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201907_uclamp_v12_lkml_junor2.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<font size=\"9\">UtilClamp v12 - JunoR2 - Mainline Validation</font><br>\n",
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Table of Contents\n",
"* [CFS Tasks Clamping](#CFS-Tasks-Clamping)\n",
"* [RT Tasks Clamping](#RT-Tasks-Clamping)\n",
"* [Per-Task API](#Per-Task-API)\n",
"* [CGroups v1 - Delegation Model](#CGroups-v1---Delegation-Model)\n",
"* [CGroups v2 - Delegation Model](#CGroups-v2---Delegation-Model)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:06:50,334 INFO : root : Using LISA logging configuration:\n",
"2019-07-18 19:06:50,336 INFO : root : /data/Code/lisa/logging.conf\n"
]
}
],
"source": [
"import logging\n",
"from lisa.utils import setup_logging\n",
"setup_logging()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"# Generate plots inline\n",
"%pylab inline\n",
"%config IPCompleter.use_jedi = False"
]
},
{
"cell_type": "markdown",
"metadata": {
"toc-hr-collapsed": true
},
"source": [
"# Utility Functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test environment setup"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:06:50,849 INFO : root : Generating grammar tables from /usr/lib/python3.5/lib2to3/Grammar.txt\n",
"2019-07-18 19:06:50,884 INFO : root : Generating grammar tables from /usr/lib/python3.5/lib2to3/PatternGrammar.txt\n",
"2019-07-18 19:06:51,324 INFO : lisa.target.Target : Creating result directory: /data/Code/lisa/results/Target-juno-20190718_190651.324480\n",
"2019-07-18 19:06:51,326 INFO : lisa.target.Target : linux juno target connection settings:\n",
"2019-07-18 19:06:51,327 INFO : lisa.target.Target : host : 192.168.90.1\n",
"2019-07-18 19:06:51,328 INFO : lisa.target.Target : port : 22\n",
"2019-07-18 19:06:51,329 INFO : lisa.target.Target : username : root\n",
"2019-07-18 19:06:51,330 INFO : lisa.target.Target : password : None\n",
"2019-07-18 19:06:51,340 INFO : lisa.target.Target : Devlib modules to load: bl, cgroups, cpufreq, cpuidle, devfreq, fastboot, gem5stats, gpufreq, hotplug, hwmon, mbed-fan, odroidxu3-fan, sched, thermal\n",
"2019-07-18 19:06:55,210 WARNING : LinuxTarget : Module devfreq is not supported by the target\n",
"2019-07-18 19:06:55,213 WARNING : LinuxTarget : Module fastboot is not supported by the target\n",
"2019-07-18 19:06:55,214 WARNING : LinuxTarget : Module gem5stats is not supported by the target\n",
"2019-07-18 19:06:55,374 WARNING : LinuxTarget : Module gpufreq is not supported by the target\n",
"2019-07-18 19:07:00,384 WARNING : LinuxTarget : Module odroidxu3-fan is not supported by the target\n",
"2019-07-18 19:07:02,943 INFO : CGroups : Available controllers:\n",
"2019-07-18 19:07:03,757 INFO : CGroups : cpuset : /home/root/devlib-target/cgroups/devlib_cgh0\n",
"2019-07-18 19:07:04,239 INFO : CGroups : cpu : /home/root/devlib-target/cgroups/devlib_cgh0\n",
"2019-07-18 19:07:04,724 INFO : CGroups : cpuacct : /home/root/devlib-target/cgroups/devlib_cgh0\n",
"2019-07-18 19:07:05,209 INFO : CGroups : blkio : /home/root/devlib-target/cgroups/devlib_cgh0\n",
"2019-07-18 19:07:05,695 INFO : CGroups : memory : /home/root/devlib-target/cgroups/devlib_cgh0\n",
"2019-07-18 19:07:06,179 INFO : CGroups : devices : /home/root/devlib-target/cgroups/devlib_cgh0\n",
"2019-07-18 19:07:06,665 INFO : CGroups : perf_event : /home/root/devlib-target/cgroups/devlib_cgh0\n",
"2019-07-18 19:07:07,151 INFO : CGroups : pids : /home/root/devlib-target/cgroups/devlib_cgh0\n",
"2019-07-18 19:07:07,313 WARNING : lisa.target.Target : Failed to initialized \"devfreq\" devlib Module\n",
"2019-07-18 19:07:07,315 WARNING : lisa.target.Target : Failed to initialized \"fastboot\" devlib Module\n",
"2019-07-18 19:07:07,316 WARNING : lisa.target.Target : Failed to initialized \"gem5stats\" devlib Module\n",
"2019-07-18 19:07:07,318 WARNING : lisa.target.Target : Failed to initialized \"gpufreq\" devlib Module\n",
"2019-07-18 19:07:07,320 WARNING : lisa.target.Target : Failed to initialized \"mbed-fan\" devlib Module\n",
"2019-07-18 19:07:07,322 WARNING : lisa.target.Target : Failed to initialized \"odroidxu3-fan\" devlib Module\n",
"2019-07-18 19:07:07,324 INFO : lisa.target.Target : Tools to install: ['rt-app', 'rt-app_uclamp']\n",
"2019-07-18 19:07:07,978 INFO : lisa.platforms.platinfo.PlatformInfo : Attempting to read energy model from target\n",
"2019-07-18 19:07:08,615 ERROR : lisa.platforms.platinfo.PlatformInfo : Couldn't read target energy model: Unable to probe for energy model on target.\n",
"2019-07-18 19:07:11,744 INFO : lisa.target.Target : Effective platform information:\n",
"|- abi from target (str): arm64\n",
"|- cpu-capacities from target (dict): {0: 810, 1: 1024, 2: 1024, 3: 810}\n",
"|- cpus-count from target (int): 4\n",
"|- freq-domains from target (list): [[0, 3], [1, 2]]\n",
"|- freqs from target (dict): {0: [450000, 800000, 950000], 1: [600000, 1000000, 1200000], 2: [600000, 1000000, 1200000], 3: [450000, 800000, 950000]}\n",
"+- kernel:\n",
" |- config from target (TypedKernelConfig): <kernel config>\n",
" |- version from target (KernelVersion): 5.2.0-rc6-00063-g15d5f1d18c2a 53 SMP PREEMPT Thu Jul 18 19:03:34 BST 2019\n",
"|- name from target-conf (str): juno\n",
"|- os from target (str): linux\n",
"+- rtapp:\n",
" |- calib from target (DeferredValue): <lazy value of RTA.get_cpu_calibrations>\n",
"|- capacity-classes from target(platform-info/cpu-capacities) (list): [[0, 3], [1, 2]]\n"
]
}
],
"source": [
"from lisa.target import Target, TargetConf\n",
"\n",
"target = Target(\n",
" name='juno',\n",
" kind='linux',\n",
" host='192.168.90.1',\n",
" username='root',\n",
" tools = ['rt-app', 'rt-app_uclamp'],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:07:12,155 INFO : root : CPUFreq governors:\n",
"{'2': 'schedutil', '1': 'schedutil', '0': 'schedutil', '3': 'schedutil'}\n"
]
}
],
"source": [
"target.cpufreq.set_all_governors('schedutil');\n",
"logging.info(\"CPUFreq governors:\\n%s\", target.cpufreq.get_all_governors())\n",
"target.write_value('/sys/devices/system/cpu/cpufreq/policy0/schedutil/rate_limit_us', 5000)\n",
"target.write_value('/sys/devices/system/cpu/cpufreq/policy1/schedutil/rate_limit_us', 5000)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|- abi from target (str): arm64\n",
"|- cpu-capacities from target (dict): {0: 810, 1: 1024, 2: 1024, 3: 810}\n",
"|- cpus-count from target (int): 4\n",
"|- freq-domains from target (list): [[0, 3], [1, 2]]\n",
"|- freqs from target (dict): {0: [450000, 800000, 950000], 1: [600000, 1000000, 1200000], 2: [600000, 1000000, 1200000], 3: [450000, 800000, 950000]}\n",
"+- kernel:\n",
" |- config from target (TypedKernelConfig): <kernel config>\n",
" |- version from target (KernelVersion): 5.2.0-rc6-00063-g15d5f1d18c2a 53 SMP PREEMPT Thu Jul 18 19:03:34 BST 2019\n",
"|- name from target-conf (str): juno\n",
"|- os from target (str): linux\n",
"+- rtapp:\n",
" |- calib from target (DeferredValue): <lazy value of RTA.get_cpu_calibrations>\n",
"|- capacity-classes from target(platform-info/cpu-capacities) (list): [[0, 3], [1, 2]]\n"
]
}
],
"source": [
"print(target.plat_info)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:08:38,344 INFO : root : Avaialble capacities at each frequency:\n",
"2019-07-18 19:08:38,345 INFO : root : LITTLEs: {450000: 384, 800000: 682, 950000: 810}\n",
"2019-07-18 19:08:38,347 INFO : root : bigs: {600000: 512, 1000000: 853, 1200000: 1024}\n"
]
}
],
"source": [
"# Maximum available frequency\n",
"big_cpus = [cpu for cpu,cap\n",
" in target.plat_info['cpu-capacities'].items()\n",
" if cap==1024]\n",
"big_cpu = big_cpus[-1]\n",
"big_max_freq = target.cpufreq.list_frequencies(big_cpu)[-1]\n",
"\n",
"# Compute capacity levels for each CPU and frequency\n",
"caps = {}\n",
"for cpu in target.list_online_cpus():\n",
" caps[cpu] = {f: int(1024. * f/big_max_freq) for f in target.cpufreq.list_frequencies(cpu)}\n",
"logging.info(\"Avaialble capacities at each frequency:\")\n",
"logging.info(\"LITTLEs: %s\", caps[target.core_cpus(target.little_core)[0]])\n",
"logging.info(\" bigs: %s\", caps[target.core_cpus(target.big_core)[0]])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:08:38,709 INFO : lisa.wlgen.rta.RTA : Creating target's run_dir: /home/root/devlib-target/lisa/wlgen/test_ramp_20190718_190838_qjGhMG\n",
"2019-07-18 19:08:38,712 INFO : lisa.target.Target : Creating result directory: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_190838.711084\n",
"2019-07-18 19:08:39,504 INFO : lisa.wlgen.rta.RTA : Calibration value: 218\n",
"2019-07-18 19:08:39,508 INFO : lisa.wlgen.rta.RTA : Default policy: SCHED_OTHER\n",
"2019-07-18 19:08:39,509 INFO : lisa.wlgen.rta.RTA : ------------------------\n",
"2019-07-18 19:08:39,511 INFO : lisa.wlgen.rta.RTA : task [test_task], sched: OTHER\n",
"2019-07-18 19:08:39,513 INFO : lisa.wlgen.rta.RTA : | start delay: 0.000000 [s]\n",
"2019-07-18 19:08:39,514 INFO : lisa.wlgen.rta.RTA : | loops count: 1\n",
"2019-07-18 19:08:39,515 INFO : lisa.wlgen.rta.RTA : + phase_000001\n",
"2019-07-18 19:08:39,517 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:08:39,518 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 10 %\n",
"2019-07-18 19:08:39,519 INFO : lisa.wlgen.rta.Phase : | run_time 1600 [us], sleep_time 14400 [us]\n",
"2019-07-18 19:08:39,521 INFO : lisa.wlgen.rta.RTA : + phase_000002\n",
"2019-07-18 19:08:39,522 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:08:39,523 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 20 %\n",
"2019-07-18 19:08:39,525 INFO : lisa.wlgen.rta.Phase : | run_time 3200 [us], sleep_time 12800 [us]\n",
"2019-07-18 19:08:39,526 INFO : lisa.wlgen.rta.RTA : + phase_000003\n",
"2019-07-18 19:08:39,527 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:08:39,528 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 30 %\n",
"2019-07-18 19:08:39,529 INFO : lisa.wlgen.rta.Phase : | run_time 4800 [us], sleep_time 11200 [us]\n",
"2019-07-18 19:08:39,531 INFO : lisa.wlgen.rta.RTA : + phase_000004\n",
"2019-07-18 19:08:39,532 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:08:39,533 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 40 %\n",
"2019-07-18 19:08:39,534 INFO : lisa.wlgen.rta.Phase : | run_time 6400 [us], sleep_time 9600 [us]\n",
"2019-07-18 19:08:39,535 INFO : lisa.wlgen.rta.RTA : + phase_000005\n",
"2019-07-18 19:08:39,536 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:08:39,537 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 50 %\n",
"2019-07-18 19:08:39,539 INFO : lisa.wlgen.rta.Phase : | run_time 8000 [us], sleep_time 8000 [us]\n",
"2019-07-18 19:08:39,540 INFO : lisa.wlgen.rta.RTA : + phase_000006\n",
"2019-07-18 19:08:39,541 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:08:39,543 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 60 %\n",
"2019-07-18 19:08:39,544 INFO : lisa.wlgen.rta.Phase : | run_time 9600 [us], sleep_time 6400 [us]\n",
"2019-07-18 19:08:39,545 INFO : lisa.wlgen.rta.RTA : + phase_000007\n",
"2019-07-18 19:08:39,546 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:08:39,547 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 70 %\n",
"2019-07-18 19:08:39,548 INFO : lisa.wlgen.rta.Phase : | run_time 11200 [us], sleep_time 4800 [us]\n"
]
}
],
"source": [
"from lisa.wlgen.rta import RTA, Ramp\n",
"\n",
"cpu = [cpu for cpu,cap\n",
" in target.plat_info['cpu-capacities'].items()\n",
" if cap==1024][-1]\n",
"\n",
"rtapp_profile = {\n",
" 'test_task': Ramp(period_ms=16, start_pct=10, end_pct=70,\n",
" delta_pct=10, cpus=[big_cpu], sched_policy='OTHER')\n",
"}\n",
"\n",
"def provide_calibration(calibration):\n",
" target.plat_info[\"rtapp\"].add_src(\"user\", {\"calib\" : calibration})\n",
"# Uncomment if you want to use this\n",
"provide_calibration({0: 580, 1: 218, 2: 218, 3: 579, 4: 579, 5: 579}) \n",
"\n",
"rta = RTA.by_profile(target, \"test_ramp\", rtapp_profile, logstats=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:08:40,351 WARNING : LinuxTarget : Event [cpu_util] not available for tracing\n",
"2019-07-18 19:08:40,355 WARNING : LinuxTarget : Event [uclamp_task_update] not available for tracing\n",
"2019-07-18 19:08:40,357 WARNING : LinuxTarget : Event [update_util_se] not available for tracing\n",
"2019-07-18 19:08:40,360 WARNING : LinuxTarget : Event [update_util_rq] not available for tracing\n",
"2019-07-18 19:08:40,362 WARNING : LinuxTarget : Event [sugov_next_freq_shared] not available for tracing\n"
]
}
],
"source": [
"from lisa.trace import Trace, FtraceCollector\n",
"import os\n",
"\n",
"events = [\n",
" \"sched_switch\",\n",
" \"sched_wakeup\",\n",
" \"cpu_frequency\",\n",
" \"cpu_idle\",\n",
" \"cpu_util\",\n",
" \"uclamp_task_update\",\n",
" \"update_util_se\",\n",
" \"update_util_rq\",\n",
" \"sugov_next_freq_shared\",\n",
" \"uclamp_se\",\n",
" \"uclamp_cfs\",\n",
" \"uclamp_rt\",\n",
"]\n",
"ftrace_coll = FtraceCollector(target, events=events, buffer_size=10240)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CGroups setup"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"cpu_ctr = target.cgroups.controller('cpu')\n",
"cpuset_ctr = target.cgroups.controller('cpuset')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"def checkGroups(cgroups):\n",
" data = []\n",
" for cg_name in cgroups:\n",
" cg = cpu_ctr.cgroup(cg_name)\n",
" cg = cg.get()\n",
" rmin = cg.get('uclamp.min', None)\n",
" if rmin is None:\n",
" rmin = float(target.execute('cat /proc/sys/kernel/sched_util_clamp_min').strip()) / 10.\n",
" if rmin == 1024:\n",
" rmin = 'max'\n",
" emin = cg['uclamp.min.effective']\n",
" rmax = cg.get('uclamp.max', None)\n",
" if rmax is None:\n",
" rmax = float(target.execute('cat /proc/sys/kernel/sched_util_clamp_max').strip()) / 10.\n",
" if rmax == 1024:\n",
" rmax = 'max'\n",
" emax = cg['uclamp.max.effective'] \n",
" data.append((cg_name, rmin, emin, rmax, emax))\n",
" df = pd.DataFrame(data, columns=['cg_name', 'uclamp.min', 'uclamp.min.effective', 'uclamp.max', 'uclamp.max.effective'])\n",
" df.set_index('cg_name', inplace=True)\n",
" return df\n",
"\n",
"def assertValue(attrib, value):\n",
" attrib = attrib.split('/')\n",
" task_group = '/'.join(attrib[:-1])\n",
" attrib = attrib[-1]\n",
" \n",
" uclamp = df[task_group:][attrib].values[0]\n",
" if uclamp == 'max':\n",
" uclamp = 100\n",
" uclamp = float(uclamp)\n",
" \n",
" if value == 'max':\n",
" value = 100.0\n",
"\n",
" logging.info(\"Assert %s/%-24s == %6.2f: %8s (measured: %6.2f%%)\",\n",
" task_group, attrib, value,\n",
" \"PASS\" if value == uclamp else \"FAILED\",\n",
" uclamp)\n",
" \n",
"def assertAttribute(attrib, op, value):\n",
" value = float(value)\n",
" values = [100.0 if v == 'max' else float(v) for v in df[attrib]]\n",
" \n",
" result = op(values)\n",
"\n",
" logging.info(\"Assert %s(%-32s) == %6.2f: %8s (measured: %6.2f%%)\",\n",
" op, attrib, value,\n",
" \"PASS\" if value == result else \"FAILED\",\n",
" result)\n",
" \n",
"def assertAttributeValues(attrib, allowed):\n",
" values = [100.0 if v == 'max' else float(v) for v in df[attrib]]\n",
" \n",
" passed = True\n",
" for v in values:\n",
" if v not in allowed:\n",
" passed = False\n",
" break\n",
"\n",
" logging.info(\"Assert %-32s in %-16s: %8s\",\n",
" attrib, allowed,\n",
" \"PASS\" if passed else \"FAILED\")\n",
"\n",
"def assertClamps(task_group, op, value):\n",
" value = float(value)\n",
" values = [100.0 if v == 'max' else float(v) for v in df[task_group:].values[0]]\n",
" \n",
" result = op(values)\n",
"\n",
" logging.info(\"Assert %s(%-32s) == %6.2f: %8s (measured: %6.2f%%)\",\n",
" op, task_group, value,\n",
" \"PASS\" if value == result else \"FAILED\",\n",
" result)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tests Functions"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import trappy\n",
"\n",
"def test(cgname):\n",
" \n",
" logging.info('#### Start RTApp execution')\n",
" with ftrace_coll:\n",
" rta.run(cgroup=cgname)\n",
"\n",
" trace_file = os.path.join(rta.res_dir, 'trace.dat')\n",
" logging.info('#### Save FTrace: %s', trace_file)\n",
" ftrace_coll.get_trace(trace_file)\n",
"\n",
" plat_info_file = os.path.join(rta.res_dir, 'plat_info.yml')\n",
" logging.info('#### Save platform description: %s', plat_info_file)\n",
" target.plat_info.to_yaml_map(plat_info_file)\n",
" logging.info('LITTLE cluster max capacity: %d',\n",
" min(target.plat_info['cpu-capacities'].values()))\n",
"\n",
" trappy.ftrace.FTrace.disable_cache = True\n",
" trace = Trace(trace_file, plat_info=target.plat_info, events=events)\n",
"\n",
" return trace"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot Functions"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from lisa.trace import TraceView\n",
"from trappy.utils import listify\n",
"\n",
"def get_view(trace, tasks):\n",
" tasks = listify(tasks)\n",
" pids = [trace.get_task_by_name(task)[0] for task in tasks]\n",
" \n",
" _df = trace.df_events('sched_switch')\n",
" t_start = _df[_df.next_pid.isin(pids)].index[0]-0.5\n",
" t_end = _df[_df.prev_pid.isin(pids)].index[-1]+0.5\n",
" \n",
" return TraceView(trace, (t_start, t_end))\n",
"\n",
"def getData(trace, rta):\n",
" df = {}\n",
" \n",
" task = rta.tasks[0]\n",
" logfile = os.path.join(rta.res_dir, \"rt-app-{name}-0.log\".format(name=task))\n",
" \n",
" tv = get_view(trace, task)\n",
" \n",
" # Get task's PID and CPU\n",
" pid = tv.get_task_by_name(task)[0]\n",
" sw_df = tv.df_events('sched_switch')\n",
" cpu = int(sw_df[sw_df.next_pid == pid].iloc[-1].__cpu)\n",
" logging.info(\"Events collected in range (%d, %d) on cpu%d\",\n",
" tv.start, tv.end, cpu)\n",
" \n",
" # Append UClamp signals\n",
" for event in ['uclamp_se', 'uclamp_cfs', 'uclamp_rt']:\n",
" _df = tv.df_events(event)\n",
" if pid and event == 'uclamp_se':\n",
" df[event] = _df[_df.pid == pid]\n",
" if cpu and event == 'uclamp_cfs':\n",
" df[event] = _df[_df.cpu == cpu]\n",
" if cpu and event == 'uclamp_rt':\n",
" df[event] = _df[_df.cpu == cpu]\n",
" \n",
" # Append RTApp execution log. RTApp header can have a variable number of\n",
" # rows depending on the configuration.\n",
" # Let's skip all the lines starting by # and assigne columns a name by hand.\n",
" logging.info(\"Loading RTApp stats from [{logfile}]...\".format(logfile=logfile))\n",
" col_names=['idx', 'perf', 'run', 'period', 'start', 'end', 'rel_st', 'slack', 'c_duration', 'c_period', 'wu_lat']\n",
" df['rtapp'] = pd.read_csv(logfile, delimiter='\\s+', comment='#', names=col_names, index_col='start')\n",
" # Re-align index to trace time\n",
" df['rtapp'].reset_index(inplace=True)\n",
" t_log = df['rtapp'].start[0]\n",
" df['rtapp']['timestamp'] = (df['rtapp'].start - t_log)/1e6 + tv.start\n",
" df['rtapp'].set_index('timestamp', inplace=True)\n",
"\n",
" # Append frequency residencies\n",
" logging.info(\"Frequency residency [%]:\")\n",
" df['opp_res'] = tv.analysis.frequency.df_cpu_frequency_residency(cpu=cpu).T\n",
" df['opp_res'] = df['opp_res'].apply(lambda v : v/tv.time_range)\n",
" \n",
" return df, tv, pid, cpu\n",
"\n",
"def doPlot(trace, rta, plot=\"cfs_util rq_util freqs rtapp\"):\n",
"\n",
" # Default lines colors\n",
" lines = [\n",
" ('uclamp_min', 'y', '-'),\n",
" ('uclamp_max', 'r', '-'),\n",
" ('util_avg', 'b', ':'),\n",
" ('uclamp_avg', 'b', '-'),\n",
" ]\n",
" columns = [l for l,_,_ in lines]\n",
" colors = [c for _,c,_ in lines]\n",
" style = [s for _,_,s in lines]\n",
"\n",
" df, tv, pid, cpu = getData(trace, rta)\n",
" \n",
" # Setup plots\n",
" plots_count = len(plot.split())\n",
" fig, ax = tv.analysis.frequency.setup_plot(ncols=1, nrows=plots_count, height=plots_count)\n",
" ax_id = 0\n",
" \n",
" if 'cfs_util' in plot:\n",
" _ax = ax[ax_id]\n",
" ax_id += 1\n",
" _df = df['uclamp_se'][tv.start:tv.end]\n",
" _df[columns].plot(\n",
" ax=_ax, drawstyle='steps-post',\n",
" color=colors, style=style, xlim=(tv.start, tv.end),\n",
" title=\"SE Clamped Utilization\", grid=True);\n",
" _ax.set_ylabel(\"Utilization\")\n",
"\n",
" if 'rq_util' in plot:\n",
" _ax = ax[ax_id]\n",
" ax_id += 1\n",
" _df = df['uclamp_rt'][tv.start:tv.end]\n",
" _df[columns].plot(\n",
" ax=_ax, drawstyle='steps-post',\n",
" color=colors, style=style, xlim=(tv.start, tv.end),\n",
" title=\"RQ Clamped Utilization\", grid=True);\n",
" _ax.set_ylabel(\"Utilization\")\n",
"\n",
" if 'freqs' in plot:\n",
" _ax = ax[ax_id]\n",
" ax_id += 1\n",
" logging.info(\"CPU capacity levels: %s\", caps[cpu])\n",
" # Ensure we injext first and last events\n",
" tv.analysis.frequency.plot_cpu_frequencies(cpu, axis=_ax);\n",
" \n",
" if 'rtapp' in plot:\n",
" _ax = ax[ax_id]\n",
" ax_id += 1\n",
" _df = df['rtapp']\n",
" _df.slack.plot(\n",
" ax=_ax,drawstyle='steps-post',\n",
" xlim=(tv.start, tv.end),\n",
" title=\"RTApp Slack\", grid=True);\n",
" _ax.set_ylabel(\"Slack\")\n",
"\n",
" \n",
" return df\n",
"\n",
"def getResidency(freq):\n",
" try:\n",
" return float(df['opp_res'][freq][0])\n",
" except:\n",
" # Requested frquency never used\n",
" return 0\n",
"\n",
"def assertResidency(freq, expected_pct, err_pct):\n",
" ok = True\n",
" res = getResidency(freq)\n",
" pct_min = expected_pct/100 - err_pct/100\n",
" pct_max = expected_pct/100 + err_pct/100\n",
" if res < pct_min or res > pct_max:\n",
" ok = False\n",
" if (res > pct_max):\n",
" err = 100*(res-expected_pct/100)\n",
" else:\n",
" err = -100*(expected_pct/100-res)\n",
" logging.info(\"Assert OPP@%9d in [%2d,%2d]: %8s (measured: %2d%%, error: %7.3f%%)\",\n",
" freq, 100*pct_min, 100*pct_max,\n",
" \"PASS\" if ok else \"FAILED\", 100*res,\n",
" err)"
]
},
{
"cell_type": "markdown",
"metadata": {
"toc-hr-collapsed": true
},
"source": [
"[Back to Table of Contents](#Table-of-Contents)"
]
},
{
"cell_type": "markdown",
"metadata": {
"toc-hr-collapsed": true
},
"source": [
"# CFS Tasks Clamping"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running on root CGroup does not set constraints"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/</th>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max uclamp.max.effective\n",
"cg_name \n",
"/ 102.4 max 102.4 max"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"checkGroups(['/'])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:08:41,534 INFO : root : #### Start RTApp execution\n",
"2019-07-18 19:08:45,973 INFO : lisa.wlgen.rta.RTA : Execution start: CGMOUNT=/home/root/devlib-target/cgroups /home/root/devlib-target/bin/shutils cgroups_run_into / /home/root/devlib-target/bin/rt-app /home/root/devlib-target/lisa/wlgen/test_ramp_20190718_190838_qjGhMG/test_ramp.json 2>&1\n",
"2019-07-18 19:08:53,166 INFO : lisa.wlgen.rta.RTA : Execution complete\n",
"2019-07-18 19:08:53,863 INFO : root : #### Save FTrace: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_190838.711084/trace.dat\n",
"2019-07-18 19:08:58,344 INFO : root : #### Save platform description: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_190838.711084/plat_info.yml\n",
"2019-07-18 19:08:58,456 INFO : root : LITTLE cluster max capacity: 810\n",
"2019-07-18 19:09:00,225 INFO : lisa.trace.Trace : Platform clusters verified to be Frequency coherent\n",
"2019-07-18 19:09:00,900 INFO : root : Events collected in range (0, 8) on cpu2\n",
"2019-07-18 19:09:00,911 INFO : root : Loading RTApp stats from [/data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_190838.711084/rt-app-test_task-0.log]...\n",
"2019-07-18 19:09:00,927 INFO : root : Frequency residency [%]:\n",
"2019-07-18 19:09:01,206 INFO : root : CPU capacity levels: {600000: 512, 1000000: 853, 1200000: 1024}\n",
"2019-07-18 19:09:01,211 INFO : lisa.analysis.frequency.FrequencyAnalysis : Average frequency for CPU2 : 0.846 GHz\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1152x1152 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"trace = test(cgname='/')\n",
"df = doPlot(trace, rta)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:09:02,138 INFO : root : Assert OPP@ 600000 in [42,52]: PASS (measured: 46%, error: -0.921%)\n",
"2019-07-18 19:09:02,139 INFO : root : Assert OPP@ 1000000 in [24,34]: PASS (measured: 32%, error: 3.478%)\n",
"2019-07-18 19:09:02,140 INFO : root : Assert OPP@ 1200000 in [17,27]: PASS (measured: 20%, error: -1.625%)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>frequency</th>\n",
" <th>600000</th>\n",
" <th>1000000</th>\n",
" <th>1200000</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>total_time</th>\n",
" <td>0.460793</td>\n",
" <td>0.324783</td>\n",
" <td>0.203751</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active_time</th>\n",
" <td>0.170623</td>\n",
" <td>0.194268</td>\n",
" <td>0.139227</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"frequency 600000 1000000 1200000\n",
"total_time 0.460793 0.324783 0.203751\n",
"active_time 0.170623 0.194268 0.139227"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assertResidency( 600000, expected_pct=47, err_pct=5)\n",
"assertResidency(1000000, expected_pct=29, err_pct=5)\n",
"assertResidency(1200000, expected_pct=22, err_pct=5)\n",
"df['opp_res']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running in uclamped group uses the constraints"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### UtilMin at minimum capacity (minus 20% margin)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/</th>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>37.00</td>\n",
" <td>37.01</td>\n",
" <td>62.00</td>\n",
" <td>62.01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max uclamp.max.effective\n",
"cg_name \n",
"/ 102.4 max 102.4 max\n",
"/uclamp 37.00 37.01 62.00 62.01"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Minimum capacity on big CPUs\n",
"big_cpas = target.plat_info['freqs'][big_cpu]\n",
"min_cap = int(100 * big_cpas[ 0] / big_cpas[-1]) * 0.75\n",
"max_cap = int(100 * big_cpas[-2] / big_cpas[-1]) * 0.75\n",
"\n",
"# Setup a child clamp group\n",
"uclamp_cg = cpuset_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set(dict(cpus=big_cpu, mems=0))\n",
"\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set({'uclamp.min': int(min_cap)}, False)\n",
"uclamp_cg.set({'uclamp.max': int(max_cap)}, False)\n",
"checkGroups(['/', '/uclamp'])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:09:04,290 INFO : root : #### Start RTApp execution\n",
"2019-07-18 19:09:08,822 INFO : lisa.wlgen.rta.RTA : Execution start: CGMOUNT=/home/root/devlib-target/cgroups /home/root/devlib-target/bin/shutils cgroups_run_into /uclamp /home/root/devlib-target/bin/rt-app /home/root/devlib-target/lisa/wlgen/test_ramp_20190718_190838_qjGhMG/test_ramp.json 2>&1\n",
"2019-07-18 19:09:16,007 INFO : lisa.wlgen.rta.RTA : Execution complete\n",
"2019-07-18 19:09:16,679 INFO : root : #### Save FTrace: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_190838.711084/trace.dat\n",
"2019-07-18 19:09:19,845 INFO : root : #### Save platform description: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_190838.711084/plat_info.yml\n",
"2019-07-18 19:09:19,957 INFO : root : LITTLE cluster max capacity: 810\n",
"2019-07-18 19:09:21,741 INFO : lisa.trace.Trace : Platform clusters verified to be Frequency coherent\n",
"2019-07-18 19:09:21,751 INFO : root : Events collected in range (0, 8) on cpu2\n",
"2019-07-18 19:09:21,756 INFO : root : Loading RTApp stats from [/data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_190838.711084/rt-app-test_task-0.log]...\n",
"2019-07-18 19:09:21,763 INFO : root : Frequency residency [%]:\n",
"2019-07-18 19:09:21,948 INFO : root : CPU capacity levels: {600000: 512, 1000000: 853, 1200000: 1024}\n",
"2019-07-18 19:09:21,953 INFO : lisa.analysis.frequency.FrequencyAnalysis : Average frequency for CPU2 : 0.809 GHz\n"
]
},
{
"data": {
"image/png": 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l9ylTgtw677wgrqlTN4T3a3u9VnI///w/9mqfd95b3Y/T09tdch81qrrX3OPHb3PJ/eyz3wv3pa0udfValpERvPbii1/vNZbkXlAQ/L554YVvhuPvdsn93HODeHNyGrv3q3PmLA/3r/UuuS9YELyXubkNLrnn5dV1/+4Se31RUe/xs7MPbcPJk9f1Wt9Fi15xyb20dJ9L7mPGbOsx97Ie63do7ti65ObWe1nZLpfcR48OttHll78a7vt3uOQ+ffoH3XNNm/ZBr7lj48Vu5eU7ut/vsWO3u+R+wQVvha8N3tNZs1Z1f36Vl+/o9fpLLnmtV3vq1DW9xg7WaaVL7rNnB/uzGTOCmEpK9np2dlOv1196ae/xYtsj2F57wu0Z5NY55ywO41zvkvuECRuPyLULLnizV3vevHe7H2dnN4bbcadL7hdd9EY4ztYw3uU9tn00nDN4v2PvV2z909I6u8ctLNzfKxdGj97Va+6srOYj3u/c3MZe4+XkNHVv89j7cyjXDoTr9scjci32fpeU1PbansXFQbusbJebBbGeccbSXq+PzR37OcrMbPUxY7aFPy+7evUZM2bnEbndM+9i8c2du/iI94Sbu6Ql8dRhLCy0nDLM7BZJ17r758P2HZIucPcv9tV//vz5vmTJku72gQMNWrZsv5qa0jRxYockad++dDU3H2rX1KSrtTVNEyYcare1pamiImjv3Zuhjg7T+PFBe8+eDHV1SePGdUqSdu/OkLs0dmzQ3rUrqHz3bJtJY8YE7erqDKWnS+Xlh9oZGdLo0UG7qipT2dlRjRrV1Wd7+/ZM5eVFNXLkoXZ+flRlZV0aN26E6upKVFwcfGsWjbqee26nCgq6NGJE0H/r1kwVF0dVWtqlkpICtbWN1siRwbeZ0ai0YUPwTeeIEcE3ZBs3BmOVlkqdndKmTcG3UyUlwTdmmzdLY8ZIxcVSe7u0ZUvwbdayZZW66KKF2ro1+MausFBqbQ2+IR0/Pvg2tKVF2r49+DYsPz/4ZnHHDmnChODb0aYmqaoq+PYsN1dqbAy+vZw0KfgWsaFBqq4+1K6vD741mzJFysqS6uqk3bsPtQ8eDL7tnDYt+FZyw4Y6ffBBvSZM6NDZZ09SfX26amqCb/fS06Xa2uBbuhkzpLS04HFtrTRzZvDta01N8K3erFkKcyWYY+ZMhbkSxDRjhsJcCdZhenCAjXbtCtY59o1ldXWwjaZODdo7dwbbdMoUhbkQvAeTJwftHTuC92zSJIW5ENxPnBjcb9sWxD1hgsL3PljviopD7czM4P3Ys+eA3nuvUePGFeicc0olBe9tbm7wfkpBLhQUBO93rF1YGOSDFOROLPek4Nvt0tIgnyRp/fogz0aODHZ7H34YPF61qlILFiwctNwrKgq+vY0n99LS9qmpqV6FhcXq7Cw74dxbunSnduyIasGCkRoxIveYuXfgQJAvsfb+/Tqu3Nu9O6p3363W5MkdOuOMSaqtTRtyuVddvU/Fxc067bQJ2r7d+s29WK7l5ATvV3+5F4m8qMsum63TTqsYlNyrr9+tkpKopk0b1yv36utb9eqrNTrzzCJNm1Ycd+7V11eprCxd48ePHfB+b/t2V0tLlcrKslVWNnrA+71du1ytrds1enS+8vJGDij3amqkmpqoOjq2qby8WGlpI3TwoDR1apc2bKhWSUmFVq7crUWLRio7OzMluffuu2/r1lsv6s61geZeIvZ7g/2Zezz7vVR95g50vzfYn7lPPvmWzjnn4pR85g5m7o0c6Vq7drtqago0Z07ZsM+9xsY6HTzYqebmQqWn79Ppp4/R0qU7VVhYoc7Oas2YMUpbttSrri5bkye7OjralJ1domXLarVwYbk2b96h8vIKLVu2T+edV6jW1hbV1aWpoSFLJSX1mjhxpJYu3aOSknGKRndqxowxWrPmgDo68jRhQoei0S51dORr/fo6XXHFKH34YZXWr9+uvLwZuvzyUu3bV6fGxmw1NKRpzJgWjR5doiVLajRy5FhFo1WaOXO8liypUU5OscrLm2VmqqvLVnV1ky69dIQ2bKhWYWGF1q7drcsvH6Xq6lo1NxeoublLY8e2a8SIQi1ZckDjxo1WW1uVTjttgt54Y4/Ky0tVWNignJxMVVWlqampTeeeW6wtW/YoO3u8tmyp1oIFY7R9+161tJSqra1V48e7cnOztGJFswoLM5WdXa/TTpugV1/do+nTR0qqVUlJvjZtisqsU6efnq+qqlpFo2O0e/cuXXbZOG3cWK329pHq7GzUhAkZSktL0/vvt2nq1GI1Ne3RzJnj9Yc/7NK8eeVqadmrUaOK9f77HSotdU2YkKW9e+vU2jpadXW7deGF47Ru3U51dpbL/aAmTsxRW1tU69dHNWNGnpqaajVx4hi9/PJuXXTRWO3bV60xY0Zq+fJmTZyYppKSNNXWtqi5uVRtbXt07rnj9f77O5SZOV7RaI0mTy5SbW2rdu5M19SpmWppqdOoUaP1+ut7tGDBWO3cuVPl5eVasaJOM2dmKysrqoMHO9TcXKS0tH2aPXuMli3bqYKCINemTx+p7dsbVVubqSlTTO3tLcrLK9V77+3TFVeMCXNtvJYv36958/L15puVOv30i9XQkK2iooOaMmW03ntvl0pKxqura6dmzCjXunUH1daWq4kTu9TV1SH3Qn3wwUFdccUobdhQpbKyCq1YUaPLLivRgQMNqq/PVENDusrLmzVmTKnee2+vRowYo87OHZo9e4KWLdunzMwijRvXKndXY2OOduxo1GWXlenDD3equHiCPvhgty6/fKR27z6gpqY8NTVFNXZsm8rKisJcK1db2w7NmlWht97aq7KyUhUXNygrK1O7d6ervr5V8+cHuZaTM16bNlXr8svHaMeOGjU1Fam9vUPjxnUpPz9Hy5c3auLEMjU379Rpp01QJLJbU6aMVHr6ARUV5WrzZpd7h+bMKdCOHfuUljZOVVXVuuyysdq8ebdaWsrU1dWoiooMZWama+XKVk2ZUqzm5iDXXnhhl84+u1xtbTUqKyvU6tWdKiqKatKkbNXU1KmlZbRqaqo0f/5oRaOuV1+tVUlJl0pKoopGg7+bS0u7VFx8qD1iRJeKiqLq6pJ27DjU7uwM/g4vK+tSYWFUHR3Szp2ZGjmySwUFUbW3m6qrMzRqVKfy811tbaZduzI0enSn8vJcra2m3bszVF7eqdzcQ+19+5Zr/vwz1NJi2rMnQ2PGdConx9XcbNq7N0Njx3YqO9vV1GSqqTnUbmxM07596Ro/vkOZmVJDQ5pqaw+16+vTtH9/uiZM6FB6+pHturo0tbam6dprJy119/DY2f6dioWniyR9292vCdtfkyR3/6e++h9eeELqVFZWamHsXAfgKMgVxIM8QbzIFcSDPEG8yBXEgzxBPFKdJ2YWV+HplLu4uKT3JM0wsylmliXpdknPpjgmAAAAAACAk84pd3Fxd+80sy9KekFSuqRfuPsHKQ4LAAAAAADgpHPKFZ4kyd2fl/R8quMAAAAAAAA4mZ2Kp9oBAAAAAAAgCSg8AQAAAAAAICEoPAEAAAAAACAhKDwBAAAAAAAgISg8AQAAAAAAICHM3VMdw5BmZjWStqU6DkiSRkral+ogMCyQK4gHeYJ4kSuIB3mCeJEriAd5gnikOk8mufuoY3Wi8IRhw8yWuPv8VMeBoY9cQTzIE8SLXEE8yBPEi1xBPMgTxGO45Amn2gEAAAAAACAhKDwBAAAAAAAgISg8YTj5WaoDwLBBriAe5AniRa4gHuQJ4kWuIB7kCeIxLPKEazwBAAAAAAAgITjiCQAAAAAAAAlB4QkAAAAAAAAJQeEJQ56Z/cLM9prZ6lTHgqHLzCaYWcTM1pjZB2Z2b6pjwtBkZjlmttjMVoa58p1Ux4Shy8zSzWy5mT2X6lgwdJnZVjNbZWYrzGxJquPB0GRmJWb2pJmtM7O1ZnZRqmPC0GNms8J9SexWb2ZfSnVcGHrM7K/D32VXm9mjZpaT6pj6wzWeMOSZ2QJJjZJ+6e5npDoeDE1mNlbSWHdfZmaFkpZK+pi7r0lxaBhizMwk5bt7o5llSnpT0r3u/k6KQ8MQZGZfljRfUpG735jqeDA0mdlWSfPdfV+qY8HQZWYPS3rD3R8ysyxJee5+MNVxYegys3RJOyVd4O7bUh0Phg4zG6/gd9jZ7t5iZo9Let7d/yO1kfWNI54w5Ln765L2pzoODG3uvsvdl4WPGyStlTQ+tVFhKPJAY9jMDG98C4MjmFmFpBskPZTqWAAMb2ZWLGmBpJ9Lkru3U3RCHK6UtImiE/qRISnXzDIk5UmqTnE8/aLwBOCkY2aTJc2T9G5qI8FQFZ4+tULSXkkvuTu5gr78q6SvSIqmOhAMeS7pRTNbamZ3pToYDElTJNVI+vfw9N2HzCw/1UFhyLtd0qOpDgJDj7vvlPR9Sdsl7ZJU5+4vpjaq/lF4AnBSMbMCSU9J+pK716c6HgxN7t7l7nMlVUg638w4jRe9mNmNkva6+9JUx4Jh4VJ3P0fSdZL+MrxMANBThqRzJP3U3edJapJ0X2pDwlAWno75J5KeSHUsGHrMrFTSTQqK2uMk5ZvZp1MbVf8oPAE4aYTX63lK0n+6+29SHQ+GvvA0h4ika1MdC4acSyT9SXjtnsckXWFmv0ptSBiqwm+e5e57JT0t6fzURoQhqEpSVY8jbJ9UUIgC+nOdpGXuvifVgWBIukrSFnevcfcOSb+RdHGKY+oXhScAJ4XwgtE/l7TW3X+Q6ngwdJnZKDMrCR/nSvqIpHWpjQpDjbt/zd0r3H2yglMdXnX3IftNIlLHzPLDf2qh8NSpqyXxn3jRi7vvlrTDzGaFi66UxD9AwdF8Qpxmh/5tl3ShmeWFfwddqeAat0MShScMeWb2qKS3Jc0ysyoz+1yqY8KQdImkOxQclRD797PXpzooDEljJUXM7H1J7ym4xtNzKY4JwPBVLulNM1spabGk37v7H1IcE4amv5L0n+Hnz1xJ/5j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\n",
"text/plain": [
"<Figure size 1152x1152 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"trace = test(cgname='/uclamp')\n",
"df = doPlot(trace, rta)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:09:22,833 INFO : root : Assert OPP@ 600000 in [45,55]: PASS (measured: 46%, error: -3.730%)\n",
"2019-07-18 19:09:22,834 INFO : root : Assert OPP@ 1000000 in [45,55]: PASS (measured: 52%, error: 2.760%)\n",
"2019-07-18 19:09:22,835 INFO : root : Assert OPP@ 1200000 in [-5, 5]: PASS (measured: 0%, error: 0.347%)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>frequency</th>\n",
" <th>600000</th>\n",
" <th>1000000</th>\n",
" <th>1200000</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>total_time</th>\n",
" <td>0.462705</td>\n",
" <td>0.527600</td>\n",
" <td>0.003473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active_time</th>\n",
" <td>0.171671</td>\n",
" <td>0.363775</td>\n",
" <td>0.002073</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"frequency 600000 1000000 1200000\n",
"total_time 0.462705 0.527600 0.003473\n",
"active_time 0.171671 0.363775 0.002073"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assertResidency( 600000, expected_pct=50, err_pct=5)\n",
"assertResidency(1000000, expected_pct=50, err_pct=5)\n",
"assertResidency(1200000, expected_pct=0, err_pct=5)\n",
"df['opp_res']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### UtilMin just above minimum capacity (minus 20% margin)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/</th>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>42.00</td>\n",
" <td>41.99</td>\n",
" <td>62.00</td>\n",
" <td>62.01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max uclamp.max.effective\n",
"cg_name \n",
"/ 102.4 max 102.4 max\n",
"/uclamp 42.00 41.99 62.00 62.01"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Minimum capacity on big CPUs\n",
"big_cpas = target.plat_info['freqs'][big_cpu]\n",
"min_cap = int(100 * big_cpas[ 0] / big_cpas[-1]) * 0.85\n",
"max_cap = int(100 * big_cpas[-2] / big_cpas[-1]) * 0.75\n",
"\n",
"# Setup a child clamp group\n",
"uclamp_cg = cpuset_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set(dict(cpus=big_cpu, mems=0))\n",
"\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set({'uclamp.min': int(min_cap)}, False)\n",
"uclamp_cg.set({'uclamp.max': int(max_cap)}, False)\n",
"checkGroups(['/', '/uclamp'])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:09:24,646 INFO : root : #### Start RTApp execution\n",
"2019-07-18 19:09:29,054 INFO : lisa.wlgen.rta.RTA : Execution start: CGMOUNT=/home/root/devlib-target/cgroups /home/root/devlib-target/bin/shutils cgroups_run_into /uclamp /home/root/devlib-target/bin/rt-app /home/root/devlib-target/lisa/wlgen/test_ramp_20190718_190838_qjGhMG/test_ramp.json 2>&1\n",
"2019-07-18 19:09:36,244 INFO : lisa.wlgen.rta.RTA : Execution complete\n",
"2019-07-18 19:09:36,936 INFO : root : #### Save FTrace: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_190838.711084/trace.dat\n",
"2019-07-18 19:09:40,214 INFO : root : #### Save platform description: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_190838.711084/plat_info.yml\n",
"2019-07-18 19:09:40,323 INFO : root : LITTLE cluster max capacity: 810\n",
"2019-07-18 19:09:42,234 INFO : lisa.trace.Trace : Platform clusters verified to be Frequency coherent\n",
"2019-07-18 19:09:42,244 INFO : root : Events collected in range (0, 8) on cpu2\n",
"2019-07-18 19:09:42,248 INFO : root : Loading RTApp stats from [/data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_190838.711084/rt-app-test_task-0.log]...\n",
"2019-07-18 19:09:42,255 INFO : root : Frequency residency [%]:\n",
"2019-07-18 19:09:42,446 INFO : root : CPU capacity levels: {600000: 512, 1000000: 853, 1200000: 1024}\n",
"2019-07-18 19:09:42,451 INFO : lisa.analysis.frequency.FrequencyAnalysis : Average frequency for CPU2 : 0.897 GHz\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1152x1152 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"trace = test(cgname='/uclamp')\n",
"df = doPlot(trace, rta)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:09:43,444 INFO : root : Assert OPP@ 600000 in [20,30]: PASS (measured: 25%, error: 0.543%)\n",
"2019-07-18 19:09:43,445 INFO : root : Assert OPP@ 1000000 in [70,80]: PASS (measured: 73%, error: -1.041%)\n",
"2019-07-18 19:09:43,446 INFO : root : Assert OPP@ 1200000 in [-5, 5]: PASS (measured: 0%, error: 0.339%)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>frequency</th>\n",
" <th>600000</th>\n",
" <th>1000000</th>\n",
" <th>1200000</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>total_time</th>\n",
" <td>0.255427</td>\n",
" <td>0.739591</td>\n",
" <td>0.003385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active_time</th>\n",
" <td>0.048410</td>\n",
" <td>0.432144</td>\n",
" <td>0.000943</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"frequency 600000 1000000 1200000\n",
"total_time 0.255427 0.739591 0.003385\n",
"active_time 0.048410 0.432144 0.000943"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assertResidency( 600000, expected_pct=25, err_pct=5)\n",
"assertResidency(1000000, expected_pct=75, err_pct=5)\n",
"assertResidency(1200000, expected_pct=0, err_pct=5)\n",
"df['opp_res']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running in more relaxed group still uses constrains from parents"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/</th>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>42.00</td>\n",
" <td>41.99</td>\n",
" <td>62.00</td>\n",
" <td>62.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>70.00</td>\n",
" <td>41.99</td>\n",
" <td>90.00</td>\n",
" <td>62.01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max uclamp.max.effective\n",
"cg_name \n",
"/ 102.4 max 102.4 max\n",
"/uclamp 42.00 41.99 62.00 62.01\n",
"/uclamp/app 70.00 41.99 90.00 62.01"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Minimum capacity on big CPUs\n",
"big_cpas = target.plat_info['freqs'][big_cpu]\n",
"min_cap = int(100 * big_cpas[ 0] / big_cpas[-1]) * 0.85\n",
"max_cap = int(100 * big_cpas[-2] / big_cpas[-1]) * 0.75\n",
"\n",
"# Setup a PARENT clamp group\n",
"uclamp_cg = cpuset_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set(dict(cpus=big_cpu, mems=0))\n",
"\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set({'uclamp.min': int(min_cap)}, False)\n",
"uclamp_cg.set({'uclamp.max': int(max_cap)}, False)\n",
"checkGroups(['/', '/uclamp'])\n",
"\n",
"# Setup a PARENT clamp group\n",
"uclamp_cg = cpuset_ctr.cgroup('/uclamp/app')\n",
"uclamp_cg.set(dict(cpus=big_cpu, mems=0))\n",
"\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp/app')\n",
"uclamp_cg.set({'uclamp.min': int(70)}, False)\n",
"uclamp_cg.set({'uclamp.max': int(90)}, False)\n",
"checkGroups(['/', '/uclamp', '/uclamp/app'])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:09:47,601 INFO : root : #### Start RTApp execution\n",
"2019-07-18 19:09:52,082 INFO : lisa.wlgen.rta.RTA : Execution start: CGMOUNT=/home/root/devlib-target/cgroups /home/root/devlib-target/bin/shutils cgroups_run_into /uclamp/app /home/root/devlib-target/bin/rt-app /home/root/devlib-target/lisa/wlgen/test_ramp_20190718_190838_qjGhMG/test_ramp.json 2>&1\n",
"2019-07-18 19:09:59,274 INFO : lisa.wlgen.rta.RTA : Execution complete\n",
"2019-07-18 19:09:59,948 INFO : root : #### Save FTrace: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_190838.711084/trace.dat\n",
"2019-07-18 19:10:03,085 INFO : root : #### Save platform description: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_190838.711084/plat_info.yml\n",
"2019-07-18 19:10:03,213 INFO : root : LITTLE cluster max capacity: 810\n",
"2019-07-18 19:10:05,180 INFO : lisa.trace.Trace : Platform clusters verified to be Frequency coherent\n",
"2019-07-18 19:10:05,191 INFO : root : Events collected in range (0, 8) on cpu2\n",
"2019-07-18 19:10:05,195 INFO : root : Loading RTApp stats from [/data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_190838.711084/rt-app-test_task-0.log]...\n",
"2019-07-18 19:10:05,202 INFO : root : Frequency residency [%]:\n",
"2019-07-18 19:10:05,400 INFO : root : CPU capacity levels: {600000: 512, 1000000: 853, 1200000: 1024}\n",
"2019-07-18 19:10:05,405 INFO : lisa.analysis.frequency.FrequencyAnalysis : Average frequency for CPU2 : 0.894 GHz\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1152x1152 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"trace = test(cgname='/uclamp/app')\n",
"df = doPlot(trace, rta)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:10:06,290 INFO : root : Assert OPP@ 600000 in [20,30]: PASS (measured: 25%, error: 0.319%)\n",
"2019-07-18 19:10:06,292 INFO : root : Assert OPP@ 1000000 in [70,80]: PASS (measured: 73%, error: -1.020%)\n",
"2019-07-18 19:10:06,293 INFO : root : Assert OPP@ 1200000 in [-5, 5]: PASS (measured: 0%, error: 0.217%)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>frequency</th>\n",
" <th>600000</th>\n",
" <th>1000000</th>\n",
" <th>1200000</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>total_time</th>\n",
" <td>0.253192</td>\n",
" <td>0.739797</td>\n",
" <td>0.002166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active_time</th>\n",
" <td>0.047263</td>\n",
" <td>0.433434</td>\n",
" <td>0.000608</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"frequency 600000 1000000 1200000\n",
"total_time 0.253192 0.739797 0.002166\n",
"active_time 0.047263 0.433434 0.000608"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assertResidency( 600000, expected_pct=25, err_pct=5)\n",
"assertResidency(1000000, expected_pct=75, err_pct=5)\n",
"assertResidency(1200000, expected_pct=0, err_pct=5)\n",
"df['opp_res']"
]
},
{
"cell_type": "markdown",
"metadata": {
"toc-hr-collapsed": true
},
"source": [
"[Back to Table of Contents](#Table-of-Contents)"
]
},
{
"cell_type": "markdown",
"metadata": {
"toc-hr-collapsed": true
},
"source": [
"# RT Tasks Clamping"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:10:06,652 INFO : lisa.wlgen.rta.RTA : Creating target's run_dir: /home/root/devlib-target/lisa/wlgen/test_ramp_rt_20190718_191006_KRfduA\n",
"2019-07-18 19:10:06,653 INFO : lisa.target.Target : Creating result directory: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp_rt-20190718_191006.653105\n",
"2019-07-18 19:10:06,987 INFO : lisa.wlgen.rta.RTA : Calibration value: 263\n",
"2019-07-18 19:10:06,988 INFO : lisa.wlgen.rta.RTA : Default policy: SCHED_OTHER\n",
"2019-07-18 19:10:06,990 INFO : lisa.wlgen.rta.RTA : ------------------------\n",
"2019-07-18 19:10:06,991 INFO : lisa.wlgen.rta.RTA : task [test_task], sched: RR\n",
"2019-07-18 19:10:06,992 INFO : lisa.wlgen.rta.RTA : | start delay: 0.000000 [s]\n",
"2019-07-18 19:10:06,994 INFO : lisa.wlgen.rta.RTA : | loops count: 1\n",
"2019-07-18 19:10:06,995 INFO : lisa.wlgen.rta.RTA : + phase_000001\n",
"2019-07-18 19:10:06,996 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:10:06,998 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 10 %\n",
"2019-07-18 19:10:06,999 INFO : lisa.wlgen.rta.Phase : | run_time 1600 [us], sleep_time 14400 [us]\n",
"2019-07-18 19:10:07,000 INFO : lisa.wlgen.rta.RTA : + phase_000002\n",
"2019-07-18 19:10:07,002 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:10:07,003 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 15 %\n",
"2019-07-18 19:10:07,004 INFO : lisa.wlgen.rta.Phase : | run_time 2400 [us], sleep_time 13600 [us]\n",
"2019-07-18 19:10:07,006 INFO : lisa.wlgen.rta.RTA : + phase_000003\n",
"2019-07-18 19:10:07,007 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:10:07,008 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 20 %\n",
"2019-07-18 19:10:07,009 INFO : lisa.wlgen.rta.Phase : | run_time 3200 [us], sleep_time 12800 [us]\n"
]
}
],
"source": [
"from lisa.wlgen.rta import RTA, Ramp\n",
"\n",
"cpu = [cpu for cpu,cap\n",
" in target.plat_info['cpu-capacities'].items()\n",
" if cap==1024][-1]\n",
"\n",
"rtapp_profile = {\n",
" 'test_task': Ramp(period_ms=16, start_pct=10, end_pct=20,\n",
" delta_pct=5, cpus=[big_cpu], sched_policy='RR', priority=99)\n",
"}\n",
"\n",
"def provide_calibration(calibration):\n",
" target.plat_info[\"rtapp\"].add_src(\"user\", {\"calib\" : calibration})\n",
"# Uncomment if you want to use this\n",
"provide_calibration({0: 586, 1: 263, 2: 263, 3: 586, 4: 585, 5: 586})\n",
"\n",
"\n",
"rta = RTA.by_profile(target, \"test_ramp_rt\", rtapp_profile, logstats=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Root RQ: Runs at max Capacity"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:10:07,347 INFO : root : #### Start RTApp execution\n",
"2019-07-18 19:10:11,720 INFO : lisa.wlgen.rta.RTA : Execution start: CGMOUNT=/home/root/devlib-target/cgroups /home/root/devlib-target/bin/shutils cgroups_run_into / /home/root/devlib-target/bin/rt-app /home/root/devlib-target/lisa/wlgen/test_ramp_rt_20190718_191006_KRfduA/test_ramp_rt.json 2>&1\n",
"2019-07-18 19:10:14,950 INFO : lisa.wlgen.rta.RTA : Execution complete\n",
"2019-07-18 19:10:15,615 INFO : root : #### Save FTrace: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp_rt-20190718_191006.653105/trace.dat\n",
"2019-07-18 19:10:18,819 INFO : root : #### Save platform description: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp_rt-20190718_191006.653105/plat_info.yml\n",
"2019-07-18 19:10:18,931 INFO : root : LITTLE cluster max capacity: 810\n",
"2019-07-18 19:10:20,491 INFO : lisa.trace.Trace : Platform clusters verified to be Frequency coherent\n",
"2019-07-18 19:10:20,500 INFO : root : Events collected in range (0, 4) on cpu2\n",
"2019-07-18 19:10:20,505 INFO : root : Loading RTApp stats from [/data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp_rt-20190718_191006.653105/rt-app-test_task-0.log]...\n",
"2019-07-18 19:10:20,512 INFO : root : Frequency residency [%]:\n",
"2019-07-18 19:10:20,599 INFO : root : CPU capacity levels: {600000: 512, 1000000: 853, 1200000: 1024}\n",
"2019-07-18 19:10:20,604 INFO : lisa.analysis.frequency.FrequencyAnalysis : Average frequency for CPU2 : 0.854 GHz\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1152x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"trace = test(cgname='/')\n",
"df = doPlot(trace, rta, plot='freqs rtapp')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:10:20,927 INFO : root : Assert OPP@ 600000 in [50,60]: PASS (measured: 51%, error: -3.098%)\n",
"2019-07-18 19:10:20,928 INFO : root : Assert OPP@ 1000000 in [-5, 5]: PASS (measured: 4%, error: 4.510%)\n",
"2019-07-18 19:10:20,930 INFO : root : Assert OPP@ 1200000 in [40,50]: PASS (measured: 41%, error: -3.522%)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>frequency</th>\n",
" <th>600000</th>\n",
" <th>1000000</th>\n",
" <th>1200000</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>total_time</th>\n",
" <td>0.519017</td>\n",
" <td>0.045099</td>\n",
" <td>0.414780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active_time</th>\n",
" <td>0.079783</td>\n",
" <td>0.013768</td>\n",
" <td>0.061281</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"frequency 600000 1000000 1200000\n",
"total_time 0.519017 0.045099 0.414780\n",
"active_time 0.079783 0.013768 0.061281"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assertResidency( 600000, expected_pct=55, err_pct=5)\n",
"assertResidency(1000000, expected_pct=0, err_pct=5)\n",
"assertResidency(1200000, expected_pct=45, err_pct=5)\n",
"df['opp_res']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Child RQ: Runs at max CGroups Capacity"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/</th>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>42.50</td>\n",
" <td>42.48</td>\n",
" <td>85.00</td>\n",
" <td>84.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>75.00</td>\n",
" <td>42.48</td>\n",
" <td>75.00</td>\n",
" <td>75.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max uclamp.max.effective\n",
"cg_name \n",
"/ 102.4 max 102.4 max\n",
"/uclamp 42.50 42.48 85.00 84.96\n",
"/uclamp/app 75.00 42.48 75.00 75.00"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"big_cpas = target.plat_info['freqs'][big_cpu]\n",
"min_cap = int(100 * big_cpas[ 0] / big_cpas[-1]) * 0.85\n",
"max_cap = int(100 * big_cpas[-1] / big_cpas[-1]) * 0.85\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set({'uclamp.min': min_cap}, False)\n",
"uclamp_cg.set({'uclamp.max': max_cap}, False)\n",
"\n",
"big_cpas = target.plat_info['freqs'][big_cpu]\n",
"min_cap = int(100 * big_cpas[-1] / big_cpas[-1]) * 0.75\n",
"max_cap = int(100 * big_cpas[-1] / big_cpas[-1]) * 0.75\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp/app')\n",
"uclamp_cg.set({'uclamp.min': min_cap}, False)\n",
"uclamp_cg.set({'uclamp.max': max_cap}, False)\n",
"\n",
"checkGroups(['/', '/uclamp', '/uclamp/app'])"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:10:22,707 INFO : root : #### Start RTApp execution\n",
"2019-07-18 19:10:27,135 INFO : lisa.wlgen.rta.RTA : Execution start: CGMOUNT=/home/root/devlib-target/cgroups /home/root/devlib-target/bin/shutils cgroups_run_into /uclamp/app /home/root/devlib-target/bin/rt-app /home/root/devlib-target/lisa/wlgen/test_ramp_rt_20190718_191006_KRfduA/test_ramp_rt.json 2>&1\n",
"2019-07-18 19:10:30,357 INFO : lisa.wlgen.rta.RTA : Execution complete\n",
"2019-07-18 19:10:31,031 INFO : root : #### Save FTrace: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp_rt-20190718_191006.653105/trace.dat\n",
"2019-07-18 19:10:35,612 INFO : root : #### Save platform description: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp_rt-20190718_191006.653105/plat_info.yml\n",
"2019-07-18 19:10:35,726 INFO : root : LITTLE cluster max capacity: 810\n",
"2019-07-18 19:10:37,299 INFO : lisa.trace.Trace : Platform clusters verified to be Frequency coherent\n",
"2019-07-18 19:10:37,310 INFO : root : Events collected in range (0, 4) on cpu2\n",
"2019-07-18 19:10:37,313 INFO : root : Loading RTApp stats from [/data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp_rt-20190718_191006.653105/rt-app-test_task-0.log]...\n",
"2019-07-18 19:10:37,320 INFO : root : Frequency residency [%]:\n",
"2019-07-18 19:10:37,407 INFO : root : CPU capacity levels: {600000: 512, 1000000: 853, 1200000: 1024}\n",
"2019-07-18 19:10:37,413 INFO : lisa.analysis.frequency.FrequencyAnalysis : Average frequency for CPU2 : 0.784 GHz\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"trace = test(cgname='/uclamp/app')\n",
"df = doPlot(trace, rta, plot='freqs rtapp')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:10:37,734 INFO : root : Assert OPP@ 600000 in [45,55]: PASS (measured: 50%, error: 0.779%)\n",
"2019-07-18 19:10:37,735 INFO : root : Assert OPP@ 1000000 in [45,55]: PASS (measured: 46%, error: -3.148%)\n",
"2019-07-18 19:10:37,736 INFO : root : Assert OPP@ 1200000 in [-5, 5]: PASS (measured: 0%, error: 0.901%)\n"
]
},
{
"data": {
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>frequency</th>\n",
" <th>600000</th>\n",
" <th>1000000</th>\n",
" <th>1200000</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>total_time</th>\n",
" <td>0.507786</td>\n",
" <td>0.468522</td>\n",
" <td>0.009012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active_time</th>\n",
" <td>0.080300</td>\n",
" <td>0.096759</td>\n",
" <td>0.007155</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"frequency 600000 1000000 1200000\n",
"total_time 0.507786 0.468522 0.009012\n",
"active_time 0.080300 0.096759 0.007155"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assertResidency( 600000, expected_pct=50, err_pct=5)\n",
"assertResidency(1000000, expected_pct=50, err_pct=5)\n",
"assertResidency(1200000, expected_pct=0, err_pct=5)\n",
"df['opp_res']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to Table of Contents](#Table-of-Contents)"
]
},
{
"cell_type": "markdown",
"metadata": {
"toc-hr-collapsed": true
},
"source": [
"# Per-Task API"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure app clamp groups"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/</th>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>44.00</td>\n",
" <td>44.04</td>\n",
" <td>66.00</td>\n",
" <td>66.02</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max uclamp.max.effective\n",
"cg_name \n",
"/ 102.4 max 102.4 max\n",
"/uclamp 44.00 44.04 66.00 66.02"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cpu_ctr = target.cgroups.controller('cpu')\n",
"cpuset_ctr = target.cgroups.controller('cpuset')\n",
"\n",
"# Setup a child clamp group\n",
"uclamp_cg = cpuset_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set(dict(cpus=target.bl.bigs[-1], mems=0))\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set({'uclamp.max': 66}, False)\n",
"uclamp_cg.set({'uclamp.min': 44}, False)\n",
"checkGroups(['/', '/uclamp'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure app with per task clamp values"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:10:39,937 INFO : lisa.wlgen.rta.RTA : Creating target's run_dir: /home/root/devlib-target/lisa/wlgen/test_ramp_20190718_191039_8pUPnW\n",
"2019-07-18 19:10:39,939 INFO : lisa.target.Target : Creating result directory: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_191039.938740\n",
"2019-07-18 19:10:40,272 INFO : lisa.wlgen.rta.RTA : Calibration value: 218\n",
"2019-07-18 19:10:40,273 INFO : lisa.wlgen.rta.RTA : Default policy: SCHED_OTHER\n",
"2019-07-18 19:10:40,274 INFO : lisa.wlgen.rta.RTA : ------------------------\n",
"2019-07-18 19:10:40,275 INFO : lisa.wlgen.rta.RTA : task [test_task], sched: OTHER\n",
"2019-07-18 19:10:40,276 INFO : lisa.wlgen.rta.RTA : | start delay: 0.000000 [s]\n",
"2019-07-18 19:10:40,277 INFO : lisa.wlgen.rta.RTA : | loops count: 1\n",
"2019-07-18 19:10:40,278 INFO : lisa.wlgen.rta.RTA : + phase_000001\n",
"2019-07-18 19:10:40,279 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:10:40,280 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 10 %\n",
"2019-07-18 19:10:40,281 INFO : lisa.wlgen.rta.Phase : | run_time 1600 [us], sleep_time 14400 [us]\n",
"2019-07-18 19:10:40,282 INFO : lisa.wlgen.rta.RTA : + phase_000002\n",
"2019-07-18 19:10:40,283 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:10:40,284 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 20 %\n",
"2019-07-18 19:10:40,284 INFO : lisa.wlgen.rta.Phase : | run_time 3200 [us], sleep_time 12800 [us]\n",
"2019-07-18 19:10:40,285 INFO : lisa.wlgen.rta.RTA : + phase_000003\n",
"2019-07-18 19:10:40,286 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:10:40,287 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 30 %\n",
"2019-07-18 19:10:40,288 INFO : lisa.wlgen.rta.Phase : | run_time 4800 [us], sleep_time 11200 [us]\n",
"2019-07-18 19:10:40,289 INFO : lisa.wlgen.rta.RTA : + phase_000004\n",
"2019-07-18 19:10:40,290 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:10:40,291 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 40 %\n",
"2019-07-18 19:10:40,292 INFO : lisa.wlgen.rta.Phase : | run_time 6400 [us], sleep_time 9600 [us]\n",
"2019-07-18 19:10:40,292 INFO : lisa.wlgen.rta.RTA : + phase_000005\n",
"2019-07-18 19:10:40,293 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:10:40,294 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 50 %\n",
"2019-07-18 19:10:40,295 INFO : lisa.wlgen.rta.Phase : | run_time 8000 [us], sleep_time 8000 [us]\n",
"2019-07-18 19:10:40,296 INFO : lisa.wlgen.rta.RTA : + phase_000006\n",
"2019-07-18 19:10:40,297 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:10:40,298 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 60 %\n",
"2019-07-18 19:10:40,299 INFO : lisa.wlgen.rta.Phase : | run_time 9600 [us], sleep_time 6400 [us]\n",
"2019-07-18 19:10:40,299 INFO : lisa.wlgen.rta.RTA : + phase_000007\n",
"2019-07-18 19:10:40,300 INFO : lisa.wlgen.rta.Phase : | duration 1.000000 [s] (62 loops)\n",
"2019-07-18 19:10:40,301 INFO : lisa.wlgen.rta.Phase : | period 16000 [us], duty_cycle 70 %\n",
"2019-07-18 19:10:40,302 INFO : lisa.wlgen.rta.Phase : | run_time 11200 [us], sleep_time 4800 [us]\n"
]
}
],
"source": [
"from lisa.wlgen.rta import RTA, Ramp\n",
"import json\n",
"\n",
"rtapp_profile = {\n",
" 'test_task': Ramp(period_ms=16, start_pct=10, end_pct=70,\n",
" delta_pct=10, cpus=[big_cpu], sched_policy='OTHER')\n",
"}\n",
"\n",
"def provide_calibration(calibration):\n",
" target.plat_info[\"rtapp\"].add_src(\"user\", {\"calib\" : calibration})\n",
"# Uncomment if you want to use this\n",
"provide_calibration({0: 580, 1: 218, 2: 218, 3: 579, 4: 579, 5: 579}) \n",
"\n",
"rta = RTA.by_profile(target, \"test_ramp\", rtapp_profile, logstats=True)\n",
"\n",
"with open(rta.local_json, 'r') as fh:\n",
" conf = json.load(fh)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:10:41,121 INFO : root : RT-App Configuration [/data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_191039.938740/test_ramp.json]:\n"
]
},
{
"data": {
"text/plain": [
"{'global': {'calibration': 218,\n",
" 'default_policy': 'SCHED_OTHER',\n",
" 'duration': -1,\n",
" 'ftrace': 'main,task,loop,event,stats',\n",
" 'logdir': '/home/root/devlib-target/lisa/wlgen/test_ramp_20190718_191039_8pUPnW',\n",
" 'logstats': True},\n",
" 'tasks': {'test_task': {'delay': 0,\n",
" 'loop': 1,\n",
" 'phases': {'p000001': {'cpus': [2],\n",
" 'loop': 62,\n",
" 'run': 1600,\n",
" 'timer': {'period': 16000, 'ref': 'test_task'}},\n",
" 'p000002': {'cpus': [2],\n",
" 'loop': 62,\n",
" 'run': 3200,\n",
" 'timer': {'period': 16000, 'ref': 'test_task'}},\n",
" 'p000003': {'boost': 200,\n",
" 'cpus': [2],\n",
" 'loop': 62,\n",
" 'policy': 'SCHED_OTHER',\n",
" 'run': 4800,\n",
" 'timer': {'period': 16000, 'ref': 'test_task'}},\n",
" 'p000004': {'cpus': [2],\n",
" 'loop': 62,\n",
" 'run': 6400,\n",
" 'timer': {'period': 16000, 'ref': 'test_task'}},\n",
" 'p000005': {'cap': 440,\n",
" 'cpus': [2],\n",
" 'loop': 62,\n",
" 'policy': 'SCHED_OTHER',\n",
" 'run': 8000,\n",
" 'timer': {'period': 16000, 'ref': 'test_task'}},\n",
" 'p000006': {'cpus': [2],\n",
" 'loop': 62,\n",
" 'run': 9600,\n",
" 'timer': {'period': 16000, 'ref': 'test_task'}},\n",
" 'p000007': {'cpus': [2],\n",
" 'loop': 62,\n",
" 'run': 11200,\n",
" 'timer': {'period': 16000, 'ref': 'test_task'}}},\n",
" 'policy': 'SCHED_OTHER'}}}"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Reduce boost on PHASE_2: from 40% to 10%\n",
"conf['tasks']['test_task']['phases']['p000003'][u'policy'] = 'SCHED_OTHER'\n",
"conf['tasks']['test_task']['phases']['p000003'][u'boost'] = 200\n",
"# cap PHASE_7: from 60% to 30%\n",
"conf['tasks']['test_task']['phases']['p000005'][u'policy'] = 'SCHED_OTHER'\n",
"conf['tasks']['test_task']['phases']['p000005'][u'cap'] = 440\n",
"\n",
"with open(rta.local_json, 'w') as fh:\n",
" json.dump(conf, fh, sort_keys=True)\n",
" \n",
"# Push new configuration and prepare command\n",
"target.push(rta.local_json, rta.remote_json)\n",
"cmdline='{} {} 2>&1'.format(target.target.get_installed('rt-app_uclamp'), rta.remote_json)\n",
"rta.command = cmdline\n",
"\n",
"# Report the new confiuration here\n",
"with open(rta.local_json, 'r') as fh:\n",
" conf = json.load(fh)\n",
"logging.info(\"RT-App Configuration [%s]:\", rta.local_json)\n",
"conf"
]
},
{
"cell_type": "markdown",
"metadata": {
"toc-hr-collapsed": false
},
"source": [
"## Run Test"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:10:41,142 INFO : root : #### Start RTApp execution\n",
"2019-07-18 19:10:45,430 INFO : lisa.wlgen.rta.RTA : Execution start: CGMOUNT=/home/root/devlib-target/cgroups /home/root/devlib-target/bin/shutils cgroups_run_into /uclamp /home/root/devlib-target/bin/rt-app_uclamp /home/root/devlib-target/lisa/wlgen/test_ramp_20190718_191039_8pUPnW/test_ramp.json 2>&1\n",
"2019-07-18 19:10:52,690 INFO : lisa.wlgen.rta.RTA : Execution complete\n",
"2019-07-18 19:10:53,386 INFO : root : #### Save FTrace: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_191039.938740/trace.dat\n",
"2019-07-18 19:10:56,605 INFO : root : #### Save platform description: /data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_191039.938740/plat_info.yml\n",
"2019-07-18 19:10:56,721 INFO : root : LITTLE cluster max capacity: 810\n",
"2019-07-18 19:10:58,560 INFO : lisa.trace.Trace : Platform clusters verified to be Frequency coherent\n",
"2019-07-18 19:10:58,570 INFO : root : Events collected in range (0, 8) on cpu2\n",
"2019-07-18 19:10:58,575 INFO : root : Loading RTApp stats from [/data/Code/lisa/results/Target-juno-20190718_190651.324480/RTA-test_ramp-20190718_191039.938740/rt-app-test_task-0.log]...\n",
"2019-07-18 19:10:58,582 INFO : root : Frequency residency [%]:\n",
"2019-07-18 19:10:58,768 INFO : root : CPU capacity levels: {600000: 512, 1000000: 853, 1200000: 1024}\n",
"2019-07-18 19:10:58,773 INFO : lisa.analysis.frequency.FrequencyAnalysis : Average frequency for CPU2 : 0.879 GHz\n"
]
},
{
"data": {
"image/png": 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1xswBG4lfsyzrL2PMSgBD4jUUrw3g8yc8ZhEREXmOdevW7Y7m4Hbr1q371zL73e8KFSp0R2+lYcOGAQC8vb3veOczfvn93c/VrFkTEydOfOD4fH194evrG/s4ODj4ns/5+/vfc51y5col22kAIiIikjSSRfBkjJkNNgfPbIw5C96dbiiAecaYdwGcAtDctvovAF4HcAxAGIB3AMCyrH+MMYMAbLetN9DeaFxERERERERERB6/ZBE8WZbV6j5P1bjHuhaALvfZzlQAUx/j0ERERESeavGrk+xWrlyJzz777I5l+fPnx+LFi5/QqEREROR5kSyCJxERERF5fOrUqYM6deo4ehgiIiLyHEjOzcVFREREREREROQppuBJRERERERERESShIInERERERERERFJEgqeREREREREREQkSSh4EhEREXnKBAcHo3jx4o4ehoiIiMgjU/AkIiIiIiIiIiJJIoWjByAiIiLyRH34IbB79wNXSx0dDTg7J2ybpUoBo0f/5yrBwcFo0KAB9u/fDwAYMWIEbty4gTZt2qBTp064dOkSnJ2dMX/+fDjH229wcDDatm2LmzdvAgDGjRuHihUrIjAwEP3790f69Omxb98+NG/eHF5eXvDz80N4eDiWLFkCT09P+Pr6wtXVFUFBQbh+/TpGjhyJBg0a3HOM/v7+WLJkCW7evImjR4/ik08+wa1btxAQEIBUqVLhl19+QcaMGTF58mRMmjQJt27dQsGCBREQEIA0adKgUaNGaNq0Kd5++218//332LBhA2bOnJmwcygiIiLPJFU8iYiIiDhQ69at0aVLF+zZswebN29G9uzZ73g+a9asWL16NXbu3Im5c+eiW7dusc/t2bMHEydOxMGDBxEQEIAjR45g27Zt6NChA8aOHRu7XnBwMLZt24aff/4ZnTp1QkRExH3Hs3//fixatAjbt29H7969kSZNGuzatQuvvvoqpk+fDgBo0qQJtm/fjj179qBo0aKYMmUKAGDSpEkYOHAgfv/9d3z77bd3jEFERESeT6p4EhERkefLAyqT7MJDQ+Hm5pakQwkNDcW5c+fg4+MDAHB1df3XOlFRUejatSt2794NZ2dnHDlyJPa58uXLxwZVnp6eqF27NgDAy8sL69evj12vefPmcHJyQqFChVCgQAEcOnQIpUqVuueYqlWrBjc3N7i5ucHd3R1vvPFG7Db37t0LgOFUnz59cPXqVdy4cQN16tQBAHh4eGDgwIGoVq0aFi9ejIwZMz7qKRIREZFkTsGTiIiIyBOQIkUKxMTExD7+r6qj+EaNGgUPDw/s2bMHMTExd4RTqVKliv3ayckp9rGTkxNu374d+5wx5o5t3v04voRs09fXF0uWLEHJkiXh7++PwMDA2Nfs27cPmTJlwvnz5xN0fCIiIvJs01Q7ERERkSfAw8MDFy9eREhICCIjI7F8+XK4ubkhV65cWLJkCQAgMjISYWFhd7zu2rVryJ49O5ycnBAQEIDo6OhE73v+/PmIiYnB8ePHceLECRQuXPiRjiU0NBTZs2dHVFTUHT2ctm3bhl9//RW7du3CiBEjcPLkyUfaj4iIiCR/Cp5EREREngAXFxf069cPFSpUQK1atVCkSBEAQEBAAMaMGYMSJUqgYsWKuHDhwh2v69y5M6ZNm4aSJUvi0KFDSJs2baL3nSdPHlSoUAH16tXDxIkT7zmlLzEGDRqEl19+GZUqVYo9jsjISLz33nuYOnUqcuTIgW+//Rbt27eHZVmPtC8RERFJ3ox+Gfhv5cqVs4KCghw9jKdDypTAJ58AQ4Y4dBiBgYHw9vZ26Bjk6aZrRBJC18nz5eDBgyhatGiiXhP6BHo8PQm+vr5o0KABmjVr5uihxHqY78fTSj9L5EF0jciD6BqRhHgarxNjzA7Lsso9aD1VPImIiIiIiIiISJJQc3ERERGRZ5i/v/+/lq1cuRKfffbZHcvy58+PxYsXP6FRiYiIyPNCwZOIiIjIc6ZOnTqoU6eOo4chIiIizwFNtRMRERERERERkSSh4ElERERERERERJKEgicREREREREREUkSCp5ERERERERERCRJKHgSERERcSB/f3+cP38+9nGHDh1w4MABAEC+fPlw+fJlRw1NRERE5JEpeBIRERFxoLuDpx9++AHFihVz4IhEREREHh8FTyIiIvLc8fYG/P35dVQUH8+YwcdhYXy8cGEKAMC1a3y8aBGfv3yZj5ct4+MLFxK2z+DgYBQvXjz28YgRI1C8eHEEBQWhdevWKFWqFMLDw+Ht7Y2goKAEbbNx48YoW7YsXnrpJUyaNAkAMHHiRPTs2TN2HX9/f3Tt2hUAMGjQIBQuXBiVK1dGq1atMGLEiIQNXkREROQhKXgSERERcZBmzZqhXLlymDlzJnbv3o3UqVMn6vVTp07Fjh07EBQUhDFjxiAkJARNmzbF4sWLY9eZO3cuWrZsie3bt2PhwoXYs2cPfv311wSHWyIiIiKPIoWjByAiIiLypAUGxn3t4nLn4zRp+Dg09DYAwN39zuczZ77zcbZsSTfOBxkzZkxsyHTmzBkcPXoUr7zyCgoUKIAtW7agUKFCOHToECpVqgQ/Pz80atQIrq6ucHV1xRtvvOG4gYuIiMhzQ8GTiIiIyBOQIkUKxMTExD6OiIh4pO0FBgZizZo1+OOPP5AmTRp4e3vHbrNly5aYN28eihQpAh8fHxhjHmlfIiIiIg9LU+1EREREngAPDw9cvHgRISEhiIyMxPLlywEAbm5uCA0NTfT2rl27hgwZMiBNmjQ4dOgQtmzZEvucj48PfvrpJ8yePRstW7YEAFSqVAnLli1DREQEbty4Ebt/ERERkaSkiicRERGRJ8DFxQX9+vVDhQoVkDNnThQpUgQA4Ovri06dOiF16tT4448/Ery9unXrYuLEiShatCgKFy6MV155Jfa5DBkyoGjRojhw4AAqVKgAAChfvjwaNmyIEiVKwMPDA15eXnB3d3+8BykiIiJyFwVPIiIiIk9It27d0K1bt38tb9q0aezXgfEaSAUHB993W6lSpcKvv/563+fvVdH0ySefYMCAAQgLC0OVKlVQtmzZhA1cRERE5CEpeBIRERF5Tvzvf//DgQMHEBERgXbt2qFMmTKOHpKIiIg84xQ8iYiIiDzFQkJCUKNGjX8tX7t2LTJlypSobc2aNetxDUtEREQkQRQ8iYiIiDzFMmXKhN27dzt6GCIiIiIPJdnf1c4Y08MY86cxZr8xZrYxxtUYk98Ys9UYc8wYM9cYk9K2birb42O25/M5dvQiIiIiIiIiIs+uZB08GWNyAugGoJxlWcUBOANoCWAYgFGWZRUEcAXAu7aXvAvgim35KNt6IiIiIvIMGjgQMAbInh2IiHD0aERERJ5PyTp4skkBILUxJgWANAD+AlAdwALb89MANLZ93cj2GLbnaxhjzBMcq4iIiIg8IUWLAgULAg0bAs7Ojh6NiIjI88lYluXoMTwSY0x3AIMBhANYBaA7gC22qiYYY3ID+NWyrOLGmP0A6lqWddb23HEAL1uWdfmubf4PwP8AwMPDo+ycOXOe2PE8zarUqoUzzZvj5HvvOXQcN27cwAsvvODQMcjTTdeIJISuk+eLu7s7ChYsmKjXREdHw1lpRZI4duwYrl275uhhPBb6WSIPomtEHkTXiCTE03idVKtWbYdlWeUetF6ybi5ujMkAVjHlB3AVwHwAdR91u5ZlTQIwCQDKlStneXt7P+omnw3GIG/evMjr4PMRGBgIfU/kv+gakYTQdfJ8OXjwINzc3BL1mtDQ0ES/5nEJDg5GgwYNsH//fofsP6m5urqidOnSSbqPa9eACROA118HSpRIuv3oZ4k8iK4ReRBdI5IQyfk6Se5T7WoCOGlZ1iXLsqIALAJQCUB629Q7AMgF4Jzt63MAcgOA7Xl3ACFPdsgiIiIiktSuXQM+/xxo0wbIlEk9nkRERBwlWVc8ATgN4BVjTBpwql0NAEEA1gNoBmAOgHYAfrKtv9T2+A/b8+us5D7XUERERBLlww+B3bsfvF50dOoE9wUqVQoYPfq/17m7imnEiBG4ceMG2rRpg06dOuHSpUtwdnbG/Pnz75jiFxwcjLZt2+LmzZsAgHHjxqFixYoIDAxE//79kT59euzbtw/NmzeHl5cX/Pz8EB4ejiVLlsDT0xO+vr5wdXVFUFAQrl+/jpEjR6JBgwb3HeO99tWyZUu0bdsW9evXBwD4+vqiQYMGeP311+Hr64v9+/ejcOHCOH/+PMaPH49y5R5YdZ/kcucGwsOBwEDg55/V40lERMRRknXwZFnWVmPMAgA7AdwGsAucIvczgDnGmK9sy6bYXjIFQIAx5hiAf8A74ImIiIg4TOvWrdGrVy/4+PggIiICMTExuHjxYuzzWbNmxerVq+Hq6oqjR4+iVatWCAoKAgDs2bMHBw8eRMaMGVGgQAF06NAB27Ztg5+fH8aOHYvRtjQsODgY27Ztw/Hjx1GtWjUcO3YMrq6u/xrL/fbVokULzJs3D/Xr18etW7ewdu1aTJgwAePHj0eGDBlw4MAB7N+/H6VKlXoyJy0BIiOBmBigbl1+iIiIiGMk6+AJACzL6g+g/12LTwCocI91IwC8+STGJSIiIk+nB1Um2YWGhid5j6fQ0FCcO3cOPj4+AHDPMCgqKgpdu3bF7t274ezsjCNHjsQ+V758eWTPnh0A4Onpidq1awMAvLy8sH79+tj1mjdvDicnJxQqVAgFChTAoUOH7hkS3W9f9erVQ/fu3REZGYkVK1agSpUqSJ06NTZu3Iju3bsDAIoXL44SSdlMKZHatgUOHAD+/NPRIxEReTg//wxUrw6kTv34thkZCZw7BxQokLD1//kHyJjx8e0f4NTnv/4C8ucHzpwBcuYEnP6jCdD160C6dAnffnQ0cOIE4OkJXLkCvPACkCrVneuEhQGXLgF58/L/iZw5gfTp+dy5c0COHIAx/97ujRuAuzvf0PD0BMaPT/i4nmfJvceTiIiISLKQIkUKxMTExD6OSGDToVGjRsHDwwN79uxBUFAQbt26Fftcqni/STs5OcU+dnJywu3bt2OfM3f99nz34wfty9XVFd7e3li5ciXmzp2LFi1aJGjsjtSmDfDxx8DIkYCbm3o8iYjjzZkDzJvHr+fOBVav5tfR0QZHjgBXrwJdugANGgBnzwINGwLLlnGd0FCG6Vev8oYJ9+oxHRgIfPYZp5NnyAB88MG/12nVioHJ+vVAnz4MtwAGMUuWAKdOAb17A1u2sFde587A2rVc58oVYPNm4O+/gdKlgfbt2UuvRQsgKorr/PUXsG8fx5o/P9Cr17/H0L490LUrQ6datQA/Py6/fh145x3g1i3gjTeAggW5vY8/BjZu5DpBQUDRotzfrVtAo0Zx2712jR9ffw1UqQKsWAEMHw506vTvMdStC+TLB/zxB9C/PzB5MpefPQvkysVQ6uWXea7s/+0OHAh8/z2/Ll0aKFz439uVe1PwJCIiIvIEeHh44OLFiwgJCUFkZCSWL18ONzc35MqVC0uWLAEAREZGIiws7I7XXbt2DdmzZ4eTkxMCAgIQHR2d6H3Pnz8fMTExOH78OE6cOIHC9/lt+b/21aJFC/z444/4/fffUdc2d61SpUqYZ/sr6sCBA9i3b1+ix5ZUGjXiHzclSwL/+596PInIk3PrFqtj7CIjGWTMng0MGsRlvXsDs2YxyFi3LisKFwZmzgRCQhhqpEsHDBvGHoJvvskw/aWXgCNHGJSUKAEsXMiAycuL1TmLFgFjxgBp0wLdugFvv8395MoFXLjA0GrkSGDrVqBaNd75c948oFIl4KOPAB8fYM0aLv/uOwZMv//OEClvXm6zUiVgwwaGNq1bA0WKcEwzZgCFCgEtWwLlygHFigHvvQd4eAB9+wJlygCHDnEc1atzO7lzc70XX+S4v/6a4dfChVz+5Zccw7x5HMfQodxXSAhw/jwwYAADsgEDgF9+4b5r1QJq1AB69uRdTc+e5VgXLgR++IFjmjsXWLoU2LOH44qJ4ToTJwKLFzPUO3EC6NeP53DuXB7nzp3AtGn8/n39Nc+HJEyyn2onIiIikhy4uLigX79+qFChAnLmzIkiRYoAAAICAtCxY0dceF3lAAAgAElEQVT069cPLi4umD9/PpzizTno3LkzmjZtiunTp6Nu3bpImzZtovedJ08eVKhQAdevX8fEiRPvOaXvQfuqXbs22rZti0aNGiFlypSx67dr1w7FihVDkSJF8NJLL8Hd3T3R43vcjh8HBg8GevTgHyA1ajh6RCLyrLMHS40asYpm0SJW9Wzbxilc773HCifbj37Mns3qHVbjFMVnn7HiZ84cPh8czG1t3AgsWMBgqGtXTgNr2pQfixcD9epxKt7+/cArr/Dn35o1DG2uXWNo4ukJ2GZl4+hRoEIFYPp0BvONGnFbx48DX3zBUOn0aSBFCm5zwQJg+XIuy5GD2xg+nMdl164dq5uCgznGQoWAgwe5vTNngDx5gDRpWKkEcFmuXPx60SJ+dO/OCqcFC3i+WrXi82vWcNnKlcC33zIMungxriqqZUseB8Cf+aNGMUj66CNWcX3zDYOq+DPBly4FTp7ksg0bGFaFhwPvv89tbd7Miip3d6B+fQZ5YWHAlCk8J8bwDY3vvtObGgml4ElERETkCenWrRu63eMt0nXr1v1rmf3ud4UKFcLevXtjlw8bNgwA4O3tDe94cy0CAwNjv777uZo1a2LixIkPHN/99gUwOPvnn3/uWN/V1RUzZsyAq6srjh8/jpo1ayJv3rwP3E9Su3aNf6y8/bajRyIiz4tp04Aff2S1zMKF7AVUtSqfc3Hh5xw5OHUtXTpW7ixaxLApVapd2L+/NEaPBipWBIYMYWXQiRPAiBEMRr74ghU8v//OwOm99xjyvP46g/aPP+Y+Jk3idLmBAxk2XbjA15Yrx9d++CFw+DCnmkVFATVrsqIpe3aGNZs3A3v3sjpozx4GUHPmAHXqAMWLM1RydQW++ophlbc3A6M5c4Dbt1kZ1bMnK40yZWLFVZ8+QOPGXGfNGvZFmjiRgdSpU5z29+GHDJ+6d2cV09KlDJzWruV06TlzGF5Vq8ag7PZtrjNrVlzQ17w5p/nNmsVArnRpBktbt7LSrHp1TkPs0gV49VWGRrlzc4rixo2szKpRg+fk3DmOpUABjsHFJS6QAzhmhU4Jp+BJRERERB5KWFgYqlWrhqioKFiWhe+++y62GsqRypThHwgA3+3u04d/3N2n0EtE5KHZexs1b84pby++yEqYVKnYY8nHh/2ZwsIYRF2+zClq27ezSuqzz4AmTbIgWzZW1NjfMxg0iEFImjTAqlUMSlq14vOlSwMBAZyCFhLCKqC+fVnJVKsWK6nWrgWyZmUAZnfyJHsfnT3LACdNGoYuDRsCHTsyzBk+nNPgAIZPfn6c7lalCn+29urF4MtemeXvz88XLrCJd9asfD59eqByZVZY2aujypbl/qpV4+NevfixbBkrldq1Y9jWpg2rtwCGeDNm8LwNHMhg6/Rp4K23OOY//+Q5adOGbzasWMHvQ2goQ6QNGxh0TZvGMMmyOGWucWNup1cvrvPbb9x+3boMsrp2jQvzLl1ihVW5cqwgs6zHfRU9+xQ8iYiIiDzD/O1/FcSzcuVKfPbZZ3csy58/PxYvXpyobbu5uSEoKOhRhpckjhzhHZIKFmRz2E8+0TvTIvJ4RUYy1GnVilPojh1jhc/06QxENm6M6wcUFgbs2MFAp2ZNhhq//MIgx9sbuHLlBYSHMzwaPpxNq6tWBbJlY0XQiy8ywKlcmT2f+vfn3eYATg+z7+PNNxmOvPUWq4UKFWIYExAANGnCAOfgQf48XLGCwbxd4cLAa6+xJ9Prr7O/VKpUrJZ69VWu4+LCiqghQxj01KoFfPopw6ls2RhkhYUxpKlUiZVJJ06wSig6mhVHq1bxjYDUqbk8JIShT6VKPJ/e3uyf9NprDK8yZGCQtXgxf5ZPmMDXt2zJ6W6DB/NjzRqeq5AQTuUD+GbD9evA1Kk8hlOnGKJFR7Oqy9mZY7AHXwsW8LgmTODUwQIFeM6yZuXUwJ49eX4l8dRcXERERJ4Llt6ijFWnTh3s3r37jo/Ehk4P60l8H9q3j7uLUdWq/CPJPtVFRORRTZ/O4ChzZiBnTi5r2BAoXx746SdOW2vWjEFQ1apAxoysGBo0iEFRQAAwdiyDmMBA4PZtgyFDOH3rk0/Y6LphQ4ZBffsydBo9mtU4AQEMk6pV4/S0Ll04hS4ggBVP06ax4qp5cwZLAQEMTRYtYjD0118Mhpo0YaPxfv04/pde4nhmzOCd7q5dYwA1bx4DJIBh1PbtDHzq1wdSpuS4goLYR8rZGVi3jj2WFi1iIFWsGIOeTJn4XMaM3HZEBIOcxo05ZW/zZh7vZ58xxGvcmOOsVo2v+9//OO1w61aGTl9+Cdy8yf2ULcupgAsXAr6+HKOrK/Duuwzfmjbl9L2//uLrvv6alWqXLzMAs1d//fYbv68ZM7LKqUcPNj3PnZuBmo8PAztjGO5Jwil4EhERkWeeq6srQkJCFD45mGVZCAkJuW9z88fl66/5x4WIyOM2axZ/xvTpw4qcIUNYRWS/F8PQoQxlLlxgBc2GDZzC9s03DGZq1mRwdPo0q3ROngQ+//wg5szhFLwePdjn6ehRVuhs3coQpG5dVva8/TZQuzaDkBw5WHGULx8reNKkYaDzwQcMuvLm5RjOnGFvo6pVWT31yivA5MkMTz76iFPHBg/mXedWr2a48v77fH22bNzG7dsMonr04NfVqjGASZWKvZ+OHeMx58/PKrCmTXmOZszg9i9fZrhUrhyrm9zcGCTt38+m5DExnOr21VcMwexVVw0acAxjxzIE+/tvTqUbMICVrRUqMGjr1YvfDycnhlDGsNLp7795rDduAFeuMFT64gsgSxagQwegbVtOXdy3j0FW2rTcX9as3Ebu3KzaOnyYUxTts8kzZXLQBZhMaaqdiIiIPPNy5cqFs2fP4tKlSwl+TURERJIHJM8jV1dX5LLfziiJvPZa3NdDhwKff847FunbKSKPKmNGViOFhLBaZsAABj1btvD5Xr1457rISAY0qVIxcPL1ZUgEsBJo1y5W8fTtC7Rp8wqqVOE0u549ebe1kSMZUk2fzmCmRQvgnXfYW2nwYFY7XbrEECp1alb2fPstp4nVrcvQx8mJIVnHjmzmvWwZQ51Dh1it5eXFiqSRI1k1VLIk91OvHiu5JkxgSNSiBcOqt9/msaZLx8beALfh6spQaN064PvveRwZMjC82rWLgdN77zGE8vdnU/F69RjGeXmx51KnTgyAnJ053W3GDO5nyBCOceJEBlyrVzMI6t+fY8uViyHekSOs/po0ied51Sr2Y5owgVVRgYF8vl07junLL4ElS3gcFSrw2Pr2ZXCXKhWPeflyhm+LFrHR+pAhDB71HlbiKXgSERGRZ56Liwvy58+fqNcEBgaidOnSSTQiSSoHDvDd7C+/5B9RlSvzD5QU+q1XRB5BcDAwcyaDnMGD+TNmzRpOpwsPZ9Bz+TLDCm9vrvvOO7wz2uDBDDW8vBhAZcrEahqAP6d8fM6iZctcaN6c26hdm9PMcuZkVU7JkqxgGjqUlThp0/Ln2ldfMYhp0IBh16JFrBS6coXh1aBBrHL6/XeGOh06sJKqTx++9tIlTi+bNo132+vZk8HLzZusTAoKYsD23XcMdlq3Zn+qqChg3DhWAy1fDrzxBgMpNzeGOx98wMqgX39ltVTZsgy7qlfn9Lft21mldesWG5J//z1Dpp49+drcuVn1dfs2p/0BDKa2bWP1UoEC3N7IkXxuzRqOf+pUBnBjx/Jn/qlTbDK+Zw+nFM6axWUbN/JcTZ7M3lu3b/O1L73EarW5cxnaHTjAZf36cfrkd99xCqAknv4LFhEREZFnRmQk/0AMD+fjypX5ISLyKL75hoFFp05xFTFLl/KjYUP2UgIYgFy6xF5z58+zR9Dhw8DKlZwON3QoexFt2sTpX4MHAyEhbsifn9VAQ4fypggZM7KCadcuBiB16gDz5zP42bSJy3PnBnr3ZvVQ5coMuwoX5nS3b79loFK5MnvchYTw85dfsnLIshjkWBbDs5QpGVD9/jurhkqWZMBz5Qp/rjZuzON85x1O6fv1V971rV49/sw9e5ahf7NmDKjWruX233iD+161itsyhtMHe/RgJdSmTfyYP5/hTnQ0w7D27TmGUqUYdMXE8Lz368dQqF49Nhz392flUrVqDOHSpmWoljIlK59OneJUyO+/Z5A3ejTHMHo0vx+HDvHjzz8ZDhYsyLvhffQRz/WZMwzrevdmcKg3MR6OTpuIiIiIPDNKl2YvE7vbt/mHTMqUcRUGIiKJ9c47DFRWroy7k9yXXzIYOX6c07BOnmSlkP1eDbt28fPHHzN8mTqVgUfZsqzoAexT2i5hzBh3zJjBapuTJ9nvKEcOVj9ducLqI/tNE27fZmiTMyfDJMtitdD8+QxaTp1i6DNgAMMigD2M+vRhdVOlSqxMcrJ1fB47FvjxRwY3AQGsPvLw4JS59Om5zg8/MCCqUIHH0LAhw6fs2TklMG9ehmZ//80qJ4DVWW+/za979OCYAIZRFy+yx1Tx4vx4/XWenzZtuJ+GDePucGcPe954g+clMJDnuXp1jrVcOVYqXbzI9fr04fTDN9/kOcqTh9uKieFxAjxX3t5sZv7iiwzs3Nx4vjdvZuVZ+fKcklilCqdwjx4NzJ79WC6n546ai4uIiMNZFv8g/PZb/pL11VeOHpGIPCtGjGD/kchIR49ERJKjo0fZu6hJE1Y3bd4MdOvGcGjHDjbRjoxkQNK6NSt1/Pz4u82CBfxcqxZDjU2bGGB89RV/5/niC97hbdmy7KhenaHHiy8yzHJx4XS5P/7gNLc2bRh6nTvH6Wevv86Kp6pV2T8pQwZOk7t0iT2TAgJ4l7yYGPaFSp2aY1m5kuN2dWW4U78+q3qWL2dQkycPw/tz5xgilSnDvlUnT7KC6O23WXWUNy8DJy8vhjbz57NSqVs3jvfSJaBRI1ZAXb/OaW1ubjyH48bx2L/4gtMOBw5kMLd6NV9Tty6rtpYsYbXYyy/HVSB16MCgqnFjns+CBTn+I0d4DEeO8PgbNWJVWv36/N5dusReUenSMbj79ltO0bt4kb979urFCq+BA+MCpnTpeH5XrWKFlT3YksRTxZOIiDhcaCjfDcyUib+glCjh6BGJSHJVuzb/UFm+nH8QVa3KSgRNjxCRh/HmmwxZcubkFLQMGdinKEsWhh+//cYKm7x5WSHj58fnTpxg4+uVKxmUrFt353b9/blO795A8+ZnULFiEbzzTtzzt29z2l7t2vz51bs3K6WcnBiUfPEF1xswgNPLGjbkjRQAVul0787qJD8/hjY//MCQxtMTGDWK09heeYWBzMsv8/ewihVZLWV36xa3kTMn+0j17s1QLH77w9OnuU7atAzA3NzYZwlgSLR2LacpDh7MMKxNm7jzCrBJ+o0b3GaPHjzenj353OjRDNNKlmQgV7s2P+w2bOAxFCnCbcfft70yKTSU0wfTpAGKFo1r9u7lxX1XrsywMCaG56Zv37jt//MPz0HBgtyum1tirhyJz+i2wv+tXLlyVlBQkKOH8XRImRL45BP+9uZAgYGB8Pb2dugY5OmmayR5ioriu3tPiq4TeRBdI8nTvn2sMvj88ydzFztdJ/IgukaSt27d2F9p/HhOv9qyhUFM3boMj+w9ngCGE82bs8Jn7Fj+6TRrFu8q16/f/ffxMNdIZCRDlIS4ejVuypwjWBYruxw5BgAIC2MAlVw9jT9LjDE7LMsq96D19N6PiIg43O3bfFfJ3V23OxeRR+PlxQ+7yMi4Oyepx5OIJNaYMQyYypbl1LN+/YALF1jd5OHBau1jx/j7y9Chcc/XqgW8+iqriKpXf/zjSmjoBDg+8DHG8WMAknfolNypx5OIiDjcoUNAtmycGpMpE5t1iog8jMBA3jHJbtQo9ulQjycRSaxvvmFo8uWXrKScPJlT7QYO5DS4jRsZNHl6sgpq7FhOEsmTh42wM2cGfHz4xprI80wVTyIi4nDZswPffcf5/e3b8+4kIiIPo0cP/tH30098XL06G4yrx5OIJJb9znMVK7Kp9vz57Ovk6cnlw4bF3RlORO5P/wWLiIjDZcoUd2vi4cMdOxYRSd5mz2bFgV2FCvwQEUmoLVuARYvYlLtPHzbg9vNjj6c8efgGWe3aDm99K5JsKHiShLPf63z8eIcOo3J0NG9FIHIfukaSH++bP2NLdHmkQgScYGFi6h5o4bIoSfep60QeRNdI8rPp9stI+WI+lN81KXZZeDgbymbMqB5PkrxERvKOYJ6evF399u3sK3Q/MTG8UUdiev/IvU2cyLvV/fkn3xzbtYsh0+3bvJNatmzA6687epTiKEt7b0XLIV7oU2sbvljl7ejhJAsKniThxo/nT18H++vsWeTOlcvRw5CnmK6R5OfYD8Vhwp3QvsRujN5VBWMz9EWL5knbhVLXiTyIrpHk5+c5JbBtTx6sibfMz493uAsP180LJOndL/w5fpzPZcnC29p7eQFVqvC28WPH3jsU3bQJ8PUFihcHJkwAKlUCQkJ4S/d//uEUryVLgO+/B/74Azh3DmjZkq+Lr0cP3uZ+925gwADggw+A+vWBn3/m1zExwFtvATNn8g5uP/0ETJ/OO6GlTg3Uq3fn8VWrxlvUN2rEu0gOGsR/W4cP805vZ88CbdowNPvsMzbkHj+eX3fvDhQpwob/kZHc3scfs3fSxx+zoui771i5WKUKAze706d5dzkPD2D9et4xbuBAIG1a/oly4wbHNXkyp8DVrAnkzg2sWQO0aMFzmCVL3Pb27QOmTQO6duW6efPyDnV//81z6uLC0KlaNaBpU4bX9esDkyaxJ6U8v/ZtC0c40mDrvtSOHkqyoeBJEq5DB0ePAABwPDAQuZ+y20jK00XXSPLz0gHeenjUH1UxxhnI9VoBdgROQrpO5EF0jSQ/9S5PQuZZKwDUiF1Wsyb/MFWPJ3nctmwB5s4F3nmHwc2rrzLkqVQJ2LkT+PVXIDgYMCYbIiJYLVOrFsOfr78GrlxhINOsGbBwIZtVh4Ux1Ni/n/2Edu1iH8S1a9msOnt24OZNXs+1a7Nx9fXrHM/Agbyb2oIFDLNy5gRatWLD/b//BmbNYtiSPTswbhwDpldeAVas4HFUrgyMHMk7QKZPD3z4IUOvq1c5Dk9Pjv+11/g6T08e46FD3MbNm5yGtnIlq4UCA4ETJxiyXb3KKqKiRYHBg4FVq4CLFxl+TZnC/o6ZMwN//cU7xPXtC+TKxaAnSxa+5sgR9mvbvZvh2siRrDpav56hWapUDLYWLGB49eWXDLhWruS0ud9/Zy/JIkUYeuXLx8kcPXrwdYUL8zw3asTvbYYM/P69+CLDrrJleU7y5nXUFSeSfOm/YBERcajz5/kL6Msv87GzM1CggGPHJCLJ02u5g/Gasx+AuGZx5crphgWSNJYuBUaPZsDRvj0rYvbuZSBz+DDQpQvXS5fOE59+yoqkxo0Zwjg7s3pp926gVCmuN24cP2fMyIqmunWBOXP4ulmzGFitXct13n+f4UiRIqz22b6dQc3s2XH/nwJ87O/Pbf71FwMvgK8rUSKu/9mSJcC1a6waGjaMy2bNYiXVW2/dedxhYayEAjiGCxcYBJ07B7z0EkOa8HAgIoKBmr2ay7IYAnXvznDnxAnegfL0aYZL9kbet24BP/7IkGngQAZo+fIBO3YwiKpfn+fwjz94PEuXxlVq3bgBREdzO61b8+u9e3mOOnfm/rdu5euXL+c4U6Vi5VdYGPDppxzfrFnAmDGslOrdG8iRgxVQ/ftryq7Iw1APfhERcajp0/k5fjNgEZGHERblghAr4x3LQkMZcFuWgwYlyd7Vq6zeCQtj5c+gQaxSeu89Xlfp0rEJdatWrIpZvx5o145hxs2bwPXrLoiJAU6e5FSxU6dY5ZMlCyuRhg8HNm7ktNDGjRnm9OzJkCRDBqBYMVYqnT/PsVgW8M03DJtee40VV336MKixLI5t+3bu74svgDJlgIYNGZ5068axlSjB6iQvL7754+7O5ZkzswqoUCHgf//jFLr8+RlajRrFSqs2bTgRokYNYN06jn/ePE59q1KFoY2fH8e+cCGDoWzZWKE0cya3t2QJx/DLL9xGsWKsUjp3jkHQ3LkM2caP57S3bNk45a1uXW4zQwYGSuHhrFjy82PAbFmcFmdM3HkpU4aVWStW8FjtVV+dO/M8li3LaX5jx7JSq3BhhoIAx5ExI6c8njyp0EnkYaniSUREHKpgQX7u3Zufo6L4C76ISGJ9takaRtzujVvxlo0frx5P8nCuXAHSpGFY0aIFg6A6deKeP36c4dPq1QxqLl1iddGCBVy/enVWBg0fvge5c5fE4cMMXuz/79Wty6Cjb19em5UqMRj6+28GJjVqsLIpf36GI82bM5TKmpX72bePU978/RkwpUjBEGf+/Lgxdu4MtG3LcObwYYYrhQszIHJyYnizcSPDtLFj+e/lk09YfXTxItc9cYLb+vBDhmVt2sRtf9069k4qWZJTDX/7jVVBAwcy8HnzTa6XJw/DnVWruJ+ffmJQd/w4Qx2AFWJdusSdg3PnOP7t27mdPHlYhbRyJZfv3MlgKEMGBmGtWzM027SJ6/r7c7sLFnCq3a5dPMe3brECqlYt9oM6e5bPAdyehwdDqw8/ZEVanToMs9zdH+vlJfJcUfAkIiIOFRHBz25u/OzsrP4JIvJwGhQ8hJxbFwEYE7usbl1WLKjHkyTGnj1s1J06NQOMceMYQBQvzqDpyBGgatW46XQA1+3alU20f/qJjat9fIC+fV3wwQesRJo+ndPn2rThdLquXRnwXLrEoKZQIU5t69KF+7SHPuvWMSQpVixuf5kzA4sXcypasWIMX4YNY8+noUO5zsqV/H+2Th1WHH3/PYOV+fM5hqlTgX79uP/69VnlNHUqQ6Q33+RUwnr1gF69eOwLFjAsyp2bAc5bb7F66OBBPt+rFxuEd+nCMGzzZoZV+fMzcDt8mM3V581j8FO/PtCgAbBsGbc7cyZ7WZUpwxDo88/ZByp7dlYstWnD8K1AAb7Ox4fNw6dMYUjWogXw7rsMjo4e5Ud0NKuwZs1iADdqFMOxGjUYWvXqxeMPCeH31dubU/1efBFo0oQ/PyZOjJteKCKJp/+CJdFu3+Y7LMWLc66ziMij6N+fn+3Bk5MTf0EVEUmsirlOo6LzRMQPnkqViuuhI5JQJUsyvLH/3tugAStjKlfmtK9s2RjYFCvGgGbbNlbopE7N3kNTpjDgyJIFOH06Db75hv2UihVjBdLZs8CBA2yaba/4/eknfi5YkGHXmTOsQJo2jcFHUBAren79lcvXrWPvo2LFGLRERgIdO3LcWbPyo04dhk+pU3N7HTsyuKlZk4/tFVzHj/M43dyATp24ztixrCyaPZvrnD/PsOa33/g3gIsLxzxoEP+NLVzI8KtoUVYp3bzJSiHL4nmMiODUwM2bWf3k4cHpi8uWcfuenuyvtGoVEBDA7X39Nbf91luszKpcmaFXUBCn5tWty9dOmsSA6uWXGeZZFkO8K1cYcu3cyef79mV/q4MH+X1o0ICvz5KF1dZdugBvv83zCXCcI0YodBJ5VOrxJIk2bhzfheja1dEjEZFngX3qS/HicctiYhwzFhFJvvbuBcr92BlfRfe6Y/m1a6wyKVmS02tE/sv69QxcADbADg9nlc3MmayMuX2blbktW3J5ixZ8w+SVV1g9s20bw41Ro7it06eBadPy4cwZTiFr04YBVa9evCNbjx4MoW7c4LSuWbNY2dO9O3sj5c4d1/vJ05Nhy8mTnMY3ZgwrkipX5jVeqBD7NC1bxqlqy5ezIXmOHJxalycPx9CjByt6vvqK2759m+Ns1IgBzNixrHhatYpVS1OnMsiZP589pnr25PO5cvEYhw7l2Pbt45S2mTP594KHBwO0vn3Zt2nhQm5/6lSGOX/+ybFOncreSd26MZRq3pxTHBcvZqA0dCi/fu01VjpNnsyqqHLlOKXv4EFOeUydmvtp3JjfkzJlOA1wzBiGcD17Mlxau5Z3uFu6lOGhmxurnBYv5nH168cxOTsz6BORR6eKJ0m0a9f4ec8ex45DRJ4N5cvzXVF7w86oKGDDBseOSUSSnwULgB0XcmEHBqJPvOUTJnC6TsGCbII8aBD/mBe5l6VLGTps2cIQaPFi3rGud29O2/L3Z4Azdy4rZ7JkYYPqUqUYfMybx6lmmzbxDZWbN4FRo3YhS5bSOHIEmDGDwQzAUGPgQFYYpU3LcLRECQZQH3/McOnzzzmlrkQJ/j85fjybbA8bxru8VanCMKh/f17jU6bwOj9wgGN7910GXJ9+ygqnsWO5nw8/ZON9exC1di1DnvnzuaxePQZWefJwStzVqwymIiPv7PFUvTrPif1ufT4+3N7o0aw+at+e61WtyrDr8895LM2aseLo+HGOEeDYxo5laHfkCLf73nv8njRrxql0w4YxGPL3B/bv52tv3GAINno0g7SNG7nurl38OHKEY27ZkpVTZ84Ahw6xsmnyZO77t9/4+epVfp8bNGDVVnBwkl9yIs8FBU+SaOvX87P9lqciIo+Ts7Om2olI4uXLd+/l9evHTY2qV49/yCp4krtFRfHz118DFy7EvcHasSNDpPBwhh0DB3LZyZNsVB0dzfVOn2ZQM3cug6EZM+KaUQ8Y4IJp01hNM3s2f5euXZv7GTqU4cacOZzGli4dw64ePdiTKTCQn69e5bYmTOB0MCcnTmNbsoS9lo4cYdXT6tWsvOrbl+vv3s1tVq3KAKpmTVb/TJ7MQGfpUoZs7u6cMujry2lu5cszIAJ4zP36carg+vUMbjw8uN8332SVcvr0XHf9ev5bq1GDAVtQEJApE/tQ5cnDN7BDQtjrKVs2Tmnz8mLF4okTDL6qVYubHnvsGKu8qlbleWnenNvMnJnhUPfurHhKlYrfi2++4foBAdzepk2sQgsM5MeYMQzlhgCyHLAAACAASURBVAzh7I0UKRha//MPnxs+nNMffX0ZlJUunUQXnMhzRlPtJNF27+ZnNekUkcfh8GH+wmfn5KTm4iLy+Hh58Y/IOnU4pUhvnMm9eHszmHF1ZXDTsCGrYpo2BT74gIHPnj0MnW7eZDXPl19y+phlAVu3spouRQqGKK1bc7v2kOjdd3kNtmzJcGP4cCBlSgah9tBm7VqGKz4+rPoxhlVN9rveWRarsCpVYqgTGMiwJTqaIUqPHgyMjhxhyDp5MgOcjh05ha1tW/7/2q0bjxVgqLR9O0O1Ll1YcRQVxX8z48dzncWL+TlfPv77yZSJr9m2jXfb69Ahrsn6p5+yJ9WiRdzO669zvZkzOW1v2zaeu+HD+W/z558ZmJ07x/O+dy+ry377jWFeQAAry2rV4vlLmZLrb9nCqqY1a3juv/iC+/3lF4ZPM2fyjawuXbjPsLC4vlq+vmzA/s8//J7t2MGxjxjBQDF7dlaticjjo+hAEq1wYf4AP3vW0SMRkeQuJoZhtmXdudz+DrKISEJt3Hjv5SEh/IN08mT+UZonD/+gzZhRt0eXOB07MrwxhoFS5878v6hMGVYRLVzIaWXlyrHip3VroE8fhjBLlrA66N13OX2ue3f2POrQgeFHqlTFMXAgt9u4McOWS5c4FW7SJO5/wgT+n7hhA6t+IyLYRHzKFO7LHux4eXE84eFAq1ashmrUiAFWzZoMnOzOnGEvpCtXWMUDMHQpUYJBjv3/3t9/5zbtd34bMIDTBjt35ofd4sW8y1utWqwkcnPj67Jn53rjxnFq2zff8HzOmcOpdlOnMhBbsYLnqEABbq9VK64THs7Kr/bted5SpOAUQoDBWM6cnHq4dy8bqgMM0A4d4tenT7OnlZ8f9zF+PI950CCeG/vdCbNkYeP3/PlZbQXwe7puHbf/0UcM7zJkeJxXlogADqp4MsZUNMa8ZYx52/7xCNtKb4xZYIw5ZIw5aIx51RiT0Riz2hhz1PY5g21dY4wZY4w5ZozZa4wp8/iO6vlh78Niv72riMjD+vNP/nL9wgtxy6Ki+EuwiEhi5Mp17+WTJ/OPSmMYCKRIwT98p09/suOTp9vbb8fdXTVjRoYg+fKxYXfPngxMPvuMYc7gwayoKVqUr/Px4RSzTp0YZIwezSqpFSsYutSvfx7ffhs3de3TTzlN7cUXgR9+YPACsDLq0iW+9qefGAzlz89eTJMm8Rru359BlLc3+0k1asTx5snD6X19bA3OBgxgGOvkxH8DGzYwoOnShdVTV66wWghgg/Tff2fl0LFjfK2PD6uWfvuN1UIzZrBn1cCBnDIIsN9S69bc3uXLnEaXOTPDpCxZuI69N+ygQRz76dPcrjEMnoC4pv/Zs3O9qlXZg+qdd7idjh25r507WaW0aROnKs6cyf2OG8fXDBjAaYUffMAQKSSE486Ykftr147VYD17xvXZcnLitMGrV7lvIO6OfCLy+DzxiidjTAAATwC7Adjf07YAPOx//34AVliW1cwYkxJAGgBfAFhrWdZQY0wvAL0AfAagHoBCto+XAUywfZZEOHqUn197zbHjEJHk78oVfh4xIm6Zs3Pcu6EiIgl1vx5PDRuyOiJjRt56PV06VoHUrPlEhydPqZgYTrtatIiVL4ULsyroxAk2/Pb2ZkBTuDBQrBinw5UowWni06fzs6cn19mwgQGIvZKnSRP+nzZgwD9wdc2BmTPZi2jbNvZ3Wr+eVVbDhzMQSZuWfYs2bOC4btxgSDJ0KL8G+NoVK1hh9Oab/AgOZjXSjBk8hn372LupdWuGKS4unK4GMJjKkoU9m375hY8LFeJd9ry8+ObPjBms9CpWjK/x9eVUvEmT4npHDR/OQOqXX1gZZg+apkxhRVfduuyZdPMm757n5sZl27czXAPYP+nQIVYfubvzODJlYsWRnx/X8fFhwFe8OMNlJyf+W9+3jw3Da9Tg/nv04Plu0iTue3vqFI9j61Y+PnCAweHhwwzj6tblfp2deQ3kzs2Aa9gw3ilQb4KJPD6OqHgqB6CSZVmdLcv6wPbR7WE2ZIxxB1AFwBQAsCzrlmVZVwE0AjDNtto0AI1tXzcCMN2iLQDSG2OyP8rBPI/stz53dnbsOEQk+Tt9mp+PH49b5uTEd25FRBIqJibuzlh3K1aMlZVp0rCC5No1/mFdtOiTHaM8nZo3Z9Dx7rtx07jSpWOAYa8I2rWLAUn//gxNpk3jh2UxoDp2jAHRoEEMlg4diqvmWbAAMMbC4MHsTxQRwRDo888ZCH30EftIAZyONncut/Xqq6weOnCAQZS9x9Pw4Wy+3aYN8P77nI53+TKDmk8/ZU+lHDk4/jZtODXw9GmOvV07hjzvvhsX0KxbxzAodWpODfTz478RHx+GSE5ObN6dLRvDr+rVGTgtXsyKqlKlWOn10UecEjdpEkOda9cY+KROzT5NU6awD9OOHRz/qFEMx/z8GP5s387gbetWHsfWrQznPviAodaePXHnadw49qwqXZr7OXCA4Vn16lz/778ZesXEsF/Url0Mp379ldVSX33FcGnePFaeLVvGzxs2sNdUwYJxQZ2IPB6O6PG0H0A2AH89hm3lB3AJwI/GmJIAdgDoDsDDsiz79i8A8LB9nRPAmXivP2tb9jjG8tzInRs4f57vIoiIPAr7naXu/gXPfnchEZGEuH077usUiALgEvv44kVOm3r/fVZrR0czKHBx4R+ihQpx2o48nxYu5OcVK4Dvv2clUZMmrIxp2jQuPNmxg5U2H37IayhrVlZB5c3LaqlatfjcgAEMjgoUYN8lywJ8fLyQLx/DqDNnWEnzyiu8Br/9lvsfMoRByZYtfPOlfn1W5pQpw+okT0+uV7w4w6LoaAZFhw+zCqtvX1Zb1akTd2wnTgDNmrEZ+cSJDL6KFWND8MqV7+yv+Pff3FbevAzjhgzhv5v27ePW+eMPBmM//MBqL3d3Bjj58sX1t/L3Z4A2Zw7P5bBhDH1/+413rHv//bhpse3bc0yzZrE6q1cvTovt25d30ANY7ZUjByvCzp7lOfPxYYC3c+ed38vp01nJOHgwm6SXLcuK6pAQBmhZsvD7+H/2zjs+imp948/spvceAgkQ6YjUIF1CERBBKSJcvQgqoKLXhopYEUXUq2L52UXFBoqXoqLYQ5UWegtSEkooIaT37J7fH88Ou0k2m01P9P1+Pvs5uzNnzpyZnWx2n3nf542K4mfB9Oncly6CvfIKf+N89lmNXFqCINigqdKOrrW9Q037A0BXAFsBFOjLlVLXVWGsGACbwQiqLZqmvQ4gE8B/lFIBNv3SlFKBmqZ9D+AFpdQGy/LfAMxWSm0vNe4MADMAIDw8vMfSpUsrO7W/NTNndsfBg37w8ipG06Z5GD78HG64oe6cxrOzs+FjawgjCKWQa6TxkJDggzvvjMH8+XvRt28qAGDQoIHo2DETb721s1b3LdeJUBFyjTQeTCZg6NBYAIArCvHzH5surfvyy+b44IPL8MYbO/DNN1FYty60xLbDh5/Fo48eqvK+5Tpp3JjNbA0G4KabeuHMGU9ce20yDh70w7hxpxAeXoAzZzzw6qvtEBRUgIsX3TF//l4sXNgW7dtnokWLXPz6azieemo/EhJ8ERRUhIEDU0rs44MPIjBlylkcOeKDxERvDB9+DmlprggMLKxWBoHt3B31Kb3+4kU3BAUVOhw7JcUdoaEFdtcx0ssbISEF8PcvttvHZNLw118+uOyyHOTkGGEwqDJ98/KMyM52QVBQIQ4f9kGrVjnQNIW8PCP8/OyPCwAZGa7w9DTBzc1cZl5K8XhNJmZnKGX1p7UlNdUNLi4KLi5mnDrliTZtsh2ex9pGPkcaF989lIZX48diROB6zF5edxVxGuJ1MmjQoHilVExF/epDeBpob7lSam0VxmoCYLNSqqXl9QDQz6k1gFil1BlLKl2cUqqdpmnvWZ4vsfRP0PuVt4+YmBi1ffv28lb/IwkKoi/LddcBP//MOz51Gf0UFxeH2NjYutuh0OiQa6TxsHYt/TM++4wpAQCNf2+6qfaNf+U6ESpCrpHGg8nEzw6AwlOhcru0LiEB2LeP6VPDhtFIeMIErhs5khEcoaFMYaoKcp00Ti5coCAREMD0ueXL+f+oY0dG3wQF0fR70SKm3M2eTRPuPn2YppmSwki7wECmsvXuTZHHnpAk14hQEXKNNC7mXx2HJ36NxXVNtmDVmbqzjG6I14mmaU4JT3WeaqeUWqtpWjiAnpZFW5VS56s41llN005qmtZOKZUAYAiAA5bHFAAvWNpVlk2+BXCPpmlLQVPxDEeik1CWL7+k6NSuHattNG0qZc8FQag6Jy3JzwkJ1mUGQ/nVqQRBEOxhNpe/rl07plF17lwytUjn22/pSbNyJcWE776j0fDixRQd9Epnwt+HvDxg0iSmqNlyxx1Aly4UnUJDmWq3aBE9gkrfhw4Otj5v1oyt+J8KgiDYpz6q2t0I4L8A4gBoAN7UNO1hpdQ3VRzyPwC+sFS0OwbgVtA0/WtN024HkATgRkvfHwCMBHAEQK6lr1AJ9Fzqd95h27q19Q6jIAhCZdGrZJb2eCp0nAEgCIJQgooC+D/7DLjqKlbYys3lZ47BQEFq9GgKET//TJPk8HD67Bw/Tq+dgACaMnfuTG+ZoiL60giNF09PRrh9+il9i7p0YaStHkiwfj19gkaPpujUtGm9TlcQBKHRUx+SweMAeupRTpqmhQL4FUCVhCel1C6wUl5phtjpqwDcXZX9COTAAQpNgwbV90wEQfg7sHAhW71kM8AfdZs22e8vCIJgj4oiTV5/nYL24MHsaxutPWUKDZFtefhhlmffu5dVN2fNAgoKgD/+AHx8gAULyt/XuXOsbGY0shKZq2v5fe2he9KU501T11RmPrZ9derrGCqad7dufNgSFcW077AwvofZ2TShFgRBEKpHfVioGUql1qXW0zyEKpCdXTKc/ehRa8SCIAhCZcnOZuSB7Zd/oxFo27b+5iQIwt+Pfv2sz7/5hpXM5s61VhR74AFg/35WCQsMZBrW2bP8bLrhBkY/JSVRSNq7l3YDV17JyloAK2cVWHyY+/dnmfbp09k3PZ3VuLKyWJmroIBpxllZ7J+WZq3K98knjMSKj2d1rsxMPr94seJjzMtjefuUFPvri4srH02al8dKbGfOABERLEXvqO/UqTxvLVrwPBsMTGOsDidPMh0yJ8f5bfLzud8dOzj/xYtLrj9xgu/z7Nkcd/RoRvGfOMFqbm3bAnPm0LdJEARBqD71Ifis0TTtJ03TpmqaNhXAajAFTmgEXHklw5MFQRBqAi8vlpy2xWCQtAZBECqHM36TgwYx+mXMGGDcOODxx4EHH6Tp+Mcfs0z9okUUir76itEvU6YA99xDAWnpUmDECOA//2GK1rlzjKRJSPDFww8D8+ZRoLn+ego8mZnAQw9R3IqKYrn5du2AGTOYwve//3Feb7zBff/0E/sNGcKS7lu3MuUrJgZYvZppYOPHU0Q7epTb3nMPza4BCjQDBjBlsGdPimcvv8z+c+dSZNH9qk6epJA0ezZf790LvPQS+z77LMf19qbgs3o1I979/Cia7dnDuezfz20ffpivV63iY98+3kBwceGx9uzJVMdp01jWfts2oFcvoGVLniOAc3Fx4fm0fYSHA2+/zXN67hzPa4cOvGHx888Ujd5/n/t4+mm+p9HRjEybPx9ITATi4ijc5eQAjz7Kc+Luzvf5pZcYwfb990CTJky7mz+fx9mrV+Wj1QRBEAT71Ie5+MOapo0HoN97el8ptaKu5yHUDK1aiceTIAhVx8uLd8ZLo0cRCIIgOMN77znX78MPGbW0Ywewaxc9m4YOpe/T778Dy5YBPXpQXLnzTmt1zUWL+Ll0660UMRITGQGVlQUUF2t4+GFGynz5JfD55zStvvVWRv28/DIFpWXLgBtv5Jj//jdwxRUc+8cfWWDhxRcp3KxezZTjnTs53rx5FHBsPxfnzqWYExDA72EDBlD8eeUVRm/l5tIM+6uv2F9PXx46lH3792e/iAguv+oqCjG2fWNjeSw//MBopo0babjt70/BaMECrjcYgIwMRhh9/TUFns2bKRqNHcux7rsPWLeOEVmHDlEkys4G3n2X20REMFLK05ORR/v2USTMyABeeAF45hmKYi4uFMsiI1mhsG9fikWA1fw7LAy45Rbg/vu5rzVrgK5dGb20ZQvQvTsFwZMneQ2MG8fX06bxnOzcydRKQRAEoeaoF8lAKfU/AP+rj30L1WPfPmsouSAIQk1QuhpVURF/tAiCIDjLhx+y7ReZiK2n7IdMJidT/MnMtH7GrF3LqJazZylc9OjB5Z07Uyg5d46ve/Zk1E1eHiNkfvmF+5w+HQC6IyGBkUojRlBE+uwzYPhwiiO9egGTJzMFLjKSUTlRURRhRo4EnnuOkUXXXUdRaMQIRuKsWEGB5pdfKOBMn85IHbOZ/R97jILMkiWMzNqwgX379WPEEsCopQ4dGD21Zw+rtd12G/vu3Qu0acPHgAHAE09QDPvzTwo24eGca1YWcO21FMGefpo+WXPmUKibOxd45BEe47x5jDwbM4YC2t13U+hq2pSCzuOP8xgiIhgZ9ttvwMSJnOcvv7BvaY4cYaphZCTFv7NnGTV29dWMbDpzhu/p6NG0fjh6lJFUjz3GCLd+/Rg5NWYMcP48hcCAAM5l1Cgel2364aJFPL8eHtW4GAVBEIQy1JnwpGnaBqVUf03TsgDY1h7RQN9vv7qai1B18vNL/kg8coR3ugRBEKrChQv8AWSL0Vi2yp0gCIIjUlMpGl3lVr7wFBEBvPoqo3a2bgUuv5wRMP/6F8WWWbOAa65hnxEj6AvUrBm3TU5m+8ADbJ9/HnjtNYo2V199BOfPt0b//ozSadLEKrCYTBSdsrL4+sQJptlt2kShCWDkZ1gY08BmzGDk05gxFFT27mXUz//9H0UjnU8+oUiyaBHFlokTgdOn+b3s/fd5jLZcfTWjmoqKWL3t5El+9urRXq+8QjsFgMLSwIE8ls6dgeXLac5+zz3c/tVX2XfBAmDlSuCLLyj6dOnC6KgPPuC5VMpq2n7TTez35psUqjp0AGbO5By6dqVAZo/WrSkMbtvG1D5XV0ZZdepEj6ZXXmHEEsBlnTrx+dKljNCKj6dIFhjIYhZjx3K/77zD93DyZIpPa9cyrXHvXqcuN0EQBKGS1JlkoJTqb2l9lVJ+Ng9fEZ0aDzExJT2eDAYRngRBqDre3rxrbYvBwB9ugiAIznLxolUcKg9No9gUFsZol+hoRhwdPEgfoIMHKRA1a8b0rPBwfu+54w6m5n34IYWpoUMpID3xBM3Co6JyMWAAo4FOn2b0Uc+eFGxWrqRnUKtWwIQJjCi6/XaKHU8/zSicX35hWpurK8WZV16hULN+PffTvz/n160bPZ6efppiy3ff8YZgQAAjoTIyGGW1cSPNsSdNYt9evShqPfwwEBzMvufOMRUwO5vRSc2a8Xh69WK00B130A9qxw5u26kTxajvvuPn85w5TGf7+WdGgQUEUMy7916e19atGfX17bdMXeveHXjySZ6XJ55gRNW99/Icu7oyiszdvazHk68vPbAmTOC5yslhStz+/TzPQ4YwOqx7dx7frFmMJlu/nu9VYiIjzW69lYLa889zXzt3cszPPmMqX0EBUysFQRCE2qHOU+00TftMKTW5omVC4+Cyy8TjSRCEquPpyR8JpcnNrfu5CILQeGnblilWOF9RT0btPPOMtXJcdDTT74YPZ7QQQEHillvYJz7eurx9e6Z6JSbSHDw9HfD0NOGxx6weT0uXUpC57Tamdr35JiN8fvuN3k6ff07x5c47GZl0+jQjbkwmClX9+lHM+eUXRgwtWADcfDNFpl27KGiNGcNUvNWrmarXsyfFqzfeoDCTk1Oy6vDWrWyHDaOYNnAgI6oGD2YkVWCg1eNJ73vVVYz62raNQtX77wMhIVaPp4kTeawffECx6t13eRxnz3K7sDDr/p96iqLVsmUUnUaP5rEsWcLz0bYtxSAfH0ZA6ZGwubmMsHrtNc7baGT/667j/49+/azz3bmTbVQUhbuXX6ZFxCefAB070oR9+3aeg5deYhTX5MkcwzaaTBAEQah56iNW5XLbF5qmuQDoUQ/zECrJhQv8xy8/CAVBqCmUspYR1ykqsv6QEARBqIjsbGD3buf7G41MT4uOZtrXDTdQNFqzhus3b6ZptVKMotq4kdFDd9xB0cTVlcLO229TsLn//m6YOpWV1M6coXn4Z58xLW/MGFbB27CBQtDbb1MIufdejqtpjGK6eJH7Cwmh/1BICOexYAFv8k2cSFFn61aKJ61bUzzq0oVi1vbtFFrCwiim/fgjo3527uRcf/qJ6WU330wh7dVXKdR5eTH1LzaWHlBFRRTIPvmEwtipUxSdRo3iMbRsybS9PXsoEA0bxjnMnk1hLCuLc92yhdFMt93GY73lFnpCFRSwOp6XFz/7n3qKUWMLFvD4s7I4b6X4OHSIQlufPjwnFy5Q9PL1ZfTYvn0U4IqK2PfHHxnBNWgQ0wGffprn7fLLGdG2ciUrGY4YwWtm1CgRnQRBEOqCuvR4mgPgMQCemqZl6osBFAJ4v67mIVSdr7/m3Th3d+uyv/6SVDtBEKpOaqq1gpKO0cjoAEEQBGf44gu2mZmO++kYDIy0ARjxM2ECPZ7uuINiyvffA3fdRZEoIoKPvn2ZynbjjYz4iYmh0DRmDHDq1EWcOROEmBimoC1YwFS4NWv4WbZwIed2330UOr75hiKNntrl60sB69ZbKUa99hqXv/MOo3EuXuTcDh9mehlAU+9166y+UYWFTMk7e5ZzP3yYoo/JRKFs2DAKUPn57HvddUzp276dUUaxsRSgDAaek8JCCkPdu7My3ttvc36axiihFi0oCG3bxv0eOUKPrVWrKFrdfTf7fPyx9b355BN6VY0axZS/efMoUK1YwQqBGzbw3Dz2GP83REXRQ8to5Puj+zV99x2FtL59+WjenDdF27XjA+C+/viDAtmDD1K4cnVl3//+l9v/9BPTHq++2mpyLgiCINQOdenxtEAp5Qvgv6X8nYKVUnPqah5C1TGZ2CYlWZe5uPAfuSAIQmXR73BfdlnJ5SYT8NFH9DP5v/+rn7kJgtB4CAhgO3Vq5be95hrg2DGKFKdOUZhwcaFgpPsM3X03BZQvvmAK3MmTTCVbtoyiyfjxpzBwIEWS55+nYPPSSxSQ9Jt1S5bQeDspiZE2a9eyPX+eEUuaxtQ23atq+XKKLH37skreV19RuNL7vvYaxZlJkyjiJCcz0ik5meLNo49SVHF3ZwqhXtktJIRRWV26MLqoe3fg2Wc5l4AAikKaxoggb2/O4+mnmWI4fDiFpZUrKb6tWEH/pP79KQ59+SV9lqKi+Pn96KNMBfzqK4pjmsYoqLg47v+BBxhtlJpKQ/XRo5n+Fh7O1LiePbnNjBk87m++4Tnq0IFRWB07UjTq0YP70uetaUylHDuWN0izsri/QYN4vJMn8zwvX05B6+LFmroSBUEQhPKo81gVpdQcTdMCNU27UtO0q/RHXc9DqDwJCWzz863LoqPL/mgUBEFwhuXL2c6aZX99YiLvbAuCIDiiqaWInV6BrjLoBQ7y8ym6vPUW07aGDOG6GTMY7XPbbRSgxo6l6PPqq9x+zBjg8GFfDBvGaKjbb2eq2PvvAx4e9HLSNEYQARSSUlP5uZebSzEqMhJwc6PoXljICKTevel75OVF0eW992jOrUd3BQZStLn9dr6+/36msE2axAj1BQsojgEUaQCOCVDwCQigmDZ9OqOk9Miko0fZ9uvHNj2d5+Kzz7gv3TMJ4P5CQijkAIyo6taNY82YQbFp0CA+Tp1in9xcikc//si0vcmTaRT+/vv0bHrqKXpWDRnC8w0weszDgxFab79NgWrBAkZZPfQQ+5w9y1aviLppE1Pxpk2jGHj4MEUxgNFqgYF8L/fvZ4SYIAiCULvUh7n4NAD3AYgEsAtAbwB/Ahhc13MRKkdaGtvSfiyCIAhVISGBd7YHDrS/3mDgjxxBEITa5s03rc+XL6cnUHY2ozLvugs4fpyRM1OmMBVs1SoKMatWAXfdZcJPPzF6KTwc+PVXRoMnJNC/KT+fgtBtt1EsOnqUbYcOFEyU4n67d6cn07BhNCd//XVG7gwYQI+kXr2skUmzZ7PK3tat7Bsfz6ifnj0ZmdS+PT2iQkJYUU9nwABrytqoUYxa+vprRnxFRFhT1QBGUDVvzghUHx+aqa9cyb5Nm1LAWrSI6XJbtnCZjw/nt2oVUxZ37qTnVXIyx3/gAX7mHzxIcSo7m8dgNJasSvj442zvuYftqFE0YZ9jyZE4epTv04IFPOfXXmvd9tFHef7XruUNjCNH2PfFF+mBtX4935dFi/ieCIIgCLVPfbjz3AegJ4AkpdQgAN0ApNfDPIRKot8p00PaAX6pOnSofuYjCELjJjCQFZ3s4e/Pu9/e3nU7J0EQGh+6sfiOHdUfa/lyej59/jlFoeJi+j6NHWtN3WrenFE9mZkUlIYPP4d16xiVM2cOI3+ee47iS9OmFHa2b6fI8dFH9BiaMoXjP/UUYDYzymraNM6hVy+2p06x35IljHo6eBDo1IljnjjBPkePMrLn4Yfp27R4MbfZs4dCVlgYxwc43yuuoKH6qFGMklq1itFbvXpRDNNtFXJzgc6dKZr5+FCE+vZbGpj37s2+27ezb0YG++bk8LN7wACKPu+8Q9HJw4PRUQBFNQCYP58pgo88wqir9eu5zV138bzs2mX17DpyhIbnnToxyv7WWymCPf8836vgYGtFPgBo0oRCltlM7yZdoBo3jvPW/b0CA6t/vQiCIAjOUR/CU75SKh8ANE1zV0odAtCugm2EBoq7e0mzcUEQBGcJD6cXSGl++41pElFRTIERBEFwRHg4Sp16AwAAIABJREFU2yZNqj/W9dczSmfECEbodOsGzJzJ6KIPPqAB9r59wBtv0ONpyRLg4EFfDBxIUeT551l5rXdvpqeFhlKQ2bqVkVBnzgBLl1KgOXSIEUBGI1Ptrr6a0U/z5jGK6fbbmT7YrBmFlocfptDl6sqUM6U4l4gIekd168aIpZUr6cs0ZQoFnyFDmO43dqxVtOrWzep7FB1No/OwMKsf1MiRFGn+9z9GEGVlUXyKjqaAFhpK/yalaNLt6ck0wPvvpwjUpQsFod9/pzB4990cNziYolKXLjRMT0mh59PnnzOF7t13rec9JobbTJvGfS5dSsHs7rspjBUV8Tz16UMBS9Osx/rkkxSlNm609v3pJ/adMYOi1Nix1b9eBEEQBOeoD+HplKZpAQBWAvhF07RVAJIq2EZoAOjlzbOyrMtatpQfhoIg1CyDB9M0VhAEwRl0wUn3eqoORiOjd/r2ZUTPr79SOFm9msLTqVMUSfbvZ/+xY4GEBF+MG8dIHD19beFCCk5PPUXfJt3jqXdvCjP33cf0vSVLgNatGU1eUMBIpfR0+juZTBR2Ro1i9NDcuRS7AIpCAKOcAApYBgOFoyVLGFWlR/ZkZ7ONjWX74IM0UJ89G7j5Zlam+/RTHvv58+wz2GKAkZNDwemjj4B//YvnZMkSbu/vzz4jR7ItKqLR9wcfcNxNm5j65+VljY7KzWUa4MyZ/P44aRK/X775Jo/pmWdozH799Vb/pptvpoD2+uvACy9QlHrySW53//3so0dHde/ONj6ez++809r3gQe4rls3ilOCIAhC3VHnHk9KKf3+wlxN0/4A4A/gx7qeh1B5Pv+crZtb/c5DEIS/BwcO8AdJeSQkMEVDEATBEbpHkp5SVhNoGkUUgFEzANPavLzozdSvH6OfTp8GOnbMw8cfU+AJD6fQsXo1TbQnT6YoMnEiRRA/P4pXoaFMW3v9dav593XX0SupTx+mDT78MKN0xo9nlE6vXvzc9PdnRNPSpdzP2rUUdj76iCLQ2LGMfNqxg2JOixbW4xowgH0//ZS+UampTAvctIkRT7Z+UMOGMdrq44+ZbpeSwoiuTZt4nE88we+GP/7IKLHwcB6fvz8FpoULre3hw1w/axYN2f/8k5FLWVmcj4cH/Zh0dCPwGTPYjhrFSK1HHuHxnD7Nc7VgAec9bBjPB8B0R6OR5yUpiRFimzZR0AoNBdatYzTU++9bzdkFQRCE2qU+zMUXAXhTKbVLKbXWsmwugLl1PReh8nTrVvKO4qFDvMMmCIJQWUJCrHfM7eHt7ViYEgRBABglBDCFqzbRRZnx49k2acJImri48wA6wseHgoi3N9PdWrQAfH2t2/v4sNWr702dao0WApg+t3Mno50WL2bUVcuWFMB0EUznxhsZJdWmDdsuXRhBFBPDR3nExtKzqk0bjh0eTpGmT5+yfXVvz6eeYpuTQ4FI7ztmDD/D27e3Hqdu/p2fz6io/HxGMYWFcfn06bRo8PQsv6KpPSIi+Hj7bUaItW7N6KfRo8v2LR0NBtBYXH+9fz+jvXS/KUEQBKH2qXPhCcBwADGapr2qlLIUesV1EOGpwePiwi9Btnh68guLIAhCZcjPp9eJo5S6yEirIa0gCEJ56Kl2ERFAWv1O5VIBlk6dKu7btGnJm3mRkXwAjCwCygpOOv3786Hj7++8Z1FAAFPZAH6PmzTJue28vSl46cTGWtP3SuPhwaii0rRvbz22qnD55dbn9kSn8rAVoS6/vHpzEARBECpPfcSqnAdwFYAbNE17S9M0FwCSad0IKC4uWzGmRQveMRMEQagM995r9TwRBEGoDrq5eERE/c5DEARBEAT71IfwpCmlMpRSowGkAIgDfZ6EBo6LC8vwCoIgVJeCArZ6Coc9Nm9mxSIfn/Lv+guCILz5JluTqX7nIQiCIAiCfepDePpWf6KUmgvgRQCJ9TAPoZLoZXBtOXjQWtlFEATBWa68kq2tka0jjh+vvbkIgtB4OXDAWuktPb1+5yIIgiAIgn3qXHhSSj1d6vV3SqnBdT0PofIoVfZLnY+P1SxTEAShNpg6VYoYCEJDIzOTFcNqspJcVXB1Zfv00yX9hwRBEARBaDjUmbm4pmkblFL9NU3LAqBsVwFQSimxkG3gFBeXrRgTFcWywGPHsszvo4/Wz9wEQWhcHDjANiuL5a0dceoUzcgFoaYpLORNlT17gLw8YOZM+gX9/jurZh05Eotrr+X1GhUFnD3La9bHh5XD3NxYCj4wkB6I114LbNtG/8MzZ1gFrLgYOH+exTlatGD5ek1j5a2NG1mJLDmZZecLC1myvmdPID6eps8GA8u/N2/OOWzfzr+H/Hz2jYnhvidOBFasoInyxYuca5cuTGuNiwMuXGDK6s6d7Lt8OQ2mN2xgFbXNm3kuoqJYzcxoZLn6yEjgjz/4f37ZMppYZ2SUPI+enhSA/Px43NHRwJEj9s+5lxePtU0bVndLS+O5iIhgBLWm8TNh/34adP/+O/slJQGdOwPdu3ObU6eADh2AhASO26ZNrV4qgiAIgiBUgzoTnpRS/S2tb0V9hYaH7pvQpUvJ5S1aAOvXMw3m119FeBIEwTlyctgWF5ffZ+hQIDEROHaMP1QFoTr89hv/V732Gj0Ld++2309PHz99mu3q1WxLp3v+9Zf1ucHAyB895Ssxsey4GzfyoWM08n9rUlLZvlu3sv3iC+uypCTOu/TfzLZtbL/+muu++sq6bu/ekn23b7ffVz9W/bhsj+3wYc5TP7bSohNA0S4vj1FQAEWh8tD/lkvvRz8ftiQnsz1xgu2ePXzoxMdbn2/aBNx8c/n7FQRBEASh/qiz5AVN04IcPepqHkLVePVVtrZlfwFg8WLecb31VkmFEQTBeXr2ZBsYWH6fX37hD9O+fR33EwR7bN/OSJ/oaEbkDh0KTJ9OYak80Qmw+o5VFIlni154w2h0fpsgB998yove0dPKbLnsMrZhYRXvU69Cq1eBc+b/docObL29K+6r42gu5VWe87dTZkY/RzEx5Y+nfzY0a+bc3ARBEARBqHvqLOIJQDyYYqfZWacAXFaHcxEqwc03r8VXX/UF4IpevZpjw4ZseHl1QLduG2AwaAgJAdzdGaIv/LOZNSsOa9aE4uWXTfj3v5ugqMj6K0nTFB5/fB927QK+/95+eURX1yJ8+eVZPPywhsTESADAmDF70L498MILne1uo2kKTz+9H5s3K6xZw3FbtfLGM8/sxNSpkSgudu5jzs8vE998U4AxY7yQm1uJX1gNkKlTd8PDA3j3XYYoBgdfxJdfFmPMGF/k5Xk6NYaHRz6WL8/AlClGpKSEAACmTeOv9Q8/7GJ3G6PRhDfeOIpPPlHYtq0dAKBv30OYMAGYNastzGbrr9zCQncAXtiypQ169HgUERG3lzsXFxem3whCRSgFnDsHPPss8Pbb1uV65Iy3tzXarjz0iJ6gIEbauLkxBc4R2dlsXVycr6zWvDlv3NjjwgX7y729GVlkS2oq24AA63GWx8WLbP38GOXk42ONUioP/XzZE73KIzTUGqVUmrQ0+8tbtbLOrzTnz9tf7upKkW7rVkZgC4IgCEJdUFjIf+Cm4gq+VAiXqMtUu+i62pdQs/Tp44KkpCNIT/dGdPQYZGZuQWbmphJ9tmyh74Xwz+bVV2MBAHv3rsPFi50xbtx6eHvTefbrr3siPh7YvTsUPj7ZGDr0WIlts7MNWLFiAHbtOoS9e69Cp077kJISgPj4EOTkXERxsStuuCG+9C6xdGkv7Nhhxo4dTeHnlwk/vxzs29cFu3f/ibS0UEyYsA4eHo5V0WPHvLBxY0/Exa3DuXNXYfDgzWjWrKBmTkods3p1B8TH+8DDoxgGgxnt2iVh69buWLduHc6fb4fhw/9EWJjjX9EpKa5Ys6YvNm78C8eOXYWYmJ04ciQK8fFeUAowGosxevSBEtsoBXz++UDs3LkPO3dejrCwCzCZDNixoyVatUpARkYwJk9ee6n/6dNBOHEiGJqWiayseIfCU0ICf1yPGAFccw1w333VO0fC35enngKee67scl9fXkPR0cC+fdbUOHu4u7PV1zsjeupiU2Uifx31LW+fISFlRSm9r4dHxfss3dcZMUnv41KJb4yODMddXZ33bNPHkWp1giAIQkPCZOYdJ6UquDMlXKIuzcXbK6UOaZrW3d56pdSOupqLUDnuuacf7rlHf/UGEhOfQVbW1hJ92rUDDh2q86kJDZyXX26D6OgmAIAffjh3aXnbtsn49NOBJfoeOXIGK1ZYX1999QVs25aHixe9AAB+fhlltgGAb79NvfS8Q4czaNMmH3/91frSsjfe6IQmTRxn83744Z8lvFf+8x83jBnTu+IDbIBcccW+S89DQy9g4sScS34xAPDII74YPLiTwzHWrz+INWusr2+4IQuffGINzYiISCnzXphMZnz+ufV1z55nUFSkYd06vhcGQ3GJbZYuBRYsAAwGO/k1pdiwge1PP/ExYULZtF/hn83MmfT4Ke1ppBMUROHJ0xLw50h40gUWPcrGmaptuohUVOT8nO15O+n4+pYf/WOvb3p6+VFStvj4MKJLj5JyZr56Hz2qyxnseVzpeHnZv1FlL0KqwKL/X3YZTdFLYzJZRalt26weT8VwwS2tNjg/4WqSl1eEjzzrbn9C40OuEaEi5BppXOw+3bO+p9DoqMtUuwcBzADwip11CsDgOpyLUMM0aVK5MHxBEP7ZTJrEx8aNFYcyuLszQuKBB4CFCx0bkgv/LPbvZ+rWRx9ZRQp76FFM9oyxS6P7NLm5sXVGnPG1lE2pqZTz8v6f2hN09L7O/F3offUILWfEJN3bqTL+VX5+5afUlRfNZW/+Xl5MLSwv2spsBt54A1i0yFrcpM8QL1y27SQ2nGju/ISriVIKmuQDCw6Qa0SoCLlGGhmKd6XCA8rJmRfKUJepdjMsT69RSpUIstY0zYkAcaE+WbgQ+PjjktVkbMnLc+6usCAIQmXRfyTrESiVibwQ/r5kZwOdHAfvlegLAGfOsHUk0uieR7Xt8RQVVb53UXnRSz4+ZdPUKuPxpPf196fHk69vxWJcVT2eyovoKi9tLjraOj8dXcg7d65sf53hw/nQGf3slRj9rPNzrQni4uIQGxtbtzsVGhVyjQgVIddI4yIpaT6OH38CwcHXAZhc39NpFNRHHbJNTi4TGhBNmjj+gr91q/wYFATBeb7/Hrj2WiA727fCvs0tgQt6yXdnolaEvz+6wBMZWXFf/f+TXtXNkb9Sx44Um264AfD2Lsa997Ji2vXXM5LHHnrq51VXWfejRwqVt40Xs4hx2WXWFEA9csq3nD8Lg8EaveXjU7Jv167lH5M+F71ynN53wAC2rVuX3Ubnyiu5r6lT+bpDB+Cee4A772SavT06d6b4NGUK2+uuowm4pnE8/RhsMZu5Xbdu1u0GDeI6CQIQBEEQhMZNXXo8NQHQDIBnKZ8nPwBedTUPoWr86198lEf79uLxJAiC8+TlMdrDttJdeXz1FbB8OUvAx8UBf/3FH7EhIbU/T6Hhoqdf+fsDp0457hseziiiqCj6QA0fztSx1avLpsd98YVVzOrXbwNiY2Px3//ydVoaK+YtXFhym7g4psFFRnI/Pj4UxlJTGTm1YQOFK1sefJBeZ76+TOdLSwMCA2mm36MHcPgwcEWpAqAzZwL/+Q/FV39/a9+EBN4cmjcP6NWL+9WNyP/9b873r7/Y99AhjjtvHivBnTvHsc6cARYvBubPL7nPL7+0CnWzZjGyShe9AODYMVaks+Wuu5j+5ojSYtLIkTy3thQXA/HxQM+ewB9/sLhAURFFu9xcoHfjtOITBEEQhH8cdenxNBzAVACRAF62WZ4F4LE6nIdQC4SHi8eTIAjOM2ECHxs3Vhy+1KkTH08+yddTplA4sDVAF/5ZbNsGvPginzuKXmrRgilfr79OgWTsWODsWaZ1eXvzhsrSpRRe9Eg6RxFUgYGsrlhaeDIYGLkElDS+Dwxka08giY4u2TfIUgOhTx+29qKMz59nv9J9dYGqVSsKMvq+L1zg65AQq1DbubO1L8BoLoBibnc75V9sz68z0WUAvxNUlrZtyy5zcaGQBgBDhljn4uvL44qJqfx+BEEQBEGoe+pSeAoB8L3lAdBQPAXABqXU8eoMrGmaEcB2AKeVUqM0TYsGsBRAMIB4AJOVUoWaprkD+BRADwCpACYqpRKrs+9/Ci+/zLuXBw/aX5+TIx5PgiDULrZVreLj628eQv3z5JOscAjYr5Cms38/xQpPTwoXQMlIuW7dKDzpRuK1hb1U9KrsUxdhHBESApw8afWJqsj3yRbdz6kyOFNNzxkCAiruoxu969FuYWE1s29BEARBEGqXuvR48in18AUQA+BHTdMmVXPs+wDYSiIvAliolGoNIA3A7ZbltwNIsyxfaOknOMGwYfRbKI/t28XjSRAE51m1Chg6FMjKKscAxw62nzEeUpLiH02LFtbnwcH2+0yYwKgm3T/JEZURgY4edb6vTmlDcMCxYFYe5VV3s0VPHdQ9sHQfJ2fYu7fyc7JnvH7xYuXH0U3dHaGn5+mtfO8QBEEQhMZBnQlPSqln7DzuA9AXwCNVHVfTtEgA1wL40PJaAzAYwDeWLosBjLE8v97yGpb1QzSpW+kUnTs79mvo2NFqlCoIglARJhN/jCvl/Edw//7W56dOAc89B6xYUQuTExosycn0HLIVf0pXkdNFSWf+u+tRdJUxrLcnIlWEPT+y8iraOaIiLyvAKgSVJ8g5wl66W0XYpgvq6JFJlcEZn0g9pV8XqbZvr/x+BEEQBEGoe+oy1c4uSqmL1RR/XgOFK70GTDCAdKWUXiz5FGhqDkt70rLfYk3TMiz9SwSKa5o2A8AMAAgPD0dcXFw1ptf4KSgw4Ntvm6Jr13ScPp0NIBEAsHZtHADN0qcVNC0CcXEban0+2dnZ//j3pOESCwA4Z6l9HR+/HUlJdKFVqj1yc3NhNptRVFRU5j1MSckGMOrStunpaSgqcoPZbEJubh6UUnbfd6WuQE5ODsxmMwoKCpBh+QWZkpICANiyZTP8/R2roqdOnQfQB+fPc99Hjx5FXJwTt98bICZTIIqKimAwFMFsNuPsWdaP18/roUOHYDA4zo05fDgVQIdL25w7dw4mUxCKigqhlAaTyVzmvTCbzQAGIy0tDYBCbm4OiosNUEohM5Pvib5NfHwgmjXLw3PP5QO4gOTk00hOLjmePbp1c8OkSZHYvj0QR4744sknAXd3E9asWe/cySmFfJY0Pu6/vyt27y6Zk9W27Qm0bGnA2rVhSEtzg7t7IfLz3XD4cBri4nY7HC8lpT2AJuBXBn4lsr0m7F0jxcWBALqUWFbRdZSW5gqgX4ll585tQ1xcRbltsSVebdp0CnFxRxxukZExAIARRUW5ALzw558piIvbX8F+SHJyCICS5lIVHVtysgeAkiZWx479iezsggr2Flvildl8AHFxjtU4k+kqAAYYjYUA3ODvX/H5qAvks0SoCLlGhIqQa6SxQaeg1NQLdfq+NebrpN6FJ03TBoHpcFXZdhSA80qpeE3TYmtqTkqp9wG8DwAxMTEqNrbGhm6UpKbSTPWNN4DYWCAxcS0SE4GBA2Oha4aff867y3VxruLi4upkP0LVCbc4y/boEYPo6CYAAE07By8vLxgMBri6uiI2tn+JbY4cOVNi24CAQLi6usJgMMLLyxOaptl93zUtFd7e3jAYDHB3d4e/pV54qCU3tFev3mjSJMjhfI8c+RMAEBbGfbdq1QqxsXZcdhsBRuM+uLq6ws1Ng8FgQJMmEQCs57V9+/aIjbXjWlxijIMltgkPD4fRaISrqxuUAoxGQ5n3wmSiyVtgYCAADV5e3igq0qBpGvz8+J7o27z3HiuAvfkmsHGjK0JDm6Ft25Ljlcf48SUjLAoKjFX+PJDPksZH6SIWbdoAU6c2xzXX8P/T2rVAWJgbMjKA7OzACt/frVuB334DvLxcLvkb2W5j7xqxl1pW0X6OHSu7rG/fnujQweFmZRg/PhKxsY4dvsPC6PGUnU3B3WQKdfo6T0oqu6yibbdsKbusT58+iIpyapeX6N27I2JjOzrso0e3eXm5IS0N6Nat4vNRF8hniVARco0IFSHXSOMiKWkjjh8HgoNDcMUVsXW238Z8ndSZ8KRp2l7QUNyWIADJAG6p4rD9AFynadpIAB4A/AC8DiBA0zQXS9RTJIDTlv6nAUQBOKVpmgsAf9BkXHBAYCCQnu7YU2XHDvFaEATBOcaNY+n2quLtXfK1mxtTe5o1AyZNYvn4rl2rN0ehYXLihPW5lxdw+LD1dWIiW12csFehrTzqw+PJGU+j0hiNFffRPZ70gh/9+5fftzT7nQuMKkFxcdllqamotPDkTLpj6fj4qpxDQRAEQRDqnrqMeBpV6rUCkKqUqkINFcsASs0BMAcALBFPDymlbtY0bRmAG8DKdlMArLJs8q3l9Z+W9b8rpUqLYUIpDAaWmnbE5ZcDCQl1Mx9BEBo3EyZUb3sfn5KvjUYKT6dPA6+8AixbZj9yQ2j82Po53Xyz/XWVqeKmV2rVPYkinQieaduW11yXLjQId8YkvFUrRmeFhwNHjgApKUCTJhVvN2oUsHs3EBEBHDgAtGtX8TY338wI5RtuYMW+Ll0q3kanVy8KetOmUchLdeLWXM+eFHzbtAF+/ZWicvv2FW83ezb/ZjUN+PlnekVWxN130+Ora1du64zZuiAIgiAI9U+d/ctWStXlz4DZAJZqmvYcgJ0AdFvsRQA+0zTtCICLAKpbTe8fQV4ev8QOHQr06GG/T0iIfAEUBME5iosZlVE6bcpZTp4s+bp9e2DXLj4fOhTYs6d68xMaLtHRVjGk9P+c5s1pvu3nxwgji0WZQ+bPZ//p0ykIOZP6NmSI/SgfR7i7l4zOcpbvvqv8Ni+8wAfguCiIPcaP56MyuLkBS5bw+bx5zm+nz7EyvP46H4IgCIIgNC7+NlKBUioOQJzl+TEAV9rpkw+gmvfa/3nk5gKPPkrxqTzhKTOzbGUhQRAEe0yezPTcqkZJpqezdXVlpIpt9S6DgdXCXnoJuOwyRn0A1vQjqWPaOBkxghXMbCNw+pX06r4kZPr78xo4fRoV0rSpVcjo5Nj6TBAEQRAEQagifxvhSag9goIoPjmKTti5E8jJAWbOpBfUY4/ZLx8tCIJw443AVVdVfXsPD37eRETQ88dWYNBTp2bPZnv2LD+fRo+mOP7kk9x/q1ZV379Q9/z8s1U8LI/jLDCDnj3pb/T447U/L0EQBEEQBKFiRHgSKkTTAE9Px3169+aX/i+/pEFoUBDwxBN1Mz9BEBoXY8dWb3svLwpP7u58bVv4oGtXpuKFhTHqpUULoMCmqvu8edZ0oN69O2HmTKbqhYQwjUtoPJT28ereHbhwgT5AX3xRP3MSBEEQBEEQyiLCk1AhubnAvfcCM2YAV5ZJYCRvv83HTz8xJcIZs1VBEP6ZbNoEdO5c1iTcWfRqXbm5bAcPtno86WKUjq3oVJrNm0OweXPJZWPGMLKmbVumAnboQDHjyBEgNhaYO7fk2OvW0VNKN5xu04ZV9wIDaXIdEMD5BgczTczfn+N5ejJaa+VKYOBAjm1LWhrnUVjIcb282L9ZM+7Xy4seRqGhFOF8fNjfaOTNguxsnotz52iY7enJFMWff+b6RYu4bOxYzs/VlXPOy+PNg9BQHlefPkC3bowWO3GCn+3NmtGvKDKS76M9cnPptxQRQb8lT09GpjVrxtdZWTwnrq6cv5sb0yTPnaMAmJTEbewZcLdty/03a1Zy+YoV5b/XgiAIgiAIQv0hwpNQIZrGyjhXXVW+8KTTvDnbypSxFgThn0NyMr15wsOZBlcVdI+n8+fZenoCL74IxMcDFy9ymY8P13t6UkxxlpUrS77+9lvr8337SgpPn35KQd4Z3N0pGBkMVuFMx9e3ZFn4JUuAm25yfs6l0b2vdIKDed7vuouf5baUPt7K4OZGgat0GnZ2Nr2TqnoDQj9XALB+PdC/f8k0uy+/LN9vUBAEQRAEQWh4iPAkVIinJ7B1q3PlkQVBEBzRtCmjeapTjMDT0ypuJCWxfPtzz3Gd7t2kCzk1WfTANqUPYKSOs+hCSmnRCShpjg5QQKsOtqITwEirggJGT9UkhYWMbPL3L7k8M7N6Ua+2UWr6+dI0q/jkTLU6QRAEQRAEoeEgwpPgFB07OtdPj2CoarUqQRD+/gQEVG97XXhyc+Nr28+bXr2AY8cAF8t/t9IijC0uLmYUFxuc3u/p07VTFS8xkRFaOTlATAyrt9U00dElK8LVFNV9Lyti4MCyy/TIWkEQBEEQBKFx4Pw3bkFwAv1Hnu69IgiCYEtKCnD//dUTV/QoGD3iqE8f67pBg+hV1LQpXzuqxuntXQyAaX8VYbD8t6yo0EJV0dMBa0N0AmpHdAKsAl9d8PjjwGuvAZdfXnf7FARBEARBEKqPCE9CjRIVxbZbt/qdhyAIDZPMTOCTTxiVVFVatmSrp2TZGopPn06fp5kz+To0tPxxjEbmbvn5VbxPPZ1MF7QcCS56X0f71tHT9/QUQUfj6iKa3rd06p8t3t5sQ0LYhoWV39doZKufVy+v8vvqtGjBVo86s4ceHdamDVtf34rH1dHPsy3PPQfcd1/tRJ0JgiAIgiAItYcIT4IgCEKd0aoVzcFvvLHqY2zfTr+fsWP5+vTpsn0mTaL594MPlj9Obi5Vnvz8ivepiz66v5CjyCddyHFG0NLHuXCh4m30vsnJbEtX8LNFP6bAQLbFxeX31eern0dnorr086CP72jcEyfYViYtzxnxSxAEQRAEQWgciPAk1ChnzrA9eLB+5yEIwt+foCC2f/1Vdp23NzB5MjBtGtCpE3DLLWX7mEwMncnIqHhfujG3LqY4Qk+bc8bMWzc/d0b80s21dVHGbEM/AAAgAElEQVTG4OA/uL5OjwpzNL7e15kILR1dcLKtNlcePj5sK2P0LunagiAIgiAIfx9EeBJqFP2HRU1XTxIE4e/BF18A/fuzUmZ1ufJKppDdfXf5ffz8gL17gcWLGf10551WT6hmzahuOBNdo1dp01PWbCuvlYczgpYuCEVGstVFK3von6u64GavQp6OHg2VlsbWUQqfnrqmp+c5ErR0dDHJUfU6XZTS51uZKoB6VUJBEARBEASh8SNV7YQaRf/x1KVL/c5DEISGSXAwo4YqE11THuPH8+Esr7zCtqiIJuc//3wAiYlXIjoaePtt+2JYcDCNuVu2BI4etaaLubiUL7D7+FBA8vSsWEBxc+M4eiSVo5Q4XSBypq+HByv/eXpSHPL1LTsXDw/7kVDORF/p+3YU8WQw8GaEM1FipQkM5PwNBscCmyAIgiAIgtDwEeFJEARBqDNGjOCjPnF1pXl1y5a5mDqVy6ZMoZhkMlGkycqiUfkDDwCHDzO6av16IDYW+PJLRk2tXs1CCkuWAO3acdv164F584AffgAGDgQ+/BBYtar8uaxZA6xcCUyYACxaBPTqBRw/DsTE0KPKaKRf1a5dQPPmwJ9/AnfcAcydy22OHgVefbVspNSKFawAd+utQFwccM01wFNPAYMHA0OG8Bz4+gJJScDZs8D77wPPPAO8+Sb3l5DAccsTod56C/jsM4576BCj2N56i8Lc6NFMtw4NBdauBWbNAhYuBEaNAg4cAAYMAP7v/+jVdf68/fE//BB44w3g3nt57MHBlXuPBUEQBEEQhIaDCE9CjaKb3h44UL/zEAShYVJYyGiZhmge7eHBdDA9SsjPD+jQgQ/Aaoh+//1se/Viq5uc2zJoENtRo6yRSnr0VJs29KUKCAD69eMDoLhly/XXW5+PG8d22jS2P/xgXXf8OMWwkBCalPfpQyGof3+uv/ZatoMHl53nFVeUHPeaa6zr9u0Dvv2WAlJKCiO5srOt/Wz7lt5WR0+DXLq0bN9x4yiQBQZaUwJ1YmOBYcP4XG8FQRAEQRCExol4PAk1ip52ceYM73RLioQgCLbMmgVERdX3LOxT2rw7Pr5mxtWFJz09LSmJraOKcJVBT2XTx6tKaps99HnqVe5qalydjh3Z2vO10s+ZIAiCIAiC0PgR4UmoUVq3ZvvBB7xjfccd9TodQRAaGNdfDzz9dH3Pwj66Z5Pe6tFANYWHB1t/f7Z+fjUz7p49bHXh7NSpmhlXT7PTjdQdeUpVBUc+X84YnAuCIAiCIAiNA/lqJ9QoLVsCv/8OvPdefc9EEISGyNCh9O1piOjCjS481bSvkF5pTjcorykhx9WV7YkTbCtTPc4Rl1/OVo90ys2tmXF19Ip4eoU8WxyZlguCIAiCIAiNCxGehBpn0CBg4kQ+11MpBEEQAIoX6en1PQv79O7NVo9E0iOTqosuouj+SDpnztTM+G3bstXnq0eeVhddGCsqKjl+TbFjB1uTqew6SdMWBEEQBEH4+yDm4oIgCEKdMXs2jbBTU+t7JmX54Qfg5EkgOprV4wYOrJlxn32WleuuuIIm2zfeyIpuQ4bUzPjXXQfs3UuD8I8+Am67rWbGnTAB2LIFmDmT52XCBOD774GePWtm/JEjOf706cDLLwOdO1Pc2rWLFQYFQRAEQRCEvwfy1U6oFTIz2W7eXL/zEAShYTF+PNCpU33Pwj5+ftb0spoShQDgiSf4AICPP665cXUmTrRGmdZkGuO//82HLTVZYW7aNGs1vblza25cQRAEQRAEoWEhwpNQK+h3q3UvE0EQBIBFB2Jj63sWgiAIgiAIgiDUFeLxJNQKejny9u3rdx6CIDQccnOZtvX77/U9E0EQBEEQBEEQ6goRngRBaNQUF7ti06b2SE9neF1Kig+Ki12xbFmHcrf5+GP75sXr1zezjKnVylzrE5Op8h/3n37K6gBpaSzHduhQK2zf3hkZGSVdpouLrQbRP/zQATk5vjCbeQ4zMgLxyy8doJSGwkLgm2+AI0eqcSCCIAiCIAiCIDQqRHgSagXd4+nPP+t3HkLjJzm5KeLj2yAnxwd5eSXXPfIIkJPjh+PHw5GaGoqiIiO++SYGgIbsbFdERiZg5Ei/MmPeeSfFp6++AqZOBSIi8mAwmPDDD60AABkZjTsLOTvbC1lZ3gCAo0cpEj3+eH8AwB9/lC1NdvfdFIRMJqCwEGjevBDt229FRgYFJxcXM/r02QRPz3wUFLgiKCgVhYVATg6wcyfg7Q08+6yGiIijyMz0gFLA6tUx8PHJg69vBjIyPKGUEd7ewPnzwIwZdXQiBEEQBEEQBEGodxr3ryuhwaJ7PAUH1+88hMbHunWRAACzWYOPTw7MZgPS032Qne2Do0eBqCggJQVo2ZIpW+PHv4d7702CyfQHDh8eAVdXb9x99xDExDyGd97ZggMHFgFwwUsvUViZMgWYPx8IDwfWrQP27we6dPGFh0c2li37BS++qHDttcFYvhx4803gjz+A/Hzg1CmgaVNrGmlD49dfgb59gaIiVyQktAMABAVdwK5dhTAaizB5cgJ+/92Ijz++DM8+y2PPzmZlsU8+AUJCGIk0Zw7w/PPBOHQoEh9++BNatRqBtm3fRbNmdyA0NBU7d16PwsKzmD9/O+bNY3W6d98FvL0V0tPDMGfOenh6fg5NG4rly3vBza0Ay5YtxuLF6TAYnq/fkyQIgiAIgiAIQp0jwpNQK+g/ztu2rd95CI0HX18zXF0L8MMPl1le5+PEicvQocNBLFz4BdauzUPz5h9i6VLgjjuA06dZkn7DhscRFjYJGRnZ6NdvD66+ugAnTlwAAFx99ReYO/d1AO5wdwfWrAGGDwceeoj7HDkSSEsDPv0UyM31R+vW6Xjmmelwc/sVP/8M+Pqy3759wKBBwKRJwAcfULiaNAlo1aoeTpQdcnOBq68Gnn8eyM31ROfOB9Ct22Hs3Nkcd9yRijlzJqBr1xcxduwcdOjwK4DuWLgQOHaM5yAnh+NkZQErVwL/+lcqNm78EbGxBpw8yXX33vsaOnVKubTPa66hkBcUBNx6K6AUEBbmh5Ytn8Hp078hNNQfRmMBzp8/gogIT9x000swGkV4EgRBEARBEIR/GiI8CbXKrFn8kf7uu8ANN9T3bISGzP79rvD3T8Hzzx9ARsaLGDs2FoWFx+Hjk4zAwBwMHPgr/PwoAH32WeUrJt53Hx+2GAyMyrvppkR06DAQ3t6vAgA0jdesTnQ0U/PGjQP27GHpd3d3Clgvvwy88QZw4gT7PvUU/aOee46vp01jSXq9ktsLLwD9+/NhMgH//S9w5ZXA4MHAxYtMXevencd3//2Aqyv3AQALFwI9egBXXcXXq1dTAFu/Hnj8cc6vbduH4ebmhejodOTlHUNk5H1ISMiAwQD4+aUhPLwIAPDWW0yrs8XXF9i8GcjKMuGhh2bA09N6EiZP/gzu7k2hFF/37s2HI0aMiMfFi2sATHXcURAEQRAEQRCEvy3i8STUCn5+wGOPAa1bM6Jky5b6npHQUMnNBc6eBdLTDbhwIRLBwfmIifkdRiNw883fYuTINSX6t2lDIacmU940DfDwyCt3fXAwhVODAejUCdixA7jlFq5r3RoYNszaNzmZaXkAsGsX8NtvwA8/WNfPmcO0OIDC05w5uBRVtGsXMHQosHcvUFAA+PsDu3dbt33wQQpgeXn8u+rWDejSBYiMpNDVrh3QsmUSmjc/U+ExN23KdEVBEARBEARBEITaRCKehFpB0xjpNH8+jYeLi+t7RkJD5dNPaRK+Z08h/vhDQ9u27+Lw4fqeVfkYDMAVV1hfjxnDh86HH1qfd+0KJCTw70EnL8/qgebqSrNt/XX37vRe6twZ8PAA5s0rue+8PEZaTZwILFvGiKUNG2r2+ARBEARBEARBEGoSEZ6EWic3V34cC2VZtIgG3zfeCKxdC/j4qPqeUq3g5lbytYeH9bmmAaGh1tcBAcCAAeWPpW/70EPA9ddTuBIEQRAEQRAEQWjIiPAk1Dpubg3HhFloOPzvf0wx27sXWLwYMJv/nsJTbXDllXwIgiAIgiAIgiA0dER4EmodFxcgKqq+ZyE0BDZt6oDk5CAAifjtN8DHh8vd3ID8/HqdmiAIgiAIgiAIglALiLm4UCcUFdX3DIT6ZsCANXB3L4KraxFatEhGs2Y0zxYEQRAEQRAEQRD+vjTqiCdN06IAfAogHIAC8L5S6nVN04IAfAWgJYBEADcqpdI0TdMAvA5gJIBcAFOVUjvqY+7/JHJzga++YgWvgQNZHUz455Gf74Xp03/CkCFvwt09EoADMyNBEARBEARBEAThb0Fjj3gqBjBLKdURQG8Ad2ua1hHAowB+U0q1AfCb5TUAXAOgjeUxA8A7dT/lfx7BwSwN/+67wP331/dshPri+PG2SEmRECdBEARBEARBEIR/Eo064kkpdQbAGcvzLE3TDgJoBuB6ALGWbosBxAGYbVn+qVJKAdisaVqApmkRlnGEWuLCBbbNmwPnztXvXIS6RSlgyxZGvX300QgEB1+HrKz6npXQ0MjK2o7ExOegaUaEh09GVtYWuLtHQakiFBVdgLd3J6Sl/YbQ0Ak4d+5zeHt3Qmbmn/D374+MjA0IDLwaZnMBCgqS4OvbCykpX4Ef81bc3SNhNHqjoOAUvL07IStrOwAvJCVthJ9fP2RlbYXZXABAg1LFMBjcYDB4wdv7cmRlxcPfvw/S09eXmbuHR3NomhsKC8/C27sjsrJ2wN+/DzIyNljG3QKzuRCABsAETXOBUsXQNBcARgfjtoSmGVBUlAIvr/bIzt4FP7/eyMjYAH///sjM3Awfn67IzT2E4uJ0aJoLzOYiGAxu0DQjfH17ITNzo+Uc/Qmlii+NCygUF6fB07MtcnJ2l+nr69sdOTkHYDJlAjBcOh8AYDYXQtNcLs1BH7c0BoMbfH2vtIw7ABkZG+HrG4OcnH1wd28GkykX+fmJMBjcAZgtc3e1jGeEn18v5OTsg8mU4/Da0TQXhIVNxIULK+DmFo68vOPw8IhCQcEZuLk1gY9PZ6Snr0Vw8ChcuLACISHjkJr6LQIDhyIzcysMBjcUFCTD1TUIJlMuAIXg4Gtx4cJKhISMA/A1srODkJm5BR4eLWE256Ow8IxlXhqMRi8UFV1EaOgNSE1dhcDA4cjM3AQPj8tgNueiuDgDXl7tkZGxEUajJ4qK0uDu3hT5+ScQEjIGmZkb4enZGiZTNoqLs+Dl1RaZmX8iKGgkLlxYCTe3MOTnn4SnZzTy8o7D378vioszYTbnwMOjFbKytiAo6BpcuLAKoaHjkJKyAu7uEZbz0AIFBSfh4dESHh7RyMrahqCgESX6Bgdfi/T0P+DtfTmKii6guDjdcj25QdNcYDJlIjT0BkvfUUhP/w1eXuwLmODuHomsrB2WvllwdQ1DYeFphISMR1rar/Dx6YLCwrMAFNzcIpCdvRtBQVfjwoVv4eoaisLC0/DwaIm8vOMICIhFYWEyAAPc3MKQk7MXAQFDkJr6PUJDxyIlZTnc3Ztajq0lCgqS4OXVHl5eHZCSstzhdeLqGoji4ixomtHmb9HV8p7bh9emBsBs+fvKg9Hoh+LiDLi4BKC4ON2m93EkJlrL92qaBqPRF8XFmZf6Go3eMJvzLX/7Gszm8o0NXVx8YTLlXuoLKGiaAWZz/qVxK5q3UgVl5sBxc6CU2cG+9b5+MJmyoWklS7M6mrfB4FHitVKFMBq9UVxc8T9+TTPAYPCEyZRTag5ZMBg8oJTZ8hld3rz9YTJlXuqrnzelimE0etmdg9mcB0CDweABkykTRqOfTZsBo5FjcuxcaJrRct1kWtbpfbIs5zodLi4BMJv53tn2BfYhMXHDpW3s982G0ehr0+ciXFyCLs3JbM6BprmW6KuPYztfa1/+P2BfPwfzzYGmuUPTDDCZcmE0+pQ5D9b9ZMNg8ICmaTCZ8ix9M0qcD9u+RqMnAFzqW958OScvKKUs17lt36wS54XjekEpM5QqgMHgbWdc2/G9LX0LYTB4Ozi2TBiNPlDKZPm/51nu9VZYeAaaZoCra3iF13ZhYTI0zQUGgxcKCk7By6t9OT2tnyMmUxYKC8/C07M1cnL2wNu7S4X7ARRycvbC27szcnJ2O7kNKtVXJzd3Pzw921k+o5yjoOAEjEYfuLgEOb1NcXEaiovT4eERXYnZmZCTsx/e3p0rsU3lz0N6elylxhcArfSX88aKpmktAawD0AnACaVUgGW5BiBNKRWgadr3AF5QSm2wrPsNwGyl1PZSY80AI6IQHh7eY+nSpXV2HI2DxQA+AfA7+I/dOZ544nIkJnrj88+3Vmvv2dnZ8NFdqYUGxaBBsQCABx9chldfnYCPPvoJX3zRFTt2BGL58ggAfQEkAAgFML/U1ikAbgTwAICFAG4FcABABpg1uwPMoC3N9QAGA9gNIBJAFJT6Gpp2F4A3AawEUFGkVRyAZ2z2/TKAHs4edgNjJgAfAO4AkgGMB/BfWI/tLQAdKxjjMIA7bLZ5AMD3AELArOaLAN4rtY0ZwBAAUwGsAlMpiwFsBTACwJdgAGpp7gWw1+b1VPDzxcWyPcCP9X0ArgWw2s4YMQD0j/GRAH6o4PiEvxc9AMSXsy4IvF47gp8nV4DXWxiA8+Vs0wbAXzZ9wwFUdNdE79sEwNlS6/zBz7HytrHFD0CmzRxK0xJ0EAAAXwBZAFoBOFrOeDo+ALIBXAbgmE3fSACnoP9It4/eNwrAyVLrvEDngvK2cQWgmzy6AygA0AJAkp1t9PcINts1B3DCwbEZAAwF8HM5cxcEQRCE2mQAgHl1treG+Dt40KBB8UqpmIr6NeqIJx1N03wA/A/A/UqpTGpNRCmlNE2rlLqmlHofwPsAEBMTo2JjY2twto2fxMS1SEwEBg6Mhe25rohmzYDUVKC65zMuLq7aYwi1S3g47wDFxnbB6NHhSEwECgvdEBzcFFlZp+HuHowrrogtsU1+/ils3gy0bdsOhw8DLVtGIzPzLIqKzPD2jkBamjv69Ikts68NG1wRFtYMGRlH4OkZCi+vFjhxAmjdug2OHAH69esPV9dgh/M9fz4FBw4Abdq0xV9/AV26dEVgYNl9NQbi4/3g4hIAo9ETeXmZiIxsj4QE63nt3r0H/Px6ORwjK8sP8fHWbdq2bYfk5LVwdw+GUgqFhUWIiYktsY1SZqxdy/ft9GlXhIY2hdlchIsX3dGkSQucOKHZ/btVajeUMgEA1q1zRYsWzZGUBFhFJ8BoPAmTCQgOdkFqatn5BgT4I90SfBAeHoKLF0PRp0/ypfXJyW/jyJH7nDl9DnFza4reva0/mE+ffh1Hjz5U7XHd/5+9O4/PqrwTv//5skgAIyoqRakiSMUFUURcx8a6V9xah9qfWmlrHa0OdubptHbsTx3bp8s8tFVrp9SpFqWOVVul1Wqto6YutUVEEApUUKMiKosKCRAIyfX8cU5iAgkJy507CZ/363W/cpZr+Z5zX7mTfHOdc3rtw5FHLmxYf/PNibz22je2ut3Gjj32Pd5++1ZeffXq1gtvhuOOq9xolsO6dYv5y1/22SbtH3bYs5SWjml2X13dKp55Zmf69evDiubyOkCWdIIdd9yBqiooLS2hshIi3qOl/7v16dOD1asbl/2gxbL16st267aCuo0mkzQfXH2dprKZLL17B2vWbFynd+9ejbZnlUtKsqeCNt9evaq8bGpStkePNaxfDy0nnT4s27NndTMPCml+xtCHsTSukM1W2WGHGtata65On0bxZ/V69lxLTU3LxxbRjd13352VK4cwZszfm43ltdeu4c03/7PZfUOH/oi99rpyo+3vvfd75sw5p9k69fr2PYTDD88Snk899SeOP/7jDfuee25PamqWbrL+qFF/YccdN/4Hx2uvfZM33/z+JusOHPhPDBt2y0bbq6tfYdq0lmZTZCJ68g//0Pz7NmvWiaxY8dQm6w8fPpk99rhgo+3vvvtL/v73zzdbZ9Cgf2HIkObfg3rPPNOPurqWZ6ABjBnzd0pKhmy0fcGCK3j77Vs3WXfvva9m8OBvNay35Vxp+zZq1F/ZccdRG21PqYann+4DwPHHb/rpSXV11TzzTGmTbYcfPoO+fUdsVLbx58hTT/UEyGeUVbH//r9gwIALN9nXjBlHUFU1k759R7Jq1SwGDvwSw4b91ybrvPnmf/Laa9ew556Xs99+N2+ybL333/8js2efQbduvTnuuJZnXzZWU/Muzz03CGj9nDVWfx6OOqqCHXbYq011nntuL2pqlnDwwVPZddcz2lTn1VevZtGiHzB48PXsvfc1bY6vfjZqNpu2fXTmv4M7feIpInqSJZ3uSinVz7V+t/4SuogYyIf/1nyL7F929Qbl29QO3nkHVrbt80ldyG67Za8//7nYkagjiojNmqrdxlbp1u3DNrfdLwRN24Vt025EoeJt3Ed2aV8h2m16Ttim72dz7ddLqdP/CrPZNuefPZ3PlhxbNHxtaZxEtHw704juLdRry/dK4z43bKf1Y2lpbG8q3sZlmq/blu+Jls9V296D5s/Zpj+3mo93c/vemnO2YQzb/ueOupqWxltd3YeJ+tbGdXM/p1r+udbc91bkdVr6rGqsW142GtZbq/Ph921bvke3ps6H5dpaZ8P6be+r/vOgLedsy+to83Xqm4vnl9HdBsxLKf2w0a7fARfnyxeTXfdRv/1zkTkKWOH9ndrPa6/BkiXw29/C3/5W7GjUHn7/+15EwGOPFTsSSZIkSVIxdOrEE3AscBHwiYiYmb8+CXwPODkiFpBd/P+9vPzDZDc3WAj8N9nNUNROTjwR1q+Hc86BIzd9pY86uTVrsv8WHHzweq6+2vdbkiRJkrZXnXouWX6T8Jbm5p7YTPkEXFHQoNSin/4UJkyACy6A+fOLHY0Kadas3YHsfiLf/W6Rg5EkSZIkFU1nn/GkTqSkBA47DE49FUpLWy+vzmvIkOxmujvt1DWemilJkiRJ2jImntTuunXLbjIeAf36wRtvFDsibWu77ZY9dql3bxNPkiRJkrQ969SX2qlzuvRSWLcuu+H03/+eJZ723rvYUWlb+sUvDgLIH9MtSZIkSdpeOeNJ7W7//eHHP4Zbbil2JNrW+vb9AICXX96ViFr69HHGkyRJkiRtz0w8qWhmz86+PvIIzJkDyRxFpzdmzB8oLV3OW2/dyhNP9GC33XxTJUmSJGl7ZuJJRdOnT/b1O9+BESPgD38objzaem+++TEqK3ctdhiSJEmSpA7CxJOK5otfhHvugeuuy9bPOit72t2PflTcuLTl9tlnHjvttLzYYUiSJEmSOggTTyqaHj1g3Dj493+Hk06CCy+EurrspuPqnHr3rqJHj5pihyFJkiRJ6iB8qp2KbocdPkw23XUXPPccnH9+lpg65xwYNQqGDClujGqbyspdqKnpVewwJEmSJEkdhIkndSgjRkBVVXYJHmSJqN69YeXKLBGlju311w+gqmrnYochSZIkSeogvNROHcoLL8Df/w7vvAPf/z6cdx6sWZMlnyKy14knfpzvfrfYkTavrg4WLoQFC2D+/GJH0/6GDp1Faan3eJIkSZIkZZxDog5pwAD42teyRNTLL8OiRfCRj8Axx8DPfx7cfDM8+SRcdRX07Qu33QavvQZf+AL8wz/AN74Bw4dDz55w+eXZpXwvvQTPPpvdxPzgg+G734UjjoCBA2HCBPjxj+GDD2DePLj44ix59MADMGYMHHYYnHYa/PSnsG4dvPIKXH89/PWvcPvtWbz/9E9QXg7f+taHx3HFFfCTn2TLvXtnx/TBBzB3brZt3Tr4r/+Cz3wG3n0Xjjwyu7n6009nfdTUwJ57wic+kcU3dy6ccgp85Svw+c/DAQdkybiLLoJXX4VnnslehxwCl1ySnZ/hw2GvvbJY7r8/O0+vvQbHHpu9rr46O0+f+ARcdhncdBNUVsLrr2d9VFdnfffsmfVz7LFw662wfn0W8//9v9kTCR94AGbP/gd69Fjf3sNFkiRJktRBmXhSh3b44TBr1ofrtbWwcOES1q7dg8ce2/hG5M8+u3Eb11/fcplnnsm+fu1rTcvcf/+m2wT41a+art97L3Trlr1OPx1+//sPk04DBmRJmv/4j43bOeig7Gu3bvDQQ9lrU5599sMnAdb79rebrv/pT1myCLJkGGQJosb+53+arj/4IPzLvzTd9stfblxmQz//eeO1jzJ69P+2ELkkSZIkaXtj4kmdSvfucN11cykr24MvfCG7LO+AA7JL8oYMgR/8ILtHVEVFlnw58cTssr2jj4Z/+7ds+e67s7aOPBL+8R+zJM4f/gDDhsGUKfDxj2eXzEVks6U+8YlsFlJ5eZZAeuIJOOGEbKbULrtkyZo33sgSTwsXwqBBcOedWf8775zNyPrYx7KE0XPPwYsvwhe/mM0q+va3YeTIrM6118I3vwkzZ2Yzkm68EZ5/PptJdNhh2UyuM87Ikldr1mSzwR58MDuuGTOydq64IovplluyY9hnn+ypgddcA488ksVz++1wxx1ZP3vsAWVlMHo0fO5zWb9r12YJuRtuyGZk7bEHjB8P77+fxbd4cTb7aeHCrO5HPpLNqPra1+D117vn79TPijE8JEmSJEkdjIkndVq3377xtp9tkO/405+arh90UJaMauymm5quv/RS6+0+/XTT9eHDs0vgGjvhhKbr9Ze2NXb22U3Xv/Odpuunnpolexqrn8lUrz6R1tittzZd/973sle9G27YuM60aU3Xf/3rjcuceeam67z++sZ1JEmSJEnbL28uLkmSJEmSpIIw8SRJkiRJkqSCMPEkSZIkSZKkgjDxJEmSJEmSpIIw8SRJkiRJkqSCMPEkSZIkSZKkgjDxJEmSJEmSpIIw8SRJkiRJkqSCMPEkSZIkSZKkgjDxJEmSJEmSpIIw8SRJkiRJkqSCMPEkSZIkSXWNY3gAACAASURBVJKkgjDxJEmSJEmSpIIw8SSpAFKxA5AkSZIkdQAmniRJkiRJklQQJp4kSZIkSZJUECaeJG1zKXmpnSRJkiTJxJMkSZIkSZIKZLtMPEXEaRHx94hYGBFXFzseSZIkSZKkrmi7SzxFRHfgJ8DpwIHAZyPiwOJGJUmSJEmS1PX0KHYARTAGWJhSehUgIn4FnA3MLWpUndDs2WcSEUXoeTmzZ/cvQr9qq8WLf9Ls9vfee4T161fQq9egFuu+9VbTuqtXv8y6dW8DLY+15csfYv365fTu/bF8y3oWL/7Z5obdYtydTVXVC0B3dthhQMO2Dc9rW2xYZ+XKaUCiV6+9W6zz7rt3sX79+w3rNTXLWLr0njb3uWTJ3Rttq61dAcDy5b9ttk5V1cyG5ffff7zNfalrWLVqTqtlVq9+ucnXlNa3WHbNmlc2KLu2ze3X1a1pteyGdZqPofl91dVvNrOtabybUl39WpOy69cvb3OcNTVLWy27YZ3mrFu3uM11amre3WR7KdXwwQdP0q1b7zbHtu20fC/DTY2vYktpXbFDaFZHjUvqPNr3b7JsLkdHtN3Nq+k0tsfE015A49/cFgFHNi4QEZcClwIMGDCA8vLydguuc+gHDOe991r/JbcQamvrWL58WVH6VtusWrUWOIrnnpsL1I+T44BZQAnLl3+sme+rGuAoVq1aDgynomInYDiwkNpagFEtfC8ez/r1c4CBLFu2H9Cf2tr9WL16HXAszz47E2jth+M64OA87kOYNesDoLm+OoORwHsA1NSM4u9/D+AgVq2qBg5jxowltH5sVcBhrFpVBRzIyy93Bw4DKvN2D23mvUjAiaxZ8wawL4sX7wnUAfuwZk0CTmrDZ+nJrFlT0cz2EqAa+CjZx/e+wGvAMGAB69fvCmR/QK9b1wcYsUFfPYEDgBXA4oZ6tbVD6d79lYZ1GAIMJfuxsDuwEliV7xvGunUbHndJ3u4HwLt53QWN2tuw3TeBAXn5Hch+BC+juvrwDdrtQzb23weW5ce7sJl29wMqgP6NypeQ/dK1vFHZgTzzzPON2l2ex7A38Eoz7Q7Lt++Rl38PqP/Dfg2wW34se/H008+x8S+764Gj89ibOx97kr2fq4FDycZJNbBLfh4/mh/XjsyY8S4tj9c64OOsX784P/Y9yb7Xe5ON4Z3y43yHurq9yN7zPcjGwV7AW2RjY/e8bA1QR0o9Nig7iGxM9MzLkL8nQfY+rqS2tm9etn6M9gLqE1a7A0vzc7kS2BFYQ21tSV6nfnyTn9tlZO/tQuBj+TnPytTVleTnELL3fTnZ2HqlUbz75OemhGz87pSf1/dbKPs60DcvS34MJfm5bHxsgxvel+z8Qvb7wIoNjm01tbW98+075dtptFz//bt/3mdvoLrR+QAoJfu8ycp+GO+Q/D0oIRs/O7JuXR1wxCY+X/rnffXgwz9I6oA6Fi7cgYULm6u3ChjRpGz2Xq9uiG3Vqg+/b6uqqjbovwyY3eiYd8rPWc98f09eeOFtmh/b9fH2bLQt5a8ewBoWL96TxYubq7sOOCKPszvZ2Kk/lzvmxzVkE+fqYLLPyNI83l75sdd/jyfmz69j/vzm6tcBB+XLkcfbDVjLokW7s2hRS33WO4VsbPfN+66PuzfZZ0pf/vrXhcAbzdTdk+zzpb5s0PRzqYY33tiVN95oHEP9uepG9v68Q/b9MY/s5+jM/Hz8nWzsv0P2nuwEvE32PTqXbJy8BByS1x2c7+9Fds7fAYZQWzuX7t0Py9utL7sv2fkuyV/LGsVwGPBC3v68vL83yT7He21Q9pC83RF5TMPysn3Jxu0ysu+d+fkxzSL77P1bo3Z3zM/F+/kxvJy3+2Jedk7e7hv5OQiyz9fB+TkakcfQuOzrwM75+f6gUbsj8nZH5mX3J/ts2YVs3Kwg+/m0gOzilJfy8/ES2WdiBdn3yXqyMVJf9uBGMcwm+1x6lexztYZsXO1J9vlzYH4e6ts9gOzn3m5kn8dreOGFxXz4+dVYAj4B9GjD7zWJ7POghOznzotMn76I+t9ZGmv6OfI54G/U1n4ceHQT33eNnQJUU1V1KrCOt98ezNtvt1anL3Agixfv3cJnSnNWAodQW1u2GX8j1wLHAJv7d/U44HWee24eH/7sa835wGPMmbOKtv8uvwdwABUVpVRUbE587W/jnzedR2xvT5+KiPOA01JKl+TrFwFHppSubK786NGj0/Tp09szRLWivLycsrKyYoehDswxorZwnKg1jhG1heNErXGMqDWOEbVFRxwnEfFCSml0a+W2x7lob5H9O6/eoHybJEmSJEmStqHtMfH0PDAsIvaNiB3I5uP9rsgxSZIkSZIkdTnb3T2eUkrrI+JK4FGyC+BvTyn9rchhSZIkSZIkdTnbXeIJIKX0MPBwseOQJEmSJEnqyrbHS+0kSZIkSZLUDkw8SZIkSZIkqSBMPEmSJEmSJKkgTDxJkiRJkiSpIEw8SZIkSZIkqSAipVTsGDq0iFgKvF7sONTEbsCyYgehDs0xorZwnKg1jhG1heNErXGMqDWOEbVFRxwn+6SUdm+tkIkndToRMT2lNLrYcajjcoyoLRwnao1jRG3hOFFrHCNqjWNEbdGZx4mX2kmSJEmSJKkgTDxJkiRJkiSpIEw8qTO6tdgBqMNzjKgtHCdqjWNEbeE4UWscI2qNY0Rt0WnHifd4kiRJkiRJUkE440mSJEmSJEkFYeJJkiRJkiRJBWHiSZ1GRNweEUsiYk6xY1HHFBEfjYgnI2JuRPwtIq4qdkzqWCKiJCKmRcSsfIz8R7FjUscUEd0j4sWIeKjYsahjioiKiJgdETMjYnqx41HHFBE7R8SvI2J+RMyLiKOLHZM6jojYP/8MqX+tjIivFDsudSwR8S/5761zIuLuiCgpdkyby3s8qdOIiOOBKuDOlNLBxY5HHU9EDAQGppRmREQp8AJwTkppbpFDUwcREQH0TSlVRURP4BngqpTSX4ocmjqYiPhXYDSwU0ppbLHjUccTERXA6JTSsmLHoo4rIu4Ank4p/TwidgD6pJQ+KHZc6ngiojvwFnBkSun1YsejjiEi9iL7ffXAlNKaiLgXeDilNLm4kW0eZzyp00gpPQW8V+w41HGllN5OKc3IlyuBecBexY1KHUnKVOWrPfOX/4FRExExCDgD+HmxY5HUeUVEP+B44DaAlNI6k07ahBOBV0w6qRk9gN4R0QPoAywucjybzcSTpC4pIgYDhwF/LW4k6mjyS6hmAkuAx1JKjhFt6Ebga0BdsQNRh5aAP0bECxFxabGDUYe0L7AU+EV+6e7PI6JvsYNSh3U+cHexg1DHklJ6C5gIvAG8DaxIKf2xuFFtPhNPkrqciNgR+A3wlZTSymLHo44lpVSbUjoUGASMiQgv3VWDiBgLLEkpvVDsWNThHZdSGgWcDlyR3xJAaqwHMAr4aUrpMGAVcHVxQ1JHlF+GeRZwX7FjUccSEbsAZ5MlsvcE+kbEhcWNavOZeJLUpeT37fkNcFdK6f5ix6OOK7/c4UngtGLHog7lWOCs/P49vwI+ERG/LG5I6ojy/0KTUloCPACMKW5E6oAWAYsazaz9NVkiStrQ6cCMlNK7xQ5EHc5JwGsppaUppRrgfuCYIse02Uw8Seoy8htH3wbMSyn9sNjxqOOJiN0jYud8uTdwMjC/uFGpI0kpfSOlNCilNJjssocnUkqd7j+LKqyI6Js/xIL80qlTAJ+6qyZSSu8Ab0bE/vmmEwEfeKLmfBYvs1Pz3gCOiog++d86J5Ldx7ZTMfGkTiMi7gaeA/aPiEUR8cVix6QO51jgIrIZCvWPpf1ksYNShzIQeDIiXgKeJ7vH00NFjklS5zMAeCYiZgHTgN+nlP5Q5JjUMf0zcFf+c+dQ4DtFjkcdTJ68PplsJovURD5j8tfADGA2WQ7n1qIGtQUiJR/mI0mSJEmSpG3PGU+SJEmSJEkqCBNPkiRJkiRJKggTT5IkSZIkSSoIE0+SJEmSJEkqCBNPkiRJkiRJKogexQ5AkiRpexUR/YHH89WPALXA0nx9dUrpmKIEJkmStI1ESqnYMUiSJG33IuJ6oCqlNLHYsUiSJG0rXmonSZLUAUVEVf61LCL+FBG/jYhXI+J7EXFBREyLiNkRMTQvt3tE/CYins9fxxb3CCRJkkw8SZIkdQYjgcuAA4CLgI+llMYAPwf+OS9zE/CjlNIRwKfzfZIkSUXlPZ4kSZI6vudTSm8DRMQrwB/z7bOBE/Llk4ADI6K+zk4RsWNKqapdI5UkSWrExJMkSVLHt7bRcl2j9To+/H2uG3BUSqm6PQOTJEnaFC+1kyRJ6hr+yIeX3RERhxYxFkmSJMDEkyRJUlcxARgdES9FxFyye0JJkiQVVaSUih2DJEmSJEmSuiBnPEmSJEmSJKkgTDxJkiRJkiSpIEw8SZIkSZIkqSBMPEmSJEmSJKkgTDxJkiRJkiSpIEw8SZIkSZIkqSBMPEmSJEmSJKkgTDxJkiRJkiSpIEw8SZIkSZIkqSBMPEmSJEmSJKkgTDxJkiQ1EhEpIvYrdhybEhGD8zh7bMM2/z0ift5c+xHxSERcvK36atTn3yKibFu3K0mSOg4TT5IkqUOKiIqIWBMRVRHxTkRMjogdNyhzTEQ8ERGVEbEiIn4XEcNbaXdgRNwWEW/n9eZHxH9ERN/CHlH7aS55FhHXR8Qv8+WyiFjUeH9K6TsppUuaay+ldHpK6Y6tjGlyRHx7g3YPSimVb027kiSpYzPxJEmSOrIzU0o7AocChwHfqN8REUcDfwR+C+wJ7Au8BDwbEYObaywidgWeA3oDR6eUSoGTgZ2BoQU7CkmSpO2UiSdJktThpZTeAR4lS0DV+0/gzpTSTSmlypTSeymlbwLTgOtaaOpfgUrgwpRSRd72mymlq1JKL21YOCLOiIgXI2JlRLwZEdc32ld/Odrn833vR8RlEXFERLwUER9ExC2Nyo+PiGcj4pZ8dtb8iDix0f5+jWZivRUR346I7vm+7hExMSKWRcSrwBlbdiYhn9n1CLBnPpusKiL2bDwjqpk65RFxSb48q1G9qvwclOX77stnp62IiKci4qB8+6XABcDX8joP5tsrIuKkfLlXRNwYEYvz140R0SvfVxYRiyLi/4mIJfk5+vyWngNJktR+TDxJkqQOLyIGAacDC/P1PsAxwH3NFL8XOKWFpk4C7k8p1bWx61XA58hmRJ0BXB4R52xQ5khgGPAZ4Ebgmryfg4BxEfHxDcq+AuxGlhy7P5+FBTAZWA/sRza76xSg/tK3LwFj8+2jgfPaGP9GUkqryM7l4pTSjvlr8WbUH1lfjyyR93dgRr77EbJzsUe+7a68zq358n/mdc9spulrgKPIkosjgTHANxvt/wjQD9gL+CLwk4jYpa1xS5Kk4jDxJEmSOrKpEVEJvAks4cOZTLuS/R7zdjN13gZ2b6G9/i3UaVZKqTylNDulVJfPiLob+PgGxb6VUqpOKf2RLFF1d0ppSUrpLeBpsmRRvSXAjSmlmpTSPWRJmzMiYgDwSeArKaVVKaUlwI+A8/N64/J6b6aU3gO+29ZjKJSIOA74NnBWSmklQErp9nz22VrgemBkRPRrY5MXADfk524p8B/ARY321+T7a1JKDwNVwP7b6HAkSVKBmHiSJEkd2Tn5fZjKgOFkM4UA3gfqgIHN1BkILGuhveUt1GlWRBwZEU9GxNKIWAFc1iiGeu82Wl7TzHrjG6K/lVJKjdZfJ7s/1T5AT+Dt/BK9D4Cfkc0cIi/z5gb1NqU2b6+xnmTJm60WER8lm1l2cUrp5Xxb94j4XkS8EhErgYq8+IbnqyV70vS46s9NveUppfWN1lfT9NxKkqQOyMSTJEnq8FJKfyK7FG1ivr6K7Cbh/9hM8XFAeQtN/S9wbkS09Xeg/wF+B3w0pdQPmAREmwPf2F4R0bj+3sBisqTSWmC3lNLO+WunlNJBebm3gY9uUG9T3gAGb7BtXz5M7CS2UET0BqaSzcB6pNGu/wOcTXaZYb9G/dcfb2t9LiZLwNWrPzeSJKkTM/EkSZI6ixuBkyNiZL5+NXBxREyIiNKI2CUivg38A/CdFtr4IbATcEdE7AMQEXtFxA8j4pBmypcC76WUqiNiDFlyZWvsAUyIiJ4R8Y/AAcDDKaW3yZ7Q94OI2CkiukXE0Eb3h7o3rzcov6/R1a30cw/wzbx8t/wG3mcCv873vwv034zL4Bq7HZifUvrPDbaXkiXPlgN92Pg9eBcYsol2785j3j0idgOuBZq92bkkSeo8TDxJkqROIb/vz51kCQlSSs8ApwKfIpsR9B5wMXBiSmlOC228R3ZT8hrgr/n9ox4HVpDfuHwDXwZuyMtdS5YA2hp/Jbv59jLg/wXOSyktz/d9DtgBmEt2KeGv+fCywP8me6rfLLKbdt/fSj83AH8Gnsnb+k/ggvrzklKaT5boeTW/tG/PFlva2Plks8YaP9nuH8jem9eBt/Jj+MsG9W4DDsz7m9pMu98GpgMvAbPz4/z2ZsQlSZI6oGh6mwFJkqTOKZ+x9CTwf1JKjxY7ng1FxHjgkpTSccWORZIkqb0440mSJHUJ+VPnzgFGRESPYscjSZIk8JcySZLUZaSUngaeLnYckiRJynipnSRJkiRJkgrCS+0kSZIkSZJUEF5q14rddtstDR48uNhhqJFVq1bRt2/fYoehDswxorZwnKg1jhG1heNErXGMqDWOEbVFRxwnL7zwwrKU0u6tlTPx1IrBgwczffr0YoehRsrLyykrKyt2GOrAHCNqC8eJWuMYUVs4TtQax4ha4xhRW3TEcRIRr7elnJfaSZIkSZIkqSBMPEmSJEmSJKkgTDxJkiRJkiSpILzHkyRJ6vJqampYtGgR1dXVba7Tr18/5s2bV8Cotk8lJSUMGjSInj17FjsUSZLUDkw8SZKkLm/RokWUlpYyePBgIqJNdSorKyktLS1wZNuXlBLLly9n0aJF7LvvvsUOR5IktQMvtZMkSV1edXU1/fv3b3PSSYUREfTv33+zZp5JkqTOzcSTJEnaLph06hh8HyRJ2r54qd3WeOopWLGi2FEUR9++UFYG3cxdqpEVK+CDD2CffYodiSRJ6gz+/Ofs94cTToCSkmJHo45k7VooL4cjjoBddy12NFJTdXXZ+Fy1qt267D97NlRWtlt/25KJp63xr/8KL7xQ7CiK5+mn4bjjih2FOpLDD4dXXoGUih2JJHVqFRUVjB07ljlz5hQ7lGZde+21HH/88Zx00knFDkWd2RtvwLHHZss/+xlcemlx41HH8stfwiWXwOc/D7ffXuxopKaefRZOPLFduxzRrr1tWyaetsaUKbB6dbGjaH+zZsEXv7h9Hrs27ZVXih2BJKkd3HDDDcUOQV1B498l23HWgDqJ+vGxbFlx45CaUz8+b78dDjmkXbqcPn06o0ePbpe+2qyN8XSKxFNE3A6MBZaklA7Ot+0K3AMMBiqAcSml9yO7ccBNwCeB1cD4lNKMvM7FwDfzZr+dUrpjqwI74ICtqt5prV1b7AgkSdpiCxZ8haqqma2Wq62tpXv37m1qc8cdD2XYsBs3WWbDWUwTJ06kqqqKCy+8kMsuu4ylS5fSvXt37rvvvib9VlRUcNFFF7Eq/8P8lltu4ZhjjqG8vJzrrruOnXfemdmzZzNu3DhGjBjBTTfdxJo1a5g6dSpDhw5l/PjxlJSUMH36dFauXMkPf/hDxo4d22yMkydPZurUqaxatYoFCxbw1a9+lXXr1jFlyhR69erFww8/zK677sr48eMZO3Ys5513HoMHD+biiy/mwQcfpKamhvvuu4/hw4e36bxJktSpDR+eXfXRDqoqK9utr22ts9ygZzJw2gbbrgYeTykNAx7P1wFOB4blr0uBn0JDouo64EhgDHBdROxS8MglSZI24YILLuCKK65g1qxZ/PnPf2bgwIFN9u+xxx489thjzJgxg3vuuYcJEyY07Js1axaTJk1i3rx5TJkyhZdffplp06ZxySWX8OMf/7ihXEVFBdOmTeP3v/89l1122SafKjdnzhzuv/9+nn/+ea655hr69OnDiy++yNFHH82dd97ZbJ3ddtuNGTNmcPnllzNx4sStPCOSJKkr6RQznlJKT0XE4A02nw2U5ct3AOXA1/Ptd6aUEvCXiNg5IgbmZR9LKb0HEBGPkSWz7i5w+JIkqQNpbWZSvcrKSkpLSwsaS2VlJW+99RbnnnsuACXN3Fy5pqaGK6+8kpkzZ9K9e3defvnlhn1HHHFEQ6Jq6NChnHLKKQCMGDGCJ598sqHcuHHj6NatG8OGDWPIkCHMnz+fQw89tNmYTjjhBEpLSyktLaVfv36ceeaZDW2+9NJLzdb51Kc+BcDhhx/O/fffv7mnQZIkdWGdIvHUggEppbfz5XeAAfnyXsCbjcotyre1tH0jEXEp2WwpBgwYQHl5+baLugvYac4cRpH9l/X9HXZo9/6rqqp8Tzqosvxrsd8fx4jawnGyfenXrx+Vm/kkmNra2s2usynV1dWsX7++oc0VK1awbt06Ukob9VNVVUVdXR2VlZV873vfY5ddduGZZ56hrq6O3XffncrKSlavXk337t0b6qaUGtqvrq6murqayspKampqWLt2bUO52tpaVq9e3eyxVVdXExEN+yKCmpoaKisrWbduXUO9mpoa1qxZQ2VlJSmlhjLV1dVN+trUuegq339+lmy5Pm+8wZh8eeHChSzqoufRMbJl9lqwgGHAsuXLmdPFz59jpPPZZdYsRgIzZsxgZTvdiqYzj5POnHhqkFJKEbHNHqOVUroVuBVg9OjRqaysbFs13TXkyaaRI0dCEc5NeXk5vicdW7HfH8eI2sJxsn2ZN2/eZs9e2tYznoYOHcqyZctYt24dO+64I4899hinnXYaH/3oR3n88cc555xzWLt2LbW1tey4445069aN0tJSqqur2WeffejXrx+/+MUvqK2tpbS0lD59+tCjR4+GGLt3707fvn032tezZ08efPBB/umf/onXXnuN119/nVGjRjU7u6qkpIQddtihoc2IYMcdd6S0tLTJvp49e9K7d29KS0ublOnbty/du3dv9byVlJRw2GGHbbNzW0x+lmyF+fMbFvfbbz/266Ln0TGyhWbPBmC3/v27/PlzjHRCebJp1KhRcPTR7dJlZx4nneUeT815N7+Ejvzrknz7W8BHG5UblG9rabskSVLB9ezZk2uvvZYxY8Zw8sknN9yAe8qUKdx8880ccsghHHPMMbzzzjtN6n35y1/mjjvuYOTIkcyfP5++fftudt977703Y8aM4fTTT2fSpEnNJp0kSZIKoTPPePodcDHwvfzrbxttvzIifkV2I/EVKaW3I+JR4DuNbih+CvCNdo5ZkiRtxyZMmNDk5uD1nnjiiY221T/9btiwYU3urfT9738fyGaXNv7PZ+Pp9xvuO+mkk5g0aVKr8Y0fP57x48c3rFdUVDS7b/Lkyc2WGT16dKe9DECSJBVGp0g8RcTdZLeP2S0iFpE9ne57wL0R8UXgdWBcXvxh4JPAQmA18HmAlNJ7EfEt4Pm83A31NxqXJEmSJEnSttcpEk8ppc+2sOvEZsom4IoW2rkduH0bhiZJktShNZ6dVO/RRx/l61//epNt++67Lw888EA7RSVJkrYXnSLxJEmSpG3n1FNP5dRTTy12GJIkaTvQmW8uLkmSJEmSpA7MxJMkSZIkSZIKwsSTJEmSJEmSCsLEkyRJkiRJkgrCxJMkSVIHU1FRwcEHH1zsMCRJkraaiSdJkiRJkiQVRI9iByBJktSuvvIVmDmz1WK9a2uhe/e2tXnooXDjjZssUlFRwdixY5kzZw4AEydOpKqqigsvvJDLLruMpUuX0r17d+677z66N+q3oqKCiy66iFWrVgFwyy23cMwxx1BeXs51113HzjvvzOzZsxk3bhwjRozgpptuYs2aNUydOpWhQ4cyfvx4SkpKmD59OitXruSHP/whY8eObTbGyZMnM3XqVFatWsWCBQv46le/yrp165gyZQq9evXi4YcfZtddd+W///u/ufXWW1m3bh377bcfU6ZMoU+fPpx99tl8+tOf5nOf+xw/+9nPeOqpp7jrrrvadg4lSVKX5IwnSZKkIrrgggu44oormDVrFn/+858ZOHBgk/177LEHjz32GDNmzOCee+5hwoQJDftmzZrFpEmTmDdvHlOmTOHll19m2rRpXHLJJfz4xz9uKFdRUcG0adP4/e9/z2WXXUZ1dXWL8cyZM4f777+f559/nmuuuYY+ffrw4osvcvTRR3PnnXcC8KlPfYrnn3+eWbNmccABB3DbbbcBcOutt3LDDTfw9NNP84Mf/KBJDJIkafvkjCdJkrR9aWVmUr01lZWUlpYWNJTKykreeustzj33XABKSko2KlNTU8OVV17JzJkz6d69Oy+//HLDviOOOKIhUTV06FBOOeUUAEaMGMGTTz7ZUG7cuHF069aNYcOGMWTIEObPn8+hhx7abEwnnHACpaWllJaW0q9fP84888yGNl966SUgS05985vf5IMPPqCqqopTTz0VgAEDBnDDDTdwwgkn8MADD7Drrrtu7SmSJEmdnIknSZKkdtCjRw/q6uoa1jc166ixH/3oRwwYMIBZs2ZRV1fXJDnVq1evhuVu3bo1rHfr1o3169c37IuIJm1uuN5YW9ocP348U6dOZeTIkUyePJny8vKGOrNnz6Z///4sXry4TccnSZK6Ni+1kyRJagcDBgxgyZIlLF++nLVr1/LQQw9RWlrKoEGDmDp1KgBr165l9erVTeqtWLGCgQMH0q1bN6ZMmUJtbe1m933fffdRV1fHK6+8wquvvsr++++/VcdSWVnJwIEDqampaXIPp2nTpvHII4/w4osvMnHiRF577bWt6keSJHV+Jp4kSZLaQc+ePbn22msZM2YMJ598MsOHDwdgypQpV++ElgAAIABJREFU3HzzzRxyyCEcc8wxvPPOO03qffnLX+aOO+5g5MiRzJ8/n759+25233vvvTdjxozh9NNPZ9KkSc1e0rc5vvWtb3HkkUdy7LHHNhzH2rVr+dKXvsTtt9/OnnvuyQ9+8AO+8IUvkFLaqr4kSVLn5qV2kiRJ7WTChAlNbg5e74knnthoW/3T74YNG9ZwbyWA73//+wCUlZVRVlbWsL3x5W4b7jvppJOYNGlSq/GNHz+e8ePHN6xXVFQ0u+/yyy/n8ssv36j+rFmzGpbPOusszjrrrFb7lCRJXZszniRJkiRJklQQzniSJEnqwiZPnrzRtkcffZSvf/3rTbbtu+++PPDAA+0UlSRJ2l6YeJIkSdrOnHrqqZx66qnFDkOSJG0HvNROkiRJkiRJBWHiSZIkSZIkSQVh4kmSJEmSJEkFYeJJkiRJkiRJBWHiSZIkqYgmT57M4sWLG9YvueQS5s6dC8DgwYNZtmxZsUKTJEnaaiaeJEmSimjDxNPPf/5zDjzwwCJGJEmStO2YeJIkSdudsjKYPDlbrqnJ1n/5y2x99eps/Te/6QHAihXZ+v33Z/uXLcvWH3wwW3/nnbb1WVFRwcEHH9ywPnHiRA4++GCmT5/OBRdcwKGHHsqaNWsoKytj+vTpbWrznHPO4fDDD+eggw7i1ltvBWDSpEn827/9W0OZyZMnc+WVVwLwrW99i/3335/jjjuOz372s0ycOLFtwUuSJG0hE0+SJElFct555zF69GjuuusuZs6cSe/evTer/u23384LL7zA9OnTufnmm1m+fDmf/vSneeCBBxrK3HPPPZx//vk8//zz/OY3v2HWrFk88sgjbU5uSZIkbY0exQ5AkiSpvZWXf7jcs2fT9T59svXKyvUA9OvXdP9uuzVd/8hHChdna26++eaGJNObb77JggULOOqooxgyZAh/+ctfGDZsGPPnz+fYY4/lpptu4uyzz6akpISSkhLOPPPM4gUuSZK2GyaeJEmS2kGPHj2oq6trWK+urt6q9srLy/nf//1fnnvuOfr06UNZWVlDm+effz733nsvw4cP59xzzyUitqovSZKkLeWldpIkSe1gwIABLFmyhOXLl7N27VoeeughAEpLS6msrNzs9lasWMEuu+xCnz59mD9/Pn/5y18a9p177rn89re/5e677+b8888H4Nhjj+XBBx+kurqaqqqqhv4lSZIKyRlPkiRJ7aBnz55ce+21jBkzhr322ovhw4cDMH78eC677DJ69+7Nc8891+b2TjvtNCZNmsQBBxzA/vvvz1FHHdWwb5ddduGAAw5g7ty5jBkzBoAjjjiCs846i0MOOYQBAwYwYsQI+vXrt20PUpIkaQMmniRJktrJhAkTmDBhwkbbP/3pTzcslze6gVRFRUWLbfXq1YtHHnmkxf3NzWj66le/yvXXX8/q1as5/vjjOfzww9sWuCRJ0hYy8SRJkrSduPTSS5k7dy7V1dVcfPHFjBo1qtghSZKkLs7EkyRJUge2fPlyTjzxxI22P/744/Tv33+z2vqf//mfbRWWJElSm5h4kiRJ6sD69+/PzJkzix2GJEnSFun0T7WLiH+JiL9FxJyIuDsiSiJi34j4a0QsjIh7ImKHvGyvfH1hvn9wcaOXJEmSJEnqujp14iki9gImAKNTSgcD3YHzge8DP0op7Qe8D3wxr/JF4P18+4/ycpIkSZIkSSqATp14yvUAekdED6AP8DbwCeDX+f47gHPy5bPzdfL9J0ZEtGOskiRJkiRJ241OfY+nlNJbETEReANYA/wReAH4IKW0Pi+2CNgrX94LeDOvuz4iVgD9gWWN242IS4FLAQYMGNDkscaCnebMYRQwa9Ys3t9hh3bvv6qqyvekgyrLvxb7/XGMqC0cJ9uXfv36UVlZuVl1amtrN7uO2qa6urrLfP/5WbLl+rzxBmPy5YULF7Koi55Hx8iW2WvBAoYBy5YvZ04XP3+Okc5nl1mzGAnMmDGDlWvXtkufnXmcdOrEU0TsQjaLaV/gA+A+4LStbTeldCtwK8Do0aNTWVnZ1jbZteTJppEjR0IRzk15eTm+Jx1bsd8fx4jawnGyfZk3bx6lpaWbVaeysnKz62wrFRUVjB07ljlz5hSl/0IrKSnhsMMOK3YY24SfJVth/vyGxf3224/9uuh5dIxsodmzAditf/8uf/4cI51QnmwaNWoUHH10u3TZmcdJZ7/U7iTgtZTS0pRSDXA/cCywc37pHcAg4K18+S3gowD5/n7A8vYNWZIkSZIkafvQqWc8kV1id1RE9CG71O5EYDrwJHAe8CvgYuC3efnf5evP5fufSCml9g5akiQVz1e+AjNntl6utrY33bu3rc1DD4Ubb9x0mQ1nMU2cOJGqqiouvPBCLrvsMpYuXUr37t2577776N6o44qKCi666CJWrVoFwC233MIxxxxDeXk51113HTvvvDOzZ89m3LhxjBgxgptuuok1a9YwdepUhg4dyvjx4ykpKWH69OmsXLmSH/7wh4wdO7bFGJvr6/zzz+eiiy7ijDPOAGD8+PGMHTuWT37yk4wfP545c+aw//77s3jxYn7yk58wevTotp04SZLU5XXqxFNK6a8R8WtgBrAeeJHsErnfA7+KiG/n227Lq9wGTImIhcB7ZE/AkyRJKpoLLriAq6++mnPPPZfq6mrq6upYsmRJw/499tiDxx57jJKSEhYsWMBnP/tZpk+fDmT3W5w3bx677rorQ4YM4ZJLLmHatGncdNNN/PjHP+bGPBtWUVHBtGnTeOWVVzjhhBNYuHAhJSUlG8XSUl+f+cxnuPfeeznjjDNYt24djz/+OD/96U/5yU9+wi677MLcuXOZM2cOhx56aPucNEmS1Gl06sQTQErpOuC6DTa/Cg33Kmxcthr4x/aIS5IkdUytzUyqV1m5puD3eKqsrOStt97i3HPPBWg2GVRTU8OVV17JzJkz6d69Oy+//HLDviOOOIKBAwcCMHToUE455RQARowYwZNPPtlQbty4cXTr1o1hw4YxZMgQ5s+f32ySqKW+Tj/9dK666irWrl3LH/7wB44//nh69+7NM888w1VXXQXAwQcfzCGHHLKNzowkSeoqOn3iSZIkqTPo0aMHdXV1DevV1dVtqvejH/2IAQMGMGvWLOrq6pokp3r16tWw3K1bt4b1bt26sX79+oZ9EdGkzQ3XW+urpKSEsrIyHn30Ue655x7OP99J45IkqW06+83FJUmSOoUBAwawZMkSli9fztq1a3nooYcoLS1l0KBBTJ06FYC1a9eyevXqJvVWrFjBwIED6datG1OmTKG2tnaz+77vvvuoq6vjlVde4dVXX2X//fdvttym+vrMZz7DL37xC55++mlOOy17iPCxxx7LvffeC8DcuXOZnT+FSpIkqZ6JJ0mSpHbQs2dPrr32WsaMGcPJJ5/M8OHDAZgyZQo333wzhxxyCMcccwzvvPNOk3pf/vKXueOOOxg5ciTz58+nb9++m9333nvvzZgxYzj99NOZNGlSs5f0tdbXKaecwp/+9CdOOukkdthhh4byS5cu5cADD+Sb3/wmBx10EP369dvs+CRJUtflpXaSJEntZMKECUyYMGGj7U888cRG2+qffjds2DBeeumlhu3f//73ASgrK6OsrKxhe3l5ecPyhvtOOukkJk2a1Gp8LfUFWeLsvffea1K+pKSEX/7yl5SUlPDKK69w0kknsc8++7TajyRJ2n6YeJIkSdIWWb16NSeccAI1NTWklPiv//qvhtlQkiRJYOJJkiSpS5s8efJG2x599FG+/vWvN9m277778sADD2xW26WlpUyfPn1rwpMkSV2ciSdJkrRdSCm1+DS37c2pp57KqaeeWpS+U0pF6VeSJBWHNxeXJEldXklJCcuXLzfpUWQpJZYvX97izc0lSVLX44wnSZLU5Q0aNIhFixaxdOnSNteprq42QVIAJSUlDBo0qNhhSJKkdmLiSZIkdXk9e/Zk33333aw65eXlHHbYYQWKSJIkafvgpXaSJEmSJEkqCBNPkiRJkiRJKggTT5IkSZIkSSoIE0+Stj2fGiVJkiRJwsSTJEmSJEmSCsTEkyRJkiRJkgrCxJOkbc9L7SRJkiRJmHiSJEmSJElSgZh4kiRJkiRJUkH0KHYA6sRuvhkeeKDdux22eDHcc0+796vNcPnl0K14eW3HiNrCcaLWOEa6iFGj4EtfKnYUkiRtt0w8afMNHgz77QfPP5+92tnuNTXQs2e796vNMHVqUbt3jKgtHCdqjWOkC1i5MksemniSJKloTDxp8+25JyxYULTu/1xeTllZWdH6V8fnGFFbOE7UGsdIF3DVVXDnncWOQpKk7Zr3eJIkSZIkSVJBmHiSJEmSJElSQZh4kiRJkiRJUkGYeJIkSZIkSVJBmHiSJEmSJElSQZh4kiRJkiRJUkGYeJIkSZIkSVJBmHiSJEmSJElSQZh4kiRJkiRJUkGYeJIkSZIkSVJB9ChGpxFxDDC4cf8ppTuLEYskSZIkSZIKo91nPEXEFGAicBxwRP4avRXt7RwRv46I+RExLyKOjohdI+KxiFiQf90lLxsRcXNELIyIlyJi1DY5KEmSJEmSJG2kGDOeRgMHppTSNmrvJuAPKaXzImIHoA/w78DjKaXvRcTVwNXA14HTgWH560jgp/lXSZIkSZIkbWPFuMfTHOAj26KhiOgHHA/cBpBSWpdS+gA4G7gjL3YHcE6+fDZwZ8r8Bdg5IgZui1gkSZIkSZLUVDFmPO0GzI2IacDa+o0ppbO2oK19gaXALyJiJPACcBUwIKX0dl7mHWBAvrwX8Gaj+ovybW832kZEXApcCjBgwADKy8u3IDQVSlVVle+JNskxorZwnKg1jpHOb79Fixiwfj3PFvB9dJxsuT5vvMGYfHnhwoUs6qLn0TGyZfZasIBhwLLly5nTxc+fY6Tz2WXWLEYCM2bMYOXata2W3xY68zgpRuLp+m3YVg9gFPDPKaW/RsRNZJfVNUgppYjYrMv6Ukq3ArcCjB49OpWVlW2jcLUtlJeX43uiTXGMqC0cJ2qNY6QLeOAB6NGjoO+j42QrzJ/fsLjffvuxXxc9j46RLTR7NgC79e/f5c+fY6QTypNNo0aNgqOPbpcuO/M4afdL7VJKfwLmA6X5a16+bUssAhallP6ar/+aLBH1bv0ldPnXJfn+t4CPNqo/KN8mSZIkSZKkbawYT7UbB0wD/hEYB/w1Is7bkrZSSu8Ab0bE/vmmE4G5wO+Ai/NtFwO/zZd/B3wuf7rdUcCKRpfkSZIkSZIkaRsqxqV21wBHpJSWAETE7sD/ks1W2hL/DNyVP9HuVeDzZAm1eyPii8DrZAkugIeBTwILgdV5WUmSJEmSJBVAMRJP3eqTTrnlbMXMq5TSTGB0M7tObKZsAq7Y0r4kSZIkSZLUdsVIPP0hIh4F7s7XP0M2E0mSJEmSJEldSLsnnlJK/xYRnwaOzTfdmlJ6oL3jkCRJkiRJUmEVY8YTKaXfAL8pRt+SJEmSJElqH+2WeIqIZ1JKx0VEJZAa7yK7/dJO7RWLJEmSJEmSCq/dEk8ppePyr6Xt1ackSZIkSZKKZ4ufJrelImJKW7ZJkiRJkiSpc2v3xBNwUOOViOgBHF6EOCRJkiRJklRA7ZZ4iohv5Pd3OiQiVuavSuBd4LftFYckSZIkSZLaR7slnlJK383v7/T/pZR2yl+lKaX+KaVvtFcckiRJkiRJah/tdnPxeimlb0TELsAwoKTR9qfaOxZJkiRJkiQVTrsnniLiEuAqYBAwEzgKeA74RHvHIkmSJEmSpMIpxs3FrwKOAF5PKZ0AHAZ8UIQ4JEmSJEmSVEDFSDxVp5SqASKiV0ppPrB/EeKQJEmSJElSAbX7pXbAoojYGZgKPBYR7wOvFyEOSZIkSZIkFVAxbi5+br54fUQ8CfQDHmnvOCRJkiRJklRY7X6pXUTcFhGHAqSU/pRS+h1wTXvHIUmSJOn/Z+/e4+yqy0P/f56Zyf1CrgyBhCRAuAqIRMAidAQroCj2eCl4ammrh6OntRfP71StPUd7sa2tp7bUHvvjiBatP0BREQQFijMiIoYg93u4hCQk5H6ZXCczz++PtSbshElmQmbNnpl83q/Xfu31/a7vWt9n7/3Mnj3PrLW2JEnVqsc1ni4AromIy2v63lWHOCRJkiRJklShehSeVgLnAu+NiH+JiCYg6hCHJEmSJEmSKlSPwlNk5obMfCewCmijuM6TJEmSJEmShpF6FJ5u6l7IzM8CnwdeqEMckiRJkiRJqtCAF54y8zN7tG/OzPMGOg5JkiRJkiRVq2mgJoqIuzPzzRGxCcjaVUBm5sSBikWSJEmSJEnVG7DCU2a+ubyfMFBzSpIkSZIkqX4G8oinKftan5lrByoWSZIkSZIkVW/ACk/A/RSn2EUP6xI4agBj0X5obX2cq69e9ar+888fxQsvbKehAWbNGsnChdt55zun8M1vrqs0ns2bt/CVr/yk0jkOJtOnJ1/4wrk0Nh74Jd8WLHiGRx9dzXnnHcVf/MUT7NjR04979YZjjkycmEyaBEuWBOefP4q2tu3s3PnK+qYm+NSnjubHP17Mz362c+87GmQi4CMfOYyzzz6u3qFIkiRJqsBAnmo3d6DmUv/60pfW8L3vncPEia8UlDZvnsDddy9l8eLd64U/+tEiXnjhdbuN7W+ZxR+rOnA7doxi69bxfPjDiznppNkHvL8zz5wHzOPv//6nfO1rLYwfv57Gxs4DD3Q/Dbcc6egYwZYtr1wG79//vbifOHEtEUlXVwObNk1m9uw2rrrqGNasmc7Yse11inb/bNgwla6un1h4kiRJkoapgTzV7vjMfDIi3tDT+sz85UDFov2TCYce+hIrVszc1fdrv3YvTz3V/KqxXV0NNDe/xPLlM1+1rr+0tbXR0tJS2f4PJl/60s/42MfOpqurf/ebWXx/wEMPbeWoo2b07877YLjlyLXX3scHPvDGV/WvWDGOMWNGsXbtRqZOLX5WM+Hiixfw3e+eU4dI99/kyavIHEZVQkmDT0cH/OhHle1+ysMPw7Ztle1/WFuy5JXlH/0ITjihfrFUyBx5jR5/vLhfurTSn+HBwBwZghYurHcEQ8pAnmr3ceAK4H/3sC6B8wYwFkmSJA13EyfC5s1w0UWVTXFKZXs+yNx+e3EbhsyRA/TAA5X+DA8G5sgQNnFi72M0oKfaXVEuXpSZu5VzI2L0QMUhSZKkg8Sf/RlcfHFxOGhFfvnLX/KGN/R4QL/6YvRoOOQQePnlekdSGXNEvTFHhqiJE+HEE+sdxZAwkEc8dbsH2POnqqc+SZIk6bUbNQrOPLPSKTZu2wZnnVXpHAeFucP3crDmiHpjjmi4G8hrPB0GHAGM2eM6TxOBsQMVhyRJkiRJkgbGQB7xdAHw28BM4As1/ZuAPx3AOCRJkiRJkjQABrLwNA34QXmD4oLiq4C7M/P5AYxDkiRJkiRJA6BhAOcav8dtAjAf+GFEXHogO46Ixoh4ICJ+ULbnRsQvImJRRFwfESPL/lFle1G5fs6BzCtJkiRJkqS9G8hvtfvznvojYgrwH8B1B7D7PwSeoLheFMDngS9m5nUR8a/Ah4Avl/frMvOYstj1eeA3DmBeSZIkSZIk7cVAHvHUo8xcC8Rr3T4iZgLvAL5StgM4D7ihHHIN8O5y+ZKyTbn+/HK8JEmSJEmS+tlAXuOpRxHxFmDdAeziH4E/oTh1D2AqsD4zd5btpRTfpkd5vwQgM3dGxIZy/Oo9YroCuAKgubmZtra2Awhv6Nu6dQfAbs/D9u1BZr5qbFdXF5lZ6XPW3t5+0L8m/WX58pUAPPzwg6xZ0x+XWmsB4OWXXwbg/vsX8uKLE/YxvhrDLUdeeGEl8MZX9f/0p3cxcuQINm/eDlzA6tWrgWPYunXrEHr8J7F5c31er+GWJ+p/5oj6wjxRb8wR9cYcUV8M5TwZsMJTRDxCcUHxWlOAl4Dfeo37vBhYmZn3R0TLgUX4isy8CrgKYP78+dnS0m+7HpKuvPKnANQ+D6NG3UtPB4s1NDQQEVT5nLW1tVW6/4PJo4/+DIBTTnk9J588u9/229zcDMDpp8/nqKNm9Nt++2q45cjy5ff12H/OOecyZswo1q7dCMC0adMAGDNmDC0t5wxYfAdmFePGjael5dwBn3m45Yn6nzmivjBP1BtzRL0xR9QXQzlPBvKIp4v3aCewJjM3H8A+zwbeFRFvB0ZTXOPpn4BJEdFUHvU0E1hWjl8GzAKWRkQTcAiw5gDmlyRJkiRJ0l4M2DWeMnPxHrcXD7DoRGZ+KjNnZuYc4FLgx5n5n4FW4L3lsMuB75fLN5VtyvU/zp7OF5MkSZIkSdIBq/vFxSvyCeDjEbGI4hpOV5f9VwNTy/6PA5+sU3ySJEmSJEnDXt0vLt5fMrMNaCuXnwPO6GHMNuB9AxqYJEmSJEnSQWq4HvEkSZIkSZKkOrPwJEmSJEmSpEpYeJIkSZIkSVIlLDxJkiRJkiSpEhaeJEmSJEmSVAkLT5IkSZIkSaqEhSdJkiRJkiRVwsKTJEmSJEmSKmHhSZIkSZIkSZWw8CRJkiRJkqRKWHiSJEmSJElSJSw8SZIkSZIkqRIWniRJkiRJklQJC0+SJEmSJEmqhIUnSZIkSZIkVcLCkyRJkiRJkiph4UmSJEmSJEmVsPAkSZIkSZKkSlh4kiRJkiRJUiUsPEmSJEmSJKkSFp4kSZIkSZJUCQtPkiRJkiRJqoSFJ0mSJEmSJFXCwpMkSZIkSZIqYeFJkiRJkiRJlbDwJEmSJEmSpEpYeJIkSZIkSVIlLDxJkiRJkiSpEhaeJEmSJEmSVAkLT5IkSZIkSaqEhSdJkiRJkiRVwsKTJEmSJEmSKjGkC08RMSsiWiPi8Yh4LCL+sOyfEhF3RMQz5f3ksj8i4sqIWBQRD0fEG+r7CCRJkiRJkoavIV14AnYC/z0zTwTOAn4vIk4EPgncmZnzgDvLNsBFwLzydgXw5YEPWZIkSZIk6eDQVO8ADkRmLgeWl8ubIuIJ4AjgEqClHHYN0AZ8ouz/emYmcG9ETIqIGeV+JEl1sHFjI489tnjA5122bENd5h0Opk2byOrVG+sdRuXMEfWFeXLgGhoaaG4+hOXL1w3YnBFBU1MjHR07GTVqJNu373jV/ciRI+jo2AnQ57ERQWNjw25jV61q57HHFu82dseODhoaGl41tqf9do9taAh27uwc8LHd942NjURQyViAzs7exzY1NZI5MGMHku8jr01TUyOdnV0Uf94PvIkTxzJr1vS6zD3UDOnCU62ImAOcBvwCaK4pJq0AmsvlI4AlNZstLfssPElSHYwc2cEtt5zNLbfUY/bZ9Zh0GJlc7wAGgDmivjBP+s/EegdQEXNEvTFHhqoHH3yeU0+dW+8wBr1hUXiKiPHAd4A/ysyNEbFrXWZmROxXCTQirqA4FY/m5mba2tr6MdqhZ+vWouJf+zxs3x49Vpa7uoqKc5XPWXt7+0H/mvSX5ctXAvDwww+yZs3z/bDHFgBefvllAO6/fyEvvjihH/a7f4Zbjrzwwkrgja/q/+lP72LkyBFs3rwduIDVq1cDx7B169Yh8/j/4i/WsHhxZ13m7ujoYMSIEXWZeyi78sq3sXnzJM455ye8+c0v1zucSpkj6gvz5MB94QuX0NExijPPvIfzzls6IHP+zd+8f7f25Mkvs25d8677fZk8eSXr1h3ap7GTJq1i/frpfRo7YcI6Nm2a3Kex48evo729b2PHjNnE1q0T+jR29Oh2tm0b36exI0duZceOMX0aO2LEdjo6Ru0a29DQSVdXY49jGxp20tXVtGtsY2MHnZ09/4xFdJHZsGvsiBHb6OgYvc9YuseOHr2ZbdvG9Tjmd37nexx2WMc+99OffB/Zf5s2NfClL70XgE996lsDPv9zz43h+uvfyd1338+6dQNztNpQ/htnyBeeImIERdHpm5n53bL75e5T6CJiBrCy7F8GzKrZfGbZt5vMvAq4CmD+/PnZ0tJSVfhDwpVX/hSA2udh1Kh7qS3wdWtoaCAiqPI5a2trq3T/B5NHH/0ZAKec8npOPrn//tPS3Fx8+Dj99PkcddSMfttvXw23HFm+/L4e+88551zGjBnF2rXFKU/Tpk0DYMyYMbS0nDNg8R2Ier5Mwy1PBspXvrKczZsnMX9+8td//f7eNxjCzBH1hXly4P7xH9vp6BjFySfvGLD3lb/5m93b48dvYd26V+73Zdy4/Ru7fv0rY7sLJT2P3cymTZN3jd1XwWXcuM20t78ytruw0/PYdrZunbBr7KhRW9i+fexex27bNn7X2H0VZ8aNa2fHjjG7xo4bt4HNmw/Zy9hNrF8/atfYsWM30d4+aa9jN22azIQJ3fvdxMaNU/Y6tr39kJoYNrF+fc+Fp+4C3Ctj2/dReDqec845ocd1VfB9ZP89//wKvvSlYrken0duuGEh118Pxx57HC0tJw/InEM5T4b0xcWjqHxcDTyRmf9Qs+om4PJy+XLg+zX9v1V+u91ZwAav7yRJkiRJklSNoX7E09nAB4FHIuLBsu9Pgb8FvhURHwIWA90l0FuBtwOLgC3A7wxsuJIkSZIkSQePIV14ysy7gVef71U4v4fxCfxepUFJkiRJkiQJGOKn2kmSJEmSJGnwsvAkSZIkSZKkSlh4kiRJkiRJUiUsPEmSJEmSJKkSFp4kSZIkSZJUCQtPkiRJkiRJqoSFJ0mSJEmSJFXCwpMkSZIkSZIqYeFJkiRJkiRJlbDwJEmSJEmSpEpYeJIkSZIkSVIlLDxJkiRJkiSpEhaeJEmSJEmSVAkLT5IkSZIkSaqEhSdJkiRJkiRVwsKTJEmSJEmSKmHhSZIkSZIkSZWw8CRJkiRJkqRKWHiSJEmSJElSJSw8SZIkSZIkqRIWniRJkiRJklQJC0+SJEmSJEmqhIUnSZIkSZIkVcKC/eK0AAAgAElEQVTCkyRJkiRJkiph4UmSJEmSJEmVsPAkSZIkSZKkSlh4kiRJkiRJUiUsPEmSJEmSJKkSFp4k9bvMekcgSZIkSRoMLDxJkiRJkiSpEhaeJEmSJEmSVAkLT5IkSZIkSaqEhSdJ/c5rPEmSJEmS4CAtPEXEhRHxVEQsiohP1jseSZIkSZKk4eigKzxFRCPwL8BFwInAZRFxYn2jkiRJkiRJGn6a6h1AHZwBLMrM5wAi4jrgEuDx/d3Rddfdzx13TGDmzM3MmLGVjo7gwQencuSR7TQ3b2PHjuChh6Yye/YmDj10O9u2NfDII1OYM2cT06dvZ+vWBh59dApz525i2rTtbNnSyGOPTeaoozYydeoONm9u5PHHJ3PMMRuYPLmDTZsaefLJyRx77AYOOaSDjRtH8NRTh3DccRuYOLGDDRtG8PTTh3D88euZMGEn69aNYNGiQzjhhHWMH9/J2rUjefbZiZx00jrGju1kzZqRPPfcRF73urWMGdPFqlWjeOGFCZx88lpGj+5i5cpRLF48geefPwSAp5+G+++H//Sfise/cePEVz0na9ZMYfz4dgAeeQQefRQuu6xY99BD8OST8Bu/UbQfeAAWLYL3va9oL1wIixfDe95TtBcsgGXL4Nd/vWjfey+sXAkTy2nvuQfWroWLLy7ad98NGzfC299etH/yE9i6FS68sGi3tkJHB7ztbUX7zjuL+/PPL+7vuAOamuAtbynat90Go0fDr/5q0b71VpgwAc45p2j/4AcweTKcfXbRvukmOPRQOOuson3jjXD44XDGGUX7O9+B2bNh/vyi/e1vw9FHwxveULSvvx6OPx5OPbVoX3stvO51cPLJ0NkJ3/oWnHIKnHRS8ThuuAFe/3o44QTYvh2++104/XQ49tjicd94I7zxjXDMMdDeDjffDGeeCUcdVTxPt9wCa9eOAuAb31jCmjXbDzj34FgA7rqr2O+6dUVte8mS4vW56CKYNKl4ne+5B97xjuL1fO45+MUv4J3vhPHji7y47z645BIYO3b33Bs1qsijBx6A974XRoyAxx6Dhx+G978fGhvhuefGsXx5/+feu95Vn9x74IFX/6zV+uEPm8pxI9i2bQwvvTSVBQsGd+696U0wZw6sXw8//GHxc3TkkcXzetttcO65cMQRsHp18fy0tMCMGfDyy/DjH8N550FzMyxfDm1t8Na3wvTpxet2113Fcz116r5zD/o/96p636tX7vX0vrd9+6hduTcU3/f2J/fuvPNQjjuu/3Ovive9gyH3Buvv3O3bG7j22qHxvjdYc2/HjtF0G6jc25tt28bufWVpx45RvY7ptnXr7vvry+UIOjpG9nn/W7aM63VMV1djed9QxhB7Hbt9++jd2p2de3+yuve35/73pS/xduvoGLHfYzo69v7adMfZbefOfe/f973B/XnvppteydV6vO/dffeU8nVbw09+8iArVoxh/vw1ALz44jhWrRrF6aevBeCFF8axdu0o3vCGov388+NYv34Up51WtJ97bjybNo3g1FPXAfDssxPYvLmRU05ZD8CiRRPYurWRKVNW8uST9/D00xPo6GjkpJOK9U89NZHOzuDEEzcA8OSTxd/vxx9ftJ944hAaGpLjjtsIwGOPHcKIEV0ce+wmAB55ZBKjR3cyb17RfvjhSYwd28kxxxTthx6azIQJHRx1VPF3/oMPTuaQQ3Ywd+5m+iwzD6ob8F7gKzXtDwJf2mPMFcBCYGFzc3O2trb2eDvmmMey+PUx/G/z5j2af/AHTydk3njj3fmOd/xwn2NbW1vz8sufT8hdz9cHPvBCNjV17mq///0v5ujRO3e1f/3Xl+TEiTt2tS++eFlOmbJtV/uCC5Znc/PWvPnmm7O1tTXPO29Fzpy5edf6c89dmXPmtO9qv+lNq3LevI272vPnr8kTT1y/q33qqevy1FPX7WqfdNL6nD9/za72scduzLPOWr2rPXfupjznnJW72rNmbc7zzluxq93cvDUvuGD5rvbUqdvyHe9Ytqs9ceKOfPe7l+5qjxnTke9734u72iNGdOYHPvDCrjZkXn7589na2pq3396WkPmhDz2bra2teeutdyVkfuQji7K1tTVvvPGnCZkf+9jT2dramjfc8LOEzD/+4yeztbU1r7325wmZn/jEE9na2ppf//q9CZmXXvrzSvPmi1/8Wba2tuZnP/tIQubVVy/I1tbW/LM/K352rrnm3mxtbc1PfOKJhMxrr/15tra25sc//mRC5re/XWxfm3utra350Y8+k5B5yy13ZWtra374w88mZN52W1u2trbmZZc9VUnudbcHOvdmzFj/que2qWl7/sd/3Jmtra156KFbsrFxx651o0dvGfS59+lPP5atra351a8uSMj87GeL941//df7EjI/97mHs7W1Nb/0pfsTMj//+QeztbU1v/jFB8rceiBbW1vz7/7uwYTMf/7n+7O1tTX/+q8fTsj8139d2Gvu3Xzzzf2ee1W979Ur93p63xs/vsjHP/qjbw3J973BkHutFbzvHQy5N1h/515zze3m3gHmXkPDzoTMK674zoDl3rhx68vfmZsTMt/0puJ1HTu2PSHzda8rXoeGho5dv2OPP7547qdPX7HbNpMnr0rInDJleUJXQuZxxz28a/vasRGdCZnTpi3LESO2JmROnVrs74wzivexWbOK5/Tkk4sYxo9fl01N23f7HHDmmT/brf361y/Ytdw9x5FHFq/dUUcVr+mppxb7mzPnmfJxLNu1zdFH7/53TXe83bdZsxbtWj788BcSMufNe6KMs8jRuXOLeQ47bPGrPrfMn7/7Z87TT7/3VWOmTHm5jHNhQubs2c/u9tgmT165a2z3Y9sz3u7HPnfuk6+Kvft1+5Vf+elu7aOOenzX2O7X+tprbxrQ972bb755SL3vDZbfuUVerKjb+563TGBhX+owURZaDhoR8V7gwsz8cNn+IHBmZv5+T+Pnz5+fCxcu7HFfjz++lKefTiZP7uSQQ7ro6oIXXxzBlCmdTJzYRWcnLFnySnvnTli6dARTp3YyYUIXHR2wbNkIpk3rZPz4LnbsCF56qYnp03cyblyyfXuwfHkThx66k7Fjk23bghUrmmhu3smYMa+0DztsJ6NHJ1u3Bi+//Ep7y5Zg5comZszYyahRyebNwapVTRx++E5Gjkza2xtYvbqRI47oYMQI2LSpgTVrGpk5s4OmJti4sYG1axuZNauDo46aDkxg5cqict7Z2cG99y5jw4YRzJ69k8xOtmwZxcqVXZxzziSmTp3A6tWwZg0cd1zxfK1aVVS3u9srVxbV72OLg2R4+eWiOj5vXtFesaKonh9zTNFevhy2bIElS9poaWnhpZdg27aiqg5F5XvHDpg7t2gvXQo7dxZVdigq4V1dxX8CAF58sbg/8sjifvFiaGiAWbOK9gsvFP+VmDmzaD//PIwcWVTloaicjx5d/KcB4Nlni4r5jBlFe9GioqJ+2GGvtCdMKKr2UFTWJ00q/nPR3Z48uajiAzz1VPGfy2nTih/pp58ulqdOLR7HM88UY6dMKf5LsWhRsa/Jk4vH/eyzxVyTJhX/tXjuuSKWQw4pnqfnny9ifemlpWzc2NEvudfcvJPOzu00NIxix45DOOusKYweDZs2wUsvFa/FqFHF67x8efFajRwJGzYUr/dRRxX/VVi/vsiHo48uXoN169iVe01NRR6tWlXkSkNDkWerV7/S/v73f8bxx5/d77l39NFFux65t3TpCqZM2c5hh03i5z/fyKxZEzj11Em7cm/jxrVEFP/1GD16NpMmNQ763Js4sfgP2gsvFD9HEyYUz+vixcXP2fjxxX/UXnyxeC7Gjet+Dyieq7FjYfPm4vk+8kgYM6Z43ZYtK57r3nLvnnvaOO20ln7Nvare9+qZe3u+723b1k5n5xpOOulInn8+huT7Xl9z79vf/gXvec+Z/Z57VbzvHQy5N1h/595550+YOfNXh8T73mDNvcWLNzJmzFpOOulIVq5sGJDcGzt2A1u37mDTpomsWrWOs88+lKeeWsqoUbNYtmwFZ5wxlVWrNrBq1WhGjkwmTdrO1KkTueeedRx33KFs3LiU44+fxZ13vsy8eVNobNzA+PGjefrpZNSoDubNG8dLL61l587DaGu7nf/6Xy/gmWeWsWNHMx0d65k7dzQ7d3bx+ONdzJkzhh071jJ37mHcdtsKzjhjBmvWLGP27GbuvXcDs2ePYsKELrZt28mqVePIXMfrX9/MY48tZcSIWWza9BKnnnooS5asZd268Uye3MHYsZ2MHj2W++/fxOmnT2XFimXMnTuL1tYVnHHGNNrb1zJ69DiefbaTQw/dyWGHjWHJkg1kHsq2bcs57bQjeOihZTQ1HUZT0xqOOGI8a9bsYNmy4NhjR9LevoEpUw7lnntW0tJyGIsXL+XQQ2fw4IPrOOWUsXR17WDjRti4cTTjx6/n6KMP5Ze/XM6ECUfQ0bGU44+fwRNPrKGjYwIzZmynqQm2bRvJokVbeNObJrN48XKmT5/JggUreOtbD2XZslVETGTp0h0cfXQybtxInnlmMyNHTqGpaTnHHTeTe+55icmTD2PMmJVMn34IixdvZ+PGBl73uibWrm2nsXEqjz22ire97TCeemopEyYczjPPrGH+/PFs3ryNVasa6ehoYsqUdubMaR7Q9722tjZmz24ZMu97g+Xz3ogRq5k0aSQjRkysy/teY+Ny2tu3snZtIxs3NjBnTgcAa9Y00t7ewOzZr7Q3b27gyCOL9urVjWzZ8kp71apGtm1rYNasV9rbtzcwc2ZH+T7aREdHsGbN/Zxyyim8/HITnZ1w+OE7y/fRJjJhxoyivXx5caRibTsCDjtsZ/m+2kRjIzQ37yxzewQjRiSHHrqzzO0RjBrVxfTpnWVuj2D06FfaL744grFju5g2rZPTTjv6/swsj7Pbu4Ox8PQm4LOZeUHZ/hRAZv5NT+P3VXhSfbS1FYUnaW/MEfWFeaLemCPqC/NEvTFH1BtzRH0xGPMkIvpUeDroLi4O3AfMi4i5ETESuBS4qc4xSZIkSZIkDTsH3cXFM3NnRPw+cBvQCHw1Mx+rc1iSJEmSJEnDzkFXeALIzFuBW+sdhyRJkiRJ0nB2MJ5qJ0mSJEmSpAFg4UmSJEmSJEmVsPAkSZIkSZKkSlh4kiRJkiRJUiUsPEmSJEmSJKkSkZn1jmFQi4hVwOJ6x6HdTANW1zsIDWrmiPrCPFFvzBH1hXmi3pgj6o05or4YjHkyOzOn9zbIwpOGnIhYmJnz6x2HBi9zRH1hnqg35oj6wjxRb8wR9cYcUV8M5TzxVDtJkiRJkiRVwsKTJEmSJEmSKmHhSUPRVfUOQIOeOaK+ME/UG3NEfWGeqDfmiHpjjqgvhmyeeI0nSZIkSZIkVcIjniRJkiRJklQJC0+SJEmSJEmqhIUnDRkR8dWIWBkRj9Y7Fg1OETErIloj4vGIeCwi/rDeMWlwiYjREbEgIh4qc+TP6x2TBqeIaIyIByLiB/WORYNTRLwQEY9ExIMRsbDe8WhwiohJEXFDRDwZEU9ExJvqHZMGj4g4rnwP6b5tjIg/qndcGlwi4o/Lz62PRsS1ETG63jHtL6/xpCEjIs4F2oGvZ+br6h2PBp+ImAHMyMxfRsQE4H7g3Zn5eJ1D0yAREQGMy8z2iBgB3A38YWbeW+fQNMhExMeB+cDEzLy43vFo8ImIF4D5mbm63rFo8IqIa4CfZuZXImIkMDYz19c7Lg0+EdEILAPOzMzF9Y5Hg0NEHEHxefXEzNwaEd8Cbs3Mf6tvZPvHI540ZGTmXcDaesehwSszl2fmL8vlTcATwBH1jUqDSRbay+aI8uZ/YLSbiJgJvAP4Sr1jkTR0RcQhwLnA1QCZucOik/bhfOBZi07qQRMwJiKagLHAS3WOZ79ZeJI0LEXEHOA04Bf1jUSDTXkK1YPASuCOzDRHtKd/BP4E6Kp3IBrUErg9Iu6PiCvqHYwGpbnAKuBr5am7X4mIcfUOSoPWpcC19Q5Cg0tmLgO+ALwILAc2ZObt9Y1q/1l4kjTsRMR44DvAH2XmxnrHo8ElMzsz8/XATOCMiPDUXe0SERcDKzPz/nrHokHvzZn5BuAi4PfKSwJItZqANwBfzszTgM3AJ+sbkgaj8jTMdwHfrncsGlwiYjJwCUUh+3BgXET8Zn2j2n8WniQNK+V1e74DfDMzv1vveDR4lac7tAIX1jsWDSpnA+8qr99zHXBeRPx7fUPSYFT+F5rMXAl8DzijvhFpEFoKLK05svYGikKUtKeLgF9m5sv1DkSDzluB5zNzVWZ2AN8FfqXOMe03C0+Sho3ywtFXA09k5j/UOx4NPhExPSImlctjgF8DnqxvVBpMMvNTmTkzM+dQnPbw48wccv9ZVLUiYlz5JRaUp069DfBbd7WbzFwBLImI48qu8wG/8EQ9uQxPs1PPXgTOioix5d8651Ncx3ZIsfCkISMirgV+DhwXEUsj4kP1jkmDztnABymOUOj+Wtq31zsoDSozgNaIeBi4j+IaTz+oc0yShp5m4O6IeAhYANySmT+qc0wanD4GfLP8vfN64K/rHI8GmbJ4/WsUR7JIuymPmLwB+CXwCEUN56q6BvUaRKZf5iNJkiRJkqT+5xFPkiRJkiRJqoSFJ0mSJEmSJFXCwpMkSZIkSZIqYeFJkiRJkiRJlbDwJEmSJEmSpEo01TsASZKkg1VETAXuLJuHAZ3AqrK9JTN/pS6BSZIk9ZPIzHrHIEmSdNCLiM8C7Zn5hXrHIkmS1F881U6SJGkQioj28r4lIn4SEd+PiOci4m8j4j9HxIKIeCQiji7HTY+I70TEfeXt7Po+AkmSJAtPkiRJQ8GpwEeAE4APAsdm5hnAV4CPlWP+CfhiZr4ReE+5TpIkqa68xpMkSdLgd19mLgeIiGeB28v+R4C3lMtvBU6MiO5tJkbE+MxsH9BIJUmSalh4kiRJGvy21yx31bS7eOXzXANwVmZuG8jAJEmS9sVT7SRJkoaH23nltDsi4vV1jEWSJAmw8CRJkjRc/AEwPyIejojHKa4JJUmSVFeRmfWOQZIkSZIkScOQRzxJkiRJkiSpEhaeJEmSJEmSVAkLT5IkSZIkSaqEhSdJkiRJkiRVwsKTJEmSJEmSKmHhSZIkSZIkSZWw8CRJkiRJkqRKWHiSJEmSJElSJSw8SZIkSZIkqRIWniRJkiRJklQJC0+SJEkHmYhojoi7ImJTRPzvescjSZKGLwtPkiRp2IiIFyJia0S019wOr3dcg9AVwGpgYmb+954GRMQZEXFrRKyPiLURsSAifqdc1xIRXeXzuykintpj3dIe9tcWER8uly+PiPsjYmNELI2Iv4uIpuoeriRJqhcLT5Ikabh5Z2aOr7m9tOcAixzMBh7PzOxpZUS8Cfgx8BPgGGAq8FHgopphL2XmeGAi8Ang/0bEiX2cfyzwR8A04EzgfOD/eQ2PQ5IkDXIWniRJ0rAXEXMiIiPiQxHxIkVRhYg4KyLuKY/qeSgiWmq2mRsRPymP6LkjIr4UEf9ernvVUT3l0VZvLZcbIuKTEfFsRKyJiG9FxJQ9Yrk8Il6MiNUR8ema/TRGxJ+W224qjwyaFRH/sudpcRFxU0T88V4e869ExH0RsaG8/5Wy/9+Ay4E/KY9YemsPm/89cE1mfj4zV2fh/sx8/54Dy3U3AuuAPhWeMvPLmfnTzNyRmcuAbwJn92VbSZI0tFh4kiRJB5NfBU4ALoiII4BbgL8CplAccfOdiJhejv3/gPspjsr5S4piTV99DHh3Od/hFEWZf9ljzJuB4yiO9vlfEXFC2f9x4DLg7RRHE/0usAW4BrgsIhoAImIa8NYyzt2URa5bgCspjlb6B+CWiJiamb9NUej5u/KIsP/YY9uxwJuAG/ryQMsi268Dk4BH+rJND84FHnuN20qSpEHMwpMkSRpubiyPYFofETfuse6zmbk5M7cCvwncmpm3ZmZXZt4BLATeHhFHAm8E/mdmbs/Mu4Cb9yOGjwCfzsylmbkd+Czw3j1O8fvzzNyamQ8BDwGnlv0fBv4sM58qjyZ6KDPXZOYCYANFoQrgUqAtM1/uYf53AM9k5jcyc2dmXgs8CbyzD7FPpviMuLyXcYdHxHqKa0V9BvhgZj7Vh/3vJiJ+F5gPfGF/t5UkSYPfwX59A0mSNPy8e8+jeGosqVmeDbwvImqLMSOAVsqjlDJzc826xcCsPsYwG/heRHTV9HUCzTXtFTXLW4Dx5fIs4Nm97PcaioLZHeX9P+1l3OFlvLUWA0f0GnlxdFYXMIOiWLU3L2XmzB76d1I8j3saAXTUdkTEu4G/Ad6amav7EJskSRpiPOJJkiQdTGovpr0E+EZmTqq5jcvMv6U42mdyRIyrGX9kzfJmigtkA8V1mYDpNeuXABftse/R5fWMerMEOHov6/4duCQiTqU4ZXDPI7q6vURR/Kp1JNDr/Jm5Bfg58J4+xNqTF4FpEdFdSCMiooxncU3fhcD/pbgY/Gs9RU+SJA1yFp4kSdLB6t+Bd0bEBeUFvUeXFw2fmZmLKU67+/OIGBkRb2b309SeBkZHxDsiYgTwZ8ComvX/CnwuImYDRMT0iLikj3F9BfjLiJgXhVMiYipAZi4F7gO+AXynPGWwJ7cCx0bEByKiKSJ+g+LC3z/oYwx/Avx2RPyP7rkj4tSIuK63DTPzReAXwOcjYnxEjAL+B8XRTveW+zqP4jpT7ylPIZQkScOUhSdJknRQyswlwCXAnwKrKI40+h+88vnoA8CZwFqKaxh9vWbbDcB/oygSLaM4Aqr2W+7+CbgJuD0iNlEUXM7sY2j/AHwLuB3YCFwNjKlZfw1wMkXxaW+PbQ1wMfDfgTUUhaSL+3o6W2beA5xX3p6LiLXAVRQFrb74DeBQYBHF83M+8I7M3Fau/5/AIcCt5TfrtUfED/u4b0mSNIREZvY+SpIk6SAXEZ8FjsnM36xzHOdSHK01O/0gJ0mSBjmPeJIkSRoiytP6/hD4ikUnSZI0FFh4kiRJGgIi4gRgPcW3zf1jncORJEnqE0+1kyRJkiRJUiU84kmSJEmSJEmVaKp3AIPdtGnTcs6cOfUOQzU2b97MuHHj6h2GBjFzRH1hnqg35oj6wjxRb8wR9cYcUV8Mxjy5//77V2fm9N7GWXjqxZw5c1i4cGG9w1CNtrY2Wlpa6h2GBjFzRH1hnqg35oj6wjxRb8wR9cYcUV8MxjyJiMV9GeepdpIkSZIkSapEZYWniPhqRKyMiEdr+v4yIh6OiAcj4vaIOLzsj4i4MiIWlevfULPN5RHxTHm7vKb/9Ih4pNzmyoiIsn9KRNxRjr8jIib3NockSZIkSZL6X5VHPP0bcOEefX+fmadk5uuBHwD/q+y/CJhX3q4AvgxFEQn4DHAmcAbwme5CUjnmv9Rs1z3XJ4E7M3MecGfZ3usckiRJkiRJqkZlhafMvAtYu0ffxprmOCDL5UuAr2fhXmBSRMwALgDuyMy1mbkOuAO4sFw3MTPvzcwEvg68u2Zf15TL1+zR39MckiRJkiRJqsCAX1w8Ij4H/BawAXhL2X0EsKRm2NKyb1/9S3voB2jOzOXl8gqguZc5lrOHiLiC4qgompubaWtr6/PjU/Xa29t9TbRP5oj6wjxRb8wR9YV5ot6YI+qNOaK+GMp5MuCFp8z8NPDpiPgU8PsUp9JVNVdGRPY+8lXbXQVcBTB//vwcbFeOP9gNxqv5a3AxR9QX5ol6Y46oL8wT9cYcUW/MEfXFUM6Ten6r3TeB95TLy4BZNetmln376p/ZQz/Ay92n0JX3K3uZQ5IkSZIkSRUY0MJTRMyraV4CPFku3wT8VvnNc2cBG8rT5W4D3hYRk8uLir8NuK1ctzEiziq/ze63gO/X7Kv72+8u36O/pzkkSZIkSZJUgcpOtYuIa4EWYFpELKU4pe7tEXEc0AUsBj5SDr8VeDuwCNgC/A5AZq6NiL8E7ivH/UVmdl+w/L9RfHPeGOCH5Q3gb4FvRcSHyjnev685JEmSJEmSVI3KCk+ZeVkP3VfvZWwCv7eXdV8FvtpD/0LgdT30rwHO3585JEmSJEmS1P/qeY0nSZIkSZIkDWMWniRJkiRJklQJC0+SJEmSJEmqhIUnSZIkSZIkVcLCkyRJkiRJkiph4UmSJEmSJEmVsPAkSZIkSZKkSlh4kiRJkiRJUiUsPEmSJEmSJKkSFp4kSZIkSZJUCQtPkiRJkiRJqoSFJ0mSJEmSJFXCwpMkSZIkSZIqYeFJkiRJkiRJlbDwJEmSJEmSpEpYeJIkSZIkSVIlLDxJkiRJkiSpEhaeJEmSJEmSVAkLT5IkSZIkSaqEhSdJkiRJkiRVwsKTJEmSJEmSKlFZ4SkivhoRKyPi0Zq+v4+IJyPi4Yj4XkRMqln3qYhYFBFPRcQFNf0Xln2LIuKTNf1zI+IXZf/1ETGy7B9VtheV6+f0NockSZIkSZL6X5VHPP0bcOEefXcAr8vMU4CngU8BRMSJwKXASeU2/yciGiOiEfgX4CLgROCycizA54EvZuYxwDrgQ2X/h4B1Zf8Xy3F7naO/H7QkSZIkSZIKlRWeMvMuYO0efbdn5s6yeS8ws1y+BLguM7dn5vPAIuCM8rYoM5/LzB3AdcAlERHAecAN5fbXAO+u2dc15fINwPnl+L3NoV7cuHAJH/3aAj76tQXcuHBJvcORJEmSJKmuPnbNQj52zcJ6hzEkNNVx7t8Fri+Xj6AoRHVbWvYBLNmj/0xgKrC+pohVO/6I7m0yc2dEbCjH72uO3UTEFcAVAM3NzbS1te3nQxtern9gB8vbE4D169czqf3ZusbT3t5+0L8m2jdzRH1hnqg35oj6wjxRb8wR9cYcGZrue247wIC9dkM5T+pSeIqITwM7gW/WY/7eZOZVwFUA8+fPz5aWlvoGVGfXP7+ASZNeabe01PdAsTUg9P0AACAASURBVLa2Ng7210T7Zo6oL8wT9cYcUV+YJ+qNOaLemCNDVOttAAP22g3lPBnwwlNE/DZwMXB+ZmbZvQyYVTNsZtnHXvrXAJMioqk86ql2fPe+lkZEE3BIOX5fc0iSJEmSJKmfVXlx8VeJiAuBPwHelZlbalbdBFxafiPdXGAesAC4D5hXfoPdSIqLg99UFqxagfeW218OfL9mX5eXy+8FflyO39sckiRJkiRJqkBlRzxFxLVACzAtIpYCn6H4FrtRwB3F9b65NzM/kpmPRcS3gMcpTsH7vczsLPfz+8BtQCPw1cx8rJziE8B1EfFXwAPA1WX/1cA3ImIRxcXNLwXY1xySJEmSJEnqf5UVnjLzsh66r+6hr3v854DP9dB/K3BrD/3P0cO30mXmNuB9+zOHJEmSJEmS+t+AnmonSZIkSZKkg4eFJ0mSJEmSJFXCwpMkSZIkSZIqYeFJkiRJkiRJlbDwJEmSJEmSpEpYeJIkSZIkSVIlLDxJkiRJkiSpEhaeJEmSJEmSVAkLT5IkSZIkSaqEhSdJkiRJkiRVwsKTJEmSJEmSKmHhSZIkSZIkSZWw8CRJkiRJkqRKWHiSJEmSJElSJSw8SZIkSZIkqRIWniRJkiRJklQJC0+SJEmSJEmqhIUnSZIkSZIkVcLCkyRJkiRJkiph4UmSJEmSJEmVsPAkSZIkSZKkSlRWeIqIr0bEyoh4tKbvfRHxWER0RcT8PcZ/KiIWRcRTEXFBTf+FZd+iiPhkTf/ciPhF2X99RIws+0eV7UXl+jm9zSFJkiRJkqT+V+URT/8GXLhH36PAfwLuqu2MiBOBS4GTym3+T0Q0RkQj8C/ARcCJwGXlWIDPA1/MzGOAdcCHyv4PAevK/i+W4/Y6R789WkmSJEmSJO2mssJTZt4FrN2j74nMfKqH4ZcA12Xm9sx8HlgEnFHeFmXmc5m5A7gOuCQiAjgPuKHc/hrg3TX7uqZcvgE4vxy/tzkkSZIkSZJUgaZ6B1A6Ari3pr207ANYskf/mcBUYH1m7uxh/BHd22TmzojYUI7f1xy7iYgrgCsAmpubaWtre00ParhYv37Hbu16Px/t7e11j0GDmzmivjBP1BtzRH1hnqg35oh6Y44MbQP12g3lPBkshadBJTOvAq4CmD9/fra0tNQ3oDq7/vkFu7VbWup7oFhbWxsH+2uifTNH1BfmiXpjjqgvzBP1xhxRb8yRIar1NoABe+2Gcp4Mlm+1WwbMqmnPLPv21r8GmBQRTXv077avcv0h5fi97UuSJEmSJEkVGCyFp5uAS8tvpJsLzAMWAPcB88pvsBtJcXHwmzIzgVbgveX2lwPfr9nX5eXye4Efl+P3NockSZIkSZIqUNmpdhFxLdACTIuIpcBnKC42/s/AdOCWiHgwMy/IzMci4lvA48BO4Pcys7Pcz+8DtwGNwFcz87Fyik8A10XEXwEPAFeX/VcD34iIReV8lwLsaw5JkiRJkiT1v8oKT5l52V5WfW8v4z8HfK6H/luBW3vof44evpUuM7cB79ufOSRJkiRJktT/BsupdpIkSZIkSRpmLDxJkiRJkiSpEhaeJEmSJEmSVAkLT5IkSZIkSaqEhSdJkiRJkiRVwsKTJEmSJEmSKmHhSZIkSZIkSZWw8CRJkiRJkqRKWHiSJEmSJElSJSw8SZIkSZIkqRJNvQ2IiAbgVOBwYCvwaGaurDowSZIkSZIkDW17LTxFxNHAJ4C3As8Aq4DRwLERsQX4f4FrMrNrIAKVJEmSJEnS0LKvI57+Cvgy8F8zM2tXREQzcBnwQeCa6sKTJEmSJEnSULXXwlNmXraPdS8D/1hJRJIkSZIkSRoWer24eEQ8GxEf2aPvB9WFJEmSJEmSpOGgL99q1wG8JSK+FhEjy74jKoxJkiRJkiRJw0BfCk9bMvM3gCeAn0bEkUD2so0kSZIkSZIOcvu6uHi3AMjMv4uIXwK3A1MqjUqSJEmSJElDXl8KT/+reyEz/yMiLgAury4kSZIkSZIkDQd7LTxFxBvKxWU1y928uLgkSZIkSZL2aV9HPP3vmuXTgYWUp91RXOPpvKqCkiRJkiRJ0tC314uLZ+Zbum/As5l5Xk1fr0WniPhqRKyMiEdr+qZExB0R8Ux5P7nsj4i4MiIWRcTDtUdYRcTl5fhnIuLymv7TI+KRcpsrIyJe6xySJEmSJEnqf335Vjt4bd9i92/AhXv0fRK4MzPnAXeWbYCLgHnl7Qrgy1AUkYDPAGcCZwCf6S4klWP+S812F76WOSRJkiRJklSNvhae9ltm3gWs3aP7EuCacvka4N01/V/Pwr3ApIiYAVwA3JGZazNzHXAHcGG5bmJm3puZCXx9j33tzxySJEmSJEmqwL4uLv7PvHKk08yIuLJ2fWb+wWuYrzkzl5fLK4DmcvkIYEnNuKVl3776l/bQ/1rmWM4B+OIPn+DpFZsAuODkGbx7/qwD2V2Pbly4hNseKcLcuqOTMSMbK5mrdp5uW3d08uRLGzltTnGg2TMrNvHRry3Y5366Y6vd3wUnFzW+2v3v2ddbu7vvqZc6uf5rC3Y9F691/92Pr3sf+xPDcNrva42lL2P687Htz+O//oEdXP/8gt222Vt8+4p7sOl+LHs+D93qmWev5fXrLz3FV8V7sQaXnn5n9bf16195L5H2xjxRb8wR9cYcGR768tmkr59b9/xbuuo6Q9Wfn/d1cfGFNcv39/fEmZkR8VpO4at8joi4guJ0PJqbm2lra9vr2KVLd7K+vYvl7cn69euZ1P7sa453b65/YAfL24uHsW1n0VfFXN3zzBhfXEN+eXvumm/OqKK4tn50J+vXr9/rPmqfh9q4u7fp3n/3uO6+Pcfsa5vOzk5e3LQOgNFlBu/v/rvHbNtZ7GN/thlO+z2QWHobs2dOHMhj29/H/9KmLmD9bjncU3w9zTNY1T4WeOV5qF1fzzzb39evP5+XnuLry/tje3v7Pt/fNbjt+TurCp2d+/6dJ4F5ot6ZI+qNOTK0dX+e7O2zyf58bt3zb+lJ7c/2+2fXnuaoyl4LT5l5zd7WHYCXI2JGZi4vT3NbWfYvA2rLazPLvmVAyx79bWX/zB7Gv5Y5XiUzrwKuApg/f362tLT0NAyA7lXdRwG1tJyx17Gv1fXPL2DSpGL5gReKgsukSZP6fa7ueb78O8V+P/q1BTzwwjpOmzOZT3ygb3PVPg+1cXfr3n/tUVM9jdnXNsUPa/EDcsLMyfsc29v+HnhhHSfMnLxf2wyn/R5ILL2N6dYfj21/Hz+s59o/ftuubYoxk14VX0/zDFa1jwXY9TzUru9Wjzzb39evv+wtvr68P7a1tbGv93cNbnv+zqqCOaK+ME/0/7N373Fy1fXh/19vwiVcYhIuBkpSE9uI3G8hYlG6GiSEWgEtglW5qGBRrGBLpX3UQhWR9ue3Kl4iUUmwUgyFFigFuVhWaivmYhCQWygBiRJuyUISiEmW9++PObPO7s7uzCYzmdnN6/l47GPP+ZzP+XzeZ+aTszPvnPM5tThGVItjZHjqmwOo9dlkKJ9b+36X7uiY3vBxUq2PZhlwjqeI+FZEHDDAtp0j4kMR8f4h9ncTUH4y3enAjRXlpxVPnjsSeLG4Xe424NiIGF9MKn4scFux7aWIOLJ4mt1pfdoaSh+SJEmSJElqgsFutfs68HcRcSDwAPAcMJrSU+FeA1wJXD3QzhFxDaWrlXaPiOWUnk53GXBtRHwYeBJ4b1H9FuB44DHgZeBMgMxcGRGfAxYW9T6bmeUJyz9G6cl5OwK3Fj8MtQ9JkiRJkiQ1x2C32t0LvDcidgGmAXsBrwAPZeYjtRrOzPcNsGlGlboJfHyAdq6klOTqW74I6HdFVma+MNQ+JEmSJEmS1HiDXfEEQGauoTSvkiRJkiRJklS3Aed4kiRJkiRJkjaHiSdJkiRJkiQ1Rc3EUzG5uCRJkiRJkjQk9Vzx9I2IWBARH4uIsU2PSJIkSZIkSSNCzcRTZr4VeD8wCVgcEf8SEe9oemSSJEmSJEka1uqa4ykzlwJ/C3wa+EPg8oh4OCLe3czgJEmSJEmSNHzVM8fTQRHxJeAh4O3AH2fmvsXyl5ocnyRJkiRJkoapbeuo81Xg28DfZOYr5cLM/HVE/G3TIpMkSZIkSdKwVk/i6Y+AVzKzGyAitgFGZ+bLmfnPTY1OkiRJkiRJw1Y9czzdCexYsb5TUSZJkiRJkiQNqJ7E0+jMXFNeKZZ3al5IkiRJkiRJGgnqSTytjYjDyisRcTjwyiD1JUmSJEmSpLrmeDoP+NeI+DUQwJ7AKU2NSpIkSZIkScNezcRTZi6MiDcC+xRFj2TmhuaGJUmSJEmSpOGuniueAI4AJhf1D4sIMvO7TYtKkiRJkiRJw17NxFNE/DPwe8C9QHdRnICJJ0mSJEmSJA2oniuepgH7ZWY2OxhJkiRJkiSNHPU81e4BShOKS5IkSZIkSXWr54qn3YEHI2IB8JtyYWa+q2lRSZIkSZIkadirJ/F0cbODkCRJkiRJ0shTM/GUmT+KiNcBUzPzzojYCRjV/NAkSZIkSZI0nNWc4ykizgKuA64oivYGbticTiPikxHxQET8IiLOK8p2jYg7ImJp8Xt8UR4RcXlEPBYR90XEYRXtnF7UXxoRp1eUHx4R9xf7XB4RMVgfkiRJkiRJarx6Jhf/OHAU8BJAZi4FXrupHUbEAcBZwHTgYOCdEfH7wIXADzNzKvDDYh1gFjC1+DkbmF20sytwEfCmoq2LKhJJs4s+yvsdV5QP1IckSZIkSZIarJ7E028yc315JSK2BXIz+twX+GlmvpyZG4EfAe8GTgCuKupcBZxYLJ8AfDdL7gHGRcRewEzgjsxcmZmrgDuA44ptr8nMezIzge/2aataH5IkSZIkSWqweiYX/1FE/A2wY0S8A/gY8B+b0ecDwOcjYjfgFeB4YBEwITOfLuqsACYUy3sDT1Xsv7woG6x8eZVyBumjl4g4m9LVVUyYMIHOzs6aB9XVVcrN1VN3qMpt9y7ranhffY+hvD6UvirbqBZ3rW0Dqdynu7ubcs60q6trk9qvrFM+vqHsM1La3dxYBqtT1ohjG+rxd3d396tTOVb6jvFqcbebvrH2/XfZDuNsKO9fowx2nqllzZo1bf2ea3DN/Ltb5hhRPRwnqsUxolocI8PTQN+fB3ovh/K5tdrn6UaPky35XaiexNOFwIeB+4GPArcA397UDjPzoYj4B+B2YC1wL9Ddp05GxOZcVVVPHAP2kZlzgDkA06ZNy46OjprtzV+2AICOjumNC7JP2wB0rQJg3LhxDe+r7zHMX7YAulYNqa/KNnrFXWGwbQOp3KeUQCi9dePGjduk9nvqVBxf3fuMoHY3K5YadcoacWxDPf6uri46Ojp69oHeY6XXGB8g7nZTeSzQ/xzQ8nE2xPevUQY7z9TS2dlJPed3tadm/t0tc4yoHo4T1eIYUS2OkeGp6vdnBv5sMpTPrdU+Tzd6nGzJ70I1b7XLzFcz81uZeXJm/kmxvFlJocz8TmYenplHA6uAR4FnitvkKH4/W1T/FTCpYveJRdlg5ROrlDNIH5IkSZIkSWqwep5qtywiHu/7szmdRsRri9+/S2l+p38BbgLKT6Y7HbixWL4JOK14ut2RwIvF7XK3AcdGxPhiUvFjgduKbS9FxJHF0+xO69NWtT4kSZIkSZLUYPXcajetYnk0cDKw62b2e30xx9MG4OOZ2RURlwHXRsSHgSeB9xZ1b6E0D9RjwMvAmQCZuTIiPgcsLOp9NjNXFssfA+YBOwK3Fj8AA/UhSZIkSZKkBquZeMrMF/oUfTkiFgN/t6mdZuZbB+hnRpXyBD4+QDtXAldWKV8EHFBvH4N6/hGY29G7bP/3wvSPwfqX4erjATh/xerStrlj4JAz4NAzYO3zcO2f9G/ziHPggFPgxafg3z7Yf/sf/AXs88elvv/jo79tG1jDBubyPjYyA56+F35wXv/9Z1wKv/sH8Mv/hR/+Tf/tx30Z9joE/u9OuPuSnuKefp7/F9h9Hw58+b85i6vYZcV2peMqe/c/w9hJ8MB8WDi7V9Pnr1jNnD2+AMCRa27mzWv+s3//6/8bgKNXX8fha3/Yb/OX9iy1ecyL3+PAV/6nVDh3DOevWM2G2IFLdrgYgDO5mret+EWvfdeOeg1wFwAnrPo6r//NA722r9r2tczb/e8BOHnlP3EWD/Yc3/krVvPMdpP4l91Kr9mfvnApEzZUzF8/dwwnr9yTf931UwCc8fxFjN/Y527NO48FTgLg7Oc+DXOz1/v38Ohp3DruwwCc+8x5bJe/6ekb4P4dj+LOsR8oXstzevV9/orV3MibeZzTYP3LvbcXfrLLH3HPLu9k5+4umNvRq2+Au8e8m8U7v4PxG5/hjOcv7rc/j1wMTGDChif50xcu67f51rFnAtOZuP5RTl75pX7bbxx3Do+PPqjX2KuM4V93PZ/l27+BN76ygFkvzu11bGvYwE0bPgNM58CX/5tjXvqXfsf/l5wPjIcH5nP+in/s1/+cPb7A2lHjOHr97TD30p52AXZZsR1fe+2X2LDNaFjwDfjFtf1eH1hc+vU/X4RHb+69absd4QNFDvtHn4PH+4zdnXaDU64vLd/51/DUT3pvf81EeM/3ihfyPFhxb+/tu70B3jWntHzT2fDCo702n7xyT5aU8u5czD/w+hUv9vp3ecKq13Hj+NKp8uznPs3O3S/1bv9HJ1J6GGjF2Ktw/45HAaX7ur/BBb3+XQAs3nkGd4/5E7Z7dR3nPnv+b3cs6syng19zCqx9vv/YnDuGw9e+gyVMY/zGZ/qfU6Hfea+fo/8Wfu+Yfue9cnzlsff6dfdxQtfs3ucsqHreO6SrC5YVc3/98RWw+z7wyH/A//6//v0Pct4D4L3Xwc67w5J5cO+8/tvffwtsv1PP2OvnzM7S7zYce+x5CMz6cmn5+g/AS8t7b5/0ZjimdN5n/nvg5T4fG14/A/7wM6Xl782CDa/03v6Gd8JRf1larjY2qvzNhYpzy5JPNPRvbqVDurpg0herjr0em/g3t4djr7Q8jMZej+Lz3nbrX6y+/2aMPWDA814Px15peRiMvUOee/q3f2+gYWOvGec9wLHXgrHX85lkmJz3HHvz4N55vXMAwHavfrbXd43+St9fen3PLe/fZ+ydv+KG3rvOnwwTPlFabtDYq/wutHz7qcD3SyubMvZqqJl4iojDKla3oXQFVD1XSkmSJEmSJGkrFrXmCY+IuypWNwJPAF/MzEeaGFfbmDZtWi5atKhmvXPmlmaEn31m42eCL7cNsOSJ0lOtDp08vuF99T2Gc+YuYMkTq4bUV2UblXFXGmzbQCr36erqYllXadweOnn8JrVfrlN5fPXuM5La3ZxYatUpa8SxDfX4u7q6uOb8Y3v2gd5jpXKMDxR3u6k8Fuh/Dmj1OBvq+9cog51navEJMsNbM//uljlGVA/HiWpxjKgWx8jwVO37c+X6QPX7qla/2ufpRo+TRnwXiojFmTmtVr16brV725B7lyRJkiRJ0lavnlvtPjXY9sz8p8aFI0mSJEmSpJGi3qfaHQHcVKz/MbAAWNqsoCRJkiRJkjT81ZN4mggclpmrASLiYuA/M/MDzQxMkiRJkiRJw9s2ddSZAKyvWF9flEmSJEmSJEkDqueKp+8CCyLi34v1E4GrmheSJEmSJEmSRoJ6nmr3+Yi4FXhrUXRmZi5pbliSJEmSJEka7uq51Q5gJ+ClzPwKsDwipjQxJkmSJEmSJI0ANRNPEXER8Gngr4ui7YDvNTMoSZIkSZIkDX/1XPF0EvAuYC1AZv4aGNPMoCRJkiRJkjT81ZN4Wp+ZCSRAROzc3JAkSZIkSZI0EtSTeLo2Iq4AxkXEWcCdwLeaG5YkSZIkSZKGu3qeavfFiHgH8BKwD/B3mXlH0yOTJEmSJEnSsDZo4ikiRgF3ZubbAJNNkiRJkiRJqtugt9plZjfwakSM3ULxSJIkSZIkaYSoeasdsAa4PyLuoHiyHUBm/nnTopIkSZIkSdKwV0/i6d+KH0mSJEmSJKluAyaeIuJ3M/OXmXnVlgxIkiRJkiRJI8NgczzdUF6IiOu3QCySJEmSJEkaQQZLPEXF8usb2WlEnB8Rv4iIByLimogYHRFTIuKnEfFYRMyPiO2LujsU648V2ydXtPPXRfkjETGzovy4ouyxiLiworxqH5IkSZIkSWq8wRJPOcDyZomIvYE/B6Zl5gHAKOBU4B+AL2Xm7wOrgA8Xu3wYWFWUf6moR0TsV+y3P3Ac8I2IGBURo4CvA7OA/YD3FXUZpA9JkiRJkiQ12GCJp4Mj4qWIWA0cVCy/FBGrI+Klzex3W2DHiNgW2Al4Gng7cF2x/SrgxGL5hGKdYvuMiIii/PuZ+ZvMXAY8Bkwvfh7LzMczcz3wfeCEYp+B+pAkSZIkSVKDDTi5eGaOakaHmfmriPgi8EvgFeB2YDHQlZkbi2rLgb2L5b2Bp4p9N0bEi8BuRfk9FU1X7vNUn/I3FfsM1EcvEXE2cDbAhAkT6OzsrHlcXV3rAeqqO1TltnuXdTW8r77HUF4fSl+VbVSLu9a2gVTu093dTTln2tXVtUntV9YpH99Q9hkp7W5uLIPVKWvEsQ31+Lu7u/vVqRwrfcd4tbjbTd9Y+/67bIdxNpT3r1EGO8/UsmbNmrZ+zzW4Zv7dLXOMqB6OE9XiGFEtjpHhaaDvzwO9l0P53Frt83Sjx8mW/C40YOKpWSJiPKWrlaYAXcC/UrpVrm1k5hxgDsC0adOyo6Oj5j7zly0AoKNjesPjKbcNQNcqAMaNG9fwvvoew/xlC6Br1ZD6qmyjV9wVBts2kMp9SgmE0t2f48aN26T2e+pUHF/d+4ygdjcrlhp1yhpxbEM9/q6uLjo6Onr2gd5jpdcYHyDudlN5LND/HNDycTbE969RBjvP1NLZ2Uk953e1p2b+3S1zjKgejhPV4hhRLY6R4anq92cG/mwylM+t1T5PN3qcbMnvQoPdatcsxwDLMvO5zNwA/BtwFDCuuPUOYCLwq2L5V8AkgGL7WOCFyvI++wxU/sIgfUiSJEmSJKnBWpF4+iVwZETsVMy7NAN4ELgL+JOizunAjcXyTcU6xfb/yswsyk8tnno3BZgKLAAWAlOLJ9htT2kC8puKfQbqQ5IkSZIkSQ22xRNPmflTShN8/wy4v4hhDvBp4FMR8Ril+Zi+U+zyHWC3ovxTwIVFO78ArqWUtPoB8PHM7C7mcDoXuA14CLi2qMsgfUiSJEmSJKnBtvgcTwCZeRFwUZ/ixyk9ka5v3XXAyQO083ng81XKbwFuqVJetQ9JkiRJkiQ1XitutZMkSZIkSdJWwMSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkppiiyeeImKfiLi34ueliDgvInaNiDsiYmnxe3xRPyLi8oh4LCLui4jDKto6vai/NCJOryg/PCLuL/a5PCKiKK/ahyRJkiRJkhpviyeeMvORzDwkMw8BDgdeBv4duBD4YWZOBX5YrAPMAqYWP2cDs6GURAIuAt4ETAcuqkgkzQbOqtjvuKJ8oD4kSZIkSZLUYK2+1W4G8H+Z+SRwAnBVUX4VcGKxfALw3Sy5BxgXEXsBM4E7MnNlZq4C7gCOK7a9JjPvycwEvtunrWp9SJIkSZIkqcG2bXH/pwLXFMsTMvPpYnkFMKFY3ht4qmKf5UXZYOXLq5QP1kcvEXE2paurmDBhAp2dnTUPpKtrPUBddYeq3Hbvsq6G99X3GMrrQ+mrso1qcdfaNpDKfbq7uynnTLu6ujap/co65eMbyj4jpd3NjWWwOmWNOLahHn93d3e/OpVjpe8YrxZ3u+kba99/l+0wzoby/jXKYOeZWtasWdPW77kG18y/u2WOEdXDcaJaHCOqxTEyPA30/Xmg93Ion1urfZ5u9DjZkt+FWpZ4iojtgXcBf913W2ZmRGQz+x+sj8ycA8wBmDZtWnZ0dNRsb/6yBQB0dExvXJB92gagaxUA48aNa3hffY9h/rIF0LVqSH1VttEr7gqDbRtI5T6lBELprRs3btwmtd9Tp+L46t5nBLW7WbHUqFPWiGMb6vF3dXXR0dHRsw/0Hiu9xvgAcbebymOB/ueAlo+zIb5/jTLYeaaWzs5O6jm/qz018+9umWNE9XCcqBbHiGpxjAxPVb8/M/Bnk6F8bq32ebrR42RLfhdq5a12s4CfZeYzxfozxW1yFL+fLcp/BUyq2G9iUTZY+cQq5YP1IUmSJEmSpAZrZeLpffz2NjuAm4Dyk+lOB26sKD+teLrdkcCLxe1ytwHHRsT4YlLxY4Hbim0vRcSRxdPsTuvTVrU+JEmSJEmS1GAtudUuInYG3gF8tKL4MuDaiPgw8CTw3qL8FuB44DFKT8A7EyAzV0bE54CFRb3PZubKYvljwDxgR+DW4mewPiRJkiRJktRgLUk8ZeZaYLc+ZS9Qespd37oJfHyAdq4ErqxSvgg4oEp51T4kSZIkSZLUeK281U6SJEmSJEkjmIknSZIkSZIkNYWJJ0mSJEmSJDWFiSdJkiRJkiQ1RUsmF5ckSWoXGzZsYPny5axbt65X+dixY3nooYdaFNXwNnr0aCZOnMh2223X6lAkSVKLmXiSJElbteXLlzNmzBgmT55MRPSUr169mjFjxrQwsuEpM3nhhRdYvnw5U6ZMaXU4kiSpxbzVTpIkbdXWrVvHbrvt1ivppE0XEey22279drvXPwAAIABJREFUriCTJElbJxNPkiRpq2fSqbF8PSVJUpmJJ0mSJEmSJDWFiSdJkqQWu/zyy9l33315//vf3+pQJEmSGsrJxSVJklrsG9/4BnfeeScTJ07sKdu4cSPbbutHNUmSNLx5xZMkSVIL/dmf/RmPP/44s2bNYuzYsXzwgx/kqKOO4oMf/CDd3d1ccMEFHHHEERx00EFcccUVQOnJceeeey777LMPxxxzDMcffzzXXXcdAJMnT+b5558HYNGiRXR0dACwdu1aPvShDzF9+nQOPfRQbrzxRgDmzZvHu9/9bo477jimTp3KX/3VX/XE9oMf/IDDDjuMgw8+mBkzZvDqq68ydepUnnvuOQBeffVVfv/3f79nXZIkqS//G02SJKnwpVsf4tEVqwHo3tjNqG1HbXabb9hzDOfP2nfA7d/85jf5wQ9+wF133cXXvvY1/uM//oMf//jH7LjjjsyZM4exY8eycOFCfvOb33DUUUdx7LHHsmTJEh555BEefPBBnnnmGfbbbz8+9KEPDRrH5z//ed7+9rdz5ZVX0tXVxfTp0znmmGMAuPfee1myZAk77LAD++yzD5/4xCcYPXo0Z511FnfffTdTpkxh5cqVbLPNNnzgAx/g6quv5rzzzuPOO+/k4IMPZo899tjs10mSJI1MJp4kSZLayLve9S523HFHAG6//Xbuu+++nquZXnzxRZYuXcrdd9/N+973PkaNGsXv/M7v8Pa3v71mu7fffjs33XQTX/ziFwFYt24dv/zlLwGYMWMGY8eOBWC//fbjySefZNWqVRx99NFMmTIFgF133RWAD33oQ5xwwgmcd955XHnllZx55pmNfQEkSdKIYuJJkiSpUHll0urVqxkzZswWj2HnnXfuWc5MvvrVrzJz5sxedW655ZYB999222159dVXgVJyqbKt66+/nn322adX/Z/+9KfssMMOPeujRo1i48aNA7Y/adIkJkyYwH/913+xYMECrr766voOTJIkbZWc40mSJKlNzZw5k9mzZ7NhwwYAHn30UdauXcvRRx/N/Pnz6e7u5umnn+auu+7q2Wfy5MksXrwYgOuvv75XW1/96lfJTACWLFkyaN9HHnkkd999N8uWLQNg5cqVPds+8pGP8IEPfICTTz6ZUaM2/3ZESZI0cpl4kiRJalMf+chH2G+//TjssMM44IAD+OhHP8rGjRs56aSTmDp1Kvvttx+nnXYab37zm3v2ueiii/jkJz/JtGnTeiWFPvOZz7BhwwYOOugg9t9/fz7zmc8M2vcee+zBnDlzePe7383BBx/MKaec0rPtXe96F2vWrPE2O0mSVJO32kmSJLXYE088AcDFF1/cq3ybbbbh0ksv5dJLL+23z9e+9rWe5TPOOKNn+a1vfSuPPvpov/o77rhjz1PxKp1xxhm99r/55pt7lmfNmsWsWbP67fPzn/+cgw8+mDe+8Y0DHZIkSRJg4kmSJElDcNlllzF79mzndpIkSXUx8SRJkjTMzZs3b4v1deGFF3LhhRdusf4kSdLw5hxPkiRJkiRJaoqWJJ4iYlxEXBcRD0fEQxHx5ojYNSLuiIilxe/xRd2IiMsj4rGIuC8iDqto5/Si/tKIOL2i/PCIuL/Y5/KIiKK8ah+SJEmSJElqvFZd8fQV4AeZ+UbgYOAh4ELgh5k5FfhhsQ4wC5ha/JwNzIZSEgm4CHgTMB24qCKRNBs4q2K/44rygfqQJEmSJElSg23xOZ4iYixwNHAGQGauB9ZHxAlAR1HtKqAT+DRwAvDdzEzgnuJqqb2Kundk5sqi3TuA4yKiE3hNZt5TlH8XOBG4tWirWh8NsXTFas6Zu6BRzfVqd+qeY5reV7V+NrWdc+Yu6NXe0hWrAXq137es1nq5bOPGrNrnUNsfKPZ6YhhJ7W5qLEOJtxHHVk+d8voeo+tvr55+hotWjrNa+zRTtfjqOT92da1n/rLGn7O1ZTTqb5YkSdKmqvzcWc9nk3o/t/b9Ln3O3AUN/+y6JT9LtWJy8SnAc8DciDgYWAx8EpiQmU8XdVYAE4rlvYGnKvZfXpQNVr68SjmD9NFLRJxN6eoqJkyYQGdnZ82DmrxDN12ju+nq6qpZd6j2GA2TdygN0K5xwfpu2H5U4/sq91M+3sk7dNM1LnqV1VL5OvSKe3R3sb3UVrleuaxvncH26e5+lW5Gsf2oTW+/p07F8dW9zwhqd7NiqVGn75jYnGMb6vHvO3ZDr33K/2b6xletn3bV+1g243Vu1jgb4vvX0NelSnz1nB+7u5tzztaW0fdv1uYYO3Ysq1ev7lfe3d1dtVz1WbduXVufVxtlzZo1W8VxatM5RlSLY2R46psDqPXZZCifW/t+l+7q6mr4Z9dyH8+uLV3Y0cwxGKULibaciJgG3AMclZk/jYivAC8Bn8jMcRX1VmXm+Ii4GbgsM39clP+Q0lVKHcDozLykKP8M8Aqlq5guy8xjivK3Ap/OzHdGRFe1PgaLd9q0ablo0aJGHb4aoLOzk46OjlaHoTbmGFE9HCcqe+ihh9h33337la9evZoxY4bnVVXd3d2MGjWqpTEM9LqONJ5LVItjRLU4RlSPdhwnEbE4M6fVqteKOZ6WA8sz86fF+nXAYcAzxS10FL+fLbb/CphUsf/Eomyw8olVyhmkD0mSpJY58cQTOfzww9l///2ZM2cO3/zmN7ngggt6ts+bN49zzz0XgO9973tMnz6dQw45hI9+9KN0d5f+93SXXXbhL/7iLzj44IP5yU9+wmc/+1mOOOIIDjjgAM4++2zK/9m4cOFCDjroIA455BAuuOACDjjgAKCUrLrgggs44ogjOOigg7jiiiu28KsgSZJGoi2eeMrMFcBTEbFPUTQDeBC4CSg/me504MZi+SbgtOLpdkcCLxa3y90GHBsR44tJxY8Fbiu2vRQRRxZPszutT1vV+pAkSSqZ2wFzO9hx/vE9yyz4Rmnb+pd/W1b5s2Reafva5/tvq8OVV17J4sWLWbRoEZdffjknnXQS//7v/96zff78+Zx66qk89NBDzJ8/n//5n//h3nvvZdSoUVx99dWlrteu5U1vehM///nPectb3sK5557LwoULeeCBB3jllVe4+eabATjzzDO54oorevYv+853vsPYsWNZuHAhCxcu5Fvf+hbLli3btNdQkiSp0Io5ngA+AVwdEdsDjwNnUkqCXRsRHwaeBN5b1L0FOB54DHi5qEtmroyIzwELi3qfLU80DnwMmAfsSGlS8VuL8ssG6EOSJKllLr/88p5E01NPPcWyZct4/etfzz333MPUqVN5+OGHOeqoo/j617/O4sWLOeKIIwB45ZVXeO1rXwvAqFGjeM973tPT5l133cU//uM/8vLLL7Ny5Ur2339/3vrWt7J69Wre/OY3A/Cnf/qnPQmp22+/nfvuu4/rrrsOgBdffJGlS5cyZcqULfY6SJKkkacliafMvBeodh/gjCp1E/j4AO1cCVxZpXwRcECV8heq9SFJktTjzE4AXqk2x9P2O/Vsr2rn3QffXkVnZyd33nknP/nJT9hpp53o6Ohg3bp1nHrqqVx77bW88Y1v5KSTTiIiyExOP/10vvCFL/RrZ/To0T1XMK1bt46PfexjLFq0iEmTJnHxxRezbt26QePITL761a8yc+bMIcUvSZI0mFbM8SRJkqTCiy++yPjx49lpp514+OGHueeeewA46aSTuPHGG7nmmms49dRTAZgxYwbXXXcdzz5bmqZy5cqVPPnkk/3aLCeZdt99d9asWdNzFdO4ceMYM2YMP/1paarN73//+z37zJw5k9mzZ7NhwwYAHn30UdauXduko5YkSVuLVt1qJ0mSJOC4447jm9/8Jvvuuy/77LMPRx55JADjx49n33335cEHH2T69OkA7LffflxyySUce+yxvPrqq2y33XZ8/etf53Wve12vNseNG8dZZ53FAQccwJ577tlzax6U5nI666yz2GabbfjDP/xDxo4dC8BHPvIRnnjiCQ477DAykz322IMbbrhhC70KkiRppDLxJEmS1EI77LADt956a9Vt5fmXKp1yyimccsop/crXrFnTa/2SSy7hkksu6Vdv//3357777gPgsssuY9q00uwH22yzDZdeeimXXnrpkI9BkiRpICaeJEmStiL/+Z//yRe+8AU2btzI6173OubNm9fqkCRJ0ghm4kmSJGkrMtAVU5IkSc3g5OKSJGmrV3qIrhrF11OSJJWZeJIkSVu10aNH88ILL5gsaZDM5IUXXmD06NGtDkWSJLUBb7WTJElbtYkTJ7J8+XKee+65XuXr1q0zebKJRo8ezcSJE1sdhiRJagMmniRJ0lZtu+22Y8qUKf3KOzs7OfTQQ1sQkSRJ0sjhrXaSJEmSJElqChNPkiRJkiRJagoTT5IkSZIkSWqK8Akug4uI54AnWx2HetkdeL7VQaitOUZUD8eJanGMqB6OE9XiGFEtjhHVox3Hyesyc49alUw8adiJiEWZOa3Vcah9OUZUD8eJanGMqB6OE9XiGFEtjhHVYziPE2+1kyRJkiRJUlOYeJIkSZIkSVJTmHjScDSn1QGo7TlGVA/HiWpxjKgejhPV4hhRLY4R1WPYjhPneJIkSZIkSVJTeMWTJEmSJEmSmsLEkyRJkiRJkprCxJOGjYi4MiKejYgHWh2L2lNETIqIuyLiwYj4RUR8stUxqb1ExOiIWBARPy/GyN+3Oia1p4gYFRFLIuLmVsei9hQRT0TE/RFxb0QsanU8ak8RMS4irouIhyPioYh4c6tjUvuIiH2Kc0j556WIOK/Vcam9RMT5xefWByLimogY3eqYhso5njRsRMTRwBrgu5l5QKvjUfuJiL2AvTLzZxExBlgMnJiZD7Y4NLWJiAhg58xcExHbAT8GPpmZ97Q4NLWZiPgUMA14TWa+s9XxqP1ExBPAtMx8vtWxqH1FxFXAf2fmtyNie2CnzOxqdVxqPxExCvgV8KbMfLLV8ag9RMTelD6v7peZr0TEtcAtmTmvtZENjVc8adjIzLuBla2OQ+0rM5/OzJ8Vy6uBh4C9WxuV2kmWrClWtyt+/B8Y9RIRE4E/Ar7d6lgkDV8RMRY4GvgOQGauN+mkQcwA/s+kk6rYFtgxIrYFdgJ+3eJ4hszEk6QRKSImA4cCP21tJGo3xS1U9wLPAndkpmNEfX0Z+Cvg1VYHoraWwO0RsTgizm51MGpLU4DngLnFrbvfjoidWx2U2tapwDWtDkLtJTN/BXwR+CXwNPBiZt7e2qiGzsSTpBEnInYBrgfOy8yXWh2P2ktmdmfmIcBEYHpEeOuuekTEO4FnM3Nxq2NR23tLZh4GzAI+XkwJIFXaFjgMmJ2ZhwJrgQtbG5LaUXEb5ruAf211LGovETEeOIFSIvt3gJ0j4gOtjWroTDxJGlGKeXuuB67OzH9rdTxqX8XtDncBx7U6FrWVo4B3FfP3fB94e0R8r7UhqR0V/wtNZj4L/DswvbURqQ0tB5ZXXFl7HaVElNTXLOBnmflMqwNR2zkGWJaZz2XmBuDfgD9ocUxDZuJJ0ohRTBz9HeChzPynVsej9hMRe0TEuGJ5R+AdwMOtjUrtJDP/OjMnZuZkSrc9/FdmDrv/WVRzRcTOxUMsKG6dOhbwqbvqJTNXAE9FxD5F0QzAB56omvfhbXaq7pfAkRGxU/FdZwaleWyHFRNPGjYi4hrgJ8A+EbE8Ij7c6pjUdo4CPkjpCoXyY2mPb3VQait7AXdFxH3AQkpzPN3c4pgkDT8TgB9HxM+BBcB/ZuYPWhyT2tMngKuLvzuHAJe2OB61mSJ5/Q5KV7JIvRRXTF4H/Ay4n1IOZ05Lg9oEkenDfCRJkiRJktR4XvEkSZIkSZKkpjDxJEmSJEmSpKYw8SRJkiRJkqSmMPEkSZIkSZKkpjDxJEmSJEmSpKbYttUBSJIkba0iYjfgh8XqnkA38Fyx/nJm/kFLApMkSWqQyMxWxyBJkrTVi4iLgTWZ+cVWxyJJktQo3monSZLUhiJiTfG7IyJ+FBE3RsTjEXFZRLw/IhZExP0R8XtFvT0i4vqIWFj8HNXaI5AkSTLxJEmSNBwcDPwZsC/wQeANmTkd+DbwiaLOV4AvZeYRwHuKbZIkSS3lHE+SJEntb2FmPg0QEf8H3F6U3w+8rVg+BtgvIsr7vCYidsnMNVs0UkmSpAomniRJktrfbyqWX61Yf5Xffp7bBjgyM9dtycAkSZIG4612kiRJI8Pt/Pa2OyLikBbGIkmSBJh4kiRJGin+HJgWEfdFxIOU5oSSJElqqcjMVscgSZIkSZKkEcgrniRJkiRJktQUJp4kSZIkSZLUFCaeJEmSJEmS1BQmniRJkiRJktQUJp4kSZIkSZLUFCaeJEmSJEmS1BQmniRJkiRJktQUJp4kSZIkSZLUFCaeJEmSJEmS1BQmniRJkiRJktQUJp4kSZJGuIg4IyJ+3IB2MiJ+vxExSZKkrYOJJ0mSpAFExBMR8UpErImIFRExLyJ2KbbdWpSviYgNEbG+Yv2bFW1MiYhXI2J2k2N9S0T8b0S8GBErI+J/IuKIZvYpSZJUi4knSZKkwf1xZu4CHAIcCvw1QGbOysxdim1XA/9YXs/MP6vY/zRgFXBKROzQjAAj4jXAzcBXgV2BvYG/B37TjP4kSZLqZeJJkiSpDpm5AriNUgKqLhERlBJPfwtsAP64z/aMiD+PiMcj4vmI+P8iYpti2xnFVUtfK65iejgiZgzQ1RuKGK/JzO7MfCUzb8/M+waI6ysR8VREvBQRiyPirRXbRkXE30TE/0XE6mL7pCptvKVoo6Pe10OSJG19TDxJkiTVISImArOAx4aw21uAicD3gWuB06vUOQmYBhwGnAB8qGLbm4D/A3YHLgL+LSJ2rdLGo0B3RFwVEbMiYnyNuBZSSqDtCvwL8K8RMbrY9ingfcDxwGuKeF6u3DkijgOuAd6TmZ01+pIkSVsxE0+SJEmDuyEiVgNPAc9SSgDV63Tg1sxcRSnBc1xEvLZPnX/IzJWZ+Uvgy5SSPmXPAl/OzA2ZOR94BPijvp1k5kuUklwJfAt4LiJuiogJ1YLKzO9l5guZuTEz/x+wA7BPsfkjwN9m5iNZ8vPMfKFi95OBK4BZmblgCK+FJEnaCpl4kiRJGtyJmTkG6ADeSOnqo5oiYkdKSZqrATLzJ8AvgT/tU/WpiuUngd+pWP9VZuYg23tk5kOZeUZmTgQOKOp9eYDY/jIiHipu4esCxlYc1yRKV1kN5Dzg2sx8YJA6kiRJgIknSZKkumTmj4B5wBfr3OUkSreqfaN4It4KSpN+973drnL+pN8Ffl2xvncxT9RA2weK9eEi1gP6bivmc/or4L3A+MwcB7wIlPt5Cvi9QZo/GTgxIj5ZKw5JkiQTT5IkSfX7MvCOiDi4jrqnA1cCB1KaT+kQ4Cjg4Ig4sKLeBRExvpjA+5PA/IptrwX+PCK2i4iTgX2BW/p2FBFvjIi/KOahomjrfcA9VeIaA2wEngO2jYi/o5QgK/s28LmImBolB0XEbhXbfw3MAD4ZEefU8TpIkqSt2LatDkCSJGm4yMznIuK7wN8B7xmoXkTsTSk5c2jxNLyyFRHxA0pJqb8sym4EFlO63W0e8J2K+j8FpgLPA88Af9JnvqWy1ZQmIv9URIwDuoCbgQuq1L0N+AGlCcnXAl+i9+1+/0RpzqfbKd1+9zClq7cqX4dfFk/Y64yIDZn57YFeC0mStHWL3tMGSJIkaUuJiASmZma/J+VFxBnARzLzLVs8MEmSpAbxVjtJkiRJkiQ1hYknSZIkSZIkNYW32kmSJEmSJKkpvOJJkiRJkiRJTeFT7WrYfffdc/Lkya0OQxXWrl3Lzjvv3Oow1MYcI6qH40S1OEZUD8eJanGMqBbHiOrRjuNk8eLFz2fmHrXqmXiqYfLkySxatKjVYahCZ2cnHR0drQ5Dbcwxono4TlSLY0T1cJyoFseIanGMqB7tOE4i4sl66nmrnSRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKaYttWB9Duul5ezzlzFwAw88C9OHHapBZHJEmSJEmSNDyYeKrhpZc3sHTFatas28iSJ1Zx2/1P92zrm4i6YdFTPdtnHrgXQK/1E6dNqlmncr1vH4PtW097mxNDrXa3ZCyP/Lqb+RXJwC11jNXaNREpSZIkSdLATDzVYeqeY5h54F69EhVLV6wG6JV4uO3+p3vK+9Yr161Vp7w+dc8x/foYaN/KugO1t7kx1Gp3S8bS1dXNc+u2/DFWa9fEkyRJkiRJAzPxVKcTp03qlWQo337X19Q9xwy6Xk+dqXuOYfaZ06v2UU/dZsXQjH1GQruSJEmSJKk6JxdvgBsWPcU5cxf0u3Km73o9dapdcXPO3AU1962nvc2JoRn7bGosT6/Jlhxj33YlSZIkSdLgvOKpAcq3X5VvyavUd72eOuX1oexbWd6sGJqxz6bE0tXV1bJjrHbbpSRJkiRJqi4ys3atrdhrJ++b77noKmafOb1Xefnql/IcQeXbttR8nZ2ddHR0tDSG8u15vuftqR3GiNqf40S1OEZUD8eJanGMqBbHiOrRjuMkIhZn5rRa9bziaRNVXglT7eoaSZIkSZKkrZ2Jp03Ud7JxSZIkSZIk9WbiSdoMNyx6qt98TzMP3IsTp03qta18RdxA6/XUqbfdyoToQPH17bsdVR5LteMo14H+r9n8JeuZv2xBS9+Leo5LkiRJkkY6E0/SZqicWB7oeerdidMm9WyrVG29PE/YYHXK6mm3MqkxUHyVfbejytcR+h9HZZ2+ywBPr0meW9e692Kg17XvcUmSJEnSSGfiSdpMlRPLlycdr9xWa332mdN77Vdrn3rr1IqvnSfE7/s6Qv94B3vN9tolGDeu9mtfrf1GvBcDva7VjkuSJEmSRjITT9ImKl+90jfxsHTF6l5PPaxWt9q+terU225lcqPa1TcDxd1uKo9loKuIBnrN9hjdu41WvBeSJEmSJBNP0iapfIrhQMvVnnY40PpAbVRbr6fdgeoO1k87Geox9902eYfV7POG9novJEmSJGlrFJnZ6hh6RMSVwDuBZzPzgKJsV2A+MBl4AnhvZq6KiAC+AhwPvAyckZk/K/Y5HfjbotlLMvOqovxwYB6wI3AL8Mms8QK8dvK++Z6LrmrbW5K2Rp2dnXR0dLQ6DLWxdh0j5Su4PJ+0h3YdJ2ofjhHVw3GiWhwjqsUxonq04ziJiMWZOa1WvW22RDBDMA84rk/ZhcAPM3Mq8MNiHWAWMLX4ORuYDT2JqouANwHTgYsiYnyxz2zgrIr9+vYlSZIkSZKkBmmrxFNm3g2s7FN8AnBVsXwVcGJF+Xez5B5gXETsBcwE7sjMlZm5CrgDOK7Y9prMvKe4yum7FW1J0hax5IlVnDN3AefMXcANi55qdTiSJEmS1FTDYY6nCZn5dLG8AphQLO8NVH5rW16UDVa+vEp5PxFxNqWrqHjNXlPo6uqis7Nz845CDbNmzRrfDw2qXcdIV9d6AB5avop1G0tJqPn//dCQ2zlkwije9DujGh3eVqddx4nah2NE9XCcqBbHiGpxjKgew3mcDIfEU4/MzIho+qRUmTkHmAOlOZ7GjRtHR4dzsrSLdry3Ve2lXcfI/GULoGsV+04cz8wD9+K2+5+uvVMfS1es5onfjOHTnpM2W7uOE7UPx4jq4ThRLY4R1eIYUT2G8zgZDomnZyJir8x8urhd7tmi/FfApIp6E4uyXwEdfco7i/KJVepL0hZ34rRJnDhtUu2KfZQnKJckSZKk4aCt5ngawE3A6cXy6cCNFeWnRcmRwIvFLXm3AcdGxPhiUvFjgduKbS9FxJHFE/FOq2hLkoaNpStWO0eUJEmSpGGhra54iohrKF2ttHtELKf0dLrLgGsj4sPAk8B7i+q3AMcDjwEvA2cCZObKiPgcsLCo99nMLE9Y/jFKT87bEbi1+JGkYWPmgXsBpeQTsElXTUmSJEnSltJWiafMfN8Am2ZUqZvAxwdo50rgyirli4ADNidGSWql8i163nInSZIkaTgYDrfaSZIkSZIkaRhqqyueJElSbzcseqrnCYgzD9zL2yslSZI0rJh4kiSpjd12/9MsXbGaNes2suSJVT1JKDVfV9d65i9r/9taTUhKkqR2ZuJJkqQ2N3XPMcw8cC+TTurHBw1IkqR2Z+JJkqRhoDyxvLaczs5OOjqmtzqMQfmgAUmS1O6cXFySJEmSJElNYeJJkiRJkiRJTWHiSZIkSZIkSU3hHE+SJEnD2JInVvXM9eQT7iRJUrsx8SRJkjTMlZ9uBz7hTpIktRcTT5I0TC1dsdonWlUx88C9ALjt/qcHrdfVtZ75y9rn9fNKFW2OqXuOATwvtEK7nUsqeV6RJLUDE0+SNAyVkyvqbckTq1jyxKqe9UMnj29hNPUrX63iF0RtDs8LquR5RZLULkw8SdIwdOK0SX6ZqOKGRU/1XOlU63/6Ozs76eiYvqVCG5RXqKgRPC+0RjudSyp5XpEktQsTT5KkEcMv3pIkSVJ72abVAUiSJEmSJGlkMvEkSZIkSZKkpjDxJEmSJEmSpKZwjidJkiRphKp86EK7KT+JsV3ja5SurvXMX9Z+k73XegiHJDWKiSdJkiRpBFq6YjVLnlgFwKGTx7c4mt6WPLGqJzZov/hGuqUrVgOYeJK0RZh4kiRJkkaY8tVE5eV2SzBUXonVjvE1UmdnJx0d01sdRi/nzG2/K7AkjVwmniRJkqQR5sRpk9o6mdPu8UmSGsfJxSVJkiRJktQUXvEkSVKb6DsJcOWtMpIkSdJwZOJJkqQ20HcS4PLEu7uM3pape45pcXSSJEnSpjHxJElSi1WbBLjvxLuSJEnScGTiSZKkFqs2ya4T70qSmmnpitUNfbpd+T8sE7UkAAAZxUlEQVRJKm8ZV326utYzf1n7PGnwDXuO4fxZ+7Y6DI0gJp5qeHl9d6tDkCRJkqSGafSVtOXbw8sOnTy+oe1LGt5MPNWw0/ajvMVBkiRJ0ojR6Ktq+94e7hW7Q9PZ2UlHx/RWhyE1jYmnGn539509cUqSJEnSALw9XNJgtml1AJIkSZIkSRqZTDxJkiRJkiSpKbbKxFNEHBcRj0TEYxFxYavjkSRJkiRJGom2usRTRIwCvg7MAvYD3hcR+7U2KkmSJEmSpJFnq0s8AdOBxzLz8cxcD3wfOKHFMUmSJEmSJI04kZmtjmGLiog/AY7LzI8U6x8E3pSZ51bUORs4G2DChAmHf//7329JrKpuzZo17LLLLq0OQ22sXcfIFUvWs6wrmTIu+Oih27c6nK1eu44TtY/hMEY8r7TecBgnai3HiGpxjKge7ThO3va2ty3OzGm16m27JYIZbjJzDjAHYNq0adnR0dHagNRLZ2cnvicaTLuOkfnLFkDXKsaNG0dHx/RWh7PVa9dxovYxHMaI55XWGw7jRK3lGFEtjhHVYziPk63xVrtfAZMq1icWZZIkSZIkSWqgrTHxtBCYGhFTImJ74FTgphbHJEmSJEmSNOJsdbfaZebGiDgXuA0YBVyZmb9ocViSJEmSJEkjzlaXeALIzFuAW1odhyRJkiRJ0ki2Nd5qJ0mSJEmSpC3AxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmSJEmSmsLEkyRJkiRJkprCxJMkSZIkSZKawsSTJEmS9P+3d//BlhbkfcC/T1h/IImsJnZFYAoTGVqCirhFm6R2K0ZAHcEEo04a0DAyjj8a26aKZUbaGDSJaW1NFWcrVEgdAVEHoiSKyK0xFfkliqjEVVRAFBUW3PiDoE//uO86h+3d3Xvv3nfPubufz8yZ+57nfc97nrPnmcvy3fcHADAKwRMAAAAAo5iZ4KmqXlBVN1fVT6tq/TbrXl9Vm6rqlqo6bqJ+/FDbVFVnTNQPrapPD/WLquqhQ/1hw/NNw/pDdtfnAwAAANjbzEzwlOTzSX4zyScmi1V1RJIXJfmVJMcneUdV7VNV+yR5e5ITkhyR5MXDtknyJ0ne2t2PT3JPktOG+mlJ7hnqbx22AwAAAGAEMxM8dfcXu/uWBVadmOTC7v5xd9+aZFOSY4bHpu7+anffn+TCJCdWVSV5RpJLhtefn+SkiX2dPyxfkuTYYXsAAAAAVtiygqeqevQCtUN3vZ0FHZjktonntw+17dV/Mcnm7n5gm/qD9jWsv3fYHgAAAIAVtmaZr/vLqjqhu+9LfnY63MVJjtzRi6rqY0keu8CqM7v70mX2suKq6vQkpyfJunXrMjc3N92GeJAtW7b4TtihWZ2RzZvvH35unsn+9jazOifMjtUwI36vTN9qmBOmy4ywM2aExVjNc7Lc4OlNmQ+fnpPk8CQXJPmdnb2ou5+5jPe6I8nBE88PGmrZTv17SdZW1ZrhqKbJ7bfu6/aqWpNk/2H7bfvcmGRjkqxfv743bNiwjLYZy9zcXHwn7MiszshFt16TbL4na9euzYYNx0y7nb3erM4Js2M1zIjfK9O3GuaE6TIj7IwZYTFW85ws61S77v5w5i/O/dEk707y/O6+cQX7mnRZkhcNd6Q7NMlhSa5Jcm2Sw4Y72D008xcgv6y7O8lVSU4eXn9qkksn9nXqsHxyko8P2wMAAACwwpZ0xFNV/XmSyaBm/yRfSfKqqkp3/5vlNlJVz0/y50kek+TDVXVjdx/X3TdX1cVJvpDkgSSv7O6fDK95VZKPJNknyXndffOwu9clubCq/ijJZ5KcO9TPTfIXVbUpyd2ZD6sAAAAAGMFST7W7bpvn169UI939wSQf3M66s5OcvUD98iSXL1D/aubverdt/UdJXrDLzQIAAACwU0sKnrr7/CSpqv2S/GjiyKN9kjxs5dsDAAAAYLVa1jWeklyZZN+J5/sm+diutwMAAADAnmK5wdPDu3vL1ifD8iNWpiUAAAAA9gTLDZ7+vqqO3vqkqp6S5Icr0xIAAAAAe4KlXlx8q9ckeV9VfTNJJXlskheuWFcAAAAArHrLCp66+9qq+idJDh9Kt3T3P6xcWwAAAACsdss94imZD52OSPLwJEdXVbr7gpVpCwAAAIDVblnBU1WdlWRD5oOny5OckOSTSQRPAAAAACRZ/sXFT05ybJJvdfdLkzwpyf4r1hUAAAAAq95yg6cfdvdPkzxQVY9McleSg1euLQAAAABWu+Ve4+m6qlqb5H8muT7JliSfWrGuAAAAAFj1lntXu1cMi++sqr9O8sju/tzKtQUAAADAarek4Kmqjt7Ruu6+YddbAgAAAGBPsNQjnv5Lkk5Sw/PeZv0zdrkjAAAAAPYISw2eXpfktu6+M0mq6tQkv5Xka0n+04p2BgAAAMCqttS72r0zyY+TpKqenuTNSc5Pcm+SjSvbGgAAAACr2VKPeNqnu+8ell+YZGN3vz/J+6vqxpVtDQAAAIDVbKlHPO1TVVvDqmOTfHxi3bLukAcAAADAnmmpYdF7k/yfqvpukh8m+ZskqarHZ/50OwAAAABIssTgqbvPrqorkxyQ5KPdvfWudj+X5NUr3RwAAAAAq9eST4/r7qsXqP3dyrQDAAAAwJ5iqdd4AgAAAIBFETwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjmJngqareUlVfqqrPVdUHq2rtxLrXV9Wmqrqlqo6bqB8/1DZV1RkT9UOr6tND/aKqeuhQf9jwfNOw/pDd+RkBAAAA9iYzEzwluSLJkd39xCR/l+T1SVJVRyR5UZJfSXJ8kndU1T5VtU+Styc5IckRSV48bJskf5Lkrd39+CT3JDltqJ+W5J6h/tZhOwAAAABGMDPBU3d/tLsfGJ5eneSgYfnEJBd294+7+9Ykm5IcMzw2dfdXu/v+JBcmObGqKskzklwyvP78JCdN7Ov8YfmSJMcO2wMAAACwwtZMu4Ht+L0kFw3LB2Y+iNrq9qGWJLdtU39qkl9MsnkixJrc/sCtr+nuB6rq3mH7706+eVWdnuT0JFm3bl3m5uZ2/ROxYrZs2eI7YYdmdUY2b75/+Ll5Jvvb28zqnDA7VsOM+L0yfathTpguM8LOmBEWYzXPyW4NnqrqY0keu8CqM7v70mGbM5M8kOQ9u7O3Sd29McnGJFm/fn1v2LBhWq2wgLm5ufhO2JFZnZGLbr0m2XxP1q5dmw0bjpl2O3u9WZ0TZsdqmBG/V6ZvNcwJ02VG2BkzwmKs5jnZrcFTdz9zR+ur6iVJnpvk2O7uoXxHkoMnNjtoqGU79e8lWVtVa4ajnia337qv26tqTZL9h+0BAAAAWGEzc42nqjo+yWuTPK+7fzCx6rIkLxruSHdoksOSXJPk2iSHDXewe2jmL0B+2RBYXZXk5OH1pya5dGJfpw7LJyf5+ETABQAAAMAKmqVrPP2PJA9LcsVwve+ru/vl3X1zVV2c5AuZPwXvld39kySpqlcl+UiSfZKc1903D/t6XZILq+qPknwmyblD/dwkf1FVm5LcnfmwCgAAAIARzEzw1N2P38G6s5OcvUD98iSXL1D/aubverdt/UdJXrBrnQIAAACwGDNzqh0AAAAAexbBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMArBEwAAAACjEDwBAAAAMIqZCZ6q6o1V9bmqurGqPlpVjxvqVVVvq6pNw/qjJ15zalV9eXicOlF/SlXdNLzmbVVVQ/3RVXXFsP0VVfWo3f9JAQAAAPYOMxM8JXlLdz+xu49K8qEkbxjqJyQ5bHicnuScZD5ESnJWkqcmOSbJWRNB0jlJXjbxuuOH+hlJruzuw5JcOTwHAAAAYAQzEzx1930TT/dL0sPyiUku6HlXJ1lbVQckOS7JFd19d3ffk+SKJMcP6x7Z3Vd3dye5IMlJE/s6f1g+f6IOAAAAwApbM+0GJlXV2UlOSXJvkn81lA9MctvEZrcPtR3Vb1+gniTruvvOYflbSdZtp4/TM390VdatW5e5ubnlfSBGsWXLFt8JOzSrM7J58/3Dz80z2d/eZlbnhNmxGmbE75XpWw1zwnSZEXbGjLAYq3lOdmvwVFUfS/LYBVad2d2XdveZSc6sqtcneVXmT6UbRXd3VfV21m1MsjFJ1q9f3xs2bBirDZZhbm4uvhN2ZFZn5KJbr0k235O1a9dmw4Zjpt3OXm9W54TZsRpmxO+V6VsNc8J0mRF2xoywGKt5TnZr8NTdz1zkpu9Jcnnmg6c7khw8se6goXZHkg3b1OeG+kELbJ8k366qA7r7zuGUvLuW+BEAAAAAWKSZucZTVR028fTEJF8ali9Lcspwd7unJbl3OF3uI0meVVWPGi4q/qwkHxnW3VdVTxvuZndKkksn9rX17nenTtQBAAAAWGGzdI2nP66qw5P8NMnXk7x8qF+e5NlJNiX5QZKXJkl3311Vb0xy7bDdH3b33cPyK5K8O8m+Sf5qeCTJHye5uKpOG97jt8f8QAAAAAB7s5kJnrr7t7ZT7ySv3M6685Kct0D9uiRHLlD/XpJjd61TAAAAABZjZk61AwAAAGDPIngCAAAAYBSCJwAAAABGIXgCAAAAYBSCJwAAAABGIXgCAAAAYBSCJwAAAABGIXgCAAAAYBSCJwAAAABGIXgCAAAAYBSCJwAAAABGIXgCAAAAYBSCJwAAAABGIXgCAAAAYBSCJwAAAABGIXgCAAAAYBSCJwAAAABGsWbaDQDsLY57wgEP+gkAALCnEzwB7CYnrT84J60/eNptAAAA7DZOtQMAAABgFIInAAAAAEYheAIAAABgFIInAAAAAEYheAIAAABgFIInAAAAAEYheAIAAABgFIInAAAAAEYheAIAAABgFIInAAAAAEYheAIAAABgFIInAAAAAEaxZtoNAACwPMc94YAH/QQAmDUzd8RTVf37quqq+qXheVXV26pqU1V9rqqOntj21Kr68vA4daL+lKq6aXjN26qqhvqjq+qKYfsrqupRu/8TAgCsjJPWH5xzXnpMTlp/8LRbAQBY0EwFT1V1cJJnJfnGRPmEJIcNj9OTnDNs++gkZyV5apJjkpw1ESSdk+RlE687fqifkeTK7j4syZXDcwAAAABGMFPBU5K3Jnltkp6onZjkgp53dZK1VXVAkuOSXNHdd3f3PUmuSHL8sO6R3X11d3eSC5KcNLGv84fl8yfqAAAAAKywmbnGU1WdmOSO7v7scGbcVgcmuW3i+e1DbUf12xeoJ8m67r5zWP5WknXb6eX0zB9dlXXr1mVubm4Zn4ixbNmyxXfCDpkRFsOcsDNmhMUwJ+yMGWFnzAiLsZrnZLcGT1X1sSSPXWDVmUn+Y+ZPs9stururqrezbmOSjUmyfv363rBhw+5qi0WYm5uL74QdMSMshjlhZ8wIi2FO2Bkzws6YERZjNc/Jbg2euvuZC9Wr6glJDk2y9Wing5LcUFXHJLkjyeQVMw8aanck2bBNfW6oH7TA9kny7ao6oLvvHE7Ju2sXPxIAAAAA2zET13jq7pu6+x919yHdfUjmT487uru/leSyJKcMd7d7WpJ7h9PlPpLkWVX1qOGi4s9K8pFh3X1V9bThbnanJLl0eKvLkmy9+92pE3UAAAAAVtjMXONpBy5P8uwkm5L8IMlLk6S7766qNya5dtjuD7v77mH5FUnenWTfJH81PJLkj5NcXFWnJfl6kt/eHR8AAAAAYG80k8HTcNTT1uVO8srtbHdekvMWqF+X5MgF6t9LcuyKNQoAAADAds3EqXYAAAAA7Hlq/oAitqeqvpP50/KYHb+U5LvTboKZZkZYDHPCzpgRFsOcsDNmhJ0xIyzGLM7JP+7ux+xsI8ETq05VXdfd66fdB7PLjLAY5oSdMSMshjlhZ8wIO2NGWIzVPCdOtQMAAABgFIInAAAAAEYheGI12jjtBph5ZoTFMCfsjBlhMcwJO2NG2BkzwmKs2jlxjScAAAAARuGIJwAAAABGIXgCAAAAYBSCJ1aNqjqvqu6qqs9PuxdmU1UdXFVXVdUXqurmqvr9affEbKmqh1fVNVX12WFG/vO0e2I2VdU+VfWZqvrQtHthNlXV16rqpqq6saqum3Y/zKaqWltVl1TVl6rqi1X1z6fdE7Ojqg4ffodsfdxXVa+Zdl/Mlqr6t8PfWz9fVe+tqodPu6elco0nVo2qenqSLUku6O4jp90Ps6eqDkhyQHffUFW/kOT6JCd19xem3BozoqoqyX7dvaWqHpLkk0l+v7uvnnJrzJiq+ndJ1id5ZHc/d9r9MHuq6mtJ1nf3d6fdC7Orqs5P8jfd/a6qemiSR3T35mn3xeypqn2S3JHkqd399Wn3w2yoqgMz//fVI7r7h1V1cZLLu/vd0+1saRzxxKrR3Z9Icve0+2B2dfed3X3DsPz9JF9McuB0u2KW9Lwtw9OHDA//AsODVNVBSZ6T5F3T7gVYvapq/yRPT3JuknT3/UInduDYJF8ROrGANUn2rao1SR6R5JtT7mfJBE/AHqmqDkny5CSfnm4nzJrhFKobk9yV5IruNiNs678leW2Sn067EWZaJ/loVV1fVadPuxlm0qFJvpPkfw2n7r6rqvabdlPMrBclee+0m2C2dPcdSf4syTeS3Jnk3u7+6HS7WjrBE7DHqaqfT/L+JK/p7vum3Q+zpbt/0t1HJTkoyTFV5dRdfqaqnpvkru6+ftq9MPN+vbuPTnJCklcOlwSASWuSHJ3knO5+cpK/T3LGdFtiFg2nYT4vyfum3QuzpaoeleTEzAfZj0uyX1X96+l2tXSCJ2CPMly35/1J3tPdH5h2P8yu4XSHq5IcP+1emCm/luR5w/V7LkzyjKr639NtiVk0/Ct0uvuuJB9Mcsx0O2IG3Z7k9okjay/JfBAF2zohyQ3d/e1pN8LMeWaSW7v7O939D0k+kORXp9zTkgmegD3GcOHoc5N8sbv/67T7YfZU1WOqau2wvG+S30jypel2xSzp7td390HdfUjmT3v4eHevun9ZZFxVtd9wE4sMp049K4m77vIg3f2tJLdV1eFD6dgkbnjCQl4cp9mxsG8keVpVPWL4f51jM38d21VF8MSqUVXvTfKpJIdX1e1Vddq0e2Lm/FqS3838EQpbb0v77Gk3xUw5IMlVVfW5JNdm/hpPH5pyT8Dqsy7JJ6vqs0muSfLh7v7rKffEbHp1kvcM/905KsmbptwPM2YIr38j80eywIMMR0xekuSGJDdlPsPZONWmlqG63cwHAAAAgJXniCcAAAAARiF4AgAAAGAUgicAAAAARiF4AgAAAGAUgicAAAAARiF4AgDYBVW1tqpeMSw/rqouGfG9jqqqZ4+1fwCAlSZ4AgDYNWuTvCJJuvub3X3yiO91VBLBEwCwalR3T7sHAIBVq6ouTHJikluSfDnJP+3uI6vqJUlOSrJfksOS/FmShyb53SQ/TvLs7r67qn45yduTPCbJD5K8rLu/VFUvSHJWkp8kuTfJM5NsSrJvkjuSvDnJrUn+e5KHJ/lhkpd29y1LeO+5JJ9N8i+TrEnye919zTh/UgDA3sgRTwAAu+aMJF/p7qOS/Idt1h2Z5DeT/LMkZyf5QXc/OcmnkpwybLMxyau7+ylJ/iDJO4b6G5Ic191PSvK87r5/qF3U3Ud190VJvpTkXwz7fEOSNy3xvZPkEUPvr0hy3q79UQAAPNiaaTcAALAHu6q7v5/k+1V1b5K/HOo3JXliVf18kl9N8r6q2vqahw0//zbJu6vq4iQf2M7+909yflUdlqSTPGSx7z2x3XuTpLs/UVWPrKq13b15mZ8XAOBBBE8AAOP58cTyTyee/zTzfw/7uSSbhyOOHqS7X15VT03ynCTXV9VTFtj/GzMfMD2/qg5JMreE9/7ZW2371jv4PAAAS+JUOwCAXfP9JL+wnBd2931Jbh2u55Sa96Rh+Ze7+9Pd/YYk30ly8ALvtX/mr/eUJC9ZXvt54fB+v57k3u6+d5n7AQD4/wieAAB2QXd/L8nfVtXnk7xlGbv4nSSnVdVnk9yc+QuVJ8lbquqmYb//N/MXAb8qyRFVdWNVvTDJnyZ5c1V9Jss/kv1Hw+vfmeS0Ze4DAGBB7moHALCXGu5q9wfdfd20ewEA9kyOeAIAAABgFI54AgAAAGAUjngCAAAAYBSCJwAAAABGIXgCAAAAYBSCJwAAAABGIXgCAAAAYBT/D/OkNHkWTbYNAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1152x1152 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"trace = test(cgname='/uclamp')\n",
"df = doPlot(trace, rta)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:10:59,657 INFO : root : Assert OPP@ 600000 in [25,35]: PASS (measured: 28%, error: -1.667%)\n",
"2019-07-18 19:10:59,659 INFO : root : Assert OPP@ 1000000 in [64,75]: PASS (measured: 70%, error: 0.584%)\n",
"2019-07-18 19:10:59,660 INFO : root : Assert OPP@ 1200000 in [-5, 5]: PASS (measured: 0%, error: 0.299%)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>frequency</th>\n",
" <th>600000</th>\n",
" <th>1000000</th>\n",
" <th>1200000</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>total_time</th>\n",
" <td>0.283331</td>\n",
" <td>0.705842</td>\n",
" <td>0.002993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active_time</th>\n",
" <td>0.098003</td>\n",
" <td>0.401381</td>\n",
" <td>0.002184</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"frequency 600000 1000000 1200000\n",
"total_time 0.283331 0.705842 0.002993\n",
"active_time 0.098003 0.401381 0.002184"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assertResidency( 600000, expected_pct=30, err_pct=5)\n",
"assertResidency(1000000, expected_pct=70, err_pct=5)\n",
"assertResidency(1200000, expected_pct=0, err_pct=5)\n",
"df['opp_res']"
]
},
{
"cell_type": "markdown",
"metadata": {
"toc-hr-collapsed": true
},
"source": [
"[Back to Table of Contents](#Table-of-Contents)"
]
},
{
"cell_type": "markdown",
"metadata": {
"toc-hr-collapsed": true
},
"source": [
"# CGroups v1 - Delegation Model"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"cpu_ctr = target.cgroups.controller('cpu')\n",
"cpuset_ctr = target.cgroups.controller('cpuset')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>40.00</td>\n",
" <td>40.04</td>\n",
" <td>60.00</td>\n",
" <td>59.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>50.00</td>\n",
" <td>40.04</td>\n",
" <td>75.00</td>\n",
" <td>59.96</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max uclamp.max.effective\n",
"cg_name \n",
"/uclamp 40.00 40.04 60.00 59.96\n",
"/uclamp/app 50.00 40.04 75.00 59.96"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Setup a child clamp group\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set({'uclamp.max': 60}, False)\n",
"uclamp_cg.set({'uclamp.min': 40}, False)\n",
"\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp/app')\n",
"uclamp_cg.set({'uclamp.max': 75}, False)\n",
"uclamp_cg.set({'uclamp.min': 50}, False)\n",
"\n",
"checkGroups(['/uclamp', '/uclamp/app'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Relaxing the parent: update child effective"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>70.00</td>\n",
" <td>70.02</td>\n",
" <td>90.00</td>\n",
" <td>90.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" <td>75.00</td>\n",
" <td>75.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max uclamp.max.effective\n",
"cg_name \n",
"/uclamp 70.00 70.02 90.00 90.04\n",
"/uclamp/app 50.00 50.00 75.00 75.00"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uclamp_cg = cpu_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set({'uclamp.max': 90}, False)\n",
"uclamp_cg.set({'uclamp.min': 70}, False)\n",
"\n",
"df = checkGroups(['/uclamp', '/uclamp/app'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:11:06,288 INFO : root : Assert /uclamp/app/uclamp.min.effective == 50.00: PASS (measured: 50.00%)\n",
"2019-07-18 19:11:06,291 INFO : root : Assert /uclamp/app/uclamp.max.effective == 75.00: PASS (measured: 75.00%)\n"
]
}
],
"source": [
"assertValue('/uclamp/app/uclamp.min.effective', 50)\n",
"assertValue('/uclamp/app/uclamp.max.effective', 75)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Subgroups creation: no default clamps"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>70.00</td>\n",
" <td>70.02</td>\n",
" <td>90.00</td>\n",
" <td>90.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" <td>75.00</td>\n",
" <td>75.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_smalls</th>\n",
" <td>0.00</td>\n",
" <td>50.00</td>\n",
" <td>max</td>\n",
" <td>75.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_bigs</th>\n",
" <td>0.00</td>\n",
" <td>50.00</td>\n",
" <td>max</td>\n",
" <td>75.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max \\\n",
"cg_name \n",
"/uclamp 70.00 70.02 90.00 \n",
"/uclamp/app 50.00 50.00 75.00 \n",
"/uclamp/app/pid_smalls 0.00 50.00 max \n",
"/uclamp/app/pid_bigs 0.00 50.00 max \n",
"\n",
" uclamp.max.effective \n",
"cg_name \n",
"/uclamp 90.04 \n",
"/uclamp/app 75.00 \n",
"/uclamp/app/pid_smalls 75.00 \n",
"/uclamp/app/pid_bigs 75.00 "
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Setup a couple of additional child groups\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp/app/pid_smalls')\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp/app/pid_bigs')\n",
"df = checkGroups(['/uclamp', '/uclamp/app', '/uclamp/app/pid_smalls', '/uclamp/app/pid_bigs'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:11:08,069 INFO : root : Assert /uclamp/app/pid_smalls/uclamp.min == 0.00: PASS (measured: 0.00%)\n",
"2019-07-18 19:11:08,072 INFO : root : Assert /uclamp/app/pid_smalls/uclamp.min.effective == 50.00: PASS (measured: 50.00%)\n",
"2019-07-18 19:11:08,073 INFO : root : Assert /uclamp/app/pid_smalls/uclamp.max == 100.00: PASS (measured: 100.00%)\n",
"2019-07-18 19:11:08,075 INFO : root : Assert /uclamp/app/pid_smalls/uclamp.max.effective == 75.00: PASS (measured: 75.00%)\n"
]
}
],
"source": [
"assertValue('/uclamp/app/pid_smalls/uclamp.min', 0)\n",
"assertValue('/uclamp/app/pid_smalls/uclamp.min.effective', 50)\n",
"assertValue('/uclamp/app/pid_smalls/uclamp.max', 'max')\n",
"assertValue('/uclamp/app/pid_smalls/uclamp.max.effective', 75)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Restricting the parent: updates child effective"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>70.00</td>\n",
" <td>70.02</td>\n",
" <td>90.00</td>\n",
" <td>90.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" <td>75.00</td>\n",
" <td>75.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_smalls</th>\n",
" <td>10.00</td>\n",
" <td>9.96</td>\n",
" <td>20.00</td>\n",
" <td>20.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_bigs</th>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" <td>70.00</td>\n",
" <td>70.02</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max \\\n",
"cg_name \n",
"/uclamp 70.00 70.02 90.00 \n",
"/uclamp/app 50.00 50.00 75.00 \n",
"/uclamp/app/pid_smalls 10.00 9.96 20.00 \n",
"/uclamp/app/pid_bigs 50.00 50.00 70.00 \n",
"\n",
" uclamp.max.effective \n",
"cg_name \n",
"/uclamp 90.04 \n",
"/uclamp/app 75.00 \n",
"/uclamp/app/pid_smalls 20.02 \n",
"/uclamp/app/pid_bigs 70.02 "
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uclamp_cg = cpu_ctr.cgroup('/uclamp/app/pid_smalls')\n",
"uclamp_cg.set({'uclamp.min': 10}, False)\n",
"uclamp_cg.set({'uclamp.max': 20}, False)\n",
"\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp/app/pid_bigs')\n",
"uclamp_cg.set({'uclamp.min': 50}, False)\n",
"uclamp_cg.set({'uclamp.max': 70}, False)\n",
"\n",
"df = checkGroups(['/uclamp', '/uclamp/app', '/uclamp/app/pid_smalls', '/uclamp/app/pid_bigs'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:11:12,231 INFO : root : Assert /uclamp/app/pid_bigs/uclamp.min == 50.00: PASS (measured: 50.00%)\n",
"2019-07-18 19:11:12,234 INFO : root : Assert /uclamp/app/pid_bigs/uclamp.min.effective == 50.00: PASS (measured: 50.00%)\n",
"2019-07-18 19:11:12,235 INFO : root : Assert /uclamp/app/pid_bigs/uclamp.max == 70.00: PASS (measured: 70.00%)\n",
"2019-07-18 19:11:12,237 INFO : root : Assert /uclamp/app/pid_bigs/uclamp.max.effective == 70.02: PASS (measured: 70.02%)\n"
]
}
],
"source": [
"assertValue('/uclamp/app/pid_bigs/uclamp.min', 50.0)\n",
"assertValue('/uclamp/app/pid_bigs/uclamp.min.effective', 50.0)\n",
"assertValue('/uclamp/app/pid_bigs/uclamp.max', 70.0)\n",
"assertValue('/uclamp/app/pid_bigs/uclamp.max.effective', 70.02)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Restrict the parent..."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>40.00</td>\n",
" <td>40.04</td>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>50.00</td>\n",
" <td>40.04</td>\n",
" <td>75.00</td>\n",
" <td>50.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_smalls</th>\n",
" <td>10.00</td>\n",
" <td>9.96</td>\n",
" <td>20.00</td>\n",
" <td>20.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_bigs</th>\n",
" <td>50.00</td>\n",
" <td>40.04</td>\n",
" <td>70.00</td>\n",
" <td>50.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max \\\n",
"cg_name \n",
"/uclamp 40.00 40.04 50.00 \n",
"/uclamp/app 50.00 40.04 75.00 \n",
"/uclamp/app/pid_smalls 10.00 9.96 20.00 \n",
"/uclamp/app/pid_bigs 50.00 40.04 70.00 \n",
"\n",
" uclamp.max.effective \n",
"cg_name \n",
"/uclamp 50.00 \n",
"/uclamp/app 50.00 \n",
"/uclamp/app/pid_smalls 20.02 \n",
"/uclamp/app/pid_bigs 50.00 "
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now let's restrict the parent\n",
"uclamp_cg = cpuset_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set(dict(cpus=target.bl.bigs[-1], mems=0))\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set({'uclamp.min': 40}, False)\n",
"uclamp_cg.set({'uclamp.max': 50}, False)\n",
"\n",
"df = checkGroups(['/uclamp', '/uclamp/app', '/uclamp/app/pid_smalls', '/uclamp/app/pid_bigs'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:11:15,963 INFO : root : Assert <built-in function max>(uclamp.min.effective ) == 40.04: PASS (measured: 40.04%)\n",
"2019-07-18 19:11:15,965 INFO : root : Assert <built-in function max>(uclamp.max.effective ) == 50.00: PASS (measured: 50.00%)\n",
"2019-07-18 19:11:15,967 INFO : root : Assert <built-in function min>(/uclamp/app/pid_smalls ) == 9.96: PASS (measured: 9.96%)\n",
"2019-07-18 19:11:15,969 INFO : root : Assert <built-in function min>(/uclamp/app/pid_bigs ) == 40.04: PASS (measured: 40.04%)\n"
]
}
],
"source": [
"assertAttribute('uclamp.min.effective', max, 40.04)\n",
"assertAttribute('uclamp.max.effective', max, 50.0)\n",
"assertClamps('/uclamp/app/pid_smalls', min, 9.96)\n",
"assertClamps('/uclamp/app/pid_bigs', min, 40.04)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ... and then relax the parent"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" <td>70.00</td>\n",
" <td>70.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" <td>75.00</td>\n",
" <td>70.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_smalls</th>\n",
" <td>10.00</td>\n",
" <td>9.96</td>\n",
" <td>20.00</td>\n",
" <td>20.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_bigs</th>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" <td>70.00</td>\n",
" <td>70.02</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max \\\n",
"cg_name \n",
"/uclamp 50.00 50.00 70.00 \n",
"/uclamp/app 50.00 50.00 75.00 \n",
"/uclamp/app/pid_smalls 10.00 9.96 20.00 \n",
"/uclamp/app/pid_bigs 50.00 50.00 70.00 \n",
"\n",
" uclamp.max.effective \n",
"cg_name \n",
"/uclamp 70.02 \n",
"/uclamp/app 70.02 \n",
"/uclamp/app/pid_smalls 20.02 \n",
"/uclamp/app/pid_bigs 70.02 "
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now let's relax the parent\n",
"uclamp_cg = cpuset_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set(dict(cpus=target.bl.bigs[-1], mems=0))\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set({'uclamp.min': 50}, False)\n",
"uclamp_cg.set({'uclamp.max': 70}, False)\n",
"\n",
"df = checkGroups(['/uclamp', '/uclamp/app', '/uclamp/app/pid_smalls', '/uclamp/app/pid_bigs'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:11:19,371 INFO : root : Assert <built-in function max>(uclamp.min.effective ) == 50.00: PASS (measured: 50.00%)\n",
"2019-07-18 19:11:19,374 INFO : root : Assert <built-in function max>(uclamp.max.effective ) == 70.02: PASS (measured: 70.02%)\n",
"2019-07-18 19:11:19,375 INFO : root : Assert <built-in function min>(uclamp.min.effective ) == 9.96: PASS (measured: 9.96%)\n",
"2019-07-18 19:11:19,377 INFO : root : Assert <built-in function min>(uclamp.max.effective ) == 20.02: PASS (measured: 20.02%)\n",
"2019-07-18 19:11:19,378 INFO : root : Assert <built-in function min>(/uclamp/app/pid_smalls ) == 9.96: PASS (measured: 9.96%)\n",
"2019-07-18 19:11:19,380 INFO : root : Assert <built-in function max>(/uclamp/app/pid_smalls ) == 20.02: PASS (measured: 20.02%)\n",
"2019-07-18 19:11:19,382 INFO : root : Assert <built-in function min>(/uclamp/app/pid_bigs ) == 50.00: PASS (measured: 50.00%)\n",
"2019-07-18 19:11:19,383 INFO : root : Assert <built-in function max>(/uclamp/app/pid_bigs ) == 70.02: PASS (measured: 70.02%)\n"
]
}
],
"source": [
"assertAttribute('uclamp.min.effective', max, 50.0)\n",
"assertAttribute('uclamp.max.effective', max, 70.02)\n",
"assertAttribute('uclamp.min.effective', min, 9.96)\n",
"assertAttribute('uclamp.max.effective', min, 20.02)\n",
"\n",
"assertClamps('/uclamp/app/pid_smalls', min, 9.96)\n",
"assertClamps('/uclamp/app/pid_smalls', max, 20.02)\n",
"assertClamps('/uclamp/app/pid_bigs', min, 50.00)\n",
"assertClamps('/uclamp/app/pid_bigs', max, 70.02)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Request protections higher then limits"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>70.00</td>\n",
" <td>9.96</td>\n",
" <td>10.00</td>\n",
" <td>9.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>50.00</td>\n",
" <td>9.96</td>\n",
" <td>75.00</td>\n",
" <td>9.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_smalls</th>\n",
" <td>10.00</td>\n",
" <td>9.96</td>\n",
" <td>20.00</td>\n",
" <td>9.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_bigs</th>\n",
" <td>50.00</td>\n",
" <td>9.96</td>\n",
" <td>70.00</td>\n",
" <td>9.96</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max \\\n",
"cg_name \n",
"/uclamp 70.00 9.96 10.00 \n",
"/uclamp/app 50.00 9.96 75.00 \n",
"/uclamp/app/pid_smalls 10.00 9.96 20.00 \n",
"/uclamp/app/pid_bigs 50.00 9.96 70.00 \n",
"\n",
" uclamp.max.effective \n",
"cg_name \n",
"/uclamp 9.96 \n",
"/uclamp/app 9.96 \n",
"/uclamp/app/pid_smalls 9.96 \n",
"/uclamp/app/pid_bigs 9.96 "
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now let's relax the parent\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set({'uclamp.min': 70}, False)\n",
"uclamp_cg.set({'uclamp.max': 10}, False)\n",
"\n",
"df = checkGroups(['/uclamp', '/uclamp/app', '/uclamp/app/pid_smalls', '/uclamp/app/pid_bigs'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:11:22,579 INFO : root : Assert <built-in function max>(uclamp.min.effective ) == 9.96: PASS (measured: 9.96%)\n",
"2019-07-18 19:11:22,581 INFO : root : Assert <built-in function min>(uclamp.max.effective ) == 9.96: PASS (measured: 9.96%)\n",
"2019-07-18 19:11:22,583 INFO : root : Assert <built-in function max>(uclamp.min ) == 70.00: PASS (measured: 70.00%)\n",
"2019-07-18 19:11:22,585 INFO : root : Assert <built-in function min>(uclamp.max ) == 10.00: PASS (measured: 10.00%)\n"
]
}
],
"source": [
"assertAttribute('uclamp.min.effective', max, 9.96)\n",
"assertAttribute('uclamp.max.effective', min, 9.96)\n",
"assertAttribute('uclamp.min', max, 70.0)\n",
"assertAttribute('uclamp.max', min, 10.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Relax the global limit..."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>70.00</td>\n",
" <td>70.02</td>\n",
" <td>80.00</td>\n",
" <td>79.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" <td>75.00</td>\n",
" <td>75.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_smalls</th>\n",
" <td>10.00</td>\n",
" <td>9.96</td>\n",
" <td>20.00</td>\n",
" <td>20.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_bigs</th>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" <td>70.00</td>\n",
" <td>70.02</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max \\\n",
"cg_name \n",
"/uclamp 70.00 70.02 80.00 \n",
"/uclamp/app 50.00 50.00 75.00 \n",
"/uclamp/app/pid_smalls 10.00 9.96 20.00 \n",
"/uclamp/app/pid_bigs 50.00 50.00 70.00 \n",
"\n",
" uclamp.max.effective \n",
"cg_name \n",
"/uclamp 79.98 \n",
"/uclamp/app 75.00 \n",
"/uclamp/app/pid_smalls 20.02 \n",
"/uclamp/app/pid_bigs 70.02 "
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uclamp_cg = cpu_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set({'uclamp.max': 80}, False)\n",
"\n",
"df = checkGroups(['/uclamp', '/uclamp/app', '/uclamp/app/pid_smalls', '/uclamp/app/pid_bigs'])\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ... but limit a local group"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>70.00</td>\n",
" <td>70.02</td>\n",
" <td>80.00</td>\n",
" <td>79.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>50.00</td>\n",
" <td>9.96</td>\n",
" <td>10.00</td>\n",
" <td>9.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_smalls</th>\n",
" <td>10.00</td>\n",
" <td>9.96</td>\n",
" <td>20.00</td>\n",
" <td>9.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_bigs</th>\n",
" <td>50.00</td>\n",
" <td>9.96</td>\n",
" <td>70.00</td>\n",
" <td>9.96</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max \\\n",
"cg_name \n",
"/uclamp 70.00 70.02 80.00 \n",
"/uclamp/app 50.00 9.96 10.00 \n",
"/uclamp/app/pid_smalls 10.00 9.96 20.00 \n",
"/uclamp/app/pid_bigs 50.00 9.96 70.00 \n",
"\n",
" uclamp.max.effective \n",
"cg_name \n",
"/uclamp 79.98 \n",
"/uclamp/app 9.96 \n",
"/uclamp/app/pid_smalls 9.96 \n",
"/uclamp/app/pid_bigs 9.96 "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uclamp_cg = cpu_ctr.cgroup('/uclamp/app')\n",
"uclamp_cg.set({'uclamp.max': 10}, False)\n",
"\n",
"df = checkGroups(['/uclamp', '/uclamp/app', '/uclamp/app/pid_smalls', '/uclamp/app/pid_bigs'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:11:26,025 INFO : root : Assert uclamp.min.effective in [9.96, 70.02] : PASS\n",
"2019-07-18 19:11:26,027 INFO : root : Assert uclamp.max.effective in [9.96, 79.98] : PASS\n"
]
}
],
"source": [
"assertAttributeValues('uclamp.min.effective', [9.96, 70.02])\n",
"assertAttributeValues('uclamp.max.effective', [9.96, 79.98])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## System-wide clamps affect root TG"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/</th>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" <td>90.00</td>\n",
" <td>90.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" <td>90.00</td>\n",
" <td>90.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_smalls</th>\n",
" <td>10.00</td>\n",
" <td>9.96</td>\n",
" <td>20.00</td>\n",
" <td>20.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_bigs</th>\n",
" <td>50.00</td>\n",
" <td>50.00</td>\n",
" <td>70.00</td>\n",
" <td>70.02</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max \\\n",
"cg_name \n",
"/ 102.4 max 102.4 \n",
"/uclamp 50.00 50.00 90.00 \n",
"/uclamp/app 50.00 50.00 90.00 \n",
"/uclamp/app/pid_smalls 10.00 9.96 20.00 \n",
"/uclamp/app/pid_bigs 50.00 50.00 70.00 \n",
"\n",
" uclamp.max.effective \n",
"cg_name \n",
"/ max \n",
"/uclamp 90.04 \n",
"/uclamp/app 90.04 \n",
"/uclamp/app/pid_smalls 20.02 \n",
"/uclamp/app/pid_bigs 70.02 "
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now let's relax the parent\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp')\n",
"uclamp_cg.set({'uclamp.min': 50}, False)\n",
"uclamp_cg.set({'uclamp.max': 90}, False)\n",
"\n",
"uclamp_cg = cpu_ctr.cgroup('/uclamp/app')\n",
"uclamp_cg.set({'uclamp.min': 50}, False)\n",
"uclamp_cg.set({'uclamp.max': 90}, False)\n",
"\n",
"df = checkGroups(['/', '/uclamp', '/uclamp/app', '/uclamp/app/pid_smalls', '/uclamp/app/pid_bigs'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/</th>\n",
" <td>10</td>\n",
" <td>9.77</td>\n",
" <td>102.4</td>\n",
" <td>max</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>50.00</td>\n",
" <td>9.77</td>\n",
" <td>90.00</td>\n",
" <td>90.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>50.00</td>\n",
" <td>9.77</td>\n",
" <td>90.00</td>\n",
" <td>90.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_smalls</th>\n",
" <td>10.00</td>\n",
" <td>9.77</td>\n",
" <td>20.00</td>\n",
" <td>20.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_bigs</th>\n",
" <td>50.00</td>\n",
" <td>9.77</td>\n",
" <td>70.00</td>\n",
" <td>70.02</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max \\\n",
"cg_name \n",
"/ 10 9.77 102.4 \n",
"/uclamp 50.00 9.77 90.00 \n",
"/uclamp/app 50.00 9.77 90.00 \n",
"/uclamp/app/pid_smalls 10.00 9.77 20.00 \n",
"/uclamp/app/pid_bigs 50.00 9.77 70.00 \n",
"\n",
" uclamp.max.effective \n",
"cg_name \n",
"/ max \n",
"/uclamp 90.04 \n",
"/uclamp/app 90.04 \n",
"/uclamp/app/pid_smalls 20.02 \n",
"/uclamp/app/pid_bigs 70.02 "
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target.execute(\"echo 100 > /proc/sys/kernel/sched_util_clamp_min\")\n",
"\n",
"df = checkGroups(['/', '/uclamp', '/uclamp/app', '/uclamp/app/pid_smalls', '/uclamp/app/pid_bigs'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/</th>\n",
" <td>10</td>\n",
" <td>9.77</td>\n",
" <td>40</td>\n",
" <td>39.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>50.00</td>\n",
" <td>9.77</td>\n",
" <td>90.00</td>\n",
" <td>39.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>50.00</td>\n",
" <td>9.77</td>\n",
" <td>90.00</td>\n",
" <td>39.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_smalls</th>\n",
" <td>10.00</td>\n",
" <td>9.77</td>\n",
" <td>20.00</td>\n",
" <td>20.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_bigs</th>\n",
" <td>50.00</td>\n",
" <td>9.77</td>\n",
" <td>70.00</td>\n",
" <td>39.06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max \\\n",
"cg_name \n",
"/ 10 9.77 40 \n",
"/uclamp 50.00 9.77 90.00 \n",
"/uclamp/app 50.00 9.77 90.00 \n",
"/uclamp/app/pid_smalls 10.00 9.77 20.00 \n",
"/uclamp/app/pid_bigs 50.00 9.77 70.00 \n",
"\n",
" uclamp.max.effective \n",
"cg_name \n",
"/ 39.06 \n",
"/uclamp 39.06 \n",
"/uclamp/app 39.06 \n",
"/uclamp/app/pid_smalls 20.02 \n",
"/uclamp/app/pid_bigs 39.06 "
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target.execute(\"echo 400 > /proc/sys/kernel/sched_util_clamp_max\")\n",
"\n",
"df = checkGroups(['/', '/uclamp', '/uclamp/app', '/uclamp/app/pid_smalls', '/uclamp/app/pid_bigs'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:11:33,892 INFO : root : Assert uclamp.min.effective in [9.77] : PASS\n",
"2019-07-18 19:11:33,894 INFO : root : Assert uclamp.max.effective in [20.02, 39.06] : PASS\n"
]
}
],
"source": [
"assertAttributeValues('uclamp.min.effective', [9.77])\n",
"assertAttributeValues('uclamp.max.effective', [20.02, 39.06])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Relax system contratins..."
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/</th>\n",
" <td>30</td>\n",
" <td>29.30</td>\n",
" <td>40</td>\n",
" <td>39.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>50.00</td>\n",
" <td>29.30</td>\n",
" <td>90.00</td>\n",
" <td>39.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>50.00</td>\n",
" <td>29.30</td>\n",
" <td>90.00</td>\n",
" <td>39.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_smalls</th>\n",
" <td>10.00</td>\n",
" <td>9.96</td>\n",
" <td>20.00</td>\n",
" <td>20.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_bigs</th>\n",
" <td>50.00</td>\n",
" <td>29.30</td>\n",
" <td>70.00</td>\n",
" <td>39.06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max \\\n",
"cg_name \n",
"/ 30 29.30 40 \n",
"/uclamp 50.00 29.30 90.00 \n",
"/uclamp/app 50.00 29.30 90.00 \n",
"/uclamp/app/pid_smalls 10.00 9.96 20.00 \n",
"/uclamp/app/pid_bigs 50.00 29.30 70.00 \n",
"\n",
" uclamp.max.effective \n",
"cg_name \n",
"/ 39.06 \n",
"/uclamp 39.06 \n",
"/uclamp/app 39.06 \n",
"/uclamp/app/pid_smalls 20.02 \n",
"/uclamp/app/pid_bigs 39.06 "
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target.execute(\"echo 300 > /proc/sys/kernel/sched_util_clamp_min\")\n",
"\n",
"df = checkGroups(['/', '/uclamp', '/uclamp/app', '/uclamp/app/pid_smalls', '/uclamp/app/pid_bigs'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:11:37,724 INFO : root : Assert uclamp.min.effective in [9.96, 29.3] : PASS\n",
"2019-07-18 19:11:37,726 INFO : root : Assert uclamp.max.effective in [20.02, 39.06] : PASS\n"
]
}
],
"source": [
"assertAttributeValues('uclamp.min.effective', [ 9.96, 29.30])\n",
"assertAttributeValues('uclamp.max.effective', [20.02, 39.06])"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uclamp.min</th>\n",
" <th>uclamp.min.effective</th>\n",
" <th>uclamp.max</th>\n",
" <th>uclamp.max.effective</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cg_name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>/</th>\n",
" <td>30</td>\n",
" <td>29.30</td>\n",
" <td>100</td>\n",
" <td>97.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp</th>\n",
" <td>50.00</td>\n",
" <td>29.30</td>\n",
" <td>90.00</td>\n",
" <td>90.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app</th>\n",
" <td>50.00</td>\n",
" <td>29.30</td>\n",
" <td>90.00</td>\n",
" <td>90.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_smalls</th>\n",
" <td>10.00</td>\n",
" <td>9.96</td>\n",
" <td>20.00</td>\n",
" <td>20.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>/uclamp/app/pid_bigs</th>\n",
" <td>50.00</td>\n",
" <td>29.30</td>\n",
" <td>70.00</td>\n",
" <td>70.02</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uclamp.min uclamp.min.effective uclamp.max \\\n",
"cg_name \n",
"/ 30 29.30 100 \n",
"/uclamp 50.00 29.30 90.00 \n",
"/uclamp/app 50.00 29.30 90.00 \n",
"/uclamp/app/pid_smalls 10.00 9.96 20.00 \n",
"/uclamp/app/pid_bigs 50.00 29.30 70.00 \n",
"\n",
" uclamp.max.effective \n",
"cg_name \n",
"/ 97.66 \n",
"/uclamp 90.04 \n",
"/uclamp/app 90.04 \n",
"/uclamp/app/pid_smalls 20.02 \n",
"/uclamp/app/pid_bigs 70.02 "
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target.execute(\"echo 1000 > /proc/sys/kernel/sched_util_clamp_max\")\n",
"\n",
"df = checkGroups(['/', '/uclamp', '/uclamp/app', '/uclamp/app/pid_smalls', '/uclamp/app/pid_bigs'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:11:59,938 INFO : root : Assert uclamp.min.effective in [9.96, 29.3] : PASS\n",
"2019-07-18 19:11:59,940 INFO : root : Assert uclamp.max.effective in [20.02, 70.02, 90.04, 97.66]: PASS\n"
]
}
],
"source": [
"assertAttributeValues('uclamp.min.effective', [ 9.96, 29.30])\n",
"assertAttributeValues('uclamp.max.effective', [20.02, 70.02, 90.04, 97.66])"
]
},
{
"cell_type": "markdown",
"metadata": {
"toc-hr-collapsed": true
},
"source": [
"[Back to Table of Contents](#Table-of-Contents)"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"toc-hr-collapsed": true
},
"source": [
"## CGroups v2 - Delegation Model"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"**NOTE:** the following cells **requires a target reboot**, since previously we mounted CGroups v1 and now we want to use v2.\n",
"\n",
"Reboot the target and wait some time for boot to complete before running the following tests"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"!ssh root@192.168.90.1 reboot"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"from time import sleep\n",
"attempt = 2\n",
"while os.system(\"ping -c1 192.168.90.1 >/dev/null\"):\n",
" sleep(2)\n",
"sleep(10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"## Target Setup"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:14:11,259 INFO : root : Using LISA logging configuration:\n",
"2019-07-18 19:14:11,260 INFO : root : /data/Code/lisa/logging.conf\n"
]
}
],
"source": [
"import logging\n",
"from lisa.utils import setup_logging\n",
"setup_logging()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:14:15,165 WARNING : LinuxTarget : Module devfreq is not supported by the target\n",
"2019-07-18 19:14:15,168 WARNING : LinuxTarget : Module fastboot is not supported by the target\n",
"2019-07-18 19:14:15,169 WARNING : LinuxTarget : Module gem5stats is not supported by the target\n",
"2019-07-18 19:14:15,328 WARNING : LinuxTarget : Module gpufreq is not supported by the target\n",
"2019-07-18 19:14:20,329 WARNING : LinuxTarget : Module odroidxu3-fan is not supported by the target\n"
]
}
],
"source": [
"from lisa.target import Target, TargetConf\n",
"\n",
"target = Target(\n",
" name='juno',\n",
" kind='linux',\n",
" host='192.168.90.1',\n",
" username='root',\n",
" tools = ['rt-app', 'rt-app_uclamp'],\n",
" devlib_excluded_modules = ['cgroups']\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"hidden": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 19:14:27,149 INFO : root : Target ABI: arm64, CPus: ['A53', 'A72', 'A72', 'A53']\n"
]
}
],
"source": [
"logging.info(\"Target ABI: %s, CPus: %s\",\n",
" target.abi,\n",
" target.cpuinfo.cpu_names)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"output = target.execute('mount')\n",
"for line in output.splitlines():\n",
" _, _, mpoint, _, ctr, _ = line.split()\n",
" if ctr != 'cgroup':\n",
" continue\n",
" target.execute('umount {}'.format(mpoint))"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"target.execute('mount -t cgroup2 none /sys/fs/cgroup');"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"target.execute('echo \"+cpu\" > /sys/fs/cgroup/cgroup.subtree_control');"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"## Create a test hierarchy"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"hidden": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'cgroup.controllers': 'cpu',\n",
" 'cgroup.events': 'populated 0\\nfrozen 0',\n",
" 'cgroup.freeze': '0',\n",
" 'cgroup.max.depth': 'max',\n",
" 'cgroup.max.descendants': 'max',\n",
" 'cgroup.stat': 'nr_descendants 0\\nnr_dying_descendants 0',\n",
" 'cgroup.subtree_control': 'cpu',\n",
" 'cgroup.type': 'domain',\n",
" 'cpu.max': 'max 100000',\n",
" 'cpu.stat': 'usage_usec 0\\nuser_usec 0\\nsystem_usec 0\\nnr_periods 0\\nnr_throttled 0\\nthrottled_usec 0',\n",
" 'cpu.uclamp.max': 'max',\n",
" 'cpu.uclamp.max.effective': 'max',\n",
" 'cpu.uclamp.min': '0.00',\n",
" 'cpu.uclamp.min.effective': 'max',\n",
" 'cpu.weight': '100',\n",
" 'cpu.weight.nice': '0'}"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target.execute('mkdir /sys/fs/cgroup/tg1');\n",
"target.execute('echo \"+cpu\" > /sys/fs/cgroup/tg1/cgroup.subtree_control');\n",
"target.read_tree_values('/sys/fs/cgroup/tg1')"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"target.execute('mkdir /sys/fs/cgroup/tg1/tg11');\n",
"target.execute('mkdir /sys/fs/cgroup/tg1/tg11/tg111');\n",
"target.execute('mkdir /sys/fs/cgroup/tg1/tg12');\n",
"target.execute('mkdir /sys/fs/cgroup/tg1/tg12/tg121');"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"hidden": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'cgroup.controllers': 'cpu',\n",
" 'cgroup.events': 'populated 0\\nfrozen 0',\n",
" 'cgroup.freeze': '0',\n",
" 'cgroup.max.depth': 'max',\n",
" 'cgroup.max.descendants': 'max',\n",
" 'cgroup.stat': 'nr_descendants 1\\nnr_dying_descendants 0',\n",
" 'cgroup.type': 'domain',\n",
" 'cpu.max': 'max 100000',\n",
" 'cpu.stat': 'usage_usec 0\\nuser_usec 0\\nsystem_usec 0\\nnr_periods 0\\nnr_throttled 0\\nthrottled_usec 0',\n",
" 'cpu.uclamp.max': 'max',\n",
" 'cpu.uclamp.max.effective': 'max',\n",
" 'cpu.uclamp.min': '0.00',\n",
" 'cpu.uclamp.min.effective': 'max',\n",
" 'cpu.weight': '100',\n",
" 'cpu.weight.nice': '0'}"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target.read_tree_values('/sys/fs/cgroup/tg1/tg11')"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"hidden": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'cgroup.events': 'populated 0\\nfrozen 0',\n",
" 'cgroup.freeze': '0',\n",
" 'cgroup.max.depth': 'max',\n",
" 'cgroup.max.descendants': 'max',\n",
" 'cgroup.stat': 'nr_descendants 0\\nnr_dying_descendants 0',\n",
" 'cgroup.type': 'domain',\n",
" 'cpu.stat': 'usage_usec 0\\nuser_usec 0\\nsystem_usec 0'}"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target.read_tree_values('/sys/fs/cgroup/tg1/tg12/tg121')"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"## Restrict the parent: restrict childs too"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"hidden": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'cgroup.controllers': 'cpu',\n",
" 'cgroup.events': 'populated 0\\nfrozen 0',\n",
" 'cgroup.freeze': '0',\n",
" 'cgroup.max.depth': 'max',\n",
" 'cgroup.max.descendants': 'max',\n",
" 'cgroup.stat': 'nr_descendants 1\\nnr_dying_descendants 0',\n",
" 'cgroup.type': 'domain',\n",
" 'cpu.max': 'max 100000',\n",
" 'cpu.stat': 'usage_usec 0\\nuser_usec 0\\nsystem_usec 0\\nnr_periods 0\\nnr_throttled 0\\nthrottled_usec 0',\n",
" 'cpu.uclamp.max': 'max',\n",
" 'cpu.uclamp.max.effective': '40.04',\n",
" 'cpu.uclamp.min': '0.00',\n",
" 'cpu.uclamp.min.effective': '0.00',\n",
" 'cpu.weight': '100',\n",
" 'cpu.weight.nice': '0'}"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target.execute('echo 0 > /sys/fs/cgroup/tg1/cpu.uclamp.min')\n",
"target.execute('echo 40 > /sys/fs/cgroup/tg1/cpu.uclamp.max')\n",
"target.read_tree_values('/sys/fs/cgroup/tg1/tg11')"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"## Reconf child: do not update effective enforced by parent"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"hidden": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'cgroup.controllers': 'cpu',\n",
" 'cgroup.events': 'populated 0\\nfrozen 0',\n",
" 'cgroup.freeze': '0',\n",
" 'cgroup.max.depth': 'max',\n",
" 'cgroup.max.descendants': 'max',\n",
" 'cgroup.stat': 'nr_descendants 1\\nnr_dying_descendants 0',\n",
" 'cgroup.type': 'domain',\n",
" 'cpu.max': 'max 100000',\n",
" 'cpu.stat': 'usage_usec 0\\nuser_usec 0\\nsystem_usec 0\\nnr_periods 0\\nnr_throttled 0\\nthrottled_usec 0',\n",
" 'cpu.uclamp.max': '80.00',\n",
" 'cpu.uclamp.max.effective': '40.04',\n",
" 'cpu.uclamp.min': '20.00',\n",
" 'cpu.uclamp.min.effective': '0.00',\n",
" 'cpu.weight': '100',\n",
" 'cpu.weight.nice': '0'}"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target.execute('echo 20 > /sys/fs/cgroup/tg1/tg11/cpu.uclamp.min')\n",
"target.execute('echo 80 > /sys/fs/cgroup/tg1/tg11/cpu.uclamp.max')\n",
"target.read_tree_values('/sys/fs/cgroup/tg1/tg11')"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"## Relaxing one of the parent constraints: keep the childs ones"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"hidden": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'cgroup.controllers': 'cpu',\n",
" 'cgroup.events': 'populated 0\\nfrozen 0',\n",
" 'cgroup.freeze': '0',\n",
" 'cgroup.max.depth': 'max',\n",
" 'cgroup.max.descendants': 'max',\n",
" 'cgroup.stat': 'nr_descendants 1\\nnr_dying_descendants 0',\n",
" 'cgroup.type': 'domain',\n",
" 'cpu.max': 'max 100000',\n",
" 'cpu.stat': 'usage_usec 0\\nuser_usec 0\\nsystem_usec 0\\nnr_periods 0\\nnr_throttled 0\\nthrottled_usec 0',\n",
" 'cpu.uclamp.max': '80.00',\n",
" 'cpu.uclamp.max.effective': '79.98',\n",
" 'cpu.uclamp.min': '20.00',\n",
" 'cpu.uclamp.min.effective': '0.00',\n",
" 'cpu.weight': '100',\n",
" 'cpu.weight.nice': '0'}"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target.execute('echo 0 > /sys/fs/cgroup/tg1/cpu.uclamp.min')\n",
"target.execute('echo 100 > /sys/fs/cgroup/tg1/cpu.uclamp.max')\n",
"target.read_tree_values('/sys/fs/cgroup/tg1/tg11')"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"## Force single OPP from parent: still keep more restrictive clamp from child"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"hidden": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'cgroup.controllers': 'cpu',\n",
" 'cgroup.events': 'populated 0\\nfrozen 0',\n",
" 'cgroup.freeze': '0',\n",
" 'cgroup.max.depth': 'max',\n",
" 'cgroup.max.descendants': 'max',\n",
" 'cgroup.stat': 'nr_descendants 1\\nnr_dying_descendants 0',\n",
" 'cgroup.type': 'domain',\n",
" 'cpu.max': 'max 100000',\n",
" 'cpu.stat': 'usage_usec 0\\nuser_usec 0\\nsystem_usec 0\\nnr_periods 0\\nnr_throttled 0\\nthrottled_usec 0',\n",
" 'cpu.uclamp.max': '80.00',\n",
" 'cpu.uclamp.max.effective': '50.00',\n",
" 'cpu.uclamp.min': '20.00',\n",
" 'cpu.uclamp.min.effective': '20.02',\n",
" 'cpu.weight': '100',\n",
" 'cpu.weight.nice': '0'}"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target.execute('echo 50 > /sys/fs/cgroup/tg1/cpu.uclamp.min')\n",
"target.execute('echo 50 > /sys/fs/cgroup/tg1/cpu.uclamp.max')\n",
"target.read_tree_values('/sys/fs/cgroup/tg1/tg11')"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"## Relaxing the parent: use all the child constraints"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"hidden": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'cgroup.controllers': 'cpu',\n",
" 'cgroup.events': 'populated 0\\nfrozen 0',\n",
" 'cgroup.freeze': '0',\n",
" 'cgroup.max.depth': 'max',\n",
" 'cgroup.max.descendants': 'max',\n",
" 'cgroup.stat': 'nr_descendants 1\\nnr_dying_descendants 0',\n",
" 'cgroup.type': 'domain',\n",
" 'cpu.max': 'max 100000',\n",
" 'cpu.stat': 'usage_usec 0\\nuser_usec 0\\nsystem_usec 0\\nnr_periods 0\\nnr_throttled 0\\nthrottled_usec 0',\n",
" 'cpu.uclamp.max': '80.00',\n",
" 'cpu.uclamp.max.effective': '79.98',\n",
" 'cpu.uclamp.min': '20.00',\n",
" 'cpu.uclamp.min.effective': '20.02',\n",
" 'cpu.weight': '100',\n",
" 'cpu.weight.nice': '0'}"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target.execute('echo 100 > /sys/fs/cgroup/tg1/cpu.uclamp.max')\n",
"target.execute('echo 100 > /sys/fs/cgroup/tg1/cpu.uclamp.min')\n",
"target.read_tree_values('/sys/fs/cgroup/tg1/tg11')"
]
},
{
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to Table of Contents](#Table-of-Contents)"
]
}
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"name": "python",
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"pygments_lexer": "ipython3",
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"toc": {
"colors": {
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},
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}
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