benchmarks-summary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "FIG_WIDTH=15\n",
    "FIG_HEIGTH=8\n",
    "\n",
    "def setup_fig():\n",
    "    fig, _ = plt.subplots()\n",
    "    fig.set_figwidth(FIG_WIDTH)\n",
    "    fig.set_figheight(FIG_HEIGTH)\n",
    "    fig.patch.set_alpha(1.0)\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Latency tests\n",
    "\n",
    "## Debian Xorg + GNOME tests\n",
    "\n",
    "Those tests were run in my regular workstation, using GNOME + Xorg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                |   count |    mean |      std |   min |     25% |    50% |     75% |   max |\n",
      "|:---------------|--------:|--------:|---------:|------:|--------:|-------:|--------:|------:|\n",
      "| mlterm         |     200 | 28.8349 | 6.23246  | 15.16 | 31.235  | 31.665 | 32.0425 | 32.65 |\n",
      "| uxterm         |     200 | 29.8086 | 5.01335  | 15.39 | 31.25   | 31.525 | 31.9025 | 32.5  |\n",
      "| konsole        |     199 | 31.7569 | 1.20074  | 16.22 | 31.52   | 31.8   | 32.12   | 35.07 |\n",
      "| alacritty      |     200 | 31.7943 | 0.560434 | 27.24 | 31.45   | 31.82  | 32.1625 | 33.11 |\n",
      "| st             |     200 | 33.5791 | 5.03363  | 30.66 | 31.64   | 31.975 | 32.3225 | 49.33 |\n",
      "| urxvt          |     200 | 47.4618 | 4.18906  | 31.81 | 48.245  | 48.58  | 49.0025 | 49.7  |\n",
      "| pterm          |     200 | 48.2193 | 2.63108  | 31.24 | 48.23   | 48.595 | 48.96   | 49.76 |\n",
      "| gnome-terminal |     200 | 48.303  | 2.40031  | 32.18 | 48.2675 | 48.635 | 48.9525 | 55.12 |\n",
      "| xfce4-terminal |     200 | 48.3829 | 2.00524  | 32.15 | 48.32   | 48.625 | 48.89   | 49.63 |\n",
      "| terminator     |     200 | 48.4853 | 2.69001  | 32.19 | 48.2375 | 48.51  | 48.8625 | 67.35 |"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": [
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hmDty5Ig2btx4SRtdvXq14uPj9ZOf/MR7W1hYmLdd2QVUGjWqq8jIiEvaNwAA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\nQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb84aa6358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = setup_fig()\n",
    "\n",
    "latency = pandas.read_csv('latency/debian9-xorg.csv')\n",
    "# remove weird outlier in konsole\n",
    "latency = latency.apply(lambda x: x[(x<100)], axis=0)\n",
    "latency = latency.reindex_axis(latency.mean().sort_values().index, axis=1)\n",
    "ax = sns.boxplot(data=latency, palette='colorblind')\n",
    "ax.set_title('Terminal input latency')\n",
    "ax.set_xlabel('terminal')\n",
    "ax.set_ylabel('latency (ms)')\n",
    "\n",
    "from tabulate import tabulate\n",
    "print(tabulate(latency.describe().transpose(), headers='keys', tablefmt='pipe'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Debian Xorg + i3\n",
    "\n",
    "Those were run using a simpler session using the i3 window manager."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdb845f5358>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": [
       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PMEoDAAAAAFtwuWJrfC5owyzvuOMOPfLII5Kko0ePqkOHDtq2bZv69esnSerTp48+/fTT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\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdbb0493dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = setup_fig()\n",
    "\n",
    "latency = pandas.read_csv('latency/debian9-xorg-i3.csv')\n",
    "latency.drop([\"urxvt-xft-monospace\", \"urxvt-fixed-16\", \"urxvt-fixed-16-float\"], axis=1, inplace=True)\n",
    "\n",
    "latency = latency.reindex_axis(latency.mean().sort_values().index, axis=1)\n",
    "\n",
    "ax = sns.boxplot(data=latency, palette='colorblind')\n",
    "ax.set_title('Terminal input latency')\n",
    "ax.set_xlabel('terminal')\n",
    "ax.set_ylabel('latency (ms)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Raw data for editors + Debian / i3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                |   count |   mean |   std |   min |   50% |   90% |   max |\n",
      "|:---------------|--------:|-------:|------:|------:|------:|------:|------:|\n",
      "| uxterm         |     200 |    1.7 |   0.3 |   0.7 |   1.7 |   2   |   2.4 |\n",
      "| mlterm         |     200 |    1.8 |   0.3 |   0.7 |   1.9 |   2.2 |   2.5 |\n",
      "| vim.athena     |     200 |    2.8 |   1.1 |   0.4 |   2.9 |   3.5 |  12.7 |\n",
      "| vim.gtk        |     200 |    3.9 |   1.2 |   0.7 |   4.2 |   4.8 |  11.9 |\n",
      "| emacs -q       |     200 |    4.8 |   2.3 |   0.5 |   4.9 |   5.8 |  32.5 |\n",
      "| gedit          |     200 |    8.9 |   3.4 |   2.8 |   9.8 |  12.5 |  14.2 |\n",
      "| konsole        |     200 |   13.4 |   1.2 |  11.5 |  13.3 |  15   |  16.1 |\n",
      "| alacritty      |     200 |   15.1 |   1.2 |  12.8 |  15.1 |  15.9 |  26.3 |\n",
      "| st             |     200 |   15.7 |   3.9 |  10.6 |  18.8 |  19.4 |  19.6 |\n",
      "| vim.gtk3       |     200 |   16.5 |   7.9 |   0.4 |  20.6 |  21.9 |  27.2 |\n",
      "| urxvt          |     200 |   18.3 |   0.3 |  17.3 |  18.3 |  18.7 |  19   |\n",
      "| pterm          |     200 |   23.4 |   0.9 |  21.7 |  23.5 |  24.5 |  25.4 |\n",
      "| gnome-terminal |     200 |   27.1 |   1   |  25.9 |  27   |  27.5 |  39.3 |\n",
      "| xfce4-terminal |     200 |   27.4 |   0.4 |  26.4 |  27.4 |  27.9 |  28.7 |\n",
      "| terminator     |     200 |   28.1 |   0.7 |  26.4 |  28.1 |  29   |  29.4 |\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdb84754cf8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": [
       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REhMTJUmrVq1q9nXbt29XcXGxMjIygo/98pe/1IMPPqiNGzeqd+/eGj9+/JnW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\nYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb845f50b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "latency = pandas.concat([latency, pandas.read_csv('editors.csv')])\n",
    "\n",
    "latency.drop(\"emacs\", axis=1, inplace=True)\n",
    "latency = latency.reindex_axis(latency.mean().sort_values().index, axis=1)\n",
    "\n",
    "from tabulate import tabulate\n",
    "print(tabulate(latency.describe(percentiles=[0.9]).round(1).transpose(), headers='keys', tablefmt='pipe'))\n",
    "\n",
    "fig = setup_fig()\n",
    "\n",
    "ax = sns.boxplot(data=latency, palette='colorblind')\n",
    "ax.set_title('Terminal input latency')\n",
    "ax.set_xlabel('terminal')\n",
    "ax.set_ylabel('latency (ms)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Fedora GNOME + Xorg\n",
    "\n",
    "Those tests were ran on Fedora 27 with the default GNOME + Xorg session. The tests crash completely in Wayland."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                |   count |    mean |     std |   min |     25% |    50% |     75% |   max |\n",
      "|:---------------|--------:|--------:|--------:|------:|--------:|-------:|--------:|------:|\n",
      "| mlterm         |     200 | 16.732  | 6.52684 |  8.2  | 12.66   | 13.36  | 17.6    | 30.9  |\n",
      "| uxterm         |     200 | 28.9527 | 3.73927 | 11.93 | 28.3675 | 29.49  | 30.63   | 37.59 |\n",
      "| konsole        |     200 | 29.8188 | 2.14301 | 21.95 | 28.5575 | 29.75  | 30.725  | 41.72 |\n",
      "| st             |     200 | 30.4384 | 3.81713 | 21.99 | 28.345  | 29.875 | 31.7525 | 46.11 |\n",
      "| pterm          |     200 | 42.1507 | 5.62529 | 29.58 | 34.2225 | 44.835 | 46.08   | 48.09 |\n",
      "| urxvt          |     200 | 43.8995 | 4.9363  | 29.68 | 44.205  | 45.59  | 46.7625 | 50.7  |\n",
      "| terminology    |     200 | 44.3843 | 4.80309 | 29.76 | 44.6625 | 46     | 46.8125 | 57.95 |\n",
      "| gnome-terminal |     200 | 45.9754 | 1.94185 | 35.14 | 45.505  | 46.345 | 46.8225 | 53.88 |\n",
      "| xfce4-terminal |     200 | 46.1249 | 1.85098 | 35.16 | 45.83   | 46.455 | 46.9925 | 48.64 |\n",
      "| terminator     |     200 | 46.189  | 1.71753 | 36.39 | 45.8475 | 46.425 | 47.115  | 48.45 |\n"
     ]
    },
    {
     "data": {
      "image/png": [
       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EmAMAAAAAByLMAQAAAIADEeYAAAAAwIHCtjUBAAAAgDrsKYlwIMwBAAAADsCekt+ySzi2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\nIUQ3JIs5IYQQQgghhOiG/gfCHYWXVy/BtwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb847ba400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = setup_fig()\n",
    "\n",
    "latency = pandas.read_csv('latency/fedora27-xorg.csv')\n",
    "latency = latency.reindex_axis(latency.mean().sort_values().index, axis=1)\n",
    "\n",
    "ax = sns.boxplot(data=latency, palette='colorblind')\n",
    "ax.set_title('Terminal input latency')\n",
    "ax.set_xlabel('terminal')\n",
    "ax.set_ylabel('latency (ms)')\n",
    "\n",
    "print(tabulate(latency.describe().transpose(), headers='keys', tablefmt='pipe'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Resource tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "deb9_xorg = pandas.read_csv('debian9-xorg-prime-100x100000.csv').assign(env='Debian 9, Xorg')\n",
    "f27_xorg = pandas.read_csv('fedora27-xorg-prime-100x100000.csv').assign(env='Fedora 27, Xorg')\n",
    "f27_wayland = pandas.read_csv('fedora27-wayland-prime-100x100000.csv').assign(env='Fedora 27, Wayland')\n",
    "\n",
    "data = pandas.concat([deb9_xorg, f27_xorg, f27_wayland])\n",
    "\n",
    "# rename debian name to more standard name also used in fedora\n",
    "data['terminal'].replace(['stterm'], 'st',inplace=True)\n",
    "\n",
    "# massage I/O data into a separate field\n",
    "data['blocks'] = data['ru_inblock'] + data['ru_oublock']\n",
    "\n",
    "# https://lkml.org/lkml/2007/3/19/84\n",
    "# \"block size\" is apparently hardcoded to 512 bytes\n",
    "data['out_bytes'] = data['ru_oublock'] * 512"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|                             |        out_bytes |   ru_maxrss |   ru_stime |   ru_utime |   wtime |\n",
      "|:----------------------------|-----------------:|------------:|-----------:|-----------:|--------:|\n",
      "| ('alacritty', 'count')      |    100           |      100    |     100    |     100    |  100    |\n",
      "| ('alacritty', 'mean')       |      0           |    29032.9  |       0.32 |       0.46 |    0.51 |\n",
      "| ('alacritty', 'std')        |      0           |      138.18 |       0.02 |       0.03 |    0.01 |\n",
      "| ('alacritty', 'min')        |      0           |    28688    |       0.27 |       0.39 |    0.5  |\n",
      "| ('alacritty', '50%')        |      0           |    29072    |       0.32 |       0.46 |    0.51 |\n",
      "| ('alacritty', '90%')        |      0           |    29157.2  |       0.36 |       0.49 |    0.51 |\n",
      "| ('alacritty', 'max')        |      0           |    29276    |       0.4  |       0.52 |    0.53 |\n",
      "| ('konsole', 'count')        |    300           |      300    |     300    |     300    |  300    |\n",
      "| ('konsole', 'mean')         |  84855.5         |    67052.1  |       0.25 |       0.79 |    0.8  |\n",
      "| ('konsole', 'std')          | 111980           |     2397    |       0.03 |       0.06 |    0.1  |\n",
      "| ('konsole', 'min')          |   8192           |    62804    |       0.16 |       0.64 |    0.63 |\n",
      "| ('konsole', '50%')          |  81920           |    68462    |       0.26 |       0.81 |    0.84 |\n",
      "| ('konsole', '90%')          | 135168           |    69153.2  |       0.28 |       0.85 |    0.89 |\n",
      "| ('konsole', 'max')          |      1.76947e+06 |    69344    |       0.3  |       0.9  |    1.01 |\n",
      "| ('mlterm', 'count')         |    300           |      300    |     300    |     300    |  300    |\n",
      "| ('mlterm', 'mean')          |   5515.95        |    11194.4  |       0.24 |       1.3  |    1.22 |\n",
      "| ('mlterm', 'std')           |   8025.74        |      181.41 |       0.02 |       0.2  |    0.21 |\n",
      "| ('mlterm', 'min')           |   4096           |    10868    |       0.16 |       0.98 |    0.9  |\n",
      "| ('mlterm', '50%')           |   4096           |    11144    |       0.24 |       1.42 |    1.35 |\n",
      "| ('mlterm', '90%')           |   4096           |    11444    |       0.26 |       1.48 |    1.41 |\n",
      "| ('mlterm', 'max')           |  53248           |    11652    |       0.28 |       1.56 |    1.51 |\n",
      "| ('pterm', 'count')          |    300           |      300    |     300    |     300    |  300    |\n",
      "| ('pterm', 'mean')           |     13.65        |    20495.5  |       0.23 |       1.02 |    0.94 |\n",
      "| ('pterm', 'std')            |    236.48        |      688.35 |       0.02 |       0.02 |    0.01 |\n",
      "| ('pterm', 'min')            |      0           |    19652    |       0.17 |       0.96 |    0.91 |\n",
      "| ('pterm', '50%')            |      0           |    20198    |       0.23 |       1.02 |    0.94 |\n",
      "| ('pterm', '90%')            |      0           |    21537.6  |       0.25 |       1.05 |    0.96 |\n",
      "| ('pterm', 'max')            |   4096           |    21728    |       0.27 |       1.1  |    1    |\n",
      "| ('st', 'count')             |    400           |      400    |     400    |     400    |  400    |\n",
      "| ('st', 'mean')              |      0           |     8546.45 |       0.23 |       0.43 |    0.38 |\n",
      "| ('st', 'std')               |      0           |      543.25 |       0.02 |       0.02 |    0.02 |\n",
      "| ('st', 'min')               |      0           |     7972    |       0.14 |       0.36 |    0.34 |\n",
      "| ('st', '50%')               |      0           |     8564    |       0.23 |       0.42 |    0.38 |\n",
      "| ('st', '90%')               |      0           |     9088    |       0.26 |       0.46 |    0.4  |\n",
      "| ('st', 'max')               |      0           |     9092    |       0.28 |       0.5  |    0.43 |\n",
      "| ('terminator', 'count')     |    300           |      300    |     300    |     300    |  300    |\n",
      "| ('terminator', 'mean')      | 202069           |    58244.8  |       0.43 |       0.92 |    1.14 |\n",
      "| ('terminator', 'std')       | 286247           |     1514.5  |       0.03 |       0.04 |    0.04 |\n",
      "| ('terminator', 'min')       |      0           |    56224    |       0.34 |       0.8  |    1.07 |\n",
      "| ('terminator', '50%')       |      0           |    58048    |       0.44 |       0.92 |    1.14 |\n",
      "| ('terminator', '90%')       | 606208           |    60792    |       0.46 |       0.97 |    1.18 |\n",
      "| ('terminator', 'max')       | 606208           |    61420    |       0.51 |       1.01 |    1.2  |\n",
      "| ('terminology', 'count')    |    100           |      100    |     100    |     100    |  100    |\n",
      "| ('terminology', 'mean')     |      0           |    39741    |       0.25 |       0.91 |    0.95 |\n",
      "| ('terminology', 'std')      |      0           |      170.98 |       0.01 |       0.02 |    0.02 |\n",
      "| ('terminology', 'min')      |      0           |    39452    |       0.23 |       0.86 |    0.9  |\n",
      "| ('terminology', '50%')      |      0           |    39716    |       0.25 |       0.91 |    0.95 |\n",
      "| ('terminology', '90%')      |      0           |    39916.4  |       0.26 |       0.94 |    0.97 |\n",
      "| ('terminology', 'max')      |      0           |    40520    |       0.28 |       0.98 |    1    |\n",
      "| ('urxvt', 'count')          |    300           |      300    |     300    |     300    |  300    |\n",
      "| ('urxvt', 'mean')           |      0           |    14473.2  |       0.31 |       0.36 |    0.38 |\n",
      "| ('urxvt', 'std')            |      0           |      514.3  |       0.02 |       0.02 |    0.01 |\n",
      "| ('urxvt', 'min')            |      0           |    13536    |       0.24 |       0.31 |    0.36 |\n",
      "| ('urxvt', '50%')            |      0           |    14746    |       0.31 |       0.36 |    0.38 |\n",
      "| ('urxvt', '90%')            |      0           |    14928.4  |       0.34 |       0.38 |    0.4  |\n",
      "| ('urxvt', 'max')            |      0           |    15012    |       0.36 |       0.42 |    0.41 |\n",
      "| ('uxterm', 'count')         |    300           |      300    |     300    |     300    |  300    |\n",
      "| ('uxterm', 'mean')          |   1365.33        |    12297.3  |       0.65 |       0.99 |    1.76 |\n",
      "| ('uxterm', 'std')           |   1934.1         |      247.81 |       0.06 |       0.46 |    0.98 |\n",
      "| ('uxterm', 'min')           |      0           |    11540    |       0.53 |       0.86 |    1.25 |\n",
      "| ('uxterm', '50%')           |      0           |    12184    |       0.65 |       0.94 |    1.32 |\n",
      "| ('uxterm', '90%')           |   4096           |    12664.4  |       0.68 |       1.01 |    2.44 |\n",
      "| ('uxterm', 'max')           |   4096           |    12756    |       1.15 |       5.68 |    9.99 |\n",
      "| ('xfce4-terminal', 'count') |    300           |      300    |     300    |     300    |  300    |\n",
      "| ('xfce4-terminal', 'mean')  | 202001           |    40117.7  |       0.4  |       0.76 |    0.94 |\n",
      "| ('xfce4-terminal', 'std')   | 286150           |     2233.5  |       0.02 |       0.04 |    0.04 |\n",
      "| ('xfce4-terminal', 'min')   |      0           |    36844    |       0.33 |       0.67 |    0.87 |\n",
      "| ('xfce4-terminal', '50%')   |      0           |    40410    |       0.4  |       0.75 |    0.94 |\n",
      "| ('xfce4-terminal', '90%')   | 606208           |    43107.2  |       0.43 |       0.81 |    0.99 |\n",
      "| ('xfce4-terminal', 'max')   | 606208           |    43572    |       0.45 |       0.85 |    1.03 |\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                       out_bytes  ru_maxrss  ru_stime  ru_utime   wtime\nterminal                                                               \nalacritty      count      100.00     100.00    100.00    100.00  100.00\n               mean         0.00   29032.92      0.32      0.46    0.51\n               std          0.00     138.18      0.02      0.03    0.01\n               min          0.00   28688.00      0.27      0.39    0.50\n               50%          0.00   29072.00      0.32      0.46    0.51\n               90%          0.00   29157.20      0.36      0.49    0.51\n               max          0.00   29276.00      0.40      0.52    0.53\nkonsole        count      300.00     300.00    300.00    300.00  300.00\n               mean     84855.47   67052.12      0.25      0.79    0.80\n               std     111979.56    2397.00      0.03      0.06    0.10\n               min       8192.00   62804.00      0.16      0.64    0.63\n               50%      81920.00   68462.00      0.26      0.81    0.84\n               90%     135168.00   69153.20      0.28      0.85    0.89\n               max    1769472.00   69344.00      0.30      0.90    1.01\nmlterm         count      300.00     300.00    300.00    300.00  300.00\n               mean      5515.95   11194.39      0.24      1.30    1.22\n               std       8025.74     181.41      0.02      0.20    0.21\n               min       4096.00   10868.00      0.16      0.98    0.90\n               50%       4096.00   11144.00      0.24      1.42    1.35\n               90%       4096.00   11444.00      0.26      1.48    1.41\n               max      53248.00   11652.00      0.28      1.56    1.51\npterm          count      300.00     300.00    300.00    300.00  300.00\n               mean        13.65   20495.49      0.23      1.02    0.94\n               std        236.48     688.35      0.02      0.02    0.01\n               min          0.00   19652.00      0.17      0.96    0.91\n               50%          0.00   20198.00      0.23      1.02    0.94\n               90%          0.00   21537.60      0.25      1.05    0.96\n               max       4096.00   21728.00      0.27      1.10    1.00\nst             count      400.00     400.00    400.00    400.00  400.00\n               mean         0.00    8546.45      0.23      0.43    0.38\n               std          0.00     543.25      0.02      0.02    0.02\n               min          0.00    7972.00      0.14      0.36    0.34\n               50%          0.00    8564.00      0.23      0.42    0.38\n               90%          0.00    9088.00      0.26      0.46    0.40\n               max          0.00    9092.00      0.28      0.50    0.43\nterminator     count      300.00     300.00    300.00    300.00  300.00\n               mean    202069.33   58244.85      0.43      0.92    1.14\n               std     286246.67    1514.50      0.03      0.04    0.04\n               min          0.00   56224.00      0.34      0.80    1.07\n               50%          0.00   58048.00      0.44      0.92    1.14\n               90%     606208.00   60792.00      0.46      0.97    1.18\n               max     606208.00   61420.00      0.51      1.01    1.20\nterminology    count      100.00     100.00    100.00    100.00  100.00\n               mean         0.00   39740.96      0.25      0.91    0.95\n               std          0.00     170.98      0.01      0.02    0.02\n               min          0.00   39452.00      0.23      0.86    0.90\n               50%          0.00   39716.00      0.25      0.91    0.95\n               90%          0.00   39916.40      0.26      0.94    0.97\n               max          0.00   40520.00      0.28      0.98    1.00\nurxvt          count      300.00     300.00    300.00    300.00  300.00\n               mean         0.00   14473.19      0.31      0.36    0.38\n               std          0.00     514.30      0.02      0.02    0.01\n               min          0.00   13536.00      0.24      0.31    0.36\n               50%          0.00   14746.00      0.31      0.36    0.38\n               90%          0.00   14928.40      0.34      0.38    0.40\n               max          0.00   15012.00      0.36      0.42    0.41\nuxterm         count      300.00     300.00    300.00    300.00  300.00\n               mean      1365.33   12297.27      0.65      0.99    1.76\n               std       1934.10     247.81      0.06      0.46    0.98\n               min          0.00   11540.00      0.53      0.86    1.25\n               50%          0.00   12184.00      0.65      0.94    1.32\n               90%       4096.00   12664.40      0.68      1.01    2.44\n               max       4096.00   12756.00      1.15      5.68    9.99\nxfce4-terminal count      300.00     300.00    300.00    300.00  300.00\n               mean    202001.07   40117.73      0.40      0.76    0.94\n               std     286150.43    2233.50      0.02      0.04    0.04\n               min          0.00   36844.00      0.33      0.67    0.87\n               50%          0.00   40410.00      0.40      0.75    0.94\n               90%     606208.00   43107.20      0.43      0.81    0.99\n               max     606208.00   43572.00      0.45      0.85    1.03"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tabulate import tabulate\n",
    "\n",
    "#print(tabulate(latency.describe().transpose(), headers='keys', tablefmt='pipe'))\n",
    "#print(tabulate(data.groupby('terminal').describe(), headers='keys', tablefmt='pipe'))\n",
    "desc = data.drop(['n', 'blocks', 'count', 'index', 'env', 'ru_idrss', 'ru_inblock', 'ru_isrss', 'ru_ixrss', 'ru_majflt', 'ru_minflt', 'ru_msgrcv', 'ru_msgsnd', 'ru_nivcsw', 'ru_nsignals', 'ru_nswap', 'ru_nvcsw', 'ru_oublock'], axis=1)\n",
    "table = desc.groupby('terminal').describe(percentiles=[0.9]).round(2)\n",
    "\n",
    "#for col in table:\n",
    "#    print(\"row: %r\" % (table[col]))\n",
    "print(tabulate(table, headers='keys', tablefmt='pipe'))\n",
    "pandas.options.display.max_rows = 2000\n",
    "table\n",
    "# that table is still not quite right, we'd need to reshuffle things around\n",
    "# this is my first try:\n",
    "#temp = table.reset_index()\n",
    "#newdf = temp.pivot(index='terminal',columns='ru_maxrss')\n",
    "#newdf.columns.name = ''\n",
    "#newdf\n",
    "#\n",
    "# but it doesn't work. it's inspired by:\n",
    "# https://stackoverflow.com/questions/32835498/pandas-python-describe-formatting-output#32835655"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Processing time (wall time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdb849b5f98>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": [
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QoYI74wAAAABAkeS2Yvfdd9/pt99+09KlS/XTTz/ptdde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\nAADA4gh2AAAAAGBx/w8hmTfvnxgSPAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdbb0493780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# not reviewed in the article\n",
    "data = data[data.terminal != 'terminology']\n",
    "\n",
    "fig = setup_fig()\n",
    "\n",
    "ax = sns.barplot(data=data, x='terminal', y='wtime', palette='colorblind', hue='env')\n",
    "ax.set_title('Processing time (smaller is better)')\n",
    "ax.set_ylabel('time (seconds)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## system time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": [
       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KRERnffPN1yotLdXRo0f1wgvPSSoLgd9/XzZNcf36dZKkDh06asuWzSouLlZxcbHS0raq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\nNH78eHXp0sW9b+nSpXrvvff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HsAMAAAAAmyPYAQAAAIDNWRrspk6dqsGDBysmJkabNm0q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\nKaWUg/s/JG5wt74aWH8AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb84a95da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = setup_fig()\n",
    "\n",
    "ax = sns.barplot(data=data, x='terminal', y='ru_stime', palette='colorblind', hue='env')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## user time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": [
       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cigKdZV73djlr6h2aod6hGeW8CgCA83cgPNayD0YB2J9lPXYnTpzQM888oy5d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\nrkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb84a95f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = setup_fig()\n",
    "\n",
    "ax = sns.barplot(data=data, x='terminal', y='ru_utime', palette='colorblind', hue='env')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Memory usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdb844c32e8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": [
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8nc+JE8fk6uqqP/74XVWrVlNKSrIMBoO8vEpIkmrVqqN9+2Ll61tJlStXkcFg\n0O+//6aqVavLaDTqiScqWPd9GBAIAQAAANjM7e4hXLx4vlq0aKnQ0C452i0WyWg0SJLMZvP/txpk\nsVis+2RmZspguDF76ODgKElasmSBHnvMUwMHDtWRI4c0YcKYW+q4ePGCPvzwAw0cOEQVK/pZ29PS\n0hQffzFPyzc3b96owoVdNWTISH366TBNnjwrT/VJFhkMf/ZjND48CzUfnkoAAAAA2IUqVarp+++3\ny2w26/r16/rii08l3QiPR47cWE4ZG/uzJMnX10//+c9BZWVlKSsrS4cO/SJfX78c/SUnJ6lkyRv3\nHG7dullZWVm3jDly5FB98EFEjieeStLx47+qdOkyudZ89epVzZgxRe+8864qVvRVpUpV9fXXS+Xm\n5iaDwaDz589Lkvbti1WlSpVzHFuy5OM6evSILBaLfv/9N50/fy4vlylfMEMIAAAAIF/5+weoZs3a\nevvtLpIsevnltpKkzp27KTJyiJYtWyRvbx9lZWXK29tHL730snr27C6z2aIXX2ypEiW8c/QXEtJc\nw4Z9os2bN6p163bauHG91qxZqebNX5J0Y9nq/v17NX36nw+N6dDhNT3zzHNKTExQsWLuOfobO3a0\n2rbtIB+fkta2yZPHq1Wr1nJ3Ly5Jeuutf6l7985q0KCx+vYdoMGDP5LJZJKPT0k1avSC1q9faz22\nUqUqKlWqtLp37yxf30oqW7b8Q/N0UoPlr/Obj6D4+CsPugRJ9/8Rv7ez4PA3Nh+jxqiH5wZYAAAA\nwBa++mqJAgOfuW9PAc3IyNCmTevVtGkLpaWl6bXX2mjJkm9y3MdoSx4ernfcxgwhAAAAAPyFl1eJ\n+/pKCCcnJx05ckjLli2W0WjQm2/2yLcwmJuHowoAAAAAeEg888yz973P997re9/7vB94qAwAAAAA\n2CkC4T2YOXOqOnRopZkzp+a+MwAAAAA8pAiEf1N6epo2bLjxxKANG2KUnp72gCsCAAAAgHvDPYT/\nr9qQJXnaz2jOVGWLRQZJZotFT45cLrPRMdfjVPp/qw8AAAAA7jcC4d9kNjrqUrGKKn75mC4Vq5C3\nMAgAAADYoXPn4tSpU4ccL4OvWNFPvXr1zvXY77/fri1bNumjjwbdl1o2bVqvRYu+lMFgVO3adfX2\n22GaM2eGfvpptyTJYrEoMTFRixYtv+XY7OxstWwZoq++WqUCBZyVnZ2tkJAGmjp1tsqVKy9J6tKl\nowYPjszKBZQxAAAgAElEQVTTS+4lacaMKSpatKhat25/z+fUvHkjrVmz6Z6PlwiE9+Scd12d8677\noMsAAAAA/pZ9/Srf1/7y8o7q0qXLaMKEB/vsjfT0dE2aNF5z5y5SwYIu6t79Db3wQlN17txNnTt3\nkyStXbtaly9fuu3xJpNJVapU0y+//Ee1atXRsWO/ytXVVfv3x6pcufK6du2qkpOT8xwGHyYEQgAA\nAAD5bsqUaB04sE9mc7ZeeaWdgoNDdOLEcQ0b9rHc3IrIx+dx675LlizUpk3rJUlBQc/p9dff0PDh\ng+Tg4KiUlCT17/+JBg8eoLS0NKWnp+u99/qoSpVq1uOdnZ01d+4iubgUkiQVKVJEKSnJ1u1ZWVn6\n+utlGj9+8h3rrVWrtvbti1WtWnV04MBetWjRUvv27VWrVm104MB+1ahRS5K0cOGX2rJlk8xms+rX\nf1qdO3dTx46tNWvWArm4uGj//n1avHi+nniignXs4cMHKT7+otLS0tS1a3c9/XSQwsO7q27deoqN\n3aOkpCSNGvWFHnvsMQ0ePEAXL15Q5cpV7svvgYfKAAAAAMhX+/fv1YUL5xUdPU1jx07WnDkzdf16\numbPnq6uXbtr7NhJMpluRJW4uLNau3aVoqOnKTp6mr77boPOnj0jSXJzc9Pw4VFKTExUixatNH78\nFPXoEa758+fcMubNMHjy5HGdP39OVav6W7dt3bpZ9erVV4ECznesuVatOtq/f6+1/pCQ5jp16g/r\n51q1alv3nThxuqZOna21a1crPT1NQUHPa8eObZKk77/fquDgJtZ9r1xJ0ZNPPqUJE6ZqyJBIzZgx\nxbqtUKFCGjt2kp56KlDbtn2nn376QVlZWZoyZZaCg5sqOfnPUHuvmCEEAAAAYDOnTv2h8PDu1s91\n69aTyWTSL78ctLZbLGYlJCTo999Pqlq1AElSzZq19cMPO3Xs2FFVreovB4cb0cXfP0DHj/8qSapS\npaokyd29uObMma6FC+cpMzNTzs63D3anT5/SoEEf6ZNPhln7k6Q1a75Rnz7973oeFSr46vTpU8rM\nzNS5c+fk41NSJUp4Ky7urPbv36sXX2wl6cZsZHh4d5lMJiUlJSklJUVNm7bQtGkT9cILIdq792d1\n69ZDJ0+ekCS5urrp8OFftHLlchkMxhwzlwEBNSVJnp6eSk5O1m+//SZ//+qSpKpVq6lAgQJ5+RXc\nFYEQAAAAgM3c7h7CxYvnq0WLlgoN7ZKj3WKRjEaDJMlsNv9/q0EWi8W6T2ZmpgyGG7OHDg43HvC4\nZMkCPfaYpwYOHKojRw5pwoQxt9Rx8eIFffjhBxo4cIgqVvSztqelpSk+/qK8vX3ueh5Go1F+fpW1\nceM6lSp14xUC1asHKDb2J12+fFklSz6u8+fPafHi+Zo5c75cXFwUGtpOkvTEExWUmJiow4d/Ubly\nT+QIchs2xCglJUXR0dOVkpKiN98MtW4zmUx/uTYWSRbruf/Z9r9hySgAAACAfFWlSjV9//12mc1m\nXb9+XV988amkG+HxyJEbD6qJjf1ZkuTr66f//OegsrKylJWVpUOHfpGvr1+O/pKTk1Sy5I17Drdu\n3aysrKxbxhw5cqg++CAixxNPJen48V/z/DCYWrXqaPnypdaZu+rVa2jVqm+ss3ZJSUkqVqyYXFxc\ndPToEZ0/f16ZmZmSpIYNg/X556MUHBySo8+kpCR5e/vIaDRq69bvrPvfzo3rc0iSdPDgfmVkZOSp\n7rshEAIAAADIV/7+AapZs7befruLwsPfkp/fjaefdu7cTZMmjVefPr3k6HhjMaO3t49eeull9ezZ\nXWFhb+nFF1uqRAnvHP2FhDTX4sXz9d57YapatZoSExO1Zs1K6/ZTp/7Q/v17NX36ZIWHd1d4eHft\n2LFVkpSYmKBixdxz9Dd27GjFxZ29pe5atero8OFfVL16DUmSr28lnThxTDVr3rh/sGJFXxUs6KJ/\n/aurNm1ar5YtX9Ho0aMkSY0aBevixYuqVatOjj6ff76hdu7crl69/qWCBQvK09NTs2dPv+11e+qp\np5WRcV3h4d21adN6eXh45u2C34XBcj/mGR9i8fFX8rRfXl9Mf6/MpWNt2r8kLTj8jc3HyMujhQEA\nAIB/sq++WqLAwGdyXUb6d6xZs1Lnz59Tt25v37c+88rDw/WO27iHEAAAAAD+wsurxH0Ng6NGDVNc\n3FlFRn523/q8XwiEAAAAAPAXzzzz7H3tr1+/Afe1v/uJewgBAAAAwE4RCAEAAADAThEIAQAAAMBO\nEQgBAAAAwE4RCAEAAADAThEIAQAAAMBO2fS1EytXrtT06dPl4OCgXr16ydfXV3379lV2drY8PDwU\nFRUlJycnrVy5UnPmzJHRaFT79u3Vpk0bZWZmKiIiQnFxcTKZTIqMjFSpUqV05MgRDRo0SJLk5+en\nwYMH2/IUAAAAAOCRZbMZwsuXLys6OloLFizQ5MmTtXHjRo0bN04dO3bUggULVKZMGS1btkypqamK\njo7W7NmzNW/ePM2ePVtJSUlavXq13NzctHDhQvXo0UOjR4+WJA0fPlz9+/fXokWLdPXqVW3dutVW\npwAAAAAAjzSbBcJdu3apfv36Kly4sDw9PTV06FDt3r1bjRo1kiQ1aNBAu3bt0v79++Xv7y9XV1c5\nOzurVq1aio2N1a5duxQcHCxJCgwMVGxsrDIyMnT27FlVr149Rx8A/plmzpyqDh1aaebMqQ+6FAAA\nALtks0B45swZpaenq0ePHurYsaN27dqltLQ0OTk5SZKKFy+u+Ph4JSQkyN3d3Xqcu7v7Le1Go1EG\ng0EJCQlyc3Oz7nuzDwD/POnpadqwYa0kacOGGKWnpz3gigAAAOyPTe8hTEpK0oQJExQXF6dOnTrJ\nYDBYt1kslhx//rXdYDDctv12bbkpVsxFDg6mez0F/BcPD9cHXQIeYh5RYXne15SRrarW/x0wq+7c\nj5XtlPu/q/F9ou+5PgAAAORks0BYvHhx1axZUw4ODipdurQKFSokk8mk9PR0OTs768KFC/L09JSX\nl5e2bNliPe7ixYuqUaOGvLy8FB8fr0qVKikzM1MWi0Wenp5KSkqy7nuzj7u5fDnVVqdol+Ljrzzo\nEvCIsBgNskgySLL8/+e84DsIAADw99xtUsdmS0afeeYZ/fDDDzKbzbp06ZJSU1MVGBiodevWSZLW\nr1+voKAgBQQE6ODBg0pJSdG1a9cUGxurOnXq6Omnn1ZMTIwkafPmzapXr54cHR1Vvnx57dmzJ0cf\nAP55zA5GJZYuKklKLF1UZgfeggMAAJDfbDZD6OXlpSZNmqhdu3aSpAEDBsjf31/9+vXT4sWL5ePj\no1atWsnR0VG9e/dWt27dZDAYFBYWJldXVzVr1kw7d+7Uq6++KicnJ40cOVKS1L9/f3388ccym80K\nCAhQYGCgrU4BgI3FVSmhuColHnQZAAAAdstgycuNeP9geV1eVm3IEpvWYS4da9P+JWnB4W9sPkaN\nUYdtPgb+uarMjrD5GIfeGGnzMQAAAB4lD2TJKAAAAADg4UYgBAAAAAA7RSAEAAAAADtFIAQAAAAA\nO0UgBAAAAAA7RSAEcFszZ05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tW+esQwAAAACAm5rTAqGXl5fmzZsnf39/x7KN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\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb841975f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = setup_fig()\n",
    "\n",
    "ax = sns.barplot(data=data, x='terminal', y='ru_maxrss', palette='colorblind', hue='env')\n",
    "ax.set_title('RSS memory usage (smaller is better)')\n",
    "ax.set_ylabel('memory (KB)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## I/O usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdb8491fc50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": [
       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PqmPHjgoPD7fWkZ6eripVKqtevZqSpAceaKMffjioRo2eLOa+/BUa+og+/XS14uLiNGFC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\ngg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb842a5630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = setup_fig()\n",
    "\n",
    "# some terminals still write minimal data, discard those that write less than 10kb\n",
    "iodata = data[data.out_bytes > 10*1024]\n",
    "\n",
    "ax = sns.barplot(data=iodata, x='terminal', y='out_bytes', palette='colorblind', hue='env')\n",
    "ax.set_title('I/O (smaller is better)')\n",
    "ax.set_ylabel('output (bytes))')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.3"
  },
  "name": "benchmarks-summary.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 2
}