path: root/ipynb/scratchpad/pelt-error/pelt-error.ipynb

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   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
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   },
   "source": [
    "# Analysis of PELT signal error due to skip last window update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "run_control": {
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     "marked": false,
     "read_only": false
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-07-25 18:34:56,088 INFO    : root         : Using LISA logging configuration:\n",
      "2017-07-25 18:34:56,090 INFO    : root         :   /home/joelaf/repo/lisa-aosp/external/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": {
     "frozen": false,
     "marked": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# Generate plots inline\n",
    "%matplotlib inline\n",
    "import json\n",
    "import os\n",
    "from trace import Trace\n",
    "import numpy\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import trappy\n",
    "\n",
    "path_to_dat = \"/home/joelaf/repo/lisa-aosp/external/lisa/ipynb/scratchpad/pelt-error/trace.dat\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Parse Trace and Profiling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "marked": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "trace = Trace(None, path_to_dat, events=[ 'sched_switch', 'pelt_update' ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Trace visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "marked": false,
     "read_only": false
    }
   },
   "outputs": [
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   "source": [
    "trappy.plotter.plot_trace(trace.ftrace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Latency DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'__comm', u'__cpu', u'__line', u'__pid', u'__tgid', u'acc_load_avg',\n",
       "       u'acc_util_avg', u'cfs_rq', u'delta_us', u'load_avg', u'load_err',\n",
       "       u'load_sum', u'sum_err', u'util_avg', u'util_err'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = trace.data_frame.trace_event('pelt_update')\n",
    "rq_df = df[df.cfs_rq == 1]\n",
    "rq_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UTIL ERROR\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<tr>\n",
       "<td style=\"border-style: hidden;\"><div class=\"ilineplot\" id=\"fig_9fb104a68b8b43debeafadd416b51f51\"></div></td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td style=\"border-style: hidden;\"><div style=\"text-align:center\" id=\"fig_9fb104a68b8b43debeafadd416b51f51_legend\"></div></td>\n",
       "</tr>\n",
       "</table>\n",
       "<script>\n",
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190], [7.598378, 0, 1], [7.598381999999999, 4, 5], [7.598386999999999, 4, 5]]}};\n",
       "</script>\n",
       "<!-- TRAPPY_PUBLISH_SOURCE_LIB = \"http://cdnjs.cloudflare.com/ajax/libs/dygraph/1.1.1/dygraph-combined.js\" -->\n",
       "<!-- TRAPPY_PUBLISH_SOURCE_LIB = \"http://dygraphs.com/extras/synchronizer.js\" -->\n",
       "<!-- TRAPPY_PUBLISH_SOURCE_LIB = \"https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.8.3/underscore-min.js\" -->\n",
       "\n",
       "            <script>\n",
       "            /* TRAPPY_PUBLISH_IMPORT = \"plotter/js/ILinePlot.js\" */\n",
       "            /* TRAPPY_PUBLISH_REMOVE_START */\n",
       "            var ilp_req = require.config( {\n",
       "\n",
       "                paths: {\n",
       "                    \"dygraph-sync\": '/nbextensions/plotter_scripts/ILinePlot/synchronizer',\n",
       "                    \"dygraph\": '/nbextensions/plotter_scripts/ILinePlot/dygraph-combined',\n",
       "                    \"ILinePlot\": '/nbextensions/plotter_scripts/ILinePlot/ILinePlot',\n",
       "                    \"underscore\": '/nbextensions/plotter_scripts/ILinePlot/underscore-min',\n",
       "                },\n",
       "\n",
       "                shim: {\n",
       "                    \"dygraph-sync\": [\"dygraph\"],\n",
       "                    \"ILinePlot\": {\n",
       "\n",
       "                        \"deps\": [\"dygraph-sync\", \"dygraph\", \"underscore\"],\n",
       "                        \"exports\":  \"ILinePlot\"\n",
       "                    }\n",
       "                }\n",
       "            });\n",
       "                /* TRAPPY_PUBLISH_REMOVE_STOP */\n",
       "                ilp_req([\"require\", \"ILinePlot\"], function() { /* TRAPPY_PUBLISH_REMOVE_LINE */\n",
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      "LOAD ERROR\n"
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298, 298], [6.799344, 348, 348], [6.799348999999999, 347, 347], [6.802653999999999, 386, 386], [6.802655999999999, 393, 393], [6.803648999999998, 398, 398], [6.803652, 405, 405], [6.8037030000000005, 0, 0], [6.803705000000001, 405, 405], [6.803808999999999, 0, 2], [6.803811, 399, 399], [6.884373999999999, 75, 75], [6.884378, 77, 77], [6.884486000000001, 75, 76], [6.884487999999999, 77, 78], [6.88599, 107, 107], [6.885994, 107, 107], [6.886018, 107, 107], [6.889315999999999, 168, 168], [6.889319, 167, 167], [6.892647999999999, 225, 225], [6.892650999999999, 225, 225], [6.895982, 273, 273], [6.895984, 279, 279], [6.899314, 323, 323], [6.899315999999999, 322, 322], [6.902647, 370, 370], [6.9026489999999985, 369, 369], [6.905106, 405, 405], [6.9051089999999995, 404, 404], [6.985346, 76, 76], [6.98535, 76, 76], [6.985462, 76, 77], [6.985464, 76, 77], [6.9860099999999985, 88, 88], [6.986013999999999, 76, 90], [6.986053, 76, 91], [6.989345999999999, 150, 150], [6.989348999999999, 149, 149], 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[7.596290999999999, 242, 251], [7.596297, 157, 157], [7.598326, 0, 0], [7.598329, 282, 282], [7.598357999999999, 196, 196], [7.598378, 0, 1], [7.598381999999999, 86, 88], [7.598386999999999, 86, 88]]}};\n",
       "</script>\n",
       "<!-- TRAPPY_PUBLISH_SOURCE_LIB = \"http://cdnjs.cloudflare.com/ajax/libs/dygraph/1.1.1/dygraph-combined.js\" -->\n",
       "<!-- TRAPPY_PUBLISH_SOURCE_LIB = \"http://dygraphs.com/extras/synchronizer.js\" -->\n",
       "<!-- TRAPPY_PUBLISH_SOURCE_LIB = \"https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.8.3/underscore-min.js\" -->\n",
       "\n",
       "            <script>\n",
       "            /* TRAPPY_PUBLISH_IMPORT = \"plotter/js/ILinePlot.js\" */\n",
       "            /* TRAPPY_PUBLISH_REMOVE_START */\n",
       "            var ilp_req = require.config( {\n",
       "\n",
       "                paths: {\n",
       "                    \"dygraph-sync\": '/nbextensions/plotter_scripts/ILinePlot/synchronizer',\n",
       "                    \"dygraph\": '/nbextensions/plotter_scripts/ILinePlot/dygraph-combined',\n",
       "                    \"ILinePlot\": '/nbextensions/plotter_scripts/ILinePlot/ILinePlot',\n",
       "                    \"underscore\": '/nbextensions/plotter_scripts/ILinePlot/underscore-min',\n",
       "                },\n",
       "\n",
       "                shim: {\n",
       "                    \"dygraph-sync\": [\"dygraph\"],\n",
       "                    \"ILinePlot\": {\n",
       "\n",
       "                        \"deps\": [\"dygraph-sync\", \"dygraph\", \"underscore\"],\n",
       "                        \"exports\":  \"ILinePlot\"\n",
       "                    }\n",
       "                }\n",
       "            });\n",
       "                /* TRAPPY_PUBLISH_REMOVE_STOP */\n",
       "                ilp_req([\"require\", \"ILinePlot\"], function() { /* TRAPPY_PUBLISH_REMOVE_LINE */\n",
       "                ILinePlot.generate(fig_1f01fdec8fa347758094d7b73cb64817_data);\n",
       "            }); /* TRAPPY_PUBLISH_REMOVE_LINE */\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the accurate and the actual signals for the RQ\n",
    "print 'UTIL ERROR'\n",
    "trappy.ILinePlot(trace.ftrace,\n",
    "                signals = [\n",
    "                    'pelt_update:util_avg',\n",
    "                    'pelt_update:acc_util_avg',\n",
    "                ]).view()\n",
    "\n",
    "print 'LOAD ERROR'\n",
    "trappy.ILinePlot(trace.ftrace,\n",
    "                signals = [\n",
    "                    'pelt_update:load_avg',\n",
    "                    'pelt_update:acc_load_avg',\n",
    "                ]).view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data showing util/load errors on the RQ (cpu 1) when thread0 running\n",
    "-------------------------------\n",
    "Note that, as expected, the error exists only for cases where delta is < 1ms (now - last_update_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of errors: 11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/joelaf/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  app.launch_new_instance()\n",
      "/home/joelaf/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:8: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n"
     ]
    },
    {
     "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>acc_load_avg</th>\n",
       "      <th>util_avg</th>\n",
       "      <th>acc_util_avg</th>\n",
       "      <th>util_err</th>\n",
       "      <th>delta_us</th>\n",
       "      <th>load_sum</th>\n",
       "      <th>sum_err</th>\n",
       "      <th>load_avg</th>\n",
       "      <th>load_err</th>\n",
       "      <th>util_err_pc</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6.986053</th>\n",
       "      <td>thread0</td>\n",
       "      <td>91</td>\n",
       "      <td>76</td>\n",
       "      <td>89</td>\n",
       "      <td>13</td>\n",
       "      <td>45</td>\n",
       "      <td>4356860</td>\n",
       "      <td>-46080</td>\n",
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       "      <td>17.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.749162</th>\n",
       "      <td>thread0</td>\n",
       "      <td>88</td>\n",
       "      <td>75</td>\n",
       "      <td>87</td>\n",
       "      <td>12</td>\n",
       "      <td>519</td>\n",
       "      <td>4219977</td>\n",
       "      <td>-531456</td>\n",
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       "      <td>12</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.749187</th>\n",
       "      <td>thread0</td>\n",
       "      <td>88</td>\n",
       "      <td>75</td>\n",
       "      <td>87</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>4248649</td>\n",
       "      <td>-28672</td>\n",
       "      <td>76</td>\n",
       "      <td>12</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.986014</th>\n",
       "      <td>thread0</td>\n",
       "      <td>90</td>\n",
       "      <td>76</td>\n",
       "      <td>88</td>\n",
       "      <td>12</td>\n",
       "      <td>590</td>\n",
       "      <td>4310780</td>\n",
       "      <td>-604160</td>\n",
       "      <td>76</td>\n",
       "      <td>14</td>\n",
       "      <td>15.789474</td>\n",
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       "    <tr>\n",
       "      <th>4.355859</th>\n",
       "      <td>thread0</td>\n",
       "      <td>85</td>\n",
       "      <td>73</td>\n",
       "      <td>83</td>\n",
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       "      <td>4059408</td>\n",
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       "      <td>11</td>\n",
       "      <td>13.698630</td>\n",
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       "    <tr>\n",
       "      <th>4.355883</th>\n",
       "      <td>thread0</td>\n",
       "      <td>85</td>\n",
       "      <td>73</td>\n",
       "      <td>83</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>4087056</td>\n",
       "      <td>-27648</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>13.698630</td>\n",
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       "    <tr>\n",
       "      <th>6.075973</th>\n",
       "      <td>thread0</td>\n",
       "      <td>86</td>\n",
       "      <td>75</td>\n",
       "      <td>85</td>\n",
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       "      <td>4125064</td>\n",
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       "      <td>75</td>\n",
       "      <td>11</td>\n",
       "      <td>13.333333</td>\n",
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       "    <tr>\n",
       "      <th>5.982620</th>\n",
       "      <td>thread0</td>\n",
       "      <td>227</td>\n",
       "      <td>211</td>\n",
       "      <td>225</td>\n",
       "      <td>14</td>\n",
       "      <td>684</td>\n",
       "      <td>10844301</td>\n",
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       "      <td>15</td>\n",
       "      <td>6.635071</td>\n",
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       "    <tr>\n",
       "      <th>0.736533</th>\n",
       "      <td>thread0</td>\n",
       "      <td>401</td>\n",
       "      <td>380</td>\n",
       "      <td>398</td>\n",
       "      <td>18</td>\n",
       "      <td>854</td>\n",
       "      <td>19171804</td>\n",
       "      <td>-874496</td>\n",
       "      <td>383</td>\n",
       "      <td>18</td>\n",
       "      <td>4.736842</td>\n",
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       "      <th>4.983089</th>\n",
       "      <td>thread0</td>\n",
       "      <td>398</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           __comm  acc_load_avg  util_avg  acc_util_avg  util_err  delta_us  \\\n",
       "Time                                                                          \n",
       "6.986053  thread0            91        76            89        13        45   \n",
       "3.749162  thread0            88        75            87        12       519   \n",
       "3.749187  thread0            88        75            87        12        28   \n",
       "6.986014  thread0            90        76            88        12       590   \n",
       "4.355859  thread0            85        73            83        10       435   \n",
       "4.355883  thread0            85        73            83        10        27   \n",
       "6.075973  thread0            86        75            85        10        27   \n",
       "5.982620  thread0           227       211           225        14       684   \n",
       "0.736533  thread0           401       380           398        18       854   \n",
       "4.983089  thread0           398       385           396        11       545   \n",
       "4.983090  thread0           398       385           396        11         1   \n",
       "\n",
       "          load_sum  sum_err  load_avg  load_err  util_err_pc  \n",
       "Time                                                          \n",
       "6.986053   4356860   -46080        76        15    17.105263  \n",
       "3.749162   4219977  -531456        76        12    16.000000  \n",
       "3.749187   4248649   -28672        76        12    16.000000  \n",
       "6.986014   4310780  -604160        76        14    15.789474  \n",
       "4.355859   4059408  -445440        74        11    13.698630  \n",
       "4.355883   4087056   -27648        74        11    13.698630  \n",
       "6.075973   4125064   -27648        75        11    13.333333  \n",
       "5.982620  10844301  -700416       212        15     6.635071  \n",
       "0.736533  19171804  -874496       383        18     4.736842  \n",
       "4.983089  19047561  -558080       387        11     2.857143  \n",
       "4.983090  19047561        0       387        11     2.857143  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errpc_fn = (lambda row: (row['util_err'] * 100.0 / row['util_avg']))\n",
    "\n",
    "err_df = rq_df[(rq_df.util_err > 10) | (rq_df.load_err > 10)][rq_df['__comm'] == 'thread0']\n",
    "err_df = err_df[['__comm',  'acc_load_avg', 'util_avg', 'acc_util_avg', \\\n",
    "                 'util_err', 'delta_us', 'load_sum', 'sum_err', 'load_avg', 'load_err']]\n",
    "\n",
    "err_df['util_err_pc'] = err_df.apply(errpc_fn ,axis=1)\n",
    "\n",
    "err_df = err_df.sort(columns=['util_err_pc'], ascending=False)\n",
    "print 'number of errors: ' + str(len(err_df))\n",
    "\n",
    "err_df.head(40)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary of issues\n",
    "\n",
    "### * At 5.98s, there is a 6% error in util_avg 225 vs 211) - this causes a glitch and makes the signal less smooth\n",
    "----\n",
    "![glitch](pelt-signal-glitch-error-6pc.png)\n",
    "\n",
    "\n",
    "### * At 3.06s, there is a 3% error in util_avg - causing lowered peak of util_avg (397 -> 387) with delta ~450us\n",
    "![lower-peak](pelt-3percent-error.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"page-break-after:always;\"></p>\n",
    "# Histogram of Errors before and after fix\n",
    "\n",
    "## BEFORE: util_avg and load_avg occurences of errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/joelaf/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "image/png": 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PAABgBJQ/AACAEVD+AAAARkD5AwAAGAHlDwAAYASUPwAAgBFQ/gAAAEZA+QMA\nABgB5Q8AAGAElD8AAIARUP4AAABGQPkDAAAYAeUPAABgBJQ/AACAEVD+AAAARkD5AwAAGAHlDwAA\nYASUPwAAgBFQ/gAAAEZA+QMAABgB5Q8AAGAElD8AAIARUP4AAABGQPkDAAAYAeUPAABgBJQ/AACA\nEVD+AAAARkD5AwAAGAHlDwAAYASUPwAAgBFQ/gAAAEZA+QMAABgB5Q8AAGAElD8AAIARUP4AAABG\nQPkDAAAYAeUPAABgBJQ/AACAEVD+AAAARkD5AwAAGAHlDwAAYASUPwAAgBFQ/gAAAEZA+QMAABgB\n5Q8AAGAElD8AAIARUP4AAABGQPkDAAAYAeUPAABgBJQ/AACAEVD+AAAARmDJy19V7VNVH66qc6rq\n7Kp62rD+VlV1WlV9Zfh6y6XOBgAAsK2axsjfNUme2d0HJLlHkidX1QFJTkhyenfvl+T0YRkAAIAF\nsOTlr7sv6u6zhstXJjk3yV5Jjkxy8rDZyUkevtTZAAAAtlXbTfPBq2ptkl9N8qkke3b3RcNV306y\n5xy3OT7J8Uly+11r8UNuS07cdSu3v3xxcgAAAEtuaid8qaqdkvxLkqd39xUzr+vuTtKz3a67X93d\n67p73e47KH8AAADzMZXyV1WrMyl+p3T3qcPqi6vqtsP1t01yyTSyAQAAbIumcbbPSvK6JOd290tn\nXPWuJMcOl49N8s6lzgYAALCtmsYxf/dM8tgkX6iq9cO65yZ5YZK3VtXjk5yf5FFTyAYAALBNWvLy\n190fTTLXwXqHL2UWAACAsZjaCV8AAABYOsofAADACEz1c/5uqi/0HbP2qr/ZqtuctzhRAAAAljUj\nfwAAACOg/AEAAIyA8gcAADACyh8AAMAIKH8AAAAjoPwBAACMwIr+qAdG6sRdt3L7yxcnBwAArCBG\n/gAAAEZA+QMAABgB5Q8AAGAElD8AAIARUP4AAABGQPkDAAAYAeUPAABgBJQ/AACAEVD+AAAARkD5\nAwAAGAHlDwAAYASUPwAAgBFQ/gAAAEZgu2kHYOmsveqNW7X9eYsTAwAAmAIjfwAAACOg/AEAAIyA\n8gcAADACjvmDhXLirlu5/eWLk+MGj7GVmZKlyQUAwJIz8gcAADACyh8AAMAIKH8AAAAjoPwBAACM\ngPIHAAAwAsofAADACCh/AAAAI6D8AQAAjIDyBwAAMALKHwAAwAhsN+0AsLXWXvXGrdr+vMWJAQAA\nK4qRPwDx83TzAAALK0lEQVQAgBFQ/gAAAEbAtE9g+Ttx15/jNpcvfA5YrvwfAWAejPwBAACMgPIH\nAAAwAsofAADACDjmDwC2xtYeX+fYOgCWCSN/AAAAI6D8AQAAjIDyBwAAMAKO+YMFsvaqN27V9uct\nTowb2NpMydLkgnnx2XXj5JhKgEVj5A8AAGAElD8AAIARMO0TALbCcpziDQDzYeQPAABgBJQ/AACA\nEVD+AAAARsAxf8Cy5yMrxsnrPk6OqQRYPEb+AAAARkD5AwAAGAHlDwAAYAQc8wcAK5zjI0fqxF23\ncvvLFyfHTbW1zyNZvs9lrLyGK4aRPwAAgBFQ/gAAAEbAtE+An9PaE9671bc574UPWYQkm9hWpoLB\nMrFc/6/7WAyWC1PPV45lN/JXVQ+sqi9X1Ver6oRp5wEAANgWLKvyV1WrkrwiyYOSHJDk0VV1wHRT\nAQAArHzLqvwlOSzJV7v76939kyRvTnLklDMBAACseNXd087wU1X1O0ke2N2/Pyw/Nsndu/spM7Y5\nPsnxw+KBSb645EG33q2TXDrtEPMg58KSc+GshIyJnAtNzoUl58JZCRkTOReanAtrJeRcCRmT5A7d\nvft8NlxxJ3zp7lcneXWSVNUZ3b1uypG2SM6FJefCWgk5V0LGRM6FJufCknPhrISMiZwLTc6FtRJy\nroSMW2u5Tfu8IMk+M5b3HtYBAABwEyy38veZJPtV1b5VdfMkxyR515QzAQAArHjLatpnd19TVU9J\n8u9JViV5fXefvZmbvHppkt1kci4sORfWSsi5EjImci40OReWnAtnJWRM5Fxoci6slZBzJWTcKsvq\nhC8AAAAsjuU27RMAAIBFoPwBAACMwIotf1X1wKr6clV9tapOmHae2VTV66vqkqpa1p9FWFX7VNWH\nq+qcqjq7qp427Uybqqo1VfXpqvrckPHPp51pc6pqVVV9tqreM+0sc6mq86rqC1W1vqrOmHaeuVTV\nLarq7VX1pao6t6r+x7Qzbaqq7jTsx+v/XVFVT592rtlU1TOG/0NfrKo3VdWaaWfaVFU9bch39nLb\nj7P9XK+qW1XVaVX1leHrLZdhxqOG/XldVS2L05bPkfPFw//1z1fVO6rqFtPMOGSaLedfDBnXV9UH\nqup208w4ZJrzPUdVPbOquqpuPY1sm2SZbX+eWFUXzPgZ+uBpZhwyzbo/q+qpw/fo2VX1omnlm5Fn\ntv35lhn78ryqWr8MMx5cVZ+8/j1IVR02zYxDptlyHlRVnxjeL727qnaZZsaFsCLLX1WtSvKKJA9K\nckCSR1fVAdNNNauTkjxw2iHm4Zokz+zuA5LcI8mTl+H+/HGS3+jug5IcnOSBVXWPKWfanKclOXfa\nIebhft198DL/DJuXJ3l/d++f5KAsw/3a3V8e9uPBSe6W5IdJ3jHlWDdSVXsl+aMk67r7wExOrHXM\ndFPdUFUdmOQPkhyWyev90Kr6pemmuoGTcuOf6yckOb2790ty+rA8TSflxhm/mOSRST6y5GnmdlJu\nnPO0JAd2968k+e8kz1nqULM4KTfO+eLu/pXh//x7kvzpkqe6sZMyy3uOqtonyQOSfHOpA83hpMz+\n3uhl1/8c7e5/W+JMszkpm+SsqvslOTLJQd19lyQvmUKuTZ2UTXJ299Ezfif9S5JTpxFshpNy49f8\nRUn+fMj4p8PytJ2UG+d8bZITuvuumfxe/+OlDrXQVmT5y+RNwVe7++vd/ZMkb87kP+Oy0t0fSfKd\naefYku6+qLvPGi5fmcmb672mm+qGeuL7w+Lq4d+yPFtRVe2d5CGZ/MDgJqiqXZPcO8nrkqS7f9Ld\n35tuqi06PMnXuvv8aQeZw3ZJtq+q7ZLskOTCKefZ1J2TfKq7f9jd1yT5z0xKy7Iwx8/1I5OcPFw+\nOcnDlzTUJmbL2N3ndveXpxRpVnPk/MDwuifJJzP5vN+pmiPnFTMWd8wy+H20mfccL0vyrCyDjMmK\nem80W84/TPLC7v7xsM0lSx5sE5vbn1VVSR6V5E1LGmoTc2TsJNePou2aZfC7aI6cv5yf/dHstCS/\nvaShFsFKLX97JfnWjOUNWWZlZaWqqrVJfjXJp6ab5MaGqZTrk1yS5LTuXnYZB3+TyS/a66YdZAs6\nyQer6syqOn7aYeawb5KNSf5xmEb72qracdqhtuCYTPkX7Vy6+4JM/lL9zSQXJbm8uz8w3VQ38sUk\n96qq3apqhyQPTrLPlDNtyZ7dfdFw+dtJ9pxmmG3I7yV537RDzKWqXlBV30rymCyPkb8bqaojk1zQ\n3Z+bdpZ5eOowlfb10546vRm/nMnPp09V1X9W1aHTDrQF90pycXd/ZdpBZvH0JC8e/g+9JMtjlH82\nZ+dnA0xHZfn/PtqilVr+WARVtVMm0wOevslfNZeF7r52mB6wd5LDhulhy0pVPTTJJd195rSzzMOv\nD/vzQZlM9b33tAPNYrskhyT5h+7+1SQ/yPSn1M2pqm6e5Igkb5t2ltkMb6iOzKRU3y7JjlX1u9NN\ndUPdfW6S/5PkA0nen2R9kmunGmor9OTzk5bFCMtKVlV/kskhCadMO8tcuvtPunufTDI+Zdp5NjX8\n8eS5WabFdBP/kOSOmRzWcVGSv55unDltl+RWmRwi88dJ3jqMri1Xj84y/WNkJqOozxj+Dz0jwwyf\nZej3kjypqs5MsnOSn0w5z022UsvfBblh8957WMfPqapWZ1L8Tunuac8N36xh2t+HszyPp7xnkiOq\n6rxMpiP/RlW9YbqRZjeMAl0/beUdmUynXm42JNkwY5T37ZmUweXqQUnO6u6Lpx1kDr+Z5BvdvbG7\nr87kOJBfm3KmG+nu13X33br73km+m8mxX8vZxVV12yQZvk59KthKVlXHJXloksf0yvgw4lOyPKeC\n/WImf+j53PA7ae8kZ1XVbaaaahbdffHwB97rkrwmy/P3UTL5nXTqcCjKpzOZ4TP1k+jMZpja/8gk\nb5l2ljkcm58di/i2LNPXvLu/1N0P6O67ZVKkvzbtTDfVSi1/n0myX1XtO/yl/Zgk75pyphVr+KvV\n65Kc290vnXae2VTV7tef9a2qtk9y/yRfmm6qG+vu53T33t29NpPvyw9197IaWUmSqtqxqna+/nIm\nJwNYdmel7e5vJ/lWVd1pWHV4knOmGGlLlvNfWZPJdM97VNUOw//7w7MMT6BTVXsMX2+fyZuXN043\n0Ra9K5M3Mhm+vnOKWVa0qnpgJtPmj+juH047z1yqar8Zi0dmef4++kJ379Hda4ffSRuSHDL8XF1W\nrv/jyeARWYa/jwb/muR+SVJVv5zk5kkunWqiuf1mki9194ZpB5nDhUnuM1z+jSTLcWrqzN9HN0vy\nvCSvnG6im267aQf4eXT3NVX1lCT/nsnZ6l7f3WdPOdaNVNWbktw3ya2rakOSP+vu5Tisfc8kj03y\nhRmnA37uMjnb1vVum+Tk4UyvN0vy1u5eth+jsALsmeQdw2yV7ZK8sbvfP91Ic3pqklOGP/R8Pcnj\nppxnVkOJvn+SJ0w7y1y6+1NV9fYkZ2Uype6zSV493VSz+peq2i3J1UmevJxO8jPbz/UkL8xk+tfj\nk5yfyQkWpmaOjN9J8ndJdk/y3qpa392/Nb2Uc+Z8TpJfSHLa8PPpk939xKmFzJw5Hzz8Ueq6TF7z\nqWZMVs57jjn2532r6uBMpkyfl2Xwc3SOnK9P8vrhowB+kuTYaY9Ob+Z1XzbHn8+xL/8gycuHEcqr\nkkz93ANz5Nypqp48bHJqkn+cUrwFUytjRgUAAAA3xUqd9gkAAMBWUP4AAABGQPkDAAAYAeUPAABg\nBJQ/AACAEVD+AAAARkD5AwAAGIH/HxwhhzDVW9h+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31e572f0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = rq_df[(rq_df.util_err > 0) | (rq_df.load_err > 0)][rq_df['__comm'] == 'thread0']\n",
    "df = df[(df.util_err > 0) | (df.load_err > 0)]\n",
    "df = df[['util_err', 'load_err']].plot(kind='hist', figsize=(15,7), bins=60, xlim=(0, 20), xticks=range(0,20), stacked=True, title = 'Occurence of errors > 10 counts', )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AFTER fix: util_avg and load_avg occurences of errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![fixed](pelt-hist-fixed.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "_draft": {
   "nbviewer_url": "https://gist.github.com/ec38b4edb2da1ef21e2aa9b1d6c64f65"
  },
  "gist": {
   "data": {
    "description": "TraceAnalysis_TasksLatencies.ipynb",
    "public": false
   },
   "id": "ec38b4edb2da1ef21e2aa9b1d6c64f65"
  },
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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