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 do_ABM_tactical(input_file, output_file, config_file, verbose=2, shock_tmp=jn(main_dir, 'abm_tactical/config/shock_tmp.dat'), bound_latlon=jn(main_dir, 'abm_tactical/config/bound_latlon.dat'), temp_nvp=jn(main_dir, 'abm_tactical/config/temp_nvp.dat'), capacity_file=main_dir+'/abm_tactical/config/sector_capacities.dat'): """ Main function of control of the tactical ABM. The function uses tactical_simulation, which has beem compiled precedently using the wrapper. Parameters ---------- input_file : string full path to M1 file (planned trajectories) output_file : string full path to save M3 file (trajectories after control) config_file : string full path to config file. verbose : integer, optional verbosity shock_tmp : string, optional full path to file containing the possible coordinates of shocks. bound_latlon : string, optional full path to file containing the coordinates of the boundary of the controlled airspace. temp_nvp : string full path to file containing temporary navigation points used in the simulations. """ for fil, name in [(shock_tmp, 'shock_tmp'), (bound_latlon, 'bound_file'), (temp_nvp, 'temp_nvp'), (capacity_file, 'capacity_file')]: choose_paras(name, fil, fil=config_file) try: inpt = ["", input_file, output_file, config_file] if verbose>1: print "M1 source:", inpt[1] print "Destination output:", inpt[2] print "Config file:", inpt[3] print print "Running ABM Tactical model..." with silence(verbose==0): # Does not work. tactical_simulation(inpt) except: pass #TODO if verbose==2: print print "Done."
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