def getRawData(path, name, nb, hip):
def getDir(path, subject, bodyPart, nb):
return (path + subject + "\\" + bodyPart + "\\" +
"DATA-00" + str(int(nb)) + ".csv")
if hip:
bodyPart = 'heup'
else:
bodyPart = 'enkel'
datadir = getDir(path, name, bodyPart, nb)
try:
data = ac.readGCDCFormat(datadir)
except:
return None
return ac.preprocessGCDC(data)
def getDataForOneBodyPart(path, hip):
data = readGCDCFormat(path)
data = preprocessGCDC(data)
data = getRunningPart(data, hip)
return data
if col in posCols()]
generatedFeatures['features'] = [f for f in generatedFeatures['features']
if f in posFeatures().keys()]
return generatedFeatures
def getSimpleFreqDomainFeatures(data, requiredFeatures=None):
requiredFeatures = checkRequiredFeatures(requiredFeatures)
data = toFreq(data[requiredFeatures['cols']])
features = dict()
for f in requiredFeatures['features']:
features.update(posFeatures()[f](data, f))
return features
if __name__ == '__main__':
import dataTransform.accproc as ac
import dataTransform.Preprocessing as pp
for i in range(9):
nb = int(i + 1)
if nb == 4:
data = ac.readGCDCFormat("..\data\Runs\Tina\enkel\DATA-00" +
`nb` + ".csv")
data = ac.preprocessGCDC(data)
filtered = pp.filterRun3(data)
print(getSimpleFreqDomainFeatures(data, None))
minx = xcandidate[0]
data = data[minx:maxx]
return data
if __name__ == '__main__':
subjects = ['Vreni', 'Annick', 'Tina', 'Tinne']
subjects = ['Ann', 'Emmy', 'Floor']
subjects = ['Hanne', 'Jolien', 'Laura']
subjects = ['Mara', 'Nina', 'Sofie', 'Yllia']
for sub in subjects:
print(sub)
for i in range(9):
nb = int(i + 1)
print(nb)
datadir = "..\..\Runs\\" + sub + "\heup\DATA-00" + `nb` + ".csv"
try:
data = ac.readGCDCFormat(datadir)
data = preprocessGCDC(data)
# data.plot()
try:
data = filterRun3(data, True)
data.plot()
except:
print("failed")
except:
continue
pylab.show()