evaluation.ipynb 138 KB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import pandas as pd\n",
    "import collections\n",
    "from operator import itemgetter\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (16,9)\n",
    "plt.rcParams.update({'figure.autolayout': True})\n",
    "from sklearn import datasets, linear_model\n",
    "\n",
    "def _evaluate_file(fileName,kind,sender=False):\n",
    "    # Remove first line, as this is the dummy line for intermittently storing data.\n",
    "    df = pd.read_csv(fileName)[1:]\n",
    "    df = df[df[\"Kind\"] == kind].drop([\"Kind\"],axis=1).set_index(\"SeqNo\")\n",
    "    if sender:\n",
    "        df.drop([\"LinkReceive_T\",\n",
    "                 \"LinkReceive_C\",\n",
    "                 \"PrrtDeliver_T\",\n",
    "                 \"PrrtDeliver_C\",\n",
    "                 \"SendFeedbackStart_T\",\n",
    "                 \"SendFeedbackStart_C\",\n",
    "                 \"SendFeedbackEnd_T\",\n",
    "                 \"SendFeedbackEnd_C\",\n",
    "                 \"DecodeStart_T\",\n",
    "                 \"DecodeStart_C\",\n",
    "                 \"DecodeEnd_T\",\n",
    "                 \"DecodeEnd_C\",\n",
    "                 \"HandlePacketStart_T\",\n",
    "                 \"HandlePacketStart_C\",\n",
    "                 \"HandlePacketEnd_T\",\n",
    "                 \"HandlePacketEnd_C\",\n",
    "                 \"CopyOutputStart_T\",\n",
    "                 \"CopyOutputStart_C\",\n",
    "                 \"CopyOutputEnd_T\",\n",
    "                 \"CopyOutputEnd_C\",\n",
    "                 \"PrrtReturnPackage_T\",\n",
    "                 \"PrrtReturnPackage_C\",\n",
    "                 \"PrrtReceivePackage_T\",\n",
    "                 \"PrrtReceivePackage_C\"\n",
    "                ],axis=1,inplace=True)\n",
    "        \n",
    "        df = df[df[\"LinkTransmitEnd_T\"] != 0] # remove empty rows\n",
    "    else:\n",
    "        df.drop([\"PrrtSendStart_T\",\n",
    "                 \"PrrtSendStart_C\",\n",
    "                 \"PrrtSendEnd_T\",\n",
    "                 \"PrrtSendEnd_C\",\n",
    "                 \"PrrtSubmitPackage_T\",\n",
    "                 \"PrrtSubmitPackage_C\",\n",
    "                 \"PrrtEncodeStart_T\",\n",
    "                 \"PrrtEncodeStart_C\",\n",
    "                 \"PrrtEncodeEnd_T\",\n",
    "                 \"PrrtEncodeEnd_C\",\n",
    "                 \"PrrtTransmitStart_T\",\n",
    "                 \"PrrtTransmitStart_C\",\n",
    "                 \"PrrtTransmitEnd_T\",\n",
    "                 \"PrrtTransmitEnd_C\",\n",
    "                 \"LinkTransmitStart_T\",\n",
    "                 \"LinkTransmitStart_C\",\n",
    "                 \"LinkTransmitEnd_T\",\n",
    "                 \"LinkTransmitEnd_C\",\n",
    "                ],axis=1,inplace=True)\n",
    "        df = df[df[\"LinkReceive_T\"] != 0] # remove empty rows\n",
    "    return df\n",
    "\n",
    "def _restore_timestamp(df, column, cycle_time, base_c, base_t):\n",
    "    df[column + \"_T\"] = ((df[column + \"_C\"] - base_c) * cycle_time + base_t).astype(int)\n",
    "\n",
    "def _diff_t_c(df, name, start, stop):\n",
    "    time = df[stop + \"_T\"] - df[start + \"_T\"]\n",
    "    cycles = (df[stop + \"_C\"] - df[start + \"_C\"])\n",
    "    return (time.astype(float), cycles.astype(float))\n",
    "    \n",
    "def _generate_processing_durations(df, name, start, stop):\n",
    "    time, cycles = _diff_t_c(df, name, start, stop)\n",
    "    df[name + \"TotalTime\"] = time\n",
    "    df[name + \"TotalCycles\"] = cycles\n",
    "\n",
    "def _generate_cycle_time(df, name, start, stop):\n",
    "    time, cycles = _diff_t_c(df, name, start, stop)\n",
    "    df[name + \"CycleTime\"] =  time / cycles\n",
    "        \n",
    "def _generate_duration(df, name, start, stop, cycleTimeColumn):\n",
    "    diff = df[stop + \"_C\"] - df[start + \"_C\"]\n",
    "    df[name + \"Cycles\"] = diff\n",
    "    df[name + \"Time\"] = diff * df[cycleTimeColumn]\n",
    "        \n",
    "def evaluate(sender_file, receiver_file, kind=0):\n",
    "    df1 = _evaluate_file(sender_file,kind,True)\n",
    "    df2 = _evaluate_file(receiver_file,kind)\n",
    "    df = df1.join(df2)\n",
    "        \n",
    "    # Processing Times and Cycle Durations\n",
    "    _generate_processing_durations(df, \"Sender\", \"PrrtSendStart\", \"LinkTransmitEnd\")\n",
    "    _generate_cycle_time(df, \"Sender\", \"PrrtSendStart\", \"LinkTransmitEnd\")\n",
    "    \n",
    "    _generate_processing_durations(df, \"Receiver\", \"LinkReceive\", \"PrrtDeliver\")\n",
    "    _generate_cycle_time(df, \"Receiver\", \"PrrtReceivePackage\", \"PrrtDeliver\")\n",
    "\n",
    "    df[\"ChannelTime\"] = df[\"LinkReceive_T\"] - df[\"LinkTransmitEnd_T\"]\n",
    "    df[\"EndToEndTime\"] = df[\"SenderTotalTime\"] + df[\"ReceiverTotalTime\"]\n",
    "\n",
    "    \n",
    "    # Correlate Receiver Times with Sender Times\n",
    "    df[\"LinkReceive_T\"] -= df[\"ChannelTime\"]\n",
    "    df[\"PrrtReceivePackage_T\"] -= df[\"ChannelTime\"]\n",
    "    df[\"PrrtDeliver_T\"] -= df[\"ChannelTime\"]\n",
    "\n",
    "    \n",
    "    # Durations\n",
    "    _generate_duration(df, \"Send\", \"PrrtSendStart\", \"PrrtSendEnd\", \"SenderCycleTime\")\n",
    "    _generate_duration(df, \"PrrtTransmit\", \"PrrtTransmitStart\", \"PrrtTransmitEnd\", \"SenderCycleTime\")\n",
    "    _generate_duration(df, \"LinkTransmit\", \"LinkTransmitStart\", \"LinkTransmitEnd\", \"SenderCycleTime\")\n",
    "    _generate_duration(df, \"Submit\", \"PrrtSendStart\", \"PrrtSubmitPackage\", \"SenderCycleTime\")\n",
    "    _generate_duration(df, \"Enqueue\", \"PrrtSubmitPackage\", \"PrrtSendEnd\", \"SenderCycleTime\")\n",
    "    _generate_duration(df, \"SenderIPC\", \"PrrtSubmitPackage\", \"PrrtTransmitStart\", \"SenderCycleTime\")\n",
    "    _generate_duration(df, \"SenderEnqueued\", \"PrrtSendEnd\", \"LinkTransmitStart\", \"SenderCycleTime\")\n",
    "    _generate_duration(df, \"Encoding\", \"PrrtEncodeStart\", \"PrrtEncodeEnd\", \"SenderCycleTime\")\n",
    "    \n",
    "    _generate_duration(df, \"ReceiverIPC\", \"PrrtReturnPackage\", \"PrrtReceivePackage\", \"ReceiverCycleTime\")\n",
    "    _generate_duration(df, \"HandlePacket\", \"HandlePacketStart\", \"HandlePacketEnd\", \"ReceiverCycleTime\")\n",
    "    _generate_duration(df, \"Feedback\", \"SendFeedbackStart\", \"SendFeedbackEnd\", \"ReceiverCycleTime\")\n",
    "    _generate_duration(df, \"Decoding\", \"DecodeStart\", \"DecodeEnd\", \"ReceiverCycleTime\")\n",
    "        \n",
    "    # Recreate missing timestamps from cycles\n",
    "    senderStamps = [\"LinkTransmitStart\",\n",
    "                   \"PrrtSubmitPackage\",\n",
    "                   \"PrrtTransmitStart\",\n",
    "                   \"PrrtTransmitEnd\",\n",
    "                   \"PrrtEncodeStart\",\n",
    "                   \"PrrtEncodeEnd\"]\n",
    "\n",
    "    for stamp in senderStamps:\n",
    "        _restore_timestamp(df, stamp, df[\"SenderCycleTime\"], df[\"PrrtSendStart_C\"], df[\"PrrtSendStart_T\"])\n",
    "        \n",
    "    receiverStamps = [\"DecodeStart\",\n",
    "                      \"DecodeEnd\",\n",
    "                      \"SendFeedbackStart\",\n",
    "                      \"SendFeedbackEnd\",\n",
    "                      \"HandlePacketStart\",\n",
    "                      \"HandlePacketEnd\",\n",
    "                      \"PrrtReturnPackage\"]\n",
    "    \n",
    "    for stamp in receiverStamps:\n",
    "        _restore_timestamp(df, stamp, df[\"ReceiverCycleTime\"], df[\"LinkReceive_C\"], df[\"LinkReceive_T\"])\n",
    "        \n",
    "    return df\n",
    "\n",
    "def hist(df):\n",
    "    return df.hist(cumulative=True, normed=1,bins=200)\n",
    "\n",
    "def scatter(df,column):\n",
    "    plt.scatter(df.index,df[column],grid=True)\n",
    "\n",
    "def regress(df,column):\n",
    "    x = df.index.values.reshape(-1,1)\n",
    "    y = df[column].values\n",
    "\n",
    "    model = linear_model.LinearRegression()\n",
    "    model.fit(x,y)\n",
    "    print(\"R-Score:\", model.score(x,y))\n",
    "    plt.scatter(x,y)\n",
    "    plt.grid()\n",
    "    plt.plot(x,model.predict(x),color=\"red\",linewidth=3)\n",
    "    \n",
    "def trace(df,title):\n",
    "    fig, ax = plt.subplots(figsize=(8, 4.5))\n",
    "    plt.grid()\n",
    "\n",
    "    base = df[\"PrrtSendStart_T\"]\n",
    "    \n",
    "    sender_color = \"#AAAAAA\"\n",
    "    receiver_color = \"#888888\"\n",
    "    \n",
    "    series = np.transpose(np.array([\n",
    "        [\"PrrtSendStart_T\", \"PrrtDeliver_T\", \"black\", \"EndToEnd\"],\n",
    "        [\"PrrtSendStart_T\", \"LinkTransmitEnd_T\", sender_color, \"SenderTotal\"],\n",
    "        [\"PrrtSendStart_T\", \"PrrtSendEnd_T\", sender_color, \"Send\"],\n",
    "        [\"PrrtSendStart_T\", \"PrrtSubmitPackage_T\", sender_color, \"Submit\"],\n",
    "        [\"PrrtSubmitPackage_T\", \"PrrtTransmitStart_T\", sender_color, \"SenderIPC\"],\n",
    "        [\"PrrtSubmitPackage_T\", \"PrrtSendEnd_T\", sender_color, \"Enqueue\"],\n",
    "        [\"PrrtSendEnd_T\", \"LinkTransmitStart_T\", sender_color, \"SenderEnqueued\"],\n",
    "        [\"PrrtTransmitStart_T\", \"PrrtTransmitEnd_T\", sender_color, \"PrrtTransmit\"],\n",
    "        [\"LinkTransmitStart_T\", \"LinkTransmitEnd_T\", sender_color, \"LinkTransmit\"],\n",
    "        [\"LinkReceive_T\", \"PrrtDeliver_T\", receiver_color, \"ReceiverTotal\"],\n",
    "        #[\"DecodeStart_T\", \"DecodeEnd_T\", receiver_color, \"Decoding\"],\n",
    "        [\"HandlePacketStart_T\", \"HandlePacketEnd_T\", receiver_color, \"HandlePacket\"],\n",
    "        [\"PrrtReturnPackage_T\", \"PrrtReceivePackage_T\", receiver_color, \"ReceiverIPC\"],\n",
    "        [\"SendFeedbackStart_T\", \"SendFeedbackEnd_T\", receiver_color, \"Feedback\"],\n",
    "    ]))\n",
    "    n = series.shape[1]\n",
    "    starts = df[series[0]] - base\n",
    "    ends = df[series[1]] - base\n",
    "    plt.hlines(range(n), starts, ends, series[2],linewidths=[5])\n",
    "    plt.xlabel(\"Time [us]\")\n",
    "    fig.canvas.draw()\n",
    "\n",
    "    ax.set_yticklabels(series[3])\n",
    "    ax.yaxis.set_ticks(np.arange(0, n, 1))\n",
    "    \n",
    "    plt.savefig(title)\n",
    "    plt.show()\n",
    "    \n",
    "def box(df_data,title):\n",
    "    ax = df_data.plot.box(vert=False,grid=True)\n",
    "    fig=ax.get_figure()\n",
    "    ax.set_yticklabels(list(map(lambda x: x.get_text().replace(\"Time\", \"\"), ax.get_yticklabels())))\n",
    "    plt.xlabel(\"Time [us]\")\n",
    "    fig.set_size_inches(8, 4.5, forward=True)\n",
    "    fig.savefig(title)\n",
    "    \n",
    "def describe_table(df):\n",
    "    stats = df.describe()\n",
    "    stats.drop([\"count\"],inplace=True)\n",
    "    stats.columns = list(map(lambda x: x.replace(\"Time\", \"\"), stats.columns))\n",
    "    table = stats.to_latex(float_format=lambda x: \"%.3f\" % x)\n",
    "    print(table)\n",
    "    return stats\n",
    "\n",
    "def get_outlier_treshold(stats):\n",
    "    q75 = stats[\"75%\"]\n",
    "    iqr = q75 - stats[\"25%\"]\n",
    "    return q75 + 1.5 * iqr\n",
    "\n",
    "def _jitter_causes(df,title=\"JitterCause.pdf\"):\n",
    "    stats = df[\"EndToEndTime\"].describe()\n",
    "    tresh = get_outlier_treshold(stats)\n",
    "    outliers = df[df[\"EndToEndTime\"] > tresh]\n",
    "\n",
    "    reasons = [\"SendTime\",\n",
    "               \"PrrtTransmitTime\",\n",
    "               \"LinkTransmitTime\",\n",
    "               \"SubmitTime\",\n",
    "               \"SenderIPCTime\",\n",
    "               \"SenderEnqueuedTime\",\n",
    "               #\"EncodingTime\",\n",
    "               \"EnqueueTime\",\n",
    "               \"DecodingTime\",\n",
    "               \"HandlePacketTime\",\n",
    "               \"ReceiverIPCTime\",\n",
    "               \"FeedbackTime\"]\n",
    "    \n",
    "    df_reasons = pd.DataFrame(index=outliers.index)\n",
    "\n",
    "    for r in reasons:\n",
    "        r_tresh = get_outlier_treshold(df[r].describe())\n",
    "        df_reasons[r] = 0\n",
    "        df_reasons[r] = outliers[outliers[r] > r_tresh].notnull()\n",
    "    \n",
    "    \n",
    "    df_sum = df_reasons.sum().sort_values(ascending=False)\n",
    "    ax = df_sum.plot.bar(x=\"Reason\",y=\"Frequency\",rot=45,grid=True,legend=False,color=\"black\")\n",
    "    fig=ax.get_figure()\n",
    "    plt.ylabel(\"Frequency\")\n",
    "    ax.set_xticklabels(list(map(lambda x: x.get_text().replace(\"Time\", \"\"), ax.get_xticklabels())))\n",
    "    fig.set_size_inches(8, 3, forward=True)\n",
    "    fig.savefig(title)\n",
    "    print(\"Outliers:\",len(outliers),\";\",\"Threshold[us]:\",tresh)\n",
    "\n",
    "def jitter_analysis(df_data):\n",
    "    df_box = df_data[[\"EndToEndTime\",\n",
    "         \"SenderTotalTime\",\n",
    "         \"SendTime\",\n",
    "         \"SubmitTime\",\n",
    "         \"EnqueueTime\",\n",
    "         \"SenderIPCTime\",\n",
    "         \"SenderEnqueuedTime\",\n",
    "         \"PrrtTransmitTime\",\n",
    "         \"LinkTransmitTime\",\n",
    "         \"ReceiverTotalTime\",\n",
    "         \"ReceiverIPCTime\",\n",
    "         \"DecodingTime\",\n",
    "         \"HandlePacketTime\",\n",
    "         \"FeedbackTime\",\n",
    "    ]]\n",
    "    thresh = get_outlier_treshold(df_box[\"EndToEndTime\"].describe())\n",
    "    df_no = df_box[df_box[\"EndToEndTime\"] <= thresh]\n",
    "    box(df_no, \"TraceJitter.pdf\")\n",
    "    print(\"No of non-outliers:\",len(df_no))\n",
    "    plt.show()\n",
    "    _jitter_causes(df_box)\n",
    "    \n",
    "def correlation(df_data,title=\"Correlation.pdf\"):\n",
    "    columns = list([\"SenderTotalTime\",\n",
    "                    \"SendTime\",\n",
    "                    #\"SubmitTime\",\n",
    "                    \"SenderIPCTime\",\n",
    "                    #\"EnqueueTime\",\n",
    "                    #\"PrrtTransmitTime\",\n",
    "                    \"LinkTransmitTime\",\n",
    "                    \"ReceiverTotalTime\",\n",
    "                    \"ReceiverIPCTime\",\n",
    "                    \"HandlePacketTime\",\n",
    "                    \"FeedbackTime\",\n",
    "                    #\"DecodingTime\",\n",
    "                    ])\n",
    "    \n",
    "    cols=4\n",
    "    rows=math.ceil(len(columns) / cols)\n",
    "    fig, axes = plt.subplots(nrows=rows, ncols=cols)\n",
    "    fig.set_size_inches(4*cols, 3.5*rows, forward=True)\n",
    "\n",
    "    i = 0\n",
    "    for column in columns:\n",
    "        ax = df_data.plot.scatter(ax=axes[i//cols,i % cols],y=\"EndToEndTime\",x=column,grid=True,marker=\"+\",color=\"black\")\n",
    "        fig2 = ax.get_figure()\n",
    "        ax.set_ylabel(\"EndToEnd [us]\")\n",
    "        ax.margins(0.1,0.1)\n",
    "        ax.set_xlabel(\"{} [us]\".format(column.replace(\"Time\", \"\")))\n",
    "        i += 1\n",
    "    fig.savefig(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df_data = evaluate(\"results/on/2017_03_28_09_33_00_Sender.csv\",\n",
    "                    \"results/on/2017_03_28_09_33_00_Receiver.csv\",kind=0)\n",
    "df_data = df_data[df_data[\"EndToEndTime\"] < 175]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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KsUtn4z958uSyQ5EkHT16dOT51taWTp06JWl0nJQDBw7o7ne/+3DdR6c7cOCA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\nnUWFt0InJwCLaUpdB0iyuro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aHJUyR9vdXl9z2d6WGcHj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N9V1Zvt+zQ493zl559X1d1wKs1+AA8EcL+qy2ATkccCuFFVr66zHDFWVPVnATwawXC9h9l31vA3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\nISIiIiIiIiJquP8P0dVhOE0VpywAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f611c360710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation(df_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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Lu9WbCcwEqKuro1AolBFK+drb2/vss6Ojo9frra2ttLW1VTCqfChnbm3gPL/V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\nnf8P2BODDpQTs08AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60d9bbc320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "element = df_data[df_data[\"DecodingTime\"] == df_data[\"DecodingTime\"].median()].iloc[0]\n",
    "trace(element, \"PacketTrace.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Jitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of non-outliers: 4018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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QV1dHIpEYZYql6enpKbrPzBuP54Upyzx27NiBJAYGBgBYtGgRyWSS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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60d95f3240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers: 67 ; Threshold[us]: 108.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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4nhpmL5Yx8QpKiYUydpxb5O5PJibUMmWZnGyMIC1lK5Kuti+V19YmY/Q+04Py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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60d9419128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jitter_analysis(df_data)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}