def test_noise_id(self):
""" test for noise-identification """
s32_rows = testutils.read_stable32( 'mdev_octave.txt' , rate )
freq = testutils.read_datafile(data_file)
y_freq = allan.frequency2fractional(freq, mean_frequency=1.0e7)
phase = allan.frequency2phase(freq, rate)
for s32 in s32_rows:
s32_tau, s32_alpha, s32_AF = s32['tau'], s32['alpha'], int(s32['m'])
# noise-ID from frequency
if len(phase)/s32_AF > 20:
alpha_int, alpha, d, rho = allan.autocorr_noise_id(freq, data_type='freq', af=s32_AF )
print( "y: ",s32_tau, s32_alpha, alpha_int, alpha, rho, d )
assert alpha_int == s32_alpha
# noise-ID from phase
if len(phase)/s32_AF > 20:
alpha_int, alpha, d, rho = allan.autocorr_noise_id( phase, data_type='phase', af=s32_AF )
print( "x: ",s32_tau, s32_alpha, alpha_int, alpha, rho, d )
assert alpha_int == s32_alpha
def test_noise_id(self):
""" test for noise-identification """
s32_rows = testutils.read_stable32( 'tic_oadev.txt' , 1.0 )
phase = testutils.read_datafile(data_file)
for s32 in s32_rows:
tau, alpha, af = s32['tau'], s32['alpha'], int(s32['m'])
try:
alpha_int = allan.autocorr_noise_id( phase , af=af)[0]
#if len(phase)/af > 30: # noise-id only works for length 30 or longer time-series
assert alpha_int == alpha
print( tau, alpha, alpha_int )
except NotImplementedError:
print ("no noise-ID: ",tau, alpha, alpha_int )
def test_noise_id(self):
""" test for noise-identification """
s32_rows = testutils.read_stable32('stable32_ADEV_decade.txt', rate)
phase = testutils.read_datafile('gps_1pps_phase_data.txt.gz')
# test noise-ID
for s32 in s32_rows:
tau, alpha, AF = s32['tau'], s32['alpha'], int(s32['m'])
try:
alpha_int = allan.autocorr_noise_id(phase, af=AF)[0]
print(tau, alpha, alpha_int)
assert alpha_int == alpha
except NotImplementedError:
print("can't do noise-ID for tau= %f" % s32['tau'])
def test_noise_id(self):
""" test for noise-identification """
s32_rows = testutils.read_stable32( 'phase_dat_oadev_octave.txt' , 1.0 )
phase = testutils.read_datafile('PHASE.DAT')
for s32 in s32_rows:
tau, alpha, af = s32['tau'], s32['alpha'], int(s32['m'])
try:
alpha_int = allan.autocorr_noise_id( phase , af=af)[0]
assert alpha_int == alpha
except:
print("can't do noise-ID for af= ",af)
pass
print( tau, alpha, alpha_int )
def test_noise_id(self):
""" test for noise-identification """
s32_rows = testutils.read_stable32('stable32_ADEV_decade.txt', rate)
phase = testutils.read_datafile('gps_1pps_phase_data.txt.gz')
# test noise-ID
for s32 in s32_rows:
tau, alpha, AF = s32['tau'], s32['alpha'], int(s32['m'])
try:
alpha_int = allan.autocorr_noise_id(phase, af=AF)[0]
print(tau, alpha, alpha_int)
assert alpha_int == alpha
except NotImplementedError:
print("can't do noise-ID for tau= %f"%s32['tau'])
def test_noise_id(self):
""" test for noise-identification """
s32_rows = testutils.read_stable32('phase_dat_oadev_octave.txt', 1.0)
phase = testutils.read_datafile('PHASE.DAT')
for s32 in s32_rows:
tau, alpha, af = s32['tau'], s32['alpha'], int(s32['m'])
try:
alpha_int = allan.autocorr_noise_id(phase, af=af)[0]
assert alpha_int == alpha
print("OK noise-ID for af = %d" % af)
except:
print("can't do noise-ID for af = %d" % af)
print("tau= %f, alpha= %f, alpha_int = %d" %
(tau, alpha, alpha_int))
nr = pow(2,14) # np.random.choice(nrrange)
brange = [0,-1, -2, -3, -4, -5]
pts=[[], [], [], [], [], []] # 6 different b-values
for n in range(N_points):
b = np.random.choice(brange)
taus=np.logspace(0,np.log10( nr/30.0 ),200) # note we stop at 30 pts series length
taus = [int(t) for t in taus]
taus = remove_duplicates(taus)
af = np.random.choice( taus )
ng.set_input(nr,qd,b)
ng.generateNoise()
x = ng.time_series
alpha_int, alpha, rho, d = at.autocorr_noise_id(x, af, data_type="phase", dmin=0, dmax=2)
pts[-1*b].append( ( af, alpha, alpha_int) )
print("calc done")
colors = { 0:'r', -1:'g', -2:'b', -3:'m', -4:'k', -5:'y'}
for (series, idx) in zip(pts, range(len(pts))):
b=-idx
af = [s[0] for s in series]
alpha = [s[1] for s in series]
alphai = [s[2] for s in series]
plt.semilogx( af, alpha, "%s."%colors[b],label='b=%d'%b)
#plt.semilogx( af, alphai, "%so"%colors[b],label='b=%d'%b) # integer alpha
print("plot done")
plt.ylim((-4,3))
plt.xlim((.8,1e3))
nr = pow(2,14) # np.random.choice(nrrange)
brange = [0,-1, -2, -3, -4, -5]
pts=[[], [], [], [], [], []] # 6 different b-values
for n in range(N_points):
b = np.random.choice(brange)
taus=np.logspace(0,np.log10( nr/30.0 ),200) # note we stop at 30 pts series length
taus = [int(t) for t in taus]
taus = remove_duplicates(taus)
af = np.random.choice( taus )
ng.set_input(nr,qd,b)
ng.generateNoise()
x = ng.time_series
alpha_int, alpha, rho, d = at.autocorr_noise_id(x, af, data_type="phase", dmin=0, dmax=2)
pts[-1*b].append( ( af, alpha, alpha_int) )
print "calc done"
colors = { 0:'r', -1:'g', -2:'b', -3:'m', -4:'k', -5:'y'}
for (series, idx) in zip(pts, range(len(pts))):
b=-idx
af = [s[0] for s in series]
alpha = [s[1] for s in series]
alphai = [s[2] for s in series]
plt.semilogx( af, alpha, "%s."%colors[b],label='b=%d'%b)
#plt.semilogx( af, alphai, "%so"%colors[b],label='b=%d'%b) # integer alpha
print "plot done"
plt.ylim((-4,3))
plt.xlim((.8,1e3))