<jupyter_start><jupyter_text>Checking simulation cosmologies<jupyter_code>import numpy as np
import matplotlib.pyplot as plt<jupyter_output><empty_output><jupyter_text>Abacus emulator_planck Fiducial + perturbationsThe emulator_planck_{00..04} boxes provide a central cosmology and four perturbations: σ8±5% and H0±5%. Thus, these 5 boxes form a “plus sign” of cosmologies.The perturbations are listed here. Note that since the cosmologies are chosen in the space of physical densities, changes to H0 result in changes to Ωm,ΩΛ.<jupyter_code>sims = [0,1,2,3,4]
h0 = [0.673, 0.673, 0.673, 0.643, 0.703]
OmegaDE = [0.686, 0.686, 0.686, 0.656, 0.712]
OmegaM = np.array([0.314, 0.314, 0.314, 0.344, 0.288])
sigma8 = np.array([0.83, 0.78, 0.88, 0.83, 0.83])
x_axis = (sigma8**1.6)*((OmegaM/0.3)**1.2)
x_axis<jupyter_output><empty_output><jupyter_text>none are low enough  AbacusCosmos<jupyter_code>abacus_cosmologies = np.loadtxt('AbacusCosmos_cosmologies.dat')
abacus_cosmologies
h = abacus_cosmologies[:,-2]/100.

ombh2 = abacus_cosmologies[:,0]
omcdmh2 = abacus_cosmologies[:,1]
omh2 = ombh2 + omcdmh2
om = omh2/(h**2)

sigma_8 = abacus_cosmologies[:,-3]<jupyter_output><empty_output><jupyter_text>![cosmology.png](attachment:cosmology.png)<jupyter_code>x_axis = (sigma_8**1.6)*((om/0.3)**1.2)
# x_axis.sort()
x_axis<jupyter_output><empty_output><jupyter_text>![cosmology.png](attachment:cosmology.png) ωm ≡ Ωmh2 abacus_cosmologies header:  ombh2, omcdmh2, w0, ns, sigma_8, H0, N_eff<jupyter_code>zipped_cosmologies = list(zip(abacus_cosmologies, x_axis))
zipped_cosmologies.sort(key=lambda x:x[1])
zipped_cosmologies[:10]
w0 = [entry[0][2] for entry in zipped_cosmologies[:10]]
ns = [entry[0][3] for entry in zipped_cosmologies[:10]]
H0 = [entry[0][-2] for entry in zipped_cosmologies[:10]]
OmegaCDMh2 = [entry[0][1] for entry in zipped_cosmologies[:10]]
print(w0)
print(ns)
print(H0)
print(OmegaCDMh2)<jupyter_output>[-0.6612194, -0.7959591, -0.8901921, -0.787883, -0.9692914, -0.6548324, -1.083356, -1.042555, -0.8656196, -0.7647829]
[0.9835354, 0.9571653, 0.9764701, 0.9873686, 0.9603143, 0.9592352, 0.9704725, 0.9730763, 0.9823772, 0.9823038]
[63.33729, 65.84599, 67.36219, 66.66885, 68.60432, 62.47385, 72.28562, 70.54661, 66.11561, 62.99374]
[0.1044778, 0.1100992, 0.1100714, 0.1063219, 0.1114661, 0.108539, 0.1139937, 0.1130902, 0.114769, 0.1096392]<jupyter_text>Mira-Titan Universe https://arxiv.org/pdf/1508.02654.pdf  0.12 ≤ ωm ≤ 0.155 (1)0.0215 ≤ ωb ≤ 0.0235 (2)0.7 ≤ σ8 ≤ 0.9 (3)0.55 ≤ h ≤ 0.85<jupyter_code>sigma8_lowest = 0.7
Omh2_lowest = 0.12
h_highest = 0.85
# h_highest = 0.7

Om_lowest = Omh2_lowest / (h_highest**2)
# Om_lowest=0.3

x_axis_lowest = (sigma8_lowest**1.6)*((Om_lowest/0.3)**1.2)
x_axis_lowest
Om_lowest<jupyter_output><empty_output><jupyter_text>11 models here: https://cosmology.alcf.anl.gov/transfer/miratitan<jupyter_code>Ocdm = np.array([0.2076,0.1718,0.2785,0.1658,0.2971,0.2200,0.1933,0.3643,0.1997,0.3276,0.2590])
Obh2 = np.array([0.02306,0.02239,0.02172,0.02350,0.02283,0.02258,0.02217,0.02150,0.02328,0.02261,0.02194])
h = np.array([0.6833,0.7833,0.6500,0.8500,0.5833,0.7100,0.8167,0.5500,0.7500,0.6167,0.7167])
sigma8 = np.array([0.7000,0.7222,0.7444,0.7667,0.7889,0.8000,0.8111,0.8333,0.8556,0.8778,0.9000])
models=[8,10,9,5,4,0,7,6,2,1,3]

Ob = Obh2/(h**2)
Om = Ob + Ocdm
x_axis = (sigma8**1.6)*((Om/0.3)**1.2)
zipped = list(zip(x_axis,models) )
zipped.sort()
zipped<jupyter_output><empty_output>
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