def __init__(self,file='MICE_matterpower',dir='',bias=1.8,mun=0,beta=0.4,sfog=0,amc=0.0,sigt=6.,sigr=10.,mult=1.,sigs=15.,gam=-1.7): dir = 'BAOtemplates/' self.f0 = loadtxt(dir+'xi0'+file+str(beta)+str(sfog)+str(sigt)+str(sigr)+str(sigs)+str(mun)+'.dat').transpose() self.f2 = loadtxt(dir+'xi2'+file+str(beta)+str(sfog)+str(sigt)+str(sigr)+str(sigs)+str(mun)+'.dat').transpose() self.f4 = loadtxt(dir+'xi4'+file+str(beta)+str(sfog)+str(sigt)+str(sigr)+str(sigs)+str(mun)+'.dat').transpose() self.f0sm = loadtxt(dir+'xi0sm'+file+str(beta)+str(sfog)+str(sigt)+str(sigr)+str(sigs)+str(mun)+'.dat').transpose() self.f2sm = loadtxt(dir+'xi2sm'+file+str(beta)+str(sfog)+str(sigt)+str(sigr)+str(sigs)+str(mun)+'.dat').transpose() self.f4sm = loadtxt(dir+'xi4sm'+file+str(beta)+str(sfog)+str(sigt)+str(sigr)+str(sigs)+str(mun)+'.dat').transpose() from Cosmo import distance if file == 'Challenge_matterpower': d = distance(.31,.69) if file == 'MICE_matterpower' or file == 'Pk_MICEcosmology_z0_Plin_Pnowig': d = distance(.25,.75) self.d = d self.beta = beta self.bias = bias self.gam = gam
def xi0dfile_dewig(r,file='Challenge_matterpower',dir='',sig=8.,max=51,ns=.95,sigz=0,pw='n'):
from scipy.integrate import quad
from Cosmo import distance
#f = open('/Users/ashleyr/BOSS/spec/camb_MWcos.dat')
#print dir
mult = 1.
dir = 'powerspectra/'
if file=='Challenge_matterpower' or file == 'TSPT_out':
om = 0.31
lam = 0.69
h = .676
nindex = .963
ombhh = .022
if file == 'MICE_matterpower':
om = 0.25
lam = .75
h = .7
ombhh = .044*0.7*.7
nindex = .949
f = open(dir+file+'.dat').readlines()
s = simulate(omega=om,lamda=lam,h=h,nindex=nindex,ombhh=ombhh)
k0 = float(f[0].split()[0])
k1 = float(f[1].split()[0])
ldk = log(k1)-log(k0)
#print ldk
xi0 = 0
xism0 = 0
#print max
dmk = r/10.
k = float(f[0].split()[0])
if file == 'camb_Nacc':
norm = 1.
else:
norm = float(f[0].split()[1])/s.Psmooth(k,0)*mult
#print norm
b = 2.
if sigz != 0:
z = .8
d = distance(.25,.75)
sigzc = d.cHz(z)*sigz
for i in range(0,len(f)-1):
k = float(f[i].split()[0])
pk = float(f[i].split()[1])*mult
pksm = s.Psmooth(k,0)*norm
dpk = pk-pksm
damp = exp(-.5*k*k*sig**2.)
pk0 = (dpk*damp+pksm)
pksm0 = pksm
dk = ldk*k
xi0 += dk*k*k*pk0*sph_jn(0,k*r)*exp(-1.*dmk*k**2.)
xism0 += dk*k*k*pksm0*sph_jn(0,k*r)*exp(-1.*dmk*k**2.)
return xi0/(2.*pi*pi),xism0/(2.*pi*pi)
def createSourcesrd_ari(sample, tile, date, ii, zmin=.5, zmax=1.1, datadir=''):
'''
prepare sv files for paircounts
'''
#from healpix import healpix,radec2thphi
from random import random
from Cosmo import distance
d = distance(
om, 1. - om
) #cosmology assumed in final BOSS analyses, make sure this conforms to current
#h = healpix()
file = sample + tile + '_' + date
fr = fitsio.read(datadir + file + '_' + str(ii) + '_clustering.ran.fits')
wz = (fr['Z'] > zmin) & (fr['Z'] < zmax)
zl = fr[wz]['Z']
cdl = np.zeros(len(zl))
for i in range(0, len(zl)):
cdl[i] = d.dc(zl[i])
sral = np.sin(np.radians(fr[wz]['RA']))
cral = np.cos(np.radians(fr[wz]['RA']))
sdecl = np.sin(np.radians(fr[wz]['DEC']))
cdecl = np.cos(np.radians(fr[wz]['DEC']))
wl = np.ones(len(cdl))
print(str(len(cdl)) + ' random objects going out for paircounts')
zw = '_zm' + str(zmin) + 'zx' + str(zmax)
gf = 'g' + file + zw
rf = 'r' + file + str(ii) + zw
fdo = open(dirpcadw + rf + 'pcadw.dat', 'w')
for i in range(0, len(cdl)):
fdo.write(
str(sral[i]) + ' ' + str(cral[i]) + ' ' + str(sdecl[i]) + ' ' +
str(cdecl[i]) + ' ' + str(cdl[i]) + ' ' + str(wl[i]) + '\n')
fdo.close()
fo = open('dopc' + rf + '.sh', 'w')
fo.write('#!/bin/bash\n')
fo.write('./pp2pt_Dmufb ' + gf + ' ' + rf + ' \n')
fo.write('./pp2pt_Dmufb ' + rf + ' ' + rf + ' \n')
fo.close()
return rf
def plotvolz():
pp = PdfPages('volz.pdf')
from Cosmo import distance
d = distance(.3,.7)
vl = []
zl = []
dz = .01
z = .01
while z < 10:
v = pi*(d.dc(z)/1000.)**3.
vl.append(v)
zl.append(z)
z += dz
#plt.loglog(zl,vl)
plt.plot(zl,vl)
plt.ylim(1.,1000)
plt.yscale('log')
plt.xlabel('Maximum Redshift')
plt.ylabel(r'Volume for 3/4 of sky (Gpc$h^{-1}$)$^3$')
pp.savefig()
pp.close()
return True
def pk3elldfile_dewig(file='Challenge_matterpower',
beta=0.4,
sigt=3.0,
sigr=3.0,
sfog=3.5,
mun=1.,
sigs=15.,
ns=.963,
sigz=0,
pw='y'):
'''
returns arrays for k, p0,2,4 multipoles for BAO and no BAO
file is input linear P(k)
mun = 0 for pre rec and 1 for post-rec with RSD removal (controls effect of smoothing scaling on RSD)
beta is fiducial f/b
sfog: the streaming velocity parameter, often denoted Sigma_s
sigz: for redshift uncertainties, ignore for most cases
sigt: transverse BAO damping, Sigma_\perp
sigr: radial BAO damping, Sigma_||
sigs: smoothing scale used in reconstruction (irrelevant if mun = 0)
ns: varying ns used in no BAO P(k); best results often come from using one that is slightly different than linear P(k) one (probably could be fixed with EH.py)
'''
from scipy.integrate import quad
from Cosmo import distance
mult = 1.
dir = 'powerspectra/'
if file == 'Challenge_matterpower' or file == 'TSPT_out':
om = 0.31
#lam = 1
h = .676
nindex = ns
ombhh = .022
if file == 'MICE_matterpower':
om = 0.25
#lam = .75
h = .7
ombhh = .044 * 0.7 * .7
nindex = .949
if file == 'DESI':
from cosmoprimo.fiducial import DESI
from cosmoprimo import PowerSpectrumBAOFilter
cosmo = DESI()
pkz = cosmo.get_fourier().pk_interpolator()
pk = pkz.to_1d(z=0)
kl = np.loadtxt(dir + 'Challenge_matterpower.dat').transpose()[
0] #this k spacing is known to work well
pkv = pk(kl)
pknow = PowerSpectrumBAOFilter(
pk, engine='wallish2018').smooth_pk_interpolator()
pksmv = pknow(kl)
if file != 'DESI':
f = np.loadtxt(dir + file + '.dat').transpose()
if pw == 'y':
fo = open(
'P02' + file + 'beta' + str(beta) + 'sigs' + str(sfog) + 'sigxy' +
str(sigt) + 'sigz' + str(sigr) + 'Sk' + str(sigs) + '.dat', 'w')
fo.write('# k P0 P2 P4 Psmooth0 Psmooth2 Psmooth4 Plin Plinsmooth\n')
if file != 'Pk_MICEcosmology_z0_Plin_Pnowig' and file != 'DESI':
s = simulate(omega=om, lamda=1 - om, h=h, nindex=nindex, ombhh=ombhh)
elif file == 'Pk_MICEcosmology_z0_Plin_Pnowig':
mult = 8. * pi**3.
pl2 = []
pl4 = []
beta0 = 0.4
for i in range(0, 100):
pl2.append(P2(i / 100. + .005))
for i in range(0, 100):
pl4.append(P4(i / 100. + .005))
mul = []
anipolyl = []
for i in range(0, 100):
mu = i / 100. + .005
mul.append(mu)
b = 2.
ff = beta * b
if sigz != 0:
z = .8
d = distance(.25, .75)
sigzc = d.cHz(z) * sigz
norm = 1
if file != 'DESI':
kl = f[0]
pml = f[1]
norm = pml[0] / s.Psmooth(kl[0], 0)
p0l = []
p2l = []
p4l = []
psm0l = []
psm2l = []
psm4l = []
for i in range(0, len(kl)):
k = kl[i]
if file != 'DESI':
pk = pml[i] * mult
else:
pk = pkv[i]
pk0 = 0
pk2 = 0
pk4 = 0
pksm0 = 0
pksm2 = 0
pksm4 = 0
if file == 'Pk_MICEcosmology_z0_Plin_Pnowig':
pksm = float(f[i].split()[2]) * mult
elif file == 'DESI':
pksm = pksmv[i]
else:
pksm = s.Psmooth(k, 0) * norm
dpk = pk - pksm
for m in range(0, 100):
#mu = (1.-mul[m])
mu = mul[m]
if mun == 'n':
mu = (1. - mul[m])
if sfog > 0:
F = 1. / (1. + k**2. * mu**2. * sfog**2. / 2.)**2.
else:
F = (1. + k**2. * sfog**2. / 2.)**2. / (
1. + k**2. * (1. - mu)**2. * sfog**2. / 2.)**2.
if mun == 'b':
mus2 = mu**2.
F = (1. + beta * mus2 *
(1. - exp(-0.5 * (k * sigs)**2.)))**2. * 1. / (
1. + k**2. * mu**2. * (sfog)**2. / 2.)**2.
#C *doesn't include damping*
S = mun * exp(-0.5 * (k * sigs)**2.)
C = (1. + beta * mu * mu * (1. - S)) * 1. / (1. + k**2. * mu**2. *
(sfog)**2. / 2.)
sigv2 = (1 - mu**2.) * sigt**2. / 4. + mu**2. * sigr**2. / 4.
damp = exp(-1. * k * k * sigv2)
if sigz != 0:
C = C * exp(-0.5 * k * k * mu * mu * sigzc * sigzc)
pkmu = C**2. * (dpk * damp**2. + pksm)
pkmusm = C**2. * pksm
pk0 += pkmu
pk2 += pkmu * pl2[m]
pk4 += pkmu * pl4[m]
pksm0 += pkmusm
pksm2 += pkmusm * pl2[m]
pksm4 += pkmusm * pl4[m]
pk0 = pk0 / 100.
pk2 = 5. * pk2 / 100.
pk4 = 9. * pk4 / 100.
pksm0 = pksm0 / 100.
pksm2 = 5. * pksm2 / 100.
pksm4 = 9. * pksm4 / 100.
p0l.append(pk0)
p2l.append(pk2)
p4l.append(pk4)
psm0l.append(pksm0)
psm2l.append(pksm2)
psm4l.append(pksm4)
print(pk0, pksm0, dpk)
if pw == 'y':
fo.write(
str(k) + ' ' + str(pk0) + ' ' + str(pk2) + ' ' + str(pk4) +
' ' + str(pksm0) + ' ' + str(pksm2) + ' ' + str(pksm4) + ' ' +
str(pk) + ' ' + str(pksm) + '\n')
if pw == 'y':
fo.close()
from matplotlib import pyplot as plt
from numpy import loadtxt as load
d = load('P02' + file + 'beta' + str(beta) + 'sigs' + str(sfog) +
'sigxy' + str(sigt) + 'sigz' + str(sigr) + 'Sk' + str(sigs) +
'.dat').transpose()
plt.xlim(0, .5)
plt.plot(d[0], d[-2] / d[-1])
plt.plot(d[0], np.ones(len(d[0])), '--')
plt.show()
p0l = np.array(p0l)
p2l = np.array(p2l)
p4l = np.array(p4l)
psm0l = np.array(psm0l)
psm2l = np.array(psm2l)
psm4l = np.array(psm4l)
return kl, p0l, p2l, p4l, psm0l, psm2l, psm4l
def xi3elldfile_dewig(r,file='Challenge_matterpower',dir='',beta=0.4,sigt=3.0,sigr=3.0,sfog=3.5,max=51,mun=1.,sigs=15.,ns=.95,sigz=0,pw='n'):
from scipy.integrate import quad
from Cosmo import distance
#f = open('/Users/ashleyr/BOSS/spec/camb_MWcos.dat')
#print dir
mult = 1.
dir = 'powerspectra/'
if file=='Challenge_matterpower' or file == 'TSPT_out':
om = 0.31
lam = 0.69
h = .676
nindex = .963
ombhh = .022
if file == 'MICE_matterpower':
om = 0.25
lam = .75
h = .7
ombhh = .044*0.7*.7
nindex = .949
f = open(dir+file+'.dat').readlines()
if pw == 'y':
fo = open('P02'+file+'beta'+str(beta)+'sigs'+str(sfog)+'sigxy'+str(sigt)+'sigz'+str(sigr)+'Sk'+str(sigs)+'.dat','w')
fo.write('# k P0 P2 P4 Psmooth\n')
if file != 'Pk_MICEcosmology_z0_Plin_Pnowig':
s = simulate(omega=om,lamda=lam,h=h,nindex=nindex,ombhh=ombhh)
else:
mult = 8.*pi**3.
pl2 = []
pl4 = []
beta0 = 0.4
for i in range(0,100):
pl2.append(P2(i/100.+.005))
for i in range(0,100):
pl4.append(P4(i/100.+.005))
mul = []
anipolyl = []
for i in range(0,100):
mu = i/100.+.005
mul.append(mu)
anipoly = (1.+beta*mu**2.)**2.
anipoly2 = (1.+beta*mu**2.)**2.*pl2[i]
anipoly4 = (1.+beta*mu**2.)**2.*pl4[i]
anipolyl.append((anipoly,anipoly2,anipoly4))
k0 = float(f[0].split()[0])
k1 = float(f[1].split()[0])
ldk = log(k1)-log(k0)
#print ldk
xi0 = 0
xi2 = 0
xi4 = 0
xism0 = 0
xism2 = 0
xism4 = 0
#print max
dmk = r/10.
k = float(f[0].split()[0])
if file == 'camb_Nacc' or file == 'Pk_MICEcosmology_z0_Plin_Pnowig':
norm = 1.
else:
norm = float(f[0].split()[1])/s.Psmooth(k,0)*mult
#print norm
b = 2.
ff =beta*b
if sigz != 0:
z = .8
d = distance(.25,.75)
sigzc = d.cHz(z)*sigz
for i in range(0,len(f)-1):
k = float(f[i].split()[0])
pk = float(f[i].split()[1])*mult
pk0 = 0
pk2 = 0
pk4 = 0
pksm0 = 0
pksm2 = 0
pksm4 = 0
if file == 'Pk_MICEcosmology_z0_Plin_Pnowig':
pksm = float(f[i].split()[2])*mult
else:
pksm = s.Psmooth(k,0)*norm
dpk = pk-pksm
for m in range(0,100):
#mu = (1.-mul[m])
mu = mul[m]
if mun == 'n':
mu = (1.-mul[m])
if sfog > 0:
F = 1./(1.+k**2.*mu**2.*sfog**2./2.)**2.
else:
F = (1.+k**2.*sfog**2./2.)**2./(1.+k**2.*(1.-mu)**2.*sfog**2./2.)**2.
if mun == 'b':
mus2 = mu**2.
F = (1.+beta*mus2*(1.-exp(-0.5*(k*sigs)**2.)))**2.*1./(1.+k**2.*mu**2.*(sfog)**2./2.)**2.
#C *doesn't include damping*
S = mun*exp(-0.5*(k*sigs)**2.)
C = (1.+beta*mu*mu*(1.-S))*1./(1.+k**2.*mu**2.*(sfog)**2./2.)
sigv2 = (1-mu**2.)*sigt**2./4.+mu**2.*sigr**2./4.
damp = exp(-1.*k*k*sigv2)
if sigz != 0:
C = C*exp(-0.5*k*k*mu*mu*sigzc*sigzc)
#damp1 = exp(-0.25*k**2.*mu**2.*sigr**2.*(1.+ff)**2.)
#damp2 = exp(-0.25*k**2.*(1.-mu**2.)*sigt**2.)
#damp3 = 1./b*S*exp(-0.5*k**2.*sigt*sigr)
#damp4 = (1.-exp(-0.25*k**2.*mu**2.*(2.*ff+ff**2.)*sigt*sigr))
#damp = damp1*damp2+damp3*damp4
anipoly = anipolyl[m]
pk0 += C**2.*(dpk*damp**2.+pksm)
pk2 += (dpk*damp**2.+pksm)*pl2[m]*C**2.
pk4 += (dpk*damp**2.+pksm)*pl4[m]*C**2.
pksm0 += pksm*C**2.
pksm2 += pksm*pl2[m]*C**2.
pksm4 += pksm*pl4[m]*C**2.
pk0 = pk0/100.
pk2 = 5.*pk2/100.
pk4 = 9.*pk4/100.
pksm0 = pksm0/100.
pksm2 = 5.*pksm2/100.
pksm4 = 9.*pksm4/100.
if pw == 'y':
fo.write(str(k)+' '+str(pk0)+' '+str(pk2)+' '+str(pk4)+' '+str(pksm0)+' '+str(pksm2)+' '+str(pksm4)+' '+str(pk)+' '+str(pksm)+'\n')
dk = ldk*k
xi0 += dk*k*k*pk0*sph_jn(0,k*r)*exp(-1.*dmk*k**2.)
xi2 += dk*k*k*pk2*sph_jn(2,k*r)*exp(-1.*dmk*k**2.)
xi4 += dk*k*k*pk4*sph_jn(4,k*r)*exp(-1.*dmk*k**2.)
xism0 += dk*k*k*pksm0*sph_jn(0,k*r)*exp(-1.*dmk*k**2.)
xism2 += dk*k*k*pksm2*sph_jn(2,k*r)*exp(-1.*dmk*k**2.)
xism4 += dk*k*k*pksm4*sph_jn(4,k*r)*exp(-1.*dmk*k**2.)
if pw == 'y':
fo.close()
from matplotlib import pyplot as plt
from numpy import loadtxt as load
d = load('P02'+file+'beta'+str(beta)+'sigs'+str(sfog)+'sigxy'+str(sigt)+'sigz'+str(sigr)+'Sk'+str(sigs)+'.dat').transpose()
plt.xlim(0,.3)
plt.plot(d[0],d[-2]/d[-1])
plt.show()
return xi0/(2.*pi*pi),-1.*xi2/(2.*pi*pi),xi4/(2.*pi*pi),xism0/(2.*pi*pi),-1.*xism2/(2.*pi*pi),xism4/(2.*pi*pi)
def mkxifile_zerrconv(z=0.8,sigz=0.029,sp=1.,bias=1.8,rmin=10.,rmax=300,rsd='',muww='muw',a='',v='y',gam=-1.7,file='MICE_matterpower',dir='',mun=0,beta=0.4,sfog=0,amc=0.0,sigt=6.,sigr=10.,mult=1.,sigs=15.,mumin=0,mumax=1):
#Santi used zspec=0.45 to 1.2
from random import gauss
dir = '/Users/ashleyross/DR12/'
if file == 'MICE_matterpower':
dir = '/Users/ashleyross/DESY1/'
f0 = loadtxt(dir+'xi0'+file+str(beta)+str(sfog)+str(sigt)+str(sigr)+str(sigs)+str(mun)+'.dat').transpose()
f2 = loadtxt(dir+'xi2'+file+str(beta)+str(sfog)+str(sigt)+str(sigr)+str(sigs)+str(mun)+'.dat').transpose()
f4 = loadtxt(dir+'xi4'+file+str(beta)+str(sfog)+str(sigt)+str(sigr)+str(sigs)+str(mun)+'.dat').transpose()
f0sm = loadtxt(dir+'xi0sm'+file+str(beta)+str(sfog)+str(sigt)+str(sigr)+str(sigs)+str(mun)+'.dat').transpose()
f2sm = loadtxt(dir+'xi2sm'+file+str(beta)+str(sfog)+str(sigt)+str(sigr)+str(sigs)+str(mun)+'.dat').transpose()
f4sm = loadtxt(dir+'xi4sm'+file+str(beta)+str(sfog)+str(sigt)+str(sigr)+str(sigs)+str(mun)+'.dat').transpose()
spf = 1.
r = rmin
from Cosmo import distance
if file == 'Challenge_matterpower':
d = distance(.31,.69)
if file == 'MICE_matterpower':
d = distance(.25,.75)
ff = d.omz(z)**.557
betad = ff/bias
betaf = betad/beta
zmin = z-sigz*(1.+z)*5.*sqrt(2.)
zmax = z+sigz*(1.+z)*5.*sqrt(2.)
#zmin = z-.15
#zmax = z+.2
nz = 4000
dz = (zmax-zmin)/float(nz)
d0 = d.dc(z)
dzl = []
rzl = []
wl = []
for i in range(0,nz):
zb = zmin + dz/2.+dz*i
#print zb
dzl.append(d.dc(zb)-d0)
rzl.append(d.dc(zb)/d0)
#rzl.append(1.)
wl.append(1./(sqrt(2.)*sigz*(1.+z)*sqrt(2.*pi))*exp(-.5*((zb-z)/(sqrt(2.)*sigz*(1.+z)))**2.))
print(sum(wl)*dz)
sumw = sum(wl)
#print wl
#fmuw = loadtxt('/Users/ashleyross/DESY1/FdaHvsmu_z0.61.0_zerr0.03_10e3n2.6_b1.5.dat').transpose()
fmuw = loadtxt('/Users/ashleyross/DESY1/FmuobsdaHvsmu_z0.61.0_zerr0.03_10e3n2.6_b1.5.dat').transpose()
#muwt = sum(fmuw[1])
muwt = 0
#for i in range(0, len(fmuw[1])):
# if i > int(mumin*100) and i < int(mumax*100):
for i in range(int(100*mumin),int(100*mumax)):
if muww == 'muw':
muwt += fmuw[1][i]
else:
muwt += 1.
muw = ''
if mumin != 0:
muw += 'mumin'+str(mumin)
if mumax != 1:
muw += 'mumax'+str(mumax)
fo = open('xizconv'+muww+file+muw+str(beta)+str(sfog)+str(sigt)+str(sigr)+str(sigz)+rsd+'sp'+str(sp)+'.dat','w')
nzs = 10000
while r < rmax:
sumxi = 0
sumxin = 0
mmin = int(100*mumin)
mmax = int(100*mumax)
for m in range(mmin,mmax):
mu = .005+0.01*m
summ = 0
summn = 0
rt = sqrt(1.-mu**2.)*r
rr = mu*r
for i in range(0,nz):
#dzt = gauss(0,sigz*(1.+z))-gauss(0,sigz*(1.+z))
#indz = int((z+dzt-zmin)/dz)
#if indz >= 0 and indz < nz:
rrp = rr + dzl[i]
#if abs(rrp) < rr:
# print rrp,rr,r
#rtp = rt*rzl[i]
rtp = rt
rp = sqrt(rrp**2.+rtp**2.)
mup = abs(rrp/rp)
if rp >= 10. and rp < 299:
indd = int((rp-10.)/spf)
indu = indd + 1
fac = (rp-10.)/spf-indd
if fac > 1 or fac < 0:
print(fac,rp,spf,indd)
xi0 = (f0[1][indu]*fac+(1.-fac)*f0[1][indd])*(1.+2/3.*betad+.2*betad**2.)/(1.+2/3.*beta+.2*beta**2.)
xi2 = (f2[1][indu]*fac+(1.-fac)*f2[1][indd])*(4/3.*betad+4/7.*betad**2.)/(4/3.*beta+4/7.*beta**2.)
xi4 = (f4[1][indu]*fac+(1.-fac)*f4[1][indd])*(betad/beta)**2.
xi0n = f0sm[1][indu]*fac+(1.-fac)*f0sm[1][indd]*(1.+2/3.*betad+.2*betad**2.)/(1.+2/3.*beta+.2*beta**2.)
xi2n = (f2sm[1][indu]*fac+(1.-fac)*f2sm[1][indd])*(4/3.*betad+4/7.*betad**2.)/(4/3.*beta+4/7.*beta**2.)
xi4n = (f4sm[1][indu]*fac+(1.-fac)*f4sm[1][indd])*(betad/beta)**2.
else:
if rp < 10.:
xi0,xi2,xi4 = wmod3(rp,mup,f0[1][0],10.,gam=gam)
xi0n,xi2n,xi4n = wmod3(rp,mup,f0sm[1][0],10.,gam=gam)
#xi2 = f2[1][0]
#xi4 = f4[1][0]
#xi2n = f2sm[1][0]
#xi4n = f4sm[1][0]
if rp >= 300:
xi0 = f0[1][-1]*(1.+2/3.*betad+.2*betad**2.)/(1.+2/3.*beta+.2*beta**2.)
xi2 = f2[1][-1]*(4/3.*betad+4/7.*betad**2.)/(4/3.*beta+4/7.*beta**2.)
xi4 = f4[1][-1]*(betad/beta)**2.
xi0n = f0sm[1][-1]*(1.+2/3.*betad+.2*betad**2.)/(1.+2/3.*beta+.2*beta**2.)
xi2n = f2sm[1][-1]*(4/3.*betad+4/7.*betad**2.)/(4/3.*beta+4/7.*beta**2.)
xi4n = f4sm[1][-1]*(betad/beta)**2.
xi0,xi2,xi4 = 0,0,0
xi0n,xi2n,xi4n = 0,0,0
if rsd == 'norsd':
xi2,xi4,xi2n,xi4n = 0,0,0,0
xip = xi0+P2(mup)*xi2+P4(mup)*xi4
xipn = xi0n+P2(mup)*xi2n+P4(mup)*xi4n
#if xip > 10:
# print xip,rp,indd,indu,f4[1][indu],f4[1][indd],P4(mup),mup
summ += xip*wl[i]
summn += xipn*wl[i]
xi = summ/sumw#/float(nzs)#
xin = summn/sumw#/float(nzs)#
if muww == 'muw':
sumxi += xi*fmuw[1][m]
sumxin += xin*fmuw[1][m]
else:
sumxi += xi
sumxin += xin
sumxi = sumxi/muwt
sumxin = sumxin/muwt
fo.write(str(r)+' '+str(sumxi)+' '+str(sumxin)+'\n')
print(r,sumxi,sumxin)
r += sp
fo.close()
# from matplotlib import pyplot as plt
# from numpy import loadtxt as load
# d = load('xizconvmuw'+file+muw+str(beta)+str(sfog)+str(sigt)+str(sigr)+str(sigz)+'sp'+str(sp)+'.dat').transpose()
# dd = load('xiavemuw'+muw+'SM.dat').transpose()
# plt.plot(d[0],d[0]**2.*d[1]*1.3,'k-',dd[0],dd[0]**2.*dd[1],'r-')
# plt.show()
# plt.plot(d[0]/.97,d[0]**2.*d[1]*1.3,'k-',dd[0],dd[0]**2.*dd[1],'r-')
# plt.show()
return True
from Cosmo import distance d = distance(.25, .75) print d.Dgn(0.65), d.Dgn(0.75), d.Dgn(0.85), d.Dgn(0.95) ## bias_bf is the bias parameter of the sample, for dark matter, you use bias_bf = 1 ## w_full is the final w you want import numpy as np dir = '/home/nil/Dropbox/w_model_code/' full_fname = dir + 'outbin_1.txt' amean = 0.75 bias = 1.8 beta = 0.4 #f = Omegam_a( amean )**0.55 ff = d.omz(amean)**.557 betad = ff / bias betaf = betad / beta theta, w0, w2, w4 = np.loadtxt(full_fname, usecols=(0, 1, 2, 3), unpack=True) b2_term = w0 bf_term = ff * (2 / 3. * w0 + 4 / 3. * w2) f2_term = ff**2 * (1 / 5. * w0 + 4 / 7. * w2 + 8 / 35. * w4) w_full = betaf**2 * b2_term + betaf * bf_term + f2_term
def xibaoSM(mn,c,rmin=50,rmax=200.,bs=5,md=1.,m=1.,mb='',v='n',p='',nbar=.002,fsky=.035,Bp=.4,mom='muw',nm='muwSA',covmd='s',N=63,mumin=0,mumax=1):
from baofit_pubtest import doxi_isolike
from Cosmo import distance
d = distance(.31,.69)
vol = fsky*4.*pi/3.*(d.dc(1.)**3.-d.dc(.6)**3.)
muw = ''
if mumin != 0:
muw += 'mumin'+str(mumin)
if mumax != 1.:
muw += 'mumax'+str(mumax)
if nm == 'avemuwSM':
dw = load('xiave'+mom+muw+'SM.dat').transpose()
if nm == 'muwSA':
dw = load('ximuwSM/xi'+mom+muw+'SA'+str(mn)+'c'+c+'.dat').transpose()
if nm == 'lampave':
dw = load('xiSM'+nm+'mu'+str(mumin)+str(mumax)+str(bs)+'.dat').transpose()
if nm == 'Y1':
munm = ''
if mumin != 0:
munm += 'mum'+str(mumin)
if mumax != 1:
munm += 'mux'+str(mumax)
dw = load('xi0gY1redBAO_mean_z_bpz_VFmz0.6xz1.0fcos'+munm+'5st0.dat').transpose()
dd = dw[1]
rl = dw[0]
mn = str(mn)
if covmd == 'th':
cov = load('covxiSM5.dat')
if covmd == 's':
cov = load('covxi'+mom+muw+'SM.dat')
if covmd == 'lamp':
cov = load('covSMlamp1.4.1'+'mu'+str(mumin)+str(mumax)+str(bs)+'.dat')
cov = cov*m
if md != 1:
for i in range(0,len(cov)):
cov[i][i] = cov[i][i]*md
if p == 'dp':
for i in range(0,len(cov)):
np = nbar*vol*nbar*5.*4.*pi*rl[i]**2.
cov[i][i] = cov[i][i]+2./np
mdi = 1
if mb == 'nobao':
mdi = 2
#mod = load('xizconv'+mom+'MICE_matterpower'+muw+'0.406.010.00.029sp1.0.dat').transpose()[mdi]
mod = load('xizconvcMICE_matterpower'+muw+'0.4010.010.0combzsiglsp1.0.dat').transpose()[mdi]
#mod = load('xizconvmuwPkMICE0.43.06.010.00.029sp1.0.dat').transpose()[mdi]
Nmock = 8*N
Nbin = (rmax-rmin)/5.
chifac = (Nmock-Nbin-1)/(float(Nmock)-2.)
chil = doxi_isolike(dd,cov,mod,rl,rmin=rmin,rmax=rmax,v=v,wo=mn+c+mb,Bp=Bp,chi2fac=chifac)
fo = open('ximuwSM/BAOxichilSM'+nm+mn+c+mb+p+str(md)+str(m)+covmd+str(Bp)+muw+'.dat','w')
for i in range(0,len(chil)):
a = .8+.001*i+.0005
fo.write(str(a)+' '+str(chil[i])+'\n')
fo.close()
from baofit import sigreg_c12
a = sigreg_c12('ximuwSM/BAOxichilSM'+nm+mn+c+mb+p+str(md)+str(m)+covmd+str(Bp)+muw)
#print a
return a
'''
Add new results to BOSS DR12 BAO likelihood
'''
from math import *
import numpy as np
from Cosmo import distance #this is other python in LSSanalysis
#directory with DR12 DM/HZ likelihood
dirDR12 = '/Users/ashleyross/Downloads/ALAM_ET_AL_2016_consensus_and_individual_Gaussian_constraints/COMBINEDDR12_BAO_consensus_dM_Hz/'
zl = [0.38, 0.38, 0.51, 0.51, 0.61, 0.61]
ml = [1512.39, 81.2087, 1975.22, 90.9029, 2306.68, 98.9647]
covt = np.loadtxt(dirDR12 + 'BAO_consensus_covtot_dM_Hz.txt')
d = distance(.31, .69, h=0.676)
def testcosmo(alphat, alphar, z):
'''
The purpose of this is to confirm that Cosmo.py reproduces the alpha_perp/|| -> DM/H conversion
alphat is the transverse alpha (alpha_perp), alphar is the radial alpha (alpha_||)
'''
print(d.dc(z) * alphat)
print(d.Hz(z) / alphar * 100.)
'''
comparison with file with ROSS in name and Ross et al. 2017 gives matches all to within 0.05%; should be good enough
'''
#def converttoalph():
fidl = np.zeros(len(ml))
mtl = np.array(ml)