def run_aggregate(self,
in_group_a_strength,
in_group_a_range,
in_group_r_strength,
in_group_r_range,
out_group_a_strength,
out_group_a_range,
out_group_r_strength,
out_group_r_range,
target_a_strength,
target_a_range,
context,
spawn_new_pedestrians=False,
simulation=True,
drawing=True):
""" initialize social force model """
force_model.set_parameters(self.parameters)
self.spawn_new_pedestrians = spawn_new_pedestrians
self.drawing = drawing
total_group_num = len(self.parameters['group_num'])
''' set parameter '''
self.parameters['in_group_a_strength'] = in_group_a_strength
self.parameters['in_group_a_range'] = in_group_a_range
self.parameters['in_group_r_strength'] = in_group_r_strength
self.parameters['in_group_r_range'] = in_group_r_range
self.parameters['out_group_a_strength'] = out_group_a_strength
self.parameters['out_group_a_range'] = out_group_a_range
self.parameters['out_group_r_strength'] = out_group_r_strength
self.parameters['out_group_r_range'] = out_group_r_range
self.parameters['target_a_strength'] = target_a_strength
self.parameters['target_a_range'] = target_a_range
self.flowrate_simulations = []
self.flowrate = 0
#we only measure pedestrians inside this area
self.turning_angles = []
self.effective_evacuation = []
self.simulation_index = "%s" % str(datetime.now().microsecond)
population_generator = PopulationGenerator(
self.parameters, in_group_a_strength, in_group_a_range,
in_group_r_strength, in_group_r_range, out_group_a_strength,
out_group_a_range, out_group_r_strength, out_group_r_range,
target_a_strength, target_a_range)
""" perform simulation over context_placement_num"""
radii_generators = context._get_radii_generators()
placement_generators = context._get_placement_generators()
t = len(placement_generators)
current_simulation_run = 0
while current_simulation_run < 1: #len(placement_generators)/total_group_num :
simulation_id = "%s" % (self.simulation_index + "_" +
str(current_simulation_run + 1))
print(">> running simulation %s" % (simulation_id))
self._init_observation_plots()
index = current_simulation_run * total_group_num
radii_generator = radii_generators[index:index + total_group_num]
placement_generator = placement_generators[index:index +
total_group_num]
generated_group_member_index = population_generator._generate_population(
radii_generator, placement_generator)
group_pedestrians1 = population_generator._get_generated_group_pedestrians_population(
)
bio_index = int(len(radii_generators) / 2) + index
radii_generator = radii_generators[bio_index:bio_index +
total_group_num]
placement_generator = placement_generators[bio_index:bio_index +
total_group_num]
population_generator._generate_population(
radii_generator, placement_generator,
generated_group_member_index)
group_pedestrians2 = population_generator._get_generated_group_pedestrians_population(
)
group_pedestrians = group_pedestrians1 + group_pedestrians2
self._run(index, simulation_id, group_pedestrians)
self.plots._dump_influential_matrix(current_simulation_run)
self.flowrate_simulations.append(self.flowrate)
#turning_angels
angels = self.plots.get_turning_angles()
#print(angels)
for item in angels:
self.turning_angles.append(item)
#get effective evacuation
effectiveness = self.plots.get_effective_evacuation()
#print(effectiveness)
for item in effectiveness:
self.effective_evacuation.append(item)
self.plots.reset_sample()
force_model.reset_model()
self.flowrate = 0
current_simulation_run += 1
def run_aggregate(self,
in_group_a_strength,
in_group_a_range,
in_group_r_strength,
in_group_r_range,
out_group_a_strength,
out_group_a_range,
out_group_r_strength,
out_group_r_range,
target_a_strength,
target_a_range,
context,
spawn_new_pedestrians=False,
simulation=True,
drawing=True):
""" initialize social force model """
force_model.set_parameters(self.parameters)
self.spawn_new_pedestrians = spawn_new_pedestrians
self.drawing = drawing
total_group_num = len(self.parameters['group_num'])
''' set parameter '''
self.parameters['in_group_a_strength'] = in_group_a_strength
self.parameters['in_group_a_range'] = in_group_a_range
self.parameters['in_group_r_strength'] = in_group_r_strength
self.parameters['in_group_r_range'] = in_group_r_range
self.parameters['out_group_a_strength'] = out_group_a_strength
self.parameters['out_group_a_range'] = out_group_a_range
self.parameters['out_group_r_strength'] = out_group_r_strength
self.parameters['out_group_r_range'] = out_group_r_range
self.parameters['target_a_strength'] = target_a_strength
self.parameters['target_a_range'] = target_a_range
self.flowrate_simulations = []
self.flowrate = 0
#we only measure pedestrians inside this area
self.turning_angles = []
self.effective_evacuation = []
self.simulation_index = "%s" % str(datetime.now().microsecond)
population_generator = PopulationGenerator(
self.parameters, in_group_a_strength, in_group_a_range,
in_group_r_strength, in_group_r_range, out_group_a_strength,
out_group_a_range, out_group_r_strength, out_group_r_range,
target_a_strength, target_a_range)
""" perform simulation over context_placement_num"""
radii_generators = context._get_radii_generators()
placement_generators = context._get_placement_generators()
current_simulation_run = 0
while current_simulation_run < 1: #len(placement_generators)/total_group_num :
simulation_id = "%s" % (self.simulation_index + "_" +
str(current_simulation_run + 1))
print(">> running simulation %s" % (simulation_id))
self._init_observation_plots()
index = current_simulation_run * total_group_num
group_pedestrians = []
if self.spawn_new_pedestrians == False:
disp_level = self.parameters[
'out_group_r_strength'] / self.parameters[
'in_group_r_strength']
frame_filename = "%s" % (str(disp_level) + "_" + str(index) +
"_0")
adaptivecontext_file = open(
"%s.json" %
os.path.join(self.framecontext_dir, frame_filename))
json_str = adaptivecontext_file.read()
currentframecontext = json.loads(json_str,
cls=FrameContextLog_Decoder)
pedestrian_list = currentframecontext.get_pedestrian_list()
adaptivecontext_file.close()
additional_ped_info = dict()
for pedestrian in pedestrian_list:
ped_id = pedestrian['pedestrian_id']
friend_zone = pedestrian['friend_zone']
additional_ped_info[ped_id] = friend_zone
radii_generator1 = radii_generators[index:index +
total_group_num]
placement_generator1 = placement_generators[index:index +
total_group_num]
population_generator._generate_population(
radii_generator1, placement_generator1)
group_pedestrians1 = population_generator._initialize_generated_group_pedestrians_friend_zone(
additional_ped_info)
bio_index = int(len(radii_generators) / 2) + index
radii_generator2 = radii_generators[bio_index:bio_index +
total_group_num]
placement_generator2 = placement_generators[
bio_index:bio_index + total_group_num]
population_generator._generate_population(
radii_generator2, placement_generator2,
len(group_pedestrians1))
group_pedestrians2 = population_generator._initialize_generated_group_pedestrians_friend_zone(
additional_ped_info)
group_pedestrians = group_pedestrians1 + group_pedestrians2
else:
radii_generator1 = radii_generators[index:index +
total_group_num]
placement_generator1 = placement_generators[index:index +
total_group_num]
population_generator._generate_population(
radii_generator1, placement_generator1)
ids_each_group = population_generator._get_ids_group()
group_pedestrians1 = population_generator._initialize_generated_group_pedestrians_population(
ids_each_group)
bio_index = int(len(radii_generators) / 2) + index
radii_generator2 = radii_generators[bio_index:bio_index +
total_group_num]
placement_generator2 = placement_generators[
bio_index:bio_index + total_group_num]
population_generator._generate_population(
radii_generator2, placement_generator2,
len(group_pedestrians1))
ids_each_group2 = population_generator._get_ids_group()
group_pedestrians2 = population_generator._initialize_generated_group_pedestrians_population(
ids_each_group2)
group_pedestrians = group_pedestrians1 + group_pedestrians2
self._run(index, simulation_id, group_pedestrians)
self.plots._dump_influential_matrix(current_simulation_run)
self.flowrate_simulations.append(self.flowrate)
#turning_angels
angels = self.plots.get_turning_angles()
#print(angels)
for item in angels:
self.turning_angles.append(item)
#get effective evacuation
effectiveness = self.plots.get_effective_evacuation()
#print(effectiveness)
for item in effectiveness:
self.effective_evacuation.append(item)
self.plots.reset_sample()
force_model.reset_model()
self.flowrate = 0
current_simulation_run += 1
def run_aggregate(self,
in_group_a_strength,
in_group_a_range,
in_group_r_strength,
in_group_r_range,
out_group_a_strength,
out_group_a_range,
out_group_r_strength,
out_group_r_range,
target_a_strength,
target_a_range,
context,
spawn_new_pedestrians=False,
simulation=True,
drawing=True):
""" initialize social force model """
force_model.set_parameters(self.parameters)
self.spawn_new_pedestrians = spawn_new_pedestrians
self.drawing = drawing
total_group_num = len(self.parameters['group_num'])
''' set parameter '''
self.parameters['in_group_a_strength'] = in_group_a_strength
self.parameters['in_group_a_range'] = in_group_a_range
self.parameters['in_group_r_strength'] = in_group_r_strength
self.parameters['in_group_r_range'] = in_group_r_range
self.parameters['out_group_a_strength'] = out_group_a_strength
self.parameters['out_group_a_range'] = out_group_a_range
self.parameters['out_group_r_strength'] = out_group_r_strength
self.parameters['out_group_r_range'] = out_group_r_range
self.parameters['target_a_strength'] = target_a_strength
self.parameters['target_a_range'] = target_a_range
#measure by x-position, start from 0 (coordinator -30)
avg_velocity_x_position = [[] for i in range(601)]
self.flowrate_simulations = []
self.flowrate = 0
self.simulation_index = "%s" % str(datetime.now().microsecond)
population_generator = PopulationGenerator(
self.parameters, in_group_a_strength, in_group_a_range,
in_group_r_strength, in_group_r_range, out_group_a_strength,
out_group_a_range, out_group_r_strength, out_group_r_range,
target_a_strength, target_a_range)
""" perform simulation over context_placement_num"""
radii_generators = context._get_radii_generators()
placement_generators = context._get_placement_generators()
current_simulation_run = 0
while current_simulation_run < 1: #len(placement_generators)/total_group_num :
simulation_id = "%s" % (self.simulation_index + "_" +
str(current_simulation_run + 1))
print(">> running simulation %s" % (simulation_id))
self._init_observation_plots()
index = current_simulation_run * total_group_num
radii_generator = radii_generators[index:index + total_group_num]
placement_generator = placement_generators[index:index +
total_group_num]
population_generator._generate_population(radii_generator,
placement_generator)
group_pedestrians = population_generator._get_generated_group_pedestrians_population(
)
self._run(index, simulation_id, group_pedestrians)
self.flowrate_simulations.append(self.flowrate)
#get velocity over x-position
average_velocity_x_position = self.plots.get_velocity_by_x_position(
)
print(average_velocity_x_position)
for scale_meter in range(len(average_velocity_x_position)):
avg_velocity_x_position[scale_meter].append(
average_velocity_x_position[scale_meter])
seperation_c_value = out_group_r_strength / in_group_r_strength
self.plots.dump_velocity_log(str(seperation_c_value),
str(current_simulation_run + 1))
self.plots._dump_influential_matrix(current_simulation_run)
self.plots.reset_sample()
force_model.reset_model()
self.flowrate = 0
current_simulation_run += 1
'''compute velocity over x-direction of 50 simulation times '''
self.avg_multi_velocity_x_position_scale = [0] * 601
for scale_meter in range(len(avg_velocity_x_position)):
if len(avg_velocity_x_position[scale_meter]) > 0:
self.avg_multi_velocity_x_position_scale[
scale_meter] = np.mean(
avg_velocity_x_position[scale_meter])