def test_project_xyz_vs_geo():
"""
ensure that project_onto works the same with xyz vs lat_lon points
"""
net_coords = {'grid-1': [0.0, 0.0],
'grid-2': [10.0, 10.0]}
net_edges = [('grid-1', 'grid-2')]
node_coords = {0: [5.0, 5.0]}
g_net = GeoGraph(gm.PROJ4_LATLONG, net_coords, data=net_edges)
g_nodes = GeoGraph(gm.PROJ4_LATLONG, node_coords)
g_project = g_net.project_onto(g_nodes, spherical_accuracy=True)
g_net_xyz = GeoGraph(gm.PROJ4_LATLONG,
g_net.lon_lat_to_cartesian_coords(),
data=net_edges)
g_nodes_xyz = GeoGraph(gm.PROJ4_LATLONG,
g_nodes.lon_lat_to_cartesian_coords())
g_project_xyz = g_net_xyz.project_onto(g_nodes_xyz, spherical_accuracy=True)
g_project_coords_ll = g_project_xyz.cartesian_to_lon_lat()
def round_coords(coords, round_precision=8):
return {i: tuple(map(lambda c: round(c, round_precision), coord))
for i, coord in coords.items()}
assert(round_coords(g_project_coords_ll) == round_coords(g_project.coords))
def test_project_xyz_vs_geo():
"""
ensure that project_onto works the same with xyz vs lat_lon points
"""
net_coords = {'grid-1': [0.0, 0.0],
'grid-2': [10.0, 10.0]}
net_edges = [('grid-1', 'grid-2')]
node_coords = {0: [5.0, 5.0]}
g_net = GeoGraph(gm.PROJ4_LATLONG, net_coords, data=net_edges)
g_nodes = GeoGraph(gm.PROJ4_LATLONG, node_coords)
g_project = g_net.project_onto(g_nodes, spherical_accuracy=True)
g_net_xyz = GeoGraph(gm.PROJ4_LATLONG,
g_net.lon_lat_to_cartesian_coords(),
data=net_edges)
g_nodes_xyz = GeoGraph(gm.PROJ4_LATLONG,
g_nodes.lon_lat_to_cartesian_coords())
g_project_xyz = g_net_xyz.project_onto(g_nodes_xyz, spherical_accuracy=True)
g_project_coords_ll = g_project_xyz.cartesian_to_lon_lat()
def round_coords(coords, round_precision=8):
return {i: tuple(map(lambda c: round(c, round_precision), coord))
for i, coord in coords.items()}
assert(round_coords(g_project_coords_ll) == round_coords(g_project.coords))
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