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Diffstat (limited to 'docker/storperf-master/storperf/utilities/math.py')
| -rw-r--r-- | docker/storperf-master/storperf/utilities/math.py | 116 |
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 |