def ProcessAndwriteSpectra(cl_vec,
filterArray,
name,
fields,
ar1,
ar2,
spTag,
binnedBeamDict=None,
iar=None):
countAll = 0
count1 = 0
for l1 in fields:
count1 += 1
count2 = 0
for l2 in fields:
count2 += 1
if count2 < count1: continue
# remove filter
cl = cl_vec[countAll * Nbin:(countAll + 1) *
Nbin] * filterArray[l1 + l2]**2
# There is an additional correction for the autos as MCM had a transfer
# function B_l_AR1*B_l_AR_2
if iar != None:
cl *= binnedBeamDict[sps[0]][iar -
1] / binnedBeamDict[sps[0]][iar]
gName = '%s/%s_%s%s_%sx%s_%s.dat' % (specDir, name, l1, l2, ar1,
ar2, spTag)
speckMisc.writeBinnedSpectrum(lBin, cl, binCount, gName)
countAll += 1
def ProcessAndwriteSpectra(cl_vec,filterArray,name,fields,ar1,ar2,spTag,binnedBeamDict=None,iar=None):
countAll=0
count1=0
for l1 in fields:
count1+=1
count2=0
for l2 in fields:
count2+=1
if count2<count1: continue
# remove filter
cl=cl_vec[countAll*Nbin:(countAll+1)*Nbin]*filterArray[l1+l2]**2
# There is an additional correction for the autos as MCM had a transfer
# function B_l_AR1*B_l_AR_2
if iar !=None:
cl*= binnedBeamDict[sps[0]][iar-1]/binnedBeamDict[sps[0]][iar]
gName = '%s/%s_%s%s_%sx%s_%s.dat'%(specDir,name,l1,l2,ar1,ar2,spTag)
speckMisc.writeBinnedSpectrum(lBin,cl,binCount,gName)
countAll+=1
U_rot = U.copy()
Q_rot.data = Q.data * numpy.cos(2 * phi) + U.data * numpy.sin(2 * phi)
U_rot.data = -Q.data * numpy.sin(2 * phi) + U.data * numpy.cos(2 * phi)
Q_rot.writeFits(patchDir + os.path.sep + 'Q_map_%s_%s_%d' %
(array, season, i),
overWrite=True)
U_rot.writeFits(patchDir + os.path.sep + 'U_map_%s_%s_%d' %
(array, season, i),
overWrite=True)
os.system('HQcompileSpectra.py global.dict')
os.system('HQcomputeAnalyticCovariance.py global.dict')
print 'only seasonTags[0] arrayTags[0] at the moment'
l, cl_EB, error_EB = numpy.loadtxt('spectra/spectrum_EB_%sx%s_%sx%s.dat' %
(array, array, season, season),
unpack=True)
speckMisc.writeBinnedSpectrum(
l, cl_EB, error_EB, 'spectrum_EB_%sx%s_%sx%s_%d.dat' %
(array, array, season, season, count))
chi2[count] = numpy.mean(cl_EB**2 / error_EB**2)
print phi, chi2[count]
count += 1
pylab.plot(ang, chi2)
pylab.show()
meanAutoSpec_B[l2+l1,spTag]=meanAutoSpec_B[l1+l2,spTag]
meanAutoSpec_AB[l2+l1,spTag]=meanAutoSpec_AB[l1+l2,spTag]
meanNoise_A[l1+l2,spTag]=meanAutoSpec_A[l1+l2,spTag]-meanCrossSpec[l1+l2,spTag]
meanNoise_A[l2+l1,spTag]=meanNoise_A[l1+l2,spTag]
meanNoise_B[l1+l2,spTag]=meanAutoSpec_B[l1+l2,spTag]-meanCrossSpec[l1+l2,spTag]
meanNoise_B[l2+l1,spTag]=meanNoise_B[l1+l2,spTag]
meanNoise_AB[l1+l2,spTag]=meanAutoSpec_AB[l1+l2,spTag]-meanCrossSpec[l1+l2,spTag]
meanNoise_AB[l2+l1,spTag]=meanNoise_AB[l1+l2,spTag]
fName = '%s/noise_%s%s_%sx%s_%sx%s.dat'%(specDir,l1,l2,arrays[0],arrays[0],sps[0],sps[0])
speckMisc.writeBinnedSpectrum(lbin,meanNoise_A[l1+l2,spTag]/nDivs,binWeight[l1+l2,spTag],fName)
fName = '%s/noise_%s%s_%sx%s_%sx%s.dat'%(specDir,l1,l2,arrays[-1],arrays[-1],sps[1],sps[1])
speckMisc.writeBinnedSpectrum(lbin,meanNoise_B[l1+l2,spTag]/nDivs,binWeight[l1+l2,spTag],fName)
fName = '%s/noise_%s%s_%sx%s_%sx%s.dat'%(specDir,l1,l2,arrays[0],arrays[-1],sps[0],sps[1])
speckMisc.writeBinnedSpectrum(lbin,meanNoise_AB[l1+l2,spTag]/nDivs,binWeight[l1+l2,spTag],fName)
c=0
count1=0
for l1 in fields:
count1+=1
for i in xrange(nDivs):
T = liteMap.liteMapFromFits('%s/%s/T_map_%s_%s_%d'%(dir,patchDir,array,season,i))
Q = liteMap.liteMapFromFits('%s/%s/Q_map_%s_%s_%d'%(dir,patchDir,array,season,i))
U = liteMap.liteMapFromFits('%s/%s/U_map_%s_%s_%d'%(dir,patchDir,array,season,i))
Q_rot=Q.copy()
U_rot=U.copy()
Q_rot.data=Q.data*numpy.cos(2*phi)+U.data*numpy.sin(2*phi)
U_rot.data=-Q.data*numpy.sin(2*phi)+U.data*numpy.cos(2*phi)
Q_rot.writeFits(patchDir+os.path.sep+'Q_map_%s_%s_%d'%(array,season,i),overWrite=True)
U_rot.writeFits(patchDir+os.path.sep+'U_map_%s_%s_%d'%(array,season,i),overWrite=True)
os.system('HQcompileSpectra.py global.dict')
os.system('HQcomputeAnalyticCovariance.py global.dict')
print 'only seasonTags[0] arrayTags[0] at the moment'
l,cl_EB,error_EB=numpy.loadtxt('spectra/spectrum_EB_%sx%s_%sx%s.dat'%(array,array,season,season),unpack=True)
speckMisc.writeBinnedSpectrum(l,cl_EB,error_EB,'spectrum_EB_%sx%s_%sx%s_%d.dat'%(array,array,season,season,count))
chi2[count]=numpy.mean(cl_EB**2/error_EB**2)
print phi,chi2[count]
count+=1
pylab.plot(ang,chi2)
pylab.show()