timeseries = cbi.benchmark_time_series(trial, subtractMean=True)
for k, model in idMat.items():
rollrsq, steerrsq, fig = cbi.input_prediction(timeseries, model)
roll[k].append(rollrsq)
steer[k].append(steerrsq)
fig.savefig('plots/' + str(runNum) + '-' + k + '.png')
plt.close(fig)
del fig
M, C1, K0, K2 = trial.bicycle.canonical(nominal=True)
rollrsq, steerrsq, fig = cbi.input_prediction(timeseries, (M, C1, K0, K2,
allH[rider]))
roll['Whipple'].append(rollrsq)
steer['Whipple'].append(steerrsq)
fig.savefig('plots/' + str(runNum) + '-whipple.png')
plt.close(fig)
del fig
v = timeseries['v'].mean()
A, B, speeds = cbi.mean_arm([rider])
M = np.linalg.inv(B[round(v * 10)][2:, [0, 1]])
C = -np.dot(M, A[round(v * 10)][2:, [2, 3]])
K = -np.dot(M, A[round(v * 10)][2:, [0, 1]])
rollrsq, steerrsq, fig = cbi.input_prediction(timeseries, (M, C, K,
allH[rider]))
roll['Arm'].append(rollrsq)
steer['Arm'].append(steerrsq)
fig.savefig('plots/' + str(runNum) + '-arm.png')
plt.close(fig)
del fig
timeseries = cbi.benchmark_time_series(trial, subtractMean=True)
for k, model in idMat.items():
rollrsq, steerrsq, fig = cbi.input_prediction(timeseries, model)
roll[k].append(rollrsq)
steer[k].append(steerrsq)
fig.savefig('plots/' + str(runNum) + '-' + k + '.png')
plt.close(fig)
del fig
M, C1, K0, K2 = trial.bicycle.canonical(nominal=True)
rollrsq, steerrsq, fig = cbi.input_prediction(timeseries,
(M, C1, K0, K2, allH[rider]))
roll['Whipple'].append(rollrsq)
steer['Whipple'].append(steerrsq)
fig.savefig('plots/' + str(runNum) + '-whipple.png')
plt.close(fig)
del fig
v = timeseries['v'].mean()
A, B, speeds = cbi.mean_arm([rider])
M = np.linalg.inv(B[round(v * 10)][2:, [0, 1]])
C = -np.dot(M, A[round(v * 10)][2:, [2, 3]])
K = -np.dot(M, A[round(v * 10)][2:, [0, 1]])
rollrsq, steerrsq, fig = cbi.input_prediction(timeseries,
(M, C, K, allH[rider]))
roll['Arm'].append(rollrsq)
steer['Arm'].append(steerrsq)
fig.savefig('plots/' + str(runNum) + '-arm.png')
plt.close(fig)
del fig
def plot(canon, H, riders, environments, idMats):
filename = ''
for rider in riders:
filename += '-' + rider
for env in environments:
filename += '-' + env.replace(' ', '')
filename = '../plots/' + filename[1:]
print filename
v0 = 0.
vf = 10.
num = 100
# identified
iM, iC1, iK0, iK2, iH = idMats
speeds, iAs, iBs = bicycle.benchmark_state_space_vs_speed(iM, iC1, iK0, iK2,
v0=v0, vf=vf, num=num)
w, v = control.eigen_vs_parameter(iAs)
iEigenvalues, iEigenvectors = control.sort_modes(w, v)
# whipple model (mean)
wM, wC1, wK0, wK2, wH = cbi.mean_canon(riders, canon, H)
speeds, wAs, wBs = bicycle.benchmark_state_space_vs_speed(wM, wC1, wK0, wK2,
v0=v0, vf=vf, num=num)
w, v = control.eigen_vs_parameter(wAs)
wEigenvalues, wEigenvectors = control.sort_modes(w, v)
# arm model (mean)
aAs, aBs, aSpeed = cbi.mean_arm(riders)
indices = np.int32(np.round(speeds * 10))
w, v = control.eigen_vs_parameter(aAs[indices])
aEigenvalues, aEigenvectors = control.sort_modes(w, v)
# eigenvalue plot
rlfig = cbi.plot_rlocus_parts(speeds, iEigenvalues, wEigenvalues,
aEigenvalues)
rlfig.savefig(filename + '-eig.png')
# root locus
v0 = 0.
vf = 10.
num = 20
speeds, iAs, iBs = bicycle.benchmark_state_space_vs_speed(iM, iC1, iK0, iK2,
v0=v0, vf=vf, num=num)
iEig, null = control.eigen_vs_parameter(iAs)
speeds, wAs, wBs = bicycle.benchmark_state_space_vs_speed(wM, wC1, wK0, wK2,
v0=v0, vf=vf, num=num)
wEig, null = control.eigen_vs_parameter(wAs)
indices = np.int32(np.round(speeds * 10))
aEig, null = control.eigen_vs_parameter(aAs[indices])
rlcfig = cbi.plot_rlocus(speeds, iEig, wEig, aEig)
rlcfig.savefig(filename + '-rlocus.png')
# bode plots
speeds = np.array([2.0, 4.0, 6.0, 9.0])
null, iAs, iBs = bicycle.benchmark_state_space_vs_speed(iM, iC1, iK0, iK2,
speeds)
null, wAs, wBs = bicycle.benchmark_state_space_vs_speed(wM, wC1, wK0, wK2,
speeds)
figs = cbi.plot_bode(speeds, iAs, iBs, wAs, wBs, aAs, aBs)
figs[0].savefig(filename + '-Tphi.png')
figs[1].savefig(filename + '-Tdel.png')
iEigenvalues, iEigenvectors = control.sort_modes(w, v)
# whipple model (mean)
wM, wC1, wK0, wK2, wH = cbi.mean_canon(allRiders, canon, H)
speeds, wAs, wBs = bicycle.benchmark_state_space_vs_speed(wM,
wC1,
wK0,
wK2,
v0=v0,
vf=vf,
num=num)
w, v = control.eig_of_series(wAs)
wEigenvalues, wEigenvectors = control.sort_modes(w, v)
# arm model (mean)
aAs, aBs, aSpeed = cbi.mean_arm(allRiders)
indices = np.int32(np.round(speeds * 10))
w, v = control.eig_of_series(aAs[indices])
aEigenvalues, aEigenvectors = control.sort_modes(w, v)
rlfig = cbi.plot_rlocus_parts(speeds, iEigenvalues, wEigenvalues, aEigenvalues)
rlfig.set_size_inches(5., 5. / goldenRatio)
rlfig.savefig('../../figures/systemidentification/A-A-eig.png')
rlfig.savefig('../../figures/systemidentification/A-A-eig.pdf')
# Root locus with respect to speed.
v0 = 0.
vf = 10.
num = 20
speeds, iAs, iBs = bicycle.benchmark_state_space_vs_speed(iM,
iC1,
# the eigenvalues and eigenvectors as a function of speed.
wM, wC1, wK0, wK2 = cbi.load_benchmark_canon(['Luke'])['Luke']
wH = cbi.lateral_force_contribution(['Luke'])['Luke']
speeds, wAs, wBs = bicycle.benchmark_state_space_vs_speed(wM,
wC1,
wK0,
wK2,
v0=v0,
vf=vf,
num=num)
w, v = control.eig_of_series(wAs)
wEigenvalues, wEigenvectors = control.sort_modes(w, v)
# Load the Arm model state space for each rider from first principles and
# generate the eigenvalues and eigenvectors as a function of speed.
aAs, aBs, aSpeed = cbi.mean_arm(['Luke'])
indices = np.int32(np.round(speeds * 10))
w, v = control.eig_of_series(aAs[indices])
aEigenvalues, aEigenvectors = control.sort_modes(w, v)
rlfig = cbi.plot_rlocus_parts(speeds, iEigenvalues, wEigenvalues, aEigenvalues)
rlfig.axes[0].set_ylabel('')
rlfig.set_size_inches(4.0, 4.0 / goldenRatio)
plt.tight_layout()
rlfig.savefig('../figures/L-P-eig.pdf')
# Root locus with respect to speed.
v0 = 0.
vf = 10.
num = 20
speeds, iAs, iBs = bicycle.benchmark_state_space_vs_speed(iM,
w, v = control.eig_of_series(iAs)
iEigenvalues, iEigenvectors = control.sort_modes(w, v)
# Load the Whipple model M, C1, K0, K2, H from first principles and generate
# the eigenvalues and eigenvectors as a function of speed.
wM, wC1, wK0, wK2 = cbi.load_benchmark_canon(['Luke'])['Luke']
wH = cbi.lateral_force_contribution(['Luke'])['Luke']
speeds, wAs, wBs = bicycle.benchmark_state_space_vs_speed(wM, wC1, wK0, wK2,
v0=v0, vf=vf,
num=num)
w, v = control.eig_of_series(wAs)
wEigenvalues, wEigenvectors = control.sort_modes(w, v)
# Load the Arm model state space for each rider from first principles and
# generate the eigenvalues and eigenvectors as a function of speed.
aAs, aBs, aSpeed = cbi.mean_arm(['Luke'])
indices = np.int32(np.round(speeds * 10))
w, v = control.eig_of_series(aAs[indices])
aEigenvalues, aEigenvectors = control.sort_modes(w, v)
rlfig = cbi.plot_rlocus_parts(speeds, iEigenvalues, wEigenvalues,
aEigenvalues)
rlfig.axes[0].set_ylabel('')
rlfig.set_size_inches(4.0, 4.0 / goldenRatio)
plt.tight_layout()
rlfig.savefig('../figures/L-P-eig.pdf')
# Root locus with respect to speed.
v0 = 0.
vf = 10.
num = 20
num = 100
# identified for all rider and all environments
iM, iC1, iK0, iK2, iH = idMat["A-A"]
speeds, iAs, iBs = bicycle.benchmark_state_space_vs_speed(iM, iC1, iK0, iK2, v0=v0, vf=vf, num=num)
w, v = control.eig_of_series(iAs)
iEigenvalues, iEigenvectors = control.sort_modes(w, v)
# whipple model (mean)
wM, wC1, wK0, wK2, wH = cbi.mean_canon(allRiders, canon, H)
speeds, wAs, wBs = bicycle.benchmark_state_space_vs_speed(wM, wC1, wK0, wK2, v0=v0, vf=vf, num=num)
w, v = control.eig_of_series(wAs)
wEigenvalues, wEigenvectors = control.sort_modes(w, v)
# arm model (mean)
aAs, aBs, aSpeed = cbi.mean_arm(allRiders)
indices = np.int32(np.round(speeds * 10))
w, v = control.eig_of_series(aAs[indices])
aEigenvalues, aEigenvectors = control.sort_modes(w, v)
rlfig = cbi.plot_rlocus_parts(speeds, iEigenvalues, wEigenvalues, aEigenvalues)
rlfig.set_size_inches(5.0, 5.0 / goldenRatio)
rlfig.savefig("../../figures/systemidentification/A-A-eig.png")
rlfig.savefig("../../figures/systemidentification/A-A-eig.pdf")
# Root locus with respect to speed.
v0 = 0.0
vf = 10.0
num = 20
speeds, iAs, iBs = bicycle.benchmark_state_space_vs_speed(iM, iC1, iK0, iK2, v0=v0, vf=vf, num=num)
iEig, null = control.eig_of_series(iAs)
