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 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 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 test_ignore_frame(self):
     """Test whether reference cycles are created
     """
     gc.collect()
     gc.disable()
     objs = muppy.get_objects()
     del objs
     self.assertEqual(gc.collect(), 0)
     objs = muppy.get_objects(include_frames=True)
     del objs
     self.assertEqual(gc.collect(), 0)
     gc.enable()

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 _initialize_component(self, modeldata, namespaces, component_name, profile_memory):
        declaration = self.component(component_name)

        if component_name in modeldata._default:
            if declaration.type() is not Set:
                declaration.set_default(modeldata._default[component_name])
        data = None

        for namespace in namespaces:
            if component_name in modeldata._data.get(namespace,{}):
                if declaration.type() is Set:
                    data = self._tuplize(modeldata._data[namespace][component_name],
                                         declaration)
                else:
                    data = modeldata._data[namespace][component_name]
            if not data is None:
                break

        if __debug__ and logger.isEnabledFor(logging.DEBUG):
            _blockName = "Model" if self.parent_block() is None \
                else "Block '%s'" % self.name
            logger.debug( "Constructing %s '%s' on %s from data=%s",
                          declaration.__class__.__name__,
                          declaration.name, _blockName, str(data) )
        try:
            declaration.construct(data)
        except:
            err = sys.exc_info()[1]
            logger.error(
                "Constructing component '%s' from data=%s failed:\n    %s: %s",
                str(declaration.name), str(data).strip(),
                type(err).__name__, err )
            raise

        if __debug__ and logger.isEnabledFor(logging.DEBUG):
                _out = StringIO()
                declaration.pprint(ostream=_out)
                logger.debug("Constructed component '%s':\n    %s"
                             % ( declaration.name, _out.getvalue()))

        if (pympler_available is True) and (profile_memory >= 2):
            mem_used = muppy.get_size(muppy.get_objects())
            print("      Total memory = %d bytes following construction of component=%s" % (mem_used, component_name))

        if (pympler_available is True) and (profile_memory >= 3):
            gc.collect()
            mem_used = muppy.get_size(muppy.get_objects())
            print("      Total memory = %d bytes following construction of component=%s (after garbage collection)" % (mem_used, component_name))

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 apply_postprocessing(data, instance=None, results=None):
    """
    Apply post-processing steps.

    Required:
        instance:   Problem instance.
        results:    Optimization results object.
    """
    #
    if not data.options.runtime.logging == 'quiet':
        sys.stdout.write('[%8.2f] Applying Pyomo postprocessing actions\n' % (time.time()-start_time))
        sys.stdout.flush()

    # options are of type ConfigValue, not raw strings / atomics.
    for config_value in data.options.postprocess:
        postprocess = pyutilib.misc.import_file(config_value, clear_cache=True)
        if "pyomo_postprocess" in dir(postprocess):
            postprocess.pyomo_postprocess(data.options, instance,results)

    for ep in ExtensionPoint(IPyomoScriptPostprocess):
        ep.apply( options=data.options, instance=instance, results=results )

    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 upon termination" % mem_used)

 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 fileCloseEnd(cntlrWinMain, filename):
    if __testContext.checkMemoryOnClose:
        gc.collect()
        try:
            xx = cntlrWinMain.prevNumObjects
            cntlrWinMain.dumpIdx
        except:
            cntlrWinMain.prevNumObjects = 0
            cntlrWinMain.dumpIdx = 0
        cntlrWinMain.dumpIdx += 1
        all_objects = muppy.get_objects()
        numObjects = len(all_objects)
        diffObjects = numObjects - cntlrWinMain.prevNumObjects
        cntlrWinMain.prevNumObjects = numObjects
        print("numObjects=" + str(numObjects) + " (" + str(diffObjects) + " more)")
        if False:
            with open(__testContext.dumpFilePrefix + str(cntlrWinMain.dumpIdx) + ".txt", "w") as text_file:
                idx = 0
                for o in all_objects:
                    idx += 1
                    otype = ""
                    try:
                        otype = str(type(o))
                    except:
                        pass
                    try:
                        print("type=" + otype + " " + str(o), file=text_file)
                    except:
                        pass
            all_objects =  None
            gc.collect()
            print(numObjects)
            print("End of close " + filename)
        __testContext.diffNumObjects = diffObjects

 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 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 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 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 __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 __init__(self, **kwargs):
        super(GlobalContainer, self).__init__(**kwargs)
        self._keyboard = None
        self.request_keyboard()
        self.working_dir = './'
        self.tutorial = None
        self.popup_stack = []
        window.Window.bind(on_resize=self.on_resize)

        if DEBUG:
            self.tracker = ClassTracker()
            self.tracker.track_object(MenuButton)
            self.all_objects = muppy.get_objects()