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
def combineAndCalibrate(p):
"""
@brief combine and calibrate the various auto/cross spectra
"""
patchDir = "patches"
labels = p['labels']
specDir = 'spectra/'
nPatches = 0
l = os.listdir(patchDir)
for il in l:
if 'patch_%s_00'%labels[0] in il and '_season' in il[-7:]:
nPatches += 1
print "Found %d season patch(es) ..."%nPatches
#Combine Autos
for label in labels:
clAuto = []
for iPatch in xrange(nPatches):
lbin,clbin,binWeight = numpy.loadtxt("%s/clBinAutoSeason_%sX%s_%03d.dat"\
%(specDir,label,label,iPatch),unpack=True)
clAuto += [clbin]
clAutoMean = numpy.mean(clAuto, axis=0)*p['calibration_%s'%label]**2
fName = "%s/clBinAutoGlobalMean_%sX%s.dat"%(specDir,label,label)
speckMisc.writeBinnedSpectrum(lbin,clAutoMean,binWeight,fName)
clCross = []
clAuto = [] #Cross- label power
for iPatch in xrange(nPatches):
lbin,clbin,binWeight = numpy.loadtxt("%s/clBinCrossSeason_%sX%s_%03d.dat"\
%(specDir,labels[0],labels[1],iPatch),\
unpack=True)
clCross += [clbin]
clCrossMean = numpy.mean(clCross,axis=0)*p['calibration_%s'%labels[0]]*p['calibration_%s'%labels[1]]
fName = "%s/clBinCrossGlobalMean_%sX%s.dat"%(specDir,labels[0],labels[1])
speckMisc.writeBinnedSpectrum(lbin,clCrossMean,binWeight,fName)
return 0 #sucess
def compileSpectra(p):
"""
@brief compile the various auto/cross spectra
"""
patchDir = "patches"
labels = p['labels']
taper = p['taper']
gaussApod = p['gaussianApodization']
applyMask = p['applyMask']
nPatches = 0
l = os.listdir(patchDir)
for il in l:
if 'patch_%s_00'%labels[0] in il and '_season' in il[-7:]:
nPatches += 1
print "Found %d season patch(es) ..."%nPatches
if taper['apply'] and gaussApod['apply']:
raise ValueError, "Both taper and Gaussian Apodization cannot be applied."+\
"Use one or the other"
trimAtL = p['trimAtL']
specDir = 'spectra/'
try:
os.makedirs(specDir)
except:
pass
lU,lL,lCen = fftTools.readBinningFile(p['binningFile'])
ii = numpy.where(lU<p['trimAtL'])
#beam transfer ()
binnedBeamWindow = []
for label in labels:
Bb = speckMisc.getBinnedBeamTransfer(p['beamFile_%s'%label],p['binningFile'],trimAtL)
binnedBeamWindow += [Bb]
hpfDict = p['highPassCosSqFilter']
filter = 1.0
if hpfDict['apply']:
print "Will take off the cos^2 high pass filter"
filter = speckMisc.getBinnedInvCosSqFilter(hpfDict['lMin'],hpfDict['lMax'],p['binningFile'],trimAtL)
for iPatch in xrange(nPatches):
mbb = pickle.load(open('mcm/'+p['mcmFileRoot']+'_%03d.pkl'%iPatch,mode="r"))
mbbInv = scipy.linalg.inv(mbb)
if applyMask:
mask = liteMap.liteMapFromFits("%s/mask%03d"%(patchDir,iPatch))
else:
mask = None
pixW = None
if p['applyPixelWeights']:
pixW = (liteMap.liteMapFromFits("%s/totalWeightMap_%03d"%(patchDir,iPatch)))
binWeightMap = pickle.load(open('noiseAndWeights/weightMap%03d.pkl'%iPatch,mode="r"))
# Get the auto spectra first
ilabel = 0
for label in labels:
print "In patch: %03d, computing %sx%s season spectrum "%(iPatch,label,label)
m0 = liteMap.liteMapFromFits("%s/patch_%s_%03d_season" %(patchDir,label,iPatch))
area = m0.Nx*m0.Ny*m0.pixScaleX*m0.pixScaleY
p2d = get2DSpectrum(m0,m0,taper,gaussApod,mask=mask,pixelWeight=pixW)
lL,lU,lbin,clbin,binCount = weightedBinInAnnuli(p2d,\
binWeightMap.powerMap,\
p['binningFile'],p['trimAtL'],\
p['powerOfL'])
clbinDecoup = numpy.dot(mbbInv,clbin)*area*filter**2
# There is an additional correction for the autos as MCM had a transfer
# function B_l_AR1*B_l_AR_2
clbinDecoup *= binnedBeamWindow[ilabel-1]/binnedBeamWindow[ilabel]
inds = numpy.where(clbinDecoup < 0.0)[0]
clbinDecoup[inds] = 0.0
fName = "%s/clBinAutoSeason_%sX%s_%03d.dat"%(specDir,label,label,iPatch)
speckMisc.writeBinnedSpectrum(lbin,clbinDecoup,binCount,fName)
ilabel += 1
# Now do the cross- spectra
m0 = liteMap.liteMapFromFits("%s/patch_%s_%03d_season"\
%(patchDir,labels[0],iPatch))
area = m0.Nx*m0.Ny*m0.pixScaleX*m0.pixScaleY
m1 = liteMap.liteMapFromFits("%s/patch_%s_%03d_season"\
%(patchDir,labels[1],iPatch))
print "In patch: %03d, computing %sx%s season spectrum "%(iPatch,labels[0],labels[-1])
p2d = get2DSpectrum(m0,m1,taper,gaussApod,mask=mask,pixelWeight=pixW)
lL,lU,lbin,clbin,binCount = weightedBinInAnnuli(p2d,\
binWeightMap.powerMap,\
p['binningFile'],p['trimAtL'],\
p['powerOfL'])
clbinDecoup = numpy.dot(mbbInv,clbin)*area*filter**2
fName = "%s/clBinCrossSeason_%sX%s_%03d.dat"%(specDir,labels[0],labels[-1],iPatch)
speckMisc.writeBinnedSpectrum(lbin,clbinDecoup,binCount,fName)
return 0 #success
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()
lmin[0], lmax[0], lcenter[0] = 0.0, 110, 110. / 2
for i in range(99):
if lmax[i] < 2000:
lmin[i + 1] = lmax[i] + 1
lmax[i + 1] = lmax[i] + 50
if ((lmax[i] >= 2000) & (lmax[i] < 2500)):
lmin[i + 1] = lmax[i] + 1
lmax[i + 1] = lmax[i] + 100
if ((lmax[i] >= 2500) & (lmax[i] < 3000)):
lmin[i + 1] = lmax[i] + 1
lmax[i + 1] = lmax[i] + 200
if ((lmax[i] >= 3000) & (lmax[i] < 6000)):
lmin[i + 1] = lmax[i] + 1
lmax[i + 1] = lmax[i] + 400
if lmax[i] >= 6000:
lmin[i + 1] = lmax[i] + 1
lmax[i + 1] = lmax[i] + 800
lcenter[i + 1] = (lmax[i + 1] + lmin[i + 1]) / 2.
print lmin[i], lmax[i], lcenter[i]
pylab.plot(lmin)
pylab.plot(lmax)
pylab.plot(lcenter)
pylab.show()
speckMisc.writeBinnedSpectrum(lmin, lmax, lcenter, 'BIN_ACTPOL_50_5')
for i in range(99):
if lmax[i]<2000:
lmin[i+1]=lmax[i]+1
lmax[i+1]=lmax[i]+50
if ((lmax[i]>=2000) & (lmax[i]<2500)):
lmin[i+1]=lmax[i]+1
lmax[i+1]=lmax[i]+100
if ((lmax[i]>=2500) & (lmax[i]<3000)):
lmin[i+1]=lmax[i]+1
lmax[i+1]=lmax[i]+200
if ((lmax[i]>=3000) & (lmax[i]<6000)):
lmin[i+1]=lmax[i]+1
lmax[i+1]=lmax[i]+400
if lmax[i]>=6000:
lmin[i+1]=lmax[i]+1
lmax[i+1]=lmax[i]+800
lcenter[i+1]=(lmax[i+1]+lmin[i+1])/2.
print lmin[i],lmax[i],lcenter[i]
pylab.plot(lmin)
pylab.plot(lmax)
pylab.plot(lcenter)
pylab.show()
speckMisc.writeBinnedSpectrum(lmin,lmax,lcenter,'BIN_ACTPOL_50_5')
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