Initialize
[1]:
# import necessary modules
# uncomment to get plots displayed in notebook
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from classy_sz import Class
import math
[2]:
# font = {'size' : 16, 'family':'STIXGeneral'}
# axislabelfontsize='large'
# matplotlib.rc('font', **font)
# plt.rcParams["figure.figsize"] = [8.0,8.0]
font = {'family':'STIXGeneral'}
axislabelfontsize='large'
matplotlib.rc('font', **font)
plt.rcParams.update({
"text.usetex": True,
"font.family": "sans-serif",
"font.sans-serif": ["Helvetica"]})
# a simple conversion from cl's to dl's
def l_to_dl(lp):
return lp*(lp+1.)/2./np.pi
[3]:
Planck = {'name': 'Planck',
'do_cib': 1, 'do_tsz': 1, 'do_cibxtsz': 1,
'freq_cib': [100., 143., 217., 353., 545., 857.],
'cc': np.array([1.076, 1.017, 1.119, 1.097, 1.068, 0.995, 0.960]),
'cc_cibmean': np.array([1.076, 1.017, 1.119, 1.097, 1.068, 0.995, 0.960]),
'freq_cibmean': np.array([100., 143., 217., 353., 545., 857.]),
'fc': np.ones(7),
}
[4]:
combo_cosmo = {
'H0':67.556,
'omega_b':0.022032,
'omega_cdm':0.12038,
# 'sigma8': 0.831,
'ln10^{10}A_s': 3.047,
'n_s': 0.9665,
'tau_reio':0.0925,
# Take fixed value for primordial Helium (instead of automatic BBN adjustment)
'YHe':0.246,
}
[5]:
%%time
M = Class()
output_str = 'cib_cib_1h,cib_cib_2h'
output_str += ',lens_cib_1h,lens_cib_2h'
output_str += ',gal_gal_1h,gal_gal_2h'
output_str += ',gal_lens_1h,gal_lens_2h'
output_str += ',gallens_lens_1h,gallens_lens_2h'
output_str += ',gallens_gallens_1h,gallens_gallens_2h'
output_str += ',tSZ_gallens_1h,tSZ_gallens_2h'
output_str += ',gal_gallens_1h,gal_gallens_2h'
output_str += ',gal_cib_1h,gal_cib_2h'
output_str += ',gallens_cib_1h,gallens_cib_2h'
output_str += ',tSZ_1h,tSZ_2h'
output_str += ',lens_1h,lens_2h'
output_str += ',tSZ_gal_1h,tSZ_gal_2h'
output_str += ',tSZ_lens_1h,tSZ_lens_2h'
output_str += ',tSZ_cib_1h,tSZ_cib_2h'
M.set({'output':output_str})
M.set(combo_cosmo)
M.set({
'mass function' : 'T08M200c',
'use_maniyar_cib_model':1,
'maniyar_cib_etamax' : 5.12572945e-01,
'maniyar_cib_zc' : 1.5,
'maniyar_cib_tau' : 8.25475287e-01,
'maniyar_cib_fsub' : 0.134*np.log(10.),
'Most_efficient_halo_mass_in_Msun' : 5.34372069e+12,
'Size_of_halo_masses_sourcing_CIB_emission' : 1.5583436676980493,
#for the Lsat tabulation:
'freq_min': 100,
'freq_max': 600,
'dlogfreq' : 0.3,
'concentration parameter':'B13', # this sets it to 5
'n_z_L_sat' :50,
'n_m_L_sat' :50,
'n_nu_L_sat':10,
'use_nc_1_for_all_halos_cib_HOD': 1,
'sub_halo_mass_function' : 'TW10',#'JvdB14',
'M_min_subhalo_in_Msun' : 1e5, # 1e5 see https://github.com/abhimaniyar/halomodel_cib_tsz_cibxtsz/blob/master/Cell_cib.py
'use_redshift_dependent_M_min': 0,
'M_min' : 1e10,
'M_max' : 1e15,
'z_min' : 0.05,
'z_max' : 5.,
'ell_min': 2.,
'ell_max':8000,
'dlogell':0.2,
'ndim_redshifts': 50,
'ndim_masses':100,
'has_cib_flux_cut': 0,
'hm_consistency':1,
# 'epsabs_L_sat': 1e-40,
# 'epsrel_L_sat': 1e-9,
# "P_k_max_1/Mpc": 50.,
# 'k_max_for_pk_class_sz':50.
'use_fft_for_profiles_transform' : 1,
'damping_1h_term' : 0,
# 'x_min_gas_density_fftw' : 1e-5,
# 'x_max_gas_density_fftw' : 1e2,
# 'n_k_pressure_profile' :50, # this is l/ls # default 80
# 'n_m_pressure_profile' :30, # default: 100, decrease for faster
# 'n_z_pressure_profile' :30, # default: 100, decrease for faster
# 'x_min_gas_pressure_fftw' : 1e-5,
# 'x_max_gas_pressure_fftw' : 1e2,
# 'k_min_samp_fftw' : 1e-3,
# 'k_max_samp_fftw' : 1e3,
# 'N_samp_fftw' : 1024,
})
M.set({
'cib_frequency_list_num' : 1,
'cib_frequency_list_in_GHz' : '150',
})
M.compute_class_szfast()
/Users/boris/venvdir/class_sz_312_brew/lib/python3.12/site-packages/IPython/core/magics/execution.py:1330: DeprecationWarning: compute_class_szfast is deprecated. Use initialize_classy_szfast instead.
out = eval(code_2, glob, local_ns)
/Users/boris/venvdir/class_sz_312_brew/lib/python3.12/site-packages/mcfit/mcfit.py:130: UserWarning: use backend='jax' if desired
warnings.warn("use backend='jax' if desired")
CPU times: user 3min 13s, sys: 2.41 s, total: 3min 15s
Wall time: 23.4 s
[6]:
specs_dict = {
'cib x cib' : M.cl_cib_cib()['150x150'],
'cib x kc' : M.cl_lens_cib()['150'],
'g x cib' : M.cl_gal_cib()['150'],
'g x g' : M.cl_gg(),
'g x kc' : M.cl_kg(),
'kg x kg' : M.cl_kg_kg(),
'kg x g' : M.cl_ggamma(),
'y x y' : M.cl_sz(),
'g x y' : M.cl_yg(),
'y x kc' : M.cl_y_kcmb(),
'kc x kc' : M.cl_kk(),
'cib x y' : M.cl_tSZ_cib()['150'],
'kg x kc' : M.cl_kg_k(),
'kg x y' : M.cl_ykg(),
'kg x cib' : M.cl_kg_cib()['150'],
}
[7]:
plot_keys = ['kc x kc',
'y x kc',
'y x y',
'cib x kc',
'cib x y',
'cib x cib',
'g x kc',
'g x y',
'g x cib',
'g x g',
'kg x kc',
'kg x y',
'kg x cib',
'kg x g',
'kg x kg']
plot_keys_latex = [r'$\kappa_\mathrm{cmb}$ x $\kappa_\mathrm{cmb}$',r'$y$ x $\kappa_{cmb}$',
r'$y$ x $y$',r'$\mathrm{CIB}$ x $\kappa_\mathrm{cmb}$',
r'$\mathrm{CIB}$ x $y$',r'$\mathrm{CIB}$ x $\mathrm{CIB}$',
r'$\delta_g$ x $\kappa_\mathrm{cmb}$',r'$\delta_g$ x $y$',r'$\delta_g$ x $\mathrm{CIB}$',r'$\delta_g$ x $\delta_g$',r'$\kappa_{g}$ x $\kappa_\mathrm{cmb}$',r'kg x $y$',r'$\kappa_{g}$ x $\mathrm{CIB}$',
r'$\kappa_{g}$ x $\delta_g$',r'$\kappa_{g}$ x $\kappa_{g}$']
[8]:
from matplotlib.lines import Line2D
# cl_cib_cib = M.cl_cib_cib()
# freq = ['100', '143', '217', '353', '545', '857']
# # freq = ['545']
# # nu1, nu2 = 4, 4
# # plot_Cell(ell, cl1h_cib, cl2h_cib, nu1, nu2, freq, 'CIB')
# two_halo = cl2h_cib
# one_halo = cl1h_cib
# faccib = 1.
plot_dim = int(1/2*(-1 + np.sqrt(1 + 8*len(plot_keys))))
# plot_dim = len(freq)
fig, axes = plt.subplots(figsize=(19, 19),
sharex=True,
#sharey=True,
ncols=plot_dim,
nrows=plot_dim)
plt.subplots_adjust(left=0.1,
bottom=0.1,
right=0.9,
top=0.9,
wspace=0.0,
hspace=0.0)
ik = 0
for i in range(plot_dim):
for j in range(plot_dim):
if i==0 and j==(plot_dim-1):
ax = axes[i, j]
custom_lines = [Line2D([0], [0], color='k', lw=2,ls='-.'),
Line2D([0], [0], color='k', lw=2,ls='--'),
Line2D([0], [0], color='k', lw=2,ls='-')]
ax.legend(custom_lines, [r'$\mathrm{1h}$', r'$\mathrm{2h}$', r'$\mathrm{Total}$'],
fontsize=25,frameon=False)
# ax.legend()
ax.axis('off')
elif i<j:
axes[i, j].axis('off')
else:
ax = axes[i, j]
clsp = specs_dict[plot_keys[ik]]
if j==0:
# ax.set_ylabel(r'$\mathrm{C_l}\: [\mathrm{Jy}^2\: \mathrm{sr}^{-1}]$', fontsize=12)
ax.set_ylabel(plot_keys_latex[ik].split(' x ')[0], fontsize=25)
if i==(plot_dim-1):
ax.set_xlabel(plot_keys_latex[ik].split(' x ')[1], fontsize=25)
ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)
ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)
ax.xaxis.set_ticks_position('both')
ax.yaxis.set_ticks_position('both')
plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=18)
plt.setp(ax.get_xticklabels(), fontsize=18)
ax.grid( visible=True, which="both", alpha=0.2, linestyle='--')
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_xlim(5,7000)
if clsp:
# print(plot_keys[ik],clsp)
l = np.asarray(clsp['ell'])
# ax.plot(l[l>5.],np.asarray(clsp['1h']/l_to_dl(l))[l>5.],ls='-.',c='k',label='class_sz 1h ')
ax.plot(l,np.asarray(clsp['1h']/l_to_dl(l)),ls='-.',c='k',label='class_sz 1h',lw=0.7)
ax.plot(l,clsp['2h']/l_to_dl(l),ls='--',c='k',label='class_sz 2h',lw=0.7)
ax.plot(l,clsp['2h']/l_to_dl(l)+np.asarray(clsp['1h']/l_to_dl(l)),ls='-',c='k',label='class_sz Total',lw=1)
# if freqs=='100' and freqsp=='100':
# ax.set_ylim(5e-3,5)
if plot_keys[ik] == 'kc x kc':
ax.set_ylim(1e-10,4e-7)
if plot_keys[ik] == 'y x kc':
ax.set_ylim(1e-5,8.5e-5)
ax.set_yscale("linear")
if plot_keys[ik] == 'y x y':
ax.set_ylim(1e-6,2e-4)
if plot_keys[ik] == 'kg x kg':
ax.set_ylim(1e-12,1e-8)
if plot_keys[ik] == 'kg x y':
ax.set_ylim(5e-10,1e-6)
ik+=1
fig.suptitle('CLASS_SZ COMBO PLOT',fontsize=20)
fig.tight_layout()
# plt.savefig('figures/class_sz_combo.pdf')
