import sys
sys.path.append('..')
from load_paths import read
import cPickle
import numpy as np
import matplotlib.pyplot as plt
import bicycledataprocessor as bdp
from canonicalbicycleid import canonical_bicycle_id as cbi
with open(read('pathToGoodRuns')) as f:
goodRuns = cPickle.load(f)
with open(read('pathToIdMat')) as f:
idMat = cPickle.load(f)
dataset = bdp.DataSet()
roll = {k : [] for k in idMat.keys() + ['Whipple', 'Arm']}
steer = {k : [] for k in idMat.keys() + ['Whipple', 'Arm']}
allH = cbi.lateral_force_contribution(['Charlie', 'Jason', 'Luke'])
for runNum in goodRuns:
trial = bdp.Run(runNum, dataset, filterFreq=15.)
rider = trial.metadata['Rider']
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')
import sys
sys.path.append('..')
from load_paths import read
import cPickle
import numpy as np
import matplotlib.pyplot as plt
import bicycledataprocessor as bdp
from canonicalbicycleid import canonical_bicycle_id as cbi
with open(read('pathToGoodRuns')) as f:
goodRuns = cPickle.load(f)
with open(read('pathToIdMat')) as f:
idMat = cPickle.load(f)
dataset = bdp.DataSet()
roll = {k: [] for k in idMat.keys() + ['Whipple', 'Arm']}
steer = {k: [] for k in idMat.keys() + ['Whipple', 'Arm']}
allH = cbi.lateral_force_contribution(['Charlie', 'Jason', 'Luke'])
for runNum in goodRuns:
trial = bdp.Run(runNum, dataset, filterFreq=15.)
rider = trial.metadata['Rider']
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')
subDat = dat.subset(**subDef)
# whipple model
speedRange = np.linspace(0.0, 10.0, num=50)
whipple = model.Whipple("Charlie").matrices(speedRange)
# arm model
m = loadmat("../../data/extensions/armsAB-Charlie.mat", squeeze_me=True) # this is charlie at 101 speeds
inputMats = np.zeros((101, 4, 2))
for i, B in enumerate(m["inputMatrices"]):
inputMats[i] = B[:, 1:]
# best identified model
with open(read("pathToIdMat")) as f:
idMat = cPickle.load(f)
M, C1, K0, K2, H = idMat["L-P"]
speeds = np.linspace(0, 10, num=50)
As = np.zeros((len(speeds), 4, 4))
Bs = np.zeros((len(speeds), 4, 2))
for i, v in enumerate(speeds):
A, BT = dtk.bicycle.benchmark_state_space(M, C1, K0, K2, v, 9.81)
As[i] = A
BF = np.dot(BT[2:, :], H)
Bs[i] = np.hstack((BT[:, 1].reshape(4, 1), np.vstack((np.zeros((2, 1)), BF))))
# A matrix box plot
nBins = 25
import sys
sys.path.append('..')
from load_paths import read
import os
import cPickle
import numpy as np
import pandas
import bicycledataprocessor as bdp
# load in the raw results
with open(read('pathToGoodRuns')) as f:
goodRuns = cPickle.load(f)
with open('rollFitPercent.p') as f:
rollFit = cPickle.load(f)
with open('steerFitPercent.p') as f:
steerFit = cPickle.load(f)
# get the rider, environment, maneuver, duration so we can sort by them
dataset = bdp.DataSet()
dataset.open()
runTable = dataset.database.root.runTable
taskTable = dataset.database.root.taskTable
runIds = [bdp.database.run_id_string(r) for r in goodRuns]
rider = []
environment = []
#!/usr/env python
# Compare the frequency response of the identified models
import sys
sys.path.append('..')
from load_paths import read
import cPickle
import numpy as np
import matplotlib.pyplot as plt
from dtk import bicycle, control
pathToIdMat = read('pathToIdMat')
with open(pathToIdMat) as f:
idMat = cPickle.load(f)
inputNames = ['$T_\phi$', '$T_\delta$']
stateNames = ['$\phi$', '$\delta$', '$\dot{\phi}$', '$\dot{\delta}$']
outputNames = ['$\phi$', '$\delta$']
C = np.array([[1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0]])
D = np.array([[0.0, 0.0],
[0.0, 0.0]])
speeds = [2.0, 5.5, 9.0]
linestyles = ['-'] * 3 + ['--'] * 3 + ['-.'] * 3 + [':'] * 3
subDat = dat.subset(**subDef)
# whipple model
speedRange = np.linspace(0.0, 10.0, num=50)
whipple = model.Whipple('Charlie').matrices(speedRange)
# arm model
m = loadmat('../../data/extensions/armsAB-Charlie.mat', squeeze_me=True) # this is charlie at 101 speeds
inputMats = np.zeros((101, 4, 2))
for i, B in enumerate(m['inputMatrices']):
inputMats[i] = B[:, 1:]
# best identified model
with open(read('pathToIdMat')) as f:
idMat = cPickle.load(f)
M, C1, K0, K2, H = idMat['L-P']
speeds = np.linspace(0, 10, num=50)
As = np.zeros((len(speeds), 4, 4))
Bs = np.zeros((len(speeds), 4, 2))
for i, v in enumerate(speeds):
A, BT = dtk.bicycle.benchmark_state_space(M, C1, K0, K2, v, 9.81)
As[i] = A
BF = np.dot(BT[2:, :], H)
Bs[i] = np.hstack((BT[:, 1].reshape(4,1), np.vstack((np.zeros((2,1)), BF))))
# A matrix box plot
nBins = 25
import sys
sys.path.append("..")
from load_paths import read
import os
import cPickle
import numpy as np
import pandas
import bicycledataprocessor as bdp
# load in the raw results
with open(read("pathToGoodRuns")) as f:
goodRuns = cPickle.load(f)
with open("rollFitPercent.p") as f:
rollFit = cPickle.load(f)
with open("steerFitPercent.p") as f:
steerFit = cPickle.load(f)
# get the rider, environment, maneuver, duration so we can sort by them
dataset = bdp.DataSet()
dataset.open()
runTable = dataset.database.root.runTable
taskTable = dataset.database.root.taskTable
runIds = [bdp.database.run_id_string(r) for r in goodRuns]
rider = []
from dtk import bicycle, control
from canonicalbicycleid import canonical_bicycle_id as cbi
goldenRatio = (1.0 + np.sqrt(5.0)) / 2.0
params = {
"axes.labelsize": 10,
"axes.grid": False,
"text.fontsize": 10,
"legend.fontsize": 8,
"xtick.labelsize": 8,
"ytick.labelsize": 8,
"text.usetex": True,
}
plt.rcParams.update(params)
pathToIdMat = read("pathToIdMat")
with open(pathToIdMat) as f:
idMat = cPickle.load(f)
# First create all of the plots for the model identified from all riders and
# all environments.
allRiders = ["Charlie", "Jason", "Luke"]
# load M, C1, K0, K2 for each rider
canon = cbi.load_benchmark_canon(allRiders)
# 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
m = loadmat('../../data/extensions/armsAB-Charlie.mat', squeeze_me=True) # this is charlie at 101 speeds
inputMats = zeros((101, 4, 1))
for i, B in enumerate(m['inputMatrices']):
inputMats[i] = B[:, 1].reshape(4, 1)
for lab, ax in coefPlot.axes.items():
row, col = int(lab[-2]), int(lab[-1])
if lab[0] == 'a':
ax.plot(m['speed'], m['stateMatrices'][:, row - 1, col - 1], 'r')
elif lab[0] == 'b':
ax.plot(m['speed'], inputMats[:, row - 1, col - 1], 'r')
# now add the model identified from the runs with Luke on the Pavilion floor
# with the canonical realization
with open(read('pathToIdMat')) as f:
idMat = cPickle.load(f)
M, C1, K0, K2, H = idMat['L-P']
speeds = linspace(0, 10, num=50)
As = zeros((len(speeds), 4, 4))
Bs = zeros((len(speeds), 4, 1))
for i, v in enumerate(speeds):
A, B = dtk.bicycle.benchmark_state_space(M, C1, K0, K2, v, 9.81)
As[i] = A
Bs[i] = B[:, 1].reshape(4, 1)
for lab, ax in coefPlot.axes.items():
row, col = int(lab[-2]), int(lab[-1])
if lab[0] == 'a':
