ipynb: profiling: update example to generate data from notebook
This patch makes use of the executor module to generate all the data
required for the profiling of a set of functions. In this what the notebook
is self-contained and it's possible to run it directly to profile a
configured set of kernel functions.
The functions to profile are defined, as part of the FTrace configuration,
by this attribute:
test_conf::ftrace::functions
Signed-off-by: Patrick Bellasi <patrick.bellasi@arm.com>
diff --git a/ipynb/profiling/kernel_functions_profiling.ipynb b/ipynb/profiling/kernel_functions_profiling.ipynb
index 858e081..1ef7d1a 100644
--- a/ipynb/profiling/kernel_functions_profiling.ipynb
+++ b/ipynb/profiling/kernel_functions_profiling.ipynb
@@ -23,9 +23,7 @@
" datefmt='%I:%M:%S')\n",
"\n",
"# Enable logging at INFO level\n",
- "logging.getLogger().setLevel(logging.INFO)\n",
- "# Comment the follwing line to disable devlib debugging statements\n",
- "logging.getLogger('ssh').setLevel(logging.DEBUG)"
+ "logging.getLogger().setLevel(logging.INFO)"
]
},
{
@@ -52,49 +50,413 @@
"\n",
"import re\n",
"import collections\n",
- "import pandas"
+ "import pandas\n",
+ "\n",
+ "# Support to tests execution\n",
+ "from executor import Executor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Define folder containgin profiling data"
+ "# Tests configuration"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
- "collapsed": false,
- "scrolled": true
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Setup a target configuration\n",
+ "target_conf = {\n",
+ "\n",
+ " # Platform and board to target\n",
+ " \"platform\" : \"linux\",\n",
+ " \"board\" : \"juno\",\n",
+ "\n",
+ " # Login credentials\n",
+ " \"host\" : \"192.168.0.1\",\n",
+ " \"username\" : \"root\",\n",
+ " \"password\" : \"\",\n",
+ "\n",
+ " # Local installation path\n",
+ " \"tftp\" : {\n",
+ " \"folder\" : \"/var/lib/tftpboot\",\n",
+ " \"kernel\" : \"kern.bin\",\n",
+ " \"dtb\" : \"dtb.bin\",\n",
+ " },\n",
+ "\n",
+ " # RTApp calibration values (comment to let LISA do a calibration run)\n",
+ " \"rtapp-calib\" : {\n",
+ " \"0\": 358, \"1\": 138, \"2\": 138, \"3\": 357, \"4\": 359, \"5\": 355\n",
+ " },\n",
+ "\n",
+ "}\n",
+ "\n",
+ "tests_conf = {\n",
+ " \n",
+ " # Kernel functions to profile for all the test\n",
+ " # configurations which have the \"ftrace\" flag enabled\n",
+ " \"ftrace\" : {\n",
+ " \"functions\" : [\n",
+ " \"select_task_rq_fair\",\n",
+ " \"enqueue_task_fair\",\n",
+ " \"dequeue_task_fair\",\n",
+ " ],\n",
+ " \"buffsize\" : 80 * 1024,\n",
+ " },\n",
+ " \n",
+ " # Platform configurations to test\n",
+ " \"confs\" : [\n",
+ " {\n",
+ " \"tag\" : \"base\",\n",
+ " \"flags\" : \"ftrace\",\n",
+ " \"sched_features\" : \"NO_ENERGY_AWARE\",\n",
+ " \"cpufreq\" : {\n",
+ " \"governor\" : \"performance\",\n",
+ " },\n",
+ " },\n",
+ " {\n",
+ " \"tag\" : \"eas\",\n",
+ " \"flags\" : \"ftrace\",\n",
+ " \"sched_features\" : \"ENERGY_AWARE\",\n",
+ " \"cpufreq\" : {\n",
+ " \"governor\" : \"performance\",\n",
+ " },\n",
+ " },\n",
+ " ],\n",
+ " \n",
+ " # Workloads to run (on each platform configuration)\n",
+ " \"wloads\" : {\n",
+ " \"rta\" : {\n",
+ " \"type\" : \"rt-app\",\n",
+ " \"conf\" : {\n",
+ " \"class\" : \"profile\",\n",
+ " \"params\" : {\n",
+ " \"p20\" : {\n",
+ " \"kind\" : \"periodic\",\n",
+ " \"params\" : {\n",
+ " \"duty_cycle_pct\" : 20,\n",
+ " },\n",
+ " \"tasks\" : \"cpus\",\n",
+ " },\n",
+ " },\n",
+ " },\n",
+ " },\n",
+ " },\n",
+ " \n",
+ " # Number of iterations for each configuration/workload pair\n",
+ " \"iterations\" : 3,\n",
+ " \n",
+ " # Tools to deploy\n",
+ " \"tools\" : [ \"rt-app\", 'trace-cmd' ],\n",
+ " \n",
+ " # Where results are collected\n",
+ " # NOTE: this folder will be wiped before running the experiments\n",
+ " \"results_dir\" : \"KernelFunctionsProfilingExample\",\n",
+ "\n",
+ " # Modules required by these experiments\n",
+ " \"exclude_modules\" : [ \"hwmon\" ],\n",
+ "\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "11:50:08 INFO : Content of the output folder ../../results_latest/\n"
+ "03:43:22 INFO : Target - Loading custom (inline) test configuration\n",
+ "03:43:22 INFO : Target - Using base path: /home/derkling/Code/lisa\n",
+ "03:43:22 INFO : Target - Loading custom (inline) target configuration\n",
+ "03:43:22 INFO : Target - Loading custom (inline) test configuration\n",
+ "03:43:22 INFO : Target - Devlib modules to load: ['bl', 'cpufreq']\n",
+ "03:43:22 INFO : Target - Connecting linux target:\n",
+ "03:43:22 INFO : Target - username : root\n",
+ "03:43:22 INFO : Target - host : 192.168.0.1\n",
+ "03:43:22 INFO : Target - password : \n",
+ "03:43:26 INFO : Target - Initializing target workdir:\n",
+ "03:43:26 INFO : Target - /root/devlib-target\n",
+ "03:43:34 INFO : Target - Topology:\n",
+ "03:43:34 INFO : Target - [[0, 3, 4, 5], [1, 2]]\n",
+ "03:43:36 INFO : Platform - Loading default EM:\n",
+ "03:43:36 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/juno.json\n",
+ "03:43:38 INFO : FTrace - Enabled tracepoints:\n",
+ "03:43:38 INFO : FTrace - sched:*\n",
+ "03:43:38 INFO : FTrace - Kernel functions profiled:\n",
+ "03:43:38 INFO : FTrace - select_task_rq_fair\n",
+ "03:43:38 INFO : FTrace - enqueue_task_fair\n",
+ "03:43:38 INFO : FTrace - dequeue_task_fair\n",
+ "03:43:38 INFO : EnergyMeter - HWMON module not enabled\n",
+ "03:43:38 WARNING : EnergyMeter - Energy sampling disabled by configuration\n",
+ "03:43:38 WARNING : Target - Using configuration provided RTApp calibration\n",
+ "03:43:38 INFO : Target - Using RT-App calibration values:\n",
+ "03:43:38 INFO : Target - {\"0\": 358, \"1\": 138, \"2\": 138, \"3\": 357, \"4\": 359, \"5\": 355}\n",
+ "03:43:38 WARNING : TestEnv - Wipe previous contents of the results folder:\n",
+ "03:43:38 WARNING : TestEnv - /home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\n",
+ "03:43:38 INFO : TestEnv - Set results folder to:\n",
+ "03:43:38 INFO : TestEnv - /home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\n",
+ "03:43:38 INFO : TestEnv - Experiment results available also in:\n",
+ "03:43:38 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n",
+ "03:43:38 INFO : \n",
+ "03:43:38 INFO : ################################################################################\n",
+ "03:43:38 INFO : Executor - Experiments configuration\n",
+ "03:43:38 INFO : ################################################################################\n",
+ "03:43:38 INFO : Executor - Configured to run:\n",
+ "03:43:38 INFO : Executor - 2 targt configurations:\n",
+ "03:43:38 INFO : Executor - base, eas\n",
+ "03:43:38 INFO : Executor - 1 workloads (3 iterations each)\n",
+ "03:43:38 INFO : Executor - rta\n",
+ "03:43:38 INFO : Executor - Total: 6 experiments\n",
+ "03:43:38 INFO : Executor - Results will be collected under:\n",
+ "03:43:38 INFO : Executor - /home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\n"
]
}
],
"source": [
- "# Base folder where tests folder are located\n",
- "res_dir = '../../results_latest/'\n",
- "logging.info('Content of the output folder %s', res_dir)\n",
- "#!tree {res_dir}"
+ "# Setup tests executions based on our configuration\n",
+ "executor = Executor(target_conf, tests_conf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Load function profiling data"
+ "# Tests execution"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "03:43:38 INFO : \n",
+ "03:43:38 INFO : ################################################################################\n",
+ "03:43:38 INFO : Executor - Experiments execution\n",
+ "03:43:38 INFO : ################################################################################\n",
+ "03:43:38 INFO : \n",
+ "03:43:38 INFO : ================================================================================\n",
+ "03:43:38 INFO : TargetConfig - configuring target for [base] experiments\n",
+ "03:43:39 INFO : SchedFeatures - Set scheduler feature: NO_ENERGY_AWARE\n",
+ "03:43:39 INFO : CPUFreq - Configuring all CPUs to use [performance] governor\n",
+ "03:43:40 INFO : WlGen - Setup new workload rta\n",
+ "03:43:40 INFO : RTApp - Workload duration defined by longest task\n",
+ "03:43:40 INFO : RTApp - Default policy: SCHED_OTHER\n",
+ "03:43:40 INFO : RTApp - ------------------------\n",
+ "03:43:40 INFO : RTApp - task [task_p20], sched: using default policy\n",
+ "03:43:40 INFO : RTApp - | calibration CPU: 1\n",
+ "03:43:40 INFO : RTApp - | loops count: 1\n",
+ "03:43:40 INFO : RTApp - + phase_000001: duration 1.000000 [s] (10 loops)\n",
+ "03:43:40 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n",
+ "03:43:40 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n",
+ "03:43:41 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+ "03:43:41 INFO : Executor - Experiment 1/6, [base:rta] 1/3\n",
+ "03:43:41 WARNING : Executor - FTrace events collection enabled\n",
+ "03:43:46 INFO : WlGen - Workload execution START:\n",
+ "03:43:46 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/run_dir/rta_00.json\n",
+ "03:43:51 INFO : Executor - Collected FTrace binary trace:\n",
+ "03:43:51 INFO : Executor - <res_dir>/rtapp:base:rta/1/trace.dat\n",
+ "03:43:52 INFO : Executor - Collected FTrace function profiling:\n",
+ "03:43:52 INFO : Executor - <res_dir>/rtapp:base:rta/1/trace_stat.json\n",
+ "03:43:52 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+ "03:43:52 INFO : Executor - Experiment 2/6, [base:rta] 2/3\n",
+ "03:43:52 WARNING : Executor - FTrace events collection enabled\n",
+ "03:43:58 INFO : WlGen - Workload execution START:\n",
+ "03:43:58 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/run_dir/rta_00.json\n",
+ "03:44:02 INFO : Executor - Collected FTrace binary trace:\n",
+ "03:44:02 INFO : Executor - <res_dir>/rtapp:base:rta/2/trace.dat\n",
+ "03:44:03 INFO : Executor - Collected FTrace function profiling:\n",
+ "03:44:03 INFO : Executor - <res_dir>/rtapp:base:rta/2/trace_stat.json\n",
+ "03:44:03 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+ "03:44:03 INFO : Executor - Experiment 3/6, [base:rta] 3/3\n",
+ "03:44:03 WARNING : Executor - FTrace events collection enabled\n",
+ "03:44:09 INFO : WlGen - Workload execution START:\n",
+ "03:44:09 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/run_dir/rta_00.json\n",
+ "03:44:14 INFO : Executor - Collected FTrace binary trace:\n",
+ "03:44:14 INFO : Executor - <res_dir>/rtapp:base:rta/3/trace.dat\n",
+ "03:44:14 INFO : Executor - Collected FTrace function profiling:\n",
+ "03:44:14 INFO : Executor - <res_dir>/rtapp:base:rta/3/trace_stat.json\n",
+ "03:44:14 INFO : \n",
+ "03:44:14 INFO : ================================================================================\n",
+ "03:44:14 INFO : TargetConfig - configuring target for [eas] experiments\n",
+ "03:44:15 INFO : SchedFeatures - Set scheduler feature: ENERGY_AWARE\n",
+ "03:44:15 INFO : CPUFreq - Configuring all CPUs to use [performance] governor\n",
+ "03:44:16 INFO : WlGen - Setup new workload rta\n",
+ "03:44:16 INFO : RTApp - Workload duration defined by longest task\n",
+ "03:44:16 INFO : RTApp - Default policy: SCHED_OTHER\n",
+ "03:44:16 INFO : RTApp - ------------------------\n",
+ "03:44:16 INFO : RTApp - task [task_p20], sched: using default policy\n",
+ "03:44:16 INFO : RTApp - | calibration CPU: 1\n",
+ "03:44:16 INFO : RTApp - | loops count: 1\n",
+ "03:44:16 INFO : RTApp - + phase_000001: duration 1.000000 [s] (10 loops)\n",
+ "03:44:16 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n",
+ "03:44:16 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n",
+ "03:44:16 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+ "03:44:16 INFO : Executor - Experiment 4/6, [eas:rta] 1/3\n",
+ "03:44:16 WARNING : Executor - FTrace events collection enabled\n",
+ "03:44:22 INFO : WlGen - Workload execution START:\n",
+ "03:44:22 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/run_dir/rta_00.json\n",
+ "03:44:28 INFO : Executor - Collected FTrace binary trace:\n",
+ "03:44:28 INFO : Executor - <res_dir>/rtapp:eas:rta/1/trace.dat\n",
+ "03:44:28 INFO : Executor - Collected FTrace function profiling:\n",
+ "03:44:28 INFO : Executor - <res_dir>/rtapp:eas:rta/1/trace_stat.json\n",
+ "03:44:28 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+ "03:44:28 INFO : Executor - Experiment 5/6, [eas:rta] 2/3\n",
+ "03:44:28 WARNING : Executor - FTrace events collection enabled\n",
+ "03:44:34 INFO : WlGen - Workload execution START:\n",
+ "03:44:34 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/run_dir/rta_00.json\n",
+ "03:44:40 INFO : Executor - Collected FTrace binary trace:\n",
+ "03:44:40 INFO : Executor - <res_dir>/rtapp:eas:rta/2/trace.dat\n",
+ "03:44:40 INFO : Executor - Collected FTrace function profiling:\n",
+ "03:44:40 INFO : Executor - <res_dir>/rtapp:eas:rta/2/trace_stat.json\n",
+ "03:44:40 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
+ "03:44:40 INFO : Executor - Experiment 6/6, [eas:rta] 3/3\n",
+ "03:44:40 WARNING : Executor - FTrace events collection enabled\n",
+ "03:44:46 INFO : WlGen - Workload execution START:\n",
+ "03:44:46 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/run_dir/rta_00.json\n",
+ "03:44:52 INFO : Executor - Collected FTrace binary trace:\n",
+ "03:44:52 INFO : Executor - <res_dir>/rtapp:eas:rta/3/trace.dat\n",
+ "03:44:52 INFO : Executor - Collected FTrace function profiling:\n",
+ "03:44:52 INFO : Executor - <res_dir>/rtapp:eas:rta/3/trace_stat.json\n",
+ "03:44:52 INFO : \n",
+ "03:44:52 INFO : ################################################################################\n",
+ "03:44:52 INFO : Executor - Experiments execution completed\n",
+ "03:44:52 INFO : ################################################################################\n",
+ "03:44:52 INFO : Executor - Results available in:\n",
+ "03:44:52 INFO : Executor - /home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Execute all the configured test\n",
+ "executor.run()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\n"
+ ]
+ }
+ ],
+ "source": [
+ "res_dir = \"/home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\"\n",
+ "out_dir = \"/home/derkling/Code/lisa/results/KernelFunctionsProfilingExample/rtapp:eas:rta/2/trace.dat\"\n",
+ "out_dir.replace(res_dir, \"<res_dir>\")\n",
+ "print executor.te.res_dir"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "03:44:52 INFO : Content of the output folder /home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[01;34m/home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\u001b[00m\r\n",
+ "├── \u001b[01;34mrtapp:base:rta\u001b[00m\r\n",
+ "│ ├── \u001b[01;34m1\u001b[00m\r\n",
+ "│ │ ├── output.log\r\n",
+ "│ │ ├── rta_00.json\r\n",
+ "│ │ ├── rt-app-task_p20-0.log\r\n",
+ "│ │ ├── trace.dat\r\n",
+ "│ │ └── trace_stat.json\r\n",
+ "│ ├── \u001b[01;34m2\u001b[00m\r\n",
+ "│ │ ├── output.log\r\n",
+ "│ │ ├── rta_00.json\r\n",
+ "│ │ ├── rt-app-task_p20-0.log\r\n",
+ "│ │ ├── trace.dat\r\n",
+ "│ │ └── trace_stat.json\r\n",
+ "│ ├── \u001b[01;34m3\u001b[00m\r\n",
+ "│ │ ├── output.log\r\n",
+ "│ │ ├── rta_00.json\r\n",
+ "│ │ ├── rt-app-task_p20-0.log\r\n",
+ "│ │ ├── trace.dat\r\n",
+ "│ │ └── trace_stat.json\r\n",
+ "│ ├── kernel.config\r\n",
+ "│ ├── kernel.version\r\n",
+ "│ └── platform.json\r\n",
+ "└── \u001b[01;34mrtapp:eas:rta\u001b[00m\r\n",
+ " ├── \u001b[01;34m1\u001b[00m\r\n",
+ " │ ├── output.log\r\n",
+ " │ ├── rta_00.json\r\n",
+ " │ ├── rt-app-task_p20-0.log\r\n",
+ " │ ├── trace.dat\r\n",
+ " │ └── trace_stat.json\r\n",
+ " ├── \u001b[01;34m2\u001b[00m\r\n",
+ " │ ├── output.log\r\n",
+ " │ ├── rta_00.json\r\n",
+ " │ ├── rt-app-task_p20-0.log\r\n",
+ " │ ├── trace.dat\r\n",
+ " │ └── trace_stat.json\r\n",
+ " ├── \u001b[01;34m3\u001b[00m\r\n",
+ " │ ├── output.log\r\n",
+ " │ ├── rta_00.json\r\n",
+ " │ ├── rt-app-task_p20-0.log\r\n",
+ " │ ├── trace.dat\r\n",
+ " │ └── trace_stat.json\r\n",
+ " ├── kernel.config\r\n",
+ " ├── kernel.version\r\n",
+ " └── platform.json\r\n",
+ "\r\n",
+ "8 directories, 36 files\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Check content of the output folder\n",
+ "res_dir = executor.te.res_dir\n",
+ "logging.info('Content of the output folder %s', res_dir)\n",
+ "!tree {res_dir}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Load function profiling data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": false
@@ -145,7 +507,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 9,
"metadata": {
"collapsed": false
},
@@ -181,7 +543,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 10,
"metadata": {
"collapsed": false
},
@@ -195,12 +557,12 @@
" func_std = func_stats.unstack(level=1)['s_2'].apply(numpy.sqrt)\n",
" func_avg.plot(kind='bar', title=fname, yerr=func_std, ax=axes);\n",
"\n",
- "#plot_stats(stats_df, 'sched_group_energy')"
+ "#plot_stats(stats_df, 'select_task_rq_fair')"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": false
@@ -210,19 +572,16 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "11:50:21 INFO : Plotting stats for [sched_group_energy] function\n",
- "11:50:21 INFO : Plotting stats for [select_task_rq_fair] function\n",
- "11:50:21 INFO : Plotting stats for [energy_diff] function\n",
- "11:50:21 INFO : Plotting stats for [schedtune_dequeue_task] function\n",
- "11:50:21 INFO : Plotting stats for [schedtune_enqueue_task] function\n",
- "11:50:21 INFO : Plotting stats for [schedtune_task_boost] function\n"
+ "03:44:53 INFO : Plotting stats for [dequeue_task_fair] function\n",
+ "03:44:53 INFO : Plotting stats for [enqueue_task_fair] function\n",
+ "03:44:53 INFO : Plotting stats for [select_task_rq_fair] function\n"
]
},
{
"data": {
- "image/png": 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AAAA6J5wCAADQOeEUAACAzgmnAAAAdE44BQAAoHPCKQAAAJ0TTgEAAOiccAoA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\nAACA7v4/WNEZI4XAvtUAAAAASUVORK5CYII=\n",
+ "image/png": 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h41/MvlwAAAAWi2qtza6DqlVJVrXWbquqw5J8KcnPJ3lJkvtba+96hPZttjUAAOxJVcXv\nGwB9DL8H10zPLZ1t5621TUk2Dbe3V9VXk6x+eOzZ9g8AAMDiNNJrSKtqbZJTk/yf4a4Lq+q2qvq9\nqjpilGMBAACwsM36DOnDhtN1P5Hk4uGZ0vcn+c3WWquqtyZ5V5JXztR2bGxsanswGGQwGIyqLAAA\nAPaj8fHxjI+P79Wxs76GNEmqammSTyf5X62198zw/Jok17XWnjrDc64hBQDmlGtIAfrZ0zWko5qy\n+8Ek66aH0eFiRw97cZIvj2gsAAAAFoFRrLL77CSfT3J7kjZ8/EaS8zJ5PenOJHcnuaC1tnmG9s6Q\nAgBzyhlSgH72dIZ0JFN2Z0MgBQDmmkAK0M/+mLILAAAAj4pACgAAQBcCKQAAAF0IpAAAAHSxtHcB\nAAAAc2l8fDzj4+NT24PBIEkyGAymtunDKrsAwKJnlV3gYb4f7H9W2QUAAGDeEUgBAADoQiAFAACg\nC4EUAACALgRSAAAAuhBIAQAA6EIgBQAAoAuBFAAAgC4EUgAAALoQSAEAAOhCIAUAAKALgRQAAIAu\nBFIAAAC6WNq7AAAAgAPN+Ph4xsfHp7YHg0GSZDAYTG0fCKq11reAqta7BgBgcauq+H0DSObn94P5\nWNMoDV9fzfScKbsAAAB0IZACAADQhUAKAABAFwIpAAAAXQikAAAAdCGQAgAA0IVACgAAQBcCKQAA\nAF0IpAAAAHQhkAIAANCFQAoAAEAXAikAAABdCKQAAAB0IZACAADQhUAKAABAFwIpAAAAXQikAAAA\ndCGQAgAA0IVACgAAQBcCKQAAAF0IpAAAAHQhkAIAANCFQAoAAEAXAikAAABdCKQAAAB0IZACAADQ\nhUAKAABAFwIpAAAAXQikAAAAdCGQAgAA0IVACgAAQBcCKQAAAF0IpAAAAHQhkAIAANCFQAoAAEAX\nsw6kVXVcVd1YVV+pqtur6j8O9x9VVddX1R1V9ZmqOmL25QIAALBYVGttdh1UrUqyqrV2W1UdluRL\nSX4+ycuT/G1r7R1V9etJjmqtvXGG9m22NQAA7ElVxe8bQDI/vx/Mx5pGafj6aqbnZn2GtLW2qbV2\n23B7e5KvJjkuk6H0w8PDPpzk7NmOBQAAwOIx0mtIq2ptklOT/GWSla21zclkaE1yzCjHAgAAYGEb\nWSAdTtf9RJKLh2dKdz3nvHjPQQMAAPCoLR1FJ1W1NJNh9COttU8Nd2+uqpWttc3D60y/s7v2Y2Nj\nU9uDwSCDwWAUZQEAALCfjY+PZ3x8fK+OnfWiRklSVf89yfdaa6+ftu+3kky01n7LokYAQE+LfcEQ\nYO/Nx+8H87GmUdrTokajWGX32Uk+n+T2TE7LbUl+I8nNST6e5Pgk65P8Ymtt6wztBVIAYE4t9l/2\ngL03H78fzMeaRmlOA+lsCaQAsPBNn541Pj4+dfnNfLkUZ7H/sgfsvfn4/WA+1jRKAikAsN/Mx1+s\n5mNNQB/z8fvBfKxplOb0PqQAAACwLwRSAAAAuhBIAQAA6EIgBQAAoAuBFAAAgC4EUgAAALoQSAEA\nAOhCIAUAAKALgRQAAIAuBFIAAAC6EEgBAADoQiAFAACgC4EUAACALgRSAAAAuhBIAQAA6EIgBQAA\noAuBFAAAgC4EUgAAALoQSAEAAOhCIAUAAKALgRQAAIAuBFIAAAC6EEgBAADoQiAFAACgi6W9C2D/\nGB8fz/j4+NT2YDBIkgwGg6ltAACYr1atWpvNm9ePpK+qGkk/K1euyaZNd4+krwNVtdb6FlDVetdw\noKmqeM8BmCvz8efMfKwJeHQmQ+Qo/j8eVT+TfY3ie8ti/x41fH0z/hXAlF0AAAC6EEgBAADoQiAF\nAACgC4EUAACALgRSAAAAuhBIAQAA6EIgBQAAoIulvQtYjMbHxzM+Pj61PRgMkiSDwWBqGwDYs1XH\nrcrmjZtH1t/kPQxnZ+Xqldn0rU0jqAaAJKneN2Ctqta7hrk0H29yOx9rAmDxGNXPmapKxmZfT5LJ\nfkbR11j8DIVOJv+oNIr//0bVz2Rfo/p+t5i/twxf34x/FTRlFwAAgC4EUgAAALoQSAEAAOhCIAUA\nAKALgRQAAIAuBFIAAAC6EEgBAADoQiAFAACgC4EUAACALpb2LgAAAKYbHx/P+Pj41PZgMEiSDAaD\nqW1gcajWWt8CqlrvGuZSVWW+vb75WBMAi8eofs5UVTI2+3qSTPYzir7G4mfofub3Fh5WVUlG8VkY\nVT+TfY3q+91i/pwPX1/N9JwpuwAAAHQhkAIAANCFQAoAAEAXAikAAABdCKQAAAB04bYvAAAA++Kg\nh1f/nb1R9bNy9cps+tamkfS1PwikAAAA++KhjOyWUqO6zdXmsc2j6Wg/MWUXAACALgRSAAAAujBl\nFxaA8fHxjI+PT20PBoMkyWAwmNoGAICFZiSBtKo+kORFSTa31p463PfmJOcn+c7wsN9orf3pKMaD\nA8304FlVU+EUAAAWslFN2f1Qkp+bYf+7Wms/PXwIowAAAEwZSSBtrd2UZMsMT41m7WIAAAAWnble\n1OjCqrqtqn6vqo6Y47EAAABYQOZyUaP3J/nN1lqrqrcmeVeSV8504NjY2NS2RVoAAAAWrukLcj6S\nOQukrbXvTvvyvyW5bnfHTg+kAAAALFy7nmR8y1vesttjRzlltzLtmtGqWjXtuRcn+fIIxwIAAGCB\nG9VtXz6WZJBkRVVtSPLmJM+rqlOT7Exyd5ILRjEWAAAAi8NIAmlr7bwZdn9oFH0DAACwOM3lokYA\nzFPTFxsYHx+fus7DwnIAwP4kkAIcgKYHz6ra65XwAABGaa7vQwoAAAAzEkgBAADoQiAFAACgC9eQ\nAgBZtWptNm9eP7L+quqRDwLggCeQAgDDMNpG1FuNqC+hFmCxM2UXAACALgRSAAAAuhBIAQAA6EIg\nBQAAoAuBFAAAgC6ssgvAvDA+Pp7x8fGp7cFgkCQZDAZT2wDA4iKQAjAvTA+eVTUVTgGAxcuUXQAA\nALoQSAEAAOjClF1gn7jeDwCA2RJIgX3iej8AAGbLlF0AAAC6EEgBAADoQiAFAACgC9eQ7saqVWuz\nefP6kfRVVSPpZ8ljlmTnD3eOpK9R1bRy9cps+tamkfQFAAAcWATS3ZgMo20EPdWI+kl2/rCSsRF0\nNJbR9JNk89jm0XQEAAAccEzZBQAAoAuBFAAAgC4EUgAAALoQSAEAAOhCIAUAYORWrVqbqpr1I8lI\n+qmqrFq1tu+bAvwDVtkFAGDk5uMdCzZvnt1t78bHxzM+Pj61PRgMkiSDwWBqG3h0BFIAANgL04Nn\nVU2FU2DfmbILAABAFwIpAAAAXQikAAAAdCGQAgAA0IVACgAAQBcCKQAAAF0IpAAAAHQhkAIAANCF\nQAoAAEAXAikAAABdCKQAAAB0IZACAADQhUAKAABAFwIpAAAAXQikAAvQqlVrU1UjeSQZST+rVq3t\n+6YAAAvO0t4FAPDobd68PkkbUW81kr42b67ZlwIAHFAEUgAAYJEbHz6S5IwkY8PtwfBBLwIpAACw\nyA0ieM5PriGF/WA+Xu/nmj8AAHpzhhT2g/l4vV/imj8AAPpyhhQAAIAuBFIAAAC6EEgBAADoQiAF\nAACgC4EUAACALgRSAAAAunDbFwAADgwHZeqe3qMwir5Wrl6ZTd/aNIJqYGEaSSCtqg8keVGSza21\npw73HZXkmiRrktyd5Bdba/eNYjwAAHjUHkoyNqK+xkbT1+axzbPvBBawUU3Z/VCSn9tl3xuT/Flr\n7eQkNya5ZERjAQAAsAiMJJC21m5KsmWX3T+f5MPD7Q8nOXsUYwEAALA4zOWiRse01jYnSWttU5Jj\n5nAsAAAAFpj9uahR290TY2NjU9uDwSCDwWA/lAMAAMCojY+PZ3x8fK+OnctAurmqVrbWNlfVqiTf\n2d2B0wMpsB+NcLXBUfVjtUEAgIVt15OMb3nLW3Z77CgDaQ0fD7s2yX9I8ltJXpbkUyMcCxiFUa02\nODaifmK1QQCAA8lIriGtqo8l+UKSJ1XVhqp6eZK3Jzmzqu5I8s+GXwMAAECSEZ0hba2dt5un/vko\n+gcAAGDx2Z+LGgGwmI3wmuTEdckAcCAQSAEYjVFdk5y4LhkADhBzeR9SAAAA2C2BFAAAgC4EUgAA\nALoQSAEAAOhCIAUAAKALgRQAAIAu3PZlTowPH0lyRv7+3gWD4QMAAACBdE4MIngCAOyr8fjjPhwY\nBFIAYATGI0AwOoP43MCBQSAFAEZgEAECgEfLokYAAAB0IZACAADQhUAKAABAFwIpAAAAXQikAAAA\ndGGV3QPFN5PcPdxek+TPh9trk5zYoR4AADiQ+f08iUB64DgxB9QHGwBg5AQIRsnv50kEUgAA2DsC\nBIycQAoALE7OZgHMewIpALA4OZsFMO8JpAAHpPHhI0nOSDI23B4MHwAAc08gBTggDSJ4AgC9uQ8p\nAAAAXQikAAAAdCGQAgAA0IVACgAAQBcCKQAAAF0IpAAAAHQhkAIAANCFQAoAAEAXAikAAABdCKQA\nAAB0IZACAADQhUAKAABAFwIpAAAAXQikAAAAdCGQAgAA0IVACgAAQBdLexcA7I3x4SNJzkgyNtwe\nDB8AALDwCKSwIAwieAIAsNgIpMC++WaSu4fba5L8+XB7bZITO9QDAMCCI5AC++bECJ4AAMyKRY0A\nAADoQiAFAACgC4EUAACALgRSAAAAuhBIAQAA6EIgBQAAoAuBFAAAgC4EUgAAALoQSAEAAOhCIAUA\nAKCLpb0LAIAkyTeT3D3cXpPkz4fba5Oc2KEeAGDOCaQAzA8nRvAEgAPMnAfSqro7yX1Jdib5UWvt\n9LkeEwAAgPlvf5wh3Zlk0Frbsh/GAgAAYIHYH4sa1X4aBwAAgAVkfwTFluSzVXVLVZ2/H8YDAABg\nAdgfU3af3Vq7t6p+IpPB9KuttZumHzA2Nja1PRgMMhgM9kNZAAAAjNr4+HjGx8f36tg5D6SttXuH\n/363qv44yelJdhtIAQAAWLh2Pcn4lre8ZbfHzumU3ao6tKoOG24/PskLknx5LscEAABgYZjrM6Qr\nk/xxVbXhWB9trV0/x2MCAACwAMxpIG2tfTPJqXM5BgAAAAuT27EAAADQhUAKAABAFwIpAAAAXQik\nAAAAdCGQAgAA0IVACgAAQBcCKQAAAF0IpAAAAHQhkAIAANCFQAoAAEAXAikAAABdCKQAAAB0IZAC\nAADQhUAKAABAFwIpAAAAXQikAAAAdCGQAgAA0IVACgAAQBcCKQAAAF0IpAAAAHQhkAIAANCFQAoA\nAEAXAikAAABdCKQAAAB0IZACAADQhUAKAABAFwIpAAAAXQikAAAAdCGQAgAA0IVACgAAQBcCKQAA\nAF0IpAAAAHQhkAIAANCFQAoAAEAXAikAAABdCKQAAAB0IZACAADQhUAKAABAFwIpAAAAXQikAAAA\ndCGQAgAA0IVACgAAQBcCKQAAAF0IpAAAAHQhkAIAANCFQAoAAEAXAikAAABdCKQAAAB0IZACAADQ\nhUAKAABAFwIpAAAAXQikAAAAdCGQAgAA0IVACgAAQBcCKQAAAF0IpAAAAHQhkAIAANDFnAfSqvoX\nVfW1qvp6Vf36XI8HAADAwjCngbSqliT5nSQ/l+QpSV5aVT81l2MCAACwMMz1GdLTk9zZWlvfWvtR\nkquT/PwcjwkAAMACMNeBdHWSe6Z9/a3hPgAAAA5w1Vqbu86r/k2Sn2utvXr49b9Pcnpr7T9OO2bu\nCgAAAKC71lrNtH/pHI+7MckJ074+brhvyu4KAwAAYHGb6ym7tyT5yapaU1WPSXJukmvneEwAAAAW\ngDk9Q9pae6iqLkxyfSbD7wdaa1+dyzEBAABYGOb0GlIAAADYnbmesgsAAAAzmutFjdhHVfVTmbxn\n68O3ydmY5FpTnoH5Yvh9anWS/9Na2z5t/79orf1pv8pYqKrq2Um2tNbWVdUZSZ6R5LbW2g2dS2MR\nqKr/3lr7f3vXweJRVT+b5PQkX26tXd+7noXKlN15qKp+PclLk1ydyXu3JpMrFJ+b5OrW2tt71cbi\nVFUvb619qHcdLBxV9R+TvDbJV5OcmuTi1tqnhs/9VWvtp3vWx8JTVZcleX4mZ2+NJ3lukj9JcmYm\n/yB7eb8FzktdAAAgAElEQVTqWGiqatdFNCvJ85LcmCSttX+934tiwauqm1trpw+3z8/kz8E/TvKC\nJNf5HX3fCKTzUFV9PclTWms/2mX/Y5J8pbX2j/pUxmJVVRtaayc88pEwqapuT/JPW2vbq2ptkk8k\n+Uhr7T1V9dettdO6FsiCU1VfSfLUJI9NsinJca21bVV1SJK/bK09rWuBLChV9VdJ1iX5vSQtk4H0\nqkz+cT+ttc/1q46FavrPt6q6Jcm/bK19t6oen8nvU/+kb4ULkym789POJMcmWb/L/icMn4NHrar+\nv909lWTl/qyFRWHJw9N0W2t3V9UgySeqak0mP1PwaP2wtfZQkgeq6q7W2rYkaa39oKr87OPRekaS\ni5NcmuQNrbXbquoHgiiztKSqjsrkTI6DWmvfTZLW2verakff0hYugXR++pUkN1TVnUnuGe47IclP\nJrmwW1UsdCuT/FySLbvsryRf2P/lsMBtrqpTW2u3JcnwTOmLknwwib8Qsy9+WFWHttYeSPL0h3dW\n1RGZPMMFe621tjPJb1fVHw7/3Ry/9zJ7RyT5UiZ/d2pV9YTW2r1VdVj8MXafmbI7T1XVkkxeJD19\nUaNbhn89hketqj6Q5EOttZtmeO5jrbXzOpTFAlVVxyXZ0VrbNMNzz26t/e8OZbGAVdVjW2t/N8P+\no5M8obV2e4eyWCSq6l8leXZr7Td618LiU1WHJlnZWvtm71oWIoEUAACALtyHFAAAgC4EUgAAALoQ\nSAEAAOhCIAUAAKALgRQAAIAuBFIAAAC6EEgBAADoQiAFAACgC4EUAACALgRSAAAAuhBIAQAA6EIg\nBQAAoAuBFAAAgC4EUgAAALoQSAEAAOhCIAUAAKALgRQAAIAuBFIAAAC6EEgBAADoQiAFAACgC4EU\nAACALgRSAAAAuhBIAQAA6EIgBQAAoAuBFAAAgC4EUgAAALoQSAEAAOhCIAUAAKALgRQAAIAuBFIA\nAAC6EEgBAADoQiAFAACgC4EUAACALgRSAAAAuhBIAQAA6EIgBQAAoAuBFAAAgC4EUgAAALoQSAEA\nAOhCIAUAAKALgRQAAIAuBFIAAAC6EEgBAADoQiAFAACgC4EUAACALgRSAAAAuhBIAQAA6EIgBQAA\noAuBFAAAgC4EUgAAALoQSAEAAOhCIAUAAKALgRQAFomq+lBV/eajbPO4qrquqrZW1TV7cfyXq+q5\n+14lAPy9pb0LAIADWVWdkeQPWmvHdyrhnCQ/keSo1lp7pINba/947ksC4EDhDCkA9FVJHjEIzqE1\nSb6+N2H0kVTVQSOoB4ADiEAKwLxVVU+oqk9U1Xeq6q6qumi4/81VdU1VfbiqtlXV7VX109PanVZV\nX6qq+6rq6qq66uGprFX1sqr6i13G2VlVTxxuP6aqLq+q9VV1b1W9v6oeO9u2u3l9hyb5n0mOrar7\nh69lVVX9TFV9oaq2VNXGqnpvVS2d1u63q2rz8PX9TVU9eYa+D6+qG6vq3XsYfyzJf05y7nDsl1fV\nE6vqhqr63vB9/4OqWjatzTer6vnT/jv8YVV9pKq2JnnZ7sYCgJkIpADMS1VVSa5L8tdJnpDknyW5\nuKrOHB5yVpKPJTlieNz7hu0OTvLHST6cZHmSP0zyb3bpftezgdO//q0kP5nkqcN/V2cytI2i7Y83\nbO2BJC9M8u3W2uGttWWttU1JHkryK8P6/2mS5yf55eHre0GSn03yk621I5L8YpK/nd5vVS1P8mdJ\n/qK19it7GH8syWVJrh6O/aFMnrG9LMmqJKckOS7J2O76SPKvk3y8tXZkko/u4TgA+AcEUgDmq59J\ncnRr7W2ttYdaa3cn+b0kLx0+f1Nr7TPDqaYfyWQITCYD3NLW2n8ZtvujJLc8wlg1bfv8JK9rrd3X\nWvt+krdPG3PUbWfUWvur1trNbdKGJL+b5Izh0z9KcniSJ1dVtdbuaK1tntZ8dZLPJbmmtfbmfRj7\nrtbaDa21Ha21v03y29PGnskXW2vXDdv+3aMdD4ADm0WNAJiv1iRZXVUTw68rk39I/Ysk65Nsmnbs\nA0keV1VLMnk2deMufa3fmwGr6ieSHJrkS5MnaJPhmLXbRiNoO0Nf/yjJu5I8I8khmfx5/aUkaa39\neVX9TibPCJ9QVf8jya+11rYPm/+rJPcnufLRjjsc+5gk70nynCSHJTkoycQemtyzL+MAQOIMKQDz\n1z1J/m9rbfnwcVRr7YjW2oseod29mTxLON0J07a/n8ngmCSpqlXTnvteJsPtU6aNe+Rwauxs2+7O\nTIsJXZHkq0lOGk6FvTTTgm1r7Xdaa89I8uQkJyd5w7S2v5vkT5P8r6o65BHGnsllSXYOX8eRSf59\n9hyqey7IBMACJ5ACMF/dnOT+qvpPw3tlHlRVT6mqZ+zm+IdD0xeT7Kiqi6pqaVW9OMnp0477myRP\nqaqnDhccenOGoWo4/fe/JXn38Ixnqmr18LrN2bbdnc1JVkxfOCiTU3K3tdYeqKqfSvKaqRdZ9Yyq\nOn24yNEPkjyYyQA5pbV2UZI7kny6qh73COPv6vAk2zP53q/Oj4ddABgpgRSAeam1tjPJi5KcmuSb\nSb6TycC3bHdNhu1+lOTFSV6eycV+/m2SP5rW751JfjPJDUm+nskpwNP9epJvJPnL4cqx1yd50mzb\n7uF13pHkqiT/t6omhmddfy3Jv6uqbZmcenv1tCbLhu/DxPB9+V6Sd87Q9aszeZb5k1X1mD3VsIu3\nJHl6kq2ZXCzqj3Z53hlRAEamRnDbsVTVBzL5S8Pm1tpTh/t+JpPXtxycyQUYfrm1duusBwOAR6mq\nPpTkntbable8BQD2v1GdIf1Qkp/bZd87kryptXZaJqc0zfTXWwAAAA5QIwmkrbWbkmzZZfe9mbw3\nXJIcmX+44iEA7C9dp5lW1SVVdX9Vbdvl8Sf7afwv7zLuw7U86lvSAMAojWTKbpJU1Zok102bsntC\nkv+dyV8CKsmzWmuWhgcAACDJ3N6H9ANJLmqtfbKqzknywSRn7npQVVkcAQAAYBFrrc14C7G5PEO6\nrbW2bNrz9810L7aqaqOqYbEbGxvL2NhY7zJYRHymGCWfJ0bNZ4pR85li1Hym9k5V7TaQjvK2L5Uf\nv3H2nVV1xrCAf5bJ5fEBAAAgyYim7FbVx5IMMnlj7w2ZXFX31UneP7z32YPDrwEAACDJiAJpa+28\n3Tz1/4yifyYNBoPeJbDI+EwxSj5PjJrPFKPmM8Wo+UzN3siuId3nAlxDCgAAsGjt6RrSuVxlFwAA\nYNFYu3Zt1q9f37uMeWvNmjW5++67H1UbZ0gBAAD2wvBMX+8y5q3dvT/7a5VdAAAA2GsCKQAAAF0I\npAAAAHQhkAIAANCFQAoAALCPVq1am6qas8eqVWt7v8Q5ZZVdAACAvTDTKrJVlWQu88zCWdnXKrsA\nAAAsGAIpAADAInDvvffmnHPOyTHHHJOTTjop733ve5Mkt9xyS571rGflqKOOyurVq3PRRRdlx44d\nU+1e97rXZeXKlTniiCPytKc9LevWrdtvNQukAAAAC1xrLWeddVZOO+203Hvvvbnhhhvynve8J5/9\n7GezdOnSvPvd787ExES++MUv5sYbb8z73//+JMn111+fm266Kd/4xjdy33335eMf/3hWrFix3+oW\nSAEAABa4W265Jd/73vdy6aWX5qCDDsratWvzqle9KldffXVOO+20nH766amqnHDCCXn1q1+dz33u\nc0mSgw8+OPfff3/WrVuX1lpOPvnkrFy5cr/VvXS/jQQAAMCcWL9+fTZu3Jjly5cnmTxjunPnzjz3\nuc/NnXfemde//vW59dZb84Mf/CA7duzI05/+9CTJ8573vFx44YV57Wtfmw0bNuTFL35xLr/88hx2\n2GH7pW5nSAEAABa4448/Pk984hMzMTGRiYmJbNmyJffdd1+uu+66vOY1r8kpp5ySu+66K1u3bs3b\n3va2H1sN98ILL8ytt96adevW5Y477sg73/nO/Va3QAoAALDAnX766Tn88MPzjne8Iw8++GAeeuih\nfOUrX8mtt96a7du3Z9myZTn00EPzta99LVdcccVUu1tvvTU333xzduzYkUMOOSSPe9zjsmTJ/ouJ\nAikAAMA+WrlyTZKas8dk/49syZIl+fSnP53bbrstJ554Yo455picf/752bZtWy6//PJ89KMfzbJl\ny3LBBRfk3HPPnWq3bdu2nH/++Vm+fHlOPPHEHH300XnDG94w6/dlb1Xvm6xWVetdAwAAwCOpqsgu\nu7e792e4v2Zq4wwpAAAAXQikAAAAdCGQAgAA0IVACgAAQBcCKQAAAF0IpAAAAHQhkAIAANCFQAoA\nAEAXS3sXAAAAMJfGx8czPj4+tT0YDJIkg8FgantfrTpuVTZv3Dy7Avdg5eqV2fStTXt17IknnpgP\nfOADef7znz9n9Yxatdb6FlDVetcAAAAcGKoq+5o/ZmpbVcnYCArbnbHsdb29A+nu3tvh/pqpjSm7\nAAAAdCGQAgAALBI333xznvKUp2TFihV55StfmR/+8IfZunVrzjrrrBxzzDFZsWJFzjrrrGzcuHGq\nze///u/npJNOyrJly3LSSSflqquumnrugx/8YJ785CdnxYoVeeELX5gNGzaMtF6BFAAAYJH42Mc+\nls9+9rO56667cscdd+Stb31rWmt5xStekXvuuScbNmzIoYcemgsvvDBJ8sADD+Tiiy/OZz7zmWzb\nti1f+MIXcuqppyZJPvWpT+Xtb397PvnJT+a73/1unvOc5+SlL33pSOsVSAEAABaJiy66KMcee2yO\nPPLIXHrppbnqqqty1FFH5Rd+4Rfy2Mc+No9//ONzySWX5POf//xUm4MOOii33357HnzwwaxcuTKn\nnHJKkuTKK6/MJZdckic96UlZsmRJ3vjGN+a2227LPffcM7J6BVIAAIBF4rjjjpvaXrNmTb797W/n\nwQcfzAUXXJC1a9fmyCOPzBlnnJGtW7emtZZDDz0011xzTa644oo84QlPyFlnnZWvf/3rSZL169fn\n4osvzvLly7N8+fKsWLEiVfVj031nSyAFAABYJKafvVy/fn2OPfbYXH755bnzzjtzyy23ZOvWrVNn\nRx9eEffMM8/M9ddfn02bNuXkk0/O+eefnyQ5/vjjc+WVV2ZiYiITExPZsmVLtm/fnmc+85kjq1cg\nBQAAWCTe9773ZePGjZmYmMhll12Wl7zkJdm+fXsOOeSQLFu2LBMTExkbG5s6/jvf+U6uvfbaPPDA\nAzn44INz2GGHZcmSyZj4S7/0S7nsssuybt26JMl9992XT3ziEyOtd+koOqmqDyR5UZLNrbWnTtt/\nUZJfTrIjyZ+01t44ivEAAADmg5WrV2bz2OY57X9vVVXOO++8vOAFL8i9996bs88+O29605uyZcuW\nnHfeeTn66KOzevXq/Oqv/mquvfbaJMnOnTvzrne9Ky972ctSVTn11FNzxRVXJEnOPvvsfP/738+5\n556bDRs25IgjjsiZZ56Zc845Z2Svr/b1prA/1knVzybZnuS/PxxIq2qQ5DeS/MvW2o6qOrq19r0Z\n2rZR1AAAAPBIqir7mj9m0/ZAsLv3Z7i/Zmozkim7rbWbkmzZZfdrkry9tbZjeMw/CKMAAAAcuOby\nGtInJXluVf1lVf15VT1jDscCAABggRnJNaR76Puo1tozq+pnknw8yRNnOnD6RbWDwSCDwWAOywIA\nAGCujI+PZ3x8fK+OHck1pElSVf8/e/ceL3ld33n+/YH2AiJXhz5KSzeS0SFmDcYsceOqpQ4xmkFJ\n1iTIZryGmKwoo8ZVNLMeNoZxlI26meg6j3gf0aizUchmIiqWrppEiDISWvEy2o0I7aW5iEgU+e4f\np/rk2Hu6abp/db6nznk+H4968DtVv/r9vnWorupX/26bk1y85BjSv0ry71trH5/8/JUkv9Ba++5u\nz3MMKQAAsCIcQzo93Y4h3bWeyW2XDyR57GQAD0xyt91jFAAAgPVrqMu+XJhklOSYqtqe5BVJ3pLk\nrVV1ZZJ/TPK0IdYFAADQw+bNm1O17IY+svD7uasG22V3f9llFwAAWCl2u115K7XLLgAAAOwzQQoA\nAEAXghQAAIAuBCkAAABdCFIAAAC6EKQAAAB0IUgBAADoQpACAADQhSAFAACgC0EKAABAF4IUAACA\nLgQpAAAAXQhSAAAAuhCkAAAAdCFIAQAA6EKQAgAA0IUgBQAAoAtBCgAAQBeCFAAAgC4EKQAAAF0I\nUgAAALoQpAAAAHQhSAEAAOhCkAIAANCFIAUAAKALQQoAAEAXghQAAIAuBCkAAABdCFIAAAC6EKQA\nAAB0IUgBAADoQpACAADQhSAFAACgC0EKAABAF4MEaVW9uap2VNXnl3nsRVV1R1UdPcS6AAAAWBuG\n2kL61iSP3/3OqtqU5NQk2wZaDwAAsA7NzW1JVR3wLckgy6mqzM1t6ftLWQMGCdLW2ieT3LDMQ69N\n8uIh1gEAAKxfO3ZsS9IGuGWg5bTJmDgQUzuGtKqelOSa1tqV01oHAAAAs2vDNBZaVYckeVkWdtdd\nvHtP88/Pzy9Oj0ajjEajaQwLAACAKRuPxxmPx/s0b7XW7nyufVlQ1eYkF7fWHlJVP5PkI0luzUKI\nbkpybZJTWmvf2u15bagxAAAAa9PC8Z9DdMNQy1lYlpa5c1WV1tqyGyiH3EJak1taa/+QZG7JAL6W\n5Odaa8sdZwoAAMA6NNRlXy5M8ukkD6yq7VX1zN1madnLLrsAAACsP4PtsrvfA7DLLgAAcCfssju7\n9rbL7tTOsgsAAAB7I0gBAADoQpACAADQhSAFAACgC0EKAABAF4IUAACALgQpAAAAXWzoPQAAgGkY\nj8cZj8eL06PRKEkyGo0WpwHoq3pfyLWqWu8xAABr2+Si7L2HARyAqkoyxJ/joZazsCyfLXdu8hlc\nyz1ml10AAAC6EKQAAAB0IUgBAADoQpACAADQhSAFAACgC0EKAABAF4IUAACALgQpAAAAXQhSAAAA\nuhCkAAAAdCFIAQAA6EKQAgAA0IUgBQAAoAtBCgAAQBeCFAAAgC429B4AAADAejMejzMejxenR6NR\nkmQ0Gi1OrwfVWus7gKrWewwAwNpWVfH3DZhtVZVkiD/HQy1nYVlDfLas9c+oyeur5R6zyy4AAABd\nCFIAAAC6EKQAAAB0IUgBAADoQpACAADQhSAFAACgC0EKAABAF4IUAACALgQpAAAAXQwSpFX15qra\nUVWfX3Lfq6vqC1V1RVX956o6fIh1AQAAsDYMtYX0rUkev9t9lyR5cGvt5CRfTnLuQOsCAABgDRgk\nSFtrn0xyw273faS1dsfkx79NsmmIdQEAALA2rNQxpM9K8l9WaF0AAADMgA3TXkFVvTzJj1prF+5p\nnvn5+cXp0WiU0Wg07WEBAAAwBePxOOPxeJ/mrdbaICutqs1JLm6tPWTJfc9IclaSx7bW/nEPz2tD\njQEAYDlVFX/fgNlWVUmG+HM81HIWljXEZ8ta/4yavL5a7rEht5DW5LZrpb+c5MVJHrWnGAUAAGD9\nGuqyLxcm+XSSB1bV9qp6ZpI/SXJYkg9X1Wer6g1DrAsAAIC1YbBddvd7AHbZBQCmbK3vDgfrgV12\nZ9fedtldqbPsAgAAwE+whRQAWPPW+tYHWA8ObAvpeHLbNT2aTI+WTO8PW0j3xd62kApSgHVo6enY\nx+Px4uW2XHqLtWqt/2UP1oPhdtkdkiDdF4IUgD1a61+CkHifw1ogSGeXY0gBAABYdQQpAAAAXQhS\nAAAAuhCkAAAAdCFIAQAA6EKQAgAA0IUgBQAAoIsNvQcAAEkyHo8zHo8Xp0ejUZJkNBotTgMAa0v1\nvgBrVbXeYwBYz1bjxbhX45iYbd5TMPuqKslq+3M8zGfLWv+Mmry+Wu4xu+wCAADQhSAFAACgC0EK\nAABAF4IUAACALgQpAAAAXQhSAAAAuhCkAAAAdCFIAQAA6EKQAgAA0IUgBQAAoAtBCgAAQBeCFAAA\ngC429B4AK2M8Hmc8Hi9Oj0ajJMloNFqcBgAAWEnVWus7gKrWewzrTVXF7xzYZTV+JqzGMTHbvKdg\n9lVVktX253iYz5a1/hk1eX213GN22QUAAKALQQoAAEAXjiGdAsdrAgAA3DnHkE7ZatwffDWOCehn\nNX4mrMYxMdu8p2D2OYZ0djmGFACYOXOb5lJVg9ySDLKcuU1znX8rAGuLLaRTthr/tWM1jgnoZzV+\nJqzGMbHyqiqZH2hh8xlmWfPx3oRObCGdXbaQAgAAsOoIUgAAALoYJEir6s1VtaOqPr/kvqOq6pKq\nurqqPlRVRwyxLgAAANaGobaQvjXJ43e776VJPtJae1CSS5OcO9C6AAAAWAMGCdLW2ieT3LDb3U9O\n8vbJ9NuTnD7EugAAAFgbpnkM6bGttR1J0lq7PsmxU1wXAAAAM2bDCq5rj+cxnp+fX5wejUYZjUYr\nMBwAAACGNh6PMx6P92newa5DWlWbk1zcWnvI5OcvJBm11nZU1VySj7XWTlrmea5DusJW45iAflbj\nZ8JqHBMrz3VIgaVch3R2rdR1SGty2+WiJM+YTD89yQcHXBcAAAAzbqjLvlyY5NNJHlhV26vqmUle\nleTUqro6yeMmPwMAAECSgY4hba2duYeH/uUQywcAAFh1Dt61K/GBG2o5G4/bmOu/cf0gy1oJK3lS\nIwBgjVp6AovxeLx4gkInKwTWtB9nsOPThzpmfsf8jmEWtEIEKQBwwJaGZ1Xt89kVAVjfpnkdUgAA\nANgjQQoAAEAXghQAAIAuBCkAAABdCFIAAAC6EKQAAAB04bIvwH5xzUEApsV3DKwf1VrrO4Cq1nsM\n01RVWW2vbzWOidnmPTXbVuP/v9U4JvbdUP//qmqwC8UPdtH5+XhvrjCfB+xSVUlW23thoM+p+Qz6\nebfa/sxM/hzXco/ZZRcAAIAuBCkAAABdCFIAAAC6EKQAAAB0IUgBAADoQpDOkLlNc6mqA74lGWQ5\nVZW5TXOdfysAAMCsch3SGbLj2h2r7rTSO+Z3DLMgAABg3bGFFGAGzc1tGWxPh2SYvSbm5rb0/aUA\nADPHFlKAGbRjx7YMd3HwYS40vmPHste7BgDYI1tIAQAA6MIWUpgB4/E44/F4cXo0GiVJRqPR4jQA\nAMwaQboHc3NbJrvEHbhdx2jB/loanlW1GKcAADDLBOkeDHd81jDHZv3TsgAAANYGx5ACAADQhSAF\nAACgC0EKAKzKa9sy24Z6TyXDvJ9cLxlWJ8eQAgCr8tq2zp0w21bj+TgO9HrJznoPwxOkAACwD5z1\nHoZnl10AAAC6EKQAAAB0IUgBAADoQpACAADQhSAFAACgC0EKAABAF4IUAACALqYepFV1blVdVVWf\nr6p3VdXdp71OAAAAVr+pBmlVbU5yVpKHttYekmRDkjOmuU4AAABmw4YpL//mJD9Mcq+quiPJoUm+\nOeV1AtDDwUlVDba4oZa18biNuf4b1w+yLABgWFMN0tbaDVX1fyTZnuTWJJe01j4yzXUC0MmPk8wP\ntKz54Za1Y37HMAsCAAY31SCtqgckeUGSzUluSvL+qjqztXbh0vnm5+cXp0ejUUaj0TSHBQAAwJSM\nx+OMx+N9mnfau+z+fJJPtdZ2JklV/d9JfjHJHoMUAACA2bX7Rsbzzjtvj/NO+yy7Vyd5eFXdsxYO\nBnpcki9MeZ0AAPD/NznWfYhbMsyy5jbNdf6lQF/TPob0v1bVO5L8fRaOLvpckv84zXUCADDrxpNb\nkjw6/3RQ+Why20+r8Fh3x7mz3k17l9201l6T5DXTXg8AAGvFKAcUnsDMmPYuuwAAALAsQQoAAEAX\nghQAAIAuBCkAAABdCFIAAAC6mPpZdgGA9WCcqVymA4A1TZCuF19L8vXJ9OYkH5tMb0lyQofxALDG\njCI8AbirBOl6cUKEJwAAsKo4hhQAAIAuBCkAAABdCFIAAAC6cAwpALA2OaEfQ/OegsEJUgBgbXJC\nP4bmPQWDE6RTMY5rsbHU3NyW7NixbbDlVdUgy9m4cXOuv/7rgywLAADuKkE6FaMIT5ZaiNE20NJq\nsGXt2DFM2AIAwP5wUiMAAAC6EKQAAAB0IUgBAADoQpACAADQhZMawXp28HBn7B3szL/Hbcz137h+\nkGUBAKxarmubRJDC+vbj/NNViQ7E/EDLSbJjfscwCwIAWM1c1zaJXXYBAADoRJACAADQhSAFAACg\nC0EKAABAF4IUAACALgQpAAAAXQhSAAAAuhCkAAAAdCFIAQAA6EKQAgAA0MWG3gMAoIfx5JYkj04y\nP5keTW4AANMnSAHWpVGEJwDQm112AQAA6GLqQVpVR1TV+6rqC1V1VVX9wrTXCQAAwOq3Ervsvj7J\nX7XWfr2qNiQ5dAXWCQAAwCo31SCtqsOTPLK19owkaa3dnuTmaa4TAACA2TDtXXZPSPKdqnprVX22\nqv5jVR0y5XUCAAAwA6a9y+6GJD+X5Lmttcur6nVJXprkFUtnmp+fX5wejUYZjUZTHhYAAADTMB6P\nMx6P92neaQfpN5Jc01q7fPLz+5O8ZPeZlgYpAAAAs2v3jYznnXfeHued6i67rbUdSa6pqgdO7npc\nkq3TXCcAAACzYSXOsvv8JO+qqrsl+W9JnrkC6wQAAGCVm3qQttb+a5L/ftrrAQAAYLZM+yy7AAAA\nsCxBCgAAQBeCFAAAgC4EKQAAAF0IUgAAALoQpAAAAHQhSAEAAOhi6tchBYYwntyS5NFJ5ifTo8kN\nAABmjyCFmTCK8AQAYK2xyy4AAABdCFIAAAC6EKQAAAB0IUgBAADoQpACAADQhSAFAACgC0EKAABA\nF4IUAACALgQpAAAAXQhSAAAAuhCkAAAAdCFIAQAA6GJD7wEAQJLka0m+PpnenORjk+ktSU7oMB4A\nYOoEKQCrwwkRngCwzthlFwAAgC4EKQAAAF0IUgAAALoQpAAAAHQhSAEAAOhCkAIAANCFIAUAAKAL\nQV+DaZ8AACAASURBVAoAAEAXghQAAIAuNvQeADCjvpbk65PpzUk+NpnekuSEDuMBAGDmCFJg/5wQ\n4QkAwAGxyy4AAABdTD1Iq+qgqvpsVV007XUBAAAwO1ZiC+k5SbauwHoAAACYIVMN0qralOSJSf5s\nmusBAABg9kx7C+lrk7w4SZvyegAAAJgxUzvLblX9SpIdrbUrqmqUpPY07/z8/OL0aDTKaDSa1rAA\nAACYovF4nPF4vE/zTvOyL49I8qSqemKSQ5Lcu6re0Vp72u4zLg1SAAAAZtfuGxnPO++8Pc47tV12\nW2sva60d31p7QJIzkly6XIwCAACwPrkOKQAAAF1Mc5fdRa21jyf5+EqsCwAAgNlgCykAAABdCFIA\nAAC6EKQAAAB0IUgBAADoQpACAADQhSAFAACgC0EKAABAF4IUAACALgQpAAAAXQhSAAAAuhCkAAAA\ndCFIAQAA6EKQAgAA0IUgBQAAoAtBCgAAQBeCFAAAgC4EKQAAAF0IUgAAALoQpAAAAHQhSAEAAOhC\nkAIAANCFIAUAAKALQQoAAEAXghQAAIAuBCkAAABdCFIAAAC6EKQAAAB0IUgBAADoQpACAADQhSAF\nAACgC0EKAABAF4IUAACALgQpAAAAXQhSAAAAuhCkAAAAdDHVIK2qTVV1aVVdVVVXVtXzp7k+AAAA\nZseGKS//9iQvbK1dUVWHJfn7qrqktfbFKa8XAACAVW6qW0hba9e31q6YTN+S5AtJjpvmOgEAAJgN\nK3YMaVVtSXJykr9bqXUCAACwek17l90kyWR33fcnOWeypfQnzM/PL06PRqOMRqOVGBYAAAADG4/H\nGY/H+zTv1IO0qjZkIUbf2Vr74HLzLA1SAAAAZtfuGxnPO++8Pc67ErvsviXJ1tba61dgXQAAAMyI\naV/25RFJ/uckj62qz1XVZ6vql6e5TgAAAGbDVHfZba19KsnB01wHAAAAs2nFzrILAAAASwlSAAAA\nuhCkAAAAdCFIAQAA6EKQAgAA0IUgBQAAoAtBCgAAQBeCFAAAgC4EKQAAAF0IUgAAALoQpAAAAHQh\nSAEAAOhCkAIAANCFIAUAAKALQQoAAEAXghQAAIAuBCkAAABdCFIAAAC6EKQAAAB0IUgBAADoQpAC\nAADQhSAFAACgC0EKAABAF4IUAACALgQpAAAAXQhSAAAAuhCkAAAAdCFIAQAA6EKQAgAA0IUgBQAA\noAtBCgAAQBeCFAAAgC4EKQAAAF0IUgAAALoQpAAAAHQx9SCtql+uqi9W1Zeq6iXTXh8AAACzYapB\nWlUHJfkPSR6f5MFJnlpV/2Ka6wQAAGA2THsL6SlJvtxa29Za+1GS9yR58pTXCQAAwAyYdpAel+Sa\nJT9/Y3IfAAAA61y11qa38Kr/KcnjW2u/M/n5t5Kc0lp7/pJ5pjcAAAAAumut1XL3b5jyeq9NcvyS\nnzdN7lu0p4EBAACwtk17l93LkvxUVW2uqrsnOSPJRVNeJwAAADNgqltIW2s/rqqzk1yShfh9c2vt\nC9NcJwAAALNhqseQAgAAwJ5Me5ddAAAAWNa0T2rEfqqqf5GFa7buukzOtUkussszsFpMPqeOS/J3\nrbVbltz/y621v+43MmZVVT0iyQ2tta1V9egkP5/kitbaRzsPjTWgqt7RWnta73GwdlTV/5jklCT/\n0Fq7pPd4ZpVddlehqnpJkqcmeU8Wrt2aLJyh+Iwk72mtvarX2FibquqZrbW39h4Hs6Oqnp/kuUm+\nkOTkJOe01j44eeyzrbWf6zk+Zk9VnZ/ksVnYe2uc5FFJ/p8kp2bhH2Qv6Dc6Zk1V7X4SzUrymCSX\nJklr7UkrPihmXlV9prV2ymT6rCx8D/5Fkl9KcrG/o+8fQboKVdWXkjy4tfaj3e6/e5KrWmv/vM/I\nWKuqantr7fg7nxMWVNWVSf6H1totVbUlyfuTvLO19vqq+lxr7aFdB8jMqaqrkjwkyT2SXJ9kU2vt\n5qo6JMnfttZ+tusAmSlV9dkkW5P8WZKWhSB9dxb+cT+ttY/3Gx2zaun3W1VdluSJrbVvV9W9svA5\n9d/1HeFsssvu6nRHkvsl2bbb/fedPAZ3WVV9fk8PJdm4kmNhTTho1266rbWvV9UoyfuranMW3lNw\nV/2wtfbjJLdW1VdbazcnSWvtB1Xlu4+76ueTnJPk5Ule3Fq7oqp+IEQ5QAdV1VFZ2JPj4Nbat5Ok\ntfb9qrq979BmlyBdnf5Nko9W1ZeTXDO57/gkP5Xk7G6jYtZtTPL4JDfsdn8l+fTKD4cZt6OqTm6t\nXZEkky2l/yrJW5L4F2L2xw+r6tDW2q1JHrbrzqo6IgtbuGCftdbuSPLaqnrf5L874u+9HLgjkvx9\nFv7u1Krqvq2166rqsPjH2P1ml91VqqoOysJB0ktPanTZ5F+P4S6rqjcneWtr7ZPLPHZha+3MDsNi\nRlXVpiS3t9auX+axR7TWPtVhWMywqrpHa+0fl7n/Pknu21q7ssOwWCOq6leSPKK19rLeY2HtqapD\nk2xsrX2t91hmkSAFAACgC9chBQAAoAtBCgAAQBeCFAAAgC4EKQAAAF0IUgAAALoQpAAAAHQhSAEA\nAOhCkAIAANCFIAUAAKALQQoAAEAXghQAAIAuBCkAAABdCFIAAAC6EKQAAAB0IUgBAADoQpACAADQ\nhSAFAACgC0EKAABAF4IUAACALgQpAAAAXQhSAAAAuhCkAAAAdCFIAQAA6EKQAgAA0IUgBQAAoAtB\nCgAAQBeCFAAAgC4EKQAAAF0IUgAAALoQpAAAAHQhSAEAAOhCkAIAANCFIAUAAKALQQoAAEAXghQA\nAIAuBCkAAABdCFIAAAC6EKQAAAB0IUgBAADoQpACAADQhSAFAACgC0EKAABAF4IUAACALgQpAAAA\nXQhSAAAAuhCkAAAAdCFIAQAA6EKQAgAA0IUgBQAAoAtBCgAAQBeCFAAAgC4EKQAAAF0IUgDWhKra\nXFV3VNXMfbdV1Suq6p0d1//AqvpcVd1UVWffybz3r6qbq6pWanwArF0z96UNAHvRDnQBVfW1qnrs\nPsw3dAAf8NgPwP+a5NLW2hGttf+wtxlba9e01g5vrfUcLwBrhCAFgP1TWYjIVbGlsKoOPoCnb05y\n1UDjWBW/DwBmgyAFYFWqqpdU1Tcmu4d+oaoeUwteWlVfqapvV9V7qurIPTz/8Kr6s6r6ZlVdU1V/\nuDSWquqsqto6Wf4/VNXJVfWOJMcnuXhy/+/vZYgfn/z3xsm8v1BVD6iqj1bVd6rqW1X1n6rq8L29\npmXGvaGqLqyq91XVhr38fl4xmeedVXVjkqdX1T2r6m1VtXPymn6/qq65k9/zR5M8JsmfTsb1U1X1\nxKr67GQX3m1V9Yol8//EluGq+lhVvbKqPllV309ywt7WBwBLCVIAVp2qemCS5yZ5WGvt8CSPT/L1\nJM9P8qQkj0xyvyQ3JHnDHhbz9iQ/TPKAJA9NcmqS354s/9eT/G9Jfmuy/Ccl+W5r7WlJtif5V5Pd\nUi/YyzAfNfnv4ZN5/y4LW0vPTzKX5KQkm5LM38lrWvq675nkA0l+kOQ3Wmu37+33NBn3e1trRya5\ncLKuEya3xyd5eu5kV+DW2uOS/L9Jnjt5HV9JckuSf91aOyLJryT53ap60tKn7baY38rC7/beSbbd\nyZgBYJEgBWA1+nGSuyf5mara0Frb3lr7WpLnJHl5a+261tqPkvzvSZ6y+3GcVbUxyROSvKC1dltr\n7TtJXpfkjMksz07y6tbaZ5OktfbfWmtLtyTeld1OF+dtrX21tfbR1trtrbXvJnltkkffyWva5Ygk\nf53ky621Z+/jMZp/01q7eLLu25L8epJXttZuaq1dm+T/vAuvY1Fr7ROttasm0/+Q5D1LXsdy3tZa\n+2Jr7Y7W2o/3Z50ArE973BUIAHpprX21qv5NFrb4Pbiq/jrJi7JwrONfVNUdk1kryY+SbNxtEccn\nuVuS6yZ76dbktn3y+P2TfHXocVfVsUlen4UtuIclOTjJzmVe009X1YeSvLC1dv3k6Q/PwvfyGbsv\ndy923x33fkm+seTn/dpaWVWnJHlVkp/JQkTfPcn77sI4AGCf2EIKwKrUWntPa+2RWYjLJPn3WQjK\nJ7TWjp7cjmqt3au1dt1uT78myW1Jjlky35GttYcsefzEPa16X4e4zH3nJ7kjyYMnu9H+Vn5yC+qu\n17R5yWva5UNJ/l2SSydhuz9j+GYWYnuXzdk/F2Zh1+HjJq/jTdn7VmNn3AVgvwhSAFadyXUxH1NV\nd8/CcaA/yMIur/9XkvOr6vjJfP9st2MbK0kmWx0vSfLaqrr35GRID6iqXcd9/lmS36+qn5ss58Sq\n2hVyO7Jw3Omd+XYW4nNp2N47C8dffq+qjkvy4jt5TXcseW4mx6xemOSjVXXMPoxhd+9Lcm5VHVlV\nm5Ls9Zqie3FYkhtaaz+abC09c7fHnUkXgEEIUgBWo3tkYZfRb2dhq98/S3JuFo6J/GCSS6rqpiSf\nTnLKkuct3VL3tCzsaro1C7vNvi8LJxtKa+39Sf4oyYVVdXOSv0hy9OR5/y7Jv52cqfaFexpga+0H\nk2V8ajLvKUnOS/KwJDcmuTjJf96H17T7cl+Zha2TH97TGYT34rwsbEX+WhaOR33HPj5v9y2c/0uS\nP5z8jv8gyZ/vZX5bRwHYbzXUda0nJ5S4PMk3WmtPqqqjsvAFtjkLZxH8jdbaTYOsDAC4U1X16CTv\nbK0df6czA0AHQ24hPScL/wq9y0uTfKS19qAkl2aZfwUGAABg/RokSCfHqTwxC8fk7PLkLFwDLpP/\nnj7EugBgpVTVmVX1vaq6ecnte1V15Qqt/692W/+u6ZfexeVs2sPruHnyHQ4AXQyyy25VvS8Lx9Ec\nkeRFk112b2itHbVknp2ttaP3uBAAAADWlQO+DmlV/UqSHa21K6pqtJdZly3fqnIyBAAAgDWstbbs\nGdoPOEiTPCLJk6rqiUkOSXLvqnpnkuuramNrbUdVzSX51l4GN8Aw1r75+fnMz8/3HgZriPcUQ/J+\nYmjeUwzNe4qheU/tm6o9Xy3sgI8hba29rLV2fGvtAUnOSHJpa+1fZ+F098+YzPb0LJymHwAAAJJM\n9zqkr0pyalVdneRxk58BAAAgyTC77C5qrX08yccn0zuT/Mshl7/ejUaj3kNgjfGeYkjeTwzNe4qh\neU8xNO+pAzfIWXYPaABVrfcYAAAAmI6qmupJjQCYMePxOOPxeHF617/wjkYj/9oLAHuwZcuWbNu2\nrfcwVq3Nmzfn61//+l16ji2kAOvc5F8tew8DAFY935l7t6ffz962kE7zpEYAAACwR4IUAACALgQp\nAAAAXQhSAAAAuhCkAAAA+2lubkuqamq3ubktvV/iVDnLLsA654yBALBvlvvOrKok0/wenZ3vaWfZ\nBQAAWKeuu+66POUpT8mxxx6bE088MX/yJ3+SJLnsssvyi7/4iznqqKNy3HHH5XnPe15uv/32xee9\n4AUvyMaNG3PEEUfkZ3/2Z7N169YVG7MgBQAAmHGttZx22ml56EMfmuuuuy4f/ehH8/rXvz4f/vCH\ns2HDhrzuda/Lzp078zd/8ze59NJL84Y3vCFJcskll+STn/xkvvKVr+Smm27Ke9/73hxzzDErNm5B\nCgAAMOMuu+yyfOc738nLX/7yHHzwwdmyZUt++7d/O+95z3vy0Ic+NKecckqqKscff3x+53d+Jx//\n+MeTJHe7293yve99L1u3bk1rLQ960IOycePGFRv3hhVbEwAAAFOxbdu2XHvttTn66KOTLGwxveOO\nO/KoRz0qX/7yl/PCF74wl19+eX7wgx/k9ttvz8Me9rAkyWMe85icffbZee5zn5vt27fn137t13LB\nBRfksMMOW5Fx20IKAAAw4+5///vnAQ94QHbu3JmdO3fmhhtuyE033ZSLL744v/d7v5eTTjopX/3q\nV3PjjTfmj/7oj37i5ENnn312Lr/88mzdujVXX311XvOa16zYuAUpAADAjDvllFNy73vfO69+9atz\n22235cc//nGuuuqqXH755bnlllty+OGH59BDD80Xv/jFvPGNb1x83uWXX57PfOYzuf3223PIIYfk\nnve8Zw46aOUyUZACAADsp40bNyepqd0Wln/nDjrooPzlX/5lrrjiipxwwgk59thjc9ZZZ+Xmm2/O\nBRdckHe96105/PDD85znPCdnnHHG4vNuvvnmnHXWWTn66KNzwgkn5D73uU9e/OIXH/DvZV+5DinA\nOuc6pACwb3xn7p3rkAIAADAzBCkAAABdCFIAAAC6EKQAAAB0IUgBAADoQpACAADQhSAFAACgC0EK\nAABAF4IUAABgDTjhhBNy6aWX9h7GXSJIAQAA9tPcprlU1dRuc5vmer/EqdrQewAAAACzase1O5L5\nKS5/fsf0Fr4K2EIKAACwRnzmM5/Jgx/84BxzzDF59rOfnR/+8Ie58cYbc9ppp+XYY4/NMccck9NO\nOy3XXnvt4nPe9ra35cQTT8zhhx+eE088Me9+97sXH3vLW96Sn/7pn84xxxyTJzzhCdm+ffug4xWk\nAAAAa8SFF16YD3/4w/nqV7+aq6++Oq985SvTWsuznvWsXHPNNdm+fXsOPfTQnH322UmSW2+9Neec\nc04+9KEP5eabb86nP/3pnHzyyUmSD37wg3nVq16VD3zgA/n2t7+dRz7ykXnqU5866HgFKQAAwBrx\nvOc9L/e73/1y5JFH5uUvf3ne/e5356ijjsqv/uqv5h73uEfuda975dxzz80nPvGJxeccfPDBufLK\nK3Pbbbdl48aNOemkk5Ikb3rTm3LuuefmgQ98YA466KC89KUvzRVXXJFrrrlmsPEKUgAAgDVi06ZN\ni9ObN2/ON7/5zdx22215znOeky1btuTII4/Mox/96Nx4441preXQQw/Nn//5n+eNb3xj7nvf++a0\n007Ll770pSTJtm3bcs455+Too4/O0UcfnWOOOSZV9RO7+x4oQQoAALBGLN16uW3bttzvfvfLBRdc\nkC9/+cu57LLLcuONNy5uHW2tJUlOPfXUXHLJJbn++uvzoAc9KGeddVaS5P73v3/e9KY3ZefOndm5\nc2duuOGG3HLLLXn4wx8+2HgFKQAAwBrxp3/6p7n22muzc+fOnH/++fnN3/zN3HLLLTnkkENy+OGH\nZ+fOnZmfn1+c/1vf+lYuuuii3Hrrrbnb3e6Www47LAcdtJCJv/u7v5vzzz8/W7duTZLcdNNNef/7\n3z/oeF32BQAAYD9tPG7jVC/NsvG4jfs8b1XlzDPPzC/90i/luuuuy+mnn54/+IM/yA033JAzzzwz\n97nPfXLcccflRS96US666KIkyR133JE//uM/ztOf/vRUVU4++eS88Y1vTJKcfvrp+f73v58zzjgj\n27dvzxFHHJFTTz01T3nKUwZ7fbVrM20vVdV6jwFgPauq+BwGgDvnO3Pv9vT7mdxfyz3HLrsAAAB0\nIUgBAADoQpACAADQhSAFAACgC0EKAABAF4IUAACALlyHFAAAYB9s3rw5VctevYQs/H7uKtchBVjn\nXFMNAJgm1yEFAABg1RGkAAAAdCFIAQAA6EKQAgAA0IUgBQAAoAtBCgAAQBeCFAAAgC4EKQAAAF0I\nUgAAALoQpAAAAHQhSAEAAOhCkAIAANCFIAUAAKALQQoAAEAXghQAAIAuBCkAAABdCFIAAAC6EKQA\nAAB0IUgBAADoQpACAADQhSAFAACgC0EKAABAF4IUAACALgQpAAAAXQhSAAAAujjgIK2qe1TV31XV\n56rqqqo6f3L/UVV1SVVdXVUfqqojDny4AAAArBXVWjvwhVQd2lq7taoOTvKpJC9K8qQk322tvbqq\nXpLkqNbaS5d5bhtiDADsn6qKz2EAYFomf9eo5R4bZJfd1tqtk8l7TJZ5Q5InJ3n75P63Jzl9iHUB\nAACwNgwSpFV1UFV9Lsn1Scatta1JNrbWdiRJa+36JMcOsS4AAADWhg1DLKS1dkeSh1bV4Uk+VFWj\nJLvv/7XH/cHm5+cXp0ejUUaj0RDDAgAAYIWNx+OMx+N9mneQY0h/YoFV/zbJD5I8O8motbajquaS\nfKy1dtIy8zuGFKAjx5ACANM01WNIq+o+u86gW1WHJDk1yeeSXJTkGZPZnp7kgwe6LgAAANaOIXbZ\nvW+St1dVZSFw39la++jkmNL3VtWzkmxL8hsDrAsAAIA1YvBddu/yAOyyC9CVXXYBgGma+mVfAAAA\n4K4SpAAAAHQhSAEAAOhCkAIAANCFIAUAAKALQQoAAEAXghQAAIAuBCkAAABdCFIAAAC6EKQAAAB0\nIUgBAADoQpACAADQhSAFAACgC0EKAABAF4IUAACALgQpAAAAXQhSAAAAuhCkAAAAdCFIAQAA6EKQ\nAgAA0IUgBQAAoAtBCgAAQBeCFAAAgC4EKQAAAF0IUgAAALoQpAAAAHQhSAEAAOhCkAIAANCFIAUA\nAKALQQoAAEAXG3oPAAAAYJrG43HG4/Hi9Gg0SpKMRqPFafqo1lrfAVS13mMAWM+qKj6HAVgvfO+t\nvMnvvJZ7zC67AAAAdCFIAQAA6EKQAgAA0IUgBQAAoAtBCgAAQBeCFAAAgC4EKQAAAF0IUgAAALoQ\npAAAAHQhSAEAAOhCkAIAANCFIAUAAKALQQoAAEAXghQAAIAuBCkAAABdCFIAAAC6EKQAAAB0IUgB\nAADoQpACAADQhSAFAACgC0EKAABAF4IUAACALgQpAAAAXQhSAAAAuhCkAAAAdCFIAQAA6EKQAgAA\n0IUgBQAAoAtBCgAAQBeCFAAAgC4EKQAAAF0IUgAAALoQpAAAAHQhSAEAAOhCkAIAANCFIAUAAKAL\nQQoAAEAXghQAAIAuDjhIq2pTVV1aVVdV1ZVV9fzJ/UdV1SVVdXVVfaiqjjjw4QIAALBWVGvtwBZQ\nNZdkrrV2RVUdluTvkzw5yTOTfLe19uqqekmSo1prL13m+e1AxwDA/quq+BwGYL3wvbfyJr/zWu6x\nA95C2lq7vrV2xWT6liRfSLIpC1H69slsb09y+oGuCwAAgLVjw5ALq6otSU5O8rdJNrbWdiQL0VpV\nxw65rtVsPB5nPB4vTo9GoyTJaDRanAYAAFjvDniX3cUFLeyuO07yh621D1bVztba0Use/25r7Zhl\nntde8YpXLP681qLNLgHAaudzCoD1xPfe9C3dQJck55133h532R0kSKtqQ5K/TPJfWmuvn9z3hSSj\n1tqOyXGmH2utnbTMc9f0MaTe8MBq53MKgPXE997Km+oxpBNvSbJ1V4xOXJTkGZPppyf54EDrAgAA\nYA0Y4iy7j0jyiSRXJmmT28uSfCbJe5PcP8m2JL/RWrtxmefbQgrQkc8pANYT33srb29bSAc7hnR/\nCVKAvnxOAbCe+N5beSuxyy4AAADcJYIUAACALgQpAAAAXQhSAAAAuhCkAAAAdCFIAQAA6EKQAgAA\n0IUgBQAAoAtBCgAAQBeCFAAAgC4EKQAAAF0IUgAAALoQpAAAAHQhSAEAAOhCkAIAANCFIAUAAFa9\nubktqaoDviUZZDlVlbm5LX1/KWtAtdb6DqCq9R7DNFVV1vLrA2afzykAZsFCTA7xfTXUchaW5Tv0\nzk3+rlHLPWYLKQAAAF0IUgAAALoQpAAAAHQhSAEAAOhCkAIAANCFIAUAAKALQQoAAEAXghQAAIAu\nBCkAAABdCFIAAAC6EKQAAAB0IUgBAADoQpACAADQhSAFAACgC0EKAABAF4IUAACALgQpAAAAXQhS\nAAAAuhCkAAAAdCFIAQAA6EKQAgAA0IUgBZhBc3NbUlWD3JIMspy5uS19fykAwMyp1lrfAVS13mOY\npqrKWn59QB8LITnUZ8tQy/J5t56Nx+OMx+PF6dFolCQZjUaL0wAHYrjvvmG/Q3333blJE9Wyj/X+\nBQpSgLtOkLKa+e4DpkGQzq69BalddgEAAOhCkAIAANCFIAUAAKALQQoAAEAXghQAAIAuBCkAAABd\nCFIAAAC6EKQAAAB0IUgBAADoQpACAADQhSAFAACgC0EKAABAF4IUAACALgQpAAAAXQhSAAAAuhCk\nAAAAdCFIAQAA6EKQAgAA0IUgBQAAoAtBCgAAQBeCFAAAgC4EKQAAAF0IUgAAALoQpAAAAHQhSAEA\nAOhiQ+8BAABMw3g8zng8XpwejUZJktFotDgNQF/VWus7gKrWewzTVP9fe/cbYtta1wH8+7tXjCwQ\nKZyxc/QewSiJzEwuhpG7Qr39oWsQor3oz4veVGQQoeaL5lUYBCFFrzSxwC4pldeystQt2D8lE/9d\n9RJ6u/fmGXxhiCmZ9/56MfuM28Occ2fOrJln7T2fD2zO2mvPPPt3Zh6eNd/9rLWeqmzz/w8Yo6qS\nTDW2TNWW8Y4Dczz2zbEm4GSmO/ZNeww1tjy21RhcR73mlF0AAACGEEgBAAAYYpJAWlVvqKr9qvrw\n2r4nVdU7q+qTVfV3VfXEKd4LAACA7TDVDOkbk7z4un2vSvIP3f0dSd6d5NUTvRcAAABbYJJA2t3v\nS/L563bfneRNq+03JXnJFO8FAADAdjjLa0if3N37SdLdV5M8+QzfCwAAgA1znuuQ3vB+yHt7e4fb\n1gYDAADYXOvrQD+WydYhrao7kry9u5+1en5fkkV371fVbpL3dPczj/g+65ACnJB1SJmzOR775lgT\ncDLWId1c57UOaa0e19yb5OdX2z+X5G0TvhcAAAAbbpIZ0qp6c5JFkm9Jsp/kt5L8ZZK3JHlqkgeS\nvLS7//uI7zVDCnBCZkiZszke++ZYE3AyZkg3181mSCc7ZfdWCaQAJyeQMmdzPPbNsSbgZATSzSWQ\nDuQACJwFgZQ5m+Oxb441ASdzumPfcvW4tr1YbS/Wtm+FseU4BNKBHACBsyCQMmdzPPbNsSbgZKY9\n9k3F2HIcNwuk57nsC3CL1m+dvVwuD5dGskwSAACbzAzpGfOJLFPTp0jMkDJvcxyn5lgTcDJmAA/B\nWAAAC91JREFUSDfXeS37AgAAAMcmkAIAADCEQAoAAMAQAikAAABDCKQAAAAMIZDewO7ulVTVqR9J\nJmmnqrK7e2XsDwUAAGBCln25geluKz3t0gxz/FlxvixdQGLZF+ZtjuPUHGsCTsayL5vLsi8AAADM\njkAKAADAEAIpAAAAQwikAAAADCGQAgAAMIRACgAAwBACKQAAAEMIpAAAAAwhkAIAADDE40YXAAAA\ncNEsl8ssl8vD7cVikSRZLBaH2xdBdffYAqp6dA1HqaokU9Q1VTsHbc3xZ8X5qtIPmHKMSqYbp/RN\nDsxxnJpjTcDJTHvsm8o0Y8u2j1Gr/18d9ZoZUgAAOAYzWjA9M6Q3sG0zpAbQ7bHtn6BxPGZImbM5\njlNzrInNpk+dPzOkm+tmM6QC6Q1sWyD9ula2vMNvO78/EoGUeZvjODXHmths+tT5E0g3180Cqbvs\nAgAAMIRACgDM0u7l3VTVJI8kk7Sze3l38E8FYLs4ZfcGnLLLXPn9kThll3mbapyqqmTv9PUkOWhn\nirb2op+TxPF4BKfsbi6n7AIAADA7ln0BAGBWrA4AF4dACgDArKwHz6o6DKfA9nHKLgAAAEMIpAAA\nAAwhkAIAADCEQAoAAMAQAikAAABDCKRwDnZ3r6SqJnkkmayt3d0rY38wAABcaJZ9gXOwv/9Akp6o\ntZqsrf39mqQdAAC4FWZIAQAAGEIgBQAAYAin7AIA2d29srq8YBrXrnkHgJsRSAGAmV7rLtQCM3f7\ndB/ATdXOzqWdXH3o6iRtnQeBFAAA4FY8kmRvgnb2Jmonyf7e/jQNnRPXkAIAcCHsXt6d3TJsu5d3\nB/9UYCwzpAAAXAj7D+9PNgs11YzWps1mwdTMkAIAADCEQAoAAMAQTtkFAGByUy4lZBkh2F4CKQAA\nk5tuKaGplhG61hYwJ07ZBQAAYAgzpADMwnK5zHK5PNxeLBZJksVicbgNAGwXgRSAWVgPnlV1GE4B\ngO0lkAK3xGwWAACnJZBuktunu8vcVO3sXNrJ1YeuTtIWm8VsFgAApyWQbpJHkuxN0M7eRO0k2d/b\nn6YhAADgwnGXXQAAAIYQSAEAABhCIAUAAGAIgRQAAIAhBFIAAACGEEgBAAAYQiAFAABgCOuQAgAA\nnLdPJ/nMavuOJO9ZbV9J8vQB9QwikAIwjduTqpqsuana2rm0k6sPXZ2kLQCYzNNzoYLnjQikAEzj\nkSR7E7W1N11b+3v70zQEYEYLJieQwkU24YyW2SxgdoSHDbZcPZLkBfnaJ1SL1WMQM1owOYEULrKp\nZrT2JmonZrOACQkPG2yRocETODfusgsAAMAQAikAAABDCKQAAAAMceaBtKruqqpPVNWnquqVZ/1+\nAAAAbIYzvalRVd2W5A+S/EiS/0rygap6W3d/4izfF7bPMrO82yAAAJzCWd9l984k93f3A0lSVfck\nuTuJQAonsojgCQDAtjnrU3YvJXlw7flDq30AAABccLNYh3Rvb+9we7FYZLFYDKvlmp2dO7K/XxO1\nNk07tz3+tjy69+gkbU21ZuTOpZ1pGtpy0/an6ehTm2v6PnX6tibtT4k+dc4uRJ+agP50fHM89ulT\nm02fOp459Knlcpnlcnmsr63uPrNCqup5Sfa6+67V81cl6e7+nbWv6bOsYbSqytz+f3Osic2mT222\nOf7+5lgTx+f3B8C61XHhyE8TzjqQ3p7kkzm4qdFnk7w/ycu7+761rxFIz9kca2Kz6VObbS6/v/VP\nU5fL5eHZMnM5c4bjm0ufAmAehgXS1ZvfleR1Obhe9Q3d/drrXhdIz9kca2Kz6VObze+PqelTAKwb\nGkgfi0B6/uZYE5vHbNb2MCYwNX0KgHUC6UBzPCjPsSZgHGMCU9OnAFh3s0B61su+AAAAwJEEUgAA\nAIYQSAEAABjCNaRnbC7X0bgBDXAjcxmn2B76FADr3NRoIAdlYO6MU0xNnwJgnZsaAQAAMDsCKQAA\nAEMIpAAAAAwhkAIAADCEQAoAAMAQAikAAABDCKQAAAAMIZACAAAwhEAKAADAEAIpAAAAQwikAAAA\nDCGQAgAAMIRACgAAwBACKQAAAEMIpAAAAAwhkAIAADCEQAoAAMAQAikAAABDVHePLaCqR9cwteVy\nmeVyebi9WCySJIvF4nAbYC6qKts2DjOWPgXAutVxoY58bfQBYxsDKcAmER6Ymj4FwLqbBVKn7AIA\nADCEQAoAAMAQAikAAABDCKQAAAAMIZACAAAwhEAKAADAEAIpAAAAQwikAAAADCGQAgAAMIRACgAA\nwBACKQAAAEMIpAAAAAwhkAIAADCEQAoAAMAQAikAAABDVHePLaCqR9cAcJFVVYzDnNZyucxyuTzc\nXiwWSZLFYnG4DcDFtPpbo458bfQfIQIpwFgCKQBwlm4WSJ2yCwAAwBACKQAAAEMIpAAAAAwhkAIA\nADCEQAoAAMAQAikAAABDCKQAAAAMIZACAAAwhEAKAADAEAIpAAAAQwikAAAADCGQAgAAMIRACgAA\nwBACKQAAAEMIpAAAAAwhkAIAADCEQAoAAMAQAikAAABDCKQAAAAMIZACAAAwhEAKAADAEAIpAAAA\nQwikAAAADCGQAgAAMIRACgAAwBACKQAAAEOcKpBW1U9X1Uer6pGqes51r726qu6vqvuq6kWnK5Mk\nWS6Xo0tgy+hTTEl/Ymr6FFPTp5iaPnV6p50h/UiSn0ry3vWdVfXMJC9N8swkP5rkD6uqTvleF54O\nz9T0KaakPzE1fYqp6VNMTZ86vVMF0u7+ZHffn+T6sHl3knu6+6vd/Zkk9ye58zTvBQAAwHY5q2tI\nLyV5cO35w6t9AAAAkCSp7r75F1T9fZKd9V1JOslruvvtq695T5Jf7+4Prp7/fpJ/7u43r56/Psk7\nuvvPj2j/5gUAAACw0br7yEs4H3eMb3zhLbzfw0meuvb88mrfsQsDAABgu015yu56sLw3ycuq6vFV\n9fQkz0jy/gnfCwAAgA132mVfXlJVDyZ5XpK/qqq/SZLu/niSP0vy8STvSPJL/VjnBgMAAHChPOY1\npAAAAHAWzuouuwAAAHBTAikAAABDPOZddhmjqr4zyd352vqtDye5t7vvG1cVwNesxqlLSf61u7+4\ntv+u7v7bcZWxqarq+Uk+390fr6oXJHlukg9197sGl8YWqKo/7u6fHV0H26OqfiDJnUk+2t3vHF3P\npnIN6QxV1SuTvDzJPUkeWu2+nORlSe7p7teOqo3tVFW/0N1vHF0Hm6OqfjXJLye5L8mzk7yiu9+2\neu2D3f2ckfWxearqt5P8cA7O3lom+cEkf53khTn4QPZ3x1XHpqmqe6/fleSHkrw7Sbr7J8+9KDZe\nVb2/u+9cbf9iDo6Df5HkRUne7m/0WyOQzlBVfSrJd3X3/123//FJPtbd3z6mMrZVVf1ndz9tdB1s\njqr6SJLv7+4vVtWVJG9N8ifd/bqq+vfu/t6hBbJxqupjSZ6V5BuSXE1yubu/UFXfmORfuvt7hhbI\nRqmqD+ZgtYfXJ+kcBNI/zcGH++nu946rjk21fnyrqg8k+bHu/lxVfVMOxqnvHlvhZnLK7jw9muTb\nkjxw3f6nrF6DE6uqD9/opSQ751kLW+G2a6fpdvdnqmqR5K1VdUe+fl1qOK6vdPcjSb5UVf/R3V9I\nku7+clU59nFSz03yiiSvSfIb3f2hqvqyIMop3VZVT8rBmRy3d/fnkqS7/6eqvjq2tM0lkM7TryV5\nV1Xdn+TB1b6nJXlGkl8ZVhWbbifJi5N8/rr9leSfzr8cNtx+VT27uz+UJKuZ0p9I8kdJfELMrfhK\nVT2hu7+U5Puu7ayqJ+ZghguOrbsfTfJ7VfWW1b/78Xcvp/fEJP+Wg7+duqqe0t2frapvjg9jb5lT\ndmeqqm7LwUXS6zc1+sDq02M4sap6Q5I3dvf7jnjtzd39MwPKYkNV1eUkX+3uq0e89vzu/scBZbHB\nquobuvt/j9j/rUme0t0fGVAWW6KqfjzJ87v7N0fXwvapqick2enuT4+uZRMJpAAAAAxhHVIAAACG\nEEgBAAAYQiAFAABgCIEUAACAIf4f23eMRvyrGDIAAAAASUVORK5CYII=\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f2fe9b29bd0>"
+ "<matplotlib.figure.Figure at 0x7fd620dcb450>"
]
},
"metadata": {},
@@ -239,7 +598,10 @@
" fig_id = 0\n",
" for fname in functions:\n",
" logging.info(\"Plotting stats for [%s] function\", fname)\n",
- " axes = pltaxes[fig_id]\n",
+ " if fcount > 1:\n",
+ " axes = pltaxes[fig_id]\n",
+ " else:\n",
+ " axes = pltaxes\n",
" plot_stats(df, fname, axes)\n",
" fig_id = fig_id + 1\n",
" \n",