diff options
Diffstat (limited to 'resources/tools/presentation/utils.py')
| -rw-r--r-- | resources/tools/presentation/utils.py | 73 |
1 files changed, 41 insertions, 32 deletions
diff --git a/resources/tools/presentation/utils.py b/resources/tools/presentation/utils.py index 8365bfad5c..bc62268937 100644 --- a/resources/tools/presentation/utils.py +++ b/resources/tools/presentation/utils.py @@ -67,59 +67,68 @@ def relative_change(nr1, nr2): return float(((nr2 - nr1) / nr1) * 100) +def remove_outliers(input_list, outlier_const=1.5, window=14): + """Return list with outliers removed, using split_outliers. -def remove_outliers(input_data, outlier_const): - """ - - :param input_data: Data from which the outliers will be removed. + :param input_list: Data from which the outliers will be removed. :param outlier_const: Outlier constant. - :type input_data: list + :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 + :rtype: list of floats """ - data = np.array(input_data) - 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 find_outliers(input_data, outlier_const=1.5): +def split_outliers(input_series, outlier_const=1.5, window=14): """Go through the input data and generate two pandas series: - - input data without outliers + - input data with outliers replaced by NAN - outliers. The function uses IQR to detect outliers. - :param input_data: Data to be examined for outliers. + :param input_series: Data to be examined for outliers. :param outlier_const: Outlier constant. - :type input_data: pandas.Series + :param window: How many preceding values to take into account. + :type input_series: pandas.Series :type outlier_const: float - :returns: Tuple: input data with outliers removed; Outliers. - :rtype: tuple (trimmed_data, outliers) + :type window: int + :returns: Input data with NAN outliers and Outliers. + :rtype: (pandas.Series, pandas.Series) """ - upper_quartile = input_data.quantile(q=0.75) - lower_quartile = input_data.quantile(q=0.25) - iqr = (upper_quartile - lower_quartile) * outlier_const - low = lower_quartile - iqr - high = upper_quartile + iqr + list_data = list(input_series.items()) + head_size = min(window, len(list_data)) + head_list = list_data[:head_size] trimmed_data = pd.Series() outliers = pd.Series() - for item in input_data.items(): - item_pd = pd.Series([item[1], ], index=[item[0], ]) - if low <= item[1] <= high: + for item_x, item_y in head_list: + item_pd = pd.Series([item_y, ], index=[item_x, ]) + trimmed_data = trimmed_data.append(item_pd) + for index, (item_x, item_y) in list(enumerate(list_data))[head_size:]: + y_rolling_list = [y for (x, y) in list_data[index - head_size:index]] + y_rolling_array = np.array(y_rolling_list) + q1 = np.percentile(y_rolling_array, 25) + q3 = np.percentile(y_rolling_array, 75) + iqr = (q3 - q1) * outlier_const + low, high = q1 - iqr, q3 + iqr + item_pd = pd.Series([item_y, ], index=[item_x, ]) + if low <= item_y <= high: trimmed_data = trimmed_data.append(item_pd) else: - trimmed_data = trimmed_data.append(pd.Series([np.nan, ], - index=[item[0], ])) outliers = outliers.append(item_pd) + nan_pd = pd.Series([np.nan, ], index=[item_x, ]) + trimmed_data = trimmed_data.append(nan_pd) return trimmed_data, outliers |