w_tensor,
v_tensor,
X_tensor[0, :],
tf.constant(ncp),
g,
h,
u=u_tensor)
Yhat = Yhat_tensor.numpy().T
x1_koopman = Yhat[0, :]
x2_koopman = Yhat[2, :]
print('FINISHED with predictions')
############################################################################
# Perform DMD for comparison
############################################################################
lam_dmd, w_dmd, v_dmd, _ = dmd.get_dmd_modes(X, Y)
Yhat_dmd = dmd.predict(lam_dmd, w_dmd, v_dmd, X[:, 0], ncp, u=u)
x1_dmd = Yhat_dmd[0, :]
x2_dmd = Yhat_dmd[2, :]
############################################################################
# Plot
############################################################################
print('PLOTS')
plt.figure()
plt.title('Simulated data and m={}'.format(m_stop))
plt.plot(t, x1_target, '-b', linewidth=2)
plt.plot(t, x1_dmd, '--r', linewidth=2)
plt.plot(t, x1_koopman, '-.k', linewidth=2)
plt.legend(('Simulated solution', 'DMD', 'Koopman'))
plt.xlim(0, dt * (ncp))
data_sim = dmd.get_koopman_snapshots_stop_friction(t_start,
t_stop,
ncp,
g,
input_force=u,
time_vec=t)
data_an = dmd.get_analytical_snapshots_damped_dual_mass(t_start,
t_stop,
ncp,
input_force=u)
# Get data matrices
X, Y = dmd.get_data_matrices(data_sim, m_stop=m_stop, u=u, q=q)
# Calculate DMD modes and eigenvalues
lam, w, v, Ahat = dmd.get_dmd_modes(X, Y)
# Predict the system
Yhat = dmd.predict(lam, w, v, X[:, 0], ncp, u=u, q=q)
# Plots
# Extract results
x1_dmd = Yhat[0, :]
x2_dmd = Yhat[2, :]
x1_sim = data_sim[0, :]
x2_sim = data_sim[2, :]
x1_an = data_an[0, :]
x2_an = data_an[2, :]
# Step size
dt = (t_stop - t_start) / ncp
method = 'DMD'
data = data_an
# Calculate stop index for training
if not t_stop_train:
m_stop = ncp + 1
elif t_stop_train > t_stop:
raise ValueError('t_stop_train must be <= t_stop.')
else:
m_stop = np.argmin(t < t_stop_train)
# Construct X,Y from data
X, Y = dmd.get_data_matrices(data, m_stop=m_stop, u=None, q=q)
# Calculate DMD modes and eigenvalues
lam, w, v, _ = dmd.get_dmd_modes(X, Y)
# Predict the system
Yhat = dmd.predict(lam, w, v, X[:, 0], ncp, u=None, q=q)
# Extract results
x1_dmd = Yhat[0, :]
x2_dmd = Yhat[1, :]
x1_sim = data_sim[0, :]
x2_sim = data_sim[1, :]
x1_an = data_an[0, :]
x2_an = data_an[1, :]
# Step size
dt = (t_stop - t_start) / ncp