CSIT-1126: Detection and reporting of failed MRR tests

[csit.git] / resources / tools / presentation / utils.py

diff --git a/resources/tools/presentation/utils.py b/resources/tools/presentation/utils.py
index f32019d..ba32932 100644 (file)
--- a/resources/tools/presentation/utils.py
+++ b/resources/tools/presentation/utils.py
@@ -19,6 +19,8 @@ import subprocess
 import numpy as np
 import pandas as pd
 import logging
+import csv
+import prettytable
 
 from os import walk, makedirs, environ
 from os.path import join, isdir
@@ -274,6 +276,71 @@ def archive_input_data(spec):
     logging.info("    Done.")
 
 
+def classify_anomalies(data, window):
+    """Evaluates if the sample value is an outlier, regression, normal or
+    progression compared to the previous data within the window.
+    We use the intervals defined as:
+    - regress: less than trimmed moving median - 3 * stdev
+    - normal: between trimmed moving median - 3 * stdev and median + 3 * stdev
+    - progress: more than trimmed moving median + 3 * stdev
+    where stdev is trimmed moving standard deviation.
+
+    :param data: Full data set with the outliers replaced by nan.
+    :param window: Window size used to calculate moving average and moving
+        stdev.
+    :type data: pandas.Series
+    :type window: int
+    :returns: Evaluated results.
+    :rtype: list
+    """
+
+    if data.size < 3:
+        return None
+
+    win_size = data.size if data.size < window else window
+    tmm = data.rolling(window=win_size, min_periods=2).median()
+    tmstd = data.rolling(window=win_size, min_periods=2).std()
+
+    classification = ["normal", ]
+    first = True
+    for build, value in data.iteritems():
+        if first:
+            first = False
+            continue
+        if np.isnan(value) or np.isnan(tmm[build]) or np.isnan(tmstd[build]):
+            classification.append("outlier")
+        elif value < (tmm[build] - 3 * tmstd[build]):
+            classification.append("regression")
+        elif value > (tmm[build] + 3 * tmstd[build]):
+            classification.append("progression")
+        else:
+            classification.append("normal")
+    return classification
+
+
+def convert_csv_to_pretty_txt(csv_file, txt_file):
+    """Convert the given csv table to pretty text table.
+
+    :param csv_file: The path to the input csv file.
+    :param txt_file: The path to the output pretty text file.
+    :type csv_file: str
+    :type txt_file: str
+    """
+
+    txt_table = None
+    with open(csv_file, 'rb') as csv_file:
+        csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+        for row in csv_content:
+            if txt_table is None:
+                txt_table = prettytable.PrettyTable(row)
+            else:
+                txt_table.add_row(row)
+        txt_table.align["Test case"] = "l"
+    if txt_table:
+        with open(txt_file, "w") as txt_file:
+            txt_file.write(str(txt_table))
+
+
 class Worker(multiprocessing.Process):
     """Worker class used to process tasks in separate parallel processes.
     """