def __init__(self, fileName=None, w=None):
"""Loads a .mat file and data from the database to construct a
data frame."""
if fileName is None:
self.fileName = PATH_TO_SYSTEM_ID_DATA
else:
self.fileName = fileName
if w is None:
w = np.logspace(-1.0, 1.0, num=100)
mat = loadmat(self.fileName, squeeze_me=True)
d = {}
d['RunID'] = [os.path.splitext(str(r))[0] for r in mat['matFiles']]
d['ActualSpeed'] = mat['speeds']
d['Duration'] = mat['durations']
for fit in mat['fits']:
for i, state in enumerate(self.states):
try:
d[state + 'Fit'].append(fit[i])
except KeyError:
d[state + 'Fit'] = [fit[i]]
d['MeanFit'] = np.mean(mat['fits'], 1)
self.stateMatrices = mat['stateMatrices']
for A in mat['stateMatrices']:
for i in range(2, 4):
for j in range(len(self.states)):
col = 'a' + str(i + 1) + str(j + 1)
try:
d[col].append(A[i, j])
except KeyError:
d[col] = [A[i, j]]
self.inputMatrices = mat['inputMatrices']
for B in mat['inputMatrices']:
for i in range(2, 4):
for j in range(0, 2):
col = 'b' + str(i + 1) + str(j + 1)
try:
d[col].append(B[i, j])
except KeyError:
d[col] = [B[i, j]]
dataset = bdp.DataSet(fileName=PATH_TO_DATABASE, pathToH5=PATH_TO_H5,
pathToCorruption=PATH_TO_CORRUPT)
dataset.open()
table = dataset.database.root.runTable
tableCols = ['Rider', 'Maneuver', 'Environment', 'Speed']
for col in tableCols:
d[col] = []
for r in d['RunID']:
i = get_row_num(r, table)
for col in tableCols:
d[col].append(table[i][col])
dataset.close()
self.dataFrame = pandas.DataFrame(d)
self.w = w
self.load_bode_data()
self.load_eig_data()
def plot_coefficients(fitThreshold):
meanFits = np.mean(a['fits'], 1)
print(runNums[np.nanargmax(meanFits)])
indices = np.nonzero(meanFits > fitThreshold)
stateMats = a['stateMatrices'][indices[0], :, :]
inputMats = a['inputMatrices'][indices[0], :]
speeds = a['speeds'][indices[0]]
dataset = bdp.DataSet(fileName=PATH_TO_DATABASE, pathToH5=PATH_TO_H5,
pathToCorruption=PATH_TO_CORRUPT)
dataset.open()
# find all the runs for Jason that were without disturbances
table = dataset.database.root.runTable
riders = []
maneuvers = []
for r in runNums[indices[0]]:
i = get_row_num(r, table)
riders.append(table[i]['Rider'])
maneuvers.append(table[i]['Maneuver'])
riders = np.array(riders)
dataset.close()
modelSpeeds = np.linspace(0., 10., num=50)
modelStateMats = {}
modelInputMats = {}
for rider in set(riders):
modelStateMats[rider] = np.zeros((len(modelSpeeds), 4, 4))
modelInputMats[rider] = np.zeros((len(modelSpeeds), 4))
# create a Bicycle object for the rider/bicycle
if rider == 'Jason':
bicycle = 'Rigid'
else:
bicycle = 'Rigidcl'
bicycle = bp.Bicycle(bicycle, pathToData=PATH_TO_PARAMETERS, forceRawCalc=True)
bicycle.add_rider(rider)
# compute the A and B matrices as a function of speed for the Whipple
# model
for i, v in enumerate(modelSpeeds):
A, B = bicycle.state_space(v, nominal=True)
modelStateMats[rider][i, :, :] = A
# the second column is for steer torque
modelInputMats[rider][i, :] = B[:, 1]
fig = plt.figure()
equations = [r'\dot{\phi}', r'\dot{\delta}', r'\ddot{\phi}', r'\ddot{\delta}']
states = [r'\phi', r'\delta', r'\dot{\phi}', r'\dot{\delta}']
aXlims = np.array([[0., 20.],
[-80., 0.],
[-1.5, 4.],
[-8., 0.],
[-0.2, 1.2],
[0., 200.],
[-175., 40.],
[-40., 60.],
[-100., 0.],
[0., 20.]])
onlyRiders = set(riders)
for i in range(2, 4):
for j in range(4):
# make a row for for the roll acceleration and steer acceleration
# equations and a colum for the four states and one for the input
ax = fig.add_subplot(2, 5, 5 * (i - 2) + j + 1)
ax.set_title('$a_{' + equations[i] + states[j] + '}$')
for rider in onlyRiders:
modelLine = ax.plot(modelSpeeds, modelStateMats[rider][:, i, j])
riderIndices = np.nonzero(riders == rider)
expLine = ax.plot(speeds[riderIndices[0]], stateMats[riderIndices[0], i, j], '.')
expLine[0].set_color(modelLine[0].get_color())
print(rider, modelLine[0].get_color())
ax.set_ylim(aXlims[5 * (i - 2) + j])
for i, p in zip(range(2, 4), [5, 10]):
ax = fig.add_subplot(2, 5, p)
ax.set_title('$b_{' + equations[i] + r'T_\delta' + '}$')
for rider in onlyRiders:
riderIndices = np.nonzero(riders == rider)
modelLine = ax.plot(modelSpeeds, modelInputMats[rider][:, i])
expLine = ax.plot(speeds[riderIndices[0]], inputMats[riderIndices[0], i], '.')
expLine[0].set_color(modelLine[0].get_color())
ax.set_ylim(aXlims[p - 1])
fig.suptitle('n = {}, Mean fit threshold: {}%'.format(len(speeds), fitThreshold))
return fig
