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plot_scaling.py
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import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import xx_power
import time
keV2erg = 1.6022e-9
deg2rad = math.pi/180.0
ster2sqdeg = 3282.80635
ster2sqarcmin = ster2sqdeg * 3600.0
ster2sqarcsec = ster2sqdeg * 3600.0 * 3600.0
Omega_m = 0.279
Omega_l = 1-Omega_m
Omega_b = 0.0461
f_baryon = Omega_b/Omega_m
h = 0.7
h70 = h/0.7
Mpiv = 3e14
XH = 0.76
efc = 1e-11 # convert cts to erg/cm^2 for ROSAT in [0.5,2.0] keV for T=1e7K N_H=2.5e20 cm^-2
def beam (ell, fwhm=0.5) :
#convert fwhm from arcmin to radian
fwhm *= (np.pi/180.0)/60.0
sigma = fwhm / (np.sqrt(8.0*np.log(2.0)))
bl = np.exp(ell*(ell+1.0) * sigma**2)
return bl
def mgas_m (mass, redshift, theta) :
eps_f = theta[0]
eps_DM = theta[1]
f_star = theta[2]
S_star = theta[3]
A_C = theta[4]
alpha0 = theta[5]
n_nt = theta[6]
beta = theta[7]
gamma_mod0 = theta[8]
gamma_mod_zslope = theta[9]
x_break = theta[10]
x_smooth = theta[11]
n_nt_mod = theta[12]
clump0 = theta[13]
alpha_clump = theta[14]
beta_clump = theta[15]
gamma_clump = theta[16]
xx_power.set_Flender_params(alpha0, n_nt, beta, eps_f*1e-6, eps_DM, f_star, S_star, A_C, gamma_mod0, gamma_mod_zslope, x_break, x_smooth, n_nt_mod, clump0, alpha_clump, beta_clump, gamma_clump)
mgas = []
m500 = []
for m in mass :
mgas.append(xx_power.return_Mgas(redshift, m))
m500.append(xx_power.Mvir_to_Mdeltac(redshift, m, 500.0))
mgas = np.array(mgas)
m500 = np.array(m500)
return mgas, m500
def tx_m (mass, redshift, theta) :
eps_f = theta[0]
eps_DM = theta[1]
f_star = theta[2]
S_star = theta[3]
A_C = theta[4]
alpha0 = theta[5]
n_nt = theta[6]
beta = theta[7]
gamma_mod0 = theta[8]
gamma_mod_zslope = theta[9]
x_break = theta[10]
x_smooth = theta[11]
n_nt_mod = theta[12]
clump0 = theta[13]
alpha_clump = theta[14]
beta_clump = theta[15]
gamma_clump = theta[16]
xx_power.set_Flender_params(alpha0, n_nt, beta, eps_f*1e-6, eps_DM, f_star, S_star, A_C, gamma_mod0, gamma_mod_zslope, x_break, x_smooth, n_nt_mod, clump0, alpha_clump, beta_clump, gamma_clump)
tx = []
m500 = []
for m in mass :
tx.append(xx_power.return_Tx(redshift, m))
m500.append(xx_power.Mvir_to_Mdeltac(redshift, m, 500.0))
tx = np.array(tx)
m500 = np.array(m500)
return tx, m500
def lx_m (mass, redshift, theta) :
eps_f = theta[0]
eps_DM = theta[1]
f_star = theta[2]
S_star = theta[3]
A_C = theta[4]
alpha0 = theta[5]
n_nt = theta[6]
beta = theta[7]
gamma_mod0 = theta[8]
gamma_mod_zslope = theta[9]
x_break = theta[10]
x_smooth = theta[11]
n_nt_mod = theta[12]
clump0 = theta[13]
alpha_clump = theta[14]
beta_clump = theta[15]
gamma_clump = theta[16]
xx_power.set_Flender_params(alpha0, n_nt, beta, eps_f*1e-6, eps_DM, f_star, S_star, A_C, gamma_mod0, gamma_mod_zslope, x_break, x_smooth, n_nt_mod, clump0, alpha_clump, beta_clump, gamma_clump)
lx = []
m500 = []
for m in mass :
lx.append(xx_power.return_Lx(redshift, m))
m500.append(xx_power.Mvir_to_Mdeltac(redshift, m, 500.0))
lx = np.array(lx)/1.e44
m500 = np.array(m500)
return lx, m500
def ysz_m (mass, redshift, theta) :
eps_f = theta[0]
eps_DM = theta[1]
f_star = theta[2]
S_star = theta[3]
A_C = theta[4]
alpha0 = theta[5]
n_nt = theta[6]
beta = theta[7]
gamma_mod0 = theta[8]
gamma_mod_zslope = theta[9]
x_break = theta[10]
x_smooth = theta[11]
n_nt_mod = theta[12]
clump0 = theta[13]
alpha_clump = theta[14]
beta_clump = theta[15]
gamma_clump = theta[16]
xx_power.set_Flender_params(alpha0, n_nt, beta, eps_f*1e-6, eps_DM, f_star, S_star, A_C, gamma_mod0, gamma_mod_zslope, x_break, x_smooth, n_nt_mod, clump0, alpha_clump, beta_clump, gamma_clump)
ysz = []
m500 = []
for m in mass :
ysz.append(yy_power.return_Ysz(redshift,m))
m500.append(xx_power.Mvir_to_Mdeltac(redshift, m, 500.0))
ysz = np.array(ysz)
m500 = np.array(m500)
E = np.sqrt(Omega_m*(1+redshift)**3+Omega_l)
ysz = ysz * E**(-2./3.)
return ysz, m500
def arnaud_lx_m(m500) :
c = 10**(0.193)
m0 = 3.e14
alpha = 1.76
return c*(m500/m0)**alpha
def arnaud_ysz_m(m500) :
c = 10**(-4.739)
m0 = 3.e14
alpha = 1.79
return c*(m500/m0)**alpha
def mantz_lx_m(m500, z) :
#Eq 5 from Mantz+16 (https://arxiv.org/abs/1606.03407)
E = np.sqrt(Omega_m*(1.+z)**3+Omega_l)
m = np.log(m500/1e15)
beta0 = 1.70
beta1 = 1.34
epsilon = np.log(E)
gamma = -4.2
lx = beta0+ beta1*(epsilon+m) - gamma*np.log(h70)
lx = np.exp(lx)*E
return lx
def cxb (theta) :
eps_f = theta[0]
eps_DM = theta[1]
f_star = theta[2]
S_star = theta[3]
A_C = theta[4]
alpha0 = theta[5]
n_nt = theta[6]
beta = theta[7]
gamma_mod0 = theta[8]
gamma_mod_zslope = theta[9]
x_break = theta[10]
x_smooth = theta[11]
n_nt_mod = theta[12]
clump0 = theta[13]
clump_zslope = theta[14]
x_clump = theta[15]
alpha_clump1 = theta[16]
alpha_clump2 = theta[17]
xx_power.set_Flender_params(alpha0, n_nt, beta, eps_f*1e-6, eps_DM, f_star, S_star, A_C, gamma_mod0, gamma_mod_zslope, x_break, x_smooth, n_nt_mod, clump0, clump_zslope, x_clump, alpha_clump1, alpha_clump2)
return (xx_power.return_total_xsb()/(4.0*math.pi))
def read_data (filename) :
ell = []
cl = []
var = []
with open(filename,'r') as f:
f.readline()
for line in f:
cols = line.split(' ')
ell.append(float(cols[0]))
cl.append(float(cols[1]))
var.append(float(cols[2]))
ell = np.array(ell)
cl = np.array(cl)
var = np.array(var)
return ell, cl, var
def main ():
# set cosmology and linear power spectrum
'''
H0=70.0
Omega_M=0.279000
Omega_b=0.046100
w0=-1.000000
Omega_k=0.000000
n_s=0.972000
inputPk="../input_pk/wmap9_fid_matterpower_z0.dat"
nH = 2.4e21
opt = 1
'''
H0=67.32117
Omega_M=0.3158
Omega_b=0.0490
w0=-1.000000
Omega_k=0.000000
n_s=0.96605
inputPk="../input_pk/planck_2018_test_matterpower.dat"
nH = 2.4e21
opt = 1
xx_power.init_cosmology(H0, Omega_M, Omega_b, w0, Omega_k, n_s, nH, inputPk, opt)
shot_noise = 0.00
ell = 10.**np.linspace(np.log10(10.),np.log10(3.e4),31)
theta_fid = [4.0, 3.e-5 ,0.0500,0.120000,1.000000,0.180000,0.800000,0.500000,0.10000,1.720000,0.195000,0.010000,0.800000, 0.9, 1.0, 6.0, 3.0]
param_ind_dict = {'eps_f':0, 'eps_DM':1, 'f_star':2, 'S_star':3, 'A_C':4, 'alpha_nt':5, 'n_nt':6, 'beta_nt':7, 'gamma_mod0':8, 'gamma_mod_zslope':9, 'x_break':10, 'x_smooth':11, 'n_nt_mod':12, 'clump0':13, 'clump_zslope':14, 'x_clump':15, 'alpha_clump1':16, 'alpha_clump2':17}
param_label_dict = {'eps_f':r'$\epsilon_f/10^{-6}$', 'eps_DM':r'$\epsilon_{DM}$', 'f_star':r'$f_\star$', 'S_star':r'$S_\star$', 'A_C':r'$A_C$','alpha_nt':r'$\alpha_{nt}$', 'n_nt':r'$n_{nt}$', 'beta_nt':r'$\beta_{nt}$', 'gamma_mod0':r'$\Gamma_0$', 'gamma_mod_zslope':r'$\beta_\Gamma$', 'n_nt_mod':'$n_{nt,mod}$', 'clump0':r'$C_0$', 'clump_zslope':r'$\beta_C$','x_clump':r'$x_{C}$', 'alpha_clump1':r'$\alpha_{C1}$', 'alpha_clump2':r'$\alpha_{C2}$'}
#rosat_ell, rosat_cl, rosat_var = read_data("../ROSAT/rosat_R4_R7_mask_hfi_R2_small_ell.txt")
#rosat_cl *= rosat_ell*(rosat_ell+1.)/(2.0*math.pi)
#rosat_cl_err = np.sqrt(rosat_var)*rosat_ell*(rosat_ell+1.)/(2.0*math.pi)
#params = ['eps_f', 'f_star', 'S_star', 'alpha_nt', 'n_nt', 'beta_nt', 'gamma_mod0', 'gamma_mod_zslope', 'clump0', 'clump_zslope', 'x_clump', 'alpha_clump1', 'alpha_clump2' ]
params = ['eps_f', 'f_star', 'clump0']
mvir = 10.**np.linspace(14.0, 15.5, 20)
z = 1e-4
#obs_list = ['mgas', 'lx', 'tx']
obs_list = ['lx']
#obs_list = ['mgas']
obs_label= {
#'lx': r'$L_X(0.15<r/R_{500}<1,z=0)\,{\rm [10^{44}ergs/s]}$',
'lx': r'$L_X(0.5-2.0\,{\rm keV})\,{\rm [10^{44}ergs/s]}$',
'mgas': r'$M_{\rm gas} (<R_{500}) {[M_\odot]}$',
'tx': r'$T_X(0.15<r/R_{500}<1,z=0)\,{\rm [keV]}$',
'ysz': r'$Y_{SZ}(<R_{500},z=0)\,{\rm [Mpc^2] }$'
}
for param in params :
obs = {}
f = {}
ax = {}
for o in obs_list :
f[o] = plt.figure( figsize=(4,4) )
ax[o] = f[o].add_axes([0.21,0.16,0.75,0.75])
param_ind = param_ind_dict[param]
param_fid = theta_fid[param_ind]
param_val_list = []
color_list = ['C0', 'C1', 'C2', 'C3', 'C4']
#multi_list = [1.0]
multi_list = [0.5,1.0,2.0]
#multi_list = [0.1,0.3,1.0,3.0,10.0]
if param == 'f_star' :
#multi_list = [1.0]
multi_list = [0.5,1.0,2.0]
for i in multi_list:
param_val = param_fid * i
param_val_list.append(param_val)
cl_list = []
for counter ,param_val in enumerate(param_val_list) :
print(param, param_val)
theta = theta_fid.copy()
theta[param_ind] = param_val
obs['lx'], m500 = lx_m(mvir, z, theta)
obs['tx'], m500 = tx_m(mvir, z, theta)
obs['mgas'], m500 = mgas_m(mvir, z, theta)
#obs['ysz'],m500 = ysz_m(mvir, z, theta)
label_str = param_label_dict[param]+r'$= %.3f $'% (param_val)
if param == 'eps_f' :
label_str = param_label_dict[param]+r'$= %.1f$'% (param_val)
if param == 'clump0' :
label_str = param_label_dict[param]+r'$= %.1f$'% (param_val+1)
#label_str = r'model'
for o in obs_list :
ax[o].plot(m500, obs[o], label=label_str)
lx_arnaud = arnaud_lx_m (m500)
lx_mantz = mantz_lx_m (m500,z)
ysz_arnaud = arnaud_ysz_m (m500)
#ax['lx'].plot(m500, lx_arnaud, color='r', label=r'Arnaud+10')
ax['lx'].plot(m500, lx_mantz, color='b', label=r'Mantz+16')
#ax['ysz'].plot(m500, ysz_arnaud, color='k', label=r'Arnaud+10')
for o in obs_list :
ax[o].set_xlabel(r'$M_{500c}$ [$M_\odot]$')
ax[o].set_ylabel(obs_label[o])
ax[o].legend(loc='best')
ax[o].set_xscale('log')
ax[o].set_yscale('log')
outname = param+'_'+o+'_m_z0.png'
f[o].savefig(outname)
f[o].clf()
if __name__ == "__main__" :
main()