contrib/perf-utils/perf-revlog-write-plot.py

interfaces: introduce and use a protocol class for the `charencoding` module

See f2832de2a46c for details when this was done for the `bdiff` module.

This lets us dump the hack where the `pure` implementation was imported during
the type checking phase to provide signatures for the module methods it
provides.  Now the protocol classes are starting to shine, because these methods
are provided by `pure.charencoding` and `cext.parsers`, and references to
`cffi.charencoding` and `cext.charencoding` are forwarded to them as appropriate
by the `policy` module.  But none of that matters, as long as the module
returned provides the listed methods.

The interface was copy/pasted from the `pure` module, but `jsonescapeu8fallback`
is omitted because it is accessed from the `pure` module directly when the
escaping fails in the primary module's `jsonescapeu8()`.

#!/usr/bin/env python3
#
#  Copyright 2018 Paul Morelle <[e-mail]>
#
# 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
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))