def cosmofromlacurved(cozold,latarget,\
Neff=3.046,mnu1=0.,mnu2=0.,mnu3=0.,mnu4=0.,NorI=-1,Smnu=-1,onuh2val=-1.,SorD=1,\
w0=-1.,wa=0.,omegak=0.):
try:
newhval = scipy.optimize.brentq(
lambda x: mysolvela(x, cozold, Neff, mnu1, mnu2, mnu3, mnu4, NorI,
Smnu, onuh2val, SorD, w0, wa, omegak) -
latarget, cozold.h * 0.5, cozold.h * 2.)
except:
print 'new h value not found in factor of 2 of old h value. recode or error!'
return None
## success!
if np.fabs(omegak) < 2.0e-6:
forceflat = 1
else:
forceflat = 0
okh2 = omegak * newhval**2
oDEh2 = newhval**2 - (cozold.och2 + cozold.obh2 + cozold.ogh2 +
cozold.onuh2(1.) + okh2)
oDE = oDEh2 / newhval**2
coznew = cosmo.cosmo(och2=cozold.och2, obh2=cozold.obh2, ogh2=cozold.ogh2, Tcmb=cozold.Tcmb, \
h=newhval,
w0=w0,
wa=wa,
omegaDE = oDE,
forceflat=forceflat,
Neff=Neff,mnu1=mnu1,mnu2=mnu2,mnu3=mnu3,mnu4=mnu4,NorI=NorI,
Smnu=Smnu,onuh2val=onuh2val,SorD=SorD)
tCMB, extra = dCMB(coznew)
assert ((tCMB[1] - latarget) / latarget < 1.0e-5)
return coznew
def cosmofromlacurved(
cozold,
latarget,
Neff=3.046,
mnu1=0.0,
mnu2=0.0,
mnu3=0.0,
mnu4=0.0,
NorI=-1,
Smnu=-1,
onuh2val=-1.0,
SorD=1,
w0=-1.0,
wa=0.0,
omegak=0.0,
):
try:
newhval = scipy.optimize.brentq(
lambda x: mysolvela(x, cozold, Neff, mnu1, mnu2, mnu3, mnu4, NorI, Smnu, onuh2val, SorD, w0, wa, omegak)
- latarget,
cozold.h * 0.5,
cozold.h * 2.0,
)
except:
print "new h value not found in factor of 2 of old h value. recode or error!"
return None
## success!
if np.fabs(omegak) < 2.0e-6:
forceflat = 1
else:
forceflat = 0
okh2 = omegak * newhval ** 2
oDEh2 = newhval ** 2 - (cozold.och2 + cozold.obh2 + cozold.ogh2 + cozold.onuh2(1.0) + okh2)
oDE = oDEh2 / newhval ** 2
coznew = cosmo.cosmo(
och2=cozold.och2,
obh2=cozold.obh2,
ogh2=cozold.ogh2,
Tcmb=cozold.Tcmb,
h=newhval,
w0=w0,
wa=wa,
omegaDE=oDE,
forceflat=forceflat,
Neff=Neff,
mnu1=mnu1,
mnu2=mnu2,
mnu3=mnu3,
mnu4=mnu4,
NorI=NorI,
Smnu=Smnu,
onuh2val=onuh2val,
SorD=SorD,
)
tCMB, extra = dCMB(coznew)
assert (tCMB[1] - latarget) / latarget < 1.0e-5
return coznew
def mysolvela(
h,
cozold,
Neff=3.046,
mnu1=0.0,
mnu2=0.0,
mnu3=0.0,
mnu4=0.0,
NorI=-1,
Smnu=-1,
onuh2val=-1.0,
SorD=1,
w0=-1.0,
wa=0.0,
omegak=0.0,
):
"""
copying from mysolveDAcurved.
"""
if np.fabs(omegak) < 2.0e-6:
forceflat = 1
else:
forceflat = 0
okh2 = omegak * h ** 2
oDEh2 = h ** 2 - (cozold.och2 + cozold.obh2 + cozold.ogh2 + cozold.onuh2(1.0) + okh2)
oDE = oDEh2 / h ** 2
coztmp = cosmo.cosmo(
och2=cozold.och2,
obh2=cozold.obh2,
ogh2=cozold.ogh2,
Tcmb=cozold.Tcmb,
h=h,
w0=w0,
wa=wa,
omegaDE=oDE,
forceflat=forceflat,
Neff=Neff,
mnu1=mnu1,
mnu2=mnu2,
mnu3=mnu3,
mnu4=mnu4,
NorI=NorI,
Smnu=Smnu,
onuh2val=onuh2val,
SorD=SorD,
)
tCMB, extra = dCMB(coztmp)
return tCMB[1] ## this is la
def chain2cosmo(elt, eltnames, mcmcfixed):
## copying Planck2cosmo in cosmo.py
global distdefaults
global distparams
## using defaults in distdefaults
mydist = distdefaults.copy()
j = 0
for i in range(len(distparams)):
if mcmcfixed[i] == 0: ## then set the value from elt.
mydist[distparams[i]] = elt[j]
j += 1
assert j == len(elt)
h = mydist['H0'] * 0.01
och2 = mydist['omegach2']
obh2 = mydist['omegabh2']
okh2 = mydist['omegak'] * h**2
w0 = mydist['w']
wa = mydist['wa']
ogh2 = 2.469e-5 ## default value givne Tcmb.
onuh2val = 0.0006450616 ## taken from camb ini file Mar13 base_planck_lowl_lowLike.ini; this is z=0 value.
SorD = 0 # single mass eigenstate.
odeh2 = h**2 - (och2 + obh2 + ogh2 + onuh2val + okh2)
oDE = odeh2 / h**2
if 'omegak' in eltnames:
forceflat = 0
else:
forceflat = 1
## set neutrino defaults:
cc = cosmo.cosmo(och2=och2,\
obh2=obh2,
h=h,\
w0=w0,\
wa=wa,\
omegaDE=oDE,\
forceflat=forceflat,\
onuh2val=onuh2val,\
SorD=SorD)
## check omegak
assert np.fabs(cc.ok - mydist['omegak']) < 2.0e-6
return cc
def setuplikelihoods(cmbfname, cmassanifname=None):
global mCMB, icovCMB
global abaolist, DVorsfidlist
global DAorsfidlist, Hrsfidlist
global rsfid
abaolist = np.array([1. / 1.57, 1. / 1.32])
mCMB, covtmp = readPlanckprior(cmbfname)
icovCMB = (np.matrix(covtmp)).I
DVorsfidlist = np.array([2])
obh2fid = 0.0224
och2fid = 0.11186
hfid = 0.7
ccfid = cosmo.cosmo(och2=och2fid, obh2=obh2fid, h=hfid, forceflat=1)
rsfid = rs(obh2fid, och2fid, 1)
DV1fid = ccfid.DVMpc(abaolist[0])
DV2fid = ccfid.DVMpc(abaolist[1])
## set up anisotropic fiducial values.
DA1fid = ccfid.DAz(abaolist[0])
DA2fid = ccfid.DAz(abaolist[1])
H1fid = ccfid.Hofz(abaolist[0])
H2fid = ccfid.Hofz(abaolist[1])
DVorsfidlist = np.array([DV1fid / rsfid, DV2fid / rsfid])
DAorsfidlist = np.array([DA1fid / rsfid, DA2fid / rsfid])
Hrsfidlist = np.array([H1fid * rsfid, H2fid * rsfid])
print 'BAO stuff:', DV1fid, DV2fid, rsfid
print DVorsfidlist
print DAorsfidlist
print Hrsfidlist
print 'chi2 for fiducial cosmology'
cmbchi2val, extra = CMBchi2(ccfid)
baochi2val, extrabao = BAOchi2(ccfid, np.array([1, 1, 0], dtype='int'))
print 'CMB: ', cmbchi2val
print 'BAO: ', baochi2val
if cmassanifname is not None:
anibaosetup(cmassanifname)
baochi2valani, extrabao = BAOchi2(ccfid,
np.array([0, 0, 1], dtype='int'))
print 'BAOani: ', baochi2valani
def setuplikelihoods(cmbfname, cmassanifname=None):
global mCMB, icovCMB
global abaolist, DVorsfidlist
global DAorsfidlist, Hrsfidlist
global rsfid
abaolist = np.array([1.0 / 1.57, 1.0 / 1.32])
mCMB, covtmp = readPlanckprior(cmbfname)
icovCMB = (np.matrix(covtmp)).I
DVorsfidlist = np.array([2])
obh2fid = 0.0224
och2fid = 0.11186
hfid = 0.7
ccfid = cosmo.cosmo(och2=och2fid, obh2=obh2fid, h=hfid, forceflat=1)
rsfid = rs(obh2fid, och2fid, 1)
DV1fid = ccfid.DVMpc(abaolist[0])
DV2fid = ccfid.DVMpc(abaolist[1])
## set up anisotropic fiducial values.
DA1fid = ccfid.DAz(abaolist[0])
DA2fid = ccfid.DAz(abaolist[1])
H1fid = ccfid.Hofz(abaolist[0])
H2fid = ccfid.Hofz(abaolist[1])
DVorsfidlist = np.array([DV1fid / rsfid, DV2fid / rsfid])
DAorsfidlist = np.array([DA1fid / rsfid, DA2fid / rsfid])
Hrsfidlist = np.array([H1fid * rsfid, H2fid * rsfid])
print "BAO stuff:", DV1fid, DV2fid, rsfid
print DVorsfidlist
print DAorsfidlist
print Hrsfidlist
print "chi2 for fiducial cosmology"
cmbchi2val, extra = CMBchi2(ccfid)
baochi2val, extrabao = BAOchi2(ccfid, np.array([1, 1, 0], dtype="int"))
print "CMB: ", cmbchi2val
print "BAO: ", baochi2val
if cmassanifname is not None:
anibaosetup(cmassanifname)
baochi2valani, extrabao = BAOchi2(ccfid, np.array([0, 0, 1], dtype="int"))
print "BAOani: ", baochi2valani
def chain2cosmo(elt, eltnames, mcmcfixed):
## copying Planck2cosmo in cosmo.py
global distdefaults
global distparams
## using defaults in distdefaults
mydist = distdefaults.copy()
j = 0
for i in range(len(distparams)):
if mcmcfixed[i] == 0: ## then set the value from elt.
mydist[distparams[i]] = elt[j]
j += 1
assert j == len(elt)
h = mydist["H0"] * 0.01
och2 = mydist["omegach2"]
obh2 = mydist["omegabh2"]
okh2 = mydist["omegak"] * h ** 2
w0 = mydist["w"]
wa = mydist["wa"]
ogh2 = 2.469e-5 ## default value givne Tcmb.
onuh2val = 0.0006450616 ## taken from camb ini file Mar13 base_planck_lowl_lowLike.ini; this is z=0 value.
SorD = 0 # single mass eigenstate.
odeh2 = h ** 2 - (och2 + obh2 + ogh2 + onuh2val + okh2)
oDE = odeh2 / h ** 2
if "omegak" in eltnames:
forceflat = 0
else:
forceflat = 1
## set neutrino defaults:
cc = cosmo.cosmo(
och2=och2, obh2=obh2, h=h, w0=w0, wa=wa, omegaDE=oDE, forceflat=forceflat, onuh2val=onuh2val, SorD=SorD
)
## check omegak
assert np.fabs(cc.ok - mydist["omegak"]) < 2.0e-6
return cc
def mysolvela(h,cozold, \
Neff=3.046,mnu1=0.,mnu2=0.,mnu3=0.,mnu4=0.,NorI=-1,Smnu=-1,onuh2val=-1.,SorD=1,\
w0=-1.,wa=0.,omegak=0.):
"""
copying from mysolveDAcurved.
"""
if np.fabs(omegak) < 2.0e-6:
forceflat = 1
else:
forceflat = 0
okh2 = omegak * h**2
oDEh2 = h**2 - (cozold.och2 + cozold.obh2 + cozold.ogh2 +
cozold.onuh2(1.) + okh2)
oDE = oDEh2 / h**2
coztmp = cosmo.cosmo(och2=cozold.och2, obh2=cozold.obh2, ogh2=cozold.ogh2, Tcmb=cozold.Tcmb, \
h=h,
w0=w0,
wa=wa,
omegaDE = oDE,
forceflat=forceflat,
Neff=Neff,mnu1=mnu1,mnu2=mnu2,mnu3=mnu3,mnu4=mnu4,NorI=NorI,
Smnu=Smnu,onuh2val=onuh2val,SorD=SorD)
tCMB, extra = dCMB(coztmp)
return tCMB[1] ## this is la
def analyzeMWchain(self, omfid, hfid, obh2fid, zeff, sig8zeff):
"""
Assumes a chain of MW format [f,nu,
Generates bsig8, DV, FAP, DA, H, fs8 columns from MW chain.
"""
import cosmo
def peak_background_bias(nu):
"""
peak_background_bias(nu):
Returns the Lagrangian biases, (b1,b2), given nu.
This is helpful if we want to make our basis set f, nu and sFog.
"""
delc = 1.686
a = 0.707
p = 0.30
anu2 = a * nu**2
b1 = (anu2 - 1 + 2 * p / (1 + anu2**p)) / delc
b2 = (anu2**2 - 3 * anu2 + 2 * p * (2 * anu2 + 2 * p - 1) /
(1 + anu2**p)) / delc**2
return ((b1, b2))
descrnew = [('bs8','float64'),('fs8','float64'),\
('DV','float64'),('FAP','float64'),\
('DA','float64'),('H','float64')]
self.chain = add_fields(self.chain, descrnew)
self.chain['fs8'][:] = self.chain['f'] * sig8zeff
for ii in range(len(self.chain['bs8'][:])):
b1, b2 = peak_background_bias(self.chain['nu'][ii])
self.chain['bs8'][ii] = (1 + b1) * sig8zeff
omh2 = omfid * hfid**2
och2fid = omh2 - obh2fid
cc = cosmo.cosmo(och2=och2fid, obh2=obh2fid, h=hfid)
aeff = 1. / (1 + zeff)
DAzeff = cc.DAz(aeff) ## Mpc.
Hzeff = cc.Hofz(aeff)
FAPzeff = DAzeff * Hzeff / (cosmo.DH * 100.
) ## c = cosmo.DH*100. in km/s units
DVzeff = (DAzeff * DAzeff * cosmo.DH * 100. * zeff / Hzeff)**(1. / 3.)
self.chain['DV'] = DVzeff * (self.chain['alpha_perp']**2 *
self.chain['alpha_par'])**(1. / 3.)
self.chain['DA'] = DAzeff * self.chain['alpha_perp']
self.chain['H'] = Hzeff / self.chain['alpha_par']
self.chain['FAP'] = FAPzeff * self.chain['alpha_perp'] / self.chain[
'alpha_par']
self.DAfid = DAzeff
self.Hfid = Hzeff
self.DVfid = DVzeff
self.zeff = zeff
self.sig8zeff = sig8zeff
## fill in 3x3 DV, FAP, fs8
pcov = np.zeros([3, 3])
m = np.zeros(3)
wgtsum = (self.chain['weight'][:]).sum()
## not if we allow prior!!
#assert (wgtsum - len(self.chain['weight'])) < 0.0001 ## MW chains are unweighted.
for ni, i in zip(['DV', 'FAP', 'fs8'], range(3)):
m[i] = (self.chain['weight'] * self.chain[ni]).sum() / wgtsum
for ni, i in zip(['DV', 'FAP', 'fs8'], range(3)):
for nj, j in zip(['DV', 'FAP', 'fs8'], range(3)):
pcov[i,j] = (self.chain[ni][:] * self.chain[nj][:] * self.chain['weight'][:]).sum()/wgtsum - \
m[i]*m[j]
self.m3x3 = m
self.pcov = pcov
self.icov = np.linalg.inv(self.pcov)
def analyzeMWchain(self,omfid,hfid,obh2fid,zeff,sig8zeff):
"""
Assumes a chain of MW format [f,nu,
Generates bsig8, DV, FAP, DA, H, fs8 columns from MW chain.
"""
import cosmo
def peak_background_bias(nu):
"""
peak_background_bias(nu):
Returns the Lagrangian biases, (b1,b2), given nu.
This is helpful if we want to make our basis set f, nu and sFog.
"""
delc = 1.686
a = 0.707
p = 0.30
anu2 = a*nu**2
b1 = (anu2-1+2*p/(1+anu2**p))/delc
b2 = (anu2**2-3*anu2+2*p*(2*anu2+2*p-1)/(1+anu2**p))/delc**2
return( (b1,b2) )
descrnew = [('bs8','float64'),('fs8','float64'),\
('DV','float64'),('FAP','float64'),\
('DA','float64'),('H','float64')]
self.chain = add_fields(self.chain, descrnew)
self.chain['fs8'][:] = self.chain['f']*sig8zeff
for ii in range(len(self.chain['bs8'][:])):
b1, b2 = peak_background_bias(self.chain['nu'][ii])
self.chain['bs8'][ii] = (1+b1)*sig8zeff
omh2 = omfid*hfid**2
och2fid = omh2 - obh2fid
cc = cosmo.cosmo(och2=och2fid,obh2=obh2fid,h=hfid)
aeff = 1./(1+zeff)
DAzeff = cc.DAz(aeff) ## Mpc.
Hzeff = cc.Hofz(aeff)
FAPzeff = DAzeff*Hzeff/(cosmo.DH*100.) ## c = cosmo.DH*100. in km/s units
DVzeff = (DAzeff*DAzeff*cosmo.DH*100.*zeff/Hzeff)**(1./3.)
self.chain['DV'] = DVzeff * (self.chain['alpha_perp']**2 * self.chain['alpha_par'])**(1./3.)
self.chain['DA'] = DAzeff * self.chain['alpha_perp']
self.chain['H'] = Hzeff / self.chain['alpha_par']
self.chain['FAP'] = FAPzeff * self.chain['alpha_perp']/self.chain['alpha_par']
self.DAfid = DAzeff
self.Hfid = Hzeff
self.DVfid = DVzeff
self.zeff = zeff
self.sig8zeff = sig8zeff
## fill in 3x3 DV, FAP, fs8
pcov = np.zeros([3,3])
m = np.zeros(3)
wgtsum = (self.chain['weight'][:]).sum()
## not if we allow prior!!
#assert (wgtsum - len(self.chain['weight'])) < 0.0001 ## MW chains are unweighted.
for ni,i in zip(['DV','FAP','fs8'],range(3)):
m[i] = (self.chain['weight']*self.chain[ni]).sum()/wgtsum
for ni,i in zip(['DV','FAP','fs8'],range(3)):
for nj,j in zip(['DV','FAP','fs8'],range(3)):
pcov[i,j] = (self.chain[ni][:] * self.chain[nj][:] * self.chain['weight'][:]).sum()/wgtsum - \
m[i]*m[j]
self.m3x3 = m
self.pcov = pcov
self.icov = np.linalg.inv(self.pcov)
import cosmo
import plot
import minimize
import plot
import numpy as np
import matplotlib.pyplot as plt
reload(constants)
reload(cosmo)
reload(plot)
reload(minimize)
reload(plot)
#set parameters for 8600, 8610
cos_8600 = cosmo.cosmo(1.0, 0.0, 0.166, 1.0, 0.83, 2, 1e5, 1e19, 1000) #SCDM
cos_8610 = cosmo.cosmo(1.0, 0.0, 0.166, 0.968, 0.83, 4, 1e5, 1e19,
1000) #WMAP ps
cos_8411 = cosmo.cosmo(0.268, 0.732, 0.166, 0.968, 0.83, 4, 1e5, 1e19,
1000) #LCDM WMAP
#
R1 = 1.
R2 = 10.
zs, s = minimize.mini_rms(cos_8600, cos_8411, 0., R1,
R2) #zs is the starting redshift of 8600
#s is the length scale ratio
plot.plot_variance(cos_8600, cos_8411, 0, zs, s)
plot.plot_fit_countor(cos_8600, cos_8411, R1, R2, 0, zs, s)
import pt import universal_test import numpy as np import matplotlib.pyplot as plt reload(constants) reload(cosmo) reload(load_data) reload(scaling) reload(verify) reload(pt) reload(universal_test) #set parameters for 8600, 8610, 8411 cos_8600 = cosmo.cosmo(1.0, 0.0, 0.166, 1.0, 0.83, 2, 1e2, 1e19, 1000) cos_8610 = cosmo.cosmo(1.0, 0.0, 0.166, 0.968, 0.83, 4, 1e2, 1e19, 1000) cos_8411 = cosmo.cosmo(0.268, 0.732, 0.166, 0.968, 0.83, 4, 1e2, 1e19, 1000) #load data load_data.load_all_data() #do the scaling fit of data scaling.get_a_alpha(vals.data_8600, vals.z_8600, cos_8600, '8600') scaling.get_a_alpha(vals.data_8610, vals.z_8610, cos_8610, '8610') scaling.get_a_alpha(vals.data_8411, vals.z_8411, cos_8411, '8411') #verify the data fit results fig_8600 = verify.verify_wf(vals.data_8600, vals.z_8600, '8600') fig_8610 = verify.verify_wf(vals.data_8610, vals.z_8610, '8610')
