def test_network_skimming(self):
# graph
g = Graph()
g.load_from_disk(test_graph)
g.set_graph(cost_field="distance", skim_fields=None)
# None implies that only the cost field will be skimmed
# skimming results
res = SkimResults()
res.prepare(g)
aux_res = MultiThreadedNetworkSkimming()
aux_res.prepare(g, res)
_ = skimming_single_origin(26, g, res, aux_res, 0)
skm = NetworkSkimming(g, res)
skm.execute()
tot = np.nanmax(res.skims.distance[:, :])
if tot > 10e10:
self.fail(
"Skimming was not successful. At least one np.inf returned.")
if skm.report:
self.fail("Skimming returned an error:" + str(skm.report))
def test_network_skimming(self):
# graph
g = Graph()
g.load_from_disk(test_graph)
g.set_graph(cost_field='distance', skim_fields=None)
# None implies that only the cost field will be skimmed
# skimming results
res = SkimResults()
res.prepare(g)
aux_res = MultiThreadedNetworkSkimming()
aux_res.prepare(g, res)
a = skimming_single_origin(26, g, res, aux_res, 0)
skm = NetworkSkimming(g, res)
skm.execute()
tot = np.nanmax(res.skims.distance[:, :])
if tot > 10e10:
self.fail('Skimming was not successful. At least one np.inf returned.')
if skm.report:
self.fail('Skimming returned an error:' + str(skm.report))
def doWork(self):
res = SkimResults()
res.prepare(self.graph)
ns = NetworkSkimming(self.graph, res)
ns.execute()
skm = res.skims
mat = (skm.get_matrix(skm.names[0]) * self.mult).astype(int)
self.depot = list(skm.index).index(self.depot)
# Create the routing index manager.
manager = pywrapcp.RoutingIndexManager(mat.shape[0], self.vehicles, self.depot)
# Create Routing Model.
routing = pywrapcp.RoutingModel(manager)
def distance_callback(from_index, to_index):
"""Returns the distance between the two nodes."""
# Convert from routing variable Index to distance matrix NodeIndex.
from_node = manager.IndexToNode(from_index)
to_node = manager.IndexToNode(to_index)
return mat[from_node, to_node]
transit_callback_index = routing.RegisterTransitCallback(distance_callback)
# Define cost of each arc.
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
# Setting first solution heuristic.
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
# Solve the problem.
solution = routing.SolveWithParameters(search_parameters)
# Print solution on console.
if not solution:
self.error = 'Solution not found'
self.report.append(self.error)
else:
self.report.append(f'Objective function value: {solution.ObjectiveValue() / self.mult}')
index = routing.Start(0)
plan_output = 'Route:\n'
route_distance = 0
while not routing.IsEnd(index):
p = skm.index[manager.IndexToNode(index)]
self.node_sequence.append(p)
plan_output += f' {p} ->'
previous_index = index
index = solution.Value(routing.NextVar(index))
route_distance += routing.GetArcCostForVehicle(previous_index, index, 0)
p = skm.index[manager.IndexToNode(index)]
self.node_sequence.append(p)
plan_output += f' {p}\n'
self.report.append(plan_output)
self.finished_threaded_procedure.emit("TSP")
## Now to skimming # %% from aequilibrae.paths import NetworkSkimming # %% # But let's say we only want a skim matrix for nodes 1, 3, 6 & 8 import numpy as np graph.prepare_graph(np.array([1, 3, 6, 8])) # %% # And run the skimming skm = NetworkSkimming(graph) skm.execute() # %% # The result is an AequilibraEMatrix object skims = skm.results.skims # Which we can manipulate directly from its temp file, if we wish skims.matrices # %% # Or access each matrix skims.free_flow_time # %%