def test_profile_args(self):
profile_args = self.subhalo.profile_args
(c) = profile_args
con = concentration(self.lens_cosmo.cosmo.h * self.mass_subhalo, '200c', self.z,
model='diemer19')
npt.assert_almost_equal(c/con, 1, 2)
profile_args = self.field_halo.profile_args
(c) = profile_args
con = concentration(self.lens_cosmo.cosmo.h * self.mass_field_halo, '200c', self.z,
model='diemer19')
npt.assert_almost_equal(c / con, 1, 2)
profile_args = self.field_halo.profile_args
(c) = profile_args
con = concentration(self.lens_cosmo.cosmo.h * self.mass_subhalo, '200c', self.z,
model='diemer19')
npt.assert_almost_equal(c / con, 1, 2)
profile_args = self.field_halo.profile_args
(c) = profile_args
con = concentration(self.lens_cosmo.cosmo.h * self.mass_field_halo, '200c', self.z,
model='diemer19')
npt.assert_almost_equal(c / con, 1, 2)
def test_profile_args(self):
profile_args = self.subhalo.profile_args
(c, rt, rho0) = profile_args
con = concentration(self.lens_cosmo.cosmo.h * self.mass,
'200c',
self.z,
model='diemer19')
npt.assert_almost_equal(c / con, 1, 2)
trunc = self.profile_args['RocheNorm'] * (10 ** 8 / 10 ** 7) ** (1. / 3) * \
(self.r3d / 50) ** self.profile_args['RocheNu']
npt.assert_almost_equal(trunc, rt, 3)
rho_central = self._cross_norm
npt.assert_almost_equal(rho0, rho_central)
profile_args = self.field_halo.profile_args
(c, rt, rho0) = profile_args
con = concentration(self.lens_cosmo.cosmo.h * self.mass,
'200c',
self.z,
model='diemer19')
npt.assert_almost_equal(c / con, 1, 2)
m_h = self.mass * self.lens_cosmo.cosmo.h
r50_comoving = self.lens_cosmo.rN_M_nfw_comoving(
m_h, self.profile_args['LOS_truncation_factor'], self.z)
r50_physical = r50_comoving * self.lens_cosmo.cosmo.scale_factor(
self.z) / self.lens_cosmo.cosmo.h
r50_physical_kpc = r50_physical * 1000
npt.assert_almost_equal(r50_physical_kpc, rt)
rho_central = self._cross_norm
npt.assert_almost_equal(rho0, rho_central)
def create_concentration_spline(self):
"""
Creates a spline for the concentration using colossus.
"""
try:
from colossus.halo import concentration
from colossus.cosmology import cosmology
except ImportError:
print("colossus not installed. No concentration spline available.")
return
cosmo = self.args['cosmology']
cos = {
'flat': True,
'H0': cosmo['h'] * 100.,
'Om0': cosmo['Omega_m'],
'Ob0': cosmo['Omega_b'],
'sigma8': cosmo['sigma8'],
'ns': cosmo['ns']
}
cosmology.addCosmology('fiducial', cos)
cosmology.setCosmology('fiducial')
z = self.args['z']
M = np.logspace(12, 17, 50)
c = np.zeros_like(M)
for i in range(len(M)):
c[i] = concentration.concentration(M[i],
'200m',
z=z,
model='diemer15')
self.args['cspline'] = interp.interp1d(M, c)
return
def mc(self, m, z):
matchCosmo()
c200 = chc.concentration(m, '200c', z, self.modelname)
return c200
def get_concentration_spline(cal=False):
from colossus.halo import concentration
from colossus.cosmology import cosmology
cosmo = get_cosmo_default(cal)
cos = {
'flat': True,
'H0': cosmo['h'] * 100.,
'Om0': cosmo['om'],
'Ob0': cosmo['ob'],
'sigma8': cosmo['sigma8'],
'ns': cosmo['ns']
}
cosmology.addCosmology('fiducial', cos)
cosmology.setCosmology('fiducial')
N = 20
M = np.logspace(12, 17, N)
z = np.linspace(0.2, 0.65, N)
c_array = np.ones((N, N))
for i in range(N):
for j in range(N):
c_array[j, i] = concentration.concentration(M[i],
'200m',
z=z[j],
model='diemer15')
return interp2d(M, z, c_array)
def compute3DProfile(halobase, mcmodel='diemer15'):
#read 3D profile
simprofile = readMXXLProfile.MXXLProfile('{}.radial_profile_3D.txt'.format(halobase))
simvolume = (4./3.)*np.pi*(simprofile.outer_radius**3 - simprofile.inner_radius**3)
simdensity = simprofile.diff_mass / simvolume #M_sol / Mpc**3
r_mpc = simprofile.median_radius
#read halo mass
sim_and_haloid = os.path.basename(halobase)
tokens = sim_and_haloid.split('_')
simid = '_'.join(tokens[:2])
haloid = '{}_0'.format(tokens[2])
#make sure cosmology always matches
curcosmo = readMXXLProfile.cosmo
nfwutils.global_cosmology.set_cosmology(curcosmo)
cmc.matchCosmo()
#compute Diemer SD prediction
r_kpch = (r_mpc*1000*curcosmo.h)
m200 = answers[simid][haloid]['m200'] #M_sol/h
zcluster = answers[simid][haloid]['redshift']
c200 = chc.concentration(m200, '200c', zcluster, model=mcmodel)
diemer_profile = dk14prof.getDK14ProfileWithOuterTerms(M = m200, c = c200, z = zcluster, mdef = '200c')
#density is returned with units M_solh^2/kpc^3.
convert_units = 1e9*curcosmo.h**2 # converts to M_sol/Mpc^3
diemer_density = diemer_profile.density(r_kpch)*convert_units
return dict(radius=r_mpc,
simprofile=simdensity,
diemerprofile=diemer_density)
def xihm_model(r, rt, m, c, alpha, bias, ma, mb, xi2h, Om):
"""Our model for the halo-mass correlation function.
Args:
r (float or array like): 3d distances from halo center in Mpc/h comoving.
rt (float): Truncation radius. The boundary of the halo.
m (float): Mass in Msun/h.
c (float): Concentration.
alpha (float): Width parameter of the exclusion function.
bias (float): Large scale halo bias.
ma (float): Mass of the first correction term in Msun/h.
mb (float): Mass of the second correction term in Msun/h.
xi2h (float or array like): 2-halo term at r.
Om (float): Omega_matter.
Returns:
float or arraylike: Halo-mass correlation function.
"""
rhom = Om * rhocrit
# Get xi1h
xi1h = 1. + cluster_toolkit.xi.xi_nfw_at_R(r, m, c, Om)
xi1h *= thetat(r, rt, alpha)
# Get concentrations
ca = concentration(M=ma, mdef='200m', z=0.)
cb = concentration(M=mb, mdef='200m', z=0.)
# Substract 1halo term
r1 = M_to_R(m, z=0., mdef='200m')
rb = M_to_R(mb, z=0., mdef='200m')
rb = rt / r1 * rb
xi2h = xi2h - mb / rhom * utconvut(r, rb, cb)
# Get xi2h
xi2h = bias * xi2h
# Get correction
C = -utconvthetae(r, rt, m, alpha, ma, ca, Om) * xi2h - utconvthetae(
r, rt, m, alpha, mb, cb, Om)
# Full xi
xi = xi1h + xi2h + C
return xi
def __init__(self, parameters, zL, mdef, chooseCosmology, part = None, \
se = None, be = None, cM_relation = None):
profile.__init__(self, zL, mdef, chooseCosmology)
self.M_mdef = parameters['M'].value #M200 in M_dot/h
if cM_relation == True:
self.c = hc.concentration(self.M, self.mdef, self.zL)
#self.c = 3.614*((1+self.zL)**(-0.424))*(self.M/self.cosmo.h/1E14)**(-0.105)
else:
self.c = parameters['c'].value
if se is None:
self.se = 1.5
else:
self.se = se
if be is None:
self.be = 1.0
else:
self.be = be
self.zL = zL
self.mdef = mdef
if part is not None:
self.part = part
else:
self.part = 'both'
#[rs] = Mpc/h
self.r_mdef = Halo.mass_so.M_to_R(self.M_mdef, self.zL,
self.mdef) / 1E3 #Mpc/h
self.rs = self.r_mdef / self.c #Mpc/h
self.Delta = int(mdef[:-1])
#[rho_mdef] = M_dot Mpc^3 from M_{\odot}h^2/kpc^3
self.rho_mdef = (Halo.mass_so.densityThreshold(self.zL, self.mdef) *
1E9 * (self.cosmo.h)**2.) / self.Delta
'''
self.dk14Prof = HaloDensityProfile.DK14Profile(M = self.M_mdef, c = self.c, z = \
self.zL, mdef = self.mdef, \
be = self.be, se = self.se, \
part = self.part)
'''
self.dk14Prof = profile_dk14.getDK14ProfileWithOuterTerms(
M=self.M_mdef,
c=self.c,
z=self.zL,
mdef=self.mdef,
outer_term_names=['pl'])
#self.dk14Prof.par.se = self.se
#self.dk14Prof.par.be = self.be
self.rmaxMult = 2.
#self.dk14Prof.par.rs = self.rs*1E3 #[rs] = kpc/h from Mpc/h
#self.dk14Prof.selected = 'by_accretion_rate' #beta = 6 gamma = 4; more accurate results
self.dk14Prof.selected = 'by_mass'
self.profile = 'dk'
#super(dkProfile, self).__init__()
return
def calc_stat(mass, redshift, mass_definition="200m"):
if mass_definition == "200m":
M200m = mass
c200m = concentration.concentration(M200m, mass_definition, redshift)
M500c, R500c, c500c = changeMassDefinition(M200m,
c200m,
redshift,
'200m',
'500c',
profile='nfw')
elif mass_definition == "500c":
M500c = mass
c500c = concentration.concentration(M500c, '500c', redshift)
M200m, R200m, c200m = changeMassDefinition(M500c, c500c, redshift,
'500c', '200m')
Rsp = sb.splashbackRadius(redshift, '200m', M=M200m)[0] * (1 + redshift)
return M200m, M500c, R200m, Rsp
def convert_mass(m, z, mdef_in='200c', mdef_out='200m',
concentration_model='diemer19', profile='nfw'):
'''
Converts between mass definitions.
'''
c = concentration.concentration(m, mdef_in, z,
model=concentration_model,
conversion_profile=profile)
return mass_defs.changeMassDefinition(m, c, z, mdef_in, mdef_out,
profile=profile)[0]
def lnlike(params, R, ds, icov, flags, z, extras):
lM = params
k, Plin, Pnl, cosmo, inparams = extras
inparams['Mass'] = 10**lM #Mpc/h
inparams["concentration"] = conc.concentration(10**lM, '200m', z, model='diemer15')
result = pyDS.calc_Delta_Sigma(k, Plin, k, Pnl, cosmo, inparams)
model = result['ave_delta_sigma']*h*(1.+z)**2 #Msun/pc^2 physical
X = ds - model
X = X[flags]
return -0.5*np.dot(X, np.dot(icov, X))
def calculate_concentration(redshift,M200m,cosmology):
from colossus.halo import concentration
from colossus.cosmology import cosmology as col_cosmology
params = {'flat': True, 'H0': cosmology['h'], 'Om0': cosmology['om'],
'Ob0': 1.0-cosmology['om'],
'sigma8': cosmology['sigma8'], 'ns': cosmology['ns']}
col_cosmology.addCosmology('fiducial_cosmology',params)
col_cosmology.setCosmology('fiducial_cosmology')
concentration = concentration.concentration(M200m,'200m',redshift,model='diemer15')
print "concentration = ",concentration,2.0
return 2.0#concentration
def r50(self, t):
# Rdisk using formula from Mo, Mao & White 1998
Mhh = self.Mh * self.cosmo.h
cvir = concentration(Mhh, 'vir', self.z, model='diemer15')
M200c, R200c, c200c = changeMassDefinition(Mhh, cvir, self.z, 'vir',
'200c')
md = self.Ms / (M200c / self.cosmo.h)
jmlam = self.etar * 0.045 # assume etar fraction of the angular momentum lost
eta50 = self.eta50_MMW98(c200c, jmlam, md)
r_50 = eta50 * R200c / self.cosmo.h
return r_50
def test_profile_args(self):
profile_args = self.subhalo.profile_args
(c) = profile_args
con = concentration(self.lens_cosmo.cosmo.h * self.mass_subhalo, '200c', self.z,
model='diemer19')
npt.assert_almost_equal(c/con, 1, 2)
profile_args = self.field_halo.profile_args
(c) = profile_args
con = concentration(self.lens_cosmo.cosmo.h * self.mass_field_halo, '200c', self.z,
model='diemer19')
npt.assert_almost_equal(c / con, 1, 2)
profile_args = self.subhalo_custom.profile_args
(c) = profile_args
c0 = self.profile_args_custom['mc_model']['c0']
beta = self.profile_args_custom['mc_model']['beta']
zeta = self.profile_args_custom['mc_model']['zeta']
h = self.lens_cosmo.cosmo.h
mh_sub = self.mass_subhalo * h
nu = peaks.peakHeight(mh_sub, self.z)
nu_ref = peaks.peakHeight(h * 10 ** 8, 0.)
con_subhalo = c0 * (1 + self.z) ** zeta * (nu / nu_ref) ** -beta
npt.assert_almost_equal(con_subhalo/c, 1, 2)
profile_args = self.field_halo_custom.profile_args
(c) = profile_args
c0 = self.profile_args_custom['mc_model']['c0']
beta = self.profile_args_custom['mc_model']['beta']
zeta = self.profile_args_custom['mc_model']['zeta']
h = self.lens_cosmo.cosmo.h
mh_sub = self.mass_field_halo * h
nu = peaks.peakHeight(mh_sub, self.z)
nu_ref = peaks.peakHeight(h * 10 ** 8, 0.)
con_field_halo = c0 * (1 + self.z) ** zeta * (nu / nu_ref) ** -beta
npt.assert_almost_equal(con_field_halo / c, 1, 2)
def hmc(model,
values,
cosmo=cosmo,
mdef='200c',
relation='diemer15',
z=0,
sigma=0.16):
"""Joint prior from the halo mass--concentration mass relation.
Relation is from Diemer & Kravtsov 2015
Parameters
----------
model : DynamicalModel instance
values : array_like
values in parameter space at which to evaluate the prior
cosmo : colossus.Cosmology instance
mdef : string
Colossus mass definition string for input halo parameters
e.g., 'vir', '200m', '200c'
relation : string
See the list here for the options.
https://bdiemer.bitbucket.io/colossus/halo_concentration.html
Defaults to the mass-concentration model of Diemer & Kravtsov 2015.
z : float
redshift
sigma : float
scatter in M-c relation, default from Diemer & Kravtsov
"""
h = cosmo.h
rho_crit = cosmo.rho_c(z) * h**2 # Msun / kpc3
kwargs = model.construct_kwargs(values)
Mh_function = lambda r: model.mass_model['dm'](r, **kwargs)
if 'M200' in kwargs:
M200 = kwargs['M200']
else:
r200 = mass._rvir(Mh_function,
rhigh=1e8,
delta_c=200,
rho_crit=rho_crit)
M200 = Mh_function(r200)
if 'c200' in kwargs:
log_c200 = np.log10(kwargs['c200'])
else:
try:
r_s = kwargs['r_s']
log_c200 = np.log10(r200 / r_s)
except KeyError:
raise ValueError('Need to have a defined halo concentration!')
# colossus uses halo mass in units of Msun / h
c200_model = concentration(M200 * h, mdef=mdef, z=z, model=relation)
return pdf.lngauss(x=log_c200, mu=np.log10(c200_model), sigma=sigma)
def log_likelihood_DS(lM200, R, ds, iCds):
c200 = concentration.concentration(10**lM200, '200c', zmean, model=cmodel)
DS = Delta_Sigma_NFW_2h(R,
zmean,
M200=10**lM200,
c200=c200,
cosmo_params=params,
terms='1h')
L_DS = -np.dot((ds - DS), np.dot(iCds, (ds - DS))) / 2.0
return L_DS
def M200(Lambda,z):
M0 = 2.21e14
alpha = 1.33
M200m = M0*((Lambda/40.)**alpha)
from colossus.cosmology import cosmology
from colossus.halo import mass_defs
from colossus.halo import concentration
c200m = concentration.concentration(M200m, '200m', z, model = 'duffy08')
M200c, R200c, c200c = mass_defs.changeMassDefinition(M200m, c200m, z, '200m', '200c')
return M200c
def get_conc(M, z):
colcos = {
"H0": cosmo['h'] * 100.,
"Om0": cosmo['om'],
'Ob0': 0.049017,
'sigma8': 0.83495,
'ns': 0.96191,
'flat': True
}
col_cosmology.addCosmology('fox_cosmology', colcos)
col_cosmology.setCosmology('fox_cosmology')
om = cosmo['om']
return conc.concentration(M, '200m', z, model='diemer15')
def delta_z(z_c, M_200, R, mean_z):
c_200 = concen.concentration(M_200, '200c', z_c, model='diemer15')
Sigma = Sig_NFW(M_200, c_200, R)
mu = np.power(1. - Sigma / cosmos.Sig_cr(z_c, z_step_g), 2)
top_integrand = np.power(mu, (beta(z_step_g) - 1.)) * p_g * z_step_g
bottom_integrand = np.power(mu, (beta(z_step_g) - 1.)) * p_g
#print(top_integrand, bottom_integrand)
if np.any(np.isnan(top_integrand)):
print(
"M_200 = {3} \nz_c = {4} \nSigma = {0} \nSigma_cr = {1} \nbeta = {2}"
.format(Sigma, cosmos.Sig_cr(z_c, z_step_g), beta(z_step_g), M_200,
z_c))
exit()
return simp_integral(dz_g, top_integrand) / (
mean_z * simp_integral(dz_g, bottom_integrand)) - 1.
def createSims(bins, nperbin, zcluster, basedir, simgroup, idstart, cosmology):
outputdir = '{}/{}'.format(basedir, simgroup)
if not os.path.exists(outputdir):
os.makedirs(outputdir)
nfwutils.global_cosmology.set_cosmology(cosmology)
cmc.matchCosmo()
#bins in delta=200
#masses are M_sol*h
m200s = []
c200s = []
answers = {}
for mlow, mhigh in bins:
curmasses = np.exp(np.random.uniform(np.log(mlow), np.log(mhigh), size=nperbin))
m200s.append(curmasses)
curconcens = chc.concentration(curmasses, '200c', zcluster, model='diemer15')
c200s.append(curconcens)
m200s = np.hstack(m200s)
c200s = np.hstack(c200s)
for i, id in enumerate(range(idstart, idstart+len(m200s))):
config = dict(max_dist = 3.,
gridlength = 1024.,
zcluster = zcluster,
m200 = float(m200s[i]),
c200 = float(c200s[i]))
with open('{}/analytic_{}.yaml'.format(outputdir, id), 'w') as output:
yaml.dump(config, output)
answers[id] = dict(m200 = m200s[i],
concen = c200s[i],
redshift = zcluster)
with open('analytic_{}_answers.pkl'.format(simgroup), 'wb') as output:
cPickle.dump(answers, output)
def computeSDProfiles(halobase, mcmodel='diemer15'):
#read 2D profile
simprofile = readMXXLProfile.MXXLProfile('{}.radial_profile.txt'.format(halobase))
simarea = np.pi*(simprofile.outer_radius**2 - simprofile.inner_radius**2)
simdensity = simprofile.diff_mass / simarea #M_sol / Mpc**2
r_mpc = simprofile.median_radius
#read halo mass
sim_and_haloid = os.path.basename(halobase)
tokens = sim_and_haloid.split('_')
simid = '_'.join(tokens[:2])
haloid = '_'.join(tokens[2:])
#make sure cosmology always matches
curcosmo = readMXXLProfile.cosmo
nfwutils.global_cosmology.set_cosmology(curcosmo)
cmc.matchCosmo()
#compute Diemer SD prediction
r_kpch = (r_mpc*1000*curcosmo.h)
m200 = answers[simid][haloid]['m200'] #M_sol/h
zcluster = answers[simid][haloid]['redshift']
c200 = chc.concentration(m200, '200c', zcluster, model=mcmodel)
diemer_profile = dk14prof.getDK14ProfileWithOuterTerms(M = m200, c = c200, z = zcluster, mdef = '200c')
surfacedensity_func, deltaSigma_func = readAnalytic.calcLensingTerms(diemer_profile, np.max(r_kpch))
convert_units = 1./(curcosmo.h*1e6) #M_sol / Mpc^2 -> diemer units
diemer_surfacedensity = surfacedensity_func(r_kpch)/convert_units
#add mean background density to Diemer prediction
acosmo = astrocosmo.FlatLambdaCDM(curcosmo.H0, curcosmo.omega_m)
density_units_3d = units.solMass / units.Mpc**3
density_units_2d = units.solMass / units.Mpc**2
back_density = (acosmo.Om(zcluster)*acosmo.critical_density(zcluster)).to(density_units_3d)
print back_density
back_sd_contrib = back_density*(200.*units.Mpc/curcosmo.h)
print back_sd_contrib
# simdensity = simdensity*density_units_2d - back_sd_contrib
diemer_surfacedensity += back_sd_contrib
return dict(radius=r_mpc,
simprofile=simdensity,
diemerprofile=diemer_surfacedensity)
def __init__(self,
parameters,
zL,
n,
mdef,
chooseCosmology,
Tau=None,
cM_relation=None,
esp=None):
profile.__init__(self, zL, mdef, chooseCosmology)
cosmo = Cosmology.setCosmology(chooseCosmology)
self.parameters = parameters
self.M_mdef = parameters['M'].value #M200 input in M_dot/h
if cM_relation == True:
self.c = hc.concentration(self.M_mdef * cosmo.h, self.mdef,
self.zL)
else:
self.c = parameters['c'].value
self.zL = zL
self.n = n #sharpness of truncation (n = 1 or 2)
if Tau == None: #dimensionless truncation radius (T = rt/rvir => fit =2.6)
self.T = 2.6
else:
self.T = Tau
self.r_mdef = Halo.mass_so.M_to_R(self.M, self.zL,
self.mdef) / 1E3 #Mpc/h
self.rs = self.r_mdef / self.c
self.rt = self.T * self.rs
self.mdef = mdef
self.chooseCosmology = chooseCosmology
self.G, self.v_c, self.H2, self.cosmo = self.calcConstants()
self.Delta = int(mdef[:-1])
#[rho_mdef] = M_dot Mpc^3 from M_{\odot}h^2/kpc^3
self.rho_mdef = (Halo.mass_so.densityThreshold(self.zL, self.mdef) *
1E9 * (self.cosmo.h)**2.) / self.Delta
self.profile = 'nfwBMO'
if esp == None:
self.esp = 1E-5
else:
self.esp = esp
return
def mass_conversion(m200m, redshift, cosmology, mass_is_log=True):
"""Convert m200m to m500c
Parameters
----------
m200m : array_like
Halo mass(es) calculated in a radius such that the mean density is 200 times
the mean density of the universe.
redshift : float
Redshift of the halos used to calculate the concentration and perform
the mass conversion
cosmology : dict
Cosmology parameters being used
mass_is_log : bool
Flag to tell the script if it should intake and output ln(M) or M
Returns
-------
output : array_like
Halo mass(es) calculated in a radius such that the mean density is 500 times
the critical density of the universe.
Notes
-----
We assume that every halo is at the same redshift.
"""
setCosmology('myCosmo', cosmology)
if mass_is_log:
m200m = np.exp(m200m)
m500c = changeMassDefinition(m200m, concentration(m200m, '200m', redshift),
redshift, '200m', '500c')[0]
if mass_is_log:
m500c = np.log(m500c)
return m500c
def log_likelihood(data_model, R, profiles, iCOV):
lM200, q = data_model
c200 = concentration.concentration(10**lM200, '200c', zmean, model=cmodel)
e = (1. - q) / (1. + q)
ds, gt, gx = profiles
iCds, iCgt, iCgx = iCOV
DS = Delta_Sigma_NFW(R, zmean, M200=10**lM200, c200=c200, cosmo=cosmo_as)
GT, GX = GAMMA_components(R,
zmean,
ellip=e,
M200=10**lM200,
c200=c200,
cosmo=cosmo_as)
L_DS = -np.dot((ds - DS), np.dot(iCds, (ds - DS))) / 2.0
L_GT = -np.dot((gt - GT), np.dot(iCgt, (gt - GT))) / 2.0
L_GX = -np.dot((gx - GX), np.dot(iCgx, (gx - GX))) / 2.0
return L_DS + L_GT + L_GX
def cmb_test_data(nber_maps,
validation_analyis=False,
clus_position_analysis=False,
extragal_bias_analysis=False):
nx, dx, ny, dy = 240, 0.25, 240, 0.25
map_params = [nx, dx, ny, dy]
l, cl = CosmoCalc().cmb_power_spectrum()
l, bl = exp.beam_power_spectrum(1.4)
l, nl = exp.white_noise_power_spectrum(2.0)
if validation_analyis is True:
sims_clus_2e14, sims_clus_6e14, sims_clus_10e14 = [], [], []
kappa_map_2e14 = lensing.NFW(2e14, 3, 1,
1100).convergence_map(map_params)
kappa_map_6e14 = lensing.NFW(6e14, 3, 1,
1100).convergence_map(map_params)
kappa_map_10e14 = lensing.NFW(10e14, 3, 1,
1100).convergence_map(map_params)
alpha_vec_2e14 = lensing.deflection_from_convergence(
map_params, kappa_map_2e14)
alpha_vec_6e14 = lensing.deflection_from_convergence(
map_params, kappa_map_6e14)
alpha_vec_10e14 = lensing.deflection_from_convergence(
map_params, kappa_map_10e14)
for i in range(nber_maps):
sim = tools.make_gaussian_realization(map_params, l, cl)
sim_clus_2e14 = lensing.lens_map(map_params, sim, alpha_vec_2e14)
sim_clus_6e14 = lensing.lens_map(map_params, sim, alpha_vec_6e14)
sim_clus_10e14 = lensing.lens_map(map_params, sim, alpha_vec_10e14)
sim_clus_2e14 = tools.convolve(sim_clus_2e14,
l,
np.sqrt(bl),
map_params=map_params)
sim_clus_6e14 = tools.convolve(sim_clus_6e14,
l,
np.sqrt(bl),
map_params=map_params)
sim_clus_10e14 = tools.convolve(sim_clus_10e14,
l,
np.sqrt(bl),
map_params=map_params)
noise_map = tools.make_gaussian_realization(map_params, l, nl)
sim_clus_2e14 += noise_map
sim_clus_6e14 += noise_map
sim_clus_10e14 += noise_map
sims_clus_2e14.append(sim_clus_2e14)
sims_clus_6e14.append(sim_clus_6e14)
sims_clus_10e14.append(sim_clus_10e14)
return sims_clus_2e14, sims_clus_6e14, sims_clus_10e14
if clus_position_analysis is True:
sims_baseline, sims_centorid_shift = [], []
kappa_map_6e14_baseline = lensing.NFW(2e14, 3, 1,
1100).convergence_map(map_params)
alpha_vec_6e14_baseline = lensing.deflection_from_convergence(
map_params, kappa_map_6e14_baseline)
for i in range(nber_maps):
x_shift, y_shift = np.random.normal(
loc=0.0, scale=0.5), np.random.normal(loc=0.0, scale=0.5)
centroid_shift = [x_shift, y_shift]
kappa_map_6e14_centroid_shift = lensing.NFW(
6e14, 3, 1, 1100).convergence_map(map_params, centroid_shift)
alpha_vec_6e14_centroid_shift = lensing.deflection_from_convergence(
map_params, kappa_map_6e14_centroid_shift)
sim = tools.make_gaussian_realization(map_params, l, cl)
sim_baseline = lensing.lens_map(map_params, sim,
alpha_vec_6e14_baseline)
sim_centroid_shift = lensing.lens_map(
map_params, sim, alpha_vec_6e14_centroid_shift)
sim_baseline = tools.convolve(sim_baseline,
l,
np.sqrt(bl),
map_params=map_params)
sim_centroid_shift = tools.convolve(sim_centroid_shift,
l,
np.sqrt(bl),
map_params=map_params)
noise_map = tools.make_gaussian_realization(map_params, l, nl)
sim_baseline += noise_map
sim_centroid_shift += noise_map
sims_baseline.append(sim_baseline)
sims_centroid_shift.append(sim_centroid_shift)
return sims_baseline, sims_centroid_shift
if extragal_bias_analysis is True:
sims_baseline, sims_tsz, sims_ksz, sims_tsz_ksz = [], [], [], []
c500 = concentration.concentration(2e14, '500c', 0.7)
M200c, _, c200c = mass_defs.changeMassDefinition(2e14,
c500,
0.7,
'500c',
'200c',
profile='nfw')
kappa_map_M200c = lensing.NFW(M200c, c200c, 0.7,
1100).convergence_map(map_params)
alpha_vec_M200c = lensing.deflection_from_convergence(
map_params, kappa_map_M200c)
fname = '/Volumes/Extreme_SSD/codes/master_thesis/code/data/mdpl2_cutouts_for_tszksz_clus_detection_M1.7e+14to2.3e+14_z0.6to0.8_15320haloes_boxsize20.0am.npz'
cutouts_dic = np.load(fname, allow_pickle=1,
encoding='latin1')['arr_0'].item()
mass_z_key = list(cutouts_dic.keys())[0]
cutouts = cutouts_dic[mass_z_key]
scale_fac = fg.compton_y_to_delta_Tcmb(145, uK=True)
tsz_cutouts, ksz_cutouts = [], []
for kcntr, keyname in enumerate(cutouts):
tsz_cutout = cutouts[keyname]['y'] * scale_fac
tsz_cutouts.append(tsz_cutout)
ksz_cutout = cutouts[keyname]['ksz'] * random.randrange(-1, 2, 2)
ksz_cutouts.append(ksz_cutout)
s, e = int((nx - 40) / 2), int((ny + 40) / 2)
for i in range(nber_maps):
sim = tools.make_gaussian_realization(map_params, l, cl)
sim_M200c = lensing.lens_map(map_params, sim, alpha_vec_M200c)
sim_baseline, sim_tsz, sim_ksz, sim_tsz_ksz = np.copy(
sim_M200c), np.copy(sim_M200c), np.copy(sim_M200c), np.copy(
sim_M200c)
tsz_cutout = tools.rotate(
tsz_cutouts[random.randint(0,
len(tsz_cutouts) - 1)],
random.randint(-180, 180))
ksz_cutout = tools.rotate(
ksz_cutouts[random.randint(0,
len(ksz_cutouts) - 1)],
random.randint(-180, 180))
tsz_ksz_cutout = tsz_cutout + ksz_cutout
sim_tsz[s:e, s:e] = sim_tsz[s:e, s:e] + tsz_cutout
sim_ksz[s:e, s:e] = sim_ksz[s:e, s:e] + ksz_cutout
sim_tsz_ksz[s:e, s:e] = sim_tsz_ksz[s:e, s:e] + tsz_ksz_cutout
sim_baseline = tools.convolve(sim_baseline,
l,
np.sqrt(bl),
map_params=map_params)
sim_tsz = tools.convolve(sim_tsz,
l,
np.sqrt(bl),
map_params=map_params)
sim_ksz = tools.convolve(sim_ksz,
l,
np.sqrt(bl),
map_params=map_params)
sim_tsz_ksz = tools.convolve(sim_tsz_ksz,
l,
np.sqrt(bl),
map_params=map_params)
noise_map = tools.make_gaussian_realization(map_params, l, nl)
sim_baseline += noise_map
sim_tsz += noise_map
sim_ksz += noise_map
sim_tsz_ksz += noise_map
sims_baseline.append(sim_baseline)
sims_tsz.append(sim_tsz)
sims_ksz.append(sim_ksz)
sims_tsz_ksz.append(sim_tsz_ksz)
return sims_baseline, sims_tsz, sims_ksz, sims_tsz_ksz
def con(M, z, model="ishiyama21"):
if model == "Bullock2001": # Employs fit from Bullock et al. 2001, to match at z=0
return 9. / (1. + z) * (M / 1.5e13)**(-0.13)
else: # Employs fit from colossus. Default here is taken as "ishiyama21"
return concentration(M, "vir", z=z, model=model)
cmap = plt.get_cmap(cmaps[i])
index = inds[i]
z = zs[i]
#Get power spectra
Plin = np.loadtxt("txt_files/P_files/Plin_z%.2f.txt" % z)
Pnl = np.loadtxt("txt_files/P_files/Pnl_z%.2f.txt" % z)
#Give the bin edges in comoving units; Mpc/h
params["R_bin_min"] = 0.0323 * h * (1 + z)
params["R_bin_max"] = 30.0 * h * (1 + z)
for j in linds:
R, DS, err, flag = np.loadtxt(datapath % (z, j), unpack=True)
lM = masses[i, j]
print 10**lM
params['Mass'] = 10**lM
params["concentration"] = conc.concentration(10**lM,
'200m',
z,
model='diemer15')
result = pyDS.calc_Delta_Sigma(k, Plin, k, Pnl, cosmo, params)
model = result['ave_delta_sigma'] * h * (1. +
z)**2 #Msun/pc^2 physical
plt.loglog(R, model, c=cmap(c[j]), ls='-')
plt.errorbar(R, DS, err, c=cmap(c[j]), marker='o', ls='')
plt.ylim(.1, 1e3)
plt.xlabel(r"$R\ [{\rm Mpc}]$")
plt.ylabel(r"$\Delta\Sigma\ [{\rm M_\odot/pc^2}]$")
plt.title("z=%.2f" % z)
plt.subplots_adjust(bottom=0.17, left=0.2)
plt.gcf().savefig("BFs_z%.2f.png" % z)
plt.show()
# catalog is updated to that of SPT-SZ
if sptsz_sim:
from astropy.io import fits
data = fits.open('SPT2500d.fits')
cluslist = data[1].data
M500c = cluslist['M500']
selectedinds = np.where(M500c >0)[0]
M500list = M500c[selectedinds]*1e14
zlist = cluslist['redshift'][selectedinds]
z_L_list = np.ones(len(M500list))*0.7
M_200_list = np.zeros(len(M500list))
mdef = '500c'
mdefout = '200m'
for i,mm in enumerate(M500list):
cval = concentration.concentration(mm, mdef, zlist[i])
Mval, r200val, c200val = mass_defs.changeMassDefinition(mm, cval, zlist[i], mdef, mdefout, profile='nfw')
M_200_list[i] = Mval
totalclus = len(M_200_list)
np.random.seed(cmbrandomseedval)
randomseeds = np.unique(np.random.randint(1e6,size= 2 * totalclus))[0:totalclus]
# add tSZ either Arnaud profile, Sehgal simulations, or Takahashi simulations
# to be done -get Arnaud from the for loop as well"
if sehgal_sims:
tSZ_emission, tSZ_emission_90ghz = cluster_stuff.fn_pick_add_sehgal_sims(M_200_list)
tSZ_emission = tSZ_emission/1e6
tSZ_emission_90ghz = tSZ_emission_90ghz/1e6
if takahashi_sims:
tSZ_emission, tSZ_emission_90ghz = cluster_stuff.fn_add_daisuke_sims(M_200_list,z_L_list)
def predict(self,
model,
separate_gal_type=False,
baryon_kwargs={},
**occ_kwargs):
"""
Predicts the number density and correlation function for a certain
model.
Parameters
----------
model : HodModelFactory
Instance of ``halotools.empirical_models.HodModelFactory``
describing the model for which predictions are made.
separate_gal_type : boolean, optional
If True, the return values are dictionaries divided by each galaxy
types contribution to the output result.
**occ_kwargs : dict, optional
Keyword arguments passed to the ``mean_occupation`` functions
of the model.
Returns
-------
ngal : numpy.array or dict
Array or dictionary of arrays containing the number densities for
each galaxy type stored in self.gal_type. The total galaxy number
density is the sum of all elements of this array.
xi : numpy.array or dict
Array or dictionary of arrays storing the prediction for the
correlation function.
"""
try:
assert (sorted(model.gal_types) == sorted(
['centrals', 'satellites']))
except AssertionError:
raise RuntimeError('The model instance must only have centrals ' +
'and satellites as galaxy types. Check the ' +
'gal_types attribute of the model instance.')
try:
assert (model._input_model_dictionary['centrals_occupation'].
prim_haloprop_key == self.attrs['prim_haloprop_key'])
assert (model._input_model_dictionary['satellites_occupation'].
prim_haloprop_key == self.attrs['prim_haloprop_key'])
except AssertionError:
raise RuntimeError('Mismatch in the primary halo properties of ' +
'the model and the TabCorr instance.')
try:
if hasattr(model._input_model_dictionary['centrals_occupation'],
'sec_haloprop_key'):
assert (model._input_model_dictionary['centrals_occupation'].
sec_haloprop_key == self.attrs['sec_haloprop_key'])
if hasattr(model._input_model_dictionary['satellites_occupation'],
'sec_haloprop_key'):
assert (model._input_model_dictionary['satellites_occupation'].
sec_haloprop_key == self.attrs['sec_haloprop_key'])
except AssertionError:
raise RuntimeError('Mismatch in the secondary halo properties ' +
'of the model and the TabCorr instance.')
try:
assert np.abs(model.redshift - self.attrs['redshift']) < 0.05
except AssertionError:
raise RuntimeError('Mismatch in the redshift of the model and ' +
'the TabCorr instance.')
mean_occupation = np.zeros(len(self.gal_type))
mask = self.gal_type['gal_type'] == 'centrals'
mean_occupation[mask] = model.mean_occupation_centrals(
prim_haloprop=self.gal_type['prim_haloprop'][mask],
sec_haloprop_percentile=(
self.gal_type['sec_haloprop_percentile'][mask]),
**occ_kwargs)
mean_occupation[~mask] = model.mean_occupation_satellites(
prim_haloprop=self.gal_type['prim_haloprop'][~mask],
sec_haloprop_percentile=(
self.gal_type['sec_haloprop_percentile'][~mask]),
**occ_kwargs)
ngal = mean_occupation * self.gal_type['n_h'].data
if self.attrs['mode'] == 'auto':
ngal_sq = np.outer(ngal, ngal)
ngal_sq = 2 * ngal_sq - np.diag(np.diag(ngal_sq))
ngal_sq = symmetric_matrix_to_array(ngal_sq)
xi = self.tpcf_matrix * ngal_sq / np.sum(ngal_sq)
elif self.attrs['mode'] == 'cross':
xi = self.tpcf_matrix * ngal / np.sum(ngal)
# baryonification
if (len(baryon_kwargs) > 0) & (has_bcm):
#params = {'flat': True, 'H0': 67.2, 'Om0': 0.31, 'Ob0': 0.049, 'sigma8': 0.81, 'ns': 0.95}
params = baryon_kwargs['cosmo_params']
params['flat'] = True
use_2h = baryon_kwargs.pop('use_2h', True)
use_clf_fsat = baryon_kwargs.pop('use_clf_fsat', False)
print(use_clf_fsat)
cosmology.addCosmology('myCosmo', params)
cosmology.setCosmology('myCosmo')
halo_mass = np.array(self.gal_type['prim_haloprop'])
par = bfc.par()
par.baryon.eta_tot = baryon_kwargs['eta_tot']
par.baryon.eta_cga = baryon_kwargs['eta_cga']
if 'transfct' in baryon_kwargs.keys():
par.files.transfct = baryon_kwargs['transfct']
else:
dirname = os.path.dirname(os.path.abspath(__file__))
par.files.transfct = '{}/files/CDM_PLANCK_tk.dat'.format(
dirname)
rbin = annular_area_weighted_midpoints(self.tpcf_args[1])
rho_r = annular_area_weighted_midpoints(np.logspace(-2, 2, 100))
n_mhalo = 10
mhalo_min = np.min(np.log10(halo_mass))
mhalo_max = np.max(np.log10(halo_mass))
mhalo_grid = np.logspace(mhalo_min, mhalo_max, n_mhalo)
halo_conc_grid = concentration.concentration(
mhalo_grid, 'vir', self.attrs['redshift'], model='diemer19')
# fudge factor accounting for scatter in mvir-c relation
halo_conc_grid = halo_conc_grid * 0.93
if use_clf_fsat:
f_sat = np.array([
model.model_dictionary['satellites_occupation'].
stellar_mass_fraction(m) for m in mhalo_grid
])
f_cen = np.array([
model.model_dictionary['centrals_occupation'].
stellar_mass_fraction(m) for m in mhalo_grid
])
f_star = f_sat + f_cen
print(f_star)
sys.stdout.flush()
else:
f_star = [None] * n_mhalo
f_cen = [None] * n_mhalo
# baryon params
par.baryon.Mc = baryon_kwargs['Mc']
par.baryon.mu = baryon_kwargs['mu']
par.baryon.thej = baryon_kwargs['thej']
if use_2h:
# 2h term
vc_r, vc_m, vc_bias, vc_corr = bfc.cosmo(par)
# print('2h term took {}s'.format(end - start))
bias_tck = splrep(vc_m, vc_bias, s=0)
corr_tck = splrep(vc_r, vc_corr, s=0)
cosmo_bias_grid = splev(mhalo_grid, bias_tck)
cosmo_corr = splev(rho_r, corr_tck)
profs = [
bfc.profiles(rho_r,
mhalo_grid[i],
halo_conc_grid[i],
cosmo_corr,
cosmo_bias_grid[i],
par,
fstar=f_star[i],
fcga=f_cen[i])[1]
for i in range(len(mhalo_grid))
]
else:
profs = [
bfc.onehalo_profiles(rho_r,
mhalo_grid[i],
halo_conc_grid[i],
par,
fstar=f_star[i],
fcga=f_cen[i])[1]
for i in range(len(mhalo_grid))
]
correction_factors_grid = [
dens_to_ds(rbin, rho_r, profs[i], epsabs=1e-1, epsrel=1e-3)[2]
for i in range(len(mhalo_grid))
]
correction_factors_grid = np.array(correction_factors_grid)
correction_factors_spl = interp1d(mhalo_grid,
correction_factors_grid.T,
fill_value='extrapolate',
bounds_error=False)
correction_factors = correction_factors_spl(halo_mass)
xi = correction_factors * xi
elif (len(baryon_kwargs) > 0):
raise ImportError(
"You passed me baryon correction module parameters, but I couldn't import the baryonification module"
)
if not separate_gal_type:
ngal = np.sum(ngal)
xi = np.sum(xi, axis=1).reshape(self.tpcf_shape)
return ngal, xi
else:
ngal_dict = {}
xi_dict = {}
for gal_type in np.unique(self.gal_type['gal_type']):
mask = self.gal_type['gal_type'] == gal_type
ngal_dict[gal_type] = np.sum(ngal[mask])
if self.attrs['mode'] == 'auto':
for gal_type_1, gal_type_2 in (
itertools.combinations_with_replacement(
np.unique(self.gal_type['gal_type']), 2)):
mask = symmetric_matrix_to_array(
np.outer(gal_type_1 == self.gal_type['gal_type'],
gal_type_2 == self.gal_type['gal_type'])
| np.outer(gal_type_2 == self.gal_type['gal_type'],
gal_type_1 == self.gal_type['gal_type']))
xi_dict['%s-%s' % (gal_type_1, gal_type_2)] = np.sum(
xi * mask, axis=1).reshape(self.tpcf_shape)
elif self.attrs['mode'] == 'cross':
for gal_type in np.unique(self.gal_type['gal_type']):
mask = self.gal_type['gal_type'] == gal_type
xi_dict[gal_type] = np.sum(xi * mask,
axis=1).reshape(self.tpcf_shape)
return ngal_dict, xi_dict
def __init__(self, z):
self.f = interp1d(np.linspace(9,15.1,200), \
concentration.concentration(10**np.linspace(9, 15.1, 200), "vir", z, model="bullock01"))
#-------------------
# saving mcmc out
mcmc_out = sampler.get_chain(flat=True).T
table = [
fits.Column(name='lM200', format='E', array=mcmc_out[0]),
fits.Column(name='q', format='E', array=mcmc_out[1])
]
tbhdu = fits.BinTableHDU.from_columns(fits.ColDefs(table))
lM = np.percentile(mcmc_out[0][1500:], [16, 50, 84])
q = np.percentile(mcmc_out[1][1500:], [16, 50, 84])
c200 = concentration.concentration(10**lM[1], '200c', zmean, model=cmodel)
h = fits.Header()
h.append(('lM200', np.round(lM[1], 4)))
h.append(('elM200M', np.round(np.diff(lM)[0], 4)))
h.append(('elM200m', np.round(np.diff(lM)[1], 4)))
h.append(('c200', np.round(c200, 4)))
h.append(('q', np.round(q[1], 4)))
h.append(('eqM', np.round(np.diff(q)[0], 4)))
h.append(('eqm', np.round(np.diff(q)[1], 4)))
primary_hdu = fits.PrimaryHDU(header=h)
hdul = fits.HDUList([primary_hdu, tbhdu])
