Add Steady State Detection module

Added a Steady State Detection module containing a steady_state(data_series)
function that is able to return a boolean indicating wether or not steady
state is reached with the data_series being passed.
This module requires a data_treatment(data_series) and an average(data_series)
modules that have been added in this commit as well. The data treatment function
aims at formatting the data series that is passed to the high level steady_state
function to reach the requirement of each sub-module (slope, average and range).

Modified the Slope and Range functions so they return None when passed an empty
data series instead of 0 which was wrong. Modified the corresponding test cases.

Modified the math_range_test.py file to fix a bug in the 2 last tests.

Change-Id: I9c3854cb0a21cc37b0787b8afca0821eefaa203d
JIRA: STORPERF-60
JIRA: STORPERF-59
JIRA: STORPERF-61
JIRA: STORPERF-62
Signed-off-by: Tim Rault <tim.rault@cengn.ca>

1 files changed, 52 insertions, 0 deletions

diff --git a/tests/utilities_tests/math_average_test.py b/tests/utilities_tests/math_average_test.py
new file mode 100644
index 0000000..3095f56
--- /dev/null
+++ b/tests/utilities_tests/math_average_test.py
@@ -0,0 +1,52 @@
+##############################################################################
+# Copyright (c) 2016 CENGN and others.
+#
+# All rights reserved. This program and the accompanying materials
+# are made available under the terms of the Apache License, Version 2.0
+# which accompanies this distribution, and is available at
+# http://www.apache.org/licenses/LICENSE-2.0
+##############################################################################
+import unittest
+from storperf.utilities import math as math
+
+
+class MathAverageTest(unittest.TestCase):
+
+    def setUp(self):
+        unittest.TestCase.setUp(self)
+
+    def test_empty_series(self):
+        expected = None
+        data_series = []
+        actual = math.average(data_series)
+        self.assertEqual(expected, actual)
+
+    def test_integer_series(self):
+        expected = 19.75
+        data_series = [5, 12, 7, 55]
+        actual = math.average(data_series)
+        self.assertEqual(expected, actual)
+
+    def test_float_series(self):
+        expected = 63.475899999999996
+        data_series = [78.6, 45.187, 33.334, 96.7826]
+        actual = math.average(data_series)
+        self.assertEqual(expected, actual)
+
+    def test_float_int_mix(self):
+        expected = 472.104
+        data_series = [10, 557.33, 862, 56.99, 874.2]
+        actual = math.average(data_series)
+        self.assertEqual(expected, actual)
+
+    def test_negative_values(self):
+        expected = -17.314
+        data_series = [-15.654, 59.5, 16.25, -150, 3.334]
+        actual = math.average(data_series)
+        self.assertEqual(expected, actual)
+
+    def test_single_value(self):
+        expected = -66.6667
+        data_series = [-66.6667]
+        actual = math.average(data_series)
+        self.assertEqual(expected, actual)