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README	4.46 KB	-rw-r--r--	2024-03-19 22:46
abitype.py	5.44 KB	-rwxr-xr-x	2024-03-19 22:46
analyze_dxp.py	4.08 KB	-rw-r--r--	2024-03-19 22:46
byext.py	3.81 KB	-rwxr-xr-x	2024-03-19 22:46
byteyears.py	1.61 KB	-rwxr-xr-x	2024-03-19 22:46
checkpip.py	793 B	-rwxr-xr-x	2024-03-19 22:46
cleanfuture.py	8.42 KB	-rwxr-xr-x	2024-03-19 22:46
combinerefs.py	4.35 KB	-rwxr-xr-x	2024-03-19 22:46
copytime.py	663 B	-rwxr-xr-x	2024-03-19 22:46
crlf.py	632 B	-rwxr-xr-x	2024-03-19 22:46
db2pickle.py	3.54 KB	-rwxr-xr-x	2024-03-19 22:46
diff.py	2.2 KB	-rwxr-xr-x	2024-03-19 22:46
dutree.doc	2.18 KB	-rw-r--r--	2024-03-19 22:46
dutree.py	1.6 KB	-rwxr-xr-x	2024-03-19 22:46
eptags.py	1.54 KB	-rwxr-xr-x	2024-03-19 22:46
find-uname.py	1.18 KB	-rwxr-xr-x	2024-03-19 22:46
find_recursionlimit.py	3.9 KB	-rwxr-xr-x	2024-03-19 22:46
finddiv.py	2.48 KB	-rwxr-xr-x	2024-03-19 22:46
findlinksto.py	1.05 KB	-rwxr-xr-x	2024-03-19 22:46
findnocoding.py	2.88 KB	-rwxr-xr-x	2024-03-19 22:46
fixcid.py	9.93 KB	-rwxr-xr-x	2024-03-19 22:46
fixdiv.py	13.91 KB	-rwxr-xr-x	2024-03-19 22:46
fixheader.py	1.33 KB	-rwxr-xr-x	2024-03-19 22:46
fixnotice.py	2.96 KB	-rwxr-xr-x	2024-03-19 22:46
fixps.py	892 B	-rwxr-xr-x	2024-03-19 22:46
generate_opcode_h.py	2.17 KB	-rw-r--r--	2024-03-19 22:46
generate_stdlib_module_names.py	4.55 KB	-rw-r--r--	2024-03-19 22:46
generate_token.py	6.81 KB	-rwxr-xr-x	2024-03-19 22:46
get-remote-certificate.py	2.25 KB	-rwxr-xr-x	2024-03-19 22:46
google.py	501 B	-rwxr-xr-x	2024-03-19 22:46
gprof2html.py	2.25 KB	-rwxr-xr-x	2024-03-19 22:46
highlight.py	8.95 KB	-rwxr-xr-x	2024-03-19 22:46
idle3	96 B	-rwxr-xr-x	2024-03-19 22:46
ifdef.py	3.62 KB	-rwxr-xr-x	2024-03-19 22:46
import_diagnostics.py	999 B	-rwxr-xr-x	2024-03-19 22:46
lfcr.py	640 B	-rwxr-xr-x	2024-03-19 22:46
linktree.py	2.38 KB	-rwxr-xr-x	2024-03-19 22:46
lll.py	748 B	-rwxr-xr-x	2024-03-19 22:46
mailerdaemon.py	7.85 KB	-rwxr-xr-x	2024-03-19 22:46
make_ctype.py	2.23 KB	-rwxr-xr-x	2024-03-19 22:46
md5sum.py	2.46 KB	-rwxr-xr-x	2024-03-19 22:46
mkreal.py	1.59 KB	-rwxr-xr-x	2024-03-19 22:46
ndiff.py	3.73 KB	-rwxr-xr-x	2024-03-19 22:46
nm2def.py	2.42 KB	-rwxr-xr-x	2024-03-19 22:46
objgraph.py	5.8 KB	-rwxr-xr-x	2024-03-19 22:46
parse_html5_entities.py	3.91 KB	-rwxr-xr-x	2024-03-19 22:46
parseentities.py	1.7 KB	-rwxr-xr-x	2024-03-19 22:46
patchcheck.py	10.51 KB	-rwxr-xr-x	2024-03-19 22:46
pathfix.py	6.63 KB	-rwxr-xr-x	2024-03-19 22:46
pdeps.py	3.91 KB	-rwxr-xr-x	2024-03-19 22:46
pep384_macrocheck.py	4.61 KB	-rw-r--r--	2024-03-19 22:46
pickle2db.py	3.93 KB	-rwxr-xr-x	2024-03-19 22:46
pindent.py	16.73 KB	-rwxr-xr-x	2024-03-19 22:46
ptags.py	1.28 KB	-rwxr-xr-x	2024-03-19 22:46
pydoc3	80 B	-rwxr-xr-x	2024-03-19 22:46
pysource.py	3.77 KB	-rwxr-xr-x	2024-03-19 22:46
reindent-rst.py	279 B	-rwxr-xr-x	2024-03-19 22:46
reindent.py	11.37 KB	-rwxr-xr-x	2024-03-19 22:46
rgrep.py	1.54 KB	-rwxr-xr-x	2024-03-19 22:46
run_tests.py	1.78 KB	-rw-r--r--	2024-03-19 22:46
serve.py	1.2 KB	-rwxr-xr-x	2024-03-19 22:46
smelly.py	4.9 KB	-rwxr-xr-x	2024-03-19 22:46
stable_abi.py	21.09 KB	-rwxr-xr-x	2024-03-19 22:46
suff.py	510 B	-rwxr-xr-x	2024-03-19 22:46
texi2html.py	68.72 KB	-rwxr-xr-x	2024-03-19 22:46
untabify.py	1.27 KB	-rwxr-xr-x	2024-03-19 22:46
update_file.py	762 B	-rw-r--r--	2024-03-19 22:46
var_access_benchmark.py	11.63 KB	-rw-r--r--	2024-03-19 22:46
verify_ensurepip_wheels.py	3.31 KB	-rwxr-xr-x	2024-03-19 22:46
which.py	1.65 KB	-rwxr-xr-x	2024-03-19 22:46
win_add2path.py	1.62 KB	-rw-r--r--	2024-03-19 22:46

Rename

"""
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())