def test_cls_camb(dict_file='orphics/tests/lcdm_baseline.pkl',
atol=1e-20,
rtol=1e-8):
"""
This reads from file with a given filename,
and compares the cls from the file
with cls created with pars from the same file.
"""
f = open(dict_file, "rb")
d = pickle.load(f)
lmax = d['lmax']
pars = d['pars']
cl_base = d['lensed_cls']
#pars['omch2']= 0.12
cl = {}
cosmo = cosmology.Cosmology(lmax=lmax, paramDict=pars)
ells = np.arange(2, lmax - 1000)
cl['ell'] = ells
cl['tt'] = cosmo.theory.lCl('TT', ells)
cl['ee'] = cosmo.theory.lCl('EE', ells)
cl['te'] = cosmo.theory.lCl('TE', ells)
cl['bb'] = cosmo.theory.lCl('BB', ells)
assert compare_cl_dicts(cl, cl_base, atol=atol, rtol=rtol)
def dcl(step=0.1):
params = cosmology.defaultCosmology.copy()
params['w0'] = -1 + step / 2.
cc = cosmology.Cosmology(params,
low_acc=low_acc,
pickling=False,
skipPower=True,
skip_growth=True,
lmax=7000)
ucltt_up = cc.theory.uCl('TT', ells)
lcltt_up = cc.theory.lCl('TT', ells)
uclte_up = cc.theory.uCl('TE', ells)
lclte_up = cc.theory.lCl('TE', ells)
uclee_up = cc.theory.uCl('EE', ells)
lclee_up = cc.theory.lCl('EE', ells)
params = cosmology.defaultCosmology.copy()
params['w0'] = -1 - step / 2.
cc = cosmology.Cosmology(params,
low_acc=low_acc,
pickling=False,
skipPower=True,
skip_growth=True,
lmax=7000)
ucltt_dn = cc.theory.uCl('TT', ells)
lcltt_dn = cc.theory.lCl('TT', ells)
uclte_dn = cc.theory.uCl('TE', ells)
lclte_dn = cc.theory.lCl('TE', ells)
uclee_dn = cc.theory.uCl('EE', ells)
lclee_dn = cc.theory.lCl('EE', ells)
dcltt = (ucltt_up - ucltt_dn) / step
dlcltt = (lcltt_up - lcltt_dn) / step
dclte = (uclte_up - uclte_dn) / step
dclee = (uclee_up - uclee_dn) / step
return dcltt, dclte, dclee #,dlcltt
def dcl(w):
params = cosmology.defaultCosmology.copy()
params['w0'] = w
cc = cosmology.Cosmology(params,low_acc=low_acc,pickling=False,skipPower=True,skip_growth=True)
ucltt = cc.theory.uCl('TT',ells)
lcltt = cc.theory.lCl('TT',ells)
uclte = cc.theory.uCl('TE',ells)
lclte = cc.theory.lCl('TE',ells)
uclee = cc.theory.uCl('EE',ells)
lclee = cc.theory.lCl('EE',ells)
return ucltt,lcltt,uclte,lclte,uclee,lclee
def test_pgm():
import halomodel as mmhm
from orphics import cosmology
cc = cosmology.Cosmology(hmvec.default_params, skipCls=True, low_acc=False)
mmhmod = mmhm.HaloModel(cc)
mthresh = np.array([10.5])
zs = np.array([0., 1., 2., 3.])
ms = np.geomspace(1e8, 1e16, 1000)
ks = np.geomspace(1e-4, 100, 1001)
# ks,pks = np.loadtxt("mm_k_pk.txt",unpack=True) # !!!
eeP = mmhmod.P_gg_2h(ks, zs, mthreshHOD=mthresh) + mmhmod.P_gg_1h(
ks, zs, mthreshHOD=mthresh)
meP = mmhmod.P_mg_2h(ks, zs, mthreshHOD=mthresh) + mmhmod.P_mg_1h(
ks, zs, mthreshHOD=mthresh)
mmP = mmhmod.P_mm_2h(ks, zs) + mmhmod.P_mm_1h(ks, zs)
hcos = hmvec.HaloModel(zs,
ks,
ms=ms,
halofit=None,
mdef='vir',
nfw_numeric=False)
hcos.add_hod("g", mthresh=(10**mthresh[0]) + zs * 0., corr="max")
# hcos.add_battaglia_profile("electron",family="AGN",xmax=100,nxs=60000)
pme_1h = hcos.get_power_1halo(name="g", name2="nfw")
pme_2h = hcos.get_power_2halo(name="g", name2="nfw")
pmm_1h = hcos.get_power_1halo("nfw")
pmm_2h = hcos.get_power_2halo("nfw")
pee_1h = hcos.get_power_1halo("g")
pee_2h = hcos.get_power_2halo("g")
for i in range(zs.size):
pl = io.Plotter(xyscale='loglin', xlabel='$k$', ylabel='$P$')
pl.add(ks, (pme_2h[i] + pme_1h[i]) / meP[i], ls="--", color="C%d" % i)
pl.add(ks, (pmm_2h[i] + pmm_1h[i]) / mmP[i], ls="-", color="C%d" % i)
pl.add(ks, (pee_2h[i] + pee_1h[i]) / eeP[i], ls=":", color="C%d" % i)
pl.vline(x=10.)
pl.hline(y=1.)
pl._ax.set_ylim(0.8, 1.3)
pl.done("pgcomp_rat_z_%d.png" % i)
def websky_halos(dirpath="./", mmin=-np.inf, mmax=np.inf):
pkfile = open(dirpath + "halos/halos.pksc", "rb")
Nhalo = np.fromfile(pkfile, dtype=np.int32, count=1)[0]
RTHMAXin = np.fromfile(pkfile, dtype=np.float32, count=1)
redshiftbox = np.fromfile(pkfile, dtype=np.float32, count=1)
nfloats_perhalo = 10
npkdata = nfloats_perhalo * Nhalo
peakdata = np.fromfile(pkfile, dtype=np.float32, count=npkdata)
peakdata = np.reshape(peakdata, (Nhalo, nfloats_perhalo))
Rth = peakdata[:, 6]
Omega_M = 0.25
h = 0.7
rho = 2.775e11 * Omega_M * h**2
M = 4.0 / 3 * np.pi * Rth**3 * rho
sel = np.logical_and(M > mmin, M <= mmax)
xpos = peakdata[:, 0][sel]
ypos = peakdata[:, 1][sel]
zpos = peakdata[:, 2][sel]
vzpos = peakdata[:, 5][sel]
h = h
M = M
from orphics import cosmology
vecs = np.swapaxes(np.array([xpos, ypos, zpos]), 0, 1)
ras, decs = hp.vec2ang(vecs, lonlat=True)
params = cosmology.defaultCosmology
params["H0"] = 70.0
params["omch2"] = 0.10331
params["ombh2"] = 0.01919
cc = cosmology.Cosmology(params,
skipCls=True,
skipPower=True,
skip_growth=True)
cspeed = 2.9979458e8 / 1e3
chis = np.sqrt(xpos**2.0 + ypos**2.0 + zpos**2.0)
zs = cc.results.redshift_at_comoving_radial_distance(chis) + vzpos / cspeed
return ras, decs, zs
def __init__(self,
lens_beam = 7.0,lens_noiseT = 33.,lens_noiseP = 56.,
lens_tellmin = 2,lens_tellmax = 3000,lens_pellmin = 2,
lens_pellmax = 3000,lens_kmin = 80,lens_kmax = 2000, lens_f_sky=0.65 ):
# get lensing noise
# Initialize cosmology and Clkk. Later parts need dimensionless spectra.
self.l_min = lens_tellmin
self.l_max = lens_tellmax
self.k_min = lens_kmin
self.k_max = lens_kmax
self.f_sky = lens_f_sky
cc = cosmology.Cosmology(lmax=self.l_max,pickling=True,dimensionless=True)
theory = cc.theory
ells = np.arange(2,self.l_max,1)
clkk = theory.gCl('kk',ells)
# Make a map template for calculating the noise curve on
shape,wcs = maps.rect_geometry(width_deg = 5.,px_res_arcmin=1.5)
# Define bin edges for noise curve
bin_edges = np.arange(80,lens_kmax,20)
nlgen = lensing.NlGenerator(shape,wcs,theory,bin_edges,lensedEqualsUnlensed=True)
# Experiment parameters, here for Planck
polCombs = ['TT','TE','EE','EB','TB']
_,_,_,_ = nlgen.updateNoise(
beamX=lens_beam,noiseTX=lens_noiseT,noisePX=lens_noiseP,
tellminX=lens_tellmin,tellmaxX=lens_tellmax,
pellminX=lens_pellmin,pellmaxX=lens_pellmax)
ls,nls,bells,nlbb,efficiency = nlgen.getNlIterative(polCombs,lens_kmin,lens_kmax,
lens_tellmax,lens_pellmin,lens_pellmax,
verbose=True,plot=False)
self.orphics_kk = clkk
self.orphics_ls = ls
self.orphics_nls = nls
self.noise_k = np.interp(np.arange(self.l_max+1), ls, nls)
self.noise_k[np.arange(self.l_max+1) <= lens_kmin] = 1e100
self.noise_k[np.arange(self.l_max+1) >= lens_kmax] = 1e100
from __future__ import print_function
from orphics import maps,io,cosmology,stats
from enlib import enmap
import numpy as np
import os,sys
from scipy import signal
cc = cosmology.Cosmology(lmax=6000,pickling=True)
deg = 15.
shape,wcs = maps.rect_geometry(width_deg=deg,px_res_arcmin=1.0)
modlmap = enmap.modlmap(shape,wcs)
modrmap = enmap.modrmap(shape,wcs)
lmax = modlmap.max()
ells = np.arange(0,lmax,1)
ps = cc.theory.lCl('TT',ells).reshape((1,1,ells.size))
mgen = maps.MapGen(shape,wcs,ps)
fwhm = 5.
kbeam = maps.gauss_beam(modlmap,fwhm)
imap = mgen.get_map()
bmap2 = maps.convolve_gaussian(imap.copy(),fwhm=fwhm,nsigma=5.0)
print(bmap2.shape)
bmap = maps.filter_map(imap.copy(),kbeam)
def __init__(self,
lens_beam = 7.0,lens_noiseT = 33.,lens_noiseP = 56.,
lens_tellmin = 2,lens_tellmax = 3000,lens_pellmin = 2,
lens_pellmax = 3000,lens_kmin = 80,lens_kmax = 3000, lens_f_sky=0.65,
bin_width=80, estimators=('TT','TE','EE','EB','TB'),
NlTT=None, NlEE=None, NlBB=None):
# get lensing noise
# Initialize cosmology and Clkk. Later parts need dimensionless spectra.
self.l_min = lens_tellmin
# CLASS can only go up to 5500
self.l_max = min(max(lens_tellmax, lens_pellmax)+1000,5500)
self.k_min = lens_kmin
self.k_max = lens_kmax
self.f_sky = lens_f_sky
# import orphics only here!
from orphics import lensing, io, stats, cosmology, maps
# generate cosmology with orphics
lmax = self.l_max
cc = cosmology.Cosmology(lmax=lmax,pickling=True,dimensionless=False)
theory = cc.theory
ells = np.arange(2,lmax,1)
clkk = theory.gCl('kk',ells)
Tcmb = 2.726
# compute noise curves
sT = lens_noiseT * (np.pi/60./180.)
sP = lens_noiseP * (np.pi/60./180.)
theta_FWHM = lens_beam * (np.pi/60./180.)
muK = Tcmb*1.0e6
# unitless white noise
exp_term = np.exp(ells*(ells+1)*(theta_FWHM**2)/(8*np.log(2)))
if NlTT is None:
NlTT = sT**2 * exp_term #/ muK**2
else:
NlTT = NlTT[ells]
if NlEE is None:
NlEE = sP**2 * exp_term #/ muK**2
else:
NlEE = NlEE[ells]
if NlBB is None:
NlBB = sP**2 * exp_term #/ muK**2
else:
NlBB = NlBB[ells]
NlTT[ells > lens_tellmax] = 1e100
NlEE[ells > lens_pellmax] = 1e100
NlBB[ells > lens_pellmax] = 1e100
self.NlTT = NlTT
self.NlEE = NlEE
self.NlBB = NlBB
# Define bin edges for noise curve
bin_edges = np.arange(2,lmax,bin_width)
# compute orphics lensing noise
ls,nlkks,theory_,qest = lensing.lensing_noise(
ells=ells,
ntt=NlTT,
nee=NlEE,
nbb=NlBB,
ellmin_t=lens_tellmin, ellmin_e=lens_pellmin,ellmin_b=lens_pellmin,
ellmax_t=lens_tellmax, ellmax_e=lens_pellmax,ellmax_b=lens_pellmax,
bin_edges=bin_edges,
estimators=estimators,
ellmin_k = 2,
ellmax_k = lens_kmax + 500, # calculate out a bit
theory=theory,
dimensionless=False)
self.orphics_kk = clkk
self.orphics_ls = ls
self.orphics_nls = nlkks['mv']
self.noise_k = np.interp(np.arange(self.l_max+1), ls, nlkks['mv'])
self.noise_k[np.arange(self.l_max+1) <= lens_kmin] = 1e100
self.noise_k[np.arange(self.l_max+1) >= lens_kmax] = 1e100
import camb
import numpy as np
from orphics import io, cosmology
import matplotlib.cm as cm
cc = cosmology.Cosmology(skipCls=True, skipPower=True, skip_growth=False)
z = 0.5
ks = np.logspace(np.log10(0.0001), np.log10(1.0), 100)
pl = io.Plotter(xscale='log', ylabel="$D$", xlabel="$k$")
#comps = ['delta_baryon','delta_tot']#,'v_newtonian_cdm','v_baryon_cdm','v_newtonian_baryon']
#comps = ['delta_tot']#,'v_newtonian_cdm','v_baryon_cdm','v_newtonian_baryon']
comps = ['growth']
zs = np.arange(0., 3., 0.1)
for z in zs:
print(z)
#comps = ['v_newtonian_cdm','v_baryon_cdm','v_newtonian_baryon']
for comp in comps:
gcomp = cc.growth_scale_dependent(ks, z, comp)
pl.add(ks, gcomp[:, 0, 0], color=cm.hot(z)) #,label=comp)
pl._ax.axhline(y=cc.Dfunc(cc.z2a(z)), ls="--")
pl._ax.axhline(y=cc.fsfunc(cc.z2a(z)), ls="--")
pl.legend()
#pl.done("output/delta_velocity.png")
pl.done("output/delta_growth_rate.png")
def test_pmm():
matt = True # set to False if you don't have Matt and Moritz's halomodel.py for comparison
if matt:
import halomodel as mmhm
from orphics import cosmology
cc = cosmology.Cosmology(hmvec.default_params,
skipCls=True,
low_acc=False)
mmhmod = mmhm.HaloModel(cc)
zs = np.array([0., 2., 3.]) #,1.,2.,3.])
#zs = np.array([0.,2.,4.,6.])
ms = np.geomspace(1e7, 1e17, 2000)
#ks = np.geomspace(1e-4,100,1001)
ks = np.geomspace(1e-5, 100, 10000)
if matt:
mmP = mmhmod.P_mm_2h(ks, zs) + mmhmod.P_mm_1h(
ks, zs) #,logmlow=log10mlow,logmhigh=log10mhigh)[z_id,:]
# print(mmhmod.halobias[:,0])
#print(mmP2h.shape)
hcos = hmvec.HaloModel(zs,
ks,
ms=ms,
halofit='mead',
mdef='vir',
nfw_numeric=True)
mmhb = mmhmod.halobias #np.load("mm_halobias.npy",)
mmnfn = mmhmod.nfn #np.load("mm_nfn.npy")
# pl = io.Plotter(xyscale='loglin')
# for i in range(zs.size):
# pl.add(ks,(hcos.nPzk[i]-mmhmod.pknl[i])/hcos.nPzk[i])
# pl.add(ks,(hcos.Pzk[i]-mmhmod.pk[i])/hcos.Pzk[i])
# pl.hline(y=0)
# pl.done()
# pl = io.Plotter(xyscale='loglog')
# for i in range(3):
# pl.add(ms,hcos.nzm[i,:],color="C%d" % i)
# pl.add(ms,mmnfn[:,i],ls='--',color="C%d" % i)
# pl.done()
# pl = io.Plotter(xyscale='loglog')
# for i in range(3):
# pl.add(ms,hcos.bh[i,:],color="C%d" % i)
# pl.add(ms,mmhb[:,i],ls='--',color="C%d" % i)
# pl.done()
# pl = io.Plotter(xyscale='loglog')
# pl.add(10**mmhmod.logm,mmhmod.halobias[:,1])
# pl.add(ms,hcos.bh[1,:])
# pl.done()
pmm_1h = hcos.get_power_1halo(name="nfw")
pmm_2h = hcos.get_power_2halo(name="nfw")
# sys.exit()
print(pmm_1h.shape)
for i in range(zs.size):
pl = io.Plotter(xyscale='loglog', xlabel='$k$', ylabel='$P$')
pl.add(ks,
pmm_1h[i],
label="z=%.1f" % zs[i],
color="C%d" % i,
ls="--",
alpha=0.2)
# pl.add(ks,pmm_2h[i],label="z=%.1f" % zs[i],ls="--",color="C%d" % i)
pl.add(ks, pmm_2h[i] + pmm_1h[i], ls="--", color="C%d" % i)
pl.add(ks, hcos.nPzk[i], ls="-", color="k", alpha=0.7)
if matt: pl.add(ks, mmP[i], ls="-.", color="C%d" % i)
pl.vline(x=10.)
pl._ax.set_ylim(1e-1, 1e5)
pl.done("nonlincomp_z_%d.png" % i)
for i in range(zs.size):
pl = io.Plotter(xyscale='loglin',
xlabel='$k$',
ylabel='$P/P_{\\mathrm{NL}}$')
pl.add(ks, (pmm_2h[i] + pmm_1h[i]) / hcos.nPzk[i],
ls="-",
color="C%d" % i)
if matt: pl.add(ks, mmP[i] / hcos.nPzk[i], ls="--", color="C%d" % i)
pl.hline(y=1)
pl.hline(y=0.9)
pl.hline(y=1.1)
pl.vline(x=10.)
pl._ax.set_ylim(0.5, 1.5)
pl.done("nonlindiff_z_%d.png" % i)
for i in range(zs.size):
pl = io.Plotter(xyscale='loglin',
xlabel='$k$',
ylabel='$P/P_{\\mathrm{L}}$')
pl.add(ks, (pmm_2h[i] + pmm_1h[i]) / hcos.Pzk[i],
ls="-",
color="C%d" % i)
if matt: pl.add(ks, mmP[i] / hcos.Pzk[i], ls="--", color="C%d" % i)
pl.vline(x=10.)
pl.hline(y=1)
# pl.hline(y=0.9)
# pl.hline(y=1.1)
# pl._ax.set_ylim(0.5,1.5)
pl.done("lindiff_z_%d.png" % i)
