def model(theta, R, h=1, Om=0.315, Ol=0.685):
from itertools import count, izip
# HMF set up parameters - fixed and not setable from config file.
expansion = 100
expansion_stars = 160
n_bins = 10000
M_min = 8.5
M_max = 15.5
step = (M_max - M_min) / 100 # or n_bins
k_min = -6.0 # ln!!! not log10!
k_max = 9.0 # ln!!! not log10!
k_step = (k_max - k_min) / n_bins
k_range = np.arange(k_min, k_max, k_step)
k_range_lin = np.exp(k_range)
M_star_min = 8.5 # Halo mass bin range
M_star_max = 15.5
step_star = (M_star_max - M_star_min) / 100
mass_range = np.arange(M_min, M_max, step)
mass_range_lin = 10.0 ** (mass_range)
mass_star_log = np.arange(M_star_min, M_star_max, step_star, dtype=np.longdouble)
mass_star = 10.0 ** (mass_star_log)
# Setting parameters from config file
omegab = 0.0455 # To be replaced by theta
omegac = 0.226 # To be replaced by theta
omegav = 0.726 # To be replaced by theta
sigma_c, alpha_s, A, M_1, gamma_1, gamma_2, b_0, b_1, b_2, alpha_star, beta_gas, r_t0, r_c0, z, M_bin_min1, M_bin_min2, M_bin_max1, M_bin_max2 = (
theta
)
M_bin_min = [M_bin_min1, M_bin_min2] # Expanded according to number of bins!
M_bin_max = [M_bin_max1, M_bin_max2] # Expanded according to number of bins!
hod_mass = [
(10.0 ** (np.arange(Mi, Mx, (Mx - Mi) / 100, dtype=np.longdouble))) for Mi, Mx in izip(M_bin_min, M_bin_max)
]
r_t0 = r_t0 * np.ones(100)
r_c0 = r_c0 * np.ones(100)
H0 = 70.4 # To be replaced by theta
rho_crit = 2.7763458 * (10.0 ** 11.0) # in M_sun*h^2/Mpc^3 # To be called from nfw_utils!
cosmology_params = {
"sigma_8": 0.81,
"H0": 70.4,
"omegab": 0.0455,
"omegac": 0.226,
"omegav": 0.728,
"n": 0.967,
"lnk_min": k_min,
"lnk_max": k_max,
"dlnk": k_step,
"transfer_fit": "CAMB",
"z": z,
} # Values to be read in from theta
# ---------------------------------------
hmf = Mass_Function(
M_min, M_max, step, k_min, k_max, k_step, "Tinker10", **cosmology_params
) # Tinker10 should also be read from theta!
mass_func = hmf.dndlnm
# power = hmf.power
rho_mean_int = rho_crit * (hmf.omegac + hmf.omegab)
rho_mean = Integrate(mass_func, mass_range_lin)
radius_range_lin = ht.mass_to_radius(mass_range_lin, rho_mean) / ((200) ** (1.0 / 3.0))
radius_range = np.log10(radius_range_lin)
radius_range_3d = 10.0 ** np.arange(-3.0, 2.9, (2.9 - (-3.0)) / (100))
radius_range_3d_i = 10.0 ** np.arange(-2.9, 1.0, (1.0 - (-2.9)) / (50))
radius_range_2d_i = 10.0 ** np.arange(-2.5, 1.0, (1.0 - (-2.5)) / (50)) # THIS IS R!
# Calculating halo model
ngal = np.array(
[
n_gal(
z,
hmf,
ngm(hmf, i[0], mass_range_lin, sigma_c, alpha_s, A, M_1, gamma_1, gamma_2, b_0, b_1, b_2),
mass_range_lin,
radius_range_lin,
)
for i in izip(hod_mass)
]
)
rho_dm = baryons.rhoDM(hmf, mass_range_lin, omegab, omegac)
rho_stars = np.array(
[
baryons.rhoSTARS(hmf, i[0], mass_range_lin, sigma_c, alpha_s, A, M_1, gamma_1, gamma_2, b_0, b_1, b_2)
for i in izip(hod_mass)
]
)
rho_gas = np.array(
[
baryons.rhoGAS(
hmf,
rho_crit,
omegab,
omegac,
i[0],
mass_range_lin,
sigma_c,
alpha_s,
A,
M_1,
gamma_1,
gamma_2,
b_0,
b_1,
b_2,
)
for i in izip(hod_mass)
]
)[:, 0]
F = np.array(
[
baryons.rhoGAS(
hmf,
rho_crit,
omegab,
omegac,
i[0],
mass_range_lin,
sigma_c,
alpha_s,
A,
M_1,
gamma_1,
gamma_2,
b_0,
b_1,
b_2,
)
for i in izip(hod_mass)
]
)[:, 1]
norm2 = rho_mean_int / rho_mean
effective_mass = np.array(
[
eff_mass(
z,
hmf,
ngm(hmf, i[0], mass_range_lin, sigma_c, alpha_s, A, M_1, gamma_1, gamma_2, b_0, b_1, b_2),
mass_range_lin,
)
for i in izip(hod_mass)
]
)
effective_mass_dm = np.array(
[
eff_mass(
z,
hmf,
ncm(hmf, i[0], mass_range_lin, sigma_c, alpha_s, A, M_1, gamma_1, gamma_2, b_0, b_1, b_2),
mass_range_lin,
)
* baryons.f_dm(omegab, omegac)
for i in izip(hod_mass)
]
)
effective_mass2 = effective_mass * (omegab / (omegac + omegab))
effective_mass_bar = np.array(
[
effective_mass2
* (baryons.f_stars(i[0], effective_mass2, sigma_c, alpha_s, A, M_1, gamma_1, gamma_2, b_0, b_1, b_2))
for i in izip(hod_mass)
]
)
T_dm = np.array(
[
T_table_multi(
expansion,
rho_dm,
z,
mass_range_lin,
radius_range_lin,
i[0],
"dm",
omegab,
omegac,
0,
0,
sigma_c,
alpha_s,
A,
M_1,
gamma_1,
gamma_2,
b_0,
b_1,
b_2,
)
for i in izip(hod_mass)
]
)
T_stars = np.array(
[
T_table_multi(
expansion_stars,
rho_mean,
z,
mass_range_lin,
radius_range_lin,
i[0],
"stars",
omegab,
omegac,
alpha_star,
r_t0,
sigma_c,
alpha_s,
A,
M_1,
gamma_1,
gamma_2,
b_0,
b_1,
b_2,
)
for i in izip(hod_mass)
]
)
T_gas = np.array(
[
T_table_multi(
expansion,
rho_mean,
z,
mass_range_lin,
radius_range_lin,
i[0],
"gas",
omegab,
omegac,
beta_gas,
r_c0,
sigma_c,
alpha_s,
A,
M_1,
gamma_1,
gamma_2,
b_0,
b_1,
b_2,
)
for i in izip(hod_mass)
]
)
T_tot = np.array([T_dm[i][0:1:1, :] + T_stars[i][0:1:1, :] + T_gas[i][0:1:1, :] for i in range(len(M_bin_min))])
F_k1 = f_k(k_range_lin)
pop_c = np.array(
[
ncm(hmf, i[0], mass_range_lin, sigma_c, alpha_s, A, M_1, gamma_1, gamma_2, b_0, b_1, b_2)
for i in izip(hod_mass)
]
)
pop_s = np.array(
[
nsm(hmf, i[0], mass_range_lin, sigma_c, alpha_s, A, M_1, gamma_1, gamma_2, b_0, b_1, b_2)
for i in izip(hod_mass)
]
)
pop_g = np.array(
[
ngm(hmf, i[0], mass_range_lin, sigma_c, alpha_s, A, M_1, gamma_1, gamma_2, b_0, b_1, b_2)
for i in izip(hod_mass)
]
)
# Galaxy - dark matter spectra
Pg_2h = np.array(
[
TwoHalo(hmf, ngal_i, pop_g_i, k_range_lin, radius_range_lin, mass_range_lin)
for ngal_i, pop_g_i in izip(ngal, pop_g)
]
)
Pg_c = np.array(
[
F_k1
* GM_cen_spectrum(
hmf,
z,
rho_dm,
rho_mean,
expansion,
pop_c_i,
ngal_i,
k_range_lin,
radius_range_lin,
mass_range_lin,
T_dm_i,
T_tot_i,
)
for pop_c_i, ngal_i, T_dm_i, T_tot_i in izip(pop_c, ngal, T_dm, T_tot)
]
)
Pg_s = np.array(
[
F_k1
* GM_sat_spectrum(
hmf,
z,
rho_dm,
rho_mean,
expansion,
pop_s_i,
ngal_i,
k_range_lin,
radius_range_lin,
mass_range_lin,
T_dm_i,
T_tot_i,
)
for pop_s_i, ngal_i, T_dm_i, T_tot_i in izip(pop_s, ngal, T_dm, T_tot)
]
)
# Galaxy - stars spectra
Ps_c = np.array(
[
F_k1
* baryons.GS_cen_spectrum(
hmf,
z,
rho_stars_i,
rho_mean,
expansion_stars,
pop_c_i,
ngal_i,
k_range_lin,
radius_range_lin,
mass_range_lin,
T_stars_i,
T_tot_i,
)
for rho_stars_i, pop_c_i, ngal_i, T_stars_i, T_tot_i in izip(rho_stars, pop_c, ngal, T_stars, T_tot)
]
)
Ps_s = np.array(
[
F_k1
* baryons.GS_sat_spectrum(
hmf,
z,
rho_stars_i,
rho_mean,
expansion,
pop_s_i,
ngal_i,
k_range_lin,
radius_range_lin,
mass_range_lin,
T_dm_i,
T_stars_i,
T_tot_i,
)
for rho_stars_i, pop_s_i, ngal_i, T_dm_i, T_stars_i, T_tot_i in izip(
rho_stars, pop_s, ngal, T_dm, T_stars, T_tot
)
]
)
# Galaxy - gas spectra
Pgas_c = np.array(
[
F_k1
* baryons.GGas_cen_spectrum(
hmf,
z,
F_i,
rho_gas_i,
rho_mean,
expansion,
pop_c_i,
ngal_i,
k_range_lin,
radius_range_lin,
mass_range_lin,
T_gas_i,
T_tot_i,
)
for F_i, rho_gas_i, pop_c_i, ngal_i, T_gas_i, T_tot_i in izip(F, rho_gas, pop_c, ngal, T_gas, T_tot)
]
)
Pgas_s = np.array(
[
F_k1
* baryons.GGas_sat_spectrum(
hmf,
z,
F_i,
rho_gas_i,
rho_mean,
expansion,
pop_s_i,
ngal_i,
k_range_lin,
radius_range_lin,
mass_range_lin,
T_dm_i,
T_gas_i,
T_tot_i,
)
for F_i, rho_gas_i, pop_s_i, ngal_i, T_dm_i, T_gas_i, T_tot_i in izip(
F, rho_gas, pop_s, ngal, T_dm, T_gas, T_tot
)
]
)
# Combined (all) by type
Pg_k_dm = np.array(
[
(ngal_i * rho_dm * (Pg_c_i + Pg_s_i + Pg_2h_i * rho_mean / rho_dm)) / (rho_mean * ngal_i)
for ngal_i, Pg_c_i, Pg_s_i, Pg_2h_i in izip(ngal, Pg_c, Pg_s, Pg_2h)
]
)
Pg_k_s = np.array(
[
(ngal_i * rho_stars_i * (Ps_c_i + Ps_s_i)) / (rho_mean * ngal_i)
for ngal_i, rho_stars_i, Ps_c_i, Ps_s_i in izip(ngal, rho_stars, Ps_c, Ps_s)
]
)
Pg_k_g = np.array(
[
(ngal_i * rho_gas_i * (Pgas_c_i + Pgas_s_i)) / (rho_mean * ngal_i)
for ngal_i, rho_gas_i, Pgas_c_i, Pgas_s_i in izip(ngal, rho_gas, Pgas_c, Pgas_s)
]
)
Pg_k = np.array(
[
(
ngal_i * rho_dm * (Pg_c_i + Pg_s_i + Pg_2h_i * rho_mean / rho_dm)
+ ngal_i * rho_stars_i * (Ps_c_i + Ps_s_i)
+ ngal_i * rho_gas_i * (Pgas_c_i + Pgas_s_i)
)
/ (rho_mean * ngal_i)
for ngal_i, Pg_c_i, Pg_s_i, Pg_2h_i, rho_stars_i, Ps_c_i, Ps_s_i, rho_gas_i, Pgas_c_i, Pgas_s_i in izip(
ngal, Pg_c, Pg_s, Pg_2h, rho_stars, Ps_c, Ps_s, rho_gas, Pgas_c, Pgas_s
)
]
) # all components
# Normalized sattelites and centrals for sigma and d_sigma
Pg_c2 = np.array([(rho_dm / rho_mean) * Pg_c_i for Pg_c_i in izip(Pg_c)])
Pg_s2 = np.array([(rho_dm / rho_mean) * Pg_s_i for Pg_s_i in izip(Pg_s)])
Ps_c2 = np.array([(rho_stars_i / rho_mean) * Ps_c_i for rho_stars_i, Ps_c_i in izip(rho_stars, Ps_c)])
Ps_s2 = np.array([(rho_stars_i / rho_mean) * Ps_s_i for rho_stars_i, Ps_s_i in izip(rho_stars, Ps_s)])
Pgas_c2 = np.array([(rho_gas_i / rho_mean) * Pgas_c_i for rho_gas_i, Pgas_c_i in izip(rho_gas, Pgas_c)])
Pgas_s2 = np.array([(rho_gas_i / rho_mean) * Pgas_s_i for rho_gas_i, Pgas_s_i in izip(rho_gas, Pgas_s)])
lnPg_k = np.array([np.log(Pg_k_i) for Pg_k_i in izip(Pg_k)]) # Total
P_inter2 = [scipy.interpolate.UnivariateSpline(k_range, np.nan_to_num(lnPg_k_i), s=0) for lnPg_k_i in izip(lnPg_k)]
"""
# Correlation functions
"""
xi2 = np.zeros((len(M_bin_min), len(radius_range_3d)))
for i in range(len(M_bin_min)):
xi2[i, :] = power_to_corr_multi(P_inter2[i], radius_range_3d)
xi2[xi2 <= 0.0] = np.nan
xi2[i, :] = fill_nan(xi2[i, :])
"""
# Projected surface density
"""
sur_den2 = np.array(
[np.nan_to_num(sigma(xi2_i, rho_mean, radius_range_3d, radius_range_3d_i)) for xi2_i in izip(xi2)]
)
sur_den2[sur_den2 >= 10.0 ** 16.0] = np.nan
sur_den2[sur_den2 <= 0.0] = np.nan
for i in range(len(M_bin_min)):
sur_den2[i, :] = fill_nan(sur_den2[i, :])
"""
# Excess surface density
"""
d_sur_den2 = np.array(
[np.nan_to_num(d_sigma(sur_den2_i, radius_range_3d_i, radius_range_2d_i)) for sur_den2_i in izip(sur_den2)]
)
# print d_sur_den2/10**12.0
return [d_sur_den2 / 10 ** 12.0, xi2, lnPg_k] # Add other outputs as needed. Total ESD should always be first!
Pgas_c2 = (rho_gas / rho_mean) * Pgas_c
Pgas_s2 = (rho_gas / rho_mean) * Pgas_s
# ----------------------------------------
lnPg_k = np.log(Pg_k) # Total
P_inter2 = scipy.interpolate.UnivariateSpline(k_range, np.nan_to_num(lnPg_k), s=0)
"""
# Correlation functions
"""
xi2 = power_to_corr_multi(P_inter2, radius_range_3d)
xi2[xi2 <= 0.0] = np.nan
xi2 = fill_nan(xi2)
"""
# Projected surface density
"""
sur_den2 = np.nan_to_num(sigma(xi2, rho_mean, radius_range_3d, radius_range_3d_i))
sur_den2[sur_den2 >= 10.0 ** 16.0] = np.nan
sur_den2[sur_den2 <= 0.0] = np.nan
sur_den2 = fill_nan(sur_den2)
"""
# Excess surface density
"""
d_sur_den2 = np.nan_to_num(d_sigma(sur_den2, radius_range_3d_i, radius_range_2d_i))
