model_path = '/Users/User/Desktop/code/models/'
DNN_Model = load_model(model_path +
'Entry28_fold_2_repeating_0_2019-06-18_10:12:53.h5')
tf = 101.83174508
trajectory = []
contorl = []
hight = 50
counter = 0
while hight > 0 and counter < 2000 / step:
prediction = DNN_Model.predict(next_state.reshape((1, 4)))
alpha = prediction[0, 0]
anti_norm_sate = anti_normalize(next_state, r, v)
state_dot = entry_model.EoM_normalized(anti_norm_sate,
alpha) # t,state, tf
# print (i,'a',alpha*180/np.pi)
#print ('state', (anti_norm_sate[0] - entry_model.constant['R0'])/1000,anti_norm_sate[1]*entry_model.constant['R0']/1000,anti_norm_sate[2])
# print ('dot',state_dot)
next_state = anti_norm_sate.reshape((4, 1)) + state_dot * step
next_state = normalize(next_state.flatten(), r, v)
trajectory.append(anti_norm_sate)
contorl.append(alpha * 180 / np.pi)
counter += 1
hight = anti_norm_sate[0] - entry_model.constant['R0']
trajectory = np.array(trajectory)
v0 = state[2]
v0 = v0 * np.sqrt(R0 * g0)
r0 = r0 * R0
return np.array([r0, state[1], v0, state[-1]])
r = entry_model.constant['r0']
v = entry_model.constant['v0']
theta = 0
gamma = entry_model.constant['gamma0']
alpha = 0.3324101876204215
state = [r / R0, theta, v / np.sqrt(R0 * g0), gamma]
state_dot = entry_model.EoM_normalized(state, alpha) # array
next_state = np.concatenate(state, state_dot)
#state = np.array(state).reshape((1,4))
mesh_size = 1011
ss = 1
step = 1 / (10**ss)
model_path = '/Users/User/Desktop/code/models/30/'
model_name = 'Entry30_fold_1_repeating_2_2019-06-23_17:18:22.h5'
#DNN_Model = load_model('model/'+model_name)
DNN_Model = load_model(model_path + model_name)
tf = 101.83174508