ipynb/examples/trace_analysis/TraceAnalysis_TasksLatencies.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trace Analysis Examples\n",
    "\n",
    "## Tasks Latencies\n",
    "\n",
    "This notebook shows the features provided for task latency profiling. It will be necessary to collect the following events:\n",
    " \n",
    "Details on idle states profiling ar given in **Latency DataFrames and Latency Plots ** below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2016-12-12 12:58:10,343 INFO    : root         : Using LISA logging configuration:\n",
      "2016-12-12 12:58:10,344 INFO    : root         :   /home/vagrant/lisa/logging.conf\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "from conf import LisaLogging\n",
    "LisaLogging.setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "# Generate plots inline\n",
    "%matplotlib inline\n",
    "\n",
    "import json\n",
    "import os\n",
    "\n",
    "# Support to access the remote target\n",
    "import devlib\n",
    "from env import TestEnv\n",
    "\n",
    "# Support for workload generation\n",
    "from wlgen import RTA, Ramp\n",
    "\n",
    "# Support for trace analysis\n",
    "from trace import Trace\n",
    "\n",
    "# Support for plotting\n",
    "import numpy\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import trappy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target Configuration\n",
    "The target configuration is used to describe and configure your test environment.\n",
    "You can find more details in **examples/utils/testenv_example.ipynb**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
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   },
   "outputs": [],
   "source": [
    "# Setup target configuration\n",
    "my_conf = {\n",
    "\n",
    "    # Target platform and board\n",
    "    \"platform\"    : 'linux',\n",
    "    \"board\"       : 'juno',\n",
    "    \"host\"        : '192.168.0.1',\n",
    "    \"password\"    : 'juno',\n",
    "\n",
    "    # Folder where all the results will be collected\n",
    "    \"results_dir\" : \"TraceAnalysis_TaskLatencies\",\n",
    "\n",
    "    # Define devlib modules to load\n",
    "    \"exclude_modules\" : [ 'hwmon' ],\n",
    "\n",
    "    # FTrace events to collect for all the tests configuration which have\n",
    "    # the \"ftrace\" flag enabled\n",
    "    \"ftrace\"  : {\n",
    "        \"events\" : [\n",
    "            \"sched_switch\",\n",
    "            \"sched_wakeup\",\n",
    "            \"sched_load_avg_cpu\",\n",
    "            \"sched_load_avg_task\",\n",
    "        ],\n",
    "        \n",
    "         \"buffsize\" : 100 * 1024,\n",
    "    },\n",
    "\n",
    "    # Tools required by the experiments\n",
    "    \"tools\"   : [ 'trace-cmd', 'rt-app' ],\n",
    "    \n",
    "    # Comment this line to calibrate RTApp in your own platform\n",
    "    \"rtapp-calib\" :  {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353},\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
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   "outputs": [
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2016-12-12 12:58:17,443 INFO    : TestEnv      : Using base path: /home/vagrant/lisa\n",
      "2016-12-12 12:58:17,444 INFO    : TestEnv      : Loading custom (inline) target configuration\n",
      "2016-12-12 12:58:17,444 INFO    : TestEnv      : Devlib modules to load: ['bl', 'cpufreq']\n",
      "2016-12-12 12:58:17,445 INFO    : TestEnv      : Connecting linux target:\n",
      "2016-12-12 12:58:17,445 INFO    : TestEnv      :   username : root\n",
      "2016-12-12 12:58:17,446 INFO    : TestEnv      :       host : 192.168.0.1\n",
      "2016-12-12 12:58:17,446 INFO    : TestEnv      :   password : juno\n",
      "2016-12-12 12:58:17,447 INFO    : TestEnv      : Connection settings:\n",
      "2016-12-12 12:58:17,447 INFO    : TestEnv      :    {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n",
      "2016-12-12 12:58:24,242 INFO    : TestEnv      : Initializing target workdir:\n",
      "2016-12-12 12:58:24,243 INFO    : TestEnv      :    /root/devlib-target\n",
      "2016-12-12 12:58:40,880 INFO    : TestEnv      : Topology:\n",
      "2016-12-12 12:58:40,881 INFO    : TestEnv      :    [[0, 3, 4, 5], [1, 2]]\n",
      "2016-12-12 12:58:42,134 INFO    : TestEnv      : Loading default EM:\n",
      "2016-12-12 12:58:42,135 INFO    : TestEnv      :    /home/vagrant/lisa/libs/utils/platforms/juno.json\n",
      "2016-12-12 12:58:45,386 INFO    : TestEnv      : Enabled tracepoints:\n",
      "2016-12-12 12:58:45,387 INFO    : TestEnv      :    sched_switch\n",
      "2016-12-12 12:58:45,387 INFO    : TestEnv      :    sched_wakeup\n",
      "2016-12-12 12:58:45,388 INFO    : TestEnv      :    sched_load_avg_cpu\n",
      "2016-12-12 12:58:45,389 INFO    : TestEnv      :    sched_load_avg_task\n",
      "2016-12-12 12:58:45,389 WARNING : TestEnv      : Using configuration provided RTApp calibration\n",
      "2016-12-12 12:58:45,390 INFO    : TestEnv      : Using RT-App calibration values:\n",
      "2016-12-12 12:58:45,390 INFO    : TestEnv      :    {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353}\n",
      "2016-12-12 12:58:45,391 INFO    : EnergyMeter  : HWMON module not enabled\n",
      "2016-12-12 12:58:45,391 WARNING : EnergyMeter  : Energy sampling disabled by configuration\n",
      "2016-12-12 12:58:45,392 INFO    : TestEnv      : Set results folder to:\n",
      "2016-12-12 12:58:45,392 INFO    : TestEnv      :    /home/vagrant/lisa/results/TraceAnalysis_TaskLatencies\n",
      "2016-12-12 12:58:45,393 INFO    : TestEnv      : Experiment results available also in:\n",
      "2016-12-12 12:58:45,393 INFO    : TestEnv      :    /home/vagrant/lisa/results_latest\n"
     ]
    }
   ],
   "source": [
    "# Initialize a test environment using:\n",
    "te = TestEnv(my_conf, wipe=False, force_new=True)\n",
    "target = te.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Workload Configuration and Execution\n",
    "\n",
    "Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def experiment(te):\n",
    "\n",
    "    # Create and RTApp RAMP task\n",
    "    rtapp = RTA(te.target, 'ramp', calibration=te.calibration())\n",
    "    rtapp.conf(kind='profile',\n",
    "               params={\n",
    "                    'ramp' : Ramp(\n",
    "                        start_pct =  60,\n",
    "                        end_pct   =  20,\n",
    "                        delta_pct =   5,\n",
    "                        time_s    =   0.5).get()\n",
    "              })\n",
    "\n",
    "    # FTrace the execution of this workload\n",
    "    te.ftrace.start()\n",
    "    rtapp.run(out_dir=te.res_dir)\n",
    "    te.ftrace.stop()\n",
    "\n",
    "    # Collect and keep track of the trace\n",
    "    trace_file = os.path.join(te.res_dir, 'trace.dat')\n",
    "    te.ftrace.get_trace(trace_file)\n",
    "    \n",
    "    # Collect and keep track of the Kernel Functions performance data\n",
    "    stats_file = os.path.join(te.res_dir, 'trace.stats')\n",
    "    te.ftrace.get_stats(stats_file)\n",
    "\n",
    "    # Dump platform descriptor\n",
    "    te.platform_dump(te.res_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2016-12-12 12:58:52,280 INFO    : Workload     : Setup new workload ramp\n",
      "2016-12-12 12:58:52,281 INFO    : Workload     : Workload duration defined by longest task\n",
      "2016-12-12 12:58:52,282 INFO    : Workload     : Default policy: SCHED_OTHER\n",
      "2016-12-12 12:58:52,282 INFO    : Workload     : ------------------------\n",
      "2016-12-12 12:58:52,283 INFO    : Workload     : task [ramp], sched: using default policy\n",
      "2016-12-12 12:58:52,283 INFO    : Workload     :  | calibration CPU: 1\n",
      "2016-12-12 12:58:52,284 INFO    : Workload     :  | loops count: 1\n",
      "2016-12-12 12:58:52,284 INFO    : Workload     : + phase_000001: duration 0.500000 [s] (5 loops)\n",
      "2016-12-12 12:58:52,285 INFO    : Workload     : |  period   100000 [us], duty_cycle  60 %\n",
      "2016-12-12 12:58:52,285 INFO    : Workload     : |  run_time  60000 [us], sleep_time  40000 [us]\n",
      "2016-12-12 12:58:52,286 INFO    : Workload     : + phase_000002: duration 0.500000 [s] (5 loops)\n",
      "2016-12-12 12:58:52,286 INFO    : Workload     : |  period   100000 [us], duty_cycle  55 %\n",
      "2016-12-12 12:58:52,287 INFO    : Workload     : |  run_time  55000 [us], sleep_time  45000 [us]\n",
      "2016-12-12 12:58:52,288 INFO    : Workload     : + phase_000003: duration 0.500000 [s] (5 loops)\n",
      "2016-12-12 12:58:52,288 INFO    : Workload     : |  period   100000 [us], duty_cycle  50 %\n",
      "2016-12-12 12:58:52,289 INFO    : Workload     : |  run_time  50000 [us], sleep_time  50000 [us]\n",
      "2016-12-12 12:58:52,290 INFO    : Workload     : + phase_000004: duration 0.500000 [s] (5 loops)\n",
      "2016-12-12 12:58:52,291 INFO    : Workload     : |  period   100000 [us], duty_cycle  45 %\n",
      "2016-12-12 12:58:52,291 INFO    : Workload     : |  run_time  45000 [us], sleep_time  55000 [us]\n",
      "2016-12-12 12:58:52,292 INFO    : Workload     : + phase_000005: duration 0.500000 [s] (5 loops)\n",
      "2016-12-12 12:58:52,293 INFO    : Workload     : |  period   100000 [us], duty_cycle  40 %\n",
      "2016-12-12 12:58:52,293 INFO    : Workload     : |  run_time  40000 [us], sleep_time  60000 [us]\n",
      "2016-12-12 12:58:52,294 INFO    : Workload     : + phase_000006: duration 0.500000 [s] (5 loops)\n",
      "2016-12-12 12:58:52,294 INFO    : Workload     : |  period   100000 [us], duty_cycle  35 %\n",
      "2016-12-12 12:58:52,295 INFO    : Workload     : |  run_time  35000 [us], sleep_time  65000 [us]\n",
      "2016-12-12 12:58:52,295 INFO    : Workload     : + phase_000007: duration 0.500000 [s] (5 loops)\n",
      "2016-12-12 12:58:52,296 INFO    : Workload     : |  period   100000 [us], duty_cycle  30 %\n",
      "2016-12-12 12:58:52,296 INFO    : Workload     : |  run_time  30000 [us], sleep_time  70000 [us]\n",
      "2016-12-12 12:58:52,296 INFO    : Workload     : + phase_000008: duration 0.500000 [s] (5 loops)\n",
      "2016-12-12 12:58:52,297 INFO    : Workload     : |  period   100000 [us], duty_cycle  25 %\n",
      "2016-12-12 12:58:52,297 INFO    : Workload     : |  run_time  25000 [us], sleep_time  75000 [us]\n",
      "2016-12-12 12:58:52,298 INFO    : Workload     : + phase_000009: duration 0.500000 [s] (5 loops)\n",
      "2016-12-12 12:58:52,298 INFO    : Workload     : |  period   100000 [us], duty_cycle  20 %\n",
      "2016-12-12 12:58:52,298 INFO    : Workload     : |  run_time  20000 [us], sleep_time  80000 [us]\n",
      "2016-12-12 12:58:58,853 INFO    : Workload     : Workload execution START:\n",
      "2016-12-12 12:58:58,853 INFO    : Workload     :    /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n"
     ]
    }
   ],
   "source": [
    "experiment(te)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse Trace and Profiling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2016-12-12 12:59:36,883 INFO    : root         : Content of the output folder /home/vagrant/lisa/results/TraceAnalysis_TaskLatencies\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/vagrant/lisa/results/TraceAnalysis_TaskLatencies\r\n",
      "├── output.log\r\n",
      "├── platform.json\r\n",
      "├── ramp_00.json\r\n",
      "├── rt-app-ramp-0.log\r\n",
      "└── trace.dat\r\n",
      "\r\n",
      "0 directories, 5 files\r\n"
     ]
    }
   ],
   "source": [
    "# Base folder where tests folder are located\n",
    "res_dir = te.res_dir\n",
    "logging.info('Content of the output folder %s', res_dir)\n",
    "!tree {res_dir}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2016-12-12 12:59:38,100 INFO    : root         : LITTLE cluster max capacity: 447\n"
     ]
    }
   ],
   "source": [
    "with open(os.path.join(res_dir, 'platform.json'), 'r') as fh:\n",
    "    platform = json.load(fh)\n",
    "logging.info('LITTLE cluster max capacity: %d',\n",
    "             platform['nrg_model']['little']['cpu']['cap_max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2016-12-12 12:59:39,175 INFO    : Trace        : Parsing FTrace format...\n",
      "2016-12-12 12:59:39,536 INFO    : Trace        : Collected events spans a 13.065 [s] time interval\n",
      "2016-12-12 12:59:39,536 INFO    : Trace        : Set plots time range to (0.000000, 13.064847)[s]\n",
      "2016-12-12 12:59:39,537 INFO    : Analysis     : Registering trace analysis modules:\n",
      "2016-12-12 12:59:39,538 INFO    : Analysis     :    tasks\n",
      "2016-12-12 12:59:39,539 INFO    : Analysis     :    status\n",
      "2016-12-12 12:59:39,539 INFO    : Analysis     :    frequency\n",
      "2016-12-12 12:59:39,540 INFO    : Analysis     :    cpus\n",
      "2016-12-12 12:59:39,541 INFO    : Analysis     :    latency\n",
      "2016-12-12 12:59:39,541 INFO    : Analysis     :    idle\n",
      "2016-12-12 12:59:39,542 INFO    : Analysis     :    functions\n",
      "2016-12-12 12:59:39,542 INFO    : Analysis     :    eas\n"
     ]
    }
   ],
   "source": [
    "trace_file = os.path.join(res_dir, 'trace.dat')\n",
    "trace = Trace(platform, trace_file, events=my_conf['ftrace']['events'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Trace visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
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1.76959399999987], [1.7732969999997295, 1.7733349999998609], [1.7735889999999017, 1.7736159999999472], [1.7736559999998462, 1.7736719999998058], [5.4690200000000004, 5.4690559999999095], [6.2739669999996295, 6.2739909999995689], [6.277730999999676, 6.2777429999996457], [9.1203259999997499, 9.12041999999974], [9.1242409999999836, 9.1242919999999685], [10.476857999999993, 10.477899999999863], [11.583134999999857, 11.583175999999639], [11.583973999999671, 11.584044999999605], [11.584073999999873, 11.58409999999958], [11.584240999999565, 11.58428399999957], [11.584426999999778, 11.584455999999591], [11.584561999999551, 11.584590999999818], [11.584755999999743, 11.584783999999672], [11.584888999999748, 11.584917999999561], [11.585056999999779, 11.585085999999592], [11.585214999999607, 11.585244999999759], [11.585347999999613, 11.585373999999774], [11.585476999999628, 11.58550699999978], [11.585620999999719, 11.585648999999648], [11.585757999999714, 11.585783999999876], [12.389678000000004, 12.389704999999594], [12.38991199999964, 12.38993599999958], [12.39012399999956, 12.390134999999646], [12.390241999999944, 12.390282999999727], [12.390715, 12.390724999999748], [12.390827999999601, 12.390853999999763], [12.390955999999733, 12.39099299999998], [12.391151999999693, 12.391162999999779], [12.391265999999632, 12.391291999999794], [12.39139699999987, 12.391421999999693], [12.391522999999779, 12.391548999999941], [12.391673999999966, 12.391699999999673], [12.416210999999748, 12.416246999999657]]}, \"sudo-938\": {\"1\": [[0.9753589999995711, 0.98125699999991411], [0.98292999999966923, 0.98295799999959854]], \"2\": [[0.99548099999992701, 0.99657999999999447], [0.99672399999963091, 0.99682399999983318], [0.99697399999968184, 0.99805499999956737]]}, \"bash-938\": {\"1\": [[0.96507699999983743, 0.97479199999997945]], \"2\": [[0.96424299999989671, 0.96493499999996857]]}, \"sshd-947\": {\"1\": [[9.0655919999999242, 9.0675169999999525], [9.0682899999997062, 9.0689409999999953]], \"2\": [[9.0690759999997681, 9.0749839999998585]]}, \"sudo-952\": {\"2\": [[11.597982999999658, 11.602201999999579], [11.602322999999615, 11.602379999999812], [11.602405999999974, 11.604216999999608], [11.605803999999807, 11.6058409999996], [11.616795999999795, 11.618988999999601]]}, \"kworker/1:1H-106\": {\"1\": [[0.50936399999955029, 0.50940499999978783], [6.2806979999995747, 6.2807109999998829], [10.526978999999756, 10.526997999999821], [11.416770999999699, 11.416783999999552], [11.418564999999944, 11.418573999999808]]}, \"bash-958\": {\"1\": [[13.042767999999796, 13.043427999999949]], \"2\": [[13.043567999999595, 13.044712999999774]]}}});\n",
       "        }); /* TRAPPY_PUBLISH_REMOVE_LINE */\n",
       "        </script>\n",
       "        </div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trappy.plotter.plot_trace(trace.ftrace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latency DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target_cpu</th>\n",
       "      <th>__cpu</th>\n",
       "      <th>curr_state</th>\n",
       "      <th>next_state</th>\n",
       "      <th>t_start</th>\n",
       "      <th>t_delta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.773515</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A</td>\n",
       "      <td>S</td>\n",
       "      <td>1.773515</td>\n",
       "      <td>0.086343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.859858</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>S</td>\n",
       "      <td>W</td>\n",
       "      <td>1.859858</td>\n",
       "      <td>0.013899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.873757</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>A</td>\n",
       "      <td>1.873757</td>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.873767</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A</td>\n",
       "      <td>S</td>\n",
       "      <td>1.873767</td>\n",
       "      <td>0.059513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.933280</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>S</td>\n",
       "      <td>W</td>\n",
       "      <td>1.933280</td>\n",
       "      <td>0.040525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          target_cpu  __cpu curr_state next_state   t_start   t_delta\n",
       "Time                                                                 \n",
       "1.773515         NaN    1.0          A          S  1.773515  0.086343\n",
       "1.859858         NaN    1.0          S          W  1.859858  0.013899\n",
       "1.873757         1.0    NaN          W          A  1.873757  0.000010\n",
       "1.873767         NaN    1.0          A          S  1.873767  0.059513\n",
       "1.933280         NaN    1.0          S          W  1.933280  0.040525"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Report full set of task status informations available from the trace\n",
    "trace.data_frame.latency_df('ramp').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>__comm</th>\n",
       "      <th>__cpu</th>\n",
       "      <th>__pid</th>\n",
       "      <th>next_comm</th>\n",
       "      <th>next_pid</th>\n",
       "      <th>next_prio</th>\n",
       "      <th>prev_comm</th>\n",
       "      <th>prev_pid</th>\n",
       "      <th>prev_prio</th>\n",
       "      <th>prev_state</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000007</th>\n",
       "      <td>trace-cmd</td>\n",
       "      <td>2</td>\n",
       "      <td>935</td>\n",
       "      <td>swapper/2</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>trace-cmd</td>\n",
       "      <td>935</td>\n",
       "      <td>120</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.000108</th>\n",
       "      <td>&lt;idle&gt;</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sh</td>\n",
       "      <td>934</td>\n",
       "      <td>120</td>\n",
       "      <td>swapper/1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.000501</th>\n",
       "      <td>&lt;idle&gt;</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>sudo</td>\n",
       "      <td>933</td>\n",
       "      <td>120</td>\n",
       "      <td>swapper/2</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.000507</th>\n",
       "      <td>sh</td>\n",
       "      <td>1</td>\n",
       "      <td>934</td>\n",
       "      <td>swapper/1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>sh</td>\n",
       "      <td>934</td>\n",
       "      <td>120</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.000691</th>\n",
       "      <td>&lt;idle&gt;</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>rcu_preempt</td>\n",
       "      <td>7</td>\n",
       "      <td>120</td>\n",
       "      <td>swapper/1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             __comm  __cpu  __pid    next_comm  next_pid  next_prio  \\\n",
       "Time                                                                  \n",
       "0.000007  trace-cmd      2    935    swapper/2         0        120   \n",
       "0.000108     <idle>      1      0           sh       934        120   \n",
       "0.000501     <idle>      2      0         sudo       933        120   \n",
       "0.000507         sh      1    934    swapper/1         0        120   \n",
       "0.000691     <idle>      1      0  rcu_preempt         7        120   \n",
       "\n",
       "          prev_comm  prev_pid  prev_prio  prev_state  \n",
       "Time                                                  \n",
       "0.000007  trace-cmd       935        120          64  \n",
       "0.000108  swapper/1         0        120           0  \n",
       "0.000501  swapper/2         0        120           0  \n",
       "0.000507         sh       934        120          64  \n",
       "0.000691  swapper/1         0        120           0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Report information on sched_switch events\n",
    "df = trace.data_frame.trace_event('sched_switch')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wakeup_latency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.873757</th>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.973805</th>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.073804</th>\n",
       "      <td>0.000023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.173801</th>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.273804</th>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          wakeup_latency\n",
       "Time                    \n",
       "1.873757        0.000010\n",
       "1.973805        0.000020\n",
       "2.073804        0.000023\n",
       "2.173801        0.000020\n",
       "2.273804        0.000020"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Report WAKEUP events and their duration\n",
    "trace.data_frame.latency_wakeup_df('ramp').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>preempt_latency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.512645</th>\n",
       "      <td>0.000019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.516644</th>\n",
       "      <td>0.000052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.512646</th>\n",
       "      <td>0.000019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.512646</th>\n",
       "      <td>0.000017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.516644</th>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          preempt_latency\n",
       "Time                     \n",
       "2.512645         0.000019\n",
       "2.516644         0.000052\n",
       "3.512646         0.000019\n",
       "4.512646         0.000017\n",
       "4.516644         0.000088"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Report PREEMPTION events and their duration\n",
    "trace.data_frame.latency_preemption_df('ramp').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latency Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2016-12-12 12:59:56,078 INFO    : Analysis     : Found:    46 WAKEUP latencies\n",
      "2016-12-12 12:59:56,104 INFO    : Analysis     : Found:     5 PREEMPT latencies\n",
      "2016-12-12 12:59:56,105 INFO    : Analysis     : Total:    51 latency events\n",
      "2016-12-12 12:59:56,180 WARNING : Analysis     : Event [sched_overutilized] not found, plot DISABLED!\n"
     ]
    },
    {
     "data": {
      "image/png": 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Dg4M577zz8sQnPjE33HBDLrzwwtx+++255JJLkiRr1qzJiSeemK99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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3943425e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot latency events for a specified task\n",
    "latency_stats_df = trace.analysis.latency.plotLatency('ramp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>50%</th>\n",
       "      <th>95%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>latency</th>\n",
       "      <td>51.0</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count     mean       std       min      50%       95%      99%  \\\n",
       "latency   51.0  0.00002  0.000012  0.000006  0.00002  0.000024  0.00007   \n",
       "\n",
       "              max  \n",
       "latency  0.000088  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot statistics on task latencies\n",
    "latency_stats_df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8k3K64oorim6C1HbmXiky90qRuVeKzL1SY+a714SKd2effTalUqnu67TTTqOv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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f394417b350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot latency events for a specified task\n",
    "trace.analysis.latency.plotLatencyBands('ramp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2016-12-12 13:00:07,509 INFO    : Trace        : Set plots time range to (3.445000, 3.450000)[s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Soer1etlFkNQjxrdUXca3VF3Gt6SiTClhnzOAgYGBAQd4lSRJkiRJkrpUr9fp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f39644f18d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Zoom into a spefific time frame\n",
    "trace.setXTimeRange(3.445, 3.45)\n",
    "trace.analysis.latency.plotLatencyBands('ramp')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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