def psvariance(R, W='th',low = 1e-7,high=100,z=0):
"""
Variance of the linear power spectrum. Use the
power spectrum from COMPOS (Ziang Yen). Numerically
and obtains the variance.
Parameters
----------
R: float
Smoothing radius
W: string, optional
Form of the normalised window function.
'th' for top-hat profile and 'ga' for
gaussian.
low: float, optional
Lower integration limit to get the variance
high: float, optional
Upper integration limit
z: float, optional
Redshift. Defualt value is 0.
Returns
-------
variance: float
Variance of the smoothed ps.
error: float
Error incurred in variance from numerical integration
"""
import numpy as np
from scipy.integrate import quad
from compos import const, matterps
const.initializecosmo(z=z)
integrand = lambda k: (k*window_th(k,R))**2*matterps.normalizedmp(k)
integral = quad(integrand,low,high)
variance = integral[0]/(2*np.pi)**2
error = integral[1]/(2*np.pi)**2
return variance, error
# This script calculates linear matter power spectrum at
# z = 0 with a given set of cosmologycal parameters#
import numpy as np
import matplotlib.pyplot as mp
from compos import const, matterps
const.initializecosmo()
k = np.linspace(-4, np.log10(6.71600e+02), 10000)
k = 10**k * const.cosmo['h']
p = matterps.normalizedmp(k)
# p_nw = matterps.normalizedmpnw(k)
mp.loglog(k / const.cosmo['h'], p * const.cosmo['h']**3)
plottextl = mp.xlim()[0] * 2
plottextd = mp.ylim()[0] * 2
mp.text(plottextl,
plottextd,
r'$h = $' + str(const.cosmo['h']) + ', $\Omega_mh^2$ = ' +
str(const.cosmo['omega_0'] * const.cosmo['h']**2) +
', $\Omega_bh^2$ = ' +
str(const.cosmo['omega_b'] * const.cosmo['h']**2),
fontsize=15)
mp.ylabel('P(k)(h$^{-3}$Mpc$^{-3}$)')
mp.xlabel('k (h Mpc$^{-1}$)')
mp.savefig('../../results/matterps/mps.jpg')
mp.savefig('../../results/matterps/mps.pdf')
mp.show()
const.initializecosmo()
x = np.linspace(-4, 1, 1000)
k = 10 ** x
d2 = growthfactor.growfunc_z(10)
d1 = growthfactor.growfunc_z(5)
pyhalofit.supparams(growthf=d2)
np2 = pyhalofit.nlpowerspec(k, 10)
pyhalofit.supparams(growthf=d1)
np1 = pyhalofit.nlpowerspec(k, 5)
pyhalofit.supparams()
np0 = pyhalofit.nlpowerspec(k, 0)
t = np.zeros((2, np.size(k)))
p2 = matterps.normalizedmp(k, 10)
p1 = matterps.normalizedmp(k, 5)
p0 = matterps.normalizedmp(k, 0)
mp.loglog(k/const.cosmo['h'], np0, label='nonlinear, z=0')
mp.loglog(k/const.cosmo['h'], p0, label='linear, z=0')
mp.loglog(k/const.cosmo['h'], np1, label='nonlinear,z=5')
mp.loglog(k/const.cosmo['h'], p1, label='linear,z=5')
mp.loglog(k/const.cosmo['h'], np2, label='nonlinear,z=10')
mp.loglog(k/const.cosmo['h'], p2, label='linear,z=10')
mp.text(0.001, 0.1, '$h = 0.7, \Omega_mh^2$=' +
str(const.cosmo['omega_0']*const.cosmo['h']**2) +
',$ \Omega_qh^2 = $' + str(const.cosmo['omega_q'] *
const.cosmo['h']**2)+',', fontsize=15)
mp.text(0.001, 0.01, '$w_0 = -1,w_1 = 0$', fontsize=15)
mp.legend(fontsize=10)
# This script calculates linear matter power spectrum at
# z = 0 with a given set of cosmologycal parameters#
import numpy as np
import matplotlib.pyplot as mp
from compos import const, matterps
const.initializecosmo()
k = np.linspace(-4, np.log10(6.71600e+02), 10000)
k = 10 ** k * const.cosmo['h']
p = matterps.normalizedmp(k)
# p_nw = matterps.normalizedmpnw(k)
mp.loglog(k / const.cosmo['h'], p * const.cosmo['h'] ** 3)
plottextl = mp.xlim()[0] * 2
plottextd = mp.ylim()[0] * 2
mp.text(plottextl, plottextd, r'$h = $' + str(const.cosmo['h']) +
', $\Omega_mh^2$ = '+str(const.cosmo['omega_0'] *
const.cosmo['h'] ** 2) +
', $\Omega_bh^2$ = '+str(const.cosmo['omega_b'] *
const.cosmo['h'] ** 2), fontsize=15)
mp.ylabel('P(k)(h$^{-3}$Mpc$^{-3}$)')
mp.xlabel('k (h Mpc$^{-1}$)')
mp.savefig('../../results/matterps/mps.jpg')
mp.savefig('../../results/matterps/mps.pdf')
mp.show()
const.initializecosmo()
x = np.linspace(-4, 1, 1000)
k = 10**x
d2 = growthfactor.growfunc_z(10)
d1 = growthfactor.growfunc_z(5)
pyhalofit.supparams(growthf=d2)
np2 = pyhalofit.nlpowerspec(k, 10)
pyhalofit.supparams(growthf=d1)
np1 = pyhalofit.nlpowerspec(k, 5)
pyhalofit.supparams()
np0 = pyhalofit.nlpowerspec(k, 0)
t = np.zeros((2, np.size(k)))
p2 = matterps.normalizedmp(k, 10)
p1 = matterps.normalizedmp(k, 5)
p0 = matterps.normalizedmp(k, 0)
mp.loglog(k / const.cosmo['h'], np0, label='nonlinear, z=0')
mp.loglog(k / const.cosmo['h'], p0, label='linear, z=0')
mp.loglog(k / const.cosmo['h'], np1, label='nonlinear,z=5')
mp.loglog(k / const.cosmo['h'], p1, label='linear,z=5')
mp.loglog(k / const.cosmo['h'], np2, label='nonlinear,z=10')
mp.loglog(k / const.cosmo['h'], p2, label='linear,z=10')
mp.text(0.001,
0.1,
'$h = 0.7, \Omega_mh^2$=' +
str(const.cosmo['omega_0'] * const.cosmo['h']**2) +
',$ \Omega_qh^2 = $' +
str(const.cosmo['omega_q'] * const.cosmo['h']**2) + ',',