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comparison contrib/perf-utils/perf-revlog-write-plot.py @ 40971:abd7b75e80bc

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contrib: provide a small script that draw performance plot We have been using this script to look into the result of various runs of the `hg perfrevlogwrite` command. It seems useful enough to be shared more widely.
author Paul Morelle <paul.morelle@octobus.net>
date Fri, 14 Dec 2018 18:15:19 +0100
parents
children c3e5ce3a9483
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40970:f960c51eebf3 40971:abd7b75e80bc
1 #!/usr/bin/env python
2 #
3 # Copyright 2018 Paul Morelle <Paul.Morelle@octobus.net>
4 #
5 # This software may be used and distributed according to the terms of the
6 # GNU General Public License version 2 or any later version.
7 #
8 # This script use the output of `hg perfrevlogwrite -T json --details` to draw
9 # various plot related to write performance in a revlog
10 #
11 # usage: perf-revlog-write-plot.py details.json
12 from __future__ import absolute_import, print_function
13 import json
14 import re
15
16 import numpy as np
17 import scipy.signal
18
19 from matplotlib import (
20 pyplot as plt,
21 ticker as mticker,
22 )
23
24
25 def plot(data):
26 items = {}
27 re_title = re.compile(r'^revisions #\d+ of \d+, rev (\d+)$')
28 for item in data:
29 m = re_title.match(item['title'])
30 if m is None:
31 continue
32
33 rev = int(m.group(1))
34 items[rev] = item
35
36 min_rev = min(items.keys())
37 max_rev = max(items.keys())
38 ary = np.empty((2, max_rev - min_rev + 1))
39 for rev, item in items.items():
40 ary[0][rev - min_rev] = rev
41 ary[1][rev - min_rev] = item['wall']
42
43 fig = plt.figure()
44 comb_plt = fig.add_subplot(211)
45 other_plt = fig.add_subplot(212)
46
47 comb_plt.plot(ary[0],
48 np.cumsum(ary[1]),
49 color='red',
50 linewidth=1,
51 label='comb')
52
53 plots = []
54 p = other_plt.plot(ary[0],
55 ary[1],
56 color='red',
57 linewidth=1,
58 label='wall')
59 plots.append(p)
60
61 colors = {
62 10: ('green', 'xkcd:grass green'),
63 100: ('blue', 'xkcd:bright blue'),
64 1000: ('purple', 'xkcd:dark pink'),
65 }
66 for n, color in colors.items():
67 avg_n = np.convolve(ary[1], np.full(n, 1. / n), 'valid')
68 p = other_plt.plot(ary[0][n - 1:],
69 avg_n,
70 color=color[0],
71 linewidth=1,
72 label='avg time last %d' % n)
73 plots.append(p)
74
75 med_n = scipy.signal.medfilt(ary[1], n + 1)
76 p = other_plt.plot(ary[0],
77 med_n,
78 color=color[1],
79 linewidth=1,
80 label='median time last %d' % n)
81 plots.append(p)
82
83 formatter = mticker.ScalarFormatter()
84 formatter.set_scientific(False)
85 formatter.set_useOffset(False)
86
87 comb_plt.grid()
88 comb_plt.xaxis.set_major_formatter(formatter)
89 comb_plt.legend()
90
91 other_plt.grid()
92 other_plt.xaxis.set_major_formatter(formatter)
93 leg = other_plt.legend()
94 leg2plot = {}
95 for legline, plot in zip(leg.get_lines(), plots):
96 legline.set_picker(5)
97 leg2plot[legline] = plot
98
99 def onpick(event):
100 legline = event.artist
101 plot = leg2plot[legline]
102 visible = not plot[0].get_visible()
103 for l in plot:
104 l.set_visible(visible)
105
106 if visible:
107 legline.set_alpha(1.0)
108 else:
109 legline.set_alpha(0.2)
110 fig.canvas.draw()
111 fig.canvas.mpl_connect('pick_event', onpick)
112
113 plt.show()
114
115
116 if __name__ == '__main__':
117 import sys
118
119 if len(sys.argv) > 1:
120 print('reading from %r' % sys.argv[1])
121 with open(sys.argv[1], 'r') as fp:
122 plot(json.load(fp))
123 else:
124 print('reading from stdin')
125 plot(json.load(sys.stdin))