<reponame>sarahaguasvivas/GPC<gh_stars>0
#!/usr/bin/env python3
from dyno_model.neural_network_predictor import *
from optimizer.newton_raphson import *
import sys
import os
import matplotlib.pyplot as plt
import time

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
plt.style.use('dark_background')
filename= "../model_data/neural_network_2.hdf5"

NNP = NeuralNetworkPredictor(model_file = filename, N1 = 0, N2= 2, Nu = 3, \
                                    ym = [2., 0.5], K = 2, yn = [0.]*2, lambd = [1., 2., -0.5])

NR_opt = NewtonRaphson(cost= NNP.Cost, d_model= NNP)

new_state_new = np.random.multivariate_normal([0.05]*12, 0.05*np.eye(12), 1)

du = [0.0]*3

elapsed=[]
u_optimal_list=[]
ym = []
state = []
yn = []

for n in range(100):
    seconds = time.time()

    future_outputs = NNP.predict(new_state_new).flatten()

    NNP.yn[0] = future_outputs[0]
    NNP.yn[1] = np.arctan2(future_outputs[-2], future_outputs[-1]) % (np.pi/4.0)
    NNP.yn[1] = NNP.yn[1]

    if np.sin(0.05*n) > 0:
        NNP.ym[0] = 1
        NNP.ym[1] = 0.5
    else:
        NNP.ym[0] = 0
        NNP.ym[1] = 0

    new_state_old = new_state_new

    u_optimal = np.reshape(NR_opt.optimize(u= future_outputs, del_u= du, rtol = 1e-8, maxit=6, verbose=False)[0], (-1, 1))

#    u_optimal[1, :] = np.clip(u_optimal[1, :], -0.5, 0.5)
#    u_optimal[2, :] = np.clip(u_optimal[2, :], -0.5, 0.5)

    u_optimal_list+=[u_optimal.flatten().tolist()]

    du = np.array(new_state_old[:, -3:].flatten()) - np.array(u_optimal.flatten())

    du = du.flatten()

    u_optimal = np.array([u_optimal])

    u_optimal = np.concatenate(([[[0.0]]], u_optimal), axis=1)

    signals = NNP.measure(u_optimal[:, :, 0])

    new_state_new = np.concatenate((signals, np.reshape(u_optimal.flatten()[1:], (1, -1))), axis=1)

    elapsed += [time.time() - seconds]

    print("yn: ", NNP.yn,"ym: ", NNP.ym, " time elapsed: ", elapsed[-1], " [s]")

    ym += [NNP.ym[0],NNP.ym[1]]
    yn += [NNP.yn[0],NNP.yn[1]]

    state += [new_state_new[:, -3], np.arctan2(new_state_new[:, -2], new_state_new[:, -1])]

elapsed = np.array(elapsed).flatten()
ym = np.reshape(ym, (-1, 2))
state = np.reshape(state, (-1, 2))
u_optimal_list = np.reshape(u_optimal_list, (-1, 3))
yn = np.reshape(yn, (-1, 2))

plt.subplot(4, 1, 1)
plt.plot(ym[:, 0], '--w', label= 'target')
plt.plot(yn[:, 0], 'r', label = 'state')

plt.legend()
plt.ylabel("block distance")

plt.subplot(4, 1, 2)
plt.plot(ym[:, 1], '--w', label='target')
plt.plot(yn[:, 1], 'r', label = 'state')
plt.ylabel("twist")
plt.legend()

plt.subplot(4, 1, 3)
plt.plot(u_optimal_list[:, 0], label='block distance')
plt.ylabel(r'u_{block}')
plt.legend()

plt.subplot(4, 1, 4)
plt.plot(u_optimal_list[:, 1], label='sin(twist)')
plt.plot(u_optimal_list[:, 2], label='cos(twist)')
plt.ylabel(r'u_{\phi}')
plt.legend()

plt.show()




#!/usr/bin/env python3
from dyno_model.neural_network_predictor import *
from optimizer.newton_raphson import *
import sys
import os
import matplotlib.pyplot as plt
import time

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
plt.style.use('dark_background')
filename= "../model_data/neural_network_2.hdf5"

NNP = NeuralNetworkPredictor(model_file = filename, N1 = 0, N2= 2, Nu = 3, \
                                    ym = [2., 0.5], K = 2, yn = [0.]*2, lambd = [1., 2., -0.5])

NR_opt = NewtonRaphson(cost= NNP.Cost, d_model= NNP)

new_state_new = np.random.multivariate_normal([0.05]*12, 0.05*np.eye(12), 1)

du = [0.0]*3

elapsed=[]
u_optimal_list=[]
ym = []
state = []
yn = []

for n in range(100):
    seconds = time.time()

    future_outputs = NNP.predict(new_state_new).flatten()

    NNP.yn[0] = future_outputs[0]
    NNP.yn[1] = np.arctan2(future_outputs[-2], future_outputs[-1]) % (np.pi/4.0)
    NNP.yn[1] = NNP.yn[1]

    if np.sin(0.05*n) > 0:
        NNP.ym[0] = 1
        NNP.ym[1] = 0.5
    else:
        NNP.ym[0] = 0
        NNP.ym[1] = 0

    new_state_old = new_state_new

    u_optimal = np.reshape(NR_opt.optimize(u= future_outputs, del_u= du, rtol = 1e-8, maxit=6, verbose=False)[0], (-1, 1))

#    u_optimal[1, :] = np.clip(u_optimal[1, :], -0.5, 0.5)
#    u_optimal[2, :] = np.clip(u_optimal[2, :], -0.5, 0.5)

    u_optimal_list+=[u_optimal.flatten().tolist()]

    du = np.array(new_state_old[:, -3:].flatten()) - np.array(u_optimal.flatten())

    du = du.flatten()

    u_optimal = np.array([u_optimal])

    u_optimal = np.concatenate(([[[0.0]]], u_optimal), axis=1)

    signals = NNP.measure(u_optimal[:, :, 0])

    new_state_new = np.concatenate((signals, np.reshape(u_optimal.flatten()[1:], (1, -1))), axis=1)

    elapsed += [time.time() - seconds]

    print("yn: ", NNP.yn,"ym: ", NNP.ym, " time elapsed: ", elapsed[-1], " [s]")

    ym += [NNP.ym[0],NNP.ym[1]]
    yn += [NNP.yn[0],NNP.yn[1]]

    state += [new_state_new[:, -3], np.arctan2(new_state_new[:, -2], new_state_new[:, -1])]

elapsed = np.array(elapsed).flatten()
ym = np.reshape(ym, (-1, 2))
state = np.reshape(state, (-1, 2))
u_optimal_list = np.reshape(u_optimal_list, (-1, 3))
yn = np.reshape(yn, (-1, 2))

plt.subplot(4, 1, 1)
plt.plot(ym[:, 0], '--w', label= 'target')
plt.plot(yn[:, 0], 'r', label = 'state')

plt.legend()
plt.ylabel("block distance")

plt.subplot(4, 1, 2)
plt.plot(ym[:, 1], '--w', label='target')
plt.plot(yn[:, 1], 'r', label = 'state')
plt.ylabel("twist")
plt.legend()

plt.subplot(4, 1, 3)
plt.plot(u_optimal_list[:, 0], label='block distance')
plt.ylabel(r'u_{block}')
plt.legend()

plt.subplot(4, 1, 4)
plt.plot(u_optimal_list[:, 1], label='sin(twist)')
plt.plot(u_optimal_list[:, 2], label='cos(twist)')
plt.ylabel(r'u_{\phi}')
plt.legend()

plt.show()
#!/usr/bin/env python3 #    u_optimal[1, :] = np.clip(u_optimal[1, :], -0.5, 0.5) #    u_optimal[2, :] = np.clip(u_optimal[2, :], -0.5, 0.5)