<jupyter_start><jupyter_text>Training data used in creating the emulatorWe provide the power spectrum boost parameters required to train the emulator for Modified Gravity Cosmology.<jupyter_code>import numpy as np 
import matplotlib.pylab as plt
from pandas.plotting import scatter_matrix
import pandas as pd
################ I/O #####################
dataDir = "../mgemu/data/" ## Data folder<jupyter_output><empty_output><jupyter_text>Cosmological parameters in the Latin-hypercube (LHC) designThe training design consists of 50 Cosmologies with varying 3 $\Lambda$CDM parameters and 2 $f(R)$ Hu-Sawicki model parameters. $\theta = \left[\Omega_m h^2, n_s, \sigma_8, f_{R_0}, n \right]$LHC Sampling ensures a good coverage in the parameter space. We choose logarithm of $f_{R_0}$ to deal with large dynamical range.<jupyter_code>paramIn = dataDir + 'mg_log_val_2.design'
parameter_array = np.loadtxt(paramIn)[:50, :]
parameter_array[:, 3] = np.log10(parameter_array[:, 3])
allLabels = ['${\Omega}_m h^2$', '$n_s$', '${\sigma}_8$', '$log(f_{R_0})$', '$n$']
df = pd.DataFrame(parameter_array, columns=allLabels)
scatter_matrix(df, figsize=(10, 10), diagonal='kde')
plt.suptitle('Cosmological parameters in the LHC design')
plt.show()<jupyter_output><empty_output><jupyter_text>Enhancement in the matter power spectra, *PMG(k)/PLCDM(k)* for *f(R)* Hu-Sawicki modelBoost in the matter power spectra are calculated from COLA simulations, for 100 timesteps between $0 \leq z \leq 50$.<jupyter_code>snap_ID = 92 # 100 redshift snapshots in ID 0-99
fileIn = dataDir + 'ratiosbinsnew_' + str(snap_ID) + '.txt'
az = np.loadtxt(dataDir + 'timestepsCOLA.txt', skiprows=1) ## Redshift corresponding to snapshot IDs

loadFile = np.loadtxt(fileIn)
PmPl_all = loadFile[:, 1:].T
kvals = loadFile[:,0]
    
print('Training snapshot: ', str(snap_ID), PmPl_all.shape, parameter_array.shape)  
print(20*'=*')
plt.figure(1, figsize=(12, 9) )
for i in range(50):
    plt.plot(kvals, PmPl_all[i])
plt.xscale('log')
plt.ylabel(r'$P_MG(k)/P_{LCDM}(k)$', fontsize=23)
plt.xlabel(r'$k$', fontsize=23)
plt.show()<jupyter_output><empty_output>
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