def test_covar_15_ip():
af, pf, ab, pb, pbv = arcovar_marple(marple_data, 15)
assert_almost_equal(pf, 0.0031358526195905032)
assert_almost_equal(pb, 0.0026095580050847235)
assert_array_almost_equal(af[0:15], array([ 3.14064291e+00 -0.53085796j, 6.71499124e+00 -2.02047795j,
1.06218919e+01 -4.91215366j, 1.40604378e+01 -8.88144555j,
1.56600743e+01-13.2925649j , 1.52808636e+01-17.26357445j,
1.29553371e+01-20.19441487j, 9.56479043e+00-21.35967801j,
5.76086019e+00-20.39407074j, 2.35478080e+00-17.25236853j,
-1.39883911e-02-12.63099132j, -1.01307484e+00 -7.71542788j,
-1.00735874e+00 -3.71449987j, -5.47782956e-01 -1.24481265j,
-1.63739470e-01 -0.22820697j]))
assert_array_almost_equal(ab[0:15], array([ 3.06854326 +0.4396126j , 6.52836187 +1.85223579j,
10.14250939 +4.53484335j, 13.27104933 +8.16295648j,
14.65282324+12.10370542j, 14.30283278+15.67072521j,
12.13984749+18.32533332j, 9.02885933+19.34952244j,
5.49933445+18.38815454j, 2.39313549+15.41172794j,
0.23240843+11.16952573j, -0.69430878 +6.74812076j,
-0.75349882 +3.21552564j, -0.42710881 +1.07407686j,
-0.13625884 +0.18990667j]))
assert_array_almost_equal(pbv, array([23.002882564886164,
14.963158025030376, 11.46060060362683,
8.8047876198403294, 8.464718707735825,
6.7595928955003961, 3.9194229830412644,
3.4283223276191257, 2.2528330561384045,
1.174361182536527, 0.53260425403862111,
0.30138304540853789, 0.1893577453852136,
0.13685257356088598]))
def test_covar_15_ip():
af, pf, ab, pb, pbv = arcovar_marple(marple_data, 15)
assert_almost_equal(pf, 0.0031358526195905032)
assert_almost_equal(pb, 0.0026095580050847235)
assert_array_almost_equal(af[0:15], array([ 3.14064291e+00 -0.53085796j, 6.71499124e+00 -2.02047795j,
1.06218919e+01 -4.91215366j, 1.40604378e+01 -8.88144555j,
1.56600743e+01-13.2925649j , 1.52808636e+01-17.26357445j,
1.29553371e+01-20.19441487j, 9.56479043e+00-21.35967801j,
5.76086019e+00-20.39407074j, 2.35478080e+00-17.25236853j,
-1.39883911e-02-12.63099132j, -1.01307484e+00 -7.71542788j,
-1.00735874e+00 -3.71449987j, -5.47782956e-01 -1.24481265j,
-1.63739470e-01 -0.22820697j]))
assert_array_almost_equal(ab[0:15], array([ 3.06854326 +0.4396126j , 6.52836187 +1.85223579j,
10.14250939 +4.53484335j, 13.27104933 +8.16295648j,
14.65282324+12.10370542j, 14.30283278+15.67072521j,
12.13984749+18.32533332j, 9.02885933+19.34952244j,
5.49933445+18.38815454j, 2.39313549+15.41172794j,
0.23240843+11.16952573j, -0.69430878 +6.74812076j,
-0.75349882 +3.21552564j, -0.42710881 +1.07407686j,
-0.13625884 +0.18990667j]))
assert_array_almost_equal(pbv, array([23.002882564886164,
14.963158025030376, 11.46060060362683,
8.8047876198403294, 8.464718707735825,
6.7595928955003961, 3.9194229830412644,
3.4283223276191257, 2.2528330561384045,
1.174361182536527, 0.53260425403862111,
0.30138304540853789, 0.1893577453852136,
0.13685257356088598]))
def create_all_psd():
f = pylab.linspace(0, 1, 4096)
pylab.clf()
pylab.figure(figsize=(12,8))
#MA 15 order
b, rho = spectrum.ma(data, 15, 30)
psd = spectrum.arma2psd(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='MA 15')
#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')
#yulewalker
ar, P,c = spectrum.aryule(data, 15, norm='biased')
psd = spectrum.arma2psd(A=ar, rho=P)
newpsd = tools.cshift(psd, len(psd)//2) # switch positive and negative freq
pylab.plot(f, 10 * pylab.log10(newpsd/max(newpsd)), label='YuleWalker 15')
#burg method
ar, P,k = spectrum.arburg(data, order=15)
psd = spectrum.arma2psd(A=ar, rho=P)
newpsd = tools.cshift(psd, len(psd)//2) # switch positive and negative freq
pylab.plot(f, 10 * pylab.log10(newpsd/max(newpsd)), label='Burg 15')
#covar method
af, pf, ab, pb, pv = spectrum.arcovar_marple(data, 15)
psd = spectrum.arma2psd(A=af, B=ab, rho=pf)
newpsd = tools.cshift(psd, len(psd)//2) # switch positive and negative freq
pylab.plot(f, 10 * pylab.log10(newpsd/max(newpsd)), label='covar 15')
#modcovar method
a, p, pv = spectrum.modcovar_marple(data, 15)
psd = spectrum.arma2psd(A=a)
newpsd = tools.cshift(psd, len(psd)//2) # switch positive and negative freq
pylab.plot(f, 10 * pylab.log10(newpsd/max(newpsd)), label='modcovar 15')
#correlogram
psd = spectrum.CORRELOGRAMPSD(data, data, lag=15)
newpsd = tools.cshift(psd, len(psd)/2) # switch positive and negative freq
pylab.plot(f, 10 * pylab.log10(newpsd/max(newpsd)), label='correlogram 15')
#minvar
psd = spectrum.minvar(data, 15)
#newpsd = tools.cshift(psd, len(psd)/2) # switch positive and negative freq
pylab.plot(f, 10 * pylab.log10(newpsd/max(newpsd)), label='MINVAR 15')
#music
psd,db = spectrum.music(data, 15, 11)
pylab.plot(f, 10 * pylab.log10(psd/max(psd)), '--',label='MUSIC 15')
#ev music
psd,db = spectrum.ev(data, 15, 11)
pylab.plot(f, 10 * pylab.log10(psd/max(psd)), '--',label='EV 15')
pylab.legend(loc='upper left', prop={'size':10}, ncol=2)
pylab.ylim([-80,10])
pylab.savefig('psd_all.png')
def test_covar_null_ip():
af, pf, ab, pb, c = arcovar_marple(marple_data, 0)
assert_almost_equal(pf, 1.7804598944893046)
def test_covar():
af, pf, ab, pb, pv = arcovar_marple(marple_data, 15)
PSD = arma2psd(af)
newpsd = cshift(PSD, len(PSD) // 2) # switch positive and negative freq
return newpsd
def test_covar_simplified():
af, pf, ab, pb, pv = arcovar_marple(marple_data, 15)
a2, e2 = arcovar(marple_data, 15)
assert_array_almost_equal(af[0:15], a2) # af contains zeros after order=15
def test_covar_null_ip():
af, pf, ab, pb, c = arcovar_marple(marple_data, 0)
assert_almost_equal(pf, 1.7804598944893046)
def test_covar():
af, pf, ab, pb, pv = arcovar_marple(marple_data, 15)
PSD = arma2psd(af)
newpsd = cshift(PSD, len(PSD)//2) # switch positive and negative freq
return newpsd
def test_covar_simplified():
af, pf, ab, pb, pv = arcovar_marple(marple_data, 15)
a2, e2 = arcovar(marple_data, 15)
assert_array_almost_equal(af[0:15], a2) # af contains zeros after order=15
def create_all_psd():
f = pylab.linspace(0, 1, 4096)
pylab.clf()
pylab.figure(figsize=(12, 8))
#MA 15 order
b, rho = spectrum.ma(data, 15, 30)
psd = spectrum.arma2psd(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='MA 15')
#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')
#yulewalker
ar, P, c = spectrum.aryule(data, 15, norm='biased')
psd = spectrum.arma2psd(A=ar, rho=P)
newpsd = tools.cshift(psd,
len(psd) // 2) # switch positive and negative freq
pylab.plot(f,
10 * pylab.log10(newpsd / max(newpsd)),
label='YuleWalker 15')
#burg method
ar, P, k = spectrum.arburg(data, order=15)
psd = spectrum.arma2psd(A=ar, rho=P)
newpsd = tools.cshift(psd,
len(psd) // 2) # switch positive and negative freq
pylab.plot(f, 10 * pylab.log10(newpsd / max(newpsd)), label='Burg 15')
#covar method
af, pf, ab, pb, pv = spectrum.arcovar_marple(data, 15)
psd = spectrum.arma2psd(A=af, B=ab, rho=pf)
newpsd = tools.cshift(psd,
len(psd) // 2) # switch positive and negative freq
pylab.plot(f, 10 * pylab.log10(newpsd / max(newpsd)), label='covar 15')
#modcovar method
a, p, pv = spectrum.modcovar_marple(data, 15)
psd = spectrum.arma2psd(A=a)
newpsd = tools.cshift(psd,
len(psd) // 2) # switch positive and negative freq
pylab.plot(f, 10 * pylab.log10(newpsd / max(newpsd)), label='modcovar 15')
#correlogram
psd = spectrum.CORRELOGRAMPSD(data, data, lag=15)
newpsd = tools.cshift(psd,
len(psd) / 2) # switch positive and negative freq
pylab.plot(f,
10 * pylab.log10(newpsd / max(newpsd)),
label='correlogram 15')
#minvar
psd = spectrum.minvar(data, 15)
#newpsd = tools.cshift(psd, len(psd)/2) # switch positive and negative freq
pylab.plot(f, 10 * pylab.log10(newpsd / max(newpsd)), label='MINVAR 15')
#music
psd, db = spectrum.music(data, 15, 11)
pylab.plot(f, 10 * pylab.log10(psd / max(psd)), '--', label='MUSIC 15')
#ev music
psd, db = spectrum.ev(data, 15, 11)
pylab.plot(f, 10 * pylab.log10(psd / max(psd)), '--', label='EV 15')
pylab.legend(loc='upper left', prop={'size': 10}, ncol=2)
pylab.ylim([-80, 10])
pylab.savefig('psd_all.png')
