def test_can_run_gym_interface_on_mixeduse_fan_fcu():
env = make("MixedUseFanFCU-v0")
episodes = 2
n_steps_per_episode = 100
for _ in range(episodes):
observation = env.get_output()
print(observation)
for _ in range(n_steps_per_episode):
action = env.sample_random_action()
observation = env.step(action)
print("Episode finished")
env.reset()
env.close()
def test_can_run_gym_interface_on_offices_thermostat():
env = make("OfficesThermostat-v0")
episodes = 2
n_steps_per_episode = 100
for _ in range(episodes):
observation = env.get_output()
print(observation)
for _ in range(n_steps_per_episode):
action = env.sample_random_action()
observation = env.step(action)
print("Episode finished")
env.reset()
env.close()
def test_can_run_gym_interface_on_seminarcenter_full():
env = make("SeminarcenterFull-v0")
episodes = 2
n_steps_per_episode = 100
for _ in range(episodes):
observation = env.get_output()
print(observation)
for _ in range(n_steps_per_episode):
action = env.sample_random_action()
observation = env.step(action)
print("Episode finished")
env.reset()
env.close()
def test_can_run_gym_interface_on_apartments2_grid():
env = make("Apartments2Grid-v0")
episodes = 2
n_steps_per_episode = 100
for _ in range(episodes):
observation = env.get_output()
print(observation)
for _ in range(n_steps_per_episode):
action = env.sample_random_action()
observation = env.step(action)
print("Episode finished")
env.reset()
env.close()
def run_model(model_name: str):
env = energym.make(model_name)
print(model_name)
# Init data record
N = round((env.stop_time - env.start_time) / env.step_size)
outputs = env.get_outputs_names()
record = pd.DataFrame(
np.zeros((N, len(outputs) + 1)),
index=np.arange(N) * env.step_size,
columns=["u"] + outputs,
)
model_param(env, model_name)
ctrl = control_param(model_name)
# Get states
out = env.get_variable_data(outputs)
# Run feedback loop
tic = time.time()
for dt in record.index:
# Get control law
uval = [[
controller(out[ctrl["sensor"][n]], ctrl["ref"][n], ctrl["kp"][n],
ctrl["bound"][n])
] for n in range(len(ctrl["inputs"]))]
# DoStep
out = env.step(dict(zip(ctrl["inputs"], uval)))
# Save into DataFrame
record.loc[dt, ctrl["inputs"]] = [val[0] for val in uval]
record.loc[dt, outputs] = [out[key] for key in outputs]
toc = time.time()
print("Elapsed time: %.2f s" % (toc - tic))
return record, env