def generate_traffic(G, paras_file=None, save_file=None, simple_setup=True, starting_date=[2010, 5, 6, 0, 0, 0],\
coordinates=True, generate_altitudes=True, put_sectors=False, save_file_capacities=None,
record_stats_file=None, remove_flights_after_midnight=False, rectificate=None, storymode=False, **paras_control):
"""
High level function to create traffic on a given network with given parameters.
It is not really intented to use as a simulation by itself, but only to generate
some synthetic traffic, mainly for the tactical ABM.
Returns a set of M1 trajectories.
If simple_setup is True, the function uses some default parameters suitable for
quick generation of traffic.
Parameters
----------
G : hybrid network
on which to generate the traffic.
paras_file : string, optional
path for reading the parameters for the simulations. If None, reads my_paras.py.
save_file : string
file for saving trajectories with the abm_tactical format.
simple_setup : boolean
if False, all parameters must be informed. Otherwise some default parameters
are used.
starting_date : list or tuple of int, optional
gives the starting date for the simulations. It is used to have the right dates
in output.
coordinates : boolean, optional
If True, return list of coordinates instead list of labels of navpoints.
generate_altitudes : boolean, optional
If True, generate synthetic altitudes in output. The altitudes are bootstrapped
using the file_traffic file informed in the paras file.
put_sectors : boolean, optional
If True, the trajectories in ouput have a fifth element which is the sector.
save_file_capacities : string, optional
If not None, the capacities of the network are written in a txt file in a
format readable by the tactical ABM.
record_stats_file : string, optional
If informed, the visual output of the funtion is written on the file.
remove_flights_after_midnight : boolean, optional
If True, remove from the trajectories all the ones which land the day after
starting_date.
rectificate : dictionary, optional
If informed, the trajctories will be rectified using the function
rectificate_trajectories_network_with_time with parameters given by the dictionary
storymode : boolean, optional
set the verbosity of the simulation itself.
paras_control : additional parameters
of values which are externally controlled. Typically,
the number of flights.
Returns
-------
trajectories_coords : list
of trajectories. Each point in the trajectories has the format (x, y, z, t), (x, y, z, t, s)
or are directly (label, z, t).
stats : dictionary
with some results about the simulation, like the number of flights rejected, etc.
Notes
-----
New in 2.9.4.
Changed in 2.9.5: Added synthetic altitudes generation.
"""
print ("Generating traffic on network...")
paras = read_paras(paras_file=paras_file, post_process=False)
if simple_setup:
paras['file_net'] = None
paras['G'] = G
paras['Nfp'] = G.Nfp # Remark: must match number of pre-computed nav-shortest paths per sec-shortest paths.
paras['Nsp_nav'] = 2
paras['unit'] = 15
paras['days'] = 24.*60.
paras['file_traffic'] = None
paras['ACtot'] = 1000
paras['control_density'] = False
paras['departure_times'] = 'uniform'
paras['noise'] = 0.
paras['nA'] = 1.
paras['par'] = [[1.,0.,0.001], [1.,0.,1000.]]
paras['STS'] = None
paras['N_shocks'] = 0.
paras['parallel'] = True
paras['old_style_allocation'] = False
paras['force'] = True
paras['capacity_factor'] = True
paras['bootstrap_mode'] = True
paras['bootstrap_only_time'] = True
#print (paras_control)
for p,v in paras_control.items():
paras[p] = v
paras = post_process_paras(paras)
G = paras['G']
print ("Average capacity:", np.mean([paras['G'].node[n]['capacity'] for n in paras['G'].nodes()]))
if 'traffic' in paras.keys():
print ("Number of flights in traffic:", len(paras['traffic']))
#print ("Capacities:", {n:G.node[n]['capacity'] for n in G.nodes()})
with clock_time():
sim = Simulation(paras, G=G, verbose=True)
sim.make_simu(storymode=storymode)
sim.compute_flags()
queue = post_process_queue(sim.queue)
M0_queue = post_process_queue(sim.M0_queue)
print
if record_stats_file!=None:
ff = open(record_stats_file, 'w')
else:
ff = sys.stdout
stats = {}
print ('Number of rejected flights:', len([f for f in sim.queue if not f.accepted]), '/', len(sim.queue), file=ff)
print ('Number of rejected flight plans:', len([fp for f in sim.queue for fp in f.FPs if not fp.accepted]), '/', len(sim.queue)*sim.Nfp, file=ff)
print ('', file=ff)
stats['rejected_flights'] = len([f for f in sim.queue if not f.accepted])
stats['rejected_flight_plans'] = len([fp for f in sim.queue for fp in f.FPs if not fp.accepted])
stats['flights'] = len(sim.queue)
print ('Global metrics for M1:', file=ff)
agg_results = extract_aggregate_values_on_queue(queue, paras['par'])
for met, res in agg_results.items():
for ac, met_res in res.items():
print ('-', met, "for companies of type", ac, ":", met_res, file=ff)
print ('', file=ff)
if paras['N_shocks']!=0:
agg_results = extract_aggregate_values_on_queue(M0_queue, paras['par'])
for met, res in agg_results.items():
for ac, met_res in res.items():
print ('-', met, "for companies of type", ac, ":", met_res, file=ff)
if record_stats_file!=None:
ff.close()
trajectories = compute_M1_trajectories(queue, sim.starting_date)
#signature at this point: (n), tt
if rectificate!=None:
eff_target = rectificate['eff_target']
del rectificate['eff_target']
trajectories, eff, G, groups_rec = rectificate_trajectories_network_with_time(trajectories, eff_target, deepcopy(G), **rectificate)
# signature at this point : (n), tt
if save_file_capacities!=None:
write_down_capacities(G, save_file=save_file_capacities)
if coordinates:
Converter = TrajConverter()
Converter.set_G(G.G_nav)
fmt_out = '(x, y, z, t, s)' if put_sectors else '(x, y, z, t)'
trajectories_coords = Converter.convert(trajectories, fmt_in='(n), t', fmt_out=fmt_out,
#put_sectors=put_sectors,
remove_flights_after_midnight=remove_flights_after_midnight,
starting_date=starting_date)
# trajectories_coords = convert_trajectories(G.G_nav, trajectories, put_sectors=put_sectors,
# remove_flights_after_midnight=remove_flights_after_midnight,
# starting_date=starting_date)
#signature at this point: (x, y, 0, tt) or (x, y, 0, tt, s)
if generate_altitudes and paras['file_traffic']!=None:
print ("Generating synthetic altitudes...")
# Insert synthetic altitudes in trajectories based on a sampling of file_traffic
with silence(True):
small_sample = G.check_all_real_flights_are_legitimate(paras['traffic'], repair=True)
print ("Kept", len(small_sample), "flights for sampling altitudes.")
sample_trajectories = convert_distance_trajectories_coords(G.G_nav, small_sample, put_sectors=put_sectors)
trajectories_coords = insert_altitudes(trajectories_coords, sample_trajectories)
#signature at this point: (x, y, z, tt) or (x, y, z, tt, s)
dummy_sector = None if not put_sectors else -1
trajectories_coords = add_first_last_points(trajectories_coords, dummy_sec=dummy_sector)
if save_file!=None:
os.system('mkdir -p '+dirname(save_file))
write_trajectories_for_tact(trajectories_coords, fil=save_file)
return trajectories_coords, stats
else:
return trajectories, stats
return trajectories, stats
if __name__=='__main__':
"""
===========================================================================
Manual single simulation
===========================================================================
"""
paras_file = None if len(sys.argv)==1 else sys.argv[1]
paras = read_paras(paras_file=paras_file)
GG = paras['G'] #ABMvars.G
print (header(paras,'SimulationO', version, paras_to_display=['ACtot']))
with clock_time():
sim = Simulation(paras, G=GG, verbose=True)
sim.make_simu(storymode=False)
sim.compute_flags()
queue = post_process_queue(sim.queue)
M0_queue = post_process_queue(sim.M0_queue)
"""
===========================================================================
Some snippets to view the results.
===========================================================================
"""
if 0:
for n in sim.G.nodes():
#print n, sim.G.node[n]['capacity'], sim.G.node[n]['load']
if max(sim.G.node[n]['load']) == sim.G.node[n]['capacity']: