example_config.yaml

####### CHANGE TO MAKE #######
# 1. Point cwd, io_dir, and model_settings to your desired directory
# 2. For Windows, make sure to switch the forward to backward slash
# You may need to change the hydra/launcher slash on line 8 of "3-CFE_calibration_loop_1.py" if you're getting an error about not finding the joblib
# 3. Choose the "soil_scheme" between 'ode' or 'classic'

defaults:
  - _self_
  - override hydra/launcher: joblib

cwd: # Change to your directory

CFE_config:
  soil_scheme: classic

io_dir:
  data_dir: ${cwd}/data # This holds all the forcing and evaluation data
  config_dir: ${cwd}/calibrate-cfe/configs/soil_{$CFE_config.soil_scheme} # This holds config files
  results_dir: ${cwd}/calibrate-cfe/results # This holds results
  cfe_dir: ${cwd}/cfe_py # This holds cfe_py models cloned from github 
  model_config_dir: ${cwd}/data/model_common_configs # This holds model configuration files (time-splitting and CFE parameter bounds)
  camels_dir: ${cwd}/data/camels # This holds camels data
  gauch_2020_dir: ${cwd}/data/camels/gauch_etal_2020 # This holds camels data downloaded from Gauch et al., 2020
  ucar_dir: ${cwd}/data/camels/ucar 
  usgs_streamflow_dir: ${cwd}/data/camels/gauch_etal_2020/usgs_streamflow
  nldas_forcing_dir: ${cwd}/data/camels/gauch_etal_2020/nldas_hourly
  basin_dir: ${cwd}/data/camels/gauch_etal_2020
  cfe_c_config_dir: ${cwd}/calibrate-cfe/configs/CFE_Config_Cver_from_Luciana

model_settings:
  basin_file: ${cwd}/data/camels/gauch_etal_2020/basin_list_516.txt
  missing_data_file: ${cwd}/data/camels/gauch_etal_2020/basin_list_missing_data_v2023.txt
  missing_data_file_testing: ${cwd}/data/camels/gauch_etal_2020/basin_list_missing_data_testing_period_v2023.txt
  time_split_file: ${cwd}/data/model_common_configs/cal-val-test-period.json
  parameter_bound_file: ${cwd}/data/model_common_configs/CFE_parameter_bounds.json

############## calibration settings ############
## Define iteration number
# Ideally, N between 1000 to 10000 would be optimal
# "Algorithms are compared for optimization problems ranging from 6 to 30 dimensions, and each problem is solved in 1000 to 10,000 total function evaluations per optimization trial."
# N = 500 is recommended by Raven document (https:\\ravenpy.readthedocs.io\_\downloads\en\latest\pdf\)
# Reference paper: Tolson, B.A. and Shoemaker, C.A., 2007. Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resources Research, 43(1)

calib_variables:
  N: 500
  max_nbasin_per_loop: 1
  print_all_process: False

basin_id: ???