import os
import cPickle
# external
import pandas
from canonicalbicycleid import canonical_bicycle_id as cbi
riders = ['Charlie', 'Jason', 'Luke']
maneuvers = ['Balance',
'Track Straight Line',
'Balance With Disturbance',
'Track Straight Line With Disturbance']
environments = ['Horse Treadmill', 'Pavillion Floor']
# Get the M, C1, K0, K2, and H matrices for the riders.
canon = cbi.load_benchmark_canon(riders)
H = cbi.lateral_force_contribution(riders)
# Pick the free parameters in the canonical model, i.e. the ones that were
# identified.
roll_parameters = ['Mpd', 'C1pd', 'K0pd']
steer_parameters = ['Mdd', 'C1dp', 'C1dd', 'K0dd', 'K2dd', 'HdF']
# Load in precomputed results.
results_directory = '../data'
with open(os.path.join(results_directory, 'good-runs.p'), 'r') as f:
good_runs = cPickle.load(f)
with open(os.path.join(results_directory, 'id-matrices.p'), 'r') as f:
id_matrices = cPickle.load(f)
# 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))
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')
########################################
allRiders = ['Charlie', 'Jason', 'Luke']
# create the comparison models
canon = cbi.load_benchmark_canon(allRiders)
H = cbi.lateral_force_contribution(allRiders)
with open('../data/idMatrices.p') as f:
idMatrices = cPickle.load(f)
riders = ['Charlie', 'Jason', 'Luke', 'All']
rMap = {x[0]: [x] for x in riders}
environments = ['Horse Treadmill', 'Pavillion Floor', 'All']
eMap = {x[0]: [x] for x in environments}
for k, v in idMatrices.items():
r, e = k.split('-')
if r == 'A':
riders = ['Charlie', 'Jason', 'Luke']
else:
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))
w, v = control.eig_of_series(aAs[indices])
aEigenvalues, aEigenvectors = control.sort_modes(w, v)
rlfig = cbi.plot_rlocus_parts(speeds, iEigenvalues, wEigenvalues,
