# profiling.py - profiling functions

#

# Copyright 2016 Gregory Szorc <gregory.szorc@gmail.com>

#

# This software may be used and distributed according to the terms of the

# GNU General Public License version 2 or any later version.

from __future__ import absolute_import, print_function

import contextlib

import time

from .i18n import _

from . import (

    error,

    util,

)

@contextlib.contextmanager

def lsprofile(ui, fp):

    format = ui.config('profiling', 'format', default='text')

    field = ui.config('profiling', 'sort', default='inlinetime')

    limit = ui.configint('profiling', 'limit', default=30)

    climit = ui.configint('profiling', 'nested', default=0)

    if format not in ['text', 'kcachegrind']:

        ui.warn(_("unrecognized profiling format '%s'"

                    " - Ignored\n") % format)

        format = 'text'

    try:

        from . import lsprof

    except ImportError:

        raise error.Abort(_(

            'lsprof not available - install from '

            'http://codespeak.net/svn/user/arigo/hack/misc/lsprof/'))

    p = lsprof.Profiler()

    p.enable(subcalls=True)

    try:

        yield

    finally:

        p.disable()

        if format == 'kcachegrind':

            from . import lsprofcalltree

            calltree = lsprofcalltree.KCacheGrind(p)

            calltree.output(fp)

        else:

            # format == 'text'

            stats = lsprof.Stats(p.getstats())

            stats.sort(field)

            stats.pprint(limit=limit, file=fp, climit=climit)

@contextlib.contextmanager

def flameprofile(ui, fp):

    try:

        from flamegraph import flamegraph

    except ImportError:

        raise error.Abort(_(

            'flamegraph not available - install from '

            'https://github.com/evanhempel/python-flamegraph'))

    # developer config: profiling.freq

    freq = ui.configint('profiling', 'freq', default=1000)

    filter_ = None

    collapse_recursion = True

    thread = flamegraph.ProfileThread(fp, 1.0 / freq,

                                      filter_, collapse_recursion)

    start_time = time.clock()

    try:

        thread.start()

        yield

    finally:

        thread.stop()

        thread.join()

        print('Collected %d stack frames (%d unique) in %2.2f seconds.' % (

            time.clock() - start_time, thread.num_frames(),

            thread.num_frames(unique=True)))

@contextlib.contextmanager

def statprofile(ui, fp):

    from . import statprof

    freq = ui.configint('profiling', 'freq', default=1000)

    if freq > 0:

        # Cannot reset when profiler is already active. So silently no-op.

        if statprof.state.profile_level == 0:

            statprof.reset(freq)

    else:

        ui.warn(_("invalid sampling frequency '%s' - ignoring\n") % freq)

    statprof.start(mechanism='thread')

    try:

        yield

    finally:

        data = statprof.stop()

        profformat = ui.config('profiling', 'statformat', 'hotpath')

        formats = {

            'byline': statprof.DisplayFormats.ByLine,

            'bymethod': statprof.DisplayFormats.ByMethod,

            'hotpath': statprof.DisplayFormats.Hotpath,

            'json': statprof.DisplayFormats.Json,

        }

        if profformat in formats:

            displayformat = formats[profformat]

        else:

            ui.warn(_('unknown profiler output format: %s\n') % profformat)

            displayformat = statprof.DisplayFormats.Hotpath

        statprof.display(fp, data=data, format=displayformat)

@contextlib.contextmanager

def profile(ui):

    """Start profiling.

    Profiling is active when the context manager is active. When the context

    manager exits, profiling results will be written to the configured output.

    """

    if profiler is None:

        profiler = ui.config('profiling', 'type', default='stat')

    if profiler not in ('ls', 'stat', 'flame'):

        ui.warn(_("unrecognized profiler '%s' - ignored\n") % profiler)

        profiler = 'stat'

    output = ui.config('profiling', 'output')

    if output == 'blackbox':

        fp = util.stringio()

    elif output:

        path = ui.expandpath(output)

        fp = open(path, 'wb')

    else:

        fp = ui.ferr

    try:

        if profiler == 'ls':

            proffn = lsprofile

        elif profiler == 'flame':

            proffn = flameprofile

        else:

            proffn = statprofile

        with proffn(ui, fp):

            yield

    finally:

        if output:

            if output == 'blackbox':

                val = 'Profile:\n%s' % fp.getvalue()

                # ui.log treats the input as a format string,

                # so we need to escape any % signs.

                val = val.replace('%', '%%')

                ui.log('profile', val)

            fp.close()

@contextlib.contextmanager

def maybeprofile(ui):

    """Profile if enabled, else do nothing.

    This context manager can be used to optionally profile if profiling

    is enabled. Otherwise, it does nothing.

    The purpose of this context manager is to make calling code simpler:

    just use a single code path for calling into code you may want to profile

    and this function determines whether to start profiling.

    """

    if ui.configbool('profiling', 'enabled'):

        with profile(ui):

            yield

    else:

        yield