def make_spectra(settings = None):
if settings is None:
from custom_settings import settings
ell = np.arange(2, settings.lmax + 1)
spectra = np.zeros((4,ell.size))
if settings.cosmo_params['source'] is "pycamb_default":
spectra = pycamb.camb(settings.lmax)
elif settings.cosmo_params['source'] is "planck_latest":
spectra = pycamb.camb(settings.lmax, settings.cosmo_params)
else:
spectra = pycamb.camb(settings.lmax, settings.cosmo_params)
if settings.plot_spectra:
plt.figure()
make_subplot(1, 'TT', spectra[0], ell, '', r'$\mu K^2$')
make_subplot(2, 'EE', spectra[1], ell, '', '')
make_subplot(3, 'BB', spectra[2], ell, 'l', r'$\mu K^2$')
make_subplot(4, 'TE', spectra[3], ell, 'l', '')
plt.show()
if settings.normalise_spectra is False:
norm_fact = ell*(ell + 1)/(2*np.pi)
spectra = spectra/norm_fact
if settings.write_spectra:
np.save(settings.spectra_file, spectra)
if settings.return_spectra:
return spectra
def get_dl(rvalue, lmaxcamb=1000):
params = {'H0':H0,'omegab':Omegab,'omegac':Omegac,'omegak':0,'scalar_index':0.968,
'reion__use_optical_depth':True,'reion__optical_depth':0.066,
'tensor_ratio':rvalue,'WantTensors':True,'scalar_amp':scalar_amp,'DoLensing':True}
T,E,B,X = pycamb.camb(lmaxcamb+1+150,**params)
params_nl = {'H0':H0,'omegab':Omegab,'omegac':Omegac,'omegak':0,'scalar_index':0.968,
'reion__use_optical_depth':True,'reion__optical_depth':0.066,
'tensor_ratio':rvalue,'WantTensors':True,'scalar_amp':scalar_amp,'DoLensing':False}
Tnl,Enl,Bnl,Xnl = pycamb.camb(lmaxcamb+1+150,**params_nl)
lll = np.arange(1,lmaxcamb+1)
return lll,T[:lmaxcamb],E[:lmaxcamb],B[:lmaxcamb],X[:lmaxcamb], Tnl[:lmaxcamb], Enl[:lmaxcamb], Bnl[:lmaxcamb], Xnl[:lmaxcamb]
def get_spectrum_bins(pars, x, stuff, spectra=None):
#### Parameters
params = stuff[0]
lmax = stuff[1]
fsky = stuff[2]
mukarcmin = stuff[3]
fwhmdeg = stuff[4]
deltal = stuff[5]
xmin = stuff[6]
xmax = stuff[7]
consistency = stuff[8]
parkeys = stuff[9]
thepars = params.copy()
for i in np.arange(len(parkeys)):
thepars[parkeys[i]] = pars[i]
#if consistency:
# thepars['tensor_index'] = -thepars['tensor_ratio']/thepars['consistency']
if spectra is None:
#### Camb for primordial B
print("")
print("Calling Camb for Primordial B-modes with lmax = {0:.0f}".format(lmax+200))
for i in np.arange(len(parkeys)):
print(parkeys[i], thepars[parkeys[i]])
amplens = thepars['lensing_amplitude']
Tprim,Eprim,Bprim,Xprim = pycamb.camb(np.max(lmax)+200,**thepars)
Bprim = Bprim[0:np.max(lmax)]
#### Camb for lensing B
thepars = params.copy()
thepars['tensor_ratio'] = 0
thepars['tensor_index'] = 0
thepars['DoLensing'] = True
print("Calling Camb for Lensing B-modeswith lmax = {0:.0f}".format(lmax+200))
Tl,El,Bl,Xl = pycamb.camb(np.max(lmax)+200,**thepars)
Bl = Bl[0:np.max(lmax)]
#### Lensing B
Blensed = amplens * Bl
##### Make linear combination
totBB = Bprim + Blensed
else:
Bprim = spectra[0]
Blensed = thepars['lensing_amplitude'] * spectra[1]
totBB = Bprim + Blensed
##### Bin the spectra
ell = np.arange(len(totBB))+1
clprim_binned = binspec(ell, Bprim, x, xmin, xmax)
cllens_binned = binspec(ell, Blensed, x, xmin, xmax)
btot_binned = clprim_binned + cllens_binned
##### Corresponding error bars
dclsb, dclnb, dclb = th_errors_bins(x, ell, totBB/(ell*(ell+1)/(2*np.pi)), fsky, mukarcmin, fwhmdeg, deltal=deltal)
return btot_binned, clprim_binned, cllens_binned, dclsb, dclnb, dclb, Bprim, Blensed, totBB
def getbbsum(pars, x): thepars = params.copy() thepars['tensor_ratio'] = pars[0] Tprim,Eprim,Bprim,Xprim = pycamb.camb(lmax+1,**thepars) Bprim = Bprim[0:599] bla = np.interp(x, ell, Bprim) + pars[1] * np.interp(x, ell, Bpl) return bla
def getbbsum_samplevar(pars, x): thepars = params.copy() thepars['tensor_ratio'] = pars[0] Tprim,Eprim,Bprim,Xprim = pycamb.camb(lmax+1,**thepars) Bprim = Bprim[0:599] totBB = Bprim + pars[1] * Bpl dclsb, dclnb, dclb = th_errors_bins(x, ell, totBB/(ell*(ell+1)/(2*np.pi)), fsky, mukarcmin, fwhmdeg, deltal=deltal) bla = np.interp(x, ell, Bprim) + pars[1] * np.interp(x, ell, Bpl) return bla, dclsb, dclnb, dclb
def get_camb_spectra(cambparams, maxell):
#### Call CAMB: first without lensing and second with lensing
print("")
print("Calling Camb (Unlensed) with lmax = {0:.0f}".format(maxell+200))
thepars = cambparams.copy()
thepars['DoLensing'] = False
Tnl,Enl,Bnl,Xnl = pycamb.camb(np.max(maxell)+200,**thepars)
Bprim = Bnl[0:np.max(maxell)]
#### Camb for lensing B
thepars = cambparams.copy()
thepars['DoLensing'] = True
print("Calling Camb (Lensed) with lmax = {0:.0f}".format(maxell+200))
Tl,El,Bl,Xl = pycamb.camb(np.max(maxell)+200,**thepars)
Tl = Tl[0:np.max(maxell)]
El = El[0:np.max(maxell)]
Xl = Xl[0:np.max(maxell)]
Bl = Bl[0:np.max(maxell)]
#### Get Pure lensing B modes
Bl = Bl - Bprim
#### build spectra list
x = np.arange(len(Bl)) + 1
basic_spectra = [x, Tl, El, Xl, Bprim, Bl]
return basic_spectra
def camb_spectra(lmax, **params):
"""
Wrapper around `pycamb.camb`. Return Cl_TT, Cl_EE, Cl_BB, Cl_EB
non-weighted by l(l+1)/2pi, starting from l=0.
"""
import pycamb
T, E, B, X = pycamb.camb(lmax + 1, **params)
ell_camb = np.arange(0, lmax + 1)
fact_camb = 2 * np.pi / (ell_camb * (ell_camb + 1))
T = np.r_[0, T * fact_camb[1:]]
E = np.r_[0, E * fact_camb[1:]]
B = np.r_[0, B * fact_camb[1:]]
X = np.r_[0, X * fact_camb[1:]]
return [T, E, B, X]
def camb_spectra(lmax, **params):
"""
Wrapper around `pycamb.camb`. Return Cl_TT, Cl_EE, Cl_BB, Cl_EB
non-weighted by l(l+1)/2pi, starting from l=0.
"""
import pycamb
T, E, B, X = pycamb.camb(lmax + 1, **params)
ell_camb = np.arange(0, lmax + 1)
fact_camb = 2 * np.pi / (ell_camb * (ell_camb + 1))
T = np.r_[0, T * fact_camb[1:]]
E = np.r_[0, E * fact_camb[1:]]
B = np.r_[0, B * fact_camb[1:]]
X = np.r_[0, X * fact_camb[1:]]
return [T, E, B, X]
def rcamblib(rvalues,lmaxcamb):
lll = np.arange(lmaxcamb+1)
fact = (lll*(lll+1))/(2*np.pi)
spec = np.zeros((lmaxcamb+1,len(rvalues)))
i=0
for r in rvalues:
print('i = {0:5.0f} over {1:5.0f}: Calling CAMB with r={2:10.8f}'.format(i,len(rvalues),r))
params = {'H0':H0,'omegab':Omegab,'omegac':Omegac,'omegak':0,'scalar_index':0.9624,'reion__use_optical_depth':True,'reion__optical_depth':0.0925,'tensor_ratio':r,'WantTensors':True,'scalar_amp':scalar_amp,'DoLensing':True}
T,E,B,X = pycamb.camb(lmaxcamb+1+150,**params)
B=B[:lmaxcamb+1]/fact
B[~np.isfinite(B)]=0
spec[:,i] = B
i = i + 1
return [lll, rvalues, spec]
def __call__(self, ctx):
p1 = ctx.getParams()[0:len(self.mapping)]
try:
params = self.constants.copy()
for k,v in self.mapping.items():
params[k] = p1[v]
self._transform(params)
cl_tt,cl_ee,cl_bb,cl_te = pycamb.camb(self.lmax, **params)
ctx.add(CL_TT_KEY, cl_tt)
ctx.add(CL_TE_KEY, cl_te)
ctx.add(CL_EE_KEY, cl_ee)
ctx.add(CL_BB_KEY, cl_bb)
except RuntimeError:
getLogger().warn("Runtime error catched from the camb so. Used params [%s]"%( ", ".join([str(i) for i in p1]) ) )
raise LikelihoodComputationException()
def __call__(self, ctx):
p1 = ctx.getParams()[0:len(self.mapping)]
try:
params = self.constants.copy()
for k, v in self.mapping.items():
params[k] = p1[v]
self._transform(params)
cl_tt, cl_ee, cl_bb, cl_te = pycamb.camb(self.lmax, **params)
ctx.add(CL_TT_KEY, cl_tt)
ctx.add(CL_TE_KEY, cl_te)
ctx.add(CL_EE_KEY, cl_ee)
ctx.add(CL_BB_KEY, cl_bb)
except RuntimeError:
getLogger().warn(
"Runtime error catched from the camb so. Used params [%s]" %
(", ".join([str(i) for i in p1])))
raise LikelihoodComputationException()
r = 0.05
H0 = 67.04
omegab = 0.022032
omegac = 0.12038
h2 = (H0/100.)**2
scalar_amp = np.exp(3.098)/1.E10
omegav = h2 - omegab - omegac
Omegab = omegab/h2
Omegac = omegac/h2
print 'Omegab = ',omegab/h2,'Omegam = ',(omegac+omegab)/h2,'Omegav = ',omegav/h2
params = {'H0':H0,'omegab':Omegab,'omegac':Omegac,'omegak':0,'scalar_index':0.9624,
'reion__use_optical_depth':True,'reion__optical_depth':0.0925,
'tensor_ratio':r,'WantTensors':True,'scalar_amp':scalar_amp,'DoLensing':True}
lmaxcamb = 1000
T,E,B,X = pycamb.camb(lmaxcamb+1+150,**params)
lll = np.arange(1,lmaxcamb+1)
T=T[:lmaxcamb]
E=E[:lmaxcamb]
B=B[:lmaxcamb]
X=X[:lmaxcamb]
fact = (lll*(lll+1))/(2*np.pi)
spectra = [lll, T/fact, E/fact, B/fact, X/fact]
params_nl = {'H0':H0,'omegab':Omegab,'omegac':Omegac,'omegak':0,'scalar_index':0.9624,
'reion__use_optical_depth':True,'reion__optical_depth':0.0925,
'tensor_ratio':r,'WantTensors':True,'scalar_amp':scalar_amp,'DoLensing':False}
lmaxcamb = 1000
Tnl,Enl,Bnl,Xnl = pycamb.camb(lmaxcamb+1+150,**params_nl)
lll = np.arange(1,lmaxcamb+1)
Tnl=Tnl[:lmaxcamb]
omegav = h2 - omegab - omegac
Omegab = omegab/h2
Omegac = omegac/h2
rvalue = 0.2
print 'Omegab = ',omegab/h2,'Omegam = ',(omegac+omegab)/h2,'Omegav = ',omegav/h2
params = {'H0':H0,'omegab':Omegab,'omegac':Omegac,'omegak':0,'scalar_index':0.9624,
'reion__use_optical_depth':True,'reion__optical_depth':0.0925,
'tensor_ratio':rvalue,'WantTensors':True,'scalar_amp':scalar_amp,'DoLensing':False,
'tensor_index':-rvalue/8}
params_nl = {'H0':H0,'omegab':Omegab,'omegac':Omegac,'omegak':0,'scalar_index':0.9624,
'reion__use_optical_depth':True,'reion__optical_depth':0.0925,
'tensor_ratio':rvalue,'WantTensors':True,'scalar_amp':scalar_amp,'DoLensing':True,
'tensor_index':-rvalue/8}
lmax=1000
ctt,cee,cbbprim,cte = pycamb.camb(lmax,**params)
ctt,cee,cbbtot,cte = pycamb.camb(lmax,**params_nl)
cbblensing = cbbtot-cbbprim
ell = np.arange(lmax-1)+2
clf()
xlim(0,500)
a0,=plot(ell, cbbprim, 'b--', label='Primordial B-modes (r={0:.1g})'.format(rvalue),lw=3)
a1,=plot(ell, cbblensing, 'g--', label='Lensing B-modes',lw=3)
a2,=plot(ell, cbbtot, 'r', label='Total B-modes',lw=3)
a3,=plot(ell, cee, color='orange', label='E-modes',lw=3)
a4,=plot(ell, -cte, ':', color='purple',label='Total',lw=3)
a4,=plot(ell, cte, color='purple',label='TE correlation',lw=3)
a5,=plot(ell, ctt, 'k', label='Temperature',lw=3)
yscale('log')
paramsnl3 = {
"H0": H0,
"omegab": Omegab,
"omegac": Omegac,
"omegak": 0,
"scalar_index": 0.9624,
"reion__use_optical_depth": True,
"reion__optical_depth": 0.0925,
"tensor_ratio": 0.001,
"WantTensors": True,
"scalar_amp": scalar_amp,
"DoLensing": False,
}
lmax = 1200
ell = np.arange(1, lmax + 1)
Tnl, Enl, Bnl, Xnl = pc.camb(lmax + 1, **paramsnl)
Tnl2, Enl2, Bnl2, Xnl2 = pc.camb(lmax + 1, **paramsnl2)
Tnl3, Enl3, Bnl3, Xnl3 = pc.camb(lmax + 1, **paramsnl3)
T, E, B, X = pc.camb(lmax + 1, **params)
Blens = B - Bnl
clf()
lw = 3
plot(ell, np.sqrt(T), "k-", label="$C_\ell^{TT}$", lw=lw)
plot(ell, np.sqrt(X), "g-", label="$C_\ell^{TE}$", lw=lw)
plot(ell, np.sqrt(-X), "g--", label="$-C_\ell^{TE}$", lw=lw)
plot(ell, np.sqrt(E), "b-", label="$C_\ell^{EE}$", lw=lw)
plot(ell, np.sqrt(Bnl), "r-", label="Primordial $C_\ell^{BB} (r=0.1)$", lw=lw)
plot(ell, np.sqrt(Bnl2), "-", label="Primordial $C_\ell^{BB} (r=0.01)$", lw=lw, color="firebrick")
plot(ell, np.sqrt(Bnl3), "-", label="Primordial $C_\ell^{BB} (r=0.001)$", lw=lw, color="darkred")
plot(ell, np.sqrt(Blens), "--", label="Lensing $C_\ell^{BB}$", lw=lw, color="darkviolet")
params = {'H0':H0,'omegab':Omegab,'omegac':Omegac,'omegak':0,'scalar_index':0.9624,
'reion__use_optical_depth':True,'reion__optical_depth':0.0925,
'tensor_ratio':rvalue,'WantTensors':True,'scalar_amp':scalar_amp,'DoLensing':False,
'tensor_index':-rvalue/8}
params_l = {'H0':H0,'omegab':Omegab,'omegac':Omegac,'omegak':0,'scalar_index':0.9624,
'reion__use_optical_depth':True,'reion__optical_depth':0.0925,
'tensor_ratio':rvalue,'WantTensors':True,'scalar_amp':scalar_amp,'DoLensing':True,
'tensor_index':-rvalue/8}
lmax = 1000
ell = np.arange(1,600)
Tprim,Eprim,Bprim,Xprim = pycamb.camb(lmax+1,**params)
Tprim = Tprim[0:599]
Eprim = Eprim[0:599]
Bprim = Bprim[0:599]
Xprim = Xprim[0:599]
Tpl,Epl,Bpl,Xpl = pycamb.camb(lmax+1,**params_l)
Tpl = Tpl[0:599]
Epl = Epl[0:599]
Bpl = Bpl[0:599]
Xpl = Xpl[0:599]
Bl = Bpl - Bprim
clf()
plot(ell,Bprim,label='Primordial $C_\ell^{BB}$'+'$ (r={0:.2f})$'.format(rvalue))
plot(ell,Bl,label='Lensing $C_\ell^{BB}$')
plot(ell,Bpl,label='Total $C_\ell^{BB}$'+'$ (r={0:.2f})$'.format(rvalue))
H0 = 67.04
omegab = 0.022032
omegac = 0.12038
h2 = (H0/100.)**2
scalar_amp = np.exp(3.098)/1.E10
omegav = h2 - omegab - omegac
Omegab = omegab/h2
Omegac = omegac/h2
print 'Omegab = ',omegab/h2,'Omegam = ',(omegac+omegab)/h2,'Omegav = ',omegav/h2
params = {'H0':H0,'omegab':Omegab,'omegac':Omegac,'omegak':0,'scalar_index':0.9624,
'reion__use_optical_depth':True,'reion__optical_depth':0.0925,
'tensor_ratio':0.,'WantTensors':True,'scalar_amp':scalar_amp,'DoLensing':False}
lmaxcamb = 3*512
T,E,B,X = pycamb.camb(lmaxcamb+1,**params)
lll = np.arange(1,lmaxcamb+1)
fact = (lll*(lll+1))/(2*np.pi)
spectra = [lll, T/fact, E/fact, B/fact, X/fact]
clf()
plot(lll,spectra[1]*(lll*(lll+1))/(2*np.pi),label='$C_\ell^{TT}$')
#yscale('log')
xlim(0,lmaxcamb+1)
#ylim(0.0001,100)
xlabel('$\ell$')
ylabel('$\ell(\ell+1)C_\ell/(2\pi)$'+' '+'$[\mu K^2]$ ')
legend(loc='lower right',frameon=False)
import pycamb
lmaxcamb = 3*nside
## parameters from Planck 2013 results XV. CMB Power spectra ..., table 8, Planck+WP
## Archiv 1303.5075
H0 = 67.04
omegab = 0.022032
omegac = 0.12038
h2 = (H0/100.)**2
scalar_amp = np.exp(3.098)/1.E10
omegav = h2 - omegab - omegac
Omegab = omegab/h2
Omegac = omegac/h2
print 'Omegab = ',omegab/h2,'Omegam = ',(omegac+omegab)/h2,'Omegav = ',omegav/h2
params = {'H0':H0,'omegab':Omegab,'omegac':Omegac,'omegak':0,'scalar_index':0.9624}
T,E,B,X = pycamb.camb(lmaxcamb+1,**params)
lll = np.arange(1,lmaxcamb+1)
fact = (lll*(lll+1))/(2*np.pi)
spectra = [lll, T/fact, E/fact, B/fact, X/fact]
## parameters from Planck 2013 results XV. CMB Power spectra ..., table 8, Planck+WP
## Archiv 1303.5075
H0 = 67.04
omegab = 0.022032
omegac = 0.12038
h2 = (H0/100.)**2
scalar_amp = np.exp(3.098)/1.E10
omegav = h2 - omegab - omegac
Omegab = omegab/h2
Omegac = omegac/h2
print 'Omegab = ',omegab/h2,'Omegam = ',(omegac+omegab)/h2,'Omegav = ',omegav/h2
print 'Omegab = ',omegab/h2,'Omegam = ',(omegac+omegab)/h2,'Omegav = ',omegav/h2
params = {'H0':H0,'omegab':Omegab,'omegac':Omegac,'omegak':0,'scalar_index':0.9624,
'reion__use_optical_depth':True,'reion__optical_depth':0.0925,
'tensor_ratio':rvalue,'WantTensors':True,'scalar_amp':scalar_amp,'DoLensing':False,
'tensor_index':-rvalue/8}
params_l = {'H0':H0,'omegab':Omegab,'omegac':Omegac,'omegak':0,'scalar_index':0.9624,
'reion__use_optical_depth':True,'reion__optical_depth':0.0925,
'tensor_ratio':0,'WantTensors':True,'scalar_amp':scalar_amp,'DoLensing':True}
lmax = 1000
ell = np.arange(1,600)
Tprim,Eprim,Bprim,Xprim = pycamb.camb(lmax+1,**params)
Tprim = Tprim[0:599]
Eprim = Eprim[0:599]
Bprim = Bprim[0:599]
Xprim = Xprim[0:599]
Tl,El,Bl,Xl = pycamb.camb(lmax+1,**params_l)
Tl = Tl[0:599]
El = El[0:599]
Bl = Bl[0:599]
Xl = Xl[0:599]
Btot = Bl + Bprim
clf()
plot(ell,Bprim,label='Primordial $C_\ell^{BB}$'+'$ (r={0:.2f})$'.format(rvalue))
plot(ell,Bl,label='Lensing $C_\ell^{BB}$')
plot(ell,Btot,label='Total $C_\ell^{BB}$'+'$ (r={0:.2f})$'.format(rvalue))