Lb1 = np.mean(bias1[Toto])
Lb2 = np.mean(bias2[Toto])
Lb3 = np.mean(bias3[Toto])
Lb4 = np.mean(bias4[Toto])
errLb1 = np.mean(errb1[Toto])
errLb2 = np.mean(errb2[Toto])
errLb3 = np.mean(errb3[Toto])
errLb4 = np.mean(errb4[Toto])
####################################################################
#### compute pt terms
Pmod_dd, Pmod_dt, Pmod_tt, A, B, C, D, E, F, G, H = pt_terms(kcamb, Pcamb)
### interpolate on simulation k
Pmod_dd, Pmod_dt, Pmod_tt, A, B, C, D, E, F, G, H = interp_simu1(z,j ,k, kcamb, Pcamb, Pmod_dd, Pmod_dt, Pmod_tt,\
A, B, C, D, E, F, G, H, 2)
#~ plt.plot(k,Pmm/Pmm)
#~ plt.plot(k,Pmod_dd/Pmm)
#~ plt.plot(k, Plin/Pmm)
#~ plt.plot(k, Pcamb/Pmm)
#~ plt.xscale('log')
#~ plt.show()
#~ kill
####################################################################
#### get fitted coefficients
#-------------------------------------------------------------------
Tm, Tcb = interp_simu3(z, j, k, kclass, Tm, Tcb, 2)
fcc = fz[j] * (Tm / Tcb)
def rescal(j, case):
z = [0.0, 0.5, 1.0, 2.0]
#~ z = [0.0,2.0]
#~ mu = 0.5
#~ kmax = 1
#~ mass_range = ['m1','m2','m3','m4']
#~ mass_range = ['m1', 'm2']
#~ mass_range = ['m1']
#~ axis = 0 #in redshift-space distortion axis
# neutrino parameters
hierarchy = 'degenerate' #'degenerate', 'normal', 'inverted'
###########################
Mnu = 0.0 #eV
###########################
Nnu = 0 #number of massive neutrinos
Neff = 3.046
# cosmological parameters
h = 0.6711
Omega_c = 0.2685 - Mnu / (93.14 * h**2)
Omega_b = 0.049
Omega_l = 0.6825
Omega_k = 0.0
Omega_m = Omega_c + Omega_b
tau = None
start = time()
########################################################################
########################################################################
####################################################################
##### scale factor
red = ['0.0', '0.5', '1.0', '2.0']
ind = red.index(str(z[j]))
fz = [0.524, 0.759, 0.875, 0.958]
Dz = [1., 0.77, 0.61, 0.42]
print 'For redshift z = ' + str(z[j])
Omeg_m_z = Omega_m * (1 + z[j])**3 / (Omega_m * (1 + z[j])**3 + Omega_l)
#~ fz = Omeg_m_z**0.55
#########################################################################
#### load data from simualtion
kcamb, Pcamb, k, Pmm, PH1, PH2, PH3 , PH4, errPhh1, errPhh2, errPhh3, errPhh4, bias1, bias2, bias3, bias4, \
bias1s, bias2s, bias3s, bias4s, errb1, errb2, errb3, errb4, Pmono1, Pmono2, Pmono3, Pmono4, errPr1, errPr2, errPr3,\
errPr4, kclass, Tm, Tcb, noise1, noise2, noise3, noise4 = ld_data(0.0, z, j)
####################################################################
##### define the maximum scale for the fit
kstop1 = [0.16, 0.2, 0.25, 0.35]
kstop2 = [0.12, 0.16, 0.2, 0.2]
kstop3 = [0.15, 0.15, 0.15, 0.15]
#### the case
if case == 1:
kstop = kstop1[ind]
elif case == 2:
kstop = kstop2[ind]
elif case == 3:
kstop = kstop3[ind]
#### other kstop
#~ kstoplim = [0.5,0.5,0.5,0.4]
#~ kstoplim = [0.25,0.25,0.25,0.25]
#~ kstop = kstoplim[ind]
print kstop
# put identation to the rest to loop over kstop
#~ #kstop_arr = np.logspace(np.log10(0.05),np.log10(0.6),20)
#~ #for kstop in kstop_arr:
#######################################################################
# interpolate to have more points and create an evenly logged array
#~ kbis = np.logspace(np.log10(np.min(k)), np.log10(np.max(k)), 250)
#~ kbis = np.logspace(np.log10(np.min(kcamb)), np.log10(np.max(kcamb)), 200)
#~ Plinbis = np.interp(kbis, k, Plin)
lim = np.where((k < kstop) & (k > 1e-2))[0]
#~ plt.figure()
#~ plt.plot(kcamb,Pcamb)
#~ plt.plot(kbis,Plinbis)
#~ plt.plot(k,Plin)
#~ plt.xscale('log')
#~ plt.yscale('log')
#~ plt.xlim(1e-3,10)
#~ plt.ylim(1e-1,4e4)
#~ plt.show()
#~ kill
####################################################################
##### compute linear bias and error
# on interpolated array
toto = np.where(k < 0.05)[0]
lb1 = np.mean(bias1[toto])
lb2 = np.mean(bias2[toto])
lb3 = np.mean(bias3[toto])
lb4 = np.mean(bias4[toto])
errlb1 = np.mean(errb1[toto])
errlb2 = np.mean(errb2[toto])
errlb3 = np.mean(errb3[toto])
errlb4 = np.mean(errb4[toto])
# on simulation array
Toto = np.where(k < 0.05)[0]
Lb1 = np.mean(bias1[Toto])
Lb2 = np.mean(bias2[Toto])
Lb3 = np.mean(bias3[Toto])
Lb4 = np.mean(bias4[Toto])
errLb1 = np.mean(errb1[Toto])
errLb2 = np.mean(errb2[Toto])
errLb3 = np.mean(errb3[Toto])
errLb4 = np.mean(errb4[Toto])
####################################################################
#### compute pt terms
Pmod_dd, Pmod_dt, Pmod_tt, A, B, C, D, E, F, G, H = pt_terms(kcamb, Pcamb)
### interpolate on simulation k
Pmod_dd, Pmod_dt, Pmod_tt, A, B, C, D, E, F, G, H = interp_simu1(z,j ,k, kcamb, Pcamb, Pmod_dd, Pmod_dt, Pmod_tt,\
A, B, C, D, E, F, G, H, 2)
#~ plt.plot(k,Pmm/Pmm)
#~ plt.plot(k,Pmod_dd/Pmm)
#~ plt.plot(k, Plin/Pmm)
#~ plt.plot(k, Pcamb/Pmm)
#~ plt.xscale('log')
#~ plt.show()
#~ kill
####################################################################
#### get fitted coefficients
print 'polynomial'
biasF1, biasF2, biasF3, biasF4, biasF1bis, biasF2bis, biasF3bis, biasF4bis = poly(kstop, lb1, lb2, lb3, lb4,\
errlb1, errlb2, errlb3, errlb4, k, bias1, bias2, bias3, bias4, errb1, errb2, errb3, errb4,0.0, z, j, case)
#~ biasF1s, biasF2s, biasF3s, biasF4s, biasF1biss, biasF2biss, biasF3biss, biasF4biss = poly(kstop, k, lb1, lb2, lb3, lb4,\
#~ errlb1, errlb2, errlb3, errlb4, kbis, bias1biss, bias2biss, bias3biss, bias4biss, errb1bis, errb2bis, errb3bis, errb4bis,Mnu, z, j, case)
#-------------------------------------------------------------------
print 'perturbation'
bias2PT1, bias2PT2, bias2PT3, bias2PT4, bias3PT1, bias3PT2, bias3PT3, bias3PT4, bias3PTbis1,\
bias3PTbis2, bias3PTbis3, bias3PTbis4 = perturb(kstop, lb1, lb2, lb3, lb4, errlb1, errlb2, errlb3, errlb4, Pmod_dd, k, bias1,\
bias2, bias3, bias4, errb1, errb2, errb3, errb4, A, B, C, D, E, F,0.0, z, j, case, PH1, noise1, noise2, noise3, noise4)
#~ bias2PT1s, bias2PT2s, bias2PT3s, bias2PT4s, bias3PT1s, bias3PT2s, bias3PT3s, bias3PT4s, bias3PTbis1s,\
#~ bias3PTbis2s, bias3PTbis3s, bias3PTbis4s = perturb(kstop, k, lb1, lb2, lb3, lb4, errlb1, errlb2, errlb3, errlb4, Pmmbis,\
#~ kbis, bias1biss, bias2biss, bias3biss, bias4biss, errb1bis, errb2bis, errb3bis, errb4bis, A, B, C, D, E, F,Mnu, z, j, case)
#~ kill
### mean ####
#~ b1 = np.array([bias2PT1*sca1/bias1bis, bias2PT2*sca2/bias2bis, bias2PT3*sca3/bias3bis, bias2PT4*sca4/bias4bis])
#~ b1bis = np.array([bias3PT1*sca1/bias1bis, bias3PT2*sca2/bias2bis, bias3PT3*sca3/bias3bis, bias3PT4*sca4/bias4bis])
#~ b1ter = np.array([bias3PTbis1*sca1/bias1bis, bias3PTbis2*sca2/bias2bis, bias3PTbis3*sca3/bias3bis, bias3PTbis4*sca4/bias4bis])
#~ b3 = np.array([biasF1*sca1/bias1bis, biasF2*sca2/bias2bis, biasF3*sca3/bias3bis, biasF4*sca4/bias4bis])
#~ b3bis = np.array([biasF1bis*sca1/bias1bis, biasF2bis*sca2/bias2bis, biasF3bis*sca3/bias3bis, biasF4bis*sca4/bias4bis])
#~ b1 = np.mean(b1,axis=0)
#~ b1bis = np.mean(b1bis,axis=0)
#~ b1ter = np.mean(b1ter,axis=0)
#~ b3 = np.mean(b3,axis=0)
#~ b3bis = np.mean(b3bis,axis=0)
sca1, sca2, sca3, sca4 = np.loadtxt(
'/home/david/codes/montepython_public/montepython/likelihoods/BE_HaPPy/coefficients/other neutrinos masses/0.15eV/rescaling_z='
+ str(z[j]) + '_.txt')
b2PT1 = bias2PT1 * sca1
b2PT2 = bias2PT2 * sca2
b2PT3 = bias2PT3 * sca3
b2PT4 = bias2PT4 * sca4
b3PT1 = bias3PT1 * sca1
b3PT2 = bias3PT2 * sca2
b3PT3 = bias3PT3 * sca3
b3PT4 = bias3PT4 * sca4
bF1 = biasF1 * sca1
bF2 = biasF2 * sca2
bF3 = biasF3 * sca3
bF4 = biasF4 * sca4
b3PTbis1 = bias3PTbis1 * sca1
b3PTbis2 = bias3PTbis2 * sca2
b3PTbis3 = bias3PTbis3 * sca3
b3PTbis4 = bias3PTbis4 * sca4
return b2PT1, b2PT2, b2PT3, b2PT4,b3PT1, b3PT2, b3PT3, b3PT4,bF1, bF2, bF3, bF4,\
b3PTbis1, b3PTbis2, b3PTbis3, b3PTbis4
#~ ####################################################################
#~ ### compute of the 4 mass bins
#~ ####################################################################
Tm, Tcb = interp_simu3(z, j, k, kclass, Tm, Tcb, 2)
fcc = fz[j] * (Tm / Tcb)
#~ kai1, kai2, kai3, kai4, sco1, sco2, sco3, sco4, tns1, tns2, tns3, tns4, etns1, etns2, etns3, etns4 = RSD(fz,fcc, Dz[ind]\
#~ , j, kstop, Pmmbis, biasF1, biasF2, biasF3, biasF4, kbis, Plinbis, Pmono1bis, Pmono2bis, Pmono3bis, \
#~ Pmono4bis, errPr1bis, errPr2bis, errPr3bis, errPr4bis, Pmod_dt, Pmod_tt, case,z,0.0, A, B, C, D, E, F, G, H )
kcamb, Pcamb, k, Pmm, PH1, PH2, PH3 , PH4, errPhh1, errPhh2, errPhh3, errPhh4, bias1, bias2, bias3, bias4, \
bias1s, bias2s, bias3s, bias4s, errb1, errb2, errb3, errb4, Pmono1, Pmono2, Pmono3, Pmono4, errPr1, errPr2, errPr3,\
errPr4, kclass, Tm, Tcb, noise1, noise2, noise3, noise4 = ld_data(0.15, z, j)
Pmod_dd, Pmod_dt, Pmod_tt, A, B, C, D, E, F, G, H = pt_terms(kcamb, Pcamb)
### interpolate on simulation k
Pmod_dd, Pmod_dt, Pmod_tt, A, B, C, D, E, F, G, H = interp_simu1(z,j ,k, kcamb, Pcamb, Pmod_dd, Pmod_dt, Pmod_tt,\
A, B, C, D, E, F, G, H, 2)
#~ return kai1*sca1, kai2*sca2, kai3*sca3, kai4*sca4, sco1*sca1, sco2*sca2, sco3*sca3, sco4*sca4, tns1*sca1, tns2*sca2,\
#~ tns3*sca3, tns4*sca4, etns1*sca1, etns2*sca2, etns3*sca3, etns4*sca4
#---------------------------------------------------
#~ kai1, kai2, kai3, kai4, sco1, sco2, sco3, sco4, tns1, tns2, tns3, tns4, etns1, etns2, etns3, etns4 = RSD1(fz[j],fcc, Dz[ind]\
#~ , j, kstop, kcamb, Pcamb, Pmod_dd, bF1, bF2, bF3, bF4, k, Pmono1, Pmono2, Pmono3, Pmono4, errPr1, errPr2,\
#~ errPr3, errPr4, Pmod_dt, Pmod_tt, case,z,0.15, A, B, C, D, E, F, G, H)
kai1, kai2, kai3, kai4, sco1, sco2, sco3, sco4, tns1, tns2, tns3, tns4, etns1, etns2, etns3, etns4 = RSD3(fz[j],fcc, Dz[ind]\
, j, kstop, kcamb, Pcamb, Pmod_dd, bF1, bF2, bF3, bF4, k, Pmono1, Pmono2, Pmono3, Pmono4, errPr1, errPr2,\
errPr3, errPr4, Pmod_dt, Pmod_tt, case,z,0.15, A, B, C, D, E, F, G, H, sca1, sca2, sca3, sca4)