def get_nlkk_single(estimator,
modlmap,
tnoise,
enoise,
bnoise,
tmask,
pmask,
pols=['TT', 'TE', 'EE', 'EB', 'TB']):
import symlens as s
feed_dict = get_feed(lcltt, lclte, lclee, lclbb, tnoise, enoise, bnoise)
alpha_estimator = estimator
beta_estimator = estimator
npols = len(pols)
masks = {'T': tmask, 'E': pmask, 'B': pmask}
bin_edges = np.arange(40, 2500, 40)
binner = stats.bin2D(modlmap, bin_edges)
cents = binner.centers
nls = np.zeros((npols, npols, cents.size))
Als = []
for i in range(npols):
a, b = pols[i]
Als.append(
s.A_l(shape,
wcs,
feed_dict,
alpha_estimator,
pols[i],
xmask=masks[a],
ymask=masks[b]))
for i in range(npols):
for j in range(i, npols):
print(pols[i], 'x', pols[j])
alpha_XY = pols[i]
beta_XY = pols[j]
a, b = alpha_XY
c, d = beta_XY
if i == j:
xmask = masks[a]
ymask = masks[b]
else:
xmask = masks['T']
ymask = masks['T']
nl = s.N_l_cross(shape,
wcs,
feed_dict,
alpha_estimator,
alpha_XY,
beta_estimator,
beta_XY,
xmask=xmask,
ymask=ymask,
Aalpha=Als[i],
Abeta=Als[j],
field_names_alpha=None,
field_names_beta=None)
cents, nl1d = binner.bin(nl)
nls[i, j] = nl1d.copy()
if i != j: nls[j, i] = nl1d.copy()
ncoadd = get_mv(nls)
return cents, nls, ncoadd
def signal_average(cov,bin_edges=None,bin_width=40,kind=3,lmin=None,dlspace=True,return_bins=False,**kwargs):
"""
dcov = cov * ellfact
bin dcov in annuli
interpolate back on to ell
cov = dcov / ellfact
where ellfact = ell**2 if dlspace else 1
"""
modlmap = cov.modlmap()
assert np.all(np.isfinite(cov))
dcov = cov*modlmap**2. if dlspace else cov.copy()
if lmin is None:
minell = maps.minimum_ell(dcov.shape,dcov.wcs)
else:
minell = modlmap[modlmap<=lmin].max()
if bin_edges is None: bin_edges = np.append([2],np.arange(minell,modlmap.max(),bin_width))
binner = stats.bin2D(modlmap,bin_edges)
cents,c1d = binner.bin(dcov)
outcov = enmap.enmap(maps.interp(cents,c1d,kind=kind,fill_value=c1d[-1],**kwargs)(modlmap),dcov.wcs)
with np.errstate(invalid='ignore'): outcov = outcov / modlmap**2. if dlspace else outcov
outcov[modlmap<2] = 0
assert np.all(np.isfinite(outcov))
if return_bins: return cents,c1d,outcov
else: return outcov
def process(kouts, name="default", ellmax=None, y=False, y_ellmin=400):
ellmax = lmax if ellmax is None else ellmax
ksilc = enmap.zeros((Ny, Nx), wcs, dtype=np.complex128).reshape(-1)
ksilc[modlmap.reshape(-1) < lmax] = np.nan_to_num(kouts.copy())
ksilc = enmap.enmap(ksilc.reshape((Ny, Nx)), wcs)
ksilc[modlmap > ellmax] = 0
if y: ksilc[modlmap < y_ellmin] = 0
msilc = np.nan_to_num(
fft.ifft(ksilc, axes=[-2, -1], normalize=True).real * bmask)
enmap.write_map(proot + "outmap_%s.fits" % name, enmap.enmap(msilc, wcs))
p2d = fc.f2power(ksilc, ksilc)
bin_edges = np.arange(100, 3000, 40)
binner = stats.bin2D(modlmap, bin_edges)
cents, p1d = binner.bin(p2d)
try:
# io.plot_img(np.log10(np.fft.fftshift(p2d)),proot+"ksilc_%s.png" % name,aspect='auto')
# io.plot_img(msilc,proot+"msilc_%s.png" % name)
# io.plot_img(msilc,proot+"lmsilc_%s.png" % name,lim=300)
tmask = maps.mask_kspace(shape,
wcs,
lmin=300,
lmax=5000 if not (y) else 1500)
fmap = maps.filter_map(msilc, tmask) * bmask
io.plot_img(fmap, proot + "hmsilc_%s.png" % name, high_res=True)
except:
pass
return cents, p1d
def get_nlkk_mixed(modlmap, stnoise, ctnoise, xnoise, enoise, bnoise, tmask,
pmask, ipols):
estimator = "hdv"
import symlens as s
feed_dict = get_feed_cross(lcltt, lclte, lclee, lclbb, stnoise, ctnoise,
xnoise, enoise, bnoise)
alpha_estimator = estimator
beta_estimator = estimator
pols = []
for pol in ipols:
if pol == 'TT':
pols.append('S_T_C_T')
pols.append('C_T_S_T')
else:
x, y = pol
pols.append('S_%s_S_%s' % (x, y))
def get_xy(ipol):
sp = ipol.split('_')
return ''.join([sp[1], sp[3]])
npols = len(pols)
masks = {'T': tmask, 'E': pmask, 'B': pmask}
bin_edges = np.arange(40, 2500, 40)
binner = stats.bin2D(modlmap, bin_edges)
cents = binner.centers
nls = np.zeros((npols, npols, cents.size))
for i in range(npols):
for j in range(i, npols):
print(pols[i], 'x', pols[j])
alpha_XY = get_xy(pols[i])
beta_XY = get_xy(pols[j])
fa, a, fb, b = pols[i].split('_')
fc, c, fd, d = pols[j].split('_')
if i == j:
xmask = masks[a]
ymask = masks[b]
else:
xmask = masks['T']
ymask = masks['T']
fnames1 = [fa, fb]
fnames2 = [fc, fd]
nl = s.N_l_cross(shape,
wcs,
feed_dict,
estimator,
alpha_XY,
estimator,
beta_XY,
xmask=xmask,
ymask=ymask,
field_names_alpha=fnames1,
field_names_beta=fnames2)
cents, nl1d = binner.bin(nl)
nls[i, j] = nl1d.copy()
if i != j: nls[j, i] = nl1d.copy()
ncoadd = get_mv(nls)
return cents, nls, ncoadd, pols
def getspec(f, lmin=50, lmax=4000, deltal=20):
p2d = enmap.read_map(f)
shape, wcs = p2d.shape, p2d.wcs
bin_edges = np.arange(lmin, lmax, deltal)
modlmap = enmap.modlmap(shape, wcs)
binner = stats.bin2D(modlmap, bin_edges)
cents, p1d = binner.bin(p2d)
return cents, p1d
def fts(result, lmin, lmax, deltal):
shape,wcs, delta, modlmap = list(result.values())
fc = omaps.FourierCalc(shape,wcs)
p2d,kgal,_ = fc.power2d(delta)
bin_edges = np.arange(lmin, lmax, deltal)
binner = stats.bin2D(modlmap,bin_edges)
cents, p1d = binner.bin(p2d)
result = OrderedDict([('cents', cents), ('p1d', p1d)])
return result
def init_geometry(ishape,iwcs):
modlmap = enmap.modlmap(ishape,iwcs)
bin_edges = np.arange(args.kellmin,args.kellmax,args.dell)
binner = stats.bin2D(modlmap,bin_edges)
if args.beam<1e-5:
kbeam = None
else:
kbeam = maps.gauss_beam(modlmap,args.beam)
lmax = modlmap.max()
ells = np.arange(2,lmax,1)
wnoise_TT = ells*0.+(args.noise*(np.pi/180./60.))**2.
wnoise_PP = 2.*wnoise_TT
nT = modlmap*0.+(args.noise*(np.pi/180./60.))**2.
nP = 2.*nT
ncomp = 3 if pol else 1
ps = np.zeros((ncomp,ncomp,ells.size))
ps[0,0] = wnoise_TT
if pol:
ps[1,1] = wnoise_PP
ps[2,2] = wnoise_PP
oshape = (3,)+ishape if pol else ishape
if not(args.flat) and args.noise_pad>1.e-5:
# Pad noise sim geometry
pad_width_deg = args.noise_pad
pad_width = pad_width_deg * np.pi/180.
res = maps.resolution(oshape[-2:],iwcs)
pad_pixels = int(pad_width/res)
template = enmap.zeros(oshape,iwcs)
btemplate = enmap.pad(template,pad_pixels)
bshape,bwcs = btemplate.shape,btemplate.wcs
del template
del btemplate
ngen = maps.MapGen(bshape,bwcs,ps)
else:
ngen = maps.MapGen(oshape,iwcs,ps)
tmask = maps.mask_kspace(ishape,iwcs,lmin=args.tellmin,lmax=args.tellmax)
pmask = maps.mask_kspace(ishape,iwcs,lmin=args.pellmin,lmax=args.pellmax)
kmask = maps.mask_kspace(ishape,iwcs,lmin=args.kellmin,lmax=args.kellmax)
qest = lensing.qest(ishape,iwcs,theory,noise2d=nT,beam2d=kbeam,kmask=tmask,noise2d_P=nP,kmask_P=pmask,kmask_K=kmask,pol=pol,grad_cut=None,unlensed_equals_lensed=True)
taper,w2 = maps.get_taper_deg(ishape,iwcs,taper_width_degrees = args.taper_width,pad_width_degrees = args.pad_width)
fc = maps.FourierCalc(oshape,iwcs,iau=args.iau)
purifier = maps.Purify(ishape,iwcs,taper) if args.purify else None
return qest,ngen,kbeam,binner,taper,fc,purifier
def fit_noise_1d(npower,lmin=300,lmax=10000,wnoise_annulus=500,bin_annulus=20,lknee_guess=3000,alpha_guess=-4,
lknee_min=0,lknee_max=9000,alpha_min=-5,alpha_max=1,allow_low_wnoise=False):
"""Obtain a white noise + lknee + alpha fit to a 2D noise power spectrum
The white noise part is inferred from the mean of lmax-wnoise_annulus < ells < lmax
npower is 2d noise power
"""
fbin_edges = np.arange(lmin,lmax,bin_annulus)
modlmap = npower.modlmap()
fbinner = stats.bin2D(modlmap,fbin_edges)
cents,dn1d = fbinner.bin(npower)
w2 = dn1d[np.logical_and(cents>=(lmax-wnoise_annulus),cents<lmax)].mean()
try:
# print(w2)
assert w2>0
# pl = io.Plotter('Dell')
# pl.add(cents,dn1d)
# pl.add(cents,cents*0+w2)
# pl.done(os.environ['WORK']+"/nonpos_white_works.png")
except:
print("White noise level not positive")
print(w2)
if not(allow_low_wnoise):
pl = io.Plotter('Dell')
pl.add(cents,dn1d)
pl.done(os.environ['WORK']+"/nonpos_white.png")
raise
else:
w2 = np.abs(w2)
print("Setting to ",w2)
wnoise = np.sqrt(w2)*180.*60./np.pi
ntemplatefunc = lambda x,lknee,alpha: fbinner.bin(rednoise(modlmap,wnoise,lknee=lknee,alpha=alpha))[1]
#ntemplatefunc = lambda x,lknee,alpha: rednoise(x,wnoise,lknee=lknee,alpha=alpha) # FIXME: This switch needs testing !!!!
res,_ = curve_fit(ntemplatefunc,cents,dn1d,p0=[lknee_guess,alpha_guess],bounds=([lknee_min,alpha_min],[lknee_max,alpha_max]))
lknee_fit,alpha_fit = res
# print(lknee_fit,alpha_fit,wnoise)
# pl = io.Plotter(xyscale='linlog',xlabel='l',ylabel='D',scalefn=lambda x: x**2./2./np.pi)
# pl.add(cents,dn1d)
# pl.add(cents,cents*0+w2)
# pl.add(cents,rednoise(cents,wnoise,lknee=lknee_fit,alpha=alpha_fit),ls="--")
# pl.add(cents,rednoise(cents,wnoise,lknee=lknee_guess,alpha=alpha_guess),ls="-.")
# pl._ax.set_ylim(1e-1,1e4)
# pl.done(os.environ['WORK']+"/fitnoise_pre.png")
# sys.exit()
return wnoise,lknee_fit,alpha_fit
#### NORM FROM FLAT-SKY CODE FOR NOW
bin_edges = np.linspace(2, lmax, 40)
with bench.show("flat sky AL"):
ls,nlkks,theory,qest = lensing.lensing_noise(ells,ntt,nee,nbb, \
ellmin_t,ellmin_t,ellmin_t, \
ellmax_t,ellmax_t,ellmax_t, \
bin_edges, \
camb_theory_file_root=None, \
estimators = ['TT'], \
delens = False, \
theory=theory, \
dimensionless=False, \
unlensed_equals_lensed=True, \
grad_cut=None,width_deg=25.,px_res_arcmin=res)
binner = stats.bin2D(qest.N.modLMap, bin_edges)
cents, albinned = binner.bin(qest.AL['TT'])
Al = maps.interp(cents, albinned)(ells)
Nl = maps.interp(ls, nlkks['TT'])(ells)
lpls, lpal = np.loadtxt("data/nls_2000.txt", unpack=True)
pl = io.Plotter(yscale='log', xscale='log')
pl.add(ells, theory.gCl('kk', ells), lw=3, color='k')
pl.add(ells, Nl, ls="--")
pl.add(lpls, lpal * (lpls * (lpls + 1.))**2. / 4., ls="-.")
#pl._ax.set_ylim(1e-10,1e-6)
pl.done(io.dout_dir + "fullsky_qe_result_al.png")
dh_nls = np.nan_to_num(lpal * (lpls * (lpls + 1.))**2. / 4.)
dh_als = np.nan_to_num(dh_nls * 2. / lpls / (lpls + 1))
Al = dh_als
bin_edges = np.arange(100,8000,40)
pfunc = lambda x,y: np.real(x*y.conj())
ffunc = lambda x: enmap.fft(x,normalize='phys')
Ncrop = 400
navg = lambda x,delta : covtools.noise_block_average(x,nsplits=1,delta_ell=delta,
radial_fit=False,lmax=None,
wnoise_annulus=None,
lmin = 40,
bin_annulus=None,fill_lmax=None,
log=False)
loc = "/home/r/rbond/sigurdkn/project/actpol/maps/mr3f_20190328/transfun/release/"
mask = sints.get_act_mr3_crosslinked_mask('deep56')
binner = stats.bin2D(mask.modlmap(),bin_edges)
bin = lambda x: binner.bin(x)
isim1 = enmap.extract(enmap.read_map(loc+'../sims/deep56_00.fits'),mask.shape,mask.wcs) * mask
isim2 = enmap.extract(enmap.read_map(loc+'../sims/deep56_01.fits'),mask.shape,mask.wcs) * mask
tmap1 = enmap.extract(enmap.read_map(loc+'s15_deep56_pa2_f150_nohwp_night_sim00_3pass_4way_coadd_transmap.fits'),mask.shape,mask.wcs) * mask
tmap2 = enmap.extract(enmap.read_map(loc+'s15_deep56_pa2_f150_nohwp_night_sim01_3pass_4way_coadd_transmap.fits'),mask.shape,mask.wcs) * mask
lmap = mask.lmap()
lymap,lxmap = lmap
# def model(x,width,amplitude,sigma):
# mmap = 1-amplitude * np.exp(-lymap**2./2./sigma**2.)
# mmap[lxmap>width/2.] = 1
# mmap[lxmap<-width/2.] = 1
# return mmap
def get_flat_power(map1, map2=None):
map2 = map1 if map2 is None else map2
power2d, _, _ = fc.power2d(emap=map1, emap2=map2)
binner = stats.bin2D(map1.modlmap(), bin_edges)
return binner.bin(power2d)
def calculate_yy(bin_edges,arrays,region,version,cov_versions,beam_version,
effective_freq,overwrite,maxval,unsanitized_beam=False,do_weights=False,
pa1_shift = None,
pa2_shift = None,
pa3_150_shift = None,
pa3_090_shift = None,
no_act_color_correction=False, ccor_exp = -1,
sim_splits=None,unblind=False,all_analytic=False,beta_samples=None):
"""
We calculate the yy power spectrum as follows.
We restrict the Fourier modes in our analysis to those within bin_edges.
This way we don't carry irrelevant pixels and thus speed up the ability to MC.
We accept two covariance versions in cov_versions, which correspond to
[act_covariance_from_split_0,act_covariance_from_split_1,other_covs].
Thus the ACT auto covariances are pre-calculated
"""
arrays = arrays.split(',')
narrays = len(arrays)
if sim_splits is not None: assert not(unblind)
def warn(): print("WARNING: no bandpass file found. Assuming array ",dm.c['id']," has no response to CMB, tSZ and CIB.")
aspecs = tutils.ASpecs().get_specs
bandpasses = not(effective_freq)
savedir = tutils.get_save_path(version,region)
assert len(cov_versions)==3
covdirs = [tutils.get_save_path(cov_versions[i],region) for i in range(3)]
for covdir in covdirs: assert os.path.exists(covdir)
if not(overwrite):
assert not(os.path.exists(savedir)), \
"This version already exists on disk. Please use a different version identifier."
try: os.makedirs(savedir)
except:
if overwrite: pass
else: raise
mask = enmap.read_map(covdir+"tilec_mask.fits")
from scipy.ndimage.filters import gaussian_filter as smooth
pm = enmap.read_map("/scratch/r/rbond/msyriac/data/planck/data/pr2/COM_Mask_Lensing_2048_R2.00_car_deep56_interp_order0.fits")
wcs = pm.wcs
mask = enmap.enmap(smooth(pm,sigma=10),wcs) * mask
shape,wcs = mask.shape,mask.wcs
Ny,Nx = shape
modlmap = enmap.modlmap(shape,wcs)
omodlmap = modlmap.copy()
ells = np.arange(0,modlmap.max())
minell = maps.minimum_ell(shape,wcs)
sel = np.where(np.logical_and(modlmap>=bin_edges[0]-minell,modlmap<=bin_edges[-1]+minell))
modlmap = modlmap[sel]
bps = []
lbeams = []
kbeams = []
shifts = []
cfreqs = []
lmins = []
lmaxs = []
names = []
for i,qid in enumerate(arrays):
dm = sints.models[sints.arrays(qid,'data_model')](region=mask,calibrated=True)
if dm.name=='act_mr3':
season,array1,array2 = sints.arrays(qid,'season'),sints.arrays(qid,'array'),sints.arrays(qid,'freq')
array = '_'.join([array1,array2])
elif dm.name=='planck_hybrid':
season,patch,array = None,None,sints.arrays(qid,'freq')
else:
raise ValueError
lmin,lmax,hybrid,radial,friend,cfreq,fgroup,wrfit = aspecs(qid)
lmins.append(lmin)
lmaxs.append(lmax)
names.append(qid)
cfreqs.append(cfreq)
if bandpasses:
try:
fname = dm.get_bandpass_file_name(array)
bps.append("data/"+fname)
if (pa1_shift is not None) and 'PA1' in fname:
shifts.append(pa1_shift)
elif (pa2_shift is not None) and 'PA2' in fname:
shifts.append(pa2_shift)
elif (pa3_150_shift is not None) and ('PA3' in fname) and ('150' in fname):
shifts.append(pa3_150_shift)
elif (pa3_090_shift is not None) and ('PA3' in fname) and ('090' in fname):
shifts.append(pa3_90_shift)
else:
shifts.append(0)
except:
warn()
bps.append(None)
else:
try: bps.append(cfreq)
except:
warn()
bps.append(None)
kbeam = tutils.get_kbeam(qid,modlmap,sanitize=not(unsanitized_beam),version=beam_version,planck_pixwin=True)
if dm.name=='act_mr3':
lbeam = tutils.get_kbeam(qid,ells,sanitize=not(unsanitized_beam),version=beam_version,planck_pixwin=False) # note no pixwin but doesnt matter since no ccorr for planck
elif dm.name=='planck_hybrid':
lbeam = None
else:
raise ValueError
lbeams.append(lbeam)
kbeams.append(kbeam.copy())
# Make responses
responses = {}
def _get_response(comp,param_override=None):
if bandpasses:
if no_act_color_correction:
r = tfg.get_mix_bandpassed(bps, comp, bandpass_shifts=shifts,
param_dict_override=param_override)
else:
r = tfg.get_mix_bandpassed(bps, comp, bandpass_shifts=shifts,
ccor_cen_nus=cfreqs, ccor_beams=lbeams,
ccor_exps = [ccor_exp] * narrays,
param_dict_override=param_override)
else:
r = tfg.get_mix(bps, comp,param_dict_override=param_override)
return r
for comp in ['tSZ','CMB','CIB']:
responses[comp] = _get_response(comp,None)
from tilec.utils import is_planck
ilcgens = []
okcoadds = []
for splitnum in range(2):
covdir = covdirs[splitnum]
kcoadds = []
for i,qid in enumerate(arrays):
lmin = lmins[i]
lmax = lmaxs[i]
if is_planck(qid):
dm = sints.models[sints.arrays(qid,'data_model')](region=mask,calibrated=True)
_,kcoadd,_ = kspace.process(dm,region,qid,mask,
skip_splits=True,
splits_fname=sim_splits[i] if sim_splits is not None else None,
inpaint=False,fn_beam = None,
plot_inpaint_path = None,
split_set=splitnum)
else:
kcoadd_name = covdir + "kcoadd_%s.npy" % qid
kcoadd = enmap.enmap(np.load(kcoadd_name),wcs)
kmask = maps.mask_kspace(shape,wcs,lmin=lmin,lmax=lmax)
dtype = kcoadd.dtype
kcoadds.append((kcoadd.copy()*kmask)[sel])
kcoadds = enmap.enmap(np.stack(kcoadds),wcs)
okcoadds.append(kcoadds.copy())
# Read Covmat
ctheory = ilc.CTheory(modlmap)
nells = kcoadds[0].size
cov = np.zeros((narrays,narrays,nells))
for aindex1 in range(narrays):
for aindex2 in range(aindex1,narrays):
qid1 = names[aindex1]
qid2 = names[aindex2]
if is_planck(names[aindex1]) or is_planck(names[aindex2]) or all_analytic:
lmin,lmax,hybrid,radial,friend,f1,fgroup,wrfit = aspecs(qid1)
lmin,lmax,hybrid,radial,friend,f2,fgroup,wrfit = aspecs(qid2)
# If both are Planck and same array, get white noise from last bin
icov = ctheory.get_theory_cls(f1,f2,a_cmb=1,a_gal=0.8)*kbeams[aindex1]*kbeams[aindex2]
if aindex1==aindex2:
pcov = enmap.enmap(np.load(covdirs[2]+"tilec_hybrid_covariance_%s_%s.npy" % (names[aindex1],names[aindex2])),wcs)
pbin_edges = np.append(np.arange(500,3000,200) ,[3000,4000,5000,5800])
pbinner = stats.bin2D(omodlmap,pbin_edges)
w = pbinner.bin(pcov)[1][-1]
icov = icov + w
else:
icov = np.load(covdir+"tilec_hybrid_covariance_%s_%s.npy" % (names[aindex1],names[aindex2]))[sel]
if aindex1==aindex2:
icov[modlmap<lmins[aindex1]] = maxval
icov[modlmap>lmaxs[aindex1]] = maxval
cov[aindex1,aindex2] = icov
cov[aindex2,aindex1] = icov
assert np.all(np.isfinite(cov))
ilcgen = ilc.HILC(modlmap,np.stack(kbeams),cov=cov,responses=responses,invert=True)
ilcgens.append(ilcgen)
solutions = ['tSZ','tSZ-CMB','tSZ-CIB']
ypowers = {}
w2 = np.mean(mask**2.)
binner = stats.bin2D(modlmap,bin_edges)
np.random.seed(100)
blinding = np.random.uniform(0.8,1.2) if not(unblind) else 1
def _get_ypow(sname,dname,dresponse=None,dcmb=False):
if dresponse is not None:
assert dname is not None
for splitnum in range(2):
ilcgens[splitnum].add_response(dname,dresponse)
ykmaps = []
for splitnum in range(2):
if dcmb:
assert dname is not None
ykmap = ilcgens[splitnum].multi_constrained_map(okcoadds[splitnum],sname,[dname,"CMB"])
else:
if dname is None:
ykmap = ilcgens[splitnum].standard_map(okcoadds[splitnum],sname)
else:
ykmap = ilcgens[splitnum].constrained_map(okcoadds[splitnum],sname,dname)
ykmaps.append(ykmap.copy())
ypower = (ykmaps[0]*ykmaps[1].conj()).real / w2
return binner.bin(ypower)[1] * blinding
# The usual solutions
for solution in solutions:
sols = solution.split('-')
if len(sols)==2:
sname = sols[0]
dname = sols[1]
elif len(sols)==1:
sname = sols[0]
dname = None
else:
raise ValueError
ypowers[solution] = _get_ypow(sname,dname,dresponse=None)
# The CIB SED samples
if beta_samples is not None:
y_bsamples = []
y_bsamples_cmb = []
for beta in beta_samples:
pdict = tfg.default_dict.copy()
pdict['beta_CIB'] = beta
response = _get_response("CIB",param_override=pdict)
y_bsamples.append( _get_ypow("tSZ","iCIB",dresponse=response,dcmb=False) )
y_bsamples_cmb.append( _get_ypow("tSZ","iCIB",dresponse=response,dcmb=True) )
else:
y_bsamples = None
y_bsamples_cmb = None
return binner.centers,ypowers,y_bsamples,y_bsamples_cmb
def build_and_save_ilc(arrays,region,version,cov_version,beam_version,
solutions,beams,chunk_size,
effective_freq,overwrite,maxval,unsanitized_beam=False,do_weights=False,
pa1_shift = None,
pa2_shift = None,
pa3_150_shift = None,
pa3_090_shift = None,
no_act_color_correction=False, ccor_exp = -1,
isotropize=False, isotropize_width=20):
print("Chunk size is ", chunk_size*64./8./1024./1024./1024., " GB.")
def warn(): print("WARNING: no bandpass file found. Assuming array ",dm.c['id']," has no response to CMB, tSZ and CIB.")
aspecs = tutils.ASpecs().get_specs
bandpasses = not(effective_freq)
savedir = tutils.get_save_path(version,region)
covdir = tutils.get_save_path(cov_version,region)
assert os.path.exists(covdir)
if not(overwrite):
assert not(os.path.exists(savedir)), \
"This version already exists on disk. Please use a different version identifier."
try: os.makedirs(savedir)
except:
if overwrite: pass
else: raise
mask = enmap.read_map(covdir+"tilec_mask.fits")
shape,wcs = mask.shape,mask.wcs
Ny,Nx = shape
modlmap = enmap.modlmap(shape,wcs)
arrays = arrays.split(',')
narrays = len(arrays)
kcoadds = []
kbeams = []
bps = []
names = []
lmins = []
lmaxs = []
shifts = []
cfreqs = []
lbeams = []
ells = np.arange(0,modlmap.max())
for i,qid in enumerate(arrays):
dm = sints.models[sints.arrays(qid,'data_model')](region=mask,calibrated=True)
lmin,lmax,hybrid,radial,friend,cfreq,fgroup,wrfit = aspecs(qid)
cfreqs.append(cfreq)
lmins.append(lmin)
lmaxs.append(lmax)
names.append(qid)
if dm.name=='act_mr3':
season,array1,array2 = sints.arrays(qid,'season'),sints.arrays(qid,'array'),sints.arrays(qid,'freq')
array = '_'.join([array1,array2])
elif dm.name=='planck_hybrid':
season,patch,array = None,None,sints.arrays(qid,'freq')
else:
raise ValueError
kcoadd_name = covdir + "kcoadd_%s.npy" % qid
kmask = maps.mask_kspace(shape,wcs,lmin=lmin,lmax=lmax)
kcoadd = enmap.enmap(np.load(kcoadd_name),wcs)
dtype = kcoadd.dtype
kcoadds.append(kcoadd.copy()*kmask)
kbeam = tutils.get_kbeam(qid,modlmap,sanitize=not(unsanitized_beam),version=beam_version,planck_pixwin=True)
if dm.name=='act_mr3':
lbeam = tutils.get_kbeam(qid,ells,sanitize=not(unsanitized_beam),version=beam_version,planck_pixwin=False) # note no pixwin but doesnt matter since no ccorr for planck
elif dm.name=='planck_hybrid':
lbeam = None
else:
raise ValueError
lbeams.append(lbeam)
kbeams.append(kbeam.copy())
if bandpasses:
try:
fname = dm.get_bandpass_file_name(array)
bps.append("data/"+fname)
if (pa1_shift is not None) and 'PA1' in fname:
shifts.append(pa1_shift)
elif (pa2_shift is not None) and 'PA2' in fname:
shifts.append(pa2_shift)
elif (pa3_150_shift is not None) and ('PA3' in fname) and ('150' in fname):
shifts.append(pa3_150_shift)
elif (pa3_090_shift is not None) and ('PA3' in fname) and ('090' in fname):
shifts.append(pa3_90_shift)
else:
shifts.append(0)
except:
warn()
bps.append(None)
else:
try: bps.append(cfreq)
except:
warn()
bps.append(None)
kcoadds = enmap.enmap(np.stack(kcoadds),wcs)
# Read Covmat
cov = maps.SymMat(narrays,shape[-2:])
for aindex1 in range(narrays):
for aindex2 in range(aindex1,narrays):
icov = enmap.enmap(np.load(covdir+"tilec_hybrid_covariance_%s_%s.npy" % (names[aindex1],names[aindex2])),wcs)
if isotropize:
bin_edges = np.append([0.],np.arange(min(lmins),modlmap.max(),isotropize_width))
binner = stats.bin2D(modlmap,bin_edges)
ls,c1d = binner.bin(icov)
icov = maps.interp(ls,c1d)(modlmap)
if aindex1==aindex2:
icov[modlmap<lmins[aindex1]] = maxval
icov[modlmap>lmaxs[aindex1]] = maxval
cov[aindex1,aindex2] = icov
cov.data = enmap.enmap(cov.data,wcs,copy=False)
covfunc = lambda sel: cov.to_array(sel,flatten=True)
assert cov.data.shape[0]==((narrays*(narrays+1))/2) # FIXME: generalize
assert np.all(np.isfinite(cov.data))
# Make responses
responses = {}
for comp in ['tSZ','CMB','CIB']:
if bandpasses:
if no_act_color_correction:
responses[comp] = tfg.get_mix_bandpassed(bps, comp, bandpass_shifts=shifts)
else:
responses[comp] = tfg.get_mix_bandpassed(bps, comp, bandpass_shifts=shifts,
ccor_cen_nus=cfreqs, ccor_beams=lbeams,
ccor_exps = [ccor_exp] * narrays)
else:
responses[comp] = tfg.get_mix(bps, comp)
ilcgen = ilc.chunked_ilc(modlmap,np.stack(kbeams),covfunc,chunk_size,responses=responses,invert=True)
# Initialize containers
solutions = solutions.split(',')
data = {}
kcoadds = kcoadds.reshape((narrays,Ny*Nx))
for solution in solutions:
data[solution] = {}
comps = solution.split('-')
data[solution]['comps'] = comps
if len(comps)<=2:
data[solution]['noise'] = enmap.zeros((Ny*Nx),wcs)
if len(comps)==2:
data[solution]['cnoise'] = enmap.zeros((Ny*Nx),wcs)
data[solution]['kmap'] = enmap.zeros((Ny*Nx),wcs,dtype=dtype) # FIXME: reduce dtype?
if do_weights and len(comps)<=2:
for qid in arrays:
data[solution]['weight_%s' % qid] = enmap.zeros((Ny*Nx),wcs)
for chunknum,(hilc,selchunk) in enumerate(ilcgen):
print("ILC on chunk ", chunknum+1, " / ",int(modlmap.size/chunk_size)+1," ...")
for solution in solutions:
comps = data[solution]['comps']
if len(comps)==1: # GENERALIZE
data[solution]['noise'][selchunk] = hilc.standard_noise(comps[0])
if do_weights: weight = hilc.standard_weight(comps[0])
data[solution]['kmap'][selchunk] = hilc.standard_map(kcoadds[...,selchunk],comps[0])
elif len(comps)==2:
data[solution]['noise'][selchunk] = hilc.constrained_noise(comps[0],comps[1])
data[solution]['cnoise'][selchunk] = hilc.cross_noise(comps[0],comps[1])
ret = hilc.constrained_map(kcoadds[...,selchunk],comps[0],comps[1],return_weight=do_weights)
if do_weights:
data[solution]['kmap'][selchunk],weight = ret
else:
data[solution]['kmap'][selchunk] = ret
elif len(comps)>2:
data[solution]['kmap'][selchunk] = np.nan_to_num(hilc.multi_constrained_map(kcoadds[...,selchunk],comps[0],*comps[1:]))
if len(comps)<=2 and do_weights:
for qind,qid in enumerate(arrays):
data[solution]['weight_%s' % qid][selchunk] = weight[qind]
del ilcgen,cov
# Reshape into maps
name_map = {'CMB':'cmb','tSZ':'comptony','CIB':'cib'}
beams = beams.split(',')
for solution,beam in zip(solutions,beams):
comps = "tilec_single_tile_"+region+"_"
comps = comps + name_map[data[solution]['comps'][0]]+"_"
if len(data[solution]['comps'])>1: comps = comps + "deprojects_"+ '_'.join([name_map[x] for x in data[solution]['comps'][1:]]) + "_"
comps = comps + version
if do_weights and len(data[solution]['comps'])<=2:
for qind,qid in enumerate(arrays):
enmap.write_map("%s/%s_%s_weight.fits" % (savedir,comps,qid), enmap.enmap(data[solution]['weight_%s' % qid].reshape((Ny,Nx)),wcs))
try:
noise = enmap.enmap(data[solution]['noise'].reshape((Ny,Nx)),wcs)
enmap.write_map("%s/%s_noise.fits" % (savedir,comps),noise)
except: pass
try:
cnoise = enmap.enmap(data[solution]['cnoise'].reshape((Ny,Nx)),wcs)
enmap.write_map("%s/%s_cross_noise.fits" % (savedir,comps),cnoise)
except: pass
ells = np.arange(0,modlmap.max(),1)
try:
fbeam = float(beam)
kbeam = maps.gauss_beam(modlmap,fbeam)
lbeam = maps.gauss_beam(ells,fbeam)
except:
qid = beam
bfunc = lambda x: tutils.get_kbeam(qid,x,version=beam_version,sanitize=not(unsanitized_beam),planck_pixwin=False)
kbeam = bfunc(modlmap)
lbeam = bfunc(ells)
kmap = enmap.enmap(data[solution]['kmap'].reshape((Ny,Nx)),wcs)
smap = enmap.ifft(kbeam*kmap,normalize='phys').real
enmap.write_map("%s/%s.fits" % (savedir,comps),smap)
io.save_cols("%s/%s_beam.txt" % (savedir,comps),(ells,lbeam),header="ell beam")
enmap.write_map(savedir+"/tilec_mask.fits",mask)
def noise_block_average(n2d,nsplits,delta_ell,lmin=300,lmax=8000,wnoise_annulus=500,bin_annulus=20,
lknee_guess=3000,alpha_guess=-4,nparams=None,
verbose=False,radial_fit=True,fill_lmax=None,fill_lmax_width=100,log=True,
isotropic_low_ell=True,allow_low_wnoise=False):
"""Find the empirical mean noise binned in blocks of dfact[0] x dfact[1] . Preserves noise anisotropy.
Most arguments are for the radial fitting part.
A radial fit is divided out before downsampling (by default by FFT) and then multplied back with the radial fit.
Watch for ringing in the final output.
n2d noise power
n2d -- the [...,Ny,Nx] 2d power to smooth
nsplits -- the number of splits from which the 2d noise power was estimated. This needs to be known
if log is True, in which case the power is log-transformed before smoothing, which changes the statistics
of the samples and hence needs a pre-determined correction based on the distribution of the original sample.
log -- whether to log transform before smoothing. Should only be used if the power is positive (so should not
be used e.g. if this is for the cross-noise of two components)
delta_ell -- the block width in ell units for the smoothing. The smoothing effectively gets done in blocks
of delta_ell x delta_ell.
radial_fit -- if True, divides out a fit to the 1d power
lmin -- lmin for the radial fit
lmax -- lmax for the radial fit (adjust based on resolution of map)
wnoise_annulus -- width of annulus in ell within which to estimate high ell white noise (adjust based on resolution)
bin_annulus -- width of 1d bins (IMPORTANT: adjust based on map size)
lknee_guess -- guess lknee for fit
alpha_guess -- guess alpha for fit
nparams -- optionally pass in a radial fit's parameters
verbose -- print more
fill_lmax -- fill power outside this lmax with the mean of the annulus between fill_lmax and fill_lmax_width
fill_lmax_width -- see above
isotropic_low_ell -- fill below lmin with an isotropic fit to the 1d power
allow_low_wnoise -- allow white noise level to be negative (for debugging)
"""
assert np.all(np.isfinite(n2d))
if log: assert np.all(n2d>0), "You can't log smooth a PS with negative or zero power. Use log=False for these."
shape,wcs = n2d.shape,n2d.wcs
modlmap = n2d.modlmap()
minell = maps.minimum_ell(shape,wcs)
Ny,Nx = shape[-2:]
if radial_fit:
with bench.show("radial fit"):
if nparams is None:
if verbose: print("Radial fitting...")
nparams = fit_noise_1d(n2d,lmin=lmin,lmax=lmax,wnoise_annulus=wnoise_annulus,
bin_annulus=bin_annulus,lknee_guess=lknee_guess,alpha_guess=alpha_guess,
allow_low_wnoise=allow_low_wnoise)
wfit,lfit,afit = nparams
nfitted = rednoise(modlmap,wfit,lfit,afit)
else:
nparams = None
nfitted = n2d*0 + 1
nfitted = np.maximum(nfitted,np.max(n2d)*1e-14)
nflat = enmap.enmap(n2d/nfitted,wcs) # flattened 2d noise power
fval = nflat[np.logical_and(modlmap>2,modlmap<2*minell)].mean()
nflat[modlmap<2] = fval
if fill_lmax is not None:
fill_avg = nflat[np.logical_and(modlmap>(fill_lmax-fill_lmax_width),modlmap<=fill_lmax)].mean()
nflat[modlmap>fill_lmax] = fill_avg
if verbose: print("Resampling...")
assert np.all(np.isfinite(nflat))
with bench.show("smooth ps grid"):
ndown = smooth_ps_grid(nflat, res=delta_ell, alpha=4, log=log, ndof=2*(nsplits-1))
# pshow(nflat)
# pshow(ndown)
outcov = ndown*nfitted
outcov[modlmap<minell] = 0
if fill_lmax is not None: outcov[modlmap>fill_lmax] = 0
assert np.all(np.isfinite(outcov))
if isotropic_low_ell:
with bench.show("isotropic low ell"):
if radial_fit:
ifunc = lambda ells,ell0,A,shell: (A*np.exp(-ell0/ells) + shell)
sel = np.logical_and(modlmap<=lmin,modlmap>=2)
ibin_edges = np.arange(minell,(lmin*2)+2*minell,2*minell)
ibinner = stats.bin2D(modlmap,ibin_edges)
cents,inls = ibinner.bin(nflat)
ys = inls
xs = cents
if radial_fit:
res,_ = curve_fit(ifunc,xs,ys,p0=[20,1,0],bounds=([2,0.,-np.inf],[lmin*2,np.inf,np.inf]))
outcov[sel] = ifunc(modlmap[sel],res[0],res[1],res[2])*nfitted[sel]
else:
deg = 5
res = np.polyfit(np.log(xs),np.log(ys*xs**2.),deg=deg)
assert res.size==(deg+1)
fitfunc = lambda x: sum([res[deg-p]*(x**p) for p in range(0,deg+1)[::-1]])
outcov[sel] = (np.exp(fitfunc(np.log(modlmap[sel])))/modlmap[sel]**2.)*nfitted[sel]
outcov[modlmap<2] = 0
# fbin_edges = np.arange(minell,lmax,bin_annulus)
# fbinner = stats.bin2D(modlmap,fbin_edges)
# cents, n1d = fbinner.bin(nflat)
# pl = io.Plotter(xyscale='loglog',xlabel='l',ylabel='D',scalefn=lambda x: x**2./2./np.pi)
# ells = np.arange(minell,2*lmin,1)
# if radial_fit:
# pl.add(ells,ifunc(ells,res[0],res[1],res[2]))
# else:
# pl.add(xs,ys,ls="--")
# pl.add(ells,np.exp(fitfunc(np.log(ells)))/ells**2.)
# pl.add(cents,n1d)
# pl.vline(x=100)
# pl.vline(x=200)
# pl.vline(x=300)
# pl.vline(x=500)
# t = "000"
# pl._ax.set_xlim(10,3000)
# pl.done(os.environ['WORK']+"/iso_fitnoise2_%s.png" % t)
# fbin_edges = np.arange(minell,lmax,bin_annulus)
# fbinner = stats.bin2D(modlmap,fbin_edges)
# cents, n1d = fbinner.bin(n2d)
# cents,dn1d = fbinner.bin(outcov)
# # cents,dn1d2 = fbinner.bin(nfitted)
# pl = io.Plotter(xyscale='linlog',xlabel='l',ylabel='D',scalefn=lambda x: x**2./2./np.pi)
# pl.add(cents,n1d)
# pl.add(cents,dn1d,ls="--")
# pl.vline(x=100)
# pl.vline(x=200)
# pl.vline(x=300)
# pl.vline(x=500)
# # pl.add(cents,dn1d2,ls="-.")
# t = "000"
# pl._ax.set_ylim(1e1,1e5)
# pl.done(os.environ['WORK']+"/fitnoise2_%s.png" % t)
# sys.exit()
return outcov,nfitted,nparams
# Theory
theory_file_root = "../alhazen/data/Aug6_highAcc_CDM"
cc = counts.ClusterCosmology(skipCls=True)
theory = cosmology.loadTheorySpectraFromCAMB(theory_file_root,unlensedEqualsLensed=False,
useTotal=False,TCMB = 2.7255e6,lpad=9000,get_dimensionless=False)
# Geometry
shape, wcs = maps.rect_geometry(width_arcmin=args.arc,px_res_arcmin=args.pix,pol=False)
modlmap = enmap.modlmap(shape,wcs)
modrmap = enmap.modrmap(shape,wcs)
# Binning
bin_edges = np.arange(0.,20.0,args.pix*2)
binner = stats.bin2D(modrmap*60.*180./np.pi,bin_edges)
# Noise model
noise_uK_rad = args.noise*np.pi/180./60.
normfact = np.sqrt(np.prod(enmap.pixsize(shape,wcs)))
kbeam = maps.gauss_beam(args.beam,modlmap)
# Simulate
lmax = int(modlmap.max()+1)
ells = np.arange(0,lmax,1)
ps = theory.uCl('TT',ells).reshape((1,1,lmax))
~ps_noise = np.array([(noise_uK_rad)**2.]*ells.size).reshape((1,1,ells.size))
mg = maps.MapGen(shape,wcs,ps)
ng = maps.MapGen(shape,wcs,ps_noise)
kamp_true = args.Amp
"""
njobs = len(qpairs)
comm,rank,my_tasks = mpi.distribute(njobs)
mask = sints.get_act_mr3_crosslinked_mask(args.region,
version=args.mask_version,
kind='binary_apod')
shape,wcs = mask.shape,mask.wcs
modlmap = mask.modlmap()
aspecs = tutils.ASpecs().get_specs
region = args.region
fbeam = lambda qname,x: tutils.get_kbeam(qname,x,sanitize=not(args.unsanitized_beam),planck_pixwin=True)
nbin_edges = np.arange(20,8000,100)
nbinner = stats.bin2D(modlmap,nbin_edges)
ncents = nbinner.centers
#cbin_edges = np.arange(20,8000,20)
cbin_edges = np.arange(20,8000,40)
cbinner = stats.bin2D(modlmap,cbin_edges)
fells = np.arange(lmax)
for task in my_tasks:
qids = qpairs[task]
qid1,qid2 = qids
ncents,n1d = np.loadtxt("%sn1d_%s_%s.txt" % (spath,qid1,qid2),unpack=True)
ncents,n1d1 = np.loadtxt("%sn1d_%s_%s.txt" % (spath,qid1,qid1),unpack=True)
ncents,n1d2 = np.loadtxt("%sn1d_%s_%s.txt" % (spath,qid2,qid2),unpack=True)
ccents,s1d = np.loadtxt("%ss1d_%s_%s.txt" % (spath,qid1,qid2),unpack=True)
fbeam1 = lambda x: tutils.get_kbeam(qid1,x,sanitize=not(args.unsanitized_beam),planck_pixwin=True)
opath = "/scratch/r/rbond/msyriac/data/act/omar/"
tpath = os.environ[
'WORK'] + "/data/depot/tilec/map_v1.0.0_rc_joint_%s/" % region
"""
We compare masks
and make sure they are identical
"""
omask = enmap.read_map("%smask_s14&15_%s.fits" % (opath, region))
tmask = enmap.read_map("%stilec_mask.fits" % tpath)
assert np.all(np.isclose(omask, tmask))
modlmap = omask.modlmap()
bin_edges = np.arange(400, 8000, 200)
binner = stats.bin2D(modlmap, bin_edges)
Nplot = 300
kbeam = tutils.get_kbeam("d56_05", modlmap, sanitize=False)
w2 = np.mean(omask**2.)
ls, bells = np.loadtxt(
"%stilec_single_tile_%s_cmb_map_v1.0.0_rc_joint_beam.txt" %
(tpath, region),
unpack=True)
tkbeam = maps.interp(ls, bells)(modlmap)
ls, bells = np.loadtxt(
"%stilec_single_tile_%s_cmb_deprojects_comptony_map_v1.0.0_rc_joint_beam.txt"
% (tpath, region),
unpack=True)
tkbeam_nosz = maps.interp(ls, bells)(modlmap)
parray_dat = aio.patch_array_from_config(Config,
expf_name,
shape_dat,
wcs_dat,
dimensionless=True)
parray_sim = aio.patch_array_from_config(Config,
expf_name,
shape_sim,
wcs_sim,
dimensionless=True)
lxmap_dat, lymap_dat, modlmap_dat, angmap_dat, lx_dat, ly_dat = fmaps.get_ft_attributes_enmap(
shape_dat, wcs_dat)
lxmap_sim, lymap_sim, modlmap_sim, angmap_sim, lx_sim, ly_sim = fmaps.get_ft_attributes_enmap(
shape_sim, wcs_sim)
lbin_edges = np.arange(kellmin, kellmax, 300)
lbinner_dat = stats.bin2D(modlmap_dat, lbin_edges)
lbinner_sim = stats.bin2D(modlmap_sim, lbin_edges)
# === COSMOLOGY ===
theory, cc, lmax = aio.theory_from_config(Config, cosmology_section)
parray_dat.add_theory(None, theory, lmax)
template_dat = fmaps.simple_flipper_template_from_enmap(shape_dat, wcs_dat)
nT = parray_dat.nT
nP = parray_dat.nP
if rank == 0: io.quickPlot2d(nT, out_dir + "nt.png")
kbeam_dat = parray_dat.lbeam
kbeampass = kbeam_dat
if rank == 0: io.quickPlot2d(kbeampass, out_dir + "kbeam.png")
fMaskCMB_T = fmaps.fourierMask(lx_dat,
ly_dat,
modlmap_dat,
# Set up SZ frequency dependence
def gnu(nu_ghz,tcmb=2.7255):
nu = 1e9*np.asarray(nu_ghz)
hplanck = 6.62607e-34
kboltzmann = 1.38065e-23
x = hplanck*nu/kboltzmann/tcmb
coth = np.cosh(x/2.)/np.sinh(x/2.)
return x*coth-4.
yresponses = gnu(freqs)
cresponses = yresponses*0 + 1.
fc = maps.FourierCalc(shape[-2:],wcs)
s = stats.Stats()
bin_edges = np.arange(300,5000,80)
binner = stats.bin2D(agen.modlmap,bin_edges)
for i in range(nsims):
cmb,y,observed = agen.get_maps()
kmaps = []
for j in range(len(freqs)):
_,kmap,_ = fc.power2d(observed[j])
km = np.nan_to_num(kmap/agen.kbeams[j])
km[agen.modlmap>ellmaxes[j]] = 0
km[agen.modlmap<ellmins[j]] = 0
kmaps.append(km.copy())
kmaps = np.stack(kmaps)
sc = maps.silc(kmaps,cinv,cresponses)
sy = maps.silc(kmaps,cinv,yresponses)
cc = maps.cilc(kmaps,cinv,cresponses,yresponses)
cy = maps.cilc(kmaps,cinv,yresponses,cresponses)
def make_sim(self, seed):
with bench.show(
"Lensing operation...") if self.rank == 0 else ignore():
full, kappa = lensing.rand_map(
self.fshape,
self.fwcs,
self.ps,
lmax=self.lmax,
maplmax=self.lmax,
seed=seed,
verbose=True if self.rank == 0 else False,
dtype=self.dtype,
output="lk")
alms = curvedsky.map2alm(full, lmax=self.lmax)
ps_data = hp.alm2cl(alms.astype(np.complex128))
del alms
self.mpibox.add_to_stats("fullsky_ps", ps_data)
south = full.submap(self.pos_south)
equator = full.submap(self.pos_eq)
ksouth = kappa.submap(self.pos_south)
kequator = kappa.submap(self.pos_eq)
del full
del kappa
if self.count == 0:
self.shape['s'], self.wcs['s'] = south.shape, south.wcs
self.shape['e'], self.wcs['e'] = equator.shape, equator.wcs
for m in ['s', 'e']:
self.taper[m], self.w2[m] = fmaps.get_taper(self.shape[m],
taper_percent=18.0,
pad_percent=4.0,
weight=None)
self.w4[m] = np.mean(self.taper[m]**4.)
self.w3[m] = np.mean(self.taper[m]**3.)
self.rotator = fmaps.MapRotatorEquator(
self.shape['s'],
self.wcs['s'],
self.wdeg,
self.hdeg,
width_multiplier=0.6,
height_multiplier=1.2,
downsample=True,
verbose=True if self.rank == 0 else False,
pix_target_override_arcmin=self.pix_intermediate)
self.taper['r'] = self.rotator.rotate(self.taper['s'])
self.w2['r'] = np.mean(self.taper['r']**2.)
self.w4['r'] = np.mean(self.taper['r']**4.)
self.w3['r'] = np.mean(self.taper['r']**3.)
self.shape['r'], self.wcs[
'r'] = self.rotator.shape_final, self.rotator.wcs_final
self.fc = {}
self.binner = {}
self.modlmap = {}
for m in ['s', 'e', 'r']:
self.fc[m] = fmaps.FourierCalc(self.shape[m], self.wcs[m])
self.modlmap[m] = enmap.modlmap(self.shape[m], self.wcs[m])
self.binner[m] = bin2D(self.modlmap[m], self.bin_edges)
self.cents = self.binner['s'].centers
self._init_qests()
self.count += 1
south *= self.taper['s']
equator *= self.taper['e']
ksouth *= self.taper['s']
kequator *= self.taper['e']
return south, equator, ksouth, kequator
from soapack import interfaces as sints
from actsims import noise
froot = "/scratch/r/rbond/msyriac/data/scratch/tilec/test_lfi_v2_00_0000_deep56/"
kroot = "/scratch/r/rbond/msyriac/data/depot/tilec/test_lfi_v2_00_0000_deep56/"
droot = "/scratch/r/rbond/msyriac/data/depot/tilec/test_lfi_data_deep56/"
lroot = "/scratch/r/rbond/msyriac/data/depot/tilec/test_lfi_v3_00_0000_deep56/"
mask = sints.get_act_mr3_crosslinked_mask("deep56")
shape,wcs = mask.shape,mask.wcs
w2 = np.mean(mask**2.)
qids = 'p01,p02,p03'.split(',')
bin_edges = np.arange(20,6000,40)
modlmap = mask.modlmap()
binner = stats.bin2D(modlmap,bin_edges)
arraynames = {'p01':'030','p02':'044','p03':'070'}
dm = sints.PlanckHybrid(region=mask)
for qid in qids:
split = enmap.read_map("%ssplit_%s.fits" % (froot,qid))
#io.hplot(enmap.downgrade(split,4),"split_%s" % qid)
arrayname = arraynames[qid]
wts = dm.get_splits_ivar(arrayname)[0,:,0,...]
coadd,_ = noise.get_coadd(split[:,0,...],wts,axis=0) * mask
def do(ymap, cmap, dmap, mask, ras, decs, wt):
combined = list(zip(ras, decs))
random.shuffle(combined)
ras[:], decs[:] = zip(*combined)
Nrand = 400
njobs = len(ras)
comm, rank, my_tasks = mpi.distribute(njobs)
print("Rank %d starting" % rank)
s = stats.Stats(comm)
i = 0
for task in my_tasks:
ra = ras[task]
dec = decs[task]
mcut, ycut, ccut, dcut, weight = get_cuts(mask, ymap, cmap, dmap, wt,
ra, dec, arcmin, pix)
if mcut is None: continue
if i == 0:
modrmap = np.rad2deg(ycut.modrmap()) * 60.
bin_edges = np.arange(0., 15., 1.0)
binner = stats.bin2D(modrmap, bin_edges)
rras, rdecs = catalogs.random_catalog(ymap.shape,
ymap.wcs,
Nrand,
edge_avoid_deg=4.)
nrej = 0
for rra, rdec in zip(rras, rdecs):
rmcut, rycut, rccut, rdcut, rweight = get_cuts(
mask, ymap, cmap, dmap, wt, rra, rdec, arcmin, pix)
if rmcut is None:
nrej = nrej + 1
continue
cents, ry1d = binner.bin(rycut)
cents, rc1d = binner.bin(rccut)
cents, rd1d = binner.bin(rdcut)
s.add_to_stats("rc1d", rc1d * 1e6)
s.add_to_stats("ry1d", ry1d * 1e6)
s.add_to_stats("rd1d", rd1d * 1e6)
if rank == 0: print(Nrand - nrej, " accepted")
cents, y1d = binner.bin(ycut)
cents, c1d = binner.bin(ccut)
cents, d1d = binner.bin(dcut)
s.add_to_stats("c1d", c1d * 1e6)
s.add_to_stats("y1d", y1d * 1e6)
s.add_to_stats("d1d", d1d * 1e6)
s.add_to_stack("cstack", ccut * 1e6 * weight)
s.add_to_stack("dstack", dcut * 1e6 * weight)
s.add_to_stack("ystack", ycut * 1e6 * weight)
s.add_to_stats("sum", (weight, ))
i = i + 1
if i % 10 == 0 and rank == 0: print(i)
print("Rank %d done " % rank)
s.get_stats()
s.get_stacks()
if rank == 0:
N = s.vectors['sum'].sum()
ystack = s.stacks['ystack'] * N
cstack = s.stacks['cstack'] * N
dstack = s.stacks['dstack'] * N
y1ds = s.vectors['y1d']
c1ds = s.vectors['c1d']
d1ds = s.vectors['d1d']
ry1d = s.stats['ry1d']['mean']
rc1d = s.stats['rc1d']['mean']
rd1d = s.stats['rd1d']['mean']
_, nwcs = enmap.geometry(pos=(0, 0),
shape=ystack.shape,
res=np.deg2rad(0.5 / 60.))
return rank, enmap.enmap(
ystack, nwcs), enmap.enmap(cstack, nwcs), enmap.enmap(
dstack, nwcs), N, cents, y1ds, c1ds, d1ds, ry1d, rc1d, rd1d
else:
return rank, None, None, None, None, None, None, None, None, None, None, None
