working-with-pandas-multiindex-dataframes-reading-and-writing-to-csv-and-hdf5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rationale\n",
    "\n",
    "For some certain loss functions, such the the negative evidence lower bound (NELBO) in variational inference, they are generally analytically intractable and thus unavailable in closed-form. As such, we might need to resort to taking stochastic estimates of the loss function. In these situations, it is very important to study and understand the robustness of the estimations we are making, particularly in terms of bias and variance. When proposing a new estimator, we may be interested in evaluating the loss at a fined-grained level - not only per batch, but perhaps even per data-point. \n",
    "\n",
    "This notebook explores storing the recorded losses in Pandas Dataframes. The recorded losses are 3d, with dimensions corresponding to epochs, batches, and data-points. Specifically, they are of shape `(n_epochs, n_batches, batch_size)`. Instead of using the deprecated Panel functionality from Pandas, we explore the preferred MultiIndex Dataframe.\n",
    "\n",
    "Lastly, we play around with various data serialization formats supported out-of-the-box by Pandas. This might be useful if the training is GPU-intensive, so the script runs and records the loss remotely on a supercomputer, and we must write the results to file, download them and finally analyze them locally. This is usually trivial, but it is unclear what the behaviour is for more complex MultiIndex dataframes. We restrict our attention to the CSV format, which is human-friendly but very slow and inefficient, and the HDF5, which is basically diametrically opposed - it's basically completely inscrutable, but is very fast and takes up laess space."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synthetic Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 600, 100)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create some noise\n",
    "a = np.random.randn(50, 600, 100)\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 600, 100)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create some noise with higher variance and add bias.\n",
    "b = 2. * np.random.randn(*a.shape) + 1.\n",
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000000,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# manufacture some loss function\n",
    "# there are n_epochs * n_batchs * batch_size \n",
    "# recorded values of the loss\n",
    "loss = 10 / np.linspace(1, 100, a.size)\n",
    "loss.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MultiIndex Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[range(0, 50), range(0, 600), range(0, 100)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we will create the indices from the \n",
    "# product of these iterators\n",
    "list(map(range, a.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the MultiIndex\n",
    "index = pd.MultiIndex.from_product(\n",
    "    list(map(range, a.shape)), \n",
    "    names=['epoch', 'batch', 'datapoint']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>loss1</th>\n",
       "      <th>loss2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>epoch</th>\n",
       "      <th>batch</th>\n",
       "      <th>datapoint</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"30\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>10.837250</td>\n",
       "      <td>10.228649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.383650</td>\n",
       "      <td>9.601012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.102928</td>\n",
       "      <td>12.792865</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.149701</td>\n",
       "      <td>11.307185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.181607</td>\n",
       "      <td>9.905578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.984361</td>\n",
       "      <td>11.646015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.935352</td>\n",
       "      <td>10.793933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.273609</td>\n",
       "      <td>9.421425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.846009</td>\n",
       "      <td>9.916008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.288851</td>\n",
       "      <td>7.250876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.360709</td>\n",
       "      <td>10.911360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9.514765</td>\n",
       "      <td>7.339939</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9.922280</td>\n",
       "      <td>10.494360</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9.094041</td>\n",
       "      <td>13.302492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9.693384</td>\n",
       "      <td>12.187093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9.675839</td>\n",
       "      <td>13.418631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11.502391</td>\n",
       "      <td>9.470244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10.958843</td>\n",
       "      <td>12.709454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10.819225</td>\n",
       "      <td>11.700684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9.412562</td>\n",
       "      <td>10.272870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.424428</td>\n",
       "      <td>10.477799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9.009290</td>\n",
       "      <td>13.920663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8.127529</td>\n",
       "      <td>13.179637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.939673</td>\n",
       "      <td>13.091603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.064599</td>\n",
       "      <td>7.311483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.924553</td>\n",
       "      <td>15.597797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8.572734</td>\n",
       "      <td>14.338683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10.294223</td>\n",
       "      <td>7.761236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11.191123</td>\n",
       "      <td>8.993673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>9.424269</td>\n",
       "      <td>12.067344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">49</th>\n",
       "      <th rowspan=\"30\" valign=\"top\">599</th>\n",
       "      <th>70</th>\n",
       "      <td>1.975951</td>\n",
       "      <td>2.746047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1.169242</td>\n",
       "      <td>4.194400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.248216</td>\n",
       "      <td>0.730370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>-0.271330</td>\n",
       "      <td>0.415903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>-0.888580</td>\n",
       "      <td>0.208060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>-0.624063</td>\n",
       "      <td>1.081515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>-1.422904</td>\n",
       "      <td>0.398015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.332523</td>\n",
       "      <td>1.892470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>-0.224471</td>\n",
       "      <td>-2.839332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.405337</td>\n",
       "      <td>0.266035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.223712</td>\n",
       "      <td>5.810484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>-0.508689</td>\n",
       "      <td>7.535930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>-1.915472</td>\n",
       "      <td>-0.275332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>-0.597498</td>\n",
       "      <td>2.799929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>-0.443378</td>\n",
       "      <td>2.202897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.610826</td>\n",
       "      <td>1.825950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.305465</td>\n",
       "      <td>0.757416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>-1.139339</td>\n",
       "      <td>3.221787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>-1.893639</td>\n",
       "      <td>0.520711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>-0.286300</td>\n",
       "      <td>3.112420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1.268100</td>\n",
       "      <td>1.341298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0.251563</td>\n",
       "      <td>1.040859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0.083156</td>\n",
       "      <td>1.311108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>-0.554107</td>\n",
       "      <td>8.272526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>-2.415105</td>\n",
       "      <td>2.607663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.335266</td>\n",
       "      <td>2.038404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.554412</td>\n",
       "      <td>2.200551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.392182</td>\n",
       "      <td>1.444542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>-0.252059</td>\n",
       "      <td>1.641488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>-0.070091</td>\n",
       "      <td>1.490349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           loss1      loss2\n",
       "epoch batch datapoint                      \n",
       "0     0     0          10.837250  10.228649\n",
       "            1           9.383650   9.601012\n",
       "            2           9.102928  12.792865\n",
       "            3           9.149701  11.307185\n",
       "            4           9.181607   9.905578\n",
       "            5           8.984361  11.646015\n",
       "            6           8.935352  10.793933\n",
       "            7           9.273609   9.421425\n",
       "            8          10.846009   9.916008\n",
       "            9          10.288851   7.250876\n",
       "            10         10.360709  10.911360\n",
       "            11          9.514765   7.339939\n",
       "            12          9.922280  10.494360\n",
       "            13          9.094041  13.302492\n",
       "            14          9.693384  12.187093\n",
       "            15          9.675839  13.418631\n",
       "            16         11.502391   9.470244\n",
       "            17         10.958843  12.709454\n",
       "            18         10.819225  11.700684\n",
       "            19          9.412562  10.272870\n",
       "            20         10.424428  10.477799\n",
       "            21          9.009290  13.920663\n",
       "            22          8.127529  13.179637\n",
       "            23          8.939673  13.091603\n",
       "            24          8.064599   7.311483\n",
       "            25          9.924553  15.597797\n",
       "            26          8.572734  14.338683\n",
       "            27         10.294223   7.761236\n",
       "            28         11.191123   8.993673\n",
       "            29          9.424269  12.067344\n",
       "...                          ...        ...\n",
       "49    599   70          1.975951   2.746047\n",
       "            71          1.169242   4.194400\n",
       "            72          0.248216   0.730370\n",
       "            73         -0.271330   0.415903\n",
       "            74         -0.888580   0.208060\n",
       "            75         -0.624063   1.081515\n",
       "            76         -1.422904   0.398015\n",
       "            77          0.332523   1.892470\n",
       "            78         -0.224471  -2.839332\n",
       "            79          0.405337   0.266035\n",
       "            80          0.223712   5.810484\n",
       "            81         -0.508689   7.535930\n",
       "            82         -1.915472  -0.275332\n",
       "            83         -0.597498   2.799929\n",
       "            84         -0.443378   2.202897\n",
       "            85          0.610826   1.825950\n",
       "            86          0.305465   0.757416\n",
       "            87         -1.139339   3.221787\n",
       "            88         -1.893639   0.520711\n",
       "            89         -0.286300   3.112420\n",
       "            90          1.268100   1.341298\n",
       "            91          0.251563   1.040859\n",
       "            92          0.083156   1.311108\n",
       "            93         -0.554107   8.272526\n",
       "            94         -2.415105   2.607663\n",
       "            95          0.335266   2.038404\n",
       "            96          0.554412   2.200551\n",
       "            97          0.392182   1.444542\n",
       "            98         -0.252059   1.641488\n",
       "            99         -0.070091   1.490349\n",
       "\n",
       "[3000000 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the dataframe that records the two losses\n",
    "df = pd.DataFrame(\n",
    "    dict(loss1=loss+np.ravel(a), \n",
    "         loss2=loss+np.ravel(b)), \n",
    "    index=index\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this contrived scenario, `loss2` is more biased and has higher variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# some basic plotting\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "df.groupby(['epoch', 'batch']).mean().plot(ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSV Read/Write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.56 s, sys: 184 ms, total: 9.74 s\n",
      "Wall time: 13.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "df.to_csv('losses.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rwxrwxrwx 1 tiao tiao 138M Nov  8 03:14 losses.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lh losses.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tiao/.virtualenvs/anmoku/lib/python3.5/site-packages/numpy/lib/arraysetops.py:463: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.47 s, sys: 108 ms, total: 1.58 s\n",
      "Wall time: 3.73 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "df_from_csv = pd.read_csv('losses.csv', index_col=['epoch', 'batch', 'datapoint'], float_precision='high')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# does not recover exactly due to insufficient floating point precision\n",
    "df_from_csv.equals(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loss1    True\n",
       "loss2    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# but it has recovered it up to some tiny epsilon\n",
    "((df-df_from_csv)**2 < 1e-25).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HDF5 Read/Write"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HDF5 writing is orders of magnitude faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 44 ms, sys: 72 ms, total: 116 ms\n",
      "Wall time: 720 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "df.to_hdf('store.h5', key='losses')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore, the file sizes are significantly smaller."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rwxrwxrwx 1 tiao tiao 58M Nov  8 03:15 store.h5\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lh store.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 28 ms, sys: 28 ms, total: 56 ms\n",
      "Wall time: 105 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "df_from_hdf = pd.read_hdf('store.h5', key='losses')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, it is far more numerical precise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.equals(df_from_hdf)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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  "nikola": {
   "category": "",
   "date": "2017-11-08 11:31:15 UTC+11:00",
   "description": "",
   "link": "",
   "slug": "working-with-pandas-multiindex-dataframes-reading-and-writing-to-csv-and-hdf5",
   "tags": "",
   "title": "Working with Pandas MultiIndex Dataframes: Reading and Writing to CSV and HDF5",
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