flasc.data_processing.dataframe_manipulations.set_pow_ref_by_n_closest_upstream_turbines

flasc.data_processing.dataframe_manipulations.set_pow_ref_by_n_closest_upstream_turbines(df: DataFrame | FlascDataFrame, df_upstream: DataFrame, turb_no: int, x_turbs: List[float], y_turbs: List[float], exclude_turbs: bool = [], N: int = 5) → DataFrame | FlascDataFrame

Add pow_ref column using N-nearest upstream turbines.

Add a column called 'pow_ref' to your dataframe, which is the mean of the columns pow_%03d for the N closest turbines that are upstream 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.

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

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

• exclude_turbs ([list, array]) -- array-like variable containing turbine indices that should be excluded in determining the column mean quantity.

• N (int) -- Number of closest turbines to consider for the calculation of the averaged column quantity. Defaults to 5.

Returns:

Dataframe which equals the inserted dataframe

plus the additional column called 'pow_ref'.

Return type:

pd.Dataframe | FlascDataFrame