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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 large_standard_deviation(x, r):
 """ Boolean variable denoting if the standard dev of x is higher than 'r' times the range = difference between max and min of x. Hence it checks if .. math:: std(x) > r * (max(X)-min(X)) According to a rule of the thumb, the standard deviation should be a forth of the range of the values. :param x: the time series to calculate the feature of :type x: numpy.ndarray :param r: the percentage of the range to compare with :type r: float :return: the value of this feature :return type: bool """

    if not isinstance(x, (np.ndarray, pd.Series)):
        x = np.asarray(x)
    return np.std(x) > (r * (np.max(x) - np.min(x)))