from metrics import Metrics
voltage_controller_metrics_vd = Metrics(
df_master_CVVd,
vd_ref[0],
ts,
max_episode_steps,
position_steady_state=position_steady_state,
position_settling_time=position_settling_time)
d = {
'Overshoot': [voltage_controller_metrics_vd.overshoot()],
'Rise Time/s ': [voltage_controller_metrics_vd.rise_time()],
'Settling Time/s ': [voltage_controller_metrics_vd.settling_time()],
'Root Mean Squared Error/V': [voltage_controller_metrics_vd.RMSE()],
'Steady State Error/V':
[voltage_controller_metrics_vd.steady_state_error()]
}
print()
df_metrics_vd = pd.DataFrame(data=d).T
df_metrics_vd.columns = ['Value']
print('Metrics of Vd')
print(df_metrics_vd)
######IMPORTANT FOR THE SCORING MODEL INNER LEVEL##############################
# Use the following code, to create a pkl-File in which the Dataframe is stored
# df_metrics_vd.to_pickle("./df_metrics_vd_controller1.pkl")
# Maybe you need to replace 'controller1.pkl' with 'controller2.pkl'
best_agent_plt.savefig('agent_plt.png')
#####################################
# Calculation of Metrics
# Here, the d- term of idq is analysed
df_master_CVId = env.history.df[['master.CVId']]
current_controller_metrics_id = Metrics(df_master_CVId, id_ref[0], ts, max_episode_steps,
position_steady_state=position_steady_state,
position_settling_time=position_settling_time)
d = {'Overshoot': [current_controller_metrics_id.overshoot()],
'Rise Time/s ': [current_controller_metrics_id.rise_time()],
'Settling Time/s ': [current_controller_metrics_id.settling_time()],
'Root Mean Squared Error/A': [current_controller_metrics_id.RMSE()],
'Steady State Error/A': [current_controller_metrics_id.steady_state_error()]}
df_metrics_id = pd.DataFrame(data=d).T
df_metrics_id.columns = ['Value']
print()
print('Metrics of id')
print(df_metrics_id)
######IMPORTANT FOR THE SCORING MODEL INNER LEVEL##############################
# Use the following code, to create a pkl-File in which the Dataframe is stored
# df_metrics_id.to_pickle("./df_metrics_id_controller1.pkl")
# Maybe you need to replace 'controller1.pkl' with 'controller2.pkl'
#####################################
#####################################
# Calculation of Metrics
# Here, the d- term of vd is analysed
df_master_CVVd = env.history.df[['master.CVVd']]
from metrics import Metrics
voltage_controller_metrics_vd = Metrics(df_master_CVVd, vd_ref[0], ts, max_episode_steps,
position_steady_state=position_steady_state,
position_settling_time=position_settling_time)
d = {'Overshoot': [voltage_controller_metrics_vd.overshoot()],
'Rise Time/s ': [voltage_controller_metrics_vd.rise_time()],
'Settling Time/s ': [voltage_controller_metrics_vd.settling_time_vd_droop()],
'Root Mean Squared Error/V': [voltage_controller_metrics_vd.RMSE()],
'Steady State Error/V': [voltage_controller_metrics_vd.steady_state_error()]}
print("\n")
print()
df_metrics_vd = pd.DataFrame(data=d).T
df_metrics_vd.columns = ['Value']
print('Metrics of Vd')
print(df_metrics_vd)
######IMPORTANT FOR THE SCORING MODEL PRIMARY LEVEL##############################
# Use the following code, to create a pkl-File in which the Dataframe is stored
# df_metrics_vd.to_pickle("./df_metrics_vd_controller1_droop.pkl")
# Maybe you need to replace 'controller1_droop.pkl' with 'controller2_droop.pkl'
