def main():
logging.basicConfig(filename=absolute_path + '/sf_abm_mp.log',
level=logging.DEBUG)
logger = logging.getLogger('main')
logger.info('{} \n\n'.format(datetime.datetime.now()))
t_start = time.time()
### Read initial graph
#global g_igraph
#g_igraph = igraph.Graph.Read_Pickle(absolute_path+'/../data_repo/data/sf/network_graph.pkl')
global g
g = interface.readgraph(
bytes(absolute_path + '/../data_repo/data/sf/network_sparse.mtx',
encoding='utf-8'))
for day in [1]:
for hour in range(9, 10):
logger.info('*************** DY{} HR{} ***************'.format(
day, hour))
t0 = time.time()
edge_volume = one_step(day, hour)
t1 = time.time()
logger.info('DY{}_HR{}: running time {}'.format(
day, hour, t1 - t0))
### Update graph
# volume_array = np.zeros(g.ecount())
# volume_array[list(edge_volume.keys())] = np.array(list(edge_volume.values()))*400 ### 400 is the factor to scale Uber/Lyft trip # to total car trip # in SF.
# g.es['volume'] = volume_array
# logger.info('DY{}_HR{}: max link volume {}'.format(day, hour, max(volume_array)))
# g.es['t_new'] = fft_array*(1.2+0.78*(volume_array/capacity_array)**4) ### BPR and (colak, 2015)
# g.es['weight'] = g.es['t_new']
#write_geojson(g, day, hour)
t_end = time.time()
logger.info('total run time is {} seconds \n\n\n\n\n'.format(t_end -
t_start))
def network(network_file_edges=None,
network_file_nodes=None,
simulation_outputs=None,
cf_files=[],
scen_nm=''):
logger = logging.getLogger("bk_evac")
links_df0 = pd.read_csv(work_dir + network_file_edges)
links_df0 = gpd.GeoDataFrame(
links_df0, crs='epsg:4326',
geometry=links_df0['geometry'].map(loads)) #.to_crs(3857)
### lane assumptions
### leave to OSM specified values for motorway and trunk
links_df0['lanes'] = np.where(
links_df0['type'].isin([
'primary', 'primary_link', 'secondary', 'secondary_link',
'tertiary', 'tertiary_link'
]), 2, links_df0['lanes'])
links_df0['lanes'] = np.where(
links_df0['type'].isin(['residential', 'unclassified']), 1,
links_df0['lanes'])
### speed assumptions
links_df0['maxmph'] = np.where(
links_df0['type'].isin(['primary', 'primary_link']), 55,
links_df0['maxmph'])
links_df0['maxmph'] = np.where(
links_df0['type'].isin(
['secondary', 'secondary_link', 'tertiary', 'tertiary_link']), 25,
links_df0['maxmph'])
links_df0['maxmph'] = np.where(
links_df0['type'].isin(['residential', 'unclassified']), 25 * 0.8,
links_df0['maxmph'])
if len(cf_files) > 0:
### read counterflow links
cf_links = []
for cf_file in cf_files:
cf_link_df = pd.read_csv(work_dir + cf_file)
cf_links.append(cf_link_df)
cf_links_df = pd.concat(cf_links)
### along counterflow direction
cf_links_id = cf_links_df.loc[cf_links_df['along'] == 1,
'edge_id_igraph']
links_df0['lanes'] = np.where(
links_df0['edge_id_igraph'].isin(cf_links_id),
links_df0['lanes'] * 2, links_df0['lanes'])
### opposite counterflow direction
opcf_links_id = cf_links_df.loc[cf_links_df['along'] == 0,
'edge_id_igraph']
links_df0['lanes'] = np.where(
links_df0['edge_id_igraph'].isin(opcf_links_id), 0,
links_df0['lanes'])
links_df0['maxmph'] = np.where(
links_df0['edge_id_igraph'].isin(opcf_links_id), 0.0000001,
links_df0['maxmph'])
links_df0['fft'] = links_df0['length'] / links_df0['maxmph'] * 2.237
links_df0['capacity'] = 1900 * links_df0['lanes']
links_df0 = links_df0[[
'edge_id_igraph', 'start_igraph', 'end_igraph', 'lanes', 'capacity',
'maxmph', 'fft', 'length', 'geometry'
]]
links_df0.to_csv(scratch_dir + simulation_outputs +
'/modified_network_edges_{}.csv'.format(scen_nm),
index=False)
# sys.exit(0)
nodes_df0 = pd.read_csv(work_dir + network_file_nodes)
nodes_df0 = gpd.GeoDataFrame(
nodes_df0,
crs='epsg:4326',
geometry=[Point(xy) for xy in zip(nodes_df0['lon'], nodes_df0['lat'])
]) #.to_crs(3857)
nodes_df0['x'] = nodes_df0['geometry'].apply(lambda x: x.x)
nodes_df0['y'] = nodes_df0['geometry'].apply(lambda x: x.y)
### Convert to mtx
wgh = links_df0['fft']
row = links_df0['start_igraph']
col = links_df0['end_igraph']
assert max(
np.max(row) + 1,
np.max(col) + 1
) == nodes_df0.shape[
0], 'nodes and links dimension do not match, row {}, col {}, nodes {}'.format(
np.max(row), np.max(col), nodes_df0.shape[0])
g_coo = ssparse.coo_matrix((wgh, (row, col)),
shape=(nodes_df0.shape[0], nodes_df0.shape[0]))
logging.info("({}, {}), {}".format(g_coo.shape[0], g_coo.shape[1],
len(g_coo.data)))
sio.mmwrite(
scratch_dir + simulation_outputs +
'/network_sparse_{}.mtx'.format(scen_nm), g_coo)
# g_coo = sio.mmread(absolute_path+'/outputs/network_sparse.mtx'.format(folder))
g = interface.readgraph(
bytes(scratch_dir + simulation_outputs +
'/network_sparse_{}.mtx'.format(scen_nm),
encoding='utf-8'))
### Create link and node objects
nodes = []
links = []
for row in nodes_df0.itertuples():
real_node = Node(getattr(row, 'node_id_igraph'), getattr(row, 'x'),
getattr(row, 'y'), 'real', getattr(row, 'node_osmid'))
virtual_node = real_node.create_virtual_node()
virtual_link = real_node.create_virtual_link()
nodes.append(real_node)
nodes.append(virtual_node)
links.append(virtual_link)
for row in links_df0.itertuples():
real_link = Link(getattr(row, 'edge_id_igraph'), getattr(row, 'lanes'),
getattr(row, 'length'), getattr(row, 'fft'),
getattr(row, 'capacity'), 'real',
getattr(row, 'start_igraph'),
getattr(row, 'end_igraph'), getattr(row, 'geometry'))
links.append(real_link)
return g, nodes, links
import sys
import scipy.sparse
import scipy.io as sio
import numpy as np
import time
import os
sys.path.insert(0, '/Users/bz247')
from sp import interface
absolute_path = os.path.dirname(os.path.abspath(__file__))
graph_file = absolute_path + '/../0_network/data/sf/network_sparse.mtx'
### Time the PQ
print('########### Priority Queue SP #############')
g_pq = interface.readgraph(bytes(graph_file, encoding='utf-8'))
ta0 = time.time()
try:
sp_pq = g_pq.dijkstra(1, 20)
except:
print('route not found')
ta1 = time.time()
route_pq = sp_pq.route(20)
ta2 = time.time()
path_pq = [vertex[1] for vertex in route_pq]
ta3 = time.time()
print('PQ: distance 1020-->20: ', sp_pq.distance(20))
print(
'PQ: total time {}, dijkstra() {}, route() {}, vertex list {}, \n'.format(
ta3 - ta0, ta1 - ta0, ta2 - ta1, ta3 - ta2))