add ultranest and snowline samplers

input/base2018TTTEEE_lite.param

@@ -33,16 +33,16 @@ data.over_sampling=[1, 10]
 
 # Cosmological parameters list
 
-data.parameters['omega_b']      = [  2.2377,   None, None,      0.015, 0.01, 'cosmo']
-data.parameters['omega_cdm']    = [ 0.12010,   None, None,     0.0013,    1, 'cosmo']
-data.parameters['100*theta_s']  = [ 1.04110,   None, None,    0.00030,    1, 'cosmo']
-data.parameters['ln10^{10}A_s'] = [  3.0447,   None, None,      0.015,    1, 'cosmo']
-data.parameters['n_s']          = [  0.9659,   None, None,     0.0042,    1, 'cosmo']
-data.parameters['tau_reio']     = [  0.0543,  0.004, None,      0.008,    1, 'cosmo']
+data.parameters['omega_b']      = [  2.2377,   2, 3,      0.015, 0.01, 'cosmo']
+data.parameters['omega_cdm']    = [ 0.12010,   0.05, 0.3,     0.0013,    1, 'cosmo']
+data.parameters['100*theta_s']  = [ 1.04110,   1.0, 1.2,    0.00030,    1, 'cosmo']
+data.parameters['ln10^{10}A_s'] = [  3.0447,   2.0, 5,      0.015,    1, 'cosmo']
+data.parameters['n_s']          = [  0.9659,   0.5, 1.5,     0.0042,    1, 'cosmo']
+data.parameters['tau_reio']     = [  0.0543,  0.004, 0.2,      0.008,    1, 'cosmo']
 
 # Nuisance parameter list, same call, except the name does not have to be a class name
 
-data.parameters['A_planck']          = [ 1.00061,   0.9,   1.1,     0.0025,     1, 'nuisance']
+data.parameters['A_planck']          = [ 1.00061,   0.99,   1.01,     0.0025,     1, 'nuisance']
 
 # Derived parameters
 
@@ -79,4 +79,4 @@ data.cosmo_arguments['P_k_max_h/Mpc'] = 1.
 #------ Mcmc parameters ----
 
 data.N=10
-data.write_step=5
+data.write_step=5

input/example_ns.param

@@ -39,3 +39,5 @@ 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
+
+#data.base_dir = test/logs/

input/jla.param

@@ -16,16 +16,16 @@ data.experiments=['JLA']
 
 
 # Cosmological parameters list
-data.parameters['Omega_cdm'] = [0.2562,  None, None, 0.008,  1, 'cosmo']
+data.parameters['Omega_cdm'] = [0.2562,  1.8, 3, 0.008,  1, 'cosmo']
 
 # Nuisance
-data.parameters['alpha']   = [0.15 ,  None, None, 0.001, 1, 'nuisance']
-data.parameters['beta']    = [3.559,  None, None, 0.02,  1, 'nuisance']
-data.parameters['M']       = [-19.02, None, None, 0.004,  1, 'nuisance']
-data.parameters['Delta_M'] = [-0.10,  None, None, 0.004,  1, 'nuisance']
+data.parameters['alpha']   = [0.15 ,  0, 1, 0.001, 1, 'nuisance']
+data.parameters['beta']    = [3.559,  3, 5, 0.02,  1, 'nuisance']
+data.parameters['M']       = [-19.02, -21, -17, 0.004,  1, 'nuisance']
+data.parameters['Delta_M'] = [-0.10,  -1, 0, 0.004,  1, 'nuisance']
 
 # Derived parameter list
-data.parameters['Omega_m'] = [0,    -1, -1, 0,1,  'derived']
+data.parameters['Omega_m'] = [0,    -1, 1, 0,1,  'derived']
 
 
 data.cosmo_arguments['Omega_b'] = 0.05