-
Notifications
You must be signed in to change notification settings - Fork 6
/
Copy pathsim.py
174 lines (131 loc) · 6 KB
/
sim.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
import numpy as np
import QuadSim
from QuadSim import QuadSim
from QuadSim import QuadState
import liu_model
import evaluation
from data_utils import *
from deep_energy_model import load_model
import time
from scipy.integrate import simps
from multiprocessing import Pool
import matplotlib.pyplot as plt
import Risk
def one_flight():
# waypoints given as x, y, z, time
waypoints = np.array([[-259.9, -260., -260., -200., -65., -64.9],
[ 125., 125., 50., -35., 100., 100.],
[ 0., 20., 20., 20., 20., 0.],
[ 0., 3., 3., 3., 3., 5.]])
groundspeed = 10
displayFig = True
wind_map_ang = np.load('wind_data/dataset_hawkins.npy')
wind_map_mag = np.load('wind_data/dataset_hawkins_mag.npy')
inlet_ang = 180
inlet_mag = 2
for i in range(1):
testsim = QuadSim(None, waypoints[:,0], wind_map_ang, wind_map_mag, inlet_ang, inlet_mag, command_airspeed=True, ghost_delta=1.0)
testsim.init_path_from_waypoints(waypoints, airspeed=groundspeed)
start_time = time.time()
states = testsim.propagate(displayFig)
end_time = time.time()
print("Time to complete sim: {} seconds".format((end_time-start_time)))
print("Flight time: {} seconds".format(states[1]/10))
print(states[3])
return states
def calc_energy(power, ts):
"""
power (list of float): power value at each time step
ts (list of float): time step value
NOTE: make sure that indices are matching up for all the inputs
Returns the energy for a flight
"""
assert len(power) == len(ts)
actual_power = simps(power, x=ts, even='avg')/1000
return actual_power
def energy_predictions(states_list, model='s-TCN'):
'''
Returns the Kilojoules predicted by the model
Parameters:
states_list (List): List of the states from sims
model (string): specified learned model to use
Returns:
energy_list (List): List of the energy for each flight
'''
configs = evaluation.get_pre_def_configs()
model, inputs = load_model(configs[model], model)
first = inputs['lookback'] - 1
all_data = {i: sim_to_network_transform(states) for i, states in enumerate(states_list)}
data, data_min, data_max, _, _, _ = process_data(all_data, eval_mode=True, **inputs)
data_tensors = create_tensors(data, range(len(states_list)), **inputs)
popt = liu_model.optimum_values()
energy_list = []
for i, states in enumerate(states_list):
# liu_pow_list = liu_model.power(states[['vertspd', 'airspeed','aoa','payload', 'density']].T, popt[0], popt[1], popt[2], popt[3], popt[4])
# liu_set_energy = simps(liu_pow_list, x=states['time'], even="avg")/1000 # Kilojoules
# print(liu_set_energy)
model.reset_states()
y_pred = model.predict(data_tensors[i]).reshape(-1)
y_pred = y_pred*(data_max[-1] - data_min[-1]) + data_min[-1]
energy_list.append(calc_energy(y_pred, states['time'].values[first:]))
return energy_list
def MC_func(arg_in):
i, waypoints, wind_map_ang, wind_map_mag, airspeed,wind_inlet_ang_dist, wind_inlet_mag_dist = arg_in
np.random.seed()
testsim = QuadSim(init_pos=waypoints[:,0], wind_map_ang=wind_map_ang, wind_map_mag=wind_map_mag, command_airspeed=True, ghost_delta=1.0)
testsim.init_path_from_waypoints(waypoints, airspeed=airspeed) #Modify set of waypoints here
inlet_ang = np.random.normal(wind_inlet_ang_dist[0], wind_inlet_ang_dist[1])
inlet_mag = np.random.normal(wind_inlet_mag_dist[0], wind_inlet_mag_dist[1])
testsim.update_inlet(inlet_ang, inlet_mag, 'rbf')
states, count, path_count, success = testsim.propagate()
states = sim_to_network_transform(states)
return (states, success)
def mc_flights(n_times=50, threads=3):
wind_map_ang = np.load('wind_data/dataset_hawkins.npy')
wind_map_mag = np.load('wind_data/dataset_hawkins_mag.npy')
wind_inlet_ang_dist = (-2.53455, 28.4662) # mean and std
wind_inlet_mag_dist = (3.0, 1.5) # mean and std
# waypoints given as x, y, z, time
waypoints = np.array([[-259.9, -260., -260., -200., -65., -64.9],
[ 125., 125., 50., -35., 100., 100.],
[ 0., 20., 20., 20., 20., 0.],
[ 0., 3., 3., 3., 3., 5.]])
airspeed = 10
state_list = []
start_time = time.time()
pool = Pool(processes=threads)
count = 0
fail_count = 0 # in situations where the wind is too strong for vehicle to reach target
for i in pool.imap_unordered(MC_func, [(i,waypoints, wind_map_ang, wind_map_mag, airspeed,wind_inlet_ang_dist, wind_inlet_mag_dist) for i in range(n_times)]):
state_list.append(i[0])
count += 1
if i[1] == False:
fail_count+=1
if count%10 == 0:
print("Sim Count: {}".format(count))
end_time = time.time()
print("Fail count: {}".format(fail_count))
print("Time to complete MC: {}".format(end_time - start_time))
return state_list
def plot_hist(energy_list):
num_bins = 20
n, bins, patches = plt.hist(np.array(energy_list), num_bins, facecolor='blue', alpha=0.5)
plt.xlabel('Kilojoules')
plt.ylabel('Frequency')
plt.grid(axis='y', alpha=0.75)
plt.title('Energy consumption')
plt.show()
# if the file is executed as the main program
if __name__ == '__main__':
# Example of running one flight
states = one_flight()
states = [states[0]]
energy_list = energy_predictions(states)
# Example of MC runs
state_list = mc_flights(n_times=50, threads=3)
energy_list = energy_predictions(state_list)
plot_hist(energy_list)
# Calculating risk from MC runs
risk = Risk.Risk(np.array(energy_list), limit=99., a = 64 ,b = 92.34 ,B = 369.36)
risk_array = risk.risk
print("CVaR is {}".format(risk.cvar()))