mirror of
https://review.haiku-os.org/buildtools
synced 2024-11-23 07:18:49 +01:00
92b3138b83
Updated dependencies: * GMP 6.2.1 * ISL 0.24 * MPL 1.2.1 * MPFR 4.1.0 The dependencies were pulled in by running the ./contrib/download_prerequisites script and then manually removing the symbolic links and archives, and renaming the directories (i.e mv isl-0.24 to isl)
606 lines
20 KiB
Python
Executable File
606 lines
20 KiB
Python
Executable File
#!/usr/bin/python
|
|
|
|
# Script to statistically compare two sets of log files with -ftime-report
|
|
# output embedded within them.
|
|
|
|
# Contributed by Lawrence Crowl <crowl@google.com>
|
|
#
|
|
# Copyright (C) 2012-2023 Free Software Foundation, Inc.
|
|
#
|
|
# This file is part of GCC.
|
|
#
|
|
# GCC is free software; you can redistribute it and/or modify
|
|
# it under the terms of the GNU General Public License as published by
|
|
# the Free Software Foundation; either version 3, or (at your option)
|
|
# any later version.
|
|
#
|
|
# GCC is distributed in the hope that it will be useful,
|
|
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
# GNU General Public License for more details.
|
|
#
|
|
# You should have received a copy of the GNU General Public License
|
|
# along with GCC; see the file COPYING. If not, write to
|
|
# the Free Software Foundation, 51 Franklin Street, Fifth Floor,
|
|
# Boston, MA 02110-1301, USA.
|
|
|
|
|
|
""" Compare two sets of compile-time performance numbers.
|
|
|
|
The intent of this script is to compare compile-time performance of two
|
|
different versions of the compiler. Each version of the compiler must be
|
|
run at least three times with the -ftime-report option. Each log file
|
|
represents a data point, or trial. The set of trials for each compiler
|
|
version constitutes a sample. The ouput of the script is a description
|
|
of the statistically significant difference between the two version of
|
|
the compiler.
|
|
|
|
The parameters to the script are:
|
|
|
|
Two file patterns that each match a set of log files. You will probably
|
|
need to quote the patterns before passing them to the script.
|
|
|
|
Each pattern corresponds to a version of the compiler.
|
|
|
|
A regular expression that finds interesting lines in the log files.
|
|
If you want to match the beginning of the line, you will need to add
|
|
the ^ operator. The filtering uses Python regular expression syntax.
|
|
|
|
The default is "TOTAL".
|
|
|
|
All of the interesting lines in a single log file are summed to produce
|
|
a single trial (data point).
|
|
|
|
A desired statistical confidence within the range 60% to 99.9%. Due to
|
|
the implementation, this confidence will be rounded down to one of 60%,
|
|
70%, 80%, 90%, 95%, 98%, 99%, 99.5%, 99.8%, and 99.9%.
|
|
|
|
The default is 95.
|
|
|
|
If the computed confidence is lower than desired, the script will
|
|
estimate the number of trials needed to meet the desired confidence.
|
|
This estimate is not very good, as the variance tends to change as
|
|
you increase the number of trials.
|
|
|
|
The most common use of the script is total compile-time comparison between
|
|
logfiles stored in different directories.
|
|
|
|
compare_two_ftime_report_sets "Log1/*perf" "Log2/*perf"
|
|
|
|
One can also look at parsing time, but expecting a lower confidence.
|
|
|
|
compare_two_ftime_report_sets "Log1/*perf" "Log2/*perf" "^phase parsing" 75
|
|
|
|
"""
|
|
|
|
|
|
import os
|
|
import sys
|
|
import fnmatch
|
|
import glob
|
|
import re
|
|
import math
|
|
|
|
|
|
####################################################################### Utility
|
|
|
|
|
|
def divide(dividend, divisor):
|
|
""" Return the quotient, avoiding division by zero.
|
|
"""
|
|
if divisor == 0:
|
|
return sys.float_info.max
|
|
else:
|
|
return dividend / divisor
|
|
|
|
|
|
################################################################# File and Line
|
|
|
|
|
|
# Should you repurpose this script, this code might help.
|
|
#
|
|
#def find_files(topdir, filepat):
|
|
# """ Find a set of file names, under a given directory,
|
|
# matching a Unix shell file pattern.
|
|
# Returns an iterator over the file names.
|
|
# """
|
|
# for path, dirlist, filelist in os.walk(topdir):
|
|
# for name in fnmatch.filter(filelist, filepat):
|
|
# yield os.path.join(path, name)
|
|
|
|
|
|
def match_files(fileglob):
|
|
""" Find a set of file names matching a Unix shell glob pattern.
|
|
Returns an iterator over the file names.
|
|
"""
|
|
return glob.iglob(os.path.expanduser(fileglob))
|
|
|
|
|
|
def lines_in_file(filename):
|
|
""" Return an iterator over lines in the named file. """
|
|
filedesc = open(filename, "r")
|
|
for line in filedesc:
|
|
yield line
|
|
filedesc.close()
|
|
|
|
|
|
def lines_containing_pattern(pattern, lines):
|
|
""" Find lines by a Python regular-expression.
|
|
Returns an iterator over lines containing the expression.
|
|
"""
|
|
parser = re.compile(pattern)
|
|
for line in lines:
|
|
if parser.search(line):
|
|
yield line
|
|
|
|
|
|
############################################################# Number Formatting
|
|
|
|
|
|
def strip_redundant_digits(numrep):
|
|
if numrep.find(".") == -1:
|
|
return numrep
|
|
return numrep.rstrip("0").rstrip(".")
|
|
|
|
|
|
def text_number(number):
|
|
return strip_redundant_digits("%g" % number)
|
|
|
|
|
|
def round_significant(digits, number):
|
|
if number == 0:
|
|
return 0
|
|
magnitude = abs(number)
|
|
significance = math.floor(math.log10(magnitude))
|
|
least_position = int(significance - digits + 1)
|
|
return round(number, -least_position)
|
|
|
|
|
|
def text_significant(digits, number):
|
|
return text_number(round_significant(digits, number))
|
|
|
|
|
|
def text_percent(number):
|
|
return text_significant(3, number*100) + "%"
|
|
|
|
|
|
################################################################ T-Distribution
|
|
|
|
|
|
# This section of code provides functions for using Student's t-distribution.
|
|
|
|
|
|
# The functions are implemented using table lookup
|
|
# to facilitate implementation of inverse functions.
|
|
|
|
|
|
# The table is comprised of row 0 listing the alpha values,
|
|
# column 0 listing the degree-of-freedom values,
|
|
# and the other entries listing the corresponding t-distribution values.
|
|
|
|
t_dist_table = [
|
|
[ 0, 0.200, 0.150, 0.100, 0.050, 0.025, 0.010, 0.005, .0025, 0.001, .0005],
|
|
[ 1, 1.376, 1.963, 3.078, 6.314, 12.71, 31.82, 63.66, 127.3, 318.3, 636.6],
|
|
[ 2, 1.061, 1.386, 1.886, 2.920, 4.303, 6.965, 9.925, 14.09, 22.33, 31.60],
|
|
[ 3, 0.978, 1.250, 1.638, 2.353, 3.182, 4.541, 5.841, 7.453, 10.21, 12.92],
|
|
[ 4, 0.941, 1.190, 1.533, 2.132, 2.776, 3.747, 4.604, 5.598, 7.173, 8.610],
|
|
[ 5, 0.920, 1.156, 1.476, 2.015, 2.571, 3.365, 4.032, 4.773, 5.894, 6.869],
|
|
[ 6, 0.906, 1.134, 1.440, 1.943, 2.447, 3.143, 3.707, 4.317, 5.208, 5.959],
|
|
[ 7, 0.896, 1.119, 1.415, 1.895, 2.365, 2.998, 3.499, 4.029, 4.785, 5.408],
|
|
[ 8, 0.889, 1.108, 1.397, 1.860, 2.306, 2.896, 3.355, 3.833, 4.501, 5.041],
|
|
[ 9, 0.883, 1.100, 1.383, 1.833, 2.262, 2.821, 3.250, 3.690, 4.297, 4.781],
|
|
[ 10, 0.879, 1.093, 1.372, 1.812, 2.228, 2.764, 3.169, 3.581, 4.144, 4.587],
|
|
[ 11, 0.876, 1.088, 1.363, 1.796, 2.201, 2.718, 3.106, 3.497, 4.025, 4.437],
|
|
[ 12, 0.873, 1.083, 1.356, 1.782, 2.179, 2.681, 3.055, 3.428, 3.930, 4.318],
|
|
[ 13, 0.870, 1.079, 1.350, 1.771, 2.160, 2.650, 3.012, 3.372, 3.852, 4.221],
|
|
[ 14, 0.868, 1.076, 1.345, 1.761, 2.145, 2.624, 2.977, 3.326, 3.787, 4.140],
|
|
[ 15, 0.866, 1.074, 1.341, 1.753, 2.131, 2.602, 2.947, 3.286, 3.733, 4.073],
|
|
[ 16, 0.865, 1.071, 1.337, 1.746, 2.120, 2.583, 2.921, 3.252, 3.686, 4.015],
|
|
[ 17, 0.863, 1.069, 1.333, 1.740, 2.110, 2.567, 2.898, 3.222, 3.646, 3.965],
|
|
[ 18, 0.862, 1.067, 1.330, 1.734, 2.101, 2.552, 2.878, 3.197, 3.610, 3.922],
|
|
[ 19, 0.861, 1.066, 1.328, 1.729, 2.093, 2.539, 2.861, 3.174, 3.579, 3.883],
|
|
[ 20, 0.860, 1.064, 1.325, 1.725, 2.086, 2.528, 2.845, 3.153, 3.552, 3.850],
|
|
[ 21, 0.859, 1.063, 1.323, 1.721, 2.080, 2.518, 2.831, 3.135, 3.527, 3.819],
|
|
[ 22, 0.858, 1.061, 1.321, 1.717, 2.074, 2.508, 2.819, 3.119, 3.505, 3.792],
|
|
[ 23, 0.858, 1.060, 1.319, 1.714, 2.069, 2.500, 2.807, 3.104, 3.485, 3.768],
|
|
[ 24, 0.857, 1.059, 1.318, 1.711, 2.064, 2.492, 2.797, 3.091, 3.467, 3.745],
|
|
[ 25, 0.856, 1.058, 1.316, 1.708, 2.060, 2.485, 2.787, 3.078, 3.450, 3.725],
|
|
[ 26, 0.856, 1.058, 1.315, 1.706, 2.056, 2.479, 2.779, 3.067, 3.435, 3.707],
|
|
[ 27, 0.855, 1.057, 1.314, 1.703, 2.052, 2.473, 2.771, 3.057, 3.421, 3.689],
|
|
[ 28, 0.855, 1.056, 1.313, 1.701, 2.048, 2.467, 2.763, 3.047, 3.408, 3.674],
|
|
[ 29, 0.854, 1.055, 1.311, 1.699, 2.045, 2.462, 2.756, 3.038, 3.396, 3.660],
|
|
[ 30, 0.854, 1.055, 1.310, 1.697, 2.042, 2.457, 2.750, 3.030, 3.385, 3.646],
|
|
[ 31, 0.853, 1.054, 1.309, 1.696, 2.040, 2.453, 2.744, 3.022, 3.375, 3.633],
|
|
[ 32, 0.853, 1.054, 1.309, 1.694, 2.037, 2.449, 2.738, 3.015, 3.365, 3.622],
|
|
[ 33, 0.853, 1.053, 1.308, 1.692, 2.035, 2.445, 2.733, 3.008, 3.356, 3.611],
|
|
[ 34, 0.852, 1.052, 1.307, 1.691, 2.032, 2.441, 2.728, 3.002, 3.348, 3.601],
|
|
[ 35, 0.852, 1.052, 1.306, 1.690, 2.030, 2.438, 2.724, 2.996, 3.340, 3.591],
|
|
[ 36, 0.852, 1.052, 1.306, 1.688, 2.028, 2.434, 2.719, 2.990, 3.333, 3.582],
|
|
[ 37, 0.851, 1.051, 1.305, 1.687, 2.026, 2.431, 2.715, 2.985, 3.326, 3.574],
|
|
[ 38, 0.851, 1.051, 1.304, 1.686, 2.024, 2.429, 2.712, 2.980, 3.319, 3.566],
|
|
[ 39, 0.851, 1.050, 1.304, 1.685, 2.023, 2.426, 2.708, 2.976, 3.313, 3.558],
|
|
[ 40, 0.851, 1.050, 1.303, 1.684, 2.021, 2.423, 2.704, 2.971, 3.307, 3.551],
|
|
[ 50, 0.849, 1.047, 1.299, 1.676, 2.009, 2.403, 2.678, 2.937, 3.261, 3.496],
|
|
[ 60, 0.848, 1.045, 1.296, 1.671, 2.000, 2.390, 2.660, 2.915, 3.232, 3.460],
|
|
[ 80, 0.846, 1.043, 1.292, 1.664, 1.990, 2.374, 2.639, 2.887, 3.195, 3.416],
|
|
[100, 0.845, 1.042, 1.290, 1.660, 1.984, 2.364, 2.626, 2.871, 3.174, 3.390],
|
|
[150, 0.844, 1.040, 1.287, 1.655, 1.976, 2.351, 2.609, 2.849, 3.145, 3.357] ]
|
|
|
|
|
|
# The functions use the following parameter name conventions:
|
|
# alpha - the alpha parameter
|
|
# degree - the degree-of-freedom parameter
|
|
# value - the t-distribution value for some alpha and degree
|
|
# deviations - a confidence interval radius,
|
|
# expressed as a multiple of the standard deviation of the sample
|
|
# ax - the alpha parameter index
|
|
# dx - the degree-of-freedom parameter index
|
|
|
|
# The interface to this section of code is the last three functions,
|
|
# find_t_dist_value, find_t_dist_alpha, and find_t_dist_degree.
|
|
|
|
|
|
def t_dist_alpha_at_index(ax):
|
|
if ax == 0:
|
|
return .25 # effectively no confidence
|
|
else:
|
|
return t_dist_table[0][ax]
|
|
|
|
|
|
def t_dist_degree_at_index(dx):
|
|
return t_dist_table[dx][0]
|
|
|
|
|
|
def t_dist_value_at_index(ax, dx):
|
|
return t_dist_table[dx][ax]
|
|
|
|
|
|
def t_dist_index_of_degree(degree):
|
|
limit = len(t_dist_table) - 1
|
|
dx = 0
|
|
while dx < limit and t_dist_degree_at_index(dx+1) <= degree:
|
|
dx += 1
|
|
return dx
|
|
|
|
|
|
def t_dist_index_of_alpha(alpha):
|
|
limit = len(t_dist_table[0]) - 1
|
|
ax = 0
|
|
while ax < limit and t_dist_alpha_at_index(ax+1) >= alpha:
|
|
ax += 1
|
|
return ax
|
|
|
|
|
|
def t_dist_index_of_value(dx, value):
|
|
limit = len(t_dist_table[dx]) - 1
|
|
ax = 0
|
|
while ax < limit and t_dist_value_at_index(ax+1, dx) < value:
|
|
ax += 1
|
|
return ax
|
|
|
|
|
|
def t_dist_value_within_deviations(dx, ax, deviations):
|
|
degree = t_dist_degree_at_index(dx)
|
|
count = degree + 1
|
|
root = math.sqrt(count)
|
|
value = t_dist_value_at_index(ax, dx)
|
|
nominal = value / root
|
|
comparison = nominal <= deviations
|
|
return comparison
|
|
|
|
|
|
def t_dist_index_of_degree_for_deviations(ax, deviations):
|
|
limit = len(t_dist_table) - 1
|
|
dx = 1
|
|
while dx < limit and not t_dist_value_within_deviations(dx, ax, deviations):
|
|
dx += 1
|
|
return dx
|
|
|
|
|
|
def find_t_dist_value(alpha, degree):
|
|
""" Return the t-distribution value.
|
|
The parameters are alpha and degree of freedom.
|
|
"""
|
|
dx = t_dist_index_of_degree(degree)
|
|
ax = t_dist_index_of_alpha(alpha)
|
|
return t_dist_value_at_index(ax, dx)
|
|
|
|
|
|
def find_t_dist_alpha(value, degree):
|
|
""" Return the alpha.
|
|
The parameters are the t-distribution value for a given degree of freedom.
|
|
"""
|
|
dx = t_dist_index_of_degree(degree)
|
|
ax = t_dist_index_of_value(dx, value)
|
|
return t_dist_alpha_at_index(ax)
|
|
|
|
|
|
def find_t_dist_degree(alpha, deviations):
|
|
""" Return the degree-of-freedom.
|
|
The parameters are the desired alpha and the number of standard deviations
|
|
away from the mean that the degree should handle.
|
|
"""
|
|
ax = t_dist_index_of_alpha(alpha)
|
|
dx = t_dist_index_of_degree_for_deviations(ax, deviations)
|
|
return t_dist_degree_at_index(dx)
|
|
|
|
|
|
############################################################## Core Statistical
|
|
|
|
|
|
# This section provides the core statistical classes and functions.
|
|
|
|
|
|
class Accumulator:
|
|
|
|
""" An accumulator for statistical information using arithmetic mean. """
|
|
|
|
def __init__(self):
|
|
self.count = 0
|
|
self.mean = 0
|
|
self.sumsqdiff = 0
|
|
|
|
def insert(self, value):
|
|
self.count += 1
|
|
diff = value - self.mean
|
|
self.mean += diff / self.count
|
|
self.sumsqdiff += (self.count - 1) * diff * diff / self.count
|
|
|
|
|
|
def fill_accumulator_from_values(values):
|
|
accumulator = Accumulator()
|
|
for value in values:
|
|
accumulator.insert(value)
|
|
return accumulator
|
|
|
|
|
|
def alpha_from_confidence(confidence):
|
|
scrubbed = min(99.99, max(confidence, 60))
|
|
return (100.0 - scrubbed) / 200.0
|
|
|
|
|
|
def confidence_from_alpha(alpha):
|
|
return 100 - 200 * alpha
|
|
|
|
|
|
class Sample:
|
|
|
|
""" A description of a sample using an arithmetic mean. """
|
|
|
|
def __init__(self, accumulator, alpha):
|
|
if accumulator.count < 3:
|
|
sys.exit("Samples must contain three trials.")
|
|
self.count = accumulator.count
|
|
self.mean = accumulator.mean
|
|
variance = accumulator.sumsqdiff / (self.count - 1)
|
|
self.deviation = math.sqrt(variance)
|
|
self.error = self.deviation / math.sqrt(self.count)
|
|
self.alpha = alpha
|
|
self.radius = find_t_dist_value(alpha, self.count - 1) * self.error
|
|
|
|
def alpha_for_radius(self, radius):
|
|
return find_t_dist_alpha(divide(radius, self.error), self.count)
|
|
|
|
def degree_for_radius(self, radius):
|
|
return find_t_dist_degree(self.alpha, divide(radius, self.deviation))
|
|
|
|
def __str__(self):
|
|
text = "trial count is " + text_number(self.count)
|
|
text += ", mean is " + text_number(self.mean)
|
|
text += " (" + text_number(confidence_from_alpha(self.alpha)) +"%"
|
|
text += " confidence in " + text_number(self.mean - self.radius)
|
|
text += " to " + text_number(self.mean + self.radius) + ")"
|
|
text += ",\nstd.deviation is " + text_number(self.deviation)
|
|
text += ", std.error is " + text_number(self.error)
|
|
return text
|
|
|
|
|
|
def sample_from_values(values, alpha):
|
|
accumulator = fill_accumulator_from_values(values)
|
|
return Sample(accumulator, alpha)
|
|
|
|
|
|
class Comparison:
|
|
|
|
""" A comparison of two samples using arithmetic means. """
|
|
|
|
def __init__(self, first, second, alpha):
|
|
if first.mean > second.mean:
|
|
self.upper = first
|
|
self.lower = second
|
|
self.larger = "first"
|
|
else:
|
|
self.upper = second
|
|
self.lower = first
|
|
self.larger = "second"
|
|
self.a_wanted = alpha
|
|
radius = self.upper.mean - self.lower.mean
|
|
rising = self.lower.alpha_for_radius(radius)
|
|
falling = self.upper.alpha_for_radius(radius)
|
|
self.a_actual = max(rising, falling)
|
|
rising = self.lower.degree_for_radius(radius)
|
|
falling = self.upper.degree_for_radius(radius)
|
|
self.count = max(rising, falling) + 1
|
|
|
|
def __str__(self):
|
|
message = "The " + self.larger + " sample appears to be "
|
|
change = divide(self.upper.mean, self.lower.mean) - 1
|
|
message += text_percent(change) + " larger,\n"
|
|
confidence = confidence_from_alpha(self.a_actual)
|
|
if confidence >= 60:
|
|
message += "with " + text_number(confidence) + "% confidence"
|
|
message += " of being larger."
|
|
else:
|
|
message += "but with no confidence of actually being larger."
|
|
if self.a_actual > self.a_wanted:
|
|
confidence = confidence_from_alpha(self.a_wanted)
|
|
message += "\nTo reach " + text_number(confidence) + "% confidence,"
|
|
if self.count < 100:
|
|
message += " you need roughly " + text_number(self.count) + " trials,\n"
|
|
message += "assuming the standard deviation is stable, which is iffy."
|
|
else:
|
|
message += "\nyou need to reduce the larger deviation"
|
|
message += " or increase the number of trials."
|
|
return message
|
|
|
|
|
|
############################################################ Single Value Files
|
|
|
|
|
|
# This section provides functions to compare two raw data files,
|
|
# each containing a whole sample consisting of single number per line.
|
|
|
|
|
|
# Should you repurpose this script, this code might help.
|
|
#
|
|
#def values_from_data_file(filename):
|
|
# for line in lines_in_file(filename):
|
|
# yield float(line)
|
|
|
|
|
|
# Should you repurpose this script, this code might help.
|
|
#
|
|
#def sample_from_data_file(filename, alpha):
|
|
# confidence = confidence_from_alpha(alpha)
|
|
# text = "\nArithmetic sample for data file\n\"" + filename + "\""
|
|
# text += " with desired confidence " + text_number(confidence) + " is "
|
|
# print text
|
|
# values = values_from_data_file(filename)
|
|
# sample = sample_from_values(values, alpha)
|
|
# print sample
|
|
# return sample
|
|
|
|
|
|
# Should you repurpose this script, this code might help.
|
|
#
|
|
#def compare_two_data_files(filename1, filename2, confidence):
|
|
# alpha = alpha_from_confidence(confidence)
|
|
# sample1 = sample_from_data_file(filename1, alpha)
|
|
# sample2 = sample_from_data_file(filename2, alpha)
|
|
# print
|
|
# print Comparison(sample1, sample2, alpha)
|
|
|
|
|
|
# Should you repurpose this script, this code might help.
|
|
#
|
|
#def command_two_data_files():
|
|
# argc = len(sys.argv)
|
|
# if argc < 2 or 4 < argc:
|
|
# message = "usage: " + sys.argv[0]
|
|
# message += " file-name file-name [confidence]"
|
|
# print message
|
|
# else:
|
|
# filename1 = sys.argv[1]
|
|
# filename2 = sys.argv[2]
|
|
# if len(sys.argv) >= 4:
|
|
# confidence = int(sys.argv[3])
|
|
# else:
|
|
# confidence = 95
|
|
# compare_two_data_files(filename1, filename2, confidence)
|
|
|
|
|
|
############################################### -ftime-report TimeVar Log Files
|
|
|
|
|
|
# This section provides functions to compare two sets of -ftime-report log
|
|
# files. Each set is a sample, where each data point is derived from the
|
|
# sum of values in a single log file.
|
|
|
|
|
|
label = r"^ *([^:]*[^: ]) *:"
|
|
number = r" *([0-9.]*) *"
|
|
percent = r"\( *[0-9]*\%\)"
|
|
numpct = number + percent
|
|
total_format = label + number + number + number + number + " kB\n"
|
|
total_parser = re.compile(total_format)
|
|
tmvar_format = label + numpct + " usr" + numpct + " sys"
|
|
tmvar_format += numpct + " wall" + number + " kB " + percent + " ggc\n"
|
|
tmvar_parser = re.compile(tmvar_format)
|
|
replace = r"\2\t\3\t\4\t\5\t\1"
|
|
|
|
|
|
def split_time_report(lines, pattern):
|
|
if pattern == "TOTAL":
|
|
parser = total_parser
|
|
else:
|
|
parser = tmvar_parser
|
|
for line in lines:
|
|
modified = parser.sub(replace, line)
|
|
if modified != line:
|
|
yield re.split("\t", modified)
|
|
|
|
|
|
def extract_cpu_time(tvtuples):
|
|
for tuple in tvtuples:
|
|
yield float(tuple[0]) + float(tuple[1])
|
|
|
|
|
|
def sum_values(values):
|
|
sum = 0
|
|
for value in values:
|
|
sum += value
|
|
return sum
|
|
|
|
|
|
def extract_time_for_timevar_log(filename, pattern):
|
|
lines = lines_in_file(filename)
|
|
tmvars = lines_containing_pattern(pattern, lines)
|
|
tuples = split_time_report(tmvars, pattern)
|
|
times = extract_cpu_time(tuples)
|
|
return sum_values(times)
|
|
|
|
|
|
def extract_times_for_timevar_logs(filelist, pattern):
|
|
for filename in filelist:
|
|
yield extract_time_for_timevar_log(filename, pattern)
|
|
|
|
|
|
def sample_from_timevar_logs(fileglob, pattern, alpha):
|
|
confidence = confidence_from_alpha(alpha)
|
|
text = "\nArithmetic sample for timevar log files\n\"" + fileglob + "\""
|
|
text += "\nand selecting lines containing \"" + pattern + "\""
|
|
text += " with desired confidence " + text_number(confidence) + " is "
|
|
print text
|
|
filelist = match_files(fileglob)
|
|
values = extract_times_for_timevar_logs(filelist, pattern)
|
|
sample = sample_from_values(values, alpha)
|
|
print sample
|
|
return sample
|
|
|
|
|
|
def compare_two_timevar_logs(fileglob1, fileglob2, pattern, confidence):
|
|
alpha = alpha_from_confidence(confidence)
|
|
sample1 = sample_from_timevar_logs(fileglob1, pattern, alpha)
|
|
sample2 = sample_from_timevar_logs(fileglob2, pattern, alpha)
|
|
print
|
|
print Comparison(sample1, sample2, alpha)
|
|
|
|
|
|
def command_two_timevar_logs():
|
|
argc = len(sys.argv)
|
|
if argc < 3 or 5 < argc:
|
|
message = "usage: " + sys.argv[0]
|
|
message += " file-pattern file-pattern [line-pattern [confidence]]"
|
|
print message
|
|
else:
|
|
filepat1 = sys.argv[1]
|
|
filepat2 = sys.argv[2]
|
|
if len(sys.argv) >= 5:
|
|
confidence = int(sys.argv[4])
|
|
else:
|
|
confidence = 95
|
|
if len(sys.argv) >= 4:
|
|
linepat = sys.argv[3]
|
|
else:
|
|
linepat = "TOTAL"
|
|
compare_two_timevar_logs(filepat1, filepat2, linepat, confidence)
|
|
|
|
|
|
########################################################################## Main
|
|
|
|
|
|
# This section is the main code, implementing the command.
|
|
|
|
|
|
command_two_timevar_logs()
|