def analyze(self, f): """Perform an analysis using as a fundamental the FFT frequency nearest to f.""" self.f_in = f self.f, rl, im, p = self.usts.f_info(f) self.prjxns = zeros((2*self.nharm)) self.prjxns[0], self.prjxns[1] = rl, -im for i in range(2,self.nharm+1): fh = i*self.f fh, rl, im, p = self.usts.f_info(fh) print 'harm: ', i, i*self.f, fh self.prjxns[2*i-2], self.prjxns[2*i-1] = rl, -im gamma = 2*pi*self.f*self.usts.dt self.metric, self.L, self.jac, self.amps, self.suf = \ bhacalc.harmonicAnalysis(self.nharm, self.usts.n, gamma, self.prjxns) #self.Q = self.dsqr - self.suf self.jac = 1. / self.jac #return (self.suf, self.Jac) self.hamps = zeros((self.nharm)) for i in range(self.nharm): self.hamps[i] = sqrt(self.amps[2*i]**2 + self.amps[2*i+1]**2)
def analyze(self, f):
"""Perform an analysis using as a fundamental the FFT frequency nearest
to f."""
self.f_in = f
self.f, rl, im, p = self.usts.f_info(f)
self.prjxns = zeros((2 * self.nharm))
self.prjxns[0], self.prjxns[1] = rl, -im
for i in range(2, self.nharm + 1):
fh = i * self.f
fh, rl, im, p = self.usts.f_info(fh)
print "harm: ", i, i * self.f, fh
self.prjxns[2 * i - 2], self.prjxns[2 * i - 1] = rl, -im
gamma = 2 * pi * self.f * self.usts.dt
self.metric, self.L, self.jac, self.amps, self.suf = bhacalc.harmonicAnalysis(
self.nharm, self.usts.n, gamma, self.prjxns
)
# self.Q = self.dsqr - self.suf
self.jac = 1.0 / self.jac
# return (self.suf, self.Jac)
self.hamps = zeros((self.nharm))
for i in range(self.nharm):
self.hamps[i] = sqrt(self.amps[2 * i] ** 2 + self.amps[2 * i + 1] ** 2)
from scipy import * from USTimeSeries import USTimeSeries import bhacalc # 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) + 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
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
gammas = arange(1, m + 1) * gamma
print 'gammas: ', gammas
metric, L, J, amps, S = bhacalc.harmonicAnalysis(m, N_s, gamma, prjxns)
#print "metric: \n", metric
#print "Lower triangle:\n", L
print "Det: ", J
print 'Amps: ', amps
hamps = zeros((m))
for i in range(m):
hamps[i] = sqrt(amps[2 * i]**2 + amps[2 * i + 1]**2)
print 'HAmps: ', hamps
A_fund = sqrt(amps[0]**2 + amps[1]**2)
print 'Fundamental: ', A_fund
print '.............'
if 1:
from bha import Harmonics
harm = Harmonics(m, usts, sig)