{
 "metadata": {
  "name": "",
  "signature": "sha256:d03f0300a24dadc9bd282e0fc96d108fd5c057ee71fb302185a4a5ffc75f7a64"
 },
 "nbformat": 3,
 "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": "iVBORw0KGgoAAAANSUhEUgAAAgYAAAEFCAYAAACYQy+TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "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": "iVBORw0KGgoAAAANSUhEUgAAAgYAAAEFCAYAAACYQy+TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f968a392150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAgYAAAEFCAYAAACYQy+TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f968a176a10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAhkAAAEFCAYAAABO5vX2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f968a118e90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAhMAAAEFCAYAAABZxGU/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6kD/Hy5oKUUzDcd9AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f968a0e96d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAhkAAAEFCAYAAABO5vX2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAf0AAAEFCAYAAADpDT78AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZaTzicOSKXUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9689fc3090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAgoAAAEFCAYAAACCf88dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f968a1119d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAg8AAAEFCAYAAABkVgRZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f968a390590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAf0AAAEFCAYAAADpDT78AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIkSZIkSZIkSZJUtP4/Y3wp7Q/ryrMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9689fe2150>"
       ]
      }
     ],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}