meta: 'An example fit using Colibri, reduced DIS dataset.'

#######################
# Data and theory specs
#######################

dataset_inputs:    
  # DIS          
- {dataset: SLAC_NC_NOTFIXED_P_EM-F2, variant: legacy_dw}
- {dataset: SLAC_NC_NOTFIXED_D_EM-F2, variant: legacy_dw}
- {dataset: BCDMS_NC_NOTFIXED_P_EM-F2, variant: legacy_dw}
- {dataset: BCDMS_NC_NOTFIXED_D_EM-F2, variant: legacy_dw}
# - {dataset: CHORUS_CC_NOTFIXED_PB_NU-SIGMARED, variant: legacy_dw}
# - {dataset: CHORUS_CC_NOTFIXED_PB_NB-SIGMARED, variant: legacy_dw}
# - {dataset: NUTEV_CC_NOTFIXED_FE_NU-SIGMARED, cfac: [MAS], variant: legacy_dw}
# - {dataset: NUTEV_CC_NOTFIXED_FE_NB-SIGMARED, cfac: [MAS], variant: legacy_dw}
# - {dataset: HERA_NC_318GEV_EM-SIGMARED, variant: legacy}
# - {dataset: HERA_NC_225GEV_EP-SIGMARED, variant: legacy}
# - {dataset: HERA_NC_251GEV_EP-SIGMARED, variant: legacy}
# - {dataset: HERA_NC_300GEV_EP-SIGMARED, variant: legacy}
# - {dataset: HERA_NC_318GEV_EP-SIGMARED, variant: legacy}
# - {dataset: HERA_CC_318GEV_EM-SIGMARED, variant: legacy}
# - {dataset: HERA_CC_318GEV_EP-SIGMARED, variant: legacy}
# - {dataset: HERA_NC_318GEV_EAVG_CHARM-SIGMARED, variant: legacy}
# - {dataset: HERA_NC_318GEV_EAVG_BOTTOM-SIGMARED, variant: legacy}
# - {dataset: NMC_NC_NOTFIXED_EM-F2, variant: legacy_dw}
# - {dataset: NMC_NC_NOTFIXED_P_EM-SIGMARED, variant: legacy}



theoryid: 40000000                     # The theory from which the predictions are drawn.
use_cuts: internal                     # The kinematic cuts to be applied to the data.

closure_test_level: false             # REAL DATA fit — no closure
                                       # for pseudodata with no noise, level 1 for pseudodata with
                                       # noise.

closure_test_pdf: LH_PARAM_20250519  # The closure test PDF used if closure_test_level is not False

#flavour_mapping: ["\\Sigma", "g", "V", "V3"]  # Masks other flavours, such that closure tests can be run on desired flavours only. 

#####################
# Loss function specs
#####################

positivity:                            # Positivity datasets, used in the positivity penalty.
  posdatasets:
  - {dataset: NNPDF_POS_2P24GEV_F2U, variant: None, maxlambda: 1e6}

positivity_penalty_settings:
  positivity_penalty: false
  alpha: 1e-7                           
  lambda_positivity: 0                 

# Integrability Settings
integrability_settings:
  integrability: False            

use_t0_covmat: True                       # Whether the t0 covariance is used in the chi2 loss.
t0pdfset: NNPDF40_nnlo_as_01180         # The t0 PDF used to build the t0 covariance matrix.
  

###################
# Methodology specs
###################
prior_settings:
  prior_distribution: uniform_parameter_prior
  prior_distribution_specs:
    bounds:
      alpha_gluon: [-0.1, 1]
      beta_gluon: [9, 13]
      alpha_up: [0.4, 0.9]
      beta_up: [3, 4.5]
      epsilon_up: [-3, 3]
      gamma_up: [1, 6]
      alpha_down: [1, 2]
      beta_down: [8, 12]
      epsilon_down: [-4.5, -3]
      gamma_down: [3.8, 5.8]
      norm_sigma: [0.1, 0.5]
      alpha_sigma: [-0.2, 0.1]
      beta_sigma: [1.2, 3]


# Nested Sampling settings
ultranest_settings:
  sampler_plot: true
  n_posterior_samples: 100      # Number of posterior samples generated.
  ReactiveNS_settings:
    vectorized: False
    ndraw_max: 500              # Maximum number of points to simultaneously propose.
  Run_settings:
    min_num_live_points: 200    # Minimum number of live points throughout the run.
    min_ess: 50                 # Target number of effective posterior samples.
    frac_remain: 0.3            # Integrate until this fraction of the integral is left in the remainder. 
  SliceSampler_settings:
    nsteps: 106                 # number of accepted steps until the sample is considered independent.


actions_:
- run_ultranest_fit                      # Choose from ultranest_fit, monte_carlo_fit, analytic_fit