<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def interpolate(self, column_hours, method='linear', limit=24, limit_direction='both', **kwargs):
 """ Wrapper function for ``pandas.Series.interpolate`` that can be used to "disaggregate" values using various interpolation methods. Parameters column_hours : dict Dictionary containing column names in ``data_daily`` and the hour values they should be associated to. method, limit, limit_direction, **kwargs These parameters are passed on to ``pandas.Series.interpolate``. Examples -------- Assume that ``mystation.data_daily.T7``, ``mystation.data_daily.T14``, and ``mystation.data_daily.T19`` contain air temperature measurements taken at 07:00, 14:00, and 19:00. We can use the interpolation functions provided by pandas/scipy to derive hourly values: """

        kwargs = dict(kwargs, method=method, limit=limit, limit_direction=limit_direction)
        data = melodist.util.prepare_interpolation_data(self.data_daily, column_hours)
        return data.interpolate(**kwargs)