def test_massfn():
from szar import counts
import hmf
from cluster_toolkit import massfunction
zs = np.linspace(0.,3.,20)
ms = np.geomspace(1e14,1e17,200)
ks = np.geomspace(1e-3,10,101)
from enlib import bench
with bench.show("init"):
hcos = hm.HaloModel(zs,ks,ms=ms,mass_function="tinker")
dndM_ct2 = np.zeros((zs.size,ms.size))
for i,z in enumerate(zs):
h = hmf.MassFunction(z=z,Mmin=np.log10(ms.min()*hcos.h),Mmax=np.log10(ms.max()*hcos.h))
if i==0: dndM_ct = np.zeros((zs.size,h.dndm.size))
dndM_ct[i,:] = h.dndm.copy()
dndM_ct2[i,:] = massfunction.dndM_at_M(ms*hcos.h, hcos.ks_sigma2/hcos.h, hcos.sPzk[i]*hcos.h**3, hcos.om0)
fsky = 0.4
hmf = counts.Halo_MF(counts.ClusterCosmology(hcos.params,skipCls=True),np.log10(ms),zs)
nz_szar = hmf.N_of_z()*fsky
print(nz_szar,nz_szar.shape)
# sys.exit()
print(hcos.nzm.shape,hcos.bh.shape)
bh = hcos.bh
nzm = hcos.nzm
# ims,ins = np.loadtxt("data/tinker2008Fig5.txt",unpack=True,delimiter=',')
# pl = io.Plotter(xyscale='linlin')
# pl.add(ims,ins,ls="--")
# pl.add(np.log10(ms*hcos.h),np.log10(nzm[0,:]*ms**2./hcos.rho_matter_z(0.)))
# pl.done()
chis = hcos.results.angular_diameter_distance(hcos.zs) * (1+hcos.zs)
nz = np.trapz(nzm,ms,axis=-1)*4.*np.pi*chis**2./hcos.results.h_of_z(hcos.zs)*fsky
nz_ct = np.trapz(dndM_ct,h.m,axis=-1)*4.*np.pi*chis**2./hcos.results.h_of_z(hcos.zs)*fsky * hcos.h**3.
nz_ct2 = np.trapz(dndM_ct2,ms,axis=-1)*4.*np.pi*chis**2./hcos.results.h_of_z(hcos.zs)*fsky * hcos.h**3.
pl = io.Plotter()
pl.add(zs,nz,label='hmvec')
pl.add(hmf.zarr,nz_szar,ls='--',label='szar')
pl.add(zs,nz_ct,ls='-.',label='hmf')
pl.add(zs,nz_ct2,ls='-.',label='ct')
pl.done()
n = np.trapz(nz,zs)
print(n)
n = np.trapz(nz_szar,hmf.zarr)
print(n)
n = np.trapz(nz_ct,zs)
print(n)
n = np.trapz(nz_ct2,zs)
print(n)
def __init__(self,
Mexp_edges,
z_edges,
cosmo_params=None,
const_params=None,
low_acc=True):
cc = counts.ClusterCosmology(
cosmo.defaultCosmology if cosmo_params is None else cosmo_params,
constDict=cosmo.defaultConstants
if const_params is None else const_params,
lmax=None,
skipCls=True,
skipPower=False,
low_acc=low_acc)
hmf = counts.Halo_MF(cc,
Mexp_edges,
z_edges,
kh=None,
powerZK=None,
kmin=1e-4,
kmax=5.,
knum=200)
delta = 200
nmzdensity = hmf.N_of_Mz(hmf.M200, delta)
Ndz = np.multiply(nmzdensity,
np.diff(z_edges).reshape((1, z_edges.size - 1)))
self.Nmz = np.multiply(
Ndz,
np.diff(10**Mexp_edges).reshape(
(Mexp_edges.size - 1, 1))) * 4. * np.pi
self.Mexp_edges = Mexp_edges
self.z_edges = z_edges
self.Medges = 10.**self.Mexp_edges
self.Mcents = (self.Medges[1:] + self.Medges[:-1]) / 2.
self.Mexpcents = np.log10(self.Mcents)
self.zcents = (self.z_edges[1:] + self.z_edges[:-1]) / 2.
self.ntot = self.Nmz.sum()
self.cc = cc
# MPI
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)))
ns = 0.958
omb = ombh2 / h**2
omc = om - omb
omch2 = omc * h**2.
As = cosmology.As_from_s8(sigma8 = 0.76,bounds=[1.9e-9,2.5e-9],rtol=1e-4,omegab = omb, omegac = omc, ns = ns, h = h)
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)