def apply_cmb_foreground_block(dc, dm, dt, dk=None):
"""
Apply the CMB x (Monopole + TSZ + KSZ) terms in one block:
(A^T N^-1 A T) x_mono
(A^T N^-1 A F) x_tsz
(A^T N^-1 A K) x_ksz
(T^T A^T N^-1 A) x_cmb
(F^T A^T N^-1 A) x_cmb
(K^T A^T N^-1 A) x_cmb
"""
ksz = False
if dk is not None: ksz = True
# (A^T N^-1 A T) x_mono; (A^T N^-1 A F) x_tsz; (A^T N^-1 A K) x_ksz
b_lt = 0; b_lk = 0; b_lm = 0
for f in range(nFreq):
mct = 0; mck = 0
for ic in range(nCluster):
mct += dt[ic] * ninvs[f] * clumaps[0][ic][f] * g_nu[f]
if ksz: mck += dk[ic] * ninvs[f] * clumaps[1][ic][f]
b_lm += fft.rfft(dm * ninvs[f],axes=[-2,-1]) * beam_prec[f]
b_lt += fft.rfft(mct,axes=[-2,-1]) * beam_prec[f]
if ksz: b_lk += fft.rfft(mck,axes=[-2,-1]) * beam_prec[f]
mcm = fft.irfft(b_lm,axes=[-2,-1],normalize=True).reshape((nx*ny,))
mct = fft.irfft(b_lt,axes=[-2,-1],normalize=True).reshape((nx*ny,))
if ksz: mck = fft.irfft(b_lk,axes=[-2,-1],normalize=True).reshape((nx*ny,))
# (T^T A^T N^-1 A) x_cmb; (F^T A^T N^-1 A) x_cmb; (K^T A^T N^-1 A) x_cmb
mc = dc.copy().reshape((nx,ny))
a_l = fft.rfft(mc,axes=[-2,-1])
mtc = numpy.zeros(nCluster)
mkc = numpy.zeros(nCluster)
mmc = 0
for f in range(nFreq):
b_l = a_l * beam_prec[f]
mc = fft.irfft(b_l,axes=[-2,-1],normalize=True)
mmc += numpy.sum(mc * ninvs[f])
for ic in range(nCluster):
mtc[ic] += numpy.sum(mc * ninvs[f] * clumaps[0][ic][f] * g_nu[f])
if ksz: mkc[ic] += numpy.sum(mc * ninvs[f] * clumaps[1][ic][f])
if ksz: return mct, mcm, mck, mtc, mmc, mkc
return mct, mcm, mtc, mmc
def apply_cmb_cmb(d0):
"""
Apply (S^-1 + A N^-1 A) x
"""
d1 = d0.copy()
d1 = numpy.reshape(d1,(nx,ny))
a_l = fft.rfft(d1,axes=[-2,-1])
c_l = 0
for f in range(nFreq):
b_l = a_l * beam_prec[f]
d2 = fft.irfft(b_l,axes=[-2,-1],normalize=True)
d2 *= ninvs[f]
b_l = fft.rfft(d2,axes=[-2,-1])
c_l += b_l * beam_prec[f]
d2 = fft.irfft(c_l,axes=[-2,-1],normalize=True)
d1 = fft.irfft(precond_2d**2 * a_l/power_2d,axes=[-2,-1],normalize=True)
d2 += d1
return d2.reshape((nx*ny,))
cosmo = experiment.cosmo
mapDir = experiment.mapDir
nsims = p['nsims']
nsamp = p['nsamp']
# Load data and instrumental specifications
template, power_2d, beams, ninvs, freqs = fist.experiment_settings(p)
# Speed up FFT by measuring
#t=time.time()
fft.rfft(ninvs[0].copy(),axes=[-2,-1],flags=['FFTW_MEASURE'])
template_l = fft.fft(ninvs[0],axes=[-2,-1])
fft.irfft(template_l,axes=[-2,-1],flags=['FFTW_MEASURE'])
#Create a directory for the results of the chain
resultDir = fist.check_dir_exists('results')
for n in range(nsims):
datamaps=[]
count=0
for k in freqs:
datamaps+=[fist.litemap_from_fits(mapDir+'/data_%03d_%d.fits'%(n,k))]
mask=fist.litemap_from_fits(mapDir+'/mask_%d.fits'%(k))
ninvs[count]*=mask.data
