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()