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ðŸ“„ analyze_dxp.py(4.11 KB)
ðŸ“„ analyze_dxp.pyc(4.64 KB)
ðŸ“„ analyze_dxp.pyo(4.64 KB)
ðŸ“„ byext.py(3.85 KB)
ðŸ“„ byext.pyc(4.42 KB)
ðŸ“„ byext.pyo(4.42 KB)
ðŸ“„ byteyears.py(1.6 KB)
ðŸ“„ byteyears.pyc(1.37 KB)
ðŸ“„ byteyears.pyo(1.37 KB)
ðŸ“„ checkappend.py(4.55 KB)
ðŸ“„ checkappend.pyc(4.77 KB)
ðŸ“„ checkappend.pyo(4.77 KB)
ðŸ“„ checkpip.py(757 B)
ðŸ“„ checkpip.pyc(1.02 KB)
ðŸ“„ checkpip.pyo(1.02 KB)
ðŸ“„ checkpyc.py(1.96 KB)
ðŸ“„ checkpyc.pyc(1.93 KB)
ðŸ“„ checkpyc.pyo(1.93 KB)
ðŸ“„ classfix.py(5.81 KB)
ðŸ“„ classfix.pyc(4.09 KB)
ðŸ“„ classfix.pyo(4.09 KB)
ðŸ“„ cleanfuture.py(8.38 KB)
ðŸ“„ cleanfuture.pyc(7.22 KB)
ðŸ“„ cleanfuture.pyo(7.19 KB)
ðŸ“„ combinerefs.py(4.28 KB)
ðŸ“„ combinerefs.pyc(4.16 KB)
ðŸ“„ combinerefs.pyo(4.12 KB)
ðŸ“„ copytime.py(663 B)
ðŸ“„ copytime.pyc(937 B)
ðŸ“„ copytime.pyo(937 B)
ðŸ“„ crlf.py(610 B)
ðŸ“„ crlf.pyc(855 B)
ðŸ“„ crlf.pyo(855 B)
ðŸ“„ cvsfiles.py(1.74 KB)
ðŸ“„ cvsfiles.pyc(2.11 KB)
ðŸ“„ cvsfiles.pyo(2.11 KB)
ðŸ“„ db2pickle.py(3.49 KB)
ðŸ“„ db2pickle.pyc(3.42 KB)
ðŸ“„ db2pickle.pyo(3.42 KB)
ðŸ“„ diff.py(2.02 KB)
ðŸ“„ diff.pyc(2.33 KB)
ðŸ“„ diff.pyo(2.33 KB)
ðŸ“„ dutree.py(1.58 KB)
ðŸ“„ dutree.pyc(2.18 KB)
ðŸ“„ dutree.pyo(2.18 KB)
ðŸ“„ eptags.py(1.45 KB)
ðŸ“„ eptags.pyc(1.83 KB)
ðŸ“„ eptags.pyo(1.83 KB)
ðŸ“„ find_recursionlimit.py(3.39 KB)
ðŸ“„ find_recursionlimit.pyc(5.54 KB)
ðŸ“„ find_recursionlimit.pyo(5.54 KB)
ðŸ“„ finddiv.py(2.46 KB)
ðŸ“„ finddiv.pyc(3.22 KB)
ðŸ“„ finddiv.pyo(3.22 KB)
ðŸ“„ findlinksto.py(1.04 KB)
ðŸ“„ findlinksto.pyc(1.39 KB)
ðŸ“„ findlinksto.pyo(1.39 KB)
ðŸ“„ findnocoding.py(2.74 KB)
ðŸ“„ findnocoding.pyc(3.13 KB)
ðŸ“„ findnocoding.pyo(3.13 KB)
ðŸ“„ fixcid.py(9.76 KB)
ðŸ“„ fixcid.pyc(7.67 KB)
ðŸ“„ fixcid.pyo(7.67 KB)
ðŸ“„ fixdiv.py(13.52 KB)
ðŸ“„ fixdiv.pyc(13.52 KB)
ðŸ“„ fixdiv.pyo(13.44 KB)
ðŸ“„ fixheader.py(1.16 KB)
ðŸ“„ fixheader.pyc(1.44 KB)
ðŸ“„ fixheader.pyo(1.44 KB)
ðŸ“„ fixnotice.py(2.98 KB)
ðŸ“„ fixnotice.pyc(3.42 KB)
ðŸ“„ fixnotice.pyo(3.42 KB)
ðŸ“„ fixps.py(893 B)
ðŸ“„ fixps.pyc(969 B)
ðŸ“„ fixps.pyo(969 B)
ðŸ“„ google.py(519 B)
ðŸ“„ google.pyc(792 B)
ðŸ“„ google.pyo(792 B)
ðŸ“„ gprof2html.py(2.12 KB)
ðŸ“„ gprof2html.pyc(2.22 KB)
ðŸ“„ gprof2html.pyo(2.22 KB)
ðŸ“„ h2py.py(5.81 KB)
ðŸ“„ h2py.pyc(4.29 KB)
ðŸ“„ h2py.pyo(4.29 KB)
ðŸ“„ hotshotmain.py(1.45 KB)
ðŸ“„ hotshotmain.pyc(1.82 KB)
ðŸ“„ hotshotmain.pyo(1.82 KB)
ðŸ“„ ifdef.py(3.63 KB)
ðŸ“„ ifdef.pyc(2.21 KB)
ðŸ“„ ifdef.pyo(2.21 KB)
ðŸ“„ lfcr.py(618 B)
ðŸ“„ lfcr.pyc(880 B)
ðŸ“„ lfcr.pyo(880 B)
ðŸ“„ linktree.py(2.37 KB)
ðŸ“„ linktree.pyc(1.98 KB)
ðŸ“„ linktree.pyo(1.98 KB)
ðŸ“„ lll.py(742 B)
ðŸ“„ lll.pyc(942 B)
ðŸ“„ lll.pyo(942 B)
ðŸ“„ logmerge.py(5.44 KB)
ðŸ“„ logmerge.pyc(4.96 KB)
ðŸ“„ logmerge.pyo(4.96 KB)
ðŸ“„ mailerdaemon.py(7.76 KB)
ðŸ“„ mailerdaemon.pyc(7.19 KB)
ðŸ“„ mailerdaemon.pyo(7.19 KB)
ðŸ“„ md5sum.py(2.33 KB)
ðŸ“„ md5sum.pyc(2.85 KB)
ðŸ“„ md5sum.pyo(2.85 KB)
ðŸ“„ methfix.py(5.33 KB)
ðŸ“„ methfix.pyc(4.03 KB)
ðŸ“„ methfix.pyo(4.03 KB)
ðŸ“„ mkreal.py(1.59 KB)
ðŸ“„ mkreal.pyc(1.93 KB)
ðŸ“„ mkreal.pyo(1.93 KB)
ðŸ“„ ndiff.py(3.72 KB)
ðŸ“„ ndiff.pyc(3.77 KB)
ðŸ“„ ndiff.pyo(3.77 KB)
ðŸ“„ nm2def.py(2.39 KB)
ðŸ“„ nm2def.pyc(2.89 KB)
ðŸ“„ nm2def.pyo(2.89 KB)
ðŸ“„ objgraph.py(5.88 KB)
ðŸ“„ objgraph.pyc(4.82 KB)
ðŸ“„ objgraph.pyo(4.82 KB)
ðŸ“„ parseentities.py(1.68 KB)
ðŸ“„ parseentities.pyc(2.03 KB)
ðŸ“„ parseentities.pyo(2.03 KB)
ðŸ“„ patchcheck.py(7.5 KB)
ðŸ“„ patchcheck.pyc(8.91 KB)
ðŸ“„ patchcheck.pyo(8.91 KB)
ðŸ“„ pathfix.py(4.23 KB)
ðŸ“„ pathfix.pyc(3.75 KB)
ðŸ“„ pathfix.pyo(3.75 KB)
ðŸ“„ pdeps.py(3.84 KB)
ðŸ“„ pdeps.pyc(3.14 KB)
ðŸ“„ pdeps.pyo(3.14 KB)
ðŸ“„ pickle2db.py(3.85 KB)
ðŸ“„ pickle2db.pyc(3.73 KB)
ðŸ“„ pickle2db.pyo(3.73 KB)
ðŸ“„ pindent.py(16.77 KB)
ðŸ“„ pindent.pyc(11.29 KB)
ðŸ“„ pindent.pyo(11.29 KB)
ðŸ“„ ptags.py(1.2 KB)
ðŸ“„ ptags.pyc(1.37 KB)
ðŸ“„ ptags.pyo(1.37 KB)
ðŸ“„ pysource.py(3.76 KB)
ðŸ“„ pysource.pyc(3.92 KB)
ðŸ“„ pysource.pyo(3.92 KB)
ðŸ“„ redemo.py(5.66 KB)
ðŸ“„ redemo.pyc(5.13 KB)
ðŸ“„ redemo.pyo(5.13 KB)
ðŸ“„ reindent-rst.py(278 B)
ðŸ“„ reindent-rst.pyc(481 B)
ðŸ“„ reindent-rst.pyo(481 B)
ðŸ“„ reindent.py(11.15 KB)
ðŸ“„ reindent.pyc(9.4 KB)
ðŸ“„ reindent.pyo(9.37 KB)
ðŸ“„ rgrep.py(1.46 KB)
ðŸ“„ rgrep.pyc(1.84 KB)
ðŸ“„ rgrep.pyo(1.84 KB)
ðŸ“„ serve.py(1.12 KB)
ðŸ“„ serve.pyc(1.56 KB)
ðŸ“„ serve.pyo(1.56 KB)
ðŸ“„ setup.py(421 B)
ðŸ“„ setup.pyc(548 B)
ðŸ“„ setup.pyo(548 B)
ðŸ“„ suff.py(621 B)
ðŸ“„ suff.pyc(904 B)
ðŸ“„ suff.pyo(904 B)
ðŸ“„ svneol.py(2.86 KB)
ðŸ“„ svneol.pyc(2.84 KB)
ðŸ“„ svneol.pyo(2.76 KB)
ðŸ“„ texcheck.py(9.04 KB)
ðŸ“„ texcheck.pyc(8.18 KB)
ðŸ“„ texcheck.pyo(8.18 KB)
ðŸ“„ texi2html.py(68.19 KB)
ðŸ“„ texi2html.pyc(81.37 KB)
ðŸ“„ texi2html.pyo(81.37 KB)
ðŸ“„ treesync.py(5.65 KB)
ðŸ“„ treesync.pyc(5.85 KB)
ðŸ“„ treesync.pyo(5.85 KB)
ðŸ“„ untabify.py(1.19 KB)
ðŸ“„ untabify.pyc(1.55 KB)
ðŸ“„ untabify.pyo(1.55 KB)
ðŸ“„ which.py(1.59 KB)
ðŸ“„ which.pyc(1.59 KB)
ðŸ“„ which.pyo(1.59 KB)
ðŸ“„ win_add2path.py(1.58 KB)
ðŸ“„ win_add2path.pyc(2.02 KB)
ðŸ“„ win_add2path.pyo(2.02 KB)
ðŸ“„ xxci.py(2.73 KB)
ðŸ“„ xxci.pyc(3.92 KB)
ðŸ“„ xxci.pyo(3.92 KB)

Editing: analyze_dxp.py

#! /usr/bin/python2.7
"""
Some helper functions to analyze the output of sys.getdxp() (which is
only available if Python was built with -DDYNAMIC_EXECUTION_PROFILE).
These will tell you which opcodes have been executed most frequently
in the current process, and, if Python was also built with -DDXPAIRS,
will tell you which instruction _pairs_ were executed most frequently,
which may help in choosing new instructions.

If Python was built without -DDYNAMIC_EXECUTION_PROFILE, importing
this module will raise a RuntimeError.

If you're running a script you want to profile, a simple way to get
the common pairs is:

$ PYTHONPATH=$PYTHONPATH:<python_srcdir>/Tools/scripts \
./python -i -O the_script.py --args
...
> from analyze_dxp import *
> s = render_common_pairs()
> open('/tmp/some_file', 'w').write(s)
"""

import copy
import opcode
import operator
import sys
import threading

if not hasattr(sys, "getdxp"):
    raise RuntimeError("Can't import analyze_dxp: Python built without"
                       " -DDYNAMIC_EXECUTION_PROFILE.")

_profile_lock = threading.RLock()
_cumulative_profile = sys.getdxp()

# If Python was built with -DDXPAIRS, sys.getdxp() returns a list of
# lists of ints.  Otherwise it returns just a list of ints.
def has_pairs(profile):
    """Returns True if the Python that produced the argument profile
    was built with -DDXPAIRS."""

return len(profile) > 0 and isinstance(profile[0], list)

def reset_profile():
    """Forgets any execution profile that has been gathered so far."""
    with _profile_lock:
        sys.getdxp()  # Resets the internal profile
        global _cumulative_profile
        _cumulative_profile = sys.getdxp()  # 0s out our copy.

def merge_profile():
    """Reads sys.getdxp() and merges it into this module's cached copy.

We need this because sys.getdxp() 0s itself every time it's called."""

with _profile_lock:
        new_profile = sys.getdxp()
        if has_pairs(new_profile):
            for first_inst in range(len(_cumulative_profile)):
                for second_inst in range(len(_cumulative_profile[first_inst])):
                    _cumulative_profile[first_inst][second_inst] += (
                        new_profile[first_inst][second_inst])
        else:
            for inst in range(len(_cumulative_profile)):
                _cumulative_profile[inst] += new_profile[inst]

def snapshot_profile():
    """Returns the cumulative execution profile until this call."""
    with _profile_lock:
        merge_profile()
        return copy.deepcopy(_cumulative_profile)

def common_instructions(profile):
    """Returns the most common opcodes in order of descending frequency.

The result is a list of tuples of the form
      (opcode, opname, # of occurrences)

"""
    if has_pairs(profile) and profile:
        inst_list = profile[-1]
    else:
        inst_list = profile
    result = [(op, opcode.opname[op], count)
              for op, count in enumerate(inst_list)
              if count > 0]
    result.sort(key=operator.itemgetter(2), reverse=True)
    return result

def common_pairs(profile):
    """Returns the most common opcode pairs in order of descending frequency.

The result is a list of tuples of the form
      ((1st opcode, 2nd opcode),
       (1st opname, 2nd opname),
       # of occurrences of the pair)

"""
    if not has_pairs(profile):
        return []
    result = [((op1, op2), (opcode.opname[op1], opcode.opname[op2]), count)
              # Drop the row of single-op profiles with [:-1]
              for op1, op1profile in enumerate(profile[:-1])
              for op2, count in enumerate(op1profile)
              if count > 0]
    result.sort(key=operator.itemgetter(2), reverse=True)
    return result

def render_common_pairs(profile=None):
    """Renders the most common opcode pairs to a string in order of
    descending frequency.

The result is a series of lines of the form:
      # of occurrences: ('1st opname', '2nd opname')

"""
    if profile is None:
        profile = snapshot_profile()
    def seq():
        for _, ops, count in common_pairs(profile):
            yield "%s: %s\n" % (count, ops)
    return ''.join(seq())

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