example_ns.param

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

# valid options are (so far): acbar, bicep, boomerang, cbi,
# hst, quad, sn, spt, fake_planck_bluebook,
# euclid_pk, euclid_lensing
# and clik_fake_planck (for Planck members)

data.experiments=['fake_planck_bluebook']
#data.experiments=['wmap','spt']

#------ Parameter list -------
# data.parameters[class name] = [mean, min, max, 1-sigma, scale, role]
# - if min max irrelevant, put to -1 or None (if you want a boundary of -1, use -1.0)
# - 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'


# Cosmological parameters list
data.parameters['omega_b'] 	= [2.249,  1.8, 3, 0.016, 0.01,'cosmo']
data.parameters['omega_cdm'] 	= [0.1120, 0.1,   0.2, 0.0016,1,   'cosmo']
data.parameters['n_s']       	= [0.963,  0.9,1.1, 0.004, 1,   'cosmo']
data.parameters['A_s']       	= [2.42,   1.8,3, 0.038, 1e-9,'cosmo']
data.parameters['h']         	= [0.703,  0.6,0.8, 0.0065,1,   'cosmo']
data.parameters['tau_reio']    	= [0.085,  0.04,0.12, 0.0044,1,   'cosmo']

# Derived parameter list
data.parameters['Omega_Lambda'] = [0,       -1, -1, 0,1,  'derived']

# Other class parameters (fixed parameters, precision parameters, etc.)
data.cosmo_arguments['sBBN file'] = data.path['cosmo']+'/bbn/sBBN.dat'


#------ Mcmc parameters ----
# Number of steps taken, by default (overwritten by the -N command)
data.N=10
# Number of accepted steps before writing to file the chain. Larger means less
# access to disc, but this is not so much time consuming.
data.write_step=5