numTemp = [df['kp_true'][i]]
denTemp = [1., -df['tau_true'][i]]
thetas = [df['theta_true'][i]]
bigU = np.transpose(u)
if thetas[0] < 1:
thetas[0] = 1
uSim = np.concatenate((np.zeros(thetas[0]), bigU[:-thetas[0]]))
numTemp = np.array(numTemp)
denTemp = np.array(denTemp)
sys = control.tf(numTemp, denTemp, 1)
_, realy, _ = control.forced_response(sys,
U=uSim,
T=np.linspace(0, 599, 600))
disturb = sig.add_disturbance()[:, 0]
amax = np.max(realy) / np.max(disturb)
ratio = np.random.normal(0, 0) * amax
disturb = disturb * ratio
disturbances[i, :] = disturb
realy = realy + disturb
#plt.plot(sig.gauss_noise(realy,'variable'),'green',linewidth=1,label='Real Response')
for k in range(0, 10):
numgauss = np.random.normal(df['kp_MATLAB'][i],
df['MATLAB_cov'][i] / 2)
dengauss = np.random.normal(df['tau_MATLAB'][i],
df['MATLAB_cov.1'][i] / 2)
numTemp = [numgauss]
denTemp = [1., -dengauss]
thetas = [df['theta_MATLAB'][i]]
