Python acovf Examples

Example #1

    xhat5, err5 = VARMA(x, B, C)
    #print err5

    #in differences
    #VARMA(np.diff(x,axis=0),B,C)

    #Note:
    # * signal correlate applies same filter to all columns if kernel.shape[1]<K
    #   e.g. signal.correlate(x0,np.ones((3,1)),'valid')
    # * if kernel.shape[1]==K, then `valid` produces a single column
    #   -> possible to run signal.correlate K times with different filters,
    #      see the following example, which replicates VAR filter
    x0 = np.column_stack([np.arange(T), 2 * np.arange(T)])
    B[:, :, 0] = np.ones((P, K))
    B[:, :, 1] = np.ones((P, K))
    B[1, 1, 1] = 0
    xhat0 = VAR(x0, B)
    xcorr00 = signal.correlate(x0, B[:, :, 0])  #[:,0]
    xcorr01 = signal.correlate(x0, B[:, :, 1])
    print np.all(
        signal.correlate(x0, B[:, :, 0], 'valid')[:-1, 0] == xhat0[P:, 0])
    print np.all(
        signal.correlate(x0, B[:, :, 1], 'valid')[:-1, 0] == xhat0[P:, 1])

    #import error
    #from movstat import acovf, acf
    from gwstatsmodels.tsa.stattools import acovf, acf
    aav = acovf(x[:, 0])
    print aav[0] == np.var(x[:, 0])
    aac = acf(x[:, 0])

Example #2

File: try_arma_more.py Project: bolliger32/pygwr

    plt.plot(wm,sdm/sdm[0], '-', wm[maxind], sdm[maxind]/sdm[0], 'o')
else:
    plt.plot(wm, sdm, '-', wm[maxind], sdm[maxind], 'o')
plt.title('matplotlib')

if hastalkbox:
    sdp, wp = stbs.periodogram(x)
    plt.subplot(2,3,3)

    if rescale:
        plt.plot(wp,sdp/sdp[0])
    else:
        plt.plot(wp, sdp)
    plt.title('stbs.periodogram')

xacov = acovf(x, unbiased=False)
plt.subplot(2,3,4)
plt.plot(xacov)
plt.title('autocovariance')

nr = len(x)#*2/3
#xacovfft = np.fft.fft(xacov[:nr], 2*nr-1)
xacovfft = np.fft.fft(np.correlate(x,x,'full'))
#abs(xacovfft)**2 or equivalently
xacovfft = xacovfft * xacovfft.conj()

plt.subplot(2,3,5)
if rescale:
    plt.plot(xacovfft[:nr]/xacovfft[0])
else:
    plt.plot(xacovfft[:nr])

Example #3

File: example_arma.py Project: zzzz123321/pygwr

arrvs = ar_generator()
##arma = ARIMA()
##res = arma.fit(arrvs[0], 4, 0)
arma = ARIMA(arrvs[0])
res = arma.fit((4,0, 0))

print res[0]

acf1 = acf(arrvs[0])
acovf1b = acovf(arrvs[0], unbiased=False)
acf2 = autocorr(arrvs[0])
acf2m = autocorr(arrvs[0]-arrvs[0].mean())
print acf1[:10]
print acovf1b[:10]
print acf2[:10]
print acf2m[:10]


x = arma_generate_sample([1.0, -0.8], [1.0], 500)
print acf(x)[:20]
import gwstatsmodels.api as sm
print sm.regression.yule_walker(x, 10)

import matplotlib.pyplot as plt
#ax = plt.axes()

Example #4

File: varma.py Project: bolliger32/pygwr

    xhat5, err5 = VARMA(x,B,C)
    #print err5

    #in differences
    #VARMA(np.diff(x,axis=0),B,C)


    #Note:
    # * signal correlate applies same filter to all columns if kernel.shape[1]<K
    #   e.g. signal.correlate(x0,np.ones((3,1)),'valid')
    # * if kernel.shape[1]==K, then `valid` produces a single column
    #   -> possible to run signal.correlate K times with different filters,
    #      see the following example, which replicates VAR filter
    x0 = np.column_stack([np.arange(T), 2*np.arange(T)])
    B[:,:,0] = np.ones((P,K))
    B[:,:,1] = np.ones((P,K))
    B[1,1,1] = 0
    xhat0 = VAR(x0,B)
    xcorr00 = signal.correlate(x0,B[:,:,0])#[:,0]
    xcorr01 = signal.correlate(x0,B[:,:,1])
    print np.all(signal.correlate(x0,B[:,:,0],'valid')[:-1,0]==xhat0[P:,0])
    print np.all(signal.correlate(x0,B[:,:,1],'valid')[:-1,0]==xhat0[P:,1])

    #import error
    #from movstat import acovf, acf
    from gwstatsmodels.tsa.stattools import acovf, acf
    aav = acovf(x[:,0])
    print aav[0] == np.var(x[:,0])
    aac = acf(x[:,0])

Example #5

File: try_arma_more.py Project: zzzz123321/pygwr

    plt.plot(wm, sdm / sdm[0], '-', wm[maxind], sdm[maxind] / sdm[0], 'o')
else:
    plt.plot(wm, sdm, '-', wm[maxind], sdm[maxind], 'o')
plt.title('matplotlib')

if hastalkbox:
    sdp, wp = stbs.periodogram(x)
    plt.subplot(2, 3, 3)

    if rescale:
        plt.plot(wp, sdp / sdp[0])
    else:
        plt.plot(wp, sdp)
    plt.title('stbs.periodogram')

xacov = acovf(x, unbiased=False)
plt.subplot(2, 3, 4)
plt.plot(xacov)
plt.title('autocovariance')

nr = len(x)  #*2/3
#xacovfft = np.fft.fft(xacov[:nr], 2*nr-1)
xacovfft = np.fft.fft(np.correlate(x, x, 'full'))
#abs(xacovfft)**2 or equivalently
xacovfft = xacovfft * xacovfft.conj()

plt.subplot(2, 3, 5)
if rescale:
    plt.plot(xacovfft[:nr] / xacovfft[0])
else:
    plt.plot(xacovfft[:nr])