Change all paths

Changes the paths of all source code so that it exists under the dockerfile location
for each container.  This way we can use COPY instead of git clone, as well as use the
existing JJB.

Change-Id: I883b2957d89659c164fff0a1ebc4d677c534796d
JIRA: STORPERF-188
Signed-off-by: mbeierl <mark.beierl@dell.com>

1 files changed, 116 insertions, 0 deletions

diff --git a/docker/storperf-master/storperf/utilities/math.py b/docker/storperf-master/storperf/utilities/math.py
new file mode 100644
index 0000000..8e04134
--- /dev/null
+++ b/docker/storperf-master/storperf/utilities/math.py
@@ -0,0 +1,116 @@
+##############################################################################
+# 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 copy
+
+
+def slope(data_series):
+    """
+    This function implements the linear least squares algorithm described in
+    the following wikipedia article :
+    https://en.wikipedia.org/wiki/Linear_least_squares_(mathematics)
+    in the case of m equations (provided by m data points) and 2 unknown
+    variables (x and y, which represent the time and the Volume performance
+    variable being tested e.g. IOPS, latency...).
+    The data_series is currently assumed to follow the pattern :
+    [[x1,y1], [x2,y2], ..., [xm,ym]].
+    If this data pattern were to change, the data_treatement function
+    should be adjusted to ensure compatibility with the rest of the
+    Steady State Detection module.
+    """
+
+    # In the particular case of an empty data series
+    if len(data_series) == 0:
+        beta2 = None
+
+    else:  # The general case
+        data_series = copy.deepcopy(data_series)
+        m = len(data_series)
+        # To make sure at least one element is a float number so the result
+        # of the algorithm be a float number
+        data_series[0][0] = float(data_series[0][0])
+
+        """
+        It consists in solving the normal equations system (2 equations,
+        2 unknowns) by calculating the value of beta2 (slope).
+        The formula of beta1 (the y-intercept) is given as a comment in
+        case it is needed later.
+        """
+        sum_xi = 0
+        sum_xi_sq = 0
+        sum_yi_xi = 0
+        sum_yi = 0
+        for i in range(0, m):
+            xi = data_series[i][0]
+            yi = data_series[i][1]
+
+            sum_xi += xi
+            sum_xi_sq += xi**2
+            sum_yi_xi += xi * yi
+            sum_yi += yi
+
+        over = (sum_xi**2 - m * sum_xi_sq)
+        if over == 0:
+            beta2 = None  # Infinite slope
+        else:
+            beta2 = (sum_yi * sum_xi - m * sum_yi_xi) / over  # The slope
+        # beta1 = (sum_yi_xi - beta2*sum_xi_sq)/sum_xi #The y-intercept if
+        # needed
+
+    return beta2
+
+
+def range_value(data_series):
+    """
+    This function implements a range algorithm that returns a float number
+    representing the range of the data_series that is passed to it.
+    The data_series being passed is assumed to follow the following data
+    pattern : [y1, y2, y3, ..., ym] where yi represents the ith
+    measuring point of the y variable. The y variable represents the
+    Volume performance being tested (e.g. IOPS, latency...).
+    If this data pattern were to change, the data_treatment function
+    should be adjusted to ensure compatibility with the rest of the
+    Steady State Dectection module.
+    The conversion of the data series from the original pattern to the
+    [y1, y2, y3, ..., ym] pattern is done outside this function
+    so the original pattern can be changed without breaking this function.
+    """
+
+    # In the particular case of an empty data series
+    if len(data_series) == 0:
+        range_value = None
+
+    else:  # The general case
+        max_value = max(data_series)
+        min_value = min(data_series)
+        range_value = max_value - min_value
+
+    return range_value
+
+
+def average(data_series):
+    """
+    This function seeks to calculate the average value of the data series
+    given a series following the pattern : [y1, y2, y3, ..., ym].
+    If this data pattern were to change, the data_treatment function
+    should be adjusted to ensure compatibility with the rest of the
+    Steady State Dectection module.
+    The function returns a float number corresponding to the average of the yi.
+    """
+    m = len(data_series)
+
+    if m == 0:  # In the particular case of an empty data series
+        average = None
+
+    else:
+        data_sum = 0
+        for value in data_series:
+            data_sum += value
+        average = data_sum / float(m)
+
+    return average