Test Reading Data from Google Cloud from Cloud Instance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import gcsfs\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fs = gcsfs.GCSFileSystem(project='polar-program-784', token='cloud')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['kemerelab-data/diba/',\n",
       " 'kemerelab-data/diba/gor01vvp01-metadata.h5',\n",
       " 'kemerelab-data/diba/gor01vvp01_processed_speed.nel',\n",
       " 'kemerelab-data/diba/gor01vvp01pin01-metadata.h5',\n",
       " 'kemerelab-data/diba/gor01vvp01pin01_processed_speed.nel']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.ls('kemerelab-data/diba')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_hdf_from_buffer(buffer, store=None, key=None):\n",
    "    with pd.HDFStore(\n",
    "            store,\n",
    "            mode=\"r\",\n",
    "            driver=\"H5FD_CORE\",\n",
    "            driver_core_backing_store=0,\n",
    "            driver_core_image=buffer.read()\n",
    "            ) as store:\n",
    "        return store[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with fs.open('kemerelab-data/diba/gor01vvp01-metadata.h5', 'rb') as f:\n",
    "    df = read_hdf_from_buffer(f, store='gor01vvp01-metadata.h5',\n",
    "                              key='Session_Metadata')\n",
    "with fs.open('kemerelab-data/diba/gor01vvp01-metadata.h5', 'rb') as f:\n",
    "    df2 = read_hdf_from_buffer(f, store='gor01vvp01-metadata.h5',\n",
    "                          key='Subset_Metadata')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>277.0</td>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>gor01</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>15-55-31</td>\n",
       "      <td>one</td>\n",
       "      <td>short</td>\n",
       "      <td>410.0</td>\n",
       "      <td>97</td>\n",
       "      <td>44</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>gor01</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>15-55-31</td>\n",
       "      <td>one</td>\n",
       "      <td>long</td>\n",
       "      <td>660.0</td>\n",
       "      <td>97</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>10-24-35</td>\n",
       "      <td>one</td>\n",
       "      <td>short</td>\n",
       "      <td>287.0</td>\n",
       "      <td>54</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>10-24-35</td>\n",
       "      <td>one</td>\n",
       "      <td>long</td>\n",
       "      <td>451.0</td>\n",
       "      <td>54</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>13-20-55</td>\n",
       "      <td>two</td>\n",
       "      <td>short</td>\n",
       "      <td>221.0</td>\n",
       "      <td>112</td>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>13-20-55</td>\n",
       "      <td>two</td>\n",
       "      <td>long</td>\n",
       "      <td>304.0</td>\n",
       "      <td>112</td>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>19-11-57</td>\n",
       "      <td>two</td>\n",
       "      <td>short</td>\n",
       "      <td>427.0</td>\n",
       "      <td>77</td>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>19-11-57</td>\n",
       "      <td>two</td>\n",
       "      <td>long</td>\n",
       "      <td>891.0</td>\n",
       "      <td>77</td>\n",
       "      <td>42</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>16-2-46</td>\n",
       "      <td>two</td>\n",
       "      <td>short</td>\n",
       "      <td>272.0</td>\n",
       "      <td>95</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>16-2-46</td>\n",
       "      <td>two</td>\n",
       "      <td>long</td>\n",
       "      <td>568.0</td>\n",
       "      <td>95</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>16-37-40</td>\n",
       "      <td>two</td>\n",
       "      <td>short</td>\n",
       "      <td>178.0</td>\n",
       "      <td>62</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>16-37-40</td>\n",
       "      <td>two</td>\n",
       "      <td>long</td>\n",
       "      <td>262.0</td>\n",
       "      <td>62</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>gor01</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1-22-43</td>\n",
       "      <td>one</td>\n",
       "      <td>short</td>\n",
       "      <td>617.0</td>\n",
       "      <td>203</td>\n",
       "      <td>71</td>\n",
       "      <td>91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>gor01</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1-22-43</td>\n",
       "      <td>one</td>\n",
       "      <td>long</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>203</td>\n",
       "      <td>62</td>\n",
       "      <td>117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>gor01</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>15-22-3</td>\n",
       "      <td>two</td>\n",
       "      <td>short</td>\n",
       "      <td>325.0</td>\n",
       "      <td>82</td>\n",
       "      <td>37</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>gor01</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>15-22-3</td>\n",
       "      <td>two</td>\n",
       "      <td>long</td>\n",
       "      <td>530.0</td>\n",
       "      <td>82</td>\n",
       "      <td>37</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>18-21-57</td>\n",
       "      <td>two</td>\n",
       "      <td>long</td>\n",
       "      <td>266.0</td>\n",
       "      <td>91</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>16-48-29</td>\n",
       "      <td>one</td>\n",
       "      <td>long</td>\n",
       "      <td>341.0</td>\n",
       "      <td>93</td>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>15-25-59</td>\n",
       "      <td>two</td>\n",
       "      <td>short</td>\n",
       "      <td>357.0</td>\n",
       "      <td>70</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>15-25-59</td>\n",
       "      <td>two</td>\n",
       "      <td>long</td>\n",
       "      <td>449.0</td>\n",
       "      <td>70</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>15-16-59</td>\n",
       "      <td>one</td>\n",
       "      <td>short</td>\n",
       "      <td>241.0</td>\n",
       "      <td>84</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>15-16-59</td>\n",
       "      <td>one</td>\n",
       "      <td>long</td>\n",
       "      <td>667.0</td>\n",
       "      <td>84</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>16-40-54</td>\n",
       "      <td>two</td>\n",
       "      <td>short</td>\n",
       "      <td>485.0</td>\n",
       "      <td>41</td>\n",
       "      <td>16</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>16-40-54</td>\n",
       "      <td>two</td>\n",
       "      <td>long</td>\n",
       "      <td>861.0</td>\n",
       "      <td>41</td>\n",
       "      <td>25</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>12-48-38</td>\n",
       "      <td>two</td>\n",
       "      <td>short</td>\n",
       "      <td>433.0</td>\n",
       "      <td>80</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>12-48-38</td>\n",
       "      <td>two</td>\n",
       "      <td>long</td>\n",
       "      <td>697.0</td>\n",
       "      <td>80</td>\n",
       "      <td>33</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>13-50-7</td>\n",
       "      <td>two</td>\n",
       "      <td>short</td>\n",
       "      <td>201.0</td>\n",
       "      <td>74</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>13-50-7</td>\n",
       "      <td>two</td>\n",
       "      <td>long</td>\n",
       "      <td>288.0</td>\n",
       "      <td>74</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>18-47-52</td>\n",
       "      <td>two</td>\n",
       "      <td>short</td>\n",
       "      <td>308.0</td>\n",
       "      <td>66</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>vvp01</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>18-47-52</td>\n",
       "      <td>two</td>\n",
       "      <td>long</td>\n",
       "      <td>420.0</td>\n",
       "      <td>66</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   animal  month  day      time track segment  duration  n_cells  \\\n",
       "0   vvp01      4    9  17-29-30   one   short     490.0       68   \n",
       "1   vvp01      4    9  17-29-30   one    long     800.0       68   \n",
       "2   vvp01      4   12  17-53-55   one   short     306.0       79   \n",
       "3   vvp01      4   12  17-53-55   one    long     466.0       79   \n",
       "4   vvp01      4   28  12-17-27   one    long     422.0      105   \n",
       "5   gor01      6    8  21-16-25   two   short     457.0      171   \n",
       "6   gor01      6    8  21-16-25   two    long     720.0      171   \n",
       "7   vvp01      4   16  14-49-24   two   short     262.0       57   \n",
       "8   vvp01      4   16  14-49-24   two    long     735.0       57   \n",
       "9   vvp01      4   19   16-48-9   one   short     242.0       53   \n",
       "10  vvp01      4   19   16-48-9   one    long     271.0       53   \n",
       "11  vvp01      4   10   21-2-40   one   short     312.0       83   \n",
       "12  vvp01      4   10   21-2-40   one    long     590.0       83   \n",
       "13  gor01      6   12  16-53-46   two   short     306.0       81   \n",
       "14  gor01      6   12  16-53-46   two    long     470.0       81   \n",
       "15  vvp01      4   21   11-19-2   two   short     214.0       80   \n",
       "16  vvp01      4   21   11-19-2   two    long     352.0       80   \n",
       "17  vvp01      4   19  13-34-40   one   short     244.0       68   \n",
       "18  vvp01      4   19  13-34-40   one    long     384.0       68   \n",
       "19  vvp01      4   18   15-38-2   two   short     194.0      109   \n",
       "20  vvp01      4   18   15-38-2   two    long     294.0      109   \n",
       "21  vvp01      4   17  12-33-47   one   short     406.0       34   \n",
       "22  vvp01      4   17  12-33-47   one    long     429.0       34   \n",
       "23  vvp01      4   18    13-6-1   one   short     276.0       69   \n",
       "24  vvp01      4   18    13-6-1   one    long     482.0       69   \n",
       "25  vvp01      4   28   17-6-14   two   short     174.0       93   \n",
       "26  vvp01      4   28   17-6-14   two    long     335.0       93   \n",
       "27  vvp01      4   25  14-28-51   one   short     211.0       80   \n",
       "28  vvp01      4   25  14-28-51   one    long     385.0       80   \n",
       "29  vvp01      4   12  14-59-23   two    long     277.0       42   \n",
       "..    ...    ...  ...       ...   ...     ...       ...      ...   \n",
       "60  gor01      6   12  15-55-31   one   short     410.0       97   \n",
       "61  gor01      6   12  15-55-31   one    long     660.0       97   \n",
       "62  vvp01      4   21  10-24-35   one   short     287.0       54   \n",
       "63  vvp01      4   21  10-24-35   one    long     451.0       54   \n",
       "64  vvp01      4   25  13-20-55   two   short     221.0      112   \n",
       "65  vvp01      4   25  13-20-55   two    long     304.0      112   \n",
       "66  vvp01      4   10  19-11-57   two   short     427.0       77   \n",
       "67  vvp01      4   10  19-11-57   two    long     891.0       77   \n",
       "68  vvp01      4   11   16-2-46   two   short     272.0       95   \n",
       "69  vvp01      4   11   16-2-46   two    long     568.0       95   \n",
       "70  vvp01      4   19  16-37-40   two   short     178.0       62   \n",
       "71  vvp01      4   19  16-37-40   two    long     262.0       62   \n",
       "72  gor01      6    9   1-22-43   one   short     617.0      203   \n",
       "73  gor01      6    9   1-22-43   one    long    1012.0      203   \n",
       "74  gor01      6   13   15-22-3   two   short     325.0       82   \n",
       "75  gor01      6   13   15-22-3   two    long     530.0       82   \n",
       "76  vvp01      4   27  18-21-57   two    long     266.0       91   \n",
       "77  vvp01      4   28  16-48-29   one    long     341.0       93   \n",
       "78  vvp01      4   12  15-25-59   two   short     357.0       70   \n",
       "79  vvp01      4   12  15-25-59   two    long     449.0       70   \n",
       "80  vvp01      4   11  15-16-59   one   short     241.0       84   \n",
       "81  vvp01      4   11  15-16-59   one    long     667.0       84   \n",
       "82  vvp01      4    9  16-40-54   two   short     485.0       41   \n",
       "83  vvp01      4    9  16-40-54   two    long     861.0       41   \n",
       "84  vvp01      4   11  12-48-38   two   short     433.0       80   \n",
       "85  vvp01      4   11  12-48-38   two    long     697.0       80   \n",
       "86  vvp01      4   19   13-50-7   two   short     201.0       74   \n",
       "87  vvp01      4   19   13-50-7   two    long     288.0       74   \n",
       "88  vvp01      4   16  18-47-52   two   short     308.0       66   \n",
       "89  vvp01      4   16  18-47-52   two    long     420.0       66   \n",
       "\n",
       "    n_placecells  n_PBEs  Notes  prescreen_z  \n",
       "0             32      46    NaN          NaN  \n",
       "1             34      33    NaN          NaN  \n",
       "2             34       1    NaN          NaN  \n",
       "3             30       0    NaN          NaN  \n",
       "4             46       8    NaN          NaN  \n",
       "5             64      37    NaN          NaN  \n",
       "6             82      57    NaN          NaN  \n",
       "7             19       9    NaN          NaN  \n",
       "8             24      12    NaN          NaN  \n",
       "9             20       6    NaN          NaN  \n",
       "10            23       6    NaN          NaN  \n",
       "11            48       1    NaN          NaN  \n",
       "12            47       4    NaN          NaN  \n",
       "13            36      23    NaN          NaN  \n",
       "14            36      36    NaN          NaN  \n",
       "15            31       1    NaN          NaN  \n",
       "16            32       7    NaN          NaN  \n",
       "17            30       6    NaN          NaN  \n",
       "18            31       9    NaN          NaN  \n",
       "19            38      10    NaN          NaN  \n",
       "20            45       6    NaN          NaN  \n",
       "21             9      10    NaN          NaN  \n",
       "22            10      11    NaN          NaN  \n",
       "23            20       9    NaN          NaN  \n",
       "24            20       7    NaN          NaN  \n",
       "25            41       2    NaN          NaN  \n",
       "26            40       5    NaN          NaN  \n",
       "27            35      12    NaN          NaN  \n",
       "28            32      36    NaN          NaN  \n",
       "29            26       4    NaN          NaN  \n",
       "..           ...     ...    ...          ...  \n",
       "60            44      27    NaN          NaN  \n",
       "61            40      32    NaN          NaN  \n",
       "62            18      24    NaN          NaN  \n",
       "63            21      18    NaN          NaN  \n",
       "64            40      15    NaN          NaN  \n",
       "65            39      18    NaN          NaN  \n",
       "66            39       9    NaN          NaN  \n",
       "67            42      20    NaN          NaN  \n",
       "68            43       0    NaN          NaN  \n",
       "69            43       3    NaN          NaN  \n",
       "70            25       1    NaN          NaN  \n",
       "71            30       0    NaN          NaN  \n",
       "72            71      91    NaN          NaN  \n",
       "73            62     117    NaN          NaN  \n",
       "74            37      21    NaN          NaN  \n",
       "75            37      31    NaN          NaN  \n",
       "76            34       4    NaN          NaN  \n",
       "77            37       9    NaN          NaN  \n",
       "78            37       3    NaN          NaN  \n",
       "79            33       2    NaN          NaN  \n",
       "80            41       2    NaN          NaN  \n",
       "81            42       0    NaN          NaN  \n",
       "82            16      37    NaN          NaN  \n",
       "83            25      42    NaN          NaN  \n",
       "84            38       8    NaN          NaN  \n",
       "85            33      14    NaN          NaN  \n",
       "86            28       6    NaN          NaN  \n",
       "87            30      11    NaN          NaN  \n",
       "88            25      20    NaN          NaN  \n",
       "89            30       8    NaN          NaN  \n",
       "\n",
       "[180 rows x 12 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "``gcs://kemerelab-data/diba`` does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-ab535a10f84e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mload_from_nel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#    with pd.HDFStore(os.path.join(fileroot,'DibaMetadata.h5')) as store:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHDFStore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gcs://kemerelab-data/diba/gor01vvp01-metadata.h5'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mstore\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m         \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Session_Metadata'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mdf2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Subset_Metadata'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pandas/io/pytables.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, path, mode, complevel, complib, fletcher32, **kwargs)\u001b[0m\n\u001b[1;32m    464\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fletcher32\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfletcher32\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 466\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    467\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    468\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pandas/io/pytables.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, mode, **kwargs)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtables\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mIOError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;34m'can not be written'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/lib/python3.6/site-packages/tables/file.py\u001b[0m in \u001b[0;36mopen_file\u001b[0;34m(filename, mode, title, root_uep, filters, **kwargs)\u001b[0m\n\u001b[1;32m    318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m     \u001b[0;31m# Finally, create the File instance, and return it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroot_uep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    321\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/lib/python3.6/site-packages/tables/file.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filename, mode, title, root_uep, filters, **kwargs)\u001b[0m\n\u001b[1;32m    782\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    783\u001b[0m         \u001b[0;31m# Now, it is time to initialize the File extension\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 784\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_g_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    785\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    786\u001b[0m         \u001b[0;31m# Check filters and set PyTables format version for new files.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mtables/hdf5extension.pyx\u001b[0m in \u001b[0;36mtables.hdf5extension.File._g_new\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/miniconda/lib/python3.6/site-packages/tables/utils.py\u001b[0m in \u001b[0;36mcheck_file_access\u001b[0;34m(filename, mode)\u001b[0m\n\u001b[1;32m    182\u001b[0m             \u001b[0mcheck_file_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r+'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0mcheck_file_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'r+'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0mcheck_file_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/lib/python3.6/site-packages/tables/utils.py\u001b[0m in \u001b[0;36mcheck_file_access\u001b[0;34m(filename, mode)\u001b[0m\n\u001b[1;32m    172\u001b[0m                 \u001b[0mparentname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'.'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparentname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mF_OK\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"``%s`` does not exist\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mparentname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    175\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparentname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"``%s`` is not a directory\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mparentname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: ``gcs://kemerelab-data/diba`` does not exist"
     ]
    }
   ],
   "source": [
    "load_from_nel = True\n",
    "\n",
    "# load from nel file:\n",
    "if load_from_nel:\n",
    "    with pd.HDFStore('gcs://kemerelab-data/diba/gor01vvp01-metadata.h5') as store:\n",
    "        df = store.get('Session_Metadata')\n",
    "        df2 = store.get('Subset_Metadata')\n",
    "    # jar = nel.load_pkl(os.path.join(fileroot,'gor01vvp01_processed_speed.nel'))\n",
    "    # exp_data = jar.exp_data\n",
    "    # aux_data = jar.aux_data\n",
    "    # del jar\n",
    "    \n"
   ]
  }
 ],
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