def __init__(self, sourceDir="captures/walksOrientPair"): self.feet = [self.parse(sourceDir+'/feet1.csv'), self.parse(sourceDir+'/feet2.csv'), self.parse(sourceDir+'/feet3.csv')] self.shins = [self.parse(sourceDir+'/shins1.csv'), self.parse(sourceDir+'/shins2.csv'), self.parse(sourceDir+'/shins3.csv')] self.thighs = [self.parse(sourceDir+'/thighs1.csv'), self.parse(sourceDir+'/thighs2.csv')] self.hips = [self.parse(sourceDir+'/hips1.csv'), self.parse(sourceDir+'/hips2.csv')] self.chest = [self.parse(sourceDir+'/chest1.csv'), self.parse(sourceDir+'/chest2.csv')] self.pca_segmenter = PCAclusteringSegmenter()
class database_walksOrientPair:
def __init__(self, sourceDir="captures/walksOrientPair"):
self.feet = [self.parse(sourceDir+'/feet1.csv'), self.parse(sourceDir+'/feet2.csv'), self.parse(sourceDir+'/feet3.csv')]
self.shins = [self.parse(sourceDir+'/shins1.csv'), self.parse(sourceDir+'/shins2.csv'), self.parse(sourceDir+'/shins3.csv')]
self.thighs = [self.parse(sourceDir+'/thighs1.csv'), self.parse(sourceDir+'/thighs2.csv')]
self.hips = [self.parse(sourceDir+'/hips1.csv'), self.parse(sourceDir+'/hips2.csv')]
self.chest = [self.parse(sourceDir+'/chest1.csv'), self.parse(sourceDir+'/chest2.csv')]
self.pca_segmenter = PCAclusteringSegmenter()
def parse(self, filepath):
with open(filepath, 'rb') as csvfile:
dialect = csv.Sniffer().sniff(csvfile.read())
date_parser = lambda x: dt.datetime.fromtimestamp(float(x))
both_orients = pd.read_table(filepath, dialect=dialect, usecols=[0,4,5,6,8,9,10,12,13,14,15], parse_dates=[10], date_parser=date_parser)
# Fixes pandas' sillyness, for some reason it reads the accelZ values as generic objects rather than numbers.
both_orients["accelZ"] = both_orients["accelZ"].convert_objects(convert_numeric=True)
left_orient = both_orients[ both_orients["nodeID"]==2 ].drop("nodeID",axis=1)
right_orient = both_orients[ both_orients["nodeID"]==3 ].drop("nodeID",axis=1)
left_orient.index = range(1,len(left_orient)+1)
right_orient.index = range(1,len(right_orient)+1)
return {'L':left_orient, 'R':right_orient}
def plotLR(self, LRdict, show=True, sensors=['gyroX','gyroY','gyroZ','accelX','accelY','accelZ','magX','magY','magZ']):
plt.figure(figsize=(12, 8), dpi=80)
top = plt.subplot(211)
plt.title("Left Orient Data")
LRdict['L'][sensors].plot(ax=top)
plt.xlabel("")
plt.setp(top.get_xticklabels(), visible=False)
bottom = plt.subplot(212, sharex=top)
plt.title("Right Orient Data")
LRdict['R'][sensors].plot(ax=bottom)
if show:
plt.show()
def plotAll(self, sensors=['gyroX','gyroY','gyroZ','accelX','accelY','accelZ','magX','magY','magZ'], LR='L', segment=None, applyPCA=False):
plt.figure(figsize=(12, 8), dpi=80)
foot = plt.subplot(511)
plt.title("Foot")
data = self.feet[2][LR][sensors]
if segment:
self.plotSegments(data, segPoints=segment, subplot=True, applyPCA=applyPCA)
elif applyPCA:
plt.plot(pca(data, n_components=1)[0])
else:
data.plot(ax=foot)
plt.xlabel("")
plt.setp(foot.get_xticklabels(), visible=False)
shin = plt.subplot(512, sharex=foot)
plt.title("Shin")
data = self.shins[2][LR][sensors]
if segment:
self.plotSegments(data, segPoints=segment, subplot=True, applyPCA=applyPCA)
elif applyPCA:
plt.plot(pca(data, n_components=1)[0])
else:
data.plot(ax=shin)
plt.xlabel("")
plt.setp(shin.get_xticklabels(), visible=False)
thigh = plt.subplot(513, sharex=foot)
plt.title("Thigh")
data = self.thighs[1][LR][sensors]
if segment:
self.plotSegments(data, segPoints=segment, subplot=True, applyPCA=applyPCA)
elif applyPCA:
plt.plot(pca(data, n_components=1)[0])
else:
data.plot(ax=thigh)
plt.xlabel("")
plt.setp(thigh.get_xticklabels(), visible=False)
hip = plt.subplot(514, sharex=foot)
plt.title("Hip")
data = self.hips[1][LR][sensors]
if segment:
self.plotSegments(data, segPoints=segment, subplot=True, applyPCA=applyPCA)
elif applyPCA:
plt.plot(pca(data, n_components=1)[0])
else:
data.plot(ax=hip)
plt.xlabel("")
plt.setp(hip.get_xticklabels(), visible=False)
chest = plt.subplot(515, sharex=foot)
plt.title("Chest")
data = self.chest[1][LR][sensors]
if segment:
self.plotSegments(data, segPoints=segment, subplot=True, applyPCA=applyPCA)
elif applyPCA:
plt.plot(pca(data, n_components=1)[0])
else:
data.plot(ax=chest)
plt.show()
def plotSegments(self, rawData, segPoints="pca", subplot=False, applyPCA=False):
if "currentTime" in rawData.columns:
rawData = rawData.drop("currentTime",axis=1)
if not subplot:
plt.figure(figsize=(11,9))
if segPoints == "pca":
segPoints = self.pca_segmenter.findSegments(rawData)
if segPoints == "minExtrema":
segPoints = self.relativeExtremaSegments(rawData, maxMin="min")
if segPoints == "maxExtrema":
segPoints = self.relativeExtremaSegments(rawData, maxMin="max")
if applyPCA:
plt.plot(pca(rawData, n_components=1)[0])
else:
plt.plot(rawData)
for s in segPoints:
if s <= len(rawData):
plt.axvline(s,color='black',linewidth=2)
if not subplot:
plt.show()
def relativeExtremaSegments(self, rawData, maxMin="max", minSegSize=50):
from scipy.signal import argrelmax, argrelmin
PCs = pca(rawData, n_components=1)[0]
if maxMin == 'max':
return argrelmax(PCs[:,0], order=minSegSize)[0]
if maxMin == 'min':
return argrelmin(PCs[:,0], order=minSegSize)[0]
