#
# Configuration file for n3fit
#

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description: NNPDF3.1 NNLO DIS only in basis which diagonalises experimental covmat with l2 regularizer

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# frac: training fraction
# ewk: apply ewk k-factors
# sys: systematics treatment (see systypes)
dataset_inputs:
# Fixed target DIS
- { dataset: NMCPD, frac: 0.5 }
- { dataset: NMC,   frac: 0.5  }
- { dataset: SLACP, frac: 0.5}
- { dataset: SLACD, frac: 0.5}
- { dataset: BCDMSP, frac: 0.5}
- { dataset: BCDMSD, frac: 0.5}
- { dataset: CHORUSNU, frac: 0.5}
- { dataset: CHORUSNB, frac: 0.5}
- { dataset: NTVNUDMN, frac: 0.5}
- { dataset: NTVNBDMN, frac: 0.5}
# EMC F2C data
# - { dataset: EMCF2C, frac: 1.0}
# HERA data
- { dataset: HERACOMBNCEM , frac: 0.5}
- { dataset: HERACOMBNCEP460, frac: 0.5}
- { dataset: HERACOMBNCEP575, frac: 0.5}
- { dataset: HERACOMBNCEP820, frac: 0.5}
- { dataset: HERACOMBNCEP920, frac: 0.5}
- { dataset: HERACOMBCCEM , frac: 0.5}
- { dataset: HERACOMBCCEP , frac: 0.5}
# Combined HERA charm production cross-sections
- { dataset: HERAF2CHARM, frac: 0.5}
# F2bottom data
- { dataset: H1HERAF2B, frac: 1.0}
- { dataset: ZEUSHERAF2B, frac: 1.0}

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datacuts:
  t0pdfset     : NNPDF40_nnlo_as_01180 # PDF set to generate t0 covmat
  q2min        : 3.49                # Q2 minimum
  w2min        : 12.5                # W2 minimum
  combocuts    : NNPDF31             # NNPDF3.0 final kin. cuts
  jetptcut_tev : 0                   # jet pt cut for tevatron
  jetptcut_lhc : 0                   # jet pt cut for lhc
  wptcut_lhc   : 30.0                # Minimum pT for W pT diff distributions
  jetycut_tev  : 1e30                # jet rap. cut for tevatron
  jetycut_lhc  : 1e30                # jet rap. cut for lhc
  dymasscut_min: 0                   # dy inv.mass. min cut
  dymasscut_max: 1e30                # dy inv.mass. max cut
  jetcfactcut  : 1e30                # jet cfact. cut

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theory:
  theoryid: 162        # database id

sampling:
  separate_multiplicative: true
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trvlseed: 1
nnseed: 2
mcseed: 3  
save: False
load: False

genrep   : True     # true = generate MC replicas, false = use real data
diagonal_basis: True

parameters: # This defines the parameter dictionary that is passed to the Model Trainer
  nodes_per_layer: [35, 25, 8]
  activation_per_layer: ['tanh', 'tanh', 'linear']
  initializer: 'glorot_normal'
  optimizer:
    learning_rate: 1.0
    optimizer_name: 'Adadelta'
  epochs: 4000
  positivity:
    multiplier: 1.09
    initial: 10.0
  stopping_patience: 0.30 # percentage of the number of epochs
  layer_type: 'dense'
  dropout: 0.0
  regularizer: l1_l2
  regularizer_args: {l1: 0.0, l2: 1.0}

fitting:
  # NN23(QED) = sng=0,g=1,v=2,t3=3,ds=4,sp=5,sm=6,(pht=7)
  # EVOL(QED) = sng=0,g=1,v=2,v3=3,v8=4,t3=5,t8=6,(pht=7)
  # EVOLS(QED)= sng=0,g=1,v=2,v8=4,t3=4,t8=5,ds=6,(pht=7)
  # FLVR(QED) = g=0, u=1, ubar=2, d=3, dbar=4, s=5, sbar=6, (pht=7)
  savepseudodata: False
  fitbasis: NN31IC # EVOL (7), EVOLQED (8), etc.
  basis:
      # remeber to change the name of PDF accordingly with fitbasis
      # smallx, largex: preprocessing ranges
      - { fl: sng,  smallx: [1.04,1.20], largex: [1.45,2.64] }
      - { fl: g,    smallx: [0.82,1.31], largex: [0.20,6.17] }
      - { fl: v,    smallx: [0.51,0.71], largex: [1.24,2.80] }
      - { fl: v3,   smallx: [0.23,0.63], largex: [1.02,3.14] }
      - { fl: v8,   smallx: [0.53,0.75], largex: [0.70,3.31] }
      - { fl: t3,   smallx: [-0.45,1.41], largex: [1.78,3.21] }
      - { fl: t8,   smallx: [0.49,1.32], largex: [1.42,3.13] }
      - { fl: cp,   smallx: [-0.07,1.13], largex: [1.73,7.37] }

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positivity:
  posdatasets:
    - { dataset: POSF2U,   maxlambda: 1e6 }  # Positivity Lagrange Multiplier
    - { dataset: POSF2DW,  maxlambda: 1e6 }
    - { dataset: POSF2S,   maxlambda: 1e6 }
    - { dataset: POSFLL,   maxlambda: 1e6 }
    - { dataset: POSDYU,   maxlambda: 1e10 }
    - { dataset: POSDYD,   maxlambda: 1e10 }
    - { dataset: POSDYS,   maxlambda: 1e10 }

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debug: False
parallel_models: True
same_trvl_per_replica: False