def lnlike3_dip(theta3_dip,x,y,yerr):
theta2params3_dip(theta3_dip)
# Compute model and chi2 on the fly
nx = x.size
chi2 = 0.
for ix in np.arange(x.size):
mod = gl.dT_model3_dip(x[ix],Cp,Gl3_dip,Fg)
sig = gl.dT_noise(x[ix],Fg)
chi2 += ((y[ix]-mod)/sig)**2
return -chi2/2.
def lnlike_trough(theta,x,y,yerr):
theta2params_trough(theta)
# Compute model and chi2 on the fly
nx = x.size
chi2 = 0.
for ix in np.arange(x.size):
mod = gl.dT_model_trough(x[ix],Cp,Glt,Fg)
sig = gl.dT_noise(x[ix],Fg)
chi2 += ((y[ix]-mod)/sig)**2
return -chi2/2.
noise_exp = np.zeros(nx,dtype='float64')
noise_ran = np.zeros(nx,dtype='float64')
fg_exp = np.zeros(nx,dtype='float64')
# for full spectrum input
sigT = np.zeros(nz,dtype='float64')
dT_mod = np.zeros(nz,dtype='float64')
#
# set up directory
odir = 'AmpC_'+str(ampC_array[iamp])+'_zC_'+str(zC_array[izc])+'_dzC_'+str(dzC_array[idzc])
outputdir = fdir+odir
if not os.path.isdir(outputdir):
os.mkdir(outputdir)
#
for iz in np.arange(nx):
nu = x[iz]
noise_ran[iz] = gl.dT_noise(nu,Fg)*np.random.randn(1)
y[iz] = gl.dT_model3(nu,Cp,Gl3,Fg) + noise_ran[iz]
yerr[iz] = gl.dT_noise(nu,Fg)
sig_exp[iz] = gl.dT_global3(nu,Cp,Gl3)
noise_exp[iz] = gl.dT_noise(nu,Fg)
fg_exp[iz] = gl.dT_fg(nu,Fg)
for iz in np.arange(nz):
nu = nu_arr[iz]
sigT[iz] = gl.dT_noise(nu,Fg)
dT_mod[iz] = gl.dT_global3(nu,Cp,Gl3)
Gl3['Amp_D']=Amp_D_default
Gl3['z_D'] = z_D_default
# preparing for MCMC
# Initial input model parameters
# === First choice for each walker (10% scatter from the truth)