def bias_numerator_integrand(logm, a, delta, cd) : import cosmocalc import numpy as np ## Set the cosmocalc cosmology cosmocalc.set_cosmology(cd) ## Need the mass in the integral, not the log10(mass) m = np.power(10., logm) ## Return the integrand return cosmocalc.tinker2010_mass_function(m, a, delta)*\ cosmocalc.tinker2010_bias(m, a, delta)*m
def get_bG(cosmo_dict, a, Masses):
return cc.growth_function(a)*np.array([cc.tinker2010_bias(Mi, a, 200) for Mi in Masses])
#Set up cosmocalc
ombh2, omch2, w0, ns, ln10As, H0, Neff, sigma8 = test_cosmo
h = H0 / 100.
Ob = ombh2 / h**2
Om = Ob + omch2 / h**2
cosmo_dict = {"om":Om,"ob":Ob,"ol":1-Om,\
"ok":0.0,"h":h,"s8":sigma8,\
"ns":ns,"w0":w0,"wa":0.0}
cc.set_cosmology(cosmo_dict) #Used to create the splines in cosmocalc
#Get the biases
bias = np.zeros_like(lM)
a = 1. / (1 + redshifts[z_index])
for i in xrange(0, len(lM)):
M = 10**lM[i]
bias[i] = cc.tinker2010_bias(M, a, 200)
#Get sigmaR
R = (3. / 4. / np.pi * volume)**(1. / 3.)
sigmaR = cc.sigmaRtophat_exact(R, a)
print sigmaR**2
for i in xrange(0, len(bias)):
for j in xrange(0, len(bias)):
if i == j: continue
cov_an[i, j] = bias[i] * bias[j] * N_emu[i] * N_emu[j] * sigmaR**2
continue
continue
print cov_an[0]
import matplotlib.pyplot as plt
z = redshifts[zind]
a = scale_factors[zind]
sigmaR = cc.sigmaRtophat_exact(R,a)
#Read in the data
MF_data = np.genfromtxt("../../all_MF_data/building_MF_data/full_mf_data/Box%03d_full/Box%03d_full_Z%d.txt"%(box,box,zind))
lM_bins = MF_data[:,:2]
lM = np.mean(lM_bins,1)
N_data = MF_data[:,2]
N_data_array.append(N_data)
logN_data_array.append(np.log(N_data))
#Get the biases
bias = np.zeros_like(lM)
for i in xrange(0,len(bias)):
bias[i] = cc.tinker2010_bias(10**lM[i],a,200)
#Predict the TMF
n = mf_emulator.predict_mass_function(test_cosmo,redshift=z,lM_bins=lM_bins)
N_emu = n*volume
N_emu_array.append(N_emu)
logN_emu_array.append(np.log(N_emu))
#Make the covariance matrix
cov_emu = np.diag(N_emu)
logcov_emu = np.diag(1./N_emu)
for i in xrange(0,len(lM)):
for j in xrange(0,len(lM)):
cov_emu[i,j] += bias[i]*bias[j]*N_emu[i]*N_emu[j]*sigmaR**2
logcov_emu[i,j] += bias[i]*bias[j]*sigmaR**2
continue #end i