[1]:

%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import numpy as np

[2]:

import sys
sys.path.append('../')
from CEmulator.Emulator import Pkmm_CEmulator

##### import the emulator for the original cosmology [only one single massive neutrino]
csstemu = Pkmm_CEmulator(verbose=True, neutrino_mass_split='single')
klists  = np.logspace(-2, 1, 500)
zlists  = np.array([0.0, 1.0, 2.0, 3.0])

Loading the PkcbLin emulator...
Using 513 training samples.
Loading the PknnLin emulator...
Using 512 training samples [remove c0001 (no massive neutrino)].
The neutrino mass is treated as a single massive component.
Loading the Bkcb emulator...
Using 129 training samples.
Loading the Bkcb_halofit emulator...
Using 129 training samples.
Loading the Bkcb_lin2hmcode emulator...
Using 513 training samples.
Loading the Bkcb_hmcode2020 emulator...
Using 129 training samples.

\(P(k)\) compare with HMCode

[3]:

%%time
csstemu.set_cosmos(Omegac=0.25, As=2e-9, mnu=0.15)
pkcbce = csstemu.get_pknl(z=zlists, k=klists,
                          Pcb=True, lintype='Emulator',
                          nltype='hmcode2020')
print(pkcbce.shape)
pkmmce = csstemu.get_pknl(z=zlists, k=klists,
                           Pcb=False, lintype='Emulator',
                           nltype='hmcode2020')
print(pkmmce.shape)

(4, 500)
(4, 500)
CPU times: user 21.1 ms, sys: 2.3 ms, total: 23.4 ms
Wall time: 21.9 ms

[4]:

%%time
camb_results = csstemu.get_camb_results(z=zlists, kmax=10.0, non_linear='mead2020')
pkcbhm = camb_results.get_matter_power_interpolator(nonlinear=True,
                                                    var1='delta_nonu', var2='delta_nonu',
                                                    hubble_units=True, k_hunit=True).P(z=zlists, kh=klists)
pkmmhm = camb_results.get_matter_power_interpolator(nonlinear=True,
                                                    var1='delta_tot', var2='delta_tot',
                                                    hubble_units=True, k_hunit=True).P(z=zlists, kh=klists)

CPU times: user 6.44 s, sys: 29.2 ms, total: 6.47 s
Wall time: 6.49 s

[5]:

from matplotlib import style
style.library.keys()

[5]:

dict_keys(['seaborn-v0_8-talk', 'dark_background', 'grayscale', 'seaborn-v0_8-poster', 'seaborn-v0_8-bright', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-white', 'classic', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', '_mpl-gallery-nogrid', 'bmh', '_mpl-gallery', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-notebook', 'seaborn-v0_8-dark', 'fast', 'seaborn-v0_8-ticks', 'Solarize_Light2', 'seaborn-v0_8-dark-palette', '_classic_test_patch', 'tableau-colorblind10', 'seaborn-v0_8-muted', 'seaborn-v0_8', 'fivethirtyeight', 'seaborn-v0_8-whitegrid', 'seaborn-v0_8-deep', 'ggplot', 'article', 'mypresentation', 'presentation'])

[6]:

gridp = plt.GridSpec(2, 1, hspace=0.05)
with plt.style.context('article'):
    ax0 = plt.subplot(gridp[0,0])
    for iz in range(len(zlists)):
        l1, = plt.plot(klists, pkcbce[iz], label=r'$z=%.0f$'%(zlists[iz]))
        l2, = plt.plot(klists, pkcbhm[iz], 'k:', lw=2.0)
    leg1 = plt.legend([l1, l2], ['Emulator', 'HMCODE-2020'], loc=0, frameon=False)
    leg2 = plt.legend(loc=3, frameon=False)
    ax0.add_artist(leg1)
    plt.grid(True)
    plt.ylabel(r'$P_{\rm cb}(k)\, [h^{-3}\,{\rm Mpc}^3]$')
    plt.xscale('log')
    plt.yscale('log')
    plt.ylim(2e-1, 9e4)
    ax0.set_xticklabels([])
    ax1 = plt.subplot(gridp[1,0])
    for iz in range(len(zlists)):
        l1, = plt.plot(klists, pkcbce[iz]/pkcbhm[iz], label=r'$z=%.0f$'%(zlists[iz]))
    plt.ylabel(r'$P_{\rm cb}^{\rm Emu}/P_{\rm cb}^{\rm HMCODE}$')
    plt.xlabel(r'$k\, [h\,{\rm Mpc}^{-1}]$')
    plt.xscale('log')
    plt.ylim(0.9401, 1.0599)
    plt.grid(True)
../_images/notebook_test-pkmm_6_0.png 
[7]:

gridp = plt.GridSpec(2, 1, hspace=0.05)
with plt.style.context('article'):
    ax0 = plt.subplot(gridp[0,0])
    for iz in range(len(zlists)):
        l1, = plt.plot(klists, pkmmce[iz], label=r'$z=%.0f$'%(zlists[iz]))
        l2, = plt.plot(klists, pkmmhm[iz], 'k:', lw=2.0)
    leg1 = plt.legend([l1, l2], ['Emulator', 'HMCODE-2020'], loc=0, frameon=False)
    leg2 = plt.legend(loc=3, frameon=False)
    ax0.add_artist(leg1)
    plt.grid(True)
    plt.ylabel(r'$P_{\rm tot}(k)\, [h^{-3}\,{\rm Mpc}^3]$')
    plt.xscale('log')
    plt.yscale('log')
    plt.ylim(2e-1, 9e4)
    ax0.set_xticklabels([])
    ax1 = plt.subplot(gridp[1,0])
    for iz in range(len(zlists)):
        l1, = plt.plot(klists, pkmmce[iz]/pkmmhm[iz], label=r'$z=%.0f$'%(zlists[iz]))
    plt.ylabel(r'$P_{\rm tot}^{\rm Emu}/P_{\rm tot}^{\rm HMCODE}$')
    plt.xlabel(r'$k\, [h\,{\rm Mpc}^{-1}]$')
    plt.xscale('log')
    plt.ylim(0.9401, 1.0599)
    plt.grid(True)
../_images/notebook_test-pkmm_7_0.png

Match \(\sigma_8\) and Compare with HMCode

[8]:

%%time
csstemu.set_cosmos(As=None, sigma8=0.73, sigma8type='CAMB')
As_camb = csstemu.Cosmo.As

The As is set to 1.714500e-09 (sigma8=0.730000) to match the input sigma8=0.730000.
CPU times: user 9.25 s, sys: 24.8 ms, total: 9.28 s
Wall time: 9.3 s

[9]:

%%time
csstemu.set_cosmos(As=None, sigma8=0.73, sigma8type='CLASS')
As_class = csstemu.Cosmo.As

The As is set to 1.715719e-09 (sigma8=0.730000) to match the input sigma8=0.730000.
CPU times: user 6.62 s, sys: 21.1 ms, total: 6.64 s
Wall time: 6.65 s

[10]:

%%time
csstemu.set_cosmos(As=None, sigma8=0.73, sigma8type='Emulator')
As_emu = csstemu.Cosmo.As

The As is set to 1.715915e-09 (sigma8=0.729921) to match the input sigma8=0.730000.
CPU times: user 15 ms, sys: 1 ms, total: 16 ms
Wall time: 15.6 ms

[11]:

print('CAMB/CLASS:', As_camb/As_class)
print('Emu/CLASS :', As_emu/As_class)

CAMB/CLASS: 0.9992894916805883
Emu/CLASS : 1.0001144405163018

The agreement of \(\sigma_8\) among Emulator CLASS and CAMB is less than 0.2% for cosmology in our emulation space.

[12]:

pkcbce = csstemu.get_pknl(z=zlists, k=klists,
                          Pcb=True, lintype='Emulator',
                          nltype='halofit')
pkmmce = csstemu.get_pknl(z=zlists, k=klists,
                           Pcb=False, lintype='Emulator',
                           nltype='halofit')

cosmo_class = csstemu.get_cosmo_class(z=zlists, kmax=10.0, non_linear='HMCODE')
pkcbhm  = np.zeros((len(zlists), len(klists)))
pkmmhm = np.zeros((len(zlists), len(klists)))
h0 = csstemu.Cosmo.h0
for iz in range(len(zlists)):
    pkcbhm [iz] = np.array([cosmo_class.pk_cb(z=zlists[iz], k=ik*h0)*h0*h0*h0 for ik in klists])
    pkmmhm[iz] = np.array([cosmo_class.pk(z=zlists[iz], k=ik*h0)*h0*h0*h0 for ik in klists])

[13]:

gridp = plt.GridSpec(2, 1, hspace=0.05)
with plt.style.context('article'):
    ax0 = plt.subplot(gridp[0,0])
    for iz in range(len(zlists)):
        l1, = plt.plot(klists, pkcbce[iz], label=r'$z=%.0f$'%(zlists[iz]))
        l2, = plt.plot(klists, pkcbhm[iz], 'k:', lw=2.0)
    leg1 = plt.legend([l1, l2], ['Emulator', 'HMCODE'], loc=0, frameon=False)
    leg2 = plt.legend(loc=3, frameon=False)
    ax0.add_artist(leg1)
    plt.grid(True)
    plt.ylabel(r'$P_{\rm cb}(k)\, [h^{-3}\,{\rm Mpc}^3]$')
    plt.xscale('log')
    plt.yscale('log')
    plt.ylim(2e-1, 9e4)
    ax0.set_xticklabels([])
    ax1 = plt.subplot(gridp[1,0])
    for iz in range(len(zlists)):
        l1, = plt.plot(klists, pkcbce[iz]/pkcbhm[iz], label=r'$z=%.0f$'%(zlists[iz]))
    plt.ylabel(r'$P_{\rm cb}^{\rm Emu}/P_{\rm cb}^{\rm HMCODE}$')
    plt.xlabel(r'$k\, [h\,{\rm Mpc}^{-1}]$')
    plt.xscale('log')
    plt.ylim(0.9401, 1.0599)
    plt.grid(True)
../_images/notebook_test-pkmm_15_0.png 
[14]:

gridp = plt.GridSpec(2, 1, hspace=0.05)
with plt.style.context('article'):
    ax0 = plt.subplot(gridp[0,0])
    for iz in range(len(zlists)):
        l1, = plt.plot(klists, pkmmce[iz], label=r'$z=%.0f$'%(zlists[iz]))
        l2, = plt.plot(klists, pkmmhm[iz], 'k:', lw=2.0)
    leg1 = plt.legend([l1, l2], ['Emulator', 'HMCODE'], loc=0, frameon=False)
    leg2 = plt.legend(loc=3, frameon=False)
    ax0.add_artist(leg1)
    plt.grid(True)
    plt.ylabel(r'$P_{\rm mm}(k)\, [h^{-3}\,{\rm Mpc}^3]$')
    plt.xscale('log')
    plt.yscale('log')
    plt.ylim(2e-1, 9e4)
    ax0.set_xticklabels([])
    ax1 = plt.subplot(gridp[1,0])
    for iz in range(len(zlists)):
        l1, = plt.plot(klists, pkmmce[iz]/pkmmhm[iz], label=r'$z=%.0f$'%(zlists[iz]))
    plt.ylabel(r'$P_{\rm mm}^{\rm Emu}/P_{\rm mm}^{\rm HMCODE}$')
    plt.xlabel(r'$k\, [h\,{\rm Mpc}^{-1}]$')
    plt.xscale('log')
    plt.ylim(0.9401, 1.0599)
    plt.grid(True)
../_images/notebook_test-pkmm_16_0.png 
[15]:

csstemu.set_cosmos(As=None, sigma8=0.73, sigma8type='CAMB')

camb_results = csstemu.get_camb_results(z=zlists, kmax=10.0, non_linear='mead2020')
pkfunc = camb_results.get_matter_power_interpolator(nonlinear=True,
                                                    hubble_units=True, k_hunit=True,
                                                    var1='delta_nonu', var2='delta_nonu')
pkcbhm20 = pkfunc.P(zlists, klists)
pkfunc = camb_results.get_matter_power_interpolator(nonlinear=True,
                                                    hubble_units=True, k_hunit=True,
                                                    var1='delta_tot', var2='delta_tot')
pkmmhm20 = pkfunc.P(zlists, klists)

The As is set to 1.714500e-09 (sigma8=0.730000) to match the input sigma8=0.730000.

[16]:

gridp = plt.GridSpec(2, 1, hspace=0.05)
with plt.style.context('article'):
    ax0 = plt.subplot(gridp[0,0])
    for iz in range(len(zlists)):
        l1, = plt.plot(klists, pkcbce[iz], label=r'$z=%.0f$'%(zlists[iz]))
        l2, = plt.plot(klists, pkcbhm20[iz], 'k:', lw=2.0)
    leg1 = plt.legend([l1, l2], ['Emulator', 'HMCODE-2020'], loc=0, frameon=False)
    leg2 = plt.legend(loc=3, frameon=False)
    ax0.add_artist(leg1)
    plt.grid(True)
    plt.ylabel(r'$P_{\rm cb}(k)\, [h^{-3}\,{\rm Mpc}^3]$')
    plt.xscale('log')
    plt.yscale('log')
    plt.ylim(2e-1, 9e4)
    ax0.set_xticklabels([])
    ax1 = plt.subplot(gridp[1,0])
    for iz in range(len(zlists)):
        l1, = plt.plot(klists, pkcbce[iz]/pkcbhm20[iz], label=r'$z=%.0f$'%(zlists[iz]))
    plt.ylabel(r'$P_{\rm cb}^{\rm Emu}/P_{\rm cb}^{\rm HMCODE-2020}$')
    plt.xlabel(r'$k\, [h\,{\rm Mpc}^{-1}]$')
    plt.xscale('log')
    plt.ylim(0.9401, 1.0599)
    plt.grid(True)
../_images/notebook_test-pkmm_18_0.png 
[17]:

gridp = plt.GridSpec(2, 1, hspace=0.05)
with plt.style.context('article'):
    ax0 = plt.subplot(gridp[0,0])
    for iz in range(len(zlists)):
        l1, = plt.plot(klists, pkmmce[iz], label=r'$z=%.0f$'%(zlists[iz]))
        l2, = plt.plot(klists, pkmmhm20[iz], 'k:', lw=2.0)
    leg1 = plt.legend([l1, l2], ['Emulator', 'HMCODE-2020'], loc=0, frameon=False)
    leg2 = plt.legend(loc=3, frameon=False)
    ax0.add_artist(leg1)
    plt.grid(True)
    plt.ylabel(r'$P_{\rm mm}(k)\, [h^{-3}\,{\rm Mpc}^3]$')
    plt.xscale('log')
    plt.yscale('log')
    plt.ylim(2e-1, 9e4)
    ax0.set_xticklabels([])
    ax1 = plt.subplot(gridp[1,0])
    for iz in range(len(zlists)):
        l1, = plt.plot(klists, pkmmce[iz]/pkmmhm20[iz], label=r'$z=%.0f$'%(zlists[iz]))
    plt.ylabel(r'$P_{\rm mm}^{\rm Emu}/P_{\rm mm}^{\rm HMCODE-2020}$')
    plt.xlabel(r'$k\, [h\,{\rm Mpc}^{-1}]$')
    plt.xscale('log')
    plt.ylim(0.9401, 1.0599)
    plt.grid(True)
../_images/notebook_test-pkmm_19_0.png 
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