Mercurial > hg > CbC > CbC_gcc
comparison contrib/compare_two_ftime_report_sets @ 111:04ced10e8804
gcc 7
author | kono |
---|---|
date | Fri, 27 Oct 2017 22:46:09 +0900 |
parents | |
children |
comparison
equal
deleted
inserted
replaced
68:561a7518be6b | 111:04ced10e8804 |
---|---|
1 #!/usr/bin/python | |
2 | |
3 # Script to statistically compare two sets of log files with -ftime-report | |
4 # output embedded within them. | |
5 | |
6 # Contributed by Lawrence Crowl <crowl@google.com> | |
7 # | |
8 # Copyright (C) 2012 Free Software Foundation, Inc. | |
9 # | |
10 # This file is part of GCC. | |
11 # | |
12 # GCC is free software; you can redistribute it and/or modify | |
13 # it under the terms of the GNU General Public License as published by | |
14 # the Free Software Foundation; either version 3, or (at your option) | |
15 # any later version. | |
16 # | |
17 # GCC is distributed in the hope that it will be useful, | |
18 # but WITHOUT ANY WARRANTY; without even the implied warranty of | |
19 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | |
20 # GNU General Public License for more details. | |
21 # | |
22 # You should have received a copy of the GNU General Public License | |
23 # along with GCC; see the file COPYING. If not, write to | |
24 # the Free Software Foundation, 51 Franklin Street, Fifth Floor, | |
25 # Boston, MA 02110-1301, USA. | |
26 | |
27 | |
28 """ Compare two sets of compile-time performance numbers. | |
29 | |
30 The intent of this script is to compare compile-time performance of two | |
31 different versions of the compiler. Each version of the compiler must be | |
32 run at least three times with the -ftime-report option. Each log file | |
33 represents a data point, or trial. The set of trials for each compiler | |
34 version constitutes a sample. The ouput of the script is a description | |
35 of the statistically significant difference between the two version of | |
36 the compiler. | |
37 | |
38 The parameters to the script are: | |
39 | |
40 Two file patterns that each match a set of log files. You will probably | |
41 need to quote the patterns before passing them to the script. | |
42 | |
43 Each pattern corresponds to a version of the compiler. | |
44 | |
45 A regular expression that finds interesting lines in the log files. | |
46 If you want to match the beginning of the line, you will need to add | |
47 the ^ operator. The filtering uses Python regular expression syntax. | |
48 | |
49 The default is "TOTAL". | |
50 | |
51 All of the interesting lines in a single log file are summed to produce | |
52 a single trial (data point). | |
53 | |
54 A desired statistical confidence within the range 60% to 99.9%. Due to | |
55 the implementation, this confidence will be rounded down to one of 60%, | |
56 70%, 80%, 90%, 95%, 98%, 99%, 99.5%, 99.8%, and 99.9%. | |
57 | |
58 The default is 95. | |
59 | |
60 If the computed confidence is lower than desired, the script will | |
61 estimate the number of trials needed to meet the desired confidence. | |
62 This estimate is not very good, as the variance tends to change as | |
63 you increase the number of trials. | |
64 | |
65 The most common use of the script is total compile-time comparison between | |
66 logfiles stored in different directories. | |
67 | |
68 compare_two_ftime_report_sets "Log1/*perf" "Log2/*perf" | |
69 | |
70 One can also look at parsing time, but expecting a lower confidence. | |
71 | |
72 compare_two_ftime_report_sets "Log1/*perf" "Log2/*perf" "^phase parsing" 75 | |
73 | |
74 """ | |
75 | |
76 | |
77 import os | |
78 import sys | |
79 import fnmatch | |
80 import glob | |
81 import re | |
82 import math | |
83 | |
84 | |
85 ####################################################################### Utility | |
86 | |
87 | |
88 def divide(dividend, divisor): | |
89 """ Return the quotient, avoiding division by zero. | |
90 """ | |
91 if divisor == 0: | |
92 return sys.float_info.max | |
93 else: | |
94 return dividend / divisor | |
95 | |
96 | |
97 ################################################################# File and Line | |
98 | |
99 | |
100 # Should you repurpose this script, this code might help. | |
101 # | |
102 #def find_files(topdir, filepat): | |
103 # """ Find a set of file names, under a given directory, | |
104 # matching a Unix shell file pattern. | |
105 # Returns an iterator over the file names. | |
106 # """ | |
107 # for path, dirlist, filelist in os.walk(topdir): | |
108 # for name in fnmatch.filter(filelist, filepat): | |
109 # yield os.path.join(path, name) | |
110 | |
111 | |
112 def match_files(fileglob): | |
113 """ Find a set of file names matching a Unix shell glob pattern. | |
114 Returns an iterator over the file names. | |
115 """ | |
116 return glob.iglob(os.path.expanduser(fileglob)) | |
117 | |
118 | |
119 def lines_in_file(filename): | |
120 """ Return an iterator over lines in the named file. """ | |
121 filedesc = open(filename, "r") | |
122 for line in filedesc: | |
123 yield line | |
124 filedesc.close() | |
125 | |
126 | |
127 def lines_containing_pattern(pattern, lines): | |
128 """ Find lines by a Python regular-expression. | |
129 Returns an iterator over lines containing the expression. | |
130 """ | |
131 parser = re.compile(pattern) | |
132 for line in lines: | |
133 if parser.search(line): | |
134 yield line | |
135 | |
136 | |
137 ############################################################# Number Formatting | |
138 | |
139 | |
140 def strip_redundant_digits(numrep): | |
141 if numrep.find(".") == -1: | |
142 return numrep | |
143 return numrep.rstrip("0").rstrip(".") | |
144 | |
145 | |
146 def text_number(number): | |
147 return strip_redundant_digits("%g" % number) | |
148 | |
149 | |
150 def round_significant(digits, number): | |
151 if number == 0: | |
152 return 0 | |
153 magnitude = abs(number) | |
154 significance = math.floor(math.log10(magnitude)) | |
155 least_position = int(significance - digits + 1) | |
156 return round(number, -least_position) | |
157 | |
158 | |
159 def text_significant(digits, number): | |
160 return text_number(round_significant(digits, number)) | |
161 | |
162 | |
163 def text_percent(number): | |
164 return text_significant(3, number*100) + "%" | |
165 | |
166 | |
167 ################################################################ T-Distribution | |
168 | |
169 | |
170 # This section of code provides functions for using Student's t-distribution. | |
171 | |
172 | |
173 # The functions are implemented using table lookup | |
174 # to facilitate implementation of inverse functions. | |
175 | |
176 | |
177 # The table is comprised of row 0 listing the alpha values, | |
178 # column 0 listing the degree-of-freedom values, | |
179 # and the other entries listing the corresponding t-distribution values. | |
180 | |
181 t_dist_table = [ | |
182 [ 0, 0.200, 0.150, 0.100, 0.050, 0.025, 0.010, 0.005, .0025, 0.001, .0005], | |
183 [ 1, 1.376, 1.963, 3.078, 6.314, 12.71, 31.82, 63.66, 127.3, 318.3, 636.6], | |
184 [ 2, 1.061, 1.386, 1.886, 2.920, 4.303, 6.965, 9.925, 14.09, 22.33, 31.60], | |
185 [ 3, 0.978, 1.250, 1.638, 2.353, 3.182, 4.541, 5.841, 7.453, 10.21, 12.92], | |
186 [ 4, 0.941, 1.190, 1.533, 2.132, 2.776, 3.747, 4.604, 5.598, 7.173, 8.610], | |
187 [ 5, 0.920, 1.156, 1.476, 2.015, 2.571, 3.365, 4.032, 4.773, 5.894, 6.869], | |
188 [ 6, 0.906, 1.134, 1.440, 1.943, 2.447, 3.143, 3.707, 4.317, 5.208, 5.959], | |
189 [ 7, 0.896, 1.119, 1.415, 1.895, 2.365, 2.998, 3.499, 4.029, 4.785, 5.408], | |
190 [ 8, 0.889, 1.108, 1.397, 1.860, 2.306, 2.896, 3.355, 3.833, 4.501, 5.041], | |
191 [ 9, 0.883, 1.100, 1.383, 1.833, 2.262, 2.821, 3.250, 3.690, 4.297, 4.781], | |
192 [ 10, 0.879, 1.093, 1.372, 1.812, 2.228, 2.764, 3.169, 3.581, 4.144, 4.587], | |
193 [ 11, 0.876, 1.088, 1.363, 1.796, 2.201, 2.718, 3.106, 3.497, 4.025, 4.437], | |
194 [ 12, 0.873, 1.083, 1.356, 1.782, 2.179, 2.681, 3.055, 3.428, 3.930, 4.318], | |
195 [ 13, 0.870, 1.079, 1.350, 1.771, 2.160, 2.650, 3.012, 3.372, 3.852, 4.221], | |
196 [ 14, 0.868, 1.076, 1.345, 1.761, 2.145, 2.624, 2.977, 3.326, 3.787, 4.140], | |
197 [ 15, 0.866, 1.074, 1.341, 1.753, 2.131, 2.602, 2.947, 3.286, 3.733, 4.073], | |
198 [ 16, 0.865, 1.071, 1.337, 1.746, 2.120, 2.583, 2.921, 3.252, 3.686, 4.015], | |
199 [ 17, 0.863, 1.069, 1.333, 1.740, 2.110, 2.567, 2.898, 3.222, 3.646, 3.965], | |
200 [ 18, 0.862, 1.067, 1.330, 1.734, 2.101, 2.552, 2.878, 3.197, 3.610, 3.922], | |
201 [ 19, 0.861, 1.066, 1.328, 1.729, 2.093, 2.539, 2.861, 3.174, 3.579, 3.883], | |
202 [ 20, 0.860, 1.064, 1.325, 1.725, 2.086, 2.528, 2.845, 3.153, 3.552, 3.850], | |
203 [ 21, 0.859, 1.063, 1.323, 1.721, 2.080, 2.518, 2.831, 3.135, 3.527, 3.819], | |
204 [ 22, 0.858, 1.061, 1.321, 1.717, 2.074, 2.508, 2.819, 3.119, 3.505, 3.792], | |
205 [ 23, 0.858, 1.060, 1.319, 1.714, 2.069, 2.500, 2.807, 3.104, 3.485, 3.768], | |
206 [ 24, 0.857, 1.059, 1.318, 1.711, 2.064, 2.492, 2.797, 3.091, 3.467, 3.745], | |
207 [ 25, 0.856, 1.058, 1.316, 1.708, 2.060, 2.485, 2.787, 3.078, 3.450, 3.725], | |
208 [ 26, 0.856, 1.058, 1.315, 1.706, 2.056, 2.479, 2.779, 3.067, 3.435, 3.707], | |
209 [ 27, 0.855, 1.057, 1.314, 1.703, 2.052, 2.473, 2.771, 3.057, 3.421, 3.689], | |
210 [ 28, 0.855, 1.056, 1.313, 1.701, 2.048, 2.467, 2.763, 3.047, 3.408, 3.674], | |
211 [ 29, 0.854, 1.055, 1.311, 1.699, 2.045, 2.462, 2.756, 3.038, 3.396, 3.660], | |
212 [ 30, 0.854, 1.055, 1.310, 1.697, 2.042, 2.457, 2.750, 3.030, 3.385, 3.646], | |
213 [ 31, 0.853, 1.054, 1.309, 1.696, 2.040, 2.453, 2.744, 3.022, 3.375, 3.633], | |
214 [ 32, 0.853, 1.054, 1.309, 1.694, 2.037, 2.449, 2.738, 3.015, 3.365, 3.622], | |
215 [ 33, 0.853, 1.053, 1.308, 1.692, 2.035, 2.445, 2.733, 3.008, 3.356, 3.611], | |
216 [ 34, 0.852, 1.052, 1.307, 1.691, 2.032, 2.441, 2.728, 3.002, 3.348, 3.601], | |
217 [ 35, 0.852, 1.052, 1.306, 1.690, 2.030, 2.438, 2.724, 2.996, 3.340, 3.591], | |
218 [ 36, 0.852, 1.052, 1.306, 1.688, 2.028, 2.434, 2.719, 2.990, 3.333, 3.582], | |
219 [ 37, 0.851, 1.051, 1.305, 1.687, 2.026, 2.431, 2.715, 2.985, 3.326, 3.574], | |
220 [ 38, 0.851, 1.051, 1.304, 1.686, 2.024, 2.429, 2.712, 2.980, 3.319, 3.566], | |
221 [ 39, 0.851, 1.050, 1.304, 1.685, 2.023, 2.426, 2.708, 2.976, 3.313, 3.558], | |
222 [ 40, 0.851, 1.050, 1.303, 1.684, 2.021, 2.423, 2.704, 2.971, 3.307, 3.551], | |
223 [ 50, 0.849, 1.047, 1.299, 1.676, 2.009, 2.403, 2.678, 2.937, 3.261, 3.496], | |
224 [ 60, 0.848, 1.045, 1.296, 1.671, 2.000, 2.390, 2.660, 2.915, 3.232, 3.460], | |
225 [ 80, 0.846, 1.043, 1.292, 1.664, 1.990, 2.374, 2.639, 2.887, 3.195, 3.416], | |
226 [100, 0.845, 1.042, 1.290, 1.660, 1.984, 2.364, 2.626, 2.871, 3.174, 3.390], | |
227 [150, 0.844, 1.040, 1.287, 1.655, 1.976, 2.351, 2.609, 2.849, 3.145, 3.357] ] | |
228 | |
229 | |
230 # The functions use the following parameter name conventions: | |
231 # alpha - the alpha parameter | |
232 # degree - the degree-of-freedom parameter | |
233 # value - the t-distribution value for some alpha and degree | |
234 # deviations - a confidence interval radius, | |
235 # expressed as a multiple of the standard deviation of the sample | |
236 # ax - the alpha parameter index | |
237 # dx - the degree-of-freedom parameter index | |
238 | |
239 # The interface to this section of code is the last three functions, | |
240 # find_t_dist_value, find_t_dist_alpha, and find_t_dist_degree. | |
241 | |
242 | |
243 def t_dist_alpha_at_index(ax): | |
244 if ax == 0: | |
245 return .25 # effectively no confidence | |
246 else: | |
247 return t_dist_table[0][ax] | |
248 | |
249 | |
250 def t_dist_degree_at_index(dx): | |
251 return t_dist_table[dx][0] | |
252 | |
253 | |
254 def t_dist_value_at_index(ax, dx): | |
255 return t_dist_table[dx][ax] | |
256 | |
257 | |
258 def t_dist_index_of_degree(degree): | |
259 limit = len(t_dist_table) - 1 | |
260 dx = 0 | |
261 while dx < limit and t_dist_degree_at_index(dx+1) <= degree: | |
262 dx += 1 | |
263 return dx | |
264 | |
265 | |
266 def t_dist_index_of_alpha(alpha): | |
267 limit = len(t_dist_table[0]) - 1 | |
268 ax = 0 | |
269 while ax < limit and t_dist_alpha_at_index(ax+1) >= alpha: | |
270 ax += 1 | |
271 return ax | |
272 | |
273 | |
274 def t_dist_index_of_value(dx, value): | |
275 limit = len(t_dist_table[dx]) - 1 | |
276 ax = 0 | |
277 while ax < limit and t_dist_value_at_index(ax+1, dx) < value: | |
278 ax += 1 | |
279 return ax | |
280 | |
281 | |
282 def t_dist_value_within_deviations(dx, ax, deviations): | |
283 degree = t_dist_degree_at_index(dx) | |
284 count = degree + 1 | |
285 root = math.sqrt(count) | |
286 value = t_dist_value_at_index(ax, dx) | |
287 nominal = value / root | |
288 comparison = nominal <= deviations | |
289 return comparison | |
290 | |
291 | |
292 def t_dist_index_of_degree_for_deviations(ax, deviations): | |
293 limit = len(t_dist_table) - 1 | |
294 dx = 1 | |
295 while dx < limit and not t_dist_value_within_deviations(dx, ax, deviations): | |
296 dx += 1 | |
297 return dx | |
298 | |
299 | |
300 def find_t_dist_value(alpha, degree): | |
301 """ Return the t-distribution value. | |
302 The parameters are alpha and degree of freedom. | |
303 """ | |
304 dx = t_dist_index_of_degree(degree) | |
305 ax = t_dist_index_of_alpha(alpha) | |
306 return t_dist_value_at_index(ax, dx) | |
307 | |
308 | |
309 def find_t_dist_alpha(value, degree): | |
310 """ Return the alpha. | |
311 The parameters are the t-distribution value for a given degree of freedom. | |
312 """ | |
313 dx = t_dist_index_of_degree(degree) | |
314 ax = t_dist_index_of_value(dx, value) | |
315 return t_dist_alpha_at_index(ax) | |
316 | |
317 | |
318 def find_t_dist_degree(alpha, deviations): | |
319 """ Return the degree-of-freedom. | |
320 The parameters are the desired alpha and the number of standard deviations | |
321 away from the mean that the degree should handle. | |
322 """ | |
323 ax = t_dist_index_of_alpha(alpha) | |
324 dx = t_dist_index_of_degree_for_deviations(ax, deviations) | |
325 return t_dist_degree_at_index(dx) | |
326 | |
327 | |
328 ############################################################## Core Statistical | |
329 | |
330 | |
331 # This section provides the core statistical classes and functions. | |
332 | |
333 | |
334 class Accumulator: | |
335 | |
336 """ An accumulator for statistical information using arithmetic mean. """ | |
337 | |
338 def __init__(self): | |
339 self.count = 0 | |
340 self.mean = 0 | |
341 self.sumsqdiff = 0 | |
342 | |
343 def insert(self, value): | |
344 self.count += 1 | |
345 diff = value - self.mean | |
346 self.mean += diff / self.count | |
347 self.sumsqdiff += (self.count - 1) * diff * diff / self.count | |
348 | |
349 | |
350 def fill_accumulator_from_values(values): | |
351 accumulator = Accumulator() | |
352 for value in values: | |
353 accumulator.insert(value) | |
354 return accumulator | |
355 | |
356 | |
357 def alpha_from_confidence(confidence): | |
358 scrubbed = min(99.99, max(confidence, 60)) | |
359 return (100.0 - scrubbed) / 200.0 | |
360 | |
361 | |
362 def confidence_from_alpha(alpha): | |
363 return 100 - 200 * alpha | |
364 | |
365 | |
366 class Sample: | |
367 | |
368 """ A description of a sample using an arithmetic mean. """ | |
369 | |
370 def __init__(self, accumulator, alpha): | |
371 if accumulator.count < 3: | |
372 sys.exit("Samples must contain three trials.") | |
373 self.count = accumulator.count | |
374 self.mean = accumulator.mean | |
375 variance = accumulator.sumsqdiff / (self.count - 1) | |
376 self.deviation = math.sqrt(variance) | |
377 self.error = self.deviation / math.sqrt(self.count) | |
378 self.alpha = alpha | |
379 self.radius = find_t_dist_value(alpha, self.count - 1) * self.error | |
380 | |
381 def alpha_for_radius(self, radius): | |
382 return find_t_dist_alpha(divide(radius, self.error), self.count) | |
383 | |
384 def degree_for_radius(self, radius): | |
385 return find_t_dist_degree(self.alpha, divide(radius, self.deviation)) | |
386 | |
387 def __str__(self): | |
388 text = "trial count is " + text_number(self.count) | |
389 text += ", mean is " + text_number(self.mean) | |
390 text += " (" + text_number(confidence_from_alpha(self.alpha)) +"%" | |
391 text += " confidence in " + text_number(self.mean - self.radius) | |
392 text += " to " + text_number(self.mean + self.radius) + ")" | |
393 text += ",\nstd.deviation is " + text_number(self.deviation) | |
394 text += ", std.error is " + text_number(self.error) | |
395 return text | |
396 | |
397 | |
398 def sample_from_values(values, alpha): | |
399 accumulator = fill_accumulator_from_values(values) | |
400 return Sample(accumulator, alpha) | |
401 | |
402 | |
403 class Comparison: | |
404 | |
405 """ A comparison of two samples using arithmetic means. """ | |
406 | |
407 def __init__(self, first, second, alpha): | |
408 if first.mean > second.mean: | |
409 self.upper = first | |
410 self.lower = second | |
411 self.larger = "first" | |
412 else: | |
413 self.upper = second | |
414 self.lower = first | |
415 self.larger = "second" | |
416 self.a_wanted = alpha | |
417 radius = self.upper.mean - self.lower.mean | |
418 rising = self.lower.alpha_for_radius(radius) | |
419 falling = self.upper.alpha_for_radius(radius) | |
420 self.a_actual = max(rising, falling) | |
421 rising = self.lower.degree_for_radius(radius) | |
422 falling = self.upper.degree_for_radius(radius) | |
423 self.count = max(rising, falling) + 1 | |
424 | |
425 def __str__(self): | |
426 message = "The " + self.larger + " sample appears to be " | |
427 change = divide(self.upper.mean, self.lower.mean) - 1 | |
428 message += text_percent(change) + " larger,\n" | |
429 confidence = confidence_from_alpha(self.a_actual) | |
430 if confidence >= 60: | |
431 message += "with " + text_number(confidence) + "% confidence" | |
432 message += " of being larger." | |
433 else: | |
434 message += "but with no confidence of actually being larger." | |
435 if self.a_actual > self.a_wanted: | |
436 confidence = confidence_from_alpha(self.a_wanted) | |
437 message += "\nTo reach " + text_number(confidence) + "% confidence," | |
438 if self.count < 100: | |
439 message += " you need roughly " + text_number(self.count) + " trials,\n" | |
440 message += "assuming the standard deviation is stable, which is iffy." | |
441 else: | |
442 message += "\nyou need to reduce the larger deviation" | |
443 message += " or increase the number of trials." | |
444 return message | |
445 | |
446 | |
447 ############################################################ Single Value Files | |
448 | |
449 | |
450 # This section provides functions to compare two raw data files, | |
451 # each containing a whole sample consisting of single number per line. | |
452 | |
453 | |
454 # Should you repurpose this script, this code might help. | |
455 # | |
456 #def values_from_data_file(filename): | |
457 # for line in lines_in_file(filename): | |
458 # yield float(line) | |
459 | |
460 | |
461 # Should you repurpose this script, this code might help. | |
462 # | |
463 #def sample_from_data_file(filename, alpha): | |
464 # confidence = confidence_from_alpha(alpha) | |
465 # text = "\nArithmetic sample for data file\n\"" + filename + "\"" | |
466 # text += " with desired confidence " + text_number(confidence) + " is " | |
467 # print text | |
468 # values = values_from_data_file(filename) | |
469 # sample = sample_from_values(values, alpha) | |
470 # print sample | |
471 # return sample | |
472 | |
473 | |
474 # Should you repurpose this script, this code might help. | |
475 # | |
476 #def compare_two_data_files(filename1, filename2, confidence): | |
477 # alpha = alpha_from_confidence(confidence) | |
478 # sample1 = sample_from_data_file(filename1, alpha) | |
479 # sample2 = sample_from_data_file(filename2, alpha) | |
480 # print | |
481 # print Comparison(sample1, sample2, alpha) | |
482 | |
483 | |
484 # Should you repurpose this script, this code might help. | |
485 # | |
486 #def command_two_data_files(): | |
487 # argc = len(sys.argv) | |
488 # if argc < 2 or 4 < argc: | |
489 # message = "usage: " + sys.argv[0] | |
490 # message += " file-name file-name [confidence]" | |
491 # print message | |
492 # else: | |
493 # filename1 = sys.argv[1] | |
494 # filename2 = sys.argv[2] | |
495 # if len(sys.argv) >= 4: | |
496 # confidence = int(sys.argv[3]) | |
497 # else: | |
498 # confidence = 95 | |
499 # compare_two_data_files(filename1, filename2, confidence) | |
500 | |
501 | |
502 ############################################### -ftime-report TimeVar Log Files | |
503 | |
504 | |
505 # This section provides functions to compare two sets of -ftime-report log | |
506 # files. Each set is a sample, where each data point is derived from the | |
507 # sum of values in a single log file. | |
508 | |
509 | |
510 label = r"^ *([^:]*[^: ]) *:" | |
511 number = r" *([0-9.]*) *" | |
512 percent = r"\( *[0-9]*\%\)" | |
513 numpct = number + percent | |
514 total_format = label + number + number + number + number + " kB\n" | |
515 total_parser = re.compile(total_format) | |
516 tmvar_format = label + numpct + " usr" + numpct + " sys" | |
517 tmvar_format += numpct + " wall" + number + " kB " + percent + " ggc\n" | |
518 tmvar_parser = re.compile(tmvar_format) | |
519 replace = r"\2\t\3\t\4\t\5\t\1" | |
520 | |
521 | |
522 def split_time_report(lines, pattern): | |
523 if pattern == "TOTAL": | |
524 parser = total_parser | |
525 else: | |
526 parser = tmvar_parser | |
527 for line in lines: | |
528 modified = parser.sub(replace, line) | |
529 if modified != line: | |
530 yield re.split("\t", modified) | |
531 | |
532 | |
533 def extract_cpu_time(tvtuples): | |
534 for tuple in tvtuples: | |
535 yield float(tuple[0]) + float(tuple[1]) | |
536 | |
537 | |
538 def sum_values(values): | |
539 sum = 0 | |
540 for value in values: | |
541 sum += value | |
542 return sum | |
543 | |
544 | |
545 def extract_time_for_timevar_log(filename, pattern): | |
546 lines = lines_in_file(filename) | |
547 tmvars = lines_containing_pattern(pattern, lines) | |
548 tuples = split_time_report(tmvars, pattern) | |
549 times = extract_cpu_time(tuples) | |
550 return sum_values(times) | |
551 | |
552 | |
553 def extract_times_for_timevar_logs(filelist, pattern): | |
554 for filename in filelist: | |
555 yield extract_time_for_timevar_log(filename, pattern) | |
556 | |
557 | |
558 def sample_from_timevar_logs(fileglob, pattern, alpha): | |
559 confidence = confidence_from_alpha(alpha) | |
560 text = "\nArithmetic sample for timevar log files\n\"" + fileglob + "\"" | |
561 text += "\nand selecting lines containing \"" + pattern + "\"" | |
562 text += " with desired confidence " + text_number(confidence) + " is " | |
563 print text | |
564 filelist = match_files(fileglob) | |
565 values = extract_times_for_timevar_logs(filelist, pattern) | |
566 sample = sample_from_values(values, alpha) | |
567 print sample | |
568 return sample | |
569 | |
570 | |
571 def compare_two_timevar_logs(fileglob1, fileglob2, pattern, confidence): | |
572 alpha = alpha_from_confidence(confidence) | |
573 sample1 = sample_from_timevar_logs(fileglob1, pattern, alpha) | |
574 sample2 = sample_from_timevar_logs(fileglob2, pattern, alpha) | |
575 print | |
576 print Comparison(sample1, sample2, alpha) | |
577 | |
578 | |
579 def command_two_timevar_logs(): | |
580 argc = len(sys.argv) | |
581 if argc < 3 or 5 < argc: | |
582 message = "usage: " + sys.argv[0] | |
583 message += " file-pattern file-pattern [line-pattern [confidence]]" | |
584 print message | |
585 else: | |
586 filepat1 = sys.argv[1] | |
587 filepat2 = sys.argv[2] | |
588 if len(sys.argv) >= 5: | |
589 confidence = int(sys.argv[4]) | |
590 else: | |
591 confidence = 95 | |
592 if len(sys.argv) >= 4: | |
593 linepat = sys.argv[3] | |
594 else: | |
595 linepat = "TOTAL" | |
596 compare_two_timevar_logs(filepat1, filepat2, linepat, confidence) | |
597 | |
598 | |
599 ########################################################################## Main | |
600 | |
601 | |
602 # This section is the main code, implementing the command. | |
603 | |
604 | |
605 command_two_timevar_logs() |