Examples
========

Streamflow simulation
---------------------
  
The configuration file ``config.yml`` and data for this example can be found `here <https://github.com/tamnva/hydroecolstm/tree/master/examples/1_streamflow_simulation>`_. 

.. code-block:: python

   from hydroecolstm.model_run import run_config
   from hydroecolstm.utility.plot import plot
   from hydroecolstm.data.read_config import read_config
   from hydroecolstm.model.create_model import create_model
   from hydroecolstm.interface.utility import write_yml_file
   from hydroecolstm.utility.evaluation_function import EvaluationFunction
   from hydroecolstm.data.read_data import read_forecast_data
   import matplotlib.pyplot as plt
   from pathlib import Path
   import torch

   #-----------------------------------------------------------------------------#
   #                        Set up, train, test model                            #
   #-----------------------------------------------------------------------------#
   
   # Read configuration file, please modify the path to the config.yml file
   config = read_config("C:/hydroecolstm/examples/1_streamflow_simulation/config.yml")
   
   # Create model and train from config 
   model, data, best_config = run_config(config)
   
   # Evaluate the model and transform to normal scale
   data['y_train_simulated'] = data["y_scaler"].inverse(model.evaluate(data["x_train_scale"]))
   data['y_valid_simulated'] = data["y_scaler"].inverse(model.evaluate(data["x_valid_scale"]))
   data['y_test_simulated'] = data["y_scaler"].inverse(model.evaluate(data["x_test_scale"]))
   
   # Plot train and validation loss with epoch
   data["loss_epoch"].plot()
   plt.show() 
   
   # Want to see all keys in data
   data.keys()
   
   # Objective function values: MAE, NSE, RMSE, MSE
   objective = EvaluationFunction(config['eval_function'], config['warmup_length'])
   objective(data['y_train'], data['y_train_simulated'])
   objective(data['y_valid'], data['y_valid_simulated'])
   objective(data['y_test'], data['y_test_simulated'])
   
   # Visualize valid and test data
   for object_id in config["object_id"]:
       for target in config["target_features"]:
           p = plot(data, object_id=str(object_id), target_feature=target)
           p.show()
   
   # Application of the model for (assumed) ungagued basins
   forecast_dataset = read_forecast_data(config)
   x_forecast_scale = data["x_scaler"].transform(forecast_dataset["x_forecast"])
   y_forecast_scale = model.evaluate(x_forecast_scale)
   
   # Application of the model for (assumed) ungagued basins
   forecast_dataset = read_forecast_data(config)
   x_forecast_scale = data["x_scaler"].transform(forecast_dataset["x_forecast"])
   y_forecast_scale = model.evaluate(x_forecast_scale)
   y_forecast_simulated = data["y_scaler"].inverse(y_forecast_scale)
   
   # Visualize result: train_test_period = "train" or "test"
   for object_id in y_forecast_simulated.keys():
       plt.plot(forecast_dataset["time_forecast"][object_id],
                forecast_dataset["y_forecast"][object_id].detach().numpy(),
                color = 'blue', label = "Observed", alpha=0.9, linewidth=0.75)
       plt.plot(forecast_dataset["time_forecast"][object_id],
                y_forecast_simulated[object_id].detach().numpy(),
                color = 'red', label = "Simulated", alpha=0.9, linewidth=0.75)
       plt.legend()
       plt.show()
   
   # Objective function for forecast
   objective(forecast_dataset['y_forecast'], y_forecast_simulated)
   
   # Save all data and model state dicts to the output_directory
   torch.save(data, Path(config["output_directory"][0], "data.pt"))
   torch.save(model.state_dict(), 
              Path(config["output_directory"][0], "model_state_dict.pt"))
   write_yml_file(config = config, 
                  out_file=Path(config["output_directory"][0], "best_config.yml"))
   #-----------------------------------------------------------------------------#
   #                   Incase you close this file and open again,                #
   #                    you can load your data, model as follows                 #
   #-----------------------------------------------------------------------------#
   config = read_config("C:/hydroecolstm/examples/1_streamflow_simulation/config.yml")
   
   model = create_model(config)
   model.load_state_dict(torch.load(Path(config["output_directory"][0], 
                                         "model_state_dict.pt")))
   
   data = torch.load(Path(config["output_directory"][0], "data.pt"))


Multiple outputs simulation
---------------------------
  
The configuration file ``config.yml`` and data for this example can be found `here <https://github.com/tamnva/hydroecolstm/tree/master/examples/2_streamflow_isotope_simulation>`_. 

.. code-block:: python

   from hydroecolstm.model_run import run_config
   from hydroecolstm.utility.plot import plot
   from hydroecolstm.data.read_config import read_config
   from hydroecolstm.model.create_model import create_model
   from hydroecolstm.interface.utility import write_yml_file
   from hydroecolstm.utility.evaluation_function import EvaluationFunction
   import matplotlib.pyplot as plt
   from pathlib import Path
   import torch
   
   #-----------------------------------------------------------------------------#
   #                        Set up, train, test model                            #
   #-----------------------------------------------------------------------------#
   
   # Read configuration file, please modify the path to the config.yml file
   config = read_config("C:/hydroecolstm/examples/2_streamflow_isotope_simulation/config.yml")
   
   # Create model and train from config 
   model, data, best_config = run_config(config)
   
   # Evaluate the model and transform to normal scale
   data['y_train_simulated'] = data["y_scaler"].inverse(model.evaluate(data["x_train_scale"]))
   data['y_valid_simulated'] = data["y_scaler"].inverse(model.evaluate(data["x_valid_scale"]))
   data['y_test_simulated'] = data["y_scaler"].inverse(model.evaluate(data["x_test_scale"]))
   
   # Plot train and validation loss with epoch
   data["loss_epoch"].plot()
   plt.show() 
   
   # Want to see all keys in data
   data.keys()
   
   # Objective function values: MAE, NSE, RMSE, MSE
   objective = EvaluationFunction(config['eval_function'], config['warmup_length'])
   objective(data['y_train'], data['y_train_simulated'])
   objective(data['y_valid'], data['y_valid_simulated'])
   objective(data['y_test'], data['y_test_simulated'])
   
   # Visualize valid and test data
   for object_id in config["object_id"]:
       for target in config["target_features"]:
           p = plot(data, object_id=str(object_id), target_feature=target)
           p.show()
   
   # Save all data and model state dicts to the output_directory
   torch.save(data, Path(config["output_directory"][0], "data.pt"))
   torch.save(model.state_dict(), 
              Path(config["output_directory"][0], "model_state_dict.pt"))
   write_yml_file(config = best_config, 
                  out_file=Path(config["output_directory"][0], "best_config.yml"))
   
   #-----------------------------------------------------------------------------#
   #                   Incase you close this file and open again,                #
   #                    you can load your data, model as follows                 #
   #-----------------------------------------------------------------------------#
   config = read_config("C:/hydroecolstm/examples/2_streamflow_isotope_simulation/results/best_config.yml")
   model = create_model(config)
   model.load_state_dict(torch.load(Path(config["output_directory"][0], 
                                         "model_state_dict.pt")))
   data = torch.load(Path(config["output_directory"][0], "data.pt"))