def __init__(self, ps=None, sigma_v=0.0, redshift=0.0, **kwargs):
if ps == None:
from os.path import join, dirname
#psfile = join(dirname(__file__),"data/ps_z1.5.dat")
#psfile = join(dirname(__file__),"data/wigglez_halofit_z1.5.dat")
psfile = join(dirname(__file__),"data/wigglez_halofit_z0.8.dat")
print "loading matter power file: " + psfile
redshift = 0.8
#pk_interp = cs.LogInterpolater.fromfile(psfile)
pwrspec_data = np.genfromtxt(psfile)
(log_k, log_pk) = (np.log(pwrspec_data[:,0]), \
np.log(pwrspec_data[:,1]))
logpk_interp = interpolate.interp1d(log_k, log_pk,
bounds_error=False,
fill_value=np.min(log_pk))
pk_interp = lambda k: np.exp(logpk_interp(np.log(k)))
kstar = 7.0
ps = lambda k: np.exp(-0.5 * k**2 / kstar**2) * pk_interp(k)
self._sigma_v = sigma_v
RedshiftCorrelation.__init__(self, ps_vv=ps, redshift=redshift)
def __init__(self, ps = None, redshift = 0.0, **kwargs):
if ps == None:
from os.path import join, dirname
psfile = join(dirname(__file__),"data/ps_z1.5.dat")
redshift = 1.5
kstar = 5.0
c1 = cs.LogInterpolater.fromfile(psfile)
ps = lambda k: np.exp(-0.5 * k**2 / kstar**2) * c1(k)
RedshiftCorrelation.__init__(self, ps_vv = ps, redshift = redshift)
def __init__(self, ps=None, redshift=0.0, **kwargs):
from os.path import join, dirname
if ps == None:
psfile = join(dirname(__file__), "data/ps_z1.5.dat")
redshift = 1.5
c1 = cs.LogInterpolater.fromfile(psfile)
ps = lambda k: np.exp(-0.5 * k**2 / self._kstar**2) * c1(k)
RedshiftCorrelation.__init__(self, ps_vv=ps, redshift=redshift)
self._load_cache(join(dirname(__file__), "data/corr_z1.5.dat"))
def __init__(self, ps = None, redshift = 0.0, sigma_v=0.0, **kwargs):
from os.path import join, dirname
if ps == None:
psfile = join(dirname(__file__),"data/ps_z1.5.dat")
redshift = 1.5
c1 = cs.LogInterpolater.fromfile(psfile)
ps = lambda k: np.exp(-0.5 * k**2 / self._kstar**2) * c1(k)
self._sigma_v = sigma_v
RedshiftCorrelation.__init__(self, ps_vv=ps, redshift=redshift)
self._load_cache(join(dirname(__file__),"data/corr_z1.5.dat"))
def angular_powerspectrum_full(self, l, nu1, nu2, redshift=False):
"""Calculate the angular powerspectrum by explicit integration.
Parameters
----------
l : np.ndarray
Multipoles to calculate at.
nu1, nu2 : np.ndarray
Frequencies/redshifts to calculate at.
redshift : boolean, optional
If `False` (default) interperet `nu1`, `nu2` as frequencies,
otherwise they are redshifts (relative to the 21cm line).
Returns
-------
aps : np.ndarray
"""
if not redshift:
z1 = units.nu21 / nu1 - 1.0
z2 = units.nu21 / nu2 - 1.0
else:
z1 = nu1
z2 = nu2
return RedshiftCorrelation.angular_powerspectrum_full(self, l, z1, z2)
def angular_powerspectrum_full(self, l, nu1, nu2, redshift=False):
"""Calculate the angular powerspectrum by explicit integration.
Parameters
----------
l : np.ndarray
Multipoles to calculate at.
nu1, nu2 : np.ndarray
Frequencies/redshifts to calculate at.
redshift : boolean, optional
If `False` (default) interperet `nu1`, `nu2` as frequencies,
otherwise they are redshifts (relative to the 21cm line).
Returns
-------
aps : np.ndarray
"""
if not redshift:
z1 = units.nu21 / nu1 - 1.0
z2 = units.nu21 / nu2 - 1.0
else:
z1 = nu1
z2 = nu2
return RedshiftCorrelation.angular_powerspectrum_full(self, l, z1, z2)
