+def remove_outliers(input_list, outlier_const=1.5, window=14):
+ """Return list with outliers removed, using split_outliers.
+
+ :param input_list: Data from which the outliers will be removed.
+ :param outlier_const: Outlier constant.
+ :param window: How many preceding values to take into account.
+ :type input_list: list of floats
+ :type outlier_const: float
+ :type window: int
+ :returns: The input list without outliers.
+ :rtype: list of floats
+ """
+
+ data = np.array(input_list)
+ upper_quartile = np.percentile(data, 75)
+ lower_quartile = np.percentile(data, 25)
+ iqr = (upper_quartile - lower_quartile) * outlier_const
+ quartile_set = (lower_quartile - iqr, upper_quartile + iqr)
+ result_lst = list()
+ for y in data.tolist():
+ if quartile_set[0] <= y <= quartile_set[1]:
+ result_lst.append(y)
+ return result_lst
+
+ # input_series = pd.Series()
+ # for index, value in enumerate(input_list):
+ # item_pd = pd.Series([value, ], index=[index, ])
+ # input_series.append(item_pd)
+ # output_series, _ = split_outliers(input_series, outlier_const=outlier_const,
+ # window=window)
+ # output_list = [y for x, y in output_series.items() if not np.isnan(y)]
+ #
+ # return output_list
+
+
+def split_outliers(input_series, outlier_const=1.5, window=14):