flasc.analysis.expected_power_analysis_output.ExpectedPowerAnalysisOutput

flasc.analysis.expected_power_analysis_output.ExpectedPowerAnalysisOutput#

class flasc.analysis.expected_power_analysis_output.ExpectedPowerAnalysisOutput(uplift_results: dict, a_in: AnalysisInput, test_turbines: list | None = None, wd_turbines: list | None = None, ws_turbines: list | None = None, use_predefined_wd: bool = False, use_predefined_ws: bool = False, wd_step: float = 2.0, wd_min: float = 0.0, wd_max: float = 360.0, ws_step: float = 1.0, ws_min: float = 0.0, ws_max: float = 50.0, bin_cols_in: list = ['wd_bin', 'ws_bin'], weight_by: str = 'min', df_freq: DataFrame | None = None, uplift_pairs: list | None = None, uplift_names: list | None = None, use_standard_error: bool = True, N: int = 1, percentiles: list = [2.5, 97.5], remove_any_null_turbine_bins: bool = False, cov_terms: str = 'zero')[source]#

Bases: object

Store the results of the expected power analysis calculations.

Additionally provide convenient methods for plotting and saving the results.

Methods

print_uplift

Print the uplift results.
Parameters:

  • uplift_results (dict)

  • a_in (AnalysisInput)

  • test_turbines (list)

  • wd_turbines (list)

  • ws_turbines (list)

  • use_predefined_wd (bool)

  • use_predefined_ws (bool)

  • wd_step (float)

  • wd_min (float)

  • wd_max (float)

  • ws_step (float)

  • ws_min (float)

  • ws_max (float)

  • bin_cols_in (list)

  • weight_by (str)

  • df_freq (DataFrame)

  • uplift_pairs (list)

  • uplift_names (list)

  • use_standard_error (bool)

  • N (int)

  • percentiles (list)

  • remove_any_null_turbine_bins (bool)

  • cov_terms (str)

_return_uplift_string()[source]#
print_uplift()[source]#

Print the uplift results.