##// END OF EJS Templates
wait-on-file: adjust the timer counter...
wait-on-file: adjust the timer counter The wait performed in increment of 0.01 second, but the timer was expressed in second. So we did not wait 20s, we waited 0.2 second. Internally we adjust the counter to be expressed in centisecond. This prevent some flackyness in the test. Differential Revision: https://phab.mercurial-scm.org/D8453

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perf-revlog-write-plot.py
126 lines | 3.2 KiB | text/x-python | PythonLexer
/ contrib / perf-utils / perf-revlog-write-plot.py
#!/usr/bin/env python
#
# Copyright 2018 Paul Morelle <Paul.Morelle@octobus.net>
#
# This software may be used and distributed according to the terms of the
# GNU General Public License version 2 or any later version.
#
# This script use the output of `hg perfrevlogwrite -T json --details` to draw
# various plot related to write performance in a revlog
#
# usage: perf-revlog-write-plot.py details.json
from __future__ import absolute_import, print_function
import json
import re
import numpy as np
import scipy.signal
from matplotlib import (
pyplot as plt,
ticker as mticker,
)
def plot(data, title=None):
items = {}
re_title = re.compile(r'^revisions #\d+ of \d+, rev (\d+)$')
for item in data:
m = re_title.match(item['title'])
if m is None:
continue
rev = int(m.group(1))
items[rev] = item
min_rev = min(items.keys())
max_rev = max(items.keys())
ary = np.empty((2, max_rev - min_rev + 1))
for rev, item in items.items():
ary[0][rev - min_rev] = rev
ary[1][rev - min_rev] = item['wall']
fig = plt.figure()
comb_plt = fig.add_subplot(211)
other_plt = fig.add_subplot(212)
comb_plt.plot(
ary[0], np.cumsum(ary[1]), color='red', linewidth=1, label='comb'
)
plots = []
p = other_plt.plot(ary[0], ary[1], color='red', linewidth=1, label='wall')
plots.append(p)
colors = {
10: ('green', 'xkcd:grass green'),
100: ('blue', 'xkcd:bright blue'),
1000: ('purple', 'xkcd:dark pink'),
}
for n, color in colors.items():
avg_n = np.convolve(ary[1], np.full(n, 1.0 / n), 'valid')
p = other_plt.plot(
ary[0][n - 1 :],
avg_n,
color=color[0],
linewidth=1,
label='avg time last %d' % n,
)
plots.append(p)
med_n = scipy.signal.medfilt(ary[1], n + 1)
p = other_plt.plot(
ary[0],
med_n,
color=color[1],
linewidth=1,
label='median time last %d' % n,
)
plots.append(p)
formatter = mticker.ScalarFormatter()
formatter.set_scientific(False)
formatter.set_useOffset(False)
comb_plt.grid()
comb_plt.xaxis.set_major_formatter(formatter)
comb_plt.legend()
other_plt.grid()
other_plt.xaxis.set_major_formatter(formatter)
leg = other_plt.legend()
leg2plot = {}
for legline, plot in zip(leg.get_lines(), plots):
legline.set_picker(5)
leg2plot[legline] = plot
def onpick(event):
legline = event.artist
plot = leg2plot[legline]
visible = not plot[0].get_visible()
for l in plot:
l.set_visible(visible)
if visible:
legline.set_alpha(1.0)
else:
legline.set_alpha(0.2)
fig.canvas.draw()
if title is not None:
fig.canvas.set_window_title(title)
fig.canvas.mpl_connect('pick_event', onpick)
plt.show()
if __name__ == '__main__':
import sys
if len(sys.argv) > 1:
print('reading from %r' % sys.argv[1])
with open(sys.argv[1], 'r') as fp:
plot(json.load(fp), title=sys.argv[1])
else:
print('reading from stdin')
plot(json.load(sys.stdin))