def test_pixwin_base(self):
# Base case
nsides = [2**p for p in np.arange(1, 14)]
[hp.pixwin(nside) for nside in nsides]
# Test invalid nside
with self.assertRaises(ValueError):
hp.pixwin(15)
def test_pixwin_lmax(self):
nside = 128
pixwin = hp.pixwin(nside, lmax=None)
self.assertEqual(len(pixwin), 3 * nside)
lmax = 200
pixwin = hp.pixwin(nside, lmax=lmax)
self.assertEqual(len(pixwin)-1, lmax)
def test_pixwin_lmax(self):
nside = 128
pixwin = hp.pixwin(nside, lmax=None)
self.assertEqual(len(pixwin), 3 * nside)
lmax = 200
pixwin = hp.pixwin(nside, lmax=lmax)
self.assertEqual(len(pixwin) - 1, lmax)
def test_pixwin_base(self):
# Base case
nsides = [2 ** p for p in np.arange(1, 14)]
[hp.pixwin(nside) for nside in nsides]
# Test invalid nside
with self.assertRaises(ValueError):
hp.pixwin(15)
def run_propag():
"""
Follows the SMICA propagation code to combine maps with set of weights.
Only runs for the chosen bins and up to the max value of bins
"""
CMB = dict()
CMB["TQU"] = dict()
almT, almE, almB = dict(), dict(), dict()
lmaxbin = int(bins[-1][1]+1)
W = io.load_data(io.fh.weight_smica_path_name)
# full maps
maps = io.load_plamap(csu.cf, field=(0,1,2), nside_out=csu.nside_out)
maps = trsf_m.process_all(maps)
for freq in csu.PLANCKMAPFREQ_f:
ns = csu.nside_out[0] if int(freq) < 100 else csu.nside_out[1]
alms = pw.map2alm_spin(maps[freq], hp.ud_grade(pmask[freq], nside_out=ns), 2, lmaxbin-1) # full sky QU->EB
# almT[det] = alms[0]
almE[freq] = alms[0]
almB[freq] = alms[1]
nalm = int((lmaxbin)*(lmaxbin-1+2)/2)
# combalmT = np.zeros((nalm), dtype=np.complex128)
combalmE = np.zeros((nalm), dtype=np.complex128)
combalmB = np.zeros((nalm), dtype=np.complex128)
beamf = io.load_beamf(freqcomb=csu.freqcomb)
xnew = np.arange(0,lmaxbin,1)
for it, det in enumerate(csu.PLANCKMAPFREQ): #weights do not depend on freqfilter, but almE/B do
if det in csu.PLANCKMAPFREQ_f:
ns = csu.nside_out[0] if int(det) < 100 else csu.nside_out[1]
W_Einterp = interpolate.interp1d(np.mean(bins, axis=1), W[1,it,:], bounds_error = False, fill_value='extrapolate')
#TODO switch to W[2,:] once BB-weights are correctly calculated
W_Binterp = interpolate.interp1d(np.mean(bins, axis=1), W[1,it,:], bounds_error = False, fill_value='extrapolate')
# combalmT += hp.almxfl(almT[name], np.squeeze(W[0,m,:]))
LHFI = "LFI" if int(det)<100 else "HFI"
combalmE += hp.almxfl(hp.almxfl(almE[det],1/beamf[str(det)+'-'+str(det)][LHFI][1].data.field(1)[:lmaxbin]), np.squeeze(W_Einterp(xnew)))
combalmE = hp.almxfl(combalmE, 1/hp.pixwin(ns, pol=True)[0][:lmaxbin])
combalmB += hp.almxfl(hp.almxfl(almB[det],1/beamf[str(det)+'-'+str(det)][LHFI][1].data.field(2)[:lmaxbin]), np.squeeze(W_Binterp(xnew)))
combalmB = hp.almxfl(combalmB, 1/hp.pixwin(ns, pol=True)[1][:lmaxbin])
CMB["TQU"]['out'] = hp.alm2map([np.zeros_like(combalmE), combalmE, combalmB], csu.nside_out[1])
io.save_data(CMB["TQU"]['out'], io.fh.cmbmap_smica_path_name)
smica_C_lmin_unsc = np.array(ps.map2cl_spin(qumap=CMB["TQU"]['out'][1:3], spin=2, mask=pmask['100'], lmax=lmaxbin-1,
lmax_mask=lmaxbin*2))*1e12 #maps are different scale than processed powerspectra from this' package pipeline, thus *1e12
io.save_data(smica_C_lmin_unsc, io.fh.clmin_smica_path_name)
def apply_ivf(self, dets, tmaps):
assert(self.lmax == 1000)
n_inv_filts = []
for det in dets:
mask_t = qcinv.util.load_map(self.mask_t)
bl = dmc.get_bl(self.sim_lib.year, det)[0:self.lmax+1]
pxw = hp.pixwin( self.sim_lib.nside )[0:self.lmax+1]
# qcinv filtering for temperature
dcf = self.lib_dir + "/dense_cache_det_" + det + ".pk"
# id preconditioners lmax nside im em tr cache
chain_descr = [ [ 2, ["split(dense("+dcf+"), 64, diag_cl)"], 256, 128, 3, 0.0, qcinv.cd_solve.tr_cg, qcinv.cd_solve.cache_mem()],
[ 1, ["split(stage(2), 256, diag_cl)"], 512, 256, 3, 0.0, qcinv.cd_solve.tr_cg, qcinv.cd_solve.cache_mem()],
[ 0, ["split(stage(1), 512, diag_cl)"], 1000, 512, np.inf, 1.0e-6, qcinv.cd_solve.tr_cg, qcinv.cd_solve.cache_mem()] ]
ninv = ( hp.read_map( dmc.get_fname_imap(self.sim_lib.year, det, self.sim_lib.forered), hdu=1, field=1 ) /
dmc.sigma0[(self.sim_lib.year, self.sim_lib.forered, 'T')][det]**2 / 1e6 * mask_t )
n_inv_filt = qcinv.opfilt_tt.alm_filter_ninv( ninv, bl*pxw, marge_monopole=True, marge_dipole=True, marge_maps=[] )
n_inv_filts.append( n_inv_filt )
n_inv_filts = qcinv.opfilt_tt_multi_simple.alm_filter_ninv_filts( n_inv_filts, degrade_single=True )
chain = qcinv.multigrid.multigrid_chain( qcinv.opfilt_tt_multi_simple, chain_descr, self.cl, n_inv_filts )
tlm = np.zeros( qcinv.util_alm.lmax2nlm(self.lmax), dtype=np.complex )
chain.solve( tlm, tmaps )
return tlm
def get_maps_cl(frec, fconv=None, lmin=20, delta_ell=40, apodization_degrees=5.):
mrec, resid, seenmap = get_maps_residuals(frec, fconv=fconv)
sh = np.shape(mrec)
print(sh, np.shape(resid))
nbsub = sh[1]
ns = hp.npix2nside(sh[2])
from qubic import apodize_mask
mymask = apodize_mask(seenmap, apodization_degrees)
# Create XPol object
from qubic import Xpol
lmax = 2 * ns
xpol = Xpol(mymask, lmin, lmax, delta_ell)
ell_binned = xpol.ell_binned
nbins = len(ell_binned)
# Pixel window function
pw = hp.pixwin(ns)
pwb = xpol.bin_spectra(pw[:lmax + 1])
# Calculate all crosses and auto
m_autos = np.zeros((nbsub, 6, nbins))
s_autos = np.zeros((nbsub, 6, nbins))
m_cross = np.zeros((nbsub, 6, nbins))
s_cross = np.zeros((nbsub, 6, nbins))
fact = ell_binned * (ell_binned + 1) / 2. / np.pi
for isub in range(nbsub):
m_autos[isub, :, :], s_autos[isub, :, :], m_cross[isub, :, :], s_cross[isub, :, :] = \
allcross_par(xpol, mrec[:, isub, :, :], silent=False, verbose=0)
return mrec, resid, seenmap, ell_binned, m_autos * fact / pwb ** 2, \
s_autos * fact / pwb ** 2, m_cross * fact / pwb ** 2, s_cross * fact / pwb ** 2
def apply_ivf(self, det, tmap, pmap):
assert(self.lmax == 1000)
mask_t = qcinv.util.load_map(self.mask_t)
mask_p = qcinv.util.load_map(self.mask_p)
bl = dmc.get_bl(self.sim_lib.year, det)[0:self.lmax+1]
pxw = hp.pixwin( self.sim_lib.nside )[0:self.lmax+1]
# qcinv filtering for temperature
dcf = self.lib_dir + "/dense_cache_det_" + det + ".pk"
# id preconditioners lmax nside im em tr cache
chain_descr = [ [ 2, ["split(dense("+dcf+"), 64, diag_cl)"], 256, 128, 3, 0.0, qcinv.cd_solve.tr_cg, qcinv.cd_solve.cache_mem()],
[ 1, ["split(stage(2), 256, diag_cl)"], 512, 256, 3, 0.0, qcinv.cd_solve.tr_cg, qcinv.cd_solve.cache_mem()],
[ 0, ["split(stage(1), 512, diag_cl)"], 1000, 512, np.inf, 1.0e-6, qcinv.cd_solve.tr_cg, qcinv.cd_solve.cache_mem()] ]
ninv = ( hp.read_map( dmc.get_fname_iqumap(self.sim_lib.year, det, self.sim_lib.forered), hdu=1, field=3 ) /
dmc.sigma0[(self.sim_lib.year, self.sim_lib.forered, 'T')][det]**2 / 1e6 * mask_t )
n_inv_filt = qcinv.opfilt_tt.alm_filter_ninv( ninv, bl*pxw, marge_monopole=True, marge_dipole=True, marge_maps=[] )
chain = qcinv.multigrid.multigrid_chain( qcinv.opfilt_tt, chain_descr, self.cl, n_inv_filt )
tlm = np.zeros( qcinv.util_alm.lmax2nlm(self.lmax), dtype=np.complex )
chain.solve( tlm, tmap )
# simple filtering for polarization.
elm, blm = hp.map2alm_spin( (pmap.real * mask_p, pmap.imag * mask_p), 2, lmax=self.lmax )
ftl, fel, fbl = self.get_ftebl(det)
hp.almxfl( elm, fel / bl / pxw, inplace=True )
hp.almxfl( blm, fbl / bl / pxw, inplace=True )
return tlm, elm, blm
def masked_cl(ellmax, mask_name, mask_fn, outfits_root, final_maps):
#Masked spectra
print "Calculating masked C_l"
mask = hp.read_map(mask_fn) #0 where holes #RING ordering
f_sky = np.sum(mask) / len(mask)
print "f_sky =", f_sky
masked_maps = [final_maps[0] * mask,
final_maps[1] * mask] #(Q*mask,U*mask)
T_map = [
np.zeros_like(final_maps[0]),
]
masked_cls = hp.anafast(T_map + masked_maps,
lmax=ellmax - 1) #(TT,EE,BB,TE,EB,TB)
pixrecip = np.concatenate(
(np.ones(2),
np.reciprocal(
hp.pixwin(
hp.get_nside(masked_maps[0]),
pol=True)[1][2:ellmax]))) #P pixwin #Not defined for l < 2
clscode = ['EE', 'BB', 'EB']
clsidx = [1, 2, 4]
for i in xrange(len(clscode)):
cl_outfits = outfits_root + '_' + clscode[
i] + 'cls_' + mask_name + '.fits'
hp.write_cl(cl_outfits,
masked_cls[clsidx[i]] * pixrecip * pixrecip / f_sky)
return 0
def get_kbeam(qid,
modlmap,
sanitize=False,
version=None,
planck_pixwin=False,
**kwargs):
dmodel = sints.arrays(qid, 'data_model')
season = sints.arrays(qid, 'season')
region = sints.arrays(qid, 'region')
array = sints.arrays(qid, 'array')
freq = sints.arrays(qid, 'freq')
dm = sints.models[dmodel]()
gfreq = array + "_" + freq if not (is_planck(qid)) else freq
if planck_pixwin and (qid in [
'p01', 'p02', 'p03', 'p04', 'p05', 'p06', 'p07', 'p08'
]):
nside = get_nside(qid)
pixwin = hp.pixwin(nside=nside, pol=False)
ls = np.arange(len(pixwin))
assert pixwin.ndim == 1
assert ls.size in [6144, 3072]
pwin = maps.interp(ls, pixwin)(modlmap)
else:
pwin = 1.
return dm.get_beam(modlmap,
season=season,
patch=region,
array=gfreq,
kind='normalized',
sanitize=sanitize,
version=version,
**kwargs) * pwin
def worker_power(map, lmax):
logger = logging.getLogger(__name__)
factor = 1 - np.sum(map == hp.UNSEEN) / len(map)
logger.info("processing map ({:.0f} deg^2)".format(4*factor*np.pi*(180/np.pi)**2))
return hp.anafast(map, lmax=lmax) / hp.pixwin(hp.npix2nside(len(map)))[:lmax + 1] ** 2 / factor
def compute_ds_dcb_line_par(map,mask,ellmin,ellmax,fwhmrad,nthreads):
npix=np.size(np.where(mask))
ellbins=ellmin.size
ellval=(ellmin+ellmax)/2
print(' Calculating dS/dCb')
pixwin=hp.pixwin(hp.npix2nside(len(map)))
maxell=np.max(ellmax)
ll=np.arange(int(maxell)+1)
bl=np.exp(-0.5*ll**2*(fwhmrad/2.35)**2)
nside=np.sqrt(map.size/12)
iprings=np.arange(map.size)
vecs=hp.pix2vec(int(nside),iprings[mask])
cosangles=np.dot(np.transpose(vecs),vecs)
nshots=npix//nthreads+1
ntot=nshots*nthreads
indices=np.arange(ntot)
indices[indices>npix-1]=npix-1
lines=np.reshape(indices,(nshots,nthreads))
ds_dcb=np.zeros((ellbins,npix,npix))
for num in np.arange(nshots):
# initiate multithreading
tasks=multiprocessing.JoinableQueue()
results = multiprocessing.Queue()
# start consumers
num_consumers = nthreads
#print 'Creating %d consumers' % num_consumers
consumers = [ Consumer(tasks, results)
for i in xrange(num_consumers) ]
for w in consumers:
w.start()
# Enqueue jobs
num_jobs = nthreads
for i in np.arange(num_jobs):
#print('Calling task ',lines[num,i])
tasks.put(Task(cosangles[lines[num,i],:],lines[num,i],ll,bl,pixwin,ellmin,ellmax,ellval))
# poison them when finished
for i in np.arange(num_jobs):
#print 'Killing Task %d ' % i
tasks.put(None)
#print('****** Now ready to join tasks ******')
tasks.join()
#print('###### Tasks were joined ######')
while num_jobs:
#print('Getting result for task %d' % num_jobs)
result = results.get()
bla=result[0]
num=result[1]
ds_dcb[:,num,num:]=bla[:,num:]
ds_dcb[:,num:,num]=bla[:,num:]
num_jobs -= 1
return(ds_dcb)
def get_pixwin_correction(self, nside):
pw = hp.pixwin(nside, pol=True, lmax=self.lmax)
pw = [pw[0][:self.lmax + 1], pw[1][:self.lmax + 1]]
ell_binned, b = self.get_binning(nside)
pwb = 2 * np.pi / (ell_binned *
(ell_binned + 1)) * b.bin_cell(np.array(pw))
return pwb
def get_ftl_eff( lmax, year, nside, det, forered, cl, mask_t ):
noiseT_uK_arcmin = dmc.get_nlev_t_uK_arcmin(year, det, forered, mask_t)
bl = dmc.get_bl( year, det)[0:lmax+1]
pxw = hp.pixwin( nside )[0:lmax+1]
ftl = 1.0 / (cl.cltt[0:lmax+1] + (noiseT_uK_arcmin * np.pi/180./60.)**2 / (bl * pxw)**2); ftl[0:2] = 0.0
return ftl
def get_pixwin_correction(self, nside):
"""Return the binned pixel window function multiplied by 2pi/(l(l+1))
for temperature and polarization """
pw = list(hp.pixwin(nside, pol=True, lmax=self.lmax))
ell_binned, b = self.get_binning(nside)
pwb = 2 * np.pi / (ell_binned * (ell_binned + 1)) * b.bin_cell(np.array(pw))
return pwb
def build_cov(cl,
nside,
mask=None,
tree_depth=0,
lmax=None,
apply_pixwin=False,
ninterp=10000,
log=False,
shift=None):
tree_depth = 2**(tree_depth)
if mask is None:
mask = np.ones(hp.nside2npix(nside), dtype=bool)
if lmax is not None and lmax > len(cl) - 1:
lmax = len(cl) - 1
cl = cl[:lmax + 1]
if apply_pixwin:
pw = hp.pixwin(nside * tree_depth, lmax=lmax)
cl = cl[:len(pw)] * (pw**2)
npix = int(mask.sum())
mask_inds = hp.ring2nest(nside, np.arange(hp.nside2npix(nside))[mask])
thetas = np.linspace(0, np.pi, ninterp)
xis = cl2xi_theta(cl, thetas)
exe_path = os.path.join(os.path.dirname(__file__), 'healcov')
proc = subprocess.Popen(exe_path,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE)
proc.stdin.write(nside.to_bytes(8, 'little'))
proc.stdin.write(npix.to_bytes(8, 'little'))
proc.stdin.write(mask_inds.tobytes())
proc.stdin.write(tree_depth.to_bytes(8, 'little'))
proc.stdin.write(ninterp.to_bytes(8, 'little'))
proc.stdin.write(thetas.tobytes())
proc.stdin.write(xis.tobytes())
proc.stdin.close()
cov = np.frombuffer(proc.stdout.read()).reshape([npix, npix])
info = proc.wait()
if info != 0:
raise Exception()
if log is True:
cov = np.log(cov / (shift**2) + 1)
return cov
def apply_ivf(self, det, tmap, pmap):
mask_t = qcinv.util.load_map(self.mask_t)
bl = self.bl
pxw = hp.pixwin(self.nside)[0:self.lmax+1]
tlm = hp.map2alm( tmap * mask_t, lmax=self.lmax, iter=0, regression=False )
hp.almxfl( tlm, self.ftl / bl / pxw, inplace=True )
return tlm
def get_ftebl_eff( lmax, year, nside, det, forered, cl, mask_t, mask_p ):
noiseT_uK_arcmin, noiseP_uK_arcmin = dmc.get_nlev_tp_uK_arcmin(year, det, forered, mask_t, mask_p)
bl = dmc.get_bl( year, det)[0:lmax+1]
pxw = hp.pixwin( nside )[0:lmax+1]
ftl = 1.0 / (cl.cltt[0:lmax+1] + (noiseT_uK_arcmin * np.pi/180./60.)**2 / (bl * pxw)**2); ftl[0:2] = 0.0
fel = 1.0 / (cl.clee[0:lmax+1] + (noiseP_uK_arcmin * np.pi/180./60.)**2 / (bl * pxw)**2); fel[0:2] = 0.0
fbl = 1.0 / (cl.clbb[0:lmax+1] + (noiseP_uK_arcmin * np.pi/180./60.)**2 / (bl * pxw)**2); fbl[0:2] = 0.0
return ftl, fel, fbl
def apply_pixwin(data: np.array, freqcomb, nside) -> Dict:
"""Applies Pixel Windowfunction with nside specified
Args:
data (Dict): powerspectra with spectrum and frequency-combinations in the columns
Returns:
Dict: Pixel Windowfunction applied Powerspectra with spectrum and frequency-combinations in the columns
"""
for fidx, freql in enumerate(data):
for sidx, specl in enumerate(freql):
lmaxp1 = data.shape[-1]
freqc = freqcomb[fidx]
if int(freqc.split("-")[0]) < 100:
data[fidx, sidx] /= hp.pixwin(nside[0], lmax=lmaxp1 - 1)
else:
data[fidx, sidx] /= hp.pixwin(nside[1], lmax=lmaxp1 - 1)
if int(freqc.split("-")[1]) < 100:
data[fidx, sidx] /= hp.pixwin(nside[0], lmax=lmaxp1 - 1)
else:
data[fidx, sidx] /= hp.pixwin(nside[1], lmax=lmaxp1 - 1)
return data
def get_pixel_window_function(self, nside, lmax, pol=True):
"""
Get the pixel window function assigned to self.spectra for the given nside
"""
pixel_window_np = hp.pixwin(nside=nside, pol=pol)
if pol:
self.spectra['TT'] = pixel_window_np[0]
self.spectra['EE'] = pixel_window_np[1]
self.spectra['BB'] = pixel_window_np[1]
self.spectra['TE'] = pixel_window_np[1]
else:
self.spectra['TT'] = pixel_window_np
self.truncate(after=lmax)
def apply_ivf(self, det, tmap):
mask_t = qcinv.util.load_map(self.mask_t)
tlm = hp.map2alm( tmap * mask_t, lmax=self.lmax, iter=0, regression=False )
bl = dmc.get_bl(self.sim_lib.year, det)[0:self.lmax+1]
pxw = hp.pixwin( self.sim_lib.nside )[0:self.lmax+1]
ftl = self.get_ftl(det)
hp.almxfl( tlm, ftl / bl / pxw, inplace=True )
return tlm
def load_chan_realization_alms(chan_fmt, chani, reali, lmax):
'''
Loads a channel and computes the spherical harmonic expansion of the map.
For polarized channels (nu < 545 GHz), returns a 3-tuple of (T, E, B)
alms, while for unpolarized channels (545 and 857 GHz) it returns T. All
alms are in healpix order.
Already adjusts for the pixel window function.
'''
fname = chan_fmt.format(chan=planck_freqs[chani], real=reali)
if chani < 7:
maps = 1e6*hp.read_map(fname,
field=(0, 1, 2))
nside = hp.npix2nside(maps[0].size)
alms = hp.map2alm(maps, pol=True, lmax=lmax)
# First adjust T by pwT, then E&B by pwE
pwT, pwE = hp.pixwin(nside=nside, pol=True, lmax=lmax)
alms[0] = hp.almxfl(alms[0], 1 / pwT)
# Set the pwE=0 \ells to 0
pol_adj = np.zeros_like(pwE)
pol_adj[pwE != 0] = 1 / pwE[pwE != 0]
alms[1] = hp.almxfl(alms[1], pol_adj)
alms[2] = hp.almxfl(alms[2], pol_adj)
return alms
factor = 1e6 / cfact_545_857[chani-7]
map = factor * hp.read_map(fname)
nside = hp.npix2nside(map.size)
alms = np.zeros((3, hp.Alm.getsize(lmax=lmax)), dtype=np.complex)
alms[0] = hp.map2alm(map, pol=False, lmax=lmax)
alms[0] = hp.almxfl(alms[0], 1 / hp.pixwin(nside=nside, lmax=lmax))
return alms
def gen_lgmca_like(lgmca_file, cov_file,
lmin=70, lmax=2000, delta_ell=30):
# Load covariance
cov = np.loadtxt(cov_file)
# Add l (l + 1) factor (i.e. convert cl -> dl)
ells = np.arange(lmax + 1)
ll1 = ells * (ells + 1) / (2 * np.pi)
# Load data vector
dls = hp.read_cl(lgmca_file)[:lmax + 1] / (getbeam(5, lmax) * hp.pixwin(2048, lmax=lmax))**2
# 1e12 is K^2 -> \mu K^2
return bare_gen_like(1e12 * ll1 * dls, cov, lmin=lmin, lmax=lmax, delta_ell=delta_ell)
def apply_ivf(self, det, tmap, pmap):
mask_t = qcinv.util.load_map(self.mask_t)
mask_p = qcinv.util.load_map(self.mask_p)
bl = self.bl
pxw = hp.pixwin(self.nside)[0:self.lmax+1]
tlm = hp.map2alm( tmap * mask_t, lmax=self.lmax, iter=0, regression=False )
elm, blm = hp.map2alm_spin( (pmap.real * mask_p, pmap.imag * mask_p), 2, lmax=self.lmax )
hp.almxfl( tlm, self.ftl / bl / pxw, inplace=True )
hp.almxfl( elm, self.fel / bl / pxw, inplace=True )
hp.almxfl( blm, self.fbl / bl / pxw, inplace=True )
return tlm, elm, blm
def get_alm_maps(pixel_maps,
fwhms,
resol_correction=False,
ref_fwhm=0,
pixwin_correction=False,
verbose=False):
"""
Compute alm maps from pixel maps and format them for FgBuster.
"""
sh = np.shape(pixel_maps)
nside = hp.npix2nside(sh[2])
n = sh[0]
lmax = 2 * nside + 1
ell = np.arange(start=0, stop=lmax + 1)
ref_sigma_rad = np.deg2rad(ref_fwhm) / 2.355
#ref_fl = np.exp(- 0.5 * np.square(ref_sigma_rad * ell))
ref_fl = hp.gauss_beam(np.deg2rad(ref_fwhm), lmax=lmax)
if verbose:
print('In get_alm_maps: FWHM = ', fwhms)
beam_sigmas_rad = np.deg2rad(fwhms) / (2 * np.sqrt(2 * np.log(2)))
pixwin = hp.pixwin(nside,
lmax=lmax) if pixwin_correction else np.ones(lmax + 1)
# compute maps
#figure()
alm_maps = None
for f in range(n):
alms = hp.map2alm(pixel_maps[f], lmax=lmax, pol=True)
correction = None
if f == 0:
sh = np.shape(alms)
alm_maps = np.empty((n, sh[0], 2 * sh[1]))
if resol_correction:
print('Applying Resol Correction')
#gauss_fl = np.exp(- 0.5 * np.square(beam_sigmas_rad[f] * ell))
gauss_fl = hp.gauss_beam(np.deg2rad(fwhms[f]), lmax=lmax)
correction = ref_fl / gauss_fl / pixwin
#plot(correction, label='freq {}'.format(f))
else:
print('No Resol Correction applied')
for i, t in enumerate(alms):
alm_maps[f, i] = format_alms(
hp.almxfl(t, correction) if resol_correction else t)
#legend()
#title('Bl ratio in get_alm_maps')
return alm_maps
def get_xpol(seenmap, ns, lmin=20, delta_ell=20, apodization_degrees=5.):
"""
Returns a Xpoll object to get spectra, the bin used and the pixel window function.
"""
# Create a mask
mymask = apodize_mask(seenmap, apodization_degrees)
# Create XPol object
lmax = 2 * ns
xpol = Xpol(mymask, lmin, lmax, delta_ell)
ell_binned = xpol.ell_binned
# Pixel window function
pw = hp.pixwin(ns)
pwb = xpol.bin_spectra(pw[:lmax + 1])
return xpol, ell_binned, pwb
def get_ftl(self, detstr):
dets = self.detstr2dets(detstr)
clnn = np.zeros(self.lmax+1)
for det in dets:
noiseT_uK_arcmin = dmc.get_nlev_t_uK_arcmin(self.sim_lib.year, det, self.sim_lib.forered, qcinv.util.load_map(self.mask_t))
bl = dmc.get_bl( self.sim_lib.year, det)[0:self.lmax+1]
pxw = hp.pixwin( self.sim_lib.nside )[0:self.lmax+1]
clnn += (bl * pxw)**2 / (noiseT_uK_arcmin * np.pi/180./60.)**2
clnn = 1./clnn
ftl = 1.0 / (self.cl.cltt[0:self.lmax+1] + clnn); ftl[0:2] = 0.0
return ftl
def apply_ivf(self, det, tmap, pmap):
mask_t = qcinv.util.load_map(self.mask_t)
mask_p = qcinv.util.load_map(self.mask_p)
tlm = hp.map2alm( tmap * mask_t, lmax=self.lmax, iter=0, regression=False )
elm, blm = hp.map2alm_spin( (pmap.real * mask_p, pmap.imag * mask_p), 2, lmax=self.lmax )
bl = dmc.get_bl(self.sim_lib.year, det)[0:self.lmax+1]
pxw = hp.pixwin( self.sim_lib.nside )[0:self.lmax+1]
ftl, fel, fbl = self.get_ftebl(det)
hp.almxfl( tlm, ftl / bl / pxw, inplace=True )
hp.almxfl( elm, fel / bl / pxw, inplace=True )
hp.almxfl( blm, fbl / bl / pxw, inplace=True )
return tlm, elm, blm
def FilterMap(pixmap, freq, mask=None, lmax=None, pixelwindow=False):
if mask is None:
mask = np.ones_like(pixmap)
else:
assert hp.isnpixok(mask.size)
Fl = GetFl(pixmap,freq, mask=mask, lmax=lmax)
alm_fname='alm_%s.fits'%freq
if not os.path.exists(alm_fname):
print "computing alms"
alm = hp.map2alm(pixmap, lmax=lmax)
hp.write_alm(alm_fname,alm)
else:
print "reading alms",alm_fname
alm = hp.read_alm(alm_fname)
if pixelwindow:
print "Correcting alms for pixelwindow"
pl=hp.pixwin(hp.npix2nside(pixmap.size))[:lmax+1]
else: pl=np.ones(len(Fl))
return hp.alm2map(hp.almxfl(alm, Fl/pl), hp.npix2nside(pixmap.size), lmax=lmax), Fl
def GetSpectra(map1, mask, lmax, map2=None, pixwin=True, beam=None, flat=True):
if pixwin:
pl = hp.pixwin(hp.npix2nside(len(map1)))[:lmax + 1]
else:
pl = np.ones(lmax + 1)
if beam is not None:
bl = hp.gauss_beam(np.radians(beam / 60.), lmax=lmax)
else:
bl = np.ones(lmax + 1)
fsky = np.mean(mask**2)
cl = hp.anafast(map1 * mask, map2=map2, lmax=lmax)
if flat:
l = np.arange(lmax + 1)
fact = l * (l + 1) / 2. / np.pi
else:
fact = 1.
return cl / fsky / bl**2 / pl**2 * fact
def simulate(self, det, idx):
assert(det == '')
tlm = self.sim_teblm_cmb.get_sim_tlm(idx)
elm = self.sim_teblm_cmb.get_sim_elm(idx)
blm = self.sim_teblm_cmb.get_sim_blm(idx)
beam = self.bl[0:self.lmax+1] * hp.pixwin(self.nside)[0:self.lmax+1]
hp.almxfl(tlm, beam, inplace=True)
hp.almxfl(elm, beam, inplace=True)
hp.almxfl(blm, beam, inplace=True)
tmap = hp.alm2map(tlm, self.nside)
qmap, umap = hp.alm2map_spin( (elm, blm), self.nside, 2, lmax=self.lmax )
npix = 12*self.nside**2
tmap += np.random.standard_normal(npix) * (self.noiseT_uK_arcmin * np.sqrt(npix / 4. / np.pi) * np.pi / 180. / 60.)
qmap += np.random.standard_normal(npix) * (self.noiseP_uK_arcmin * np.sqrt(npix / 4. / np.pi) * np.pi / 180. / 60.)
umap += np.random.standard_normal(npix) * (self.noiseP_uK_arcmin * np.sqrt(npix / 4. / np.pi) * np.pi / 180. / 60.)
return tmap, qmap + 1.j*umap
def analworker(i):
print "This is analysis worker starting for map", i + 1, "/", nmaps
QU_maps = hp.read_map(fits[i], field=(1, 2)) #(Q,U)
pixrecip = np.concatenate((np.ones(2),
np.reciprocal(
hp.pixwin(hp.get_nside(QU_maps[0]),
pol=True)[1][2:smoothing_lmax])
)) #P pixwin #Not defined for l < 2
pm_alms = hp.map2alm_spin(QU_maps, spin, lmax=smoothing_lmax - 1)
del QU_maps
hp.almxfl(pm_alms[0], pixrecip, inplace=True) #Correcting for pixwin
hp.almxfl(pm_alms[1], pixrecip, inplace=True) #Correcting for pixwin
#Reorder to S2LET alms
pm_alms[0] = ps.lm_hp2lm(pm_alms[0], smoothing_lmax)
pm_alms[1] = ps.lm_hp2lm(pm_alms[1], smoothing_lmax)
P_alms = -1. * pm_alms[0] - 1.j * pm_alms[1] #CHECK THIS IS CORRECT!
del pm_alms
wav_maps, scal_maps = ps.analysis_lm2wav_manualtiling(
P_alms, smoothing_lmax, ndir, spin, scal_tiles, wav_tiles.T.ravel(),
scal_bandlims, wav_bandlims)
del P_alms
np.save(scal_outfits[i], scal_maps)
del scal_maps
#Splitting up output wavelet maps
offset = 0
for j in xrange(jmax + 1):
for n in xrange(ndir):
bandlim = wav_bandlims[j]
nelem = bandlim * (2. * bandlim - 1.)
wav_outfits = wav_outfits_root[i] + '_j' + str(j) + '_n' + str(
n + 1) + '.npy'
np.save(wav_outfits, wav_maps[offset:offset + nelem])
offset += nelem
del wav_maps
return 0
def estimate_cl(sky_map, lmax, binary_mask=None, beam_fwhm=0.0, pixwin=False):
"""
Estimates the TT, EE, BB and TE auto/cross power spectrum.
The sky map(s) will be masked by the binary mask provided.
beam_fwhm input is in arcmins. It is converted internally to radians.
If pixwin is True, the spectra is debeamed with the pixel window function.
"""
if sky_map.ndim == 1:
pol = False
else:
pol = True
if binary_mask is not None:
sky_map_masked = mu.mask_map(sky_map, binary_mask=binary_mask, pol=pol)
f_sky = mu.get_sky_fraction(binary_mask)
else:
sky_map_masked = sky_map
if hp.maptype == 0:
f_sky = 1.0
else:
f_sky = np.aray([1.0, 1.0, 1.0])
Bl = hp.gauss_beam(fwhm=np.radians(fwhm / 60.0), lmax=lmax, pol=pol)
if pixwin:
pixel_window = hp.pixwin(hp.get_nside(sky_map), pol)
if pol:
spectra = hp.anafast(sky_map_masked.filled(), lmax=lmax)[:4]
spectra /= f_sky[1] * Bl.T**2
if pixwin:
spectra[0] /= pixwin[0]**2
spectra[1:] /= pixwin[1]**2
else:
spectra = hp.anafast(sky_map_masked.filled(), lmax=lmax)
spectra /= f_sky * Bl**2
if pixwin:
spectra /= pixel_window**2
return spectra
def compute_ds_dcb(map,mask,ellmin,ellmax,fwhmrad):
npix=np.size(np.where(mask))
maxell=np.max(ellmax)
ellbins=ellmin.size
ellval=(ellmin+ellmax)/2
print(' Calculating dS/dCb')
pixwin=hp.pixwin(hp.npix2nside(len(map)))[0:maxell+1]
ll=np.arange(int(maxell)+1)
bl=np.exp(-0.5*ll**2*(fwhmrad/2.35)**2)
nside=np.sqrt(map.size/12)
iprings=np.arange(map.size)
vecs=hp.pix2vec(int(nside),iprings[mask])
cosangles=np.dot(np.transpose(vecs),vecs)
ds_dcb=np.zeros((ellbins,npix,npix))
for i in np.arange(npix):
bla,aa=do_a_line(cosangles[i,:],i,ll,bl,pixwin,ellmin,ellmax,ellval)
ds_dcb[:,i,i:]=bla[:,i:]
ds_dcb[:,i:,i]=bla[:,i:]
return(ds_dcb)
def apply_ivf(self, det, tmap, pmap):
assert(self.lmax == 1000)
mask_t = qcinv.util.load_map(self.mask_t)
mask_p = qcinv.util.load_map(self.mask_p)
bl = dmc.get_bl(self.sim_lib.year, det)[0:self.lmax+1]
pxw = hp.pixwin( self.sim_lib.nside )[0:self.lmax+1]
# qcinv filtering for temperature
dcf = self.lib_dir + "/dense_cache_det_" + det + ".pk"
# id preconditioners lmax nside im em tr cache
chain_descr = [ [ 2, ["split(dense("+dcf+"), 32, diag_cl)"], 256, 128, 3, 0.0, qcinv.cd_solve.tr_cg, qcinv.cd_solve.cache_mem()],
[ 1, ["split(stage(2), 256, diag_cl)"], 512, 256, 3, 0.0, qcinv.cd_solve.tr_cg, qcinv.cd_solve.cache_mem()],
[ 0, ["split(stage(1), 512, diag_cl)"], 1000, 512, np.inf, 1.0e-6, qcinv.cd_solve.tr_cg, qcinv.cd_solve.cache_mem()] ]
# temperature noise covariance
ninv_t = ( hp.read_map( dmc.get_fname_iqumap(self.sim_lib.year, det, self.sim_lib.forered), hdu=1, field=3 ) /
dmc.sigma0[(self.sim_lib.year, self.sim_lib.forered, 'T')][det]**2 / 1e6 * mask_t )
# polarization noise covariance
tfname = dmc.get_fname_iqumap(self.sim_lib.year, det, False) #NOTE: Nobs counts taken from non-FG reduced maps.
n_inv_qq = hp.read_map( tfname, hdu=2, field=1 ) / (dmc.sigma0[(self.sim_lib.year, self.sim_lib.forered, 'P')][det] * 1.e3)**2 * mask_p
n_inv_qu = hp.read_map( tfname, hdu=2, field=2 ) / (dmc.sigma0[(self.sim_lib.year, self.sim_lib.forered, 'P')][det] * 1.e3)**2 * mask_p
n_inv_uu = hp.read_map( tfname, hdu=2, field=3 ) / (dmc.sigma0[(self.sim_lib.year, self.sim_lib.forered, 'P')][det] * 1.e3)**2 * mask_p
# construct filter
if self.diag_n == True:
n_inv_filt = qcinv.opfilt_tp.alm_filter_ninv( [ninv_t, 0.5*(n_inv_qq + n_inv_uu)], bl*pxw, marge_monopole=True, marge_dipole=True )
else:
n_inv_filt = qcinv.opfilt_tp.alm_filter_ninv( [ninv_t, n_inv_qq, n_inv_qu, n_inv_uu], bl*pxw, marge_monopole=True, marge_dipole=True )
chain = qcinv.multigrid.multigrid_chain( qcinv.opfilt_tp, chain_descr, self.cl, n_inv_filt )
alm = qcinv.opfilt_tp.teblm( [np.zeros( qcinv.util_alm.lmax2nlm(self.lmax), dtype=np.complex ) for i in xrange(0,3)] )
chain.solve( alm, [tmap, pmap.real, pmap.imag] )
return alm.tlm, alm.elm, alm.blm
def Clgg(gal_map, gal_mask, N_side, lmax, normalized=False):
# CMASS map, number counts. Should convert to delta_n/n
gal = hp.read_map(gal_map)
if gal_mask != False:
gal_mask = hp.read_map(gal_mask)
gal_mask = hp.ud_grade(gal_mask, N_side) # Bring to same resolution
else:
gal_mask = np.ones_like(gal)
# The mask is product of PLANCK lensing and CMASS masks
mask = gal_mask
fsky = np.sum(mask) * 1. / len(mask)
# Apply the mask. Same for all, for now
masked_pl = mask
masked_gal = gal * mask
# Compute shot noise
mean_gal = np.sum(masked_gal) / np.sum(mask)
A_per_healpix = (4 * np.pi) / (12 * N_side**2)
shot_noise = (mean_gal / A_per_healpix)**(-1.)
# Normalized tells you if the map has already been normalized or is a raw map of counts
if normalized == False:
mean_gal = np.sum(masked_gal) / np.sum(mask)
masked_gal_dn = masked_gal / mean_gal - 1.
masked_gal_dn = mask * masked_gal_dn
else:
masked_gal_dn = masked_gal
Clgg = hp.anafast(masked_gal_dn, lmax=lmax)
ls = np.arange(len(Clgg))
pixwinf = hp.pixwin(N_side)[0:len(Clgg)]
Clgg = Clgg / (pixwinf**2)
Clgg = Clgg / fsky
return ls, Clgg, fsky, shot_noise
def load_chan_realization(chan_fmt, chani, reali, lmax,
doppler=True, beam=r2_channel_beams):
'''
Loads a single array of `D_\ells` from an FFP8 fits file, adjusting for the
observation beam and (possibly) for doppler modulation. Returns the TT
power spectrum for `\ell = 0` to `\ell = lmax`, inclusive.
chan_fmt: Path to the .fits file.
chani: Planck observation channel, 0 <= chani < 9. I.e. 0 means 30 GHz.
reali: The realization number.
doppler: Whether to remove doppler modulation from the realization.
returns: An array of `D_\ell = \ell (\ell + 1) / (2 \pi) C_\ell`.
A `numpy.array` of `dtype == np.double` and size `lmax + 1`.
'''
m = hp.read_map(chan_fmt.format(freq=planck_freqs[chani], real=reali))
if chani >= 7:
m /= cfact_545_857[chani - 7]
if doppler:
m = doppler_adjust(m, chani)
cls = hp.anafast(1e6*m, lmax=lmax)
ells = np.arange(lmax + 1)
ll1 = ells * (ells + 1) / (2 * np.pi)
nside = hp.npix2nside(m.size)
beam = beam[chani, :lmax + 1] * hp.pixwin(nside)[:lmax + 1]
beam_msk = beam != 0
# Avoid divide-by-zero warnings
adj_dls = np.zeros_like(cls)
adj_dls[beam_msk] = (ll1 * cls)[beam_msk] / beam[beam_msk]**2
return adj_dls
), 'Set env. variable PLENS to a writeable folder'
TEMP = os.path.join(os.environ['PLENS'], 'temp', 'anisofilt_example')
cls_path = os.path.join(os.path.dirname(os.path.abspath(plancklens.__file__)),
'data', 'cls')
#--- definition of simulation and inverse-variance filtered simulation libraries:
lmax_ivf = 2048
lmin_ivf = 100 # We will use in the QE only CMB modes between lmin_ivf and lmax_ivf
lmax_qlm = 4096 # We will calculate lensing estimates until multipole lmax_qlm.
nside = 2048 # Healpix resolution of the data and sims.
nlev_t = 35. # Filtering noise level in temperature (here also used for the noise simulations generation).
nlev_p = 55. # Filtering noise level in polarization (here also used for the noise simulations generation).
nsims = 300 # Total number of simulations to consider.
transf = hp.gauss_beam(5. / 60. / 180. * np.pi,
lmax=lmax_ivf) * hp.pixwin(nside)[:lmax_ivf + 1]
#: CMB transfer function. Here a 5' Gaussian beam and healpix pixel window function.
cl_unl = utils.camb_clfile(
os.path.join(cls_path, 'FFP10_wdipole_lenspotentialCls.dat'))
cl_len = utils.camb_clfile(
os.path.join(cls_path, 'FFP10_wdipole_lensedCls.dat'))
#: Fiducial unlensed and lensed power spectra used for the analysis.
cl_weight = utils.camb_clfile(
os.path.join(cls_path, 'FFP10_wdipole_lensedCls.dat'))
cl_weight['bb'] *= 0.
#: CMB spectra entering the QE weights (the spectra multplying the inverse-variance filtered maps in the QE legs)
libdir_pixphas = os.path.join(TEMP, 'pix_phas_nside%s' % nside)
pix_phas = phas.pix_lib_phas(libdir_pixphas, 3, (hp.nside2npix(nside), ))
#Smoothing W-beam for 're-convolving'
'''smoothbeam = cp.deepcopy(combbeam[4])
smoothbegin = 875
smoothend = 1024
smoothbeam[smoothbegin:smoothend+1] = smoothfunc(np.arange(smoothbegin,smoothend+1),smoothbeam[smoothbegin])'''
#For Planck, effective beam is 5' FWHM gaussian
smoothbeam = hp.gauss_beam(mh.radians(5. / 60.), lmax=ellmax - 1)
'''smoothbegin = 3400
smoothend = ellmax-1
smoothbeam[smoothbegin:smoothend+1] = smoothfunc(np.arange(smoothbegin,smoothend+1),smoothbeam[smoothbegin])
smoothbeam_orig = hp.gauss_beam(mh.radians(5./60.),lmax=ellmax-1)'''
#Take reciprocal of beam transfer functions
#pixrecip = np.reciprocal(hp.pixwin(hp.get_nside(maps))[:ellmax+1]) #TESTING pixwin
pixrecip1024 = np.reciprocal(
hp.pixwin(1024)[:ellmax]) #30 & 44 GHz at Nside=1024
pixrecip2048 = np.reciprocal(
hp.pixwin(2048)[:ellmax]) #70 & HFI @ Nside=2048 #Not 70 for MC
recipcombbeam = np.reciprocal(rawbeam) #combbeam)
#Thresholding of deconvolved beams to 1/b_l <= 1000
recipcombbeam[recipcombbeam > 1000] = 1000
deconbeam = [None] * len(recipcombbeam)
for i in xrange(2): #len(recipcombbeam)):
deconbeam[i] = recipcombbeam[
i] * smoothbeam * pixrecip1024 #Deconvolving to tapered 5' gaussian beam
for i in xrange(2, len(recipcombbeam)):
deconbeam[i] = recipcombbeam[
i] * smoothbeam * pixrecip2048 #Deconvolving to tapered 5' gaussian beam
deconbeam = np.array(deconbeam)
clf()
plot(theell, sigcosvardee / samplevar_th_ee, 'r', label = 'EE: MC / Knox')
plot(theell, sigcosvardee / deltacl_ee_like, 'r--', label = 'EE: MC / Likelihood')
plot(theell, sigcosvardbb / samplevar_th_bb, 'b', label = 'BB: MC / Knox')
plot(theell, sigcosvardbb / deltacl_bb_like, 'b--', label = 'BB: MC / Likelihood')
ylim(0,2)
legend(numpoints=1)
##### Noise variance
signoisedee = sigdee-sigdee[0,:]
signoisedbb = sigdbb-sigdbb[0,:]
pixwin=hp.pixwin(nside)[0:max(ll)+1]
omega = 4* pi * fsky
fact = 2 * np.interp(theell, ell, np.sqrt(2./((2*ell+1)*fsky))) / np.sqrt(ellmax-ellmin) * theell * (theell +1) / (2*pi) / np.interp(theell, ll,bl**2*pixwin**2) * omega
clf()
subplot(1,2,1)
for i in xrange(4, len(allvalnoise)):
plot(theell, signoisedee[i,:], label='$\sigma$ = {}'.format(allvalnoise[i]), color=cm.jet(allvalnoise[i]/max(allvalnoise)))
plot(theell, (fact * allvalnoise[i]**2)/100, '--', color=cm.jet(allvalnoise[i]/max(allvalnoise)))
legend(fontsize=8, loc='upper left')
yscale('log')
subplot(1,2,2)
for i in xrange(4, len(allvalnoise)):
plot(theell, signoisedbb[i,:], label='$\sigma$ = {}'.format(allvalnoise[i]), color=cm.jet(allvalnoise[i]/max(allvalnoise)))
plot(theell, (fact * allvalnoise[i]**2)/1000, '--', color=cm.jet(allvalnoise[i]/max(allvalnoise)))
def test(useCLASS=1,
useLensing=1,
classCamb=1,
nSims=1000,
lmax=100,
lmin=2,
newSMICA=False,
newDeg=False,
suppressC2=False,
suppFactor=0.23,
filterC2=False,
filtFacLow=0.1,
filtFacHigh=0.2,
R1=False):
"""
code for testing the other functions in this module
Inputs:
useCLASS: set to 1 to use CLASS, 0 to use CAMB
CLASS Cl has early/late split at z=50
CAMB Cl has ISWin/out split: ISWin: 0.4<z<0.75, ISWout: the rest
Note: CAMB results include primary in ISWin and ISWout (not as intended)
default: 1
useLensing: set to 1 to use lensed Cl, 0 for non-lensed
default: 1
classCamb: if 1: use the CAMB format of CLASS output, if 0: use CLASS format
Note: parameter not used if useCLASS = 0
default: 1
nSims: the number of simulations to do for ensemble
default: 1000
lmax: the highest l to include in Legendre transforms
default: 100
lmin: the lowest l to include in S_{1/2} = CIC calculations
default: 2
newSMICA: set to True to recalculate SMICA results
default: False
newDeg: set to True to recalculate map and mask degredations
(only if newSMICA is also True)
default: False
suppressC2: set to True to suppress theoretical C_2 (quadrupole) by
suppFactor before creating a_lm.s
Default: False
suppFactor: multiplies C_2 if suppressC2 is True
Default: 0.23 # from Tegmark et. al. 2003, figure 13 (WMAP)
filterC2 : set to true to filter simulated CMBs after spice calculates
cut sky C_l. Sims will pass filter if C_2 * filtFacLow < C_2^sim <
C_2 * filtFacHigh.
Default: False
filtFacLow,filtFacHigh: defines C_2 range for passing simulated CMBs
Default: 0.1,0.2
R1: set to True to use SMICA and Mask R1. Otherwise, R2 used.
Only affects calculation of newly degraded map.
Default: False
"""
# load data
ell, fullCl, primCl, lateCl, crossCl = gcp.loadCls(useCLASS=useCLASS,
useLensing=useLensing,
classCamb=classCamb)
# fill beginning with zeros
startEll = int(ell[0])
ell = np.append(np.arange(startEll), ell)
fullCl = np.append(np.zeros(startEll), fullCl)
primCl = np.append(np.zeros(startEll), primCl)
lateCl = np.append(np.zeros(startEll), lateCl)
crossCl = np.append(np.zeros(startEll), crossCl)
# suppress C_2 to see what happens in enesmble
if suppressC2:
fullCl[2] *= suppFactor
primCl[2] *= suppFactor
lateCl[2] *= suppFactor
crossCl[2] *= suppFactor
conv = ell * (ell + 1) / (2 * np.pi)
#print ell,conv #ell[0]=2.0
"""
# verify statistical properties of alm realizations
nSims = 1000
lmax = 100
Clprim_sum = np.zeros(lmax+1)
Cllate_sum = np.zeros(lmax+1)
Clcros_sum = np.zeros(lmax+1) # prim x late
for nSim in range(nSims):
print 'starting sim ',nSim+1, ' of ',nSims
#alm_prim,alm_late = getAlms(A_lij,lmax=lmax) #AKW method defunct
# see if synalm can do it
alm_prim,alm_late = hp.synalm((primCl,lateCl,crossCl),lmax=lmax,new=True)
Clprim_sum = Clprim_sum + hp.alm2cl(alm_prim)
Cllate_sum = Cllate_sum + hp.alm2cl(alm_late)
Clcros_sum = Clcros_sum + hp.alm2cl(alm_prim,alm_late)
Cl_prim_avg = Clprim_sum/nSims
Cl_late_avg = Cllate_sum/nSims
Cl_cros_avg = Clcros_sum/nSims
doPlot = True
if doPlot:
plt.plot(ell[:lmax+1],Cl_prim_avg*conv[:lmax+1])
plt.plot(ell[:lmax+1],primCl[:lmax+1]*conv[:lmax+1])
plt.title('primary')
plt.ylabel('D_l')
plt.show()
plt.plot(ell[:lmax+1],Cl_late_avg*conv[:lmax+1])
plt.plot(ell[:lmax+1],lateCl[:lmax+1]*conv[:lmax+1])
plt.title('late')
plt.ylabel('D_l')
plt.show()
plt.plot(ell[:lmax+1],Cl_cros_avg*conv[:lmax+1])
plt.plot(ell[:lmax+1],crossCl[:lmax+1]*conv[:lmax+1])
plt.title('cross')
plt.ylabel('D_l')
plt.show()
"""
# get covariances from SMICA map and mask
theta_i = 0.0 #degrees
theta_f = 180.0 #degrees
nSteps = 1800
#lmax = 100
""" # don't want anafast after all
# get unmasked and masked SMICA covariances
# note: getSMICA uses linspace in theta for thetaArray
#newSMICA = False#True
thetaArray2, C_SMICA, C_SMICAmasked, S_SMICAnomask, S_SMICAmasked = \
getSMICA(theta_i=theta_i,theta_f=theta_f,nSteps=nSteps,lmax=lmax,lmin=lmin,
newSMICA=newSMICA,newDeg=newDeg,useSPICE=False,R1=R1)
print ''
print 'S_{1/2}(anafast): SMICA, no mask: ',S_SMICAnomask,', masked: ',S_SMICAmasked
print ''
"""
# get C_l from SPICE to compare to above method
# note: getSMICA uses linspace in theta for thetaArray
#newSMICA = False#True
thetaArray2sp, C_SMICAsp, C_SMICAmaskedsp, S_SMICAnomasksp, S_SMICAmaskedsp = \
getSMICA(theta_i=theta_i,theta_f=theta_f,nSteps=nSteps,lmax=lmax,lmin=lmin,
newSMICA=newSMICA,newDeg=newDeg,useSPICE=True,R1=R1)
print ''
print 'S_{1/2}(spice): SMICA, no mask: ', S_SMICAnomasksp, ', masked: ', S_SMICAmaskedsp
print ''
# Find S_{1/2} in real space to compare methods
nTerms = 10000
#SSnm2 = SOneHalf(thetaArray2, C_SMICA, nTerms=nTerms)
#SSmd2 = SOneHalf(thetaArray2, C_SMICAmasked, nTerms=nTerms)
SSnm2sp = SOneHalf(thetaArray2sp, C_SMICAsp, nTerms=nTerms)
SSmd2sp = SOneHalf(thetaArray2sp, C_SMICAmaskedsp, nTerms=nTerms)
# create ensemble of realizations and gather statistics
covEnsembleFull = np.zeros([nSims, nSteps + 1]) # for maskless
covEnsembleCut = np.zeros([nSims, nSteps + 1]) # for masked
sEnsembleFull = np.zeros(nSims)
sEnsembleCut = np.zeros(nSims)
covTheta = np.array([])
#nSims = 1000
# apply beam and pixel window functions to power spectra
# note: to ignore the non-constant pixel shape, W(l) must be > B(l)
# however, this is not true for NSIDE=128 and gauss_beam(5')
# Here I ignore this anyway and proceed
myNSIDE = 128 # must be same NSIDE as in getSMICA function
Wpix = hp.pixwin(myNSIDE)
Bsmica = hp.gauss_beam(5. / 60 * np.pi / 180) # 5 arcmin
WlMax = Wpix.size
if WlMax < lmax:
print 'die screaming!!!'
return 0
primCl = primCl[:WlMax] * (Wpix * Bsmica)**2
lateCl = lateCl[:WlMax] * (Wpix * Bsmica)**2
crossCl = crossCl[:WlMax] * (Wpix * Bsmica)**2
# note: i tried sims without this scaling, and results seemed the same at a glance
# collect simulated Cl for comparison to model
Clsim_full_sum = np.zeros(lmax + 1)
# get Jmn matrix for harmonic space S_{1/2} calc.
myJmn = getJmn(lmax=lmax)
# set up ramdisk for SpICE
# super lame that spice needs to read/write from disk, but here goes...
RAMdisk = '/Volumes/ramdisk/'
ClTempFile = RAMdisk + 'tempCl.fits'
mapTempFile = RAMdisk + 'tempMap.fits'
mapDegFile = RAMdisk + 'smicaMapDeg.fits' # this should have been created by sims.getSMICA
maskDegFile = RAMdisk + 'maskMapDeg.fits' # this should have been created by sims.getSMICA
# create RAM Disk for SpICE and copy these files there using bash
RAMsize = 4 #Mb
ramDiskOutput = subprocess.check_output('./ramdisk.sh create ' +
str(RAMsize),
shell=True)
print ramDiskOutput
diskID = ramDiskOutput[
31:41] # this might not grab the right part; works for '/dev/disk1'
subprocess.call('cp smicaMapDeg.fits ' + RAMdisk, shell=True)
subprocess.call('cp maskMapDeg.fits ' + RAMdisk, shell=True)
doTime = True # to time the run and print output
startTime = time.time()
#for nSim in range(nSims):
nSim = 0
while nSim < nSims:
print 'starting sim ', nSim + 1, ' of ', nSims
alm_prim, alm_late = hp.synalm((primCl, lateCl, crossCl),
lmax=lmax,
new=True)
# calculate C(theta) of simulation
Clsim_prim = hp.alm2cl(alm_prim)
Clsim_late = hp.alm2cl(alm_late)
Clsim_cros = hp.alm2cl(alm_prim, alm_late)
Clsim_full = Clsim_prim + 2 * Clsim_cros + Clsim_late
# use Cl_sim_full to omit prim/late distinction for now
# start with a mask
# -> for optional C2 filtering based on cut sky map
# alm2map should create map with default RING ordering
# pixel window and beam already accounted for in true Cls
#mapSim = hp.alm2map(alm_prim+alm_late,myNSIDE,lmax=lmax,pixwin=True,sigma=5./60*np.pi/180)
mapSim = hp.alm2map(alm_prim + alm_late, myNSIDE, lmax=lmax)
hp.write_map(mapTempFile, mapSim)
ispice(mapTempFile,
ClTempFile,
maskfile1=maskDegFile,
subav="YES",
subdipole="YES")
Cl_masked = hp.read_cl(ClTempFile)
ell2 = np.arange(Cl_masked.shape[0])
# Check for low power of cut sky C_2
if (filterC2 == True and fullCl[2] * filtFacHigh > Cl_masked[2] and
Cl_masked[2] > fullCl[2] * filtFacLow) or filterC2 == False:
# note: getCovar uses linspace in x for thetaArray
thetaArray, cArray2 = getCovar(ell2[:lmax + 1],
Cl_masked[:lmax + 1],
theta_i=theta_i,
theta_f=theta_f,
nSteps=nSteps,
lmin=lmin)
covEnsembleCut[nSim] = cArray2
# S_{1/2}
sEnsembleCut[nSim] = np.dot(
Cl_masked[lmin:lmax + 1],
np.dot(myJmn[lmin:, lmin:], Cl_masked[lmin:lmax + 1]))
doPlot = False #True
if doPlot:
plt.plot(thetaArray, cArray)
plt.xlabel('theta (degrees)')
plt.ylabel('C(theta)')
plt.title('covariance of CMB simulation ' + str(nSim + 1))
plt.show()
# now without the mask
# uses the same sims that passed the C2 filter
Clsim_full_sum += Clsim_full
# note: getCovar uses linspace in x for thetaArray
thetaArray, cArray = getCovar(ell[:lmax + 1],
Clsim_full[:lmax + 1],
theta_i=theta_i,
theta_f=theta_f,
nSteps=nSteps,
lmin=lmin)
covEnsembleFull[nSim] = cArray
covTheta = thetaArray
# S_{1/2}
sEnsembleFull[nSim] = np.dot(
Clsim_full[lmin:],
np.dot(myJmn[lmin:, lmin:], Clsim_full[lmin:]))
nSim += 1
if doTime:
print 'time elapsed: ', int(
(time.time() - startTime) / 60.), ' minutes'
# free the RAM used by SpICE's RAM disk
ramDiskOutput = subprocess.check_output('./ramdisk.sh delete ' + diskID,
shell=True)
print ramDiskOutput
avgEnsembleFull = np.average(covEnsembleFull, axis=0)
stdEnsembleFull = np.std(covEnsembleFull, axis=0)
# do I need a better way to describe confidence interval?
avgEnsembleCut = np.average(covEnsembleCut, axis=0)
stdEnsembleCut = np.std(covEnsembleCut, axis=0)
Clsim_full_avg = Clsim_full_sum / nSims
# save results
saveFile1 = "simStatResultC.npy"
np.save(
saveFile1,
np.vstack((thetaArray, avgEnsembleFull, stdEnsembleFull,
avgEnsembleCut, stdEnsembleCut)))
saveFile2 = "simStatC_SMICA.npy"
np.save(saveFile2, np.vstack((thetaArray2sp, C_SMICAsp, C_SMICAmaskedsp)))
saveFile3 = "simStatResultS.npy"
np.save(
saveFile3,
np.vstack((np.hstack((np.array(S_SMICAnomasksp), sEnsembleFull)),
np.hstack((np.array(S_SMICAmaskedsp), sEnsembleCut)))))
doPlot = True
if doPlot:
print 'plotting C_l... '
#print ell.size,conv.size,primCl.size,crossCl.size,lateCl.size
plt.plot(ell[:lmax + 1],
conv[:lmax + 1] * (primCl + 2 * crossCl + lateCl)[:lmax + 1],
label='model D_l')
plt.plot(ell[:lmax + 1],
conv[:lmax + 1] * Clsim_full_avg,
label='ensemble average D_l')
plt.legend()
plt.show()
makePlots(saveFile1=saveFile1,
saveFile2=saveFile2,
saveFile3=saveFile3)
# S_{1/2} output
print ''
print 'using CIC method: '
#print 'S_{1/2}(anafast): SMICA, no mask: ',S_SMICAnomask,', masked: ',S_SMICAmasked
print 'S_{1/2}(spice): SMICA, no mask: ', S_SMICAnomasksp, ', masked: ', S_SMICAmaskedsp
print ''
print 'using CCdx method: '
#print 'S_{1/2}(anafast): SMICA, no mask: ',SSnm2,', masked: ',SSmd2
print 'S_{1/2}(spice): SMICA, no mask: ', SSnm2sp, ', masked: ', SSmd2sp
print ''
#### Get MC results
mclsout = np.zeros(nbins)
sclsout = np.zeros(nbins)
for j in np.arange(nbins):
mclsout[j] = np.mean(allclsout[:,j])
sclsout[j] = np.std(allclsout[:,j])/sqrt(nbmc)
mcls = np.zeros(lmax+1)
scls = np.zeros(lmax+1)
for j in np.arange(lmax+1):
mcls[j] = np.mean(allcls[:,j])
scls[j] = np.std(allcls[:,j])/sqrt(nbmc)
ell = np.arange(lmax+1)
pw = hp.pixwin(nside)[0:lmax+1]
pwb = np.interp(newl,ell,pw)
fact = ell * (ell + 1) / (2 * np.pi) * len(maskmap) / maskok.sum()
factth = lll * (lll + 1) / (2 * np.pi)
clf()
subplot(2,1,1)
title('TT')
xlim(0,lmax)
errorbar(newl,mclsout/pwb**2,yerr=sclsout,fmt='bo',label='Corrected')
plot(ell,fact*mcls/pw**2,'g',label='Anafast rescaled')
plot(spectra[0],spectra[1]*factth,'r',label='Input')
legend(loc='lower right',frameon=False,fontsize=10)
subplot(2,1,2)
title('Residuals')
def test(useCLASS=1,
useLensing=1,
classCamb=1,
nSims=1000,
lmax=100,
lmin=2,
newSMICA=False,
newDeg=False,
suppressC2=False,
suppFactor=0.23):
"""
code for testing the other functions in this module
Inputs:
useCLASS: set to 1 to use CLASS, 0 to use CAMB
CLASS Cl has early/late split at z=50
CAMB Cl has ISWin/out split: ISWin: 0.4<z<0.75, ISWout: the rest
Note: CAMB results include primary in ISWin and ISWout (not as intended)
default: 1
useLensing: set to 1 to use lensed Cl, 0 for non-lensed
default: 1
classCamb: if 1: use the CAMB format of CLASS output, if 0: use CLASS format
Note: parameter not used if useCLASS = 0
default: 1
nSims: the number of simulations to do for ensemble
default: 1000
lmax: the highest l to include in Legendre transforms
default: 100
lmin: the lowest l to include in S_{1/2} = CIC calculations
default: 2
newSMICA: set to True to recalculate SMICA results
default: False
newDeg: set to True to recalculate map and mask degredations
(only if newSMICA is also True)
default: False
suppressC2: set to True to suppress theoretical C_2 by suppFactor
before creating a_lm.s
Default: False
suppFactor: multiplies C_2 if suppressC2 is True
Default: 0.23 # from Tegmark et. al. 2003, figure 13 (WMAP)
"""
##############################################################################
# load theoretical power spectra
# load data
ell, fullCl, primCl, lateCl, crossCl = gcp.loadCls(useCLASS=useCLASS,
useLensing=useLensing,
classCamb=classCamb)
# fill beginning with zeros
startEll = int(ell[0])
ell = np.append(np.arange(startEll), ell)
fullCl = np.append(np.zeros(startEll), fullCl)
primCl = np.append(np.zeros(startEll), primCl)
lateCl = np.append(np.zeros(startEll), lateCl)
crossCl = np.append(np.zeros(startEll), crossCl)
# suppress C_2 to see what happens in enesmble
#suppressC2 = False
#suppFactor = 0.23 # from Tegmark et. al. 2003, figure 13 (WMAP)
if suppressC2:
fullCl[2] *= suppFactor
primCl[2] *= suppFactor
lateCl[2] *= suppFactor
crossCl[2] *= suppFactor
conv = ell * (ell + 1) / (2 * np.pi)
#print ell,conv #ell[0]=2.0
# apply beam and pixel window functions to power spectra
# note: to ignore the non-constant pixel shape, W(l) must be > B(l)
# however, this is not true for NSIDE=128 and gauss_beam(5')
# Here I ignore this anyway and proceed
myNSIDE = 128 # must be same NSIDE as in sims.getSMICA function
Wpix = hp.pixwin(myNSIDE)
Bsmica = hp.gauss_beam(5. / 60 * np.pi / 180) # 5 arcmin
WlMax = Wpix.size
if WlMax < lmax:
print 'die screaming!!!'
return 0
fullCl = fullCl[:WlMax] * (Wpix * Bsmica)**2
primCl = primCl[:WlMax] * (Wpix * Bsmica)**2
lateCl = lateCl[:WlMax] * (Wpix * Bsmica)**2
crossCl = crossCl[:WlMax] * (Wpix * Bsmica)**2
# note: i tried sims without this scaling, and results seemed the same at a glance
##############################################################################
# load SMICA data, converted to C(theta), via SpICE
if newSMICA:
theta_i = 0.0 #degrees
theta_f = 180.0 #degrees
nSteps = 1800
thetaArray2sp, C_SMICAsp, C_SMICAmaskedsp, S_SMICAnomasksp, S_SMICAmaskedsp = \
sims.getSMICA(theta_i=theta_i,theta_f=theta_f,nSteps=nSteps,lmax=lmax,lmin=lmin,
newSMICA=newSMICA,newDeg=newDeg,useSPICE=True)
# filenames for SpICE to use
# super lame that spice needs to read/write from disk, but here goes...
RAMdisk = '/Volumes/ramdisk/'
ClTempFile = RAMdisk + 'tempCl.fits'
mapTempFile = RAMdisk + 'tempMap.fits'
mapDegFile = RAMdisk + 'smicaMapDeg.fits' # this should have been created by sims.getSMICA
maskDegFile = RAMdisk + 'maskMapDeg.fits' # this should have been created by sims.getSMICA
# create RAM Disk for SpICE and copy these files there using bash
RAMsize = 4 #Mb
ramDiskOutput = subprocess.check_output('./ramdisk.sh create ' +
str(RAMsize),
shell=True)
print ramDiskOutput
diskID = ramDiskOutput[
31:41] # this might not grab the right part; works for '/dev/disk1'
subprocess.call('cp smicaMapDeg.fits ' + RAMdisk, shell=True)
subprocess.call('cp maskMapDeg.fits ' + RAMdisk, shell=True)
ispice(mapDegFile,
ClTempFile,
maskfile1=maskDegFile,
subav="YES",
subdipole="YES")
ClsmicaCut = hp.read_cl(ClTempFile)
# find S_{1/2} for SMICA. Should actually optimize but see what happens here first.
#myJmn = legprodint.getJmn(endX=0.5,lmax=lmax,doSave=False)
#Ssmica = np.dot(ClsmicaCut[lmin:lmax+1],np.dot(myJmn[lmin:,lmin:],
# ClsmicaCut[lmin:lmax+1]))*1e24 #K^4 to microK^4
##############################################################################
# create ensemble of realizations and gather statistics
spiceMax = myNSIDE * 3 # should be lmax+1 for SpICE
ClEnsembleCut = np.zeros([nSims, spiceMax])
simEll = np.arange(spiceMax)
doTime = True # to time the run and print output
startTime = time.time()
for nSim in range(nSims):
print 'starting masked Cl sim ', nSim + 1, ' of ', nSims
alm_prim, alm_late = hp.synalm((primCl, lateCl, crossCl),
lmax=lmax,
new=True)
mapSim = hp.alm2map(alm_prim + alm_late, myNSIDE, lmax=lmax)
hp.write_map(mapTempFile, mapSim)
ispice(mapTempFile,
ClTempFile,
maskfile1=maskDegFile,
subav="YES",
subdipole="YES")
ClEnsembleCut[nSim] = hp.read_cl(ClTempFile)
doPlot = False #True
if doPlot:
gcp.showCl(simEll[:lmax + 1],
ClEnsembleCut[nSim, :lmax + 1],
title='power spectrum of simulation ' + str(nSim + 1))
timeInterval1 = time.time() - startTime
if doTime: print 'time elapsed: ', int(timeInterval1 / 60.), ' minutes'
# free the RAM used by SpICE's RAM disk
ramDiskOutput = subprocess.check_output('./ramdisk.sh delete ' + diskID,
shell=True)
print ramDiskOutput
# put SMICA in as 0th member of the ensemble; 1e12 to convert K^2 to microK^2
ClEnsembleCut = np.vstack((ClsmicaCut * 1e12, ClEnsembleCut))
nSims += 1
##############################################################################
# create S(x) for each C_l, using interpolation
nXvals = 181
thetaVals = np.linspace(0, 180, nXvals) # one degree intervals
xVals = np.cos(thetaVals * np.pi / 180)
Jmnx = np.empty([nXvals, lmax + 1, lmax + 1])
for index, xVal in enumerate(xVals):
Jmnx[index] = legprodint.getJmn(endX=xVal, lmax=lmax, doSave=False)
SxToInterpolate = np.empty(nXvals)
# create list of functions
dummy = lambda x: x**2
SofXList = [dummy for i in range(nSims)]
for nSim in range(nSims):
print 'starting S(x) sim ', nSim + 1, ' of ', nSims
for index, xVal in enumerate(xVals):
SxToInterpolate[index] = np.dot(
ClEnsembleCut[nSim, lmin:lmax + 1],
np.dot(Jmnx[index, lmin:, lmin:],
ClEnsembleCut[nSim, lmin:lmax + 1]))
SofX = interp1d(xVals, SxToInterpolate)
#SofXList = SofXList.append(SofX)
# Apparently appending a function to an empty list is not allowed. Instead:
SofXList[nSim] = SofX
#print SofXList#[nSim]
doPlot = False #True
if doPlot:
nplotx = (nXvals - 1) * 10 + 1
plotTheta = np.linspace(0, 180, nplotx)
plotx = np.cos(plotTheta * np.pi / 180)
plotS = SofXList[nSim](plotx)
plt.plot(plotx, plotS)
plt.title('S(x) for simulation ' + str(nSim + 1))
plt.show()
doPlot = True
if doPlot:
for nSim in range(nSims):
nplotx = (nXvals - 1) * 10 + 1
plotTheta = np.linspace(0, 180, nplotx)
plotx = np.cos(plotTheta * np.pi / 180)
plotS = SofXList[nSim](plotx)
plt.plot(plotx, plotS, label='sim ' + str(nSim + 1))
#plt.legend()
plt.title('S(x) for ' + str(nSims) + ' simulations')
plt.xlabel('x')
plt.ylabel('S_x')
plt.show()
##############################################################################
# create Pval(x) for each S(x), using ensemble
# Pval: probability of result equal to or more extreme
# create list of functions
PvalOfXList = [dummy for i in range(nSims)]
for nSim in range(nSims):
print 'starting Pval(x) sim ', nSim + 1, ' of ', nSims
def PvalOfX(x):
nUnder = 0 # will also include nEqual
nOver = 0
threshold = SofXList[nSim](x)
for nSim2 in range(nSims):
Sx = SofXList[nSim2](x)
if Sx > threshold:
nOver += 1
#print "Over! mySx: ",Sx,", threshold: ",threshold
else:
nUnder += 1
#print "Under! mySx: ",Sx,", threshold: ",threshold
#print "nUnder: ",nUnder,", nOver: ",nOver
return nUnder / float(nUnder + nOver)
PvalOfXList[nSim] = PvalOfX
##############################################################################
# find global minimum for each Pval(x)
# simply use same xVals as above, at one degree intervals
# if there are equal p-values along the range, the one with the highest xVal
# will be reported
PvalMinima = np.empty(nSims)
xValMinima = np.empty(nSims)
doTime = True # to time the run and print output
startTime = time.time()
for nSim in range(nSims):
print 'starting minimum Pval(x) search for sim ', nSim + 1, ' of ', nSims
PvalOfX = PvalOfXList[nSim]
#print 'function: ',PvalOfX
PvalMinima[nSim] = PvalOfX(1.0)
xValMinima[nSim] = 1.0
Pvals = np.empty(nXvals)
for index, xVal in enumerate(
xVals): # will start from 1 and go down to -1
myPval = PvalOfX(xVal)
Pvals[index] = myPval
#print "nSim: ",nSim,", n: ",index,", myPval: ",myPval,", PvalMinima[nSim]: ",PvalMinima[nSim]
if myPval < PvalMinima[
nSim] and xVal > -0.999: #avoid the instabililility
PvalMinima[nSim] = myPval
xValMinima[nSim] = xVal
#print 'nSim: ',nSim+1,', new x for minimum Pval: ',xVal
#raw_input("Finished sim "+str(nSim+1)+" of "+str(nSims)+". Press enter to continue")
doPlot = True #False#True
if doPlot: # and np.random.uniform() < 0.1: #randomly choose about 1/10 of them
plt.plot(xVals, Pvals)
plt.vlines(xValMinima[nSim], 0, 1)
plt.xlabel('x = cos(theta), min at ' + str(xValMinima[nSim]))
plt.ylabel('P-value')
plt.title('P-values for simulation ' + str(nSim + 1) + ' of ' +
str(nSims) + ', p_min = ' + str(PvalMinima[nSim]))
plt.xlim(-1.05, 1.05)
plt.ylim(-0.05, 1.05)
plt.show()
timeInterval2 = time.time() - startTime
if doTime: print 'time elapsed: ', int(timeInterval2 / 60.), ' minutes'
"""
# A MYSTERY! Something about the following code causes Pvals to always take
# the values of PvalOfXList[nSims](xVals) WTF? Omit for now.
# Testing seems to indicate that PvalOfXList functions still have different
# locations in memory, but they all seem to be evaluating the same.
# However, when the previous block of code is copied to come again after
# this one, it behaves properly again.
# see how well it did
doPlot = False#True
if doPlot:
nPlots = 10
for nPlot in range(nPlots):
print 'plot ',nPlot+1,' of ',nPlots
toPlot = nPlot#np.random.randint(0,high=nSims)
#for nSim in range(nSims):
Pvals = np.empty(nXvals)
PvalOfX = PvalOfXList[nPlot]
print 'function: ',PvalOfX
for index, xVal in enumerate(xVals):
Pvals[index] = PvalOfX(xVal)
#print index,Pvals[index]
#print Pvals
plt.plot(xVals,Pvals)
plt.vlines(xValMinima[toPlot],0,1)
plt.xlabel('x = cos(theta), min at '+str(xValMinima[toPlot]))
plt.ylabel('P-value')
plt.title('P-values for simulation '+str(toPlot+1)+' of '+str(nSims))
plt.show()
"""
##############################################################################
# create distribution of S(xValMinima)
SxEnsembleMin = np.empty(nSims)
for nSim in range(nSims):
SxEnsembleMin[nSim] = SofXList[nSim](xValMinima[nSim])
# extract SMICA result
Ssmica = SxEnsembleMin[0]
##############################################################################
# plot/print results
print 'plotting S_x distribution... '
myBins = np.logspace(1, 7, 100)
plt.axvline(x=Ssmica, color='g', linewidth=3, label='SMICA masked')
plt.hist(SxEnsembleMin[1:], bins=myBins, histtype='step', label='cut sky')
# [1:] to omit SMICA value
plt.gca().set_xscale("log")
plt.legend()
plt.xlabel('S_x (microK^4)')
plt.ylabel('Counts')
plt.title('S_x of ' + str(nSims - 1) +
' simulated CMBs') #-1 due to SMICA in zero position
plt.show()
print ' '
print 'nSims = ', nSims - 1
print 'time interval 1: ', timeInterval1, 's, time interval 2: ', timeInterval2, 's'
print ' => ', timeInterval1 / (nSims - 1), ' s/sim, ', timeInterval2 / (
nSims - 1), ' s/sim'
print 'SMICA optimized S_x: S = ',Ssmica,', for x = ',xValMinima[0], \
', with p-value ',PvalMinima[0]
print ' '
print 'step 3: profit'
print ''
def test_pixwin_pol(self):
pixwin = hp.pixwin(128, pol=True)
self.assertEqual(len(pixwin), 2)
#theoretical power spectra.
cl = qcinv.util.camb_clfile('inputs/wmap/bestfit_lensedCls.dat', lmax=lmax)
# noise level
# http://lambda.gsfc.nasa.gov/product/map/dr4/skymap_info.cfm
ninv = hp.read_map("inputs/wmap/wmap_band_forered_iqumap_r9_7yr_V_v4.fits", field=3) / (3.137*1.0e3)**2
assert( len(ninv) == npix )
ncov = np.zeros(npix)
ncov[ np.where(ninv != 0) ] = 1.0 / ninv[ np.where(ninv != 0) ]
assert( npix == len(ninv) )
beam = 0.5 * (np.loadtxt('inputs/wmap/wmap_ampl_bl_V1_7yr_v4.txt')[:,1][0:(lmax+1)] + np.loadtxt('inputs/wmap/wmap_ampl_bl_V2_7yr_v4.txt')[:,1][0:(lmax+1)])
beam *= hp.pixwin(nside)[0:(lmax+1)]
clnn = np.sum(ncov) * (4.*np.pi) / npix**2 / beam**2
ncov *= mask
ninv *= mask
# noise map
print "generating noise map"
nmap = np.random.standard_normal(npix) * np.sqrt(ncov)
# cmb map
print "generating cmb map"
tmap, talm = hp.synfast(cl.cltt * beam * beam, nside, lmax=lmax, alm=True)
hp.almxfl(talm, 1.0/beam, inplace=True)
def get_beam(self, det):
pxw = hp.pixwin(self.nside)[0:self.lmax+1]
beam = dmc.get_bl(self.year, det)[0:self.lmax+1]
return beam * pxw
lmax = len(cross_cls) beam_lmax = lmax l = np.arange(beam_lmax) ll = l*(l+1)/(2*np.pi) beam_14 = hp.gauss_beam(14.4*np.pi/(180.*60.),beam_lmax-1) beam_5 = hp.gauss_beam(5.*np.pi/(180.*60.),beam_lmax-1) b14_180 = hp.gauss_beam(np.sqrt((3*60.)**2 - (14.4)**2)*np.pi/(180.*60.),beam_lmax-1) b5_180 = hp.gauss_beam(np.sqrt((3*60.)**2 - (5.)**2)*np.pi/(180.*60.),beam_lmax-1) beam_14 *= (1. - b14_180) beam_5 *= (1. - b5_180) pix = hp.pixwin(256)[:beam_lmax] theory_cls= hp.read_cl(theory_cl_file) theory_cls=theory_cls[0][:beam_lmax] #theory_cls[:2]=1e-10 cross_cls = cross_cls[:beam_lmax] radio_cls = radio_cls[:beam_lmax] cmb_cls = cmb_cls[:beam_lmax] wls = hp.anafast((~radio_fr.mask).astype(float))[:beam_lmax] fskyw2 = np.sum([(2*m+1)*wls[mi] if m > 0 else 0 for mi,m in enumerate(xrange(len(wls)))])/(4*np.pi) wls = wls fsky = 1. - np.sum(mask_bool).astype(float)/len(mask_bool) L = np.sqrt(4*np.pi*fsky)
plot(lll,np.sqrt(abs(spectra[4])*(lll*(lll+1))/(2*np.pi)),label='$C_\ell^{TE}$')
plot(lll,np.sqrt(spectra[2]*(lll*(lll+1))/(2*np.pi)),label='$C_\ell^{EE}$')
plot(lll,np.sqrt(spectra[3]*(lll*(lll+1))/(2*np.pi)),label='$C_\ell^{BB}$')
yscale('log')
xlim(0,lmaxcamb+1)
ylim(0.0001,100)
xlabel('$\ell$')
ylabel('$\sqrt{\ell(\ell+1)C_\ell/(2\pi)}$'+' '+'$[\mu K]$ ')
legend(loc='lower right',frameon=False)
#############################################################################
nside = 128
lmax = 2*nside-1
maps=hp.synfast(spectra[1:],nside,fwhm=0,pixwin=True,new=True)
pw = hp.pixwin(nside, pol=True)
lmax = 1*nside-1
cls1 = hp.anafast(maps,pol=True, lmax=lmax)
ell1= np.arange(lmax+1)
pw1 = [pw[0][0:lmax+1], pw[1][0:lmax+1]]
lmax = 2*nside-1
cls2 = hp.anafast(maps,pol=True, lmax=lmax)
ell2= np.arange(lmax+1)
pw2 = [pw[0][0:lmax+1], pw[1][0:lmax+1]]
lmax = 3*nside-1
cls3 = hp.anafast(maps,pol=True, lmax=lmax)
ell3= np.arange(lmax+1)
pw3 = [pw[0][0:lmax+1], pw[1][0:lmax+1]]
def covth_bins(ellbins,nside,ipok,bl,spectra,polar=True,temp=True,allinone=True):
#### define bins in ell
nbins=len(ellbins)-1
minell=np.array(ellbins[0:nbins])
maxell=np.array(ellbins[1:nbins+1])-1
ellval=(minell+maxell)*0.5
lmax=np.max(ellbins)
#maxell[nbins-1]=lmax
print('minell:',minell)
print('maxell:',maxell)
#### define Stokes
all=['I','Q','U']
if not temp:
all=['Q','U']
if not polar:
all=['I']
#### define pixels
print('rpix calculation')
rpix=np.array(hp.pix2vec(nside,ipok))
print('rpix done: ',rpix.shape)
allcosang=np.dot(np.transpose(rpix),rpix)
print('cosang done')
#### define Pixel window function
pixwin=hp.pixwin(nside)[0:lmax+1]
#### get ell values from spectra and restrict spectra to good values
nspec,nell=np.shape(spectra)
ell=spectra[0]
maskl=ell<(lmax+1)
ell=ell[maskl]
ctt=spectra[1][maskl]
cee=spectra[2][maskl]
cbb=spectra[3][maskl]
cte=spectra[4][maskl]
if nspec==5:
print('Assuming EB and TB are zero')
ceb=0
ctb=0
else:
ceb=spectra[5][maskl]
ctb=spectra[6][maskl]
# Effective spectra
print('Calculating effective spectra')
norm=(2*ell+1)/(4*np.pi)*(pixwin**2)*(bl[maskl]**2)
norm[1:]=norm[1:]/(ell[1:]*(ell[1:]+1))
effctt=norm*ctt
effcte=norm*cte
effcee=norm*cee
effcbb=norm*cbb
effceb=norm*ceb
effctb=norm*ctb
#### define masks for ell bins
masks=[]
for i in np.arange(nbins):
masks.append((ell>=minell[i]) & (ell<=maxell[i]))
### Create array for covariances matrices per bin
nbpixok=ipok.size
nstokes=np.size(all)
print('creating array')
cov=np.zeros((nbins,nstokes,nstokes,nbpixok,nbpixok))
for i in np.arange(nbpixok):
print(i)
pyquad.progress_bar(i,nbpixok)
for j in np.arange(i,nbpixok):
if nstokes==1:
pl=pl0(allcosang[i,j],lmax)
TT=effctt*pl
for b in np.arange(nbins):
cov[b,0,0,i,j]=np.sum(TT[masks[b]])*ellval[b]*(ellval[b]+1)
cov[b,0,0,j,i]=cov[b,0,0,i,j]
if nstokes==2:
cij,sij=polrotangle(rpix[:,i],rpix[:,j])
cji,sji=polrotangle(rpix[:,j],rpix[:,i])
f12=F1l2(allcosang[i,j],lmax)
f22=F2l2(allcosang[i,j],lmax)
QQ=f12*effcee-f22*effcbb
UU=f12*effcbb-f22*effcee
QU=(f12+f22)*effceb
cQQpsQU = ( cij*QQ + sij*QU )
cQUpsUU = ( cij*QU + sij*UU )
cQUmsQQ = ( cij*QU - sij*QQ )
cUUmsQU = ( cij*UU - sij*QU )
for b in np.arange(nbins):
cov[b,0,0,i,j] = np.sum( cji*cQQpsQU[masks[b]] + sji*cQUpsUU[masks[b]] )*ellval[b]*(ellval[b]+1)
cov[b,0,0,j,i] = cov[b,0,0,i,j]
cov[b,0,1,i,j] = np.sum(-sji*cQQpsQU[masks[b]] + cji*cQUpsUU[masks[b]] )*ellval[b]*(ellval[b]+1)
cov[b,1,0,j,i] = cov[b,0,1,i,j]
cov[b,1,1,i,j] = np.sum(-sji*cQUmsQQ[masks[b]] + cji*cUUmsQU[masks[b]] )*ellval[b]*(ellval[b]+1)
cov[b,1,1,j,i] = cov[b,1,1,i,j]
cov[b,0,1,j,i] = np.sum( cji*cQUmsQQ[masks[b]] + sji*cUUmsQU[masks[b]] )*ellval[b]*(ellval[b]+1)
cov[b,1,0,i,j] = cov[b,0,1,j,i]
if nstokes==3:
cij,sij=polrotangle(rpix[:,i],rpix[:,j])
cji,sji=polrotangle(rpix[:,j],rpix[:,i])
pl=pl0(allcosang[i,j],lmax)
f10=F1l0(allcosang[i,j],lmax)
f12=F1l2(allcosang[i,j],lmax)
f22=F2l2(allcosang[i,j],lmax)
TT=effctt*pl
QQ=f12*effcee-f22*effcbb
UU=f12*effcbb-f22*effcee
TQ=-f10*effcte
TU=-f10*effctb
QU=(f12+f22)*effceb
cQQpsQU = ( cij*QQ + sij*QU )
cQUpsUU = ( cij*QU + sij*UU )
cQUmsQQ = ( cij*QU - sij*QQ )
cUUmsQU = ( cij*UU - sij*QU )
for b in np.arange(nbins):
cov[b,0,0,i,j] = np.sum( TT[masks[b]] )*ellval[b]*(ellval[b]+1)
cov[b,0,0,j,i] = cov[b,0,0,i,j]
cov[b,0,1,i,j] = np.sum( cji*TQ[masks[b]] + sji*TU[masks[b]] )*ellval[b]*(ellval[b]+1)
cov[b,1,0,j,i] = cov[b,0,1,i,j]
cov[b,1,0,i,j] = np.sum( cij*TQ[masks[b]] + sij*TU[masks[b]] )*ellval[b]*(ellval[b]+1)
cov[b,0,1,j,i] = cov[b,1,0,i,j]
cov[b,0,2,i,j] = np.sum(-sji*TQ[masks[b]] + cij*TU[masks[b]] )*ellval[b]*(ellval[b]+1)
cov[b,2,0,j,i] = cov[b,0,2,i,j]
cov[b,2,0,i,j] = np.sum(-sij*TQ[masks[b]] + cij*TU[masks[b]] )*ellval[b]*(ellval[b]+1)
cov[b,0,2,j,i] = cov[b,2,0,i,j]
cov[b,1,1,i,j] = np.sum( cji*cQQpsQU[masks[b]] + sji*cQUpsUU[masks[b]] )*ellval[b]*(ellval[b]+1)
cov[b,1,1,j,i] = cov[b,1,1,i,j]
cov[b,1,2,i,j] = np.sum(-sji*cQQpsQU[masks[b]] + cji*cQUpsUU[masks[b]] )*ellval[b]*(ellval[b]+1)
cov[b,2,1,j,i] = cov[b,1,2,i,j]
cov[b,2,1,i,j] = np.sum( cji*cQUmsQQ[masks[b]] + sji*cUUmsQU[masks[b]] )*ellval[b]*(ellval[b]+1)
cov[b,1,2,j,i] = cov[b,2,1,i,j]
cov[b,2,2,i,j] = np.sum(-sji*cQUmsQQ[masks[b]] + cji*cUUmsQU[masks[b]] )*ellval[b]*(ellval[b]+1)
cov[b,2,2,j,i] = cov[b,2,2,i,j]
if allinone:
newcov=np.zeros((nbins,nstokes*nbpixok,nstokes*nbpixok))
for i in np.arange(nbins):
for si in np.arange(nstokes):
for sj in np.arange(nstokes):
newcov[i,si*nbpixok:(si+1)*nbpixok,sj*nbpixok:(sj+1)*nbpixok]=cov[i,si,sj,:,:]
return(newcov)
else:
return(cov)
def pixfunc(self):
if self._pixfunc is None:
self._pixfunc = hp.pixwin(self.nside, pol=False)[:self.lmax]
return self._pixfunc
# add noise
map=mapin.copy()
map[mask]+=np.random.randn(npix)*signoise
allmaps[:,i]=map[mask]
allmaps_nosig[:,i]=map[mask]-mapin[mask]
mcl=np.zeros(251)
for i in arange(251):
mcl[i]=np.mean(allcl[i,:])
clf()
plot(ell,spectra[1]*(ell*(ell+1))/(2*np.pi),'r')
xlim(0,251)
thel=np.arange(251)
thebell=np.exp(-0.5*(thel**2)*((fwhm/2.35)**2))
pixwin=hp.pixwin(hp.npix2nside(len(mapin)))[0:251]
plot(thel,mcl*thel*(thel+1)/(2*np.pi)/thebell**2/pixwin**2)
covmc=np.zeros((npix,npix))
covmc_nonoise=np.zeros((npix,npix))
covmc_nosig=np.zeros((npix,npix))
for i in np.arange(npix):
print(i)
for j in np.arange(i,npix):
covmc[i,j]=mean( (allmaps[i,:]-mean(allmaps[i,:]))*(allmaps[j,:]-mean(allmaps[j,:])))
covmc_nonoise[i,j]=mean( (allmaps_nonoise[i,:]-mean(allmaps_nonoise[i,:]))*(allmaps_nonoise[j,:]-mean(allmaps_nonoise[j,:])))
covmc_nosig[i,j]=mean( (allmaps_nosig[i,:]-mean(allmaps_nosig[i,:]))*(allmaps_nosig[j,:]-mean(allmaps_nosig[j,:])))
covmc[j,i]=covmc[i,j]
covmc_nosig[j,i]=covmc_nosig[i,j]
def fix_for_mask(config, logger=log.null_logger):
with log.Timer(logger,
'Step 4: Accounting for mask').with_level(logging.INFO):
coupling_matrix_fname = config.get('mask_coupling_matrix_fname')
# If the coupling matrix is not specified, we need to make it
if not coupling_matrix_fname:
output_file = os.path.join(config['output_dir'],
'master_deconv_spectra.fits')
maxl = config.get('matmask_maxl', 2500)
# mrsp_matmask -t -w -v -z -l 1024 -o TQU <smap> <mask> <output_file>
cmd = [
matmask_command, '-t', '-w', '-v', '-z', '-l',
str(maxl), '-o', 'TQU', config['aggregated_map_fname'],
config['mask_fname'], output_file
]
rc = log.run_command(cmd, timeout=5e-2, logger=logger)
if rc != 0:
raise RuntimeError(matmask_command +
' failed with exit code: ' + str(rc))
# Outputs are:
# master_deconv_spectra.fits
# master_deconv_spectra_maskedmap_pspec.fits
# master_deconv_spectra_mask_pspec.fits
# master_deconv_spectra_mask_spec_radii.fits
# master_deconv_spectra_coupling_matrices.fits
if config.get('delete_tmps', True):
tmps = [
os.path.join(config['output_dir'], fname) for fname in (
'master_deconv_spectra.fits',
'master_deconv_spectra_maskedmap_pspec.fits',
'master_deconv_spectra_mask_pspec.fits',
'master_deconv_spectra_mask_spec_radii.fits',
'master_deconv_spectra_coupling_matrices.fits')
]
for tmp in tmps:
logger.debug('deleting ' + tmp)
os.remove(tmp)
coupling_matrix_fname = os.path.join(
config['output_dir'],
'master_deconv_spectra_coupling_matrices.fits')
coupling_matrix = fitsio.FITS(coupling_matrix_fname)[0].read()
# FIXME: mrsp_matmask does not produce the correct spectrum, but it does
# produce the correct coupling matrix. we can use the coupling matrix
# and invert it ourselves.
# TODO: get to the bottom of this. Why does mrsp_matmask give the wrong
# answers? Can Florent or JLS figure this out?
mask = hp.read_map(config['mask_fname'], verbose=False)
aggregated_cmb = hp.read_map(config['aggregated_map_fname'],
verbose=False)
# 1e6 to convert K -> mu K
masked_powerspec = hp.anafast(mask * 1e6 * aggregated_cmb,
lmax=coupling_matrix.shape[0] - 1)
recovered_pspec = np.linalg.solve(coupling_matrix, masked_powerspec)
ells = np.arange(recovered_pspec.size)
ll1 = ells * (ells + 1) / (2 * np.pi) / hp.pixwin(
2048, lmax=ells.size - 1)**2
hp.write_cl(
os.path.join(config['output_dir'], 'mask_corrected_spectra.fits'),
ll1 * recovered_pspec)
clnoiseless[j,:,:] = np.mean(allclnoiseless, axis = 2) dclnoiseless[j,:,:] = np.std(allclnoiseless, axis = 2) clnoisy[j,:,:] = np.mean(allclnoisy, axis = 2) dclnoisy[j,:,:] = np.std(allclnoisy, axis = 2) clres[j,:,:] = np.mean(allclres, axis = 2) dclres[j,:,:] = np.std(allclres, axis = 2) clspoiled_noiseless[j,:,:] = np.mean(allclspoiled_noiseless, axis = 2) dclspoiled_noiseless[j,:,:] = np.std(allclspoiled_noiseless, axis = 2) clspoiled_noisy[j,:,:] = np.mean(allclspoiled_noisy, axis = 2) dclspoiled_noisy[j,:,:] = np.std(allclspoiled_noisy, axis = 2) clspoiled_res[j,:,:] = np.mean(allclspoiled_res, axis = 2) dclspoiled_res[j,:,:] = np.std(allclspoiled_res, axis = 2) #### Pixel Window function wpix = hp.pixwin(ns,pol=True) #### Synthesized Beam window function fwhm = 0.47 ll = np.arange(len(wpix[0])) bl = exp(-ll**2*(np.radians(fwhm/2.35))**2/2) #### Correction corrT = np.interp(newl, ll, wpix[0]**2*bl**2) corrP = np.interp(newl, ll, wpix[1]**2*bl**2) corr=corrT cl = clinit col = ['black','red', 'blue', 'green', 'magenta'] clf() for i in np.arange(len(sigptgvals)): for j in np.arange(6): subplot(2,3,j+1)
def plot_mc():
f=open('cl_theory_FR_QxaU.json','r')
theory1_array_in=json.load(f)
f.close()
f=open('cl_theory_FR_UxaQ.json','r')
theory2_array_in=json.load(f)
f.close()
f=open('cl_array_FR_QxaU.json','r')
cross1_array_in=json.load(f)
f.close()
f=open('cl_array_FR_UxaQ.json','r')
cross2_array_in=json.load(f)
f.close()
# f=open('cl_noise_FR_QxaU.json','r')
# noise1_array_in=json.load(f)
# f.close()
# f=open('cl_noise_FR_UxaQ.json','r')
# noise2_array_in=json.load(f)
# f.close()
f=open('cl_Nau_FR_QxaU.json','r')
Nau_array_in=json.load(f)
f.close()
f=open('cl_Ndq_FR_QxaU.json','r')
Ndq_array_in=json.load(f)
f.close()
f=open('cl_Naq_FR_UxaQ.json','r')
Naq_array_in=json.load(f)
f.close()
f=open('cl_Ndu_FR_UxaQ.json','r')
Ndu_array_in=json.load(f)
f.close()
bins=[1,5,10,20,25,50]
N_runs=500
# bls=hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),383)**2
#bls=hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),3*nside_out-1)**2
bls=(hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),3*nside_out-1)*hp.pixwin(nside_out)[:3*nside_out])**2
#bls=np.repeat(1,3*nside_out)
fsky=225.*(np.pi/180.)**2/(4*np.pi)
l=np.arange(len(cross1_array_in[0]))
ll=l*(l+1)/(2*np.pi)
L=np.sqrt(fsky*4*np.pi)
dl_eff=2*np.pi/L
theory1_array_in=np.array(theory1_array_in)/(fsky*bls)
theory2_array_in=np.array(theory2_array_in)/(fsky*bls)
cross1_array_in=np.array(cross1_array_in)/(fsky*bls)
cross2_array_in=np.array(cross2_array_in)/(fsky*bls)
Ndq_array_in=np.array(Ndq_array_in)/(fsky)
Ndu_array_in=np.array(Ndu_array_in)/(fsky)
Nau_array_in=np.array(Nau_array_in)/(fsky)
Naq_array_in=np.array(Naq_array_in)/(fsky)
#noise1_array_in=np.array(noise1_array_in)/(fsky*bls)
#noise2_array_in=np.array(noise2_array_in)/(fsky*bls)
Ndq_array_in.shape += (1,)
Ndu_array_in.shape += (1,)
Nau_array_in.shape += (1,)
Naq_array_in.shape += (1,)
for b in bins:
theory_cls=hp.read_cl('/home/matt/Planck/data/faraday/correlation/fr_theory_cl.fits')
# N_dq=np.mean(Ndq_array_in)
# N_au=np.mean(Nau_array_in)
# #delta1=np.sqrt(2.*abs((np.mean(cross1_array_in,axis=0)-np.mean(noise1_array_in,axis=0))**2+(np.mean(cross1_array_in,axis=0)-np.mean(noise1_array_in,axis=0))/2.*(N_dq+N_au)+N_dq*N_au/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
# delta1=np.sqrt(2.*((np.mean(theory1_array_in,axis=0))**2+(np.mean(theory1_array_in,axis=0))/2.*(N_dq+N_au)+N_dq*N_au/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
#
cosmic1=np.sqrt(2./((2.*l+1)*np.sqrt(b**2+dl_eff**2)*fsky)*np.mean(theory1_array_in,axis=0)**2)
# N_du=np.mean(Ndu_array_in)
# N_aq=np.mean(Naq_array_in)
# #delta2=np.sqrt(2.*abs((np.mean(cross2_array_in,axis=0)-np.mean(noise2_array_in,axis=0))**2+(np.mean(cross2_array_in,axis=0)-np.mean(noise2_array_in,axis=0))/2.*(N_dq+N_au)+N_dq*N_au/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
# delta2=np.sqrt(2.*((np.mean(theory2_array_in,axis=0))**2+(np.mean(theory2_array_in,axis=0))/2.*(N_du+N_aq)+N_du*N_aq/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
cosmic2=np.sqrt(2./((2*l+1)*np.sqrt(b**2+dl_eff**2)*fsky)*np.mean(theory2_array_in,axis=0)**2)
theory1_array=[]
theory2_array=[]
cross1_array=[]
cross2_array=[]
# noise1_array=[]
# noise2_array=[]
Ndq_array=[]
Ndu_array=[]
Nau_array=[]
Naq_array=[]
plot_l=[]
if( b != 1):
tmp_t1=bin_llcl.bin_llcl(ll*theory1_array_in,b)
tmp_t2=bin_llcl.bin_llcl(ll*theory2_array_in,b)
tmp_c1=bin_llcl.bin_llcl(ll*cross1_array_in,b)
tmp_c2=bin_llcl.bin_llcl(ll*cross2_array_in,b)
# tmp_n1=bin_llcl.bin_llcl(ll*noise1_array_in,b)
# tmp_n2=bin_llcl.bin_llcl(ll*noise2_array_in,b)
theory1_array=tmp_t1['llcl']
theory2_array=tmp_t2['llcl']
theory1_array.shape += (1,)
theory2_array.shape += (1,)
theory1_array=theory1_array.T
theory2_array=theory2_array.T
plot_l= tmp_t1['l_out']
cross1_array=tmp_c1['llcl']
cross2_array=tmp_c2['llcl']
# noise1_array=tmp_n1['llcl']
# noise2_array=tmp_n2['llcl']
Ndq_array=bin_llcl.bin_llcl(ll*Ndq_array_in,b)['llcl']
Ndu_array=bin_llcl.bin_llcl(ll*Ndu_array_in,b)['llcl']
Naq_array=bin_llcl.bin_llcl(ll*Naq_array_in,b)['llcl']
Nau_array=bin_llcl.bin_llcl(ll*Nau_array_in,b)['llcl']
tmp_c1=bin_llcl.bin_llcl((ll*cosmic1)**2,b)
#tmp_d1=bin_llcl.bin_llcl((ll*delta1)**2,b)
cosmic1=np.sqrt(tmp_c1['llcl'])
#delta1=np.sqrt(tmp_d1['llcl'])
tmp_c2=bin_llcl.bin_llcl((ll*cosmic2)**2,b)
#tmp_d2=bin_llcl.bin_llcl((ll*delta2)**2,b)
cosmic2=np.sqrt(tmp_c2['llcl'])
#delta2=np.sqrt(tmp_d2['llcl'])
t_tmp=bin_llcl.bin_llcl(ll*theory_cls,b)
theory_cls=t_tmp['llcl']
else:
plot_l=l
theory1_array=np.multiply(ll,theory1_array_in)
cross1_array=np.multiply(ll,cross1_array_in)
# noise1_array=np.multiply(ll,noise1_array_in)
theory2_array=np.multiply(ll,theory2_array_in)
cross2_array=np.multiply(ll,cross2_array_in)
# noise2_array=np.multiply(ll,noise2_array_in)
cosmic1*=ll
cosmic2*=ll
#delta1*=ll
#delta2*=ll
Ndq_array=np.multiply(ll,Ndq_array_in)
Ndu_array=np.multiply(ll,Ndu_array_in)
Naq_array=np.multiply(ll,Naq_array_in)
Nau_array=np.multiply(ll,Nau_array_in)
theory_cls*=ll
#ipdb.set_trace()
bad=np.where(plot_l < 24)
N_dq=np.mean(Ndq_array,axis=0)
N_du=np.mean(Ndu_array,axis=0)
N_aq=np.mean(Naq_array,axis=0)
N_au=np.mean(Nau_array,axis=0)
#noise1=np.mean(noise1_array,axis=0)
#noise2=np.mean(noise2_array,axis=0)
theory1=np.mean(theory1_array,axis=0)
theory2=np.mean(theory1_array,axis=0)
theory_array = np.add(theory1_array,theory2_array)
theory=np.mean(theory_array,axis=0)
#dtheory=np.sqrt(np.var(theory1_array,ddof=1) + np.var(theory2_array,ddof=1))
#cross_array = np.add(np.subtract(cross1_array,noise1),np.subtract(cross2_array,noise2))
cross_array = np.add(cross1_array,cross2_array)
cross=np.mean(cross_array,axis=0)
#dcross=np.std(cross_array,axis=0,ddof=1)
dcross=np.sqrt( ( np.var(cross1_array,axis=0,ddof=1) + np.var(cross2_array,axis=0,ddof=1)))
cosmic=np.sqrt(cosmic1**2+cosmic2**2)
delta1=np.sqrt(2./((2*plot_l+1)*fsky*np.sqrt(b**2+dl_eff**2))*(theory1**2 + theory1*(N_dq+N_au)/2. + N_dq*N_au/2.))
delta2=np.sqrt(2./((2*plot_l+1)*fsky*np.sqrt(b**2+dl_eff**2))*(theory2**2 + theory2*(N_du+N_aq)/2. + N_du*N_aq/2.))
delta=np.sqrt(delta1**2+delta2**2)
#cosmic=np.abs(theory_cls)*np.sqrt(2./((2*plot_l+1)*fsky*np.sqrt(dl_eff**2+b**2)))
#theory1=np.mean(theory1_array,axis=0)
#dtheory1=np.std(theory1_array,axis=0,ddof=1)
#cross1=np.mean(cross1_array,axis=0)
#dcross1=np.std(np.subtract(cross1_array,noise1),axis=0,ddof=1)
#ipdb.set_trace()
good_l=np.logical_and(plot_l <= 250,plot_l >25)
plot_binned.plotBinned((cross)*1e12,dcross*1e12,plot_l,b,'Cross_43x95_FR', title='QUIET FR Correlator',theory=theory*1e12,delta=delta*1e12,cosmic=cosmic*1e12)
#theory2=np.mean(theory2_array,axis=0)
#dtheory2=np.std(theory2_array,axis=0,ddof=1)
#cross2=np.mean(cross2_array,axis=0)
##delta2=np.mean(delta2_array,axis=0)
#dcross2=np.std(np.subtract(cross2_array,noise2),axis=0,ddof=1)
##ipdb.set_trace()
#plot_binned.plotBinned((cross2-noise2)*1e12,dcross2*1e12,plot_l,b,'Cross_43x95_FR_UxaQ', title='Cross 43x95 FR UxaQ',theory=theory2*1e12,dtheory=dtheory2*1e12,delta=delta2*1e12,cosmic=cosmic2*1e12)
#ipdb.set_trace()
if b == 25 :
good_l=np.logical_and(plot_l <= 250,plot_l >25)
likelihood(cross[good_l],delta[good_l],theory[good_l],'field1','c2bfr')
#if b == 1 :
# xbar= np.matrix(ll[1:]*(cross-np.mean(cross))[1:]).T
# vector=np.matrix(ll[1:]*cross[1:]).T
# mu=np.matrix(ll[1:]*theory[1:]).T
# fact=len(xbar)-1
# cov=(np.dot(xbar,xbar.T)/fact).squeeze()
## ipdb.set_trace()
# U,S,V =np.linalg.svd(cov)
# _cov= np.einsum('ij,j,jk', V.T,1./S,U.T)
# likelhd=np.exp(-np.dot(np.dot((vector-mu).T,_cov),(vector-mu))/2. )/(np.sqrt(2*np.pi*np.prod(S)))
## print('Likelihood of fit is #{0:.5f}'.format(likelihood[0,0]))
# f=open('FR_likelihood.txt','w')
# f.write('Likelihood of fit is #{0:.5f}'.format(likelhd[0,0]))
# f.close()
subprocess.call('mv *01*.png bin_01/', shell=True)
subprocess.call('mv *05*.png bin_05/', shell=True)
subprocess.call('mv *10*.png bin_10/', shell=True)
subprocess.call('mv *20*.png bin_20/', shell=True)
subprocess.call('mv *25*.png bin_25/', shell=True)
subprocess.call('mv *50*.png bin_50/', shell=True)
subprocess.call('mv *.eps eps/', shell=True)
def test_pixwin_pol(self):
pixwin = hp.pixwin(128, pol=True)
self.assertEqual(len(pixwin), 2)
def main():
##Parameters for Binning, Number of Runs
## Beam correction
use_beam=0
# bls=hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),383)**2
#bls=hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),3*nside_out-1)**2
bls=(hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),3*nside_out-1)*hp.pixwin(nside_out)[:3*nside_out])**2
N_runs=500
bins=[1,5,10,20,50]
map_prefix='/home/matt/quiet/quiet_maps/'
i_file=map_prefix+'quiet_simulated_43.1'
j_file=map_prefix+'quiet_simulated_94.5'
alpha_file='/data/wmap/faraday_MW_realdata.fits'
bands=[43.1,94.5]
names=['43','95']
wl=np.array([299792458./(band*1e9) for band in bands])
cross1_array_in=[]
cross2_array_in=[]
Ndq_array_in=[]
Ndu_array_in=[]
Nau_array_in=[]
Naq_array_in=[]
noise1_array_in=[]
noise2_array_in=[]
theory1_array_in=[]
theory2_array_in=[]
#simulate_fields.main()
ttmp1,ttmp2=faraday_theory_quiet(i_file+'.fits',j_file+'.fits',wl[0],wl[1],alpha_file,names[0]+'x'+names[1],beam=use_beam)
theory1_array_in.append(ttmp1)
theory2_array_in.append(ttmp2)
#for n in xrange(N_runs):
for i in xrange(N_runs):
print(Fore.WHITE+Back.GREEN+Style.BRIGHT+'Correlation #{:03d}'.format(i+1)+Back.RESET+Fore.RESET+Style.RESET_ALL)
tmp1,tmp2,n1,n2,n3,n4=faraday_correlate_quiet(i_file+'.fits',j_file+'.fits',wl[0],wl[1],alpha_file,names[0]+'x'+names[1],beam=use_beam)
# ntmp1,ntmp2=faraday_noise_quiet(i_file+'.fits',j_file+'.fits',wl[0],wl[1],alpha_file,names[0]+'x'+names[1],beam=use_beam)
cross1_array_in.append(tmp1)
cross2_array_in.append(tmp2)
Ndq_array_in.append(n1)
Ndu_array_in.append(n2)
Nau_array_in.append(n3)
Naq_array_in.append(n4)
# noise1_array_in.append(ntmp1)
# noise2_array_in.append(ntmp2)
f=open('cl_theory_FR_QxaU.json','w')
json.dump(np.array(theory1_array_in).tolist(),f)
f.close()
f=open('cl_theory_FR_UxaQ.json','w')
json.dump(np.array(theory2_array_in).tolist(),f)
f.close()
theory1=np.mean(theory1_array_in,axis=0)
theory2=np.mean(theory2_array_in,axis=0)
hp.write_cl('cl_theory_FR_QxaU.fits',theory1)
hp.write_cl('cl_theory_FR_UxaQ.fits',theory2)
#f=open('cl_theory_FR_QxaU.json','r')
#theory1_array=json.load(f)
#f.close()
#f=open('cl_theory_FR_UxaQ.json','r')
#theory2_array=json.load(f)
#f.close()
f=open('cl_array_FR_QxaU.json','w')
json.dump(np.array(cross1_array_in).tolist(),f)
f.close()
f=open('cl_array_FR_UxaQ.json','w')
json.dump(np.array(cross2_array_in).tolist(),f)
f.close()
f=open('cl_Ndq_FR_QxaU.json','w')
json.dump(np.array(Ndq_array_in).tolist(),f)
f.close()
f=open('cl_Ndu_FR_UxaQ.json','w')
json.dump(np.array(Ndu_array_in).tolist(),f)
f.close()
f=open('cl_Nau_FR_QxaU.json','w')
json.dump(np.array(Nau_array_in).tolist(),f)
f.close()
f=open('cl_Naq_FR_UxaQ.json','w')
json.dump(np.array(Naq_array_in).tolist(),f)
f.close()
#f=open('cl_noise_FR_QxaU.json','w')
#json.dump(np.array(noise1_array_in).tolist(),f)
#f.close()
#f=open('cl_noise_FR_UxaQ.json','w')
#json.dump(np.array(noise2_array_in).tolist(),f)
#f.close()
bins=[1,5,10,20,25,50]
fsky=225.*(np.pi/180.)**2/(4*np.pi)
l=np.arange(len(cross1_array_in[0]))
ll=l*(l+1)/(2*np.pi)
L=np.sqrt(fsky*4*np.pi)
dl_eff=2*np.pi/L
theory1_array_in=np.array(theory1_array_in)/(fsky*bls)
theory2_array_in=np.array(theory2_array_in)/(fsky*bls)
cross1_array_in=np.array(cross1_array_in)/(fsky*bls)
cross2_array_in=np.array(cross2_array_in)/(fsky*bls)
Ndq_array_in=np.array(Ndq_array_in)/(fsky)
Ndu_array_in=np.array(Ndu_array_in)/(fsky)
Nau_array_in=np.array(Nau_array_in)/(fsky)
Naq_array_in=np.array(Naq_array_in)/(fsky)
#noise1_array_in=np.array(noise1_array_in)/(fsky*bls)
#noise2_array_in=np.array(noise2_array_in)/(fsky*bls)
Ndq_array_in.shape += (1,)
Ndu_array_in.shape += (1,)
Nau_array_in.shape += (1,)
Naq_array_in.shape += (1,)
for b in bins:
theory_cls=hp.read_cl('/home/matt/Planck/data/faraday/correlation/fr_theory_cl.fits')
# N_dq=np.mean(Ndq_array_in)
# N_au=np.mean(Nau_array_in)
# #delta1=np.sqrt(2.*abs((np.mean(cross1_array_in,axis=0)-np.mean(noise1_array_in,axis=0))**2+(np.mean(cross1_array_in,axis=0)-np.mean(noise1_array_in,axis=0))/2.*(N_dq+N_au)+N_dq*N_au/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
# delta1=np.sqrt(2.*((np.mean(theory1_array_in,axis=0))**2+(np.mean(theory1_array_in,axis=0))/2.*(N_dq+N_au)+N_dq*N_au/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
#
cosmic1=np.sqrt(2./((2.*l+1)*np.sqrt(b**2+dl_eff**2)*fsky)*np.mean(theory1_array_in,axis=0)**2)
# N_du=np.mean(Ndu_array_in)
# N_aq=np.mean(Naq_array_in)
# #delta2=np.sqrt(2.*abs((np.mean(cross2_array_in,axis=0)-np.mean(noise2_array_in,axis=0))**2+(np.mean(cross2_array_in,axis=0)-np.mean(noise2_array_in,axis=0))/2.*(N_dq+N_au)+N_dq*N_au/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
# delta2=np.sqrt(2.*((np.mean(theory2_array_in,axis=0))**2+(np.mean(theory2_array_in,axis=0))/2.*(N_du+N_aq)+N_du*N_aq/2.)/((2.*l+1.)*np.sqrt(b**2+dl_eff**2)*fsky))
cosmic2=np.sqrt(2./((2*l+1)*np.sqrt(b**2+dl_eff**2)*fsky)*np.mean(theory2_array_in,axis=0)**2)
theory1_array=[]
theory2_array=[]
cross1_array=[]
cross2_array=[]
# noise1_array=[]
# noise2_array=[]
Ndq_array=[]
Ndu_array=[]
Nau_array=[]
Naq_array=[]
plot_l=[]
if( b != 1):
tmp_t1=bin_llcl.bin_llcl(ll*theory1_array_in,b)
tmp_t2=bin_llcl.bin_llcl(ll*theory2_array_in,b)
tmp_c1=bin_llcl.bin_llcl(ll*cross1_array_in,b)
tmp_c2=bin_llcl.bin_llcl(ll*cross2_array_in,b)
# tmp_n1=bin_llcl.bin_llcl(ll*noise1_array_in,b)
# tmp_n2=bin_llcl.bin_llcl(ll*noise2_array_in,b)
theory1_array=tmp_t1['llcl']
theory2_array=tmp_t2['llcl']
theory1_array.shape += (1,)
theory2_array.shape += (1,)
theory1_array=theory1_array.T
theory2_array=theory2_array.T
plot_l= tmp_t1['l_out']
cross1_array=tmp_c1['llcl']
cross2_array=tmp_c2['llcl']
# noise1_array=tmp_n1['llcl']
# noise2_array=tmp_n2['llcl']
Ndq_array=bin_llcl.bin_llcl(ll*Ndq_array_in,b)['llcl']
Ndu_array=bin_llcl.bin_llcl(ll*Ndu_array_in,b)['llcl']
Naq_array=bin_llcl.bin_llcl(ll*Naq_array_in,b)['llcl']
Nau_array=bin_llcl.bin_llcl(ll*Nau_array_in,b)['llcl']
tmp_c1=bin_llcl.bin_llcl((ll*cosmic1)**2,b)
#tmp_d1=bin_llcl.bin_llcl((ll*delta1)**2,b)
cosmic1=np.sqrt(tmp_c1['llcl'])
#delta1=np.sqrt(tmp_d1['llcl'])
tmp_c2=bin_llcl.bin_llcl((ll*cosmic2)**2,b)
#tmp_d2=bin_llcl.bin_llcl((ll*delta2)**2,b)
cosmic2=np.sqrt(tmp_c2['llcl'])
#delta2=np.sqrt(tmp_d2['llcl'])
t_tmp=bin_llcl.bin_llcl(ll*theory_cls,b)
theory_cls=t_tmp['llcl']
else:
plot_l=l
theory1_array=np.multiply(ll,theory1_array_in)
cross1_array=np.multiply(ll,cross1_array_in)
# noise1_array=np.multiply(ll,noise1_array_in)
theory2_array=np.multiply(ll,theory2_array_in)
cross2_array=np.multiply(ll,cross2_array_in)
# noise2_array=np.multiply(ll,noise2_array_in)
cosmic1*=ll
cosmic2*=ll
#delta1*=ll
#delta2*=ll
Ndq_array=np.multiply(ll,Ndq_array_in)
Ndu_array=np.multiply(ll,Ndu_array_in)
Naq_array=np.multiply(ll,Naq_array_in)
Nau_array=np.multiply(ll,Nau_array_in)
theory_cls*=ll
#ipdb.set_trace()
bad=np.where(plot_l < 24)
N_dq=np.mean(Ndq_array,axis=0)
N_du=np.mean(Ndu_array,axis=0)
N_aq=np.mean(Naq_array,axis=0)
N_au=np.mean(Nau_array,axis=0)
#noise1=np.mean(noise1_array,axis=0)
#noise2=np.mean(noise2_array,axis=0)
theory1=np.mean(theory1_array,axis=0)
theory2=np.mean(theory1_array,axis=0)
theory_array = np.add(theory1_array,theory2_array)
theory=np.mean(theory_array,axis=0)
#dtheory=np.sqrt(np.var(theory1_array,ddof=1) + np.var(theory2_array,ddof=1))
#cross_array = np.add(np.subtract(cross1_array,noise1),np.subtract(cross2_array,noise2))
cross_array = np.add(cross1_array,cross2_array)
cross=np.mean(cross_array,axis=0)
#dcross=np.std(cross_array,axis=0,ddof=1)
dcross=np.sqrt( ( np.var(cross1_array,axis=0,ddof=1) + np.var(cross2_array,axis=0,ddof=1)))
cosmic=np.sqrt(cosmic1**2+cosmic2**2)
delta1=np.sqrt(2./((2*plot_l+1)*fsky*np.sqrt(b**2+dl_eff**2))*(theory1**2 + theory1*(N_dq+N_au)/2. + N_dq*N_au/2.))
delta2=np.sqrt(2./((2*plot_l+1)*fsky*np.sqrt(b**2+dl_eff**2))*(theory2**2 + theory2*(N_du+N_aq)/2. + N_du*N_aq/2.))
delta=np.sqrt(delta1**2+delta2**2)
#cosmic=np.abs(theory_cls)*np.sqrt(2./((2*plot_l+1)*fsky*np.sqrt(dl_eff**2+b**2)))
#theory1=np.mean(theory1_array,axis=0)
#dtheory1=np.std(theory1_array,axis=0,ddof=1)
#cross1=np.mean(cross1_array,axis=0)
#dcross1=np.std(np.subtract(cross1_array,noise1),axis=0,ddof=1)
#ipdb.set_trace()
plot_binned.plotBinned((cross)*1e12,dcross*1e12,plot_l,b,'Cross_43x95_FR', title='QUIET FR Correlator',theory=theory*1e12,delta=delta*1e12,cosmic=cosmic*1e12)
#theory2=np.mean(theory2_array,axis=0)
#dtheory2=np.std(theory2_array,axis=0,ddof=1)
#cross2=np.mean(cross2_array,axis=0)
##delta2=np.mean(delta2_array,axis=0)
#dcross2=np.std(np.subtract(cross2_array,noise2),axis=0,ddof=1)
##ipdb.set_trace()
#plot_binned.plotBinned((cross2-noise2)*1e12,dcross2*1e12,plot_l,b,'Cross_43x95_FR_UxaQ', title='Cross 43x95 FR UxaQ',theory=theory2*1e12,dtheory=dtheory2*1e12,delta=delta2*1e12,cosmic=cosmic2*1e12)
#ipdb.set_trace()
if b == 25 :
good_l=np.logical_and(plot_l <= 200,plot_l >25)
likelihood(cross[good_l],delta[good_l],theory[good_l],'field1','c2bfr')
#if b == 1 :
# xbar= np.matrix(ll[1:]*(cross-np.mean(cross))[1:]).T
# vector=np.matrix(ll[1:]*cross[1:]).T
# mu=np.matrix(ll[1:]*theory[1:]).T
# fact=len(xbar)-1
# cov=(np.dot(xbar,xbar.T)/fact).squeeze()
## ipdb.set_trace()
# U,S,V =np.linalg.svd(cov)
# _cov= np.einsum('ij,j,jk', V.T,1./S,U.T)
# likelhd=np.exp(-np.dot(np.dot((vector-mu).T,_cov),(vector-mu))/2. )/(np.sqrt(2*np.pi*np.prod(S)))
## print('Likelihood of fit is #{0:.5f}'.format(likelihood[0,0]))
# f=open('FR_likelihood.txt','w')
# f.write('Likelihood of fit is #{0:.5f}'.format(likelhd[0,0]))
# f.close()
subprocess.call('mv *01*.png bin_01/', shell=True)
subprocess.call('mv *05*.png bin_05/', shell=True)
subprocess.call('mv *10*.png bin_10/', shell=True)
subprocess.call('mv *20*.png bin_20/', shell=True)
subprocess.call('mv *25*.png bin_25/', shell=True)
subprocess.call('mv *50*.png bin_50/', shell=True)
subprocess.call('mv *.eps eps/', shell=True)
def getSMICA(theta_i=0.0,
theta_f=180.0,
nSteps=1800,
lmax=100,
lmin=2,
newSMICA=False,
useSPICE=True,
newDeg=False,
R1=False):
"""
Purpose:
load CMB and mask maps from files, return correlation function for
unmasked and masked CMB
Mostly follows Copi et. al. 2013 for cut sky C(theta)
Uses:
get_crosspower.py (for plotting)
C(theta) save file getSMICAfile.npy
Inputs:
theta_i,theta_f: starting and ending points for C(theta) in degrees
nSteps: number of intervals between i,f points
lmax: the maximum l value to include in legendre series for C(theta)
lmin: the lowest l to use in C(theta,Cl) and S_{1/2} = CIC calculation
newSMICA: set to True to reload data from files and recompute
if False, will load C(theta) curves from file
useSPICE: if True, will use SPICE to find power spectra
if False, will use anafast, following Copi et. al. 2013
Default: True
newDeg: set to True to recalculate map and mask degredations
Note: the saved files are dependent on the value of lmax that was used
Default: False
R1: set to True to use R1 versions of SMICA and mask. Otherwise, R2 is used
Only affects which Planck files are used; irrelevant if newDeg=False.
Default: False
Outupts:
theta: nSteps+1 angles that C(theta) arrays are for (degrees)
unmasked: C(theta) unmasked (microK^2)
masked: C(theta) masked (microK^2)
"""
saveFile = 'getSMICAfile.npy' #for anafast
saveFile2 = 'getSMICAfile2.npy' #for spice
if newSMICA:
# start with map degredations
mapDegFile = 'smicaMapDeg.fits'
maskDegFile = 'maskMapDeg.fits'
if newDeg:
# load maps; default files have 2048,NESTED,GALACTIC
dataDir = '/Data/'
if R1:
smicaFile = 'COM_CompMap_CMB-smica-field-I_2048_R1.20.fits'
maskFile = 'COM_Mask_CMB-union_2048_R1.10.fits'
else:
smicaFile = 'COM_CMB_IQU-smica-field-int_2048_R2.01_full.fits'
maskFile = 'COM_CMB_IQU-common-field-MaskInt_2048_R2.01.fits'
print 'opening file ', smicaFile, '... '
smicaMap, smicaHead = hp.read_map(dataDir + smicaFile,
nest=True,
h=True)
print 'opening file ', maskFile, '... '
maskMap, maskHead = hp.read_map(dataDir + maskFile,
nest=True,
h=True)
if R1:
smicaMap *= 1e-6 #microK to K
# degrade map and mask resolutions from 2048 to 128; convert NESTED to RING
useAlm = True # set to True to do harmonic space scaling, False for ud_grade
NSIDE_big = 2048
NSIDE_deg = 128
while 4 * NSIDE_deg < lmax:
NSIDE_deg *= 2
print 'resampling maps at NSIDE = ', NSIDE_deg, '... '
order_out = 'RING'
if useAlm:
# transform to harmonic space
smicaMapRing = hp.reorder(smicaMap, n2r=True)
maskMapRing = hp.reorder(maskMap, n2r=True)
smicaCl, smicaAlm = hp.anafast(smicaMapRing,
alm=True,
lmax=lmax)
maskCl, maskAlm = hp.anafast(maskMapRing, alm=True, lmax=lmax)
# this gives 101 Cl values and 5151 Alm values. Why not all 10201 Alm.s?
# scale by pixel window functions
bigWin = hp.pixwin(NSIDE_big)
degWin = hp.pixwin(NSIDE_deg)
winRatio = degWin / bigWin[:degWin.size]
degSmicaAlm = hp.almxfl(smicaAlm, winRatio)
degMaskAlm = hp.almxfl(maskAlm, winRatio)
# re-transform back to real space
smicaMapDeg = hp.alm2map(degSmicaAlm, NSIDE_deg)
maskMapDeg = hp.alm2map(degMaskAlm, NSIDE_deg)
else:
smicaMapDeg = hp.ud_grade(smicaMap,
nside_out=NSIDE_deg,
order_in='NESTED',
order_out=order_out)
maskMapDeg = hp.ud_grade(maskMap,
nside_out=NSIDE_deg,
order_in='NESTED',
order_out=order_out)
# note: degraded resolution mask will no longer be only 0s and 1s.
# Should it be? Yes.
# turn smoothed mask back to 0s,1s mask
threshold = 0.9
maskMapDeg[np.where(maskMapDeg > threshold)] = 1
maskMapDeg[np.where(maskMapDeg <= threshold)] = 0
#testing
#hp.mollview(smicaMapDeg)
#plt.show()
#hp.mollview(maskMapDeg)
#plt.show()
#return 0
hp.write_map(mapDegFile, smicaMapDeg,
nest=False) # use False if order_out='RING' above
hp.write_map(maskDegFile, maskMapDeg, nest=False)
else: # just load previous degradations (dependent on previous lmax)
print 'loading previously degraded map and mask...'
smicaMapDeg = hp.read_map(mapDegFile, nest=False)
maskMapDeg = hp.read_map(maskDegFile, nest=False)
# find power spectra
print 'find power spectra... '
if useSPICE:
ClFile1 = 'spiceCl_unmasked.fits'
ClFile2 = 'spiceCl_masked.fits'
# note: lmax for spice is 3*NSIDE-1 or less
ispice(mapDegFile, ClFile1, subav="YES", subdipole="YES")
Cl_unmasked = hp.read_cl(ClFile1)
ispice(mapDegFile,
ClFile2,
maskfile1=maskDegFile,
subav="YES",
subdipole="YES")
Cl_masked = hp.read_cl(ClFile2)
Cl_mask = np.zeros(Cl_unmasked.shape[0]) # just a placeholder
ell = np.arange(Cl_unmasked.shape[0])
else: # use anafast
Cl_unmasked = hp.anafast(smicaMapDeg, lmax=lmax)
Cl_masked = hp.anafast(smicaMapDeg * maskMapDeg, lmax=lmax)
Cl_mask = hp.anafast(maskMapDeg, lmax=lmax)
ell = np.arange(lmax + 1) #anafast output seems to start at l=0
# plot them
doPlot = False #True
if doPlot:
gcp.showCl(ell,
np.array([Cl_masked, Cl_unmasked]),
title='power spectra of unmasked, masked SMICA map')
# Legendre transform to real space
print 'Legendre transform to real space... '
# note: getCovar uses linspace in x for thetaArray
thetaDomain, CofTheta = getCovar(ell[:lmax + 1],
Cl_unmasked[:lmax + 1],
theta_i=theta_i,
theta_f=theta_f,
nSteps=nSteps,
lmin=lmin)
thetaDomain, CCutofThetaTA = getCovar(ell[:lmax + 1],
Cl_masked[:lmax + 1],
theta_i=theta_i,
theta_f=theta_f,
nSteps=nSteps,
lmin=lmin)
CofTheta *= 1e12 # K^2 to microK^2
CCutofThetaTA *= 1e12 # K^2 to microK^2
if useSPICE:
CCutofTheta = CCutofThetaTA #/(4*np.pi)
else:
thetaDomain, AofThetaInverse = getCovar(
ell[:lmax + 1],
Cl_mask[:lmax + 1],
theta_i=theta_i,
theta_f=theta_f,
nSteps=nSteps,
lmin=0) # don't zilch the mask
# note: zilching the mask's low power drastically changed C(theta) for masked anafast
# Not sure why.
CCutofTheta = CCutofThetaTA / AofThetaInverse
xArray = np.cos(thetaDomain * np.pi / 180.)
# back to frequency space for S_{1/2} = CIC calculation
if useSPICE:
CCutofL = Cl_masked[:lmax + 1] * 1e12 #K^2 to microK^2
else:
legCoefs = legfit(xArray, CCutofTheta, lmax)
CCutofL = legCoefs * (4 * np.pi) / (2 * ell[:lmax + 1] + 1)
# S_{1/2}
myJmn = getJmn(lmax=lmax)
SMasked = np.dot(CCutofL[lmin:],
np.dot(myJmn[lmin:, lmin:], CCutofL[lmin:]))
SNoMask = np.dot(
Cl_unmasked[lmin:lmax + 1],
np.dot(myJmn[lmin:, lmin:],
Cl_unmasked[lmin:lmax +
1])) * 1e24 #two factors of K^2 to muK^2
# save results
if useSPICE:
np.save(
saveFile2,
np.array(
[thetaDomain, CofTheta, CCutofTheta, SNoMask, SMasked]))
else:
np.save(
saveFile,
np.array(
[thetaDomain, CofTheta, CCutofTheta, SNoMask, SMasked]))
else: # load from file
if useSPICE:
fileData = np.load(saveFile2)
else:
fileData = np.load(saveFile)
thetaDomain = fileData[0]
CofTheta = fileData[1]
CCutofTheta = fileData[2]
SNoMask = fileData[3]
SMasked = fileData[4]
return thetaDomain, CofTheta, CCutofTheta, SNoMask, SMasked
# ok so this is a bit overcomplicated because we need to take into account CAR and HEALPIX
# for CAR the pixel window function deconvolution is done in Fourier space and take into account
# the anisotropy if the pixwin
# In HEALPIX it's a simple 1d function in multipole space
# we also need to take account the case where we have projected Planck into a CAR pixellisation since
# the native pixel window function of Planck need to be deconvolved
if win_T.pixel == "CAR":
wy, wx = enmap.calc_window(win_T.data.shape)
inv_pixwin_lxly = (wy[:, None] * wx[None, :])**(-1)
pixwin_l[sv] = np.ones(2 * lmax)
if sv == "Planck":
print("Deconvolve Planck pixel window function")
# we include this special case for Planck projected in CAR taking into account the Planck native pixellisation
# we should check if the projection doesn't include an extra pixel window
inv_pixwin_lxly = None
pixwin_l[sv] = hp.pixwin(2048)
elif win_T.pixel == "HEALPIX":
pixwin_l[sv] = hp.pixwin(win_T.nside)
else:
inv_pixwin_lxly = None
t = time.time()
for k, map in enumerate(maps):
if win_T.pixel == "CAR":
split = so_map.read_map(map, geometry=win_T.data.geometry)
if d["src_free_maps_%s" % sv] == True:
#det_eff=.85 #ndays=15.*24*3600 #FPU=np.array([5,5,3]) # #noise_const_array=ukdet*np.sqrt(4125.*60**2)/np.sqrt(FPU*ndet*det_eff*ndays)/pix_area_array*1e-6 #gamma_dust=6.626e-34/(1.38e-23*21) #dust_factor=krj_to_kcmb*np.array([1e-6*(np.exp(gamma_dust*353e9)-1)/(np.exp(gamma_dust*x*1e9)-1)* (x/353.)**2.54 for x in bands]) #krj_to_kcmb=np.array([1.,1.,1.]) krj_to_kcmb=np.ones_like(bands) # sync_factor=krj_to_kcmb*np.array([1e-6*(30./x)**2 for x in bands]) beam=hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),3*nside_out-1) bls=(hp.gauss_beam(smoothing_scale*np.pi/(180.*60.),3*nside_out-1)*hp.pixwin(nside_out)[:3*nside_out])**2 l=np.arange(3*nside_out) ll=l*(l+1)/(2*np.pi) def likelihood(cross,dcross,theory,name,title): Sig=np.sum(cross/(dcross**2))/np.sum(1./dcross**2) #Noise=np.std(np.sum(cross_array/dcross**2,axis=1)/np.sum(1./dcross**2)) \ Noise=np.sqrt(1./np.sum(1./dcross**2)) Sig1=np.sum(cross*(theory/dcross)**2)/np.sum((theory/dcross)**2) Noise1=np.sqrt(np.sum(dcross**2*(theory/dcross)**2)/np.sum((theory/dcross)**2)) SNR=Sig/Noise SNR1=Sig1/Noise1
def covth(nside,ipok,lmax,bl,spectra,polar=True,temp=True,allinone=True):
all=['I','Q','U']
if not temp:
all=['Q','U']
if not polar:
all=['I']
rpix=np.array(hp.pix2vec(nside,ipok))
allcosang=np.dot(np.transpose(rpix),rpix)
pixwin=hp.pixwin(nside)[0:lmax+1]
nspec,nell=np.shape(spectra)
ell=spectra[0]
maskl=ell<(lmax+1)
ell=ell[maskl]
ctt=spectra[1][maskl]
cee=spectra[2][maskl]
cbb=spectra[3][maskl]
cte=spectra[4][maskl]
if nspec==5:
print('Assuming EB and TB are zero')
ceb=0
ctb=0
else:
ceb=spectra[5][maskl]
ctb=spectra[6][maskl]
norm=(2*ell+1)/(4*np.pi)*(pixwin**2)*(bl[maskl]**2)
effctt=norm*ctt
effcte=norm*cte
effcee=norm*cee
effcbb=norm*cbb
effceb=norm*ceb
effctb=norm*ctb
nbpixok=ipok.size
nstokes=np.size(all)
print(nstokes)
cov=np.zeros((nstokes,nstokes,nbpixok,nbpixok))
for i in np.arange(nbpixok):
pyquad.progress_bar(i,nbpixok)
for j in np.arange(i,nbpixok):
if nstokes==1:
pl=pl0(allcosang[i,j],lmax)
TT=effctt*pl
cov[0,0,i,j]=np.sum(TT)
cov[0,0,j,i]=cov[0,0,i,j]
if nstokes==2:
cij,sij=polrotangle(rpix[:,i],rpix[:,j])
cji,sji=polrotangle(rpix[:,j],rpix[:,i])
f12=F1l2(allcosang[i,j],lmax)
f22=F2l2(allcosang[i,j],lmax)
QQ=f12*effcee-f22*effcbb
UU=f12*effcbb-f22*effcee
QU=(f12+f22)*effceb
cQQpsQU = ( cij*QQ + sij*QU )
cQUpsUU = ( cij*QU + sij*UU )
cQUmsQQ = ( cij*QU - sij*QQ )
cUUmsQU = ( cij*UU - sij*QU )
cov[0,0,i,j] = np.sum( cji*cQQpsQU + sji*cQUpsUU )
cov[0,0,j,i] = cov[0,0,i,j]
cov[0,1,i,j] = np.sum(-sji*cQQpsQU + cji*cQUpsUU )
cov[1,0,j,i] = cov[0,1,i,j]
cov[1,1,i,j] = np.sum(-sji*cQUmsQQ + cji*cUUmsQU )
cov[1,1,j,i] = cov[1,1,i,j]
cov[0,1,j,i] = np.sum( cji*cQUmsQQ + sji*cUUmsQU )
cov[1,0,i,j] = cov[0,1,j,i]
if nstokes==3:
cij,sij=polrotangle(rpix[:,i],rpix[:,j])
cji,sji=polrotangle(rpix[:,j],rpix[:,i])
pl=pl0(allcosang[i,j],lmax)
f10=F1l0(allcosang[i,j],lmax)
f12=F1l2(allcosang[i,j],lmax)
f22=F2l2(allcosang[i,j],lmax)
TT=effctt*pl
QQ=f12*effcee-f22*effcbb
UU=f12*effcbb-f22*effcee
TQ=-f10*effcte
TU=-f10*effctb
QU=(f12+f22)*effceb
cQQpsQU = ( cij*QQ + sij*QU )
cQUpsUU = ( cij*QU + sij*UU )
cQUmsQQ = ( cij*QU - sij*QQ )
cUUmsQU = ( cij*UU - sij*QU )
cov[0,0,i,j] = np.sum( TT )
cov[0,0,j,i] = cov[0,0,i,j]
cov[0,1,i,j] = np.sum( cji*TQ + sji*TU )
cov[1,0,j,i] = cov[0,1,i,j]
cov[1,0,i,j] = np.sum( cij*TQ + sij*TU )
cov[0,1,j,i] = cov[1,0,i,j]
cov[0,2,i,j] = np.sum(-sji*TQ + cij*TU )
cov[2,0,j,i] = cov[0,2,i,j]
cov[2,0,i,j] = np.sum(-sij*TQ + cij*TU )
cov[0,2,j,i] = cov[2,0,i,j]
cov[1,1,i,j] = np.sum( cji*cQQpsQU + sji*cQUpsUU )
cov[1,1,j,i] = cov[1,1,i,j]
cov[1,2,i,j] = np.sum(-sji*cQQpsQU + cji*cQUpsUU )
cov[2,1,j,i] = cov[1,2,i,j]
cov[2,1,i,j] = np.sum( cji*cQUmsQQ + sji*cUUmsQU )
cov[1,2,j,i] = cov[2,1,i,j]
cov[2,2,i,j] = np.sum(-sji*cQUmsQQ + cji*cUUmsQU )
cov[2,2,j,i] = cov[2,2,i,j]
if allinone==True:
return(allmat2bigmat(cov))
else:
return cov
