def __init__(self, params): #omr=0.0, omb=0.05, omc=0.25, oml=0.7, H0=70. ):
assert (1 - np.abs(params['omegab'] + params['omegac'] + params['omegav']) <= 0.05)
self.omegar = 0. # TODO: add radiation density
self.omegab = params['omegab']
self.omegac = params['omegac']
self.omegav = params['omegav']
self.H0 = params['H0']
self.h = self.H0 / 100.
self.omegam = params['omegab']+params['omegac']
self.zvec = np.concatenate( [np.linspace(0., 20., 500., endpoint=False),
np.linspace( 20., 200., 200., endpoint=False),
np.linspace(200., 1500., 100)] )
# self.xvec = np.array( [ integrate.quad( lambda z : (const.c.value * 1.e-3) / self.H_z(z), 0., zmax )[0] for zmax in self.zvec ] )
self.xvec = np.array( [ integrate.quad( lambda z : (const.c.to('km/s').value) / self.H_z(z), 0., zmax )[0] for zmax in self.zvec ] )
self.zmin = np.min(self.zvec)
self.zmax = np.max(self.zvec)
self.xmin = np.min(self.xvec)
self.xmax = np.max(self.xvec)
self.spl_x_z = interpolate.UnivariateSpline( self.zvec, self.xvec, k=1, s=0 )
self.spl_z_x = interpolate.UnivariateSpline( self.xvec, self.zvec, k=1, s=0 )
self.chi_rec = self.spl_x_z(1090.)
# Evaluating Matter PS
max_kappa = 80.
kappa = np.logspace(-3,np.log10(max_kappa),50)
z = np.logspace(-3,1,num=50)
z = np.insert(z,0,0.)
redshifts = z[::-1]
mps = pycamb.matter_power(redshifts, k=kappa, **params)
mps = np.asarray(mps)
@np.vectorize
def pk_hi(k, z):
return mps[1,-1,z]*(k/mps[0,-1,z])**-3
k_hi = np.linspace(mps[0,-1,0],2000,1000)[1:]
mps_hi = np.zeros((2, k_hi.size, redshifts.size))
for i in xrange(0, redshifts.size):
mps_hi[1,:,i] = pk_hi(k_hi,i)
mps_hi[0,:,i] = k_hi
mps_ext_hi = np.hstack((mps, mps_hi))
self.pkz = interpolate.RectBivariateSpline(mps_ext_hi[0,:,0], z, mps_ext_hi[1,:,::-1], kx=3, ky=2, s=0) # (Mpc/h)^3
def __init__(self,cosmo,z,kmax=10,nk=32768):
self.cosmo=cosmo.copy()
kvals = np.linspace(0,kmax,nk)
self.k = kvals.copy()
pk=pycamb.matter_power(redshifts=[z], k=kvals, **cosmo)[1]
pk[0]=0
self.pk=pk
r=2*pi/kmax*np.arange(nk)
pkk=self.k*self.pk
cric=-np.imag(fft(pkk)/nk)/r/2/pi**2*kmax
cric[0]=0
h=cosmo['H0']/100
self.xi=interpolate.interp1d(r,cric)
def PK ( koverh , redshiftlist = [0] , maxk = 1.0 , logk_spacing =0.02 , **params ) : """returns the interpolated matter power spectrum """ kh, PK , sigma8 = pyc.matter_power (redshifts = redshiftlist, maxk = maxk , logk_spacing = logk_spacing , get_sigma8 = True, **params ) return np.interp( koverh, kh, PK )
def __init__(self, cosmomo, NonLinear=0):
self.omegab=cosmomo.omegab
self.omegam=cosmomo.omegam
self.omegal=cosmomo.omegal
self.omegac= self.omegam-self.omegab
self.H0=cosmomo.H0
self.h=self.H0/100.
self.camb_params={"omegab":self.omegab, "omegac":self.omegac, 'omegav':self.omegal, "H0":self.H0, "scalar_amp":2.1e-9, "reion__optical_depth":0.09, "reion__reionization":True, "reion__use_optical_depth":True, "reion__redshift":11, "scalar_index":0.9692, "reion__delta_redshift":1.5, "TCMB":2.726, "Num_Nu_massless":3.046, "yhe":0.24, "NonLinear":NonLinear}
self.z_tab=[0, 0.1, 0.2, 0.3, 0.5, 0.7,1., 1.5,2,2.5,3.,3.5,4,5,7,9.,10.,15,20, 50, 70, 100, 200, 400, 600, 1000,1100, 2000]
self.AA=pc.matter_power(redshifts=self.z_tab[::-1], maxk=1000, **self.camb_params)
self.Pdek=scipy.interpolate.RectBivariateSpline( nm.log10(self.AA[0][:,0]*self.h),self.z_tab,(self.AA[1][:,::-1]/self.h**3), kx=3, ky=3, s=0)
def powerspectrum(A):
assert A.WhichSpectrum == 2
import pycamb
a = dict(H0=A.h * 100.,
omegac=A.OmegaM- A.OmegaB,
omegab=A.OmegaB,
omegav=A.OmegaL,
omegak=0.0, omegan=0,
scalar_index=A.PrimordialIndex
)
print a
fakesigma8 = pycamb.transfers(scalar_amp=1, **a)[2]
scalar_amp = fakesigma8 ** -2 * A.Sigma8 ** 2
k, p = pycamb.matter_power(scalar_amp=scalar_amp, maxk=500, **a)
p[numpy.isnan(p)] = 0
numpy.savetxt(A.PowerSpectrumFile, zip(k, p), fmt='%g')
def __init__(self, cosmomo, NonLinear=0):
self.omegab=cosmomo.omegab
self.omegam=cosmomo.omegam
self.omegal=cosmomo.omegal
self.omegac= self.omegam-self.omegab
self.H0=cosmomo.H0
self.h=self.H0/100.
self.camb_params={"omegab":self.omegab, "omegac":self.omegac, 'omegav':self.omegal, 'omegan':0.0, "H0":self.H0, "scalar_amp":2.215e-9, "reion__optical_depth":0.0925, "reion__reionization":True, "reion__use_optical_depth":True, "reion__redshift":11.37, "scalar_index":0.9619, "reion__delta_redshift":1.5, "TCMB":2.725, "Num_Nu_massless":3.046, "yhe":0.248, "NonLinear":NonLinear, "lSampleBoost":1, "AccuracyBoost":1, "lAccuracyBoost":1}
self.z_tab=[0.001, 0.1, 0.2, 0.3,0.4, 0.5,0.6, 0.7,0.8,0.9,1., 1.1, 1.2, 1.3, 1.4, 1.5,1.7, 2,2.3, 2.5,2.7, 3.,3.5,4,5,7,9.,10.,15,20, 50, 70, 100, 200, 400, 600, 1000,1100, 2000]
#self.z_tab=[0, 0.1, 0.2, 0.3, 0.5, 0.7, 70, 100, 200, 400, 600, 1000,1100, 2000]
self.AA=pc.matter_power(redshifts=self.z_tab[::-1], maxk=100, logk_spacing=0.1, **self.camb_params)
self.Pdek=scipy.interpolate.RectBivariateSpline( (self.AA[0][:,0]*self.h),self.z_tab,(self.AA[1][:,::-1]/self.h**3), kx=3, ky=3, s=0)
def powerspectrum(A):
assert A.WhichSpectrum == 2
import pycamb
a = dict(H0=A.h * 100.,
omegac=A.OmegaM - A.OmegaB,
omegab=A.OmegaB,
omegav=A.OmegaL,
omegak=0.0,
omegan=0,
scalar_index=A.PrimordialIndex)
print a
fakesigma8 = pycamb.transfers(scalar_amp=1, **a)[2]
scalar_amp = fakesigma8**-2 * A.Sigma8**2
k, p = pycamb.matter_power(scalar_amp=scalar_amp, maxk=500, **a)
p[numpy.isnan(p)] = 0
numpy.savetxt(A.PowerSpectrumFile, zip(k, p), fmt='%g')
def sigma8(redshiftlist = [0] ,
maxk = 1.0 ,
logk_spacing = 0.02,
**params ):
"""
returns the value of sigma8 at the redshift specified
for the set of parameters passed in.
args:
redshiftlist: list of floats, optional, defaults to [0]
maxk : float, optional, defaults to 1.0
max value of k to which the matter power spectrum
is calculated
logk_spacing: float, optional , defaults to 0.02
spacing in log(k *Mpc/h)
**params: dictionary/keyword argument for CAMB parameters
returns:
float, sigma8 for the parameters passed
example usage:
>>> import camb as cp
>>> params = {"scalar_amp":2.3e-9}
>>> cp.sigma8(**params)
>>> 0.8339590551185299
>>> #This value may change if the defaults of pycamb change
status:
Tested to work correctly for single redshifts.
See testlinearityofsigma8withAs() in tests.
R. Biswas, Mon Aug 26 10:31:00 CDT 2013
"""
sigma8 = pyc.matter_power(redshifts = redshiftlist,
maxk = maxk ,
logk_spacing = logk_spacing ,
get_sigma8 = True,
**params )[-1]
return float(sigma8)
def Pk(self):
import pycamb
a = dict(H0=self.h * 100.,
omegac=self.M- self.B,
omegab=self.B,
omegav=self.L,
omegak=1 - self.M - self.L, omegan=0)
DH = 299792.468 / 100.
fakesigma8 = pycamb.transfers(scalar_amp=1, **a)[2]
scalar_amp = fakesigma8 ** -2 * self.sigma8 ** 2
k, p = pycamb.matter_power(scalar_amp=scalar_amp, maxk=500, **a)
k *= DH
p /= DH ** 3
p[numpy.isnan(p)] = 0
func = interp1d(k, p, kind=5, bounds_error=False, fill_value=0)
func.__doc__ = \
"""power spectrum P(k) normalized to sigma8.
k is in 1/DH and p is in DH**3
"""
return func
def Pk(self):
import pycamb
a = dict(H0=self.h * 100.,
omegac=self.M - self.B,
omegab=self.B,
omegav=self.L,
omegak=1 - self.M - self.L,
omegan=0)
DH = 299792.468 / 100.
fakesigma8 = pycamb.transfers(scalar_amp=1, **a)[2]
scalar_amp = fakesigma8**-2 * self.sigma8**2
k, p = pycamb.matter_power(scalar_amp=scalar_amp, maxk=500, **a)
k *= DH
p /= DH**3
p[numpy.isnan(p)] = 0
func = interp1d(k, p, kind=5, bounds_error=False, fill_value=0)
func.__doc__ = \
"""power spectrum P(k) normalized to sigma8.
k is in 1/DH and p is in DH**3
"""
return func
def power(self, OmegaB, sigma8):
""" returns matter power spectrum. need pycamb
the power spectrum will be in GADGET normalization,
normalized to (2 * numpy.pi) ** -3 * P_phys(k)
This takes a long time to calculate; and no caching
is implemented.
returns a function P_k(k).
"""
import pycamb
from scipy.interpolation import interp1d
assert self.M + self.L + self.K == 1.0
args = dict(H0=self.h * 100,
omegac=self.M - OmegaB,
omegab=OmegaB,
omegav=self.L,
omegak=0,
omegan=0)
fakesigma8 = pycamb.transfers(scalar_amp=1, **args)[2]
scalar_amp = fakesigma8 ** -2 * sigma8 ** 2
k, p = pycamb.matter_power(scalar_amp=scalar_amp, maxk=10, **args)
k /= self.U.MPC_h
p *= (self.U.MPC_h) ** 3 * (2 * numpy.pi) ** -3 # xiao to GADGET
return interp1d(k, p, kind='linear', copy=True,
bounds_error=False, fill_value=0)
def get_pk(cosmo,z, kmax=10, nk=32768): h = cosmo['H0']/100 kvals = np.linspace(0,kmax/h,nk) pk=pycamb.matter_power(redshifts=[z], k=kvals, **cosmo)[1] pk[0]=0 return kvals*h, pk / h**3
def get_pk(cosmo,z, kmax=10, nk=32768): kvals = np.linspace(0,kmax,nk) pk=pycamb.matter_power(redshifts=[z], k=kvals, **cosmo)[1] pk[0]=0 return kvals, pk
