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* Add imports * Add field LOS paths * Add basic flow model * Edit script * Add nb * Add nb * Update nb * Add some docs * Add RA reading * Add imoprts * Updates to the flow model * Update script * Bring back A2 * Update imports * Update imports * Add Carrick to ICs * Add Carrick boxsize * Add Carrick and fix minor bugs * Add Carrick box * Update script * Edit imports * Add fixed flow! * Update omega_m and add it * Update nb * Update nb * Update nb * Remove old print statements * Update params * Add thinning of chains * Add import * Add flow validation script * Add submit script * Add ksmooth * Update nb * Update params * Update script * Update string * Move where distributions are defined * Add density bias parameter * Add lognorm mean * Update scripts * Update script
4 KiB
4 KiB
$P(k, H_0)$¶
Quick notebook to see how the power spectrum depends on $H_0$.
In [1]:
import numpy as np
import matplotlib.pyplot as plt
import camb
from camb import model
%matplotlib inline
In [19]:
def get_pk(h):
pars = camb.CAMBparams()
pars.set_cosmology(H0=h*100, ombh2=0.04825 * h**2, omch2=(0.307 - 0.04825) * h**2)
pars.InitPower.set_params(ns=0.9611)
pars.set_matter_power(redshifts=[0.], kmax=40)
#Non-Linear spectra (Halofit)
pars.NonLinear = model.NonLinear_both
results = camb.get_results(pars)
results.calc_power_spectra(pars)
kh_nonlin, z_nonlin, pk_nonlin = results.get_matter_power_spectrum(minkh=1e-3, maxkh=50, npoints = 200)
return kh_nonlin, pk_nonlin[0]
kh_nonlin, pk_nonlin_reference = get_pk(0.705)
In [25]:
plt.figure()
for h in [0.65, 0.70, 0.75]:
__, pk = get_pk(h)
plt.plot(kh_nonlnie, pk / pk_nonlin_reference, label=r"$h = {}$".format(h))
plt.legend()
plt.xscale("log")
plt.xlabel(r"$k ~ [h / \mathrm{Mpc}]$")
plt.ylabel(r"$P(k, h) / P(k, h=0.705)$")
plt.tight_layout()
plt.savefig("../plots/pk_h0_dependence.png", dpi=450)
plt.show()
In [ ]: