examples: restructure the rtapp notebooks
Implement the rtapp_example notebook as a merged version between the
original rtapp_examples and simple_rtapp.
Also the final rtapp_example notebook is modified to present more details
on the functionality provided by this workload generator.
Signed-off-by: Ionela Voinescu <ionela.voinescu@arm.com>
diff --git a/ipynb/examples/wlgen/rtapp_example.ipynb b/ipynb/examples/wlgen/rtapp_example.ipynb
new file mode 100644
index 0000000..51d4f54
--- /dev/null
+++ b/ipynb/examples/wlgen/rtapp_example.ipynb
@@ -0,0 +1,1083 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# RTA workload\n",
+ "\n",
+ "The RTA or RTApp workload represents a type of workload obtained using the rt-app test application.\n",
+ "More details on the test application can be found at https://github.com/scheduler-tools/rt-app."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2016-12-08 12:02:29,102 INFO : root : Using LISA logging configuration:\n",
+ "2016-12-08 12:02:29,103 INFO : root : /home/vagrant/lisa/logging.conf\n"
+ ]
+ }
+ ],
+ "source": [
+ "import logging\n",
+ "from conf import LisaLogging\n",
+ "LisaLogging.setup()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Generate plots inline\n",
+ "%pylab inline\n",
+ "\n",
+ "import json\n",
+ "import os\n",
+ "\n",
+ "# Support to initialise and configure your test environment\n",
+ "import devlib\n",
+ "from env import TestEnv\n",
+ "\n",
+ "# Support to configure and run RTApp based workloads\n",
+ "from wlgen import RTA, Periodic, Ramp, Step, Pulse\n",
+ "\n",
+ "# Suport for FTrace events parsing and visualization\n",
+ "import trappy\n",
+ "\n",
+ "# Support for performance analysis of RTApp workloads\n",
+ "from perf_analysis import PerfAnalysis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Test environment setup\n",
+ "\n",
+ "For more details on this please check out **examples/utils/testenv_example.ipynb**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Setup a target configuration\n",
+ "my_target_conf = {\n",
+ " \n",
+ " # Define the kind of target platform to use for the experiments\n",
+ " \"platform\" : 'linux', # Linux system, valid other options are:\n",
+ " # android - access via ADB\n",
+ " # linux - access via SSH\n",
+ " # host - direct access\n",
+ " \n",
+ " # Preload settings for a specific target\n",
+ " \"board\" : 'juno', # juno - JUNO board with mainline hwmon\n",
+ " \n",
+ " # Define devlib module to load\n",
+ " \"modules\" : [\n",
+ " 'bl', # enable big.LITTLE support\n",
+ " 'cpufreq' # enable CPUFreq support\n",
+ " ],\n",
+ "\n",
+ " # Account to access the remote target\n",
+ " \"host\" : '192.168.0.1',\n",
+ " \"username\" : 'root',\n",
+ " \"password\" : 'juno',\n",
+ "\n",
+ " # Comment the following line to force rt-app calibration on your target\n",
+ " \"rtapp-calib\" : {\n",
+ " '0': 361, '1': 138, '2': 138, '3': 352, '4': 360, '5': 353\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "# Setup the required Test Environment supports\n",
+ "my_tests_conf = {\n",
+ " \n",
+ " # Binary tools required to run this experiment\n",
+ " # These tools must be present in the tools/ folder for the architecture\n",
+ " \"tools\" : ['rt-app', 'taskset', 'trace-cmd'],\n",
+ " \n",
+ " # FTrace events end buffer configuration\n",
+ " \"ftrace\" : {\n",
+ " \"events\" : [\n",
+ " \"sched_switch\",\n",
+ " \"cpu_frequency\"\n",
+ " ],\n",
+ " \"buffsize\" : 10240\n",
+ " },\n",
+ "\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2016-12-07 10:25:45,152 INFO : TestEnv : Using base path: /home/vagrant/lisa\n",
+ "2016-12-07 10:25:45,153 INFO : TestEnv : Loading custom (inline) target configuration\n",
+ "2016-12-07 10:25:45,153 INFO : TestEnv : Loading custom (inline) test configuration\n",
+ "2016-12-07 10:25:45,153 INFO : TestEnv : Devlib modules to load: ['bl', 'cpufreq', 'hwmon']\n",
+ "2016-12-07 10:25:45,154 INFO : TestEnv : Connecting linux target:\n",
+ "2016-12-07 10:25:45,154 INFO : TestEnv : username : root\n",
+ "2016-12-07 10:25:45,155 INFO : TestEnv : host : 192.168.0.1\n",
+ "2016-12-07 10:25:45,155 INFO : TestEnv : password : juno\n",
+ "2016-12-07 10:25:45,156 INFO : TestEnv : Connection settings:\n",
+ "2016-12-07 10:25:45,156 INFO : TestEnv : {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n",
+ "2016-12-07 10:26:02,855 INFO : TestEnv : Initializing target workdir:\n",
+ "2016-12-07 10:26:02,856 INFO : TestEnv : /root/devlib-target\n",
+ "2016-12-07 10:26:23,640 INFO : TestEnv : Topology:\n",
+ "2016-12-07 10:26:23,643 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n",
+ "2016-12-07 10:26:28,585 INFO : TestEnv : Enabled tracepoints:\n",
+ "2016-12-07 10:26:28,587 INFO : TestEnv : sched_switch\n",
+ "2016-12-07 10:26:28,589 INFO : TestEnv : cpu_frequency\n",
+ "2016-12-07 10:26:28,591 WARNING : TestEnv : Using configuration provided RTApp calibration\n",
+ "2016-12-07 10:26:28,591 INFO : TestEnv : Using RT-App calibration values:\n",
+ "2016-12-07 10:26:28,592 INFO : TestEnv : {\"0\": 361, \"1\": 138, \"2\": 138, \"3\": 352, \"4\": 360, \"5\": 353}\n",
+ "2016-12-07 10:26:28,592 INFO : EnergyMeter : Scanning for HWMON channels, may take some time...\n",
+ "2016-12-07 10:26:28,594 INFO : EnergyMeter : Channels selected for energy sampling:\n",
+ "2016-12-07 10:26:28,595 INFO : EnergyMeter : BOARDBIG_energy\n",
+ "2016-12-07 10:26:28,595 INFO : EnergyMeter : BOARDLITTLE_energy\n",
+ "2016-12-07 10:26:28,596 INFO : TestEnv : Set results folder to:\n",
+ "2016-12-07 10:26:28,596 INFO : TestEnv : /home/vagrant/lisa/results/20161207_102628\n",
+ "2016-12-07 10:26:28,597 INFO : TestEnv : Experiment results available also in:\n",
+ "2016-12-07 10:26:28,597 INFO : TestEnv : /home/vagrant/lisa/results_latest\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Initialize a test environment using\n",
+ "# - the provided target configuration (my_target_conf)\n",
+ "# - the provided test configuration (my_test_conf)\n",
+ "te = TestEnv(target_conf=my_target_conf, test_conf=my_tests_conf)\n",
+ "target = te.target"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Workload configuration\n",
+ "\n",
+ "To create an instance of an RTApp workload generator you need to provide the following:\n",
+ "- target: target device configuration\n",
+ "- name: name of workload. This is the name of the JSON configuration file reporting the generated RTApp configuration.\n",
+ "- calibration: CPU load calibration values, measured on each core.\n",
+ "\n",
+ "An RTApp workload is defined by specifying a **kind**, provided below through **rtapp.conf**, which represents the way we want to define the behavior of each task.\n",
+ "The possible kinds of workloads are **profile** and **custom**. It's very important to notice that **periodic** is no longer considered a \"kind\" of workload but a \"class\" within the **profile** kind.\n",
+ "<br><br>\n",
+ "As you see below, when \"kind\" is \"profile\", the tasks generated by this workload have a profile which is defined by a sequence of phases. These phases are defined according to the following grammar:<br>\n",
+ " - params := {task, ...} <br>\n",
+ " - task := NAME : {SCLASS, PRIO, [phase, ...]}<br>\n",
+ " - phase := (PTIME, PERIOD, DCYCLE)<br> <br>\n",
+ " \n",
+ "There are some pre-defined task classes for the **profile** kind:\n",
+ " - **Step**: the load of this task is a step with a configured initial and final load. \n",
+ " - **Pulse**: the load of this task is a pulse with a configured initial and final load.The main difference with the 'step' class is that a pulse workload is by definition a 'step down', i.e. the workload switches from an initial load to a final one which is always lower than the initial one. Moreover, a pulse load does not generate a sleep phase in case of 0[%] load, i.e. the task ends as soon as the non null initial load has completed.\n",
+ " - **Ramp**: the load of this task is a ramp with a configured number of steps determined by the input parameters.\n",
+ " - **Periodic**: the load of this task is periodic with a configured period and duty-cycle.<br><br>\n",
+ "The one below is a workload mix having all types of workloads described above, but each of them can also be specified serapately in the RTApp parameters. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2016-12-07 10:27:16,946 INFO : Workload : Setup new workload simple\n",
+ "2016-12-07 10:27:16,947 INFO : Workload : Workload duration defined by longest task\n",
+ "2016-12-07 10:27:16,947 INFO : Workload : Default policy: SCHED_OTHER\n",
+ "2016-12-07 10:27:16,948 INFO : Workload : ------------------------\n",
+ "2016-12-07 10:27:16,948 INFO : Workload : task [task_per20], sched: {'policy': 'FIFO'}\n",
+ "2016-12-07 10:27:16,949 INFO : Workload : | calibration CPU: 1\n",
+ "2016-12-07 10:27:16,949 INFO : Workload : | loops count: 1\n",
+ "2016-12-07 10:27:16,949 INFO : Workload : + phase_000001: duration 5.000000 [s] (50 loops)\n",
+ "2016-12-07 10:27:16,950 INFO : Workload : | period 100000 [us], duty_cycle 20 %\n",
+ "2016-12-07 10:27:16,950 INFO : Workload : | run_time 20000 [us], sleep_time 80000 [us]\n",
+ "2016-12-07 10:27:16,951 INFO : Workload : ------------------------\n",
+ "2016-12-07 10:27:16,951 INFO : Workload : task [task_pls5-80], sched: using default policy\n",
+ "2016-12-07 10:27:16,952 INFO : Workload : | start delay: 0.500000 [s]\n",
+ "2016-12-07 10:27:16,952 INFO : Workload : | calibration CPU: 1\n",
+ "2016-12-07 10:27:16,952 INFO : Workload : | loops count: 1\n",
+ "2016-12-07 10:27:16,953 INFO : Workload : + phase_000001: duration 1.000000 [s] (10 loops)\n",
+ "2016-12-07 10:27:16,953 INFO : Workload : | period 100000 [us], duty_cycle 65 %\n",
+ "2016-12-07 10:27:16,954 INFO : Workload : | run_time 65000 [us], sleep_time 35000 [us]\n",
+ "2016-12-07 10:27:16,954 INFO : Workload : + phase_000002: duration 1.000000 [s] (10 loops)\n",
+ "2016-12-07 10:27:16,955 INFO : Workload : | period 100000 [us], duty_cycle 5 %\n",
+ "2016-12-07 10:27:16,955 INFO : Workload : | run_time 5000 [us], sleep_time 95000 [us]\n",
+ "2016-12-07 10:27:16,955 INFO : Workload : ------------------------\n",
+ "2016-12-07 10:27:16,956 INFO : Workload : task [task_rmp20_5-60], sched: using default policy\n",
+ "2016-12-07 10:27:16,956 INFO : Workload : | calibration CPU: 1\n",
+ "2016-12-07 10:27:16,957 INFO : Workload : | loops count: 1\n",
+ "2016-12-07 10:27:16,957 INFO : Workload : | CPUs affinity: 0\n",
+ "2016-12-07 10:27:16,958 INFO : Workload : + phase_000001: duration 1.000000 [s] (10 loops)\n",
+ "2016-12-07 10:27:16,958 INFO : Workload : | period 100000 [us], duty_cycle 5 %\n",
+ "2016-12-07 10:27:16,958 INFO : Workload : | run_time 5000 [us], sleep_time 95000 [us]\n",
+ "2016-12-07 10:27:16,959 INFO : Workload : + phase_000002: duration 1.000000 [s] (10 loops)\n",
+ "2016-12-07 10:27:16,959 INFO : Workload : | period 100000 [us], duty_cycle 25 %\n",
+ "2016-12-07 10:27:16,960 INFO : Workload : | run_time 25000 [us], sleep_time 75000 [us]\n",
+ "2016-12-07 10:27:16,960 INFO : Workload : + phase_000003: duration 1.000000 [s] (10 loops)\n",
+ "2016-12-07 10:27:16,961 INFO : Workload : | period 100000 [us], duty_cycle 45 %\n",
+ "2016-12-07 10:27:16,961 INFO : Workload : | run_time 45000 [us], sleep_time 55000 [us]\n",
+ "2016-12-07 10:27:16,962 INFO : Workload : + phase_000004: duration 1.000000 [s] (10 loops)\n",
+ "2016-12-07 10:27:16,962 INFO : Workload : | period 100000 [us], duty_cycle 65 %\n",
+ "2016-12-07 10:27:16,963 INFO : Workload : | run_time 65000 [us], sleep_time 35000 [us]\n",
+ "2016-12-07 10:27:16,963 INFO : Workload : ------------------------\n",
+ "2016-12-07 10:27:16,964 INFO : Workload : task [task_stp10-50], sched: using default policy\n",
+ "2016-12-07 10:27:16,964 INFO : Workload : | start delay: 0.500000 [s]\n",
+ "2016-12-07 10:27:16,964 INFO : Workload : | calibration CPU: 1\n",
+ "2016-12-07 10:27:16,965 INFO : Workload : | loops count: 1\n",
+ "2016-12-07 10:27:16,965 INFO : Workload : + phase_000001: sleep 1.000000 [s]\n",
+ "2016-12-07 10:27:16,966 INFO : Workload : + phase_000002: duration 1.000000 [s] (10 loops)\n",
+ "2016-12-07 10:27:16,966 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n",
+ "2016-12-07 10:27:16,967 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create a new RTApp workload generator using the calibration values\n",
+ "# reported by the TestEnv module\n",
+ "rtapp = RTA(target, 'simple', calibration=te.calibration())\n",
+ "\n",
+ "# Configure this RTApp instance to:\n",
+ "rtapp.conf(\n",
+ "# 1. generate a \"profile based\" set of tasks\n",
+ " kind='profile',\n",
+ " \n",
+ " # 2. define the \"profile\" of each task\n",
+ " params={\n",
+ "\n",
+ " # 3. PERIODIC task\n",
+ " # \n",
+ " # This class defines a task which load is periodic with a configured\n",
+ " # period and duty-cycle.\n",
+ " # \n",
+ " # This class is a specialization of the 'pulse' class since a periodic\n",
+ " # load is generated as a sequence of pulse loads.\n",
+ " # \n",
+ " # Args:\n",
+ " # cuty_cycle_pct (int, [0-100]): the pulses load [%]\n",
+ " # default: 50[%]\n",
+ " # duration_s (float): the duration in [s] of the entire workload\n",
+ " # default: 1.0[s]\n",
+ " # period_ms (float): the period used to define the load in [ms]\n",
+ " # default: 100.0[ms]\n",
+ " # delay_s (float): the delay in [s] before ramp start\n",
+ " # default: 0[s]\n",
+ " # sched (dict): the scheduler configuration for this task\n",
+ " # cpus (list): the list of CPUs on which task can run\n",
+ " 'task_per20': Periodic(\n",
+ " period_ms=100, # period\n",
+ " duty_cycle_pct=20, # duty cycle\n",
+ " duration_s=5, # duration\n",
+ " cpus=None, # run on all CPUS\n",
+ " sched={\n",
+ " \"policy\": \"FIFO\", # Run this task as a SCHED_FIFO task\n",
+ " },\n",
+ " delay_s=0 # start at the start of RTApp\n",
+ " ).get(),\n",
+ "\n",
+ " # 4. RAMP task\n",
+ " #\n",
+ " # This class defines a task which load is a ramp with a configured number\n",
+ " # of steps according to the input parameters.\n",
+ " # \n",
+ " # Args:\n",
+ " # start_pct (int, [0-100]): the initial load [%], (default 0[%])\n",
+ " # end_pct (int, [0-100]): the final load [%], (default 100[%])\n",
+ " # delta_pct (int, [0-100]): the load increase/decrease [%],\n",
+ " # default: 10[%]\n",
+ " # increase if start_prc < end_prc\n",
+ " # decrease if start_prc > end_prc\n",
+ " # time_s (float): the duration in [s] of each load step\n",
+ " # default: 1.0[s]\n",
+ " # period_ms (float): the period used to define the load in [ms]\n",
+ " # default: 100.0[ms]\n",
+ " # delay_s (float): the delay in [s] before ramp start\n",
+ " # default: 0[s]\n",
+ " # loops (int): number of time to repeat the ramp, with the\n",
+ " # specified delay in between\n",
+ " # default: 0\n",
+ " # sched (dict): the scheduler configuration for this task\n",
+ " # cpus (list): the list of CPUs on which task can run\n",
+ " 'task_rmp20_5-60': Ramp(\n",
+ " period_ms=100, # period\n",
+ " start_pct=5, # intial load\n",
+ " end_pct=65, # end load\n",
+ " delta_pct=20, # load % increase...\n",
+ " time_s=1, # ... every 1[s]\n",
+ " cpus=\"0\" # run just on first CPU\n",
+ " ).get(),\n",
+ " \n",
+ " # 5. STEP task\n",
+ " # \n",
+ " # This class defines a task which load is a step with a configured\n",
+ " # initial and final load.\n",
+ " # \n",
+ " # Args:\n",
+ " # start_pct (int, [0-100]): the initial load [%]\n",
+ " # default 0[%])\n",
+ " # end_pct (int, [0-100]): the final load [%]\n",
+ " # default 100[%]\n",
+ " # time_s (float): the duration in [s] of the start and end load\n",
+ " # default: 1.0[s]\n",
+ " # period_ms (float): the period used to define the load in [ms]\n",
+ " # default 100.0[ms]\n",
+ " # delay_s (float): the delay in [s] before ramp start\n",
+ " # default 0[s]\n",
+ " # loops (int): number of time to repeat the ramp, with the\n",
+ " # specified delay in between\n",
+ " # default: 0\n",
+ " # sched (dict): the scheduler configuration for this task\n",
+ " # cpus (list): the list of CPUs on which task can run\n",
+ " 'task_stp10-50': Step(\n",
+ " period_ms=100, # period\n",
+ " start_pct=0, # intial load\n",
+ " end_pct=50, # end load\n",
+ " time_s=1, # ... every 1[s]\n",
+ " delay_s=0.5 # start .5[s] after the start of RTApp\n",
+ " ).get(),\n",
+ " \n",
+ " # 6. PULSE task\n",
+ " #\n",
+ " # This class defines a task which load is a pulse with a configured\n",
+ " # initial and final load.\n",
+ " # \n",
+ " # The main difference with the 'step' class is that a pulse workload is\n",
+ " # by definition a 'step down', i.e. the workload switch from an finial\n",
+ " # load to a final one which is always lower than the initial one.\n",
+ " # Moreover, a pulse load does not generate a sleep phase in case of 0[%]\n",
+ " # load, i.e. the task ends as soon as the non null initial load has\n",
+ " # completed.\n",
+ " # \n",
+ " # Args:\n",
+ " # start_pct (int, [0-100]): the initial load [%]\n",
+ " # default: 0[%]\n",
+ " # end_pct (int, [0-100]): the final load [%]\n",
+ " # default: 100[%]\n",
+ " # NOTE: must be lower than start_pct value\n",
+ " # time_s (float): the duration in [s] of the start and end load\n",
+ " # default: 1.0[s]\n",
+ " # NOTE: if end_pct is 0, the task end after the\n",
+ " # start_pct period completed\n",
+ " # period_ms (float): the period used to define the load in [ms]\n",
+ " # default: 100.0[ms]\n",
+ " # delay_s (float): the delay in [s] before ramp start\n",
+ " # default: 0[s]\n",
+ " # loops (int): number of time to repeat the ramp, with the\n",
+ " # specified delay in between\n",
+ " # default: 0\n",
+ " # sched (dict): the scheduler configuration for this task\n",
+ " # cpus (list): the list of CPUs on which task can run\n",
+ " 'task_pls5-80': Pulse(\n",
+ " period_ms=100, # period\n",
+ " start_pct=65, # intial load\n",
+ " end_pct=5, # end load\n",
+ " time_s=1, # ... every 1[s]\n",
+ " delay_s=0.5 # start .5[s] after the start of RTApp\n",
+ " ).get(),\n",
+ " \n",
+ " \n",
+ " },\n",
+ " \n",
+ " # 7. use this folder for task logfiles\n",
+ " run_dir=target.working_directory\n",
+ " \n",
+ ");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The output of the previous cell reports the main properties of the generated\n",
+ "tasks. Thus for example we see that the first task is configure to be:\n",
+ " - named **task_per20**\n",
+ " - executed as a **SCHED_FIFO** task\n",
+ " - generating a load which is **calibrated** with respect to the **CPU 1**\n",
+ " - with one single \"phase\" which defines a peripodic load for the **duration** of **5[s]**\n",
+ " - that periodic load consistes of **50 cycles**\n",
+ " - each cycle has a **period** of **100[ms]** and a **duty-cycle** of **20%**,\n",
+ " which means that the task, for every cycle, will **run** for **20[ms]** and then sleep for **80[ms]** \n",
+ "\n",
+ "All these properties are translated into a JSON configuration file for RTApp which you can see in **Collected results** below.<br>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Workload composition\n",
+ "\n",
+ "Another way of specifying the phases of a task is through workload composition, described in the next cell.<br>\n",
+ "**NOTE:** We are just giving this as an example of specifying a workload, but this configuration won't be the one used for the following execution and analysis cells. You need to uncomment these lines if you want to use the composed workload."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Initial phase and pinning parameters\n",
+ "ramp = Ramp(period_ms=100, start_pct=5, end_pct=65, delta_pct=20, time_s=1, cpus=\"0\")\n",
+ "\n",
+ "# Following phases\n",
+ "medium_slow = Periodic(duty_cycle_pct=10, duration_s=5, period_ms=100)\n",
+ "high_fast = Periodic(duty_cycle_pct=60, duration_s=5, period_ms=10)\n",
+ "medium_fast = Periodic(duty_cycle_pct=10, duration_s=5, period_ms=1)\n",
+ "high_slow = Periodic(duty_cycle_pct=60, duration_s=5, period_ms=100)\n",
+ "\n",
+ "#Compose the task\n",
+ "complex_task = ramp + medium_slow + high_fast + medium_fast + high_slow\n",
+ "\n",
+ "# Configure this RTApp instance to:\n",
+ "# rtapp.conf(\n",
+ "# # 1. generate a \"profile based\" set of tasks\n",
+ "# kind='profile',\n",
+ "# \n",
+ "# # 2. define the \"profile\" of each task\n",
+ "# params={\n",
+ "# 'complex' : complex_task.get()\n",
+ "# },\n",
+ "#\n",
+ "# # 6. use this folder for task logfiles\n",
+ "# run_dir='/tmp'\n",
+ "#)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Workload execution"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2016-12-07 10:27:30,511 INFO : root : #### Setup FTrace\n",
+ "2016-12-07 10:27:38,104 INFO : root : #### Start energy sampling\n",
+ "2016-12-07 10:27:38,723 INFO : root : #### Start RTApp execution\n",
+ "2016-12-07 10:27:38,726 INFO : Workload : Workload execution START:\n",
+ "2016-12-07 10:27:38,728 INFO : Workload : /root/devlib-target/bin/rt-app /root/devlib-target/simple_00.json 2>&1\n",
+ "2016-12-07 10:27:54,778 INFO : root : #### Read energy consumption: /home/vagrant/lisa/results/20161207_102628/energy.json\n",
+ "2016-12-07 10:27:55,401 INFO : root : #### Stop FTrace\n",
+ "2016-12-07 10:27:57,663 INFO : root : #### Save FTrace: /home/vagrant/lisa/results/20161207_102628/trace.dat\n",
+ "2016-12-07 10:28:03,718 INFO : root : #### Save platform description: /home/vagrant/lisa/results/20161207_102628/platform.json\n"
+ ]
+ }
+ ],
+ "source": [
+ "logging.info('#### Setup FTrace')\n",
+ "te.ftrace.start()\n",
+ "\n",
+ "logging.info('#### Start energy sampling')\n",
+ "te.emeter.reset()\n",
+ "\n",
+ "logging.info('#### Start RTApp execution')\n",
+ "rtapp.run(out_dir=te.res_dir, cgroup=\"\")\n",
+ "\n",
+ "logging.info('#### Read energy consumption: %s/energy.json', te.res_dir)\n",
+ "nrg_report = te.emeter.report(out_dir=te.res_dir)\n",
+ "\n",
+ "logging.info('#### Stop FTrace')\n",
+ "te.ftrace.stop()\n",
+ "\n",
+ "trace_file = os.path.join(te.res_dir, 'trace.dat')\n",
+ "logging.info('#### Save FTrace: %s', trace_file)\n",
+ "te.ftrace.get_trace(trace_file)\n",
+ "\n",
+ "logging.info('#### Save platform description: %s/platform.json', te.res_dir)\n",
+ "(plt, plt_file) = te.platform_dump(te.res_dir)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Collected results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2016-12-07 10:29:04,768 INFO : root : Generated RTApp JSON file:\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\n",
+ " \"global\": {\n",
+ " \"calibration\": 138, \n",
+ " \"default_policy\": \"SCHED_OTHER\", \n",
+ " \"duration\": -1, \n",
+ " \"logdir\": \"/root/devlib-target\"\n",
+ " }, \n",
+ " \"tasks\": {\n",
+ " \"task_per20\": {\n",
+ " \"loop\": 1, \n",
+ " \"phases\": {\n",
+ " \"p000001\": {\n",
+ " \"loop\": 50, \n",
+ " \"run\": 20000, \n",
+ " \"timer\": {\n",
+ " \"period\": 100000, \n",
+ " \"ref\": \"task_per20\"\n",
+ " }\n",
+ " }\n",
+ " }, \n",
+ " \"policy\": \"SCHED_FIFO\"\n",
+ " }, \n",
+ " \"task_pls5-80\": {\n",
+ " \"loop\": 1, \n",
+ " \"phases\": {\n",
+ " \"p000000\": {\n",
+ " \"delay\": 500000\n",
+ " }, \n",
+ " \"p000001\": {\n",
+ " \"loop\": 10, \n",
+ " \"run\": 65000, \n",
+ " \"timer\": {\n",
+ " \"period\": 100000, \n",
+ " \"ref\": \"task_pls5-80\"\n",
+ " }\n",
+ " }, \n",
+ " \"p000002\": {\n",
+ " \"loop\": 10, \n",
+ " \"run\": 5000, \n",
+ " \"timer\": {\n",
+ " \"period\": 100000, \n",
+ " \"ref\": \"task_pls5-80\"\n",
+ " }\n",
+ " }\n",
+ " }, \n",
+ " \"policy\": \"SCHED_OTHER\"\n",
+ " }, \n",
+ " \"task_rmp20_5-60\": {\n",
+ " \"cpus\": [\n",
+ " 0\n",
+ " ], \n",
+ " \"loop\": 1, \n",
+ " \"phases\": {\n",
+ " \"p000001\": {\n",
+ " \"loop\": 10, \n",
+ " \"run\": 5000, \n",
+ " \"timer\": {\n",
+ " \"period\": 100000, \n",
+ " \"ref\": \"task_rmp20_5-60\"\n",
+ " }\n",
+ " }, \n",
+ " \"p000002\": {\n",
+ " \"loop\": 10, \n",
+ " \"run\": 25000, \n",
+ " \"timer\": {\n",
+ " \"period\": 100000, \n",
+ " \"ref\": \"task_rmp20_5-60\"\n",
+ " }\n",
+ " }, \n",
+ " \"p000003\": {\n",
+ " \"loop\": 10, \n",
+ " \"run\": 45000, \n",
+ " \"timer\": {\n",
+ " \"period\": 100000, \n",
+ " \"ref\": \"task_rmp20_5-60\"\n",
+ " }\n",
+ " }, \n",
+ " \"p000004\": {\n",
+ " \"loop\": 10, \n",
+ " \"run\": 65000, \n",
+ " \"timer\": {\n",
+ " \"period\": 100000, \n",
+ " \"ref\": \"task_rmp20_5-60\"\n",
+ " }\n",
+ " }\n",
+ " }, \n",
+ " \"policy\": \"SCHED_OTHER\"\n",
+ " }, \n",
+ " \"task_stp10-50\": {\n",
+ " \"loop\": 1, \n",
+ " \"phases\": {\n",
+ " \"p000000\": {\n",
+ " \"delay\": 500000\n",
+ " }, \n",
+ " \"p000001\": {\n",
+ " \"loop\": 1, \n",
+ " \"sleep\": 1000000\n",
+ " }, \n",
+ " \"p000002\": {\n",
+ " \"loop\": 10, \n",
+ " \"run\": 50000, \n",
+ " \"timer\": {\n",
+ " \"period\": 100000, \n",
+ " \"ref\": \"task_stp10-50\"\n",
+ " }\n",
+ " }\n",
+ " }, \n",
+ " \"policy\": \"SCHED_OTHER\"\n",
+ " }\n",
+ " }\n",
+ "}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Inspect the JSON file used to run the application\n",
+ "with open('{}/simple_00.json'.format(te.res_dir), 'r') as fh:\n",
+ " rtapp_json = json.load(fh, )\n",
+ "logging.info('Generated RTApp JSON file:')\n",
+ "print json.dumps(rtapp_json, indent=4, sort_keys=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2016-12-07 10:29:06,513 INFO : root : Content of the output folder /home/vagrant/lisa/results/20161207_102628\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "total 3592\r\n",
+ "drwxrwxr-x 2 user user 4096 Dec 7 10:28 .\r\n",
+ "drwxrwxr-x 22 user user 4096 Dec 7 10:26 ..\r\n",
+ "-rw-rw-r-- 1 user user 68 Dec 7 10:27 energy.json\r\n",
+ "-rw-rw-r-- 1 user user 705 Dec 7 10:27 output.log\r\n",
+ "-rw-rw-r-- 1 user user 673 Dec 7 10:28 platform.json\r\n",
+ "-rw-r--r-- 1 user user 6360 Dec 7 10:27 rt-app-task_per20-0.log\r\n",
+ "-rw-r--r-- 1 user user 2764 Dec 7 10:27 rt-app-task_pls5-80-1.log\r\n",
+ "-rw-r--r-- 1 user user 5120 Dec 7 10:27 rt-app-task_rmp20_5-60-2.log\r\n",
+ "-rw-r--r-- 1 user user 1648 Dec 7 10:27 rt-app-task_stp10-50-3.log\r\n",
+ "-rw-r--r-- 1 user user 3104 Dec 7 10:27 simple_00.json\r\n",
+ "-rw-r--r-- 1 user user 3629056 Dec 7 10:28 trace.dat\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "# All data are produced in the output folder defined by the TestEnv module\n",
+ "logging.info('Content of the output folder %s', te.res_dir)\n",
+ "!ls -la {te.res_dir}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2016-12-07 10:29:09,792 INFO : root : Energy: /home/vagrant/lisa/results/20161207_102628/energy.json\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\n",
+ " \"LITTLE\": 1.8224460000000136, \n",
+ " \"big\": 0.5827259999999796\n",
+ "}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Dump the energy measured for the LITTLE and big clusters\n",
+ "logging.info('Energy: %s', nrg_report.report_file)\n",
+ "print json.dumps(nrg_report.channels, indent=4, sort_keys=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2016-12-07 10:29:14,705 INFO : root : Platform description: /home/vagrant/lisa/results/20161207_102628/platform.json\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\n",
+ " \"clusters\": {\n",
+ " \"big\": [\n",
+ " 1, \n",
+ " 2\n",
+ " ], \n",
+ " \"little\": [\n",
+ " 0, \n",
+ " 3, \n",
+ " 4, \n",
+ " 5\n",
+ " ]\n",
+ " }, \n",
+ " \"cpus_count\": 6, \n",
+ " \"freqs\": {\n",
+ " \"big\": [\n",
+ " 450000, \n",
+ " 625000, \n",
+ " 800000, \n",
+ " 950000, \n",
+ " 1100000\n",
+ " ], \n",
+ " \"little\": [\n",
+ " 450000, \n",
+ " 575000, \n",
+ " 700000, \n",
+ " 775000, \n",
+ " 850000\n",
+ " ]\n",
+ " }, \n",
+ " \"nrg_model\": null, \n",
+ " \"topology\": [\n",
+ " [\n",
+ " 0, \n",
+ " 3, \n",
+ " 4, \n",
+ " 5\n",
+ " ], \n",
+ " [\n",
+ " 1, \n",
+ " 2\n",
+ " ]\n",
+ " ]\n",
+ "}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Dump the platform descriptor, which could be useful for further analysis\n",
+ "# of the generated results\n",
+ "logging.info('Platform description: %s', plt_file)\n",
+ "print json.dumps(plt, indent=4, sort_keys=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Trace inspection\n",
+ "\n",
+ "More information on visualization and trace inspection can be found in **examples/trappy**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>\n",
+ "/*\n",
+ " * Copyright 2015-2016 ARM Limited\n",
+ " *\n",
+ " * Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+ " * you may not use this file except in compliance with the License.\n",
+ " * You may obtain a copy of the License at\n",
+ " *\n",
+ " * http://www.apache.org/licenses/LICENSE-2.0\n",
+ " *\n",
+ " * Unless required by applicable law or agreed to in writing, software\n",
+ " * distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+ " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+ " * See the License for the specific language governing permissions and\n",
+ " * limitations under the License.\n",
+ " */\n",
+ "\n",
+ ".d3-tip {\n",
+ " line-height: 1;\n",
+ " padding: 12px;\n",
+ " background: rgba(0, 0, 0, 0.6);\n",
+ " color: #fff;\n",
+ " border-radius: 2px;\n",
+ " position: absolute !important;\n",
+ " z-index: 99999;\n",
+ "}\n",
+ "\n",
+ ".d3-tip:after {\n",
+ " box-sizing: border-box;\n",
+ " pointer-events: none;\n",
+ " display: inline;\n",
+ " font-size: 10px;\n",
+ " width: 100%;\n",
+ " line-height: 1;\n",
+ " color: rgba(0, 0, 0, 0.6);\n",
+ " content: \"\\25BC\";\n",
+ " position: absolute !important;\n",
+ " z-index: 99999;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ ".d3-tip.n:after {\n",
+ " margin: -1px 0 0 0;\n",
+ " top: 100%;\n",
+ " left: 0;\n",
+ "}\n",
+ "\n",
+ ".contextRect {\n",
+ " fill: lightgray;\n",
+ " fill-opacity: 0.5;\n",
+ " stroke: black;\n",
+ " stroke-width: 1;\n",
+ " stroke-opacity: 1;\n",
+ " pointer-events: none;\n",
+ " shape-rendering: crispEdges;\n",
+ "}\n",
+ "\n",
+ ".chart {\n",
+ " shape-rendering: crispEdges;\n",
+ "}\n",
+ "\n",
+ ".mini text {\n",
+ " font: 9px sans-serif;\n",
+ "}\n",
+ "\n",
+ ".main text {\n",
+ " font: 12px sans-serif;\n",
+ "}\n",
+ "\n",
+ ".axis line, .axis path {\n",
+ " stroke: black;\n",
+ "}\n",
+ "\n",
+ ".miniItem {\n",
+ " stroke-width: 8;\n",
+ "}\n",
+ "\n",
+ ".brush .extent {\n",
+ "\n",
+ " stroke: #000;\n",
+ " fill-opacity: .125;\n",
+ " shape-rendering: crispEdges;\n",
+ "}\n",
+ "</style>\n",
+ "<div id=\"fig_4cfaa00f230747688c3fccc3fa58d6e9\" class=\"eventplot\">\n",
+ "<!-- TRAPPY_PUBLISH_SOURCE_LIB = \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.6/d3.min.js\" -->\n",
+ "<!-- TRAPPY_PUBLISH_SOURCE_LIB = \"http://labratrevenge.com/d3-tip/javascripts/d3.tip.v0.6.3.js\" -->\n",
+ "\n",
+ " <script>\n",
+ " /* TRAPPY_PUBLISH_IMPORT = \"plotter/js/EventPlot.js\" */\n",
+ " /* TRAPPY_PUBLISH_REMOVE_START */\n",
+ " var req = require.config( {\n",
+ "\n",
+ " paths: {\n",
+ "\n",
+ " \"EventPlot\": '/nbextensions/plotter_scripts/EventPlot/EventPlot',\n",
+ " \"d3-tip\": '/nbextensions/plotter_scripts/EventPlot/d3.tip.v0.6.3',\n",
+ " \"d3-plotter\": '/nbextensions/plotter_scripts/EventPlot/d3.min'\n",
+ " },\n",
+ " waitSeconds: 15,\n",
+ " shim: {\n",
+ " \"d3-plotter\" : {\n",
+ " \"exports\" : \"d3\"\n",
+ " },\n",
+ " \"d3-tip\": [\"d3-plotter\"],\n",
+ " \"EventPlot\": {\n",
+ "\n",
+ " \"deps\": [\"d3-tip\", \"d3-plotter\" ],\n",
+ " \"exports\": \"EventPlot\"\n",
+ " }\n",
+ " }\n",
+ " });\n",
+ " /* TRAPPY_PUBLISH_REMOVE_STOP */\n",
+ " \n",
+ " req([\"require\", \"EventPlot\"], function() { /* TRAPPY_PUBLISH_REMOVE_LINE */\n",
+ " EventPlot.generate('fig_4cfaa00f230747688c3fccc3fa58d6e9', '/nbextensions/', {\"lanes\": [{\"id\": 0, \"label\": \"CPU :0\"}, {\"id\": 1, \"label\": \"CPU :1\"}, {\"id\": 2, \"label\": \"CPU :2\"}, {\"id\": 3, \"label\": \"CPU :3\"}, {\"id\": 4, \"label\": \"CPU :4\"}, {\"id\": 5, \"label\": \"CPU :5\"}], \"colorMap\": null, \"keys\": [\"task_pls5-80-9364\", \"task_stp10-50-9366\", \"task_per20-9363\", \"task_rmp20_5-60-9365\", \"rt-app-9362\", \"sshd-9367\", \"bash-9395\", \"bash-9401\", \"bash-9399\", \"sshd-9382\", \"sshd-9388\", \"sshd-9372\", \"bash-9387\", \"sshd-9377\", \"sshd-9370\", \"sshd-9375\", \"sshd-9385\", \"sshd-9371\", \"sshd-9391\", \"sshd-9376\", \"sshd-9381\", \"sudo-9395\", \"sshd-9392\", \"sudo-9401\", \"sshd-9380\", \"bash-9376\", \"sudo-9399\", \"bash-9381\", \"bash-9371\", \"bash-9392\", \"sh-9397\", \"kworker/u12:0-9374\", \"kworker/u12:0-9379\", \"kworker/u12:0-9369\", \"sshd-9387\", \"sudo-9400\", \"sudo-9402\", \"sudo-9396\", \"bash-9393\", 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+ " }); /* TRAPPY_PUBLISH_REMOVE_LINE */\n",
+ " </script>\n",
+ " </div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# NOTE: The interactive trace visualization is available only if you run\n",
+ "# the workload to generate a new trace-file\n",
+ "trappy.plotter.plot_trace(te.res_dir)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## RTApp task performance plots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2016-12-07 10:29:27,425 INFO : PerfAnalysis : PerfIndex, Task [task_rmp20_5-60] avg: -5.34, std: 8.79\n",
+ "2016-12-07 10:29:27,795 INFO : PerfAnalysis : PerfIndex, Task [task_pls5-80] avg: -8.28, std: 7.04\n",
+ "2016-12-07 10:29:28,161 INFO : PerfAnalysis : PerfIndex, Task [task_stp10-50] avg: -24.23, std: 1.75\n",
+ "2016-12-07 10:29:28,546 INFO : PerfAnalysis : PerfIndex, Task [task_per20] avg: 0.56, std: 0.06\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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Jpi/eJr4QyF5r02ZNvg08l8TzFHGtLrSu7Gfsy8R1eG4S53Rgn7zxEyj98jaZ\n5vtzeeK+8BJx/r1KfDGRbSalPtnWscl65wPPAIfSXHdif/+qwLisD4l7QDmyCc9yP4MQTV4dTe6l\n+F2ixPe4vGnqqcx9TpLUfhMo/Cx2TDL8m8AlxL3rHGBn4tnmaaI9zvxaSFOJZ6KXiev6DsRzy1bE\n+9pH5GpdrZrMU0/hhOeLwC3E/WN3oomsuUDfArFfTyRoDyDu+y/T9Nm0G1Er4kMi6bkjcBjxTPgk\ncY9O3ZKsJ33WOTRZx2FIklSGyTRPeJ5L3BjHECVmjiW+ebs8b5rPETfRh4h2IIcRL6EXES/bqfyE\n53LES/h7xA26lFoKJzzXBs5Lxu1B8yr4PwF+QNxohxPt08ykecJsBpFoOCCJ/evEg0N+CaF6cjf9\nbsRL5ifk2rkpV7EHjjTh+RJwA7BLEs/sJLa/E8mE0cl2fEBULc/3EvGAUE+04zgG+Au5pEs5sb1B\nJKJTs4lkcNbXkuV+tYzlpiYk86SNos8lErflfiu7TjL/eUSi/E3iRfxJmp8bv0imLVR69i3gygLD\nJ1JewvMXRMKgZ2b474lE5bHEcd2FqGb83WT8+sRxTBOx6U+aeCrnswbFz6EtiMTeo3nLzlbf/Q3w\nPi239z2BlhOefYlrxkFEgmpn4nPzKU0/F1sTD9I35cWVXhfWIKphvwh8CxhFNEWwgKaJnVrKf8ge\nQ3yJ8r8kjhHJ9vw1b5ppRPtXWQ/TcsnlQUTCcz9yx/lS4pzOv2a0JuYJlPdiUMxVRabbnLimzwQO\nJ47TAcn0KybTbERcT54jvigYS+yrBiLhnhpJ7hp1OXG802vRVODfRCJ0p2S+T8klmVMNyTY9QTQL\nsRtwazI8vw3hdF35Cc9RxDW3jijFuTNxjuTfG/qR+zLmSJq/vE3O7Kca4HbifJmYxH5css+m0TQp\n/BJxrj6Z7KevAtck6xpOc9dS+MuirDuT9Z8ObEbTRH9WNuFZ7mcQ4M9E6Zg/EPt9NPHFz1F509RT\nmfucJKn9JpB7ZuxB3Le/Rjwbvk/cIxuI58V8axPPXb/MG1ZH8doPkylcw6+ewgnPx2j6rvDlZHj6\n5WU3ooBGtlmWdcl9sZ3aj9x7XL6h5O7lqc8T9+EHiYI2HxH3NkmSyjKZ0lXauxE33IOIF6q0BEx6\nUxpXZL5UmvD8PJFseAXYtIy4amm5Snuhkk35aojY04eDdL2rJv+3VAqynrjpL08k6d4hXsBbq47C\nDxy1yfBs2qg4AAAgAElEQVTpmeHpt7jZ5Oa5yfDPZWL8jOb79A4isVyq6vy3kuUdlRn+CZHcydqW\npg835RgD/JRI6AwjknWvEOdcOefBNsk63yMSJnsTSYc0ifitvGkvIZJmhTxLfJucNZHyEp53E8m0\nrCeIc6OUctvwLPZZg9IPrU/StPRkVlqCsaX9PYHWV2nvTsR8GZEsyvchhavi/p54aM928HJcsv60\n6YFaynvIhkj6PkfptiwPSebLTwhvlQwbX2K+QtLtvoumx7/cmFvzYlDMTJpfIyDO1bnkEn6FXEUk\n8NfODL+FSOCnidmRSdw3ZKZLr0XZ5Ob1REnPfA3JMlfLG9aNSAw+lzcsXVd+wvMZosR0Nll/I7H/\nUqXa8JxM0xKeYyj8hdA+NL+m1BMvV/nn6nLElw/ZpjMgV2K+b4Fx+dYnSnOnpVE/Is6l79K8dHdL\nVdqLfQbTUs0/bSGWeipzn5Mktd8ECtdaeIx4Dv8Z8UVWP+Lan//zAE2/wK0j7leFTKZ1Cc+fZ6Zb\njqZfkg5O/v9BgWVOpelzzZXEc0o2/h4ULnSxLfElYVpzL1uTRMsAe2mXVElbEje7OUQybSFRHbIb\nUTII4Hmietw5RFXFTUosb33iJrwikcB6okOizq3rb+RKFC4kbviQS6S8QyQLTiRuzFtS+DraSDxQ\nTCUSvMMov921rHeI0lCF3Jr5f0by+5Yiw7OJ3qdovk+vIl66tyyyzl2I0rj/oPW9jrfGHUQS4Fai\nV/iLiRfxRpq+iKfJ6fQnLfGUHpfliKTpdUTTA/sSieLTOjD2fGtQ+KHxIeKb918QCZvWts1azmct\nVeocKiWNuzVNEZSyD/Bf4kH5UyLmw2hasruU3YjP0WyaHvPbk/HZpO4tNO3dMz3X08/BIOJzf3kS\nSzFXEcm47+UNO4ooNXFNGXEfSZxzC8ht904U3u6WYt6IOB7ZTm5eIaonl6PQOdmb2H9/J14mitmR\nSIy+lhk+OVnGNpnhN2f+L3WNWpXmLyN30zQR2pDEuCGwVpEYNyT201XkvgxIf24j9l/2M1KOtD3b\nyZnh1xKJx2x7t48RVd5TnxCJ2kJfuKXHY40WYniRSLyPIK5h/wK+QlyLHyCud6WU8xncJfl9UQvL\nquR9TpJUGQcRX5ZuQdzvtiDuD6sTz8xvEdf+/J+taf5l5+wKxZN9pvgk+Z0+96brfaPAvG/S9Evg\n1YnOaLPxL0zGZbfhYSLRuTzxHjG/9eGrqzPhKalS1iXaFFyTKGk4jLjhfo+4WaXtqnxAvKw9RlQ1\nfpJ4eZ5I8xIqWwEDiRfc1zsw9hWJUqRfIarIjkhi3ysZn8beSCQq7iCSntOIB4fzyFX5hNjeQUn8\nt1NeVcViSj1wvJP5f2ELw7NJtUIPF+mwQqW8xhAlse4gqmlmzaVwicfP541vj5eJl/X8pMoVNH3g\nuSuzrhlE1f18dxIlr1bNm3Y5mrb9k/p8BeIu5Bii+tCeRCnLuUSpu3I6Lin3s5aq1ENre+xFJAdn\nEefONkTMf6T8ZO/qRDXvNFGT/jxJfDaz52xLD9lpycFXKW0hUbX3AOLLgNWIxPllSSylHEc8ZD9A\n7IOtievM7RTe7va+GJSjscCwVYhnwpb2xecpfD6lw7LHoLXXqOy529prFOTaFf41zV+ILiKXqGut\nVck1r5Gvkdj3LZ1/JDG0t+O5RuJ+9TOiWt9axGdrKKXbJiv3M7gasZ0tnU+VvM9JkirjGeJL1sdp\neh2fQ9w/tieu/dmfPTPLKfSs0BHSe2WhL9fXyMQxJ5m+UPxfJtcsVOoMot3xR4EziVKnWsZkkwuS\n1FZ7ElWm96JpgqlQNdcnic4sINohm0CUVllAtOuWupq4Wf+cXKdFHWFH4kY7gqZt9RVK3r1Cruri\nhkRV04lEldjvJMMbidJW15JrU/E7tO3hoSMfOIo9XEDzl/UxRPXUqUT18M8KzPsEcTyz0irRT7Yh\nxkLy98npNO3cKq1mM5ModVXOch5Pfm9G06rCaxBJjPbEPZumVXJT84nzZmIyflciAXoThXuFz9ea\nzxq0/RxKk0KFkk6tNZ4onZbt/Gd5yo/vbaJNyx8XGd/axG5acnCdMqb9HdFB2DeJ5FB3oop9S8YT\nn5nvZYa3VHW5mJZeDMrxBs0Tfu8QVd1a2hdzKVyyMh1WrApcWxXapmLXqFQaw1nEFzSFPFdkeClp\nNbp+NN3OmiSmh9qwzFR7PmvziZLi36B0G8elPoP53ia2c40W4qnkfU6S1LFuIp5j+lNeh5SlVPI6\n/yzx/LY/0exNagCwHU2/iL2JuNf1oHnTPlk7E+10n0kUTHmMKECzPS1/Wa2liCU8JbVHY4G/86uG\n1hCdX5TyOFEK6n0KV6P+OfB94obV3oRnWloqW22yUOwQVe5LeYGI70maxp5Wv/gz8XJ5KLnqxp3J\nF2meoDyAKIWb3z7oaCLZeS+RbCv2oDCFqBqZ39t5D+JF+0Hanzhbn6jW/kDesJeTWNOf55PhnxE9\nSW9CPDSlaogqmzPJlTK7negBekJmfROIcyPbDmFrPEpUn812WpTvbeL8uDqZNk1ApOdrNiHR1s9a\n1ieUbs8o7cTmqVYut5AGmp83a9C84fk0rkKl4G4mkucv0vSYpz+tTXg+R5wHh1G6DU+SZV9LlB74\nNtGcQEulISG2O3td2YxoV6ot8l8M8qUvBuV4hKjilm8B0ezBPpRuw/Nu4guibCLyYOILhpY6cWqt\nnWjac2x34mXnBYqX+n+WuA5sQeHzZDrRNigUvyek8u9x/0p+Z9tt3TuZP9vBXWtsTpRG/6CF6Yo1\nL5E2DVOqJkSpz2D+dqZNpXyH0rrKfU6SFF9QXULUjDqbaCZoFPHcfzFNO/yBplXJs0qNa60G4FSi\nlsIUormnA4kaW7Mz67qaaJrm1mSescRzwiHEdqWlVNck2vucSpTyfI94dtiCaFJNyxBLeEpqj/yb\nUNp77FXEzWQF4oVp5cw8uxFJgylEpwo1REm1lchVR846n3hBvYQo2ZbtYbBcaUm+Y4mXtE+Jl8z/\nEu2K/p64MX5G3GyzycDNiLbS/k68cC8kXv43JUrYFHIdcQO+lngp3p/WfbNYyYeKrNlE4mYikYwc\nT3TscyKRAISoLn1DMu0vaF6K8ClypSr/SJRk+wfxrerbxLEeSOt6aIc4F+5Jlj+P2McnEsfm1DKX\ncRpRcvJ2Yhs/JErnbkrTjmveJaqHnkkkQe8iqh2fTvSoPYO2uzWJe1siYZx6iPim+olk/YOJ/f9f\ncvs+PV9PSrZhEVHCsdzPWqrYOfQ4kaj4BpFE/JimbbqOIJI8i1rcyrATkZTOuoVIVu5FVCm+jihJ\n+BMiQTMwM/0TxEP4bsR5mfYKfhrxjf39xDXhOSIZXEsksY+keduSLfkecRweJDrSmUU0GTCa5omt\n84lkeyPNk+PF3EycrxOJ479R8v+LtO0ZLH0xuIy4hl5GHPfTaf5iUMxtxDFflyixnjqOaC/3IaK0\n8Uyievg4Isk7j7g+7ka0b/xT4tw9kPicnUDpjuzaYi5xHTiTKMn4XaIadbaUYta3ie28nWhz83Wi\nxP5g4supfZPp0vP9CGL7PiaOTfplSP7+vItozuNsooTu/cQ94QwiifqXMrcpe4y6EdfZK8uY9yni\nM3lbEufyRDMJPyQ+K5fnTZtdT7mfwfuIbfkJcfxvIRLDaS+3hdpubu99TpLUfi2VvDySeN75NnE/\n7UbcA9J7f/5yii2r2Lj2lPpMO9g7ibifvEQUKBlJ0/bZG4imjY4l2io9hXgveJV4Lnmc2KariGfX\n/Oa3HkqmP4dIhOZ3sCRJUjNX0Lw0yteIHqnnE4mDXxJVoReR6wV3EPBXogTOR8QL8wPEjStf2kt7\nvm8QiZ7LKP5iX0vxXtohbqCvEjfI/Li2IZJN84hq9H8gvgnMX9ZqxE35aeLF/oNke4+haamWl2h+\nIx2RTH8LhduKLGQquaRXvtokruMyw0cm27RXZviEZHh+srI+ifHrxEv/x0SC45jMvKcn61pE854f\n8/df6gtEgmEOcR78l+adeZTjXKLk7PvEMX+VKD1UThuX+b5IJLTez4tn1yLTHk0kNz8mjuFp5DpB\nyppIeb20Q+zfSzPDziKq48wlStc9T7Q5uEreND2JJP+bxL5eRK7Dk3I+a1D8HCJZ1u3EvmmgaU+Y\naa+Z5SSq017MC/3kx3xiso4FxLE9jDi/sgnVzYimJeYly8jvSX5V4LfEufoJcZ49RCTf0lJ6tRT+\nfJAMz3ZYtTXxuXyXpseikJdoXRMHPYmH61nEsXqESCBeQdP93dqYDyNKMn5MtNeVlm4op5f25Ykv\nIwo1DbAx0c7j28my64kkWn4J2C8SpaffTaaZTvPr7UjKvxZB7jzI/zyl94AjiWPyCZHwyyY703Vl\nr0WbEqVB3kjmfZ1IWmZLQh9DnE+fJstJt6XQ/lye+OLnpWSZrxIJwGwTBYXuARCfx3syw3ZJ1ptN\n/BdyOJFUfIFcgvY5IomZbWqgUC/t5X4Ga4gXyseTdbxLvBDnXzsrdZ+TJEla6pxCPPh/QLzMTSES\nIVkTiRIb84kHxWyPzssBFxAP5/OIh/C1M9OsQnxb/V7y82eiNFm+dYmX4nnJss6jeRXETYkqX/OJ\nh9xySxlJWnJqiRflQ7EUeyn1+O1mW/UgEmzlJjz3IxLkhdry7KwupvLVk7u6zYhjnq321RWdTJTu\nbKlX72oq9KXX0uhWIsksSa1VqfdpSVKF3UZ8iz6YeIm4iXgBz29H6SQiQbknUaLgKuJind8b8u+I\nkhM7EqWx7iZKveSXtrqNqAa4NVGK63Gavuh3J0rg/ItoR2knIqGZ/6Ddlygl8FfiJvF1olRModIY\nkqqnlqYlzLIljBTqMeHZFt+naenFchKeEC8YhaqCdkYbEiXAvlztQDqJDYhnjAeJZ4OloeRaT6KE\n6PHVDqSEZSHhuQPxZUj/agciqUuq1Pu0JKmD9SMebocl/9cQ7VGdkDdNL6JazRHJ/ysRVYr2yZtm\nTaKq6ujk/7Ra3lfyptk6GZZWH9olmSe/Ef5vEC986c3gO0SbTvmlPk+ivE4LJC05PYnqkulPsXYN\nq6k7UUqw2E+xatSVVKzKZUerofS2d/ZSuavR9PxaEsdK1TWZeEZ4nLZ3NqTWWxYSnpJUSW15n5Yk\nLQEbEhfotIj9+sn/m2emu4F4+YAocdFA8+rpjxFtEkG0T/RugfW9S7R5BVE18X+Z8asky04byv0z\nUU0g35bJNAOQpPLVUbzNw2xbikubyZTe9nI7yJEkSVJOW96nJWmp1VlK0tQQvaP+h+gMBHKlLd/M\nTPsWuQ4Q1iA6s3g/M82befOvkcyT9VZmmux63k2WnT9NNgnxZt64lwusQ5IKOYLSVYk+WVKBVMHp\nWGpLkiSpktr6Pi1JS63OkvC8kGhTZFhLEyYaWxhfrPfm9szT0jqz1iR65V2VSJzmux24o5XLk7Ts\nWI7mvRgvS5blbZckSdUzBhibGdYLmAscTlQR74wq+T69ZvIjSZ3JbFp5De4MCc8LgN2Ihttfzxv+\nRvJ79by/s/+/QdyAVqJpKc/Vgf/mTfOFAuv9QmY5W2XGr5IsO3+aNTLTrJ6JNd+awNcKDIfY1rOK\njJMkSZIkdS5r0jkTnu15n85ac+WVV379vffeq3iQktROrxF985R9Ha5mwrOGuDjvAYykeZXwl4gL\n8Wiih3WIBOQIcg0vTwM+Tab5RzJsTeLbrbTn0QeIhOhXgEeSYVsnw+5P/r8f+BFx8U+L/I8mqpVO\ny1vOWUSHKJ/mTfNagdgXu/LKKxk8eHCx0ZIkSZKkTuqZZ55h/Pjx1Q6jkEq8T2et+d5773WJd9i9\n996b6667rtphtMg4K8s4K+fdd9/lb3+byvXX/4lDD53cbPzHH8/jk08e4YADRrHKKqss+QDzJNfh\ntWnlF0/VTHheBOxPXKA/Ild68j3gY6KY/W+JROTzwAvJ3/OAvyXTvg9cDvyGqGbwLvBroifVfyXT\nPENUIb8U+DZxY7gEuClZLsCdRFsnVxIX/1WBXyXTzUum+RvR9txkIvE5CDgFOKPURg4ePJghQ6yd\nKUmSJEmqmEq8TxfUFd5he/Xq1eljBOOsNOOsnDlz5nDPPTPp0WM5Nt54p2bj582bw9y5b7L55pvT\nr1+/KkTYftVMeB5JXITrMsMnED2iA5wDrABcTFQxf5D4huqjvOm/D3wG/D2Z9l/AwTRtl+QA4tuv\nO5P//wkclTe+gah+fjFRFX4BueRn6gNgZ+LG8ijwDpFonVTm9kqSJEmSVAmVep/ukjbaaKNqh1AW\n46ws46y8L3xhw2qH0GGqmfDsVuZ0Z1C6FOVC4Jjkp5j3gINaWM8sYFwL0zxJVAGQJEmSJKlaKvU+\nLUlLpc7QaZEkSZIkSZLUJZ17LnzwAfTtW+1IlDLhKUmSJEmSuozddtut2iGUxTgrqzPHee658Npr\nsPbacNppnTfOrE022bnaIXQYE56SJEmSlrjnn3+eDz/8sNphaBnXp08fBg4cWO0w1Eo333wzRxxx\nRLXDaJFxVpZxVt7TT9/Fzjv/sNphdAgTnpIkSZKWqOeff55BgwZVOwwJgOeee86kZxczceLEaodQ\nFuOsLOOsvDFjTmh5oi7KhKckSZKkJSot2XnllVcyePDgKkejZdUzzzzD+PHjLWncBQ0ZMqTaIZTF\nOCvLOCuvf//Nqx1ChzHhKUmSJKkqBg8e3KVeDCVJUtfQrdoBSJIkSZIkSVKlmPCUJEmSJEldxuWX\nX17tEMpinJVlnJX30ENXVjuEDmPCU5IkSZIkdRnTp0+vdghlMc7K6sxxDhoEm2wSvztznFmvvvp4\ntUPoMLbhKUmSJEmSuoyLLrqo2iGUxTgrqzPHec89+f913jiz9t77nGqH0GEs4SlJkiRJFfLQQw/x\n9a9/nQEDBrD88suzxhprsN1223H88ccvnmbkyJGMGjWqw2IYOXIkm266aYctX5Kkzs6EpyRJkiRV\nwC233MJ2223HvHnz+NWvfsVdd93F+eefz/bbb8/f//73xdPV1NRQU1PTobF09PIlSerMrNIuSZIk\nSRVwzjnnsMEGG3DHHXfQrVuubMm+++7Lr371q8X/NzY2mpCUJKkDWcJTkiRJkipg7ty59OvXr0my\ns1xnnHEGW2+9NauuuiorrbQSQ4cO5Y9//GPBaf/2t7+x7bbb0qdPH/r06cOWW25ZdNrUlClT6N27\nN0cccQSLFi1qdXxSZ7L77rtXO4SyGGdlGWflXX75+GqH0GFMeEqSJElSBWy33XY8+OCDHHvssTz8\n8MN8+umnZc9bX1/PEUccwTXXXMOUKVPYa6+9OOaYYzjzzDObTHfaaacxfvx4+vfvz5/+9CduuOEG\nDjnkEF555ZWiy540aRL77rsvp556Kpdccgndu3dv8zZKncFRRx1V7RDKYpyVZZyVN2zYN6sdQoex\nSrskSZKkzmv+fJgxo+PXs/HG0Lt3uxbxy1/+khkzZnDBBRdwwQUX0LNnT77yla8wbtw4jj76aHqX\nWP4VV1yx+O+GhgZ22GEHGhoaOP/88zn11FMBeOmllzjrrLMYP348f/7znxdPv9NOOxVcZmNjI8cc\ncwyXXnopf/7zn9l///3btX1SZzF69Ohqh1AW46ws46y8jTbquA70qs2EpyRJkqTOa8YMGDq049cz\nbRoMGdKuRXz+85/n3nvvZdq0adx9991MmzaNqVOncsopp/CHP/yBRx55hFVXXbXgvPfccw9nnXUW\njz76KB988MHi4TU1Nbz99tusttpq3HXXXTQ0NPC9732vxVgWLFjAHnvswX//+1/uuusuhg8f3q5t\nkySpKzHhKUmSJKnz2njjSEYuifVUyNChQxmaJGk/++wzTjrpJCZNmsQ555zD2Wef3Wz6hx9+mDFj\nxjBq1Cguu+wy+vfvT69evZgyZQo///nPWbBgAQBvv/02AP37928xhrfeeotZs2ax8847s+2221Zs\n2yRJze24I7z5Jqy+OtxzT7WjEZjwlCRJktSZ9e7d7pKX1dSjRw9OP/10Jk2axFNPPVVwmquvvppe\nvXpx880306tXr8XDr7/++ibTrbbaagDMmjWLtddeu+R6BwwYwLnnnsuee+7JXnvtxbXXXttk2VJX\ndsMNN7DnnntWO4wWGWdldeY4n3sOXnsN3n+/c8eZ9cQTt7LttgdXO4wOYadFkiRJklQBs2fPLjj8\n6aefBmCttdYqOL6mpobu3bs36d19wYIF/OUvf6GmpmbxsDFjxtC9e3d+97vflRXPV7/6VW6//Xbu\nvfdevva1rzF//vxyN0Xq1K666qpqh1AW46ws46y8//3v+pYn6qIs4SlJkiRJFTBmzBjWWWcdxo0b\nx0YbbURDQwOPPfYYv/nNb+jTpw/HHnvs4mkbGxsX/73bbrsxadIkDjjgAA4//HDmzp3Lr3/9a5Zf\nfvkm0w0YMIAf/ehHnHnmmSxYsID99tuPlVZaiaeffpq5c+cyceLEZssfNmwYd999N2PHjmXMmDHc\ncsst9O3bt+N3htSBrrnmmmqHUBbjrCzjrLyDD76s2iF0GBOekiRJklQBp556Kv/85z+ZNGkSs2fP\n5pNPPmGttdZi9OjRnHLKKWy00UZAlOjML7k5atQo/vjHP3L22Wez++67079/fw4//HBWW201vvWt\nbzVZxxlnnMHAgQO54IILGD9+PD169GDQoEEcc8wxi6fJLn/o0KHU1dWx8847s9NOO3HHHXfw+c9/\nvoP3hiRJ1WPCU5IkSZIqYJ999mGfffZpcbqpU6c2GzZhwgQmTJjQbPihhx7abNj48eMZP358q5b/\nxS9+kddff73F2CRJWhrYhqckSZIkSZKkpYYJT0mSJEmS1GUUKvncGRlnZRln5V199dHVDqHDWKVd\nkiRJkiR1GaNHj652CGUxzsrqzHEedxx88AH07Qtrrtl548waNGhUtUPoMCY8JUmSJElSl7H//vtX\nO4SyGGdldeY4jzsu/7/OG2fWkCF7VTuEDmOVdkmSJEmSJElLDROekiRJkiRJkpYaJjwlSZIkSVKX\ncd9991U7hLIYZ2UZZ+W9+OKD1Q6hw5jwlCRJkiRJXcY555xT7RDKYpyVZZyVN3XqhdUOocOY8JQk\nSZIkSV3G1VdfXe0QymKclWWclXfQQZdUO4QOY8JTkiRJkiR1Gb179652CGUxzsoyzsrr1avrxNpa\nJjwlSZIkqQKuu+46unXrxjXXXNNs3BZbbEG3bt244447mo3bcMMNGTJkCAC1tbWMGzeu2TSXXXYZ\n3bt3Z88992ThwoUAdOvWrcnPyiuvzKhRo7j11lubzFtsmW1x//33c8YZZ/D+++9XZHmStDR49ll4\n6qn4rc7BhKckSZIkVcDIkSOpqamhrq6uyfB33nmHxx9/nBVXXLHZuFmzZvHiiy+y4447Lh5WU1PT\nZJpf/epXHHHEERx00EFcf/319OrVa/G4ffbZhwcffJD777+fiy66iDfeeINx48Y1SXrW1NQ0W2Zb\nmfCUpOZ22gm+9KX4rc7BhKckSZIkVcCqq67Kpptu2iyp+e9//5uePXty2GGHMXXq1Cbj0mlHjRpV\ncJk/+tGPOOmkkzj22GOZPHky3bo1fYVbffXV2Wqrrdhmm2048MADueWWW2hsbOS8885bPE1jY2P7\nNy6jI5YpleuEE06odghlMc7KMs7Ku+mmidUOocOY8JQkSZKkChk5ciTPPvssb7755uJhdXV1bLXV\nVuy6665MmzaNjz76qMm4Hj16sMMOOzRZTmNjI9/5znf45S9/ycSJE5k0aVJZ619//fXp168fL7/8\ncqvivuuuu9hjjz1YZ511WGGFFRg4cCBHHnkkc+fOXTzNxIkTOfHEEwFYb731Flelv/feexdPc801\n17Dtttuy4oor0qdPH8aOHctjjz3WZF0TJkygT58+zJw5k1133ZU+ffqw7rrrcvzxxy+urp/65JNP\n+OlPf8rgwYNZYYUV6NevHzvuuCMPPPAAADvttBODBw9utj2NjY1suOGGfO1rX2vVflDXsO6661Y7\nhLIYZ2UZZ+WtvPLa1Q6hw5jwlCRJkqQKSaum55fknDp1KiNGjGD77benpqamSYJw6tSpbLnllvTp\n0weI6ucLFy7kgAMO4JJLLuH888/ntNNOK3v97777LnPnzmW11VZrVdwzZ85km2224aKLLuLOO+/k\ntNNO46GHHmLYsGF89tlnABx++OEcffTRAEyZMoUHH3yQBx98kC233BKAs846iwMOOIAvfelL/OMf\n/+Avf/kLH374IcOHD+eZZ55psr5PP/2UcePGsfPOO3PjjTdy2GGHMWnSJM4+++zF03z22Wfssssu\n/OxnP2P33XfnhhtuYPLkyWy33XbMmjULgGOPPZZnn32Wu+++u8nyb7vtNl588cXF8Wrp0lWOq3FW\nlnFW3vDhh1c7hA7To9oBSJIkSVIx8z+dz4w5Mzp8PRv325jePdvfW+3w4cPp1q0bdXV17Lfffsyd\nO5ennnqK3/zmN3zuc59jyJAhTJ06lV122YVXXnmF+vp69t1338XzNzY2cueddwLw4x//mKOOOqrk\n+hoaGli0aBENDQ3MnDmT4447jsbGRg488MBWxX3kkUc2iWHbbbdlxIgR1NbWcttttzFu3DjWXntt\n1llnHQC23HLLJqWYZs2axemnn87RRx/Nb3/728XDd955ZwYOHMgZZ5zB1VdfvXj4woULOfPMM9l7\n772BqNL/6KOP8re//Y1TTz0VgKuuuoq6ujouu+wyDjvssMXz7rbbbov/HjduHOuttx4XXnghO+U1\nnnfhhRey4YYbMnbs2FbtB0nS0sGEpyRJkqROa8acGQy9ZGiHr2faEdMYsuaQdi9nlVVWYYsttljc\nNue///1vunfvzvbbbw/AiBEjuOeee4DC7XfW1NSwxRZb8M4773DhhRcybtw4ttpqq6Lru/jii7n4\n4js5dIcAACAASURBVIsX/7/yyitz5plnNklgluOtt97itNNO45ZbbmH27Nk0NDQsHjdjxowWe3m/\n4447WLRoEQcddNDiEqEAyy23HDvssEOzdk1ramqaLXPTTTddvG8gSmmusMIKTZKdWTU1NRx11FGc\neOKJzJo1i3XWWYeZM2dyxx138Jvf/KacTZckLYVMeEqSJEnqtDbutzHTjpi2RNZTKSNHjuTcc89l\n9uzZTJ06lS9/+cv07h2lR3fYYQfOPfdcPvjgA6ZOnUrPnj0ZPnz44nkbGxvp378/119/PaNGjWL0\n6NHcfvvtbLPNNgXX9Y1vfIMTTjiBmpoa+vTpwwYbbNDqHtkbGhoYPXo0b7zxBqeeeiqbbropn/vc\n51i0aBHbbLMNCxYsaHEZaZulX/nKVwqO7969e5P/P/e5zzXpbR4iOfrxxx8v/v/tt99mrbXWanHd\n3/zmNzn99NP5/e9/z89//nMuuugievfuXTJRqq5txowZbLxx5T6zHcU4K8s4K+/NN59nxRX7VTuM\nDmHCU5IkSVKn1btn74qUvFySdtxxR84991zq6ur497//3aTjnGHDhtHY2Mi9995LXV1dk2Rovtra\nWurq6hg1ahRjxozh9ttvZ9ttt2023WqrrcaQIe3bP08++SSPP/44f/rTnzjooIMWD3/hhRfKXka/\nfvHCfN111zFgwIAWpy+nl/fVVluN+++/n8bGxpJJ3L59+3LwwQdz2WWXccIJJ3DFFVdwwAEH0Ldv\n37LjV9dy4okncuONN1Y7jBYZZ2UZZ+XdfPMZHHvs7dUOo0PYaZEkSZIkVdCwYcPo3r071157LU89\n9RQjR45cPG6llVZiiy22YPLkybz88stNqrNnDRgwgLq6Ovr168fYsWO5//77OyTeNJmYLXH5hz/8\nodm0yy23HADz589vMnzs2LH06NGDF154gSFDhhT8KbTOUnbddVcWLFjA5MmTW5z2mGOOYc6cOey1\n1168//77LbZ9qq7twgsvrHYIZTHOyurMcd59Nzz5ZPzuzHFm7bXXL6sdQoexhKckSZIkVVDfvn0Z\nOnQoU6ZMoUePHovb70yNGDGCSZMmAZRMeAKsu+66i0t6jh07lltvvZVhw4a1OqbZs2dz7bXXNhu+\n3nrrsfnmm7PBBhtw8skn09jYyCqrrMJNN93Ev/71r2bTb7bZZgCcd955HHzwwfTs2ZONN96YAQMG\n8NOf/pQf//jHvPjii4wZM4ZVVlmFN954g0ceeYQVV1yRiRMnLl5OOSU8999/f6644gqOPPJInn32\nWUaOHElDQwMPPfQQm2yyCd/4xjcWTzto0CBGjx7NHXfcwfDhw9l0001bvY/UdeR3mNWZGWdldeY4\nN9oo/7/OG2fWKqv0r3YIHcYSnpIkSZJUYWkic8stt2TFFVdsMm7EiBFAlJbMJkMLlXxcZ511qKur\nY/XVV2fXXXflvvvua1UsNTU1TJ8+nX333bfZz0UXXUSPHj246aabGDRoEN/+9rc54IADmDNnTsGE\n54gRIzjllFO46aabGD58OFtvvTXTp08H4OSTT+baa6/lueeeY8KECYwdO5aTTz6ZWbNmLd7mNJ5C\n25kd3r17d2699VZOOeUUpkyZwp577skhhxzC/fffT21tbbP599tvPwBLd0qSaF1r1mqNIcC0adOm\ntbtNHUmSJGlpMn36dIYOHYrPyqqkvffem4cffpj6+vpmnSQVUs55mE4DDAWmVzTgzsd3WGkZMWfO\nHCZNup5VV92rYKdF8+bNYe7c6/nBD/Za3EZztbT1OmwJT0mSJElSl7Rw4UIeeOABzjvvPG644QZO\nOOGEspKd6trOPvvsaodQFuOsLOOsvHvuOb/aIXQY2/CUJEmSJHVJr7/+Ottvvz0rrbQSRx55JEcf\nfXS1Q9ISkO00q7MyzsoyzspbuHBBtUPoMCY8JUmSJEldUm1tLQ0NDdUOQ0vYGWecUe0QymKclWWc\nlTd27EnVDqHDWKVdkiRJkiRJ0lLDEp6SJEmSJElSG517LnzwAfTtC8cdV+1oBJbwlCRJkiRJXcic\nOXOqHUJZjLOyOnOc554LZ5wRvztznFnz5s2tdggdxoSnJEmSJEnqMg477LBqh1AW46ws46y8a645\nttohdBgTnpIkSZIkqcuYOHFitUMoi3FWlnFW3pgxJ1Q7hA5jG56SJEmSquKZZ56pdghahnn+dV1D\nhgypdghlMc7KMs7K699/82qH0GFMeEqSJElaovr06QPA+PHjqxyJlDsfJUlLDxOekiRJkpaogQMH\n8txzz/Hhhx9WOxQt4/r06cPAgQOrHYYkqcJMeEqSJEla4kwySWqryy+/nG9+85vVDqNFxllZxll5\nDz10JTvt9P1qh9Eh7LRIkiRJkiR1GdOnT692CGUxzsrqzHEOGgSbbBK/O3OcWa+++ni1Q+gwlvCU\nJEmSJEldxkUXXVTtEMpinJXVmeO85578/zpvnFl7731OtUPoMCY8lzXPPw+dqa2kPn3A6kySJEmS\nJEmqEBOeHe2ZZ6odQc6rr8Iee1Q7iub++U/o37/aUai1TFZLkiRJkqROyIRnRxs/vtoRNNdZEoxp\nArYzJmFVnueeM+kpSZIkSZI6FROeHe3KK2Hw4GpHkdOZSuUNGRIJs85UxV7leeaZSOZ77CRJkiQt\nYbvvvjs33nhjtcNokXFWlnFW3uWXj+fYY2+vdhgdwoRnRxs8OBJ7KqyzJF8lSZIkSV3CUUcdVe0Q\nymKclWWclTds2DerHUKH6VbtACRJkiRJkso1evToaodQFuOsLOOsvI02GlXtEDqMCU9JkiRJkiRJ\nSw0TnpIkSZIkSVIb7bgjfPGL8VudgwlPSZIkSZLUZdxwww3VDqEsxllZnTnO556Dp5+O3505zqwn\nnri12iF0GBOekiRJkiSpy7jqqquqHUJZjLOyjLPy/ve/66sdQocx4SlJkiRJkrqMa665ptohlMU4\nK8s4K+/ggy+rdggdxoSnJEmSJEmSpKWGCU9JkiRJkiRJSw0TnpIkSZIkSZKWGiY8JUmSJElSl3Ho\noYdWO4SyGGdlGWflXX310dUOocP0qHYAkiRJkiRJ5Ro9enS1QyiLcVZWZ47zuOPggw+gb19Yc83O\nG2fWoEGjqh1ChzHhKUmSJEmSuoz999+/2iGUxTgrqzPHedxx+f913jizhgzZq9ohdBirtEuSJEmS\nJElaapjwlCRJkiRJkrTUMOEpSZIkSZK6jPvuu6/aIZTFOCvLOCvvxRcfrHYIHcaEpyRJkiRJ6jLO\nOeecaodQFuOsLOOsvKlTL6x2CB3GhKckSZIkSeoyrr766mqHUBbjrCzjrLyDDrqk2iF0GBOekiRJ\nkiSpy+jdu3e1QyiLcVaWcVZer15dJ9bW6lHtACRJkiRJkqSu6tln4bPPoEcP2GijakcjMOEpSZIk\nSZIktdlOO8Frr8Haa8Orr1Y7GoFV2iVJkiRJUhdywgknVDuEshhnZRln5d1008Rqh9BhTHhKkiRJ\nkqQuY9111612CGUxzsoyzspbeeW1qx1ChzHhKUmSJEmSuoyjjz662iGUxTgryzgrb/jww6sdQocx\n4SlJkiRJkiRpqWHCU5IkSZIkSdJSw4SnJEmSJEnqMmbMmFHtEMpinJVlnJX35pvPVzuEDtOj2gFI\n6sKeeab9y+jTBwYObP9yJEn6/+zdf7xc913f+XfIxayNr2pvvA/HSKiFzZUcu5BwXXg01C61vVx7\nQ3sT5C5BreyNlKQFYsXOBcmPtlsikS6PSIDiJDJQ14Js8SIp3RrFNiE1a7mhahNvYhni4itfg8kP\nqYlAhlRyFWpEsn+c0SOjsWwdyd+j78zo+Xw87uNoZr73nNfIihR9dM4cAM4J69evz/33318745R0\nlqWzvAcf3Jjbbvt47YxOGHgCp29ystmuWlVmfwsLhp4AAEArW7durZ3Qis6yhrnz4YeTY8eSiYnk\n/POHt3PQihXvq53QGQNP4PRNTTVDyiNHXt5+5ueboenL3Q8AAHDOWLp0ae2EVnSWNcydy5f3Pxre\nzkEXX7ykdkJnDDyBM+OMTAAAAGAIGXgC4+Hpp4fvTNGSn09a6v35zFQAAADGnIEnUN/LvfnR/v3J\nm95UpqW0j340WfIyLxMo/f58ZioAACNs06ZNueOOO2pnnJLOsnSWt3v3BzM7+zO1Mzph4AnUU/rm\nRyWGi6UcH1KWHFS+3PfnM1MBABgDR48erZ3Qis6ydJb3/PNfrZ3QGQNPoJ5SNz9Khu9S7enpcu8t\nGb73BwAAlWzcuLF2Qis6y9JZ3o03jsaZqGfCwBOoa5yHeMP63l7uRwicCwyYAQAARpaBJ8C5ovRH\nCIw7n3UKAAC0sGVLcvhwsmhRMjdXu4bEwBPg3FHyIwTGmc86BQAYaocOHcoll1xSO+OUdJY1zJ1b\ntiQHDiSLFye33DK8nYOee+7ZXHjhaLSeLgNPgHOJMxYBABhxa9asyf33318745R0lqWzvJ07b8tt\nt328dkYnvql2AAAAAEBbGzZsqJ3Qis6ydJZ3ww3raid0xsATAAAAGBnT09O1E1rRWZbO8pYseV3t\nhM4YeAIAAAAAY8PAEwAAAAAYGwaep+8nkvxRkq8m+UySq+vmAAAAwLlj27ZttRNa0VmWzvIeffTe\n2gmdMfA8PW9J8v4k703y+iT/IclvJfn2mlEAAABwrti7d2/thFZ0ljXMncuWJVdc0WyHuXPQ/v2f\nrZ3QmYnaASNmLsk9SX6l9/jdSW5I8uNJ/mmtKAAAADhX3HXXXbUTWtFZ1jB37t7d/2h4OwfddNPm\n2gmdcYZne+clmU7y0MDzDyX5/rOfAwAAAAAMcoZne5ckeWWSgwPP/3GSV7/YN83/yXzypS6zTs/k\neZOZetXUy97P088+nSPPHylQNN5K/XwPo1K/Bsb512TJ//7D+P7G1lfmM/k/JuP5v1wAAIDxZ+DZ\nsVX3rUo+VbviRB/90Y9myaIlZ/z9+w/vz5t2vKlg0Xh7uT/fw6j0r4Fx/jVZ4r//ML+/sfWu5KNf\n/kSWDNE/WAEAZ9/8n8zXTgDgDBh4tncoyV8muXTg+UvzEudwfs/vfU8m/2jyhOduePMNufGHbywe\neCrHhyalBifjOMgrqfTP9zAqNagcx1+TXfz3H6b3N872P/6JvOnTc3nTp+eST9euAQDOmid6X/3+\nvEYIpzI7O5v777+/dsYp6SxLZ3nbtq3Kbbd9vHZGJww823s+yWNJZpJ8tO/5H0zyGy/2Tff84j2Z\nnp7uOK2d6cums3DrwlBdhjzOSv58D6MSvwbG+ddk6f/+w/b+xtn0l5KFDyZH/s29yWtfWzsHAKho\n/rPzWXXjqtoZDLj11ltrJ7Sisyyd5V199dtqJ3TGwPP0bEnya0k+k+ZC9X+UZEmSX64ZdToMTM4u\nP9+nNs4/R+P83sbd1J8muei1yWXD8Q9WAEAlPt5mKM3MzNROaEVnWTrLW7782toJnTHwPD0fSfKq\nJD+d5LI0Fzy8MckXa0YBAAAAAA0Dz9P3S70vAAAAAM5x112XHDyYXHppsnt37RqS5JtqBwAAAAC0\ntWvXrtoJregsa5g7FxaSJ59stsPcOeiJJz5WO6EzBp4AAADAyNi+fXvthFZ0lqWzvMcfv692QmcM\nPAEAAICRsXPnztoJregsS2d5t9xyT+2Ezhh4AgAAAABjw8ATAAAAABgbBp4AAAAAwNgw8AQAAABG\nxurVq2sntKKzLJ3l7dixtnZCZyZqBwAAAAC0NTMzUzuhFZ1lDXPn3Fxy+HCyaFFy2WXD2zlo2bJr\nayd0xsATAAAAGBkrV66sndCKzrKGuXNurv/R8HYOmp5eUTuhMy5pBwAAAADGhoEnAAAAADA2DDwB\nAACAkbFnz57aCa3oLEtnec8886naCZ0x8AQAAABGxubNm2sntKKzLJ3lPfLI1toJnTHwBAAAAEbG\njh07aie0orMsneXdfPPdtRM6Y+AJAAAAjIwLLrigdkIrOsvSWd55541O6+maqB0AAAAAAKPqqaeS\nY8eSiYlk+fLaNSQGngAAAABwxq6/PjlwIFm8ONm/v3YNiUvaAQAAgBGybt262gmt6CxLZ3kPPLCh\ndkJnDDwBAACAkbF06dLaCa3oLEtneRddtLh2Qmdc0g4AJzM/3/0xJieTqanujwMAMEbWrl1bO6EV\nnWXpLO+aa95RO6EzBp4A0G9ystmuWnV2jrewYOgJAABQkIEnAPSbmmqGkEeOdHuc+flmqNr1cQAA\nAM4xBp4AMMgZlwAAQ2vfvn25/PLLa2ecks6ydJZ38ODTufDCS2pndMJNiwAAAICRsX79+toJregs\nS2d5Dz64sXZCZ5zhCQAAAIyMrVu31k5oRWdZw9z58MPJsWPJxERy/vnD2zloxYr31U7ojIEnAAAA\nMDKWLl1aO6EVnWUNc+fy5f2Phrdz0MUXL6md0BmXtAMAAAAAY8PAEwAAAAAYGwaeAAAAwMjYtGlT\n7YRWdJals7zduz9YO6EzBp4AAADAyDh69GjthFZ0lqWzvOef/2rthM4YeAIAAAAjY+PGjbUTWtFZ\nls7ybrzxjtoJnTHwBAAAAADGxkTtAAAAAAAYVVu2JIcPJ4sWJXNztWtInOEJAAAAjJBDhw7VTmhF\nZ1nD3LllS7JxY7Md5s5Bzz33bO2Ezhh4AgAAACNjzZo1tRNa0VmWzvJ27rytdkJnDDwBAACAkbFh\nw4baCa3oLEtneTfcsK52QmcMPAEAAICRMT09XTuhFZ1l6SxvyZLX1U7ojIEnAAAAADA2DDwBAAAA\ngLFh4AkAAACMjG3bttVOaEVnWTrLe/TRe2sndMbAEwAAABgZe/furZ3Qis6yhrlz2bLkiiua7TB3\nDtq//7O1EzozUTsAAAAAoK277rqrdkIrOssa5s7du/sfDW/noJtu2lw7oTPO8AQAAAAAxoaBJwAA\nAAAwNlzSDgA1zc93f4zJyWRqqvvjAAAADAEDTwCoYXKy2a5adXaOt7Bg6AkAjIXZ2dncf//9tTNO\nSWdZOsvbtm1Vbrvt47UzOmHgCQA1TE01Q8gjR7o9zvx8M1Tt+jgAAGfJrbfeWjuhFZ1l6Szv6qvf\nVjuhMwaeAFCLMy4BAE7bzMxM7YRWdJals7zly6+tndAZNy0CAAAAAMaGgScAAAAAnKHrrkuuvLLZ\nMhwMPAEAAICRsWvXrtoJregsa5g7FxaSJ59stsPcOeiJJz5WO6EzBp4AAADAyNi+fXvthFZ0lqWz\nvMcfv692QmcMPAEAAICRsXPnztoJregsS2d5t9xyT+2Ezhh4AgAAAABjw8ATAAAAABgbBp4AAAAA\nwNgw8AQAAABGxurVq2sntKKzLJ3l7dixtnZCZyZqBwAAAAC0NTMzUzuhFZ1lDXPn3Fxy+HCyaFFy\n2WXD2zlo2bJrayd0xsATAAAAGBkrV66sndCKzrKGuXNurv/R8HYOmp5eUTuhMy5pBwAAAADGhoEn\nAAAAADA2DDwBAACAkbFnz57aCa3oLEtnec8886naCZ0x8AQAAABGxubNm2sntKKzLJ3lPfLI1toJ\nnTHwBAAAAEbGjh07aie0orMsneXdfPPdtRM6Y+AJAAAAjIwLLrigdkIrOsvSWd55541O6+maqB0A\nAAAAAKPqqaeSY8eSiYlk+fLaNSQGngAAAABwxq6/PjlwIFm8ONm/v3YNiUvaAQAAgBGybt262gmt\n6CxLZ3kPPLChdkJnDDwBAACAkbF06dLaCa3oLEtneRddtLh2Qmdc0g4A54L5+e6PMTmZTE11fxwA\n4Jy2du3a2gmt6CxLZ3nXXPOO2gmdMfAEgHE2OdlsV606O8dbWDD0BAAAqjLwBIBxNjXVDCGPHOn2\nOPPzzVC16+MAAACcgoEnAIw7Z1wCAGNk3759ufzyy2tnnJLOsnSWd/Dg07nwwktqZ3TCTYsAAACA\nkbF+/fraCa3oLEtneQ8+uLF2Qmec4QkAAACMjK1bt9ZOaEVnWcPc+fDDybFjycREcv75w9s5aMWK\n99VO6IyBJwAAADAyli5dWjuhFZ1lDXPn8uX9j4a3c9DFFy+pndAZl7QDAAAAAGPDwBMAAAAAGBsG\nngAAAMDI2LRpU+2EVnSWpbO83bs/WDuhMwaeAAAAwMg4evRo7YRWdJals7znn/9q7YTOGHgCAAAA\nI2Pjxo21E1rRWZbO8m688Y7aCZ0x8AQAAAAAxsZE7QAAAAAAGFVbtiSHDyeLFiVzc7VrSJzhCQAA\nAIyQQ4cO1U5oRWdZw9y5ZUuycWOzHebOQc8992zthM4YeAIAAAAjY82aNbUTWtFZls7ydu68rXZC\nZww8AQAAgJGxYcOG2gmt6CxLZ3k33LCudkJnDDwBAACAkTE9PV07oRWdZeksb8mS19VO6IyBJwAA\nAAAwNgw8AQAAAICxYeAJAAAAjIxt27bVTmhFZ1k6y3v00XtrJ3TGwBMAAAAYGXv37q2d0IrOsoa5\nc9my5Iormu0wdw7av/+ztRM6M1E7AAAAAKCtu+66q3ZCKzrLGubO3bv7Hw1v56CbbtpcO6EzzvAE\nAAAAAMaGgScAAAAAMDZc0g4AlDM/3/0xJieTqanujwMAAIwkA08A4OWbnGy2q1adneMtLBh6AsA5\nanZ2Nvfff3/tjFPSWZbO8rZtW5Xbbvt47YxOGHgCAC/f1FQzhDxypNvjzM83Q9WujwMADK1bb721\ndkIrOsvSWd7VV7+tdkJnDDwBgDKccQkAnAUzMzO1E1rRWZbO8pYvv7Z2QmfctAgAAAAAGBsGngAA\nAABwhq67LrnyymbLcDDwBAAAAEbGrl27aie0orOsYe5cWEiefLLZDnPnoCee+FjthM4YeAIAAAAj\nY/v27bUTWtFZls7yHn/8vtoJnTHwBAAAAEbGzp07aye0orMsneXdcss9tRM6Y+AJAAAAo+dvJ3kg\nyYEkX0vypoHXP9x7vv/rP53FPoBqDDwBAABg9FyQ5PEk7+w9/vrA619P8ltJXt339cazVgdQ0UTt\nAAAAAOC0fbz39WJekeT5JH98dnIAhoczPAEAAGD8fD3J30lyMMlTSe5O8j/VDCpl9erVtRNa0VmW\nzvJ27FhbO6EztQaefy3JtiTPJDma5A+SbEjyzQPrlqb5TJLnkvxJkg+cZM13JflEbz/7k/zzkxzv\nB5I8luSrSf4wyT8+yZqbkjyZ5M+T/H6SN59kzU8k+aPefj6T5OoXe4MAAABQ0W8l+QdJrk3yk0m+\nN8nuJOfVjCphZmamdkIrOssa5s65ueQ972m2w9w5aNmya2sndKbWJe3L05xe/4/SDDu/K8m/SvKt\nSdb11rwyyW+m+deov5XkkiT/V+/73tVbsyjJbyd5OMmP9/b74ST/LcmW3prvSPKxJP8yzW/2Vyf5\nxTQD1Pt6a96QZEeSf5ZkV5IVST7SW/v/9da8Jcn7e8f5j0l+LM0fIFck+eLL++kAAACAoj7S9+Mn\n05y087kkP5TkN2oElbJy5craCa3oLGuYO+fm+h8Nb+eg6ekVtRM6U+sMz3+XZE2S/zfNb7gPJPn5\nNIPG42aSvDbJqiS/l2ao+ZNJ3pHkwt6af5jmX6femuY38N9I8rNJ+n+p/VjvGHNpTuPfluRXkvxU\n35rbkzyUZHOShSTv6x3v9r41c0nu6X3vU0nenWbQ+eOn/e4BAADg7Ppyki8kec1LLXrjG9+Y2dnZ\nE77e8IY3ZNeuXSese+ihhzI7O/uC73/nO9+Zbdu2nfDc3r17Mzs7m0OHDp3w/Hve855s2rTphOe+\n8IUvZHZ2Nvv27Tvh+Q996ENZt27dCc8dPXo0s7Oz2bNnzwnPb9++/aSXFb/lLW/xPrwP72P79qxd\n+8JL2e+++y353d898X088sgjZ/V9vPWtb81rXvOaE37/ede73pUz8Yoz+q5u/Is0Q87v6z3+mSR/\nL8n39K25OMmzaU7J/0SSf51kMskP9635njSXr39Hks8n+Z3e43f3rfnhJDuTnJ/kL3vrtqS5ZP64\ndye5Lc3l9+elOWv07yf5aN+aO5O8Ps3nogyaTvLYY489lunp6Zd+5wBAO3v3JlddlTz2WOLPVwA6\ntnfv3lx11VVJclWSvZVzXsrX0nws2/0vseaSNCftvCPJvSd53d9h4Rxx6NChvP/99+VVr1qRCy+8\n5AWvP/fcoTz77H1597tX5JJLXvj62XSmvw8Py13a/+ckt+bEMzNfneZy9n5/luYuc6/uW/PMwJqD\nfa99PsmlJ9nPwTTv/ZLej092rOPPp7fulSdZ88d9awCAs2V+vrt9T04mU1Pd7R8AyvjWJP1/YH1n\nmhNynk3yp0k2Jvl/0pzZ+dfSXA35Jxnxy9mTZM+ePbn66uG/pYbOsnSW98wzn8p3f/ffrZ3RidID\nzw1JfvoUa/5GTpzIfluSj6f5fJFfGVh7qjNQv346cQDAiJucbLarVnV7nIUFQ08Aht3xmxAlzd+N\nj9/H4sNpbrj715PcnOSiJF/qrf3f0ly9ONI2b948EgMlnWXpLO+RR7YaeLb0oSS/foo1n+/78bcl\neSTNTYD+0cC6L+Ubl7cfd3Gay8u/3Hv85bzwDMtL+157qTXHkhzqW3PpSdYc38ehNJe+n2zNl/IS\nbr/99lx00UUnPLdy5cqh/rBdABhaU1PNMPLIkW72Pz/fDFO72j8AQ2v79u3Zvn37Cc995StfqVTT\nyr/PS9+X48az1HHW7dixo3ZCKzrL0lnezTffXTuhM6UHns/2vtpYnGbY+ekkL/zU1+STae6a3n9J\n+kyS/57mMzmPr/nZJN+c5C/61hzINwarn0zzWaD9ZnrH/cu+NTM58TM8Z9IMYpPmMvrHes/1f4bn\nD+YUlwPceeedPv8EAEpy5iUAHTjZiSl9nx3HELngggtqJ7Sisyyd5Z133ui0nq5ad2lfnOZfoz6f\nZF2aoearc+KZmA+lufP6vWk+h+T6JD+X5O4kz/XW/HqaAeiHk1yZ5mZE/yTfOJU/SX45yV9NKVki\nwAAAIABJREFU8gtp7vq+pvf1831rPpBmmLk+yeVJ7ugd786+NVuSvD3NcPa1Sd6fZElv/wAAAACc\ng556Kvn932+2DIdaNy36wTQ3KvrOJPv7nv96mpsDJc1d5n4oyS+mOdPyq2mGn+v61h/u7euuJJ9J\n88HMv5BmGHnc55K8sffcO9Oc/bk2J56Z+ckkP5rmTvHvTfIHSX4kzVmgx30kyavSfEbpZUme6O33\ni6f1zgEAAAAYG9dfnxw4kCxenOzff+r1dK/WGZ4f7h37lb3tN/U97vfFNJejf2uaO6Xfnm9cun7c\nf07yA0nOT3Pm6HtPcrzfSXP7+v8hzaD1ZB9S8G/TnLn5LWnOFt11kjW/lOQ7evv53iR7XvQdAgAA\nAMWtW7fu1IuGgM6ydJb3wAMbaid0ptbAEwAAAOC0LV26tHZCKzrL0lneRRctrp3QGQNPAAAAYGSs\nXbu2dkIrOsvSWd4117yjdkJnDDwBAAAAgLFh4AkAAAAAjI1ad2kHABhe8/Pl9zk5mUxNld8vAJxj\n9u3bl8svv7x2xinpLEtneQcPPp0LL7ykdkYnDDwBAI6bnGy2q1Z1s/+FBUNPAHiZ1q9fn/vvv792\nxinpLEtneQ8+uDG33fbx2hmdMPAEADhuaqoZSh45Una/8/PNELX0fgHgHLR169baCa3oLGuYOx9+\nODl2LJmYSM4/f3g7B61Y8b7aCZ0x8AQA6OcMTAAYakuXLq2d0IrOsoa5c/ny/kfD2zno4ouX1E7o\njJsWAQAAAABjw8ATAAAAABgbBp4AAADAyNi0aVPthFZ0lqWzvN27P1g7oTMGngAAAMDIOHr0aO2E\nVnSWpbO855//au2Ezhh4AgAAACNj48aNtRNa0VmWzvJuvPGO2gmdMfAEAAAAAMbGRO0AAIBzxvx8\n+X1OTiZTU+X3CwBAK1u2JIcPJ4sWJXNztWtIDDwBALo3OdlsV63qZv8LC4aeAJwzDh06lEsuuaR2\nxinpLGuYO7dsSQ4cSBYvTm65ZXg7Bz333LO58MLRaD1dBp4AAF2bmmqGkkeOlN3v/HwzRC29XwAY\nYmvWrMn9999fO+OUdJals7ydO2/Lbbd9vHZGJww8AQDOBmdgAkARGzZsqJ3Qis6ydJZ3ww3raid0\nxk2LAAAAgJExPT1dO6EVnWXpLG/JktfVTuiMgScAAAAAMDYMPAEAAACAsWHgCQAAAIyMbdu21U5o\nRWdZOst79NF7ayd0xsATAAAAGBl79+6tndCKzrKGuXPZsuSKK5rtMHcO2r//s7UTOuMu7QAAAMDI\nuOuuu2ontKKzrGHu3L27/9Hwdg666abNtRM6Y+AJADDq5ufL7m9yMpmaKrtPAAA4Sww8AQBG1eRk\ns121qvy+FxYMPQEAGEkGngAAo2pqqhlMHjlSbp/z880AteQ+AQDgLDLwBAAYZc7CBOAcMzs7m/vv\nv792xinpLEtnedu2rcptt328dkYn3KUdAAAAGBm33npr7YRWdJals7yrr35b7YTOGHgCAAAAI2Nm\nZqZ2Qis6y9JZ3vLl19ZO6IyBJwAAAAAwNgw8AQAAAOAMXXddcuWVzZbhYOAJAAAAjIxdu3bVTmhF\nZ1nD3LmwkDz5ZLMd5s5BTzzxsdoJnTHwBAAAAEbG9u3baye0orMsneU9/vh9tRM6Y+AJAAAAjIyd\nO3fWTmhFZ1k6y7vllntqJ3RmonYAAABDaH6+/D4nJ5OpqfL7BQCAPgaeAAB8w+Rks121qpv9LywY\negIA0CkDTwAAvmFqqhlKHjlSdr/z880QtfR+AQBggIEnAAAncgYmAENs9erV+dVf/dXaGaeksyyd\n5e3YsTZvf/vo3GTpdBh4AgAAACNjZmamdkIrOssa5s65ueTw4WTRouSyy4a3c9CyZdfWTuiMgScA\nAAAwMlauXFk7oRWdZQ1z59xc/6Ph7Rw0Pb2idkJnvql2AAAAAABAKQaeAAAAAMDYMPAEAAAARsae\nPXtqJ7Sisyyd5T3zzKdqJ3TGwBMAAAAYGZs3b66d0IrOsnSW98gjW2sndMZNiwAAOHvm58vvc3Iy\nmZoqv18AhtKOHTtqJ7Sisyyd5d188921Ezpj4AkAQPcmJ5vtqlXd7H9hwdAT4BxxwQUX1E5oRWdZ\nOss777zRaT1dBp4AAHRvaqoZSh45Una/8/PNELX0fgEAWnrqqeTYsWRiIlm+vHYNiYEnAABnizMw\nAYAxdP31yYEDyeLFyf79tWtI3LQIAAAAGCHr1q2rndCKzrJ0lvfAAxtqJ3TGwBMAAAAYGUuXLq2d\n0IrOsnSWd9FFi2sndMbAEwAAABgZa9eurZ3Qis6ydJZ3zTXvqJ3QGQNPAAAAAGBsGHgCAAAAAGPD\nwBMAAAAYGfv27aud0IrOsnSWd/Dg07UTOmPgCQAAAIyM9evX105oRWdZOst78MGNtRM6M1E7AAAA\nAKCtrVu31k5oRWdZw9z58MPJsWPJxERy/vnD2zloxYr31U7ojIEnAAAAMDKWLl1aO6EVnWUNc+fy\n5f2Phrdz0MUXL6md0BmXtAMAAAAAY8PAEwAAAAAYGwaeAAAAwMjYtGlT7YRWdJals7zduz9YO6Ez\nBp4AAADAyDh69GjthFZ0lqWzvOef/2rthM4YeAIAAAAjY+PGjbUTWtFZls7ybrzxjtoJnTHwBAAA\nAADGxkTtAAAAAAAYVVu2JIcPJ4sWJXNztWtInOEJAAAAjJBDhw7VTmhFZ1nD3LllS7JxY7Md5s5B\nzz33bO2Ezhh4AgAAACNjzZo1tRNa0VmWzvJ27rytdkJnDDwBAACAkbFhw4baCa3oLEtneTfcsK52\nQmcMPAEAAICRMT09XTuhFZ1l6SxvyZLX1U7ojIEnAAAAADA2DDwBAAAAgLFh4AkAAACMjG3bttVO\naEVnWTrLe/TRe2sndMbAEwAAABgZe/furZ3Qis6yhrlz2bLkiiua7TB3Dtq//7O1EzozUTsAAAAA\noK277rqrdkIrOssa5s7du/sfDW/noJtu2lw7oTPO8AQAAAAAxoaBJwAAAAAwNgw8AQAAAICxYeAJ\nAAAAjIzZ2dnaCa3oLEtnedu2raqd0BkDTwAAAGBk3HrrrbUTWtFZls7yrr76bbUTOmPgCQAAAIyM\nmZmZ2gmt6CxLZ3nLl19bO6EzBp4AAAAAwNgw8AQAAACAM3TddcmVVzZbhoOBJwAAADAydu3aVTuh\nFZ1lDXPnwkLy5JPNdpg7Bz3xxMdqJ3TGwBMAAAAYGdu3b6+d0IrOsnSW9/jj99VO6IyBJwAAADAy\ndu7cWTuhFZ1l6SzvllvuqZ3QGQNPAAAAAGBsGHgCAAAAAGNjonYAAAC8bPPzdY47OZlMTdU5NgAA\nJ2XgCQDA6JqcbLarVtVrWFgw9AQ4i1avXp1f/dVfrZ1xSjrL0lnejh1r8/a3j85Nlk6HgScAAKNr\naqoZOB45cvaPPT/fDFprHBvgHDYzM1M7oRWdZQ1z59xccvhwsmhRctllw9s5aNmya2sndMbAEwCA\n0ebsSoBzysqVK2sntKKzrGHunJvrfzS8nYOmp1fUTuiMmxYBAAAAAGPDwBMAAAAAGBsGngAAAMDI\n2LNnT+2EVnSWpbO8Z575VO2Ezhh4AgAAACNj8+bNtRNa0VmWzvIeeWRr7YTOGHgCAAAAI2PHjh21\nE1rRWZbO8m6++e7aCZ0x8AQAAABGxgUXXFA7oRWdZeks77zzRqf1dE3UDgAAAACAUfXUU8mxY8nE\nRLJ8ee0aEgNPAAAAADhj11+fHDiQLF6c7N9fu4bEJe0AAADACFm3bl3thFZ0lqWzvAce2FA7oTMG\nngAAAMDIWLp0ae2EVnSWpbO8iy5aXDuhMwaeAAAAwMhYu3Zt7YRWdJals7xrrnlH7YTOGHgCAAAA\nAGPDwBMAAAAAGBsGngAAAMDI2LdvX+2EVnSWpbO8gwefrp3QGQNPAAAAYGSsX7++dkIrOsvSWd6D\nD26sndCZidoBAAAAAG1t3bq1dkIrOssa5s6HH06OHUsmJpLzzx/ezkErVryvdkJnhuEMz29J8rtJ\nvpbkuwdeW5rkgSTPJfmTJB9I8s0Da74rySeSHE2yP8k/P8kxfiDJY0m+muQPk/zjk6y5KcmTSf48\nye8nefNJ1vxEkj/q7eczSa5+yXcGAAAAFLV06dLaCa3oLGuYO5cvT668stkOc+egiy9eUjuhM8Mw\n8Nyc5MBJnn9lkt9Mcn6Sv5XkR9MMJX+hb82iJL+dZtD5N5KsTfJTSeb61nxHko+lGYq+PsnPJvlg\nkhV9a96QZEeSD6cZuv5ako8k+b6+NW9J8v4k7+3t5z8k+a0k335a7xYAAAAA6Eztgef/muR/STOk\nHDST5LVJViX5vSQPJ/nJJO9IcmFvzT9Mcl6St6Y5O/M30gw0+weeP5bkc73nnkqyLcmvDBzz9iQP\npRm+LiR5X+94t/etmUtyT+97n0ry7iRfTPLjp/eWAQAAAICu1Bx4Xprk7iQ3p7lEfNAbkjyR5Mt9\nzz2U5hL4q/rWfCLJXwys+bYkf7VvzUMD+34ozRmhr+w9/psvsub7ez8+L8n0KdYAAAAAHdu0aVPt\nhFZ0lqWzvN27P1g7oTO1Bp6vSHP5+C8l2fsia16d5ODAc3+W5Pneay+25mDfa0kzWD3Zmokkl5xi\nP8f3cUma4ejgmj/uWwMAAAB07OjRo7UTWtFZls7ynn/+ZOcfjofSA88NaW4+9FJfV6X5rM0L01w6\n3u8Vp3g86OsvLxcAAAAYJRs3bqyd0IrOsnSWd+ONd9RO6MxE4f19KMmvn2LN55P8H2kuNf/vA699\nJsm9SVanuZT9+wZevzjN5eXHL3P/cl54huWlfa+91JpjSQ71rbn0JGuO7+NQkr98kTVfyku4/fbb\nc9FFF53w3MqVK7Ny5cqX+jYAAADOou3bt2f79u0nPPeVr3ylUg0AL0fpgeezva9TeVeSf9b3eHGS\nf5fkR5I82nvuPyX5pznxkvSZNEPSx3qPP5nmJkXfnG98judMmru+f75vzd8bOP5Mkk+nGWIeXzOT\n5AMDa/5j78fP9445k+SjfWt+MM2Nkl7UnXfemenp6ZdaAgAAQGUnOzFl7969ueqqq17kOwAaW7Yk\nhw8nixYlc3OnXk/3an2G5xfT3FX9+NfTvef/MMl/6f34od5r9yZ5fZLrk/xcmhsdPddb8+tpBqAf\nTnJlkh9O8k+SbOk71i+nuYHRL6S56/ua3tfP9635QJph5voklye5o3e8O/vWbEny9jRnn742yfuT\nLOntHwAAADgLDh06dOpFQ0BnWcPcuWVLsnFjsx3mzkHPPdfmnMXRVPMu7YMGP4/za0l+KMmfpznT\ncmeS+5L8VN+aw2nOslyS5nL4rWkGm+/vW/O5JG9M8neSPJ7mzNK1OfHMzE8m+dE0w8zfS3JLmrNN\nP9235iNJbk/y0739XN3b7xdP+50CAAAAZ2TNmjW1E1rRWZbO8nbuvK12QmdKX9J+pj6X5i7og76Y\nF16OPug/J/mBU6z5nTQ3S3op/7b39VJ+qfcFAAAAVLBhw4baCa3oLEtneTfcsK52QmeG6QxPAAAA\ngJc0KvfJ0FmWzvKWLHld7YTOGHgCAAAAAGPDwBMAAAAAGBsGngAAAMDI2LZtW+2EVnSWpbO8Rx+9\nt3ZCZww8AQAAgJGxd+/e2gmt6CxrmDuXLUuuuKLZDnPnoP37P1s7oTPDcpd2AAAAgFO66667aie0\norOsYe7cvbv/0fB2Drrpps21EzrjDE8AAAAAYGwYeAIAAAAAY8PAEwAAAAAYGwaeAAAAwMiYnZ2t\nndCKzrJ0lrdt26raCZ1x0yIAAHg55ufrHHdyMpmaqnNsgIpuvfXW2gmt6CxLZ3lXX/222gmdMfAE\nAIAzMTnZbFdVPDtiYcHQEzjnzMzM1E5oRWdZOstbvvza2gmdMfAEAIAzMTXVDByPHDn7x56fbwat\nNY4NADDkDDwBAOBMObsSAM55112XHDyYXHppsnt37RoSNy0CAAAARsiuXbtqJ7Sis6xh7lxYSJ58\nstkOc+egJ574WO2Ezhh4AgAAACNj+/bttRNa0VmWzvIef/y+2gmdMfAEAAAARsbOnTtrJ7Sisyyd\n5d1yyz21Ezpj4AkAAAAAjA0DTwAAAABgbBh4AgAAAABjw8ATAAAAGBmrV6+undCKzrJ0lrdjx9ra\nCZ2ZqB0AAAAA0NbMzEzthFZ0ljXMnXNzyeHDyaJFyWWXDW/noGXLrq2d0BkDTwAAAGBkrFy5snZC\nKzrLGubOubn+R8PbOWh6ekXthM64pB0AAAAAGBsGngAAAADA2DDwBAAAAEbGnj17aie0orMsneU9\n88ynaid0xsATAAAAGBmbN2+undCKzrJ0lvfII1trJ3TGwBMAAAAYGTt27Kid0IrOsnSWd/PNd9dO\n6IyBJwAAADAyLrjggtoJregsS2d55503Oq2na6J2AAAAAACMqqeeSo4dSyYmkuXLa9eQGHgCAAAA\nwBm7/vrkwIFk8eJk//7aNSQuaQcAAABGyLp162ontKKzLJ3lPfDAhtoJnTHwBAAAAEbG0qVLaye0\norMsneVddNHi2gmdMfAEAAAARsbatWtrJ7Sisyyd5V1zzTtqJ3TGwBMAAAAAGBsGngAAAADA2DDw\nBAAAAEbGvn37aie0orMsneUdPPh07YTOGHgCAAAAI2P9+vW1E1rRWZbO8h58cGPthM5M1A4AAAAA\naGvr1q21E1rRWdYwdz78cHLsWDIxkZx//vB2Dlqx4n21Ezpj4AkAAACMjKVLl9ZOaEVnWcPcuXx5\n/6Ph7Rx08cVLaid0xiXtAAAAAMDYMPAEAAAAAMaGgScAAAAwMjZt2lQ7oRWdZeksb/fuD9ZO6IyB\nJwAAADAyjh49WjuhFZ1l6Szv+ee/WjuhMwaeAAAAwMjYuHFj7YRWdJals7wbb7yjdkJnDDwBAAAA\ngLExUTsAAAAAAEbVli3J4cPJokXJ3FztGhJneAIAAAAj5NChQ7UTWtFZ1jB3btmSbNzYbIe5c9Bz\nzz1bO6EzBp4AAADAyFizZk3thFZ0lqWzvJ07b6ud0BkDTwAAAGBkbNiwoXZCKzrL0lneDTesq53Q\nGQNPAAAAYGRMT0/XTmhFZ1k6y1uy5HW1EzrjpkUAADCq5ufrHHdyMpmaqnNsAIBTMPAEAIBRMznZ\nbFetqtewsGDoCQAMJQNPAAAYNVNTzcDxyJGzf+z5+WbQWuPYAEm2bduWt73tbbUzTklnWTrLe/TR\ne3P99bfXzuiEgScAAIwiZ1cC56i9e/eOxEBJZ1nD3LlsWfJX/kpy6aXD3Tlo//7P1k7ojIEnAAAA\nMDLuuuuu2gmt6CxrmDt37+5/NLydg266aXPthM64SzsAAAAAMDYMPAEAAACAsWHgCQAAAACMDQNP\nAAAAYGTMzs7WTmhFZ1k6y9u2bVXthM4YeAIAAAAj49Zbb62d0IrOsnSWd/XVo3E3+TNh4AkAAACM\njJmZmdoJregsS2d5y5dfWzuhMwaeAAAAAMDYMPAEAAAAgDN03XXJlVc2W4aDgScAAAAwMnbt2lU7\noRWdZQ1z58JC8uSTzXaYOwc98cTHaid0xsATAAAAGBnbt2+vndCKzrJ0lvf44/fVTuiMgScAAAAw\nMnbu3Fk7oRWdZeks75Zb7qmd0BkDTwAAAABgbBh4AgAAAABjw8ATAAAAABgbBp4AAADAyFi9enXt\nhFZ0lqWzvB071tZO6MxE7QAAAACAtmZmZmontKKzrGHunJtLDh9OFi1KLrtseDsHLVt2be2Ezhh4\nAgAAACNj5cqVtRNa0VnWMHfOzfU/Gt7OQdPTK2ondMYl7QAAAADA2DDwBAAAAADGhoEnAAAAMDL2\n7NlTO6EVnWXpLO+ZZz5VO6EzBp4AAADAyNi8eXPthFZ0lqWzvEce2Vo7oTMGngAAAMDI2LFjR+2E\nVnSWpbO8m2++u3ZCZww8AQAAgJFxwQUX1E5oRWdZOss777zRaT1dE7UDAAAAAGBUPfVUcuxYMjGR\nLF9eu4bEwBMAAAAAztj11ycHDiSLFyf799euIXFJOwAAADBC1q1bVzuhFZ1l6SzvgQc21E7ojIEn\nAAAAMDKWLl1aO6EVnWXpLO+iixbXTuiMgScAAAAwMtauXVs7oRWdZeks75pr3lE7oTMGngAAAADA\n2DDwBAAAAADGhoEnAAAAMDL27dtXO6EVnWXpLO/gwadrJ3TGwBMAAAAYGevXr6+d0IrOsnSW9+CD\nG2sndGaidgAAAABAW1u3bq2d0IrOsoa58+GHk2PHkomJ5Pzzh7dz0IoV76ud0BkDTwAAAGBkLF26\ntHZCKzrLGubO5cv7Hw1v56CLL15SO6EzLmkHAAAAAMaGgScAAAAAMDZc0g4AAJy++fk6x52cTKam\n6hwbGAqbNm3KHXfcUTvjlHSWpbO83bs/mNnZn6md0QkDTwAAoL3JyWa7alW9hoUFQ084hx09erR2\nQis6y9JZ3vPPf7V2QmcMPAEAgPamppqB45EjZ//Y8/PNoLXGsYGhsXHjxtoJregsS2d5N944Gmei\nngkDTwAA4PQ4uxIAGGIGngAAAABwhrZsSQ4fThYtSubmateQuEs7AAAAMEIOHTpUO6EVnWUNc+eW\nLcnGjc12mDsHPffcs7UTOmPgCQAAAIyMNWvW1E5oRWdZOsvbufO22gmdMfAEAAAARsaGDRtqJ7Si\nsyyd5d1ww7raCZ0x8AQAAIDR87eTPJDkQJKvJXnTSdZs6L1+NMkjSa44W3Fdmp6erp3Qis6ydJa3\nZMnraid0xsATAAAARs8FSR5P8s7e468PvH5Hktt7r39vki8n+e0kF56tQIBa3KUdAAAARs/He18n\n84o0w87/M8mu3nP/e5KDSf5Bkrs7rwOoyBmeAAAAMF6+I8mlSR7qe+75JJ9I8v1Vigratm1b7YRW\ndJals7xHH723dkJnDDwBAABgvLy6tz048Pwf9702svbu3Vs7oRWdZQ1z57JlyRVXNNth7hy0f/9n\nayd0xiXtAAAAcO4Y/KzPkXPXXXfVTmhFZ1nD3Ll7d/+j4e0cdNNNm2sndMYZngAAADBevtzbXjrw\n/KV9r53UG9/4xszOzp7w9YY3vCG7du06Yd1DDz2U2dnZF3z/O9/5zhdc0rt3797Mzs7m0KFDJzz/\nnve8J5s2bTrhuS984QuZnZ3Nvn37Tnj+Qx/6UNatW3fCc0ePHs3s7Gz27NlzwvPbt2/P6tWrX9D2\nlre8xfvwPryP7duzdu3aF7Tdffdb8ru/e+L7eOSRR87q+3jrW9+a17zmNSf8/vOud73rBcdv4xVn\n9F3l/FCSn07yXUn+W5LfSXJT3+tL04zGr03y1SS/nuSnkvxF35rvSrI1zV3n/jTJv0zy3oHj/ECS\nLUmuSPJfkmzuret3U+/7vjPJHyb5Z/nGhzsf9xNJ1qW5BOD303wI9J6c3HSSxx577LFMT0+/yBIA\nAKC1vXuTq65KHnss8f+xOQv27t2bq666KkmuSjLM16l+Lcmbk9zfe/yKJAeSvD/Jz/WeOy/NJe3r\nkvyrk+zD32HhHHHo0KG8//335VWvWpELL7zkBa8/99yhPPvsfXn3u1fkkkte+PrZdKa/D9c8w/Om\nJP86ybYk353mg5P/777XX5nkN5Ocn+RvJfnR3vf8Qt+aRUl+O8n+JH8jydo0A9G5vjXfkeRjaT6c\n+fVJfjbJB5Os6FvzhiQ7kny41/JrST6S5Pv61rwlzR8W7+3t5z8k+a0k337a7xwAAABenm9N83fT\n1/cef2fvx9+e5rL1O5P80zSD0L+e5u+7z6U5kQhgrNUaeE4k+UCa4eTdSf4gydNJ7utbM5PktUlW\nJfm9JA8n+ckk70hyYW/NP0zzr1RvTfJkkt9IM9DsH3j+WJLP9Z57Ks2A9Vd6xz7u9jR3r9ucZCHJ\n+3rHu71vzVySe3rf+1SSdyf5YpIfP/23DwAAAC/L96Y522lvmgHnlt6PN/Ze35xm6PmLST6d5LI0\nf8/+b2e9tLCTXWI7jHSWpbO8bdtW1U7oTK2B53SSb0vzm/LjaS4z/1iSK/vWvCHJEznx80UeSvIt\naU5jPb7mEznxEveHevv+q31rHho4/kNpzgh9Ze/x33yRNd/f+/F5veaXWgMAAABny79P83f6b0rz\nd9vjP17Tt2Zjmr8fn5/mo+KePLuJ3bj11ltrJ7Sisyyd5V199dtqJ3Sm1sDzO3vbDUl+JsnfTfJn\naX7Dvrj32quTHBz4vj9L8nzvtRdbc7DvtaT5UOaTrZlIcknf2pOtOb6PS9L8ATK45o/71gAAAAAd\nm5mZqZ3Qis6ydJa3fPm1tRM6U3rguSHNhyW/1NdVfcf9F2kuQ9+bZHWaMz7/ft/+TnVTpa8X6gYA\nAAAAxsBE4f19KKf+AOTPp7nZUHLi6fTPJ3kmzZ3Zk+ZS9v6bBiXN2Z/n5RuXuX85LzzD8tK+115q\nzbEkh/rWXHqSNcf3cSjJX77Imi/lJdx+++256KKLTnhu5cqVWbly5Ut9GwAAAGfR9u3bs3379hOe\n+8pXvlKpBhgl112XHDyYXHppsnt37RqS8gPPZ3tfp/JYkv+e5PIk/6n33DenuaP653uPP5nmjnL9\nl6TP9L7vsb41P9v73r/oW3NgYD9/b+D4M2k+tPkv+9bMpLmRUv+a/9j78fO9Y84k+Wjfmh9Mc4bq\ni7rzzjszPT39UksAAACo7GQnpuzduzdXXXXVi3wHtezatStvfvOba2ecks6yhrlzYSE5cCD5r/91\nuDsHPfHEx/KGN9xSO6MTtT7D83CSX07zAco/mGR5kl9Kc8n7v+mt+XdpzgC9N8nrk1yf5OfS3NX9\nud6aX08zAP1wmhse/XCSf5Lm7nTH/XKaGxj9Qpq7vq/pff1835oPpBlmrk8zhL2jd7w7+9ZsSfL2\nNJfevzbJ+5Ms6e0fAAAAOAsGz8QdVjrL0lne44/fVzuhM6XP8Dwd69JcVv5rae4Y96k4tNP9AAAg\nAElEQVQk1yX5r73Xv5bkh5L8YpozLb+aZvi5rm8fh9MMTO9K8pkkf5pmsPn+vjWfS/LG3nPvTHP2\n59qceGbmJ5P8aJrPFH1vkj9I8iNpzgI97iNJXpXkp5NcluYO8m9M8sUzefMAAADA6du5c2fthFZ0\nlqWzvFtuuad2QmdqDjyPpRlernuJNV/MCy9HH/Sfk/zAKdb8TpqbJb2Uf9v7eim/1PsCAAAAAIZQ\nrUvaAQAAAACKM/AEAAAAAMaGgScAAAAwMlavXl07oRWdZeksb8eOtbUTOlPzMzwBAAAATsvMzEzt\nhFZ0ljXMnXNzyeHDyaJFyWWXDW/noGXLrq2d0BkDTwAAAGBkrFy5snZCKzrLGubOubn+R8PbOWh6\nekXthM64pB0AAAAAGBsGngAAAADA2DDwBAAAAEbGnj17aie0orMsneU988ynaid0xsATAAAAGBmb\nN2+undCKzrJ0lvfII1trJ3TGwBMAAAAYGTt27Kid0IrOsnSWd/PNd9dO6IyBJwAAADAyLrjggtoJ\nregsS2d55503Oq2na6J2AAAAAACMqqeeSo4dSyYmkuXLa9eQGHgCAAAAwBm7/vrkwIFk8eJk//7a\nNSQuaQcAAABGyLp162ontKKzLJ3lPfDAhtoJnTHwBAAAAEbG0qVLaye0orMsneVddNHi2gmdcUk7\nAAAwWubn6x17cjKZmqp3fCBr166tndCKzrJ0lnfNNe+ondAZA08AAGA0TE4221Wr6nYsLBh6AsAQ\nM/AEAABGw9RUM2w8cqTO8efnm2FrreMDAK0YeAIAAKPDmZVwztu3b18uv/zy2hmnpLMsneUdPPh0\nLrzwktoZnXDTIgAAAGBkrF+/vnZCKzrL0lnegw9urJ3QGWd4AgAAACNj69attRNa0VnWMHc+/HBy\n7FgyMZGcf/7wdg5aseJ9tRM6Y+AJAAAAjIylS5fWTmhFZ1nD3Ll8ef+j4e0cdPHFS2ondMYl7QAA\nAADA2DDwBAAAAADGhoEnAAAAMDI2bdpUO6EVnWXpLG/37g/WTuiMgScAAAAwMo4ePVo7oRWdZen8\n/9m79zip6sP+/y9g1YBCvDWIINWmgEi8dPESDcZbs1ovqPhLzDaosGpqFdRsBJsYI1ttFBLRGIjG\nn0QatYBpDFGiUSuoJUbaiImiXDTegiIRE0UCFgl8//icgdlhL2fhHD4zw+v5eOxjmDOfOec9s7Mo\n7/2c88ne2rVrYkfIjYWnJEmSJEmqGE1NTbEjpGLObJkzeyeddGXsCLmx8JQkSZIkSZJUNWpiB5Ak\nSZIkSZIq1cSJsHIl9OgBjY2x0wic4SlJkiRJkirIihUrYkdIxZzZKuecEydCU1O4LeecpVatejd2\nhNxYeEqSJEmSpIrR0NAQO0Iq5syWObM3Y8ZlsSPkxsJTkiRJkiRVjHHjxsWOkIo5s2XO7J144pjY\nEXJj4SlJkiRJkipGbW1t7AipmDNb5sxenz4Hx46QGwtPSZIkSZIkSVXDwlOSJEmSJElS1bDwlCRJ\nkiRJFWPKlCmxI6RizmyZM3vz5t0dO0JuLDwlSZIkSVLFmD9/fuwIqZgzW+Wcs39/OOCAcFvOOUst\nXfpc7Ai5qYkdQJIkSZIkKa3JkyfHjpCKObNVzjlnzy6+V745S5111oTYEXLjDE9JkiRJkiRJVcPC\nU5IkSZIkSVLVsPCUJEmSJEmSVDUsPCVJkiRJUsUYOnRo7AipmDNb5szelCnDY0fIjYWnJEmSJEmq\nGKNGjYodIRVzZsuc2Rsy5PzYEXJj4SlJkiRJkipGXV1d7AipmDNb5szegAHHxY6QGwtPSZIkSZIk\nSVXDwlOSJEmSJEnaQscfD4MGhVuVBwtPSZIkSZJUMWbOnBk7QirmzFY551yyBF58MdyWc85Szz//\nYOwIubHwlCRJkiRJFWPatGmxI6RizmyZM3vPPntf7Ai5sfCUJEmSJEkVY8aMGbEjpGLObJkze+ee\ne0fsCLmx8JQkSZIkSZJUNSw8JUmSJEmSJFUNC09JkiRJkiRJVcPCU5IkSZIkVYyRI0fGjpCKObNl\nzuxNnz46doTc1MQOIEmSJEmSlFZdXV3sCKmYM1vlnLOxEVauhB49oFev8s1Zqn//42JHyI2FpyRJ\nkiRJqhj19fWxI6RizmyVc87GxuJ75ZuzVG3tsNgRcuMp7ZIkSZIkSZKqhoWnJEmSJEmSpKrhKe2S\nJEmS1BELF8Y5bvfu0K9fnGNLZWTu3LkMGTIkdox2mTNb5szeK688zUEHnRo7Ri4sPCVJkiQpje7d\nw+3w4fEyLFli6ant3oQJEyqiUDJntsyZvTlzJll4SpIkSdJ2rV+/UDh+8MG2P/bChaFojXFsqcxM\nnz49doRUzJktc2bvnHNujx0hNxaekiRJkpSWsyul6Lp16xY7QirmzJY5s7fjjpWTtaMsPCVJkiRJ\nkqQttHgxrFsHNTUwYEDsNAILT0mSJEmSJGmLnXACvPkm9O4NS5fGTiOAzrEDSJIkSZIkpTVmzJjY\nEVIxZ7bMmb0HHhgXO0JuLDwlSZIkSVLF6Nu3b+wIqZgzW+bM3q679o4dITcWnpIkSZIkqWKMHj06\ndoRUzJktc2bv6KMvjB0hNxaekiRJkiRJkqqGhackSZIkSZKkqmHhKUmSJEmSKsaiRYtiR0jFnNky\nZ/aWL38pdoTcWHhKkiRJkqSKMXbs2NgRUjFntsyZvVmzmmJHyE1N7ACSJEmSJElpTZo0KXaEVMyZ\nrXLO+dhjsG4d1NRA167lm7PUsGE3xI6QGwtPSZIkSZJUMfr27Rs7QirmzFY55xwwoPhe+eYstdtu\nfWJHyI2ntEuSJEmSJEmqGhaekiRJkiRJkqqGhackSZIkSaoY48ePjx0hFXNmy5zZmz37ltgRcmPh\nKUmSJEmSKsbq1atjR0jFnNkyZ/bWrl0TO0JuLDwlSZIkSVLFaGpqih0hFXNmy5zZO+mkK2NHyI2F\npyRJkiRJkqSqURM7gCRJkiRJklSpJk6ElSuhRw9obIydRuAMT0mSJEmSVEFWrFgRO0Iq5sxWOeec\nOBGamsJtOecstWrVu7Ej5MbCU5IkSZIkVYyGhobYEVIxZ7bMmb0ZMy6LHSE3Fp6SJEmSJKlijBs3\nLnaEVMyZLXNm78QTx8SOkBsLT0mSJEmSVDFqa2tjR0jFnNkyZ/b69Dk4doTcWHhKkiRJkiRJqhoW\nnpIkSZIkSZKqhoWnJEmSJEmqGFOmTIkdIRVzZsuc2Zs37+7YEXJj4SlJkiRJkirG/PnzY0dIxZzZ\nKuec/fvDAQeE23LOWWrp0udiR8hNTewAkiRJkiRJaU2ePDl2hFTMma1yzjl7dvG98s1Z6qyzJsSO\nkBtneEqSJEmSJEmqGhaekiRJkiRJkqqGhackSZIkSZKkqmHhKUmSJEmSKsbQoUNjR0jFnNkyZ/am\nTBkeO0JuLDwlSZIkSVLFGDVqVOwIqZgzW+bM3pAh58eOkBsLT0mSJEmSVDHq6upiR0jFnNkyZ/YG\nDDgudoTcWHhKkiRJkiRJqhoWnpIkSZIkSdIWOv54GDQo3Ko8WHhKkiRJkqSKMXPmzNgRUjFntso5\n55Il8OKL4bacc5Z6/vkHY0fIjYWnJEmSJEmqGNOmTYsdIRVzZsuc2Xv22ftiR8hNTewAkiRJkqSU\nFi6Mc9zu3aFfvzjHlkrMmDEjdoRUzJktc2bv3HPviB0hNxaekiRJklTuuncPt8OHx8uwZImlpySp\nIlh4SpIkSVK569cvFI4ffLDtj71wYShaYxxbkqQtYOEpSZIkSZXA2ZWSJKXiokWSJEmSJKlijBw5\nMnaEVMyZLXNmb/r00bEj5MYZnpIkSZIkqWLU1dXFjpCKObNVzjkbG2HlSujRA3r1Kt+cpfr3Py52\nhNxYeEqSJEmSpIpRX18fO0Iq5sxWOedsbCy+V745S9XWDosdITee0i5JkiRJkiSpalh4SpIkSZIk\nSaoaMQvP/YEHgBXA+8Bc4NiSMX2TMauAd4DvAjuUjDkQeAJYDSwFrm7hWMcAzwBrgN8B/9TCmLOA\nF4EPgReAM1oYczHwarKfXwNDWn95kiRJkiQpa3Pnzo0dIRVzZsuc2XvlladjR8hNzMLzweT2WGAw\n8BtgFtAz2d4F+DnQFfgM8EVCKXlj0T56AI8Sis5DgdHAFUDx1RP2S471BHAI8C3gFqD4QgVHAtOB\nqcBBwF3AvcDhRWPOBm4Crk3289/AQ8A+HX3hkiRJkiRpy0yYMCF2hFTMmS1zZm/OnEmxI+QmVuG5\nJ7AvcAOwAHgZ+BrQDTggGVMHDASGA78FHgO+ClwI7JKM+RKwIzCCMDvzp4RCs7jwvAh4Ldm2GJgC\n/JBQjBZcDjwCTACWJLkeS7YXNAJ3JM9dDHwF+D3wz1vyBkiSJEmSpI6bPn167AipmDNb5szeOefc\nHjtCbmIVniuAecB5hJKzhlBMvk049RzCrMvnk20FjwA7EWaEFsY8AXxUMmZv4K+LxjxScvxHCDNC\nuyT3P93KmKOSP+8I1LYzRpIkSZIk5axbt26xI6RizmyZM3s77lg5WTuqJuKxTwceBj4A1gPLgX8A\nViaP75VsK/YnYG3yWGHMKyVjlhc99jrhFPnS/SwnvPY9kz+3dKzCdpJxXVoY84eiMZIkSZIkSdrO\nLF4M69ZBTQ0MGBA7jSD7GZ7jCOVlW1+1hLLxAeBNwsI/hwE/I1zDs7hA7NTO8TZkF12SJEmSJEnq\nmBNOgE99KtyqPGRdeH6PsPp6W18vAJ8jnJb+ReBXhAWLLiGsfn5esq+32bSAUcFuhNPL3y4aUzrD\nsmfRY22NWUc4tb61Y/Us2scK4C+tjFlGGy6//HKGDh3a7GvatGltPUWSJEmStI1NmzZts3+7XX75\n5e0/UdvcmDFjYkdIxZzZMmf2HnhgXOwIucn6lPZ3k6/2dCbMzlxfsn0Dm2Z1/gr4Os1PSa8D/o9N\n1/n8FWGRoh3YdB3POsLM0deLxpxWcpw64H8JJWZhTB3w3ZIxv0z+vDY5Zh1hJmrB5wgLJbXq5ptv\npra2tq0hkiRJkqTI6uvrqa+vb7Zt/vz5DB48uJVnKJa+ffvGjpCKObNlzuztumvv2BFyE2vRol8C\nfwR+BBwE9Ae+TVho6OfJmIcJK6/fDRwCnJCMuR1YlYz5D0IBOhUYBJxJWO19YtGxbkv2eyNh1feG\n5Os7RWO+SygzxxJmoV6ZHO/mojETgQuAkcl+bgL6JPuXJEmSJEnbwOjRo2NHSMWc2TJn9o4++sLY\nEXITa9Gi94ATgeuBxwinqS8gLGT0fDJmPXAK8H1CQbqGUH4Wzw1eSZhlORn4NaFEvZFQRha8Bpyc\nbLuEMPtzNM1nZv6KcHr9dcC1wMvAFwizQAvuBfYAvgn0SnKeDPx+C16/JEmSJEmSpBzEXKX9N4RV\n2dvyezY/Hb3UAuCYdsY8SbhmaFt+kny15dbkS5IkSZIkSVIZinVKuyRJkiRJUoctWrQodoRUzJkt\nc2Zv+fKXYkfIjYWnJEmSJEmqGGPHjo0dIRVzZsuc2Zs1qyl2hNzEPKVdkiRJkiSpQyZNmhQ7Qirm\nzFY553zsMVi3DmpqoGvX8s1ZatiwG2JHyI2FpyRJkiRJqhh9+/aNHSEVc2arnHMOGFB8r3xzltpt\ntz6xI+TGU9olSZIkSZIkVQ0LT0mSJEmSJElVw8JTkiRJkiRVjPHjx8eOkIo5s2XO7M2efUvsCLmx\n8JQkSZIkSRVj9erVsSOkYs5smTN7a9euiR0hNxaekiRJkiSpYjQ1NcWOkIo5s2XO7J100pWxI+TG\nwlOSJEmSJElS1aiJHUCSJEmSJEmqVBMnwsqV0KMHNDbGTiNwhqckSZIkSaogK1asiB0hFXNmq5xz\nTpwITU3htpxzllq16t3YEXJj4SlJkiRJkipGQ0ND7AipmDNb5szejBmXxY6QGwtPSZIkSZJUMcaN\nGxc7QirmzJY5s3fiiWNiR8iNhackSZIkSaoYtbW1sSOkYs5smTN7ffocHDtCbiw8JUmSJEmSJFUN\nC09JkiRJkiRJVcPCU5IkSZIkVYwpU6bEjpCKObNlzuzNm3d37Ai5sfCUJEmSJEkVY/78+bEjpGLO\nbJVzzv794YADwm055yy1dOlzsSPkpiZ2AEmSJEmSpLQmT54cO0Iq5sxWOeecPbv4XvnmLHXWWRNi\nR8iNMzwlSZIkSZIkVQ1neEqSJEmS2rdwYZzjdu8O/frFObYkqSJZeEqSJEmSWte9e7gdPjxehiVL\nLD0lSalZeEqSJEmSWtevXygcP/hg2x974cJQtMY4tsrW0KFDuf/++2PHaJc5s2XO7E2ZMpzLLvtF\n7Bi5sPCUJEmSJLXN2ZUqI6NGjYodIRVzZsuc2Rsy5PzYEXLjokWSJEmSJKli1NXVxY6QijmzZc7s\nDRhwXOwIubHwlCRJkiRJklQ1LDwlSZIkSZKkLXT88TBoULhVebDwlCRJkiRJFWPmzJmxI6RizmyV\nc84lS+DFF8NtOecs9fzzD8aOkBsLT0mSJEmSVDGmTZsWO0Iq5syWObP37LP3xY6QGwtPSZIkSZJU\nMWbMmBE7QirmzJY5s3fuuXfEjpAbC09JkiRJkiRJVcPCU5IkSZIkSVLVsPCUJEmSJEmSVDUsPCVJ\nkiRJUsUYOXJk7AipmDNb5sze9OmjY0fITU3sAJIkSZIkSWnV1dXFjpCKObNVzjkbG2HlSujRA3r1\nKt+cpfr3Py52hNxYeEqSJEmSpIpRX18fO0Iq5sxWOedsbCy+V745S9XWDosdITee0i5JkiRJkiSp\nalh4SpIkSZIkSaoaFp6SJEmSJKlizJ07N3aEVMyZLXNm75VXno4dITcWnpIkSZIkqWJMmDAhdoRU\nzJktc2ZvzpxJsSPkxsJTkiRJkiRVjOnTp8eOkIo5s2XO7J1zzu2xI+TGwlOSJEmSJFWMbt26xY6Q\nijmzZc7s7bhj5WTtqJrYASRJkiRJkqRKtXgxrFsHNTUwYEDsNAILT0mSJEmSJGmLnXACvPkm9O4N\nS5fGTiPwlHZJkiRJklRBxowZEztCKubMljmz98AD42JHyI2FpyRJkiRJqhh9+/aNHSEVc2bLnNnb\nddfesSPkxsJTkiRJkiRVjNGjR8eOkIo5s2XO7B199IWxI+TGwlOSJEmSJElS1bDwlCRJkiRJklQ1\nLDwlSZIkSVLFWLRoUewIqZgzW+bM3vLlL8WOkBsLT0mSJEmSVDHGjh0bO0Iq5syWObM3a1ZT7Ai5\nqYkdQJIkSZIkKa1JkybFjpCKObNVzjkfewzWrYOaGujatXxzlho27IbYEXJj4SlJkiRJkipG3759\nY0dIxZzZKuecAwYU3yvfnKV2261P7Ai58ZR2SZIkSZIkSVXDwlOSJEmSJElS1bDwlCRJkiRJFWP8\n+PGxI6RizmyZM3uzZ98SO0JuLDwlSZIkSVLFWL16dewIqZgzW+bM3tq1a2JHyI2FpyRJkiRJqhhN\nTU2xI6RizmyZM3snnXRl7Ai5sfCUJEmSJEmSVDVqYgeQJEmSJEmSKtXEibByJfToAY2NsdMInOEp\nSZIkSZIqyIoVK2JHSMWc2SrnnBMnQlNTuC3nnKVWrXo3doTcOMNTkiRJklTeFi7cvo6rNjU0NHD/\n/ffHjtEuc2bLnNmbMeMyLrvsF7Fj5MLCU5IkSZJUnrp3D7fDh8fNobIybty42BFSMWe2zJm9E08c\nEztCbiw8JUmSJEnlqV8/WLIEPvggzvEffBCuvjrOsdWq2tra2BFSMWe2zJm9Pn0Ojh0hNxaekiRJ\nkqTy1a9fvGN7SrskVSQXLZIkSZIkSZJUNSw8JUmSJElSxZgyZUrsCKmYM1vmzN68eXfHjpAbC09J\nkiRJklQx5s+fHztCKubMVjnn7N8fDjgg3JZzzlJLlz4XO0JuvIanJEmSJEmqGJMnT44dIRVzZquc\nc86eXXyvfHOWOuusCbEj5MYZnpIkSZIkSZKqhoWnJEmSJEmSpKph4SlJkiRJkiSpalh4SpIkSZKk\nijF06NDYEVIxZ7bMmb0pU4bHjpAbC09JkiRJklQxRo0aFTtCKubMljmzN2TI+bEj5MbCU5IkSZIk\nVYy6urrYEVIxZ7bMmb0BA46LHSE3Fp6SJEmSJEmSqoaFpyRJkiRJkrSFjj8eBg0KtyoPFp6SJEmS\nJKlizJw5M3aEVMyZrXLOuWQJvPhiuC3nnKWef/7B2BFyY+EpSZIkSZIqxrRp02JHSMWc2TJn9p59\n9r7YEXJj4SlJkiRJkirGjBkzYkdIxZzZMmf2zj33jtgRcmPhKUmSJEmSJKlqWHhKkiRJkiRJqhoW\nnpIkSZIkSZKqhoWnJEmSJEmqGCNHjowdIRVzZsuc2Zs+fXTsCLmpiR1AkiRJkiQprbq6utgRUjFn\ntso5Z2MjrFwJPXpAr17lm7NU//7HxY6QGwtPSZIkSZJUMerr62NHSMWc2SrnnI2NxffKN2ep2tph\nsSPkxlPaJUmSJEmSJFUNC09JkiRJkiRJVcPCU5IkSZIkVYy5c+fGjpCKObNlzuy98srTsSPkxsJT\nkiRJkiRVjAkTJsSOkIo5s2XO7M2ZMyl2hNxYeEqSJEmSpIoxffr02BFSMWe2zJm9c865PXaE3Fh4\nSpIkSZKkitGtW7fYEVIxZ7bMmb0dd6ycrB1VEzuAJEmSJEmSVKkWL4Z166CmBgYMiJ1GYOEpSZIk\nSZIkbbETToA334TevWHp0thpBJ7SLkmSJEmSKsiYMWNiR0jFnNkyZ/YeeGBc7Ai5sfCUJEmSJEkV\no2/fvrEjpGLObJkze7vu2jt2hNxYeEqSJEmSVH3GAetLvt6KGSgro0ePjh0hFXNmy5zZO/roC2NH\nyI3X8JQkSZIkqTotAP6+6P5fYgWRpG3JwlOSJEmSpOr0F+APsUNI0rbmKe2SJEmSJFWnfsCbwCvA\nNGC/uHGysWjRotgRUjFntsyZveXLX4odITcWnpIkSZIkVZ+ngXOAOuBCYC/gKWD3mKGyMHbs2NgR\nUjFntsyZvVmzmmJHyI2FpyRJkiRJ1ecXwE+BF4DHgFOS7ee19aSTTz6ZoUOHNvs68sgjmTlzZrNx\njzzyCEOHDt3s+ZdccglTpkxptm3+/PkMHTqUFStWNNt+zTXXMH78+Gbb3njjDYYOHbrZLLnvfe97\njBkzBoBJkyYBsHr1aoYOHcrcuXObjZ02bRojR47cLNvZZ5+9TV/HmjVr2nwdBbFfR+H93NLvx7Z6\nHUOGDGnzdRTEeB1f+9pMFiyAxx4L72fMn480r6OwsNKwYTds3H777Wfzm980/37MmTNnm76OESNG\n8Ld/+7fN/v659NJLNzt+Gp226FlKoxZ45plnnqG2tjZ2FkmSJElSB82/5x4GDx8OMBiYHzlOFh4B\nXgIuaeEx/w0rbSdWrFjBTTfdxx57DGOXXfbc7PFVq1bw7rv38ZWvDGPPPTd/fFuaP38+gwcPhg7+\nPewMT0mSJEmSqt9OwAHAsthBJClvFp6SJEmSJLVk331jJ9ga3wE+S1io6AjgP4FdgH+PGUqStgUL\nT0mSJEmSWtK1a+wEW6M3YWX2RcBPgA+BTwO/jxkqC6XXAyxX5syWObM3e/YtsSPkpiZ2AEmSJEmS\nlLn62AHysnr16tgRUjFntsyZvbVr18SOkJu8ZnheBTwFrAb+1MqYvsADwCrgHeC7wA4lYw4Enkj2\nsxS4uoX9HAM8A6wBfgf8UwtjzgJeJPxG6wXgjBbGXAy8muzn18CQFsaMA95M8swhXP9EkiRJkiRt\nI01NTbEjpGLObJkzeyeddGXsCLnJq/DcAZgBfL+Vx7sAPwe6Ap8BvkgoJW8sGtMDeJRQdB4KjAau\nABqLxuwHPEgoRQ8BvgXcAgwrGnMkMB2YChwE3AXcCxxeNOZs4Cbg2mQ//w08BOxTNOZK4HLCanaH\nAW8n+XZp9V2QJEmSJEmStE3ldUr7uOR2RCuP1wEDgc8RikOArxJKya8TZn1+Cdgx2cdHhBma/QmF\n58TkORcBr7GpBF1MKEevAO5Ltl0OPAJMSO7fQJgVejnwj8m2RuAO4IfJ/a8AJwL/nOTplIz/N2Bm\nMuY8YHmyj9tbeZ2SJEmSJEmqYhMnwsqV0KMHNDa2P175i7Vo0ZHA82wqOyGUkjsBg4vGPEEoO4vH\n7A38ddGYR0r2/Qih9OyS3P90K2OOSv68I1Dbzpj9gJ4lY9Ym+Y5CkiRJkiRtEytWrIgdIRVzZquc\nc06cCE1N4bacc5Zaterd2BFyE2vRor0IsyOL/YlQIu5VNOaVkjHLix57nVBClu5nOeF17Zn8uaVj\nFbaTjOvSwpg/lGShlTF9acPChQvbeliSJEmSVKb891x5amho4P77748do13mzJY5szdjxmVcdtkv\nYsfIRUcKz3HAN9sZcygwP+X+OrXz+IaU+4mttZzLgIXDhw8fuC3DSJIkSZIytZDw7zuViXHjxsWO\nkIo5s2XO7J144pjYEXLTkcLze8B/tDPm9ZT7WkbzRYMAdiOcXl44zf1tNs2sLOhZ9FhbY9YBK4rG\n9GxhTGEfK4C/tDKm8B+1t1t4Xkv3iy0DTgB6tfK4JEmSJKn8LcPCs6zU1tbGjpCKObNlzuz16XNw\n7Ai56Ujh+W7ylYVfAVfR/JT0OuD/gGeKxnyLsOL7R0Vj3mRTsfor4LSSfdcB/0soMQtj6oDvloz5\nZfLntckx64CfFY35HPDT5M+vEorNOuC3ybYdCYsftVWH+x9GSZIkSZIkaRvKa9GivsAhyW0X4ODk\n/s7J448QVl2/O9l+AvBtwmrnq5Ix/0EoQKcCg4Azga+xaYV2gNsICxjdSFj1vSH5+k7RmO8Sisqx\nwP7Alcnxbi4aMxG4ABiZ7OcmoE+yfwinrd9MWLH9DOBTSa5VtD/rVZIkSZIkSdI2klfh+a+Ea3mO\nI5SczxJmURZWYF8PnAJ8SJhpOQO4D7iiaB8rCbMs+wC/BiYRis2bisa8BpwMHAvplCcAACAASURB\nVJsc4ypgNJtmZkKY4flFQpn5W+Bc4AuEWaAF9wKXE65R+iwwJNnv74vGTCCUnt9PntuLUKT+OcX7\nIUmSJEmSMjBlypTYEVIxZ7bMmb158+6OHSE3eRWeI5J9dybM8CzcPlk05veE09F3JqyUfjmbTl0v\nWEA4bbwr0Bu4toVjPUkoUj8GfJIwS7TUTwgzN3cizBad2cKYW4H9kv0cBsxtYUwTsHeS5zjCLFVJ\nkiRJkrSNzJ+fdq3kuMyZrXLO2b8/HHBAuC3nnKWWLn0udoTcdOQanpIkSZIkSVFNnjw5doRUzJmt\ncs45e3bxvfLNWeqssybEjpCbvGZ4SpIkSZIkSdI2Z+EpSZIkSZIkqWpYeEqSJEmSJEmqGhaekiRJ\nkiSpYgwdOjR2hFTMmS1zZm/KlOGxI+TGwlOSJEmSJFWMUaNGxY6QijmzZc7sDRlyfuwIubHwlCRJ\nkiRJFaOuri52hFTMmS1zZm/AgONiR8iNhackSZIkSZKkqmHhKUmSJEmSJG2h44+HQYPCrcqDhack\nSZIkSaoYM2fOjB0hFXNmq5xzLlkCL74Ybss5Z6nnn38wdoTcWHhKkiRJkqSKMW3atNgRUjFntsyZ\nvWefvS92hNxYeEqSJEmSpIoxY8aM2BFSMWe2zJm9c8+9I3aE3Fh4SpIkSZIkSaoaFp6SJEmSJEmS\nqoaFpyRJkiRJkqSqYeEpSZIkSZIqxsiRI2NHSMWc2TJn9qZPHx07Qm5qYgeQJEmSJElKq66uLnaE\nVMyZrXLO2dgIK1dCjx7Qq1f55izVv/9xsSPkxsJTkiRJkiRVjPr6+tgRUjFntso5Z2Nj8b3yzVmq\ntnZY7Ai58ZR2SZIkSZIkSVXDwlOSJEmSJElS1bDwlCRJkiRJFWPu3LmxI6RizmyZM3uvvPJ07Ai5\nsfCUpG1rfcqvz2Z4vO9twfP2LclTfHGXo4BrgI9vbbh2vAY8kPMxtpX+wE3Ab4H3gHeBucBZrYz/\nBDAVeAf4M/AUcPwWHHcErX/GPtGB/ewANALPA6uBPwG/BI5sYexoYBHwIfAK8E02v2b45SVZdm/n\n+FNLxn+YHGMcsFMHXkca+wI/J3yP1gMTM97/9uZxwucmS+MI3xtJ0nZqwoQJsSOkYs5smTN7c+ZM\nih0hNy5aJEnb1qeL/twJuBo4ls3LrIUZHnPDVjz3WkL581LRtkLheSfw/lbsuz0b2Lrs5aQOOBm4\nC3ga6AJ8Efgx4b28tmjsTsBjQA/gUuAPwCjgF8DfA09uwfFHEArCYn9M+dwuwE+BzwDjCeXrLsDf\nAd1Kxl4F/CtwPfAIcDhwHdAb+KeicdOS/VwINKTMsQYoLCO5G/CPhDJ1f8J7mZWbCLlHAm8DyzLc\n9/Yqj5/javm7QZK0BaZPnx47QirmzJY5s3fOObfHjpAbC09J2rb+p+T+CsI/3Eu3l4vf0Xq2Tjkf\nO8/9dyUUaNvKdKD016cPA38FXAncAHyUbD8fGESYPTkv2fY4YXboBJqX5mktAOZvwfMgzNg8iVB0\nF38WHiwZtwfwDeD25BZCObsDofS8mU1F/vLk62TSf5/Xlxz/YcJszC8QZp++lXI/LelEKJo/BD5F\neN/v34r9FeuSfK3NaH8K8v77R5JUxrp1K/2da3kyZ7bMmb0dd6ycrB3lKe2SVH4uIRRFy4FVwHPA\nGDb/JdXfAbOScR8Cbyb3e7ex707Atwjly/lbkG0coXQDeJXNT8E/mzCz7y3Cqc8vEmb7lf6X9G8I\nJeCbSfa3gf8CDm7n+BcTisFrOpD5ccIptZ8lzCr8MzAF+Osk+xWE0vH1JPPjwABCATYhyfgn4CfA\nniX7fo1w2v2ZhO/TGkJJPLpk3IpWsv0P4b0pPqX7TMJszHlF2/4C3E2Yedir7Zfboq0phy4DnqD9\nUv4kwnt2Z8n2O5Pjn7EVGVpTeI/6Jrc9gO8QPpv/BywlzNgs/fwVLvVwEaGE/RA4L9n+SUIRW/hs\nF/bdl/A9KPy8vUgoWovf232T54whlL6vJmOPY9Np2AcSZva+T/hcTCQUogMJJe7K5HlfLcm8E3Aj\n8CybLovwFDC0hfel8PrOSV7fn4HfAKe0MHZ/wozbt5OsrwP/DuxYNGYv4AfA7wnva+FSBV1a2F8a\nHcl3SvJY4RIJpe9LQSfC3w+/Ifwc/5HwPu9XNOaLybEvKXnuOGAdYQa1JElShy1eDC+8EG5VHpzh\nKUnl55OEMvB3hH/kH0I4VXh/NpWUOwOPJmMuJpQwvQinx3dvZb87Ea6F+A+EEuHRLcj2/xNOJx5N\nKOYKp/sWZu71Ax4izOb7gFDiXEko6k4o2s+DhIJiDPAGYabjkcCurRy3M/BtwqndDYRTw9PaQHhv\n7iKckv0vNL/+3yWE2ZMXJa/tRsLsvt8SSqmRhCLrO8nrP7Nk34cQSrVrCKXRcOC7hMLoxnayHUc4\nZf0PRds+RSgYSxWugziIjp9mPYvwHr9PKHS/CbyQ4nn7EIrh+wlF+fmEcnYxoQz+UUnu4pwFbxOK\nvUEdzJzG3ya37xBKzSeAvZOszyWZ/pVQMpaWWWcAQwhl19uEUvtIwun7LxOK8EL+vyKUizWEIvM1\n4DTCZ+KTbF6gXUp4jxoJBebLbLre6b2Ez+KthEsdjCX8PB9H+NyMB75E+Ly/DPwsed5OhFm0EwnF\n4w7A5whFfEs/E6cAhyZ5/5wc56eEMv/VZMzBhGvJ/oFweY2XkvfvNMLndy2h7PwfQiHYRPg756hk\nv/uS/pIEpdLkOyF5/b8k/DKlJhm3F5uf0v4DQmn9XcLfK3sQPudPJa/zD4S/Vz9L+Ll8GngmOcY3\nCJ+Z/9rC1yJJkrZzJ5wAb74JvXvD0qWx00iSFN9UQjHYms6Ef+SfQ5jZWFgoaDChtDutnf2vB24h\nlFT/TSgXD0yRa9/kuee28NgVNJ/51ppOhOyfZdPMNghFxHo2nwVZ6jVC0fYxQqnzRzZdw7EjHk+O\nd0zJ9n2T7aWnel+abP9pyfaJyfadSzKuY/P39GHCLLyubeS6INnfqJLt/wd8v4XxRybjz25jn6VO\nJBR+JxPKvYsJn4EPWsjckk8nx3yPUGSeRSgO7022X1A09nZav0zAYkIRXmoc6Rct+oAwo7CGMNP2\nUsLM18LSkv9C+F7Uljx3WHKMk4q2rSd8nlpaeOs1Nj+d/frkOYeWbJ+cZOiX3N83GbeEzWc/jkse\nu7xk+/xk++lF27oQfonx4xbyFY+pAe4gFHfF1hNmWRd/Vj9BeH+uLNr2GGGm6B5tHOc2QlHep2R7\nY3KcgW08F8LP33NbmO9pQrlbPNt0lyTzX4q2FT6nl5UcpzehTL2haNuOhPfrd0n2t4HZeIq8JEH4\nb+iGZ555ZkO5u+KKK2JHSMWc2SrnnL17b9gA4baccxa88847G77+9R9sOPbYSzb84AcbNvu68cbw\n+DvvvBM76oZnnnmmsLZD6f/nt8lT2iWp/PwdoXBZQSgA1hJOMe1MmP0EYSbWnwiz7P4JOKCN/f0N\n8CtCUfBpsl8xufRY/0GYgVjI/njyWKEY+SOhbBgLfIXwelv679EGQrE1h1DwDkn+vCX+SMuzJmHz\na1EWFvf5eSvbS4veF9j8PZ1GOL3671o55j8QyrIfs/m1PbP0MGGW24OEmXzfB44mvLf/WjSuUE4X\nvgplXeH7shOhNP0JYRbcFwhF3TdzzF5qZ0Lpv5YwW+8mwusqzLg9lfB9+C3NX8sjhNd7bMn+ZpN+\n0a3jCd/nX5dsn0p470qL+PtpXsgVm1VyfxGhrCsuhP9C+Bkp/ax9njDb8QM2vRcNhNnfpeYQyr6C\nwkziwj67EX4JcC+hQGzNqcm+ltH8ff1F8njpLxLSai/fzsBhwH00v/7pKsJlJIoLylMJ3+N7SjIu\nJ5StxxaNXUv4/O7Bpl92/CMugiRJFaVv3/Z+714ezJktc2Zv113buhpaZbPwlKTy0pdw/c5ehBls\nQwizyi4h/AP/Y8m4lYSi4TeEUzEXEK41OY7NL1dyOGEG2r1s3cIu7dmFMIv0MMIp+Mck2Ycljxey\nbyCcRvowofR8hlB0fDfZR0EnoH+S/xeEayZuqbZOAS9drXxtO9tLZ22+3cI+C9tamjl3IqHEeZhw\n6nKpd2l5xuPuRY9vjdcJpVnx4kd3El5f4atwuYPCsRYRZtoVe4Qw62+PorE7sen7XGz3DHKvIXye\nDiXMTv04YYZz4Xvbk3DqcqEILHytTB4v/V505LIAe7QyflnR42n33dLnajWbL2q0luaftWHADML3\n4UuE79+hwA9peSZxS+/3/xWN3Y3w/4HtnXTVk3Cd0NL3dQHhZ7mt2aFtSZOvE23/fBVn7ET4e2Rt\nydcRLWT8HeEXADsRrsva0jEkSWVs9Oj2ThQqD+bMljmzd/TRF8aOkBuv4SlJ5eUMwsymYTQvmFqa\nvr8AqE/+fBAwgjDjbg3hOoAF0wkznf6NTYsW5eF4QlF7DKH4LGipvHuDTadD/y3hNO1xhNNN/znZ\nvoFw/b3/JCwyRPLYlszEynP2VkuLCO2V3JaWOicCMwmz284izIIt9Tzh+1mqcAr6gi3I2JLi9+Qa\nwqUPCgqXWfgdzWfhtbWfwmnLB9F8gaO9CIXT1uZu6fIDxd4hZG3tmpKlC0d15DPxLuHalqUK27Zm\n35DudOrhhEV7vliy/WNbcDwIxetfCNdpbcs7hFmzV7XyeEevJ5vWnwiva68WHivdtiIZO4RQmpYq\n3XYBYcbyPMKlNe6l/UW5JEmSVEGc4SlJ8W1o4c/Fs706Ae396u05wjX13qfl06j/jXDtwGvZ+sKz\nUB6UrnzdUnYIp9y35WVCvgU0z14ogX5EKHlGsunU/nIyiM0Lyn8kzCwsLujqCGXnk4Ri+6NW9vdT\nwinKhxdtqyEUXk+z9bPR/oZwWvuvira9nmQtfL2UbF9HWDTmAMLiRQWdCKfl/45NMxZ/QVhka0TJ\n8UYQPhsztzJ3e6XeLEJ5/keav5bC1xtbcez/IrwHpT9b5ya5tvRSCwVpCsv1bP6Z2Yvm1/7siDWE\nyzx8nrZnac4ilO2v0PL7mlfh+WdCCXkWYSZmQXfCzN7i96xwinufVjIWL9B1IKHc/3fC9YWfI8yc\nbW3BNEmSJFWgcvtHoyRtj4pndz1CKAynERZZOZNw6nPpP8ZPJVy/8ELCIjKfI6z6/HFaX339lmT8\nWMLp41uqMJPvMsJCOocSTkX/JWFW1m2EQu/U5HWUloEHEUq/UYTXeDxwHaGIaC37T5J9nkWYjbVD\nBzPnuSDJMsI1G0cQXs/dhO/JdYQCEMLMs5nJ2OsJM3Y/XfTVvWh/PyQUND8mzOAtLBLUj+YLuqTx\nKPA1winJxxO+Z/9NKDKvTrmPbxLKp18QZuIWruV5IGGhoII/EV7zPyW3xxAWuLqGsLr9IrZOe9/D\nmwmLIz1JuDbs3xNK5gsIhdbhrT+1XTcRLhnx82R/dYSfoYsJ10V9eSv2Da2/tuLtswjX8J1M+F6e\nR/hevtXG89s7TiPhZ2ke4XUdR/jlwj1surzENwlF61PARcmxTya89gcICwN19Lhpx11NKHUfJRS7\nZxEWWlpVMvYpwqJZdxJmt5+avJZ/JHx/LkrG7Uz4Wfpdkv8jwvU8dyP83EmSKsSiRVv7vxXbhjmz\nZc7sLV/+UvuDKpSFpyTFVVhxrmAx4R/1uxGu83gLYYbSpSXjlhAKprGEGXj3AocQSpAptO6HhOv/\n/TNhdectKQKfIJR2pxEKl3mEAu+PwCmE6xHeneRYyearii8jFEQXE0q9mcnzGgnlWEHprLeHCEVL\nYaZkS9eKbEnpe5z2OWm3zycUbF9Nch1JmE37naIxJxDy/jVhsZynir5+SfOZg2uT8XOA7xHK1J6E\nGZXFlwpI43nC9/suQmE5hjBb8VDSXxP1FcKM0JcJpdJ/sum6jj8pGfstwmv//whF/SWEz8olHcxd\nKs33cHWScyqh2H+AUHSOJlwe4rUOHKvUCuAowvfu+mTfnyMUumkv0tTaa0i7fSqhYP4HQvE6Jsny\nH608v7UMxZ4jFMHPJPt6iLCi+Ydsmqn9NuHz8khyzIcIs67PA54l/D3U3jG3NN9/EX7R0YPwvfwO\n4e+MH7Yw9iLCL1E+S/hFyyygiXBN0HnJmNsIs0A/T5jhCvAqcH5ynEtT5pQkRTZ27NjYEVIxZ7bM\nmb1Zs5piR8hNnjNeytnXCNfHG0D4H96nCLNmlrTzvGOAiYTT2t4irI78g/xiSlI0+xKKrvMJZVlL\n15pUKNGeI5R/6pgawuzBbwB7svliPpIkaduqBZ555plnqK1t6fLx5eONN96oiJWwzZmtcs65eDGs\nWwc1NdC1a/nmLFixYgU33XQfnTsfzj77HLLZ46tWreDdd+/jK18Zxp577hkh4Sbz589n8ODBAINp\n+5r+zWyvMzw/S5g1cwRhhkYNYeZC6fXoiu1HOH30CcIsqm8RZl4Na+M5klTpphBmevl3nbJ0OeFz\n9Q3yXVBKkiRVoXIvkwrMma1yzjlgAAwaFG7LOWep3XbrEztCbrbXVdr/oeT+SOAPhN9ozW3lORcR\nZvI0JvcXE07xuoJw2qkkVZM3CX/HFbwSK0gbutD2mQobCKtQ5ylWWdeJ8PrbUs6zcu8hXGuz4P1Y\nQSRJkiRVn+218CxVWAykrdPpjiTMAi32COF0zy7k/49qSdqWPqIDpwtE8hhhxn5rXiOsSJ6n/XLe\nf2vuJKwQ3poNtF+IxvRO8iVJkiRJmbPwDLNkbiIsBNHWAg49geUl25YT3sM9W3gMoFfyJUnK3s2E\nRXRas5Ywc78a3cvmv4QrVa2vXZKkbW1Z8qUyMX78eK688srYMdplzmyZM3uzZ9/C0KH/GjtGLiw8\nYRIwCBiS8X577b333m+99dZbGe9WkiRJkrQNLQROwNKzbKxevTp2hFTMmS1zZm/t2jWxI+Rmey88\nvwecSjglsr1m8m1gr5JtPQnXSFvRwvheb731FnfffTcDBw7c6qBSubn88su5+eabY8eQcuHnW9XM\nz7eqmZ9vZW3hwoUMHz58IOHMPQvPMtHU1BQ7QirmzJY5s3fSSZUxE3VLbK+FZydC2Xk6cCzweorn\n/Ao4rWRbHfC/tHH9zoEDB1Jb61mFqj677rqrn21VLT/fqmZ+vlXN/HxLkiTYfgvPyUA9ofD8M5tm\nbr4HfJj8+Xpgb+C85P5twCjgRuAOwiJGDcAXt01kSZIkSZIklZuJE2HlSujRAxobY6cRQOfYASK5\nCOgBPE44lb3w9YWiMXsB+xTdfw04mTAj9FngKmA08NO8w0qSJEmSpGDFipauKld+zJmtcs45cSI0\nNYXbcs5ZatWqd2NHyM32Wnh2Brokt8VfPyoaMxI4vuR5TwKDgY8Bn6Tt1YElSZIkSVLGGhoaYkdI\nxZzZMmf2Zsy4LHaE3GyvhaekrVRfXx87gpQbP9+qZn6+Vc38fEvbh3HjxsWOkIo5s2XO7J144pjY\nEXJj4Slpi/gPClUzP9+qZn6+Vc38fEvbh0pZnMyc2TJn9vr0OTh2hNxsr4sWSZIkSdoOvPTSS3zw\nwQexY6hMde/enX79+sWOIUnKmIWnJEmSpKr00ksv0b9//9gxVOaWLFli6SlJVcbCU5IkSVJVKszs\nvPvuuxk4cGDkNCo3CxcuZPjw4c4ArkBTpkzh/PPPjx2jXebMljmzN2/e3ZxwwuWxY+TCwlOSJElS\nVRs4cGBFXVNNUtvmz59fEYWSObNVzjn794ePfxx69izvnKWWLn0udoTcWHhKkiRJkqSKMXny5NgR\nUjFntso55+zZxffKN2eps86aEDtCblylXZIkSZIkSVLVsPCUJEmSJEmSVDUsPCVJkiRJkiRVDQtP\nSZIkSaogU6dOpXPnzhu/dthhB/bZZx8aGhp46623MjvO2rVrueiii+jVqxc1NTUbF37ad999Oe20\n0zI7DsCIESPYb7/9Mt2nqtfQoUNjR0jFnNkyZ/amTBkeO0JuXLRIkiRJkirQ1KlT2X///VmzZg1P\nPPEE119/PU888QQLFiyga9euW73/W2+9ldtvv51JkyYxePBgdtllFwA6depEp06dtnr/pfLYp6rT\nqFGjYkdIxZzZMmf2hgypjNXkt4SFpyRJkiRVoE996lMbZ10ec8wx/OUvf+Haa69l5syZ1NfXb/F+\n16xZQ9euXVmwYAHdunXj4osvbvb4hg0btip3a/Lar6pPXV1d7AipmDNb5szegAHHxY6QG09plyRJ\nkqQqcMQRRwDw+uuvA/D973+fQw45hG7durH77rvz+c9/nldffbXZc4499lgOPPBAnnzySY466ih2\n3nlnGhoa6Ny5M1OmTGH16tUbT53/0Y9+1OJxX3vtNTp37syNN97IxIkT2W+//ejevTtHHXUU8+bN\n22z81KlTGTBgAB/72Mc44IADuOuuu1rc79q1a7nuuuvYf//9+djHPsYnPvEJGhoaWLFixcYxN9xw\nA126dGHWrFnNnjtixAh23nlnXnjhhfRvoCSpalh4SpIkSVIVePnllwH4q7/6K7785S/zla98hbq6\nOn72s5/x/e9/nxdeeIGjjjqKP/zhDxuf06lTJ5YtW8Y555zD8OHDeeihh7jkkkt4+umnOfnkk+na\ntStPP/00Tz/9NKecckqbx588eTKPPfYYt9xyC/fccw9//vOfOfnkk1m5cuXGMVOnTqWhoYFBgwZx\n33338Y1vfINrr72WOXPmNDulff369Zx++umMHz+e4cOH8+CDD3LDDTfw6KOPcuyxx/Lhhx8C8C//\n8i+cdNJJnHfeebzxxhsA3HnnnfzoRz/ie9/7HoMGDcrs/ZWk1hx/PAwaFG5VHjylXZIkSZKA1ath\n0aJ8j7H//tCtWzb7WrduHevWrePDDz/kiSee4LrrrqN79+7069ePCy+8kJtuuonLLrts4/ijjz6a\n/v37M3HiRG644QYgnEb+xz/+kZ/85Cccc8wxzfa/55570rlzZw4//PBUeXr06MGsWbM2Fpd77703\nhx9+OA899BBnn30269ev56qrruLQQw/lvvvu2/i8IUOG0K9fP3r37r1x27333svDDz/MT3/6U04/\n/fSN2w8++GAOO+wwpk6dykUXXQTAXXfdxSGHHMIXvvAFbr31VkaNGsXw4cNpaGjo4DuqSjFz5kzO\nOOOM2DHaZc5slXPOJUvgzTfh/ffLO2ep559/kCOPPDd2jFxYeEqSJEkSoewcPDjfYzzzDCSX3dxq\nn/70p5vdP+igg7j11lv5+c9/TqdOnfjSl77EunXrNj7es2dPDjroIB5//PFmz9t99903Kzu3xCmn\nnNJsluaBBx4IsHHm5eLFi1m2bBlXXHFFs+f17duXo446auOp+ACzZs1it91245RTTmn2Gg4++GB6\n9uzJ448/vrHw3H333ZkxYwbHHHMMn/nMZ9h333257bbbtvr1qHxNmzatIgolc2bLnNl79tn7LDwl\nSZIkqZrtv38oJPM+RlbuuusuBg4cSE1NDT179qRnz54A/PCHP2TDhg184hOfaPF5n/zkJ5vd79Wr\nVyZ59thjj2b3d9ppJyAsggTw7rvvArDXXntt9tyePXvy2muvbby/fPly/vSnP7Hjjju2eKzCvgoO\nP/xwDjjgAJ577jkuvvhiumU1jVZlacaMGbEjpGLObJkze+eee0fsCLmx8JQkSZIkwqnmWc2+3BYG\nDhy4cZX2YnvuuSedOnVi7ty5G0vHYqXbimdl5qlQiC5btmyzx95+++1mOfbcc0/22GMPHn744Rb3\n1b1792b3r7nmGhYsWMChhx7K1Vdfzamnnsq+++6bXXhJUkVx0SJJkiRJqiKnnXYaGzZsYOnSpdTW\n1m721ZGFfLIsQwcMGECvXr2YNm1as+2vv/46Tz31VLNtp512Gu+++y7r1q1r8TX069dv49hHH32U\nG264gauvvppHHnmEj3/843zhC1/go48+yiy7JKmyOMNTkiRJkqrIUUcdxZe//GVGjhzJr3/9a44+\n+mh23nlnli1bxty5cznooIM2Xv8SwsJFrWnrsY7q3Lkz1157LRdccAFnnnkmF1xwAe+99x5NTU30\n6tWr2bG++MUvcs8993DyySdz2WWXcdhhh7HDDjuwdOlSHn/8cU4//XTOOOMMli1bxvDhwznuuOO4\n5pprgHA66dFHH83YsWO56aabMssvSaoczvCUJEmSpArT3szL2267jUmTJvHkk09SX1/PqaeeyjXX\nXMOaNWs44ogjmu2ntX219tjWzPpsaGjgjjvu4MUXX+Sss87iuuuu46qrruL4449vtt/OnTtz//33\n8/Wvf5377ruPYcOGceaZZzJ+/Hi6du3KQQcdxPr166mvr6dLly7cc889G597xBFHcP3113PLLbdw\n//33b3FWla+RI0fGjpCKObNlzuxNnz46doTcOMNTkiRJkirIiBEjGDFiRCbj5syZ0+pjd955J3fe\needm21999dVm9/fdd1/Wr1/f4j5a2t7Q0EBDQ0Ozbeedd95m47p06UJjYyONjY2tZixdcb7gq1/9\nKl/96ldbfZ4qW11dXewIqZgzW+Wcs7ERVq6EHj2gV6/yzVmqf//jYkfIjYWnJEmSJEmqGPX19bEj\npGLObJVzzua/lynfnKVqa4fFjpAbT2mXJEmSJEmSVDUsPCVJkiRJkiRVDQtPSZIkSZJUMebOnRs7\nQirmzJY5s/fKK0/HjpAbC09JkiRJklQxJkyYEDtCKubMljmzN2fOpNgRcmPhKUmSJEmSKsb06dNj\nR0jFnNkyZ/bOOef22BFyY+EpSZIkSZIqRrdu3WJHSMWc2TJn9nbcsXKydlRN7ACSJEmSlKeFCxfG\njqAy5OdCUlYWL4Z166CmBgYMiJ1GYOEpSZIkqUp1794dgOHDh0dOonJW+JxI0pY64QR4803o3RuW\nLoX333+fjz76qM3n7LDDDnz84x/fRgm3PxaekiRJkqpSv379WLJkCR98wTpcWgAAIABJREFU8EHs\nKCpT3bt3p1+/frFjqIPGjBnDt7/97dgx2mXObFVKzksvvZR99vkU773X9rhdd4Uvf/nsqKXnAw+M\no76+Ohcu2p4Lz88CY4BaoBdwJvCzNsYfC8xuYfv+wJKsw0mSJEnaepZZUvXp27dv7AipmDNblZKz\nV69evPcedO16PN267drimNWr3+O992a3Ows0b7vu2jvq8fO0PS9a1A14Frgkub8h5fP6AXsVfb2c\nfTRJkiRJktSS0aNHx46QijmzVSk5L7zwQgC6dduVXXbZs8Wv1orQbe3ooy+MHSE32/MMz18kXx21\nAng/4yySJEmSJEmSMrA9z/DcUs8CbwH/RTjNXZIkSZIkSVKZsPBM7y3gQmBY8rUYeAwYEjOUJEmS\nJEnbk0WLFsWOkIo5s1UpOV966aXYEVJbvrxysnaUhWd6S4ApwG+ApwnX/vw5YeEjSZIkSZK0DYwd\nOzZ2hFTMma1KydnU1BQ7QmqzZlVO1o7anq/hmYV5wJfaGnD55Zez667NL0ZbX19PfX19nrkkSZIk\nSR0wbdo0pk2b1mzbe++9FymN2jJp0qTYEVIxZ7bKOedjj8G6dVBTA2vW3MCPf/w/sSOlMmzYDbEj\n5MbCc+v8HeFU91bdfPPN1NbWbqM4kiRJkqQt0dLElPnz5zN48OBIidSavn37xo6QijmzVc45BwzY\n9OcVK/oAlVF47rZbn9gRcrM9F547A/2K7v8NcAjwLvB74Hpgb+C85PHLgVeBF4EdgeFsup6nJEmS\nJEmSpDKwPReehwGzkz9vACYmf54KNAB7AfsUjd8B+DbQB1gDLABOBn6xDbJKkiRJkiRJSmF7XrTo\nccLr7wx0KfpzQ/L4SOD4ovHfBvoD3YA9gGOw7JQkSZIkaZsaP3587AipmDNblZLzlltuiR0htdmz\nKydrR23PhackSZIkSaowq1evjh0hFXNmq1JyrlmzJnaE1NaurZysHWXhKUmSJEmSKkZTU1PsCKmY\nM1uVkvPKK6+MHSG1k06qnKwdZeEpSZIkSZIkqWpsz4sWSZIkSZL+X3v3H2dXXd95/E2AEEJCgsES\nEKNSQIEiNlSKLiBEDa4PjFtksUGkEKutFSrkUXDrrj5CbVWgTaiFllLi1l1KyK7lly7yS7ACQlYJ\nv7oOxhJUkCSSlEkISSQk7h9n8mCYzCRnknvne8/M8/l43MfMvffcm1e+DJO5nznnHgB2yty5yZo1\nyd57J2edVbqGxB6eAAAAQIOsXLmydEItOlurkzvnzk0uvrj6uGrVqtI5ta1d25zWwTLwBAAAABpj\n1qxZpRNq0dlaTen89Kc/XTqhtoULm9M6WAaeAAAAQGPMmTOndEItOlurKZ0XXnhh6YTaTj65Oa2D\nZeAJAAAANMbUqVNLJ9Sis7Wa0nnUUUeVTqjtwAOb0zpYBp4AAAAAwLBh4AkAAAAADBsGngAAAEBj\nzJ8/v3RCLTpbqymd1157bemE2hYtak7rYBl4AgAAAI2xePHi0gm16GytTu489NDk8MOrj4899ljp\nnNqeeaY5rYO1W+kAAAAAgLquvPLK0gm16GytTu68++5XPl+58tLMm3dDuZhB+NCHLi2d0Db28AQA\nAAAAhg0DTwAAAABg2DDwBAAAAACGDQNPAAAAoDFmzJhROqEWna3VlM4zzzyzdEJt8+c3p3WwDDwB\nAACAxjj33HNLJ9Sis7Wa0vmxj32sdEJtxx3XnNbBMvAEAAAAGmP69OmlE2rR2VpN6TzppJNKJ9T2\n5jc3p3WwDDwBAAAAgGHDwBMAAAAAdtC0ackRR1Qf6QwGngAAAEBj3HTTTaUTatHZWp3cuWRJ8sMf\nVh9vvfXW0jm1Pf54c1oHy8ATAAAAaIwFCxaUTqhFZ2s1pfOGG24onVDbww83p3WwDDwBAACAxli4\ncGHphFp0tlZTOq+55prSCbWddVZzWgfLwBMAAAAAGDYMPAEAAACAYcPAEwAAAAAYNgw8AQAAgMY4\n55xzSifUorO1mtJ53nnnlU6o7frrm9M6WLuVDgAAAACoa/r06aUTatHZWp3cOXt2smZNsvfeybhx\nJ+WnPy1dVM+hh55UOqFtDDwBAACAxpg5c2bphFp0tlYnd86e/crnK1eemnnzbigXMwhTp55aOqFt\nHNIOAAAAAAwbBp4AAAAAwLBh4AkAAAA0xn333Vc6oRadrdWUzgcffLB0Qm1LlzandbBG8sDzhCTf\nSPLzJJuTfLDGY96V5KEk65M8meQP2lYHAAAAbOXSSy8tnVCLztZqSucVV1xROqG2e+5pTutgjeST\nFo1N8nCS+UluSPKr7Wz/piS3Jvn7JGckOS7J3yZ5rufxAAAAQJtdf/31pRNq0dlandS5evXqbNy4\nsd/7vvzlL+faa7+73efYsGF9Vq1aNeD9u+++eyZMmLDDjXV89KNXt/X5SxrJA8/bei51/WGSnyTZ\ncu6tHyX5rSR/EgNPAAAAGBJjx44tnVCLz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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4041e30550>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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IxM3/O8TN/OFEfzrnU7w/0Wxm5mVEAO5oIsi2M9Hs9fC8ZSYk70s7BZ8NXE/r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\nTjq8xtTwMGBvHY/PNG1E6SBnA/8J/JFcDV7UgG08/qo5aWwND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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4040223d90>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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RnRh9nri+GkdcL/yHuL9YJSP2a4nv4YOI758XyX2XQ1y3/JW4rvoBcf19OPFd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\nE+ibn8iSJEmSpBS8AZwNJPvbefP8HVZSKXsPOIME9sOlesTop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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f40412b6090>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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O5eeAv8ssNzOJ8U/E9eRQIum4HriI+NHndOLYTRPn2f5Y+5Jp9wG/BI4irq83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j8DvAa0Ak8D0/sY/3TgmcL4rwB/X9V0UsYsXbo0dgQp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\nJEmSJEmSJElSFP8PJgFspvAZoS4AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f4040235650>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Parse the RT-App generate log files to compute performance metrics\n",
+ "pa = PerfAnalysis(te.res_dir)\n",
+ "\n",
+ "# For each task which has generated a logfile, plot its performance metrics\n",
+ "for task in pa.tasks():\n",
+ " pa.plotPerf(task, \"Performance plots for task [{}] \".format(task))"
+ ]
+ }
+ ],
+ "metadata": {
+ "celltoolbar": "Raw Cell Format",
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/ipynb/examples/wlgen/rtapp_examples.ipynb b/ipynb/examples/wlgen/rtapp_examples.ipynb
deleted file mode 100644
index a109d14..0000000
--- a/ipynb/examples/wlgen/rtapp_examples.ipynb
+++ /dev/null
@@ -1,737 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "from conf import LisaLogging\n",
- "LisaLogging.setup()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Populating the interactive namespace from numpy and matplotlib\n"
- ]
- }
- ],
- "source": [
- "# Generate plots inline\n",
- "%pylab inline\n",
- "\n",
- "import json\n",
- "import os\n",
- "\n",
- "# Support to access the remote target\n",
- "import devlib\n",
- "from env import TestEnv\n",
- "\n",
- "# Support to configure and run RTApp based workloads\n",
- "from wlgen import RTA, Ramp, Step, Pulse, Periodic"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Test environment setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "11:39:33 INFO : Target - Using base path: /home/derkling/Code/lisa\n",
- "11:39:33 INFO : Target - Loading custom (inline) target configuration\n",
- "11:39:33 INFO : Target - Devlib modules to load: []\n",
- "11:39:33 INFO : Target - Connecting host target:\n",
- "11:39:33 INFO : Target - username : put_here_your_username\n",
- "11:39:33 INFO : Target - password : \n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "sudo password:········\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "11:39:36 INFO : Target - Initializing target workdir:\n",
- "11:39:36 INFO : Target - /tmp\n",
- "11:39:36 INFO : Target - Topology:\n",
- "11:39:36 INFO : Target - [[0, 1, 2, 3, 4, 5, 6, 7]]\n",
- "11:39:36 WARNING : Target - Unable to identify cluster frequencies\n",
- "11:39:36 INFO : TestEnv - Set results folder to:\n",
- "11:39:36 INFO : TestEnv - /home/derkling/Code/lisa/results/20160226_113936\n",
- "11:39:36 INFO : TestEnv - Experiment results available also in:\n",
- "11:39:36 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n"
- ]
- }
- ],
- "source": [
- "# Let's use the local host as a target\n",
- "te = TestEnv(\n",
- " target_conf={\n",
- " \"platform\": 'host',\n",
- " \"username\": 'put_here_your_username'\n",
- " })"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Create a new RTA workload generator object"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The wlgen::RTA class is a workload generator which exposes an API to configure\n",
- "RTApp based workload as well as to execute them on a target."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "11:39:36 INFO : WlGen - Setup new workload example\n"
- ]
- }
- ],
- "source": [
- "# Create a new RTApp workload generator\n",
- "rtapp = RTA(\n",
- " \n",
- " target=te.target, # Target execution on the local machine\n",
- " \n",
- " name='example', # This is the name of the JSON configuration file reporting\n",
- " # the generated RTApp configuration\n",
- " \n",
- " calibration={0: 10, 1: 11, 2: 12, 3: 13} # These are a set of fake\n",
- " # calibration values\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Workload Generation Examples"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Single periodic task"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "An RTApp workload is defined by specifying a **kind**, which represents the way\n",
- "we want to defined the behavior of each task.<br>\n",
- "The most common kind is **profile**, which allows to define each task using one\n",
- "of the predefined **profile** supported by the RTA base class.<br>\n",
- "<br>\n",
- "The following example shows how to generate a \"periodic\" task<br>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "code_folding": [],
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "11:39:36 INFO : RTApp - Workload duration defined by longest task\n",
- "11:39:36 INFO : RTApp - Default policy: SCHED_OTHER\n",
- "11:39:36 INFO : RTApp - ------------------------\n",
- "11:39:36 INFO : RTApp - task [task_per20], sched: {'policy': 'FIFO'}\n",
- "11:39:36 INFO : RTApp - | calibration CPU: 0\n",
- "11:39:36 INFO : RTApp - | loops count: 1\n",
- "11:39:36 INFO : RTApp - + phase_000001: duration 5.000000 [s] (50 loops)\n",
- "11:39:36 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n",
- "11:39:36 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n"
- ]
- }
- ],
- "source": [
- "# Configure this RTApp instance to:\n",
- "rtapp.conf(\n",
- " \n",
- " # 1. generate a \"profile based\" set of tasks\n",
- " kind='profile',\n",
- " \n",
- " # 2. define the \"profile\" of each task\n",
- " params={\n",
- " \n",
- " # 3. PERIODIC task\n",
- " # \n",
- " # This class defines a task which load is periodic with a configured\n",
- " # period and duty-cycle.\n",
- " # \n",
- " # This class is a specialization of the 'pulse' class since a periodic\n",
- " # load is generated as a sequence of pulse loads.\n",
- " # \n",
- " # Args:\n",
- " # cuty_cycle_pct (int, [0-100]): the pulses load [%]\n",
- " # default: 50[%]\n",
- " # duration_s (float): the duration in [s] of the entire workload\n",
- " # default: 1.0[s]\n",
- " # period_ms (float): the period used to define the load in [ms]\n",
- " # default: 100.0[ms]\n",
- " # delay_s (float): the delay in [s] before ramp start\n",
- " # default: 0[s]\n",
- " # sched (dict): the scheduler configuration for this task\n",
- " 'task_per20': Periodic(\n",
- " period_ms=100, # period\n",
- " duty_cycle_pct=20, # duty cycle\n",
- " duration_s=5, # duration\n",
- " cpus=None, # run on all CPUS\n",
- " sched={\n",
- " \"policy\": \"FIFO\", # Run this task as a SCHED_FIFO task\n",
- " },\n",
- " delay_s=0 # start at the start of RTApp\n",
- " ).get(),\n",
- " \n",
- " },\n",
- " \n",
- " # 4. use this folder for task logfiles\n",
- " run_dir='/tmp'\n",
- " \n",
- ");"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The output of the previous cell reports the main properties of the generated\n",
- "tasks. Thus for example we see that the first task is configure to be:\n",
- "1. named **task_per20**\n",
- "2. will be executed as a **SCHED_FIFO** task\n",
- "3. generating a load which is **calibrated** with respect to the **CPU 0**\n",
- "3. with one single \"phase\" which defines a peripodic load for the **duration** of **5[s]**\n",
- "4. that periodic load consistes of **50 cycles**\n",
- "5. each cycle has a **period** of **100[ms]** and a **duty-cycle** of **20%**\n",
- "6. which means that the task, for every cycle, will **run** for **20[ms]** and then sleep for **20[ms]** \n",
- "\n",
- "All these properties are translated into a JSON configuration file for RTApp.<br>\n",
- "Let see what it looks like the generated configuration file:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{\n",
- " \"tasks\": {\n",
- " \"task_per20\": {\n",
- " \"policy\": \"SCHED_FIFO\", \n",
- " \"phases\": {\n",
- " \"p000001\": {\n",
- " \"run\": 20000, \n",
- " \"timer\": {\n",
- " \"ref\": \"task_per20\", \n",
- " \"period\": 100000\n",
- " }, \n",
- " \"loop\": 50\n",
- " }\n",
- " }, \n",
- " \"loop\": 1\n",
- " }\n",
- " }, \n",
- " \"global\": {\n",
- " \"duration\": -1, \n",
- " \"logdir\": \"/tmp\", \n",
- " \"default_policy\": \"SCHED_OTHER\", \n",
- " \"calibration\": 10\n",
- " }\n",
- "}\n"
- ]
- }
- ],
- "source": [
- "# Dump the configured JSON file for that task\n",
- "with open(\"./example_00.json\") as fh:\n",
- " rtapp_config = json.load(fh)\n",
- "print json.dumps(rtapp_config, indent=4)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Workload mix"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Using the wlgen::RTA workload generator we can easily create multiple tasks, each one with different \"profiles\", which are executed once the rtapp application is started in the target.<br>\n",
- "<br>\n",
- "In the following example we configure a workload mix composed by a RAMP task, a STEP task and a PULSE task:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "code_folding": [],
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "11:39:37 INFO : RTApp - Workload duration defined by longest task\n",
- "11:39:37 INFO : RTApp - Default policy: SCHED_OTHER\n",
- "11:39:37 INFO : RTApp - ------------------------\n",
- "11:39:37 INFO : RTApp - task [task_pls5-80], sched: using default policy\n",
- "11:39:37 INFO : RTApp - | start delay: 0.500000 [s]\n",
- "11:39:37 INFO : RTApp - | calibration CPU: 0\n",
- "11:39:37 INFO : RTApp - | loops count: 1\n",
- "11:39:37 INFO : RTApp - + phase_000001: duration 1.000000 [s] (10 loops)\n",
- "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 65 %\n",
- "11:39:37 INFO : RTApp - | run_time 65000 [us], sleep_time 35000 [us]\n",
- "11:39:37 INFO : RTApp - + phase_000002: duration 1.000000 [s] (10 loops)\n",
- "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 5 %\n",
- "11:39:37 INFO : RTApp - | run_time 5000 [us], sleep_time 95000 [us]\n",
- "11:39:37 INFO : RTApp - ------------------------\n",
- "11:39:37 INFO : RTApp - task [task_rmp20_5-60], sched: using default policy\n",
- "11:39:37 INFO : RTApp - | calibration CPU: 0\n",
- "11:39:37 INFO : RTApp - | loops count: 1\n",
- "11:39:37 INFO : RTApp - | CPUs affinity: 0\n",
- "11:39:37 INFO : RTApp - + phase_000001: duration 1.000000 [s] (10 loops)\n",
- "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 5 %\n",
- "11:39:37 INFO : RTApp - | run_time 5000 [us], sleep_time 95000 [us]\n",
- "11:39:37 INFO : RTApp - + phase_000002: duration 1.000000 [s] (10 loops)\n",
- "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 25 %\n",
- "11:39:37 INFO : RTApp - | run_time 25000 [us], sleep_time 75000 [us]\n",
- "11:39:37 INFO : RTApp - + phase_000003: duration 1.000000 [s] (10 loops)\n",
- "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 45 %\n",
- "11:39:37 INFO : RTApp - | run_time 45000 [us], sleep_time 55000 [us]\n",
- "11:39:37 INFO : RTApp - + phase_000004: duration 1.000000 [s] (10 loops)\n",
- "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 65 %\n",
- "11:39:37 INFO : RTApp - | run_time 65000 [us], sleep_time 35000 [us]\n",
- "11:39:37 INFO : RTApp - ------------------------\n",
- "11:39:37 INFO : RTApp - task [task_stp10-50], sched: using default policy\n",
- "11:39:37 INFO : RTApp - | start delay: 0.500000 [s]\n",
- "11:39:37 INFO : RTApp - | calibration CPU: 0\n",
- "11:39:37 INFO : RTApp - | loops count: 1\n",
- "11:39:37 INFO : RTApp - + phase_000001: sleep 1.000000 [s]\n",
- "11:39:37 INFO : RTApp - + phase_000002: duration 1.000000 [s] (10 loops)\n",
- "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 50 %\n",
- "11:39:37 INFO : RTApp - | run_time 50000 [us], sleep_time 50000 [us]\n"
- ]
- }
- ],
- "source": [
- "# Configure this RTApp instance to:\n",
- "rtapp.conf(\n",
- " # 1. generate a \"profile based\" set of tasks\n",
- " kind='profile',\n",
- " \n",
- " # 2. define the \"profile\" of each task\n",
- " params={\n",
- " \n",
- " # 3. RAMP task\n",
- " #\n",
- " # This class defines a task which load is a ramp with a configured number\n",
- " # of steps according to the input parameters.\n",
- " # \n",
- " # Args:\n",
- " # start_pct (int, [0-100]): the initial load [%], (default 0[%])\n",
- " # end_pct (int, [0-100]): the final load [%], (default 100[%])\n",
- " # delta_pct (int, [0-100]): the load increase/decrease [%],\n",
- " # default: 10[%]\n",
- " # increase if start_prc < end_prc\n",
- " # decrease if start_prc > end_prc\n",
- " # time_s (float): the duration in [s] of each load step\n",
- " # default: 1.0[s]\n",
- " # period_ms (float): the period used to define the load in [ms]\n",
- " # default: 100.0[ms]\n",
- " # delay_s (float): the delay in [s] before ramp start\n",
- " # default: 0[s]\n",
- " # loops (int): number of time to repeat the ramp, with the\n",
- " # specified delay in between\n",
- " # default: 0\n",
- " # sched (dict): the scheduler configuration for this task\n",
- " # cpus (list): the list of CPUs on which task can run\n",
- " 'task_rmp20_5-60': Ramp(\n",
- " period_ms=100, # period\n",
- " start_pct=5, # intial load\n",
- " end_pct=65, # end load\n",
- " delta_pct=20, # load % increase...\n",
- " time_s=1, # ... every 1[s]\n",
- " cpus=\"0\" # run just on first CPU\n",
- " ).get(),\n",
- " \n",
- " # 4. STEP task\n",
- " # \n",
- " # This class defines a task which load is a step with a configured\n",
- " # initial and final load.\n",
- " # \n",
- " # Args:\n",
- " # start_pct (int, [0-100]): the initial load [%]\n",
- " # default 0[%])\n",
- " # end_pct (int, [0-100]): the final load [%]\n",
- " # default 100[%]\n",
- " # time_s (float): the duration in [s] of the start and end load\n",
- " # default: 1.0[s]\n",
- " # period_ms (float): the period used to define the load in [ms]\n",
- " # default 100.0[ms]\n",
- " # delay_s (float): the delay in [s] before ramp start\n",
- " # default 0[s]\n",
- " # loops (int): number of time to repeat the ramp, with the\n",
- " # specified delay in between\n",
- " # default: 0\n",
- " # sched (dict): the scheduler configuration for this task\n",
- " # cpus (list): the list of CPUs on which task can run\n",
- " 'task_stp10-50': Step(\n",
- " period_ms=100, # period\n",
- " start_pct=0, # intial load\n",
- " end_pct=50, # end load\n",
- " time_s=1, # ... every 1[s]\n",
- " delay_s=0.5 # start .5[s] after the start of RTApp\n",
- " ).get(),\n",
- " \n",
- " # 5. PULSE task\n",
- " #\n",
- " # This class defines a task which load is a pulse with a configured\n",
- " # initial and final load.\n",
- " # \n",
- " # The main difference with the 'step' class is that a pulse workload is\n",
- " # by definition a 'step down', i.e. the workload switch from an finial\n",
- " # load to a final one which is always lower than the initial one.\n",
- " # Moreover, a pulse load does not generate a sleep phase in case of 0[%]\n",
- " # load, i.e. the task ends as soon as the non null initial load has\n",
- " # completed.\n",
- " # \n",
- " # Args:\n",
- " # start_pct (int, [0-100]): the initial load [%]\n",
- " # default: 0[%]\n",
- " # end_pct (int, [0-100]): the final load [%]\n",
- " # default: 100[%]\n",
- " # NOTE: must be lower than start_pct value\n",
- " # time_s (float): the duration in [s] of the start and end load\n",
- " # default: 1.0[s]\n",
- " # NOTE: if end_pct is 0, the task end after the\n",
- " # start_pct period completed\n",
- " # period_ms (float): the period used to define the load in [ms]\n",
- " # default: 100.0[ms]\n",
- " # delay_s (float): the delay in [s] before ramp start\n",
- " # default: 0[s]\n",
- " # loops (int): number of time to repeat the ramp, with the\n",
- " # specified delay in between\n",
- " # default: 0\n",
- " # sched (dict): the scheduler configuration for this task\n",
- " # cpus (list): the list of CPUs on which task can run\n",
- " 'task_pls5-80': Pulse(\n",
- " period_ms=100, # period\n",
- " start_pct=65, # intial load\n",
- " end_pct=5, # end load\n",
- " time_s=1, # ... every 1[s]\n",
- " delay_s=0.5 # start .5[s] after the start of RTApp\n",
- " ).get(),\n",
- " \n",
- " \n",
- " },\n",
- " \n",
- " # 6. use this folder for task logfiles\n",
- " run_dir='/tmp'\n",
- " \n",
- ");"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{\n",
- " \"tasks\": {\n",
- " \"task_rmp20_5-60\": {\n",
- " \"policy\": \"SCHED_OTHER\", \n",
- " \"phases\": {\n",
- " \"p000004\": {\n",
- " \"run\": 65000, \n",
- " \"timer\": {\n",
- " \"ref\": \"task_rmp20_5-60\", \n",
- " \"period\": 100000\n",
- " }, \n",
- " \"loop\": 10\n",
- " }, \n",
- " \"p000003\": {\n",
- " \"run\": 45000, \n",
- " \"timer\": {\n",
- " \"ref\": \"task_rmp20_5-60\", \n",
- " \"period\": 100000\n",
- " }, \n",
- " \"loop\": 10\n",
- " }, \n",
- " \"p000002\": {\n",
- " \"run\": 25000, \n",
- " \"timer\": {\n",
- " \"ref\": \"task_rmp20_5-60\", \n",
- " \"period\": 100000\n",
- " }, \n",
- " \"loop\": 10\n",
- " }, \n",
- " \"p000001\": {\n",
- " \"run\": 5000, \n",
- " \"timer\": {\n",
- " \"ref\": \"task_rmp20_5-60\", \n",
- " \"period\": 100000\n",
- " }, \n",
- " \"loop\": 10\n",
- " }\n",
- " }, \n",
- " \"cpus\": [\n",
- " 0\n",
- " ], \n",
- " \"loop\": 1\n",
- " }, \n",
- " \"task_pls5-80\": {\n",
- " \"policy\": \"SCHED_OTHER\", \n",
- " \"phases\": {\n",
- " \"p000002\": {\n",
- " \"run\": 5000, \n",
- " \"timer\": {\n",
- " \"ref\": \"task_pls5-80\", \n",
- " \"period\": 100000\n",
- " }, \n",
- " \"loop\": 10\n",
- " }, \n",
- " \"p000001\": {\n",
- " \"run\": 65000, \n",
- " \"timer\": {\n",
- " \"ref\": \"task_pls5-80\", \n",
- " \"period\": 100000\n",
- " }, \n",
- " \"loop\": 10\n",
- " }, \n",
- " \"p000000\": {\n",
- " \"delay\": 500000\n",
- " }\n",
- " }, \n",
- " \"loop\": 1\n",
- " }, \n",
- " \"task_stp10-50\": {\n",
- " \"policy\": \"SCHED_OTHER\", \n",
- " \"phases\": {\n",
- " \"p000002\": {\n",
- " \"run\": 50000, \n",
- " \"timer\": {\n",
- " \"ref\": \"task_stp10-50\", \n",
- " \"period\": 100000\n",
- " }, \n",
- " \"loop\": 10\n",
- " }, \n",
- " \"p000001\": {\n",
- " \"sleep\": 1000000, \n",
- " \"loop\": 1\n",
- " }, \n",
- " \"p000000\": {\n",
- " \"delay\": 500000\n",
- " }\n",
- " }, \n",
- " \"loop\": 1\n",
- " }\n",
- " }, \n",
- " \"global\": {\n",
- " \"duration\": -1, \n",
- " \"logdir\": \"/tmp\", \n",
- " \"default_policy\": \"SCHED_OTHER\", \n",
- " \"calibration\": 10\n",
- " }\n",
- "}\n"
- ]
- }
- ],
- "source": [
- "# Dump the configured JSON file for that task\n",
- "with open(\"./example_00.json\") as fh:\n",
- " rtapp_config = json.load(fh)\n",
- "print json.dumps(rtapp_config, indent=4)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## Workload composition"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Initial phase and pinning parameters\n",
- "ramp = Ramp(period_ms=100, start_pct=5, end_pct=65, delta_pct=20, time_s=1,\n",
- " cpus=\"0\")\n",
- "\n",
- "# Following phases\n",
- "medium_slow = Periodic(duty_cycle_pct=10, duration_s=5, period_ms=100)\n",
- "high_fast = Periodic(duty_cycle_pct=60, duration_s=5, period_ms=10)\n",
- "medium_fast = Periodic(duty_cycle_pct=10, duration_s=5, period_ms=1)\n",
- "high_slow = Periodic(duty_cycle_pct=60, duration_s=5, period_ms=100)\n",
- "\n",
- "#Compose the task\n",
- "complex_task = ramp + medium_slow + high_fast + medium_fast + high_slow"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "11:39:37 INFO : RTApp - Workload duration defined by longest task\n",
- "11:39:37 INFO : RTApp - Default policy: SCHED_OTHER\n",
- "11:39:37 INFO : RTApp - ------------------------\n",
- "11:39:37 INFO : RTApp - task [complex], sched: using default policy\n",
- "11:39:37 INFO : RTApp - | calibration CPU: 0\n",
- "11:39:37 INFO : RTApp - | loops count: 1\n",
- "11:39:37 INFO : RTApp - | CPUs affinity: 0\n",
- "11:39:37 INFO : RTApp - + phase_000001: duration 1.000000 [s] (10 loops)\n",
- "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 5 %\n",
- "11:39:37 INFO : RTApp - | run_time 5000 [us], sleep_time 95000 [us]\n",
- "11:39:37 INFO : RTApp - + phase_000002: duration 1.000000 [s] (10 loops)\n",
- "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 25 %\n",
- "11:39:37 INFO : RTApp - | run_time 25000 [us], sleep_time 75000 [us]\n",
- "11:39:37 INFO : RTApp - + phase_000003: duration 1.000000 [s] (10 loops)\n",
- "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 45 %\n",
- "11:39:37 INFO : RTApp - | run_time 45000 [us], sleep_time 55000 [us]\n",
- "11:39:37 INFO : RTApp - + phase_000004: duration 1.000000 [s] (10 loops)\n",
- "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 65 %\n",
- "11:39:37 INFO : RTApp - | run_time 65000 [us], sleep_time 35000 [us]\n",
- "11:39:37 INFO : RTApp - + phase_000005: duration 5.000000 [s] (50 loops)\n",
- "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 10 %\n",
- "11:39:37 INFO : RTApp - | run_time 10000 [us], sleep_time 90000 [us]\n",
- "11:39:37 INFO : RTApp - + phase_000006: duration 5.000000 [s] (500 loops)\n",
- "11:39:37 INFO : RTApp - | period 10000 [us], duty_cycle 60 %\n",
- "11:39:37 INFO : RTApp - | run_time 6000 [us], sleep_time 4000 [us]\n",
- "11:39:37 INFO : RTApp - + phase_000007: duration 5.000000 [s] (5000 loops)\n",
- "11:39:37 INFO : RTApp - | period 1000 [us], duty_cycle 10 %\n",
- "11:39:37 INFO : RTApp - | run_time 100 [us], sleep_time 900 [us]\n",
- "11:39:37 INFO : RTApp - + phase_000008: duration 5.000000 [s] (50 loops)\n",
- "11:39:37 INFO : RTApp - | period 100000 [us], duty_cycle 60 %\n",
- "11:39:37 INFO : RTApp - | run_time 60000 [us], sleep_time 40000 [us]\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'example_00'"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Configure this RTApp instance to:\n",
- "rtapp.conf(\n",
- " # 1. generate a \"profile based\" set of tasks\n",
- " kind='profile',\n",
- " \n",
- " # 2. define the \"profile\" of each task\n",
- " params={\n",
- " 'complex' : complex_task.get()\n",
- " },\n",
- "\n",
- " # 6. use this folder for task logfiles\n",
- " run_dir='/tmp'\n",
- ")"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/ipynb/examples/wlgen/simple_rtapp.ipynb b/ipynb/examples/wlgen/simple_rtapp.ipynb
deleted file mode 100644
index 866a0c4..0000000
--- a/ipynb/examples/wlgen/simple_rtapp.ipynb
+++ /dev/null
@@ -1,882 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "from conf import LisaLogging\n",
- "LisaLogging.setup()\n",
- "\n",
- "# Comment the follwing line to disable devlib debugging statements\n",
- "# logging.getLogger('ssh').setLevel(logging.DEBUG)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Populating the interactive namespace from numpy and matplotlib\n"
- ]
- }
- ],
- "source": [
- "# Generate plots inline\n",
- "%pylab inline\n",
- "\n",
- "import json\n",
- "import os"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Test environment setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# Setup a target configuration\n",
- "my_target_conf = {\n",
- " \n",
- " # Define the kind of target platform to use for the experiments\n",
- " \"platform\" : 'linux', # Linux system, valid other options are:\n",
- " # android - access via ADB\n",
- " # linux - access via SSH\n",
- " # host - direct access\n",
- " \n",
- " # Preload settings for a specific target\n",
- " \"board\" : 'juno', # load JUNO specific settings, e.g.\n",
- " # - HWMON based energy sampling\n",
- " # - Juno energy model\n",
- " # valid options are:\n",
- " # - juno - JUNO Development Board\n",
- " # - tc2 - TC2 Development Board\n",
- " # - oak - Mediatek MT63xx based target\n",
- "\n",
- " # Define devlib module to load\n",
- " #\"modules\" : [\n",
- " # 'bl', # enable big.LITTLE support\n",
- " # 'cpufreq' # enable CPUFreq support\n",
- " #],\n",
- "\n",
- " # Account to access the remote target\n",
- " \"host\" : '192.168.0.1',\n",
- " \"username\" : 'root',\n",
- " \"password\" : '',\n",
- "\n",
- " # Comment the following line to force rt-app calibration on your target\n",
- " \"rtapp-calib\" : {\n",
- " '0': 361, '1': 138, '2': 138, '3': 352, '4': 360, '5': 353\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "# Setup the required Test Environment supports\n",
- "my_tests_conf = {\n",
- " \n",
- " # Binary tools required to run this experiment\n",
- " # These tools must be present in the tools/ folder for the architecture\n",
- " #\"tools\" : ['rt-app', 'taskset', 'trace-cmd'],\n",
- " \n",
- " # FTrace events end buffer configuration\n",
- " \"ftrace\" : {\n",
- " \"events\" : [\n",
- " \"sched_switch\",\n",
- " \"cpu_frequency\"\n",
- " ],\n",
- " \"buffsize\" : 10240\n",
- " },\n",
- "\n",
- "}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false,
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "06:28:57 INFO : Target - Using base path: /home/derkling/Code/lisa\n",
- "06:28:57 INFO : Target - Loading custom (inline) target configuration\n",
- "06:28:57 INFO : Target - Loading custom (inline) test configuration\n",
- "06:28:57 INFO : Target - Devlib modules to load: ['bl', 'hwmon', 'cpufreq']\n",
- "06:28:57 INFO : Target - Connecting linux target:\n",
- "06:28:57 INFO : Target - username : root\n",
- "06:28:57 INFO : Target - host : 192.168.0.1\n",
- "06:28:57 INFO : Target - password : \n",
- "06:29:34 INFO : Target - Initializing target workdir:\n",
- "06:29:34 INFO : Target - /root/devlib-target\n",
- "06:29:37 INFO : Target - Topology:\n",
- "06:29:37 INFO : Target - [[0, 3, 4, 5], [1, 2]]\n",
- "06:29:39 INFO : Platform - Loading default EM:\n",
- "06:29:39 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/juno.json\n",
- "06:29:40 INFO : FTrace - Enabled tracepoints:\n",
- "06:29:40 INFO : FTrace - sched_switch\n",
- "06:29:40 INFO : FTrace - cpu_frequency\n",
- "06:29:40 INFO : EnergyMeter - Scanning for HWMON channels, may take some time...\n",
- "06:29:40 INFO : EnergyMeter - Channels selected for energy sampling:\n",
- "06:29:40 INFO : EnergyMeter - a57_energy\n",
- "06:29:40 INFO : EnergyMeter - a53_energy\n",
- "06:29:40 INFO : TestEnv - Set results folder to:\n",
- "06:29:40 INFO : TestEnv - /home/derkling/Code/lisa/results/20160225_182940\n",
- "06:29:40 INFO : TestEnv - Experiment results available also in:\n",
- "06:29:40 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n"
- ]
- }
- ],
- "source": [
- "# Support to access the remote target\n",
- "import devlib\n",
- "from env import TestEnv\n",
- "\n",
- "# Initialize a test environment using:\n",
- "# the provided target configuration (my_target_conf)\n",
- "# the provided test configuration (my_test_conf)\n",
- "te = TestEnv(target_conf=my_target_conf, test_conf=my_tests_conf)\n",
- "target = te.target"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Workload configuration"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "06:29:40 INFO : WlGen - Setup new workload simple\n",
- "06:29:40 INFO : RTApp - Workload duration defined by longest task\n",
- "06:29:40 INFO : RTApp - Default policy: SCHED_OTHER\n",
- "06:29:40 INFO : RTApp - ------------------------\n",
- "06:29:40 INFO : RTApp - task [task_p20], sched: using default policy\n",
- "06:29:40 INFO : RTApp - | calibration CPU: 1\n",
- "06:29:40 INFO : RTApp - | loops count: 1\n",
- "06:29:40 INFO : RTApp - | CPUs affinity: 0\n",
- "06:29:40 INFO : RTApp - + phase_000001: duration 5.000000 [s] (50 loops)\n",
- "06:29:40 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n",
- "06:29:40 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n",
- "06:29:40 INFO : RTApp - ------------------------\n",
- "06:29:40 INFO : RTApp - task [task_r20_5-60], sched: using default policy\n",
- "06:29:40 INFO : RTApp - | calibration CPU: 1\n",
- "06:29:40 INFO : RTApp - | loops count: 1\n",
- "06:29:40 INFO : RTApp - | CPUs affinity: 5\n",
- "06:29:40 INFO : RTApp - + phase_000001: duration 1.000000 [s] (10 loops)\n",
- "06:29:40 INFO : RTApp - | period 100000 [us], duty_cycle 5 %\n",
- "06:29:40 INFO : RTApp - | run_time 5000 [us], sleep_time 95000 [us]\n",
- "06:29:40 INFO : RTApp - + phase_000002: duration 1.000000 [s] (10 loops)\n",
- "06:29:40 INFO : RTApp - | period 100000 [us], duty_cycle 25 %\n",
- "06:29:40 INFO : RTApp - | run_time 25000 [us], sleep_time 75000 [us]\n",
- "06:29:40 INFO : RTApp - + phase_000003: duration 1.000000 [s] (10 loops)\n",
- "06:29:40 INFO : RTApp - | period 100000 [us], duty_cycle 45 %\n",
- "06:29:40 INFO : RTApp - | run_time 45000 [us], sleep_time 55000 [us]\n",
- "06:29:40 INFO : RTApp - + phase_000004: duration 1.000000 [s] (10 loops)\n",
- "06:29:40 INFO : RTApp - | period 100000 [us], duty_cycle 65 %\n",
- "06:29:40 INFO : RTApp - | run_time 65000 [us], sleep_time 35000 [us]\n"
- ]
- }
- ],
- "source": [
- "# Support to configure and run RTApp based workloads\n",
- "from wlgen import RTA, Periodic, Ramp\n",
- "\n",
- "# Create a new RTApp workload generator using the calibration values\n",
- "# reported by the TestEnv module\n",
- "rtapp = RTA(target, 'simple', calibration=te.calibration())\n",
- "\n",
- "# Configure this RTApp instance to:\n",
- "rtapp.conf(\n",
- " # 1. generate a \"profile based\" set of tasks\n",
- " kind='profile',\n",
- " \n",
- " # 2. define the \"profile\" of each task\n",
- " params={\n",
- " \n",
- " # 3. PERIODIC task with\n",
- " 'task_p20': Periodic(\n",
- " period_ms=100, # period\n",
- " duty_cycle_pct=20, # duty cycle\n",
- " duration_s=5, # duration \n",
- " cpus='0' # pinned on CPU0\n",
- " ).get(),\n",
- " \n",
- " # 4. RAMP task (i.e. increasing load) with\n",
- " 'task_r20_5-60': Ramp(\n",
- " start_pct=5, # intial load\n",
- " end_pct=65, # end load\n",
- " delta_pct=20, # load % increase...\n",
- " time_s=1, # ... every 1[s]\n",
- " # pinned on last CPU of the target\n",
- " cpus=str(len(target.core_names)-1)\n",
- " ).get(),\n",
- " },\n",
- " \n",
- " # 4. use this folder for task logfiles\n",
- " run_dir=target.working_directory\n",
- " \n",
- ");"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Workload execution"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "06:29:40 INFO : #### Setup FTrace\n",
- "06:29:43 INFO : #### Start energy sampling\n",
- "06:29:44 INFO : #### Start RTApp execution\n",
- "06:29:44 INFO : WlGen - Workload execution START:\n",
- "06:29:44 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/simple_00.json\n",
- "06:29:50 INFO : #### Read energy consumption: /home/derkling/Code/lisa/results/20160225_182940/energy.json\n",
- "06:29:51 INFO : EnergyReport - Energy [ a53]: 9.734375\n",
- "06:29:51 INFO : EnergyReport - Energy [ a57]: 4.190309\n",
- "06:29:51 INFO : #### Stop FTrace\n",
- "06:29:52 INFO : #### Save FTrace: /home/derkling/Code/lisa/results/20160225_182940/trace.dat\n",
- "06:29:54 INFO : #### Save platform description: /home/derkling/Code/lisa/results/20160225_182940/platform.json\n"
- ]
- }
- ],
- "source": [
- "logging.info('#### Setup FTrace')\n",
- "te.ftrace.start()\n",
- "\n",
- "logging.info('#### Start energy sampling')\n",
- "te.emeter.reset()\n",
- "\n",
- "logging.info('#### Start RTApp execution')\n",
- "rtapp.run(out_dir=te.res_dir, cgroup=\"\")\n",
- "\n",
- "logging.info('#### Read energy consumption: %s/energy.json', te.res_dir)\n",
- "nrg_report = te.emeter.report(out_dir=te.res_dir)\n",
- "\n",
- "logging.info('#### Stop FTrace')\n",
- "te.ftrace.stop()\n",
- "\n",
- "trace_file = os.path.join(te.res_dir, 'trace.dat')\n",
- "logging.info('#### Save FTrace: %s', trace_file)\n",
- "te.ftrace.get_trace(trace_file)\n",
- "\n",
- "logging.info('#### Save platform description: %s/platform.json', te.res_dir)\n",
- "(plt, plt_file) = te.platform_dump(te.res_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Collected results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "06:29:54 INFO : Content of the output folder /home/derkling/Code/lisa/results/20160225_182940\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "total 4064\r\n",
- "drwxrwxr-x 2 derkling derkling 4096 Feb 25 18:29 .\r\n",
- "drwxrwxr-x 99 derkling derkling 4096 Feb 25 18:29 ..\r\n",
- "-rw-rw-r-- 1 derkling derkling 52 Feb 25 18:29 energy.json\r\n",
- "-rw-rw-r-- 1 derkling derkling 450 Feb 25 18:29 output.log\r\n",
- "-rw-rw-r-- 1 derkling derkling 1075 Feb 25 18:29 platform.json\r\n",
- "-rw-r--r-- 1 derkling derkling 6360 Feb 25 18:29 rt-app-task_p20-0.log\r\n",
- "-rw-r--r-- 1 derkling derkling 6360 Feb 25 18:29 rt-app-task_p20-3.log\r\n",
- "-rw-r--r-- 1 derkling derkling 5120 Feb 25 18:29 rt-app-task_r20_5-60-1.log\r\n",
- "-rw-r--r-- 1 derkling derkling 3880 Feb 25 18:29 rt-app-task_r20_5-60-3.log\r\n",
- "-rw-r--r-- 1 derkling derkling 3880 Feb 25 18:29 rt-app-task_r20_5-60-6.log\r\n",
- "-rw-r--r-- 1 derkling derkling 1831 Feb 25 18:29 simple_00.json\r\n",
- "-rw-r--r-- 1 derkling derkling 4104192 Feb 25 18:29 trace.dat\r\n"
- ]
- }
- ],
- "source": [
- "# All data are produced in the output folder defined by the TestEnv module\n",
- "logging.info('Content of the output folder %s', te.res_dir)\n",
- "!ls -la {te.res_dir}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "06:29:54 INFO : Generated RTApp JSON file:\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{\n",
- " \"global\": {\n",
- " \"calibration\": \"CPU1\", \n",
- " \"default_policy\": \"SCHED_OTHER\", \n",
- " \"duration\": -1, \n",
- " \"logdir\": \"/root/devlib-target\"\n",
- " }, \n",
- " \"tasks\": {\n",
- " \"task_p20\": {\n",
- " \"cpus\": [\n",
- " 0\n",
- " ], \n",
- " \"loop\": 1, \n",
- " \"phases\": {\n",
- " \"p000001\": {\n",
- " \"loop\": 50, \n",
- " \"run\": 20000, \n",
- " \"timer\": {\n",
- " \"period\": 100000, \n",
- " \"ref\": \"task_p20\"\n",
- " }\n",
- " }\n",
- " }, \n",
- " \"policy\": \"SCHED_OTHER\"\n",
- " }, \n",
- " \"task_r20_5-60\": {\n",
- " \"cpus\": [\n",
- " 5\n",
- " ], \n",
- " \"loop\": 1, \n",
- " \"phases\": {\n",
- " \"p000001\": {\n",
- " \"loop\": 10, \n",
- " \"run\": 5000, \n",
- " \"timer\": {\n",
- " \"period\": 100000, \n",
- " \"ref\": \"task_r20_5-60\"\n",
- " }\n",
- " }, \n",
- " \"p000002\": {\n",
- " \"loop\": 10, \n",
- " \"run\": 25000, \n",
- " \"timer\": {\n",
- " \"period\": 100000, \n",
- " \"ref\": \"task_r20_5-60\"\n",
- " }\n",
- " }, \n",
- " \"p000003\": {\n",
- " \"loop\": 10, \n",
- " \"run\": 45000, \n",
- " \"timer\": {\n",
- " \"period\": 100000, \n",
- " \"ref\": \"task_r20_5-60\"\n",
- " }\n",
- " }, \n",
- " \"p000004\": {\n",
- " \"loop\": 10, \n",
- " \"run\": 65000, \n",
- " \"timer\": {\n",
- " \"period\": 100000, \n",
- " \"ref\": \"task_r20_5-60\"\n",
- " }\n",
- " }\n",
- " }, \n",
- " \"policy\": \"SCHED_OTHER\"\n",
- " }\n",
- " }\n",
- "}\n"
- ]
- }
- ],
- "source": [
- "# Inspect the JSON file used to run the application\n",
- "with open('{}/simple_00.json'.format(te.res_dir), 'r') as fh:\n",
- " rtapp_json = json.load(fh, )\n",
- "logging.info('Generated RTApp JSON file:')\n",
- "print json.dumps(rtapp_json, indent=4, sort_keys=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "06:29:54 INFO : Energy: /home/derkling/Code/lisa/results/20160225_182940/energy.json\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{\n",
- " \"LITTLE\": \"9.734375\", \n",
- " \"big\": \"4.190309\"\n",
- "}\n"
- ]
- }
- ],
- "source": [
- "# Dump the energy measured for the LITTLE and big clusters\n",
- "logging.info('Energy: %s', nrg_report.report_file)\n",
- "print json.dumps(nrg_report.channels, indent=4, sort_keys=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "06:29:54 INFO : Platform description: /home/derkling/Code/lisa/results/20160225_182940/platform.json\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{\n",
- " \"clusters\": {\n",
- " \"big\": [\n",
- " 1, \n",
- " 2\n",
- " ], \n",
- " \"little\": [\n",
- " 0, \n",
- " 3, \n",
- " 4, \n",
- " 5\n",
- " ]\n",
- " }, \n",
- " \"cpus_count\": 6, \n",
- " \"freqs\": {\n",
- " \"big\": [\n",
- " 450000, \n",
- " 625000, \n",
- " 800000, \n",
- " 950000, \n",
- " 1100000\n",
- " ], \n",
- " \"little\": [\n",
- " 450000, \n",
- " 575000, \n",
- " 700000, \n",
- " 775000, \n",
- " 850000\n",
- " ]\n",
- " }, \n",
- " \"nrg_model\": {\n",
- " \"big\": {\n",
- " \"cluster\": {\n",
- " \"nrg_max\": 64\n",
- " }, \n",
- " \"cpu\": {\n",
- " \"cap_max\": 1024, \n",
- " \"nrg_max\": 616\n",
- " }\n",
- " }, \n",
- " \"little\": {\n",
- " \"cluster\": {\n",
- " \"nrg_max\": 57\n",
- " }, \n",
- " \"cpu\": {\n",
- " \"cap_max\": 447, \n",
- " \"nrg_max\": 93\n",
- " }\n",
- " }\n",
- " }, \n",
- " \"topology\": [\n",
- " [\n",
- " 0, \n",
- " 3, \n",
- " 4, \n",
- " 5\n",
- " ], \n",
- " [\n",
- " 1, \n",
- " 2\n",
- " ]\n",
- " ]\n",
- "}\n"
- ]
- }
- ],
- "source": [
- "# Dump the platform descriptor, which could be useful for further analysis\n",
- "# of the generated results\n",
- "logging.info('Platform description: %s', plt_file)\n",
- "print json.dumps(plt, indent=4, sort_keys=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Trace inspection"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "/*\n",
- "\n",
- " * Copyright 2015-2016 ARM Limited\n",
- "\n",
- " *\n",
- "\n",
- " * Licensed under the Apache License, Version 2.0 (the \"License\");\n",
- "\n",
- " * you may not use this file except in compliance with the License.\n",
- "\n",
- " * You may obtain a copy of the License at\n",
- "\n",
- " *\n",
- "\n",
- " * http://www.apache.org/licenses/LICENSE-2.0\n",
- "\n",
- " *\n",
- "\n",
- " * Unless required by applicable law or agreed to in writing, software\n",
- "\n",
- " * distributed under the License is distributed on an \"AS IS\" BASIS,\n",
- "\n",
- " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
- "\n",
- " * See the License for the specific language governing permissions and\n",
- "\n",
- " * limitations under the License.\n",
- "\n",
- " */\n",
- "\n",
- "\n",
- "\n",
- ".d3-tip {\n",
- "\n",
- " line-height: 1;\n",
- "\n",
- " padding: 12px;\n",
- "\n",
- " background: rgba(0, 0, 0, 0.6);\n",
- "\n",
- " color: #fff;\n",
- "\n",
- " border-radius: 2px;\n",
- "\n",
- " position: absolute !important;\n",
- "\n",
- " z-index: 99999;\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- ".d3-tip:after {\n",
- "\n",
- " box-sizing: border-box;\n",
- "\n",
- " pointer-events: none;\n",
- "\n",
- " display: inline;\n",
- "\n",
- " font-size: 10px;\n",
- "\n",
- " width: 100%;\n",
- "\n",
- " line-height: 1;\n",
- "\n",
- " color: rgba(0, 0, 0, 0.6);\n",
- "\n",
- " content: \"\\25BC\";\n",
- "\n",
- " position: absolute !important;\n",
- "\n",
- " z-index: 99999;\n",
- "\n",
- " text-align: center;\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- ".d3-tip.n:after {\n",
- "\n",
- " margin: -1px 0 0 0;\n",
- "\n",
- " top: 100%;\n",
- "\n",
- " left: 0;\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- ".contextRect {\n",
- "\n",
- " fill: lightgray;\n",
- "\n",
- " fill-opacity: 0.5;\n",
- "\n",
- " stroke: black;\n",
- "\n",
- " stroke-width: 1;\n",
- "\n",
- " stroke-opacity: 1;\n",
- "\n",
- " pointer-events: none;\n",
- "\n",
- " shape-rendering: crispEdges;\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- ".chart {\n",
- "\n",
- " shape-rendering: crispEdges;\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- ".mini text {\n",
- "\n",
- " font: 9px sans-serif;\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- ".main text {\n",
- "\n",
- " font: 12px sans-serif;\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- ".axis line, .axis path {\n",
- "\n",
- " stroke: black;\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- ".miniItem {\n",
- "\n",
- " stroke-width: 8;\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- ".brush .extent {\n",
- "\n",
- "\n",
- "\n",
- " stroke: #000;\n",
- "\n",
- " fill-opacity: .125;\n",
- "\n",
- " shape-rendering: crispEdges;\n",
- "\n",
- "}\n",
- "\n",
- "</style>\n",
- "<div id=\"fig_071d2212c17d417c93598b874c9aeede\" class=\"eventplot\">\n",
- " <script>\n",
- " var req = require.config( {\n",
- "\n",
- " paths: {\n",
- "\n",
- " \"EventPlot\": '/nbextensions/plotter_scripts/EventPlot/EventPlot',\n",
- " \"d3-tip\": '/nbextensions/plotter_scripts/EventPlot/d3.tip.v0.6.3',\n",
- " \"d3-plotter\": '/nbextensions/plotter_scripts/EventPlot/d3.min'\n",
- " },\n",
- " shim: {\n",
- " \"d3-plotter\" : {\n",
- " \"exports\" : \"d3\"\n",
- " },\n",
- " \"d3-tip\": [\"d3-plotter\"],\n",
- " \"EventPlot\": {\n",
- "\n",
- " \"deps\": [\"d3-tip\", \"d3-plotter\" ],\n",
- " \"exports\": \"EventPlot\"\n",
- " }\n",
- " }\n",
- " });\n",
- " req([\"require\", \"EventPlot\"], function() {\n",
- " EventPlot.generate('fig_071d2212c17d417c93598b874c9aeede', '/nbextensions/');\n",
- " });\n",
- " </script>\n",
- " </div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Suport for FTrace events parsing and visualization\n",
- "import trappy\n",
- "\n",
- "# NOTE: The interactive trace visualization is available only if you run\n",
- "# the workload to generate a new trace-file\n",
- "trappy.plotter.plot_trace(te.res_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# RTApp task performance plots"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "06:29:55 INFO : PerfIndex, Task [task_p20] avg: 0.59, std: 0.00\n",
- "06:29:55 INFO : PerfIndex, Task [task_r20_5-60] avg: -0.25, std: 1.33\n"
- ]
- },
- {
- "data": {
- "image/png": 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Ar2fmJTNkm8qmlBOW3SQiHkbpo/7UhPWOAP4xIu4La2ZZPL5mgBs/52DtSVl+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\n7n5eg9fPqP6VnwEAGVRR0fQySKd8XMjqS0kXufvC8JS3Z81sjru/Xru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- "text/plain": [
- "<matplotlib.figure.Figure at 0x7f08f8a11490>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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QWTXkhTqhKAZ6ZoKke5WmSA9TGn3Lj+CeHREbK53UaUOlwrTkZ5L+KukPtpfp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\nwuVnnHGGRo4cqUcffVR/+ct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0FAPoWqv2FAP1wPsd6HNF9xTbHiPpdkkfiYi3bV8j6U8R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\nDAAAwGQJxQAAAEzWNwclZcsKYgYiAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7f08f7685f10>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Support for performance analysis of RTApp workloads\n",
- "from perf_analysis import PerfAnalysis\n",
- "\n",
- "# Parse the RT-App generate log files to compute performance metrics\n",
- "pa = PerfAnalysis(te.res_dir)\n",
- "\n",
- "# For each task which has generated a logfile, plot its performance metrics\n",
- "for task in pa.tasks():\n",
- " pa.plotPerf(task, \"Performance plots for task [{}] \".format(task))"
- ]
- }
- ],
- "metadata": {
- "celltoolbar": "Raw Cell Format",
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}