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  1. #------Experiments to test (separated with commas)-----
  2.  
  3. data.experiments=['act_s1316_sim','Planck_lensing','Planck_lowl','Planck_highl_TTTEEE_lite']
  4.  
  5.  
  6. #------ Parameter list -------
  7.  
  8. # data.parameters[class name] = [mean, min, max, 1-sigma, scale, role]
  9. # - if min max irrelevant, put to None
  10. # - if fixed, put 1-sigma to 0
  11. # - if scale irrelevant, put to 1, otherwise to the appropriate factor
  12. # - role is either 'cosmo', 'nuisance' or 'derived'. You should put the derived
  13. # parameters at the end, and in case you are using the `-j fast` Cholesky
  14. # decomposition, you should order your nuisance parameters from slowest to
  15. # fastest.
  16.  
  17. # Cosmological parameters list
  18.  
  19. data.parameters['omega_b']      = [ 2.2253,   1.0, 3.0,  0.028, 0.01, 'cosmo']
  20. data.parameters['omega_dmeff']  = [0.11919,   0.05, 0.2, 0.0027,    1, 'cosmo']
  21. data.parameters['100*theta_s']  = [ 1.0418,   1.0, 1.1,   3e-4,    1, 'cosmo']
  22. data.parameters['ln10^{10}A_s'] = [ 3.0753,   2.5, 3.5, 0.0029,    1, 'cosmo']
  23. data.parameters['n_s']          = [0.96229,   0.8, 1.2, 0.0074,    1, 'cosmo']
  24. data.parameters['tau_reio']     = [0.09463, 0.04, 0.13, 0.013,     1, 'cosmo']
  25. data.parameters['log10sigma_dmeff'] = [-28, -35, -18, 2, 1, 'cosmo']
  26.  
  27.  
  28. # Nuisance parameter list, same call, except the name does not have to be a class name
  29.  
  30. data.parameters['yp']         = [0.97,    0.9, 1.0,   0.015, 1,  'nuisance']
  31. data.parameters['A_planck']          = [100.028,   90,  110,  0.25,  0.01,'nuisance']
  32.  
  33. # Derived parameters
  34.  
  35. data.parameters['h']              = [0, None, None, 0,     1,   'derived']
  36. data.parameters['A_s']             = [0, None, None, 0,  1e-9,   'derived']
  37. data.parameters['sigma8']          = [0, None, None, 0,     1,   'derived']
  38.  
  39. # data.parameters['z_reio']          = [1, None, None, 0,     1,   'derived']
  40. # data.parameters['Omega_Lambda']    = [1, None, None, 0,     1,   'derived']
  41. # data.parameters['YHe']             = [1, None, None, 0,     1,   'derived']
  42. # data.parameters['H0']              = [0, None, None, 0,     1,   'derived']
  43.  
  44. # Other cosmo parameters (fixed parameters, precision parameters, etc.)
  45.  
  46. data.cosmo_arguments['sBBN file'] = data.path['cosmo']+'/bbn/sBBN.dat'
  47. data.cosmo_arguments['k_pivot'] = 0.05
  48.  
  49. # The base model features two massless
  50. # and one massive neutrino with m=0.06eV.
  51. # The settings below ensures that Neff=3.046
  52. # and m/omega = 93.14 eV
  53.  
  54. data.cosmo_arguments['N_ur'] = 2.0328
  55. data.cosmo_arguments['N_ncdm'] = 1
  56. data.cosmo_arguments['m_ncdm'] = 0.06
  57. data.cosmo_arguments['T_ncdm'] = 0.71611
  58.  
  59. # These two are required to get sigma8 as a derived parameter
  60. # (class must compute the P(k) until sufficient k)
  61.  
  62. data.cosmo_arguments['output'] = 'mPk'
  63. data.cosmo_arguments['P_k_max_h/Mpc'] = 5.
  64. data.cosmo_arguments['k_per_decade_for_pk'] = 100
  65. data.cosmo_arguments['omega_cdm'] = 1e-22
  66. data.cosmo_arguments['gauge']='synchronous'
  67. data.cosmo_arguments['tight_coupling_trigger_tau_c_over_tau_h']=0
  68. data.cosmo_arguments['tight_coupling_trigger_tau_c_over_tau_k']=0
  69. data.cosmo_arguments['background_verbose'] = 0
  70. data.cosmo_arguments['m_dmeff'] = 1e-3
  71. data.cosmo_arguments['npow_dmeff'] = 0.
  72.  
  73. #------ Mcmc parameters ----
  74.  
  75. data.N=100000
  76. data.write_step=5
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