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 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_delta_vir():
r""" Compute the calculated value of `~halotools.empirical_models.halo_boundary_functions.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)
def test_density_threshold():
r""" Verify that the `~halotools.empirical_models.halo_boundary_functions.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 as np from astropy.cosmology import WMAP9 as cosmo from astropy import units as u ''' This code calculate the half-mode mass of WDM ''' wdm_m = 2.0 # keV Owdm = cosmo.Om0 h=cosmo.h mu=1.12 rho = cosmo.critical_density(0) # g/cm^3 rho = rho.to(u.Msun/u.Mpc**3) alpha = 0.049*(wdm_m)**(-1.11)*(Owdm/0.25)**0.11*(h/0.7)**1.22*(u.Mpc/u.h) # Mpc h-1 Mfs = 4.0*np.pi/3.0*rho*(alpha/2.0)**3 Mhm = 2.7e3*Mfs print Mhm*h**3
'''
Defines mass profiles
'''
import os
import numpy as np
import astropy.units as u
import astropy.constants as const
import pandas as pd
from scipy.integrate import cumtrapz
from scipy.interpolate import UnivariateSpline
from astropy.cosmology import WMAP9 as cosmo
RHO_CRIT = cosmo.critical_density(0.)
class MassProfile:
def __init__(self, profiletype, **kwargs):
self.type = profiletype
self.kwargs = kwargs
def M(self):
raise NotImplementedError("""Woops! You need to specify an actual halo
type before you can get the mass profile!""")
pass
def Mprime(self, bs, profile):
mprime = np.gradient(profile, bs, edge_order=2)
self.mprime = mprime
return mprime
def crit_density(z): #Msun/h /kpc^3
gcm3_to_msunkpc3 = 1.477543e31
density = cosmo.critical_density(z).value * gcm3_to_msunkpc3
#print "crit desity(%f): %f Msun/kpc^3"%(z,density)
#print "crit desity(%f): %e Msun/kpc^3"%(z,density*1000**3/cosmo.h**2)
return density
def get_box_density(dataset):
""" gets the total box density in Msun / Mpc***3 for a yt dataset"""
return WMAP9.critical_density(0).value * \
(3.086e24)**3 / 1.989e33 * dataset.omega_matter \
* YTArray(1., 'Msun') / YTArray(1., 'Mpc')**3
