def main():
seed(0) #set seed
#get graph info
G = nx.read_gpickle("input/graphMTC_CentroidsLength6.gpickle") #noCentroidsLength15.gpickle") #does not have centroidal links. There is also the choice of a proper multidigraph: nx.read_gpickle("input/graphMTC_CentroidsLength5.gpickle")
G = nx.freeze(G) #prevents edges or nodes to be added or deleted
#get od info. This is in format of a dict keyed by od, like demand[sd1][sd2] = 200000.
demand = bd.build_demand('input/BATS2000_34SuperD_TripTableData.csv', 'input/superdistricts_centroids.csv')
#get earthquake info
q = QuakeMaps('input/20130210_mtc_total_lnsas3.pkl', 'input/20130210_mtc_magnitudes3.pkl', 'input/20130210_mtc_faults3.pkl', 'input/20130210_mtc_weights3.pkl', 'input/20130210_mtc_scenarios3.pkl') #input/20130107_mtc_total_lnsas1.pkl', 'input/20130107_mtc_magnitudes1.pkl','input/20130107_mtc_faults1.pkl', 'input/20130107_mtc_weights1.pkl', 'input/20130107_mtc_scenarios1.pkl') #'input/20130210_mtc_total_lnsas3.pkl', 'input/20130210_mtc_magnitudes3.pkl', 'input/20130210_mtc_faults3.pkl', 'input/20130210_mtc_weights3.pkl', 'input/20130210_mtc_scenarios3.pkl') #('input/20130107_mtc_total_lnsas1.pkl', 'input/20130107_mtc_magnitudes1.pkl', #totalfilename=None, magfilename=None, faultfilename=None, weightsfilename=None, scenariofilename=None):
print 'weights: ', q.weights
q.num_sites = len(q.lnsas[0])
#determine which scenarios you want to run
good_indices = pick_scenarios(q.lnsas, q.weights)
travel_index_times = []
index = 0
#loop over scenarios
print 'size of lnsas: ', len(q.lnsas)
for scenario in q.lnsas: #each 'scenario' has 1557 values of lnsa, i.e. one per site
if index in good_indices:
print 'index: ', index
(bridges, flow, path, path2) = run_simple_iteration(G, scenario, demand, False)
travel_index_times.append((index, bridges, flow, path, path2))
# print 'new travel times: ', travel_index_times
if index%1000 ==0:
util.write_2dlist(time.strftime("%Y%m%d")+'_bridges_flow_paths4.txt',travel_index_times)
index += 1 #IMPORTANT
util.write_2dlist(time.strftime("%Y%m%d")+'_bridges_flow_paths4.txt',travel_index_times)
print 'the number of scenarios I considered doing: ', index
print 'the number of scenarios I actually did: ', len(travel_index_times)
def main():
seed(0) #set seed
#get graph info
G = nx.read_gpickle("input/graphMTC_CentroidsLength5.gpickle") #noCentroidsLength15.gpickle") #does not have centroidal links
print '|V| = ', len(G.nodes())
print '|E| = ', len(G.edges())
G = nx.freeze(G) #prevents edges or nodes to be added or deleted
#get od info. This is in format of a dict keyed by od, like demand[sd1][sd2] = 200000.
demand = bd.build_demand('input/BATS2000_34SuperD_TripTableData.csv', 'input/superdistricts_centroids.csv') #bd.build_demand('input/BATS2000_34SuperD_TripTableData.csv', 'input/superdistricts_centroids.csv')
#get earthquake info
q = QuakeMaps('input/20130210_mtc_total_lnsas3.pkl', 'input/20130210_mtc_magnitudes3.pkl', 'input/20130210_mtc_faults3.pkl', 'input/20130210_mtc_weights3.pkl', 'input/20130210_mtc_scenarios3.pkl') #(input/20130107_mtc_total_lnsas1.pkl', 'input/20130107_mtc_magnitudes1.pkl', 'input/20130107_mtc_faults1.pkl', 'input/20130107_mtc_weights1.pkl', 'input/20130107_mtc_scenarios1.pkl') #totalfilename=None, magfilename=None, faultfilename=None, weightsfilename=None, scenariofilename=None): 'input/20130210_mtc_total_lnsas3.pkl', 'input/20130210_mtc_magnitudes3.pkl', 'input/20130210_mtc_faults3.pkl', 'input/20130210_mtc_weights3.pkl', 'input/20130210_mtc_scenarios3.pkl') #(
q.num_sites = len(q.lnsas[0])
#determine which scenarios you want to run
good_indices = pick_scenarios(q.lnsas, q.weights)
travel_index_times = []
index = 0
#loop over scenarios
for scenario in q.lnsas: #each 'scenario' has 1557 values of lnsa, i.e. one per site
if index in good_indices:
print 'index: ', index
(travel_time, vmt) = run_iteration(G, scenario, demand)
travel_index_times.append((index, travel_time, vmt))
# print 'new travel times: ', travel_index_times
if index%100 ==0:
util.write_2dlist(time.strftime("%Y%m%d")+'_travel_time.txt',travel_index_times)
index += 1 #IMPORTANT
util.write_2dlist(time.strftime("%Y%m%d")+'_travel_time.txt',travel_index_times)
def main():
'''can change the number of epsilons below'''
seed(0) #set seed
simple = False #False #simple is just %bridges out, which is computationally efficient
#get graph info
# G = nx.read_gpickle("input/graphMTC_CentroidsLength6.gpickle") #noCentroidsLength15.gpickle") #does not have centroidal links. There is also the choice of a proper multidigraph: nx.read_gpickle("input/graphMTC_CentroidsLength5.gpickle")
G = nx.read_gpickle("input/graphMTC_CentroidsLength6highways.gpickle") #noCentroidsLength15.gpickle") #does not have centroidal links. Directed! only one edge between nodes
# G1 = nx.read_gpickle("input/graphMTC_CentroidsLength5.gpickle") #undirected, multiple edges. It is a little funky because it has two links between A and B and two between B and A so is that double-counting?
# '''a multigraph: An undirected graph class that can store multiedges.
# Multiedges are multiple edges between two nodes. Each edge
# can hold optional data or attributes.
# A MultiGraph holds undirected edges. Self loops are allowed.'''
print 'nodes: ', len(G.nodes())
G = nx.freeze(G) #prevents edges or nodes to be added or deleted
# G1 = nx.freeze(G1)
#get od info. This is in format of a dict keyed by od, like demand[sd1][sd2] = 200000.
demand = bd.build_demand('input/BATS2000_34SuperD_TripTableData.csv', 'input/superdistricts_centroids.csv') #we just take a percentage in ita.py, namely #to get morning flows, take 5.3% of daily driver values. 11.5/(4.5*6+11.5*10+14*4+4.5*4) from Figure S10 of http://www.nature.com/srep/2012/121220/srep01001/extref/srep01001-s1.pdf
#get path
#get earthquake info #UPDATED May 23, 2013
#TODO
q = QuakeMaps('input/20130612_mtc_total_lnsas5.pkl', 'input/20130612_mtc_magnitudes5.pkl', 'input/20130612_mtc_faults5.pkl', 'input/20130612_mtc_weights5.pkl', 'input/20130612_mtc_scenarios5.pkl') #input/20130107_mtc_total_lnsas1.pkl', 'input/20130107_mtc_magnitudes1.pkl','input/20130107_mtc_faults1.pkl', 'input/20130107_mtc_weights1.pkl', 'input/20130107_mtc_scenarios1.pkl') #'input/20130210_mtc_total_lnsas3.pkl', 'input/20130210_mtc_magnitudes3.pkl', 'input/20130210_mtc_faults3.pkl', 'input/20130210_mtc_weights3.pkl', 'input/20130210_mtc_scenarios3.pkl') #('input/20130107_mtc_total_lnsas1.pkl', 'input/20130107_mtc_magnitudes1.pkl', #totalfilename=None, magfilename=None, faultfilename=None, weightsfilename=None, scenariofilename=None):
q.num_sites = len(q.lnsas[0])
numeps = 5 #CAHNGE THIS CHANGE THIS!!!!!!!!
#determine which scenarios you want to run
good_indices = pick_scenarios(q.lnsas, q.weights,True, numeps)
targets = good_indices #[12, 35, 55, 71, 75, 82, 86, 87, 88, 106, 108, 115, 121, 231, 241, 247, 256, 258, 260, 261, 676, 730, 733, 1231, 1548] #indices between 0 and 2110. the scenarios for which you want to save the damaged bridge data
print 'the number of scenarios for which I want to save bridge info: ', len(targets)
travel_index_times = []
index = 0
good_index = 0
# pdb.set_trace()
#figure out what the travel time and vmt are if no damage to any bridges
no_damage_travel_time = -1
no_damage_vmt = -1
found_no_damage = False
for scenario in q.lnsas: #each 'scenario' has 1xxx values of lnsa, i.e. one per site
while found_no_damage == False:
(bridges, flow, path, path2, newG) = run_simple_iteration(G, scenario, demand, False, good_index, targets, True) #since looking for no damage case, it is ok to clean up
if bridges == 0:
found_no_damage = True
print 'found case with no damage so I will save those and save you work later on'
(no_damage_travel_time, no_damage_vmt) = run_iteration(G, scenario, demand, newG)
#loop over scenarios
print 'size of lnsas: ', len(q.lnsas)
for scenario in q.lnsas: #each 'scenario' has 1xxx values of lnsa, i.e. one per site
if index in good_indices:
print 'index: ', index
if simple == True:
(bridges, flow, path, path2, newG) = run_simple_iteration(G, scenario, demand, False, good_index, targets)
travel_index_times.append((index, bridges, flow, path, path2, -1, -1, bridges/float(q.num_sites), -1))
else:
(bridges, flow, path, path2, newG) = run_simple_iteration(G, scenario, demand, False, good_index, targets, False) #doesn't clean up the damage
print 'what i found for bridges: ', bridges
if bridges == 0:
travel_time = no_damage_travel_time;
vmt = no_damage_vmt;
else:
print 'attempting new'
(travel_time, vmt) = run_iteration(G, scenario, demand, newG, True)
print 'what i have for (tt, vmt): ', (travel_time, vmt)
travel_index_times.append((index, bridges, flow, path, path2, travel_time, vmt, bridges/float(q.num_sites), -1))
good_index += 1
# travel_index_times.append((index, travel_time, vmt))
# print 'new travel times: ', travel_index_times
if index%1000 ==0:
print 'index: ', index
util.write_2dlist(time.strftime("%Y%m%d")+'_bridges_flow_paths_5eps_extensive.txt',travel_index_times)
index += 1 #IMPORTANT
util.write_2dlist(time.strftime("%Y%m%d")+'_bridges_flow_paths_5eps_extensive.txt',travel_index_times)
print 'the number of scenarios I considered doing: ', index
print 'the number of scenarios I actually did: ', len(travel_index_times)
print 'i.e.: ', good_index
print 'and now, I will save a dataset of damaged bridges in each scenario'
util.write_2dlist(time.strftime("%Y%m%d")+'_damaged_bridges_5eps_extensive.txt',BRIDGE_DAMAGE_DATASET)
with open(time.strftime("%Y%m%d")+'_damaged_bridges_5eps_extensive.pkl', 'wb') as f:
pickle.dump(BRIDGE_DAMAGE_DATASET, f)