def process_response(self, request, response):
req = request.META['PATH_INFO']
if req.find('static') == -1 and req.find('media') == -1:
print req
self.end_objects = muppy.get_objects()
sum_start = summary.summarize(self.start_objects)
sum_end = summary.summarize(self.end_objects)
diff = summary.get_diff(sum_start, sum_end)
summary.print_(diff)
#print '~~~~~~~~~'
#cb = refbrowser.ConsoleBrowser(response, maxdepth=2, \
#str_func=output_function)
#cb.print_tree()
print '~~~~~~~~~'
a = asizeof(response)
print 'Total size of response object in kB: %s' % \
str(a / 1024.0)
print '~~~~~~~~~'
a = asizeof(self.end_objects)
print 'Total size of end_objects in MB: %s' % \
str(a / 1048576.0)
b = asizeof(self.start_objects)
print 'Total size of start_objects in MB: %s' % \
str(b / 1048576.0)
print '~~~~~~~~~'
return response
def file_test(rows=500000, cols=50):
"File test"
print("Creating file with {} rows and {} columns".format(rows, cols))
file = create_file(rows, cols)
print("Size of the file: {:.2f} MiB".format(getsize(file) / (1024 * 1024)))
print("Reading file")
sum1 = summarize(get_objects())
las = read(file)
sum2 = summarize(get_objects())
diff = get_diff(sum1, sum2)
print_(diff)
for curve in las.curves:
print("Name: {}, Min: {:.2f}, Mean: {:.2f}, Max: {:.2f}"
.format(curve.mnemonic, nanmin(curve.data), nanmean(curve.data),
nanmax(curve.data)))
del las
las = read(file)
del las
las = read(file)
del las
las = read(file)
del las
print("Happy end")
def __exit__(self, exc_type, exc_val, exc_tb):
if self.debug:
try:
gc.collect()
end_memory = self.process.memory_info().rss
net_memory = end_memory-self.start_memory
if net_memory > 100 * 1000 * 1000:
Log.warning(
"MEMORY WARNING (additional {{net_memory|comma}}bytes): "+self.description,
default_params=self.params,
net_memory=net_memory
)
from pympler import summary
from pympler import muppy
sum1 = sorted(summary.summarize(muppy.get_objects()), key=lambda r: -r[2])[:30]
Log.warning("{{data}}", data=sum1)
elif end_memory > 1000*1000*1000:
Log.warning(
"MEMORY WARNING (over {{end_memory|comma}}bytes): "+self.description,
default_params=self.params,
end_memory=end_memory
)
from pympler import summary
from pympler import muppy
sum1 = sorted(summary.summarize(muppy.get_objects()), key=lambda r: -r[2])[:30]
Log.warning("{{data}}", data=sum1)
except Exception as e:
Log.warning("problem in memory measure", cause=e)
def _get_summaries(function, *args):
"""Get a 2-tuple containing one summary from before, and one summary
from after the function has been invoked.
"""
s_before = summary.summarize(get_objects())
function(*args)
s_after = summary.summarize(get_objects())
return (s_before, s_after)
def profile_expose_method(profiled_method_wrapper, accept, args, func, kw, exclude_from_memory_profiling):
"""
Targeted to profile a specific method that wraps HTTP request processing endpoints into database context.
:param profiled_method_wrapper: method wrapped around profiled call to be passed in to memory profiler
:param accept: param specific to profiled call
:param args: args of a function that is being wrapped by a profiled method
:param func: function that is being wrapped by a profiled method
:param kw: kwargs of a function that is being wrapped by a profiled method
:return: output of a profiled method without modification
"""
if not exclude_from_memory_profiling and get_memory_profile_logging_on() and \
check_memory_profile_package_wide_disable(func):
controller_class = args[0].__class__.__name__ if args and len(args) > 0 else ''
end_point_name_parts = [s for s in [func.__module__, controller_class, func.__name__] if s != '']
end_point_name = ".".join(end_point_name_parts)
is_pympler_on = _is_pympler_profiling_value_on(end_point_name)
profile_output = {'output': {}}
if is_pympler_on:
all_objects = muppy.get_objects()
all_objects_summary_before = summary.summarize(all_objects)
memory_profile = memory_usage((_profile_me,
(profile_output, profiled_method_wrapper, func, accept, args, kw),
{}),
interval=0.1)
output = profile_output['output']
if is_pympler_on:
all_objects_summary_after = summary.summarize(all_objects)
diff = summary.get_diff(all_objects_summary_before, all_objects_summary_after)
diff_less = summary.format_(diff)
diff_out = ''
for s in diff_less:
diff_out += s+'\n'
thread_log.info("================ PYMPLER OUTPUT <{}> ==============\n{}".format(end_point_name, diff_out))
try:
message = json.dumps({'log_type': 'memory_profile',
'proc_id': os.getpid(),
'name': func.__name__,
'module': func.__module__,
'mem_profile': memory_profile,
'min': min(memory_profile),
'max': max(memory_profile),
'diff': max(memory_profile) - min(memory_profile),
'leaked': memory_profile[-1] - memory_profile[0],
'args': [arg for arg in args[1:]], # exclude self
'kwargs': kw})
memory_log.info(message,
extra={'controller_module': func.__module__,
'controller_class': controller_class,
'endpoint': func.__name__})
except Exception as e:
thread_log.exception('Logger failed: {}'.format(e))
else:
output = profiled_method_wrapper(accept, args, func, kw)
return output
def create_summary(self):
"""Return a summary.
See also the notes on ignore_self in the class as well as the
initializer documentation.
"""
if not self.ignore_self:
res = summary.summarize(muppy.get_objects())
else:
# If the user requested the data required to store summaries to be
# ignored in the summaries, we need to identify all objects which
# are related to each summary stored.
# Thus we build a list of all objects used for summary storage as
# well as a dictionary which tells us how often an object is
# referenced by the summaries.
# During this identification process, more objects are referenced,
# namely int objects identifying referenced objects as well as the
# correspondind count.
# For all these objects it will be checked wether they are
# referenced from outside the monitor's scope. If not, they will be
# subtracted from the snapshot summary, otherwise they are
# included (as this indicates that they are relevant to the
# application).
all_of_them = [] # every single object
ref_counter = {} # how often it is referenced; (id(o), o) pairs
def store_info(o):
all_of_them.append(o)
if id(o) in ref_counter:
ref_counter[id(o)] += 1
else:
ref_counter[id(o)] = 1
# store infos on every single object related to the summaries
store_info(self.summaries)
for k, v in self.summaries.items():
store_info(k)
summary._traverse(v, store_info)
# do the summary
res = summary.summarize(muppy.get_objects())
# remove ids stored in the ref_counter
for _id in ref_counter:
# referenced in frame, ref_counter, ref_counter.keys()
if len(gc.get_referrers(_id)) == (3):
summary._subtract(res, _id)
for o in all_of_them:
# referenced in frame, summary, all_of_them
if len(gc.get_referrers(o)) == (ref_counter[id(o)] + 2):
summary._subtract(res, o)
return res
def test_print_diff(self):
"""Test summary can be printed."""
try:
self._stdout = sys.stdout
sys.stdout = self.DevNull()
sum1 = summary.summarize(muppy.get_objects())
sum2 = summary.summarize(muppy.get_objects())
sumdiff = summary.get_diff(sum1, sum2)
summary.print_(sumdiff)
finally:
sys.stdout = self._stdout
def print_diff(self, ignore=[]):
"""Print the diff to the last time the state of objects was measured.
keyword arguments
ignore -- list of objects to ignore
"""
# ignore this and the caller frame
ignore.append(inspect.currentframe()) #PYCHOK change ignore
diff = self.get_diff(ignore)
print("Added objects:")
summary.print_(summary.summarize(diff['+']))
print("Removed objects:")
summary.print_(summary.summarize(diff['-']))
# manual cleanup, see comment above
del ignore[:]
def memusage_before_n_after(fun, *args, **kwargs):
from pympler import muppy
from pympler import summary
from datetime import datetime
before = summary.summarize(muppy.get_objects())
before_time = datetime.now()
fun_ret = fun(*args, **kwargs)
after_time = datetime.now()
after = summary.summarize(muppy.get_objects())
diff = summary.get_diff(before, after)
print "execution time: ", after_time - before_time
summary.print_(diff)
return fun_ret, diff
def format_diff(self, ignore=()):
"""Format the diff to the last time the state of objects was measured.
keyword arguments
ignore -- list of objects to ignore
"""
# ignore this and the caller frame
lines = []
diff = self.get_diff(ignore+(inspect.currentframe(),))
lines.append("Added objects:")
for line in summary.format_(summary.summarize(diff['+'])):
lines.append(line)
lines.append("Removed objects:")
for line in summary.format_(summary.summarize(diff['-'])):
lines.append(line)
return lines
def dump_objs():
global TRACKER
if TRACKER is None:
TRACKER = tracker.SummaryTracker()
with open("obj_log.txt", "a") as fp:
fp.write("Memory at {}\n".format(str(datetime.datetime.now())))
try:
all_objects = muppy.get_objects()
sum1 = summary.summarize(all_objects)
str_sum = summary.format_(sum1)
fp.write("Summary:\n")
for line in str_sum:
fp.write(" {}\n".format(line))
except Exception:
err = traceback.format_exc()
fp.write("Error: \n")
fp.write(err)
try:
str_diff = TRACKER.format_diff()
fp.write("Diff:\n")
for line in str_diff:
fp.write(" {}\n".format(line))
except Exception:
err = traceback.format_exc()
fp.write("Error: \n")
fp.write(err)
fp.write("\n")
def test_sweep(self):
"""Test that all and only empty entries are removed from a summary."""
objects = ['the', 'quick', 'brown', 'fox', 1298, 123, 234, [], {}]
summ = summary.summarize(objects)
# correct removal of rows when sizes are empty
summary._subtract(summ, {})
summary._subtract(summ, [])
summ = summary._sweep(summ)
found_dict = found_tuple = False
for row in summ:
if row[0] == "<type 'dict'>":
found_dict = True
if row[0] == "<type 'tuple'>":
found_tuple = True
self.assert_(found_dict == False)
self.assert_(found_tuple == False)
# do not remove row if one of the sizes is not empty
# e.g. if the number of objects of a type did not change, but the
# total size did
summ = summary._subtract(summ, 'the')
summ = summary._subtract(summ, 'quick')
summ = summary._subtract(summ, 'brown')
summ = summary._subtract(summ, '42')
summ = summary._sweep(summ)
found_string = False
for row in summ:
if row[0] == summary._repr(''):
found_string = True
self.assert_(row[1] == 0)
totalsize = _getsizeof('fox') - _getsizeof('42')
self.assert_(row[2] == totalsize)
self.assert_(found_string == True)
def format_diff(self, ignore=[]):
"""Format the diff to the last time the state of objects was measured.
keyword arguments
ignore -- list of objects to ignore
"""
# ignore this and the caller frame
ignore.append(inspect.currentframe()) # PYCHOK change ignore
diff = self.get_diff(ignore)
yield "Added objects:"
for line in summary.format_(summary.summarize(diff["+"])):
yield line
yield "Removed objects:"
for line in summary.format_(summary.summarize(diff["-"])):
yield line
# manual cleanup, see comment above
del ignore[:]
def printListingUsage(self, args):
all_objects = muppy.get_objects()
sum1 = summary.summarize(all_objects)
summary.print_(sum1)
print(" ")
print("Summary: ")
tr = tracker.SummaryTracker()
tr.print_diff()
def memory_usage(where):
"""
Print out a basic summary of memory usage.
"""
mem_summary = summary.summarize(muppy.get_objects())
print("Memory summary:", where)
summary.print_(mem_summary, limit=2)
print("VM: %.2fMb" % (get_virtual_memory_usage_kb() / 1024.0))
def handle_signal_abort(self, signum, frame):
Log.warn("Someone want to kill me! But I'll not die now! Hahahaha!")
s = summary.summarize(muppy.get_objects())
Log.debug("Current memory usage:")
summary.print_(s)
diff = summary.get_diff(self.mem_sum, s)
self.mem_sum = s
Log.debug("New memory usage:")
summary.print_(diff)
def test_summarize(self):
"""Test summarize method. """
objects = [1, 'a', 'b', 'a', 5, [], {}]
expected = [[summary._repr(''), 3, 3*_getsizeof('a')],\
[summary._repr(1), 2, 2*_getsizeof(1)],\
[summary._repr([]), 1, _getsizeof([])],\
[summary._repr({}), 1, _getsizeof({})]]
res = summary.summarize(objects)
for row_e in res:
self.assert_(row_e in expected)
def memory_summary(self):
# Only import Pympler when we need it. We don't want it to
# affect our process if we never call memory_summary.
caller = sys._getframe(1).f_code.co_name # So we can reference the caller
from pympler import summary, muppy
mem_summary = summary.summarize(muppy.get_objects())
rows = summary.format_(mem_summary)
indigo.server.log ('\n\nCALLED BY: ' + caller + '\n\n' + '\n'.join(rows) )
def sig_usr(self, a, b):
import threading
import gc
held_locks = {}
code = {}
curthreads = threading.enumerate()
for threadId, stack in sys._current_frames().items():
name = str(threadId)
for ct in curthreads:
if ct.ident == threadId:
name = ct.name
code[name] = ["NAME: %s" % name]
for filename, lineno, fname, line in traceback.extract_stack(stack):
code[name].append('FILE: "%s", line %d, in %s' % (filename, lineno, fname))
if line:
code[name].append(" %s" % (line.strip()))
held_locks[name] = ""
for lock in alllocks:
if lock.writer_id == threadId:
held_locks[name] += ("%s(w)" % lock.name)
continue
for reader_id, reader_stack in lock.reader_stacks:
if reader_id == threadId:
held_locks[name] += ("%s(r)" % lock.name)
for k in code:
log.info('\n\nLOCKS: %s \n%s' % (held_locks[k], '\n'.join(code[k])))
log.info("\n\nSTACKS:")
for lock in alllocks:
for (reader_id, reader_stack) in lock.reader_stacks:
log.info("Lock %s (%s readers)" % (lock.name, lock.readers))
log.info("Lock reader (thread %s):" % (reader_id,))
log.info(''.join(reader_stack))
for writer_stack in lock.writer_stacks:
log.info("Lock %s (%s readers)" % (lock.name, lock.readers))
log.info("Lock writer (thread %s):" % (lock.writer_id,))
log.info(''.join(writer_stack))
self.shelf.sync()
gc.collect()
# If we've got pympler installed, output a summary of memory usage.
try:
from pympler import summary, muppy
from pympler.asizeof import asizeof
summary.print_(summary.summarize(muppy.get_objects()))
except:
pass
def memory_summary(self, summarize=True):
"Using pympler summarize module to view memory summary."
if summarize:
all_objects = muppy.get_objects()
Logger.info("ENV: \nMemory Footprint:")
Logger.info("-----------------")
return summary.print_(summary.summarize(all_objects), limit=50)
else:
Logger.info("ENV: \nMemory Tracker:")
Logger.info("---------------")
self.mem_tracker.print_diff()
def print_muppy_sumary():
# http://pythonhosted.org/Pympler/index.html
try:
from pympler import muppy, summary
except ImportError:
print("WARNING: pympler not installed")
return
# from pympler.classtracker import ClassTracker
# from pympler.classtracker_stats import HtmlStats
global all_objects, obj_summary, class_tracker
if all_objects is None:
all_objects = muppy.get_objects()
obj_summary = summary.summarize(all_objects)
summary.print_(obj_summary)
# class_tracker = ClassTracker()
# class_tracker.track_class(FICSPlayer, trace=1)
# class_tracker.track_class(ICGameModel, resolution_level=2, trace=1)
else:
obj_summary2 = summary.summarize(muppy.get_objects())
diff = summary.get_diff(obj_summary, obj_summary2)
summary.print_(diff, limit=200)
def print_memory(count=30):
'''
Print the statistics of the objects in the memory.
Need pympler to use.
'''
from pympler import muppy, summary
gc.collect()
all_objects = muppy.get_objects()
my_types = muppy.filter(all_objects, Type=wx.Object)
sum1 = summary.summarize(my_types)
# sum1 = summary.summarize(all_objects)
summary.print_(sum1, limit=count)
def __init__(self, ignore_self=True):
"""Constructor.
The number of summaries managed by the tracker has an performance
impact on new summaries, iff you decide to exclude them from further
summaries. Therefore it is suggested to use them economically.
Keyword arguments:
ignore_self -- summaries managed by this object will be ignored.
"""
self.s0 = summary.summarize(muppy.get_objects())
self.summaries = {}
self.ignore_self = ignore_self
def get_report(self):
all_objects = muppy.get_objects()
size = get_size(all_objects)
report = summary.summarize(all_objects)
sort_index = self.cleaned_data['sort_by']
limit = self.cleaned_data['limit']
report.sort(key=lambda item: item[sort_index], reverse=True)
if limit:
report = report[:limit]
return size, report
def analyzeAllMFCC():
client = MongoClient()
db = client.audiograins
grainEntries = db.grains
query = grainEntries.find({ "mfcc00" : { "$exists": False }})
print("Analyzing MFCC for " + str(query.count()) + " grains")
for grain in tqdm(query):
mfccs = analyzeMFCC(grain)
for mfccIndex in range(0, len(mfccs)):
update = {"mfcc" + format(mfccIndex, '02') : mfccs[mfccIndex]}
grainEntries.update_one({"_id": grain["_id"]}, {"$set" : update})
summary.print_(summary.summarize(muppy.get_objects()))
client.close()
def getDebugInfo(self, itemname):
"""Give debug info about a particular item."""
global profile
outf=StringIO()
if itemname == "":
outf.write("the item was empty")
if profile:
all_objects = muppy.get_objects()
sum1 = summary.summarize(all_objects)
summary.print_(sum1, 100)
ib = refbrowser.InteractiveBrowser(self)
ib.main()
return outf.getvalue()
itemname=keywords.fixID(itemname)
itemlist=vtype.parseItemList(itemname)
item=self.getSubValue(itemlist)
item.writeDebug(outf)
return outf.getvalue()
def test_subtract(self):
"""Test that a single object's data is correctly subtracted from a summary.
- result in correct total size and total number of objects
- if object was not listed before, it should be listed negative
afterwards
"""
objects = ['the', 'quick', 'brown', 'fox', 1298, 123, 234, [], {}]
summ = summary.summarize(objects)
summary._subtract(summ, 'the')
summary._subtract(summ, {})
summary._subtract(summ, (1,))
# to verify that these rows where actually included afterwards
checked_str = checked_dict = checked_tuple = False
for row in summ:
if row[0] == summary._repr(''):
totalsize = _getsizeof('quick') + _getsizeof('brown') +\
_getsizeof('fox')
self.assert_(row[1] == 3, "%s != %s" % (row[1], 3))
self.assert_(row[2] == totalsize, totalsize)
checked_str = True
if row[0] == summary._repr({}):
self.assert_(row[1] == 0)
self.assert_(row[2] == 0)
checked_dict = True
if row[0] == summary._repr((1,)):
self.assert_(row[1] == -1)
self.assert_(row[2] == -_getsizeof((1,)))
checked_tuple = True
self.assert_(checked_str, "no str found in summary")
self.assert_(checked_dict, "no dict found in summary")
self.assert_(checked_tuple, "no tuple found in summary")
summary._subtract(summ, 'quick')
summary._subtract(summ, 'brown')
checked_str = False
for row in summ:
if row[0] == summary._repr(''):
self.assert_(row[1] == 1)
self.assert_(row[2] == _getsizeof('fox'))
checked_str = True
self.assert_(checked_str, "no str found in summ")
def pympler_snapshot(rows=None, limit=15, sort="size", order="descending"):
"""Print the rows as a summary.
Keyword arguments:
limit -- the maximum number of elements to be listed
sort -- sort elements by 'size', 'type', or '#'
order -- sort 'ascending' or 'descending'
"""
if not rows:
rows = summary.summarize(muppy.get_objects())
localrows = []
for row in rows:
localrows.append(list(row))
# input validation
sortby = ['type', '#', 'size']
if sort not in sortby:
raise ValueError("invalid sort, should be one of" + str(sortby))
orders = ['ascending', 'descending']
if order not in orders:
raise ValueError("invalid order, should be one of" + str(orders))
# sort rows
if sortby.index(sort) == 0:
if order == "ascending":
localrows.sort(key=lambda x: _repr(x[0]))
elif order == "descending":
localrows.sort(key=lambda x: _repr(x[0]), reverse=True)
else:
if order == "ascending":
localrows.sort(key=lambda x: x[sortby.index(sort)])
elif order == "descending":
localrows.sort(key=lambda x: x[sortby.index(sort)], reverse=True)
# limit rows
localrows = localrows[0:limit]
for row in localrows:
row[2] = stringutils.pp(row[2])
# print rows
localrows.insert(0, ["types", "# objects", "total size"])
return pympler_prepare(localrows)
def __init__(self):
Thread.__init__(self)
self.E = builder.ElementMaker(namespace = self.__xmlns__)
self._rosters = {}
self._resources = None
self._session_cache = {}
self._conns = {}
self.xmpp_read = {}
self.term_read = {}
self.term_stealed = {}
self.update_sessions()
self.mem_sum = summary.summarize(muppy.get_objects())
signal.signal(signal.SIGUSR2, self.handle_signal_message)
signal.signal(signal.SIGABRT, self.handle_signal_abort)
signal.signal(signal.SIGHUP, self.handle_signal_abort)
self._running = True
self._updater = Updater(self)
self._pinger = Pinger(self)
self._stealer = Stealer(self)
self.start()
def log_summary(self, msg=None):
"""
Generates a summary of all memory used. The summary is
returned as a string and logged to a file
"""
if self.on:
all_objs=muppy.get_objects()
all_summ=summary.summarize(all_objs)
formatted_summ="\n".join(
summary.format_(
all_summ,
limit=15,
sort='size',
order='descending'
)
)
if msg is not None:
self.logger.debug('Full Summary:\n' + msg + '\n' +\
formatted_summ)
else:
self.logger.debug('Full Summary:\n' + formatted_summ)
return formatted_summ
# Shallow size (например, размер листа, но не объектов, которые в нем есть)
print(sys.getsizeof(1)) # 28
print(sys.getsizeof(54234.000342314000)) # 24
print(sys.getsizeof(None)) # 16
print(sys.getsizeof([])) # 72
print(sys.getsizeof([1, 2, 3, 4, ['s', 'l', ['a', 1]]])) # 112
print(sys.getsizeof({})) # 248
print(sys.getsizeof(tuple())) # 56
print()
# Deep size (идет по иерархии объектов вглубь, суммирует)
print(asizeof.asizeof(1)) # 32
print(asizeof.asizeof(54234.000342314000)) # 24
print(asizeof.asizeof(None)) # 16
print(asizeof.asizeof([])) # 72
print(asizeof.asizeof([1, 2, 3, 4, ['s', 'l', ['a', 1]]])) # 592
print(
asizeof.asizeof(
[1, 2, 3, 4, ['s', 'l', ['a', 1, [None, 1, {
'a': 1,
'b': 'a'
}]]]])) # 1016
print(asizeof.asizeof({})) # 248
print(asizeof.asizeof(tuple())) # 56
print(asizeof.asizeof(set())) # 232
all_objects_2 = muppy.get_objects()
sum_1 = summary.summarize(all_objects_1)
sum_2 = summary.summarize(all_objects_2)
summary.print_(summary.get_diff(sum_1, sum_2))
def learn(self):
while True:
experiences = []
for i in range(0, self.REPLAY_BUFFER_SIZE):
episode_experience = self.play_episode()
experiences.append(episode_experience)
step = self.global_step + i
reward = sum(episode_experience.rewards)
self.log('reward', reward, step, False)
if reward > self.record_run:
frames = episode_experience.get_frames()
self.log_gif('best_run', frames, step)
self.record_run = reward
self.sess.run(tf.assign(self.RECORD, self.record_run))
logging.info('Record beaten: {0}'.format(self.record_run))
logging.info('Episode: {0} | reward: {1}'.format(step, reward))
states, actions, values, advantages = self.get_experience_batches(
experiences)
opt_results = []
for i in range(len(actions)):
opt_res = self.policy.optimize(states[i], actions[i],
values[i], advantages[i],
self.sess)
opt_results.append(opt_res)
value_loss = np.array([q["value_loss"]
for q in opt_results]).mean()
policy_loss = np.array([q["policy_loss"]
for q in opt_results]).mean()
entropy = np.array([q["entropy"] for q in opt_results]).mean()
total_loss = np.array([q["total_loss"]
for q in opt_results]).mean()
self.log('value_loss', value_loss, self.global_step)
self.log('policy_loss', policy_loss, self.global_step)
self.log('entropy', entropy, self.global_step)
self.log('total_loss', total_loss, self.global_step)
print('=======')
if self.batch_count % self.SAVE_MODEL_STEPS == 0:
self.check_model()
self.save_model()
all_objects = muppy.get_objects()
sum1 = summary.summarize(all_objects)
summary.print_(sum1)
all_objects.clear()
sum1.clear()
del all_objects
del sum1
opt_results.clear()
experiences.clear()
self.global_step = self.sess.run(
tf.assign(self.GS, self.GS + self.REPLAY_BUFFER_SIZE))
self.batch_count = self.sess.run(tf.assign(self.BC, self.BC + 1))
def summarize_memory():
print("Virtual machine: {:.2f}Mb".format(
psutil.Process().memory_info_ex().vms / (1024 * 1024)))
summary.print_(summary.summarize(muppy.get_objects()), limit=1)
def memusage(o):
summary.print_(
summary.summarize(o))
def main():
adv_messages = rawdata.load_dict_from_json(ADV_MESSAGES_PATH)
banned_users = []
while True:
accounts = rawdata.load_dict_from_json(ACCOUNTS_PATH)
users = init(accounts)
accounts.clear()
try:
queue = Queue()
users_signed = []
for user in users:
if not user in banned_users:
queue.put(user)
for i in range(len(users) - len(banned_users)):
thread = Thread(target=signing, args=(queue, users_signed, banned_users))
thread.daemon = True
thread.start()
queue.join()
del users[:]
users = users_signed.copy()
del users_signed[:]
queue = Queue()
for user in users:
if not user in banned_users:
queue.put(user)
theard_count = 3 if len(users) >= 5 else 1
for i in range(theard_count):
thread = Thread(target=group_creating_desire_c, args=(queue, COUNT_OF_GROUPS, NAME_OF_GROUPS))
thread.daemon = True
thread.start()
queue.join()
arg_list = {"counter":0, "adv_counter":0, "iteration":0}
while True:
queue = Queue()
del_targ = []
targets = rawdata.load_dict_from_json(TARGETS_PATH)
len_targets = len(targets)
time_start = time.time()
for user in users:
if not user in banned_users:
queue.put(user)
print("незабаненных пользователей %s/%s | забаненны %s" % (len(users) - len(banned_users), len(users), banned_users))
for i in range(len(users) - len(banned_users)):
thread = Thread(target=posting_for_all_users, args=(queue, targets, adv_messages, arg_list, del_targ, banned_users))
thread.daemon = True
thread.start()
queue.join()
time_stop = time.time()
time_dif = time_stop - time_start
wait_time = 60*(24 + random.uniform(0, 5)) - time_dif
u_banned_users = [user.username for user in banned_users]
rawdata.delete_from_json(data=del_targ, path_to_data=TARGETS_PATH)
rawdata.delete_from_json(data=u_banned_users, path_to_data=ACCOUNTS_PATH)
del del_targ[:]
targets.clear()
del targets
gc.collect()
summary.print_(summary.summarize(muppy.get_objects()))
print("Осталось целей %s" % len_targets)
print("Всего/рекламных %s/%s за %s мин" % (arg_list["counter"], arg_list["adv_counter"], str(time_dif / 60)))
if wait_time > 0:
print("Пауза %s минут\n" % str(wait_time / 60))
time.sleep(wait_time)
else:
print("Продолжаем без паузы %s мин запаздывания\n" % str(-1 * wait_time / 60))
arg_list["iteration"] += 1
except:
print("Ошибка в posting.py/main %s" % sys.exc_info()[0])
raise
def log_mem(mes=''):
if options.debug:
all_objects = muppy.get_objects()
sum1 = summary.summarize(all_objects)
app_log.log(logging.DEBUG, 'mem (%s): %d' % (mes, len(all_objects)))
summary.print_(sum1)
def cli_mem_summary(self, event):
"""Output memory usage summary"""
all_objects = muppy.get_objects()
state = summary.summarize(all_objects)
summary.print_(state)
def process_response(self, request, response):
req = request.META['PATH_INFO']
if not self.is_ignored(req):
if SHOW_ON_DJANGO_DEBUG_TOOLBAR_LOGGIN_PANEL:
logger.info(
'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
'~~~~~~~~~~~~~~')
print '\n\n'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'\
'~~~~~~~~~~~~~~~~~~~~~~~~~~'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'\
'~~~~~~~~~~~~~~~~~~~~~~~~~~'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'\
'~~~~~~~~~~~~~~~~~~~~~~~~~~'
if SHOW_ON_DJANGO_DEBUG_TOOLBAR_LOGGIN_PANEL:
logger.info(u'REQUESTED URL: {}'.format(req))
print u'REQUESTED URL: {}'.format(req)
self.end_objects = muppy.get_objects()
if SHOW['request_summary']:
sum_start = summary.summarize(self.start_objects)
if SHOW_ON_DJANGO_DEBUG_TOOLBAR_LOGGIN_PANEL:
logger.info(
'~~~~~~~~~ SUMMARIZE REQUEST OBJECTS ~~~~~~~~~')
for row in sorted(sum_start,
key=lambda i: i[2],
reverse=True)[:15]:
logger.info(
"type: %60s , # objects: %10d, total size: %s",
*(row[0], row[1], filesizeformat(row[2])))
print '~~~~~~~~~ SUMMARIZE REQUEST OBJECTS ~~~~~~~~~'
summary.print_(sum_start)
if SHOW['response_summary']:
sum_end = summary.summarize(self.end_objects)
if SHOW_ON_DJANGO_DEBUG_TOOLBAR_LOGGIN_PANEL:
logger.info(
'~~~~~~~~~ SUMMARIZE RESPONSE OBJECTS ~~~~~~~~~')
for row in sorted(sum_end,
key=lambda i: i[2],
reverse=True)[:15]:
logger.info(
"type: %60s , # objects: %10d, total size: %s",
*(row[0], row[1], filesizeformat(row[2])))
print '~~~~~~~~~ SUMMARIZE RESPONSE OBJECTS ~~~~~~~~~'
summary.print_(sum_end)
if SHOW['compared_request_response_summaries']:
diff = summary.get_diff(sum_start, sum_end)
if SHOW_ON_DJANGO_DEBUG_TOOLBAR_LOGGIN_PANEL:
logger.info(
'~~~~~~~~~ COMPARED REQUEST & RESPONSE SUMMARIES '
'~~~~~~~~~')
for row in sorted(diff, key=lambda i: i[2],
reverse=True)[:15]:
logger.info(
"type: %60s , # objects: %10d, total size: %s",
*(row[0], row[1], filesizeformat(row[2])))
print \
'~~~~~~~~~ COMPARED REQUEST & RESPONSE SUMMARIES ~~~~~~~~~'
summary.print_(diff)
# print '~~~~~~~~~'
# cb = refbrowser.ConsoleBrowser(
# response, maxdepth=2, str_func=output_function)
# cb.print_tree()
a = asizeof(response)
a_string = 'Total size of response object in kB: %s' % \
str(a/1024.0)
b = asizeof(self.end_objects)
b_string = 'Total size of end_objects in MB: %s' % str(
b / 1048576.0)
c = asizeof(self.start_objects)
c_string = 'Total size of start_objects in MB: %s' % \
str(c/1048576.0)
if SHOW_ON_DJANGO_DEBUG_TOOLBAR_LOGGIN_PANEL:
logger.info(
'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
'~~~~~~~~~~~~~~')
logger.info(a_string)
logger.info(b_string)
logger.info(c_string)
logger.info(
'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
'~~~~~~~~~~~~~~')
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'\
'~~~~~~~~~~~~~~~~~~~~~~~~~~'
print a_string
print b_string
print c_string
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'\
'~~~~~~~~~~~~~~~~~~~~~~~~~~'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'\
'~~~~~~~~~~~~~~~~~~~~~~~~~~'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'\
'~~~~~~~~~~~~~~~~~~~~~~~~~~'
return response
def memory_profiler(self):
all_objects = muppy.get_objects()
stats = summary.summarize(all_objects)
return {'Memory_profiler': [l for l in summary.format_(stats, LIMIT_OBJECTS_FOR_PROFILER)]}
def before_after_each_function(request):
global _global_collect_info
try:
import psutil # Don't fail if not there
except ImportError:
yield
return
current_pids = set(proc.pid for proc in psutil.process_iter())
before_curr_proc_memory_info = psutil.Process().memory_info()
if _global_collect_info and DEBUG_MEMORY_INFO:
try:
from pympler import summary, muppy
sum1 = summary.summarize(muppy.get_objects())
except:
pydev_log.exception()
sys.stdout.write('''
===============================================================================
Memory before: %s
%s
===============================================================================
''' % (request.function,
format_memory_info(psutil.virtual_memory(),
before_curr_proc_memory_info)))
yield
processes_info = []
for proc in psutil.process_iter():
if proc.pid not in current_pids:
try:
try:
cmdline = proc.cmdline()
except:
cmdline = '<unable to get>'
processes_info.append(
'New Process: %s(%s - %s) - %s' %
(proc.name(), proc.pid, cmdline,
format_process_memory_info(proc.memory_info())))
except (psutil.NoSuchProcess, psutil.AccessDenied):
pass # The process could've died in the meanwhile
after_curr_proc_memory_info = psutil.Process().memory_info()
if DEBUG_MEMORY_INFO:
try:
if after_curr_proc_memory_info.rss - before_curr_proc_memory_info.rss > 10 * 1000 * 1000:
# 10 MB leak
if _global_collect_info:
sum2 = summary.summarize(muppy.get_objects())
diff = summary.get_diff(sum1, sum2)
sys.stdout.write(
'===============================================================================\n'
)
sys.stdout.write('Leak info:\n')
sys.stdout.write(
'===============================================================================\n'
)
summary.print_(diff)
sys.stdout.write(
'===============================================================================\n'
)
_global_collect_info = True
# We'll only really collect the info on the next test (i.e.: if at one test
# we used too much memory, the next one will start collecting)
else:
_global_collect_info = False
except:
pydev_log.exception()
sys.stdout.write(
'''
===============================================================================
Memory after: %s
%s%s
===============================================================================
''' % (request.function,
format_memory_info(psutil.virtual_memory(),
after_curr_proc_memory_info),
'' if not processes_info else '\nLeaked processes:\n' +
'\n'.join(processes_info)), )
def process_response(self, request, response):
path = request.META['PATH_INFO']
if self.is_ignored(path):
return response
self.end_objects = muppy.get_objects()
sum_start = summary.summarize(self.start_objects)
sum_end = summary.summarize(self.end_objects)
diff = summary.get_diff(sum_start, sum_end)
if SHOW_ON_DJANGO_DEBUG_TOOLBAR_LOGGIN_PANEL:
logger.info(
'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
'~~~~~~~~~~~~~~')
logger.info("Top %d memory deltas after processing URL: %s",
SHOW_TOP_X_MEMORY_DELTAS, path)
logger.info("%-60s %10s %10s", "type", "# objects", "total size")
print '\n\n'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'\
'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'\
'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'\
'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print "Top %d memory deltas after processing URL: %s" % (
SHOW_TOP_X_MEMORY_DELTAS, path)
print "%60s %10s %10s" % ("type", "# objects", "total size")
for row in sorted(diff, key=lambda i: i[2],
reverse=True)[:SHOW_TOP_X_MEMORY_DELTAS]:
if SHOW_ON_DJANGO_DEBUG_TOOLBAR_LOGGIN_PANEL:
logger.info("type: %60s , # objects: %10d, total size: %s",
*(row[0], row[1], filesizeformat(row[2])))
print "%60s %10d %s" % (row[0], row[1], filesizeformat(row[2]))
start_size = asizeof(self.start_objects)
end_size = asizeof(self.end_objects)
if SHOW_ON_DJANGO_DEBUG_TOOLBAR_LOGGIN_PANEL:
logger.info(
'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
'~~~~~~~~~~~~~~')
logger.info(
"Processed %s: memory delta %0.1f kB (%0.1f -> %0.1fMB), "
"response size: %0.1f kB",
path,
(end_size - start_size) / 1024.0,
start_size / 1048576.0,
end_size / 1048576.0,
len(response.content) / 1024.0,
)
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'\
'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print(
"Processed %s: memory delta %0.1f kB (%0.1f -> %0.1fMB), "
"response size: %0.1f kB" % (
path,
(end_size - start_size) / 1024.0,
start_size / 1048576.0,
end_size / 1048576.0,
len(response.content) / 1024.0,
))
if SHOW_ON_DJANGO_DEBUG_TOOLBAR_LOGGIN_PANEL:
logger.info(
'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
'~~~~~~~~~~~~~~')
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'\
'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'\
'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'\
'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print '\n\n'
return response
def _load_model_data(self, modeldata, namespaces, **kwds):
"""
Load declarations from a DataPortal object.
"""
#
# As we are primarily generating objects here (and acyclic ones
# at that), there is no need to run the GC until the entire
# model is created. Simple reference-counting should be
# sufficient to keep memory use under control.
#
with PauseGC() as pgc:
#
# Unlike the standard method in the pympler summary
# module, the tracker doesn't print 0-byte entries to pad
# out the limit.
#
profile_memory = kwds.get('profile_memory', 0)
#
# It is often useful to report timing results for various
# activities during model construction.
#
report_timing = kwds.get('report_timing', False)
if (pympler_available is True) and (profile_memory >= 2):
mem_used = muppy.get_size(muppy.get_objects())
print("")
print(" Total memory = %d bytes prior to model "
"construction" % mem_used)
if (pympler_available is True) and (profile_memory >= 3):
gc.collect()
mem_used = muppy.get_size(muppy.get_objects())
print(" Total memory = %d bytes prior to model "
"construction (after garbage collection)" % mem_used)
#
# Do some error checking
#
for namespace in namespaces:
if not namespace is None and not namespace in modeldata._data:
msg = "Cannot access undefined namespace: '%s'"
raise IOError(msg % namespace)
#
# Initialize each component in order.
#
if report_timing is True:
import pyomo.core.base.expr as EXPR
construction_start_time = time.time()
for component_name, component in iteritems(self.component_map()):
if component.type() is Model:
continue
if report_timing is True:
start_time = time.time()
clone_counters = EXPR.generate_expression.clone_counter
self._initialize_component(modeldata, namespaces,
component_name, profile_memory)
if report_timing is True:
total_time = time.time() - start_time
if isinstance(component, IndexedComponent):
clen = len(component)
else:
assert isinstance(component, Component)
clen = 1
print(" %%6.%df seconds required to construct component=%s; %d indicies total" \
% (total_time>=0.005 and 2 or 0, component_name, clen) \
% total_time)
tmp_clone_counters = EXPR.generate_expression.clone_counter
if clone_counters != tmp_clone_counters:
clone_counters = tmp_clone_counters
print(
" Cloning detected! (clone counters: %d)"
% clone_counters)
# Note: As is, connectors are expanded when using command-line pyomo but not calling model.create(...) in a Python script.
# John says this has to do with extension points which are called from commandline but not when writing scripts.
# Uncommenting the next two lines switches this (command-line fails because it tries to expand connectors twice)
#connector_expander = ConnectorExpander()
#connector_expander.apply(instance=self)
if report_timing is True:
total_construction_time = time.time() - construction_start_time
print(" %6.2f seconds required to construct instance=%s" %
(total_construction_time, self.name))
if (pympler_available is True) and (profile_memory >= 2):
print("")
print(
" Summary of objects following instance construction")
post_construction_summary = summary.summarize(
muppy.get_objects())
summary.print_(post_construction_summary, limit=100)
print("")
def create_instance(self,
filename=None,
data=None,
name=None,
namespace=None,
namespaces=None,
profile_memory=0,
report_timing=False,
**kwds):
"""
Create a concrete instance of an abstract model, possibly using data
read in from a file.
Optional:
filename: The name of a Pyomo Data File that will be used
to load data into the model.
data: A dictionary containing initialization data for
the model to be used if there is no filename
name: The name given to the model.
namespace: A namespace used to select data.
namespaces: A list of namespaces used to select data.
profile_memory: A number that indicates the profiling level.
report_timing: Report timing statistics during construction.
"""
#
# Generate a warning if this is a concrete model but the
# filename is specified. A concrete model is already
# constructed, so passing in a data file is a waste of time.
#
if self.is_constructed() and isinstance(filename, string_types):
msg = "The filename=%s will not be loaded - supplied as an " \
"argument to the create_instance() method of a "\
"concrete instance with name=%s." % (filename, name)
logger.warning(msg)
if 'clone' in kwds:
kwds.pop('clone')
logger.warning(
"""DEPRECATION WARNING: Model.create_instance() no longer accepts the
'clone' argument: the base abstract model is always cloned.""")
if 'preprocess' in kwds:
kwds.pop('preprocess')
logger.warning(
"""DEPRECATION WARNING: Model.create_instance() no longer accepts the
'preprocess' argument: preprocessing is always deferred to when the
model is sent to the solver""")
if kwds:
msg = \
"""Model.create_instance() passed the following unrecognized keyword
arguments (which have been ignored):"""
for k in kwds:
msg = msg + "\n '%s'" % (k, )
logger.error(msg)
if self.is_constructed():
logger.warning(
"""DEPRECATION WARNING: Cannot call Model.create_instance() on a
constructed model; returning a clone of the current model instance.""")
return self.clone()
if name is None:
name = self.name
if filename is not None:
if data is not None:
logger.warning(
"Model.create_instance() passed both 'filename' "
"and 'data' keyword arguments. Ignoring the "
"'data' argument")
data = filename
if data is None:
data = {}
#
# Clone the model and load the data
#
instance = self.clone()
if name is not None:
instance._name = name
# If someone passed a rule for creating the instance, fire the
# rule before constructing the components.
if instance._rule is not None:
instance._rule(instance)
if namespaces:
_namespaces = list(namespaces)
else:
_namespaces = []
if namespace is not None:
_namespaces.append(namespace)
if None not in _namespaces:
_namespaces.append(None)
instance.load(data,
namespaces=_namespaces,
profile_memory=profile_memory,
report_timing=report_timing)
#
# Preprocess the new model
#
if False and preprocess is True:
if report_timing is True:
start_time = time.time()
instance.preprocess()
if report_timing is True:
total_time = time.time() - start_time
print(" %6.2f seconds required for preprocessing" %
total_time)
if (pympler_available is True) and (profile_memory >= 2):
mem_used = muppy.get_size(muppy.get_objects())
print(
" Total memory = %d bytes following instance preprocessing"
% mem_used)
print("")
if (pympler_available is True) and (profile_memory >= 2):
print("")
print(
" Summary of objects following instance preprocessing"
)
post_preprocessing_summary = summary.summarize(
muppy.get_objects())
summary.print_(post_preprocessing_summary, limit=100)
#
# Indicate that the model is concrete/constructed
#
instance._constructed = True
return instance
return subs
def sub_string_brute(seq1, seq2):
""" Sub-string by brute force """
subs = []
for item in seq2:
for parent in seq1:
if item in parent:
subs.append(item)
return subs
def test(N):
random_strings(10, N)
subs=sub_string(seq1, seq2)
def test2():
subs=sub_string(seq1, seq2)
if __name__ == "__main__":
from pympler import summary
from pympler import muppy
test(10000)
all_objects = muppy.get_objects()
sum1 = summary.summarize(all_objects)
summary.print_(sum1)
parameters, errors = data.GetAllData(peakFileList)
parameterList_bg = mapp.CreatePeakList(peakFileList)
PlotParameterMappings(parameters, parameterList_bg, mapdims, step,
background=background, msize=msize, area=area)
dict_bg = CreateMinMaxDict(parameters, parameterList_bg, folder)
dict_minmax_global = UpdateGlobalDict(dict_minmax_global, dict_bg)
print('\nList of minima and maxima.')
PrintMinMax(dict_minmax, parameterList)
print(linebreaker + '\n' + linebreaker)
if debug:
all_objects = muppy.get_objects()
sum1 = summary.summarize(all_objects)# Prints out a summary of the large objects
summary.print_(sum1)# Get references to certain types of objects such as dataframe
dataframes = [ao for ao in all_objects if isinstance(ao, pd.DataFrame)]
for dat in dataframes:
print(dat.columns.values)
print(len(dat))
stats = time2.compare_to(time1, 'lineno')
for stat in stats[:10]:
print(stat)
if scaled:
print('List of global minima and maxima '
+ 'and the mappings they are taken from.')
PrintMinMax(dict_minmax_global, dict_minmax_global.keys())
print(linebreaker + '\n' + linebreaker)
def print_summary():
"""Print a summary of all known objects."""
summary.print_(summary.summarize(get_objects()))
def mem():
all_objects = muppy.get_objects()
summary.print_(summary.summarize(all_objects))
def memory_summary():
mem_summary = summary.summarize(muppy.get_objects())
rows = summary.format_(mem_summary)
return '\n'.join(rows)
# MAX_RESULTS results.
# flake8: noqa
from operator import itemgetter
from pympler import muppy, summary
MODULES = ["netmiko", "napalm", "sqlalchemy"]
MAX_RESULTS = 100
def format_size(size):
for unit in ["B", "KiB", "MiB", "GiB"]:
if size < 1024.0 or unit == "GiB":
break
size /= 1024.0
return f"{size:.2f} {unit}"
profile = sorted(
(object_ for object_ in summary.summarize(muppy.get_objects())
if any(module in object_[0] for module in MODULES)),
key=itemgetter(2),
reverse=True,
)
for object_ in profile[:MAX_RESULTS]:
print(f"Name: {object_[0]}")
print(f"Number of objects: {object_[1]}")
print(f"Total size: {format_size(object_[2])}", end="\n\n")
def create_model(data):
"""
Create instance of Pyomo model.
Return:
model: Model object.
instance: Problem instance.
symbol_map: Symbol map created when writing model to a file.
filename: Filename that a model instance was written to.
"""
#
if not data.options.runtime.logging == 'quiet':
sys.stdout.write('[%8.2f] Creating model\n' %
(time.time() - start_time))
sys.stdout.flush()
#
if (pympler_available is True) and (data.options.runtime.profile_memory >=
1):
global memory_data
mem_used = muppy.get_size(muppy.get_objects())
data.local.max_memory = mem_used
print(" Total memory = %d bytes prior to model construction" %
mem_used)
#
# Find the Model objects
#
_models = {}
for _name, _obj in iteritems(data.local.usermodel.__dict__):
if isinstance(_obj, Model):
_models[_name] = _obj
model_name = data.options.model.object_name
if len(_models) == 1:
_name = list(_models.keys())[0]
if model_name is None:
model_name = _name
elif model_name != _name:
msg = "Model '%s' is not defined in file '%s'!"
raise SystemExit(msg % (model_name, data.options.model.filename))
elif len(_models) > 1:
if model_name is None:
msg = "Multiple models defined in file '%s'!"
raise SystemExit(msg % data.options.model.filename)
elif not model_name in _models:
msg = "Unknown model '%s' in file '%s'!"
raise SystemExit(msg % (model_name, data.options.model.filename))
ep = ExtensionPoint(IPyomoScriptCreateModel)
if model_name is None:
if len(ep) == 0:
msg = "A model is not defined and the 'pyomo_create_model' is not "\
"provided in module %s"
raise SystemExit(msg % data.options.model.filename)
elif len(ep) > 1:
msg = 'Multiple model construction plugins have been registered in module %s!'
raise SystemExit(msg % data.options.model.filename)
else:
model_options = data.options.model.options.value()
model = ep.service().apply(
options=pyutilib.misc.Container(*data.options),
model_options=pyutilib.misc.Container(*model_options))
else:
if model_name not in _models:
msg = "Model '%s' is not defined in file '%s'!"
raise SystemExit(msg % (model_name, data.options.model.filename))
model = _models[model_name]
if model is None:
msg = "'%s' object is 'None' in module %s"
raise SystemExit(msg % (model_name, data.options.model.filename))
elif len(ep) > 0:
msg = "Model construction function 'create_model' defined in " \
"file '%s', but model is already constructed!"
raise SystemExit(msg % data.options.model.filename)
#
# Print model
#
for ep in ExtensionPoint(IPyomoScriptPrintModel):
ep.apply(options=data.options, model=model)
#
# Create Problem Instance
#
ep = ExtensionPoint(IPyomoScriptCreateDataPortal)
if len(ep) > 1:
msg = 'Multiple model data construction plugins have been registered!'
raise SystemExit(msg)
if len(ep) == 1:
modeldata = ep.service().apply(options=data.options, model=model)
else:
modeldata = DataPortal()
if model._constructed:
#
# TODO: use a better test for ConcreteModel
#
instance = model
elif len(data.options.data.files) > 1:
#
# Load a list of *.dat files
#
for file in data.options.data.files:
suffix = (file).split(".")[-1]
if suffix != "dat":
msg = 'When specifiying multiple data files, they must all ' \
'be *.dat files. File specified: %s'
raise SystemExit(msg % str(file))
modeldata.load(filename=file, model=model)
instance = model.create_instance(
modeldata,
namespaces=data.options.data.namespaces,
profile_memory=data.options.runtime.profile_memory,
report_timing=data.options.runtime.report_timing)
elif len(data.options.data.files) == 1:
#
# Load a *.dat file or process a *.py data file
#
suffix = (data.options.data.files[0]).split(".")[-1].lower()
if suffix == "dat":
instance = model.create_instance(
data.options.data.files[0],
namespaces=data.options.data.namespaces,
profile_memory=data.options.runtime.profile_memory,
report_timing=data.options.runtime.report_timing)
elif suffix == "py":
userdata = pyutilib.misc.import_file(data.options.data.files[0],
clear_cache=True)
if "modeldata" in dir(userdata):
if len(ep) == 1:
msg = "Cannot apply 'pyomo_create_modeldata' and use the" \
" 'modeldata' object that is provided in the model"
raise SystemExit(msg)
if userdata.modeldata is None:
msg = "'modeldata' object is 'None' in module %s"
raise SystemExit(msg % str(data.options.data.files[0]))
modeldata = userdata.modeldata
else:
if len(ep) == 0:
msg = "Neither 'modeldata' nor 'pyomo_create_dataportal' " \
'is defined in module %s'
raise SystemExit(msg % str(data.options.data.files[0]))
modeldata.read(model)
instance = model.create_instance(
modeldata,
namespaces=data.options.data.namespaces,
profile_memory=data.options.runtime.profile_memory,
report_timing=data.options.runtime.report_timing)
elif suffix == "yml" or suffix == 'yaml':
try:
import yaml
except:
msg = "Cannot apply load data from a YAML file: PyYaml is not installed"
raise SystemExit(msg)
modeldata = yaml.load(open(data.options.data.files[0]))
instance = model.create_instance(
modeldata,
namespaces=data.options.data.namespaces,
profile_memory=data.options.runtime.profile_memory,
report_timing=data.options.runtime.report_timing)
else:
raise ValueError("Unknown data file type: " +
data.options.data.files[0])
else:
instance = model.create_instance(
modeldata,
namespaces=data.options.data.namespaces,
profile_memory=data.options.runtime.profile_memory,
report_timing=data.options.runtime.report_timing)
#
modify_start_time = time.time()
for ep in ExtensionPoint(IPyomoScriptModifyInstance):
if data.options.runtime.report_timing is True:
tick = time.time()
ep.apply(options=data.options, model=model, instance=instance)
if data.options.runtime.report_timing is True:
print(" %6.2f seconds to apply %s" %
(time.time() - tick, type(ep)))
tick = time.time()
#
for transformation in data.options.transform:
with TransformationFactory(transformation) as xfrm:
instance = xfrm.create_using(instance)
if instance is None:
raise SystemExit("Unexpected error while applying "
"transformation '%s'" % transformation)
#
if data.options.runtime.report_timing is True:
total_time = time.time() - modify_start_time
print(" %6.2f seconds required for problem transformations" %
total_time)
if logger.isEnabledFor(logging.DEBUG):
print("MODEL INSTANCE")
instance.pprint()
print("")
for ep in ExtensionPoint(IPyomoScriptPrintInstance):
ep.apply(options=data.options, instance=instance)
fname = None
smap_id = None
if not data.options.model.save_file is None:
if data.options.runtime.report_timing is True:
write_start_time = time.time()
if data.options.model.save_file == True:
if data.local.model_format in (ProblemFormat.cpxlp,
ProblemFormat.lpxlp):
fname = (data.options.data.files[0])[:-3] + 'lp'
else:
fname = (data.options.data.files[0])[:-3] + str(
data.local.model_format)
format = data.local.model_format
else:
fname = data.options.model.save_file
format = data.options.model.save_format
io_options = {}
if data.options.model.symbolic_solver_labels:
io_options['symbolic_solver_labels'] = True
if data.options.model.file_determinism != 1:
io_options[
'file_determinism'] = data.options.model.file_determinism
(fname, smap_id) = instance.write(filename=fname,
format=format,
io_options=io_options)
if not data.options.runtime.logging == 'quiet':
if not os.path.exists(fname):
print("ERROR: file " + fname + " has not been created!")
else:
print("Model written to file '" + str(fname) + "'")
if data.options.runtime.report_timing is True:
total_time = time.time() - write_start_time
print(" %6.2f seconds required to write file" % total_time)
if (pympler_available is True) and (data.options.runtime.profile_memory
>= 2):
print("")
print(" Summary of objects following file output")
post_file_output_summary = summary.summarize(muppy.get_objects())
summary.print_(post_file_output_summary, limit=100)
print("")
for ep in ExtensionPoint(IPyomoScriptSaveInstance):
ep.apply(options=data.options, instance=instance)
if (pympler_available is True) and (data.options.runtime.profile_memory >=
1):
mem_used = muppy.get_size(muppy.get_objects())
if mem_used > data.local.max_memory:
data.local.max_memory = mem_used
print(" Total memory = %d bytes following Pyomo instance creation" %
mem_used)
return pyutilib.misc.Options(model=model,
instance=instance,
smap_id=smap_id,
filename=fname,
local=data.local)
def get_summary(self):
gc.collect()
# exclude everything in this object itself
excluded = set(id(o) for o in muppy.get_referents(self))
return summary.summarize(o for o in muppy.get_objects()
if not id(o) in excluded)
def estimate_callback(req):
global image_data
global state_data
global imu_data
global objpoints
global imgpoints
global db
global obs_n
global cov_n
global good_corners
global last_frame_corners
global goodenough
global num_step
rospy.loginfo(rospy.get_caller_id() + 'I heard image %s', len(image_data))
rospy.loginfo(rospy.get_caller_id() + 'I heard imu %s', len(imu_data))
rospy.loginfo(rospy.get_caller_id() + 'I heard state %s', len(state_data))
local_img_data = image_data
t = 0
#stop record
if req.reset == 1:
#stop record
#stop_recording()
#remove history data
os.chdir(kalibr_path[:-1])
os.system("rm data.bag")
os.system("rm data_copy.bag")
os.system("rm data_tmp.bag")
# bag = rosbag.Bag('data_tmp.bag', 'w')
# bag.close()
#clear all the record data
del image_data[:]
del imu_data[:]
del state_data[:]
del objpoints[:]
del imgpoints[:]
del good_corners[:]
del db[:]
obs_n = 0
last_frame_corners = None
goodenough = False
num_step = 0
#feed back the update
res = estimateSrvResponse()
res.par_upd = [0, 0, 0, 0, 0, 0]
res.obs = 0
res.coverage = 0
#start recording
#start_recording()
return res
if not req.reset == 1:
num_step += 1
rospy.loginfo(rospy.get_caller_id() + 'start estimate')
res = estimateSrvResponse()
res.par_upd = [0, 0, 0, 0, 0, 0]
res.obs = 0
res.coverage = 0
# estimation
# camera intrinsic calibration
# imgpoints, best_mtx = camera_intrinsic_calibration(req, image_data)
# best_mtx, ipts, opts, progress = camera_intrinsic_calibration2(
# req, image_data)
# rospy.loginfo(rospy.get_caller_id() + 'I get parameters %s',
# best_mtx[0, 0])
#merge bags
#stop_recording()
time.sleep(2)
merge_bag(num_step)
#cam imu calibration
reproj_err = 1
extrinsic = np.identity(4)
H_Aopt = 0
H_eig = np.zeros((6, ))
# if num_step == 3 or num_step==7:
# #call kalibr
# os.chdir(kalibr_path[:-1])
# try_kalibr_total()
# reproj_err, extrinsic = get_kalibr_results()
# extrinsic = np.asarray(extrinsic)
# H_Aopt, H_Dopt, H_Eopt, H_eig = read_H()
# if num_step<3:
# #get reprojection error and extrinsics
# os.chdir(kalibr_path[:-1])
# #try_kalibr()
# reproj_err, extrinsic = get_kalibr_results()
# extrinsic = np.asarray(extrinsic)
# # compute observability
# H_Aopt, H_Dopt, H_Eopt, H_eig = read_H()
########not calculate observability
# else:
# os.system(k_command)
##check for leakage
all_objects = muppy.get_objects()
sum1 = summary.summarize(all_objects)
# Prints out a summary of the large objects
summary.print_(sum1)
rospy.loginfo(rospy.get_caller_id() + ' ' + 'get kalibr reproj err %s',
reproj_err)
rospy.loginfo(rospy.get_caller_id() + ' ' + 'get kalibr extrinsic %s',
extrinsic)
#process extrinsic
e_flat = extrinsic[0:3, 0:3].flatten()
rotation = PyKDL.Rotation(e_flat[0], e_flat[1], e_flat[2], e_flat[3],
e_flat[4], e_flat[5], e_flat[6], e_flat[7],
e_flat[8])
rpy = np.asarray(rotation.GetRPY())
position = extrinsic[0:3, 3]
state = np.concatenate([position.reshape(3, ), rpy.reshape(3, )])
# compute entropy
# orientation_x_en, orientation_y_en, orientation_z_en, orientation_w_en, angular_velocity_x_en, angular_velocity_y_en, angular_velocity_z_en, linear_acceleration_x_en, linear_acceleration_y_en, linear_acceleration_z_en = compute_imu_entropy(
# req, imu_data)
# # compute the coverage
# if len(good_corners) > 0:
res = estimateSrvResponse()
# for c, b in good_corners:
# imgpoints.append(c) # just for coverage calculation.
# rospy.loginfo(rospy.get_caller_id() + 'I get corners %s',
# len(imgpoints))
# ####progress measures camera coverage
# res.coverage = np.sum(progress) - cov_n
# cov_n = np.sum(progress)
# get parameter update
# compute the observation
res.obs = H_Aopt * 10000000
res.par_upd = np.concatenate([state, np.asarray(H_eig) * 10000000])
#res.coverage = (orientation_x_en+orientation_w_en+orientation_y_en+orientation_z_en)*0.1
res.coverage = 0
rospy.loginfo(rospy.get_caller_id() + 'I get par_upd %s', res.par_upd)
rospy.loginfo(rospy.get_caller_id() + 'I get obs %s', res.obs)
rospy.loginfo(rospy.get_caller_id() + 'I get coverage %s',
res.coverage)
# res.obs = 1.0 * len(db) / 20.0 - obs_n
# obs_n = 1.0 * len(db) / 20.0
# rospy.loginfo(rospy.get_caller_id() + 'I get db %s', len(db))
# rospy.loginfo(rospy.get_caller_id() + 'I get good corners %s',
# len(good_corners))
#start_recording()
return res
def profile_expose_method(profiled_method_wrapper, accept, args, func, kw,
exclude_from_memory_profiling):
"""
Targeted to profile a specific method that wraps HTTP request processing endpoints into database context.
:param profiled_method_wrapper: method wrapped around profiled call to be passed in to memory profiler
:param accept: param specific to profiled call
:param args: args of a function that is being wrapped by a profiled method
:param func: function that is being wrapped by a profiled method
:param kw: kwargs of a function that is being wrapped by a profiled method
:return: output of a profiled method without modification
"""
if not exclude_from_memory_profiling and get_memory_profile_logging_on() and \
check_memory_profile_package_wide_disable(func):
controller_class = args[0].__class__.__name__ if args and len(
args) > 0 else ''
end_point_name_parts = [
s for s in [func.__module__, controller_class, func.__name__]
if s != ''
]
end_point_name = ".".join(end_point_name_parts)
is_pympler_on = _is_pympler_profiling_value_on(end_point_name)
profile_output = {'output': {}}
if is_pympler_on:
all_objects = muppy.get_objects()
all_objects_summary_before = summary.summarize(all_objects)
memory_profile = memory_usage(
(_profile_me, (profile_output, profiled_method_wrapper, func,
accept, args, kw), {}),
interval=0.1)
output = profile_output['output']
if is_pympler_on:
all_objects_summary_after = summary.summarize(all_objects)
diff = summary.get_diff(all_objects_summary_before,
all_objects_summary_after)
diff_less = summary.format_(diff)
diff_out = ''
for s in diff_less:
diff_out += s + '\n'
thread_log.info(
"================ PYMPLER OUTPUT <{}> ==============\n{}".
format(end_point_name, diff_out))
try:
message = json.dumps({
'log_type':
'memory_profile',
'proc_id':
os.getpid(),
'name':
func.__name__,
'module':
func.__module__,
'mem_profile':
memory_profile,
'min':
min(memory_profile),
'max':
max(memory_profile),
'diff':
max(memory_profile) - min(memory_profile),
'leaked':
memory_profile[-1] - memory_profile[0],
'args': [arg for arg in args[1:]], # exclude self
'kwargs':
kw
})
memory_log.info(message,
extra={
'controller_module': func.__module__,
'controller_class': controller_class,
'endpoint': func.__name__
})
except Exception as e:
thread_log.exception('Logger failed: {}'.format(e))
else:
output = profiled_method_wrapper(accept, args, func, kw)
return output
def process(self):
#get printable version of indata
self.infostr = ""
for t in self.indata:
if self.indata[t]:
self.infostr = self.infostr + "|" + t + ":" + self.indata[t]
for res in self.config:
curt = time.time()
self.linecount = 0
#check for URL
if "level" in res:
#Check IGNORE option
if "IGNORE" in DataContainer.optionsrec:
if res["level"]["path"] in DataContainer.optionsrec[
"IGNORE"]:
logging.debug("IGNORING LEVEL " + res["level"]["path"])
continue
self.indata["type"] = res["level"]["path"]
#TODO: not optimal solution: eg. type:instances appears after type:classes in logfile
self.infostr = self.infostr + "|type:" + self.indata["type"]
if "RESUME" in DataContainer.optionsrec:
#check skip -> We are at the lowest level
if not "resources" in res:
existing = False
if DataContainer.dbrec != None:
reddict = dict((k, v)
for k, v in self.indata.iteritems()
if k in DataContainer.fieldnames
and v is not None)
logging.debug("Looking for existing resource " +
repr(reddict))
#try:
# key=raw_input("ENTER KEY :")
#except:
# pass
existing = DataContainer.dbrec.findOne(
reddict, res["level"]["path"])
if existing:
logging.warning(
"Resume option set, Skipping existing resource "
+ json.dumps(self.indata))
continue
logging.info("GETTING LEVEL " + res["level"]["path"] + " " +
self.infostr)
if "url" not in res:
#No URL, we assume that there is exactly one "thing" (Here usually a Repository)
outdata = copy.deepcopy(self.indata)
self.processObject(res, outdata)
else:
#get response url
self.zeroResCount = 0
self.iterateUrl(res, res["url"]["path"])
#after iteration, do some object counting
all_objects = muppy.get_objects(include_frames=True)
sum1 = summary.summarize(all_objects)
#code.interact(local=locals())
totalmem = 0
counter = 0
for entry in sorted(sum1, key=itemgetter(2), reverse=True):
out = ""
for data in entry:
out = out + "\t" + str(data)
totalmem += entry[2]
if counter < 15:
logging.debug(out)
counter += 1
logging.debug("TOTAL MEM : " + str(totalmem))
logging.debug(
"Process: Memory usage : %s (kb)" %
resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
logging.info(
str(self.linecount) + " terms written to DB in " +
str(int(time.time() - curt)) + " sec. " + self.infostr)
def process_response(self, request, response):
if not self.is_active:
return False
if self.track_usage:
self.tracked = self.tracker.diff()
self.summary = summary.summarize(muppy.get_objects())
def uc_init(self,
input_file,
wait: bool = False,
trace: bool = False,
verbose: bool = False) -> Tuple[Uc, int, List[int]]:
"""
Initializes unicorn with the given params
:param input_file: input file to drop into the emulator with config.init_func
:param wait: block until state dir becomes available
:param trace: if we should add trace hooks to unicorn
:param verbose: enables some more logging
:return: Tuple of (unicorn, entry_point, exits)
"""
config = self.config
uc = Uc(self.arch.unicorn_arch, self.arch.unicorn_mode)
if trace:
print("[+] Settings trace hooks")
uc.hook_add(UC_HOOK_BLOCK, unicorn_debug_block)
uc.hook_add(UC_HOOK_CODE, unicorn_debug_instruction, self)
uc.hook_add(
UC_HOOK_MEM_WRITE | UC_HOOK_MEM_READ | UC_HOOK_MEM_FETCH,
unicorn_debug_mem_access,
)
if wait:
self.wait_for_probe_wrapper()
if verbose:
print("[*] Reading from file {}".format(input_file))
# we leave out gs_base and fs_base on x64 since they start the forkserver
self.uc_load_registers(uc)
# let's see if the user wants a change.
config.init_func(self, uc)
# get pc from unicorn state since init_func may have altered it.
pc = self.uc_read_pc(uc)
self.map_known_mem(uc)
exits = self.calculate_exits(pc)
if not exits:
raise ValueError(
"No exits founds. Would run forever... Please set an exit address in config.py."
)
# On error: map memory, add exits.
uc.hook_add(UC_HOOK_MEM_UNMAPPED, unicorn_debug_mem_invalid_access,
self)
if os.getenv("UCF_DEBUG_MEMORY"):
from pympler import muppy, summary
all_objects = muppy.get_objects()
sum1 = summary.summarize(all_objects)
summary.print_(sum1)
# Last chance to hook before forkserver starts (if running as afl child)
fork_sleep = os.getenv("UCF_DEBUG_SLEEP_BEFORE_FORK")
if fork_sleep:
print("[d] Sleeping. Forkserver will start in {} seconds.".format(
fork_sleep))
time.sleep(float(fork_sleep))
return uc, pc, exits
def _muppy_object_summary(self):
all_objects = muppy.get_objects()
sum1 = summary.summarize(all_objects)
summary.print_(sum1)
sleep(5)
def create_instance(self,
filename=None,
data=None,
name=None,
namespace=None,
namespaces=None,
preprocess=False,
profile_memory=0,
report_timing=False,
clone=None):
"""
Create a concrete instance of an abstract model, possibly using data
read in from a file.
Optional:
filename: The name of a Pyomo Data File that will be used to load
data into the model.
data: A dictionary containing initialization data for the model
to be used if there is no filename
name: The name given to the model.
namespace: A namespace used to select data.
namespaces: A list of namespaces used to select data.
preprocess: If True, then preprocess the constructed model.
profile_memory: A number that indicates the profiling level.
report_timing: Report timing statistics during construction.
clone: Force a clone of the model if this is True.
"""
if self._constructed:
logger.warning(
"DEPRECATION WARNING: Cannot call Model.create_instance() on a concrete model."
)
return self
if name is None:
name = self.name
if not filename is None:
data = filename
if data is None:
data = {}
#
# Generate a warning if this is a concrete model but the filename is specified.
# A concrete model is already constructed, so passing in a data file is a waste
# of time.
#
if self.is_constructed() and isinstance(filename, basestring):
msg = "The filename=%s will not be loaded - supplied as an argument to the create_instance() method of a ConcreteModel instance with name=%s." % (
filename, name)
logger.warning(msg)
#
# If construction is deferred, then clone the model and
#
if not self._constructed:
instance = self.clone()
if namespaces is None or len(namespaces) == 0:
instance.load(data,
namespaces=[None],
profile_memory=profile_memory,
report_timing=report_timing)
else:
instance.load(data,
namespaces=list(namespaces) + [None],
profile_memory=profile_memory,
report_timing=report_timing)
else:
if clone:
instance = self.clone()
else:
instance = self
#
# Preprocess the new model
#
if preprocess is True:
print(
" Model preprocessing during construction has been deprecated."
)
if False and preprocess is True:
if report_timing is True:
start_time = time.time()
instance.preprocess()
if report_timing is True:
total_time = time.time() - start_time
print(" %6.2f seconds required for preprocessing" %
total_time)
if (pympler_available is True) and (profile_memory >= 2):
mem_used = muppy.get_size(muppy.get_objects())
print(
" Total memory = %d bytes following instance preprocessing"
% mem_used)
print("")
if (pympler_available is True) and (profile_memory >= 2):
print("")
print(
" Summary of objects following instance preprocessing"
)
post_preprocessing_summary = summary.summarize(
muppy.get_objects())
summary.print_(post_preprocessing_summary, limit=100)
if not name is None:
instance.name = name
#
# Indicate that the model is concrete/constructed
#
instance._constructed = True
return instance
def print_memory(self):
all_objects = muppy.get_objects()
sum1 = summary.summarize(all_objects)
# Prints out a summary of the large objects
summary.print_(sum1)
def mem():
objs = muppy.get_objects()
summ = summary.summarize(objs)
return '\n'.join(summary.format_(summ)) + '\n'
