def moments(KF):
WT = KF.WT
z_test = fastlib.ED_TwrGag(WT.ED) - WT.ED['TowerBsHt']
EI = np.interp(z_test, WT.Twr.s_span, WT.Twr.EI[0, :])
kappa = np.interp(z_test, WT.Twr.s_span, WT.Twr.PhiK[0][0, :])
qx = KF.X_hat[KF.iX['ut1']]
KF.M_sim = [qx * EI[i] * kappa[i] / 1000
for i in range(len(z_test))] # in [kNm]
KF.M_ref = [
KF.df['TwHt{:d}MLyt'.format(i + 1)].values
for i in range(len(z_test))
] # in [kNm]
return KF.M_sim, KF.M_ref
def plot_moments(KF, fig=None, scaleByMean=False):
import matplotlib
import matplotlib.pyplot as plt
z_test = list(fastlib.ED_TwrGag(KF.WT.ED) - KF.WT.ED['TowerBsHt'])
print('z test:', z_test)
n = len(z_test)
# z_test.reverse()
# --- Compare measurements
cmap = matplotlib.cm.get_cmap('viridis')
COLRS = [(cmap(v)[0], cmap(v)[1], cmap(v)[2])
for v in np.linspace(0, 1, n + 1)]
if fig is None:
fig = plt.figure()
fig.set_size_inches(6.4, 15.0, forward=True) # default is (6.4,4.8)
for i, z in enumerate(z_test):
ax = fig.add_subplot(n, 1, i + 1)
M_sim = KF.M_sim[i]
if scaleByMean:
M_sim += -np.mean(KF.M_sim[i]) + np.mean(KF.M_ref[i])
ax.plot(KF.time,
KF.M_ref[i],
'k-',
color='k',
label='Reference',
lw=1)
ax.plot(KF.time,
M_sim,
'--',
color=COLRS[i],
label='Estimation',
lw=0.8)
ax.set_ylabel('My z={:.1f}'.format(z))
ax.tick_params(direction='in')
# if ii<2:
if i < n - 1:
ax.set_xticklabels([])
else:
ax.set_xlabel('Time [s]')
ax.legend()
# # plt.ylim(0.05*10**8,0.8*10**8)
ax.set_title('KalmanLoads')