PAL: Add NFV reconf tests

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

diff --git a/resources/tools/presentation/utils.py b/resources/tools/presentation/utils.py
index a2aa0dc..3f0d6ff 100644 (file)
--- a/resources/tools/presentation/utils.py
+++ b/resources/tools/presentation/utils.py
@@ -16,6 +16,7 @@
 
 import multiprocessing
 import subprocess
 
 import multiprocessing
 import subprocess

+import math

 import numpy as np
 import logging
 import csv
 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 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
 
 from errors import PresentationError
 from jumpavg.BitCountingClassifier import BitCountingClassifier


@@ -50,11 +52,7 @@ def stdev(items):
     :returns: Stdev.
     :rtype: float
     """
     :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):
 
 
 def relative_change(nr1, nr2):


@@ -71,6 +69,32 @@ def relative_change(nr1, nr2):
     return float(((nr2 - nr1) / nr1) * 100)
 
 
     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.
 
 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
 
     :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()
     """
 
     env = environ.copy()


@@ -178,6 +202,29 @@ def get_last_completed_build_number(jenkins_url, job_name):
     return execute_command(cmd)
 
 
     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.
 
 def archive_input_data(spec):
     """Archive the report.
 


@@ -188,9 +235,11 @@ def archive_input_data(spec):
 
     logging.info("    Archiving the input data files ...")
 
 
     logging.info("    Archiving the input data files ...")
 

-    extension = spec.input["file-format"]
-    data_files = get_files(spec.environment["paths"]["DIR[WORKING,DATA]"],
-                           extension=extension)
+    extension = spec.input["arch-file-format"]
+    data_files = list()
+    for ext in extension:
+        data_files.extend(get_files(
+            spec.environment["paths"]["DIR[WORKING,DATA]"], extension=ext))

     dst = spec.environment["paths"]["DIR[STATIC,ARCH]"]
     logging.info("      Destination: {0}".format(dst))
 
     dst = spec.environment["paths"]["DIR[STATIC,ARCH]"]
     logging.info("      Destination: {0}".format(dst))
 


@@ -217,13 +266,13 @@ 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.
     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.
     :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)
                  for _, sample in data.iteritems()]
     # TODO: Put analogous iterator into jumpavg library.
     groups = BitCountingClassifier().classify(bare_data)


@@ -234,9 +283,9 @@ def classify_anomalies(data):
     values_left = 0
     avg = 0.0
     for _, sample in data.iteritems():
     values_left = 0
     avg = 0.0
     for _, sample in data.iteritems():

-        if np.isnan(sample):
+        if np.isnan(sample.avg):

             classification.append("outlier")
             classification.append("outlier")

-            avgs.append(sample)
+            avgs.append(sample.avg)

             continue
         if values_left < 1 or active_group is None:
             values_left = 0
             continue
         if values_left < 1 or active_group is None:
             values_left = 0