def do_all(z_arr, k_arr, cpar, prefix, wig):
pcs = csm.PcsPar()
pcs.background_set(cpar['om'], cpar['ol'], cpar['ob'], cpar['w0'],
cpar['wa'], cpar['hh'], TCMB)
if wig:
typ = 'EH'
else:
typ = 'EH_smooth'
pcs.set_linear_pk(typ, -3, LKMAX, 0.01, cpar['ns'], cpar['s8'])
gf0 = pcs.growth_factor(1)
a_arr = 1. / (z_arr + 1)
gf_arr = np.array([pcs.growth_factor(a) for a in a_arr])
pk_arr = np.array([[pcs.Pk_linear_0(k) * (gf / gf0)**2 for k in k_arr]
for gf in gf_arr])
if PLOT_STUFF == 1:
for i in np.arange(len(pk_arr)):
plt.plot(k_arr, pk_arr[i], label='$z=%.2lf$' % (z_arr[i]))
plt.xlabel('$k\\,[h\\,{\\rm Mpc}^{-1}]$', fontsize=FS)
plt.ylabel('$P(k)\\,[{\\rm Mpc}\\,h^{-1}]^3$', fontsize=FS)
plt.gca().set_xscale('log')
plt.gca().set_yscale('log')
plt.legend(loc='lower left')
plt.show()
if WRITE_STUFF == 1:
header_pk = "[0] k (Mpc/h)^-1"
for i in np.arange(len(z_arr)):
header_pk += ", [%d]" % (i + 2) + " P(k,z=%.1lf) (Mpc/h)^3" % (
z_arr[i])
np.savetxt(prefix + "_pk_eh.txt",
np.transpose(np.vstack((k_arr, pk_arr))),
header=header_pk)
def get_pz(om, ol, ob, hh, get_nm=False):
"""Average electron pressure in the relevant redshift range
for a set of cosmological parameters"""
pcs = csm.PcsPar()
pcs.unset_gsl_eh()
pcs.background_set(om, ol, ob, -1., 0., hh, 2.7255)
g_ratio = pcs.growth_factor(
1.) / dgrowth0 #Growth ratio with respect to background universe
pcs.set_linear_pk('EH', -5., 3., 0.01, NS, S8 * g_ratio)
pcs.set_mf_params(LMMIN, LMMAX, DLM)
cospar = {'OM': om, 'OB': ob, 'h': hh, 'pcs': pcs}
marr = np.logspace(LMMIN, LMMAX, NLM)
zarr = np.array([Z_EFF])
pzarr = np.zeros_like(zarr)
for iz, z in enumerate(zarr):
zeff = get_zeff(om, ol, 1 - om - ol, hh,
z) #Equivalent redshift in the effective Universe
nmarr = np.zeros_like(marr)
for im, m in enumerate(marr):
bp = get_battaglia(m, zeff, cospar)
pmean = integrated_profile(bp)
nmarr[im] = pmean * pcs.mass_function_logarithmic(
m, zeff, 'Tinker10_200')
nmf = interp1d(np.log10(marr), nmarr, bounds_error=False, fill_value=0)
pzarr[iz] = itg.quad(nmf, 7.5, LMMAX + 0.5)[0]
if get_nm:
return marr, nmarr, np.mean(pzarr)
else:
return np.mean(pzarr)
def do_all(z_arr,cpar,prefix) :
pcs=csm.PcsPar()
pcs.background_set(cpar['om'],cpar['ol'],cpar['ob'],cpar['w0'],cpar['wa'],cpar['hh'],TCMB)
a_arr=1./(z_arr+1)
chi_arr=np.array([pcs.radial_comoving_distance(a) for a in a_arr])
if PLOT_STUFF==1 :
plt.plot(z_arr,chi_arr); plt.xlabel('$z$',fontsize=FS); plt.ylabel('$\\chi(z)\\,[{\\rm Mpc}\\,h^{-1}]$',fontsize=FS); plt.show()
if WRITE_STUFF==1 :
np.savetxt(prefix+"_chi.txt",np.transpose([z_arr,chi_arr]),header="[1] z, [2] chi(z) (Mpc/h)")
def get_s8(cp):
cospar = cp.copy()
pcs = csm.PcsPar()
if DEBUG:
pcs.set_verbosity(1)
else:
pcs.set_verbosity(0)
om = (cospar['och2'] + cospar['obh2'] +
cospar['mnu'] * 0.001 / 93.14) / cospar['hh']**2
ob = cospar['obh2'] / cospar['hh']**2
pcs.background_set(om, 1 - om, ob, cospar['w0'], cospar['wa'],
cospar['hh'], TCMB)
write_class_param_file(cospar, np.array([0]))
pcs.set_linear_pk(PREFIX_SAVE + "pk.dat", -4., 3., 0.01, cospar['ns'], -1)
return np.sqrt(pcs.sig0_L(8, 8, 'TopHat', 'TopHat'))
def get_mfunc(cospar, z_min, z_max, nz, lm_min, lm_max, nm, fsky):
z_arr = z_min + (z_max - z_min) * (np.arange(nz) + 0.5) / nz
z0_arr = z_min + (z_max - z_min) * (np.arange(nz) + 0.0) / nz
zf_arr = z_min + (z_max - z_min) * (np.arange(nz) + 1.0) / nz
pcs = csm.PcsPar()
if DEBUG:
pcs.set_verbosity(1)
else:
pcs.set_verbosity(0)
om = (cospar['och2'] + cospar['obh2'] +
cospar['mnu'] * 0.001 / 93.14) / cospar['hh']**2
ob = cospar['obh2'] / cospar['hh']**2
pcs.background_set(om, 1 - om, ob, cospar['w0'], cospar['wa'],
cospar['hh'], TCMB)
write_class_param_file(cospar, z_arr)
m_arr = 10.**(lm_min + (lm_max - lm_min) * (np.arange(nm) + 0.5) / nm)
r_arr = np.array([pcs.M2R(m) for m in m_arr])
m2_arr = 10.**(lm_min + (lm_max - lm_min) * (np.arange(nm + 1) + 0.0) / nm)
lmarr = np.log10(m2_arr)
r2_arr = np.array([pcs.M2R(m) for m in m2_arr])
nm_arr = np.zeros([nz, nm])
for iz in np.arange(nz):
z = z_arr[iz]
z0 = z0_arr[iz]
zf = zf_arr[iz]
r0 = pcs.radial_comoving_distance(1. / (1 + z0))
rf = pcs.radial_comoving_distance(1. / (1 + zf))
pcs.set_linear_pk(PREFIX_SAVE + "z%d_pk.dat" % (iz + 1), -4., 3., 0.01,
cospar['ns'], -1)
volz = 4 * np.pi * fsky * (rf**3 - r0**3) / (
3 * (zf - z0)
) #pcs.radial_comoving_distance(1./(1+z))**2/pcs.hubble(1./(1+z))
sigM_arr = np.sqrt(
np.array([pcs.sig0_L(r, r, 'TopHat', 'TopHat') for r in r_arr]))
sig2_arr = np.sqrt(
np.array([pcs.sig0_L(r, r, 'TopHat', 'TopHat') for r in r2_arr]))
dlsdlm_arr = -np.log10(sig2_arr[1:] / sig2_arr[:-1]) / (lmarr[1:] -
lmarr[:-1])
nm_arr[iz, :] = (np.log(10.) * volz * dlsdlm_arr * om * RHOCRIT0 *
g_nu(sigM_arr, z, cospar['mfName']) / m_arr)
os.system("rm " + PREFIX_SAVE + "*")
return pcs, nm_arr
def do_all(z_arr, cpar, prefix):
pcs = csm.PcsPar()
pcs.background_set(cpar['om'], cpar['ol'], cpar['ob'], cpar['w0'],
cpar['wa'], cpar['hh'], TCMB)
a_arr = 1. / (z_arr + 1)
gf_arr = np.array([pcs.growth_factor(a) for a in a_arr])
if PLOT_STUFF == 1:
plt.plot(a_arr, gf_arr)
plt.xlabel('$a$', fontsize=FS)
plt.ylabel('$D(z)$', fontsize=FS)
plt.show()
if WRITE_STUFF == 1:
np.savetxt(prefix + "_gf.txt",
np.transpose([z_arr, gf_arr]),
header="[1] z, [2] D(z)")
def do_all(m_arr, cpar, prefix):
pcs = csm.PcsPar()
pcs.background_set(cpar['om'], cpar['ol'], cpar['ob'], cpar['w0'],
cpar['wa'], cpar['hh'], TCMB)
pcs.set_linear_pk('BBKS', -3, LKMAX, 0.01, cpar['ns'], cpar['s8'])
r_arr = np.array([pcs.M2R(m) for m in m_arr])
sm_arr = np.sqrt(
np.array([pcs.sig0_L(r, r, 'TopHat', 'TopHat') for r in r_arr]))
if PLOT_STUFF == 1:
plt.plot(m_arr, sm_arr)
plt.xlabel('$M\\,[M_{\\odot}\\,h^{-1}]$', fontsize=FS)
plt.ylabel('$\\sigma(M)$', fontsize=FS)
plt.gca().set_xscale('log')
plt.gca().set_yscale('log')
plt.show()
if WRITE_STUFF == 1:
np.savetxt(prefix + "_sm.txt",
np.transpose([m_arr, sm_arr]),
header="[1] M (M_sun/h), [2] sigma(M)")
RHOCRIT0 = 2.7744948E11
OM0 = 0.3
HH0 = 0.7
FB0 = 0.1666666
NS = 0.96
S8 = 0.8
NRMAX = 10.
LMMIN = 8.
LMMAX = 16.
DLM = 0.1
NLM = 64
B_CMASS = 2.
Z_EFF = 0.5
#Cosmological parameters outside the voide
pcs_BG0 = csm.PcsPar()
pcs_BG0.unset_gsl_eh()
pcs_BG0.background_set(OM0, 1 - OM0, OM0 * FB0, -1., 0., HH0, 2.7255)
dgrowth0 = pcs_BG0.growth_factor(1.) #Normalization of the growth factor
#Factor needed to scale the void profile at z=Z_EFF to z=0
scale_delta = B_CMASS * pcs_BG0.growth_factor(1. / (1 + Z_EFF)) / dgrowth0
def rDelta(m, z, Delta, cpar):
"""Returns r_Delta
"""
hn = (cpar['pcs']).hubble(1. / (1 + z)) / (cpar['pcs']).hubble(1.)
rhoc = RHOCRIT0 * hn * hn
return (3 * m / (4 * np.pi * Delta * rhoc))**0.333333333 * (1 + z)
import numpy as np
import py_cosmo_mad as csm
KMAX = 0.2
pcs = csm.PcsPar()
pcs.set_verbosity(1)
pcs.background_set(0.315, 0.685, 0.049, -1., 0., 0.67, 2.725)
def reprocess_file(fname, forgetlmax):
data = np.loadtxt(fname, unpack=True)
z0 = data[0]
zf = data[1]
sz = data[2]
marg = data[3]
zm = (z0 + zf) / 2
if forgetlmax:
lmax = 2000 * np.ones_like(sz)
else:
lmax = np.array([
int(pcs.radial_comoving_distance(1. / (1 + z)) * KMAX) for z in zm
])
np.savetxt(fname,
np.transpose([z0, zf, sz, marg, lmax]),
fmt='%lf %lf %lf %d %d',
header='[0]z0 [1]zf [2]sigma_z [3]marg_sz [4]lmax')
reprocess_file('bins_red.txt', False)
reprocess_file('bins_blue.txt', False)
reprocess_file('bins_gold.txt', True)
