def grid_and_non_grid():
"""
return networkplan GeoGraph with grid and non-grid components
0 2
| |
+-6-+ 1 3-4-5
(inf)
where node 3 is a fake node connecting node 0 to the grid
"""
node_coords = np.array([[0.0, 1.0],
[4.0, 0.0],
[4.0, 1.0],
[5.0, 0.0],
[6.0, 0.0],
[7.0, 0.0],
[0.0, 0.0]])
grid = GeoGraph(gm.PROJ4_FLAT_EARTH, dict(enumerate(node_coords)))
budget_values = [5, 5, 5, 5, 5, 5, np.inf]
nx.set_node_attributes(grid, 'budget', dict(enumerate(budget_values)))
grid.add_edges_from([(0, 6), (1, 2), (3, 4), (4, 5)])
return grid
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 dataset_store_to_geograph(dataset_store):
"""
convenience function for converting a network stored in a dataset_store
into a GeoGraph
Args:
dataset_store containing a network
Returns:
GeoGraph representation of dataset_store network
TODO: determine projection from dataset_store?
"""
all_nodes = list(dataset_store.cycleNodes()) + \
list(dataset_store.cycleNodes(isFake=True))
# nodes in output GeoGraph are id'd from 0 to n (via enumerate)
np_to_nx_id = {node.id: i for i, node in enumerate(all_nodes)}
coords = [node.getCommonCoordinates() for node in all_nodes]
coords_dict = dict(enumerate(coords))
G = GeoGraph(coords=coords_dict)
# only set population, system and budget for now
# TODO: Do we need all from the output?
for i, node in enumerate(all_nodes):
if not node.is_fake:
properties = {
'budget': node.metric,
'population': node.output['demographics']['population count'],
'system': node.output['metric']['system']
}
G.node[i] = properties
def seg_to_nx_ids(seg):
"""
Return the networkx segment ids
"""
return (np_to_nx_id[seg.node1_id],
np_to_nx_id[seg.node2_id])
edges = [seg_to_nx_ids(s) for s in
dataset_store.cycleSegments(is_existing=False)]
edge_weights = {seg_to_nx_ids(s): s.weight for s in
dataset_store.cycleSegments(is_existing=False)}
edge_is_existing = {seg_to_nx_ids(s): s.is_existing for s in
dataset_store.cycleSegments(is_existing=False)}
edge_subnet_id = {seg_to_nx_ids(s): s.subnet_id for s in
dataset_store.cycleSegments(is_existing=False)}
G.add_edges_from(edges)
nx.set_edge_attributes(G, 'weight', edge_weights)
nx.set_edge_attributes(G, 'is_existing', edge_is_existing)
nx.set_edge_attributes(G, 'subnet_id', edge_subnet_id)
return G
def dataset_store_to_geograph(dataset_store):
"""
convenience function for converting a network stored in a dataset_store
into a GeoGraph
Args:
dataset_store containing a network
Returns:
GeoGraph representation of dataset_store network
TODO: determine projection from dataset_store?
"""
all_nodes = list(dataset_store.cycleNodes()) + \
list(dataset_store.cycleNodes(isFake=True))
np_to_nx_id = {node.id: i for i, node in enumerate(all_nodes)}
coords = [node.getCommonCoordinates() for node in all_nodes]
coords_dict = dict(enumerate(coords))
budget_dict = {i: node.metric for i, node in enumerate(all_nodes)}
G = GeoGraph(coords=coords_dict)
nx.set_node_attributes(G, 'budget', budget_dict)
def seg_to_nx_ids(seg):
"""
Return the networkx segment ids
"""
return (np_to_nx_id[seg.node1_id],
np_to_nx_id[seg.node2_id])
edges = [seg_to_nx_ids(s) for s in
dataset_store.cycleSegments(is_existing=False)]
edge_weights = {seg_to_nx_ids(s): s.weight for s in
dataset_store.cycleSegments(is_existing=False)}
edge_is_existing = {seg_to_nx_ids(s): s.is_existing for s in
dataset_store.cycleSegments(is_existing=False)}
edge_subnet_id = {seg_to_nx_ids(s): s.subnet_id for s in
dataset_store.cycleSegments(is_existing=False)}
G.add_edges_from(edges)
nx.set_edge_attributes(G, 'weight', edge_weights)
nx.set_edge_attributes(G, 'is_existing', edge_is_existing)
nx.set_edge_attributes(G, 'subnet_id', edge_subnet_id)
return G
def generate_mst(coords):
"""
Generate a min spanning tree based on coordinate
distances
"""
input_proj = gm.PROJ4_LATLONG
if gm.is_in_lon_lat(coords):
input_proj = gm.PROJ4_LATLONG
else:
input_proj = gm.PROJ4_FLAT_EARTH
node_dict = dict(enumerate(coords))
geo_nodes = GeoGraph(input_proj, node_dict)
geo_full = geo_nodes.get_connected_weighted_graph()
geo_mst = nx.minimum_spanning_tree(geo_full)
geo_nodes.add_edges_from(geo_mst.edges(data=True))
return geo_nodes
def test_load_write_json():
"""
ensure that reading/writing js 'node-link' format works
"""
os.mkdir('test/tmp')
node_dict = {0: [0,0], 1: [0,1], 2: [1,0], 3: [1,1]}
g = GeoGraph(gm.PROJ4_LATLONG, node_dict)
g.add_edges_from([(0,1),(1,2),(2,3)])
nio.write_json(g, open('test/tmp/g.js', 'w'))
g2 = nio.load_json(open('test/tmp/g.js', 'r'))
os.remove('test/tmp/g.js')
os.rmdir('test/tmp')
assert nx.is_isomorphic(g, g2,
node_match=operator.eq,
edge_match=operator.eq),\
"expected written and read graphs to match"
def test_load_write_json():
"""
ensure that reading/writing js 'node-link' format works
"""
os.mkdir(os.path.join('test', 'tmp'))
node_dict = {0: [0,0], 1: [0,1], 2: [1,0], 3: [1,1]}
g = GeoGraph(gm.PROJ4_LATLONG, node_dict)
g.add_edges_from([(0,1),(1,2),(2,3)])
json_file_path = os.path.join('test', 'tmp', 'g.json')
nio.write_json(g, open(json_file_path, 'w'))
g2 = nio.read_json_geograph(json_file_path)
os.remove(json_file_path)
os.rmdir(os.path.join('test', 'tmp'))
assert nx.is_isomorphic(g, g2,
node_match=operator.eq,
edge_match=operator.eq),\
"expected written and read graphs to match"
def grid_and_non_grid():
"""
return networkplan GeoGraph with grid and non-grid components
0 2
| |
+-6-+ 1 3-4-5
(inf)
where node 3 is a fake node connecting node 0 to the grid
"""
node_coords = np.array([[0.0, 1.0], [4.0, 0.0], [4.0, 1.0], [5.0, 0.0],
[6.0, 0.0], [7.0, 0.0], [0.0, 0.0]])
grid = GeoGraph(gm.PROJ4_FLAT_EARTH, dict(enumerate(node_coords)))
budget_values = [5, 5, 5, 5, 5, 5, np.inf]
nx.set_node_attributes(grid, 'budget', dict(enumerate(budget_values)))
grid.add_edges_from([(0, 6), (1, 2), (3, 4), (4, 5)])
return grid
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 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
def dataset_store_to_geograph(dataset_store):
"""
convenience function for converting a network stored in a dataset_store
into a GeoGraph
Args:
dataset_store containing a network
Returns:
GeoGraph representation of dataset_store network
TODO: determine projection from dataset_store?
"""
all_nodes = list(dataset_store.cycleNodes()) + \
list(dataset_store.cycleNodes(isFake=True))
# nodes in output GeoGraph are id'd from 0 to n (via enumerate)
np_to_nx_id = {node.id: i for i, node in enumerate(all_nodes)}
coords = [node.getCommonCoordinates() for node in all_nodes]
coords_dict = dict(enumerate(coords))
G = GeoGraph(coords=coords_dict)
# only set population, system and budget for now
# TODO: Do we need all from the output?
for i, node in enumerate(all_nodes):
if not node.is_fake:
properties = {
'budget': node.metric,
'population': node.output['demographics']['population count'],
'system': node.output['metric']['system']
}
G.node[i] = properties
def seg_to_nx_ids(seg):
"""
Return the networkx segment ids
"""
return (np_to_nx_id[seg.node1_id], np_to_nx_id[seg.node2_id])
edges = [
seg_to_nx_ids(s)
for s in dataset_store.cycleSegments(is_existing=False)
]
edge_weights = {
seg_to_nx_ids(s): s.weight
for s in dataset_store.cycleSegments(is_existing=False)
}
edge_is_existing = {
seg_to_nx_ids(s): s.is_existing
for s in dataset_store.cycleSegments(is_existing=False)
}
edge_subnet_id = {
seg_to_nx_ids(s): s.subnet_id
for s in dataset_store.cycleSegments(is_existing=False)
}
G.add_edges_from(edges)
nx.set_edge_attributes(G, 'weight', edge_weights)
nx.set_edge_attributes(G, 'is_existing', edge_is_existing)
nx.set_edge_attributes(G, 'subnet_id', edge_subnet_id)
return G