benchmarks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set(rc={'image.cmap': 'cubehelix'})\n",
    "FIG_WIDTH=20\n",
    "FIG_HEIGTH=8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Latency tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>xterm</th>\n",
       "      <th>mlterm</th>\n",
       "      <th>kterm</th>\n",
       "      <th>mrxvt</th>\n",
       "      <th>xvt</th>\n",
       "      <th>konsole</th>\n",
       "      <th>urxvt</th>\n",
       "      <th>st</th>\n",
       "      <th>alacritty</th>\n",
       "      <th>putty</th>\n",
       "      <th>xfce4</th>\n",
       "      <th>gnome</th>\n",
       "      <th>terminator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.17</td>\n",
       "      <td>4.93</td>\n",
       "      <td>17.94</td>\n",
       "      <td>13.42</td>\n",
       "      <td>19.62</td>\n",
       "      <td>25.38</td>\n",
       "      <td>19.40</td>\n",
       "      <td>23.69</td>\n",
       "      <td>32.33</td>\n",
       "      <td>31.17</td>\n",
       "      <td>31.46</td>\n",
       "      <td>29.38</td>\n",
       "      <td>38.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.26</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.79</td>\n",
       "      <td>13.80</td>\n",
       "      <td>13.80</td>\n",
       "      <td>17.09</td>\n",
       "      <td>20.14</td>\n",
       "      <td>29.13</td>\n",
       "      <td>20.87</td>\n",
       "      <td>25.49</td>\n",
       "      <td>32.56</td>\n",
       "      <td>30.03</td>\n",
       "      <td>33.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.75</td>\n",
       "      <td>13.70</td>\n",
       "      <td>2.61</td>\n",
       "      <td>12.77</td>\n",
       "      <td>15.70</td>\n",
       "      <td>23.21</td>\n",
       "      <td>19.37</td>\n",
       "      <td>23.98</td>\n",
       "      <td>17.88</td>\n",
       "      <td>25.69</td>\n",
       "      <td>31.03</td>\n",
       "      <td>32.89</td>\n",
       "      <td>33.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.56</td>\n",
       "      <td>4.99</td>\n",
       "      <td>14.45</td>\n",
       "      <td>8.39</td>\n",
       "      <td>7.76</td>\n",
       "      <td>16.74</td>\n",
       "      <td>21.16</td>\n",
       "      <td>22.06</td>\n",
       "      <td>25.66</td>\n",
       "      <td>28.10</td>\n",
       "      <td>28.76</td>\n",
       "      <td>31.90</td>\n",
       "      <td>33.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.71</td>\n",
       "      <td>13.78</td>\n",
       "      <td>12.03</td>\n",
       "      <td>3.99</td>\n",
       "      <td>12.36</td>\n",
       "      <td>14.28</td>\n",
       "      <td>25.82</td>\n",
       "      <td>26.93</td>\n",
       "      <td>30.67</td>\n",
       "      <td>29.32</td>\n",
       "      <td>32.28</td>\n",
       "      <td>31.95</td>\n",
       "      <td>28.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.97</td>\n",
       "      <td>14.19</td>\n",
       "      <td>13.80</td>\n",
       "      <td>18.23</td>\n",
       "      <td>13.88</td>\n",
       "      <td>22.43</td>\n",
       "      <td>23.90</td>\n",
       "      <td>26.52</td>\n",
       "      <td>25.72</td>\n",
       "      <td>28.25</td>\n",
       "      <td>31.10</td>\n",
       "      <td>32.33</td>\n",
       "      <td>30.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.23</td>\n",
       "      <td>4.87</td>\n",
       "      <td>12.68</td>\n",
       "      <td>8.04</td>\n",
       "      <td>17.02</td>\n",
       "      <td>18.71</td>\n",
       "      <td>21.24</td>\n",
       "      <td>20.53</td>\n",
       "      <td>28.59</td>\n",
       "      <td>27.41</td>\n",
       "      <td>30.29</td>\n",
       "      <td>28.34</td>\n",
       "      <td>33.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.88</td>\n",
       "      <td>12.92</td>\n",
       "      <td>13.88</td>\n",
       "      <td>6.45</td>\n",
       "      <td>13.09</td>\n",
       "      <td>20.50</td>\n",
       "      <td>25.18</td>\n",
       "      <td>21.21</td>\n",
       "      <td>25.50</td>\n",
       "      <td>24.52</td>\n",
       "      <td>31.48</td>\n",
       "      <td>29.46</td>\n",
       "      <td>34.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.97</td>\n",
       "      <td>7.84</td>\n",
       "      <td>14.00</td>\n",
       "      <td>7.30</td>\n",
       "      <td>11.00</td>\n",
       "      <td>20.71</td>\n",
       "      <td>25.72</td>\n",
       "      <td>20.86</td>\n",
       "      <td>28.69</td>\n",
       "      <td>29.01</td>\n",
       "      <td>29.04</td>\n",
       "      <td>30.87</td>\n",
       "      <td>29.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13.90</td>\n",
       "      <td>13.80</td>\n",
       "      <td>4.55</td>\n",
       "      <td>13.31</td>\n",
       "      <td>11.82</td>\n",
       "      <td>21.35</td>\n",
       "      <td>21.77</td>\n",
       "      <td>25.75</td>\n",
       "      <td>31.85</td>\n",
       "      <td>27.92</td>\n",
       "      <td>29.92</td>\n",
       "      <td>33.64</td>\n",
       "      <td>32.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14.46</td>\n",
       "      <td>4.77</td>\n",
       "      <td>5.92</td>\n",
       "      <td>7.38</td>\n",
       "      <td>12.59</td>\n",
       "      <td>20.49</td>\n",
       "      <td>25.14</td>\n",
       "      <td>22.83</td>\n",
       "      <td>25.58</td>\n",
       "      <td>28.01</td>\n",
       "      <td>31.13</td>\n",
       "      <td>32.20</td>\n",
       "      <td>30.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8.29</td>\n",
       "      <td>5.78</td>\n",
       "      <td>5.98</td>\n",
       "      <td>18.72</td>\n",
       "      <td>15.15</td>\n",
       "      <td>20.66</td>\n",
       "      <td>22.74</td>\n",
       "      <td>23.88</td>\n",
       "      <td>27.57</td>\n",
       "      <td>26.14</td>\n",
       "      <td>31.03</td>\n",
       "      <td>31.54</td>\n",
       "      <td>33.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11.84</td>\n",
       "      <td>13.97</td>\n",
       "      <td>12.84</td>\n",
       "      <td>16.72</td>\n",
       "      <td>11.09</td>\n",
       "      <td>19.90</td>\n",
       "      <td>24.10</td>\n",
       "      <td>23.92</td>\n",
       "      <td>31.02</td>\n",
       "      <td>30.15</td>\n",
       "      <td>32.43</td>\n",
       "      <td>32.21</td>\n",
       "      <td>33.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.47</td>\n",
       "      <td>13.14</td>\n",
       "      <td>13.57</td>\n",
       "      <td>9.69</td>\n",
       "      <td>7.68</td>\n",
       "      <td>23.75</td>\n",
       "      <td>25.13</td>\n",
       "      <td>22.82</td>\n",
       "      <td>26.06</td>\n",
       "      <td>29.34</td>\n",
       "      <td>33.45</td>\n",
       "      <td>33.68</td>\n",
       "      <td>30.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.69</td>\n",
       "      <td>13.24</td>\n",
       "      <td>13.31</td>\n",
       "      <td>5.77</td>\n",
       "      <td>12.07</td>\n",
       "      <td>21.66</td>\n",
       "      <td>26.41</td>\n",
       "      <td>25.54</td>\n",
       "      <td>30.81</td>\n",
       "      <td>24.97</td>\n",
       "      <td>30.65</td>\n",
       "      <td>34.06</td>\n",
       "      <td>32.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9.80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>14.60</td>\n",
       "      <td>14.28</td>\n",
       "      <td>10.69</td>\n",
       "      <td>19.89</td>\n",
       "      <td>25.41</td>\n",
       "      <td>26.86</td>\n",
       "      <td>21.89</td>\n",
       "      <td>27.90</td>\n",
       "      <td>29.89</td>\n",
       "      <td>32.28</td>\n",
       "      <td>29.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.39</td>\n",
       "      <td>3.92</td>\n",
       "      <td>5.33</td>\n",
       "      <td>7.21</td>\n",
       "      <td>17.89</td>\n",
       "      <td>19.67</td>\n",
       "      <td>25.66</td>\n",
       "      <td>23.61</td>\n",
       "      <td>11.90</td>\n",
       "      <td>27.86</td>\n",
       "      <td>28.78</td>\n",
       "      <td>34.22</td>\n",
       "      <td>31.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13.70</td>\n",
       "      <td>5.21</td>\n",
       "      <td>7.41</td>\n",
       "      <td>13.54</td>\n",
       "      <td>13.26</td>\n",
       "      <td>15.98</td>\n",
       "      <td>22.87</td>\n",
       "      <td>23.98</td>\n",
       "      <td>27.07</td>\n",
       "      <td>23.64</td>\n",
       "      <td>29.98</td>\n",
       "      <td>33.09</td>\n",
       "      <td>31.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.99</td>\n",
       "      <td>13.03</td>\n",
       "      <td>14.17</td>\n",
       "      <td>18.93</td>\n",
       "      <td>10.03</td>\n",
       "      <td>25.68</td>\n",
       "      <td>23.43</td>\n",
       "      <td>25.72</td>\n",
       "      <td>25.51</td>\n",
       "      <td>29.59</td>\n",
       "      <td>32.46</td>\n",
       "      <td>33.24</td>\n",
       "      <td>29.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13.45</td>\n",
       "      <td>12.31</td>\n",
       "      <td>14.86</td>\n",
       "      <td>16.80</td>\n",
       "      <td>12.61</td>\n",
       "      <td>24.22</td>\n",
       "      <td>23.39</td>\n",
       "      <td>24.62</td>\n",
       "      <td>29.94</td>\n",
       "      <td>30.56</td>\n",
       "      <td>31.55</td>\n",
       "      <td>33.04</td>\n",
       "      <td>32.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.64</td>\n",
       "      <td>6.96</td>\n",
       "      <td>13.09</td>\n",
       "      <td>12.96</td>\n",
       "      <td>20.05</td>\n",
       "      <td>20.96</td>\n",
       "      <td>23.58</td>\n",
       "      <td>20.55</td>\n",
       "      <td>25.76</td>\n",
       "      <td>25.90</td>\n",
       "      <td>29.64</td>\n",
       "      <td>31.02</td>\n",
       "      <td>35.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.56</td>\n",
       "      <td>6.55</td>\n",
       "      <td>8.18</td>\n",
       "      <td>14.08</td>\n",
       "      <td>13.21</td>\n",
       "      <td>20.13</td>\n",
       "      <td>25.46</td>\n",
       "      <td>26.19</td>\n",
       "      <td>25.44</td>\n",
       "      <td>27.90</td>\n",
       "      <td>30.17</td>\n",
       "      <td>33.74</td>\n",
       "      <td>31.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14.29</td>\n",
       "      <td>12.47</td>\n",
       "      <td>14.82</td>\n",
       "      <td>17.85</td>\n",
       "      <td>12.35</td>\n",
       "      <td>21.22</td>\n",
       "      <td>25.78</td>\n",
       "      <td>25.35</td>\n",
       "      <td>30.66</td>\n",
       "      <td>29.48</td>\n",
       "      <td>29.77</td>\n",
       "      <td>33.65</td>\n",
       "      <td>30.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>14.07</td>\n",
       "      <td>13.81</td>\n",
       "      <td>14.49</td>\n",
       "      <td>17.57</td>\n",
       "      <td>11.61</td>\n",
       "      <td>16.09</td>\n",
       "      <td>22.94</td>\n",
       "      <td>23.16</td>\n",
       "      <td>25.44</td>\n",
       "      <td>24.83</td>\n",
       "      <td>33.86</td>\n",
       "      <td>29.21</td>\n",
       "      <td>31.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6.52</td>\n",
       "      <td>14.11</td>\n",
       "      <td>13.05</td>\n",
       "      <td>12.79</td>\n",
       "      <td>15.11</td>\n",
       "      <td>22.74</td>\n",
       "      <td>23.75</td>\n",
       "      <td>23.17</td>\n",
       "      <td>16.30</td>\n",
       "      <td>27.56</td>\n",
       "      <td>30.23</td>\n",
       "      <td>31.34</td>\n",
       "      <td>30.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.68</td>\n",
       "      <td>12.02</td>\n",
       "      <td>4.87</td>\n",
       "      <td>6.93</td>\n",
       "      <td>13.07</td>\n",
       "      <td>24.41</td>\n",
       "      <td>21.82</td>\n",
       "      <td>26.05</td>\n",
       "      <td>25.03</td>\n",
       "      <td>30.77</td>\n",
       "      <td>29.88</td>\n",
       "      <td>32.43</td>\n",
       "      <td>34.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10.32</td>\n",
       "      <td>13.91</td>\n",
       "      <td>5.23</td>\n",
       "      <td>13.86</td>\n",
       "      <td>18.89</td>\n",
       "      <td>20.02</td>\n",
       "      <td>24.57</td>\n",
       "      <td>24.01</td>\n",
       "      <td>26.18</td>\n",
       "      <td>28.12</td>\n",
       "      <td>31.36</td>\n",
       "      <td>31.67</td>\n",
       "      <td>35.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.10</td>\n",
       "      <td>14.38</td>\n",
       "      <td>9.75</td>\n",
       "      <td>13.96</td>\n",
       "      <td>18.87</td>\n",
       "      <td>20.72</td>\n",
       "      <td>24.88</td>\n",
       "      <td>23.30</td>\n",
       "      <td>25.91</td>\n",
       "      <td>27.81</td>\n",
       "      <td>33.15</td>\n",
       "      <td>33.84</td>\n",
       "      <td>33.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11.52</td>\n",
       "      <td>3.75</td>\n",
       "      <td>14.95</td>\n",
       "      <td>13.71</td>\n",
       "      <td>9.80</td>\n",
       "      <td>21.79</td>\n",
       "      <td>21.80</td>\n",
       "      <td>23.66</td>\n",
       "      <td>30.21</td>\n",
       "      <td>25.06</td>\n",
       "      <td>33.46</td>\n",
       "      <td>32.65</td>\n",
       "      <td>29.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8.27</td>\n",
       "      <td>5.33</td>\n",
       "      <td>13.90</td>\n",
       "      <td>12.85</td>\n",
       "      <td>10.76</td>\n",
       "      <td>15.53</td>\n",
       "      <td>19.89</td>\n",
       "      <td>23.95</td>\n",
       "      <td>24.57</td>\n",
       "      <td>31.53</td>\n",
       "      <td>33.41</td>\n",
       "      <td>34.71</td>\n",
       "      <td>32.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>9.96</td>\n",
       "      <td>14.63</td>\n",
       "      <td>13.30</td>\n",
       "      <td>16.38</td>\n",
       "      <td>11.79</td>\n",
       "      <td>20.83</td>\n",
       "      <td>23.49</td>\n",
       "      <td>22.76</td>\n",
       "      <td>24.18</td>\n",
       "      <td>24.96</td>\n",
       "      <td>32.09</td>\n",
       "      <td>34.42</td>\n",
       "      <td>33.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>4.10</td>\n",
       "      <td>11.16</td>\n",
       "      <td>9.21</td>\n",
       "      <td>12.95</td>\n",
       "      <td>12.29</td>\n",
       "      <td>21.74</td>\n",
       "      <td>23.35</td>\n",
       "      <td>21.28</td>\n",
       "      <td>26.07</td>\n",
       "      <td>25.41</td>\n",
       "      <td>33.14</td>\n",
       "      <td>32.31</td>\n",
       "      <td>32.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>5.34</td>\n",
       "      <td>4.74</td>\n",
       "      <td>14.49</td>\n",
       "      <td>13.97</td>\n",
       "      <td>11.68</td>\n",
       "      <td>20.68</td>\n",
       "      <td>25.96</td>\n",
       "      <td>20.86</td>\n",
       "      <td>16.03</td>\n",
       "      <td>27.71</td>\n",
       "      <td>31.20</td>\n",
       "      <td>33.37</td>\n",
       "      <td>34.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>6.37</td>\n",
       "      <td>13.92</td>\n",
       "      <td>14.92</td>\n",
       "      <td>13.88</td>\n",
       "      <td>10.39</td>\n",
       "      <td>20.39</td>\n",
       "      <td>23.67</td>\n",
       "      <td>25.08</td>\n",
       "      <td>26.36</td>\n",
       "      <td>27.86</td>\n",
       "      <td>31.88</td>\n",
       "      <td>32.26</td>\n",
       "      <td>32.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>14.46</td>\n",
       "      <td>13.18</td>\n",
       "      <td>13.54</td>\n",
       "      <td>12.56</td>\n",
       "      <td>10.74</td>\n",
       "      <td>20.27</td>\n",
       "      <td>25.25</td>\n",
       "      <td>24.42</td>\n",
       "      <td>26.04</td>\n",
       "      <td>28.53</td>\n",
       "      <td>28.88</td>\n",
       "      <td>34.67</td>\n",
       "      <td>33.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>14.73</td>\n",
       "      <td>14.33</td>\n",
       "      <td>14.30</td>\n",
       "      <td>12.37</td>\n",
       "      <td>13.39</td>\n",
       "      <td>21.58</td>\n",
       "      <td>20.25</td>\n",
       "      <td>22.65</td>\n",
       "      <td>24.57</td>\n",
       "      <td>25.47</td>\n",
       "      <td>31.05</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>8.37</td>\n",
       "      <td>14.08</td>\n",
       "      <td>14.73</td>\n",
       "      <td>13.53</td>\n",
       "      <td>11.04</td>\n",
       "      <td>20.55</td>\n",
       "      <td>25.00</td>\n",
       "      <td>22.89</td>\n",
       "      <td>16.47</td>\n",
       "      <td>24.64</td>\n",
       "      <td>29.50</td>\n",
       "      <td>33.45</td>\n",
       "      <td>32.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>6.56</td>\n",
       "      <td>11.89</td>\n",
       "      <td>12.15</td>\n",
       "      <td>6.97</td>\n",
       "      <td>10.92</td>\n",
       "      <td>20.85</td>\n",
       "      <td>23.97</td>\n",
       "      <td>29.17</td>\n",
       "      <td>29.92</td>\n",
       "      <td>28.57</td>\n",
       "      <td>33.56</td>\n",
       "      <td>33.83</td>\n",
       "      <td>33.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>14.55</td>\n",
       "      <td>5.09</td>\n",
       "      <td>13.16</td>\n",
       "      <td>14.37</td>\n",
       "      <td>18.44</td>\n",
       "      <td>24.64</td>\n",
       "      <td>21.10</td>\n",
       "      <td>24.37</td>\n",
       "      <td>25.48</td>\n",
       "      <td>29.51</td>\n",
       "      <td>33.58</td>\n",
       "      <td>29.65</td>\n",
       "      <td>33.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>8.16</td>\n",
       "      <td>11.80</td>\n",
       "      <td>4.93</td>\n",
       "      <td>13.43</td>\n",
       "      <td>17.70</td>\n",
       "      <td>22.12</td>\n",
       "      <td>24.71</td>\n",
       "      <td>26.61</td>\n",
       "      <td>28.72</td>\n",
       "      <td>27.40</td>\n",
       "      <td>29.60</td>\n",
       "      <td>33.45</td>\n",
       "      <td>34.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>8.97</td>\n",
       "      <td>13.72</td>\n",
       "      <td>4.43</td>\n",
       "      <td>20.26</td>\n",
       "      <td>14.11</td>\n",
       "      <td>20.51</td>\n",
       "      <td>24.76</td>\n",
       "      <td>23.70</td>\n",
       "      <td>27.72</td>\n",
       "      <td>28.17</td>\n",
       "      <td>31.71</td>\n",
       "      <td>29.57</td>\n",
       "      <td>31.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>6.81</td>\n",
       "      <td>8.29</td>\n",
       "      <td>6.94</td>\n",
       "      <td>20.00</td>\n",
       "      <td>10.25</td>\n",
       "      <td>17.09</td>\n",
       "      <td>20.70</td>\n",
       "      <td>25.82</td>\n",
       "      <td>29.81</td>\n",
       "      <td>27.28</td>\n",
       "      <td>29.60</td>\n",
       "      <td>35.18</td>\n",
       "      <td>30.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>8.59</td>\n",
       "      <td>4.02</td>\n",
       "      <td>13.33</td>\n",
       "      <td>13.89</td>\n",
       "      <td>9.38</td>\n",
       "      <td>22.38</td>\n",
       "      <td>23.33</td>\n",
       "      <td>23.44</td>\n",
       "      <td>29.83</td>\n",
       "      <td>27.62</td>\n",
       "      <td>30.67</td>\n",
       "      <td>30.94</td>\n",
       "      <td>33.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>5.57</td>\n",
       "      <td>13.99</td>\n",
       "      <td>12.51</td>\n",
       "      <td>13.25</td>\n",
       "      <td>10.35</td>\n",
       "      <td>20.90</td>\n",
       "      <td>27.01</td>\n",
       "      <td>23.74</td>\n",
       "      <td>22.60</td>\n",
       "      <td>26.81</td>\n",
       "      <td>33.45</td>\n",
       "      <td>29.55</td>\n",
       "      <td>33.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>14.00</td>\n",
       "      <td>4.69</td>\n",
       "      <td>12.96</td>\n",
       "      <td>12.87</td>\n",
       "      <td>10.66</td>\n",
       "      <td>19.88</td>\n",
       "      <td>20.86</td>\n",
       "      <td>25.68</td>\n",
       "      <td>24.43</td>\n",
       "      <td>27.90</td>\n",
       "      <td>33.07</td>\n",
       "      <td>32.45</td>\n",
       "      <td>33.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>8.98</td>\n",
       "      <td>14.58</td>\n",
       "      <td>14.31</td>\n",
       "      <td>9.93</td>\n",
       "      <td>12.00</td>\n",
       "      <td>23.36</td>\n",
       "      <td>24.14</td>\n",
       "      <td>21.94</td>\n",
       "      <td>11.80</td>\n",
       "      <td>27.94</td>\n",
       "      <td>33.94</td>\n",
       "      <td>34.07</td>\n",
       "      <td>32.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>9.74</td>\n",
       "      <td>14.12</td>\n",
       "      <td>13.11</td>\n",
       "      <td>13.59</td>\n",
       "      <td>11.16</td>\n",
       "      <td>17.24</td>\n",
       "      <td>23.25</td>\n",
       "      <td>23.87</td>\n",
       "      <td>30.91</td>\n",
       "      <td>29.49</td>\n",
       "      <td>34.79</td>\n",
       "      <td>32.73</td>\n",
       "      <td>34.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>4.18</td>\n",
       "      <td>13.14</td>\n",
       "      <td>13.99</td>\n",
       "      <td>6.73</td>\n",
       "      <td>12.78</td>\n",
       "      <td>20.91</td>\n",
       "      <td>20.50</td>\n",
       "      <td>25.88</td>\n",
       "      <td>25.65</td>\n",
       "      <td>25.27</td>\n",
       "      <td>29.97</td>\n",
       "      <td>33.54</td>\n",
       "      <td>34.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>5.29</td>\n",
       "      <td>13.89</td>\n",
       "      <td>13.49</td>\n",
       "      <td>9.60</td>\n",
       "      <td>12.72</td>\n",
       "      <td>20.86</td>\n",
       "      <td>22.41</td>\n",
       "      <td>25.67</td>\n",
       "      <td>28.98</td>\n",
       "      <td>24.91</td>\n",
       "      <td>30.33</td>\n",
       "      <td>33.56</td>\n",
       "      <td>32.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>9.30</td>\n",
       "      <td>4.50</td>\n",
       "      <td>13.48</td>\n",
       "      <td>7.27</td>\n",
       "      <td>19.49</td>\n",
       "      <td>21.35</td>\n",
       "      <td>25.79</td>\n",
       "      <td>18.78</td>\n",
       "      <td>25.46</td>\n",
       "      <td>29.46</td>\n",
       "      <td>32.43</td>\n",
       "      <td>31.60</td>\n",
       "      <td>31.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>7.02</td>\n",
       "      <td>7.87</td>\n",
       "      <td>13.64</td>\n",
       "      <td>21.48</td>\n",
       "      <td>12.72</td>\n",
       "      <td>18.09</td>\n",
       "      <td>20.80</td>\n",
       "      <td>22.71</td>\n",
       "      <td>16.02</td>\n",
       "      <td>27.71</td>\n",
       "      <td>33.62</td>\n",
       "      <td>34.41</td>\n",
       "      <td>29.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>13.98</td>\n",
       "      <td>14.18</td>\n",
       "      <td>13.94</td>\n",
       "      <td>3.95</td>\n",
       "      <td>11.15</td>\n",
       "      <td>23.08</td>\n",
       "      <td>23.88</td>\n",
       "      <td>21.10</td>\n",
       "      <td>28.48</td>\n",
       "      <td>28.50</td>\n",
       "      <td>32.90</td>\n",
       "      <td>29.73</td>\n",
       "      <td>32.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>14.46</td>\n",
       "      <td>14.22</td>\n",
       "      <td>8.83</td>\n",
       "      <td>17.74</td>\n",
       "      <td>20.20</td>\n",
       "      <td>22.54</td>\n",
       "      <td>25.54</td>\n",
       "      <td>23.56</td>\n",
       "      <td>26.69</td>\n",
       "      <td>24.95</td>\n",
       "      <td>29.97</td>\n",
       "      <td>31.87</td>\n",
       "      <td>33.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>13.99</td>\n",
       "      <td>8.57</td>\n",
       "      <td>13.39</td>\n",
       "      <td>17.31</td>\n",
       "      <td>17.48</td>\n",
       "      <td>20.44</td>\n",
       "      <td>24.83</td>\n",
       "      <td>22.55</td>\n",
       "      <td>31.01</td>\n",
       "      <td>25.31</td>\n",
       "      <td>34.32</td>\n",
       "      <td>33.84</td>\n",
       "      <td>31.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>4.73</td>\n",
       "      <td>5.17</td>\n",
       "      <td>13.06</td>\n",
       "      <td>13.15</td>\n",
       "      <td>12.19</td>\n",
       "      <td>20.87</td>\n",
       "      <td>23.94</td>\n",
       "      <td>24.47</td>\n",
       "      <td>11.01</td>\n",
       "      <td>29.02</td>\n",
       "      <td>29.50</td>\n",
       "      <td>34.29</td>\n",
       "      <td>33.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>14.39</td>\n",
       "      <td>4.88</td>\n",
       "      <td>10.65</td>\n",
       "      <td>12.12</td>\n",
       "      <td>18.00</td>\n",
       "      <td>20.91</td>\n",
       "      <td>25.50</td>\n",
       "      <td>16.65</td>\n",
       "      <td>22.45</td>\n",
       "      <td>28.83</td>\n",
       "      <td>32.84</td>\n",
       "      <td>33.47</td>\n",
       "      <td>33.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>14.53</td>\n",
       "      <td>5.70</td>\n",
       "      <td>5.70</td>\n",
       "      <td>8.26</td>\n",
       "      <td>20.17</td>\n",
       "      <td>21.65</td>\n",
       "      <td>19.22</td>\n",
       "      <td>23.95</td>\n",
       "      <td>25.98</td>\n",
       "      <td>30.00</td>\n",
       "      <td>33.49</td>\n",
       "      <td>30.62</td>\n",
       "      <td>33.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>8.89</td>\n",
       "      <td>4.42</td>\n",
       "      <td>14.46</td>\n",
       "      <td>16.57</td>\n",
       "      <td>13.50</td>\n",
       "      <td>16.84</td>\n",
       "      <td>20.75</td>\n",
       "      <td>15.99</td>\n",
       "      <td>29.86</td>\n",
       "      <td>25.85</td>\n",
       "      <td>30.51</td>\n",
       "      <td>33.76</td>\n",
       "      <td>33.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>14.01</td>\n",
       "      <td>13.81</td>\n",
       "      <td>5.55</td>\n",
       "      <td>6.72</td>\n",
       "      <td>11.51</td>\n",
       "      <td>22.10</td>\n",
       "      <td>24.56</td>\n",
       "      <td>22.24</td>\n",
       "      <td>11.34</td>\n",
       "      <td>28.28</td>\n",
       "      <td>33.45</td>\n",
       "      <td>30.94</td>\n",
       "      <td>34.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>8.01</td>\n",
       "      <td>9.04</td>\n",
       "      <td>13.35</td>\n",
       "      <td>16.79</td>\n",
       "      <td>12.23</td>\n",
       "      <td>20.75</td>\n",
       "      <td>25.08</td>\n",
       "      <td>22.74</td>\n",
       "      <td>22.97</td>\n",
       "      <td>28.04</td>\n",
       "      <td>29.14</td>\n",
       "      <td>35.78</td>\n",
       "      <td>32.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     xterm  mlterm  kterm  mrxvt    xvt  konsole  urxvt     st  alacritty  \\\n",
       "0     4.17    4.93  17.94  13.42  19.62    25.38  19.40  23.69      32.33   \n",
       "1    12.26    4.00   4.79  13.80  13.80    17.09  20.14  29.13      20.87   \n",
       "2    11.75   13.70   2.61  12.77  15.70    23.21  19.37  23.98      17.88   \n",
       "3    11.56    4.99  14.45   8.39   7.76    16.74  21.16  22.06      25.66   \n",
       "4    11.71   13.78  12.03   3.99  12.36    14.28  25.82  26.93      30.67   \n",
       "5     8.97   14.19  13.80  18.23  13.88    22.43  23.90  26.52      25.72   \n",
       "6    14.23    4.87  12.68   8.04  17.02    18.71  21.24  20.53      28.59   \n",
       "7    14.88   12.92  13.88   6.45  13.09    20.50  25.18  21.21      25.50   \n",
       "8     9.97    7.84  14.00   7.30  11.00    20.71  25.72  20.86      28.69   \n",
       "9    13.90   13.80   4.55  13.31  11.82    21.35  21.77  25.75      31.85   \n",
       "10   14.46    4.77   5.92   7.38  12.59    20.49  25.14  22.83      25.58   \n",
       "11    8.29    5.78   5.98  18.72  15.15    20.66  22.74  23.88      27.57   \n",
       "12   11.84   13.97  12.84  16.72  11.09    19.90  24.10  23.92      31.02   \n",
       "13    5.47   13.14  13.57   9.69   7.68    23.75  25.13  22.82      26.06   \n",
       "14    4.69   13.24  13.31   5.77  12.07    21.66  26.41  25.54      30.81   \n",
       "15    9.80   13.40  14.60  14.28  10.69    19.89  25.41  26.86      21.89   \n",
       "16    4.39    3.92   5.33   7.21  17.89    19.67  25.66  23.61      11.90   \n",
       "17   13.70    5.21   7.41  13.54  13.26    15.98  22.87  23.98      27.07   \n",
       "18    5.99   13.03  14.17  18.93  10.03    25.68  23.43  25.72      25.51   \n",
       "19   13.45   12.31  14.86  16.80  12.61    24.22  23.39  24.62      29.94   \n",
       "20    5.64    6.96  13.09  12.96  20.05    20.96  23.58  20.55      25.76   \n",
       "21    5.56    6.55   8.18  14.08  13.21    20.13  25.46  26.19      25.44   \n",
       "22   14.29   12.47  14.82  17.85  12.35    21.22  25.78  25.35      30.66   \n",
       "23   14.07   13.81  14.49  17.57  11.61    16.09  22.94  23.16      25.44   \n",
       "24    6.52   14.11  13.05  12.79  15.11    22.74  23.75  23.17      16.30   \n",
       "25    4.68   12.02   4.87   6.93  13.07    24.41  21.82  26.05      25.03   \n",
       "26   10.32   13.91   5.23  13.86  18.89    20.02  24.57  24.01      26.18   \n",
       "27    7.10   14.38   9.75  13.96  18.87    20.72  24.88  23.30      25.91   \n",
       "28   11.52    3.75  14.95  13.71   9.80    21.79  21.80  23.66      30.21   \n",
       "29    8.27    5.33  13.90  12.85  10.76    15.53  19.89  23.95      24.57   \n",
       "..     ...     ...    ...    ...    ...      ...    ...    ...        ...   \n",
       "170   9.96   14.63  13.30  16.38  11.79    20.83  23.49  22.76      24.18   \n",
       "171   4.10   11.16   9.21  12.95  12.29    21.74  23.35  21.28      26.07   \n",
       "172   5.34    4.74  14.49  13.97  11.68    20.68  25.96  20.86      16.03   \n",
       "173   6.37   13.92  14.92  13.88  10.39    20.39  23.67  25.08      26.36   \n",
       "174  14.46   13.18  13.54  12.56  10.74    20.27  25.25  24.42      26.04   \n",
       "175  14.73   14.33  14.30  12.37  13.39    21.58  20.25  22.65      24.57   \n",
       "176   8.37   14.08  14.73  13.53  11.04    20.55  25.00  22.89      16.47   \n",
       "177   6.56   11.89  12.15   6.97  10.92    20.85  23.97  29.17      29.92   \n",
       "178  14.55    5.09  13.16  14.37  18.44    24.64  21.10  24.37      25.48   \n",
       "179   8.16   11.80   4.93  13.43  17.70    22.12  24.71  26.61      28.72   \n",
       "180   8.97   13.72   4.43  20.26  14.11    20.51  24.76  23.70      27.72   \n",
       "181   6.81    8.29   6.94  20.00  10.25    17.09  20.70  25.82      29.81   \n",
       "182   8.59    4.02  13.33  13.89   9.38    22.38  23.33  23.44      29.83   \n",
       "183   5.57   13.99  12.51  13.25  10.35    20.90  27.01  23.74      22.60   \n",
       "184  14.00    4.69  12.96  12.87  10.66    19.88  20.86  25.68      24.43   \n",
       "185   8.98   14.58  14.31   9.93  12.00    23.36  24.14  21.94      11.80   \n",
       "186   9.74   14.12  13.11  13.59  11.16    17.24  23.25  23.87      30.91   \n",
       "187   4.18   13.14  13.99   6.73  12.78    20.91  20.50  25.88      25.65   \n",
       "188   5.29   13.89  13.49   9.60  12.72    20.86  22.41  25.67      28.98   \n",
       "189   9.30    4.50  13.48   7.27  19.49    21.35  25.79  18.78      25.46   \n",
       "190   7.02    7.87  13.64  21.48  12.72    18.09  20.80  22.71      16.02   \n",
       "191  13.98   14.18  13.94   3.95  11.15    23.08  23.88  21.10      28.48   \n",
       "192  14.46   14.22   8.83  17.74  20.20    22.54  25.54  23.56      26.69   \n",
       "193  13.99    8.57  13.39  17.31  17.48    20.44  24.83  22.55      31.01   \n",
       "194   4.73    5.17  13.06  13.15  12.19    20.87  23.94  24.47      11.01   \n",
       "195  14.39    4.88  10.65  12.12  18.00    20.91  25.50  16.65      22.45   \n",
       "196  14.53    5.70   5.70   8.26  20.17    21.65  19.22  23.95      25.98   \n",
       "197   8.89    4.42  14.46  16.57  13.50    16.84  20.75  15.99      29.86   \n",
       "198  14.01   13.81   5.55   6.72  11.51    22.10  24.56  22.24      11.34   \n",
       "199   8.01    9.04  13.35  16.79  12.23    20.75  25.08  22.74      22.97   \n",
       "\n",
       "     putty  xfce4  gnome  terminator  \n",
       "0    31.17  31.46  29.38       38.79  \n",
       "1    25.49  32.56  30.03       33.44  \n",
       "2    25.69  31.03  32.89       33.49  \n",
       "3    28.10  28.76  31.90       33.30  \n",
       "4    29.32  32.28  31.95       28.72  \n",
       "5    28.25  31.10  32.33       30.73  \n",
       "6    27.41  30.29  28.34       33.34  \n",
       "7    24.52  31.48  29.46       34.23  \n",
       "8    29.01  29.04  30.87       29.32  \n",
       "9    27.92  29.92  33.64       32.89  \n",
       "10   28.01  31.13  32.20       30.91  \n",
       "11   26.14  31.03  31.54       33.64  \n",
       "12   30.15  32.43  32.21       33.71  \n",
       "13   29.34  33.45  33.68       30.81  \n",
       "14   24.97  30.65  34.06       32.27  \n",
       "15   27.90  29.89  32.28       29.55  \n",
       "16   27.86  28.78  34.22       31.10  \n",
       "17   23.64  29.98  33.09       31.24  \n",
       "18   29.59  32.46  33.24       29.78  \n",
       "19   30.56  31.55  33.04       32.69  \n",
       "20   25.90  29.64  31.02       35.87  \n",
       "21   27.90  30.17  33.74       31.72  \n",
       "22   29.48  29.77  33.65       30.29  \n",
       "23   24.83  33.86  29.21       31.32  \n",
       "24   27.56  30.23  31.34       30.72  \n",
       "25   30.77  29.88  32.43       34.11  \n",
       "26   28.12  31.36  31.67       35.33  \n",
       "27   27.81  33.15  33.84       33.53  \n",
       "28   25.06  33.46  32.65       29.51  \n",
       "29   31.53  33.41  34.71       32.73  \n",
       "..     ...    ...    ...         ...  \n",
       "170  24.96  32.09  34.42       33.26  \n",
       "171  25.41  33.14  32.31       32.44  \n",
       "172  27.71  31.20  33.37       34.14  \n",
       "173  27.86  31.88  32.26       32.70  \n",
       "174  28.53  28.88  34.67       33.29  \n",
       "175  25.47  31.05  32.77       31.87  \n",
       "176  24.64  29.50  33.45       32.43  \n",
       "177  28.57  33.56  33.83       33.34  \n",
       "178  29.51  33.58  29.65       33.82  \n",
       "179  27.40  29.60  33.45       34.19  \n",
       "180  28.17  31.71  29.57       31.78  \n",
       "181  27.28  29.60  35.18       30.11  \n",
       "182  27.62  30.67  30.94       33.64  \n",
       "183  26.81  33.45  29.55       33.33  \n",
       "184  27.90  33.07  32.45       33.23  \n",
       "185  27.94  33.94  34.07       32.79  \n",
       "186  29.49  34.79  32.73       34.84  \n",
       "187  25.27  29.97  33.54       34.36  \n",
       "188  24.91  30.33  33.56       32.07  \n",
       "189  29.46  32.43  31.60       31.43  \n",
       "190  27.71  33.62  34.41       29.59  \n",
       "191  28.50  32.90  29.73       32.93  \n",
       "192  24.95  29.97  31.87       33.36  \n",
       "193  25.31  34.32  33.84       31.75  \n",
       "194  29.02  29.50  34.29       33.66  \n",
       "195  28.83  32.84  33.47       33.13  \n",
       "196  30.00  33.49  30.62       33.66  \n",
       "197  25.85  30.51  33.76       33.52  \n",
       "198  28.28  33.45  30.94       34.09  \n",
       "199  28.04  29.14  35.78       32.67  \n",
       "\n",
       "[200 rows x 13 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latency = pandas.read_csv('latency-all.csv')\n",
    "\n",
    "for terminal in []:  # use ignored_terms to skip the same as above\n",
    "    try:\n",
    "        latency.drop(terminal, axis=1, inplace=True)\n",
    "    except ValueError:\n",
    "        pass\n",
    "latency.describe().transpose()\n",
    "latency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|            |   count |    mean |     std |   min |     25% |    50% |     75% |   max |\n",
      "|:-----------|--------:|--------:|--------:|------:|--------:|-------:|--------:|------:|\n",
      "| xterm      |     200 |  9.5595 | 3.61008 |  3.03 |  5.6475 |  9.46  | 13.1625 | 15.04 |\n",
      "| mlterm     |     200 | 10.0466 | 3.98002 |  3.75 |  5.4075 | 11.82  | 13.74   | 15.24 |\n",
      "| kterm      |     200 | 10.3916 | 4.07847 |  2.21 |  5.4875 | 12.605 | 13.98   | 17.94 |\n",
      "| mrxvt      |     200 | 13.6173 | 4.09194 |  3.95 | 11.9375 | 13.425 | 17.1275 | 21.63 |\n",
      "| xvt        |     200 | 13.9408 | 3.65453 |  7.54 | 11.0675 | 12.915 | 17.895  | 21.09 |\n",
      "| konsole    |     200 | 19.5795 | 3.00694 | 13.75 | 16.75   | 20.41  | 21.78   | 28.35 |\n",
      "| urxvt      |     200 | 23.472  | 1.89033 | 19.22 | 22.2725 | 23.67  | 25.005  | 27.01 |\n",
      "| st         |     200 | 23.8257 | 1.94372 | 15.99 | 22.8175 | 23.8   | 25.3725 | 29.17 |\n",
      "| alacritty  |     200 | 24.1864 | 6.14005 |  8.28 | 22.5625 | 26.045 | 28.485  | 32.33 |\n",
      "| putty      |     200 | 27.7548 | 1.93073 | 23.64 | 25.8875 | 28.095 | 29.23   | 31.65 |\n",
      "| xfce4      |     200 | 31.9299 | 1.7065  | 28.16 | 30.9075 | 31.83  | 33.235  | 36.08 |\n",
      "| gnome      |     200 | 32.4747 | 1.71692 | 28.26 | 31.4325 | 32.645 | 33.715  | 36.79 |\n",
      "| terminator |     200 | 32.6866 | 1.51636 | 28.72 | 31.7425 | 32.85  | 33.5725 | 38.79 |\n"
     ]
    }
   ],
   "source": [
    "from tabulate import tabulate\n",
    "print(tabulate(latency.describe().transpose(), headers='keys', tablefmt='pipe'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6913a14358>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6913a6b898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\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)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latency test, editors\n",
    "\n",
    "Validates the results from Falin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>emacs -q</th>\n",
       "      <td>200.0</td>\n",
       "      <td>4.80690</td>\n",
       "      <td>2.318233</td>\n",
       "      <td>0.50</td>\n",
       "      <td>4.2275</td>\n",
       "      <td>4.910</td>\n",
       "      <td>5.2625</td>\n",
       "      <td>32.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emacs</th>\n",
       "      <td>200.0</td>\n",
       "      <td>6.46015</td>\n",
       "      <td>4.353637</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.3775</td>\n",
       "      <td>6.280</td>\n",
       "      <td>6.8725</td>\n",
       "      <td>57.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vim.gtk3</th>\n",
       "      <td>200.0</td>\n",
       "      <td>16.50690</td>\n",
       "      <td>7.922458</td>\n",
       "      <td>0.37</td>\n",
       "      <td>15.7725</td>\n",
       "      <td>20.555</td>\n",
       "      <td>21.1900</td>\n",
       "      <td>27.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vim.gtk</th>\n",
       "      <td>200.0</td>\n",
       "      <td>3.88750</td>\n",
       "      <td>1.239527</td>\n",
       "      <td>0.67</td>\n",
       "      <td>3.6250</td>\n",
       "      <td>4.230</td>\n",
       "      <td>4.5300</td>\n",
       "      <td>11.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vim.athena</th>\n",
       "      <td>200.0</td>\n",
       "      <td>2.80030</td>\n",
       "      <td>1.144322</td>\n",
       "      <td>0.37</td>\n",
       "      <td>2.3875</td>\n",
       "      <td>2.870</td>\n",
       "      <td>3.2325</td>\n",
       "      <td>12.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gedit</th>\n",
       "      <td>200.0</td>\n",
       "      <td>8.90400</td>\n",
       "      <td>3.365788</td>\n",
       "      <td>2.83</td>\n",
       "      <td>6.2325</td>\n",
       "      <td>9.835</td>\n",
       "      <td>11.9825</td>\n",
       "      <td>14.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count      mean       std   min      25%     50%      75%    max\n",
       "emacs -q    200.0   4.80690  2.318233  0.50   4.2275   4.910   5.2625  32.52\n",
       "emacs       200.0   6.46015  4.353637  0.97   5.3775   6.280   6.8725  57.06\n",
       "vim.gtk3    200.0  16.50690  7.922458  0.37  15.7725  20.555  21.1900  27.22\n",
       "vim.gtk     200.0   3.88750  1.239527  0.67   3.6250   4.230   4.5300  11.90\n",
       "vim.athena  200.0   2.80030  1.144322  0.37   2.3875   2.870   3.2325  12.67\n",
       "gedit       200.0   8.90400  3.365788  2.83   6.2325   9.835  11.9825  14.22"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latency_editors = pandas.read_csv('editors.csv')\n",
    "latency_editors.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f69134ef978>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ASRRFAAAAAHRQFAEAAACQRFEEAAAAQAdFEQAAAABJFEUAAAAAdFAUAQAAAJBE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\n/AVDvRmqMb0FLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69138979b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "latency_editors.drop(\"emacs\", axis=1, inplace=True)\n",
    "\n",
    "\n",
    "ax = sns.boxplot(data=latency_editors, palette='colorblind')\n",
    "ax.set_title('Editor input latency')\n",
    "ax.set_ylabel('latency (ms)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6913791668>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TTugZAwAAAJWnIoKipqbL+tXpM2vW+UmSZcvuL6qkQUknEQAAAFS2igiK2D86iQAAAKCy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\niIiIiIiIiAAA/w/uLgeLKnN3eQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f691388b438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "latency = pandas.concat([pandas.read_csv('editors.csv').assign(family='editors'), pandas.read_csv('latency-all.csv').assign(family='terminals')])\n",
    "latency.drop(\"emacs\", axis=1, inplace=True)\n",
    "\n",
    "ax = sns.boxplot(data=latency, palette='colorblind')\n",
    "ax.set_title('Input latency')\n",
    "ax.set_ylabel('latency (ms)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resources tests, preliminary results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>time (s)</th>\n",
       "      <th>cpu (%)</th>\n",
       "      <th>memory (k)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.961111</td>\n",
       "      <td>49.444444</td>\n",
       "      <td>1890.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.437045</td>\n",
       "      <td>33.388288</td>\n",
       "      <td>72.027772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.520000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.150000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1824.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.900000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1880.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.670000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>1944.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>26.050000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time (s)    cpu (%)   memory (k)\n",
       "count   9.000000   9.000000     9.000000\n",
       "mean   10.961111  49.444444  1890.666667\n",
       "std     9.437045  33.388288    72.027772\n",
       "min     2.520000  13.000000  1800.000000\n",
       "25%     3.150000  29.000000  1824.000000\n",
       "50%     6.900000  38.000000  1880.000000\n",
       "75%    18.670000  79.000000  1944.000000\n",
       "max    26.050000  99.000000  2000.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resources = pandas.read_csv('performance.csv')\n",
    "\n",
    "ignored_terms = ['xvt',  # rare\n",
    "                 'eterm',  # too old\n",
    "                 'mrxvt',  # mostly like rxvt\n",
    "                 'xfce4-terminal',  # like gnome-terminal\n",
    "                 'xfce4',  # synonym\n",
    "                 'terminator',  # synonym\n",
    "                 'Terminator']  # like gnome-terminal\n",
    "for terminal in ignored_terms:\n",
    "    resources = resources[resources.terminal != terminal]  # rare\n",
    "resources = resources.sort_values(by='time (s)')\n",
    "resources.sort_values(by='time (s)')\n",
    "resources.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f691327e5f8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v+dv9n//8Z83/e82fPz8//vGPM378+FRVVWXs2LFZd91163u3KBg2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f691326cf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "ax = sns.barplot(data=resources, x='terminal', y='time (s)', palette='colorblind')\n",
    "ax.set_title('Processing time (smaller is better)')\n",
    "\n",
    "#g = sns.PairGrid(resources,\n",
    "#                 x_vars='terminal',\n",
    "#                 y_vars=['time (s)', 'cpu (%)', 'memory (k)'])\n",
    "#g.map(sns.barplot, palette=\"colorblind\")\n",
    "#g.fig.set_figwidth(FIG_WIDTH)\n",
    "#g.fig.set_figheight(FIG_HEIGTH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resources tests, Debian 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>time</th>\n",
       "      <th>cpu</th>\n",
       "      <th>memory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.840050</td>\n",
       "      <td>138.830000</td>\n",
       "      <td>28079.235000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.289404</td>\n",
       "      <td>20.396289</td>\n",
       "      <td>20618.593129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.370000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>7788.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.590000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>12089.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.910000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>17282.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.035000</td>\n",
       "      <td>145.500000</td>\n",
       "      <td>42401.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.360000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>64120.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             time         cpu        memory\n",
       "count  800.000000  800.000000    800.000000\n",
       "mean     0.840050  138.830000  28079.235000\n",
       "std      0.289404   20.396289  20618.593129\n",
       "min      0.370000  116.000000   7788.000000\n",
       "25%      0.590000  121.000000  12089.000000\n",
       "50%      0.910000  133.000000  17282.000000\n",
       "75%      1.035000  145.500000  42401.000000\n",
       "max      1.360000  180.000000  64120.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pandas.read_csv('debian9-xorg/times-100x100000.csv')\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             cpu        memory        time\n",
      "terminal                                                  \n",
      "konsole        count  100.000000    100.000000  100.000000\n",
      "               mean   140.330000  63447.480000    0.679200\n",
      "               std      2.300878    260.941980    0.041382\n",
      "               min    133.000000  62512.000000    0.620000\n",
      "               25%    139.000000  63342.000000    0.650000\n",
      "               50%    141.000000  63430.000000    0.675000\n",
      "               75%    142.000000  63609.000000    0.700000\n",
      "               max    144.000000  64120.000000    0.830000\n",
      "mlterm         count  100.000000    100.000000  100.000000\n",
      "               mean   133.100000  11313.040000    0.934100\n",
      "               std      1.114641     70.529587    0.015048\n",
      "               min    130.000000  11196.000000    0.910000\n",
      "               25%    132.000000  11272.000000    0.920000\n",
      "               50%    133.000000  11296.000000    0.930000\n",
      "               75%    134.000000  11332.000000    0.940000\n",
      "               max    135.000000  11528.000000    0.980000\n",
      "pterm          count  100.000000    100.000000  100.000000\n",
      "               mean   132.580000  21153.000000    0.919700\n",
      "               std      0.654124    182.575403    0.012264\n",
      "               min    131.000000  20692.000000    0.900000\n",
      "               25%    132.000000  21115.000000    0.910000\n",
      "               50%    133.000000  21186.000000    0.920000\n",
      "               75%    133.000000  21281.000000    0.930000\n",
      "               max    134.000000  21432.000000    0.950000\n",
      "stterm         count  100.000000    100.000000  100.000000\n",
      "               mean   174.730000   7884.720000    0.417600\n",
      "               std      3.741266     50.785761    0.019701\n",
      "               min    155.000000   7788.000000    0.370000\n",
      "               25%    174.000000   7848.000000    0.400000\n",
      "               50%    175.500000   7880.000000    0.420000\n",
      "               75%    177.000000   7908.000000    0.430000\n",
      "               max    180.000000   7996.000000    0.460000\n",
      "terminator     count  100.000000    100.000000  100.000000\n",
      "               mean   117.410000  57693.120000    1.097200\n",
      "               std      0.779731    198.132762    0.009648\n",
      "               min    116.000000  57332.000000    1.080000\n",
      "               25%    117.000000  57524.000000    1.090000\n",
      "               50%    117.000000  57698.000000    1.100000\n",
      "               75%    118.000000  57857.000000    1.100000\n",
      "               max    120.000000  58112.000000    1.130000\n",
      "urxvt          count  100.000000    100.000000  100.000000\n",
      "               mean   168.720000  13647.120000    0.456500\n",
      "               std      2.885281    111.032735    0.013881\n",
      "               min    150.000000  13408.000000    0.430000\n",
      "               25%    168.000000  13568.000000    0.450000\n",
      "               50%    169.000000  13660.000000    0.460000\n",
      "               75%    170.000000  13733.000000    0.460000\n",
      "               max    173.000000  13872.000000    0.500000\n",
      "uxterm         count  100.000000    100.000000  100.000000\n",
      "               mean   120.630000  12474.240000    1.319100\n",
      "               std      0.630135     61.347350    0.012561\n",
      "               min    119.000000  12276.000000    1.300000\n",
      "               25%    120.000000  12436.000000    1.310000\n",
      "               50%    121.000000  12474.000000    1.320000\n",
      "               75%    121.000000  12509.000000    1.330000\n",
      "               max    122.000000  12608.000000    1.360000\n",
      "xfce4-terminal count  100.000000    100.000000  100.000000\n",
      "               mean   123.140000  37021.160000    0.897000\n",
      "               std      1.154875    215.269468    0.028692\n",
      "               min    119.000000  36588.000000    0.870000\n",
      "               25%    123.000000  36856.000000    0.880000\n",
      "               50%    123.000000  36972.000000    0.890000\n",
      "               75%    124.000000  37215.000000    0.900000\n",
      "               max    125.000000  37424.000000    1.020000\n"
     ]
    }
   ],
   "source": [
    "print(data.groupby('terminal').describe().to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6912dcb518>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zjQAA1gOtrZvlu98YXHYM/g6WpwHQaMw0AgCg5qZdNjfTLptbdgwAoBeURgAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6912e049b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "# lots of outliers in xterm, some in pterm, but others have too little variability\n",
    "ax = sns.boxplot(data=data, x='terminal', y='time', palette='colorblind')\n",
    "ax = sns.barplot(data=data, x='terminal', y='time', palette='colorblind')\n",
    "ax.set_title('Processing time (smaller is better)')\n",
    "ax.set_ylabel('time (s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KCwszl51Op9xut7kcEhLSYtvdu3friiuuUEREhPlcVlaWpkyZorS0NNXW1lpTNAAANmTZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6912e1ceb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "small = pandas.read_csv('times-10x10000.csv')\n",
    "ax = sns.boxplot(data=small, x='terminal', y='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6912f7ca58>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6912e1c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "ax = sns.barplot(data=data, x='terminal', y='cpu', palette='colorblind')\n",
    "ax = sns.boxplot(data=data, x='terminal', y='cpu', palette='colorblind')\n",
    "\n",
    "ax.set_title('CPU usage (correlates inversly with speed)')\n",
    "ax.set_ylabel('CPU (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6912ba3ef0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6deuUmZmpI0eOyGazKTc3V1evXtXzzz+vqKgoSVJYWJii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\nDEOyCQAAAAAAAIb5P+Dvq0hBx3XtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6912b82e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "# no visible variability, apart from an outlier in xterm\n",
    "ax = sns.boxplot(data=data, x='terminal', y='memory', palette='colorblind')\n",
    "ax = sns.barplot(data=data, x='terminal', y='memory', palette='colorblind')\n",
    "ax.set_title('Memory usage')\n",
    "ax.set_ylabel('memory (k)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resources tests, Fedora 27, Xorg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>time</th>\n",
       "      <th>cpu</th>\n",
       "      <th>memory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.900937</td>\n",
       "      <td>136.458750</td>\n",
       "      <td>28904.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.339163</td>\n",
       "      <td>21.010774</td>\n",
       "      <td>21843.871481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.360000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>7380.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.680000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>11835.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.930000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>17316.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.165000</td>\n",
       "      <td>142.500000</td>\n",
       "      <td>44603.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.370000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>69372.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             time         cpu        memory\n",
       "count  800.000000  800.000000    800.000000\n",
       "mean     0.900937  136.458750  28904.140000\n",
       "std      0.339163   21.010774  21843.871481\n",
       "min      0.360000  101.000000   7380.000000\n",
       "25%      0.680000  122.000000  11835.000000\n",
       "50%      0.930000  125.000000  17316.000000\n",
       "75%      1.165000  142.500000  44603.000000\n",
       "max      1.370000  177.000000  69372.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pandas.read_csv('fedora27-xorg/times-100x100000.csv')\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             cpu        memory        time\n",
      "terminal                                                  \n",
      "konsole        count  100.000000    100.000000  100.000000\n",
      "               mean   127.210000  68649.960000    0.835600\n",
      "               std      3.720799    309.098393    0.034416\n",
      "               min    101.000000  67936.000000    0.770000\n",
      "               25%    126.000000  68408.000000    0.810000\n",
      "               50%    128.000000  68638.000000    0.840000\n",
      "               75%    129.000000  68823.000000    0.850000\n",
      "               max    132.000000  69372.000000    0.960000\n",
      "mlterm         count  100.000000    100.000000  100.000000\n",
      "               mean   123.840000  11071.880000    1.328300\n",
      "               std      0.837505     89.913907    0.021275\n",
      "               min    122.000000  10848.000000    1.270000\n",
      "               25%    123.000000  11020.000000    1.320000\n",
      "               50%    124.000000  11078.000000    1.330000\n",
      "               75%    124.000000  11133.000000    1.340000\n",
      "               max    126.000000  11268.000000    1.370000\n",
      "pterm          count  100.000000    100.000000  100.000000\n",
      "               mean   133.550000  19989.520000    0.932400\n",
      "               std      0.538891    197.640858    0.014079\n",
      "               min    132.000000  19656.000000    0.900000\n",
      "               25%    133.000000  19828.000000    0.920000\n",
      "               50%    134.000000  20014.000000    0.930000\n",
      "               75%    134.000000  20189.000000    0.940000\n",
      "               max    135.000000  20320.000000    0.970000\n",
      "st             count  100.000000    100.000000  100.000000\n",
      "               mean   170.270000   7548.200000    0.389600\n",
      "               std      1.516608    105.677803    0.008980\n",
      "               min    165.000000   7380.000000    0.370000\n",
      "               25%    169.000000   7456.000000    0.380000\n",
      "               50%    170.000000   7560.000000    0.390000\n",
      "               75%    171.000000   7628.000000    0.392500\n",
      "               max    173.000000   7748.000000    0.410000\n",
      "terminator     count  100.000000    100.000000  100.000000\n",
      "               mean   118.850000  56631.880000    1.127300\n",
      "               std      0.715979    250.492173    0.007366\n",
      "               min    118.000000  56096.000000    1.110000\n",
      "               25%    118.000000  56426.000000    1.120000\n",
      "               50%    119.000000  56650.000000    1.130000\n",
      "               75%    119.000000  56840.000000    1.130000\n",
      "               max    121.000000  57140.000000    1.140000\n",
      "urxvt          count  100.000000    100.000000  100.000000\n",
      "               mean   173.700000  14840.120000    0.380600\n",
      "               std      1.708564     93.539953    0.008018\n",
      "               min    169.000000  14624.000000    0.360000\n",
      "               25%    173.000000  14783.000000    0.380000\n",
      "               50%    174.000000  14862.000000    0.380000\n",
      "               75%    175.000000  14908.000000    0.390000\n",
      "               max    177.000000  14976.000000    0.400000\n",
      "uxterm         count  100.000000    100.000000  100.000000\n",
      "               mean   121.720000  12140.280000    1.280800\n",
      "               std      1.326345     89.513622    0.019263\n",
      "               min    118.000000  12024.000000    1.240000\n",
      "               25%    121.000000  12075.000000    1.270000\n",
      "               50%    122.000000  12120.000000    1.280000\n",
      "               75%    122.250000  12165.000000    1.290000\n",
      "               max    125.000000  12388.000000    1.330000\n",
      "xfce4-terminal count  100.000000    100.000000  100.000000\n",
      "               mean   122.530000  40361.280000    0.932900\n",
      "               std      0.673525    228.259558    0.007148\n",
      "               min    121.000000  39728.000000    0.920000\n",
      "               25%    122.000000  40227.000000    0.930000\n",
      "               50%    123.000000  40374.000000    0.930000\n",
      "               75%    123.000000  40534.000000    0.940000\n",
      "               max    124.000000  40772.000000    0.950000\n"
     ]
    }
   ],
   "source": [
    "print(data.groupby('terminal').describe().to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f69129ab828>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WGg0bNiytra2d24sWLUpzc3OS5L777svixYvz+c9/Pq+//nqefvrp\nTJs2LVOmTFnj8draOioVlQ1YS0t7rSPQQzhX6ArnC+vLuUJXOF9YX9U8V0444V/S2tpStfGqraVl\nUZJkwoSJNU5SOc3NQzJjxkW1jlERRxxxbI444tiqjPXWUsbLL/+Pqoz3lp7ws2FtxVbFSqO99tor\nF154YSZNmpRHH300Q4cO7VyaNnHixEyc+Ob/qJ999tmcdtppay2MAAAA6Lq//vXJvNLRkaR6syuq\nqa6UlEqlLFq0MKvK6359z1PuXFZVDb25ZHyrYHyrPOqNKlEwVqw0Gj16dEaOHJlJkyalVCrljDPO\nyOzZs9PY2Jjx48dXalgAAADeZmW/nr08Zk1KKaf+jTdXpJRKpazqOyDlXlmOrazaSK2tLXlx0YtZ\nOaBf1caslrr6N8+N55a11ThJZdR3vFaR41asNEqSk08+ebXtHXfcsfCaESNG5IorrqhkDAAAgI1S\nY+NmeXKH3jmzom7VG/nwY9eklKScUv7PjodkVV2fWsfqdsMfr+6d4VYO6JeFn9mtqmNWw9aPvpjm\np5ekdZst8vxHtqx1nG437PoHKnLcipZGAAAAUAmr6vqkvX5gNlu5LIsHbd8rC6Nqa29fmvqO17L1\nz39f6yjdrr5Ul7Yk9W1t2frhJ2sdp/uVk/Zy9y9lVBoBAAD0Us3NQ2odoaJefXlxXk3S8pHdax2l\nYqr5HW6yyYAsLVVvOVzVlJP65W90bq7cpF+vvMzXJv0HdPsxlUYAAAC91IwZF2WnM6t7tyi6VzXv\nnHbVVb/IR35yatXGq6btfz0/Axr6pnWbLbKwFy5PS5I7jp7e7cdUGgEAANAtGv/yu/RvfaJq49W/\ntixJMmTuT6o25vLm7dP+wTFVG6832+zBv2aTp1+qylgdr7yWjiQrly/LsMefrcqYr24zOEtHb1uV\nsSpFaQQAAECPVK53HSOoJKURAAAA3aL9g2PMwmG9LR29bVVm4mz6f57LFg/+V5Jk2Ye2yisfHl7x\nMXuLuloHAAAAAKiUzf70zDs+Zt2URgAAAAAUKI0AAACAXmvpzu97x8esm9IIAAAA6LVe+fDwrOpT\nn1V96l3PqItcCBsAAADo1cwweneURgAAAECvZobRu2N5GgAAAAAFSiMAAAAACpRGAAAAABQojQAA\nAAAoUBoBAAAAUKA0AgAAAKBAaQQAAABAgdIIAAAAgAKlEQAAAAAFSiMAAAAACpRGAAAAABQojQAA\nAAAoUBoBAAAAUKA0AgAAAKBAaQQAAABAgdIIAAAAgAKlEQAAAAAFSiMAAAAACpRGAAAAABQojQAA\nAAAoUBoBAAAAUKA0AgAAAKBAaQQAAABAgdIIAAAAgAKlEQAAAAAFSiMAAAAACpRGAAAAABQojQAA\nAAAoUBoBAAAAUKA0AgAAAKBAaQQAAABAgdIIAAAAgAKlEQAAAAAFSiMAAAAACpRGAAAAABQojQAA\nAAAoUBoBAAAAUKA0AgAAAKBAaQQAAABAgdIIAAAAgIKGSh582rRpmTdvXkqlUqZMmZJRo0Z17rvv\nvvty3nnnpa6uLttuu22mTp2aujodFgAAAMCGoGItzdy5c/PUU0/l6quvztSpUzN16tTV9p9++um5\n4IILctVVV+WVV17J3XffXakoAAAAAHRRxUqje++9N+PGjUuSbLfddnn55ZezbNmyzv2zZ8/Olltu\nmSRpampKW1tbpaIAAAAA0EUVW57W2tqakSNHdm43NTWlpaUlAwcOTJLOfy9atCj33HNPjj/++LUe\nb9CgAWloqK9UXDZQQ4Y01joCPYRzha6o9vnyWFVHoztV+1xZ8kJVh6Ob+VnE+nKu0BXOF9ZXJc6V\nipVG5XK5sF0qlVZ77qWXXsqXvvSlnH766Rk0aNBaj9fW1tHtGdnwtbS01zoCPYRzha5wvrC+nCt0\nhfOF9eVcoSucL6yvd3uurK1sqtjytGHDhqW1tbVze9GiRWlubu7cXrZsWY499tgcf/zxGTNmTKVi\nAAAAAPAuVKw02muvvTJnzpwkyaOPPpqhQ4d2LklLkunTp+eoo47KvvvuW6kIAAAAALxLFVueNnr0\n6IwcOTKTJk1KqVTKGWeckdmzZ6exsTFjxozJ9ddfn6eeeirXXnttkuTAAw/MYYcdVqk4AAAAAHRB\nxUqjJDn55JNX295xxx07Hy9YsKCSQwMAAADwHlRseRoAAAAAPZfSCAAAAIACpREAAAAABUojAAAA\nAAqURgAAAAAUKI0AAAAAKFAaAQAAAFCgNAIAAACgQGkEAAAAQIHSCAAAAIACpREAAAAABUojAAAA\nAAqURgAAAAAUKI0AAAAAKFAaAQAAAFCgNAIAAACgQGkEAAAAQIHSCAAAAIACpREAAAAABUojAAAA\nAAqURgAAAAAUKI0AAAAAKFAaAQAAAFCgNAIAAACgQGkEAAAAQIHSCAAAAIACpREAAAAABUojAAAA\nAAqURgAAAAAUKI0AAAAAKFAaAQAAAFCgNAIAAACgQGkEAAAAQIHSCAAAAIACpREAAAAABUojAAAA\nAAqURgAAAAAUKI0AAAAAKFAaAQAAAFCgNAIAAACgQGkEAAAAQIHSCAAAAIACpREAAAAABUojAAAA\nAAqURgAAAAAUKI0AAAAAKFAaAQAAAFCgNAIAAACgQGkEAAAAQIHSCAAAAIACpREAAAAABRUtjaZN\nm5bDDjsskyZNyvz581fbd8899+SQQw7JYYcdlpkzZ1YyBgAAAABdVLHSaO7cuXnqqady9dVXZ+rU\nqZk6depq+88+++xceOGFufLKK/P73/8+TzzxRKWiAMD/3979R0VV538cfw7DkMeTCiO/jJZajcxE\nUfvhWX9QR8F+2nZSTyJCq2SdPSrGqkf0YO2aP7Y0PawQWpkarpLaqKuukZpr5RGjolihbGzLRAoQ\nBQdMR8b5/uHXWWCw9ceMI/p6/DXD537u53PnfnzP57793DsiIiIiInKJvJY02rt3L3FxcQB06dKF\n2tpa6urqADh8+DAdOnSgU6dO+Pn58cADD7B3715vdUVERERERERERC6R15JGR48eJSgoyPXebDZT\nVVUFQFVVFWazucUyERERERERERHxPX9v7djpdLq9NxgMLZYBrrILCQlpd1n9qFiccln15MZUNVXP\n15KLE/92ma+7IK1ISEaCr7sgrURIyBpfd0FakZwn7/J1F6SV0DWRXApdE0ljXltpFBYWxtGjR13v\nKysrCQ4ObrGsoqKCkJAQb3VFREREREREREQukdeSRv379yc/Px+A0tJSQkNDufnmmwG49dZbqaur\no6ysjIaGBnbt2kX//v291RUREREREREREblEBmdL94p5yIIFC/jss88wGAy89NJLlJaW0q5dO+Lj\n4yksLGTBggUADBkyhJQULZkUEREREREREblWeDVpJCIiIiIiIiIirZPXbk8TEREREREREZHWS0kj\nERERERERERFxo6SRD1ksFl555RWvtzNo0CDq6+u93o74XmFhIdXV1b7uhlxl53904Ep99NFHrF69\n+pLrvf/++x5pX1oHxZnrz5kzZxgxYgTTpk27rPp5eXkMGjSoxTJPj5e0tDROnTp1SXXS09PZtWuX\nx/ognlNWVsZTTz0FKLbIlfPUfEhubOXl5RQXF/u6G9cUJY1EriPvvfeeJlw3mLKyMrZu3eqRfcXG\nxjJq1KhLrvfGG294pH1pHRRnrj9VVVXY7fbL+o+s6upqtm/ffsFyT4+XRYsW0aZNG4/tT64dii1y\nJTw5H5IbW0FBgZJGzfj7ugNyzmuvvUabNm0oKyvj8OHD2O12UlNTGTBgAPHx8Tz99NPs2rULu93O\n8uXLOXHiBFOnTsXPzw+Hw8H8+fMJDQ3lxRdfdKt/XkVFBRkZGdjtdoxGI7Nnz+aWW27x4VHLlbBY\nLHz88cfU1dXx888/k5SUxI4dO7BarSxevJj9+/fz9ttv4+/vT3R0NOnp6VgsFj766CMqKyuZPHky\nr732GpGRkRQVFZGQkMCBAwf46quvSExMJDEx0deHKBdh1qxZFBcXk5WVxbfffkttbS0Oh4OMjAzu\nuusuhgwZQmxsLB07duTQoUOYzWZKSko4duwY48aNw2KxcPz4cVatWsX27duxWq0kJiaSnp7Ob37z\nGw4cOEC3bt2YM2cO33zzDX/5y1/w9/fHz8+PzMxM1q9fz4EDB5gwYQJZWVm8+uqrfPHFFzgcDhIT\nE3nyySdJSkoiKioKgBdffNHHn5hciMViwWq1Mm3aNOrr6xk4cCARERHk5eXhcDgYNWoU48ePV5y5\nDmRlZdG2bVvGjh1LdnY2WVlZtGnThunTpzN9+nSmTJlCXV0d7dq1Y+HChTidTmbMmOEWXwDmz59P\namoqaWlpbu3s2bPH4+Nl0KBBbN68mZdffpnQ0FBKSkooLy9nwYIFdO/enXnz5lFcXMzp06dJSEhg\nxIgRV/vjlWYsFguFhYUcP34cq9VKWloaW7Zs4bvvvnP9krI3xopc38rLy5tcCxmNRqxWK1lZWUyY\nMMHX3RMfaD6PGTp0KP7+/q558KeffkpaWho9e/Zk7NixjB8/nhkzZrhWqFksFj777DMKCgrw9/en\nU6dODB482MdHdW1Q0uga8P7771NeXs7AgQOprKxk1apVVFRUkJycTH5+Pg6Hg86dO/Pss8+SlpZG\nQUEBhw8fpl+/fowfP56SkhKqqqooLCwkICDArf55mZmZjBkzhn79+rF7925ef/11Zs+e7cMjlyt1\n8OBBNmzYwIkTJ/j9739P165deemll+jQoQM5OTm8++67BAQEMGnSJD7//HMAfvrpJ/Ly8jhy5Ahf\nf/012dnZ1NbW8vjjj7Nz505Onz7NxIkTNeFqJVJSUvj73/+OwWBg4MCBjBgxgoMHDzJnzhyWL19O\nQ0MDsbGxxMbGkp6ejr+/PytXrmTy5MkUFRWxYsUKpk6dyr59+5rst6SkhEWLFtGxY0diY2M5ceIE\n1dXVzJw5k7vvvpvMzEw2b97Ms88+y5tvvklWVhaFhYVYrVby8vI4efIkTzzxBHFxcQBERUWRkJDg\ni49ILlNgYCBjxozhjTfe4PTp0zz//PM88sgjrF69mpkzZyrOtGLPPfcciYmJDBgwgN27d7Nt2zb+\n9Kc/MW/ePBYtWsSAAQNITk5mxYoV7N27lwMHDrQYX/bt28dNN91ETExMi+3079+fbt26eW282O12\nli1bxpo1a9i4cSN33HEHERERTJ8+nVOnThEXF6ek0TXihx9+YPXq1axbt46lS5eyceNGLBYLS5cu\nBbw/VuT6k5+f3+RaaM+ePQQFBSlhJE00ngc/9NBDzJo1i+TkZCIiIrjnnnsICwvDarUSFRXFhx9+\nSEpKCp06dSIoKEgJo0aUNPIxq9XKBx98wD//+U/mz59P3759AQgLCyMgIICamhoA7r33XgDCw8Ox\n2Wz079+fCRMmYLPZeOihh+jduzdbtmy5YH2AoqIivv/+e3JycnA4HJjN5qt8tOJp9913H/7+/pjN\nZjp06MDx48eBc8mk8vJyUlJSALDZbJSXlwPQo0cPDAYDAJGRkQQFBREQEIDZbCYsLIz6+npsNptv\nDkguW1FREceOHeMf//gHAL/88ourrGfPnm6vQ0ND6dy5MwDBwcFu5zwyMpKQkBDXtjabjY4dO7Jg\nwQJOnTpFZWUlQ4cObVJn//793HfffQC0bduWO+64g0OHDrn1QVqPp556ipSUFPz8/EhPT29SpjjT\negUEBJCWlsaoUaNYsmQJ/v7/nQ6WlpYyadIkAP7whz8A555Z1Dy+2O12Fi9eTHZ29kW16Y3x0nhu\nVFxczE033URtbS0jR47EZDK5vhPF96KjozEYDISEhNC1a1eMRmOL3z2g2CIXp/m1UExMDPv37/d1\nt+QadH4O2rlzZ3r16sW8efNYt24dAEOGDGHXrl1ERkZy8OBBevXqxSeffOLL7l6TlDTysSNHjhAV\nFeV6kKzT6XSV2e12/PzOPXbKaDS6/u50OrnzzjvZtGkTe/bsYeHChQwbNuxX6wOYTCYyMzMJDQ31\n6jHJ1XP27FnXa6fT6ZpImUwmoqOjWbZsWZPtLRYLJpPJ9b7xuGp80SCtj8lkYubMmfTu3bvFsvMa\nn/PmcaWxxmXny+fMmcO4ceOIjY1l2bJlnDx5ssk258ffeWfOnHHFoMZ9kGtT4/PX0NAAnDuHv/zy\nC2fPnuXMmTNNzqPiTOt29OhR2rdvz88//0x4eLjr70ajscl3C7QcX7766iuqqqoYN24cAJWVlaSl\npfHoo4/yzjvvALBixYom+/D0eGkewz799FMKCgrIzc3FZDK1GA/FNxqfy8avIyIi+Pbbb5tsq9gi\nF+NC10Jy42ppHgNN56BHjx7FZDJhs9kIDAwkPj6eF154gaioKAYMGOA2l5Vz9CBsH3vwwQeZO3cu\nOTk5dO/e3XWLyE8//YSfnx/t27dvsd7WrVuxWq3ExcUxadIk9u/fT48ePX61fkxMDDt27ABg7969\nbN682ctHJ9725Zdf4nA4OHbsGPX19XTs2BGHw8Fvf/tbvvvuO9cDJf/2t79RUVHh496KN/j5+dHQ\n0NDk3/fBgwdZvny5x9uqqakhMjISu93O7t27OXPmDPDfhFN0dLQrBtXX1/Pjjz9y2223ebwf4h03\n33wzlZWVAK5bQZYvX86jjz5KXFyca0wZDAbFmVbOZrOxcuVK1q5dy1tvvUVdXZ2rLDo6moKCAuDc\nCqMNGza0GF9iYmLIz89n7dq1rF27ltDQUBYtWkR8fDy5ubnk5uZiNBqv6ng5fvw44eHhmEwmdu7c\nicPhwG63e7QN8R7FFrkUza+FLBZLk0SB3Hhamsc0VlRUhM1m469//Ssvv/wycO7uHIAtW7bw8MMP\nA+dikcZSU0oaXQPMZjMTJ050TXCSkpJIS0tj1qxZF6xz++23u+7JzM7OJiEhgccee+xX60+YMIGd\nO3eSmJhIdnY2vXr18vahiZdFREQwadIknnnmGV544QXuv/9+UlNTKSsrY8aMGYwbN46RI0dSU1Oj\nFWbXqS5dulBaWsqxY8f48ccfGTVqFBkZGa7bNjxp9OjRjB8/ntTUVJKSkti4cSPffPMN3bp1Y/jw\n4dx7771ER0eTmJjI2LFjmTx5Mm3btvV4P8Q7fve73/H999+TlJTEf/7zH+DcMyNGjhxJcnIyW7du\n5ciRI4oz14GFCxcyZswYgoODGT16NAsXLnSVPfPMMxQVFZGUlMS//vUv4uPjGT169GXHl6s5Xvr1\n68ehQ4cYPXo0hw8f5sEHH+TPf/6zR9sQ71FskUvR/FooNTWV0tJS5s6d6+uuiY80n8c0X3k0d+5c\npsh9uwYAAANiSURBVEyZQs+ePQkMDGTbtm0ADB48mMLCQvr06QNA7969eeutt1y3ZAsYnM3vSRCR\nVqHxLwSIiIiIiIiIeJpWGomIiIiIiIiIiButNBIRERERERERETdaaSQiIiIiIiIiIm6UNBIRERER\nERERETdKGomIiIiIiIiIiBsljURERET+36ZNmzy+z6SkJBwOxyXX27dvHwkJCR7vj4iIiMjFUtJI\nREREBHA4HLz++use329ubi5Go9Hj+xURERHxNn9fd0BERETkWjBjxgyOHDnC2LFjGT58OKtWrcLp\ndGI2m5k9ezZBQUH06dOH4cOHc/bsWeLj41myZAnh4eH8+9//JiYmhq5du7J9+3Zqamp48803CQ8P\np2vXrpSUlJCTk0NNTQ0VFRX88MMP9O3bl5kzZ3Ly5EmmTZtGTU0N9fX1PPzwwzz33HO+/jhERERE\ntNJIREREBGDixImYzWbmzJnDkiVLWLFiBWvWrOH+++9n6dKlAJw8eZIHHniAjIwMAIqLi5k2bRrr\n169n8+bNtG/fntzcXLp3705+fr5bG6WlpWRmZrJ+/XosFgu1tbVUV1czePBgcnNzycvLY+nSpdTV\n1V3VYxcRERFpiVYaiYiIiDRSVFREVVUVKSkpANjtdm699VYAnE4nffr0cW3bpUsXAgMDAQgMDKR3\n794AhIWFYbPZ3PZ9zz33YDQaMRqNBAUFUVtbS3BwMJ9//jl5eXmYTCZOnz5NTU2Ntw9TRERE5H9S\n0khERESkkYCAAHr27OlaXdScyWRyvW7+rKLG751Op1vd5ts7nU5WrlyJ3W5nzZo1GAwG+vbteyXd\nFxEREfEY3Z4mIiIiAvj5+dHQ0ECPHj0oLi6mqqoKgG3btrFjxw6vtVtdXU2XLl0wGAzs3LmTU6dO\nYbfbvdaeiIiIyMVS0khEREQECA0NJTg4mD/+8Y9Mnz6d559/nsTERNavX0+vXr281u6wYcPYsGED\nycnJlJWVMXToUKZMmeK19kREREQulsHZ0tppERERERERERG5oWmlkYiIiIiIiIiIuFHSSERERERE\nRERE3ChpJCIiIiIiIiIibpQ0EhERERERERERN0oaiYiIiIiIiIiIGyWNRERERERERETEjZJGIiIi\nIiIiIiLiRkkjERERERERERFx83/VF4JIWjXO2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6912a04ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "# lots of outliers in xterm, some in pterm, but others have too little variability\n",
    "ax = sns.boxplot(data=data, x='terminal', y='time', palette='colorblind')\n",
    "ax = sns.barplot(data=data, x='terminal', y='time', palette='colorblind')\n",
    "ax.set_title('Processing time (smaller is better)')\n",
    "ax.set_ylabel('time (s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f691287e780>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69128e1b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "ax = sns.barplot(data=data, x='terminal', y='cpu', palette='colorblind')\n",
    "ax = sns.boxplot(data=data, x='terminal', y='cpu', palette='colorblind')\n",
    "\n",
    "ax.set_title('CPU usage (correlates inversly with speed)')\n",
    "ax.set_ylabel('CPU (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6912c58f60>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69128e10b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "# no visible variability, apart from an outlier in xterm\n",
    "ax = sns.boxplot(data=data, x='terminal', y='memory', palette='colorblind')\n",
    "ax = sns.barplot(data=data, x='terminal', y='memory', palette='colorblind')\n",
    "ax.set_title('Memory usage')\n",
    "ax.set_ylabel('memory (k)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resources tests, Fedora 27, Wayland"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>time</th>\n",
       "      <th>cpu</th>\n",
       "      <th>memory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.945612</td>\n",
       "      <td>132.775000</td>\n",
       "      <td>28913.71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.335552</td>\n",
       "      <td>16.261532</td>\n",
       "      <td>21858.23772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>7380.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.745000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>11832.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.980000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>17328.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.212500</td>\n",
       "      <td>137.500000</td>\n",
       "      <td>44597.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.580000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>69424.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             time         cpu       memory\n",
       "count  800.000000  800.000000    800.00000\n",
       "mean     0.945612  132.775000  28913.71000\n",
       "std      0.335552   16.261532  21858.23772\n",
       "min      0.400000  116.000000   7380.00000\n",
       "25%      0.745000  121.000000  11832.00000\n",
       "50%      0.980000  125.000000  17328.00000\n",
       "75%      1.212500  137.500000  44597.00000\n",
       "max      1.580000  172.000000  69424.00000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pandas.read_csv('fedora27-wayland/times-100x100000.csv')\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             cpu        memory        time\n",
      "terminal                                                  \n",
      "konsole        count  100.000000    100.000000  100.000000\n",
      "               mean   125.660000  68735.080000    0.909000\n",
      "               std      2.046036    284.498271    0.023677\n",
      "               min    117.000000  68004.000000    0.840000\n",
      "               25%    125.000000  68552.000000    0.890000\n",
      "               50%    126.000000  68742.000000    0.910000\n",
      "               75%    127.000000  68902.000000    0.930000\n",
      "               max    130.000000  69424.000000    0.970000\n",
      "mlterm         count  100.000000    100.000000  100.000000\n",
      "               mean   123.270000  11073.320000    1.378900\n",
      "               std      0.736563     89.963503    0.025382\n",
      "               min    120.000000  10860.000000    1.340000\n",
      "               25%    123.000000  11034.000000    1.370000\n",
      "               50%    123.000000  11084.000000    1.380000\n",
      "               75%    124.000000  11124.000000    1.390000\n",
      "               max    125.000000  11268.000000    1.580000\n",
      "pterm          count  100.000000    100.000000  100.000000\n",
      "               mean   132.600000  20008.360000    0.980400\n",
      "               std      0.550482    183.771571    0.013327\n",
      "               min    131.000000  19676.000000    0.950000\n",
      "               25%    132.000000  19875.000000    0.970000\n",
      "               50%    133.000000  20024.000000    0.980000\n",
      "               75%    133.000000  20117.000000    0.990000\n",
      "               max    134.000000  20324.000000    1.010000\n",
      "st             count  100.000000    100.000000  100.000000\n",
      "               mean   158.670000   7544.240000    0.436100\n",
      "               std      2.522845     96.061967    0.008396\n",
      "               min    148.000000   7380.000000    0.420000\n",
      "               25%    157.000000   7476.000000    0.430000\n",
      "               50%    158.500000   7552.000000    0.440000\n",
      "               75%    160.000000   7581.000000    0.440000\n",
      "               max    165.000000   7748.000000    0.460000\n",
      "terminator     count  100.000000    100.000000  100.000000\n",
      "               mean   116.970000  56612.640000    1.178300\n",
      "               std      0.688359    225.253147    0.008415\n",
      "               min    116.000000  56132.000000    1.160000\n",
      "               25%    117.000000  56456.000000    1.170000\n",
      "               50%    117.000000  56602.000000    1.180000\n",
      "               75%    117.000000  56796.000000    1.180000\n",
      "               max    119.000000  57144.000000    1.200000\n",
      "urxvt          count  100.000000    100.000000  100.000000\n",
      "               mean   161.010000  14839.280000    0.417600\n",
      "               std      2.217902     88.857857    0.007670\n",
      "               min    156.000000  14640.000000    0.400000\n",
      "               25%    160.000000  14776.000000    0.410000\n",
      "               50%    161.000000  14850.000000    0.420000\n",
      "               75%    162.000000  14913.000000    0.420000\n",
      "               max    172.000000  14980.000000    0.440000\n",
      "uxterm         count  100.000000    100.000000  100.000000\n",
      "               mean   123.390000  12146.480000    1.282400\n",
      "               std      1.278375     85.814515    0.019803\n",
      "               min    119.000000  12020.000000    1.250000\n",
      "               25%    123.000000  12083.000000    1.270000\n",
      "               50%    123.000000  12124.000000    1.280000\n",
      "               75%    124.000000  12188.000000    1.290000\n",
      "               max    126.000000  12360.000000    1.340000\n",
      "xfce4-terminal count  100.000000    100.000000  100.000000\n",
      "               mean   120.630000  40350.280000    0.982200\n",
      "               std      0.691288    226.087142    0.009054\n",
      "               min    119.000000  39692.000000    0.960000\n",
      "               25%    120.000000  40214.000000    0.980000\n",
      "               50%    121.000000  40370.000000    0.980000\n",
      "               75%    121.000000  40536.000000    0.990000\n",
      "               max    122.000000  40752.000000    1.000000\n"
     ]
    }
   ],
   "source": [
    "print(data.groupby('terminal').describe().to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f69126cfc18>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dOzN58mRycnLIzs6mU6dOzJ07l7lz5zJr1iwaGxvp0qUL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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69126adeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "# lots of outliers in xterm, some in pterm, but others have too little variability\n",
    "ax = sns.boxplot(data=data, x='terminal', y='time', palette='colorblind')\n",
    "ax = sns.barplot(data=data, x='terminal', y='time', palette='colorblind')\n",
    "ax.set_title('Processing time (smaller is better)')\n",
    "ax.set_ylabel('time (s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f691253c7f0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6912586a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "ax = sns.barplot(data=data, x='terminal', y='cpu', palette='colorblind')\n",
    "ax = sns.boxplot(data=data, x='terminal', y='cpu', palette='colorblind')\n",
    "\n",
    "ax.set_title('CPU usage (correlates inversly with speed)')\n",
    "ax.set_ylabel('CPU (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f69124b7898>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69123feac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "# no visible variability, apart from an outlier in xterm\n",
    "ax = sns.boxplot(data=data, x='terminal', y='memory', palette='colorblind')\n",
    "ax = sns.barplot(data=data, x='terminal', y='memory', palette='colorblind')\n",
    "ax.set_title('Memory usage')\n",
    "ax.set_ylabel('memory (k)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f691228feb8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAAtVNbQCAAAAICdU9mmpwEAAACw8xIaAQAA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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69122d76a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "deb9_xorg = pandas.read_csv('debian9-xorg/times-100x100000.csv').assign(env='Debian 9, Xorg')\n",
    "f27_xorg = pandas.read_csv('fedora27-xorg/times-100x100000.csv').assign(env='Fedora 27, Xorg')\n",
    "f27_wayland = pandas.read_csv('fedora27-wayland/times-100x100000.csv').assign(env='Fedora 27, Wayland')\n",
    "\n",
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "data = pandas.concat([deb9_xorg, f27_xorg, f27_wayland])\n",
    "data['terminal'].replace(['stterm'], 'st',inplace=True)\n",
    "ax = sns.barplot(data=data, x='terminal', y='time', palette='colorblind', hue='env')\n",
    "ax.set_title('Processing time (smaller is better)')\n",
    "ax.set_ylabel('time (seconds)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6911eb0160>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dOjSDBg1KSUlJBg8enJYtW+aII47IY489ln79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\nABRG2AQAAABAYf4vBXm2qKd3XEQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f69121a5b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "ax = sns.barplot(data=data, x='terminal', y='memory', palette='colorblind', hue='env')\n",
    "ax.set_title('Memory use (smaller is better)')\n",
    "ax.set_ylabel('memory (KB)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f6911ebe4a8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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000/PPffck6uuuso4s5/60pe+lKFDh+bkk0/O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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6911dad198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "ax = sns.barplot(data=data, x='terminal', y='cpu', palette='colorblind', hue='env')\n",
    "ax.set_title('CPU use (smaller is better)')\n",
    "ax.set_ylabel('CPU (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6911cd25c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots()\n",
    "fig.set_figwidth(FIG_WIDTH)\n",
    "fig.set_figheight(FIG_HEIGTH)\n",
    "\n",
    "ax = sns.barplot(data=data, x='terminal', y='time', palette='colorblind', hue='env')\n",
    "#ax = sns.swarmplot(data=data, x='terminal', y='time', palette='colorblind', hue='env')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>terminal</th>\n",
       "      <th>time</th>\n",
       "      <th>cpu</th>\n",
       "      <th>memory</th>\n",
       "      <th>env</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.66</td>\n",
       "      <td>141</td>\n",
       "      <td>63732</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.62</td>\n",
       "      <td>144</td>\n",
       "      <td>63256</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.65</td>\n",
       "      <td>142</td>\n",
       "      <td>63612</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.64</td>\n",
       "      <td>143</td>\n",
       "      <td>63412</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.64</td>\n",
       "      <td>143</td>\n",
       "      <td>63668</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.67</td>\n",
       "      <td>143</td>\n",
       "      <td>63344</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.66</td>\n",
       "      <td>143</td>\n",
       "      <td>62704</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.64</td>\n",
       "      <td>143</td>\n",
       "      <td>63184</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.63</td>\n",
       "      <td>141</td>\n",
       "      <td>63724</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.67</td>\n",
       "      <td>141</td>\n",
       "      <td>63400</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.63</td>\n",
       "      <td>142</td>\n",
       "      <td>63708</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.63</td>\n",
       "      <td>142</td>\n",
       "      <td>63668</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.63</td>\n",
       "      <td>142</td>\n",
       "      <td>63252</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.68</td>\n",
       "      <td>142</td>\n",
       "      <td>62848</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.64</td>\n",
       "      <td>143</td>\n",
       "      <td>63284</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.64</td>\n",
       "      <td>142</td>\n",
       "      <td>63664</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.62</td>\n",
       "      <td>144</td>\n",
       "      <td>63588</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.67</td>\n",
       "      <td>143</td>\n",
       "      <td>63416</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.66</td>\n",
       "      <td>142</td>\n",
       "      <td>63540</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.64</td>\n",
       "      <td>144</td>\n",
       "      <td>63492</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.63</td>\n",
       "      <td>141</td>\n",
       "      <td>62648</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.63</td>\n",
       "      <td>142</td>\n",
       "      <td>63680</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.63</td>\n",
       "      <td>143</td>\n",
       "      <td>63120</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.65</td>\n",
       "      <td>142</td>\n",
       "      <td>63364</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.66</td>\n",
       "      <td>143</td>\n",
       "      <td>63608</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.83</td>\n",
       "      <td>133</td>\n",
       "      <td>63376</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.83</td>\n",
       "      <td>134</td>\n",
       "      <td>63616</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.69</td>\n",
       "      <td>141</td>\n",
       "      <td>63440</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.69</td>\n",
       "      <td>139</td>\n",
       "      <td>63460</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>konsole</td>\n",
       "      <td>0.69</td>\n",
       "      <td>139</td>\n",
       "      <td>62720</td>\n",
       "      <td>Debian 9, Xorg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>st</td>\n",
       "      <td>0.43</td>\n",
       "      <td>161</td>\n",
       "      <td>7568</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>st</td>\n",
       "      <td>0.43</td>\n",
       "      <td>159</td>\n",
       "      <td>7572</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>st</td>\n",
       "      <td>0.43</td>\n",
       "      <td>155</td>\n",
       "      <td>7536</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>st</td>\n",
       "      <td>0.44</td>\n",
       "      <td>159</td>\n",
       "      <td>7604</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>st</td>\n",
       "      <td>0.44</td>\n",
       "      <td>160</td>\n",
       "      <td>7740</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>st</td>\n",
       "      <td>0.43</td>\n",
       "      <td>158</td>\n",
       "      <td>7560</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>st</td>\n",
       "      <td>0.43</td>\n",
       "      <td>159</td>\n",
       "      <td>7512</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>st</td>\n",
       "      <td>0.43</td>\n",
       "      <td>159</td>\n",
       "      <td>7564</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>st</td>\n",
       "      <td>0.44</td>\n",
       "      <td>160</td>\n",
       "      <td>7684</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>st</td>\n",
       "      <td>0.43</td>\n",
       "      <td>162</td>\n",
       "      <td>7544</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>st</td>\n",
       "      <td>0.45</td>\n",
       "      <td>156</td>\n",
       "      <td>7508</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>st</td>\n",
       "      <td>0.42</td>\n",
       "      <td>161</td>\n",
       "      <td>7432</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>st</td>\n",
       "      <td>0.44</td>\n",
       "      <td>156</td>\n",
       "      <td>7432</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>st</td>\n",
       "      <td>0.44</td>\n",
       "      <td>157</td>\n",
       "      <td>7580</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>st</td>\n",
       "      <td>0.42</td>\n",
       "      <td>157</td>\n",
       "      <td>7472</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>st</td>\n",
       "      <td>0.44</td>\n",
       "      <td>157</td>\n",
       "      <td>7744</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>st</td>\n",
       "      <td>0.44</td>\n",
       "      <td>158</td>\n",
       "      <td>7684</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>st</td>\n",
       "      <td>0.44</td>\n",
       "      <td>158</td>\n",
       "      <td>7428</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>st</td>\n",
       "      <td>0.44</td>\n",
       "      <td>157</td>\n",
       "      <td>7548</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>st</td>\n",
       "      <td>0.45</td>\n",
       "      <td>157</td>\n",
       "      <td>7408</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>st</td>\n",
       "      <td>0.45</td>\n",
       "      <td>148</td>\n",
       "      <td>7576</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791</th>\n",
       "      <td>st</td>\n",
       "      <td>0.43</td>\n",
       "      <td>159</td>\n",
       "      <td>7748</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>st</td>\n",
       "      <td>0.44</td>\n",
       "      <td>160</td>\n",
       "      <td>7748</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>st</td>\n",
       "      <td>0.43</td>\n",
       "      <td>156</td>\n",
       "      <td>7432</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>st</td>\n",
       "      <td>0.45</td>\n",
       "      <td>156</td>\n",
       "      <td>7480</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>st</td>\n",
       "      <td>0.44</td>\n",
       "      <td>158</td>\n",
       "      <td>7676</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>st</td>\n",
       "      <td>0.45</td>\n",
       "      <td>161</td>\n",
       "      <td>7556</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>st</td>\n",
       "      <td>0.43</td>\n",
       "      <td>160</td>\n",
       "      <td>7740</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>st</td>\n",
       "      <td>0.44</td>\n",
       "      <td>160</td>\n",
       "      <td>7476</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>st</td>\n",
       "      <td>0.42</td>\n",
       "      <td>160</td>\n",
       "      <td>7560</td>\n",
       "      <td>Fedora 27, Wayland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    terminal  time  cpu  memory                 env\n",
       "0    konsole  0.66  141   63732      Debian 9, Xorg\n",
       "1    konsole  0.62  144   63256      Debian 9, Xorg\n",
       "2    konsole  0.65  142   63612      Debian 9, Xorg\n",
       "3    konsole  0.64  143   63412      Debian 9, Xorg\n",
       "4    konsole  0.64  143   63668      Debian 9, Xorg\n",
       "5    konsole  0.67  143   63344      Debian 9, Xorg\n",
       "6    konsole  0.66  143   62704      Debian 9, Xorg\n",
       "7    konsole  0.64  143   63184      Debian 9, Xorg\n",
       "8    konsole  0.63  141   63724      Debian 9, Xorg\n",
       "9    konsole  0.67  141   63400      Debian 9, Xorg\n",
       "10   konsole  0.63  142   63708      Debian 9, Xorg\n",
       "11   konsole  0.63  142   63668      Debian 9, Xorg\n",
       "12   konsole  0.63  142   63252      Debian 9, Xorg\n",
       "13   konsole  0.68  142   62848      Debian 9, Xorg\n",
       "14   konsole  0.64  143   63284      Debian 9, Xorg\n",
       "15   konsole  0.64  142   63664      Debian 9, Xorg\n",
       "16   konsole  0.62  144   63588      Debian 9, Xorg\n",
       "17   konsole  0.67  143   63416      Debian 9, Xorg\n",
       "18   konsole  0.66  142   63540      Debian 9, Xorg\n",
       "19   konsole  0.64  144   63492      Debian 9, Xorg\n",
       "20   konsole  0.63  141   62648      Debian 9, Xorg\n",
       "21   konsole  0.63  142   63680      Debian 9, Xorg\n",
       "22   konsole  0.63  143   63120      Debian 9, Xorg\n",
       "23   konsole  0.65  142   63364      Debian 9, Xorg\n",
       "24   konsole  0.66  143   63608      Debian 9, Xorg\n",
       "25   konsole  0.83  133   63376      Debian 9, Xorg\n",
       "26   konsole  0.83  134   63616      Debian 9, Xorg\n",
       "27   konsole  0.69  141   63440      Debian 9, Xorg\n",
       "28   konsole  0.69  139   63460      Debian 9, Xorg\n",
       "29   konsole  0.69  139   62720      Debian 9, Xorg\n",
       "..       ...   ...  ...     ...                 ...\n",
       "770       st  0.43  161    7568  Fedora 27, Wayland\n",
       "771       st  0.43  159    7572  Fedora 27, Wayland\n",
       "772       st  0.43  155    7536  Fedora 27, Wayland\n",
       "773       st  0.44  159    7604  Fedora 27, Wayland\n",
       "774       st  0.44  160    7740  Fedora 27, Wayland\n",
       "775       st  0.43  158    7560  Fedora 27, Wayland\n",
       "776       st  0.43  159    7512  Fedora 27, Wayland\n",
       "777       st  0.43  159    7564  Fedora 27, Wayland\n",
       "778       st  0.44  160    7684  Fedora 27, Wayland\n",
       "779       st  0.43  162    7544  Fedora 27, Wayland\n",
       "780       st  0.45  156    7508  Fedora 27, Wayland\n",
       "781       st  0.42  161    7432  Fedora 27, Wayland\n",
       "782       st  0.44  156    7432  Fedora 27, Wayland\n",
       "783       st  0.44  157    7580  Fedora 27, Wayland\n",
       "784       st  0.42  157    7472  Fedora 27, Wayland\n",
       "785       st  0.44  157    7744  Fedora 27, Wayland\n",
       "786       st  0.44  158    7684  Fedora 27, Wayland\n",
       "787       st  0.44  158    7428  Fedora 27, Wayland\n",
       "788       st  0.44  157    7548  Fedora 27, Wayland\n",
       "789       st  0.45  157    7408  Fedora 27, Wayland\n",
       "790       st  0.45  148    7576  Fedora 27, Wayland\n",
       "791       st  0.43  159    7748  Fedora 27, Wayland\n",
       "792       st  0.44  160    7748  Fedora 27, Wayland\n",
       "793       st  0.43  156    7432  Fedora 27, Wayland\n",
       "794       st  0.45  156    7480  Fedora 27, Wayland\n",
       "795       st  0.44  158    7676  Fedora 27, Wayland\n",
       "796       st  0.45  161    7556  Fedora 27, Wayland\n",
       "797       st  0.43  160    7740  Fedora 27, Wayland\n",
       "798       st  0.44  160    7476  Fedora 27, Wayland\n",
       "799       st  0.42  160    7560  Fedora 27, Wayland\n",
       "\n",
       "[2400 rows x 5 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  }
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