def number_peaks(self, x, n=None):
        """
            As in tsfresh `number_peaks <https://github.com/blue-yonder/tsfresh/blob/master/tsfresh/feature_extraction/\
            feature_calculators.py#L1003>`_
            
            Calculates the number of peaks of at least support n in the time series x. A peak of support n is defined \
            as a subsequence of x where a value occurs, which is bigger than its n neighbours to the left and to the right.

            Hence in the sequence

            >>> x = [3, 0, 0, 4, 0, 0, 13]

            4 is a peak of support 1 and 2 because in the subsequences

            >>> [0, 4, 0]
            >>> [0, 0, 4, 0, 0]

            4 is still the highest value. Here, 4 is not a peak of support 3 because 13 is the 3th neighbour to the \
            right of 4 and its bigger than 4.

            :param x: the time series to calculate the feature of
            :type x: pandas.Series
            :param n: the support of the peak
            :type n: int
            :return: the value of this feature
            :rtype: float
            """
        if n is None:
            n = 5
        peaks = feature_calculators.number_peaks(x, n)
        logging.debug("agg linear trend by tsfresh calculated")
        return peaks
As in tsfresh `number_peaks <https://github.com/blue-yonder/tsfresh/blob/master/tsfresh/feature_extraction/\
            feature_calculators.py#L1003>`_
            
            Calculates the number of peaks of at least support n in the time series x. A peak of support n is defined \
            as a subsequence of x where a value occurs, which is bigger than its n neighbours to the left and to the right.

            Hence in the sequence

            >>> x = [3, 0, 0, 4, 0, 0, 13]

            4 is a peak of support 1 and 2 because in the subsequences

            >>> [0, 4, 0]
            >>> [0, 0, 4, 0, 0]

            4 is still the highest value. Here, 4 is not a peak of support 3 because 13 is the 3th neighbour to the \
            right of 4 and its bigger than 4.

            :param x: the time series to calculate the feature of
            :type x: pandas.Series
            :param n: the support of the peak
            :type n: int
            :return: the value of this feature
            :rtype: float