#------Experiments to test (separated with commas)-----

data.experiments=['act_s1316_sim','Planck_lensing','Planck_lowl','Planck_highl_TTTEEE_lite']


#------ Parameter list -------

# data.parameters[class name] = [mean, min, max, 1-sigma, scale, role]
# - if min max irrelevant, put to None
# - if fixed, put 1-sigma to 0
# - if scale irrelevant, put to 1, otherwise to the appropriate factor
# - role is either 'cosmo', 'nuisance' or 'derived'. You should put the derived
# parameters at the end, and in case you are using the `-j fast` Cholesky
# decomposition, you should order your nuisance parameters from slowest to
# fastest.

# Cosmological parameters list

data.parameters['omega_b']      = [ 2.2253,   1.0, 3.0,  0.028, 0.01, 'cosmo']
data.parameters['omega_dmeff']  = [0.11919,   0.05, 0.2, 0.0027,    1, 'cosmo']
data.parameters['100*theta_s']  = [ 1.0418,   1.0, 1.1,   3e-4,    1, 'cosmo']
data.parameters['ln10^{10}A_s'] = [ 3.0753,   2.5, 3.5, 0.0029,    1, 'cosmo']
data.parameters['n_s']          = [0.96229,   0.8, 1.2, 0.0074,    1, 'cosmo']
data.parameters['tau_reio']     = [0.09463, 0.04, 0.13, 0.013,     1, 'cosmo']
data.parameters['log10sigma_dmeff'] = [-28, -35, -18, 2, 1, 'cosmo']


# Nuisance parameter list, same call, except the name does not have to be a class name

data.parameters['yp']         = [0.97,    0.9, 1.0,   0.015, 1,  'nuisance']
data.parameters['A_planck']          = [100.028,   90,  110,  0.25,  0.01,'nuisance']

# Derived parameters

data.parameters['h']              = [0, None, None, 0,     1,   'derived']
data.parameters['A_s']             = [0, None, None, 0,  1e-9,   'derived']
data.parameters['sigma8']          = [0, None, None, 0,     1,   'derived']

# data.parameters['z_reio']          = [1, None, None, 0,     1,   'derived']
# data.parameters['Omega_Lambda']    = [1, None, None, 0,     1,   'derived']
# data.parameters['YHe']             = [1, None, None, 0,     1,   'derived']
# data.parameters['H0']              = [0, None, None, 0,     1,   'derived']

# Other cosmo parameters (fixed parameters, precision parameters, etc.)

data.cosmo_arguments['sBBN file'] = data.path['cosmo']+'/bbn/sBBN.dat'
data.cosmo_arguments['k_pivot'] = 0.05

# The base model features two massless
# and one massive neutrino with m=0.06eV.
# The settings below ensures that Neff=3.046
# and m/omega = 93.14 eV

data.cosmo_arguments['N_ur'] = 2.0328
data.cosmo_arguments['N_ncdm'] = 1
data.cosmo_arguments['m_ncdm'] = 0.06
data.cosmo_arguments['T_ncdm'] = 0.71611

# These two are required to get sigma8 as a derived parameter
# (class must compute the P(k) until sufficient k)

data.cosmo_arguments['output'] = 'mPk'
data.cosmo_arguments['P_k_max_h/Mpc'] = 5.
data.cosmo_arguments['k_per_decade_for_pk'] = 100
data.cosmo_arguments['omega_cdm'] = 1e-22
data.cosmo_arguments['gauge']='synchronous'
data.cosmo_arguments['tight_coupling_trigger_tau_c_over_tau_h']=0
data.cosmo_arguments['tight_coupling_trigger_tau_c_over_tau_k']=0
data.cosmo_arguments['background_verbose'] = 0
data.cosmo_arguments['m_dmeff'] = 1e-3
data.cosmo_arguments['npow_dmeff'] = 0.

#------ Mcmc parameters ----

data.N=100000
data.write_step=5