def test_multiplet(): from USTimeSeries import USTimeSeries from numpy.random import normal # Specify the parameters for the underlying signal & sampling. dt = 1/48000. f1 = 984.3750 # The value near 1k for 1024 samples f1 = 1007.8125 # The value near 1k for 2048 samples (1/2 way between for 1024) #fs = 1031.25 #fs = 1017. # A value between grid pts #fs = 1000. A1 = 100. phi1 = 0. f2 = f1 - 30. A2 = 0.01*A1 phi2 = pi/2. sig = 1. N_s = 1024 nf = 2 # Simulate some real-valued data from a sinusoid with white noise. times = arange(N_s)*dt data = A1*cos(2*pi*f1*times - phi1) + A2*cos(2*pi*f2*times - phi2) \ + 0*normal(0.,sig,(N_s)) usts = USTimeSeries(data, dt) usts.transform() print 'unpadded f range: ', usts.f_range() usts.transform(16) print 'padded f range: ', usts.f_range() # Try it with multiplet. print '... Multiplet version:' mult = Multiplet(usts, sig, exact=1) flist = (f1, f2) print 'flist: ', flist mult.analyze(flist) print 'used: ', mult.flist print "Jac: ", mult.jac print 'Suff. stat: ', mult.suf amps = mult.amplitudes() print 'Amps: ', amps print 'Sigs: ', mult.sigmas() print 'Ratio: ', amps/mult.sigmas() # Now estimate the amplitudes using a nonpadded FFT. usts.transform() fund, rl, im, p_fund = usts.f_info(flist[0]) for i in range(nf): f, rl, im, p = usts.f_info(flist[i]) print i, flist[i], mult.flist[i], amps[i]/amps[0], f, sqrt(p/p_fund) print "Done!\n==============="
#f = 1000.
A1 = 1000.
phi1 = 0.
A2 = 0.01 * A1
phi2 = pi / 2.
A3 = 0.0001 * A1
phi3 = pi / 4.
sig = 1.
N_s = 1024
# Simulate some real-valued data from a sinusoid with white noise.
times = arange(N_s) * dt
data = A1*cos(2*pi*f*times - phi1) + A2*cos(4*pi*f*times - phi2) + \
A3*cos(6*pi*f*times - phi3) + random.normal(0.,sig,(N_s))
usts = USTimeSeries(data, dt)
usts.transform(8)
print 'f range: ', usts.f_range()
m = 3
prjxns = zeros((2 * m))
fund, rl, im, p_fund = usts.f_info(f)
for i in range(1, m + 1):
fh = i * f
fh, rl, im, p = usts.f_info(fh)
print fh, i, 2 * i - 2, 2 * i - 1
prjxns[2 * i - 2] = rl
prjxns[2 * i - 1] = -im
print 'Prjxns: ', prjxns
gamma = 2 * pi * fund * dt
def test_harmonics(m): from USTimeSeries import USTimeSeries from numpy.random import normal # Specify the parameters for the underlying signal & sampling. dt = 1/48000. f = 984.3750 # The value near 1k for 1024 samples f = 1007.8125 # The value near 1k for 2048 samples (1/2 way between for 1024) #f = 1031.25 #f = 1017. # A value between grid pts #f = 1000. A1 = 1000. phi1 = 0. A2 = 0.01*A1 phi2 = pi/2. A3 = 0.0001*A1 phi3 = pi/4. sig = 1. N_s = 1024 # Simulate some real-valued data from a sinusoid with white noise. times = arange(N_s)*dt data = A1*cos(2*pi*f*times - phi1) + A2*cos(4*pi*f*times - phi2) + \ A3*cos(6*pi*f*times - phi3) + normal(0.,sig,(N_s)) usts = USTimeSeries(data, dt) usts.transform(8) print 'f range: ', usts.f_range() harm = Harmonics(m, usts, sig) harm.analyze(f) print 'prjxns: ', harm.prjxns #print "metric: \n", harm.metric #print "Lower triangle:\n", harm.L print "Jac: ", harm.jac print 'Suff. stat: ', harm.suf amps = harm.amplitudes() print 'Amps: ', amps print 'Sigs: ', harm.sigmas() print 'Ratio: ', amps/harm.sigmas() # Now try it with multiplet. print '... Multiplet version:' mult = Multiplet(usts, sig) flist = [] for i in range(m): flist.append((i+1)*f) print 'flist: ', flist mult.analyze(flist) print "Jac: ", mult.jac print 'Suff. stat: ', mult.suf amps = mult.amplitudes() print 'Amps: ', amps print 'Sigs: ', mult.sigmas() print 'Ratio: ', amps/mult.sigmas() # Now estimate the amplitudes using a nonpadded FFT. usts.transform() fund, rl, im, p_fund = usts.f_info(f) A_fund = sqrt( amps[0]**2 + amps[1]**2 ) for i in range(1,m+1): f, rl, im, p = usts.f_info(i*fund) print i, i*harm.f, amps[i-1]/amps[0], f, sqrt(p/p_fund) print "Done!\n==============="
