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 = 2.4e+21
xx_power.init_cosmology(H0, Omega_M, Omega_b, w0, Omega_k, n_s, nH,
inputPk, 1)
yy_power.init_cosmology(H0, Omega_M, Omega_b, w0, Omega_k, n_s, nH,
inputPk)
#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}
#params = [ 'eps_f', 'f_star', 'S_star', 'A_C', 'alpha_nt', 'n_nt', 'beta_nt', 'gamma_mod0', 'gamma_mod_zslope', 'n_nt_mod', 'clump0', 'clump_zslope', 'x_clump', 'alpha_clump1', 'alpha_clump2' ]
#params = [ 'eps_f', 'f_star', 'clump0' ]
#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']
fish = {}
covar = {}
for survey in surveys:
if 'ROSAT' in survey:
ell = np.linspace(10, 700)
elif 'Chandra' in survey:
ell = np.linspace(10, 10000)
fish[survey] = xxfisher(ell, params, survey)
covar[survey] = np.linalg.inv(fish[survey])
#print(covar[survey])
'''
for i, p in enumerate(params) :
if p in priors.keys() :
fish[survey] += 1.0/priors[p]**2.0
'''
for i1, p1 in enumerate(params):
for i2, p2 in enumerate(params):
f = plt.figure(figsize=(5, 5))
ax = f.add_axes([0.20, 0.18, 0.72, 0.72])
if i1 != i2:
for survey in surveys:
cov = extract_submat(covar[survey], i1, i2)
ax = plot_error_ellipse(ax, cov, p1, p2, survey)
print(survey, p1, p2, cov)
# recompute the ax.dataLim
ax.relim()
# update ax.viewLim using the new dataLim
ax.autoscale_view()
ax.set_xlabel(param_label_dict[p1])
ax.set_ylabel(param_label_dict[p2])
#ax.set_xlim(param_lim_dict[p1])
#ax.set_ylim(param_lim_dict[p2])
#ax.set_xlabel(param_label_dict[p1]+'/'+param_label_dict[p1]+r'$({\rm fid)}$')
#ax.set_ylabel(param_label_dict[p2]+'/'+param_label_dict[p2]+r'$({\rm fid})$')
ax.legend(loc='best')
f.savefig("cov_" + p1 + "_" + p2 + ".png")
else:
continue
Omega_k=0.000000
n_s=0.972000
inputPk="../input_pk/wmap9_fid_matterpower_z0.dat"
nH = 2.45e21
'''
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 = 0
opt = 1
xx_power.init_cosmology(H0, Omega_M, Omega_b, w0, Omega_k, n_s, nH, inputPk,
opt)
def beam(ell, fwhm=13.7):
#convert fwhm from arcmin to radian
fwhm = math.radians(fwhm / 60.0)
sigma = fwhm / (np.sqrt(8.0 * np.log(2.0)))
bl = np.exp(ell * (ell + 1.0) * sigma**2)
return bl
def lnlike(theta, x, y, invcov):
'''
double alpha0; // fiducial : 0.18
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 = 2.4e+20
xx_power.init_cosmology(H0, Omega_M, Omega_b, w0, Omega_k, n_s, nH, inputPk)
shot_noise = 3.e-22
ell = 10.**np.linspace(np.log10(1.),np.log10(3.e4), 1000)
theta = [5.0,0.000000,0.026000,0.120000,1.000000,0.180000,0.800000,0.500000,0.100000,1.720000,0.195000,0.010000,0.800000,0.670000,0.730000,1.230000,0.880000,3.850000]
param_ind_dict = {'eps_f':0, 'eps_DM':1, 'f_star':2, 'S_star':3, 'A_C':4, 'gamma_mod0':8, 'gamma_mod_zslope':9, 'clump0':13, 'clump_zslope':14}
param_label_dict = {'eps_f':r'\epsilon_f', 'eps_DM':r'\epsilon_{DM}', 'f_star':r'f_\star', 'S_star':r'S_\star', 'A_C':r'A_C','gamma_mod0':r'\Gamma_0', 'gamma_mod_zslope':r'\beta_\Gamma', 'clump0':r'C_0', 'clump_zslope':r'\beta_C'}
rosat_ell, rosat_cl, rosat_var = read_data("../ROSAT/rosat_R6_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)
theta_bf = [ 9.94086977e+00, 3.19781381e-02, 1.98917895e-01, 2.85712644e-03,
-2.28851210e+01]
theta[param_ind_dict['eps_f']] = theta_bf[0]
theta[param_ind_dict['f_star']] = theta_bf[1]
theta[param_ind_dict['S_star']] = theta_bf[2]
theta[param_ind_dict['clump0']] = theta_bf[3]
shot_noise = 10.0**theta_bf[4]
f = plt.figure( figsize=(5,5) )
ax = f.add_axes([0.18,0.16,0.75,0.75])
cl = power (ell, theta)
cl *= ell*(ell+1)/(2.0*math.pi)
psn = np.full(ell.shape, shot_noise, dtype = np.float64)
psn *= ell*(ell+1)/(2.0*math.pi)
total = cl + psn
ax.plot (ell, total, ls = '-' )
ax.plot (ell, cl, ls = '--')
ax.plot (ell, psn, ls = ':')
ax.errorbar(rosat_ell, rosat_cl, yerr = rosat_cl_err, color='k', label=r"ROSAT")
ax.set_xlim ( 10, 3e4 )
#ax.set_ylim ( 1e-19, 1e-13)
ax.set_xlabel(r'$\ell$')
ax.set_ylabel(r'$\ell(\ell+1)C_{\ell}/2\pi\,[{\rm erg^{2}s^{-2}cm^{-4}str^{-2}}]$')
ax.set_xscale('log')
ax.set_yscale('log')
ax.legend(loc='best')
outname = '../plots/bf_xxpower.pdf'
f.savefig(outname)
f.clf()
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 = 2.4e+20
opt = 1
xx_power.init_cosmology(H0, Omega_M, Omega_b, w0, Omega_k, n_s, nH,
inputPk, opt)
shot_noise = 3.e-22
ell = 10.**np.linspace(np.log10(10.), np.log10(3e4), 40)
theta_fid = [
4.0, 3.e-5, 0.026000, 0.120000, 1.000000, 0.180000, 0.800000, 0.500000,
0.100000, 1.720000, 0.195000, 0.010000, 0.800000, 0.670000, 0.730000,
1.230000, 0.880000, 3.85000
]
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'$\epsilon_f$',
'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'$\alpha_{C2}$'
}
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'
]
#param_list = ['eps_f', 'f_star', 'S_star', 'clump0', 'alpha0']
#param_list = ['f_star', 'S_star', 'clump0']
#param_list = ['gamma_mod0']
params = ['eps_f']
planck_ell, planck_cl, planck_var = read_data(
"../Planck/planck_mask_hfi_R2_small_ell.txt")
planck_cl *= planck_ell * (planck_ell + 1.) / (2.0 * math.pi)
planck_cl_err = np.sqrt(planck_var) * planck_ell * (planck_ell +
1.) / (2.0 * math.pi)
for param in params:
param_ind = param_ind_dict[param]
param_fid = theta_fid[param_ind]
print(param_fid)
param_val_list = []
color_list = ['C0', 'C1', 'C2', 'C3', 'C4']
varlist = [0.1, 0.5, 1.0, 1.5, 2.0]
#for i in [0.01, 0.1, 1.0, 1.67]:
for i in [1.0]:
#for i in varlist:
param_val = param_fid * i
#param_val = i
param_val_list.append(param_val)
f = plt.figure(figsize=(5, 5))
ax = f.add_axes([0.18, 0.16, 0.75, 0.75])
#ax.errorbar(planck_ell, planck_cl, yerr = planck_cl_err, color='k', label=r"Planck")
cl_list = []
for counter, param_val in enumerate(param_val_list):
theta = theta_fid.copy()
theta[param_ind] = param_val
start = time.time()
cl = power(ell, theta)
end = time.time()
print("Elapsed time: %s" % (end - start))
cl *= ell * (ell + 1) / (2.0 * math.pi)
cl *= (1.e6 * Tcmb)**2
#cl *= Tcmb**2.0 * 1.e6
label_str = param_label_dict[param] + '$= %.3f $' % (param_val)
if param == 'eps_f':
label_str = param_label_dict[
param] + r'$= %.1f \times 10^{-6}$' % (param_val)
ax.plot(ell,
cl,
ls='-',
color=color_list[counter],
label=label_str)
ax.set_xlim(10, 3e3)
ax.set_xlabel(r'$\ell$')
ax.set_ylabel(r'$10^{12}\ell(\ell+1)C_{\ell}^{yy}/2\pi$')
ax.set_xscale('log')
ax.set_yscale('log')
ax.legend(loc='best')
outname = param + '_yy_power.png'
#outname = param+'_xx_power.png'
f.savefig(outname)
f.clf()
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 = 0.0
opt = 1
xx_power.init_cosmology(H0, Omega_M, Omega_b, w0, Omega_k, n_s, nH,
inputPk, opt)
ell = 10.**np.linspace(np.log10(10.), np.log10(1.e4), 31)
theta_fid = [
4.0, 3.e-5, 0.0250, 0.120000, 1.000000, 0.180000, 0.800000, 0.500000,
0.10000, 1.720000, 0.195000, 0.010000, 0.800000, 0.2, 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,
'alpha_clump': 14,
'beta_clump': 15,
'gamma_clump': 16
}
param_label_dict = {
'eps_f': r'$\epsilon_f$',
'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'$C_0$',
'alpha_clump': r'$\alpha_C$',
'beta_clump': r'$\beta_{C}$',
'gamma_clump': r'$\gamma_{C}$'
}
#rosat_ell, rosat_cl, rosat_var = read_data("../ROSAT/rosat_R4_R7_counts.txt")
rosat_ell, rosat_cl, rosat_var = read_data(
"../ROSAT/rosat_R4_R7_unabsorbed.txt")
#rosat_cl *= rosat_ell*(rosat_ell+1.)/(2.0*math.pi)
rosat_cl_err = np.sqrt(rosat_var)
#rosat_cl_err *= rosat_ell*(rosat_ell+1.)/(2.0*math.pi)
k_k, ell_k, beam_k, cl_k, cl_k_err = read_kolodzig()
cl_k /= beam_k
#cl_k *= ell_k*(ell_k+1)/(2.0*math.pi)
#params = [ 'eps_f', 'f_star', 'S_star', 'alpha_nt', 'n_nt', 'beta_nt', 'gamma_mod0', 'gamma_mod_zslope', 'clump0', 'alpha_clump', 'beta_clump', 'gamma_clump' ]
params = [
'eps_f', 'f_star', 'S_star', 'clump0', 'alpha_clump', 'beta_clump',
'gamma_clump', 'gamma_mod0'
]
#params = [ 'gamma_mod0' ]
param_values = {
#'eps_f':[4.0 ],
'eps_f': [2.0, 4.0, 6.0, 8.0],
'f_star': [0.015, 0.02, 0.025, 0.03],
'S_star': [0.06, 0.12, 0.24, 0.48],
'clump0': [0.1, 1.0, 3.0, 10.0],
'alpha_clump': [0.05, 1.0, 2.0, 4.0],
'beta_clump': [0.05, 1.0, 2.0, 4.0],
'gamma_clump': [2.0, 4.0, 8.0, 10.0],
'gamma_mod0': [-0.1, 0.1, 1.0, 1.6667]
}
for param in params:
param_ind = param_ind_dict[param]
param_val_list = param_values[param]
color_list = ['C0', 'C1', 'C2', 'C3']
ls_list = [':', '-', '--', '-.']
f = plt.figure(figsize=(4, 4))
ax = f.add_axes([0.21, 0.16, 0.75, 0.75])
#ax.errorbar(rosat_ell, rosat_cl, yerr = rosat_cl_err, color='k', fmt='o', label=r"ROSAT", markersize = 3)
#ax.errorbar(ell_k, cl_k, yerr = cl_k_err, label=r'Chandra', color='r', fmt='+', markersize=3)
cl_list = []
for counter, param_val in enumerate(param_values[param]):
theta = theta_fid.copy()
theta[param_ind] = param_val
print(theta[param_ind])
start = time.time()
cl = power(ell, theta)
end = time.time()
print("Elapsed time: %s" % (end - start))
#cl *= ell*(ell+1)/(2.0*math.pi)
#psn = psn*ell*(ell+1)/(2.0*math.pi)
#cl_total *= ell*(ell+1)/(2.0*math.pi)
#cl_list.append(cl)
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$'% (param_val+1)
ax.plot(ell,
cl,
ls='--',
color=color_list[counter],
label=label_str)
ax.set_xlim(30, 1e4)
ax.set_ylim(1e-25, 1e-19)
ax.set_xlabel(r'$\ell$')
#ax.set_ylabel(r'$\ell(\ell+1)C_{\ell}^{xx}/2\pi\,[{\rm cts^2 s^{-2}arcmin^{-4}}]$')
#ax.set_ylabel(r'$C_{\ell}\,[{\rm erg^{2}s^{-2}cm^{-4}sr^{-2}}]$')
ax.set_ylabel(r'$C_{\ell}\,[{\rm ergs^{2}s^{-2}cm^{-4}sr^{-2}}]$')
ax.set_xscale('log')
ax.set_yscale('log')
ax.legend(loc='lower left', prop={'size': 10})
#outname = '../plots/'+param+'_xx_power.pdf'
outname = param + '_xx_power.pdf'
f.savefig(outname)
f.clf()
def main():
# set cosmology and linear power spectrum
'''
H0=70.0
Omega_M=0.279000
Omega_b=0.046100
w0=-1.000000
Omega_k=0.000000
n_s=0.972000
inputPk="../input_pk/wmap9_fid_matterpower_z0.dat"
nH = 2.4e21
opt = 1
'''
H0 = 67.32117
Omega_M = 0.3158
Omega_b = 0.0490
w0 = -1.000000
Omega_k = 0.000000
n_s = 0.96605
inputPk = "../input_pk/planck_2018_test_matterpower.dat"
nH = 2.4e21
opt = 1
xx_power.init_cosmology(H0, Omega_M, Omega_b, w0, Omega_k, n_s, nH,
inputPk, opt)
shot_noise = 0.00
#ell = 10.**np.linspace(np.log10(1.),np.log10(1.e5),40)
ell = np.linspace(1.0, 10000.0, 10000)
theta_fid = [
4.0, 3.e-5, 0.026000, 0.120000, 1.000000, 0.180000, 0.800000, 0.500000,
0.100000, 1.720000, 0.195000, 0.010000, 0.800000, 0.670000, 0.730000,
1.230000, 0.880000, 3.85000
]
param_ind_dict = {
'eps_f': 0,
'eps_DM': 1,
'f_star': 2,
'S_star': 3,
'A_C': 4,
'alpha_nt': 5,
'n_nt': 6,
'beta_nt': 7,
'gamma_mod0': 8,
'gamma_mod_zslope': 9,
'x_break': 10,
'x_smooth': 11,
'n_nt_mod': 12,
'clump0': 13,
'clump_zslope': 14,
'x_clump': 15,
'alpha_clump1': 16,
'alpha_clump2': 17
}
param_label_dict = {
'eps_f': r'$\epsilon_f$',
'eps_DM': r'$\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}$'
}
'''
rosat_ell, rosat_cl, rosat_var = read_data("../ROSAT/rosat_R4_R7_mask_hfi_R2_small_ell.txt")
rosat_cl *= rosat_ell*(rosat_ell+1.)/(2.0*math.pi)
rosat_cl_err = np.sqrt(rosat_var)*rosat_ell*(rosat_ell+1.)/(2.0*math.pi)
'''
params = [
'eps_f', 'f_star', 'S_star', 'alpha_nt', 'n_nt', 'beta_nt',
'gamma_mod0', 'gamma_mod_zslope', 'clump0', 'clump_zslope', 'x_clump',
'alpha_clump1', 'alpha_clump2'
]
f = plt.figure(figsize=(5, 5))
ax = f.add_axes([0.18, 0.16, 0.75, 0.75])
#ax.errorbar(rosat_ell, rosat_cl, yerr = rosat_cl_err, color='k', label=r"ROSAT")
cl = power(ell, theta_fid)
nside = 2048
fwhm = math.radians(12. / 60.)
recon_map = hp.sphtfunc.synfast(cl,
nside,
fwhm=fwhm,
pixwin=True,
new=True)
cmap = hp.mollview(recon_map)
hp.graticule()
plt.savefig('recon_map.png')
cl_recon = hp.sphtfunc.anafast(recon_map)
ell_recon = (np.arange(cl_recon.size)).astype(float)
print(ell_recon, cl_recon)
cl *= ell * (ell + 1) / (2.0 * math.pi)
cl_recon *= ell_recon * (ell_recon + 1) / (2.0 * math.pi)
ax.plot(ell, cl, ls='-', label='original')
ax.plot(ell_recon, cl_recon, ls='-', label='healpix')
ax.set_xlim(10, 3e3)
ax.set_ylim(1e-19, 1e-15)
ax.set_xlabel(r'$\ell$')
ax.set_ylabel(
r'$\ell(\ell+1)C_{\ell}^{xx}/2\pi\,[{\rm erg^{2}s^{-2}cm^{-4}str^{-2}}]$'
)
ax.set_xscale('log')
ax.set_yscale('log')
ax.legend(loc='best')
#outname = '../plots/'+param+'_xx_power.pdf'
outname = 'recon_xx_power.png'
f.savefig(outname)
f.clf()
def main():
# set cosmology and linear power spectrum
'''
H0=70.0
Omega_M=0.279000
Omega_b=0.046100
w0=-1.000000
Omega_k=0.000000
n_s=0.972000
inputPk="../input_pk/wmap9_fid_matterpower_z0.dat"
nH = 2.4e21
opt = 1
'''
H0 = 67.32117
Omega_M = 0.3158
Omega_b = 0.0490
w0 = -1.000000
Omega_k = 0.000000
n_s = 0.96605
inputPk = "../input_pk/planck_2018_test_matterpower.dat"
nH = 0
opt = 1
xx_power.init_cosmology(H0, Omega_M, Omega_b, w0, Omega_k, n_s, nH,
inputPk, opt)
shot_noise = 3.0e-21
ell = 10.**np.linspace(np.log10(10.), np.log10(3.e4), 31)
theta_best = [
2.3932, 3.e-5, 0.0254, 0.09520, 1.000000, 0.45200, 0.841000, 1.628000,
0.102400, 1.720000, 0.195000, 0.010000, 1.6960, 0.9227, 2.4602, 4.5937
]
theta_best = np.array(theta_best, dtype='double')
param_ind_dict = {
'eps_f': 0,
'eps_DM': 1,
'f_star': 2,
'S_star': 3,
'A_C': 4,
'A_nt': 5,
'B_nt': 6,
'gamma_nt': 7,
'gamma_mod0': 8,
'gamma_mod_zslope': 9,
'x_break': 10,
'x_smooth': 11,
'clump0': 12,
'alpha_clump': 13,
'beta_clump': 14,
'gamma_clump': 15
}
param_label_dict = {
'eps_f': r'$\epsilon_f/10^{-6}$',
'eps_DM': r'$\epsilon_{DM}$',
'f_star': r'$f_\star$',
'S_star': r'$S_\star$',
'A_C': r'$A_C$',
'A_nt': r'$A_{nt}$',
'B_nt': r'$B_{nt}$',
'gamma_nt': r'$\gamma_{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}$'
}
rosat_ell, rosat_cl, rosat_var = read_data(
"../ROSAT/rosat_R4_R7_chandra_unabsorbed.txt")
#rosat_cl *= rosat_ell*(rosat_ell+1.)/(2.0*math.pi)
rosat_cl_err = np.sqrt(rosat_var)
#k_k, ell_k, beam_k, cl_k, cl_k_err = read_kolodzig ()
#cl_k /= beam_k
#cl_k *= ell_k*(ell_k+1)/(2.0*math.pi)
#params = [ 'eps_f', 'f_star', 'S_star', 'alpha_nt', 'n_nt', 'beta_nt', 'gamma_mod0', 'gamma_mod_zslope', 'clump0', 'clump_zslope', 'x_clump', 'alpha_clump1', 'alpha_clump2' ]
f = plt.figure(figsize=(4, 4))
ax = f.add_axes([0.21, 0.16, 0.75, 0.75])
ax.errorbar(rosat_ell,
rosat_cl,
yerr=rosat_cl_err,
color='k',
fmt='o',
label=r"ROSAT")
#ax.errorbar(ell_k, cl_k, yerr = cl_k_err, label=r'Kolodzig+18')
start = time.time()
cl = power(ell, theta_best)
#cl_up = power (ell, theta_cup)
#cl_dn = power (ell, theta_cdn)
end = time.time()
print("Elapsed time: %s" % (end - start))
with open('best_fit.txt', 'w') as outf:
print('# ell C_ell [ergs^2/s^2/cm^4/sr^2]', file=outf)
for i in np.arange(len(ell)):
print(ell[i], cl[i], file=outf)
#cl *= ell*(ell+1)/(2.0*math.pi)
ax.plot(ell, cl, ls='-', label='best fit model')
ax.set_xlim(100, 3e4)
#ax.set_ylim ( 1e-19, 1e-14)
ax.set_xlabel(r'$\ell$')
ax.set_ylabel(r'$C_{\ell}^{xx},[{\rm erg^{2}s^{-2}cm^{-4}sr^{-2}}]$')
#ax.set_ylabel(r'$\ell(\ell+1)C_{\ell}^{xx}/2\pi\,[{\rm cts^{2}s^{-2}arcmin^{-2}}]$')
ax.set_xscale('log')
ax.set_yscale('log')
ax.legend(loc='upper left')
#outname = '../plots/'+param+'_xx_power.pdf'
outname = 'bf_xx_power.png'
f.savefig(outname)
f.clf()
def main():
# set cosmology and linear power spectrum
H0 = 70.0
Omega_M = 0.279000
Omega_b = 0.046100
w0 = -1.000000
Omega_k = 0.000000
n_s = 0.972000
inputPk = "../input_pk/wmap9_fid_matterpower_z0.dat"
nH = 0
opt = 1
xx_power.init_cosmology(H0, Omega_M, Omega_b, w0, Omega_k, n_s, nH,
inputPk, opt)
shot_noise = 0.00
ell = 10.**np.linspace(np.log10(10.), np.log10(3.e4), 31)
theta_fid = [
4.0, 3.e-5, 0.0250, 0.120000, 1.000000, 0.180000, 0.800000, 0.500000,
0.10000, 1.720000, 0.195000, 0.010000, 0.800000, 0.2, 1.0, 6.0, 3.0
]
param_ind_dict = {
'eps_f': 0,
'eps_DM': 1,
'f_star': 2,
'S_star': 3,
'A_C': 4,
'alpha_nt': 5,
'n_nt': 6,
'beta_nt': 7,
'gamma_mod0': 8,
'gamma_mod_zslope': 9,
'x_break': 10,
'x_smooth': 11,
'n_nt_mod': 12,
'clump0': 13,
'alpha_clump': 14,
'beta_clump': 15,
'gamma_clump': 16
}
param_label_dict = {
'eps_f': r'$\epsilon_f$',
'eps_DM': r'$\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$',
'alpha_clump': r'$\alpha_C$',
'beta_clump': r'$\beta_{C}$',
'gamma_clump': r'$\gamma_{C}$'
}
#rosat_ell, rosat_cl, rosat_var = read_data("../ROSAT/rosat_R4_R7_mask_hfi_R2_small_ell.txt")
#rosat_cl *= rosat_ell*(rosat_ell+1.)/(2.0*math.pi)
#rosat_cl_err = np.sqrt(rosat_var)*rosat_ell*(rosat_ell+1.)/(2.0*math.pi)
#params = ['eps_f', 'f_star', 'S_star', 'alpha_nt', 'n_nt', 'beta_nt', 'gamma_mod0', 'gamma_mod_zslope', 'clump0', 'clump_zslope', 'x_clump', 'alpha_clump1', 'alpha_clump2' ]
params = ['gamma_mod0']
param_values = {
'eps_f': [1.0, 2.0, 4.0, 6.0, 8.0],
'f_star': [0.01, 0.015, 0.02, 0.025, 0.03],
'S_star': [0.03, 0.06, 0.12, 0.24, 0.48],
'clump0': [0.1, 1.0, 3.0, 10.0, 30.0],
'alpha_clump': [0.02, 0.05, 1.0, 2.0, 4.0],
'beta_clump': [0.02, 0.05, 1.0, 2.0, 4.0],
'gamma_clump': [1.0, 2.0, 4.0, 8.0, 10.0],
#'gamma_mod0':[0.0, 0.1, 1.0, 1.2, 1.667]
'gamma_mod0': [0.001, 0.01, 0.1, 0.3, 1.0]
}
mvir = 3.e13
#mvir = 10.**np.linspace(13.0, 15.8, 25)
z = 0.01
#obs_list = ['mgas', 'lx', 'tx', 'ysz']
profile_list = ['density']
profile_label = {
'pressure': r'$P(r/R_{500})\,{\rm [keV cm^-3]}$',
'density': r'$\rho_{\rm gas}/\rho_{\rm crit}$'
}
x = 10**np.linspace(-3., 0.5, 100)
mgas, m500 = mgas_m([mvir], z, theta_fid)
for param in params:
pro = {}
f = {}
ax = {}
for o in profile_list:
f[o] = plt.figure(figsize=(5, 5))
ax[o] = f[o].add_axes([0.18, 0.16, 0.75, 0.75])
param_ind = param_ind_dict[param]
param_fid = theta_fid[param_ind]
param_val_list = []
color_list = ['C0', 'C1', 'C2', 'C3', 'C4']
for i in [0.1, 0.5, 1.0, 1.5, 2.0]:
#for i in [1.0]:
param_val = param_fid * i
param_val_list.append(param_val)
cl_list = []
for counter, param_val in enumerate(param_values[param]):
theta = theta_fid.copy()
theta[param_ind] = param_val
#pro['pressure'] = pressure_profile(x, mvir, z, theta)
pro['density'] = density_profile(x, mvir, z, theta)
#print(x, pro['density'])
label_str = param_label_dict[param] + r'$= %.3f $' % (param_val)
if param == 'eps_f':
label_str = param_label_dict[
param] + r'$= %.1f \times 10^{-6}$' % (param_val)
for o in profile_list:
ax[o].plot(x, pro[o], label=label_str)
arnaud_pro = P_gnfw(x, m500, z, *mod_param['A10'])
robs, rho_obs, rho_obs_err = read_observed_density()
ax['density'].errorbar(robs,
rho_obs,
yerr=rho_obs_err,
color='k',
label='M17')
#ax['pressure'].plot(x, arnaud_pro, color='k', label=r'Arnaud+10')
for o in profile_list:
ax[o].set_xlabel(r'$r/R_{500c}$')
ax[o].set_ylabel(profile_label[o])
ax[o].legend(loc='lower left')
ax[o].set_xscale('log')
ax[o].set_yscale('log')
outname = param + '_' + o + '_profile.png'
f[o].savefig(outname)
f[o].clf()
def power_model (x, params) :
# set cosmology and linear power spectrum
H0=70.000000
Omega_M=0.279000
Omega_b=0.046100
w0=-1.000000
Omega_k=0.000000
n_s=0.972000
nH = 1.e22
inputPk="../input_pk/wmap9_fid_matterpower_z0.dat"
xx_power.init_cosmology(H0, Omega_M, Omega_b, w0, Omega_k, n_s, nH, inputPk)
'''
double alpha0; // fiducial : 0.18
double n_nt; // fiducial : 0.80
double beta; // fiducial : 0.50
double eps_f; // fiducial : 3.97e-6
double eps_DM; // fiducial : 0.00
double f_star; // fiducial : 0.026
double S_star; // fiducial : 0.12
double A_C; // fiducial : 1.00
double gamma_mod0; // fiducial : 0.10
double gamma_mod_zslope; // fiducial : 1.72
double x_break; // fiducial : 0.195
double x_smooth; // fiducial : 0.01
double n_nt_mod; // fiducial : 0.80
'''
#alpha0, n_nt, beta, eps_f, eps_DM, f_star, S_star, A_C, gamma_mod0, gamma_mod_zslope, x_break, x_smooth, n_nt_mod = theta
#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)
#eps_f, f_star, S_star, gamma_mod0, gamma_mod_zslope, clump0, clump_zslope = theta
eps_f, f_star, S_star, clump0, noise = params[0], params[1], params[2], params[3], param[4]
#fix DM profile
eps_DM = 0.006
A_C = 1.0
#fix non-thermal pressure term
alpha0 = 0.18
n_nt = 0.80
beta = 0.50
x_smooth = 0.01
n_nt_mod = 0.80
x_break = 0.195
gamma_mod0 = 0.10
gamma_mod_zslope = 1.72
#clumping terms
#clump0 = 0.0
clump_zslope = 0.0
x_clump = 1.23
alpha_clump1 = 0.88
alpha_clump2 = 3.85
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, noise)
model = xx_power.return_xx_power(x) # [erg cm^-2 s^-1 str^-1]^2
return model
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, 0.0800, 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,
}
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}$'
}
#rosat_ell, rosat_cl, rosat_var = read_data("../ROSAT/rosat_R4_R7_mask_hfi_R2_small_ell.txt")
#rosat_cl *= rosat_ell*(rosat_ell+1.)/(2.0*math.pi)
#rosat_cl_err = np.sqrt(rosat_var)*rosat_ell*(rosat_ell+1.)/(2.0*math.pi)
#params = ['eps_f', 'f_star', 'S_star', 'alpha_nt', 'n_nt', 'beta_nt', 'gamma_mod0', 'gamma_mod_zslope', 'clump0', 'clump_zslope', 'x_clump', 'alpha_clump1', 'alpha_clump2' ]
params = ['eps_f', 'f_star', 'clump0']
redshift, dlz = np.linspace(-4, np.log10(3.0), 10, retstep=True)
redshift = 10**redshift
print(dlz)
my_cosmo = cosmo.Cosmology()
hubble = my_cosmo.cosmo.h
print(hubble)
mvir, dlm = np.linspace(13, 16, 20, retstep=True)
flux, dlf = np.linspace(-20, -10, 20, retstep=True)
mvir = 10**mvir
flux = 10**flux
Nsperster = np.zeros(flux.shape)
redo = False
if redo:
for iv, f in enumerate(flux):
for iz, z in enumerate(redshift):
dVdz = my_cosmo.cosmo.differential_comoving_volume(
z).value * hubble**3
s = []
for mass in mvir:
ff, m500 = xray_flux(mass, z, theta_fid)
s.append(ff)
s = np.array(s)
mlim = np.interp(f, s, mvir)
#h = hmf.MassFunction(z=z, Mmin=13, Mmax=16, dlog10m=0.1)
h = hmf.MassFunction(z=z)
Nm = 0.0
for im, mass in enumerate(h.m):
if mass >= mlim:
Nm += h.dlog10m * h.dndlog10m[im] * dVdz * z * dlz
Nsperster[iv] += Nm
np.save("logNlogS.npy", Nsperster)
surveys = ["CDFS", "COSMOS", "XXL", "S82", "RASS"]
areas = [0.25, 2.0, 50.0, 31.0, 4.0 * 3.141592 * ster2sqdeg * 0.25]
sens = [0.66e-15, 1.7e-15, 5e-15, 0.87e-15, 5.6e-13]
fig = plt.figure(figsize=(4, 4))
ax = fig.add_axes([0.21, 0.16, 0.75, 0.75])
print(areas, sens)
ax.scatter(areas, sens, s=1.0, marker='o')
for i, txt in enumerate(surveys):
ax.annotate(txt, (areas[i], sens[i]))
ax.set_xlabel(r'Area [deg$^2$]')
ax.set_ylabel(r'flux [erg/s/cm$^2$]')
ax.set_xscale('log')
ax.set_yscale('log')
#ax.set_xlim(1e-2, 1e5 )
#ax.set_ylim(1e-16, 1e-12)
#ax.legend(loc='best')
plt.show()
fig.savefig("wedding_test.png")
fig.clf()
def main():
# set cosmology and linear power spectrum
'''
H0=70.0
Omega_M=0.279000
Omega_b=0.046100
w0=-1.000000
Omega_k=0.000000
n_s=0.972000
inputPk="../input_pk/wmap9_fid_matterpower_z0.dat"
nH = 2.4e21
opt = 1
'''
H0 = 67.32117
Omega_M = 0.3158
Omega_b = 0.0490
w0 = -1.000000
Omega_k = 0.000000
n_s = 0.96605
inputPk = "../input_pk/planck_2018_test_matterpower.dat"
nH = 2.4e21
opt = 1
xx_power.init_cosmology(H0, Omega_M, Omega_b, w0, Omega_k, n_s, nH,
inputPk, opt)
shot_noise = 0.00
ell = 10.**np.linspace(np.log10(10.), np.log10(3.e4), 31)
theta_fid = [
4.0, 3.e-5, 0.0500, 0.120000, 1.000000, 0.180000, 0.800000, 0.500000,
0.10000, 1.720000, 0.195000, 0.010000, 0.800000, 0.9, 1.0, 6.0, 3.0
]
param_ind_dict = {
'eps_f': 0,
'eps_DM': 1,
'f_star': 2,
'S_star': 3,
'A_C': 4,
'alpha_nt': 5,
'n_nt': 6,
'beta_nt': 7,
'gamma_mod0': 8,
'gamma_mod_zslope': 9,
'x_break': 10,
'x_smooth': 11,
'n_nt_mod': 12,
'clump0': 13,
'clump_zslope': 14,
'x_clump': 15,
'alpha_clump1': 16,
'alpha_clump2': 17
}
param_label_dict = {
'eps_f': r'$\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()
