def test_pcovar_plot():
p = pcovar([1, 2, 3, 4, 5, 6, 7, 8], 2)
p.plot()
assert len(p.psd) == 5
p = pcovar([1, 2, 3, 4, 5, 6, 7, 8, 9], 2)
p.plot()
assert len(p.psd) == 5
def test_pcovar():
p = pcovar(data_cosine(), 15, NFFT=4096, scale_by_freq=True)
p()
p = pcovar(marple_data, 15, NFFT=4096)
p()
print(p.get_converted_psd('centerdc'))
return p.psd
def test_pcovar():
p = pcovar(data_cosine(), 15, NFFT=4096, scale_by_freq=True)
p()
print(p)
p = pcovar(marple_data, 15, NFFT=4096)
p()
print(p)
print(p.get_converted_psd('centerdc'))
return p.psd
def pcovar(var):
"""
Input: SSTA time series
"""
nw = 48 # order of an autoregressive prediction model for the signal, used in estimating the PSD.
nfft = 256 # NFFT (int) – total length of the final data sets (padded with zero if needed
fs = 1 # default value
p = pcovar(var, nw, nfft, fs)
return 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])