Report soak comparison: Add stdev for delta

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

diff --git a/resources/tools/presentation/utils.py b/resources/tools/presentation/utils.py
index a2aa0dc..8f56300 100644 (file)
--- a/resources/tools/presentation/utils.py
+++ b/resources/tools/presentation/utils.py
@@ -16,6 +16,7 @@
 
 import multiprocessing
 import subprocess
+import math
 import numpy as np
 import logging
 import csv
@@ -24,7 +25,8 @@ import prettytable
 from os import walk, makedirs, environ
 from os.path import join, isdir
 from shutil import move, Error
-from math import sqrt
+from datetime import datetime
+from pandas import Series
 
 from errors import PresentationError
 from jumpavg.BitCountingClassifier import BitCountingClassifier
@@ -50,11 +52,7 @@ def stdev(items):
     :returns: Stdev.
     :rtype: float
     """
-
-    avg = mean(items)
-    variance = [(x - avg) ** 2 for x in items]
-    stddev = sqrt(mean(variance))
-    return stddev
+    return Series.std(Series(items))
 
 
 def relative_change(nr1, nr2):
@@ -71,6 +69,32 @@ def relative_change(nr1, nr2):
     return float(((nr2 - nr1) / nr1) * 100)
 
 
+def relative_change_stdev(mean1, mean2, std1, std2):
+    """Compute relative standard deviation of change of two values.
+
+    The "1" values are the base for comparison.
+    Results are returned as percentage (and percentual points for stdev).
+    Linearized theory is used, so results are wrong for relatively large stdev.
+
+    :param mean1: Mean of the first number.
+    :param mean2: Mean of the second number.
+    :param std1: Standard deviation estimate of the first number.
+    :param std2: Standard deviation estimate of the second number.
+    :type mean1: float
+    :type mean2: float
+    :type std1: float
+    :type std2: float
+    :returns: Relative change and its stdev.
+    :rtype: float
+    """
+    mean1, mean2 = float(mean1), float(mean2)
+    quotient = mean2 / mean1
+    first = std1 / mean1
+    second = std2 / mean2
+    std = quotient * math.sqrt(first * first + second * second)
+    return (quotient - 1) * 100, std * 100
+
+
 def get_files(path, extension=None, full_path=True):
     """Generates the list of files to process.
 
@@ -120,8 +144,8 @@ def execute_command(cmd):
 
     :param cmd: Command to execute.
     :type cmd: str
-    :returns: Return code of the executed command.
-    :rtype: int
+    :returns: Return code of the executed command, stdout and stderr.
+    :rtype: tuple(int, str, str)
     """
 
     env = environ.copy()
@@ -178,6 +202,29 @@ def get_last_completed_build_number(jenkins_url, job_name):
     return execute_command(cmd)
 
 
+def get_build_timestamp(jenkins_url, job_name, build_nr):
+    """Get the timestamp of the build of the given job.
+
+    :param jenkins_url: Jenkins URL.
+    :param job_name: Job name.
+    :param build_nr: Build number.
+    :type jenkins_url: str
+    :type job_name: str
+    :type build_nr: int
+    :returns: The timestamp.
+    :rtype: datetime.datetime
+    """
+
+    url = "{jenkins_url}/{job_name}/{build_nr}".format(jenkins_url=jenkins_url,
+                                                       job_name=job_name,
+                                                       build_nr=build_nr)
+    cmd = "wget -qO- {url}".format(url=url)
+
+    timestamp = execute_command(cmd)
+
+    return datetime.fromtimestamp(timestamp/1000)
+
+
 def archive_input_data(spec):
     """Archive the report.
 
@@ -217,13 +264,13 @@ def classify_anomalies(data):
     the first value of changed average as a regression, or a progression.
 
     :param data: Full data set with unavailable samples replaced by nan.
-    :type data: pandas.Series
+    :type data: OrderedDict
     :returns: Classification and trend values
     :rtype: 2-tuple, list of strings and list of floats
     """
     # Nan mean something went wrong.
     # Use 0.0 to cause that being reported as a severe regression.
-    bare_data = [0.0 if np.isnan(sample) else sample
+    bare_data = [0.0 if np.isnan(sample.avg) else sample
                  for _, sample in data.iteritems()]
     # TODO: Put analogous iterator into jumpavg library.
     groups = BitCountingClassifier().classify(bare_data)
@@ -234,9 +281,9 @@ def classify_anomalies(data):
     values_left = 0
     avg = 0.0
     for _, sample in data.iteritems():
-        if np.isnan(sample):
+        if np.isnan(sample.avg):
             classification.append("outlier")
-            avgs.append(sample)
+            avgs.append(sample.avg)
             continue
         if values_left < 1 or active_group is None:
             values_left = 0