def ff(a, gamma=0.55):
# RSD function to be used for numerical derivatives
c = copy.deepcopy(rf.experiments.cosmo)
c['gamma'] = gamma
H, r, D, f = rf.background_evolution_splines(c)
z = 1. / a - 1.
return f(z)
def Frsd(a, gamma=0.55, mu=1.):
# RSD function to be used for numerical derivatives
c = copy.deepcopy(rf.experiments.cosmo)
c['gamma'] = gamma
H, r, D, f = rf.background_evolution_splines(c)
z = 1. / a - 1.
# F_RSD
b = 1. #np.sqrt(1.+z)
Frsd = (b + mu**2. * f(z))**2. * D(z)**2.
return Frsd
def Frsd_deriv(a, gamma=0.55, mu=1.):
# Analytic deriv. used in baofisher code
H, r, D, f = rf.background_evolution_splines(rf.experiments.cosmo)
z = 1. / a - 1.
b = 1. #np.sqrt(1.+z)
#ff = np.poly1d( np.polyfit(a, f(z), deg=6) )
#df_da = ff.deriv(1)(a)
df_da = np.gradient(f(z), a[1] - a[0])
#dD_da = D(z)*f(z)/a
dD_da = np.gradient(D(z), a[1] - a[0])
# Get derivative of F_RSD w.r.t. f(z)
dD_df = dD_da / df_da
#dD_df = np.log(a)
dF_df = 2. * (b + f(z)*mu**2.) * mu**2. * D(z)**2. \
+ 2. * (b + f(z)*mu**2.)**2. * D(z) * dD_df
# Chain rule, for deriv. w.r.t. gamma
logOma = np.log(f(z)) / gamma
return dF_df, dF_df * f(z) * logOma
#!/usr/bin/python
"""
Re-bin Euclid n(z)
"""
import numpy as np
import pylab as P
import scipy.integrate
import radiofisher as rf
# Precompute cosmo fns.
cosmo_fns = rf.background_evolution_splines(rf.experiments.cosmo)
HH, rr, DD, ff = cosmo_fns
expt = rf.experiments_galaxy.EuclidRef
def vol(zmin, zmax):
"""
Calculate volume of redshift bin.
"""
C = 3e5
_z = np.linspace(zmin, zmax, 1000)
vol = C * scipy.integrate.simps(rr(_z)**2. / HH(_z), _z)
vol *= 4. * np.pi
return vol
# Load Euclid n(z) and b(z)
e = rf.experiments_galaxy.load_expt(expt)
nz = expt['nz']
zmin = expt['zmin']
zmin = dat[0]
bias = dat[4]
#zs = np.concatenate((zmin, [zmin[1] - zmin[0],]))
#zc = 0.5 * (zs[:-1] + zs[1:])
# Single bin between 800 - 1000 MHz
zs = np.array([1420. / 1000. - 1., 1420. / 800. - 1.])
zc = 0.5 * (zs[:-1] + zs[1:])
# Define kbins (used for output)
kbins = np.arange(0., 5. * cosmo['h'], 0.1 * cosmo['h']) # Bins of 0.1 h/Mpc
################################################################################
# Precompute cosmological functions and P(k)
cosmo_fns = rf.background_evolution_splines(cosmo)
# Load P(k) and split into smooth P(k) and BAO wiggle function
k_in, pk_in = np.genfromtxt("slosar_pk_z0.dat").T # Already in non-h^-1 units
cosmo['pk_nobao'], cosmo['fbao'] = rf.spline_pk_nobao(k_in, pk_in)
cosmo['k_in_max'] = np.max(k_in)
cosmo['k_in_min'] = np.min(k_in)
# Switch-off massive neutrinos, fNL, MG etc.
mnu_fn = None
transfer_fn = None
Neff_fn = None
switches = []
H, r, D, f = cosmo_fns
#!/usr/bin/python
"""
Project a Fisher matrix for f, H etc. to alpha = f.H, used for the KSZ paper.
"""
import numpy as np
import pylab as P
import radiofisher as rf
import os
# Precompute cosmo fns.
H, r, D, f = rf.background_evolution_splines(rf.experiments.cosmo)
# Load precomputed Fisher matrix
fname = "/home/phil/oslo/bao21cm/fisher_bao_EuclidRef.dat"
F0 = np.genfromtxt(fname).T
# Get parameter names from header
ff = open(fname, 'r')
line = ff.readline()[2:-1]
ff.close()
pnames = line.split(", ")
# Load redshift bin array
zc = np.array([
0.65,
0.75,
0.85,
0.95,
1.05,
1.15,
1.25,
