def __init__(self, parameters: dict = {}):
"""
@param path where results go, like "Experiment ID"
@param parameters the configuration parameters.
gui: whether we show a GUI.
scenario: the path to the scenario to use
"""
logging.debug(parameters)
self._parameters = copy.deepcopy(self._DEFAULT_PARAMETERS)
self._parameters.update(parameters)
dirname = os.path.dirname(__file__)
tlPhasesFile = os.path.join(dirname, "../../scenarios/Sumo/",
self._parameters['scene'],
self._parameters['tlphasesfile'])
self._tlphases = TrafficLightPhases(tlPhasesFile)
self.ldm = ldm(using_libsumo=self._parameters['libsumo'])
self._takenActions = {}
self._yellowTimer = {}
self._chosen_action = None
self.seed(42) # in case no seed is given
self._action_space = self._getActionSpace()
self.stats_control = Control(self._parameters['tripinfofolder'])
self.factor_graph = self._parameters['factored_agents']
self.n_factors = len(list(self.factor_graph.keys()))
self.factored_coords = self._parameters['factored_coords']
#self.pixelsPerMeter = self._parameters['pixelsPerMeter']
self.testseed = list(self._parameters['test_seed'])
self.seed_cntr = 0
def test_load(self):
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, "test.tll.xml")
res = TrafficLightPhases(filename)
self.assertEqual(['0', '1'], res.getIntersectionIds())
self.assertEqual(1, res.getNrPhases('1')) # one has yellow
self.assertEqual(4, res.getNrPhases('0')) # 4 of 8 have yellow
self.assertEqual("GGggrrrrGGggrrrr", res.getPhase("0", 0))
def __init__(self, parameters: dict = {}, init_state=True):
"""
@param path where results go, like "Experiment ID"
@param parameters the configuration parameters.
gui: whether we show a GUI.
scenario: the path to the scenario to use
"""
logging.debug(parameters)
self._parameters = copy.deepcopy(self._DEFAULT_PARAMETERS)
self._parameters.update(parameters)
self._scenario_path = os.path.join(self._parameters['scenarios_path'],
self._parameters['scene'])
if self._parameters['tlphasesfile'] is None:
tlPhasesFile = self._parameters[
'tlphasesfile'] = self.get_net_file()
else:
tlPhasesFile = os.path.join(self._parameters['scenarios_path'],
self._parameters['scene'],
self._parameters['tlphasesfile'])
self._tlphases = TrafficLightPhases(tlPhasesFile)
self.ldm = ldm(using_libsumo=self._parameters['libsumo'])
self._takenActions = {}
self._yellowTimer = {}
self._chosen_action = None
self.seed(parameters['seed']) # in case no seed is given
self._action_space = self._getActionSpace()
# TODO: Wouter: make state configurable ("state factory")
if init_state:
self._state = LdmMatrixState(self.ldm, [
self._parameters['box_bottom_corner'],
self._parameters['box_top_corner']
], self._parameters["reward_range"], "byCorners")
else:
self._state = None
# Computed when needed instead of in __init__:
self._observation_space = None
def __init__(self, parameters:dict={}):
"""
@param path where results go, like "Experiment ID"
@param parameters the configuration parameters.
gui: whether we show a GUI.
scenario: the path to the scenario to use
"""
logging.debug(parameters)
self._parameters = copy.deepcopy(self._DEFAULT_PARAMETERS)
self._parameters.update(parameters)
dirname = os.path.dirname(__file__)
tlPhasesFile = os.path.join(dirname, "../../scenarios/Sumo/", self._parameters['scene'], self._parameters['tlphasesfile'])
self._tlphases = TrafficLightPhases(tlPhasesFile)
self.ldm = ldm(using_libsumo=self._parameters['libsumo'])
self._takenActions = {}
self._yellowTimer = {}
self._chosen_action = None
self.seed(42) # in case no seed is given
self._action_space = self._getActionSpace()
class SumoGymAdapter(Env):
"""
An adapter that makes Sumo behave as a proper Gym environment.
At top level, the actionspace and percepts are in a Dict with the
trafficPHASES as keys.
@param maxConnectRetries the max number of retries to connect.
A retry is needed if the randomly chosen port
to connect to SUMO is already in use.
"""
_DEFAULT_PARAMETERS = {
'gui': True, # gui or not
'scene':
'four_grid', # subdirectory in the aienvs/scenarios/Sumo directory where
'tlphasesfile': 'cross.net.xml', # file
'box_bottom_corner': (0,
0), # bottom left corner of the observable frame
'box_top_corner': (10, 10), # top right corner of the observable frame
'resolutionInPixelsPerMeterX': 1, # for the observable frame
'resolutionInPixelsPerMeterY': 1, # for the observable frame
'y_t': 6, # yellow time
'car_pr':
0.5, # for automatic route/config generation probability that a car appears
'car_tm':
2, # for automatic route/config generation when the first car appears?
'route_starts':
[], # for automatic route/config generation, ask Rolf
'route_min_segments':
0, # for automatic route/config generation, ask Rolf
'route_max_segments':
0, # for automatic route/config generation, ask Rolf
'route_ends': [], # for automatic route/config generation, ask Rolf
'generate_conf': True, # for automatic route/config generation
'libsumo': False, # whether libsumo is used instead of traci
'waiting_penalty': 1, # penalty for waiting
'new_reward': False, # some other type of reward ask Miguel
'lightPositions': {}, # specify traffic light positions
'scaling_factor': 1.0, # for rescaling the reward? ask Miguel
'maxConnectRetries': 50, # maximum reattempts to connect by Traci
}
def __init__(self, parameters: dict = {}):
"""
@param path where results go, like "Experiment ID"
@param parameters the configuration parameters.
gui: whether we show a GUI.
scenario: the path to the scenario to use
"""
logging.debug(parameters)
self._parameters = copy.deepcopy(self._DEFAULT_PARAMETERS)
self._parameters.update(parameters)
dirname = os.path.dirname(__file__)
tlPhasesFile = os.path.join(dirname, "../../scenarios/Sumo/",
self._parameters['scene'],
self._parameters['tlphasesfile'])
self._tlphases = TrafficLightPhases(tlPhasesFile)
self.ldm = ldm(using_libsumo=self._parameters['libsumo'])
self._takenActions = {}
self._yellowTimer = {}
self._chosen_action = None
self.seed(42) # in case no seed is given
self._action_space = self._getActionSpace()
self.stats_control = Control(self._parameters['tripinfofolder'])
self.factor_graph = self._parameters['factored_agents']
self.n_factors = len(list(self.factor_graph.keys()))
self.factored_coords = self._parameters['factored_coords']
#self.pixelsPerMeter = self._parameters['pixelsPerMeter']
self.testseed = list(self._parameters['test_seed'])
self.seed_cntr = 0
def step(self, actions: dict):
self._set_lights(actions)
self.ldm.step()
obs = self._observe()
done = self.ldm.isSimulationFinished()
global_reward = self._computeGlobalReward()
'''self.action_switches(actions)
actual_reward = self.actual_global_reward(global_reward)'''
# as in openai gym, last one is the info list
return obs, global_reward, done, []
def reset_test_cntr(self):
self.seed_cntr = 0
'''def actual_global_reward(self, global_reward):
global_reward['result'] += -0.1*self._action_switches['0']
return global_reward
def action_switches(self, actions:spaces.Dict):
self._action_switches = {}
for intersectionId in actions.keys():
if len(self._takenActions[intersectionId])==1:
self._action_switches[intersectionId] = 0
else:
prev_action = self._takenActions[intersectionId][-1]
if prev_action != self._intToPhaseString(intersectionId, actions.get(intersectionId)):
self._action_switches[intersectionId] = 1
else:
self._action_switches[intersectionId] = 0
return self._action_switches'''
def reset(self, episode=None):
try:
logging.debug("LDM closed by resetting")
self.ldm.close()
except:
logging.debug(
"No LDM to close. Perhaps it's the first instance of training")
if episode != None:
average_travel_times, average_travel_time = self.stats_control.log(
)
logging.info("Starting SUMO environment...")
self._startSUMO()
# TODO: Wouter: make state configurable ("state factory")
self._state = FactoredLDMMatrixState(
self.ldm, [
self._parameters['box_bottom_corner'],
self._parameters['box_top_corner']
],
factored_agents=self.factor_graph,
factored_coords=self.factored_coords)
return self._observe(), average_travel_times, average_travel_time
else:
logging.info("Starting SUMO environment...")
self._startSUMO()
# TODO: Wouter: make state configurable ("state factory")
self._state = FactoredLDMMatrixState(
self.ldm, [
self._parameters['box_bottom_corner'],
self._parameters['box_top_corner']
],
factored_agents=self.factor_graph,
factored_coords=self.factored_coords)
return self._observe()
# TODO: change the defaults to something sensible
def render(self, delay=0.0):
import colorama
colorama.init()
def move_cursor(x, y):
print("\x1b[{};{}H".format(y + 1, x + 1))
def clear():
print("\x1b[2J")
clear()
move_cursor(100, 100)
import numpy as np
np.set_printoptions(linewidth=100)
print(self._observe())
time.sleep(delay)
def seed(self, seed):
random.seed(seed)
self._seed = int(time.time())
def close(self):
self.__del__()
# TODO: Wouter: this needs to return a space and be somehow unified with gym.spaces
@property
def observation_space(self):
return self._state.update_state()
@property
def action_space(self):
return self._action_space
########## Private functions ##########################
def __del__(self):
logging.debug("LDM closed by destructor")
if 'ldm' in locals():
self.ldm.close()
def _startSUMO(self):
"""
Start the connection with SUMO as a subprocess and initialize
the traci port, generate route file.
"""
val = 'sumo-gui' if self._parameters['gui'] else 'sumo'
maxRetries = self._parameters['maxConnectRetries']
sumo_binary = checkBinary(val)
self.dirname = os.path.dirname(__file__)
outfile = self._parameters['tripinfofolder']
self.out = os.path.join(
*[self.dirname, "../../test/Stats", outfile, "tripinfo.xml"])
# Try repeatedly to connect
while True:
try:
# this cannot be seeded
self._port = random.SystemRandom().choice(
list(range(10000, 20000)))
if self._parameters['test'] == False:
self._seed = self._seed + random.randint(0, 276574)
else:
try:
self._seed = self.testseed[self.seed_cntr]
except:
self._seed = self._seed + random.randint(0, 276574)
self.seed_cntr += 1
self._sumo_helper = SumoHelper(self._parameters, self._port,
int(self._seed))
conf_file = self._sumo_helper.sumocfg_file
logging.info("Configuration: " + str(conf_file))
sumoCmd = [
sumo_binary, "-c", conf_file, "--tripinfo-output",
self.out, "--seed",
str(self._seed)
]
self.ldm.start(sumoCmd, self._port)
except Exception as e:
if str(e) == "connection closed by SUMO" and maxRetries > 0:
maxRetries = maxRetries - 1
continue
else:
raise
else:
break
self.ldm.init(
waitingPenalty=self._parameters['waiting_penalty'],
new_reward=self._parameters['new_reward']) # ignore reward for now
# used to set boundaries to compute the network space and it computes the states, i can use this to compute states based on the fatored graphs.
self.ldm.setResolutionInPixelsPerMeter(
self._parameters['resolutionInPixelsPerMeterX'],
self._parameters['resolutionInPixelsPerMeterY'])
self.ldm.setPositionOfTrafficLights(self._parameters['lightPositions'])
if list(self.ldm.getTrafficLights()
) != self._tlphases.getIntersectionIds():
raise Exception(
"environment traffic lights do not match those in the tlphasesfile "
+ self._parameters['tlphasesfile'] +
str(self.ldm.getTrafficLights()) +
str(self._tlphases.getIntersectionIds()))
def _intToPhaseString(self, intersectionId: str, lightPhaseId: int):
"""
@param intersectionid the intersection(light) id
@param lightvalue the PHASES value
@return the intersection PHASES string eg 'rrGr' or 'GGrG'
"""
logging.debug("lightPhaseId" + str(lightPhaseId))
return self._tlphases.getPhase(intersectionId, lightPhaseId)
def _observe(self):
"""
Fetches the Sumo state and converts in a proper gym observation.
The keys of the dict are the intersection IDs (roughly, the trafficLights)
The values are the state of the TLs
"""
return self._state.update_state()
def _computeGlobalReward(self):
"""
Computes the global reward
"""
return self._state.update_reward()
def _getActionSpace(self):
"""
@returns the actionspace: a dict containing <id,phases> where
id is the intersection id and value is
all possible actions for each id as specified in tlphases
"""
return spaces.Dict({inters:spaces.Discrete(self._tlphases.getNrPhases(inters)) \
for inters in self._tlphases.getIntersectionIds()})
def _set_lights(self, actions: spaces.Dict):
"""
Take the specified actions in the environment
@param actions a list of
"""
for intersectionId in actions.keys():
action = self._intToPhaseString(intersectionId,
actions.get(intersectionId))
# Retrieve the action that was taken the previous step
try:
prev_action = self._takenActions[intersectionId][-1]
except KeyError:
# If KeyError, this is the first time any action was taken for this intersection
prev_action = action
self._takenActions.update({intersectionId: []})
self._yellowTimer.update({intersectionId: 0})
# Check if the given action is different from the previous action
if prev_action != action:
# Either the this is a true switch or coming grom yellow
action, self._yellowTimer[
intersectionId] = self._correct_action(
prev_action, action, self._yellowTimer[intersectionId])
# Set traffic lights
self.ldm.setRedYellowGreenState(intersectionId, action)
self._takenActions[intersectionId].append(action)
def _correct_action(self, prev_action, action, timer):
"""
Check what we are going to do with the given action based on the
previous action.
"""
# Check if the agent was in a yellow state the previous step
if 'y' in prev_action:
# Check if this agent is in the middle of its yellow state
if timer > 0:
new_action = prev_action
timer -= 1
# Otherwise we can get out of the yellow state
else:
new_action = self._chosen_action
if not isinstance(new_action, str):
raise Exception("chosen action is illegal")
# We are switching from green to red, initialize the yellow state
else:
self._chosen_action = action
if self._parameters['y_t'] > 0:
new_action = prev_action.replace('G', 'y')
timer = self._parameters['y_t'] - 1
else:
new_action = action
timer = 0
return new_action, timer
class SumoGymAdapter(Env):
"""
An adapter that makes Sumo behave as a proper Gym environment.
At top level, the actionspace and percepts are in a Dict with the
trafficPHASES as keys.
@param maxConnectRetries the max number of retries to connect.
A retry is needed if the randomly chosen port
to connect to SUMO is already in use.
"""
_DEFAULT_PARAMETERS = {
'gui':
True, # gui or not
'scenarios_path':
os.path.join(os.path.dirname(__file__), "../../scenarios/Sumo/"),
'scene':
'four_grid', # subdirectory in the aienvs/scenarios/Sumo directory where
'tlphasesfile':
None, # Use None to read phases from net file, otherwise only relative name
'box_bottom_corner':
(0, 0), # bottom left corner of the observable frame
'box_top_corner': (10, 10), # top right corner of the observable frame
'resolutionInPixelsPerMeterX':
1, # for the observable frame
'resolutionInPixelsPerMeterY':
1, # for the observable frame
'y_t':
6, # yellow time
'generate_conf':
True, # for automatic route/config generation
'simulation_start_time':
'0', # The start time of the sumo simulation in seconds
'reward_range': [100],
'route_generation_method':
'undefined', # One of ['legacy', 'randomTrips.py', 'activitygen']
# Options for 'route_generation_method' 'activitygen'
'activitygen_options': [], # e.g. ["--end", endtime]
'stat_file':
None, # stat file used. Leave none for automatic search.
# Options for 'route_generation_method' 'randomTrips.py'
'trips_generate_options':
[], # sumo/tools/randomTrips.py additional options. -n, -o, --validate already handled!
# Custom route and trip generation if route_generation_method is set to 'legacy'
'car_pr':
0.5, # for automatic route/config generation probability that a car appears
'car_tm':
2, # for automatic route/config generation when the first car appears?
'route_starts': [], # for automatic route/config generation, ask Rolf
'route_min_segments':
0, # for automatic route/config generation, ask Rolf
'route_max_segments':
0, # for automatic route/config generation, ask Rolf
'route_ends': [], # for automatic route/config generation, ask Rolf
'seed':
None, # Used to seed sumo, and to generate the traffic by all generation methods.
'libsumo':
False, # whether libsumo is used instead of traci
'waiting_penalty':
1, # penalty for waiting
'new_reward':
False, # some other type of reward ask Miguel
'lightPositions': {}, # specify traffic light positions
'scaling_factor':
1.0, # for rescaling the reward? ask Miguel
'maxConnectRetries':
50, # maximum reattempts to connect by Traci
'seed':
None,
'reward_function':
"default", #options include default, eval and elise
'maxConnectRetries':
50 # maximum reattempts to connect by Traci
}
def __init__(self, parameters: dict = {}, init_state=True):
"""
@param path where results go, like "Experiment ID"
@param parameters the configuration parameters.
gui: whether we show a GUI.
scenario: the path to the scenario to use
"""
logging.debug(parameters)
self._parameters = copy.deepcopy(self._DEFAULT_PARAMETERS)
self._parameters.update(parameters)
self._scenario_path = os.path.join(self._parameters['scenarios_path'],
self._parameters['scene'])
if self._parameters['tlphasesfile'] is None:
tlPhasesFile = self._parameters[
'tlphasesfile'] = self.get_net_file()
else:
tlPhasesFile = os.path.join(self._parameters['scenarios_path'],
self._parameters['scene'],
self._parameters['tlphasesfile'])
self._tlphases = TrafficLightPhases(tlPhasesFile)
self.ldm = ldm(using_libsumo=self._parameters['libsumo'])
self._takenActions = {}
self._yellowTimer = {}
self._chosen_action = None
self.seed(parameters['seed']) # in case no seed is given
self._action_space = self._getActionSpace()
# TODO: Wouter: make state configurable ("state factory")
if init_state:
self._state = LdmMatrixState(self.ldm, [
self._parameters['box_bottom_corner'],
self._parameters['box_top_corner']
], self._parameters["reward_range"], "byCorners")
else:
self._state = None
# Computed when needed instead of in __init__:
self._observation_space = None
def update_parameters(self, updated_params: dict):
"""
Updates the parameters. Please note that some of the
parameter changes may only be propagated after resetting
the environment.
"""
self._parameters.update(updated_params)
def set_stat_file(self, stat_file: str):
self.update_parameters({"stat_file": stat_file})
def _compute_observation_space(self):
self._startSUMO(gui=False)
_s = self._observe()
self.frame_height = _s.shape[0]
self.frame_width = _s.shape[1]
return Box(low=0,
high=1.0,
shape=(self.frame_height, self.frame_width),
dtype=np.float32)
def step(self, actions: dict):
self._set_lights(actions)
self.ldm.step()
obs = self._observe()
done = self.ldm.isSimulationFinished()
global_reward_list = self._computeGlobalReward(
self._parameters['reward_function'])
if len(self._parameters['reward_range']) == 1:
return obs, global_reward_list[self._parameters['reward_range']
[0]], done, []
else:
return obs, global_reward_list, done, []
# as in openai gym, last one is the info list
# return obs, global_reward, done, []
def reset(self):
try:
logging.debug("LDM closed by resetting")
self.ldm.close()
except:
logging.debug(
"No LDM to close. Perhaps it's the first instance of training")
logging.debug("Starting SUMO environment...")
self._startSUMO()
return self._observe()
# TODO: change the defaults to something sensible
def render(self, delay=0.0):
import colorama
colorama.init()
def move_cursor(x, y):
print("\x1b[{};{}H".format(y + 1, x + 1))
def clear():
print("\x1b[2J")
clear()
move_cursor(100, 100)
import numpy as np
np.set_printoptions(linewidth=100)
print(self._observe())
time.sleep(delay)
def seed(self, seed):
self._seed = seed
def close(self):
self.__del__()
@property
def observation_space(self):
# # this is the previous method, which does not take resolution into consideration
# size = self._state.size()
# return Box(low=0, high=np.inf, shape=(size[0], size[1]), dtype=np.int32)
if self._observation_space is None:
self._observation_space = self._compute_observation_space()
return self._observation_space
@property
def action_space(self):
return self._action_space
########## Private functions ##########################
def __del__(self):
logging.debug("LDM closed by destructor")
if 'ldm' in locals():
self.ldm.close()
def _startSUMO(self, gui=None):
"""
Start the connection with SUMO as a subprocess and initialize
the traci port, generate route file.
"""
val = 'sumo'
if gui is True:
val = 'sumo-gui'
elif gui is None:
val = 'sumo-gui' if self._parameters['gui'] else 'sumo'
maxRetries = self._parameters['maxConnectRetries']
sumo_binary = checkBinary(val)
# Try repeatedly to connect
while True:
try:
# this cannot be seeded
self._port = random.SystemRandom().choice(
list(range(10000, 20000)))
self._sumo_helper = SumoHelper(self._parameters, self._port,
self._seed)
conf_file = self._sumo_helper.sumocfg_file
logging.debug("Configuration: " + str(conf_file))
sumoCmd = [sumo_binary, "-c", conf_file, "-W", "-v",
"false"] # shut up SUMO
if self._seed is not None:
sumoCmd += ["--seed", str(self._seed)]
self.ldm.start(sumoCmd, self._port)
except Exception as e:
if str(e) == "connection closed by SUMO" and maxRetries > 0:
maxRetries = maxRetries - 1
continue
else:
raise
else:
break
self.ldm.init(
waitingPenalty=self._parameters['waiting_penalty'],
new_reward=self._parameters['new_reward']) # ignore reward for now
self.ldm.setResolutionInPixelsPerMeter(
self._parameters['resolutionInPixelsPerMeterX'],
self._parameters['resolutionInPixelsPerMeterY'])
self.ldm.setPositionOfTrafficLights(self._parameters['lightPositions'])
if list(self.ldm.getTrafficLights()
) != self._tlphases.getIntersectionIds():
raise Exception(
"environment traffic lights do not match those in the tlphasesfile "
+ self._parameters['tlphasesfile'] +
str(self.ldm.getTrafficLights()) +
str(self._tlphases.getIntersectionIds()))
def _intToPhaseString(self, intersectionId: str, lightPhaseId: int):
"""
@param intersectionid the intersection(light) id
@param lightvalue the PHASES value
@return the intersection PHASES string eg 'rrGr' or 'GGrG'
"""
logging.debug("lightPhaseId" + str(lightPhaseId))
return self._tlphases.getPhase(intersectionId, lightPhaseId)
def _observe(self):
"""
Fetches the Sumo state and converts in a proper gym observation.
The keys of the dict are the intersection IDs (roughly, the trafficLights)
The values are the state of the TLs
"""
return self._state.update_state()
def _computeGlobalReward(self, function):
"""
Computes the global reward
"""
rewards: dict = self._state.update_reward(function)
for k in rewards.keys():
rewards[k] = rewards[k] / self._parameters['scaling_factor']
return rewards
def _getActionSpace(self):
"""
@returns the actionspace: a dict containing <id,phases> where
id is the intersection id and value is
all possible actions for each id as specified in tlphases
"""
return spaces.Dict({inters:spaces.Discrete(self._tlphases.getNrPhases(inters)) \
for inters in self._tlphases.getIntersectionIds()})
def _set_lights(self, actions: spaces.Dict):
"""
Take the specified actions in the environment
@param actions a list of
"""
for intersectionId in actions.keys():
action = self._intToPhaseString(intersectionId,
actions.get(intersectionId))
# Retrieve the action that was taken the previous step
try:
prev_action = self._takenActions[intersectionId]
except KeyError:
# If KeyError, this is the first time any action was taken for this intersection
prev_action = action
self._takenActions.update({intersectionId: action})
self._yellowTimer.update({intersectionId: 0})
# Check if the given action is different from the previous action
if prev_action != action:
# Either the this is a true switch or coming grom yellow
action, self._yellowTimer[
intersectionId] = self._correct_action(
prev_action, action, self._yellowTimer[intersectionId])
# Set traffic lights
self.ldm.setRedYellowGreenState(intersectionId, action)
self._takenActions[intersectionId] = action
def _correct_action(self, prev_action, action, timer):
"""
Check what we are going to do with the given action based on the
previous action.
"""
# Check if the agent was in a yellow state the previous step
if 'y' in prev_action:
# Check if this agent is in the middle of its yellow state
if timer > 0:
new_action = prev_action
timer -= 1
# Otherwise we can get out of the yellow state
else:
new_action = self._chosen_action
if not isinstance(new_action, str):
raise Exception("chosen action is illegal")
# We are switching from green to red, initialize the yellow state
else:
self._chosen_action = action
if self._parameters['y_t'] > 0:
new_action = prev_action.replace('G', 'y')
timer = self._parameters['y_t'] - 1
else:
new_action = action
timer = 0
return new_action, timer
# Returns full path
def get_net_file(self):
net_files = glob.glob(self._scenario_path + '/*.net.xml')
assert len(
net_files
) == 1, f"Expected exactly one netfile, but netfiles: {net_files}"
return net_files[0]