def bias_eff(k, Pk, OmegaM, z, M1, M2, author, bins=100):
M = np.logspace(np.log10(M1), np.log10(M2), bins + 1) #bins in mass
M_middle = 10**(0.5 * (np.log10(M[1:]) + np.log10(M[:-1]))
) #center of the bin
deltaM = M[1:] - M[:-1] #size of the bin
if author == 'SMT01':
dndM = MFL.ST_mass_function(k, Pk, OmegaM, None, None, None,
M_middle)[1]
if author == 'Tinker':
dndM = MFL.Tinker_mass_function(k, Pk, OmegaM, z, None, None, None,
M_middle)[1]
b = np.empty(bins, dtype=np.float64)
for i in range(bins):
b[i] = bias(k, Pk, OmegaM, M_middle[i], author)
bias_eff = np.sum(dndM * deltaM * b) / np.sum(dndM * deltaM)
return bias_eff
M_max = 1e16 #Msun/h
bins = 200
#From Crighton et al. 2015
Omega_HI = 4e-4 * (1.0 + z)**0.60
#define the bins in mass
M = np.logspace(np.log10(M_min), np.log10(M_max), bins + 1)
#read the matter power spectrum
if do_CDM: [k, Pk] = BL.CDM_Pk(f_Pk_DM, f_transfer, Omega_CDM, Omega_B)
else: [k, Pk] = BL.DM_Pk(f_Pk_DM)
#compute/read the halo mass function
if not (os.path.exists(f_MF)):
dndM = MFL.ST_mass_function(k, Pk, OmegaM, None, None, None, M)[1]
np.savetxt(f_MF, np.transpose([M, dndM]))
else:
M_MF, dndM = np.loadtxt(f_MF, unpack=True)
if np.any(M_MF != M):
print 'error!!\nbins in mass function are different'
sys.exit()
#compute/read the halo bias
if not (os.path.exists(f_bias)):
b = np.empty(bins + 1, dtype=np.float64)
for i, mass in enumerate(M):
b[i] = BL.bias(k, Pk, OmegaM, mass, 'SMT01')
np.savetxt(f_bias, np.transpose([M, b]))
else:
M_b, b = np.loadtxt(f_bias, unpack=True)