/usr/share/pyshared/pycallgraph/tracer.py is in python-pycallgraph 1.0.1-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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import inspect
import sys
import os
import time
from distutils import sysconfig
from collections import defaultdict
from threading import Thread
try:
from Queue import Queue, Empty
except ImportError:
from queue import Queue, Empty
from .util import Util
class SyncronousTracer(object):
def __init__(self, outputs, config):
self.processor = TraceProcessor(outputs, config)
self.config = config
def tracer(self, frame, event, arg):
self.processor.process(frame, event, arg, self.memory())
return self.tracer
def memory(self):
if self.config.memory:
from .memory_profiler import memory_usage
return int(memory_usage(-1, 0)[0] * 1000000)
def start(self):
sys.settrace(self.tracer)
def stop(self):
sys.settrace(None)
def done(self):
pass
class AsyncronousTracer(SyncronousTracer):
def start(self):
self.processor.start()
SyncronousTracer.start(self)
def tracer(self, frame, event, arg):
self.processor.queue(frame, event, arg, self.memory())
return self.tracer
def done(self):
self.processor.done()
self.processor.join()
class TraceProcessor(Thread):
'''
Contains a callback used by sys.settrace, which collects information about
function call count, time taken, etc.
'''
def __init__(self, outputs, config):
Thread.__init__(self)
self.trace_queue = Queue()
self.keep_going = True
self.outputs = outputs
self.config = config
self.updatables = [a for a in self.outputs if a.should_update()]
self.init_trace_data()
self.init_libpath()
def init_trace_data(self):
self.previous_event_return = False
# A mapping of which function called which other function
self.call_dict = defaultdict(lambda: defaultdict(int))
# Current call stack
self.call_stack = ['__main__']
# Counters for each function
self.func_count = defaultdict(int)
self.func_count_max = 0
self.func_count['__main__'] = 1
# Accumulative time per function
self.func_time = defaultdict(float)
self.func_time_max = 0
# Accumulative memory addition per function
self.func_memory_in = defaultdict(int)
self.func_memory_in_max = 0
# Accumulative memory addition per function once exited
self.func_memory_out = defaultdict(int)
self.func_memory_out_max = 0
# Keeps track of the start time of each call on the stack
self.call_stack_timer = []
self.call_stack_memory_in = []
self.call_stack_memory_out = []
def init_libpath(self):
self.lib_path = sysconfig.get_python_lib()
path = os.path.split(self.lib_path)
if path[1] == 'site-packages':
self.lib_path = path[0]
self.lib_path = self.lib_path.lower()
def queue(self, frame, event, arg, memory):
data = {
'frame': frame,
'event': event,
'arg': arg,
'memory': memory,
}
self.trace_queue.put(data)
def run(self):
while self.keep_going:
try:
data = self.trace_queue.get(timeout=0.1)
except Empty:
pass
self.process(**data)
def done(self):
while not self.trace_queue.empty():
time.sleep(0.1)
self.keep_going = False
def process(self, frame, event, arg, memory=None):
'''This function processes a trace result. Keeps track of
relationships between calls.
'''
if memory is not None and self.previous_event_return:
# Deal with memory when function has finished so local variables
# can be cleaned up
self.previous_event_return = False
if self.call_stack_memory_out:
full_name, m = self.call_stack_memory_out.pop(-1)
else:
full_name, m = (None, None)
# NOTE: Call stack is no longer the call stack that may be
# expected. Potentially need to store a copy of it.
if full_name and m:
call_memory = memory - m
self.func_memory_out[full_name] += call_memory
self.func_memory_out_max = max(
self.func_memory_out_max, self.func_memory_out[full_name]
)
if event == 'call':
keep = True
code = frame.f_code
# Stores all the parts of a human readable name of the current call
full_name_list = []
# Work out the module name
module = inspect.getmodule(code)
if module:
module_name = module.__name__
module_path = module.__file__
if not self.config.include_stdlib \
and self.is_module_stdlib(module_path):
keep = False
if module_name == '__main__':
module_name = ''
else:
module_name = ''
if module_name:
full_name_list.append(module_name)
# Work out the class name
try:
class_name = frame.f_locals['self'].__class__.__name__
full_name_list.append(class_name)
except (KeyError, AttributeError):
class_name = ''
# Work out the current function or method
func_name = code.co_name
if func_name == '?':
func_name = '__main__'
full_name_list.append(func_name)
# Create a readable representation of the current call
full_name = '.'.join(full_name_list)
if len(self.call_stack) > self.config.max_depth:
keep = False
# Load the trace filter, if any. 'keep' determines if we should
# ignore this call
if keep and self.config.trace_filter:
keep = self.config.trace_filter(full_name)
# Store the call information
if keep:
if self.call_stack:
src_func = self.call_stack[-1]
else:
src_func = None
self.call_dict[src_func][full_name] += 1
self.func_count[full_name] += 1
self.func_count_max = max(
self.func_count_max, self.func_count[full_name]
)
self.call_stack.append(full_name)
self.call_stack_timer.append(time.time())
if memory is not None:
self.call_stack_memory_in.append(memory)
self.call_stack_memory_out.append([full_name, memory])
else:
self.call_stack.append('')
self.call_stack_timer.append(None)
if event == 'return':
self.previous_event_return = True
if self.call_stack:
full_name = self.call_stack.pop(-1)
if self.call_stack_timer:
start_time = self.call_stack_timer.pop(-1)
else:
start_time = None
if start_time:
call_time = time.time() - start_time
self.func_time[full_name] += call_time
self.func_time_max = max(
self.func_time_max, self.func_time[full_name]
)
if memory is not None:
if self.call_stack_memory_in:
start_mem = self.call_stack_memory_in.pop(-1)
else:
start_mem = None
if start_mem:
call_memory = memory - start_mem
self.func_memory_in[full_name] += call_memory
self.func_memory_in_max = max(
self.func_memory_in_max,
self.func_memory_in[full_name],
)
def is_module_stdlib(self, file_name):
'''
Returns True if the file_name is in the lib directory. Used to check
if a function is in the standard library or not.
'''
return file_name.lower().startswith(self.lib_path)
def __getstate__(self):
'''Used for when creating a pickle. Certain instance variables can't
pickled and aren't used anyway.
'''
odict = self.__dict__.copy()
dont_keep = [
'outputs',
'config',
'updatables',
'lib_path',
]
for key in dont_keep:
del odict[key]
return odict
def group(self, name):
return name.split('.', 1)[0]
def groups(self):
grp = defaultdict(list)
for node in self.nodes():
grp[self.group(node.name)].append(node)
for g in grp.iteritems():
yield g
def stat_group_from_func(self, func, calls):
stat_group = StatGroup()
stat_group.name = func
stat_group.group = self.group(func)
stat_group.calls = Stat(calls, self.func_count_max)
stat_group.time = Stat(self.func_time.get(func, 0), self.func_time_max)
stat_group.memory_in = Stat(
self.func_memory_in.get(func, 0), self.func_memory_in_max
)
stat_group.memory_out = Stat(
self.func_memory_in.get(func, 0), self.func_memory_in_max
)
return stat_group
def nodes(self):
for func, calls in self.func_count.iteritems():
yield self.stat_group_from_func(func, calls)
def edges(self):
for src_func, dests in self.call_dict.iteritems():
if not src_func:
continue
for dst_func, calls in dests.iteritems():
edge = self.stat_group_from_func(dst_func, calls)
edge.src_func = src_func
edge.dst_func = dst_func
yield edge
class Stat(object):
'''Stores a "statistic" value, e.g. "time taken" along with the maximum
possible value of the value, which is used to calculate the fraction of 1.
The fraction is used for choosing colors.
'''
def __init__(self, value, total):
self.value = value
self.total = total
try:
self.fraction = value / total
except ZeroDivisionError:
self.fraction = 0
@property
def value_human_bibyte(self):
'''Mebibyte of the value in human readable a form.'''
return Util.human_readable_bibyte(self.value)
class StatGroup(object):
pass
def simple_memoize(callable_object):
'''Simple memoization for functions without keyword arguments.
This is useful for mapping code objects to module in this context.
inspect.getmodule() requires a number of system calls, which may slow down
the tracing considerably. Caching the mapping from code objects (there is
*one* code object for each function, regardless of how many simultaneous
activations records there are).
In this context we can ignore keyword arguments, but a generic memoizer
ought to take care of that as well.
'''
cache = dict()
def wrapper(*rest):
if rest not in cache:
cache[rest] = callable_object(*rest)
return cache[rest]
return wrapper
inspect.getmodule = simple_memoize(inspect.getmodule)
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