HOOPLApy/main.py at main · ulaval-rs/HOOPLApy · GitHub

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import multiprocessing

import hoopla
from hoopla import data, models
from hoopla.calibration.calibration import make_calibration
from hoopla.config import DATA_PATH
from hoopla.initialization import list_catchments
from hoopla.simulation import make_simulation
from hoopla.forecast import make_forecast


def run_hoopla(combinations: dict):
    config = combinations['config']

    catchment_names = list_catchments(config.general.time_step)

    # Load models
    print('Loading models ...')
    catchment_name = catchment_names[0]
    hydro_model = models.load_hydro_model(combinations['hydro_model_name'])
    pet_model = models.load_pet_model(combinations['pet_model_name'])
    sar_model = models.load_sar_model(combinations['sar_model_name'])
    da_model = models.load_da_model(combinations['da_model_name'])

    # Load observations
    print('Loading data ...')
    observations = data.load_observations(
        path=f'{DATA_PATH}/{config.general.time_step}/Hydromet_obs/Hydromet_obs_{catchment_name}.mat',
        file_format='mat',
        config=config,
        pet_model=pet_model,
        sar_model=sar_model
    )

    calibration_file_results = f'./results/calibration-C={catchment_name}-H={hydro_model.name()}-E={pet_model.name()}-S={sar_model.name()}.json'
    simulation_file_results = f'./results/simulation-C={catchment_name}-H={hydro_model.name()}-E={pet_model.name()}-S={sar_model.name()}.json'
    forecast_file_results = f'./results/forecast-C={catchment_name}-H={hydro_model.name()}-E={pet_model.name()}-S={sar_model.name()}.json'

    # Calibration
    # -----------
    if config.operations.calibration:
        model_parameters = data.load_model_parameters(
            filepath=f'{DATA_PATH}/{config.general.time_step}/Model_parameters/model_param_boundaries.mat',
            model_name=hydro_model.name(),
            file_format='mat',
        )

        if config.general.compute_snowmelt:
            sar_model_parameters = data.load_sar_model_parameters(
                filepath=f'{DATA_PATH}/{config.general.time_step}/Model_parameters/snow_model_param_boundaries.mat',
                model_name=sar_model.name(),
                file_format='mat',
                calibrate_snow=config.calibration.calibrate_snow
            )
            # Add the SAR model's parameters at the end of the parameters to calibrate
            model_parameters += sar_model_parameters

        # Crop observed data according to specified dates and warm up
        print('Removing unused data ...')
        observations_for_calibration, _, observations_for_warm_up = data.crop_data(
            config=config,
            observations=observations.copy(),
            hydro_model=hydro_model,
            pet_model=pet_model,
            sar_model=sar_model,
            ini_type='ini_calibration'
        )

        print('Starting calibration ...')
        make_calibration(
            observations=observations_for_calibration,
            observations_for_warm_up=observations_for_warm_up,
            config=config,
            hydro_model=hydro_model,
            pet_model=pet_model,
            sar_model=sar_model,
            model_parameters=model_parameters,
            filepath_results=calibration_file_results
        )

    # Simulation
    # ----------
    if config.operations.simulation:
        calibrated_params = data.load_calibrated_model_parameters(
            filepath=calibration_file_results
        )

        # Crop data for the simulation
        print('Removing unused data ...')
        observations_for_simulation, _, observations_for_warm_up = data.crop_data(
            config=config,
            observations=observations.copy(),
            hydro_model=hydro_model,
            pet_model=pet_model,
            sar_model=sar_model,
            ini_type='ini_simulation'
        )

        print('Starting simulation ...')
        make_simulation(
            observations=observations_for_simulation,
            observations_for_warm_up=observations_for_warm_up,
            config=config,
            hydro_model=hydro_model,
            pet_model=pet_model,
            sar_model=sar_model,
            da_model=da_model,
            parameters=calibrated_params,
            filepath_results=simulation_file_results
        )

    # Forecast
    if config.operations.forecast:
        # Load meteo forecast data
        if config.forecast.meteo_ens:
            # Meteorological ensemble prediction data
            forecast_data = data.load_ens_met_data(
                filepath=f'{DATA_PATH}/{config.general.time_step}/Ens_met_fcast/Met_fcast_{catchment_name}.mat',
                file_format='mat',
                config=config,
                sar_model=sar_model
            )
        else:
            forecast_data = data.load_forecast_data(
                filepath=f'{DATA_PATH}/{config.general.time_step}/Det_met_fcast/Met_fcast_{catchment_name}.mat',
                file_format='mat',
                config=config,
                sar_model=sar_model
            )

        # Load calibrated model parameters
        calibrated_params = data.load_calibrated_model_parameters(
            filepath=calibration_file_results
        )

        # Crop data for the simulation
        print('Removing unused data ...')
        observations, observations_for_forecast, observations_for_warm_up = data.crop_data(
            config=config,
            observations=observations.copy(),
            hydro_model=hydro_model,
            pet_model=pet_model,
            sar_model=sar_model,
            ini_type='ini_forecast',
            forecast_data=forecast_data
        )

        print('Starting forecast')
        make_forecast(
            observations=observations,
            observations_for_warm_up=observations_for_warm_up,
            observations_for_forecast=observations_for_forecast,
            config=config,
            hydro_model=hydro_model,
            pet_model=pet_model,
            sar_model=sar_model,
            da_model=da_model,
            parameters=calibrated_params,
            filepath_results=forecast_file_results
        )


if __name__ == '__main__':
    config = hoopla.load_config('./config.toml')

    # Listing models
    print('Available models\n----------------')
    print('hydro_models', models.list_hydro_models())
    print('evapotranspiration_models', models.list_pet_models())
    print('snow_models', models.list_sar_models())
    print('da_models', models.list_da_models())

    models_combination = hoopla.util.make_combinations(config)

    if config.general.parallelism:
        pool = multiprocessing.Pool()
        pool.map(run_hoopla, models_combination)
    else:
        for combination in models_combination:
            run_hoopla(combination)