flasc.data_processing.dataframe_manipulations.set_pow_ref_by_upstream_turbines_in_radius

flasc.data_processing.dataframe_manipulations.set_pow_ref_by_upstream_turbines_in_radius(df: DataFrame | FlascDataFrame, df_upstream: DataFrame, turb_no: int, x_turbs: List[float], y_turbs: List[float], max_radius: float, include_itself: bool = False) → DataFrame | FlascDataFrame

Add pow_ref column using upstream turbines within a radius.

Add a column called 'pow_ref' to your dataframe, which is the mean of the columns pow_%03d for turbines that are upstream and also within radius [max_radius] of the turbine of interest [turb_no].

Parameters:

• df (pd.Dataframe | FlascDataFrame) -- Dataframe with measurements. This dataframe typically consists of wd_%03d, ws_%03d, ti_%03d, pow_%03d, and potentially additional measurements.

• df_upstream (pd.Dataframe) -- Dataframe containing rows indicating wind direction ranges and the corresponding upstream turbines for that wind direction range. This variable can be generated with flasc.utilities.floris_tools.get_upstream_turbs_floris(...). turb_no (int): Turbine number from which the radius should be calculated.

• turb_no (int) -- Turbine number from which the radius should be

• x_turbs ([list, array]) -- Array containing x locations of turbines.

• y_turbs ([list, array]) -- Array containing y locations of turbines.

• max_radius (float) -- Maximum radius for the upstream turbines until which they are still considered as relevant/used for the calculation of the averaged column quantity.

• include_itself (bool, optional) -- Include the measurements of turbine turb_no in the determination of the averaged column quantity. Defaults to False.

Returns:

Dataframe which equals the inserted dataframe

plus the additional column called 'pow_ref'.

Return type:

pd.Dataframe | FlascDataFrame