code: def run_model(self, weather_df):
          r
          wind_speed_hub = self.wind_speed_hub(weather_df)
          density_hub = (None if (self.power_output_model == 'power_curve' and
                                  self.density_correction is False)
                         else self.density_hub(weather_df))
          self.power_output = self.calculate_power_output(wind_speed_hub,
                                                          density_hub)
          return self
docs: r"""
          Runs the model.
  
          Parameters
          ----------
          weather_df : pandas.DataFrame
              DataFrame with time series for wind speed `wind_speed` in m/s, and
              roughness length `roughness_length` in m, as well as optionally
              temperature `temperature` in K, pressure `pressure` in Pa and
              density `density` in kg/m³ depending on `power_output_model` and
              `density_model chosen`.
              The columns of the DataFrame are a MultiIndex where the first level
              contains the variable name (e.g. wind_speed) and the second level
              contains the height at which it applies (e.g. 10, if it was
              measured at a height of 10 m). See below for an example on how to
              create the weather_df DataFrame.
  
          Other Parameters
          ----------------
          roughness_length : Float, optional.
              Roughness length.
          turbulence_intensity : Float, optional.
              Turbulence intensity.
  
          Returns
          -------
          self
  
          Examples
          ---------
          >>> import numpy as np
          >>> import pandas as pd
          >>> weather_df = pd.DataFrame(np.random.rand(2,6),
          ...                           index=pd.date_range('1/1/2012',
          ...                                               periods=2,
          ...                                               freq='H'),
          ...                           columns=[np.array(['wind_speed',
          ...                                              'wind_speed',
          ...                                              'temperature',
          ...                                              'temperature',
          ...                                              'pressure',
          ...                                              'roughness_length']),
          ...                                    np.array([10, 80, 10, 80,
          ...                                             10, 0])])
          >>> weather_df.columns.get_level_values(0)[0]
          'wind_speed'