@task()
def fetch_and_upload_data(load_type: str) -> FlyteFile:
    import os

    timestamps = pd.date_range(start="2024-01-01", periods=96, freq='15T')

    # Sample data for site PV and temperature
    np.random.seed(0)
    pv_data = np.random.uniform(low=0, high=100, size=len(timestamps))  # PV generation in kW
    temperature_data = np.random.uniform(low=-10, high=35, size=len(timestamps))  # Temperature in Celsius

    # Create a DataFrame
    data = pd.DataFrame({
        'Timestamp': timestamps,
        'site': pv_data,
        'temperature': temperature_data
    })
    execution_id = current_context().execution_id.name

    raw_data_path_remote = f"<s3://jielian-dev/{execution_id}/raw_data_{load_type}_2022-08-31T17:00:00Z_2023-03-03T11:00:00Z.csv>"
    # write to local path
    raw_data_path_local = Path(flytekit.current_context().working_directory) / f"{execution_id}/raw_data_{load_type}_2022-08-31T17:00:00Z_2023-03-03T11:00:00Z.csv"
    directory = Path(flytekit.current_context().working_directory) / f"{execution_id}"
    if not os.path.exists(directory):
        os.makedirs(directory)
    data.to_csv(raw_data_path_local)
    return FlyteFile(path=raw_data_path_local.__str__(),
                     remote_path=raw_data_path_remote
                     )

s3 path