def get_correlation_function(self, k=None, pk=None, \
Sigma_nl=0., r=None, r0=1., inverse=0, update=0):
if k is None or pk is None:
k = self.k
pk = self.pk
#-- transform to log space for Hankel Transform
klog = 10**N.linspace(N.log10(k.min()), N.log10(k.max()), k.size)
pklog = N.interp(klog, k, pk)
#-- apply isotropic damping
pklog *= N.exp(-0.5 * klog**2 * Sigma_nl**2)
rout, xiout = fftlog.HankelTransform(klog, pklog, q=1.5, mu=0.5, \
output_r_power=-3, output_r=r, r0=r0)
norm = 1 / (2 * N.pi)**1.5
if inverse:
xiout /= norm
else:
xiout *= norm
if update:
self.r = rout
self.xi = xiout
return rout, xiout
def get_correlation_function(self, k=None, pk=None,
sigma_nl=0., r=None,
r0=1., inverse=0):
''' This computes isotropic xi(r) from isotropic P(k)
Currently not used by fitter but useful for tests
'''
if k is None or pk is None:
k = self.k
pk = self.pk
#-- transform to log space for Hankel Transform
klog = 10**np.linspace( np.log10(k.min()), np.log10(k.max()), k.size)
pklog = np.interp(klog, k, pk)
#-- apply isotropic damping
pklog *= np.exp(-0.5*klog**2*sigma_nl**2)
rout, xiout = fftlog.HankelTransform(klog, pklog,
q=1.5, mu=0.5,
output_r_power=-3,
output_r=r, r0=r0)
norm = 1/(2*np.pi)**1.5
if inverse:
xiout /= norm
else:
xiout *= norm
return rout, xiout
def get_multipoles_from_pk(self, k, pk_mult, r0=1., r=None):
xi_mult = pk_mult * 0
ell_max = xi_mult.shape[0] * 2
for ell in range(0, ell_max, 2):
rout, xiout = fftlog.HankelTransform(k, pk_mult[ell/2], q=1.5, mu=0.5+ell, \
output_r_power=-3, output_r=r, r0=r0)
norm = 1 / (2 * N.pi)**1.5 * (-1)**(ell / 2)
xi_mult[ell / 2] = xiout * norm
return rout, xi_mult
def get_xi_multipoles_from_pk(self, k, pk_mult, output_r=None, r0=1.):
nell = len(pk_mult)
xi_mult = []
for i in range(nell):
pk = pk_mult[i]
ell = i*2
r, xi = fftlog.HankelTransform(k, pk, mu=0.5+ell, output_r=output_r,
output_r_power=-3, q=1.5, r0=r0)
norm = 1/(2*np.pi)**1.5 * (-1)**(ell/2)
xi_mult.append(xi*norm)
xi_mult = np.array(xi_mult)
return r, xi_mult
def get_pk_multipoles_from_xi(self, r, xi_mult, output_k=None, r0=1.):
nell = len(xi_mult)
pk_mult = []
for i in range(nell):
xi = xi_mult[i]
ell = i*2
k, pk = fftlog.HankelTransform(r, xi, mu=0.5+ell, output_r=output_k,
output_r_power=-3, q=1.5, r0=r0)
#norm = 1/(2*np.pi)**1.5 * (-1)**(ell/2)
norm = 1/(2*np.pi)**1.5 * (-1)**(ell/2) * (2*np.pi)**3
pk_mult.append(pk*norm)
pk_mult = np.array(pk_mult)
return k, pk_mult
def get_xi_multipoles_from_pk(self, k, pk_mult, output_r=None, r0=1.):
xi_mult = []
ell = 0
for pk in pk_mult:
rout, xiout = fftlog.HankelTransform(k,
pk,
mu=0.5 + ell,
output_r=output_r,
output_r_power=-3,
q=1.5,
r0=r0)
norm = 1 / (2 * np.pi)**1.5 * (-1)**(ell / 2)
xi_mult.append(xiout * norm)
ell += 2
return rout, np.array(xi_mult)
