from hmf import MassFunction from astropy.cosmology import FlatLambdaCDM import astropy.units as u cosmo = FlatLambdaCDM(H0=67.77*u.km/u.s/u.Mpc, Om0=0.307115, Ob0=0.048206) #lib.covariance_factor #lib.f_BH(sigma, 0.333, 0.788, 0.807, 1.795) bias = lambda sigma : lib.b_BH(sigma, a=0.908, p=0.671, q=1.737) # diagonal error dn_n_L04 = lambda sigma : ((bias(sigma) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[0]) ) dn_n_L10 = lambda sigma : ((bias(sigma) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[1]) ) dn_n_L25 = lambda sigma : ((bias(sigma) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[2]) ) dn_n_L40 = lambda sigma : ((bias(sigma) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[3]) ) dn_L04 = lambda sigma : (((bias(sigma) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[0]))**2. + lib.shot_noise(sigma, 400.**3.) )**0.5 dn_L10 = lambda sigma : (((bias(sigma) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[1]) )**2. + lib.shot_noise(sigma, 1000.**3.) )**0.5 dn_L25 = lambda sigma : (((bias(sigma) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[2]) )**2. + lib.shot_noise(sigma, 2500.**3.) )**0.5 dn_L40 = lambda sigma : (((bias(sigma) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[3]) )**2. + lib.shot_noise(sigma, 4000.**3.) )**0.5 dn_n_sn_L04 = lambda sigma : ( lib.shot_noise(sigma, 400.**3.) )**0.5 dn_n_sn_L10 = lambda sigma : ( lib.shot_noise(sigma, 1000.**3.) )**0.5 dn_n_sn_L25 = lambda sigma : ( lib.shot_noise(sigma, 2500.**3.) )**0.5 dn_n_sn_L40 = lambda sigma : ( lib.shot_noise(sigma, 4000.**3.) )**0.5 # off diagonal error # (lib.nbar(s1) * lib.nbar(s2) )**0.5 * dn_cov_L04 = lambda s1, s2 : (dn_L04(s1)*dn_L04(s2))**0.5 # ( (bias(s1)*bias(s2) * lib.hmf.growth_factor**2. * (lib.covariance_factor[0]) )**2.+ lib.shot_noise(n.min([s1,s2]), 400.**3.) )**0.5 dn_cov_L10 = lambda s1, s2 : (dn_L10(s1)*dn_L10(s2) )**0.5#( (bias(s1)*bias(s2) * lib.hmf.growth_factor**2. * (lib.covariance_factor[1]) )**2.+ lib.shot_noise(n.min([s1,s2]), 1000.**3.) )**0.5 dn_cov_L25 = lambda s1, s2 : (dn_L25(s1)*dn_L25(s2) )**0.5#( (bias(s1)*bias(s2) * lib.hmf.growth_factor**2. * (lib.covariance_factor[2]) )**2.+ lib.shot_noise(n.min([s1,s2]), 2500.**3.) )**0.5 dn_cov_L40 = lambda s1, s2 : (dn_L40(s1)*dn_L40(s2) )**0.5#( (bias(s1)*bias(s2) * lib.hmf.growth_factor**2. * (lib.covariance_factor[3]) )**2.+ lib.shot_noise(n.min([s1,s2]), 4000.**3.) )**0.5
from hmf import MassFunction from astropy.cosmology import FlatLambdaCDM import astropy.units as u cosmo = FlatLambdaCDM(H0=67.77*u.km/u.s/u.Mpc, Om0=0.307115, Ob0=0.048206) #lib.covariance_factor #lib.f_BH(sigma, 0.333, 0.788, 0.807, 1.795) A0=0.333 a0=0.786 p0=0.807 q0=1.795 bias = lambda sigma, a0, p0, q0 : lib.b_BH(sigma, a0, p0, q0) fsigma = lambda sigma : lib.f_BH(sigma, A0, a0, p0, q0) # diagonal error dn_L04 = lambda sigma, a, p, q : (((bias(sigma, a, p, q) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[0]))**2. + lib.shot_noise(sigma, 400.**3.) )**0.5 dn_L10 = lambda sigma, a, p, q : (((bias(sigma, a, p, q) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[1]) )**2. + lib.shot_noise(sigma, 1000.**3.) )**0.5 dn_L25 = lambda sigma, a, p, q : (((bias(sigma, a, p, q) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[2]) )**2. + lib.shot_noise(sigma, 2500.**3.) )**0.5 dn_L40 = lambda sigma, a, p, q : (((bias(sigma, a, p, q) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[3]) )**2. + lib.shot_noise(sigma, 4000.**3.) )**0.5 # off diagonal error dn_cov_L04 = lambda s1, s2, a, p, q : (dn_L04(s1, a, p, q)*dn_L04(s2, a, p, q))**0.5 dn_cov_L10 = lambda s1, s2, a, p, q : (dn_L10(s1, a, p, q)*dn_L10(s2, a, p, q) )**0.5 dn_cov_L25 = lambda s1, s2, a, p, q : (dn_L25(s1, a, p, q)*dn_L25(s2, a, p, q) )**0.5 dn_cov_L40 = lambda s1, s2, a, p, q : (dn_L40(s1, a, p, q)*dn_L40(s2, a, p, q) )**0.5 # opens the data #Quantity studied qty = "mvir" # working directory
Om0=0.307115, Ob0=0.048206) #lib.covariance_factor #lib.f_BH(sigma, 0.333, 0.788, 0.807, 1.795) A0 = 0.333 a0 = 0.786 p0 = 0.807 q0 = 1.795 bias = lambda sigma, a0, p0, q0: lib.b_BH(sigma, a0, p0, q0) fsigma = lambda sigma: lib.f_BH(sigma, A0, a0, p0, q0) # diagonal error dn_L04 = lambda sigma, a, p, q: ( ((bias(sigma, a, p, q) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[0]))**2. + lib.shot_noise(sigma, 400.**3.))**0.5 dn_L10 = lambda sigma, a, p, q: ( ((bias(sigma, a, p, q) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[1]))**2. + lib.shot_noise(sigma, 1000.**3.))**0.5 dn_L25 = lambda sigma, a, p, q: ( ((bias(sigma, a, p, q) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[2]))**2. + lib.shot_noise(sigma, 2500.**3.))**0.5 dn_L40 = lambda sigma, a, p, q: ( ((bias(sigma, a, p, q) * lib.hmf.growth_factor)**2. * (lib.covariance_factor[3]))**2. + lib.shot_noise(sigma, 4000.**3.))**0.5 # off diagonal error dn_cov_L04 = lambda s1, s2, a, p, q: (dn_L04(s1, a, p, q) * dn_L04( s2, a, p, q))**0.5 dn_cov_L10 = lambda s1, s2, a, p, q: (dn_L10(s1, a, p, q) * dn_L10( s2, a, p, q))**0.5