]
# Get snapshots, both simulated and analytical
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, :]
g = [x1, x2, x1**2]
data = koopman_prob(data_an, g, ncp)
else:
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, :]