flasc.data_processing.dataframe_manipulations.set_ti_by_upstream_turbines_in_radius

flasc.data_processing.dataframe_manipulations.set_ti_by_upstream_turbines_in_radius#

flasc.data_processing.dataframe_manipulations.set_ti_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 = True) → DataFrame | FlascDataFrame[source]#

Add TI column by upstream turbines within a radius.

Add a column called 'ti' to your dataframe, which is the mean of the columns ti_%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 'ti'.

Return type:

pd.Dataframe | FlascDataFrame