How to use the vector.norm function in vector

self.s.CompassCalEllipsoidOffset = tuple(value[0][:3])
                #rtimu.resetFusion()
            elif name == 'imu.rate':
                self.rate = value

        if not self.lastdata:
            return
        gyro, compass = self.lastdata

        # see if gyro is out of range, sometimes the sensors read
        # very high gyro readings and the sensors need to be reset by software
        # this is probably a bug in the underlying driver with fifo misalignment
        d = .05/self.rate # filter constant
        for i in range(3): # filter gyro vector
            self.avggyro[i] = (1-d)*self.avggyro[i] + d*gyro[i]
        if vector.norm(self.avggyro) > .8: # 55 degrees/s
            print('too high standing gyro bias, resetting sensors', gyro, self.avggyro)
            self.init()

        # detects the problem even faster:
        if any(map(lambda x : abs(x) > 1000, compass)):
            print('compass out of range, resetting', compass)
            self.init()

def dip(y, z):
            n = min(max(vector.dot(y, z)/vector.norm(y), -1), 1)
            return n
        r1 = lmap(lambda y, z : fac*beta[3]*(beta[4]-dip(y, z)), m, g)

def ComputeDeviation(points, fit):
    m, d  = 0, 0
    for p in points:
        v = vector.sub(p[:3], fit[:3])
        m += (1 - vector.dot(v, v) / fit[3]**2)**2

        if len(fit) > 4:
            n = vector.dot(v, p[3:]) / vector.norm(v)
            if abs(n) <= 1:
                ang = math.degrees(math.asin(n))
                d += (fit[4] - ang)**2
            else:
                d += 1e111
    m /= len(points)
    d /= len(points)
    return [m**.5, d**.5]

def dip(y, z):
            n = min(max(vector.dot(y, z)/vector.norm(y), -1), 1)
            return n
        r1 = lmap(lambda y, z : fac*beta[2]*(beta[3]-dip(y, z)), m, g)

def dip(y, z):
            n = min(max(vector.dot(y, z)/vector.norm(y), -1), 1)
            return n
        r1 = lmap(lambda y, z : fac*beta[1]*(beta[2]-dip(y, z)), m, g)