def calculate(self):
"""
calculate various properties of the gaussian, fnl, gnl maps i.e. compute
* Ais
* As
* Cls
* dipoles using remove_dipole
"""
inpCls=self.inputCls[:self.lmax+1]
if (self.gausmap0!=None):
self.gausCls0=hp.alm2cl(self.gausalm0)[0:self.lmax+1]
self.gausCls1=hp.alm2cl(self.gausalm1)[0:self.lmax+1]
self.gausA0=get_A0(self.gausCls0[1:], inpCls[1:])
self.gausAi=Ais(self.gausmap1, self.lmax); self.gausA=AistoA(self.gausAi)
self.gausAi2=Ais(self.gausmap0, self.lmax); self.gausA2=AistoA(self.gausAi2)
self.gausmp=hp.remove_monopole(self.gausmap0, fitval=True)[1]
self.gausdipole=get_dipole(self.gausmap1)
if (self.fnlmaps0!=None):
self.fnlCls0=[]; self.fnlCls1=[]; self.fnlA0=[]; self.fnlAi=[]; self.fnlAi2=[]
self.fnlmp=[]; self.fnldipole=[]; self.fnlA=[]; self.fnlA2=[]
for i in range(self.Nfnls):
self.fnlCls0.append(hp.anafast(self.fnlmaps0[i], nspec=self.lmax))
self.fnlCls1.append(hp.anafast(self.fnlmaps1[i], nspec=self.lmax))
self.fnlA0.append(get_A0(self.fnlCls0[i][1:], inpCls[1:]))
self.fnlAi.append(Ais(self.fnlmaps1[i], self.lmax)); self.fnlA.append(AistoA(self.fnlAi[i]))
self.fnlAi2.append(Ais(self.fnlmaps0[i], self.lmax)); self.fnlA2.append(AistoA(self.fnlAi2[i]))
self.fnlmp.append(hp.remove_monopole(self.fnlmaps0[i], fitval=True)[1])
self.fnldipole.append(get_dipole(self.fnlmaps1[i]))
def get_hem_Cls(skymap, direction, LMAX=256, deg=90.):
"""
from the given healpix skymap, return Cls for two hemispheres defined by the
direction given, useful to study the possible scale dependence of power modulation
direction should be a unit vector
"""
# generate hemispherical mask
NPIX=len(skymap)
NSIDE=hp.npix2nside(NPIX)
maskp=np.array([0.]*NPIX)
disc=hp.query_disc(nside=NSIDE, vec=direction, radius=0.0174532925*deg)
maskp[disc]=1.
#skymap=hp.remove_monopole(skymap)
map1=hp.ma(skymap)
map1.mask=maskp
Clsp=hp.anafast(map1, lmax=LMAX)
if (deg<90.):
maskm=np.array([0.]*NPIX)
disc=hp.query_disc(nside=NSIDE, vec=-direction, radius=0.0174532925*deg)
maskm[disc]=1.
map1.mask=maskm
else:
map1.mask=np.logical_not(maskp)
Clsm=hp.anafast(map1, lmax=LMAX)
return [Clsp, Clsm]
def get_spectra(maps, maskmap, lmax, min_ell, delta_ell, wl=None, Mllmat=None, MllBinned=None, ellpq=None, MllBinnedInv=None): nside = hp.npix2nside(len(maps[0])) if wl == None: wl = hp.anafast(maskmap,regression=False) wl = wl[0:lmax+1] if Mllmat == None: Mllmat=Mll(lmax,wl) cutmaps = np.array([maps[0] * maskmap, maps[1] * maskmap, maps[2] * maskmap]) cls = hp.anafast(cutmaps,pol=True) allcls= np.array([cls[0][0:lmax+1], cls[1][0:lmax+1], cls[2][0:lmax+1], cls[3][0:lmax+1], cls[4][0:lmax+1], cls[5][0:lmax+1]]) if ellpq == None: all_ell = min_ell-1 + np.arange(1000)*delta_ell max_ell = np.max(all_ell[all_ell < lmax]) nbins = np.int((max_ell + 1 - min_ell) / delta_ell) ell_low = min_ell + np.arange(nbins)*delta_ell ell_hi = ell_low + delta_ell - 1 the_ell = np.arange(lmax+1) newl, p, q = make_pq(the_ell, ell_low, ell_hi) else: newl = ellpq[0] p = ellpq[1] q = ellpq[2] if MllBinned == None: MllBinned = MllBin(Mllmat, p, q) if MllBinnedInv == None: MllBinnedInv = np.linalg.inv(MllBinned) ClsBinned = BinCls(p,allcls) newcls = np.dot(MllBinnedInv, np.ravel(np.array(ClsBinned))) newcls = np.reshape(newcls, (6, p.shape[0])) return newcls, newl, Mllmat, MllBinned, MllBinnedInv, p, q, allcls
def G_plus_C_vs_G_four(freq):
"""plots fourier of GSM and CMB added together
compared to GSM alone
User defines GSM frequency in MHz
WARNING: Takes some time to run
Note:
GSM default shape is 512
CMB default shape is 2048
GSM and CMB shape must match to add arrays
GSM has been cast to a higher nside of 2048
"""
gsm=GlobalSkyModel()
#generates a model of the GSM
gsmdata=gsm.generate(freq)
#upgrade the GSM data to 2048
gsmdata=hp.ud_grade(gsmdata,2048)
#the raw CMB data
cmbdata=hp.read_map('/home/amber/data/cmb/COM_CMB_IQU-commander-field-Int_2048_R2.01_full.fits')
LMAX=1024
bothmap=hp.anafast(gsmdata+cmbdata,lmax=LMAX)
ell=np.arange(len(bothmap))
plt.figure()
freq_str=str(freq)
plt.plot(ell,ell*(ell+1)*bothmap,color='b',label='GSM at '+freq_str+' MHz +CMB')
plt.xlabel('ell');plt.ylabel('ell(ell+1)');plt.grid()
#delete this line?
#hp.write_bothmap('bothmap.fits',bothmap)
gsmdataonly=gsm.generate(freq)
gsmmaponly=hp.anafast(gsmdataonly,lmax=LMAX)
ell=np.arange(len(gsmmaponly))
plt.plot(ell,ell*(ell+1)*gsmmaponly,color='r',label='GSM at '+freq_str+' MHz')
#delete this line?
#hp.write_gsmmaponly('gsmmaponly.fits',gsmmaponly)
plt.legend(loc='upper right')
plt.show()
def run(self, dataSlice, slicePoint=None):
# Calculate the power spectrum.
if self.removeDipole:
cl = hp.anafast(hp.remove_dipole(dataSlice[self.colname]))
else:
cl = hp.anafast(dataSlice[self.colname])
l = np.arange(np.size(cl))
condition = np.where((l <= self.lmax) & (l >= self.lmin))[0]
totalpower = np.sum(cl[condition]*l[condition]*(l[condition]+1)/2.0/np.pi)
return totalpower
def toy_test_real_vs_harmonic(amp=100., nside=2**5, nexp=1000):
# generate the signal map
npix = hp.nside2npix(nside)
signal = np.zeros(npix)
ind_hpix = hp.ang2pix(nside, 0., 0., nest=False)
signal[ind_hpix] = 1.
# get autospectrum of signal
lmax_tmp = 4*nside-1
cl_signal = hp.anafast(signal, lmax=lmax_tmp)
l = np.arange(len(cl_signal))
# create white CL's with unit variance
nl = np.ones_like(l, dtype=np.float)
nl /= (np.sum(nl*(2.*l+1.))/4./np.pi)
# create harmonic space weights. the factor of
# 2L+1 is because we'll be dealing with 1d spectra rather than 2d.
weight = (2.*l+1.)/nl
est_real = []
est_harm = []
for i in range(nexp):
print '%i/%i'%(i,nexp)
# make noise map
noise = hp.synfast(nl, nside)
data = amp*signal + noise
# get real space estimate of signal amplitude
est_real.append(data[ind_hpix])
# get harmonic space estimate of signal amplitude
amp_tmp = np.sum(hp.anafast(signal, map2=data, lmax=lmax_tmp)*weight) / np.sum(cl_signal*weight)
est_harm.append(amp_tmp)
est_real = np.array(est_real)
est_harm = np.array(est_harm)
print ' '
print ' mean(est_real): %0.2f'%est_real.mean()
print ' mean(est_harm): %0.2f'%est_harm.mean()
print ' var(est_real): %0.2f'%est_real.var()
print ' var(est_harm): %0.2f'%est_harm.var()
print ' VarRatio: %0.2f'%(est_harm.var()/est_real.var())
print ' std(est_real): %0.2f'%est_real.std()
print ' std(est_harm): %0.2f'%est_harm.std()
print ' StdRatio: %0.2f'%(est_harm.std()/est_real.std())
print ' '
nbins=15
pl.clf()
pl.hist(est_real,bins=nbins, alpha=0.5)
pl.hist(est_harm,bins=nbins, alpha=0.5)
pl.legend(['real space','harmonic space'])
pl.title('AMP=%0.3f, NEXP=%i, VarRatio=%0.3f'%(amp, nexp, est_harm.var()/est_real.var()))
ipdb.set_trace()
def wpixel(instr, ell, nside=1024, idet=231, theta_max=30):
nu = instr.filter.name
NPOINTS = instr.detector.NPOINTS
if NPOINTS == 1:
return np.ones(len(ell))
path_ = '/home/fincardona/Qubic/Compare_poly/maps/poly/interfero'
pathN = '/home/fincardona/Qubic/spatial_extension/maps/poly/interfero'
beam_ = read_map('%s/sb_nside{}_mono_{}Ghz_idet{}_tmax{}.fits'.
format(nside, int(nu/1e9), idet, theta_max) % path_)
beamN = read_map('%s/sb_nside{}_mono_{}Ghz_idet{}_tmax{}_Npoints{}.fits'.
format(nside, int(nu/1e9), idet, theta_max, NPOINTS) % pathN)
return (hp.anafast(beamN, lmax=ell[-1], pol=False) /
hp.anafast(beam_, lmax=ell[-1], pol=False))[ell[0]:]
def get_spectra(self, map1, map2=None):
"""
Return biased and Xpol-debiased estimations of the power spectra of
a Healpix map or of the cross-power spectra if *map2* is provided.
xpol = Xpol(mask, lmin, lmax, delta_ell)
biased, unbiased = xpol.get_spectra(map1, [map2])
The unbiased Cls are binned. The number of bins is given by
(lmax - lmin) // delta_ell, using the values specified in the Xpol's
object initialisation. As a consequence, the upper bound of the highest
l bin may be less than lmax. The central value of the bins can be
obtained through the attribute `xpol.ell_binned`.
Parameters
----------
map1 : Nx3 or 3xN array
The I, Q, U Healpix maps.
map2 : Nx3 or 3xN array, optional
The I, Q, U Healpix maps.
Returns
-------
biased : float array of shape (6, lmax+1)
The anafast's pseudo (cross-) power spectra for TT, EE, BB, TE, EB,
TB. The corresponding l values are given by `np.arange(lmax + 1)`.
unbiased : float array of shape (6, nbins)
The Xpol's (cross-) power spectra for TT, EE, BB, TE, EB, TB.
The corresponding l values are given by `xpol.ell_binned`.
"""
map1 = np.asarray(map1)
if map1.shape[-1] == 3:
map1 = map1.T
if map2 is None:
biased = hp.anafast(map1 * self.mask, pol=True)
else:
map2 = np.asarray(map2)
if map2.shape[-1] == 3:
map2 = map2.T
biased = hp.anafast(map1 * self.mask, map2 * self.mask, pol=True)
biased = np.array([cl[:self.lmax+1] for cl in biased])
binned = self.bin_spectra(biased)
fact_binned = self.ell_binned * (self.ell_binned + 1) / (2 * np.pi)
binned *= fact_binned
unbiased = np.dot(self.mll_binned_inv, binned.ravel()).reshape(6, -1)
unbiased /= fact_binned
return biased, unbiased
def compute_pcl_estimate(data_file,inv_noise_file,beam_file):
d = hp.read_map(data_file)
inv_n = hp.read_map(inv_noise_file)
print "zero pixels= ",len(inv_n[inv_n<=0])
if len(inv_n[inv_n<=0]) > 0:
inv_n[inv_n<=0] = min(inv_n[inv_n>0])
n = 1./np.sqrt(inv_n)
nside = hp.npix2nside(np.shape(d)[0])
# compute the total power spectrum
C_l = hp.anafast(d,lmax=2*nside)
num_samps = int(1000)
N_l = np.zeros(np.shape(C_l))
B_l_in = np.loadtxt(beam_file,delimiter=",")
B_l = B_l_in[:,1]
#apply beam to the cls
C_l /= B_l**2
# Monte Carlo
mu = np.zeros(np.shape(d))
for samp in range(num_samps):
if samp % 100 == 0 :
print "samples =",samp
# draw a realisation from noise
n_i = np.random.normal(mu,n)
# find the power spectrum of this realisation
N_l_i = hp.anafast(n_i,lmax=2*nside)
# accumulate
N_l += N_l_i
N_l /= float(num_samps)
#apply beam to the nls
N_l /= B_l**2
# subtract
S_l = C_l - N_l
return (C_l,N_l,S_l)
def generate_map(t, p, nside, plot=False):
"""Generate map and bands for the given galaxy locations"""
npix = 12*nside**2
ix = hp.ang2pix(nside, t, p, nest=True)
M = np.zeros(npix)
for i in xrange(len(ix)):
M[ix[i]] += 1.
ave = 1.*len(t)/npix
M = M/ave-1.0 #Calculate overdensity
cl = hp.anafast(M)
ell = np.arange(len(cl))+1
ell2 = (ell[::3]+ell[1::3]+ell[2::3])/3.
cl2 = (cl[::3]+cl[1::3]+cl[2::3])/3.
if plot:
plot_galaxies_and_density(t, p, M)
plot_anafast_band_powers(ell2, cl2)
plt.show()
ell3 = ell2[1:]
cl3 = cl2[1:] #Alex to Matias: Why are these removed?
cl_err = np.ones(len(ell3))*0.00002
index = np.arange(len(ell3))
ell_min = ell3-1
ell_max = ell3+1
cl3 = cl3*1000 #Bigger c_l values for convergence
bands = zip(index, ell3, ell_min, ell_max, cl3, cl_err, cl3)
return M, bands
def __init__(self, mask, lmin, lmax, delta_ell):
"""
Parameters
----------
mask : boolean Healpix map
Mask defining the region of interest (of value True)
lmin : int
Lower bound of the first l bin.
lmax : int
Highest l value to be considered. The inclusive upper bound of
the last l bin is lesser or equal to this value.
delta_ell :
The l bin width.
"""
mask = np.asarray(mask)
lmin = int(lmin)
lmax = int(lmax)
if lmin < 1:
raise ValueError('Input lmin is less than 1.')
if lmax < lmin:
raise ValueError('Input lmax is less than lmin.')
delta_ell = int(delta_ell)
wl = hp.anafast(mask)[:lmax+1]
self.mask = mask
self.lmin = lmin
self.lmax = lmax
self.delta_ell = delta_ell
self.wl = wl
self.ell_binned, self._p, self._q = self._bin_ell()
mll_binned = self._get_Mll()
self.mll_binned_inv = np.linalg.inv(mll_binned)
def study_multiband_planck(quick=True):
savename = datadir+'cl_multiband.pkl'
bands = [100, 143, 217, 'mb']
if quick: cl = pickle.load(open(savename,'r'))
else:
cl = {}
mask = load_planck_mask()
mask_factor = np.mean(mask**2.)
for band in bands:
this_map = load_planck_data(band)
this_cl = hp.anafast(this_map*mask, lmax=lmax)/mask_factor
cl[band] = this_cl
pickle.dump(cl, open(savename,'w'))
cl_theory = {}
pl.clf()
for band in bands:
l_theory, cl_theory[band] = get_cl_theory(band)
this_cl = cl[band]
pl.plot(this_cl/cl_theory[band])
pl.legend(bands)
pl.plot([0,4000],[1,1],'k--')
pl.ylim(.7,1.3)
pl.ylabel('data/theory')
def test_anafast_iqu(self):
cl = hp.anafast([m.filled() for m in self.map1], lmax = 1024)
cliqu = np.array(pyfits.open(os.path.join(self.path, 'data', 'cl_iqu_wmap_fortran.fits'))[1].data)
self.assertEqual(len(cl[0]), 1025)
self.assertEqual(len(cl), 6)
for comp in range(6):
np.testing.assert_array_almost_equal(cl[comp], np.double(cliqu[cliqu.dtype.names[comp]]), decimal=4)
def test_anafast_iqu(self):
self.map1[0] = hp.remove_monopole(self.map1[0])
cl = hp.anafast(self.map1, lmax=self.lmax)
self.assertEqual(len(cl[0]), 65)
self.assertEqual(len(cl), 6)
for i in range(6):
np.testing.assert_array_almost_equal(cl[i], self.cliqu[i], decimal=8)
def G_plus_C_cross_C_four(freq,scale):
"""plot of fourier of
GSM(scale)+CMB crossed with CMB
compared to CMB only
User defines GSM freq in MHz
User defines scale of GSM such as 0.1 or 0.01
WARNING: takes some time to run
"""
gsm=GlobalSkyModel()
#create the GSM model
gsmdata=gsm.generate(freq)
#rather than upgrading GSM, downgrade the CMB to 512
cmbdata=hp.read_map('/home/amber/data/cmb/COM_CMB_IQU-commander-field-Int_2048_R2.01_full.fits')
cmbmap=hp.ud_grade(cmbdata,512)
LMAX=1024
both=(gsmdata*scale)+cmbmap
#cross cmb+gsm with the cmb
c_plus_g_cross_c=hp.sphtfunc.anafast(both,map2=cmbmap,lmax=LMAX)
ell=np.arange(len(c_plus_g_cross_c))
plt.figure()
#format variables as text for labels
freq_str=str(freq)
scale_str=str(scale)
plt.plot(ell,ell*(ell+1)*c_plus_g_cross_c,color='b',label='GSM('+scale_str+') at '+freq_str+' MHZ+CMB cross CMB')
cmbmaponly=hp.anafast(cmbdata,lmax=LMAX)
ell=np.arange(len(cmbmaponly))
plt.plot(ell,ell*(ell+1)*cmbmaponly,color='r',label='CMB')
#delete this line?
#hp.write_cmbmaponly('cmbmaponly.fits',cmbmaponly)
plt.xlabel('ell');plt.ylabel('ell(ell+1)');plt.grid()
#delete this line?
#hp.write_cp_plus_g_cross_c('c_plus_g_cross_c.fits',c_plus_g_cross_c)
plt.legend(loc='upper right')
plt.show()
def main(nsim=0):
nl = h._nl
# Load map, mask, mll
print ""
print "Loading map, mask, mll, and calculating mll_inv..."
map_data = hp.read_map(h._fn_map)
mask = hp.read_map(h._fn_mask)
mll = np.load(h._fn_mll)
mll_inv = np.linalg.inv(mll)
# Read in Planck map: normalize, remove mono-/dipole, mask
print "Normalizing, removing mono-/dipole, and masking map..."
map_masked = map_data * mask
# Create cltt (cltt_data_masked) and correct it (cltt_data_corrected)
print "Calculating cltt_data_masked, cltt_data_corrected..."
cltt_data_masked = hp.anafast(map_masked)
cltt_data_masked = cltt_data_masked[:nl]
cltt_data_corrected = np.dot(mll_inv, cltt_data_masked)
# Create simulation of map (map_sim) from cltt_data_corrected
print "Creating and saving map_sim_%i..." % nsim
map_sim = hp.synfast(cltt_data_corrected, h._nside)
hp.write_map('output/map_sim_%i.fits' % nsim, map_sim)
def read_and_diff_files_fast(f1,f2,nside=256,tmask=None,return_map=False):
#assume tmask input is already degraded
mm1=hp.read_map(f1,[0,1,2],verbose=False)
mm2=hp.read_map(f2,[0,1,2],verbose=False)
mmm1=[]
mmm2=[]
for m1,m2 in zip(mm1,mm2):
m1=hp.ud_grade(hp.ma(m1),nside_out=nside)
m2=hp.ud_grade(hp.ma(m2),nside_out=nside)
tmask=m1.mask | m2.mask | tmask
mmm1.append(m1)
mmm2.append(m2)
diff=[]
for m1,m2 in zip(mmm1,mmm2):
d=m1-m2
d.mask=tmask
diff.append(d)
skyfrac=1-float(tmask.sum())/len(tmask)
cldata=hp.anafast(diff)
cldata_out=[]
for cl in cldata:
cldata_out.append(cl/skyfrac)
if return_map is False:
return cldata_out
if return_map is True:
return cldata_out,diff
def GetPs(freq):
name = "21cm_Syn_35beam_noise"
# plt.close('all')
re = tt.ICA.rebuild(N, A_, S_, freq, Plot)
rebuild_21cm = map[freq, :] - re
rebuild_21cm = rebuild_21cm - rebuild_21cm.mean()
cl1 = hp.anafast(map1[freq, pol])
cl2 = hp.anafast(rebuild_21cm)
l = np.arange(len(cl1))
plt.figure("APS")
plt.semilogy(l, l * (l + 1) * cl1, "r-", label="simulation")
plt.semilogy(l, l * (l + 1) * cl2, "b-", label="result")
plt.xlabel("$l$")
plt.ylabel("$l(l+1)C_l$")
plt.title(name)
plt.legend()
plt.savefig("./result/APS/" + name + ".eps")
def test_rotate_map_polarization_with_spectrum():
"""Rotation of reference frame should not change the angular power spectrum.
In this test we create a map from a spectrum with a pure EE signal and check
that the spectrum of this map and the spectrum of the same map rotated
from Galactic to Ecliptic agrees.
This test checks if the QU rotation is correct"""
nside = 32
cl = np.zeros((6, 96), dtype=np.double)
# Set ell=1 for EE to 1
cl[1][2] = 1
gal2ecl = Rotator(coord=["G", "E"])
m_original = hp.synfast(cl, nside=nside, new=True)
cl_from_m_original = hp.anafast(m_original)
m_rotated = gal2ecl.rotate_map_pixel(m_original)
cl_from_m_rotated = hp.anafast(m_rotated)
assert np.abs(cl_from_m_rotated - cl_from_m_original).sum() < 1e-2
def estimate_cl(sky_map, lmax, binary_mask=None, fwhm=0.0, pol=True):
sky_map_masked, binary_mask = mask_map(sky_map, binary_mask=binary_mask, pol=pol, ret_mask=True, fill_zeros=True)
f_sky = float(np.sum(binary_mask))/binary_mask.size
Bl = hp.gauss_beam(fwhm=fwhm, lmax=lmax, pol=pol)
if pol:
spectra = hp.anafast((sky_map_masked[0].filled(), sky_map_masked[1].filled(), sky_map_masked[2].filled()), lmax=lmax)[:4]
spectra = np.array(spectra)
spectra /= f_sky*Bl.T**2
else:
spectra = hp.anafast(sky_map_masked.filled(), lmax=lmax)
spectra /= f_sky*Bl**2
spectra = np.array(spectra)
return spectra
def generate_powerspectrum(mask,lmax): print "Reading mask file" m = hp.read_map(mask) clfn = mask + '.cl' nside = hp.npix2nside(m.size) print "Generating power spectrum" cl = hp.anafast(m,lmax=lmax) np.savetxt(clfn,cl) return clfn
def test_anafast_iqu(self):
cl = hp.anafast([m.filled() for m in self.map1], lmax = 1024)
cliqu = pyfits.open(os.path.join(self.path, 'data', 'cl_iqu_wmap_fortran.fits'))[1].data
self.assertEqual(len(cl[0]), 1025)
self.assertEqual(len(cl), 6)
for comp in range(6):
comphealpix = comp
if comp in [4,5]: # order of HEALPIX is TB, EB while in healpy is EB, TB
comphealpix = {4:5, 5:4}[comp]
np.testing.assert_array_almost_equal(cl[comp], cliqu.field(comphealpix), decimal=8)
def plotPowerSpectrum(self, metricValue, title=None, fignum=None, maxl=500.,
label=None, addLegend=False, legendloc='upper right',
removeDipole=True,
logPlot=True, verbose=False, **kwargs):
"""Generate and plot the power spectrum of metricValue.
maxl = maximum ell value to plot (default 500 .. to plot all l, set to value > 3500)
title = plot Title (default None)
fignum = figure number (default None and create new plot)
label = label to add in figure legend (default None)
addLegend = flag to add legend (default False).
removeDipole = remove dipole when calculating power spectrum (default True)
(monopole removed automatically.)
"""
if fignum:
fig = plt.figure(fignum)
else:
fig = plt.figure()
# If the mask is True everywhere (no data), just plot zeros
if False not in metricValue.mask:
return None
else:
if removeDipole:
cl = hp.anafast(hp.remove_dipole(metricValue.filled(self.badval), verbose=verbose))
else:
cl = hp.anafast(metricValue.filled(self.badval))
l = np.arange(np.size(cl))
# Plot the results.
if removeDipole:
condition = ((l < maxl) & (l > 1))
else:
condition = (l < maxl)
plt.plot(l[condition], (cl[condition]*l[condition]*(l[condition]+1))/2.0/np.pi, label=label)
if cl[condition].max() > 0 and logPlot:
plt.yscale('log')
plt.xlabel(r'$l$')
plt.ylabel(r'$l(l+1)C_l/(2\pi)$')
if addLegend:
plt.legend(loc=legendloc, fancybox=True, prop={'size':'smaller'})
if title!=None:
plt.title(title)
# Return figure number (so we can reuse/add onto/save this figure if desired).
return fig.number
def __init__(self,threshold,sig,freq,nside,LMAX,CMB_file_name='/home/amber/data/cmb/COM_CMB_IQU-commander-field-Int_2048_R2.01_full.fits'):
self.threshold=threshold
self.sig=sig
self.freq=freq
#check that nside value is valid
if not np.all(hp.isnsideok(nside)):
raise ValueError("%s is not a valid nside parameter (must be a power of 2, less than 2**30)"%str(nside))
self.nside=nside
self.LMAX=LMAX
self.CMB_file_name=CMB_file_name
#can read and downgrade cmb in same step
#use nside instead of 512. change both cmb and gsm to nside
self.CMB_map=hp.ud_grade(hp.read_map(CMB_file_name),nside)
#power spectrum of downgraded CMB map
self.CMB_PS=hp.anafast(self.CMB_map,lmax=LMAX)
self.ell=np.arange(len(self.CMB_PS))
self.signal=np.sqrt((np.sum((self.ell*(self.ell+1)*self.CMB_PS)**2))/(len(self.CMB_PS)))
self.GSM_map=hp.ud_grade(gsm.generate(self.freq),nside)
self.GSM_PS=hp.anafast(self.GSM_map,lmax=LMAX)
def make_cl_planck_data():
savename = datadir+'cl_planck_217.pkl'
# get mask
mask = load_planck_mask()
mask_factor = np.mean(mask**2.)
# get planck data
planck = load_planck_data()
cl_planck = hp.anafast(mask*planck, lmax=4000)/mask_factor
l_planck = np.arange(len(cl_planck))
pickle.dump((l_planck, cl_planck), open(savename,'w'))
def w_function(
lmax, nside, n, nu_cent, idet, theta_max, syb_f=None, NPOINTS=1,
pol=False):
cls = np.zeros((len(n), len(NPOINTS), lmax))
ell = np.arange(lmax)
for i, enn in enumerate(n):
for j, N in enumerate(NPOINTS):
beam = read_(
nside, enn, nu_cent, idet, theta_max, syb_f=syb_f, NPOINTS=N)
cls[i, j] = hp.anafast(beam, lmax=lmax-1, pol=pol) * (2*ell+1)
return cls, cls.max()
def __call__(self, metricValue, slicer, userPlotDict, fignum=None):
"""
Generate and plot the power spectrum of metricValue (calculated on a healpix grid).
"""
if 'Healpix' not in slicer.slicerName:
raise ValueError('HealpixPowerSpectrum for use with healpix metricBundles.')
plotDict = {}
plotDict.update(self.defaultPlotDict)
plotDict.update(userPlotDict)
fig = plt.figure(fignum, figsize=plotDict['figsize'])
ax = fig.add_subplot(plotDict['subplot'])
# If the mask is True everywhere (no data), just plot zeros
if False not in metricValue.mask:
return None
if plotDict['removeDipole']:
cl = hp.anafast(hp.remove_dipole(metricValue.filled(slicer.badval)), lmax=plotDict['maxl'])
else:
cl = hp.anafast(metricValue.filled(slicer.badval), lmax=plotDict['maxl'])
ell = np.arange(np.size(cl))
if plotDict['removeDipole']:
condition = (ell > 1)
else:
condition = (ell > 0)
ell = ell[condition]
cl = cl[condition]
# Plot the results.
plt.plot(ell, (cl * ell * (ell + 1)) / 2.0 / np.pi,
color=plotDict['color'], linestyle=plotDict['linestyle'], label=plotDict['label'])
if cl.max() > 0 and plotDict['logScale']:
plt.yscale('log')
plt.xlabel(r'$l$', fontsize=plotDict['fontsize'])
plt.ylabel(r'$l(l+1)C_l/(2\pi)$', fontsize=plotDict['fontsize'])
if plotDict['labelsize'] is not None:
plt.tick_params(axis='x', labelsize=plotDict['labelsize'])
plt.tick_params(axis='y', labelsize=plotDict['labelsize'])
if plotDict['title'] is not None:
plt.title(plotDict['title'])
# Return figure number (so we can reuse/add onto/save this figure if desired).
return fig.number
def make_multiband_map(quick=True):
savename = datadir+'map_mb.pkl'
if quick: return pickle.load(open(savename,'r'))
mask = load_planck_mask()
mask_factor = np.mean(mask**2.)
bands=[100, 143, 217]
# first calculate the band-dependent, ell-dependent weights
weight = {}
cl_theory = {}
total_weight = 0.
for band in bands:
# load biased, beam-convolved spectrum
l_theory, this_cl_theory = get_cl_theory(band)
# debias (correct for beam)
bl, l_bl = load_planck_bl(band)
this_cl_theory /= (bl**2.)
cl_theory[band] = this_cl_theory
weight[band] = 1./this_cl_theory
total_weight += 1./this_cl_theory
for k in weight.keys(): weight[k]/=total_weight
alm_multiband = 0.
for band in bands:
# load map
this_map = load_planck_data(band)
# get alm's
this_cl, this_alm = hp.anafast(this_map*mask, lmax=lmax, alm=True)
# you might be tempted to correct alm by sqrt(mask_factor), but we're going to
# transfer back to map space anyway. we'll account for the mask later.
# debias these alm's (i.e. correct for one power of beam)
bl, l_bl = load_planck_bl(band)
assert len(bl)==(lmax+1)
this_alm = hp.sphtfunc.almxfl(this_alm, 1./bl, inplace=False)
# multiply by ell-dependent weights
this_alm = hp.sphtfunc.almxfl(this_alm, weight[band], inplace=False)
alm_multiband += this_alm
# apply a nominal multi-band beam, which we'll take to be a FWHM=5' gaussian
bl_mb, l_bl = load_planck_bl('mb')
alm_multiband = hp.sphtfunc.almxfl(alm_multiband, bl_mb, inplace=False)
# transfer back to real space
map_mb = hp.sphtfunc.alm2map(alm_multiband, nside, lmax=lmax)
# save
pickle.dump(map_mb, open(savename,'w'))
return map_mb
def correlated_cl(par,mp) :
nb=len(par.bins)-1
lmax=3*par.nside-1
pixsize=4*np.pi/hp.nside2npix(par.nside)
cb=np.zeros(nb)
larr=np.arange(lmax+1)
z=invert_covar(par,mp)
clz_scaled=(2*larr+1.)*hp.anafast(z)
for ib in np.arange(nb) :
cb[ib]=np.sum(clz_scaled[par.bins[ib]:par.bins[ib+1]])/pixsize**2
return cb
def Beam_G_plus_C_cross_C_four(freq,scale,itheta,iphi,nside,sig=np.pi/10):
"""plot of fourier for
GSM(scale)+CMB crossed with CMB
beam applied then accounted for
all compared to CMB alone
User defines frequency of GSM and scale to decrease it
User selects point of interest: theta and phi
User may designate sigma for beam
User selects nside
Default sigma is pi/10
"""
gsm=GlobalSkyModel()
#generates GSM model
gsmdata=gsm.generate(freq)
#rather than upgrading GSM, downgrade the CMB to 512
cmbdata=hp.read_map('/home/amber/data/cmb/COM_CMB_IQU-commander-field-Int_2048_R2.01_full.fits')
cmbmap=hp.ud_grade(cmbdata,512)
LMAX=1024
both=(gsmdata*scale)+cmbmap
#run the beam function with the given input
showbeam=beam(itheta,iphi,nside,sig)
npix=12*nside**2
#correct the data to account for the beam
correction=npix/(np.sum(showbeam))
both=both*showbeam*correction
#cross GSM+CMB with CMB and transform
c_plus_g_cross_c=hp.sphtfunc.anafast(both,map2=cmbmap,lmax=LMAX)
ell=np.arange(len(c_plus_g_cross_c))
plt.figure()
#convert variables into text for labels
freq_str=str(freq)
scale_str=str(scale)
plt.plot(ell,ell*(ell+1)*c_plus_g_cross_c,color='b',label='GSM('+scale_str+') at '+freq_str+' MHZ+CMB cross CMB')
#transform CMB
cmbmaponly=hp.anafast(cmbmap,lmax=LMAX)
ell=np.arange(len(cmbmaponly))
plt.plot(ell,ell*(ell+1)*cmbmaponly,color='r',label='CMB')
#delete this line?
#hp.write_cmbmaponly('cmbmaponly.fits',cmbmaponly)
plt.xlabel('ell');plt.ylabel('ell(ell+1)');plt.grid()
#delete this line?
#hp.write_c_plus_g_cross_c('c_plus_g_cross_c.fits',c_plus_g_cross_c)
#convert variable into text for labels
theta_str=str(itheta)
phi_str=str(iphi)
plt.legend(loc='upper right')
plt.title('Point of Interest:('+theta_str+','+phi_str+')')
#set the limits for the y axis
plt.ylim(-0.00000001,0.00000006)
plt.show()
)
noise = sim.other_components["noise"]
hitmaps, _ = noise.get_hitmaps(tube)
for i, ch in enumerate(mapsims.parse_channels("tube:" + tube)[0]):
filename = glob(folder + f"*{ch.tag}*1_of_1*")[0]
print("reading " + filename)
m[i] = hp.read_map(filename, (0, 1, 2))
npix = len(m[i][0])
nside = hp.npix2nside(npix)
sky_fraction = hp.mask_good(m[i][0]).sum() / npix
print("sky_fraction", sky_fraction)
m[i] *= hitmaps[i]
cl.append(
np.array(
hp.anafast(m[i], lmax=3*nside-1, use_pixel_weights=True)
)
/ np.mean(hitmaps[i] ** 2)
/ sky_fraction
)
cl.append(
np.array(
hp.anafast(m[0], m[1], lmax=3*nside-1, use_pixel_weights=True)
)
/ np.mean(hitmaps[i] ** 2)
/ sky_fraction
)
cl = np.array(cl)
with open(f"output/noise/N_ell_tube_{tube}.pkl", "wb") as f:
pickle.dump(cl, f, protocol=-1)
# y = data[0]
# popt, pcov = optimize.curve_fit(f1, x, y)
# a, b, c = popt
# y_fit = f(x_fit, *popt)
# lable = r'$a \, \exp{(- \frac{|x - \mu|} {c})}$' + '\n\n' + r'$a = %f$' % a + '\n' + r'$\mu = %f$' % b + '\n' + r'$\sigma = %f$' % np.abs(c)
# plt.plot(x_fit, y_fit, lw=2, color="g", label=lable)
# plt.legend()
plt.savefig(out_dir + 'hist_%d.png' % i)
plt.close()
# compute cl
cl_sim = healpy.anafast(cm_map[cind])
cl_est = healpy.anafast(rec_cm[cind])
cl_simxest = healpy.anafast(cm_map[cind], rec_cm[cind])
# plot cl
plt.figure()
plt.plot(cl_sim, label='Input HI')
plt.plot(cl_est, label='Recovered HI')
plt.plot(cl_simxest, label='cross', color='magenta')
plt.legend(loc='best')
plt.xlabel(r'$l$')
plt.ylabel(r'$C_l^{TT}$')
plt.savefig(out_dir + 'cl_%d.png' % i)
plt.close()
# plot transfer function cl_out / cl_in
#print(lines[i+1].split()[0])
thetas.append(float(lines[i].split()[1]))
phis.append(float(lines[i].split()[2]))
i += 1
indices = hp.ang2pix(nside, thetas, phis)
hpxmap = np.zeros(npix, dtype=np.float)
for k in range(len(thetas)):
hpxmap[indices[k]] += 1.0
#hpxmap[indices[k]] += npix*(1.0/len(thetas))
hpxmapFinal = np.zeros(npix, dtype=np.float)
for v in range(len(thetas2)):
hpxmapFinal[indices2[v]] += (
hpxmap[indices2[v]] / hpxmap2[indices2[v]]) * (npix *
(1.0 / len(thetas)))
c = hp.anafast(hpxmapFinal)
print(c)
print("")
print("")
DPI = 100
SIZE = 400
hp_smoothed = hp.sphtfunc.smoothing(hpxmap, fwhm=np.radians(3.), iter=1)
hp.mollview(hp_smoothed,
cmap=cm.jet,
norm="hist",
xsize=SIZE,
title='Real Data smoothed')
plt.savefig("AMPT_smoothed1.png", dpi=DPI)
#copy input data as we need it later
outputMap = copy.copy(inputMap)
#transform map
lens.transformModelVector(outputMap, 5 * crpropa.EeV)
#plot transformed map
healpy.mollview(map=outputMap, title='Lensed')
# In[17]:
#Calculate power spectra
lmax = 20
mmUnlensed = healpy.anafast(inputMap, lmax=lmax)
mmLensed = healpy.anafast(outputMap, lmax=lmax)
#plot power spectra
figure()
l = arange(0, 20.01)
step(l, mmUnlensed / mmUnlensed[0], c='k', label='Unlensed', where='mid')
step(l, mmLensed / mmLensed[0], c='r', label='Lensed', where='mid')
semilogy()
ylim(1E-7,1)
xlabel('$l$')
ylabel(' $C_l / C_0$')
legend()
# In[17]:
def clerr_jack(delta, mask, weight, njack=20, lmax=512):
'''
'''
npix = delta.size
hpix = np.argwhere(mask).flatten()
dummy = np.ones(mask.sum())
hpixl, wl, deltal,_ = split_jackknife(hpix, weight[mask],
delta[mask], dummy, njack=njack)
print('# of jackknifes %d, input : %d'%(len(hpixl), njack))
cljks = {}
# get the cl of the jackknife mask
wlt = wl.copy()
hpixt = hpixl.copy()
wlt.pop(0)
hpixt.pop(0)
wlc = np.concatenate(wlt)
hpixc = np.concatenate(hpixt)
maski = np.zeros(npix, '?')
maski[hpixc] = True
map_i = hp.ma(maski.astype('f8'))
map_i.mask = np.logical_not(maski)
clmaskj = hp.anafast(map_i.filled(), lmax=lmax)
sfj = ((2*np.arange(clmaskj.size)+1)*clmaskj).sum()/(4.*np.pi)
for i in range(njack):
hpixt = hpixl.copy()
wlt = wl.copy()
deltalt = deltal.copy()
#
hpixt.pop(i)
wlt.pop(i)
deltalt.pop(i)
#
hpixc = np.concatenate(hpixt)
wlc = np.concatenate(wlt)
deltac = np.concatenate(deltalt)
#
maski = np.zeros(npix, '?')
deltai = np.zeros(npix)
wlci = np.zeros(npix)
#
maski[hpixc] = True
deltai[hpixc] = deltac
wlci[hpixc] = wlc
#
map_i = hp.ma(deltai * wlci)
map_i.mask = np.logical_not(maski)
cljks[i] = hp.anafast(map_i.filled(), lmax=lmax)/sfj
#
hpixt = hpixl.copy()
wlt = wl.copy()
deltalt = deltal.copy()
#
hpixc = np.concatenate(hpixt)
wlc = np.concatenate(wlt)
deltac = np.concatenate(deltalt)
#
maski = np.zeros(npix, '?')
deltai = np.zeros(npix)
wlci = np.zeros(npix)
#
maski[hpixc] = True
deltai[hpixc] = deltac
wlci[hpixc] = wlc
#
map_i = hp.ma(maski.astype('f8'))
map_i.mask = np.logical_not(maski)
clmask = hp.anafast(map_i.filled(), lmax=lmax)
sf = ((2*np.arange(clmask.size)+1)*clmask).sum()/(4.*np.pi)
map_i = hp.ma(deltai * wlci)
map_i.mask = np.logical_not(maski)
cljks[-1] = hp.anafast(map_i.filled(), lmax=lmax)/sf # entire footprint
#
clvar = np.zeros(len(cljks[-1]))
for i in range(njack):
clvar += (cljks[-1] - cljks[i])*(cljks[-1] - cljks[i])
clvar *= (njack-1)/njack
return dict(clerr=np.sqrt(clvar), cljks=cljks, clmaskj=clmaskj, clmask=clmask, sf=sf, sfj=sfj)
deltai[~mask] = hp.UNSEEN
deltai[mask] = deltai[mask] / deltai[mask].mean() - 1.0
hp.fitsfunc.write_map(
'Maps/delta_' + params.runflag +
'_matter_fullsky_{}.fits'.format(str(round(zl, 4))),
deltai,
overwrite=True,
dtype=np.float32)
kappai = (1.0+zl) * ( ( 1.0 - cosmology.lcdm.comoving_distance(zl)/cosmology.lcdm.comoving_distance(params.zsource) ) *\
cosmology.lcdm.comoving_distance(zl) *\
( cosmology.lcdm.comoving_distance(z2) - cosmology.lcdm.comoving_distance(z1) ) ).to_value() * deltai
kappai *= (3.0 * cosmology.lcdm.Om0 * cosmology.lcdm.H0**2 / 2.0 /
cosmology.cspeed**2).to_value()
kappa[mask] += kappai[mask]
kappai[~mask] = hp.UNSEEN
hp.fitsfunc.write_map(
'Maps/kappa_' + params.runflag +
'_field_fullsky_{}.fits'.format(str(round(zl, 4))),
kappai,
overwrite=True,
dtype=np.float32)
print("++++++++++++++++++++++")
print("Computing convergence Cl")
cl = hp.anafast(kappa, lmax=512)
ell = np.arange(len(cl))
np.savetxt("Maps/Cls_kappa_z{}.txt".format(params.zsource),
np.transpose([ell, cl, ell * (ell + 1) * cl]))
print("All done!")
def proj_all(dens_type=0,
ngrid=512,
nside=256,
lmax=750,
rsd=True,
write=True):
npix = hp.nside2npix(nside)
dirin = os.path.join("batch", "ngrid{}".format(ngrid),
"dens_type{}".format(dens_type))
files = glob.glob(os.path.join(dirin, "cat*.fits"))
ncat = len(files)
print("{} files in {}".format(len(files), dirin))
zval = (0, 0.1, 0.2, 0.3, 0.4, 0.5)
cpt = 1
cls = zeros((4, lmax + 1))
for file in files:
cat = Catalog(file, rsd)
print("OK {}".format(cpt))
for i in range(0, 4):
ishell = i + 2
zmax = zval[ishell]
zmin = zval[ishell - 1]
zrec, ra, dec = cat.get([zmin, zmax])
Nsamp = len(ra)
nbar = Nsamp / (4 * np.pi)
mp = np.bincount(hp.ang2pix(nside, np.radians(90 - dec),
np.radians(ra)),
minlength=npix)
Nmean = mp.mean()
map = mp.astype(float) / Nmean - 1.
#anafast
cl = hp.anafast(map,
lmax=lmax,
iter=0,
pol=False,
use_weights=True,
datapath=os.environ['HEALPIXDATA'])
l = arange(len(cl))
s2 = sum((2 * l + 1) * cl) / (4 * pi)
print(
"\t -> shell={}: z in [{},{}] Nsamp={:5.2}M sigma={:5.2} shot noise={:5.2}"
.format(ishell, zmin, zmax, Nsamp / 1e6, sqrt(s2), 1 / nbar))
#remove SN
cl -= 1. / nbar
cls[i, :] += cl
cpt += 1
#normalize
for i in range(0, 4):
cls[i, :] /= ncat
if write:
for i in range(0, 4):
ishell = i + 2
dirout = os.path.join(dirin, "shell{:d}".format(ishell))
os.makedirs(dirout, exist_ok=True)
clname = "clmean.fits"
if not rsd:
clname = "clmean_norsd.fits"
f1 = os.path.join(dirout, clname)
print("writing {}".format(f1))
hp.write_cl(f1, cls[i], overwrite=True)
return cls
def proj(dens_type=0,
ishell=5,
ngrid=512,
nside=256,
lmax=750,
rsd=True,
write=True):
dirin = get_path(dens_type, ishell, ngrid)
os.makedirs(dirin, exist_ok=True)
zval = (0, 0.1, 0.2, 0.3, 0.4, 0.5)
zmax = zval[ishell]
zmin = zval[ishell - 1]
files = glob.glob(os.path.join(dirin, "..", "cat*.fits"))
print("Analyzing: {}".format(dirin))
print("shell #{}: z=[{},{}] ".format(ishell, zmin, zmax))
print("there are {} files".format(len(files)))
zcut = [zmin, zmax]
print(" --> slice z=[{},{}] onto nside={} map".format(
zcut[0], zcut[1], nside))
npix = hp.nside2npix(nside)
cls = []
cpt = 1
for file in files:
#zrec,ra,dec=read_catalog(file,zcut,rsd)
catalog = Catalog(file, rsd)
zrec, ra, dec = catalog.get(zcut)
Nsamp = len(ra)
nbar = Nsamp / (4 * np.pi)
print(" {} -> Nsamp={}, SN={}".format(cpt, Nsamp, 1 / nbar))
mp = np.bincount(hp.ang2pix(nside, np.radians(90 - dec),
np.radians(ra)),
minlength=npix)
Nmean = mp.mean()
map = mp.astype(float) / Nmean - 1.
#anafast
cl = hp.anafast(map,
lmax=lmax,
iter=0,
pol=False,
use_weights=True,
datapath=os.environ['HEALPIXDATA'])
#remove SN
cl -= 1. / nbar
cls.append(cl)
cpt += 1
clm = np.mean(cls, 0)
covmat = np.cov(np.transpose(cls))
if write:
dirout = os.path.join(dirin, "..", "shell{:d}".format(ishell))
os.makedirs(dirout, exist_ok=True)
clname = "clmean.fits"
if not rsd:
clname = "clmean_norsd.fits"
f1 = os.path.join(dirout, clname)
hp.write_cl(f1, clm, overwrite=True)
covname = "covmat.fits"
if not rsd:
clname = "covmat_norsd.fits"
f2 = os.path.join(dirout, "covmat.fits")
hdu = fits.ImageHDU(covmat)
hdu.writeto(f2, overwrite=True)
print("writing {} and {}".format(f1, f2))
return clm, covmat
tickfs(ax)
plt.loglog()
plt.savefig("../plots_paper/cls_lss.pdf",bbox_inches='tight')
plt.show()
if (o.whichfig==4) or (o.whichfig==-1) :
#Plotting LSS contaminant power spectra
l,cltt,clee,clbb,clte,nltt,nlee,nlbb,nlte=np.loadtxt("cls_lss.txt",unpack=True)
ll=l[:3*nside_lss]; cl_x=cltt[:3*nside_lss]; cw_x=clee[:3*nside_lss]
msk_s=hp.read_map("mask_lss_ns%d.fits"%nside_lss,verbose=False); fsky=np.mean(msk_s)
l_s=hp.read_map("cont_lss_star_ns%d.fits"%nside_lss,verbose=False)
l_d=hp.read_map("cont_lss_dust_ns%d.fits"%nside_lss,verbose=False)
w_pq,w_pu=hp.read_map("cont_wl_psf_ns%d.fits"%nside_lss,verbose=False,field=[0,1])
w_sq,w_su=hp.read_map("cont_wl_ss_ns%d.fits"%nside_lss ,verbose=False,field=[0,1])
cl_d,cw_pe,cw_pb,_,_,_=hp.anafast([l_d*msk_s,w_pq*msk_s,w_pu*msk_s],pol=True,iter=0)/fsky
cl_s,cw_se,cw_sb,_,_,_=hp.anafast([l_s*msk_s,w_sq*msk_s,w_su*msk_s],pol=True,iter=0)/fsky
def rebin_and_plot(x,y,b,lt,c,ax,lb=None) :
xp=np.mean(x.reshape([len(x)//b,b]),axis=1)
yp=np.mean(y.reshape([len(x)//b,b]),axis=1)
if lb is None :
ax.plot(xp,yp,lt,c=c)
else :
ax.plot(xp,yp,lt,c=c,label=lb)
plt.figure(figsize=(7,8))
plt.subplots_adjust(hspace=0)
ax1=plt.subplot(311)
c_x='#000099'; c_d='#00CCCC'; c_s='#CC0000'; c_t='#FF9933'; rb=8
rebin_and_plot(ll,cl_x,rb,'-',c_x,ax1,lb='Signal')
rebin_and_plot(ll,cl_d,rb,'--',c_d,ax1,lb='Dust')
def CreateAnafastPartialSky(cl,
nside,
lmin,
lmax,
delta_ell,
f_sky=1.,
plot_results=False,
noise_rms=0):
import qubic.NamasterLib as nam
npix = 12 * nside**2
# Determine SEEN pixels from f_sky using query_disc
vec = hp.pixelfunc.ang2vec(np.pi / 2, np.pi * 3 / 4)
radius = f_sky * np.pi
ipix_disc = hp.query_disc(nside=nside, vec=vec, radius=radius, nest=False)
while len(ipix_disc) < f_sky * npix:
radius += 0.01 * np.pi
ipix_disc = hp.query_disc(nside=nside,
vec=vec,
radius=radius,
nest=False)
#print("npix_partial_sky: ", len(ipix_disc))
m = np.arange(npix)
m = np.delete(m, ipix_disc, axis=None)
# Define the seen pixels
seenpix = ipix_disc
### Making mask - it will be automaticall apodized when instanciating the object with default (tunable) parameters
mask = np.zeros(npix)
mask[seenpix] = 1
Namaster = nam.Namaster(mask, lmin=lmin, lmax=lmax, delta_ell=delta_ell)
if plot_results:
if not os.path.isdir(path_):
os.mkdir(path_)
plt.figure()
hp.mollview(mask)
figname = 'fig_map_partial_mask.png'
dest = os.path.join(path_, figname)
plt.savefig(dest) # write image to file
plt.clf()
plt.figure()
mask_apo = Namaster.mask_apo
hp.mollview(mask_apo)
figname = 'fig_map_partial_mask_apo.png'
dest = os.path.join(path_, figname)
plt.savefig(dest) # write image to file
plt.clf()
ell_binned, b = Namaster.get_binning(nside)
# Get binned input spectra
cl_theo_binned = np.zeros(shape=(4, ell_binned.shape[0]))
for i in range(4):
cl_theo_binned[i, :] = Namaster.bin_spectra(cl.T[i, :], nside)
print(cl_theo_binned.shape)
# Create map
map_ = hp.synfast(cl.T, nside, pixwin=False, verbose=False, new=True)
noise = np.random.randn(npix) * noise_rms
map_ = map_ + noise
# constructing partial map
map_partial = map_.copy()
# Set UNSEEN pixels to zero for NaMaster
map_partial[:, m] = 0
# Get spectra
leff, dells, w = Namaster.get_spectra(map_partial,
purify_e=True,
purify_b=False,
beam_correction=None,
pixwin_correction=None,
verbose=False)
dells = dells.T
if plot_results:
clnames = ['TT', 'EE', 'BB', 'TE']
ll = np.arange(cl.shape[0])
#rc('figure', figsize=(12, 8))
plt.figure(figsize=(12, 8))
for i in range(dells.shape[0]):
plt.subplot(2, 2, i + 1)
plt.plot(ll,
ll * (ll + 1) * cl[:, i] / (2 * np.pi),
label="Input spectra")
plt.plot(leff,
leff * (leff + 1) * cl_theo_binned[i, :] / (2 * np.pi),
"o",
label="Binned input spectra")
plt.plot(leff, dells[i], label="NaMaster")
plt.xlabel('$\\ell$')
plt.ylabel('$D_\\ell$')
plt.title(clnames[i])
plt.legend()
plt.tight_layout()
figname = 'fig_theor_namaster_all_Dl.png'
dest = os.path.join(path_, figname)
plt.savefig(dest) # write image to file
plt.clf()
# Anafast spectrum of this map
# Set UNSEEN pixels to hp.UNSEEN for Anafast
map_partial[:, m] = hp.UNSEEN
cl_ana, alm_ana = hp.anafast(map_partial, alm=True, lmax=lmax)
# Get binned anafast spectra
cl_ana_binned = np.zeros(shape=(4, ell_binned.shape[0]))
for i in range(4):
cl_ana_binned[i, :] = Namaster.bin_spectra(cl_ana[i, :], nside)
if plot_results:
mapnames = ['T', 'Q', 'U']
for i in range(3):
plt.figure()
hp.mollview(map_partial[i])
figname = 'fig_map_partial_{}.png'.format(mapnames[i])
dest = os.path.join(path_, figname)
plt.savefig(dest) # write image to file
plt.clf()
for i in range(4):
# Plot theoretical and anafast spectra
plt.figure()
plt.plot(ll, ll * (ll + 1) * cl_ana[i][:lmax + 1], label="Anafast")
plt.plot(leff,
leff * (leff + 1) * cl_ana_binned[i],
"o",
label="Anafast binned")
plt.plot(ll, ll * (ll + 1) * cl.T[i], 'r', label="Input spectra")
plt.xlim(0, max(ll))
plt.title(clnames[i])
plt.ylabel(r"$\ell (\ell + 1) C_{\ell}^{" + clnames[i] + r"}$")
plt.legend()
figname = 'fig_theor_anaf_{}.png'.format(clnames[i])
dest = os.path.join(path_, figname)
plt.savefig(dest) # write image to file
plt.clf()
return dells, alm_ana, cl_theo_binned, cl_ana_binned
#np.save('cl_PySM_dust_TQU_ns4096.fits', cl_dust)
#SYNCH
synchT = hp.read_map(
'/global/homes/k/krach/usr/python/PySM_public/pysm/SO_template/synch_T.fits'
)
synchQ = hp.read_map(
'/global/homes/k/krach/usr/python/PySM_public/pysm/SO_template/synch_Q.fits'
)
synchU = hp.read_map(
'/global/homes/k/krach/usr/python/PySM_public/pysm/SO_template/synch_U.fits'
)
synch_pysm = np.array([synchT, synchQ, synchU])
synch4096 = add_gaussian_small_scales(synch_pysm, 4096, pol=True)
hp.write_map('maps/PySM_synch_TQU_ns4096_2.fits', synch4096, overwrite=True)
cl_synch = hp.anafast(synch4096)
np.save('cl_PySM_synch_TQU_ns4096_2.fits', cl_synch)
##FREE-FREE
#ffT = hp.read_map('/global/homes/k/krach/usr/python/PySM_public/pysm/SO_template/freefree_T.fits')
#ff4096 = add_gaussian_small_scales(ffT, 4096, pol=False)
#hp.write_map('maps/PySM_freefree_T_ns4096.fits', ff4096, overwrite=True)
#cl_ff = hp.anafast(ff4096)
#np.save('cl_PySM_freefree_T_ns4096.fits', cl_ff)
##AME 1
#ame1 = hp.read_map('/global/homes/k/krach/usr/python/PySM_public/pysm/SO_template/ame_T.fits')
#ame1_4096 = add_gaussian_small_scales(ame1, 4096, pol=False)
#hp.write_map('maps/PySM_ame1_T_ns4096.fits', ame1_4096, overwrite=True)
#cl_ame1 = hp.anafast(ame1_4096)
#np.save('cl_PySM_ame1_T_ns4096.fits', cl_ame1)
def polsample_test(niter=1000):
import camb
from camb import model, initialpower
pars = camb.CAMBparams()
#This function sets up CosmoMC-like settings, with one massive neutrino and helium set using BBN consistency
pars.set_cosmology(H0=67.5,
ombh2=0.022,
omch2=0.122,
mnu=0.06,
omk=0,
tau=0.06)
pars.WantTensors = True
pars.InitPower.set_params(ns=0.965, r=0.05)
pars.set_for_lmax(2500, lens_potential_accuracy=0)
results = camb.get_results(pars)
powers = results.get_cmb_power_spectra(pars, CMB_unit='muK')
var = 1e-4
nside = 8
lmax = 3 * nside - 1
ell = np.arange(lmax + 1.)
Cl_arr = powers['total'][:lmax + 1].T
Z = ell * (ell + 1) / (2 * np.pi)
Z[:2] = 1.
Cls = np.array([Cl_arr[0]/Z, Cl_arr[1]/Z, Cl_arr[2]/Z,\
Cl_arr[3]/Z, 0*Cl_arr[0], 0*Cl_arr[0]])
plt.figure()
for i in range(4):
plt.subplot(2, 2, i + 1)
plt.loglog(ell[2:], Cls[i][2:], color='C{0}'.format(i))
s_true = hp.synfast(Cls, nside, new=True)
m = np.copy(s_true)
inds = (mask == 0)
for i in range(3):
m[i] = s_true[i] + np.random.randn(npix) * var**0.5
m[i][inds] = 0
Clhat_true = hp.anafast(s_true)
Clhat_noise = hp.anafast(m)
for i in range(4):
plt.subplot(2, 2, i + 1)
plt.loglog(ell[2:], Clhat_true[i][2:], '.', color='C{0}'.format(i))
for i in range(4):
plt.subplot(2, 2, i + 1)
plt.loglog(ell[2:], Clhat_noise[i][2:], 'o', color='C{0}'.format(i))
plt.savefig('1.png')
#plt.show()
plt.figure()
plt.suptitle('Input')
hp.mollview(s_true[0], min=-200, max=200, sub=131, title='')
hp.mollview(s_true[1], min=-0.5, max=0.5, sub=132, title='')
hp.mollview(s_true[2], min=-0.5, max=0.5, sub=133, title='')
plt.savefig('2.png')
#plt.show()
plt.figure()
Clhat = hp.anafast(m)
s = np.copy(m)
Cli = np.copy(Cls)
for n in range(niter):
print(n, niter)
si = maparr_sample(Cli, m, var=var)
Cli = clarr_sample(si)
s = np.vstack((s, si))
Clhat = np.vstack((Clhat, Cli))
for j in range(4):
plt.subplot(2, 2, j + 1)
plt.plot(ell[2:], Cli[j][2:], '.', alpha=0.05,\
color='C{0}'.format(j))
plt.figure()
hp.mollview(si[0],
min=-200,
max=200,
sub=131,
title='',
cbar=False,
cmap=cmap)
hp.mollview(si[1],
min=-.5,
max=.5,
sub=132,
title='',
cbar=False,
cmap=cmap)
hp.mollview(si[2],
min=-.5,
max=.5,
sub=133,
title='',
cbar=False,
cmap=cmap)
fnames = glob('realization_*')
plt.savefig('realization_{0}.png'.format(str(len(fnames)).zfill(4)))
plt.close()
for i in range(4):
plt.subplot(2, 2, i + 1)
plt.plot(ell[2:], Cls[i][2:], 'k')
for i in range(4):
plt.subplot(2, 2, i + 1)
plt.plot(ell[2:], Clhat_true[i][2:], 'k.')
plt.yscale('log')
plt.xscale('log')
plt.savefig('3.png')
#plt.show()
np.save('map_samples.npy', s)
np.save('cl_samples.npy', clhat)
plt.figure()
plt.suptitle('Output')
hp.mollview(si[0], min=-200, max=200, sub=131, title='')
hp.mollview(si[1], min=-0.5, max=0.5, sub=132, title='')
hp.mollview(si[2], min=-0.5, max=0.5, sub=133, title='')
plt.savefig('4.png')
#plt.show()
return
cldict_normal_test = calculations.ilc_foreground_cleaning.polarized_ilc_reconstruction(
frequencies=np.array([200., 270., 350., 600.]) * 1.e9,
fname=fname,
regnoise=regnoise,
lensed=lensed,
modcov=modcov,
verbose=True)
save_dict_to_hd5(cntpath, cldict_normal_test)
clnpath = os.path.abspath(datapath + 'cldict_noise.hd5')
if os.path.exists(clnpath):
print("cldict_noise already exists at {0}. Loading it.".format(clnpath))
cldict_noise = read_dict_from_hd5(clnpath)
else:
print("Computing noise C_l's.")
noisemap = cldict_normal_test['noisemaps'][0][0]
cls_noise = hp.anafast([noisemap, noisemap, noisemap])
labels = ['TT', 'EE', 'BB', 'TE', 'EB', 'TB']
cldict_noise = {labels[i]: cls_noise[i] for i in range(len(labels))}
save_dict_to_hd5(clnpath, cldict_noise)
cldict_noise['ell'] = np.arange(len(cldict_noise['TT']), dtype='float')
prefix_noise = cldict_noise['ell'] * (cldict_noise['ell'] + 1) / (2 * np.pi)
print("Finished importing calibration_gain.")
print("=" * 80)
def myplot(cld,
name,
label,
nf=1.,
bbmax=0.1,
