X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Futils.py;h=df543c17ea3c437146a82334a2a68f4e253a553c;hb=52cb667958d954d6233d0865a59d90cca82db026;hp=0bf78f09bfa1c3290fa80ffa66a4827d22e3efbb;hpb=f2f19bad6847e315366e5e9ab4952fded0097b1f;p=csit.git

diff --git a/resources/tools/presentation/utils.py b/resources/tools/presentation/utils.py
index 0bf78f09bf..df543c17ea 100644
--- a/resources/tools/presentation/utils.py
+++ b/resources/tools/presentation/utils.py
@@ -68,58 +68,69 @@ def relative_change(nr1, nr2):
     return float(((nr2 - nr1) / nr1) * 100)
 
 
-def remove_outliers(input_data, outlier_const):
-    """
+def remove_outliers(input_list, outlier_const=1.5, window=14):
+    """Return list with outliers removed, using split_outliers.
 
-    :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)
+    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():
+    for y in input_list:
         if quartile_set[0] <= y <= quartile_set[1]:
             result_lst.append(y)
     return result_lst
 
 
-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 = q1 - iqr
+        item_pd = pd.Series([item_y, ], index=[item_x, ])
+        if low <= item_y:
             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
 
@@ -129,7 +140,7 @@ def get_files(path, extension=None, full_path=True):
 
     :param path: Path to files.
     :param extension: Extension of files to process. If it is the empty string,
-    all files will be processed.
+        all files will be processed.
     :param full_path: If True, the files with full path are generated.
     :type path: str
     :type extension: str
@@ -187,8 +198,8 @@ def execute_command(cmd):
 
     stdout, stderr = proc.communicate()
 
-    logging.debug(stdout)
-    logging.debug(stderr)
+    logging.info(stdout)
+    logging.info(stderr)
 
     if proc.returncode != 0:
         logging.error("    Command execution failed.")