def _headMoveMeasure(A):
lags = np.arange(0,24)
i0 = 3
C = tsstats.acf(A[:,i0], lags)
Cm = C.min()
Ct = tsstats.acf(A[:,-i0], lags)
Cmt = Ct.min()
return -(Cm-Cmt)
def s_acf(traj, maxMissing):
sigma2 = ma.array([speedWithoutReversals(trajw).var()
for trajw in traj.asWindows(100.)
if fractionMissing(trajw)>maxMissing])
C = ma.array([tsstats.acf(speedWithoutReversals(trajw), lags, exclude=trajw.excluded)
for trajw in traj.asWindows(100.)
if fractionMissing(trajw)>maxMissing])
C[np.isnan(C)] = ma.masked
C = (sigma2[:,np.newaxis]*C).mean(axis=0)
if len(C) == 0:
C = ma.zeros((len(lags),))
C[:] = ma.masked
return C
def s_acf(traj, maxMissing):
sigma2 = ma.array([
speedWithoutReversals(trajw).var() for trajw in traj.asWindows(100.)
if fractionMissing(trajw) > maxMissing
])
C = ma.array([
tsstats.acf(speedWithoutReversals(trajw), lags, exclude=trajw.excluded)
for trajw in traj.asWindows(100.)
if fractionMissing(trajw) > maxMissing
])
C[np.isnan(C)] = ma.masked
C = (sigma2[:, np.newaxis] * C).mean(axis=0)
if len(C) == 0:
C = ma.zeros((len(lags), ))
C[:] = ma.masked
return C
def plotACFComparison(self, t, y, tau,
dataStyle='k.',
theoryStyle='r-',
showPlot=True):
dts = t[1:] - t[:-1]
dt = dts[0]
if not np.all(dts - dt < 1e-12):
raise Exception('Time must be evenly sampled.')
lags = np.round(tau/dt)
Cdata = tsstats.acf(y, lags)
plt.plot(lags*dt, Cdata, dataStyle)
Ctheory = self.acf(tau)
plt.plot(tau, Ctheory, theoryStyle)
plt.xlabel(r'$\tau$ (s)')
plt.ylabel(r'$\langle y(t) \cdot y(t+\tau) \rangle / \langle y(t) \cdot y(t) \rangle$')
plt.ylim((-1, 1))
plt.xlim((tau.min(), tau.max()))
plt.grid(True)
if showPlot:
plt.show()