# Rows: Data Set
roll_r_squared = {}
steer_r_squared = {}
for model_name, canonical_matrices in id_matrices.items():
roll_r_squared[model_name] = []
steer_r_squared[model_name] = []
for i, queries in enumerate(combinations_of_queries):
print('Computing the coefficient of determination for data ' +
'set {} with model {}.'.format(data_set_names[i], model_name))
if model_name == 'Whipple':
canonical_matrices = cbi.mean_canon(queries[0], canon, H)
elif model_name == 'Arm':
pass
#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]])
# I need some way to compute the A and b matrices using just the
# M, C, and K matrices.
# Select the subset of runs and remove any that have errors.
run_numbers = cbi.select_runs(queries[0], maneuvers, queries[1])
run_numbers = list(set(run_numbers).intersection(good_runs))
print('Number of runs: {}'.format(len(run_numbers)))
roll_r_squared[model_name].append(
for combo in combinations:
print('Computing the estimate for riders: {} and environments: {}'.format(
combo[0], combo[1]))
if combo[0] == 'All':
riders = ['Charlie', 'Jason', 'Luke']
else:
riders = [combo[0]]
if combo[1] == 'All':
environments = ['Horse Treadmill', 'Pavillion Floor']
else:
environments = [combo[1]]
runs = cbi.select_runs(riders, maneuvers, environments)
runs = list(set(runs).difference(errors))
means = cbi.mean_canon(riders, canon, H)
idMat, rollCovar, steerCovar = cbi.enforce_symmetry(
runs, trials, rollParams, steerParams, *means)
idMatrices[combo[0][0] + '-' + combo[1][0]] = idMat
covarMatrices[combo[0][0] + '-' + combo[1][0]] = (rollCovar, steerCovar)
print('Done.')
with open('../data/goodRuns.p', 'w') as f:
cPickle.dump(goodRuns, f)
# save all of the identified matrices to file
with open('../data/idMatrices.p', 'w') as f:
cPickle.dump(idMatrices, f)
if combo[0] == 'All':
riders = ['Charlie', 'Jason', 'Luke']
else:
riders = [combo[0]]
if combo[1] == 'All':
environments = ['Horse Treadmill', 'Pavillion Floor']
else:
environments = [combo[1]]
# Select the subset of runs and remove any that have errors.
runs = cbi.select_runs(riders, maneuvers, environments)
runs = list(set(runs).difference(errors))
# Compute the mean first principles model with respect to the subset of
# riders. This returns a tuple: (M, C1, K0, K2, H)
means = cbi.mean_canon(riders, canon, H)
# Solve the linear least squares: roll equation first, steer equation
# second with the matrix symmetry enforced from the roll results.
id_matrix, roll_covariance, steer_covariance = \
cbi.enforce_symmetry(runs, trials, roll_parameters,
steer_parameters, *means)
id_matrices[combo[0][0] + '-' + combo[1][0]] = id_matrix
covariance_matrices[combo[0][0] + '-' + combo[1][0]] = (roll_covariance,
steer_covariance)
print('Done.')
# Store the identified models, the parameter covariances, and the list of
# good runs.
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')
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)
# load the H lateral force vector for each rider
H = cbi.lateral_force_contribution(allRiders)
# Eigenvalues versus speed plot.
v0 = 0.0
vf = 10.0
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")
