### base config ###
# -*- coding: utf-8 -*-
full_field: &FULL_FIELD
  loss: 'l2'
  max_epochs: 200
  batch_size: 32
  num_data_workers: 4
  dt: 1                     # how many timesteps ahead the model will predict
  n_history: 0              # how many previous timesteps to consider
  prediction_type: 'iterative'
  prediction_length: 41     # applicable only if prediction_type == 'iterative'
  n_initial_conditions: 5  # applicable only if prediction_type == 'iterative'
  ics_type: "default"       # default or datetime
  date_strings: ["2018-09-14 00:00:00"]
  save_raw_forecasts: !!bool True
  save_channel: !!bool False
  masked_acc: !!bool False
  maskpath: None
  perturb: !!bool False
  add_grid: !!bool False
  N_grid_channels: 0
  gridtype: 'sinusoidal'    # options 'sinusoidal' or 'linear'
  roll: !!bool False
  enable_nhwc: !!bool False
  optimizer_type: 'FusedAdam'

  # directory path to store training checkpoints and other output
  exp_dir: './exp'

  # data
  train_data_path: 'data/train'
  valid_data_path: 'data/valid'  
  test_data_path:  'data/test'

  # land mask
  land_mask: !!bool True
  land_mask_path: 'data/land_mask.h5'

  # normalization
  normalize: !!bool True
  normalization: 'zscore' # options zscore, minmax, zscore_lat
  global_means_path: 'data/mean_s_t_ssh.npy' 
  global_stds_path:  'data/std_s_t_ssh.npy' 

  
  add_noise: !!bool False

  log_to_screen: !!bool True
  log_to_wandb: !!bool False
  save_checkpoint: !!bool True
  plot_animations: !!bool False

#############################################
Triton: &Triton
  <<: *FULL_FIELD
  nettype: 'Triton'
  log_to_wandb: !!bool False

  # Train params
  lr: 1E-3
  batch_size: 32 
  scheduler: 'CosineAnnealingLR'

  loss_channel_wise: True
  loss_scale: False 
  use_loss_scaler_from_metnet3: True

  atmos_channels: [93, 94, 95, 96]

  ocean_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92]

  in_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96]

  out_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92]


  out_variables: ["S0", "S2", "S5", "S7", "S11", "S15", "S21", "S29", "S40", "S55", "S77", "S92", "S109", "S130", "S155", "S186", "S222", "S266", "S318", "S380", "S453", "S541", "S643", "U0", "U2", "U5", "U7", "U11", "U15", "U21", "U29", "U40", "U55", "U77", "U92", "U109", "U130", "U155", "U186", "U222", "U266", "U318", "U380", "U453", "U541", "U643", "V0", "V2", "V5", "V7", "V11", "V15", "V21", "V29", "V40", "V55", "V77", "V92", "V109", "V130", "V155", "V186", "V222", "V266", "V318", "V380", "V453", "V541", "V643", "T0", "T2", "T5", "T7", "T11", "T15", "T21", "T29", "T40", "T55", "T77", "T92", "T109", "T130", "T155", "T186", "T222", "T266", "T318", "T380", "T453", "T541", "T643", "SSH"]
  
  N_in_channels: 97 # 72
  N_out_channels: 93 # 67

#############################################
Fourcastnet: &Fourcastnet
  <<: *FULL_FIELD
  nettype: 'Fourcastnet'
  log_to_wandb: !!bool False

  # Train params
  lr: 1E-3
  batch_size: 32 
  scheduler: 'CosineAnnealingLR'

  loss_channel_wise: True
  loss_scale: False 
  use_loss_scaler_from_metnet3: True

  atmos_channels: [93, 94, 95, 96]

  ocean_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92]

  in_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96]

  out_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92]


  out_variables: ["S0", "S2", "S5", "S7", "S11", "S15", "S21", "S29", "S40", "S55", "S77", "S92", "S109", "S130", "S155", "S186", "S222", "S266", "S318", "S380", "S453", "S541", "S643", "U0", "U2", "U5", "U7", "U11", "U15", "U21", "U29", "U40", "U55", "U77", "U92", "U109", "U130", "U155", "U186", "U222", "U266", "U318", "U380", "U453", "U541", "U643", "V0", "V2", "V5", "V7", "V11", "V15", "V21", "V29", "V40", "V55", "V77", "V92", "V109", "V130", "V155", "V186", "V222", "V266", "V318", "V380", "V453", "V541", "V643", "T0", "T2", "T5", "T7", "T11", "T15", "T21", "T29", "T40", "T55", "T77", "T92", "T109", "T130", "T155", "T186", "T222", "T266", "T318", "T380", "T453", "T541", "T643", "SSH"]
  
  N_in_channels: 97 # 72
  N_out_channels: 93 # 67