def find_good_scaling_capacity(G, file_traffic, max_value=1., min_value=0., step=-0.1, target=0.015): """ This function scales down uniformly the capacities of all sectors of G in order to meet the target of fraction of flights rejected. Parameters ---------- G: hybrid network file_traffic: string full path of file with traffic to use to infer capacities max_value: float, optional maximum scale value. min_value: float, optional minimum scale value step: float, optional step of scale value for the search. target: float, optional Target of fraction of flight rejected. Returns ------- cap: float scale factor which meets the target frac: float exact fraction of flights rejected with this target H: hybrid network with modified value of capacities. """ caps = np.arange(max_value, min_value, step) last_ratio = None # Sweep the scaling factor for i, cap in enumerate(caps): with silence(True): H = deepcopy(G) trajs, stats = generate_traffic(H, file_traffic=file_traffic, capacity_factor=cap, put_sectors=True, remove_flights_after_midnight=True) # Get the fraction of flights rejected ratio = stats['rejected_flights']/float(stats['flights']) #print "ratio:", ratio # If the fraction is over the target, it is our value. if ratio>=target: break last_ratio = ratio # Refinement if ratio!=last_ratio and i!=0: cap = cap - (ratio - target)*(cap - caps[i-1])/(ratio - last_ratio) with silence(True): H = deepcopy(G) trajs, stats = generate_traffic(H, file_traffic=file_traffic, capacity_factor=cap, put_sectors=True, remove_flights_after_midnight=True) # Get the fraction of flights rejected ratio = stats['rejected_flights']/float(stats['flights']) return cap, ratio, H
def find_good_scaling_capacity(G, file_traffic, silent=True, max_value=1., min_value=0., step=-0.1, target=0.015, **paras_control): caps = np.arange(max_value, min_value, step) last_ratio = None for i, cap in enumerate(caps): with silence(silent): H = deepcopy(G) trajs, stats = generate_traffic(H, file_traffic=file_traffic, capacity_factor=cap, put_sectors=True, remove_flights_after_midnight=True, **paras_control) ratio = stats['rejected_flights']/float(stats['flights']) #print "cap=", cap, "; ratio=", ratio if ratio>=target: break last_ratio = ratio if ratio!=last_ratio and i!=0: #cap = (cap + caps[i-1])/2. cap = cap - (ratio - target)*(cap - caps[i-1])/(ratio - last_ratio) return cap, stats['rejected_flights']/float(stats['flights']), H
for x, y in boundary:
f.write(str(x) + '\t' + str(y) + '\n')
print "Finding best capacity factor..."
capacity_factor, rejected_flights, H = find_good_scaling_capacity(G, _result_dir + "/networks/" + name_G + '_flights_selected.pic', target=target_rejected_flights)
print "Found best capacity factor:", capacity_factor, "(rejected fraction", rejected_flights, "of flights)"
#print "Capacities:", {n:H.node[n]['capacity'] for n in H.nodes()}
write_down_capacities(H, save_file=_result_dir + '/trajectories/capacities/' + G.name + '_capacities_rec_rej' + str(target_rejected_flights) + '_new.dat')
#print "Capacities saved as", _result_dir + '/trajectories/capacities/' + G.name + '_capacities_rec_rej' + str(target_rejected_flights) + '_new.dat'
if zone in targets_eff_per_ACC.keys():
for eff_target in targets_eff_per_ACC[zone]:
for i in range(n_iter):
counter(i, n_iter, message="Doing simulations...")
name_results = name_sim(name_G) + '_eff_' + str(eff_target) + '_rej' + str(target_rejected_flights) + '_new_' + str(i) + '.dat'
with silence(True):
trajs, stats = generate_traffic(deepcopy(G), save_file=_result_dir + '/trajectories/M1/' + name_results,
record_stats_file=_result_dir + '/trajectories/M1/' + name_results.split('.dat')[0] + '_stats.dat',
file_traffic=_result_dir + "/networks/" + name_G + '_flights_selected.pic',
put_sectors=True,
remove_flights_after_midnight=True,
capacity_factor=capacity_factor,
rectificate={'eff_target':eff_target, 'inplace':False, 'hard_fixed':False, 'remove_nodes':True, 'resample_trajectories':True}
)
#trajs_rec, eff, G, groups_rec = rectificate_trajectories_network(trajs, eff_target, deepcopy(G), inplace=False)
#print "Ratio rejected:", stats['rejected_flights']/float(stats['flights'])
print
print
with open(result_dir + '/trajectories/bounds/' + G.name + '_bound_latlon.dat', 'w') as f:
for x, y in boundary:
f.write(str(x) + '\t' + str(y) + '\n')
# --------------------------------------------------------------------------------------
paras_control = {'bootstrap_mode':True, 'bootstrap_only_time':True, 'ACtot':ACtot}
#print "Finding best capacity factor..."
#capacity_factor, rejected_flights, H = find_good_scaling_capacity(G, result_dir + "/networks/" + name_G + '_flights_selected.pic',
# target=target_rejected_flights, silent=True, **paras_control)
#print "Found best capacity factor:", capacity_factor, "(rejected fraction", rejected_flights, "of flights)"
write_down_capacities(G, save_file=result_dir + '/trajectories/capacities/' + G.name + '_capacities_no_target_rej.dat')
for i in range(n_iter):
counter(i, n_iter, message="Doing simulations...")
name_results = name_sim(name_G) + '_rej' + str(target_rejected_flights) + '_ACtot' + str(ACtot) + '_' + str(i) + '.dat'
with silence(True):
trajs, stats = generate_traffic(deepcopy(G), save_file= result_dir + '/trajectories/M1/' + name_results,
record_stats_file=result_dir + '/trajectories/M1/' + name_results.split('.dat')[0] + '_stats.dat',
file_traffic=result_dir + "/networks/" + name_G + '_flights_selected.pic',
put_sectors=True,
remove_flights_after_midnight=True,
capacity_factor=1.,
**paras_control)
#print "Ratio rejected:", stats['rejected_flights']/float(stats['flights'])
print
print
sys.path.insert(1, '../..')
sys.path.insert(1, '../../abm_strategic')
import pickle
import os
from os.path import join as jn
from abm_strategic.simulationO import generate_traffic
from abm_strategic import result_dir, main_dir
#result_dir = jn(main_dir, 'tests', 'example')
if __name__ == '__main__':
with open(jn(result_dir, 'networks', 'Example.pic'), 'r') as f:
G = pickle.load(f)
os.system('mkdir -p ' + jn(result_dir, 'trajectories', 'M1'))
save_file = jn(result_dir, 'trajectories', 'M1', 'trajectories_example.dat')
paras_file = jn(main_dir, 'abm_strategic', 'my_paras.py')
trajectories = generate_traffic(G, paras_file=paras_file,
save_file=save_file,
# file_traffic=file_traffic,
# coordinates=True,
# put_sectors=True,
# save_file_capacities=save_file_capacities,
# capacity_factor=0.05,
# remove_flights_after_midnight=True,
# record_stats_file='../trajectories/M1/trajectories_stats.dat'
# rectificate={'eff_target':0.99, 'inplace':False, 'hard_fixed':False, 'remove_nodes':True, 'resample_trajectories':True},
# storymode=False,
# ACtot=4000
)
