def get_theory_dicts(nells=None, lmax=9000, grad=True):
thloc = os.path.dirname(
os.path.abspath(__file__)) + "/../data/" + config['theory_root']
ls = np.arange(lmax + 1)
ucls = {}
tcls = {}
theory = cosmology.loadTheorySpectraFromCAMB(thloc,
get_dimensionless=False)
ells, gt, ge, gb, gte = np.loadtxt(f"{thloc}_camb_1.0.12_grads.dat",
unpack=True,
usecols=[0, 1, 2, 3, 4])
if nells is None: nells = {'TT': 0, 'EE': 0, 'BB': 0}
ucls['TT'] = maps.interp(ells, gt)(ls) if grad else theory.lCl('TT', ls)
ucls['TE'] = maps.interp(ells, gte)(ls) if grad else theory.lCl('TE', ls)
ucls['EE'] = maps.interp(ells, ge)(ls) if grad else theory.lCl('EE', ls)
ucls['BB'] = maps.interp(ells, gb)(ls) if grad else theory.lCl('BB', ls)
ucls['kk'] = theory.gCl('kk', ls)
tcls['TT'] = theory.lCl('TT', ls) + nells['TT']
tcls['TE'] = theory.lCl('TE', ls)
tcls['EE'] = theory.lCl('EE', ls) + nells['EE']
tcls['BB'] = theory.lCl('BB', ls) + nells['BB']
return ucls, tcls
comm = mpi.MPI.COMM_WORLD
rank = comm.Get_rank()
numcores = comm.Get_size()
# Paths
PathConfig = io.load_path_config()
pout_dir = PathConfig.get("paths","plots")+"qest_hdv_"+str(args.noise)+"_"
io.mkdir(pout_dir,comm)
# 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)
mlmax = lmax + 500
polcomb = 'TT'
nside = 2048
# lmin = 300
# lmax = 1500
# mlmax = lmax + 500
# polcomb = 'TT'
# nside = 1024
config = io.config_from_yaml(
os.path.dirname(os.path.abspath(__file__)) + "/../input/config.yml")
opath = config['data_path']
thloc = os.path.dirname(
os.path.abspath(__file__)) + "/../data/" + config['theory_root']
theory = cosmology.loadTheorySpectraFromCAMB(thloc, get_dimensionless=False)
def get_mv_kappa(polcomb, xalm, yalm):
shape, wcs = enmap.fullsky_geometry(res=np.deg2rad(0.5 * 8192 / nside /
60.))
res = qe.qe_all(shape,
wcs,
lambda x, y: theory.lCl(x, y),
mlmax,
yalm[0],
yalm[1],
yalm[2],
estimators=[polcomb],
xfTalm=xalm[0],
xfEalm=xalm[1],
comm = mpi.MPI.COMM_WORLD
rank = comm.Get_rank()
numcores = comm.Get_size()
Njobs = Nsims
num_each,each_tasks = mpi.mpi_distribute(Njobs,numcores)
if rank==0: print ("At most ", max(num_each) , " tasks...")
my_tasks = each_tasks[rank]
mlmax = 4000 if not(pure_healpix) else None # lmax used for harmonic transforms
# load theory
camb_root = "data/cosmo2017_10K_acc3"
theory = cosmology.loadTheorySpectraFromCAMB(camb_root,get_dimensionless=False)
# ells corresponding to modes in the alms
nside = 2048
ells = np.arange(0,3*nside+1,1)
#lpls,lpal = np.loadtxt("data/nls_%d.txt" % lmax,unpack=True)
lpfile = "/gpfs01/astro/workarea/msyriac/data/sims/msyriac/lenspix/cosmo2017_lmax_fix_lens_lmax_%d_qest_lmax_%d_AL.txt" % (lmax+2000,lmax)
lpls,lpal = np.loadtxt(lpfile,unpack=True,usecols=[0,1])
lpal = lpal / (lpls) / (lpls+1.)
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 = maps.interp(lpls,dh_als)(ells)
sim_location = "/gpfs01/astro/workarea/msyriac/data/sims/msyriac/lenspix/"
beamPath = simRoot + "beam_0.txt"
l,beamells = np.loadtxt(beamPath,unpack=True,usecols=[0,1])
cmbellmin = 100
cmbellmax = 3000
kellmin = 100
kellmax = 3000
#cambRoot = "/astro/u/msyriac/repos/cmb-lensing-projections/data/TheorySpectra/ell28k_highacc"
cambRoot = "/gpfs01/astro/workarea/msyriac/data/sims/alex/non-linear"
TCMB = 2.7255e6
#theory = loadTheorySpectraFromCAMB(cambRoot,unlensedEqualsLensed=False,useTotal=False,TCMB = TCMB,lpad=4000)
# !!!!!!!!!!!!! N2 bias
theory = loadTheorySpectraFromCAMB(cambRoot,unlensedEqualsLensed=True,useTotal=False,TCMB = TCMB,lpad=(cmbellmax + 500))
ellkk = np.arange(2,9000,1)
Clkk = theory.gCl("kk",ellkk)
N = int(sys.argv[1])
assert N%numcores==0
num_each = (N / numcores)
startIndex = rank*num_each
endIndex = startIndex + num_each
myIs = list(range(N))[startIndex:endIndex]
listCrossPower = {}
if nIter == 1:
iterName = '_iterOff'
else:
iterName = '_iterOn'
print "Run with testNlkk lensing or lensTT ", iterName
outDir = 'output/' + saveName + '_'
i = 0
#cambRoot = 'data/Aug6_highAcc_CDM'
cambRoot = '../quicklens/quicklens/data/cl/planck_wp_highL/planck_lensing_wp_highL_bestFit_20130627'
#cambRoot = '/home/hnnguyen/CAMB-0.1.6.1/base_plikHM_TT_lowTEB_minimum_fudgedtotaup06_lmax5000'
theoryOverride = cosmo.loadTheorySpectraFromCAMB(
cambRoot,
unlensedEqualsLensed=False,
useTotal=False,
TCMB=TCMB,
lpad=bigell) #,get_dimensionless=True)
#theoryOverride = None
tellmin, tellmax = list_from_config(Config, expName, 'tellrange')
pellmin, pellmax = list_from_config(Config, expName, 'pellrange')
px = np.round(180. / max(tellmax, pellmax) * 60. * 0.95, 2)
ls, Nls, ellbb, dclbb, efficiency, cc = lensNoise(
Config,
expName,
lensName,
beamOverride=None,
lkneeTOverride=None,
lkneePOverride=None,
def __init__(self,
full_sky_pix,
wdeg,
hdeg,
yoffset,
mpi_comm=None,
nsims=1,
lmax=7000,
pix_intermediate=None,
bin_lmax=3000):
self.dtype = np.float32
self.bin_edges = np.arange(80, bin_lmax, 100) # define power bin edges
# Create a full sky geometry
self.fshape, self.fwcs = enmap.fullsky_geometry(res=full_sky_pix *
np.pi / 180. / 60.,
proj="car")
self.fshape = self.fshape[-2:]
# Holders for geometries and window corrections for each of e,s,r
self.shape = {}
self.wcs = {}
self.taper = {}
self.w2 = {}
self.w3 = {}
self.w4 = {}
# Intermediate pixelization for rotation
self.pix_intermediate = full_sky_pix if (
pix_intermediate is None) else pix_intermediate
self.wdeg = wdeg
self.hdeg = hdeg
degree = u.degree
vwidth = hdeg / 2.
hwidth = wdeg / 2.
# Box locations to slice from
self.pos_south = np.array([[-vwidth + yoffset, -hwidth],
[vwidth + yoffset, hwidth]]) * degree
self.pos_eq = np.array([[-vwidth, -hwidth], [vwidth, hwidth]]) * degree
# Get MPI comm
self.comm = mpi_comm
try:
self.rank = mpi_comm.Get_rank()
self.numcores = mpi_comm.Get_size()
except:
self.rank = 0
self.numcores = 1
if self.rank == 0:
self.logger = io.get_logger("rotrecon")
# Distribute MPI tasks
num_each, each_tasks = mpi_distribute(nsims, self.numcores)
self.mpibox = MPIStats(self.comm, num_each, tag_start=333)
if self.rank == 0:
self.logger.info("At most " + str(max(num_each)) + " tasks...")
self.tasks = each_tasks[self.rank]
# Initialize theory power spectra
theory_file_root = "data/Aug6_highAcc_CDM"
powspec_file = "data/Aug6_highAcc_CDM_lenspotentialCls.dat"
self.ps = powspec.read_camb_full_lens(powspec_file).astype(self.dtype)
self.theory = cosmology.loadTheorySpectraFromCAMB(
theory_file_root,
unlensedEqualsLensed=False,
useTotal=False,
TCMB=2.7255e6,
lpad=9000,
get_dimensionless=False)
self.lmax = lmax
self.count = 0
def __init__(self, loc='./data/spectra/'):
# with open(loc+'Planck_Planck.pickle', 'rb') as handle:
# self.pdict = pickle.load(handle)
# with open(loc+'ACT_ACT.pickle', 'rb') as handle:
# self.adict = pickle.load(handle)
# with open(loc+'ACT_planck.pickle', 'rb') as handle:
# self.apdict = pickle.load(handle)
self.pdict = {}
self.apdict = {}
self.adict = {}
for patch in ['deep56', 'boss']:
for i in range(len(pfreqs)):
for j in range(i, len(pfreqs)):
array1 = pfreqs[i]
array2 = pfreqs[j]
self.pdict[patch, array1,
array2] = load_spec(patch, array1, array2)
acts = {
'boss': {
's15': ['pa1_f150', 'pa2_f150', 'pa3_f090', 'pa3_f150']
},
'deep56': {
's14': ['pa1_f150', 'pa2_f150'],
's15': ['pa1_f150', 'pa2_f150', 'pa3_f090', 'pa3_f150']
}
}
for patch in ['deep56', 'boss']:
combs = []
for season in acts[patch].keys():
for array in acts[patch][season]:
combs.append((season, array))
ncombs = len(combs)
for i in range(ncombs):
season1, array1 = combs[i]
for k in range(len(pfreqs)):
self.apdict[patch, pfreqs[k], season1,
array1] = load_spec(patch, pfreqs[k],
(season1, array1))
for j in range(i, ncombs):
season2, array2 = combs[j]
self.adict[patch, season1, array1, season2,
array2] = load_spec(patch, (season1, array1),
(season2, array2))
croot = "data/cosmo2017_10K_acc3"
self.theory = cosmology.loadTheorySpectraFromCAMB(
croot, get_dimensionless=False)
import pandas as pd
self.adf = pd.read_csv("./data/spectra/wnoise.csv")
# 545 and 857 are wrong
self.planck_rms = {
'030': 195,
'044': 226,
'070': 199,
'100': 77,
'143': 33,
'217': 47,
'353': 153,
'545': 1000,
'857': 1000
}
def comp_cltt(alex):
if alex:
lmax = 5000
res = 1.0
camb_root = "/gpfs01/astro/workarea/msyriac/data/act/theory/cosmo2017_10K_acc3"
sim_name = "fullskyLensedMapUnaberrated_T_00002.fits"
#sim_name = "fullskyLensedMap_T_00000.fits"
ksim_name = "kappaMap_00002.fits"
sim_location = "/gpfs01/astro/workarea/msyriac/data/sims/dw/"
else:
lmax = 3000
res = 1.5
camb_root = "/astro/u/msyriac/repos/quicklens/quicklens/data/cl/planck_wp_highL/planck_lensing_wp_highL_bestFit_20130627"
sim_name = "lensed_map_seed_1.fits"
ksim_name = "kappa_map_seed_1.fits"
sim_location = "/gpfs01/astro/workarea/msyriac/data/depot/falafel/"
theory = cosmology.loadTheorySpectraFromCAMB(camb_root,
get_dimensionless=False)
Xmap = enmap.read_map(sim_location + sim_name)
ikappa = enmap.read_map(sim_location + ksim_name)
if alex:
shape, wcs = enmap.fullsky_geometry(res=np.deg2rad(res / 60.),
proj="car")
Xmap.wcs = wcs
ikappa.wcs = wcs
calm = cs.map2alm(Xmap, lmax=lmax).astype(np.complex128)
kalm = cs.map2alm(ikappa, lmax=lmax).astype(np.complex128)
cls = hp.alm2cl(calm)
clskk = hp.alm2cl(kalm)
lsc = np.arange(0, len(cls), 1)
print(cls[:5])
print(clskk[:5])
cls[lsc < 2] = np.nan
clskk[lsc < 2] = np.nan
bin_edges = np.logspace(np.log10(2), np.log10(lmax), 40)
binner = stats.bin1D(bin_edges)
ls = np.arange(0, lmax, 1)
cltt = theory.lCl('TT', ls)
clkk = theory.gCl('kk', ls)
cents, btt = binner.binned(ls, cltt)
cents, bcc = binner.binned(lsc, cls)
cents, bttkk = binner.binned(ls, clkk)
cents, bcckk = binner.binned(lsc, clskk)
# plot
pl = io.Plotter(yscale='log',
xscale='log',
xlabel='$\\ell$',
ylabel='$C_{\\ell}$')
pl.add(ls, ls**2. * theory.lCl('TT', ls), lw=3, color='k')
pl.add(cents, cents**2. * btt, ls="none", marker="x")
pl.add(cents, cents**2. * bcc, ls="none", marker="o")
pl.done(io.dout_dir + "fullsky_sim_test_cmb_alex_" + str(alex) + ".png")
pl = io.Plotter(yscale='log', xscale='log', xlabel='$L$', ylabel='$C_L$')
pl.add(ls, theory.gCl('kk', ls), lw=3, color='k')
pl.add(cents, bttkk, ls="none", marker="x")
pl.add(cents, bcckk, ls="none", marker="o")
pl.done(io.dout_dir + "fullsky_sim_test_kk_alex_" + str(alex) + ".png")
pl = io.Plotter(xscale='log', xlabel='$L$', ylabel='$\\delta C_L / C_L$')
pl.add(cents, (bcc - btt) / btt, ls="-", marker="o", label="cmb")
pl.add(cents, (bcckk - bttkk) / bttkk, ls="-", marker="o", label="kk")
pl.hline()
pl._ax.set_ylim(-0.2, 0.2)
pl.done(io.dout_dir + "fullsky_sim_test_alex_diff_" + str(alex) + "_2.png")
