def example_one():
"""
20003 function calls in 0.778 seconds
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.778 0.778 item_58_profile.py:38(<lambda>)
1 0.001 0.001 0.778 0.778 item_58_profile.py:19(insertion_sort)
10000 0.764 0.000 0.777 0.000 item_58_profile.py:26(insert_value)
9989 0.013 0.000 0.013 0.000 {method 'insert' of 'list' objects}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
11 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}
- notes for Nick:
- we can see that the biggest use of CPU in our test is the cumulative
time spent in the 'insert_value' function.
"""
max_size = 10**4
data = [randint(0, max_size) for _ in range(max_size)]
test = lambda: insertion_sort(data)
profiler = Profile()
profiler.runcall(test)
# to extract statistics about the 'test' function's performance, we use pstats
stats = Stats(profiler)
stats.strip_dirs()
stats.sort_stats('cumulative')
stats.print_stats()
def write_profile(pfile='./logs/profile.out'):
global BUBBLE_PROFILE
if not BUBBLE_PROFILE:
return
BUBBLE_PROFILE.disable()
#s = io.StringIO()
s = StringIO()
sortby = 'cumulative'
#ps = Stats(BUBBLE_PROFILE).sort_stats(sortby)
ps = Stats(BUBBLE_PROFILE, stream=s).sort_stats(sortby)
ps.print_stats()
# print(s.getvalue())
# now=arrow.now()
#pstats_file='./logs/profiling'+str(now)+'.pstats'
#profile_text='./logs/profile'+str(now)+'.txt'
pstats_file = './logs/profiling.pstats'
profile_text = './logs/profile.txt'
BUBBLE_PROFILE.dump_stats(pstats_file)
with open(profile_text, 'a+') as pf:
pf.write(s.getvalue())
print("end_profile")
print('BUBBLE_PROFILE:pstats_file:' + pstats_file)
print('BUBBLE_PROFILE:profile_text:' + profile_text)
def __run(self, *args, **kwargs):
__start = time.time()
# notify if we don't process quickly
if __start - self.__time_submitted > 0.05:
self.log.warning(f'Starting of {self.name} took too long: {__start - self.__time_submitted:.2f}s. '
f'Maybe there are not enough threads?')
# start profiler
pr = Profile()
pr.enable()
# Execute the function
try:
self._func(*args, **kwargs)
except Exception as e:
self.__format_traceback(e, *args, **kwargs)
# disable profiler
pr.disable()
# log warning if execution takes too long
__dur = time.time() - __start
if self.__warn_too_long and __dur > 0.8:
self.log.warning(f'Execution of {self.name} took too long: {__dur:.2f}s')
s = io.StringIO()
ps = Stats(pr, stream=s).sort_stats(SortKey.CUMULATIVE)
ps.print_stats(0.1) # limit to output to 10% of the lines
for line in s.getvalue().splitlines()[4:]: # skip the amount of calls and "Ordered by:"
if line:
self.log.warning(line)
def write_profile(pfile='./logs/profile.out'):
global BUBBLE_PROFILE
if not BUBBLE_PROFILE:
return
BUBBLE_PROFILE.disable()
#s = io.StringIO()
s = StringIO()
sortby = 'cumulative'
#ps = Stats(BUBBLE_PROFILE).sort_stats(sortby)
ps = Stats(BUBBLE_PROFILE,stream=s).sort_stats(sortby)
ps.print_stats()
# print(s.getvalue())
# now=arrow.now()
#pstats_file='./logs/profiling'+str(now)+'.pstats'
#profile_text='./logs/profile'+str(now)+'.txt'
pstats_file='./logs/profiling.pstats'
profile_text='./logs/profile.txt'
BUBBLE_PROFILE.dump_stats(pstats_file)
with open(profile_text,'a+') as pf:
pf.write(s.getvalue())
print("end_profile")
print('BUBBLE_PROFILE:pstats_file:'+pstats_file)
print('BUBBLE_PROFILE:profile_text:'+profile_text)
def _execute(self, func, phase_name, n, *args):
if not self.profile_dir:
return func(*args)
basename = '%s-%s-%d-%02d-%d' % (
self.contender_name, phase_name, self.objects_per_txn, n, self.rep)
txt_fn = os.path.join(self.profile_dir, basename + ".txt")
prof_fn = os.path.join(self.profile_dir, basename + ".prof")
profiler = cProfile.Profile()
profiler.enable()
try:
res = func(*args)
finally:
profiler.disable()
profiler.dump_stats(prof_fn)
with open(txt_fn, 'w') as f:
st = Stats(profiler, stream=f)
st.strip_dirs()
st.sort_stats('cumulative')
st.print_stats()
return res
def example_two():
"""
30003 function calls in 0.018 seconds
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.018 0.018 item_58_profile.py:98(<lambda>)
1 0.001 0.001 0.018 0.018 item_58_profile.py:38(insertion_sort)
10000 0.002 0.000 0.017 0.000 item_58_profile.py:88(insert_value_better)
10000 0.012 0.000 0.012 0.000 {method 'insert' of 'list' objects}
10000 0.003 0.000 0.003 0.000 {built-in method _bisect.bisect_left}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
"""
max_size = 10**4
data = [randint(0, max_size) for _ in range(max_size)]
test = lambda: insertion_sort(data)
profiler = Profile()
profiler.runcall(test)
# to extract statistics about the 'test' function's performance, we use pstats
stats = Stats(profiler)
stats.strip_dirs()
stats.sort_stats('cumulative')
stats.print_stats()
def to_txt(self, filename):
"""
Saves all profiles into one file.
@param filename filename where to save the profiles,
can be a stream
"""
if len(self) == 0:
raise ValueError( # pragma: no cover
"No profile was done.")
if isinstance(filename, str):
with open(filename, "w") as f: # pylint: disable=W1514
self.to_txt(f)
return
f = filename
f.write(self.name + "\n")
for i, (prof, kw) in enumerate(self):
f.write("------------------------------------------------------\n")
f.write("profile %d\n" % i)
if kw:
for a, b in sorted(kw.items()):
f.write("%s=%s\n" % (a, str(b).replace('\n', '\\n')))
f.write("--\n")
if hasattr(prof, 'output_text'):
f.write(prof.output_text(unicode=False, color=False))
else:
s = StringIO()
sortby = SortKey.CUMULATIVE
ps = Stats(prof, stream=s).sort_stats(sortby)
ps.print_stats()
f.write(s.getvalue())
f.write("\n")
def new_func(*args, **kwargs):
# create a Profile and enable tracking
prof = Profile()
prof.enable()
# call the profiled function
result = func(*args, **kwargs)
# disable tracking
prof.disable()
# simulate writing to file via string buffer
buff = StringIO()
# write stats to string buffer
prof_stats = Stats(prof, stream=buff)
prof_stats.print_stats()
# print the output from string buffer
print('-' * 79)
path = func.__code__.co_filename.split(sep)
path = path[len(path) - list(reversed(path)).index(package) - 1:]
print(f'{".".join(path)} :: ' f'{func.__name__}')
print(buff.getvalue())
# propagate back the real func's output
return result
def main():
"""Conduct a peridynamics simulation."""
parser = argparse.ArgumentParser()
parser.add_argument('--profile', action='store_const', const=True)
args = parser.parse_args()
if args.profile:
profile = cProfile.Profile()
profile.enable()
model = Model(mesh_file, horizon=0.1, critical_strain=0.005,
elastic_modulus=0.05, initial_crack=is_crack)
# Set left-hand side and right-hand side of boundary
indices = np.arange(model.nnodes)
model.lhs = indices[model.coords[:, 0] < 1.5*model.horizon]
model.rhs = indices[model.coords[:, 0] > 1.0 - 1.5*model.horizon]
integrator = Euler(dt=1e-3)
u, damage, *_ = model.simulate(
steps=100,
integrator=integrator,
boundary_function=boundary_function,
write=1000
)
if args.profile:
profile.disable()
s = StringIO()
stats = Stats(profile, stream=s).sort_stats(SortKey.CUMULATIVE)
stats.print_stats()
print(s.getvalue())
def __call__(self, environ, start_response):
response_body = []
def catching_start_response(status, headers, exc_info=None):
start_response(status, headers, exc_info)
return response_body.append
def runapp():
appiter = self._app(environ, catching_start_response)
response_body.extend(appiter)
if hasattr(appiter, 'close'):
appiter.close()
p = Profile()
p.runcall(runapp)
body = ''.join(response_body)
stats = Stats(p, stream=self._stream)
stats.sort_stats(*self._sort_by)
self._stream.write('-' * 80)
self._stream.write('\nPATH: %r\n' % environ.get('PATH_INFO'))
stats.print_stats(*self._restrictions)
self._stream.write('-' * 80 + '\n\n')
return [body]
def stopTest(self, test):
super(BenchTestResult, self).stopTest(test)
if self._benchmark:
self._profiler.disable()
stats = Stats(self._profiler)
stats.sort_stats(self._sort)
stats.print_stats(self._limit)
def profile():
prof = Profile()
prof.runcall(f1)
stat = Stats(prof)
stat.strip_dirs()
stat.sort_stats('cumulative')
stat.print_stats()
def expose(self, widget, event):
context = widget.window.cairo_create()
#r = (event.area.x, event.area.y, event.area.width, event.area.height)
#context.rectangle(r[0]-.5, r[1]-.5, r[2]+1, r[3]+1)
#context.clip()
if False:
import profile
profile.runctx("self.draw(context, event.area)", locals(),
globals(), "/tmp/pychessprofile")
from pstats import Stats
s = Stats("/tmp/pychessprofile")
s.sort_stats('cumulative')
s.print_stats()
else:
self.drawcount += 1
start = time()
self.animationLock.acquire()
self.draw(context, event.area)
self.animationLock.release()
self.drawtime += time() - start
#if self.drawcount % 100 == 0:
# print "Average FPS: %0.3f - %d / %d" % \
# (self.drawcount/self.drawtime, self.drawcount, self.drawtime)
return False
def expose(self, widget, event):
context = widget.window.cairo_create()
#r = (event.area.x, event.area.y, event.area.width, event.area.height)
#context.rectangle(r[0]-.5, r[1]-.5, r[2]+1, r[3]+1)
#context.clip()
if False:
import profile
profile.runctx("self.draw(context, event.area)", locals(), globals(), "/tmp/pychessprofile")
from pstats import Stats
s = Stats("/tmp/pychessprofile")
s.sort_stats('cumulative')
s.print_stats()
else:
self.drawcount += 1
start = time()
self.animationLock.acquire()
self.draw(context, event.area)
self.animationLock.release()
self.drawtime += time() - start
#if self.drawcount % 100 == 0:
# print "Average FPS: %0.3f - %d / %d" % \
# (self.drawcount/self.drawtime, self.drawcount, self.drawtime)
return False
def __analyze2 ():
import profile
profile.runctx("self.__analyze2()", locals(), globals(), "/tmp/pychessprofile")
from pstats import Stats
s = Stats("/tmp/pychessprofile")
s.sort_stats('cumulative')
s.print_stats()
def main():
"""Main sequence"""
analyser = Analyser(config=ProfilingConfig)
data = import_all(config=ProfilingConfig)
analyser.run(data)
del analyser
del data
profiler = Profile()
tracemalloc.start(10)
time1 = tracemalloc.take_snapshot()
profiler.runcall(test)
time2 = tracemalloc.take_snapshot()
time_stats = Stats(profiler)
time_stats.strip_dirs()
time_stats.sort_stats('cumulative')
print("\n===Time Profiler Stats===\n")
time_stats.print_stats(TOP_STATS)
print("\n===Time Profiler Callers===\n")
time_stats.print_callers(TOP_STATS)
memory_stats = time2.compare_to(time1, 'lineno')
print("\n===Memory Profiler Callers===\n")
for stat in memory_stats[:3]:
print(stat)
print("\n===Top Memory Consumer===\n")
top = memory_stats[0]
print('\n'.join(top.traceback.format()))
def tearDown(self):
"""Report profiling results"""
p = Stats(self.pr)
p.strip_dirs()
p.sort_stats('cumtime')
p.print_stats()
print "\n--->>>"
def stopTest(self, test):
super(BenchTestResult, self).stopTest(test)
if self._benchmark:
self._profiler.disable()
stats = Stats(self._profiler)
stats.sort_stats(self._sort)
stats.print_stats(self._limit)
def tearDown(self):
if self.should_profile:
results = Stats(self.profile)
results.strip_dirs()
results.sort_stats('cumulative')
results.print_stats(50)
super().tearDown()
def tearDown(self):
if self.should_profile:
results = Stats(self.profile)
results.strip_dirs()
results.sort_stats('cumulative')
results.print_stats(50)
super().tearDown()
def profile(to=None, sort_by='cumtime'):
'''Profiles a chunk of code, use with the ``with`` statement::
from halonctl.debug import profile
with profile('~/Desktop/stats'):
pass # Do something performance-critical here...
Results for individual runs are collected into ``to``. The specifics of how
reports are done varies depending on what type ``to`` is.
* **File-like objects**: Stats are dumped, according to ``sort_by``, into the stream, separated by newlines - watch out, the file/buffer may grow very big when used in loops.
* **List-like objects**: A number of pstats.Stats objects are appended.
* **str and unicode**: Treated as a path and opened for appending. Tildes (~) will be expanded, and intermediary directories created if possible.
* **None or omitted**: Results are printed to sys.stderr.
'''
if isinstance(to, six.string_types):
to = open_fuzzy(to, 'a')
to_is_stream = hasattr(to, 'write')
to_is_list = hasattr(to, 'append')
p = Profile()
p.enable()
yield
p.disable()
ps = Stats(p, stream=to if to_is_stream else sys.stderr)
ps.sort_stats('cumtime')
if to_is_stream or to is None:
ps.print_stats()
elif to_is_list:
to.append(ps)
def __call__(self, environ, start_response):
response_body = []
def catching_start_response(status, headers, exc_info=None):
start_response(status, headers, exc_info)
return response_body.append
def runapp():
appiter = self._app(environ, catching_start_response)
response_body.extend(appiter)
if hasattr(appiter, 'close'):
appiter.close()
p = Profile()
p.runcall(runapp)
body = ''.join(response_body)
stats = Stats(p)
stats.sort_stats(*self._sort_by)
self._stream.write('-' * 80)
self._stream.write('\nPATH: %r\n' % environ.get('PATH_INFO'))
stats.print_stats(*self._restrictions)
self._stream.write('-' * 80 + '\n\n')
return [body]
def tearDown(self):
'''Disconnect from statseg'''
self.stat.disconnect()
profile = Stats(self.profile)
profile.strip_dirs()
profile.sort_stats('cumtime')
profile.print_stats()
print("\n--->>>")
def print_stats(statsfile, statstext):
with open(statstext, 'w') as f:
mystats = Stats(statsfile, stream=f)
mystats.strip_dirs()
mystats.sort_stats('cumtime')
# mystats.print_callers('_strptime')
mystats.print_stats()
startfile(statstext)
def tearDown(self):
"""finish any test"""
if hasattr(self, "prof"):
p = Stats(self.prof)
p.strip_dirs()
p.sort_stats('cumtime')
p.print_stats()
print("\n--->>>")
def tearDown(self):
""" """
p = Stats(self.prof)
p.sort_stats("cumtime")
if self.verbose is True:
p.dump_stats("profiles/test_graphsearcher.py.prof")
p.print_stats()
p.strip_dirs()
def wrap(*args, **kwargs):
prof = Profile()
res = prof.runcall(func, *args, **kwargs)
stats = Stats(prof)
stats.strip_dirs()
stats.sort_stats('tottime')
stats.print_stats(20)
return res
def tearDown(self):
if PROFILE:
p = Stats(self.pr)
p.strip_dirs()
p.sort_stats('cumtime')
p.print_stats()
if DEBUG:
print('\n{}>>>'.format('-' * 77))
def tearDown(self):
return
"""finish any test"""
p = Stats(self.pr)
p.strip_dirs()
p.sort_stats('cumtime')
p.print_stats()
print("\n--->>>")
def tearDown(self):
"""Disconnect from statseg"""
self.stat.disconnect()
profile = Stats(self.profile)
profile.strip_dirs()
profile.sort_stats("cumtime")
profile.print_stats()
print("\n--->>>")
def _run(self):
"""
Do the work
"""
# Init with the very first library section. This will block!
section = self.queue.get()
self.queue.task_done()
if section is None:
return
while not self.isCanceled():
if section is None:
break
LOG.debug('Start processing section %s (%ss)', section.name,
section.plex_type)
self.current = 1
self.processed = 0
self.total = section.total
self.section_name = section.name
self.section_type_text = utils.lang(
v.TRANSLATION_FROM_PLEXTYPE[section.plex_type])
profile = Profile()
profile.enable()
with section.context(self.last_sync) as context:
while not self.isCanceled():
# grabs item from queue. This will block!
item = self.queue.get()
if isinstance(item, dict):
context.add_update(item['xml'][0],
section_name=section.name,
section_id=section.id,
children=item['children'])
self.title = item['xml'][0].get('title')
self.processed += 1
elif isinstance(item, UpdateLastSyncAndPlaystate):
context.plexdb.update_last_sync(
item.plex_id, section.plex_type, self.last_sync)
if section.plex_type != v.PLEX_TYPE_ARTIST:
context.update_userdata(item.xml_item,
section.plex_type)
elif isinstance(item, InitNewSection) or item is None:
section = item
break
else:
context.remove(item.plex_id,
plex_type=section.plex_type)
self.update_progressbar()
self.current += 1
if self.processed == 500:
self.processed = 0
context.commit()
self.queue.task_done()
self.queue.task_done()
profile.disable()
string_io = StringIO()
stats = Stats(profile, stream=string_io).sort_stats('cumulative')
stats.print_stats()
LOG.info('cProfile result: ')
LOG.info(string_io.getvalue())
def profileit(fun, *args, **kwargs):
pr = Profile()
pr.enable()
res = fun(*args, **kwargs)
pr.disable()
ps = Stats(pr).sort_stats('tottime')
#ps = Stats(pr).sort_stats('cumtime')
ps.print_stats()
return res, ps
def profile(func, file_path):
pr = Profile()
pr.enable()
func()
pr.disable()
s = open(file_path, "w")
sortby = "cumulative"
ps = Stats(pr, stream=s).sort_stats(sortby)
ps.print_stats()
def profile(func, file_path):
pr = Profile()
pr.enable()
func()
pr.disable()
s = open(file_path, 'w')
sortby = 'cumulative'
ps = Stats(pr, stream=s).sort_stats(sortby)
ps.print_stats()
def wrapper(*args, **kwargs):
pr = cProfile.Profile()
pr.enable()
retval = func(*args, **kwargs)
s = StringIO()
ps = Stats(pr, stream=s).sort_stats(sortby)
ps.print_stats()
print(s.getvalue())
return retval
def print_profile_data():
"""
Print the collected profile data.
"""
stream = StringIO()
statistics = Stats(profiler, stream=stream)
statistics.sort_stats('cumulative')
statistics.print_stats()
print(stream.getvalue())
def run():
if sys.version_info < (3, 0, 0):
sys.stderr.write("You need python 3.0 or later to run this script\n")
sys.exit(1)
arg_parser = get_arg_parser()
args = arg_parser.parse_args()
if not args.p1_path and (not args.p1_draft or not args.p1_battle):
arg_parser.error("You should use either p1-path or both "
"p1-draft and p1-battle.\n")
elif not args.p2_path and (not args.p2_draft or not args.p2_battle):
arg_parser.error("You should use either p2-path or both "
"p2-draft and p2-battle.\n")
if args.p1_path is not None:
player_1 = agents.NativeAgent(args.p1_path)
player_1 = (player_1, player_1)
else:
player_1 = parse_draft_agent(args.p1_draft)(), \
parse_battle_agent(args.p1_battle)()
if args.p2_path is not None:
player_2 = agents.NativeAgent(args.p2_path)
player_2 = (player_2, player_2)
else:
player_2 = parse_draft_agent(args.p2_draft)(), \
parse_battle_agent(args.p2_battle)()
if args.profile:
profiler = cProfile.Profile()
result = io.StringIO()
profiler.enable()
for i in range(args.games):
evaluate((i, player_1, player_2, args.seed))
profiler.disable()
profiler_stats = Stats(profiler, stream=result)
profiler_stats.sort_stats('cumulative')
profiler_stats.print_stats()
print(result.getvalue())
else:
params = ((j, player_1, player_2, args.seed, args.silent)
for j in range(args.games))
with Pool(args.processes) as pool:
pool.map(evaluate, params)
wins, games = wins_by_p0
ratio = 100 * wins / games
print(f"{'%.2f' % ratio}% {'%.2f' % (100 - ratio)}%")
def inner(*args, **kwargs):
pro = Profile()
pro.runcall(func, *args, **kwargs)
stats = Stats(pro)
stats.strip_dirs()
stats.sort_stats(field)
print("Profile for {}()".format(func.__name__))
stats.print_stats()
stats.print_callers()
def search_method():
"""Match for applicable methods and their arguments.
Input:
* username: username.
* theory_name: name of the theory.
* thm_name: name of the theorem.
Returns:
* search_res: list of search results.
* ctxt: current proof context.
"""
data = json.loads(request.get_data().decode("utf-8"))
if data['profile']:
pr = cProfile.Profile()
pr.enable()
if not proof_cache.check_cache(data):
start_time = time.perf_counter()
proof_cache.create_cache(data)
print("Load: %f" % (time.perf_counter() - start_time))
if data['thm_name'] != '':
limit = ('thm', data['thm_name'])
else:
limit = None
basic.load_theory(data['theory_name'],
limit=limit,
username=data['username'])
start_time = time.perf_counter()
state = proof_cache.states[data['index']]
fact_ids = data['step']['fact_ids']
goal_id = data['step']['goal_id']
search_res = state.search_method(goal_id, fact_ids)
with settings.global_setting(unicode=True):
for res in search_res:
if '_goal' in res:
res['_goal'] = [printer.print_term(t) for t in res['_goal']]
if '_fact' in res:
res['_fact'] = [printer.print_term(t) for t in res['_fact']]
vars = state.get_vars(goal_id)
with settings.global_setting(unicode=True, highlight=True):
print_vars = dict((k, printer.print_type(v)) for k, v in vars.items())
print("Response:", time.perf_counter() - start_time)
if data['profile']:
p = Stats(pr)
p.strip_dirs()
p.sort_stats('cumtime')
p.print_stats()
return jsonify({'search_res': search_res, 'ctxt': print_vars})
def print_profile_data():
"""
Print the collected profile data.
"""
stream = StringIO()
statistics = Stats(profiler, stream=stream)
statistics.sort_stats('cumulative')
statistics.print_stats()
print(stream.getvalue())
def profile_call(_func, *args, **kwargs):
p = Profile()
rv = []
p.runcall(lambda: rv.append(_func(*args, **kwargs)))
p.dump_stats('/tmp/sentry-%s-%s.prof' % (time.time(), _func.__name__))
stats = Stats(p, stream=sys.stderr)
stats.sort_stats('time', 'calls')
stats.print_stats()
return rv[0]
def tearDown(self):
for worker in self.driver._workers:
worker.stop()
worker.wait()
self.cvx.endpoint_data.clear()
super(MechTestBase, self).tearDown()
if ENABLE_PROFILER:
p = Stats(self.pr)
p.strip_dirs()
p.sort_stats('cumtime')
p.print_stats()
def show_time_profiler_results(pr, top_records):
"""
Show results of timed profiling.
:param pr: profiler instance
:param top_records: how many top function calls to show.
"""
if pr:
st = Stats(pr)
st.strip_dirs()
st.sort_stats('cumulative')
st.print_stats(top_records)
def wrapper(*args, **kwg):
f = func
res = None
try:
cProfile.runctx("res = f(*args, **kwg)", globals(), locals(), filename)
return res
finally:
if filename:
pstats = Stats(filename)
pstats.sort_stats(*sort_fields)
pstats.print_stats(*p_amount)
def profile(func, args=None, kwargs=None, sort="time"):
prof = profile_.Profile()
if args is None:
args = ()
if kwargs is None:
kwargs = {}
ret = prof.runcall(func, *args, **kwargs)
stats = Stats(prof)
stats.sort_stats(sort)
stats.print_stats()
return ret
def print_stats(profiler, printCallers=False):
from pstats import Stats
stats = Stats(profiler)
stats.strip_dirs()
stats.sort_stats('cumulative')
if printCallers is True:
stats.print_callers()
else:
stats.print_stats()
def tearDown(self):
if ENABLE_PROFILE:
if DUMP_PROFILE:
self.pr.dump_stats('profile.out')
p = Stats(self.pr)
p.strip_dirs()
p.sort_stats('time')
p.print_stats(40)
p.print_callees('types.py:846\(validate_value', 20)
p.print_callees('types.py:828\(_validate_primitive_value', 20)
p.print_callees('uploadsession.py:185\(write', 20)
TestBase.teardown(self)
def tearDownClass(cls):
# stop swarm
print("TEST: stopping swarm")
for node in cls.swarm:
node.stop()
shutil.rmtree(STORAGE_DIR)
# get profiler stats
stats = Stats(cls.profile)
stats.strip_dirs()
stats.sort_stats('cumtime')
stats.print_stats()
def run(self):
"""method calling cProfile and printing the output"""
tests=self.tests()
for test in tests:
tmpBuffer=StringIO.StringIO()
profile=cProfile.Profile()
profile.runctx('self.'+str(test[0])+"()",globals(),locals())
stats=Stats(profile,stream=tmpBuffer)
stats.sort_stats('time','calls')
stats.print_stats(1)
match=re.findall(r'\bin\b(.*?)\bCPU\b',tmpBuffer.getvalue())
print str(test[1].__doc__ )+":"+str(match[0])+" CPU Time"
def profile_func(func):
from cProfile import Profile
from pstats import Stats
p = Profile()
rv = []
p.runcall(lambda: rv.append(func()))
p.dump_stats('/tmp/lektor-%s.prof' % func.__name__)
stats = Stats(p, stream=sys.stderr)
stats.sort_stats('time', 'calls')
stats.print_stats()
return rv[0]
def process_response(self, request, response):
if self.is_on(request):
self.prof.create_stats()
out = StringIO.StringIO()
stats = Stats(self.prof, stream=out)
stats.sort_stats(*sort_tuple)
stats.print_stats()
stats_str = out.getvalue()
if response and response.content and stats_str:
response.content = "<pre>" + stats_str + "</pre>"
return response
def _wrap(*kl, **kw):
prof = Profile()
prof.enable()
try:
return func(*kl, **kw)
finally:
prof.disable()
result = StringIO()
ps = Stats(prof, stream = result).sort_stats('cumulative')
ps.print_stats()
print result.getvalue()
def tearDownClass(cls):
# stop swarm
print("TEST: stopping swarm")
for node in cls.swarm:
node.stop()
time.sleep(0.1) # not to fast
cls.test_get_unl_peer.stop()
shutil.rmtree(STORAGE_DIR)
# get profiler stats
if PROFILE:
stats = Stats(cls.profile)
stats.strip_dirs()
stats.sort_stats('cumtime')
stats.print_stats()
def main():
"""Profiling main function."""
profiler = Profile()
profiler = profiler.runctx("run_app()", globals(), locals())
iostream = StringIO()
stats = Stats(profiler, stream=iostream)
stats.sort_stats("time") # or cumulative
stats.print_stats(80) # 80 == how many to print
# optional:
# stats.print_callees()
# stats.print_callers()
logging.info("Profile data:\n%s", iostream.getvalue())
def test_api_me_post_success(self):
self.login(self.get_default_test_username(), self.get_default_test_password())
self.pr = cProfile.Profile()
self.pr.enable()
#CREATING y BILLS
for x in range(0, 100):
self.apiCreateNewBill(testUtils.random_name_generator(), testUtils.random_number_generator())
p = Stats (self.pr)
p.strip_dirs()
p.sort_stats ('cumtime')
p.print_stats ()
self.logout()
def run(self):
"""method calling cProfile and printing the output"""
tests=self.tests()
for test in tests:
tmpBuffer=StringIO.StringIO()
profile=cProfile.Profile()
profile.runctx('self.'+str(test[0])+"()",globals(),locals())
stats=Stats(profile,stream=tmpBuffer)
stats.sort_stats('time','calls')
stats.print_stats(1)
#match=re.findall(r'\bin\b(.*?)\bCPU\b',tmpBuffer.getvalue())
match=re.findall(r'\bin\b(.*?)\bseconds\b',tmpBuffer.getvalue())
#There is some difference between 2.6 and 2.7, re with seconds fits both version
#but in 2.6 it returns CPU string, the filter will clean it
print str(test[1].__doc__ )+":"+filter(lambda x: x.isdigit() or x==".", str(match[0]))+" CPU Time"
def profiling():
from cProfile import Profile
from pstats import Stats
ir_params = { 'ev_params' : {'space':'e3', 'F': [0.1,0.1,0.1], 'j_max':30},
'duration' : 1,
'nu' : 1.7e-5,
'sampling_rate' : 8000,
}
p = Profile()
p.runcall(lambda : get_ir(ir_params))
stats = Stats(p, stream=sys.stdout)
stats.sort_stats('time')
stats.print_stats(10)
def __profile_code():
"""
@return: None
@rtype: None
Edit this function to do all profiling.
"""
import cProfile
from pstats import Stats
profile_file = "C:\\Users\\PBS Biotech\\Documents\\Personal\\PBS_Office\\MSOffice\\officelib\\pbslib\\test\\profile.txt"
cProfile.run('full_scan(manyfile3, manyrecipesteps)', filename=profile_file)
with open("C:\\Users\\Public\\Documents\\PBSSS\\Functional Testing\\tpid.txt", 'w') as f:
stats = Stats(profile_file, stream=f)
# stats.strip_dirs()
stats.sort_stats('time')
stats.print_stats('MSOffice')
def profile():
import cProfile
from pstats import Stats
path_to_stat_file = '/tmp/_profiling_'
cProfile.run('solver_profiling()', path_to_stat_file)
s = Stats(path_to_stat_file)
# s.strip_dirs()
s.sort_stats('time')
# s.sort_stats('cum')
s.print_stats(20)
times = [v[2] for v in s.stats.values()]
times.sort(reverse=True)
# times = times[:N]
sum_of_times = sum(times)
print sum_of_times, [round(t / sum_of_times * 100, 2) for t in times][:10]
