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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def refresh(self) -> None: """Update the actual simulation values based on the toy-value pairs. Usually, one does not need to call refresh explicitly. The "magic" methods __call__, __setattr__, and __delattr__ invoke it automatically, when required. Instantiate a 1-dimensional |SeasonalParameter| object: When a |SeasonalParameter| object does not contain any toy-value pairs yet, the method |SeasonalParameter.refresh| sets all actual simulation values to zero: 0.0 When there is only one toy-value pair, its values are relevant for all actual simulation values: 2.0 Method |SeasonalParameter.refresh| performs a linear interpolation for the central time points of each simulation time step. Hence, in the following example, the original values of the toy-value pairs do not show up: 2.00274 3.99726 3.0 If one wants to preserve the original values in this example, one would have to set the corresponding toy instances in the middle of some simulation step intervals: 2.0 2.005479 3.994521 4.0 """

        if not self:
            self.values[:] = 0.
        elif len(self) == 1:
            values = list(self._toy2values.values())[0]
            self.values[:] = self.apply_timefactor(values)
        else:
            for idx, date in enumerate(
                    timetools.TOY.centred_timegrid(self.simulationstep)):
                values = self.interp(date)
                self.values[idx] = self.apply_timefactor(values)