def getSimulationProperties(sim_name):
if sim_name in sims:
sim = SimulationProperties()
sim.name = sim_name
sim.cosmo_name = sims[sim_name]['cosmo']
sim.box_size = sims[sim_name]['box_size']
sim.Np = sims[sim_name]['Np']
sim.epsilon = sims[sim_name]['epsilon']
sim.folder = sims[sim_name]['folder']
sim.Nfiles = sims[sim_name]['Nfiles']
sim.Nsnapdigits = sims[sim_name]['Nsnapdigits']
sim.code = sims[sim_name]['code']
else:
print("Invalid sim name, %s. Valid names are:" % sim_name)
printBoxes()
exit()
try:
previous_cosmo = cosmology.getCurrent()
except Exception:
previous_cosmo = None
cosmo = cosmology.setCosmology(sim.cosmo_name)
sim.mp = getParticleMass(sim.box_size, sim.Np, cosmo.Om0)
if previous_cosmo is not None:
cosmology.setCurrent(previous_cosmo)
return sim
def mass_function_rseppi(Mass):
cosmo = cosmology.getCurrent()
delta_c = peaks.collapseOverdensity(z=z)
R = peaks.lagrangianR(Mass)
sigma = cosmo.sigma(R=R, z=z)
nu = delta_c / sigma
nu2 = nu**2
zp1 = 1.0 + z
A = 0.333 * zp1**-0.11
a = 0.788 * zp1**-0.01
p = 0.807
q = 1.795
f = A * np.sqrt(2 / np.pi) * np.exp(
-a * nu2 * 0.5) * (1.0 + (nu2 * a)**-p) * (nu * np.sqrt(a))**q
d_ln_sigma_d_ln_R = cosmo.sigma(R, z, derivative=True)
rho_Mpc = cosmo.rho_m(0.0) * 1E9
mass_func_model = -1 / 3 * f * rho_Mpc / Mass * d_ln_sigma_d_ln_R
return mass_func_model
def matchCosmo():
curcosmo = nfwutils.global_cosmology
isMatch = False
try:
cur_ccosmo = cCosmo.getCurrent()
isMatch = curcosmo.H0 == cur_ccosmo.H0 and \
curcosmo.omega_m == cur_ccosmo.Om0 and \
curcosmo.omega_l == cur_ccosmo.OL0
except:
pass
if isMatch is False:
cCosmo.setCosmology(
'curCosmo',
dict(H0=curcosmo.H0,
Om0=curcosmo.omega_m,
OL0=curcosmo.omega_l,
Ob0=0.049,
sigma8=0.9,
ns=0.95))
def t_d(r, M, z, c, R, beta=beta_def):
Menc = NFWM(r, M, z, c, R)
t_dyn = 2. * np.pi * (r**3 / (G*Menc))**(1./2.) * km_per_kpc / (cosmology.getCurrent().H0 / 100.)
return beta * t_dyn / s_per_Gyr / 2.
from colossus.cosmology import cosmology
from colossus.lss import bias
# Specify a set of concentration models for comparison.
models = [('cole89', 'NA'), ('sheth01', 'NA'), ('tinker10', '200c')]
# Build a range of halo masses at which to compute concentrations.
massLogarithmic = np.linspace(10.0, 15.0, 10)
mass = 10.0**massLogarithmic
# Specify redshifts.
redshifts = (0.0, 0.5, 1.0)
# Specify cosmology.
cosmology.setCosmology('planck15')
cosmo = cosmology.getCurrent()
# Iterate over models.
for model in models:
outputFile = open(
"testSuite/data/haloBiasesColossus/" + model[0] + "_" + model[1] +
".txt", "w")
# Write cosmological parameters to file.
outputFile.write("# OmegaMatter = " + str(cosmo.Om0) + "\n")
outputFile.write("# OmegaDarkEnergy = " + str(cosmo.Ode0) + "\n")
outputFile.write("# OmegaBaryon = " + str(cosmo.Ob0) + "\n")
outputFile.write("# HubbleConstant = " + str(cosmo.H0) + "\n")
outputFile.write("# sigma_8 = " + str(cosmo.sigma8) + "\n")
outputFile.write("# index = " + str(cosmo.ns) + "\n")
def cm_relation():
""""""
cosmology.setCosmology('planck15')
#for model_name in concentration.models:
#print(model_name)
#cosmology.setCosmology('bolshoi')
csm = cosmology.getCurrent()
M = 10**np.arange(5.0, 12.4, 0.1)
z_ = 0
rho_c = csm.rho_c(z_)
h_ = csm.Hz(z_) * 1e-2
delta = 200
plt.close()
fig, ax = plt.subplots(1, 1, figsize=(8, 5))
models = ['ludlow16', 'diemer18']
colors = [mpl.cm.Blues(0.5), mpl.cm.Blues(0.8)]
#for model_name in concentration.models:
for e, model_name in enumerate(models):
c, mask = concentration.concentration(M,
'200c',
0.0,
model=model_name,
range_return=True)
R = ((3 * M) / (4 * np.pi * delta * rho_c))**(1 / 3)
rs = R / c * 1e3
#plt.plot(M[mask]*h_, rs[mask]*h_, lw=2, label=model_name)
plt.fill_between(M[mask] * h_,
rs[mask] * h_ * 0.84,
rs[mask] * h_ * 1.16,
lw=2,
label=model_name,
alpha=0.5,
color=colors[e])
#rerkal = 1.05*1e3*np.sqrt(M*h_*1e-8)
#plt.plot(M*h_, rerkal, 'r-')
plt.axvline(1e6, ls=':', color='0.3')
plt.axvline(1e7, ls=':', color='0.3')
plt.axvline(1e8, ls=':', color='0.3')
#plt.xlim(1E3, 4E15)
#plt.ylim(2.0, 18.0)
plt.xscale('log')
plt.yscale('log')
#plt.xlabel('$M_{200,c}$ [$M_\odot$ h$^{-1}$]')
##plt.ylabel('Concentration')
#plt.ylabel('$r_s$ [kpc h$^{-1}$]')
plt.xlabel('$M_{200,c}$ [$M_\odot$]')
#plt.ylabel('Concentration')
plt.ylabel('$r_s$ [pc]')
plt.legend(fontsize='small', ncol=1, frameon=False, loc=4)
plt.tight_layout()
plt.savefig('../plots/rs_mass_cdm.png')