#~ #compute tinker stuff
limM = [4.2e11,1e12,3e12,1e13, 5e15]
loglim = [ 11.623, 12., 12.477, 13., 15.698]
cname = 'Pk_cc_z='+str(z[j])+'00.txt'
camb = np.loadtxt(cname)
kcamb = camb[:,0]
Pcamb = camb[:,1]
massf = np. loadtxt('/home/david/codes/Paco/data2/0.15eV/hmf_z='+str(z[j])+'.txt')
m_middle = massf[:,10]
dm = massf[:,11]
#### get tinker and crocce hmf
bt=np.empty(len(m_middle),dtype=np.float64)
for i in range(len(m_middle)):
bt[i]=bias(kcamb,Pcamb,Omega_m,m_middle[i],'Tinker')
dndM=MFL.Tinker_mass_function(kcamb,Pcamb,Omega_m,z[j],limM[0],limM[4],len(m_middle),Masses=m_middle)[1]
with open('/home/david/codes/Paco/data2/other neutrinos masses/'+str(nv)+'/thmf_z='+str(z[j])+'.txt', 'w+') as fid_file:
#~ with open('/home/david/codes/Paco/data2/other neutrinos masses/'+"%.3" % nv+'/thmf_z='+str(z[j])+'.txt', 'w+') as fid_file:
for m in xrange(0, len(m_middle)):
fid_file.write('%.8g\n' % (dndM[m]))
fid_file.close()
dndMbis=MFL.Crocce_mass_function(kcamb,Pcamb,Omega_m,z[j],limM[0],limM[4],len(m_middle),Masses=m_middle)[1]
with open('/home/david/codes/Paco/data2/other neutrinos masses/'+str(nv)+'/chmf_z='+str(z[j])+'.txt', 'w+') as fid_file:
#~ with open('/home/david/codes/Paco/data2/other neutrinos masses/'+"%.2" % nv+'/chmf_z='+str(z[j])+'.txt', 'w+') as fid_file:
for m in xrange(0, len(m_middle)):
fid_file.write('%.8g\n' % (dndMbis[m]))
fid_file.close()
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)
if np.any(M_b!=M):
print 'error!!\nbins in halo bias are different'; sys.exit()
#compute the M_HI function
M_HI = M_HI_model(M,z,h)
#compute the bias of the HI
numerator = integral1(M,dndM,b,M_HI)
denominator = integral2(M,dndM,M_HI)
if fix_Omega_HI:
# Omega_HI = f3/rho_crit*\int_0^infty n(M)M_HI(M)dM
if compute_MF:
dndM=MFL.ST_mass_function(k,Pk,Omega_m,None,None,None,M)[1]
np.savetxt(f_MF,np.transpose([M,dndM]))
else:
M_MF,MF=np.loadtxt(f_MF,unpack=True)
if Mmin<np.min(M_MF): #check that the file explores the minimum mass
print 'compute the mass function for smaller masses'; sys.exit()
if Mmax>np.max(M_MF): #check that the file explores the maximum mass
print 'compute the mass function for larger masses'; sys.exit()
dndM=10**(np.interp(np.log10(M),np.log10(M_MF),np.log10(MF))); del M_MF,MF
#compute the halo bias or read it from a file
if compute_bias:
bias=np.zeros(bins,dtype=np.float64)
for i in xrange(bins):
bias[i]=BL.bias(k,Pk,Omega_m,M[i],author='SMT01') #'ST01' 'Tinker'
print 'M = %1.3e Msun/h ---> bias = %1.3f'%(M[i],bias[i])
np.savetxt(f_bias,np.transpose([M,bias]))
else:
M_b,b=np.loadtxt(f_bias,unpack=True)
if Mmin<np.min(M_b): #check that the file explores the minimum mass
print 'compute the bias for smaller masses'; sys.exit()
if Mmax>np.max(M_b): #check that the file explores the maximum mass
print 'compute the bias for larger masses'; sys.exit()
bias=10**(np.interp(np.log10(M),np.log10(M_b),np.log10(b))); del M_b,b
#compute the DLA cross section as a function of the dark matter halo mass
cross_section=np.zeros(bins,dtype=np.float64)
f=open(f_out_cross_section,'w')
for i in xrange(bins):
cross_section[i]=cross_section_model(M[i],redshift,model)
#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)
if np.any(M_b != M):
print 'error!!\nbins in halo bias are different'
sys.exit()
#compute the M_HI function
M_HI = M_HI_model(M, z, h)
#compute the bias of the HI
numerator = integral1(M, dndM, b, M_HI)
denominator = integral2(M, dndM, M_HI)
if fix_Omega_HI:
else:
M_MF, MF = np.loadtxt(f_MF, unpack=True)
if Mmin < np.min(M_MF): #check that the file explores the minimum mass
print 'compute the mass function for smaller masses'
sys.exit()
if Mmax > np.max(M_MF): #check that the file explores the maximum mass
print 'compute the mass function for larger masses'
sys.exit()
dndM = 10**(np.interp(np.log10(M), np.log10(M_MF), np.log10(MF)))
del M_MF, MF
#compute the halo bias or read it from a file
if compute_bias:
bias = np.zeros(bins, dtype=np.float64)
for i in xrange(bins):
bias[i] = BL.bias(k, Pk, Omega_m, M[i],
author='SMT01') #'ST01' 'Tinker'
print '%e %f' % (M[i], bias[i])
np.savetxt(f_bias, np.transpose([M, bias]))
else:
M_b, b = np.loadtxt(f_bias, unpack=True)
if Mmin < np.min(M_b): #check that the file explores the minimum mass
print 'compute the bias for smaller masses'
sys.exit()
if Mmax > np.max(M_b): #check that the file explores the maximum mass
print 'compute the bias for larger masses'
sys.exit()
bias = 10**(np.interp(np.log10(M), np.log10(M_b), np.log10(b)))
del M_b, b
#compute the DLA cross section as a function of the dark matter halo mass
cross_section = np.zeros(bins, dtype=np.float64)
#M1,sigma_DLAs = np.loadtxt('DLAs_sigma_mean_z=%d.txt'%z, unpack=True)
# interpolate HMF and DLAs cross-sections
MF = np.interp(M,M_mean,MF,left=0,right=0)
sigma_DLAs = np.interp(M,M_mean,sigma_DLAs,left=0,right=0)
indexes = np.where((MF>0.0) & (sigma_DLAs>0.0))[0]
MF = MF[indexes]
sigma_DLAs = sigma_DLAs[indexes]
M = M[indexes]
Mmin = np.min(M)
Mmax = np.max(M)
# compute halo bias 'SMT01'
b = BL.bias(k, Pk, Omega_m, M, 'SMT01', 10000)
np.savetxt('bias_z=%d.txt'%z, np.transpose([M,b]))
#sigma_DLAs = np.zeros(bins+1, dtype=np.float64)
#if z==2: M0 = 10.22955643 #value of 10^20
#if z==3: M0 = 9.89018152 #value of 10^20
#for i in xrange(bins+1):
# sigma_DLAs[i] = sigma_DLAs_func(M[i], M0, N_HI=10**20.0)
# integral value, its limits and precision parameters
eps = 1e-14
h1 = 1e-15
hmin = 0.0
bins_k = 10000 #number of bins to use in the input Pk
# For neutrinos use Omega_{CDM+B} instead of Omega_m
Omega_m = 0.3175
Mmin = 1e10 #Msun/h
Mmax = 1e16 #Msun/h
bins_MF = 300 #number of bins in the HMF
author = 'SMT01' #'SMT01', 'Tinker'
f_out = 'bias_SMT_z=0.txt'
##################################################################################
# read input Pk
k, Pk = np.loadtxt(f_in, unpack=True)
# define the array with the masses where to evaluate the halo bias
Masses = np.logspace(np.log10(Mmin), np.log10(Mmax), bins_MF)
# compute halo bias
b = BL.bias(k, Pk, Omega_m, Masses, author, bins_k)
# save results to file
np.savetxt(f_out, np.transpose([Masses, b]))
# This computes the effective bias for halos in the range
# [min(Masses), max(Masses)]
z = 0.0
b_eff = BL.bias_eff(k, Pk, Omega_m, Masses, z, author)
limM[4],
len(m_middle4),
Masses=m_middle4)[1]
hmf2 = MFL.Crocce_mass_function(kcamb2,
Pcamb2,
Omega_m,
z[j],
limM[3],
limM[4],
len(m_middle4),
Masses=m_middle4)[1]
bt1 = np.empty(len(m_middle4), dtype=np.float64)
bt2 = np.empty(len(m_middle4), dtype=np.float64)
for i in range(len(m_middle4)):
bt1[i] = bias(kcamb1, Pcamb1, Omega_m, m_middle4[i], 'Tinker')
bt2[i] = bias(kcamb2, Pcamb2, Omega_m, m_middle4[i], 'Tinker')
Bias_eff_t1 = np.sum(hmf1 * dm4 * bt1) / np.sum(dm4 * hmf1)
Bias_eff_t2 = np.sum(hmf2 * dm4 * bt2) / np.sum(dm4 * hmf2)
#-------------------------------------------------------------
### massive case
# neutrino parameters
hierarchy = 'degenerate' #'degenerate', 'normal', 'inverted'
Mnun = 0.15 #eV
Nnun = 3 #number of massive neutrinos
Neffn = 3.046
# cosmological parameters
hn = 0.6711
bt4 = np.empty(len(m_middle4), dtype=np.float64)
#~ bst1=np.empty(len(m_middle1),dtype=np.float64)
#~ bst2=np.empty(len(m_middle2),dtype=np.float64)
#~ bst3=np.empty(len(m_middle3),dtype=np.float64)
#~ bst4=np.empty(len(m_middle4),dtype=np.float64)
#~ bsmt1=np.empty(len(m_middle1),dtype=np.float64)
#~ bsmt2=np.empty(len(m_middle2),dtype=np.float64)
#~ bsmt3=np.empty(len(m_middle3),dtype=np.float64)
#~ bsmt4=np.empty(len(m_middle4),dtype=np.float64)
bm1 = np.empty(len(m_middle1), dtype=np.float64)
bm2 = np.empty(len(m_middle2), dtype=np.float64)
bm3 = np.empty(len(m_middle3), dtype=np.float64)
bm4 = np.empty(len(m_middle4), dtype=np.float64)
for i in range(len(m_middle1)):
bt1[i] = bias(kcamb, Pcamb, Omega_m, m_middle1[i], 'Tinker')
bt2[i] = bias(kcamb, Pcamb, Omega_m, m_middle2[i], 'Tinker')
bt3[i] = bias(kcamb, Pcamb, Omega_m, m_middle3[i], 'Tinker')
bt4[i] = bias(kcamb, Pcamb, Omega_m, m_middle4[i], 'Tinker')
#~ #-------------------------------------------
#~ bst1[i]=bias(kcamb,Pcamb,Omega_m,m_middle1[i],'Crocce')
#~ bst2[i]=bias(kcamb,Pcamb,Omega_m,m_middle2[i],'Crocce')
#~ bst3[i]=bias(kcamb,Pcamb,Omega_m,m_middle3[i],'Crocce')
#~ bst4[i]=bias(kcamb,Pcamb,Omega_m,m_middle4[i],'Crocce')
#~ #------------------------------------------
#~ bsmt1[i]=bias(kcamb,Pcamb,Omega_m,m_middle1[i],'SMT01')
#~ bsmt2[i]=bias(kcamb,Pcamb,Omega_m,m_middle2[i],'SMT01')
#~ bsmt3[i]=bias(kcamb,Pcamb,Omega_m,m_middle3[i],'SMT01')
#~ bsmt4[i]=bias(kcamb,Pcamb,Omega_m,m_middle4[i],'SMT01')
#-----------------------------------------
bm1[i] = bias(kcamb, Pcamb, Omega_m, m_middle1[i], 'Mice')
