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()