text: # coding: utf-8
  from __future__ import division
  from __future__ import print_function
  from __future__ import absolute_import
  from __future__ import unicode_literals
  
  import os
  
  RANDOM_STATE = 42
  SEED = 42
  SAVING_DIR = 'OUTPUT'
  DEFAULT_DIR = os.path.join(SAVING_DIR, "Default")
  MODEL_SAVING_DIR = os.path.join(SAVING_DIR, "MODELS")
  
  PRECISION = 1e-3
  N_CV = 6
  
  _ERROR = '_error'
  _MINOS_UP = '_minos_up'
  _MINOS_LOW = '_minos_low'
  _TRUTH = '_truth'
  
  PLOT_CONTOUR = False
  
  # FIT PARAMETERS
  #---------------
  # TODO : one day maybe put all these values as input arguments
  #       and correctly sort experiments according to the paramters
  
  # Parameter of interest
  CALIBRATED_MU = 1.0
  TRUE_MU = 1.0  # FIXME maybe I should try TRUE != CALIBRATED ?
  
  # Nuisance parameters
  CALIBRATED_TAU_ENERGY_SCALE = 1.0
  CALIBRATED_TAU_ENERGY_SCALE_ERROR = 0.05
  TRUE_TAU_ENERGY_SCALE = 1.03
  
  CALIBRATED_JET_ENERGY_SCALE = 1.0
  CALIBRATED_JET_ENERGY_SCALE_ERROR = 0.05
  TRUE_JET_ENERGY_SCALE = 0.97
  
  CALIBRATED_LEP_ENERGY_SCALE = 1.0
  CALIBRATED_LEP_ENERGY_SCALE_ERROR = 0.01
  TRUE_LEP_ENERGY_SCALE = 1.001
  
  # FIXME What are the usual values for soft term ?
  CALIBRATED_SIGMA_SOFT = 2.7
  CALIBRATED_SIGMA_SOFT_ERROR = 0.5
  TRUE_SIGMA_SOFT = 3.3
  
  # FIXME What are the usual values for nasty bkg ?
  CALIBRATED_NASTY_BKG = 1.0
  CALIBRATED_NASTY_BKG_ERROR = 0.5
  TRUE_NASTY_BKG = 1.5
  