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))
requiredFeatures = checkRequiredFeatures(requiredFeatures)
data = getVelocity(data, requiredFeatures['cols'])
features = dict()
for f in requiredFeatures['features']:
fun, useGetFun = posFeatures()[f]
if useGetFun:
ff = getFun(data, fun, f)
else:
ff = fun(data, f)
features.update(ff)
return features
if __name__ == '__main__':
import dataTransform.Preprocessing as pp
data = ac.readGCDCFormat("..\data\Runs\Example\enkel\DATA-001.csv")
data = ac.preprocessGCDC(data)
filtered = pp.filterRun3(data)
velocity = getVelocity(filtered, ['Vx', 'Vz'])
print velocity
features = getVelocityFeatures(filtered,
{'cols': ['Vx'], 'features': ['covar', 'av']})
print(features)
# velocity.plot()
# ppl.plot(t,filtered.Ax)
# ppl.show()
