def test_pcorrelogram_class():
p = pcorrelogram(marple_data, lag=16)
p()
print(p)
p = pcorrelogram(data_two_freqs(), lag=16)
p.plot()
print(p)
def test_pcorrelogram_class():
p = pcorrelogram(marple_data, lag=16)
p()
print(p)
p = pcorrelogram(data_two_freqs(), lag=16)
p.plot()
print(p)
def create_all_psd():
f = pylab.linspace(0, 1, 4096)
pylab.figure(figsize=(12,8))
# MA model
p = spectrum.pma(xx, 64,128); p(); p.plot()
"""
#ARMA 15 order
a, b, rho = spectrum.arma_estimate(data, 15,15, 30)
psd = spectrum.arma2psd(A=a,B=b, rho=rho)
newpsd = tools.cshift(psd, len(psd)//2) # switch positive and negative freq
pylab.plot(f, 10 * pylab.log10(newpsd/max(newpsd)), label='ARMA 15,15')
"""
# YULE WALKER
p = spectrum.pyule(xx, 7 , NFFT=4096, scale_by_freq=False); p.plot()
# equivalent to
# plot([x for x in p.frequencies()] , 10*log10(p.psd)); grid(True)
#burg method
p = spectrum.pburg(xx, 7, scale_by_freq=False); p.plot()
#pcovar
p = spectrum.pcovar(xx, 7, scale_by_freq=False); p.plot()
#pmodcovar
p = spectrum.pmodcovar(xx, 7, scale_by_freq=False); p.plot()
# correlogram
p = spectrum.pcorrelogram(xx, lag=60, NFFT=512, scale_by_freq=False); p.plot()
# minvar
p = spectrum.pminvar(xx, 7, NFFT=256, scale_by_freq=False); p.plot()
# pmusic
p = spectrum.pmusic(xx, 10,4, scale_by_freq=False); p.plot()
# pmusic
p = spectrum.pev(xx, 10, 4, scale_by_freq=False); p.plot()
# periodogram
p = spectrum.Periodogram(xx, scale_by_freq=False); p.plot()
#
legend( ["MA 32", "pyule 7", "pburg 7", "pcovar", "pmodcovar", "correlogram",
"minvar", "pmusic", "pev", "periodgram"])
pylab.ylim([-80,80])
def create_all_psd():
f = pylab.linspace(0, 1, 4096)
pylab.figure(figsize=(12, 8))
# MA model
p = spectrum.pma(xx, 64, 128)
p()
p.plot()
"""
#ARMA 15 order
a, b, rho = spectrum.arma_estimate(data, 15,15, 30)
psd = spectrum.arma2psd(A=a,B=b, rho=rho)
newpsd = tools.cshift(psd, len(psd)//2) # switch positive and negative freq
pylab.plot(f, 10 * pylab.log10(newpsd/max(newpsd)), label='ARMA 15,15')
"""
# YULE WALKER
p = spectrum.pyule(xx, 7, NFFT=4096, scale_by_freq=False)
p.plot()
# equivalent to
# plot([x for x in p.frequencies()] , 10*log10(p.psd)); grid(True)
#burg method
p = spectrum.pburg(xx, 7, scale_by_freq=False)
p.plot()
#pcovar
p = spectrum.pcovar(xx, 7, scale_by_freq=False)
p.plot()
#pmodcovar
p = spectrum.pmodcovar(xx, 7, scale_by_freq=False)
p.plot()
# correlogram
p = spectrum.pcorrelogram(xx, lag=60, NFFT=512, scale_by_freq=False)
p.plot()
# minvar
p = spectrum.pminvar(xx, 7, NFFT=256, scale_by_freq=False)
p.plot()
# pmusic
p = spectrum.pmusic(xx, 10, 4, scale_by_freq=False)
p.plot()
# pmusic
p = spectrum.pev(xx, 10, 4, scale_by_freq=False)
p.plot()
# periodogram
p = spectrum.Periodogram(xx, scale_by_freq=False)
p.plot()
#
legend([
"MA 32", "pyule 7", "pburg 7", "pcovar", "pmodcovar", "correlogram",
"minvar", "pmusic", "pev", "periodgram"
])
pylab.ylim([-80, 80])
eps = np.finfo(float).tiny # grilla de sampleo temporal tt = np.linspace(0, (N-1)/fs, N).flatten() dd = 0.5 # prestar atención que las tuplas dentro de los diccionarios también pueden direccionarse mediante "ii" #data = np.sin( 2*np.pi*(N/4+dd)*df*tt) + np.sqrt(0.1)*np.random.randn(N) data = np.sin( 2*np.pi*(N/4+dd)*df*tt) + np.sin( 2*np.pi*(N/4+10+dd)*df*tt) + np.sqrt(0.1)*np.random.randn(N) # No paramétricos pP = sp.Periodogram(data, sampling=fs, NFFT = N ) _, pW = sg.welch(data, fs=fs, nfft=N, window='hanning', nperseg=int(np.round(N/3)) ) pBT = sp.pcorrelogram(data, sampling=fs, NFFT = N, lag=int(np.round(N/5)), window='hamming') pMT = sp.MultiTapering(data, sampling=fs, NFFT = N, NW=2 ) # Paramétricos pEig = sp.pev(data, 30, NFFT=N ) pCov = sp.pmodcovar(data, 30, NFFT = N ) pARMA = sp.parma(data, 8, 8, 30, NFFT=N) sp.pma description = ['Per', 'Welch', 'BT', 'MT', 'Eig', 'MCov', 'ARMA' ]
def test_pcorrelogram_class():
p = pcorrelogram(marple_data, lag=16)
p()
print(p)