doc/Displaying results.ipynb

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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import sys\n",
      "sys.path.append('..')\n",
      "import trappy.wa"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Displaying Results"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "At the end of running an agenda, Workload Automation produces a summary of the results for the workloads. trappy can parse the results and tabulate it in a notebook. Additionally, an optional id argument an be passed which if supplied overrides the id in the results file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = trappy.wa.get_results(\"../tests\")\n",
      "results"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">geekbench</th>\n",
        "      <th colspan=\"2\" halign=\"left\">antutu</th>\n",
        "      <th colspan=\"2\" halign=\"left\">egypt_offscreen</th>\n",
        "      <th colspan=\"2\" halign=\"left\">thechase</th>\n",
        "      <th colspan=\"2\" halign=\"left\">t-rex_offscreen</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>power_allocator</th>\n",
        "      <th>step_wise</th>\n",
        "      <th>power_allocator</th>\n",
        "      <th>step_wise</th>\n",
        "      <th>power_allocator</th>\n",
        "      <th>step_wise</th>\n",
        "      <th>power_allocator</th>\n",
        "      <th>step_wise</th>\n",
        "      <th>power_allocator</th>\n",
        "      <th>step_wise</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>3</td>\n",
        "      <td>8</td>\n",
        "      <td>5</td>\n",
        "      <td>4</td>\n",
        "      <td>652</td>\n",
        "      <td>504</td>\n",
        "      <td>491.615669</td>\n",
        "      <td>242.052226</td>\n",
        "      <td>1777</td>\n",
        "      <td>2365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
        "      <td>4</td>\n",
        "      <td>3</td>\n",
        "      <td>9</td>\n",
        "      <td>555</td>\n",
        "      <td>2507</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>397</td>\n",
        "      <td>429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>5</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "      <td>7</td>\n",
        "      <td>790</td>\n",
        "      <td>325</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>512</td>\n",
        "      <td>424</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "        geekbench                    antutu           egypt_offscreen  \\\n",
        "  power_allocator step_wise power_allocator step_wise power_allocator   \n",
        "0               3         8               5         4             652   \n",
        "1               1         4               3         9             555   \n",
        "2               5         2               2         7             790   \n",
        "\n",
        "                   thechase             t-rex_offscreen            \n",
        "  step_wise power_allocator   step_wise power_allocator step_wise  \n",
        "0       504      491.615669  242.052226            1777      2365  \n",
        "1      2507             NaN         NaN             397       429  \n",
        "2       325             NaN         NaN             512       424  "
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In addition to displaying tabulated results, trappy can graphically plot individual benchmarks as well as all the benchmarks in the result."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results.plot_results_benchmark(\"antutu\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f968a4d5950>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results.plot_results()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HIkmSJEmSJEmSJEmSJEmSJEmSJOmk/H8Y6byOfAp6jQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f968a392150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f968a176a10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZCdDw/1LKorZevKUUeBT4m2j5daAC2EZIhTwNnEJjbcat0e1SYA6wKdpmVNQ+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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f968a118e90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SRqyZiKdHayYkZZ01E8pVrkxIkqSMGExIacy75y/nTkqGwYQkScqINRPx9GjN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\n6kD/Hy5oKUUzDcd9AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f968a0e96d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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G88BrwP8M23oDu8Pt3eE+QAmwI+G5O4B+p9i/pCxUWNiDvLy8tN0KC3tk+pCl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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9689f75c10>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Combining Results"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "trappy allows combination of results across different agenda runs as well. This is useful if you want to compare / display results from different runs of Workload Automation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "unconstrained = trappy.wa.get_results(\"../tests/unconstrained.csv\", name=\"Unconstrained\")\n",
      "constrained = trappy.wa.get_results(\"../tests/constrained.csv\", name=\"Constrained\")\n",
      "\n",
      "results = trappy.wa.combine_results([unconstrained, constrained])\n",
      "results"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">antutu</th>\n",
        "      <th colspan=\"2\" halign=\"left\">egypt_offscreen</th>\n",
        "      <th colspan=\"2\" halign=\"left\">t-rex_offscreen</th>\n",
        "      <th colspan=\"2\" halign=\"left\">geekbench</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>Unconstrained</th>\n",
        "      <th>Constrained</th>\n",
        "      <th>Unconstrained</th>\n",
        "      <th>Constrained</th>\n",
        "      <th>Unconstrained</th>\n",
        "      <th>Constrained</th>\n",
        "      <th>Unconstrained</th>\n",
        "      <th>Constrained</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "      <td>864</td>\n",
        "      <td>334</td>\n",
        "      <td>185</td>\n",
        "      <td>560</td>\n",
        "      <td>6</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>802</td>\n",
        "      <td>242</td>\n",
        "      <td>878</td>\n",
        "      <td>872</td>\n",
        "      <td>6</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>6</td>\n",
        "      <td>7</td>\n",
        "      <td>47</td>\n",
        "      <td>190</td>\n",
        "      <td>262</td>\n",
        "      <td>918</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>4</td>\n",
        "      <td>4</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>588</td>\n",
        "      <td>5</td>\n",
        "      <td>7</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>6</td>\n",
        "      <td>2</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>559</td>\n",
        "      <td>494</td>\n",
        "      <td>9</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>9</td>\n",
        "      <td>8</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "         antutu             egypt_offscreen             t-rex_offscreen  \\\n",
        "  Unconstrained Constrained   Unconstrained Constrained   Unconstrained   \n",
        "0             2           2             864         334             185   \n",
        "1           NaN         NaN             802         242             878   \n",
        "2             6           7              47         190             262   \n",
        "3             4           4             NaN         NaN             588   \n",
        "4             6           2             NaN         NaN             559   \n",
        "5             9           8             NaN         NaN             NaN   \n",
        "\n",
        "                  geekbench              \n",
        "  Constrained Unconstrained Constrained  \n",
        "0         560             6           1  \n",
        "1         872             6           1  \n",
        "2         918             1           3  \n",
        "3           5             7           2  \n",
        "4         494             9           8  \n",
        "5         NaN           NaN         NaN  "
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results.plot_results()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f9689fc3090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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du7cZMGCAmTx5stm4caM5fvy4ufvuu01eXp7Jy8sz99xzjzlx4oQxxpgNGzaY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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f968a1119d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RZ/AgSZISYvAgSSlmzoOizuBBkiQlxOBBvaq4eBA5OTkJD1ImM+dBUZff2wVQ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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f968a390590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f9689fe2150>"
       ]
      }
     ],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}