def get_transfer(transfer_file, camb_dict, transfer_fit, k_bounds=None):
"""
We use either CAMB or the EH approximation to get the transfer function.
The transfer function is in terms of k/h -- IN UNITS OF h/Mpc??!!
"""
#If no transfer file uploaded, but it was custom, execute CAMB
if transfer_file is None:
if transfer_fit == "CAMB":
k, T, sig8 = pycamb.transfers(**camb_dict)
T = np.log(T[[0, 6], :, 0])
del sig8, k
elif transfer_fit == "EH":
k = np.exp(np.linspace(np.log(k_bounds[0]), np.log(k_bounds[1]), 250))
#Since the function natively calculates the transfer based on k in Mpc^-1,
# we need to multiply by h.
t, T = pert.transfer_function_EH(k * camb_dict['H0'] / 100,
omega_M_0=(camb_dict['omegac'] + camb_dict['omegab']), omega_lambda_0=camb_dict['omegav'],
h=camb_dict["H0"] / 100, omega_b_0=camb_dict['omegab'], omega_n_0=camb_dict['omegan'],
N_nu=camb_dict['Num_Nu_massive'])
T = np.vstack((np.log(k), np.log(T)))
del t
else:
#Import the transfer file
T = read_transfer(transfer_file)
return T
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 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 lnt(self, lnk):
"""
Generate transfer function with CAMB
.. note :: This should not be called by the user!
"""
for k in self.option_defaults:
if k not in self.t.transfer_options:
self.t.transfer_options[k] = self.option_defaults[k]
cdict = dict(self.t.pycamb_dict,
**self.t.transfer_options)
T = pycamb.transfers(**cdict)[1]
T = np.log(T[[0, 6], :, 0])
lnkout, lnT = self._check_low_k(T[0, :], T[1, :])
return spline(lnkout, lnT, k=1)(lnk)
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 lnt(self, lnk):
"""
Natural log of the transfer function
Parameters
----------
lnk : array_like
Wavenumbers [Mpc/h]
Returns
-------
lnt : array_like
The log of the transfer function at lnk.
"""
pycamb_dict = {
"w_lam": self.cosmo.w(0),
"TCMB": self.cosmo.Tcmb0.value,
"Num_Nu_massless": self.cosmo.Neff,
"omegab": self.cosmo.Ob0,
"omegac": self.cosmo.Om0 - self.cosmo.Ob0,
"H0": self.cosmo.H0.value,
"omegav": self.cosmo.Ode0,
"omegak": self.cosmo.Ok0,
"omegan": self.cosmo.Onu0,
# "scalar_index":self.n,
}
cdict = dict(pycamb_dict, **self.params)
T = pycamb.transfers(**cdict)[1]
T = np.log(T[[0, 6], :, 0])
if lnk[0] < T[0, 0]:
lnkout, lnT = self._check_low_k(T[0, :], T[1, :], lnk[0])
else:
lnkout = T[0, :]
lnT = T[1, :]
return spline(lnkout, lnT, k=1)(lnk)
def lnt(self, lnk):
"""
Natural log of the transfer function
Parameters
----------
lnk : array_like
Wavenumbers [Mpc/h]
Returns
-------
lnt : array_like
The log of the transfer function at lnk.
"""
pycamb_dict = {
"w_lam": self.cosmo.w(0),
"TCMB": self.cosmo.Tcmb0.value,
"Num_Nu_massless": self.cosmo.Neff,
"omegab": self.cosmo.Ob0,
"omegac": self.cosmo.Om0 - self.cosmo.Ob0,
"H0": self.cosmo.H0.value,
"omegav": self.cosmo.Ode0,
"omegak": self.cosmo.Ok0,
"omegan": self.cosmo.Onu0,
# "scalar_index":self.n,
}
cdict = dict(pycamb_dict, **self.params)
T = pycamb.transfers(**cdict)[1]
T = np.log(T[[0, 6], :, 0])
if lnk[0] < T[0, 0]:
lnkout, lnT = self._check_low_k(T[0, :], T[1, :], lnk[0])
else:
lnkout = T[0, :]
lnT = T[1, :]
return spline(lnkout, lnT, k=1)(lnk)
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)
