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analyze_dxp.py
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analyze_dxp.pyc
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byteyears.py
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checkappend.py
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checkpyc.py
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classfix.py
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classfix.pyo
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cleanfuture.py
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combinerefs.py
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copytime.py
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cvsfiles.py
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db2pickle.py
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find_recursionlimit.py
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findnocoding.py
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fixcid.py
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gprof2html.py
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hotshotmain.py
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linktree.py
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mailerdaemon.py
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md5sum.py
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methfix.py
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ndiff.py
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objgraph.py
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objgraph.pyo
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parseentities.py
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patchcheck.py
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pathfix.py
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pickle2db.py
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pindent.py
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ptags.py
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pysource.py
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redemo.py
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reindent-rst.py
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rgrep.py
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texi2html.py
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Code Editor : analyze_dxp.py
#! /usr/bin/env python """ 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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