def plot(canon, H, riders, environments, idMats):
filename = ''
for rider in riders:
filename += '-' + rider
for env in environments:
filename += '-' + env.replace(' ', '')
filename = 'canonical-id-plots/' + filename[1:]
print filename
v0 = 0.
vf = 10.
num = 100
mM, mC1, mK0, mK2, mH = csi.mean_canon(riders, canon, H)
speeds, mAs, mBs = bicycle.benchmark_state_space_vs_speed(mM, mC1, mK0, mK2,
v0=v0, vf=vf, num=num)
w, v = control.eigen_vs_parameter(mAs)
mEigenvalues, mEigenvectors = control.sort_modes(w, v)
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)
aAs, aBs, aSpeed = csi.mean_arm(riders)
w, v = control.eigen_vs_parameter(aAs)
aEigenvalues, aEigenvectors = control.sort_modes(w, v)
rlfig = plt.figure()
ax = rlfig.add_subplot(1, 1, 1)
ax.plot(speeds, iEigenvalues.real, 'k-')
ax.plot(speeds, abs(iEigenvalues.imag), 'k--')
ax.plot(speeds, mEigenvalues.real, 'b-')
ax.plot(speeds, abs(mEigenvalues.imag), 'b--')
ax.plot(aSpeed, aEigenvalues.real, 'r-')
ax.plot(aSpeed, abs(aEigenvalues.imag), 'r--')
ax.set_ylim((-15, 10))
ax.set_xlabel('Speed [m/s]')
ax.set_ylabel('Real and Imaginary Parts of the Eigenvalues [1/s]')
rlfig.savefig(filename + '-eig.png')
rlfig.clf()
# bode plots
speeds = [2.0, 3.0, 5.8, 9.0]
null, mAs, mBs = bicycle.benchmark_state_space_vs_speed(mM, mC1, mK0, mK2,
speeds)
null, iAs, iBs = bicycle.benchmark_state_space_vs_speed(iM, iC1, iK0, iK2,
speeds)
C = np.array([[1.0, 0.0, 0.0, 0.0]])
D = np.array([[0.0, 0.0]])
systems = []
inputNames = ['$T_\phi$', '$T_\delta$']
stateNames = ['$\phi$', '$\delta$', '$\dot{\phi}$', '$\dot{\delta}$']
outputNames = ['$\phi$']
for A, B in zip(mAs, mBs):
systems.append(control.StateSpace(A, B, C, D, name='Whipple',
stateNames=stateNames, inputNames=inputNames,
outputNames=outputNames))
for A, B in zip(iAs, iBs):
systems.append(control.StateSpace(A, B, C, D, name='Canon ID',
stateNames=stateNames, inputNames=inputNames,
outputNames=outputNames))
C = np.zeros((1, 19))
C[0, 3] = 1
D = 0.
indices = [20, 30, 58, 90]
for A, B in zip(aAs[indices], aBs[indices]):
systems.append(control.StateSpace(A, B[:, [0, 2]], C, D, name='Arms',
inputNames=inputNames, outputNames=outputNames))
w = np.logspace(-1, 2)
linestyles = ['-', '--', '-.', ':'] * 3
colors = ['k'] * len(speeds) + ['b'] * len(speeds) + ['r'] * len(speeds)
bode = control.Bode(w, *tuple(systems), linestyles=linestyles, colors=colors)
bode.plot()
bode.figs[0].savefig(filename + '-Tphi.png')
bode.figs[1].savefig(filename + '-Tdel.png')
bode.figs[0].clf()
bode.figs[1].clf()
# Eigenvalues versus speed plot.
v0 = 0.
vf = 10.
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)
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')
id_matrices = cPickle.load(f)
# Eigenvalues versus speed parameters.
v0 = 0.
vf = 10.
num = 100
# Load the identified model from data for rider L and in pavillion floor and
# generate the eigenvalues an eigenvectors as a function of speed.
iM, iC1, iK0, iK2, iH = id_matrices['L-P']
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)
# 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))
