c = 3.2
cc = cutils.get_hdv_cc()
# Set up lensing
def lens_map(imap):
return plensing.displace_map(imap, alpha, order=args.lens_order)
# Map of distances from center
modrmap = enmap.modrmap(shape, wcs)
# The convergence ~ projected mass density
kappa = lensing.nfw_kappa(massOverh,
modrmap,
cc,
zL=z,
concentration=c,
overdensity=180.,
critical=False,
atClusterZ=False)
if rank == 0: enmap.write_map(f'{savedir}kappa.fits', kappa)
# Deflection field
alpha = lensing.alpha_from_kappa(kappa)
# Fourier magnitude map
modlmap = enmap.modlmap(shape, wcs)
# Unlensed CMB theory
theory = cosmology.default_theory()
cltt2d = theory.uCl('TT', modlmap)
clte2d = theory.uCl('TE', modlmap)
clee2d = theory.uCl('EE', modlmap)
# 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
kappa = lensing.nfw_kappa(kamp_true*1e15,modrmap,cc,overdensity=200.,critical=True,atClusterZ=True)
phi,_ = lensing.kappa_to_phi(kappa,modlmap,return_fphi=True)
grad_phi = enmap.grad(phi)
posmap = enmap.posmap(shape,wcs)
pos = posmap + grad_phi
alpha_pix = enmap.sky2pix(shape,wcs,pos, safe=False)
lens_order = 5
if rank==0: print("Starting sims...")
# Stats
Nsims = args.Nclusters
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]
modrmap = enmap.modrmap(shape, wcs)
#kamps = np.linspace(0.01,0.8,40)
amp_min = -0.3
amp_max = 0.5
# amp_min = 0.76
# amp_max = 0.84
kamps = np.linspace(amp_min, amp_max, 10)
print(kamps)
Ccovs = []
Cinvs = []
logdets = []
kappa = lensing.nfw_kappa(1e15, modrmap, cc)
phi, _ = lensing.kappa_to_phi(kappa, modlmap, return_fphi=True)
grad_phi = enmap.grad(phi)
lens_order = 5
posmap = enmap.posmap(shape, wcs)
for k, kamp in enumerate(kamps):
pos = posmap + kamp * grad_phi
alpha_pix = enmap.sky2pix(shape, wcs, pos, safe=False)
Scov = lensing.lens_cov(Ucov,
alpha_pix,
lens_order=lens_order,
kbeam=kbeam)
# io.plot_img(np.nan_to_num((Scov-Ucov)*100./Ucov))
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)
# Simulate
lmax = int(bmodlmap.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(bshape,bwcs,ps)
ng = maps.MapGen(bshape,bwcs,ps_noise)
kamp_true = args.Amp
kappa = lensing.nfw_kappa(kamp_true*1e15,bmodrmap,cc,overdensity=200.,critical=True,atClusterZ=True)
phi,_ = lensing.kappa_to_phi(kappa,bmodlmap,return_fphi=True)
grad_phi = enmap.grad(phi)
posmap = enmap.posmap(bshape,bwcs)
pos = posmap + grad_phi
alpha_pix = enmap.sky2pix(bshape,bwcs,pos, safe=False)
lens_order = 5
### FG PROFS
famps = np.linspace(fmin,fmax,fnum)
fg = kappa * 50.
fg_true = maps.filter_map(fg.copy()*ftrue,kbeam)
np.random.seed(2)
# 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)
lens_func = lambda x: lensing.nfw_kappa(mass,
x,
cc,
zL=0.7,
concentration=3.2,
overdensity=200.,
critical=True,
atClusterZ=True)
#sigma = 1.0 * np.pi/180./60.
#lens_func = lambda x: 0.2 * np.exp(-x**2./sigma**2./2.)
rshape, rwcs = maps.rect_geometry(width_arcmin=5., px_res_arcmin=0.001)
fshape, fwcs = maps.rect_geometry(width_arcmin=20., px_res_arcmin=0.1)
cshape, cwcs = maps.rect_geometry(width_arcmin=20., px_res_arcmin=0.5)
rmodrmap = enmap.modrmap(rshape, rwcs)
fmodrmap = enmap.modrmap(fshape, fwcs)
cmodrmap = enmap.modrmap(cshape, cwcs)
rmodlmap = enmap.modlmap(rshape, rwcs)
fmodlmap = enmap.modlmap(fshape, fwcs)
cmodlmap = enmap.modlmap(cshape, cwcs)
get_dimensionless=False)
# 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(oshape, owcs, ps_noise)
kamp_true = args.Amp
if args.hdv:
kappa = lensing.nfw_kappa(kamp_true * 1e15,
modrmap,
cc,
overdensity=180.,
critical=False,
atClusterZ=False)
else:
kappa = lensing.nfw_kappa(kamp_true * 1e15,
modrmap,
cc,
overdensity=200.,
critical=True,
atClusterZ=True)
phi, _ = lensing.kappa_to_phi(kappa, modlmap, return_fphi=True)
grad_phi = enmap.grad(phi)
posmap = enmap.posmap(shape, wcs)
pos = posmap + grad_phi
alpha_pix = enmap.sky2pix(shape, wcs, pos, safe=False)
print(As)
params = {}
params['As'] = As
params['H0'] = h * 100.
params['omch2'] = omch2
params['ombh2'] = ombh2
params['ns'] = ns
params['mnu'] = 0.
conc = 3.2
cc = counts.ClusterCosmology(params,skipCls=True,skipPower=True,skip_growth=True)
z = 0.7
mass = 2e14
thetas = np.geomspace(0.1,10,1000)
kappa = lensing.nfw_kappa(mass,thetas*utils.arcmin,cc,zL=z,concentration=conc,overdensity=180,critical=False,atClusterZ=False)
hthetas,hkappa = np.loadtxt("data/hdv_unfiltered.csv",unpack=True,delimiter=',')
pl = io.Plotter(xyscale='loglog', xlabel='$\\theta$ [arcmin]', ylabel='$\\kappa$')
pl.add(thetas,kappa)
pl.add(hthetas,hkappa,ls='--')
pl.done('test_uhdv.png')
pl = io.Plotter(xyscale='linlin', xlabel='$\\theta$ [arcmin]', ylabel='$\\kappa$')
pl.add(hthetas,hkappa/maps.interp(thetas,kappa)(hthetas),ls='--')
pl.hline(y=1)
pl.done('test_uhdv_ratio.png')
shape,wcs = enmap.geometry(pos=(0,0),shape=(512,512),res=0.2 * utils.arcmin,proj='plain')
kmask = maps.mask_kspace(shape,wcs,lmax=8095)
Nsims = num_amps
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]
# File I/O
io.mkdir(GridName,comm)
cov_name = lambda x: GridName+"/cov_"+str(x)+".npy"
if rank==0: print("Rank 0 starting ...")
for k,my_task in enumerate(my_tasks):
kamp = kamps[my_task]
kappa_template = lensing.nfw_kappa(kamp*1e15,bmodrmap,cc,overdensity=200.,critical=True,atClusterZ=True)
phi,_ = lensing.kappa_to_phi(kappa_template,bmodlmap,return_fphi=True)
grad_phi = enmap.grad(phi)
pos = posmap + grad_phi
alpha_pix = enmap.sky2pix(bshape,bwcs,pos, safe=False)
def do_the_thing():
return lensing.lens_cov(Ucov,alpha_pix,lens_order=lens_order,kbeam=kbeam,bshape=shape)
if rank==0:
with bench.show("rank 0 lensing cov"):
Scov = do_the_thing()
else:
Scov = do_the_thing()
lcltt2d = theory.lCl('TT', modlmap)
# Define our NFW cluster
mass = 2e14
c = 3.2
z = 0.7
#cc = counts.ClusterCosmology(skipCls=True,skipPower=True)
cc = Cosmology()
massOverh = mass / cc.h
print(cc.h)
# Generate its kappa
kappa = lensing.nfw_kappa(massOverh,
modrmap,
cc,
zL=z,
concentration=c,
overdensity=500.,
critical=True,
atClusterZ=True)
#io.plot_img(kappa)
# Convert kappa to deflection
alpha = lensing.alpha_from_kappa(kappa)
# Change number of sims here
nsims = 600
comm, rank, my_tasks = mpi.distribute(nsims)
# Final beam to apply to kappa map
ffwhm = None #2.0