)))
grid_coords = np.array([[-5.0, 0.0], [15.0, 0.0]])
grid = GeoGraph(gm.PROJ4_FLAT_EARTH, {'grid-' + str(n): c for n, c in
enumerate(grid_coords)})
nx.set_node_attributes(grid, 'budget', {n: 0 for n in grid.nodes()})
grid.add_edges_from([('grid-0', 'grid-1')])
# now find projections onto grid
rtree = grid.get_rtree_index()
projected = grid.project_onto(nodes, rtree_index=rtree)
projected.remove_nodes_from(grid)
projected.remove_nodes_from(nodes)
# populate disjoint set of subgraphs
subgraphs = UnionFind()
# only one connected component, so just associate all nodes
# with first node of grid
parent = grid.nodes()[0]
subgraphs.add_component(parent, budget=grid.node[parent]['budget'])
for node in grid.nodes()[1:]:
subgraphs.add_component(node, budget=grid.node[node]['budget'])
subgraphs.union(parent, node, 0)
# and the projected "fake" nodes
for node in projected.nodes():
subgraphs.add_component(node, budget=np.inf)
subgraphs.union(parent, node, 0)
# add projected nodes to node set
nodes.add_nodes_from(projected, budget=np.inf)
# merge coords
nodes.coords = dict(nodes.coords, **projected.coords)
return nodes, subgraphs, rtree