def tsXML_to_psdXML(inxmlpath, outxmldir):
'''
Not too nice function for opening a TDIsignal xml file, reading the time-series' inside, calculate the PSDs for each, and then saving them in a similar xml file, but with the time-series replaced by PSDs. Will have to do for now.
'''
inxml = lisaxml.readXML(inxmlpath)
source = inxml.SourceData[0]
lisa = inxml.LISAData
tdiobs = inxml.TDIData[0]
inxml.close()
hr = 60.0**2
patches = int(tdiobs.TimeSeries.Duration / hr)
specAA = synthlisa.spect(tdiobs.A, tdiobs.TimeSeries.Cadence, patches)
specEE = synthlisa.spect(tdiobs.E, tdiobs.TimeSeries.Cadence, patches)
specTT = synthlisa.spect(tdiobs.T, tdiobs.TimeSeries.Cadence, patches)
f = specAA[:, 0]
pAA = specAA[:, 1]
pEE = specEE[:, 1]
pTT = specTT[:, 1]
pobs = lisaxml.Observable('f,pAA,pEE,pTT', DataType='FractionalFrequency')
pobs.TimeSeries = lisaxml.TimeSeries([f, pAA, pEE, pTT], 'f,pAA,pEE,pTT')
pobs.TimeSeries.Cadence = f[1] - f[0]
pobs.TimeSeries.Cadence_Unit = 'Hertz'
pobs.TimeSeries.TimeOffset = 0
pobs.TimeSeries.TimeOffset_Unit = 'Hertz'
inxmlname = os.path.basename(inxmlpath)
outxmlpath = outxmldir + re.sub('\.xml$', '', inxmlname) + '-psd.xml'
outxml = lisaxml.lisaXML(outxmlpath, author='J.Yu')
outxml.TDIData(pobs)
outxml.LISAData(lisa)
outxml.SourceData(source)
outxml.close()
return
def tsXML_to_psdXML(inxmlpath,outxmldir):
"""
Not too nice function for opening a TDIsignal xml file, reading the time-series' inside, calculate the PSDs for each, and then saving them in a similar xml file, but with the PSDs saved as lisaxml's TimeSeries objects. Will have to do for now. """
inxml = lisaxml.readXML(inxmlpath)
sources = inxml.SourceData
lisa = inxml.LISAData
tdiobs = inxml.TDIData[0]
inxml.close()
segduration = 2*60.0**2
patches = int(tdiobs.TimeSeries.Duration/segduration)
specAA = synthlisa.spect(tdiobs.A,tdiobs.TimeSeries.Cadence,patches)
specEE = synthlisa.spect(tdiobs.E,tdiobs.TimeSeries.Cadence,patches)
specTT = synthlisa.spect(tdiobs.T,tdiobs.TimeSeries.Cadence,patches)
f = specAA[:,0]
pAA = specAA[:,1]
pEE = specEE[:,1]
pTT = specTT[:,1]
pobs = lisaxml.Observable('f,pAA,pEE,pTT',DataType = 'FractionalFrequency')
pobs.TimeSeries = lisaxml.TimeSeries([f,pAA,pEE,pTT],'f,pAA,pEE,pTT')
pobs.TimeSeries.Cadence = f[1] - f[0]
pobs.TimeSeries.Cadence_Unit = 'Hertz'
pobs.TimeSeries.TimeOffset = 0
pobs.TimeSeries.TimeOffset_Unit = 'Hertz'
inxmlname = os.path.basename(inxmlpath)
outxmlpath = outxmldir + re.sub('\.xml$','',inxmlname) + '-psd.xml'
outxml = lisaxml.lisaXML(outxmlpath,author='J.Yu')
outxml.TDIData(pobs)
outxml.LISAData(lisa)
for source in sources:
outxml.SourceData(source)
outxml.close()
return
def ComputeNorm(ser, sampling, PSD):
size = Numeric.shape(ser)[0]
if(Numeric.shape(PSD)[0] != size/2):
print "wrong size of psd: ", pdlen, Numeric.shape(PSD)
sys.exit(1)
ser2 = synthlisa.spect(ser, sampling,0)
norm = 0.0
indx = 0
#fout = open("psdTest.dat", 'w')
for i in xrange(size/2-1):
if (ser2[i][0] > fLow and ser2[i][0]<= fHigh):
norm += ser2[i][1]/PSD[i]
# record = str(ser2[i][0]) + spr + str(PSD[i]) + spr + str(2.0*norm) + "\n"
# fout.write(record)
indx = indx + 1
norm = sqrt(2.0*norm)
# fout.close()
#print "indx = ", indx
#norm = sqrt(2.0 *numpy.sum(ser2[1:,1]/PSD))
return norm
def ComputeNorm(ser, sampling, PSD):
size = Numeric.shape(ser)[0]
if(Numeric.shape(PSD)[0] != size/2):
print "wrong size of psd: ", pdlen, Numeric.shape(PSD)
sys.exit(1)
ser2 = synthlisa.spect(ser, sampling,0)
norm = 0.0
indx = 0
# fout = open("dChiTest.dat", 'w')
for i in xrange(size/2-1):
if (ser2[i][0] > 1.e-5 and ser2[i][0]<0.009):
norm += ser2[i][1]/PSD[i]
# record = str(ser2[i][0]) + " " + str(2.0*norm) + "\n"
# fout.write(record)
indx = indx + 1
norm = sqrt(2.0*norm)
# fout.close()
#print "indx = ", indx
#norm = sqrt(2.0 *numpy.sum(ser2[1:,1]/PSD))
return norm
# reading Key file
Injtdifile = lisaxml.readXML(Injfile)
Injsources = Injtdifile.getLISASources()[0]
tdi = Injtdifile.getTDIObservables()[0]
Injtdifile.close()
sampling = tdi.TimeSeries.Cadence
X = tdi.Xf
A = (2.0*tdi.Xf -tdi.Yf - tdi.Zf)/3.0
E = (tdi.Zf - tdi.Yf)/math.sqrt(3.0)
#XX = synthlisa.spect(X, sampling, 0)
#fr = XX[1:,0]
Specdat = synthlisa.spect(Adata,sampling,0)
fr = Specdat[1:,0]
PSDdat = Specdat[1:,1]
"""
Sigfilout = open("AEsigCheck1.dat", 'w')
for i in xrange(len(A)):
record = str(sampling*float(i)) + spr + str(A[i]) + spr + str(E[i]) + "\n"
Sigfilout.write(record)
Sigfilout.close()
sys.exit(0);
"""
if (EMRIchal):
IndX = len(A)
for i in xrange(len(A)-1, 1, -1):
if (A[i] != 0.0):
parser.error("I don't recognize the set of TDI observables!")
[t, X, Y, Z] = numpy.transpose(
synthlisa.getobs(samples,
options.timestep,
obsset,
options.inittime,
display=options.verbose))
# Computing SNR....
if options.debugSNR or hasattr(allsources[0], 'RequestSN'):
# this is |\tilde{h}|^2 * dt/N
sampling = ts.Cadence
hX = synthlisa.spect(X, sampling, 0)
hY = synthlisa.spect(Y, sampling, 0)
hZ = synthlisa.spect(Z, sampling, 0)
# get frequencies, skip DC
fr = hX[1:, 0]
om = 2.0 * pi * fr
# instrument noise (was 2.5, 1.8)
if options.armlength == 'Standard':
Spm = 2.5e-48 * (1.0 + (fr / 1.0e-4)**-2) * fr**(-2)
Sop = 1.8e-37 * fr**2
else:
print "Warning: adopting eLISA noise parameters for non-standard armlength"
Spm = 6.00 - 48 * (1.0 + (fr / 1.0e-4)**-2) * fr**(-2)
obsstr = 't,X1f,X2f,X3f'
elif options.observables == 'Sagnac':
obsset = [tdi.alpham,tdi.betam,tdi.gammam,tdi.zetam]
obsstr = 'alphaf,betaf,gammaf,zetaf'
else:
parser.error("I don't recognize the set of TDI observables!")
[t,X,Y,Z] = numpy.transpose(synthlisa.getobs(samples,options.timestep,obsset,options.inittime,display=options.verbose))
# Computing SNR....
if options.debugSNR or hasattr(allsources[0],'RequestSN'):
# this is |\tilde{h}|^2 * dt/N
sampling = ts.Cadence
hX = synthlisa.spect(X,sampling,0)
hY = synthlisa.spect(Y,sampling,0)
hZ = synthlisa.spect(Z,sampling,0)
# get frequencies, skip DC
fr = hX[1:,0]
om = 2.0 * pi * fr
# instrument noise (was 2.5, 1.8)
if options.armlength == 'Standard':
Spm = 2.5e-48 * (1.0 + (fr/1.0e-4)**-2) * fr**(-2)
Sop = 1.8e-37 * fr**2
else:
print "Warning: adopting eLISA noise parameters for non-standard armlength"
Spm = 6.00e-48 * (1.0 + (1.0e-4/fr)) * fr**(-2)
fout1.close() ### Computing quadratures if needed (we do not need pi/2) if options.phasemax: # injfile = Injfile[:-18]+'_piby2-tdi-frequency.xml' # Injtdifile = lisaxml.readXML(injfile) # tdipiby2 = Injtdifile.getTDIObservables()[0] # Xpiby2 = tdipiby2.Xf # Injtdifile.close() injfile = Injfile[:-15]+'_0-tdi-strain.xml' Injtdifile = lisaxml.readXML(injfile) tdi0 = Injtdifile.getTDIObservables()[0] X0 = tdi0.Xs XX = synthlisa.spect(X, sampling, 0) fr = XX[1:,0] # Compute noise spectral density om = 2.0 * pi * fr L = 16.6782 # instrument noise Spm = 2.5e-48 * (1.0 + (fr/1.0e-4)**-2) * fr**(-2) Sop = 1.8e-37 * fr**2 Synx = 16.0 * numpy.sin(om*L)**2 * (2.0 * (1.0 + numpy.cos(om*L)**2) * Spm + Sop) Synxy = -4.0 * numpy.sin(2.0*om*L)*numpy.sin(om*L) * ( Sop + 4.*Spm ) Syn = 2.0 * (Synx - Synxy)/3.0
nyquistf = 0.5/sampling
print "Cadence = ", sampling, " Nyquist freq = ", nyquistf
print "Deaing with data file:", Datafile
challname = ""
if ( re.search('challenge3.2', Datafile) != None ):
challname = "3.2"
elif ( re.search('challenge3.3', Datafile) != None ):
challname = "3.3"
elif ( re.search('challenge3.4', Datafile) != None ):
challname = "3.4"
fHigh = nyquistf
print "challenge3.4 -> upper frequency is extended to nyquist frequency"
Specdat = synthlisa.spect(Adata,sampling,0)
fr = Specdat[1:,0]
PSDdat = Specdat[1:,1]
# computing useful number of points
pdlen3 = samples/2
freqs = Numeric.arange(0,pdlen3+1,dtype='d') * (nyquistf / pdlen3)
ind = 0
for i in xrange(pdlen3):
if (freqs[i] > fLow and freqs[i]<= fHigh):
ind += 1
ind = ind*2
print "data size = ", samples
print "number of used points ", ind, "corresponds to the freq range: ", fLow, "-", fHigh
nyquistf = 0.5 / sampling
print "Cadence = ", sampling, " Nyquist freq = ", nyquistf
print "Deaing with data file:", Datafile
challname = ""
if (re.search('challenge3.2', Datafile) != None):
challname = "3.2"
elif (re.search('challenge3.3', Datafile) != None):
challname = "3.3"
elif (re.search('challenge3.4', Datafile) != None):
challname = "3.4"
fHigh = nyquistf
print "challenge3.4 -> upper frequency is extended to nyquist frequency"
Specdat = synthlisa.spect(Adata, sampling, 0)
fr = Specdat[1:, 0]
PSDdat = Specdat[1:, 1]
# computing useful number of points
pdlen3 = samples / 2
freqs = Numeric.arange(0, pdlen3 + 1, dtype='d') * (nyquistf / pdlen3)
ind = 0
for i in xrange(pdlen3):
if (freqs[i] > fLow and freqs[i] <= fHigh):
ind += 1
ind = ind * 2
print "data size = ", samples
print "number of used points ", ind, "corresponds to the freq range: ", fLow, "-", fHigh
"""
X.append(tdiData.Zs)
print " --> time step =" , stime , "s"
patches = 64
# use Blackman windowing for spectra
mywindow = 'blackman'
# compute spectra, averaging "patches" overlapping segments of time series
if (options.verbose):
print " --> Compute spectrums ..."
sX = []
for iSim in xrange(len(X)):
sX.append(synthlisa.spect(X[iSim],stime,patches,win=mywindow))
if (options.verbose):
print " --> Normalized spectrums ..."
f = sX[0][1:,0]
for iSim in xrange(len(sX)):
if ( (UsedSim[iSim] == 'SL') or (UsedSim[iSim] == 'LC') ):
# go to a spectrum of phase (from frequency)
sX[iSim][1:,1] = sX[iSim][1:,1] / (2 * math.pi * f)**2
else:
# go to a spectrum of phase (from strain)
scale = 1e10 / 299792458.0
sX[iSim][1:,1] = scale**2 * sX[iSim][1:,1]
# Record output data
