From f0ca15ec410a73b8f223a48e1be234c8561c6c2c Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Thu, 21 Jul 2016 12:35:43 +0100 Subject: [PATCH 01/24] libs/utils/trace: cosmetics: rename datadir Signed-off-by: Patrick Bellasi --- libs/utils/trace.py | 18 +++++++++--------- libs/utils/trace_analysis.py | 2 +- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/libs/utils/trace.py b/libs/utils/trace.py index 3c72377d4..9bceb01b6 100644 --- a/libs/utils/trace.py +++ b/libs/utils/trace.py @@ -34,7 +34,7 @@ import logging class Trace(object): - def __init__(self, platform, datadir, events, + def __init__(self, platform, data_dir, events, tasks=None, window=(0,None), normalize_time=True, trace_format='FTrace'): @@ -42,9 +42,6 @@ class Trace(object): # The platform used to run the experiments self.platform = platform - # Folder containing all perf data - self.datadir = None - # TRAPpy Trace object self.ftrace = None @@ -76,14 +73,17 @@ class Trace(object): # Cluster frequency coherency flag self.freq_coherency = True + # Folder containing all trace data + self.data_dir = None + # Folder containing trace - if not os.path.isdir(datadir): - self.datadir = os.path.dirname(datadir) + if not os.path.isdir(data_dir): + self.data_dir = os.path.dirname(data_dir) else: - self.datadir = datadir + self.data_dir = data_dir self.__registerTraceEvents(events) - self.__parseTrace(datadir, tasks, window, normalize_time, trace_format) + self.__parseTrace(data_dir, tasks, window, normalize_time, trace_format) self.__computeTimeSpan() def __registerTraceEvents(self, events): @@ -326,7 +326,7 @@ class Trace(object): Return the PANDAS dataframe with the performance data for the specified event """ - if self.datadir is None: + if self.data_dir is None: raise ValueError("trace data not (yet) loaded") if self.ftrace and hasattr(self.ftrace, event): return getattr(self.ftrace, event).data_frame diff --git a/libs/utils/trace_analysis.py b/libs/utils/trace_analysis.py index ab8229e89..1f24f842d 100644 --- a/libs/utils/trace_analysis.py +++ b/libs/utils/trace_analysis.py @@ -55,7 +55,7 @@ class TraceAnalysis(object): # Plotsdir is byb default the trace dir if self.plotsdir is None: - self.plotsdir = self.trace.datadir + self.plotsdir = self.trace.data_dir # Minimum and Maximum x_time to use for all plots self.x_min = 0 -- GitLab From 69a445cdc130949aa54b84fb9b3dfbcb55fa2d53 Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Thu, 21 Jul 2016 12:36:03 +0100 Subject: [PATCH 02/24] libs/utils/trace: add support for plots directory --- libs/utils/trace.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/libs/utils/trace.py b/libs/utils/trace.py index 9bceb01b6..bf0184af2 100644 --- a/libs/utils/trace.py +++ b/libs/utils/trace.py @@ -37,7 +37,9 @@ class Trace(object): def __init__(self, platform, data_dir, events, tasks=None, window=(0,None), normalize_time=True, - trace_format='FTrace'): + trace_format='FTrace', + plots_dir=None, + plots_prefix=''): # The platform used to run the experiments self.platform = platform @@ -82,6 +84,12 @@ class Trace(object): else: self.data_dir = data_dir + # By deafult, use the trace dir to save plots + self.plots_dir = plots_dir + if self.plots_dir is None: + self.plots_dir = self.data_dir + self.plots_prefix = plots_prefix + self.__registerTraceEvents(events) self.__parseTrace(data_dir, tasks, window, normalize_time, trace_format) self.__computeTimeSpan() -- GitLab From 8224360b6dbfe7e28c28ef7a06fa100b2556c656 Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Thu, 21 Jul 2016 12:36:34 +0100 Subject: [PATCH 03/24] libs/utils/trace: add support for X time range definition Signed-off-by: Patrick Bellasi --- libs/utils/trace.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/libs/utils/trace.py b/libs/utils/trace.py index bf0184af2..bd842f7aa 100644 --- a/libs/utils/trace.py +++ b/libs/utils/trace.py @@ -94,6 +94,29 @@ class Trace(object): self.__parseTrace(data_dir, tasks, window, normalize_time, trace_format) self.__computeTimeSpan() + # Minimum and Maximum x_time to use for all plots + self.x_min = 0 + self.x_max = self.time_range + + # Reset x axis time range to full scale + t_min = self.window[0] + t_max = self.window[1] + self.setXTimeRange(t_min, t_max) + + # Initialize supported analysis modules + + def setXTimeRange(self, t_min=None, t_max=None): + if t_min is None: + self.x_min = 0 + else: + self.x_min = t_min + if t_max is None: + self.x_max = self.time_range + else: + self.x_max = t_max + logging.info('Set plots time range to (%.6f, %.6f)[s]', + self.x_min, self.x_max) + def __registerTraceEvents(self, events): if isinstance(events, basestring): -- GitLab From fae44f68c443723378b83ab146fc20bc5a844a7d Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Fri, 22 Jul 2016 12:44:46 +0100 Subject: [PATCH 04/24] libs/utils/trace: deprecate df The df method returns just Trappy's DataFrame built from parsed trace_event, while the Trace call want to be a generic interface to get access to other PANDAs DataFrames created by trace analysis modules. This patch deprecate the current df() method in preparation for the introduction of a more generic API to get access to data frames, either created by Trappy or other trace analysis modules. Signed-off-by: Patrick Bellasi --- libs/utils/trace.py | 46 +++++++++++++++++++++++++++++++-------------- 1 file changed, 32 insertions(+), 14 deletions(-) diff --git a/libs/utils/trace.py b/libs/utils/trace.py index bd842f7aa..5e6b3c707 100644 --- a/libs/utils/trace.py +++ b/libs/utils/trace.py @@ -26,6 +26,7 @@ import re import sys import trappy import json +import warnings from trappy.utils import listify @@ -193,14 +194,14 @@ class Trace(object): def __loadTasksNames(self, tasks): # Try to load tasks names using one of the supported events if 'sched_switch' in self.available_events: - self.getTasks(self.df('sched_switch'), tasks, + self.getTasks(self._dfg_trace_event('sched_switch'), tasks, name_key='next_comm', pid_key='next_pid') - self._scanTasks(self.df('sched_switch'), + self._scanTasks(self._dfg_trace_event('sched_switch'), name_key='next_comm', pid_key='next_pid') return if 'sched_load_avg_task' in self.available_events: - self.getTasks(self.df('sched_load_avg_task'), tasks) - self._scanTasks(self.df('sched_load_avg_task')) + self.getTasks(self._dfg_trace_event('sched_load_avg_task'), tasks) + self._scanTasks(self._dfg_trace_event('sched_load_avg_task')) return logging.warning('Failed to load tasks names from trace events') @@ -215,7 +216,7 @@ class Trace(object): te = 0 for events in self.available_events: - df = self.df(events) + df = self._dfg_trace_event(events) if len(df) == 0: continue if (df.index[0]) < ts: @@ -229,7 +230,7 @@ class Trace(object): # Build a stat on trace overutilization if self.hasEvents('sched_overutilized'): - df = self.df('sched_overutilized') + df = self._dfg_trace_event('sched_overutilized') self.overutilized_time = df[df.overutilized == 1].len.sum() self.overutilized_prc = 100. * self.overutilized_time / self.time_range @@ -352,7 +353,20 @@ class Trace(object): tname, self.tasks[tname]['pid']) return self.tasks + +################################################################################ +# DataFrame Getter Methods +################################################################################ + def df(self, event): + warnings.simplefilter('always', DeprecationWarning) #turn off filter + warnings.warn("\n\tUse of Trace::df() is deprecated and will be soon removed." + "\n\tUse Trace::data_frame.trace_event(event_name) instead.", + category=DeprecationWarning) + warnings.simplefilter('default', DeprecationWarning) #reset filter + return self._dfg_trace_event(event) + + def _dfg_trace_event(self, event): """ Return the PANDAS dataframe with the performance data for the specified event @@ -365,13 +379,17 @@ class Trace(object): 'Supported events are: {}'\ .format(event, self.available_events)) +################################################################################ +# Trace Events Sanitize Methods +################################################################################ + def _sanitize_SchedCpuCapacity(self): # Add more columns if the energy model is available if not self.hasEvents('cpu_capacity') \ or 'nrg_model' not in self.platform: return - df = self.df('cpu_capacity') + df = self._dfg_trace_event('cpu_capacity') # Add column with LITTLE and big CPUs max capacities nrg_model = self.platform['nrg_model'] @@ -390,7 +408,7 @@ class Trace(object): def _sanitize_SchedLoadAvgCpu(self): if not self.hasEvents('sched_load_avg_cpu'): return - df = self.df('sched_load_avg_cpu') + df = self._dfg_trace_event('sched_load_avg_cpu') if 'utilization' in df: # Convert signals name from v5.0 to v5.1 format df.rename(columns={'utilization':'util_avg'}, inplace=True) @@ -399,7 +417,7 @@ class Trace(object): def _sanitize_SchedLoadAvgTask(self): if not self.hasEvents('sched_load_avg_task'): return - df = self.df('sched_load_avg_task') + df = self._dfg_trace_event('sched_load_avg_task') if 'utilization' in df: # Convert signals name from v5.0 to v5.1 format df.rename(columns={'utilization':'util_avg'}, inplace=True) @@ -414,7 +432,7 @@ class Trace(object): def _sanitize_SchedBoostCpu(self): if not self.hasEvents('sched_boost_cpu'): return - df = self.df('sched_boost_cpu') + df = self._dfg_trace_event('sched_boost_cpu') if 'usage' in df: # Convert signals name from to v5.1 format df.rename(columns={'usage':'util'}, inplace=True) @@ -424,7 +442,7 @@ class Trace(object): def _sanitize_SchedBoostTask(self): if not self.hasEvents('sched_boost_task'): return - df = self.df('sched_boost_task') + df = self._dfg_trace_event('sched_boost_task') if 'utilization' in df: # Convert signals name from to v5.1 format df.rename(columns={'utilization':'util'}, inplace=True) @@ -447,7 +465,7 @@ class Trace(object): em_lcluster['nrg_max'] + em_bcluster['nrg_max'] print "Maximum estimated system energy: {0:d}".format(power_max) - df = self.df('sched_energy_diff') + df = self._dfg_trace_event('sched_energy_diff') df['nrg_diff_pct'] = SCHED_LOAD_SCALE * df.nrg_diff / power_max # Tag columns by usage_delta @@ -466,7 +484,7 @@ class Trace(object): if not self.hasEvents('sched_overutilized'): return # Add a column with overutilized status duration - df = self.df('sched_overutilized') + df = self._dfg_trace_event('sched_overutilized') df['start'] = df.index df['len'] = (df.start - df.start.shift()).fillna(0).shift(-1) df.drop('start', axis=1, inplace=True) @@ -478,7 +496,7 @@ class Trace(object): if not self.hasEvents('cpu_frequency'): return # Verify that all platform reported clusters are frequency choerent - df = self.df('cpu_frequency') + df = self._dfg_trace_event('cpu_frequency') clusters = self.platform['clusters'] for c, cpus in clusters.iteritems(): cluster_df = df[df.cpu.isin(cpus)] -- GitLab From f493b3388f11c03a2ad4522d69aaf49c82da787f Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Fri, 22 Jul 2016 12:49:47 +0100 Subject: [PATCH 05/24] libs/utils/trace: add support to register DataFrame getters A DataFrame getter is a method or attribute of a module which: 1) reference or point to a DataFrame 2) its name starts with "_dfg_" (short for DataFrameGetter) This patch add the required support to populate a Trace::data_frame attribute with references to DataFrame getters exposed by a module. The new _registerDataFrameGetters() method is used to register SFG exposed by the Trace module itself. Signed-off-by: Patrick Bellasi --- libs/utils/trace.py | 21 ++++++++++++++++++++- 1 file changed, 20 insertions(+), 1 deletion(-) diff --git a/libs/utils/trace.py b/libs/utils/trace.py index 5e6b3c707..ee6223176 100644 --- a/libs/utils/trace.py +++ b/libs/utils/trace.py @@ -104,7 +104,18 @@ class Trace(object): t_max = self.window[1] self.setXTimeRange(t_min, t_max) - # Initialize supported analysis modules + self.data_frame = TraceData() + self._registerDataFrameGetters(self) + + def _registerDataFrameGetters(self, module): + logging.debug("Registering [%s] local data frames", module) + for func in dir(module): + if not func.startswith('_dfg_'): + continue + dfg_name = func.replace('_dfg_', '') + dfg_func = getattr(module, func) + logging.debug(" %s", dfg_name) + setattr(self.data_frame, dfg_name, dfg_func) def setXTimeRange(self, t_min=None, t_max=None): if t_min is None: @@ -510,3 +521,11 @@ class Trace(object): return logging.info("Platform clusters verified to be Frequency choerent") +################################################################################ +# Utility Methods +################################################################################ + +# A DataFrame collector exposed to Trace's clients +class TraceData: + pass + -- GitLab From ff49ae1f0c3dcf5eba2b063c3571b692355f5320 Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Fri, 22 Jul 2016 12:52:03 +0100 Subject: [PATCH 06/24] libs/utils/trace: turn "functions_stats" into a DataFrame getter This patch rename the _functions_stats method to be registered as a DataFrame getter at Trace module initialization time. The code is also moved in the proper section of the Trace module source file. Signed-off-by: Patrick Bellasi --- libs/utils/trace.py | 94 ++++++++++++++++++++++----------------------- 1 file changed, 47 insertions(+), 47 deletions(-) diff --git a/libs/utils/trace.py b/libs/utils/trace.py index ee6223176..2f9ef9b13 100644 --- a/libs/utils/trace.py +++ b/libs/utils/trace.py @@ -248,53 +248,6 @@ class Trace(object): logging.info('Overutilized time: %.6f [s] (%.3f%% of trace time)', self.overutilized_time, self.overutilized_prc) - def _loadFunctionsStats(self, path='trace.stats'): - if os.path.isdir(path): - path = os.path.join(path, 'trace.stats') - if path.endswith('dat') or path.endswith('html'): - pre, ext = os.path.splitext(path) - path = pre + '.stats' - if not os.path.isfile(path): - return False - - # Opening functions profiling JSON data file - logging.debug('Loading functions profiling data from [%s]...', path) - with open(os.path.join(path), 'r') as fh: - trace_stats = json.load(fh) - - # Build DataFrame of function stats - frames = {} - for cpu, data in trace_stats.iteritems(): - frames[int(cpu)] = pd.DataFrame.from_dict(data, orient='index') - - # Build and keep track of the DataFrame - self._functions_stats_df = pd.concat(frames.values(), keys=frames.keys()) - - return len(self._functions_stats_df) > 0 - - def functions_stats_df(self, functions=None): - """ - Get a DataFrame of specified kernel functions profile data - - For each profiled function a DataFrame is returned which reports stats - on kernel functions execution time. The reported stats are per-CPU and - includes: number of times the function has been executed (hits), - average execution time (avg), overall execution time (time) and samples - variance (s_2). - By default returns a DataFrame of all the functions profiled. - - :param functions: the name of the function or a list of function names - to report - :type functions: str or list - - """ - if not hasattr(self, '_functions_stats_df'): - return None - df = self._functions_stats_df - if not functions: - return df - return df.loc[df.index.get_level_values(1).isin(listify(functions))] - def _scanTasks(self, df, name_key='comm', pid_key='pid'): df = df[[name_key, pid_key]] self._tasks_by_name = df.set_index(name_key) @@ -390,6 +343,29 @@ class Trace(object): 'Supported events are: {}'\ .format(event, self.available_events)) + def _dfg_functions_stats(self, functions=None): + """ + Get a DataFrame of specified kernel functions profile data + + For each profiled function a DataFrame is returned which reports stats + on kernel functions execution time. The reported stats are per-CPU and + includes: number of times the function has been executed (hits), + average execution time (avg), overall execution time (time) and samples + variance (s_2). + By default returns a DataFrame of all the functions profiled. + + :param functions: the name of the function or a list of function names + to report + :type functions: str or list + + """ + if not hasattr(self, '_functions_stats_df'): + return None + df = self._functions_stats_df + if not functions: + return df + return df.loc[df.index.get_level_values(1).isin(listify(functions))] + ################################################################################ # Trace Events Sanitize Methods ################################################################################ @@ -525,6 +501,30 @@ class Trace(object): # Utility Methods ################################################################################ + def _loadFunctionsStats(self, path='trace.stats'): + if os.path.isdir(path): + path = os.path.join(path, 'trace.stats') + if path.endswith('dat') or path.endswith('html'): + pre, ext = os.path.splitext(path) + path = pre + '.stats' + if not os.path.isfile(path): + return False + + # Opening functions profiling JSON data file + logging.debug('Loading functions profiling data from [%s]...', path) + with open(os.path.join(path), 'r') as fh: + trace_stats = json.load(fh) + + # Build DataFrame of function stats + frames = {} + for cpu, data in trace_stats.iteritems(): + frames[int(cpu)] = pd.DataFrame.from_dict(data, orient='index') + + # Build and keep track of the DataFrame + self._functions_stats_df = pd.concat(frames.values(), keys=frames.keys()) + + return len(self._functions_stats_df) > 0 + # A DataFrame collector exposed to Trace's clients class TraceData: pass -- GitLab From 6721d173e18d3c56a33fd3f2eb9b50acd675ec27 Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Fri, 22 Jul 2016 13:02:09 +0100 Subject: [PATCH 07/24] libs/utils/analysis: add base support to register trace analysis modules A TraceModule is a LISA module which can: a) expose new DataFrame getters b) expose analysis functions to report/plot DataFrame data This patch adds the basic support to add new TraceAnalysis modules which can now be added by simply: 1) creating a new module under libs/utils/analysis 2) derive the top level class from AnalysisModule 3) prefix with "_dfg_" all its methods which generated a DataFrame The AnalysisRegister class provides the run-time support to register all the available trace analysis module, which are available as attributes of this class with a name derived from the module filename. For example: libs/utils/analysis/frequency_analysis.py will be registered as: AnalysisRegister::frequency The Trace module initialize and keep track of the AnalysisRegister in its "analysis" attribute. Thus for example the previous module can be accessed using: trace = Trace(platform, trace_dir) trace.analysis.frequency.plotClusterFrequencies() Signed-off-by: Patrick Bellasi --- libs/utils/__init__.py | 3 ++ libs/utils/analysis_module.py | 32 ++++++++++++++++ libs/utils/analysis_register.py | 66 +++++++++++++++++++++++++++++++++ libs/utils/trace.py | 3 ++ 4 files changed, 104 insertions(+) create mode 100644 libs/utils/analysis_module.py create mode 100644 libs/utils/analysis_register.py diff --git a/libs/utils/__init__.py b/libs/utils/__init__.py index cdcfbe38f..68b2ab3ce 100644 --- a/libs/utils/__init__.py +++ b/libs/utils/__init__.py @@ -32,3 +32,6 @@ from filters import Filters from report import Report import android + +from analysis_register import AnalysisRegister +from analysis_module import AnalysisModule diff --git a/libs/utils/analysis_module.py b/libs/utils/analysis_module.py new file mode 100644 index 000000000..0b399fc80 --- /dev/null +++ b/libs/utils/analysis_module.py @@ -0,0 +1,32 @@ +# SPDX-License-Identifier: Apache-2.0 +# +# Copyright (C) 2015, ARM Limited and contributors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +class AnalysisModule(object): + + def __init__(self, trace): + """ + Support for CPUs Signals Analysis + """ + self._trace = trace + self._platform = trace.platform + self._tasks = trace.tasks + self._data_dir = trace.data_dir + + self._dfg_trace_event = trace._dfg_trace_event + + trace._registerDataFrameGetters(self) + diff --git a/libs/utils/analysis_register.py b/libs/utils/analysis_register.py new file mode 100644 index 000000000..e01807cc2 --- /dev/null +++ b/libs/utils/analysis_register.py @@ -0,0 +1,66 @@ +# SPDX-License-Identifier: Apache-2.0 +# +# Copyright (C) 2015, ARM Limited and contributors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import os +import sys + +from glob import glob +from inspect import isclass +from importlib import import_module + +from analysis_module import AnalysisModule + +# Configure logging +import logging + +# Define list of supported Analysis Classes +class AnalysisRegister(object): + + def __init__(self, trace): + + # Add workloads dir to system path + analysis_dir = os.path.dirname(os.path.abspath(__file__)) + analysis_dir = os.path.join(analysis_dir, 'analysis') + logging.debug('%14s - Analysis: %s', 'Analysis', analysis_dir) + + sys.path.insert(0, analysis_dir) + logging.debug('%14s - Syspath: %s', 'Analysis', format(sys.path)) + + logging.info("Registering trace analysis modules:") + for filepath in glob(os.path.join(analysis_dir, '*.py')): + filename = os.path.splitext(os.path.basename(filepath))[0] + + # Ignore __init__ files + if filename.startswith('__'): + continue + + logging.debug('%14s - Filename: %s', 'Analysis', filename) + + # Import the module for inspection + module = import_module(filename) + for member in dir(module): + # Ignore the base class + if member == 'AnalysisModule': + continue + handler = getattr(module, member) + if handler and isclass(handler) and \ + issubclass(handler, AnalysisModule): + class_name = handler.__name__ + module_name = module.__name__.replace('_analysis', '') + setattr(self, module_name, handler(trace)) + logging.info(" %s", module_name) + diff --git a/libs/utils/trace.py b/libs/utils/trace.py index 2f9ef9b13..db87824f6 100644 --- a/libs/utils/trace.py +++ b/libs/utils/trace.py @@ -28,6 +28,7 @@ import trappy import json import warnings +from analysis_register import AnalysisRegister from trappy.utils import listify # Configure logging @@ -107,6 +108,8 @@ class Trace(object): self.data_frame = TraceData() self._registerDataFrameGetters(self) + self.analysis = AnalysisRegister(self) + def _registerDataFrameGetters(self, module): logging.debug("Registering [%s] local data frames", module) for func in dir(module): -- GitLab From 5824ef082910363b2258cedecdabc60f6a2ea4c7 Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Fri, 22 Jul 2016 14:34:55 +0100 Subject: [PATCH 08/24] libs/utils/analysis: add FREQUENCY module This patch move the CPUs/Clusters frequency residency analysis code, originally part of the trace_analysis.py module, to be a new TraceAnalysis module whith dynamic registration via the AnalysisRegister. Signed-off-by: Patrick Bellasi --- libs/utils/analysis/frequency_analysis.py | 640 ++++++++++++++++++++++ libs/utils/trace.py | 17 + libs/utils/trace_analysis.py | 550 ------------------- 3 files changed, 657 insertions(+), 550 deletions(-) create mode 100644 libs/utils/analysis/frequency_analysis.py diff --git a/libs/utils/analysis/frequency_analysis.py b/libs/utils/analysis/frequency_analysis.py new file mode 100644 index 000000000..c0d626097 --- /dev/null +++ b/libs/utils/analysis/frequency_analysis.py @@ -0,0 +1,640 @@ +# SPDX-License-Identifier: Apache-2.0 +# +# Copyright (C) 2015, ARM Limited and contributors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import matplotlib.gridspec as gridspec +import matplotlib.pyplot as plt +import pandas as pd +import pylab as pl +import operator +from trappy.utils import listify +from devlib.utils.misc import memoized +from collections import namedtuple + +from analysis_module import AnalysisModule + +# Configure logging +import logging + +NON_IDLE_STATE = 4294967295 + +ResidencyTime = namedtuple('ResidencyTime', ['total', 'active']) +ResidencyData = namedtuple('ResidencyData', ['label', 'residency']) + +class FrequencyAnalysis(AnalysisModule): + + def __init__(self, trace): + """ + Support for plotting Frequency Analysis data + """ + super(FrequencyAnalysis, self).__init__(trace) + +################################################################################ +# DataFrame Getter Methods +################################################################################ + + def _dfg_cpu_frequency_residency(self, cpu, total=True): + """ + Get per-CPU frequency residency, i.e. amount of + time CPU `cpu` spent at each frequency. + + :param cpu: CPU ID + :type cpu: int + + :param total: if true returns the "total" time, otherwise the "active" + time is returned + :type total: bool + + :returns: :mod:`pandas.DataFrame` - "total" or "active" time residency + at each frequency. + """ + residency = self._getCPUFrequencyResidency(cpu) + if not residency: + return None + if total: + return residency.total + return residency.active + + def _dfg_cluster_frequency_residency(self, cluster, total=True): + """ + Get per-Cluster frequency residency, i.e. amount of time CLUSTER + `cluster` spent at each frequency. + + :param cluster: this can be either a single CPU ID or a list of CPU IDs + belonging to a cluster or the cluster name as specified in the + platform description + :type cluster: str or int or list(int) + + :param total: if true returns the "total" time, otherwise the "active" + time is returned + :type total: bool + + :returns: :mod:`pandas.DataFrame` - "total" or "active" time residency + at each frequency. + """ + residency = self._getClusterFrequencyResidency(cluster) + if not residency: + return None + if total: + return residency.total + return residency.active + + +################################################################################ +# Plotting Methods +################################################################################ + + def plotClusterFrequencies(self, title='Clusters Frequencies'): + if not self._trace.hasEvents('cpu_frequency'): + logging.warn('Events [cpu_frequency] not found, '\ + 'plot DISABLED!') + return + df = self._dfg_trace_event('cpu_frequency') + + pd.options.mode.chained_assignment = None + + # Extract LITTLE and big clusters frequencies + # and scale them to [MHz] + if len(self._platform['clusters']['little']): + lfreq = df[df.cpu == self._platform['clusters']['little'][-1]] + lfreq['frequency'] = lfreq['frequency']/1e3 + else: + lfreq = [] + if len(self._platform['clusters']['big']): + bfreq = df[df.cpu == self._platform['clusters']['big'][-1]] + bfreq['frequency'] = bfreq['frequency']/1e3 + else: + bfreq = [] + + # Compute AVG frequency for LITTLE cluster + avg_lfreq = 0 + if len(lfreq) > 0: + lfreq['timestamp'] = lfreq.index; + lfreq['delta'] = (lfreq['timestamp'] - lfreq['timestamp'].shift()).fillna(0).shift(-1); + lfreq['cfreq'] = (lfreq['frequency'] * lfreq['delta']).fillna(0); + timespan = lfreq.iloc[-1].timestamp - lfreq.iloc[0].timestamp; + avg_lfreq = lfreq['cfreq'].sum()/timespan; + + # Compute AVG frequency for big cluster + avg_bfreq = 0 + if len(bfreq) > 0: + bfreq['timestamp'] = bfreq.index; + bfreq['delta'] = (bfreq['timestamp'] - bfreq['timestamp'].shift()).fillna(0).shift(-1); + bfreq['cfreq'] = (bfreq['frequency'] * bfreq['delta']).fillna(0); + timespan = bfreq.iloc[-1].timestamp - bfreq.iloc[0].timestamp; + avg_bfreq = bfreq['cfreq'].sum()/timespan; + + pd.options.mode.chained_assignment = 'warn' + + # Setup a dual cluster plot + fig, pltaxes = plt.subplots(2, 1, figsize=(16, 8)); + plt.suptitle(title, y=.97, fontsize=16, + horizontalalignment='center'); + + # Plot Cluster frequencies + axes = pltaxes[0] + axes.set_title('big Cluster'); + if avg_bfreq > 0: + axes.axhline(avg_bfreq, color='r', linestyle='--', linewidth=2); + axes.set_ylim( + (self._platform['freqs']['big'][0] - 100000)/1e3, + (self._platform['freqs']['big'][-1] + 100000)/1e3 + ); + if len(bfreq) > 0: + bfreq['frequency'].plot(style=['r-'], ax=axes, + drawstyle='steps-post', alpha=0.4); + else: + logging.warn('NO big CPUs frequency events to plot') + axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.set_ylabel('MHz') + axes.grid(True); + axes.set_xticklabels([]) + axes.set_xlabel('') + self._trace.analysis.status.plotOverutilized(axes) + + axes = pltaxes[1] + axes.set_title('LITTLE Cluster'); + if avg_lfreq > 0: + axes.axhline(avg_lfreq, color='b', linestyle='--', linewidth=2); + axes.set_ylim( + (self._platform['freqs']['little'][0] - 100000)/1e3, + (self._platform['freqs']['little'][-1] + 100000)/1e3 + ); + if len(lfreq) > 0: + lfreq['frequency'].plot(style=['b-'], ax=axes, + drawstyle='steps-post', alpha=0.4); + else: + logging.warn('NO LITTLE CPUs frequency events to plot') + axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.set_ylabel('MHz') + axes.grid(True); + self._trace.analysis.status.plotOverutilized(axes) + + # Save generated plots into datadir + figname = '{}/{}cluster_freqs.png'\ + .format(self._trace.plots_dir, self._trace.plots_prefix) + pl.savefig(figname, bbox_inches='tight') + + logging.info('LITTLE cluster average frequency: %.3f GHz', + avg_lfreq/1e3) + logging.info('big cluster average frequency: %.3f GHz', + avg_bfreq/1e3) + + return (avg_lfreq/1e3, avg_bfreq/1e3) + + def plotCPUFrequencyResidency(self, cpus=None, pct=False, active=False): + """ + Plot per-CPU frequency residency. big CPUs are plotted first and then + LITTLEs. + + Requires the following trace events: + - cpu_frequency + - cpu_idle + + :param cpus: List of cpus. By default plot all CPUs + :type cpus: list(str) + + :param pct: plot residencies in percentage + :type pct: bool + + :param active: for percentage plot specify whether to plot active or + total time. Default is TOTAL time + :type active: bool + """ + if not self._trace.hasEvents('cpu_frequency'): + logging.warn('Events [cpu_frequency] not found, plot DISABLED!') + return + if not self._trace.hasEvents('cpu_idle'): + logging.warn('Events [cpu_idle] not found, plot DISABLED!') + return + + if cpus is None: + # Generate plots only for available CPUs + cpufreq_data = self._dfg_trace_event('cpu_frequency') + _cpus = range(cpufreq_data.cpu.max()+1) + else: + _cpus = listify(cpus) + + # Split between big and LITTLE CPUs ordered from higher to lower ID + _cpus.reverse() + big_cpus = [c for c in _cpus if c in self._platform['clusters']['big']] + little_cpus = [c for c in _cpus if c in + self._platform['clusters']['little']] + _cpus = big_cpus + little_cpus + + # Precompute active and total time for each CPU + residencies = [] + xmax = 0.0 + for c in _cpus: + r = self._getCPUFrequencyResidency(c) + residencies.append(ResidencyData('CPU{}'.format(c), r)) + + max_time = r.total.max().values[0] + if xmax < max_time: + xmax = max_time + + self._plotFrequencyResidency(residencies, 'cpu', xmax, pct, active) + + def plotClusterFrequencyResidency(self, clusters=None, + pct=False, active=False): + """ + Plot the frequency residency in a given cluster, i.e. the amount of + time cluster `cluster` spent at frequency `f_i`. By default, both 'big' + and 'LITTLE' clusters data are plotted. + + Requires the following trace events: + - cpu_frequency + - cpu_idle + + :param clusters: name of the clusters to be plotted (all of them by + default) + :type clusters: str ot list(str) + + :param pct: plot residencies in percentage + :type pct: bool + + :param active: for percentage plot specify whether to plot active or + total time. Default is TOTAL time + :type active: bool + """ + if not self._trace.hasEvents('cpu_frequency'): + logging.warn('Events [cpu_frequency] not found, plot DISABLED!') + return + if not self._trace.hasEvents('cpu_idle'): + logging.warn('Events [cpu_idle] not found, plot DISABLED!') + return + + # Assumption: all CPUs in a cluster run at the same frequency, i.e. the + # frequency is scaled per-cluster not per-CPU. Hence, we can limit the + # cluster frequencies data to a single CPU + if not self._trace.freq_coherency: + logging.warn('Cluster frequency is not coherent, plot DISABLED!') + return + + # Sanitize clusters + if clusters is None: + _clusters = self._platform['clusters'].keys() + else: + _clusters = listify(clusters) + + # Precompute active and total time for each cluster + residencies = [] + xmax = 0.0 + for c in _clusters: + r = self._getClusterFrequencyResidency( + self._platform['clusters'][c.lower()]) + residencies.append(ResidencyData('{} Cluster'.format(c), r)) + + max_time = r.total.max().values[0] + if xmax < max_time: + xmax = max_time + + self._plotFrequencyResidency(residencies, 'cluster', xmax, pct, active) + +################################################################################ +# Utility Methods +################################################################################ + + @memoized + def _getCPUActiveSignal(self, cpu): + """ + Build a square wave representing the active (i.e. non-idle) CPU time, + i.e.: + cpu_active[t] == 1 if at least one CPU is reported to be + non-idle by CPUFreq at time t + cpu_active[t] == 0 otherwise + + :param cpu: CPU ID + :type cpu: int + """ + if not self._trace.hasEvents('cpu_idle'): + logging.warn('Events [cpu_idle] not found, '\ + 'cannot compute CPU active signal!') + return None + + idle_df = self._dfg_trace_event('cpu_idle') + cpu_df = idle_df[idle_df.cpu_id == cpu] + + cpu_active = cpu_df.state.apply( + lambda s: 1 if s == NON_IDLE_STATE else 0 + ) + + start_time = 0.0 + if not self._trace.ftrace.normalized_time: + start_time = self._trace.ftrace.basetime + if cpu_active.index[0] != start_time: + entry_0 = pd.Series(cpu_active.iloc[0] ^ 1, index=[start_time]) + cpu_active = pd.concat([entry_0, cpu_active]) + + return cpu_active + + @memoized + def _getClusterActiveSignal(self, cluster): + """ + Build a square wave representing the active (i.e. non-idle) cluster + time, i.e.: + cluster_active[t] == 1 if at least one CPU is reported to be + non-idle by CPUFreq at time t + cluster_active[t] == 0 otherwise + + :param cluster: list of CPU IDs belonging to a cluster + :type cluster: list(int) + """ + cpu_active = {} + for cpu in cluster: + cpu_active[cpu] = self._getCPUActiveSignal(cpu) + + active = pd.DataFrame(cpu_active) + active.fillna(method='ffill', inplace=True) + + # Cluster active is the OR between the actives on each CPU + # belonging to that specific cluster + cluster_active = reduce( + operator.or_, + [cpu_active.astype(int) for _, cpu_active in + active.iteritems()] + ) + + return cluster_active + + @memoized + def _getClusterFrequencyResidency(self, cluster): + """ + Get a DataFrame with per cluster frequency residency, i.e. amount of + time spent at a given frequency in each cluster. + + :param cluster: this can be either a single CPU ID or a list of CPU IDs + belonging to a cluster or the cluster name as specified in the + platform description + :type cluster: str or int or list(int) + + :returns: namedtuple(ResidencyTime) - tuple of total and active time + dataframes + + :raises: KeyError + """ + if not self._trace.hasEvents('cpu_frequency'): + logging.warn('Events [cpu_frequency] not found, '\ + 'frequency residency computation not possible!') + return None + if not self._trace.hasEvents('cpu_idle'): + logging.warn('Events [cpu_idle] not found, '\ + 'frequency residency computation not possible!') + return None + + if isinstance(cluster, str): + try: + _cluster = self._platform['clusters'][cluster.lower()] + except KeyError: + logging.warn('%s cluster not found!', cluster) + return None + else: + _cluster = listify(cluster) + + freq_df = self._dfg_trace_event('cpu_frequency') + # Assumption: all CPUs in a cluster run at the same frequency, i.e. the + # frequency is scaled per-cluster not per-CPU. Hence, we can limit the + # cluster frequencies data to a single CPU. This assumption is verified + # by the Trace module when parsing the trace. + if len(_cluster) > 1 and not self._trace.freq_coherency: + logging.warn('Cluster frequency is NOT coherent,'\ + 'cannot compute residency!') + return None + cluster_freqs = freq_df[freq_df.cpu == _cluster[0]] + + ### Compute TOTAL Time ### + time_intervals = cluster_freqs.index[1:] - cluster_freqs.index[:-1] + total_time = pd.DataFrame({ + 'time' : time_intervals, + 'frequency' : [f/1000.0 for f in cluster_freqs.iloc[:-1].frequency] + }) + total_time = total_time.groupby(['frequency']).sum() + + ### Compute ACTIVE Time ### + cluster_active = self._getClusterActiveSignal(_cluster) + + # In order to compute the active time spent at each frequency we + # multiply 2 square waves: + # - cluster_active, a square wave of the form: + # cluster_active[t] == 1 if at least one CPU is reported to be + # non-idle by CPUFreq at time t + # cluster_active[t] == 0 otherwise + # - freq_active, square wave of the form: + # freq_active[t] == 1 if at time t the frequency is f + # freq_active[t] == 0 otherwise + available_freqs = sorted(cluster_freqs.frequency.unique()) + new_idx = sorted(cluster_freqs.index.tolist() + \ + cluster_active.index.tolist()) + cluster_freqs = cluster_freqs.reindex(new_idx, method='ffill') + cluster_active = cluster_active.reindex(new_idx, method='ffill') + nonidle_time = [] + for f in available_freqs: + freq_active = cluster_freqs.frequency.apply( + lambda x: 1 if x == f else 0 + ) + active_t = cluster_active * freq_active + # Compute total time by integrating the square wave + nonidle_time.append(self._trace.integrate_square_wave(active_t)) + + active_time = pd.DataFrame({'time' : nonidle_time}, + index=[f/1000.0 for f in available_freqs]) + active_time.index.name = 'frequency' + return ResidencyTime(total_time, active_time) + + def _getCPUFrequencyResidency(self, cpu): + """ + Get a DataFrame with per-CPU frequency residency, i.e. amount of + time CPU `cpu` spent at each frequency. Both total and active times + will be computed. + + :param cpu: CPU ID + :type cpu: int + + :returns: namedtuple(ResidencyTime) - tuple of total and active time + dataframes + """ + return self._getClusterFrequencyResidency(cpu) + + def _plotFrequencyResidencyAbs(self, axes, residency, n_plots, + is_first, is_last, xmax, title=''): + """ + Private method to generate frequency residency plots. + + :param axes: axes over which to generate the plot + :type axes: matplotlib.axes.Axes + + :param residency: tuple of total and active time dataframes + :type residency: namedtuple(ResidencyTime) + + :param n_plots: total number of plots + :type n_plots: int + + :param is_first: if True this is the first plot + :type is_first: bool + + :param is_first: if True this is the last plot + :type is_first: bool + + :param xmax: x-axes higher bound + :param xmax: double + + :param title: title of this subplot + :type title: str + """ + yrange = 0.4 * max(6, len(residency.total)) * n_plots + residency.total.plot.barh(ax = axes, color='g', + legend=False, figsize=(16,yrange)) + residency.active.plot.barh(ax = axes, color='r', + legend=False, figsize=(16,yrange)) + + axes.set_xlim(0, 1.05*xmax) + axes.set_ylabel('Frequency [MHz]') + axes.set_title(title) + axes.grid(True) + if is_last: + axes.set_xlabel('Time [s]') + else: + axes.set_xticklabels([]) + + if is_first: + # Put title on top of the figure. As of now there is no clean way + # to make the title appear always in the same position in the + # figure because figure heights may vary between different + # platforms (different number of OPPs). Hence, we use annotation + legend_y = axes.get_ylim()[1] + axes.annotate('OPP Residency Time', xy=(0, legend_y), + xytext=(-50, 45), textcoords='offset points', + fontsize=18) + axes.annotate('GREEN: Total', xy=(0, legend_y), + xytext=(-50, 25), textcoords='offset points', + color='g', fontsize=14) + axes.annotate('RED: Active', xy=(0, legend_y), + xytext=(50, 25), textcoords='offset points', + color='r', fontsize=14) + + def _plotFrequencyResidencyPct(self, axes, residency_df, label, + n_plots, is_first, is_last, res_type): + """ + Private method to generate PERCENTAGE frequency residency plots. + + :param axes: axes over which to generate the plot + :type axes: matplotlib.axes.Axes + + :param residency_df: residency time dataframe + :type residency_df: :mod:`pandas.DataFrame` + + :param label: label to be used for percentage residency dataframe + :type label: str + + :param n_plots: total number of plots + :type n_plots: int + + :param is_first: if True this is the first plot + :type is_first: bool + + :param is_first: if True this is the last plot + :type is_first: bool + + :param res_type: type of residency, either TOTAL or ACTIVE + :type title: str + """ + # Compute sum of the time intervals + duration = residency_df.time.sum() + residency_pct = pd.DataFrame( + {label : residency_df.time.apply(lambda x: x*100/duration)}, + index=residency_df.index + ) + yrange = 3 * n_plots + residency_pct.T.plot.barh(ax=axes, stacked=True, figsize=(16, yrange)) + + axes.legend(loc='lower center', ncol=7) + axes.set_xlim(0, 100) + axes.grid(True) + if is_last: + axes.set_xlabel('Residency [%]') + else: + axes.set_xticklabels([]) + if is_first: + legend_y = axes.get_ylim()[1] + axes.annotate('OPP {} Residency Time'.format(res_type), + xy=(0, legend_y), xytext=(-50, 35), + textcoords='offset points', fontsize=18) + + def _plotFrequencyResidency(self, residencies, entity_name, xmax, + pct, active): + """ + Generate Frequency residency plots for the given entities. + + :param residencies: + :type residencies: namedtuple(ResidencyData) - tuple containing: + 1) as first element, a label to be used as subplot title + 2) as second element, a namedtuple(ResidencyTime) + + :param entity_name: name of the entity ('cpu' or 'cluster') used in the + figure name + :type entity_name: str + + :param xmax: upper bound of x-axes + :type xmax: double + + :param pct: plot residencies in percentage + :type pct: bool + + :param active: for percentage plot specify whether to plot active or + total time. Default is TOTAL time + :type active: bool + """ + n_plots = len(residencies) + gs = gridspec.GridSpec(n_plots, 1) + fig = plt.figure() + + figtype = "" + for idx, data in enumerate(residencies): + label = data[0] + r = data[1] + if r is None: + plt.close(fig) + return + + axes = fig.add_subplot(gs[idx]) + is_first = idx == 0 + is_last = idx+1 == n_plots + if pct and active: + self._plotFrequencyResidencyPct(axes, data.residency.active, + data.label, n_plots, + is_first, is_last, + 'ACTIVE') + figtype = "_pct_active" + continue + if pct: + self._plotFrequencyResidencyPct(axes, data.residency.total, + data.label, n_plots, + is_first, is_last, + 'TOTAL') + figtype = "_pct_total" + continue + + self._plotFrequencyResidencyAbs(axes, data.residency, + n_plots, is_first, + is_last, xmax, + title=data.label) + + figname = '{}/{}{}_freq_residency{}.png'\ + .format(self._trace.plots_dir, + self._trace.plots_prefix, + entity_name, figtype) + pl.savefig(figname, bbox_inches='tight') + diff --git a/libs/utils/trace.py b/libs/utils/trace.py index db87824f6..8a2332fcd 100644 --- a/libs/utils/trace.py +++ b/libs/utils/trace.py @@ -369,6 +369,7 @@ class Trace(object): return df return df.loc[df.index.get_level_values(1).isin(listify(functions))] + ################################################################################ # Trace Events Sanitize Methods ################################################################################ @@ -504,6 +505,22 @@ class Trace(object): # Utility Methods ################################################################################ + def integrate_square_wave(self, sq_wave): + """ + Compute the integral of a square wave time series. + + :param sq_wave: square wave assuming only 1.0 and 0.0 values + :type sq_wave: :mod:`pandas.Series` + """ + sq_wave.iloc[-1] = 0.0 + # Compact signal to obtain only 1-0-1-0 sequences + comp_sig = sq_wave.loc[sq_wave.shift() != sq_wave] + # First value for computing the difference must be a 1 + if comp_sig.iloc[0] == 0.0: + return sum(comp_sig.iloc[2::2].index - comp_sig.iloc[1:-1:2].index) + else: + return sum(comp_sig.iloc[1::2].index - comp_sig.iloc[:-1:2].index) + def _loadFunctionsStats(self, path='trace.stats'): if os.path.isdir(path): path = os.path.join(path, 'trace.stats') diff --git a/libs/utils/trace_analysis.py b/libs/utils/trace_analysis.py index 1f24f842d..f8c3ad2e6 100644 --- a/libs/utils/trace_analysis.py +++ b/libs/utils/trace_analysis.py @@ -123,102 +123,6 @@ class TraceAnalysis(object): axes.set_ylabel(ylabel) axes.get_xaxis().set_visible(False) - def plotClusterFrequencies(self, title='Clusters Frequencies'): - if not self.trace.hasEvents('cpu_frequency'): - logging.warn('Events [cpu_frequency] not found, '\ - 'plot DISABLED!') - return - df = self.trace.df('cpu_frequency') - - pd.options.mode.chained_assignment = None - - # Extract LITTLE and big clusters frequencies - # and scale them to [MHz] - if len(self.platform['clusters']['little']): - lfreq = df[df.cpu == self.platform['clusters']['little'][-1]] - lfreq['frequency'] = lfreq['frequency']/1e3 - else: - lfreq = [] - if len(self.platform['clusters']['big']): - bfreq = df[df.cpu == self.platform['clusters']['big'][-1]] - bfreq['frequency'] = bfreq['frequency']/1e3 - else: - bfreq = [] - - # Compute AVG frequency for LITTLE cluster - avg_lfreq = 0 - if len(lfreq) > 0: - lfreq['timestamp'] = lfreq.index; - lfreq['delta'] = (lfreq['timestamp'] - lfreq['timestamp'].shift()).fillna(0).shift(-1); - lfreq['cfreq'] = (lfreq['frequency'] * lfreq['delta']).fillna(0); - timespan = lfreq.iloc[-1].timestamp - lfreq.iloc[0].timestamp; - avg_lfreq = lfreq['cfreq'].sum()/timespan; - - # Compute AVG frequency for big cluster - avg_bfreq = 0 - if len(bfreq) > 0: - bfreq['timestamp'] = bfreq.index; - bfreq['delta'] = (bfreq['timestamp'] - bfreq['timestamp'].shift()).fillna(0).shift(-1); - bfreq['cfreq'] = (bfreq['frequency'] * bfreq['delta']).fillna(0); - timespan = bfreq.iloc[-1].timestamp - bfreq.iloc[0].timestamp; - avg_bfreq = bfreq['cfreq'].sum()/timespan; - - pd.options.mode.chained_assignment = 'warn' - - # Setup a dual cluster plot - fig, pltaxes = plt.subplots(2, 1, figsize=(16, 8)); - plt.suptitle(title, y=.97, fontsize=16, - horizontalalignment='center'); - - # Plot Cluster frequencies - axes = pltaxes[0] - axes.set_title('big Cluster'); - if avg_bfreq > 0: - axes.axhline(avg_bfreq, color='r', linestyle='--', linewidth=2); - axes.set_ylim( - (self.platform['freqs']['big'][0] - 100000)/1e3, - (self.platform['freqs']['big'][-1] + 100000)/1e3 - ); - if len(bfreq) > 0: - bfreq['frequency'].plot(style=['r-'], ax=axes, - drawstyle='steps-post', alpha=0.4); - else: - logging.warn('NO big CPUs frequency events to plot') - axes.set_xlim(self.x_min, self.x_max); - axes.set_ylabel('MHz') - axes.grid(True); - axes.set_xticklabels([]) - axes.set_xlabel('') - self.plotOverutilized(axes) - - axes = pltaxes[1] - axes.set_title('LITTLE Cluster'); - if avg_lfreq > 0: - axes.axhline(avg_lfreq, color='b', linestyle='--', linewidth=2); - axes.set_ylim( - (self.platform['freqs']['little'][0] - 100000)/1e3, - (self.platform['freqs']['little'][-1] + 100000)/1e3 - ); - if len(lfreq) > 0: - lfreq['frequency'].plot(style=['b-'], ax=axes, - drawstyle='steps-post', alpha=0.4); - else: - logging.warn('NO LITTLE CPUs frequency events to plot') - axes.set_xlim(self.x_min, self.x_max); - axes.set_ylabel('MHz') - axes.grid(True); - self.plotOverutilized(axes) - - # Save generated plots into datadir - figname = '{}/{}cluster_freqs.png'.format(self.plotsdir, self.prefix) - pl.savefig(figname, bbox_inches='tight') - - logging.info('LITTLE cluster average frequency: %.3f GHz', - avg_lfreq/1e3) - logging.info('big cluster average frequency: %.3f GHz', - avg_bfreq/1e3) - - return (avg_lfreq/1e3, avg_bfreq/1e3) def __addCapacityColum(self): df = self.trace.df('cpu_capacity') @@ -894,457 +798,3 @@ class TraceAnalysis(object): figname = '{}/{}schedtune_conf.png'.format(self.plotsdir, self.prefix) pl.savefig(figname, bbox_inches='tight') - @memoized - def getCPUActiveSignal(self, cpu): - """ - Build a square wave representing the active (i.e. non-idle) CPU time, - i.e.: - cpu_active[t] == 1 if at least one CPU is reported to be - non-idle by CPUFreq at time t - cpu_active[t] == 0 otherwise - - :param cpu: CPU ID - :type cpu: int - """ - if not self.trace.hasEvents('cpu_idle'): - logging.warn('Events [cpu_idle] not found, '\ - 'cannot compute CPU active signal!') - return None - - idle_df = self.trace.df('cpu_idle') - cpu_df = idle_df[idle_df.cpu_id == cpu] - - cpu_active = cpu_df.state.apply( - lambda s: 1 if s == NON_IDLE_STATE else 0 - ) - - start_time = 0.0 - if not self.trace.ftrace.normalized_time: - start_time = self.trace.ftrace.basetime - if cpu_active.index[0] != start_time: - entry_0 = pd.Series(cpu_active.iloc[0] ^ 1, index=[start_time]) - cpu_active = pd.concat([entry_0, cpu_active]) - - return cpu_active - - @memoized - def getClusterActiveSignal(self, cluster): - """ - Build a square wave representing the active (i.e. non-idle) cluster - time, i.e.: - cluster_active[t] == 1 if at least one CPU is reported to be - non-idle by CPUFreq at time t - cluster_active[t] == 0 otherwise - - :param cluster: list of CPU IDs belonging to a cluster - :type cluster: list(int) - """ - cpu_active = {} - for cpu in cluster: - cpu_active[cpu] = self.getCPUActiveSignal(cpu) - - active = pd.DataFrame(cpu_active) - active.fillna(method='ffill', inplace=True) - - # Cluster active is the OR between the actives on each CPU - # belonging to that specific cluster - cluster_active = reduce( - operator.or_, - [cpu_active.astype(int) for _, cpu_active in - active.iteritems()] - ) - - return cluster_active - - def _integrate_square_wave(self, sq_wave): - """ - Compute the integral of a square wave time series. - - :param sq_wave: square wave assuming only 1.0 and 0.0 values - :type sq_wave: :mod:`pandas.Series` - """ - sq_wave.iloc[-1] = 0.0 - # Compact signal to obtain only 1-0-1-0 sequences - comp_sig = sq_wave.loc[sq_wave.shift() != sq_wave] - # First value for computing the difference must be a 1 - if comp_sig.iloc[0] == 0.0: - return sum(comp_sig.iloc[2::2].index - comp_sig.iloc[1:-1:2].index) - else: - return sum(comp_sig.iloc[1::2].index - comp_sig.iloc[:-1:2].index) - - @memoized - def getClusterFrequencyResidency(self, cluster): - """ - Get a DataFrame with per cluster frequency residency, i.e. amount of - time spent at a given frequency in each cluster. - - :param cluster: this can be either a single CPU ID or a list of CPU IDs - belonging to a cluster or the cluster name as specified in the - platform description - :type cluster: str or int or list(int) - - :returns: namedtuple(ResidencyTime) - tuple of total and active time - dataframes - - :raises: KeyError - """ - if not self.trace.hasEvents('cpu_frequency'): - logging.warn('Events [cpu_frequency] not found, '\ - 'frequency residency computation not possible!') - return None - if not self.trace.hasEvents('cpu_idle'): - logging.warn('Events [cpu_idle] not found, '\ - 'frequency residency computation not possible!') - return None - - if isinstance(cluster, str): - try: - _cluster = self.platform['clusters'][cluster.lower()] - except KeyError: - logging.warn('%s cluster not found!', cluster) - return None - else: - _cluster = listify(cluster) - - freq_df = self.trace.df('cpu_frequency') - # Assumption: all CPUs in a cluster run at the same frequency, i.e. the - # frequency is scaled per-cluster not per-CPU. Hence, we can limit the - # cluster frequencies data to a single CPU. This assumption is verified - # by the Trace module when parsing the trace. - if len(_cluster) > 1 and not self.trace.freq_coherency: - logging.warn('Cluster frequency is NOT coherent,'\ - 'cannot compute residency!') - return None - cluster_freqs = freq_df[freq_df.cpu == _cluster[0]] - - ### Compute TOTAL Time ### - time_intervals = cluster_freqs.index[1:] - cluster_freqs.index[:-1] - total_time = pd.DataFrame({ - 'time' : time_intervals, - 'frequency' : [f/1000.0 for f in cluster_freqs.iloc[:-1].frequency] - }) - total_time = total_time.groupby(['frequency']).sum() - - ### Compute ACTIVE Time ### - cluster_active = self.getClusterActiveSignal(_cluster) - - # In order to compute the active time spent at each frequency we - # multiply 2 square waves: - # - cluster_active, a square wave of the form: - # cluster_active[t] == 1 if at least one CPU is reported to be - # non-idle by CPUFreq at time t - # cluster_active[t] == 0 otherwise - # - freq_active, square wave of the form: - # freq_active[t] == 1 if at time t the frequency is f - # freq_active[t] == 0 otherwise - available_freqs = sorted(cluster_freqs.frequency.unique()) - new_idx = sorted(cluster_freqs.index.tolist() + \ - cluster_active.index.tolist()) - cluster_freqs = cluster_freqs.reindex(new_idx, method='ffill') - cluster_active = cluster_active.reindex(new_idx, method='ffill') - nonidle_time = [] - for f in available_freqs: - freq_active = cluster_freqs.frequency.apply( - lambda x: 1 if x == f else 0 - ) - active_t = cluster_active * freq_active - # Compute total time by integrating the square wave - nonidle_time.append(self._integrate_square_wave(active_t)) - - active_time = pd.DataFrame({'time' : nonidle_time}, - index=[f/1000.0 for f in available_freqs]) - active_time.index.name = 'frequency' - return ResidencyTime(total_time, active_time) - - def getCPUFrequencyResidency(self, cpu): - """ - Get a DataFrame with per-CPU frequency residency, i.e. amount of - time CPU `cpu` spent at each frequency. Both total and active times - will be computed. - - :param cpu: CPU ID - :type cpu: int - - :returns: namedtuple(ResidencyTime) - tuple of total and active time - dataframes - """ - return self.getClusterFrequencyResidency(cpu) - - def _plotFrequencyResidencyAbs(self, axes, residency, n_plots, - is_first, is_last, xmax, title=''): - """ - Private method to generate frequency residency plots. - - :param axes: axes over which to generate the plot - :type axes: matplotlib.axes.Axes - - :param residency: tuple of total and active time dataframes - :type residency: namedtuple(ResidencyTime) - - :param n_plots: total number of plots - :type n_plots: int - - :param is_first: if True this is the first plot - :type is_first: bool - - :param is_first: if True this is the last plot - :type is_first: bool - - :param xmax: x-axes higher bound - :param xmax: double - - :param title: title of this subplot - :type title: str - """ - yrange = 0.4 * max(6, len(residency.total)) * n_plots - residency.total.plot.barh(ax = axes, color='g', - legend=False, figsize=(16,yrange)) - residency.active.plot.barh(ax = axes, color='r', - legend=False, figsize=(16,yrange)) - - axes.set_xlim(0, 1.05*xmax) - axes.set_ylabel('Frequency [MHz]') - axes.set_title(title) - axes.grid(True) - if is_last: - axes.set_xlabel('Time [s]') - else: - axes.set_xticklabels([]) - - if is_first: - # Put title on top of the figure. As of now there is no clean way - # to make the title appear always in the same position in the - # figure because figure heights may vary between different - # platforms (different number of OPPs). Hence, we use annotation - legend_y = axes.get_ylim()[1] - axes.annotate('OPP Residency Time', xy=(0, legend_y), - xytext=(-50, 45), textcoords='offset points', - fontsize=18) - axes.annotate('GREEN: Total', xy=(0, legend_y), - xytext=(-50, 25), textcoords='offset points', - color='g', fontsize=14) - axes.annotate('RED: Active', xy=(0, legend_y), - xytext=(50, 25), textcoords='offset points', - color='r', fontsize=14) - - def _plotFrequencyResidencyPct(self, axes, residency_df, label, - n_plots, is_first, is_last, res_type): - """ - Private method to generate PERCENTAGE frequency residency plots. - - :param axes: axes over which to generate the plot - :type axes: matplotlib.axes.Axes - - :param residency_df: residency time dataframe - :type residency_df: :mod:`pandas.DataFrame` - - :param label: label to be used for percentage residency dataframe - :type label: str - - :param n_plots: total number of plots - :type n_plots: int - - :param is_first: if True this is the first plot - :type is_first: bool - - :param is_first: if True this is the last plot - :type is_first: bool - - :param res_type: type of residency, either TOTAL or ACTIVE - :type title: str - """ - # Compute sum of the time intervals - duration = residency_df.time.sum() - residency_pct = pd.DataFrame( - {label : residency_df.time.apply(lambda x: x*100/duration)}, - index=residency_df.index - ) - yrange = 3 * n_plots - residency_pct.T.plot.barh(ax=axes, stacked=True, figsize=(16, yrange)) - - axes.legend(loc='lower center', ncol=7) - axes.set_xlim(0, 100) - axes.grid(True) - if is_last: - axes.set_xlabel('Residency [%]') - else: - axes.set_xticklabels([]) - if is_first: - legend_y = axes.get_ylim()[1] - axes.annotate('OPP {} Residency Time'.format(res_type), - xy=(0, legend_y), xytext=(-50, 35), - textcoords='offset points', fontsize=18) - - def _plotFrequencyResidency(self, residencies, entity_name, xmax, - pct, active): - """ - Generate Frequency residency plots for the given entities. - - :param residencies: - :type residencies: namedtuple(ResidencyData) - tuple containing: - 1) as first element, a label to be used as subplot title - 2) as second element, a namedtuple(ResidencyTime) - - :param entity_name: name of the entity ('cpu' or 'cluster') used in the - figure name - :type entity_name: str - - :param xmax: upper bound of x-axes - :type xmax: double - - :param pct: plot residencies in percentage - :type pct: bool - - :param active: for percentage plot specify whether to plot active or - total time. Default is TOTAL time - :type active: bool - """ - n_plots = len(residencies) - gs = gridspec.GridSpec(n_plots, 1) - fig = plt.figure() - - figtype = "" - for idx, data in enumerate(residencies): - label = data[0] - r = data[1] - if r is None: - plt.close(fig) - return - - axes = fig.add_subplot(gs[idx]) - is_first = idx == 0 - is_last = idx+1 == n_plots - if pct and active: - self._plotFrequencyResidencyPct(axes, data.residency.active, - data.label, n_plots, - is_first, is_last, - 'ACTIVE') - figtype = "_pct_active" - continue - if pct: - self._plotFrequencyResidencyPct(axes, data.residency.total, - data.label, n_plots, - is_first, is_last, - 'TOTAL') - figtype = "_pct_total" - continue - - self._plotFrequencyResidencyAbs(axes, data.residency, - n_plots, is_first, - is_last, xmax, - title=data.label) - - figname = '{}/{}{}_freq_residency{}.png'\ - .format(self.plotsdir, self.prefix, entity_name, figtype) - - pl.savefig(figname, bbox_inches='tight') - - def plotCPUFrequencyResidency(self, cpus=None, pct=False, active=False): - """ - Plot per-CPU frequency residency. big CPUs are plotted first and then - LITTLEs. - - Requires the following trace events: - - cpu_frequency - - cpu_idle - - :param cpus: List of cpus. By default plot all CPUs - :type cpus: list(str) - - :param pct: plot residencies in percentage - :type pct: bool - - :param active: for percentage plot specify whether to plot active or - total time. Default is TOTAL time - :type active: bool - """ - if not self.trace.hasEvents('cpu_frequency'): - logging.warn('Events [cpu_frequency] not found, plot DISABLED!') - return - if not self.trace.hasEvents('cpu_idle'): - logging.warn('Events [cpu_idle] not found, plot DISABLED!') - return - - if cpus is None: - # Generate plots only for available CPUs - cpufreq_data = self.trace.df('cpu_frequency') - _cpus = range(cpufreq_data.cpu.max()+1) - else: - _cpus = listify(cpus) - - # Split between big and LITTLE CPUs ordered from higher to lower ID - _cpus.reverse() - big_cpus = [c for c in _cpus if c in self.platform['clusters']['big']] - little_cpus = [c for c in _cpus if c in - self.platform['clusters']['little']] - _cpus = big_cpus + little_cpus - - # Precompute active and total time for each CPU - residencies = [] - xmax = 0.0 - for c in _cpus: - r = self.getCPUFrequencyResidency(c) - residencies.append(ResidencyData('CPU{}'.format(c), r)) - - max_time = r.total.max().values[0] - if xmax < max_time: - xmax = max_time - - self._plotFrequencyResidency(residencies, 'cpu', xmax, pct, active) - - def plotClusterFrequencyResidency(self, clusters=None, - pct=False, active=False): - """ - Plot the frequency residency in a given cluster, i.e. the amount of - time cluster `cluster` spent at frequency `f_i`. By default, both 'big' - and 'LITTLE' clusters data are plotted. - - Requires the following trace events: - - cpu_frequency - - cpu_idle - - :param clusters: name of the clusters to be plotted (all of them by - default) - :type clusters: str ot list(str) - - :param pct: plot residencies in percentage - :type pct: bool - - :param active: for percentage plot specify whether to plot active or - total time. Default is TOTAL time - :type active: bool - """ - if not self.trace.hasEvents('cpu_frequency'): - logging.warn('Events [cpu_frequency] not found, plot DISABLED!') - return - if not self.trace.hasEvents('cpu_idle'): - logging.warn('Events [cpu_idle] not found, plot DISABLED!') - return - - # Assumption: all CPUs in a cluster run at the same frequency, i.e. the - # frequency is scaled per-cluster not per-CPU. Hence, we can limit the - # cluster frequencies data to a single CPU - if not self.trace.freq_coherency: - logging.warn('Cluster frequency is not coherent, plot DISABLED!') - return - - # Sanitize clusters - if clusters is None: - _clusters = self.platform['clusters'].keys() - else: - _clusters = listify(clusters) - - # Precompute active and total time for each cluster - residencies = [] - xmax = 0.0 - for c in _clusters: - r = self.getClusterFrequencyResidency( - self.platform['clusters'][c.lower()]) - residencies.append(ResidencyData('{} Cluster'.format(c), r)) - - max_time = r.total.max().values[0] - if xmax < max_time: - xmax = max_time - - self._plotFrequencyResidency(residencies, 'cluster', xmax, pct, active) - -- GitLab From 84422589a5999d2d53c6e38fb66f77fbddaa059c Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Fri, 22 Jul 2016 14:38:58 +0100 Subject: [PATCH 09/24] libs/utils/analysis: add STATUS analysis module This patch move the Overutilized analysis code, originally part of the trace_analysis.py module, to be a new TraceAnalysis module with dynamic registration via the AnalysisRegister. Signed-off-by: Patrick Bellasi --- libs/utils/analysis/status_analysis.py | 91 ++++++++++++++++++++++++++ libs/utils/trace_analysis.py | 37 ----------- 2 files changed, 91 insertions(+), 37 deletions(-) create mode 100644 libs/utils/analysis/status_analysis.py diff --git a/libs/utils/analysis/status_analysis.py b/libs/utils/analysis/status_analysis.py new file mode 100644 index 000000000..32f8d359f --- /dev/null +++ b/libs/utils/analysis/status_analysis.py @@ -0,0 +1,91 @@ +# SPDX-License-Identifier: Apache-2.0 +# +# Copyright (C) 2015, ARM Limited and contributors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import matplotlib.gridspec as gridspec +import matplotlib.pyplot as plt +import pandas as pd + +from analysis_module import AnalysisModule + +# Configure logging +import logging + +class StatusAnalysis(AnalysisModule): + + def __init__(self, trace): + """ + Support for System Status analysis + """ + super(StatusAnalysis, self).__init__(trace) + + +################################################################################ +# DataFrame Getter Methods +################################################################################ + + def _dfg_overutilized(self): + if not self._trace.hasEvents('sched_overutilized'): + return None + + # Build sequence of overutilization "bands" + df = self._dfg_trace_event('sched_overutilized') + + # Remove duplicated index events, keep only last event which is the + # only one with a non null length + df = df[df.len != 0] + # This filtering can also be achieved by removing events happening at + # the same time, but perhaps this filtering is more complex + # df = df.reset_index()\ + # .drop_duplicates(subset='Time', keep='last')\ + # .set_index('Time') + + return df[['len', 'overutilized']] + + +################################################################################ +# Plotting Methods +################################################################################ + + def plotOverutilized(self, axes=None): + if not self._trace.hasEvents('sched_overutilized'): + logging.warn('Events [sched_overutilized] not found, '\ + 'plot DISABLED!') + return + + df = self._dfg_overutilized() + + # Compute intervals in which the system is reported to be overutilized + bands = [(t, df['len'][t], df['overutilized'][t]) for t in df.index] + + # If not axis provided: generate a standalone plot + if not axes: + gs = gridspec.GridSpec(1, 1) + plt.figure(figsize=(16, 1)) + axes = plt.subplot(gs[0,0]) + axes.set_title('System Status {white: EAS mode, red: Non EAS mode}'); + axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.set_yticklabels([]) + axes.set_xlabel('Time [s]') + axes.grid(True); + + # Otherwise: draw overutilized bands on top of the specified plot + for (t1,td,overutilized) in bands: + if not overutilized: + continue + t2 = t1+td + axes.axvspan(t1, t2, facecolor='r', alpha=0.1) + diff --git a/libs/utils/trace_analysis.py b/libs/utils/trace_analysis.py index f8c3ad2e6..38cfd6453 100644 --- a/libs/utils/trace_analysis.py +++ b/libs/utils/trace_analysis.py @@ -222,43 +222,6 @@ class TraceAnalysis(object): lcpus = set(cpus) & set(self.platform['clusters']['little']) self.__plotCPU(lcpus, "LITTLE") - def plotOverutilized(self, axes=None): - if not self.trace.hasEvents('sched_overutilized'): - logging.warn('Events [sched_overutilized] not found, '\ - 'plot DISABLED!') - return - - # Build sequence of overutilization "bands" - df = self.trace.df('sched_overutilized') - - # Remove duplicated index events, keep only last event which is the - # only one with a non null length - df = df[df.len != 0] - # This filtering can also be achieved by removing events happening at - # the same time, but perhaps this filtering is more complex - # df = df.reset_index()\ - # .drop_duplicates(subset='Time', keep='last')\ - # .set_index('Time') - - # Compute intervals in which the system is reported to be overutilized - bands = [(t, df['len'][t], df['overutilized'][t]) for t in df.index] - - # If not axis provided: generate a standalone plot - if not axes: - gs = gridspec.GridSpec(1, 1) - plt.figure(figsize=(16, 1)) - axes = plt.subplot(gs[0,0]) - axes.set_title('System Status {white: EAS mode, red: Non EAS mode}'); - axes.set_xlim(self.x_min, self.x_max); - axes.grid(True); - - # Otherwise: draw overutilized bands on top of the specified plot - for (t1,td,overutilized) in bands: - if not overutilized: - continue - t2 = t1+td - axes.axvspan(t1, t2, facecolor='r', alpha=0.1) - def _plotTaskSignals(self, axes, tid, signals, is_last=False): # Get dataframe for the required task util_df = self.trace.df('sched_load_avg_task') -- GitLab From 707733058e30e847ecae5f7f4441ade05fa86710 Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Fri, 22 Jul 2016 14:40:37 +0100 Subject: [PATCH 10/24] libs/utils/analysis: add EAS module This patch move the EAS/SchedTune analysis code, originally part of the trace_analysis.py module, to be a new TraceAnalysis module with dynamic registration via the AnalysisRegister. Signed-off-by: Patrick Bellasi --- libs/utils/analysis/eas_analysis.py | 371 ++++++++++++++++++++++++++++ libs/utils/trace_analysis.py | 323 ------------------------ 2 files changed, 371 insertions(+), 323 deletions(-) create mode 100644 libs/utils/analysis/eas_analysis.py diff --git a/libs/utils/analysis/eas_analysis.py b/libs/utils/analysis/eas_analysis.py new file mode 100644 index 000000000..8167ee5ef --- /dev/null +++ b/libs/utils/analysis/eas_analysis.py @@ -0,0 +1,371 @@ +# SPDX-License-Identifier: Apache-2.0 +# +# Copyright (C) 2015, ARM Limited and contributors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import matplotlib.gridspec as gridspec +import matplotlib.pyplot as plt +import pylab as pl + +from analysis_module import AnalysisModule + +# Configure logging +import logging + + +class EasAnalysis(AnalysisModule): + + def __init__(self, trace): + """ + Support for EAS signals anaysis + """ + super(EasAnalysis, self).__init__(trace) + +################################################################################ +# DataFrame Getter Methods +################################################################################ + + +################################################################################ +# Plotting Methods +################################################################################ + + def plotEDiffTime(self, tasks=None, + min_usage_delta=None, max_usage_delta=None, + min_cap_delta=None, max_cap_delta=None, + min_nrg_delta=None, max_nrg_delta=None, + min_nrg_diff=None, max_nrg_diff=None): + if not self._trace.hasEvents('sched_energy_diff'): + logging.warn('Events [sched_energy_diff] not found, plot DISABLED!') + return + df = self._dfg_trace_event('sched_energy_diff') + + # Filter on 'tasks' + if tasks is not None: + logging.info('Plotting EDiff data just for task(s) [%s]', tasks) + df = df[df['comm'].isin(tasks)] + + # Filter on 'usage_delta' + if min_usage_delta is not None: + logging.info('Plotting EDiff data just with minimum usage_delta of [%d]', min_usage_delta) + df = df[abs(df['usage_delta']) >= min_usage_delta] + if max_usage_delta is not None: + logging.info('Plotting EDiff data just with maximum usage_delta of [%d]', max_usage_delta) + df = df[abs(df['usage_delta']) <= max_usage_delta] + + # Filter on 'cap_delta' + if min_cap_delta is not None: + logging.info('Plotting EDiff data just with minimum cap_delta of [%d]', min_cap_delta) + df = df[abs(df['cap_delta']) >= min_cap_delta] + if max_cap_delta is not None: + logging.info('Plotting EDiff data just with maximum cap_delta of [%d]', max_cap_delta) + df = df[abs(df['cap_delta']) <= max_cap_delta] + + # Filter on 'nrg_delta' + if min_nrg_delta is not None: + logging.info('Plotting EDiff data just with minimum nrg_delta of [%d]', min_nrg_delta) + df = df[abs(df['nrg_delta']) >= min_nrg_delta] + if max_nrg_delta is not None: + logging.info('Plotting EDiff data just with maximum nrg_delta of [%d]', max_nrg_delta) + df = df[abs(df['nrg_delta']) <= max_nrg_delta] + + # Filter on 'nrg_diff' + if min_nrg_diff is not None: + logging.info('Plotting EDiff data just with minimum nrg_diff of [%d]', min_nrg_diff) + df = df[abs(df['nrg_diff']) >= min_nrg_diff] + if max_nrg_diff is not None: + logging.info('Plotting EDiff data just with maximum nrg_diff of [%d]', max_nrg_diff) + df = df[abs(df['nrg_diff']) <= max_nrg_diff] + + # Grid: setup stats for gris + gs = gridspec.GridSpec(4, 3, height_ratios=[2,4,2,4]); + gs.update(wspace=0.1, hspace=0.1); + + # Configure plot + fig = plt.figure(figsize=(16, 8*2+4*2+2)); + plt.suptitle("EnergyDiff Data", + y=.92, fontsize=16, horizontalalignment='center'); + + # Plot1: src and dst CPUs + axes = plt.subplot(gs[0,:]); + axes.set_title('Source and Destination CPUs'); + df[['src_cpu', 'dst_cpu']].plot(ax=axes, style=['bo', 'r+']); + axes.set_ylim(-1, self._platform['cpus_count']+1) + axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.grid(True); + axes.set_xticklabels([]) + axes.set_xlabel('') + self._trace.analysis.status.plotOverutilized(axes) + + # Plot2: energy and capacity variations + axes = plt.subplot(gs[1,:]); + axes.set_title('Energy vs Capacity Variations'); + + for color, label in zip('gbyr', ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject', 'Suboptimal Reject']): + subset = df[df.nrg_payoff_group == label] + if (len(subset) == 0): + continue + subset[['nrg_diff_pct']].plot(ax=axes, style=[color+'o']); + axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.set_yscale('symlog') + axes.grid(True); + axes.set_xticklabels([]) + axes.set_xlabel('') + self._trace.analysis.status.plotOverutilized(axes) + + # Plot3: energy payoff + axes = plt.subplot(gs[2,:]); + axes.set_title('Energy Payoff Values'); + for color, label in zip('gbyr', ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject', 'Suboptimal Reject']): + subset = df[df.nrg_payoff_group == label] + if (len(subset) == 0): + continue + subset[['nrg_payoff']].plot(ax=axes, style=[color+'o']); + axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.set_yscale('symlog') + axes.grid(True); + axes.set_xticklabels([]) + axes.set_xlabel('') + self._trace.analysis.status.plotOverutilized(axes) + + # Plot4: energy deltas (kernel and host computed values) + axes = plt.subplot(gs[3,:]); + axes.set_title('Energy Deltas Values'); + df[['nrg_delta', 'nrg_diff_pct']].plot(ax=axes, style=['ro', 'b+']); + axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.grid(True); + self._trace.analysis.status.plotOverutilized(axes) + + # Save generated plots into datadir + figname = '{}/{}ediff_time.png'\ + .format(self._trace.plots_dir, self._trace.plots_prefix) + pl.savefig(figname, bbox_inches='tight') + + + # Grid: setup stats for gris + gs = gridspec.GridSpec(1, 3, height_ratios=[2]); + gs.update(wspace=0.1, hspace=0.1); + + fig = plt.figure(figsize=(16, 4)); + + # Plot: usage, capacity and energy distributuions + axes = plt.subplot(gs[0,0]); + df[['usage_delta']].hist(ax=axes, bins=60) + axes = plt.subplot(gs[0,1]); + df[['cap_delta']].hist(ax=axes, bins=60) + axes = plt.subplot(gs[0,2]); + df[['nrg_delta']].hist(ax=axes, bins=60) + + # Save generated plots into datadir + figname = '{}/{}ediff_stats.png'\ + .format(self._trace.plots_dir, self._trace.plots_prefix) + pl.savefig(figname, bbox_inches='tight') + + + def plotEDiffSpace(self, tasks=None, + min_usage_delta=None, max_usage_delta=None, + min_cap_delta=None, max_cap_delta=None, + min_nrg_delta=None, max_nrg_delta=None, + min_nrg_diff=None, max_nrg_diff=None): + if not self._trace.hasEvents('sched_energy_diff'): + logging.warn('Events [sched_energy_diff] not found, plot DISABLED!') + return + df = self._dfg_trace_event('sched_energy_diff') + + # Filter on 'tasks' + if tasks is not None: + logging.info('Plotting EDiff data just for task(s) [%s]', tasks) + df = df[df['comm'].isin(tasks)] + + # Filter on 'usage_delta' + if min_usage_delta is not None: + logging.info('Plotting EDiff data just with minimum usage_delta of [%d]', min_usage_delta) + df = df[abs(df['usage_delta']) >= min_usage_delta] + if max_usage_delta is not None: + logging.info('Plotting EDiff data just with maximum usage_delta of [%d]', max_usage_delta) + df = df[abs(df['usage_delta']) <= max_usage_delta] + + # Filter on 'cap_delta' + if min_cap_delta is not None: + logging.info('Plotting EDiff data just with minimum cap_delta of [%d]', min_cap_delta) + df = df[abs(df['cap_delta']) >= min_cap_delta] + if max_cap_delta is not None: + logging.info('Plotting EDiff data just with maximum cap_delta of [%d]', max_cap_delta) + df = df[abs(df['cap_delta']) <= max_cap_delta] + + # Filter on 'nrg_delta' + if min_nrg_delta is not None: + logging.info('Plotting EDiff data just with minimum nrg_delta of [%d]', min_nrg_delta) + df = df[abs(df['nrg_delta']) >= min_nrg_delta] + if max_nrg_delta is not None: + logging.info('Plotting EDiff data just with maximum nrg_delta of [%d]', max_nrg_delta) + df = df[abs(df['nrg_delta']) <= max_nrg_delta] + + # Filter on 'nrg_diff' + if min_nrg_diff is not None: + logging.info('Plotting EDiff data just with minimum nrg_diff of [%d]', min_nrg_diff) + df = df[abs(df['nrg_diff']) >= min_nrg_diff] + if max_nrg_diff is not None: + logging.info('Plotting EDiff data just with maximum nrg_diff of [%d]', max_nrg_diff) + df = df[abs(df['nrg_diff']) <= max_nrg_diff] + + # Grid: setup grid for P-E space + gs = gridspec.GridSpec(1, 2, height_ratios=[2]); + gs.update(wspace=0.1, hspace=0.1); + + fig = plt.figure(figsize=(16, 8)); + + # Get min-max of each axes + x_min = df.nrg_diff_pct.min() + x_max = df.nrg_diff_pct.max() + y_min = df.cap_delta.min() + y_max = df.cap_delta.max() + axes_min = min(x_min, y_min) + axes_max = max(x_max, y_max) + + + # # Tag columns by usage_delta + # ccol = df.usage_delta + # df['usage_delta_group'] = np.select( + # [ccol < 150, ccol < 400, ccol < 600], + # ['< 150', '< 400', '< 600'], '>= 600') + # + # # Tag columns by nrg_payoff + # ccol = df.nrg_payoff + # df['nrg_payoff_group'] = np.select( + # [ccol > 2e9, ccol > 0, ccol > -2e9], + # ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject'], 'Suboptimal Reject') + + # Plot: per usage_delta values + axes = plt.subplot(gs[0,0]); + + for color, label in zip('bgyr', ['< 150', '< 400', '< 600', '>= 600']): + subset = df[df.usage_delta_group == label] + if (len(subset) == 0): + continue + plt.scatter(subset.nrg_diff_pct, subset.cap_delta, + s=subset.usage_delta, + c=color, label='task_usage ' + str(label), + axes=axes) + + # Plot space axes + plt.plot((0, 0), (-1025, 1025), 'y--', axes=axes) + plt.plot((-1025, 1025), (0,0), 'y--', axes=axes) + + # # Perf cuts + # plt.plot((0, 100), (0,100*delta_pb), 'b--', label='PB (Perf Boost)') + # plt.plot((0, -100), (0,-100*delta_pc), 'r--', label='PC (Perf Constraint)') + # + # # Perf boost setups + # for y in range(0,6): + # plt.plot((0, 500), (0,y*100), 'g:') + # for x in range(0,5): + # plt.plot((0, x*100), (0,500), 'g:') + + axes.legend(loc=4, borderpad=1); + + plt.xlim(1.1*axes_min, 1.1*axes_max); + plt.ylim(1.1*axes_min, 1.1*axes_max); + + # axes.title('Performance-Energy Space') + axes.set_xlabel('Energy diff [%]'); + axes.set_ylabel('Capacity diff [%]'); + + + # Plot: per usage_delta values + axes = plt.subplot(gs[0,1]); + + for color, label in zip('gbyr', ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject', 'Suboptimal Reject']): + subset = df[df.nrg_payoff_group == label] + if (len(subset) == 0): + continue + plt.scatter(subset.nrg_diff_pct, subset.cap_delta, + s=60, + c=color, + marker='+', + label='{} Region'.format(label), + axes=axes) + # s=subset.usage_delta, + + # Plot space axes + plt.plot((0, 0), (-1025, 1025), 'y--', axes=axes) + plt.plot((-1025, 1025), (0,0), 'y--', axes=axes) + + # # Perf cuts + # plt.plot((0, 100), (0,100*delta_pb), 'b--', label='PB (Perf Boost)') + # plt.plot((0, -100), (0,-100*delta_pc), 'r--', label='PC (Perf Constraint)') + # + # # Perf boost setups + # for y in range(0,6): + # plt.plot((0, 500), (0,y*100), 'g:') + # for x in range(0,5): + # plt.plot((0, x*100), (0,500), 'g:') + + axes.legend(loc=4, borderpad=1); + + plt.xlim(1.1*axes_min, 1.1*axes_max); + plt.ylim(1.1*axes_min, 1.1*axes_max); + + # axes.title('Performance-Energy Space') + axes.set_xlabel('Energy diff [%]'); + axes.set_ylabel('Capacity diff [%]'); + + plt.title('Performance-Energy Space') + + # Save generated plots into datadir + figname = '{}/{}ediff_space.png'\ + .format(self._trace.plots_dir, self._trace.plots_prefix) + pl.savefig(figname, bbox_inches='tight') + + + def plotSchedTuneConf(self): + """ + Plot the configuration of the SchedTune + """ + if not self._trace.hasEvents('sched_tune_config'): + logging.warn('Events [sched_tune_config] not found, plot DISABLED!') + return + # Grid + gs = gridspec.GridSpec(2, 1, height_ratios=[4,1]); + gs.update(wspace=0.1, hspace=0.1); + + # Figure + plt.figure(figsize=(16, 2*6)); + plt.suptitle("SchedTune Configuration", + y=.97, fontsize=16, horizontalalignment='center'); + + # Plot: Margin + axes = plt.subplot(gs[0,0]); + axes.set_title('Margin'); + data = self._dfg_trace_event('sched_tune_config')[['margin']] + data.plot(ax=axes, drawstyle='steps-post', style=['b']); + axes.set_ylim(0, 110); + axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.xaxis.set_visible(False); + + # Plot: Boost mode + axes = plt.subplot(gs[1,0]); + axes.set_title('Boost mode'); + data = self._dfg_trace_event('sched_tune_config')[['boostmode']] + data.plot(ax=axes, drawstyle='steps-post'); + axes.set_ylim(0, 4); + axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.xaxis.set_visible(True); + + # Save generated plots into datadir + figname = '{}/{}schedtune_conf.png'\ + .format(self._trace.plots_dir, self._trace.plots_prefix) + pl.savefig(figname, bbox_inches='tight') + diff --git a/libs/utils/trace_analysis.py b/libs/utils/trace_analysis.py index 38cfd6453..69622f53c 100644 --- a/libs/utils/trace_analysis.py +++ b/libs/utils/trace_analysis.py @@ -438,326 +438,3 @@ class TraceAnalysis(object): self.plotsdir, self.prefix, tid, task_name) pl.savefig(figname, bbox_inches='tight') - def plotEDiffTime(self, tasks=None, - min_usage_delta=None, max_usage_delta=None, - min_cap_delta=None, max_cap_delta=None, - min_nrg_delta=None, max_nrg_delta=None, - min_nrg_diff=None, max_nrg_diff=None): - if not self.trace.hasEvents('sched_energy_diff'): - logging.warn('Events [sched_energy_diff] not found, plot DISABLED!') - return - df = self.trace.df('sched_energy_diff') - - # Filter on 'tasks' - if tasks is not None: - logging.info('Plotting EDiff data just for task(s) [%s]', tasks) - df = df[df['comm'].isin(tasks)] - - # Filter on 'usage_delta' - if min_usage_delta is not None: - logging.info('Plotting EDiff data just with minimum usage_delta of [%d]', min_usage_delta) - df = df[abs(df['usage_delta']) >= min_usage_delta] - if max_usage_delta is not None: - logging.info('Plotting EDiff data just with maximum usage_delta of [%d]', max_usage_delta) - df = df[abs(df['usage_delta']) <= max_usage_delta] - - # Filter on 'cap_delta' - if min_cap_delta is not None: - logging.info('Plotting EDiff data just with minimum cap_delta of [%d]', min_cap_delta) - df = df[abs(df['cap_delta']) >= min_cap_delta] - if max_cap_delta is not None: - logging.info('Plotting EDiff data just with maximum cap_delta of [%d]', max_cap_delta) - df = df[abs(df['cap_delta']) <= max_cap_delta] - - # Filter on 'nrg_delta' - if min_nrg_delta is not None: - logging.info('Plotting EDiff data just with minimum nrg_delta of [%d]', min_nrg_delta) - df = df[abs(df['nrg_delta']) >= min_nrg_delta] - if max_nrg_delta is not None: - logging.info('Plotting EDiff data just with maximum nrg_delta of [%d]', max_nrg_delta) - df = df[abs(df['nrg_delta']) <= max_nrg_delta] - - # Filter on 'nrg_diff' - if min_nrg_diff is not None: - logging.info('Plotting EDiff data just with minimum nrg_diff of [%d]', min_nrg_diff) - df = df[abs(df['nrg_diff']) >= min_nrg_diff] - if max_nrg_diff is not None: - logging.info('Plotting EDiff data just with maximum nrg_diff of [%d]', max_nrg_diff) - df = df[abs(df['nrg_diff']) <= max_nrg_diff] - - # Grid: setup stats for gris - gs = gridspec.GridSpec(4, 3, height_ratios=[2,4,2,4]); - gs.update(wspace=0.1, hspace=0.1); - - # Configure plot - fig = plt.figure(figsize=(16, 8*2+4*2+2)); - plt.suptitle("EnergyDiff Data", - y=.92, fontsize=16, horizontalalignment='center'); - - # Plot1: src and dst CPUs - axes = plt.subplot(gs[0,:]); - axes.set_title('Source and Destination CPUs'); - df[['src_cpu', 'dst_cpu']].plot(ax=axes, style=['bo', 'r+']); - axes.set_ylim(-1, self.platform['cpus_count']+1) - axes.set_xlim(self.x_min, self.x_max); - axes.grid(True); - axes.set_xticklabels([]) - axes.set_xlabel('') - self.plotOverutilized(axes) - - # Plot2: energy and capacity variations - axes = plt.subplot(gs[1,:]); - axes.set_title('Energy vs Capacity Variations'); - - for color, label in zip('gbyr', ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject', 'Suboptimal Reject']): - subset = df[df.nrg_payoff_group == label] - if (len(subset) == 0): - continue - subset[['nrg_diff_pct']].plot(ax=axes, style=[color+'o']); - axes.set_xlim(self.x_min, self.x_max); - axes.set_yscale('symlog') - axes.grid(True); - axes.set_xticklabels([]) - axes.set_xlabel('') - self.plotOverutilized(axes) - - # Plot3: energy payoff - axes = plt.subplot(gs[2,:]); - axes.set_title('Energy Payoff Values'); - for color, label in zip('gbyr', ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject', 'Suboptimal Reject']): - subset = df[df.nrg_payoff_group == label] - if (len(subset) == 0): - continue - subset[['nrg_payoff']].plot(ax=axes, style=[color+'o']); - axes.set_xlim(self.x_min, self.x_max); - axes.set_yscale('symlog') - axes.grid(True); - axes.set_xticklabels([]) - axes.set_xlabel('') - self.plotOverutilized(axes) - - # Plot4: energy deltas (kernel and host computed values) - axes = plt.subplot(gs[3,:]); - axes.set_title('Energy Deltas Values'); - df[['nrg_delta', 'nrg_diff_pct']].plot(ax=axes, style=['ro', 'b+']); - axes.set_xlim(self.x_min, self.x_max); - axes.grid(True); - self.plotOverutilized(axes) - - # Save generated plots into datadir - figname = '{}/{}ediff_time.png'.format(self.plotsdir, self.prefix) - pl.savefig(figname, bbox_inches='tight') - - - # Grid: setup stats for gris - gs = gridspec.GridSpec(1, 3, height_ratios=[2]); - gs.update(wspace=0.1, hspace=0.1); - - fig = plt.figure(figsize=(16, 4)); - - # Plot: usage, capacity and energy distributuions - axes = plt.subplot(gs[0,0]); - df[['usage_delta']].hist(ax=axes, bins=60) - axes = plt.subplot(gs[0,1]); - df[['cap_delta']].hist(ax=axes, bins=60) - axes = plt.subplot(gs[0,2]); - df[['nrg_delta']].hist(ax=axes, bins=60) - - # Save generated plots into datadir - figname = '{}/{}ediff_stats.png'.format(self.plotsdir, self.prefix) - pl.savefig(figname, bbox_inches='tight') - - - def plotEDiffSpace(self, tasks=None, - min_usage_delta=None, max_usage_delta=None, - min_cap_delta=None, max_cap_delta=None, - min_nrg_delta=None, max_nrg_delta=None, - min_nrg_diff=None, max_nrg_diff=None): - if not self.trace.hasEvents('sched_energy_diff'): - logging.warn('Events [sched_energy_diff] not found, plot DISABLED!') - return - df = self.trace.df('sched_energy_diff') - - # Filter on 'tasks' - if tasks is not None: - logging.info('Plotting EDiff data just for task(s) [%s]', tasks) - df = df[df['comm'].isin(tasks)] - - # Filter on 'usage_delta' - if min_usage_delta is not None: - logging.info('Plotting EDiff data just with minimum usage_delta of [%d]', min_usage_delta) - df = df[abs(df['usage_delta']) >= min_usage_delta] - if max_usage_delta is not None: - logging.info('Plotting EDiff data just with maximum usage_delta of [%d]', max_usage_delta) - df = df[abs(df['usage_delta']) <= max_usage_delta] - - # Filter on 'cap_delta' - if min_cap_delta is not None: - logging.info('Plotting EDiff data just with minimum cap_delta of [%d]', min_cap_delta) - df = df[abs(df['cap_delta']) >= min_cap_delta] - if max_cap_delta is not None: - logging.info('Plotting EDiff data just with maximum cap_delta of [%d]', max_cap_delta) - df = df[abs(df['cap_delta']) <= max_cap_delta] - - # Filter on 'nrg_delta' - if min_nrg_delta is not None: - logging.info('Plotting EDiff data just with minimum nrg_delta of [%d]', min_nrg_delta) - df = df[abs(df['nrg_delta']) >= min_nrg_delta] - if max_nrg_delta is not None: - logging.info('Plotting EDiff data just with maximum nrg_delta of [%d]', max_nrg_delta) - df = df[abs(df['nrg_delta']) <= max_nrg_delta] - - # Filter on 'nrg_diff' - if min_nrg_diff is not None: - logging.info('Plotting EDiff data just with minimum nrg_diff of [%d]', min_nrg_diff) - df = df[abs(df['nrg_diff']) >= min_nrg_diff] - if max_nrg_diff is not None: - logging.info('Plotting EDiff data just with maximum nrg_diff of [%d]', max_nrg_diff) - df = df[abs(df['nrg_diff']) <= max_nrg_diff] - - # Grid: setup grid for P-E space - gs = gridspec.GridSpec(1, 2, height_ratios=[2]); - gs.update(wspace=0.1, hspace=0.1); - - fig = plt.figure(figsize=(16, 8)); - - # Get min-max of each axes - x_min = df.nrg_diff_pct.min() - x_max = df.nrg_diff_pct.max() - y_min = df.cap_delta.min() - y_max = df.cap_delta.max() - axes_min = min(x_min, y_min) - axes_max = max(x_max, y_max) - - - # # Tag columns by usage_delta - # ccol = df.usage_delta - # df['usage_delta_group'] = np.select( - # [ccol < 150, ccol < 400, ccol < 600], - # ['< 150', '< 400', '< 600'], '>= 600') - # - # # Tag columns by nrg_payoff - # ccol = df.nrg_payoff - # df['nrg_payoff_group'] = np.select( - # [ccol > 2e9, ccol > 0, ccol > -2e9], - # ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject'], 'Suboptimal Reject') - - # Plot: per usage_delta values - axes = plt.subplot(gs[0,0]); - - for color, label in zip('bgyr', ['< 150', '< 400', '< 600', '>= 600']): - subset = df[df.usage_delta_group == label] - if (len(subset) == 0): - continue - plt.scatter(subset.nrg_diff_pct, subset.cap_delta, - s=subset.usage_delta, - c=color, label='task_usage ' + str(label), - axes=axes) - - # Plot space axes - plt.plot((0, 0), (-1025, 1025), 'y--', axes=axes) - plt.plot((-1025, 1025), (0,0), 'y--', axes=axes) - - # # Perf cuts - # plt.plot((0, 100), (0,100*delta_pb), 'b--', label='PB (Perf Boost)') - # plt.plot((0, -100), (0,-100*delta_pc), 'r--', label='PC (Perf Constraint)') - # - # # Perf boost setups - # for y in range(0,6): - # plt.plot((0, 500), (0,y*100), 'g:') - # for x in range(0,5): - # plt.plot((0, x*100), (0,500), 'g:') - - axes.legend(loc=4, borderpad=1); - - plt.xlim(1.1*axes_min, 1.1*axes_max); - plt.ylim(1.1*axes_min, 1.1*axes_max); - - # axes.title('Performance-Energy Space') - axes.set_xlabel('Energy diff [%]'); - axes.set_ylabel('Capacity diff [%]'); - - - # Plot: per usage_delta values - axes = plt.subplot(gs[0,1]); - - for color, label in zip('gbyr', ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject', 'Suboptimal Reject']): - subset = df[df.nrg_payoff_group == label] - if (len(subset) == 0): - continue - plt.scatter(subset.nrg_diff_pct, subset.cap_delta, - s=60, - c=color, - marker='+', - label='{} Region'.format(label), - axes=axes) - # s=subset.usage_delta, - - # Plot space axes - plt.plot((0, 0), (-1025, 1025), 'y--', axes=axes) - plt.plot((-1025, 1025), (0,0), 'y--', axes=axes) - - # # Perf cuts - # plt.plot((0, 100), (0,100*delta_pb), 'b--', label='PB (Perf Boost)') - # plt.plot((0, -100), (0,-100*delta_pc), 'r--', label='PC (Perf Constraint)') - # - # # Perf boost setups - # for y in range(0,6): - # plt.plot((0, 500), (0,y*100), 'g:') - # for x in range(0,5): - # plt.plot((0, x*100), (0,500), 'g:') - - axes.legend(loc=4, borderpad=1); - - plt.xlim(1.1*axes_min, 1.1*axes_max); - plt.ylim(1.1*axes_min, 1.1*axes_max); - - # axes.title('Performance-Energy Space') - axes.set_xlabel('Energy diff [%]'); - axes.set_ylabel('Capacity diff [%]'); - - plt.title('Performance-Energy Space') - - # Save generated plots into datadir - figname = '{}/{}ediff_space.png'.format(self.plotsdir, self.prefix) - pl.savefig(figname, bbox_inches='tight') - - - def plotSchedTuneConf(self): - """ - Plot the configuration of the SchedTune - """ - if not self.trace.hasEvents('sched_tune_config'): - logging.warn('Events [sched_tune_config] not found, plot DISABLED!') - return - # Grid - gs = gridspec.GridSpec(2, 1, height_ratios=[4,1]); - gs.update(wspace=0.1, hspace=0.1); - - # Figure - plt.figure(figsize=(16, 2*6)); - plt.suptitle("SchedTune Configuration", - y=.97, fontsize=16, horizontalalignment='center'); - - # Plot: Margin - axes = plt.subplot(gs[0,0]); - axes.set_title('Margin'); - data = self.trace.df('sched_tune_config')[['margin']] - data.plot(ax=axes, drawstyle='steps-post', style=['b']); - axes.set_ylim(0, 110); - axes.set_xlim(self.x_min, self.x_max); - axes.xaxis.set_visible(False); - - # Plot: Boost mode - axes = plt.subplot(gs[1,0]); - axes.set_title('Boost mode'); - data = self.trace.df('sched_tune_config')[['boostmode']] - data.plot(ax=axes, drawstyle='steps-post'); - axes.set_ylim(0, 4); - axes.set_xlim(self.x_min, self.x_max); - axes.xaxis.set_visible(True); - - # Save generated plots into datadir - figname = '{}/{}schedtune_conf.png'.format(self.plotsdir, self.prefix) - pl.savefig(figname, bbox_inches='tight') - -- GitLab From 09977a5e67227b1da1ef46d026232feb4ca6301a Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Fri, 22 Jul 2016 14:41:52 +0100 Subject: [PATCH 11/24] libs/utils/analysis: add TASKS module This patch move the tasks's signals analysis code, originally part of the trace_analysis.py module, to be a new TraceAnalysis module with dynamic registration via the AnalysisRegister. Signed-off-by: Patrick Bellasi --- libs/utils/analysis/tasks_analysis.py | 266 ++++++++++++++++++++++++++ libs/utils/trace_analysis.py | 216 --------------------- 2 files changed, 266 insertions(+), 216 deletions(-) create mode 100644 libs/utils/analysis/tasks_analysis.py diff --git a/libs/utils/analysis/tasks_analysis.py b/libs/utils/analysis/tasks_analysis.py new file mode 100644 index 000000000..6ed364e5c --- /dev/null +++ b/libs/utils/analysis/tasks_analysis.py @@ -0,0 +1,266 @@ +# SPDX-License-Identifier: Apache-2.0 +# +# Copyright (C) 2015, ARM Limited and contributors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import matplotlib.gridspec as gridspec +import matplotlib.pyplot as plt +import pylab as pl +import re + +from analysis_module import AnalysisModule + +# Configure logging +import logging + +class TasksAnalysis(AnalysisModule): + + def __init__(self, trace): + """ + Support for Tasks signals analysis + """ + super(TasksAnalysis, self).__init__(trace) + + +################################################################################ +# DataFrame Getter Methods +################################################################################ + + +################################################################################ +# Plotting Methods +################################################################################ + + def plotTasks(self, tasks=None, signals=None): + """ + Generate a common set of useful plots for each of the specified tasks + + This method allows to filter which signals should be plot, if data are + available in the input trace. The list of signals supported are: + Tasks signals plot: + load_avg, util_avg, boosted_util, sched_overutilized + Tasks residencies on CPUs: + residencies, sched_overutilized + Tasks PELT signals: + load_sum, util_sum, period_contrib, sched_overutilized + + Note: + sched_overutilized: enable the plotting of overutilization bands on + top of each subplot + residencies: enable the generation of the CPUs residencies plot + + :param tasks: the list of task names and/or PIDs to plot. + Numerical PIDs and string task names can be mixed + in the same list. + default: all tasks defined in Trace + creation time are plotted + :type tasks: list + + :param signals: list of signals (and thus plots) to generate + default: all the plots and signals available in the + current trace + :type signals: list + """ + if not signals: + signals = ['load_avg', 'util_avg', 'boosted_util', + 'sched_overutilized', + 'load_sum', 'util_sum', 'period_contrib', + 'residencies'] + + # Check for the minimum required signals to be available + if not self._trace.hasEvents('sched_load_avg_task'): + logging.warn('Events [sched_load_avg_task] not found, '\ + 'plot DISABLED!') + return + + # Defined list of tasks to plot + if tasks: + tasks_to_plot = tasks + elif self._tasks: + tasks_to_plot = sorted(self._tasks) + else: + raise ValueError('No tasks to plot specified') + + # Compute number of plots to produce + plots_count = 0 + plots_signals = [ + # Fist plot: task's utilization + {'load_avg', 'util_avg', 'boosted_util'}, + # Second plot: task residency + {'residencies'}, + # Third plot: tasks's load + {'load_sum', 'util_sum', 'period_contrib'} + ] + for signals_to_plot in plots_signals: + signals_to_plot = signals_to_plot.intersection(signals) + if len(signals_to_plot): + plots_count = plots_count + 1 + + # Grid + gs = gridspec.GridSpec(plots_count, 1, height_ratios=[2,1,1]); + gs.update(wspace=0.1, hspace=0.1); + + # Build list of all PIDs for each task_name to plot + pids_to_plot = [] + for task in tasks_to_plot: + # Add specified PIDs to the list + if isinstance(task, int): + pids_to_plot.append(task) + continue + # Otherwise: add all the PIDs for task with the specified name + pids_to_plot.extend(self._trace.getTaskByName(task)) + + for tid in pids_to_plot: + task_name = self._trace.getTaskByPid(tid) + if len(task_name) == 1: + task_name = task_name[0] + logging.info('Plotting %5d: %s...', tid, task_name) + else: + logging.info('Plotting %5d: %s...', tid, ', '.join(task_name)) + plot_id = 0 + + # Figure + plt.figure(figsize=(16, 2*6+3)); + plt.suptitle("Task Signals", + y=.94, fontsize=16, horizontalalignment='center'); + + # Plot load and utilization + signals_to_plot = {'load_avg', 'util_avg', + 'boosted_util', 'sched_overutilized'} + signals_to_plot = list(signals_to_plot.intersection(signals)) + if len(signals_to_plot) > 0: + axes = plt.subplot(gs[plot_id,0]); + axes.set_title('Task [{0:d}:{1:s}] Signals'\ + .format(tid, task_name)); + plot_id = plot_id + 1 + is_last = (plot_id == plots_count) + self._plotTaskSignals(axes, tid, signals_to_plot, is_last) + + # Plot CPUs residency + signals_to_plot = {'residencies', 'sched_overutilized'} + signals_to_plot = list(signals_to_plot.intersection(signals)) + if len(signals_to_plot) > 0: + axes = plt.subplot(gs[plot_id,0]); + axes.set_title('Task [{0:d}:{1:s}] Residency (green: LITTLE, red: big)'\ + .format(tid, task_name)); + plot_id = plot_id + 1 + is_last = (plot_id == plots_count) + self._plotTaskResidencies(axes, tid, signals_to_plot, is_last) + + # Plot PELT signals + signals_to_plot = { + 'load_sum', 'util_sum', + 'period_contrib', 'sched_overutilized'} + signals_to_plot = list(signals_to_plot.intersection(signals)) + if len(signals_to_plot) > 0: + axes = plt.subplot(gs[plot_id,0]); + axes.set_title('Task [{0:d}:{1:s}] PELT Signals'\ + .format(tid, task_name)); + plot_id = plot_id + 1 + self._plotTaskPelt(axes, tid, signals_to_plot) + + # Save generated plots into datadir + if isinstance(task_name, list): + task_name = re.sub('[:/]', '_', task_name[0]) + else: + task_name = re.sub('[:/]', '_', task_name) + figname = '{}/{}task_util_{}_{}.png'.format( + self._trace.plots_dir, self._trace.plots_prefix, tid, task_name) + pl.savefig(figname, bbox_inches='tight') + + +################################################################################ +# Utility Methods +################################################################################ + + def _plotTaskSignals(self, axes, tid, signals, is_last=False): + # Get dataframe for the required task + util_df = self._dfg_trace_event('sched_load_avg_task') + + # Plot load and util + signals_to_plot = list({'load_avg', 'util_avg'}.intersection(signals)) + if len(signals_to_plot): + data = util_df[util_df.pid == tid][signals_to_plot] + data.plot(ax=axes, drawstyle='steps-post'); + + # Plot boost utilization if available + if 'boosted_util' in signals and \ + self._trace.hasEvents('sched_boost_task'): + boost_df = self._dfg_trace_event('sched_boost_task') + data = boost_df[boost_df.pid == tid][['boosted_util']] + if len(data): + data.plot(ax=axes, style=['y-'], drawstyle='steps-post'); + else: + task_name = self._trace.getTaskByPid(tid) + logging.warning("No 'boosted_util' data for task [%d:%s]", + tid, task_name) + + # Add Capacities data if avilable + if 'nrg_model' in self._platform: + nrg_model = self._platform['nrg_model'] + max_lcap = nrg_model['little']['cpu']['cap_max'] + max_bcap = nrg_model['big']['cpu']['cap_max'] + tip_lcap = 0.8 * max_lcap + tip_bcap = 0.8 * max_bcap + logging.debug('LITTLE capacity tip/max: %d/%d, big capacity tip/max: %d/%d', + tip_lcap, max_lcap, tip_bcap, max_bcap) + axes.axhline(tip_lcap, color='g', linestyle='--', linewidth=1); + axes.axhline(max_lcap, color='g', linestyle='-', linewidth=2); + axes.axhline(tip_bcap, color='r', linestyle='--', linewidth=1); + axes.axhline(max_bcap, color='r', linestyle='-', linewidth=2); + + axes.set_ylim(0, 1100); + axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.grid(True); + if not is_last: + axes.set_xticklabels([]) + axes.set_xlabel('') + if 'sched_overutilized' in signals: + self._trace.analysis.status.plotOverutilized(axes) + + def _plotTaskResidencies(self, axes, tid, signals, is_last=False): + util_df = self._dfg_trace_event('sched_load_avg_task') + data = util_df[util_df.pid == tid][['cluster', 'cpu']] + for ccolor, clabel in zip('gr', ['LITTLE', 'big']): + cdata = data[data.cluster == clabel] + if (len(cdata) > 0): + cdata.plot(ax=axes, style=[ccolor+'+'], legend=False); + # Y Axis - placeholders for legend, acutal CPUs. topmost empty lane + cpus = [str(n) for n in range(self._platform['cpus_count'])] + ylabels = [''] + cpus + axes.set_yticklabels(ylabels) + axes.set_ylim(-1, self._platform['cpus_count']) + axes.set_ylabel('CPUs') + # X Axis + axes.set_xlim(self._trace.x_min, self._trace.x_max); + + axes.grid(True); + if not is_last: + axes.set_xticklabels([]) + axes.set_xlabel('') + if 'sched_overutilized' in signals: + self._trace.analysis.status.plotOverutilized(axes) + + def _plotTaskPelt(self, axes, tid, signals): + util_df = self._dfg_trace_event('sched_load_avg_task') + data = util_df[util_df.pid == tid][['load_sum', 'util_sum', 'period_contrib']] + data.plot(ax=axes, drawstyle='steps-post'); + axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.ticklabel_format(style='scientific', scilimits=(0,0), + axis='y', useOffset=False) + axes.grid(True); + if 'sched_overutilized' in signals: + self._trace.analysis.status.plotOverutilized(axes) + diff --git a/libs/utils/trace_analysis.py b/libs/utils/trace_analysis.py index 69622f53c..5373c51c3 100644 --- a/libs/utils/trace_analysis.py +++ b/libs/utils/trace_analysis.py @@ -222,219 +222,3 @@ class TraceAnalysis(object): lcpus = set(cpus) & set(self.platform['clusters']['little']) self.__plotCPU(lcpus, "LITTLE") - def _plotTaskSignals(self, axes, tid, signals, is_last=False): - # Get dataframe for the required task - util_df = self.trace.df('sched_load_avg_task') - - # Plot load and util - signals_to_plot = list({'load_avg', 'util_avg'}.intersection(signals)) - if len(signals_to_plot): - data = util_df[util_df.pid == tid][signals_to_plot] - data.plot(ax=axes, drawstyle='steps-post'); - - # Plot boost utilization if available - if 'boosted_util' in signals and \ - self.trace.hasEvents('sched_boost_task'): - boost_df = self.trace.df('sched_boost_task') - data = boost_df[boost_df.pid == tid][['boosted_util']] - if len(data): - data.plot(ax=axes, style=['y-'], drawstyle='steps-post'); - else: - task_name = self.trace.getTaskByPid(tid) - logging.warning("No 'boosted_util' data for task [%d:%s]", - tid, task_name) - - # Add Capacities data if avilable - if 'nrg_model' in self.trace.platform: - nrg_model = self.trace.platform['nrg_model'] - max_lcap = nrg_model['little']['cpu']['cap_max'] - max_bcap = nrg_model['big']['cpu']['cap_max'] - tip_lcap = 0.8 * max_lcap - tip_bcap = 0.8 * max_bcap - logging.debug('LITTLE capacity tip/max: %d/%d, big capacity tip/max: %d/%d', - tip_lcap, max_lcap, tip_bcap, max_bcap) - axes.axhline(tip_lcap, color='g', linestyle='--', linewidth=1); - axes.axhline(max_lcap, color='g', linestyle='-', linewidth=2); - axes.axhline(tip_bcap, color='r', linestyle='--', linewidth=1); - axes.axhline(max_bcap, color='r', linestyle='-', linewidth=2); - - axes.set_ylim(0, 1100); - axes.set_xlim(self.x_min, self.x_max); - axes.grid(True); - if not is_last: - axes.set_xticklabels([]) - axes.set_xlabel('') - if 'sched_overutilized' in signals: - self.plotOverutilized(axes) - - def _plotTaskResidencies(self, axes, tid, signals, is_last=False): - util_df = self.trace.df('sched_load_avg_task') - data = util_df[util_df.pid == tid][['cluster', 'cpu']] - for ccolor, clabel in zip('gr', ['LITTLE', 'big']): - cdata = data[data.cluster == clabel] - if (len(cdata) > 0): - cdata.plot(ax=axes, style=[ccolor+'+'], legend=False); - # Y Axis - placeholders for legend, acutal CPUs. topmost empty lane - cpus = [str(n) for n in range(self.platform['cpus_count'])] - ylabels = [''] + cpus - axes.set_yticklabels(ylabels) - axes.set_ylim(-1, self.platform['cpus_count']) - axes.set_ylabel('CPUs') - # X Axis - axes.set_xlim(self.x_min, self.x_max); - - axes.grid(True); - if not is_last: - axes.set_xticklabels([]) - axes.set_xlabel('') - if 'sched_overutilized' in signals: - self.plotOverutilized(axes) - - def _plotTaskPelt(self, axes, tid, signals): - util_df = self.trace.df('sched_load_avg_task') - data = util_df[util_df.pid == tid][['load_sum', 'util_sum', 'period_contrib']] - data.plot(ax=axes, drawstyle='steps-post'); - axes.set_xlim(self.x_min, self.x_max); - axes.ticklabel_format(style='scientific', scilimits=(0,0), - axis='y', useOffset=False) - axes.grid(True); - if 'sched_overutilized' in signals: - self.plotOverutilized(axes) - - def plotTasks(self, tasks=None, signals=None): - """ - Generate a common set of useful plots for each of the specified tasks - - This method allows to filter which signals should be plot, if data are - available in the input trace. The list of signals supported are: - Tasks signals plot: - load_avg, util_avg, boosted_util, sched_overutilized - Tasks residencies on CPUs: - residencies, sched_overutilized - Tasks PELT signals: - load_sum, util_sum, period_contrib, sched_overutilized - - Note: - sched_overutilized: enable the plotting of overutilization bands on - top of each subplot - residencies: enable the generation of the CPUs residencies plot - - :param tasks: the list of task names and/or PIDs to plot. - Numerical PIDs and string task names can be mixed - in the same list. - default: all tasks defined at TraceAnalysis - creation time are plotted - :type tasks: list - - :param signals: list of signals (and thus plots) to generate - default: all the plots and signals available in the - current trace - :type signals: list - """ - if not signals: - signals = ['load_avg', 'util_avg', 'boosted_util', - 'sched_overutilized', - 'load_sum', 'util_sum', 'period_contrib', - 'residencies'] - - # Check for the minimum required signals to be available - if not self.trace.hasEvents('sched_load_avg_task'): - logging.warn('Events [sched_load_avg_task] not found, '\ - 'plot DISABLED!') - return - - # Defined list of tasks to plot - if tasks: - tasks_to_plot = tasks - elif self.tasks: - tasks_to_plot = sorted(self.tasks) - else: - raise ValueError('No tasks to plot specified') - - # Compute number of plots to produce - plots_count = 0 - plots_signals = [ - # Fist plot: task's utilization - {'load_avg', 'util_avg', 'boosted_util'}, - # Second plot: task residency - {'residencies'}, - # Third plot: tasks's load - {'load_sum', 'util_sum', 'period_contrib'} - ] - for signals_to_plot in plots_signals: - signals_to_plot = signals_to_plot.intersection(signals) - if len(signals_to_plot): - plots_count = plots_count + 1 - - # Grid - gs = gridspec.GridSpec(plots_count, 1, height_ratios=[2,1,1]); - gs.update(wspace=0.1, hspace=0.1); - - # Build list of all PIDs for each task_name to plot - pids_to_plot = [] - for task in tasks_to_plot: - # Add specified PIDs to the list - if isinstance(task, int): - pids_to_plot.append(task) - continue - # Otherwise: add all the PIDs for task with the specified name - pids_to_plot.extend(self.trace.getTaskByName(task)) - - for tid in pids_to_plot: - task_name = self.trace.getTaskByPid(tid) - if len(task_name) == 1: - task_name = task_name[0] - logging.info('Plotting %5d: %s...', tid, task_name) - else: - logging.info('Plotting %5d: %s...', tid, ', '.join(task_name)) - plot_id = 0 - - # Figure - plt.figure(figsize=(16, 2*6+3)); - plt.suptitle("Task Signals", - y=.94, fontsize=16, horizontalalignment='center'); - - # Plot load and utilization - signals_to_plot = {'load_avg', 'util_avg', - 'boosted_util', 'sched_overutilized'} - signals_to_plot = list(signals_to_plot.intersection(signals)) - if len(signals_to_plot) > 0: - axes = plt.subplot(gs[plot_id,0]); - axes.set_title('Task [{0:d}:{1:s}] Signals'\ - .format(tid, task_name)); - plot_id = plot_id + 1 - is_last = (plot_id == plots_count) - self._plotTaskSignals(axes, tid, signals_to_plot, is_last) - - # Plot CPUs residency - signals_to_plot = {'residencies', 'sched_overutilized'} - signals_to_plot = list(signals_to_plot.intersection(signals)) - if len(signals_to_plot) > 0: - axes = plt.subplot(gs[plot_id,0]); - axes.set_title('Task [{0:d}:{1:s}] Residency (green: LITTLE, red: big)'\ - .format(tid, task_name)); - plot_id = plot_id + 1 - is_last = (plot_id == plots_count) - self._plotTaskResidencies(axes, tid, signals_to_plot, is_last) - - # Plot PELT signals - signals_to_plot = { - 'load_sum', 'util_sum', - 'period_contrib', 'sched_overutilized'} - signals_to_plot = list(signals_to_plot.intersection(signals)) - if len(signals_to_plot) > 0: - axes = plt.subplot(gs[plot_id,0]); - axes.set_title('Task [{0:d}:{1:s}] PELT Signals'\ - .format(tid, task_name)); - plot_id = plot_id + 1 - self._plotTaskPelt(axes, tid, signals_to_plot) - - # Save generated plots into datadir - if isinstance(task_name, list): - task_name = re.sub('[:/]', '_', task_name[0]) - else: - task_name = re.sub('[:/]', '_', task_name) - figname = '{}/{}task_util_{}_{}.png'.format( - self.plotsdir, self.prefix, tid, task_name) - pl.savefig(figname, bbox_inches='tight') - -- GitLab From 962383ae1bfa853fed20c6e9f0d676abb5297eba Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Fri, 22 Jul 2016 14:42:49 +0100 Subject: [PATCH 12/24] libs/utils/analysis: add CPUs module This patch move the CPUs signals analysis code, originally part of the trace_analysis.py module, to be a new TraceAnalysis module with dynamic registration via the AnalysisRegister. Signed-off-by: Patrick Bellasi --- libs/utils/analysis/cpus_analysis.py | 125 +++++++++++++++++++++++++++ libs/utils/trace_analysis.py | 80 ----------------- 2 files changed, 125 insertions(+), 80 deletions(-) create mode 100644 libs/utils/analysis/cpus_analysis.py diff --git a/libs/utils/analysis/cpus_analysis.py b/libs/utils/analysis/cpus_analysis.py new file mode 100644 index 000000000..7c3da5701 --- /dev/null +++ b/libs/utils/analysis/cpus_analysis.py @@ -0,0 +1,125 @@ +# SPDX-License-Identifier: Apache-2.0 +# +# Copyright (C) 2015, ARM Limited and contributors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import matplotlib.gridspec as gridspec +import matplotlib.pyplot as plt +import pylab as pl + +from trappy.utils import listify + +from analysis_module import AnalysisModule + +# Configure logging +import logging + +class CpusAnalysis(AnalysisModule): + + def __init__(self, trace): + """ + Support for CPUs Signals Analysis + """ + super(CpusAnalysis, self).__init__(trace) + +################################################################################ +# Plotting Methods +################################################################################ + + def plotCPU(self, cpus=None): + if not self._trace.hasEvents('sched_load_avg_cpu'): + logging.warn('Events [sched_load_avg_cpu] not found, '\ + 'plot DISABLED!') + return + + # Filter on specified cpus + if cpus is None: + cpus = sorted(self._platform['clusters']['little'] + self._platform['clusters']['big']) + cpus = listify(cpus) + + # Plot: big CPUs + bcpus = set(cpus) & set(self._platform['clusters']['big']) + self._plotCPU(bcpus, "big") + + # Plot: LITTLE CPUs + lcpus = set(cpus) & set(self._platform['clusters']['little']) + self._plotCPU(lcpus, "LITTLE") + + +################################################################################ +# Utility Methods +################################################################################ + + def _plotCPU(self, cpus, label=''): + if label != '': + label1 = '{} '.format(label) + label2 = '_{}s'.format(label.lower()) + + # Plot required CPUs + fig, pltaxes = plt.subplots(len(cpus), 1, figsize=(16, 3*(len(cpus)))); + plt.suptitle("{}CPUs Signals".format(label1), + y=.99, fontsize=16, horizontalalignment='center'); + + idx = 0 + for cpu in cpus: + + # Reference axes to be used + axes = pltaxes + if (len(cpus) > 1): + axes = pltaxes[idx] + + # Add CPU utilization + axes.set_title('{0:s}CPU [{1:d}]'.format(label1, cpu)); + df = self._dfg_trace_event('sched_load_avg_cpu') + df = df[df.cpu == cpu] + if len(df): + df[['util_avg']].plot(ax=axes, drawstyle='steps-post', alpha=0.4); + + # if self._trace.hasEvents('sched_boost_cpu'): + # df = self._dfg_trace_event('sched_boost_cpu') + # df = df[df.cpu == cpu] + # if len(df): + # df[['usage', 'boosted_usage']].plot( + # ax=axes, + # style=['m-', 'r-'], + # drawstyle='steps-post'); + + # Add Capacities data if avilable + if self._trace.hasEvents('cpu_capacity'): + df = self._dfg_trace_event('cpu_capacity') + df = df[df.cpu == cpu] + if len(df): + # data = df[['capacity', 'tip_capacity', 'max_capacity']] + # data.plot(ax=axes, style=['m', 'y', 'r'], + data = df[['capacity', 'tip_capacity' ]] + data.plot(ax=axes, style=['m', '--y' ], + drawstyle='steps-post') + + axes.set_ylim(0, 1100); + axes.set_xlim(self._trace.x_min, self._trace.x_max); + + # Disable x-axis timestamp for top-most cpus + if (len(cpus) > 1 and idx < len(cpus)-1): + axes.set_xticklabels([]) + axes.set_xlabel('') + axes.grid(True); + + idx+=1 + + # Save generated plots into datadir + figname = '{}/{}cpus{}.png'.format(self._trace.plots_dir, + self._trace.plots_prefix, label2) + pl.savefig(figname, bbox_inches='tight') + diff --git a/libs/utils/trace_analysis.py b/libs/utils/trace_analysis.py index 5373c51c3..c544a0e56 100644 --- a/libs/utils/trace_analysis.py +++ b/libs/utils/trace_analysis.py @@ -142,83 +142,3 @@ class TraceAnalysis(object): [df.cpu.isin(self.platform['clusters']['little'])], [tip_lcap], tip_bcap) - def __plotCPU(self, cpus=None, label=''): - if cpus is None or len(cpus) == 0: - return - if label != '': - label1 = '{} '.format(label) - label2 = '_{}s'.format(label.lower()) - - # Plot required CPUs - fig, pltaxes = plt.subplots(len(cpus), 1, figsize=(16, 3*(len(cpus)))); - plt.suptitle("{}CPUs Signals".format(label1), - y=.99, fontsize=16, horizontalalignment='center'); - - idx = 0 - for cpu in cpus: - - # Reference axes to be used - axes = pltaxes - if (len(cpus) > 1): - axes = pltaxes[idx] - - # Add CPU utilization - axes.set_title('{0:s}CPU [{1:d}]'.format(label1, cpu)); - df = self.trace.df('sched_load_avg_cpu') - df = df[df.cpu == cpu] - if len(df): - df[['util_avg']].plot(ax=axes, drawstyle='steps-post', alpha=0.4); - - # if self.trace.hasEvents('sched_boost_cpu'): - # df = self.trace.df('sched_boost_cpu') - # df = df[df.cpu == cpu] - # if len(df): - # df[['usage', 'boosted_usage']].plot( - # ax=axes, - # style=['m-', 'r-'], - # drawstyle='steps-post'); - - # Add Capacities data if avilable - if self.trace.hasEvents('cpu_capacity'): - df = self.trace.df('cpu_capacity') - df = df[df.cpu == cpu] - if len(df): - # data = df[['capacity', 'tip_capacity', 'max_capacity']] - # data.plot(ax=axes, style=['m', 'y', 'r'], - data = df[['capacity', 'tip_capacity' ]] - data.plot(ax=axes, style=['m', '--y' ], - drawstyle='steps-post') - - axes.set_ylim(0, 1100); - axes.set_xlim(self.x_min, self.x_max); - - # Disable x-axis timestamp for top-most cpus - if (len(cpus) > 1 and idx < len(cpus)-1): - axes.set_xticklabels([]) - axes.set_xlabel('') - axes.grid(True); - - idx+=1 - - # Save generated plots into datadir - figname = '{}/{}cpus{}.png'.format(self.plotsdir, self.prefix, label2) - pl.savefig(figname, bbox_inches='tight') - - def plotCPU(self, cpus=None): - if not self.trace.hasEvents('sched_load_avg_cpu'): - logging.warn('Events [sched_load_avg_cpu] not found, '\ - 'plot DISABLED!') - return - - # Filter on specified cpus - if cpus is None: - cpus = sorted(self.platform['clusters']['little'] + self.platform['clusters']['big']) - - # Plot: big CPUs - bcpus = set(cpus) & set(self.platform['clusters']['big']) - self.__plotCPU(bcpus, "big") - - # Plot: LITTLE CPUs - lcpus = set(cpus) & set(self.platform['clusters']['little']) - self.__plotCPU(lcpus, "LITTLE") - -- GitLab From 3435f9dcb63d6f13b8008631f2ca95b93ff06e69 Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Fri, 22 Jul 2016 14:44:48 +0100 Subject: [PATCH 13/24] libs/utils/analysis: add FUNCTIONs module This patch move the Functions Profiling analysis code, originally part of the trace_analysis.py module, to be a new TraceAnalysis module with dynamic registration via the AnalysisRegister. Signed-off-by: Patrick Bellasi --- libs/utils/analysis/functions_analysis.py | 77 +++++++++++++++++++++++ libs/utils/trace_analysis.py | 46 -------------- 2 files changed, 77 insertions(+), 46 deletions(-) create mode 100644 libs/utils/analysis/functions_analysis.py diff --git a/libs/utils/analysis/functions_analysis.py b/libs/utils/analysis/functions_analysis.py new file mode 100644 index 000000000..dfdb0aa22 --- /dev/null +++ b/libs/utils/analysis/functions_analysis.py @@ -0,0 +1,77 @@ +# SPDX-License-Identifier: Apache-2.0 +# +# Copyright (C) 2015, ARM Limited and contributors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from trappy.utils import listify + +from analysis_module import AnalysisModule + +# Configure logging +import logging + +class FunctionsAnalysis(AnalysisModule): + + def __init__(self, trace): + """ + Support for kernel functions profiling and analysis + """ + super(FunctionsAnalysis, self).__init__(trace) + + def plotProfilingStats(self, functions=None, metrics='avg'): + """ + Plot functions profiling metrics for the specified kernel functions. + + For each speficied metric a barplot is generated which report the value + of the metric when the kernel function has been executed on each CPU. + By default all the kernel functions are plotted. + + :param functions: the name of list of name of kernel functions to plot + :type functions: str or list + + :param metrics: the metrics to plot + avg - average execution time + time - total execution time + :type metrics: srt or list + """ + if not hasattr(self._trace, '_functions_stats_df'): + logging.warning('Functions stats data not available') + return + + metrics = listify(metrics) + df = self._trace.data_frame.functions_stats(functions) + + # Check that all the required metrics are acutally availabe + available_metrics = df.columns.tolist() + if not set(metrics).issubset(set(available_metrics)): + msg = 'Metrics {} not supported, available metrics are {}'\ + .format(set(metrics) - set(available_metrics), + available_metrics) + raise ValueError(msg) + + for _m in metrics: + if _m.upper() == 'AVG': + title = 'Average Completion Time per CPUs' + ylabel = 'Completion Time [us]' + if _m.upper() == 'TIME': + title = 'Total Execution Time per CPUs' + ylabel = 'Execution Time [us]' + data = df[_m.lower()].unstack() + axes = data.plot(kind='bar', + figsize=(16,8), legend=True, + title=title, table=True) + axes.set_ylabel(ylabel) + axes.get_xaxis().set_visible(False) + diff --git a/libs/utils/trace_analysis.py b/libs/utils/trace_analysis.py index c544a0e56..007bfc1d1 100644 --- a/libs/utils/trace_analysis.py +++ b/libs/utils/trace_analysis.py @@ -78,52 +78,6 @@ class TraceAnalysis(object): logging.info('Set plots time range to (%.6f, %.6f)[s]', self.x_min, self.x_max) - def plotFunctionStats(self, functions=None, metrics='avg'): - """ - Plot functions profiling metrics for the specified kernel functions. - - For each speficied metric a barplot is generated which report the value - of the metric when the kernel function has been executed on each CPU. - By default all the kernel functions are plotted. - - :param functions: the name of list of name of kernel functions to plot - :type functions: str or list - - :param metrics: the metrics to plot - avg - average execution time - time - total execution time - :type metrics: srt or list - """ - if not hasattr(self.trace, '_functions_stats_df'): - logging.warning('Functions stats data not available') - return - - metrics = listify(metrics) - df = self.trace.functions_stats_df(functions) - - # Check that all the required metrics are acutally availabe - available_metrics = df.columns.tolist() - if not set(metrics).issubset(set(available_metrics)): - msg = 'Metrics {} not supported, available metrics are {}'\ - .format(set(metrics) - set(available_metrics), - available_metrics) - raise ValueError(msg) - - for _m in metrics: - if _m.upper() == 'AVG': - title = 'Average Completion Time per CPUs' - ylabel = 'Completion Time [us]' - if _m.upper() == 'TIME': - title = 'Total Execution Time per CPUs' - ylabel = 'Execution Time [us]' - data = df[_m.lower()].unstack() - axes = data.plot(kind='bar', - figsize=(16,8), legend=True, - title=title, table=True) - axes.set_ylabel(ylabel) - axes.get_xaxis().set_visible(False) - - def __addCapacityColum(self): df = self.trace.df('cpu_capacity') # Rename CPU and Capacity columns -- GitLab From 450f3be527c1c947a8adb44000a22b1992319aab Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Fri, 22 Jul 2016 14:47:34 +0100 Subject: [PATCH 14/24] libs/utils/trace: cleanup unused imports Signed-off-by: Patrick Bellasi --- libs/utils/trace.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/libs/utils/trace.py b/libs/utils/trace.py index 8a2332fcd..608ae3592 100644 --- a/libs/utils/trace.py +++ b/libs/utils/trace.py @@ -15,14 +15,9 @@ # limitations under the License. # -import glob -import matplotlib.gridspec as gridspec -import matplotlib.pyplot as plt import numpy as np import os import pandas as pd -import pylab as pl -import re import sys import trappy import json -- GitLab From ed84c9525d40e2f35e424f3b7cd839a322d865ca Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Fri, 22 Jul 2016 14:51:13 +0100 Subject: [PATCH 15/24] libs/utils/trace_analysis: remove useless original module The AnalysisRegister module provides now a support to dynamically register all the trace analysis module available under the libs/utils/analysis folder. The code originally part of libs/utils/trace_analysis.py has been moved into a set of new AnalysisModules. Thus, this patch get rides of the not more used original trace_analysis.py file. Signed-off-by: Patrick Bellasi --- libs/utils/__init__.py | 1 - libs/utils/trace_analysis.py | 98 ------------------------------------ 2 files changed, 99 deletions(-) delete mode 100644 libs/utils/trace_analysis.py diff --git a/libs/utils/__init__.py b/libs/utils/__init__.py index 68b2ab3ce..934d90b94 100644 --- a/libs/utils/__init__.py +++ b/libs/utils/__init__.py @@ -24,7 +24,6 @@ from energy import EnergyMeter from conf import JsonConf from trace import Trace -from trace_analysis import TraceAnalysis from perf_analysis import PerfAnalysis from filters import Filters diff --git a/libs/utils/trace_analysis.py b/libs/utils/trace_analysis.py deleted file mode 100644 index 007bfc1d1..000000000 --- a/libs/utils/trace_analysis.py +++ /dev/null @@ -1,98 +0,0 @@ -# SPDX-License-Identifier: Apache-2.0 -# -# Copyright (C) 2015, ARM Limited and contributors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); you may -# not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT -# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -import glob -import matplotlib.gridspec as gridspec -import matplotlib.pyplot as plt -import numpy as np -import os -import pandas as pd -import pylab as pl -import re -import sys -import trappy -import operator -from trappy.utils import listify -from devlib.utils.misc import memoized -from collections import namedtuple - -# Configure logging -import logging - -NON_IDLE_STATE = 4294967295 - -ResidencyTime = namedtuple('ResidencyTime', ['total', 'active']) -ResidencyData = namedtuple('ResidencyData', ['label', 'residency']) - -class TraceAnalysis(object): - - def __init__(self, trace, tasks=None, plotsdir=None, prefix=''): - """ - Support for plotting a standard set of trace singals and events - """ - - self.trace = trace - self.tasks = tasks - self.plotsdir = plotsdir - self.prefix = prefix - - # Keep track of the Trace::platform - self.platform = trace.platform - - # Plotsdir is byb default the trace dir - if self.plotsdir is None: - self.plotsdir = self.trace.data_dir - - # Minimum and Maximum x_time to use for all plots - self.x_min = 0 - self.x_max = self.trace.time_range - - # Reset x axis time range to full scale - t_min = self.trace.window[0] - t_max = self.trace.window[1] - self.setXTimeRange(t_min, t_max) - - def setXTimeRange(self, t_min=None, t_max=None): - if t_min is None: - self.x_min = 0 - else: - self.x_min = t_min - if t_max is None: - self.x_max = self.trace.time_range - else: - self.x_max = t_max - logging.info('Set plots time range to (%.6f, %.6f)[s]', - self.x_min, self.x_max) - - def __addCapacityColum(self): - df = self.trace.df('cpu_capacity') - # Rename CPU and Capacity columns - df.rename(columns={'cpu_id':'cpu'}, inplace=True) - # Add column with LITTLE and big CPUs max capacities - nrg_model = self.platform['nrg_model'] - max_lcap = nrg_model['little']['cpu']['cap_max'] - max_bcap = nrg_model['big']['cpu']['cap_max'] - df['max_capacity'] = np.select( - [df.cpu.isin(self.platform['clusters']['little'])], - [max_lcap], max_bcap) - # Add LITTLE and big CPUs "tipping point" threshold - tip_lcap = 0.8 * max_lcap - tip_bcap = 0.8 * max_bcap - df['tip_capacity'] = np.select( - [df.cpu.isin(self.platform['clusters']['little'])], - [tip_lcap], tip_bcap) - -- GitLab From c67f06c5d9f81ac6a277d63fb51102da3b882d0e Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Fri, 22 Jul 2016 16:46:19 +0100 Subject: [PATCH 16/24] tools/plots: replace TraceAnalysis with refactored analysis API Signed-off-by: Michele Di Giorgio --- tools/plots.py | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/tools/plots.py b/tools/plots.py index 0de8ddd08..d73813e56 100755 --- a/tools/plots.py +++ b/tools/plots.py @@ -19,11 +19,9 @@ import sys # sys.path.insert(1, "./libs") -#from utils.perf_analysis import PerfAnalysis -#from utils.trace_analysis import TraceAnalysis + from perf_analysis import PerfAnalysis from trace import Trace -from trace_analysis import TraceAnalysis import os import re @@ -145,31 +143,30 @@ def plotdir(run_dir, platform): # Load Trace Analysis modules trace = Trace(platform, run_dir, tasks) - ta = TraceAnalysis(trace, tasks) # Define time ranges for all the temporal plots - ta.setXTimeRange(args.tmin, args.tmax) + trace.setXTimeRange(args.tmin, args.tmax) # Tasks plots if 'tasks' in args.plots: - ta.plotTasks() + trace.analysis.tasks.plotTasks() if pa: for task in tasks: pa.plotPerf(task) # Cluster and CPUs plots if 'clusters' in args.plots: - ta.plotClusterFrequencies() + trace.analysis.frequency.plotClusterFrequencies() if 'cpus' in args.plots: - ta.plotCPU() + trace.analysis.cpus.plotCPU() # SchedTune plots if 'stune' in args.plots: - ta.plotSchedTuneConf() + trace.analysis.eas.plotSchedTuneConf() if 'ediff' in args.plots: - ta.plotEDiffTime(); + trace.analysis.eas.plotEDiffTime(); if 'edspace' in args.plots: - ta.plotEDiffSpace(); + trace.analysis.eas.plotEDiffSpace(); if __name__ == "__main__": main() -- GitLab From b1423a814f1bf7c2583dc47741cb0c18931e492b Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Fri, 22 Jul 2016 17:39:22 +0100 Subject: [PATCH 17/24] libs/utils: cosmetics: remove unnecessary empty lines Signed-off-by: Michele Di Giorgio --- libs/utils/analysis/eas_analysis.py | 6 ------ libs/utils/trace.py | 5 ----- 2 files changed, 11 deletions(-) diff --git a/libs/utils/analysis/eas_analysis.py b/libs/utils/analysis/eas_analysis.py index 8167ee5ef..5aae18798 100644 --- a/libs/utils/analysis/eas_analysis.py +++ b/libs/utils/analysis/eas_analysis.py @@ -24,7 +24,6 @@ from analysis_module import AnalysisModule # Configure logging import logging - class EasAnalysis(AnalysisModule): def __init__(self, trace): @@ -153,7 +152,6 @@ class EasAnalysis(AnalysisModule): .format(self._trace.plots_dir, self._trace.plots_prefix) pl.savefig(figname, bbox_inches='tight') - # Grid: setup stats for gris gs = gridspec.GridSpec(1, 3, height_ratios=[2]); gs.update(wspace=0.1, hspace=0.1); @@ -173,7 +171,6 @@ class EasAnalysis(AnalysisModule): .format(self._trace.plots_dir, self._trace.plots_prefix) pl.savefig(figname, bbox_inches='tight') - def plotEDiffSpace(self, tasks=None, min_usage_delta=None, max_usage_delta=None, min_cap_delta=None, max_cap_delta=None, @@ -235,7 +232,6 @@ class EasAnalysis(AnalysisModule): axes_min = min(x_min, y_min) axes_max = max(x_max, y_max) - # # Tag columns by usage_delta # ccol = df.usage_delta # df['usage_delta_group'] = np.select( @@ -283,7 +279,6 @@ class EasAnalysis(AnalysisModule): axes.set_xlabel('Energy diff [%]'); axes.set_ylabel('Capacity diff [%]'); - # Plot: per usage_delta values axes = plt.subplot(gs[0,1]); @@ -329,7 +324,6 @@ class EasAnalysis(AnalysisModule): .format(self._trace.plots_dir, self._trace.plots_prefix) pl.savefig(figname, bbox_inches='tight') - def plotSchedTuneConf(self): """ Plot the configuration of the SchedTune diff --git a/libs/utils/trace.py b/libs/utils/trace.py index 608ae3592..bc0a66e8d 100644 --- a/libs/utils/trace.py +++ b/libs/utils/trace.py @@ -136,7 +136,6 @@ class Trace(object): else: raise ValueError('Events must be a string or a list of strings') - def __parseTrace(self, path, tasks, window, normalize_time, trace_format): logging.debug('Loading [sched] events from trace in [%s]...', path) logging.debug("Parsing events: %s", self.events) @@ -179,7 +178,6 @@ class Trace(object): self.__loadTasksNames(tasks) - # Compute plot window if not normalize_time: start = self.window[0] @@ -199,7 +197,6 @@ class Trace(object): for evt in self.available_events: logging.debug(' - %s', evt) - def __loadTasksNames(self, tasks): # Try to load tasks names using one of the supported events if 'sched_switch' in self.available_events: @@ -355,7 +352,6 @@ class Trace(object): :param functions: the name of the function or a list of function names to report :type functions: str or list - """ if not hasattr(self, '_functions_stats_df'): return None @@ -424,7 +420,6 @@ class Trace(object): df.rename(columns={'usage':'util'}, inplace=True) df['boosted_util'] = df['util'] + df['margin'] - def _sanitize_SchedBoostTask(self): if not self.hasEvents('sched_boost_task'): return -- GitLab From f93efbe21b68cf8f422a221d81fcd723a2ced356 Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Fri, 22 Jul 2016 18:34:15 +0100 Subject: [PATCH 18/24] libs/utils/analysis: docstrings everywhere Signed-off-by: Michele Di Giorgio --- libs/utils/analysis/cpus_analysis.py | 18 +- libs/utils/analysis/eas_analysis.py | 18 +- libs/utils/analysis/frequency_analysis.py | 24 ++- libs/utils/analysis/functions_analysis.py | 13 +- libs/utils/analysis/status_analysis.py | 22 +- libs/utils/analysis/tasks_analysis.py | 55 ++++- libs/utils/analysis_module.py | 9 +- libs/utils/analysis_register.py | 7 +- libs/utils/trace.py | 246 +++++++++++++++++++--- 9 files changed, 347 insertions(+), 65 deletions(-) diff --git a/libs/utils/analysis/cpus_analysis.py b/libs/utils/analysis/cpus_analysis.py index 7c3da5701..196faca3f 100644 --- a/libs/utils/analysis/cpus_analysis.py +++ b/libs/utils/analysis/cpus_analysis.py @@ -27,11 +27,14 @@ from analysis_module import AnalysisModule import logging class CpusAnalysis(AnalysisModule): + """ + Support for CPUs Signals Analysis + + :param trace: input Trace object + :type trace: :mod:`libs.utils.Trace` + """ def __init__(self, trace): - """ - Support for CPUs Signals Analysis - """ super(CpusAnalysis, self).__init__(trace) ################################################################################ @@ -39,6 +42,9 @@ class CpusAnalysis(AnalysisModule): ################################################################################ def plotCPU(self, cpus=None): + """ + Plot CPU-related signals for both big and LITTLE clusters. + """ if not self._trace.hasEvents('sched_load_avg_cpu'): logging.warn('Events [sched_load_avg_cpu] not found, '\ 'plot DISABLED!') @@ -63,6 +69,12 @@ class CpusAnalysis(AnalysisModule): ################################################################################ def _plotCPU(self, cpus, label=''): + """ + Internal method that generates plots for all input CPUs. + + :param cpus: list of CPUs to be plotted + :type cpus: list(int) + """ if label != '': label1 = '{} '.format(label) label2 = '_{}s'.format(label.lower()) diff --git a/libs/utils/analysis/eas_analysis.py b/libs/utils/analysis/eas_analysis.py index 5aae18798..a33dddc17 100644 --- a/libs/utils/analysis/eas_analysis.py +++ b/libs/utils/analysis/eas_analysis.py @@ -25,11 +25,14 @@ from analysis_module import AnalysisModule import logging class EasAnalysis(AnalysisModule): + """ + Support for EAS signals anaysis + + :param trace: input Trace object + :type trace: :mod:`libs.utils.Trace` + """ def __init__(self, trace): - """ - Support for EAS signals anaysis - """ super(EasAnalysis, self).__init__(trace) ################################################################################ @@ -46,6 +49,9 @@ class EasAnalysis(AnalysisModule): min_cap_delta=None, max_cap_delta=None, min_nrg_delta=None, max_nrg_delta=None, min_nrg_diff=None, max_nrg_diff=None): + """ + Plot energy_diff()-related signals on time axes. + """ if not self._trace.hasEvents('sched_energy_diff'): logging.warn('Events [sched_energy_diff] not found, plot DISABLED!') return @@ -176,6 +182,10 @@ class EasAnalysis(AnalysisModule): min_cap_delta=None, max_cap_delta=None, min_nrg_delta=None, max_nrg_delta=None, min_nrg_diff=None, max_nrg_diff=None): + """ + Plot energy_diff()-related signals on the Performance-Energy space + (PxE). + """ if not self._trace.hasEvents('sched_energy_diff'): logging.warn('Events [sched_energy_diff] not found, plot DISABLED!') return @@ -326,7 +336,7 @@ class EasAnalysis(AnalysisModule): def plotSchedTuneConf(self): """ - Plot the configuration of the SchedTune + Plot the configuration of SchedTune. """ if not self._trace.hasEvents('sched_tune_config'): logging.warn('Events [sched_tune_config] not found, plot DISABLED!') diff --git a/libs/utils/analysis/frequency_analysis.py b/libs/utils/analysis/frequency_analysis.py index c0d626097..11e6ddc46 100644 --- a/libs/utils/analysis/frequency_analysis.py +++ b/libs/utils/analysis/frequency_analysis.py @@ -35,11 +35,14 @@ ResidencyTime = namedtuple('ResidencyTime', ['total', 'active']) ResidencyData = namedtuple('ResidencyData', ['label', 'residency']) class FrequencyAnalysis(AnalysisModule): + """ + Support for plotting Frequency Analysis data + + :param trace: input Trace object + :type trace: :mod:`libs.utils.Trace` + """ def __init__(self, trace): - """ - Support for plotting Frequency Analysis data - """ super(FrequencyAnalysis, self).__init__(trace) ################################################################################ @@ -98,9 +101,16 @@ class FrequencyAnalysis(AnalysisModule): ################################################################################ def plotClusterFrequencies(self, title='Clusters Frequencies'): + """ + Plot frequency trend for all clusters. If sched_overutilized events are + available, the plots will also show the intervals of time where the + cluster was overutilized. + + :param title: user-defined plot title + :type title: str + """ if not self._trace.hasEvents('cpu_frequency'): - logging.warn('Events [cpu_frequency] not found, '\ - 'plot DISABLED!') + logging.warn('Events [cpu_frequency] not found, plot DISABLED!') return df = self._dfg_trace_event('cpu_frequency') @@ -485,8 +495,8 @@ class FrequencyAnalysis(AnalysisModule): :param is_first: if True this is the first plot :type is_first: bool - :param is_first: if True this is the last plot - :type is_first: bool + :param is_last: if True this is the last plot + :type is_last: bool :param xmax: x-axes higher bound :param xmax: double diff --git a/libs/utils/analysis/functions_analysis.py b/libs/utils/analysis/functions_analysis.py index dfdb0aa22..7500b2cb3 100644 --- a/libs/utils/analysis/functions_analysis.py +++ b/libs/utils/analysis/functions_analysis.py @@ -23,11 +23,14 @@ from analysis_module import AnalysisModule import logging class FunctionsAnalysis(AnalysisModule): + """ + Support for kernel functions profiling and analysis + + :param trace: input Trace object + :type trace: :mod:`libs.utils.Trace` + """ def __init__(self, trace): - """ - Support for kernel functions profiling and analysis - """ super(FunctionsAnalysis, self).__init__(trace) def plotProfilingStats(self, functions=None, metrics='avg'): @@ -39,12 +42,12 @@ class FunctionsAnalysis(AnalysisModule): By default all the kernel functions are plotted. :param functions: the name of list of name of kernel functions to plot - :type functions: str or list + :type functions: str or list(str) :param metrics: the metrics to plot avg - average execution time time - total execution time - :type metrics: srt or list + :type metrics: srt or list(str) """ if not hasattr(self._trace, '_functions_stats_df'): logging.warning('Functions stats data not available') diff --git a/libs/utils/analysis/status_analysis.py b/libs/utils/analysis/status_analysis.py index 32f8d359f..29e512756 100644 --- a/libs/utils/analysis/status_analysis.py +++ b/libs/utils/analysis/status_analysis.py @@ -25,11 +25,14 @@ from analysis_module import AnalysisModule import logging class StatusAnalysis(AnalysisModule): + """ + Support for System Status analysis + + :param trace: input Trace object + :type trace: :mod:`libs.utils.Trace` + """ def __init__(self, trace): - """ - Support for System Status analysis - """ super(StatusAnalysis, self).__init__(trace) @@ -38,6 +41,9 @@ class StatusAnalysis(AnalysisModule): ################################################################################ def _dfg_overutilized(self): + """ + Get data frame with sched_overutilized data. + """ if not self._trace.hasEvents('sched_overutilized'): return None @@ -61,6 +67,16 @@ class StatusAnalysis(AnalysisModule): ################################################################################ def plotOverutilized(self, axes=None): + """ + Draw a plot that shows intervals of time where the system was reported + as overutilized. + + The optional axes parameter allows to plot the signal on an existing + graph. + + :param axes: axes on which to plot the signal + :type axes: :mod:`matplotlib.axes.Axes` + """ if not self._trace.hasEvents('sched_overutilized'): logging.warn('Events [sched_overutilized] not found, '\ 'plot DISABLED!') diff --git a/libs/utils/analysis/tasks_analysis.py b/libs/utils/analysis/tasks_analysis.py index 6ed364e5c..8e4a8c484 100644 --- a/libs/utils/analysis/tasks_analysis.py +++ b/libs/utils/analysis/tasks_analysis.py @@ -26,11 +26,14 @@ from analysis_module import AnalysisModule import logging class TasksAnalysis(AnalysisModule): + """ + Support for Tasks signals analysis. + + :param trace: input Trace object + :type trace: :mod:`libs.utils.Trace` + """ def __init__(self, trace): - """ - Support for Tasks signals analysis - """ super(TasksAnalysis, self).__init__(trace) @@ -66,12 +69,12 @@ class TasksAnalysis(AnalysisModule): in the same list. default: all tasks defined in Trace creation time are plotted - :type tasks: list + :type tasks: list(str) or list(int) :param signals: list of signals (and thus plots) to generate default: all the plots and signals available in the current trace - :type signals: list + :type signals: list(str) """ if not signals: signals = ['load_avg', 'util_avg', 'boosted_util', @@ -186,6 +189,21 @@ class TasksAnalysis(AnalysisModule): ################################################################################ def _plotTaskSignals(self, axes, tid, signals, is_last=False): + """ + For task with ID `tid` plot the specified signals. + + :param axes: axes over which to generate the plot + :type axes: :mod:`matplotlib.axes.Axes` + + :param tid: task ID + :type tid: int + + :param signals: signals to be plot + :param signals: list(str) + + :param is_last: if True this is the last plot + :type is_last: bool + """ # Get dataframe for the required task util_df = self._dfg_trace_event('sched_load_avg_task') @@ -231,6 +249,21 @@ class TasksAnalysis(AnalysisModule): self._trace.analysis.status.plotOverutilized(axes) def _plotTaskResidencies(self, axes, tid, signals, is_last=False): + """ + For task with ID `tid` plot residency information. + + :param axes: axes over which to generate the plot + :type axes: :mod:`matplotlib.axes.Axes` + + :param tid: task ID + :type tid: int + + :param signals: signals to be plot + :param signals: list(str) + + :param is_last: if True this is the last plot + :type is_last: bool + """ util_df = self._dfg_trace_event('sched_load_avg_task') data = util_df[util_df.pid == tid][['cluster', 'cpu']] for ccolor, clabel in zip('gr', ['LITTLE', 'big']): @@ -254,6 +287,18 @@ class TasksAnalysis(AnalysisModule): self._trace.analysis.status.plotOverutilized(axes) def _plotTaskPelt(self, axes, tid, signals): + """ + For task with ID `tid` plot PELT-related signals. + + :param axes: axes over which to generate the plot + :type axes: :mod:`matplotlib.axes.Axes` + + :param tid: task ID + :type tid: int + + :param signals: signals to be plot + :param signals: list(str) + """ util_df = self._dfg_trace_event('sched_load_avg_task') data = util_df[util_df.pid == tid][['load_sum', 'util_sum', 'period_contrib']] data.plot(ax=axes, drawstyle='steps-post'); diff --git a/libs/utils/analysis_module.py b/libs/utils/analysis_module.py index 0b399fc80..6cdebd48e 100644 --- a/libs/utils/analysis_module.py +++ b/libs/utils/analysis_module.py @@ -16,11 +16,14 @@ # class AnalysisModule(object): + """ + Base class for Analysis modules. + + :param trace: input Trace object + :type trace: :mod:`libs.utils.Trace` + """ def __init__(self, trace): - """ - Support for CPUs Signals Analysis - """ self._trace = trace self._platform = trace.platform self._tasks = trace.tasks diff --git a/libs/utils/analysis_register.py b/libs/utils/analysis_register.py index e01807cc2..e9c05fd22 100644 --- a/libs/utils/analysis_register.py +++ b/libs/utils/analysis_register.py @@ -27,8 +27,13 @@ from analysis_module import AnalysisModule # Configure logging import logging -# Define list of supported Analysis Classes class AnalysisRegister(object): + """ + Define list of supported Analysis Classes. + + :param trace: input Trace object + :type trace: :mod:`libs.utils.Trace` + """ def __init__(self, trace): diff --git a/libs/utils/trace.py b/libs/utils/trace.py index bc0a66e8d..8a0997e81 100644 --- a/libs/utils/trace.py +++ b/libs/utils/trace.py @@ -30,6 +30,39 @@ from trappy.utils import listify import logging class Trace(object): + """ + The Trace object is the LISA trace events parser. + + :param platform: a dictionary containing information about the target + platform + :type platform: dict + + :param data_dir: folder containing all trace data + :type data_dir: str + + :param events: events to be parsed (everything in the trace by default) + :type events: list(str) + + :param tasks: filter data for the specified tasks only + :type tasks: list(str) + + :param window: time window to consider when parsing the trace + :type window: tuple(int, int) + + :param normalize_time: normalize trace time stamps + :type normalize_time: bool + + :param trace_format: format of the trace. Possible values are: + - FTrace + - SysTrace + :type trace_format: str + + :param plots_dir: directory where to save plots + :type plots_dir: str + + :param plots_prefix: prefix for plots file names + :type plots_prefix: str + """ def __init__(self, platform, data_dir, events, tasks=None, window=(0,None), @@ -106,6 +139,13 @@ class Trace(object): self.analysis = AnalysisRegister(self) def _registerDataFrameGetters(self, module): + """ + Internal utility function that looks up getter functions with a "_dfg_" + prefix in their name and bounds them to the specified module. + + :param module: module to which the function is added + :type module: class + """ logging.debug("Registering [%s] local data frames", module) for func in dir(module): if not func.startswith('_dfg_'): @@ -116,6 +156,15 @@ class Trace(object): setattr(self.data_frame, dfg_name, dfg_func) def setXTimeRange(self, t_min=None, t_max=None): + """ + Set x axis time range to the specified values. + + :param t_min: lower bound + :type t_min: int or float + + :param t_max: upper bound + :type t_max: int or float + """ if t_min is None: self.x_min = 0 else: @@ -128,7 +177,12 @@ class Trace(object): self.x_min, self.x_max) def __registerTraceEvents(self, events): + """ + Save a copy of the parsed events. + :param events: single event name or list of events names + :type events: str or list(str) + """ if isinstance(events, basestring): self.events = events.split(' ') elif isinstance(events, list): @@ -137,6 +191,27 @@ class Trace(object): raise ValueError('Events must be a string or a list of strings') def __parseTrace(self, path, tasks, window, normalize_time, trace_format): + """ + Internal method in charge of performing the actual parsing of the + trace. + + :param path: path to the trace folder (or trace file) + :type path: str + + :param tasks: filter data for the specified tasks only + :type tasks: list(str) + + :param window: time window to consider when parsing the trace + :type window: tuple(int, int) + + :param normalize_time: normalize trace time stamps + :type normalize_time: bool + + :param trace_format: format of the trace. Possible values are: + - FTrace + - SysTrace + :type trace_format: str + """ logging.debug('Loading [sched] events from trace in [%s]...', path) logging.debug("Parsing events: %s", self.events) if trace_format.upper() == 'SYSTRACE' or path.endswith('html'): @@ -189,6 +264,12 @@ class Trace(object): self.ftrace.basetime + duration) def __checkAvailableEvents(self, key=""): + """ + Internal method used to build a list of available events. + + :param key: key to be used for TRAPpy filtering + :type key: str + """ for val in self.ftrace.get_filters(key): obj = getattr(self.ftrace, val) if len(obj.data_frame): @@ -198,7 +279,12 @@ class Trace(object): logging.debug(' - %s', evt) def __loadTasksNames(self, tasks): - # Try to load tasks names using one of the supported events + """ + Try to load tasks names using one of the supported events. + + :param tasks: list of task names + :type tasks: list(str) + """ if 'sched_switch' in self.available_events: self.getTasks(self._dfg_trace_event('sched_switch'), tasks, name_key='next_comm', pid_key='next_pid') @@ -212,12 +298,21 @@ class Trace(object): logging.warning('Failed to load tasks names from trace events') def hasEvents(self, dataset): + """ + Returns True if the specified event is present in the parsed trace, + False otherwise. + + :param dataset: trace event name or list of trace events + :type dataset: str or list(str) + """ if dataset in self.available_events: return True return False def __computeTimeSpan(self): - # Compute time axis range, considering all the parsed events + """ + Compute time axis range, considering all the parsed events. + """ ts = sys.maxint te = 0 @@ -244,11 +339,31 @@ class Trace(object): self.overutilized_time, self.overutilized_prc) def _scanTasks(self, df, name_key='comm', pid_key='pid'): + """ + Extract tasks names and PIDs from the input data frame. The data frame + should contain a task name column and PID column. + + :param df: data frame containing trace events from which tasks names + and PIDs will be extracted + :type df: :mod:`pandas.DataFrame` + + :param name_key: The name of the dataframe columns containing task names + :type name_key: str + + :param pid_key: The name of the dataframe columns containing task PIDs + :type pid_key: str + """ df = df[[name_key, pid_key]] self._tasks_by_name = df.set_index(name_key) self._tasks_by_pid = df.set_index(pid_key) def getTaskByName(self, name): + """ + Get the PIDs of all tasks with the specified name. + + :param name: task name + :type name: str + """ if name not in self._tasks_by_name.index: return [] if len(self._tasks_by_name.ix[name].values) > 1: @@ -257,6 +372,12 @@ class Trace(object): return [self._tasks_by_name.ix[name].values[0]] def getTaskByPid(self, pid): + """ + Get the names of all tasks with the specified PID. + + :param name: task PID + :type name: int + """ if pid not in self._tasks_by_pid.index: return [] if len(self._tasks_by_pid.ix[pid].values) > 1: @@ -266,28 +387,33 @@ class Trace(object): def getTasks(self, dataframe=None, task_names=None, name_key='comm', pid_key='pid'): - # """ Helper function to get PIDs of specified tasks - # - # This method requires a Pandas dataset in input to be used to - # fiter out the PIDs of all the specified tasks. - # In a dataset is not provided, previouslt filtered PIDs are - # returned. - # If a list of task names is not provided, the workload defined - # task names is used instead. - # The specified dataframe must provide at least two columns - # reporting the task name and the task PID. The default values of - # this colums could be specified using the provided parameters. - # - # :param task_names: The list of tasks to get the PID of (by default - # the workload defined tasks) - # :param dataframe: A Pandas datafram containing at least 'pid' and - # 'task name' columns. If None, the previously - # filtered PIDs are returned - # :param name_key: The name of the dataframe columns containing - # task names - # :param pid_key: The name of the dataframe columns containing - # task PIDs - # """ + """ + Helper function to get PIDs of specified tasks. + + This method requires a Pandas dataset in input to be used to fiter out + the PIDs of all the specified tasks. If a dataset is not provided, + previously filtered PIDs are returned. + + If a list of task names is not provided, the workload defined task + names is used instead. The specified dataframe must provide at least + two columns reporting the task name and the task PID. The default + values of this colums could be specified using the provided parameters. + + :param dataframe: A Pandas datafram containing at least 'pid' and + 'task name' columns. If None, the previously filtered PIDs are + returned. + :type dataframe: :mod:`pandas.DataFrame` + + :param task_names: The list of tasks to get the PID of (by default the + workload defined tasks) + :type task_names: list(str) + + :param name_key: The name of the dataframe columns containing task names + :type name_key: str + + :param pid_key: The name of the dataframe columns containing task PIDs + :type pid_key: str + """ if dataframe is None: return self.tasks df = dataframe @@ -318,6 +444,13 @@ class Trace(object): ################################################################################ def df(self, event): + """ + Get a dataframe containing all occurrences of the specified trace event + in the parsed trace. + + :param event: Trace event name + :type event: str + """ warnings.simplefilter('always', DeprecationWarning) #turn off filter warnings.warn("\n\tUse of Trace::df() is deprecated and will be soon removed." "\n\tUse Trace::data_frame.trace_event(event_name) instead.", @@ -327,8 +460,11 @@ class Trace(object): def _dfg_trace_event(self, event): """ - Return the PANDAS dataframe with the performance data for the specified - event + Get a dataframe containing all occurrences of the specified trace event + in the parsed trace. + + :param event: Trace event name + :type event: str """ if self.data_dir is None: raise ValueError("trace data not (yet) loaded") @@ -351,7 +487,7 @@ class Trace(object): :param functions: the name of the function or a list of function names to report - :type functions: str or list + :type functions: str or list(str) """ if not hasattr(self, '_functions_stats_df'): return None @@ -366,7 +502,10 @@ class Trace(object): ################################################################################ def _sanitize_SchedCpuCapacity(self): - # Add more columns if the energy model is available + """ + Add more columns to cpu_capacity data frame if the energy model is + available. + """ if not self.hasEvents('cpu_capacity') \ or 'nrg_model' not in self.platform: return @@ -388,20 +527,24 @@ class Trace(object): [tip_lcap], tip_bcap) def _sanitize_SchedLoadAvgCpu(self): + """ + If necessary, rename certain signal names from v5.0 to v5.1 format. + """ if not self.hasEvents('sched_load_avg_cpu'): return df = self._dfg_trace_event('sched_load_avg_cpu') if 'utilization' in df: - # Convert signals name from v5.0 to v5.1 format df.rename(columns={'utilization':'util_avg'}, inplace=True) df.rename(columns={'load':'load_avg'}, inplace=True) def _sanitize_SchedLoadAvgTask(self): + """ + If necessary, rename certain signal names from v5.0 to v5.1 format. + """ if not self.hasEvents('sched_load_avg_task'): return df = self._dfg_trace_event('sched_load_avg_task') if 'utilization' in df: - # Convert signals name from v5.0 to v5.1 format df.rename(columns={'utilization':'util_avg'}, inplace=True) df.rename(columns={'load':'load_avg'}, inplace=True) df.rename(columns={'avg_period':'period_contrib'}, inplace=True) @@ -412,15 +555,26 @@ class Trace(object): ['LITTLE'], 'big') def _sanitize_SchedBoostCpu(self): + """ + Add a boosted utilization signal as the sum of utilization and margin. + + Also, if necessary, rename certain signal names from v5.0 to v5.1 + format. + """ if not self.hasEvents('sched_boost_cpu'): return df = self._dfg_trace_event('sched_boost_cpu') if 'usage' in df: - # Convert signals name from to v5.1 format df.rename(columns={'usage':'util'}, inplace=True) df['boosted_util'] = df['util'] + df['margin'] def _sanitize_SchedBoostTask(self): + """ + Add a boosted utilization signal as the sum of utilization and margin. + + Also, if necessary, rename certain signal names from v5.0 to v5.1 + format. + """ if not self.hasEvents('sched_boost_task'): return df = self._dfg_trace_event('sched_boost_task') @@ -430,6 +584,10 @@ class Trace(object): df['boosted_util'] = df['util'] + df['margin'] def _sanitize_SchedEnergyDiff(self): + """ + If a energy model is provided, some signals are added to the + sched_energy_diff trace event data frame. + """ if not self.hasEvents('sched_energy_diff') \ or 'nrg_model' not in self.platform: return @@ -462,21 +620,34 @@ class Trace(object): ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject'], 'Suboptimal Reject') def _sanitize_SchedOverutilized(self): + """ Add a column with overutilized status duration. """ if not self.hasEvents('sched_overutilized'): return - # Add a column with overutilized status duration df = self._dfg_trace_event('sched_overutilized') df['start'] = df.index df['len'] = (df.start - df.start.shift()).fillna(0).shift(-1) df.drop('start', axis=1, inplace=True) def _chunker(self, seq, size): + """ + Given a data frame or a series, generate a sequence of chunks of the + given size. + + :param seq: data to be split into chunks + :type seq: :mod:`pandas.Series` or :mod:`pandas.DataFrame` + + :param size: size of each chunk + :type size: int + """ return (seq.iloc[pos:pos + size] for pos in range(0, len(seq), size)) def _sanitize_CpuFrequency(self): + """ + Verify that all platform reported clusters are frequency coherent (i.e. + frequency scaling is performed at a cluster level). + """ if not self.hasEvents('cpu_frequency'): return - # Verify that all platform reported clusters are frequency choerent df = self._dfg_trace_event('cpu_frequency') clusters = self.platform['clusters'] for c, cpus in clusters.iteritems(): @@ -489,7 +660,7 @@ class Trace(object): logging.warn(chunk) self.freq_coherency = False return - logging.info("Platform clusters verified to be Frequency choerent") + logging.info("Platform clusters verified to be Frequency coherent") ################################################################################ # Utility Methods @@ -512,6 +683,13 @@ class Trace(object): return sum(comp_sig.iloc[1::2].index - comp_sig.iloc[:-1:2].index) def _loadFunctionsStats(self, path='trace.stats'): + """ + Read functions profiling file and build a data frame containing all + relevant data. + + :param path: path to the functions profiling trace file + :type path: str + """ if os.path.isdir(path): path = os.path.join(path, 'trace.stats') if path.endswith('dat') or path.endswith('html'): -- GitLab From 9d69537e219bc1052a28be114cb14ad47cc22c8f Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Mon, 25 Jul 2016 14:52:41 +0100 Subject: [PATCH 19/24] libs/utils/trace: Add min_cluster_cap to task utilization DataFrame The minimum cluster utilization columns reports the minimum of the maximum capacities of a cluster which can host the task utilization. For example, on a big.LITTLE system, if a task has a utilization above the capacity of the LITTLE cluster, than its min_cluster_cap is 1024 (which is the capacity of the big cluster where this task should be scheduled). Signed-off-by: Patrick Bellasi --- libs/utils/trace.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/libs/utils/trace.py b/libs/utils/trace.py index 8a0997e81..c05ea77ce 100644 --- a/libs/utils/trace.py +++ b/libs/utils/trace.py @@ -553,6 +553,13 @@ class Trace(object): df['cluster'] = np.select( [df.cpu.isin(self.platform['clusters']['little'])], ['LITTLE'], 'big') + # Add a column which represents the max capacity of the smallest + # clustre which can accomodate the task utilization + little_cap = self.platform['nrg_model']['little']['cpu']['cap_max'] + big_cap = self.platform['nrg_model']['big']['cpu']['cap_max'] + df['min_cluster_cap'] = df.util_avg.map( + lambda util_avg : + big_cap if util_avg > little_cap else little_cap) def _sanitize_SchedBoostCpu(self): """ -- GitLab From eca5c66709821e9c9447bfeeb34325625b94f7d4 Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Mon, 25 Jul 2016 15:22:52 +0100 Subject: [PATCH 20/24] libs/utils/analysis: move filters APIs into TASKs module All the functions defined from libs/utils/filters.py, in the new refactoring of the TraceAnalysis code, they better belong to the tasks_analysis module. Let's move this code under this module as well as refactor a big the DataFrame getter methods to return a more complete set of tables. Signed-off-by: Patrick Bellasi --- libs/utils/__init__.py | 2 - libs/utils/analysis/tasks_analysis.py | 360 ++++++++++++++++++++++++++ libs/utils/analysis_module.py | 5 + libs/utils/filters.py | 295 --------------------- 4 files changed, 365 insertions(+), 297 deletions(-) delete mode 100644 libs/utils/filters.py diff --git a/libs/utils/__init__.py b/libs/utils/__init__.py index 934d90b94..1ad7ce895 100644 --- a/libs/utils/__init__.py +++ b/libs/utils/__init__.py @@ -26,8 +26,6 @@ from conf import JsonConf from trace import Trace from perf_analysis import PerfAnalysis -from filters import Filters - from report import Report import android diff --git a/libs/utils/analysis/tasks_analysis.py b/libs/utils/analysis/tasks_analysis.py index 8e4a8c484..5faf98e98 100644 --- a/libs/utils/analysis/tasks_analysis.py +++ b/libs/utils/analysis/tasks_analysis.py @@ -17,10 +17,12 @@ import matplotlib.gridspec as gridspec import matplotlib.pyplot as plt +import numpy as np import pylab as pl import re from analysis_module import AnalysisModule +from devlib.utils.misc import memoized # Configure logging import logging @@ -41,6 +43,137 @@ class TasksAnalysis(AnalysisModule): # DataFrame Getter Methods ################################################################################ + def _dfg_top_big_tasks(self, min_samples=100, min_utilization=None): + """ + Tasks which had 'utilization' samples bigger than the specified + threshold + + :param min_samples: minumum number of samples over the min_utilization + :type min_samples: int + + :param min_utilization: minimum utilization used to filter samples + default: capacity of a little cluster + :type min_utilization: int + """ + if not self._trace.hasEvents('sched_load_avg_task'): + logging.warn('Events [sched_load_avg_task] not found') + return None + + if min_utilization is None: + min_utilization = self._little_cap + + # Get utilization samples >= min_utilization + df = self._dfg_trace_event('sched_load_avg_task') + big_tasks_events = df[df.util_avg > min_utilization] + if not len(big_tasks_events): + logging.warn('No tasks with with utilization samples > %d', + min_utilization) + return None + + # Report the number of tasks which match the min_utilization condition + big_tasks = big_tasks_events.pid.unique() + logging.info('%5d tasks with samples of utilization > %d', + len(big_tasks), min_utilization) + + # Compute number of samples above threshold + big_tasks_stats = big_tasks_events.groupby('pid')\ + .describe(include=['object']) + big_tasks_stats = big_tasks_stats.unstack()['comm']\ + .sort_values(by=['count'], ascending=False) + + # Filter for number of occurrences + big_tasks_stats = big_tasks_stats[big_tasks_stats['count'] > min_samples] + if not len(big_tasks_stats): + logging.warn(' but none with more than %d samples', + min_samples) + return None + + logging.info(' %d with more than %d samples', + len(big_tasks_stats), min_samples) + + # Add task name column + big_tasks_stats['comm'] = big_tasks_stats.index.map( + lambda pid : ', '.join(self._trace.getTaskByPid(pid))) + + # Filter columns of interest + big_tasks_stats = big_tasks_stats[['count', 'comm']] + big_tasks_stats.rename(columns={'count' : 'samples'}, inplace=True) + + return big_tasks_stats + + def _dfg_top_wakeup_tasks(self, min_wakeups=100): + """ + Tasks which wakeups more frequent than a specified threshold + """ + if not self._trace.hasEvents('sched_wakeup'): + logging.warn('Events [sched_wakeup] not found') + return None + + df = self._dfg_trace_event('sched_wakeup') + + # Compute number of wakeups above threshold + wkp_tasks_stats = df.groupby('pid').describe(include=['object']) + wkp_tasks_stats = wkp_tasks_stats.unstack()['comm']\ + .sort_values(by=['count'], ascending=False) + + # Filter for number of occurrences + wkp_tasks_stats = wkp_tasks_stats[ + wkp_tasks_stats['count'] > min_wakeups] + if not len(df): + logging.warn('No tasks with more than %d wakeups', len(wkp_tasks_stats)) + return None + logging.info('%5d tasks with more than %d wakeups', len(wkp_tasks_stats)) + + # Add task name column + wkp_tasks_stats['comm'] = wkp_tasks_stats.index.map( + lambda pid : ', '.join(self._trace.getTaskByPid(pid))) + + # Filter columns of interest + wkp_tasks_stats = wkp_tasks_stats[['count', 'comm']] + wkp_tasks_stats.rename(columns={'count' : 'samples'}, inplace=True) + + return wkp_tasks_stats + + def _dfg_rt_tasks(self, min_prio = 100): + """ + Tasks with RT priority + + NOTE: priorities uses scheduler values, thus: the lower the value the + higher is the task priority. + RT Priorities: [ 0..100] + FAIR Priorities: [101..120] + + :param min_prio: minumum priority + :type min_prio: int + """ + if not self._trace.hasEvents('sched_switch'): + logging.warn('Events [sched_switch] not found') + return None + + df = self._dfg_trace_event('sched_switch') + + # Filters tasks which have a priority bigger than threshold + df = df[df.next_prio <= min_prio] + + # Filter columns of interest + rt_tasks = df[['next_pid', 'next_prio']] + + # Remove all duplicateds + rt_tasks = rt_tasks.drop_duplicates() + + # Order by priority + rt_tasks.sort_values(by=['next_prio', 'next_pid'], ascending=True, inplace=True) + rt_tasks.rename(columns={'next_pid' : 'pid', 'next_prio' : 'prio'}, inplace=True) + + # Set PID as index + rt_tasks.set_index('pid', inplace=True) + + # Add task name column + rt_tasks['comm'] = rt_tasks.index.map( + lambda pid : ', '.join(self._trace.getTaskByPid(pid))) + + return rt_tasks + ################################################################################ # Plotting Methods @@ -183,6 +316,233 @@ class TasksAnalysis(AnalysisModule): self._trace.plots_dir, self._trace.plots_prefix, tid, task_name) pl.savefig(figname, bbox_inches='tight') + def plotBigTasks(self, max_tasks=10, min_samples=100, + min_utilization=None): + + # Get PID of big tasks + big_frequent_task_df = self._dfg_top_big_tasks( + min_samples, min_utilization) + if max_tasks > 0: + big_frequent_task_df = big_frequent_task_df.head(max_tasks) + big_frequent_task_pids = big_frequent_task_df.index.values + + big_frequent_tasks_count = len(big_frequent_task_pids) + if big_frequent_tasks_count == 0: + logging.warn("No big/frequent tasks to plot") + return + + # Get the list of events for all big frequent tasks + df = self._dfg_trace_event('sched_load_avg_task') + big_frequent_tasks_events = df[df.pid.isin(big_frequent_task_pids)] + + # Define axes for side-by-side plottings + fig, axes = plt.subplots(big_frequent_tasks_count, 1, + figsize=(16, big_frequent_tasks_count*4)) + plt.subplots_adjust(wspace=0.1, hspace=0.2) + + plot_idx = 0 + for pid, group in big_frequent_tasks_events.groupby('pid'): + + # # Build task names (there could be multiple, during the task lifetime) + task_name = 'PID: {} | {}'.format( + pid, ' | '.join(self._trace.getTaskByPid(pid))) + + # Plot title + if big_frequent_tasks_count == 1: + ax = axes + else: + ax = axes[plot_idx] + ax.set_title(task_name) + + # Left axis: utilization + ax = group.plot(y=['util_avg', 'min_cluster_cap'], + style=['r.', '-b'], + drawstyle='steps-post', + linewidth=1, + ax=ax) + ax.set_xlim(self._trace.x_min, self._trace.x_max) + ax.set_ylim(0, 1100) + ax.set_ylabel('util_avg') + ax.set_xlabel('') + ax.grid(True) + self._trace.analysis.status.plotOverutilized(ax) + + plot_idx+=1 + + ax.set_xlabel('Time [s]') + + logging.info('Tasks which have been a "utilization" of %d for at least %d samples', + self._little_cap, min_samples) + + def plotWakeupTasks(self, max_tasks=10, min_wakeups=0, per_cluster=False): + + if per_cluster is True and \ + not self._trace.hasEvents('sched_wakeup_new'): + logging.warn('Events [sched_wakeup_new] not found, ' + 'plots DISABLED!') + return + elif not self._trace.hasEvents('sched_wakeup') and \ + not self._trace.hasEvents('sched_wakeup_new'): + logging.warn('Events [sched_wakeup, sched_wakeup_new] not found, ' + 'plots DISABLED!') + return + + # Define axes for side-by-side plottings + fig, axes = plt.subplots(2, 1, figsize=(14, 5)); + plt.subplots_adjust(wspace=0.2, hspace=0.3); + + if per_cluster: + + # Get per cluster wakeup events + df = self._dfg_trace_event('sched_wakeup_new') + big_frequent = ( + (df.target_cpu.isin(self._big_cpus)) + ) + ntbc = df[big_frequent] + ntbc_count = len(ntbc) + little_frequent = ( + (df.target_cpu.isin(self._little_cpus)) + ) + ntlc = df[little_frequent]; + ntlc_count = len(ntlc) + + logging.info("%5d tasks forked on big cluster (%3.1f %%)", + ntbc_count, 100. * ntbc_count / (ntbc_count + ntlc_count)) + logging.info("%5d tasks forked on LITTLE cluster (%3.1f %%)", + ntlc_count, 100. * ntlc_count / (ntbc_count + ntlc_count)) + + ax = axes[0] + ax.set_title('Tasks Forks on big CPUs'); + ntbc.pid.plot(style=['g.'], ax=ax); + ax.set_xlim(self._trace.x_min, self._trace.x_max); + ax.set_xticklabels([]) + ax.set_xlabel('') + ax.grid(True) + self._trace.analysis.status.plotOverutilized(ax) + + ax = axes[1] + ax.set_title('Tasks Forks on LITTLE CPUs'); + ntlc.pid.plot(style=['g.'], ax=ax); + ax.set_xlim(self._trace.x_min, self._trace.x_max); + ax.grid(True) + self._trace.analysis.status.plotOverutilized(ax) + + return + + # Keep events of defined big tasks + wkp_task_pids = self._dfg_top_wakeup_tasks(min_wakeups) + if len(wkp_task_pids): + wkp_task_pids = wkp_task_pids.index.values[:max_tasks] + logging.info("Plotting %d frequent wakeup tasks", len(wkp_task_pids)) + + ax = axes[0] + ax.set_title('Tasks WakeUps Events'); + df = self._dfg_trace_event('sched_wakeup') + if len(df): + df = df[df.pid.isin(wkp_task_pids)] + df.pid.astype(int).plot(style=['b.'], ax=ax); + ax.set_xlim(self._trace.x_min, self._trace.x_max); + ax.set_xticklabels([]) + ax.set_xlabel('') + ax.grid(True) + self._trace.analysis.status.plotOverutilized(ax) + + ax = axes[1] + ax.set_title('Tasks Forks Events'); + df = self._dfg_trace_event('sched_wakeup_new') + if len(df): + df = df[df.pid.isin(wkp_task_pids)] + df.pid.astype(int).plot(style=['r.'], ax=ax); + ax.set_xlim(self._trace.x_min, self._trace.x_max); + ax.grid(True) + self._trace.analysis.status.plotOverutilized(ax) + + def plotBigTasksVsCapacity(self, + min_samples=1, + min_utilization=None, + big_cluster=True): + + if not self._trace.hasEvents('sched_load_avg_task'): + logging.warn('Events [sched_load_avg_task] not found') + return + if not self._trace.hasEvents('cpu_frequency'): + logging.warn('Events [cpu_frequency] not found') + return + + if big_cluster: + cluster_correct = 'big' + cpus = self._big_cpus + else: + cluster_correct = 'LITTLE' + cpus = self._little_cpus + + # Get all utilization update events + df = self._dfg_trace_event('sched_load_avg_task') + + # Keep events of defined big tasks + big_task_pids = self._dfg_top_big_tasks( + min_samples, min_utilization) + if big_task_pids is not None: + big_task_pids = big_task_pids.index.values + df = df[df.pid.isin(big_task_pids)] + if not df.size: + logging.warn('No events for tasks with more then %d utilization ' + 'samples bigger than %d, plots DISABLED!') + return + + fig, axes = plt.subplots(2, 1, figsize=(14, 5)); + plt.subplots_adjust(wspace=0.2, hspace=0.3); + + # Add column of expected cluster depending on: + # a) task utilization value + # b) capacity of the selected cluster + bu_bc = ( \ + (df['util_avg'] > self._little_cap) & \ + (df['cpu'].isin(self._big_cpus)) + ) + su_lc = ( \ + (df['util_avg'] <= self._little_cap) & \ + (df['cpu'].isin(self._little_cpus)) + ) + # The Cluster CAPacity Matches the UTILization (ccap_mutil) iff: + # - tasks with util_avg > little_cap are running on a BIG cpu + # - tasks with util_avg <= little_cap are running on a LITTLe cpu + df.loc[:,'ccap_mutil'] = np.select( + [(bu_bc | su_lc)], [True], False) + + df_freq = self._dfg_trace_event('cpu_frequency') + df_freq = df_freq[df_freq.cpu == cpus[0]] + + ax = axes[0] + ax.set_title('Tasks Utilization vs Allocation'); + for ucolor, umatch in zip('gr', [True, False]): + cdata = df[df['ccap_mutil'] == umatch] + if (len(cdata) > 0): + cdata['util_avg'].plot(ax=ax, + style=[ucolor+'.'], legend=False); + ax.set_xlim(self._trace.x_min, self._trace.x_max); + ax.set_xticklabels([]) + ax.set_xlabel('') + ax.grid(True) + self._trace.analysis.status.plotOverutilized(ax) + + ax = axes[1] + ax.set_title('Frequencies on "{}" cluster'.format(cluster_correct)) + df_freq['frequency'].plot(style=['-b'], ax=ax, drawstyle='steps-post'); + ax.set_xlim(self._trace.x_min, self._trace.x_max); + ax.grid(True) + self._trace.analysis.status.plotOverutilized(ax) + + legend_y = axes[0].get_ylim()[1] + axes[0].annotate('Utilization-Capacity Matches', + xy=(0, legend_y), + xytext=(-50, 45), textcoords='offset points', + fontsize=18) + axes[0].annotate('Task schduled (green) or not (red) on min cluster', + xy=(0, legend_y), + xytext=(-50, 25), textcoords='offset points', + fontsize=14) + ################################################################################ # Utility Methods diff --git a/libs/utils/analysis_module.py b/libs/utils/analysis_module.py index 6cdebd48e..5b58f3605 100644 --- a/libs/utils/analysis_module.py +++ b/libs/utils/analysis_module.py @@ -31,5 +31,10 @@ class AnalysisModule(object): self._dfg_trace_event = trace._dfg_trace_event + self._big_cap = self._platform['nrg_model']['big']['cpu']['cap_max'] + self._little_cap = self._platform['nrg_model']['little']['cpu']['cap_max'] + self._big_cpus = self._platform['clusters']['big'] + self._little_cpus = self._platform['clusters']['little'] + trace._registerDataFrameGetters(self) diff --git a/libs/utils/filters.py b/libs/utils/filters.py deleted file mode 100644 index b241a9ea2..000000000 --- a/libs/utils/filters.py +++ /dev/null @@ -1,295 +0,0 @@ -# SPDX-License-Identifier: Apache-2.0 -# -# Copyright (C) 2015, ARM Limited and contributors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); you may -# not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT -# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# import glob -import matplotlib.gridspec as gridspec -import matplotlib.pyplot as plt -import numpy as np -# import os -# import pandas as pd -# import pylab as pl -# import re -# import sys -# import trappy - -# Configure logging -import logging - -class Filters(object): - - def __init__(self, trace, tasks=None): - self.trace = trace - self.tasks = tasks - - self.big_tasks = {} - - self.big_frequent_tasks_pids = None - self.big_frequent_tasks_tasks = None - self.wkp_frequent_tasks_pids = None - self.wkp_frequent_tasks_tasks = None - - self.big_cap = self.trace.platform['nrg_model']['big']['cpu']['cap_max'] - self.little_cap = self.trace.platform['nrg_model']['little']['cpu']['cap_max'] - self.big_cpus = self.trace.platform['clusters']['big'] - self.little_cpus = self.trace.platform['clusters']['little'] - - # Minimum and Maximum x_time to use for all plots - self.x_min = 0 - self.x_max = self.trace.time_range - - # Reset x axis time range to full scale - self.setXTimeRange() - - def setXTimeRange(self, t_min=None, t_max=None): - if t_min is None: - self.x_min = 0 - else: - self.x_min = t_min - if t_max is None: - self.x_max = self.trace.time_range - else: - self.x_max = t_max - logging.info('Set plots time range to (%.6f, %.6f)[s]', - self.x_min, self.x_max) - - - def topBigTasks(self, max_tasks=10, min_samples=100, min_utilization=None): - """ - Tasks which had a 'utilization' bigger than the specified threshold - """ - - if min_utilization is None: - min_utilization = self.little_cap - - df = self.trace.df('sched_load_avg_task') - big_tasks_events = df[df.util_avg > min_utilization] - big_tasks = big_tasks_events.pid.unique() - - big_tasks_count = big_tasks.size - print 'Total {} tasks with at least {} "utilization" samples > {}'\ - .format(big_tasks_count, min_samples, min_utilization) - - big_tasks_stats = big_tasks_events.groupby('pid')\ - .describe(include=['object']); - big_tasks_pids = big_tasks_stats.unstack()['comm']\ - .sort_values(by=['count'], ascending=False) - big_tasks_pids = big_tasks_pids[big_tasks_pids['count'] > min_samples] - - big_topmost = big_tasks_pids.head(max_tasks) - print 'Top {} "big" tasks:'.format(max_tasks) - print big_topmost - - self.big_frequent_tasks_tasks = dict(zip(big_topmost.top.values, big_topmost.index)) - self.big_frequent_tasks_pids = list(big_topmost.index) - - # Keep track of big tasks tload events - self.big_tasks['sched_load_avg_task'] = big_tasks_events - - return self.big_frequent_tasks_tasks - - def _taskIsBig(self, utilization): - if utilization > self.little_cap: - return self.big_cap - return self.little_cap - - def plotBigTasks(self, max_tasks=10, min_samples=100, min_utilization=None): - - # Get the list of big and frequent tasks - if self.big_frequent_tasks_pids is None: - self.topBigTasks(max_tasks, min_samples, min_utilization) - - big_frequent_tasks_count = len(self.big_frequent_tasks_pids) - if big_frequent_tasks_count == 0: - print "No big/frequent tasks to plot" - return - - # Get the list of events for all big frequent tasks - df = self.trace.df('sched_load_avg_task') - big_frequent_tasks_events = df[df.pid.isin(self.big_frequent_tasks_pids)] - - # Add a column to represent big status - big_frequent_tasks_events.loc[:,'isbig'] = \ - big_frequent_tasks_events['util_avg'].map(self._taskIsBig) - - - # Define axes for side-by-side plottings - fig, axes = plt.subplots(big_frequent_tasks_count, 1, - figsize=(14, big_frequent_tasks_count*5)); - plt.subplots_adjust(wspace=0.1, hspace=0.2); - - plot_idx = 0 - for i, group in big_frequent_tasks_events.groupby('pid'): - - # Build task names (there could be multiple, during the task lifetime) - big_frequent_task_i = big_frequent_tasks_events[big_frequent_tasks_events['pid'] == i] - task_names = big_frequent_task_i.comm.unique() - task_name = 'PID: ' + str(i) - for s in task_names: - task_name += ' | ' + s - - # Plot title - if (big_frequent_tasks_count == 1): - ax_ratio = axes - else: - ax_ratio = axes[plot_idx] - ax_ratio.set_title(task_name); - - # Left axis: utilization - ax_ratio = group.plot(y=['util_avg', 'isbig'], - style=['r.', '-b'], - drawstyle='steps-post', - linewidth=1, - ax=ax_ratio) - ax_ratio.set_xlim(self.x_min, self.x_max); - ax_ratio.set_ylim(0, 1100) - ax_ratio.set_ylabel('util_avg') - - plot_idx+=1 - - print 'Tasks which have been a "utilization" of {0:d} for at least {1:d} samples'\ - .format(self.little_cap, min_samples) - - def topWakeupTasks(self, max_tasks=10, min_wakeups=100): - """ - Tasks which wakeups more frequent than a specified threshold - """ - - df = self.trace.df('sched_wakeup') - - wkp_tasks_stats = df.groupby('pid').describe(include=['object']) - wkp_tasks_pids = wkp_tasks_stats.unstack()['comm']\ - .sort_values(by=['count'], ascending=False) - wkp_tasks_pids = wkp_tasks_pids[wkp_tasks_pids['count'] > min_wakeups] - - wkp_topmost = wkp_tasks_pids.head(max_tasks) - print 'Top {} "big" tasks:'.format(max_tasks) - print wkp_topmost - - self.wkp_frequent_tasks_tasks = dict(zip(wkp_topmost.top.values, wkp_topmost.index)) - self.wkp_frequent_tasks_pids = list(wkp_topmost.index) - - return self.wkp_frequent_tasks_tasks - - def plotWakeupTasks(self, max_tasks=10, min_wakeups=0, per_cluster=False): - - # Get the list of big and frequent tasks - if self.wkp_frequent_tasks_pids is None: - self.topWakeupTasks(max_tasks, min_wakeups) - - wkp_frequent_tasks_count = len(self.wkp_frequent_tasks_pids) - if wkp_frequent_tasks_count == 0: - print "No big/frequent wakeups tasks to plot" - return - - # Define axes for side-by-side plottings - fig, axes = plt.subplots(2, 1, figsize=(14, 5)); - plt.subplots_adjust(wspace=0.2, hspace=0.3); - - if per_cluster: - - # Get per cluster wakeup events - df = self.trace.df('sched_wakeup_new') - big_frequent = ( - (df.target_cpu.isin(self.big_cpus)) - ) - ntbc = df[big_frequent] - little_frequent = ( - (df.target_cpu.isin(self.little_cpus)) - ) - ntlc = df[little_frequent]; - - ax = axes[0] - ax.set_title('Tasks Forks on big CPUs'); - ax.set_xlim(self.x_min, self.x_max); - ntbc.pid.plot(style=['g.'], ax=ax); - - ax = axes[1] - ax.set_title('Tasks Forks on LITTLE CPUs'); - ax.set_xlim(self.x_min, self.x_max); - ntlc.pid.plot(style=['g.'], ax=ax); - - else: - - ax = axes[0] - ax.set_title('Tasks WakeUps Events'); - df = self.trace.df('sched_wakeup') - df.pid.astype(int).plot(style=['b.'], ax=ax); - ax.set_xlim(self.x_min, self.x_max); - ax.xaxis.set_visible(False); - - ax = axes[1] - ax.set_title('Tasks Forks Events'); - df = self.trace.df('sched_wakeup_new') - df.pid.astype(int).plot(style=['r.'], ax=ax); - ax.set_xlim(self.x_min, self.x_max); - ax.xaxis.set_visible(False); - - def plotTasksVsFrequency(self, big_cluster=True): - - if big_cluster: - cluster_correct = 'big' - cluster_wrong = 'LITTLE' - cpus = self.big_cpus - else: - cluster_correct = 'LITTLE' - cluster_wrong = 'big' - cpus = self.little_cpus - - fig, axes = plt.subplots(2, 1, figsize=(14, 5)); - plt.subplots_adjust(wspace=0.2, hspace=0.3); - - df_wkp = self.big_tasks['sched_load_avg_task'] - # Add column of expected cluster, depending on utilization value and - # capacity of the selected cluster - bu_bc = ( \ - (df_wkp['util_avg'] > 500) & \ - (df_wkp['cpu'].isin([2,3])) - ) - su_lc = ( \ - (df_wkp['util_avg'] <= 500) & \ - (df_wkp['cpu'].isin([0,1])) - ) - df_wkp.loc[:,'ccap_mutil'] = np.select( - [(bu_bc | su_lc)], [True], False) - - df_freq = self.trace.df('cpu_frequency') - rd_freq = df_freq[df_freq['cpu'].isin(cpus)] - - ax = axes[0] - ax.set_title('Big Tasks Utilization vs Allocation'); - for ucolor, umatch in zip('gr', [True, False]): - cdata = df_wkp[df_wkp['ccap_mutil'] == umatch] - if (len(cdata) > 0): - cdata['util_avg'].plot(ax=ax, - style=[ucolor+'.'], legend=False); - ax.set_xlim(self.x_min, self.x_max); - ax.xaxis.set_visible(False); - - ax = axes[1] - ax.set_title('Frequencies on "{}" cluster'.format(cluster_correct)) - df_freq['frequency'].plot(style=['-b'], ax=ax, drawstyle='steps-post'); - ax.set_xlim(self.x_min, self.x_max); - - def rtTasks(self, max_prio = 100): - df = self.trace.df('sched_switch') - df = df[df.next_prio <= max_prio] - df = df[['next_pid', 'next_comm']] - df = df.drop_duplicates() - rt_tasks = {} - for pid,task in df.values: - rt_tasks[pid] = task - return rt_tasks -- GitLab From 985e393b38536d532396bf3b180db48d69a1bd34 Mon Sep 17 00:00:00 2001 From: Patrick Bellasi Date: Tue, 26 Jul 2016 11:08:53 +0100 Subject: [PATCH 21/24] libs/utils/analysis: add check on plotTasks parameters Signed-off-by: Patrick Bellasi --- libs/utils/analysis/tasks_analysis.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/libs/utils/analysis/tasks_analysis.py b/libs/utils/analysis/tasks_analysis.py index 5faf98e98..03a194335 100644 --- a/libs/utils/analysis/tasks_analysis.py +++ b/libs/utils/analysis/tasks_analysis.py @@ -221,6 +221,10 @@ class TasksAnalysis(AnalysisModule): 'plot DISABLED!') return + if not isinstance(tasks, str) and \ + not isinstance(tasks, list): + raise ValueError('Wrong format for tasks parameter') + # Defined list of tasks to plot if tasks: tasks_to_plot = tasks -- GitLab From b9c02fe3f7682ceb9e1c703d218bb4c9449358a1 Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Mon, 25 Jul 2016 15:39:26 +0100 Subject: [PATCH 22/24] libs/utils: make pylint and pep8 a bit happier Signed-off-by: Michele Di Giorgio --- libs/utils/analysis/cpus_analysis.py | 45 ++-- libs/utils/analysis/eas_analysis.py | 249 ++++++++++++---------- libs/utils/analysis/frequency_analysis.py | 126 +++++------ libs/utils/analysis/functions_analysis.py | 14 +- libs/utils/analysis/status_analysis.py | 34 +-- libs/utils/analysis/tasks_analysis.py | 228 ++++++++++++-------- libs/utils/analysis_module.py | 10 +- libs/utils/analysis_register.py | 5 +- libs/utils/trace.py | 98 +++++---- 9 files changed, 465 insertions(+), 344 deletions(-) diff --git a/libs/utils/analysis/cpus_analysis.py b/libs/utils/analysis/cpus_analysis.py index 196faca3f..5b161ac90 100644 --- a/libs/utils/analysis/cpus_analysis.py +++ b/libs/utils/analysis/cpus_analysis.py @@ -15,7 +15,8 @@ # limitations under the License. # -import matplotlib.gridspec as gridspec +""" CPUs Analysis Module """ + import matplotlib.pyplot as plt import pylab as pl @@ -26,6 +27,7 @@ from analysis_module import AnalysisModule # Configure logging import logging + class CpusAnalysis(AnalysisModule): """ Support for CPUs Signals Analysis @@ -37,22 +39,23 @@ class CpusAnalysis(AnalysisModule): def __init__(self, trace): super(CpusAnalysis, self).__init__(trace) -################################################################################ +############################################################################### # Plotting Methods -################################################################################ +############################################################################### def plotCPU(self, cpus=None): """ Plot CPU-related signals for both big and LITTLE clusters. """ if not self._trace.hasEvents('sched_load_avg_cpu'): - logging.warn('Events [sched_load_avg_cpu] not found, '\ - 'plot DISABLED!') + logging.warn('Events [sched_load_avg_cpu] not found, ' + 'plot DISABLED!') return # Filter on specified cpus if cpus is None: - cpus = sorted(self._platform['clusters']['little'] + self._platform['clusters']['big']) + cpus = sorted(self._platform['clusters']['little'] + + self._platform['clusters']['big']) cpus = listify(cpus) # Plot: big CPUs @@ -64,9 +67,9 @@ class CpusAnalysis(AnalysisModule): self._plotCPU(lcpus, "LITTLE") -################################################################################ +############################################################################### # Utility Methods -################################################################################ +############################################################################### def _plotCPU(self, cpus, label=''): """ @@ -80,24 +83,25 @@ class CpusAnalysis(AnalysisModule): label2 = '_{}s'.format(label.lower()) # Plot required CPUs - fig, pltaxes = plt.subplots(len(cpus), 1, figsize=(16, 3*(len(cpus)))); + _, pltaxes = plt.subplots(len(cpus), 1, figsize=(16, 3*(len(cpus)))) plt.suptitle("{}CPUs Signals".format(label1), - y=.99, fontsize=16, horizontalalignment='center'); + y=.99, fontsize=16, horizontalalignment='center') idx = 0 for cpu in cpus: # Reference axes to be used axes = pltaxes - if (len(cpus) > 1): + if len(cpus) > 1: axes = pltaxes[idx] # Add CPU utilization - axes.set_title('{0:s}CPU [{1:d}]'.format(label1, cpu)); + axes.set_title('{0:s}CPU [{1:d}]'.format(label1, cpu)) df = self._dfg_trace_event('sched_load_avg_cpu') df = df[df.cpu == cpu] if len(df): - df[['util_avg']].plot(ax=axes, drawstyle='steps-post', alpha=0.4); + df[['util_avg']].plot(ax=axes, drawstyle='steps-post', + alpha=0.4) # if self._trace.hasEvents('sched_boost_cpu'): # df = self._dfg_trace_event('sched_boost_cpu') @@ -115,23 +119,24 @@ class CpusAnalysis(AnalysisModule): if len(df): # data = df[['capacity', 'tip_capacity', 'max_capacity']] # data.plot(ax=axes, style=['m', 'y', 'r'], - data = df[['capacity', 'tip_capacity' ]] - data.plot(ax=axes, style=['m', '--y' ], + data = df[['capacity', 'tip_capacity']] + data.plot(ax=axes, style=['m', '--y'], drawstyle='steps-post') - axes.set_ylim(0, 1100); - axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.set_ylim(0, 1100) + axes.set_xlim(self._trace.x_min, self._trace.x_max) # Disable x-axis timestamp for top-most cpus - if (len(cpus) > 1 and idx < len(cpus)-1): + if len(cpus) > 1 and idx < len(cpus)-1: axes.set_xticklabels([]) axes.set_xlabel('') - axes.grid(True); + axes.grid(True) - idx+=1 + idx += 1 # Save generated plots into datadir figname = '{}/{}cpus{}.png'.format(self._trace.plots_dir, self._trace.plots_prefix, label2) pl.savefig(figname, bbox_inches='tight') +# vim :set tabstop=4 shiftwidth=4 expandtab diff --git a/libs/utils/analysis/eas_analysis.py b/libs/utils/analysis/eas_analysis.py index a33dddc17..395d58e8b 100644 --- a/libs/utils/analysis/eas_analysis.py +++ b/libs/utils/analysis/eas_analysis.py @@ -15,6 +15,8 @@ # limitations under the License. # +""" EAS-specific Analysis Module """ + import matplotlib.gridspec as gridspec import matplotlib.pyplot as plt import pylab as pl @@ -24,6 +26,7 @@ from analysis_module import AnalysisModule # Configure logging import logging + class EasAnalysis(AnalysisModule): """ Support for EAS signals anaysis @@ -35,25 +38,25 @@ class EasAnalysis(AnalysisModule): def __init__(self, trace): super(EasAnalysis, self).__init__(trace) -################################################################################ +############################################################################### # DataFrame Getter Methods -################################################################################ +############################################################################### -################################################################################ +############################################################################### # Plotting Methods -################################################################################ +############################################################################### def plotEDiffTime(self, tasks=None, - min_usage_delta=None, max_usage_delta=None, - min_cap_delta=None, max_cap_delta=None, - min_nrg_delta=None, max_nrg_delta=None, - min_nrg_diff=None, max_nrg_diff=None): + min_usage_delta=None, max_usage_delta=None, + min_cap_delta=None, max_cap_delta=None, + min_nrg_delta=None, max_nrg_delta=None, + min_nrg_diff=None, max_nrg_diff=None): """ Plot energy_diff()-related signals on time axes. """ if not self._trace.hasEvents('sched_energy_diff'): - logging.warn('Events [sched_energy_diff] not found, plot DISABLED!') + logging.warn('Event [sched_energy_diff] not found, plot DISABLED!') return df = self._dfg_trace_event('sched_energy_diff') @@ -64,130 +67,140 @@ class EasAnalysis(AnalysisModule): # Filter on 'usage_delta' if min_usage_delta is not None: - logging.info('Plotting EDiff data just with minimum usage_delta of [%d]', min_usage_delta) + logging.info('Plotting EDiff data just with minimum ' + 'usage_delta of [%d]', min_usage_delta) df = df[abs(df['usage_delta']) >= min_usage_delta] if max_usage_delta is not None: - logging.info('Plotting EDiff data just with maximum usage_delta of [%d]', max_usage_delta) + logging.info('Plotting EDiff data just with maximum ' + 'usage_delta of [%d]', max_usage_delta) df = df[abs(df['usage_delta']) <= max_usage_delta] # Filter on 'cap_delta' if min_cap_delta is not None: - logging.info('Plotting EDiff data just with minimum cap_delta of [%d]', min_cap_delta) + logging.info('Plotting EDiff data just with minimum ' + 'cap_delta of [%d]', min_cap_delta) df = df[abs(df['cap_delta']) >= min_cap_delta] if max_cap_delta is not None: - logging.info('Plotting EDiff data just with maximum cap_delta of [%d]', max_cap_delta) + logging.info('Plotting EDiff data just with maximum ' + 'cap_delta of [%d]', max_cap_delta) df = df[abs(df['cap_delta']) <= max_cap_delta] # Filter on 'nrg_delta' if min_nrg_delta is not None: - logging.info('Plotting EDiff data just with minimum nrg_delta of [%d]', min_nrg_delta) + logging.info('Plotting EDiff data just with minimum ' + 'nrg_delta of [%d]', min_nrg_delta) df = df[abs(df['nrg_delta']) >= min_nrg_delta] if max_nrg_delta is not None: - logging.info('Plotting EDiff data just with maximum nrg_delta of [%d]', max_nrg_delta) + logging.info('Plotting EDiff data just with maximum ' + 'nrg_delta of [%d]', max_nrg_delta) df = df[abs(df['nrg_delta']) <= max_nrg_delta] # Filter on 'nrg_diff' if min_nrg_diff is not None: - logging.info('Plotting EDiff data just with minimum nrg_diff of [%d]', min_nrg_diff) + logging.info('Plotting EDiff data just with minimum ' + 'nrg_diff of [%d]', min_nrg_diff) df = df[abs(df['nrg_diff']) >= min_nrg_diff] if max_nrg_diff is not None: - logging.info('Plotting EDiff data just with maximum nrg_diff of [%d]', max_nrg_diff) + logging.info('Plotting EDiff data just with maximum ' + 'nrg_diff of [%d]', max_nrg_diff) df = df[abs(df['nrg_diff']) <= max_nrg_diff] # Grid: setup stats for gris - gs = gridspec.GridSpec(4, 3, height_ratios=[2,4,2,4]); - gs.update(wspace=0.1, hspace=0.1); + gs = gridspec.GridSpec(4, 3, height_ratios=[2, 4, 2, 4]) + gs.update(wspace=0.1, hspace=0.1) # Configure plot - fig = plt.figure(figsize=(16, 8*2+4*2+2)); + fig = plt.figure(figsize=(16, 8*2+4*2+2)) plt.suptitle("EnergyDiff Data", - y=.92, fontsize=16, horizontalalignment='center'); + y=.92, fontsize=16, horizontalalignment='center') # Plot1: src and dst CPUs - axes = plt.subplot(gs[0,:]); - axes.set_title('Source and Destination CPUs'); - df[['src_cpu', 'dst_cpu']].plot(ax=axes, style=['bo', 'r+']); + axes = plt.subplot(gs[0, :]) + axes.set_title('Source and Destination CPUs') + df[['src_cpu', 'dst_cpu']].plot(ax=axes, style=['bo', 'r+']) axes.set_ylim(-1, self._platform['cpus_count']+1) - axes.set_xlim(self._trace.x_min, self._trace.x_max); - axes.grid(True); + axes.set_xlim(self._trace.x_min, self._trace.x_max) + axes.grid(True) axes.set_xticklabels([]) axes.set_xlabel('') self._trace.analysis.status.plotOverutilized(axes) # Plot2: energy and capacity variations - axes = plt.subplot(gs[1,:]); - axes.set_title('Energy vs Capacity Variations'); + axes = plt.subplot(gs[1, :]) + axes.set_title('Energy vs Capacity Variations') - for color, label in zip('gbyr', ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject', 'Suboptimal Reject']): + colors_labels = zip('gbyr', ['Optimal Accept', 'SchedTune Accept', + 'SchedTune Reject', 'Suboptimal Reject']) + for color, label in colors_labels: subset = df[df.nrg_payoff_group == label] - if (len(subset) == 0): + if len(subset) == 0: continue - subset[['nrg_diff_pct']].plot(ax=axes, style=[color+'o']); - axes.set_xlim(self._trace.x_min, self._trace.x_max); + subset[['nrg_diff_pct']].plot(ax=axes, style=[color+'o']) + axes.set_xlim(self._trace.x_min, self._trace.x_max) axes.set_yscale('symlog') - axes.grid(True); + axes.grid(True) axes.set_xticklabels([]) axes.set_xlabel('') self._trace.analysis.status.plotOverutilized(axes) # Plot3: energy payoff - axes = plt.subplot(gs[2,:]); - axes.set_title('Energy Payoff Values'); - for color, label in zip('gbyr', ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject', 'Suboptimal Reject']): + axes = plt.subplot(gs[2, :]) + axes.set_title('Energy Payoff Values') + for color, label in colors_labels: subset = df[df.nrg_payoff_group == label] - if (len(subset) == 0): + if len(subset) == 0: continue - subset[['nrg_payoff']].plot(ax=axes, style=[color+'o']); - axes.set_xlim(self._trace.x_min, self._trace.x_max); + subset[['nrg_payoff']].plot(ax=axes, style=[color+'o']) + axes.set_xlim(self._trace.x_min, self._trace.x_max) axes.set_yscale('symlog') - axes.grid(True); + axes.grid(True) axes.set_xticklabels([]) axes.set_xlabel('') self._trace.analysis.status.plotOverutilized(axes) # Plot4: energy deltas (kernel and host computed values) - axes = plt.subplot(gs[3,:]); - axes.set_title('Energy Deltas Values'); - df[['nrg_delta', 'nrg_diff_pct']].plot(ax=axes, style=['ro', 'b+']); - axes.set_xlim(self._trace.x_min, self._trace.x_max); - axes.grid(True); + axes = plt.subplot(gs[3, :]) + axes.set_title('Energy Deltas Values') + df[['nrg_delta', 'nrg_diff_pct']].plot(ax=axes, style=['ro', 'b+']) + axes.set_xlim(self._trace.x_min, self._trace.x_max) + axes.grid(True) self._trace.analysis.status.plotOverutilized(axes) # Save generated plots into datadir figname = '{}/{}ediff_time.png'\ - .format(self._trace.plots_dir, self._trace.plots_prefix) + .format(self._trace.plots_dir, self._trace.plots_prefix) pl.savefig(figname, bbox_inches='tight') # Grid: setup stats for gris - gs = gridspec.GridSpec(1, 3, height_ratios=[2]); - gs.update(wspace=0.1, hspace=0.1); + gs = gridspec.GridSpec(1, 3, height_ratios=[2]) + gs.update(wspace=0.1, hspace=0.1) - fig = plt.figure(figsize=(16, 4)); + fig = plt.figure(figsize=(16, 4)) # Plot: usage, capacity and energy distributuions - axes = plt.subplot(gs[0,0]); + axes = plt.subplot(gs[0, 0]) df[['usage_delta']].hist(ax=axes, bins=60) - axes = plt.subplot(gs[0,1]); + axes = plt.subplot(gs[0, 1]) df[['cap_delta']].hist(ax=axes, bins=60) - axes = plt.subplot(gs[0,2]); + axes = plt.subplot(gs[0, 2]) df[['nrg_delta']].hist(ax=axes, bins=60) # Save generated plots into datadir figname = '{}/{}ediff_stats.png'\ - .format(self._trace.plots_dir, self._trace.plots_prefix) + .format(self._trace.plots_dir, self._trace.plots_prefix) pl.savefig(figname, bbox_inches='tight') def plotEDiffSpace(self, tasks=None, - min_usage_delta=None, max_usage_delta=None, - min_cap_delta=None, max_cap_delta=None, - min_nrg_delta=None, max_nrg_delta=None, - min_nrg_diff=None, max_nrg_diff=None): + min_usage_delta=None, max_usage_delta=None, + min_cap_delta=None, max_cap_delta=None, + min_nrg_delta=None, max_nrg_delta=None, + min_nrg_diff=None, max_nrg_diff=None): """ Plot energy_diff()-related signals on the Performance-Energy space (PxE). """ if not self._trace.hasEvents('sched_energy_diff'): - logging.warn('Events [sched_energy_diff] not found, plot DISABLED!') + logging.warn('Event [sched_energy_diff] not found, plot DISABLED!') return df = self._dfg_trace_event('sched_energy_diff') @@ -198,41 +211,49 @@ class EasAnalysis(AnalysisModule): # Filter on 'usage_delta' if min_usage_delta is not None: - logging.info('Plotting EDiff data just with minimum usage_delta of [%d]', min_usage_delta) + logging.info('Plotting EDiff data just with minimum ' + 'usage_delta of [%d]', min_usage_delta) df = df[abs(df['usage_delta']) >= min_usage_delta] if max_usage_delta is not None: - logging.info('Plotting EDiff data just with maximum usage_delta of [%d]', max_usage_delta) + logging.info('Plotting EDiff data just with maximum ' + 'usage_delta of [%d]', max_usage_delta) df = df[abs(df['usage_delta']) <= max_usage_delta] # Filter on 'cap_delta' if min_cap_delta is not None: - logging.info('Plotting EDiff data just with minimum cap_delta of [%d]', min_cap_delta) + logging.info('Plotting EDiff data just with minimum ' + 'cap_delta of [%d]', min_cap_delta) df = df[abs(df['cap_delta']) >= min_cap_delta] if max_cap_delta is not None: - logging.info('Plotting EDiff data just with maximum cap_delta of [%d]', max_cap_delta) + logging.info('Plotting EDiff data just with maximum ' + 'cap_delta of [%d]', max_cap_delta) df = df[abs(df['cap_delta']) <= max_cap_delta] # Filter on 'nrg_delta' if min_nrg_delta is not None: - logging.info('Plotting EDiff data just with minimum nrg_delta of [%d]', min_nrg_delta) + logging.info('Plotting EDiff data just with minimum ' + 'nrg_delta of [%d]', min_nrg_delta) df = df[abs(df['nrg_delta']) >= min_nrg_delta] if max_nrg_delta is not None: - logging.info('Plotting EDiff data just with maximum nrg_delta of [%d]', max_nrg_delta) + logging.info('Plotting EDiff data just with maximum ' + 'nrg_delta of [%d]', max_nrg_delta) df = df[abs(df['nrg_delta']) <= max_nrg_delta] # Filter on 'nrg_diff' if min_nrg_diff is not None: - logging.info('Plotting EDiff data just with minimum nrg_diff of [%d]', min_nrg_diff) + logging.info('Plotting EDiff data just with minimum ' + 'nrg_diff of [%d]', min_nrg_diff) df = df[abs(df['nrg_diff']) >= min_nrg_diff] if max_nrg_diff is not None: - logging.info('Plotting EDiff data just with maximum nrg_diff of [%d]', max_nrg_diff) + logging.info('Plotting EDiff data just with maximum ' + 'nrg_diff of [%d]', max_nrg_diff) df = df[abs(df['nrg_diff']) <= max_nrg_diff] # Grid: setup grid for P-E space - gs = gridspec.GridSpec(1, 2, height_ratios=[2]); - gs.update(wspace=0.1, hspace=0.1); + gs = gridspec.GridSpec(1, 2, height_ratios=[2]) + gs.update(wspace=0.1, hspace=0.1) - fig = plt.figure(figsize=(16, 8)); + fig = plt.figure(figsize=(16, 8)) # Get min-max of each axes x_min = df.nrg_diff_pct.min() @@ -252,14 +273,15 @@ class EasAnalysis(AnalysisModule): # ccol = df.nrg_payoff # df['nrg_payoff_group'] = np.select( # [ccol > 2e9, ccol > 0, ccol > -2e9], - # ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject'], 'Suboptimal Reject') + # ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject'], + # 'Suboptimal Reject') # Plot: per usage_delta values - axes = plt.subplot(gs[0,0]); + axes = plt.subplot(gs[0, 0]) for color, label in zip('bgyr', ['< 150', '< 400', '< 600', '>= 600']): subset = df[df.usage_delta_group == label] - if (len(subset) == 0): + if len(subset) == 0: continue plt.scatter(subset.nrg_diff_pct, subset.cap_delta, s=subset.usage_delta, @@ -268,11 +290,13 @@ class EasAnalysis(AnalysisModule): # Plot space axes plt.plot((0, 0), (-1025, 1025), 'y--', axes=axes) - plt.plot((-1025, 1025), (0,0), 'y--', axes=axes) + plt.plot((-1025, 1025), (0, 0), 'y--', axes=axes) # # Perf cuts - # plt.plot((0, 100), (0,100*delta_pb), 'b--', label='PB (Perf Boost)') - # plt.plot((0, -100), (0,-100*delta_pc), 'r--', label='PC (Perf Constraint)') + # plt.plot((0, 100), (0, 100*delta_pb), 'b--', + # label='PB (Perf Boost)') + # plt.plot((0, -100), (0, -100*delta_pc), 'r--', + # label='PC (Perf Constraint)') # # # Perf boost setups # for y in range(0,6): @@ -280,21 +304,23 @@ class EasAnalysis(AnalysisModule): # for x in range(0,5): # plt.plot((0, x*100), (0,500), 'g:') - axes.legend(loc=4, borderpad=1); + axes.legend(loc=4, borderpad=1) - plt.xlim(1.1*axes_min, 1.1*axes_max); - plt.ylim(1.1*axes_min, 1.1*axes_max); + plt.xlim(1.1*axes_min, 1.1*axes_max) + plt.ylim(1.1*axes_min, 1.1*axes_max) # axes.title('Performance-Energy Space') - axes.set_xlabel('Energy diff [%]'); - axes.set_ylabel('Capacity diff [%]'); + axes.set_xlabel('Energy diff [%]') + axes.set_ylabel('Capacity diff [%]') # Plot: per usage_delta values - axes = plt.subplot(gs[0,1]); + axes = plt.subplot(gs[0, 1]) - for color, label in zip('gbyr', ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject', 'Suboptimal Reject']): + colors_labels = zip('gbyr', ['Optimal Accept', 'SchedTune Accept', + 'SchedTune Reject', 'Suboptimal Reject']) + for color, label in colors_labels: subset = df[df.nrg_payoff_group == label] - if (len(subset) == 0): + if len(subset) == 0: continue plt.scatter(subset.nrg_diff_pct, subset.cap_delta, s=60, @@ -306,11 +332,13 @@ class EasAnalysis(AnalysisModule): # Plot space axes plt.plot((0, 0), (-1025, 1025), 'y--', axes=axes) - plt.plot((-1025, 1025), (0,0), 'y--', axes=axes) + plt.plot((-1025, 1025), (0, 0), 'y--', axes=axes) # # Perf cuts - # plt.plot((0, 100), (0,100*delta_pb), 'b--', label='PB (Perf Boost)') - # plt.plot((0, -100), (0,-100*delta_pc), 'r--', label='PC (Perf Constraint)') + # plt.plot((0, 100), (0, 100*delta_pb), 'b--', + # label='PB (Perf Boost)') + # plt.plot((0, -100), (0, -100*delta_pc), 'r--', + # label='PC (Perf Constraint)') # # # Perf boost setups # for y in range(0,6): @@ -318,20 +346,20 @@ class EasAnalysis(AnalysisModule): # for x in range(0,5): # plt.plot((0, x*100), (0,500), 'g:') - axes.legend(loc=4, borderpad=1); + axes.legend(loc=4, borderpad=1) - plt.xlim(1.1*axes_min, 1.1*axes_max); - plt.ylim(1.1*axes_min, 1.1*axes_max); + plt.xlim(1.1*axes_min, 1.1*axes_max) + plt.ylim(1.1*axes_min, 1.1*axes_max) # axes.title('Performance-Energy Space') - axes.set_xlabel('Energy diff [%]'); - axes.set_ylabel('Capacity diff [%]'); + axes.set_xlabel('Energy diff [%]') + axes.set_ylabel('Capacity diff [%]') plt.title('Performance-Energy Space') # Save generated plots into datadir figname = '{}/{}ediff_space.png'\ - .format(self._trace.plots_dir, self._trace.plots_prefix) + .format(self._trace.plots_dir, self._trace.plots_prefix) pl.savefig(figname, bbox_inches='tight') def plotSchedTuneConf(self): @@ -339,37 +367,38 @@ class EasAnalysis(AnalysisModule): Plot the configuration of SchedTune. """ if not self._trace.hasEvents('sched_tune_config'): - logging.warn('Events [sched_tune_config] not found, plot DISABLED!') + logging.warn('Event [sched_tune_config] not found, plot DISABLED!') return # Grid - gs = gridspec.GridSpec(2, 1, height_ratios=[4,1]); - gs.update(wspace=0.1, hspace=0.1); + gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1]) + gs.update(wspace=0.1, hspace=0.1) # Figure - plt.figure(figsize=(16, 2*6)); + plt.figure(figsize=(16, 2*6)) plt.suptitle("SchedTune Configuration", - y=.97, fontsize=16, horizontalalignment='center'); + y=.97, fontsize=16, horizontalalignment='center') # Plot: Margin - axes = plt.subplot(gs[0,0]); - axes.set_title('Margin'); + axes = plt.subplot(gs[0, 0]) + axes.set_title('Margin') data = self._dfg_trace_event('sched_tune_config')[['margin']] - data.plot(ax=axes, drawstyle='steps-post', style=['b']); - axes.set_ylim(0, 110); - axes.set_xlim(self._trace.x_min, self._trace.x_max); - axes.xaxis.set_visible(False); + data.plot(ax=axes, drawstyle='steps-post', style=['b']) + axes.set_ylim(0, 110) + axes.set_xlim(self._trace.x_min, self._trace.x_max) + axes.xaxis.set_visible(False) # Plot: Boost mode - axes = plt.subplot(gs[1,0]); - axes.set_title('Boost mode'); + axes = plt.subplot(gs[1, 0]) + axes.set_title('Boost mode') data = self._dfg_trace_event('sched_tune_config')[['boostmode']] - data.plot(ax=axes, drawstyle='steps-post'); - axes.set_ylim(0, 4); - axes.set_xlim(self._trace.x_min, self._trace.x_max); - axes.xaxis.set_visible(True); + data.plot(ax=axes, drawstyle='steps-post') + axes.set_ylim(0, 4) + axes.set_xlim(self._trace.x_min, self._trace.x_max) + axes.xaxis.set_visible(True) # Save generated plots into datadir figname = '{}/{}schedtune_conf.png'\ - .format(self._trace.plots_dir, self._trace.plots_prefix) + .format(self._trace.plots_dir, self._trace.plots_prefix) pl.savefig(figname, bbox_inches='tight') +# vim :set tabstop=4 shiftwidth=4 expandtab diff --git a/libs/utils/analysis/frequency_analysis.py b/libs/utils/analysis/frequency_analysis.py index 11e6ddc46..7aad0020f 100644 --- a/libs/utils/analysis/frequency_analysis.py +++ b/libs/utils/analysis/frequency_analysis.py @@ -15,6 +15,8 @@ # limitations under the License. # +""" Frequency Analysis Module """ + import matplotlib.gridspec as gridspec import matplotlib.pyplot as plt import pandas as pd @@ -34,6 +36,7 @@ NON_IDLE_STATE = 4294967295 ResidencyTime = namedtuple('ResidencyTime', ['total', 'active']) ResidencyData = namedtuple('ResidencyData', ['label', 'residency']) + class FrequencyAnalysis(AnalysisModule): """ Support for plotting Frequency Analysis data @@ -45,9 +48,9 @@ class FrequencyAnalysis(AnalysisModule): def __init__(self, trace): super(FrequencyAnalysis, self).__init__(trace) -################################################################################ +############################################################################### # DataFrame Getter Methods -################################################################################ +############################################################################### def _dfg_cpu_frequency_residency(self, cpu, total=True): """ @@ -96,9 +99,9 @@ class FrequencyAnalysis(AnalysisModule): return residency.active -################################################################################ +############################################################################### # Plotting Methods -################################################################################ +############################################################################### def plotClusterFrequencies(self, title='Clusters Frequencies'): """ @@ -132,76 +135,75 @@ class FrequencyAnalysis(AnalysisModule): # Compute AVG frequency for LITTLE cluster avg_lfreq = 0 if len(lfreq) > 0: - lfreq['timestamp'] = lfreq.index; - lfreq['delta'] = (lfreq['timestamp'] - lfreq['timestamp'].shift()).fillna(0).shift(-1); - lfreq['cfreq'] = (lfreq['frequency'] * lfreq['delta']).fillna(0); - timespan = lfreq.iloc[-1].timestamp - lfreq.iloc[0].timestamp; - avg_lfreq = lfreq['cfreq'].sum()/timespan; + lfreq['timestamp'] = lfreq.index + lfreq['delta'] = (lfreq['timestamp'] -lfreq['timestamp'].shift()).fillna(0).shift(-1) + lfreq['cfreq'] = (lfreq['frequency'] * lfreq['delta']).fillna(0) + timespan = lfreq.iloc[-1].timestamp - lfreq.iloc[0].timestamp + avg_lfreq = lfreq['cfreq'].sum()/timespan # Compute AVG frequency for big cluster avg_bfreq = 0 if len(bfreq) > 0: - bfreq['timestamp'] = bfreq.index; - bfreq['delta'] = (bfreq['timestamp'] - bfreq['timestamp'].shift()).fillna(0).shift(-1); - bfreq['cfreq'] = (bfreq['frequency'] * bfreq['delta']).fillna(0); - timespan = bfreq.iloc[-1].timestamp - bfreq.iloc[0].timestamp; - avg_bfreq = bfreq['cfreq'].sum()/timespan; + bfreq['timestamp'] = bfreq.index + bfreq['delta'] = (bfreq['timestamp'] - bfreq['timestamp'].shift()).fillna(0).shift(-1) + bfreq['cfreq'] = (bfreq['frequency'] * bfreq['delta']).fillna(0) + timespan = bfreq.iloc[-1].timestamp - bfreq.iloc[0].timestamp + avg_bfreq = bfreq['cfreq'].sum()/timespan pd.options.mode.chained_assignment = 'warn' # Setup a dual cluster plot - fig, pltaxes = plt.subplots(2, 1, figsize=(16, 8)); - plt.suptitle(title, y=.97, fontsize=16, - horizontalalignment='center'); + fig, pltaxes = plt.subplots(2, 1, figsize=(16, 8)) + plt.suptitle(title, y=.97, fontsize=16, horizontalalignment='center') # Plot Cluster frequencies axes = pltaxes[0] - axes.set_title('big Cluster'); + axes.set_title('big Cluster') if avg_bfreq > 0: - axes.axhline(avg_bfreq, color='r', linestyle='--', linewidth=2); + axes.axhline(avg_bfreq, color='r', linestyle='--', linewidth=2) axes.set_ylim( (self._platform['freqs']['big'][0] - 100000)/1e3, (self._platform['freqs']['big'][-1] + 100000)/1e3 - ); + ) if len(bfreq) > 0: bfreq['frequency'].plot(style=['r-'], ax=axes, - drawstyle='steps-post', alpha=0.4); + drawstyle='steps-post', alpha=0.4) else: logging.warn('NO big CPUs frequency events to plot') - axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.set_xlim(self._trace.x_min, self._trace.x_max) axes.set_ylabel('MHz') - axes.grid(True); + axes.grid(True) axes.set_xticklabels([]) axes.set_xlabel('') self._trace.analysis.status.plotOverutilized(axes) axes = pltaxes[1] - axes.set_title('LITTLE Cluster'); + axes.set_title('LITTLE Cluster') if avg_lfreq > 0: - axes.axhline(avg_lfreq, color='b', linestyle='--', linewidth=2); + axes.axhline(avg_lfreq, color='b', linestyle='--', linewidth=2) axes.set_ylim( (self._platform['freqs']['little'][0] - 100000)/1e3, (self._platform['freqs']['little'][-1] + 100000)/1e3 - ); + ) if len(lfreq) > 0: lfreq['frequency'].plot(style=['b-'], ax=axes, - drawstyle='steps-post', alpha=0.4); + drawstyle='steps-post', alpha=0.4) else: logging.warn('NO LITTLE CPUs frequency events to plot') - axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.set_xlim(self._trace.x_min, self._trace.x_max) axes.set_ylabel('MHz') - axes.grid(True); + axes.grid(True) self._trace.analysis.status.plotOverutilized(axes) # Save generated plots into datadir figname = '{}/{}cluster_freqs.png'\ - .format(self._trace.plots_dir, self._trace.plots_prefix) + .format(self._trace.plots_dir, self._trace.plots_prefix) pl.savefig(figname, bbox_inches='tight') logging.info('LITTLE cluster average frequency: %.3f GHz', - avg_lfreq/1e3) + avg_lfreq/1e3) logging.info('big cluster average frequency: %.3f GHz', - avg_bfreq/1e3) + avg_bfreq/1e3) return (avg_lfreq/1e3, avg_bfreq/1e3) @@ -248,11 +250,11 @@ class FrequencyAnalysis(AnalysisModule): # Precompute active and total time for each CPU residencies = [] xmax = 0.0 - for c in _cpus: - r = self._getCPUFrequencyResidency(c) - residencies.append(ResidencyData('CPU{}'.format(c), r)) + for cpu in _cpus: + res = self._getCPUFrequencyResidency(cpu) + residencies.append(ResidencyData('CPU{}'.format(cpu), res)) - max_time = r.total.max().values[0] + max_time = res.total.max().values[0] if xmax < max_time: xmax = max_time @@ -303,20 +305,21 @@ class FrequencyAnalysis(AnalysisModule): # Precompute active and total time for each cluster residencies = [] xmax = 0.0 - for c in _clusters: - r = self._getClusterFrequencyResidency( - self._platform['clusters'][c.lower()]) - residencies.append(ResidencyData('{} Cluster'.format(c), r)) + for cluster in _clusters: + res = self._getClusterFrequencyResidency( + self._platform['clusters'][cluster.lower()]) + residencies.append(ResidencyData('{} Cluster'.format(cluster), + res)) - max_time = r.total.max().values[0] + max_time = res.total.max().values[0] if xmax < max_time: xmax = max_time self._plotFrequencyResidency(residencies, 'cluster', xmax, pct, active) -################################################################################ +############################################################################### # Utility Methods -################################################################################ +############################################################################### @memoized def _getCPUActiveSignal(self, cpu): @@ -331,7 +334,7 @@ class FrequencyAnalysis(AnalysisModule): :type cpu: int """ if not self._trace.hasEvents('cpu_idle'): - logging.warn('Events [cpu_idle] not found, '\ + logging.warn('Events [cpu_idle] not found, ' 'cannot compute CPU active signal!') return None @@ -397,11 +400,11 @@ class FrequencyAnalysis(AnalysisModule): :raises: KeyError """ if not self._trace.hasEvents('cpu_frequency'): - logging.warn('Events [cpu_frequency] not found, '\ + logging.warn('Events [cpu_frequency] not found, ' 'frequency residency computation not possible!') return None if not self._trace.hasEvents('cpu_idle'): - logging.warn('Events [cpu_idle] not found, '\ + logging.warn('Events [cpu_idle] not found, ' 'frequency residency computation not possible!') return None @@ -420,20 +423,20 @@ class FrequencyAnalysis(AnalysisModule): # cluster frequencies data to a single CPU. This assumption is verified # by the Trace module when parsing the trace. if len(_cluster) > 1 and not self._trace.freq_coherency: - logging.warn('Cluster frequency is NOT coherent,'\ + logging.warn('Cluster frequency is NOT coherent,' 'cannot compute residency!') return None cluster_freqs = freq_df[freq_df.cpu == _cluster[0]] - ### Compute TOTAL Time ### + # Compute TOTAL Time time_intervals = cluster_freqs.index[1:] - cluster_freqs.index[:-1] total_time = pd.DataFrame({ - 'time' : time_intervals, - 'frequency' : [f/1000.0 for f in cluster_freqs.iloc[:-1].frequency] + 'time': time_intervals, + 'frequency': [f/1000.0 for f in cluster_freqs.iloc[:-1].frequency] }) total_time = total_time.groupby(['frequency']).sum() - ### Compute ACTIVE Time ### + # Compute ACTIVE Time cluster_active = self._getClusterActiveSignal(_cluster) # In order to compute the active time spent at each frequency we @@ -446,7 +449,7 @@ class FrequencyAnalysis(AnalysisModule): # freq_active[t] == 1 if at time t the frequency is f # freq_active[t] == 0 otherwise available_freqs = sorted(cluster_freqs.frequency.unique()) - new_idx = sorted(cluster_freqs.index.tolist() + \ + new_idx = sorted(cluster_freqs.index.tolist() + cluster_active.index.tolist()) cluster_freqs = cluster_freqs.reindex(new_idx, method='ffill') cluster_active = cluster_active.reindex(new_idx, method='ffill') @@ -459,7 +462,7 @@ class FrequencyAnalysis(AnalysisModule): # Compute total time by integrating the square wave nonidle_time.append(self._trace.integrate_square_wave(active_t)) - active_time = pd.DataFrame({'time' : nonidle_time}, + active_time = pd.DataFrame({'time': nonidle_time}, index=[f/1000.0 for f in available_freqs]) active_time.index.name = 'frequency' return ResidencyTime(total_time, active_time) @@ -479,7 +482,7 @@ class FrequencyAnalysis(AnalysisModule): return self._getClusterFrequencyResidency(cpu) def _plotFrequencyResidencyAbs(self, axes, residency, n_plots, - is_first, is_last, xmax, title=''): + is_first, is_last, xmax, title=''): """ Private method to generate frequency residency plots. @@ -505,10 +508,10 @@ class FrequencyAnalysis(AnalysisModule): :type title: str """ yrange = 0.4 * max(6, len(residency.total)) * n_plots - residency.total.plot.barh(ax = axes, color='g', - legend=False, figsize=(16,yrange)) - residency.active.plot.barh(ax = axes, color='r', - legend=False, figsize=(16,yrange)) + residency.total.plot.barh(ax=axes, color='g', + legend=False, figsize=(16, yrange)) + residency.active.plot.barh(ax=axes, color='r', + legend=False, figsize=(16, yrange)) axes.set_xlim(0, 1.05*xmax) axes.set_ylabel('Frequency [MHz]') @@ -564,7 +567,7 @@ class FrequencyAnalysis(AnalysisModule): # Compute sum of the time intervals duration = residency_df.time.sum() residency_pct = pd.DataFrame( - {label : residency_df.time.apply(lambda x: x*100/duration)}, + {label: residency_df.time.apply(lambda x: x*100/duration)}, index=residency_df.index ) yrange = 3 * n_plots @@ -613,9 +616,7 @@ class FrequencyAnalysis(AnalysisModule): figtype = "" for idx, data in enumerate(residencies): - label = data[0] - r = data[1] - if r is None: + if data.residency is None: plt.close(fig) return @@ -648,3 +649,4 @@ class FrequencyAnalysis(AnalysisModule): entity_name, figtype) pl.savefig(figname, bbox_inches='tight') +# vim :set tabstop=4 shiftwidth=4 expandtab diff --git a/libs/utils/analysis/functions_analysis.py b/libs/utils/analysis/functions_analysis.py index 7500b2cb3..a82f63ea8 100644 --- a/libs/utils/analysis/functions_analysis.py +++ b/libs/utils/analysis/functions_analysis.py @@ -15,6 +15,8 @@ # limitations under the License. # +""" Functions Analysis Module """ + from trappy.utils import listify from analysis_module import AnalysisModule @@ -22,6 +24,7 @@ from analysis_module import AnalysisModule # Configure logging import logging + class FunctionsAnalysis(AnalysisModule): """ Support for kernel functions profiling and analysis @@ -64,17 +67,18 @@ class FunctionsAnalysis(AnalysisModule): available_metrics) raise ValueError(msg) - for _m in metrics: - if _m.upper() == 'AVG': + for metric in metrics: + if metric.upper() == 'AVG': title = 'Average Completion Time per CPUs' ylabel = 'Completion Time [us]' - if _m.upper() == 'TIME': + if metric.upper() == 'TIME': title = 'Total Execution Time per CPUs' ylabel = 'Execution Time [us]' - data = df[_m.lower()].unstack() + data = df[metric.lower()].unstack() axes = data.plot(kind='bar', - figsize=(16,8), legend=True, + figsize=(16, 8), legend=True, title=title, table=True) axes.set_ylabel(ylabel) axes.get_xaxis().set_visible(False) +# vim :set tabstop=4 shiftwidth=4 expandtab diff --git a/libs/utils/analysis/status_analysis.py b/libs/utils/analysis/status_analysis.py index 29e512756..771fdfe3f 100644 --- a/libs/utils/analysis/status_analysis.py +++ b/libs/utils/analysis/status_analysis.py @@ -15,15 +15,19 @@ # limitations under the License. # +# pylint: disable=E1101 + +""" System Status Analaysis Module """ + import matplotlib.gridspec as gridspec import matplotlib.pyplot as plt -import pandas as pd from analysis_module import AnalysisModule # Configure logging import logging + class StatusAnalysis(AnalysisModule): """ Support for System Status analysis @@ -36,9 +40,9 @@ class StatusAnalysis(AnalysisModule): super(StatusAnalysis, self).__init__(trace) -################################################################################ +############################################################################### # DataFrame Getter Methods -################################################################################ +############################################################################### def _dfg_overutilized(self): """ @@ -62,9 +66,9 @@ class StatusAnalysis(AnalysisModule): return df[['len', 'overutilized']] -################################################################################ +############################################################################### # Plotting Methods -################################################################################ +############################################################################### def plotOverutilized(self, axes=None): """ @@ -78,8 +82,8 @@ class StatusAnalysis(AnalysisModule): :type axes: :mod:`matplotlib.axes.Axes` """ if not self._trace.hasEvents('sched_overutilized'): - logging.warn('Events [sched_overutilized] not found, '\ - 'plot DISABLED!') + logging.warn('Event [sched_overutilized] not found, ' + 'plot DISABLED!') return df = self._dfg_overutilized() @@ -91,17 +95,19 @@ class StatusAnalysis(AnalysisModule): if not axes: gs = gridspec.GridSpec(1, 1) plt.figure(figsize=(16, 1)) - axes = plt.subplot(gs[0,0]) - axes.set_title('System Status {white: EAS mode, red: Non EAS mode}'); - axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes = plt.subplot(gs[0, 0]) + axes.set_title('System Status {white: EAS mode, ' + 'red: Non EAS mode}') + axes.set_xlim(self._trace.x_min, self._trace.x_max) axes.set_yticklabels([]) axes.set_xlabel('Time [s]') - axes.grid(True); + axes.grid(True) # Otherwise: draw overutilized bands on top of the specified plot - for (t1,td,overutilized) in bands: + for (start, delta, overutilized) in bands: if not overutilized: continue - t2 = t1+td - axes.axvspan(t1, t2, facecolor='r', alpha=0.1) + end = start + delta + axes.axvspan(start, end, facecolor='r', alpha=0.1) +# vim :set tabstop=4 shiftwidth=4 expandtab diff --git a/libs/utils/analysis/tasks_analysis.py b/libs/utils/analysis/tasks_analysis.py index 03a194335..be3f966a2 100644 --- a/libs/utils/analysis/tasks_analysis.py +++ b/libs/utils/analysis/tasks_analysis.py @@ -15,6 +15,8 @@ # limitations under the License. # +""" Tasks Analysis Module """ + import matplotlib.gridspec as gridspec import matplotlib.pyplot as plt import numpy as np @@ -27,6 +29,7 @@ from devlib.utils.misc import memoized # Configure logging import logging + class TasksAnalysis(AnalysisModule): """ Support for Tasks signals analysis. @@ -39,9 +42,9 @@ class TasksAnalysis(AnalysisModule): super(TasksAnalysis, self).__init__(trace) -################################################################################ +############################################################################### # DataFrame Getter Methods -################################################################################ +############################################################################### def _dfg_top_big_tasks(self, min_samples=100, min_utilization=None): """ @@ -52,7 +55,7 @@ class TasksAnalysis(AnalysisModule): :type min_samples: int :param min_utilization: minimum utilization used to filter samples - default: capacity of a little cluster + default: capacity of a little cluster :type min_utilization: int """ if not self._trace.hasEvents('sched_load_avg_task'): @@ -93,17 +96,20 @@ class TasksAnalysis(AnalysisModule): # Add task name column big_tasks_stats['comm'] = big_tasks_stats.index.map( - lambda pid : ', '.join(self._trace.getTaskByPid(pid))) + lambda pid: ', '.join(self._trace.getTaskByPid(pid))) # Filter columns of interest big_tasks_stats = big_tasks_stats[['count', 'comm']] - big_tasks_stats.rename(columns={'count' : 'samples'}, inplace=True) + big_tasks_stats.rename(columns={'count': 'samples'}, inplace=True) return big_tasks_stats def _dfg_top_wakeup_tasks(self, min_wakeups=100): """ - Tasks which wakeups more frequent than a specified threshold + Tasks which wakeup more frequently than a specified threshold. + + :param min_wakeups: minimum number of wakeups + :type min_wakeups: int """ if not self._trace.hasEvents('sched_wakeup'): logging.warn('Events [sched_wakeup] not found') @@ -114,27 +120,29 @@ class TasksAnalysis(AnalysisModule): # Compute number of wakeups above threshold wkp_tasks_stats = df.groupby('pid').describe(include=['object']) wkp_tasks_stats = wkp_tasks_stats.unstack()['comm']\ - .sort_values(by=['count'], ascending=False) + .sort_values(by=['count'], ascending=False) # Filter for number of occurrences wkp_tasks_stats = wkp_tasks_stats[ wkp_tasks_stats['count'] > min_wakeups] if not len(df): - logging.warn('No tasks with more than %d wakeups', len(wkp_tasks_stats)) + logging.warn('No tasks with more than %d wakeups', + len(wkp_tasks_stats)) return None - logging.info('%5d tasks with more than %d wakeups', len(wkp_tasks_stats)) + logging.info('%5d tasks with more than %d wakeups', + len(df), len(wkp_tasks_stats)) # Add task name column wkp_tasks_stats['comm'] = wkp_tasks_stats.index.map( - lambda pid : ', '.join(self._trace.getTaskByPid(pid))) + lambda pid: ', '.join(self._trace.getTaskByPid(pid))) # Filter columns of interest wkp_tasks_stats = wkp_tasks_stats[['count', 'comm']] - wkp_tasks_stats.rename(columns={'count' : 'samples'}, inplace=True) + wkp_tasks_stats.rename(columns={'count': 'samples'}, inplace=True) return wkp_tasks_stats - def _dfg_rt_tasks(self, min_prio = 100): + def _dfg_rt_tasks(self, min_prio=100): """ Tasks with RT priority @@ -162,22 +170,24 @@ class TasksAnalysis(AnalysisModule): rt_tasks = rt_tasks.drop_duplicates() # Order by priority - rt_tasks.sort_values(by=['next_prio', 'next_pid'], ascending=True, inplace=True) - rt_tasks.rename(columns={'next_pid' : 'pid', 'next_prio' : 'prio'}, inplace=True) + rt_tasks.sort_values(by=['next_prio', 'next_pid'], ascending=True, + inplace=True) + rt_tasks.rename(columns={'next_pid': 'pid', 'next_prio': 'prio'}, + inplace=True) # Set PID as index rt_tasks.set_index('pid', inplace=True) # Add task name column rt_tasks['comm'] = rt_tasks.index.map( - lambda pid : ', '.join(self._trace.getTaskByPid(pid))) + lambda pid: ', '.join(self._trace.getTaskByPid(pid))) return rt_tasks -################################################################################ +############################################################################### # Plotting Methods -################################################################################ +############################################################################### def plotTasks(self, tasks=None, signals=None): """ @@ -217,8 +227,8 @@ class TasksAnalysis(AnalysisModule): # Check for the minimum required signals to be available if not self._trace.hasEvents('sched_load_avg_task'): - logging.warn('Events [sched_load_avg_task] not found, '\ - 'plot DISABLED!') + logging.warn('Events [sched_load_avg_task] not found, ' + 'plot DISABLED!') return if not isinstance(tasks, str) and \ @@ -249,8 +259,8 @@ class TasksAnalysis(AnalysisModule): plots_count = plots_count + 1 # Grid - gs = gridspec.GridSpec(plots_count, 1, height_ratios=[2,1,1]); - gs.update(wspace=0.1, hspace=0.1); + gs = gridspec.GridSpec(plots_count, 1, height_ratios=[2, 1, 1]) + gs.update(wspace=0.1, hspace=0.1) # Build list of all PIDs for each task_name to plot pids_to_plot = [] @@ -272,18 +282,18 @@ class TasksAnalysis(AnalysisModule): plot_id = 0 # Figure - plt.figure(figsize=(16, 2*6+3)); + plt.figure(figsize=(16, 2*6+3)) plt.suptitle("Task Signals", - y=.94, fontsize=16, horizontalalignment='center'); + y=.94, fontsize=16, horizontalalignment='center') # Plot load and utilization signals_to_plot = {'load_avg', 'util_avg', 'boosted_util', 'sched_overutilized'} signals_to_plot = list(signals_to_plot.intersection(signals)) if len(signals_to_plot) > 0: - axes = plt.subplot(gs[plot_id,0]); - axes.set_title('Task [{0:d}:{1:s}] Signals'\ - .format(tid, task_name)); + axes = plt.subplot(gs[plot_id, 0]) + axes.set_title('Task [{0:d}:{1:s}] Signals' + .format(tid, task_name)) plot_id = plot_id + 1 is_last = (plot_id == plots_count) self._plotTaskSignals(axes, tid, signals_to_plot, is_last) @@ -292,9 +302,11 @@ class TasksAnalysis(AnalysisModule): signals_to_plot = {'residencies', 'sched_overutilized'} signals_to_plot = list(signals_to_plot.intersection(signals)) if len(signals_to_plot) > 0: - axes = plt.subplot(gs[plot_id,0]); - axes.set_title('Task [{0:d}:{1:s}] Residency (green: LITTLE, red: big)'\ - .format(tid, task_name)); + axes = plt.subplot(gs[plot_id, 0]) + axes.set_title( + 'Task [{0:d}:{1:s}] Residency (green: LITTLE, red: big)' + .format(tid, task_name) + ) plot_id = plot_id + 1 is_last = (plot_id == plots_count) self._plotTaskResidencies(axes, tid, signals_to_plot, is_last) @@ -305,9 +317,9 @@ class TasksAnalysis(AnalysisModule): 'period_contrib', 'sched_overutilized'} signals_to_plot = list(signals_to_plot.intersection(signals)) if len(signals_to_plot) > 0: - axes = plt.subplot(gs[plot_id,0]); - axes.set_title('Task [{0:d}:{1:s}] PELT Signals'\ - .format(tid, task_name)); + axes = plt.subplot(gs[plot_id, 0]) + axes.set_title('Task [{0:d}:{1:s}] PELT Signals' + .format(tid, task_name)) plot_id = plot_id + 1 self._plotTaskPelt(axes, tid, signals_to_plot) @@ -316,12 +328,27 @@ class TasksAnalysis(AnalysisModule): task_name = re.sub('[:/]', '_', task_name[0]) else: task_name = re.sub('[:/]', '_', task_name) - figname = '{}/{}task_util_{}_{}.png'.format( - self._trace.plots_dir, self._trace.plots_prefix, tid, task_name) + figname = '{}/{}task_util_{}_{}.png'\ + .format(self._trace.plots_dir, self._trace.plots_prefix, + tid, task_name) pl.savefig(figname, bbox_inches='tight') def plotBigTasks(self, max_tasks=10, min_samples=100, - min_utilization=None): + min_utilization=None): + """ + For each big task plot utilization and show the smallest cluster + capacity suitable for accommodating task utilization. + + :param max_tasks: maximum number of tasks to consider + :type max_tasks: int + + :param min_samples: minumum number of samples over the min_utilization + :type min_samples: int + + :param min_utilization: minimum utilization used to filter samples + default: capacity of a little cluster + :type min_utilization: int + """ # Get PID of big tasks big_frequent_task_df = self._dfg_top_big_tasks( @@ -371,7 +398,7 @@ class TasksAnalysis(AnalysisModule): ax.grid(True) self._trace.analysis.status.plotOverutilized(ax) - plot_idx+=1 + plot_idx += 1 ax.set_xlabel('Time [s]') @@ -379,7 +406,19 @@ class TasksAnalysis(AnalysisModule): self._little_cap, min_samples) def plotWakeupTasks(self, max_tasks=10, min_wakeups=0, per_cluster=False): + """ + Show waking up tasks over time and newly forked tasks in two separate + plots. + + :param max_tasks: maximum number of tasks to consider + :param max_tasks: int + :param min_wakeups: minimum number of wakeups of each task + :type min_wakeups: int + + :param per_cluster: if True get per-cluster wakeup events + :type per_cluster: bool + """ if per_cluster is True and \ not self._trace.hasEvents('sched_wakeup_new'): logging.warn('Events [sched_wakeup_new] not found, ' @@ -392,8 +431,8 @@ class TasksAnalysis(AnalysisModule): return # Define axes for side-by-side plottings - fig, axes = plt.subplots(2, 1, figsize=(14, 5)); - plt.subplots_adjust(wspace=0.2, hspace=0.3); + fig, axes = plt.subplots(2, 1, figsize=(14, 5)) + plt.subplots_adjust(wspace=0.2, hspace=0.3) if per_cluster: @@ -411,9 +450,11 @@ class TasksAnalysis(AnalysisModule): ntlc_count = len(ntlc) logging.info("%5d tasks forked on big cluster (%3.1f %%)", - ntbc_count, 100. * ntbc_count / (ntbc_count + ntlc_count)) + ntbc_count, + 100. * ntbc_count / (ntbc_count + ntlc_count)) logging.info("%5d tasks forked on LITTLE cluster (%3.1f %%)", - ntlc_count, 100. * ntlc_count / (ntbc_count + ntlc_count)) + ntlc_count, + 100. * ntlc_count / (ntbc_count + ntlc_count)) ax = axes[0] ax.set_title('Tasks Forks on big CPUs'); @@ -440,31 +481,44 @@ class TasksAnalysis(AnalysisModule): logging.info("Plotting %d frequent wakeup tasks", len(wkp_task_pids)) ax = axes[0] - ax.set_title('Tasks WakeUps Events'); + ax.set_title('Tasks WakeUps Events') df = self._dfg_trace_event('sched_wakeup') if len(df): df = df[df.pid.isin(wkp_task_pids)] - df.pid.astype(int).plot(style=['b.'], ax=ax); - ax.set_xlim(self._trace.x_min, self._trace.x_max); + df.pid.astype(int).plot(style=['b.'], ax=ax) + ax.set_xlim(self._trace.x_min, self._trace.x_max) ax.set_xticklabels([]) ax.set_xlabel('') ax.grid(True) self._trace.analysis.status.plotOverutilized(ax) ax = axes[1] - ax.set_title('Tasks Forks Events'); + ax.set_title('Tasks Forks Events') df = self._dfg_trace_event('sched_wakeup_new') if len(df): df = df[df.pid.isin(wkp_task_pids)] - df.pid.astype(int).plot(style=['r.'], ax=ax); - ax.set_xlim(self._trace.x_min, self._trace.x_max); + df.pid.astype(int).plot(style=['r.'], ax=ax) + ax.set_xlim(self._trace.x_min, self._trace.x_max) ax.grid(True) self._trace.analysis.status.plotOverutilized(ax) - def plotBigTasksVsCapacity(self, - min_samples=1, - min_utilization=None, - big_cluster=True): + def plotBigTasksVsCapacity(self, min_samples=1, + min_utilization=None, big_cluster=True): + """ + Draw a plot that shows whether tasks are placed on the correct cluster + based on their utilization and cluster capacity. Green dots mean the + task was placed on the correct cluster, Red means placement was wrong + + :param min_samples: minumum number of samples over the min_utilization + :type min_samples: int + + :param min_utilization: minimum utilization used to filter samples + default: capacity of a little cluster + :type min_utilization: int + + :param big_cluster: + :type big_cluster: bool + """ if not self._trace.hasEvents('sched_load_avg_task'): logging.warn('Events [sched_load_avg_task] not found') @@ -481,7 +535,7 @@ class TasksAnalysis(AnalysisModule): cpus = self._little_cpus # Get all utilization update events - df = self._dfg_trace_event('sched_load_avg_task') + df = self._dfg_trace_event('sched_load_avg_task') # Keep events of defined big tasks big_task_pids = self._dfg_top_big_tasks( @@ -494,8 +548,8 @@ class TasksAnalysis(AnalysisModule): 'samples bigger than %d, plots DISABLED!') return - fig, axes = plt.subplots(2, 1, figsize=(14, 5)); - plt.subplots_adjust(wspace=0.2, hspace=0.3); + fig, axes = plt.subplots(2, 1, figsize=(14, 5)) + plt.subplots_adjust(wspace=0.2, hspace=0.3) # Add column of expected cluster depending on: # a) task utilization value @@ -511,20 +565,19 @@ class TasksAnalysis(AnalysisModule): # The Cluster CAPacity Matches the UTILization (ccap_mutil) iff: # - tasks with util_avg > little_cap are running on a BIG cpu # - tasks with util_avg <= little_cap are running on a LITTLe cpu - df.loc[:,'ccap_mutil'] = np.select( - [(bu_bc | su_lc)], [True], False) + df.loc[:,'ccap_mutil'] = np.select([(bu_bc | su_lc)], [True], False) df_freq = self._dfg_trace_event('cpu_frequency') df_freq = df_freq[df_freq.cpu == cpus[0]] ax = axes[0] - ax.set_title('Tasks Utilization vs Allocation'); + ax.set_title('Tasks Utilization vs Allocation') for ucolor, umatch in zip('gr', [True, False]): - cdata = df[df['ccap_mutil'] == umatch] - if (len(cdata) > 0): + cdata = df[df['ccap_mutil'] == umatch] + if len(cdata) > 0: cdata['util_avg'].plot(ax=ax, - style=[ucolor+'.'], legend=False); - ax.set_xlim(self._trace.x_min, self._trace.x_max); + style=[ucolor+'.'], legend=False) + ax.set_xlim(self._trace.x_min, self._trace.x_max) ax.set_xticklabels([]) ax.set_xlabel('') ax.grid(True) @@ -532,7 +585,7 @@ class TasksAnalysis(AnalysisModule): ax = axes[1] ax.set_title('Frequencies on "{}" cluster'.format(cluster_correct)) - df_freq['frequency'].plot(style=['-b'], ax=ax, drawstyle='steps-post'); + df_freq['frequency'].plot(style=['-b'], ax=ax, drawstyle='steps-post') ax.set_xlim(self._trace.x_min, self._trace.x_max); ax.grid(True) self._trace.analysis.status.plotOverutilized(ax) @@ -548,9 +601,9 @@ class TasksAnalysis(AnalysisModule): fontsize=14) -################################################################################ +############################################################################### # Utility Methods -################################################################################ +############################################################################### def _plotTaskSignals(self, axes, tid, signals, is_last=False): """ @@ -575,15 +628,15 @@ class TasksAnalysis(AnalysisModule): signals_to_plot = list({'load_avg', 'util_avg'}.intersection(signals)) if len(signals_to_plot): data = util_df[util_df.pid == tid][signals_to_plot] - data.plot(ax=axes, drawstyle='steps-post'); + data.plot(ax=axes, drawstyle='steps-post') # Plot boost utilization if available if 'boosted_util' in signals and \ - self._trace.hasEvents('sched_boost_task'): + self._trace.hasEvents('sched_boost_task'): boost_df = self._dfg_trace_event('sched_boost_task') data = boost_df[boost_df.pid == tid][['boosted_util']] if len(data): - data.plot(ax=axes, style=['y-'], drawstyle='steps-post'); + data.plot(ax=axes, style=['y-'], drawstyle='steps-post') else: task_name = self._trace.getTaskByPid(tid) logging.warning("No 'boosted_util' data for task [%d:%s]", @@ -596,16 +649,18 @@ class TasksAnalysis(AnalysisModule): max_bcap = nrg_model['big']['cpu']['cap_max'] tip_lcap = 0.8 * max_lcap tip_bcap = 0.8 * max_bcap - logging.debug('LITTLE capacity tip/max: %d/%d, big capacity tip/max: %d/%d', - tip_lcap, max_lcap, tip_bcap, max_bcap) - axes.axhline(tip_lcap, color='g', linestyle='--', linewidth=1); - axes.axhline(max_lcap, color='g', linestyle='-', linewidth=2); - axes.axhline(tip_bcap, color='r', linestyle='--', linewidth=1); - axes.axhline(max_bcap, color='r', linestyle='-', linewidth=2); - - axes.set_ylim(0, 1100); - axes.set_xlim(self._trace.x_min, self._trace.x_max); - axes.grid(True); + logging.debug( + 'LITTLE capacity tip/max: %d/%d, big capacity tip/max: %d/%d', + tip_lcap, max_lcap, tip_bcap, max_bcap + ) + axes.axhline(tip_lcap, color='g', linestyle='--', linewidth=1) + axes.axhline(max_lcap, color='g', linestyle='-', linewidth=2) + axes.axhline(tip_bcap, color='r', linestyle='--', linewidth=1) + axes.axhline(max_bcap, color='r', linestyle='-', linewidth=2) + + axes.set_ylim(0, 1100) + axes.set_xlim(self._trace.x_min, self._trace.x_max) + axes.grid(True) if not is_last: axes.set_xticklabels([]) axes.set_xlabel('') @@ -632,8 +687,8 @@ class TasksAnalysis(AnalysisModule): data = util_df[util_df.pid == tid][['cluster', 'cpu']] for ccolor, clabel in zip('gr', ['LITTLE', 'big']): cdata = data[data.cluster == clabel] - if (len(cdata) > 0): - cdata.plot(ax=axes, style=[ccolor+'+'], legend=False); + if len(cdata) > 0: + cdata.plot(ax=axes, style=[ccolor+'+'], legend=False) # Y Axis - placeholders for legend, acutal CPUs. topmost empty lane cpus = [str(n) for n in range(self._platform['cpus_count'])] ylabels = [''] + cpus @@ -641,9 +696,9 @@ class TasksAnalysis(AnalysisModule): axes.set_ylim(-1, self._platform['cpus_count']) axes.set_ylabel('CPUs') # X Axis - axes.set_xlim(self._trace.x_min, self._trace.x_max); + axes.set_xlim(self._trace.x_min, self._trace.x_max) - axes.grid(True); + axes.grid(True) if not is_last: axes.set_xticklabels([]) axes.set_xlabel('') @@ -664,12 +719,15 @@ class TasksAnalysis(AnalysisModule): :param signals: list(str) """ util_df = self._dfg_trace_event('sched_load_avg_task') - data = util_df[util_df.pid == tid][['load_sum', 'util_sum', 'period_contrib']] - data.plot(ax=axes, drawstyle='steps-post'); - axes.set_xlim(self._trace.x_min, self._trace.x_max); - axes.ticklabel_format(style='scientific', scilimits=(0,0), + data = util_df[util_df.pid == tid][['load_sum', + 'util_sum', + 'period_contrib']] + data.plot(ax=axes, drawstyle='steps-post') + axes.set_xlim(self._trace.x_min, self._trace.x_max) + axes.ticklabel_format(style='scientific', scilimits=(0, 0), axis='y', useOffset=False) - axes.grid(True); + axes.grid(True) if 'sched_overutilized' in signals: self._trace.analysis.status.plotOverutilized(axes) +# vim :set tabstop=4 shiftwidth=4 expandtab diff --git a/libs/utils/analysis_module.py b/libs/utils/analysis_module.py index 5b58f3605..160ab1bcf 100644 --- a/libs/utils/analysis_module.py +++ b/libs/utils/analysis_module.py @@ -15,6 +15,9 @@ # limitations under the License. # +""" Helper module for Analysis classes """ + + class AnalysisModule(object): """ Base class for Analysis modules. @@ -31,10 +34,11 @@ class AnalysisModule(object): self._dfg_trace_event = trace._dfg_trace_event - self._big_cap = self._platform['nrg_model']['big']['cpu']['cap_max'] - self._little_cap = self._platform['nrg_model']['little']['cpu']['cap_max'] - self._big_cpus = self._platform['clusters']['big'] + self._big_cap = self._platform['nrg_model']['big']['cpu']['cap_max'] + self._little_cap = self._platform['nrg_model']['little']['cpu']['cap_max'] + self._big_cpus = self._platform['clusters']['big'] self._little_cpus = self._platform['clusters']['little'] trace._registerDataFrameGetters(self) +# vim :set tabstop=4 shiftwidth=4 expandtab diff --git a/libs/utils/analysis_register.py b/libs/utils/analysis_register.py index e9c05fd22..0f9f238bc 100644 --- a/libs/utils/analysis_register.py +++ b/libs/utils/analysis_register.py @@ -15,6 +15,8 @@ # limitations under the License. # +""" Helper module for registering Analysis classes methods """ + import os import sys @@ -27,6 +29,7 @@ from analysis_module import AnalysisModule # Configure logging import logging + class AnalysisRegister(object): """ Define list of supported Analysis Classes. @@ -64,8 +67,8 @@ class AnalysisRegister(object): handler = getattr(module, member) if handler and isclass(handler) and \ issubclass(handler, AnalysisModule): - class_name = handler.__name__ module_name = module.__name__.replace('_analysis', '') setattr(self, module_name, handler(trace)) logging.info(" %s", module_name) +# vim :set tabstop=4 shiftwidth=4 expandtab diff --git a/libs/utils/trace.py b/libs/utils/trace.py index c05ea77ce..209f60a93 100644 --- a/libs/utils/trace.py +++ b/libs/utils/trace.py @@ -15,6 +15,8 @@ # limitations under the License. # +""" Trace Parser Module """ + import numpy as np import os import pandas as pd @@ -29,6 +31,7 @@ from trappy.utils import listify # Configure logging import logging + class Trace(object): """ The Trace object is the LISA trace events parser. @@ -65,7 +68,7 @@ class Trace(object): """ def __init__(self, platform, data_dir, events, - tasks=None, window=(0,None), + tasks=None, window=(0, None), normalize_time=True, trace_format='FTrace', plots_dir=None, @@ -121,7 +124,8 @@ class Trace(object): self.plots_prefix = plots_prefix self.__registerTraceEvents(events) - self.__parseTrace(data_dir, tasks, window, normalize_time, trace_format) + self.__parseTrace(data_dir, tasks, window, normalize_time, + trace_format) self.__computeTimeSpan() # Minimum and Maximum x_time to use for all plots @@ -174,7 +178,7 @@ class Trace(object): else: self.x_max = t_max logging.info('Set plots time range to (%.6f, %.6f)[s]', - self.x_min, self.x_max) + self.x_min, self.x_max) def __registerTraceEvents(self, events): """ @@ -287,7 +291,7 @@ class Trace(object): """ if 'sched_switch' in self.available_events: self.getTasks(self._dfg_trace_event('sched_switch'), tasks, - name_key='next_comm', pid_key='next_pid') + name_key='next_comm', pid_key='next_pid') self._scanTasks(self._dfg_trace_event('sched_switch'), name_key='next_comm', pid_key='next_pid') return @@ -327,7 +331,7 @@ class Trace(object): self.time_range = te - ts logging.info('Collected events spans a %.3f [s] time interval', - self.time_range) + self.time_range) # Build a stat on trace overutilization if self.hasEvents('sched_overutilized'): @@ -336,7 +340,7 @@ class Trace(object): self.overutilized_prc = 100. * self.overutilized_time / self.time_range logging.info('Overutilized time: %.6f [s] (%.3f%% of trace time)', - self.overutilized_time, self.overutilized_prc) + self.overutilized_time, self.overutilized_prc) def _scanTasks(self, df, name_key='comm', pid_key='pid'): """ @@ -347,15 +351,16 @@ class Trace(object): and PIDs will be extracted :type df: :mod:`pandas.DataFrame` - :param name_key: The name of the dataframe columns containing task names + :param name_key: The name of the dataframe columns containing task + names :type name_key: str :param pid_key: The name of the dataframe columns containing task PIDs :type pid_key: str """ - df = df[[name_key, pid_key]] + df = df[[name_key, pid_key]] self._tasks_by_name = df.set_index(name_key) - self._tasks_by_pid = df.set_index(pid_key) + self._tasks_by_pid = df.set_index(pid_key) def getTaskByName(self, name): """ @@ -386,7 +391,7 @@ class Trace(object): return [self._tasks_by_pid.ix[pid].values[0]] def getTasks(self, dataframe=None, - task_names=None, name_key='comm', pid_key='pid'): + task_names=None, name_key='comm', pid_key='pid'): """ Helper function to get PIDs of specified tasks. @@ -408,7 +413,8 @@ class Trace(object): workload defined tasks) :type task_names: list(str) - :param name_key: The name of the dataframe columns containing task names + :param name_key: The name of the dataframe columns containing task + names :type name_key: str :param pid_key: The name of the dataframe columns containing task PIDs @@ -422,26 +428,26 @@ class Trace(object): logging.debug("Lookup dataset for tasks...") for tname in task_names: logging.debug("Lookup for task [%s]...", tname) - results = df[df[name_key] == tname][[name_key,pid_key]] - if len(results)==0: + results = df[df[name_key] == tname][[name_key, pid_key]] + if len(results) == 0: logging.error(' task %16s NOT found', tname) continue (name, pid) = results.head(1).values[0] - if name!=tname: + if name != tname: logging.error(' task %16s NOT found', tname) continue - if (tname not in self.tasks): + if tname not in self.tasks: self.tasks[tname] = {} pids = list(results[pid_key].unique()) self.tasks[tname]['pid'] = pids logging.info(' task %16s found, pid: %s', - tname, self.tasks[tname]['pid']) + tname, self.tasks[tname]['pid']) return self.tasks -################################################################################ +############################################################################### # DataFrame Getter Methods -################################################################################ +############################################################################### def df(self, event): """ @@ -470,8 +476,8 @@ class Trace(object): raise ValueError("trace data not (yet) loaded") if self.ftrace and hasattr(self.ftrace, event): return getattr(self.ftrace, event).data_frame - raise ValueError('Event [{}] not supported. '\ - 'Supported events are: {}'\ + raise ValueError('Event [{}] not supported. ' + 'Supported events are: {}' .format(event, self.available_events)) def _dfg_functions_stats(self, functions=None): @@ -497,9 +503,9 @@ class Trace(object): return df.loc[df.index.get_level_values(1).isin(listify(functions))] -################################################################################ +############################################################################### # Trace Events Sanitize Methods -################################################################################ +############################################################################### def _sanitize_SchedCpuCapacity(self): """ @@ -534,8 +540,8 @@ class Trace(object): return df = self._dfg_trace_event('sched_load_avg_cpu') if 'utilization' in df: - df.rename(columns={'utilization':'util_avg'}, inplace=True) - df.rename(columns={'load':'load_avg'}, inplace=True) + df.rename(columns={'utilization': 'util_avg'}, inplace=True) + df.rename(columns={'load': 'load_avg'}, inplace=True) def _sanitize_SchedLoadAvgTask(self): """ @@ -545,21 +551,21 @@ class Trace(object): return df = self._dfg_trace_event('sched_load_avg_task') if 'utilization' in df: - df.rename(columns={'utilization':'util_avg'}, inplace=True) - df.rename(columns={'load':'load_avg'}, inplace=True) - df.rename(columns={'avg_period':'period_contrib'}, inplace=True) - df.rename(columns={'runnable_avg_sum':'load_sum'}, inplace=True) - df.rename(columns={'running_avg_sum':'util_sum'}, inplace=True) + df.rename(columns={'utilization': 'util_avg'}, inplace=True) + df.rename(columns={'load': 'load_avg'}, inplace=True) + df.rename(columns={'avg_period': 'period_contrib'}, inplace=True) + df.rename(columns={'runnable_avg_sum': 'load_sum'}, inplace=True) + df.rename(columns={'running_avg_sum': 'util_sum'}, inplace=True) df['cluster'] = np.select( [df.cpu.isin(self.platform['clusters']['little'])], ['LITTLE'], 'big') # Add a column which represents the max capacity of the smallest # clustre which can accomodate the task utilization - little_cap = self.platform['nrg_model']['little']['cpu']['cap_max'] - big_cap = self.platform['nrg_model']['big']['cpu']['cap_max'] + little_cap = self.platform['nrg_model']['little']['cpu']['cap_max'] + big_cap = self.platform['nrg_model']['big']['cpu']['cap_max'] df['min_cluster_cap'] = df.util_avg.map( - lambda util_avg : - big_cap if util_avg > little_cap else little_cap) + lambda util_avg: big_cap if util_avg > little_cap else little_cap + ) def _sanitize_SchedBoostCpu(self): """ @@ -572,7 +578,7 @@ class Trace(object): return df = self._dfg_trace_event('sched_boost_cpu') if 'usage' in df: - df.rename(columns={'usage':'util'}, inplace=True) + df.rename(columns={'usage': 'util'}, inplace=True) df['boosted_util'] = df['util'] + df['margin'] def _sanitize_SchedBoostTask(self): @@ -587,7 +593,7 @@ class Trace(object): df = self._dfg_trace_event('sched_boost_task') if 'utilization' in df: # Convert signals name from to v5.1 format - df.rename(columns={'utilization':'util'}, inplace=True) + df.rename(columns={'utilization': 'util'}, inplace=True) df['boosted_util'] = df['util'] + df['margin'] def _sanitize_SchedEnergyDiff(self): @@ -608,7 +614,7 @@ class Trace(object): SCHED_LOAD_SCALE = 1024 power_max = em_lcpu['nrg_max'] * lcpus + em_bcpu['nrg_max'] * bcpus + \ - em_lcluster['nrg_max'] + em_bcluster['nrg_max'] + em_lcluster['nrg_max'] + em_bcluster['nrg_max'] print "Maximum estimated system energy: {0:d}".format(power_max) df = self._dfg_trace_event('sched_energy_diff') @@ -624,7 +630,8 @@ class Trace(object): ccol = df.nrg_payoff df['nrg_payoff_group'] = np.select( [ccol > 2e9, ccol > 0, ccol > -2e9], - ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject'], 'Suboptimal Reject') + ['Optimal Accept', 'SchedTune Accept', 'SchedTune Reject'], + 'Suboptimal Reject') def _sanitize_SchedOverutilized(self): """ Add a column with overutilized status duration. """ @@ -657,21 +664,21 @@ class Trace(object): return df = self._dfg_trace_event('cpu_frequency') clusters = self.platform['clusters'] - for c, cpus in clusters.iteritems(): + for _, cpus in clusters.iteritems(): cluster_df = df[df.cpu.isin(cpus)] for chunk in self._chunker(cluster_df, len(cpus)): f = chunk.iloc[0].frequency if any(chunk.frequency != f): - logging.warn('Cluster Frequency is not coherent! '\ - 'Failure in [cpu_frequency] events at:') + logging.warn('Cluster Frequency is not coherent! ' + 'Failure in [cpu_frequency] events at:') logging.warn(chunk) self.freq_coherency = False return logging.info("Platform clusters verified to be Frequency coherent") -################################################################################ +############################################################################### # Utility Methods -################################################################################ +############################################################################### def integrate_square_wave(self, sq_wave): """ @@ -716,11 +723,14 @@ class Trace(object): frames[int(cpu)] = pd.DataFrame.from_dict(data, orient='index') # Build and keep track of the DataFrame - self._functions_stats_df = pd.concat(frames.values(), keys=frames.keys()) + self._functions_stats_df = pd.concat(frames.values(), + keys=frames.keys()) return len(self._functions_stats_df) > 0 -# A DataFrame collector exposed to Trace's clients + class TraceData: + """ A DataFrame collector exposed to Trace's clients """ pass +# vim :set tabstop=4 shiftwidth=4 expandtab -- GitLab From d1ac554269d81538cd63fc1f773b08b94efd9e11 Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Fri, 22 Jul 2016 16:42:04 +0100 Subject: [PATCH 23/24] ipynb: replace TraceAnalysis in notebooks according to refactoring Signed-off-by: Michele Di Giorgio --- ipynb/android/Android_Workloads.ipynb | 5 +- ipynb/android/benchmarks/Android_PCMark.ipynb | 7 +- .../workloads/Android_Recents_Fling.ipynb | 7 +- ipynb/android/workloads/Android_YouTube.ipynb | 5 +- .../TraceAnalysis_FunctionsProfiling.ipynb | 250 +++--- ipynb/sched_dvfs/smoke_test.ipynb | 10 +- ipynb/sched_tune/stune_juno_rampL.ipynb | 24 +- .../stune_juno_taskonly_rampL.ipynb | 14 +- ipynb/sched_tune/stune_oak_rampL.ipynb | 16 +- .../ThermalSensorCharacterisation.ipynb | 5 +- ipynb/trappy/example_custom_events.ipynb | 3 +- ipynb/tutorial/00_LisaInANutshell.ipynb | 20 +- ipynb/tutorial/06_TraceAnalysis.ipynb | 755 ++++++++++++++---- .../UseCaseExamples_SchedTuneAnalysis.ipynb | 95 +-- 14 files changed, 793 insertions(+), 423 deletions(-) diff --git a/ipynb/android/Android_Workloads.ipynb b/ipynb/android/Android_Workloads.ipynb index b6963eb9e..d09902473 100644 --- a/ipynb/android/Android_Workloads.ipynb +++ b/ipynb/android/Android_Workloads.ipynb @@ -60,10 +60,11 @@ "\n", "# Support for trace events analysis\n", "from trace import Trace\n", - "from trace_analysis import TraceAnalysis\n", "\n", "# Suport for FTrace events parsing and visualization\n", - "import trappy" + "import trappy\n", + "\n", + "import datetime" ] }, { diff --git a/ipynb/android/benchmarks/Android_PCMark.ipynb b/ipynb/android/benchmarks/Android_PCMark.ipynb index fc39e0f28..e8f432fd1 100644 --- a/ipynb/android/benchmarks/Android_PCMark.ipynb +++ b/ipynb/android/benchmarks/Android_PCMark.ipynb @@ -70,7 +70,6 @@ "\n", "# Support for trace events analysis\n", "from trace import Trace\n", - "from trace_analysis import TraceAnalysis\n", "\n", "# Suport for FTrace events parsing and visualization\n", "import trappy" @@ -515,6 +514,12 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" + }, + "toc": { + "toc_cell": false, + "toc_number_sections": true, + "toc_threshold": 6, + "toc_window_display": false } }, "nbformat": 4, diff --git a/ipynb/android/workloads/Android_Recents_Fling.ipynb b/ipynb/android/workloads/Android_Recents_Fling.ipynb index 9781e566e..c83a3f30b 100644 --- a/ipynb/android/workloads/Android_Recents_Fling.ipynb +++ b/ipynb/android/workloads/Android_Recents_Fling.ipynb @@ -68,7 +68,6 @@ "\n", "# Support for trace events analysis\n", "from trace import Trace\n", - "from trace_analysis import TraceAnalysis\n", "\n", "# Suport for FTrace events parsing and visualization\n", "import trappy" @@ -403,7 +402,6 @@ " # Parse trace\n", " tr = Trace(te.platform, exp_dir,\n", " events=my_conf['ftrace']['events'])\n", - " ta = TraceAnalysis(tr, te.platform)\n", " \n", " # return all the experiment data\n", " return {\n", @@ -411,8 +409,7 @@ " 'framestats_file' : framestats_file,\n", " 'trace_file' : trace_file,\n", " 'ftrace' : tr.ftrace,\n", - " 'trace' : tr,\n", - " 'ta' : ta\n", + " 'trace' : tr\n", " }" ] }, @@ -590,7 +587,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11+" + "version": "2.7.6" }, "toc": { "toc_cell": false, diff --git a/ipynb/android/workloads/Android_YouTube.ipynb b/ipynb/android/workloads/Android_YouTube.ipynb index 1f0b6c761..7a3c27a5f 100644 --- a/ipynb/android/workloads/Android_YouTube.ipynb +++ b/ipynb/android/workloads/Android_YouTube.ipynb @@ -67,7 +67,6 @@ "\n", "# Support for trace events analysis\n", "from trace import Trace\n", - "from trace_analysis import TraceAnalysis\n", "\n", "# Suport for FTrace events parsing and visualization\n", "import trappy" @@ -393,15 +392,13 @@ " # Parse trace\n", " tr = Trace(te.platform, trace_file,\n", " events=my_tests_conf['ftrace']['events'])\n", - " ta = TraceAnalysis(tr, te.platform)\n", "\n", " # return all the experiment data\n", " return {\n", " 'dir' : exp_dir,\n", " 'framestats_file' : framestats_file,\n", " 'trace' : trace_file,\n", - " 'ftrace' : tr.ftrace,\n", - " 'ta' : ta\n", + " 'ftrace' : tr.ftrace\n", " }" ] }, diff --git a/ipynb/examples/trace_analysis/TraceAnalysis_FunctionsProfiling.ipynb b/ipynb/examples/trace_analysis/TraceAnalysis_FunctionsProfiling.ipynb index 86f562cbe..860bb1cd2 100644 --- a/ipynb/examples/trace_analysis/TraceAnalysis_FunctionsProfiling.ipynb +++ b/ipynb/examples/trace_analysis/TraceAnalysis_FunctionsProfiling.ipynb @@ -66,8 +66,7 @@ "from wlgen import RTA, Ramp\n", "\n", "# Support for trace events analysis\n", - "from trace import Trace\n", - "from trace_analysis import TraceAnalysis" + "from trace import Trace" ] }, { @@ -353,16 +352,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "03:35:53 INFO : Parsing FTrace format...\n", - "03:35:54 INFO : Trace contains onlt functions stats\n", - "03:35:54 INFO : Collected events spans a 0.000 [s] time interval\n", - "03:35:54 INFO : Set plots time range to (0.000000, 0.000000)[s]\n" + "03:21:10 INFO : Parsing FTrace format...\n", + "03:21:10 INFO : Trace contains only functions stats\n", + "03:21:10 INFO : Collected events spans a 0.000 [s] time interval\n", + "03:21:10 INFO : Set plots time range to (0.000000, 0.000000)[s]\n", + "03:21:10 INFO : Registering trace analysis modules:\n", + "03:21:10 INFO : frequency\n", + "03:21:10 INFO : functions\n", + "03:21:10 INFO : tasks\n", + "03:21:10 INFO : eas\n", + "03:21:10 INFO : status\n", + "03:21:10 INFO : cpus\n" ] } ], "source": [ - "trace = Trace(platform, res_dir, events=[])\n", - "ta = TraceAnalysis(trace)" + "trace = Trace(platform, res_dir, events=[])" ] }, { @@ -398,127 +403,127 @@ " \n", " 0\n", " dequeue_task_fair\n", - " 534\n", - " 3.139\n", - " 1676.40\n", - " 4.969\n", + " 538\n", + " 3.372\n", + " 1814.54\n", + " 5.544\n", " \n", " \n", " enqueue_task_fair\n", - " 791\n", - " 3.289\n", - " 2601.82\n", - " 2.528\n", + " 571\n", + " 3.214\n", + " 1835.56\n", + " 2.027\n", " \n", " \n", " 1\n", " dequeue_task_fair\n", - " 96\n", - " 3.960\n", - " 380.24\n", - " 3.462\n", + " 12\n", + " 3.501\n", + " 42.02\n", + " 1.593\n", " \n", " \n", " enqueue_task_fair\n", - " 88\n", - " 2.929\n", - " 257.80\n", - " 1.382\n", + " 17\n", + " 3.076\n", + " 52.30\n", + " 0.593\n", " \n", " \n", " 2\n", " dequeue_task_fair\n", - " 191\n", - " 2.775\n", - " 530.20\n", - " 2.693\n", + " 1160\n", + " 2.469\n", + " 2864.78\n", + " 2.218\n", " \n", " \n", " enqueue_task_fair\n", - " 194\n", - " 2.400\n", - " 465.62\n", - " 1.237\n", + " 1164\n", + " 2.177\n", + " 2535.18\n", + " 0.999\n", " \n", " \n", " 3\n", " dequeue_task_fair\n", - " 282\n", - " 5.181\n", - " 1461.24\n", - " 9.018\n", + " 304\n", + " 2.362\n", + " 718.34\n", + " 3.015\n", " \n", " \n", " enqueue_task_fair\n", - " 78\n", - " 3.766\n", - " 293.82\n", - " 4.445\n", + " 279\n", + " 2.538\n", + " 708.18\n", + " 1.082\n", " \n", " \n", " 4\n", " dequeue_task_fair\n", - " 55\n", - " 3.101\n", - " 170.60\n", - " 2.611\n", + " 199\n", + " 2.646\n", + " 526.58\n", + " 2.827\n", " \n", " \n", " enqueue_task_fair\n", - " 32\n", - " 3.203\n", - " 102.52\n", - " 2.020\n", + " 215\n", + " 2.571\n", + " 552.78\n", + " 0.903\n", " \n", " \n", " 5\n", " dequeue_task_fair\n", - " 1145\n", - " 5.705\n", - " 6532.86\n", - " 12.424\n", + " 88\n", + " 2.407\n", + " 211.82\n", + " 2.773\n", " \n", " \n", " enqueue_task_fair\n", - " 1127\n", - " 5.332\n", - " 6009.62\n", - " 7.407\n", + " 59\n", + " 2.774\n", + " 163.70\n", + " 1.491\n", " \n", " \n", "\n", "" ], "text/plain": [ - " hits avg time s_2\n", - "0 dequeue_task_fair 534 3.139 1676.40 4.969\n", - " enqueue_task_fair 791 3.289 2601.82 2.528\n", - "1 dequeue_task_fair 96 3.960 380.24 3.462\n", - " enqueue_task_fair 88 2.929 257.80 1.382\n", - "2 dequeue_task_fair 191 2.775 530.20 2.693\n", - " enqueue_task_fair 194 2.400 465.62 1.237\n", - "3 dequeue_task_fair 282 5.181 1461.24 9.018\n", - " enqueue_task_fair 78 3.766 293.82 4.445\n", - "4 dequeue_task_fair 55 3.101 170.60 2.611\n", - " enqueue_task_fair 32 3.203 102.52 2.020\n", - "5 dequeue_task_fair 1145 5.705 6532.86 12.424\n", - " enqueue_task_fair 1127 5.332 6009.62 7.407" + " hits avg time s_2\n", + "0 dequeue_task_fair 538 3.372 1814.54 5.544\n", + " enqueue_task_fair 571 3.214 1835.56 2.027\n", + "1 dequeue_task_fair 12 3.501 42.02 1.593\n", + " enqueue_task_fair 17 3.076 52.30 0.593\n", + "2 dequeue_task_fair 1160 2.469 2864.78 2.218\n", + " enqueue_task_fair 1164 2.177 2535.18 0.999\n", + "3 dequeue_task_fair 304 2.362 718.34 3.015\n", + " enqueue_task_fair 279 2.538 708.18 1.082\n", + "4 dequeue_task_fair 199 2.646 526.58 2.827\n", + " enqueue_task_fair 215 2.571 552.78 0.903\n", + "5 dequeue_task_fair 88 2.407 211.82 2.773\n", + " enqueue_task_fair 59 2.774 163.70 1.491" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the DataFrame for the specified list of kernel functions\n", - "df = trace.functions_stats_df(['enqueue_task_fair', 'dequeue_task_fair'])\n", + "df = trace.data_frame.functions_stats(['enqueue_task_fair', 'dequeue_task_fair'])\n", "df" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -542,73 +547,73 @@ " \n", " 0\n", " select_task_rq_fair\n", - " 810\n", - " 9.760\n", - " 7906.32\n", - " 52.352\n", + " 783\n", + " 1.617\n", + " 1266.88\n", + " 0.896\n", " \n", " \n", " 1\n", " select_task_rq_fair\n", - " 64\n", - " 4.512\n", - " 288.80\n", - " 16.507\n", + " 17\n", + " 0.782\n", + " 13.30\n", + " 0.031\n", " \n", " \n", " 2\n", " select_task_rq_fair\n", - " 164\n", - " 3.560\n", - " 583.90\n", - " 13.352\n", + " 777\n", + " 1.042\n", + " 810.16\n", + " 3.248\n", " \n", " \n", " 3\n", " select_task_rq_fair\n", - " 71\n", - " 10.311\n", - " 732.12\n", - " 65.864\n", + " 259\n", + " 1.575\n", + " 408.12\n", + " 8.924\n", " \n", " \n", " 4\n", " select_task_rq_fair\n", - " 40\n", - " 4.777\n", - " 191.10\n", - " 29.821\n", + " 186\n", + " 1.837\n", + " 341.72\n", + " 4.420\n", " \n", " \n", " 5\n", " select_task_rq_fair\n", - " 1015\n", - " 5.306\n", - " 5386.04\n", - " 17.775\n", + " 51\n", + " 2.557\n", + " 130.42\n", + " 13.227\n", " \n", " \n", "\n", "" ], "text/plain": [ - " hits avg time s_2\n", - "0 select_task_rq_fair 810 9.760 7906.32 52.352\n", - "1 select_task_rq_fair 64 4.512 288.80 16.507\n", - "2 select_task_rq_fair 164 3.560 583.90 13.352\n", - "3 select_task_rq_fair 71 10.311 732.12 65.864\n", - "4 select_task_rq_fair 40 4.777 191.10 29.821\n", - "5 select_task_rq_fair 1015 5.306 5386.04 17.775" + " hits avg time s_2\n", + "0 select_task_rq_fair 783 1.617 1266.88 0.896\n", + "1 select_task_rq_fair 17 0.782 13.30 0.031\n", + "2 select_task_rq_fair 777 1.042 810.16 3.248\n", + "3 select_task_rq_fair 259 1.575 408.12 8.924\n", + "4 select_task_rq_fair 186 1.837 341.72 4.420\n", + "5 select_task_rq_fair 51 2.557 130.42 13.227" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the DataFrame for the single specified kernel function\n", - "df = trace.functions_stats_df('select_task_rq_fair')\n", + "df = trace.data_frame.functions_stats('select_task_rq_fair')\n", "df" ] }, @@ -628,9 +633,9 @@ "outputs": [ { "data": { - "image/png": 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cccet8jKAxYsXp6enJ88880x222237LjjjknS+xkv+2x+8IMf9G5z3nnnZeLE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\n27Ytx44dq1TG19cXPz8/69mFf//735w9e5bGjRtz+vTpauPw9fWtUOb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IiIiIiIjIjDDRJyIiIiIiIjIjTPSJiIiIiIiIzAgTfSKi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\nTBw8eBDz58+Xy1Z/YEdWVhZWrlyJU6dO1fNtkLnIzc3FlStXEBgYCG9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CQ4vlEcBzZXWeA7ZooXxRUS5JkiRJ9OzZk9WrV/Poo4/S0NDAVlttxbbbbsu/\n/uu/8u1vf5sRI0bQu3dvpk2bxi9/+cu33Re/dOlS/vu//5sf/ehH9OnThyFDhnDaaadx/fXXA/Dz\nn/+cr3/96+y2224AbLfddmy11Vbt6mNLU+y322479t13X3r37s3gwYM5/fTT+f3vf7/eY4LSLQIr\nVqzggAMOYPTo0fz7v/97m+7T33vvvZkwYQIAL7zwAnPmzOGCCy6gd+/ejB07lvHjx3foVoDDDjuM\nYcOGAXDEEUcwevRo7r///la3P/vssxkwYACbbrppu9tqSa8Nshe1WURsAowHvt78u8zMiNhgt9us\nveoFMG7cOMaNG7ehdi1JkiRpI7X99ttz8cUXU1dXx6OPPsoBBxzAD3/4Q55++mn+7u/+jh493hrr\n7dWr19tGmRcuXEhDQwPDhw9vKmtsbGwK8s899xzbbbfdBu/30qVLOfXUU7nnnnuor6+nsbGRQYMG\ntXpMF110EcOHDyczue+++1izZk3ThYi2GDlyZNPy4sWLGThwIH369GkqGzVqFM8++2y7j2PGjBn8\n6Ec/4umnnwZg5cqVLFu2rNXtt9xyyzbtd9asWcyaNesdtzPkd72DgAcz88VifWlEDMvMJcVU/BeK\n8kVA+a89ktII/qJiubx8UUsNlYd8SZIkSd3H5MmTmTx5MvX19Zxwwgl8/etfZ6uttuLKK69kr732\netv2awMplELnpptuyrJly9a5IFD+/YIFC1pst61Pum9pu3POOYeePXsyd+5cBgwYwM0338wpp5yy\n3mOaMWMGAPvvvz8777wz++67L7NmzeL973//O7Zf3ofhw4fz8ssvs2rVKvr27QuULna0dPzrs3Dh\nQqZOncpdd93FXnvtRUTwkY98ZL0zAtp6zpoP3J5//vktbud0/a43mbem6gPMBI4ulo8Gbi4rnxQR\nm0TENsBoYHZmLgFWRMSexYP4ppTVkSRJktTNzZ8/n7vuuovVq1ez6aabstlmm9GrVy/+4R/+gXPO\nOYdnnnkGgBdffJGZM2e+rf7w4cPZf//9OeOMM5pG1J988knuvvtuAL70pS9x4YUX8tBDD5GZLFiw\noGmfQ4cO5cknn3zHPg4ZMoQePXqss+3KlSvp168ftbW1LFq0iB/84AfrPaaePXuus8+vfe1rHHnk\nkey7777rHTmHt98uMGrUKD760Y8ybdo0GhoauOeee7jlllva/Xq+V199lYhg8ODBNDY2cuWVVzJ3\n7tx27eMlj/vaAAAgAElEQVTdMuR3oYjoR+mhezeVFX8P+ExEzAf2KdbJzHnADcA84L+Bk/Ktf4kn\nAT8HnqD0AL/buuYIJEmSJK1PdOKnrVavXs3ZZ5/NkCFDGD58OH/961/5p3/6J0499VQmTJjA/vvv\nT21tLXvttRezZ89uqlceaGfMmMEbb7zBmDFjGDRoEIcffjhLliwBSvecn3vuuRx55JHU1tZyyCGH\nND2l/+yzz+bb3/42AwcO5KKLLmq1j3379uXcc8/l4x//OIMGDWL27NlMmzaNhx56iP79+zN+/HgO\nPfTQpj61dkxr+712u2984xscfPDB7Lfffrzyyiuttt98JB/g2muv5f7772fQoEF861vf4otf/GKb\n7skv38+YMWP46le/yl577cWwYcOYO3cun/jEJ1ptt70XEdoiNsQ7BbXxiYj0t+0cEdHp7ykNNsz7\nPiVJktQ52vs+eL33nH/++SxYsICrr766ov1o7d9aUf62qwSO5EuSJEmS1Mx79SKOIV+SJEmStMFd\nc8011NTUvO3zoQ99qEvaP+igg1ps/3vf+16b6q+dWn/ttddW9Djay+n6Vcrp+p3H6fqSJElyur66\nitP1JUmSJEnqpgz5kiRJkiRVCUO+JEmSJElVolelOyBJkiRJ70Wd8Y5z6d0y5EuSJElSO/nQPW2s\nnK4vSZIkSVKVMORLkiRJklQlDPmSJEmSJFUJQ74kSZIkSVXCkC9JkiRJUpUw5EuSJEmSVCUM+ZIk\nSZIkVQlDviRJkiRJVcKQL0mSJElSlTDkS5IkSZJUJQz5kiRJkiRVCUO+JEmSJElVwpAvSZIkSVKV\n6FXpDkiSJEmSul5EdEk7mdkl7ajEkC9JkiRJ3VZnB/CuuZCgtzhdX5IkSZKkKmHIlyRJkiSpShjy\nJUmSJEmqEoZ8SZIkSZKqhCFfkiRJkqQqYciXJEmSJKlKGPIlSZIkSaoShnxJkiRJkqqEIV+SJEmS\npCphyJckSZIkqUoY8iVJkiRJqhKGfEmSJEmSqoQhX5IkSZKkKmHIlyRJkiSpShjyJUmSJEmqEoZ8\nSZIkSZKqhCFfkiRJkqQqYciXJEmSJKlKGPIlSZIkSaoShnxJkiRJkqqEIV+SJEmSpCphyJckSZIk\nqUoY8iVJkiRJqhKGfEmSJEmSqoQhvwtFxICI+GVEPBYR8yJiz4gYFBF3RMT8iLg9IgaUbX92RDwR\nEY9HxP5l5btFxJ+L7y6pzNFIkiRJkjY2hvyudQnwm8z8G2Bn4HHgLOCOzNwBuLNYJyLGABOBMcCB\nwGUREcV+LgeOy8zRwOiIOLBrD0OSJEmStDEy5HeRiOgPjM3MfwfIzDWZuRyYAEwvNpsOHFwsfx64\nLjMbMvNpYAGwZ0QMB2oyc3ax3YyyOpIkSZKkbqxXpTvQjWwDvBgRVwK7AA8CpwFDM3Npsc1SYGix\nPAK4r6z+c8AWQEOxvNaiolySJEmSNjpvTUjuPJnZ6W28Vxjyu04vYFfg5Mx8ICIuppiav1ZmZkRs\nsH+ddXV1Tcvjxo1j3LhxG2rXkiRJktQ2de/x/W8kZs2axaxZs95xu/CKR9eIiGHAvZm5TbH+CeBs\nYFvg05m5pJiK/7vM3CkizgLIzO8V298GTAMWFtv8TVE+GfhUZv5Ds/bS37ZzRASdfWYDr0ZKkiSp\nc5VG2LvgL9u6Tm6irnv+7RwRZObbpkl4T34XycwlwLMRsUNRtB/wKPBr4Oii7Gjg5mJ5JjApIjaJ\niG2A0cDsYj8riifzBzClrI4kSZIkqRtzun7XOgW4JiI2AZ4EjgV6AjdExHHA08ARAJk5LyJuAOYB\na4CTyobmTwKuAvpQelr/bV15EJIkSZKkjZPT9auU0/U7j9P1JUmSVA2crv/e5nR9SZIkSZKqnCFf\nkiRJkqQqYciXJEmSJKlKGPIlSZIkSaoShnxJkiRJkqqEIV+SJEmSpCphyJckSZIkqUoY8iVJkiRJ\nqhKGfEmSJEmSqoQhX5IkSZKkKmHIlyRJkiSpShjyJUmSJEmqEoZ8SZIkSZKqhCFfkiRJkqQqYciX\nJEmSJKlKGPIlSZIkSaoShnxJkiRJkqqEIV+SJEmSpCphyJckSZIkqUoY8iVJkiRJqhKGfEmSJEmS\nqoQhX5IkSZKkKmHIlyRJkiSpShjyJUmSJEmqEoZ8SZIkSZKqhCFfkiRJkqQqYciXJEmSJKlKGPIl\nSZIkSaoShnxJkiRJkqpEr0p3QJIkSZKkdyMiOr2NzOz0NjYEQ74kSZIk6T2ts+N3519C2HCcri9J\nkiRJUpUw5EuSJEmSVCUM+ZIkSZIkVQlDviRJkiRJVcKQL0mSJElSlTDkS5IkSZJUJQz5kiRJkiRV\nCUO+JEmSJElVwpAvSZIkSVKVMORLkiRJklQlDPmSJEmSJFUJQ74kSZIkSVXCkC9JkiRJUpUw5EuS\nJEmSVCUM+ZIkSZIkVQlDviRJkiRJVcKQ34Ui4umI+FNEPBwRs4uyQRFxR0TMj4jbI2JA2fZnR8QT\nEfF4ROxfVr5bRPy5+O6SShyLJEmSJGnjY8jvWgmMy8yPZOYeRdlZwB2ZuQNwZ7FORIwBJgJjgAOB\nyyIiijqXA8dl5mhgdEQc2JUHIUmSJEnaOBnyu140W58ATC+WpwMHF8ufB67LzIbMfBpYAOwZEcOB\nmsycXWw3o6yOJEmSJKkbM+R3rQT+JyLmRMTxRdnQzFxaLC8FhhbLI4Dnyuo+B2zRQvmiolySJEmS\n1M31qnQHupmPZ+bzETEEuCMiHi//MjMzInJDNVZXV9e0PG7cOMaNG7ehdi1JkiRJ6kKzZs1i1qxZ\n77hdZG6wTKl2iIhpwErgeEr36S8ppuL/LjN3ioizADLze8X2twHTgIXFNn9TlE8GPpWZ/9Bs/+lv\n2zkigs4+swH4+0mSJKkzlR751QV/2dZ1chN1XXIUG93f5xFBZja/Hdzp+l0lIvpGRE2x3A/YH/gz\nMBM4utjsaODmYnkmMCkiNomIbYDRwOzMXAKsiIg9iwfxTSmrI0mSJEnqxpyu33WGAr8qHpDfC7gm\nM2+PiDnADRFxHPA0cARAZs6LiBuAecAa4KSyofmTgKuAPsBvMvO2rjwQSZIkSdLGyen6Vcrp+p3H\n6fqSJEmqBk7Xb7uN8e9zp+tLkiRJklTlDPmSJEmSJFUJQ74kSZIkSVXCB++1Q0QcSul2j7fd91Dm\ntcz8TRd1SZIkSZKkJob89vkZpVfbtSaAsYAhX5IkSZLU5Qz57XNbZh67vg0i4pqu6owkSZIkSeW8\nJ78dMvMLG2IbSZIkSZI6gyG/AyLiiIioLZbPi4hfRcSule6XJEmSJKl7M+R3zHmZuSIiPgHsC/wb\ncHmF+yRJkiRJ6uYM+R3zZvG/fwtckZm3AJtUsD+SJEmSJBnyO2hRRPwMmAjcGhGb4bmUJEmSJFWY\nwbRjjgB+C+yfma8AA4GvVbZLkiRJkqTuzlfodczmwBwgI2KrouzxCvZHkiRJkiRDfgf9BshieTNg\nG+D/gA9UrEeSJEmSpG7PkN8BmfnB8vXi9XlfrlB3JEmSJEkCvCd/g8jMh4A9K90PSZIkSVL35kh+\nB0TEV8tWewC7Aosq1B1JkiRJkgBDfkfV8NY9+WuAW4D/rFx3JEmSJEky5HdIZtZVug+SJEmSJDXn\nPfntEBF1G2IbSZIkSZI6gyP57fOliFgBxHq2mQzUdU13JEmSJEl6iyG/fX5O6X789flZV3REkiRJ\nkqTmDPnt4L34kiRJkqSNmffkS5IkSZJUJQz5kiRJkiRVCUO+JEmSJElVwpDfARGxY0TcGRGPFus7\nR8Q3Kt0vSZIkSVL3ZsjvmCuAc4A3ivU/U3p1niRJkiRJFWPI75i+mXn/2pXMTKChgv2RJEmSJMmQ\n30EvRsT2a1ci4jDg+Qr2R5IkSZIkelW6A+9RJwM/A3aKiMXAX4AvVLZLkiRJkqTuzpDfAZn5JLBv\nRPQDemRmfaX7JEmSJEmSIb8DImIg8EVga6BXREDp1vyvVLJfkiRJkqTuzZDfMb8B7gX+BDQCAWRF\neyRJkiRJ6vYM+R2zaWaeUelOSJIkSZJUzqfrd8y1ETE1IoZHxKC1n0p3SpIkSZLUvTmS3zGvAz8A\nzqU0XR9K0/W3rViPJEmSJEndniG/Y74KbJeZf610RyRJkiRJWsvp+h3zBPBapTshSZIkSVI5R/I7\nZhXwx4j4HbC6KPMVepIkSZKkijLkd8zNxaecr9CTJEmSJFWUIb8DMvOqSvdBkiRJkqTmDPntEBE3\nZubhEfHnFr7OzNy5yzslSZIkSVLBkN8+FxX/O76ivZAkSZIkqQWG/Pa5DPhIZj5d6Y5IkiRJktSc\nr9CTJEmSJKlKOJLfPltExKVAtPBdm16hFxE9gTnAc5k5PiIGAb8ARgFPA0dk5ivFtmcDfw+8CXwl\nM28vyncDrgI2A36Tmae+2wOTJEmSJL33OZLfPq8BDxafOWWftWVtcSowj7deuXcWcEdm7gDcWawT\nEWOAicAY4EDgsohYe3HhcuC4zBwNjI6IA9/lcUmSJEmSqoAj+e3zUmZO72jliBgJfBb4DnBGUTwB\n+FSxPB2YRSnofx64LjMbgKcjYgGwZ0QsBGoyc3ZRZwZwMHBbR/slSZIkSaoOjuS3z+p3Wf9HwNeA\nxrKyoZm5tFheCgwtlkcAz5Vt9xywRQvli4pySZIkSVI3Z8hvh8z8WEfrRsTfAi9k5sO0fE8/mZm8\nNY1fkiRJkqR2cbp+19kbmBARn6X0wLzaiLgaWBoRwzJzSUQMB14otl8EbFlWfySlEfxFxXJ5+aKW\nGqyrq2taHjduHOPGjdswRyJJkiRJ6lKzZs1i1qxZ77hdlAaP1ZUi4lPAmcXT9f8ZWJaZ34+Is4AB\nmXlW8eC9a4E9KE3H/x9g+8zMiLgf+AowG7gVuDQzb2vWRvrbdo6I6PTpFgH4+0mSJKkzlZ7r3QV/\n2dZ1chN1XXIUG93f5xFBZr5tlrgj+R1UvApvKGXnMDOfaccu1v4L+R5wQ0QcR/EKvWJf8yLiBkpP\n4l8DnFSW2k+i9Aq9PpReoedD9yRJkiRJjuR3REScAkyjNLX+zbXlmfmhinWqGUfyO48j+ZIkSaoG\njuS33cb497kj+RvWacCOmbms0h2RJEmSJGktn67fMc8AKyrdCUmSJEmSyjmS3zF/AX4XEbcCbxRl\nmZkXVbBPkiRJkqRuzpDfMc8Un02KT1fczCJJkiRJ0noZ8jsgM+sAIqKmWK+vaIckSZIkScJ78jsk\nIj4UEQ8DjwKPRsSDEfHBSvdLkiRJktS9GfI75mfAGZm5VWZuBXy1KJMkSZIkqWIM+R3TNzN/t3Yl\nM2cB/SrXHUmSJEmSvCe/o/4SEecBV1N66N4XgKcq2yVJkiRJUnfnSH7H/D3wfuAm4D+BIUWZJEmS\nJEkV40h+B2TmS8Aple6HJEmSJEnlDPntEBGXZOapEfHrFr7OzJzQ5Z2SJEmSJKlgyG+fGcX//rCF\n77IrOyJJkiRJUnOG/HbIzAeLxQ9n5sXl30XEacDvu75XkiRJkiSV+OC9jjm6hbJjuroTkiRJkiSV\ncyS/HSJiMnAksE2z+/JrgGWV6ZUkSZIkSSWG/Pb5X+B5Sq/MuxCIorweeKRSnZIkSZIkCQz57ZKZ\nC4GFwMcq3RdJkiRJkpoz5HdARNSXrW4C9AZWZmZthbokSZIkSZIhvyMys2btckT0ACbg6L4kSZIk\nqcJ8uv67lJmNmXkzcGCl+yJJkiRJ6t4cye+AiDi0bLUHsBvwWoW6I0mSJEkSYMjvqPFAFstrgKeB\nz1esN5IkSZIkYcjvkMw8ptJ9kCRJkiSpOe/J74CImB4RA8rWB0bEv1eyT5IkSZIkGfI7ZpfMfGXt\nSma+DOxawf5IkiRJkmTI76CIiEFlK4OAnhXsjyRJkiRJ3pPfQT8E7o2IG4AADge+U9kuSZIkSZK6\nO0N+B2TmjIh4EPh0UfR3mTmvkn2SJEmSJMnp+h03CHg1M38CvBgR21S6Q5IkSZKk7s2Q3wERUQf8\nI3B2UbQJ8B8V65AkSZIkSRjyO+rvgM8DrwJk5iKgpqI9kiRJkiR1e4b8jlmdmY1rVyKiXyU7I0mS\nJEkSGPI76saI+FdgQERMBe4Efl7hPkmSJEmSujmfrt8BmfmDiNgfqAd2AM7LzDsq3C1JkiRJUjdn\nyO+AiDguM/8NuL1Y7xUR0zLz/Ap3TZIkSZLUjTldv2P2i4jfRMSIiPggcC9QW+lOSZIkSZK6N0fy\nOyAzJ0fEJOBPlJ6w/4XMvKfC3ZIkSZIkdXOO5HdAROwAfAW4CXgGOMon7EuSJEmSKs2Q3zEzgW9m\n5lTgU8ATwAOV7ZIkSZIkqbtzun7H7JmZywEysxH4YUT8usJ9kiRJkiR1c47kt0NE/CNAZi6PiMOb\nfX1M1/dIkiRJkqS3GPLbZ3LZ8jnNvjuoKzsiSZIkSVJzhnxJkiRJkqqEIV+SJEmSpCrhg/faZ+eI\nqC+W+5QtA/SpRIckSZIkSVrLkN8Omdmz0n2QJEmSJKk1TtfvIhGxWUTcHxF/jIh5EfFPRfmgiLgj\nIuZHxO0RMaCsztkR8UREPB4R+5eV7xYRfy6+u6QSxyNJkiRJ2vgY8rtIZr4OfDozPwzsDHw6Ij4B\nnAXckZk7AHcW60TEGGAiMAY4ELgsIqLY3eXAcZk5GhgdEQd27dFIkiRJkjZGhvwulJmrisVNgJ7A\ny8AEYHpRPh04uFj+PHBdZjZk5tPAAmDPiBgO1GTm7GK7GWV1JEmSJEndmCG/C0VEj4j4I7AU+F1m\nPgoMzcylxSZLgaHF8gjgubLqzwFbtFC+qCiXJEmSJHVzPnivC2VmI/DhiOgP/DYiPt3s+4yI3FDt\n1dXVNS2PGzeOcePGbahdS5IkSZK60KxZs5g1a9Y7bheZGyxTqh0i4jzgNeBLwLjMXFJMxf9dZu4U\nEWcBZOb3iu1vA6YBC4tt/qYonwx8KjP/odn+09+2c0QEnX1mA/D3kyRJUmcqPfKrC/6yrevkJuq6\n5Cg2ur/PI4LMjOblTtfvIhExeO2T8yOiD/AZ4GFgJnB0sdnRwM3F8kxgUkRsEhHbAKOB2Zm5BFgR\nEXsWD+KbUlZHkiRJktSNOV2/6wwHpkdED0oXV67OzDsj4mHghog4DngaOAIgM+dFxA3APGANcFLZ\n0PxJwFVAH+A3mXlblx6JJEmSJGmj5HT9KuV0/c7jdH1JkiRVA6frt93G+Pe50/UlSZIkSapyhnxJ\nkiRJkqqEIV+SJEmSpCphyJckSZIkqUoY8iVJkiRJqhKGfEmSJEmSqoQhX5IkSZKkKmHIlyRJkiSp\nShjyJUmSJEmqEoZ8SZIkSZKqhCFfkiRJkqQqYciXJEmSJKlKGPIlSZIkSaoShnxJkiRJkqqEIV+S\nJEmSpCphyJckSZIkqUoY8iVJkiRJqhKGfEmSJEmSqkSvSndA2pAiotJdkCRJkqSKMeSr+tS9x/cv\nSZIkSR3kdH1JkiRJkqqEIV+SJEmSpCphyJckSZIkqUoY8iVJkiRJqhKGfEmSJEmSqoQhX5IkSZKk\nKmHIlyRJkiSpShjyJUmSJEmqEoZ8SZIkSZKqhCFfkiRJkqQqYciXJEmSJKlKGPIlSZIkSaoShnxJ\nkiRJkqqEIV+SJEmSpCphyJckSZIkqUoY8iVJkiRJqhKGfEmSJEmSqoQhX5IkSZKkKmHIlyRJkiSp\nShjyJUmSJEmqEoZ8SZIkSZKqhCFfkiRJkqQqYciXJEmSJKlKGPIlSZIkSaoShnzp/7d372FRVfv/\nwN/b2zdUvGSkxGgQAjLAXABRNAgVL+UtLxmYlQrn/KqTWadMPaFf/Xa0TM30dDmdTmpqeSnLS492\ntNREDYmLYVhKOSAY3lJULjZcPr8/BvYBmUHUAZzx/Xqengf27LX2Gvm09vrsvfbaREREREREToJJ\nPhEREREREZGTYJJPRERERERE5CSY5DcSRVG6KoqyW1GUTEVRflQU5bnK7XcqirJTUZRjiqLsUBSl\nQ7UyMxVFyVIU5WdFUQZV2x6iKMrhys+WNsX3ISIiIiIiolsPk/zGUwrgBREJANAbwF8URfEHMAPA\nThHxBfBN5e9QFEUL4FEAWgBDALyrKIpSWdd7AOJExAeAj6IoQxr3qxAREREREdGtiEl+IxGRUyJy\nqPLnQgA/AfAAMALAR5W7fQTg4cqfRwJYKyKlIpIN4BcAvRRFcQfgKiLJlfutqlaGiIiIiIiIbmNM\n8puAoiieAIwADgLoLCKnKz86DaBz5c/3AMirViwPlosCV28/WbmdiIiIiIiIbnMtmroBtxtFUdoC\n2Ahgqohc/u8MfEBERFEUsdex5syZo/4cFRWFqKgoe1VNREREREREjWjPnj3Ys2fPNfdjkt+IFEVp\nCUuCv1pENlVuPq0oShcROVU5Ff9M5faTALpWK66B5Q7+ycqfq28/ae141ZN8IiIiIiIiclxX37id\nO3eu1f04Xb+RVC6a9yGAIyLyVrWPtgB4svLnJwFsqrY9RlGUVoqieAHwAZAsIqcAXFIUpVdlnY9X\nK0NERERERES3Md7Jbzx9AUwAkKEoSnrltpkAXgewQVGUOADZAMYBgIgcURRlA4AjAMoAPCMiVVP5\nnwGwEoALgG0i8lVjfQkiIiIiIiK6dTHJbyQisg+2Z05E2ygzH8B8K9tTAQTZr3VERERERETkDDhd\nn4iIiIiIiMhJMMknIiIiIiIichJM8omIiIiIiIicBJN8IiIiIiIiIifBJJ+IiIiIiIjISTDJJyIi\nIiIiInISTPKJiIiIiIiInASTfCIiIiIiIiInwSSfiIiIiIiIyEkwySciIiIiIiJyEkzyiYiIiIiI\niJwEk3wiIiIiIiIiJ8Ekn4iIiIiIiMhJtGjqBtDtQ1GUpm4CERERERGRU2OST41MGrh+XkggIiIi\nIqLbF6frExERERERETkJJvlEREREREREToJJPhEREREREZGTYJJPRERERERE5CSY5BMRERERERE5\nCSb5RERERERERE6CST4RERERERGRk2CST0REREREROQkmOQTEREREREROQkm+UREREREREROokVT\nN4CIiIjIWSmK0uDHEJEGPwYRETkOJvlEREREDaohk/CGv4hARESOhdP1iYiIiIiIiJwEk3wiIiIi\nIiIiJ8Ekn4iIiIiIiMhJMMknIiIiIiIichJM8omIiIiIiIicBJN8IiIiIiIiIifBJJ+IiIiIiIjI\nSTDJJyIiIiIiInISTPKJiIiIiIiInASTfCIiIiIiIiInwSSfiIiIiIiIyEkwySciIiIiIiJyEkzy\niYiIiIiIiJwEk3wiIiIiIiIiJ8Ekn4iIiIiIiMhJMMknIiIiIiIichJM8omIiIiIiIicBJN8IiIi\nIiIiIifBJJ+IiIiIiIjISTDJJyIiIiIiInISTPIbiaIoyxVFOa0oyuFq2+5UFGWnoijHFEXZoShK\nh2qfzVQUJUtRlJ8VRRlUbXuIoiiHKz9b2tjfg4iIiIiIiG5dTPIbzwoAQ67aNgPAThHxBfBN5e9Q\nFEUL4FEA2soy7yqKolSWeQ9AnIj4APBRFOXqOomIiIiIiOg2xSS/kYhIIoALV20eAeCjyp8/AvBw\n5c8jAawVkVIRyQbwC4BeiqK4A3AVkeTK/VZVK0NERERERES3OSb5TauziJyu/Pk0gM6VP98DIK/a\nfnkAPKxsP1m5nYiIiIiIiAgtmroBZCEioiiK2LPOOXPmqD9HRUUhKirKntUTERERERFRI9mzZw/2\n7Nlzzf2Y5Det04qidBGRU5VT8c9Ubj8JoGu1/TSw3ME/Wflz9e0nbVVePcknIiIiIiIix3X1jdu5\nc+da3Y/T9ZvWFgBPVv78JIBN1bbHKIrSSlEULwA+AJJF5BSAS4qi9KpciO/xamWIiIiIiIjoNsc7\n+Y1EUZS1AB4AcJeiKLkAZgN4HcAGRVHiAGQDGAcAInJEUZQNAI4AKAPwjIhUTeV/BsBKAC4AtonI\nV435PYiIiIiIiOjWxSS/kYhIrI2Pom3sPx/AfCvbUwEE2bFpRERERERE5CQ4XZ+IiIiIiIjISTDJ\nJyIiIiIiInISTPKJiIiIiIiInASTfCIiIiIiIiInwSSfiIiIiIiIyEkwySciIiIiIiJyEkzyiYiI\niIiIiJwEk3wiIiIiIiIiJ8Ekn4iIiIiIiMhJMMknIiIiIiIichItmroBRERERHTrUhSlUY4jIo1y\nHCIiZ8ckn4iIiIjq1NDpd+NcRiAiuj1wuj4RERERERGRk2CST0REREREROQkmOQTEREREREROQkm\n+UREREREREROgkk+ERERERERkZNgkk9ERERERETkJPgKPSIiIifCd5oTERHd3pjkExERORm+05yI\niOj2xen6RERERERERE6Cd/KJiIgqcao7EREROTom+URERDVwsjsRERE5Lk7XJyIiIiIiInISTPKJ\niIiIiIiInASn6xMRETWyxnr2n4iIiG4/TPKJiIga2xwHrZuIiIhueZyuT0REREREROQkmOQTERER\nEREROQkm+UREREREREROgs/kE5HDaqzFy0Qa+r3pRERERET2wSSfiBxcQyfgXAWdiIiIiBwHp+sT\nEREREREROQkm+UREREREREROgtP1iYiIiBxYY61PQkREjoFJPhFRE2uMAToXDyRyYnMcvH4iIrIr\nJvlERNfQKEl4A9bNe3xEREREtw8m+URE1zLHwesnIiIiotsGF94jIiIiIiIichJM8omIiIiIiIic\nBKfrExERERFRo2msN0Jw0Vm6XTHJJyIiIiJyAM6UHDf0EbjoLN3OmOQTERERETkMpsdEVDc+k09E\nRERERETkJJjkExERERERETkJTtcnIiIiIiJVYz37T0QNg0k+ERERERH91xwHr5/oNsfp+g5KUZQh\niqL8rChKlqIo05u6PURERERERNT0mOQ7IEVRmgN4G8AQAFoAsYqi+Ddtq4iIiIiIiKipMcl3TGEA\nfneFAAUAACAASURBVBGRbBEpBbAOwMgmbhMRERERERE1MSb5jskDQG613/MqtxEREREREdFtTBGR\npm4DXSdFUcYAGCIif6r8fQKAXiIypdo+/MMSERERERE5MRGp9ToMrq7vmE4C6Frt966w3M2vgRdw\nyJ4URWFMkV0xpsieGE9kb4wpsjfGFNmbrdddcrq+Y0oB4KMoiqeiKK0APApgSxO3yeF89dVX6NGj\nB3x8fLBgwYKmbg45uMmTJ6Nz584ICgpq6qaQk8jNzUW/fv0QEBCAwMBALFu2rKmbRA7sypUr6NWr\nFwwGA7RaLWbOnNnUTSInUV5eDqPRiOHDhzd1U8gJeHp6QqfTwWg0IiwsrKmb47A4Xd9BKYryIIC3\nADQH8KGIvHbV58K/rW3l5eXw8/PD119/DQ8PD/Ts2RNr166Fvz9fUmALrz7XLTExEW3btsUTTzyB\nw4cPN3VzHAJjqm6nTp3CqVOnYDAYUFhYiJCQEGzatIn9lA2Mp2srLi5G69atUVZWhvvvvx+LFi3C\n/fff39TNumUxpurnzTffRGpqKi5fvowtW3jPqS6MqWvz8vJCamoq7rzzzqZuikOojKlat/N5J99B\nich2EfETke5XJ/h0bcnJyejevTs8PT3RsmVLxMTEYPPmzU3dLHJgERER6NixY1M3g5xIly5dYDAY\nAABt27aFv78/fvvttyZuFTmy1q1bAwDMZjPKy8s5iKablpeXh23btiE+Pp7JK9kNY+nmMcmn29LJ\nkyfRtet/lzXQaDQ4efJkE7aIiMi27OxspKeno1evXk3dFHJgFRUVMBgM6Ny5M/r16wetVtvUTSIH\n98ILL2DhwoVo1owpBdmHoiiIjo5GaGgoPvjgg6ZujsPi/5F0W7K1SAUR0a2msLAQY8eOxdKlS9G2\nbdumbg45sGbNmuHQoUPIy8vD3r17sWfPnqZuEjmwL7/8EnfffTeMRiPvvJLd7N+/H+np6di+fTve\neecdJCYmNnWTHBKTfLoteXh4IDc3V/09NzcXGo2mCVtERFRbaWkpxowZgwkTJuDhhx9u6uaQk2jf\nvj2GDh2KlJSUpm4KObADBw5gy5Yt8PLyQmxsLHbt2oUnnniiqZtFDs7d3R0A4ObmhlGjRiE5ObmJ\nW+SYmOTTbSk0NBRZWVnIzs6G2WzG+vXrMWLEiKZuFhGRSkQQFxcHrVaL559/vqmbQw7u3LlzKCgo\nAACUlJRg586dMBqNTdwqcmTz589Hbm4uTCYT1q1bh/79+2PVqlVN3SxyYMXFxbh8+TIAoKioCDt2\n7OBbi24Qk3y6LbVo0QJvv/02Bg8eDK1Wi0cffZQrVtNNiY2NRZ8+fXDs2DF07doVK1asaOomkYPb\nv38/1qxZg927d8NoNMJoNOKrr75q6maRg8rPz0f//v1hMBjQq1cvDB8+HAMGDGjqZpET4aOQdLNO\nnz6NiIgItZ8aNmwYBg0a1NTNckh8hZ6T4iv0yN742heyN8YU2RPjieyNMUX2xpgie+Mr9IiIiIiI\niIicXJ138hVF4aUmIiIiIiIioluQtTv5LepRqGFaQw2K04HI3hhTZG+MKbInxhPZG2OK7I0xRfZm\nay2MBpuuP2fOHCxevLihqre7nJwcrF279obLX8+7i6dNm4bAwEBMnz7d5j5bt27FggULbrg9dGuZ\nPHkyOnfuXGuF0H/84x/w9/dHYGAgZsyYAQD4+OOP1UW2jEYjmjdvjoyMDACA2WzGn//8Z/j5+cHf\n3x+ff/65zWOeOHECbdu2daj/D6n+rly5gl69esFgMECr1WLmzJkAgOTkZISFhcFoNKJnz574/vvv\n1TKvvfYafHx80KNHD+zYscNqvdOmTYO/vz/0ej1Gjx6Nixcv1viccXX78vT0hE6ng9FoRFhYGABg\n1qxZ0Ov1MBgMGDBgQI1Xk9oj3sgx2DrHAcDixYvRrFkznD9/Xt2WkZGB8PBwBAYGQqfTwWw2AwBe\neeUVdOvWDa6urjaPtXPnToSGhkKn0yE0NBS7d++utc+IESO4IvdtxloMxsTEqGMpLy+vGm+TqE//\nBNQcp9U1bqdbX0FBAcaOHQt/f39otVokJSVhzpw50Gg0tRa3TU5OVrfpdDqsX78egGX1/6FDh6ox\nUTX2utqVK1cQGxsLnU4HrVaL119/Xf3sesbyN0VEbP5n+fjGzJkzRxYtWnTD5Rvb7t27ZdiwYTdc\nvm3btvXet3379lJRUXFDxykrK6vXfjfztyP727t3r6SlpUlgYKC6bdeuXRIdHS1ms1lERM6cOVOr\n3OHDh6V79+7q77Nnz5ZZs2apv587d87mMceMGSPjxo2z2/+HjKlbT1FRkYiIlJaWSq9evSQxMVGi\noqLkq6++EhGRbdu2SVRUlIiIZGZmil6vF7PZLCaTSby9vaW8vLxWnTt27FC3T58+XaZPn17jc3vG\nFWPKsXh6esrvv/9eY9ulS5fUn5ctWyZxcXEiYr94ux6Mp6Zj7RwnInLixAkZPHhwjdgpLS0VnU4n\nGRkZIiJy/vx5NQYOHjwo+fn5dY6p0tPTJT8/X0REfvzxR/Hw8Kjx+caNG2X8+PESFBR009+LMeU4\nbMVglRdffFFeffVVEal//1Sfcdr1Ykw1nSeeeEI+/PBDEbH0QwUFBTJnzhxZvHhxrX2Li4vVmMjP\nz5dOnTpJWVmZFBcXy549e0RExGw2S0REhGzfvr1W+RUrVkhMTIxal6enp+Tk5IjI9Y3l66Mypmrl\n8Xa9kz9v3jz4+fkhIiICR48eBQD8+uuvePDBBxEaGorIyEh1u8lkQnh4OHQ6HRISEtSrtnv27MHw\n4cPVOp999ll89NFHAIDU1FRERUUhNDQUQ4YMwalTpwAAUVFRSE1NBWB5D6yXlxcAoLy8HNOmTUNY\nWBj0ej3+9a9/2Wz7jBkzkJiYCKPRiKVLlyInJweRkZEICQlBSEgIvvvuOwCWV9BERkbCaDQiKCgI\n+/fvr1HPuXPn0KdPH2zfvt3qcUaMGIHCwkIEBwdjw4YN+PLLL9G7d28EBwdj4MCBOHPmDABg5cqV\nmDJlCgBg4sSJeOqpp9C7d29eRXRQERER6NixY41t7733HmbOnImWLVsCANzc3GqV++STTxATE6P+\nvmLFihpXDTt16mT1eJs2bcJ9990HrVZrj+bTLap169YALFeFy8vL0bFjR3Tp0kW9G1pQUAAPDw8A\nwObNmxEbG4uWLVvC09MT3bt3R3Jycq06Bw4ciGbNLKeGXr16IS8vT/2McUVy1TTT6ndcCwsLcddd\ndwGwT7yR47B2jgOAv/71r3jjjTdqbNuxYwd0Op16x7Vjx45qDISFhaFLly51HstgMKj7aLValJSU\noLS0FIAlBpcsWYKEhAROib7N2IpBwNJvbdiwAbGxsQDq3z/VZ5xGjuHixYtITEzE5MmTAVhepd2+\nfXsA1h9Nd3FxUfulkpIStG/fHs2bN4eLiwseeOABAEDLli0RHByMkydP1irv7u6OoqIilJeXo6io\nCK1atUK7du0A1H8sf7PsluSnpqZi/fr1+OGHH7Bt2zZ1iuj/+3//D//4xz+QkpKChQsX4plnngEA\nTJ06FX/5y1+QkZGBe+65x2a9iqJAURSUlpZiypQp2LhxI1JSUjBp0iS88sorNfa52ocffogOHTog\nOTkZycnJ+OCDD5CdnW31OAsWLEBERATS09MxdepU3H333di5cydSU1Oxbt06PPfccwAsSdeQIUOQ\nnp6OH374AXq9Xq3jzJkzGDZsGF599VU8+OCDVo+zZcsWuLi4ID09HePGjcP999+PpKQkpKWl4dFH\nH1VPhld/n99++w3fffcdFi1aZPPfihxLVlYW9u7di969eyMqKgopKSm19ql+UiooKAAAJCQkICQk\nBOPGjVMvClVXWFiIN954A3PmzGnQ9lPTq6iogMFgQOfOndGvXz8EBATg9ddfx4svvohu3bph2rRp\neO211wBY+hCNRqOW1Wg0Vk9M1S1fvhwPPfQQAMYVWc5L0dHRCA0NxQcffKBur5pivXLlSnXgcrPx\nRo5v8+bN0Gg00Ol0NbZnZWVBURQMGTIEISEhWLhw4Q0fY+PGjQgJCVGTsFmzZuGll15SL4ASAUBi\nYiI6d+4Mb29vAPXvn+ozTiPHYDKZ4ObmhkmTJiE4OBh/+tOfUFxcDMDySIZer0dcXJw61gYsU/YD\nAgIQEBCAN998s1adBQUF2Lp1KwYMGFDrs8GDB6Ndu3Zwd3eHp6cnpk2bhg4dOtR7LG8PdkvyExMT\nMXr0aNxxxx1wdXXFiBEjcOXKFRw4cACPPPIIjEYjnnrqKfXu+4EDB9TkZcKECXXWLSI4evQoMjMz\nER0dDaPRiHnz5l1zwLBjxw6sWrUKRqMRvXv3xvnz5/HLL7/YPEZ1ZrMZ8fHx0Ol0GDduHH766ScA\nlqvMK1aswNy5c3H48GH1WXyz2YwBAwZg4cKFVv/YtuTm5mLQoEHQ6XRYtGgRjhw5Uqs9iqLgkUce\nsbmwAjmmsrIyXLhwAUlJSVi4cCHGjRtX4/ODBw+idevW6l3TsrIy5OXloW/fvkhNTUV4eDheeuml\nWvXOmTMHL7zwAlq3bs07GU6uWbNmOHToEPLy8rB3717s2bMHcXFxWLZsGU6cOIElS5aoV62tqatP\nmTdvHlq1aoXx48cDYFwRsH//fqSnp2P79u145513kJiYCMASKydOnMCkSZPw/PPP2yx/PfFGjq24\nuBjz58/H3Llz1W1V/UZpaSn27duHTz75BPv27cMXX3yBXbt2XfcxMjMzMWPGDLz//vsAgEOHDuH4\n8eMYOXIk+yiqYe3atdfsW6z1T9cap5HjKCsrQ1paGp555hmkpaWhTZs2eP311/HMM8/AZDLh0KFD\ncHd3x4svvqiWCQsLQ2ZmJtLS0jB16tQaa8aUlZUhNjYWU6dOhaenZ63jrVmzBiUlJcjPz4fJZMKi\nRYuQnZ1d77G8Pdgtybe2WmRFRQU6dOiA9PR09b/MzMw662nRogUqKirU369cuaL+HBAQoNaTkZGh\nLo5QvUz1/QHg7bffVsv8+uuviI6Ortf3WbJkCdzd3ZGRkYGUlBT88ccfACzTgRITE+Hh4YGJEydi\n9erVACxTNkJDQ9U21deUKVPw3HPPISMjA++//z5KSkqs7ser0s5Ho9Fg9OjRAICePXuiWbNm+P33\n39XP161bV+Ok1KlTJ7Ru3VotM3bsWKSlpdWqNzk5GS+//DK8vLywdOlSzJ8/H++++24DfxtqSu3b\nt8fQoUORkpKC5ORkjBo1CoAlRqqmIHp4eNRYFC0vL0+dyn+1lStXYtu2bfj444/VbYwrcnd3B2CZ\nsjpq1Kha01vHjx+vzuK72Xgjx/brr78iOzsber0eXl5eyMvLQ0hICE6fPo2uXbsiMjISd955J1xc\nXPDQQw9ZPZfVJS8vD6NHj8bq1avVRzSTkpKQkpICLy8vRERE4NixY+jfv39DfD1yIGVlZfjiiy/w\n6KOPqtvq2z9da5xGjkOj0UCj0aBnz54A/juGdnNzU2eEx8fHW31so0ePHvD29q5xo7hq4byqmd5X\nO3DgAEaNGoXmzZvDzc0Nffv2RUpKSr3H8vZgtyQ/MjISmzZtwpUrV3D58mVs3boVrVu3hpeXFz77\n7DMAlqu4VauE9+3bF+vWrQOAGif2e++9F0eOHIHZbEZBQQG++eYbKIoCPz8/nD17FklJSQAsV4Kr\n7np7enqqU2iqjgVYpkq8++67KCsrAwAcO3ZMnZpxtXbt2uHy5cvq75cuXVKf+Vq1ahXKy8sBWFaW\ndnNzQ3x8POLi4pCeng7AcpFj+fLl+Pnnn2s9f1aXS5cuqY8rrFy5st7lyPE9/PDD6t2LY8eOwWw2\nq8/lVFRU4NNPP63xPL6iKBg+fLi6kvA333yDgICAWvXu3bsXJpMJJpMJzz//PF555RX1MRlyHufO\nnVOnfZWUlGDnzp0wGAzo3r07vv32WwDArl274OvrC8CyHsi6detgNpthMpmQlZWlrpBe3VdffYWF\nCxdi8+bNuOOOO9TtjKvbW3FxsXqOLCoqwo4dOxAUFFRj0LN582Z19eqbjTdybEFBQTh9+rTaZ2g0\nGqSlpaFz584YPHgwDh8+jJKSEpSVleHbb7+1ei6zpaCgAEOHDsWCBQsQHh6ubn/qqadw8uRJmEwm\n7Nu3D76+vjc0Q4Ccy9dffw1/f/8ajwbXt3+qa5xGjqVLly7o2rUrjh07BsASFwEBAeoMcwD44osv\n1LVCqu66A5Y3sGVlZcHHxweAZar9pUuXsGTJEpvH69Gjhxo7RUVFSEpKQo8ePeo9lrcLa6vxyQ2u\nrj9v3jzx9fWV+++/Xx577DFZvHixmEwmGTJkiOj1etFqterKliaTScLDwyUoKEgSEhJqrKT68ssv\ni4+PjwwaNEjGjBkjH330kYiIHDp0SCIjI0Wv10tAQID8+9//FhGRn3/+WXQ6nRiNRklISBAvLy8R\nEamoqJC//e1vEhQUJIGBgdK/f3+5ePGi1baXlpZK//79Ra/Xy1tvvSVZWVmi0+lEr9fL9OnTxdXV\nVUREVq5cKYGBgWI0GiUyMlKys7NFRNTP//jjDxk8eLC89957Nv+dqvYVEdm8ebPcd999EhISItOm\nTZN+/fqpx5kyZYqIiEycOFE2btx4XX+L6/3bUcOKiYkRd3d3adWqlWg0Glm+fLmYzWaZMGGCBAYG\nSnBwsOzevVvdf/fu3RIeHl6rnpycHImMjBSdTifR0dGSm5srIiJbtmyR2bNn19rf1qqhN4IxdWvJ\nyMgQo9Eoer1egoKC5I033hARke+//17CwsJEr9dL7969JS0tTS0zb9488fb2Fj8/P3UFfhGR+Ph4\nSU1NFRGR7t27S7du3cRgMIjBYJCnn3661rHtFVeMKcdx/Phx0ev16vl3/vz5ImJ520JgYKDo9XoZ\nPXq0nD59Wi1jr3irL8ZT07F2jqvOy8urxpsZ1qxZIwEBARIYGFjjjQrTpk0TjUYjzZs3F41GI3Pn\nzhWRmue4V199Vdq0aaPGjMFgkLNnz9Y4nslk4ur6txlbMThx4kR5//33a+1fV/+UkpIiIlLnOO1G\nMaaazqFDhyQ0NFR0Op2MGjVKLly4II8//rgEBQWJTqeTkSNHyqlTp0REZPXq1RIQECAGg0F69uyp\nrqCfm5sriqKIVqtV+5+qFfur91NXrlyRxx57TAIDA0Wr1dZ4I5GtsfyNgo3V9RWp47klRVGkrs/t\nydXVtcaddLo51h6fILoZjCmyN8YU2RPjieyNMUX2xpgie6uMqVqLStj1FXo3g4vKEREREREREd2c\na97Jb8S2EBEREREREVE9WbuT36IehRqmNdSgOB2I7I0xRfbGmCJ7YjyRvTGmyN4YU2RvtmbD3zLT\n9ZtaTk4O1q5de8Pl27ZtW+99p02bhsDAQEyfPt3mPlu3bsWCBQtuuD3UNHJzc9GvXz8EBAQgMDAQ\ny5YtUz/7xz/+AX9//1p/+9deew0+Pj7o0aMHduzYoW5/5ZVX0K1bN7i6uto83pUrVxAbGwudTget\nVovXX38dgGUl7KFDh6rHmzlzZgN8W7oV2YrBOXPmQKPRwGg0wmg0qq/7/Pjjj9VtRqMRzZs3V9+C\nUl1ycjLCwsJgNBrRs2dP9VVp5HzKy8thNBoxfPhwdZu1/is7OxsuLi5q7NT1tgVr5Xfu3InQ0FDo\ndDqEhoaqqw2T87DVH/3www8IDw+HTqfDiBEj1DWZkpOT1XjS6XRYv3691Xpt9UeMKec3efJkdO7c\nWV0FHQDOnz+PgQMHwtfXF4MGDVLfPFPfeLB1fmQ8OZeCggKMHTsW/v7+0Gq1OHjwoM3YAWyPz9ev\nXw+9Xo/AwEDMmDHD5vEyMjIQHh6OwMBA6HQ6mM3mxh2fW1uNr+o/3EYrQO7evVuGDRt2w+Wrvx3g\nWtq3by8VFRU3dJyysrJ67Xc7/e1uJfn5+ZKeni4iIpcvXxZfX185cuSI7Nq1S6Kjo8VsNouIyJkz\nZ0REJDMzU/R6vZjNZjGZTOLt7a3GxsGDByU/P7/O2FqxYoXExMSIiEhxcbF4enpKTk6OFBcXy549\ne0TEsjpsRESEujLojWJMOQZbMVifFfEPHz4s3bt3t/rZAw88oK5AvG3bNomKirrptjKmbk2LFy+W\n8ePHy/Dhw0VEbPZfJpNJAgMDr1mfrfLp6emSn58vIiI//vijeHh43FS7GU+3Hlv9UWhoqOzdu1dE\nRJYvXy6zZs0SEct5rLy8XC3bqVMnq+MeW/0RY8r57d27V9LS0mr0PdOmTZMFCxaIiMjrr7+uvrGh\nvvFg6/xo73gSYUw1pSeeeEJdCb+0tFQKCgpsxo6t8fm5c+ekW7ducu7cORERefLJJ+Wbb76pdazS\n0lLR6XSSkZEhIiLnz5+X8vLyhhyf18rj7Xonf82aNejVqxeMRiOeeuoplJeXo23btkhISIDBYEB4\neDjOnDkDADCZTOpV3ISEBPVu5Z49e2rcPXj22Wfx0UcfAQBSU1MRFRWF0NBQDBkyRH23YVRUFFJT\nUwFY3h3t5eUFwHI3Ytq0aQgLC4Ner8e//vUvm22fMWMGEhMTYTQasXTpUuTk5CAyMhIhISEICQnB\nd999BwDIz89HZGQkjEYjgoKCsH///hr1nDt3Dn369MH27dutHmfEiBEoLCxEcHAwNmzYgC+//BK9\ne/dGcHAwBg4cqP77rFy5ElOmTAEATJw4EU899RR69+5d591/anpdunSBwWAAYJnd4e/vj5MnT+Kf\n//wnZs6ciZYtWwIA3NzcAFjeKx0bG4uWLVvC09MT3bt3x8GDBwEAYWFh6NKlS53Hc3d3R1FREcrL\ny1FUVIRWrVqhXbt2cHFxwQMPPAAAaNmyJYKDg3Hy5MmG+tp0C7EVg8C1H7/65JNPEBMTY/Uzd3d3\nXLx4EYDlariHh4cdW023iry8PGzbtg3x8fFqvLz33ntW+6/6slXeYDCofZxWq0VJSQlKS0vt9VXo\nFmCrP8rKykJERAQAIDo6Ghs3bgQAuLi4oFkzy9C0pKQE7du3R/PmzWvVa6s/Ykw5v4iICHTs2LHG\nti1btuDJJ58EADz55JPYtGkTgOuLB2vnR8aT87h48SISExMxefJkAECLFi3Qvn17m7Fja3x+/Phx\n+Pj4oFOnTgCAAQMGqP1XdTt27IBOp1NnnHTs2BHNmjVr1PG53ZL8n376CRs2bMCBAweQnp6O5s2b\n4+OPP0ZxcTHCw8Nx6NAhREZG4oMPPgAATJ06FX/5y1+QkZGBe+65x2a9iqJAURSUlpZiypQp2Lhx\nI1JSUjBp0iS88sorNfa52ocffogOHTogOTkZycnJ+OCDD5CdnW31OAsWLEBERATS09MxdepU3H33\n3di5cydSU1Oxbt06PPfccwAsg+AhQ4YgPT0dP/zwA/R6vVrHmTNnMGzYMLz66qt48MEHrR5ny5Yt\ncHFxQXp6OsaNG4f7778fSUlJSEtLw6OPPoo33nhD/U7V/fbbb/juu++waNEim/9WdGvJzs5Geno6\nevXqhWPHjmHv3r3o3bs3oqKikJKSAsDyd9VoNGoZjUZzXf+zDx48GO3atYO7uzs8PT0xbdo0dOjQ\nocY+BQUF2Lp1KwYMGGCfL0YOoyoGe/fuDcAyZVqv1yMuLq7GlLQqGzZsQGxsrNW6Xn/9dbz44ovo\n1q0bpk2bhtdee61B205N44UXXsDChQvVRAsAsrKyrPZfgOWCvdFoRFRUFPbt22e1zrrKV9m4cSNC\nQkLUCwHkfKqfEwMCArB582YAwKefforc3Fx1v+TkZAQEBCAgIABvvvmm1brq0x8xpm4fp0+fRufO\nnQEAnTt3xunTp2vtc614uNb5kfHk2EwmE9zc3DBp0iQEBwfjT3/6E4qKimzGjrXx+W+//QYfHx8c\nPXoUOTk5KCsrw6ZNm2r0X1WysrKgKAqGDBmCkJAQLFy4sNY+DT0+t1uS/8033yA1NRWhoaEwGo3Y\ntWsXTCYTWrVqhaFDhwIAQkJC1CT7wIED6mBywoQJddYtIjh69CgyMzMRHR0No9GIefPmXTMZ2rFj\nB1atWgWj0YjevXvj/Pnz+OWXX2weozqz2Yz4+HjodDqMGzcOP/30EwDL3dUVK1Zg7ty5OHz4sPos\nvtlsxoABA7Bw4cLr+mPl5uZi0KBB0Ol0WLRoEY4cOVKrPYqi4JFHHuFrBh1IYWEhxo4di6VLl8LV\n1RVlZWW4cOECkpKSsHDhQowbN85m2ev5O69ZswYlJSXIz8+HyWTCokWLYDKZ1M/LysoQGxuLqVOn\nwtPT82a+EjmY6jHYtm1bPP300zCZTDh06BDc3d3x4osv1tj/4MGDaN26NbRardX64uLisGzZMpw4\ncQJLlixRr4aT8/jyyy9x9913w2g01jgH2eq/7rnnHuTm5iI9PR1vvvkmxo8frz5bXd21+r/MzEzM\nmDED77//fsN+QWoyV58Tly9fjnfffRehoaEoLCxEq1at1H3DwsKQmZmJtLQ0TJ06Vb1jX921+iPG\n1O3L2o2/a8XDtc6PjCfHV1ZWhrS0NDzzzDNIS0tDmzZt1HWsqti6aVxdhw4d8N577+HRRx9FZGQk\nvLy8rM42Ki0txb59+/DJJ59g3759+OKLL7Br164a7Wno8bldp+s/+eSTSE9PR3p6On766Sf87//+\nb40rXs2aNUNZWVmddbRo0QIVFRXq71euXFF/DggIUOvPyMhQF8aoXqb6/gDw9ttvq2V+/fVXREdH\n1+u7LFmyBO7u7sjIyEBKSgr++OMPAJZpQomJifDw8MDEiROxevVqAJYpF6GhoWqb6mvKlCl47rnn\nkJGRgffffx8lJSVW92vduvV11UtNp7S0FGPGjMGECRPw8MMPA7BcARw9ejQAoGfPnmjWrBnOnTsH\nDw+PGlcA8/Lyrmsa9IEDBzBq1Cg0b94cbm5u6Nu3b427ZH/+85/h5+enzkSh24O1GLz77rvV/YMT\nUAAAFMFJREFUE1h8fDySk5NrlFm3bh3Gjx9vs87k5GSMGjUKADB27Nha5cnxHThwAFu2bIGXlxdi\nY2Oxa9cuPP7441b7r99//x2tWrVSp80GBwfD29sbWVlZteq1VR6w9HmjR4/G6tWr1UftyLlY64/8\n/Pzwn//8BykpKYiJiYG3t3etcj169IC3t7fVmzN19UeMqdtP586d1Ud48/Pzcffdd6uf1Sce6jo/\nMp6cg0ajgUajQc+ePQFY+o20tDR06dLFauzUNT4fNmwYkpKScODAAfj6+sLPz6/W8bp27YrIyEjc\neeedcHFxwUMPPYS0tDT188YYn9styR8wYAA+++wznD17FoBlpcucnByb+/ft2xfr1q0DYFnducq9\n996LI0eOwGw2o6CgAN988w0URYGfnx/Onj2LpKQkAJaTRtVdb09PTzWx+eyzz9S6Bg8ejHfffVe9\nsHDs2DEUFxdbbU+7du1q3IG4dOmS+hzOqlWrUF5eDgA4ceIE3NzcEB8fj7i4OKSnpwOwXP1Zvnw5\nfv75Z3XKfX1cunRJfVxh5cqV9S5HtyYRQVxcHLRaLZ5//nl1+8MPP6xewTt27BjMZjPuuusujBgx\nAuvWrYPZbIbJZEJWVhbCwsLqfbwePXqo9RYVFSEpKQn+/v4AgISEBFy6dAlLliyx4zekW52tGMzP\nz1d//uKLL2qsTFxRUYFPP/3U5vP4ANC9e3d8++23AIBdu3bB19e3AVpPTWn+/PnIzc2FyWTCunXr\n0L9/f6xevdpq/9WpUyecO3dOPTceP34cWVlZuO+++2rVa6t8QUEBhg4digULFiA8PLzxvig1Glv9\nUdVYsaKiAn//+9/x9NNPA7BM6a8as+Xk5CArKws+Pj616rXVHzGmbk8jRoxQ1+/66KOP1ItJ9Y0H\nW+dHxpPz6NKlC7p27Ypjx44BAL7++msEBARg+PDhVmOnrvF51fppFy5cwHvvvYf4+Phaxxs8eDAO\nHz6MkpISlJWV4dtvv0VAQACARhyfW1uNT25wdf3169eLwWAQnU4noaGhkpSUJK6ururnn332mUya\nNElELKvyhoeHS1BQkCQkJNRYQfzll18WHx8fGTRokIwZM0Y++ugjERE5dOiQREZGil6vl4CAAPn3\nv/8tIiI///yz6HQ6MRqNkpCQIF5eXiIiUlFRIX/7298kKChIAgMDpX///nLx4kWrbS8tLZX+/fuL\nXq+Xt956S7KyskSn04ler5fp06er32PlypUSGBgoRqNRIiMjJTs7W0RE/fyPP/6QwYMHy3vvvWfz\n36n6v8nmzZvlvvvuk5CQEJk2bZr069dPPc6UKVNERGTixImycePG+v4ZRISrdzaVxMREURRF9Hq9\nGAwGMRgMsn37djGbzTJhwgQJDAyU4OBg2b17t1pm3rx54u3tLX5+fupqwSKW1WI1Go00b95cNBqN\nzJ07V0REtmzZIrNnzxYRkStXrshjjz0mgYGBotVqZdGiRSIikpubK4qiiFarVdtRtaLojWJMOQZr\nMbht2zZ5/PHHJSgoSHQ6nYwcOVJOnTqlltm9e7eEh4fXqis+Pl5SUlJEROT777+XsLAw0ev10rt3\nb0lLS7vptjKmbl179uxRV9e31X9t3LhRAgICxGAwSHBwsHz55Zdq+eqxY6v8q6++Km3atFHj1GAw\nyNmzZ2+4zYynW4+t/mjp0qXi6+srvr6+MnPmTHX/1atXqzHVs2fPGqtO16c/Ykw5v5iYGHF3d5eW\nLVuKRqOR5cuXy++//y4DBgwQHx8fGThwoFy4cEFE6o6H+Ph4SU1NFRGxeX60dzyJMKaa0qFDhyQ0\nNFR0Op2MGjVKCgoKbMaOiO3xeWxsrGi1WtFqtbJ+/Xp1e/XxuYjImjVrJCAgQAIDA9VV+xtwfF4r\nj1ekjtWWFUWRuj63J1dXV6vP8tGNURTlmitpE10PxhTZG2OK7InxRPbGmCJ7Y0yRvVXGVK3FBOz6\nTP7N4KJyRERERERERDfnmnfyG7EtRERERERERFRP1u7kt6hHoYZpDTUoTgcie2NMkb0xpsieGE9k\nb4wpsjfGFNmbrdnwTTJdf+LEidi4ceN1l8vJycHatWvr3OeHH37A9u3bb6hd2dnZNVacbkiffvop\ntFotBgwYYHOf3377DY888kijtIfs4+jRozAajep/7du3x7Jly/DDDz8gPDwcOp0OI0aMqLH+xGuv\nvQYfHx/06NEDO3bsULebzWb1FRv+/v74/PPPax0vOTlZPZZOp8P69esBAMXFxRg6dCj8/f0RGBiI\nmTNnNvyXp1tCbm4u+vXrh4CAAAQGBmLZsmUALLESFhYGo9GInj174vvvvwdgee1obGwsdDodtFpt\nrffGVrFVnm4Pnp6e0Ol0MBqN6grDtmLCVr90tbr6RXIO1s6JS5cuxezZs6HX62EwGDBgwAD1VVU7\nd+5EaGgodDodQkNDsXv3bqv1fvrppwgICEDz5s2Rmpqqbq9veXI+kydPRufOnWuM4231MefPn0e/\nfv3g6uqKKVOm2KyzepxVf/0ZOSZr57FZs2ZZ7YvqGhvVZ3wO2B7fVxkxYkTD5p3WVuOr+g8NtALk\njawWL2JZAXrYsGF17rNixQp59tlnb6hdJpNJAgMDr7tcaWnpdZcZPHiw7N+//7rL1fd4DfW3o/or\nLy+XLl26SE5OjoSGhsrevXtFRGT58uUya9YsERHJzMwUvV4vZrNZTCaTeHt7S0VFhYiIzJ49W91P\nROTcuXO1jlFcXCzl5eUiIpKfny+dOnWSsrIyKS4ulj179oiIZWXriIiIGqsU3wjGlGPIz8+X9PR0\nERG5fPmy+Pr6ypEjR+SBBx5QV4fdtm2bREVFiYilz4yJiRERSzx5enpKTk5OrXptlb8ZjCnH4enp\nKb///nuNbbZiwla/dDVb/eKNYjzd2qrOiSdOnJBLly6p25ctWyZxcXEiIpKeni75+fkiIvLjjz+K\nh4eH1bp++uknOXr0qERFRakrpF9P+fpiTDmOvXv3SlpaWo1xvK0+pqioSPbt2yf//Oc/68wZbMXZ\nzWBMNR1r5zFbfVFdY6P6jM+tje+rzosilrfTjB8/XoKCgm76e8HG6vp2u5NfVFSEoUOHwmAwICgo\nCBs2bEBqaiqioqIQGhqKIUOG4NSpUzUuLgCwuc8vv/yC6OhoGAwGhIaG4vjx45gxYwYSExNhNBqx\ndOnSWm0wm82YPXs21q9fD6PRiA0bNuD7779Hnz59EBwcjL59+6rvR8zMzESvXr1gNBqh1+vx66+/\n1qjr+PHjCA4OrnGFuLqVK1dixIgRGDBgAAYOHIgrV64gJiYGWq0Wo0ePRu/evW2W/b//+z/s378f\nkydPxssvv4ycnBxERkYiJCQEISEh+O677wDUnFlw9fHo1vf111+je/fu6NatG7KyshAREQEAiI6O\nVmeybN68GbGxsWjZsiU8PT3RvXt3JCcnAwBWrFhR4w58p06dah3DxcUFzZpZ/jcuKSlB+/bt0bx5\nc7i4uOCBBx4AALRs2RLBwcE4efJkg35fujV06dIFBoMBANC2bVv4+/vj5MmTcHd3x8WLFwFY3v3r\n4eEBAHB3d0dRURHKy8tRVFSEVq1aoV27drXqtVWebh9y1RRTWzFhq1+6mq1+kZzT119/DW9vb3Tt\n2hWurq7q9sLCQtx1110AAIPBgC5dugAAtFotSkpKUFpaWquuHj16wNfXt9b2+pYn5xMREYGOHTvW\n2Garj2ndujX69u2L//mf/6mzTltxRo7r6vOYrb6orrFRfcbndY3vCwsLsWTJEiQkJDTsoxvWMn+5\ngTv5n332mfzpT39Sf7948aL06dNHvbqxbt06mTx5soj8906+2WyW8PBwq/uEhYXJpk2bRMTy7vmq\nO5PXupNf/f3yIpYrNFV3EHbu3CljxowREZFnn31WPv74YxGx3BkvKSlR7+T//PPPYjQaJSMjw+Zx\nVqxYIRqNRn2f4uLFi9WrPxkZGdKiRYs6r/pVvypYXFwsV65cERGRY8eOSWhoqIjUnFlw9fGu5Xr+\ndtQwJk2aJO+8846IiPTp00eN58WLF4urq6uIWOJwzZo1apm4uDjZuHGjXLhwQbp27Sp//etfJTg4\nWB555BE5ffq01eMcPHhQtFqtuLi4qMeo7sKFC3LfffeJyWS6qe/DmHI8JpNJunXrJpcvX5bs7GzR\naDTStWtX8fDwqHG3/rHHHhM3Nzdp06aNfPDBB1brurr8iRMnbrp9jCnH4eXlJQaDQUJCQuRf//qX\niNSOieoxda1+ScR2v3ijGE+3turnRBGRv/3tb9K1a1fx8/OzOrb59NNPZeDAgXXWWdcd1vqUvxbG\nlGO5ekbutfqYlStX1mv2L+/kOwdr5zER232RtbFRfcfntsb3IiLPP/+8bNq0SbKzs29oBvnV0NB3\n8nU6HXbu3IkZM2Zg3759OHHiBH788UdER0fDaDRi3rx5Ne4kigiOHj2KzMzMWvsUFhbit99+w8iR\nIwEArVq1gouLS72udsh/L1AAsNxdGDt2LIKCgvDXv/4VR44cAQD06dMH8+fPxxtvvIHs7Gzccccd\nAIAzZ87g4YcfxieffFLncxKKomDgwIHo0KEDACAxMRETJkwAAAQFBUGn09WrrYBlBkJ8fDx0Oh3G\njRuntvFqgwYNUo9Htzaz2YytW7eqayosX74c7777LkJDQ1FYWIhWrVrVWb6srAx5eXno27cvUlNT\nER4ejpdeesnqvmFhYcjMzERaWhqmTp2q3lmrqic2NhZTp06Fp6en3b4f3foKCwsxduxYLF26FG3b\ntkVcXByWLVuGEydOYMmSJYiLiwMArFmzBiUlJcjPz4fJZMKiRYtgMplq1Xd1+cmTJzf2V6ImtH//\nfqSnp2P79u145513kJiYaDOmgLr7pSrX2y+S47r6nAgA8+bNw4kTJzBx4kS88MILNfbPzMzEjBkz\n8P7779/Q8W62PDkH9jFUnbXzGGC9L7I2NsrOzr6u8fnVRASHDh3C8ePHMXLkyAZfgNFuSb6Pjw/S\n09MRFBSEhIQEbNy4EQEBAUhPT0d6ejoyMjLw1Vdf1SpnbZ+b+dJXrzA4a9YsDBgwAIcPH8bWrVtR\nUlICAIiNjcXWrVvh4uKChx56CLt374aiKOjQoQPuvfde9Q9flzZt2tT4/XrbXdXWJUuWwN3dHRkZ\nGUhJSYHZbLa6f+vWra+rfmo627dvR0hICNzc3AAAfn5++M9//oOUlBTExMTA29sbAODh4aEu8gEA\neXl58PDwQKdOndC6dWuMHj0aADB27NhrLvrSo0cPeHt745dfflG3VS0M8txzz9n7K9ItrLS0FGPG\njMGECRPw8MMPA7AshjZq1CgAlniqmjZ24MABjBo1Cs2bN4ebmxv69u2LlJSUWnXaKk+3B3d3dwCA\nm5sbRo0aheTk5HrFhLV+qYqtfpGcz9XnxOrGjx9fYyHPvLw8jB49GqtXr4aXl9d1H+tmy5PzYB9D\n1Vk7j1VXvS+yNTaq7/jc2vheo9EgKSkJKSkp8PLyQkREBI4dO4b+/fs3yPe1W5Kfn5+PO+64A489\n9hheeuklJCcn49y5c0hKSgJgGXRWv0OtKAr8/Pxw9uzZWvu4urpCo9Fg8+bNAIA//vgDJSUlaNeu\n3TVX33V1da2xz6VLl3DPPfcAsDxDUeX48ePw8vLClClTMHLkSBw+fBiAZdbA559/jlWrVtW5kv/V\nCX1kZCQ++eQTAMCPP/6IjIyMuv/Bqrl06ZL6DNmqVatQXl5e77J0a1q7di1iY2PV38+ePQsAqKio\nwN///nc8/fTTACwra65btw5msxkmkwlZWVkICwuDoigYPny4ujLwN998g4CAgFrHqbqqCFjePpGV\nlQUfHx8AQEJCAi5duoQlS5Y06HelW4uIIC4uDlqtFs8//7y6vXv37vj2228BALt27VKfM+zRowd2\n7doFwLK2SlJSEvz9/WvVa6s8Ob/i4mL1vFpUVIQdO3YgMDDQZkzU1S9VZ6tfJOdz9TkxKytL/Xnz\n5s0wGo0ALLMvhw4digULFiA8PLxedV89e/N6y5PzulYfcz035xr6ris1LGvnsaC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/NAzlHzt2LJMnT244juacdNJJfOhDH6JLly507dp1q/psqvVLHJIkSZKkD5Q9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\n54dt27aZXGdV+c3VgWQ7SkpKYDQaMWTIEAClT2Tq27cvfHx80K9fP/V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uQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -650,7 +655,7 @@ "source": [ "# Plot Average and Total execution time for the specified\n", "# list of kernel functions\n", - "ta.plotFunctionStats(\n", + "trace.analysis.functions.plotProfilingStats(\n", " functions = [\n", " 'select_task_rq_fair',\n", " 'enqueue_task_fair',\n", @@ -665,16 +670,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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Fi/LGN74xf/zHf5ybb745y5YtSzII1JtttlmuuOKKLFu2LF/+8pfzkY98ZNp6zz777Jx4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\nRERERPYhTdhFRERERERE9qGfhAfwbyDrC/4AAAAASUVORK5CYII=\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -683,10 +688,19 @@ ], "source": [ "# Plot Average execution time for the single specified kernel function\n", - "ta.plotFunctionStats(\n", + "trace.analysis.functions.plotProfilingStats(\n", " functions = 'update_curr_fair',\n", ")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -705,7 +719,13 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.9" + "version": "2.7.6" + }, + "toc": { + "toc_cell": false, + "toc_number_sections": true, + "toc_threshold": 6, + "toc_window_display": false } }, "nbformat": 4, diff --git a/ipynb/sched_dvfs/smoke_test.ipynb b/ipynb/sched_dvfs/smoke_test.ipynb index a00e400d7..0817b3e8c 100644 --- a/ipynb/sched_dvfs/smoke_test.ipynb +++ b/ipynb/sched_dvfs/smoke_test.ipynb @@ -55,7 +55,6 @@ "\n", "# Support for trace events analysis\n", "from trace import Trace\n", - "from trace_analysis import TraceAnalysis\n", "\n", "# Suport for FTrace events parsing and visualization\n", "import trappy" @@ -340,15 +339,14 @@ " # Parse trace\n", " tr = Trace(te.platform, exp_dir,\n", " events=my_tests_conf['ftrace']['events'])\n", - " ta = TraceAnalysis(tr)\n", " \n", " # return all the experiment data\n", " return {\n", " 'dir' : exp_dir,\n", " 'energy' : nrg,\n", " 'trace' : trace_file,\n", - " 'ftrace' : tr.ftrace,\n", - " 'ta' : ta\n", + " 'tr' : tr,\n", + " 'ftrace' : tr.ftrace\n", " }\n", "\n", " \n", @@ -541,9 +539,9 @@ " # Plot Custer Frequencies and report averate frequencies\n", " for governor in confs:\n", " plot_title = \"Cluster frequencies, {}\".format(governor.upper())\n", - " ta = results[tid][governor]['ta']\n", + " trace = results[tid][governor]['tr']\n", " logging.info(\"%s:\", plot_title)\n", - " ta.plotClusterFrequencies(title=plot_title)\n", + " trace.analysis.frequency.plotClusterFrequencies(title=plot_title)\n", " \n", " # Plot RTApp performance index\n", " for governor in confs:\n", diff --git a/ipynb/sched_tune/stune_juno_rampL.ipynb b/ipynb/sched_tune/stune_juno_rampL.ipynb index cc263a518..61e9e5297 100644 --- a/ipynb/sched_tune/stune_juno_rampL.ipynb +++ b/ipynb/sched_tune/stune_juno_rampL.ipynb @@ -32,27 +32,18 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], + "outputs": [], "source": [ "# Generate plots inline\n", - "%pylab inline\n", + "%matplotlib inline\n", "\n", "import json\n", "import os\n", "\n", - "# Support for performance analysis of RTApp workloads\n", - "from trace_analysis import TraceAnalysis\n", - "\n", "# Support for trace events analysis\n", "from trace import Trace\n", + "\n", + "# Support for performance analysis of RTApp workloads\n", "from perf_analysis import PerfAnalysis\n", "\n", "# Suport for FTrace events parsing and visualization\n", @@ -217,18 +208,17 @@ " \"cpu_capacity\"\n", " ]\n", " trace = Trace(platform, run_dir, events, tasks=pa.tasks())\n", - " ta = TraceAnalysis(trace, tasks=pa.tasks())\n", " \n", " # Define time ranges for all the temporal plots\n", - " ta.setXTimeRange(t_min, t_max)\n", + " trace.setXTimeRange(t_min, t_max)\n", " \n", " # Tasks plots\n", - " ta.plotTasks()\n", + " trace.analysis.tasks.plotTasks()\n", " for task in pa.tasks():\n", " pa.plotPerf(task)\n", "\n", " # Cluster and CPUs plots\n", - " ta.plotClusterFrequencies()\n" + " trace.analysis.frequency.plotClusterFrequencies()\n" ] }, { diff --git a/ipynb/sched_tune/stune_juno_taskonly_rampL.ipynb b/ipynb/sched_tune/stune_juno_taskonly_rampL.ipynb index 15776bc06..7c0808db0 100644 --- a/ipynb/sched_tune/stune_juno_taskonly_rampL.ipynb +++ b/ipynb/sched_tune/stune_juno_taskonly_rampL.ipynb @@ -43,16 +43,15 @@ ], "source": [ "# Generate plots inline\n", - "%pylab inline\n", + "%matplotlib inline\n", "\n", "import json\n", "import os\n", "\n", - "# Support for performance analysis of RTApp workloads\n", - "from trace_analysis import TraceAnalysis\n", - "\n", "# Support for trace events analysis\n", "from trace import Trace\n", + "\n", + "# Support for performance analysis of RTApp workloads\n", "from perf_analysis import PerfAnalysis\n", "\n", "# Suport for FTrace events parsing and visualization\n", @@ -187,18 +186,17 @@ " \"cpu_capacity\"\n", " ]\n", " trace = Trace(platform, run_dir, events, tasks=pa.tasks())\n", - " ta = TraceAnalysis(trace, tasks=pa.tasks())\n", " \n", " # Define time ranges for all the temporal plots\n", - " ta.setXTimeRange(t_min, t_max)\n", + " trace.setXTimeRange(t_min, t_max)\n", " \n", " # Tasks plots\n", - " ta.plotTasks()\n", + " trace.analysis.tasks.plotTasks()\n", " for task in pa.tasks():\n", " pa.plotPerf(task)\n", "\n", " # Cluster plots\n", - " ta.plotClusterFrequencies()\n" + " trace.analysis.frequency.plotClusterFrequencies()\n" ] }, { diff --git a/ipynb/sched_tune/stune_oak_rampL.ipynb b/ipynb/sched_tune/stune_oak_rampL.ipynb index b5ecc8abb..a6ebbc4e3 100644 --- a/ipynb/sched_tune/stune_oak_rampL.ipynb +++ b/ipynb/sched_tune/stune_oak_rampL.ipynb @@ -43,16 +43,15 @@ ], "source": [ "# Generate plots inline\n", - "%pylab inline\n", + "%matplotlib inline\n", "\n", "import json\n", "import os\n", "\n", - "# Support for performance analysis of RTApp workloads\n", - "from trace_analysis import TraceAnalysis\n", - "\n", "# Support for trace events analysis\n", "from trace import Trace\n", + "\n", + "# Support for performance analysis of RTApp workloads\n", "from perf_analysis import PerfAnalysis\n", "\n", "# Suport for FTrace events parsing and visualization\n", @@ -174,19 +173,18 @@ " \"cpu_capacity\"\n", " ]\n", " trace = Trace(platform, run_dir, events, tasks=pa.tasks())\n", - " ta = TraceAnalysis(trace, tasks=pa.tasks())\n", " \n", " # Define time ranges for all the temporal plots\n", - " ta.setXTimeRange(t_min, t_max)\n", + " trace.setXTimeRange(t_min, t_max)\n", " \n", " # Tasks plots\n", - " ta.plotTasks()\n", + " trace.analysis.tasks.plotTasks()\n", " for task in pa.tasks():\n", " pa.plotPerf(task)\n", "\n", " # Cluster and CPUs plots\n", - " ta.plotClusterFrequencies()\n", - " ta.plotCPU()\n" + " trace.analysis.frequency.plotClusterFrequencies()\n", + " trace.analysis.cpus.plotCPU()\n" ] }, { diff --git a/ipynb/thermal/ThermalSensorCharacterisation.ipynb b/ipynb/thermal/ThermalSensorCharacterisation.ipynb index 30e2a0fc8..a55b196ac 100644 --- a/ipynb/thermal/ThermalSensorCharacterisation.ipynb +++ b/ipynb/thermal/ThermalSensorCharacterisation.ipynb @@ -30,7 +30,7 @@ "logging.basicConfig(format=log_fmt)\n", "\n", "# Change to info once the notebook runs ok\n", - "logging.getLogger().setLevel(logging.DEBUG)" + "logging.getLogger().setLevel(logging.INFO)" ] }, { @@ -54,7 +54,6 @@ "\n", "# Support for trace events analysis\n", "from trace import Trace\n", - "from trace_analysis import TraceAnalysis\n", "\n", "# Suport for FTrace events parsing and visualization\n", "import trappy" @@ -90,7 +89,7 @@ " \"board\" : 'juno',\n", " \n", " # Target board IP/MAC address\n", - " \"host\" : '10.1.205.135',\n", + " \"host\" : '192.168.0.1',\n", " \n", " # Login credentials\n", " \"username\" : 'root',\n", diff --git a/ipynb/trappy/example_custom_events.ipynb b/ipynb/trappy/example_custom_events.ipynb index 3786c54fe..261a9897a 100644 --- a/ipynb/trappy/example_custom_events.ipynb +++ b/ipynb/trappy/example_custom_events.ipynb @@ -37,7 +37,7 @@ ], "source": [ "# Generate plots inline\n", - "%pylab inline\n", + "%matplotlib inline\n", "\n", "import copy\n", "import json\n", @@ -56,7 +56,6 @@ "\n", "# Support for trace events analysis\n", "from trace import Trace\n", - "from trace_analysis import TraceAnalysis\n", "\n", "# Suport for FTrace events parsing and visualization\n", "import trappy" diff --git a/ipynb/tutorial/00_LisaInANutshell.ipynb b/ipynb/tutorial/00_LisaInANutshell.ipynb index 347acb33d..c4edc58c1 100644 --- a/ipynb/tutorial/00_LisaInANutshell.ipynb +++ b/ipynb/tutorial/00_LisaInANutshell.ipynb @@ -1636,7 +1636,7 @@ "source": [ "# Trace events are converted into tables, let's have a look at one\n", "# of such tables\n", - "df = trace.df('sched_load_avg_task')\n", + "df = trace.data_frame.trace_event('sched_load_avg_task')\n", "df.head()" ] }, @@ -2158,7 +2158,7 @@ "# Plot residency time\n", "import matplotlib.pyplot as plt\n", "# Enable generation of Notebook emebedded plots\n", - "%pylab inline\n", + "%matplotlib inline\n", "\n", "fig, axes = plt.subplots(1, 1, figsize=(16, 5));\n", "df.plot(kind='bar', ax=axes);" @@ -2166,23 +2166,20 @@ }, { "cell_type": "markdown", - "metadata": { - "heading_collapsed": true - }, + "metadata": {}, "source": [ "# Example of Custom Plotting" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from perf_analysis import PerfAnalysis\n", - "from trace_analysis import TraceAnalysis\n", "\n", "# Full analysis function\n", "def analysis(t_min=None, t_max=None):\n", @@ -2209,25 +2206,24 @@ " \"cpu_capacity\",\n", " ]\n", " trace = Trace(platform, test_dir, events, tasks=pa.tasks())\n", - " ta = TraceAnalysis(trace, tasks=pa.tasks())\n", " \n", " # Define time ranges for all the temporal plots\n", - " ta.setXTimeRange(t_min, t_max)\n", + " trace.setXTimeRange(t_min, t_max)\n", " \n", " # Tasks performances plots\n", " for task in pa.tasks():\n", " pa.plotPerf(task)\n", " \n", " # Tasks plots\n", - " ta.plotTasks()\n", + " trace.analysis.tasks.plotTasks()\n", "\n", " # Cluster and CPUs plots\n", - " ta.plotClusterFrequencies()" + " trace.analysis.frequency.plotClusterFrequencies()" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 32, "metadata": { "collapsed": false, "scrolled": false diff --git a/ipynb/tutorial/06_TraceAnalysis.ipynb b/ipynb/tutorial/06_TraceAnalysis.ipynb index 09e13fa26..78afa3861 100644 --- a/ipynb/tutorial/06_TraceAnalysis.ipynb +++ b/ipynb/tutorial/06_TraceAnalysis.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", "# Tutorial Goal" ] }, @@ -13,7 +14,7 @@ "source": [ "This tutorial aims to show some example of **trace analysis and visualization**\n", "using a pre-defined set of analysis and plotting functions provided by the\n", - "**Filters** and **TraceAnalysis** modules of LISA." + "**Filters** and **Trace** modules of LISA." ] }, { @@ -39,18 +40,10 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], + "outputs": [], "source": [ "# Generate plots inline\n", - "%pylab inline\n", + "%matplotlib inline\n", "\n", "# Python modules required by this notebook\n", "import json\n", @@ -76,7 +69,20 @@ "output_type": "stream", "text": [ "\u001b[01;34m./example_results\u001b[00m\r\n", + "├── \u001b[01;35mcluster_freqs.png\u001b[00m\r\n", + "├── \u001b[01;35mediff_stats.png\u001b[00m\r\n", + "├── \u001b[01;35mediff_time.png\u001b[00m\r\n", "├── platform.json\r\n", + "├── \u001b[01;35mtask_util_20331_sh.png\u001b[00m\r\n", + "├── \u001b[01;35mtask_util_20552_chrome.png\u001b[00m\r\n", + "├── \u001b[01;35mtask_util_20615_chrome.png\u001b[00m\r\n", + "├── \u001b[01;35mtask_util_20672_keygen.png\u001b[00m\r\n", + "├── \u001b[01;35mtask_util_20678_df.png\u001b[00m\r\n", + "├── \u001b[01;35mtask_util_20687_chrome.png\u001b[00m\r\n", + "├── \u001b[01;35mtask_util_20705_chrome.png\u001b[00m\r\n", + "├── \u001b[01;35mtask_util_20803_sh.png\u001b[00m\r\n", + "├── \u001b[01;35mtask_util_20805_lsof.png\u001b[00m\r\n", + "├── \u001b[01;35mtask_util_650_permission_brok.png\u001b[00m\r\n", "├── \u001b[01;35mtask_util_chrome.png\u001b[00m\r\n", "├── \u001b[01;35mtask_util_keygen.png\u001b[00m\r\n", "├── \u001b[01;35mtask_util_lsof.png\u001b[00m\r\n", @@ -84,7 +90,7 @@ "├── trace.raw.txt\r\n", "└── trace.txt\r\n", "\r\n", - "0 directories, 7 files\r\n" + "0 directories, 20 files\r\n" ] } ], @@ -107,9 +113,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:42:19 INFO : CPUs max capacities:\n", - "11:42:19 INFO : big: 1024 (cpus: [2, 3])\n", - "11:42:19 INFO : LITTLE: 591 (cpus: [0, 1])\n" + "05:45:57 INFO : CPUs max capacities:\n", + "05:45:57 INFO : big: 1024 (cpus: [2, 3])\n", + "05:45:57 INFO : LITTLE: 591 (cpus: [0, 1])\n" ] } ], @@ -167,7 +173,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:42:46 INFO : Collected events spans a 35.314 [s] time interval\n" + "05:45:58 INFO : Parsing FTrace format...\n", + "05:46:29 INFO : Platform clusters verified to be Frequency coherent\n", + "05:46:29 INFO : Collected events spans a 35.314 [s] time interval\n", + "05:46:29 INFO : Set plots time range to (0.000000, 35.313536)[s]\n", + "05:46:29 INFO : Registering trace analysis modules:\n", + "05:46:29 WARNING : No performance data found in:\n", + "05:46:29 WARNING : ./example_results\n", + "05:46:29 INFO : perf\n", + "05:46:29 INFO : eas\n", + "05:46:29 INFO : tasks\n", + "05:46:29 INFO : cpus\n", + "05:46:29 INFO : functions\n", + "05:46:29 INFO : status\n", + "05:46:29 INFO : frequency\n" ] }, { @@ -208,22 +227,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:42:46 INFO : List of events identified in the trace:\n", - "11:42:46 INFO : sched_load_avg_task\n", - "11:42:46 INFO : cpu_frequency\n", - "11:42:46 INFO : cpu_capacity\n", - "11:42:46 INFO : sched_tune_boostgroup_update\n", - "11:42:46 INFO : sched_load_avg_cpu\n", - "11:42:46 INFO : sched_boost_cpu\n", - "11:42:46 INFO : sched_wakeup_new\n", - "11:42:46 INFO : sched_tune_config\n", - "11:42:46 INFO : sched_boost_task\n", - "11:42:46 INFO : sched_tune_tasks_update\n", - "11:42:46 INFO : sched_tune_filter\n", - "11:42:46 INFO : sched_energy_diff\n", - "11:42:46 INFO : sched_switch\n", - "11:42:46 INFO : sched_contrib_scale_f\n", - "11:42:46 INFO : sched_wakeup\n" + "05:46:29 INFO : List of events identified in the trace:\n", + "05:46:29 INFO : sched_load_avg_task\n", + "05:46:29 INFO : cpu_frequency\n", + "05:46:29 INFO : cpu_capacity\n", + "05:46:29 INFO : sched_tune_boostgroup_update\n", + "05:46:29 INFO : sched_load_avg_cpu\n", + "05:46:29 INFO : sched_boost_cpu\n", + "05:46:29 INFO : sched_wakeup_new\n", + "05:46:29 INFO : sched_tune_config\n", + "05:46:29 INFO : sched_boost_task\n", + "05:46:29 INFO : sched_tune_tasks_update\n", + "05:46:29 INFO : sched_tune_filter\n", + "05:46:29 INFO : sched_energy_diff\n", + "05:46:29 INFO : sched_switch\n", + "05:46:29 INFO : sched_contrib_scale_f\n", + "05:46:29 INFO : sched_wakeup\n" ] } ], @@ -263,6 +282,7 @@ " util_avg\n", " util_sum\n", " cluster\n", + " min_cluster_cap\n", " \n", " \n", " Time\n", @@ -278,6 +298,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -295,6 +316,7 @@ " 33\n", " 1601714\n", " LITTLE\n", + " 591\n", " \n", " \n", " 0.000229\n", @@ -310,6 +332,7 @@ " 33\n", " 1612418\n", " LITTLE\n", + " 591\n", " \n", " \n", " 0.000334\n", @@ -325,6 +348,7 @@ " 0\n", " 0\n", " LITTLE\n", + " 591\n", " \n", " \n", " 0.000982\n", @@ -340,6 +364,7 @@ " 3\n", " 164585\n", " LITTLE\n", + " 591\n", " \n", " \n", " 0.001178\n", @@ -355,6 +380,7 @@ " 3\n", " 205364\n", " LITTLE\n", + " 591\n", " \n", " \n", "\n", @@ -369,13 +395,13 @@ "0.000982 sh 0 20277 sh 0 5 285169 \n", "0.001178 sh 0 20277 sh 0 5 355825 \n", "\n", - " period_contrib pid util_avg util_sum cluster \n", - "Time \n", - "0.000014 916 20278 33 1601714 LITTLE \n", - "0.000229 111 20278 33 1612418 LITTLE \n", - "0.000334 957 20277 0 0 LITTLE \n", - "0.000982 710 20277 3 164585 LITTLE \n", - "0.001178 903 20277 3 205364 LITTLE " + " period_contrib pid util_avg util_sum cluster min_cluster_cap \n", + "Time \n", + "0.000014 916 20278 33 1601714 LITTLE 591 \n", + "0.000229 111 20278 33 1612418 LITTLE 591 \n", + "0.000334 957 20277 0 0 LITTLE 591 \n", + "0.000982 710 20277 3 164585 LITTLE 591 \n", + "0.001178 903 20277 3 205364 LITTLE 591 " ] }, "execution_count": 7, @@ -386,14 +412,14 @@ "source": [ "# Original TRAPpy::FTrace DataSet are still accessible by specifying the\n", "# trace event name of interest\n", - "trace.df('sched_load_avg_task').head()" + "trace.data_frame.trace_event('sched_load_avg_task').head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Predefined LISA Filtering Functions" + "# LISA Tasks Filtering Functions" ] }, { @@ -407,16 +433,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:42:47 INFO : Set plots time range to (0.000000, 35.313536)[s]\n", - "11:42:47 INFO : Set plots time range to (0.000000, 35.313536)[s]\n" + "05:46:29 INFO : Set plots time range to (0.000000, 35.313536)[s]\n" ] } ], "source": [ - "from filters import Filters\n", - "\n", - "fl = Filters(trace)\n", - "fl.setXTimeRange(t_min, t_max)" + "trace.setXTimeRange(t_min, t_max)" ] }, { @@ -434,23 +456,17 @@ }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Total 120 tasks with at least 100 \"utilization\" samples > 591\n", - "Top 3 \"big\" tasks:\n", - " count unique top freq\n", - "pid \n", - "20672 2426 2 keygen 2422\n", - "20705 1866 1 chrome 1866\n", - "20803 662 2 lsof 653\n" + "05:46:29 INFO : 120 tasks with samples of utilization > 591\n", + "05:46:30 INFO : 10 with more than 100 samples\n" ] } ], "source": [ "# Get a list of tasks which are the most big in the trace\n", - "top_big_tasks = fl.topBigTasks(\n", - " max_tasks=3, # Maximum number of tasks to report\n", + "top_big_tasks = trace.data_frame.top_big_tasks(\n", " min_utilization=None, # Minimum utilization to be considered \"big\"\n", " # default: LITTLE CPUs max capacity\n", " min_samples=100, # Number of samples over the minimum utilization \n", @@ -466,8 +482,89 @@ "outputs": [ { "data": { + "text/html": [ + "
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samplescomm
pid
206722426keygen, session_manager
207051866chrome
20803662sh, lsof
20615587chrome
20805576lsof
20331440sh, lsof
650290permission_brok
20552157chrome, Chrome_IOThread
20678136df, sshd, bash
20687120chrome
\n", + "
" + ], "text/plain": [ - "{'chrome': 20705, 'keygen': 20672, 'lsof': 20803}" + " samples comm\n", + "pid \n", + "20672 2426 keygen, session_manager\n", + "20705 1866 chrome\n", + "20803 662 sh, lsof\n", + "20615 587 chrome\n", + "20805 576 lsof\n", + "20331 440 sh, lsof\n", + "650 290 permission_brok\n", + "20552 157 chrome, Chrome_IOThread\n", + "20678 136 df, sshd, bash\n", + "20687 120 chrome" ] }, "execution_count": 10, @@ -482,24 +579,36 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 11, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Tasks which have been a \"utilization\" of 591 for at least 100 samples\n" + "05:46:30 INFO : 120 tasks with samples of utilization > 591\n", + "05:46:31 INFO : 10 with more than 100 samples\n", + "05:46:31 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:31 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:31 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:31 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:31 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:32 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:32 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:32 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:32 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:32 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:32 INFO : Tasks which have been a \"utilization\" of 591 for at least 100 samples\n" ] }, { "data": { - "image/png": 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dE90vm3sKAN2AliQAqDTTprVN2iC1HSeSMGpUstzRPEudueii0CrV0BCym+Vi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\n3LLbdexoX46Wz6fWL+raDRg71vt54S78etVVZrmw0Cx36mymAM/PDzx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KAAAg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\no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8rlarBz++1FGygHQtUhan3NWZoxo9ab83/+T+thbM880/ozNH6eZuXGeVDlYYNVTz/d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\nJEmSJEklBkmSJEmSVGKQJEmSJEkl/z9qL00aL+P9CQAAAABJRU5ErkJggg==\n", 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5VH5YgfVMJRiliIiItDeNsRURkezRuUv0OaqOp7KP/TH6vPRSjbEVERERQIWt\n5ACNwQiXcp/DvvIVKCyEJUsgVfOprMbYhkn3e7iU+zAp7+HSe2xFRCR3fPe78M47cPjh8OqrNbdp\njK2IiIjUojG2IiKScUryrmKZnx6Nse3aFT76qHqbxtiKiIiES2NsRUQke+TF/16ZwQsv1NzWLT/6\n7NUz2r5gQfvGJiIiIhlHha1kPY3BCJdyn8NuvAk6dICyMhg5ssam1atLa+47dmz7xSWJ0f0eLuU+\nTMp7uNr0PbZm9m3AgapHvg5sBf7i7mXNurKIiEh9Bg2CK/4NRh58V4YNa/NwREREJLM1aoytmc0C\nRgHziIrb/wH8FRgCPOLud7RlkI2lMbYiIrmh5MwZ0XtsPzUNZs3aPzPypElMe+o4Jq+5lkqs+q+t\n6He/iIhIEFo6xnYQ8Cl3/7a7fws4CTgcOBOY2GpRioiIAGzaGH0tqPVKn1WrYM2a5OISERGRjNTY\nwrYPsDutvQfo6+47gV2tHpVIE2gMRriU+xzWuUv0OarWK33y81nN2mRikkTpfg+Xch8m5T1cbf0e\n25nAC2Z2k5lNAf4MzDKzQ4CVzbmwmaXM7BEz+7uZrTSzT5tZLzNbYmarzGyxmaXS9r/ezFab2etm\ndk5zrikiIlniK1+BwkJYsmR/N2SIuiWLiIiI1NLo99ia2cnAZ4kmjnrW3Ve06MJm9wFL3f23ZtYR\nOAT4L+ADd7/TzL4H9HT368xsODALOBkYADwBHOPulbXOqTG2IiI5oKQEli2LPmubZl9nMtP2j7Et\nLIS3327nCEVERCQJLRpja2bTgE7ufpe7390KRW0P4Ax3/y2Au+91963A+cB98W73ARfGyxcAs919\nj7uXA2uAU1oSg4iIZLAZM6LxtWPHQkVFw/uWl7dHRCIiIpLBGtsV+S/AD8zsLTP7sZmNauF1jwTe\nN7MSM3vJzO6NuzX3dfdN8T6bgL7xcn+oMahqLdGTWxGNwQiYcp/D6ps8CjTGNlC638Ol3IdJeQ9X\nm46xdfcZ7j6WqCvwG8CdZtaSaSk7Ap8Cfu7unwL+CVxX65pO1O253rBacH0REclk9U0eVZeCgraP\nR0RERDJaxybuPxQYRvT+2mZNGhVbC6x19xfj9iPA9cBGM+vn7hvN7AjgvXj7OqJXDlUZGK87wMSJ\nEyksLAQglUpRVFREcXExsL/6V1tttXOnXaU9r79qFTzzTNQ++eRo+4svqt2a7cePOo5t762snjwq\n/ed/NAMev3TLAAAgAElEQVSBUkqBswAKCjLmv0e127ZdJVPiUbt92lXrMiUetdVWu31/35eVlVER\nD0sqb2D4UaMmjzKzO4FxwFvAQ8Af3P0gg54Oes5lwL+7+6p4puX8eNNmd7/DzK4DUrUmjzqF/ZNH\nDa09U5QmjxKR9nDhhfDmm0lHkdu2bYNvfAO++c0Dt71oJ3MKL+6fPKq8HIYMaecIRUREJAn1TR7V\n2ML2q8Aj7v5BKwZ0AvBroDPwJnAl0AGYAwwGyoHxVQW0mX0f+DKwF7jW3RfVcU4VtgEqTfsrroRF\nuQ9TqRnF6Sv0ez8Iut/DpdyHSXkP18FyX19h26iuyO7+SzPraWanAF3T1i9rRqxVx75CNGa3ttH1\n7P9D4IfNvZ6IiOSgPn2SjkBEREQyQGOf2F4NTCYa5/oycCrwnLuf3bbhNY2e2IqIBMBq/ZFWv/dF\nRESC0aL32ALXEo1vLXf3s4ATga2tGJ+IiMh+kyZBcXHj3mMrIiIiwWtsYbvL3T8CMLOu7v468Mm2\nC0uk8WrPnibhUO5z2KpVsHRpne+xLU1vTJ3anlFJgnS/h0u5D5PyHq7m5r6xhe27ZtYTeAxYYmZz\niSZ3EhERaX358UT5db3H9soro8+pU+GGG9o3LhEREclIjRpjW+MAs2KgAFjo7rvjdb3c/cPWD69p\nNMZWRCRHVFRET2qnT4dUKuloREREJEO06HU/jTj5y+5+YotP1PI4VNiKiIiIiIjkqJZOHiWSsTQG\nI1zKfQ4bNix6UtunD7zzTo1NynuYlPdwKfdhUt7D1dzcN+o9tiIiIu3qrbdgz55o+bTTYN26ZOMR\nERGRjKauyCIiknk6dYK9e6PlsWNh/vxk4xEREZGMoK7IIiKSPT772ehz5EiYOTPZWERERCTjNVjY\nmlmvhr7Sdh3dxnGK1EtjMMKl3Oewxx6Diy6K3mVba1Zk5T1Mynu4lPswKe/haqsxti8BDfXtPRLA\n3Tc36+oiIiJ1SaVgzpykoxAREZEs0SpjbDOFxtiKiIiIiIjkrvrG2Db4xNbMhrn762b2qbq2u/tL\nrRWgiIiIiIiISHMcbPKob8efPwV+UseXSOI0BiNcyn0OmzQJioujGZErKmpsUt7DpLyHS7kPk/Ie\nrjYZY+vuV8eL57r7rvRtZta1WVcUERE5mFWroomjICpyNd5WREREGtCoMbZm9pK7f+pg65KmMbYi\nIjli7FhYsABGjYIlSw6YGVlERETC1NwxtkcA/YH8eJytEc2SXADkt0WgIiIizJoVPamdPl1FrYiI\niBzUwcbYngP8GBhANKb2x/Hnt4Dvt21oIo2jMRjhUu5zWNXrfuooapX3MCnv4VLuw6S8h6utxtje\nB9xnZn8Dal/heODRZl1VREREREREpJU0doztd4i6IAN0Bc4D/u7uX27D2JpMY2xFRERERERyV31j\nbBtV2NZxsi7AYnc/szWCay0qbEVEcsSkSdHMyPn50XhbjbMVERER6i9sDzbGtj6HEI27FUmcxmCE\nS7nPYVWv+1mwICpy0yjvYVLew6Xch0l5D1ebjLGtYmZ/TWvmAYcDU5t1RRERkYPJjyfeHzUqmhlZ\nREREpAGNHWNbmNbcC2xy9z1tFFOzqSuyiEiOqKjQ635ERETkAK06xjZTqbAVERERERHJXa09xrZV\nmFkHM3vZzObF7V5mtsTMVpnZYjNLpe17vZmtNrPXzeyc5KKWTKMxGOFS7sOkvIdJeQ+Xch8m5T1c\nzc19ooUtcC2wkv2vEroOWOLuxwBPxm3MbDhwMTAcOBf4uZklHbuIiIiIiIhkgMS6IpvZQGAG8H+A\nb7n7v5rZ68CZ7r7JzPoBpe4+zMyuByrd/Y742IXAFHd/vtY51RVZREREREQkR2ViV+T/C3wXqExb\n19fdN8XLm4C+8XJ/YG3afmvR64ZERHLXpElQXAxjx0YTSYmIiIg0oFGv+2ltZnYe8J67v2xmxXXt\n4+5uZg09fq1z28SJEyksLAQglUpRVFREcXF0iar+2mrnVrtqXabEo3b7tcvKyvjGN76RMfGo3Yrt\n5cvhlVcoBpg0idJrrqneXvvez4h41W7ztu73cNt33XWX/n8uwHbVukyJR+32a9f+fV9WVkZF/Efu\n8vJy6pNIV2Qz+yFwBdGrg7oCBcCjwMlAsbtvNLMjgKfirsjXAbj77fHxC4Gb3P2FWudVV+QAlZaW\nVt8MEhblPoeNHQsLFkTvsV2ypMYrf5T3MCnv4VLuw6S8h+tguc/Y1/2Y2ZnAd+IxtncCm939jriY\nTbn7dfHkUbOAU4i6ID8BDK1dxaqwFRHJEXqPrYiIiNShvsI2ka7IdaiqRm8H5pjZVUA5MB7A3Vea\n2RyiGZT3AteoghURyWGpFMyZk3QUIiIikiXykg7A3Ze6+/nx8ofuPtrdj3H3c9y9Im2/H7r7UHcf\n5u6LkotYMk36WAwJi3IfJuU9TMp7uJT7MCnv4Wpu7hMvbEVERERERERaIvExtq1JY2xFRERERERy\nVya+x1ZERERERESkxVTYStbTGIxwKfc5KpWCjh2hc2d49dUDNivvYVLew6Xch0l5D5fG2IqISG7Y\nuhX27YM9e+CEE6JX/4iIiIg0QGNsRUQks1itYTMXXaRX/4iIiAhQ/xhbFbYiIpJZahe2W7ZE3ZNF\nREQkeJo8SnKWxmCES7kPRK2iVnkPk/IeLuU+TMp7uDTGVkREck9JSdIRiIiISBZQV2QREcksM2bA\nlVdGRe3EiUlHIyIiIhlEY2xFREREREQkq2mMreQsjcEIl3IfJuU9TMp7uJT7MCnv4dIYWxERERER\nEQmSuiKLiEhmSaVgxw7Iy4MVK2DkyKQjEhERkQyhMbYiIpIdOnaEffui5a5d4aOPko1HREREMobG\n2ErO0hiMcCn3OSov/qfJDF544YDNynuYlPdwKfdhUt7DpTG2IiKSG1asiJ7UlpWpG7KIiIg0iroi\ni4iIiIiISFZQV2QRERERERHJSSpsJetpDEa4lPswKe9hUt7DpdyHSXkPl8bYioiIiIiISJA0xlZE\nRDLHpEmwahXk58OsWdE7bUVERERieo+tiIhkvvz8/e+tHTsW5s9PNh4RERHJKJo8SnKWxmCES7nP\nQVVFLcCf/1znLsp7mJT3cCn3YVLew6UxtiIiklsqKqIvERERkYNIpCuymQ0C7gcOBxyY7u73mFkv\n4HfAEKAcGO/uFfEx1wNfBvYBk919cR3nVVdkEZFsZrV6Fl10EcyZk0wsIiIiknEyaoytmfUD+rl7\nmZkdCvwFuBC4EvjA3e80s+8BPd39OjMbDswCTgYGAE8Ax7h7Za3zqrAVEclm6YVtp07w3nuaQEpE\nRESqZdQYW3ff6O5l8fIO4O9EBev5wH3xbvcRFbsAFwCz3X2Pu5cDa4BT2jVoyVgagxEu5T7H7dkD\nW7cesFp5D5PyHi7lPkzKe7iydoytmRUCJwIvAH3dfVO8aRPQN17uD6xNO2wtUSEsIiK5bMSIpCMQ\nERGRLJDo637ibshLgVvc/TEz2+LuPdO2f+juvcxsGvC8u8+M1/8aeNzdH611PnVFFhHJZrXH2ALo\n97qIiIjE6uuK3DGJYADMrBPwe+ABd38sXr3JzPq5+0YzOwJ4L16/DhiUdvjAeN0BJk6cSGFhIQCp\nVIqioiKKi4uB/Y+11VZbbbXVztA2kdL4s7qdKfGprbbaaqutttrt2i4rK6MifktCeXk59Ulq8igj\nGkO72d2/mbb+znjdHWZ2HZCqNXnUKeyfPGpo7cezemIbptLS0ur/+CUsyn0OasQTW+U9TMp7uJT7\nMCnv4TpY7jPtie1ngcuBV83s5Xjd9cDtwBwzu4r4dT8A7r7SzOYAK4G9wDWqYEVEAlBXoSsiIiJS\nS6JjbFubntiKiGS5c86BJUtqrtPvdREREYll1Ot+RERE6rR8ec32nDnJxCEiIiJZRYWtZL2qQeYS\nHuU+B6W/t9YMLrrogF2U9zAp7+FS7sOkvIerublXYSsiIpmpY2IT94uIiEiW0RhbERHJHOmTRZ16\nKjz3XHKxiIiISMapb4ytClsREckc6YVtly6wa1dysYiIiEjG0eRRkrM0BiNcyn0OSu9+XHsiqZjy\nHiblPVzKfZiU93BpjK2IiGS/v/wFunaFV16BkSOTjkZERESyhLoii4hI5kjvijx1KtxwQ3KxiIiI\nSMbRGFsREcl8VuvfKf1OFxERkTQaYys5S2MwwqXc55jaRe3UqXXupryHSXkPl3IfJuU9XBpjKyIi\nuUXdkEVERKSR1BVZREQyg7ohi4iIyEGoK7KIiGSPCy5IOgIRERHJIipsJetpDEa4lPsc06HD/uV6\nxteC8h4q5T1cyn2YlPdwaYytiIhkt3379i+ffHJycYiIiEjW0RhbERFJxrBhsHEjdOoEK1ZAYWHN\n7fp9LiIiIrXoPbYiIpJZUinYujVaHjgQ1q6tuV2/z0VERKQWTR4lOUtjMMKl3Ge5Tp2iz/x8OP30\nRh+mvIdJeQ+Xch8m5T1cGmMrIiLZZfRo6Nw5Gk/73/9dc1ue/nkSERGRxlNXZBERSUbt99bWpt/n\nIiIiUou6IouIiIiIiEhOUmErWU9jMMKl3OewE06od5PyHiblPVzKfZiU93BpjK2IiGSXY4+tf9sx\nx7RfHCIiIpL1NMZWRESSMWjQga/4ARg1CpYsiV4HJCIiIpJGY2xFRCSzDBly4LoTT1RRKyIiIk2W\nVYWtmZ1rZq+b2Woz+17S8Uhm0BiMcCn3Wa6gIPqsep8twNChBy1qlfcwKe/hUu7DpLyHK+fH2JpZ\nB+D/AecCw4EJZtbAAC0JRVlZWdIhSEKU+yw3axZcdBGceWbUHjUKpk8/6GHKe5iU93Ap92FS3sPV\n3NxnTWELnAKscfdyd98DPARckHBMkgEqKiqSDkESkljuUyno2BE6d4ZXX00mhlyQSsGcOfDww1GB\n28guyLrnw6S8h0u5D5PyHq7m5r5jK8fRlgYA76a11wKfTigWEQnZjh2wb1/0dcIJ0Ls3rFhR95jR\nhkyaBKtWwZtvRscWFECfPvDOOzXXzZoVFXxV++fn171f1br8/P3H1HW9+rY3N9amxDNsGGzcCLt2\nRfvs3QsnnRQVtxpXKyIiIs2UTYWtpjuWOpWXlycdgiQksdzv21ez/cEHcOSRkJcXbTv0UOjaNSrc\nNm+G7dvhkEOgS5f96zp1AjN4//3oHFWzA3fpAh9/XHPdoEHQoQNs3br/mp07w+7d9R/br19UUH78\n8f6ny+nX69MnKiQrKqLiEqIYu3WrO+6Dxdqx4/7z1LW9rutVne+JJ6LCec6cRv34dc+HSXkPl3If\nJuU9XM3Nfda87sfMTgWmuPu5cft6oNLd70jbJzu+GREREREREWmWul73k02FbUfgDeDzwHpgOTDB\n3f+eaGAiIiIiIiKSqKzpiuzue83sP4BFQAfgNypqRUREREREJGue2IqIiIiIiIjUJZte91MvMzvX\nzF43s9Vm9r2k45H2Y2blZvaqmb1sZsuTjkfahpn91sw2mdlf09b1MrMlZrbKzBabmabUzUH15H6K\nma2N7/uXzezcJGOU1mdmg8zsKTN7zcz+ZmaT4/W673NYA3nXPZ/jzKyrmb1gZmVmttLMbovX657P\nYQ3kvVn3fNY/sTWzDkRjb0cD64AX0djbYJjZ28BJ7v5h0rFI2zGzM4AdwP3uPiJedyfwgbvfGf9B\nq6e7X5dknNL66sn9TcB2d/9posFJmzGzfkA/dy8zs0OBvwAXAlei+z5nNZD38eiez3lmlu/uO+N5\ndZ4BvgOcj+75nFZP3j9PM+75XHhiewqwxt3L3X0P8BBwQcIxSfs6YFY0yS3u/jSwpdbq84H74uX7\niP7nR3JMPbkH3fc5zd03untZvLwD+DvR++x13+ewBvIOuudznrvvjBc7E82nswXd8zmvnrxDM+75\nXChsBwDvprXXsv+XoOQ+B54wsxVmdnXSwUi76uvum+LlTUDfJIORdvd1M3vFzH6jrmm5zcwKgROB\nF9B9H4y0vD8fr9I9n+PMLM/Myoju7afc/TV0z+e8evIOzbjnc6Gwze6+1NJSn3X3E4ExwNfibosS\nGI/GVOh3QTh+ARwJFAEbgJ8kG460lbg76u+Ba919e/o23fe5K877I0R534Hu+SC4e6W7FwEDgc+Z\n2Vm1tuuez0F15L2YZt7zuVDYrgMGpbUHET21lQC4+4b4833gD0Rd0yUMm+LxWJjZEcB7Cccj7cTd\n3/MY8Gt03+ckM+tEVNQ+4O6Pxat13+e4tLw/WJV33fNhcfetwHzgJHTPByMt76Oae8/nQmG7Ajja\nzArNrDNwMTA34ZikHZhZvpl1j5cPAc4B/trwUZJD5gJfipe/BDzWwL6SQ+L/uakyDt33OcfMDPgN\nsNLd70rbpPs+h9WXd93zuc/Meld1NzWzbsAXgJfRPZ/T6st71R8zYo2+57N+VmQAMxsD3EU04Pg3\n7n5bwiFJOzCzI4me0gJ0BGYq97nJzGYDZwK9icZg3Aj8EZgDDAbKgfHuXpFUjNI26sj9TUAxUfck\nB94GvpI2BktygJmdDiwDXmV/18PrgeXovs9Z9eT9+8AEdM/nNDMbQTQ5VF789YC7/8jMeqF7Pmc1\nkPf7acY9nxOFrYiIiIiIiIQrF7oii4iIiIiISMBU2IqIiIiIiEhWU2ErIiIiIiIiWU2FrYiIiIiI\niGQ1FbYiIiIiIiKS1VTYioiIiIiISFZTYSsiIiIiIiJZTYWtiIiIiIiIZDUVtiIiIiIiIpLVVNiK\niIiIiIhIVlNhKyIiksPMrNTMzmzGceVm9vkm7F/ZwLYpZvZAU2Oo4zwlZvahmT3f0nOJiEhuUWEr\nIiI5IS7EdprZdjPbGBdBh8TbSs3sqni52Mwq4/22m9m7ZvY7MxvVhGsdY2Z/NLP3zGyzmS00s2Nq\n7fNNM9tgZlvN7Ddm1jlt24Pxtm1m9paZ/VfatuFmtiIu4CrM7FkzOz1t+1lm9lS87e1GhOvxV1M1\n97j6ztUiZnYGMBro7+6ntjwkERHJJSpsRUQkVzhwnrt3Bz4FjAJ+kLYtvbha5+7d431PBV4Hnjaz\nsxt5rR7AY8AxQF9gOfDHqo1m9i/A94CzgSHAUcDNacffBhzp7gXAGODr8TEA64CLgMOAnsBDwCNp\nx+4Afg18t5GxZgJrhXMMAcrdfVcrnEtERHKMClsREck57r4eWAgc14h917n7TUTF4h2NPP+L7l7i\n7hXuvhe4C/ikmfWMd/kS8Gt3/7u7VwBTgYlpx79Wq0DbC7wfb9vq7m+7uwMdgEpgQ61rzwQa87S2\nQWbW28z+ZGZb4ifPy2rtcqKZvRI/HX7IzLq0wjW7xk+sP4ivu9zMDo+39TezuXEsq83s3+P1VwH3\nAp+Jn7Lf1NI4REQkt3RMOgAREZFWZABmNojoSejvm3DsH4BrzKybu39kZvOAp939zkYc+zlgg7tv\nidvD4/NVeRXoa2Y9q/Yxs58TFcBdgP9w95dqfCNmFcAhwHqiJ79t4dvAu0DvuJ3exdeInhz/C/Ax\n8CxRcf6rFl7zS0ABMDA+bxHwUbztIaKf1ReBY4ElZvamu//GzPYC/+7uZ7Tw+iIikoP0xFZERHKF\nAY+Z2RbgaaAU+GETjl8fnyMF4O7/2pii1swGAv8P+Fba6kOBrWntbfFn96oV7n5NvN9o4FYzOyX9\nvO6eIury/BDwsJm1Rnfe2nYDRwCF7r7P3Z9NDwG4x903xsX4PKIitDWueRhwtEdedvft8R8jTgO+\n5+673f0Voqfo/xYf1xbfv4iI5AgVtiIikiscuMDde7p7obv/h7t/3ITjB8TnqGjsAWbWB1gM/Mzd\nf5e2aQfRU8kqPeLP7TUCjpQCDwMTap/f3XcC1xGN5R3R2Lia4EfAGmCxmb1pZt+rtX1j2vJHRIV4\nSz0ALAIeMrN1ZnaHmXUE+gMfuvs/0/b9B1FeREREGqTCVkREJDIO+Iu7f3TQPYF4PO1i4DF3v63W\n5teo+XTzBGBTWlfl2joB/6xnWweif693NiaupnD3He7+HXf/BHA+8C0zO6u+3VtyqbRr7nX3qe5+\nHNET2vOInsquA3qZWXrxPBhY24LriohIIFTYiohIsCwyIJ6M6Crg+408roDoqeMz7l7XMfcDV5nZ\nsXEBfANQEh/bx8wuMbNDzKxDPBvyRcSzKpvZaDMrircVAD8F3nD3NWkxdyUqhs3MuqS/SqiJ3///\nMLOhcTfnbcA+osmq6ty9OdeofWz8uqURZtaB6An2HmCfu68F/gzcFn9PI4EvAw+24LoiIhIIFbYi\nIhKi/ma2naiwWk40e/KZ7v5E1Q5m9riZXVfP8eOIXid0Zdr7cLfF421x90XAncBTQDnwJlA1k68D\nXyV6ErkZuAW4wt1fjLengNlEXaLfAPoQPU2tcibR09v5wCCiLsILm/lzOBpYQvRz+DNRl+ql9ezb\nkvfaph/bj6jr9VZgJdFY6AfibROAQqLxzo8CN7r7f7fC9UVEJMdZ9DYBERERyUVm9hRwk7vXfpVP\na1+n0t31B3MREUmE/gESERERERGRrKbCVkRERFqDuoCJiEhi1BVZREREREREspqe2IqIiIiIiEhW\n65h0AK3JzPT4WUREREREJIe5+wGvoMupwhZAXavDM3HiRGbMmJF0GJIA5T5MynuYlPdwKfdhUt7D\ndbDcR69eP5C6IkvWKywsTDoESYhyHyblPUzKe7iU+zAp7+Fqbu5V2IqIiIiIiEhWU2ErWS+VSiUd\ngiREuQ+T8h4m5T1cyn2YlPdwNTf3Kmwl6xUVFSUdgiREuQ+T8h4m5T1cyn2YlPdwNTf3OfUeWzPz\nXPp+RERERERySX0T/4jUpa7azszCmBVZREREREQylx5ESWM09Y8g6oosWa+0tDTpECQhyn2YlPcw\nKe/hUu5FpDFU2IqIiIiIiEhW0xhbERERERFpF/H4yKTDkCxQ338r9Y2x1RNbERERERGRJrjtttu4\n+uqrASgvLycvL4/KysqEowpbmxa2ZvZbM9tkZn9NW9fLzJaY2SozW2xmqbRt15vZajN73czOSVt/\nkpn9Nd52d1vGLNlHY2/CpdyHSXkPk/IeLuVeklZaWsqgQYNqrLv++uu59957E4pI6tLWT2xLgHNr\nrbsOWOLuxwBPxm3MbDhwMTA8Pubntn8qrF8AV7n70cDRZlb7nJIgs5pfIiIiIiIi7alNC1t3fxrY\nUmv1+cB98fJ9wIXx8gXAbHff4+7lwBrg02Z2BNDd3ZfH+92fdoxkAPf46+pJ+JnFMHYsVFS02/WL\ni4vb7VqSWZT7MCnvYVLew6XcS3vIy8vjrbfeqm5PnDiRG264gZ07dzJmzBjWr19P9+7dKSgoYMOG\nDUyZMoUrrriiSdcoKSlh+PDhFBQU8IlPfILp06dXbzv22GOZP39+dXvv3r306dOHsrIyAO6//36G\nDBlC7969ufXWWyksLOTJJ59s4XedW5IYY9vX3TfFy5uAvvFyf2Bt2n5rgQF1rF8Xr5dMMmkS/PrX\nsHQpLFgAl12WdEQiIiIikk0mTYLi4pY9JGmNcxBNUGRm5Ofns3DhQvr378/27dvZtm0bRxxxRJPf\nsQrQt29f5s+fz7Zt2ygpKeGb3/xmdeF66aWXMnv27Op9Fy1axOGHH05RURErV67ka1/7GrNnz2bD\nhg1s3bqV9evXNyuGXJbo5FHxFMaaFi2bVfU/vvfe6LFtlRUr2i0Ejb0Jl3IfJuU9TMp7uJT7gKxa\ntf8hyaRJyZ0jVjUjb10z8zZnZuexY8dy5JFHAvC5z32Oc845h2XLlgEwYcIE5s6dy65duwCYNWsW\nEyZMAOCRRx7h/PPP57TTTqNTp05MnTpVRW0dOiZwzU1m1s/dN8bdjN+L168D0kdlDyR6UrsuXk5f\nv66+k0+cOJHCwkIAUqkURUVF1V1Yqn4xqt3KbSKl8WcxQGVlu12/Ssb8PNRut3ZZWVlGxaO22mq3\nXVv3e7jtqidamRKP2i1rNyg/P/ocNQrSuuk2SWuco40sWLCAm2++mdWrV1NZWcnOnTsZOXIkAEOH\nDuXYY49l7ty5nHfeecybN49bbrkFgA0bNjBw4P5yqFu3bhx22GGJfA/trer3f0X89L28vLzefdv8\nPbZmVgjMc/cRcftOYLO732Fm1wEpd78unjxqFnAKUVfjJ4Ch7u5m9gIwGVgOzAfucfeFdVxL77Ft\nb/X9tahfP9iwoX1jEREREZGM1uB7bCsqoqes06dDKlX3PgfTzHMceuihPP/88xx//PEAnHvuuZxy\nyilMnTqVpUuXcvnll/Puu+9W73/zzTezZs0aHnjgAcrLyznqqKPYu3cveXl5dZ7/448/pmfPnjz4\n4INccMEFdOjQgXHjxjFixAimTp0KwF133cXSpUsZP348d999N88//zwAU6dO5Y033mDmzJkAfPTR\nR6RSKRYsWMDZZ5/drB9TNsio99ia2Wzgz8AnzexdM7sSuB34gpmtAs6O27j7SmAOsBJYAFyTVqVe\nA/waWA2sqauolQyzaFHSEYiIiIhINkmlYM6c5he1LThHUVERM2fOZN++fSxcuLC6izBEY2M3b97M\ntm3bqtc19WHa7t272b17N7179yYvL48FCxawePHiGvtccsklLFq0iF/+8pdcljZfzRe/+EXmzZvH\nc889x+7du5kyZUqzukLnujYtbN19grv3d/fO7j7I3Uvc/UN3H+3+/9m79/i66jrf/69PWy4F2ibl\nVlpaQq2IRaBVZABFghYEzgzQc2ZaEP1ZZPTwGC/MjOMIjEIFRoEDjuA5XkC5SVsH0cF2gHL7ERCH\n2yBFpghYOFELbYHatMWCtPZz/tg7ZTckbZIm2dl7vZ6PRx7s715rr/XdeWeXfPP9ftbK/TLz2Mxs\nq9j/q5k5KTP3z8w7Kp5/LDMPLG/7XH/2WX3koosG7FTtS1xUPGZfTOZeTOZeXGavgXDFFVewYMEC\nGpmTEAcAACAASURBVBsbmTt3LtOnT9+0bf/99+fUU09l4sSJjB49mmXLlm26uFS7rdW8jhgxgiuv\nvJIZM2YwevRo5s2bx0knnbTZPmPGjOGII47gwQcfZObMmZuenzx5Mt/85jc55ZRTGDt2LCNGjGCP\nPfZghx126KN3Xx/6fSnyQHIpchV09SFetWrb/trWAy0tLd2r21DdMftiMvdiMvfiMvv6ssWlyOqW\nV199lcbGRpYsWcI+++xT7e70m54uRXZgq23T2cB2xAh48kmo4w+aJEmSes6Bbe8sWLCAD33oQ2Qm\nn//853n00Ud57LHHqt2tfjWoamxVUGvXwvvfX+1eSJIkSQNql112YcSIEW/5+vnPf75Nx50/fz7j\nxo1j3LhxPPfcc/zwhz/sox7XD2dstW06m7HdaSd46qkBm7F1iVJxmX0xmXsxmXtxmX19ccZW3eWM\nrarvjTdg9epq90KSJElSQThjq23T1cWjdtwRXnttYPsiSZKkQc0ZW3WXM7aqvgh4+OFq90KSJElS\nQTiwVd9btAgOOmjATuf97YrL7IvJ3IvJ3IvL7CV1hwNb9a3m5gEd1EqSJEmSNbbaNh1rbFetgoaG\n6vRFkiRJg1qt19j+9re/5YADDmDNmjVEV9ea2QazZs1i/PjxXHjhhX1+7Fpjja2qy0GtJEmS6tSE\nCRNYu3ZtvwxqoTRo29Zjt7S0MH78+D7qUe1wYKuaZ+1NcZl9MZl7MZl7cZm9iqbaM9obNmyo6vl7\ny4GtJEmSpEJramrisssu46CDDmLEiBGcccYZrFixguOPP55Ro0ZxzDHH0NbWRmtrK0OGDGHjxo0A\nNDc3c9555/H+97+fkSNH8uEPf5iVK1du9XwPPPAARxxxBI2NjUyYMIEbbrhh07b2GdvrrruOI488\ncrPXDRkyhOeffx6A2267jQMOOICRI0ey99578/Wvf51169Zx/PHH8+KLLzJixAhGjhzJ8uXLyUwu\nvvhiJk2axG677cbMmTNZtWoVwKb3dM0117DPPvswbdq0HvX9+uuvB+DWW29l6tSpjBo1igkTJvCV\nr3xl02vaz3H11Vczbtw4xo4dy+WXX77V71NPOLBVzWtubq52F1QlZl9M5l5M5l5cZq+BEBH85Cc/\n4Z577uGZZ57h3//93zn++OO5+OKLeemll9i4cSNXXnllp6+dN28e1113HS+99BJvvPEGl1122RbP\n9Zvf/IYTTjiBs846i1deeYVFixZx8MEH97jPZ5xxBldddRVr1qxh8eLFHH300ey0004sXLiQsWPH\nsnbtWtasWcOYMWO48sormT9/Pvfffz/Lli2jsbGRT3/605sd7/777+fpp5/mjjvu6FHfp0yZAsAu\nu+zCjTfeyOrVq7n11lv59re/zU9/+tPNXt/S0sKSJUu48847ueSSS7jnnnt6/L674sBWkiRJ0qAQ\n0TdfvfHZz36W3XffnbFjx3LkkUdy+OGHc/DBB7PDDjswffp0Hn/88bfUv0YEp59+OpMmTWLHHXdk\nxowZLFq0aIvnmTt3LscccwwzZ85k6NChjB49ulcD2+23357FixezZs0aRo0axdSpU4HOlzJ/97vf\n5aKLLmLs2LFst912nH/++dx8882bZp4BZs+ezfDhw9lhhx161fejjjqKAw44AIADDzyQU045hfvu\nu2+z159//vkMHz6cd73rXZx++unMmzevx++7Kw5s1XdGjqzKaa29KS6zLyZzLyZzLy6zL5bMvvnq\njT333HPT4+HDh2/W3nHHHXn11Vc7fd2YMWM2e11X+7VbunQpEydO7F0nK/z4xz/mtttuo6mpiebm\nZh566KEu921tbWX69Ok0NjbS2NjI5MmTGTZsGCtWrNi0T3cuOLWlvj/88MMcffTR7LHHHjQ0NPDd\n7373LcuyK88xYcIEXnzxxa2es7sc2GrbtA9mR46E1aur2xdJkiSpj/TXRZzGjx/Pc889t9X9dt55\nZ9atW7epvXz58s22H3LIIdxyyy28/PLLnHzyycyYMQOg06sqT5gwgYULF7Jq1apNX+vWrWOvvfba\ntE93rsa8pb5/5CMf4eSTT2bp0qW0tbVx5plnbjYjDKXbJVU+Hjdu3FbP2V0ObLVtVq8u/VmsioNa\na2+Ky+yLydyLydyLy+w12PV0AHzaaadx991386Mf/YgNGzawcuVKnnjiiU3Haj/ewQcfzOLFi3ni\niSd4/fXXmT179qZjrF+/njlz5rB69WqGDh3KiBEjGDp0KFCaeV65ciVr1qzZtP+ZZ57Jueeeu2lg\n+fLLLzN//vwev9ct9f3VV1+lsbGR7bffnkceeYS5c+e+ZbB80UUX8dprr7F48WKuu+46Zs6c2eM+\ndMWBrSRJkiR1UDkoq7y/bGd1tp3t15Xx48dz2223cfnll7PrrrsydepUfvnLX77l9fvttx/nnXce\n06ZN4x3veAdHHnnkZse+8cYb2XfffRk1ahRXXXUVc+bMAWD//ffn1FNPZeLEiYwePZrly5dz1lln\nceKJJ3LssccycuRIDj/8cB555JFO30Nv+/6tb32L8847j5EjR3LhhRd2Omg96qijmDRpEtOmTeML\nX/jCVq/A3BNR7fsk9aWIyHp6P+qelpYW/5pbUGZfTOZeTOZeXGZfXyKi6vdp1cBrbW1l4sSJbNiw\ngSFDuje32tXPSvn5t4zEnbGVJEmSJNU0Z2wlSZIkDYiizNjOmTOHM8888y3PNzU18eSTT1ahR93X\nH31vbW3lbW97G+vXr++3GVsHtpIkSZIGRFEGttp2LkVW4Xh/u+Iy+2Iy92Iy9+Iye0nd4cBWkiRJ\nklTTXIosSZIkaUC4FFnd1dOlyMMGpFediIhzgI8CG4EngdOBnYF/BfYBWoEZmdlWsf8ngD8Bn8vM\nO6vQbUmSJEnboLv3TJV6oipLkSOiCfgk8O7MPBAYCpwCnA3clZn7AfeU20TEZGAmMBk4DvhWRLiM\nWoC1N0Vm9sVk7sVk7sVl9vUlM7v1de+993Z7X7/q66sy+56o1uBwDbAe2CkihgE7AS8CJwLXl/e5\nHji5/PgkYF5mrs/MVmAJcOiA9liSJEmSNChVrcY2Ij4FXA68BtyRmR+LiFWZ2VjeHsDvM7MxIr4J\nPJSZc8rbvgfcnpk/7nDMrNb7kSRJkiT1r0FVYxsRbwP+FmgCVgM/ioiPVu6TmRkRWxqldrpt1qxZ\nNDU1AdDQ0MCUKVNobm4G3lzKYtu2bdu2bdu2bdu2bdu2B3970aJFtLW1AdDa2kpXqjJjGxEzgWMy\n86/L7Y8BhwEfBI7OzOURsRdwb2buHxFnA2TmxeX9FwLnZ+bDHY7rjG0BtbS0bPrhV7GYfTGZezGZ\ne3GZfTGZe3FtLfuuZmyH9GentuBp4LCIGF5ecjwNeApYAHy8vM/HgVvKj+cDp0TE9hGxL/B24JEB\n7rMkSZIkaRCqZo3tP1IavG4EfgH8NTACuAmYwFtv93Mupdv9bADOysw7OjmmM7aSJEmSVKe6mrGt\n2sC2PziwlSRJkqT6NdiWIkt9pr3IXMVj9sVk7sVk7sVl9sVk7sXV2+wd2EqSJEmSappLkSVJkiRJ\nNcGlyJIkSZKkuuTAVjXPGoziMvtiMvdiMvfiMvtiMvfissZWkiRJklRI1thKkiRJkmqCNbaSJEmS\npLrkwFY1zxqM4jL7YjL3YjL34jL7YjL34rLGVpIkSZJUSNbYSpIkSZJqgjW2kiRJkqS65MBWNc8a\njOIy+2Iy92Iy9+Iy+2Iy9+KyxlaSJEmSVEjW2EqSJEmSaoI1tpIkSZKkuuTAVjXPGoziMvtiMvdi\nMvfiMvtiMvfissZWkiRJklRI1thKkiRJkmqCNbaSJEmSpLrkwFY1zxqM4jL7YjL3YjL34jL7YjL3\n4upt9sO6s1NEfB5IoH3KN4HVwGOZuahXZ5YkSZIkqQ90q8Y2IuYChwALKA1u/xvwJLAPcHNmXtKf\nnewua2wlSZIkqX51VWPb3YHtz4DjM/PVcnsX4DbgOEqztu/s4/72igNbSZIkSapf23rxqN2BNyra\n64E9M3Md8Hof9E/qNWswisvsi8nci8nci8vsi8nci6u/72M7B3g4Is6PiNnAfwBzI2Jn4KnenDgi\nGiLi5oj4VUQ8FRF/FhGjI+KuiHg2Iu6MiIaK/c+JiF9HxNMRcWxvzilJkiRJqj/dvo9tRLwXeB+l\nC0f9PDP/c5tOHHE9cF9mXhMRw4CdgX8CXsnMSyPii0BjZp4dEZOBucB7gXHA3cB+mbmxwzFdiixJ\nkiRJdWpba2y/CczLzP/oo86MAh7PzIkdnn8aOCozV0TEGKAlM/ePiHOAje0XqYqIhcDszHyow+sd\n2EqSJElSndrWGtvHgC9FxPMRcVlEHLKN/dkXeDkiro2IX0TE1eVlzXtm5oryPiuAPcuPxwJLK16/\nlNLMrWQNRoGZfTGZezGZe3GZfTGZe3H1a41tZl6XmSdQWgr8DHBpRCzp1RlLhgHvBr6Vme8G/gCc\n3eGcSWnZc5fd2obzS5IkSZLqxLAe7j8J2J/S/Wt7ddGosqXA0sx8tNy+GTgHWB4RYzJzeUTsBbxU\n3v4CML7i9XuXn3uLWbNm0dTUBEBDQwNTpkyhubkZeHP0b9u27fpptxss/bHd/+3m5uZB1R/bft5t\n92+7/bnB0h/btm0P7L/3ixYtoq2tDYDW1la60t0a20uB6cDzwA+Bf8vMtq2+cMvHvB/468x8tnyl\n5Z3Km1Zm5iURcTbQ0OHiUYfy5sWjJnUsqLXGVpIkSZLq17bW2D4PHJ6ZH87Ma7d1UFv2WWBORDwB\nHAT8M3AxcExEPAt8sNwmM58CbqI0S3w78DeOYNWu4192VBxmX0zmXkzmXlxmX0zmXly9zb5bS5Ez\n8zsR0RgRhwI7Vjx/f6/OWnrtE5Rqdjua1sX+XwW+2tvzSZIkSZLqU3eXIn8S+BylOtfHgcOABzPz\ng/3bvZ5xKbIkSZIk1a9tXYp8FqX61tbMPBqYCqzuw/5JkiRJktQr3R3Yvp6ZrwFExI6Z+TTwjv7r\nltR91mAUl9kXk7kXk7kXl9kXk7kXV7/W2AK/i4hG4BbgrohYBbT26oySJEmSJPWhbtXYbvaCiGZg\nJLAwM98oPzc6M3/f993rGWtsJUmSJKl+dVVj2+OBbRcHfzwzp27zgba9Hw5sJUmSJKlObevFo6RB\nyxqM4jL7YjL3YjL34jL7YjL34upt9g5sJUmSJEk1zaXIkiRJkqSa4FJkSZIkSVJd2uLANiJGb+mr\nYtdp/dxPqUvWYBSX2ReTuReTuReX2ReTuRdXf93H9hfAltb27guQmSt7dXZJkiRJkrZRn9TYDhbW\n2EqSJElS/eqqxnaLM7YRsX9mPh0R7+5se2b+oq86KEmSJElSb2zt4lGfL//368DlnXxJVWcNRnGZ\nfTGZezGZe3GZfTGZe3H1S41tZn6y/PC4zHy9cltE7NirM0qSJEmS1Ie6VWMbEb/IzHdv7blqs8ZW\nkiRJkupXb2ts9wLGAjuV62yD0lWSRwI79UdHJUmSJEnqia3V2B4LXAaMo1RTe1n5v38PnNu/XZO6\nxxqM4jL7YjL3YjL34jL7YjL34uqvGtvrgesj4r+Ajmd4F/CTXp1VkiRJkqQ+0t0a23+gtAQZYEfg\nz4FfZeYn+rFvPWaNrSRJkiTVr65qbLs1sO3kYDsAd2bmUX3Rub7iwFaSJEmS6ldXA9ut1dh2ZWdK\ndbdS1VmDUVxmX0zmXkzmXlxmX0zmXlz9UmPbLiKerGgOAfYALujVGSVJkiRJ6kPdrbFtqmhuAFZk\n5vp+6lOvuRRZkiRJkupXn9bYDlYObCVJkiSpfvV1jW2fiIihEfF4RCwot0dHxF0R8WxE3BkRDRX7\nnhMRv46IpyPi2Or1WoONNRjFZfbFZO7FZO7FZfbFZO7F1dvsqzqwBc4CnuLNWwmdDdyVmfsB95Tb\nRMRkYCYwGTgO+FZEVLvvkiRJkqRBoGpLkSNib+A64J+Bv8/Mv4iIp4GjMnNFRIwBWjJz/4g4B9iY\nmZeUX7sQmJ2ZD3U4pkuRJUmSJKlODcalyP8CfAHYWPHcnpm5ovx4BbBn+fFYYGnFfkvxdkOSJEmS\nJLp5u5++FhF/DryUmY9HRHNn+2RmRsSWpl873TZr1iyampoAaGhoYMqUKTQ3l07Rvl7bdn21258b\nLP2xPXDtRYsW8bd/+7eDpj+2B6bd8bNf7f7Y9vNuu3/b3/jGN/x9roDt9ucGS39sD1y747/3ixYt\noq2tDYDW1la6UpWlyBHxVeBjlG4dtCMwEvgJ8F6gOTOXR8RewL3lpchnA2TmxeXXLwTOz8yHOxzX\npcgF1NLSsunDoGIx+2Iy92Iy9+Iy+2Iy9+LaWvaD9nY/EXEU8A/lGttLgZWZeUl5MNuQmWeXLx41\nFziU0hLku4FJHUexDmwlSZIkqX51NbCtylLkTrSPRi8GboqIM4BWYAZAZj4VETdRuoLyBuBvHMFK\nkiRJkqD6t/shM+/LzBPLj3+fmdMyc7/MPDYz2yr2+2pmTsrM/TPzjur1WINNZS2GisXsi8nci8nc\ni8vsi8nci6u32Vd9YCtJkiRJ0raoeo1tX7LGVpIkSZLq12C8j60kSZIkSdvMga1qnjUYxWX2xWTu\nxWTuxWX2xWTuxWWNrSRJkiSpkKyxlSRJkiTVBGtsJUmSJEl1yYGtap41GMVl9sVk7sVk7sVl9sVk\n7sVlja0kSZIkqZCssZUkSZIk1QRrbCVJkiRJdcmBrWqeNRjFZfbFZO7FZO7FZfbFZO7FZY2tJEmS\nJKmQrLGVJEmSJNUEa2wlSZIkSXXJga1qnjUYxWX2xWTuxWTuxWX2xWTuxWWNrSRJkiSpkKyxlSRJ\nkiTVBGtsJUmSJEl1yYGtap41GMVl9sVk7sVk7sVl9sVk7sVlja0kSZIkqZCssZUkSZIk1QRrbCVJ\nkiRJdcmBrWqeNRjFZfbFZO7FZO7FZfbFZO7FZY2tJEmSJKmQqlJjGxHjgRuAPYAErsrMKyNiNPCv\nwD5AKzAjM9vKrzkH+ATwJ+BzmXlnJ8e1xlaSJEmS6lRXNbbVGtiOAcZk5qKI2AV4DDgZOB14JTMv\njYgvAo2ZeXZETAbmAu8FxgF3A/tl5sYOx3VgK0mSJEl1alBdPCozl2fmovLjV4FfURqwnghcX97t\nekqDXYCTgHmZuT4zW4ElwKED2mkNWtZgFJfZF5O5F5O5F5fZF5O5F1fN1thGRBMwFXgY2DMzV5Q3\nrQD2LD8eCyyteNlSSgNhSZIkSVLBVfU+tuVlyPcBF2bmLRGxKjMbK7b/PjNHR8Q3gYcyc075+e8B\nt2XmTzocz6XIkiRJklSnulqKPKwanQGIiO2AHwM/yMxbyk+viIgxmbk8IvYCXio//wIwvuLle5ef\ne4tZs2bR1NQEQENDA1OmTKG5uRl4c1rbtm3btm3btm3btm3btm0P/vaiRYtoa2sDoLW1la5U6+JR\nQamGdmVm/l3F85eWn7skIs4GGjpcPOpQ3rx41KSO07PO2BZTS0vLph9+FYvZF5O5F5O5F5fZF5O5\nF9fWsh9sM7bvAz4K/DIiHi8/dw5wMXBTRJxB+XY/AJn5VETcBDwFbAD+xhGsJEmSJAmqXGPb15yx\nlSRJkqT6Nahu9yNJkiRJUl9xYKua115kruIx+2Iy92Iy9+Iy+2Iy9+LqbfYObCVJkiRJNc0aW0mS\nJElSTbDGVpIkSZJUlxzYquZZg1FcZl9M5l5M5l5cZl9M5l5c1thKkiRJkgrJGltJkiRJUk2wxlaS\nJEmSVJcc2KrmWYNRXGZfTOZeTOZeXGZfTOZeXNbYSpIkSZIKyRpbSZIkSVJNsMZWkiRJklSXHNiq\n5lmDUVxmX0zmXkzmXlxmX0zmXlzW2EqSJEmSCskaW0mSJElSTbDGVpIkSZJUlxzYquZZg1FcZl9M\n5l5M5l5cZl9M5l5c1thKkiRJkgrJGltJkiRJUk2wxlaSJEmSVJcc2KrmWYNRXGZfTOZeTOZeXGZf\nTOZeXNbYSpIkSZIKyRpbSZIkSVJNsMZWkiRJklSXampgGxHHRcTTEfHriPhitfujwcEajOIy+2Iy\n92Iy9+Iy+2Iy9+Kq+xrbiBgK/G/gOGAycGpEvLO6vdJgsGjRomp3QVVi9sVk7sVk7sVl9sVk7sXV\n2+xrZmALHAosyczWzFwP/BA4qcp90iDQ1tZW7S6oSsy+mMy9mMy9uMy+mMy9uHqbfS0NbMcBv6to\nLy0/J0mSJEkqsFoa2Hq5Y3WqtbW12l1QlZh9MZl7MZl7cZl9MZl7cfU2+5q53U9EHAbMzszjyu1z\ngI2ZeUnFPrXxZiRJkiRJvdLZ7X5qaWA7DHgG+BDwIvAIcGpm/qqqHZMkSZIkVdWwaneguzJzQ0R8\nBrgDGAp830GtJEmSJKlmZmwlSZIkSepMLV08qksRcVxEPB0Rv46IL1a7Pxo4EdEaEb+MiMcj4pFq\n90f9IyKuiYgVEfFkxXOjI+KuiHg2Iu6MiIZq9lH9o4vsZ0fE0vLn/vGIOK6afVTfi4jxEXFvRCyO\niP+KiM+Vn/dzX8e2kLuf+ToXETtGxMMRsSginoqIr5Wf9zNfx7aQe68+8zU/YxsRQynV3k4DXgAe\nxdrbwoiI/wu8JzN/X+2+qP9ExJHAq8ANmXlg+blLgVcy89LyH7QaM/PsavZTfa+L7M8H1mbm16va\nOfWbiBgDjMnMRRGxC/AYcDJwOn7u69YWcp+Bn/m6FxE7Zea68nV1HgD+ATgRP/N1rYvcP0QvPvP1\nMGN7KLAkM1szcz3wQ+CkKvdJA+stV0VTfcnMnwGrOjx9InB9+fH1lH75UZ3pInvwc1/XMnN5Zi4q\nP34V+BWle9f7ua9jW8gd/MzXvcxcV364PaXr6azCz3zd6yJ36MVnvh4GtuOA31W0l/LmP4Kqfwnc\nHRH/GRGfrHZnNKD2zMwV5ccrgD2r2RkNuM9GxBMR8X2XptW3iGgCpgIP4+e+MCpyf6j8lJ/5OhcR\nQyJiEaXP9r2ZuRg/83Wvi9yhF5/5ehjY1vZaam2r92XmVOB44NPlZYsqmCzVVPhvQXF8G9gXmAIs\nAy6vbnfUX8rLUX8MnJWZayu3+bmvX+Xcb6aU+6v4mS+EzNyYmVOAvYEPRMTRHbb7ma9DneTeTC8/\n8/UwsH0BGF/RHk9p1lYFkJnLyv99Gfg3SkvTVQwryvVYRMRewEtV7o8GSGa+lGXA9/BzX5ciYjtK\ng9ofZOYt5af93Ne5itxvbM/dz3yxZOZq4FbgPfiZL4yK3A/p7We+Hga2/wm8PSKaImJ7YCYwv8p9\n0gCIiJ0iYkT58c7AscCTW36V6sh84OPlxx8HbtnCvqoj5V9u2k3Hz33diYgAvg88lZnfqNjk576O\ndZW7n/n6FxG7tS83jYjhwDHA4/iZr2td5d7+x4yybn/ma/6qyAARcTzwDUoFx9/PzK9VuUsaABGx\nL6VZWoBhwByzr08RMQ84CtiNUg3GecBPgZuACUArMCMz26rVR/WPTrI/H2imtDwpgf8L/M+KGizV\ngYh4P3A/8EveXHp4DvAIfu7rVhe5nwucip/5uhYRB1K6ONSQ8tcPMvN/RcRo/MzXrS3kfgO9+MzX\nxcBWkiRJklRc9bAUWZIkSZJUYA5sJUmSJEk1zYGtJEmSJKmmObCVJEmSJNU0B7aSJEmSpJrmwFaS\nJEmSVNMc2EqSJEmSapoDW0mSJElSTXNgK0mSJEmqaQ5sJUmSJEk1zYGtJKlwIqIlIo7q5evO6I8+\n9bXevseK17dGxIf6sk+1pprfg4iYFRE/q8a5JakWObCVpDpW/sV8XUSsjYjlEXFtROxc3rZpkBYR\nzRGxsbzf2oj4XUT8a0Qc0oNz7RcRP42IlyJiZUQsjIj9OuzzdxGxLCJWR8T3I2L7im0tEfFaRR9+\nVbGtqUP/1kbEP1Vs/0JEPBkRayLi+Yj4h610N8tfPdXb11XDFvsaESMj4hsR8Zvy93NJRPxLROza\nndcPNhU/I0MqnjsiIv7/8s9FW0TMj4h3lredVvGztK7Dz9ea8iFq6nsgSUXmwFaS6lsCf56ZI4B3\nA4cAX6rYVvlL+wuZOaK872HA08DPIuKD3TzXKOAWYD9gT+AR4KftGyPiw8AXgQ8C+wATga906Oun\n2/uQme/s5BwjK7b/c4dtHwMagOOAz0TEzG72u19ExLBqnn9Lyn9QuAd4J/DhcuaHA68A7+3F8Qbd\ne42Iw4E7gH8D9gL2BZ4Afh4R+2bmnIqf9+Op+PnPzJG9ON+g+x5IUpE4sJWkgsjMF4GFwAHd2PeF\nzDwf+B5wSTeP/2hmXpuZbZm5AfgG8I6IaCzv8nHge5n5q8xsAy4AZnU4TGzlNJ3+fysz/1dmLsrM\njZn5LKUB9fu60+/ORMRJEbGoPLO8JCKOrdjcFBEPlGcB72if4ayYMfxERPwGuDtKvlSeOV8REddH\nxMgO+8+KiN+WZ7nPjIj3RsQvI2JVRHyzQ78+ERFPRcTvyzPiE3r5Fv8/YDwwPTOfBsjMlzPznzNz\nYcV+UyPiifJs5w8jYodyP5ojYmlE/GNELAO+HxHbl2eAXyh//Uv7jHzF/l8oz+i/GBEnR8QJEfFs\n+b2fXfE+IyLOLn/vX4nS6oFGeuZS4PrM/GZm/iEzV2Xml4GHgNkd9t3Sz11Pvgdb7HdE/ChKKxba\nIuK+iJhcsW3X8ozy6oh4GHhbD9+vJBWaA1tJqn8BEBHjKc1MPd6D1/4b8O6IGF4+xoKI+MduvvYD\nwLLMXFVuT6Y0Y9bul8CeHQYsX4uIl8sDx87qQ38TpWXS18SbS2Y3ExFRPvd/dbOfHV9/KHA98PnM\nHFU+1m/aNwMfoTQg3wPYHui47PkDwP6UZo5PpzSgb6Y0Q70L8L877H8oMAk4BbgCOJfSrPYBwIyI\n+EC5XycB5wDTgd2AnwHzevMegWnA7Zm5bgv7BPBXwIcpzXYexOZ/iNgTaAQmAP+T0kqAQ4GDCvc7\nwwAAIABJREFUy1+H8ubqgPb9d6A0e3oepT+anAZMBY4EzouIfcr7fg44kdL3ci9gFfB/uvvmImIn\nSjPQP+pk803AMd09FD37Hmyt37dSynp34BfAnIpt/wdYB4wBPkHpZ8dl0JLUTQ5sJam+BXBLRKyi\nNBBqAb7ag9e/WD5GA0Bm/kVmXrrVk0bsTWkA9/cVT+8CrK5ot9cxjij/94uUBg9jgauABRExsbzt\nZUrLqCcA7ym/pnJQUGl2+b/Xbq2fXTgD+H5m3gOlme7MfKa8LYFrMnNJZr5OaZA0peP5M/O18vbT\ngMszszUz/0BpYHpKVNSBAhdm5huZeRewFpibma+UZ9h/VnH8M4GvZeYzmbkR+BowpfwHi54aDSzb\nyj4JXJmZy8t/nFjQ4b1uBM7PzPXl9/oR4IJy31+htMz8YxX7rwf+OTP/BPxruQ/fKM+mPgU8RWlA\n3P5ev1T+3q8vH+svO3zftvb+hnTxHpdT+sNAd/T0e/A/t9TvzLyu/H7btx0cESMiYijw34Hzyj87\niyn9cWVrKxgkSWUObCWpviVwUmY2ZmZTZn4mM//Yg9ePKx+jrbsviIjdgTuB/5OZ/1qx6VWgsnZx\nVPm/awEy85H2X/oz8wbg58AJ5W1/yMxflJcavwR8Bjg2yhfCqjj3Z4CPAv+tPHjojb2B57awfXnF\n49coDdgr/a7i8V68OdsL8FtgGKWZvnYrOhyvY7v9+PsAV5SXKK8CVpafH7eFvnZlJaU/IGzNlt7r\ny5n5RkV7LG99r5XnWJmZ7TOQr5X/u6X3+m8V7/UpYAObf9+2ZBWlQedenWzbi1ItcXf15HvQRBf9\njoihEXFxeZnyauD/Uvps7UZpBncYm//s/LYHfZSkwnNgK0nakunAY5n52lb3BMrLiu8EbsnMr3XY\nvJjNZ7sOBlZULFXujcor4H4C+EfgQ+XZzt76HaXlor1VuXz0RUqDnXYTKA10Kgd03fVb4FPlP1K0\nf+2cmQ/14lh3Ax8uL9ntrY7LZDt7r73N4bfAcR3e606ZubVZ5lLHSrPjDwIzOtk8g9L77wsdvwdb\n6vdHKC1T/lB5ifu+lGZkg9KKhA2Uvmftels/LUmF5MBWkrSZ8gVwxkXE+ZSW5Z7bzdeNpHQV2gcy\ns7PX3ACcERHvLA+Av0x5uXBEjIqID0fEjhExLCJOo1R3ubC8/dCIeEdEDCnX1l4J3JuZa8vbTwP+\nGTg2M1u34e0DfB84PSI+WD7fuIh4R+Vb7cGx5gF/F6ULRe1CaRn4D8tLibur/XzfAc5tv+BQ+Xv2\nVz04TqUfUBrA/7jy+xoR50bE8b085jzgSxGxW0TsRqmO9ge9PNZ3gK9G+eJYEbF7RJzYw2OcDXw8\nIj5bXu7bGBEXAX/G5lfj7ktb6vcuwB+B35dXGmwqCSgvz/4JMDsihpcz/jjW2EpStzmwlSS1GxsR\nayktDX6E0sWLjsrMTbNbEXFbVFy9toPplOpgT4+K+4GW623JzDsoXan2XqCV0nLf88uv3Q64EHiJ\n0uzVpyktoV5S3j4RuJ1SXe6TlJaEnlpx7gsp1VU+WnHub/Xmm5CZj1K6cM+/UFqC3cLms2fZ4XHH\ndqVrKA3u7geep3RxoM9uYf9Ou1Tu1y2UrlD9w/JS1icpXdSox8rLZ6dRuqXTXZRqnx+m9D3sagZ4\na+/1IuA/KV0U7JflxxdtYf8tvfcrgPnAnVG6p+yDlC5GtTWbjpmZP6f0/fnvlGaOWymtEnh/Zna2\n1Ly7WWzpe7Clft9Aaan2C5QubPZgh9d/htLgdzmln5trutEfSVJZvFnuIklSMUTEvZQu+nN/tfvS\nX4rwHiVJaueMrSRJkiSppjmwlSRJNSEiTqtYal759WS1+yZJqi6XIkuSJEmSapoztpIkSZKkmjas\n2h3oSxHh9LMkSZIk1bHMfMut9+pqYAvg0urimTVrFtddd121u6EqMPtiMvdiMvfiMvtiMvfi2lr2\nEZ3fTt6lyKp5TU1N1e6CqsTsi8nci8nci8vsi8nci6u32TuwlSRJkiTVNAe2qnkNDQ3V7oKqxOyL\nydyLydyLy+yLydyLq7fZO7BVzZsyZUq1u6AqMftiMvdiMvfiMvtiMvfi6m32dXUf24jIeno/kiRJ\nUj3p6sI/Umc6G9tFRDGuiixJkiRp8HIiSt3R0z+CuBRZNa+lpaXaXVCVmH0xmXsxmXtxmb2k7nBg\nK0mSJEmqadbYSpIkSRoQ5frIandDNaCrn5WuamydsZUkSZKkHvja177GJz/5SQBaW1sZMmQIGzdu\nrHKviq1fB7YRcU1ErIiIJyueGx0Rd0XEsxFxZ0Q0VGw7JyJ+HRFPR8SxFc+/JyKeLG+7oj/7rNpj\n7U1x1VL2f//38D/+B8yYAf/0T9XuTW2rpdzVd8y9uMxe1dbS0sL48eM3e+6cc87h6quvrlKP1Jn+\nnrG9Fjiuw3NnA3dl5n7APeU2ETEZmAlMLr/mW/HmpbC+DZyRmW8H3h4RHY8pSYPav/wLHHssHH00\nXH99tXsjSZJUX/p1YJuZPwNWdXj6RKD917rrgZPLj08C5mXm+sxsBZYAfxYRewEjMvOR8n43VLxG\norm5udpdUJXUUvYNDaXZ2r/4i2r3pPbVUu7qO+ZeXGavgTBkyBCef/75Te1Zs2bx5S9/mXXr1nH8\n8cfz4osvMmLECEaOHMmyZcuYPXs2H/vYx3p0jmuvvZbJkyczcuRI3va2t3HVVVdt2vbOd76TW2+9\ndVN7w4YN7L777ixatAiAG264gX322YfddtuNiy66iKamJu65555tfNf1pRo1tntm5ory4xXAnuXH\nY4GlFfstBcZ18vwL5eclSZIk1YtPfQqam+GEE6CtrXrHoHSBoohgp512YuHChYwdO5a1a9eyZs0a\n9tprrx7fYxVgzz335NZbb2XNmjVce+21/N3f/d2mgetHPvIR5s2bt2nfO+64gz322IMpU6bw1FNP\n8elPf5p58+axbNkyVq9ezYsvvtirPtSzql48qnwJYy+Lpm1i7U1xmX0xmXsxmXtxmX2BPPss3Hcf\n3H57aYBarWOUtV+Rt7Mr8/bmys4nnHAC++67LwAf+MAHOPbYY7n//vsBOPXUU5k/fz6vv/46AHPn\nzuXUU08F4Oabb+bEE0/kiCOOYLvttuOCCy5wUNuJYVU454qIGJOZy8vLjF8qP/8CUFmVvTelmdoX\nyo8rn3+hq4PPmjWLpqYmABoaGpgyZcqmJSzt/zDarq92u8HSH9sD1160aNGg6s+W2uvXt/DAAzB1\n6uDoj23btdaupc+77b5tt89oDZb+2N629hbttFPpv4ccAhXLdHukL47RT26//Xa+8pWv8Otf/5qN\nGzeybt06DjroIAAmTZrEO9/5TubPn8+f//mfs2DBAi688EIAli1bxt57vzkcGj58OLvuumtV3sNA\na//3v608+97a2trlvv1+H9uIaAIWZOaB5falwMrMvCQizgYaMvPs8sWj5gKHUlpqfDcwKTMzIh4G\nPgc8AtwKXJmZCzs5l/exlTQoNTbC88/DH/4Ahx0GS5du/TWSJNWbLd7Htq2tNMt61VWli1P0Ri+P\nscsuu/DQQw/xrne9C4DjjjuOQw89lAsuuID77ruPj370o/zud7/btP9XvvIVlixZwg9+8ANaW1uZ\nOHEiGzZsYMiQIZ0e/49//CONjY3ceOONnHTSSQwdOpTp06dz4IEHcsEFFwDwjW98g/vuu48ZM2Zw\nxRVX8NBDDwFwwQUX8MwzzzBnzhwAXnvtNRoaGrj99tv54Ac/2KtvUy0YVPexjYh5wH8A74iI30XE\n6cDFwDER8SzwwXKbzHwKuAl4Crgd+JuKUerfAN8Dfg0s6WxQK0mSJKmGNTTATTf1flC7DceYMmUK\nc+bM4U9/+hMLFy7ctEQYSrWxK1euZM2aNZue6+lk2htvvMEbb7zBbrvtxpAhQ7j99tu58847N9vn\nlFNO4Y477uA73/kOp5122qbn//Iv/5IFCxbw4IMP8sYbbzB79uxeLYWud/06sM3MUzNzbGZun5nj\nM/PazPx9Zk7LzP0y89jMbKvY/6uZOSkz98/MOyqefywzDyxv+1x/9lm1p32Ji4rH7IvJ3IvJ3IvL\n7DUQrrjiChYsWEBjYyNz585l+vTpm7btv//+nHrqqUycOJHRo0ezbNmyTReXare1mtcRI0Zw5ZVX\nMmPGDEaPHs28efM46aSTNttnzJgxHHHEETz44IPMnDlz0/OTJ0/mm9/8Jqeccgpjx45lxIgR7LHH\nHuywww599O7rQ78vRR5ILkUuppaWlu7Vbaju1FL2LkXuO7WUu/qOuReX2deXLS5FVre8+uqrNDY2\nsmTJEvbZZ59qd6ff9HQpsgNbSRoADmwlSXJg21sLFizgQx/6EJnJ5z//eR599FEee+yxanerXw2q\nGltJkiRJKopddtmFESNGvOXr5z//+TYdd/78+YwbN45x48bx3HPP8cMf/rCPelw/HNiq5ll7U1xm\nX0zmXkzmXlxmr1ry6quvsnbt2rd8ve9979um41599dWsWrWKtrY27rrrLt7+9rf3UY/rhwNbSZIk\nSVJNs8ZWkgaANbaSJFljq+6zxlaSJEmSVCgObFXzrL0pLrMvJnMvJnMvLrOX1B0ObCVJkiRJNc0a\nW0kaANbYSpJU+zW2v/3tbznggANYs2YNEW8p89xms2bNYvz48Vx44YV9fuxaY42tJEmSJPWDCRMm\nsHbt2n4Z1EJp0Latx25paWH8+PF91KPa4cBWNc/am+Iy+2Iy92Iy9+IyexVNtWe0N2zYUNXz95YD\nW0mSJEmF1tTUxGWXXcZBBx3EiBEjOOOMM1ixYgXHH388o0aN4phjjqGtrY3W1laGDBnCxo0bAWhu\nbua8887j/e9/PyNHjuTDH/4wK1eu3Or5HnjgAY444ggaGxuZMGECN9xww6Zt7TO21113HUceeeRm\nrxsyZAjPP/88ALfddhsHHHAAI0eOZO+99+brX/8669at4/jjj+fFF19kxIgRjBw5kuXLl5OZXHzx\nxUyaNInddtuNmTNnsmrVKoBN7+maa65hn332Ydq0aT3q+/XXXw/ArbfeytSpUxk1ahQTJkzgK1/5\nyqbXtJ/j6quvZty4cYwdO5bLL798q9+nnnBgq5rX3Nxc7S6oSsy+mMy9mMy9uMxeAyEi+MlPfsI9\n99zDM888w7//+79z/PHHc/HFF/PSSy+xceNGrrzyyk5fO2/ePK677jpeeukl3njjDS677LItnus3\nv/kNJ5xwAmeddRavvPIKixYt4uCDD+5xn8844wyuuuoq1qxZw+LFizn66KPZaaedWLhwIWPHjmXt\n2rWsWbOGMWPGcOWVVzJ//nzuv/9+li1bRmNjI5/+9Kc3O97999/P008/zR133NGjvk+ZMgWAXXbZ\nhRtvvJHVq1dz66238u1vf5uf/vSnm72+paWFJUuWcOedd3LJJZdwzz339Ph9d8WBrSRJkqRBIaJv\nvnrjs5/9LLvvvjtjx47lyCOP5PDDD+fggw9mhx12YPr06Tz++ONvqX+NCE4//XQmTZrEjjvuyIwZ\nM1i0aNEWzzN37lyOOeYYZs6cydChQxk9enSvBrbbb789ixcvZs2aNYwaNYqpU6cCnS9l/u53v8tF\nF13E2LFj2W677Tj//PO5+eabN808A8yePZvhw4ezww479KrvRx11FAcccAAABx54IKeccgr33Xff\nZq8///zzGT58OO9617s4/fTTmTdvXo/fd1cc2KrmWXtTXGZfTOZeTOZeXGZfLJl989Ube+6556bH\nw4cP36y944478uqrr3b6ujFjxmz2uq72a7d06VImTpzYu05W+PGPf8xtt91GU1MTzc3NPPTQQ13u\n29rayvTp02lsbKSxsZHJkyczbNgwVqxYsWmf7lxwakt9f/jhhzn66KPZY489aGho4Lvf/e5blmVX\nnmPChAm8+OKLWz1ndzmwlSRJkqQO+usiTuPHj+e5557b6n4777wz69at29Revnz5ZtsPOeQQbrnl\nFl5++WVOPvlkZsyYAdDpVZUnTJjAwoULWbVq1aavdevWsddee23apztXY95S3z/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19DJvtxt2kTPlezSP5Oc+VkDlvzNe/F90GsczY2siUIEeU99rElIkqnt9/OdA7yWyCgg8dsCWoj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uux9y9wpgE3B6u0YsIiLpVVAAjz9ee9+6dc2f98Ybtct5nVIXU6ocOJDuCCTduncPnlPZxV1E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\nB67KlardG7gq7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DPYdxZg4MC6j/9qa81nJ39iL01JcQ+vbPex7QQER53YDuztnKsGtjbo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\nTC9kitTVPdHgNFZOr9fsryvS9EdE5EsMbCntMQfDv1j3/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rmA8M+7OT91bq6e+9etxTY/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\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D7Mtk7mUy93KZfZnMvVyNZt+qpcjvBj4C3B0Rd1bbTgbOAi6PiOOpbvcDkJn3RcTlwH3A\ncuATnpqVJEmSJEELlyI3g0uRJUmSJKl7tdVSZEmSJEmSRosTW3U8ezDKZfZlMvcymXu5zL5M5l6u\nRrN3YitJkiRJ6mj22EqSJEmSOoI9tpIkSZKkruTEVh3PHoxymX2ZzL1M5l4usy+TuZfLHltJkiRJ\nUpHssZUkSZIkdQR7bCVJkiRJXcmJrTqePRjlMvsymXuZzL1cZl8mcy+XPbaSJEmSpCLZYytJkiRJ\n6gj22EqSJEmSupITW3U8ezDKZfZlMvcymXu5zL5M5l4ue2wlSZIkSUWyx1aSJEmS1BHssZUkSZIk\ndSUntup49mCUy+zLZO5lMvdymX2ZzL1c9thKkiRJkopkj60kSZIkqSPYYytJkiRJ6kpObNXx7MEo\nl9mXydzLZO7lMvsymXu57LGVJEmSJBXJHltJkiRJUkewx1aSJEmS1JU6amIbEYdExAMR8XBEfK7V\n9ag92INRLrMvk7mXydzLZfZlMvdydX2PbUSMBf4JOASYBhwbEW9pbVVqB319fa0uQS1i9mUy9zKZ\ne7nMvkzmXq5Gs++YiS2wD/BIZvZn5svA94DDW1yT2sDAwECrS1CLmH2ZzL1M5l4usy+TuZer0ew7\naWI7BXi0bryo2iZJkiRJKlgnTWy93LFWq7+/v9UlqEXMvkzmXiZzL5fZl8ncy9Vo9h1zu5+IeCcw\nKzMPqcYnA69k5pfq9umMH0aSJEmS1JDV3e6nkya2GwAPAu8HHgduB47NzPtbWpgkSZIkqaU2aHUB\nI5WZyyPir4DrgLHAt53USpIkSZI65oytJEmSJEmr00kXjxpSRBwSEQ9ExMMR8blW16P1JyL6I+Lu\niLgzIm5vdT1qjoj4TkQsiYh76rZNiojrI+KhiFgQERNaWaOaY4jsZ0XEouq4vzMiDmlljRp9ETE1\nIm6KiHsj4pcR8elqu8d9Fxsmd4/5LhcRm0TEbRHRFxH3RcSZ1XaP+S42TO4NHfMdf8Y2IsZS6709\nEHgM+Bn23hYjIv4D2Cszf9vqWtQ8EbEf8DxwSWbuXm07G3g6M8+u/kFrYmae1Mo6NfqGyP5UYFlm\nfrmlxalpImIbYJvM7IuILYA7gCOA4/C471rD5H4UHvNdLyI2y8wXquvq/AT4G+AwPOa72hC5v58G\njvluOGO7D/BIZvZn5svA94DDW1yT1q/XXBVN3SUz/xV4dpXNhwGzq+ezqf3lR11miOzB476rZeYT\nmdlXPX8euJ/aves97rvYMLmDx3zXy8wXqqcbUbuezrN4zHe9IXKHBo75bpjYTgEerRsvYuUfgup+\nCdwQET+PiBNaXYzWq60zc0n1fAmwdSuL0Xr3qYi4KyK+7dK07hYRPcCewG143BejLvdbq00e810u\nIsZERB+1Y/umzLwXj/muN0Tu0MAx3w0T285eS6119e7M3BOYAXyyWraowmStp8I/C8rxdWBHYDqw\nGDi3teWoWarlqFcAJ2bmsvrXPO67V5X7D6nl/jwe80XIzFcyczqwHfDeiDhgldc95rvQanLvpcFj\nvhsmto8BU+vGU6mdtVUBMnNx9etTwL9QW5quMiyp+rGIiG2BJ1tcj9aTzHwyK8C38LjvShGxIbVJ\n7Xcz88pqs8d9l6vL/dLB3D3my5KZzwFXA3vhMV+Mutz3bvSY74aJ7c+BN0VET0RsBBwNzGtxTVoP\nImKziBhXPd8cOBi4Z/h3qYvMAz5aPf8ocOUw+6qLVH+5GXQkHvddJyIC+DZwX2aeV/eSx30XGyp3\nj/nuFxFbDS43jYhNgYOAO/GY72pD5T74jxmVER/zHX9VZICImAGcR63h+NuZeWaLS9J6EBE7UjtL\nC7ABMMfsu1NEzAX2B7ai1oNxCvAj4HJge6AfOCozB1pVo5pjNdmfCvRSW56UwH8Af1HXg6UuEBHv\nAW4B7mbl0sOTgdvxuO9aQ+T+eeBYPOa7WkTsTu3iUGOqx3cz8/9ExCQ85rvWMLlfQgPHfFdMbCVJ\nkiRJ5eqGpciSJEmSpII5sZUkSZIkdTQntpIkSZKkjubEVpIkSZLU0ZzYSpIkSZI6mhNb9DsZzQAA\nIABJREFUSZIkSVJHc2IrSZIkSepoTmwlSZIkSR3Nia0kSZIkqaM5sZUkSZIkdTQntpIk6VUiYmFE\n7D/Ea70R8egofMdfRsSSiFgaERPX9fMkSWVzYitJKlZE9EfECxGxLCKeiIiLImLz6rWFEXF89bw3\nIl6p9lsWEY9GxPcjYu+1+K5dIuJHEfFkRDwTEddGxC6r7PPXEbE4Ip6LiG9HxEZ1r20XEVdV710c\nEV+NiLF1r78/Ih6IiP+KiP8/Irave21WRLxcV//SiOgZptysHk0RERsC5wLvz8zxmflss75LklQG\nJ7aSpJIl8IHMHAe8Hdgb+Pu61+ond49l5rhq33cCDwD/GhHvG+F3bQlcCewCbA3cDvxo8MWI+EPg\nc8D7gB2AnYDT6t5/PvA0sC0wHdgf+ET13q2AK4C/AyYCPwe+v8rPOXew/moy2T/CupthG2AT4P4W\n1iBJ6iJObCVJAjLzceBaYLcR7PtYZp4KfAv40gg//2eZeVFmDmTmcuA84M11y3A/CnwrM+/PzAHg\ndGBm3UfsBnw/M1/KzCVVrdOq1/4I+GVmXpGZLwGzgD3qzghH9Rh1EfG5iFhUnQV+YHCiHxEbR8R5\nEfFY9fi/EbFRVdPghHYgIm5oRl2SpLI4sZUklS4AImIqMAO4cy3e+y/A2yNi0+ozroqI/z3C974X\nWFy3DHcacFfd63cDW9dNfK8DPhQRm0bElKrWa6vXdqt/b2a+ADzCykl6Ah+sljH/MiI+vhY/45Ai\n4s3AJ4G9M3M8cDDQX738d8A+wB7VYx/g7zPzobq6tszMA0ejFklS2TZodQGSJLVQAFdGxHLgOeDH\nwBfX4v2PV58xAXgxMz84oi+N2A74J+B/1W3eoqph0NLq13HAs9TOwt5QbR8LXJyZg0uZNweeWuVr\nllbvBbgc+CawhNoy6isiYiAzvzeSeofxe2BjYLeIeCYz/7PutQ8Bf5WZTwNExGlVDafQpLPHkqRy\necZWklSyBA7PzImZ2ZOZf5WZ/70W759SfcbASN8QEa8HFgD/X2bW98E+D4yvG29Z/bosIoLaGdsf\nAJsBWwGTIuJLQ7x38P3LAKrlzU9kzU+BrwB/PNKah5KZjwCfoTbpXhIRcyNi2+rlycBv6nb/z2qb\nJEmjzomtJEmNOxK4IzNfHMnO1bLiBcCVmXnmKi/fS+2iUIP2AJZUS5W3AvYC/ikzX87M3wIXA4fW\nvXePuu/ZHHhjtb2pMnNuZu5H7YJXycqe48eBnrpdt6+2SZI06pzYSpK0FqJmSkScChwPfH6E7xtP\n7azrTzJzde+5BDg+It5STYD/Abioeu1pYDHwlxExNiImULvY1GBf7ZXAWyPijyJiE+BUoK/qZyUi\nDo+IiVXt+wCfpu6KzI2qbmH0vojYGPhv4HfUlicDzAX+PiK2qq7afArw3XX9TkmSVseJrSRJIzM5\nIpZRW957O7ULIO2fmSuu6hsR10TESUO8/0hqtxM6bpX7yW4HkJnXAWcDN1G7ANOvqE1QycykduXj\nD1Kb5D5MbSL519XrTwH/E/hH4LfV9xxT991HV+9ZCswGzszMdZlkDt4GaWPgTGr9vYupnVk+uXrt\nC9RuO3R39fh5tW3Vz5AkaZ1F7f+VkiRJNRFxE3BqZt7S6lokSRoJz9hKkiRJkjqaE1tJkiRJUkdz\nKbIkSZIkqaN5xlaSJEmS1NE2aHUBoykiPP0sSZIkSV0sM2PVbV01sQVwaXV5Zs6cycUXX9zqMtQC\nZl8mcy+TuZfL7Mtk7uVaU/YRr5nTAi5FVhfo6elpdQlqEbMvk7mXydzLZfZlMvdyNZq9E1tJkiRJ\nUkdzYquON2HChFaXoBYx+zKZe5nMvVxmXyZzL1ej2TuxVcebPn16q0tQi5h9mcy9TOZeLrMvk7mX\nq9Hsu+o+thGRq/t5hmowllanm44JSZIkqZtERBlXRR6KkxWNhP8IIkmSJHUelyJL6lgLFy5sdQlq\nAXMvk7mXy+zLZO7lajR7J7aSJEmSpI5WTI9tN/2cah5/r0iSJEnta6geW8/YdpAzzzyTE044AYD+\n/n7GjBnDK6+80uKqJEmSJKm1mjqxjYjvRMSSiLinbtukiLg+Ih6KiAURMaHutZMj4uGIeCAiDq7b\nvldE3FO99pVm1twuFi5cyNSpU1+17eSTT+bCCy9sUUVS+7H/pkzmXiZzL5fZl6mdcl+yBN7xjlc/\nZs1qdVXdq9Hsm31V5IuArwKX1G07Cbg+M8+OiM9V45MiYhpwNDANmALcEBFvqtYWfx04PjNvj4hr\nIuKQzLy2ybVLkiRJKtyECXD++SvHt90GP/5x6+rR6jX1jG1m/ivw7CqbDwNmV89nA0dUzw8H5mbm\ny5nZDzwCvCMitgXGZebt1X6X1L2no40ZM4Zf//rXK8YzZ87kH/7hH3jhhReYMWMGjz/+OOPGjWP8\n+PEsXryYWbNm8ad/+qdr9R0XXXQR06ZNY/z48bzxjW/kggsuWPHaW97yFq6++uoV4+XLl/P617+e\nvr4+AC655BJ22GEHttpqK77whS/Q09PDjTfeuI4/tTR6ent7W12CWsDcy2Tu5TL7MrVT7htv/Oqz\ntbvu2uqKuluj2beix3brzFxSPV8CbF09nwwsqttvEbUzt6tuf6zaPjo+9jHo7YVDD4WBgdZ9BrVG\n6Ihgs80249prr2Xy5MksW7aMpUuXsu222zZ0j9Wtt96aq6++mqVLl3LRRRfx13/91ysmrh/60IeY\nO3fuin2vu+463vCGNzB9+nTuu+8+PvnJTzJ37lwWL17Mc889x+OPP+59XiVJklSWUfq7vpqrpReP\nqpYZt/YStA89BDffDPPn137TtuozKoNX5F3dlXkbuVrvoYceyo477gjAe9/7Xg4++GBuueUWAI49\n9ljmzZvH7373OwAuu+wyjj32WAB++MMfcthhh7Hvvvuy4YYbcvrppzupVdtpp/4brT/mXiZzL5fZ\nl6mtch/Fv+trzdq1x3Z1lkTENpn5RLXM+Mlq+2NA/dWStqN2pvax6nn99seG+vCZM2fS09MDwIQJ\nE5g+ffrw1Wy2We3XvfeGumW6a2U0PqNJ5s+fz2mnncbDDz/MK6+8wgsvvMDb3vY2AHbeeWfe8pa3\nMG/ePD7wgQ9w1VVXccYZZwCwePFitttu5X/2TTfdlNe97nUt+RlaZfCgGlwO4bj9xn19fW1Vj2PH\njps39ngvdzy40qxd6nG8fsaD2qKem2+md7CevfbirrsWAi2sp8vHq/5539fXx0B1pry/v5+hNP0+\nthHRA1yVmbtX47OBZzLzSxFxEjAhMwcvHnUZsA/VxaOAnTMzI+I24NPA7cDVwPmru3hUQ/exHRio\n/cvLBRfUOsMb0eBnbLHFFtx666289a1vBeCQQw5hn3324fTTT+fmm2/mIx/5CI8++uiK/U877TQe\neeQRvvvd79Lf389OO+3E8uXLGTNmzGo//7//+7+ZOHEil156KYcffjhjx47lyCOPZPfdd+f0008H\n4LzzzuPmm2/mqKOO4itf+Qq33norAKeffjoPPvggc+bMAeDFF19kwoQJzJ8/n/e9730N/WfqBN7H\nVpIkSa9Sv2pxzBgWzP8955wDCxa0rqSSteQ+thExF/h34M0R8WhEHAecBRwUEQ8B76vGZOZ9wOXA\nfcB84BN1s9RPAN8CHgYeGdUrIk+YAJdf3vikdh0+Y/r06cyZM4ff//73XHvttSuWCEOtN/aZZ55h\n6dKlK7at7YTrpZde4qWXXmKrrbZizJgxzJ8/nwWrHIHHHHMM1113Hd/4xjf48Ic/vGL7H//xH3PV\nVVfx05/+lJdeeolZs2Y54ZMkSVJ56ie2N9/cujo0rKZObDPz2MycnJkbZebUzLwoM3+bmQdm5i6Z\neXBmDtTt/8XM3Dkzd83M6+q235GZu1evfbqZNa9PX/nKV7jqqquYOHEil112GUceeeSK13bddVeO\nPfZYdtppJyZNmsTixYtXXFxq0Jp6XseNG8f555/PUUcdxaRJk5g7dy6HH374q/bZZptt2Hffffnp\nT3/K0UcfvWL7tGnT+OpXv8oxxxzD5MmTGTduHG94wxvYeOONR+mnl9bdqsuVVAZzL5O5l8vsy9RW\nuX/gA7Vf3/EOqFZaqnkazb7pS5HXp4aWImtEnn/+eSZOnMgjjzzCDjvs0OpymsbfK51l4cKFK3ow\nVA5zL5O5l8vsy9RWuff2rjxT+yd/woI/v9ylyE20puyHWorsxFZDuuqqq3j/+99PZvLZz36Wn/3s\nZ9xxxx2tLqup/L0iSZKkVzn00NoVkffeG66/ngW3T3Bi20It6bHV+rHFFlswbty41zz+7d/+bZ0+\nd968eUyZMoUpU6bwq1/9iu9973ujVLEkSZLUIS67DP7kT+D669ftujxqKie2XeD5559n2bJlr3m8\n+93vXqfPvfDCC3n22WcZGBjg+uuv501vetMoVSyNjrbqv9F6Y+5lMvdymX2Z2ir3iRPhBz+o/fqJ\nT7S6mq7XaPZObCVJkiRpJL7+9VZXoCHYYyvV8feKJEmSXmWVO5Es+LubOef299pj2yL22EqSJEnS\nuvrHL7S6Aq2GE1tJHaut+m+03ph7mcy9XGZfprbO/e/+vtUVdDV7bCVJkiRptF1zzavHt9zSmjo0\nLHtsO8B//ud/sttuu7F06VIiXrOcfJ3NnDmTqVOncsYZZ4z6Z3eaTv+9IkmSpCao+zv4Ag7inIMW\n2GPbIvbYdrDtt9+eZcuWNWVSC7XfHOv62QsXLmTq1KmjVJEkSZLUpjbdrNUVaDWc2Aqg5Wcply9f\n3tLvV2dq6/4bNY25l8ncy2X2ZWrr3F98odUVdDV7bDtQT08P55xzDm9729sYN24cxx9/PEuWLGHG\njBlsueWWHHTQQQwMDNDf38+YMWN45ZVXAOjt7eWUU07hPe95D+PHj+cP//APeeaZZ9b4fT/5yU/Y\nd999mThxIttvvz2XXHLJitcGz9hefPHF7Lfffq9635gxY/j1r38NwDXXXMNuu+3G+PHj2W677fjy\nl7/MCy+8wIwZM3j88ccZN24c48eP54knniAzOeuss9h5553ZaqutOProo3n22WcBVvxM3/nOd9hh\nhx048MAD16r22bNnA3D11Vez5557suWWW7L99ttz2mmnrXjP4HdceOGFTJkyhcmTJ3Puueeu8b+T\nJEmSNKQNN2p1BVoNJ7YtFBH88z//MzfeeCMPPvggP/7xj5kxYwZnnXUWTz75JK+88grnn3/+at87\nd+5cLr74Yp588kleeuklzjnnnGG/6ze/+Q2HHnooJ554Ik8//TR9fX3ssccea13z8ccfzwUXXMDS\npUu59957OeCAA9hss8249tprmTx5MsuWLWPp0qVss802nH/++cybN49bbrmFxYsXM3HiRD75yU++\n6vNuueUWHnjgAa677rq1qn369OkAbLHFFlx66aU899xzXH311Xz961/nRz/60avev3DhQh555BEW\nLFjAl770JW688ca1/rnVnnp7e1tdglrA3Mtk7uUy+zK1de4vv9TqCrpao9k7saXWCz4aj0Z86lOf\n4vWvfz2TJ09mv/32413vehd77LEHG2+8MUceeSR33nnna/pfI4LjjjuOnXfemU022YSjjjqKvr6+\nYb/nsssu46CDDuLoo49m7NixTJo0qaGJ7UYbbcS9997L0qVL2XLLLdlzzz2B1S9l/uY3v8kXvvAF\nJk+ezIYbbsipp57KD3/4wxVnngFmzZrFpptuysYbb9xQ7fvvvz+77bYbALvvvjvHHHMMN99886ve\nf+qpp7Lpppvy1re+leOOO465c+eu9c8tSZIkAZ6xbVNObIHM0Xk0Yuutt17xfNNNN33VeJNNNuH5\n559f7fu22WabV71vqP0GLVq0iJ122qmxIutcccUVXHPNNfT09NDb28utt9465L79/f0ceeSRTJw4\nkYkTJzJt2jQ22GADlixZsmKfkVxwarjab7vtNg444ADe8IY3MGHCBL75zW++Zll2/Xdsv/32PP74\n42v8TnWGtu6/UdOYe5nMvVxmX6a2zt0ztk1lj22XaNZFnKZOncqvfvWrNe63+eab88ILKxvin3ji\niVe9vvfee3PllVfy1FNPccQRR3DUUUcBrPaqyttvvz3XXnstzz777IrHCy+8wLbbbrtin5FcjXm4\n2j/0oQ9xxBFHsGjRIgYGBvj4xz/+qjPCULtdUv3zKVOmrPE7JUmSJHUOJ7Ydam0nwB/+8Ie54YYb\n+MEPfsDy5ct55plnuOuuu1Z81uDn7bHHHtx7773cdddd/O53v2PWrFkrPuPll19mzpw5PPfcc4wd\nO5Zx48YxduxYoHbm+ZlnnmHp0qUr9v/4xz/O5z//+RUTy6eeeop58+at9c86XO3PP/88EydOZKON\nNuL222/nsssue81k+Qtf+AIvvvgi9957LxdffDFHH330Wteg9tTW/TdqGnMvk7mXy+zL1Na5v32v\nVlfQ1eyx7RL1k7L6+8uurs92dfsNZerUqVxzzTWce+65vO51r2PPPffk7rvvfs37d9llF0455RQO\nPPBA3vzmN7Pffvu96rMvvfRSdtxxR7bccksuuOAC5syZA8Cuu+7Ksccey0477cSkSZN44oknOPHE\nEznssMM4+OCDGT9+PO9617u4/fbbV/szNFr71772NU455RTGjx/PGWecsdpJ6/7778/OO+/MgQce\nyN/+7d+u8QrMkiRJ0pCWLV3zPlrvotX3Lx1NEZGr+3kiouX3adX619/fz0477cTy5csZM2Zk/4bj\n75XOsnDhwvb+F101hbmXydzLZfZlarvc607ILOAgzjloAQsWtLCeLram7Ku/r7/mDJlnbCVJkiRJ\nHc2JbReZM2cO48aNe81j9913b3Vpa9Ss2ke63Fmdqa3+JVfrjbmXydzLZfZlauvcN9m01RV0tUaz\ndymyVMffK5IkSXoNlyK3DZciS+o6bX2POzWNuZfJ3Mtl9mVq69w33qTVFXQ172MrSZIkSc32379r\ndQVaDZciS3X8vSJJkqTXcCly2xhqKfIGrSgGICJOBj4CvALcAxwHbA58H9gB6AeOysyBuv3/DPg9\n8OnMXKvfSl5ESJIkSdI623CjVleg1WjJUuSI6AFOAN6embsDY4FjgJOA6zNzF+DGakxETAOOBqYB\nhwBfi4gR156ZPrr4cdNNN43q56lztHX/jZrG3Mtk7uUy+zK1de4vv9TqCrpap/XYLgVeBjaLiA2A\nzYDHgcOA2dU+s4EjqueHA3Mz8+XM7AceAfZZrxVLkiRJktpSy3psI+JjwLnAi8B1mfmnEfFsZk6s\nXg/gt5k5MSK+CtyamXOq174FzM/MK1b5zPSMmyRJkqRRVd9ju+nhnPOeK+2xbZG26rGNiDcCnwF6\ngOeAH0TER+r3ycyMiOFmqat9bebMmfT09AAwYcIEpk+fvuImv4OntR07duzYsWPHjh07dux4xGNq\nFgJ3vbiIQW1TXxeP+/r6GBgYAKC/v5+htOSMbUQcDRyUmX9ejf8UeCfwPuCAzHwiIrYFbsrMXSPi\nJIDMPKva/1rg1My8bZXP9YxtgRYuXLjiN7/KYvZlMvcymXu5zL5MbZd7/RnbjT/IOe+d5xnbJllT\n9kOdsR3TzKKG8QDwzojYtFp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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -508,7 +617,7 @@ ], "source": [ "# Plot utilization of \"big\" tasks decorated with platform specific capacity information\n", - "fl.plotBigTasks()" + "trace.analysis.tasks.plotBigTasks()" ] }, { @@ -526,34 +635,19 @@ }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Top 5 \"big\" tasks:\n", - " count unique top freq\n", - "pid \n", - "20527 6184 1 chrome 6184\n", - "1364 5731 1 ksdioirqd/mmc2 5731\n", - "7 5432 1 rcu_preempt 5432\n", - "20552 4192 1 Chrome_IOThread 4192\n", - "20596 4096 1 Chrome_ChildIOT 4096\n" + "05:46:34 INFO : 81621 tasks with more than 79 wakeups\n" ] } ], "source": [ - "top_wakeup_tasks = fl.topWakeupTasks(\n", - " max_tasks=5, # Maximum number of tasks to report\n", + "top_wakeup_tasks = trace.data_frame.top_wakeup_tasks(\n", " min_wakeups=100 # Minimum number of wakeup to be reported\n", ")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Wakeup vs Forks" - ] - }, { "cell_type": "code", "execution_count": 13, @@ -563,24 +657,75 @@ "outputs": [ { "data": { - "image/png": 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f3YHrJUVWzrcj4hZJvcC1kv4SeII0whsR8ZCka4GHgNeAc3ORzHnAVcBkoLtc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\n8sqB3d4KD6bs7h6dQGIohWeF1Kre/GZ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samplescomm
pid
205276184chrome, session_manager
13645731ksdioirqd/mmc2
75432rcu_preempt
205524192chrome, Chrome_IOThread
205964096chrome, Chrome_ChildIOT
\n", + "
" + ], "text/plain": [ - "" + " samples comm\n", + "pid \n", + "20527 6184 chrome, session_manager\n", + "1364 5731 ksdioirqd/mmc2\n", + "7 5432 rcu_preempt\n", + "20552 4192 chrome, Chrome_IOThread\n", + "20596 4096 chrome, Chrome_ChildIOT" ] }, + "execution_count": 13, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "fl.plotWakeupTasks(per_cluster=False)" + "top_wakeup_tasks.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Wakeup per cluster" + "### Wakeup vs Forks" ] }, { @@ -590,11 +735,21 @@ "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "05:46:37 INFO : 81621 tasks with more than 481 wakeups\n", + "05:46:39 INFO : Plotting 10 frequent wakeup tasks\n", + "05:46:39 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:39 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n" + ] + }, { "data": { - "image/png": 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KEjAx75p7UxsAZwFN/0rZzHarstvLCpbf9PmbUPpv1pmziKnBlu9sYe231jrw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\nzMzMzLoEBz9mZmZmZtYlOPgxMzMzM7M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HHgjnnQcXXgi/+qtr6p/xjCYpADj44DX1/d/122679rGe8hQ48sjm8yGHwDnnwBe/uGbI\n1hZbwJe+1AzlA3je89bsu8MO8O1vw157NeULL1xz/D32aBKi8c777Geve9xewtRrw2ACtdVWa9b3\nhpL1tp1M/3F7v/sSuPbatdcPDk9buRJGR9e+hqlYvHj9bdLMWm9PT5KFNAnPBVV1cVu9KsmuVXVX\nkt2AXt59B7Bn3+5Po+nhuaP9PFjf2+fpwJ1JFgDbT9TLs3z5chYvXgzA0NAQS5cu/Xmm1xvbZ3lu\nlXt1c6U9licvf+UrwxxxBLztbSN84AOjfPCDp/LCF8IznjHCd74D++/f/F+xv/zLEQ47rNl/9Wr4\n9V9v9v/MZ4YZGoJPfGKE17wGfvKT5vgw0v47u+UttxzmyCPh/vtHuOmm5nqf85y1fx5f+xoccsgI\n7343fOlLwxx6KLzpTWuO99Snwr//+wg//ema43/60yP81V/Bm988zLHHrjnfu941zJveNHF7Dj98\nmMsug9HREd7/fjj55GEWLoSnP32Ef/u3tbc/6yz4H/9jmAcegKc8ZYQ3vhH+7M/Wvd6zzhph773h\nj/+46a1buRJ23rlZv3TpMMuXwze+se7xN8XP1/LGlnt1c6U9lqdS3nLLEf7TfxrliitO5ZRT4J57\nRjj9dDjiiOZ+u+eeEY44As48c+Lj7bQT/PjHwyxaBA8+uOHt2XtveO5zh/nIR5rvE4Arrmi+n487\nrvm+GBpqtj/llBF+8hPYeedh3vc+ePnLm+0/9rFhTjkF7rprhAULmu9zWHv/I46AL3yh2X7w98fF\nFw+zYgW86lUjbLMN7LXX2uu///016wE+/OFhEjjhhBH+9/9u9h8aarZ/9NE113fSSSM8/PCa9Sed\nNMLdd8M3vjHM858Pr3nNmv0H29t//sH2Xnjh8M+3XbUK/uAPhrn2WrjvvhFGRpr1vfYOrr/tNrjr\nrmEOOwxuuaU53kc+0mz/2GPN/K5Fi4Z5+OEmPs96FlxzzZrrG689ljes3KsbGRlhdHSU1W3X29jY\nGBOZ9JHV7UMIzqd50MBpffXvbOvOTHI6MFRVp7cPMrgQOJRm2NrngGdWVSW5FjgZuA64BDirqi5P\nchJwYFW9Lsky4LiqWjZOW2qytmpuGhkZ+fl/qOoWY9dNxq27jF13GbtuMm7dNVnsJnpk9fqSniOA\nLwI3sGYI2xk0ictKmh6aMeD4qlrd7vMnwGuAR2mGw322rT8YOA/YCri0qnqPv14EXEAzX+heYFlV\njY3TFpMeSZIkSROaVtIzl5j0SJIkSZrMtF5OKm2s/rGX6hZj103GrbuMXXcZu24ybt01ndiZ9EiS\nJEma1xzeJkmSJGlecHibJEmSpM2SSY9mlONlu8vYdZNx6y5j113GrpuMW3c5p0eSJEmSBjinR5Ik\nSdK84JweSZIkSZslkx7NKMfLdpex6ybj1l3GrruMXTcZt+5yTo8kSZIkDXBOjyRJkqR5wTk9kiRJ\nkjZLJj2aUY6X7S5j103GrbuMXXcZu24ybt3lnB5JkiRJGuCcHkmSJEnzgnN6JEmSJG2W1pv0JPm7\nJKuS3NhX97Yktye5vl1e1LfujCS3Jrk5ydF99QcnubFd9/6++kVJPtnWX5Nkr015gZpdjpftLmPX\nTcatu4xddxm7bjJu3TVTc3o+AhwzUFfAe6rqoHa5DCDJ/sAJwP7tPh9K0uteOhs4saqWAEuS9I55\nInBvW/9e4MwNvgpJkiRJmsCU5vQkWQx8uqoObMtvBR6qqncPbHcG8HhVndmWLwfeBnwP+EJV7dfW\nLwOGq+r3223eWlXXJlkA/LCqdh6nDc7pkSRJkjShmZjT84dJvp7k3CRDbd3uwO1929wO7DFO/R1t\nPe2/PwCoqkeBB5LsuBHtkiRJkqSfWzDN/c4G/rz9/BfAu2mGqc2o5cuXs3jxYgCGhoZYunQpw8PD\nwJqxfZbnVrlXN1faY3nq5dHRUU499dQ50x7LUysP3nuz3R7Lfl9uDmW/L7tZHrz3Zrs9lqf3fTk6\nOsrq1asBGBsbYyLTGt420bokpwNU1V+26y4H3kozvO2qvuFtLwWeX1Wv6w2Bq6prHN42/4yMjPz8\nP1R1i7HrJuPWXcauu4xdNxm37posdhMNb5vunJ7dquqH7efTgP9cVS9rH2RwIXAozbC1zwHPrKpK\nci1wMnAdcAlwVlVdnuQk4MA2AVoGHFdVy8Zpg0mPJEmSpAlNlPSsd3hbko8DRwE7JfkBTc/NcJKl\nNE9xuw34PYCquinJSuAm4FHgpL5M5STgPGAr4NKqurytPxe4IMmtwL3AOgmPJEmSJE3XFuvboKpe\nWlW7V9WTqmrPqvq7qnpVVT2nqp5bVcdV1aq+7d9eVc+sqn2r6rN99V+tqgPbdSf31f+sqo6vqiVV\ndXhVjW3yq9Ss6R97qW4xdt1k3LrL2HWXsesm49Zd04ndepMeSZIkSeqyKc3pmQuc0yNJkiRpMjPx\nnh5JkiRJmvNMejSjHC/bXcaum4xbdxm77jJ23WTcuss5PZIkSZI0wDk9kiRJkuYF5/RIkiRJ2iyZ\n9GhGOV62u4xdNxm37jJ23WXsusm4dZdzeiRJkiRpgHN6JEmSJM0LzumRJEmStFky6dGMcrxsdxm7\nbjJu3WXsusvYdZNx6y7n9EiSJEnSAOf0SJIkSZoXnNMjSZIkabO03qQnyd8lWZXkxr66HZNcmeSW\nJFckGepbd0aSW5PcnOTovvqDk9zYrnt/X/2iJJ9s669JstemvEDNLsfLdpex6ybj1l3GrruMXTcZ\nt+6aqTk9HwGOGag7HbiyqvYBPt+WSbI/cAKwf7vPh5L0upfOBk6sqiXAkiS9Y54I3NvWvxc4c4Ov\nQpIkSZImMKU5PUkWA5+uqgPb8s3AUVW1KsmuwEhV7ZvkDODxqjqz3e5y4G3A94AvVNV+bf0yYLiq\nfr/d5q1VdW2SBcAPq2rncdrgnB5JkiRJE9rUc3p2qapV7edVwC7t592B2/u2ux3YY5z6O9p62n9/\nAFBVjwIPJNlxmu2SJEmSpLVs9IMM2u4Xu2A0LsfLdpex6ybj1l3GrruMXTcZt+6aTuwWTPNcq5Ls\nWlV3JdkNuLutvwPYs2+7p9H08NzRfh6s7+3zdODOdnjb9lV133gnXb58OYsXLwZgaGiIpUuXMjw8\nDKy5eMtzq9wzV9pjeerl0dHROdUey5bne7lnrrTH8tTLfl9atvzElnt699/q1asBGBsbYyLTndPz\nTpqHD5yZ5HRgqKpObx9kcCFwKM2wtc8Bz6yqSnItcDJwHXAJcFZVXZ7kJODAqnpdO9fnuKpaNk4b\nnNMjSZIkaUITzelZb9KT5OPAUcBONPN33gL8E7CSpodmDDi+qla32/8J8BrgUeCUqvpsW38wcB6w\nFXBpVZ3c1i8CLgAOAu4FllXV2DjtMOmRJEmSNKFpP8igql5aVbtX1ZOqas+q+khV3VdVL6yqfarq\n6F7C027/9qp6ZlXt20t42vqvVtWB7bqT++p/VlXHV9WSqjp8vIRH3TXYDanuMHbdZNy6y9h1l7Hr\nJuPWXdOJ3XqTHkmSJEnqsinN6ZkLHN4mSZIkaTKb+j09kiRJktQJJj2aUY6X7S5j103GrbuMXXcZ\nu24ybt3lnB5JkiRJGuCcHkmSJEnzgnN6JEmSJG2WTHo0oxwv213GrpuMW3cZu+4ydt1k3LrLOT2S\nJEmSNMA5PZIkSZLmBef0SJIkSdosmfRoRjletruMXTcZt+4ydt1l7LrJuHWXc3okSZIkaYBzeiRJ\nkiTNC87pkSRJkrRZ2qikJ8lYkhuSXJ/kurZuxyRXJrklyRVJhvq2PyPJrUluTnJ0X/3BSW5s171/\nY9qkucXxst1l7LrJuHWXsesuY9dNxq27ZmNOTwHDVXVQVR3a1p0OXFlV+wCfb8sk2R84AdgfOAb4\nUJJe19PZwIlVtQRYkuSYjWyXJEmSJAEbOacnyW3AIVV1b1/dzcBRVbUqya7ASFXtm+QM4PGqOrPd\n7nLgbcD3gC9U1X5t/TKaROr3B87lnB5JkiRJE5qpOT0FfC7JV5K8tq3bpapWtZ9XAbu0n3cHbu/b\n93Zgj3Hq72jrJUmSJGmjbWzS87yqOgh4EfD6JEf2r2y7Zuye2Yw5Xra7jF03GbfuMnbdZey6ybh1\n13Rit2BjTlhVP2z//VGSfwQOBVYl2bWq7kqyG3B3u/kdwJ59uz+NpofnjvZzf/0d451v+fLlLF68\nGIChoSGWLl3K8PAwsObiLc+tcs9caY/lqZdHR0fnVHssW57v5Z650h7LUy/7fWnZ8hNb7undf6tX\nrwZgbGyMiUx7Tk+SrYEtq+rBJE8BrgD+DHghcG9VnZnkdGCoqk5vH2RwIU1itAfwOeCZVVVJrgVO\nBq4DLgHOqqrLB87nnB5JkiRJE5poTs/G9PTsAvxj+wC2BcDHquqKJF8BViY5ERgDjgeoqpuSrARu\nAh4FTurLYk4CzgO2Ai4dTHgkSZIkabq2mO6OVXVbVS1tl2dX1Tva+vuq6oVVtU9VHV1Vq/v2eXtV\nPbOq9q2qz/bVf7WqDmzXnbxxl6S5ZLAbUt1h7LrJuHWXsesuY9dNxq27phO7aSc9kiRJktQFG/We\nnieSc3okSZIkTWam3tMjSZIkSXOaSY9mlONlu8vYdZNx6y5j113GrpuMW3c5p0eSJEmSBjinR5Ik\nSdK84JweSZIkSZslkx7NKMfLdpex6ybj1l3GrruMXTcZt+5yTo8kSZIkDXBOjyRJkqR5wTk9kiRJ\nkjZLJj2aUY6X7S5j103GrbuMXXcZu24ybt01ndgt2PTNmDlZp6NKkiRJkibXqTk90I22SpIkSZoN\nzumRJEmStBky6dEMG5ntBmjaRma7AZqWkdlugKZtZLYboGkbme0GaFpGZrsBmraRDd5jziQ9SY5J\ncnOSW5O8ebxtqly6trz3vaOz3gYXY7c5Lcatu4ux6+5i7Lq5GLfuLpPFbiJzIulJsiXwQeAYYH/g\npUn2m91WaVNYvXr1bDdB02Tsusm4dZex6y5j103GrbumE7s5kfQAhwLfqaqxqnoE+ATwm7PcJkmS\nJEnzQVXN+gL8DvC3feVXAB8Y2KagaqutqsbGah2DnVvPeEbVdtutXfeud627XzJ+59gznlF1//3r\nbj/R+b70pbXXb7991ZZbVi1cOPXOupUrZ7uzcCaWV2/yY65cObWYzMay5ZaTrz/ttHXrxvtvNan6\n+tfX/e9nov9eoerSSyf+WWyxRdVTn9rcO8961uzFzuWJWIxbdxdj191lbsRu4cLx/0aa6HfHggVr\nl/fee+3yeH83Df6dM1l7lixZ87dU/++zBQua33GT6f87arzfhxMtr3nN5Mft9+pXv3qdtrlMvPz1\nX68bo8Fttthiw+Lcb7y/Tyb622b77V+9zt/pa9ZTVevmG3PikdVJ/itwTFW9ti2/Ajisqv6wb5vZ\nb6gkSZKkOa3GeWT1XHk56R3Ann3lPYHb+zcYr/GSJEmStD5zZU7PV4AlSRYneRJwAvCpWW6TJEmS\npHlgTvT0VNWjSf4A+CywJXBuVX1rlpslSZIkaR6YE3N6JEmSJGmmzJXhbZIkSZI0I0x6JEmSJM1r\nJj2SJEmS5jWTHkmSJEnzmkmPJEmSpHnNpEeSJEnSvGbSI0mSJGleM+mRJEmSNK+Z9EiSJEma10x6\nJEmSJM1rJj2SJEmS5jWTHkmSJEnzmkmPJEmSpHnNpEeSJEnSvGbSI0mSJGleM+mRJEmSNK+Z9EiS\nJEma10x6JElTkuS8JH8x2+3oSTKS5MTZbockae4z6ZGkeSrJQ0kebJfHkzzcV37pNA5Z7bIxbVqe\n5LG+djyY5KxpHm5TtOdtSR4ZaM99G3PMKZ53LMmvzPR5JEmNBbPdAEnSzKiqbXqfk9wGnFhVX9jI\nw2Yj9wf4f1X1/Gk3IMkmagc0SdPHq+pVm+h4G3LeTXUNkqT1sKdHkjYzSQ5N8i9J7k9yZ5IPJFnY\nt/69SVYleSDJDUn2H+cY2ya5Ksn72vKxSb6Z5MdJbk/yhsmaMEG7fjnJvyZZneS6JL/Ut24kyf9M\n8v+Ah4C9B/bdrW3rG9ry8iT/1rbnu0leNklbJmrP2UneNVD3T0lOaz/vnuSiJHe35/jDvu3elmRl\nkvPbNnwfNjZnAAAdS0lEQVQjycHtuguApwOfbnuW3phkUZK/T3JPG5frkvzCJD9DSdIGMOmRpM3P\no8ApwFOBXwJeAJwEkOTXgCOBJVW1PfASoH+4VyV5KvB54EtVdWpbfy6woqq2Aw4ANqhHKcmOwCXA\n+4AdgfcAlyTZoW+zVwD/DdgW+F7fvnsDI8BZVfXuJE8B3g8c07bnl4DRDWlP60LghL7z7AD8KvDx\nJFsAnwauB3an+RmemuTovv1/A/g4sD3wKeCDAFX1SuD7wK9X1bZV9VfAcmA74Gnt9f8e8JNptFmS\nNA6THknazFTV16rquqp6vKq+B5wDHNWufoQmqdgvyRZV9e2quqtv9z1oEoxPVtVb+ur/AzggyXZV\n9UBVXT9JEw5vezPuT3JfksOA/wJ8u6o+1rbrE8DNwIt7zQbOq6pvtesfbet7CdZbqurDfed4HDgw\nyVZVtaqqbpqkPcf3tef+JJ9v679Mk+Qd2ZZ/B/jn9ufxn4Gdqup/VtWjVXUb8GFgWd9xv1RVl1dV\nAX8PPHeSNvwHTRK6pBrXV9WDk2wvSdoAJj2StJlJsk+SzyT5YZIHgP9F8wc37ZyfDwJ/DaxK8jdJ\ntu3tSpOcPBn4m4HD/lfgWGCsHYp2+CRNuKaqdmiXHavqWpreku8PbPe9tr7nB4OXArwcuB24qFdZ\nVf9O00Pz+8Cd7bU+a5L2fLKvPTtU1Qva4xTwCaD30IeXAR9rP+8F7N6fLAFnAP1D0lb1fX4YeHLb\nQzSeC4DPAp9IckeSM5M471aSNhGTHkna/JwN3AQ8sx3C9t/p+31QVR+oqkOA/YF9gDf1VgF/S/PH\n+aVJtu7b5ytVdRywM3AxsHID23QHTSLRb6+2/uenGVhfwFuBe4EL+xOKqrqiqo4GdqXpMfrbCc67\nvgcKfBz4nSR7AYeyJrn6PnDbQLK0XVX9+gRtHe+8awpNb9GfV9UBwC8Dvw480Q9XkKR5y6RHkjY/\n2wAPAg8n2Rd4He0f4UkOSXJY+2CDh4GfAo+1+wWgqv4A+DbNRPwnJ1mY5OVJtq+qx9pjP8aGuRTY\nJ8lLkyxIcgKwL/CZvm3GS04eoZl39BTgo2n8QpLfbOf2PAL8+yTtmfQJalU1CtxDM3Tt8qr6cbvq\nOuDBJH+cZKskWyZ5dpJDpnJcml6g//TzRiTDSQ5MsiXNz++RSdosSdpAJj2StPl5I81QrR/TzOf5\nRN+67dq6+4Axmj/4e08w638vzgqaYWUX0wx3ewVwWztcbgXNsLPxjPtunaq6j6Z34w3tOd9IM9H/\nvoF91z1g1SPAbwO70DxQYQFwGk0v0b00D2Z43STtOSFrv6fnx0l26tvmQuBX2n9753y8be9S4LvA\nj2h+bttNcp395XcAf9oOjXsDTY/UPwAP0PTCjdAMeZMkbQJphixPsDLZE/gozRjlAs6pqrPap+x8\nkmbowRhwfFWtTvJyml9UPc8BDqqqG5KM0Hyp955Gc3RV/SjJovYcv0jzy+mEdmKtJEmSJG209SU9\nuwK7VtVokm2ArwLHAb8L3FNV70zyZmCHqjp9YN9nA/9YVUva8lXAG6rqawPbnQQ8u6pOaocz/FZV\n9T/9RpIkSZKmbdLhbVV1Vzuemap6CPgWzeNKXwyc3252Pk0iNOhlrD1kAsYf49x/rIto3nUgSZIk\nSZvElOf0JFkMHARcC+xSVb1Hca6iGUc96Hiap970Oz/J9Un+tK9uD9rHkLbvXXigHT4nSZIkSRtt\nSu8AaIe2XQScUlUPJms6bKqqktTA9ocBDw+8DO7lVXVn71hJXllVU56kOXgOSZIkSRpUVeuMLltv\n0tM+tvQi4IKquritXpVk16q6K8luwN0Duy2j7yk37cnvbP99KMmFNO87uIDm6TpPp3mB3AJg+4Gn\n9fQfY33N1RyzfPlyzjvvvNluhqbB2HWTcesuY9ddxq6bjFt3TRa7/s6ZfpMOb0uz17nATVX1vr5V\nnwJe3X5+Nc0jS3v7bEHzzoRP9NVt2Xv8Z5tE/QZw4zjH+h3g85O1SZIkSZI2xPp6ep5H8+6FG5Jc\n39adAfwlsDLJibSPrO7b5/nA96tqrK9uEXB5m/BsCVzJmrdjnwtckORWmkdW++S2eWTx4sWz3QRN\nk7HrJuPWXcauu4xdNxm37ppO7CZNeqrqy0zcG/TCCfYZAX55oO5h4JAJtv8ZaydNmkeGh4dnuwma\nJmPXTcatu4xddxm7bjJu3TWd2E356W2SJEmS1EUmPZIkSZLmtXTliWhJqittlSRJkvTESzLuI6vt\n6ZEkSZI0r5n0aEaNjIzMdhM0Tcaum4xbdxm77jJ23WTcums6sTPpkSRJkjSvOadHkiRJ0rzgnB5J\nkiRJmyWTHs0ox8t2l7HrJuPWXcauu4xdNxm37nJOjyRJkiQNcE6PJEmSpHnBOT2SJEmSNksmPZpR\njpftLmPXTcatu4xddxm7bjJu3eWcHkmSJEka4JweSZIkSfPCtOb0JNkzyVVJvpnkG0lObut3THJl\nkluSXJFkqK1/eZLr+5bHkjynXXdwkhuT3Jrk/X3nWJTkk239NUn22rSXLkmaUStWwPAwHHssrF49\n262RJGkd6xve9ghwWlUdABwOvD7JfsDpwJVVtQ/w+bZMVX2sqg6qqoOAVwK3VdUN7bHOBk6sqiXA\nkiTHtPUnAve29e8FztyE16dZ5njZ7jJ23fSEx23FCli5Eq6+Gi67rClrWrznusvYdZNx665NPqen\nqu6qqtH280PAt4A9gBcD57ebnQ8cN87uLwM+DpBkN2DbqrquXffRvn36j3UR8IINvgpJ0uy45RZ4\n4IHm8w47wDnnzG57JEkax5Tn9CRZDFwNPBv4flXt0NYHuK9X7tv+O8CLq+qmJIcA76iqX23XHQn8\ncVX9RpIbgV+rqjv79ju0qu4bOJ5zeiRprjn22KaHZ4cd4PrrYS9HKEuSZs9GvacnyTY0vTCnVNWD\n/evaTKQGtj8MeLiqbpp+kyVJc96FF8JLXgLf/a4JjyRpzlqwvg2SLKRJeC6oqovb6lVJdq2qu9qh\na3cP7LYMuLCvfAfwtL7y04Db+9Y9HbgzyQJg+8Fenp7ly5ezePFiAIaGhli6dCnDw8PAmrF9ludW\nuVc3V9pjeerl0dFRTj311DnTHstTKw/eezN+/qEhRk46CUZH58T1d7ncq5sr7bE89bLfl90sD957\ns90ey9P7vhwdHWV1+xCdsbExJjLp8LZ26Nr5NA8aOK2v/p1t3ZlJTgeGqur0dt0WwPeBI6pqrG+f\na4GTgeuAS4CzquryJCcBB1bV65IsA46rqmXjtMXhbR00MjLy8/9Q1S3GrpuMW3cZu+4ydt1k3Lpr\nsthNNLxtfUnPEcAXgRtYM4TtDJrEZSVND80YcHxVrW73GQbeXlW/PHCsg4HzgK2AS6uq9/jrRcAF\nwEHAvcCy/mSpb3+THkmSJEkTmlbSM5eY9EiSJEmazEY9yECarv6xl+oWY9dNxq27jF13GbtuMm7d\nNZ3YmfRIkiRJmtcc3iZJkiRpXnB4myRJkqTNkkmPZpTjZbvL2HWTcesuY9ddxq6bjFt3OadHkiRJ\nkgY4p0eSJEnSvOCcHkmSJEm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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -602,14 +757,14 @@ } ], "source": [ - "fl.plotWakeupTasks(per_cluster=True)" + "trace.analysis.tasks.plotWakeupTasks(per_cluster=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## RT Tasks" + "### Wakeup per cluster" ] }, { @@ -619,45 +774,36 @@ "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "05:46:39 INFO : 653 tasks forked on big cluster (91.8 %)\n", + "05:46:39 INFO : 58 tasks forked on LITTLE cluster (8.2 %)\n", + "05:46:39 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:39 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n" + ] + }, { "data": { + "image/png": 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PObk5uZQkS/ZZSU1EREQ0p0dE2plsiUmyOElxWTE7du1g9KDRPDrx0X2ONSaZ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\nUrEzwCulf0yZOYU116/h03d/mi9P/PLRhKbz9Yq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"text/plain": [ - "{10: 'migration/0',\n", - " 11: 'watchdog/0',\n", - " 12: 'watchdog/1',\n", - " 13: 'migration/1',\n", - " 17: 'watchdog/2',\n", - " 18: 'migration/2',\n", - " 22: 'watchdog/3',\n", - " 23: 'migration/3',\n", - " 118: 'kschedfreq:0',\n", - " 119: 'kschedfreq:2',\n", - " 136: 'kworker/0:1H',\n", - " 138: 'kworker/1:1H',\n", - " 239: 'loop0',\n", - " 264: 'kworker/2:1H',\n", - " 286: 'kworker/3:1H',\n", - " 492: 'daisydog',\n", - " 1364: 'ksdioirqd/mmc2',\n", - " 2298: 'kworker/u9:4',\n", - " 20088: 'kworker/u9:0',\n", - " 20693: 'kworker/u9:1'}" + "" ] }, - "execution_count": 15, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "fl.rtTasks(max_prio=100)" + "trace.analysis.tasks.plotWakeupTasks(per_cluster=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Predefined LISA TraceAnslysis Functions" + "## RT Tasks" ] }, { @@ -668,22 +814,189 @@ }, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "11:44:30 INFO : Set plots time range to (0.000000, 35.313536)[s]\n" - ] + "data": { + "text/html": [ + "
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priocomm
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\n", + "
" + ], + "text/plain": [ + " prio comm\n", + "pid \n", + "10 0 migration/0\n", + "11 0 watchdog/0\n", + "12 0 watchdog/1\n", + "13 0 migration/1\n", + "17 0 watchdog/2\n", + "18 0 migration/2\n", + "22 0 watchdog/3\n", + "23 0 migration/3\n", + "492 0 daisydog\n", + "118 49 kschedfreq:0\n", + "119 49 kschedfreq:2\n", + "1364 98 ksdioirqd/mmc2\n", + "136 100 kworker/0:1H\n", + "138 100 kworker/1:1H\n", + "239 100 loop0\n", + "264 100 kworker/2:1H\n", + "286 100 kworker/3:1H\n", + "2298 100 kworker/u9:4\n", + "20088 100 kworker/u9:0\n", + "20693 100 kthreadd, kworker/u9:1" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# Import the LISA::TraceAnalysis module\n", - "from trace_analysis import TraceAnalysis\n", + "trace.data_frame.rt_tasks(min_prio=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Predefined LISA analysis Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Trace** class provides an **analysis** object that allows to perform several types of analysis on data contained in the trace. Currently available analysis types are:\n", "\n", - "ta = TraceAnalysis(\n", - " trace, # LISA::Trace object\n", - " tasks=top_big_tasks, # (optional) list of tasks to plot\n", - " plotsdir=res_dir\n", - ")" + "| Analysis Object | Description |\n", + "|-----------------|---------------------------------------|\n", + "| `cpus` | CPUs Analysis |\n", + "| `eas` | EAS-specific functionalities Analysis |\n", + "| `functions` | Functions Profiling Analysis |\n", + "| `frequency` | Frequency Analysis |\n", + "| `status` | System Status Analysis |\n", + "| `tasks` | Tasks Analysis |\n", + "\n", + " \n", + " Those are easily accessible via:\n", + " \n", + " ```python\n", + " trace.analysis.\n", + " ```" ] }, { @@ -697,13 +1010,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:44:33 INFO : Set plots time range to (0.000000, 35.313536)[s]\n" + "05:46:40 INFO : Set plots time range to (0.000000, 35.313536)[s]\n" ] } ], "source": [ "# Define time ranges for all the time based plots\n", - "ta.setXTimeRange(t_min, t_max)" + "trace.setXTimeRange(t_min, t_max)" ] }, { @@ -718,28 +1031,53 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:44:44 INFO : task chrome found, pid: [19933, 19990, 20052, 20302, 19949, 20017, 20324, 20019, 20026, 19950, 19938, 20477, 20481, 20482, 20484, 20486, 20488, 20490, 20491, 20492, 20527, 20531, 20532, 20533, 20535, 20537, 20539, 20540, 20541, 20542, 20543, 20544, 20545, 20546, 20547, 20548, 20549, 20550, 20551, 20552, 20553, 20554, 20555, 20558, 20560, 20592, 20593, 20594, 20595, 20596, 20598, 20600, 20602, 20604, 20612, 20613, 20615, 20616, 20617, 20618, 20619, 20620, 20621, 20622, 20623, 20624, 20628, 20629, 20630, 20635, 20638, 20666, 20677, 20679, 20680, 20681, 20682, 20683, 20684, 20685, 20687, 20688, 20689, 20690, 20691, 20692, 20694, 20695, 20699, 20700, 20701, 20702, 20703, 20704, 20705, 20706, 20707, 20708, 20709, 20710, 20711, 20712, 20713, 20714, 20721, 20722, 20723, 20730, 20731, 20732, 20733, 20734, 20735, 20736, 20737, 20738, 20757, 20791, 20792, 20960, 20963, 20964, 20965, 20971, 20973, 20975, 20976, 20977]\n", - "11:44:44 INFO : task lsof found, pid: [20331, 20333, 20803, 20805]\n", - "11:44:44 INFO : task keygen found, pid: [20672]\n", - "11:44:45 INFO : 472 591 819 1024\n", - "11:44:45 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n", - "11:44:45 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n", - "11:44:45 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n", - "11:44:50 INFO : 472 591 819 1024\n", - "11:44:50 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n", - "11:44:50 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n", - "11:44:50 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n", - "11:44:51 INFO : 472 591 819 1024\n", - "11:44:51 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n", - "11:44:51 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n", - "11:44:51 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n" + "05:46:40 INFO : Plotting 20672: keygen, session_manager...\n", + "05:46:40 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:40 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:40 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:41 INFO : Plotting 20705: chrome...\n", + "05:46:41 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:41 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:41 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:41 INFO : Plotting 20803: sh, lsof...\n", + "05:46:42 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:42 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:42 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:42 INFO : Plotting 20615: chrome...\n", + "05:46:42 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:42 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:42 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:43 INFO : Plotting 20805: lsof...\n", + "05:46:43 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:43 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:43 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:43 INFO : Plotting 20331: sh, lsof...\n", + "05:46:43 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:43 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:43 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:44 INFO : Plotting 650: permission_brok...\n", + "05:46:44 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:44 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:44 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:44 INFO : Plotting 20552: chrome, Chrome_IOThread...\n", + "05:46:44 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:44 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:44 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:45 INFO : Plotting 20678: df, sshd, bash...\n", + "05:46:45 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:45 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:45 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:46 INFO : Plotting 20687: chrome...\n", + "05:46:46 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:46 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:46 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n" ] }, { "data": { - "image/png": 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t+1Um3PZxx1nLTfz00y527y7h+OMPByrHQ/p+1Ik1Pt1O77bPjBnWWFJfV7hp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v1Mba3yfjueG1lNlxQpoqVegiAIL+NXk+B0hbU7YEuswruUMRZijK1hjP2jb7uKMbaYMVbLGPsX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\npp2S4/9dWEjhcgEWi0GSQYTRb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JZlHsNjqWOgjNTBimmHaw8fG3VC/fhleu6pQ0XlecaC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\nFkd2kv+dybrSO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6V3lnL36XcjF0iGdRjoKtu1dT7We638cvVhxpk041Hv18vhm7QNidQT\nI9h+9VFHGYfA6q2u52DezLoJbddfD2eeCZMnwznn1OlSfqmt1WLTrl0Le/bAL7/A+PHw+uv6ckY/\nMZBKcGpSQj3XtOnwnmv8cPAH1k3M4qf9v0RvzNw60OQCqxBiihBimxCiQAhxh79y06YZf2bM0Lxi\n1ZXPP4fhwzUNN+/PxIlwyy11vwdAQkLw4MsWq74HSq+AwU6PuFYrdOwInTrBaw5/Ct72rwBXDbnK\ndWaaxW24Paj1EN/CUUSosZoGthlI1d0GhgxeFFUU8e7x+/igbD533mk80AWitBROOSW8cxTG1NbC\njLO0F3a8yWuJtLSDLibjabJhvTzYHQKrMxZgl/KLDMtJQovDauTx0onTdsrfhDfX1NBb9+GEtfGf\n51TDrXeCxOIcmnu6T1q82XeJ/Y7RdzCozaD6qpUOo52cUBxiBOOZqc/U+RpNwf8N0mwI5QLpUl0F\nqJxf6VNWLpBcNvAyn4lWvDmeSzMu5bkZvuqm1w27LiQHZb3T3YNzRmIGrdP0Ksht0toA+A2/EsrC\nWO/8Hpzc6mQfldZAOAXxcV3GGeZfPeRqDt5y0Cf99M6+z7oTIQSLZy1m/ez1gGYr7EnF/ArWz16P\nXCC5cvCVRpfQ8cNsd3z2BHPwCf2YTmPqvDCXk5xDvz7xsMtYgdNzHL1gsNuW9qUJ2i5zpl2/Jd8t\nu5vuODUhibyUPIa1G8aGazfw0jn+nTOdbrrDcIHCZBIc//O3yAWSnbOPs2uOZuvrfFYyUhORCyTj\nhXfIK/e1enTIJe3EqR55eoHVJEykJ6b7rZvZZKZn+3xW3Pug/nwH+VkpyAWS8ifX+LRJrc092RRV\nOYyxa57OkxLqNn5bLPDEE/DOO5HbSwab3336qTb3vvJKOOsssNTaIWeHzxzXWyNRI5BKcBQvKNeR\n+06/D/uR7syeW8zcKI540yzjsApNmfwZYDLQD7hYCGFo7d2jl5Xrrvf97D9oZUc9LG6XVVVz8jmf\n8fiHS30+F9+7lGL73jrfw24HsgoNhUpPLF7xvKot+g7puXp0wQWaimqlY25g3Hmd5+mdLhlN9KKJ\nDWG4OkuKS3KtmiaVaDvKI9qN0pWpuaeGIYWvgrDxpz/Bxo3hhRBKT4fdu+G//9VW/Ty56y5NBXnO\nHC1UiyIwBQXw+XIr2M0+LxCZchicWmm2+BAdHQXH7ucy8Qn6Dpkcb7y76qhdRPd2hpCKF9puoD+B\ndfaI+rUpb1+jX0mXoaiqOQj3xX7pgEt1x71ytd2KTpma6rDtPhu2+2wMr73N51zXZPGQ+/v7537u\nU+6qXvO5Ou/loHV5bMJj/DjnR05pdwoZiRnh/Ix6pUdODyrmV1B4UyFPTXkqYNnrh1/PlO5T6nxP\np8ZJY/Gfs/9DzT2ammKiWes7665ZF5Y9KGjj/aiO+jH77d++zX1j7wvpXfX02X/mxB0nqJxfScmd\nJa52d2oo3TrqVjZdtymACnnw531CtwlsvHaj3xiwzv/9P6f90yUg/efs/7jub0ScKc4lTHsSTCjO\nSMygX34/1zU8SYlP0ZlZBGNw28EU3VrE0duOBi9cj7RpbdLGenvg9s1Ocu/69s3RFqKCRTxIiPc/\nvfV+p5gMw+Lp6do2my5tnPXwemf5vBe8zbrcq+OZ/twjNAA2nUqwySNiRATcL2CMZj9bnPgzq0sW\n8vH+hZRnRWY06Tm/++tfYfZsuPlm7fPMM1BaVUnfmYu57on/UZ62gY93vwNzezB3yTyGztc+w+bf\nwrKffeeJJ50Uu0JpIB4Y9wAmzEybDitWwKZNDX/P996DrCwYFMb6cDhze0+a2oZ1OLBdSrkbQAjx\nJnAOsM274NNZCfxjve8AZDvLztayb5iMkdpG6GwoXc4PXa/g8TW+g/zmE/tJzh4O1M21rNUq4Q9d\nmTTVwtRJvgN0fj783/9BjZcUZTYb77CSvYv13EJcHHS0zgF6UlEVeNvQ5rFb67OzFWUUFxfX6fxJ\nXafpjhPMCQyevpYfNr8ObTYw9IzPoLwNSBOZmdqCQlkZ9O4Nl10GPXrAqlWAh1O5tWvhyBFtN37d\nOhgwAL75RhtgMzLg3//WPu+/D0uXQocOMHas5g5+715th7ZrV9i+XROYnTvuP/ygtX/37pqK98GD\nWtnevbUwPPv2aULP2LHaquOePfD225CdDVdfrdXp8ss1deUxY7QFjEGDNA2EI0fg0Ue16917r3Z9\nIeCMM8BpOmSxuFXLU1MhJ8f9m6uqtPoWF8OhQ1BSAv37a/X56ivYtk1T0+nXD047DXr10n5HWpr2\nv6ms1NKTkrRPURG8+/EJuPY0MPk+r1IKcGpa2eOR5voRWP0tTgwZYodP3I4qZgwbaFwQhypZkHeh\n0Q7csGGw4pvgdcxIr/uL9r0L3uOvny3kmxMfkGb2Ni7y/V962hbWhUXnLmLRuYvYfmw73XO6+xV4\n27YR4PDr8cZv3uDidy92Z3po2PXPG4S5rDO29N36+vpR/TVa2Pj2qvr3QBFIkJ/V9n5eP3i/z25N\n56zOzB0xl7kj5gb8X39yySec8+Y5rN69mkv7X8oza8PfdX3pnJd4a/NbfvMfn/Q4N596c720+fdX\nf48QwrUr9/CZD3NhvwuDOr4yori42KfvGIWe8UfXnI6GIWKcz0tqQir9WvXzye+c2ZndJbtdC8FP\nT32aGz+50fAe806dF1Ad+NXzXmXhuQtJMCfwygb3Tm44NrOZiZmU1JSEpHacGJdIxXxNJbRdejte\nPfdVuudEpqXRKrVV8EL1TEZiBlf1vYraMl81ZM9uNrKDe35nd608an97V1zDttTnfc7PSc7xSXPi\nPVYYx3EPhJfAKr0FVP2xXbjfcwP6122fKBxbXZ0NqxRYZWh6okdLKnWafq2yHCGkJsznPTRnX98e\nbMvJ1kHs7ncEq+074szh/S7P+d1tj+4gt/suzv+toKwM3ni+NZfcuIMf2v8eS0Ev9g8/xtIVf4As\nsGcU0iFjDACfFb3GexvzwUsp4LyBE1i74rOw6hMr2Nt9z8eMhD6/o/+wv0ONe8H2pptg5Eh44w34\n7DOYMkXTNPzoI80OuXt3bS6anq7NNSsqtDlcx47aZ/BgbV5XWKipaJtM2uZDp07afPahhzRTvDvu\ngGPHtGuYTPD119r5HTrA0aNQVFTM229rWovJydCmjbYZVFyszW390dQCa3vAc+tyH5oQ68MVOS/w\n4o2+qi0p105kZdVheqTrPVHa7fDYMwfZdHgLVlnNqEFuD1t7diYz75IBDBvk3knZWmAhvWQ0Sy/9\nwOcel/zlZd459D/efK/MJ88fe/dB144JJHt4yC+v1GbMq0peIrVEv4tjt8OnL2XTsfNU1u7Qq7eO\nPt3CO0vdx547rEUpX3D8qJmfX2/Nf1+ZzU9bqsB4IRekYNBgO/sLtcOM1NjR5dcRwOB+5oAJ7K/5\nhU/5FG5xO40oAahJg8RytgF3H28Dq7Ig3q3Wds+Ke7j3w44c2taJRx4vYdi1xVCdBdm/wtnfYO+4\nHZK3w/5TOPexSwABW0/A5xISyqDrCl7dfQS+HaoJam02QGUeVHSEs5ZDfBXLrQnQ1gIDYQ3AzraQ\nfhBygNpkHluWC4sGaIJ21m6wxzHnzVMgsQx6LYGhh/jKlggpx/gBeHHZQEg+DlfspfDAUN659Qww\n10BtKszro/0+k037m7kbOq+GwyfD3lFgrtX+l8669liinWdJhXXpYI+DlKOQ+yvcCKvX3MTqJQPh\n+y2Q9wuUtoekEkg6AV/lgtkClblQnQ1dvoCOmw3bKDHF4pJbhD0eafJdSf/Xv7S/vxQAoYVjRJqM\ndZcGtdMmsS9cN4f3/3YLU7qd7f8aIcQxNvJu6UzKzYWdh32yXXiGrvCHOcjc6tw+5/L2lrf55gSM\n7q835O7Zri2FQQzy3/rtW1z438h36Xrk9giY36sX4BDeG9rjbigOmQIxsdtEPtsZ+sTnpJQhQYUT\nuUCy7eg29pXuY0K3CUgpXaHHAD686ENAC0e2Zv8a1h1Yx4tjvuTW9efQL78fX+75koGtB7KxaKPh\n9ZPjk1kyawnTXze2Mbz51JsB+PSST5nyWmg7upcPvJwlBUs4VqX3IHpKe72NRO+83vTO8x8SIxgR\nt1dNul+17GAaGp9e+il9/tHH9SzeMPwGbhhu7IHWn32nc8fVc6ezZ25P0hP0qp5ygeSqp1/mxeP+\n1XS/vPJLrvjwCtfx3yb9LWD9U+K1Scb+ecE96kcjHTI60G1UEXyoT/dcGPJ08OTcMXc6y6usthq+\nA0JRbXZiEuFNhYOpj9fa9aujnu+NW0d7eCgPMFfxR2KAn+VtjuIpsAoEVnvgHday7G/4/box/H4d\nYHGE/jFZGGKZB17RnkbkT+QkJvJt1mXEP2xmTOFS2mWEtuhht8P3nx2jS2+H0Dq3B8eA7a3HU5xW\nRdF53/DSpglgyuOOAX/n/KMDWcXVAPzntrO4aqjWf3LmfMfqPZvAywfZHWNu464Vfi0MY5LBCdri\n3t+n/J2/r3iLHYNegUF684en7Cae+nQsdNsJd+zmfUsqJFTAEDhWk8YxBEwt4zjwfm0yxFfBKNha\nncFWk41lCRVwpBuYcmCCIxTfqdkcKmsLg9px39cnQ0I53Khplayym7X541j41ZLKr2aLNq/8Ao4O\new7SHap0UsCAFK0uAcZrEYrXy4ZCCPEbYLKUcrbj+FJguJRyrlc5ua17L3r18l05XFywxFnKK0f/\nu9LM2mqbTdqosmshNEw2t8qS3VwFljRmnDzW5x4rtvxEZdzesFbh3CogQpfqpIOXhz2blBwsO+Ao\n7y43rcc09pbu5eein5nRczqLC5aQa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QV5vZakmTJW0TL79WUZKp+P/p4avbSFoVf5lPe13tX/qnSPqPrP0eKGlixvqZfzzYpCjR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\ne9+EGpQdsg8TuYcr1+xzPvlRMXF9rs6DEdwAAORm7PZNO6Y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vfvc7/eEPf9ATTzzRsP2z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\nj65371LsvUlx965oY6/EVEREWg1jzGFbT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sZrazpG9IGiZpsAozrSdp43uiMNkOAAAAIBkmZlhT1bWEr7WfpIfdfbkkmdnt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OhAZxBzNfg+kGfASABsmuJ3oQAKjqXAA/gAlQVwHoDXtCJpezYboAL3LvtMdBngDgHADr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CAVw4dzzhnzXqvVDV6yICv3f3M/euf2ATBVc0c2uvKuWqX3HTOGJsnq2dP5eKNfGoa1e9fQ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\nz8pP6to9gd3tLuzhqpoO/f1fsNQfEuzZREcGb65CM3h1u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tnt95ws6d/njRUNe5f+An7//JQDcplWot7igOcQ9X0tjz5z0AQGq1Zdq6rTMz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TAAAg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\nvKioviLwl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n37Vj3PzMxk27ZtbNq0ifvvv5+0tDTS0tLo1q0bW7ZsYdu2bfXuV9u2bdvo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\nTkB8LQAAAABJRU5ErkJggg==\n", 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HexpTv2QjSdowLEwlSd19FDicxlTXbwI/6PLeyGrdAzRuHHM/z/Zjdn2e5jtpTMf8aVUI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\n2YeJ3MOVtxpTM7vBzPqaWVcze8rMlpvZuJzODgAAAAAIXqt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MfP7d/deS/inpXxtpVnM/\nNwCANkhVx9Tdn5e03cinme1jZn+06EIXz8XfZNc3VtE3oQAA1V2FdbaZLTWz1ZK+r2j0VO7+tKIR\no59J+sDMbrNtFxkyRR2gHpJuy/HcPSU9KulFd78ua9U6RSNW2fpJynRc75X0lqJOaF9J7yrqRGT2\nrV+L2E9R53Q78ZTTyYqmtraau78taaKi+tYPLLqg0+CsTT7I+vcGST3MrD3/f/pdRTnMiaedToif\nHyKp/mjsQjUcHWxPLulkdy929zJ3v9DdN2Wtuyhel3lMabcTu//N3U9z910VjVT+u6QrG9l0iKT3\nsvbbqIYjmU1mFk8DfjXTuZZ0oOKflXrt2dHPDQCgjVLVMW3CLxT9j2+kpO9o+wtlyMyGSiqT9HT+\nmwYAqXWLpH9I2jeednilsn7nu/tP4t+rByiaSvudzCpJtyuaVvsHM+vVmpOaWXdFtaGL3P38eqvn\nKmukysz2UTTVd3781GhJt7n7xrgG9TZJX8zad0S9442In29MV0UdkJy4+yx3P0rRBYVc0nXN7NJu\n4hrd89y9VNL5kn4ev1fvx+3JNjR+vlNz91ck/U6N17cuUTRKLqnui5EGHcvGxH9D/ELR1O0B7l4s\n6e9qYuR5Bz83AIA2SnXHNK4pOVzSb8zsVUVXc9yt3manS/pNPD0HABDpo2gkcoOZ7S/p/yme3mhm\nI83sXy26GNIGSZ9o29VOTZLc/UJFo5ePmlmPlpwwPt6D8THHN7LJvZJOjGtBeyuqpXwo7oRKUQ3p\nuWbWI+5cnKdtNamVkmrM7GIz625mF0uqVfylpEW3I9kj/vdQRSPEOZV4xKPNx8Sd7E3a/v1pzXHG\nW8tuQVN/v1PMLNPRWqUotxpJf5S0n5mNMbMuZnaapP0lzc7s2tpztYMdnjPOqkfm0eKDmh1pZt8w\ns13i5f0V1Yn+tZHNH1L0uTrcoqs3T22uXVl6K3p/l0sqiEenD2yiTTv6uQEAtFGqO6aK2rcqU0sa\nP+p/W3qamMYLAPVdpqjMYY2iEaH7s9b1jZ9bqeiiPsu1rR4z+36a5ymaIvlw3ElrTHYH4AhF04CP\nlbTKtt2n8khJcvd/SPqmog7qB5J6Kr4gU2y8olGo9+Pzlkk6K953s6QvKZqe+3H83y+5+9Z4309J\netHM1inqxL6kaEps1EizW8wsU6/aWNuzdZd0raSPFF3Zt0TSFfG6xu432tQXo3tIeqGJdTsyUtJf\nzWytolvkXBxfnGmFpBMkfVtRZpcpupVL5sJVLbkX6nav2cza2pn9qW1/H9OXs9aVStqoqBO3QdJ6\nM9s7q607skpR7ewb8fvwR0m/lXR91v6Z+7HOlXSRos/4EkVfyHyo6EuF7bbNktn3H5JmKPq8LFPU\nKX2h3naZfXf0cwMAaCPL50CjmVUr+iOpRtIWdz+0kW3KJD3q7gfFy3+R9CN3fzD+H+hB7v56vG5/\nSX90973y8woAABlm9qaiq6D+1t0nNLd9mpjZRkUdl5vbsy6y3jmeUNSpfGtnHL+VbRkm6WVJXST9\np7vfFY8O/lBRR/wAd69OsIntJp5t9bGiaewLk24PAKBl8t0xXSDps1nf7tZfP0vRVSNLFH2bfpWk\nZxTVSg1WVDM0y92nx9tPkdTd3b+Xh+YDAIAUMrMTFd3eyBSNgB7i7rneXxYAkIAkOqYj4+lIAAAA\nbWZmtyu6bYwpGhn+T3f/Z7KtAgC0Rr47pu8qug9djaIrL96et5MDAAAAAFKpS57Pd6S7L42vsvcn\nM3szvncpAAAAACBQee2YuvvS+L8fmdnvJB0qqa5jambc8gUAAAAAOjF3b3BV+LzdLsbMeplZUfzv\n3pKOk/RG/e3cnUdgjylTpiTeBh5kz4PceZA7D7LnQe48dn72TcnniOkgSb+Lb5nWRdK97v5kHs8P\nAAAAAEihvHVM3X2BpPJ8nQ8dR3V1ddJNQELIPkzkHiZyDxfZh4ncw5Vr9nmbygs0pbyc7ytCRfZh\nIvcwkXu4yD5Mact97dqkWxCOXLPP6+1immNmnqb2AAAAAOjY5s2TDjhAopuRDmYmb+TiR/m+XUxO\n4rpUBIwvLAAAAJCLBQsaLv/sZ9KNNybTHjSuw0zlTfrqUjySe6DzqqysTLoJSAC5h4ncw0X2YUpT\n7r33nK+Ciw6oW957b2nGjAQb1Mnlmn2H6ZgCAAAAQGtt8vWqHThPmzdnP8vgR9p0iBrTeB5yAi1C\nGpA/AAAAcjV30VId+OPPaN20perdW7Ld50jjjpVfuzrppgWpqRpTRkwBAAAAdFolAwqkghrVjXN8\n6Sypx5pE24SG6JgCSEya6k+QP+QeJnIPF9mHKU25FxYUygpqVVMjXXmlpFVlSTepU6PGNAFlZWV6\n6qmn2vWYU6dO1bhx49r1mAAAAECoCqxAshpt3So995ykLpuSbhIaQce0Dcys3W9lw61xEJKKioqk\nm4AEkHuYyD1cZB+mNOVeaIWSRSOmI0dK2uuZpJvUqeWaPR1TAAAAAJ1WgRXIrUZVVdJNN0XP3fWl\nu5JtFBqgY9oONm/erIkTJ6q0tFSlpaW69NJLtTm+HvWqVat0wgknaNddd9WAAQN04okn6v3336/b\nd8GCBTr66KPVt29fHXfccVq+fHmz5/vkk0905plnqqSkRMXFxTr00EP10UcfSWo4vTh7anB1dbUK\nCgo0c+ZM7bnnnho4cKBuvfVWvfzyyxoxYoSKi4t10UUXtedbA+xQmupPkD/kHiZyDxfZhylNuRcW\nRCOmL78sSS6TaexBY5NuVqcVdI2pWfs8cuHumj59uubMmaPXXntNr732mubMmaPp06dLkmpra3XO\nOedo0aJFWrRokXr27KkLL7ywbv+xY8fqkEMO0YoVKzR58mTdeeedzU7nvfPOO7VmzRq99957Wrly\npW677Tb16NEjfi+2n17c2LHmzJmjt99+W/fff78uueQS/eAHP9DTTz+tuXPn6te//rWee+653N4M\nAAAAIGUKrEDqulGfbHL17V+jAiuIOqtIlU7RMXVvn0eu7rvvPl111VUqKSlRSUmJpkyZorvvvluS\nNGDAAH35y19Wjx491KdPH33ve9/Ts88+K0latGiRXnnlFV1zzTXq2rWrjjrqKJ144onN3rOzW7du\nWrFihf75z3/KzPTpT39aRUVFTbw3DY81efJkdevWTccee6yKioo0duxYlZSUaMiQITrqqKP06quv\n5v5mAK2QpvoT5A+5h4ncw0X2YUpT7t0Ku0mS9tnvE3m3NarxmoRb1LlRY5qgJUuWaOjQoXXLe+65\np5YsWSJJ2rBhg84//3yVlZWpX79+Ovroo7V69Wq5u5YsWaLi4mL17Nmzbt/s4zRl3Lhx+sIXvqDT\nTz9dpaWluvzyy7V169YWt3fQoEF1/+7Zs2eD5XXr1rX4WAAAAECaFViBtKWHuveQNpc9ppJeJUk3\nCY2gY9oOhgwZourq6rrlRYsWqbS0VJI0Y8YMzZ8/X3PmzNHq1av17LPPyt3l7ho8eLA+/vhjbdiw\noW7fhQsXNjuVt0uXLrrqqqs0d+5cvfjii5o9e7buuisq4O7du7fWr19ft+2yZcta/Xq4MjDyJU31\nJ8gfcg8TuYeL7MOUuty9QGPH1mrTrn/Rl/f/ctKt6dSCrjFN2pgxYzR9+nQtX75cy5cv19VXX60z\nzzxTkrRu3Tr17NlT/fr108qVKzVt2rS6/YYOHaqRI0dqypQp2rJli1544QXNnj272fNVVlbqjTfe\nUE1NjYqKitS1a1cVFkbz5MvLy3X//fdr69ateuWVV/TQQw+1uqPZ3FRiAAAAoEPxAqmgRsX9CzVi\n0IikW4NG0DFtIzPTpEmTNHLkSI0YMUIjRozQyJEjNWnSJEnSxIkTtXHjRpWUlOiII47Q8ccfv11H\n8b777tPf/vY3DRgwQFdffbXOOuusZs+5bNkynXLKKerXr58OOOAAVVRU1F1595prrtE777yj4uJi\nTZ06VWeccUaD9rbkNQH5kKb6E+QPuYeJ3MNF9mFKXe4eXZl36F5b1LWga9Kt6dRyzd7SNDpmZt5Y\ne8yMUbyAkT8AAADaovB7A2Q/eVuHTPqWzjvuaE349ISkmxSs+G/7BiNhjJgCSEzq6k+QF+QeJnIP\nF9mHKW2519YUqKa2VrW2RV0LGTHdmagx7WTuvfdeFRUVNXgcdNBBSTcNAAAA6Fi8QLIavbe1KumW\noAlM5UXqkT8AAADawi4bLN32fxr4X4fpqXMe0cG7HZx0k4LFVF4AAAAAQerVs0CyWq3YukiDiwYn\n3Rw0go4pgMSkrf4E+UHuYSL3cJF9mNKW+4YuS6TiBSpQoQb0HJB0czo1akwBAAAAoClDXlGtalRo\nhUm3BI2gxhSpR/4AAABoC5tm0j1/VJevn6gtk7ck3ZygUWMKAAAAIEg9Vh8kfdJfXQq6JN0UNIGO\naR5de+21OvfccyVJ1dXVKigoUG1tbcKtApKTtvoT5Ae5h4ncw0X2YUpb7p9sKJS6rdMnWz9Juimd\nXq7Z85XBTlJZWalx48Zp8eLFdc9dccUVCbYIAAAACJQXSP0Wql/3fkm3BE1gxBRAYioqKpJuAhJA\n7mEi93CRfZhSl7sXSt3Wa7+B+yXdkk4v1+zz3jE1s0Ize9XMHs33udtbQUGB3n333brl8ePHa/Lk\nydqwYYOOP/54LVmyREVFRerbt6+WLl2qqVOnaty4ca06x8yZM7XPPvuob9++2nvvvXXfffdJUoNj\n1Z8aXFFRocmTJ+vII49UUVGRTjrpJC1fvlxnnHGG+vXrp0MPPVQLFy5sh3cBAAAASLfSIdGIaWEB\nV+RNqySm8l4i6R+SitrrgDatwUWdcuJT2nblVzOTmalXr156/PHHdeaZZ243ldesde1cv369Lrnk\nEr3yyisaNmyYPvjgA61YsaLFx3rggQf0xBNPaODAgTr88MN1+OGH67bbbtNdd92ls88+W9OmTdMv\nf/nL1r1IoB1VVlam7xtV7HTkHiZyDxfZhyltuZcMLNSHfT9mKm8e5Jp9XjumZra7pC9K+r6kb7XX\ncdvaoWxPmduaNHZ7k1xueVJQUKA33nhDu+++uwYNGqRBgwa16FhmpgkTJmivvfaSJB1//PGaN2+e\njjnmGEnSKaecosmTJ7e6PQAAAEBHU9Nljb506jqV9No76aagCfmeyvsjSd+RxKVoW6B379564IEH\ndOutt2rIkCE64YQT9NZbb7V4/0wnVpJ69OihXXfddbvldevWtWt7gdZK0zepyB9yDxO5h4vsw5S2\n3P/+4d/1m3/8Rt0LuyfdlE4v9TWmZnaCpA/d/VVJ7TP3NmG9evXShg0b6paXLl1aN8W2sam2rZ3K\nK0nHHXecnnzySS1btkz7779/3e1mevfuvd25ly1btsPj5HJuAAAAoDNxpWemJbaXz6m8R0g6ycy+\nKKmHpL5mdpe7fz17o/Hjx6usrEyS1L9/f5WXl+exia1TXl6ue++9V9OnT9ef/vQnPffcczr00EMl\nRaOVK1as0Jo1a9S3b19JrZ/K++GHH+qll17S5z//efXs2VO9e/dWYWFh3bmvv/56LV68WH379tW1\n117bYP/s8+UyjThtMvdEynwLw3LHX66qqtLEiRNT0x6W87Oc+Xda2sMyP+8s79zlm266SeXl5alp\nD8v5Wc48l5b2lPQq0YYtG+QLXJWVlYm3pzMv1/99X1VVpVWrVkmKLtjaJHfP+0PS0ZIebeR5b0xT\nzyftlVde8eHDh3tRUZGPGzfOx44d65MnT65bf/bZZ/vAgQO9uLjYlyxZ4lOnTvVx48a5u/uCBQu8\noKDAa2pqmjz+0qVL/eijj/Z+/fp5//79/XOf+5zPmzevbv0FF1zg/fv392HDhvntt9++3fEqKir8\njjvuqNt20qRJPmHChLrlP/3pTz5s2LB2ey92prTmj7Z75plnkm4CEkDuYSL3cJF9mNKW+1ce+IqX\nXF/it7x8S9JN6fSayz7+275BH9E8gZE0Mzta0rfd/aR6z3tj7TGzTjHih9yQPwAAANri1N+cqife\neUI3Hnujzv3suUk3J2jx3/YN6gyTuF2M3P1ZSc8mcW4AAAAAYSmwAm2u2cx9TFOsIOkGQOrTp4+K\niooaPP7yl78k3TRgp8quQ0E4yD1M5B4usg9T2nIvLCiMOqZGx3RnyzX7REZMsT1u2wIAAADsPIVW\nqFqvZcQ0xRKpMW0KNaZoDPkDAACgLXa5YRct37Bc933lPo05aEzSzQlaUzWmTOUFAAAA0Kkt37Bc\nUlRrinQiGQCJSVv9CfKD3MNE7uEi+zClLfeSXiWSpL7d+ybcks4v1+zpmAIAAADo1M46+CxJ0i69\nd0m4JWgKHdOUOPDAA/Xcc8/ltG9BQYHefffddm5R+7n22mt17rnR/aKqq6tVUFCg2trahFuFNKio\nqEi6CUgAuYeJ3MNF9mFKW+6Zq/F2KeDarztbrtmTTEr8/e9/T7oJrVZZWalx48Zp8eLFO9zuiiuu\nyFOLAAAAgIYyV+PtWtA14ZagKYyYJmzr1q1JN2GnqqmpSboJSLG01Z8gP8g9TOQeLrIPU9pyZ8Q0\nf6gxTUBZWZn++7//W8OHD9eAAQN09tlna9OmTZKk2bNnq7y8XMXFxTryyCP1xhtvbLff9ddfrxEj\nRqioqEg1NTUqKyvTU089JUnatGmTJk6cqNLSUpWWlurSSy/V5s2b6/a/4YYbNGTIEO2+++765S9/\n2aK2bty4Ud/+9rdVVlam/v3766ijjtInn3wiSXrkkUc0fPhwFRcX63Of+5zefPPN7do6Y8YMHXzw\nwerfv79OP/10bdq0SevXr9fxxx+vJUuWqKioSH379tXSpUs1depUfe1rX9O4cePUr18/zZw5U1On\nTtW4ceO2a88dd9yh0tJSDRkyRDNmzMgtAAAAAKAFMh1SOqbpRce0je677z49+eSTeueddzR//nxN\nnz5dr776qs455xzdfvvtWrlypc4//3yddNJJ2rJlS91+999/v/74xz9q1apVKiwslJnJLLqdz/e/\n/33NmTNHr732ml577TXNmTNH06dPlyQ9/vjjmjFjhv785z9r/vz5+vOf/9yidl522WV69dVX9dJL\nL2nlypW64YYbVFBQoPnz52vs2LH68Y9/rOXLl+uLX/yiTjzxxLqRXDPTb37zGz3xxBNasGCBXn/9\ndc2cOVO9e/fW448/riFDhmjt2rVas2aNBg8eLCnq6J5yyilavXq1zjjjjLrXla2yslJvv/22nnzy\nSV133XV1nXKEJW31J8gPcg8TuYeL7MOUttz/b9n/SZJK+5Ym3JLOL9fsO0fH1Kx9Hq0+renCCy9U\naWmpiouLdeWVV2rWrFm6/fbbdf755+uQQw6RmenrX/+6unfvrr/+9a91+1188cUqLS1V9+7dGxz3\nvvvu01VXXaWSkhKVlJRoypQpuvvuuyVJv/71r3X22WfrgAMOUK9evTRt2rRm21lbW6tf/epXuvnm\nmzV48GAVFBTosMMOU7du3fTAAw/ohBNO0KhRo1RYWKjLLrtMGzdu1Isvvli3/8UXX6zddttNxcXF\nOvHEE1VVVSVJcvdGz3fEEUfopJNOkiT16NGj0e2mTJminj176sADD9SECRM0a9asZl8HAAAAkItH\n3npEktStsFvCLUFTOkfH1L19HjnYY4896v695557asmSJVq4cKFmzJih4uLiusd7772nJUuWNLpf\nfUuWLNHQoUMbHFeSli5d2uCczVm+fLk++eQT7bPPPg3WLV26dLtjmJn22GMPvf/++3XP7bbbbnX/\n7tmzp9atW7fD8+2+++7Ntqmx9w3hSVv9CfKD3MNE7uEi+zClLfdvHfYtSVKBdY7uT5pRY5qQRYsW\nbffvIUOGaM8999SVV16pjz/+uO6xbt06nXbaaXXbNja9NWPIkCGqrq7e7rilpdG0g8GDBzc4Z3NK\nSkrUo0cPvf32242ea+HChXXL7q7FixfXnW9HGnsN2VOSd7Rd/dfQkvMBAAAAudizX/ODOUgWHdM2\ncHf9/Oc/1/vvv6+VK1fq+9//vk4//XR94xvf0K233qo5c+bI3bV+/Xo99thjzY40ZowZM0bTp0/X\n8uXLtXz5cl199dU688wzJUmnnnqqZs6cqXnz5mnDhg0tmspbUFCgs88+W9/61re0dOlS1dTU6KWX\nXtLmzZt16qmn6rHHHtPTTz+tLVu2aMaMGerRo4eOOOKIZo87aNAgrVixQmvWrNnuPWnsfapv+vTp\n2rhxo+bOnauZM2du12lHONJWf4L8IPcwkXu4yD5M5B6usGtME2JmGjt2rI477jjts88+GjZsmCZN\nmqTPfvazuv3223XhhRdqwIABGjZsmO66664djpJmmzRpkkaOHKkRI0ZoxIgRGjlypCZNmiRJGj16\ntCZOnKhjjjlG++23n0aNGtWi495444066KCDdMghh2jgwIG64oorVFtbq/3220/33HOPLrroIu2y\nyy567LHH9Oijj6pLl8avWJY9Irr//vtrzJgx2nvvvTVgwAAtXbq0yRHT7OfMTEcffbT23Xdfff7z\nn9d3vvMdff7zn2/RewMAAACg87GmLmCTBDPzxtpjZk1eaCdJe+21l+644w4dc8wxSTelU0tr/mi7\nyspKvlENELmHidzDRfZhSlvu3/3Td3XDizfIp/A35c7WXPbx3/YNRtYYMQUAAADQqd3w4g1JNwHN\nYMS0DdI2Yjp8+PBGL4b0i1/8QmPGjEmgRe0jrfkDAACgY7Bp0QAdI6bJa2rElI4pUo/8AQAA0BaX\n/+lyXf/i9XRMU4CpvABSJ233OEN+kHuYyD1cZB+mtOXer0e/pJsQjFyzb/zSqwAAAADQSVx06EU6\nYo/mb4eI5DCVF6lH/gDw/9u7/1Df77oO4M/XdidqRndmubLF0UjImJyhiKXWGWZMArUfGoPA6x/R\nH/3wn8gMSv8J8UciJQjRjM2FEkamxTKFDY1oa3ZPm9s0BQ84p7OYJzZmtNq7P873ttP1nqscPJ/P\n5/t9PR5wuN/393zH5z2ee9/d1/1+nt8DAJvhqFt51+Yd02/1Z4ACAACwXtaiYzrG8LXBX7fccss3\nfQ2baWn9E6Yh957k3pfse5J7X8fNfi0GUwAAADbXWnRMAQAAWH9+XAwAAACLZDBldjoIfcm+J7n3\nJPe+ZN+T3PvSMQUAAGAt6ZgCAAAwidk7plX1xKq6rap2q+qeqnrLVNcGAABguSYbTMcY/5nkmjHG\ndpLnJrmmql481fVZLh2EvmTfk9x7kntfsu9J7n2tRcd0jPHI6uETklya5MEprw8AAMDyTNoxrapL\nkvxzkh9K8p4xxm+d930dUwAAgA11VMf01JSbGGM8lmS7qr4ryUerameMcevh15w5cyZbW1tJktOn\nT2d7ezs7OztJHn9b2Nra2tra2tra2tra2nr5693d3ezv7ydJ9vb2cpTZPpW3qn43ydfHGO849Jx3\nTBu69dZb/+8/XnqRfU9y70nufcm+J7n39c2yX8Kn8j6tqk6vHj8pycuSnJ3q+gAAACzTZO+YVtVV\nSW7IwTB8SZL3jTHeft5rvGMKAACwoY56x3S2W3kvxGAKAACwuWa/lReOcq4kTT+y70nuPcm9L9n3\nJPe+jpu9wRQAAIBZuZUXAACASbiVFwAAgEUymDI7HYS+ZN+T3HuSe1+y70nufemYAgAAsJZ0TAEA\nAJiEjikAAACLZDBldjoIfcm+J7n3JPe+ZN+T3PvSMQUAAGAt6ZgCAAAwCR1TAAAAFslrD8g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IaTvEXVpPcQ+XYh8mxT1cqjEVEUmhnoV/ogh6p6VLJkvTbgeLxaGpvH3F3amu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\n6brpuYi+g+J274aZM8OvIiIiUlvMDOdcwbC1RIQNMOAW4PliS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MJUmSJHVpK1atYPbC2bxr2Lu4fsZsTvjF8ay4yLsfFcGbH6m0ytaDoPox+zyZ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\nSrRjKul3kk5J+JjIOGoQwkX2YSL3MJF7uMg+TFnM/dDbDk2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Is9vNrKJetaovSnrDzD6W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MJUmSJEkDkj2mkiRJkqSm6Pce04gYGhE/jIg/RMQdEfGaiBgeEddHxN0RcV1E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9pmadP6ZOLb7t1KlsXwvbX3ttdY2H7Su3/Qc+UF3jYfseb9/aYzpFU3XmWab9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\n9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vxt5OSY97nz7mtrSHVKQ4MQ17Nt6sS3rcG/XAA/U9r2/fcNuRRFmPPflj3O3F\nGlMiIqIQuLOrVlvPibDs9Prr/seIOzvzE08Em1X5jTfMLYeCExEZTEwpdqxBsFeaYu/2lHl7zKg+\nSY/7+vXmdv78no8x/vVLetyDevnlyo9feikwdWr115k9O5z2pEFWYk+1YdztxRpTIqIIufVj/frF\n2w6Knjsb7z//Wby+dCgv2cnvWrZe++xTqFOuZMIEc8tjiojIYGJKsWMNgr3SFHvOxhuepMfdTRQ+\n8IHi9aXDN/fZpzntyYqkxz2oXr3CeR03ebUhMc1K7Kk2jLu9WGNKRBSha6+NuwXULG7CcOyxwHe/\na+779W69+GJTm0UJ4f5AEaRXtBL3O8WGxJSIKAgmphQ71iDYK42xHzUq7hakX9Ljfs895ra7uzBs\ns6WFQ3kblfS4B+UeA6WJ6ZlnArfcEvx1bOoxzUrsqTaMu71YY0pEFKGjjjK348fH2w6K3rhx5tYv\nMSV7HHkk8PTTPdcPHGhuSxPKXK62a5Iec4z/6xAR2YqJKcWONQj2SlPs3boynkQ2Lulxd2Pc1VVI\nTDdvLn6Mapf0uJd68klgypSe60ePNrelPabd3bXVn7qXlLHhmEpb7CkcjLu9WGNKRBQhm4bd2e6C\nC8xtd3ehl3SbbTiU10Z+8XZn6C5NTGvtMV22rPw+iIhsxMSUYscaBHulKfbuSWijE55QeuI+a1Zh\nNuZddvEfynvXXc1tU5qlJe5efkljLuf/WK09pm5vvA2JaRpjT41j3O1Vb+xr+G2PiMheTEztscsu\nwMyZwLRp5t+iRcCwYcCdd/ZMIvr2jaeNFJ/Fi81toz2mGzeaWxsSUyKiINhjSrFjDYK90hT7TZvM\nLU8iG5f0uJ97buH+oEFmMqT+/f2H8oZ1TUsbJD3ufvw+76ef7v/Yq6+aXtOg3n23/D6yJo2xp8Yx\n7vZijSkRUYTWrDG37DHNvm98w9zusEPx9Wv9hvIyMc22Sklj6XfB3LnA4MHBX5sTqhERFWNiSrFj\nDYK90hR79+SRJ5GNS3rc3Rj71QyWxr+WoZu2S3rc/Qwf3nPdJz9pbr3Hgjtr88KFwV9782ZTZ2rD\nd0oaY0+NY9ztxeuYEhFFqLPT3LLH1B4LFhQv+w3l9UtcKDt69+65bscdza37XbByZaHW+Mgjg792\nZyfQr58diSkRURBMTCl2rEGwV5piP2eOuWVi2rg0xX2XXQr3vUN53eNg332b2540S1Pc3VrRPn3K\nb+MmlG/1PVmUAAAgAElEQVS8UVg3ZEjwfbgJrQ2JaZpiT+Fh3O3FGlMiogi5J6oPPBBvO6i5Xnml\neNlNInI5M8zXr+6U0s+dMXerrcpv4/44Ue8lgzihGhFRMSamFDvWINgrTbF3a8iWLIm3HVmQpri7\nQ7iB4qG8bmJKwaUp7hs2mNtKPaZr15pbb2JZyzGRywGjR9fetjRKU+wpPIy7vVhjSkQUITcxpfDM\nmQO89VbcrajM2yPqvc/ENNvcHlM///ynuXVHUeRy5nbAgNpm5d2wwVyGiD2mREQGE1OKHWsQ7JWm\n2C9aBBxzDHDIIXG3JP3cuO+0k7kkS5qwx7R+afq8u8N0/ZLGZ58tfsxNUL2960G8/bY5hmxITNMU\newoP424v1pgSEUVo1SozsQlnYbVLi+f/khzKa49KiWk5I0fWto9Bg0wN6+rVtT2PiCirmJhS7FiD\nYK80xb5PH2DsWDt6N6Lmxr0lof8H2msvE2sAOPDAwnoO5W1Mmj7vlRLTww8vfuzEE81tLcN4AdPT\nmssVhgJnWZpiT+Fh3O3FGlMiogjl80BrK/DQQ8Af/xh3a7IhqZfeUS0kndts0/MxwNQgupPfUPYE\n6TF1H2ttNbe1ztDc1WWOr6T+QENE1Gz8OqTYsQbBXmmKfS5XOAGdMiXetqRdGuLuJhnlhvKuXl3b\nNSspHXF31ZKYutvWk5j27l1729IoTbGn8DDu9mKNKRFRhPJ5Dt2Mym23xd2CYqqFhLTSrLxjxjS3\nXdQ8tSSm9Q7FdRNTlgcQERlMTCl2rEGwV5piz8Q0PG7cDz/czMr7r3/F255S3qG8pb1g3mTE7UGn\nYNL2eQeCJaYrVhTfBrVkCdC3b+1tS6M0xZ7Cw7jbizWmREQR8iam7OEIR1sbsMcecbeiJ9VCL1i5\nobzd3fyhIsuC1D+7x4J7HNR6PPTrBwwcyO8TIiIXE1OKHWsQ7JWm2HsT0xEj4m1L2rlxz+eTOfGL\naiExGTSosN7bezpvHvDCC81tV9ql6fPu/jDhlzQuWVL8WL3XMe3utmcob5piT+Fh3O3FGlMiogi5\ns/ICQP/+8bYlK/L52ieMaYaurkKyUG4o79lnN7dN1FxLl5pbv6Rx48bix9wktpbJsN57r5CYEhGR\nwcSUYscaBHulKfb5PLBc3gAGLmVtYYPcuHsnGUqKzk5gzhz/oZylQ3mpNmn6vJ93nrn1S0z79jXD\ncF3usbDttsFf//XXza33mMqyNMWewsO424s1pkREEcrngV+u3wc47jzWFoYkiUN5ly83t21tPR/z\n9p4eemhz2kPx2Hdfc+uXNM6eDWzaVHjs0kvNbS3fC11d5jaJIwaIiOKSsFMCshFrEOyVptjn80Cu\nZSMwZCF7TBvkrTFN6ol5uUvBuMnIyScD55wT3f7fey+6145Lmj7vn/ykuT3lFOBLX+r5+OjRhWPh\nE58wt7Ukpt7jy4Ye0zTFnsLDuNuLNaZERBFyh94h1ztxvXxplcShvK2twNZblx/K617aJsrLB40e\nbRJTt5aRmu+3vzW3qsDNNxfWu8fFVlsVEsqRI81tLcdy797ATjsl94cZIqI4JOyUgGzEGgR7pSn2\nWyYpybO7tFFu3JM4lNdt0zvv9Hzswx8urM/lomv7yJHAgAHBLlmSJmn6vD/xhP96d6h3a2shMXXj\ntGlT8Ne37fJTaYo9hYdxt1e9secZFhFRAFuSkBbOehOWJA7ldRPTcePMJEheo0YV7t9xB/Duu9G2\nxYaEJW281y5177//vrmdNi3467jHmS2THxERBZGw36rJRqxBsFeaYu/OvDl8cBtPJBvkxj2JQ3nd\nhMG9BEg5ffsCEyZE146kJexhSNPn/dRT/de7vaPexNTt+axl6LXb457FOPtJU+wpPIy7veqNPXtM\niYiqUC0kKv36MSsNS5KH8volpt4k4qCDgKFDm9cuaq499vBfr2pqgL09nTfeWPvrd3YCmzcXXpOI\niNhjSgnAGgR7pSX21XrPqDZpqDGtFvNmtD1rCUtaPu9A+fjncqaHtNEhuCtXmtewpcc0TbGn8DDu\n9uJ1TImIIvK733kWJGPZQoySXGNaKTFVjb7tSXtfbBMkMS21227BX7+lBRg/3tzP2g8QRET1YmJK\nsWMNgr3SEvuZMwv38+jmiWSD3LjPmhVvO/xUmpXXqxn1sVk7ztLyeQeAl17yX9/djS3XMS6NT7lr\n3/rx1phmLc5+0hR7Cg/jbi9ex5SIKCLeJOXdvh14esMN8TUmQzZvLvQaJYWbmP7pT8C991bejj2m\n2fXQQ/7rN20C3nrLP6Gs5YeKSj2vRES2YmJKsWMNgr3SEvvukivELM/NjachGeHGfcAAz/VhE8JN\nTA84APjMZ/y3UU3mjMJJl5bPOwCcdlrx8pNPAqtXA9dea5bDSkwBO3pM0xR7Cg/jbi/WmBIRRaQ0\nMRV+dYYiny+cnCdFV5eZMbUct4drxYqex0XYbEhYksq9LIzrjDOAW28FDjzQLHsT04MPNrdtbcFf\nnz2mREQ98eyKYscaBHulJfZMTMPlxj2Js/IuXBgsIbzzTuDRR6NrRxYTlrR83oHiY+CRR4CTTjLJ\n5PjxwJFHFiem+Txw2WXAlVcGf33vjzI2/ACRpthTeBh3e2WnxtSdP937b/Jk/20nT+b2Wdj+ttuS\n1R5u37ztjzgiWe0ps33PnjFJ5vuZsu2LEtMEtAcw7dlll/Lb/0AnQxU46ijg7LOjbU9RwpKQ96eh\n7VPyeQeKe0xbW4Gjn52Mb14oOOojgn88IfjXs4JDDzPb5/PAMccAO+5YeM62v638+j0mP0pivLg9\nt290+9LPfNzt4fbJ2b4M0QT9VCcimqT2EFE8pk2biN12uwttbRPjbgoA01ty330AJpsv02MGfBeP\nfutH8TYqA9ragP/7P+CNN4Cbb467NcZf/wrccAPwt7/5P96rl5m06VOfMsM7//u/o2nH0KHA/Pnm\nlprv5JMLk191dAAPPgiMHQvsuSdw1VVmEqSLLwY+9jFgv/2A6683twBw2GHAFVeY23Luvhv4y1+A\ngw4ykyldc03kfxIRUWKICFS1R4Zad4+piFwsIq+JyAwRuUtE+orIMBF5XERmicjfRWRoyfazReRN\nETm63v0SETXb5z4H7LRTYbklgYNN0iiJNaZB2+SdvCYq/J02Pt5Zl/feu7Be1awfNKjQq/rCC8WX\nlApixQpz++qrwAMPNN5eIqIsqOvsSkQmAPgigA+p6kQAvQCcAuAiAI+r6s4AnnCWISK7AzgZwO4A\nPgrgehHhmR0BYA2CzdISexFg1129a/j11Ygk15i6QywrUY0+Ma0w0im10vJ5B8yx6f4wMHhwYb2b\nmLa0FA/3Xby4ttdvbTW3v/lN7c9NozTFnsLDuNur2TWmawB0ARggIq0ABgB4B8AJAG53trkdwCec\n+ycCuFtVu1R1PoA5APavc99ERE317W8DDz1U6L7i5EeN6+42s98mMTGtlHC6CWMzekwpPuV6q72J\naS5XWH/CCbW9fi4HDBtWf/uIiLKorlMCVV0J4CoAb8MkpKtU9XEAo1R1qbPZUgCjnPtjASzyvMQi\nAOPqajFlDq9zZa+0xH7mTAC9N25Z5lDexrS3t+PZZ839tCWmQHN6TN39ZElaPu9Az8vFuNzEtFev\nwjbbbAMMHFjb69v2w0aaYk/hYdzt1dTrmIrIDgAuADABJulsE5Giy1E7sxhV+t9qxv6XS0SZJoUz\nVYFFZ5QRGTTI3CYtMa1WY+r2mHZ3F4ZjRiGLQ3nTpFpi6vaYHnecufZtrUmmm5iefz4wZEjj7SUi\nyoJ6/7e6L4BnVXUFAIjIHwEcBGCJiIxW1SUiMgbAe872iwGM9zx/G2ddD2eeeSYmTJgAABg6dCgm\nTZq0Jet2xytzOVvL7rqktIfLzVuePn06LrjgAt/Hp0yZhv79VySmvdAOYB6A7YGBLSNib0+alzs6\nOrB4MTBmDNDSEn97vMu5XDtaWip9X5nlxYs78J//AEccEU17uro6MGUKcOKJ8b4fzfq8J2152TKz\n7MZ74cIObNgADB7cjlWrgA0bOjBjBvDww+3o0wf49787MHx44fkvv9yBXK78699xRwc6O4GLL27H\n8uXx/71RL1999dU8n7Nw2V2XlPZwuXnLpd/306dPx6pVqwAA8+fPRzl1XS5GRD4I4PcA9gOwCcBt\nAKYC2A7AClW9UkQuAjBUVS9yJj+6C6audByAfwDYsfTaMLxcjJ06Ojq2HMxkl3KxT9rlYkQA9F8J\nfGc4AOCUgTfi7gu/HG+jUqyjowPjxrXj2GNN/e7Uqcm5XMxPfgLMmAH8/vf+j/fuDaxfD3zwg+ay\nMlF9dQ0fDsyaZW6zIk3f9ccdBzz8sLmvCnzzm+ZyMSNGAE88YXpJjz/ezNgNAEuWAKOc4qUgl4tx\ne8TvvNNcmuiuu6L7W5IgTbGn8DDu9qoW+3KXi6mrx1RVXxGR3wF4AUAewEsAbgIwCMC9IvIFAPMB\nnORs/7qI3AvgdQDdAM5jBkoufmnZK1WxlzJj+6hm7e3tmDkzmcNVczmgb9/yj7tt7teveLZWqi5N\nn/dyZyjuENxczgzndtVbLyqSvVpiP2mKPYWHcbdXvbGvu0JGVX8K4Kclq1cCOKrM9lcAuKLe/RER\nxaWlBfjfb+VwVdwNyRC3Vi9p8nlgXICp+ZpxqRsbEpakKldj6iamIsDy5fW//gEHAHvsYZLbWbPq\nfx0ioiyJ+H+rRNV5axHILmmJ/X77AYe359ECYPuBnLmtUR0dHVBN3sRHQLCEU7X6JEmNSmLS3qi0\nfN4B4LHH/Ne7iWlnZ3H8+/Wr7fUnTQL23RcYP96OHyDSFHsKD+Nur3pjH+GcgkRE2aAKQHI4Ymvg\n+7sBf531GlauLHPmWkKkFUOHtkOEM/l65fPJTL6qJaZum9ljaic3MR02DHjrrcL6trbaXsf9YaNv\n38pDx4mIbMLElGLHGgR7pSX2+TygyOP9zWb5g6P+gYULZwZ67po1z2LSpCkYNGhShC1Ml/b2drz6\najIT01wu2GVgcrloE9MkvjeNSsvnHQC23x6YN6/n+smTzRDec88tP9w3CPeHjSzG2U+aYk/hYdzt\n1fQaUyKiLOrsNImJd5ieKpBHDhtywBtrgKdnn4/Pn3huoNd74YUPwcwRR15JrjGtNkRXFZg5E9i8\nuTltouRw60pbWhrr6fT2uLNnnIjISGCFD9mGNQj2SmLsR4wAvvSl4nW5HLC2ewUAoAWtUFaZNiTN\nNabeZHr9+mjbkrWEJYmf93q1tJhLxtTL22OatTj7yVLsKTjG3V6sMSUiCsG6dcArrxSvM7Vkgl1G\n7IK++ZVWnEhGLa01pl6c/Mg+Bx1k6ksbTUzdoeCMMxFRARNTih1rEOyVltiPGAEMbMujBQKeRzau\nvb0dL7+czJPyILWj7g8TI0dG25as/QCSls+764QTgNGji9eNGgUcfDDwzjvF1zGtlW1DedMWewoH\n424v1pgSEYVk5cri5VwOEMlDJIFjT1NqxQpg48a4W9FT0KG8vXqZS31EJYlJuy3eftvc/uIXwAc+\nUPzYn/8MzJgBnHhiYz2m69YVrodKREQGz7IodqxBsFdSYz9gQPFyLgeo5NCyJTG1oIsjQh0dHdiw\nARgyJO6W9BT0+qTNqJHNWk9aUj/vXmvXAttt5/4Y5Z84Ll9efShvtditWVN4ftbi7CcNsafwMe72\nqjf2TEyJiEqsWVO8nM8DgtXYq20lALHiRDJq+TwwdmzcrehpzpzqPWGqya2RpcYEmWm5u9skpuvW\n1b+fPn2ArbfmMURE5MXElGLHGgR7JTX2CxcWL+dyQGt+Bg4b+g7+uXCPeBqVIe3t7TVNMtRMLS3A\nmDHlH/fOohplUpHFhCWpn3evXK76Nm5iWm7bILHzXi/Xhh+60hB7Ch/jbq96Y5/A0wIiomTJ5QBI\nHrM3DMPz7+wad3MyIamJaa9ePYdyl2pGYurdDzWPm2y6daZ+jj/exH716vr3091dqDFlnImIjASe\nFpBtWINgr7TEfvlyQFoUhSl5eSbZiI6OjsQOha1WOyrSnKQ6ie9No9LwefdOyDVhQs/HDzsM+PrX\ngU2bgvWuluP2mGYxzn7SEHsKH+NuL9aYEhFFpG9foFevvHOxGGFaGoJmTB5UjyAJc7OSavakNZ83\n2ax0fI4Y0dh+3B5TgHEmInIl8LSAbMMaBHulJfaqgCIfdzMyI8k1pqrBElP2mNYuDZ/3fJWPuZu4\nijR2HVPbekzTEHsKH+NuL9aYEhFFJJcD0KJOjymFIcmJaZChvLYkFLapNjx3/nyzTaXJj4JYtow9\npkREpRJ4WkC2YQ2CvZIa+0MPLV7O5YD1XWuhWwbx8kyyEW6NaRIT0yBJZ7OGIWctYUnq592rWrI5\nfLj519LSWI/pmjWmRMCWyY/SEHsKH+NuL9aYEhFFwD1p7NXSC+L8Z8OJZNSSmpgGGcrr9phFiT2y\n8aiWmLqxbzQxbWsDBg1inImIvBJ4WkC2YQ2CvZIa+3ffLdzfkoRoHr179Y6tTVmS9BrTpAzlzdoP\nIEn9vHtVS0zzeTME15uYjhtX+368x3/W4uwnDbGn8DHu9mKNKRFRSLyJSS5nTkQtOHdsqqQmpkkZ\nysuetHgEmfyopcX8eDVzplnn1orWup+WFsaZiMgrgacFZBvWINgrqbHP5wu9GG4PiWp+y+RHyjS1\nIR0dHXjvvbhb4S/orLxMKGqX1M+7V9Ae05/9rLCukcQUsKPHNA2xp/Ax7vZijSkRUUjmzgV+/Wtz\n3+0hyUMBAWfmDcmmTck8Ia+WmLpDeTn5UTZt3Fj5cb/64noSU/d1+AMHEVEBE1OKHWsQ7JXE2B98\nMHDIIcCqVWa5qwvYsMHTY8oTyYa1t7dDFdhuu7hb0lOQYbqrVhWOj6hkMWFJ4ue91MMPV37c7TH1\nYo9pdWmIPYWPcbcXa0yJiEJQ2mPW2Wku62DDyWMzBRkyG4cgw3Q7O4Edd4y+LTzmmu/wwys//vbb\nQGsrcM01hXX1Jqa9etlzuRgioiCYmFLsWINgryTGvrTHLJ8Hhg0rqTHlmWRDOjo6appAKJ+vPilN\nWIIO5W1tjbYdSUzaG5XEz3upah/t1lZg6FBg7NjCOk5+VF0aYk/hY9ztxRpTIqIQlPaYuSeQeWfC\nI9aYhqOWCYQOPhg49tho2+MKkjAndUZhaly1H0BaW3smlBMm1Lcfm4byEhEFwf+1UuxYg2CvJMa+\ntMfMPYFUzUNs6d6ImFtjGvTtfP554LHHom2TK0jC7DcBThSylrAk8fNeqtp77h4fbvw//Wng7rtr\n349tPaZpiD2Fj3G3F2tMiYhCUC4xzWctS4hZUmtMkzIrbxLfGxtU6zF1jw83/qNGAQMH1rcf9pgS\nERVjYkqxYw2CvZIYe78aU7OsvI5pSGqpMe3ujr49XkHaxR7T+iTx816q2nvuHh9u/Os9DjZutKvH\nNA2xp/Ax7vZijSkRUQg2b65QYyoAIHjttbhalx1Ba0xzuejb4vXGG5V7zdhjmm3Vekzd49aNTz3H\nwYYN5rZ3b3ObtR8giIjqxcSUYscaBHslMfYzZgB9+hSWtySmeTMr7w47AHNmx9e+LKilxrRZs/G6\nWlqA8eMrb7N+vfkBI2pZS1iS+HkvFaTHVKTwg0k9iWlXFzBokF2Xi0lD7Cl8jLu9WGNKRBSCkSNN\n3ZjLTUwXrVmI7nw3xowBhN+cDQs6lLfZJ+39+lW/FEwzhhezxzQeQWtMhw0zy0uX1r4P77HPOBMR\nFfD0imLHGgR7JTH25S4X06+1P9r6tDlrLejiiFBHR0fgobzN7jGt1i63h2vkyOa1KSuS+HkvVel4\nc38kEQH69jX3n3yyvn14f5Sxocc0DbGn8DHu9mKNKRFRCMpNfqQwQ3l5HdNwJHUob5KuY2pDwpI0\nld5z91j01pied17t+/AeP+wxJSIqYGJKsWMNgr2SGPvSHrNczgzdVADMScPh1pgGSe7i6DFNwqy8\nWUxYkvh5L1XpeFuxonDfjU+vXvXtwxtfG36ASEPsKXyMu71YY0pEFILShOnOO4GZMwHVJmdIGRd0\nKK/3pP3FF6Nrj6taYtqsWXkBOxKWpKn0nvfqVagtbWRW3tIaU8aZiMhgYkqxYw2CvZIY+9KEae+9\nza0qCsN4hWeSjXCvYxokMe3sLNzfd9/o2uQKknQGTaobkcUe0yR+3kutWlX+Me+x0ch1TG0cypuG\n2FP4GHd7scaUiKgCVWDRomDbeU82R44Ejjqq0GMqtpxJRuxXvwLuuqv6dpdcEn1bvN5/v/rwzGb1\nmFLzVbpurl9C2WhiCrDHlIjIxf+1UuxYg2CvZsb+vvuqX58S8J+VVwRQKCc+Cokb93//u/q2f/1r\ntG0BgHXrgOHDzf0+fQozrvrhUN76peG7vtLvTlEkprb8zpWG2FP4GHd7scaUiKiCNWuCbVc6xNRd\nHttrHlqwKZrG0RarVgGbPG/z7rtHv8/164GVK839pMzKa0vCkjTVZuUtTSjriZONkx8REQXBxJRi\nxxoEezUz9kFnzyxNTNzEtAVdWNs6yV0bevts4sa9T5+ej221FXDWWYXlL34x+vaUzsKchFl533+/\neBbYLEjDd707K69ffMPqMS2d/ChrcfaThthT+Bh3e7HGlIiogqCJqV9vhhnKK8hLG4fzhujyy/3X\nz5tXuN+MIbNuvFVN/CsdK80ayjtuHLB5c7T7oJ7cxLR/f//HwkhMN2wA1q4t7Me9T0RkOyamFDvW\nINirmbFvbTW3lSY3Acr3mLLGNDzt7e04+mhgr738H1+3rrntcZMMNympNjxz+fLqx1GjhgzJ3hDP\nNHzXu++5X8K5caMZ9g00XmM6cKC537eviXXWpSH2FD7G3V6sMSUiqmCHHcxttaSnXI8ptDAjr+55\ndzSNtEily8Vss03xdlHbsMHcdndXTzRETI9qMy4Xk7XENA0qDeXt7i5MjNXo5WKGDjX3GWciogIm\nphQ71iDYK44a02ongX49pi0thR7TU/c8DRj7QnQNtUC165gOGlS4741XVMNn3WtX5nLBhnyrFmbx\njUoWE5Y0fNdX6jFVNTXQQLiz8mYtzn7SEHsKH+NuL9aYEhGFoLTHdMUKd5ZYhYhgr1F7AfmABatU\nVqXEtNb1YbQFCNZjCjRn8iNbEpakqdRj6j1mG0lMvccP40xEVMDElGLHGgR7xRH7SieB7mPeBKh3\nb2DAADMPL2tMw9He3h44MW1Gj6mru7t6j2mzJj+KO2GZPz/4JZaCSsN3fa2JaT0x8k6wFXecmyUN\nsafwMe72Yo0pEVGD/BJTVXdoqW6pMaXGVUru4uoxDdoTakNiuv32wJlnxrf/uFQbyhtGj6n3+Glp\nsSMxJSIKgokpxY41CPaKI/aVTgKXLOm5zh3a25XrYo9pSKrVmMbVY7pmTbAZgUuHe0ch7sQUKNTe\nhiUN3/W19ph666Fr2Yd3KK+7zyxLQ+wpfIy7vVhjSkRUQZCTv+7u4hlhgcLJ6PquDVCwayMs9dSY\nRpWYugngnDnVt7VlKC9gLotjg82bC5f/ca8dGzQxDXp9ZC8bJz8iIgqCiSnFjjUI9mpm7GfNMrfV\nTgJLkyJ3Vt7WllYM6z8smsZZppYa04ce8l8fJveYGDwYmDix+ra2JKb1JF2VJPW7fpttgLPOMveX\nLTO31RLTRi8XY1timtTYU7QYd3uxxpSIqAL3+oOV+J0geodtcihveIImpvfcU7jf2hptm4L0qr/3\nHtDZaUdiOn16vPtvlmXLgJdeMvf79TO3fscmZ+UlIooWE1OKHWsQ7JW0GlO/ZKlwXVONvK7QFrXU\nmHoNGRJNe9xjIkiCMG5c5aQ6LFlMWNLwXe/+OGEuEVXMG/dKQ36D7MO2WXnTEHsKH+NuL9aYEhE1\nyC/hcHtMzckjM9OwVEvu/E7Yo04GgyQIixcDTz1lR4+pjdxa06237vmY95jt7DS39Qx39ta0clZe\nIqICJqYUO9Yg2Ctp1zEF/HtMOZQ3XEFrTLu7/deHrZYe09NPB155Bbjqqmja4spiYprk7/rXXgM2\nbgw+K++OO5bfrpp16wo9rrbMypvk2FN0GHd7scaUiKiCICf5ftt4h/JSeIIkpvPm+a+Poi1AoRes\nErcGcVjE82BlMTFNqs98xtxu2FDck1kqrBrT3r2BkSMLr8M4ExEZTEwpdqxBsFcaaky3DOUFICwy\nDUW5GlM3Nu76f/yj+PGo3/6urmDbDR0KXHhhtG1JQsIS9mRTSf2ud39sAArXbg06K289Q3kLP3Yl\nI87NkNTYU7QYd3uxxpSIKATlJz/iUN4wVUpMXc36HcDd74IFwNKllbd1h17aMPmR26uXdd6YV7oM\nTGlCWW67amy8XAwRURBMTCl2rEGwV7XY/+lPwE47hbvPSieBzz0HvPVW8bpCEsKzx7CUqzGttf43\nbKrAvvsG286GxHTAgHBfL6nf9YMGFe5XSkyLLh1VMjtvLUoTUxskNfYULcbdXqwxJaLMefJJYM6c\ncF9zw4byjz3/fM91bhKiyh7TMFVKTEsnPXJFXWPa1VV9aGazEsYkJKZRzzycFN7Jr9z7QYfybrVV\n7fvzS3DjjjURURJY8r8dSjLWINirWuzDPDF2T/xWriy/jd/smLyOafiq1ZjOnu3/vKje/yVLzG2Q\nxBSwp8c07MQ0qd/13s990Fl5GxnK6x0S7L5W1mfmTWrsKVqMu71YY0pEmRNFj02lk/2jj+65zjv5\nEa9jGp733iufmE6dWrzsiioZPPlkc9vdzcS0tA028L7P7v3ly/23CyMx9Q7ldV8r7lgTESUBE1OK\nHWsQ7FUt9lEkppV6JgYPBkqbVDzhiSVn6hFrb2/H0qXFtX1AfCfnbW3mNuhQXlsmP3r77XBfL6nf\n9d7vBPc9nzWr53Z+P0jUcxzYmJgmNfYULcbdXk2vMRWRoSJyv4i8ISKvi8gBIjJMRB4XkVki8ncR\nGe/9YOUAACAASURBVOrZ/mIRmS0ib4qIT78EEVGxZiemuVzPfW5JQrJ+5thkQ4eaf15xTX707W+b\n27Vr2WPqVakeO0vc7wTVwv2Pf7zndmEmpt7n5XLxx5qIKAkaOe27BsDDqrobgL0AvAngIgCPq+rO\nAJ5wliEiuwM4GcDuAD4K4HoRYW8tAWANgs2qxX7u3PD3WS0xLU1Mtkx+BE5+FJaOjg7fXkd3CG+5\ny5RElQy6CfJTTyWnxzSXAzZtinYfzZbU73q/obx+x0FYiWlpjSlQufY9C5Iae4oW426vptaYisgQ\nAIep6m8BQFW7VXU1gBMA3O5sdjuATzj3TwRwt6p2qep8AHMA7F9Xi4nIGo8/bm5XrDD/1q1r/DVr\nTUzfftsM8QSYmIbJ7yS/X7/CY15jx5rbqGflXbYs2D6a0WOqCrz4YrT7IMNv8qPW1p7beXtUXWEM\n5R0zJvuTHxERBVFvr+X2AJaJyK0i8pKI3CwiAwGMUlX3UtVLAYxy7o8FsMjz/EUAxtW5b8oY1iDY\nq1rsL7/c3O68s/m39db1D3lzn1fpBDCf75mY9u3rXhJCOfdRSMpdx3TYMHNbGmM3SYiiB92rszM5\nQ3kPOADo3z/afTRbUr/r/WpM/Y6DuXOB9euL14Ux+ZENkhp7ihbjbq96Y+/zm2Dg530IwNdUdZqI\nXA1n2K5LVVVEKp1C+j525plnYsKECQCAoUOHYtKkSVv+OLdbmMtc5nL2l6dMmYZ161bg+OPb8eCD\n5vEjjgCA+l7v9dfNcj5ffvvnnis8/tZbHejoAFTb0dYGzJnRhYGr52PSB1DT/t2JdeJ+P5O2vHlz\nB/71L8Abz8WLzbJJFDowc6ZZNklCBzZvLt4+zPYAHdi0Cdhuu8rbi5j2zZljjo+o3p+33upAZ2d0\nf2+Q98OIZ//NXDbJqDke3c//8uXF8V24sANdXcDuuxc/X8T/9V9+2QxX99tfPg8sWVL8+s8+24ER\nI5LxfnCZy1zmctjL06dPx6pVqwAA8+fPR1mqWvM/AKMBzPMsHwrgbwDeADDaWTcGwJvO/YsAXOTZ\n/lEAB/i8rpJ9nnrqqbibQDEpF/upU/fUtWv/o3/+s+oJJxTWA6r5fH37uvtu8/wnnyy/zTXXqB53\nnOp3vqP64x+bdd/4hurPf6565Z+30uffvEw3dnYpLu0VeL/Tpu2ta9a8WF+jM+qpp57SoUNVV6xQ\nvekm1XPOMetnzTIxcv9dd5253XHHwroo3Hijee3hw1Uvu6zytl/9qtn2mmuiaYvrhz9U/e53o91H\nJVG830n9rj/ySPO3Ll2q+tGPmvunnVa8zTe+obrnnqqnn15YB6i+6PPRPuww1aefLr+/r35Vdffd\nC8tjxqguXtzY35B0SY09RYtxt1e12Ds5X48cU7TOcXEi8gyAc1R1lohMBjDAeWiFql4pIhcBGKqq\nFzmTH90FU1c6DsA/AOyoJTsXEcXkuppDaTYPZnA42YextxPjbifG3V6MvZ0Yd3tVi/1kQFV7FMXU\nO5QXAL4O4Pci0gfAXABnAegF4F4R+QKA+QBOAgBVfV1E7gXwOoBuAOeVJqVkMX5p2YuxtxPjbifG\n3V6MvZ0Yd3vVGfu6e0yjICLMV4kI06ZNxG673YVBgyYCKExI0silOu6+Gzj1VODRR4FjjvHf5sc/\nBtasMfsbOhS46CLg/POBHXYANm03FP+124WYuP1F6P+jftDLugPt94UXPoRddvkNBg36UO2NzrDB\ng4GFC4F77zWXibn5ZmD2bDPJlevaa4Gvf93MTrvPPmZCmu5gb3tNbrwR+MpXTD3wJZcUrmvq5+tf\nB667zrTta18Lvy2uH/3IXEf0Rz+Kbh+VuJ8xG/6XfPjhwDPPAEuXAp/7HHDOOcCnPlU8M+83vwk8\n/DBw8MHALbdUfr0Pf9hM3Hb44f6P/+xnZl8//7lZ/s1vgH//29wSEdlARHx7TFviaAyRl1skTfaJ\nI/aVZuXt7u55mQj3moMK80VKjevo6PCd2bY0CXJ/hHCT0SAz5jYil6s+W6pNh0DfvuG+XlK/60sv\nFzN8ePnLxQSJf7VtmjGrc9IkNfYULcbdXvXGnokpEVnFmRTO17x5PZMfbw8tr2MaHjfhr7aNSOE6\nslEnpn6XCyrHvbYqpd8i52J27pRP5Y7LsBLKID/KEBHZiIkpxc6dTprsE0fsKyUeuVzPa0d2dron\nquY6prb1dESh3blkRrX30t3GTUyjeu/dpCBIj6nLhuMg7GQpqd/1Q4cW7i9ZAucyPT1FlZjacCwl\nNfYULcbdXvXGnokpEVmlWo3i6NHFy+vWmVq/1ZvWsMc0REFO8t3eKzdmUfcqdXdX7zHd0ntuwaFg\nYy/egAHFiapXkF7+IGwcyktEFAQTU4odaxDs1czYuyfZbu+bn3y+54ln//6m5gwAtuq3VTSNs0y5\nGtNS69ebeB16KPCLXzSnbZWOD9uEnZgm9bve+3e2tJRPPuudeM1vf7YlpkmNPUWLcbcXa0yJKJM+\n/OFwX69Sj6lfYuqejLa2tLK/NERB6uzWrDGz9/brB3z5y83pwdthh8qP29Rjagt38iPWmBIRxYuJ\nKcWONQj2qhb7D30IuOqqcPe5cGH5x/wS08KsvBZ2c0Skvb098FBed1hls976oPux4VCwpcbU+3f6\nfQd4t2ONaX2SGnuKFuNuL9aYElHmrFgRTk2XV6WTQL/JbzgrbzSCJqaVlsNsi4uTHxXY0otXmpiW\ni22YQ3BtOH6IiGrFxJRixxoEe1WL/YIF5WfIjILf5UIKQ/vMrLzUuKA1pt5tktJjyqG89Uvqd733\nOqbNGsobZF2WJDX2FC3G3V6sMSWiTHFP1Jp5wlapxlSVPaZhClJnV7pNM46FsHvoKflq6TGNYlZe\n/shBRGTwf8EUO9Yg2KtS7L29GGFwTz7nzi2/TaUaU4CJaVjcGtNqJ/kvvQTMn2/uN+vknUN5C2yp\nMQ06+RFn5a1fUmNP0WLc7cUaUyLKlFzO3O68c7iv++yz5R+r1GMKZHysXZNVOznfdlvgmWd6Pidq\nHMpbkPXhpa64Jz8qbQMRka2YmFLsWINgr0qxz+fNZUJGjAhvf7vsAgwZUv7xyrPyAmJDNtIETz3V\nAaC2k/wo33pOftRTnz7hv2ZSv+u9ozOaMfmRjUN5kxp7ihbjbi/WmBJRpvhNRBS1RYuqzcpLYdi4\n0X+9N0H0O1lPUo9p1kWRmCaV97iK6zqmRETExJQSgDUI9qoU+1wOaG1tXlsAYM0aoK2teF3RrLxM\nTUMxcWL7luuTBsUa0/RL6nd9aY8pE9PwJTX2FC3G3V6sMSWiTOnuBlavDu/1gvS29e4NbL118TrO\nyhs+VWDQoPqeF7WgCUPWEwubah69M4CvXt38WXm9bSAishkTU4odaxDsVSn2s2aFv79qyURXl0lO\nvYpORjOejDTLs8921JzYJaXG1JbJj6JIlJL6Xb9gQeH+2rXlR2pENStv1o8lILmxp2gx7vZijSkR\nZcrw4cBuuzV3n/Pm9UxMi2tMLTiDbIJyPU/VkqEk9ZhSdowYUahnHzzYTLrmZ/lyYMOGxvdn41Be\nIqIgmJhS7FiDYK9q1zENe/Kjri5zbcxyBg3yrzEVMRkRZ+UNx/77twc+MZ840dwmpcbUlh7TKCT1\nu96bKFYbrjt+fDj7tG0ob1JjT9Fi3O3FGlMiypywT/7nzq2+P79ZeSEZP2tssiC1eiLmh4JPf7ry\ndlOnmh8cwpKUGtOurmiGswfl/n3r18fXhmbZckkorT5cN4zZikuTUP7IQURkMDGl2LEGwV6VYh/2\ncLcnn6y+jV8v7SOPAPPmMTEN03PPBasxLT0G/HqVDjgAuOWW8NqWlFl5R41q/uWSvNwh7e+/H95r\nJvW73vtDSbUfTaKa/Cjrkhp7ihbjbi/WmBJRpoR98rZ8ObDrrpW3yeX8Tzzvu1+tO5GMUtDZTb3H\nQKX3/9JLG2+PK+hQ3qgNHhxvYgoAAwZkf4gpUNxLWq3HlJeLISKKDhNTih1rEOxVKfZhXZrB1doK\nTJhQeZtczj8ZOOUUXsM0TPvt519jWpoElR4D5ZKkZcvCa9u4ccG2y3pi4R3eGpakfteX9phWii0v\nF1OfpMaeosW424s1pkSUKWFdmqH0Nas9XpqYHnggsOfEjJ81NlnQHx2KZkSucCyMGhVOu6rtp57t\n0iqKxDSpvMdZtWOTl4shIooOE1OKHWsQ7FUt9mGfsFU7yfYbyqsKQJT9pSF6/vlgNaabNgU7Bs46\nq/E2uTgrrznm16wx70W1H3NqkdTv+lomP2KNaX2SGnuKFuNuL9aYElGmRHHyttdelffn11tSuFyM\nZWeSEQrSYzp/vrltRiJYS42pK8uJhXutzkGD7OkxDTr5UVQ1pja8z0RE1TAxpdixBsFe1WpMaz0J\n3H574I9/LP/4QQeVf8w9GS/dJy8XE7599glWYwoEO4HP5cJpl9/+an08C/J5YOBAU5dtS41p0MmP\nougxteGYSmrsKVqMu71YY0pEmVJPYjp/PvCf/1R/Pb+T7bfeqvQ8zsobpiA9pm6tb5D3fe3axtvk\nCpp4hDnENWncz4qIHT15jVwuRi4T7HPTPjXt7+23a2wgEZElmJhS7FiDYK9q1zGtp3ciyKUe/E62\nt9oKGD7cvx0Ah/KGaerU6jWmbi9okB7TG28Mp11++ytn/Pjw9pk0USWmSf2ub/RyMS+9+1JN++vu\nBvr1K16X9R8Akhp7ihbjbi/WmBJRpixfDqxbV/vzqiWzLS3mxLBUPm+u21hKFdiQW4tcPsTxopar\n5UcH73blEoZq16etBSc/KiSmtszK6538qNYe03r06lU8k3SWjyUiolowMaXYsQbBXtVqTEePDv5a\n69eb22onefm8f2+XdwKU0nZs7F6FPYf4XOCU6rLLLu3YtKnn+nprTPfeu7H21DL5UZj1rEnl7TEN\nc8hyUr/rvZ/9enpMa2XjrLxJjT1Fi3G3F2tMiShT8nlgyJDg27uzuAbp0XjvvZ7rNm0qJLdeqkCL\nvIVx/XNoa2swAyIAwMqVQJ8+tT+vGSfzQSc/ynpiYXONKS8XQ0QUDyamFDvWINgrzBrTWibL8ZPL\nAX37lmuH4p1NfdDWVuF6MxTYq692YMKEYNuuWVO8HHWiVO2Yc38siTqx6O4GFi+Odh/luO+xLTWm\npROj8XIx4Utq7ClajLu9WGNKRJlSa6+Cm5jW26ORz5sJkPzWm8mPKCy1xHbEiML9KJNBd9h4UnpM\nb70VePrpaPdRjk2z8pYm4bVcLqY771OsHnCftl0uhogoCCamFDvWINirWo1pmD2m1U6wc7nyNaZA\nz0K7l18O3jYqtsce/tcx9dPsnqW6f9jQPPIaXkFmrxhLmqNKTJP4Xe/XOxy0xnTWill17bNa8ptF\nSYw9RY9xtxdrTIkoU8pNRlROkB5T78lg6bUEczn/ZMA9WS09P//+94O3jYqV+9HBLwnyrovqZL6W\nyY/GjvVvy9l/ORuffeCzDbdlY9dGzFw+E1ddBYwc2fDL1cWmWXk3by7cd2frrnSceSftqveHiKVL\n0WPyr6y/z0REQTAxpdixBsFe1WpMa0lEWlvN7YIFwba//vri5YqJqU87wpgExVYzZlS/jqnLmzgA\n0Z3ABx2iu/vu5rZ//+L1t79yO+597d6G27HPTftg11/tir59gWHDGn65ukQ1K28Sv+u7uwuXieru\n9q8z9/JOyFZvYjpkSHFsbeg9TWLsKXqMu71YY0pEmbJ+fc9ehUrWrjW3110XbPvSBKdSYvrG6pew\nOVecITExbUy1k3H3/e3sLP+cZcvMbbVkIgj3tYIOoQ07/gtXL8TKjSvxxvI3AMSbrLiJ6YwZwBNP\nxNeOZiidkTfodWwBQEp+sTri9iPwzIJnqu7TxqG8RET/n70zj5OjrPP/56nqnp6eM5ncgVwQwo0E\n5EYIIMjKgsoqoILgsbqyqy6KC67+JKwCiqy7iOgKCyu4LopyiAgikAxXwiGQi4SQ+z4mM8nc00c9\n398fT1fXXV3dUzV9Pe+8Jl3HU8/zVD/V9dS3vlcQ5KOVpOxIH4T6xW/sb7kFeOSR4HU99NDo+sK5\nj2C6/w3HdimYls6RRy4o+P2dd577dvMLhQMHxOeKFaPvUxAzTjOlChZ9qT48t/E5x/aZ/zkTE26f\nYNlWLvNOs7XC4sXh1VuJ93qzkGgXGK967Cq8vuN1S3nz/qZ4k2Vf5+ZOvLL1lYJtFus/XwtU4thL\nokeOe/0ifUwlEkld09JSXHk3janbw6J4WFUc5evtwTJMvDRGxfqY5v1/RynAlXJ8qeN/56t34vxf\nnV+wXLkj4urfdTFWC9WI3WzZfI3974r/xV82/MVS3rzfzZSXAkTw5hzYk9qMax6/xtIPiUQiqXfk\no5Wk7EgfhPrFb+w/9Sng618PXteZZ46uL36mvKdMOcexvZxRU6ud1asL+5i6CZ3ptLug1NgYXt+C\nUqpgqirBLpxym/LqhCmYVuK93p7D1P6924VP836zEMpuZrk6CkuYRMC7/a/jgeUPOOqsVSpx7CXR\nI8e9fil17GPhdmP06Dd3MzedfRMWLljo2L6wcyFufuFmWb7Ky1/dfrWryr9a+i/Lj6L8L28GXnCW\nv6hZCBtmLejCzoXAwpuh/FuJ/ZkP/GE5gAU3AZ3O8r9YuxAvnHMz2M0AkgBSwLduBtiMm9ASOxhq\nQ7Ol/OrJC8FurrDvs0rKEznTxeTL56p6DgDOBJ7L3oR/zW1sagIGBkTwGHP5pYAYtxL7047g5b8w\n9yYACz2FCfscZu9PR7LDt34zZhlnzMfry+Jj+xZxvqHU7/F7L/v1eR3QC+C4R4GG043zndkEgIST\n85LEQmDhzfjI2wB8UkU9tOohdOBG/M+m72FBp9f1vxDM9mJj1UR5P5Hla7D8Jlh+82XvjyxfUeVd\nIaKK+RPdkUgk9c7rrx9D3/veCrrtNut2gIhz92NeeUXs97qNXHIJ0eOPG2Wuv966/w9/IDrrLLF8\nww2Ub/ugg4h+t+Qe+s0zzURENJLOEL6r0ic/Gexc3nhjPvX1vRmscJ3wwANEV10llu+5h+gLXxDL\ny5YZ4/PFL4rP73/fOO6gg4i2bTPW16wRZU49dXT9+fGP/a8dM6+/Lsr191u3YyEICwtX8H8r/s9R\nru22tvzx+t/qdzN06KHFnEV4bN9ONG0a0SGHEN1/f3n6MFZ0dRF1dBDNnCmuv7Y2Y9/ixaB7Fn2I\niIi+/nUx7n/8o7H/G898wzFuWAg69iPPUWend5tnnEF08+8fzl8HDz1EdNllUZydRCKRVCY5mc8h\nC8aCia8SiURS22gaMH68c7tr2hpFwzsT/w3Ad8eiazVHEB9TN1NeRbGmLymnX16YPsZuvoqv7V0M\noLAvahTo1/xJJznT4tQadh9TI0KvuLhaYk5T3huevQEHtR2EPYN7XOvkylCgNs11SiQSiUT6mEoq\nAOmDUL+EOfajFVLMD6X27cSMyvV29iWXjq7BOmbNmuB5TO3Bj8yC6dCQs0wplHJ8mMLEQHrAse2z\niy6oiKi8YVKJ93pzhFzzC5NXt78KwJkShjHg9iW342t//hqeWPuEa51cSbtuz+/ngFJn0mgljr0k\neuS41y8yj6lEIqlr9If4Cy8srryOX/AjgADbA6pC8WK7KMlRaroMRfGP0juWlKoxHcwMhtuRCIhK\nMK1EzMKo+by7hkRiW2LWlDDm76Uv1edaZyw7zrdNIgC5l11/eu9PxjaJRCKpc6RgKik7Ms9V/RLm\n2I/2wc5PMFVM0TdjOQcIJm+fJTNvnjP4kZ1qNeXN8qz/cfaoNz7Umsa0Eu/1XlF5dfNqrhSZhwpA\nPDPBd7+4hnOC6bo/1cVLgEoce0n0yHGvX2QeU4lEUteMNqell2DKOfLaDTNSMC0dL8Gn0NjZTXnD\nEtz2uLsKFuyL0Q+jI3sH97qWT2dH8H9PM8zq/Qr+cDrw8ssdWLv2iwCAiw67yFL2ihnA46cDhxxS\nPnPxehCWAG/BlLgGAGDMahkRxvdCBLy493EAwEi2xhPFSiQSSRHIJytJ2ZE+CPVLFGPvJazYtxdj\nyutaJcnbZ6m8+67wMX17yyMYSdwNVXUGi9HHxyyI2k15R/syQuf224s/xqwx1bVrjQqQyqZcy6/v\neQ9TGoGNzd/Dla8D8+b9AiMjWwAIrVm+ru9yTGoA2uNAW5u7kBs1+vfZ2QmsXh1evUF+7z09QEND\neG0WQjcrdwQ/GhY+po0xa/SnsATTv+x6CICRPqjWkfN8fSLHvX6RPqYSiaSuCSKc+D1UegU/8tLu\nMbhIsZJA6MLAX1degWNn/hPe975ve5ZtNqWPjUpjCgDHHSc+Oed4eoV3tGW9TfM1oZHQrj39AeDZ\nd25xPW7FnhUgAgayDP1ZIBZrc5R56bMvgZkqLrcp7549wPe+N7Zt790LZDJj157Zx9S8nM0IH9O4\nak1eEIZgar6GT5h2AgDpYyqRSCSAFEwlFYD0QahfovAxLfUBb98+6wOjjjDlde6Qprylc9hhwsf0\n7QMqetKAqnpHMb3uOmPZS2M6WmbNAk48USxv3Pcqkj3e0pibYDqUMTS+g8ObPY4jR1lLH9pn4cyZ\nZ7q2NdaU08d0LLWlgLcp7xNrHshts//2Cw8KweVGYmtT59OPfrouzKblPF+fyHGvX6SPqUQiqWuC\nCqb33w9cfrmzXEODEdjIWa8zKi+TprwlowsALWoKDMDBsx60+GkCwMaN4tM8Jl7Bj8IQ4PR6Kaf9\n9GLIRa7UuHGMAncpg0CIKUCGZyxbdVgFSSf6+Fx5JXDTTWPbdiUIpkSEi6bq22xCpou/eVvCqv1+\n++QTsX5guWebbi/AJBKJRCIFU0kFIH0Q6pdy5DH97GeBk092buccaG/3qtct+JE05S2VJUs6AQAz\n22fjub1AU2IAAFnGcMoU53F2U14v8+tisWph/S+kDRuc2zSTMOslXsYz6wEAN79wszjGJp3Y82UC\nQouv+cvJkaALaNOnA8lk4fJBKeb3PjwcXrt+mFMX5QVTEKYl9W22AXCxnui9sdeynhiehaGseyoZ\nvR0zQ1pfzZvyynm+PpHjXr9IH1OJRCJBcAHVLfiRm5Bj9jsTx+kHVo6Gq9pgTASaaowlMJCaBe4y\nZnfcASy1BaV1M+UNS9FomOiKiyCV2u1abvx45zazxhQeZpwq77Ks/3rl/xbsE2NA1j/7TCSUM4+p\nPg7r1o1Ne/rvmzEglQIGB41gVgDQn7YLmIVvMInUDN/9KVt8rDXDLwTtrkRS9zz5JLB9e7l7IYkK\nKZhKyo70Qahf/Ma+2AfyKPOYmh9GucPnTFIss2cvyGlEuWd044MOAk491bpteFhEbdXRtV1haJsM\nwVRIZJu63U0x3drSSMP4RiGxHjHxcNfjYorVTnzV3ncs65869lP55XPmnAPA/XocCwYGxF/YBLnX\nHzggPt3M6qPALIRns6LdjGaYWzvkcxdT3mLpsr6jQFJpHXWdlY6c5+uTKMb94ouBGf7vfiQVgPQx\nlUgkNcXatUDaOyaOg0L+hubtbtogzgsIprmDGtQGzIi/L3jHJA6MseAgCi59tbQYgoteT1jpO4z0\nNOKNSJq7X3xu/oHLdi/D/pH9AACF3PNSTmg9AjtM5qnL9ywHQPk8lufMPie/L64YuTPLYeLZ22tE\nQx7r9vXxHCvBNJMx/IaJgI4OYHWXOUeO7QtwMeUtlg5HhhhpfSGRSCSAFEwlFYD0Qahf/Ma+tRU4\n+ujgdQUJhGM1ybXu8zLlJQKYaTtjDB9qv85ZUBKYLVtEHlMQh/mhvJAQNHOmdT0KU14tF5zIy9fU\nTTB9YPkD+eWd+992PW7/cBd2m2RWvfaH/nozFp8NDGcNqbXcKWOIhEbi9tuBb33Lu9z+/SK9S1CC\n3Ov18x0rbXEm4wx+lIwbjrXOYFiEhJrIrz12+WMAkNeYl8Jfen4ufUwlNYkc9/pF+phKJJKaotgH\nNXMQk1LwMuXlXARDkUQBBxURREpETDXWwzTl1bXznHKCqUc5N8E0rRna1ZnqMtfjMloKmkulc0Z+\nAAB5zSlgBEIqZ6DeIG2fc457kKrRoI/lWD7PTpxoBNZizBpl1/7bJxAU05sqXSC95PBLArdnv16P\nbP5ACb2WSCSS2mOMjGUkEm+k70n9Umjsi3kwf++9nBAZQEhxq3ffPvcIpLoprzS2C4+ZMxfkljhQ\nhCmvm2AalvC2c6f41Cibqzu4xtQsmHpBlHUN8qQzft+1WLr0egBAY3qP6biCVYdO0DaL9UMNcq/X\n2w4j2nIQ7MHNGLP6kdvTxRC4RaOtC6lPvvdk4Db1c/zUsZ9CX6oPk0ZmYn8Jfa8m5Dxfn0Q17occ\nEkm1khCRPqYSiaSmKPaBfPduYPLk0utvavJJF8OkzjRsGAPmJrahpXFzUcdEIZia6+nqW6tvdS3r\nJpgOZVySm9qPo6xFYzqjbYZF+B2ecCuOP74Txx/fCUy/D8v6klAUKptgGuR7nTo1mrYBkZZn06bw\n63drz2zKKz4Jr+wD9qWAjGb1GWbMqjHVUwV1D3cHblNTxPXy1ZO/CoUp4NBq3pRXIgmTxsZy90AS\nFVIwlZQd6YNQvxQa+2KEDsaET2qpD3icAw0N7ttzhpWlVSxxsGVLZ375iLbB/HKhsYvSlFfPF5rV\nhNDw6Uc/7VrOTTAdTA86N9pg2gHruu3ibmiciWRyNpLJ2ZjUdiwUJgyaKlkwLZZifExvuQU4//zw\n++DWnt3HlBOHwoCJCWDb3qes5Rm35Jw1m/0GRhHm4jElBpWpII8UQ7WEnOfrk6jGfawsKiSlI31M\nJRJJTVHKA7ldcDGzbZu1nB2v4EdCYJHqjDAxj1G3zQp2+nTgzjvdj7OPL+fhPaCcfTYwe7YR/GjQ\nQwvqJphefvTlhRugEV/x45DxTtu0cvuY3nrr2LdrHt8NG8amPbtgSibT/XMm28uT5aWCPQ0QwxhJ\nQAAAIABJREFUAHAN2OaTZ1G3v2hNtEJhSl0IphJJmNjv+888A/zud+XpiyRcRjWlM8ZUxtjbjLE/\n5tY7GGPPMsbeY4z9hTE2zlT2W4yxdYyxdxljF4y245LaQfqe1C9+Y1+K1sav/PLlwLRp1vrNuKWL\n0XOpMkUKpmEya9aC/Fht7f6kZd+0acBXv+p+XJQ+ptdeK0xHuUeaGB03wbQx1ohPHvNJ5w7zccSt\nUXkJ2Nm7Pr8+t2Ou63Hl9DE97TTgrLOc+4eGgD17nNsLUYyP6VihX0P6tSU+CYrHdTVpsjDl1c15\nmYslRaIRSLlnDQJg+LDGlJgw5aXaN+WV83x9EtW42wXTRYuAxYsjaUpSIuXyMf0agNUw1Ak3AniW\niOYBeD63DsbYUQAuB3AUgAsB/IwxJrW1EonEl1KEDq8HvPHj/QMmuGlMORf5FKURb3TEKHhYV7tg\n2tsL9PWNvg/mOrVcVF4v3ATTuBq3BMxxbyNraWdr71Y00RYAwDb1b12P8bMAiBJDQAOWuQQZ/ud/\nBt7//ujaHkvcfEw5ccxp9jggZ8q7YPYCALD4m+o0NBSwsWBir8pUqEp9mPJKJGHi9mxgTycmqU5K\nFg4ZYwcD+DCA/4bx3HYJAD2h2wMAPppb/giAh4goQ0SbAawHcHKpbUtqC+l7Ur+EPfZBH+TdJjVd\nY5rV0iDokVkN0z5JeGze3JlfTmXHeRe0YR/fhgaR6iMMYUa/JjgvXjDVuIYJyQlQYh2exxFxT/Hj\nYyf90uc43+5EBmPArFlAS4tz3/LlwPbtxfetGB/TscKcZsr8e0/Z05fmYEyY8v7o/B8BcBdMC73I\n0gVR3Qx4QKv1mLxynq9XxsrHNCq/eEnplMPH9D8AfBOwzLVTiEg38NkDQH8VPh2A2eNiO4CDRtG2\nRCKpA8KcaOwPvPZ1XWP6p0WNaDvm+HwZIQxxV5M9yWgQU0dKa81vKSX4UTwebq8Ghw1nZDct6Ekn\nCe27mSzPQlVUIFfenJNUZyDd55kupqVxguv2cj1o6d8xY94BwaJue6wwp4uxBD9SXE4cwAgfRM9w\nD+KKuPDcBFNA+Jl6oUdjjikxZLQMFBY8ZZJEInHeg6RgWjuUlMeUMfa3APYS0duMsQVuZYiIGGN+\nU4zrvmuuuQazZ88GAIwbNw7HH3983k5Zl77lulyX67WzrmPfv3PnG1i5shuXXGIv717f5s2d2L8f\naGx035/JdOKVV4CLLxbr27d3orPT2L99eyeam4EjjyRMbduE1zZ24oUXAEUR+1ctz2ByvDNffnjb\nbnR2dhY8P13jVCnfdyWsz5q1AO/svB5Ny4Djjo7l969dq0CfUtyO37sXIDLWV64EGFuAd98Fnn22\nE/F46f1burQTkyYBGhPJOS/LAs8veg7nn3eBo3xPj3VdIw27Vu7CqpYmHHXUAVx8bxLfPnKxpf53\n3tqLHfsA5NIc9K8DlhFwvHgH4ujPupVZ9PSsAtFHQ//+C60TAfv3d+LVVwG331smAwCd6O113++3\nruO1P5lcoJfIfUZ7vo2Nwt95eLgTq1aJ64mIsHoFx+bmE3D60W8BALZtE+UzNA1TW6bir0v+Cmwy\nCaa51DZ7796L42+7FHf/+9uYQRpuuMHZPo8NAJuAV19+FRObJoL6OLq6rPejSvq9hrGub6uU/sj1\n6l7PZKy/l61bO9HfD0R9v5Drxa3rdHZ2YtmyZThwQESn37x5MzwhoqL/ANwKYBvErXgXgEEAvwLw\nLoCpuTLTALybW74RwI2m4/8M4BSXekkikUhef/0YuuaaFfTEE9btABHn7sd85ztEF1xAdMYZ7vvH\njSPq6RHLP/kJ0T/9k3X/5z5HdO+9RIsXg55bBLrtNqKBAaJkkuixZT+g3/5lXL7sF376SzrkG58J\ndC5vvDGf+vreDFR2NHz840RNTda/tjaiDRsib7pobriB6Ft33k2LF4Pu6PwpPb8IxLlGr79O9P73\nex/3qU8R/epXxvqjj4rxBoheeqn0/kyfTrRtm1j+n8Xn0uLFoMWLQQMjPYGO/+HLP6Trn7meXnvt\niPyxdv7r2TPos78EYaH4O+E/4Vl2xe4VdOcTrXTBBY/lr9mx5OmniT70IaKNG8V3a+eyy8T2yZPd\n94+GpUtFnfpf1CxZQnTKKURz5xI9/DDRcccRrdqzin79dAOt3Pjz/Ph8/euiP52r1tDhdx1Oa7rW\nEBaC1nWvIyLKjysR0fTvnEmY+SL9/OfubU458j3CQtD+4f107ZPX0t/f+1P6yEeiP1eJpBYAiM46\ny7rtG98guv328vRHUho5mc8hYyreIquvMPuvRDSDiOYAuALAIiK6CsATAK7OFbsawOO55ScAXMEY\na2CMzQFwGIDXS2lbUnvY36xI6ge/sS/VpC/ocfZy998PPPGEWFbt6SOqIGTm9u2i/3v3Gn+zZwMH\nDhQ8dMzZurUTavYwAEBMbQh8HGMiIqx+TpwDkycDZ545ehNQwwzMsMFMZYcDHatxTZjy+peymiEX\n1buxRe/nwID1087evcXVG+ReH6WZsBve6WIYsi7+xkzhlqi8Xqa8vm3mzNhbGlryUXlrHTnP1ydR\njbvdbFea8lYepY59SYKpC/oc+wMA5zPG3gNwbm4dRLQawMMQEXyfBnAtVcOTnkQiKSulpIvxurME\nueM884z43JdS8seIB1WOSo/NSwQkk0Bzs/FnT39TSTBw7EnFLd9qEB/TL33J8PGM4mGETH6lqXSw\nnCj/uuhfcfcbd/vXC57PjtkUbwpUbzrtLag98wywbl2gaopG/14HB8X6WAqL5Y7Km38RxUQkZQDY\n1mOEJo7FORhj2DMgro1Sgh9xcMxtPwIxJQZVUcFR++liJJIokYJp7TBqwZSIXiCiS3LLPUT0QSKa\nR0QXENEBU7lbiWguER1BRM+Mtl1J7WD2QZHUF35jH8WDmj5xeU1gV10lPicmeL4PSliv7yKmmibm\nGTMWgKDB7RHe7xzG5i25IYUNDb0X+KiB9ICnZn1DzwYMpHrzb3Dv+pu7Ar3myGYpr8W3c+GFwLe+\nFbh7RcNY+IGlgtzrK0Ew1YNe8ZxgmtEMzTlTRB7TLBf7StWYmjWuhNrXmMp5vj6JatylxrTyKXXs\nq+SRSyKR1CNhakzt2Mudey5wxRXWbXrEzmrQZlTbxMyUrBDUiuhzVA8j1ki/hpCgxKf6HnfN49fg\nzPvPxFXHXYX/d9b/8yw396656B1YC/1kPzf/c/l+b80F63DjvPNEtGgvhoNZGheN/n2ceKL//ijb\nHiv0l0/675wx4NE1jyKVTUFzTR0kInS3JkQ0addo3blNnhYLzBBMhzJD2Jfe7lFQIpG44Xbfr6b5\nT+KNFEwlZUf6ntQvYfuYjmZiWrTI6UtXbXlMq2Vi3rq1E0AWRMV1OMq35Pl6TKa8WoH+PbfxL1i7\n5xWMbxyPCUn3lC9mhk1Cpj75fmbBYteyhMIvRaIab/P32trqvr8UgtzrRaTfsePNN4GXXxbL+nkZ\nGlMXwZQJjen8qfMBWDWmdu1pIuHeJkHLlz247WA0qs2jOIPqQM7z9UlU4+42V0sqi3L7mEokEkno\nlPLgHcTH1KveRYuM5cHG3+cf0LuHuipeNK0mjem2bcKc0Z4d9ic/Abq6vI9zy0Ubtqm12axScxNM\nTHxn7i787rRc3kumwDekEQP6ssbZKgHGqlyCadR1+9EQPBZWKFx3nbGs/4bOnn02muNN7kGJmPAx\ndQt2pQub+lfnpTElcCi54xWm5KJRjuYsJJL6oq/Pul5N85/EHymYSsqO9D2pX8L2MS3mQd6tXCIB\ndKVEXk113H/nBR9O4mG0kqmmiTmZXICmZudDf18fcPPN3sfZg/DoptajxXItEOFnG8Tirv1v+x53\nRFuuH8Qd2jIiwkB6AGktDUBMto0xQzMWVDD1CzwUpcY0CirRx9TethH8iCHZILTg+we35csoOR9T\nHW7SsNuvAc+XJszuYzrGoYjLgJzn65Ooxr3JFj+umua/ekH6mEokkppjrCeaU04BBqkZ63qFAGH4\nmBIalDFW5RRJNU3MDQ1AS8daxFnW4mOqqkBLi/dxkydb1yOJygsNPCccbdv9+0DH/Oad3+SP1nlg\n+QNou60Vie8Le06FAWktm99faPIdTIuQuOXQmHZ1ASMj0dRdiEoQTDlxMADzpp4PAFi24UdGGeZ8\nCaGT354bFy/BlMf7MJwZzB9D4GOeJkciqWZSKee9olrmP4k/UjCVlB3pe1K/hD32QQMVuU1gs2cD\nJ5wA6FkMAbOPaeVTTYJpd3cnOI1giJy+dX7nYH/Qj8LHlGV3AwCe3Am0NB/le8y6fvHZM9yDXQO7\nLPvWdK3BT44H/vFQsa4wYCAzZLTnU++scbOgEQdXRsoimMZi7r6loyXM3/sPfwgsWTL6esyRuvXv\n2uFTbhkEqwH6+OT4/LLKrLa7XoIpkYLmuHgDI/KYcrxe45nd5Txfn0Q17qtXA7fcYqxLU/jKQ/qY\nSiSSmqIUocOvvJt/ohlNExo7BsoH5dFNeashnUM1CaaA+E6zCJbPUydKwdRUKwaz+ssI/6ed9YPG\nsj0664aeNTimHTh7klhXgLwmFgBiPrNvW6ItJ+T4+x5GacrrJ5hWQlTeG28Ebrtt9O2Zryl7uhid\nt/esMPrFDFNeuonQ0mCo+AOb8oKjpaE1f0xrG8eUKaM5C4mk/jD7mVbb/CfxRgqmkrIjfU/ql0Jj\nH2bwo0L16YKpWWNqSRdT4bNeNU3MEyYsAJAFAyvo92smKsHU2q6C/pxgWigas950s4p88KPB5k8D\nAI5sWA4A2C9cTKEyIBk3NMSTPSK2WuqugKi8/f3AG2+EU2/YPqZbt5beF522NmvbeR9TEyPZTN7U\nduP+jXhtx2uuddmDHx044FoMxDTpYyqpC8Zq3Ktp/qsXpI+pRCKpKaIIfmQuZ4dzd42p2eeskqm2\niZmQBblMQX7n4JYuJqyovEbdhjhqF1Ds6E1PShhCyWnzRJjX97cIqUnXkjIGXHTYxUh/R0iqyw4A\nb+336xDAWPk0pvr3+uEPA/tt/XzkkWja1dsOij0ASil84xvAYYcJIXL9eiM9lFkD3psxglB9d/F3\nXes5ZPwhmD9NpJDRD/29h4sygUPJmf0yMBC4NEWUSEZBtc1/Em+kYCopO9L3pH4pNPZhTjRBTHn1\nh3ECw+7dIgiMkce0sme9apqYu7s7AWgAUyzfaqGH83Tauh5WVF4rhFntswOZ8k5LGo3HkAERB8tN\nq+1xsf2INuA/3wec0gE0qxnEVbGjuWU+vrHCr3YGlEkwNX+vyWR49Qa51xcjoIURMKijAzj3XODI\nI4GVK3P12kx5pycZtJw1/2BmEG6s+vIq/PnTfzY2MPIJ5MWhWjSmtZ8uRs7z9clYjnu1zH/1gvQx\nlUgkNUUpD2qqCqxbZw2KYMYc6MSO2cc0FmPIZoGnn84Jq1Xw1NjdHc6D+tihwW0K8nu4OPxw63pU\nPqafed/VOHzC4SAq9IWK6+J/TgJOxY+Qze4HU53SyP2bgYe2Ah886ob8tnsvvhePXOatemQoXx7T\ncr7kKOanFkbAoP5cAKtTTrGnizHKfOwgykcp1ri7v3kynkQiZrXPluliJJLwaW4GPvc567YqmKIl\nAZGCqaTsSN+T+iVsH9PjjgO+9z3g0UeLO25kBOjpEdqhGY1DaIynMa7d0BxxcFS6xhQAGhvL3YNg\nTJiwIBdQSrF8rcU+XEThY8rAwZgCBobB4Y2+xx3bDqzqBc55AXiRbsSZZ/agJTnbUW5FL3DPJmDG\nhPn5bSdOPxGXHnmpb/2M+acRiVIw/fVBE7Fyz0rX/RdcUFq9lZjHNBYDhoeNnLGMiSjL3PbF6/3S\nqHAgNN0M2DMqL6yCKafaN+WV83x9Eva4axowOAhMnWrdvnkzMDTkeoikTEgfU4lEUlOU8qAWiwFn\nnFF8/SMjIghKcy42TVqZni+jm49WuplQMinOv1ogZF2/1GK+5yjSxYhl4f83iy3F21uCOVROahbh\nd2NqHGkPYdKeTqRAj8pmyksEpNRubNzvL5j75ZwNC12jGSUTJohP/XpqjDUiphhjtX7koPwLgiMn\nHjnq9tyCH9W6YCqRhIEeiTcet25vawMOPnjs+yMJHymYSsqO9D2pX8rlY2qvVw98BABDWQCsAYAQ\nWEdGnD5nlUj1+ZgOA7D6mO7Zg7zJZCEOHIjOlFdhKtpoAwBgJOsvGf06Fxk2rhhPSg0eMysrsrMM\n5TPl9aOnp7R6S/Ex3bSptLaCwrnQbJrNpjXScl8uw4NbgJd278jvu/p9V+Oq467yrVMroFQlls1b\nCihMQYana14wlfN8fRL2uMdi4s8e+Kxa5r56QvqYSiQSSQG8Jq9Mxog8Ksoo+eUpU/TorJU981WT\nYAoAYBsAskYzSiSA8eODHa4LpqONyjsyAuzda+oWCIwpaFSyAICBkV2ex24ZbsSBjFjWgxo5yrjH\nyglGmXxMC/kqn3RSNO0CY2/KqwumetuMCT9S/avllMtBm/tOCITG2Ohs5inRi5HsMABgODuMgUzv\nqOqTSOoFzt2jcdf6i516QgqmkrIjfU/qlyjymAbFPJE995ypTVPDRqReKZiGichj2ohYYo5lu6oa\nmutCcC40d4W0U4W44w7xaTzsEBjUfC5bTUt5HqsA0HLXUYPakN/enzHK3LUBSMaSoJtKyX9UPlNe\nPxoaSmu/En1Mdb9Ss4+psJAQJ3dox1wc3DYtL5hyMvxDvdDPwfNcMo2YmBSm3xOSEzCY7a/5B2s5\nz9cnYY+7+UWSnWqZ/+oF6WMqkUhqiigf1OwT2Ny5xrLCkE/5oU+C0pQ3CghuwY+CaEAVRZQNI9jT\nsFBcobVVfLKcKW+TynO99B77LE/l85Sar5HV/HxTGWBG+4yi+8VyGS7LJZguPhtQsjs890fV/lgL\naPo1ZxZMNTI0pod1HIGDWqfl+yVyGvufeKFzIFNU3sZYI3pSe2teMJVIwsArRZj8/dQOUjCVlB3p\ne1K/lDOPqZlkEjjmmFybAGASTFVVHFzpMl81CabCx1REvzUTNC+pqoqyRIZAWSoNDfYtBMbUQAk8\nGCivMW1tMDry9Q/+Mb+sEdDSUEKUIOHiWFaNKeP7Xffr2sNizajDzmMaBmYNTDot/vYM7IF+J2BM\nBciIjkxEBTWm7e3+7aVSHEqu0emt09GoJrFhw2jPpLKR83x9Eva4+728rJb5r16QPqYSiaSmiOIB\nldm0czqaZkS0VYQtLwCTxlSa8obCRRcB992nr3EAimNMihVMR3vO9uiOwseUQc2ve0+TCkNegDUL\nK3E1ge2JzwIAtg4BX37/l0vqG2NUlnQx+TY9UqPov50DB8Jvuxi/4SOOGH175uBHO3aIaMTfWfyd\n/H4FGpJswKIxLSSY6rjdw4aGADCORNyIytuQ8B9niUQisJvy/vrXwFe+Ur7+SMJHCqaSsiN9T+oX\nv7HfuTO6B297vYODQDbrLKBPgr39b4LRaKLYRE81CKZPPQU8+aTuY8odUWoLncPEieIzSsEUEFqy\nmKIve0+TM5uAk6afAgAOYeXYg/8O+9NAXxa4eN7FRfeLgUHjhI0+GVui1pju2HG3736guJQxQX1M\ngwqmow18BVgfdONxYPacnISY+241pQUaFIuPaaHoyn4v1YiAhkZDuGWM5c3Fa9kcUc7z9UkUPqbm\nn9+VVwI//Wlt/3aqFeljKpFIaoodO4Bp08Krr9DDov7gqZp8TPXgRxeOfxctrDu8zkRANQimgBFk\niOkaU9O+Queg56nThdIwovImEtb1NnUQAEHN9SMR97HLBHDfpU8AAFTFGrXpfTMuwvtOEvaZyXiy\npL41N7sLzjq9EQVz1X8rbUoPUilgyxb3/QAwMBB++0Gv4zCud7PGlHOAFBHsKqPpEaPjyKS2Yfxx\nX8H4g1aBUNiU1w9xjXNrHtPcFyofriUSf7zu+el0dcx/ksJIwVRSdqTvSf3iN/ZtbcCkSeG252XK\nyzkwYQKg8ayloDm/aaVTLYKpoug+puTQRgYVNIeHgQ0bgvuk+tHcbF1PMI4pbccYbaXdX0hoXJi5\nxhThpOomrExqEhew2f+0GJJJf0ll8uSSqi2I/tvop0no6HB+x6UKUEF9TPVr4JBD/MuGJZjq9RAB\nPCeYprLic+60i6DEJuKsE36BD37y20UFP3L7nogAKCaNKVhOCzv6c6lk5Dxfn4Q97nZTXj292Hvv\nhdqMJASkj6lEIqk5xsqUVxdAt/csc2zXJ8HVAy7J0yqIahJMAQBMpGUxE1TQPPpooLs7nHOeN8+6\nniYFTYlx+fV9O/8d6/e+ipGMVTX46jph5qoqQqWpMucbjGQ8ibNmnVXQ9NOLQj6mU6eWVG1BdIGq\npX0BJk1y5jW1C1xhavrMgqlbvkIzYZjy2qPygomT1TXg7599Of7+wq1Yvf04gPFAwY8KtceYYQ6s\nMAUEqTGVSIKgaeLer6PnH3/33fL0RxI+UjCVlB3pe1K/+I39WD6k6QJoWhvBvrTq2A4AR8+9bew6\nVALVIpiqqtnH1Jkuxu8c9H3HHhusfBAYA844w1hXQIgpCaxknwcAzGgcwvbVp+G+p1txy++Nxkay\n/aI8i+U+ndNpTInhhWteKL1vBdLFrF5dctW+GIIoieBfBQTT9euD1RvUx1Qf00JjG5bGNMuGQEzL\nCaZCE27XiuqnfOdrd+Jnf/1Zye0RAWBk8THVNaa1LJjKeb4+CXvcf/c7IOWRWroa5r96QvqYSiSS\nmiPKdDF2U16RGzMLIqPR7m7gtdeA3ak4JrYeZjmea7aASWWmWgRTRRHfm/AxLS74kY7Z9DLMc85o\nGcQVQkxtxFfO/m90m15SHN0GnDHR1Nectjem5jSmStg236xguhhnqptwyLdJhQXTww8P93dQzJiG\nMfb79gE/z56Mxew7OaHRfLJGAznLbWzr24aR7IhvnX5abiKAKU4f01oXTCWSMKikOVcSDVIwlZQd\n6XtSv4xlHlNzfW6mvIoCcNJgfjZ8/XWxb2oig2w+GIrIU7htO3D99eH2bzRUk2D63HOdwpSXFRf8\nyFxHMeWD0ju8B0lVpHsBgP041LPsSDYj+mISMEJHKU+6mCwfyddfSDAtpg9BfUyD1hmGKW9TE7AX\n72AbXnYRTE39yn2eN+c8XHDoBb51muR6575cG+brRvqYSmqVsMf9Ax8Apk9331frv6Fqo9Sxj4Xb\nDYlEIgmHcpjyajwNMolKJ54IHHecWE5rho/hsccAJ+8EBteNXR8LUU2C6cSJQGvDHnA+y7KvUPAj\n+4uFsM+5f3gXACCuNOq99Syb4irMmVLcfExHg/BB9DfljSr3JWc9AICEGg9kyhsm3d3CjywIbW2j\nb08/lwyGECfkU7d4cdL0k9CWKNBwoQjg7RvRmxIhlfXgR+a+SCQSb2bOFJ/y91KbSI2ppOxI35P6\npZCPaZSClpspLycN3Gy+x4XZHQCcMOvy6DoTAtUkmDY2LsDc5r1oHv6TxZo3aPAjs2AahtZMRxdK\nEnERqrdruMez7Ixx87B1SCxfPO9iHD356PA6koOhcJqjKDjQL+zlMtn+UAXTIPf6eHxsTXn1c0mw\ndnAOvDz5M+5158qFkcc0Nf9OPLj8QQBG8KNaN+WV83x9Eva4m+/5tfx7qQWkj6lEIqkpwha0/MwP\n9XylnGuAycdU04BYwxCyHFCVyjYwqRbBVFWNsUiq1ieLYn1Mw0gXY4ZTBt1pNR9pt2dor09pQkIV\nmtUnPvkExjWO8ylbGkzx15hG9WDWmBAVD2dTroJpVJpaHT0FxFigf4cMDJwD+xuW+5cHjT5djAk9\n+FEmA/zv/wbttURSn9jTO5mphvlPUhgpmErKjvQ9qV/K5WNqR08Xo1HGYoXHORBLHECsCu6U1SKY\nKgowPNwJjYD9jZ9w5Jb1O4e8EGEz5R0ZAXq8lZuB4TwLbnoxMegTaEPLdqNJjS4ShxB+/H1MoxJM\niYnzUhU1VI1pUB9Tr6ibdsLUmG7CInDFHNTIPSqvCFRUesP2704PfgQAK1aUXG3FI+f5+iTscTfP\nEVJjWtnIPKYSiaSmiHrS8TLlNfuYahqgJHqQsT2YK0zB0uEHsa7pV9F2sgiqRTDVI8luGpqACe3H\nWfYFFUzt5RMJYGDA/ZhC9PcLwRYANG59MZHx1VZmsD+bKK3RIiiHxpQgBNM3drwufK9tPp9R/jaJ\nRKTcIIQlmM6KnQwA0JRBn4L6R3CNaRDMPqbV8PuVSMqJFExrHymYSsqO9D2pX8bSx9TPlHf/fiGc\nEmUdgqkaT6E/a71VfuzIj+Hc5q9id8OL4XVwlFSLYKooQCKxAOJJ35kuxs9n1EswnTmz9HMfGjLa\n5JS1+BgnffuiIcWjNe9mir856LJlUbUsBNO9g124/Xbg1lvd2y+WoHlMW1uD1ReWYNrP94hlv6hF\n+fJUMAJzMS8TGGPYPbAbgLDaqFXkPF+fROFjKk15qwPpYyqRSGqOsTLl3b9ffKayA1BMUTn7+4GR\nVMoirABAS0MLDoodE27nRkm1CKZ6kBfG4EgXU8hn1EswHS0HHyw+hcbUqPDwjoM9j+HEA4gxo4Vc\nNcH697DXzwV2FPBcAy0x8Rvwaj8KiIzx8CsDhCeY9vAtAKwmy6ms1Z5YP+UgwY/MdRfa1p5ox0h2\nBBdeCHR0BO21RFKfSI1p7SMFU0nZkb4n9Us5x948qSkKcMghwL6+1RbBZOlSYMnSFDhV9q1yyxag\nt7c6BFMAGBnpBEBQTClWiIB02v8curqs62FE5TU/6GR5GopJ3IzHhaSwPjXbeRw47BrfMOHEoWmE\nvj7nPv3ajSpIkG7KO6XRfX867b69EEF9TMfygdPclmXZ47XDaE15iQC1bw7u+dt7AAAxJYaWhhac\ncELgLlclcp6vT8bSx7Ra5r96QfqYSiSSmiJKDaC9Xt3HFACGMdGy76jTHsbkRCaajoSpW+DRAAAg\nAElEQVTED34gPqtlYhYPFE5T3u5u/4ivvb3W9RUrgD17Rt8X/Xvbtf9ti2AKyo07c5rsEmmIUjAl\n4qBkNxIubqyR+1/nBNNZ4w/DHXcAkyZZ95u1uKlUuP0JUpde5t13w22P2xp304wGCX7kdwpEgNo7\nFzPbRTJGVVHzPqYSicSfffuAV14Ry0GDpEmqCymYSsqO9D2pXwqNfVQ+pnaMdDEZgFkdvc5d8Mfw\nOhERusBWLYJpIrEADAAzTUFf+IIwZWz00NIBwMUXA/fea6w/8QRw992j74/+vSmKij4y1JAK7/U4\nQgimFKFg2pJoRUxhjsBDou3ImrU0sL57HY44AjjpJOvupiZjedMm4J57glUb1Me0uTlQ90LBHgSt\nULkgGtODpgNnnuljyss4VEXcZxSmQCOXQa4x5Dxfn4Q97rfeCmRy7wt37Ai1aknISB9TiURSU0Tx\n8G1PTaKjp4vhPG3Rjs2ZA6T4oeF3JGSWLhWf1SKY6hpT3ZSXAXjkEbHP7xza2oQAyzmwW8SLwdSp\n7mXXrSumLwLOs7BMi6zB87jh7LBDwxYmDAxQCFmXjDR6s1u3Am+9FX7bHMWlwdm8Oby2iYAzzgA2\nbPAvA1gF5NG0ZywbKy0NVulYf0EQJPhRPA5c8CGf9hQtX4fKpMZUIgmKX1qwapn/JP5IwVRSdqTv\nSf3iN/blMOXVeBpAzLI91XchNo5MiKYjIaELL9UwMTMGpFKdYHAOcFA5LxYTfrWf/zzwve859+/e\nDcyb51/Hli1Gm3o3OGVBpmnx0jNewPQjFgMmjRbPqdUGU/2RakyFuM4Lakwfeyz8lil3vh+e5rXf\nSPsDAGefHazeoPd6xoTP95YthnbE3n5YmF8u+NVrXCPBgx+5MTwMpDMcKjM0pvUgmMp5vj4Je9w/\n97lQq5NEiPQxlUgkNUdYghbn7g+45v2KAgwOrTH8CiG0JEzJIkpfwjDQTRCrLd0Es01BQV9GzJ8v\nhFNdkOjtBb75TWO/n0mmzuzZwNq1TsHUPC12tMzEvKkLcoGOcn3UlxmhKV7A5nQUMCaEpq1bnfsi\nN+Vl/hpTc/uf/CQwZUp4TZvr7usD/vxn/zJB+M1vgCuvdN+nC4XNmASKu4QgtrcdwJQ3X9bDlLcx\naWhMFaYgrZUYTUoiqTPmzQOuuKLcvZBEiRRMJWVH+p7UL4XymIbFc8+JT7PgZjflVRQALIZYwshV\nYQimlY0uiFWDxhQAFGUBAIKiWCXpUrTkjAFr1gA7dxrb4vFgxw4M2KM8ZkXyUBsJbUt+eXfvmlzZ\naKPyMjA0JrnruUQe/MimwdN/P6PtQ1AfU308PvhB4NhjR9/2o48Cv/61+z5OGlTEwMAwpLmEQM43\nqrddOPgRAGER4FaNzcdU/xzAKKN4VThynq9Pwh533e3GjWqZ/+oF6WMqkUhqijBNedNp4KKLjAmN\nc2DRImO/LpgSaVCUZst2tWErFFR2VN7TThOfo02dMlb09ACMOaPyAsHH/L33xB8AHGNLKauPcyEB\nRtNsGlO7j2mOSabIuDsPLM/VHa1gCjDEYpQ/RzOlpmsJCjWszy+fcoqzvShTupjHI5kMp06/3wWH\nBgZVvABghVXty/csL6jh9BNchWBqaEx17Suh9gMgSSSjxRxBX1KbyOGVlB3pe1K/FBr7sATT558H\n/vQnY33lSmtEv/xkRxqYKSqvpgEMSWRZRAkjQ0L38auWCVtVO0VUXuY05Q3KX/4CvPyyuEbsx+nf\nw5Il/nXowXMsGlOXabErZfgd7+h5DYBu9huhxpQxNDVztLY690VuyqsY+WBiMaC9PZz2g+YxLfS7\n19tfu9bfRF/HVzAlDQpTEY8zi2DqZa67dPtS/Mer/1G4UbhHDSUCMi0b8ybEjDFMa/Fw5q0h5Dxf\nn4Q97mbBVOYxrWykj6lEIpF48J//aV23+yDq6WIIGhRm2E52dwOMZR0pZCoNfYKu5InZGv1UmDoq\nbPSmvH5tvfmmfzmhJTfWU5keuAmbHQ3CnHt9aqaRNoS4q9lvWDAAYOTqLxu5YArC3hHVIpCPVR+K\nEUyBYFGJCwmmWcolRPTRmJpPt3fEO42QTvs4YNkyl3oIUFOTMK5xXH7broFdeFX9UcE6JZJ6R5+r\nJbWLHF5J2ZG+J/VLIR/TsYrKOziYM/+0aUwbG3O5TSv8Vqk/qFfLhM3YAoA5tVKl+ph68c1vwjd4\nkN2UN5XeC7iYVLbk5TOW978UkWuj1ZgC5RFMCRo4sZy5tXv7eh8eegi46qpg9RbrY+pXRidIoCu/\n30WGDeXMeYXGtI3PLFhfSksVLDNtGjBpknO77mNqTjnz07/5KVIoLOxWM3Ker0/G0sdUUllIH1OJ\nRFJzRCWY7t9vXd+6VTcJ1KAo1mgzDQ1ZAJU9E1ZD8CM3jSljTo1cKecwfbr79nQaeOkl777YBVPG\nVKgNszzbEaKiOFjlB6AG8EksHQZivKBg6pbndPRwaGSPmezefkdHMOEwKEEF02QSOPXUYHX6+5hm\n0aHmAp4xno8UzUz/2/mHE/8hWMMuuAmmiVgicKRfiaSeEVZM7vsqef6TBEcKppKyI31P6pdCeUyj\n4ne/s64nkyJvItN6wGx5TMG0SE02wyBMwSAqDBNYgPPOnGBaWh5TM4wJP8hS+rJrlz0qr+bwewWA\n/ozeFssLpvuGe9AXYUwsBo7U0F89x7ajQ3ymCivvioZA4KSgJeYdkEf/Dn/7W+C884LVW6yPKefe\nKVeKeQjVy27fDnziE9Z9nLJQWExci4y7jr+Zs2adhY8e8dHgjdvQBVOzIMpgXFe1ipzn65Owxz0e\nj+aeJwkf6WMqkUhqiihNef/u78TnJz4h8jDu2CG0KgpSUJWGfDkRaKF6NKbVxMGNw1CVhGVbqWNe\n6kuMTF7g1Ldwiym3sdXclmjspS0vYOW+TaU1HIBubTLaNXfBlAjIdqwCmroiaZuII63F0eDxhBBl\nVF7AGI++PuCFF/zLBEHXmK5fD/z+99Z9GrJQdc19TmCc1T4LTfGm4jpdFGTRmAZJPyORSMR9Z+JE\n6zY3k3lJ9SIFU0nZkb4n9UuhsY/qee3gnOXeJz4hApS8957+8MowrmVuvly1aEy1Ksg0oQsyf/wj\nACzAgQzDtPajHeXC9DEt1Jds1m5izOE2LepbGBh47oDGWByTm6cW33hA4i0LkEXCUzDtu/JY4ILr\nI/Er7u3VkOEqUh7XValCaVAfU50LLjAiJ4+mfa8ongAwPJLN5zHVTXk3//NmtDdaQxEbGv/RSeRE\nANlMeXN7RlVvpSPn+fok7HG3WrgIa5lvflOsy/c7lYX0MZVIJJKA3HAD8OqrwGWXibetRgh6skTl\n5RxgbABMakxHjf48r6c/UeDUFJVqyms+rre3cD1ePqbwMOW1dpNydWTQPXyg+A4XiZcpKwCgqTsS\nwfTddzlGRlhZHvRSqWA+psX0Tc+Hes89zn298bXYmXlXrLBguWmDajh7epw+wHlTXlZfprwSSViY\nf36HHw4kEt5lJdWHFEwlZUf6ntQvY5XH1E57O3DKKca6EECFX5+iGA6LREBM7QJRhM6EIdDeDsws\nHEy0IhCCVKdYtgU/CipwmIW1dNpYXrcOGDdOaMCDHK9rTPNv4MFdX0IMZHMBcZihMW1QgIHMSOHO\nhoBdOM2vE4skQiVjBM4ZGhQgq6UxOOjfn6AEudcPDcHRnh2i4qwEZs8Wn+vXO/e9M/OrxgrjUEJ8\nLBoZAe64w7ptODuMTNNWF1Pe2hZM5Txfn4Q97n73HqkxrSykj6lEIpF48O1vA9dd572fSAhMDATV\nFpUXiCHRWPlS38UXl7sH/piDHwH6iwDFUSbIw8VHTbFn/uu/jOVHHhGfXQVcL70EU7HiojE1Lemm\nnDOSQCziByEGgqL4acRZJBrT9gnb88s7epcjmxVCv86I2gV0uEh5IZBIOH3I7GzaBAwPB6/T72E2\nq/YBgBH8KOTHIrswPJQRUncqa0RwkRF5JZJgRBl7QlIZFBnLUCIJH+l7Ur94jX3YgVViMaCtzXu/\nbsrLwB1aPMY0S6TeSqSaJmvxEmABGADmIlUFOY/GRmM546LMNkdt7OsT0Vh1v2K9D4CLKa+XxpTa\nkMz0oUXpB6ce0QYHejWfi2rUiE7pgqlZM2rWmN56K3DLLeG2rGkxDKcS6E6rSI6IL3hoyNj/9tyP\nAye8CCws7ocaVh7T3hJTfuoC/vr1wNycK3lGNVXGuHfjuVPt7V8BXoTtvL06PdASJ2sdtW7KW8o8\nv2EDsGKFddvUqcBpp4XTJ0n0ROljakfmN60sSh37yn7akkgkdUuYgtbNNwMnnAAsXOi+XxdMx8eG\nHHlMwTQwpbJnvGoQTM0aU6EtJVdT3iCY5dm//Vvhy2dmyxZj+dprxZ+5bj0ya1eXSLtimPK6+5he\ncMpijGR6sWPNAgylnkPPcA+mNQJnzPt8sA6PgmwW2LZNpDPSMQTTaIyeFIVjcKQJnIDmVuEkaTad\n7Wl7MZJ2dQpdy4ceWlx9+velm32vWWMIpqZWgTNuRxfeca8j93nncb3AlnPw4jbvqL3fnjmC+MCF\neOopBYoCvPgioKrNOPXULXlTcIuPaR2Y8pbCd78LPPsscMYZYn1kBFi+HNi5s7z9kpQX/aeTTlvN\n/ufNK09/JOEiTXklZUf6ntQvfmMftqC1YYP3Pt3HtDVG2L3/r5Z9caUPSoUHPwKqSzAl6gQDoNhM\nGIMK2OYyV17pNBO+/npj/4wZzuPfeEN8LlliE4aJXNPFzOg4HodNORurtfOwKzsb+4b2YUojoKhR\nakwFRx3l9Lk0+hzNoDPGwYmBwBCLZXDiiYV9OgcH3bXXZoLmMQ3C9OnBypnrLKjonNNpPsq1yN4R\n4A38A844Y6/n3+3bTsM9uz6GS69ciZtuEts4T+X+RL0xky97PZjyljLPMwb8+MfAY4+Jv/vvjzZN\nkSR8ovQx3bpV5DWVVCbSx1QikUhKRPcxBYBEvMO6DzHwCg9+VE0Pa3nhk8ESaMqyrwDmMooC9PeL\n5YEBZ9nJk53bzAKKuU1F2wnO084Dcoxr7ACBkGAckxIAseifitx8SM2mvNG0yUFcAYGBUxaqWlgw\nbWmx+v6WSpBroFgLAf37GjfOu4y7cGjb1tgHlQFnz/4QVLXZ8y9DKn7z3kMYmfMsslmxTa9L91GO\n2ywzat2UVyIJA8v9WhFReSW1hRRMJWVH+pjWL35jP5YaQCNdDHDa3C/Z96KxobIzeFebKa+qLsil\niynNx9QsrCmKITS5acTefNO57d57jWOt3x1Da7O3najoL0dMyTVE5U4gG5HGVNEQaxAaU869BdPX\nXrOuP/WUf71h+ZgCpQmmbszp+idcMeVmsZL2Ns8FALVlDyYkgGRQZzZGjrZ1U97xyfFGsTow5ZXz\nfH0SdR5T832g0ufAekPmMZVIJDVDsekgguA3aemmvCMaoCoNln0K406/0wqjmgRTwPTGu0QfU7vG\n9KyzijteD47EuT1djIK42uzTrgqAoPFsrr3oEsiSj9AZtY9pSwvH5MkKOAEaz3gKpiefHEnzgTSm\nxaCX1zXrZhSeQEJpEsKhknUWMNFMIiEqIfjNaft26zonQiw9AS0NLflt9WDKK5GEhVkwjSIquaS8\nyCGVlB3pY1q/eI19Oj22gpY+wSkMiKkNtr0aGKv8OHGVLpjqEAGcd0JhcOSMLEVbxpgIbnTjjaUJ\nLNY2CYqLj2m+LSgAcXDSBZjoBFMA4B4aWf08jzyC4fTTw2+3qUmDwoTGVHMx5W0cmeU4JsgDYlg+\npqWa8sZcfsYEMuryEUyJDAHS7xqxwFVHP4WPqbPztW7KK+f5+iRKH1POpca0kpE+phKJpGYgAg47\nbOzaS6XEg7XKAJVZBdNYjEMdA1/C0VANPqb2AEVA6elivB5Gisji4VqegVzNi40CCgACcV1Ki+6L\n14jnNbN29O9w4kSG1tbw22aMQPDzMXURrEL6KsxC5wMPuKfCKVUwde8jgeX+QfG/gBQm9h85/cJg\nDWsJRz+zPAst1mfZVg+mvBJJGNhNec1TSDXMg5LCSMFUUnak70n9MpY+poXqi8WEYGrXmDJIU94w\nMAsHsZjIYxpGuphmk+VtsQ8mdlNewD0qb77dnI/pWATDmtk+E4wxpNPO89LXGWORjHs2sRv92V6k\nsiNYuXsZBgdF2g4dN9PTIN99sT6mIyPe5YoVTBlz76PQmAarjDFCb0aFGvR+wFXs3m3dlOUZKDxh\nrRcMGgfWrg1WbTUi5/n6JCofUyJg40axfPHFxj5J5SB9TCUSSU0x1oJWpqkTAKDaIsVOTeyF5hOp\ntRKoBsFUR++rwnKmsS77CmEuo8ehYUwImk3+8WsscA7ccQfwP/+T74GvxpRB+JjqJrZnzLs2eGMl\nMjwMvPee+z6FKZE8jA1NfAF7RrYjrgCDI1txwQWGX27UDGEfHkyL8L7PPw9MnOgsU8o569eHszIh\nGDLGgG2n+tahsiINbl18gAeHCEp6vGN7MklIJByb65b77gN+/ety90JSiTAGdHeLZUUB5swxtkuq\nHymYSsqO9D2pX8Yyj2kh+lLuT//jGzj27f3l2HamSA4cKHcPCmPWmHLeCcD5EqDUdDE6nBeX145z\n8dY9nXvvwAr5mDIRxpfzDLpSMbQnpwVvrEROOMHqG7l5M9CXswRlYJEIpuNjKhoVIKtMxNyOw9HR\nYc/3WtqPM8i9fh9bjdX8DwCAxkYgmXSWKfZFjP59eWlM8wrgLWfh9IYvutahZQGmEHgx5z5+o2OT\nopLDt5oxBjVGNR3Ipdh5/oEHoumHZGyJysdUf4nzu9+FWr0kRMbUx5QxNoMxtpgx9g5jbBVj7Ku5\n7R2MsWcZY+8xxv7CGBtnOuZbjLF1jLF3GWMXlNRbiUQiKZFCD7INsXbPfe0dnwi5N+HjZ/ZYSaxY\nIfqqstLTxeRJ9uCe5T/Jr953H9DbW/iwL3wBuPRSN0GFclpRd4SZL4emDaAlFn2qGAYheJs1fXPm\nAB//uFge0PZjwwZg375w2+XbP4CZzWeBoIBT1sUMNrq3Rn7fv6UcA159NZhWTVUNs20nBEW/6Ji/\nWa9SrMZ01kuOTRrnUBRrGzIqrxNplilxQ38p5fYzlRrT2qDU93MZANcR0dEATgXwj4yxIwHcCOBZ\nIpoH4PncOhhjRwG4HMBRAC4E8DPmG2FCUk9I35P6pZLGfkf31yzrO3eJzy1DzZjecVIZehQcxoAp\nU8rdC39E/lJ9TeR3cTPlLYrjfoUbX/ga+lJCJTYwUFxfOAfOOQf47GeR64+/YML04Efg2JeO1u9Y\n4xxZnoWiAK+/LoQwHd20d196GzZsAP7hH8JtO5HQoKgKAAWcZxyCKU9uxdWzgHTG+RbA7wVJoN97\nzvz12J8fi+e2/tG9iKkve/cWrpIx4YvsZsorqsoFP2I8LySSy8Woqrw4wTTb6NjEObma+NZ6VN5i\n7/VhpwuTlIco5ngpgFYHY+pjSkS7iWhZbnkAwBoABwG4BIBugPEAgI/mlj8C4CEiyhDRZgDrAUSU\nAU0ikVQ75fCZPKJlj+t2GfwoHPr7jYfNpvE7AQCKUpqPqWpTqg2kB4oyZzYLpjNmAEccYezzC36k\na0z7UwcwmInW7/itXW9hJDsCRQF++EPgtNOcZfalRZLMXbvCbTuZGASgeGpMD0udjmtmA8PDG3Dg\ngPAF1cdkeHiUjeeEtlV7V+G6l65yL5K7Tj7+ceCjH3Ut4lre1ZSXzBlj7S8mrBej0JgWE3XJWTar\ncYeGlOU6t3598KprnWIjbEvqA6lJr31GrbVkjM0GMB/AawCmEJH+dLcHgP4OfzoAc5rp7RCCrEQi\nfUzrmLH0MS1U34tdwEYXjRtjqIp0MZUumA4PA9Oni+V44xPoz7pHdg1yHg164OQd4v0mp+KeYs2C\nKWPApEn5Pb4+pipLYG5iGx5+5/+QjfgB6ar3fQYxRXX1O9S3tcScQXTC4OLj3sLsxsVg0HCgf6VD\nqGOkO71SXoDQXzr4abqC3OvNprx9aXe7bCJg13l/g65xTxesTy/vJZjqwihjDGBkl0UtKIzAixl3\nF82oxp1aeQaGhkQw7W+1Uuw8LwXT2iAKH1PG3H8rlT4H1htlyWPKGGsB8AiArxFRv3kfCTsYv1u4\nfO8hkUjGjEJvWs+aBGwYdG5nBVKIVALVIJgSGYFsFKYhzd2nn6LOQxFSECfu0KLaefBBa18UEcfI\n8t0VymN63jHfBgCMp404xtslORTiahwKY67npT+0H97y/sjab1IJpE4A4BTqyLR07rnAhAnOvpWK\n2YT2sHFHeZYbPujPeOHgD+Pxx4PU6R2Vlyx+pYTUiPcF+L7ZGzAlUUSqIDfBVCO4PXolk+Qa6Kle\nkYKpxA39t/yLX5S7J5KoiBUu4g5jLA4hlP6KiPSpYQ9jbCoR7WaMTQOgv9PYAWCG6fCDc9scXHPN\nNZg9ezYAYNy4cTj++OPzdsq69C3X5bpcr511Hfv+7u430NnZ7VLevb7NmztzEVnd9//933eis9Ot\nfbG+bBmQV5wB2LOlB0AnGAiqEnfUt3v9Wgx17fTsv77e0uK/P6z1TZuc59ff7/19jPX6q6925sw8\nF6CheT5WLSdMaOgEWgEAyGY7QaRgS+9sHNc+u2B9QCeQWgZACDPbt3fmtpv2m9avvroTM2eK44mA\nvXs70dMDzJy5AIyJ+tesyODQQ1Xf8wGAGQ1dWLZMtBHV9/XG0uXYspGbNKZifPPntwnYv18YKPX2\nht/+smXATq7gmBOFKe/27abriwjLlgEDA3/FqafqwrE4XtP868+fjcd+omT+/NZhdf7BwVyeSOwH\njKicfudDBGhaJwYHAfvvQU8Xk94wAvRsxeCAiLT81lvDOHDgNVx44VwAwI4N/diXyOLg4wu3p/f/\nhBldWJM7nbffziKVehkaPwQMzFKeMYb97+3FUG+no3/lvj+Hta5vC1q+t7czd5SxX6QIqYzzkevl\nWQcW5F4kGuuCTrz6qrifV1J/631dp7OzE8uWLcOBnM/N5s2b4QkRFf0HYezyIID/sG2/HcANueUb\nAfwgt3wUgGUAGgDMAbABAHOplyQSiaSz8xi65JIVju0AEefux3znO0T/9m9Ef/0r0QknWPdNm0a0\nY4dY3t67nX676rf5fR/4gKh38WLQw69eQ0RE9z1/Cn3yWx8igOj/nm6g5Vv/4Gjvqv+4hw6//gsF\nz+WNN+ZTX9+bBcuNhhtuILr1Vuf2+fOJ3oy26cCsXUt02GHiu552xGJ6+M8xIiK676376PlFoNZW\njQCiKbeBVu1cUrA+gAhznicsBG3s2Uj/+I+6/tP4+/CHres6n/kM0ec/TzRpEtGVVxI98IDY/sDT\nzfTKunt9231+Eeg3S66ke585rOTvIghrdi2ih5+J0emnW/ufP5+FoEsf+gQBRK2t4ba9eDFo8WLQ\nfYs+QL9cfDb97GdEX/qSsf+YG8+hxYtB+w+8Rl1dRBMmEM2eLfq1ffvo2v7E118hLET+b8YMZ5l3\n3yWjTIDHhn/5F6Jx44gOOUT08YknjH0Hf+lauvaXd9Ghdx5K+Juv0NHX/xMRES1deigNDq7Ll/vy\nD07Mfy+F0Pt26UOXUVOT2Pbii+2UTu+nH/9qFbXeeJSl/O/f+T2d+1+X0vz5hc+lXjjoIDFWDz5o\nbNu5k2jq1PL1SVJ+vv1tMc/b7+sA0dat5e2bpDhyMp9DxlRQGmcAuBLAOYyxt3N/FwL4AYDzGWPv\nATg3tw4iWg3gYQCrATwN4NpcpyQSx5sVSf1QjrG/6/W7cPnvL3ds70qpOH725x3bVcahyuBHo8Z8\nx08PvZU3B/3YER+z7P/NqcALb38kWKWHPAfA3cf0tNOAn/zEsTnf1iGHAF1d1u+uRRkumLqDAHDK\nIsqUKQCgQAEDsGSJdxndzDeqsWdMBZHmNOXNLac0IwSv3dfUjSC/d45sUX287rrCZcw5dF32Gj6m\nyZ6i2vbi1INPzbVnbZBIRFt2vcYY1bT5arH3+h05m7qNzlSwkioi7DnePtfddpuxXOlzYL1R6tiX\nZMpLRC/D2z/1gx7H3Arg1lLak0gk9UdUk0yWiwdfThyKyZ9QYYS4mnCUHx/PQmElez1EzvbtwHPP\nAZddVu6eFCbvy6kYT+D6GGQyxv5GtcFx7A9f/iHGJ8fjiyd+0djYL8wuOXGH0HHssUBHh3s/iIDW\nVqCtzfA3BYAmlaMtOd33HHJ6e98yYcCYAlbmUAwMKohSIAL+8AfDr6u3V/Rre+82zMqZYqdS4nO0\nwlWvUlgS4ZwwswloiwEUIBqz/jC7KWf++9//LX43ALB9h2YIitkENOad76Y3zdATOzVvPOjFxKaJ\nzmN7gSuvBE78MMH+UoMxJpIQ1bBgWipDQ+XugaSS0H/LH/gA8NJLwI03GvsanNOGpAopVWMqkYSG\n2QdFUl+UY+xf2PICAOCWF2+xbFcBxPKCKcNnznsGh570MAayCloaJ6FS+f73gbfeqvy3xWbBsant\n2HzaDT3wTDoNLFqUyyHpolG68fkb8S/P/ot149DEXHmjcuGHJvAKiGQWRvXIvAAwzBW0Jaf5n4c4\nClFrTOOxdkxo0KCoRlqawXxwLsr9H63gqvEUtMwO7NgB7DFnU2LO9ufPzx3jozEN8ntXuPPlkB1O\nhG/OA+6aHyzfpV3LMnUqsGIF8OabAN5/D57c8/N8HtMEn+BZj8KKC4RmH5+nngK6ughkC/yVy6Ja\n06kwSr3XH354uP2QjC1RzPGMAd/6lnO7FEwri1LHvioEU93MRv5V/59EEoTRmKb29gpBbeFC9/1/\nd+TfAQD600YgcaXxAMY1cMQU8VB8/vE/x7td7Zhz7GKojPLbKxFdi1XpPy9L9FtFyz+yu5vOup9M\nb8qaPuSU04yovPpDvVlLqnjMcGZhtJiovIAQCYf7FmM8ok06OavjWGQ4cPwJhh3FhhAAACAASURB\nVGCqayV1wbDYNDnFsE27FK1NhwCs0dOckpMhFTY25raNNipv7vOWY4DnzgLuvbcZmUy3tQwRWmLB\n27PfT37xC+DnPwfuvFOsnzTuQrHAyDX3KADMmQMoDAWvD8DrmhYkkhwN8foz5S0V/bqSSADj5U3W\nxeJfCqa1QVUIpkBpQZrkX2X9eSF9TOsXv7EvVdAayOUi7epy3z+jTcT5NF+TTVPWAgDGNYn0yjM6\njsdQVuSIVBghHmsqrTNjSKULpoDRx9TAcofG1FbSseVLhwALbdlDCE7B1IyXYGoWVKyCKXzzmOrl\nD012Y0JDAFXdKNC/l6eeMiVnIWF+DCYkmD+v/zMA7/MslZXdSRw8/lIoLA5Aw0MP2UvkNKY5wbi7\n23hQHPG2hA14rxd1j4sD/7wMGBjogKZZ8zhpJoG8FMHUwupLcWTbKeL7ZhzGY5H1guroEIJpkMcm\nfezc5j0icqSRqQdT3lLn+Wq4r0m8icrHVAqmlU+pY181gqlEIqkvSn0g0U37vI7XtUxmM7txrQeQ\n4UBjvMXYNk58qgyuvqeS4jA/oyuK5qqZyugP5i5aqStmAGebLKq//nXg9DPFYGd5Focd5mwz7hGz\nSn+46euz+rYyAKzAtEgA1mePw2ac7VsuDAhAe7voY0eH6Hdfn74HaEu0AQAOHAAWLw63ZYUxKEoc\nIA3XXGPdq39fnHg+h+mTT4rPt98eXcsjzAhApOWTAFjhJgkuyLOPr2DKhM/ngZEDwIS1luvS/tKE\nQQSEKoSuMf3DukegzejM1SX2DfM+pJUDjvKMmTTikjxSMJWY0X/LGVs64WwWiFVuKAhJEUjBVFJ2\npI9p/eI39qMVTN9+G3j8cZf9OfNDszajdeIW7ElZb4f6s68QTCvfnqwaHuD0PjaNP9LVlFfJazEL\nn8y//ztw1NFiLL/8py9j7lzr/nhc/O3b5zyWSAh8ALBs6yNYrzIQERhDwUBXBGBubAUwsqxgH8Mi\nFhNa0fwlmzPlbTAFiVq5Mrz2hAAGIZiC4+67bfvz4yQi9prNp/0eDu2/9yVLgK9+1VY3WSvIZp0P\noWaN6Zo13u3p+AqmOSH86ElHA1NW+r6YEKa8AQRTU2PaYY9Z64CKJHf6rDNG2LTJiEZba5Q6z1fD\nfU3iTVQ+pm1t1m1e8QQk5aOmfUwlEokkKLpgumQJ8LGPiaA6u3aZgt24+OXF1SxizKoV1TThc9ig\nVLZg2twsPiv9Ac6sMWWMXE15VZYvEKjOriFhr71k2xLLIb/9LfDNb4rlCS6xbCgX/Ki1FTjxgv/C\nGROBvYN7hUBWwC5WP4/Zjb2+5cJDNMiYeFmiKMib8kbZpqqqYCwGQEMyKbbmgy/lBOObOm8CJ44e\nU5aV558P3spjjwF33WXd5vb7vPpq7zJu42unkMaUMYaTDzoZYB6pXHI0KIDdxLcQ/KCl1nXiUNHo\n2LZzZD2mTRP3qnrHeq8oXz8klYd+bXzoQ/K3UqtIwXQUzJ49G88XMwsHYOHChbjqqqtCrbPSkT6m\n9UsUY2+P0qn7nE6ZktvPcxpT0wOmomYxyG12nwSoMRF4ppIFU32irvQHOLNwMDKwIr+9mOBHaW7d\n3hQ3fH/N53/ZZcCsWYX70t8PpEaaTa1SII3pWNGgAFpWBOliuVyiU6cCYE7/1q99TQT+CgNiYlwU\nFkOMBsAYkEwaEY/1386hjbuxrXeb5dj77/euN8jvneAUTF95xbo+lDF8TufNK1glgMIaUz0qr1tE\naCuFcxqbr2muDuXNnAEhhNq1stNbpyOlpTDdP1PRmPDd7wJ794ZfbzH3enME6Eq/r0n8CXuO/9GP\nhOsCY7l7oaRikT6mZSCKSLMycq1EIhitKa+Z8eOtfnGA1ZRXGbcVB1J9lmMSjcCcOVmkqyQgSTXc\nOvK+nB4aU1NJ1+P3Z6z2WvpYxpRYUedvFpK7eoRZZTbTJYIfRZwGplheWvN9AIZgumcPADXjWnbb\nNtfNJaEqQljTx2lkBFivByLO/XSumAG8s/WXJbdh1ozdfTdw7rkugilzvgpY17cqv/z/2TvvMCnK\n+4F/Znb3eufguDt65xA4aaIIGBEUwRZFRaJYfnZNMCFG0KggamIvMRZiJEbA3hUwKk1FmkrvcLQ7\nyvW+beb3x7uzO9v37va4Q+bzPPvs7sw77/tOf7/vt61dGz5lTIjYe24fU1mSQXIiqaGHRTGWIMlx\n9VXqL0bZzkUXefqg4i+YxlviSbQk0hp45JHALhAnkl9zECiDphNNtwWD1ochmBq0OIaP6alLsHMf\n2icsNOEGqYGCH5kG/RvFZ/BqNsGo0U6/5a2NkIPuVoS+n8kZnuSEoUwnfbH75H/UzmXHlI6NFkyr\njwnn1CU/3QgSyHJojalyAgXXHfVdqbTVAaK/x4+7rm+dxnT48Oi2WVOjpc2RSIhthwkR/vI3vxFm\n8QLdybTuiLjuYPe7wwFLlogATn6Cqezw8x9rG5vttW1VFSGJxMdUkiSQlaDpYtzdCXN9gLfvL1Zv\nZ7hAGtPWRqCIp02lIe95/fk+GSbcDIJjjO9OXQwf0xbEZrMxbdo0cnNzyc3N5Z577sHmeoOXl5cz\nceJE2rVrR0ZGBhdddBGHddEN9u3bx+jRo0lJSWHcuHEUB4rUEYBJkyaRnZ1NWloao0ePZuvWrQCs\nXr2a7OxsL23QRx99xMCBAwGoq6tj6tSpZGRkkJeXxxNPPEHHjh2jdSgMDKJGcwmmNqe4N/X3iGnH\nZRDAhFNR7K1eMK0TcgsrV7ZsPyLBk8dU0S1r/MhTE0x/2/e3jRZMsSUDEO/YiEz44EcnEpNsdk+g\naD6mAMieaMSrVgXZuIE4HLBggceP1GKWiYtJpX2MsIX3inCs02Kqqr8UE6kpaFtXDKCbb/YIQwoK\n47IgTyfP+d7TCeZk9+/09Mg0puF8TD0TJGEEUym8Ke+g9oP0DYjtdD7uvoKpWTaz6VjrUQPt39+y\n7evPpyGYGvgydmxL98CgOWk9b+CTFFVVmTNnDmvWrGHDhg0AXHLJJcyZM4fZs2ejKAo33XQT77//\nPg6HgxtvvJG77rqLjz4SkfquueYaRowYwddff82PP/7IhAkTuPTSS8O2O2HCBObNm0dMTAz33nsv\nU6ZM4eeff+aMM84gMTGRb775hvPOOw+ABQsWMGXKFABmzZrFgQMH2LdvH9XV1YwfP77FzYeXLVtm\nzKqdooQ79/X2at76pieSK1/l/a8koCh7MZmCz6klJQVdBcC0JdMAH42pLZnM+Cy/sqrqOKEasqbg\n0Wa1TvQaU5PpU2IkYY7aEI1ph3gb245vo0taF+It8SKSLhJrDq/hvOzg223aBEOGePfF97HnUM1I\nWJECpKrx3Y/C+lhi0n8Xcb8bjz51iecY5vVT2Oq0UFhViNVhBZqezmjfPpgyBXbuBFBJSpJoEzuI\n9Xs9x8Ntjqo7l6ouEFFODhQWwtdfwzXX+Lfhe7936SK+166FLVu0+pxc28l7O99UQIreDF8Ob/qp\nqqHK6DSm4Jdj1L+u8OpE/bNFq89dfQBT3r6ZfXXbNfx5s2kT9OkTPD1SQ/ngA2HSG828kA15zyuK\nCExWVWUIpicahwM+/NA/EnZj2bp1GXl550RUtl8/yM8PXSYtDa67run9Mmh+Gju2/1UIptF6cDXW\nJG7BggX84x//IDMzE4CHHnqIW2+9ldmzZ5ORkcFll13mLjtz5kzOPfdcAA4cOMC6dev49ttvsVgs\njBw5kosuuihgUm5frtcllnvooYd4/vnnqaqqIjk5mcmTJ7Nw4ULOO+88qqqqWLRoEc888wwA7733\nHq+88gqpqamkpqbyhz/8gYcffrhxO25g0IxIEhRX76dH3BHq2zwMQPe4hwkXfmbSJPjxR3juudD1\ne/mYyg4CGZCoaus35R06VAzsWzt6YdDptFCpCnVZpBNj9U6IM0HeP/MYlD2I9besR1EVTmt3GpXW\nypDvgZwcSPDESWL3bv+ckU4sSJLQUobcD6DEaqWmvDCifjcZ1/VXWAivvip+53RwsrU2k4SMChyK\ng6YKps8+C489Jn7X1LhSxUgyMeZETJJHY+vXKUDFI6gdOdKwdrVbUBNKAVRJAUzgmoxKTMQdFdh3\nOxBmn5EIpkEH2pIKqsvHFOjePfT1WFm+OHRj+EQW9jENVgIIpp57oHGC6YABIijM9OkN3jQge/bA\n5s0waFD4ss2B0ykEEEMwPfFs3y4sGCZOjE59R49CQUH4coWFEBcHixZFp12Dk5dfhWDa0j5WhYWF\ndNaFgOzUqROFhWLQUltbyz333MOSJUsoKysDoLq6GlVVKSwsJD09nXjdW7dz584cDBPBQlEUZs6c\nyfvvv8/x48eRZRlJkiguLnYLpiNGjODll1/mww8/ZPDgwW5z3cLCQi/T3Q4dOkTtODQWQ1t66hIu\nj6ndKcwIL+j/EADfLn04bJ2yDN27B1+/dDT8WAIOeZ97WYesImJkf3tAVXW2eo3pyTRw03JeZrT3\nPHci1ZjaFJk4kxjw/1T0EyAEgDYJbbA6rA06DhkZkO3SsJot9QA4zZ2RKA/r/5dkVumfCj9WRZBA\nM8pogmlMrAKKiVp7LfWOeqBpgXO+/tqT71UzqZWQSI5tS4pFpc5WCehsa3XHWnXlBSauHMUeTygh\nOZJnvZ0ad8ClBDPExnqiAWscP65ic50mWY7MlFcTTP/xD7+1JCRI1LvaHDxILyT6E8n1qkX+FtV4\nX09Fzs3sj/nSbxshGDd8MKOZ8j/wQHQE09NPFzmgoz2uash7XlHEhMMVV8BZZ0W3HwahUVXo1Anm\nz49WjedEVGrx4vCTyQYnF4aPaQuSk5NDgW5K6MCBA+Tm5gLw9NNPs3PnTtasWUNFRQXLly9HVVVU\nVSU7O5uysjJqa2vd2+7fvz+sBmH+/Pl8+umnfPPNN1RUVLBv3z53nQB5eXl07tyZRYsWsWDBAq7R\n2VRlZ2d7Cb7hhGADg5bE4bRSbIt+5uzhbaCjeZenHUWiwpnuV07F2eITX+Fo7f3T8Oqn7MkXGanG\nNNBuCn89bx++SPuiNZverkD0w/UJ52O6q1o01NZyYmyn1QB7fu11TlJTRD9eWvtSVNsTgqmKJMkk\nxmVQ74R6eyU2m9B8+F5vmmB697/SuePVCJKK6nCFRvDCiQ2nS+MYL4vz6mue/9+3VMpcgmakGlMt\nQNKdd/qs1HxMXReEHMaU2xTfP3Rj+GhMO/3gte6zuvuCbqOY6sPW7YsmmPpaADSW1pAbUsvX+957\nnjRfBieGk+V9YvDrxRBMo8DkyZOZM2cOxcXFFBcXM3v2bH73O+F/VF1dTXx8PKmpqZSWljJr1iz3\ndp07d2bIkCE89NBD2O12vvvuOz7XJzwLQnV1NbGxsWRkZFBTU8PMmTP9ylxzzTU899xzrFy5kkmT\nJrmXX3nllTz++OOUl5dz+PBh/vGPf7QKH1ODU5NQ516SwKFYcYbx+WooVXbYbh+GJurU14NJdkIA\nE06nUufW0vmiAvXW5olg2RBOpoGEJImBdMXR/aiu506kGlP9bmqpNVRUtyAxYgS89Vbw7X19cCVJ\naGOcTourLgWzDHIYCdcU64oobG7+JHrBjkzf/AoqpAMA1Nnrotqm3S4a1s6LZsqelibypIrrzTv4\n0W23wW9zYVL3GgYMCF637/3+yCPiW/8Kqq02I7mMuUrVDqgq7NghTAw19BrSSDSmop9B1yDhCX5k\nd+ptfv3PwHkDw6t1vATTBnC0x98bvE20U6skJQmTymjTkPf8Dz/A3r3R74NBZERzSGiM705djDym\nLYQkSTzwwAMMGTKEAQMGMGDAAIYMGcIDDzwAwLRp06irqyMzM5OzzjrLL9jQggULWL16NRkZGcye\nPZupU6eGbfO6666jc+fO5Obmctppp3HmmWf6CZeTJ09mxYoVjBkzhowMT961Bx98kA4dOtC1a1fG\njRvHpEmTiIlmhAMDgyigDSI37HudrNgoRWFwUeOUiTHHow2u7XbhYyrL/ppZVbFT4wyssV2/DvYX\nQIB5oRahLGENb218y+tTlvs2dqV1REXSzmmPHiKPaWN86TQsJiFMKqriFkzj40XwnkDExQnfSc2w\nRR/ER9Ki3jpLATBJYZ6HrmftufkvN7r/kRP4GD35w5Pu39qxaAp6v67SUkB2IEsec9qKuiJ3ACJV\nxSsqr01K47HHoNo1QdOvX8Pb19KDWCxgMqkoLr9MiznNLXjpI8Xq/cPLyryF1kCEy2MqSx4fU3MY\nH+PU2PBaYacaXFLuKo0Ouq4xk0zRFkxNppZ3D/jww5Zt/1TmZJroNPh18qvwMW0p9u3z+Kg9//zz\nPP/8835lsrOzWbp0qdeyW265xf27a9eurFixokHtJiYm8rFPBuxrr73W63/Hjh1xBphGTkhI4M03\n33T/f/nll1s8XYzhY3rqEs7HNKHmXWjiuPv668XgVY8syW6lj6qCnHwMm9Nf9akqVqxK4A5cfAkc\nXwQ6S/wWwR1AptM0/rsxmbYJbd3rDg74gj1VvTmD01uodx4089nRo2FLZQ7oTHllCc648NnQ2+t+\naz58iqp4TcrV2GpIejwJ9SHv0VVCgohaqpk9an2prIS2biFLpcwuh9WYaq3FmcOEf44SgUx5x3Yb\ny/xN8139aLp6Ky7Oc2w++KKMMyZCjFn4ipbbZTYcWAgMFf3x6U6drSziaLC+9/uFF8KXX3rMsE0m\nQFbcPqbBGH+hSpqrzdGXfkJJySUhy7/2Wqi13qa88Zb4IKWEBBhnCX/eEywJQddlHrmKpLS+fstn\nnj2Tx76J5Ykn4N13wzbhJtqCaVNySIeiIe/5iy8GnyGOwQkkmuffGN+duhg+pgYRceTIEb7//nsU\nRWHHjh0888wzXlGDDQxaC5IEKZamT99+9pn/MlkyoYk6qgomVSYzoZ1fOUWpCVqvM3UnxZ3/xUHL\nN03uY1PQBIUZQ7Yxo8NX3NbuC/fng1HVyOqhFu2fHm3AI8sKem3gtpqOTLnpLyG3VXXRTTUtoaqq\nXj6BmqZKpFAJVZfoS0wMSK6gV0VVhRFqC0Q/THKUcnOEainAAHHaNGif1J6x3cYyKHsQ7RL9r9uG\ncvHFnt8KTiTFRLxZCFfFzmwvc2tx7DwHymEvCZuiKRhDhazrFkzr64Ug3iZWnD8fq2E37dqpHKwR\nata22UcjbzBzG+bZPvPxPnlMg5mWxzr3ufoa3sLonuH38OU13gGOtGtLCXORvfde2OoD1hstVLVh\n/trNQfv2cP75LduHUxVDY2rQ0hiCaStl/vz5JCcn+3369w8feCEUNpuN2267jZSUFMaMGcOll17K\nHXfcEaVeNw7DB+HUJdi5t1ph9ermaVNFRfIRTGWT4lrmjayUoAZ5TFZaKwHYF/tR83S0gbSLtxKX\n+w/OOGOv+1Nab0amsqW7Bnibz5YdOYheMB0/aDPPbQ+d8sQseVRDminnnJVzWLx7McdqjnmZdx6t\nCSysFBcLDZMmmDocHiErNWI588QJpuDZV+1+qK3VrmGJ09tHRxOuFyzrbDaQgqvhhIbOc6wPl/0M\nRGaYHex+N5k8OTNVVSHGdcupbkNrb1QVKuuFn/GatcFNuH0577KiAGa2wpS3e4YI5R0s+FGaSdxH\ngZ4TvsSaY+md2TvIWjWkX/XpDTylu3c3rHw4FKV5NKYNec+/8w4sWRL9PhiEp7Q0ulZAxvju1MXw\nMf2VMWXKFKqqqvw+mzZtalK9nTp1YtOmTVRXV3Po0CGefPJJzGbDotugdVFUJAKaHK5vXH7GULO+\nIvKqJzWDqoJJciIFicZaL2UGXP7PCf/k0oSnGtW/aOLRxIBsSsNiSXd/nGoLO4v5IEl6U0FP3zIy\nIDaMIkqvPS+r97bN3lO2hzWH17iFuFq7/8iqogJGjYL33/f0oXt3MFuEoHJaKmFNSF17AWha9+al\nor6cw5UiyFGs61YwmYSwGkq4ef31hvk+63N8Himud6XM8a7faoVVq/zvrbRYYUpsbYJJqSzr61U5\nUpcKQGldKZaYQGmcGq7WGTAA/nJfgE66/J3HdR8n/gaRyqocDXtPdkvv5vlz5tOeiRmCa2UnTYK+\n/la+IZkzp2Hlw9FcprwN4T//adn2T2WqqiA5uaV7YXAqYwimBi2O4YNw6hLu3Neb+3HIdFGD6/0o\niBJz69H1tI9T/bQiOenlgQekqkKwx6RJNmEOkbPxRKENeGVJxRQmcEtLoteYthn/P8qtjdPkBvLf\nO7vT2dicNrc/5qaj/hN4w4aJb83fWJJg4UK44XpdhNlIOuC6TswnQGNaUV9CXfVaAHJyxDK73aMx\n1dCEUO0YP/EEPP545O3oo7DKZgeS6n8dDRzo0Tbrgx8lxoj8ptWO8IK67/2u9bdrV4/5aE2dQpWt\nL3YplR4ZPQCVUaO863EqDRdMJcknv6inF+5ATxBcaLSrTTjf50/3EryjmRq5vuEZZkLSGnxM0/0z\ndxmcIFQVohl2xBjfnboYPqYGBga/GuJd8UdUnBGZzvlSXBx4eZ1dJDOUJbPbSFBVxUBflf0d5brH\nl9AjtqDB7bcEbRPqURuZpuJEoA14VRUkSz0ltaUBy9kVkBRvoVXRRXhxOL21oZ1SO/nVsaNkh9+y\ni1zzG3rtXFwcWCwR5BrxQsub2vyTAH06XEF2SlcA2rpiWuXm+mtMtcOj+ScmBI+9ExB9AB2n4gw4\nSZOSIqLmClNPj2BY6Z5gEMucTv9gY6EYMkQMhLXg8dXVKpBIz85/IiU2JeA2+v2PVMMoy0HSuEg+\ngmkQqSxBjo4EKLS90ZP8IkmVo0dRYNmy4Nu1Bo3pjBkwfnzL9uFUpTX4GBuc2hiXn0GLY/ggnLoE\nO/dpafC73wGqE1lquKYiWG7R1356AwCnyRMwxq3JkwJHcCm1te7HpNb/SpuZpLjIA+H8/vegCxB+\nQtAGvKU7/bWT2rojlonEUuW1zqnacLo2aOtSUtc7hKDw1mWe5KWaiWcoM9ctW7wH3yoqVS5T1swA\nZqN+uNqwmAJHb40mEhbAu0+DB/trTF1ps9371NA8lKoKvXqJ3w7VEfL42WygZK93/69znYcah+Ku\nqzTwnEPIZ72quoRTSQmb21YvSGfnhCzqRpZFcKxuiVBStoKKih+oqvqBfm3LiWULNVWr6ZcC8c69\nVFT8gKJ4C6JKlIZLoUx5G4Nvft5w7NkDv/mNyA0biOYSTBryno+LE2b2BieeaPsYR3N8t3cvlJdH\nrTqDZsbwMTUwMPhVIbRrTqRGmEzag6Q+/fHQj9Q5tQAmuuBHcuDgR8etZipjRvktb03oTXkbIsS/\n+SbMndtMnQqAd+5Qf19FLZdlelIeTrzPhd1Zj1OFYpuZNi5f1KPVR+mW3o2cZI9kopnyBtSMuXj+\neR/BVHW6hd7I9kMIijHmxMg3aiSybEZCH/RJaH41jWG1rZrtxdvp1w8eftiz3eDBDWtHL4w4FSGY\nBtIc7toFH3wgfteSwffWizirDfz4Y1d6uuZ1vvwSHnww8rYrK+GLL6CkxHXP64IDldeXY3X6S176\nyLYXRBi9VZZFAK3XBsMv225jz57pHDw4nduG7CVDeoXD+//Kbd2gnfUD9uyZTkJCbywWT87SwGGY\nGs7WrZF5MkdKQ015tUm7YGlmmiv4UUNwOg2tXUvRmjWmR10x7YywKL9uWunlZ3AqYfggnLoEO/ea\n4CDhRA4SlCgUWk5GX7xTQuij8joCtiNLKlIjNLYtgSSB2RR5X62hM6pEBb02R2/K+9uzITHIaZXl\nGD8h4L8rxhEjQ50SQ4oZYk1Cbeqbx9TuFDMSgXJ/atx2m6+5okqxmhvxPpmchyMu21RkyQyq/yzL\nws0L+WLXF/TI6OE+Fk1BL4zsLaxEr8+WcFJTdxCAzZvh5pvFcpNkZvLQ57j6R0hOPgOzazSRnuWt\n7dYTyMdU82ez24WpsIritnQd0XFEkP56zGETEyLTEMuyuO+dKtS2+TuDBv1AXt4P3L14IMflf9Lr\ntK+4+xc4nDSDQYN+ID9/KWZzqu44NJx5l8zzW2a3hzblPdyAy2vnTmhoPMRwpr+twcdUUTwTVQYn\nlmhPTERzfJeVJaypjOBMJweGj+lJwOOPP87Nrrd6QUEBsix7+U4ZGBjokBQkpYJIQtIUF8OGDZ7/\nYf2udG9eVQU5tpJAd6IQTFv39KxbY4raICFeCwbUXCiKiCS7Zo1nmSTB8eNwRgYMbxN4O6eq0imu\nhk0HPQloe5tFACCrlI5JEoGnwNvXUEV1a0pDRW195RVfjakCUgL76iOLuNIt/sTZkimqA4vq77C5\ncPNCAOLMcUF9IhuC/niUV9q9BqaqZMHhrPELKJYcm4xdsXPUCk5dELAHZx/lqqsib3vUKI+GNzER\n9OlUEmMSA4pwpWXe57e+PriVhIbZTIBUMUDKYa/lwcxs9X61kXJF3hW6ChTy8ivIyQnux2qJEdGj\nI0FRRPCuhhJuyNEafEx37myYn7JB9GgN5z8URmCsXz+GYNpMLFu2jI4+oc1mzJjB3BNpO3eSYPiY\nnroEO/excbs4Z3Q+KjKyHF4j9OmnsHWr8J0CaBNE6NHw05iqZtonZfuVS5AVZClMHpNWgkkGsxx5\nXy065Wp5ORw5Et3+aAPgv/1NfGuyot0Ov/yCO1elL/UurefBku8CrDVhkjy5JjVfy3WF6xg9b7Rb\nwAilMdXwCKaaBisywaMi5Y8RlYsGaUk9STVVey2bu97zDlFUhY1HN/ptV1PTsHa8zPdkO7Iag1ur\nJ6dird9Ob31aTlsCEtAuUfg0H646jN11vk3mej7+OPDkULhnfW0t1NYpYYVts9kjQBZU7QFg27bg\n5U8f/zRjb5oaxMRbJTXGM9qNhqCvEW/x+CEnJVaROWEi5RXBTXmTk8S9EY4lS4RGUfPlHT5cfC9f\nHn7bltKYNuQ9/+qrMG9e9PtgEJ5om/Ia47tTF8PH1MDA4FeDSfbYmcZZqTcCHAAAIABJREFUMsKW\nVxQYPRrOPtu1jcu078wzvctp0VqtDhsHK4R5oqqG1owqapTzMUQZVYVPVn5HeoyCqQH+uHql4sSJ\nkO0vlze5X+DtDyRJUGWtDryBC1MI02mr0+EtmLo0plogJE3w+GLjLL7d+nTQerzNH4OnBApEUlyU\nD1QIctLySbN4C1O3fO6JWNUxpaNXzlabTQS3aeig3st8z2TzEs56WDbTI2Yv99yj20ASfUqPFwLd\nzpKdbmHrjDPrsVqDm9Pr0fsdgxYx2T9Hq80mcqhqOBwqWsanguKV9O8fuo17bp/B6K5vUlJb4l/A\nEY9FZwIfVGMadm/88U1Lde+Zm6mpCW7Kq92S4bSFWgAYbQKi0hUYOZLJJW3C6KefRAA0ffTbffvE\n5EBr1pgZNC+twcfY4NTGEEybgCzL7N271/3/+uuv569//Su1tbWMHz+ewsJCkpOTSUlJoaioiIcf\nfphrr722QW288cYb5OXlkZKSQvfu3Xnttdfc6/r27csXX3zh/u9wOGjbti2/uKZc33zzTTp37kxm\nZiZz5syhS5cufPPNN03c6+hj+JieuoQ79xJO5AZoATU+/1xEnQxm0bm/fAcjMsVvTTCVg6SliYuJ\nPNJtS6CqsPm4kBoaEvxIb9KnDXTvv19onqPVL9//cRmbuON2G/n5wbdLT+wWdF1xTSEdE0SKkoLy\nAr/otBuOCHvuR/pB9eE/B63HZtNH5RUHItLgNqECK0WbTm2GYg2i4ZqUN4ns5GzMurQ1f/sb9Ojh\nKROpFlxVdQJKTA2ybmBa5opKHas3XJC8hSvNtxcgNq7eZZLrT6D7XZI81+LhwwgTflfVVocVp+qk\nd284eNCzjUMXrWpg+9PD7tueI+0Bf0HRVcJLGA2mMY1W8KNgbTy/+nn+tedBMjLCm9tqaOWyssT3\n11+H30bTmE6dCi++CIsXe9Z16yaeBU3VmP3rX97nCxr2nj/jDJEyJhwOBzzyiHhuBfv8978N6/up\nTrQ1ptEc36lq8NgIzy06jf9+d0XglQYtguFj2gqQJBHJMCEhgcWLF5OTk0NVVRWVlZVkZ2c3ykQo\nKyuLL774gsrKSt544w3uuecet+B5zTXXsFDnZLJkyRLatWtHfn4+W7du5c4772ThwoUUFRVRUVFB\nYWFhVM2UDAyam+5xx1DVILlfdPian7VvL9JfBBvgjUxYAcDz3z+EM38Ostke0D9TaOdaf/CjFNMB\nAEymyIV4veB4viuy6bJlsGhRdPoUSDB9+N4BlCeNDbldh7Q+4odLiNAEwUq7RJUdsl3a8I+2f+TW\nrllcmuK3t7wNQJwJEs2hBQnteqmu2YasFCMT/joT+3HiBNMYU4I7qJAvD4x6AFmSQwrK69ZF1o6i\nwIABCHPduDKX4CmIzboP8A1G422OeqTaIwE7nA2PqqVFih00CJA8guKjKx+l2lZD377eg2WHw1Nm\n2b7Qk62qCnaHxdXrANeEbx7TKGpMA1cU+Lq86fSbGN15dIOq0p5vmlXCDz9Evo0e33u1qcOEm2+G\n999v/Pa9e3vSF4XiyBERATohIfCnuhoee6zx/TgVac0a0+pqodEPRH78FuJrPzmxHTJoFlp3VI8I\nkWZF5y5SH2r6jKgWdCNQ8I1QATmCceGFF7p/jxo1inHjxrFixQry8/OZPHkygwYNor6+nri4OBYs\nWMDkyZMBeP/997n44os566yzAJg9ezYvvPBCY3ap2Vm2bJmhNT1FieTcHy9fHVFdgV6mwW65g3UW\nOsbb6W17itdur8UsBx54mmVaXfCjJUtgxQp49FHxX1XB7BLMzA3oq36AKklwxx0Nz38ZimDH/mjF\nL9QeIqjWtE1iB1bUZ5Htmje1udKFJOXMoU/MWrYXfsyYrmPIb5/Pe1vfQ5Zkt8ARoxPMTRIoineq\nEbMZHA4LILmvlx7mX8AMux0DAH9/TX9OnGBqMcW59kNBdklmPTN6sqt0F2lxaRyrORYVDa42sVNZ\nCSRZMCGOEcCgLtexvewxn/sr+Lts3/HlwJiA64Ld78XF4vvAAeD0TThUEXbzw6s+xFpwmX8b+yDH\n5UcepxSE2jVUFRxOcX8Ulq2nfaBCup1zKIEnKKI3WPc3VQbIa5vHjpIdlJZCUVF4P3nwaD81wbS+\nXqT06dkz+DaBcjzX1wuzXo1o7KtvHQ15z0eqtVNV6NBBaEYDsW0bfPVVRE0auIi2j3E0x3eyDLkh\nAqjXK4aurTXR2HPfukZcjSQaAmVrZdGiRcyaNYtdu3ahKAq1tbUMGDAAgB49etC3b18+/fRTJk6c\nyGeffcYjjzwCQFFRER06dHDXEx8fT5tI3nQGBq2MGEv46zaYEBRseXX8RVTYP+SSsTW8+F4q/dtW\nYorxHrLuryigRzuQG5FHtTl59lkhnOoFU0U+HThMjDkh4nqqXFk9bDYRCMlkCjxobSyBNKYAyWYh\nSNX5maiKAinx7XXbqDy3qC/Dk6Ftclf+89MzXJAJfeWdtK15ibu7llC091Ye6ONABTrFL+P71Ewq\n7MWkWmDlyiR3Xd26wZIlDl588QU+/vgu9+CrzCZTo8Rhio0sB8GJNOXVhNEjldvISesHQEZ8Bg+O\nepBOqZ3YXbqbzcc241ScLF/e+PwamiBw8cXw6non+mkasymOGt/rQqf1u6b/NcQ5PgCEprTeFsCP\nM0S7AMeOAUlFmLKLoeOP7KxJYwIjGZQ9iLUH/PdLklVkGWyk0rNNN96ugIICofUNxNFjHYEdlB77\nD7T16wUSkBwjzv/W44Ft2aNqyhtAME2JTeFwlcgVc//98EkI5Y8myL/1lvjWBNO9e4WmMdQc+JQp\ngZf/61+6/gURTKxW2LLFpdl2sWMHfPwx/OUv3mWbYg4aqdZOUVpvzs2TldacxzSU0LzX3pu4lKEn\ntkMGzUIrvfxODhISEqjV2RUUFRW5TWUDmcw21IzWarVy+eWXc++993Ls2DHKysq48MILvTSvkydP\nZuHChXzyySfk5eXRrZvwz8rOzubQoUPucnV1dZSURD5gOJEY2tJTl0jO/WVDXw9bJtgLK5gp7+3n\nfMAlY71Hb77Rf8e4XEtDBeNpCboFdMEUO9oQwVSbt9q1Sxy/tLSm902P7+C4ziGyoydY4snPB3u6\nd3RbTeAz67SeL615geHJBQD07zSZ0Xl/5dW9sM/WmRrLMFaVxpGScTFLj8GyY9DGVExOvMzKYnh6\nJ4webXN/du60sWDBDBITRT4O7Xopphux6VdFbKLbkMjH0aDSLnG0Yrv7/+rDqymsKgSE9vRozVG2\nFW9rVOoQDU0QePppQHZ6+ZiCyDlbVnNId05Vt+x6Q/4NZCR4Tx7V1IjJEz1Ll8K1157j17YkQUoK\nMD2Ho5cPgOJe9Esa6VVm0SJvwUn4FgOSCVQnXbt6Jlp8UVWor0t2/RY7MPPbmdz62a3c/dWtkLkT\nWZbcKYgCppSh+U15u6Z3JdEinHOD7YuG2Uel8NlngcsF4sCB8GWCDVU+/tiT2kfjo4/gvvtEMCU9\nvsJNQ97zkQpHrdns9GSlNecxDSWYiokj42JoTRg+pi1Afn4+8+fPx+l0snjxYlasWOFel5WVRUlJ\nCZVauDwabsprs9mw2WxkZmYiyzKLFi3iKx+7lKuvvpolS5bwyiuvMEU3FXrFFVfw2WefsWrVKmw2\nGw8//HCjTIkNDFqa+JiUiMpFYso7ptu5WGSfUZ0WETRI8CPJt3wLk+RSAu7eLb5VFXD54Qbyh1VV\nT9ROPZp2tKameWbJfY/9sbplAMRaMjhuNXPxIN+ouf6C4YG907z+W8wZLC+GffZOHKMvP5bFkpJx\nMcuLYXkx1CsxaBow3/a1qMPXXgvxaYeptR9zd1RCQnFGlp/07F534Gz794jKRoNqp7cgPLLTSK7p\nfw0AHVM7clq701BV1R0AR0+kAos24EtMBCSnS6MnbiibQ0hJBcU6k3pJIdwg8Nlnvf9v2QK6uVIv\nysshToYuCdBFaY/F6W2CvdrVtHuiSQWQsCsKR6r2ceCAMG8Ptm+ySxic0L4ekwR3Db2LQdmD6Jsp\ntNB6DaZTCSyYxsjRen+qYSep9cLjjh1CcJ8/37NMUQgaYEqjrKzxFhDBngVaICFFEf5+R49CH5dL\neEmJd8AjQ2N6cnKyakwlVFRDpPlVYJzFJvD888/z2WefkZ6ezoIFC7jsMo8vTJ8+fZg8eTLdunUj\nIyPDrU3Vv5DCvZySk5N54YUXuPLKK8nIyGDhwoVccsklXmXat2/PWWedxapVq7hKl9U8Ly+PF198\nkauvvpqcnBySk5Np164dsbHhc0KeaIw8V6cu0Tr3weZcfDWmMaZYt2bEva3r21czeqBejPwCBUVq\nSbQUEX36CJM+VQXVZUYpBxhRfPMNpKb612N3BVJVlOYZ4PmeE5MsVLJ21cTmDYEisHiWWdQyEsoe\n40KfzCx6bdYvR35xBz/qmCJyRludVmJMscj4e0EWCiUj7drBU3N7s/GIsPt0R/ZVgkTV8CHOksSY\nfvdGVDYaxMt2jlZ48tuoSi1mtQKrtRCrtZAUsx3FfgyrtRBZ9pZEdEHcQ+I1GJUUL41cUqwwHZD0\nEW2l4Lk4FUVcWL6vGiEkLaOgwLtdjem94OmB8OCYn8hI2EZCQh/3uu7dxbdmoKT5FNucNs7OqOLQ\nIWvAfd2+XeuHp6G7f4Zbh9zKrUNuZeppN7n2zbNNMI1pXOMtpf0IZMpbUlvC6sOrIeUgXbp4lm/Z\nIjSo+/bp+uj0vl+fe86/jYwM71zFofANKBNsaKIlAaipEQGO2rf3nMOCAujUCY4fF/997/+GPOtt\ntsh9TFurEHWyEm2NaTTHd8EE01kfdSRZ2cXe8n3+Kw1ajMae+9Y14jrJGDx4MJs3bw66/vXXX+f1\n1z1miA899JD7d5cuXXCGy3QN3HHHHdxxxx0hy3wdJEb81KlTmTp1KgDV1dXMmjXLy+/UwKC1M3j4\n8YjKlZYGFk41IU4jYFROF76pJAb1f5fiXRPITMmLqA+REA0BcNgwoVFZt84jXMpSEvVBHidFReK7\nvNzbXFfb9rvvToxg6hE8g5nMejZIlIXGrNwGaTqFYYJFmCoPzx3OzpKdbqFSy2MqIdElrQs4DqEi\ntF/aRMRZZ4ljlp4OeZk11Dg8o3FJksnIuJD9JW81cm+bjzIlDbPDcyH/qeN6nAevZv0Rkd/33i7H\nKS+YzJp9dVx99UwWLIggz4YPXoNR2ekVgTclvh1ldpkDBXPJ7+SafPUZHOqtccqrhE2nr2Cq5UH9\n/HO46y7Pcq3dJAss2G/mg+UjuX3InVxywQQOVBwIWJfq0oqbUy6F2gVcduUxyo519NsvTZiTJQVF\nBVmCCrtfMa9J4lA+xMHusQYhqRwMoDlunyR8qx974Sg/f+HZl0DPtb/8xVuYjI2Fb7+Fc8/1L2uz\nQUwY63NfbbN+8P/VV+LeSUryLnP4sHf/tGetK2kATz4Jd94Zut1gbNwIV0SQ+cPQmEYfbQKvNRJM\nMB2dLm6o3Yf2+q80OOkwbulfMZ999hm1tbXU1NQwffp0BgwYQOfOnVu6W34YPqanLqHOfZXNQnJc\nZsR12QMMOI8e9VkQwpw91hzv9V/TashSdHwKbTYRYGjhQuGX9dFHeGmPGkJGhue3qgKSymGGBCw7\nYKD49h18asFiYmObx1fr55+9/xeXaMdeJW9gIPWTRyBIdx3yNJ9Df0XeFRyYJoSVnaU73RrTAVke\n7adJMiEDigrnv3W+e9sBA2DmTM9+JppVquqLQVWQkPjt0H8y9YIANs8tjCIlsfvAU2woXAWAExOd\n85Zz1lmFnHVWIQ/s7kNGz2/o2HE6MTH1XttOmBBZG/oB319mODGZvC+GEvphtxVppb3WLS9YTlFV\nkft/d9N6QKTr8OecoH1wKFDjTANUFKd3+74ClxCEJQZmD6bCDmeODKzt1m53Ka4Sz9Xnj16DGUow\nbUzYq6xEbxvrs/ttoFfqLo4f/8jr08lygOt6dEeWvPfF5m3VDPhrOH2Fs++/9/zets173ZVXwq23\nei/77W+9/+/bBytXit/nn+/vL/zOOx5rFM3MWpt4+Otfxff+/d55dBvyns/MhEMJr/PGsvNCljN8\nTKNPVVXrzmPqe75/LvK8aJQoBigzaDqGj+lJTFJSEsnJyX6f7/Vvl0bw6aefkpubS25uLnv27OHt\nt9+OUo8NDJofpQGBDFQVcnL8l/sGEamwBhc8MhO8w3VqpouJMQHsYBuBJjgvXAhvvgmzZsHf/tb0\nesXgW0EK8jjXAtl8/LH38poaISgXFAjfv/p6v02bhO/Mu8UiRrKSGn744Bt8x7NcpmNqR3pk9OCr\nPV8JryJJpm2iOHed4mq4qs1yzsuCGgd8EyTH5ZF6YeO448g3rpAZrfdV2NlygNPT4LtNLrNT1KD+\n0I3hllvEtaENRisoQFEVLy1iXFw33GpSn+A97RKFqa9+6W234efzev314nvjxuDzQ5WOYpBUYmL0\n6Vuc3HGnM6BPpYSEikRmWxurVvmvdwumquz+7R8N2seUN4iPqb6+hrDqplXcefp0AEqsMO6MZYzJ\n28rRo2/6fX6bdQDZ/gPvvedpK5JgRbIsrEY0HnjA81svHAK8+64neFKwKMYATzyB+5hqx6ejS5F7\n880ewfSdd7y369HD87uxE28AOyqeoCvhc9QaGtPoIsve57A1oQmmxbXFHKsRMQKWrj3Hvf5EB6Yz\naB6MW7oVUF1dTVVVld9nxIgRTap37ty5lJWVUV5ezv/+9z96hkpu1oIYPqanLqHOfWpMAFVBECI1\n6dKimQYi2GDfbIpOck9VFQFLPv1UaEtvv71xA92AdaOAFPoAvPGGd3txcUKr+cwz8P77oPM0iAra\nwPW994Sgorr0Td3jijiyzV+9HUwYPVQf72dCOTRXpAUorStFkiTq7HWiTRXmFcBNP7fnuxK4ZdAt\nAeusV8UAZvX2vxJJMJqW5JBNCN39YrdRWXeMjBgFUwQpjF59Fb7+OnyE17lzxbc2cRJviffKBwvg\nVB38fERoJnr28s7DObn/ZPfv2LRJAJw+6GeSk7d7mfj26wewjLlzPXkntdV6n0pQOfdcUX+HFOF6\nUmWroqZGpEMR23nqVVSJ7j2sZAYwrtCeCZLsdAvOzgD3nHb+c5NzGdh+oH8Bra1GRP3smt6VNi7L\nj8d3wINLhvPIp5dx2mkf+X321CajuAI/WYXbeES5hWVZBEnS0HsJXXABvPiid3ktCFWvXjB9uigD\nYjJhiMvwoqhImPACXH65eFZovr4TJ3ru7/37veveuTNwHxv6nlcj0E87nYEtZQxaD83hY3r/B9ms\nXZXFez9czKAUz2RzWlyUQ8sbNInGnntDMDUwMDjp8Q0GorFpk3fi+OSY5ICBR8BfMM1I7AJETzBt\nLrMzVQUJhUge54oiyu/ZI3zEHnjAkzZm0qTo9kvvNbBjhzCzPVAL1UEihQY7NFkd/8pvRnk7C5t0\n50of/GjCd1CTeDnbf7+PVye+GjSQjRa9sW/sLpLkmlYtmF43rpBD9fFU2iVqbWUAVFuLw253+eVC\nuNmzJ3Q5LUCOdgicitPvXFRZy7miAxw8+Cxr19YFPV4dM89mZxW07XAJY8cOpK5ud8Byjz/u+S1J\nQmPr1lonFxEfq5nRy1hk2FcmdsLhED7RlVWKW3CRJRWHEljd7/bBlp1uMad7Ri+/crJrf3b/fjd3\nD7s7YF0gJj6aRoIrcFTg43e0ppS8QULF+e9/u9qMwH5Ylr1Nfn19cvXPQBDC7uWXCy12aiosXiys\nJw4dgrVrhRbdl0WLRF/OPx+WLQver7Vrw/c3EoZn+Pph+FNREdjU2eDXiSaYXtXBQaIZ2tq8w47/\nbtBfgmxpcDJhCKYGLY7hY3rqEq1zH0xjetppHsELQvuPST7RetsmC/VArCVyP9dQhAp139R6URXv\nqKkhuOgij6nW8OGQmwsXXyx8z6Ldr2HD4JNPtAUiAI1TlcjP9y+vd2vcq5zp/l1rPU6sxdthUR+o\nSpIknjn/GT6f/Dn1Cjwz7lnizHFYZEtQwdSB8CcucSQQL1tbtSmvSTaTmXsvKRaVvceF41/3dh5r\nms3HNrNws3cS06IiaNMGBgZX/rmx24GkI/zXOolHVzyKU/VOFwOQ4RR+o/sKZqGy1+v4awGphHAn\ns8w2mac//RsHD3ZHVT3qLCHInOP+73CICaVDyjqyuh8h1mxBAtp1PeYl+KZaYPWeuXTuLISekSPh\nq5onqVGF7apZgj2l8ykoENezHi2IfZvkSvcZ/mKKj8MkHsE0zhwXcpIieCzi0GhB1zwa1+D1/LD7\nZQDq6lzbBhCGhw3z9jOXZbhbJ0+ffXaAPqie9DGzZgkriQkTQIvfqCgeE99AEY7vv1/4qW/aJFLF\nVFcH3YWANPRZn2wOrgq124XrgckU2IXDoPXQHD6mwaxr0hO7Rq0tg6Zj+JgaGBj8qthd2iXisoqC\nVyRRPaef7lknBFPfkKLiK1hamBhLcsT9CNfHcGaVjUUNoTH93/88vz/+2JPyAaBtWzFYbQ6hWVFE\nNNDkZE8fQ6VAN+u6f+O5P7h/52ac4VdWn/JHQsJishBnFprtjqkdXfWZcSiB1bOqHM/bh2Iora+l\ntZvygic/bXldMTYFLD5a/He26B39VGasvoGyurKI6s7KAsbey6e73+eBpQ8IH0uf4+FAHG+HlOwO\nOKWhCaZ689pA6Yn+4qPMsFiEH+PLzqFc/f7V2BU7STHJfhrFbZVQUfEDZ58tIksDkOHRxB5xdkJV\nxMWzerUnZ68+vYq9nSfdTlZS4ySZ2vQHyOj0fKO2vbKfmPWpstdA34+ClttTA6mxYtJE03pqh/Wv\nfxXm2QcPwpo13j6lsiwmIjR++QUY+hK0/8W97MEHPdpx/WSddqq1du768i7+PPuwX9/iXbHhNN9x\n36BKviQmwvr1ocuEwiyJe7e02t/JNidH1P/DD4aPabQpLPQ2BW9NNNfkrkHrwrilDVocw8f01CXU\nuY9EO1FRAUuXwqOPinydgTjvPI+mIJTG1ORjymuSzcR0nE9mQruQfdi6VZi+6ZPLB6K8PHzahkg4\ndsx74CAGlFVBNabZulygvikYYmKaVzDVTxaoqurKuaq6U0q4y6oKliDtD+lyld8yL1PeIB3/au9X\nvLUxSPoXVSW/w0QkIMNc3ao1pgDjTrufSrvEUytn+AXvuXbAtVzR13Nib/w/J/N+mcfhKn/hIhAT\nJkD/YSW8Ngi+GQXxzu2YVG/T2GvGlnLzerBbD3HVu5cEvDP1ir3f/jaYwLAsYB+W71+OU1GoslVx\nrMZbY5qSfgGZ8Qnk5nq0iGy6xr3e5DzMwbJ/kp+/lPz8pfzpT0spK1vGdddZ3WVkCWyK9tvfPzeS\niYkLBz7CqN53hS0XiOx0IalrpsDBTHmvGXi7+3mjPSv0JrNPPeURvPXoTXfz812BzibcBbed7l6u\nCZKJiXgFkvI1+z1w6CVquz7m14b72EdITY1IC1RSIv435D1/+DCkWsRDe5VLg6ynvBz3REWwCUmD\nxmGx+F8TTeFE5DHVUkAFu68MWgbDx9TAwOBXRviXzNy5nlQSeg1CMBQ1ePgSWfbXmJ7V/ZqQg9b8\nfIgdMp+Pqu5jxU9HgpYDIUw2NY1wQQHs3u0xuXOjKijOwCkzOnSA664TZs2+HDsGGzY0n2CqF05U\nFCQ1JuDxjzfHE+PzNipNuosqR+BXlP6caIORfu36cUP+De7lSRaReLGgvCBADSrZyTn0cmlzbfbI\ntIstiQJc11mYturpm9nXbVp7883w7HNCkvl237cR1auqMLjdKnomCwHu9LjN2OU2xMR4NItmUwy7\nqyHWBDN77PfL+XthNlhkkOU4fir6iaUHhJp+5Uro2tU/n7CudQBSYlPc5rTgPcDcVX6U9qYi1q0T\nJqgAlHehnUmY2ndNcDCqLdx++2yuu242/fvPZsuWK1AUj8muSWf+F+h+loPZBkaZ7uk9I2qvSxf/\nPKEg7lFtUkoTHlav9k73EizS9wcf4Grbe7mvduyPveCKDv8kq+d3Aes5w9+AwY/Zsz2/tYBVDaG2\nVlxPANZjT7C+4F3WF7zLlqPvktZhtfuYGHlMm4e0VhpDSHtPVdgl9lo9z6ftNaLDofKUG5w8GLe0\nQYtj+JieuoQ696oafrCoj8joO3M+b9lv+M/iFK9lIX1MafjU+5ShE3lozAxqO3/EntqfQ5Z1OAII\nlA2ka1fh/6VPISFSOspYgvjCaoO3QGZ1ZS557IsvmpbaIVS7nn6qoMpI4OdjGmuOxexzun875EUu\nOi+wTZlmtgsef9P2Se359yX/di+/c9idAHx/wD/tlqravLSkNXVBQom2ItIsKl0Sodg0OmiZ7Gyw\nxIhjVu+ILP+PqkLvRE+kqteL+mHNfJTY2PZBt3k4z4qk0zzGdpxHTq9PyM6+kR0lO5i/ZR6KIqIx\nFxRAUpJW8hzvik5/A4BKa6WXKbBeeLxs4P1Y1Ri+9ZWzXUXeqriOsSvg2WeX8sc/LmXOnKUkJIxE\nlj3XjkmC+FagWctr1x8QfrLBMKvljBvn0TTqBdOKCvEc6d1bHNd166Bffg1fL5dQVSeLF8OYMaH7\n4OtO4B0RGQ7UCYk3tW0BILS0evQmw6Ndl+Irr3iXGT/eP11QQ97znusFMmIUtm+fyvbtUzl25Foe\nnj3CLaAYgmn0iXYKnubwMXWoMk7ZIz13bBvmojdoEQwf0xagS5cufBPMfrAVIssyexszfRmClStX\n0qdPH/f/k+2YGLReItHg6Qdtfi/Tup/oHFfFfxen8cbSUUizJPaWBbj+3ZqUhj8Ou6R14d4R95Jg\n7Ra2rNPZdMFUY+lSkZ/wtdd0pn1B+h9ooDFjhvi+9FLPsqNHRWCT6dNh1Cj/z+jRImVEpPhFSnZN\nCgQ7r+fk/ZXZWyOvXyOYRjvWFNwerXf8UY4Ue5xtLSHKtjZqFP91K7SOAAAgAElEQVSLaPn+5e7f\n2uSLoips2CA07KFQFKh1esxel+9f7mfW7ktuPAwc+LX7/4geU+mVc7H3PST5ay+uvVb4lbpNyi+5\nyb1OBXdEXL3G1GyKIcNi47mXhvLcc6N48smxJMbVkOwSXmaMfBCHCqcNFOFZO3f2pEPRMIV5lsgn\nyHFt5QHhOz14UOD2ahwOcmIq+HZFPS+9JPz9fKPfOp1CcGvfHgYPhiprBbEyfL3UzPnn+z9j/vRQ\n6Oi2Dz8sUrxohyBe9vhlZ2TAZZd5l582zfNbSx9z883iOz9fPG+GDBF+sI0lKcvzoKlM+SNTLqhj\nygV19Om2GrOkugWUYNHYDRpPc0WPjwbaeTdJKqrqeUZddebbgGHK+2vBuKWbgCRJJyxoxrx58xgZ\napr1BOEr3I4cOZLt27e7/zfmmBg+pqcuoc59FUVht9cP2nwHKIrr8dYxrgK17ifPCp/LM94sNCtm\nc3SCHAUjGhpTX63wt9/C5MlgMStB/SQDCabaf70vkSR5ciHOmeP/keXgOQoD4edjioIW1sbXxxSg\nXXInlh6PvP7L+ogRszmACTZAzzY9GdV5FL/76HesK1znt96MlX21QuuXk3lu5A23MHabt6Bxbtdz\nWXVolQhahEcwnfGNmH24557Q9RUViXO/rLwHk1aJZVlJWX7lfNOoxMZm+5UBKLnXpeoLYFZ3443L\n+POfYezYwH3p1UakctG/Q/rljOeZfTlk9LmKf/97Dn37bmbqtVXuwXPXdBGJs9cI8R5at87bdHjK\nFDhTF8E2ECdKMB3ZWVxnwQbQ5/WdRowMTzwrHNaff9578s1kEsKY/r6SEYKkJcho7trbjvhpL/WY\nTNCzp+dZmm5xUu+EV18TAaSyfU7z2LHwhz+I3y+8IPz7ZRn+9S+PuTBAp06enKjQsPd8p94eM+y4\nGP+Tp9eYGj6m0SXabh3N4WOaZlHcpvlZvb/C5HoHmM0JIbY2ONEYPqYGJwy9yZWBQXORJAU3JdTQ\nX4paaohA2J019EoKvE5yDaClMFqiphINwVTzB3vhBfH92GPw4osgy2pQjW8gwdRXCxN3wZ/4rk0n\nuo14ksT0AwE1pu1Cx4Dyo7bWtx2XpiNI+evzr2fHXTsirv/dSe8CYJH9g9lodEgRTr1D5w71Wu5U\n4bQes3EgTIJH9n0w4nZbmri47l7/z+hwhpdw7mWubrJSWirORTBMJpAkhcE5Qyl25YSMMflH6aq1\n1zI1ghyVGfEZJFoSQ17r7jyZ30/3Wq6ZH5fUlriXmWQzUtxptOvUn40bR6Gq8Yw9z7ONLMn0b9ef\nq66Ed8Ul4Y4gCzB4sEqyBfKH7CKzhy4ktZ5mFkxjzbFMXQszf/MsANW2wLlWstPyqLBDl97C3vbj\nj72fcbGx/pYX7270Dw6k57HvHqN9+EepmxqnRI3TREI8pKSIY+lJO6Ty588v4bzrZrN9uwiiNHOm\nWHPTTdBNZziyvnA969Y7GTZMpHZpCOWl4r5t0+MzzsubEbCMYcrbPETblDeaqCpIsnhISbJ4RvXN\n9sxyxVsChAM3OOlopZffycOaNWvo168fGRkZ3HjjjVitwiRq7ty59OzZkzZt2nDJJZdQVOTR/vzw\nww8MHTqUtLQ0hg0bxqpVq9zr5s2bR/fu3UlJSaFbt24sWLCA7du3c9ttt7Fq1SqSk5PJcCUws1qt\nTJ8+nc6dO9O+fXtuv/126nVvgCeffJKcnBw6dOjAv7Vs3WE455xzeP311736o2lqR40aBcDAgQNJ\nTk7mvffeY9myZXTs2LGRR8/TpsGpSahzb5HiAi7/873C3HT6dJECQeP++73LdYsvd//unQyvDob0\nAFFxT5TZUmmpJzpwY9APULUULO5UDwRIg+Mi0OBNH5UT4ObLX+eh/IPcdu0MLrjxD43vpA6bTXw0\nRB8hwezvYwpC86lpzCJBlmQGZw8OaaGxYNOCgMuPWmNIiM2kQ4c/UmQKor5rZZw9sp5VNf25YniQ\nSMMu9Obqdz3zLbW1riitQZAkkGTFKwVPoDQ7N+TfwKWn/R4Qgn0oauw1ZGT5SMOJR9mTssf9t18/\nwOZtpVDjEthK64JHMgs0aN50bBPzN853B+bZvVtE4xb+1wpOFVITu3JahwsD1tncj4CkmCT2TXeS\nES/e3W9tCn0On/9JRMXdv1/cv9qzraBA+JdXVHjKfrljPkd8BL+dO4GDIhfwu1vexRkX2BRh19Hv\ncOrO9f7idVgCmGBrFg5Pf/EpffiUpMqH6N07eP///t3fGTJ3CGQLSxWbrWHv+QmTRG6h/h0mIgeR\nkiQJnnnG+xlj0HSibcobbR/T+Hau2TG7fxqhOEuQ2WeDFuGU9jFdtkyKyqehqKrKggUL+Oqrr9iz\nZw87d+5kzpw5fPvtt8ycOZP33nuPoqIiOnfuzNVXXw1AaWkpEyZMYNq0aZSWlvLHP/6RCRMmUFZW\nRk1NDX/4wx9YvHgxlZWVrFq1ivz8fPr06cOrr77KmWeeSVVVFaWu8KP33Xcfu3fvZsOGDezevZvD\nhw8z2xUOb/HixTz99NN8/fXX7Ny5k6+//jrofugJZYq7YsUKADZu3EhVVRWTJk1q8DEzMIicwNdh\n20zhX9UQLYDGkwPwszCUAgzEmgO7vWnpYvQCZoorppN7zKaG1pjqb+nnnvPkMwShgemd6uBoXQzf\nb8tHlpsgPeswmUSeVH1HVCSWhA5eHDGyJLPuFn8T3chQkSUz4wc8zOSRX0WnQ82M2RTLjAkbww6+\nEiwJdErtxJltU0nImQuEzkso1jmRdRYDWm5SPSM6jeD58SKPZzifTYAapYQNG6CXa66h84XvMX3x\n/7GndBcggnF16gz3jbiPlFhxQVsdYmI3WP5Z8J6g0fPED0+4f9fVeXKp/t/Nqqu/wYc7J8IlR5Zk\nt2Y7VBA2pwrjkz7jm2/68fLLp/HWW+u9hPHbbwddWAckoNjqXUdWFlhiPSf9/hc2snGjd5lVe+Zx\neNtI3lo5wb1s3+ahJJgDGWELrWe/XnHEuS6Ttfvm8/nPfwlQEu775j4A/vFPp9ezJlJ2KIVhy2in\n7KWXGl6/QQgStlEozWzpXgREVSEm6RBHrBa6+0y2VDsk4g3B9FdBlEJxtCznnNMypqWSJHHXXXeR\nm5sLwP3338/dd99NUVERN910E/kutcDjjz9Oeno6+/fvZ8WKFfTu3ZspU6YAcPXVV/PCCy/w6aef\nMmnSJGRZZtOmTXTo0IGsrCyyXM4hvuazqqoyd+5cNm7cSJortveMGTOYMmUKjz32GO+++y433ngj\neXl5AMyaNYu33377hByXhrJs2TJDa3qKEurcB/PD+stfPALZn//csPZy4/ELEhQVwVQVg+GqKiH0\nxQVQ9jqdjROmNTTBVO9X5RlPq8F9TPHWMo0cCdXVcMND/8eC78u5ZsT7bNjVj/btM1DVBjh5RtBf\n/Xhf05h2SGnPL78c4UTc8sumLuOc/4iG1h5eixZQ1KHYqbGHsG89iSmvL+dAxQEeGwa58R/xRJjy\nigIKDkySiRcueIHfL/49eW3zmtwPk2zCbBZC1M6dMGbSh/Q/DD9uf5DuZy0kNhZuuhEcioVKq4jg\nNShnMFf2u5KrT7vaq65qWzWLdy8Gzg+oMb1l0C289tNrXv6QW7aI79hYLehWcOHTdILSxWiECtJS\nlPwQaw8t48ULXuKXX35Pu3YHkKTB7NwphPyKCu/ny8CsgeDwTqKckgL2dp7oQ/tt6/j0wIu89M5S\nsky7gHbsPfIFuUBn9SscThsVdR6rrkAR0WNjhXDd1uWXvnnH3XSNKwP+HnRf7vrlTBIzDwG5DXrP\nFx3IZ1niL74xnAOiN9s2aDpZA37PsNyveW1tA303grBj/W56D+4Rttzu4lpSemQCtwQtc6h2OdOv\nvzrguonnBZ/sMWgZGju2/1UIpi2J3oy1U6dOFBYWUlhYyKBBg9zLExMTadOmDYcPH6aoqIhOnTp5\n1dG5c2cKCwtJSEjgnXfe4amnnuKmm25ixIgRPP300/QOYDNz/PhxamtrGTx4sHuZqqooLqeuoqIi\nhg71+FX5tmlg0PqJ/mBxq7Ufk8/8r3cr5gY6QAWgOHkpfzu4mCfvuBjVmkTFvP+QlOD9eG2qj6le\nY+ovmCrBo/L6mPJKkvAZVZTXwZVuJyXFSXq6ibIIcsFGiq+vkoqCqkrEye5mmx29L9+wfw1j05U3\ncbhsAzlx0CGlaS4IrQmH4sDqtBJvjmPlgZUApMfGAtbQGyImTI5bD2AxxXD3oLu5+4y7w24TjjuH\n3onVKbS1EyYIk9QtR5bSH/hsx4ccUv5OSt37ZNoKKE6Y6t4uxhTDO1e841dfdlI2z61+jsLCZ9m/\nH3yV7g+OfpDXfnqNSnsJuKYfNP9mFWdY0+Pk5o171iBKbDL/2bqcZyfmUFMjNMmSJAIUafzvf57f\nfTL7EF+9BjjmXra8QERp/s+l/2Hqx1O575v7yIqFW4ZDz7yfeWfJReTGeu5Cq72aVT92ISnM82nD\nxnEMSRe/Y6gD4PtdrzGiZ3BhQk0sBHLD77iObHtnuqRWhS8IJBjxbqKK1SFU3F2r741KfZV1Cl2r\nwxtn9upghysglGBq0oks5XaZNIshjP4aMQTTJnJAl1DwwIED5OTkkJOTw/79+93La2pqKCkpoUOH\nDn7rAPbv38/48eMBGDduHOPGjcNqtXL//fdz8803s2LFCr/Z3szMTOLj49m6dSvZvmHzgOzsbL++\nRUJiYiI1upCGR45Eye4uBIa29NQl5Llvgnmd5jdV6xA+jQBFdXDruDWYTN4jGRkZaOILziwEgA/u\nv5FL519FZd0rJCV4j3aPHfPPIdgQQgumwU15V66EgbpxoVbHMauZdrHiOEmZW3DGm0OafDYU/+iO\nIv25hD2gj2lzUFBe4PW/+OjrxALIkJkcwknuJGRnyU4GZg2gf7v+jOs+joSYLeA4THafZezefU7w\n7br9gb4StE2MTENyPP562tbNC1nm233fMqSLjT2lu7m1qB9v//dNslzxjPKxMXfnfVzQHjomwkbb\njwxKg7QQZu7bi0XE3ZQ2NezfeIB7lt/Jn/p1da/X/GMzn8xk8DBxL2paRafiCGr+qxFjboS9aRMI\npb3905l/4qFlD/Hmhjfdy3SB7wHvYFY2awFJrsi8BcVr6frSMPe6yadNZurHU722PVa1laxY76mh\nnw684yWU2nWpODQ3p/3KMLdQCpAbJyb0jh58BHreQkXdEd77bhy9ut7HRdnwWRGMy4K8eyficB5s\n4HveiYPIUjg1xlTYIDhZGbuptEtcPDY6zrtjfxNZuX98OAe7/bmQWnK7Uun+Xax2o6y+OCKtukHL\ncEr7mLYUqqry0ksvcfjwYUpLS3n00Ue5+uqrmTx5Mm+88QYbNmzAarUyc+ZMhg8fTqdOnRg/fjw7\nd+5k4cKFOBwO3nnnHbZv387EiRM5duwYn3zyCTU1NVgsFhITEzG5RqBZWVkcOnQIu128UGRZ5uab\nb2batGkcPy7M7w4fPsxXXwl/qSuvvJJ58+axbds2amtrmTVrVkT7lJ+fz4cffkhdXR27d+/2CoSk\n9WPPnj1BtjYwiB6R5CSLTSqm+9B3kdtu5Y9L/sj6wvUAOBU7ThXK7Z4BlmzJ8hNKAUxRegrGlQ/g\nkj6XgBJ4pBQT4+SsKXms3vsma/fNp8w0HzVmX8T1+wmmUy7ksqVtxcDRWUEwDbMkOxh48ensK14N\nCJNjgHpVDPzq7dUkmeo5VBHdHMd+prwh/Oqai1uH3Mq6m9dxQY8L/NZZfkURHCf0nMDPRSL3o12x\nY5bNtM8UQZ0SUo8yezYsWhTYP/NwR1eI5wgtFCad8UZY95kZZ8+gSyK8t+ZGMmJg/ebrvNaPbgsd\nXbdiX/P3vHDWcIZ1GE18fM8AtUFanHBX2XB0A07FSddEcDg8ZudxZo9ta9W1/QGPFlRVnWGnnVJi\nT5xv2gsXvMA/L/xn0PWJMSIy2es/v86Y8WIgrg92BJ7nwBs/v0EP5X1URQiJK9ed41XOLJsZ0XGE\n17Jj+z2RkHdbuwDgLLzDvaygPhUZB1ZHJS9+2cG9vLO8hgo72H0OZplNQVVVlu6YSw/LJpRDU/hj\nL1gxpg0z+sAlPY9RbT1GQzDJTmxqYtD1vXuDFjbDEEyjy6jOu0ixtM7MC4rqsYD5v3G7uOGCshbs\njUFzYQimTUCSJKZMmcK4cePo3r07PXv25IEHHmDMmDE88sgjXH755eTk5LBv3z63f2ebNm34/PPP\nefrpp8nMzOSpp57i888/JyMjA0VRePbZZ8nNzaVNmzasXLmSl18WoeDHjBlDv379aN++Pe1ceRv+\n/ve/06NHD4YPH05qaipjx45lpyvR4AUXXMC0adM499xz6dWrF2PGjIkowMM999xDTEwMWVlZ3HDD\nDfzud7/z2u7hhx9m6tSppKen8/7770cll6uRx/TUJfS5D39dXXzbTfzriavIv+7/ePbHZ/ly15cA\nOBQbigrgUQFmmYMlmj8xL+FSxwbO7bqN/btuYseOm+nRcSrtBl5MQQFBP/p8jHv2wLnX/44r750I\ncj202cnzI0W0bRN1SAROd3PplIc5u+MvFO0cDijsq9jC7e/0pK1FVP75z9Npm9iesb2uAZqe0kZD\nb8p79CgUl6ioqsS4M75lx5ETk57FLJsZnDPYzzy0JOGmE9L+ieL2IbcDsPX4Fi5aeBFf7vrSHZfg\nUlcapQsv9OSp1ZNUMYyOCT1IiU2LWn8uz7scgDFtSnjpdLjQZdSjRXf9WienvHUogxFDV/0/e/ce\nH0V1/g/88+wmIUBCwq1BFBJUEChRVMQLWIIXRIsVWq3a2oKt1X6tF6xa+/XXiqi1Wu3X2FZrW2tR\nq6WtVqpovVQJFm8Va6wX8EqCiiAICSQQyOX5/XFmNrO7s7dks7vJ+bxfr33B3M6cOc/MZM+ec2Zw\n2CE1GDhwgm965x9mKk6z/zgb/20E5pcDQ4JNGDDAPAWotLAUJf08PzRIB2TiFXj8lbOxbdenCVtM\n89L161QSLjz8Qpz2+cQPDnz909dR32h6V/WLaDz8whc/xj2rTsPGrc8BAIbmmybUUYU7sWIGsPRw\nYMkU4OWXJ+JnEzdhyRTg584rX/oFOmuWEhwUtd8FsxsworAd7Ru+h8oBH4ctK8kHdg/+AXa0dd6b\n9yvcgCffvg+lDeHX9OFT38SrOzpfoJrK3/lA3m5ojB4gQPgQAVZMc1t632NqWnE/bIk+byn3dDX2\n7MrbDevWmdaOK66IfjLdeeedh/POO893u2nTpmH16uinSY4YMSJmIPPz87F8+fKwef369cNPfvIT\n/OQnP/Hd5oorrgjL29lnn+27ntfQoUPxxBNPhM1btGhR6P9+x+XtJuyWCVH3Ja6YFvQzzX8/n/MC\n3mgERE2ft/b2VgQE2Bw4DCNhHi9fv3u4bxrpG8nqn1Jzs3n5/MPPrsNBFwJfPcH0erjklq8jEPhP\nzIcANTcDs2YB991npn//e+DS0+7Dng5g8+4XMWLUTkzadwg+WQOYrrz++9+wsRyYUAcA+PU/JmGv\nfmtweuf3RQzb+Rts1OEIBsy333Q9oNTtyltWBnzyCfDw3ztw3neBfQYfiANGpHEwaxIG9RuEgfkD\ncc7qZlw89TxcOPWOjO6/pykUdY312HvQyLC5ADBs0lXAgGOAncNRVxf+RFcAaNo8GBUDRqT1ybTe\np/q+tR0Y4TRo7mkH1u8agGtOeQXPvjAB44qBs6dcmjC9r1V+DV//29exY88O3OhUrgWC75x8Ymid\nnx3/M5y3/Dy889k7KD9yDwahDoU73sW9Ty/BkUNjJOzIZMU0GRdNvQi/+PcvkNf2Ps67/F4cNGYe\nADMUQBV46u1bMKTpAbS27wf396iG4otQusO0ft/xAfBBM/DW+X8DAJx5u3mY1d3hr/PF3mXz8J8P\nd+GQAeZJyQX7/C5h3oKBPHzUtheK8zZgHWZgDFai38ZvhK1Th2NQVVCGS07eiEf/KXj8cWBcRfLH\nr+XP4KUNwPdiLD/nqiHIk3bcdN1fkJ9/QvIJU0LPf1iOvH6H5GQX2Z27dmNt+wB8d15j4pWp18qt\nuzFZiWNM7RUv9sl8URbpbBGdVAK0bn8U21u2o2btTQgKcPEJ/4aW/RwAcPox/l1Ve/p5nKqvYp9x\nR+GAqTdi3Y7OLoftTsNFrNbSG24A7r8fOPlk8/nFLxRtHUBTG3DJazOxsfkTNLSYP9BjChtijjFt\n2dXZTXF84ZqwZe/vMq1kW3duRn3jRxgwYAeOPmQFlr1yUdSnpPJi7Or4KOnjdrvyTpwIbNsGjBmj\ngPO0z2xc82MGj8H7zcCFX+hblVIAWLfN/CDovm5laP+hcCumRxS/gyvueBAAcOKJwAknAKeearar\nqwMQaMXO5gDSfSUMHPtvXPAqsM++d+CMF4GC0ffjW+fVYd7MeowfNh5fO2EHvvICcPR4/1eORHrk\nzEcAAFdMc95xWVYZtvzcQ8/FE2eZH1Xrt9eFXmmTqFIKAIEMP5U3kZ+fYO5Zw/sB+wx6CO/vWYj1\nn/0HRUVAe34DhjSZ5fsVmGE1W/cE0dj0Zmj7G2bdgfrLFQMHTsDAgROwduFOvHZhZ7fHz/YEUVWl\nmF15NYqC5h7a1gEctf85AIB6OS5m3mZVLsb0A5fgswHfxpSx/+u7zvEH3Ro2/e67qV3z+w0EDhji\nPwZcRHHIiG3ol9eGsopXfZ/STF3XoYIg0vNEXiDd7zHdEfobQrmPY0wpaZ///OdRXFwc9fnTn/6U\n7awReST+AxQIhj+tZ/KgRvznxRK0tm3He7vLAQAzJ3wfVVUa8/2PeYE0deWNk93hA3ejavLLaG9L\nvt/ZJqfn8bnnmg/E1GSHFABXjgcuHQvsbu18GETjdv93eu5b8RYAoGTMA1HLNM+0sH1hOBBs24Cp\n+7+P8UObsWXLsqjP8VNuw6f4edL5b+1owdlnC+580rzIMtivAYMH7Ep6+3Tb1Zq9ffe086aYXixr\nt6zBrP1mYe0FawEoNufNwp4O4LADakPrPvkk8OCDwOzZ5l2iCLZi5AhBuiumh+19GN64RHHulPOw\n8YeKo/Y9E8OKylFcOAwAUFRQhM/+V0MPLkpkzrg5AICDRxyMys9V4sT9T4xa56Ay01+1NB8oTuJS\nc6/8YI7VbvICedj9o91Y6QyjLd1xK6559Cjc+9q9WP3BPVHrt5d8E0ce8CMAwIbdhZiyb3hX9f75\n/VFaWIr+o5cAAL4yq/M9sYcecD0+DJ6M447pvA8OGXRw6P9HTNuBd3ebB01t2p2PYCAPE0Yej69M\nvRMDCoZE5aWqSrH34Elh8/6wBJg/v/OzZk3UZiGqwLRhwOSB7/guFwAdCuxo8XknF3VbQBSIMSwk\n2zq0I23PhKDcxa68FnrzzTcTr5RBfI+pvbryHlOvgPg/1mRI8x0YktxDHUPv5es2zy+5bW3AlVea\nbpOjR5unA4/sD5TmN8VJwN/JJ5t/S4a0hUrk8yUB7DuwA9rSWeFAh3/anxtsWkomjDwBLzo97de3\nT8To4FuYPPYqNNWZ98IFsAcBp5J+zgnRT/G+8b7RkEDyDzB6q+URjAUANePfDp16B/YuMl+Is3HN\nnzT2JPzy37/M6D4zpSBYgOnDBGOLduHMV/6LYQOG4VNVlA0+DM+89SRk6x8w7ubfAG2FQMsgoOhT\nrLv3cVx55VCUnrYW/ftPR8/3Heh+3F865yVM3XsqTp90uu/ysqIy3HT8TajcY15wvP+Eh/GP//4Q\nY/Pf8l3ffaVMMveaTCsIFuCeevOgKADY3b4bP3jqUvzs8KMQ+aKjgBRg3IgqbNt5N86oOAuBGBXt\nw/edD+wb/pTeQytOx6EV4eVZOeo0vLX1Joyd9C8U5hfhOyd8gI6Ojqh09xt+GPar0tCTewtHLYna\n58A84OqffIKP1r6HCROqcMcd5geRCf7DieE8yxHb8mf4Lg+IqZgOHGhaT1+tfxB72pt9192wBSge\nORbAkf47oygiHRBJX8U0nfd6RRuaWjmouLfge0yJqE8JBJOomAbS+H6TbuoIdLbIbd7Shp8+fw3G\nvnEgfvv9znUG5HW2SuzcCZSk8AyHQP6u0K/F7zT1x8fNzZg1cFnnCuLfgvDeh2MwqHQbjnDzMGoJ\nDhDB6+9ehXMqTsdzrY1o/fg8DBgwDsNank8+QwkMDJjWlOY9O7Ft5ydY8/ZstLU+HnNMbU+7dfat\nqJ5dnZ2dZ0B+3iAAjVhUOQx1ddeiufl1jN1nFjYNAob324MR/QCgxfkAuHY2ilvKsKZjM9raW9M6\nxrSnTN17asJ1LjvqMtTUXI73WitRVXYyBg78M7AnVsXUXI+5euxvf78Vq541X8S/PQb49pjNQPvf\nAQDtCgQFWN8yEEeMNz8uHb7vN2OmlYqKYYeh4tjwniSxKrsA0DL0arS370bVfvOjljW1CQaMvgiz\nK36Kqqrwd7D62bABeHXjIEw95Cu+y0UUHTAPhDrr7LVoXHclPmzxf5lpQNvxPxcDoXOeEhJoWium\n6dShbehQNpn2dayYUtaxtdRe8WKfn5dMxbQDm1qAskJgXctgjCnchh2DLkPx9pvTmMvk7FW4b+j/\ndz77KDBzETY0mC/S7rtUH9tUHHqoREnpBzi5ci3ueTz8faeuskkduPvhNvzpOfNY1S9d82hoWWH/\n8fjpf17BSUfdibYPjkZ9SzGOOfQu33QCAQU8408DwXxMqfha6EvstLHnomHvkzCocC88++ySLhx5\nDM4TFA8q3oZXXtob2vEdtLaZgsjGNS8iOdkyli6nH7MeLz1fglH9tkN1D4YNOwWDB8/ElENexFfu\nPwKvNkRvUzV8Ey4ZCwwONiATLaaZivshh2/C0f3MDyPjRp6C9e8s9V2vrT3zrzBKRV4gD6/t3B8H\nDXgvbP72VsHm9uHYr/BTfHN26r0w0m125aKYy7YWzMa+Hel69XgAACAASURBVP/A+paTcc/jwBdO\nBT7dcSuAb/mu//e/A4PivGI4IM5rjwQo7m/uid+Y7d9i+ugLy/HZFv8KLvnr6OiAdqSvYpreMaZt\nUI4x7TW6GvteUzHN1V80iahnBHx+tQ0IsOTJEVDnS/TXp21GszNcasqke/Dmh/ejX8fOqO3ieVfm\n4cj9zupWXm8/5k+YMrpzXFVrexsCzSOj1rvk2IdC/y8t3ggA2G8//3Gbj9f8DMeOfR/v7TQDsk4a\n1Xlcg0umAngFAafC2a9kDvYdHt1dTRU4+eBabG4diLxAAQCguSX6nYKlAzrfV7ijNT332qFFqwAA\nje2DURLk++Z6Wv+CQdj/wFrsVToBQSfWADCmcBRuOvmfOO7ezgfaPPPNZ3DFP6/A2q0v45lPgfPL\nD0RpqX/Xyd5oUP/Oh7dMqTgNUyr8X8/S3pHbFVMAuPikd3Hnkwdg/4LOMZcN/WZjQF4x1m9/LIs5\nS86XptyJNz9ejv2c6dW1V6I1+G/EqphWVCjKR2xHyx7/e0ZBwHyam3egclBrz2TaYgJFcXFutpju\nbN+Kgrw0PROCclavqJhqopeQUa/GMab2ihf7fsH+vvNH7HUeAuLcurYuxsbmATjs4LtQuc8cVO4z\nBw+8ZJ4suf9BryWVh+/M+FvK+Y70P0efETbd1tEO0Tw09zddCB/8CPjjemBb1bGdK4mpUU8be65v\nmn9/6i8A3sc5x78OAPjBH/vhpH324IP2QzFv4pXAY79Gv2A/7AFQkFfimwYAFAaBUcFm5AULMGS/\nv2PiyNlxj6WpIz1jeHbuGYKW9gAGjfwR1tVfG7aM13zP2GfIQb7zj933WOii8L+jL53zEk5Zegre\n1XaMG3dbJrKXc3F3u/LmunNm+byAtpcoHTAS08aeG4p9zYs3IC8Yu9xfWvsqysuBocXjfZcXBIBd\nbZJUpbS9zfw4941vJFw1SiAAXHMNUF6e+ra9mQQU+Xm5OcZUpANQjjHtLXrFGFMRmQ2gGuaRX3eq\n6o2Z3D/lptra2pz6skKZEy/2fr0kqqrCv9DU1CyGADhwdOfDOwYXHwDsMu/LzIqCJnzwWT0GYwz2\nGnwA7lv/FP67cSKuPPySsNU0GH/ckwbCW34H5JuK7LeONU/fXf2d1Thkr0Ow8h2gqP8+UduHGWHG\nVh446ktxVxv9+ecxLt9/4GtHB/DUE8DPLvTfdsgQ4B3PgzRbOwT12wdh3EhgUGAHRpW/gOIBpssd\nr/nsExE8fObDGf3hN9fi3q6532LaV7ixN71dYpf7p0HzI9ZBo0+NWtawzbxK4sMdhRi69zcwdOdv\n8X7b5Jjv3GxyejnPmpV6fm++GXjzTfsqpsOKGtL6+qRkr/lPNppnLhQVAcccA/zoR37rtGOfvdL1\ntELqaV2932esYipmNPWvABwH4GMAL4vIw6oa58HhZIOGBp/BT2SFeLHvkDFJpbF1V3hF6tiJl6N9\n/CUx1s6MfwWuRZ6OwebLP4AsFpxe/D+4/NhzwtYxj+WPrV3CK65VZeFf5g4deSgAoONzN+OYA/xr\ni/3yzMOhDi73f4ppJL/uwK4OBYaN+ADLVz3tu/yLs4YCmByabst7DfuWNGCv0oOwoT4fJ054PbSM\n13zuyOQwmVyL++DBrJhmiht78ztI7HtfaaF5T5bfednWDhQA0A7B3Cm3oWbt/vj2xMtjptXa1o7y\nQXtQN2hmyvk95lsB7Gj7FYAYjw/uo/rntaNfwYi0pZfsNd/cZCqmY8cCjzwCbNwYvc6gSW0YPIgP\nP+otunq/z2SL6VQA76lqHQCIyFIApwBgxZSIohQOHJ3Ueu3t0V17goHsjlKQncOhTqvAESVfxoUn\nHx21TmG/RF+Ko5f7PavlmImXxkxhdLF5AFFJ/+5/0Sgu3IHzT1qO9fWP+y5fuqQNRxzhPuUUmHfR\n08gPAOP3Ohbj99qFex4vwuhC/4eUEGVDQR67BWaaqmDPbsXWrf7Ljz74Bfxn+76+raD779+O9W8B\nBwzdiWAgD8fGqZQCQFPre84+U/8BYsr+z+HTlj8DuDrlbXuzPR2Cz/XPfDNxVRXw8SdtkIFbEChq\nw+MrC6LW+Z/rcuuHLeoZmfz2tjeADz3THwE4PIP7pxxVV1eX7SxQlsSK/W/WFeIHs5L8lTuQW18u\ndZFi6/Zd2NxoKmEvLHzQd70hnzsG7zWvjNkNTST6VTjtKfa6jPUaha4YP8x8KfjqCf5ju2pqBPN/\ndD4A4LnngG0N/bGqbWLo+PKLT8J7zbWoAq95W+Va3EsLS7HxUp+mGUo7N/ZT93kfhcH3cedjD0et\nI6I4bG9g0j7+o7x2tm5KaZ+tbY0AgLNnrkwtswB+vnQYNjf/BTfcH/2wuL5s/yFtKMzzf75DVyR7\nza9reguDgo044/vD8YMBwLfui/7btfD41B5sSNnV1fu9ZGp8iYh8BcBsVf2OM30WgMNV9ULPOr3j\nSQRERERERETUJerz/p9Mtph+DGCUZ3oUTKtpiF8GiYiIiIiIqG/L5Cji1QDGikiFiBQAOB1AdF8O\nIiIiIiIiskrGWkxVtU1ELgDwBMzrYn7PJ/ISERERERFRxsaYEhEREREREfnhC4GIiIiIiIgoq1gx\nJSIiIiIioqxixZSIiIiIiIiyihVTIiIiIiIiyipWTImIiIiIiCirWDElIiIiIiKirGLFlIiIiIiI\niLKKFVMiiiIiS0Tk2iTXrRORnSJyd0/nK91EpENEmpI9Vs92C0TkXz2Vr1wkIhVOeVn7dyPbZRDv\nuhSR90Vkt4jc60yPc87tNhH5dgbzOFpEdoiIxFh+tZvHXCEiJ4jIQ9nOByUvxb9Rce/XIvKYiHwj\nybQeEJHZyeaTiFJj7RcMor7E+QK6w/l0OBVFd/rMLiSpzifZdeeo6nwnL8NF5E8i8rGINIjIKhGZ\nGpHfr4lIvZPvh0RksGfZEucLtpv/7d4vuZ7KpLv8t55l80VktYg0isiHInKjiAQT5P9AVf2xs32F\niKxL8rhzklN+85Nct0ZEZnimx4nIX0VksxO710Tkkt5aGfX7QurMe11EmkXkExG5XURKnGV3eM6r\n3SKyxzP9KJK/JnpK6LoUkSoRWRFaoLofgOs90++oahGAfyFGviOuta0i8rSIfL5bGVRdr6rFqhqr\nrLJdhn5+AuCn2c5EquJVuJxr+9vOvdY9h3c69093us3z/6aIZdtFZJSbjk/6FRHru5/Tev7IAaT2\nNyp+QqonqWqyP5bcCOC6dOyXiKL1yi8bRBROVYucL4PFAOphKorFzudPXUzWt8UjCUUAXgJwCIDB\nAO4G8KiIDAQA54vvHQC+DqAMwE4At3u2VwA3evI/yOdLbqVn+bme+f0BXAxgKIDDARwL4LIuHkda\niCODu0zly5q3orMfTNzqAUxS1VIApwE4FCamKUniB4GME5FLAdwA4FIAgwAcAaAcwFMikq+q3/Vc\nR9cDWOo5z76IFK+JLJRBqudZ6FoDMBLAegB/SHuucpiIHAZgkKr+u4vb59x57lAAqqr3e87pEwF8\n7Dmn8zzL3B8kSjz33Q+RuAJY4kmvWFX/mmpGRSQv1W3cTbu4XZep6ssABonIoZneN5ENWDEl6sNE\nZKqIvCAi20Rkg4j8UkTyPctvEZFNTgvjf0Vkok8axSKyQkSqk9mnqq5T1WpV3aTG7wAUABjnrPJ1\nAA+r6ipVbQbwYwBfdiuu7m4T7Mb33qWqd6jqc6rapqobANwHYFoy+fYmE8qEaTH4m4h8KiJbROSX\n3hVF5CanpekD8XTvcloZrhOR5wA0AxgjIkeJyMtOS+S/ReTIiPWvFZHnnFaHh0VkmIjc58Tm3yJS\n7ll/vIg8JSKfichan1aKrrQkLAawSlUvU9VNQKjV7SxV3e5Z7ywxrd2bReRKT56uFtPN7V4RaQQw\nX0RGOsfymYi8KyLnRKz/V2f97c75N1ZE/tc5J+tF5HjP+iUi8nvnPP7IKa+k/4aJyCAAVwO4QFWf\nVNV2Va0H8FUAFQDOitwEsc/DVMogZr5FZD8RecY5tzaLyB/Fab11lh8sIv9xymcpgMKIPKWt9VFV\nWwD8FZ0VFDjxe9A5/z8QkQs9y6ZKZ++EjSLyc2d+WHdnERkjIiudY3gSwDDvfkXkCBF53rlH1Up4\nC36NiFwjptfFdhF5QkSGepZP92y7XkyPicOc/Hh7WXxZRGpjHPqJAGoi8jRLRN52rtXbnPx/21m2\nwLlO/09EtgBYJCIFInKzc05sFJFfi0ihJ705zrFtc7at9CyrE5FLxfROaBCRpSLSL4mQdUW8+2pG\nK3nOOXK+iLwL4G1nXrxy8rsWUtyl/NIp4zUicoxnQahVWESCIvJz53r8QEQukOju+zUAvtjVYyei\n2FgxJerb2tDZgngkTAvi+YAZVwXgaABjVbUEpnVsq2dbdb4EPg3gX6q6sCsZEJHJMBXT95xZEwG8\nFtqJ6gcAdqOz4goA54upzKwWkS/7JPusmG6YD4qnwuZjBoA3PHm5TURui7Wyqtap6r7OukEAywGs\ng2lV2xuAt/X5cABrYcr2ZwB+H5HcWQDOgWltbAbwKIBqAEMA/B9MK/Jgz/qnO9vsDWA/AC84aQ4B\nsAbAIidfAwE8BeCPAIYDOAPA7SIywTmGs1X1njhl4j3emar6rDN5LIAHkthsGkysjgVwlYgc4Fn2\nJQB/dc6n+wEshWmF2wvAqQCuF5GZnvXnALgHpmX9Vee4ANN6dy2A33jWXQJgD0zZHAxgFkz5Juso\nmC+zf/POdH4ceQzA8X4bxZBKGSTK909gymcCgFEwlWeISAGAZTA9DgbDVBq/Aqcyqqo1qnoMuk+c\n/Q0EcCZMqzmcL+KPwMRlpHOsC0VklrPdrQBucY5zXwB/iZH+/QBehrlOrgUwH52t9HvDXGPXqOpg\nmN4ND3orn06eFgD4HMx95DJn23KYuN0KU9mdDOBVp0XrMwAneNL4Bkw5+pkEp2LkpDsMpqyvgLn2\n3oa5d3p/BJgK4H0nT9fDdO/cH8BBzr97A7jKSe9gmOv4O056vwHwsHT+QKgw994TAIwBcKBzvG5+\ntonIUTHyngu6U6E9BcBhACbGK6dE1wKQVDkdDvM3aCjMvfRvIlLqLPO2Cn8HwGyYWB4CYC6ifwBa\n4ywnojRjxZSoD1PV/6jqv1W1w2kd+i1MZQ0AWgEUA5ggIgFVfVtVN3o23xvml+E/q+pVXdm/mFaq\newFcrao7nNlFABojVt3u5AUAfgHz5W44TGvqkogvHF+AqSiOB7ABwHLx6U4nIt+C+WJxsztPVb+n\nqt9LMvtTYSoMl6vqLlXdrarPe5bXq+rvnW7G9wDYS0Q+5+4KwBJVXaOqHTCVkbdV9T4nFkthKrVf\n8qz/B6e1eTuAfwB4R1WfUdV2mC9iBzvrzgGwTlXvdtKqhalsdXds11AAnySx3mKnLP4L8wOD9wva\n86r6sPP/4TCVwStUdY+qvgbgTgDf9Kz/rKo+5RzjA04ebnCm/wygQkQGiUgZTMvWJU4sNsNU8s9I\n4fiGAdjixCPSRkS05CWQbBmUxMu3qr6vqk+raquqbgFwCzqvzyMA5KnqrU7r7oMwFbx0EgCXicg2\nmGvwKJgWZMBUGIap6nVOD4R1MPFzy3wPgLEiMkxVd6rqS1GJi4wGMAXAj51j/BdMZdd1FoDHVPVx\nAFDVfwJYjc7WKPe6eM9p0f0LTAUUAL4G4ClV/bNTPludeADmejzLycMQmOvv/hhlUApgh2f6JABv\nqOoy5/r6Bcz54bVBVW9zzqXdMJWZ76tqg6o2wYxXdcvpXAC/UdWXnR4k9zjbHOFJ7xequlFVtznl\n4x4jVHVwxH0n12xxKoXu54DEm4T81Cmz3YhdTkciiWshiXL61LP9X2B+cJjjs95XAVSr6gZVbYCJ\nZWTluwnmvCGiNOtqv34i6gVEZBxM69yhAAbAXPOrAUBVnxGRXwG4DUC5iPwNwGVOBVJgvhzuQHir\nVSr77g/zJet5Vb3Rs6gJ5gu7V4mzL6jqq575/xCR+wB8GcDzzvJVzrJGEbkYppI7HsCbnn3PhWnJ\nOFZVva3AqRgFU/n0q8gAni+rqrrT6TlYBOBTZ/aHnnXd8Xte9c581ybP/1s86bjT7jjPcgCHO5UJ\nVx7Ml/Hu+CwiP7F4v6TvRPj40488/x8JYKvTIulaD1NRcXmPcRdMxVE903DS3wdAPoBPPD00A4gu\n03i2ABjm/AgTGdO9AGxOIa1ky6AccfLtVLhvBTAd5oeZADp7LYwE8HHEfuuR3i6XCuAmVb1KREYB\neALmh4P/c/I+MuI8CwJwW9i/DeAaAGvEPDBssao+GpH+SADbVHWXZ149zLUFZx+nicjJnuV5AJ7x\nTHvLehc6y3oUgA9iHNd9AN4UkQEwFY1n1eme7mMbzHhjb54/ilgnctp7bQ+Hube+4u09jM4f/ssB\nfFM83aBhzgnvtRZ5jMlch7liaJx7ZCLecoxVTnvBlGd3rwW/7ffyWW+viHxFxh4w12pDCvsmoiSx\nxZSob/s1gLcA7O90uft/8Fz3qvpLVZ0C0712HIDL3UUAfgfzRfUx5wte0pwxUssArFfV8yIWvwlP\nC5OYh+4UAHgnlX24m0f8CzFjPX8L8wCoN323Ss6HAEb7tcYmydv962OYL15e5Yj+suS3baT1AFY6\nLQTupziFluBY/gnTPa47vPneAGCIiHgrbaPh/0UvkQ9hWk+Geo65RFUrE23o8YKTRtgxOvmbDdNl\n3aur4ze92yXK9/UA2mEeNlUC0+XUvT4/gem14FXejXzFIgCg5kE3FwH4sdPT4UOYlnnveTZIVec4\n67+nql9T1eEwXVkfcH6M8voEwOCI+4f3GNYDuNfnXP5ZEvleD9M9OoqqfgTgRZgftM6C6bURy38R\nPoxgA8wPIQDMwETvtLsLz/+3wFQmJ3qOoVRV3cruegA/iTjGIlX9c4z85OJTi3uK91jjlVM6rgW/\n7Tf4rPcJOn84QcT/XRMAxBqzTETdwIopUd9WBNMSuVNExgP4H3SO75oiIoc7Y512wrTKtTvbuV9W\nL4Dp8vSI92Ee8TjpPeCkucBnlfsAnCzmwSUDYcadPei2rInIqSJSJCIBZzzb1wE87CybKCKTnQdU\nFMG07HwEM+YHzgMt7gPwZVVdnXQp+XsJ5kvKDSIyQEQKUxzr5f01/zEA40TkTBHJE5HTYVp5l8dY\nP15LwKNOWmc546/yxTzwZXxUBjofRDM6ifwuAnCUiPzMacmDiOwv5kE+gxJsG8Wp6DwP4Kci0k9E\nDgTwLZixsamm9QmAJwH8n5iHcQXEPDjoCymk0QjzgKdfinlvZb6IVMB0D/0Q0ZWXbrdMJpFvd/zx\ndme85eWezV8A0CYiFzl5/TJM99q4u0wxi2HH6HSlfQ/mPvESgB0i8gMR6e9cc5NEZAoAOOffcGfT\nRmffHRHp1cP00FjsHMN0hHef/CPMvWCWk36hmNfgeCsRseJwP4DjROQ055oaKiLeLtX3wIwTnYSI\nccURHkNn92nAXF+VInKKmKfFfg/AiFgbO62FvwNQ7ZaHiOwtnWNxfwfgu2IeFiUiMlBEvhjxg41X\nquedONdXofvpRlqx5HvTl/Cn6MZ6X+0CSe3VW/HK6Xmkfi1E+pxn+9Ng7r+P+az3FwAXi3nwVynM\nORR5XX0BZrgFEaUZK6ZEfdtlMGOxtsO0Ii71LBvkzNsKoA7ml/+bnGXeh0GcC1P5Wyaxnxbp/XJy\nFEw34OMBNEjn++2mAYCqvgXguzAVyE0wr3g537P9Rc7+tsG0xJyjnQ/oKXOOoRHm4SOjYFpG3Qr1\nj2C6Wf1Dwt8/aTJpnpb56zh5D3G+cJ4MM951PUzlxR1/5/cKhZjTTnfiOTCvKdkCE5c5Ed2MNeL/\nvuk5Xa1nwYxh+xim8vxTmFbnSKNgYhurZbYzcfMQqiNhnlD7pog0wPzA8DJM92u/Y4zMX+TyM530\nNsBUDq5S1WfirB9v+pswx/gWzDn7V8SpMPjtQ1VvAnAlzLjjRphWtXqYLt+tSRyPXx4TbRMv34th\nxkE3wnR7fxCdcd4D0+K3AKab9Ved5fH4ncvxKid++b0J5hoMwpyzk2G6zG6GuV+4P1KcAOANEdkB\nMzb2DGesoJuu62swD57ZCvNAoNBDiJyWzVNgYvIpzHV2aUSefa8LVV0PMx70UpjyeRXmwUGuv8G0\n0D+kZnyqfwGYoQON4rxrWVU/gxmv/TOYa3UCTOXae2yRZXYFTIX+RTFPY34KTiusqr4CMwb1V04Z\nvAtzTsR7z6v3oT6he2eMdY+CabHd6XyapbOXR1fOXz+/9qS/E8BdnnW99/gdIuI+JG8UgFXRSfnv\nK045wbk2414LSZTTiwDGwpzH1wL4ijOmN9LvYH5M+i+AV2B+qGh3uyuLeb3QjjT88ElEPkRjvgOb\niCgxEVkLMy7nb6p6drbzkwoR2QXzhfNWVV2U7fykm4j8P5iHfvwu23mhniUib8OMTfyzqp4jImNh\nflTIA3C+Jvmk5r5EzKtIzvP8GBJrveNhymiez7IAzI9SX1PVlT2T075HRJ4AcJGqvp1w5RwmIicC\n+LWqVjjTDwC4U50HdhFRerFiSkRERH2K093zBlUdl3Dl6G1nAfg3TEvk5TBdm/f1tAhTH+V0hT4G\nptW0DKZl9nlV/X5WM0ZkCXblJSKiXktE7ojoSuh+bs923ig7RKQGwO0w40O74kiYrrmbYYYlzGWl\n1BoC8y7hrQD+A/Owvi69Lo2IUscWUyIiIiIiIsqqnHqPqYiwlkxERERERNSHqWrUw/lyriuvqvJj\n2WfRokVZzwM/jD0/jDs/jDs/jD0/jDs/PR/7WHKuYkpERERERER2YcWUsq6uri7bWaAsYeztxLjb\niXG3F2NvJ8bdXl2NPSumlHWTJ0/OdhYoSxh7OzHudmLc7cXY24lxt1dXY59TT+UVEc2l/BARERER\nEVH6iAi0Nzz8iIiIiIiIiOzCiillXU1NTbazQFnC2NuJcbcT424vxt5OjLu9uhp7VkyJiIiIiIgo\nqzjGlIiIiIiIiDKCY0yJiIiIiIgoJ7FiSlnHMQj2YuztxLjbiXG3F2NvJ8bdXhxjSkRERERERL0S\nx5gSERERERFRRnCMKREREREREeUkVkwp6zgGwV6MvZ0Ydzsx7vZi7O3EuNuLY0yJiIiIiIioV+IY\nUyIiIiIiIsoIjjElIiIiIiKinMSKKWUdxyDYi7G3E+NuJ8bdXoy9nRh3e3GMKREREREREfVKHGNK\nREREREREGcExpkRERERERJSTWDGlrOMYBHsx9nZi3O3EuNuLsbcT424vjjElIiIiIiKiXoljTImI\niIiIiCgjOMaUiIiIiIiIchIrppR1HINgL8beToy7nRh3ezH2dmLc7cUxpkRERERERNQrcYwpERER\nERERZQTHmBIREREREVFOYsWUso5jEOzF2NuJcbcT424vxt5OjLu9OMaUiIiIiIiIeiWOMSUiIiIi\nIqKM4BhTIiIiIiIiykkZq5iKSKGIvCQitSLyloj8NFP7ptzGMQj2YuztxLjbiXG3F2NvJ8bdXl2N\nfV56sxGbqraIyExV3SkieQBWich0VV2VqTwQERERERFR7sloV15V3en8twBAEMDWTO6fclNVVVW2\ns0BZEjf2/ft3fwf9+wPV1eHzRMxn3rzup0+pKy5G1axZXd/ejZ/fp7oaKCgw6xUUdM5zjRgRvU06\nzoPINJPNr7tudXX85cmWwQUXhE+7x+uXvls+gUDnvLy82Pt205g3Dxgzxvw/GAzf3t3GW7aevFTN\nnBn/WGN9iotjL8vL6zwWv0/kr/aR58T06bG3nT49tfKP93HPy0TrRZ6jBQWd+Yi8l/nxK99g0Mwv\nLg5ft7Ky8/+Ry2IRSe7ePG9eWH75d95OjLu9uhr7jFZMRSQgIrUANgFYoapvZXL/RNSLtLSkJ41l\ny/yXrVjR/fQpdU1NQGtrz6S9bFln2u6/3vhv2hS9TS6cB7HO0VQtXx4+7R6vX/pu+XgfONjeHjtt\nN40VK4D6evP/jo7w7SN5yzZeXhJpaoq9rL09/vnk153Mm4fVq2NvG29ZqpI95yPP0dbWznwkU3Z+\n63R0mPmR5bhmTef/45VxpGTuzStWpO+8JiJrZLrFtENVJwPYB8AXRKQqk/un3MQxCPZi7O1Uk+0M\nUFbUZDsDlDW819uJcbdXV2OftdfFiMiPAexS1Zs983T+/PmoqKgAAJSWlmLy5Mmh5mD3IDndt6bd\nebmSH05nbrq2thYLFy7sXD5rFqqclgWzNlAFAIWFqPnHP5JL/8QTgZaW8O0j04ucnjsXNRdfnPHj\nt2a6uBg1TotMFcIrKFX5+cCePfG3F0ktnl2dnjYNVatWJXd8TrfVHs1PH5uuBbAwh/LTK6cHDQIO\nPhg1kyYBp55qzsfqatRcckn69ldUhJpHHjHTia6/wkJg1y5zffzoR6h65RWgtRU1Tut7VTAIlJai\n+uijMfnii3PjfsTpjE2783IlP5zO3HTk97va2lo0NDQAAOrq6nD33Xf7vi4mYxVTERkGoE1VG0Sk\nP4AnACxW1ac96/A9pkRkiMTvJphsGjNmAJ4/kqFxcCUlgHOTpAxyy7+rsY017hIwsV650qTtrueN\nv9+26TgPItP1Hlu8/LrrVlWZfMdanmh/rvLyzm62Xm65pMq7bzePJSXA9u2x4+ct+5ISoLExPXnp\nqkWLgKuv7pyOvCcUFgK7d/tv26+ff7fVRDGNxVs2qXDzUVUVfi/zE+tc8l4brrw8oK3N/D/Z+22y\n129pKTB5cuL8EpGVYr3HNGNP5QWwF4C7RSQA04X4Xm+llIiIiIiIiOwUyNSOVPV1VT1EVSer6oGq\nelOm9k25rYa/qForbuwLC7u/g8JCYO5c/2UzZ3Y/fUpdURFqgsGeSXvuXCA/3/zf/dcb/7Ky6G1y\n4TyIdY6mas6c8Gn3eP3Sd8vH24IXLy5uGjNnmpZZHOeO7gAAIABJREFUoPOJvLF4y7aszHT/7Mqx\nFhXFXhYMdh6LH6drWRhvHqZMib1tvGWpipdHr8hzND+/Mx/JlJ3fOoGAmR9ZjhMmdP4/XhlHSube\nPHNmWF74d95OjLu9uhr7rI0x9cOuvHaqqakJ9UsnuzD2dmLc7cS424uxtxPjbq9EsY/VlZcVUyIi\nIiIiIsqIWBXTjHXlJSIiIiIiIvLDiillHccg2IuxtxPjbifG3V6MvZ0Yd3t1NfasmBIREREREVFW\ncYwpERERERERZQTHmBIREREREVFOYsWUso5jEOzF2NuJcbcT424vxt5OjLu9OMaUiIiIiIiIeiWO\nMSUiIiIiIqKM4BhTIiIiIiIiykmsmFLWcQyCvRh7OzHudmLc7cXY24lxtxfHmBIREREREVGvxDGm\nRERERERElBEcY0pEREREREQ5iRVTyjqOQbAXY28nxt1OjLu9GHs7Me724hhTIiIiIiIi6pU4xpSI\niIiIiIgygmNMiYiIiIiIKCexYkpZxzEI9mLs7cS424lxtxdjbyfG3V4cY0pERERERES9EseYEhER\nERERUUZwjCkRERERERHlJFZMKes4BsFejL2dGHc7Me72YuztxLjbi2NMiYiIiIiIqFfiGFMiIiIi\nIiLKCI4xJSIiIiIiopzEiillHccg2IuxtxPjbifG3V6MvZ0Yd3txjCkRERERERH1ShxjSkRERERE\nRBmR9TGmIjJKRFaIyJsi8oaIXJSpfRMREREREVHuymRX3lYAl6jq5wEcAeB7IjIhg/unHMUxCPZi\n7O3EuNuJcbcXY28nxt1eOT/GVFU3qmqt8/8mAGsAjMzU/omIMG8eMGZM9Pzq6sTbioR/RowwH3fb\n/v07162sNOtUV5uPO50ublrz5nVv+1xQXW2Oo6Cga/kSAdw/gN74FBdHxywQAC64IHz7MWPC0/Cm\nS5RL8vK6tu68eZ3XQKa4+6uszNw+yU6VleZvMfUJWRljKiIVAFYC+LxTSXXnc4wpEfWc0lJg+3ag\noyN8flVVdMUkUqwvdTNmmG1FAPf+lZcHtLebZQCwapWZTtf9zd1XaSnQ0ND17XNBVRVQWws0Nprp\nVPMlAixaBFx9dXJfvMvLgbq6zulAwOzTTcObbq6UERGQ2jnpXbe0tOvXV1e512IwCLS1ZWafZCf3\n7y3v171K1seYejJSBOABABd7K6VERERERERkp4y2mIpIPoDlAP6hqlF950RE58+fj4qKCgBAaWkp\nJk+ejKqqKgCd/ZU53bem3Xm5kh9OZ266trYWCxcu7Nn93XorsGwZzBRQ5fxbAwD5+ag66ihg5UrU\nHHSQWb5gAbBwodl+5szw9SO37870ihWpHY/TAuGbXkkJaiZNAq67rmvbq2Y0/jUXXADcey+wfXv8\n8lqxwj89kfTHI9F0qvHidNR0Rq73vjZ93HFAe3v0+RgIAE8/Hb7+sceiyukNErW+33Ss66ur03Hu\nl9UAJldUoGrduvTtj9M5P+3OS3v6Y8YAdXX+51tZGWqWLs2J47d5OvJ+X1tbiwanh1ddXR3uvvtu\n3xbTjFVMRUQA3A3gM1W9JMY67MproZqamtDJTHbJeOzZlTd8+ywJi3tVFbvyWoL3+m7qxV15GXs7\nZSTu7MqbkxLFPhe68k4DcBaAmSLyqvOZncH9U47iHyt7MfZ2YtztxLjbi7G3E+Nur67GPmMVU1Vd\npaoBVZ2sqgc7n8cztX8iIsycaVrMIs2dm3paZWXm425bWNi5bMKEznTnzu2cTreZM3sm3UyaO9cc\nR35+19Pw+wNYVBQ9TwSYMyd8nns+8AsU5bpgsGvrZvM+0VP3PiLXhAnmbzH1CVl5Km8s7MprJ3bx\nsRdjbyfG3U6Mu70Yezsx7vbqDV15iYiIiIiIiKKwxZSIiIiIiIgygi2mRERERERElJNYMaWsq0n0\nmg7qsxh7OzHudmLc7cXY24lxt1dXY59yxVREbhKRQSKSLyJPi8gWEflGl/ZORERERERE1kt5jKmI\nvKaqB4nIPABzAHwfwL9U9cBuZ4ZjTImIiIiIiPqsdI4xzXP+nQPgAVVtBMDaJBEREREREXVJVyqm\nj4jIWgCHAnhaRD4HoCW92SKbcAyCvRh7OzHudmLc7cXY24lxt1fGxpiq6g8BTANwqKruAdAM4JQu\n7Z2IiIiIiIisl/QYUxH5CsK77CqALQBqVXVHWjLDMaZERERERER9Vqwxpnl+K8dwMqLHkg4BcJCI\nfFtVn+5OBomIiIiIiMhOSXflVdUFqnp2xOcUADMA/LTnskh9Hccg2IuxtxPjbifG3V6MvZ0Yd3tl\nbIxpJFWtB5Df3XSIiIiIiIjITim/xzQqAZHxAP6gqkd2OzMcY0pERERERNRndXuMqYg84jN7MICR\nAM7qRt6IiIiIiIjIYql05f05gJsjPucBmKCqz/dA3sgSHINgL8beToy7nRh3ezH2dmLc7ZWJMaYr\nYZ7COxVAoaquVNU3VXV3l/ZMREREREREhNTeY/prABMBPA/gWADLVfWatGaGY0yJiIiIiIj6rFhj\nTFOpmL4J4EBVbReRAQBWqeohac4kK6ZERERERER9VKyKaSpdefeoajsAqOpOAFGJEXUFxyDYi7G3\nE+NuJ8bdXoy9nRh3e3U19kk/lRfAeBF53TO9n2daVfXALuWAiIiIiIiIrJZKV96xAMoAfBSxaBSA\nT1T1vW5nhl15iYiIiIiI+qx0dOWtBtCoqnXeD4BGALekKZ9ERERERERkmVQqpmWq+nrkTFX9L4Ax\n6csS2YZjEOzF2NuJcbcT424vxt5OjLu9MvEe09I4ywq7tHciIiIiIiKyXipjTJcCeEZVfxsx/zsA\njlPV07udGY4xJSIiIiIi6rPS8R7TEQAeArAHwCvO7EMB9AMwT1U/SUMmWTElIiIiIiLqo7r98CNV\n3QjgKACLAdQBWAdgsaoekY5KKdmLYxDsxdjbiXG3E+NuL8beToy7vTLxHlM4zZnPOB8iIiIiIiKi\nbku6K28msCsvERERERFR35WO95imIxN3icgmEYl67QwRERERERHZKaMVUwB/ADA7w/ukHMcxCPbq\nqdgXXFuA6herAQCyOOoHOUy/azoKri3AvKXzAADF1xf3SD4Sqby9Mu7yCx67wHd+/+v6R82TxeJ7\nrJW3V0Ydn3e/sbZLJ7ecZbHggscugCyQ0H7j7bv/df3D1ksmr4nKNLA4EMqL+6l+sRrT75qOmrqa\nsHVT2a9XZDqxJJP2vKXzUHBtQZfyUnl7JcZUj8GIm0fETd/Ni/e8Kr6+GMXXF0MWC0bcPCK0T+/+\nI/PiTrvnbeXtlci7Jg/VL1abZQuSz7ssFuRd4z/ayHttF19fHDoGL/ecl8USKoN4acbKgyyWsHgm\nG9tY6SVSfH1x6PgAYMiNQ0LH6c73xsPdJtlzo+DagtD//a6V4OJgaB9unt0yCMXR83Hz5z3H3GsK\nMOfXiJtHhN3r5y2dF3aMXjV1NWHpRa43/a7pofQLri1IeO/2Hm+qYuWxr0vn34NU/sa75R2v3N3r\nt/L2yoz87aKuy8R7TLtNVf8FYFsm90lE9mntaMWytctiLl+9YTVaO1qxom4FAKCptSlTWQuzZsua\nuMuXv7Pcd35Le0tK+4g8vkT7TTe3nIHYx+QnleN0JTo2RfRwkWVrl2H1htXdqnR4pSsdwJRda0dr\nl7Zds2UN6hvrsal5U9z0Xd7ybmptCp038bb348Z4zZY1aNf2uNdiPO3a7jvfm15Ta1PYMXjnu7xl\nECvNeNJVMU1GU2tT2PFta9kWNT8yHqncv7znkt+10oEO333U1NX4xtHNX+T67ror6lZELVtRtyLm\nOeGWr7tN5HqrN6wOzW/taE147F29dvz2TT3LLe945e5ev5n+G0aZk+kWU6IoVVVV2c4CZQljb6kx\n2c4AZQXjbi3e6+3EuNurq7HP+MOPRKQCwCOqGtWHRER0/vz5qKioAACUlpZi8uTJoYNzm4U5zWlO\nczpyOu9beWjvaO/88rvO+TfF6aJxRdhx5Y4ey++Fb12INVvWQNcpOrQDwX1N17lRW0fhD3P/gAd2\nPoDl7yxHy3st2NS0CeWTywEA62vXm9a+bh5fMtO6SLt9vNOvmo7ajbVo3rs5pf0X7l9oWu6SXF+X\nKCpvr8RbL78FAOio6EBQgtB1itElo7Gueh0CiwPQddrl8vCbXjFjRdjxVi+tRu3GWlRMrsDilYsx\nv2Q+AGDB3AWoqqgKlc/MlTMTpj/3gLlY9viypPPjjdeFb12INza/EXP9skllOHKfI/HUM0+heU9z\nRs6neNO6xMSlpqYGM5fMjLv+QSMOwmv9X0vb/gMSQPsf2kP7B5KLz6IZi1BXW4fJIyZj4RkLw7aP\nvB7ipbdiwQpUVVWh+PpiNL3T1O3j8ZvWJYqCawvQ+n6r7/LgvkHTCpWF+AckgMETBuOMSWfgtr/c\n1u30CvMKsevOXTGPNxgIou2uNgCx41VbWItla5ehYW0DXtv4GmZUzQAATNo5CadOPDVn/t6le1oW\nOF1ie+jvQaLyfveVd7FhxwaUjC9B4+5GjNwyEsMHDseCuQtw2ZOXof2D9pj580577yc9kV9Od326\ntrYWDQ0NAIC6ujrcfffdvg8/yrmKKZ/Ka5+amprQyUt26anYy2LBjPIZqFlgxivpovD7SuF1hdjd\nvhsl/UrQ8MMG33UyIe+aPLRd1RZzeUV1BeoW1kXN98uvO9Ymcn7eNXlo1/aw+d79xtounUpvKA2V\nc3lJOepr68Naz2LtO9b4oXh5TVSmbtl5055RPgMvfvQifjj9h7i66uqY+0+2jK6uuTosnXh5SZR2\n6Q2laNzdGDU/mbzkXZOHDu2AQuOm78bGm65f2XvLLfL/3m3KS8pRt7AudO7NKJ+BlfUrzZfHMcnl\nPd55WbWkKnRtAwhdx5Hbu3kUSFgX7mTj6Ka/aMaiUDyTjW2s9BLt23vv8uYBQNg9DYgdq3j78ObB\n71rxlps3xotmLEJNXY2JYwze/Lh5dc/fFTNWhO71pTeUYvKIyaFj9Lq65mosXrk4lJ4ba5d77w6d\nUykcb6oi922LdP49TOVvvFve8crdzZt7bwF69m8XdV2i2OfEU3mJiIiIiIiIImW0YioifwLwPIBx\nIvKhiJydyf1TbmJrqb16Kvb5gXzMHT835vIpI6cgP5CPmRWmm11RflGP5CORCcMmxF0+Z9wc3/mF\nwcKU9hF5fIn2m25uOQPOMSU51jCV43QlOjZBdEvg3PFzMWXkFFRVVKW8Pz/pSgcwZZcfyO/SthOG\nTUB5STnKBpbFTd/lLe+i/KLQeRNvez/ueTth2AQEJdh5LaY4xjQoQd/53mu7KL8o7Bi8813eMoiV\nZjzeeKYztn6K8ovCjm9w4eCo+ZHxSOX+5T2X/K6VgPO1MHIfVRVVvvdUN3+R67vrzqyYibKBZWH3\n+pkVM2Pen93yddOLXG/KyCmh+fmB/ITH3tVrx2/flLpU/sa75R2v3N3rN9N/wyh1Xf1+l/GuvPGw\nKy8REREREVHfxa68lLPcQdJkH8beToy7nRh3ezH2dmLc7dXV2LNiSkRERERERFnFrrxERERERESU\nEezKS0RERERERDmJFVPKOo5BsBdjbyfG3U6Mu70Yezsx7vbiGFMiIiIiIiLqlTjGlIiIiIiIiDKC\nY0yJiIiIiIgoJ7FiSlnHMQj2YuztxLjbiXG3F2NvJ8bdXhxjSkRERERERL0Sx5gSERERERFRRnCM\nKREREREREeUkVkwp6zgGwV6MvZ0Ydzsx7vZi7O3EuNuLY0yJiIiIiIioV+IYUyIiIiIiIsoIjjEl\nIiIiIiKinMSKKWUdxyDYi7G3E+NuJ8bdXoy9nRh3e3GMKREREREREfVKHGNKREREREREGcExpkRE\nRERERJSTWDGlrOMYBHsx9nZi3O3EuNuLsbcT424vjjElIiIiIiKiXoljTImIiIiIiCgjOMaUiIiI\niIiIchIrppR1HINgL8beToy7nRh3ezH2dmLc7cUxpkRERERERNQrcYwpERERERERZQTHmBIRERER\nEVFOYsWUso5jEOzF2NuJcbcT424vxt5OjLu9OMaUiIiIiIiIeqWMjjEVkdkAqgEEAdypqjdGLOcY\nUyIiIiIioj4q1hjTjFVMRSQI4G0AxwH4GMDLAM5U1TWedVgxJSIAQGBxAApzP9BFnfeF6XdNx6pv\nrQpNy2LBtFHT8MqGVwAAe9r3YOLwiXhj8xsAgLKBZWhsacSe9j0YXTIa6xauAwAEFwfRvqg9lEak\nuQfMxePvPY6W9pbQPG8+usLNq5v/6hersfCIhTHXn7d0Hh4646HQ9PS7pqOxpRFrtqxB21VtYenG\nyl/1i9W45IlLAABF+UVoam1C2cAy7D9kfzz34XPQRYoLHrsAt718G3SRYsiNQ7D1iq2++Sm4tgB7\nfrwnZn69x1P9YjUufeJSfOmAL4UdQzx+cehqmcti8d12TPUYfLzjY+z58R6MuHkENjVv8t1+7gFz\nseztZaE0/PLmCkowFI94xyCLBSvmr0BVRVXC/Lsx8brlhFvini8jbh6B4QOG4/XzXweQ+PyK3Hbr\nrq2h+EYex9wD5uKhMx7CvKXz8M8P/on5k+fjVyf9KrS88vZKvH7+62HnrPca8+7nh9N/GMpX5Dnu\nJ1Ys3WP8wVM/8D0vq1+sxu//83vMqJgROr9TKRPAXHPudeKWyaThk0JlnAq/8kiVe19085MfyEdr\nR2vS10m88zgW77FH7se9J3jTLcovwo4rd6S8n8h0WztaE5Z1vHOD+h7veRZ5XvJc6F1y4eFHUwG8\np6p1qtoKYCmAUzK4f8pRHINgr3ixdyulkVZvWO07r6W9BS3tLehAB9ZsCf3ehU3Nm0Lz6xvrQ/M7\n0BE3byvqVoRVStPFm/9la5clzEPktmu2rEG7Jv/l1ruPptYmAKZMvPlY/s7y0P+3tWyLmVZrR2vS\n+1q2dhk60BF1DEB2r/n6xvrQccSqlALRZR9PKvGoqatJaj1vTFyJzpdNzZvCzv1E60duGy++bnms\nqFuBptamqPy5+/WWW+Q1VlNTg03Nm8LylUo5+1m2dlnMfC9buwxrtqwJy2sqZQL432+8ZZyKRPec\nZETeFxNdkz3Nb//ufcYr1WveTberZU25gd/v7NUbxpjuDeBDz/RHzjwiIiIiIiKyWCa78n4FwGxV\n/Y4zfRaAw1X1Qs86On/+fFRUVAAASktLMXnyZFRVVQHorH1zmtOc7pvTM5fMBMbAWOf8m6PTK2as\nSOr4Zq6cGTe9GVUzAACTdk7CqRNPxa0bb8WKuhVo+6ANzXuaERgTMC0tWTjeooIi7B6127ReRCwP\n1gfxz2/+E7WFtVi2dhka1jbgtY2vxU1v2uhpWHXNqpTKxzu9YsGKPnP+YB1wy+xbsPCMhaH83/ri\nrVi2e1lS239v+Pdw6sRTccbqM0zLb5Lnl7e8vvznL2PbiG1ZO353uqRfCdo+aMPkEZND54cskJjr\n33LCLbjkjkt8lwfGBFAYLMTO93bG3f9Buw7C9NHT8avzTXdk7/k0/a7peO7Z55LK/6SppqtprOv/\n2JXH+l6/sk7wzIJnkro/BhYHoOs0qfxgjOnW6N1eFkvW4ls0znTrTeb+f9w9x6G9vD1mehWlFVhX\nvS7u8egSjZk+p3vndFfP30R/Lzid2ena2lo0NDQAAOrq6nD33XdnfYzpEQCuVtXZzvT/AujwPgCJ\nY0yJyBU5lsRVeF0hWn7UErZev2A/7G7fHZoXlKBv90qBoGNRR2i7eOMHS/qVoHF3Y9i8dIwx7Rfs\nF8p/1ZIq1Cyoibl+6Q2laPhhQ2i68LpCtHW0oV3bw/ISb4xp1ZIqrKxfGTXfLTNdpKiorkB9Y33C\ncTqJxvB4j8fdb0m/krBjiCcTY0wjx+jF4sY/mTGm3nwmGmO6aMYiXF11dcL8uzHxmlE+I+75Iosl\nbLxrovMrctvIvHq5cSy9oRSNuxtRXlKOuoV1oeV51+Sh7aq2sHPWLwayWMKOI/Icj5W3WOeBe57F\nOvdXrV+FfQbtEzq/UykTwFxz7nXilom3jFORjjFwkWPqXNkaY+oeU2S66TrORGXNcYV24RjTviMX\nxpiuBjBWRCpEpADA6QAezuD+KUe5v6yQfRh7OzHudmLc7cXY24lxt1dXY5+xiqmqtgG4AMATAN4C\n8GfvE3mJiLwE/r/sTxk5xXdeYbAQhcFCBBDAhGETQsvKBpaF5peXlIfmBxLc/mZWzERhsLCLuY/N\nm/+54+cmzEPkthOGTUBQgknvz7uPovwiAKZMvPmYM25O6P+DCwfHTCs/kJ/0vuaOn4sAAlHHkG3l\nJeWh4ygbWBZzvVTynUo8knkiLxAeE1ei86VsYFnYuZ9o/cht48XXLY+ZFTNRlF8UlT93v95y87vG\nygaWheWru+fH3PFzY+Z77vi5mDBsQlheUykTwP9+4y3jVCS65yQj8r6Y6JrsaX77d+8z6Ui3q2VN\nRL1TRt9jmgi78hIREREREfVdudCVl4iIiIiIiCgKK6aUdRyDYC/G3k6Mu50Yd3sx9nZi3O2V82NM\niYiIiIiIiPxwjCkRERERERFlBMeYEhERERERUU5ixZSyjmMQ7MXY24lxtxPjbi/G3k6Mu704xpSI\niIiIiIh6JY4xJSIiIiIioozgGFMiIiIiIiLKSayYUtZxDIK9GHs7Me52YtztxdjbiXG3F8eYEhER\nERERUa/EMaZERERERESUERxjSkRERERERDmJFVPKOo5BsBdjbyfG3U6Mu70Yezsx7vbiGFMiIiIi\nIiLqlTjGlIiIiIiIiDKCY0yJiIiIiIgoJ7FiSlnHMQj2YuztxLjbiXG3F2NvJ8bdXhxjSkRERERE\nRL0Sx5gSERERERFRRnCMKREREREREeUkVkwp6zgGwV6MvZ0Ydzsx7vZi7O3EuNuLY0yJiIiIiIio\nV+IYUyIiIiIiIsoIjjElIiIiIiKinMSKKWUdxyDYi7G3E+NuJ8bdXoy9nRh3e3GMKREREREREfVK\nHGNKREREREREGcExpkRERERERJSTWDGlrOMYBHsx9nZi3O3EuNuLsbcT424vjjGlXqu2tjbbWaAs\nYeztxLjbiXG3F2NvJ8bdXl2NPSumlHUNDQ3ZzgJlCWNvJ8bdToy7vRh7OzHu9upq7FkxJSIiIiIi\noqxixZSyrq6uLttZoCxh7O3EuNuJcbcXY28nxt1eXY19zr0uJtt5ICIiIiIiop7j97qYnKqYEhER\nERERkX3YlZeIiIiIiIiyihVTIiIiIiIiyipWTImIiIiIiCirWDElIiIiIiKirGLFlIiIiIiIiLKK\nFVMiIiIiIiLKKlZMiYiIiIiIKKtYMSUiIiIiIqKsYsWUiIiIiIiIsooVUyIiIiIiIsoqVkyJiBIQ\nkSUicm2S69aJyE4Rubun85VuItIhIk3JHqtnuwUi8q+eylcuEpEKp7ys/Tua7TKId12KyPsisltE\n7nWmxznndpuIfDuzOe0eETlaRNZmYD/Wn9NElF28+RBRn+N8Ad3hfDqciqI7fWYXklTnk+y6c1R1\nvpOX4SLyJxH5WEQaRGSViEyNyO/XRKTeyfdDIjLYs2yJ8wXbzf92ERHP8o7/z96Zh0lRXQ/7vT0w\nwMAMqyIzbC4gghKMuIHo4IKaiBq/uKEYRI1Z1OD2UxMQVKJRozEmbiEmqBHFfdeIQmtckQhKEFcY\nBpgBGbZhGZiZ7vv9caumq7urt5ne+7zP009tt+49Vaequk6dc88NOd6/Obb9TCm1SCm1VSm1Wil1\nu1KqKIb8w7XW06z9ByqlVsZ53FmJdf5+FmdZr1LqGMfyYKXU00qpDZbuPlNKXZmrL+5uHxCsdUuV\nUjuUUrVKqfuVUl2tbQ86rqvdSqlGx/KrxH9PpIqW+1IpVamUWtCyQet9gVsdy19rrbsA/yGC3CH3\n2kal1JtKqf2tbTOUUk2O49+mlNrk2NevlNrHmu8fUi70Hh3t0vYwq72NSqnN1n17siX7f7TWQ5Jx\nwgRBELKZnPxzFQRBiIbWuovWulRrXQqswhiKpdbviVZWq2IXcaUL8DHwQ6A78AjwqlKqM5gXUuBB\n4DygN7ATuN+xvwZud8hfprUOfbE+yLH95471nYDfAD2Bw4HjgGtaeRxJQVmksclEjCenobMvRm+r\ngAO11t2AM4FDMDpNiDg+CKQdpdTVwB+Aq4Ey4AhgADBPKdVea/0Lx310K/Ck4zr7MQneExk4B4le\nZy33GtAX+B6Y7dj+hOP4S7XWPVwr0braWc5aPdyx7n2X3V4G/o15BuwJXAHUJyi/IAhCTiOGqSAI\nBYNS6jCl1IeWR6JGKfUXpVR7x/Y/KaXWWx7Gz5VSQ13qKFVKLVBK3RNPm1rrlVrre7TW67VhFlAM\nDLaKnAe8pLV+T2u9A5gGnGEbrnazMZpxfZZrrR/UWr+vtW7WWtcAjwNh3ppYh9AihFL9lFLPKaW+\nV0rVKaX+4iyolLpTKbVJKbVCKXWSY71XKTVTKfU+sAPYWyk1Sin1ieWJXKiUOjKk/C1KqfctD9NL\nSqleSqnHLd0sVEoNcJQfopSaZ3mbvlRKnRnpGBLgJuA9rfU1Wuv10OJ1O19r7TQYzlfG271BKfVb\nh0wzlFLPKKUeU0ptBX6mlCq3jmWjUuobpdTFIeWftsrXW9ffIKXUDdY1uUopdYKjfFel1MPWdbzG\nOl9x/6crpcqAGcBlWus3tdY+rfUq4CxgIHB+6C5Evg4TOQcR5VZK7auUmm9dWxuUUv9SlvfW2n6w\nUupT6/w8CXQMkSlpHlytdQPwBHCgY3VKPqgopXphzvks615t0lp/YBuwyniDVzvK/1Aptdg6D08p\npeYqK6TZKrtGKXWVdd3UKKUmOfb9sbXvVqVUtVJqehS5JikTEl1v3dMTUnH8giAINlllmCql/mE9\nSJfGUfZu6+G6WCn1lVJqczpkFAQhp2km4EHc4iM2AAAgAElEQVQ8EuNB/BWAUupEYAwwSGvdFeMd\n2+TYVyulegJvA//RWk9pjQBKqREYw/Rba9VQ4LOWRrReAewmYLgC/MoyZhYppc5wqfZdZcIwn3Ua\nbC4cA/zPIct9Sqn7IhXWWldpre3wxCLgFWAlxqtWgXlxtzkc+BJzbu8AHg6p7nzgYoy3cQfwKnAP\n0AO4G+NF7u4of7a1TwWwL/ChVWcPYDkw3ZKrMzAP+BewB3AOcL9S6gDrGC7UWj8a5Zw4j3es1vpd\na/E44Jk4dhuN0dVxwI3KCv20OBV42rqe5gBPAtVAH+CnwK1KqbGO8qcAj2I864ut4wIoB24BHnKU\nnQ00Ys7NwcA4zPmNl1EYw+4550rr48hrwAluO0UgkXMQS+7fY87PAUA/jPGMUqoYeAETcdAdeBr4\nf1jGqNbaq7U+NgGZI6Gs9rpgPhp9moQ6Y7ER8zx4XCl1mlKqd0ThzHl4HvgH5jw8AZxOsFHeG+MB\nLwcuAu5zGPjbgfMtffwY+KVS6jSXdjoDfwZO0lqXYZ6XS9p0lIIgCDHIKsMU+CdwUsxSgNb6Kq31\nwVrrg4G/AM+mVDJBEHIerfWnWuuFWmu/5R36G8ZYA2gCSoEDlFIerfVXWut1jt0rAC8wV2t9Y2va\nt7xUjwEztNbbrNVdgK0hRestWQDuBfbDGF3TgNlKqVGOskdjDMUhQA3winIJmVRKTcaEE//RXqe1\n/rXW+tdxin8YxmC4VmvdoLXerbX+wLF9ldb6YSvM+FGgj1JqT7spYLbWernW2o8xRr7SWj9u6eJJ\njFF7qqP8Py1vcz3wOvC11nq+1tqHMUoOtsqeAqzUWj9i1bUEY2yFek0TpSdQG0e5m6xz8TnmA8MP\nHNs+0Fq/ZM3vgTEGr9NaN2qtPwP+DlzgKP+u1nqedYzPWDL8wVqeCwxUSpVZhsvJwJWWLjZgjPxz\nEji+XkCdpY9Q1lnb4yXec9A1mtxa6++01m9bHsM64E8E7s8jgHZa6z9b3t1ngU8SkDEeFHCN9aH7\nG6AEmOTYfpYy0Rb27+1kNGrdM2OBKuAuoEYp9Y5Saj+X4kcARVrrv1jn4XlgYUiZJuBma/vrGGN0\nf6utd7TWy6z5pZiPJcfgjh84SCnVyYr4+KJtRyoIghCdrDJMtdb/AYI8n1Zoz+uWp+DdkC+xNhMI\n/nIvCIIQhjLJbF6xvItbMd6ZngBa6/nAX4H7gPVKqYeUUrZxqDDehY4Ee60SabsTph/ZB1rr2x2b\ntmNe2J10BbZZci3WWm+2jK7XMeG4LV5TKwS4WWu9FeMNHogxUp1tn47pI3iy1trpBU6Efhjj082Q\nAWPM2DLttGadfTFXO+bLMZ5DJ6us9TbrHfO7MP39nMt23QOAw50GA+Y/IaLXKU42hsgTCefHi50E\nH/Max3w5sMnySNpUYz542DiPsQFjOGrHMlb9A4D2QK3jmB/EGL/xUgf0ihD+2wfYkEBd8Z6DqHIr\npXorpZ60QlG3Yj7i9LT2LQfWhrS7iuSG12rgTq11d611H6316VprZ/KvudY2+3dc0hrWeq3W+nKt\n9X6Y87QD84EnFLfzsDpkeWPIfdqiE6XU4cp0RfheKbUFuJTAOXbKswMTtfALjKH8SoT3L0EQhKSR\nVYZpBP4GXK61HglcS3BSEKywtYHA/PSLJghCjvEA8AWwnxXK9jscz0HLCzESE147GPPMAfPCOguT\nnOQ1pVRJIo0qpTpgwhCrtdaXhmxehsPDpEzSnWLg60TasHcPmaJMX8+/YRJALWtFnTargf5u3tg4\ncYYarsW8fDsZQPgLt9u+oVQD74QYDKUJeIIj8RYmVLQtOOWuAXpYIaI2/Qk23OJlNSbcu6fjmLtq\nrQ9KoI4PrTqCjtGS7yRMyLqT1vbfdO4XS+5bAR8m2VRXYCKB+7OWYCMezDWT7MzAkQxdHWVbUtFa\nr8G86xzostntPPRPoPo5mGdRX20Sej1I5D7qb2qtxwF7YSIaZiXQjiAIQsJktWFq/UEeCTytlFqM\neYDuFVLsHEz/lUynrRcEIfvpgvFE7lRKDQF+SSAL60jLm9Ae42HYhXlJBuuFVGt9GfAV8LJSqmM8\nDVr1PWPVOcmlyOPAeKXUUVa/rluAZ23PmlLqp0qpLkopj1JqHFayJGvbUKXUCKVUkfW8vBtj6Cy3\nth9r1X+G1npR3GfJnY8xL8V/UEqVKKU6hoQUx8L5Uv8aMFgpda5Sqp1S6myMl/eVCOWjGQSvWnWd\nr5Rqb/0OtfQbLEBgnMZ4XuSnA6OUUnfYff6UUvspk8inLI79g9BarwY+AG5TSnVQSg0HJmP6xiZa\nVy3wJnC3Msm4PFZ00dEJ1LEVk+DpL0qpE63zNhB4CmNAPhayS5uNsjjktvsf1yulKgh8GAJjSDcr\npa6wZD0DODRWkwmKGO0Y4zn+DtZ9Yf/iesdSSnVTSt1knQuPMsmQJmOOOZQPAZ9S6jLr3jmN2OfB\nSRdgs9a6UZlhqybgcp6UUnsq09+1MyY0eAeB56EgCEJKyGrDFCPfFm31JbV+w0LKnI2E8QqCEB/X\nYF7E6jFexCcd28qsdZswfb3qgDutbc5xTH+OMf5esDyhbjhfYkdhwoBPALaokLEMrX5bv8AYkOsx\nQ7z8yrH/FVZ7m4HbgYt1IEFPb+sYtgLfYcJtT7H6JAJMxfRVfV0Fjz9phFTqAaXUA1Fkb8EKDRyP\n6e9ajTFeznI5PzjWuS5b4cSnYIYpqcPo5ZSQMGMdMu9anzZ9dcdhPlKuxRjPt2G8zqH0w+g2kmc2\nULlJQnUkJiJnmRX2+AymX+P2CMcYKl/o9nOt+mow/WBvtELII5WPtnwB5hi/wFyzTxP+4TaqTFrr\nO4HfYvodbwU+woTHHqe1borjeNxkjLVPNLlvwvSD3ooJe3+WgJ4bMSHskzBh1mcRO7eE27UczcCM\nNl6xBs5WweOT1ltGpM0yzAco+zfJsW80GjHe37cwx74UE7o9yVEm9DxchHkmnIf5oNMYWjYCvwJu\nVkrVY/qsz3U5TjDvX1di7pWNmMRwv4xxHIIgCG1CpdPRqJSqwrwQ+oAmrfVhLmUGAi/boT3KDC/w\nJ631M0ophRmv73Nr2xDgda313uk5AkEQhOgopb7E9NF7Tmt9YablSQSlVAMm1PLPWuuIw0jkKkqp\n3wHfazNkj5DHKKW+wvTHnKu1vlgpNQjzUaEd8CsdZ6bmXEAp9TFwv9b6kUzLIgiC0BbSbZiuBA6J\nlHxDKfUEJjtcL4zn4EZgAaZfWB9M0oQntNYzrfLTgQ5a69+61ScIgiAIgpBPWKHPX2OiDc7D9Efd\nR1vj7QqCIOQq7TLQZsQwGq31uRE2nRyh/E1JkUgQBEEQ8gCl1IMYYyWUx7TWv3JZL+Qe+2P6AnfG\nhPD/VIxSQRDygXR7TFdg+k/4gIcknEoQBEEQBEEQBEFIt8d0tNa6Vim1BzBPKfWlNXapIAiCIAiC\nIAiCUKCk1TC1UsWjtd6glHoeOAxoMUyVUjLkiyAIgiAIgiAIQh6jtQ7r3pm24WKsce9KrfnOmPT+\nS0PLaa3lV2C/6dOnZ1wG+Ynu5Sd6l5/oXX6ie/mJ3uWXet1HIp0e097A82bEF9oBj2ut30xj+4Ig\nCIIgCIIgCEIWkjaPqdZ6pdZ6hPU7UGt9W7raFrKbqqqqTIsgZAjRfWFSKHr/7jtoaAClzO/LL6OX\nf/nlQNm77kqPjOmkUPQuhCO6L0xE74VLa3WfNsNUECIxYsSITIsgZAjRfWGSr3r3+eDmmwPG5X77\nQUlJYPsBB8DTT7vvqxScempg+ZprUitrJshXvQuxEd0XJqL3wqW1uk/rcDGxUErpbJJHEARBEOLh\nxRfh9NPjK3vttXDJJTBokFlWEUb3lr9DQRAEIR9RSqFdkh+JYSoIgiAIbWD1aujfP/H9eveGefNg\n+HD37fJ3KAhCrqMifXkTCgY32y6SYSqhvELG8Xq9mRZByBCi+8IkV/WuFHz+efj61hilAOvXRzZK\n85Fc1bvQdkT3hYmt90xniJVf5n6JIoapIAiCIERh2DCorjbzU6dmVhZBEARByFcklFcQBEEQIlBd\nDQMGBK9z/k1VVcHeewdv79LFJEJ6+23Yc0/Yd1+zPlZE29atUFYG//wnvPMOzJ7dVukFQRAyixWy\nmWkxhAwRSf+RQnnTOY6pIAiCIOQUoUZpKKFGaWMjtG+feDtVVcYoBfB4pH+pIAiCUHhIKK+QcaTv\nSeEiui9Mclnv0QxVrVtnlIbW6/GA39+6erKZXNa70DZE94WJ6F1IFDFMBUEQBCFOpk93X2/3QW0N\nL74YvJyvhqkQzjffwK5dmZZCEAqPgQMH8vbbbye1zhkzZjBx4sSk1lloiGEqZJzKyspMiyBkCNF9\nYZLLevf53Nf36xd732nT3NefemrwcnU1zJmTmFy5QC7rPV6UgkWL4i8/eDAMHJgycbKGQtC9EE42\n610plfShbGRonLYjhqkgCIIgxEkkwzQebr45vr6jjz3W+jaEzLF2rZk++mhi+61fn3xZBEEQchEx\nTIWMI30QChfRfWGSy3r/4ovA/Ouvp6YNT57+M+ey3uPhoIPMtLX9jPOZfNe94E4u6L2xsZEpU6ZQ\nUVFBRUUFV155JY2NjQBs2bKFU045hT333JMePXowfvx41tpfoICVK1dyzDHHUFZWxrhx46irq4vZ\n3q5duzj//PPp1asX3bt357DDDmPDhg1AeHixMzS4qqoKj8fD7Nmz6d+/Pz179uTBBx/kk08+Yfjw\n4XTv3p3LL788macmI+Tp358gCELyefddWLEi01II6SLUOzpsWLDH88knzdRprMbD2Web6f33w/z5\n4dv//OfE6hOyg82bzXTEiPjK19amThZByBWUSs6vNWitmTlzJgsXLuSzzz7js88+Y+HChcycORMA\nv9/PRRddRHV1NdXV1XTq1InLLrusZf8JEyZw6KGHsnHjRqZNm8YjjzwSM5z3kUceob6+njVr1rBp\n0yYeeughOnbsaJ2L4PBit7oWLlzIt99+y5NPPslvfvMbbr31VubPn8+yZct46qmnePfdd1t3MrIE\nMUyFjJPNfRCE1JJruj/mGJgyJdNS5D7ZrveGBvOic+KJwet/9rPgF6Dt2810n30Sq//RR6G+Hn75\nSxg7Nnz7nnvCgQcmVmcukO16TxbxJjT63e/MtFOn1MqTDRSK7oVg4tG71sn5tZY5c+Zw44030qtX\nL3r16sX06dN5zOpP0aNHD37yk5/QsWNHunTpwm9/+1veeecdAKqrq1m0aBG33HIL7du3Z8yYMYwf\nPz7mmK3FxcVs3LiRb775BqUUBx98MKWlpRHOTXhd06ZNo7i4mBNOOIHS0lImTJhAr169KC8vZ8yY\nMSxevLj1JyMLEMNUEAQhAbZty7QEQqr59FMzDU3YWFwcvPzcc2baoUNi9RcXQ4T3ECEH2LULjj02\n8vZbbokvAZIVLchNNyVHLkEQEqempoYBjvG6+vfvT01NDQA7d+7k0ksvZeDAgXTt2pVjjjmGrVu3\norWmpqaG7t2708nxZWlArIGvgYkTJ3LiiSdyzjnnUFFRwXXXXUdzc3Pc8vbu3btlvlOnTmHL2+0v\npjmKGKZCxsmFPghCasgl3dvDd7Rrl1k58oFs1/tLL5np6NGZlSPfyHa9x8sLL8CCBdHLxOPBefxx\nM+3fv+0yZTv5onshMXJB7+Xl5VRVVbUsV1dXU1FRAcBdd93F119/zcKFC9m6dSvvvPMOWmu01vTp\n04fNmzezc+fOln1XrVoVM5S3Xbt23HjjjSxbtowPPviAV155hUetjGmdO3dmx44dLWXXrVuX8PHk\nemZgMUwFQRDiwP6vsPuRCfnLyy+b6fvvRy93wQWpl0XIPhoaom8/8sj46rE98B991DZ5BEFoPeee\ney4zZ86krq6Ouro6br75Zs4//3wAtm/fTqdOnejatSubNm3iJkd4w4ABAxg5ciTTp0+nqamJ9957\nj1deeSVme16vl6VLl+Lz+SgtLaV9+/YUFRUBMGLECJ588kmam5tZtGgRzz77bMKGZqxQ4mxHDFMh\n40jfk8Ill3Q/a5aZ/ve/mZUjH8h2vTuSLkYl0WFBCp1s13u8PPxw9O3WO2ZM7FDee+5pmzy5QL7o\nXkiMbNe7UoqpU6cycuRIhg8fzvDhwxk5ciRTp04FYMqUKTQ0NNCrVy9GjRrFySefHGQozpkzh48/\n/pgePXpw880387Of/Sxmm+vWrePMM8+ka9euDB06lMrKypbMu7fccgvfffcd3bt3Z8aMGZx33nlh\n8sZzTLmMyibLWimls0keQRAEmxdegJ/8BE4+GV57LdPSCKkk0v/6PfeYrMx21ly7XLL/tpYuhQkT\nzFTIHOPGwdSpcPTRwettvfv9wdeKPX/UUXDrrTBmTPT6nfvKq4+Qryilct6LJ7SeSPq31of924rH\nVMg4udAHQUgNuaT7Ll3MdOjQzMqRD+SS3m1OOinTEuQ+uab3efPMEFGRcHvX7tcv8XZuvz3xfXKN\nXNO9kBxE70KiiGEqCIIQB3bSvASS5wl5wplnwkUXuW97/vn0yiKkFzvpmRtbt4avO+20xNvo3Dnx\nfQRByF4ef/xxSktLw34HHXRQpkXLesQwFTJOtvdBEFJHLul+/Xoz9fkyK0c+kEt6B3jqKfjpT923\ndeuWXllymVzTO0BTU+Rt1dVm2tgIkyebeTdjNRbRjN98IRd1L7SdQtX7eeedx7Zt28J+S6V/RkzE\nMBUEQYgDa1gzHFnlhQLgkEOCl+3wTTtxTY8e6ZVHSC99+kTeZn+kuuoq+Oc/zfyECYm3UQiGqSAI\nQjyIYSpkHOmDULjkku7vv99M48gGL8Qgl/R+zjmBeWeymtWrzVQis+Inl/Ruf3goK4tcpqTETO+7\nL7CuNddDIRimuaR7IXmI3oVEEcNUEAQhDgo0Iiml/P3vcMstmZYiOtu3u6+3x1TP8cz8QgQ2bjTT\n7t0jl3HrG1pennhbhWCYCoIgxEO7TAsgCIXaB0HILd1/9x388IdQW5tpSXIfW++XXGKWp03LnCyx\niDTKQbxjVQoBcul+b2gw02ijXLgZlK35ULF4ceL75Bq5pHsheYjehUQRj6kgCEIc+P0wejRs3hxI\nhCTkJ4MHQ0WFmT/hBPcyJSXQt2/6ZBLSi519O5o3M1meTrv/uiAIQqEjhqmQcaQPQuGSS7ovKjKh\ne7t2wZ/+lGlpcptQvd9wQ2bkiESHDtC+vZk/4gj3Mk1NppwQP7l0v6faMLU9sYceCkce2fp6coVc\n0r2QPPJJ77fddhuXWGE+VVVVeDwe/BKHn3TEMBUEQYgDnw+Ki818aWlmZckX+vUz07q6zMrhRGtY\nuhS2bDHL7SJ0eFmzxnjPhfzENkwTDeWNlx07zPTww6O3IQhC+vF6vfSz/6AsbrjhBmbNmpUhiQoH\n6WMqZBzpg1C45JLum5th1Soz36lTZmXJdWy9H310ILtttrBrl5nG6kO6fbsJ+RXiJ5fudzsrbzTj\n0x4uplevxD+u2PX37h0wUvOZXNK9kDxE70KipN1jqpQqUkotVkq9nO62BUEQWktzM7z87XOw1+IW\nz6nQNpqbI3skM4VtiLjp+P334S9/MfOnnw4ffZQ+uYT0YifkimaYvviimTY1JV6/8wOIeEwFIf14\nPB5WrFjRsjxp0iSmTZvGzp07Ofnkk6mpqaG0tJSysjJqa2uZMWMGEydOTKiN2bNns++++1JWVsY+\n++zDnDlzAMLqCg0NrqysZNq0aYwePZrS0lJOPfVU6urqOO+88+jatSuHHXYYq+wv5XlGJl4JfgN8\nAUgwnACYkAn5qlaY5JLud+yATT/5f7D/KNq1ez/T4uQ0tt6z0TC1sQ0HJ19+GZhPZdei9evhf//L\nTsO9LeTS/X744fDGGzBzJnz7baAf9Lp1gTL2kDJbtyZef3298bQqVRiGaS7pXkge8fQxVTclZ8wt\nPb1tN5JSCqUUJSUlvPHGG5x//vmsdoT0qARTbu/YsYPf/OY3LFq0iEGDBrF+/Xo2Wg+NeOqaO3cu\n//73v+nZsydHHnkkRx55JA899BCPPvookydP5qabbuIf//hHYgeZA6T1L08p1Rf4EfB74Kp0ti0I\ngtAWvv3WmvG3wyO985PCc8/BscdmWopg5s83U7f+oxdfDFdckXoZDjvMTHftgi5dUt+eEM7995vp\n55+bn22YOjPojh/f+vq3bzfJswrFMBWESLTVoEwm2roZtctN6bYuFh6Ph6VLl9K3b1969+5N7969\n46pLKcWFF17I3nvvDcDJJ5/M8uXLOdb6wzzzzDOZls3jrLWBdL9e/Qm4FpA0VkIL8hW1cMkl3beE\ndnp88iLZRmy9H3QQ7L9/ZmUJxf5AftBB4dsuv9xMU63/sjKTATrfyKX73R7HNBRbL0ceSZs+UP35\nz7B2beEYprmkeyF5FLLeO3fuzNy5c3nwwQcpLy/nlFNO4auvvop7f9uIBejYsSN77rln0PL27duT\nKm+2kDbDVCl1CvC91noxkBy/vSAIQhrw+QLJSir2LMmsMHnE7t3ZN+SKref6+ujlzjsPrrwytbIU\ngsGSrVx/vfv64mITguvxhIdzJxLpt2hRYB/RsyCkn5KSEnbu3NmyXFtb2xJi6xZqm2goL8C4ceN4\n8803WbduHUOGDGkZbqZz585Bba9z9hFwoTVt5yrpDOUdBZyqlPoR0BEoU0o9qrW+wFlo0qRJDBw4\nEIBu3boxYsSIli8udqy6LOfXsr0uW+SR5fQtL1myhClTpmSNPJGWH3kEwAsrYWffGvx+jdf7TtbI\nl2vL9nxNDRQXZ14e5/Iee5jlHTvMMgRvh0q0hoYGr2XEpkYen8/Lu+/Cj3+c3uOX+90sf/yxWQ7V\n78qVldTVQZ8+Xj79FMaMqbTKeRk2LFB+8WIvPl/k+jdvNssej7meMn28qV6+55575H2uAJezmREj\nRvD4448zc+ZM5s2bx7vvvsthVj+K3r17s3HjRurr6ykrKwMSD+X9/vvv+fDDDzn++OPp1KkTnTt3\npshK9z5ixAjuuOMOVq9eTVlZGbfddlvY/s72WhNGnE3Yz/8t1jhsVVVVkQtrrdP+A44BXnZZr4XC\nY8GCBZkWQcgQuaL7Cy7QGrRmBpoZ6LPuujfTIuU0tt47ddJ6xgytL744s/I4+c9/tB49Wusnn9T6\nXhc1K6V1c7PWv/iF1vffnzo5Sku13rIldfVngly537XWer/9rHve+t13n9arVml9/fVm+eijtbYP\nZ8QIs+7kk83yUUdp/e670eu//HKzz113aT1lSkoPJSvIJd0LyWPBggU6W9/tFy1apIcNG6ZLS0v1\nxIkT9YQJE/S0adNatk+ePFn37NlTd+/eXdfU1OgZM2boiRMnaq21XrlypfZ4PNrn80Wsv7a2Vh9z\nzDG6a9euulu3bnrs2LF6+fLlLdt//etf627duulBgwbpWbNmBdVXWVmpH3744ZayU6dO1RdeeGHL\n8rx58/SgQYOSdi5SSST9W+vDbESlM2CFK6WOAa7WWp8asl5nQh5BEIRo7LsvrFgBzDDhNGNLfsP8\na+/JrFB5QFkZ3HgjfPUVZMu45V4vzJhhpm4UFZlw35Ej4bTTTNlU0LUrVFebqZB+zjsPrJEdABg+\nHC64AA49FCZPhn79zLU7dmwghPfSS+HBB2HMGLj1VjONxJw58Morpr5Vq+AeeZwIeYpSKuc9fkLr\niaR/a31YjHJGEtFrrd8B3slE24IgCIniGOoMgC5qj8wIkmds25Z9fUzXrIk9LqXWsGFD6odykXe5\nzOHzBebnzYPXXw+s79fPGKPOPqZ77gl//Wv89f/f/5nkR336wAcfJEdmQRCEXMeTaQEEIRf6Igip\nIbd0H7AS2qkss6ZyDK/XS12dmS8uzqwsoTQ3Q8+ekbfbyWratYOhQ1MnRz4mxcml+91pmO6zT2B+\nyxaorYUFC+DZZ+Hdd836s89O7EPF2rVmevfd8MknbZc328kl3QvJoxD03qVLF0pLS8N+778v4523\nhjwaulsQBCGFeJoDs1oenW2loQH69m3bkBupoLnZeL8iYYdtrloFzzwDZ5yRGjny0TDNJSIZppMn\nG+MU4IEHzA/g3HMTq7+sLHbmZ0EQsp98HbYlU2TZK4FQiNhZ3ITCI6d03253y6xHiWHaFiorK9mx\nw4TNZhvNzbE9X7bBmMoxWPNxdIBcut+dhqmTH/84MH/CCYH5Aw5IrP4BA0z/1OHDE5ctF8kl3QvJ\nQ/QuJIq8XQmCIMSDJ9DxUDymbWf27ExL4E4sw9T2ZP7gB3DqqZHLJQPxmGaOSIbpUUcZL3/79iaU\ne948s94aBSJuli4103/9C159tfVyCoIg5BPiMRUyTiH0QRDcySXdl3QJhPIq8tCdlUa8Xi87dmRa\nCnfiMUzBGC6pTH6Uj6G8uXS/NzS4r//lL+Gxx4whumtX29t5+ml44om215Pt5JLuheQhehcSRQxT\nQRCEOLj+dwGPqSbPLIYMUFGRaQnciTeUt7k5cS9ZIuSjYZorNDfD/PnRyxQVwcKFgeVOnRJr45JL\nTP/UiRMTl08QBCFfEcNUyDjSB6FwyUbdf/WVGQoklCOPaky/MHlKZWUlRx1lxgLNNlaujJ6QyfaY\nfvllavuB5qNhmo33eyibN5sw3VjDGHk85hqwSdR77vOZdsrL4fDDE5cz18gF3QvJp1D0fuCBB/Ku\nnaI7QTweDytCx6TLIm677TYuueQSAKqqqvB4PPidY2UlGekoJQiC4GDIEDj6aHjHMdJyv36wy2PG\nN/HoYvz5ZjFkAJ8vcS9TOvD7oaQkehlb/Vu3pk6OfEx+lAvYIebl5eYjRSSamwPZeVuD7ZnPxw8Q\nglBo/O9//8u0CAnj9XqZOHEiq1evjlruhhtuSJNEBvGYChlH+iAULtmq+1CPaXMzoPyMLB/J0F2T\nMyJTPuH1evH5UhsK21q0hr32irzdad1euaAAACAASURBVEiUl6delnwiW+93Jy++aKa1tXD88TBh\ngnu5v/0Nvv8+cj2xdLdjR2EZprmgeyH55Lvem5ubYxfKYXyRssClEDFMBUEQQgh9Fjc3A55m2nlM\nkIkuhDfJFOP3Z6dh6vPFF8qrVGr7yRaKwZJtrF9vprt2mT6gjz3mXm7SJDj4YPdt8Xi7v//e3AOi\nZ0HIDAMHDuQPf/gDw4YNo0ePHkyePJndu82wcK+88gojRoyge/fujB49mqV2Gm1rvzvuuIPhw4dT\nWlqKz+dj4MCBvP322wDs3r2bKVOmUFFRQUVFBVdeeSWNjYGuQHfeeSfl5eX07duXf/zjH3HJ2tDQ\nwNVXX83AgQPp1q0bY8aMYZeVfe2ll15i2LBhdO/enbFjx/Klo4/BwIEDueuuu/jBD35At27dOOec\nc9i9ezc7duzg5JNPpqamhtLSUsrKyqitrWXGjBn89Kc/ZeLEiXTt2pXZs2czY8YMJoZ0hn/44Yep\nqKigvLycu+66q3UKiIAYpkLGKZQ+CEI42ar7r78OXt6wAXY1NtPe0x4lMZZtprKyMqYBmCni8eRq\nbX6plD8fDZZsvd+dhI6t66bjDh1g770DQ760hqIi6NvX1J9venYjF3QvJJ9s1/ucOXN48803+e67\n7/j666+ZOXMmixcv5qKLLmLWrFls2rSJSy+9lFNPPZWmpkACxCeffJLXX3+dLVu2UFRUhFKq5d3g\n97//PQsXLuSzzz7js88+Y+HChcycOROAN954g7vuuou33nqLr7/+mrfeeisuOa+55hoWL17Mhx9+\nyKZNm7jzzjvxeDx8/fXXTJgwgXvvvZe6ujp+9KMfMX78+BZPrlKKp59+mn//+9+sXLmSzz//nNmz\nZ9O5c2feeOMNysvL2bZtG/X19fTp0wcwhu6ZZ57J1q1bOe+881zfebxeL99++y1vvvkmt99+e4tR\nngyy8LVAEAQhe7BfGlVRwGOKZOVtM2+9FRgDMpuIZZgqZcoolfrkR0L6+eEPA/Pdu7uXuftuc420\nJYpv3TrTx1op4zkVhILFfpi29Zdws4rLLruMiooKunfvzu9+9zueeOIJZs2axaWXXsqhhx6KUooL\nLriADh068NFHH7Xsd8UVV1BRUUEHlyxpc+bM4cYbb6RXr1706tWL6dOn85gVevHUU08xefJkhg4d\nSklJCTfddFNMOf1+P//85z/585//TJ8+ffB4PBxxxBEUFxczd+5cTjnlFI477jiKioq45ppraGho\n4IMPPmjZ/4orrmCvvfaie/fujB8/niVLlgCRI79GjRrFqdYg3R07dnQtN336dDp16sSBBx7IhRde\nyBNJHPNKDFMh4+R7HwQhMrmge/ulsbSrbZgqMUvbiNfrZc6cTEvhTjyGaaqHirHJN09aLtzvY8YE\n5nv2dC/To0fb9b9xY8AwzTc9u5ELuheST1x6t0NQ2vprBf369WuZ79+/PzU1NaxatYq77rqL7t27\nt/zWrFlDTU2N636h1NTUMGDAgLB6AWpra8PajEVdXR27du1i3333DdtWW1sbVIdSin79+rF27dqW\ndXs5kiZ06tSJ7du3R22vb9++MWVyO2/JQgxTQRCEKNhekY07N+LXtmujAN4kU8x110VOLJNJ4un7\nmo7ETYVisGQb8XhBu3Vru/47djT1iJ4FIXNUV1cHzZeXl9O/f39+97vfsXnz5pbf9u3bOfvss1vK\nRuvSU15eTlVVVVC9FVZCgj59+oS1GYtevXrRsWNHvv32W9e2Vq1a1bKstWb16tUt7UXD7RicIcnR\nyoUeQzztxYsYpkLGyfY+CELqyGbd79xppvaQEUopijxFKJS8SLYRW+89emRWDjfiSX60fTtYOTJS\nRj4aLNl8v9u89lr07fvsA4MGGcO0ffvWt9PcbPbPRz27kQu6F5JPNutda83999/P2rVr2bRpE7//\n/e8555xzuPjii3nwwQdZuHAhWmt27NjBq6++GtPTaHPuuecyc+ZM6urqqKur4+abb+b8888H4Kyz\nzmL27NksX76cnTt3xhXK6/F4mDx5MldddRW1tbX4fD4+/PBDGhsbOeuss3j11VeZP38+TU1N3HXX\nXXTs2JFRo0bFrLd3795s3LiR+vr6oHPidp5CmTlzJg0NDSxbtozZs2cHGe1tRQxTQRAEFx54wEzt\noWN8fh+9SnoBoMVj2maydbiY9etjh/K+/nrq5ZA+ppnhlVeib/f7zYeLl14COxfKfvsl3k5tbWEN\nFyMI2YZSigkTJjBu3Dj23XdfBg0axNSpUznkkEOYNWsWl112GT169GDQoEE8+uijcSc+nDp1KiNH\njmT48OEMHz6ckSNHMnXqVABOOukkpkyZwrHHHsvgwYM57rjj4qr3j3/8IwcddBCHHnooPXv25IYb\nbsDv9zN48GD+9a9/cfnll7PHHnvw6quv8vLLL9OuXTvXepwe0SFDhnDuueeyzz770KNHD2prayN6\nTJ3rlFIcc8wx7Lfffhx//PFce+21HH/88XGdm3hQ2TTsgVJKZ5M8Qnrwer1Z/VVNSB3ZqHv7+Xvb\nbXD99fD222Y8w9mLH+HtlW/z+cKu7NNtMM9dd3lmBc1hvF4vixZVUlsLQ4bAwoUwa1ampTIMHw4z\nZ4KV+yGMsjK4/Xb41a9Sa1D07QsffghRujLlHNl4v4fyxz/Ctdeaead+r77ajFt7993wwQcwcGBg\n2xFHGF3ZHH20uYaOPjpyO0pBTY358HXeeW3L8JsL5ILuheTj9XoZO3ZsVg6xtvfee/Pwww9z7LHH\nZlqUvEYp5ap/a32YVS4eU0EQBAeHHx78QmnnMPBpH0WeIlAyjmkySMRj+sYb4UP4pIriYmOAREIp\nGDYs9XKIJy0zxPJ+1tSY/qFTppjlrl3hxRdb19aee4qeBUEQnIhhKmQc+YpauGSj7kOT32gN++5r\nQnnbqXYoJMayrdjjmEaINgpi5044+WTYf//UywWm7180uWxDYvjw1MqRjwZLNt7vocRKftShgzFG\njzrKLG/dagzMRLCHGyoqyk89u5ELuheSj+g9foYNG0ZpaWnYL5lDseQCcbwWCIIgFA6ffBJ46YSA\nZ6/Z30yRpwifD3bsyJx8+cL330Npaexy69enXhYnsQxTiJ0gKRkUisGSbfh80bfbQwXZ18gBByTe\nRlNTIHGS6FkQMsNKO7NhlrBs2bJMi5AViMdUyDgyvlnhkq2679UrMO/zmRdHn/ZRpIpYuhTmzZM3\nybbg9XrZujW+F/LGxtTL4yQej2lzc3oM03wjW+93J7E8pvaHKjuqIlJf5GgUomGaC7oXko/oXUgU\nMUwFQRAcdOsWHJr39tvwzTfw9cav2e3bzc8uyEOLIQMoZYbeiEVJSWB+y5bUyWNTXx/bMN20KT2y\nFILBkm3861+Rt/mtYYw9noBh2prM0o2NgaiLQjFMBUEQ4kEMUyHjSB+EwiUbdR8apjl8OHTpAu09\n7RnQdQClZaA88ibZFiorK1tCImPhDJvu3j11MtXUmGltrek3GA2lYO+9UyeL3Ua+GSzZeL+HMnRo\n5G3ffx+Yt6MqWuM5b24O3j/f9OxGLuheSD6idyFRpI+pIAiCg9DkRwCHHAJ+7adzcWfUjo2ZESzP\nsJMfxQrVtYfuSCVbtkBFRcBAGDQoclk7lDeexE1toaoqPV5ZIZj+/SNvc+rcNkhb4zFtbjbZn8Fc\nT7YnVhDylXjHABUE8ZgKGUf6IBQu6dS93w/ffRdfOacXxPag2n1MAVAF4OJIIV6vlzlzIB7119en\nXJygfoUdOwb6/0WiqSn1hinAs8+mvo10kgvP+mjJj7QOeDrbEsrrvH7y0TPuRi7oXkg+Xq8XrbX8\nCvC3YMGClvlEEMNUEISC4OmnY49RCOGhvLYH1ec345jKl9/k8fzz4euGD4cZMwLL/fqlXg5bpVrH\nzrirFDzyCLz6aurl2rkz9W0IwdiGqbNvs3ObfW3Y082bE29j1y6orjbzWsf3wUwQBKEQEMNUyDjS\nB6FwSafu7ZDRWMNBhIby2i+jfu0PeEwpABdHCrH1PmJE+LalS+G99wLLrcl6mij2B12/P5B1NRJK\nwYcfpl4miN7fMRfJhWe9/XxwuwaczwbbMP3TnxJv49FHA/Nr1ya+fy6SC7oXko/ovXBpre7FMBUE\noSCwvaXbtkUvFxrKe//98OabJpTXozw8/cVc9LirUydogXD44XDVVe7bnDpKMAqoVdh9/Px+9z7G\noVx/PfzsZ6mVqbIyPg+/kFxsw9QtVNvNY3rWWYm34QxP/8EPoFOnxOsQBEHIR8QwFTKO9D0pXNKp\n+1j9BsEYQaGG6csvm6kdynvFYVNSI2AB4fV6KS2NrJMBAwLzTsM0VVHUtbVmumlT7HaUMgZKly6p\nkcXG48m/pDjZ+qzfsgUaGsz855+babwe0/HjE2/v2GMD80VF8T2bcp1s1b2QWkTvhUtrdS+GqSAI\nBUU0D5xzbEGba6+1jBEr+dGUw68CfysynghBhH4AcOJcf+65gflUv8DH09cvXVl5M52tdcaM9PSj\nzQZ69AhcZ089ZaZuhqmbxzRWBIYbvXrBmDGBevLtA4QgCEJrEcNUyDjSB6FwyYTuoxmmmzeHe8IG\nDYLJk6F6azUeJY/MZFBZWRm3YRrP+rZiGwbxeEG//94kr0m1YZrp8S1vugnuuy+5dWbrs15r+Phj\nMz92rJmuXx9ezukxtad33pl4e84PG4VimGar7oXUInovXLK+j6lSqqNS6mOl1BKl1BdKqdvS1bYg\nCMK335pptJd9raF79+B1djKc3b7dlLR3SdUptIpohmlRkfv4pqkyTO1roqgo+jiWNu+8E/Cup4ps\nMFg2FtCQvevWmWm0fr1+f/DQQgArVybeVnNzcEhwpvUsCIKQLaTNMNVa7wLGaq1HAMOBsUqpo9LV\nvpC9SB+EwiWdurdfBKMZpn5/eP9C2zBt72nPHp33SJ2ABYTX643pMe3QIdww6tgxNfLY10RjY3ye\n0EGDoHfv1Mhik+lQXoCFC5NbX7Y/6+2szJF44glYtcrMtyWb7po18NZbZr5QDNNs172QGkTvhUtO\n9DHVWtujshUDRcCmdLYvCIIQy2PqZpi2a2eGi5FQ3uThZpiG6ubee4OXy8pSI4vd7oYNsQ3TAQPg\n7bdT5721yXQobyGyeXPAI3rIIeHb99wzMD9kSOvbmTcvMF8ohqkgCEI8pPUtSynlUUotAdYDC7TW\nX6SzfSE7kT4IhUs6dW+/5McyTEMNjk2bLO+VGKZJI1IfU/sFfetWM929O3h7qrLy2tfE+vXuIcRO\nTjzRTFNtmGaDxzTZZOuzfty4wLx97bklPxowAE46yczbIf9uY/HG4ic/gZ/+1MwXimGarboXUovo\nvXBpre5TnL4hGK21HxihlOoK/FspVam19jrLTJo0iYEDBwLQrVs3RowY0XJwtltYlmVZlgtjecUK\nWLGikpkz217fsmVmubExcvk334SVK83yihVevF7o1KmSDRtg8/LNLOmyhJGnH5E15yeXl7ds8bJ4\nMbRvH9huvFWVvPgigNcawsNsB69lqCZfnoceMvUvWgQlJdHLK1WJzwdVVeb6SNX52bTJy+efw/jx\nqak/1jJ42WMPSMX5zrbl0lJzvO+9B36/2b59e7B+V6/2snEj9O1rlv/zH7P/IYe41794sQlXd2vP\n54ONG039xxxjPtJk0/mQZVmWZVlO9vKSJUvYsmULAFVVVUREa52RHzANuCZknRYKjwULFmRaBCFD\nxNL9mWdqnazHwty5pq433ohc5le/MmWuu07r224z62bO1PqGG7Q+fNbh+sPVH+qG3U2aG4uSI1SB\nsmDBAj1ypNYLF2r9t79pffHFZn1Dgzn/9u+++4KX99svNfKUlpr677hD69NOi172F7/QuqwscH2k\nih49tL7lltS2EQ3QuqIiuXVm67O+stIc74YNWp9+upkfMya4zFVXaX3UUVqfempgHWj93/+G1zdm\njNbvvBO5vUcf1fr884PraW5u2zFkO9mqeyG1iN4Ll1i6t2y+MPvQE9lkTS5KqV5KqW7WfCfgBGBx\nutoXBCH3SGZWUDtcMzTrrpOjXNKxSR/T1LBkSXho7mefRd/Hk6LTP2CAmd58s0m6FAvneJapYtMm\nmDYttW3E4vvvM9t+urCHCdI6kPzok0/Cy9mJ0JzEc72E4jYObjxj6AqCIOQ76XzL6gPMt/qYfgy8\nrLV+O43tC1mK7eoXCo9YurcNhqeeMr8332x7m6HDPTjp2RNOOCG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AhqD3qt/DjdBMvKHL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\n+kPl+86o8KFEV337PpojpgOBHc65fAAz+x0wE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SJElSr2SPqSSp4ewxlSSpTPaYSpIkSZJakoWpms4ehHKZfZnMvUzmXi6zL5O5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LzCZLOkXSZEnvlXS1mVXfMmJkgh6EuMg+plJz3749+Vx4Ft4Je2/v3oBQERzv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\nmtmGuO1T4vvHmtkjKdt9y6JR79Vmdl3acTMz+7mZfRivf9T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QlUhMkThqEMJF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\nN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+ "text/plain": [ + "" ] }, "metadata": {}, @@ -767,7 +1175,7 @@ } ], "source": [ - "ta.plotTasks(top_big_tasks)" + "trace.analysis.tasks.plotTasks(top_big_tasks.index.tolist())" ] }, { @@ -781,16 +1189,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:45:13 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n", - "11:45:13 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n", - "11:45:13 INFO : LITTLE cluster average frequency: 0.918 GHz\n", - "11:45:13 INFO : big cluster average frequency: 1.169 GHz\n" + "05:46:49 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:49 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:49 INFO : LITTLE cluster average frequency: 0.918 GHz\n", + "05:46:49 INFO : big cluster average frequency: 1.169 GHz\n" ] }, { "data": { "text/plain": [ - "(0.91760603617064707, 1.1693335360754311)" + "(0.9176060361706474, 1.1693335360754313)" ] }, "execution_count": 19, @@ -799,9 +1207,9 @@ }, { "data": { - "image/png": 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sHtSpvVb38zr3+nb3uSr71Wn7btqv2pdO/ayaTy9fsxSU5t7tudrNc1SnvHrV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\nAFOPzi0AAAAAYOr9P5P3dAfJxHKeAAAAAElFTkSuQmCC\n", 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fUbTvIHkW1y1rw2HlnTOSYx4rHnEfUL9r7KjP3wOU2TP2ZeehfuXVOU/3qluv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\nbah7ziyel0Zh0Laf1L0N2qm77/U6Rka9T2bKrnv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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -810,12 +1218,12 @@ ], "source": [ "# Cluster frequencies\n", - "ta.plotClusterFrequencies()" + "trace.analysis.frequency.plotClusterFrequencies()" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -824,22 +1232,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "11:45:58 INFO : Plotting EDiff data just for task(s) [{'chrome': 20705, 'lsof': 20803, 'keygen': 20672}]\n", - "11:45:58 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n", - "/home/derkling/.local/lib/python2.7/site-packages/matplotlib/axes/_base.py:2767: UserWarning: Attempting to set identical left==right results\n", + "05:46:49 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "/usr/lib/python2.7/dist-packages/matplotlib/axes/_base.py:2544: UserWarning: Attempting to set identical left==right results\n", "in singular transformations; automatically expanding.\n", "left=25.777079, right=25.777079\n", " 'left=%s, right=%s') % (left, right))\n", - "11:45:58 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n", - "11:45:58 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n", - "11:45:58 WARNING : Events [sched_overutilized] not found, plot DISABLED!\n" + "05:46:49 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:49 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n", + "05:46:50 WARNING : Event [sched_overutilized] not found, plot DISABLED!\n" ] }, { "data": { - "image/png": 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0FzZoTU1NNDY2rvZx1qhKkiRJkirCGlVJkiRJUo9goqqqU801DFq3jH0xGfdi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OSpLkMiCEEK6oPO9WoDuEcNjSX2tUJUmS5qYe6kProQ+SGsNSHE+TVB6jvgSsr3x8EfDFCdcv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\nGhQXqpIkSZKkQXGhKkmSJEkalP8Cuykik4Bjv08AAAAASUVORK5CYII=\n", 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P/w22PYF2h8ws28z6m9ltwFCCpyBDMJ34dDMbEdbJCx961MvM9jOzM8K1qVUEiX1NeNyn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\ntBQWUrZ6NQB3vngCY/Z8jDeAHcz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k5rEeeGPNmnzidHc6zeb77qN81y72Mo+rSDGJeZz9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\nJEmSJI141qRKkiRJkoacNamSJEmSpKJnkqqiYs1Cchn7ZDLuyWX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k8m35PKUR8WvAt4GfAXZn5mpE7AQuy8xT17m9PaWSZs4+rG7zPUB9YS1rnvVE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\n7P7772f37h9jaekJB//9yI8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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -858,7 +1265,7 @@ ], "source": [ "# Plots SchedTune's Energy-Diff Space Filtering\n", - "ta.plotEDiffTime(top_big_tasks)" + "trace.analysis.eas.plotEDiffTime()" ] } ], @@ -879,6 +1286,12 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" + }, + "toc": { + "toc_cell": false, + "toc_number_sections": true, + "toc_threshold": 6, + "toc_window_display": false } }, "nbformat": 4, diff --git a/ipynb/tutorial/UseCaseExamples_SchedTuneAnalysis.ipynb b/ipynb/tutorial/UseCaseExamples_SchedTuneAnalysis.ipynb index 7c1fef2e2..45881f6c4 100644 --- a/ipynb/tutorial/UseCaseExamples_SchedTuneAnalysis.ipynb +++ b/ipynb/tutorial/UseCaseExamples_SchedTuneAnalysis.ipynb @@ -10,26 +10,10 @@ "marked": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: pylab import has clobbered these variables: ['axes', 'trace', 'f']\n", - "`%matplotlib` prevents importing * from pylab and numpy\n" - ] - } - ], + "outputs": [], "source": [ "# Enable in-notebook generation of plots\n", - "%pylab inline" + "%matplotlib inline" ] }, { @@ -810,7 +794,7 @@ "source": [ "# Trace events are converted into tables, let's have a look at one\n", "# of such tables\n", - "load_df = trace.df('sched_load_avg_task')\n", + "load_df = trace.data_frame.trace_event('sched_load_avg_task')\n", "load_df.head()" ] }, @@ -953,7 +937,7 @@ } ], "source": [ - "cap_df = trace.df('cpu_capacity')\n", + "cap_df = trace.data_frame.trace_event('cpu_capacity')\n", "cap_df.head()" ] }, @@ -1096,49 +1080,27 @@ "A graphical representation can always be on hand" ] }, - { - "cell_type": "markdown", - "metadata": { - "hidden": true - }, - "source": [ - "#### Load the trace analysis module" - ] - }, { "cell_type": "code", "execution_count": 21, "metadata": { - "collapsed": true, - "hidden": true + "collapsed": true }, "outputs": [], "source": [ - "trace = Trace(platform, boost15_trace, [\n", - " \"sched_switch\",\n", - " \"sched_overutilized\",\n", - " \"sched_load_avg_cpu\",\n", - " \"sched_load_avg_task\",\n", - " \"sched_boost_cpu\",\n", - " \"sched_boost_task\",\n", - " \"cpu_frequency\",\n", - " \"cpu_capacity\",\n", - " ])" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false, - "hidden": true - }, - "outputs": [], - "source": [ - "from trace_analysis import TraceAnalysis\n", - "\n", - "# Initialize a trace analysis module\n", - "ta = TraceAnalysis(trace, tasks=['task_ramp'], prefix='boost15_')" + "trace = Trace(platform, boost15_trace,\n", + " [\"sched_switch\",\n", + " \"sched_overutilized\",\n", + " \"sched_load_avg_cpu\",\n", + " \"sched_load_avg_task\",\n", + " \"sched_boost_cpu\",\n", + " \"sched_boost_task\",\n", + " \"cpu_frequency\",\n", + " \"cpu_capacity\",\n", + " ],\n", + " tasks=['task_ramp'],\n", + " plots_prefix='boost15_'\n", + " )" ] }, { @@ -1179,16 +1141,14 @@ } ], "source": [ - "ta.plotTasks(\n", + "trace.analysis.tasks.plotTasks(\n", " signals=['util_avg', 'boosted_util', 'sched_overutilized', 'residencies'],\n", ")" ] }, { "cell_type": "markdown", - "metadata": { - "hidden": true - }, + "metadata": {}, "source": [ "#### Example of Clusters related singals" ] @@ -1197,8 +1157,7 @@ "cell_type": "code", "execution_count": 131, "metadata": { - "collapsed": false, - "hidden": true + "collapsed": false }, "outputs": [ { @@ -1213,7 +1172,7 @@ } ], "source": [ - "ta.plotClusterFrequencies();" + "trace.analysis.frequency.plotClusterFrequencies()" ] }, { @@ -1677,13 +1636,13 @@ "outputs": [], "source": [ "# Get the boost events related to the RTApp task\n", - "df = trace.df('sched_boost_task')\n", + "df = trace.data_frame.trace_event('sched_boost_task')\n", "df = df[df.comm == 'task_ramp']\n", "\n", "# Use them to create a new \"boost_task_rtapp\" TRAPpy class\n", "trace.ftrace.add_parsed_event(\"boost_task_rtapp\", df)\n", "\n", - "# df = trace.df('boost_task_rtapp')\n", + "# df = trace.data_frame.trace_event('boost_task_rtapp')\n", "# df.head()" ] }, @@ -1769,8 +1728,8 @@ "outputs": [], "source": [ "# Get the two dataset of interest\n", - "df1 = trace.df('cpu_capacity')[['cpu', 'capacity']]\n", - "df2 = trace.df('boost_task_rtapp')[['__cpu', 'boosted_util']]\n", + "df1 = trace.data_frame.trace_event('cpu_capacity')[['cpu', 'capacity']]\n", + "df2 = trace.data_frame.trace_event('boost_task_rtapp')[['__cpu', 'boosted_util']]\n", "\n", "# Join the information from these two\n", "df3 = df2.join(df1, how='outer')\n", @@ -1904,7 +1863,7 @@ "import pandas as pd\n", "\n", "# Focus on cpu_frequency events for CPU0\n", - "df = trace.df('cpu_frequency')\n", + "df = trace.data_frame.trace_event('cpu_frequency')\n", "df = df[df.cpu == 0]\n", "\n", "# Compute the residency on each OPP before switching to the next one\n", -- GitLab From fc1be6e8682756bdcc5ff8bd6098766bdb05697d Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Tue, 26 Jul 2016 16:37:59 +0100 Subject: [PATCH 24/24] ipynb: bug fix - copy.deepcopy throws an exception with memoized methods Calling copy.deepcopy on the results dictionary of each experiment throws an exception if among the results objects there are some with memoized methods. Nevertheless, the deep copy was used to correctly copy the energy data dictionary, therefore deep copy is now performed on that data only. Signed-off-by: Michele Di Giorgio --- ipynb/android/Android_Workloads.ipynb | 6 +++--- ipynb/android/workloads/Android_Recents_Fling.ipynb | 4 +--- ipynb/android/workloads/Android_YouTube.ipynb | 5 ++--- ipynb/sched_dvfs/smoke_test.ipynb | 5 ++--- 4 files changed, 8 insertions(+), 12 deletions(-) diff --git a/ipynb/android/Android_Workloads.ipynb b/ipynb/android/Android_Workloads.ipynb index d09902473..d57bde61a 100644 --- a/ipynb/android/Android_Workloads.ipynb +++ b/ipynb/android/Android_Workloads.ipynb @@ -394,7 +394,7 @@ " return {\n", " 'dir' : exp_dir,\n", " 'db_file' : db_file,\n", - " 'nrg_data' : nrg_data,\n", + " 'nrg_data' : copy.deepcopy(nrg_data),\n", " 'nrg_file' : nrg_file,\n", " 'trace_file' : trace_file,\n", " }\n", @@ -402,7 +402,7 @@ " return {\n", " 'dir' : exp_dir,\n", " 'db_file' : db_file,\n", - " 'nrg_data' : nrg_data,\n", + " 'nrg_data' : copy.deepcopy(nrg_data),\n", " 'nrg_file' : nrg_file,\n", " }" ] @@ -573,7 +573,7 @@ " logging.info('Test %d/%d: %s in %s configuration',\n", " idx+1, len(test_wloads), wload_kind.upper(), conf_name.upper())\n", " res = experiment(wl, te.res_dir, conf_name, wload_name, iterations, collect)\n", - " results[conf_name][wload_name] = copy.deepcopy(res)\n", + " results[conf_name][wload_name] = res\n", "\n", " # Save collected results\n", " res_file = os.path.join(te.res_dir, conf_name, 'results.json')\n", diff --git a/ipynb/android/workloads/Android_Recents_Fling.ipynb b/ipynb/android/workloads/Android_Recents_Fling.ipynb index c83a3f30b..7926371dd 100644 --- a/ipynb/android/workloads/Android_Recents_Fling.ipynb +++ b/ipynb/android/workloads/Android_Recents_Fling.ipynb @@ -56,7 +56,6 @@ "source": [ "%pylab inline\n", "\n", - "import copy\n", "import os\n", "from time import sleep\n", "\n", @@ -517,8 +516,7 @@ "# Run the benchmark in all the configured governors\n", "for governor in confs:\n", " test_dir = os.path.join(te.res_dir, governor)\n", - " res = experiment(governor, test_dir)\n", - " results[governor] = copy.deepcopy(res)" + " results[governor] = experiment(governor, test_dir)" ] }, { diff --git a/ipynb/android/workloads/Android_YouTube.ipynb b/ipynb/android/workloads/Android_YouTube.ipynb index 7a3c27a5f..a64829013 100644 --- a/ipynb/android/workloads/Android_YouTube.ipynb +++ b/ipynb/android/workloads/Android_YouTube.ipynb @@ -54,7 +54,6 @@ "source": [ "%pylab inline\n", "\n", - "import copy\n", "import os\n", "import pexpect as pe\n", "from time import sleep\n", @@ -442,8 +441,8 @@ "# Run the benchmark in all the configured governors\n", "for governor in confs:\n", " test_dir = os.path.join(te.res_dir, governor)\n", - " res = experiment(governor, test_dir, collect='systrace', trace_time=15)\n", - " results[governor] = copy.deepcopy(res)" + " results[governor] = experiment(governor, test_dir,\n", + " collect='systrace', trace_time=15)" ] }, { diff --git a/ipynb/sched_dvfs/smoke_test.ipynb b/ipynb/sched_dvfs/smoke_test.ipynb index 0817b3e8c..9eab63f45 100644 --- a/ipynb/sched_dvfs/smoke_test.ipynb +++ b/ipynb/sched_dvfs/smoke_test.ipynb @@ -343,7 +343,7 @@ " # return all the experiment data\n", " return {\n", " 'dir' : exp_dir,\n", - " 'energy' : nrg,\n", + " 'energy' : copy.deepcopy(nrg),\n", " 'trace' : trace_file,\n", " 'tr' : tr,\n", " 'ftrace' : tr.ftrace\n", @@ -360,8 +360,7 @@ " results[tid] = {}\n", " for governor in confs:\n", " test_dir = os.path.join(res_dir, governor)\n", - " res = experiment(governor, rtapp, test_dir)\n", - " results[tid][governor] = copy.deepcopy(res)\n", + " results[tid][governor] = experiment(governor, rtapp, test_dir)\n", " \n", "def plot(tid):\n", " global results\n", -- GitLab