def virial_radius(logMhalo, redshift, delta=200., rhocrit=True,
BryanNorman=False, WMAP=False, COSHalos=False):
"""Calculate the virial radius of a galaxy.
--- Inputs: ---
logMhalo: Halo mass(es).
redshift: Galaxy redshift
delta=200: Overdensity (Default=200)
rhocrit: Use the critical density (Default=True); alternately the mean density of the universe.
WMAP: Use WMAP9 cosmology. (Default=False)
BryanNorman: Use the Bryan & Norman (1998) scaling (Default=False)
COSHalos: Use the COS-Halos assumptions (Default=False)
"""
import numpy as np
import astropy.units as u
import astropy.constants as c
# Set some terms based on the COSHalos flag, which matches the calculations
# from the COS-Halos work (Prochaska+ 2017).
#
# This overrides the other user-set flags.
if COSHalos:
BryanNorman=True
WMAP=True
rhocrit=True
# Choose the cosmology. Default is Plank15.
if WMAP:
from astropy.cosmology import WMAP9 as cosmo
else:
from astropy.cosmology import Planck15 as cosmo
# Use the Bryan & Norman (2008) definition of Rvir? Default: False
#
# This overrides user-set flags for delta and rhocrit.
if BryanNorman:
# Overdensity depends on redshift:
x = cosmo.Om(redshift)-1.
delta = (18.*np.pi**2+82.*x-39.*x**2)
# Also assume critical density scaling:
rhocrit = True
# Choose whether to scale by mean or critical density. Default: Critical
if rhocrit == True:
rho = cosmo.critical_density(redshift)
else:
rho = cosmo.Om(redshift)*cosmo.critical_density(redshift)
# Linear halo mass (requires logMhalo in numpy array for calculations.
Mhalo = (10.**np.array(logMhalo))*u.M_sun
# Calculate the virial radius.
Rvir3 = (3./(4.*np.pi))*(Mhalo/(delta*rho.to('Msun/kpc3')))
Rvir = (Rvir3)**(1./3.)
return Rvir
def mstar_mhalo(z, mhalo):
## Moster+09, eqns 16,17 in PS11
mhalo0 = np.power(10, 11.88 + 0.018 * (1 + z))
mMz = 0.0282 * np.power(1 + z, -0.72)
gammaz = 0.556 * np.power(1 + z, -0.26)
betaz = 0.17 * z + 1.06
denom = np.power(mhalo / mhalo0, -1.0 * betaz) + np.power(
mhalo / mhalo0, gammaz)
mstar = mhalo * 2 * mMz / denom
## vvir, eqn 19 PS11
d = cosmo.Om(z) - 1
Delta = 18 * pi * pi + 82 * d - 39 * d * d
factor = np.power(
(cosmo.Om0 / 0.25) * (1 / cosmo.Om(z)) * (Delta / (18 * pi * pi)),
1 / 6.)
vvir = 112.6 * np.power(mhalo / 1e12, 1 / 3.) * factor * np.sqrt(1 + z)
# +0.05 for Chabrier
return (np.log10(mstar) + 0.05), vvir
def test_delta_vir(self):
""" Compute the calculated value of `~halotools.empirical_models.profile_helpers.delta_vir`
at high-redshift where :math:`\\Omega_{\\rm m} = 1` should be a good approximation, and
compare it to the analytical top-hat collapse result in this regime.
"""
bn98_result = delta_vir(WMAP9, 10.0)
assert np.allclose(bn98_result, 18. * np.pi**2, rtol=0.01)
# Choose a high-redshift where Om = 1 is a good approximation
z = 10
rho_crit = WMAP9.critical_density(z)
rho_crit = rho_crit.to(u.Msun / u.Mpc**3).value / WMAP9.h**2
rho_m = WMAP9.Om(z) * rho_crit
wmap9_delta_vir_z10 = density_threshold(WMAP9, z, 'vir') / rho_m
assert np.allclose(wmap9_delta_vir_z10, bn98_result, rtol=0.01)
def test_density_threshold(self):
""" Verify that the `~halotools.empirical_models.profile_helpers.density_threshold`
method returns the correct multiple of the appropriate density contrast over a range
of redshifts and cosmologies.
"""
zlist = [0, 1, 5, 10]
for z in zlist:
for cosmo in (WMAP9, Planck13):
rho_crit = WMAP9.critical_density(z)
rho_crit = rho_crit.to(u.Msun / u.Mpc**3).value / WMAP9.h**2
rho_m = WMAP9.Om(z) * rho_crit
wmap9_200c = density_threshold(WMAP9, z, '200c') / rho_crit
assert np.allclose(wmap9_200c, 200.0, rtol=0.01)
wmap9_2500c = density_threshold(WMAP9, z, '2500c') / rho_crit
assert np.allclose(wmap9_2500c, 2500.0, rtol=0.01)
wmap9_200m = density_threshold(WMAP9, z, '200m') / rho_m
assert np.allclose(wmap9_200m, 200.0, rtol=0.01)
import numpy.random as rm
import scipy.stats as stats
from multiprocessing import Pool, Process, freeze_support
import multiprocessing
import argparse
G = ct.G
c = ct.c
Msun = 1.989e30
pi = ct.pi
noisecurvepts = 900
Hubble_Parameter = WMAP9.H(0.0).value #km/sec/Mpc
h = Hubble_Parameter / 100.0
Omega_M = WMAP9.Om(0.0)
Omega_vac = 1.0 - Omega_M
#this is to cut out scale factors that occur before the first real mergers?????
def scaleadjustfunc(scale_factor):
change = (scale_factor <= 1.0 / 138.0)
keep = (scale_factor > 1.0 / 138.0)
return scale_factor * keep + (1.0 / 138.0) * change
def Least_squared_binned_fit(counts):
atest = np.linspace(0.001, 20.0, 10000)
chisquared = np.zeros(len(atest))
for i in np.arange(len(counts[0])):
if counts[0][i] != 0:
