def test_loading_columns():
network = pg.read_network()
print('\nstart loading columns\n')
st = time()
pg.load_columns(network)
print('processing time of loading columns :{0: .2f}'.format(time() - st) +
's')
print('\nstart column generation\n')
st = time()
iter_num = 0
column_update_num = 10
pg.perform_network_assignment(1, iter_num, column_update_num, network)
print('processing time of column generation:{0: .2f}'.format(time() - st) +
's'
f' for {iter_num} assignment iterations and '
f'{column_update_num} iterations in column generation')
pg.output_columns(network)
pg.output_link_performance(network)
print('\npath finding results can be found in agent.csv')
def test_find_shortest_path_for_agents():
network = pg.read_network()
st = time()
network.find_path_for_agents()
print('\nprocessing time of finding shortest paths for all agents:{0: .2f}'
.format(time() - st) + 's')
agent_id = 300
print('\norigin node id of agent is ' +
str(network.get_agent_orig_node_id(agent_id)))
print('destination node id of agent is ' +
str(network.get_agent_dest_node_id(agent_id)))
print('shortest path (node id) of agent is ' +
str(network.get_agent_node_path(agent_id)))
print('shortest path (link id) of agent is ' +
str(network.get_agent_link_path(agent_id)))
agent_id = 1000
print('\norigin node id of agent is ' +
str(network.get_agent_orig_node_id(agent_id)))
print('destination node id of agent is ' +
str(network.get_agent_dest_node_id(agent_id)))
print('shortest path (node id) of agent is ' +
str(network.get_agent_node_path(agent_id)))
print('shortest path (link id) of agent is ' +
str(network.get_agent_link_path(agent_id)))
def test_find_shortest_path_for_agents():
network = pg.read_network()
st = time()
network.find_path_for_agents()
print('\nprocessing time of finding shortest paths for all agents: '
f'{time()-st:.2f} s')
agent_id = 300
print('\norigin node id of agent is '
f'{network.get_agent_orig_node_id(agent_id)}')
print('destination node id of agent is '
f'{network.get_agent_dest_node_id(agent_id)}')
print('shortest path (node id) of agent, '
f'{network.get_agent_node_path(agent_id)}')
print('shortest path (link id) of agent, '
f'{network.get_agent_link_path(agent_id)}')
agent_id = 1000
print('\norigin node id of agent is '
f'{network.get_agent_orig_node_id(agent_id)}')
print('destination node id of agent is '
f'{network.get_agent_dest_node_id(agent_id)}')
print('shortest path (node id) of agent, '
f'{network.get_agent_node_path(agent_id)}')
print('shortest path (link id) of agent, '
f'{network.get_agent_link_path(agent_id)}')
# output unique agent paths to a csv file
# if you do not want to include geometry info in the output file,
# you can do pg.output_agent_paths(network, False)
pg.output_agent_paths(network)
def test_loading_columns():
network = pg.read_network()
print('\nstart loading columns\n')
st = time()
pg.load_columns(network)
print(f'processing time of loading columns: {time()-st:.2f} s')
print('\nstart column generation')
st = time()
column_gen_num = 0
column_update_num = 10
# pg.perform_network_assignment(assignment_mode=1, column_gen_num,
# column_update_num, network)
# has been deprecated starting from v0.7.2, and will be removed in later.
pg.perform_column_generation(column_gen_num, column_update_num, network)
print(f'processing time of column generation: {time()-st:.2f} s'
f' for {column_gen_num} iterations in column generation and '
f'{column_update_num} iterations in column update')
pg.output_columns(network)
pg.output_link_performance(network)
def test_find_shortest_path():
load_demand = False
network = pg.read_network(load_demand)
print('\nshortest path (node id) from node 1 to node 2 is ' +
network.find_shortest_path(1, 2))
print('\nshortest path (link id) from node 1 to node 2 is ' +
network.find_shortest_path(1, 2, 'link'))
def test_accessibility():
network = pg.read_network()
print('\nstart accessibility evaluation\n')
st = time()
pg.evaluate_accessiblity(network)
print('complete accessibility evaluation.\n')
print('processing time of accessibility evaluation:{0: .2f}'.format(
time() - st) + 's')
print('accessibility matrices can be found in accessibility.csv '
'and accessibility_aggregated.csv')
def test_find_shortest_path():
network = pg.read_network(load_demand=False)
print('\nshortest path (node id) from node 1 to node 2, ' +
network.find_shortest_path(1, 2))
print('\nshortest path (link id) from node 1 to node 2, ' +
network.find_shortest_path(1, 2, seq_type='link'))
# retrieve the shortest path under a specific mode (which must be defined
# in settings.yaml)
print('\nshortest path (node id) from node 1 to node 2, ' +
network.find_shortest_path(1, 2, mode='w'))
print('\nshortest path (link id) from node 1 to node 2, ' +
network.find_shortest_path(1, 2, mode='w', seq_type='link'))
def test_column_generation_py():
network = pg.read_network()
print('\nstart column generation\n')
st = time()
iter_num = 20
column_update_num = 20
pg.perform_network_assignment(1, iter_num, column_update_num, network)
print(f'processing time of column generation: {time()-st:.2f} s'
f' for {iter_num} assignment iterations and '
f'{column_update_num} iterations in column generation')
# if you do not want to include geometry info in the output file,
# use pg.output_columns(network, False)
pg.output_columns(network)
pg.output_link_performance(network)
def test_accessibility():
load_demand = False
network = pg.read_network(load_demand)
print('\nstart accessibility evaluation\n')
st = time()
pg.evaluate_accessibility(network)
print('complete accessibility evaluation.\n')
print(f'processing time of accessibility evaluation: {time()-st:.2f} s')
# get accessible nodes and links starting from node 1 with a 5-minitue
# time window for the default mode auto (i.e., 'p')
network.get_accessible_nodes(1, 5)
network.get_accessible_links(1, 5)
# get accessible nodes and links starting from node 1 with a 15-minitue
# time window for mode walk (i.e., 'w')
network.get_accessible_nodes(1, 15, 'w')
network.get_accessible_links(1, 15, 'w')
def test_column_generation_py():
network = pg.read_network()
print('\nstart column generation\n')
st = time()
column_gen_num = 10
column_update_num = 10
# pg.perform_network_assignment(assignment_mode=1, assignment_num,
# column_update_num, network)
# has been deprecated starting from v0.7.2, and will be removed later.
pg.perform_column_generation(column_gen_num, column_update_num, network)
print(f'processing time of column generation: {time()-st:.2f} s'
f' for {column_gen_num} iterations in column generation and '
f'{column_update_num} iterations in column update')
# if you do not want to include geometry info in the output file,
# use pg.output_columns(network, False)
pg.output_columns(network)
pg.output_link_performance(network)
def test_loading_columns():
network = pg.read_network()
print('\nstart loading columns\n')
st = time()
pg.load_columns(network)
print(f'processing time of loading columns: {time()-st:.2f} s')
print('\nstart column generation\n')
st = time()
iter_num = 0
column_update_num = 10
pg.perform_network_assignment(1, iter_num, column_update_num, network)
print(f'processing time of column generation: {time()-st:.2f} s'
f' for {iter_num} assignment iterations and '
f'{column_update_num} iterations in column generation')
pg.output_columns(network)
pg.output_link_performance(network)
import path4gmns as pg
if __name__ == "__main__":
network = pg.read_network()
print('the shortest path from node 1 to node 2 is ' +
str(pg.find_shortest_path(network, 1, 2)))
pg.find_path_for_agents(network)
agent = network.agent_list[0]
print('the shortest path (node sequence) of agent 0 is ' +
str(agent.path_node_seq_no_list))
print('the shortest path (link sequence) of agent 0 is ' +
str(agent.path_link_seq_no_list))
