def main():
nr = 2**14 # number of datapoints in time-series
tau0=1.0 # sampling interval, sets nyquist frequency to 0.5/tau0
adev0 = 1.0e-11
#
# for each noise type generated with cn.noiseGen()
# compute
# - phase PSD and coefficient g_b
# - frequency PSD and coefficient h_a
# - ADEV
# - MDEV
#
qd0 = qd1 = qd2 = qd3 = qd4 = pow(adev0, 2) # discrete variance for noiseGen()
tau0 = 1.0 # sample interval
sample_rate = 1/tau0
x0 = cn.noiseGen(nr, qd0, 0) # white phase noise (WPM)
x1 = cn.noiseGen(nr, qd1, -1) # flicker phase noise (FPM)
x2 = cn.noiseGen(nr, qd2, -2) # white frequency noise (WFM)
x3 = cn.noiseGen(nr, qd3 , -3) # flicker frequency noise (FFM)
x4 = cn.noiseGen(nr, qd4 , -4) # random walk frequency noise (RWFM)
# compute frequency time-series
y0 = at.phase2frequency(x0, sample_rate)
y1 = at.phase2frequency(x1, sample_rate)
y2 = at.phase2frequency(x2, sample_rate)
y3 = at.phase2frequency(x3, sample_rate)
y4 = at.phase2frequency(x4, sample_rate)
# compute phase PSD
(f0, psd0) = at.noise.scipy_psd(x0, fs=sample_rate, nr_segments=4)
(f1, psd1) = at.noise.scipy_psd(x1, fs=sample_rate, nr_segments=4)
(f2, psd2) = at.noise.scipy_psd(x2, fs=sample_rate, nr_segments=4)
(f3, psd3) = at.noise.scipy_psd(x3, fs=sample_rate, nr_segments=4)
(f4, psd4) = at.noise.scipy_psd(x4, fs=sample_rate, nr_segments=4)
# compute phase PSD prefactor g_b
g0 = cn.phase_psd_from_qd( qd0, 0, tau0 )
g1 = cn.phase_psd_from_qd( qd0, -1, tau0 )
g2 = cn.phase_psd_from_qd( qd0, -2, tau0 )
g3 = cn.phase_psd_from_qd( qd0, -3, tau0 )
g4 = cn.phase_psd_from_qd( qd0, -4, tau0 )
# compute frequency PSD
(ff0, fpsd0) = at.noise.scipy_psd(y0, fs=sample_rate, nr_segments=4)
(ff1, fpsd1) = at.noise.scipy_psd(y1, fs=sample_rate, nr_segments=4)
(ff2, fpsd2) = at.noise.scipy_psd(y2, fs=sample_rate, nr_segments=4)
(ff3, fpsd3) = at.noise.scipy_psd(y3, fs=sample_rate, nr_segments=4)
(ff4, fpsd4) = at.noise.scipy_psd(y4, fs=sample_rate, nr_segments=4)
# compute frequency PSD prefactor h_a
a0 = cn.frequency_psd_from_qd( qd0, 0, tau0 )
a1 = cn.frequency_psd_from_qd( qd1, -1, tau0 )
a2 = cn.frequency_psd_from_qd( qd2, -2, tau0 )
a3 = cn.frequency_psd_from_qd( qd3, -3, tau0 )
a4 = cn.frequency_psd_from_qd( qd4, -4, tau0 )
# compute ADEV
(t0,d0,e,n) = at.oadev(x0, rate=sample_rate)
(t1,d1,e,n) = at.oadev(x1, rate=sample_rate)
(t2,d2,e,n) = at.oadev(x2, rate=sample_rate)
(t3,d3,e,n) = at.oadev(x3, rate=sample_rate)
(t4,d4,e,n) = at.oadev(x4, rate=sample_rate)
# compute MDEV
(mt0,md0,e,n) = at.mdev(x0, rate=sample_rate)
(mt1,md1,e,n) = at.mdev(x1, rate=sample_rate)
(mt2,md2,e,n) = at.mdev(x2, rate=sample_rate)
(mt3,md3,e,n) = at.mdev(x3, rate=sample_rate)
(mt4,md4,e,n) = at.mdev(x4, rate=sample_rate)
plt.figure()
# Phase PSD figure
plt.subplot(2,2,1)
plt.loglog(f0,[ g0*pow(xx, 0.0) for xx in f0],'--',label=r'$g_0f^0$', color='black')
plt.loglog(f0,psd0,'.', color='black')
plt.loglog(f1[1:],[ g1*pow(xx, -1.0) for xx in f1[1:]],'--',label=r'$g_{-1}f^{-1}$', color='red')
plt.loglog(f1,psd1,'.' ,color='red')
plt.loglog(f2[1:],[ g2*pow(xx,-2.0) for xx in f2[1:]],'--',label=r'$g_{-2}f^{-2}$', color='green')
plt.loglog(f2,psd2,'.', color='green')
plt.loglog(f3[1:], [ g3*pow(xx,-3.0) for xx in f3[1:]],'--',label=r'$g_{-3}f^{-3}$', color='pink')
plt.loglog(f3, psd3, '.', color='pink')
plt.loglog(f4[1:], [ g4*pow(xx,-4.0) for xx in f4[1:]],'--',label=r'$g_{-4}f^{-4}$', color='blue')
plt.loglog(f4, psd4, '.', color='blue')
plt.grid()
plt.legend(framealpha=0.9)
plt.title(r'Phase Power Spectral Density')
plt.xlabel(r'Frequency (Hz)')
plt.ylabel(r' $S_x(f)$ $(s^2/Hz)$')
# frequency PSD figure
plt.subplot(2,2,2)
plt.loglog(ff0[1:], [ a0*pow(xx,2) for xx in ff0[1:]],'--',label=r'$h_{2}f^{2}$', color='black')
plt.loglog(ff0,fpsd0,'.', color='black')
plt.loglog(ff1[1:], [ a1*pow(xx,1) for xx in ff1[1:]],'--',label=r'$h_{1}f^{1}$', color='red')
plt.loglog(ff1,fpsd1,'.', color='red')
plt.loglog(ff2[1:], [ a2*pow(xx,0) for xx in ff2[1:]],'--',label=r'$h_{0}f^{0}$', color='green')
plt.loglog(ff2,fpsd2,'.', color='green')
plt.loglog(ff3[1:], [ a3*pow(xx,-1) for xx in ff3[1:]],'--',label=r'$h_{-1}f^{-1}$', color='pink')
plt.loglog(ff3,fpsd3,'.', color='pink')
plt.loglog(ff4[1:], [ a4*pow(xx,-2) for xx in ff4[1:]],'--',label=r'$h_{-2}f^{-2}$', color='blue')
plt.loglog(ff4,fpsd4,'.', color='blue')
plt.grid()
plt.legend(framealpha=0.9)
plt.title(r'Frequency Power Spectral Density')
plt.xlabel(r'Frequency (Hz)')
plt.ylabel(r' $S_y(f)$ $(1/Hz)$')
# ADEV figure
plt.subplot(2,2,3)
plt.loglog(t0, [ cn.adev_from_qd(qd0, 0, tau0)/xx for xx in t0],'--', label=r'$\sqrt{Eh_{2}}\tau^{-1}$', color='black')
plt.loglog(t0,d0,'o', color='black')
plt.loglog(t1, [ cn.adev_from_qd(qd1, -1, tau0)/xx for xx in t1],'--', label=r'$\sqrt{Dh_{1}}\tau^{-1}$', color='red')
plt.loglog(t1,d1,'o', color='red')
plt.loglog(t2, [ cn.adev_from_qd(qd2, -2, tau0)/math.sqrt(xx) for xx in t2],'--', label=r'$\sqrt{Ch_{0}}\tau^{-1/2}$', color='green')
plt.loglog(t2,d2,'o', color='green')
plt.loglog(t3, [ cn.adev_from_qd(qd3, -3, tau0)*1 for xx in t3],'--', label=r'$\sqrt{Bh_{-1}}\tau^0$', color='pink')
plt.loglog(t3,d3,'o', color='pink')
plt.loglog(t4, [ cn.adev_from_qd(qd4, -4, tau0)*math.sqrt(xx) for xx in t4],'--', label=r'$\sqrt{Ah_{-2}}\tau^{+1/2}$', color='blue')
plt.loglog(t4,d4,'o', color='blue')
plt.legend(framealpha=0.9, loc='lower left')
plt.grid()
plt.title(r'Allan Deviation')
plt.xlabel(r'$\tau$ (s)')
plt.ylabel(r'ADEV')
# MDEV
plt.subplot(2, 2, 4)
plt.loglog(t0, [ cn.adev_from_qd(qd0, 0, tau0)/pow(xx,3.0/2.0) for xx in t0],'--', label=r'$\sqrt{Eh_{2}}\tau^{-3/2}$', color='black')
plt.loglog(mt0,md0,'o', color='black')
plt.loglog(t1, [ cn.adev_from_qd(qd1, -1, tau0)/xx for xx in t1],'--', label=r'$\sqrt{Dh_{1}}\tau^{-1}$', color='red')
plt.loglog(mt1,md1,'o', color='red')
plt.loglog(t2, [ cn.adev_from_qd(qd2, -2, tau0)/math.sqrt(xx) for xx in t2],'--', label=r'$\sqrt{Ch_{0}}\tau^{-1/2}$', color='green')
plt.loglog(mt2,md2,'o', color='green')
plt.loglog(t3, [ cn.adev_from_qd(qd3, -3, tau0)**1 for xx in t3],'--', label=r'$\sqrt{Bh_{-1}}\tau^0$', color='pink')
plt.loglog(mt3,md3,'o', color='pink')
plt.loglog(t4, [ cn.adev_from_qd(qd4, -4, tau0)*math.sqrt(xx) for xx in t4],'--', label=r'$\sqrt{Ah_{-2}}\tau^{+1/2}$', color='blue')
plt.loglog(mt4,md4,'o', color='blue')
plt.legend(framealpha=0.9, loc='lower left')
plt.grid()
plt.title(r'Modified Allan Deviation')
plt.xlabel(r'$\tau$ (s)')
plt.ylabel(r'MDEV')
plt.show()
taus=my_taus)
(totdev_taus, totdev_devs, totdev_errs, ns) = allan.totdev(phase=phase,
rate=rate,
taus=my_taus)
(tie_taus, tie_devs, tie_errs, ns) = allan.tierms(phase=phase,
rate=rate,
taus=my_taus)
#(mtie_taus,mtie_devs,mtie_errs,ns) = allan.mtie(phase=phase, rate=rate, taus=my_taus)
(tdev_taus, tdev_devs, tdev_errs, ns) = allan.tdev(phase=phase,
rate=rate,
taus=my_taus)
(tdev2_taus, tdev2_devs, tdev2_errs,
ns2) = allan.tdev(frequency=allan.phase2frequency(phase, 1.0),
rate=rate,
taus=my_taus)
plt.subplot(111, xscale="log", yscale="log")
plt.errorbar(adev_taus, adev_devs, yerr=adev_errs, label='ADEV')
plt.errorbar(oadev_taus, oadev_devs, yerr=oadev_errs, label='OADEV')
plt.errorbar(mdev_taus, mdev_devs, yerr=mdev_errs, label='MDEV')
plt.errorbar(hdev_taus, hdev_devs, yerr=hdev_errs, label='HDEV')
plt.errorbar(ohdev_taus, ohdev_devs, yerr=ohdev_errs, label='OHDEV')
plt.errorbar(tdev_taus, tdev_devs, yerr=tdev_errs, label='TDEV(phase)')
plt.errorbar(tdev2_taus, tdev2_devs, yerr=tdev2_errs, label='TDEV(frequency)')
plt.errorbar(totdev_taus, totdev_devs, yerr=totdev_errs, label='TOTDEV')
plt.errorbar(tie_taus, tie_devs, yerr=tie_errs, label='TIERMS')
(adev_taus,adev_devs,adev_errs,ns) = allan.adev(phase=phase, rate=rate, taus=my_taus) (oadev_taus,oadev_devs,oadev_errs,ns) = allan.oadev(phase=phase, rate=rate, taus=my_taus) (hdev_taus,hdev_devs,hdev_errs,ns) = allan.hdev(phase=phase, rate=rate, taus=my_taus) (ohdev_taus,ohdev_devs,ohdev_errs,ns) = allan.ohdev(phase=phase, rate=rate, taus=my_taus) (mdev_taus,mdev_devs,mdev_errs,ns) = allan.mdev(phase=phase, rate=rate, taus=my_taus) (totdev_taus,totdev_devs,totdev_errs,ns) = allan.totdev(phase=phase, rate=rate, taus=my_taus) (tie_taus,tie_devs,tie_errs,ns) = allan.tierms(phase=phase, rate=rate, taus=my_taus) #(mtie_taus,mtie_devs,mtie_errs,ns) = allan.mtie(phase=phase, rate=rate, taus=my_taus) (tdev_taus,tdev_devs,tdev_errs,ns) = allan.tdev(phase=phase, rate=rate, taus=my_taus) (tdev2_taus,tdev2_devs,tdev2_errs,ns2) = allan.tdev(frequency=allan.phase2frequency(phase,1.0), rate=rate, taus=my_taus) plt.subplot(111, xscale="log", yscale="log") plt.errorbar(adev_taus, adev_devs, yerr=adev_errs, label='ADEV') plt.errorbar(oadev_taus, oadev_devs, yerr=oadev_errs, label='OADEV') plt.errorbar(mdev_taus, mdev_devs, yerr=mdev_errs, label='MDEV') plt.errorbar(hdev_taus, hdev_devs, yerr=hdev_errs, label='HDEV') plt.errorbar(ohdev_taus, ohdev_devs, yerr=ohdev_errs, label='OHDEV') plt.errorbar(tdev_taus, tdev_devs, yerr=tdev_errs, label='TDEV(phase)') plt.errorbar(tdev2_taus, tdev2_devs, yerr=tdev2_errs, label='TDEV(frequency)') plt.errorbar(totdev_taus, totdev_devs, yerr=totdev_errs, label='TOTDEV') plt.errorbar(tie_taus, tie_devs, yerr=tie_errs, label='TIERMS') #plt.errorbar(mtie_taus, mtie_devs, yerr=mtie_errs, label='MTIE')
def main():
nr = 2**14 # number of datapoints in time-series
adev0 = 1.0e-11
#
# for each noise type generated Noise class
# compute
# - phase PSD and coefficient g_b
# - frequency PSD and coefficient h_a
# - ADEV
# - MDEV
#
# discrete variance for noiseGen()
qd0 = qd1 = qd2 = qd3 = qd4 = pow(adev0, 2)
tau0 = 1.0 # sample interval
sample_rate = 1.0 / tau0
x0 = at.Noise(nr, qd0, 0) # white phase noise (WPM)
x0.generateNoise()
x1 = at.Noise(nr, qd1, -1) # flicker phase noise (FPM)
x1.generateNoise()
x2 = at.Noise(nr, qd2, -2) # white frequency noise (WFM)
x2.generateNoise()
x3 = at.Noise(nr, qd3, -3) # flicker frequency noise (FFM)
x3.generateNoise()
x4 = at.Noise(nr, qd4, -4) # random walk frequency noise (RWFM)
x4.generateNoise()
# compute frequency time-series
y0 = at.phase2frequency(x0.time_series, sample_rate)
y1 = at.phase2frequency(x1.time_series, sample_rate)
y2 = at.phase2frequency(x2.time_series, sample_rate)
y3 = at.phase2frequency(x3.time_series, sample_rate)
y4 = at.phase2frequency(x4.time_series, sample_rate)
# compute phase PSD
(f0, psd0) = at.noise.scipy_psd(x0.time_series,
f_sample=sample_rate,
nr_segments=4)
(f1, psd1) = at.noise.scipy_psd(x1.time_series,
f_sample=sample_rate,
nr_segments=4)
(f2, psd2) = at.noise.scipy_psd(x2.time_series,
f_sample=sample_rate,
nr_segments=4)
(f3, psd3) = at.noise.scipy_psd(x3.time_series,
f_sample=sample_rate,
nr_segments=4)
(f4, psd4) = at.noise.scipy_psd(x4.time_series,
f_sample=sample_rate,
nr_segments=4)
# compute phase PSD prefactor g_b
g0 = x0.phase_psd_from_qd(tau0)
g1 = x1.phase_psd_from_qd(tau0)
g2 = x2.phase_psd_from_qd(tau0)
g3 = x3.phase_psd_from_qd(tau0)
g4 = x4.phase_psd_from_qd(tau0)
# compute frequency PSD
(ff0, fpsd0) = at.noise.scipy_psd(y0, f_sample=sample_rate, nr_segments=4)
(ff1, fpsd1) = at.noise.scipy_psd(y1, f_sample=sample_rate, nr_segments=4)
(ff2, fpsd2) = at.noise.scipy_psd(y2, f_sample=sample_rate, nr_segments=4)
(ff3, fpsd3) = at.noise.scipy_psd(y3, f_sample=sample_rate, nr_segments=4)
(ff4, fpsd4) = at.noise.scipy_psd(y4, f_sample=sample_rate, nr_segments=4)
# compute frequency PSD prefactor h_a
a0 = x0.frequency_psd_from_qd(tau0)
a1 = x1.frequency_psd_from_qd(tau0)
a2 = x2.frequency_psd_from_qd(tau0)
a3 = x3.frequency_psd_from_qd(tau0)
a4 = x4.frequency_psd_from_qd(tau0)
# compute ADEV
(t0, d0, e, n) = at.oadev(x0.time_series, rate=sample_rate)
(t1, d1, e, n) = at.oadev(x1.time_series, rate=sample_rate)
(t2, d2, e, n) = at.oadev(x2.time_series, rate=sample_rate)
(t3, d3, e, n) = at.oadev(x3.time_series, rate=sample_rate)
(t4, d4, e, n) = at.oadev(x4.time_series, rate=sample_rate)
# compute MDEV
(mt0, md0, e, n) = at.mdev(x0.time_series, rate=sample_rate)
(mt1, md1, e, n) = at.mdev(x1.time_series, rate=sample_rate)
(mt2, md2, e, n) = at.mdev(x2.time_series, rate=sample_rate)
(mt3, md3, e, n) = at.mdev(x3.time_series, rate=sample_rate)
(mt4, md4, e, n) = at.mdev(x4.time_series, rate=sample_rate)
plt.figure()
# Phase PSD figure
plt.subplot(2, 2, 1)
plt.loglog(f0, [g0 * pow(xx, 0.0) for xx in f0],
'--',
label=r'$g_0f^0$',
color='black')
plt.loglog(f0, psd0, '.', color='black')
plt.loglog(f1[1:], [g1 * pow(xx, -1.0) for xx in f1[1:]],
'--',
label=r'$g_{-1}f^{-1}$',
color='red')
plt.loglog(f1, psd1, '.', color='red')
plt.loglog(f2[1:], [g2 * pow(xx, -2.0) for xx in f2[1:]],
'--',
label=r'$g_{-2}f^{-2}$',
color='green')
plt.loglog(f2, psd2, '.', color='green')
plt.loglog(f3[1:], [g3 * pow(xx, -3.0) for xx in f3[1:]],
'--',
label=r'$g_{-3}f^{-3}$',
color='pink')
plt.loglog(f3, psd3, '.', color='pink')
plt.loglog(f4[1:], [g4 * pow(xx, -4.0) for xx in f4[1:]],
'--',
label=r'$g_{-4}f^{-4}$',
color='blue')
plt.loglog(f4, psd4, '.', color='blue')
plt.grid()
plt.legend(framealpha=0.9)
plt.title(r'Phase Power Spectral Density')
plt.xlabel(r'Frequency (Hz)')
plt.ylabel(r' $S_x(f)$ $(s^2/Hz)$')
# frequency PSD figure
plt.subplot(2, 2, 2)
plt.loglog(ff0[1:], [a0 * pow(xx, 2) for xx in ff0[1:]],
'--',
label=r'$h_{2}f^{2}$',
color='black')
plt.loglog(ff0, fpsd0, '.', color='black')
plt.loglog(ff1[1:], [a1 * pow(xx, 1) for xx in ff1[1:]],
'--',
label=r'$h_{1}f^{1}$',
color='red')
plt.loglog(ff1, fpsd1, '.', color='red')
plt.loglog(ff2[1:], [a2 * pow(xx, 0) for xx in ff2[1:]],
'--',
label=r'$h_{0}f^{0}$',
color='green')
plt.loglog(ff2, fpsd2, '.', color='green')
plt.loglog(ff3[1:], [a3 * pow(xx, -1) for xx in ff3[1:]],
'--',
label=r'$h_{-1}f^{-1}$',
color='pink')
plt.loglog(ff3, fpsd3, '.', color='pink')
plt.loglog(ff4[1:], [a4 * pow(xx, -2) for xx in ff4[1:]],
'--',
label=r'$h_{-2}f^{-2}$',
color='blue')
plt.loglog(ff4, fpsd4, '.', color='blue')
plt.grid()
plt.legend(framealpha=0.9)
plt.title(r'Frequency Power Spectral Density')
plt.xlabel(r'Frequency (Hz)')
plt.ylabel(r' $S_y(f)$ $(1/Hz)$')
# ADEV figure
plt.subplot(2, 2, 3)
plt.loglog(t0, [x0.adev_from_qd(tau0, xx) / xx for xx in t0],
'--',
label=r'$\propto\sqrt{h_{2}}\tau^{-1}$',
color='black')
plt.loglog(t0, d0, 'o', color='black')
plt.loglog(t1, [x1.adev_from_qd(tau0, xx) / xx for xx in t1],
'--',
label=r'$\propto\sqrt{h_{1}}\tau^{-1}$',
color='red')
plt.loglog(t1, d1, 'o', color='red')
plt.loglog(t2, [x2.adev_from_qd(tau0, xx) / math.sqrt(xx) for xx in t2],
'--',
label=r'$\propto\sqrt{h_{0}}\tau^{-1/2}$',
color='green')
plt.loglog(t2, d2, 'o', color='green')
plt.loglog(t3, [x3.adev_from_qd(tau0, xx) * 1 for xx in t3],
'--',
label=r'$\propto\sqrt{h_{-1}}\tau^0$',
color='pink')
plt.loglog(t3, d3, 'o', color='pink')
plt.loglog(t4, [x4.adev_from_qd(tau0, xx) * math.sqrt(xx) for xx in t4],
'--',
label=r'$\propto\sqrt{h_{-2}}\tau^{+1/2}$',
color='blue')
plt.loglog(t4, d4, 'o', color='blue')
plt.legend(framealpha=0.9, loc='lower left')
plt.grid()
plt.title(r'Allan Deviation')
plt.xlabel(r'$\tau$ (s)')
plt.ylabel(r'ADEV')
# MDEV
plt.subplot(2, 2, 4)
plt.loglog(t0,
[x0.mdev_from_qd(tau0, xx) / pow(xx, 3.0 / 2.0) for xx in t0],
'--',
label=r'$\propto\sqrt{h_{2}}\tau^{-3/2}$',
color='black')
plt.loglog(mt0, md0, 'o', color='black')
plt.loglog(t1, [x1.mdev_from_qd(tau0, xx) / xx for xx in t1],
'--',
label=r'$\propto\sqrt{h_{1}}\tau^{-1}$',
color='red')
plt.loglog(mt1, md1, 'o', color='red')
plt.loglog(t2, [x2.mdev_from_qd(tau0, xx) / math.sqrt(xx) for xx in t2],
'--',
label=r'$\propto\sqrt{h_{0}}\tau^{-1/2}$',
color='green')
plt.loglog(mt2, md2, 'o', color='green')
plt.loglog(t3, [x3.mdev_from_qd(tau0, xx)**1 for xx in t3],
'--',
label=r'$\propto\sqrt{h_{-1}}\tau^0$',
color='pink')
plt.loglog(mt3, md3, 'o', color='pink')
plt.loglog(t4, [x4.mdev_from_qd(tau0, xx) * math.sqrt(xx) for xx in t4],
'--',
label=r'$\propto\sqrt{h_{-2}}\tau^{+1/2}$',
color='blue')
plt.loglog(mt4, md4, 'o', color='blue')
plt.legend(framealpha=0.9, loc='lower left')
plt.grid()
plt.title(r'Modified Allan Deviation')
plt.xlabel(r'$\tau$ (s)')
plt.ylabel(r'MDEV')
plt.show()
def main():
nr = 2**14 # number of datapoints in time-series
adev0 = 1.0e-11
#
# for each noise type generated with cn.noiseGen()
# compute
# - phase PSD and coefficient g_b
# - frequency PSD and coefficient h_a
# - ADEV
# - MDEV
#
tau0 = 0.1 # sample interval for simulated time-series
sample_rate = 1.0 / tau0
qd0 = qd1 = qd2 = qd3 = qd4 = pow(adev0,
2) # discrete variance for noiseGen()
# This scaling makes all g_i coefficients equal
# -> PSDs cross at 1 Hz.
qd1 = qd0 * 2 * np.pi / sample_rate
qd2 = qd1 * 2 * np.pi / sample_rate
qd3 = qd2 * 2 * np.pi / sample_rate
qd4 = qd3 * 2 * np.pi / sample_rate
print("generating timeseries..."),
x0 = cn.noiseGen(nr, qd0, 0) # white phase noise (WPM)
x1 = cn.noiseGen(nr, qd1, -1) # flicker phase noise (FPM)
x2 = cn.noiseGen(nr, qd2, -2) # white frequency noise (WFM)
x3 = cn.noiseGen(nr, qd3, -3) # flicker frequency noise (FFM)
x4 = cn.noiseGen(nr, qd4, -4) # random walk frequency noise (RWFM)
# compute frequency time-series
y0 = at.phase2frequency(x0, sample_rate)
y1 = at.phase2frequency(x1, sample_rate)
y2 = at.phase2frequency(x2, sample_rate)
y3 = at.phase2frequency(x3, sample_rate)
y4 = at.phase2frequency(x4, sample_rate)
print("done.")
print("computing PSDs..."),
# compute phase PSD
(f0, psd0) = at.noise.scipy_psd(x0, f_sample=sample_rate, nr_segments=4)
(f1, psd1) = at.noise.scipy_psd(x1, f_sample=sample_rate, nr_segments=4)
(f2, psd2) = at.noise.scipy_psd(x2, f_sample=sample_rate, nr_segments=4)
(f3, psd3) = at.noise.scipy_psd(x3, f_sample=sample_rate, nr_segments=4)
(f4, psd4) = at.noise.scipy_psd(x4, f_sample=sample_rate, nr_segments=4)
# compute phase PSD prefactor g_b
g0 = cn.phase_psd_from_qd(qd0, 0, tau0)
g1 = cn.phase_psd_from_qd(qd1, -1, tau0)
g2 = cn.phase_psd_from_qd(qd2, -2, tau0)
g3 = cn.phase_psd_from_qd(qd3, -3, tau0)
g4 = cn.phase_psd_from_qd(qd4, -4, tau0)
print g0
print g1
print g2
print g3
print g4
# compute frequency PSD
(ff0, fpsd0) = at.noise.scipy_psd(y0, f_sample=sample_rate, nr_segments=4)
(ff1, fpsd1) = at.noise.scipy_psd(y1, f_sample=sample_rate, nr_segments=4)
(ff2, fpsd2) = at.noise.scipy_psd(y2, f_sample=sample_rate, nr_segments=4)
(ff3, fpsd3) = at.noise.scipy_psd(y3, f_sample=sample_rate, nr_segments=4)
(ff4, fpsd4) = at.noise.scipy_psd(y4, f_sample=sample_rate, nr_segments=4)
# compute frequency PSD prefactor h_a
a0 = cn.frequency_psd_from_qd(qd0, 0, tau0)
a1 = cn.frequency_psd_from_qd(qd1, -1, tau0)
a2 = cn.frequency_psd_from_qd(qd2, -2, tau0)
a3 = cn.frequency_psd_from_qd(qd3, -3, tau0)
a4 = cn.frequency_psd_from_qd(qd4, -4, tau0)
print "a_i == g_i*(4pi^2)"
print a0, g0 * pow(2 * np.pi, 2)
print a1, g1 * pow(2 * np.pi, 2)
print a2, g2 * pow(2 * np.pi, 2)
print a3, g3 * pow(2 * np.pi, 2)
print a4, g4 * pow(2 * np.pi, 2)
print("done.")
print("computing ADEV/MDEV..."),
# compute ADEV
(t0, d0, e, n) = at.oadev(x0, rate=sample_rate)
(t1, d1, e, n) = at.oadev(x1, rate=sample_rate)
(t2, d2, e, n) = at.oadev(x2, rate=sample_rate)
(t3, d3, e, n) = at.oadev(x3, rate=sample_rate)
(t4, d4, e, n) = at.oadev(x4, rate=sample_rate)
# compute MDEV
(mt0, md0, e, n) = at.mdev(x0, rate=sample_rate)
(mt1, md1, e, n) = at.mdev(x1, rate=sample_rate)
(mt2, md2, e, n) = at.mdev(x2, rate=sample_rate)
(mt3, md3, e, n) = at.mdev(x3, rate=sample_rate)
(mt4, md4, e, n) = at.mdev(x4, rate=sample_rate)
print("done.")
plt.figure()
# Phase PSD figure
plt.subplot(2, 2, 1)
plt.loglog(f0, [g0 * pow(xx, 0.0) for xx in f0],
'--',
label=r'$g_0f^0 = {h_2\over4\pi^2}f^0$',
color='black')
plt.loglog(f0, psd0, '.', color='black')
plt.loglog(f1[1:], [g1 * pow(xx, -1.0) for xx in f1[1:]],
'--',
label=r'$g_{-1}f^{-1} = {h_1\over4\pi^2}f^{-1} $',
color='red')
plt.loglog(f1, psd1, '.', color='red')
plt.loglog(f2[1:], [g2 * pow(xx, -2.0) for xx in f2[1:]],
'--',
label=r'$g_{-2}f^{-2} = {h_0\over4\pi^2}f^{-2} $',
color='green')
plt.loglog(f2, psd2, '.', color='green')
plt.loglog(f3[1:], [g3 * pow(xx, -3.0) for xx in f3[1:]],
'--',
label=r'$g_{-3}f^{-3} = {h_{-1}\over4\pi^2}f^{-3} $',
color='pink')
plt.loglog(f3, psd3, '.', color='pink')
plt.loglog(f4[1:], [g4 * pow(xx, -4.0) for xx in f4[1:]],
'--',
label=r'$g_{-4}f^{-4} = {h_{-2}\over4\pi^2}f^{-4}$',
color='blue')
plt.loglog(f4, psd4, '.', color='blue')
plt.grid()
plt.legend(framealpha=0.9, fontsize=22)
plt.title(r'Phase Power Spectral Density')
plt.xlabel(r'Frequency (Hz)')
plt.ylabel(r' $S_x(f)$ $(s^2/Hz)$')
# frequency PSD figure
plt.subplot(2, 2, 2)
plt.loglog(ff0[1:], [a0 * pow(xx, 2) for xx in ff0[1:]],
'--',
label=r'$h_{2}f^{2}$',
color='black')
plt.loglog(ff0, fpsd0, '.', color='black')
plt.loglog(ff1[1:], [a1 * pow(xx, 1) for xx in ff1[1:]],
'--',
label=r'$h_{1}f^{1}$',
color='red')
plt.loglog(ff1, fpsd1, '.', color='red')
plt.loglog(ff2[1:], [a2 * pow(xx, 0) for xx in ff2[1:]],
'--',
label=r'$h_{0}f^{0}$',
color='green')
plt.loglog(ff2, fpsd2, '.', color='green')
plt.loglog(ff3[1:], [a3 * pow(xx, -1) for xx in ff3[1:]],
'--',
label=r'$h_{-1}f^{-1}$',
color='pink')
plt.loglog(ff3, fpsd3, '.', color='pink')
plt.loglog(ff4[1:], [a4 * pow(xx, -2) for xx in ff4[1:]],
'--',
label=r'$h_{-2}f^{-2}$',
color='blue')
plt.loglog(ff4, fpsd4, '.', color='blue')
plt.grid()
plt.legend(framealpha=0.9, fontsize=22)
plt.title(r'Frequency Power Spectral Density')
plt.xlabel(r'Frequency (Hz)')
plt.ylabel(r' $S_y(f)$ $(1/Hz)$')
# ADEV figure
plt.subplot(2, 2, 3)
f_h = 0.5 / tau0
K0 = 3.0 * f_h / (4.0 * pow(np.pi, 2))
plt.loglog(t0, [np.sqrt(K0 * a0) / xx for xx in t0],
'--',
label=r'$\sqrt{3f_H\over4\pi^2} \sqrt{h_{2}}\tau^{-3/2}$',
color='black')
plt.loglog(t0, d0, 'o', color='black')
# IEEE 1139-2008, table B.2
def K1(f_H, tau):
gamma = 1.038 / 2
return ((3.0 / 2.0) * np.log(2.0 * np.pi * f_h * tau) +
gamma) / (2.0 * pow(np.pi, 2))
plt.loglog(
t1, [np.sqrt(K1(f_h, xx) * a1) / xx for xx in t1],
'--',
label=
r'$\sqrt{ 3\ln{(2 \pi f_H \tau)} + \gamma \over 4\pi^2 }\sqrt{h_{1}}\tau^{-1}$',
color='red')
plt.loglog(t1, d1, 'o', color='red')
K2 = 0.5
plt.loglog(t2, [np.sqrt(K2 * a2) / math.sqrt(xx) for xx in t2],
'--',
label=r'$\sqrt{1\over2} \sqrt{h_{0}}\tau^{-1/2}$',
color='green')
plt.loglog(t2, d2, 'o', color='green')
K3 = 2 * np.log(2)
plt.loglog(t3, [np.sqrt(K3 * a3) for xx in t3],
'--',
label=r'$ \sqrt{2\ln{2}}\sqrt{h_{-1}}\tau^0$',
color='pink')
plt.loglog(t3, d3, 'o', color='pink')
K4 = (2 * np.pi**2.0) / 3.0
plt.loglog(t4, [np.sqrt(K4 * a4) * math.sqrt(xx) for xx in t4],
'--',
label=r'$\sqrt{2\pi^2\over3}\sqrt{h_{-2}}\tau^{+1/2}$',
color='blue')
plt.loglog(t4, d4, 'o', color='blue')
plt.legend(framealpha=0.9, loc='lower left', fontsize=22)
plt.grid()
plt.title(r'Allan Deviation')
plt.xlabel(r'$\tau$ (s)')
plt.ylabel(r'ADEV')
# MDEV
plt.subplot(2, 2, 4)
M0 = 3.0 / (8.0 * pow(np.pi, 2))
plt.loglog(t0, [np.sqrt(M0 * a0) / pow(xx, 3.0 / 2.0) for xx in t0],
'--',
label=r'$\sqrt{ 3\over 8\pi^2 } \sqrt{h_{2}}\tau^{-3/2}$',
color='black')
plt.loglog(mt0, md0, 'o', color='black')
M1 = (24.0 * np.log(2) - 9.0 * np.log(3)) / (8.0 * pow(np.pi, 2))
plt.loglog(
t1, [np.sqrt(M1 * a1) / xx for xx in t1],
'--',
label=r'$\sqrt{ 24\ln(2)-9\ln(3) \over 8\pi^2 } \sqrt{h_{1}}\tau^{-1}$',
color='red')
plt.loglog(mt1, md1, 'o', color='red')
M2 = 0.25
plt.loglog(t2, [np.sqrt(M2 * a2) / math.sqrt(xx) for xx in t2],
'--',
label=r'$ \sqrt{1\over4}\sqrt{h_{0}}\tau^{-1/2}$',
color='green')
plt.loglog(mt2, md2, 'o', color='green')
M3 = 27.0 / 20.0 * np.log(2)
plt.loglog(t3, [np.sqrt(M3 * a3) for xx in t3],
'--',
label=r'$\sqrt{ {27\over20}\ln(2) } \sqrt{h_{-1}}\tau^0$',
color='pink')
plt.loglog(mt3, md3, 'o', color='pink')
M4 = 11.0 / 20.0 * pow(np.pi, 2)
plt.loglog(t4, [np.sqrt(M4 * a4) * math.sqrt(xx) for xx in t4],
'--',
label=r'$ \sqrt{ {11\over20}\pi^2 } \sqrt{h_{-2}}\tau^{+1/2}$',
color='blue')
plt.loglog(mt4, md4, 'o', color='blue')
plt.legend(framealpha=0.9, loc='lower left', fontsize=22)
plt.grid()
plt.title(r'Modified Allan Deviation')
plt.xlabel(r'$\tau$ (s)')
plt.ylabel(r'MDEV')
plt.show()
