def eval(m, pars, k, mu):
if not m.use_so_correction:
return numpy.zeros(len(k))
kprime = k_AP(k, mu, m.alpha_perp, m.alpha_par)
muprime = mu_AP(mu, m.alpha_perp, m.alpha_par)
G = m.FOG(kprime, muprime, m.sigma_c)
G2 = m.FOG(kprime, muprime, m.sigma_so)
Gprime = m.FOG.derivative_sigma(kprime, muprime, m.sigma_so)
with m.preserve(use_so_correction=False):
# Pcc with no FOG kernels
m.sigma_c = 0.
Pcc = m.Pgal_cc(k, mu)
# derivative of the SO correction terms
term1 = 2 * m.f_so * (1 - m.f_so) * G * Pcc
term2 = 2 * m.f_so**2 * G2 * Pcc
term3 = 2 * G * m.f_so * m.fcB * m.NcBs / (m.alpha_perp**2 *
m.alpha_par)
toret = (term1 + term2 + term3) * Gprime
return (1 - m.fs)**2 * toret
def eval(m, pars, k, mu):
kprime = k_AP(k, mu, m.alpha_perp, m.alpha_par)
muprime = mu_AP(mu, m.alpha_perp, m.alpha_par)
G = m.FOG(kprime, muprime, m.sigma_sB)
return (m.fs * m.fsB * G)**2 / (m.alpha_perp**2 * m.alpha_par)
def eval(m, pars, k, mu):
kprime = k_AP(k, mu, m.alpha_perp, m.alpha_par)
muprime = mu_AP(mu, m.alpha_perp, m.alpha_par)
G = m.FOG(kprime, muprime, m.sigma_c)
Gprime = m.FOG.derivative_sigma(kprime, muprime, m.sigma_c)
with m.preserve():
m.sigma_c = 0
if not m.use_so_correction:
term1 = 2 * G * Gprime * m.Pgal_cc(k, mu)
else:
# turn off SO correction
m.use_so_correction = False
Pcc = m.Pgal_cc(k, mu)
# derivative of the SO correction terms
G2 = m.FOG(kprime, muprime, m.sigma_so)
term1_a = 2 * G * (1 - m.f_so)**2 * Pcc
term1_b = 2 * m.f_so * (1 - m.f_so) * G2 * Pcc
term1_c = 2 * G2 * m.f_so * m.fcB * m.NcBs / (m.alpha_perp**2 *
m.alpha_par)
term1 = (term1_a + term1_b + term1_c) * Gprime
term2 = Gprime * m.Pgal_cs(k, mu)
return (1 - m.fs)**2 * term1 + 2 * m.fs * (1 - m.fs) * term2
def eval(m, pars, k, mu):
kprime = k_AP(k, mu, m.alpha_perp, m.alpha_par)
muprime = mu_AP(mu, m.alpha_perp, m.alpha_par)
# finger of god
G = m.FOG(kprime, muprime, m.sigma_fog)
# do the volume rescaling
rescaling = (m.alpha_drag**3) / (m.alpha_perp**2 * m.alpha_par)
mu2 = muprime**2
return rescaling * G**2 * mu2 * (m.P_mu2(kprime) +
2 * m.P_mu4(kprime) * mu2) / m.f
def eval(m, pars, k, mu):
kprime = k_AP(k, mu, m.alpha_perp, m.alpha_par)
muprime = mu_AP(mu, m.alpha_perp, m.alpha_par)
G = m.FOG(kprime, muprime, m.sigma_fog)
if hasattr(G, 'values'):
G = G.values
Gprime = m.FOG.derivative_sigma(kprime, muprime, m.sigma_fog)
toret = np.zeros_like(G)
valid = np.nonzero(G)
toret[valid] = 2 * Gprime[valid] * m.power(k, mu)[valid] / G[valid]
return toret
def eval(m, pars, k, mu):
kprime = k_AP(k, mu, m.alpha_perp, m.alpha_par)
muprime = mu_AP(mu, m.alpha_perp, m.alpha_par)
# derivative of scale-dependent bias term
dbtot_db1 = 1 + 2 * m.f_nl * m.delta_crit / m.alpha_png(kprime)
# finger-of-god
G = m.FOG(kprime, muprime, m.sigma_fog)
# the final derivative
btot = m.btot(kprime)
rescaling = (m.alpha_drag**3) / (m.alpha_perp**2 * m.alpha_par)
return rescaling * G**2 * (2 * m.P_mu0(kprime) + muprime**2 *
m.P_mu2(kprime)) * dbtot_db1 / btot
def eval(m, pars, k, mu):
kprime = k_AP(k, mu, m.alpha_perp, m.alpha_par)
muprime = mu_AP(mu, m.alpha_perp, m.alpha_par)
# finger-of-god
G = m.FOG(kprime, muprime, m.sigma_fog)
# derivative of scale-dependent bias term
ddb_dfnl = 2 * (m.b1 - m.p) * m.delta_crit / m.alpha_png(kprime)
# do the volume rescaling
rescaling = (m.alpha_drag**3) / (m.alpha_perp**2 * m.alpha_par)
# the final derivative
btot = m.btot(kprime)
return rescaling * G**2 * (2 * m.P_mu0(kprime) + m.P_mu2(kprime) *
muprime**2) / btot * ddb_dfnl
def eval(m, pars, k, mu):
# AP shifted
kprime = k_AP(k, mu, m.alpha_perp, m.alpha_par)
muprime = mu_AP(mu, m.alpha_perp, m.alpha_par)
Gc = m.FOG(kprime, muprime, m.sigma_c)
GsA = m.FOG(kprime, muprime, m.sigma_sA)
GsB = m.FOG(kprime, muprime, m.sigma_sB)
toret = 2*m.fs*(1-m.fs)*m.fcB*Gc * ((1-m.fsB)*GsA + m.fsB*GsB)
# additional term from SO correction
if m.use_so_correction:
G1 = m.FOG(kprime, muprime, m.sigma_c)
G2 = m.FOG(kprime, muprime, m.sigma_so)
toret += (1-m.fs)**2 * 2*G1*G2*m.f_so*m.fcB
return toret / (m.alpha_perp**2 * m.alpha_par)
def eval(m, pars, k, mu):
term1 = 2 * (-(1 - m.fcB) * m.Pgal_cAcA(k, mu) +
(1 - 2 * m.fcB) * m.Pgal_cAcB(k, mu) +
m.fcB * m.Pgal_cBcB(k, mu))
term2 = (1 - m.fsB) * (-m.Pgal_cAsA(k, mu) + m.Pgal_cBsA(
k, mu)) + m.fsB * (-m.Pgal_cAsB(k, mu) + m.Pgal_cBsB(k, mu))
# AP shifted
kprime = k_AP(k, mu, m.alpha_perp, m.alpha_par)
muprime = mu_AP(mu, m.alpha_perp, m.alpha_par)
# additional term from SO correction
if m.use_so_correction:
G1 = m.FOG(kprime, muprime, m.sigma_c)
G2 = m.FOG(kprime, muprime, m.sigma_so)
term1 *= (((1 - m.f_so) * G1)**2 + 2 * m.f_so *
(1 - m.f_so) * G1 * G2 + (m.f_so * G2)**2)
term1 += 2 * G1 * G2 * m.f_so * m.NcBs / (m.alpha_perp**2 *
m.alpha_par)
return (1 - m.fs)**2 * term1 + 2 * m.fs * (1 - m.fs) * term2
def eval(m, pars, k, mu):
if not m.use_so_correction:
return numpy.zeros(len(k))
# sum the individual components of Pcc
PcAcA = (1. - m.fcB)**2 * m.Pgal_cAcA(k, mu)
PcAcB = 2 * m.fcB * (1 - m.fcB) * m.Pgal_cAcB(k, mu)
PcBcB = m.fcB**2 * m.Pgal_cBcB(k, mu)
Pcc = PcAcA + PcAcB + PcBcB
# FOG
kprime = k_AP(k, mu, m.alpha_perp, m.alpha_par)
muprime = mu_AP(mu, m.alpha_perp, m.alpha_par)
G1 = m.FOG(kprime, muprime, m.sigma_c)
G2 = m.FOG(kprime, muprime, m.sigma_so)
# the SO correction
term1 = -2 * (1. - m.f_so) * G1**2 * Pcc
term2 = 2 * (1 - 2 * m.f_so) * G1 * G2 * Pcc
term3 = 2 * m.f_so * G2**2 * Pcc
term4 = 2 * G1 * G2 * m.fcB * m.NcBs / m.alpha_perp**2 / m.alpha_par
return (1 - m.fs)**2 * (term1 + term2 + term3 + term4)